diff --git a/.github/workflows/test-package.yml b/.github/workflows/test-package.yml index 1d2b699..31db2bb 100644 --- a/.github/workflows/test-package.yml +++ b/.github/workflows/test-package.yml @@ -1,16 +1,6 @@ name: tests on: - push: - branches: - - 'main' - paths: - - '**.py' - - '!docs/**' - - pull_request: - branches: ['main'] - schedule: - cron: '0 11 1 * *' @@ -39,6 +29,7 @@ jobs: python -m pip install flake8 python -m pip install jinja2 python -m pip install -e . + python -m pip install kvxopt - name: Lint with flake8 run: | diff --git a/.github/workflows/test-wheel.yml b/.github/workflows/test-wheel.yml index c93b43c..3ea0136 100644 --- a/.github/workflows/test-wheel.yml +++ b/.github/workflows/test-wheel.yml @@ -1,10 +1,6 @@ name: build on: - push: - tags: - - '[0-9]+.[0-9]+.[0-9]+' - schedule: - cron: '0 12 1 * *' @@ -31,6 +27,10 @@ jobs: python -m pip install --upgrade pip python -m pip install build + - name: Run pre-build script + run: | + python ./pre-build.py + - name: Build wheel run: python -m build @@ -44,7 +44,7 @@ jobs: tar --list -f ./dist/*.tar.gz | grep "deareis/gui/licenses/LICENSE-DearEIS.txt" dist="$(tar --list -f ./dist/*.tar.gz | grep "deareis/gui/licenses/LICENSE-.*\.txt" | sort)" repo="$(ls LICENSES | grep "LICENSE-.*.txt" | sort)" - python -c "from sys import argv; from os.path import basename; dist = list(map(basename, argv[1].split('\n'))); dist.remove('LICENSE-DearEIS.txt'); repo = list(map(basename, argv[2].split('\n'))); assert dist == repo; list(map(print, dist))" "$dist" "$repo" + python -c "from sys import argv; from os.path import basename; dist = set(list(map(basename, argv[1].split('\n')))); dist.remove('LICENSE-DearEIS.txt'); repo = set(list(map(basename, argv[2].split('\n')))); assert dist == repo, 'Incorrect set of bundled licenses! An extra .txt file has probably been left in the \'/src/deareis/gui/licenses\' folder.'; list(map(print, sorted(dist)))" "$dist" "$repo" - name: Check wheel contents run: | @@ -56,7 +56,7 @@ jobs: unzip -Z1 ./dist/*.whl | grep "deareis/gui/licenses/LICENSE-DearEIS.txt" dist="$(unzip -Z1 ./dist/*.whl | grep "deareis/gui/licenses/LICENSE-.*\.txt" | sort)" repo="$(ls LICENSES | grep "LICENSE-.*.txt" | sort)" - python -c "from sys import argv; from os.path import basename; dist = list(map(basename, argv[1].split('\n'))); dist.remove('LICENSE-DearEIS.txt'); repo = list(map(basename, argv[2].split('\n'))); assert dist == repo; list(map(print, dist))" "$dist" "$repo" + python -c "from sys import argv; from os.path import basename; dist = set(list(map(basename, argv[1].split('\n')))); dist.remove('LICENSE-DearEIS.txt'); repo = set(list(map(basename, argv[2].split('\n')))); assert dist == repo, 'Incorrect set of bundled licenses! An extra .txt file has probably been left in the \'/src/deareis/gui/licenses\' folder.'; list(map(print, sorted(dist)))" "$dist" "$repo" - name: Install wheel working-directory: ./dist diff --git a/.gitignore b/.gitignore index cc1489f..3acecb2 100644 --- a/.gitignore +++ b/.gitignore @@ -131,6 +131,12 @@ dmypy.json docs/API*.md docs/API*.pdf docs/documentation +examples/example-2.json +src/deareis/CHANGELOG.md +src/deareis/CONTRIBUTORS +src/deareis/COPYRIGHT +src/deareis/LICENSE +src/deareis/LICENSES/* +src/deareis/README.md src/deareis/gui/changelog/*.md src/deareis/gui/licenses/*.txt -examples/example-2.json diff --git a/.gitmodules b/.gitmodules deleted file mode 100644 index 020ca9b..0000000 --- a/.gitmodules +++ /dev/null @@ -1,3 +0,0 @@ -[submodule "docs/api_documenter"] - path = docs/api_documenter - url = https://github.com/vyrjana/python-api-documenter diff --git a/CHANGELOG.md b/CHANGELOG.md index 4cf441f..f5cf910 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,20 +1,49 @@ -# 3.4.3 +# 4.0.0 + +- Added `Getting started` window when running DearEIS for the first time. +- Added ability to replace outliers with interpolated points. +- Added `Process` button to the `Data sets` tab in the spot where the `Average` button used to be and clicking the button opens a popup with `Average`, `Interpolate`, and `Subtract` buttons. +- Added multiple plot types to most tabs and several modal windows as sub-tabs for each plot type. +- Added ability to perform batch analyses via the GUI. +- Added `Series resistance/capacitance/inductance` rows to the statistics table in the `Kramers-Kronig` tab. +- Added `Z-HIT analysis` tab for reconstructing modulus data from phase data. +- Added `Timeout` setting for the TR-RBF method in the `DRT analysis` tab. +- Added a row for the log pseudo chi-squared to the statistics table in the `Fitting` tab. +- Added highlighting of fitted parameters with values that were restricted by the lower or upper limit in the `Fitting` tab. +- Added an `Adjust parameters` button, which brings up a window for adjusting initial values with a real-time preview, to the `Fitting` and `Simulation` tabs. +- Added `Duplicate` button to the `Plotting` tab. +- Added a window for defining a path to a Python script/package that defines one or more user-defined elements using pyimpspec's API. +- Added setting for specifying how many parallel processes to use when, e.g., fitting circuits. +- Updated to use version 4.0.0 of pyimpspec. +- Updated project file and configuration file structures. +- Updated DRTSettings implementation regarding the settings for the m(RQ)fit method. +- Updated how "save as" works for projects (a new project tab is created alongside the original project tab after saving). +- Updated how result labels are displayed in their respective drop-down lists. +- Updated labels on plot axes, table headers, etc. +- Updated the layout of the window for averaging data sets. +- Updated tooltips. +- Updated the `Subtract impedances` function to result in the creation of a new data set instead of replacing the existing data set. +- Updated the keybindings in most of the modal windows to be based on similar keybindings that are available in other contexts (i.e., these keybindings are also affected by changes made by the user). +- Switched to Sphinx for documentation. + + +# 3.4.3 (2022/12/14) - Updated dependency versions. - Fixed a bug that caused `utility.format_number` to produce results with two exponents when given certain inputs. -# 3.4.2 +# 3.4.2 (2022/11/28) - Updated documentation. -# 3.4.1 +# 3.4.1 (2022/11/26) - Updated minimum version for pyimpspec. -# 3.4.0 +# 3.4.0 (2022/11/26) - Added labels above the circuit previes in the `Fitting` and `Simulation` tabs to clarify that those correspond to the circuits used in the chosen result rather than what is specified in the settings on the left-hand side. - Added button that opens URL to tutorials. @@ -25,7 +54,7 @@ - Fixed a bug that caused DRT results to not always load properly. -# 3.3.0 +# 3.3.0 (2022/11/22) - Added clickable hyperlinks to the `About` window. - Added the ability to subtract the impedance of a fitted circuit in the `Subtract impedance` window by choosing an existing fit result. @@ -35,7 +64,7 @@ - Fixed the BHT method (DRT analysis) so that it works properly when `num_procs` is set to greater than one and NumPy is using OpenBLAS. -# 3.2.0 +# 3.2.0 (2022/11/01) - Added support for calculating the distribution of relaxation times using the `m(RQ)fit` method. - Added support (including a keybinding) for loading a simulated spectrum as a data set. @@ -49,13 +78,13 @@ - Fixed a bug that was triggered by having too few unmasked data points when performing Kramers-Kronig tests. -# 3.1.3 +# 3.1.3 (2022/09/21) - Fixed bugs that caused the toggling of a plottable series (e.g., a data set or a Kramers-Kronig test result) in the `Plotting` tab to apply the change to the wrong plot under certain circumstances. - Fixed bugs that caused a failure to properly adjust the axis limits in cases where the difference between the maximum and minimum values being plotted was zero or all values were zero. -# 3.1.2 +# 3.1.2 (2022/09/15) - Added the 3-sigma CI series to the legends of DRT plots. - Updated the order that the mean and 3-sigma CI series are plotted in DRT plots. @@ -65,12 +94,12 @@ - Updated labels in the `DRT plots` section of the appearance settings window. -# 3.1.1 +# 3.1.1 (2022/09/13) - Updated API documentation. -# 3.1.0 +# 3.1.0 (2022/09/11) - Added the ability to copy circuit diagrams to the clipboard as SVG from the `Fitting` and `Simulation` tabs. - Updated to use version 3.1.0 of `pyimpspec`, which resulted in the following changes: @@ -87,7 +116,7 @@ - Pinned Dear PyGui at version 1.6.2 until the automatic adjustment of axis limits in plots can be made to work properly with version 1.7.0. -# 3.0.0 +# 3.0.0 (2022/09/05) **Breaking changes in the API!** @@ -130,7 +159,7 @@ - Fixed bugs that caused (un)selecting groups of items in the `Plotting` tab to not work properly. -# 2.2.0 +# 2.2.0 (2022/08/10) - Added `num_per_decade` argument to the `deareis.mpl.plot_fit` function. - Added sorting of elements to the `to_dataframe` methods in the `FitResult` and `SimulationResult` classes. @@ -140,7 +169,7 @@ - Removed `tabulate` as explicit dependency since it was added as an explicit dependency to `pyimpspec`. -# 2.1.0 +# 2.1.0 (2022/08/04) - Added a setting for the interval for saving automatic backups to the `Settings - defaults` window. - Added a changelog window that is shown automatically when DearEIS has been updated. @@ -152,13 +181,13 @@ - Refactored code. -# 2.0.1 +# 2.0.1 (2022/08/01) - Added GitHub Actions workflow for testing the package (API only) on Linux (Ubuntu), MacOS, and Windows. - Fixed issues that prevented tests from passing. -# 2.0.0 +# 2.0.0 (2022/07/31) - Added a window for exporting plots using matplotlib. - Testing showed that attempting to free the memory allocated to the plot previews caused DearEIS to always crash on one of the computers used for testing. @@ -189,18 +218,18 @@ - Refactored code and removed deprecated code. -# 1.1.0 +# 1.1.0 (2022/07/13) - Added support for `.dfr` data format. -# 1.0.2 +# 1.0.2 (2022/07/09) - Updated classifiers in `setup.py`. - Fixed a bug that caused an error when deleting any data set. -# 1.0.1 +# 1.0.1 (2022/07/05) - Added an Inno Setup Script for producing an installer for Windows. - Updated About window. @@ -210,7 +239,7 @@ - Refactored code. -# 1.0.0 +# 1.0.0 (2022/06/16) - Rewrote large parts of the program. - Added the ability to create a project by merging two or more existing projects. @@ -222,7 +251,7 @@ - Various bug fixes. -# 0.3.0 +# 0.3.0 (2022/04/04) - Added a `Plotting` tab that can be used to plot multiple data sets, test results, fit results, and simulation results in a single figure. Currently supports Nyquist, Bode (magnitude), and Bode (phase) plot types. - Added a setting for specifying the number of points per decade to use when drawing simulated responses as lines. @@ -247,14 +276,14 @@ - Fixed a bug that allowed specifying an invalid number of RC circuits for the Kramers-Kronig test. -# 0.2.1 +# 0.2.1 (2022/03/28) - Fixed the handling of unsupported file formats when loading data sets. - Fixed erroneous extension on state file for storing recently opened projects. - Fixed the effect of editing notes on whether or not a project is dirty. - Fixed a bug that caused an exception just before the program terminates. -# 0.2.0 +# 0.2.0 (2022/03/28) - Added a confirmation window when possibly overwriting a file while saving a project under a new name. - Added file extension filters to the file dialog when loading data sets. @@ -263,21 +292,21 @@ - Fixed a bug that prevented saving a project that had previously been saved, then modified just prior to an abrupt termination of the program, and finally restored from the snapshot created before the program terminated. -# 0.1.3 +# 0.1.3 (2022/03/28) - Fixed a packaging bug that prevented the `console_script` entry point from working on Windows. -# 0.1.2 +# 0.1.2 (2022/03/28) - Fixed a packaging bug that prevented the `console_script` entry point from working on Windows. -# 0.1.1 +# 0.1.1 (2022/03/28) - Fixed a packaging bug that prevented third-party licenses from being included in the generated wheel. -# 0.1.0 +# 0.1.0 (2022/03/28) - Initial public beta release. diff --git a/COPYRIGHT b/COPYRIGHT index 5178fe8..889fda1 100644 --- a/COPYRIGHT +++ b/COPYRIGHT @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by diff --git a/DEV-LICENSES/LICENSE-build.txt b/DEV-LICENSES/LICENSE-build.txt new file mode 100644 index 0000000..c3713cd --- /dev/null +++ b/DEV-LICENSES/LICENSE-build.txt @@ -0,0 +1,20 @@ +Copyright © 2019 Filipe Laíns + +Permission is hereby granted, free of charge, to any person obtaining a +copy of this software and associated documentation files (the "Software"), +to deal in the Software without restriction, including without limitation +the rights to use, copy, modify, merge, publish, distribute, sublicense, +and/or sell copies of the Software, and to permit persons to whom the +Software is furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice (including the next +paragraph) shall be included in all copies or substantial portions of the +Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL +THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +DEALINGS IN THE SOFTWARE. diff --git a/DEV-LICENSES/LICENSE-flake8.txt b/DEV-LICENSES/LICENSE-flake8.txt new file mode 100644 index 0000000..e5e3d6f --- /dev/null +++ b/DEV-LICENSES/LICENSE-flake8.txt @@ -0,0 +1,22 @@ +== Flake8 License (MIT) == + +Copyright (C) 2011-2013 Tarek Ziade +Copyright (C) 2012-2016 Ian Cordasco + +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/DEV-LICENSES/LICENSE-setuptools.txt b/DEV-LICENSES/LICENSE-setuptools.txt new file mode 100644 index 0000000..353924b --- /dev/null +++ b/DEV-LICENSES/LICENSE-setuptools.txt @@ -0,0 +1,19 @@ +Copyright Jason R. Coombs + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to +deal in the Software without restriction, including without limitation the +rights to use, copy, modify, merge, publish, distribute, sublicense, and/or +sell copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS +IN THE SOFTWARE. diff --git a/DEV-LICENSES/LICENSE-sphinx-rtd-theme.txt b/DEV-LICENSES/LICENSE-sphinx-rtd-theme.txt new file mode 100644 index 0000000..211dd9c --- /dev/null +++ b/DEV-LICENSES/LICENSE-sphinx-rtd-theme.txt @@ -0,0 +1,20 @@ +The MIT License (MIT) + +Copyright (c) 2013-2018 Dave Snider, Read the Docs, Inc. & contributors + +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of +the Software, and to permit persons to whom the Software is furnished to do so, +subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS +FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR +COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/DEV-LICENSES/LICENSE-sphinx.txt b/DEV-LICENSES/LICENSE-sphinx.txt new file mode 100644 index 0000000..12779b2 --- /dev/null +++ b/DEV-LICENSES/LICENSE-sphinx.txt @@ -0,0 +1,67 @@ +License for Sphinx +================== + +Unless otherwise indicated, all code in the Sphinx project is licenced under the +two clause BSD licence below. + +Copyright (c) 2007-2023 by the Sphinx team (see AUTHORS file). +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + +* Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + +Licenses for incorporated software +================================== + +The included implementation of NumpyDocstring._parse_numpydoc_see_also_section +was derived from code under the following license: + +------------------------------------------------------------------------------- + +Copyright (C) 2008 Stefan van der Walt , Pauli Virtanen + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + + 1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in + the documentation and/or other materials provided with the + distribution. + +THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR +IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) +HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, +STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING +IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE. + +------------------------------------------------------------------------------- diff --git a/LICENSES/LICENSE-numdifftools.txt b/LICENSES/LICENSE-numdifftools.txt new file mode 100644 index 0000000..46f390f --- /dev/null +++ b/LICENSES/LICENSE-numdifftools.txt @@ -0,0 +1,27 @@ +Copyright (c) 2009-2022, Per A. Brodtkorb, John D'Errico +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of the copyright holders nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/LICENSES/LICENSE-statsmodels.txt b/LICENSES/LICENSE-statsmodels.txt new file mode 100644 index 0000000..47cd54e --- /dev/null +++ b/LICENSES/LICENSE-statsmodels.txt @@ -0,0 +1,34 @@ +Copyright (C) 2006, Jonathan E. Taylor +All rights reserved. + +Copyright (c) 2006-2008 Scipy Developers. +All rights reserved. + +Copyright (c) 2009-2018 statsmodels Developers. +All rights reserved. + + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + a. Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + b. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + c. Neither the name of statsmodels nor the names of its contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL STATSMODELS OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT +LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY +OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH +DAMAGE. diff --git a/LICENSES/README.md b/LICENSES/README.md index 9453d25..d9843c3 100644 --- a/LICENSES/README.md +++ b/LICENSES/README.md @@ -43,6 +43,11 @@ - License: custom license - Dependency via _pyimpspec_. +# numdifftools +- https://github.com/pbrod/numdifftools +- License: BSD 3-clause +- Dependency via _pyimpspec_. + # numpy - https://github.com/numpy/numpy - License: BSD 3-clause @@ -86,7 +91,12 @@ # SciPy - https://github.com/scipy/scipy - License: BSD 3-clause -- Dependency via _pyimpspec_. +- Dependency (primarily via _pyimpspec_). + +# statsmodels +- https://github.com/statsmodels/statsmodels +- License: BSD 3-clause +- Dependency (primarily via _pyimpspec_). # SymPy - https://github.com/sympy/sympy diff --git a/MANIFEST.in b/MANIFEST.in index 53af6b2..01e4015 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,8 +1,8 @@ -include COPYRIGHT -include CONTRIBUTORS -include LICENSE -include README.md -include LICENSES/README.md -include LICENSES/*.txt +include src/deareis/CHANGELOG.md +include src/deareis/CONTRIBUTORS +include src/deareis/COPYRIGHT +include src/deareis/LICENSE +include src/deareis/LICENSES/* +include src/deareis/README.md include src/deareis/gui/changelog/CHANGELOG.md include src/deareis/gui/licenses/*.txt diff --git a/README.md b/README.md index 231c7d9..b1a71f2 100644 --- a/README.md +++ b/README.md @@ -13,19 +13,6 @@ A GUI program for analyzing, simulating, and visualizing impedance spectra. ## Table of contents - [About](#about) -- [Getting started](#getting-started) - - [Requirements](#requirements) - - [Installing](#installing) - - [Running](#running) - - [Settings and keybindings](#settings-and-keybindings) -- [Features](#features) - - [Projects](#projects) - - [Data sets](#data-sets) - - [Data validation](#data-validation) - - [Distribution of relaxation times](#distribution-of-relaxation-times) - - [Circuit fitting and simulation](#circuit-fitting-and-simulation) - - [Visualization](#visualization) - - [Scripting](#scripting) - [Changelog](#changelog) - [Contributing](#contributing) - [License](#license) @@ -33,176 +20,25 @@ A GUI program for analyzing, simulating, and visualizing impedance spectra. ## About -DearEIS is a Python package that includes both a program with a graphical user interface (GUI) and an application programming interface (API) for working with impedance spectra. +DearEIS is a Python package that includes a program with a graphical user interface (GUI) for working with impedance spectra. +An application programming interface (API) is also included that is primarily for batch processing. The target audience is researchers who use electrochemical impedance spectroscopy (EIS) though the program may also be useful in educational settings. -The program implements: -- projects that can contain multiple experimental data sets -- reading experimental data from several different data formats -- validation of impedance spectra by checking if the data is Kramers-Kronig transformable -- construction of equivalent circuits either by parsing a circuit definition code or by using the included graphical editor -- equivalent circuit fitting -- simulation of impedance spectra -- composition of complex plots +The GUI program implements features such as: -GIFs showcasing parts of the GUI can be found [here](https://vyrjana.github.io/DearEIS/). -See the [Features](#features) section and [pyimpspec](https://github.com/vyrjana/pyimpspec) for more details about, e.g., supported data formats and implementation details. +- projects that can contain multiple experimental data sets and analysis results +- reading certain data formats and parsing the experimental data contained within +- validation of impedance spectra using linear Kramers-Kronig tests or the Z-HIT algorithm +- estimation of the distribution of relaxation times (DRT) +- construction of circuits, e.g., by parsing circuit description codes (CDC) or by using the included graphical editor +- support for user-defined circuit elements +- complex non-linear least squares fitting of equivalent circuits +- simulation of the impedance spectra of circuits +- visualization of impedance spectra and/or various analysis results -The API is built upon the API provided by [pyimpspec](https://github.com/vyrjana/pyimpspec) and can be used to, e.g., perform batch processing. -Documentation about the API can be found [here](https://vyrjana.github.io/DearEIS/api/). -[This Jupyter notebook](examples/examples.ipynb) contains some examples of how to use the API though the focus is on the additions available in the DearEIS API. -API documentation and examples for pyimpspec can be found [here](https://vyrjana.github.io/pyimpspec/api). +See the [official documentation](https://vyrjana.github.io/DearEIS/) for instructions on how to install DearEIS, screenshots and guides, and the API reference. -If you encounter issues, then please open an issue on [GitHub](https://github.com/vyrjana/DearEIS/issues). - - -## Getting started - -### Supported platforms - -- Linux -- Windows -- MacOS - -The package **may** also work on other platforms depending on whether or not those platforms are supported by DearEIS' [dependencies](setup.py). - - -### Requirements - -- [Python](https://www.python.org) -- The following Python packages - - [Dear PyGui](https://github.com/hoffstadt/DearPyGui): cross-platform GUI toolkit - - [pyimpspec](https://github.com/vyrjana/pyimpspec): data parsing, data validation, circuits, and fitting - - [requests](https://github.com/psf/requests): checking the latest version of DearEIS available on PyPI - - [xdg](https://github.com/srstevenson/xdg): XDG Base Directory Specification compliant paths - -These Python packages (and their dependencies) are installed automatically when DearEIS is installed using [pip](https://pip.pypa.io/en/stable/). - -The following Python packages can be installed as optional dependencies for additional functionality: - -- Alternatives to cvxopt (used by default by pyimpspec) in DRT calculations using the [TR-RBF method](https://doi.org/10.1016/j.electacta.2015.09.097) - - [kvxopt](https://github.com/sanurielf/kvxopt): convex optimization - - This fork of cvxopt may support additional platforms (e.g., Apple Silicon hardware like M1). - - [cvxpy](https://github.com/cvxpy/cvxpy): convex optimization - - **IMPORTANT!** Windows and MacOS users must follow the steps described in [the CVXPY documentation](https://www.cvxpy.org/install/index.html) before installing this optional dependency! - - -### Installing - -Make sure that a **recent version of Python (3.8+, 64-bit)** and pip are installed first and then type the following command in a terminal of your choice (e.g., PowerShell in Windows): - -``` -pip install deareis -``` - -**NOTE!** You may wish use the `--user` option when installing with pip if you are not using a virtual environment. -If you **only** intend to use DearEIS via the GUI, then you may wish to use, e.g., [pipx](https://pypa.github.io/pipx/) to install DearEIS inside of a virtual environment. - -If you wish to install the optional dependencies, then they must be specified explicitly when installing pyimpspec: - -``` -pip install deareis[cvxpy] -``` - -Alternatively, use the Windows installer available in the [releases section](https://github.com/vyrjana/DearEIS/releases). -The installer will take care of installing DearEIS using pip and then create shortcuts in the start menu. - -Newer versions of DearEIS can be installed at a later date by appending the `--upgrade` option to the command: - -``` -pip install --upgrade deareis -``` - - -### Running - -Once installed, DearEIS can be started, e.g., from a terminal or the Windows start menu by searching for the command `deareis`. -If the Windows installer was used, then there should be shortcuts in the start menu. -The program may also show up in some application launchers. -Check out [these tutorials](https://vyrjana.github.io/DearEIS/tutorials/) to find out how to use the program. - -There is also a `deareis-debug` command that prints additional information to the terminal and can be useful when troubleshooting issues. - - -### Settings and keybindings - -DearEIS has several user-configurable settings. -It is possible to configure the default values of the settings on the Kramers-Kronig, fitting, and simulation tabs as well as some aspects of the plots (e.g., colors and markers). -Several keybindings, which are also user-configurable, are supported for more keyboard-centric navigation although a mouse or trackpad is required in some circumstances. - - -## Features - -Below is a brief overview of the main features of DearEIS. -See the included tooltips and instructions in the program for more information. - - -### Projects - -DearEIS has a project-based workflow and multiple projects can be open at the same time. -Each project has a user-definable label and a section for keeping notes. -Multiple projects can also be merged to form a single project. - - -### Data sets - -The experimentally obtained impedance spectra are referred to as _data sets_. -Each project can contain multiple data sets. -Multiple noisy data sets can be averaged to produce a single data set. -Individual data points can be masked to exclude outliers or to focus on a part of the spectrum. -Corrections can be made by subtracting either a constant complex value, the impedance of an equivalent circuit, or another spectrum. -See [pyimpspec's](https://github.com/vyrjana/pyimpspec/) documentation for information about which data formats are currently supported. - - -### Data validation - -Data sets can be validated by checking if they are Kramers-Kronig transformable. -A few different implementations are included. - -See [pyimpspec](https://github.com/vyrjana/pyimpspec/) for more details regarding the implementation of the tests. - - -### Distribution of relaxation times - -The distribution of relaxation times can be calculated using a few different methods (TR-NNLS, TR-RBF, and BHT). - -See [pyimpspec](https://github.com/vyrjana/pyimpspec/) for more details regarding the implementation of the calculations. - - -### Circuit fitting and simulation - -Equivalent circuits can be constructed either by means of inputting a circuit description code (CDC) or by using the graphical, node-based circuit editor. -More information about the CDC syntax can be found in the program. -The circuits can be fitted to the experimental data to obtain values for the element parameters. -Initial values as well as upper and lower limits can be defined for each element parameter. -Element parameters can also be fixed at a constant value. -The impedance spectra produced by the circuits can also be simulated in a wide frequency range. -Various aspects of the circuits and the fitting results can be copied to the clipboard in different formats. -For example, a table of fitted element parameters can be obtained in the form of a Markdown or LaTeX table. -The mathematical expression for a circuit's impedance as a function of the applied frequency can also be obtained as, e.g., a SymPy expression. - - -### Visualization - -Data sets and their corresponding results (Kramers-Kronig tests and equivalent circuit fits) are visualized using simple Nyquist plots, Bode plots, and residual plots. -More complex plots containing multiple data sets, Kramers-Kronig test results, equivalent circuit fitting results, and/or simulation results can also be created. -These complex plots can be used to overlay and compare results. -The plots can also be exported using matplotlib to render them as either bitmap graphics or vector graphics. -However, they can also be used to compose plots that can be turned into publication-ready figures with the help of a Python script (see the [Scripting](#scripting) section for more details) or by copying the plot's data to another program. - - -### Scripting - -DearEIS projects can also be used in Python scripts for the purposes of batch processing results. -This capability could be used to: -- generate project files from large numbers of measurements in an automated fashion -- export the processed data to another format -- generate tables that can be included in a document written in, e.g., LaTeX or Markdown -- plot publication-ready figures using, e.g., matplotlib - -See [the Jupyter notebook](examples/examples.ipynb) for some examples. -Documentation about the API can be found [here](https://vyrjana.github.io/DearEIS/api/). -See [this other Jupyter notebook](https://github.com/vyrjana/pyimpspec/blob/main/examples/examples.ipynb) and [pyimpspec's API](https://vyrjana.github.io/pyimpspec/api/) for examples and documentation, respectively, regarding the API that DearEIS extends. +Those who would prefer to only use an API (or a command-line interface (CLI)) for everything may wish to use [pyimpspec](https://github.com/vyrjana/pyimpspec) instead. ## Changelog @@ -236,7 +72,7 @@ See [CONTRIBUTORS](CONTRIBUTORS) for a list of people who have contributed to th ## License -Copyright 2022 DearEIS developers +Copyright 2023 DearEIS developers DearEIS is licensed under the [GPLv3 or later](https://www.gnu.org/licenses/gpl-3.0.html). diff --git a/build.sh b/build.sh index 2142352..9b3f683 100644 --- a/build.sh +++ b/build.sh @@ -2,10 +2,73 @@ # Stop when a non-zero exit code is encountered set -e +docs_html(){ + sphinx-build "./docs/source" "./docs/build/html" +} + +docs_test(){ + sphinx-build -b doctest "./docs/source" "./docs/build/html" +} + +docs_latex(){ + sphinx-build -M latexpdf "./docs/source" "./docs/build/latex" +} + +validate_tar(){ + echo + echo "Listing changelogs and licenses that were bundled in *.tar.gz:" + # Check if the changelog was bundled properly + tar --list -f ./dist/*.tar.gz | grep "deareis/gui/changelog/CHANGELOG\.md" + # Check if the package license was included + tar --list -f ./dist/*.tar.gz | grep "LICENSE$" + # Check if the other licenses were bundled properly + tar --list -f ./dist/*.tar.gz | grep "deareis/gui/licenses/LICENSE-DearEIS.txt" + dist="$(tar --list -f ./dist/*.tar.gz | grep "deareis/gui/licenses/LICENSE-.*\.txt" | sort)" + repo="$(ls LICENSES | grep "LICENSE-.*.txt" | sort)" + python3 -c "from sys import argv; from os.path import basename; dist = set(list(map(basename, argv[1].split('\n')))); dist.remove('LICENSE-DearEIS.txt'); repo = set(list(map(basename, argv[2].split('\n')))); assert dist == repo, 'Incorrect set of bundled licenses! An extra .txt file has probably been left in the \'/src/deareis/gui/licenses\' folder.'; list(map(print, sorted(dist)))" "$dist" "$repo" +} + +validate_wheel(){ + echo + echo "Listing changelogs and licenses that were bundled in *.whl:" + # Check if the changelog was bundled properly + unzip -Z1 ./dist/*.whl | grep "deareis/gui/changelog/CHANGELOG\.md" + # Check if the package license was included + unzip -Z1 ./dist/*.whl | grep "LICENSE$" + # Check if the other licenses were bundled properly + unzip -Z1 ./dist/*.whl | grep "deareis/gui/licenses/LICENSE-DearEIS.txt" + dist="$(unzip -Z1 ./dist/*.whl | grep "deareis/gui/licenses/LICENSE-.*\.txt" | sort)" + repo="$(ls LICENSES | grep "LICENSE-.*.txt" | sort)" + python3 -c "from sys import argv; from os.path import basename; dist = set(list(map(basename, argv[1].split('\n')))); dist.remove('LICENSE-DearEIS.txt'); repo = set(list(map(basename, argv[2].split('\n')))); assert dist == repo, 'Incorrect set of bundled licenses! An extra .txt file has probably been left in the \'/src/deareis/gui/licenses\' folder.'; list(map(print, sorted(dist)))" "$dist" "$repo" +} + +if [ "$1" == "docs" ]; then + docs_html + docs_test + docs_latex + exit +fi +if [ "$1" == "docs-html" ]; then + docs_html + exit +fi +if [ "$1" == "docs-test" ]; then + docs_test + exit +fi +if [ "$1" == "docs-latex" ]; then + docs_latex + exit +fi + + # Check for uncommitted changes and untracked files if [ "$(git status --porcelain=v1 | wc -l)" -ne 0 ]; then echo "Detected uncommitted changes and/or untracked files!" - exit + if ! [ "$1" == "override" ]; then + echo "Continue with the build process anyway by providing 'override' as an argument to this script." + exit + fi fi # Check for major issues @@ -13,42 +76,28 @@ flake8 . --select=E9,F63,F7,F82 --show-source --statistics echo "flake8 didn't find any issues..." echo +# Update non-source code files that should be included +python3 ./pre-build.py + # Build wheel python3 -m build # Validate the source and wheel distributions -echo -echo "Listing changelogs and licenses that were bundled in *.tar.gz:" -# Check if the changelog was bundled properly -tar --list -f ./dist/*.tar.gz | grep "deareis/gui/changelog/CHANGELOG\.md" -# Check if the package license was included -tar --list -f ./dist/*.tar.gz | grep "LICENSE$" -# Check if the other licenses were bundled properly -tar --list -f ./dist/*.tar.gz | grep "deareis/gui/licenses/LICENSE-DearEIS.txt" -dist="$(tar --list -f ./dist/*.tar.gz | grep "deareis/gui/licenses/LICENSE-.*\.txt" | sort)" -repo="$(ls LICENSES | grep "LICENSE-.*.txt" | sort)" -python3 -c "from sys import argv; from os.path import basename; dist = list(map(basename, argv[1].split('\n'))); dist.remove('LICENSE-DearEIS.txt'); repo = list(map(basename, argv[2].split('\n'))); assert dist == repo, 'Incorrect set of bundled licenses! An extra .txt file has probably been left in the \'/src/deareis/gui/licenses\' folder.'; list(map(print, dist))" "$dist" "$repo" - -# Validate the source and wheel distributions -echo -echo "Listing changelogs and licenses that were bundled in *.whl:" -# Check if the changelog was bundled properly -unzip -Z1 ./dist/*.whl | grep "deareis/gui/changelog/CHANGELOG\.md" -# Check if the package license was included -unzip -Z1 ./dist/*.whl | grep "LICENSE$" -# Check if the other licenses were bundled properly -unzip -Z1 ./dist/*.whl | grep "deareis/gui/licenses/LICENSE-DearEIS.txt" -dist="$(unzip -Z1 ./dist/*.whl | grep "deareis/gui/licenses/LICENSE-.*\.txt" | sort)" -repo="$(ls LICENSES | grep "LICENSE-.*.txt" | sort)" -python3 -c "from sys import argv; from os.path import basename; dist = list(map(basename, argv[1].split('\n'))); dist.remove('LICENSE-DearEIS.txt'); repo = list(map(basename, argv[2].split('\n'))); assert dist == repo, 'Incorrect set of bundled licenses! An extra .txt file has probably been left in the \'/src/deareis/gui/licenses\' folder.'; list(map(print, dist))" "$dist" "$repo" +validate_tar +validate_wheel # Update documentation +# - The contents of ./docs/build/html should be committed to the gh-pages branch +# - ./docs/build/latex/latex/deareis.pdf should be uploaded as an attachment to a release echo -echo "Generating API documentation..." -# The package at https://github.com/vyrjana/python-api-documenter is required for -# generating the API documentation. -# The "documentation" folder should be copied to the gh-pages branch in the end. -python3 ./docs/generate-api-reference.py +echo "Generating documentation..." +# Generate HTML, run tests, and finally generate PDF +docs_html +docs_test +docs_latex + +# Copy documentation assets +python3 ./post-build.py # Everything should be okay echo diff --git a/dev-requirements.txt b/dev-requirements.txt index 332171a..ebcda69 100644 --- a/dev-requirements.txt +++ b/dev-requirements.txt @@ -1,3 +1,5 @@ -flake8 -setuptools -build \ No newline at end of file +build~=0.10 +flake8~=6.0 +setuptools~=67.2 +sphinx~=5.3 +sphinx-rtd-theme~=1.2 \ No newline at end of file diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d0c3cbf --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/README.md b/docs/README.md deleted file mode 100644 index 9e2ff2f..0000000 --- a/docs/README.md +++ /dev/null @@ -1,23 +0,0 @@ -# Documentation - -## API documentation - -### How to generate - -This folder contains a script (`generate-api-reference.py`) for generating Markdown files containing the API reference that is hosted as part of the project's GitHub pages site. -The script can be run independently but it is also executed by `build.sh`, which can be found in the repository root directory. -The generated Markdown files should be copied to the `documentation` folder found in the `gh-pages` branch. -The recommended setup is to: - -- Clone the project repository as a separate local repository dedicated to just working with the `gh-pages` branch. -- Symlink the `documentation` directory that exists in the repository dedicated to the `gh-pages` branch to this directory. - -The generated Markdown files will then automatically be placed in the repository dedicated to the `gh-pages` branch where they can then be committed and pushed to GitHub. - -### Dependencies - -The script shares code with the [pyimpspec](https://github.com/vyrjana/pyimpspec) project, which also has a script for generating API documentation in the same way. -The shared code is contained in [this repository](https://github.com/vyrjana/python-api-documenter), which has been added to this repository as a submodule. -The submodule can be updated with the following command when new commits are pushed to the shared repository: - -`git submodule update --remote --merge` diff --git a/docs/api_documenter b/docs/api_documenter deleted file mode 160000 index 996ffda..0000000 --- a/docs/api_documenter +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 996ffda4df25642f74c3e70f5eb4c16cc70e2641 diff --git a/docs/generate-api-reference.py b/docs/generate-api-reference.py deleted file mode 100644 index 5f2a9d4..0000000 --- a/docs/generate-api-reference.py +++ /dev/null @@ -1,353 +0,0 @@ -#!/usr/bin/env python3 -from os import makedirs -from os.path import ( - dirname, - exists, - join, -) -from typing import IO -import deareis - -# Import github.com/vyrjana/python-api-documenter, which has been added as a submodule -import sys - -submodule_path: str = join(dirname(__file__), "api_documenter", "src") -assert exists(submodule_path), submodule_path -sys.path.append(submodule_path) -from api_documenter import ( - process, - process_classes, - process_functions, -) - - -def write_file(path: str, content: str): - fp: IO - with open(path, "w") as fp: - fp.write(content) - - -def jekyll_header(title: str, link: str) -> str: - return f"""--- -layout: documentation -title: API - {title} -permalink: /api/{link}/ ---- -""" - - -if __name__ == "__main__": - version: str = "" - with open(join(dirname(dirname(__file__)), "version.txt"), "r") as fp: - version = fp.read().strip() - assert version.strip() != "" - output_dir: str = dirname(__file__) - root_folder: str = join(output_dir, "documentation") - if not exists(root_folder): - makedirs(root_folder) - multiprocessing_disclaimer: str = """ -**NOTE!** The API makes use of multiple processes where possible to perform tasks in parallel. Functions that implement this parallelization have a `num_procs` keyword argument that can be used to override the maximum number of processes allowed. Using this keyword argument should not be necessary for most users under most circumstances. - -If NumPy is linked against a multithreaded linear algebra library like OpenBLAS or MKL, then this may in some circumstances result in unusually poor performance despite heavy CPU utilization. It may be possible to remedy the issue by specifying a lower number of processes via the `num_procs` keyword argument and/or limiting the number of threads that, e.g., OpenBLAS should use by setting the appropriate environment variable (e.g., `OPENBLAS_NUM_THREADS`). Again, this should not be necessary for most users and reporting this as an issue to the pyimpspec or DearEIS repository on GitHub would be preferred. -""" - # Markdown - write_file( - join(root_folder, "API.md"), - process( - title=f"DearEIS - API reference ({version})", - description=f""" -DearEIS is built on top of the pyimpspec package. -See the [API reference for pyimpspec](https://vyrjana.github.io/pyimpspec/api/) for information more information about classes and functions that are provided by that package and referenced below (e.g. the `Circuit` class). -The API of DearEIS can be used for automatic some tasks (e.g., batch importing data or batch exporting plots). -However, the pyimpspec API may be a bit easier to use if you just want to have an API to use in Python scripts or Jupyter notebooks. -Primarily because the DearEIS API uses settings objects (e.g., `DRTSettings` that can be (de)serialized easily) instead of keyword arguments in the function signatures. - -{multiprocessing_disclaimer} - """.strip(), - modules_to_document=[ - deareis, - deareis.mpl, - ], - minimal_classes=[ - # Connections - deareis.Parallel, - deareis.Series, - # Elements - deareis.Capacitor, - deareis.ConstantPhaseElement, - deareis.Gerischer, - deareis.HavriliakNegami, - deareis.HavriliakNegamiAlternative, - deareis.Inductor, - deareis.ModifiedInductor, - deareis.Resistor, - deareis.Warburg, - deareis.WarburgOpen, - deareis.WarburgShort, - deareis.DeLevieFiniteLength, - # Exceptions - deareis.DRTError, - deareis.FittingError, - deareis.ParsingError, - deareis.UnexpectedCharacter, - ], - objects_to_ignore=[ - deareis.Project.parse, - deareis.Project.update, - ], - latex_pagebreak=False, - ), - ) - # Jekyll - root_url: str = "https://vyrjana.github.io/DearEIS/api" - # - index - write_file( - join(root_folder, "index.md"), - f"""--- -layout: documentation -title: API documentation -permalink: /api/ ---- - -## API documentation - -Check out [this Jupyter notebook](https://github.com/vyrjana/DearEIS/blob/main/examples/examples.ipynb) for examples of how to use the API. -A single Markdown file of the API reference is available [here](https://raw.githubusercontent.com/vyrjana/DearEIS/gh-pages/documentation/API.md). -The [pyimpspec API](https://vyrjana.github.io/pyimpspec/api/) may be a bit easier to use if you just want to have an API to use in Python scripts or Jupyter notebooks. -Primarily because the DearEIS API uses settings objects (e.g., `DRTSettings` that can be (de)serialized easily) instead of keyword arguments in the function signatures. - -- [Project]({root_url}/project) -- [Data set]({root_url}/data-set) -- [Kramers-Kronig testing]({root_url}/kramers-kronig) -- [Distribution of relaxation times]({root_url}/drt) -- [Circuit]({root_url}/circuit) -- [Elements]({root_url}/elements) -- [Fitting]({root_url}/fitting) -- [Simulating]({root_url}/simulating) -- [Plotting]({root_url}/plotting) - - [matplotlib]({root_url}/plot-mpl) - -The DearEIS API is built upon the [pyimpspec](https://vyrjana.github.io/pyimpspec) package. - -{multiprocessing_disclaimer} - -""", - ) - # Project - write_file( - join(root_folder, "project.md"), - jekyll_header("project", "project") - + """ -`Project` objects can be created via the API for, e.g., the purposes of batch processing multiple experimental data files rather than manually loading files via the GUI program. -`Project` objects can also be used to, e.g., perform statistical analysis on multiple equivalent circuit fitting result and then generate a Markdown/LaTeX table. - -""" - + process_classes( - classes_to_document=[ - deareis.Project, - ], - objects_to_ignore=[ - deareis.Project.parse, - deareis.Project.update, - ], - module_name="deareis", - ), - ) - # Data sets - write_file( - join(root_folder, "data-set.md"), - jekyll_header("data set", "data-set") - + process( - title="", - modules_to_document=[ - deareis.api.data, - ], - minimal_classes=[ - deareis.UnsupportedFileFormat, - ], - description=""" -The `DataSet` class in the DearEIS API differs slightly from the base class found in the pyimpspec API. -The `parse_data` function is a wrapper for the corresponding function in pyimpspec's API with the only difference being that the returned `DataSet` instances are the variant used by DearEIS. - """, - ), - ) - # Kramers-Kronig results - write_file( - join(root_folder, "kramers-kronig.md"), - jekyll_header("Kramers-Kronig testing", "kramers-kronig") - + process( - title="", - modules_to_document=[ - deareis.api.kramers_kronig, - ], - objects_to_ignore=[ - deareis.DataSet, - ], - description="", - ), - ) - # Circuit - write_file( - join(root_folder, "circuit.md"), - jekyll_header("Circuit", "circuit") - + process( - title="", - modules_to_document=[ - deareis.api.circuit, - ], - minimal_classes=[ - deareis.Parallel, - deareis.Series, - deareis.ParsingError, - deareis.UnexpectedCharacter, - ], - objects_to_ignore=[ - deareis.get_elements, - deareis.Element, - ] - + list(deareis.get_elements().values()), - description=""" -Circuits can be generated in one of two ways: -- by parsing a circuit description code (CDC) -- by using the `CircuitBuilder` class - -The basic syntax for CDCs is fairly straighforward: - -```python -# A resistor connected in series with a resistor and a capacitor connected in parallel -circuit: deareis.Circuit = deareis.parse_cdc("[R(RC)]") -``` - -An extended syntax, which allows for defining initial values, lower/upper limits, and labels, is also supported: - -```python -circuit: deareis.Circuit = deareis.parse_cdc("[R{R=50:sol}(R{R=250f:ct}C{C=1.5e-6/1e-6/2e-6:dl})]") -``` - -Alternatively, the `CircuitBuilder` class can be used: - -```python -with deareis.CircuitBuilder() as builder: - builder += ( - deareis.Resistor(R=50) - .set_label("sol") - ) - with builder.parallel() as parallel: - parallel += ( - deareis.Resistor(R=250) - .set_fixed("R", True) - ) - parallel += ( - deareis.Capacitor(C=1.5e-6) - .set_label("dl") - .set_lower_limit("C", 1e-6) - .set_upper_limit("C", 2e-6) - ) -circuit: deareis.Circuit = builder.to_circuit() -``` - -""" - + f"Information about the supported circuit elements can be found [here]({root_url}/elements).\n\n", - ), - ) - # Elements - write_file( - join(root_folder, "elements.md"), - jekyll_header("Elements", "elements") - + process( - title="", - modules_to_document=[ - deareis.api.circuit, - ], - minimal_classes=list(deareis.get_elements().values()), - objects_to_ignore=[ - deareis.Circuit, - deareis.CircuitBuilder, - deareis.Connection, - deareis.Parallel, - deareis.Series, - deareis.ParsingError, - deareis.UnexpectedCharacter, - deareis.parse_cdc, - ], - description="", - ), - ) - # Fitting - write_file( - join(root_folder, "fitting.md"), - jekyll_header("fitting", "fitting") - + process( - title="", - modules_to_document=[ - deareis.api.fitting, - ], - objects_to_ignore=[ - deareis.Circuit, - deareis.DataSet, - ], - minimal_classes=[ - deareis.FittingError, - ], - description="", - ), - ) - # Simulating - write_file( - join(root_folder, "simulating.md"), - jekyll_header("simulating", "simulating") - + process( - title="", - modules_to_document=[ - deareis.api.simulation, - ], - description="", - ), - ) - # Plots - write_file( - join(root_folder, "plotting.md"), - jekyll_header("plotting", "plotting") - + process( - title="", - modules_to_document=[ - deareis.api.plotting, - ], - minimal_classes=[ - deareis.PlotType, - ], - description="", - ), - ) - # DRT results - write_file( - join(root_folder, "drt.md"), - jekyll_header("drt", "drt") - + process( - title="", - modules_to_document=[ - deareis.api.drt, - ], - objects_to_ignore=[ - deareis.DataSet, - ], - minimal_classes=[ - deareis.DRTError, - ], - description="", - ), - ) - # Plotting - matplotlib - write_file( - join(root_folder, "plot-mpl.md"), - jekyll_header("plotting - matplotlib", "plot-mpl") - + process( - title="", - modules_to_document=[ - deareis.mpl, - ], - description=""" -These functions are for basic visualization of various objects (e.g., `DataSet`, `TestResult`, and `FitResult`) using the [matplotlib](https://matplotlib.org/) package. - """, - ), - ) diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..dc1312a --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/source/apidocs.rst b/docs/source/apidocs.rst new file mode 100644 index 0000000..ea0229d --- /dev/null +++ b/docs/source/apidocs.rst @@ -0,0 +1,58 @@ +.. include:: ./substitutions.rst + +API Documentation +================= + +DearEIS includes an API that is primarily intended for batch processing (e.g., importing data into a project or exporting results/plots/tables). + +.. doctest:: + + >>> import deareis # The main functions, classes, etc. + >>> from deareis import mpl # Plotting functions based on matplotlib. + + +.. warning:: + + DearEIS provides wrapper functions for some of pyimpspec's functions since DearEIS uses classes that store the relevant arguments/settings in a way that can be serialized and deserialized as part of project files. + Consequently, the API might feel somewhat cumbersome to use for some tasks where these settings classes must be instantiated and using pyimpspec directly might be more convenient. + + DearEIS also implements subclasses or entirely new classes to contain some of the information that pyimpspec's various result classes would contain. + This has been done so that various results can be serialized and deserialized as part of project files. + + However, DearEIS' classes can in several cases be used directly with functions from pyimpspec (e.g., the various plotting functions) since pyimpspec checks for the presence of attributes and/or methods with specific names rather than whether or not an object is an instance of some class. + +.. note:: + + The API makes use of multiple processes where possible to perform tasks in parallel. + Functions that implement this parallelization have a ``num_procs`` keyword argument that can be used to override the maximum number of processes allowed. + Using this keyword argument should not be necessary for most users under most circumstances. + Call the |get_default_num_procs| function to get the automatically determined value for your system. + There is also a |set_default_num_procs| function that can be used to set a global override rather than using the ``num_procs`` keyword argument when calling various functions. + + If NumPy is linked against a multithreaded linear algebra library like OpenBLAS or MKL, then this may in some circumstances result in unusually poor performance despite heavy CPU utilization. + It may be possible to remedy the issue by specifying a lower number of processes via the ``num_procs`` keyword argument and/or limiting the number of threads that, e.g., OpenBLAS should use by setting the appropriate environment variable (e.g., ``OPENBLAS_NUM_THREADS``). + Again, this should not be necessary for most users and reporting this as an issue to the DearEIS or pyimpspec repository on GitHub would be preferred. + + +.. automodule:: deareis + :members: get_default_num_procs, set_default_num_procs + + +.. raw:: latex + + \clearpage + +.. toctree:: + :maxdepth: 1 + :caption: Contents: + + apidocs_data + apidocs_project + apidocs_kramers_kronig + apidocs_zhit + apidocs_drt + apidocs_circuit + apidocs_fitting + apidocs_plot_mpl + apidocs_typing + apidocs_exceptions diff --git a/docs/source/apidocs_circuit.rst b/docs/source/apidocs_circuit.rst new file mode 100644 index 0000000..6e5eda2 --- /dev/null +++ b/docs/source/apidocs_circuit.rst @@ -0,0 +1,48 @@ +.. include:: ./substitutions.rst + +Equivalent circuits +=================== + +Functions +--------- +.. automodule:: deareis + :members: parse_cdc, register_element + + +Base classes +------------ +.. automodule:: deareis + :members: Element, Container, Connection + + +Connection classes +------------------ +.. automodule:: deareis + :members: Series, Parallel + + +Circuit classes +--------------- +.. automodule:: deareis + :members: Circuit + +.. automodule:: deareis + :members: CircuitBuilder + + +Element classes +--------------- +.. automodule:: deareis.api.circuit.elements + :members: + :imported-members: + + +Element registration classes +---------------------------- +.. automodule:: deareis + :members: ElementDefinition, ContainerDefinition, ParameterDefinition, SubcircuitDefinition + + +.. raw:: latex + + \clearpage diff --git a/docs/source/apidocs_data.rst b/docs/source/apidocs_data.rst new file mode 100644 index 0000000..c02f422 --- /dev/null +++ b/docs/source/apidocs_data.rst @@ -0,0 +1,15 @@ +.. include:: ./substitutions.rst + +Data parsing +============ + +.. automodule:: deareis + :members: parse_data + + +.. autoclass:: deareis.DataSet + :inherited-members: + +.. raw:: latex + + \clearpage diff --git a/docs/source/apidocs_drt.rst b/docs/source/apidocs_drt.rst new file mode 100644 index 0000000..5e587f3 --- /dev/null +++ b/docs/source/apidocs_drt.rst @@ -0,0 +1,22 @@ +.. include:: ./substitutions.rst + +Distribution of relaxation times analysis +========================================= + +.. automodule:: deareis + :members: calculate_drt + + +Classes +------- +.. automodule:: deareis + :members: DRTResult, DRTSettings + +Enums +----- +.. automodule:: deareis + :members: DRTMethod, DRTMode, RBFShape, RBFType + +.. raw:: latex + + \clearpage diff --git a/docs/source/apidocs_exceptions.rst b/docs/source/apidocs_exceptions.rst new file mode 100644 index 0000000..b940f60 --- /dev/null +++ b/docs/source/apidocs_exceptions.rst @@ -0,0 +1,13 @@ +.. include:: ./substitutions.rst + +Exceptions +========== + +.. automodule:: deareis.exceptions + :members: + :imported-members: + +.. raw:: latex + + \clearpage + diff --git a/docs/source/apidocs_fitting.rst b/docs/source/apidocs_fitting.rst new file mode 100644 index 0000000..bd0511a --- /dev/null +++ b/docs/source/apidocs_fitting.rst @@ -0,0 +1,22 @@ +.. include:: ./substitutions.rst + +Circuit fitting +=============== + +.. automodule:: deareis + :members: fit_circuit + + +Classes +------- +.. automodule:: deareis + :members: FitResult, FitSettings + +Enums +----- +.. automodule:: deareis + :members: CNLSMethod, Weight + +.. raw:: latex + + \clearpage diff --git a/docs/source/apidocs_kramers_kronig.rst b/docs/source/apidocs_kramers_kronig.rst new file mode 100644 index 0000000..6539428 --- /dev/null +++ b/docs/source/apidocs_kramers_kronig.rst @@ -0,0 +1,22 @@ +.. include:: ./substitutions.rst + +Kramers-Kronig testing +====================== + +.. automodule:: deareis + :members: perform_test, perform_exploratory_tests + + +Classes +------- +.. automodule:: deareis + :members: TestResult, TestSettings + +Enums +----- +.. automodule:: deareis + :members: CNLSMethod, TestMode, Test + +.. raw:: latex + + \clearpage diff --git a/docs/source/apidocs_plot_mpl.rst b/docs/source/apidocs_plot_mpl.rst new file mode 100644 index 0000000..b69fada --- /dev/null +++ b/docs/source/apidocs_plot_mpl.rst @@ -0,0 +1,340 @@ +.. include:: ./substitutions.rst + +Plotting - matplotlib +===================== + +Wrappers +-------- + +These functions provide a high-level API for visualizing various objects/results (e.g., :class:`~deareis.DataSet`). +Most of them are the same functions included in pyimpspec_ with the notable exception of :func:`~deareis.mpl.plot`. + +.. automodule:: deareis.mpl + :members: plot, plot_circuit, plot_data, plot_drt, plot_fit, plot_tests + + + +Primitives +---------- + +These functions are used by the wrapper functions to make a more complex figure with multiple subplots. +These are all the same as those included in pyimpspec_. + +.. automodule:: deareis.mpl + :members: plot_bht_scores, plot_bode, plot_complex, plot_gamma, plot_imaginary, plot_magnitude, plot_mu_xps, plot_nyquist, plot_phase, plot_real, plot_residuals + + +Examples +-------- + +Below are some examples of plots created using the plotting functions listed above. +The circuit and the data set are based on test circuit 1 (TC-1) from this 1995 article by `Bernard Boukamp`_. + +Legends are disabled and colored axes are used instead in the figures generated by the wrapper functions (i.e., the first figure in each series). +This has been done due to the small size of the figures in this documentation. +However, the figures generated by the individual primitive functions are also included with the legends enabled and without colored axes. + +The default color scheme is based on the *Vibrant qualitative* color scheme presented in `Paul Tol`_'s blog. +Colors and markers can be defined when calling any of the functions. + +.. _Bernard Boukamp: https://doi.org/10.1149/1.2044210 +.. _Paul Tol: https://personal.sron.nl/~pault/ + + +plot +~~~~ +:func:`~deareis.mpl.plot` + +.. plot:: + + from deareis import Project + from deareis import mpl + project = Project.from_file("../../tests/example-project-v5.json") + for plot in project.get_plots(): + if "DRT" not in plot.get_label(): + continue + figure, axes = mpl.plot(plot, project) + break + +.. raw:: latex + + \clearpage + + +plot_circuit +~~~~~~~~~~~~ +:func:`~deareis.mpl.plot_circuit` + +* :func:`~deareis.mpl.plot_nyquist` +* :func:`~deareis.mpl.plot_bode` + +.. plot:: + + from deareis import mpl + import pyimpspec + from pyimpspec.mock_data import EXAMPLE, TC1 + import matplotlib.pyplot as plt + from numpy import logspace, log10 as log + + f = EXAMPLE.get_frequencies() + figure, axes = mpl.plot_circuit(TC1, frequencies=f, label="TC-1", title="", legend=False, colored_axes=True) + figure.tight_layout() + plt.show() + + data = pyimpspec.simulate_spectrum( + TC1, + logspace( + log(max(f)), + log(min(f)), + num=int(log(max(f)) - log(min(f))) * 100 + 1, + ), + label="TC-1", + ) + figure, axes = mpl.plot_nyquist(data, line=True) + figure.tight_layout() + plt.show() + + figure, axes = mpl.plot_bode(data, line=True) + figure.tight_layout() + plt.show() + +.. raw:: latex + + \clearpage + + +plot_data +~~~~~~~~~ +:func:`~deareis.mpl.plot_data` + +* :func:`~deareis.mpl.plot_nyquist` +* :func:`~deareis.mpl.plot_bode` + +.. plot:: + + from deareis import mpl + from pyimpspec.mock_data import EXAMPLE + import pyimpspec.plot.colors as colors + import matplotlib.pyplot as plt + + figure, axes = mpl.plot_data(EXAMPLE, legend=False, colored_axes=True) + figure.tight_layout() + plt.show() + + figure, axes = mpl.plot_nyquist(EXAMPLE) + figure.tight_layout() + plt.show() + + figure, axes = mpl.plot_bode(EXAMPLE) + figure.tight_layout() + plt.show() + +.. raw:: latex + + \clearpage + + +plot_drt +~~~~~~~~ +:func:`~deareis.mpl.plot_drt` + +* :func:`~deareis.mpl.plot_complex` +* :func:`~deareis.mpl.plot_gamma` +* :func:`~deareis.mpl.plot_residuals` + +.. plot:: + + from deareis import mpl + import pyimpspec + from pyimpspec.mock_data import EXAMPLE + import pyimpspec.plot.colors as colors + import matplotlib.pyplot as plt + + drt = pyimpspec.calculate_drt_tr_nnls(EXAMPLE) + figure, axes = mpl.plot_drt( + drt, + EXAMPLE, + legend=False, + colored_axes=True, + ) + figure.tight_layout() + plt.show() + + figure, axes = mpl.plot_complex( + EXAMPLE, + colors={ + "real": colors.COLOR_BLACK, + "imaginary": colors.COLOR_BLACK, + }, + legend=False, + ) + _ = mpl.plot_complex( + drt, + line=True, + figure=figure, + axes=axes, + ) + figure.tight_layout() + plt.show() + + figure, axes = mpl.plot_gamma(drt) + figure.tight_layout() + plt.show() + + + figure, axes = mpl.plot_residuals(drt) + figure.tight_layout() + plt.show() + +.. raw:: latex + + \clearpage + + +plot_fit +~~~~~~~~ +:func:`~deareis.mpl.plot_fit` + +* :func:`~deareis.mpl.plot_nyquist` +* :func:`~deareis.mpl.plot_bode` +* :func:`~deareis.mpl.plot_residuals` + +.. plot:: + + from deareis import mpl + import pyimpspec + from pyimpspec.mock_data import EXAMPLE + import pyimpspec.plot.colors as colors + import matplotlib.pyplot as plt + + circuit = pyimpspec.parse_cdc("R(RC)(RW)") + fit = pyimpspec.fit_circuit(circuit, data=EXAMPLE) + + figure, axes = mpl.plot_fit( + fit, + EXAMPLE, + legend=False, + colored_axes=True, + ) + figure.tight_layout() + plt.show() + + figure, axes = mpl.plot_nyquist( + EXAMPLE, + colors={"impedance": colors.COLOR_BLACK}, + legend=False, + ) + _ = mpl.plot_nyquist( + fit, + line=True, + figure=figure, + axes=axes, + ) + figure.tight_layout() + plt.show() + + figure, axes = mpl.plot_bode( + EXAMPLE, + colors={ + "magnitude": colors.COLOR_BLACK, + "phase": colors.COLOR_BLACK, + }, + legend=False, + ) + _ = mpl.plot_bode( + fit, + line=True, + figure=figure, + axes=axes, + ) + figure.tight_layout() + plt.show() + + figure, axes = mpl.plot_residuals(fit) + figure.tight_layout() + plt.show() + +.. raw:: latex + + \clearpage + + +plot_tests +~~~~~~~~~~ +:func:`~deareis.mpl.plot_tests` + +* :func:`~deareis.mpl.plot_mu_xps` +* :func:`~deareis.mpl.plot_residuals` +* :func:`~deareis.mpl.plot_nyquist` +* :func:`~deareis.mpl.plot_bode` + +.. plot:: + + from deareis import mpl + import pyimpspec + from pyimpspec.mock_data import EXAMPLE + import pyimpspec.plot.colors as colors + import matplotlib.pyplot as plt + + mu_criterion = 0.85 + tests = pyimpspec.perform_exploratory_tests( + EXAMPLE, + mu_criterion=mu_criterion, + add_capacitance=True, + ) + + figure, axes = mpl.plot_tests( + tests, + mu_criterion, + EXAMPLE, + legend=False, + colored_axes=True, + ) + figure.tight_layout() + plt.show() + + figure, axes = mpl.plot_mu_xps( + tests, + mu_criterion, + ) + figure.tight_layout() + plt.show() + + figure, axes = mpl.plot_residuals(tests[0]) + figure.tight_layout() + plt.show() + + figure, axes = mpl.plot_nyquist( + EXAMPLE, + colors={"impedance": colors.COLOR_BLACK}, + legend=False, + ) + _ = mpl.plot_nyquist( + tests[0], + line=True, + figure=figure, + axes=axes, + ) + figure.tight_layout() + plt.show() + + figure, axes = mpl.plot_bode( + EXAMPLE, + colors={ + "magnitude": colors.COLOR_BLACK, + "phase": colors.COLOR_BLACK, + }, + legend=False, + ) + _ = mpl.plot_bode( + tests[0], + line=True, + figure=figure, + axes=axes, + ) + figure.tight_layout() + plt.show() + +.. raw:: latex + + \clearpage diff --git a/docs/source/apidocs_project.rst b/docs/source/apidocs_project.rst new file mode 100644 index 0000000..24d075a --- /dev/null +++ b/docs/source/apidocs_project.rst @@ -0,0 +1,12 @@ +.. include:: ./substitutions.rst + +Projects +======== + +.. automodule:: deareis + :members: Project + +.. raw:: latex + + \clearpage + diff --git a/docs/source/apidocs_typing.rst b/docs/source/apidocs_typing.rst new file mode 100644 index 0000000..44ec6a8 --- /dev/null +++ b/docs/source/apidocs_typing.rst @@ -0,0 +1,33 @@ +.. include:: ./substitutions.rst + +Typing +====== + +Some type hints/annotations that are used throughout DearEIS. + + +Aliases +------- + +.. autoclass:: deareis.ComplexImpedance +.. autoclass:: deareis.ComplexImpedances +.. autoclass:: deareis.ComplexResidual +.. autoclass:: deareis.ComplexResiduals +.. autoclass:: deareis.Frequencies +.. autoclass:: deareis.Frequency +.. autoclass:: deareis.Gamma +.. autoclass:: deareis.Gammas +.. autoclass:: deareis.Impedance +.. autoclass:: deareis.Impedances +.. autoclass:: deareis.Indices +.. autoclass:: deareis.Phase +.. autoclass:: deareis.Phases +.. autoclass:: deareis.Residual +.. autoclass:: deareis.Residuals +.. autoclass:: deareis.TimeConstant +.. autoclass:: deareis.TimeConstants + + +.. raw:: latex + + \clearpage diff --git a/docs/source/apidocs_zhit.rst b/docs/source/apidocs_zhit.rst new file mode 100644 index 0000000..de9c244 --- /dev/null +++ b/docs/source/apidocs_zhit.rst @@ -0,0 +1,22 @@ +.. include:: ./substitutions.rst + +Z-HIT analysis +============== + +.. automodule:: deareis + :members: perform_zhit + + +Classes +------- +.. automodule:: deareis + :members: ZHITResult, ZHITSettings + +Enums +----- +.. automodule:: deareis + :members: ZHITInterpolation, ZHITSmoothing, ZHITWindow + +.. raw:: latex + + \clearpage diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 0000000..fdc62d2 --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,77 @@ +from os.path import abspath, dirname, exists, join +from inspect import getmodule + +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +project = "DearEIS" +copyright = "2023, DearEIS developers" +author = "DearEIS developers" +release = "X.Y.Z" +version_path = join(dirname(dirname(dirname(abspath(__file__)))), "version.txt") +if exists(version_path): + with open(version_path, "r") as fp: + release = fp.read().strip() + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = [ + "sphinx.ext.doctest", + "sphinx.ext.napoleon", + "sphinx.ext.autodoc", + "sphinx.ext.mathjax", + "matplotlib.sphinxext.plot_directive", +] + +numfig = True +templates_path = ["_templates"] +exclude_patterns = [] + +autodoc_typehints = "description" +autodoc_typehints_format = "short" + + +def autodoc_skip_member_handler(app, what, name, obj, skip, options): + module_string = str(getmodule(obj)) + conditions = [ + skip, + name.startswith("_"), + "deareis" not in module_string and "pyimpspec" not in module_string, + ] + to_skip = any(conditions) + # print(to_skip, name, module_string, skip) + return to_skip + + +def autodoc_process_docstring(app, what, name, obj, options, lines): + replacements = { + "|DataFrame|": "`pandas.DataFrame `_", + "|Drawing|": "`schemdraw.Drawing `_", + "|Expr|": "`sympy.Expr `_", + "|MinimizerResult|": "`lmfit.MinimizerResult `_", + "|Figure|": "`matplotlib.Figure `_", + "|Axes|": "`matplotlib.Axes `_", + } + for i, line in enumerate(lines): + for key, value in replacements.items(): + if key in line: + line = line.replace(key, value) + lines[i] = line + + +def setup(app): + app.connect("autodoc-skip-member", autodoc_skip_member_handler) + app.connect("autodoc-process-docstring", autodoc_process_docstring) + + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = "sphinx_rtd_theme" +html_static_path = ["_static"] diff --git a/docs/source/guide.rst b/docs/source/guide.rst new file mode 100644 index 0000000..1585c4e --- /dev/null +++ b/docs/source/guide.rst @@ -0,0 +1,26 @@ +Getting started +=============== + +Here are some quick guides to getting started with DearEIS. + +.. note:: + + There are numerous tooltips available throughout the GUI. + These can be viewed by hovering the mouse cursor over, e.g., labels next to settings. + +.. toctree:: + :maxdepth: 1 + :caption: Contents: + + guide_installing + guide_projects + guide_data + guide_validation + guide_drt + guide_fitting + guide_simulation + guide_plotting + guide_batch + guide_settings + guide_command_palette + guide_api diff --git a/docs/source/guide_api.rst b/docs/source/guide_api.rst new file mode 100644 index 0000000..ca3c14f --- /dev/null +++ b/docs/source/guide_api.rst @@ -0,0 +1,530 @@ +.. include:: ./substitutions.rst + +Application programming interface +================================= + +The GUI is the primary interface of DearEIS but an API is also included for some batch processing capabilities. +However, if an API is the desired interface for performing various tasks, then using pyimpspec_ directly may be preferable. + + +Creating/loading a project +-------------------------- + + +.. doctest:: + + >>> from deareis import Project + >>> + >>> # Create a new project + >>> project: Project = Project() + >>> + >>> # Load an existing project + >>> project = Project.from_file("./tests/example-project-v5.json") + + +Batch importing data sets +------------------------- + +.. doctest:: + + >>> from deareis import DataSet, Project, parse_data + >>> + >>> project: Project = Project() + >>> + >>> data: DataSet + >>> for data in parse_data("./tests/data-1.idf"): + ... project.add_data_set(data) + >>> + >>> # Remember to save the project somewhere as well! + >>> # project.save("./tests/batch-imported-data.json") + + +Batch plotting results +---------------------- + +In the following example we will load a project, iterate over data sets, various results, simulations, and plots. +A single example of each of these will be plotted. + +.. doctest:: + + >>> from deareis import ( + ... DRTResult, + ... DataSet, + ... FitResult, + ... PlotSettings, + ... Project, + ... SimulationResult, + ... TestResult, + ... ZHITResult, + ... mpl, + ... ) + >>> import matplotlib.pyplot as plt + >>> + >>> project: Project = Project.from_file("./tests/example-project-v5.json") + >>> + >>> data: DataSet + >>> for data in project.get_data_sets(): + ... figure, axes = mpl.plot_data( + ... data, + ... colored_axes=True, + ... legend=False, + ... ) + ... + ... # Iterate over Kramers-Kronig results + ... test: TestResult + ... for test in project.get_tests(data): + ... figure, axes = mpl.plot_fit( + ... test, + ... data=data, + ... colored_axes=True, + ... legend=False, + ... ) + ... break + ... + ... # Iterate over Z-HIT results + ... zhit: ZHITResult + ... for zhit in project.get_zhits(data): + ... figure, axes = mpl.plot_fit( + ... zhit, + ... data=data, + ... colored_axes=True, + ... legend=False, + ... ) + ... break + ... + ... # Iterate over DRT results + ... drt: DRTResult + ... for drt in project.get_drts(data): + ... figure, axes = mpl.plot_drt( + ... drt, + ... data=data, + ... colored_axes=True, + ... legend=False, + ... ) + ... break + ... + ... # Iterate over circuit fits + ... fit: FitResult + ... for fit in project.get_fits(data): + ... figure, axes = mpl.plot_fit( + ... fit, + ... data=data, + ... colored_axes=True, + ... legend=False, + ... ) + ... break + ... break + >>> + >>> # Iterate over simulations + >>> sim: SimulationResult + >>> for sim in project.get_simulations(): + ... figure, axes = mpl.plot_nyquist( + ... sim, + ... line=True, + ... colored_axes=True, + ... legend=False, + ... ) + ... break + >>> + >>> # Iterate over plots + >>> plot: PlotSettings + >>> for plot in project.get_plots(): + ... figure, axes = mpl.plot(plot, project) + ... break + >>> + >>> plt.close("all") + +.. raw:: latex + + \clearpage + + +.. plot:: + + from deareis import Project, mpl + project = Project.from_file("../../tests/example-project-v5.json") + for data in project.get_data_sets(): + figure, axes = mpl.plot_data( + data, + colored_axes=True, + legend=False, + ) + figure.tight_layout() + break + + +.. plot:: + + from deareis import Project, mpl + project = Project.from_file("../../tests/example-project-v5.json") + for data in project.get_data_sets(): + for test in project.get_tests(data): + figure, axes = mpl.plot_fit( + test, + data=data, + colored_axes=True, + legend=False, + ) + figure.tight_layout() + break + break + + +.. plot:: + + from deareis import Project, mpl + project = Project.from_file("../../tests/example-project-v5.json") + for data in project.get_data_sets(): + for zhit in project.get_zhits(data): + figure, axes = mpl.plot_fit( + zhit, + data=data, + colored_axes=True, + legend=False, + ) + figure.tight_layout() + break + break + + +.. plot:: + + from deareis import Project, mpl + project = Project.from_file("../../tests/example-project-v5.json") + for data in project.get_data_sets(): + for drt in project.get_drts(data): + figure, axes = mpl.plot_drt( + drt, + data=data, + colored_axes=True, + legend=False, + ) + figure.tight_layout() + break + break + + +.. plot:: + + from deareis import Project, mpl + project = Project.from_file("../../tests/example-project-v5.json") + for data in project.get_data_sets(): + for fit in project.get_fits(data): + figure, axes = mpl.plot_fit( + fit, + data=data, + colored_axes=True, + legend=False, + ) + figure.tight_layout() + break + break + + +.. plot:: + + from deareis import Project, mpl + project = Project.from_file("../../tests/example-project-v5.json") + for sim in project.get_simulations(): + figure, axes = mpl.plot_nyquist( + sim, + line=True, + colored_axes=True, + legend=False, + ) + figure.tight_layout() + break + + +.. plot:: + + from deareis import Project, mpl + project = Project.from_file("../../tests/example-project-v5.json") + for plot in project.get_plots(): + figure, axes = mpl.plot(plot, project) + figure.tight_layout() + break + + +.. raw:: latex + + \clearpage + + +Customized plots +---------------- + +The approach used in the previous example could be used as the basis for creating more complicated plots (i.e., select the data sets and results programmatically). +However, it may be more convenient to use DearEIS' GUI to select the data sets, results, etc. and assign colors, markers, etc. +The resulting |PlotSettings| and |PlotSeries| objects can then be used as the foundation for generating the final plot using either the plotting functions included with DearEIS or another plotting library. + +.. doctest:: + + >>> from deareis import ( + ... PlotSeries, # Wrapper class for DataSet, TestResult, etc. + ... PlotSettings, # The settings class for plots created via DearEIS' GUI + ... PlotType, # Enum for different types of plots (e.g., Nyquist) + ... Project, + ... mpl, + ... ) + >>> import matplotlib.pyplot as plt + >>> from matplotlib.figure import Figure + >>> from typing import ( + ... Optional, + ... Tuple, + ... ) + >>> + >>> # Prepare the figure that will be used to create a custom Nyquist plot. + >>> figure, axis = plt.subplots() + >>> axes = [axis] + >>> + >>> # Load the project of interest. + >>> project: Project = Project.from_file("./tests/example-project-v5.json") + >>> + >>> # Get the settings for the plot that contains the series (data sets, + >>> # fit results, etc.) that we wish to plot. + >>> plot: PlotSettings = [ + ... plot for plot in project.get_plots() + ... if plot.get_label() == "Noisy" + ... ][0] + >>> + >>> # Each data set, fit result, etc. can be represented as a PlotSeries + >>> # object that contains the required data and the style (color, marker, etc.). + >>> series: PlotSeries + >>> for series in project.get_plot_series(plot): + ... # Figure out if the series should be included in the figure legend. + ... label: Optional[str] = None + ... if series.has_legend(): + ... label = series.get_label() + ... + ... # Figure out the color and marker. + ... color: Tuple[float, float, float, float] = series.get_color() + ... marker: Optional[str] = mpl.MPL_MARKERS.get(series.get_marker()) + ... + ... # Determine whether or not the series should be plotted using markers, + ... # a line, or both. + ... # We will use the plotting functions provided by DearEIS in this example + ... # but you could use any plotting library that you wish. However, you + ... # would need to call, e.g., series.get_frequencies() and/or + ... # series.get_impedances() to get the relevant data. + ... if series.has_line(): + ... _ = mpl.plot_nyquist( + ... series, + ... colors={"impedance": color}, + ... markers={"impedance": marker}, + ... line=True, + ... label=label if marker is None else "", + ... figure=figure, + ... axes=axes, + ... num_per_decade=50, + ... ) + ... if marker is not None: + ... _ = mpl.plot_nyquist( + ... series, + ... colors={"impedance": color}, + ... markers={"impedance": marker}, + ... line=False, + ... label=label, + ... figure=figure, + ... axes=axes, + ... num_per_decade=-1, + ... ) + ... elif marker is not None: + ... _ = mpl.plot_nyquist( + ... series, + ... colors={"impedance": color}, + ... markers={"impedance": marker}, + ... line=False, + ... label=label, + ... figure=figure, + ... axes=axes, + ... num_per_decade=-1, + ... ) + >>> + >>> # Add the figure title and legend. + >>> _ = figure.suptitle(plot.get_label()) + >>> _ = axis.legend() + + +.. plot:: + + from deareis import ( + PlotSeries, + PlotSettings, + PlotType, + Project, + mpl, + ) + import matplotlib.pyplot as plt + from matplotlib.figure import Figure + from typing import ( + Optional, + Tuple, + ) + figure, axis = plt.subplots() + axes = [axis] + project: Project = Project.from_file("../../tests/example-project-v5.json") + plot: PlotSettings = [plot for plot in project.get_plots() if plot.get_label() == "Noisy"][0] + assert plot.get_type() == PlotType.NYQUIST + series: PlotSeries + for series in project.get_plot_series(plot): + label: Optional[str] = None + if series.has_legend(): + label = series.get_label() + color: Tuple[float, float, float, float] = series.get_color() + marker: Optional[str] = mpl.MPL_MARKERS.get(series.get_marker()) + if series.has_line(): + _ = mpl.plot_nyquist( + series, + colors={"impedance": color}, + markers={"impedance": marker}, + line=True, + label=label if marker is None else "", + figure=figure, + axes=axes, + num_per_decade=50, + ) + if marker is not None: + _ = mpl.plot_nyquist( + series, + colors={"impedance": color}, + markers={"impedance": marker}, + line=False, + label=label, + figure=figure, + axes=axes, + num_per_decade=-1, + ) + elif marker is not None: + _ = mpl.plot_nyquist( + series, + colors={"impedance": color}, + markers={"impedance": marker}, + line=False, + label=label, + figure=figure, + axes=axes, + num_per_decade=-1, + ) + _ = figure.suptitle(plot.get_label()) + _ = axis.legend() + +.. raw:: latex + + \clearpage + + +Generating tables +----------------- + +Several of the various ``*Result`` classes have ``to_*_dataframe`` methods that return tables as ``pandas.DataFrame`` objects, which can be used to output, e.g., Markdown or LaTeX tables. + +.. doctest:: + + >>> from deareis import DataSet, FitResult, Project + >>> project: Project = Project.from_file("./tests/example-project-v5.json") + >>> data: DataSet = project.get_data_sets()[0] + >>> fit: FitResult = project.get_fits(data)[0] + >>> print(fit.to_parameters_dataframe().to_markdown(index=False)) + | Element | Parameter | Value | Std. err. (%) | Unit | Fixed | + |:----------|:------------|--------------:|----------------:|:----------|:--------| + | R_1 | R | 99.9527 | 0.0270272 | ohm | No | + | R_2 | R | 200.295 | 0.0161674 | ohm | No | + | C_1 | C | 7.98618e-07 | 0.00251014 | F | No | + | R_3 | R | 499.93 | 0.0228817 | ohm | No | + | W_1 | Y | 0.000400664 | 0.0303242 | S*s^(1/2) | No | + >>> print(fit.to_parameters_dataframe(running=True).to_markdown(index=False)) + | Element | Parameter | Value | Std. err. (%) | Unit | Fixed | + |:----------|:------------|--------------:|----------------:|:----------|:--------| + | R_0 | R | 99.9527 | 0.0270272 | ohm | No | + | R_1 | R | 200.295 | 0.0161674 | ohm | No | + | C_2 | C | 7.98618e-07 | 0.00251014 | F | No | + | R_3 | R | 499.93 | 0.0228817 | ohm | No | + | W_4 | Y | 0.000400664 | 0.0303242 | S*s^(1/2) | No | + +.. raw:: latex + + \clearpage + + +Generating circuit diagrams +--------------------------- + +``Circuit`` objects can be used to draw circuit diagrams. + +.. doctest:: + + >>> from deareis import DataSet, FitResult, Project + >>> project: Project = Project.from_file("./tests/example-project-v5.json") + >>> data: DataSet = project.get_data_sets()[0] + >>> fit: FitResult = project.get_fits(data)[0] + >>> print(fit.circuit.to_circuitikz()) + \begin{circuitikz} + \draw (0,0) to[short, o-] (1,0); + \draw (1.0,0.0) to[R=$R_{\rm 1}$] (3.0,0.0); + \draw (3.0,-0.0) to[R=$R_{\rm 2}$] (5.0,-0.0); + \draw (3.0,-1.5) to[capacitor=$C_{\rm 1}$] (5.0,-1.5); + \draw (3.0,-0.0) to[short] (3.0,-1.5); + \draw (5.0,-0.0) to[short] (5.0,-1.5); + \draw (5.0,-0.0) to[R=$R_{\rm 3}$] (7.0,-0.0); + \draw (5.0,-1.5) to[generic=$W_{\rm 1}$] (7.0,-1.5); + \draw (5.0,-0.0) to[short] (5.0,-1.5); + \draw (7.0,-0.0) to[short] (7.0,-1.5); + \draw (7.0,0) to[short, -o] (8.0,0); + \end{circuitikz} + >>> print(fit.circuit.to_circuitikz(running=True)) + \begin{circuitikz} + \draw (0,0) to[short, o-] (1,0); + \draw (1.0,0.0) to[R=$R_{\rm 0}$] (3.0,0.0); + \draw (3.0,-0.0) to[R=$R_{\rm 1}$] (5.0,-0.0); + \draw (3.0,-1.5) to[capacitor=$C_{\rm 2}$] (5.0,-1.5); + \draw (3.0,-0.0) to[short] (3.0,-1.5); + \draw (5.0,-0.0) to[short] (5.0,-1.5); + \draw (5.0,-0.0) to[R=$R_{\rm 3}$] (7.0,-0.0); + \draw (5.0,-1.5) to[generic=$W_{\rm 4}$] (7.0,-1.5); + \draw (5.0,-0.0) to[short] (5.0,-1.5); + \draw (7.0,-0.0) to[short] (7.0,-1.5); + \draw (7.0,0) to[short, -o] (8.0,0); + \end{circuitikz} + >>> figure = fit.circuit.to_drawing().draw() + >>> figure = fit.circuit.to_drawing(running=True).draw() + +.. plot:: + + from deareis import Project + project = Project.from_file("../../tests/example-project-v5.json") + data = project.get_data_sets()[0] + fit = project.get_fits(data)[0] + fit.circuit.to_drawing().draw() + fit.circuit.to_drawing(running=True).draw() + +.. raw:: latex + + \clearpage + +.. _generating_equations: + +Generating equations +-------------------- + +Equations for the impedances of elements and circuits can be obtained in the form of SymPy_ expressions or LaTeX strings. + +.. note:: + + Equations **always** make use of a running count of the elements as the lower index in variables to avoid conflicting/duplicate variable names from different elements. + Circuit diagrams and tables can also make use of running counts as lower indices if explicitly told to (e.g., ``circuit.to_drawing(running=True)``, ``circuit.to_circuitikz(running=True)``, or ``fit.to_parameters_dataframe(running=True)``). + +.. doctest:: + + >>> from deareis import DataSet, FitResult, Project + >>> project: Project = Project.from_file("./tests/example-project-v5.json") + >>> data: DataSet = project.get_data_sets()[0] + >>> fit: FitResult = project.get_fits(data)[0] + >>> print(fit.circuit.to_sympy()) + R_0 + 1/(2*I*pi*C_2*f + 1/R_1) + 1/(sqrt(2)*sqrt(pi)*Y_4*sqrt(I*f) + 1/R_3) + >>> print(fit.circuit.to_latex()) + Z = R_{0} + \frac{1}{2 i \pi C_{2} f + \frac{1}{R_{1}}} + \frac{1}{\sqrt{2} \sqrt{\pi} Y_{4} \sqrt{i f} + \frac{1}{R_{3}}} + +.. raw:: latex + + \clearpage diff --git a/docs/source/guide_batch.rst b/docs/source/guide_batch.rst new file mode 100644 index 0000000..e8b285d --- /dev/null +++ b/docs/source/guide_batch.rst @@ -0,0 +1,29 @@ +.. include:: ./substitutions.rst + +Batch analysis +============== + +The various tabs dedicated to performing some form of analysis also have a **Batch** button. +These buttons bring up a window (:numref:`batch_window`)where multiple data sets can be selected for inclusion in a batch analysis. + +.. _batch_window: +.. figure:: images/batch-analysis.png + :alt: The Batch analysis window + + An example of the **Batch analysis** window where a few data sets have been selected. + +.. note:: + + If any errors are encountered while performing the analyses, then those errors are presented at the end. + There is no way to cancel a batch analysis once it has been started. + + +.. note:: + + Performing Kramers-Kronig tests in **Exploratory** mode would usually bring up a window for inspection of the intermediate results. + This window is **NOT** shown when performing a batch analysis. + +.. raw:: latex + + \clearpage + diff --git a/docs/source/guide_command_palette.rst b/docs/source/guide_command_palette.rst new file mode 100644 index 0000000..bea4088 --- /dev/null +++ b/docs/source/guide_command_palette.rst @@ -0,0 +1,23 @@ +.. include:: ./substitutions.rst + +Command palette +=============== + +DearEIS supports the use of keybindings to perform many but not all of the actions available in the various windows and tabs (e.g., switch to a specific tab, switch to a certain plot type, a Kramers-Kronig test, or perform a Kramers-Kronig test). +These keybindings are in many cases similar from window to window and tab to tab, and the keybindings can be reassigned via the corresponding settings window. +However, in some cases the keybindings are unique to the window (e.g., the file dialog). + +When there isn't a modal/popup window open, then it is possible to perform actions via the **Command palette** (:numref:`command_palette`) that can be opened by default via ``Ctrl+P``. +The contents of the list of actions depends upon the context (e.g., which tab is currently open). + +.. _command_palette: +.. figure:: images/command-palette.png + :alt: The Command palette window + + Various actions can be performed via the **Command palette**, which only requires memorization of a single keybinding (``Ctrl+P`` by default). + Actions can be navigated with the ``Up/Down`` arrow keys, ``Page Up/Down`` keys, and ``Home/End`` keys. + The window also supports fuzzy matching for finding a specific action (e.g., ``saw`` should bring the ``Show the 'About' window`` action to the top). + +.. raw:: latex + + \clearpage diff --git a/docs/source/guide_data.rst b/docs/source/guide_data.rst new file mode 100644 index 0000000..dc0562c --- /dev/null +++ b/docs/source/guide_data.rst @@ -0,0 +1,177 @@ +.. include:: ./substitutions.rst + +Processing data +=============== + +The **Data sets** tab +--------------------- + +Multiple impedance spectra (or *data sets*) can be loaded and processed in the **Data sets** tab (:numref:`data_tab`) that contains the following: + +- **Data set** combo for switching between data sets that have been loaded via the **Load** button. +- **Label** input for modifying the label assigned to the current data set. +- **Path** input for modifying the file path assigned to the current data set. +- **Process** button for opening a popup window with buttons for accessing features that enable further processing of the current data set. +- A table of data points with the ability to mask (i.e., hide/exclude) individual points (e.g., outliers). +- **Toggle points** button for (un)masking multiple points at once. +- **Copy mask** button for copying a mask from another data set and applying it to the current data set. +- **Enlarge/show plot** button for viewing a larger version of the current plot type. +- **Adjust limits** checkbox for enabling/disabling automatic adjustment of plot limits when switching between data sets. +- Plot type combo for switching between plot types. This is primarily for use when the program window is so narrow that the plots are hidden in order to keep the table of data points from becoming to narrow. + + +.. _data_tab: +.. figure:: images/data-sets-tab.png + :alt: The Data sets tab of a project + + The data point around 50 Hz has been omitted from this rather noisy data set as an outlier. + +.. raw:: latex + + \clearpage + + +Supported file formats +---------------------- + +Several different file formats are supported: + +- BioLogic: ``.mpt`` +- Eco Chemie: ``.dfr`` +- Gamry: ``.dta`` +- Ivium: ``.idf`` and ``.ids`` +- Spreadsheets: ``.xlsx`` and ``.ods`` +- Plain-text character-separated values (CSV): ``.csv`` and ``.txt`` + +Additional file formats may be supported in the future. + +Not all CSV files and spreadsheets are necessarily supported as-is but the parsing of those types of files should be quite flexible in terms of, e.g., the characters that are used as separators. +The parsers expect to find at least a column with frequencies (Hz) and columns for either the real and imaginary parts of the impedance (ohm), or the absolute magnitude (ohm) and the phase angle/shift (degrees). +The supported column headers are: + +- frequency: ``frequency``, ``freq``, or ``f`` +- real: ``z'``, ``z_re``, ``zre``, ``real``, or ``re`` +- imaginary: ``z"``, ``z''``, ``z_im``, ``zim``, ``imaginary``, ``imag``, or ``im`` +- magnitude: ``|z|``, ``z``, ``magnitude``, ``modulus``, ``mag``, or ``mod`` +- phase: ``phase``, ``phz``, or ``phi`` + +The identification of column headers is case insensitive (i.e., ``Zre`` and ``zre`` are considered to be the same). +The sign of the imaginary part of the impedance and/or the phase angle/shift may be negative, but then that has to be indicated in the column header with a ``-`` prefix (e.g., ``-Zim`` or ``-phase``). + +.. raw:: latex + + \clearpage + + +Masking data points +------------------- + +Masks can be applied to hide data points in several ways and masked data points are excluded from plots and analyses. +This feature can be used to get rid of outliers or to analyze a fragment of a data set. +Individual data points can be masked via the checkboxes along the left-hand side of the table of data points (:numref:`data_tab`). +Ranges of data points can be toggled via the window that is accessible via the **Toggle points** button below the table of data points. +This can be used to, e.g., quickly mask multiple points or to remove the mask from all points (:numref:`toggle_figure`). +Middle-mouse clicking and dragging a region in a plot in that window can also be used to choose the points to toggle. + + +.. _toggle_figure: +.. figure:: images/data-sets-tab-toggle.png + :alt: Masking multiple points + + The **Toggle points** can be used to (un)mask multiple data points in several ways. + A preview of what the current data set would look like with the new mask is also included. + Here a region has been highlighted in one of the plots by holding down the middle-mouse button and dragging. + All of the points are included, which means that the points within the highlighted region will be toggled (i.e., excluded) when the **Accept** button is clicked. + +.. raw:: latex + + \clearpage + +If multiple data sets will need to have the same (or very similar) masks, then the **Copy mask** window can be used to copy the applied mask from another data set to the current data set (:numref:`mask_figure`). + + +.. _mask_figure: +.. figure:: images/data-sets-tab-copy.png + :alt: Copying masks from another data set + + The **Copy mask** includes a preview of what the current data set would look like with the mask that was applied to another data set in :numref:`toggle_figure`. + +.. raw:: latex + + \clearpage + + +Processing data sets +-------------------- + +DearEIS includes a few functions for processing data sets: averaging, interpolation, and subtraction. +All of these functions are available via the **Process** button that can be found above the table of data points (:numref:`data_tab`). +The results of these functions are added to the project as a new data set (i.e., without getting rid of the original data set). + + +Averaging +~~~~~~~~~ + +The averaging feature can be used to obtain a less noisy spectrum by averaging multiple measurements (:numref:`averaging_figure`). +This can be useful in cases where the noise cannot be reduced by adjusting some aspect of the experimental setup (e.g., by improving the shielding). +Only data sets with the same frequencies can be averaged. + +.. note:: + + Make sure that the measurements differ due to random noise rather than, e.g., drift before using this feature. + +.. _averaging_figure: +.. figure:: images/data-sets-tab-averaging.png + :alt: Averaging of multiple data sets + + Two data sets have been chosen (markers) and an average data set has been generated (line). + The other data sets do not have the same frequencies as the two chosen data sets and thus cannot be selected while at least one of those two data sets is selected. + +.. raw:: latex + + \clearpage + + +Interpolation +~~~~~~~~~~~~~ + +The interpolation feature can be used to replace an outlier rather than simply omitting it (:numref:`interpolation_figure`). +Some specific methods of analysis may be sensitive to the spacing of data points, which is why interpolation may be preferred over omission. +The data set is smoothed using `LOWESS `_ and interpolated using an `Akima spline `_ while ignoring any masked points. +Individual data points can then be replaced with a point on this spline by ticking the checkbox next to that data point. +Alternatively, if the smoothing and interpolation cannot provide a reasonable result, then values for the real and/or imaginary part of the data point can be inputted directly. + + +.. _interpolation_figure: +.. figure:: images/data-sets-tab-interpolation.png + :alt: Interpolation of data points + + The outlier (red marker), which was masked in :numref:`data_tab`, has been replaced with a value (orange marker) along the interpolated spline (green line). + +.. raw:: latex + + \clearpage + + +Subtraction +~~~~~~~~~~~ + +The recorded impedances can also be corrected by subtracting one of the following (:numref:`subtraction_figure`): + +- a fixed impedance +- a circuit +- a fitted circuit +- another data set + +This feature can be used to correct for some aspect of a measurement setup that is independent of the sample itself. + +.. _subtraction_figure: +.. figure:: images/data-sets-tab-subtraction.png + :alt: Subtraction of impedances from a recorded spectrum + + A resistance of 100 ohm is subtracted from a data set. + + +.. raw:: latex + + \clearpage diff --git a/docs/source/guide_drt.rst b/docs/source/guide_drt.rst new file mode 100644 index 0000000..07c7d51 --- /dev/null +++ b/docs/source/guide_drt.rst @@ -0,0 +1,105 @@ +.. include:: ./substitutions.rst + +Distribution of relaxation times analysis +========================================= + +Performing analyses +------------------- + +The distribution of relaxation times (DRT) can be calculated using multiple different approaches (see the corresponding publications for details): + +- `Bayesian Hilbert transform (BHT) `_ +- `Tikhonov regularization and non-negative least squares fitting (TR-NNLS) `_ +- `Tikhonov regularization and radial basis function (or piecewise linear) discretization (TR-RBF) `_ +- `multi-(RQ) fit (m(RQ)fit) `_ + + +This type of analysis can be used, e.g., as an aid when developing equivalent circuits by revealing the number of time constants. +The peak shapes (e.g., symmetry and sharpness) can also help with identifying circuit elements that could be suitable. + +DRT calculations can be performed in the **DRT analysis** tab (:numref:`drt_tab`): + +- the various settings that determine how the DRT calculations are performed +- combo boxes that can be used to choose the active data set and the active result, and a button for deleting the active result +- a table of statistics related to the active result +- a table of the settings that were used to obtain the active result + +.. _drt_tab: +.. figure:: images/drt-tab-interpolated.png + :alt: The DRT analysis tab of a project + + An example of a result obtained with the noisy data set with the interpolated data point near 50 Hz and the TR-RBF method. + +The results are presented in the form of one or more tables (e.g., statistics, scores), a plot of gamma versus time constant, and other plots. +Some results can be copied to the clipboard in different plain-text formats via the **Output** combo box and the **Copy** button. + +It was mentioned in the :doc:`/guide_data` subchapter that some forms of analysis can be sensitive to the omission of a data point. +Below are some examples of this. +The overlay plots shown below are created using the **Plotting** tab (more information about that can be found in the :doc:`/guide_plotting` subchapter). + +.. _drt_overlay: +.. figure:: images/drt-overlaid.png + :alt: Three overlaid DRT spectra + + Three overlaid DRT spectra that were obtained with the TR-RBF method using the same settings: with outlier (original), without outlier (omitted), and with the outlier replaced (interpolated). + The presence of the outlier clearly has a significant effect on peak positions in the range above 0.001 s. + However, omitting the outlier resulted in additional peaks appearing within the 0.01 to 0.1 s range. + +.. raw:: latex + + \clearpage + + +.. _drt_overlay_2: +.. figure:: images/drt-overlaid-2.png + :alt: Six overlaid DRT spectra + + Additional DRT spectra, which were obtained by fitting **R(RC)(RQ)** circuits and calculating the DRT using the m(RQ)fit method, overlaid on top of :numref:`drt_overlay`. + The presence of the outlier has shifted the peaks toward lower time constants (original). + The m(RQ)fit method is less sensitive to the omission of the outlier as can be seen from the two DRT spectra (omitted and interpolated) that are almost identical. + The two latter spectra also have, e.g., their left-most peaks in the correct position of approximately 0.00016 s which is the expected value based on the known resistance and capacitance values (200 ohm and 0.8 :math:`\mathrm{\mu F}`, respectively) of the circuit that was used to generate the data sets. + +.. raw:: latex + + \clearpage + + +One can see based on :numref:`drt_overlay_2` that different DRT methods can produce very different results, but the settings and amount of noise in the data also have a significant effect as can be seen in :numref:`drt_overlay_3`. + +.. _drt_overlay_3: +.. figure:: images/drt-overlaid-3.png + :alt: Four overlaid DRT spectra + + In this example two DRT spectra are shown for each of the data sets: ideal (no noise) and interpolated (noisy with the outlier replaced). + The DRT spectra have been obtained using the TR-RBF method with otherwise identical settings apart from the regularization parameters, |lambda|, that are indicated in the labels found in the plot legend. + + +References: + +- Boukamp, B.A., 2015, Electrochim. Acta, 154, 35-46, (https://doi.org/10.1016/j.electacta.2014.12.059) +- Boukamp, B.A. and Rolle, A, 2017, Solid State Ionics, 302, 12-18 (https://doi.org/10.1016/j.ssi.2016.10.009) +- Ciucci, F. and Chen, C., 2015, Electrochim. Acta, 167, 439-454 (https://doi.org/10.1016/j.electacta.2015.03.123) +- Effat, M. B. and Ciucci, F., 2017, Electrochim. Acta, 247, 1117-1129 (https://doi.org/10.1016/j.electacta.2017.07.050) +- Kulikovsky, A., 2021, J. Electrochem. Soc., 168, 044512 (https://doi.org/10.1149/1945-7111/abf508) +- Liu, J., Wan, T. H., and Ciucci, F., 2020, Electrochim. Acta, 357, 136864 (https://doi.org/10.1016/j.electacta.2020.136864) +- Wan, T. H., Saccoccio, M., Chen, C., and Ciucci, F., 2015, Electrochim. Acta, 184, 483-499 (https://doi.org/10.1016/j.electacta.2015.09.097) + +.. raw:: latex + + \clearpage + + +Applying old settings and masks +------------------------------- + +The settings that were used to perform the active analysis result are also presented as a table and these settings can be applied by pressing the **Apply settings** button. + +The mask that was applied to the data set when the analysis was performed can be applied by pressing the **Apply mask** button. +If the mask that is applied to the data set has changed since an earlier analysis was performed, then that will be indicated clearly above the statistics table. + +These features make it easy to restore old settings/masks in case, e.g., DearEIS has been closed and relaunched, or after trying out different settings. + + +.. raw:: latex + + \clearpage diff --git a/docs/source/guide_fitting.rst b/docs/source/guide_fitting.rst new file mode 100644 index 0000000..ad0c0b8 --- /dev/null +++ b/docs/source/guide_fitting.rst @@ -0,0 +1,166 @@ +.. include:: ./substitutions.rst + +Fitting +======= + +The **Fitting** tab is where equivalent circuits can be fitted to data sets (:numref:`fitting_tab`): + +- the various settings that determine how the fitting is performed +- combo boxes that can be used to choose the active data set, the active fit result, and the active output +- a table of fitted parameter values and estimated errors (if possible to estimate) +- a table of statistics related to the active fit result +- a table of the settings that were used to obtain the active result + + +.. _fitting_tab: +.. figure:: images/fitting-tab.png + :alt: The Fitting tab of a project. + + An example of an **R(RC)(RW)** circuit that has been fitted to a data set. + The obtained fitted parameters are close to the parameters that were used to generate the data set in the first place. + +Equivalent circuits can be constructed either by typing in a corresponding `circuit description code (CDC) `_ or by using the graphical circuit editor, which is accessible by pressing the **Edit** button. + +Different iterative methods and weights are available. +If one or both of these settings are set to **Auto**, then combinations of iterative method(s) and weight(s) are used to perform multiple fits in parallel and the best fit is returned. + +The results are presented in the form of a table containing the fitted parameter values (and, if possible, error estimates for the fitted parameter values), a table containing statistics pertaining to the quality of the fit, three plots (Nyquist, Bode, and relative errors of the fit), and a preview of the circuit that was fitted to the data set. +If you hover the mouse cursor over cells in the tables, then you can get additional information (e.g., more precise values or explanations). + + +Equivalent circuits +------------------- + +The CDC syntax is quite simple: + +- Circuit elements are represented by one or more letter symbols such as ``R`` for a resistor, ``C`` for a capacitor, and ``Wo`` for a Warburg diffusion of finite-length with a reflective boundary. +- Two or more circuit elements enclosed in parentheses, ``()``, are connected in parallel. +- Two or more circuit elements enclosed in square brackets, ``[]``, are connected in series. This can be used to construct, e.g., a parallel connection that contains a nested series connection (``(C[RW])`` where ``R`` and ``W`` are connected together in series and that series connection is in parallel with ``C``). + +DearEIS also supports an extended CDC syntax. +This extended syntax allows for defining circuit elements with, e.g., labels, initial values for parameters, and parameter limits. +Circuit elements can be followed up by curly braces and the aforementioned things can be defined within these curly braces. +For example, ``R{R=250f:ct}`` defines a resistor with: + +- an initial value of 250 ohms for the resistance ``R`` +- a fixed initial value (i.e., a constant value) +- the label ``ct``, which stands for charge transfer + +Engineering notation (e.g., ``1e-6`` or ``1E-6`` instead of ``0.000001``) is supported by the extended syntax. +All parameters do not need to be defined if a circuit element has multiple parameters. +If parameters are omitted, then the default values are used (e.g., the default initial value for the ``R`` parameter of a resistor is 1000 ohms). +If parameter limits are completely omitted, then the default values are used (e.g., the default lower limit for the ``R`` parameter of a resistor i 0 and the upper limit is infinity). +``Q{Y=1.3e-7//1e-5,n=0.95/0.9/1.0:dl}`` defines a constant phase element with: + +- an initial value of 1.3 \* 10^-7 F\*s^(n-1) for the ``Y`` parameter (other sources may use the notation ``A`` or ``Q0`` for this parameter) with no lower limit and an upper limit of 1 \* 10^-5 F\*s^(n-1) +- an initial value of 0.95 for the ``n`` parameter (other sources may use the notation ``alpha`` or ``psi`` for this parameter) with a lower limit of 0.9 and an upper limit of 1.0 +- the label ``dl``, which stands for double-layer (i.e., double-layer capacitance) + +The valid symbols are listed in the **Element** combo box positioned below the **CDC input** field. + +Alternatively, nodes representing the circuit elements can be added to the node editor and connected together to form an equivalent circuit. +This is done by choosing the type of circuit element one wants to add, clicking the **Add** button, and finally linking nodes together by clicking and dragging between the terminals of the nodes (i.e., the yellow dots on either side of a node). +If two parallel circuits are connected in series like in :numref:`circuit_editor`, then it is necessary to place a node between them. +This node could be an element (e.g., a resistor) that is also connected in series to the two parallel circuits or it could be a dummy node. +This dummy node, which can be added via the **Add dummy** button, does not affect the impedance of the system at all. +Links between nodes can be deleted by either clicking on a link and then pressing the **Delete** button on the keyboard, or by holding down **Ctrl** when clicking on a link. +Multiple nodes can be moved or deleted by clicking and dragging a selection box around them. + +.. raw:: latex + + \clearpage + +Clicking a node will update the area on the left-hand side. +This is where a label such as ``ct`` for charge transfer can be added to a circuit element. +More importantly, this is where initial values and limits can be defined for the parameters of a circuit element. +One can also set a parameter to have a fixed value. + +.. note:: + + Due to technical reasons, one must click on the upper part of a node (i.e., where the label is) that represents a circuit element in order to be able to define, e.g., custom initial values. + Also, any values typed into the input fields must be confirmed by pressing ``Enter`` or the value will not actually be set. + Click and hold on the lower part of the node to move it around. + +.. note:: + + Click and hold on a terminal/pin (yellow dot) to start creating a link and then drag and release near another terminal/pin. + Hold down Ctrl while clicking on a link to remove the link. + + +.. _circuit_editor: +.. figure:: images/fitting-tab-editor.png + :alt: Example of the circuit editor window + + The graphical circuit editor can be used to construct equivalent circuits and to define the initial values and limits of parameters. + +.. _container_elements: +.. figure:: images/fitting-tab-container.png + :alt: Example of the parameters and subcircuits of a container element + + Container elements such as the general transmission line model have subcircuits that can also be modified. + +Press the **Accept circuit** button in the bottom right-hand corner once the equivalent circuit is complete. +If there is an issue with the equivalent circuit (e.g., a missing or invalid connection), then the button will be labeled **Cancel** instead. +The **Status** field at the bottom of the window should offer some help regarding the nature of the issue and the affected node should be highlighted with a red label (:numref:`invalid_circuit`). + +.. _invalid_circuit: +.. figure:: images/fitting-tab-invalid.png + :alt: Example of a status message for an invalid circuit (e.g., missing connection) + + If the circuit is invalid because of, e.g., a missing connection, then that is indicated by highlighting the affected node and by showing a relevant error message in the status field near the bottom of the window. + + +Adjusting parameters +-------------------- + +The parameters of each circuit element can be adjusted via the circuit editor. +However, there is also a separate parameter adjustment window that provides a real-time preview of the impedance spectrum produced by the circuit. +This window is accessible from the **Fitting** tab (i.e., not from the circuit editor). + +.. figure:: images/fitting-tab-adjustment.png + :alt: The parameter adjustment window in the Fitting tab + + The parameter adjustment window provides a convenient way of dialing in the initial values before performing a fit. + + +Applying old settings and masks +------------------------------- + +The settings that were used to perform the active fitting result are also presented as a table and these settings can be applied by pressing the **Apply settings** button. + +The mask that was applied to the data set when the fitting was performed can be applied by pressing the **Apply mask** button. +If the mask that is applied to the data set has changed since an earlier fitting was performed, then that will be indicated clearly above the statistics table. + +These features make it easy to restore old settings/masks in case, e.g., DearEIS has been closed and relaunched, or after trying out different settings. + + +Copying results to the clipboard +-------------------------------- + +Different aspects of the results can be copied to the clipboard in different plain-text formats via the **Output** combo box and the **Copy** button. +For example, the following results can be copied: + +- the basic or extended CDC of the fitted circuit +- a table of the impedance response of the fitted circuit as character-separated values +- a table of the fitted parameters as, e.g., character-separated values. +- a circuit diagram of the fitted circuit as, e.g., LaTeX or Scalable Vector Graphics (see example below) +- the SymPy_ expression describing the impedance of the fitted circuit + +.. note:: + + Variables in SymPy expressions use a different set of lower indices to avoid conflicting variable names. See :ref:`generating_equations` for more information. + + +.. plot:: + :caption: Example of a circuit diagram as it would look if it was copied as Scalable Vector Graphics. + + from pyimpspec import Circuit, parse_cdc + circuit: Circuit = parse_cdc("R(RC)(RW)") + drawing = circuit.to_drawing() + drawing.draw() + + + +.. raw:: latex + + \clearpage diff --git a/docs/source/guide_installing.rst b/docs/source/guide_installing.rst new file mode 100644 index 0000000..a1f80ec --- /dev/null +++ b/docs/source/guide_installing.rst @@ -0,0 +1,103 @@ +.. include:: ./substitutions.rst + +Installing +========== + +Supported platforms +------------------- + +- Linux +- Windows +- MacOS + +The package **may** also work on other platforms depending on whether or not those platforms are supported by DearEIS' dependencies. + + +Requirements +------------ + +- `Python `_ (3.8, 3.9, or 3.10) +- The following Python packages + + - `dearpygui `_ + - pyimpspec_ + - `requests `_ + +These Python packages (and their dependencies) are installed automatically when DearEIS is installed using `pip `_. + +The following Python packages can be installed as optional dependencies for additional functionality: + +- DRT calculations using the `TR-RBF method `_ (at least one of the following is required): + - `cvxopt `_ + - `kvxopt `_ (this fork of cvxopt may support additional platforms) + - `cvxpy `_ + + +.. note:: + + Windows and MacOS users who wish to install CVXPY **must** follow the steps described in the `CVXPY documentation `_! + + +Installing +---------- + +Make sure that Python and pip are installed first (see previous section for supported Python versions). +For example, open a terminal and run the command: + +.. code:: bash + + pip --version + +.. note:: + + If you only intend to use DearEIS via the GUI or are familiar with `virtual environments `_, then you should consider using `pipx `_ instead of pip to install DearEIS. + Pipx will install DearEIS inside of a virtual environment, which can help with preventing potential version conflicts that may arise if DearEIS requires an older or a newer version of a dependency than another package. + Pipx also manages these virtual environments and makes it easy to run applications/packages. + + +If there are no errors, then run the following command to install pyimpspec and its dependencies: + +.. code:: bash + + pip install deareis + +DearEIS should now be available as a command in the terminal and possibly also some application launchers. + +If you wish to install the optional dependencies, then they must be specified explicitly when installing DearEIS: + +.. code:: bash + + pip install deareis[cvxpy] + +Newer versions of DearEIS can be installed at a later date by adding the ``--upgrade`` option to the command: + +.. code:: bash + + pip install --upgrade deareis + + +Running the GUI program +----------------------- + +You should now be able to run DearEIS via, e.g., a terminal or the Windows start menu by typing in the command ``deareis``. +There is also a ``deareis-debug`` command that can be used for troubleshooting purposes and prints a lot of potentially useful information to a terminal window. +DearEIS can also be launched as a Python module: + +.. code:: bash + + python -m deareis + + +Using the API +------------- + +The ``deareis`` package should now be accessible in Python: + +.. doctest:: + + >>> import deareis + + +.. raw:: latex + + \clearpage diff --git a/docs/source/guide_plotting.rst b/docs/source/guide_plotting.rst new file mode 100644 index 0000000..5a66b35 --- /dev/null +++ b/docs/source/guide_plotting.rst @@ -0,0 +1,120 @@ +.. include:: ./substitutions.rst + +Plotting +======== + +The **Plotting** tab (:numref:`plotting_tab`) can be used to compose plots with multiple data sets and/or analysis results. +These plots can be used just within DearEIS to compare different results side-by-side. +However, these plots can also be used to prepare relatively simple plots for sharing and/or publication. +The plot settings can also be used via the API provided by DearEIS, which means that the basic layout can be prepared via the GUI and then the final plot can be generated programmatically. +This approach would also mean that a plotting library other than `matplotlib`_, which is the default backend for exporting plots using DearEIS, could be used instead. + +.. _plotting_tab: +.. figure:: images/plotting-tab.png + :alt: The Plotting tab of a project + + The **Plotting** tab can be used to create plots containing multiple data sets and/or results. + +.. raw:: latex + + \clearpage + + +Selecting items to plot +----------------------- + +The **Available** tab (:numref:`available`) contains entries for all of the data sets, analysis results, and simulations contained within a project. +Individual items can be selected by ticking their corresponding checkboxes. + +.. _available: +.. figure:: images/plotting-tab-available.png + :alt: The Available tab within the Plotting tab + + Data sets and/or results can be selected from the **Available** tab. + In this example the substring ``randles`` has been used to filter items. + +The **Filter** input field can be used to search for specific items. +The labels of analysis results are treated as if they also contain the label of the data set that they belong to, which means that filtering based on a data set's label will also include the analysis results belonging to that data set. +Using a hyphen, "-", as a prefix is equal to a logical not (i.e., "-noisy" excludes items with "noisy" in their labels). +Multiple filter terms can be used by separating them with commas. +If one or more spaces, " ", are typed in this field, then all of the headings are expanded. +Similarly, if the input field is cleared, then all of the headings are collapsed. + +Each heading contains buttons for (un)selecting all items within that heading and buttons for expanding/collapsing all subheadings. +There are also buttons for (un)selecting all items regardless of the heading they fall under. +Items that have been selected are added to the **Active** tab. + +.. raw:: latex + + \clearpage + + +Customizing selected items +-------------------------- + +Selected items are listed in a table in the **Active** tab and several aspects of those items can be edited. +An item's label, which is used in the plot's legend, can be overridden by typing in a new label. +If one or more spaces, " ", are given as the new label, then the item will not have an entry in the legend. + +.. _active: +.. figure:: images/plotting-tab-active.png + :alt: The Active tab within the Plotting tab + + The label and appearance of the selected items can be modified in the **Active** tab. + All three items have been given new labels that are used in the plot's legend. + +An item's appearance (color, marker shape, and whether or not it should have a line) can be edited from the popup window that appears when clicking the **Edit** button (:numref:`edit`). +The **U** and **D** buttons can be used to adjust the order of an item when generating a plot, which affects the legend and whether or not an item will either be covered by or be covering some other item. + +.. _edit: +.. figure:: images/plotting-tab-edit.png + :alt: The Edit appearance window within the Plotting tab + + The **Edit appearance** window that can be used to define the color associated with a plottable item. + The type of marker (if any) can also be chosen. + Whether or not the item should (also) be plotted using a line can also be chosen. + +If specific items should have the same appearance across multiple plots, then there are two approaches for conveniently copying the relevant settings from one plot to another. +The first approach is to have a main plot (e.g., labeled **Appearance template**) that is used simply to define the appearance of items. +This main plot can then be duplicated, which also copies each item's settings, and modified. +The second approach is to use the menu that is accessible via the **Copy appearance** button (:numref:`copy`) to copy item settings from another plot. +One can choose which plot to copy from, which items to copy settings from, and which categories of settings to copy (label, colors, etc.). + +.. _copy: +.. figure:: images/plotting-tab-copy.png + :alt: The Copy appearance settings window within the Plotting tab + + The **Copy appearance settings** window is for copying the appearance settings for multiple items from one plot to another. + Alternatively, one can define the settings in one plot that is then duplicate the plot and make minor adjustments to the newly duplicated plot. + +.. raw:: latex + + \clearpage + + +Exporting plots +--------------- + +Plots can be exported (i.e., saved as files) using `matplotlib`_ (:numref:`export`). +The plots (|PlotSettings|) and the items (|PlotSeries|) included in the plots are also accessible via the API. +This means means that one can compose a plot using the GUI and generate the final plot using the API, which allows for batch exporting and also for greater control of a plot's appearance. + +.. _export: +.. figure:: images/plotting-tab-export.png + :alt: The Export plot window within the Plotting tab + + The **Export plot** window is for previewing and preparing to save a plot as a file. + Some amount of customization is available, but users who desire a greater degree of control are directed to use the API of DearEIS to extract the data (and possibly also the various plot settings) in order to programmatically generate the final plots. + +.. warning:: + + A subset of users may encounter crashes when attempting to export plots. + This issue appears to affect systems running Linux together with an nVidia GPU and proprietary drivers. + + Unticking the ``Clear texture registry`` setting in ``Settings > Defaults > Plotting tab - Export plot > Miscellaneous`` may resolve the issue. + However, unticking this setting means that any memory allocated to previewing matplotlib plots is not freed until DearEIS is closed. + + +.. raw:: latex + + \clearpage diff --git a/docs/source/guide_projects.rst b/docs/source/guide_projects.rst new file mode 100644 index 0000000..fa5358a --- /dev/null +++ b/docs/source/guide_projects.rst @@ -0,0 +1,68 @@ +.. include:: ./substitutions.rst + +Projects +======== + +The workflow of DearEIS is based upon projects, which are stored as `JavaScript Object Notation (JSON) `_. +These projects can contain multiple impedance spectra (or data sets) as well as multiple analysis results. + +Projects can be created from the **Home** tab (:numref:`home_tab`) or the **File** menu (top of the window). +Recent projects are listed in this tab for quick access. +The entire list can be cleared and individual entries can be also be removed. +Two or more projects can also be merged to form a new project. + +.. _home_tab: +.. figure:: images/home-tab.png + :alt: The Home tab of the program + + Recent projects are easily accessible from the **Home** tab. + + +DearEIS maintain a snapshot of a project while that project is open. +The snapshot is updated every *N* actions (configurable in the settings) and this snapshot is recoverable in case DearEIS crashes or is closed while a project has unsaved changes. +Any such snapshots are loaded automatically the next time that DearEIS is started. +The snapshots are stored in ``XDG_STATE_HOME`` paths specified by the `XDG Base Directory Specification `_: + + +.. list-table:: Default location of project snapshots files on different operating systems. + :widths: 33 67 + :header-rows: 1 + + * - Operating system + - Location + * - Linux + - ``~/.local/state/DearEIS`` + * - MacOS + - ``~/Library/Application Support/DearEIS`` + * - Windows + - ``%LOCALAPPDATA%\DearEIS`` + +.. raw:: latex + + \clearpage + + +Multiple projects can be open at the same time as separate tabs and each project is split into multiple tabs: + +- The **Overview** tab is where the project's label can be specified and notes can be kept. + +- The **Data sets** tab is for importing and processing experimental data before it is analyzed. + +- The **Kramers-Kronig** and **Z-HIT analysis** tabs provide the primary means of validating impedance spectra. + +- The **DRT analysis** and **Fitting** tabs are for extracting quantitative information. + +- The **Simulation** tab can be used to familiarize oneself with or to demonstrate the impedance spectra of different circuits and how parameter values affect the resulting spectra. + +- The **Plotting** tab is for composing figures where multiple results can be overlaid on top of each other. + +.. _overview_tab: +.. figure:: images/overview-tab.png + :alt: The Overview tab of a project + + Notes about a project can be kept in the **Overview** tab. + + +.. raw:: latex + + \clearpage diff --git a/docs/source/guide_settings.rst b/docs/source/guide_settings.rst new file mode 100644 index 0000000..397f24a --- /dev/null +++ b/docs/source/guide_settings.rst @@ -0,0 +1,105 @@ +.. include:: ./substitutions.rst + +.. _settings_page: + +Settings +======== + +The configuration for DearEIS is stored as `JavaScript Object Notation (JSON) `_ that is stored in ``XDG_CONFIG_HOME`` paths specified by the `XDG Base Directory Specification `_: + +.. list-table:: Default location of the configuration file on different operating systems. + :widths: 33 67 + :header-rows: 1 + + * - Operating system + - Location + * - Linux + - ``~/.config/DearEIS`` + * - MacOS + - ``~/Library/Application Support/DearEIS`` + * - Windows + - ``%LOCALAPPDATA%\DearEIS`` + + +Appearance +---------- + +Some aspects of the appearances of various plots can be defined (:numref:`appearance`). +Some of these settings are also mixed and matched in some plots when there are more items to plot than shown in the plots in this window (e.g., see the plots in the window for interpolating data points in the **Data sets** tab). + +.. _appearance: +.. figure:: images/settings-appearance.png + :alt: The Appearance settings window + + Most changes made to plot appearances should take effect immediately. + Changing the number of points in simulated lines requires switching back and forth between data sets or results to update the plots. + + +Defaults +-------- + +The default settings that are used in, e.g., the tabs for performing analyses can be defined here (:numref:`defaults`). + +.. _defaults: +.. figure:: images/settings-defaults.png + :alt: The Default settings window + + Changes made to defaults should take effect immediately. + +.. raw:: latex + + \clearpage + + +Keybindings +----------- + +Many actions can be performed via keybindings. +If an update to DearEIS changes or adds keybindings for actions, then those keybindings may not change or be assigned. +In such cases it may be necessary to manually assign keybindings to those actions or to simply reset the keybindings. + +.. _keybindings: +.. figure:: images/settings-keybindings.png + :alt: The Keybinding settings window + + The keybindings defined in this window apply to a great extent also to modal/popup windows with similar functionality (e.g., for cycling results or plot types). + +.. raw:: latex + + \clearpage + + +User-defined elements +--------------------- + +Both pyimpspec and DearEIS include support for user-defined elements since version 4.0.0. +The support has been implemented in DearEIS by providing a setting where the user can specify a Python script that defines one or more new elements. +See the source code and the documentation for pyimpspec (specifically the **User-defined elements** section of the **Equivalent circuits** subchapter) for examples. +The relevant functions and classes are available via the APIs of both DearEIS (:doc:`/apidocs_circuit`) and pyimpspec. + +.. warning:: + + User-defined elements are not stored in project files. + If a project is dependent on a user-defined element, then that project cannot be opened unless the user-defined element has been loaded. + A project is dependent on a user-defined element if it is used in, e.g., a circuit fit or a simulation. + + The circuits used in these types of results are stored in a project file in the form of a circuit description code (CDC), which DearEIS needs to parse when a project is loaded. + User-defined elements are thus required at the moment of parsing and DearEIS/pyimpspec will expect to find elements that match the symbols encountered while parsing the CDC. + Changing the symbol of a user-defined element while it is in use can thus cause issues and symbols can conflict with, e.g., new elements that have been added to pyimpspec. + Changing the parameters/subcircuits of a user-defined element is also likely to cause issues if an older version is being used by a project. + + So keep track of your script(s) that define user-defined elements and consider creating new elements when changes have to be made. + +.. _user_defined_elements: +.. figure:: images/settings-user-defined-elements.png + :alt: The User-defined elements window + + Specify the path to a Python script and then click the **Refresh** button. + The new circuit elements should show up in the table. + Hovering over a row in the table should show the automatically generated extended description for a circuit element. + If the path is left empty and then the **Refresh** button is clicked, then the user-defined elements are cleared. + + +.. raw:: latex + + \clearpage diff --git a/docs/source/guide_simulation.rst b/docs/source/guide_simulation.rst new file mode 100644 index 0000000..d34d3b1 --- /dev/null +++ b/docs/source/guide_simulation.rst @@ -0,0 +1,26 @@ +.. include:: ./substitutions.rst + +Simulation +========== + +The layout of the **Simulation** tab (:numref:`simulation_tab`) is similar to that of the **Fitting** tab: + +- the various settings that determine the simulation parameters +- combo boxes that can be used to choose the active data set (or none), the active simulation result, and the active output +- a table of the parameter values +- a table of the settings that were used to obtain the active result + +However, the purpose of the **Simulation** tab is to provide a means to simulate impedance spectra of circuits within an arbitrary range of frequencies. +The simulated impedance spectra can be loaded as data sets, which means that they can then be subjected to the various forms of analysis included in DearEIS. +The **Simulation** tab can thus be very useful for teaching, demonstration, and development purposes. + +.. _simulation_tab: +.. figure:: images/simulation-tab.png + :alt: The Simulation tab of a project. + + An example of where a fitted circuit's impedance response has been extrapolated outside of the frequency range of the original experimental data. + + +.. raw:: latex + + \clearpage diff --git a/docs/source/guide_validation.rst b/docs/source/guide_validation.rst new file mode 100644 index 0000000..a9302f8 --- /dev/null +++ b/docs/source/guide_validation.rst @@ -0,0 +1,141 @@ +.. include:: ./substitutions.rst + +Validation +========== + +The two primary approaches to validating experimental data included in DearEIS are linear Kramers-Kronig testing and Z-HIT analysis. +The former is a widely adopted approach based on attempting to fit a specific type of equivalent circuit, which is known *a priori* to be Kramers-Kronig transformable. +The latter approach reconstructs the modulus data from the (typically) more stable phase data, which can reveal issues such as drift at low frequencies due to time invariant behavior exhibited by the measured system. + + +Kramers-Kronig testing +---------------------- + +Data validation based on linear Kramers-Kronig testing can be performed in the **Kramers-Kronig** tab (:numref:`kk_tab`) which contains the following: + +- various settings that determine how the Kramers-Kronig test is performed +- combo boxes that can be used to choose the active data set and the active test result +- a table of statistics related to the active test result +- a table of settings that were used to obtain the active result +- different plots + +.. _kk_tab: +.. figure:: images/kramers-kronig-tab.png + :alt: The Kramers-Kronig tab of a project + + A Kramers-Kronig test result for the noisy data set with the omitted outlier. + The relative residuals are rather large, but most importantly they are randomly distributed around zero. + +The three variants of the linear Kramers-Kronig test described by `Boukamp (1995) `_ have been included as well as an implementation that uses complex non-linear least squares fitting. +These tests can be performed with a fixed number of parallel RC elements (**Manual** mode) or that number can be determined automatically based on an algorithm described by `Schönleber et al. (2014) `_ (**Auto** mode). +The intermediate results of the latter approach can be inspected in the **Exploratory** mode as a means of detecting and dealing with false negatives (i.e., cases where valid data is indicated as invalid because the algorithm stops increasing the number of parallel RC elements too early). +An additional weight is also used in the **Exploratory** mode when suggesting the number of parallel RC elements as a means of increasing the probability of avoiding false negatives. + +The test results are presented in the form of a table of statistics (e.g., |pseudo chi-squared|) and different plots such as one of the relative residuals of the fit. + + +Exploratory mode +~~~~~~~~~~~~~~~~ + +If the **Exploratory** mode is used, then the Kramers-Kronig test is performed with a range of number of parallel RC elements like when using the **Auto** mode. +However, these results are presented in a modal window for inspection (:numref:`exploratory_window`) in the form of the following plots: + +- |mu| and the base-10 logarithm of |pseudo chi-squared| versus the number of parallel RC elements +- relative residuals of the real and imaginary impedances vs frequency +- Nyquist plot +- Bode plot + +.. _exploratory_window: +.. figure:: images/kramers-kronig-tab-exploratory.png + :alt: Window of exploratory test results + + The plot of |mu| fluctuates a bit at low numbers of parallel RC elements in this example, but wilder fluctuations can in some cases (depending on the chosen |mu|-criterion) result in a false negative when using the **Auto** mode. + Another number of parallel RC elements could be chosen, if necessary, from this window. + +The |mu| values range from 0.0 to 1.0 and these extremes represent over- and underfitting, respectively (see Schönleber et al. (2014) for the more information about how |mu| is calculated). +The |mu|-criterion is the threshold that is used to decide when to stop adding more parallel RC elements (i.e., when |mu| drops below the chosen |mu|-criterion). + +The main advantage of using the **Exploratory** mode is that one can see how |mu| changes as a function of the number of parallel RC elements. +In some cases |mu| can fluctuate wildly at low numbers of parallel RC elements, which would otherwise lead to the algorithm stopping too early and make it seem like the data is invalid when that is not necessarily the case. + + +References: + +- Boukamp, B.A., 1995, J. Electrochem. Soc., 142, 1885-1894 +- Schönleber, M., Klotz, D., and Ivers-Tiffée, E., 2014, Electrochim. Acta, 131, 20-27 + +.. raw:: latex + + \clearpage + + +Z-HIT analysis +-------------- + +Data validation using the `Z-HIT algorithm `_ can be performed in the **Z-HIT analysis** tab (:numref:`zhit_tab`) that contains the following: + +- the various settings that determine how the Z-HIT analysis is performed +- combo boxes that can be used to choose the active data set and the active analysis result +- a table of statistics related to the active analysis result +- a table of the settings that were used to obtain the active result +- different plots + + +.. _zhit_tab: +.. figure:: images/zhit-tab.png + :alt: The Z-HIT analysis tab of a project + + The modulus data that is plotted in the upper plot has been reconstructed (red line) based on the phase data (orange markers and green line) of some example data that exhibits drift at low frequencies (blue markers). + + +The Z-HIT algorithm was first described by Ehm et al. (2000) and provides a means of validating recorded impedance spectra using a modified logarithmic Hilbert transformation. +The phase data is typically smoothed before it is interpolated using a spline, and then it is integrated and derivated to reconstruct the modulus data. +The final step is an adjustment of the offset of the reconstructed modulus data by fitting to a subset of the experimental data that is unaffected by, e.g., drift. +This subset of data points is typically in the range of 1 Hz to 1000 Hz. + +.. note:: + + The modulus data is not reconstructed perfectly. + There are often minor deviations even with ideal data. + +DearEIS offers a few options for smoothing algorithm and interpolation spline, and several different window functions for the weights to use during offset adjustment. +The weights can also be previewed in a window (:numref:`weights_window`) that is accessible via the **Preview weights** button that is located below the section for settings. +This window can help with selecting a window function and appropriate parameters for it. + +.. _weights_window: +.. figure:: images/zhit-tab-weights.png + :alt: A window for previewing weights + + It is possible to preview the weights that could be applied when fitting the approximated modulus data to the experimental modulus data. + The shaded region shows the position of the window function while the orange markers show the weight (from 0.0 to 1.0) that could be applied. + +The results are presented in the form of a table of statistics and different plots. + +References: + +- Ehm, W., Göhr, H., Kaus, R., Röseler, B., and Schiller, C.A., 2000, Acta Chimica Hungarica, 137 (2-3), 145-157. + +.. raw:: latex + + \clearpage + + +Applying old settings and masks +------------------------------- + +The settings that were used to perform the active test result are also presented as a table and these settings can be applied by pressing the **Apply settings** button. + +The mask that was applied to the data set when the test was performed can be applied by pressing the **Apply mask** button. +If the mask that is applied to the data set has changed since an earlier analysis was performed, then that will be indicated clearly above the statistics table. + +.. figure:: images/kramers-kronig-tab-warning.png + :alt: Invalid result because the data set mask has changed + + An example of the warning (red text on the left-hand side) that could be shown if, e.g., the mask applied to a data set has been changed after an analysis has been performed. + In this case, three points near the apex of the semi-circle on the left-hand side in the Nyquist plot have been omitted. + +These features make it easy to restore old settings/masks in case, e.g., DearEIS has been closed and relaunched, or after trying out different settings. + +.. raw:: latex + + \clearpage diff --git a/docs/source/images/batch-analysis.png b/docs/source/images/batch-analysis.png new file mode 100644 index 0000000..29c39dd Binary files 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DearEIS documentation master file, created by + sphinx-quickstart on Wed Jan 11 19:11:26 2023. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +.. include:: ./substitutions.rst + +Welcome to DearEIS's documentation! +===================================== + +.. only:: html + + .. image:: https://github.com/vyrjana/DearEIS/actions/workflows/test-package.yml/badge.svg + :alt: tests + :target: https://github.com/vyrjana/DearEIS/actions/workflows/test-package.yml + + .. image:: https://github.com/vyrjana/DearEIS/actions/workflows/test-wheel.yml/badge.svg + :alt: build + :target: https://github.com/vyrjana/DearEIS/actions/workflows/test-wheel.yml + + .. image:: https://img.shields.io/pypi/pyversions/DearEIS + :alt: Supported Python versions + + .. image:: https://img.shields.io/github/license/vyrjana/DearEIS + :alt: GitHub + :target: https://www.gnu.org/licenses/gpl-3.0.html + + .. image:: https://img.shields.io/pypi/v/DearEIS + :alt: PyPI + :target: https://pypi.org/project/deareis/ + + .. image:: https://joss.theoj.org/papers/10.21105/joss.04808/status.svg + :alt: DOI + :target: https://doi.org/10.21105/joss.04808 + +DearEIS is a Python package for processing, analyzing, and visualizing impedance spectra. +The primary interface for using DearEIS is a graphical user interface (GUI). + +.. figure:: images/fitting-tab.png + :alt: The graphical user interface of DearEIS + + +The GUI can be started via the following command + +.. code:: bash + + deareis + +or by running DearEIS as a Python module + +.. code:: bash + + python -m deareis + + +An application programming interface (API) is also included and it can be used to, e.g., batch process results into tables and plots. + +.. doctest:: + + >>> import deareis + +The changelog can be found `here `_. +If you encounter bugs or wish to request a feature, then please open an `issue on GitHub `_. +If you wish to contribute to the project, then please read the `readme `_ before submitting a `pull request via GitHub `_. + +DearEIS is licensed under GPLv3_ or later. + +.. only:: html + + PDF copies of the documentation are available in the `releases section `_. + + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + guide + apidocs diff --git a/docs/source/substitutions.rst b/docs/source/substitutions.rst new file mode 100644 index 0000000..7285b8b --- /dev/null +++ b/docs/source/substitutions.rst @@ -0,0 +1,40 @@ +.. classes +.. |PlotSettings| replace:: :class:`~deareis.PlotSettings` +.. |PlotSeries| replace:: :class:`~deareis.PlotSeries` + +.. type hints +.. |ComplexImpedance| replace:: :class:`~pyimpspec.ComplexImpedance` +.. |ComplexImpedances| replace:: :class:`~pyimpspec.ComplexImpedances` +.. |ComplexResidual| replace:: :class:`~pyimpspec.ComplexResidual` +.. |ComplexResiduals| replace:: :class:`~pyimpspec.ComplexResiduals` +.. |Frequencies| replace:: :class:`~pyimpspec.Frequencies` +.. |Frequency| replace:: :class:`~pyimpspec.Frequency` +.. |Gamma| replace:: :class:`~pyimpspec.Gamma` +.. |Gammas| replace:: :class:`~pyimpspec.Gammas` +.. |Impedance| replace:: :class:`~pyimpspec.Impedance` +.. |Impedances| replace:: :class:`~pyimpspec.Impedances` +.. |Indices| replace:: :class:`~pyimpspec.Indices` +.. |Phase| replace:: :class:`~pyimpspec.Phase` +.. |Phases| replace:: :class:`~pyimpspec.Phases` +.. |Residual| replace:: :class:`~pyimpspec.Residual` +.. |Residuals| replace:: :class:`~pyimpspec.Residuals` +.. |TimeConstant| replace:: :class:`~pyimpspec.TimeConstant` +.. |TimeConstants| replace:: :class:`~pyimpspec.TimeConstants` + +.. math +.. |mu| replace:: :math:`{\rm \mu}` +.. |lambda| replace:: :math:`\lambda` +.. |chi-squared| replace:: :math:`\chi^2` +.. |pseudo chi-squared| replace:: :math:`\chi^2_{ps.}` + +.. functions +.. |get_default_num_procs| replace:: :func:`~deareis.get_default_num_procs` +.. |set_default_num_procs| replace:: :func:`~deareis.set_default_num_procs` + +.. links +.. _circuitikz: https://github.com/circuitikz/circuitikz +.. _gplv3: https://www.gnu.org/licenses/gpl-3.0.en.html +.. _matplotlib: https://matplotlib.org/ +.. _pyimpspec: https://vyrjana.github.io/pyimpspec/ +.. _sympify: https://docs.sympy.org/latest/modules/core.html#sympy.core.sympify.sympify +.. _sympy: https://www.sympy.org/en/index.html diff --git a/examples/README.md b/examples/README.md deleted file mode 100644 index 05e1726..0000000 --- a/examples/README.md +++ /dev/null @@ -1,7 +0,0 @@ -# Examples - -The `example-data.csv` file contains the impedance response generated by a circuit (test circuit 1 or TC-1) that was used in the article "A linear Kronig-Kramers transform test for immittance data validation" by B.A. Boukamp (DOI:10.1149/1.2044210). - -The `example-project.json` file contains the example data with and without added noise, some analysis results, and some plots. - -The `examples.ipynb` is a Jupyter notebook that demonstrates how one could use the API of DearEIS. The `example-data.json` is used in the Jupyter notebook as input for the custom JSON parser and contains data for the same impedance response as `example-data.csv` except it is given in terms of modulus and phase shift. diff --git a/examples/example-data.csv b/examples/example-data.csv deleted file mode 100644 index 814e074..0000000 --- a/examples/example-data.csv +++ /dev/null @@ -1,30 +0,0 @@ -f Z' Z'' -10000.0 109.00918219439028 -26.55567987651522 -7196.856730011521 112.05775995468218 -35.16622560165986 -5179.474679231213 116.90624584231648 -46.46637728652976 -3727.593720314942 124.83456650484118 -60.852292416758615 -2682.6957952797275 137.77047905254426 -78.08935305235111 -1930.6977288832495 157.97170110042765 -96.48058538206396 -1389.4954943731375 186.6369160728213 -112.20462986265139 -1000.0 221.69082501913698 -120.39912459346031 -719.6856730011522 257.4374275323009 -118.65060098612625 -517.9474679231213 288.11836356808647 -109.31032122364688 -372.7593720314942 311.5631153667847 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"metadata": {}, - "outputs": [], - "source": [ - "import deareis" - ] - }, - { - "cell_type": "markdown", - "id": "4f507662-e48e-437a-bb07-edbf418616e4", - "metadata": {}, - "source": [ - "The API provides access to various functions (e.g., `parse_data`, `perform_test`, and `fit_circuit`) and classes (e.g., `DataSet`, `TestResult`, and `FitResult`).\n", - "Many of these functions are wrappers for similar functions found in the pyimpspec package, which DearEIS is based on.\n", - "However, it should be noted that the function signatures may differ somewhat (e.g., the number of arguments, the argument types, the return type).\n", - "Check out the [API documentation](https://vyrjana.github.io/DearEIS/api) for further information.\n", - "\n", - "The API includes some functions for basic visualization using matplotlib.\n", - "For the sake of convenience, this module will be imported as follows for use throughout the rest of this notebook:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d974a1a1-021c-41c4-b4bb-4c70cac9ff9a", - "metadata": {}, - "outputs": [], - "source": [ - "from deareis import mpl" - ] - }, - { - "cell_type": "markdown", - "id": "2bfd4a08-190e-4178-b6b6-4b8d9f42eea2", - "metadata": {}, - "source": [ - "Below is a an example of how one might prepare a JupyterLab notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "cafaefce-9ff3-4a58-a164-20e0a166ac21", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib\n", - "matplotlib.rcParams[\"figure.figsize\"] = [12, 6]\n", - "from IPython.display import (\n", - " Latex,\n", - " Markdown,\n", - " Math,\n", - " display,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "6a0e999c-f6aa-470c-8809-cfaf2dc5331c", - "metadata": {}, - "source": [ - "Some specific classes will also be imported for the purposes of adding type annotations throughout the notebook:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a7b88b5e-d634-4262-a567-8a54f24893f7", - "metadata": {}, - "outputs": [], - "source": [ - "from deareis import ( # See the API documentation for more details about these classes: https://vyrjana.github.io/DearEIS/api/\n", - " DataSet, # A class that represents an impedance spectrum\n", - " DRTResult, # A class that contains the result of a distribution of relaxation times (DRT) analysis\n", - " DRTSettings, # A class that contains the settings to use when performing a DRT analysis\n", - " FitResult, # A class that contains the result of an equivalent circuit fit\n", - " PlotSettings, # A class that represents a complex plot where multiple DataSet, DRTResult, etc. have been overlaid\n", - " PlotType, # An enum for the valid types of plots that a PlotSettings object can represent\n", - " PlotSeries, # A class that contains the data required to plot DataSet, DRTResult, etc. objects according to the settings defined by a PlotSettings object\n", - " SimulationResult, # A class that contains the result of simulating a circuit's impedance response\n", - " TestResult, # A class that contains the result of a Kramers-Kronig test\n", - " TestSettings, # A class that contains the settings to use when performing a Kramers-Kronig test\n", - " Project, # A collection of multiple DataSet, DRTResult, FitResult, SimulationResult, etc. objects \n", - ")\n", - "from matplotlib.figure import Figure\n", - "from numpy import ndarray\n", - "from typing import (\n", - " List,\n", - " Optional,\n", - " Tuple,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "8fee4527-85c8-41a4-8929-27fe79e3db20", - "metadata": { - "tags": [] - }, - "source": [ - "## Examples of use cases\n", - "\n", - "DearEIS is primarily intended to be used via the graphical user interface (GUI).\n", - "However, the GUI and the API can be combined to implement hybrid workflows that make use of the strengths of both approaches when performing specific tasks.\n", - "\n", - "### Example 1 - Batch processing results\n", - "\n", - "Let's say that we are working on a thesis or an article that is going to be typeset using $\\LaTeX$.\n", - "We would like to include results that have been obtained by characterizing some samples using electrochemical impedance spectroscopy (EIS).\n", - "The experimental data is imported into a DearEIS project and the following work has been done in the GUI program:\n", - "\n", - "- A few outliers have been excluded.\n", - "- The data has been validated using Kramers-Kronig analysis.\n", - "- Suitable equivalent circuits have been developed and fitted to the data.\n", - "- Plots that compare multiple spectra have been composed.\n", - "\n", - "The GUI program does include the functionality required for copying the results as character-separated values (CSV) so that tables and plots could be prepared using, e.g., a spreadsheet program.\n", - "However, an advantage of using the API instead is that if, e.g., style changes needed to be made, then the tables and/or figures could be updated simply by executing the relevant scripts again.\n", - "The API could be used to:\n", - "\n", - "- Generate plots as vector graphics using, e.g., [matplotlib](https://matplotlib.org/) or another plotting library.\n", - "- Generate `pandas.DataFrame` objects that can be used to, e.g., produce $\\LaTeX$ or Markdown tables containing the results of circuit fits.\n", - "- Draw circuit diagrams using the `Circuit.to_drawing` method, which returns a `schemdraw.Drawing` object ([SchemDraw package](https://schemdraw.readthedocs.io/en/latest/)), or the `Circuit.to_circuitikz` method, which generates $\\LaTeX$ source that can be used to draw the diagram using the [CircuiTikZ package](https://ctan.org/pkg/circuitikz).\n", - "\n", - "These scripts could even be included in a build system that used, e.g., a Docker image to provide a reproducible environment for generating a PDF from the source files ($\\LaTeX$ files, figures, etc.).\n", - "\n", - "#### Loading a project\n", - "\n", - "Existing projects can be loaded using the `Project.from_file` method." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f85bb27c-5c2a-429d-bbef-be897457cb86", - "metadata": {}, - "outputs": [], - "source": [ - "project_ex1: Project = Project.from_file(\"example-project.json\")" - ] - }, - { - "cell_type": "markdown", - "id": "9d11e17f-b693-4c70-bbda-e8a73c51c8eb", - "metadata": {}, - "source": [ - "#### Generating plots\n", - "\n", - "##### Data sets\n", - "\n", - "Individual data sets could be plotted as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "15243451-a98c-4611-8a55-4c4a85179b83", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data: DataSet\n", - "for data in project_ex1.get_data_sets():\n", - " fig, axes = mpl.plot_data(data)\n", - " \n", - " # Alternatively, the required data can be accessed as follows for use with another plotting library.\n", - " # - Raw data\n", - " f: ndarray = data.get_frequency()\n", - " Z: ndarray = data.get_impedance()\n", - " \n", - " # - Data for a Nyquist plot (re = Z.real, im = -Z.imag)\n", - " re: ndarray\n", - " im: ndarray\n", - " real, imag = data.get_nyquist_data()\n", - " \n", - " # - Data for a Bode plot (log_f = log10(f), log_mag = log10(abs(Z)), log_phi = -numpy.angle(Z, deg=True))\n", - " log_f: ndarray\n", - " log_mag: ndarray\n", - " log_phi: ndarray\n", - " log_f, log_mag, log_phi = data.get_bode_data()" - ] - }, - { - "cell_type": "markdown", - "id": "0ba475c4-ab37-4daa-b65a-a93f6dc40e23", - "metadata": {}, - "source": [ - "The `plot_data` function uses the `plot_nyquist` and `plot_bode` functions but those can also be used directly.\n", - "These functions have arguments that can be used to define, e.g., colors." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9ec5498c-4956-4914-b2ef-0cceefa5fd77", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data: DataSet = project_ex1.get_data_sets()[0]\n", - "fig, axes = mpl.plot_nyquist(data, )\n", - "fig, axes = mpl.plot_bode(data)" - ] - }, - { - "cell_type": "markdown", - "id": "96652838-163b-43e8-9161-1c7592daeb20", - "metadata": {}, - "source": [ - "##### Kramers-Kronig test results\n", - "\n", - "Similarly, Kramers-Kronig test results and circuit fit results can also be accessed and plotted:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "ff403d67-65d9-4c88-b897-76743c10f586", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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XLl0ypO3fvx8uLi7o1KlTpdupbEwHDx5Ez549zcpfslZTyXRDALh8+TIAIDg42Civm5sbHB0dDefLcvfuXdy7d8+ozsq0X5aqxKXX66HVapGamoo5c+bg5s2bmDp1aoVtVUe85rp8+TKcnJzg6upqlN6yZUvD+bIcPHgQ9+7dw9ixY0v9uw8LCwPHcUhMTCy3fa1Wi2PHjpV5TTqdDhqNBomJiXj//ffBGEN4eLjZbTHGoNVqkZOTg61btyIyMhJTp04tt8OsvOsCnux1SwghhBDq7CKEEEKeiE6ng1arRWFhIfbt24evvvoKL7zwAgYPHmyU7+TJk2jbtm2V60lKSgIA+Pr6Viq+tm3bQq/X49SpU5W8Mhg65kpGeJX4999/ARSveVQiKSmp0rFVRVxcHBQKRbn3skRKSgpmzJiB9u3bG0ajAcVxi8VikyOx7O3tja7rcVqtFuPGjYOlpSU++uijctu/dOkSlixZgmHDhhk6dspTlbgGDRoEoVAIDw8P/PDDD9i6dStefPHFCtsypaz79aSysrJgb29fKl0mk0EkEpV7v9euXQs+n4+wsLBS5/h8Pvh8foWdv3PnzsXt27cxZ84ck+dbtGgBkUgEX19f7Nq1C1FRUWjXrp3ZbS1evBhCoRD29vYYOXIkJk+ejAULFpQbU3nXBTzZ65YQQgghtEA9IYQQ8kQeH8nUvHlz/P3336VGdaSmpqJDhw5PXE9llUzrenQnRHMNHDgQfn5++OSTT+Di4oIOHTrg1KlTmDlzJvh8/hNNbauq1NRUAKanqz0qOzsbgwYNAmMMW7duLRVreR0kZZ1jjGHcuHE4duwY/vrrL3h5eZVZR2JiIl566SV4eXkZLVQOoNROfY92olQ2rmXLliE3NxdpaWnYuHEjRowYgfXr12PkyJEAikd+6fV6ozpMTa+r6H49qarc7+zsbOzYsQMDBgyAh4dHqfNr167F2rVry213zZo1mD9/PqZOnYohQ4aYzPPXX3+hqKgISUlJWL16NQYOHIidO3ca7ThZXlthYWHo06cPsrOzcfjwYXz99dfIy8szudukOdcFPNnrlhBCCCE0sosQQgh5Ir/++itiY2Nx+PBhvPvuu7h27Zqho+FRCoUCEomkyvU0atQIwH/TG81V0qZCoahUOQAQiUT4559/0KhRI/Tr1w92dnZ45ZVXMHPmTNjZ2Rl9UW/UqFGlY6uKkuso717m5OSgb9++SElJwYEDB9C4cWOj8w4ODlAqlZDL5aXKZmdnmxyFxBjD+PHjsXHjRkRGRpbZcQIA9+7dQ8+ePSEQCHDo0KFS9QmFQqNj/fr1VY6radOm6NChAwYPHozff/8dvXv3Rnh4uKGDa+zYsUZt9e7du9L360k5ODiYHL1VVFQEtVpt8roAYOPGjVCpVBg/fnyV2o2IiMC7776Ld955B19//XWZ+Vq0aIGOHTvilVdeQVRUFLy9vfHhhx+a3Y6rqyvat2+Pfv36YdGiRfjiiy+wfPnyMnceNee6nuR1SwghhBDq7CKEEEKeSPPmzdG+fXv07NkTq1evxvjx4xEVFYU///zTKJ+joyOys7OrXE/79u1hb2+Pv//+G4wxs+MradPR0bEKVwf4+fnh5MmTuH//Pi5duoSMjAy8+uqryMzMxAsvvGDI179/fzx48KDGp12VXEdZ9zInJwd9+vRBQkICDhw4YHL6YMmaWPHx8Ubp6enpyMzMRFBQkFF6SUdXREQE1qxZgzfffLPM+O7du4cePXqAMYYjR47A09OzVJ7Y2FijIyQkpEpxmdKxY0fk5OTg4cOHAIqn8D3a1k8//WSU35z79aSCg4Px8OFDpKenG6WXXGdZ17V27Vq4uLhUaUplREQExo8fj9DQUKxevdrsde4EAgHatm2LmzdvVrrNEiWL/JdVhznX9aSvW0IIIeRZR51dhBBCSDVasmQJ7Ozs8PnnnxtNHwsICMCdO3eqXI9QKMQnn3yC69ev48svvzRZJiMjo9T6Wnfv3gUAsxfGL4uHhweCg4NhYWGBr7/+GjKZDOPGjTOc/+ijjyCTyTBhwgTk5eWVKs8Yw/bt258oBqC4UxCAyXtZ0nFz9+5d7N+/H23atDFZx4ABAyCRSBAZGWmUHhkZCY7jMHToUKO43377bUREROCnn34qtaPgo5KSktCjRw/odDocPnwY3t7eJvO1b9/e6CjZFbEycZnCGENMTAxsbW0Ndfr4+Bi19egOluberydVsqlCyQi2EpGRkZBKpRgwYECpMmfPnsWlS5cQGhpa6am8kZGRGD9+PN58802sWbOmUhs6KJVKnDp1Cn5+fpVq81FHjhwBAJN1mHtd1fW6JYQQQp5VtGYXIYQQUo3s7Ozw6aef4uOPP8Zvv/1mGAXUo0cPfPHFF5DL5bCwsKhSPdOnT8e1a9cwZ84cnDlzBqNGjYKXlxfy8vJw9OhR/Pzzz5g3bx6ef/55Qz2nTp2Cg4NDqR3+SkaMlXypPnv2LCwtLQEAr7zyiiHfkiVL4OrqikaNGuHBgwf4/fffsWPHDmzYsMFoGqOvry+2bNmCESNGoHXr1pg4caKh8+Tq1atYt24dGGMYNmyYoYxOpys1Ag4oXrh84MCBJu+Lp6cnGjdujFOnTuGDDz4wpCsUCvTv3x8XLlzADz/8AK1WazTKzMnJCU2aNAFQvNj7Z599htmzZ8Pe3h79+vVDbGws5s6di/Hjxxt1MHzwwQdYu3Ytxo4di+DgYKM6xWKx4RozMjLQs2dPpKWlYe3atcjIyEBGRoZR3KZGeT2qMnENGTIErVq1QuvWreHg4IDU1FRERkYiJiYGK1asqLCDqDL3Cyj+91GyE2F+fj4YY4bnrkOHDmV27AHF0wTHjRuHOXPmgM/no0OHDti/fz9+/vlnfPXVVyanMZasj/Voh+rjxo0bh/Xr1+POnTuG9v/44w+MGzcOrVu3xrvvvoszZ84YlWnTpo1hN9EuXbpg8ODBaN68OWxsbJCYmIhVq1bhzp07pTpmTbU1Z84cPHjwAC+88AI8PDyQm5uLqKgo/PLLL3j11VdLLXJv7nUBZb9uCSGEEGImRgghhJBKi4iIYABYbGxsqXMKhYI1atSINW3alGm1WsYYY7dv32Ycx7Hff//9iephjLG///6bvfjii8zJyYkJBAJmZ2fHevbsyVavXs1UKpUhn16vZ97e3mzSpEml6gZQ5vGoefPmsSZNmjCxWMxsbW3ZgAED2NGjR8u8L3fu3GETJkxgfn5+TCwWM6lUygIDA9mUKVNYQkKCIV9oaGiZ7Xt7e5dZP2OMzZ49m9nZ2TGlUmlIS0hIKPeaQkNDS9WzdOlS1qxZMyYSiVijRo3YnDlzmFqtNsrj7e1tVpxHjhwpt/05c+aUe02VjWvx4sWsQ4cOzM7OjvH5fObg4MD69+/Pdu/ebVYblb1f5T1fERERFbanVqvZnDlzWKNGjZhIJGLNmjVjP/74o8m8crmc2djYsBdeeKHcOktiMvff1eN5p06dylq1asVsbGyYQCBgrq6ubNiwYez48eNmtbVz507Wp08f5uLiwgQCAbO0tGQdO3ZkP/74I9NoNFW+rvJet4QQQggxD8dYJRb+IIQQQkiVhYSEQKvV4p9//qmV9g4dOoR+/frhypUrCAgIqJU2a0Nqaip8fX3x66+/YsSIEXUdDiHVqqG+bgkhhJDaRJ1dhBBCSC25fPky2rRpgxMnTqBDhw413l7Pnj3h5+eHX375pcbbqm2ffPIJ/vnnH8TFxYHHoyVIScPRkF+3hBBCSG2hNbsIIYSQWhIUFISIiIhSu9LVhJycHHTv3h0TJkyo8bbqwmeffQYLCwukpKTAy8urrsMhpFo09NctIYQQUltoZBchhBBCCCGEEEIIaTBo3D8hhBBCCCGEEEIIaTCos4sQQgghhBBCCCGENBjU2UUIIYQQQgghhBBCGgzq7CKEEEIIIYQQQgghDQZ1dhFCCCGEEEIIIYSQBoM6uwghhBBCCCGEEEJIg0GdXYQQQgghhBBCCCGkwaDOLkIIIYQQQgghhBDSYFBnFyGEEELIM6KgoAAdOnRA69atERwcjF9++aWuQyKEEEIIqXYcY4zVdRCEEEIIIaTm6XQ6qFQqWFhYQC6XIygoCLGxsXBwcKjr0AghhBBCqg2N7CKEEEIIeUbw+XxYWFgAAJRKJXQ6Heh3T0IIIYQ0NNTZRQghhBBSTxw9ehQhISFwd3cHx3HYsWNHqTwrV66Er68vJBIJ2rVrh2PHjhmdz83NRatWreDp6YmPP/4Yjo6OtRQ9IYQQQkjtqLedXQsXLkSHDh1gZWUFZ2dnDB06FDdu3DDKExYWBo7jjI5OnToZ5VGpVJg0aRIcHR0hk8kwePBg3L9/vzYvhRBCCCHELEVFRWjVqhWWL19u8vzWrVsxefJkzJo1CxcuXEC3bt0wcOBAJCUlGfLY2tri4sWLSEhIwG+//YYHDx7UVviEEEIIIbWi3q7ZNWDAALz++uvo0KEDtFotZs2ahfj4eFy9ehUymQxAcWfXgwcPEBERYSgnEolgb29vePz+++9j165diIyMhIODA6ZOnYrs7GycO3cOfD6/wjj0ej1SU1NhZWUFjuOq/0IJIYQQUgpjDAUFBXB3dwePV29/u3siHMdh+/btGDp0qCHtueeeQ9u2bbFq1SpDWvPmzTF06FAsXLiwVB3vv/8+evXqhVdffdVkGyqVCiqVyvBYq9Xi2rVr8PLyembvOyGEEFLb9Ho9Hjx4gDZt2kAgENR1OPUDayAyMjIYABYTE2NICw0NZUOGDCmzTG5uLhMKhWzLli2GtJSUFMbj8VhUVJTJMkqlkuXl5RmOq1evMgB00EEHHXTQQUcdHMnJydX2WaK+AcC2b99ueKxSqRifz2fbtm0zyvfBBx+wF154gTHGWHp6OsvLy2OMMZaXl8cCAwPZxYsXy2xjzpw5df4c00EHHXTQQQcdxceZM2eq/wNFA9VgugTz8vIAwGjUFgBER0fD2dkZtra26N69O+bPnw9nZ2cAwLlz56DRaNCvXz9Dfnd3dwQFBeHEiRPo379/qXYWLlyIefPmlUpPTk6GtbV1dV4SIYQQQsqQn58PLy8vWFlZ1XUoT43MzEzodDq4uLgYpbu4uCA9PR0AcP/+fYwbNw6MMTDGMHHiRLRs2bLMOj/99FNMmTLF8Dg5ORlBQUE4c+YM3NzcauZCCCGEEGIkLS0NHTt2LPUeT8rWIDq7GGOYMmUKunbtiqCgIEP6wIED8eqrr8Lb2xsJCQmYPXs2evXqhXPnzkEsFiM9PR0ikQh2dnZG9T36ofBxj3/oK/mwbW1tTZ1dhBBCSC2jJQRKe/yeMMYMae3atUNcXJzZdYnFYojFYsNjGxsbAICbmxs8PT2fPFhCCCGEmI2WEDBfg+jsmjhxIi5duoR///3XKH3EiBGG/w8KCkL79u3h7e2NPXv2YPjw4WXW9+iHwsc9/qGPEEIIIeRp4OjoCD6fX+oHu4yMDPolmBBCCCHPlHrfLThp0iTs3LkTR44cqfAXRjc3N3h7e+PWrVsAAFdXV6jVauTk5Bjlow+FhBBCCKlvRCIR2rVrhwMHDhilHzhwAF26dKmjqAghhBBCal+97ewqWWdi27ZtOHz4MHx9fSssk5WVheTkZMMaE+3atYNQKDT6UJiWlobLly/Th0JCCCGEPHUKCwsRFxdnmIqYkJCAuLg4JCUlAQCmTJmCNWvWYN26dbh27Ro++ugjJCUl4b333qvDqAkhhBBCale9ncYYHh6O3377DX///TesrKwMQ/ZtbGwglUpRWFiIuXPn4uWXX4abmxsSExMxc+ZMODo6YtiwYYa848aNw9SpU+Hg4AB7e3tMmzYNwcHB6NOnT11eHiFPJZ1OB41GU9dhEEKeAUKhEHw+v67DeOqcPXsWPXv2NDwuWUc0NDQUkZGRGDFiBLKysvDFF18gLS0NQUFB2Lt3L7y9vWs1Tnq/IIQ0VPT+REj9wDHGWF0HURVlrakVERGBsLAwKBQKDB06FBcuXEBubi7c3NzQs2dPfPnll/Dy8jLkVyqVmD59On777TcoFAr07t0bK1euNMpTnvz8fNjY2CAvL48WqCcNFmMM6enpyM3NretQCCHPEFtbW7i6upp8z6f337px//59eHl5ITk52eTyEfR+QQh5FpT3/kRITajo/ZeUVm9HdlXURyeVSrFv374K65FIJFi2bBmWLVtWXaER0uCUfHFxdnaGhYUFvbETQmoUYwxyuRwZGRkAYFh+gDz96P2CENKQNfT3J11mEphKXm4eTmwBvmOjWoqIkKqrt51dhJDaodPpDF9cHBwc6jocQsgzQiqVAijeNMbZ2ZmmjNQD9H5BCHkWNNT3J11mEvK/GW5WXutp26jDizz1qLOLEFKukjVXLCws6jgSQsizpuTvjkajaTBfJhoyer8ghDwrGuL7U8mILosRX4LvbHrzN11GAuRbZ1c4+ouQpwF1dhFCzEJTUQghtY3+7tRP9LwRQhq6hvx3ju/sC4FHQF2HQcgT49V1AIQQQgghhBBCCKldW25fxeCov7Du+iWjdKVWW0cREVJ9qLOLEPJM69GjByZPnlzndc6dOxetW7eu1jgIIaS6rFixAoGBgejRo0ddh0IIIaQcuswkaFOumzwUyVeheXjPkDf2YTp2Jd3GlZzM/8ozBtv1P8Btw3I8kBfVxSUQUi1oGiMhpNZodXqkFqgh4nNwsRTV+BDwsLAw5ObmYseOHTXaTl3hOA7bt2/H0KFD6zoUQkgDFx4ejvDwcMPW54QQQp4+5S0yrwWHcc6dYMm0iHzrE4icfPBm0xZoamOHYHsnQFcIAEhVKqHS6ZClUsBRIjWU1zNWK9dASHWhzi5CSI1TanRYGJ2In06n4EGhGgDQ1sMKn3T3wWstXeo4OkIIIcQ8PXr0QOvWrfHDDz/UaX3VHceTiI6ORlhYGBITE2ulHCGkbOUtMn88Owt7T/4LPtNh8sN0dHTyQRtHF7RxLP4srk25DgDwkkqRHfoh7hcVgM8rngh2LScTow7vwsqAANBqXqS+oGmMhJAapdLqMSgiDkti7uGVYGfsHdMaW0cFw1kmwojf4rEoOrHWYikqKsJbb70FS0tLuLm54dtvvy2VR61W4+OPP4aHhwdkMhmee+45REdHG85nZWVh5MiR8PT0hIWFBYKDg7F58+ZKx7Jo0SK4uLjAysoK48aNg1KpNDofGxuLvn37wtHRETY2NujevTvOnz9vOO/j4wMAGDZsGDiOMzy+c+cOhgwZAhcXF1haWqJDhw44ePBgpeMjhJC6kl6gwqLoRIz54wo+2HkDR+/mgNXCiIKwsLAGO1K2Jqbsl2fv3r3gOK7M47XXXqu1WAh5FpUsMv/o0T34eWxu2wHrHpxCWxvbcsvbiSXFo73+b/6Fk4jLysCUK/Gg8V2kvqDOLkJIjVp96j6OJeZi/7g2WD4kAAP9HfFaSxf8M7YNZvX0wcx9t3E7s3a2L54+fTqOHDmC7du3Y//+/YiOjsa5c+eM8owZMwbHjx/Hli1bcOnSJbz66qsYMGAAbt26BQBQKpVo164ddu/ejcuXL+Odd97B6NGjcfr0abPj+P333zFnzhzMnz8fZ8+ehZubG1auXGmUp6CgAKGhoTh27BhOnTqFpk2bYtCgQSgoKABQ3BkGABEREUhLSzM8LiwsxKBBg3Dw4EFcuHAB/fv3R0hICJKSkqp83wghpLasPJmMRov+xReH7uLGQzl2XXuI7j+fQ+9fziNXoanr8IiZevbsibS0NKPj/v37hh9xZs+eXdchEvJMYIxBof3vb+cwN3cMkqeWW0aXkVBqra+ljX0Q5tkIv/u4oeHuQ0kaGursIoTUqJ/OpOCVYGd087UrdW5WL1/YSYX4JTalxuMoLCzE2rVr8c0336Bv374IDg7G+vXrodPpDHnu3LmDzZs3448//kC3bt3QpEkTTJs2DV27dkVERAQAwMPDA9OmTUPr1q3RuHFjTJo0Cf3798cff/xhdiw//PADxo4di/Hjx8Pf3x9fffUVAgMDjfL06tULb775Jpo3b47mzZvjp59+glwuR0xMDADAyan41zZbW1u4uroaHrdq1QrvvvsugoOD0bRpU3z11Vdo3Lgxdu7c+UT3jxBCatqOKxkI//sG3u3ogdSZ3XBiQgfcmf48doe1RlxaAV7ffLlW46loNDBjDEuWLEHjxo0hlUrRqlUr/Pnnn0Z5oqKi0LVrV9ja2sLBwQEvvfQS7ty5U61xVNROWFgYYmJisHTpUsPIqpKpg9URnylSqRSurq6Gw8nJCdOmTcOFCxdw+PBhBAcHP3EbhJDyMcbw8elo9N6zBXlqVYX5ObEFAEC+dTYKlr1pdPBWj8F3R7+F5bZ5RnkJeZrRml2EkBrDGMP1h0X48HnTixlLhXx0bmSDaxk1v9PLnTt3oFar0blzZ0Oavb09/P39DY/Pnz8PxhiaNWtmVFalUsHBwQEAoNPpsGjRImzduhUpKSlQqVRQqVSQyWRmx3Lt2jW89957RmmdO3fGkSNHDI8zMjLw+eef4/Dhw3jw4AF0Oh3kcnmFI7SKioowb9487N69G6mpqdBqtVAoFDSyixDy1FsYnYjefvb4cbC/YQMTHo/DiwGO+Hl4c7y6KR7nU/LR1sO6VuJ5dDSwq6srZs6ciXPnzhl2zv3ss8+wbds2rFq1Ck2bNsXRo0fx5ptvwsnJCd27dwdQ/Dd5ypQpCA4ORlFRET7//HMMGzYMcXFx4PHM+825ojgqamfp0qW4efMmgoKC8MUXXwD47weT6oivIjqdDm+++SYOHDhAHV2E1KJUeSHW3riEHJUSB+4n4JXG5a+2xXdsBOtp2wzrfpWFE1vguIaDIvku+ns1rs6QCalW1NlFCKkxHMfBUsRHeoG6zDxpBSr4O9b8r0PmrPei1+vB5/Nx7tw58Pl8o3OWlpYAgG+//Rbff/89fvjhBwQHB0Mmk2Hy5MlQq8u+xqoICwvDw4cP8cMPP8Db2xtisRidO3eusJ3p06dj3759+Oabb+Dn5wepVIpXXnml2uMjhJDqlJqvwpnkfGwdFWxyp96hgU5wlAmx48rDWunsKhkN/Ouvv6Jv374AgPXr18PT0xNAcSfRd999h8OHDxt+RGncuDH+/fdf/PTTT4bOrpdfftmo3rVr18LZ2RlXr15FUFDQE8dRoqJ2RCIRLCws4OrqWqlyT0qn02H06NE4cOAADh06hJYtWz5xnYQQ83jIrHDkpZE4nZFaYUdXCb5jowrzHE65h357t8BKKML54WHwtbZ9wkgJqRk0jZEQUqNeDnJGxNlUKDS6UufOJOfhfEoBXgmu+R0Z/fz8IBQKcerUKUNaTk4Obt68aXjcpk0b6HQ6ZGRkwM/Pz+go+YJw7NgxDBkyBG+++SZatWqFxo0bG9bzMlfz5s2N4gBQ6vGxY8fwwQcfYNCgQWjRogXEYjEyMzON8giFQqNpmCXlwsLCMGzYMAQHB8PV1ZV2uiKEPPXk6uK/ZY4WQpPnBXwe7KRCKLT6WomnotHAV69ehVKpRN++fWFpaWk4fv31V6NpgHfu3MGoUaPQuHFjWFtbw9e3eHc0c0fbmjMq+UnaedL4ylPS0bV//34cOnQIrVq1euI6CSEVK+T4hnW3WiizMdZaYlh7S5eR8MT1d3X1RAcnN7zUyA/OUprOSJ5eNLKLEFKjpr3gjd8vPcDQXy9i+ZAANHW0AGMMB25lY8yfV9HazRKDmzvWeByWlpYYN24cpk+fDgcHB7i4uGDWrFlG0zSaNWuGN954A2+99Ra+/fZbtGnTBpmZmYZpF4MGDYKfnx/++usvnDhxAnZ2dvjuu++Qnp6O5s2bmx3Lhx9+iNDQULRv3x5du3bFpk2bcOXKFTRu/N9QcD8/P2zYsAHt27dHfn4+pk+fDqlUalSPj48PDh06hOeffx5isRh2dnbw8/PDtm3bEBISAo7jMHv2bOj1tfPlkBBCqsrTRgxbiQD7b2Whl599qfN3suS4nSVHkIv5U8afREWjgUv+ru7ZswceHh5G58RiseH/Q0JC4OXlhV9++QXu7u7Q6/UICgoye7StubtQVrWdJ42vLCUdXfv27aOOLkJq0U8pKVjk2R/b/1qMJtrCMvM9yZpbIj4f+we9BkuhyORIXEKeFtTZRQipUS1cLLErtDVGbI5Hs29OIMDJAgUqHVLyVejUyAbbR7eEgF87g0y//vprFBYWYvDgwbCyssLUqVORl5dnlCciIgJfffUVpk6dipSUFDg4OKBz584YNGgQAGD27NlISEhA//79YWFhgXfeeQdDhw4tVU95RowYgTt37uCTTz6BUqnEyy+/jPfffx/79u0z5Fm3bh3eeecdtGnTBo0aNcKCBQswbdo0o3q+/fZbTJkyBb/88gs8PDyQmJiI77//HmPHjkWXLl3g6OiITz75BPn5+U9w1wghpOZJhHyMae+OVafuY1RrV7R0szKcU2v1mLL7JuykQrxaCyOBAePRwI0aFU/rKRkN3L17dwQGBkIsFiMpKckwZfFxWVlZuHbtGn766Sd069YNAPDvv/9WaxzmtiMSiUqNBK6O+EzR6XR46623sG/fPhw8eNBobTFCSM1R6bRYeS8B94UyRPWdghl+zUzm48QWZk1XLI+VSGz0OLEgDz5WNk9UJyHVjTq7CCE1rpefPZJndMUf8Rk4l5IPsYCHF/0d0c3XtkZ/EYqMjDR6bGlpiQ0bNmDDhg2GtOnTpxvlEQqFmDdvHubNm2eyTnt7e+zYsaPcdqOjoyuMbebMmZg5c6ZR2uLFiw3/36ZNG8TGxhqdf+WVV4weh4SEICQkxCjNx8cHhw8fNkoLDw+vMB5CCKlrc/o0RvTdHHRZdRZh7dzQo7EdUvJU+PlMCm5lybHtzZawEPErrqgaVDQa2MrKCtOmTcNHH30EvV6Prl27Ij8/HydOnIClpSVCQ0NhZ2cHBwcH/Pzzz3Bzc0NSUhJmzJhRrXEAMKsdHx8fnD59GomJibC0tIS9vX21xPc4vV6Pt956Czt27MCff/4JNzc3pKenG+VxcnIqtS4mIeTJifkCHHlpJH69eRlTW3aslVFXWr0e004dxuprcfh38Bto7+RW420SYi7q7CKE1AqJkI/Rbd0wui29CRJCCCnNRiJA9Dvt8PXRe/jlTApWnLwPHgcMCXRCxKuB6OhVu6MGKhoN/OWXX8LZ2RkLFy7E3bt3YWtri7Zt2xp+yODxeNiyZQs++OADBAUFwd/fHz/++CN69OhRrXGY0860adMQGhqKwMBAKBQKJCQkwMfHp8JykZGRGDNmjNnTKWNjY/Hbb78BgGFE9ONycnJga2tbqXtACDGPs1SGaa2eq7X2+ByHxIJ8qHQ6HE1Lps4u8lThmLnvXsSk/Px82NjYIC8vD9bWtbMVNiG1SalUIiEhAb6+vpBIJHUdDiHkGVLe3x96/60b9+/fh5eXF5KTk0vtCFid7xd6PUOeUgupkAeJkEYB1ZW5c+ciOjq63BHL0dHRCAsLq/RmKFUtR8jT4Gn6fJyvVuFabhaec3avk/bz1CqcSL+PgY2a1En7z4ry3n9NWbVqFVatWmX4G9uiRQt8/vnnGDhwIIDiNSHnzZuHn3/+GTk5OXjuueewYsUKtGjRoiYvo1bRboyEEEIIIeSpwuNxsLMQUkdXHdu3bx+WLFlS12EQQsqx/Mp5dNqxAeH/7q+T9m1EYuroegp5enpi0aJFOHv2LM6ePYtevXphyJAhuHLlCgBgyZIl+O6777B8+XLExsbC1dUVffv2RUFBQR1HXn2os4sQQgghhJRrxYoVCAwMrPQUPFK/nTx5Eh07dqzrMAgh5chWKcDnODzvWvFon5p2vzAfH586goT83LoOpcEqKChAfn6+4VCpVCbzhYSEYNCgQWjWrBmaNWuG+fPnw9LSEqdOnQJjDD/88ANmzZqF4cOHIygoCOvXr4dcLjdMRW8IqLOLEEIIIYSUKzw8HFevXjVrAw7ybPHx8cHkyZNrrRwhxNg3nXrh1oh3MKJxQF2HgrcP7cDXl87g+1MHoE25XurQZSbVdYj1XmBgIGxsbAzHwoULKyyj0+mwZcsWFBUVoXPnzkhISEB6ejr69etnyCMWi9G9e3ecOHGiJsOvVbRAPSGEEEIIIaRKqLOLkLrna21b1yFAl5mEcXFbUWAbgG4nj6Lg8AOT+aynbQPfsVEtR9dwXL16FR4eHobHYrG4zLzx8fHo3LkzlEolLC0tsX37dgQGBho6tFxcXIzyu7i44N69ezUTeB2gzi5CCCGEEEIIIaQeOZxyDy3sHOFiIavrUAAATCVHb8UDhAyeCL7z26XO6zISIN86G0wlr4PoGg4rKyuzN+bx9/dHXFwccnNz8ddffyE0NBQxMTGG8xzHGeVnjJVKq8+os4sQQgghhBBCCKknCtQqvHpwBxRaLf4d8gbaOrrWdUgGfGdfCDzqfkolAUQiEfz8/AAA7du3R2xsLJYuXYpPPvkEAJCeng43NzdD/oyMjFKjveozWrOLEEIIIYQQQgipJ9IVRfCztoOXpTVa2jvXdTilKLVarL1+EXuT7tR1KOQRjDGoVCr4+vrC1dUVBw4cMJxTq9WIiYlBly5d6jDC6kWdXYQQAuDw4cMICAiAXq+v9rpfeeUVfPfdd9Veb03LysqCs7MzEhMT6zoU0gBNmzYNH3zwQV2HQQghhNQ7TW3scWroaMSEjISA9/R9pV925RzGH43CrNijYIzVdTjPpJkzZ+LYsWNITExEfHw8Zs2ahejoaLzxxhvgOA6TJ0/GggULsH37dly+fBlhYWGwsLDAqFGj6jr0avP0vTIIIaSahIWFgeM4cBwHgUCARo0a4f3330dOTk6pvB9//DFmzZoF3v8/MERGRhrKchwHFxcXhISE4MqVK0bl1Go1lixZglatWsHCwgKOjo54/vnnERERAY1GAwD4/PPPMX/+fOTn5xvKKZVKhIWFITg4GAKBAEOHDjV5DStWrEDz5s0hlUrh7++PX3/9tVSe/Px8zJo1CwEBAZBIJHB1dUWfPn2wbds2wweMHj16VHoh4IULFyIkJAQ+Pj4AgIsXL2LkyJHw8vKCVCpF8+bNsXTp0lLl4uPj0b17d0ilUnh4eOCLL74w+qCzbds29O3bF05OTrC2tkbnzp2xb98+ozp++eUXdOvWDXZ2drCzs0OfPn1w5syZCmNmjGHu3Llwd3eHVCpFjx49Sj1n7777Lpo0aQKpVAonJycMGTIE169fL7dec67d3Of0cTk5ORg9erRhV53Ro0cjNzfXcP7xf4uPHhkZGWXWu3DhQnTo0AFWVlZwdnbG0KFDcePGDaM8c+fORUBAAGQymeE+nz59utx4b9y4gZ49e8LFxQUSiQSNGzfGZ599Zvj3Dhi/9h49WrRoYcjz8ccfIyIiAgkJCWbdJ0IIIYT8h+M4uFpY1nUYJo31bwl/G3u84RcILav+H5JJxR48eIDRo0fD398fvXv3xunTpxEVFYW+ffsCKP4cNnnyZEyYMAHt27dHSkoK9u/fDysrqzqOvPpQZxchpEEbMGAA0tLSkJiYiDVr1mDXrl2YMGGCUZ4TJ07g1q1bePXVV43Sra2tkZaWhtTUVOzZswdFRUV48cUXoVarARR3dPXv3x+LFi3CO++8gxMnTuDMmTMIDw/HsmXLDJ0sLVu2hI+PDzZt2mSoW6fTQSqV4oMPPkCfPn1Mxr5q1Sp8+umnmDt3Lq5cuYJ58+YhPDwcu3btMuTJzc1Fly5d8Ouvv+LTTz/F+fPncfToUYwYMQIff/wx8vLyqnTfFAoF1q5di/HjxxvSzp07BycnJ2zcuBFXrlzBrFmz8Omnn2L58uWGPPn5+ejbty/c3d0RGxuLZcuW4ZtvvjEa2Xb06FH07dsXe/fuxblz59CzZ0+EhITgwoULhjzR0dEYOXIkjhw5gpMnT6JRo0bo168fUlJSyo17yZIl+O6777B8+XLExsbC1dUVffv2RUFBgSFPu3btEBERgWvXrmHfvn1gjKFfv37Q6XRl1mvOtZvznJoyatQoxMXFISoqClFRUYiLi8Po0aMN50eMGIG0tDSjo3///ujevTucncueuhATE4Pw8HCcOnUKBw4cgFarRb9+/VBUVGTI06xZMyxfvhzx8fH4999/4ePjg379+uHhw4dl1isUCvHWW29h//79uHHjBn744Qf88ssvmDNnjiHP0qVLjeJNTk6Gvb290WvM2dkZ/fr1w+rVq82+V4QQQsizrFCjxq83L0NbAzMRqpODRIprr43HtFbPQcjj13U4z6S1a9ciMTERKpUKGRkZOHjwoKGjCyjuLJ07dy7S0tKgVCoRExODoKCgOoy4BjDyRPLy8hgAlpeXV9ehEFIjFAoFu3r1KlMoFKXOaeWFZR46pcL8vAq5WXkrKzQ0lA0ZMsQobcqUKcze3t4obdKkSeyVV14xSouIiGA2NjZGaTt37mQA2KVLlxhjjC1evJjxeDx2/vz5Um2r1WpWWPhfzHPnzmXdunUzO07GGOvcuTObNm2aUdqHH37Inn/+ecPj999/n8lkMpaSklKqfEFBAdNoNIwxxrp3784+/PBDk+2b8tdffzFHR8cK802YMIH17NnT8HjlypXMxsaGKZVKQ9rChQuZu7s70+v1ZdYTGBjI5s2bV+Z5rVbLrKys2Pr168vMo9frmaurK1u0aJEhTalUMhsbG7Z69eoyy128eJEBYLdv3y4zjymPX/ujynpOH3f16lUGgJ06dcqQdvLkSQaAXb9+3WSZjIwMJhQK2a+//lqpeDMyMhgAFhMTU2aekve0gwcPVqrujz76iHXt2rXM89u3b2ccx7HExESj9MjISObl5VVmufL+/tD7b91ITk5mAFhycnKpc+U9X4QQ0pDU1d+7RRdOMvy0iA3Yu7VW2zWH5v41lv1JO6a5f61K50n5ynv/JabV25Fd5kzPYGZMZ1GpVJg0aRIcHR0hk8kwePBg3L9/vzYvhZB6K66bV5nHnY9DjfJe6utfZt5bH7xmlPdySGuT+Z7U3bt3ERUVBaFQaJR+9OhRtG/fvtyyubm5+O233wDAUH7Tpk3o06cP2rRpUyq/UCiETPbfVtAdO3bEmTNnoFKpzI5XpVJBIpEYpUmlUpw5cwYajQZ6vR5btmzBG2+8AXd391LlLS0tIRBUbdNdc+4JAOTl5cHe3t7w+OTJk+jevTvEYrEhrX///khNTS1z7S+9Xo+CggKjeh4nl8uh0WjKzZOQkID09HT069fPkCYWi9G9e3ecOHHCZJmioiJERETA19cXXl6V+zf2+LVXxcmTJ2FjY4PnnnvOkNapUyfY2NiUGfOvv/4KCwsLvPLKK0bpHMchMjKy3HgBlBmzWq3Gzz//DBsbG7Rq1cqQHhYWhh49epRZ7+3btxEVFYXu3buXmWft2rXo06cPvL29jdI7duyI5ORk3Lt3r8yyhBBCCClmIxLDUSLFqCaBdR1KmXQZCdCmXIc25To0968hJv44Vp6Igi6Dli0gtatq34KeAiXTMzp06ACtVotZs2ahX79+uHr1quELZsl0lsjISDRr1gxfffUV+vbtixs3bhjmok6ePBm7du3Cli1b4ODggKlTp+Kll17CuXPnwOfTkEtC6rvdu3fD0tISOp0OSqUSAEotFp+YmGiysygvLw+WlpZgjEEulwMABg8ejICA4u2Ub926VW4nwKM8PDygUqmQnp5e6gt/Wfr37481a9Zg6NChaNu2Lc6dO4d169ZBo9EgMzMTfD4fOTk5hniqU1n35FEnT57E77//jj179hjS0tPTDWt8lSjZwjg9PR2+vr6l6vn2229RVFSE1157rdS5EjNmzICHh0e50wPT09ON2nu0/cc7U1auXImPP/4YRUVFCAgIwIEDByASicqs+3Gmrr0q0tPTTU5FdHZ2NlzP49atW4dRo0ZBKpUapfv7+8PGxsZkGcYYpkyZgq5du5Yaor579268/vrrkMvlcHNzw4EDB+Do6Gg47+bmZnLjhi5duuD8+fNQqVR455138MUXX5hsOy0tDf/884+hs/hRHh4eAIr/vZn7uiCEEEKeVe8FtsHopi0g5j99X+M5sQUAQL51tiHtosgWPT37QqzXoU/SHjg9ko+Qmvb0vUrMFBUVZfQ4IiICzs7OOHfuHF544QUwxvDDDz9g1qxZGD58OABg/fr1cHFxwW+//YZ3330XeXl5WLt2LTZs2GD4ArVx40Z4eXnh4MGD6N+/f61fFyH1SetjyWWe4x6bn9/ywI0ycgIcZzzINGhX3BPF9aiePXti1apVkMvlWLNmDW7evIlJkyYZ5VEoFKVGUAGAlZUVzp8/D61Wi5iYGHz99ddG6wsxxsBxnFlxlHRMlHSamWP27NlIT09Hp06dwBiDi4sLwsLCsGTJEvD5fMOi7+bGUBll3ZMSV65cwZAhQ/D5558bzf83FU95cW7evBlz587F33//Xeb6U0uWLMHmzZsRHR1tiGnTpk149913DXn++ecfww8Uptp/PO2NN95A3759kZaWhm+++QavvfYajh8/DolEgoEDB+LYsWMAAG9v71Ijgsu79qowdV/K+rd18uRJXL161eRGBeUtsj9x4kRcunQJ//77b6lzPXv2RFxcHDIzM/HLL7/gtddew+nTpw3Px8KFC03WuXXrVhQUFODixYuYPn06vvnmG3z88cel8kVGRsLW1tbkgv1VeV0QUteysrLQvHlznDlzplTnfnV45ZVX0KVLF0yZMqXa665JNX1fyLOtqq+L6OhohIWFVXpn6aqWqw0yofk/ztUmvmMjWE/bBqb67z39ecbQ7dRx+MlkEPddDmsbB/AdG9VhlORZUm+nMT7u8ekZ5kxnOXfuHDQajVEed3d3BAUFlTl9RKVSIT8/3+gg5FnFl8rKPHhiifl5JVKz8laFTCaDn58fWrZsiR9//BEqlQrz5s0zyuPo6Ghyh0Yejwc/Pz8EBATg3XffxejRozFixAjD+WbNmuHatWtmxZGdnQ0AcHJyMjt2qVSKdevWQS6XIzExEUlJSfDx8YGVlRUcHR3h5OQEOzs7s2OojLLuCQBcvXoVvXr1wttvv43PPvvM6Jyrq2upEUklOwY+PuJq69atGDduHH7//fcyR2x98803WLBgAfbv34+WLVsa0gcPHoy4uDjD0b59e7i6ugKAyfYfb9vGxgZNmzbFCy+8gD///BPXr1/H9u3bAQBr1qwx1Lt3716zr70qXF1d8eDBg1LpDx8+LBVzSWytW7dGu3btzG5j0qRJ2LlzJ44cOQJPT89S50teI506dcLatWshEAiwdu3aCuv18vJCYGAgRo4ciUWLFmHu3LmlFvlnjGHdunUYPXq0yZFzVXldEFJTzN3B9/GdaitTNj09HZMmTULjxo0hFovh5eWFkJAQHDp0yJDH1A6+R48eRUhICNzd3cFxHHbs2FEq/oKCAkyePBne3t6QSqXo0qULYmNjS+UzJ4awsDCzd5Qt676Ys+QIUDzS1tfXFxKJBO3atTP82PBovRXVY25bppTX/qpVq9CyZUtYW1sbdg/+559/zLoXFcVjznNa2XgBwMfHx+RuuOHh4U8cc1XuhzllzInZ1Ouiqvbu3VvmLsccx5U70rw26DKTDNMAS44/zx3GqSsnDY91mUl1GmNZ+I6NIPAIMBxCz+aIfnkc1g0cCe8mbaiji9SqBtHZZWp6RnnTWUrOpaenQyQSwc7Orsw8j1u4cKFhe3gbG5tKr/FCCKlbc+bMwTfffIPU1FRDWps2bXD16tUKy3700Ue4ePGioVNk1KhROHjwoNEugiW0Wq3RrneXL1+Gp6en0fQwcwmFQnh6eoLP52PLli146aWXwOPxwOPxMGLECGzatMnoekoUFRVBq9VWuj2g7Hty5coV9OzZE6GhoZg/f36p8507d8bRo0cNO1YCwP79++Hu7m70xXDz5s0ICwvDb7/9hhdffNFkDF9//TW+/PJLREVFlVo/zMrKCn5+foZDKpXC19cXrq6uOHDggCGfWq1GTEwMunTpUu71MsYM66l5eHgY6n10al1F114VnTt3Rl5eHs6cOWNIO336NPLy8krFXFhYiN9//x3jxo0zq27GGCZOnIht27bh8OHDJqeQllWuMmvLlZTRaDSGUXwlYmJicPv27TJjvnz5MoRCIVq0aFGp9gipKRXt4Gtqp1pzyyYmJqJdu3Y4fPgwlixZgvj4eERFRaFnz55GX+pN7eBbVFSEVq1aGe0A+7jx48fjwIED2LBhA+Lj49GvXz/06dPHaBdbc2OoLFP3xZwdYbdu3YrJkydj1qxZuHDhArp164aBAwciKSmpUvWYk8eUitr39PTEokWLcPbsWZw9exa9evXCkCFDSo34fZw58ZjznFY2XgCIjY012g235D3x8R2nqxJzVe6HOWXMidnU66KqevbsWWqX4/v376Nv375wdHTE7NmzK66khugyk5D/zXAULHvTcKQvD8V7Z/5F5+NHsXPNFBQsexP53wx/aju8HsergRkIhJil1pfErwETJkxg3t7eRjsTHD9+nAFgqampRnnHjx/P+vfvzxhjbNOmTUwkEpWqr0+fPuzdd9812ZZSqWR5eXmGo2RXBNoNijRU9Xl3rbJ2xGvXrh0LDw83PP7xxx9Zu3btjPKY2o2RseLdHIODg5ler2dKpZJ169aN2dnZseXLl7O4uDh2584dtnXrVta2bVt24cIFo1jGjh1rVNeVK1fYhQsXWEhICOvRowe7cOGCUZkbN26wDRs2sJs3b7LTp0+zESNGMHt7e5aQkGDIk52dzQICApinpydbv349u3LlCrt58yZbu3Yt8/PzYzk5OYyx4t0YR40aZWij5EhLSzN57y5dusQEAgHLzs42pF2+fJk5OTmxN954g6WlpRmOjIwMQ57c3Fzm4uLCRo4cyeLj49m2bduYtbU1++abbwx5fvvtNyYQCNiKFSuM6snNzTXkWbx4MROJROzPP/80ylNQUGAy3hKLFi1iNjY2bNu2bSw+Pp6NHDmSubm5sfz8fMYYY3fu3GELFixgZ8+eZffu3WMnTpxgQ4YMYfb29uzBgwdl1mvOtTNW8XNqyoABA1jLli3ZyZMn2cmTJ1lwcDB76aWXSuVbs2YNk0gkRs/Jo/z9/dm2bdsMj99//31mY2PDoqOjjWKWy4t3Py0sLGSffvopO3nyJEtMTGTnzp1j48aNY2KxmF2+fNlQz4wZM9jo0aMNjzdu3Mi2bt3Krl69yu7cucN+//135uHhwd54441SMb355pvsueeeK/Pa58yZw3r16lXmedqN8elT1d0Yn/bdexkzbwffsnaqNafswIEDmYeHh9FOvSVK/laXKG8HXwBs+/btRmlyuZzx+Xy2e/duo/RWrVqxWbNmVToGc3eULWHODr6mdoTt2LEje++994zyBQQEsBkzZlSqnqrkqWr7dnZ2bM2aNeXWW9l4TD2n1RXvhx9+yJo0aVLujshViblEVe5HRWXKirm810VZjhw5wry9vcvNo9Vq2euvv84cHR0NO25XVK6mPh+X7FioPL+Xae5fY5r711jKnTj25u5NzH/jcqZIusKU5/fWy10NUwrz2eILp5iukv8WSTHajbHy6n1n18SJE5mnpye7e/euUfqdO3cYAHb+/Hmj9MGDB7O33nqLMcbYoUOHGIBSXxxatmzJPv/8c7Papw/bpKFriJ1dJR3dSUlJjLHiDiOpVMquX79uyFNWZ9e9e/eYQCBgW7cWb/msVCrZwoULWXBwMJNIJMze3p49//zzLDIykmk0GsZY8T20trZmJ0+eNKrL29ubASh1lLh69Spr3bo1k0qlzNramg0ZMsQoxhK5ublsxowZrGnTpkwkEjEXFxfWp08ftn37dsMHxe7du5tsa86cOWXev06dOrHVq1cbHs+ZM8dkHY9/GLx06RLr1q0bE4vFzNXVlc2dO9foA2tZsYSGhlZ4b8qLlzHG9Ho9mzNnDnN1dWVisZi98MILLD4+3nA+JSWFDRw4kDk7OzOhUMg8PT3ZqFGjTN7XR5l77RU9p6ZkZWWxN954g1lZWTErKyv2xhtvlPriyxhjnTt3ZqNGjSqzHgAsIiLC6LGpoySPQqFgw4YNY+7u7kwkEjE3Nzc2ePBgdubMGaN6Q0NDWffu3Q2Pt2zZwtq2bcssLS2ZTCZjgYGBbMGCBaX+RuTm5jKpVMp+/vnnMmNu1qwZ27x5c5nnqbPr6VPVzq6z7ezKPG5+8JpR3vPPe5SZ9/rbxh3Bcb39TOarisffM+7cucMCAwOZi4uLIe3DDz9kAwYMqHTZrKwsxnEcW7BggVmx7N27l4nFYqZUKkudM9Uxkp+fzwCwgwcPGqV36tTJ8PqtTAyV7ewq67486tatWwyA4W+ySqVifD7fqJOeMcY++OAD9sILL5hdT1XzVLZ9rVbLNm/ezEQiEbty5UqZ9VYlHnM6u6pyv1QqFXNwcGDz5883So+IiKjwvamimE3dj4rqNecelhUzY+W/LspSUadVSUeXg4MDu3jxotnlarqzy1RHluL/nyvLy/O0Uuu0zGn9jww/LWI7E2/VdTj1EnV2VV697ezS6/UsPDycubu7s5s3b5o87+rqyhYvXmxIU6lUzMbGxvDlLTc3lwmFQsOXVsYYS01NZTwej0VFRZkVB33YJg1dfe7sqozp06ezd955p0bqXr58Oevbt2+N1F2T9uzZw5o3b850Ol1dh0IaoN27d7PmzZsbOoVNoc6up8fy5ctZ8+bNWZMmTRp0Zxefz2cymYxJJBJDB/F3331nyDNkyJBSo3TNKXv69GkGoFRHRVkuXrzIALDExMRS58rqGOncuTPr3r07S0lJYVqtlm3YsIFxHMeaNWtW6Rgq29lV1n0podfrWUhICOvatashLSUlhQFgx48fN8o7f/58Q8zm1FOVPJVp/9KlS0wmkzE+n89sbGzYnj17yq23KvGY09lVlfu1detWxufzWUpKilH6tm3bmL+/f5ViLu9+lFVvZe5hWTEzVv7roizldVpptVo2cuRI5uDgwOLi4swux1jddHZVJs/TaPrJw6zr3xvZ0dSkug6lXqLOrsqrt7sxhoeH47fffsPff/8NKysrwxpbNjY2kEql4DgOkydPxoIFC9C0aVM0bdoUCxYsgIWFBUaNGmXIO27cOEydOhUODg6wt7fHtGnTEBwcXO729oSQhmfWrFlYsWIFdDqdYVe/6iIUCrFs2bJqrbM2DBo0CLdu3UJKSgqtT0iqXVFRESIiIiAQ1NuPIs+U8PBwhIeH4/79+1X6e1Afdu8FKt7Bt7ydassryyq5e25VdirdsGEDxo4dCw8PD/D5fLRt2xajRo3C+fPnqxRDZVS0g295O8Kas4OuOfWUl8fUDr5NmjQxq31/f3/ExcUhNzcXf/31F0JDQxETE4PAwECT9Xbr1q3SMVdGZe7X2rVrMXDgQLi7uxulDxs2DMOGDSuzjfJiLu9+lFVveWXMjRmo3h18dTodRo8ejf379+PQoUNo1arVE9dZ3fSMYdXVC3jZtxlcLSzrOpwntqBjdwh4DWLJcFJP1NtPmKtWrQIA9OjRwyg9IiICYWFhAICPP/4YCoUCEyZMQE5ODp577jns378fVlZWhvzff/89BAIBXnvtNSgUCvTu3RuRkZHV/mWXEPJ0s7GxwcyZM2uk7nfeeadG6q0NH374YV2HQBqout7titSuyuyoW1N5zVGyOykA/Pjjj+jZsyfmzZuHL7/8EkD5O9WWV7Zp06bgOA7Xrl0za5fDquxU2qRJE8TExKCoqAj5+flwc3PDiBEjDJtTVDaGyijvvpTsCHv06FGjHWEdHR3B5/PN2kG3vHrMyTN48GA899xzhsclHYLmtC8SiQzPa/v27REbG4ulS5fip59+MllvZWM2V2Xv171793Dw4EFs27atUu1UFHN596Ms5papKObq2sG3pKNr3759T21HFwDEpCVh4vED+PzsMaSPngghr35/P6WOLlLb6u2/OFY8BbPUUdLRBRT/8jF37lykpaVBqVQiJibGsFtjCYlEgmXLliErKwtyuRy7du2iEQyEEEIIIbVIp2fQ6vWldvSsa4/v4Gvu7r2Pl7W3t0f//v2xYsUKkzsE5ubmGj1+kh18ZTIZ3NzckJOTg3379mHIkCEAUOkYKsPUfWEV7AgrEonQrl07ox10AeDAgQNGu9FWVI85eUzt4Gtu+6baKtmx1lS95sZcWZWNNyIiAs7OzmXuePy4qsb86P0wV1llKor5SV4XJXQ6Hd566y3s27cPBw8eROvWratcV23o5OyOl339631H16NUOi3W34xHcmF+XYdCGrh6O7KLEEIIIYTUb7kKDdIL1ShU6QAAYgEPzjIhnC1FNTLdrrJ69OiBFi1aYMGCBVi+fDn69++PTz/9FDk5ObCzs6tU2ZUrV6JLly7o2LEjvvjiC7Rs2RJarRYHDhzAqlWrcO3aNUPZY8eOoV+/fobHhYWFuH37tuFxQkIC4uLiYG9vj0aNGgEA9u3bB8YY/P39cfv2bUyfPh3+/v4YM2aMoVxlYsjLy0NcXJzRNT3a3qNM3ZeKlhwBgClTpmD06NFo3749OnfujJ9//hlJSUl47733DHWbU485eUypqP2ZM2di4MCB8PLyQkFBAbZs2YLo6GhERUWVWae58ZjznFY23hJ6vR4REREIDQ01OVV8+/bt+PTTT3H9+vVKxVzR/TBVr7n3sKKYgdKvi8rS6/V46623sGPHDvz5559wc3MrNVLOycnpqZjho8tIQDdnXxzr0AEavR7alOtG5+qz0Ud244+7NzC1ZQd806lXXYdDGrJaXSGsAaIFcklDV7IAp1wurzgzIYRUI7lcTgvUP2WquhujKen5ShabnMeuPShkGYUqllWkZney5Cw2OY/delhktItrbTB3B9/Hd6qtTNnU1FQWHh7OvL29mUgkYh4eHmzw4MHsyJEjhjKmdvA9cuRIhbvYbt26lTVu3JiJRCLm6urKwsPDWW5ubqmYzIkhNDS0wvYe9/h9MVUej+0ayxhjK1asMMTStm1bFhMTY3TenHrMbcuU8tofO3as4ZyTkxPr3bs3279/f4V1mhOPOc9pZeMtsW/fPgaA3bhxw2QdpnZNNCfmiu6HqXrNvYcVxWzqdWHOrpKPLjR/6tSpMq+z5CjZDbmuFqjXPrzHsj9pZ9ahfXivWtuuLbvv3WbuG5azZfFn6zqUeoUWqK88jrGnbLx4PZOfnw8bGxvk5eXB2tq6rsMhpNrpdDrcvHkTzs7OcHBwqOtwCCHPkKysLGRkZKBZs2alfmmn99+6UbJAfXJycqn1fJRKJRISEuDr61vuYuUAoNTqcDm9CC5WInhai41GceUqNLidpYC3nQROMlGNXMeT2Lt3L6ZNm4bLly+DVwNr0KxYsQJ///039u/fX+1116Savi/k2WbqdTF37lxER0cjOjq6zHLR0dEICwtDYmJipdqrqFxl/t5Vli4zCdGpSehiZw9hGa8lTmwBvqPpEYBPOz0rnrYuegpG0NUn5b3/EtNoGiMhpFx8Ph+2trbIyMgAAFhYWDwVU0sIIQ0XYwxyuRwZGRmwtbV9KqaUkOqVWaSBgMfBw+q/ji69Rg1OIIStVAgbiQYPC9VmdXbp9QxqvR58joOQX/OdLDW9Uy3t4EtIaaZeF/v27cPSpUvrKKKac1sgQ59Tx+FuYYnbr78DqUBY1yFVKx7HUUcXqRXU2UUIqZCrqysAGDq8CCGkNtja2hr+/pCGRa7Rw1LEB4/3348nquQEMLUKPAsZHAVSpGmFYExW5g8sGp0eqfkqZMk10P9/noKliA83azFsJDX7Ebcmd6qlHXwJKc3U6+LkyZN1EEnNu5ufCxepDG0cXRpcR9ejGGM4n/kA1iIRmtrY13U4pAGizi5CSIU4joObmxucnZ2h0WjqOhxCyDNAKBTSiK4GjMcBWt1/K2kwvR5MowbT66ArzIcI+fAGoLiRBoGdA0QuHkblNTo9bjyUQ6tncLEUwUosgEavx8NCDW5lyuFrL4GDxdM3BZIQUrt8fHwwefLkWitXHQY2aoL7b0xAllJRJ+3Xlk9OR+PrS2fwfmAbrOxa9Y0HCCkLdXYRQszG5/PpyychhJAnZicRICFHCYVGB6mQD47Hg7RZEPQqBXRFhcjNzoWFTgnodQD+G9nFdDqoUhKRz5OAx8QIcLWDRPjf+5K9VIjEHCXu5ShhKxGCz6Np94Q8y+pjZxcACHg8uFjI6qz92tDX0wfLr5yv6zBIA0adXYQQQgghpFbZSYVILVDjdpYCje2lkIn44DgOOoEESQzIs5CguZMFxHo1ON5/nVk6RRF0hfmQIR8yAHpFGlQyK/BkVuDLrMATiuBhLUaWXINsuQZOljS6ixBSf6QWFcBdZlXXYdSKXu7eeDB6IqxE4roOhTRQ1NlFCCGEEEJqFY/HoZmjFLcyFbiWUQSJgAc+j0ORWgc+B/g5SGEhEuDxj6o8sQQ8J3fk5+TCUq8E02qhzcsB8nIAACL3RhDZOkAi4EGp1Zcbg07PkCXXIEdRvOaXWMCDk0wIy/93vBFCSG3KVirgu/kntHZwxv4XR8CmgXcC8Xk86ugiNYo6uwghhBBCSK0TC/ho4SJDnlKLPKUWjAEOFkI4WJQ9/ZAnFEFg74RklRS+dmLYQgNdUQF0RQXQK+XgS2X/39aeQaLIgyI3CXyZFfgya/Ck/+0mrNLqcTNTDpVWDxuJAGIBhwKVDtlyDZwthfCykVCHFyGkVp14kAI9Y9Do9Q2+o+txWUoFZAIhJALqniDVh/41EUIIIYSQOsFxHGylQthKzd9xTMjnwVLMR0aRFvZOMvBllgDcwHRagMdHtkIDrZ5Bpi+CXl58aB6mg+MLiqc6WlohQSEAOD6CXGSGNb8YY3hYpEFSrhISAR/ONAWSEFKLXvL2w/03JiBVXljXodSq8H/34+drF7Gh54t43S+wrsMhDQivrgMghBBCCCGkMtytxChS65CQo4T6/9MV2f87uu7lKGErFUDi5gmRWyMIrG3B8flgOi20+TlQpybBMycBPjZCo8XtOY6Ds6UI9lIBHhSqwRgrq3lCCKkRLhYytHF0qeswapW9WAIt0+NkRmpdh0IaGBrZRQghhBBC6hVriQC+9lLcy1EgW66BRMCDVl88fdFWKoCvnRQ8HgeenQNg5wDGGPSKIugKC6DMy4WGAfaS/0ZuKZPvguM48CytYS+2QLZCD7WOQSwofyojYwx6BnAcwKNpj4SQKtLq9RDwns1xKO8HtsEov0A0t3Os61BIA0OdXYQQQgghpN5xsBDCViJAtkIDpUYPPq94l0fpI6O1SnAcB76FJfgWlnggtkOuXAOH/3dOMZ0WusJ8gDEgPxdCcPDhi6HPtoPe2gacuPT6XSVTHjMK1YaF8K3EfLhaiWAjMX9KJiGEqHU6NN36M15w9cLSLr1hL5HWdUi1yl1m9czsQElqF3V2EUIIIYSQeonP4+Akq9zaWpYiPh4UqCFX62Ah4gM8PiTeTaErzIeuMB96pRxSnRL6zDQoMtMgsHWA2L2RoTxjDHezFchRaGEnFcDNSgQdA7LkGtzKVMDLVg8Xy2drcWlCSNUdTElEUmE+DqXeg/UztjD94xhjtDkIqTbU2UUIIYQQQsq1YsUKrFixAmq1uq5DeWK2EgFEfA6JOQo0c7SAgM8D30IGvoUMuVaOuJdZAE+BGjJNEXRFheBJLQxl9WoVClNToGdi+Dk5wNZSYjjnJBPifp4Kybkq2EgEkAhKjzAjhJDHDfRqjNNDRyNVXvjMTmXUM4ZPTkdj651rOD7kTXhZWtd1SKQBeDZfTYQQQgghxGzh4eG4evUqoqOj6zqUJ8ZxHPwcLKDWMVxKL0RijgKp+SrceFiE21kKyCwksHd1gaRRE1j4B0FgY2co27NXL3w6+zO4KzIgTL4O5b3b0GRnQq/RgOM4eFiLwedxyCzSmBVLjx49MHny5EpfQ1XL1YaAgACsWbOmrsMgpN7gOA4dnd0x1KdZXYdSZ3gchzMP05BcVIDf716v63BIA0EjuwghhBBCyFNBl5kEppKXeZ4TW4Dv2KjM8+ayEPER6CLDw0I1chRa6JgWcz96H6qifOzZ+bdhGg3HMx6dxfH4UAok0Asl4GmU0BUVQFdUAKTfB8/CAmK3RrAS8yHX6J84xurUo0cPtG7dGj/88EONtqNQKHD79m20atWqRtshhDQ8s9t0QVGwBgO8fOs6FNJAUGcXIYQQQgipc7rMJOR/M7zCfNbTtlVLh5eIz4OHjQQeNsWPbSQC5Cp55a8Xw+NBwZcg38UXbmJAV5AHbUEe9Ioi6BUKcAIhtHolBBwHnbwQHI9vcoF7jU6Ph0VqFKl1yChU43J6IRxkQjjLRODz6u96NZcvXwZjDEFBQXUdCiFPPcYYXoz6E52c3fFBUDvYiiUVF2rA+nj61HUIpIGhaYyEEEIIIaTOlYzoshjxJawmbSx1WIz40ihfTSsqKsJbb70FS0tLuLm54dtvvwUAiAU8ZBVpwIQifL9uPVr0HgDHdl3Q+bU3sPmPv1Co0sFWKoA6PQU716/F8x3aw9bGBg4ODnjppZdw9cYtXMsoQnqBGnweB0sRHzIRH6n5Klx/WASNTm9WHI+KiopC165dYWtra2jnzp07AICwsDDExMRg6dKl4DgOHMchMTGxwnKVERcXh169eqFr167Q6/Vo1KgRvv/++0rXQ8iz5HRGKv5JvotFcafAo0XZCal21NlFCCGEEEKeGnxnXwg8AkodfOfandoyffp0HDlyBNu3b8f+/fsRHR2Nc+fOwULIh0bPMGnqDKyLiMCqVatw5coVhH84BWPC3kL8mROwk/DBCYUoUqowcfRIxGz5Fbt/XgGmkOOVIYMh1CgR5GIJiYAHCxEfvvZSBDrLoNExJOUqzYrjUUVFRZgyZQpiY2Nx6NAh8Hg8DBs2DHq9HkuXLkXnzp3x9ttvIy0tDWlpafDy8qqwnLnu3LmD7t27o1evXhg8eDCGDx+OqVOnYsqUKTh79uyTPxGENFAtHZyxoedLmNuu6zO/C2OJgvS7WHUyCiN3b4Tm/jVoU64bHbrMpLoOkdQjNI2REEIIIYSQRxQWFmLt2rX49ddf0bdvXwDA+vXr4enpCSGfg6tYj3WrlmHV1l3wbtMZCsbQbsArePHlGET9vh5jhg8A36sxRk2cDF1hPrT5udAV5WPFvM/g26038q+fhajRQKM2pUI+3K3FSMpVQq3TQ8TnlRvHo15++WWjx2vXroWzszOuXr2KoKAgiEQiWFhYwNXVtVLlzPHee+9h+PDh+Oyzz/Dcc8/h1VdfxZQpU7B48WIcO3YM7du3N6seQp41FgIh3mzaoq7DeGroMpOQt3QkpnsPRhFPgLGxv6K9KrtUvuqayk4aPursIoQQQggh5BF37tyBWq1G586dDWn29vbw9/cHAKQm3IJKpcTEN4YaznMA1Go12rRpY0i7m5iI2bNn49SpU8jMzIRepwMAPMgvMORhOi0Ud66Bb20LG1nxAmJytQ4iKa/COB6N16id/4/MSkpKKrfTqqrlSqSnp+Pw4cM4ceIEdDod4uPjsWDBAvB4PAgEAohEogrrIIQQoHiKuozp8KGnO6ytHRHYpz+sHlnHTJeRAPnW2bU2lZ3Uf9TZRQghhBBCyCMYY+WeL+kU2rtnDzw8PIzOicX/TUcKCQmBl5cXfvnlF7i7uyO7UIWuHdtAw/vvI7herYJepYT+YTrwMB2NeSLwBXbQ8x3KjUOl1eN+nhKMAYNeegk+jRoZ2tHr9QgKCoJarS73Oh6Pz9xyJU6dOgW9Xo/WrVvj+vXrUCgUaN26NZKTk5GZmYnnn3/erHoIedZ8eiYGLe2dMMynGSQC+kr+qHltOkPgEVDXYZAGgNbsIoQQQggh5BF+fn4QCoU4deqUIS0nJwc3b94EAAQGBkIsFiMpKQl+fn5GR8l6WFlZWbh27Ro+++wz9O7dG82bN4e6KB8AUKDSGerlSSwg9vAG38oG4DiI9Wrwsh9AcfsqPAT6UnGkZWTixo2byFVqkS3XIDH1AW5ev46R709B52490Lx5c+Tk5Bhdj0gkgk6nM0ozFd/j5SpS0immVCoRFxcHT09PODg44KeffkJgYCBat25dqfoIeRbcK8jD4rhTGHV4F9IVRXUdDiENFnUjE0IIIYQQ8ghLS0uMGzcO06dPh4ODA1xcXDBr1izweMW/E1tZWWHatGn46KOPoNfr0bVrV+Tn5+PEiROwtLREaGgo7Ozs4ODggJ9//hlubm5ISkrCjBkzAABZcg3ylFoAAMdx4FvbIV9ohSSuCO48Jaw1hdDJCyET8DF27FhDHPbWVpjy2VxwPB7spQIEu1qCucjg4OCAbZsiYe/kArE8E7NnzTS6Hh8fH5w+fRqJiYmwtLSEvb19ufGZq1OnThAIBPjiiy9QWFiIJk2aYOXKlfj+++9x5MiRangmCGl4pAIBZrftgnuF+fCxsqnrcJ5KOr0ex9LvI7EgD2H+wXUdDqmnqLOLEEIIIYQ8NXQZCZVKrylff/01CgsLMXjwYFhZWWHq1KnIy8sznP/yyy/h7OyMhQsX4u7du7C1tUXbtm0xc2ZxRxOPx8OWLVvwwQcfICgoCP7+/vjxxx/Ro0cPSAU83MqUo0itQ55SgysPiqDU6mEnFcHR3gY8zhV6rQZMqcQ333yDoqIiDB48GJYWUkwKHQ1dXhb4WjWg14PH52PLli2Y9MEHeLV3JzRp2gyrli9Djx49DLFOmzYNoaGhCAwMhEKhQEJCAnx8fMqMr0RkZCTGjBlT5nTKRo0aYd26dfjkk0+QlpYGgUAAuVyOvXv3omPHjjXyvBBS3zlLZZjXvltdh/FUO5WRip67N8NKKMKIJgGQCoR1HRKphzhW0aIEpFz5+fmwsbFBXl4erK2t6zocQggh5JlA77914/79+/Dy8kJycnKpHQGVSiUSEhLg6+sLiURSRg1l02UmIf+b4RXmawg7cTHGkK/UIkuugUbPIOLz4GAhhJWYD47jTJfRapF39yaEWtV/iTweBFY24FvbgW9phXs5KhSqdQhytayWOOfOnYvo6GhER0dXmNfe3h7r1q3D0KFDq6VtQp5mT/r3jpSmTbmOgmVvwmrSRvDc/dF2WyQ6OLlifocX4CyVGZ1/Ftf0Ku/9l5hGI7sIIYQQQkid4zs2gvW0beXutMWJLep9RxdQPHXRRiqEjdT80QqcQIBUm0aw5ungDDl0eTnQq1XQ5uVAm5cDgZ0jhBZO0Ffj79j79u3D0qVLK8x3//595OTkIDiYphsRUp49SXfgIrVAO0fXMju2CcDjOFwYHkb3iDwR6uwihBBCCCFPhYbQkVWTJAIecjWAh4srhI6u0CuLO720+bkQWNmiQK6DRMCDTqmALj8XAhs78MTGo050eobMIjUy5RqotHoIeBzsLYRwlokgEhjvXXXy5Emz4oqPj4dMJkPjxo2r7VoJaWj0jGHCv/uRVJiPnf1fRoi3X12H9FR6Wqayk/qv3u7GePToUYSEhMDd3R0cx2HHjh1G58PCinuCHz06depklEelUmHSpElwdHSETCbD4MGDcf/+/Vq8CkIIIYQQQszjJBNBqdUjs0hTvLC9VAaRqyekTVsgnydBoVoHR5kIutxsaDLTobhzDYq7N6DJyoBeo4FOz3AzU477eSpIBDx4WIthKxXiYZEGVzOKoNDoKg7ChIEDB6KwsJBGYRBSjny1Cp2d3eEilaGPh3ddh/PU4cQWAAD51tkoWPam4bi28m2cXj0B8q2zjfIRUpF6O7KrqKgIrVq1wpgxY/Dyyy+bzDNgwABEREQYHotEIqPzkydPxq5du7BlyxY4ODhg6tSpeOmll3Du3Dnw+fwajZ8QQgghhJDKsBTz4SQT4l6uEgUqLewthOAA5Ci0yJRrYCcVwE4qgE5nCb5GBV1hPvRKOdRKOZCRCo3IAkK+JQLcnCET//c1wM1KhJuZctzNViDQWUadVoTUAFuxBFv6DIFWr4eAV2/HnNQYU1PZN9xPwriLF9DL0QlRz3VpMFPZSe2ot51dAwcOxMCBA8vNIxaL4erqavJcXl4e1q5diw0bNqBPnz4AgI0bN8LLywsHDx5E//79qz1mQgghhBBCqorjODSylUAq5ONBoRrZWQoAgIjPwdNGDBdLETiOg8DKBgIrGzCtFtr8XGjzs6GXF0GoKoIrXwULkZtRvUI+D142EtzMlKNQpYOVpN5+RSDkqUcdXWV7vCOrh5UrcPECdEIpmFtTCHg0IIWYr0G/0qKjo+Hs7IxmzZrh7bffRkZGhuHcuXPnoNFo0K9fP0Oau7s7goKCcOLEiTLrVKlUyM/PNzoIIYQQQgipDRzHwdlShCAXGVq6WiL4/4erlbjUiCxOIIDQ3hFSn2ZgjfyRKbYDZ+dkyMcYgyLhBtQPUiCDBnwOKKriVEZCSNnuF+YjS6mo6zDqHV9rW6S9ORFHQkZCSB1dlbJw4UJ06NABVlZWcHZ2xtChQ3Hjxg2jPOYs/VSfNdifbQYOHIhXX30V3t7eSEhIwOzZs9GrVy+cO3cOYrEY6enpEIlEsLOzMyrn4uKC9PT0MutduHAh5s2bV9PhE0IIIYQQUiaO4yASmD/dkBOJ8VBsDweb/9a70csLoVfIoVfIocnKgDdfDHC20EucwBOWvVMkYwxyjR56xiAR8CDkN+jfzwmpNF1mktF0vM8vXcCG+8n4unkQJvo2pul4leBiIavrEOqlmJgYhIeHo0OHDtBqtZg1axb69euHq1evQib7755WtPRTfdZgO7tGjBhh+P+goCC0b98e3t7e2LNnD4YPH15mOcZYuesUfPrpp5gyZYrhcX5+Pry8vKonaEIIIYQQQmqAVMiDkM8hS66B5f/X6+JJZRB7+kKblwNdYR7EOhWQ+wCKvAzwZVYQOrmCL/3vSxFjDA+LNEgvUEGtYwAAjgPspEJ42Yip04sQFHd05X9j/H3ztlt3aKXOaHxwGQqUDwEA1tO2UYdXJWj0Omj1ekgFZXfEk/9ERUUZPY6IiICzszPOnTuHF154wZBe3tJP9V2D7ex6nJubG7y9vXHr1i0AgKurK9RqNXJycoxGd2VkZKBLly5l1iMWiyEWi2s8XkIIIYQQQqoLj+PgYinC/TwVLIR8OMqE4Hg8CKxtoRBbIoFnB0e9HHa6QujlRdAV5kPo9N8XIKbTIbVQg7QCNRwshHCUCSHgcchTavGgQI3rah2aO1lAQB1e5BlXMqLLYsSX4Dv7AgAOA7heWICmssHAw0TIt842GvlFyrc47hQWXzyNue2exwdB7es6nDpVUFBgtJSSuf0TeXl5AAB7e3uj9JKln2xtbdG9e3fMnz8fzs7O1Rt0HXlm3o2ysrKQnJwMN7fiBTnbtWsHoVCIAwcOGPKkpaXh8uXL5XZ2EUIIIYQQUh+5WIoMuzleflCEO1lyXMsowo2HcgiFQji7u0Lq0wzSJs0hcvEAT/LflEdFegpkabfRGPnwtuTBSiyAVMiHq5UY/s4W0Or0SC9U1+HVEfJ04Tv7QuARYDiC/DtA7Nnc0AFGzCfi8ZGjUuJwSlJdh1LnAgMDYWNjYzgWLlxYYRnGGKZMmYKuXbsiKCjIkD5w4EBs2rQJhw8fxrfffovY2Fj06tULKpWqJi+h1tTbkV2FhYW4ffu24XFCQgLi4uJgb28Pe3t7zJ07Fy+//DLc3NyQmJiImTNnwtHREcOGDQMA2NjYYNy4cZg6dSocHBxgb2+PadOmITg42LA7IyGEEEIIAVasWIEVK1ZArabOjPqsZDdHBwshMuUaqLR6iPgcXB2ksJUIDEt58MQS8MQSQznGWPFIL70GyH8IRf5D8CxkENjYQ2BtC4lAAEeZCJlFGnhYl14on5BnlVavB4/jwKPXxBMZ6dcc7Zxc8byLR12HUueuXr0KD4//7oM5o7omTpyIS5cu4d9//zVKr+rST/VFve3sOnv2LHr27Gl4XLKOVmhoKFatWoX4+Hj8+uuvyM3NhZubG3r27ImtW7fCysrKUOb777+HQCDAa6+9BoVCgd69eyMyMhJ8Pu30QAghhBBSIjw8HOHh4bh//z6tVVrPcRwHS7HAsG6XuWUyHX0hkBfAiRVCV1Q81VEtL4I6PQVCe0dYWDnjQaEaegbw6Xs9IQCAbQk38NHJw/gwqD0+bv1cXYdTb7laWMLVwrKuw3gqWFlZwdra2uz8kyZNws6dO3H06FF4enqWm/fxpZ/qu3rb2dWjRw8wxso8v2/fvgrrkEgkWLZsGZYtW1adoRFCCCGEENKg8Pl85Ahk8HR1AdNqoMvLgTYvG3qVEuB4UGv14DiAA4NOqQRfIjVZj54x5Cq0UGh04HEcbKXF0yEJaYh2JN5CqrwQuWplXYdCnjGMMUyaNAnbt29HdHQ0fH0rnj77+NJP9d0zs2YXIYQQQgghTyorKwvOzs5ITEyskfpfeeUVfPfddzVS95Owlwqg1jHkKLTgCUUQOrpA0jgAEl9/8GwdcCs5Df1b++Hu1ctQ3r0Oxd3r0GRlQK/RGOrIV2oRn16Iu9kKZMo1SCtQ4cqDItzOlEOnL/tHbEKq63UREBCANWvWVENE5onoMQh/9xuOcQEta63Nhkqr12NR3Cl0/Xsj8tUNY02pmhQeHo6NGzfit99+g5WVFdLT05Geng6FQgGgeFmoadOm4eTJk0hMTER0dDRCQkKMln6q76izixBCCCGEPPPCwsLAcRw4joNAIECjRo3w/vvvIycnxyjfwoULERISAh8fn0qXTU9Px6RJk9C4cWOIxWJ4eXkhJCQEhw4dMuT5/PPPMX/+fKPdto4ePYqQkBC4u7uD4zjs2LGjVPwFBQWYPHkyvL29IZVK0aVLF8TGxpbKZ04MYWFhGDp0qFE5mYgPW4kAiTkKZBSqDZ1TCp4It3I1WLPsW7z40kvwdnMBOB6WLFuBTt17wsbWFs6ODggZNAiHTsdBKuChhYsMrdys0NrdCkf+iET3ts1hYSFFu3btcOzYsVL3u0OHDrCysoKzszOGDh2KGzduVDpPWVauXAlfX19IJJJS7a9atQotW7aEtbU1rK2t0blzZ/zzzz8V1mlOPOY8p5WNFwB8fHwM/xYfPcLDw5845qrcD3PKmBOzqddFZSkUCty+fRutWrWqch2VJeYLMNinKZpY29Vamw0Vn+MQeSMexx+kYG/SnboO56m3atUq5OXloUePHnBzczMcW7duBVA8Wjc+Ph5DhgxBs2bNEBoaimbNmuHkyZNGSz/VZ9TZRQghhBBCCIABAwYgLS0NiYmJWLNmDXbt2oUJEyYYzisUCqxduxbjx4+vdNnExES0a9cOhw8fxpIlSxAfH4+oqCj07NnT6Et9y5Yt4ePjg02bNhnSioqK0KpVKyxfvrzM2MePH48DBw5gw4YNiI+PR79+/dCnTx+kpKRUOgZTOI6Dr70UdlIhknKViEsrwIXUAlx/KEdhkRy7tm7Ee++8DaGDCyyatsCJS1fwbuhoHN68Hjt/XgmtvAAfjhwMV6HaMG3xj99/x6xPpuHjGTOx8Z9j6NTleQwcOBBJSf/tuBYTE4Pw8HCcOnUKBw4cgFarRb9+/VBUVFSpPKZs3boVkydPxqxZs3DhwgV069bNqH1PT08sWrQIZ8+exdmzZ9GrVy8MGTIEV65cKbdec+Ix5zmtbLwAEBsbi7S0NMNRsvP8q6+++sQxV+V+mFPGnJhNvS4q6/Lly2CMGe1GV5N0GQnQplwvdegyEmql/YaG4zh82qYTVnftj14e3nUdzlOPMWbyCAsLAwBIpVLs27cPGRkZUKvVuHfvHiIjIxvWupyMPJG8vDwGgOXl5dV1KIQQQsgzg95/60ZycjIDwJKTk0udUygU7OrVq0yhUJQ6V6hWsUK1iun1ekOaSqtlhWoVU2o1JvPqHsmr1hXnVWjMy1sVoaGhbMiQIUZpU6ZMYfb29obHf/31F3N0dKxS2YEDBzIPDw9WWFhYqnxOTo7R47lz57Ju3bqZjBMA2759u1GaXC5nfD6f7d692yi9VatWbNasWZWOwdT1PEqp0bEHBSqWlq9keQoN+/PPP03eF8YY0ykVTPkghd06HsMAsJiYGMO5Du3asnfffpvp9Xp2MTWf3ctRsICAADZjxowy287IyChVT1XyMMZYx44d2XvvvWeUVlH7dnZ2bM2aNeXWW9l4TD2n1RXvhx9+yJo0aWL02quOmEtU5X5UVKasmMt7XZTnwoULrGfPnkwkEjEAzNHRkX333XeVrudR5f290z68x25+2pl1/WEK+/arV1nWJ+1YtolD+/DeE8VAni3lvf8S02hkFyGEEEIIqVGWEd/DMuJ7ZCoVhrSvL56GZcT3mHj8gFFe5w3LYRnxPZIK/5uutOLKeVhGfI9xRx+b/rR5NSwjvse1nExDWuSN+GqJ+e7du4iKioJQKDSkHT16FO3bt6902ezsbERFRSE8PBwymaxUfltbW6PHHTt2xJkzZ6BSmbcujVarhU6ng0QiMUqXSqWGreYrG0N5xAIenC1FcLUSw1oiwLFjx8q8LzyxBHxHV8TriqfF2NvbAwBUCgXOx11E9yB/KBNvwl6TD6bVoF+/fjhx4kSZbefl5RnVU9U8arUa586dQ79+/YzSy2pfp9Nhy5YtKCoqQufOncust6rxVKSy8ZaU2bhxI8aOHQuO+2+LzMjISKPHVYnZ1P2oqF5z7mFZMQOVf10AwJ07d9C9e3f06tULgwcPxvDhwzF16lRMmTIFZ8+eNbueyuA7NsL+oV/iX6kzNvn1gvWkjbB67LCetg18x0Y10j4hpFi93Y2REEIIIYSQ6rR7925YWlpCp9NBqSzePe3RRbETExPh7u5e6bK3b98GYwwBAQFmxeHh4QGVSoX09HR4e1c8XcfKygqdO3fGl19+iebNm8PFxQWbN2/G6dOn0bRp0yrFUBnl3RegeN2UH76chQ6duhimkD18kA6dTgdnRwfoFXLYQQ4UPYSDRIj01FQwvR4cz/h3eY1WhwkfTEbb5zpD7+CN6w+L4GQhhJ2FELz/d4wwxjBlyhR07dq13OlqmZmZ0Ol0cHFxMUp3cXFBenq64XF8fDw6d+4MpVIJS0tLbN++HYGBgWbfG3PjqYi58T5qx44dyM3NNUxbKmFjYwN/f/8qxVze/Sir3srcw7JiBir/ugCA9957D8OHD8dnn32G5557Dq+++iqmTJmCxYsXl9tJ+6SGtugIhdgSHjIrCDyq/zX3LJNrNdh/PwGZSgXGB9Te+muk/qHOLkIIIYQQUqMKx3wEALAQ/DdKanqr5zA5uD0Ej3VoZIyeCACQPpI3vEVbvB3QCnzOOG/iyPdK5Q3zD65ynD179sSqVasgl8uxZs0a3Lx5E5MmTTKcVygUpUZPmVOWseLF3CsaTVNCKpUCAORyudmxb9iwAWPHjoWHhwf4fD7atm2LUaNG4fz581WKoTLKuy8AMGnSJNy9fgU//xUFhUYHqZAPnkgMAJB4NYHcxgW8wlxIdCrolAownRbanIcQOvzXsaPU6hA2/j1cjo/HH3sPwsFCiCK1Dgk5SmQptPCzl4LH4zBx4kRcunTJMKINADZt2oR3333X8Piff/5BkyZNAJS+H4wxozR/f3/ExcUhNzcXf/31F0JDQxETE4PAwECT9Xbr1s2oPlPxPImK4n3U2rVrMXDgwFIdkcOGDSt3t7XyYi7vfpRVb3llzI0ZqPzrIj09HYcPH8aJEyeg0+kQHx+PBQsWgMfjQSAQQCQSmVVPVXjIrDCt1XM1Vv+zLDYjDcP2b4e9WILQZkEQ8vh1HRJ5StE0RkIIIYQQUqNkQhFkQpHRl3IRnw+ZUAQxX2AyL++RvEJecV6JwLy8VY5TJoOfnx9atmyJH3/8ESqVCvPmzTOcd3R0LLXDojllmzZtCo7jcO3aNbPiyM7OBgA4OTmZHXuTJk0QExODwsJCJCcn48yZM9BoNPD19a1SDJVR3n2ZNGkSdu7cicNHDqNRIy9cfyhHar4SEitb8Pl8XLybgnvMEhr3JpA2aY4suQoujo7gP7J7nbYgFxPGjUXM/r04fPgwng9uCg8bCZo5ydDM0QIFKi1SC1SGto4cOQJPT09D+cGDByMuLs5wtG/fHo6OjuDz+aVGRWVkZBiNnhKJRPDz80P79u2xcOFCtGrVCkuXLi2zXlPX/ng8Vb3H5sRb4t69ezh48KDJzRTKU1HM5d2PsphbpqKYK/u6OHXqFPR6PVq3bo3r169DoVCgdevWSE5ORmZmJp5//nmz6iFPl66unujg5Ia3mgahSKOp63DIU4w6uwghhBBCCDFhzpw5+Oabb5CamgoAaNOmDa5evVrpsvb29ujfvz9WrFhhcofA3Nxco8eXL1+Gp6cnHB0dKx2zTCaDm5sbcnJysG/fPgwZMgQAKh1DZZi6L4wxTJw4Edu2bcPhw4fRtEkT+DtawF4qQHqBGnfztAgIbo2jRw7B114KVysxeGIJDh8/ged79QZPKDLUEx4+CVFR/yBq7Up489TQZD6AXqMGAFhLBHCWCTF18geGtko6+EpYWVnBz8/PcEilUohEIrRr186w81+JAwcOoEuXLmVeK2PMsGaUqXpNXfvj8VRFZeONiIiAs7MzXnzxRbPqr2rMj94Pc5VVpqKYK/u6UKuL/40olUrExcXB09MTDg4O+OmnnxAYGIjWrVtXKm5zfXn+OA6n3INOr6+R+p91fB4PZ4a9he+79IatuOwRpYRQZxchhBBCCCEm9OjRAy1atMCCBQsAAP3798eVK1fKHMVUXtmVK1dCp9OhY8eO+Ouvv3Dr1i1cu3YNP/74Y6nFuo8dO2a0EHlhYaFh9BAAJCQkIC4uDklJSYY8+/btQ1RUFBISEnDgwAH07NkT/v7+GDNmjCFPZWLIy8szGrX0eHuPMnVfwsPDsXHjRvz222+wsrJCeno6Mh9mwFkCtHKzQqCLDJ9Mm4ptv63H31s24Nq1a/joo4+QlJSE999/36ieLX/vxPLvvoeVzBJp9+8j+colZMefgzLxFjQ5mfjik4+wd9vvWLt+g6Gt9PR0KBQKU+EaTJkyBWvWrMG6deuM2n/vveLpsTNnzsSxY8eQmJiI+Ph4zJo1C9HR0XjjjTfKrdfUtT8ejznPaWXjLaHX6xEREYHQ0FAIBKVXrdm+fXuptdvMibmi+2GqXnPvYUUxA6VfFxXp1KkTBAIBvvjiCxw9ehRNmjTBypUr8f333yMiIsLseirjVl42Pj/7L/rt3YpslbJG2iCEmKn2N4BsWGjrc0IIIaT20ftv3Shv63OFQsGuXr3KFApFHUT25EJDQ9mQIUNKpW/atImJRCKWlJTEGGOsU6dObPXq1VUqm5qaysLDw5m3tzcTiUTMw8ODDR48mB05csRQRqFQMGtra3by5ElD2pEjRxiAUkdoaKghz9atW1njxo2ZSCRirq6uLDw8nOXm5paKyZwYQkNDK2zvcY/fF1PlAbCIiAijcitWrDDE0rZtWxYTE2N0vqx6Vn81lxVeOc8UCTfNbkuv17MCpYal5ytZer6SFam15bY/duxYwzknJyfWu3dvtn///jLvQWWu3Zzn1JSK7hdjjO3bt48BYDdu3DBZR0REBHv8a6A5MVd0P0zVa+49rChmU68LU+097tdff2Vubm4MABMIBKxDhw4sOjq63DLmKOvv3e28bPZ2zD9s5MG/n7gNUrFrOZnsXsGz8TmgvPdfYhrH2P9XqyRVkp+fDxsbG+Tl5cHa2rquwyFVkKvQIE+phbOlCFIhLXBICCH1Ab3/1o379+/Dy8sLycnJpdbzUSqVSEhIgK+vb7mLldd3e/fuxbRp03D58mXweNU/SWLFihX4+++/sX///mqvuybV5H0pUutwLaMIfg5S2EqLNyPQa9TQ5eWAE4mRqhcjW65FsJMEqrvXwLe0Bt/aDnyZlWFHR5VWj7vZChSpdeBx/+/JYYC1mA9feymEfJrw8jQz9bqYO3cuoqOjER0dXWF5e3t7rFu3DkOHDq2WeJ6Vv3dPs6knD+O7+FhMb9kRSzr1rOtwalx577/ENNqNkTyzziTn4YtDCdh7IxOMARZCHt5s44Y5fRrD3Vpc1+ERQggh5Ck0aNAg3Lp1CykpKfDy8qr2+oVCIZYtW1bt9da0mrwvMhEfliI+knKVEAt4xbs5CkXgHJyRo9AiI1sBd2sx9EX5YDodtHk50OblgOMLwLe2AWdpixuFHDiOQ1MHKawlAjAAuQotkvOUuJkpR3MnGXi86t+pklQPU6+Lffv2Vbg4PlDcSZCTk4Pg4Krv1EqePp1d3CG8wkOeunJrxpFnB43sekL0y3L9tP9mFkLWx8HfSYbwzp7wsZPiVFIeVp66D4mAh+Pvt4enTfX/SsMYQ/TdHGw4n4aHRRp42Uowtr072nvSvx1CCKkMev+tGzSyi9QVtVaPm5lyKLV6WEsEEPM5FKl1kGv0sJMK0Ni+eHF4vVIOXV4OtPm5YNr/dmrT8gSQePpCbGlpVK9crcPVjCL42EngKBPV6jWR2vHPP//g1VdfRUFBgdGOsE/C1N+7Y2nJ8LGygZclvSfVBqVWC5VeBxvRszFIgUZ2VR6N1yXPHI1OjzF/XkXPJvY4O7Ej3n3OE/2bOWBOn8Y4N6kjdHqG6XtvVXu7crUOL0XGodcv53EiKQ8AsOd6JjosP4Nxf16FTk/9zoQQQgghpogEPDR3lsHbTgLGGIrUOogEPDR1lKKxvRQcVzxyiy+VQeTqCWnTFpB4+0Fg6wA9x4eA6SB6pBNWJy+ETlEEqZAHKzEf2XJNOa2T+mzgwIEoLCysto4uUxhjCI3eg0a/rcLB+4k11g75j0QgeGY6ukjV0DRG8szZcz0Tqfkq/DOmNYQcgzLxJlT/f1Oy4jhM7+qH6fvuIrNIDcuHidBkZxav98BxAI8HjuMV/5fHg9QvEJygeO0ITeYD6ArzDecEtg7gP/LLzsS/ryP6bg52jG6JwYFO4DgOOj1DxNlUvLv9GrxsxJjbt0ld3BJCCCGEkKcen8fBSSaCkxkjsDiOA19mBb7MCjdgCxehDrJHdvlTZ6RCLy8CJxLDXmSJPIEMjFmU2yHCGINco4NaxyDkcZCJ+DXagULqj2yVEl6W1shUKtDF1aOuw3nmKLQaSP//nYyQEtTZRZ45WYd2Yc6tgxDNXIK4W1ehV8qNzvf48wY0OoY7WQo4r/se2Xu3lllXq4O3ILB1AACk/rIEmX8Zb2Ms9moMi4CW0PoGYcfNxlgwtC2GtHA2nOfzOIzv6IGrGUX48UQyPunhQ4vkE0IIIYRUI6GAj3xOAJf/P2aMgScUgfEUYGoVLNQqWCALSvkD8K1tIbC2A08iNaojT6lFcq4SSq3ekCYW8OBpI4adlL5kP+scJFLEhIxCnloFC+p0qTWMMbx+aCd23buNS6+MhZ+NXV2HRJ4i1NlFGiRtXg7kN+OhuHEJ8ltX4TN7qWEElvv1o2h9bx+K/p+XJ7GAuFETcAIBwBiSi4qHsVuK+RA6ukDi0xRMzwCmB9PrAaYH9Howpgd4/3VM8SRS8K1twXQ6QK+HXlEEVfJdqJLvAtgB9sIahLV3BwBk/fMH1CmJsAhoBYuAlhjT3h3f/5uEU0l56NnEvjZvFSGEEEJIg+YkEyIxR4kClRZWYgE4joPYwwdMr0N+dg7kOTmw1sqhV6ugz3wAvUoJiVdjQ/k8pQa3MhWwEvPRyNYCUiEPSq0e6QVq3MlSoIk9YGdBHRwENK2ulnEch2yVAgqdFlHJdzHRpl1dh0SeItTZRZ4ajDEcTcjF2fv5EPE59G/mgGZOMrPKFl0+h7zjByC/eRmKG5egTr9vdN519ERI/QIBAE1fHIZlGRye694Fw0N6QOzVBByfb4jh0w2XEOBkgUBnGbgP5sLzg7lmxeD10Vfw+ugrw2Ntbhbk1y9Bfv0S4uKuIk9sDUtRcTvZe7ci/+RhQ17O3hk/cB7gb+6MnOc7wbbbgOLON0IIIaSeob2PyNPG3kKILLkGtzLlcLEUwU4qBANDtlyLDKUItvYecLUVQ1eYD21+LgQ2toayerUKSLwFL7ElHOwcwRcXT10U8nmwFPFxJ0uB5DwlbKUCmtL4DHn079xDhRzWIhHEfPrsXhfmd3gBXz/HRysH54ozk2cKvSLJU+FyeiFGbo7H5QdFsBTxodbp8cGumxjWwgkRr7aAjaT4nyrTapF38hAKzh6DyxsTIHIuHimVf+oI0n5ZYlSnyMMHFs2CYOHf0mjtLL8Xh0KubIaxF9Ihz5BhtCcHMYDMIjW+OpyAv68+xKbXg574A4vA1gHWnXrCulNPON7PB1t+BnuvZyIk0Al2vQdDYOsI+Y2LUCbeAsvOQFdkADsu4N7BjbA9kmCoJ2vPVvBlVrDu0hs8+rWIkP+xd9/hTZVfAMe/GU269160QMsqe++9FFmCIIoiiiiKIuLEAT8RRBQnKCiIDAEHiAIiiLL3KHvTTUv3btM2ub8/CoHKltJ0nM/z8GjufXNzUkLvzbnnPa8QopyysiqubMnNzcXGxuYWo4UoO2qVipputsRlGriYXUB8VgEAWrUKbwcdvo56VCoVWicXtP+aBpWXlobWVIh9XhqGqDRUWis0Dk5oHZxR29nh46jnRGIOWQYjjtby1aqqyM0tboNiZWXFhC3r+C3qDLPa9WDYpZvrouy0uPR9UIh/k9/IwuJi0vPp/M1+fB30/D2qCZ2qu2AoMrHs0EVeWn2avt+Hs66vN2m/LSZ51SIKE+MBsG/UypzscmjWDrc+D2NTqz62ofWxCQ1D6+B0w9f8sl9t8otMjFpxgtfWncXXQceZlDwUReHTPqEMa+Rdqu+xmb8jLQIcefWPM7QIcMSr/2O4938MgDOxKYz6ZAU9NfE87JCMSlPyzuCFudMpiItE4+iMS/cBuN0/BLv6zW+ZjMs2FJGRX4S7nQ69VhZeFUIIcW9pNBqcnZ1JTEwEwNb25s2+hShrntbgprMiv8gIgI1Wg1oNBoPhhs/JtrIlVe+Gj6YQJS8bpaAAUpIgJQmVRovOxx+KTOTkgo6bN87PLTCSXWAEFKy1avOUSlFxKIpCbm4uiYmJODs7o1Kr2ZN0gfQCA9WuurkuhLA8lSK15nclMzMTJycnMjIycHSUX3D/xfjVp1l4IJ5TL7fG7arVdRSjkS0rVrF/3td0SDmASiluCKp1dsOlWz/c+w/HtnbDu3rtE4k5LDuUQGpuIdVdbXi0sQ8e9rde4ee/OJWUQ6e5+ykwKoxo4kMtD1sOXMhi8cEE/J30bH66KV4OJSu3FJOJmA9fJX3LH+YkHxQ3vne7fwiuvR9C71etxHMOxGUy5e8IfjuRjNGkYK/TMLyJD293CcbHUSrDhBCVg5x/LSM2NpaAgABiYmLw9/e/Zr+iKCQkJJCenl72wQlxDxgu9ebyctCh16hQCgyY8nMx5eeBooC7D/FZhXjaW6EvMqAoJtR6G3OLDACjSSE5p5D8IhNqVfEC30ZTcWWZh50VOrkpWeE4Ozvj7e19aXV1E9sSYungEyDJSwu5kJPFrGMHScrPZW6HXpYO55641flXXEuSXXdJLrbvnteULTzayJuP+4SW2G6IjeBo/ytNBu2btMVj4Aicu/SpsNP54jLymbk1msXhCSTlFODvqGdkM19ebBt408amitFI1r6tpKxZTvrfv5tXkHQf8DjVJn5iHrf5fBq95h8k2NWGMa38CXa1YU9MBl/tisXGSsP2Z5vh72R9z9+nEELca3L+tYzbvdg2Go0UFhaWYWRC3Bsmk0Kv7w5S3dWG2f1qo1YXJzMUo5H82EjePmpkd0wGf49qSuSERyiIPg8qFTahYTg074Btk3YM35xJYk4hk7pVp3MNVzRqFUfis/jfxgii0/NZ8WgD/Jzl+qyisLKyQqOR1dPLk3OZadRcNheNSsXF4WNxs658U+kl2XXnZBqjsLjU3EKqu+jJ3PU3uWeO4T18LAB6/2BcuvfnjyQtu+rcz6KX+1k40rvn52TNx31C+bhPKIqi3PbdH5VGg2PLTji27ITx9Rmk/7OGlDXLcLt/iHlM5uG9hL87hRH1ejHz2ZHY2BQnBO+v7c6oFn60nr2X8atP8+MjDe7JexNCCCEu02g08mVQVBovdazJg0sOk1F4mre6BNPQx54T6bl8cFDhh/BEFgyui721HvembUjPziD3RDh5CdHkbVkLwFO2PgT0f5jm9dubj9k82JpFjzhTc8YOvtx7kU8eqGWptyf+I5OioJZKrnKhhqML4+s3p5mHNzayyJe4RCq77pLcWb47hSmJvDFhKvdFb8ApIx40GuqvPozOwweAIqOJ6h9up08dD2b3r23haMu3ra89h+3GpQBo3Txx7TUIt/uHYhsaBsAX26MZv+YMsW+0u2a6pBDizimKgqJgvssvypacfy1D7iyLqurnIxcZv/o0MRlX+nt5O+j4oFdNHm9askF2wcU4Mrb+SfqWP0jdvQWtsRDX+4cSPHk2UHz+SFu/AocWHXlteyrLDiVwYWKHW8ZgNCmk5xViq9NgYyXJZEv7+vhBvj4RzisNWvBISD1LhyMqOTn/3jlJe4oyp5hMZO3bSvKKBaT9s4aHjUXFO+wc8egztMTYb/ZeICbDwJPNZJWNWznWsC/R5zIYkr6bopREEpfMJnHJbGxq1sX1/iF0af8QRSaFMyl5kuwSVYrJpLDmZDLf7I3jdHIuztZahjTw4olmvjjb3Hj68I0ciMvkoy1R/Ho8ibxCE2FedjzTyp+nW/hhpZG+K6JymjVrFrNmzaKgoMDSoQhhEYPqe9G/rgd/n0sjNiMfbwc93UNcr/t7X+flh8egkXgMGsl9s7fR9OIBXh/c1rw/79RhIiaOApWKBwLDyLeqS85xB2xrN0SlvvZ4GflFfLApkm/3xpGcU4haBX1quzOxSzAtAm68IJO4t5afO8mhlETic7MtHYoQ4jqksusuyZ3lO5f86yKiprxofmxdrylfOXXkF6fmjO0SygN1PMg2FPH9gXjm7onjmZb+UtV1G77aFcsLv50i+c02KAc2k7JmGRlb1qEUFmDl7k3KJ3/R5fvjHHyhJY18HSwdrhC3lJpbyIqjiSTlFBDgbM2Aep7Y6e7sTnaR0cSjy4+x/PBFWgQ40raaMzEZ+aw6noSvg56No5pQw832to/32/EkBi05TJCLDU809cHdTsf6MymsPJZEzxBXVg5veEeNhouMJpYdvsjc3cWJOAe9hsH1vXiutT9+0l/vpuT8axlyZ1mIO/PMyhOsPZnM+Vfbor2UGMvat5WYmW+Rd/pIibFaN0+c2nTFc+gz2NaqD0B6XiEd5+znfGoeTzb3pUOwC3EZ+czZU3zeWDm8IffXdi/z9yUgJT+Pn8+f4oFqNfC1k2vr8iDNkM/vUWep4ehMW+/KdY6S8++d+8/JrsLCQhISEsjNzcXDwwNXV9fSjq1CkIvtW1OKCilKT8HK3RuAoqwMjg1qiUvnPrgPHIFtaBjpeYW8svYMiw8mkF9UvOqit4OOl9oFMqF9NZkmdBtiM/IJmr6dj+4LYVy7QACKMtNJ2/ArGkcnnk2qyf64TE6Na0nKiu9w6zMUjZyYRTmkKArvbYxg2qZICk0KTtZa0vIKcdRrmXl/CCOb+932sab9E8HbG86zdGgYgxt4mbdHpeXRfd5BbK3UHHyh5W31z8vIL8J/6lZ6hLiybFj9EnfzN5xJ4f4F4UztWZMJHard5ChXFBpNDFp8mN9OJNOtpisdgp2JyzSwNDwBnVbNhiebSGL6JuT8axlysS3Endkfm0mzL/fwaZ9QXrx0fXbZjgMn+ejThTyjPY3H2T2YLlUIhX69Codmxf293vpuA3+FR/Dta0MI871SxVVQZGLQksNsj8og9o12Mq1RCOC13Zv48NBuhlSvzbJuFb/f89Xk/Hvn7mgaY3Z2NkuWLGHp0qXs2bMHg+HKnHV/f3969OjB008/TfPmzUs9UFEx5Rw/SNSUF1FZ6ag9/09UGg1aBycarDmK6qrmgc42VnzzYF0+7B3C8cQcdBoVjXwdZErQHfB3smZkM19eX3cWR72GRxv7oHN0xuq+R/hgUyQ/Ho5i/qC6ZGz8lZgZr3Fh7nS8H3sBj4eeRGNjZ+nwRSWTV2hEq1b9p3/DH2yK5N2/zvNax2qMb18NT3sdEal5/G/jeZ785QS2Og1DG3rf8jiFRhNf7ozlyWa+5kSXoiiY8nLwNWYzt70jjy45xOYdejq1bXwl9ohTGDPTUUwmMBahFBWhGItYfTSB5hfi+HLCS+b3VZAQi8mQT0cHa56ooWPBllO81NILtU5/ywTa9E2R/HE6hdUjGpW4K/9+jxr0nH+QgYsPc2ZCGzR3kOxPzilg4YF4TiVdqRJrEeAoS6ELIYSFNPV3ZFzbAMatPs3e2Ewea+KDnU7DquNJzNp5gWZtB9JhZGN0GMkO30Xmjo3YNWwJFJ9LTWu+Z1bkWooe/YDzLTri2Kozjq26oPP2Z+b9oYR8tIOfjyQyvImPhd+pEJZlTI6mr52OtQ6ONNapKYo7WWK/Sm+Lxj3wBs8WldFtV3Z98sknvP/++wQFBdG3b19atGiBn58fNjY2pKamcvToUbZu3crKlStp1aoVX3zxBSEhIfcs8C1btjBjxgz2799PfHw8K1eupH///ub9iqIwefJk5s6dS1paGi1btmTWrFnUq3eleaDBYGDChAksXbqUvLw8unbtyuzZs+8oUyp3lq/PmJfDha+mkrhsDphMaJxcqPXtWmyCZaWZe8lQZOLJn4+zJDwBdzsrApysOZWUQ4FRYUqPGrzWKYiMbRuImTkRQ/RZALSuHng//iIeDz6BuhIu0yvKjtGkMHdPHLN2xnDsYg4A3Wq68kqHavQIdbutY2QZivB9fytPt/Dj4z6hJfYpisLARYc5ejGbUy+3If/MUQouxlGUnoIxJwtTThbGnCyM2ZmorW3JGT6ROjN3svGpJgR8PpqcI/sw5mSByVTiuAY7Z9psPm9+fGp0X7L3b7tufIVqK1rtuWh+fHb8I2Rs+eO6Y1V6axpvjkKlLe4LlvD95+Qc2YvWxQ21kxvv78+gRnV/RndvgNbZDZua9cw3AS5XAvw6vAH96nne1s/umz1xjP3tFABhXnYkZBUQl2mgZ6gbPw6rj6N15WrTKedfy5A7y0LcOUVR+GpXLB9vjeZ8ah4ALjZanmrux6Ru1bG9wRT900k5fDX6WR5J3owqN6vEPuugEBxbdaZHUU/6NvRneu8bf+86mZjDx1uj+PlIItkFRup42jG6pR+jmvvd0dR7UexIahLvHdjOiND63BdYw9LhCIoTXZkfDbzlOMcJKypswkvOv3futq98d+zYwT///EP9+vWvu79FixaMHDmSr7/+mnnz5rF58+Z7muzKycmhYcOGPPHEEzz44IPX7P/www+ZOXMmCxYsIDQ0lClTptC9e3dOnTqFg0PxtJBx48bx+++/s2zZMtzc3Hj55Zfp06cP+/fvl+Wy70LGjr+InvYyBfExALj2Hoz/+PexcpF+AveaXqtm8dAw3ugcxNLwBFJyCxna0IvHmvjgfakpvVO77ji26kzqup+58M2HFMRFEvvJW1xc9CXeT7yEx6CRqK76/BtNCtsi07mYXUCAk55WgU5SJSKuYTQpDFt2lJ+PXGRgmCevdqhGTqGJ7/dfoOf8g3zZrxbPtQ645XH+3HqQkItHeKYwkYtL/6Yw+SJFKYkUJiegtrVn3DOf0mnufvbFZWL39tPknz913eNo3TzRDZ9ofmzKy8WYlXFlgFqNykpHjhGwKpnk1Xn6oPcPBo0WlUaDSqtFpdESmVFAUpGWVleNVeut0Tg4YSowoBjySxxHKTCA5sppNvvQ7hKJsVEAR+DMquLHjbZEo9HaA+C96jOWHFiLKSGA2Kb10PtVQ+9bDZ1fNXTe/qitdCVea/WJJJ5ecYKnW/jxfs8auNvpMJoUfjuexBM/H+eRZUf5fUSjm/7shRBC3BsqlYoxrQN4pqU/Z1JyKTQq1HCzueXUQ1udhk9DH6PN29PorYkjc9c/ZO76h5yj+8iPPIMxP4/E5r2wvXSc1PUr0Hn6Yluvifk8selcKvcvCMfV1orn2wTg7aBj0/k0Xvz9NL8eS2L1iEboJeF1RxafOcZP509RZFIk2VVOKIZcAGyHvIfGM/ia/cbECHKXv20eJ6qG2052/fTTT7c1Tq/XM2bMmP8c0O3q3bs3vXv3vu4+RVH49NNPmThxIgMHFmd4v//+e7y8vPjhhx8YPXo0GRkZzJs3j0WLFtGtWzcAFi9eTEBAAH/99Rc9e/a85++hIjocn8V3+4pXSPSws2J4Ex9aX0p+GHOziZ72Mql/FH9WdD4BBL7xMU5tulk46qqnnpc9U3rWvOF+lVaLW5+huPZ6kJTVS4n/9iMKEmJJ/fNnPB56yjxu+aEEXvvjLFHpV77E1/aw5ZM+ofSqJclLccWiA/H8ePgivzzagIFhVyqRnmnpx/jVp3nht1P0DnUjUGvAEBuBISYCQ2wEirEI39Gvm8d7fTaGb+LPkbkfMv/1GlpnN4JdixNTqbmFuIeEoba2RevsisbOEY29Ixp7BzR2Dmid3XB2s8HHQcePRy7y6VufgaKgsXdAbeeA2tqWzefT6PzNATY+1aTE6wS/N+e67/H8yWQeXxBOaHQGrQKL+6ZUnzYPKD7vtPtqL7YqI+uG18NkyMdUYCiRGPYa9ixObbpRlJ5ManwCv+06RUc3E84FmRhzs1FfNZ047+wJaqWegtRTXDz6V8lA1Goa/X0ejX1xNVPGjr9Y//M+Rjj78Hm7uuhsiyvJNGoVA8I8KTCaGLr0KOEXsu6oB1hugZFTSTlYadTU9rA1N1YWQgjx36jVKmp53H7rCD9HPU39HJi7/yKDnmyOfYMW+D79GkVZGWTt3cLOU/GkRhXRr64HitFI9LSXMWZloLaxw75xK2yatOeNIw60qV2fVY83NleQPdc6gE3nUun1XTjTN0XyTrfq9+otV0qP1KxLkclEd/8gS4ci/kXjGYzWrzaKorA3KZ4m7t5or7PCqagaSnVOg8lkIjY2lsBAy5YGRkREkJCQQI8ePczb9Ho9HTt2ZMeOHYwePZr9+/dTWFhYYoyvry9hYWHs2LHjhskug8FQoldZZua/v45VTiaTwvO/neKrXbH4OOgI87Znf1wmX++OY0A9D34YGoZeb0N+1DlQq/EcOhrfZ95AY2tv6dDFTai0Vrj3fwzX+4eSsmox1tVrm7+cL9txmsVfL6JJt4EsH1afWh62HIrPZsrfEfT5/hBrRzS67alpovzLKTCy5GA8Px1JJMtQRKi7LU+39Kdttdur5Ptqdyy9a7kxMMwTU4EBta64klClUvHC6UU027uei/0TScsr+TtT4+hcItml8Q0iOj2HwOBAXPx8sXLzxMrdGyt3L6zcvVgXXVydFexqQ/X3v7llXM+1DmDSX+fpEVK/RBIuJj2fZ349SX1vezrXcLmtn1GvUDfqe9vz8NIjrHi0AY39ipNN2YYiJv55jh3Rmax9otGlpNu10+ocmrXDoVk7ALxMCv0/3sFhf0eWPnxtxXTcA88zOaMe/2uoI9CQTMGFKAxxkRguRKPW6UscP2bJHB7fvRGAo7+B2tYe6+BQbGrUwaZ6bQYMehI3WytWHku8rWRXToGRt/48y/x9F8g0GAEIcNLzUrtAxrULlMpOIYQoIyqViomdgxm4+DAv/n6ayd2q42JrhdrOka3erXhsyzHur+1MI18HijLScGzVmay9WylKTyFzx0Yyd2zkS4ADzqQbn8L2mTfNx+5Uw5Unmvrw9e5Y3uwcdMsbGoVGE3EZBnRaNT4Ouip9Lmjg5snHrbtYOgxxA4qi0GTFAsJTEvmnz8N08q2Y0xbF3ftPya7vvvuO5cuXExUVhaOjI+3bt+ell15Cq9USHByM0Wgs7TjvSEJCAgBeXl4ltnt5eREVFWUeo9PpcHFxuWbM5edfz7Rp05g8eXIpR1z+Tf0ngq93xzKrXy1GtfDDSqPGZFL49e/9PLHxAi/+rmPOwDoEvfsFpvw87Oo1ufVBRbmhttLhMWik+XGh0cTe2Z8w+eRybNW7CGw+A7vARnSs7kLbak70nH+Ql9ec5nBIqyp9sVNZRKXl0e3bA5xPzaNHiBthXvZsiUxn0cF9jGnlz5f9al3371kxGjHERZJ35hjNt6ymr20yR36NQDEYaLDuuHlcUdQpaqefNT+2cvdGHxCM3j8IvX91FKPRPHW2xezlVP9wO2He9vz2WMMSF9/ZhiKmfr2P9kHOt31n/LWO1TgUn8WDiw/TKtCJdkHOxKTns/JYIt4OelY/3ui2P8MatYrVIxrRa/5Bmnyxh2b+jrjbWrE9Kp3cQhNf9qtF79useFSrVbzULpDnVp2iQ7ALo1v4mVedPZ2UwxP7FJwbdaPt2BYl4lMUBWNGWsmD1Qhj7+kEmmozUKdcwJSbTe6xA+QeO4DazgHPR8bgamtFbqGJuNnvU5SWhHX12thUr41NjTpo3TzNr5FfaKTnvAOEx2fzYtsA+tbxIK/QyMID8Yxfc4azKXnM6l/7tt6jEEKIuzcgzJNZ/WoxbvVpvt0bR0MfBy5kGohOz6d7iCs/DA0DQOvkQvVp81FMJvLOnSBrz2Z2rV6LW8RBbLLSSxyzKCON6A8m8JB/I9YlOBCX2ZxqLtfv25pXaGTaP5HM2RNHYnYBAA197Hm1YxDDGt16sRghyppKpaKxmxdnM9OJyEqnE5LsutcKCwtJSEggNzcXDw8PXF1dLR0ScIfJLqPRyMCBA1m3bh333Xcfffv2JS0tjZ9//pm5c+fyxRdf3Ks4/5N/f4FRFOWWX2puNeaNN95g/Pjx5seZmZkEBNy6D01Flldo5JPtMYxtHcCYSz13lKIiEn+YTdCc6XzX7mGG7u/N5O7V8a5Rx8LRitKw/kwqMYod2NiTe3Q/Jx/riseDT+D77ES0Ti681jGInvMPciAui6b+0hi6IlMUhf4LD2FS4MT41oReSiKZLjWbf/bXk9TzsmN0facS1URR014mdc1yTPnFvQ+euLS94NJ/C9OSzX36vB59nk/t2lHoEchXz/a46eqfWo2arwbUod/CQ3Scu5/x7QKp5WHH/rhMZmyJIiotny2jm972+9Nq1Cx7uD6PNEpi7p44Vh1Pwtlay9SeNRnZzBeXS1P+blegszWHXmzJr8eS+PV4ErkFRsa1DeSpFn4EOlvf0bGebeXP8cQcxvx6kplbo+gQ7EJcpoH1Z1Ko4WrDyuENrjkfqVQqtM4lLyBqjH2LthkdeL5NAO91CcQQc5788yfJO3cCpaiIyLR8zqbkUtfTjrRvV5kXp7hM4+SCbe2G2DdowS9hw9gVk8m2Z5qZp2pCcQVA8wAnxvx6kseb+tAiwAlxe8rrBaAQouIY0zqAB8M8WbA/ntPJubQKcGRwAy9zK5GrqdRqbEPqYRtSj+0u3fnxYCxH+tihc79SBJB9cCdpG1biyEp+BlIfmoqxaVvsm7bDoUkbc6W/ocjE/d+FszM6g6ea+/JAHQ+yDEUs2B/PI8uOcj4ll7e6Vp0pkAZjER+E7+Kh6rWpI72Iy7UPWnZkdrseWGsr1+I85Ul2djZLlixh6dKl7Nmzp8TsN39/f3r06MHTTz9N8+bNLRbjHf3tf/LJJ+zevZvw8HDq1LmS1DCZTMycOZOnn3661AP8L7y9i+8yJCQk4ONzZRnexMREc7WXt7c3BQUFpKWllajuSkxMpE2bNjc8tl6vR6/X36PIy6dd0Rmk5hbyZHNfAHJPHiLyvRfJO3UYgNqZ5ylyNLL+dAqPNfW1ZKiilFzINPBTQC8Wf/oycZ9PIvWPn0j6eT5pf63C7/l3qN95kHnc7acdRFm4vMDu7VYr/XMujfD4bP4e1eRKosuQT+6pwww4vx/72L9xff0Y4bkXabQpEo1d8TQ4ldYKU34uKr01NjXqsFfjy26ND++N6oNjrbASCZn44GbMVgr5rkPdmya6Lru/tjt/jmzMW+vPMWjJEfP2HiGuLBkSRsM76DsFxVVU/ep53vaqhrdipVEzuIEXgxt43XrwTahUKr7sV5uHG3ozZ3csxy5m42itZc6AOgxr5I3dDVbn+jcbKw0jmvoye2csjzTypu6lqi2Xbv0putSvy0mv5aEGXhiefYO8M8eLk2HnT2KIjcCYkUbW7k0UpafybV4n+tf1oFWgE7GfvYvWyQXbek2wq9OIp1v4MX1TJN/uvSDJrluoCBeAQoiKxctBz2udgu7oOb1ruTFjSxR77GrS1e/Kedm6ei18Rr/O9tXr8E84ji4tmbS/VpH2V/GKKdXe/RL3B4YxZ3csu84nsX5Uc9rXuNK64sH6XkzacI63N5xncAOvO+pDVpGti4lg0v7tzD1xiJhHxqCW2Q3lludtXG+K/+6TTz7h/fffJygoiL59+/L666/j5+eHjY0NqampHD16lK1bt9K9e3datWrFF198cU8XL7yRO0p2LViwgBkzZpRIdAGo1WomTJiAoii89tprpRrgfxEcHIy3tzcbNmygcePGABQUFLB582amT58OQNOmTbGysmLDhg089NBDAMTHx3P06FE+/PBDi8VeHhmKTAA4aYzEfvYOF5fMBpMJjaMz/uPew/n+h+Gtv8m/NE5UfN72xSv4nDE5UO+9Obj3f4zo6a+Qf/4kUVNeJP/oSaAzPo5VK/FbXimKwspjSXy+PZod0RmoVSq61HDhpXaBdA+5eV+1jedS8XHQ0am6Cymrl5H44zfknT6KUlQIwNW/7fMjz5inKHsNexbPwU+iD6iOSqOh4EIWo2btIf60E1/VteNyGuhwfBYP/XCEIBdrhjS8/eRQ15qudK3pytnkXJJyCvB3sibgDiunKoq2Qc60DXK+q2NM6ladf86n0Xr2Xp5q7ken6sVVYnN2x3L0Yg4/PVIfO50Gu+4DoPsA8/NM+XnkR54m59hB1Hprzu3JY0RTH0wFBhKXfm3+HKBSYR0UwmSbYM7Eh5Bbfwi2IfVuGtOu6Aw+3x7N1sh0VECn6i680DaQZpW8GrSiXAAKISq/TtVdaO7vyIifjrHqsYY0udRrUuUbzJygB/lfnSYsezuU+zRxZO/fTtaB7WQf2oND49YAzNkdx7tFO3EYNYazjVrj0KQ19o3bYFurAa93CmLWrli+2RPHR/eH3jQOo0lhR1Q6idkFBDpb08zfsUK2wXC3tqFPYA3qu3pIoqucMiZGXLOt0GRCnRxlgWgqrx07dvDPP/9Qv/61fWcBWrRowciRI/n666+ZN28emzdvtsi1jkq5XAZwG2xsbDh8+HC5uCjLzs7m7NniqRiNGzdm5syZdO7cGVdXVwIDA5k+fTrTpk3ju+++IyQkhKlTp7Jp0yZOnTqFg0NxVcCzzz7L6tWrWbBgAa6urkyYMIGUlBT279+PRnN7d9QzMzNxcnIiIyMDR8fKeQEfm5FPvff+ZFXMFzicOwCAS4+BBLw8FSs3T9acTKbPgnB2P9dc7vZXEgVFJgI+2Eb3mq4sGlIPlUqFUlRI4rK5JCz6kin3fcJxoxNHX5KeXZamKAqv/nGWj7ZE0SHYmQfDPCk0Kiw9lMD+uCxm3h/CS+2rmccb83LIObyX7PCd5Bzdz4/NRzP3gg1Rr7cjcdlcYj4qbhavdXHHLqwp8d61eeWsPYvefJiQ4JtXbv52PIlhy45SYDTRMsCJbEMR4fHZ1HCz4Y8nGhPibntPfxZVXXpeIVP/iWT+vguk5BaiUkHvUDfe7Bx828m0gGlbGVjPk5nd/Ej65Ttyjx8k59gBCi5Elxjn1KE3NWcuMT8uTE7Ayv1K75Yvd8Qw9rdThLjbMijMEwX48fBFItLymDOgDqNa+N31+y2v59/Bgwfzzjvv3PAC8DKDwcC8efPQ6XQ89dRTNx1bnsTGxhIQEEBMTAz+/v6WDkcIcQsXMg30mn+QIwnZtAhwxMdBz/aodJJzCpnSowYTuwSXGG8qLECltSqeNv/mRtamzMNt/7oSY9S29tg3aM6vBLO7fn9+Ht3uhq+//FACr687S2TalZW963nZ8dkDtehas2JO676d1jiibBmTo8n8aGCJbRFaO8a7NyVBa82O2PWoAMcJK9C4V8weXnL+vXN3lOxyd3fnt99+u+E0v/DwcD7//HPmz59fagHeyKZNm+jcufM12x9//HEWLFiAoihMnjyZOXPmkJaWRsuWLZk1axZhYWHmsfn5+bzyyiv88MMP5OXl0bVrV2bPnn1HPbjK68V2aRs9cwWP/jgWO52W6lPm4NyhFwDJOQV0nrsfaysNe55rLr/4K5Hv919gxE/HGdrQi1c7VKO2px3hF7KYtuEUa85l8fvjjbivtjsxH72OTWh93Po8jEqW9i1z60+n0HP+QT7pE8q4dldO3oqi8Ma6s3y58QTb2ptwjzxI9sEd5Jw4BMYi87i0J9+ne1QIe59vQX11OjlH9mIX1gydb/Gqe8+sPMHvJ5KJeq3tLVdqAkjLLeT7A/Hsjc1Ep1HRu5Y7A+p5YHUbzxWlo8hoIiW3EDudBnv9nfWqmLDmNPP3XeDMhDa42enM2wvTkjm4aRvzfljLo/YXqdN/MO79HgXAEBfF0X6Nsa5eC8eWnblYsyXtt2kY0zGUGfeFmBvvG00KL/5evKrvoRdbEeZ9d6v1VpXzb3kjF9tCVDyFRhO/Hkvi56OJZBuKqONpx6gWfrecfugyaRNjW/rwWmAO2Qd3kHVgJ9nhOzFmpgNgVGn4cuwaFj7WAoCMnRtRabTYN2iO2tqWJQfjeXT5MQbU8+CVDtUIdbfl4IUs3v8nkm2R6fw5sjFdymnCy5gcjWLIveF+ld62wiZNKqt//51lFRXivWEdBSYTxzp2pZabZ4X+O5Pz7527o2TXgw8+iJ2dHQsXLrxmX0JCAp06deLMmTMWX42xLFWVi+0LmQae+d98YozWdOjchiZ+DpxKymXevgsoisKmp5tS1+vuvriI8mfh/gu88ec5LmRe6TdT3dWGT/qE0reuB9nhuzj11H0A2DVoTuAbM81Tm2Iz8ll4IJ6otHzcbK0Y1sj7rr/cVhV30ner/8JDRKXlceCFlqhUKgqTLwJg5e5FodHEoHGf8s7OKSWeo/P2x75xa+watMC+VVfClsbhYqNl/cjGJRIca08m03/RId7pElylGtBWZTHp+TT5YjcBTtZ80bcWbao5YTQprDqexJhVpwhw0rNzTPMSycu0jb9x/o2RYLoylb1QrcWlcSucWnfBpfsA9H7F1YWFRhPVPthGv7oefDXg7hY0qcjnX5PJRGxsLIGBFe+iWy62hag6nvr5OH+eSeHUy22wvdRH8vJqj8f+3siCDYfoOmkaA8OKe2KeGN6F3BPhqLRW2NRtwvz8ADRhrZjy4mC0dlf6bRYaTXT95gBZhiLz9Ut5cr0qoY02XrTIT8FBuXLDsCJXCVUVP58/SSM3L2o6udx6cDkn5987d0fJrsOHD9O6dWsGDRrEK6+8Qs2aNUlNTeX3339nypQpBAUFsWPHDkl2VRLZ4btArca+QfHdmuScAj7eGs38fRdIzC7AyVrL8MbeTOhQ7YbLFYuKr9Bo4p9zaVzMLiDASU+HYBdzpUbx1MY5XJj7IabcbNBo8Hz4WZbUGsJbWxOw0aqp7WlHVFoeSTmFPNLIm3mD6qLXSpXPvymKwi9HE/l8e8ylvlvQpYYrL7ULpGfojftu1Xl3DS84xnGf4SRZ+7ZhiD6L98iX8RszEYAxi3dx33ejqd+lM/aN22DfuDV6n5LVq+EXsug+7wAFRSYebuSNr4OeLZHpbDybSt867vz8aAOpzKpCDl3IYsjSI5xKysXdzgpDkYksg5FuNYuXuPew113znKKMNLL2biFz9z+c+HMdHrmJ5n3VP/wely4PmB8/u/IEO6IyODSu1V3FWRHOv9999x3Lly8nKioKR0dH2rdvz0svvYRWq8XX17dCXi/JxbYQVceJxByaf7mHlgGOzOpXm9qedphMCn+dTeWJn4/jYWfFvudboNWoUUwmIic/T9a+LRRevFDyQBotzu17UuOjReZNa08mc/+CcMJfaHnLhWeMJoWotDwUoJqz9W1Vmt+NoriTZH3xKLZD3kPjGUx8fj5BG//EWqPhXJfuuKRfIHf52ziMXYzWr/Y9jUWIy+T8e+fuaH5DgwYNWLt2LSNHjmTx4sVXDqLV8uKLLzJ27FiqVat2kyOIiiJ90xrOTxyF2tqG2t+txzqwBu52Oqb1qsm0XjUpKDJhpVGVuzsxovRZadT0uEGyRaW1wuvR53HpMZCYj98kfeNvJC7+kvr6pXw26HWGP/s4jtZaCo0mFh9M4NlfT2Kv1/D1XVZ0VDaKovDK2jN8vDWajsHOfHJ/CEUmhaWHLtJr/kFm3BfChA5X9d3KzSb+mxlk7tnMwlNHUKOQfHmnSkVhUrx5bLLKlg8Gfcu2Z2+86lsjXwcOvdiK2Ttj+PFIIpn5RdTysGXxkHoMbeiNRi3/zquShr4OHH+pNRvPpbI7OgOtRk2vUDca3eTLiNbJBZdu/XDp1o9e+u0McM1hnH00mbv+waF5hxJjC4wKlf0jZTQaGThwIOvWreO+++6jb9++pKWl8fPPPzN37ly++OILS4cohBC3VMfTjjUjGjHkhyPUmbmTEHdbsg1FxGcV0CrQiZXDG5gTTyq1muDJs1EUhYK4KH7/6XeO/72RAURQEB+DSndlUSNFUQj4cgyjLzqRuKcQ0/1dUVtdeyPFZFL4bHs0n22PISq9uOeXv5Oe51sH8HL7wHue9NJ4BqP1q03cxThqOLngbm2Dd3BDiuJkgSYhKoI7a+YBdOzYkTNnzrBnzx4iIiJwdHSkdevWuLq6kpOTw7vvvnsv4hRlKOmXBURPnwAmE/YtOqHz9LlmjE4qc8RVdJ6+1Ji+gLSt69k58QW8cxMJ+PNj7J4ZCmix0qh5opkvaXmFvPbHWd7pWh1fWcnRbMOZVD7eGs2nfUJ58aq+W+PaBfLW2lN8v3QNXVN9aNy/eBU9td6G5FWLMGamowYiHQJp0rsXLi07Yt+4NVpHZ6C4GvP3E8m80zX4Oq9akq+jnik9azKlZ8178RZFBaNWq+ge4nbL1Tyvp2uIG/OPGHn39ZF4PlSy8XpugZGVxxJ5svndN6gvzz755BN2795NeHh4iRWsTSYTM2fO5Omnn7ZgdEIIcfs6Vnch6vV2/HI0kX2xmei1au6v7U7bak7XvemtUqnQ+weh6T6Edy/WYcjLrQktSkUpuNISI//8SQp2/cUogCm/cOhjO+wbt8axRUccWnbCpkYdUKl46pfjLDgQz/DGPjzc0Au1SsWPhy8ycf05DlzIYunQMPNsg3uplZcfpx4aRZoh/9aDRblzIDmB704doYm7F0/UamDpcCqFoqIiFi5ciKIoPPbYY1hZWVk6pOu67WRXdHS0ubeEWq2mVatWtGpVcgqCnZ2dOdkVFxeHn1/lvpitbBRFIX7udOK/+RAA9/7DCXz9Y1TaO86JiirqfHArBrb8mE3OO6jesgUam+LGp4qigLGIJ5v78eofZ1l9IomnW1b+8tvYjHzS8orwd9TjYnvjk8CsnTE08rHnhbbF0wsLEmLJ2LmRzB0bGbR3CwOzM8k85YXSrz8qlQqVRoPvM2+idXQmMbgpjy6KoIeXK/Oa1UV7qedWbEY+Dy89io2VutInFkT58lzrAObuieOJn47x7YN1zX1esg1FPP7jMXILTTzbsnJ/JhcsWMCMGTNKJLqg+PppwoQJKIrCa6+9ZqHohBDizui1aoY18mZYI+9bD76kdy033Gyt+GBTJPMH1S2RGFO7evFzx5fxj9xLu8zjFKUlk7njLzJ3/AWA95Mvc6zrM3y3P57vB9flsaZXVoPuEepG71puDFpyhKENvBhwqV/YvaZSqXC1lrYtFdGOhDi+PHaAtl5+kuwqJW+++SZt27alqKiI119/nY8//tjSIV3XbWcxmjdvTt++fRk1ahQtWrS47piMjAx+/PFHPvvsM0aPHs3YsWNLLVBxbylFRURPn0DyyuLFB3xGvYLP06/LNEVxR3IKjBg0elyefhOXq1b4SV37IxcXfUHA6x9hrVWTU1Dx+tTciY1nU3l3wzm2R2UAYKVRMSjMk6k9axLkeu2F0oELWTzexIeLi78kZdVi8iPPlNifb+3AaZcQ2uXloLEtbvJ/uWLGFVgx3IHBS47gP20bHYOdKTQqbIlMx9lay9onGuN5nR5LQtwrdTzt+GFoGI8sO8ofp1LoU8cdkwKrTyRRYFT4cVh9qrvZWjrMe+rcuXPX3BC82iuvvMIrr7xShhEJIUTZsrbSMK1XTZ5ecQKjSeHVjkHm1Rj/tzGKdbqWrJ46mgYhruSdPU7Wnk1k7tlM9oGdODRuzdw9cTTwtqe/8QwnHn0Ep/Y9ce7YG5taDXiwvhetA6OZsyfunie74nOz8baxk+9EFVj/oBAOplzkweBalg6l0jCZTOTm5qJWqzFdtUBReXPbya4TJ04wdepUevXqhZWVFc2aNcPX1xdra2vS0tI4fvw4x44do1mzZsyYMYPevXvfy7hFKbu4ZHZxokutJvDVGXgMesLSIYkKKNTDFo1axcazqeblrBWTiYQFn5AfcZrTT93HRK/W1O8+BbjSg0pRFPbFZnIyKRd7nYbuIa7Y6ytmReHKo4kMWnKYlgFOLH04jCAXG3ZEpfPptmhaf7WXHc82J8jFmvzI02Tu/BuPQSPRadRkGowUJl4oTnSp1diFNcOpTVccW3dl5AEVcdlFjLK9/mqWvWu5E/laW77bd4HtURnY6eCzB0IZ3tgHR+uK+XMUFdug+l409XNkzu5YNkekowKebxPA6Jb+BDpbWzq8e87Ozo6kpCRCQkKuuz88PJzPP/+c+fPnl3FkQghRdka18EOtgol/nmPRwQTz9mBXG34d3pDetdwBsA0NwzY0DK9Hn8dUWIBKpeL0rv10DHYmY+v35J48RO7JQ8R/8yFWXr44t+/FENsGzL4YcKOXLhWKotBu1RJUKljZYyD1XT3u6euJe8Pf3pF5He+zdBiVyrRp01i+fDlFRUVMnz7d0uHc0B2txgiQn5/P2rVr2bp1K5GRkeTl5eHu7k7jxo3p2bMnYWFh9yrWcqkirAZ1WbahiB/CE/jjVAoFRhPN/B0Z1cIPfydrTPl5nB0/DI/BT+LSuY+lQxUV2KDFh9kdk8GuMc3xcyr+UluUnkLUZ5NJ/X0JahRUWis8hozCZ+TLHM7WMGrFCQ7EZZmP4aDX8HL7arzdJbhMejGUlvxCIwEfbKNDkDM/PtKgRGP3+IRkXnh/Ab2yj9Ai6SAFCbEAhHz1K2/Fe7HsUAKnBrlAXAQOLTqW6LsVMG0b73QN5o3Ot+69JURVUZ7Pvw8++CB2dnYsXLjwmn0JCQl06tSJM2fOyGqMQogqoaDIxF9nU0nMLiDQ2ZqO1V1uufhN26/24mWvY/n9vmRs30DGlnVk7vwbU36ueUy+1ppmK3ah9w287jFyCozM3R3LvH0XiEzLx83WimGNvHmhTQA+N+kde3k1xoR+b9Hs+FlUQFy3Xthdau1iTIyQ1RhFmZPz752742SXKKk8X2xf7XB8Fr2/Cychy0DHYBfs9Rq2nEkiV1Ezf1BdHm3sg6IoUqIr7lpMej5tv9pLfpGJZ1r609zfkXOpeczeFYs+5iTzslehObS1eLC9E5NDnyKufjem9KhBp+ouJGQV8NWuWD7aGsVLbQP5uE+oZd8QcOxiNp9vj+GPU8kUGBWa+zvyXGt/el26I3nZ8kMJDF16lFMvtyb0UmVb9pG9xH35Htnhu8BYZB6r0umxb9wanycnkBDYiIaf7aJLDVfmD6prnnYYk57Pw0uPcCIplxPjW8t0RCGuUp7Pv4cPH6Z169YMGjSIV155hZo1a5Kamsrvv//OlClTCAoKYseOHZLsEkKIG/h0WzSvrD3DmQltzC0gTIZ8svZu5cLG1UStX4O9tRVt/zpu/v6SuPwb1LZ2uHR5gGyNDd2+PcCh+CweDPOkxaXr0cUHE7CxUvPPqKbU9rS77msbk6PJ/GggADkqDUd0zrQypFwzznHCCjTu10+0ifInNjuTXyPP8GhIPZz1Fa/KXM6/d06SXXepPF9sX5ZtKCL0ox14O+hZ8WgDglxtMBUYODlmIFttQnjO7j62jWlJq0AnS4cqKon4TAPv/R3BogPxZBcY0apVDKjnwcTOwTT0dSBj50biPnuXvLPHeavrDH783+M4/Gva4oebI3lj3VnOv9qWai6Wawj667FEhvxwBHc7HcMaeWNnpea3E8kcvJDFKx2qMb13TfNF1oe/7mX/H+uYN6YH9g1bApB76ggnHulYfDDfYJZpavHQ4w/RtFcP1NZX+hb9eTqFQYsPU2A00SHYhSKTwtbIdFxstKx+vBEt5d+nECWU9/Pv5s2bGTlyJJGRkeZtWq2WF198kbFjx1KtWrVy3efiRuRiWwhRFjLyi2j46S70WjXfDKxD+2BnAHbHZDLql+Ok5BSwf1g1fKoHAWAqMHC4V53ilaqtbTlZsz3f2LXio9ceo0mgi/m4idkFdJ67H51GxYEXWt7wRr8xORrFkHvdfQAqva0kuiqY+j/N42haMos69+HRkHqWDueO3en5d9q0aaxYsYKTJ09iY2NDmzZtmD59OrVqXeldpigKkydPZu7cuaSlpdGyZUtmzZpFvXoV7+dzPZLsukvl/WIbYO7uWJ799STnXmlLkKsNiqIQNfl5UlYvRW3nwNgun+IfUpNlw+pbOlRRyRQUmUjNK8RRrzWvyHZZZq6Bni99xZDhAxnXrvhiIf7bGWid3XG9bzD5Wht83t/C652CeLNzMLkFRhYdjOf7/fEkZBfg46BjRFNfHm3sjY2V5novf1NFRhO/nUjmj1PJFJkUWgQ48Ugj7xI9rhKyDARP306fOu4sGRKGTqsGik8Mn2+P4fVVh/m1cT51L4aTtWczeWeOAeDQ6yFCp3xtHpu88nscW3RkfaY9fRceKlH5dbWUnAIW7I9ne1Q6apWKLjVcGN7E55pEoBCiYpx/TSYTe/bsISIiAkdHR1q3bo2rqys5OTl89NFH5hWsKxJJdgkhysrZ5FwGLDrE0Ys5+DrqUasgNsNAqLstK4c3oK7XlV6mxtxsEpfOIWXNMgzR58zbrbx8cev9EG59HsY6qLiP4vrTKfScf5BtzzSjbZDzdV+7wGhEp7nz60tRfk3at40NcZG81rAlfYOu31OzPLvT82+vXr0YOnQozZs3p6ioiIkTJ3LkyBGOHz+OnV3x95Dp06fz/vvvs2DBAkJDQ5kyZQpbtmzh1KlTODg43PDY0dHRBAbefrI3Li4OP7+yX4lbkl13qSJcbA9YeIiM/CL+fropAAkLPiPuy8mgVlPzs+V8mVedDzZFkvW/zhaOVFQlkal5BH+4nfVPNqZ7iBtF6akcvq8eSoEBtZ0D7n2H8UR2U+o2rs+7XYPpPu8gh+Kz6FPbnXpe9hxJyGbNqWSa+jmyfmRjXGytAIjLyGfunjj+PpeGSVFoU82ZZ1r6UeOq1d/OJOdy/4JwziTnUt/bHlsrNfvisrDXaVg+rD49Q90AmLLxPFM3RXLhzfY421iZn28qLODM84NIP7gLramoxPs65RCEVYcHGDx5contiqLQc/5BknMK2T+2hUwZFuIuldfzb0W5APyvJNklhChLJpPCxnOp/HMuDQVoH+RMr1C3G/Z0VRSFnRs28fusrxmUvhtyMgHwHjEOv+ffMR/T4d1/mNKjBi+1r3bd47RcuRA3axs+bd2VUGfXe/LeRNmq6C177vb8m5SUhKenJ5s3b6ZDhw4oioKvry/jxo3jtddeA8BgMODl5cX06dMZPXr0DY/l5eVF3759GTVqFC1atLjumIyMDH788Uc+++wzRo8ezdixY+845rsl5QJVQIHRhL2++M5E2t+/Fye6gIAJH+DUuiv226IpMFa8qRSiYnO3s8JKo+JwfDbdQ9xQ6XT4Pf8OST/NwxBznsSlc5gOpJxqxUfhfbmghHBgbEsa+l65y3AgLpPu8w7y3KqT/PBwff48ncLARYdQq1T0qeOORqVi3t44PtsezfeD6/FwI29yC4z0mHcAvVbNgbEtaOxX/CU5LiOf0StP0n/hIfaPbUFdL3uOnjjH88pxsr7aSEZ+HtXenAmA2kqHMTMNramIeGsP6vfqhUOLjjg078B3W5KZszuOuG3RPNXcF3u9luj0fN5Zf44NZ1L5dXiDCn2iFULcXPPmzSvEBaAQQlQEarWK7iFudA9xu63xKpUKTe0mfFBnFAOe/oKQiF2krF6G630Pmcekbv+LieFf4Fj/Ba5eHfyyMxmp7EmKR6fW4FIBezuJ66ss199ZWVlkZmaaH+v1evT6Gy+4cFlGRgYArq7FyduIiAgSEhLo0aNHiWN17NiRHTt23DTZdeLECaZOnUqvXr2wsrKiWbNm+Pr6Ym1tTVpaGsePH+fYsWM0a9aMGTNm0Lt37//6du+KVHbdpfJ6Z/lq7244xyfbojk30JGYMX1RDHl4PDSKwFeLlwnt9u0B8guNbHu2uYUjFVXNI8uOsiMqnYMvtDRXTikmE5m7/mbHV1/gfmIbaop/RUU8+j8GjXseAGNeDiqtFWorHV/uiOGl1afZOaY5Hefso0sNV5YMDTNPR8wtMPLMyhMsPXSRAy+0ZG9MBk+tOMGpl9sQ4m5bIp6U3Vv431e/0NqUQL20kxTEx5j3qXR6Gm2OQm1V3CQ++/Ae5p3OZ+KhIrLf62IeV2g08eLvp5mzOxZrrRp3Ox2xGfnY6TR80bcWjzf1vXc/UCGqkPJ6/k1NTWXq1KnMnz//lheAb731lsUuAP8rqewSQpR3hiITAdO28lADL77sd+1qiX+Pfhin/X8C4NSuB94jxmHfqFWJMWcz0tiTFM+wmnXLJGZRdgpNRk6np1LP1cPSodyRy+fff3v33XeZNGnSTZ+rKAr9+vUjLS2NrVuLFwrbsWMHbdu2JS4uDl/fK99Pnn76aaKiovjzzz9vGVN+fj5r165l69atREZGkpeXh7u7O40bN6Znz56EhYXd2ZssZVLZVQWMauHHp3+d4PiLz2BvyMOxdRcCxr8PwPf7L7DxbCo/DLXsB1FUTe92DabV7L20/3ofk7pVp3MNVxKyDMxO8meW31im3Deelsd+h40/0b5PT/Pzkn9dRNyX72HfoDk9ajehf1QWq+YcpnmqmvkPdcBec6VSUWfIZnYLPdH7ovhlcSx5Gem8YsjC9qfdnI+JIHjKXFTq4l5cGSu/47GjqwAoABSVmtP2gTTu0gmv5m3gqmbSdvWbM+/v3XQLKdk43kqjZnb/2rzeKYifDl8kLa+Q6q42PNTAC3vpvSVEpefq6spHH33ElClTrnsB+Mgjj5SLC0AhhKis9Fo1L7YN5J0N52ji68CIpr7maY9bI9J43bYbY2vmU/fcFjK2rSdj23rsG7XCe8RLOLbthkqloqaTC362jny//wK/n0gmv8hEIx97RrXws+jCSeLunM1Io+WvCzEqConDx1bIvmzHjx8v0f7gdqq6nn/+eQ4fPsy2bduu2ffvirc7me5pbW3NwIEDGThw4G2NL2ul8s3r3LlzfPHFF0RFRZVYRvu3334rjcOLu+TvZM2soU2ZfnEoI+PWcq7XO1jvusDvJ5PZeDaVp5r7MrShl6XDFFVQqIcdW0Y349lfTzJoyRHzdjdbKz7sXZMJHarx05HaDC3swUXvKz1wco+HoxjyyNq7BfZu4XWAU9AfiN4Njr+Fo/ctHh/7yVuk/LaEj//12glbiv/rN/Zd9D7Fd0kcWnTkZGoBv2U78+G4hyiq0YCesw5T18OOFe0aoNYXV3UZiky89scZjiRk80XfWlxPoLM1L3e4fh8IIUTlV94vAIUQojJ7vVMQZ1NyefKXE0zdFEkzP0fOpeaxLzaT1iEN6PP+Y9gkR3Nx0RekrF5Gdvguzo4bgnOn+6nx0SLOJOfSc/5BIlLzaBfkjIuNli92xPDB5ihm9avF6JZS2VoRVXd0RqfWUKSYOJNR8aq7ABwcHO6oon3s2LH89ttvbNmypURFtre3NwAJCQn4+PiYtycmJuLlVTlyA6WS7Orfvz/PP/88Q4YMQX2pQkKUL4829qHm/8bz6ZYBrNmdQoExieb+jvwwNIyhDb0qzRxmUfGEeduz9ZlmHLuYzcnEHOz1WjoGO2N9aYXFdkHOqDUalh9O5Pk2xUmpoP99hfcT48g+sIPdm3dy8lwsvuTgbsrBS8lB63RliWmNgxNqO3uytXYkKdbYODsTYdDSq2Ud7KrXQm195e6cx8ARfJ7ekMz8IhxbFE/rXTOiEfd9d5CAD7Zxfy037PVa1p1OISmngFn9atGxugtCCCGEEKL80KhVzB9Ul1Et/Ph2TxyRafkEuVgzsXMQfWq7o9WoIbAG1SZ+is/Tr5H4w1ck/bKAp+s1oc62DazepcVepeH4842p41/cLyynwMira8/wzMqT1HSzpWtNaVxf0ahVKrb2fYRgByc0lTxvoSgKY8eOZeXKlWzatIng4OAS+4ODg/H29mbDhg00btwYgIKCAjZv3sz06dMtEXKpK5WeXa1atWLXrl2lEU+FU157hlyWtGIBzh16Y+VeObKzomoatvQIf5xKYd3IxrQMvDJtcHtkOvd9d5AB9TxxttHyQ3gCMW+0R6+9cvJSFAVFgdozd9LE14HJ3atTZ+ZOXusYxNSeNUoken89lsiARYf5fnBdHruqt1ZyTgHz911g7clkCowKzf0deaaVP3U87crmByCEuEZ5P/9WVtKzSwhRWR2IPkfTdT+jUakxRoRxsPYFVEtm4DvqVdwHPI5Ko8FkUmgxaw+e9jrWPtHY0iGLKuROz79jxozhhx9+YNWqVdSqdWUmipOTEzY2xTf7p0+fzrRp0/juu+8ICQlh6tSpbNq0iVOnTuHg4HCjQ1cYpZLsWrVqFTt27KBbt24l5ox26NDhbg9d7pXni+2UNcuJfPdZrLz8qLd8Oxr78hWfELcrI7+IXvMPsis6gy41XKjnZc/RhGz+OZ9G22pO/DGyMRcyDdT7ZBcjm/nyVf/aaC71ZlAUhbfWn2PqP5Fse6YZbYOc+WhLFK+sPUP7IGcea+KDrU7DquNJ/HzkIg+GebL04frm5wshyqfyfP6tzCTZJYSorIwmE3/FRfLWP8cwZXiy9MQHZO3ZDIBt3SZUe/NjbGs35PPt0YxbfZqCKV2KK8REhWVSFNQVZIbTnZ5/bzRz67vvvmPEiBFA8fekyZMnM2fOHNLS0mjZsiWzZs2qNH1FS2Ua459//smmTZs4e/aseRqjSqWqEsmu8soQF0X0BxMAcLt/qCS6RIXmZK1l89NN+fHIRb7fH8/f51LxddSzZGgYg8I80WnV1PLQ8u3AOjz5y3E2nElhSAMvtGoVvxxN5GRSLh/2rknbIGcAJnSoRi13Wz7eGsWoFScAqOVhy2cP1OLZVv6S6BJCCCGEqGI0ajU9A6qzwCqHeJ2BkM9/JOmXBcTNnkLu8QOceKwrHoOfwqnVEygKGBVZ7a2iWhN9jv/t305bbz9mtu5q6XDuidupaVKpVEyaNOmWqzlWVKXy73Pz5s0cO3ZM+j6VE4rJRNR7L2DKy8G+SRt8n3nD0iEJcdd0WjWPNvbh0cY+NxwzopkvDXzs+Wx7DEvCEzAp0LaaE988WJd2lxJdlz1Q14MH6nqQV2ik0KjgoNfI7zAhhBBCiCquqZ8jE4+eJcWg4DlkFC5dHyBm5lukrV9B0vK5VFv9Cw83fwG9tpulQxX/UaHJyJ6keJLyc/m4VRf5DnCXDhw4wNatW9HpdLRt25YGDRpYOiSglJJdLVq04Ny5c9SsWbM0DifuUPiFLObtLW686Gprxcikjdjv24pKb0PQO1+gquTN94S4WhM/R75/qN5tj7ex0mBjdQ8DEkIIIYQQ5doTm9bQzMOHx0PDGNHUh3c2nOP5VadYNKQeVu7eVJ/6LZl9H+H4e+NxuBjFfY0CLB2yuAs9/IP5ul1P+gXVlETXXfr0008ZP348zs7OaLVakpOTqVevHgsWLKBp06YWja1Ukl0HDx6kXr161K5dG71ej6IoqFQq9uzZUxqHFzegKAov/n6aL3bE4Ouop4mvA5Enz6BeMw0A92feQu8ffIujCCGEEKK0BAcH/6cL53HjxvHCCy/cg4iEEELczKGURBacPsriM8cZFFwLLzs7Fg8JY8gPR9gfl8njTXxwsbVi7UlnNtZ7n5dbRvK/Yf3Mz88O34VtnUao9dYWfBfiTthqrRhdt5Glw6iw5s+fT6NGjahXrx5Tp07lgw8+4JVXXkGlUhETE8OcOXPo1KkTf/zxB+3atbNYnKWS7Fq1atU12yRDeu99si2aL3bE8PkDoTzTyh8rjZqIt78i1WjgsGtd1tCany0dpBBCCFGFLFiw4D89LygoqFTjKG2zZs1i1qxZFBQUWDoUIYQoVUEOTnzephtxOVl42RavtD0wzJNdY5rzybZoPtwSRX6RiYY+Dnz1UCMea9Lb3N/VcCGa0889iM7Th8DXZuDYqrMl34oQZWLGjBmcPXsWAJPJxN69e/nkk09o0qQJjRo1YsqUKfj5+TFhwgR27dplsTjvajXG4cOHs2jRIpo3b37d5FZVqOyy1GpQhUYTQdO3c39td+YOrGPebszOJG7WFLaEDWD41hzOTGhDTXfbMotLCCGEKAuyGqNlyGqMQghxRdbBnUS88SSFyQkAuD/4BAEvT0Wt0wOQmV9ERGoetjoNNd1spCCknPkz5jyLzxxnQsMWNHTztHQ4N1Xezr/5+fkcOXKE9u3b069fP6Kiojhy5Aj5+fn4+/tTo0YNtm/fzs8//0xYWBjBwWU/4+yuKrs+/PBDAH7+WeqHytqh+GwuZBoY0bRks26NvSOBr33Ig4VGntyxiT9OJTPWPdBCUQohhBBVz9ChQ3nnnXeoW7eupUMRQghxDzk0bk29n3dxYc40EpfNJfmX78g7fQTnSd/y9r5sloQnkFdoAqCelx0TOwfzcCNvC0ctLvv25GF+jjiFr519uU92lTfW1tY0b96ctm3b0rBhQ5YvX47JZOLEiRMcOnSILVu2sGnTJkaMGEFaWhr29vZkZmaWaYx31Ll86NChHD9+3PzYx6c40VKtWrXr/hH3ToGx+Jemg16Loiikb1pbYnlRa60anUaNwfifC/eEEEII8R/8+OOPdOnSpcQ109VMJlOZX/AJIYS41sQ9W1gfG4Hpv092QmPvSMDL06j5yTI0Dk7kHNnHsUc6cWbrZt7sFMSuMc35/fGG1HC1Ydiyo8zcGlWK70DcjZG16vNCWFMeql7b0qFUWB9//DEffvghTz31FAcOHCA0NJQHHngAe3t7fH19SUlJITo6mh9//LHMY7ujZNetLt4URZGLtzJSx8MOa62aNSeTSf5lAecmPMq5CcPNCa/N59PILjDS1M/BwpEKIYQQVU/Dhg3p3Lkzx44du2ZfUlISLi4uFohKCCHEZUdSk5gavpP7//iZxLycuz6eU7vu1F64kXSvmjjnp/OFTyxvda1Oy0An+tTx4NfHGvJy+0Be++MsFzINpfAOxN3qHViDz9p0o6mHVNv9V40aNWL//v1ERUXRqlUrrK2tcXZ25osvvmD69OkA+Pv706tXrzKP7Y6SXXDzi7fExES5eCsjLrZWDGvkzdwNh4n+7F0AHJq1Q6VSkZpbyPg1Z6jraUen6vL3IYQQQpQllUrFggUL6NKlC507d+bo0aPXjLmLlqlCCCFKgaOVjrH1mvJ4aBjetvalckytbxCPNX2PQ12fJezlySX2qVQq3u5aHb1WzYL9F0rl9YQoD2rUqMGGDRuIi4tjxYoVLFu2jLNnz/LII49YNK47SnbJxVv58tF9ITwduQLyskn0CuXXoPt47Y8z1P54B9Hp+fwwNEyaIAohhBBlTFEUNBoNS5YsoWvXrnTp0uWaayY5PwshhGVVc3Di87bd+LZj71I7Znp+ERcKNLgMfxGVtrg9tqmwgKj3X8JwIRonay1hXnacS8krtdcUd+90eiozDu2+q+msAuLi4ujduzeDBw8mICDA0uHcWbJLLt7KF9vUOLqf+wOAr2o/xrO/nWbe3gsMbejNvudb0NBXpjAKIYQQlqJWq1myZAndunWjS5cuHDlyxNIhCSGEuIfsdRo0ahVR6fnmbRdmv0/yyu85MbwL6bv+ITbDgJP1Xa0TJ0pRoclIi18X8uruTWxLiLV0OBVa8+bNiYyMtHQYZnc8jREqzsXbpEmTUKlUJf54e1+Zj6soCpMmTcLX1xcbGxs6dep03emZ5VXc7ClQVIhjq8789vEYjFO7kvxORz7vW4sgVxtLhyeEEEJUSVff+FOr1SxevNh8zXT48GELRiaEEAJg9rEDnMtMK/XjWltp6FfHnTm7YzEUFS8o5jlkFLZ1G2PMSOXs2MF0PfwjDzeQlf/KCyu1hoeq16ZXQDBW6v+UHhGXlLdZfnc8jdH8xApy8VavXj3i4+PNf65OzH344YfMnDmTL7/8kr179+Lt7U337t3JysqyYMQ3pygKiqKQc/wgaetXgEqF39hJgFTVCSGEEOXBvy/2Ll8zde/ena5duxIeHm6ZwIQQQnAsNYnntm+gzo/fkppf+tMJJ3YJJiItn34LD3HsYjY6b3+qzf6N1Db9USkmXji7BNdZ4zCWQlN8UTrmtO/JH70forWXn6VDEaXojuonb3Tx9uijj9K1a1cWL15cqsGVBq1WW6Ka6zJFUfj000+ZOHEiAwcOBOD777/Hy8uLH374gdGjR5d1qDe1KzqDj7ZEseZkMgVGEwOs4hjnXwvf+g2wrVXf0uEJIYQQ4pI1a9bg5ORUYtvla6bhw4fz4IMPWigyIYQQhSYTPf2DsbeywtW69GfDNPFz5PfHG/LYj8cI+2QXvo56MvOLyLZ5mPe7BNNzy5ekb/yNsymJ1PxsORo7aT1jaVI0UjndUbKrIl68nTlzBl9fX/R6PS1btmTq1KlUr16diIgIEhIS6NGjh3msXq+nY8eO7Nix44bJLoPBgMFwZanYzMzMe/4efghP4LEfjxHqbsukbtVxstay5qQ7HQsm80h1R74zKWjU8g9UCCGEKA96975+s2O1Ws2iRYsYPnw4S5cuLeOohBBCADRy92LdfQ9RZDLds9foHuJG1Gvt+O1EEkcTsrHVaehX14NaHt3JDu/O2XFDyT1zDENcFLahYfcsDnFncgoLOJ6WQnNPH0uHIkrBHSW7bufibdmyZaUSWGlo2bIlCxcuJDQ0lIsXLzJlyhTatGnDsWPHSEhIAMDLy6vEc7y8vIiKirrhMadNm8bkyZNvuL+0JWQZeOKnYzzSyJv5g+qak1rPtPJnxdFEBi05TId9F3iqhZRcCiGEEOXd5b6nL774oqVDEUKIKk17j/sz6bRqBtX3YlD9kt837Ru1IuSrX1GKCiXRVY4cT0um+cqF6DUaEh59Hp1GY+mQxF2662UgDhw4QFhYGDqdjsWLF5eri7erk3P169endevW1KhRg++//55WrVoB15YsKopy0zLGN954g/Hjx5sfZ2Zm3tNlNeftvYBGreKzB0LRqFWkb1pL3rkTeD3yLAPDPOlT251ZO2Mk2SWEEEKUA8HBwf9pOsS4ceN44YUX7kFEQgghAIpMJr47dZhHQuphq7WyaCx2dRqVeJx76gg6nwC0js4WiUdALSdXHK102Gi1RGZlEOrsaumQxF2662RX8+bNOXHiBKGhoahUKlq0aFEacd0TdnZ21K9fnzNnztC/f38AEhIS8PG5UqaYmJh4TbXX1fR6PXq9/l6HanYgLpN2Qc4421ihKArx8z4i90Q4Ko0G7xHj6FPbndErT2KUqYxCCCGExS1YsOA/PS8oKKhU4xBCiKrMmByNYsgtsW1ZXCxPh+/nk4PbOdShC2prOzTugRaK8IqcE+GcGTMAnW81QmetQCtJFstIjWVn67b4WVujykmkKCexxG6V3rZcfF7E7bvrZFd5W17yZgwGAydOnKB9+/YEBwfj7e3Nhg0baNy4MQAFBQVs3ryZ6dOnWzjSK3RaNZmZxT3Csg/sKE506a1x6/coAJkGI1YaFZLnEkIIISyvY8eOlg5BCCGqNGNyNJkfDbxmu8nWj2puDegffZTsL78DwHHCCosnMNRWOlRaK/JOHeb0mP6Ezl6J1tnNojFVNZc/M05A9k3GlYfPS3n27rvv4u7ubukwzO462VWeTZgwgQceeIDAwEASExOZMmUKmZmZPP7446hUKsaNG8fUqVMJCQkhJCSEqVOnYmtry7Bhwywdutn9td0ZvvwYxy9mo1s8CwC3Pg9j5eKOyaSw8EA899dylxUkhBBCCCGEEFXe5You2yHvofEMNm8fBjxkMlGoKOhSosld/vY11V+WYFOzLqFzfuP0s/3JO32U08/0I2T2ShLUDvx85CJpeUXUdLNhUH0vbHXSR+pe+PdnRlEUco1G7LTF6RJjYkS5+byUZ++++66lQyihUie7YmNjefjhh0lOTsbDw4NWrVqxa9cuqlWrBsCrr75KXl4eY8aMIS0tjZYtW7J+/XocHMrP8q+D63vxzobzPD97DTO2rgOVCq9hz5KRX8TLq09z7GI2s/vXtnSYQgghhBBCCFFuaDyD0fqV/J6kBayBonLWfNymem1qzfmN08/0J+/scTYP68Wwum+QbeeKq40VF7IMvPj7aWb1r82wRt6WDrfS0ngGs9Gk44UdG2ns5smybv0sHZK4C5U62XWrlSFVKhWTJk1i0qRJZRPQf6DXqln3RCNWPvclAIcDWvHWxiy2Rm6lwGjiu0F1aRfkbNkghRBCCCGEEKIc2psYT3pBPt38gsr1bBjroFBC5/7OnsfvwyU5gl9OTqX+vNU4+/gSkZrHW+vP8ejyozhba7mvdvmZKlbZuFnbcDojleT8XApNRqzU5SsxKm5fpU52VWQpOQXM33eBX48noclIYUbUPwAcaT0MvVbNKx2qMaqFH76OZdcsXwghhBBCCCEqktf3bObvC1F80KIjrzVqZelwbirZyY9HG7zN0mNT8QqqhqOLCwDBrjYseqge8ZkGJv11XpJd91BTd29+7NaPnv7Bkuiq4CTZVQ4djs+ix7yDpOUV0reuB8F6Dbv9WmGdmYSuXnNW9g4p13clhBBCCCGEEMLSikwm6ru6sz85gYdr1rV0OLf0y5FEkhx8qTNvLc5enqitbcz71GoVz7cJ4MHFhzmfkkt1N1sLRlp5qVQqBleXNkGVgSS7yhlDkYk+C8LxddRz8IWW+Fyq3DKN6MbMLZFMX3eOhr6OPCxztYUQQgghhBDihrRqNZ+26cbUFh2x1VpZOpxbSs8vwsVGi2u1IPM2RVFIWb0Uly4PUM3Z2jxOCHFzd53sKm/LS1Z0K44mEpNhYN3IxuZEFxRn8id0Cuav8+nM3BYtyS4hhBBCCCGEuA0VIdEFUNPNhvisAs6l5FLjUuXWhdnvk/DdTNL+WsX2B6ehVasIvJT0EqXLmBhh/v91iReZExXJEF8/BlsVWjAq8V+VSrJLlJ6/z6XS0Meeul72AKSs/RHbOg2xCa4FwMMNvRjx03GyDEU46KUwTwghhBBCCCH+bd6x/XTNzSXI9trpflcnNcqTgWGeuPx2iol/nuOHoWGo1SqcO/bm4g9fkbl9A5kJVgzs+yrudjpLh1qpqPTFn5Hc5W+bt21zrstq13oYIg9yf8K2EuNExSDZknJGUUCjLu7HVZSRRtSUF1EKDNT9aSc2wbXQXtqnKJaMUgghhBBCCCHKH5XelnNae8ZExKCOiOZg9Fr8jHk3HFue2Fhp+HpAHR5eeoT4LANj2wRQzbkmpx55n1rzX6b3ubUMzGkGNLB0qJWKxj0QxwkrUAy55m2P52SjjY1hsG9nHByeQaW3ReMeaMEoxZ2SZFc50zbImfn7L3AuJReHDT+hFBiwCQ3DOigUgJ+OJFLf2x4HvawMIYQQQgghhBBX07gHohv5BV3C96JTq6nd55vrjiuvyYuHGnjhZK1l0l/nGbzkCABWmgCmdnyWzptnk/fNe6SF1MSlcx8LR1q5/PuzUAeYEtrMMsGIUiHJrnJEURT8HfXoNWoafrabhXvmUQ2w6/0wAPP3xrHqeBLfPlhHVmMUQgghhBBCiOtoUL0+G6rXJ7+oCK224n3l7RnqRs9QN6LS8kjPKyLA2RoXmy7EfGgg6ad5RLw1Gt2c37ALa2rpUIUot9SWDkAUUxSFl1afpsf8g3jYWeGVGUe15DMUqdS0O+pD/U938eQvJ3i6hR8jm/laOlwhhBBCCCGEKNesK2Ci62rVXGxo6OuAq60VKpWKgJen4di2O0qhgbyzxy0dXpWwPymBV3b9w8XcHEuHIu5Qxf7XX4nM33eBz7bH8EXfWjzX2p9Ts7aTsw2OeTXkosaB1NQ8fh3egL51PaSqSwghhBBCCCH+JTEvh5/On2JkrfrYVJAVGO+ESqul+rR55Bzdj2OLjpYOp0p4dtt69ibFE+TgxHP1mlg6HHEHpLKrHFAUhU+2RTOgngfPtwkAwLhlFQB9Rz/Jrueak1toQgFJdAkhhBBCCCHEdXx6ZB/Pb9/AoA2/WjqUe0Zja18i0VWUlYGpsMCCEVVuI0LDeKh6beq7elg6FHGHJNlVDiTlFHLsYg4PN/QGoCjlIoXJF1FZ6XDp3Icmfo6EuNvy97k0C0cqhBBCCCGEEOVTqJMr1ewdebpOI0uHUiYMsZGceqIH0e+/hKIolg6nUhpTrwnLu/Wjg0+ApUMRd0imMZYDl38xadTFVVtW7t40+PMkeaePorF3BECrVskvMCGEEEIIIYS4gRG16vNISF00qqpR05Efc478mPPkR55B7x+Ez1OvWDokIcqNqvFboJzztNcR4m7Lz0cumreprXTYXZoTfOxiNicSc2gX5GyhCIUQQgghhBCi/LNSa1BXkdYvTq27EvjKhwBc+HoaKX/8ZOGIKq/U/DwWnzkmBSgViCS7ygGVSsXY1v4sP3yRpeEJJfal5hby5M/H8XfSM6Cep4UiFEIIIYQQQojyaWXEaf6Oi6qSiQiPQU/gNfx5AKL+N5bsQ7stHFHlk19URPVlcxj+z2oOply89RNEuSDTGMuJ51oHsDc2k2HLjvL5jhi61nDhYnYByw9fxEqjZv3Ixui0kpsUQgghhBBCiMsMxiLG7thAXE42P3Xrx6DqtS0dUpnzGzsJQ2wk6f+s5vzrI6mz+B+s3KRQorRYa7X0DqjOifQUMgtkMYCKQpJd5YRarWLB4Ho8GObJ17vjWHggHnu9lhfbBvJMSz/8nKwtHaIQQgghhBBClCtGReGh6rVZEXGaB6rVtHQ4FqFSqwmaNIuTEafIjzzDzrfH826jl9gZnYFGBd1qujK+fTU6VnexdKgV1ryOvbHVWlk6DHEHVEpVrPUsRZmZmTg5OZGRkYGjo6OlwxFCCCGqBDn/WkZsbCwBAQHExMTg7+9v6XCEEMIsp7AAOyudpcOwqNzzJ/lr4gRGej9O3VrBDKjnQYFR4YfwBA4nZDOrXy3GtJZVBSsiOf/eOansEkIIIYQQQghRoVX1RBfA5kJ3+lUbx+cPhDK2baB5+6sdq/HS6tM8/9spOtdwpY6nnQWjrNiMJhMHUy7SzMPH0qGIW5AmUEIIIYQQQgghKpQ/os/x6N+/E5+bbelQyo1ZO2Np5u9oTnSlrl9BfuRpVCoV03uH4GGn46tdsRaOsuLKLiyg+rI5tFi5kKisDEuHI25Bkl1CCCGEEEIIISoMo8nEizs3suTscT47ss/S4ZQb+2Iz6VPbHYCkn+cT8eZTnHvlMYy52ei1anqGuLIvNtPCUVZc9lY6ajg646y35nhasqXDEbcgyS4hhBBCCCGEEBWGRq3mhy4P0LdaTSY2bm3pcMoNnUZFdoERAOfOfbDy8CE/4jRR772AoihkFxjRaVQWjrJi+67jfVx45Dl6B9awdCjiFiTZJYQQQgghhBCiQmnm4cOqng/ioNNbOpRyo3ctd5aGJ1BQZMLKzZPqH8wHjZa0Db9yfsGXrD2Vwn2XKr/Ef1PNwQlrrbQ+rwgk2SWEEEIIIYQQotxTFIWMAoOlwyi3XmgbQFJOAY8sP0pabiH2DVsSMP59AFJmT6ZV1klGNvO1cJSVR0p+nqVDuKktW7bwwAMP4Ovri0ql4tdffy2xf8SIEahUqhJ/WrVqZZlg7wFJdgkhhBBCCCGEKPd+izpL9aVfM//kYUuHUi7V87Lnx2H1WXMyGb9pW7n/u4M8ktmEdd7t0CgmPjr2KY65KZYOs8JLN+TT+felBP7wVblOvubk5NCwYUO+/PLLG47p1asX8fHx5j9r164twwjvLam/E0IIIYQQQghRrhiTo1EMuSW2fRu+m1RDPqfizmJ0d0bjHmih6MqvfvU8iXi1LfP2XWBHVAa2Og3O42eg+3okBedPkLH1TzwGjrB0mBWak05PYn4OeUWFbImP4YFqNS0d0nX17t2b3r1733SMXq/H29u7jCIqW5LsEkIIIYQQQghRbhiTo8n8aOA12+ejYrFDMIMjVpC5wYjjhBWS8LoOLwc9b3YOLrEtv+ZC8s4ex6XLAxaKqvJQqVR826E3fnYOBNo7lulrZ2VlkZl5ZUVNvV6PXv/f+9Zt2rQJT09PnJ2d6dixI++//z6enp6lEarFSbJLCCGEEEIIIUS5cbmiy3bIe2g8SyZtxgLGxAhyl799TeWXuDHrwBpYywqCpaa1l59FXrdu3bolHr/77rtMmjTpPx2rd+/eDB48mGrVqhEREcHbb79Nly5d2L9//10l0MoLSXYJIYQQQgghhCh3NJ7BaHxrsT42gh7+wahUKkuHVCkUXIwjcvLzBLw8FZsadSwdToVnUhTUZfTZPH78OH5+VxJtd5OUGjJkiPn/w8LCaNasGdWqVWPNmjUMHHhtZWVFIw3qhRBCCCGEEEKUS79EnKLXHz/Rc+2PmBTF0uFUCrGfvUvWns2cf/0JjLnZlg6nwkoz5DN6yzpqLf+GQpOxTF7TwcEBR0dH85/SrMDy8fGhWrVqnDlzptSOaUmS7AJmz55NcHAw1tbWNG3alK1bt1o6JCGEEEIIIYSo8lIN+dhqrWjl5Vtm1TOVXcCEaVi5e5MfcZroD15BkSTif2JvZcWqqLOczUxjfWykpcO5aykpKcTExODj42PpUEpFlU92LV++nHHjxjFx4kQOHjxI+/bt6d27N9HR0ZYOTQghhBBCCCGqtKfrNOLEQ0/xRqNWlg6l0rBy9SB46regVpO6djkpqxZZOqQKyUqt4bM2Xfmnz8P0Dqhu6XCukZ2dTXh4OOHh4QBEREQQHh5OdHQ02dnZTJgwgZ07dxIZGcmmTZt44IEHcHd3Z8CAAZYNvJRU+WTXzJkzefLJJ3nqqaeoU6cOn376KQEBAXz11VeWDk0IIYQQolyYNWsWdevWpVOnTpYORQhRBQXaO2KjtbJ0GJWKQ5M2+I15C4DoGa+Te/qohSOqmIbUqEMn38ByWXW4b98+GjduTOPGjQEYP348jRs35p133kGj0XDkyBH69etHaGgojz/+OKGhoezcuRMHBwcLR146qnSD+oKCAvbv38/rr79eYnuPHj3YsWPHdZ9jMBgwGAzmx1cv+ymEEEIIURk999xzPPfcc8TGxhIQEGDpcIQQlZyiKPzPJYyRUSe4Xvt0Y2JEmcdUGXk99gJZB3eSuX0D519/gjoLN6Kxd7R0WKKUdOrU6aZTVP/8888yjKbsVelkV3JyMkajES8vrxLbvby8SEhIuO5zpk2bxuTJk8siPCGEEEIIIYSocr6MjeVTlzrMP3SUI79Px0Epuu44ld62jCOrXFRqNcGTZ3P8kY6orHQUZaZLsus/yCowMOdEONsvxrGi+wBZNbScqNLJrsv+/WFUFOWGH9A33niD8ePHmx9nZmbKHU4hhBBCCCGEKCXDGrRhRVwMj/r64dt38HXHqPS2aNwDyziyykfr7EborBXovP1RW0vy8L9QgHf2bSPPWMTuxAu08vKzdEiCKp7scnd3R6PRXFPFlZiYeE2112V6vb5Ul/cUQgghhBBCCHGFl60dmwaMQKOu8i2my4R1UGiJx8lpWfx8KoO4TAM+DjoeauCFu53OQtGVf3aZF3mzZgjuOh2heakUxWVdM0aSs2WvSie7dDodTZs2ZcOGDSVWHNiwYQP9+vWzYGRCCCGEEEIIUXVkFxZwOCWRNt7+AJLosgCT0chPkyah37yCN1tNw97FhYTsAsavOcPEzkG81SVYpuj9izE5msyPBvLcVduuTXUVc5ywQhJeZahKJ7ugeEWC4cOH06xZM1q3bs3cuXOJjo7mmWeesXRoQgghhBBCCFHpKYrCE5vW8mvkGeZ26MkTtRpYOqQqac6mU/j98yN++UlsK/yROq8vIiW3kJlbo3lnw3lsrTS83KGapcMsVxRDLgC2Q95D4xl83THGxAhyl79tHivKRpVPlw8ZMoRPP/2U//3vfzRq1IgtW7awdu1aqlWTf8RCCCGEEEIIca8VmIzoNBpUKqjl5GrpcKqkgiITk3Yms2voNFRWOvK2riVx2Rzc7XRM7VWTMa38mbopkrxCo6VDLZc0nsEoPiGsKdQw6UIiWr/a5j83SoKJe6vKJ7sAxowZQ2RkJAaDgf3799OhQwdLhySEEEIIIYQQVYJeo2Vx5z7sG/C4eRqjKFubI9JIzC5g6IPd8R/3HgCxn75D9pG9ALzQNoDU3EI2nk21ZJjlWkJuDgPWr+D9gzs5l5lm6XCqPEl2CSGEEEIIIYQoc7lFheb/V6lUNHDztGA0VVt6XhEAgc7WeDz0FC7d+oGxiIg3nqQoI41AZ+vicflFlgyzXAuwd+SRmvWY0KAF1poq3zHK4iTZJYQQQgghhBCiTOUUFtDq10WM37mRIpPJ0uFUeTXdbADYHpWBSqWi2lufoQ+oTkFCLJHvPsv2iLRL42wtGWa5t6hLH2a06oyfnYOlQ6nyJNklhBBCCCGEEKJMrYk+x5HUJH44e4LkfGncbWmNfB1o6ufApL/Ok1tgRGPvSPUPvkNt54Bt2568s+E89b3taRngaOlQhbgtUlsnhBBCCCGEEKJMPVSjDlq1Gi8bO7xt7S0dTpWnUqn4qn9tOn9zgBaz9vBSu0Dqewdy8n/rePxgBmdTstnwZBNUKpWlQxXitkiySwghhBBCCCFEqTImR6MYbl6x1c/BFo27NKQvL5oHOLHtmWZM/PMso1acQFGKt/cMdWP+oLo0cjBSkBCLThYRuIYxMeI/7RP3jiS7hBBCCCGEEEKUGmNyNJkfDbxme6TWjhkudfggORwHpbjRueOEFWjcA8s6RHEDjXwdWPNEYxKyDCRkFeBlr8PHUU/euROceG4YGjtHas//A7W19O4CUOmLfw65y9++7bGibEiySwghhBBCCCFEqblc0WU75D00nsHm7S/v2s4/Kcmoardnvq8rucvfvmX1l7AMbwc93g5682ONvSOm3BwK4qKImjaBoEmzZEojoHEPxHHCilt+jlV6W0nqljFJdgkhhBBCCCGEKHUaz2C0frXNjz/s4MSwv3/nw0590aTHWTAycad0Xn5UnzaP02MGkLpmGXZhTfEc/KSlwyoXJIlVPslqjEIIIYQQQggh7okik8n8/808fDg2+En87BwsGJH4rxyatcdv7LsAxHz0BtmHdls4IiFuTJJdQgghhBBCCCFK3brEi9T58VsiszLM26zUGgtGJO6W16PP49KtHxiLOP/aExQmX7R0SEJclyS7hBBCCCGEEEKUKhPwzqkTnM1M44PwXZYOR5QSlUpFtbc/x7p6LQqTE4j99NaN2YWwBEl2CSGEEEIIIYQoVWpgZfOWjK/fnM/bdLN0OKIUaewcqPHhQpy79iVgwgeWDkeI65IG9UIIIYQQQggh7prRZOJIahJhlx57ZyYwPTAYEs5Q9O+xiRFlHZ4oRdZBIdSYvsDSYQhxQ5LsEkIIIYQQQghxVwzGIob/s5rfo86xrm0nGgC5y289xU2lt733wYl7LvnXRdjWa4JtSD1LhyIEIMkuIYQQQgghhBB3SaNSk19kxKiYSNTZ4ThhBYoh96bPUelt0bgHllGE4l5JXP4NMTNeQ+8fTO1Ff6N1cLJ0SEJIsksIIYQQQgghxN3RqtUs69aXQymJtPbys3Q4ogy59nyQi4u/xBAbwbHXnmRhr/f481w6RSaFlgFOPNfanxYBkgATZUuSXUIIIYQQQgghrmFMjr5hdZZJUVhy8SLHCk181KoLALZaK0l0VUFaZ1dqzFjI8Sd6UbTnb+ySrGn38JvYWGlYdSKZhQfimXFfCBM6VLN0qKIKkWSXEEIIIYQQQogSjMnRZH408Ib7j+qcGOnfA4DB1WvT0tO3rEIT5VCGb23eqfcc74XPpF/EWvxzW+P16HN8fH8ob60/xytrz9DMz4FONVwtHaqoIiTZJYQQQgghhLjnblYlBNK/qby5/HdlO+Q9NJ7BQHE1l1qlAqBFYgRP//ULwS370djNy2JxivLhmz1xbPFti1tbR1JmTSL207fRefvj0q0f7/esweqTyXy+I0aSXaLMSLJLCCGEEEIIcUt3k6y6VZXQZY4TVlz3GLd67Vu9vvjvNJ7BqH1r8f3pI3x0eA9bH3gEV2sbAD5ICcehxgS0Go2FoxSWtiUynZ4hrlR7dCzqpDiSfvqWwpREAFQqFYPre/LJtmgLRymqEkl2CSGEEEIIIW7qbpNV16sSKnH8xAhyl7993YTW7b72zV5f3B2jYuLjw3s5npbC50f3M6lZO0uHJMoZFaBQnNgKeHkqrj0GYN+olXm/ohTvE6KsSLJLCCGEEEKIKuBuqqPuJll1NY1nMFq/2ncQ9a1f+3ZeX6ZQ3rmMwkIupyas1Bpmte3OvuQEXghratG4RPnUMdiZaZsiSc8rxNnGqkSiqygrgz/2nqZTsI8FIxRVjSS7hBBCCCGEqORKqzrqcrJKURRyiwrRqtXoNcVfKbJUWnYmJaIyWtE7sIb5OUvOHCM86iSdrd3pcWlbZoGBr4+Ho9doeLF+M/PY7akpxOUdp5GbJ3Vd3AFQFIU8lRp7j6A7TpTdyXuvjFVh/zXJ98PZ47y47S+m2QUw4tK2jr6BdPStXD8fUXqeauHHtE2RDF9+jGXD6mOnK57amhcfy44nBzA6X031WSstHKWoSiTZJYQQQgghRCX3X6ujUvLz+DM2gpzkGAZdNfa+dT+xLiaCeR16M7J2AwBitLbcv2cnHta2JD421jx2bcx5fjh/FhediznZlWrI57U9m7DRaEskuxbGxjA/ZhuTm7bjnabFya70okL8gh9Ev241mU+EorvUH2r+ycP8cyGaAcEh9NVdep+KQkRmOj629lhrtbf13u+2KgzKZ2XYnST58py8sdFo0ajVAFzMzSG5oIAlDkHmZJcQN+PtoOeXRxswcPFh/KduZWCYJzZWavbvCef91ATqF+XgPP91lA++QyU93kQZkGSXEEIIIYQQVcTNphFOP3uaNT6deOViPAMujYnJzuSRv3/HQ6crkezysrEDIKuwwLzN0VRIQ0cnPBycSxy3T2ANPIwGGl74x7zNVqvl8dAwrC4lVy6r5+BAF99qhDq5mLelFxYCoEZlTnQB7E68wOKzxwhxcqGvt1txPEVFVF82B4CckeOx1VoBsNrWl1Np+fT0tqO9T8Atf06XlUZFnKWmUN5uku/FA7v4LjaWVT0G0s0/CICHa9bBOieFB1f9jDGxxzXPvfx8Ia7Wq5Y7x19qzVe7Y/nzdApFJoWWjRvg1PE7VO8+Svo/q4n99C0CXp5m6VBFFSDJLiGEEEIIISqI0kicXMjJ4vU9m7mYl8Of9w0xbz+Vk81OGw+OZ2Ux4NK2IAcnOvkEEqBRMJ28coyZrbrwRZtu2FnpzNv8jXnsa9/pmmTawzXrMthGTdbGZPM2Txs7FnS6/5rYXgiuwfh/PT/IxpaoiJUoT31VYvuwmnWp6eRCO29/KMoCIKmgAGuNFr1GY050Aay39WHx2dPonDzNya7swgLCfppHkE7HMtQ4XBobk52JAvjZ2t91v7DSmEJ5t3/nVyc4FUXhZHoKdS5NEQUoMhVPSV0Xe96c7PK2tefpkLpkopC7/O2bxq7S2950v6haglxtmN47hOm9Q67aWpdUZhPx5lMkLp2DzicQr2HPWixGUTVIsksIIYQQQogK4L8kTlZEnOKHs8fp5WDLg5f221npWHTmGFA8TdHN2gaAJwOq0e7Az3To1NV8LGe9Nf888DBFcSfJ2nLlNVwvPacsqFQqHJQiHP71mlf3kCqKK87E1bCzI3fk+BIVZwCd8y5iE9aFtt5+5m2RWRlEZWeSaWWFHpN5+/8ObOfbk4eZ1LQtE72Lk0IGtwC+SkonyMGJwdVro77NVeVKYwrl3STLjP963HrVInYnxhPx8DP4X9r2YvUajGzSnuYeJZuHa9wDcZywQhr7i1Lh2mMgBQmxxH0+idhP3kLn7Y9LlwcsHZaoxCTZJYQQQgghRAVws8SJoigciz7JX5uW8VxeNpfTQsfSkvkl4jQ6X39zsstJp+eT1l2o4eiCrfbK14G2rm40yI7Gwc7+hjHcaOra7U5pu5vn32zM1ftUKhWOOn2J/QNyYnmsfkO0fkHmbdUdndne9xFS4s/BqR/M2w1GI1qVmuCrpmNG5Oby+p7NOOv0DKlRx7x9yoEd7Ig6zQgbbwaan1/EsbRk/GwdcLu07b+sQgm3nywz5uegmExoL00L3XQhmqc3/YWPTwc2XjXeSq3BWqPlQHIC/peK8kLs7NF6+l739SWRJUqT1/CxFMTHkPTTPOK+fA/nDr1QXVWBKURpkmSXEEIIIYQQFYjGMxiNby0yCgw4660BMCkKXTf8Qap7E1plpNPuUluqAUGh6NQa2mlNsO3KMcbVb35Hr3l5qtp/ndJ2N8+/3efe7PWvx1ZrRRtvf4qM2WRdtX1h5z581/E+jIoCCWcAsFKpebRmvWt6jG2Oj+avpIv00VxJrp3JSKPpiu9x09uQ0O1Kv6svju5nX1ICT9ZuQIdLUylTCwpY5FAdnwtxDLsqGXY+M53YnCyC8vNwoPjvPMnFn29OHMJKreaNxq3NY992bcDC9Wv5vrOGAcGhQHFC80xODkk6FxRFMY/9vE03ajg646jTm6vhhCgrKpWKgJenoba2wfPhZyTRJe6pSpvsCgoKIioqqsS21157jQ8++MD8ODo6mueee46///4bGxsbhg0bxkcffYROp/v34YQQQgghhCgXNiUn8cSmv/G3c2Bn/+EAqFUqenp4ceHs/uIkzSVhrh6EuXoUT0Pk9quj/u1up7TdzfNv57m3en24s6oyjVqNBii69DjU3p5FXfpcM+6dJm0Z7OpCszVrzduyCgvwtrHD29auxNjfo86yIS6SLn6B5mRXVF4uEzya4nP8CMOaX5k++truTfwccYqv6zfioUvbUvLzeHf/NtytbUokuxI11mQVFXEoJdGc7Krn4s66lq0JXv4GKtVg89jG7l43/PkIURZUWi3+L/6vxLaizHS0js6WCUhUWpU22QXwv//9j1GjRpkf29tfKck2Go3cf//9eHh4sG3bNlJSUnj88cdRFIUvvvjCEuEKIYQQQohK7lbNxqFk0iazwMC6mPPUdHKhwaX91e3siM3JItWQT35REdaXpiIubNyUrG2f4OD69HWPCXdXHXW3U9ru5vl389y7rUq7mfY+AbQ25ZBVlGPe1trLj/jhz2NSFEwXTpm3P1W7Ad38gmjm7m3eZqfR0icnFlf/1iWOa2dlRS0nV2yvWn3Sy8aOp2s3vKZf2nMZp5l4/wjqhjY1b9NpNHR19yTLZLjj9yREWUpdv4LoqeOpMfMHHJq0sXQ4ohKp1MkuBwcHvL29r7tv/fr1HD9+nJiYGHx9i+eof/zxx4wYMYL3338fR0fHsgxVCCGEEEJUcrfbbByuNBx/c88WZh0/wNO1GzKrRhAAgTa2bHlgGE09vM2Jrlspreqoiqg0Gq3/l4o4tUp1Vdt7eOiqXl+Xhdrbs/DiThweeq7E9ssrVV6uyAPwsLFlTode1xyjQUE6Dg6OaNWaa/bdLL7b7bMmxL2iKAopq5dizM7kzNjB1PzkBxxbdORoQjZnknNxstbSLsgZnVZ964MJ8S+VOtk1ffp03nvvPQICAhg8eDCvvPKKeYrizp07CQsLMye6AHr27InBYGD//v107tz5usc0GAwYDFfukGRmZt7bNyGEEEIIISqFWzWY/+T8OVbGRDD7xM/UvzS2X1BNNsRFUsPRpcT49pemwd2JypbEuhP/9b3fq35hZeFeVrQJURpUKhU1PlzIuVcfI3PHRs68OJTZnd5mvhJqHuPtoOPtLsE828of1W2ugioEVOJk14svvkiTJk1wcXFhz549vPHGG0RERPDtt98CkJCQgJdXyTnrLi4u6HQ6EhISbnjcadOmMXny5HsauxBCCCGEqLwur8xnUhTUV315W33gALtycllr50v9S9u6+QVxakhxW47LDcWlUqfsWKJfWGk9vzQq2oS419TWNtT4aDGHXnoM0+4NjNo4icFjP6fZwIHEZhj4YkcMz606RabByOudgiwdrqhAKlSya9KkSbdMNO3du5dmzZrx0ksvmbc1aNAAFxcXBg0axPTp03FzK14E+HqZYUVRbpoxfuONNxg/frz5cWZmJgEBd35nTQghhBBCVE0ZhYW8uWUdf8VFcfyhJ9Frii/J32zcmjPRp+i29nfz2KuvS6VSxzIs2S/sbp8viSxREah1et5r9irdLuTSMmY7Hl++gMbHhkbd+jFvUF3cbK2Y9Nd5RjX3xc1OFpMTt6dCJbuef/55hg4detMxQUFB193eqlUrAM6ePYubmxve3t7s3r27xJi0tDQKCwuvqfi6ml6vR6/X33C/EEIIIYQQ/1ZgNHK564y9VsuamHPE5WSzLiaCfkEhAPQKqE43dQFZxvzrHkMqdSoeS65iKURFkZxTwKrTafSY8AWuf0wldd3PZB/ajUu3fgC82rEan22PZtnhizzXWgpNxO2pUMkud3d33N3d/9NzDx48CICPjw8ArVu35v333yc+Pt68bf369ej1epo2bXrD4wghhBBCiKrtVisqXp18OJ2eytgdG0g15LOzRUsANCoVn7buipu1DR197ixJIUmNiseSq1gKUREkZBVgUqChvzNBk7/CsXVXXO97yLzf3U6Hr6Oe2AxZXVTcvgqV7LpdO3fuZNeuXXTu3BknJyf27t3LSy+9RN++fQkMLD5Z9OjRg7p16zJ8+HBmzJhBamoqEyZMYNSoUbISoxBCCCGEuK7bWVFRAZwuraboZm3DPxeiKTSZOJuTzeX5A4Oq177nsQohREXgaa9DpYITiTm0ruaM2/1DzPtMBQbit/5NfJYeb3uZwihuX6VMdun1epYvX87kyZMxGAxUq1aNUaNG8eqrr5rHaDQa1qxZw5gxY2jbti02NjYMGzaMjz76yIKRCyGEEEKI8uxmKyqeyclm6pEDqCL2M//SODdrG77vdD8tPH2olnWRLG7eVFyazAshqhpPex29Qt34ZFs0DzfyxsZKA4BSVETEm0+RvmkND9V6jCENPrBwpBXLli1bmDFjBvv37yc+Pp6VK1fSv39/835FUZg8eTJz584lLS2Nli1bMmvWLOrVq2e5oEtRpUx2NWnShF27dt1yXGBgIKtXry6DiIQQQgghRGVyeUXFq+UkxbM4ZSNWDkFMNxjwubT94Zp1ATAWZAG3bjYO0mReCFG1vNe9Bu3n7KP7twf4X/catA92JjrdwA6DE3WBF08tJG9GPqaJM1Fby+/H25GTk0PDhg154oknePDBB6/Z/+GHHzJz5kwWLFhAaGgoU6ZMoXv37pw6dQoHBwcLRFy6KmWySwghhBBCiHspvbCQ7w/vwU5rxTN1GwPQzMOHt0Jq0X7TbDz0A655zu00GwdpOC6EqHqa+juy4ckmPL3iBF2/PWDe7uT6ILMe8Kb22s9I/eNH8s4dp8aHC9H7B1ku2Aqid+/e9O7d+7r7FEXh008/ZeLEiQwcWDw1//vvv8fLy4sffviB0aNHl2Wo94Qku4QQQgghhLhDaxMTmBB+AG8bO0bWaoBOUzzt5t3Q2mT9mXrD50kSSwghrq9tkDNHX2rFjqgMziTn4mStpUeoG3a6zmT16cz5N0aSd/ooJ4Z3Jvi9uTi1627pkMtcVlYWmZmZ5sd6vR69Xn/Hx4mIiCAhIYEePXqUOFbHjh3ZsWNHpUh2qW89RAghhBBCiKrtaGoS+5LizY8H+fjR3S+I95q3t2BUQghRuahUKtoGOTOimS8Dwjyx0xXfSHBo2pY6i/7Grn4zjFkZRE15EVP+zatkK6O6devi5ORk/jNt2rT/dJyEhAQAvLy8Smz38vIy76vopLJLCCGEEEJUKcbk6JtOJfz3NMIFp47wxOa1tPL0ZUuzZgDo1GrWX7VimBBCiHtL5+VH6Jzfif3kbVx6DKiSvbuOHz+On5+f+fF/qeq6mkqlKvFYUZRrtlVUkuwSQgghhBBVN0W3EgAAj71JREFUhjE5msyPBt50jALoXvoRe6/qAPQKCMZWa4W/nQO5RmPxcW6waqKspiiEEPeOWqcn8LUPS2zL2LYenbc/GV41mLM7jhVHE8kpNFLHw45nWvlzXy23SpPAcXBwwNHR8a6P4+3tDRRXePn4+Ji3JyYmXlPtVVFJsksIIYQQQlQZlyu6bIe8h8Yz+Jr9284d5eXwvTQ8sJv5vYuTXd629kQNewZ3a9viZBm3XlFRVlMUQoh7L+/8Sc6/8SQmk4kPwp5lrWcbBtX3wttBx19nU+mzIJwnmvrw7YN1UasrR8KrNAQHB+Pt7c2GDRto3Lh4kZWCggI2b97M9OnTLRxd6ZBklxBCCCGEqHI0nsFo/Wpfuz0tlQPWrpyLv8DnhQXYW+kAcL80XeZ2VlSU1RSFEKJsWLl6Ylu/Odl7NvH6/pm8/2AWIQOnoNJaoSgKiw7EM+Ln4zTzd2RM6wBLh1umsrOzOXv2rPlxREQE4eHhuLq6EhgYyLhx45g6dSohISGEhIQwdepUbG1tGTZsmAWjLj2S7BJCCCGEEFVSocnI4jPH0KrUDA8NA6C1iyszk/Yx9OG3zImuf5NElhBClA9aZ1cOP/0lmxLfZmTkSrJ++YaTx/YQ+PpH2IU15bGmvqw9lcJn22N4tpV/pZnOeDv27dtH586dzY/Hjx8PwOOPP86CBQt49dVXycvLY8yYMaSlpdGyZUvWr1+Pg4ODpUIuVbIaoxBCCCGEqJKWnT3ByM1/8OruTeQVFZq3j8iKwOMum/4KIYQoG1uiMvm79ZPU+GgxGgcnck8e4uQTPYj5+A0AHmrgxenkXC5mF1g40rLVqVMnFEW55s+CBQuA4ub0kyZNIj4+nvz8fDZv3kxYWJhlgy5FkuwSQgghhBBVgsFYRExenvnxkBp1aOHhw4SGLSwYlRBCiLuhUqlQAOdO91Hv59243jcEFAWNfXEjd5OiFI+zYIyi7Mk0RiGEEEIIUeltS4hl2MbfCNBZ8fulbTqNhl39h1epaS1CCFHZdK7uwhc7Yvg/e/cdX0WV/3/8NbemJyQhhRq69BJ6UURAwQLqWhYWZBcR17KrrquLfq3ryv5cV9217aoodlkLVqpIk95C7y0EkkASSE9um98fN1yIBARDCuH9fDzuAzJz7sxnzpk7d+5nzpxJOZRPlwZxNHv6dWKvH0to2y4AfLI+kyvthwnP3APhbWs2WKk2SnaJiIiIyAXHm5V6ToPENw+P5HBJEV6PnUxrEMcf3K5El4jIhe3atrE0jw7mtv9tZubvutIgwkl41z6YpskbK9KYvjGDBTteYevoXcSP+j2JE/6MNSSspsOWKqZkl4iIiIhcULxZqeQ9f8Np55di4f2IZhQMGMtTA64GoEFoOHOG30I3dx7ure/jPby34mWfZrqIiNRONquFr8Z2ZsiUtTR/bgkj2tUnPszBvF05bDlcyB+71iPR04zcA9vIfP9lcuZ8QeMHJxM18OpTLnjkl3rYcaQIh81Cu7hQrBZdELlQKdklIiIiIheU4z26Qm75K9a4ZqfMX7N7Iw9t24N922Z+16U/TcMjAbg0sTHerFTcQNG0x864DsMZct7jFhGRqtEhIYxN9/Xm7dWH+HzTYTZnFtA+PpSXr2vD5S3qYdz6IccWzeLA83/BdSiVPX8eS2T/oTT841MEN2vDsWI3j8zezXtr0yl0eQFIqhfEny9tetE9xbGuULJLRERERC5I1rhm2BpegtfnY29+Li0j6wEwELh51Xz6976WhJDQ8u+JbULEg1+c0y2QIiJS+8WEOvjzZUn8+bKkCudHXXoVET0vJf3tF8l879/k/jiH3B/n0OSdeVw538XenGL+fGlTrr4klrwSD++sOcTdX21n/7ES/t+wVtW7MVJpSnaJiIiIyAVrV+5RRs75gmOuEnbdMpEgm//09j9HVhKe9Ads1lNPd5XIEhG5OFmCQmh416PEDL+Jg6/8FVdGGm8eiWTr4X2suLsHrUszcDYIw7BYGNQymk4JYTw0cxe3dUukXbzG+bqQWGo6ABERERGRX6pRaDi5rlIK3W425hyp6XBEROQCEJTUmhbPv0+bKTN5c9UhRnVJoH0EbPvtULbc0pesrz/E5yrlj/2aEBfmYMqqQzUdspwj9ewSERERkQvG/EP7+WrLJp4o+zvIZuOLIdfTKrIeUc6gGo1NREQuMHYn+46W0KdJJEU7NmH6fJTs3cH+p+/l0OvPEvfrifSL7crunOKajlTOkZJdIiIiInJBOFSYz5Uz/ofb52NQUH2Gl03vEZdYo3GJiMiFyWIxiHBa2X+shPChfej03UaOTH+Xwx/9B/eRdA7++0nut4ewtdtIPNc9hS0q5rTLyi3x8NXmwxwpdNO0XhDXXBJLkN1afRsj5SjZJSIiIiLVzpuVelaDxLu8XhxW/4+FBqHh/LFDdwpzj9B6/9d4D++teNmnmS4iIvJTv+6SwNurD/HnS5sSERZBwph7ibt1IjkzP2XPlJcIObibbis/xud+NPAe0+PGsNn9/zdN/r5gH3+bv48it5dQu5UCl5fYUDsvXdOa0V11QaYmKNklIiIiItXKm5VK3vM3nLGMB4M3rn2aV/bsZvUNt9EwNByA53oNxJd9gLz5z1E07bEzLsNwhpy3mEVEpG56cEBTPlmfyZVvr+Ola1rTs3EEbsPGrMaD+EOXRG5pv5lHErJx1D+RtNr5x1sw3S6ih97Ae0FdeeTHbP40oAkPDGhKgwgnO44U8uT3e/jNtM0E263c0CGuBrfw4qRkl4iIiIhUq+M9ukJu+SvWuGanzPce3kvhtMeYcSiNjOJC3ty6nie79wfAMAyssU2IePCLs+oZJiIiciYtY0OYO74rt368id6vrSImxE6Jx0ehy8u1bWOZfPOdRAXbA+U9uUfJX70YvF4K1i5lgGHh6xY96dt/DFGWBMBJ6/qhfHhrB3JLPPzfnN1c374+hmHU3EZehJTsEhEREZEaYY1rhq3hJYG/l2Sk0TMuEStgAP9s14Hdjgh+3bLdqe9VIktERM6THo0j2fFgX+bszGbtwXycNgvD28TQLj7slLK2yHp0/GodOXOns3v6NIJSt9Bg13L2Pbkcw/EAib97gMTb/4xhGDwwoCmD31pLyqF8ujaMqIEtu3gp2SUiIiIiNe72hTOZsn0Dr/QbwsQo/+2H3aPq0fukZJiIiEhVsVoMhrWJZVib2J8t60hoRMKYe/m0+Qj+/dkiFrY7xNHZn1Oybyf22PhAuRbRwQBkF7mrLG6pmKWmAxARERER6RYbj9UwSC8qqOlQREREzkpSvWB2ORPIuub3tPt0OW0/WkS9wSMD81el5QHQtF5wDUV48VLPLhERERGpcRPaduaKhk1pExWD5+C2mg5HRETkZ13VJoYGEU6emLuHz3/TiZDWHQLzilxenp2/lwFJUbSK1QNTqpt6domIiIhIjbNbrLSJiqnpMERERM6a3Wrh5eva8PXWIwyZspbvtmWx40ghH6dk0Pf1VezMLubFa1rXdJgXJfXsEhERERERERH5BW7oEMe347rwyKxdXDM1JTD98ub1WHhHO5IbaWD6mqBkl4iIiIjUCO/hvec0XUREpDYa1iaWq1rHsPVwIUcK3TSJCqJZtMbpqklKdomIiIhItTKc/rFLiqY9dlblREREajvDMGgXH1bTYUiZCzLZ9be//Y3vvvuOlJQUHA4Hx44dO6VMamoqd999Nz/88APBwcGMGjWK559/HofDESizceNG7rnnHlauXEl0dDQTJ07ksccewzCMatwaERERkYuLNbYJEQ9+gVladNoyhjMEa2yTaoxKRERE6ooLMtnlcrm46aab6NOnD1OmTDllvtfr5eqrr6Z+/fr8+OOPZGdnc9ttt2GaJi+//DIAeXl5DBkyhMsvv5xVq1axY8cOxo0bR2hoKH/605+qe5NERERELipKZImIiEhVuSCTXU899RQAU6dOrXD+nDlz2LJlCwcOHKBBgwYA/POf/2TcuHH87W9/IyIigg8//JCSkhKmTp2K0+mkQ4cO7NixgxdeeIEHHnhAvbtERESkTjpw4ABjxozh8OHD2Gw2HnvsMW666aaaDktERETkvLHUdABVYdmyZXTo0CGQ6AK48sorKS0tZc2aNYEyl112GU6ns1yZQ4cOsW/fvtMuu7S0lLy8vHIvERERkQuFzWbjpZdeYsuWLXz//ffcf//9FBYW1nRYIiIiIudNnUx2ZWRkEB8fX25avXr1cDgcZGRknLbM8b+Pl6nI5MmTiYyMDLwaN258nqMXERERqTqJiYl06dIFgLi4OKKjo8nJyanZoERERETOo1qT7HryyScxDOOMr9WrV5/18iq6DdE0zXLTf1rGNM3Tvve4SZMmkZubG3gdOHDgrGMSERERqaxFixZx7bXX0qBBAwzD4MsvvzylzGuvvUazZs0ICgoiOTmZxYsXV7is1atX4/P5dPFORERE6pRaM2bXPffcw6233nrGMklJSWe1rISEBFasWFFu2tGjR3G73YHeWwkJCaf04Dp8+DDAKT2+TuZ0Osvd+igiIiJSnQoLC+ncuTO//e1vufHGG0+ZP23aNO677z5ee+01+vXrx3//+1+GDRvGli1baNLkxKDw2dnZjB07lrfeeqs6wxcRERGpcrUm2RUbG0tsbOx5WVafPn3429/+Rnp6OomJiYB/0Hqn00lycnKgzCOPPILL5cLhcATKNGjQ4KyTanCiN5jG7hIREak+x793j38PX0yGDRvGsGHDTjv/hRdeYPz48dx+++0AvPTSS8yePZvXX3+dyZMnA/4xSK+//nomTZpE3759T7us0tJSSktLA3/n5uYCkJ6efj42RURERM7C8e9dn89Xw5FcOGpNsutcpKamkpOTQ2pqKl6vl5SUFABatmxJWFgYQ4cOpV27dowZM4Z//OMf5OTk8OCDDzJhwgQiIiIAGDVqFE899RTjxo3jkUceYefOnTz77LM8/vjj5/Qkxvz8fAB1/xcREakB+fn5REZG1nQYtYbL5WLNmjX85S9/KTd96NChLF26FPAnCMeNG8egQYMYM2bMGZc3efLkwFOwT9azZ8/zF7SIiIiclczMzHK9tOX0Lshk1+OPP867774b+Ltr164AzJ8/n4EDB2K1Wvnuu++466676NevH8HBwYwaNYrnn38+8J7IyEjmzp3L3XffTffu3alXrx4PPPAADzzwwDnF0qBBAw4cOEB4ePg5Jcny8vJo3LgxBw4cCCTg5NypHitPdVh5qsPzQ/VYeRdTHZqmSX5+frknLwtkZWXh9XorfAjP8eEblixZwrRp0+jUqVNgvK/333+fjh07nrK8SZMmlTs38ng8bN26lcaNG2OxnBj6deDAgSxYsKDCmCqa99Np+fn5tGvXji1bthAeHn4OW3x+nWk7qmtZZ/u+syl3ru1ypnlqs8q/T21WcVw1saxzed/PlVWbVc+yqqvNzjS/JtvM5/ORmZkZyH3Iz7sgk11Tp05l6tSpZyzTpEkTvv322zOW6dixI4sWLapULBaLhUaNGv3i90dERNT5HyTVQfVYearDylMdnh+qx8q7WOpQPbpOr6KH8Byf1r9//7O+DaKisUr79et3SjmHw3Ha86GK5v102vHbUhs2bFij++6ZtqO6lnW27zubcufaLmeapzar/PvUZn4XUpudTVm1WfUsq7ra7Ezza7rN1KPr3NSapzGKiIiISOXExsZitVorfAjPmR7AU1l33333Oc07U/madD7j+qXLOtv3nU25c22XM81Tm1X+fWozvwupzc6mrNqsepZVXW12pvkXUpsJGObFOLJrLZCXl0dkZCS5ubkXxdX3qqJ6rDzVYeWpDs8P1WPlqQ4vPoZhMH36dEaOHBmY1qtXL5KTk3nttdcC09q1a8eIESMCA9TXNtp3LzxqswuP2uzCoza78KjNao8L8jbGusDpdPLEE0+ccmuAnBvVY+WpDitPdXh+qB4rT3V4cSgoKGDXrl2Bv/fu3UtKSgrR0dE0adKEBx54gDFjxtC9e3f69OnDG2+8QWpqKnfeeWcNRn1m2ncvPGqzC4/a7MKjNrvwqM1qD/XsEhEREbmALFiwgMsvv/yU6bfddltgTNPXXnuN5557jvT0dDp06MCLL77IpZdeWs2RioiIiNQMJbtERERERERERKTO0AD1IiIiIiIiIiJSZyjZJSIiIiIiIiIidYaSXSIiIiIiIiIiUmco2XWeLVq0iGuvvZYGDRpgGAZffvllufmmafLkk0/SoEEDgoODGThwIJs3by5XprS0lHvvvZfY2FhCQ0O57rrrSEtLq8atqDmTJ0+mR48ehIeHExcXx8iRI9m+fXu5MqrDn/f666/TqVMnIiIiiIiIoE+fPsycOTMwX3V47iZPnoxhGNx3332BaarHM3vyyScxDKPcKyEhITBf9Xf2Dh48yG9+8xtiYmIICQmhS5curFmzJjBfdSkiIiIicoKSXedZYWEhnTt35pVXXqlw/nPPPccLL7zAK6+8wqpVq0hISGDIkCHk5+cHytx3331Mnz6dTz75hB9//JGCggKuueYavF5vdW1GjVm4cCF33303y5cvZ+7cuXg8HoYOHUphYWGgjOrw5zVq1Ii///3vrF69mtWrVzNo0CBGjBgR+PGrOjw3q1at4o033qBTp07lpqsef1779u1JT08PvDZu3BiYp/o7O0ePHqVfv37Y7XZmzpzJli1b+Oc//0lUVFSgjOpS6rJvv/2WNm3a0KpVK956662aDkfO0vXXX0+9evX41a9+VdOhyFk4cOAAAwcOpF27dnTq1IlPP/20pkOSn5Gfn0+PHj3o0qULHTt25M0336zpkOQsFRUV0bRpUx588MGaDqVuM6XKAOb06dMDf/t8PjMhIcH8+9//HphWUlJiRkZGmv/5z39M0zTNY8eOmXa73fzkk08CZQ4ePGhaLBZz1qxZ1RZ7bXH48GETMBcuXGiapuqwMurVq2e+9dZbqsNzlJ+fb7Zq1cqcO3euedlll5l//OMfTdPUvng2nnjiCbNz584VzlP9nb2HH37Y7N+//2nnqy6lLnO73WarVq3MtLQ0My8vz2zZsqWZnZ1d02HJWfjhhx/Mr7/+2rzxxhtrOhQ5C4cOHTLXrVtnmqZpZmZmmg0bNjQLCgpqNig5I4/HYxYWFpqmaZqFhYVms2bNzKysrBqOSs7GI488Yt50003mn/70p5oOpU5Tz65qtHfvXjIyMhg6dGhgmtPp5LLLLmPp0qUArFmzBrfbXa5MgwYN6NChQ6DMxSQ3NxeA6OhoQHX4S3i9Xj755BMKCwvp06eP6vAc3X333Vx99dUMHjy43HTV49nZuXMnDRo0oFmzZtx6663s2bMHUP2di6+//pru3btz0003ERcXR9euXctdvVVdSl22cuVK2rdvT8OGDQkPD2f48OHMnj27psOSs3D55ZcTHh5e02HIWUpMTKRLly4AxMXFER0dTU5OTs0GJWdktVoJCQkBoKSkBK/Xi2maNRyV/JydO3eybds2hg8fXtOh1HlKdlWjjIwMAOLj48tNj4+PD8zLyMjA4XBQr16905a5WJimyQMPPED//v3p0KEDoDo8Fxs3biQsLAyn08mdd97J9OnTadeunerwHHzyySesXbuWyZMnnzJP9fjzevXqxXvvvcfs2bN58803ycjIoG/fvmRnZ6v+zsGePXt4/fXXadWqFbNnz+bOO+/kD3/4A++99x6gfVFqt58byxTgtddeo1mzZgQFBZGcnMzixYsD8w4dOkTDhg0Dfzdq1IiDBw9WR+gXtcq2m1S/89lmq1evxufz0bhx4yqO+uJ2Ptrs2LFjdO7cmUaNGvHQQw8RGxtbTdFfnM5Hmz344IMV/raQ80/JrhpgGEa5v03TPGXaT51NmbrmnnvuYcOGDXz88cenzFMd/rw2bdqQkpLC8uXL+f3vf89tt93Gli1bAvNVh2d24MAB/vjHP/LBBx8QFBR02nKqx9MbNmwYN954Ix07dmTw4MF89913ALz77ruBMqq/n+fz+ejWrRvPPvssXbt2ZeLEiUyYMIHXX3+9XDnVpdRGPzeW6bRp07jvvvt49NFHWbduHQMGDGDYsGGkpqYCVNhLQfts1atsu0n1O19tlp2dzdixY3njjTeqI+yL2vlos6ioKNavX8/evXv56KOPyMzMrK7wL0qVbbOvvvqK1q1b07p16+oM+6KlZFc1Ov4Usp9eRT98+HDginxCQgIul4ujR4+etszF4N577+Xrr79m/vz5NGrUKDBddXj2HA4HLVu2pHv37kyePJnOnTvzr3/9S3V4ltasWcPhw4dJTk7GZrNhs9lYuHAh//73v7HZbIF6UD2evdDQUDp27MjOnTu1H56DxMRE2rVrV25a27ZtAydOqkupzYYNG8YzzzzDDTfcUOH8F154gfHjx3P77bfTtm1bXnrpJRo3bhxI5jZs2LBcT660tDQSExOrJfaLWWXbTarf+Wiz0tJSrr/+eiZNmkTfvn2rK/SL1vn8nMXHx9OpUycWLVpU1WFf1CrbZsuXL+eTTz4hKSmJBx98kDfffJOnn366OjfhoqJkVzVq1qwZCQkJzJ07NzDN5XKxcOHCwBdKcnIydru9XJn09HQ2bdp0UXzpmKbJPffcwxdffMEPP/xAs2bNys1XHf5ypmlSWlqqOjxLV1xxBRs3biQlJSXw6t69O6NHjyYlJYXmzZurHs9RaWkpW7duJTExUfvhOejXrx/bt28vN23Hjh00bdoU0HFRLlwul4s1a9aUG0sOYOjQoYGx5Hr27MmmTZs4ePAg+fn5zJgxgyuvvLImwpUyZ9NuUrucTZuZpsm4ceMYNGgQY8aMqYkw5SRn02aZmZnk5eUBkJeXx6JFi2jTpk21xyp+Z9NmkydP5sCBA+zbt4/nn3+eCRMm8Pjjj9dEuBcFW00HUNcUFBSwa9euwN979+4lJSWF6OhomjRpwn333cezzz5Lq1ataNWqFc8++ywhISGMGjUKgMjISMaPH8+f/vQnYmJiiI6O5sEHHwzcBlTX3X333Xz00Ud89dVXhIeHB3oqREZGEhwcjGEYqsOz8MgjjzBs2DAaN25Mfn4+n3zyCQsWLGDWrFmqw7MUHh4eGCvuuNDQUGJiYgLTVY9n9uCDD3LttdfSpEkTDh8+zDPPPENeXh633Xab9sNzcP/999O3b1+effZZbr75ZlauXMkbb7wRuMVEdSkXqqysLLxe7xnHm7PZbPzzn//k8ssvx+fz8dBDDxETE1MT4UqZs2k3gCuvvJK1a9dSWFhIo0aNmD59Oj169KjucIWza7MlS5Ywbdo0OnXqFBiH6P3336djx47VHa5wdm2WlpbG+PHjMU0z0GGgU6dONRGucPbHRqk+SnadZ6tXr+byyy8P/P3AAw8AcNtttzF16lQeeughiouLueuuuzh69Ci9evVizpw55Z5W8+KLL2Kz2bj55pspLi7miiuuYOrUqVit1mrfnup2vIvnwIEDy01/5513GDduHIDq8CxkZmYyZswY0tPTiYyMpFOnTsyaNYshQ4YAqsPzRfV4Zmlpafz6178mKyuL+vXr07t3b5YvXx7okaT6Ozs9evRg+vTpTJo0iaeffppmzZrx0ksvMXr06EAZ1aVcyH5uvLnrrruO6667rrrDkp/xc+2mp2bWPmdqs/79++Pz+WoiLDmDM7VZcnIyKSkpNRCVnMnZjqF6/LetVB3D1PNJRURERKSKGYbB9OnTGTlyJOC/5SMkJIRPP/2U66+/PlDuj3/8IykpKSxcuLCGIpWTqd0uPGqzC4/a7MKjNqv9NGaXiIiIiFQ7h8NBcnJyubHkAObOnaux5GoxtduFR2124VGbXXjUZrWPbmMUERERkSrxc2OZPvDAA4wZM4bu3bvTp08f3njjDVJTU7nzzjtrMGpRu1141GYXHrXZhUdtdoExRURERESqwPz5803glNdtt90WKPPqq6+aTZs2NR0Oh9mtWzdz4cKFNRewmKapdrsQqc0uPGqzC4/a7MKiMbtERERERERERKTO0JhdIiIiIiIiIiJSZyjZJSIiIiIiIiIidYaSXSIiIiIiIiIiUmco2SUiIiIiIiIiInWGkl0iIiIiIiIiIlJnKNklIlImOzubuLg49u3bd87v/dWvfsULL7xw/oMSERERERGRc6Jkl4hcEGbMmIFhGKd93XzzzZVex+TJk7n22mtJSkoqN33Dhg3ccMMNxMTEEBQURPv27fnHP/6Bx+MJlHn88cf529/+Rl5eXqXjEBERERERkV9OyS4RuSBcfvnlpKenl3ulpaUxZMgQYmNjeeyxxyq1/OLiYqZMmcLtt99ebvrChQvp3bs3wcHBfPXVV6xfv56HHnqI559/nhtuuAGfzwdAp06dSEpK4sMPP6xUHCIiIiIiIlI5hmmaZk0HISJyrrxeL7/5zW/4/vvv+eGHH+jYsWOllvfFF18wceJEjhw5Um4drVq1om/fvnzwwQflym/ZsoUuXbrw+uuvM378eACeeuop5s2bx6JFiyoVi4iIiIiIiPxy6tklIhec44muuXPnMm/evEonugAWLVpE9+7dy01buXIle/fu5c9//vMp5du1a8fw4cOZNm1aYFrPnj1ZuXIlpaWllY5HREREREREfhklu0TkguL1ehkzZkwg0dWpU6fzstx9+/bRoEGDctP27t0LQKtWrSp8T+vWrdm/f3/g74YNG1JaWkpGRsZ5iUlERETkp1599VWSkpKw2WwVXpCrzAN3zkQP4xGRC4mSXSJywTie6JozZw7z5s2jc+fOpy13roqLiwkKCio3LSIiAoCcnJwK33P06NFAGYDg4GAAioqKznn9IiIiIj9n06ZN3Hfffbz66qscOHCAp5566pQyP33gzrhx4xg5cuQp5RYsWIBhGBw7duys1q2H8YjIhUTJLhG5IBxPdM2ePZvvv//+lETXvn376Ny5MxMmTKBr166Ulpbyzjvv0LNnTzp16sTjjz9+xuXHxsZy9OjRctP69OmD3W7nm2++qTCeOXPm0L9//8C040mx+vXr/9LNFBERETmtr7/+muTkZK6++moSExMJCQkpN/90D9w5H/QwHhG5kCjZJSK1ntfrZezYsYFEV5cuXSost3nzZu699142bNjA7t27mTFjBsuWLSMlJYV169axbNmy066ja9eubNmypdy0mJgY/vCHP/DMM89w6NChcvNefPFFsrOzuf/++wPTNm3aRKNGjYiNjf3lGysiIiJSgRYtWvDoo4+yYsUKDMNgzJgxp5SZOXMmNpuNPn36nPPy9+3bh2EYp7wGDhwYKHPdddfx8ccfV2YzRESqhZJdIlKr+Xw+xo4dy5dffskHH3xAYmIiGRkZ5V7Hb1ts3bp1YAyvefPmsWzZMpKTk+nWrRtbt25l9+7dp13PlVdeyebNm8v17iooKOAPf/gDzZo14/LLL2ft2rUA/OMf/+CRRx7h5ZdfxuFwBNa/ePFihg4dWlVVISIiIhexZcuW0bx5c/7xj3+Qnp7Oa6+9dkqZih64c7YaN25Menp64LVu3TpiYmK49NJLA2X0MB4RuVDYajoAEZEzWbVqFR999BEAw4cPr7DM8QTVyV35TdPkjjvu+NnbF4/r2LEj3bt353//+x8TJ04E4Pnnny83Fsa///1vpk6dykMPPQTA7373O8A/kH1CQgLTp09n9uzZ57iFIiIiIj8vLCyMffv20b9/fxISEiosU9EDdwC+/fZbwsLCyk376RinVqs1sNySkhJGjhxJnz59ePLJJwNlTn4YT9OmTSu5RSIiVUc9u0SkVuvVqxemaZ7xFRUVdcr7Bg0axLRp0wKJsLS0NLKzs8+4rscee4x//etf+Hw+AJ588sly65k6dSrAKetPSkpiypQp9OrVi969e5/X7RcREREB2LBhA+C/QHc6FT1wB+Dyyy8nJSWl3Outt9467XLGjx9Pfn4+H330ERbLiZ+MehiPiFwo1LNLROqkDh068PDDDzNw4EB8Ph/h4eF88sknxMTEnPY9w4cPZ+fOnRw8eJDGjRuf0/rsdjsvv/xyZcMWERERqVBKSgotW7YkNDT0tGUqeuAOQGhoKC1btiw3LS0trcJlPPPMM8yaNYuVK1cSHh5ebp4exiMiFwolu0SkTkhKSmL16tXlpo0dO5axY8ee03L++Mc//qL133HHHb/ofSIiIiJnIyUl5ZSnUf9U165d+eCDD37xOj7//HOefvppZs6cSYsWLU6Zr4fxiMiFQrcxioiIiIiI1HIpKSmnfSL1cRU9cOdsbdq0ibFjx/Lwww/Tvn37wIOAjvfmAj2MR0QuHEp2iYiIiIiI1GI+n4+NGzf+bM+ukx+4c65Wr15NUVERzzzzDImJiYHXDTfcAPgHrZ8+fToTJkz4RdsgIlKdDNM0zZoOQkRERERERCpvxowZPPjgg2zatKnc4PKV9eqrr/LVV18xZ86c87ZMEZGqojG7RERERERE6ojKPHDnTPQwHhG5kKhnl4iIiIiIiIiI1Bkas0tEREREREREROoMJbtERERERERERKTOULJLRERERERERETqDCW7RERERERERESkzlCyS0RERERERERE6gwlu0REREREREREpM5QsktEREREREREROoMW00HcKHzeDysW7eO+Ph4LBblDkVERERERETk/PH5fGRmZtK1a1dsNqVxzoZqqZLWrVtHz549azoMEREREREREanDVq5cSY8ePWo6jAuCkl2VFB8fD/h3usTExBqORkRERERERETqkvT0dHr27BnIP8jPU7Krko7fupiYmEijRo1qOBoRERERERERqYs0dNLZU02JiIiIiIiIiEidoWSXiIiIiIiIiIjUGUp2iYiIiIiIiIhInaExu0RERERERESkWni9Xtxud02HUes4HA6NyXUeKdklIiIiIiIiIlXKNE0yMjI4duxYTYdSK1ksFpo1a4bD4ajpUOoEJbtEREREREREpEodT3TFxcUREhKCYRg1HVKt4fP5OHToEOnp6TRp0kR1cx4o2SUiIiIiIiIiVcbr9QYSXTExMTUdTq1Uv359Dh06hMfjwW6313Q4FzzdECoiIiIiIiIiVeb4GF0hISE1HEntdfz2Ra/XW8OR1A11Ltn12muv0axZM4KCgkhOTmbx4sVn9b4lS5Zgs9no0qVL1QYoIiIiIiIichHS7XmnVxV1czHnR+pUsmvatGncd999PProo6xbt44BAwYwbNgwUlNTz/i+3Nxcxo4dyxVXXFFNkYqIiIiIiIiIVI2LPT9Sp5JdL7zwAuPHj+f222+nbdu2vPTSSzRu3JjXX3/9jO+bOHEio0aNok+fPtUUqYiIiIiIiIhI1bjY8yN1JtnlcrlYs2YNQ4cOLTd96NChLF269LTve+edd9i9ezdPPPHEWa2ntLSUvLy8wCs/P79ScYuIiIiIiIiI/Jz8/Pxy+YjS0tIKy1VXfqQ2qzPJrqysLLxeL/Hx8eWmx8fHk5GRUeF7du7cyV/+8hc+/PBDbLazezDl5MmTiYyMDLzatWtX6dhFREREREREpHZbunQphmFw1VVX1cj627VrVy4fMXny5ArLVVd+pDa78LfgJ346qJtpmhUO9Ob1ehk1ahRPPfUUrVu3PuvlT5o0iQceeCDw98GDB5XwEhEREREREalCpteH+4dNlE5fiXf/EYxQJ47BnXDe2AtLTHi1xPD222/z61//ms8//5zU1FSaNGlSLes9bsuWLTRs2DDwt9PpPGP5qs6P1GZ1JtkVGxuL1Wo9JUt5+PDhU7KZ4O/+t3r1atatW8c999wDgM/nwzRNbDYbc+bMYdCgQae8z+l0ltuh8vLyzvOWiIiIiIiIiMhxpsdL4aSPcM/fjK1bM5zXJOM7nEvJ+4so/Xw54a/ejrVlQpXGUFhYyLRp05g3bx5Hjx5l6tSpPP7441W6zp8KDw8nIiLiZ8tVV36kNqsztzE6HA6Sk5OZO3duuelz586lb9++p5SPiIhg48aNpKSkBF533nknbdq0ISUlhV69elVX6CIiIiIiIiJyGiXvLcK9aCuh/xxL+BsTCf79UEKfuInIrx7CEhNOwUMfYHp9VRrDtGnTSEhIoGfPnowePZp33nkH0zSrdJ2/lPIjdahnF8ADDzzAmDFj6N69O3369OGNN94gNTWVO++8E/Dfgnjw4EHee+89LBYLHTp0KPf+uLg4goKCTpkuIiIiIiIiItXP9Hgp/WwZjuu647is/BBClugwQh65gfxxr+JZtgN7/0uqLI4pU6YwevRoAEaOHMnEiROZN28egwcPrrJ1VsbFnh+pU8muW265hezsbJ5++mnS09Pp0KEDM2bMoGnTpgCkp6eTmppaw1GKiIiIiIiIyNnwpR/FPJyH44qOFc63tm+EER+JZ/2+Kkt2bd++naVLl/LOO+8AEBYWxogRI3j77bdPSXZ5vV6sVmuVxHEuLvb8iGHW1n53F4i0tDQaN27MgQMHaNSoUU2HIyIiIiIiIlKrlJSUsHfvXpo1a0ZQUNA5vdeblk3eyH8Q9u/fYu/b5pT5pmmSO+xZnNd2J/juK89XyOU89NBDLFiwgJUrVwamfffdd9x4442kp6eTm5vLiBEj6NmzJytWrGDVqlV89NFHvP7665SUlDBy5EiefvrpM67jTHWkvMO5qzNjdomIiIiIiIhI3WJpUA9Lw2hcs9dXON+zdi9mVj62Hi2qZP0ej4f33nuPUaNGlZt+5ZVXEh4ezocffgjA5s2buffee9mwYQO7d+9mxowZLFu2jJSUFNatW8eyZcuqJD6pWJ26jVFERERERERE6g7DYsF5a1+KX/gOW3JzHNcmYxgG4O/1VfTM51hbJVRZsuvbb78lMzOTDh06sGnTpnLzBgwYwJQpU7jmmmto3bo1nTp1AmDevHksW7aM5ORkAAoKCti9ezd9+vSpkhjlVEp2iYiIiIiIiEit5bylL97dmRQ9/RklHyzC3q05vsxc3Eu3Y0mIIvQfYwIJsPNtypQpAAwZMuS0ZXJycggJCQn8bZomd9xxB48//niVxCQ/T7cxioiIiIiIiEitZVgshDx6A2Gv3461RQKejamYhSUE/+laIj76I9ZGMVW27m+++QbTNM/4io6OLveeQYMGMW3aNI4ePQr4x9zKzs6ushjlVOrZJSIiIiIiIiK1mmEY2Hu0xN6jZU2H8rM6dOjAww8/zMCBA/H5fISHh/PJJ58QE1N1STkpT8kuEREREREREZFfKCkpidWrV5ebNnbsWMaOHVtDEYluYxQRERERERERkTpDyS4REREREREREakzlOwSEREREREREZE6Q8kuERERERERERGpM5TsEhERERERERGROkPJLhERERERERERqTOU7BIRERERERERkTpDyS4REREREREREakzlOwSEREREREREZE6Q8kuERERERERERGpM5TsEhERERERERH5GUuXLsUwDK666qqaDkV+hq2mAxAREREREREROZOduTnku12nnR9ud9AqMrpKY3j77bf59a9/zeeff05qaipNmjSp0vXJL1fnena99tprNGvWjKCgIJKTk1m8ePFpy37xxRcMGTKE+vXrExERQZ8+fZg9e3Y1RisiIiIiIiIiZ7IzN4fW094k+Yt3T/tqPe1NdubmVFkMhYWFTJs2jfvuu49BgwYxderUKlvX+XIx50fqVLLr+I736KOPsm7dOgYMGMCwYcNITU2tsPyiRYsYMmQIM2bMYM2aNVx++eVce+21rFu3rpojFxEREREREZGKHO/R9cHl17DmhttOeX1w+TXlylWFadOmkZCQQM+ePRk9ejTvvPMOpmlW2foq62LPjxhmbW6dc9SrVy+6devG66+/HpjWtm1bRo4cyeTJk89qGe3bt+eWW27h8ccfP6vyaWlpNG7cmAMHDtCoUaNfFLeIiIiIiIhIXVVSUsLevXsDvYzO1dqsDJK/eJc1N9xGt9iEc55/PvTr148hQ4bw5JNPUlBQQHx8PF999RWDBw/m0KFD/PnPf+bDDz/8xcs/Ux39krxDTeRHapM607PL5XKxZs0ahg4dWm760KFDWbp06Vktw+fzkZ+fT3T06e/zLS0tJS8vL/DKz8+vVNwiIiIiIiIiUntt376dpUuXMmrUKADCwsIYMWIEb7/9NgANGjSoVKLrbOXn55fLR5SWllZYrrryI7VZnUl2ZWVl4fV6iY+PLzc9Pj6ejIyMs1rGP//5TwoLC7n55ptPW2by5MlERkYGXu3atatU3CIiIiIiIiJSe02ZMoUePXrQunXrwLTRo0fzxRdfcPToUfbt20f37t0B2LdvH507d2bcuHG0a9eO3//+93z55Zf06tWL9u3bs3Pnzl8cR7t27crlI07XQ6u68iO1WZ1Jdh1nGEa5v03TPGVaRT7++GOefPJJpk2bRlxc3GnLTZo0idzc3MBry5YtlY5ZRERERERERGofj8fDe++9F+jVddyVV15JeHh4hT26tm7dyqRJk9i4cSMLFixgyZIlrFixgnvvvZdXXnnlF8eyZcuWcvmISZMmnbF8VedHajNbTQdwvsTGxmK1Wk/JUh4+fPiUbOZPTZs2jfHjx/Ppp58yePDgM5Z1Op04nc7A33l5eb88aBERERERERGptb799lsyMzPp0KEDmzZtKjdvwIABTJkyhWuuuabc9DZt2tCmTRvAP07W8TxDp06dmDlz5i+OJTw8nIiIiJ8tV135kdqsziS7HA4HycnJzJ07l+uvvz4wfe7cuYwYMeK07/v444/53e9+x8cff8zVV19dHaGKiIiIiIiIyDnaejT7nKafD1OmTAFgyJAhpy2Tk5NT7u+TO8hYLJbA3xaLBa/XWwVRlqf8SB1KdgE88MADjBkzhu7du9OnTx/eeOMNUlNTufPOOwH/LYgHDx7kvffeA/wNOXbsWP71r3/Ru3fvQNYzODiYyMjIGtsOEREREREREfELtzsA+M38b8+q3Pn0zTff/GyZffv2nff1VtbFnh+pU8muW265hezsbJ5++mnS09Pp0KEDM2bMoGnTpgCkp6eTmpoaKP/f//4Xj8fD3Xffzd133x2YfttttzF16tTqDl9EREREREREfqJVZDQ7bplAvtt12jLhdgetIi/MJwdWhQslP+J2u8nIyKCoqIj69euft6c/GqZpmudlSVRdkLVZWloajRs35sCBAzRq1KimwxERERERERGpVUpKSti7dy/NmjUjKCiopsOplc5UR3Ut71BQUMCHH37Ixx9/zMqVKyktLQ3Ma9SoEUOHDuWOO+6gR48ev3gdlX4aY0FBAf/9738ZOHAgkZGRJCUl0a5dO+rXr0/Tpk2ZMGECq1atquxqRERERERERETkAvbiiy+SlJTEm2++yaBBg/jiiy9ISUlh+/btLFu2jCeeeAKPx8OQIUO46qqr2Llz5y9aT6VuY3zxxRf529/+RlJSEtdddx1/+ctfaNiwIcHBweTk5LBp0yYWL17MkCFD6N27Ny+//DKtWrWqzCpFREREREREROQCtHTpUubPn0/Hjh0rnN+zZ09+97vf8Z///IcpU6awcOHCX5RHqlSyq7qCFBERERERERGRC9unn356VuWcTid33XXXL15PpZJd1RWkiIiIiIiIiIjUTVlZWaxYsQKv10uPHj1ITEys1PLq1NMYRURERERERKR2Oo/Px6tzLua6+fzzzxk/fjytW7fG7Xazfft2Xn31VX7729/+4mVWSbLrfGfkREREREREROTCZLfbASgqKiI4OLiGo6mdXC4XAFartYYjqXoFBQWEhYUF/n7qqadYuXIlrVu3BuC7775jwoQJtSvZVRUZORERERERERG5MFmtVqKiojh8+DAAISEhGIZRw1HVHj6fjyNHjhASEoLNVvdvwEtOTua5555jxIgRANhsNg4fPhxIdmVmZuJwOCq1DsOsZF+5n2bkOnXqxGeffXZKRu7QoUOVCrS2SktLo3Hjxhw4cIBGjRrVdDgiIiIiIiIitY5pmmRkZHDs2LGaDqVWslgsNGvWrMIkT13LO+zbt4+77roLp9PJq6++yu7du7n11lvxer14PB4sFgtTp05l+PDhv3gdlU4ZVkdGTkREREREREQuXIZhkJiYSFxcHG63u6bDqXUcDgcWi6Wmw6gWSUlJzJgxg48++ojLLruMP/7xj+zatYtdu3bh9Xq55JJLCAoKqtQ6Kt2zqzoycrVZXcuwioiIiIiIiEjtUZfzDkePHuXBBx9k06ZNvPHGG3Tu3Pm8LLfSPbuqIyMnIiIiIiIiIiJ1w8yZM9myZQudO3dmypQpLFiwgFGjRjF8+HCefvrpSj/I4Lz1kRs1ahQrV65k3bp1DBw4EJ/PR5cuXZToEhERERERERERAB566CHGjRvHqlWrmDhxIn/9618ZOHAg69atw+l00qVLF2bOnFmpdVT6NkYon5EbPHgwCxYs4O677z5vGbnarC53JxQRERERERGRmlXX8g6xsbHMnj2b5ORkcnJy6N27Nzt27AjM37x5MxMnTuTHH3/8xeuodM+u6sjISd1lFpRQMm0pBfe/S/4f36H4rXn4svJqOiwRERERERERqQIhISHs3bsXgAMHDpxyR2D79u0rleiC89CzqzoycrVZXcuwVifPpgMU3DcVM78YW3JzDKcN96rdYJqEPn0Ljis61nSIIiIiIiIiIjWqruUdPvzwQyZMmEBUVBRFRUW8++67jBgx4ryuo9ID1B/PyCUnJ1dZRk7qHl9uEesfnUrhJVGE3PtrLDFh/umFpZS8PR/PC/8jPtZB285tqi+mvCLcP27HLCjB2jQWW48WGNX46NeduTnku11nLBNud9AqMrraYjGLXHiP5GI47FgSIjEMo1rjkPJq0z4iIiJSlfSdJyJSd40ePZqrrrqKPXv20KpVK6Kios77Oiqd7Jo8eTJjx47lD3/4QyAjJ/Jztny1mO5XO4AiWPh5+ZkNgYZhsOJLdiRNqPKTGNPro+Q/cyj5aAmUusFqAa8PS6MYQv7vBuzdW1Tp+sF/Qtd62ptnVXbHLVVbJ2cbS1XHcZzp8eJeuAXPWn83V1vXZtgHtsOwWat83cf93Al3dZxs16Z95DizoATX7PV4U49ghDhxXNERa8uEKl/vyWpD2/w0DtM0wQTDYtRYHJ7ULNxLt0NhKUZsBI4Bl2CJDqvWWGqL2rSP5BUW4V6+E++mVEwfWFvGY+9/CZawoGpvF9M08aVmYRaUYGkYjSUqtNrWDbUrkbEzN4dje9JxzduELy0LHDbs3Vtg79MGI9herW1j+nx4Vu7Cs2YvYGLr1hxbr5a16uLbxfqd591/hNIvV+FLzcIIcWIf3BF7/0swrBdX2xyP49jeDFyzU/Cs2wceL5akOByDO2JLbkaEw1m9x7MSF+6lOzDzirAk1sPWvUWtaheo7uNZBq55G/HuO4xhs2Dr3Az7wLZYwoMv2nMAs9iNZ8N+zCIXlgZRWFsnYhjGRVcftU1MTAwxMTFVtvxKJ7uqIyMndYfp9eHLOEb2kq2QDO91vIz2rZIA8OzKoPiteVjCgtjmLuSOziaH35lHo7BojPBg7MnNAz+kTZcHs7AEIzy40kmPjf+eTs6cdTh+0x3HkE4YkcF4d2VS+r+leJ98l/gnbqFtj/aV3fQzOv4F+cHl19C2Xgy+ghLMvCKM8GAs4f4HPGw9ms1v5n/7s1+mlZWb4x8z7b/L3XTo0wlbxyaQX4Lrh0141u5l76+78TvXriqPA2Dbhh1kPjcd80geRmI9AMzvV2BMDSf4/qup17JhrTnhruqT7eP1/U50R5p9noKZUxiYZ8RFEDR6ALtaRFbLPgKw+asfOfLO9+DyYMRFQX4R5hcLsPVsSfCdg4kIC71o2qbWxuEoe3mBBZurPZa84hLcK3binr8ZX1YeRmgQ9j6tsV/eHkuos1b9YK72tjmeEy7KgDnrqy2O47HkLN1K6fSV+PZn+SdaDWw9WuK8tR9RDWJqTbtANbdNBNCubMaxNTBzTbXFAbBtyy4y//kVZloORj1/8tH8ejFGw2hC7htOVNP4WtM21fWd98YmK612HcPWrRmWxGh8B7LwpOzH0iKO1DsvZcyy2dXynbfxze/I/mwpRngQlhbxmJnZ+J7dgKV5HCF/vo7ImMiLpm3KxVEPGGQFrMAxSF3sf1VDHAA7jmWT/fVKXF+uxCw8sR8Y9SMIHjcQW5emVf5dU6uPZ53KZrg3wtyN1RbH8ViO7UnHNWc9no2p4DOxtkjAMbQTtrYNa/Yc4Chw0mlRdSXM5YTU1FSaNGly1uUPHjxIw4YNz3k9lUp2HQ/ybDNyvzTIc/Haa6/xj3/8g/T0dNq3b89LL73EgAEDTlt+4cKFPPDAA2zevJkGDRrw0EMPceedd1ZpjLWBZ/MBSj9bjnfbQbDbsPdrg/OGXljqR5zX9Xj3Hab0i5X4DmThTc3GdzAHPF7MGCskR9Bqfz7d+vjP+vM3HGblwRxiSkxaOQzoHIF7Zgol2V7/wh4aEUh2eTamUjDxDf/0EAeWiBCMiGB/8isiGMd13XEMaAuA71ghnhW7/POPv8L9rx37DtA5dBdcHw5shx+2nwi+LdA2FNZ9y47WidVyEGxdbND6ze9xL9wCXh8YBra+rQn+/VCIPfUzZpomLp8Xn2kSZLUFbjMscLso8rgJttoIdzj99WCa7M0/htdn0jKyHpaysulFBRwqLCA2KJim4ZGUfrMGgsD83UBKmjWmX0JDnFYb5uBe5Pz7O4o/Xw7Xhld5XWw/cJC2y6fDpeD/xi7bDyjbR9fMgDXVd8I9NaELzWZuw7fnsH9GsB37gLbsGdKascvnVMvJNkDSm0tI7tqOoCcGYk2Kw7sznZIpP+B+7Bv427XVEsOWBWvpkLkEhh9/0q4XcJa9DsN3HwHV+GNovUGrXbkYUSHg9WHml2BplcD+3/Zm7Jofqrxtji//v/MLaRMSiWNAW4xgB56UfXjW7WN3/6bcHvfzV33PWxyLiul0w6XY+16CYbNgFrko/WY1rq/XsO+3vflt3pYqjeWUk8t2AAZQCq6NMLv6TrjLtY3bjrV9Y8xiF95NqRhhQey/eyC3bV9S5W2Tl1sAwJtb7XT+3TCsZcl737EiSqb8wJYDaUy8NLjK4yjXNt2B7id/52fC4i+Aakzeh7el2f/WYRa7MUIdmEUusFtxjuzJ7gFNGTP/uyqvk5wlWwF420yi04gBGDZ/bxBvZi5F//yWHcFe7uhsVnkc2w8eou2Pn0MvoNfJ7RIBeGD517C8Gr/zErvSbPZ2fDszADBCHdgGtGXv4NbVlmACaJ1ZQv+X78XaIj4wzbMljYK738L61UaIq/oYNn/1I53MTXDj8XY56v+newRQAt//D6i+tnk7uA3N5+3ETD8GgKVZHM7hXdnVNqaaLormAvBWehRd7rgGw37iorNr/mY2fL6QiYNCq+V41uZ/b/n/uCoIKD+UDnsWwB7/f6uybQKfmbjOJH20GjO3GILtUOoBmwXH1d3YM6glYxZU/fHs6LrdAExxNaLzjQMDbePLLaboxW/ZVpzHxF626v2uqQdcauA/DzgMW78H/2G3+s4Bfiikfd/OOIZ0whIdhnf7QUo+Xc62o9lMvDSo2o5nNaU25kd69OjBddddx4QJE+jZs2eFZXJzc/nf//7Hv/71LyZOnMi99957zuupVLKruoI8W9OmTeO+++7jtddeo1+/fvz3v/9l2LBhbNmypcLM4d69exk+fDgTJkzggw8+YMmSJdx1113Ur1+fG2+8scrirGkb355J9rQfMeqHY+uWBMWluGcswZi1lOAHr6Veh6SfPfCYXh++zGP4UrPxHsjCdyDbn9A6kE3Q2MtwXtcdAHdOPru+XkqO06Dz8aSV3cor3UMA+MFWQO+yZR5sFc3g6yOIMGx8vdDtL3pNN17w5LDEKOSuei5+VVa2ML+A6c3t1C826Z/uwlfkgoxjgfhsPVoG/u/dk0nhox9XuB3pCQ64LpT3+15Fu4R4vEfycX21EkKCMMKcbM3M5PaYLLIXbaJZ82ZYGtTDUi/snOobwOPzkV5UQKHHzSVRJ5JWX+3bycrD6TSL8J9AFT35KXsdQYy8PZ4Sm8GeqN6UfLyE/PH/4e/3+5N3b25dz+sD/Em/o6UlxLz3b39d3/5nbGUJrCfX/Mg/N6zioc69+H+9Bvrn+7y0/MSfIDw27j4iy5Jgr29Zx1/XLuWudl15pc9gPIu2wFAHd29ZineLyZ74ATSwOsFq4e04Fw/9JNFlerzcOP0TMOCFVj1oFBKOYbWwpSSP1fnZtI6JpV9Lf3uYpklefgGhTidWhz2QnKtI9tx1ALzXfTDtm5RPkvtyi1n7xHvV+gXV9NVFdG3ZHOefB+GJCuboqh24p6/Eu/UglB3+9uYdI9/toml4ZKB+c0qKWZ2VQbDVxoDExoHlzUnbS1pBPpc1aEyLCP8P30OF+UzdsYlwu4N7OyQHyv53SwpzD5Tdxtm7FaFPjiK1II8/Lf6WKEcQb74wloL7plLy0VLoC+/v9F+u6hbr309KvR6WZBwkxGajd/yJuiz2uDEwcFqtZ2yLn8r6fCl0hPcHXk276Nhy81yLtrLhf/Or5UTXd9Tfu62N207f5ydi7dAYTBPPsp0UPvsF5quLCBxgzhOX10u+24XFMKjnDCoXh7VLEl3+8Gsig/1JwKMjS9j0/UpsU7+HISduETtcXIjXNIl2BuG0+r+Gjz8r5lza4ad82f6ESseretP7hkHlZ97dhIK0EozvtsDpz2/Oi5OTbl1uvxpbhxP7vfdIHkXPTmdnvJMJrUurfB/xbD0IQPvu7el7zw0YTjsAvsxcCh58D15bCFdUupP7z3It9p/Zd5l4Ld0vaXViRiyYTzbGe9tzVR4DnEi6TTkWR+ffXlVuf/MdLWLtP6Yxsbul+pL3U1fQvV9Xgm4fhLVBNL4jeZR8sIjS1xaD7fzcgmSaJh7Th880cVhOHOuKPG4K3C6yZ62FDtD5xsvoFpvAnrxj2C0WEtvGwcO/xvzDq9D5/F4IrEjOvBQA3us2iPZJjcvN8+UUsPbpD6olIXpc01cW0jWpKc77b8KoF4p75S7/RdKtB/2J0l8oz1VKocdNuN1BmN0B+NtiQ/ZhDMOgV1wDwJ9sBJh/XWvqR1sDHe5KS13MMHIJHt0F5xerYWQ4ZqkbX4nLf6un1RK4fe18HFdN0yTrmxXQ80Tv+5O51+5h/duzquU7z/T5t6fZR6tJ7toex+grMd1eXDPX4Xl6Bt7be8N5+NiUej24vF5CbHasZbfPFrhdZBYX4rRYcS/0H886jRmK4TPJPXKMdsGR1A8JgUE9KfpxK1CIr8Rd+WDOIHA8K25Il9GDy80zPT6Knp3Odoe7Wr5rAJr+90eSO15C8J1DsDaPx5dTQOm0pZRMmQ8m/s5vVcw1KwWaQ+dbBpFcP/HEjFjw/d8YfL97Hqj6C9bHDmUDMCU7hs6/HR64iGCaJqVfrmLTvNXVep7Y4Yru9J1w3YkZ8YmYPTtj3vNvwFOlMdS02pof2bp1K88++yxXXXUVdrud7t2706BBA4KCgjh69Chbtmxh8+bNdO/enX/84x8MGzbsF62nUmd41RXk2XrhhRcYP348t99+OwAvvfQSs2fP5vXXX2fy5MmnlP/Pf/5DkyZNeOmllwBo27Ytq1ev5vnnnz/nxvR6vXi93p8vWMO2zl9DJ8+Gk65O7YdI4OqyHhqb58Jm2Pqr8bQMi8TMzMV3IBsjLgJrM//lM8+mAxTd+Sa4y2/vvEY21sTZuHLvHgZ4uwKwIQJ6jYok3rCzv8PVWBrHYMRFUvzN/yA7jf0HM/G43RgWC0VOK41Cwwkr9uA5kgVE4bi5D+vXLWV+ag43NwgP1PGhjomMHxxGpN3B4YGjIL8YM7+E+/evY3bRYf6S4OZ3ZWXzDC+vDk8kOt/D6B0uyC+GIhclVvCVbUOb+vXpXK8+nr0FFH12oteBK8YKN0ZQ8spsso56cd4+iIjxVwCQtT2V6f+ehifYztjCsECPseeiC5jjLOSe5h359eX9Adh+5DAdvn6PSLuDrFF3B269/HLvDqbu3MTv23YBwBcVSuzkcez5agpWj4HtumRCh3Yi/54puDYfgER/4ux4PZg+34lYj+ZjFrkxC0owM/wnie6c/EBZb8Yxwn0GFhOO/fk9jFwXZkEJzoRSGrQwCNlwEE/bAjz5RYCDprkeHF5wvf8NRcX+E6ys5CBI9u8rXq8/Dl9uITOOHKDUZvDUHzaRX+gv+00HJ4/2DeFXhSH0/sNd/nhL3LR++xUOh1j48dNc2uWaYLMyt7Gdty+x0z8slkkPjPdvz4qdkAx7Pv6BSzNsRGHDsNvIDLeyJ8KCPToM8ATi+Pj9r8nDy3Vh8cSGhEKwg/UU8UVBBi3rRfO7Pn0CdfXAsh9IK8rn6eT+geTj9wf388iqRXSOqc+bA64KlB0/7zsAUq5sQf8/jMUwDGam7makdSPdf9uIv087AITg9fr49byvWXEknc+vGMGIJP8P2nVZGVw543+0jYpm442/Cyz3uZQVzDu0n3f7XUnTBnbMUjf7Dx/i0VWLaOIM5a6yfQLg25QUvi3I9P8RFUzxm99zxFXA544dxJo2/tt/KPbR/fE8/g4QxUsbV5OwbB+tCyMBSDVcXBF3gGDTIP/2PwWWe8877/O2eYRHcyJ4+Gg4+EyOGj6GND5CiGlh5V1/CPQAfHfKZ8zPSWdYjpXEHenQMZIGry9kfoiXIB9MePR2rMFOzOH1WDN9AQCr3ptFG2LBacNntzDFehTTZmHi9cNwhvr3oyVrN7EyPY2O4fUYGN0Aw2EDp42X0rbitRr8vksPwssefLIs8xDz01PpFF2fa5q0oGR2Cjhg2s2t+b5kH/cWRxPtDMbSuyWrHr+Sl7/4FnAG9hGAsQu+I6ukmNf6DSEp3F8/0/ft4Mk1S7g0sTEv9z1xwtzli6nszjvGD1ffQo+yk8ZPtm3ktiWzuaJ+Q2b2Hg5eH8XfrIIwuL3eEZoeOsDlSf5x/hZs28YNB5fRtm8YYFK6Zg8l9iNce2AJK0tz+azHEEZ26uzf/9as56qUuXQ0Qlhube/vAev1cRt7WWUU8WKHvozo0wOAjas2ccfq+TR2W5maGQ0eL3h9vBl6BJrAmhAPPcu2N3XtDp77biaRpoVJqQa+rFwgkm/fmM5nVrg6uQu9L/dna3P2p/POd/MIsloZb4nzXxG2WllmFLDX4qL7Ja3o2M3/k7P4WD4zl6zGZrMyvF4DsFnBbmWXu5CVuf59tW2P9iQP6IbH5WZjejpeTJITG+AePRTPi/+D1lF8v3sXhstLp3h/Ytbl9fD6prW4fT7+0LYrdqsVDIMZaXuYfXA/AxIbcVPzS/zHE9Pkhu+/xO3z8cHAq4kqSz5O2b6Bf29aw4ikVgz9dju0APvo/iRM+w+57lI23PBbmsdE4nzqJqY/9x/AxmPLFvD18Jv8y/X5aP/xmxxxlzCvxaW0tYSAx8u0owd46Oh2BkfE896NtwT2k0vfep1UXHxS1IjOJXZwe5hlyefhqBx62SN5//bf4Vq9G9rC/d9+RdYMHy9vNOhxFLAarI6ChzuX9SY6aV/9+3NvcsBbwtijQXR0O8Bq4aDDx5cRLhLCwxlz962BGBa/8RW5hYV08AWTYPjLFltNDlm9hEaEkTT6cgBKlmwDoE1EPdp9vxsshn+cSrsNw2ahuHEDIANPfjHeel6yt+wnv6iIqKAgwh1BYLdSbDFZV3gUbBYGtG4diGFR+gF25h2lZ/1EOkbXB/wXZF7YuBrDgKeT+wfKfrbLH8eiSxvS/+GRYBhkFRVy9Y9f4mtqMu/GHhR/uQKuCubtrRsYdegbbm/TifvadwOXh6LCYtrM/ACfabLjljsIc/iTJk/NmcU/0rYwMbghk+1NMUvcmKUuwvBfPDn4qwnER/o/98+99ylPeQ4wPNQFOMi/712OHXbTcaSNIpvBxst+RduWSZgN6wFervpoKtcf8PH/1vv89WYY/KmLhVIrPDX6ZpJaNwVgw8ffs3jlepoVG1x61OIvazHYHWxiMwyaP3wTwS39xxPXjHW4P18BhkFpzmG4Opim76+kZfFaMAyC7r0Ka9uGUK8+hfXjgHyKpy2lKDgKguwYwQ5/AjfYgbV948DDftwFxf5EQJCd6MjwwDnHxpwjHC0toUN0LNFO/zE4rTCf2Wl7iXI4ubFZGzxH/QmEr4Y14fPuSfy2dTydY+Jwdm/Ojn5N+Mu3XwK2cvvqXUvmsjjjAM92v5Rrm/ovbq05nM6gmf8jwRHMlt4joagUs8jFb3Yv5ZvCTF5r15c7+vQFYGfKDvqs/ZZor4W9m6OhqJSi4mNwuZWnCvcSPHMhbW4Z6W/DVdu4YctMbD6TOV7/+Ub+7/7DnW0cfNjGyV+CGvPIOP9nI3/jPsZ9/BGRLpN/LCnGDmAYbIyxciDcSscrkmk/fjgAnr2HyRv/H/9FQ8NfzjAM/9h29lLoGUnriHp0rlcfX04BRXe8ASEOjCAHRfn+c7Gi/86l0B6BtUsS9is6+I8nHi+eBVsg2IER4sAIdkCIE6/TRrHTiiXYTnjZscs0TX7MPEixx8PAxMY4rP52W3kkncUZaVh3+HvZOX4/hOCr+3P7olkU4uZfk39F+LSVFP9vHtwYxR2LZnFNkxY81rXviePUtx9zuLiQb4beSMvQCChy8d72jdy/eRlDYxvyyTW/CsTb+r1XSTVdLCxsRrcCC2ZhKdPsx5jQuIDLPaE8kRYESWCGOPj1u++yo56Vz7/L5/KD/qTByvZOIITffPYJm+99IBDD+P97jj1BPh5Z76bXUTCsFvaHW/gkyUrTyCgmPHZHoOzqSW9Tkl9Ec7eNSKz+41RZItNoEEXwQyMoWbkTgNYuK61fWeQvY7Ng2G3gsOENjsSdshlaRwX21+NjWeKwgd2K4bDhsVnItfjw2G00bNs0EMPag2kcLC6kQ/04mkX6L0pmFhfy1rYN2CwWHu7cy7/vePyfg0mDo/jziGRGNI3F6/WSbvdydf00HLcn8vdvVsN14Xi9Pv66Zglf7tvJxLad+V1SWyhxk5NXwDXLZ4BpsuSmcYFzr3/NnsunmXv5tbU+t/tiodRFcUkpI2x7wWcyc/wEQpz+i6tvvPEJb4YdBmwU/GEqxzJdmKVuRg0KxorBlDvHY7ROBApYPH0+b6cepEehnevznOCwYtisvBvrAquFW8eNpF6C//x49/x17Ni2h0RbEG3tof7vC7uVNKsHq91Gg0s7YS8b89F3KAdfZi7FM5dCPLS7vDudSm3gs4LF4u+B/9vh/nN8PLj3H8F1xO3vle81wevjqKuEYo+buLZJBEX5j2mHd6WxffteQk0LHW2hmF4feH3MKs4iy1vK0H7dadTUfzF3a8o2Plmzhjifjc47c6EN+PKKefzlt9lhlHJ/735079oe7BZsQzpC7jo8Rwvw1qv9v+N/Sa6hJvMjZxIdHc3zzz/PM888w4wZM1i8eDH79u2juLiY2NhYRo8ezZVXXkmHDh0qtZ5KJbuqK8iz4XK5WLNmDX/5y1/KTR86dChLly6t8D3Lli1j6NCh5aZdeeWVTJkyBbfbjd1uP+U9paWllJaWBv7Oz88HYOXKlezdu7eym1HlNrw3C/rX45H45jR1hgSmW0rchG9IZ+/GPUwaEsv+u/9D/T2FmD6T9BCDgj5NKL3K37MoOyePfw8NptBu8M1SH6WxIZTGhvJukotvg4o5FlYCi/336+d5PdgwsNusLC45iG1XOuyCZHsQM4AmX69h1vSN2Hu1xLBZ+W/KPry7Mth6TUcA1q1bx1Wmkw4JzQk7eJjFR/zLTXUV0yk4HIdhYen+rYHtWG/mscdXwracDBYvPlH2iUYlhFqsdBhRlvDw+vjHod3MKsoBYM2PyyiMicF1JJ/XRiXg8PiYvNdOXq5//hMDwlgUZ+HRwj0MXuw/qc7alcHvuxlElLoY/e6Jtt99WQgr2jjps3YDjWz+E7GitKPYvSbOohKO9X8c7Fa8wXZ6tLRDfRsR2dshBrZ2CIc1a3nvaDQhNjtb/t+HWIvdGJYSRi7L4KsbEui9PoPFpn/bnAfz2PNRHrYSLyVvTOb4nvko8H9A5qB0Fgf7yzqyCtn/Vk5ZiZzATYF37YO7lkNW30h+XLOSjSVHgCheW+ujfakVX+No8gwDw2dy91EfPb7M4aaR0axbt47CoFAsBaW8sNZDvhXCnQ7cFhPDaxLvNhiU5qaZxR1oC0uJh1yH/ws8wmWCzwSXh13BVmY3tGLPyg2UTcncBzTgyVY+Bq3PIiLHH/HXbR082D6UPg4PYAvE8XDONg6FWWj71jpCs/xl17Vy8NzloVy6CVp5Tly5mbFqNbsiLYx9Yz2xRw18DiupTaysTbZCahaLOdELJ+/QYQi1kFlSwLa/f4jPYeWQoxRCID83j63N/ScZa9esIXhfFrEOg5zXZ5CabWBxeykN8tLuEpNGOccC2wbQfsEerEFeIr/9jPxD/thCwyyM7hZEPcNTruywRYeICfLwbjsnnlkbKM32EuMweK6lA4vDyuI2i/HlF7Ox6DAQxZD9Llpu2ou77MTTFW7hkivDsJuUW+7R9COQAM7tGXg27gcgN9RgR8soHF4PS056ku4PGal8mOCjwd5iGlDWM2LnIR68zp84v2TZUqx2/1fKnAT//PkHU7l5of9HbakF7r+9HnjhkkWLCQ7z/9D6bPFaXo13M35eCT2XFAfW98jtUXgtBu3S84iM9a/jm6XreSG2hFt3u+m9xkOhrQRuqMc7OzZSbDNoty+HhPpR/nWv3co3Lfxtk/3nqWQe8+9v3w+1cTjIYPHMHziQ6O+Vtm3FFjbXyydxUyZHH16E4TMxfCZFN4VTHGll48xFlDTz9zY4snwrREPx+n0U/M3fG8dblhSPK/Kx+4eV2FodAuDwyp00sXiJzfdBqJ3SF76jONuLeV04RryVtOUbWJzrHyNvz7rdEArmkTxKv5gTqIf0a8JIbWBn97rNLPaU+Mtu3McqZynZxV483x8MlF1/XRhgZ/OB1EA7H9pygP80cJNQ6OPh7bkYMf4fTz+UZLOwkR1j/TrcNv+RI2tnOg87Mwh3mYx5e1NguVMuC+HjNk4eWHCIawv9V2sLUrO4uWQ3Vp/JkcePBcq+2C+YKe39P9w2h5fiXbwYV0YuV+b594Mjbx7FaoK1LI6H1y9h5/zVjO7dBQAzu4A/Zft7Jt567zdElHUMWJwcxGvJweQu2UxCnyMA2PJLmXFwF16LQcawZzDKdp+DnZxs7hlMm3nHqL9iO7RoyYaVayjJK6TUYZB384vkHfNgmODt6d8P87amsji87LPh9XE0J5ejIRYKn51OcdmxJ7eNg8OXhZKxJ63c5+hgfh5pEVYK567Hfdhf9lgLO3uuCCM+J4fFixezIXMftI0hzXCzL8xCQXYevrLPfQZ21pQlKY4fzwA+J4d1cVYuW5tN21R/RWxtaGPS1eG0zTtG0kkx/F/2dpbEWXhzXgE37vaXXRtn5cqRETTJNnl3sf+zuWntOmgFl7GNf31TxKgd/qvqG2KsDBsRTmS4CVhYu3Q5Rfv38/fFy5kdb/DC4kLGbfWX3R5l4fKbI4ku8fF55omLCC8vWsH0BHh6RTGNtrkxrRbSwq1MHhGM02sypMgMlF29fCVEw46iYxwc9y9Mm0GOA1Z298e+vHlrtsw4ALTmwNINbK/nJfWt2eQvnw5AiRUyxvt/fC5ZtJgQp/97OWflNkoSfRSt3EnJ0g2B9RkTojANg5WLlxEV6a/fgrRMSPB3uAD/Z8/M8RJRGonLAjs3bSIr/QAbCjKA+hwJMsg1PZg5RYHlTk+M5GiQhatT1nEgMxWAhft38GQHk6v3ltJ/3YmxFUf8OoK0cCtTlv9I83R/cnfdxi38vy55XHbQze8PuYFgfNsO8XBrBzlBBqNWLicyy3+hcY4vG3Dw0r5NvDH/RAzXXhPG1mgrz21rRIu2/h5ha9bt5MHQHHpleJj5dT6mxf89N+6aENZHW3nJTKBzm7Lk3O40/ug9yCXFBl32LiA1Nwf62JltzWfrlnXE7c8ir6H/+LcjLZNlCf596eBznxKTa2It9bLrEg9b68O6OUuIuiQdgMwNqRQGuSnIK6Xw1n8F4rVcEYrR3E7a6s0sLksS5G8/RJN8L/VKPXiX+xMYZowViKB7pgf70SOBz1z+zgy6Z/o/Oyf318p1GhTZDXJzjp4ou/8IX7VwYPWZvLj4eJ2ZfNDSyZsdgph4bA+3lpU1D+UyaHQIoW6Tbe8fI9Tj3zc+aeXgfy3958vHP5/mkXyWRRZQbDO4P6UkcFydtXcXL8TY6PvjPro7/Lc6evOKuWvrOoptsPTTPELLTkWe6RHEC12DuS3TyrgBZV3lXB4G7V2NaRisWmoQgx2f08pnDb28kOBiwMFSaOhkk6MA1+LFfLpjM4WGyZgNuSQV+Ths8X9G12RlEnMol8UF/vo1XF527U/jcJBB5m9eIv6wP4jCNg7yLgsle9t+FkeWHU9ME+uxYoi0UnzSMc3WzE5YfCiWvEI2ZGVCUhgpKSk0LTTxGV7qucG0+M8Vi8t6MNk9vnLHytWRJpujbRSsK4Z8DyawPdzG39uG0yE3l0tOKjsp4gjLW1qZ8n0B1+/xHxdWxFu5aVg4bQoyeX7xYjZvXA8N4Y3svRT5vIzfXELvTH+826Is/L17ML4uQeXa7tUflrEkCiYvK+K6vf7lrqlvZcj1ETQu8PFe1olj2t9/XMHsOHhhcSG3bXfjs1nYE2PliWtDqecy6Zvnr+9tB/1JyOXhXta++S1dM22YNoODVh8bkt0E+WBjfgYQzrp169i8egtr43zsf/Er8tdN8++/QQarxkYB8OOiRYEHUmxctZHliT76rMykdJX/HKDECkvKjn9LFy7GGew/19l4JIN1ZZ9PM6cAM8+LD5jV2D9t5do17D2aBkSx6sghPmzsZczWIq5LOXE8efi3URTZDeKXL6N+jP/CwJxlKUyOL+WGXS7e+uHEMa3vmEiygi28PzuDRg3851M/LtnEM/UK6OvyAHZKnviUgmwvNw4LIz3UwhMNW5HQuD6b4v1H3oErvuPyNDcfzjlpubdEsDfSypvbG9Oyhf/Ys3rlNv4clUv/Q26+/rYgUPbxG8LZFGvjle8LaN/a30tp05qd/C08h65ZHl7YVARtIvDM2cDsfiGsTLDRfcVqigv8v4e25fmPV2uXr6ToYBq13ZEj/vOf/Px88vLyAtOdTifOsqTnyaorP1IZQUFB3HDDDdxwww3ndbnHnZe++1Ud5NnIysrC6/USHx9fbnp8fDwZGRkVvicjI6PC8h6Ph6ysLBITE095z+TJk3nqqafOX+DVyDRN/2C0/evR1BlC66ATP+jdRQXs2ZJOarz/hNuZU4zFa3Ln5aH8r5WDB455OT4akC0iiEUN/Tv6hgd74Cw7ILfPPYKvOJ+mYSe6+4dbrMxq1QPrT7qQN3L4v3yib+mPdd52XDPX+QcubBZH0MQh2NvWhwP+HzztgsNpF1y+y20TRzD/atyOn7o3LolbPS4S7Sc+8HbDYGhELLaTT4usFo5ZfPjKJnmWbIfr+pIeYeOrsFJCLVbubN+KPa9/CzTG0ywaCo+R0eREHEGJUfRKP0Kkw2Df6KbYij1Yi92M8BXTd6+Hhk1PdHWP8cDBDwuwlZSd5bi8WF1efr2mhF8Dc/tYIAYsTWIJPlbKNZ/uPmXbkmL8J1tRR04kBEynlaiCE1l+n82CN9iGN8iGL8iOJ+JEPXjCnKRf2RJfkA1vkP2kcv6/PWEODIcNayv/fr9vVCec4eVv3XCv2UPalzuAE7e5+sKctPuN/4r9vpPKtgAe+8k2+JxWvmvejSK3h+wHreSa/sRYE1cxf3EVEt/yxHgL1hh/XbfBQf6NHdnjtWDx+vD58kny5BJcXEzgEGaa9Cm1k+s28TWpR36kD4vbS5LFy/idHpLs5cdxuH99KUUWk5aHXTjKfoANKDCYdthKSHQ4dD1R9sklhYwdGs5lP2bS8LA/iXEz8CsDShtG8PXlscBRzBIXH8/Ix3m0uNy6+gA/pkBJ/VC2n3RL3cPbfARnnPhyNw1o4DJ4cY0bV7SdnSctY4gziiY5ObwL5CZFkN0yFJ/dynU2C94gG4fxX007bqK9Pu3a20jv6N/BHQZ8XmTgs1nIOmm5f6nXlIcOF2FtYSGthb+nQqlhMiXXhfsnd330SkwguriANq1i2L9rPRBB5pAWDPEUUmL4/FdU8d9uE5NdCK2Cia0fyeGB8RgeLz6PlyuPlmCYYDQ90Ze/peFk5MFS2pfaKK0XjMXjw/B4uXm323/b0YATZdsVGIw5UkqvTA+OYy6cZT8wrt7rIsJlEt7/xP0bXY76GJdawtR2QQRlFhJcdhv135bYcVsMEq85UbZ/kY0vl+QTW+zD6jrRW/KL7/wnVPm3tQx0ch/sCuLwm/uxAD6rv8eGt6yuPlpQQsS1IRxv1V6OMJbPzmC9082whnaKE0IpDLPw+SYDc6uF9KERHN9bOkdEsWJVHqY1iJweEZhWA9Ni4fFiH3n7IbJtvUBciXH1+PeeEhyGQdrIhmAxMK0W+pVksgIPicaJOgtJrMfvMgoJcRjs7mxh/94DQASt69UjvshLw7gTy7VHBnN1qp0gH2T1aexP+nl8JAW7ufSYl7iwE98ZFquVbsdMrD4oTgzD8JgYPh/1TCuNCnykhVkIpBG8PuKLfFh94LaA9aQLk+2zPCT4gk/EYBrcsMuFw2eW+yHbL92DubaY1jHBnOzFxUVYfRBZamKUNd2Nu1wkZ3oIbhbLZvNEgmXh53lYfSYJRSZG2eSr9rt4pUsQdxw86d4fq4WvFrrxWA0aBAVT1MiCabVwOQYzV1uwxpf/Pno5O4LSbIjtEEmGxYpps9DGAe8d9eEM9x97LPH+Hw23hcQRj52wEc3ZabFh+EwifS5uX7+JtzqVX+7wyDi6uVyE9I8n1esv6zFcXFVSQHxQ+ZPNhPBQ2rhcGO0SyWzqL5vtdBPqLSTIfuJUzwi2A268FoPSxpEcc1r8P06DfBTbvDiP7/5lySOrzYrD68UVYscdbmB4fTgtJs1zvUT95A6l9nlQVOqiaa4Xa6kX8BLtdnPHJgO7afjHwSxzWZqbudE2+u8uJmKX/4Q92AIfZNuxmmCbeGK4gOFZFu5YfIyGhSc+mw4vzJ+e77/V8U78wwUCo3yRjF6STagRyrGOEfgcVnx2K8t3Ghh2K4WNTtTbr5Ka8eucItZvWs3MZvGk3tqRIGcon9st/mNhpBNvVh5m+lGgPk8ENaB912C2d3OCaWKYJre7j1Jo+gg/aXiD0BZxXJp/hKbNY9h1Zz3/vmaaWEnFaXqw1jvxOcpsEk5mUT6ZzaPYn+bvEXloeGu+cR7hoMXLwCg7kWVls90lgINtjYLJ6VEPi8uL4fKSE+4hJwgKnSc+986yHk/ust3a8JlYSzw0OealyAKOk3a1xGNurspxkZTno/6yHGJjrICdqzYXMMyANm1c/qdkAy2z3Dy4tZjnk4OJ2nSYemXH1ScOW7nPYRBy6Ymn8TZxBLHu41zsJniC/Ukbr9PGv3dZ+fcBK0f6R5NfVja6fj1mphXjddpIvcWGL8jKAU8hkMXEkmBad2/B8dYPbxHP883icM1ez/rSfUAEu+7uxTibk+u9bkKanGhjIzGSP+Y0xGWabHm8K5QdD+wFh2lXmktksxO34edEO6EASuwGqff3w8AAE34oOsgCzzFOVhTh4NG+/nOyYY0asn+B/ztxU/t6fBVaSojXEbjT024a7Kjnb5tj8aHYCzxYS70ElR0Hi+wnjnRWl492Zcn1kD0FRJWdn/Q8bOOWFg6i810sbujf2Q2PjyeWFOIzoMPObUS5TLJj/Mebx1YU0SXUDmV9DUybhfdm5+MDWuScOABfk+qhx9dF0DSakl7HK81g+v5wLBYDW7tI0rv5265rkI1Fpg1XoyBWW9KAQnxHC/i/fr3w2a3Q02AD/p6xUa99B4Qw/qSenwB3tGhNjseFc1Qo27CCz6TUXcz17qPExpU/pgXFRRBnllB6WQv29Q/C8JnsMYoocGSRH+U/PhkRIUAuqxs62Or00icqhqR8O4bXR6rNxddJRcQVlL9FLTvCTnqYlyOJoRS5fBgeH9ZQfz17rOVPfFoUmHTDS1SJ/wKY1eUl/qiPsVtLCfcZgfqlyH/B6KHVxQzf6yLqqH9vDbLC5xn+8XT3O08ch8cdsnDDmlxa5J44poV64YMfivDarFiamZhln93rjEh6b8klyRJBTvdofHYrLoeFFzN9mDYLtqQTn/u+zRrjXbGRNzqFsX90J5xBoXisFh4pPYrb58Pu8/l/B/aLokGj+owtLqJ9Kyf7koIxvD4sHh+X+rIpKfUFEmgAzphwWpe4iYkIJbtnVNl5molhLcVigsV5ou2KnRZKbUbg2OMNtuF1wtZoKxmhFop9Zftf2Z0pJTaDwiArrqggzLJesDbDgtVn4naeaI/gYCdNiiDaYie/dQymxcA0DLp4fMQcNQlNPBFvbFQYtx7Oo5HXQXaiv22zO8dxExYuzzJp0jQyUNabmesf9y/0J+O91XLt2pX/DfzEE0/w5JNPnlKuuvIjtVnVD1RRzX56X75pmme8V7+i8hVNP27SpEk88MCJLrkHDx6kXbt29OzZk0aNGv3SsKuFaZr4Qr4HoGvXrnSLPbEjZxUVMjTrxGMpgh6+jrBWLWh5cBO2jatwXtaRAd37B5bzbuNEmoRF0DsuEbvFf6A9l2FgQrMyIXUTYZd1I3bEkFPmhxzLhgObT4nzl7q5gmlzvX2Yf+gA18z5nLab8+nqPEjSiC4808ZJRHoeXd7eCE7/rRhPXTqUrjFxRDicga7FAMM4NfY2FQUwABhzHabHi1lYCnnFmGW3Xpr5xTTMzYJjawiKicWIrs+mYUcwS9wYDhtGiAMw2LbBP3h+w6t606O3/9Yn0+PFl9wdIyzI/3KU/0gn/CSMCsI9RVBwFKyfjbE8leDrL8XSOBbT7cW9bDveRfsIHZgM5J+3toGKh1VyepywZxFTIjvQ+1cnxh/qBNz9zWqWffUlPyQFB+IYcOmlpywjkYo3uV/HblDswixxQ4n/36RiF0nFboywIGy9Toz59uP/1gMlhPZtg63A6n+fx4vhsBHSJJbOMVYoOkq3Ht2JKI6D4lJw2v23ljjtGE4bOO2EhAczoGtSYLm+SzqDxSgrZwNb+XGzyrXdgAEczTwE335EVP1Ykp68rVzZ1l4fBdOmYm15CeCjye+vo/XZtM1pPrRdK5h28jiWSzyfAGl0GNSbce1O7PGm10fhE//DzAnhHeC2395Ey5Pi+K7C5Q6gopEc3+fU43G/TsmMyS0ClwfcHnjhU8DLA4MH09USiq1va4wg/0mwJ6El9Z+YytR24PzT1YSERoPNwpiyMV2srRIwQsoe3tChG+axwhNjvVj9tx61s1r9t0SEOgO3AZn9+sEkAlddAYK+WghZqwh74ia6dTrpF/0AMO/y4bzzX4CX2OfGkXhSfZQbiW4AMO7UemheQd0ADK1g2i2H03nhmw+5bJebfuN7Y5QlRK4FfBnHyPvmdQr7dQOOMGHU9RV+fq+pYLl/PU0MVx7/z59PTPs7cNOhg/Sa+THtjznoW7bjpHmv8f/QvMvEtWAz3hc/BeDtMWPoVj/ef9sJ/h9K04YMPLFA0wQTrjFNrgYMuzXQbqbXR69evQEzUA7TpK0JbTEhyIH9sXeAYjr16Eb7d/v4y1kt4LBhFrsIuts/5uElj4wqXx8VDNyayIkH9ZVzmkFe+570/+CIGFg9gyvcEfSZcH25z2/pt2sI3rmetzqV/26uaPDY9kBFlxUrKtuSU7/7gmPiYNk3TA9ux6CnBhNaNmZSvNfLjrxcNjz1Ib/qWErygD50i42nf//+p5wPRQM7Koihf9cemCUu8PjA7cX0eAl1e3m57JZcW8cTY4M4cnyQtYoOPToQdGszcHsJ8vq4wWH1f4ZjIzFD/D2auo4bThdnpH9/dtoD/w6wVjA40RkG3D2dkth6ULSBlrEN6DG8X2C6WeKi4M8fYNT395a6+sorTvnMVDR01QDg/gqmn3oJCzqUlnBzQR7BNhvHmu+E/YtJatKEv7btwpGSYq5q1oYmYRG45m7g0CGTN7rAPYMHk3RPp8AyPj2Wjdc0SQqLCLRn3379GG/6sHlMjLs9mMX+25k+K3FhFruxJtXHiPQna7wN23D1yl3+78JL3BjrtwIurk9Moku+BcfAAYG288anY919jOcpxD4iGWdELEaok54hTghxYG2ZgCXO/0PS7OOF20accl5y3CmPqxpR/k9PViZ89T7Nt+XT65ZW2NqfGMfMvWYPBct+wHn1ZcBBOvfqftrzkYoGUOlTwTSfaZJR2o98t4tm4VGB6fdkpNHgy7n8255Np/BEevTujNvn5daFJQQbFpqtPUhJnH/dV185kIEeN11j4uifcOI3wbz0Swiy2mgxLi5wa+IjHjeTXB6chgVLWWLBdHlYE9PU314dXVDsv/XzmmIX1xS5WLl0La/jP050DY6iy3ubMSxW6OrACA2CVH/vwiu7dyG5ZXPsA07cWeOp1xQjyIER5oTQIIwQBxF2GxU+C+1nPkfGwTSY9QmXLDhEnyeHnGhzl4fiV2fhy/fvh/169SrXLhUttScwqoLp31d0TPO4uaKoAJ9p0joympAWzWDmJ/ymOALHgM5c3aQFrcpuN4w4lstzk98hP9jJX8NKA8fWVzq2p9DjJuk3EYFbeeubJqWmGRif7Lj+vfuAy4vp9pSdd3gJdXl40+3xX6C/xL8Xh8Q3gMXTuaZZK7oObwVuD6bbS5DdxrAgO2ZBMcumzgD8bdelUx/wesHpKDue2YiwWbmVCpymLS6pcCo0j0zgjYxltLRG0vNG/xlwX/xtU/jox9jD/WeXN105uMLPzLcVhjCA/6tgenrZvyf/1u7Wuxe3l5awYdp8fmQn4f+8jXpJTZmWeZBSr5fusfGE2+yYb6+AZjB98Eh6xzUg9tETdxtt8vlOaYsBAwZQ0fDoUyuqhAFwU9l/1+zdBz98RlzDBoy8p/y3p1lQ4o8jDpL79T5vv2mqUlqav/fZli1byj30r6JeXSer6vxIbVZnkl2xsbFYrdZTspSHDx8+JTt5XEJCQoXlbTbbaZ8u+dNugse7EFqtVqzWahh5sJIcbRoC+VitlnLxxoWFc2V8E4yVu5nV1I6jU1PssbE8mtiPp3teespBZ2ybjpWKI6psDJ6xC2f8bLmqqtcQq5XEMP/V2NAJQ+DdlUTO3chdZfNtPVsSfufVsPwbooODiQkJPf3CzpbV6r9iHl3+Cn797Cz4fA2/3bncP6FxBe+9wh9rTIekE3VitUKz83twjrmkCayHiU0LYe1MWHvSzKtDoexabFW2DYCjcxLsWYT77QUUrzuMY3AnMMD1/UbcczcQdFMn4MAp+/LZsMac/WDDwWMuhY1z2NMpntC+ncvNMz0+tjz7EXQAW7CTkNFn/0PLmlDv5wudxObwJy6279qP9Yl3cVyTjLVhNN79Ryj9ejXefQdIvWcgHFzzi+rkXITc3Be+/x8lT32Oa0hvbF2b4TuSR+nny/FtOkDY/w2DjGXnPQ5rdHi5z07Q1d0gexVWp52ggeXbxrPlIMaxYsCOs1tznLGnpH5PLDc2AmLPcp+oYHuCereGb1exYcpMLBMsWBv6ez6aRS5K3l/EzuI8ILTK28V2PGG07wiFY17B+aveWBpG4918gNLPV2ALcRA65lJY8HmVxmIL9v/Q8c7bhLdbBxwD2wfqzbv3MK6XZ+No2wgowBbswBZ00kma1QqRZ9lF3mqFuMgzFgm+sgtkLGPLwnXYBvcom+rD9LgofnsOu6NsZYuq2raxN6kPq2Hr0o2YOzKw922DYbfhWbMbz7p97B/cBsis+jgS/ftm1CcrcVjrYRnRAyPUieNADomvzuLg7izoGP7L4ogM9b/OQtCl7eCLVWw7lE7wrf38FwXKmB4fRf/8hj3NowCwxUfhOMPnt7KChnSBrzaw6cPv8S7Ygq19Y3wFJXiWbMMsdnHg7stg/6oqaZvYkFD/GJPAmm4e2L8Y1xvzuLl3ZxxDO8PWI5TMmYfr2zU0ufoSIIMe8Ynl4mgfc+rjCAPzHUAI/qehnYa1VSKOVieu1AcvjYdNc3D0bk34lX3LlbW0SsQo8d+6FXxzX0LO1C6VrCtrWTJzT7NILH9+E2uHxlga1MOXmoV32yGs3RuSel1HWHLwvLSNFYi3hfPTM6rLGjYlbOSV/Pu7jyh5djolXXdgS27O24dDKP16NWZeMWHPjoCt35MclxB4MMzJBjVKOmVaqNUa6JEYEGzF1qfCS6YAOIwiYC8WlxdbfAiR7524VOQ9lAO3vwCEE3xTH4J+Eoe16+kunZw7W9kFhx2eIhj/ArbO/kSaZ9MBzPwSUn/TE4p3nPfPTJjVShvniR44trIkYd95++meFULQ6AQMnxPP5gOETfmB23cfY+cLv+Kv62cFYmkXU/+U5Z42Qqv1lAc8VsRW9iT7bQcPETJ6QNlFaj/TNCl5bQ67G4aWLdKCPb7qnj7o7NMKpi9j8zdL8S7ejq1bM8ziUjzLdmAWlJJ256VwaG2VfddEWIOJCAome1AX+GEnpf+Zi/H4WC5t4E+rmi4Pxf/8FuNwPhBOk4hIEsLK/yY6n3HZysYS885IoeQYOG/piyUuEs/avZS8NQ9LaQkQVOXfvefL8RjDw8OJiPj589bqyo/UZnUm2eVwOEhOTmbu3Llcf/31gelz585lxIgRFb6nT58+fPPNN+WmzZkzh+7du5/3+1FrC8dVXSB1MaXfrMUcN+xEhrbEzaffF7PmgIdZTU9se4itauqhVWQ0O26ZcMancITbHVX6ONqTOQa2I+L6gXi3pGEWlmBpFIO1UQzWrIq7eJ5vrWNi2RTZgyNvz8N+dTec13bDEh6MWeSidM56XJ+twHFDT+rf0r/K6+R42+QVFeNZuxdvWjaGw44tOQlrA/+6q6Ntju+b+27rhTF3B+Y/tvinJ9bD+eAV7E1OhPkHqjQGgKi2TWAjjMtIgS9STi1QduE03O44dd55dHz5EweFAlmwcTYcf5ZCK6BVOBxcUy2xWCL8V0N3922KOXMJfLEAAGvbhjifu4FdDUOgGj469n6XwNerSHnrO9xr92Dv0RLc/gFo3Yu3sWdoa6ojkIhQ/xXJOzr5YEX57xTqA5f7T7aqul2OS/3zEGxzduL5YBZ4TQi2Yx/WFueIHmwP3DhZ9Xb3agSTP8IyLQ5riwR8R3LxbkjFSKxH6thesHpelcdQ79L28L9l/DZvC3yxpfzMhkBD/75cvZ/fIijwD5hOc6B5BJBZLXEct2dQS3h/Fnw0ByPUgXmsGCMimP13DoCMlCpf//HtnNCmFL778NQCrU4tW1UiypKt/rbJBU+u/wfuFTbABvtXVUscx7/znLf2xfPpBlzf+I/nRnwkwXdfRfBVreGr96o0BgBrqwTYBOs/ngceH7bk5hg2C94jeZR+uJhthbn4M2hVK7CPtC6F1hFArv/VHmgfAeTDklnlylaV409xdd7SB8+XW/xPvAt14riiI0FjLsUW7oOtZ17G+eAY2hmW7GXda1/i+81QrGW3R3v2Hqb4P3PZ0fg8XJQ9C4HjWS8b/if7lQ2jkGgH7FC8o1y5qhZ011C8b68gf/zrgWnW9o0J/88EbIkOWF+16z++nXd08sGsCp7+nkhZ3VTD8cxx8vGsBNgKwcCgsuPZobXVEoclyn+M2JqZgfc3/w9b12YYdguelP2YBSXsG9cT8qrhQ1Nm75geGN9uxvxjSmCatU0D9t80GLb+ePo3XuCUH6lDyS6ABx54gDFjxtC9e3f69OnDG2+8QWpqKnfe6e/4OGnSJA4ePMh77/lPFu68805eeeUVHnjgASZMmMCyZcuYMmUKH39cwYGqjrB3b+5Pdn2yhLwZ27D3vwSzsBT39xsw3V5Cnr4Gds6vlliqK5F1NrYe9Q+6TKID/6VQN2RlnJheDdrdfDmlxRaK/zsXPlmHJS4S35E88HhxjupP8O1Xlbt1qiq1iowue0rnKTcaVJvjX8S/Ldhads/B8SsYXshbC/PLl6sqraNi2H7NWA6//C2etfswwoMgxImZmYsRHkTQHYOJ7tWm2pKQ+W4XptfEu+MQZm4RRnSY/5a8sh9K1ZGIPF7n48PSYXgw/jMpgHzY8UPgHqcqP6kL9l9ynXhpMLAPtu7zz4ij7ImzGdUSx8kJYvfKXXjW7gWPF0tSfRwD22OpF1qt7XLb1iX+3qGNT75aug/m7zulbFXGcXvCsbJ2KPGvPwJoEQ54Aomuavn83nw7R5duwzVvI769mWC3YuvcDPtVnbE1ia2WtvnpBR7T4x+v5Pjj2KGaP7vhGXD9yftH2QlsWaKruj4zR7enUfrJErybTwwMbGmVQNDNfbC1a1QjbVOR6rz4tqdvUxxXdcWX5b9zwBIbgWE12FZN5yOBH8t9HXBwif91XBOgif9HbHXtI2dql+NxVFvb9GmKc3g3TJ+JYTl+O4+v2s4Voxr5eyVNaFYES74sP7MXgP94UhvapjrbZXe7WOxvjMG7Kx2zoARLXCTWxv4eKNXRNsfr49iedEo++hHvhtTAPKNxNEG/6oO9e/OL6ngWSIj2P941ruzC45CyhGhZoqu6LjT9rmh7WcLv5N5QBYFEV3UlZmvCxZ4fMUzzpNFb64DXXnuN5557jvT0dDp06MCLL77IpWVj+YwbN459+/axYMGCQPmFCxdy//33s3nzZho0aMDDDz8caPyzkZaWRuPGjTlw4ECtH7MLYG1WBslfvMvUpJ60+HEf3r2HwW7D1iUJx+CO7LC7+c38b1lzw20VdsWua3bm5tB62ps/W27HLROq7Uvbd7QA1+z1+DJzsUSH4biyc2A8hIvNztycGv/CPplnZzru+ZsxS9xYW8TjuKJjYFyki01taZvjcZhuL77MXLAaWOKiMKzVl/yrTX6uXaB66qS2xCGnqi2f3ZN5D+VgHs7zJ++bxP78G+qg2nQ+cnwf8ew9jGf1HkyXG2vDGOy9W2EE2S+6z25ta5u8giLcK3bg3ZkBFgu2Do39PWdslouqbc62XaB6z+O96UfxHTqKJTIES4v4C3Kco/OhtnzX1JY4zodfmneo7vxIZe3evZuXX36Z/fv34/WeeKjG119/fc7LqtJk14oVK9i9ezejRo0iJyeHoqKiCyIhdC4utGRXbfrCri3q0kFQRERELkw6H6m91Da1ky6syMXkQss7/FIdO3bknnvuoVOnTlhOuqupV69eZ3hXxarsNsYnn3yStWvXsm3bNkaNGkVxcTG33norP/5Yd++LvRDUlu6ttcnFtK0iIiJSO+l8pPZS29ROaheRuic0NJSJEyeel2VVWbLryy+/ZN26dXTr1g2Ahg0bkp+fX1Wrk3OgLwYRERERERERqU0mTZrEww8/zODBg3E6Tzy29vitl+eiypJdxwM7fp/ysWPHLtp7lkVERERERERE5PRmz57NggUL2LVrV+A2RsMwaley6/e//z233HILWVlZPPPMM0ybNo2HH364qlYnIiIiIiIiIiIXqIULF7J58+bz0lGqypJdo0ePplevXsybNw/TNPnkk09o3759Va1OREREREREREQuUD179mT37t20bNmy0suqkmSXz+ejR48epKSk0LZt26pYhYiIiIiIiIiI1BHr1q2jQ4cOtGnTBqfTiWmaGIbBypUrz3lZVZLsslgs9OzZk82bN6s3l4iIiIiIiIiInNFXX3113pZVZbcxrly5kq5du9K6dWtCQkIqlZETEREREREREZG6q2nTpudtWVWW7Do5I5eTk0N0dHRVrUpERERERERERC5AY8aM4f3336dHjx7lBqevdbcxAkRFRfHhhx8yZcoU1q9fj8fjqapViYiIiIiIiIjIBei5554D4LPPPgtMq2ynqfOe7Prhhx94++23+eKLLwgPD6d///6kpKSc79WIiIiIiIiIiMgFLjExETi/naYs5yOwtLQ0nnnmGVq0aMF1112HaZp89tlnHDp0iKeeeup8rEJEREREREREROqYH374gd/85jckJiby1FNPkZSUhGmalVpmpZNdw4cPp1WrVixbtoynn36azMxMPvzwQ4YPH47Vai13v6WIiIiIiIiIiFzcqrrTVKVvY5w1axajRo3ivvvuo3v37pUOSERERERERERE6qbhw4czf/58Bg0axNNPP83IkSMJDQ0NzD8fnaYq3bNryZIlBAcHM2jQINq0acPTTz/Nrl27Kh2YiIiIiIiIiIjULbNmzeLGG2/kqaeeYvTo0eUSXedLpZNdffr04c033yQjI4OHH36YOXPm0KZNG3r37s3LL79MZmbm+YhTREREREREREQucNXRaco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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data: DataSet\n", - "for data in project_ex1.get_data_sets():\n", - " test: TestResult\n", - " for test in project_ex1.get_tests(data): # Get the test results for a specific data set.\n", - " fig, axes = mpl.plot_fit(test, data, num_per_decade=10) # The number of points per decade used when plotting lines can be changed.\n", - " \n", - " # The raw data and plot-specific data can be obtained with TestResult objects as was shown earlier with the DataSet objects.\n", - " f: ndarray = test.get_frequency()\n", - " Z: ndarray = test.get_impedance()\n", - " \n", - " # The number of points can also be increased here to achieve smoother lines.\n", - " f = test.get_frequency(num_per_decade=100)\n", - " Z = test.get_impedance(num_per_decade=100)" - ] - }, - { - "cell_type": "markdown", - "id": "74f83a42-ea4f-4b5c-bd29-604ab146fb23", - "metadata": {}, - "source": [ - "##### DRT analysis results\n", - "\n", - "DRT analysis results have their own plotting function." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "105aba99-ec34-4bff-b4ed-4cceb86d06ca", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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UIRkZGaSnp5OWlqatpZSQkMDgwYOz9VUUhfT09Gw3jUZT5HH98ccfPPfcc/lKjBw8eJCMjAx8fX21bUFBQQDUr19fp6+xsTG1a9fWHs9NTEwM//77r86Yucla6pifvgWJa9iwYdja2vLOO+9w48YN4uLi2L59O//3f//HqFGjsLCweOr5smRkZJCWlsbNmzd55513UBRFZxnesz5fxSUgIEBbD+5p9u/fj6OjY7bk5YULF5g1axZff/01lpaWBTpX1nXXq1dPp6+rqysODg45Pi8ajYb09HTCwsKYPn06V65cYfz48dn6tW/fnlu3buntuRVCCCFE/hk9vYsQQgghSouWLVvq3Fer1SxbtoyuXbtm67t8+XKWL19e7DFFRkZy48YNRowY8dS+cXFxvPvuu3h6ejJ8+HBte1YtJDs7u2yPsbOze2px/FGjRpGQkMCUKVPy7BcaGspHH31E06ZN6dmz51PjLUhcVapU4dixY/Tr14/q1atr299///0CFzf39fXl8uXLQGai5q+//qJJkyaFiqskGRgYYGhomONMrsd99913HDhwgCVLlmBoaKht12g0DB8+nP79+z+1PlhO53r48CFqtTrHxKKdnV2ONbeef/55du7cCYC1tTUbN26kR48e2fo1btwYgL///jtbMk0IIYQQpYsku4QQQogy5Mcff6ROnToAPHjwgC1btjBq1CgyMjKy7b44YMAAPvzww2xj7Nu3j0mTJhVZTGFhYUDOywsfl5ycTP/+/bl16xb79u3LcdZObkmSvJIn06ZNY+3atSxdulQnIfSkqKgonn/+eRRFYePGjTqz0DIyMnSWpxkYGOgcz09cN2/epFevXjg7O7Np0yYcHR35559/mDVrFvHx8Xz//fdAZkLn8dl1KpVKJ+ED8Ouvv5KQkMDt27dZsWIF3bt35/fff8+2Y2Jhnq/i9Mknn/DJJ5/k2efPP/9k1KhRvPjii4wePVrn2MKFC7l69Sq///57oc+V17XndGzp0qU8evSI8PBw1qxZw8CBA1m9enW2TQWyfr5DQ0OfGpsQQggh9EuSXUIIIUQZUqdOHZo2baq9361bN27dusXEiRMZMmQItra22mOOjo46fbMU9ayfrF0NTU1Nc+2TkpJCv379OHLkCNu3b6dFixY6x7Nqaz18+DDb7ntRUVE5zmACmDFjBrNmzWL27NnZkn2Pi46Oxt/fn9DQUPbt20e1atV0jlevXp1bt25p70+fPp2AgIACxfXRRx8RGxvLmTNntDOLnnvuORwcHBg+fDivvvoqfn5+zJw5kxkzZmgf5+Xlle3fJGuJZfPmzenbty+NGjVizJgxnD179pmeL33buXMn/fv3x9/fn7Vr1+okn27fvs0nn3zCZ599homJCY8ePQLQLr199OgRarUaMzOzXMe3t7cnOTmZxMREzM3NdY5FRUXlmAytUaOG9v979+5N9+7dGTVqFAMHDtRJeGb9fOd3F08hhBBC6I/U7BJCCCHKuPr165OUlMSVK1f0cn4HBweAHAuNQ2aiq2/fvuzfv5+tW7fSqVOnbH2yloWdP39epz09PZ1Lly5Rt27dbI+ZMWMGAQEBBAQE8PHHH+caX3R0NJ07dyYkJITdu3dnq3MFsG3bNk6ePKm9ZS3JLEhcZ86cwcfHJ9sSumbNmgH/1ZMaMWKEzrm2bduWa+wARkZGNG7cWOfftzDPl77t3LmTvn374ufnx6+//pqtYP+NGzdISkpizJgxVKpUSXv7+++/uXjxIpUqVWLy5Ml5niO35yUiIoIHDx7k63lp3rw50dHR3L9/X6c96+c76+ddCCGEEKWXzOwSQgghyrgzZ84AmTO59MHLywszMzOuX7+e7VjWjK59+/axefPmHGuLAbRo0QJXV1dWrVrFwIEDte2bNm0iPj6e/v376/T/9NNPCQgIYOrUqUyfPj3X2LISXTdu3GD37t00atQox3651WAqSFxubm4EBQURHx+vs0Tz2LFjAHh4eGj7ubm55Rrzk5KTkzl+/Dje3t6Fiqs02LVrF3379qVt27Zs3boVtVqdrU/Dhg3Zv39/tvaxY8cSExPDypUrtc9hbrp164apqSmrVq3SmT24atUqVCoVffv2zfPxiqJw8OBBbG1ts+3keePGDQB8fHzyHEMIIYQQ+ifJLiGEEKIMCQoKIj09HchcwrZ582Z2795Nv379qFq1ql5iMjExoVWrVhw/fjzbsRdffJE///yTKVOmYG9vr9PH2tpamzgwNDRk/vz5DB06lJEjRzJo0CCuXr3KxIkT8ff3p1u3btrHffHFF3zyySd069aNHj16ZDtvVhH/pKQkunbtyunTp1m8eDHp6ek6fR0dHXUKyeekIHGNHTuWvn374u/vzwcffICDgwPHjx9n7ty5+Pj40L1796c+l61bt6Z3797UqVMHGxsbbt68yddff83169fZsmVLoeLKzalTp7TLJ2NjY1EUhU2bNgGZs9G8vLy0fbPasxI+p06d0ib0XnzxRW2/mTNnMnPmTPbu3Yufnx8AR44coW/fvri4uPDxxx9rk7NZfHx8sLa2xtbWNltNMgBbW1vS09OzHcvpXHZ2dkydOpVp06ZhZ2dHly5dOHnyJAEBAbz55ps6iao+ffrQoEEDGjZsiL29PWFhYaxatYqDBw/y1VdfYWSk+zb5+PHjGBoa8txzzz31uRVCCCGEnilCCCGEKPVWrlypADo3GxsbpWHDhsrChQuV5ORknf6AMmrUqBzH+uWXXxRA2b9/f47H/fz8FF9f3wLF9/333yuGhoZKWFhYtjhyu/n5+WUbZ926dUr9+vUVExMTxcXFRXn//feVuLi4bPHlNW6WkJCQPPsNGzYs39eXn7gURVH27dundOnSRXFxcVHMzMyUmjVrKuPHj1cePHiQr/OMHz9eadCggWJjY6MYGRkpLi4uSr9+/ZS///77meLKybBhw3J9blauXKnTNz/Pt6IoyvTp07P9bGW15XbL7ecwS24/jzmdK8uSJUuUmjVrKiYmJkrlypWV6dOnK6mpqTp95s2bpzRr1kypVKmSYmhoqNjb2ytdu3ZVtm/fnmMc7dq1U3r16pVnrEIIIYQoHVSK8tjWQ0IIIYQQhZCcnEzlypUZP358ke70KERpcP36dWrUqMHOnTvx9/fXdzhCCCGEeApJdgkhhBCiSHz99dcEBARw48aNbEXahSjLXn/9de7evcvu3bv1HYoQQggh8kFqdgkhhBCiSIwYMYJHjx5x48aNXAu+C1HWpKenU7169afuBCmEEEKI0kNmdgkhhBBCCCGEEEKIcsNA3wEIIYQQQgghhBBCCFFUJNklhBBCCCGEEEIIIcoNSXYJIYQQQgghhBBCiHJDkl1CCCGEEEIIIYQQotyQZJcQQgghhBBCCCGEKDeM9B1AaZOens7p06dxdnbGwEBygUIIIUReNBoN9+7do1GjRhgZydsKUXw0Gg1hYWFYWVmhUqn0HY4QQghRqimKQlxcHG5ubhUytyHvSp9w+vRpmjdvru8whBBCiDLlxIkTNGvWTN9hiHIsLCwMT09PfYchhBBClCl37tzBw8ND32GUOEl2PcHZ2RnIfNPu6uqq52iEEEKI0i08PJzmzZtr/34KUVysrKyAzDft1tbWeo5GCCGEKN1iY2Px9PTU/v2saCTZ9YSs6X2urq4VMvsphBBCFEZFnB4vSlbW0kVra2tJdgkhhBD5VFGX/ss7UyGEEEIIIYQQQghRbkiySwghhBBCCCGEEEKUG5LsEkIIIYQQQgghhBDlhtTsEkIIIYQQQohnlJGRQVpamr7DEEJUIMbGxhgaGuo7jFJJkl1CCCGEEEIIUUiKohAREcGjR4/0HYoQogKytbXFxcWlwhaiz40ku4QQQhRaxoPbKCmJuR5Xqc0xdKhcghEJIYQQJSsr0eXk5IS5ubl84BRClAhFUUhMTCQyMhIAV1dXPUdUukiyq5gF34tn0/lI4lLSqelgzssNXLA2laddCFH2ZTy4TeyC/k/tZz1hsyS8hBBClEsZGRnaRJe9vb2+wxFCVDBmZmYAREZG4uTkJEsaHyNZl2KSmJrBa78E88v5SCqZGeFoYcL1qCTG/3GVFf1q80ojyboKIcq2rBld5gM/xdCparbjGZEhJG6clufMLyGEEKIsy6rRZW5urudIhBAVVdbrT1pamiS7HiPJrmLy+qYL/HHpAate8mFQAxdMjAy4G5PM5L+uMfTnYBwtTOhSU779EUKUfYZOVTFyr63vMIQQQgi9kaWLQgh9kdefnBnoO4Dy6MK9eH4+d4+v+tRmWBM3TIwyn2YPG1NWv+RLGy9bPt0XoucohRBCCCFEWacoCt999x1nzpzRdyhCCCFEqSHJrmKw6XwktqZGDG7oku2YkhjHOy09OHLzERFxKXqITgghhBBClBdz587lrbfeQq1W6zsUIQrs4cOHODk5cfPmTX2HIsqhCRMm8P777+s7DB1VqlRh8eLF+g6jQpBkVzGIS83A0dJEO6MriyY1hUvDu1Jt1WSs0+KJS8nQU4RCCCGEEKKs02g0nDhxAoDY2Fg9RyPKktdeew2VSqW92dvb061bN86dO6fTT6VSsXXr1hwf37dvX22fvG6vvfZarnHMnTuXXr16UaVKFQDOnj3LoEGD8PT0xMzMjDp16rBkyZJsjzt//jx+fn6YmZnh7u7OzJkzURRFe3zz5s34+/vj6OiItbU1rVq1YufOnTpjfPvtt7Rr145KlSpRqVIlOnfurP19youiKAQEBODm5oaZmRnt27cnODhYp09ERARDhw7FxcUFCwsLGjduzKZNm/Ic9+HDh3Tr1g03NzfUajWenp6899572X63n3btOZk9ezatW7fG3NwcW1vbHPuMGTOGJk2aoFaradiw4VOfB8j892vWrBlWVlY4OTnRt29fLl++nGv/kSNHolKp8pXsedp1HjlyhDZt2mBvb4+ZmRm1a9dm0aJFOmNMnDiRlStXEhIiq6oqIkl2FYNaDuZcf5jInUfJOu1xgUdIvnkV4yO/8cuxcVid3qunCIUQQgghRFlnYGDAr7/+ytatW2nRogUA+/btIyAgQL+BiTKhW7duhIeHEx4ezt69ezEyMqJnz54FHidrjPDwcBYvXoy1tbVOW07JKoCkpCS+//573nzzTW1bYGAgjo6OrFmzhuDgYKZMmcLkyZNZtmyZtk9sbCz+/v64ublx8uRJli5dyoIFC1i4cKG2z6FDh/D392fHjh0EBgbSoUMHevXqxenTp7V9Dhw4wKBBg9i/fz/Hjh2jcuXKdOnShdDQ0Dyvd/78+SxcuJBly5Zx8uRJXFxc8Pf3Jy4uTttn6NChXL58md9//53z58/Tv39/Bg4cqHP+JxkYGNCnTx9+//13rly5wqpVq9izZw9vv/12ga49J6mpqbz00ku88847ufZRFIXhw4czcODAPMd63MGDBxk1ahTHjx9n9+7dpKen06VLFxISErL13bp1K//88w9ubm5PHTc/12lhYcF7773HoUOHuHjxIlOnTmXq1Kl888032j5OTk506dKFFStW5PuacpK1EYUoYxSh486dOwqg3Llzp9BjxCWnKdaf7FMGrz+vZGRodI5dPnJY+dWvnnKqSSXlVJNKyvWP31TSoh8+a9hCCFHi0u5eVKImNVHS7l4s1HFRPhTF301Ruk2fPl0BdG7Ozs7a4xqNRpk+fbri6uqqmJqaKn5+fkpQUJDOGMnJycp7772n2NvbK+bm5kqvXr0K/DMTExOjAEpMTEyRXFd5dPXqVQVQjI2Nlfv37+s7nAohKSlJuXDhgpKUlKTvUApk2LBhSp8+fXTaDh06pABKZGSktg1QtmzZkq/HK4qirFy5UrGxsclXDL/++qvi4ODw1H7vvvuu0qFDB+395cuXKzY2NkpycrK2be7cuYqbm5ui0WhyGkJRFEXx8fFRZsyYkevx9PR0xcrKSlm9enWufTQajeLi4qJ89tln2rbk5GTFxsZGWbFihbbNwsJC+fHHH3Uea2dnp3z33Xe5jp2TJUuWKB4eHtr7hb32LPn595k+fbrSoEGDAsWZJTIyUgGUgwcP6rTfvXtXcXd3V4KCghQvLy9l0aJFeY5T2Ovs16+fMmTIEJ22VatWKZ6engW6jqzn4Pvvv1eqVq2qqFQqRaPRKI8ePVLeeustxdHRUbGyslI6dOignDlzRvu4a9euKb1791acnJwUCwsLpWnTpsru3bt1xs7P9RdUbq9DFf3vpszsKgaWaiNW9KvDhrMRtF1xijWnw9l3LYqA3ddpvTeDqd2WYDV4NBgYEL3zV4IHtCJ6/3adMTQahT1XHzJy80UGrz9PwO7r3H5ippgQQpQGGZEhpIdeynbLiJQp4yJnhw4dolevXri5uWVbIpOWlsakSZOoV68eFhYWuLm58eqrrxIWFqYzRkpKCqNHj8bBwQELCwt69+7N3bt3S/hKKg5fX1+dmRrnz5/XHsvPLIexY8eyZcsWNmzYwJEjR4iPj6dnz55kZEhJh8K6fv16ttkG3t7e+Pv7M2LECFJTU/UUmVAUhYSEhBK/KU9ZypaX+Ph41q5di7e3N/b2JbNj/KFDh2jatOlT+8XExGBnZ6e9f+zYMfz8/HTq1HXt2pWwsLBca39pNBri4uJ0xnlSYmIiaWlpefYJCQkhIiKCLl26aNvUajV+fn4cPXpU29a2bVs2btxIVFQUGo2GDRs2kJKSQvv27Z96vVnCwsLYvHkzfn5+2rbCXHtRUqlUrFq1KtfjMTExADrPoUajYejQoXz44Yf4+vrm+LjXXntN57kpzHWePn2ao0eP6jxfAM2bN+fOnTvcunXrKVen69q1a/z888/8+uuv2g1AevToQUREhHbGYOPGjenUqRNRUVFA5u/R888/z549ezh9+jRdu3alV69e3L59u0DnFkXDSN8BlFeDGrrgaGHMp/tCGLoxcw23hYkhQxu5MMO/Ok6WHUjo2oebM0aRfOMy93/+Dtv2PVCpVEQlptHnx7McufmIGg7muFur2X7pAZ/uC+Hz52swrp2Xnq9OCCFApTYHIHHjtHz1EyJLQkICDRo04PXXX+eFF17QOZaYmMi///7LtGnTaNCgAdHR0YwdO5bevXtz6tQpbb+xY8eybds2NmzYgL29PePHj6dnz54EBgZiaGhY0pdU7hkZGeHiksPGO4rC4sWLmTJlCv379wdg9erVODs7s27dOkaOHElMTAzff/89P/30E507dwZgzZo1eHp6smfPHrp27Vqi11IeKIpC9+7defToEX/88QfNmjXTHtu1a5ceIxOQ+TpmaWlZ4ueNj4/HwsIi3/23b9+ujTMhIQFXV1e2b9+OgYHufIhBgwZle11NSUmhR48ezxTvzZs3n7qk7dixY/z888/88ccf2raIiAhtja8szs7O2mNVq1bNNs4XX3xBQkICAwYMyPVcH330Ee7u7trXqZxERETonO/x8z+eTNm4cSMDBw7E3t4eIyMjzM3N2bJlC9WrV8/9Yv9n0KBB/PbbbyQlJdGrVy++++47nfMX9NqLUq1atbCxscnxmKIojBs3jrZt21K3bl1t+7x58zAyMsqzULyrqysajUZ7vyDX6eHhwf3790lPTycgIEBnWSyAu7s7kPnz5uWV/8/Rqamp/PTTTzg6OgKZS8TPnz9PZGSkNgm3YMECtm7dyqZNmxgxYgQNGjSgQYMG2jFmzZrFli1b+P3333nvvffyfW5RNCTZVYw617Cncw177sWlEJeSgZu1GnOT//5QWPg2ps6aA0T88AX2vV5BpVIBMGjNGS5GJrLrjUZ09rZDpVIRl5LOzL0hjP/jKp42prxU3zm30wohRIkwdKiM9YTNKCmJufZRqc0xdKhcglGJsqB79+507949x2M2Njbs3r1bp23p0qU0b96c27dvU7lyZUme6MHVq1e1RZNbtGjBnDlzqFat2lNnOYwcOZLAwEDS0tJ0+ri5uVG3bl2OHj2a679XSkoKKSn/7VwtBdj/ExERQXh4OKmpqdSpU0ff4YgyqkOHDnz99dcAREVFsXz5crp3786JEyd0kgKLFi3KlgCaNGnSM8/MTEpKwtTUNNfjwcHB9OnTh08++QR/f3+dY1mfm7JkzWp7sh1g/fr1BAQE8Ntvv+Hk5JTjuebPn8/69es5cOCANqa1a9cycuRIbZ8///xTm/TL6fyPt02dOpXo6Gj27NmDg4MDW7du5aWXXuLw4cPUq1eP7t27c/jwYQC8vLx0CtwvWrSI6dOnc/nyZT7++GPGjRvH8uXLC3XtRe3SpUu5Hnvvvfc4d+4cR44c0bYFBgayZMkS/v333zzjmzt3bra2/F7n4cOHiY+P5/jx43z00Ud4e3szaNAg7XEzMzMgMwldEF5eXtpEV9a1xMfHZ5v5mJSUxPXr14HMpPGMGTPYvn07YWFhpKenk5SUJDO79ESSXSXA2UqNs1XOxwxM1Li9/bH2/sk7MTTc8QVTXA1o5fCl9pfZSm3E/O7eBEXEM/fATV6s51QiL2hCiPIv48HtQiesJJElssTFxekkI9Rqtc7yg2cRExODSqXS7iBV2OSJKJwWLVrw448/UrNmTe7du8esWbNo3bo1wcHB+ZrlEBERgYmJCZUqVcrWJ+vxOZk7dy4zZswo4qspH1xdXYmOjubGjRtYWlpy5coVFEWhVq1a2j43btwgOjqaJk2a6DHSisnc3Jz4+Hi9nLcgLCws8Pb21t5v0qQJNjY2fPvtt8yaNUvb7uLiotMPwMrKikePHj1TvA4ODkRHR+d47MKFC3Ts2JG33nqLqVOn6hxzcXHJ9toRGRkJZH8t2rhxI2+88Qa//PJLrjO2FixYwJw5c9izZw/169fXtvfu3Vu78QNkzhAKDw8HMl/XXF1ddc6fde7r16+zbNkygoKCtMv2GjRowOHDh/nqq69YsWIF3333HUlJSQAYGxtnuz4XFxdq166Nvb097dq1Y9q0abi6uhbo2kvS6NGj+f333zl06BAeHh7a9sOHDxMZGUnlyv+9X8zIyGD8+PEsXrw41yWJBbnOrFle9erV4969ewQEBOgku7KWGD6euMqPJ2dJajQaXF1dOXDgQLa+We9PPvzwQ3bu3MmCBQvw9vbGzMyMF198UZaV64kku0qZPUfP0y90H8Z307nwUisqf/Q5lTr3BTKz2G80c+OltecJjU3Bwyb3b0KEECI/Mh7cJnZB/6f2s56wWRJbIk8+Pj4696dPn14kO8IlJyfz0UcfMXjwYKytrYHCJ09E4Tw+C69evXq0atWK6tWrs3r1alq2bAk8fZZDTp7WZ/LkyYwbN057PzY2Fk9Pz8JcQrlkZGREzZo12bZtG/369SMjI4P69euzadMmjh8/zquvvkrbtm21s0dEyVGpVAVaTlhaqFQqDAwMtEmY4taoUSPWrFmTrT04OJiOHTsybNgwZs+ene14q1at+Pjjj0lNTcXExATIXL7r5uams/Rt/fr1DB8+nPXr1+e65PLzzz9n1qxZ7Ny5M1v9MCsrK6ysdGcsVK1aFRcXF3bv3k2jRo2AzOVuBw8eZN68ecB/M4ieXA5qaGioXaqXtbTuabJmM2XNcs3vtZcURVEYPXo0W7Zs4cCBA9mWUQ4dOjRbkrFr164MHTqU119/PddxC3udiqLozAgGCAoKwtjYONd6YfnVuHFjIiIiMDIyyjWGw4cP89prr9GvXz8gc2lxSdRSEzmTAvWlTJSVM9M6f46Ztw/pjx5y46PhXJ/0GumPHgJgZ5aZ+U9O0+Q1jBBC5EvWjC7zgZ9iNXpNtpv5wE91+gmRmwsXLhATE6O9TZ48+ZnHTEtL4+WXX0aj0egs4chNfhIs4tlZWFhQr149rl69qq3jldM38Fnfvru4uJCampptBsfjfXKiVquxtrbWuQld//zzDwMGDNAuJzt37hyvvvoqrVq1wsjICBMTE9kEQOQqJSWFiIgIIiIiuHjxIqNHjyY+Pp5evXqVyPm7du1KcHCwzmtDcHAwHTp0wN/fn3Hjxmnju3//vrbP4MGDUavVvPbaawQFBbFlyxbmzJnDuHHjtH8D1q9fz6uvvsoXX3xBy5YtteNkFVCHzKWLU6dO5YcffqBKlSraPnnNylOpVIwdO5Y5c+awZcsWgoKCeO211zA3N2fw4MEA1K5dG29vb0aOHMmJEye4fv06X3zxBbt376Zv3765jr1jxw5WrlxJUFAQN2/eZMeOHbzzzju0adNGm1zJz7Xn5Pbt25w5c4bbt2+TkZHBmTNnOHPmjM61Xrt2jTNnzhAREUFSUpK2z+OzkmrXrs2WLVu090eNGsWaNWtYt24dVlZW2ucwK2Fqb29P3bp1dW7Gxsa4uLjozESdPHkyr776aoH+jb/66iu2bdvG1atXuXr1KitXrmTBggUMGTJE59oPHz5Mu3bttMsZC6tz5860atWKvn37snPnTm7evMnRo0eZOnWqtqaot7c3mzdv5syZM5w9e5bBgwfr1CITJazkN4As3fS9hfpP/4YpTNqtXAqNUkJXzFFONXdUTjWppJzt5qPEnjykfPjHFaVSwH4lKTVdL/EJIcqXtLsXlahJTZS0uxcLdVyIZ/27SS7b2qempip9+/ZV6tevrzx48EDn2N69exVAiYqK0mmvX7++8sknnxQqDpF/ycnJiru7uzJjxgxFo9EoLi4uyrx587THU1JSFBsbG2XFihWKoijKo0ePFGNjY2Xjxo3aPmFhYYqBgYHy119/5fu8FX0L9SxxcXFKhw4dlI8//ljp1KmTAig9evRQrl27plhZWSmA8uWXXyqxsbH6DrVCSEpKUi5cuKAkJSXpO5QCGTZsmAJob1ZWVkqzZs2UTZs26fTL7TV62LBhSp8+fbK1r1y5UrGxscl3HC1bttS+ViiKokyfPl0nrqybl5eXzuPOnTuntGvXTlGr1YqLi4sSEBCgaDQa7XE/P78cxxk2bJi2j5eXV459pk+fnmfMGo1GmT59uuLi4qKo1WrlueeeU86fP6/T58qVK0r//v0VJycnxdzcXKlfv77y448/5jnuvn37lFatWik2NjaKqampUqNGDWXSpElKdHR0ga49J0/+e2fd9u/f/9TnLCQkRNsHUFauXKlzP6fb432e5OXlpSxatChbfH5+fgW6zi+//FLx9fVVzM3NFWtra6VRo0bK8uXLlYyMDJ1xatasqaxfvz5bDHn9O0+fPl1p0KBBtvbY2Fhl9OjRipubm2JsbKx4enoqr7zyinL79m1FURQlJCRE6dChg2JmZqZ4enoqy5YtU/z8/JQxY8bkef3PKrfXoYr+d1OSXU/Qd7IrKTVdcZx5QOn2/b9KclqGknDxjHK+f3PlVJNKyqn2VRWnj/9Qxm+/rJfYhBDljyS7xLMqjmRXVqLL19dXiYyMzPaYokqeiPwZP368cuDAAeXGjRvK8ePHlZ49eypWVlbKzZs3FUVRlM8++0yxsbFRNm/erJw/f14ZNGiQ4urqqpNsefvttxUPDw9lz549yr///qt07NhRadCggZKenv8v7yr6m/YsWcled3d3RaVSKYBy48YNRVEU5auvvlIAxcHBQUlJSdFzpBVDWU12lRZ//PGHUqdOnWwJCiGKwvbt25U6deooaWlp2rbExETF1NRU2bdvnx4jK1qS7MqZ1OwqZUyNDVkzsC59fjxL3UXHeKu5Ox4frkPz3UzWURMvVwemd66m7zCFEEKIQouPj+fatWva+yEhIZw5cwY7Ozvc3Nx48cUX+ffff9m+fTsZGRnaJXJ2dnaYmJhgY2PDG2+8wfjx47G3t8fOzo4JEyZQr169PLeMF4Vz9+5dBg0axIMHD3B0dKRly5YcP35cu1vbxIkTSUpK4t133yU6OpoWLVqwa9cunVo3ixYtwsjIiAEDBpCUlESnTp1YtWqVdmczkX8+Pj588803bNu2jdDQUNq3b6+tkzNixAg+/fRTIiIi+OOPP+jXr58s7xWl2vPPP8/Vq1cJDQ2VmnyiyCUkJLBy5UqMjP5Lexw8eJCOHTvSoUMHPUYmSoJKUf5X9U4AmW/oPD09uXPnjs5OEiXtdGgs8w/d4tegSNIyFDxs1LzVzJ1x7SpjqTYi5uge0h9FYf/8AL3FKIQo+9JDLxG3dAhWo9dg5F67wMcfN+rILqJTklnUqhPO5mWvMK8onML83Txw4ECObzKHDRtGQEBAtgK3Wfbv30/79u2BzML1H374IevWrdMmT5YvXy4flsqx2NhYbGxsiImJqfD1uxRFwdvbmxs3brB69WqdWjeTJk1i/vz59OjRA0NDQ44cOcKFCxf0ulNbeZacnExISAhVq1bF1FQ2jxJClLzcXocq+t9NmdlVSjVyt2b9oHpkaBRS0jWYGRtov5VLi7rPzenvkh79gNhj+6g8aT6GlhXvh1cIoR//RIYx69+jOJtZ8J3ff7u0Hb0XypmHkbR0duP9uk3zGEFUdO3btyev79ry8z2cqakpS5cuZenSpUUZmhBlwtWrV7lx4wZqtZoXXnhB59jrr7/O/Pnz+fPPP6lVqxZRUVH8/fff9O//9J13hRBCiPJCdmMs5QwNVJibGOpMPzeyroTjS2+AgQFRf/7MhVfakxAUqMcohRBlXUZkCOmhl7LdAk4fw9+tIwce/LcLUmpGBttvX2fn3RCdMYbW8OXL1p0l0SWEEMVEo9Hw119/8dtvvwHQvHlzLCx0Z9LWrl2bli1botFo8Pf358SJEyW2u54QQghRWsjMrjJIZWSE24hJWDf3I2TqCFJDb3Lpje64v/Mxzq++j8pAcphCiPxRqc0BSNw4DYD7BmocNSna40FOLQm09CQwIZGsSkgN7Z1Y2rozDeyddGrBjKvfvERjF0KIiubWrVt0794dg/+912vbtm2O/Xr37s3x48e5ceMGzZo1K8kQhRBCiFJBsiJlmGXDltRZd4hKnftARjqhy2Zy9b0X0KQk6zs0IUQZYehQGesJm7EavYadL86jcfV+bO47B6vRa7AavYbRnV9mffM2vFK/lfYxViZq3qvbhHaunrkWPU5KT+ODo3u5Ex9bUpcihBDlXnR0ND4+PhgbGwO5J7u6d89cYr5v3z6Sk+V9oRBCiIpHZnaVcUbWtlSd+wPWrdZy5/OPMHHxwECdWZQuJjmdhNQMHMyNMTGSvKYQImeGDpUB2HThIgkZGVwzUGuL0XdyL9yY7xzZxeorQZy4H86R3q/ITmBCCFEEGjduzP79+3F2dkalUtGqVasc+zVo0ABXV1fCw8P56quvSExMpG/fvtSrV6+EIxZCCCH0Q5Jd5YBKpcKhzxAsG7bE2NGFwyHRzN5/k2PBt0gxMMHMwpzXm7oxtWNV7MyN9R2uEKKU2tipD6uvBDG0pu8zj/VJ4zYcuxfGjCZtJdElhBBF6O+//wbA19eXSpUq5dhHpVLRvXt3fvjhB77++muuX7+OhYWFJLuEEEJUGKVquk9oaChDhgzB3t4ec3NzGjZsSGDgf4XXFUUhICAANzc3zMzMaN++PcHBwTpjpKSkMHr0aBwcHLCwsKB3797cvXu3pC9FL0y9vPntRiIdvv2XB/HJbIn8jgPXPmV8tTR+OBVGuxWniEpM03eYQohSytDAgOG162NsYPjMY1WztuXCS2/Q2aPKswcmhBBC68SJEwC0bt06z37dunUDMrekf+WVV6hTp06xxyaEEEKUFqUm2RUdHU2bNm0wNjbmzz//5MKFC3zxxRfY2tpq+8yfP5+FCxeybNkyTp48iYuLC/7+/sTFxWn7jB07li1btrBhwwaOHDlCfHw8PXv2JCMjQw9XVbISUjN4Y9MF+vk6crCfC/b3rmJ85zI9vnuDv+vfIzwuhU92X9d3mEKIUmRf6C3mnTmORlGKfGzDxzbLeJicxM24mCI/hxBCVBSKotC0aVNWrlwJQP369fPs/9xzzwEQFhbG0qVLtXW8hChNHj58iJOTEzdv3tR3KKIcmjBhAu+//36BHxcQEEDDhg2LPiBRokpNsmvevHl4enqycuVKmjdvTpUqVejUqRPVq1cHMv/AL168mClTptC/f3/q1q3L6tWrSUxMZN26dQDExMTw/fff88UXX9C5c2caNWrEmjVrOH/+PHv27NHn5ZWIn8/dIyYlnc+718DCsyo+6w5h2aQtmsR4kue8w9cPf2btqTskppb/xJ8Q4uliUlMYsn87H504yJLzp4rtPGcfRtJ48yr67tpMUrrMLhVCiMJ49OgRgYGB3Lt3D8hcxpgXZ2dnatasiaIo2qWPQmR57bXXUKlU2pu9vT3dunXj3LlzOv1UKhVbt27N8fF9+/bV9snr9tprr+Uax9y5c+nVqxdVqlQB4OzZswwaNAhPT0/MzMyoU6cOS5Ysyfa48+fP4+fnh5mZGe7u7sycORPlsS/uNm/ejL+/P46OjlhbW9OqVSt27typM8a3335Lu3btqFSpEpUqVaJz587amZN5yc9qo4iICIYOHYqLiwsWFhY0btyYTZs25Tnuw4cP6datG25ubqjVajw9PXnvvfeIjdXd7Odp156T2bNn07p1a8zNzXUmkzxuzJgxNGnSBLVane9Ez9y5c2nWrBlWVlY4OTnRt29fLl++nGv/kSNHolKpWLx48VPHftp1HjlyhDZt2mBvb4+ZmRm1a9dm0aJFOmNMnDiRlStXEhISkq/ryTJhwgT27t1boMeI0qfUJLt+//13mjZtyksvvYSTkxONGjXi22+/1R4PCQkhIiKCLl26aNvUajV+fn4cPXoUgMDAQNLS0nT6uLm5UbduXW2fJ6WkpBAbG6u9PT5LrKy5EJlAdTszqtiZAWDs4EzNrzbjPGwMAN5HN7Dg6CfcuXFTj1EKIUoLa2MTZjZtS2MHZ0bUaVBs53EwNSMpPZ34tFTCExOK7TxCCFGemZmZ8dtvv2nvPy3ZBdCuXTsADh8+zKNHj4iOji62+ETZ061bN8LDwwkPD2fv3r0YGRnRs2fPAo+TNUZ4eDiLFy/G2tpapy2nZBVAUlIS33//PW+++aa2LTAwEEdHR9asWUNwcDBTpkxh8uTJLFu2TNsnNjYWf39/3NzcOHnyJEuXLmXBggUsXLhQ2+fQoUP4+/uzY8cOAgMD6dChA7169eL06dPaPgcOHGDQoEHs37+fY8eOUblyZbp06UJoaGie15uf1UZDhw7l8uXL/P7775w/f57+/fszcOBAnfM/ycDAgD59+vD7779z5coVVq1axZ49e3j77bcLdO05SU1N5aWXXuKdd97JtY+iKAwfPpyBAwfmOdbjDh48yKhRozh+/Di7d+8mPT2dLl26kJCQ/f3e1q1b+eeff3Bzc3vquPm5TgsLC9577z0OHTrExYsXmTp1KlOnTuWbb77R9nFycqJLly6sWLEi39cEYGlpib29fYEeI0ohpZRQq9WKWq1WJk+erPz777/KihUrFFNTU2X16tWKoijK33//rQBKaGiozuPeeustpUuXLoqiKMratWsVExOTbGP7+/srI0aMyPG806dPV4Bstzt37hTxFRa/6buuKXYzDiip6RnZjkXv/0P5p21l5VSTSsrZoZ0VjUajhwiFEKVRekb214yiFng/XHmUklzs5xEl786dO2X276YoW2JiYhRAiYmJ0XcoenPy5EkFUBwdHfPVf+XKlQqguLi4KIAyd+7cYo6w4klKSlIuXLigJCUl6TuUAhk2bJjSp08fnbZDhw4pgBIZGaltA5QtW7bk6/GKkvkzZ2Njk68Yfv31V8XBweGp/d59912lQ4cO2vvLly9XbGxslOTk/95XzJ07V3Fzc8vzM46Pj48yY8aMXI+np6crVlZW2s+fOdFoNIqLi4vy2WefaduSk5MVGxsbZcWKFdo2CwsL5ccff9R5rJ2dnfLdd9/lOnZOlixZonh4eGjvF/bas+Tn32f69OlKgwYNChRnlsjISAVQDh48qNN+9+5dxd3dXQkKClK8vLyURYsW5TlOYa+zX79+ypAhQ3TaVq1apXh6ehboOp58DrJ+3mfPnq04OTkpNjY2SkBAgJKWlqZMmDBBqVSpkuLu7q58//33OuNMnDhRqVGjhmJmZqZUrVpVmTp1qpKamqrT59NPP1UcHR0VS0tL5Y033lAmTZpU4Oc/t9ehiv53s9TM7NJoNDRu3Jg5c+bQqFEjRo4cyVtvvcXXX3+t0+/JXb0URXnqTl959Zk8eTIxMTHa24ULF57tQvSof10nohLT+OV8ZLZjNn7dmdd7KSGOdfD++AvZHU2ICuxWXAypj9UxfLy2VnFp7OCCjYlae18phhphQghR3mUtlcrPrC74b2ZXZGTme8MbN24UT2AiRwkJCSQkJOj8zUtNTSUhIYGUlJQc+2o0Gm1bWloaCQkJJCcnP7Xvs4qPj2ft2rV4e3uX2IyWQ4cO0bRp06f2i4mJwc7OTnv/2LFj+Pn5oVb/976ia9euhIWF5Vr7S6PREBcXpzPOkxITE0lLS8uzT35WGwG0bduWjRs3EhUVhUajYcOGDaSkpNC+ffunXm+WsLAwNm/ejJ+fn7atMNdelFQqFatWrcr1eExMZn3Wx59DjUbD0KFD+fDDD3N97Xrttdd0npvCXOfp06c5evSozvMF0Lx5c+7cucOtW7eecnV527dvH2FhYRw6dIiFCxcSEBBAz549qVSpEv/88w9vv/02b7/9Nnfu3NE+xsrKilWrVnHhwgWWLFnCt99+q7PUcu3atcyePZt58+YRGBhI5cqVs+U/ROGVmmSXq6srPj4+Om116tTh9u3bALi4uACZ658fFxkZibOzs7ZPampqtinaj/d5klqtxtraWnuzsrIqkuvRh/quVvTxceTtLRf5+dw9MjSZf1gj41MZsfkimx+aY/rFFsxr/1fQNObv3aTHPtJTxEKIkhaflkqXHT/T+rc13NJTwfjtt67R9ve1xKel6uX8QghRFh09epQdO3YA+U92VatWDRcXFzQaDdu3b9dZ3iOKn6WlJZaWljx48EDb9vnnn2Npacl7772n09fJyQlLS0vtZx+Ar776CktLS9544w2dvlWqVMHS0pKLFy8+U3zbt2/XxmhlZcXvv//Oxo0bMXjiS7BBgwZp+2Xd1q5d+0znBrh58+ZTl7QdO3aMn3/+mZEjR2rbIiIisn22y7r/5GfFLF988QUJCQkMGDAg13N99NFHuLu707lz51z7ZI2f0/kfP/fGjRtJT0/H3t4etVrNyJEj2bJli7YedV4GDRqEubk57u7uWFtb89133+mcv6DXXpRq1aqFjY1NjscURWHcuHG0bduWunXratvnzZuHkZFRnoXiXV1dqVy5svZ+Qa7Tw8MDtVpN06ZNGTVqlM6yWAB3d3eAZ04G2tnZ8eWXX1KrVi2GDx9OrVq1SExM5OOPP6ZGjRpMnjwZExMTnRqJU6dOpXXr1lSpUoVevXoxfvx4fv75Z+3xpUuX8sYbb/D6669Ts2ZNPvnkE+rVq/dMcYr/lJpkV5s2bbIVs7ty5QpeXl4AVK1aFRcXF3bv3q09npqaysGDB7VbLzdp0gRjY2OdPuHh4QQFBT11e+by4qeBvvhVrcTAdefxmHuYhkuO4zn3MGtOR/Bt/zr09HHS9k0I/pfrE4ZycWgHEi+dy2NUIUR5cenRQx4kJ3IvKQErY5MSP39SehrvHNnF0XuhLDj79CKwQgghMs2ZM0f7ISm/yS6VSkXLli0BuHTpUrHFJsqmDh06cObMGc6cOcM///xDly5d6N69e7YZMIsWLdL2y7r17t37mc+flJSEqalprseDg4Pp06cPn3zyCf7+/jrHclrtk1M7wPr16wkICGDjxo04OTllOw6ZdbjWr1/P5s2btTGtXbtWJ8F3+PDhPM//eNvUqVOJjo5mz549nDp1inHjxvHSSy9x/vx5ALp3764d98nf50WLFvHvv/+ydetWrl+/zrhx4wp97UXt0qVL9OvXL8dj7733HufOnWP9+vXatsDAQJYsWcKqVavyjG/u3Ln8+OOPOm35vc7Dhw9z6tQpVqxYweLFi3XOD5n1DiFz5t6z8PX11UkEOzs76ySmDA0Nsbe3186kBdi0aRNt27bFxcUFS0tLpk2bppPQvnz5Ms2bN9c5z5P3ReEZ6TuALB988AGtW7dmzpw5DBgwgBMnTvDNN99ov4FSqVSMHTuWOXPmUKNGDWrUqMGcOXMwNzdn8ODBANjY2PDGG28wfvx47O3tsbOzY8KECdSrVy/PDH15YqU2YttrDQm8G8sv5+8Rm5LBa03cGNrIBXsL3Q+2KiNjjJ1cSQ29xaXhXak8cT4OfYfqKXIhRElo6ujK2RdeJyIpATtTsxI/v5mRMRs69WbTjctMquxOemjuH75UanMMHSrnelwIISqSypUrY2xsTFpaWr6TXZD5wWnr1q2cPHmyGKMTOYmPjwfA3Nxc2/bhhx8yduxYjIx0P4ZlfUDO+mAOMGrUKN566y0MDQ11+mbNUHm8b2FYWFjg7e2tvd+kSRNsbGz49ttvmTVrlrbdxcVFpx9kLs969OjRM53fwcEh100TLly4QMeOHXnrrbeYOnWqzjEXF5ccV/tA9hlXGzdu5I033uCXX37J9fPgggULmDNnDnv27KF+/f9WwPTu3ZsWLVpo77u7uxMeHg5kzi5ydXXVOX/Wua9fv86yZcsICgrS/q42aNCAw4cP89VXX7FixQq+++47kpKSADA2Ns52fS4uLtSuXRt7e3vatWvHtGnTcHV1LdC1l6TRo0fz+++/c+jQITw8PLTthw8fJjIyUmfWVkZGBuPHj2fx4sW5zrYqyHVWrVoVgHr16nHv3j0CAgIYNGiQ9nhUVBQAjo6Ohb9Asv87qVSqHNuylhcfP36cl19+mRkzZtC1a1dsbGzYsGEDX3zxRbbHPE5KfRSdUpPsatasGVu2bGHy5MnMnDmTqlWrsnjxYl555RVtn4kTJ5KUlMS7775LdHQ0LVq0YNeuXTpLDxctWoSRkREDBgwgKSmJTp06sWrVqmx/JMq7Jh7WNPGwzrOPea161PlpPzenv0vM4b+4NWsM8Wf/ofKkzzHQw4dgIUTJ8LC0xsMy79eH4tTGxYOWRhpiF/Qn+Sl9rSdsloSXEEKQ+R43a0ex2rVr5/txzZo1A2D//v289tpr9O3bl759+xZHiOIJFhYW2dpMTEwwMck+szqnvsbGxtk+TOfWtyioVCoMDAy0SZji1qhRI9asWZOtPTg4mI4dOzJs2DBmz56d7XirVq34+OOPSU1N1T6Xu3btws3NjSpVqmj7rV+/nuHDh7N+/Xp69OiRYwyff/45s2bNYufOndnqh1lZWWUrcfP4aqNGjRoB/602mjdvHvDfDKInl4MaGhpqEyFZS+ueJivxkVXjLb/XXlIURWH06NFs2bKFAwcOaBNPWYYOHZotydi1a1eGDh3K66+/nuu4hb1ORVGy1cMLCgrC2Ni4QF8SFIW///4bLy8vpkyZom17ctZkrVq1OHHiBEOH/jfh5NSpUyUWY3lXapJdAD179sxzu1uVSkVAQAABAQG59jE1NWXp0qUsXbq0GCIsf4ysban+xRoiVi8h7OvZPNy2jrunA1nhN4VbZi7UdDBnZAt3mnvmvDZbCFH6pWZkMOzAH0yo35wmji76DgcAJSXzjaDZgJmsTzOip7MLlR5bVpkRGULixmnafkIIUdHdunULRVGwsLAo0AyFrA/wkZGRrF69GktLS0l2CSAzgZI1eyY6Opply5YRHx9Pr169SuT8Xbt2ZfLkyURHR1OpUiUgM9HVoUMHunTpwrhx47TxGRoaan/uBw8ezIwZM3jttdf4+OOPuXr1KnPmzOGTTz7RzpJZv349r776KkuWLKFly5bacczMzLQ1p+bPn8+0adNYt24dVapU0fbJWl6Yk/ysNqpduzbe3t6MHDmSBQsWYG9vz9atW9m9ezfbt2/P9fnYsWMH9+7do1mzZlhaWnLhwgUmTpxImzZttAme/Fx7Tm7fvk1UVBS3b98mIyODM2fOAODt7a291mvXrhEfH09ERARJSUnaPj4+PtqEU+3atZk7d652KeOoUaNYt24dv/32G1ZWVtrn0MbGBjMzM+zt7bNteGBsbIyLiwu1atXStk2ePJnQ0FDtUsb8XOdXX31F5cqVtcn/I0eOsGDBAkaPHq1zvsOHD9OuXbtnnglZUN7e3ty+fZsNGzbQrFkz/vjjD7Zs2aLTZ/To0bz11ls0bdqU1q1bs3HjRs6dO0e1atW0fbImBMlS9ELQxxaQpVlF3kL92r7dyt7WVZRTTSopM979UBm64bxS5bPDCpN2K2N/v5Sv7WyFEKXPzMAjCv/3meLy41IlKS1N3+EoiqIoaXcvKlGTmigT9/yq8H+fKb3/2qTzGpN1PO3uRT1GKfKjIv/dFCWrom+h/ueffyqAUq9evQI/tlatWgqgvPLKK8rBgweLIbqKKykpSblw4YKSlJSk71AKZNiwYQqgvVlZWSnNmjVTNm3apNMPULZs2ZLj4/v06ZOtfeXKlYqNjU2+42jZsqWyYsUK7f3p06frxJV18/Ly0nncuXPnlHbt2ilqtVpxcXFRAgICdN5H+Pn55TjOsGHDtH28vLxy7DN9+vQ8Y9ZoNMr06dMVFxcXRa1WK88995xy/vx5nT5XrlxR+vfvrzg5OSnm5uZK/fr1lR9//DHPcfft26e0atVKsbGxUUxNTZUaNWookyZNUqKjowt07Tl58t8767Z///6nPmchISHaPoCycuVKnfs53R7v8yQvLy9l0aJF2eLz8/Mr0HV++eWXiq+vr2Jubq5YW1srjRo1UpYvX65kZGTojFOzZk1l/fr12WLI6995+vTpSoMGDXTie/Ln3c/PTxkzZkye1/bhhx8q9vb2iqWlpTJw4EBl0aJF2X4/Zs6cqTg4OCiWlpbK8OHDlffff19p2bKl9vjKlSuVp6Vtcnsdquh/N1WKIotCH3f37l08PT25c+eOznrj8k5RFJotO4HmQQQrLQKpPyEAlYEBGRqFr47dYcy2K/xfv9qMaFFxnhMhyouo5CTeOvQXQ2v60rdKTX2HA0B66CXilg7h6qvL6Hj8KJ80bsOHDZprv63LOm41eg1G7vlfriNKXkX9uylKXmxsLDY2NsTExGBtrb+l2Prw22+/8e677xIWFkafPn3YunVrgR7/6quv8tNPPxEQEMD06dOLJ8gKKjk5mZCQEKpWrZpnsXWRsx07djBhwgSCgoKyLfsT4ln98ccffPjhh5w7d05bJy8pKQk7Ozt27NhBhw4d9Bxhdv7+/ri4uPDTTz/l+zG5vQ5V5L+bUMqWMQr9OXgjmsDQOPa82Y4G3n207arUJPqd+IZTNZ/ni8O3ebOZOwYGxb/ThxCi6NiZmrHJv2+J7NJTUI1tbAkZ9DaOZuZP7yyEEBXU1atXCQsLA8hWEyc/mjVrxk8//SRF6kWp8/zzz3P16lVCQ0Px9PTUdziinElISGDlypU6G0IcPHiQjh07lopEV2JiIitWrKBr164YGhqyfv169uzZw+7du/UdWrkg6XMBwK6rUbhamdCxeiWd9tvzJxG5djmjfn2P1FtXuBPztHLSQojSQKMonLofrr1fGhNdWSTRJYQQeRs0aBCtW7cG0Knlkl9ZhbTPnDnDv//+y+3bt4s0PiGexZgxYyTRJYrFgAEDdHbUBOjWrRt//PGHniLSpVKp2LFjB+3ataNJkyZs27aNX3/9NdedQ0XBSLJLAJChKKiNDLJ9IHYa8CbGzu4Y3bvJqhNTSDzyp54iFELkJePBbdJDL2lvnx/eTvMtPxJw4DfSQy+R8aB0f7BRFIWjEXd5+/BOEtJS9R2OEEKUKu7u7tod8gqT7Kpfvz4AoaGhNGnSRFsEWgghhP6YmZmxZ88eoqKiSEhI4N9//6V///76DqvckGWMAoCWnjbMP3iLM2FxNHT7b4td89oNqLNmP3+9ORC3W6dJnPEmYWGXcX1rEqrH1tVnPLid565pKrU5hg6Vi/UahKioMh7cJnaB7h/Ga/aNUGy8sf17DXF/hQBgPWFzqfs9zIjMjE1RFF49sJfriQk0M1YxRJ2h58iEEKL0UBSF69evA4VbxmhtbU316tW5fv06VlZWZGTIa6wQQojyTZJdAoBedRzwsjVl5JaL7BzeCFszY+2x/fdVDKw5kR89fqfq3+sJ//ZzEi+dp+qnKzC0tM7xg3ZOSuMHbSHKg6xEs/nATzF0yvwQ9DUwNCqKVpV6o7l/k8SN0/JMSJc0lTpz6WLixmnatuHWNQhS2+K9YzeJqY90+gkhREW2cuVKYmNjAahSpUqhxmjYsCHXr19n+vTpjB8/vgijE5CZkBRCCH2Q15+cSbJLAGBkaMCmIfXp+sNpqs3/m1cauuBuY8rBG9H8deUhz9dyovfQpcT91Y5bcz4gIegUGfGxGFpa5/hB+3EZkSGl7oO2EOWRoVNVnZ0Ln3PP/G96KazXZehQGesJm3VeFyY+0UdmhAohBGg0GkaMGAGAk5MT5uaF+xKgQYMG/Prrr5w9e7Yow6vwjI0zvyBOTEzEzMxMz9EIISqixMTM99NZr0cikyS7hFZTD2tOv9+CZUfv8Mv5e8SmZFDTwZyVL/owpJELRoYG2Pd8GdNqtdCkJGPiorvFvKFTVQxca/FvWBxxKel425vjaStbMAtRUtI1GpacO8GbtRtgY6LWdzhPJYksIYR4usTEROrWrcvZs2cLVa8rS8OGDYHMIvWi6BgaGmJra0tkZCQA5ubmpXpTGCFE+aEoComJiURGRmJra4uhoaG+QypVJNkldFS2NWX+8zWY/3yNXPtY+DTSuR936jAA24PvMW7tQ0KiMguoqlTQvaY9y5qDbbFFLITIsib0DhPOneH/Lp7h0oC3MCijb7bDE+NZf+0Co+s2wdhA/mgLISo2S0tLhgwZwtmzZwtVrytLVrIrODiY1q1bM2rUKF555ZUiirJic3FxAdAmvIQQoiTZ2tpqX4fEfyTZJZ5J2sNIwv5vLm717YheMZPWPWex8sUmuFurORgSzdwDNxm+6Rab9R2oEBWAm6kZPpXseb1mvTKb6NIoCs22rCY0IZ7q1pXoUyX3xLsQQlQUd+/eBcDT07PQY3h4eGBnZ0dUVBTHjh2jadOmkuwqIiqVCldXV5ycnEhLS9N3OEKICsTY2FhmdOVCkl3imRjbO2E18B24uJGm0cE0//MDqndag6mDN94O5vSu48gri2/pO0whKoQujk50rd+WjDJcpNJApWJQdR+O3gvFwkjqDgghBPyX7PLw8HhKz9ypVCoaNmzIvn37GDFiBKNHjy6q8MT/GBoayodOIYQoJQz0HYAo+w47NAXA0NaB5JArXBrWmZgjuwBwtDRhUANXABJS0nMdI0Oj4fi9UH66EkSGRlP8QQtRDmVEhqCEX8Eg4irpoZe0t4zIEH2HViCfNffj7z5D6OxRRd+hCCGE3s2bN48dO3YAz5bsgv+WMpqamlKjhsycFUIIUX7JzC7xzMLjUwGoNuv/uLVoNvFnjnPtg0G4vf0xLsPH4etsAcDDxDRsgFP3w9l+6zq1bO0Y5O0DgAI8t20daRoNz7l64mVlA8DRiLscjrhLN89qNLB30sflCVHqrb13D8XCnV4bp+X5DYZKXbgdvEqaoYF8DyOEEFkuX75MUlJmPdRnTXY1aNAAkCL1Qgghyj9JdolnZmuaudQoNfER1abN495Py4je+xvpkdcZ/ucGjodHsMnQFGfTzB+34/fCmPHv3/TxqqFNdhkZGND2f7s7JmX8NwPs5xuXWBIUyM24GL5u11Xb/k9kGI3snTGRqeKigotLTWH8+bM8dG7NhsbNeMHVLcd+KrV5mdv9MDUjg92hN3nes5rsbCWEqLAmT57MqlWrUBSlyGZ2BQYG8vPPP9O8eXOqVKny7EEKIYQQpYwku8Qz6+LrgXIM/t7+JfcNTemVGIpFCw+IOcGhEDtuGFtx3diK2raZs7VaOLnxRq36tHFx1xlnX89B2cZu6eTGrSqxdPf8b6vtm3ExtNz6E9bGJkS+Ohq1YeaPsaIo8oFYVDgqlYpRvo3YcecGLzRuj1E5mRWVodFQ5+fvuBH3iIO9BvOca+GLMgshRFlmZmaGoigYGRnh5PRss9xr166NiYkJCQkJDBw4kBUrVjBy5MgiilQIIYQoPSTZJZ5ZZe+avOw7hY1JV7AxMMb/uU44WqgJvhdHleMXMbiXgFWMLQkhN7F2qEwzJ1eaObnma+yXvX14+X+zv7KExD3CwdSM6ta22kQXwCv7tvEwJYl5zdvT0MG5SK9RiNLK0tiEGU3bMb1J2zK7A2NODA0MaO/mSeLtNO4lJeg7HCGE0Jus4vRubm7PXPzcxMQEX19fTp8+TY0aNbCysiqKEIUQQohSp3xMARAl7kL0A05Ghmvv/zCwJzaG5sTF2OC2OhzrFWE0/DmG67HVWGkZh/XhnVx97wXurV+B8ow7xXVw8+Le0NFs7/aitk2jKOy6e5Ndd29yNyHumcYXoiwqT4muLAtaduTOK+/yUrXa+g5FCCH0Ii0tjbVr1wLg7u7+lN75k7WUcdCgQQwePLhIxhRCCCFKG5nZJQps1eXzvH5wB22c3TnSZwgA5ibGPHh9FNGJ6Wy/9IDY5HRqOprTpYY9qtQm3IoOIWrHRu5+8TFJl89TefIXGKhNCx2DgUqFg+l/xbZVwKHegzkUfoeeXt7a9nSNptws6xLicfcSExh/fB9TGrWiTiUHfYdTLCo9w2uEEEKUB/fu3WPZsmXAsxenz1K/fn0Azp8/XyTjCSGEEKWRJLvEU4UmxJGu0Wh3SOzqWRVTQyOczCxITk/H1Cjzx8jIwABHSxNeb/pEgWxTM6rMWI55rXrcXfIJD7evJynkMtU//xETJzcyHtxGSUnM9fz5KaytUqnwqeSAz2Mf+uPTUmm59SferF2f9+s2LZczX0TF9dmZ46y9doEbcY842meovsMpdldjoqhhY6fvMIQQokSlp6fj5eXFrVu38PQsmtqFdevWBSA4OBiQmqdCCCHKJ0l2iTwtDQrkg2N7GVrDl5XtewDgam5J2JBRBZp1oVKpcH7lXcy8fbgxeTiJwf9ycWgnqk//grTfP3nq460nbC7wTnI/XD5HcPQDFp0/xRu16mNloi7Q44UozYbXrs/N+Bje9Wmk71CKVYZGg9+2dfx9L5RzLw6nnp2jvkMSQogSU6VKFVq2bMmtW7eKbGaXr68vAFeuXMHV1ZVNmzbRpk2bIhlbCCGEKC0k2SWyefwbvqaOLmQoCuGJCTrthV1eZN2iPXV+3Mu18UNIDb+DyjBzvD313mfxZQPiUzO0fWs5mDOjoQEue+bmOfMrN6N9m6A2MKKmbSVJdIlyp56dI1u69Nd3GMXO0MAAZzMLjFQGnIwMl2SXEKLCySpQX1TJLhcXF+zs7IiKiiIiIoILFy5IsksIIUS5I8kuoXUj9hEfnThAOxdPRtdtAkBLJzcuDXiTWrb2RXYetUdVaq/cSdK1C6jtrUkFZp1X6Ni6KaNaV8bdRs3BG9FM232D4Qcus6OQ51GpVIz0aajTtvPODZYG/8s37briZiE7EAlRFsxv2Z6v23XBycxC36EIIUSJy0p2FVWBepVKRd26dTl06BDTp09n0KBBRTKuEEIIUZpI5W6htfvuTX65cZmZ//5Ncno6kPmGqCgTXVkMzS2xrN+cyPgUACZ6xPHOzo/xMkzEzNiQbrUcODCiCY6WJkV2zgyNhlF/7+aP29dZfP5UkY0rREl6/+89zPr3KHGpKfoOpcRUt64kiS4hRIU0ZcoUbt++DYCbm9tTeudf1lLG5ORkLC0ti2xcIYQQorSQZFcFlq7REJ4Yr73/Ru36jKzTkL09XtYWnS9uO69EAdDo0PfE/XOAS692IvFy5u5AFiaGDGrgDEBscvozn8vQwIDfuvRnQLXaBDRt+8zjCVHSLj96yLLgQKadOszlmCh9h6MXielp+g5BiApv7ty5qFQqxo4dq21TFIWAgADc3NwwMzOjffv22gLoWVJSUhg9ejQODg5YWFjQu3dv7awlkbPg4GAURQEylx8WlSeL1AshhBDljSS7KqizDyNp+OtK+u3arH0TZWRgwIp2Xalv71RicTxMTAXAa/wc1J7VSA2/w6Xh3Yj661cAqtmZAfAoqWg+4PraObKxcx/MjYy1beOP7WP7rWtFMr4QxamGjR1rO/big3pNaeroqu9wSlS6RsOQfdtw/HEpt+Nj9R2OEBXWyZMn+eabb6hfv75O+/z581m4cCHLli3j5MmTuLi44O/vT1xcnLbP2LFj2bJlCxs2bODIkSPEx8fTs2dPMjIynjyN+J933nkHACsrK0xNC1cvNSdZM7tOnDjBZ599RmysvK4KIYQoXyTZVUE5mZlzMy6GKzHRXIuN1lscjhaZyxRTHT2pvXoP1q06oqQkETL1Le4snMK1yMw3yZXMjPMaptB23L7OwvMn6bNrMzdiHxXLOYQoKgYqFYO8fVjYqpO+QylxRgYGhCbEk5iexu83r+o7HCEqpPj4eF555RW+/fZbKlWqpG1XFIXFixczZcoU+vfvT926dVm9ejWJiYmsW7cOgJiYGL7//nu++OILOnfuTKNGjVizZg3nz59nz549+rqkUs/YOPP9T1HV68qSleyKjIxk8uTJBAUFFen4QgghhL5JsquCeJCcyC83Lmnvu5pbsqVLP64NHEENGzu9xdWtZmY9sDWnwzGytsV78UZcXv8AgMh1X2O5KgAAK9OiX1aZ8eA2z5HMuGreTKpeg8pxEaSHXtLeMh7cLvJzClFYWTMwK7K5zZ/jRN9XGeXbWN+hCFEhjRo1ih49etC5c2ed9pCQECIiIujSpYu2Ta1W4+fnx9GjRwEIDAwkLS1Np4+bmxt169bV9nlSSkoKsbGxOreKJiIiAijaJYwADg4O2jG7du2Kubl5kY4vhBBC6JvsxlgBhCXE4fPL98SnpVLPzpHa/ys47+9RVc+Rgb2FCXHAoZNniE5KZ2B9Z5y69SPB0AKLTUvIMDMFkov8vBkPbhO7oD8AUwEFiNuVeSwFAwxQMEbBesJmDB0qF/n5hSiIY/dCeefILmY3e44elavrOxy9aelctDMbhP4cOnSIzz//nMDAQMLDw9myZQt9+/bVHlcUhRkzZvDNN98QHR1NixYt+Oqrr7SzUSAzETJhwgTWr19PUlISnTp1Yvny5Xh4eOjhisq/DRs28O+//3Ly5Mlsx7ISMs7Ozjrtzs7O3Lp1S9vHxMREZ0ZYVp+sxz9p7ty5zJgxoyjCL5NSUlL4/fffgaJPdkHm7K6IiAgGDBhAw4YNi3x8IYQQQp8k2VUBuFlY8ZyLJ3cSYvMs7pzx4DZKSmKux1Vq8yJP/KjUmd8kfsNGCNoI/5tFXw+gqSPVeKTtp6SnoTIqmuWMWddpPvBTDJ3+S/olZWQwOPAElukpLA/8Ps/nQ4iSMvf0cc4+jGTrzSsVOtn1OEVRUKlU+g5DFFJCQgINGjTg9ddf54UXXsh2PKv+06pVq6hZsyazZs3C39+fy5cvY2VlBWTWf9q2bRsbNmzA3t6e8ePH07NnTwIDAzE0NCzpSyrX7ty5w5gxY9i1a1eedaOe/J3Mz+9pXn0mT57MuHHjtPdjY2Px9PQsQORlW1hYGBs3bgSKJ9lVt25d9u7dK0XqhRBClEuS7CqHHiQnMu/MP3zatJ12V8XVHXpgbWyCoUHOK1cfn+mUl6Ke6WToUBnrCZtRUhJJy9AQeDeWhLQMKtuaUsPBAshMdBnYeXBj4jCM7BzxnDAXAxN10ZzfqSpG7rW1989H3GXvwwcYoeKyiTUti+QsQjyble2fZ8G5E7zj00jfoehdmiaDOaePsf7aRY72GYKdqZm+QxKF0L17d7p3757jsSfrPwGsXr0aZ2dn1q1bx8iRI7X1n3766Sftkro1a9bg6enJnj176Nq1a4ldS0UQGBhIZGQkTZo00bZlZGRw6NAhli1bxuXLl4HM2Vuurv9tnhEZGamd7eXi4kJqairR0dE6s7siIyNp3bp1judVq9Wo1UXz974s0mg0uLq6Eh4ejpubW5GPnzVTMigoiKSkJMzM5PVUCCFE+SHJrnJGURT8/9jImYeRGKpUfNaiPQCV1Hnv4JPbTKcsGZEhJG6cViwznbKSZ0ZA21zyaPFnjvPo4A5QFJKuBlFt/mpMimE3utYuHvzSuS82cZH4XttQ5OMLURj2pmbMbe6n7zBKBSOVAVtuXuVyTBTrrl3gvbpNnv4gUSLi4uJ0aioVNlHxtPpPI0eOfGr9J0l2Fa1OnTpx/vx5nbbXX3+d2rVrM2nSJKpVq4aLiwu7d++mUaPMpHxqaioHDx5k3rx5ADRp0gRjY2N2797NgAEDAAgPDycoKIj58+eX7AWVEdWrV8fHx4fw8PBim9kFsHfvXiwtLYmLi5PaXUIIIcoNSXaVMyqVioAmbZl68hAvVK1V4Mc/OdOptLBs2BLvRRsImTaChPOnuDikA9XnrcKyYdHPvepTpQbpoRlkbZYek5qCtbGJLJkSJS4lIx21obxMP06lUjGjSVviosPpY21KeuilnPsVw7JrkTcfHx+d+9OnTycgIKDA4xRX/SdReFZWVtrESBYLCwvs7e217WPHjmXOnDnUqFGDGjVqMGfOHMzNzRk8eDAANjY2vPHGG4wfPx57e3vs7OyYMGEC9erVy1bwXvwn6+f58RlzRSVrZldGRgYAV69epUGDBkV+HiGEEEIf5FNUGacoCisvn8fbphLPuWbWsehTpQY9KlfHKJcli2WVTVt/av+4lxsfvkrStQtcHtkbz3GzcRzwJiqViocJqcSnZuBsaYKpcdHUawlNTqLb1h/pVdmbeS3aaxNe+qhvJioWjaLQ5re11LG1Z37L9riaW+o7pFKjp6Wa2BXjSAdtUjonssFEybpw4QLu7v9tIvCsy8+Kuv6TKF4TJ04kKSmJd999V7upwK5du7Q11gAWLVqEkZERAwYM0G4qsGrVKqmxlofw8HCgeGp2WVtb4+npyZ07d9i2bZskuoQQQpQrkuwq474MCmTssb1Ut7bl3IvDMf9fAffylujKYupZjVord3Lr0zFE79pM+LIp3L3wL8sdu3DybubyGQtjA3rWdmBkCw9sbW2e6cPuwYcPuPQoioS0i0xq2BJ7UzO91TcTFcuxe6EEPojgSkwUi1t30nc4pYo+l12L3FlZWWFtbf3M42R9qC/q+k+iaB04cEDnvkqlIiAgIM/ZfKampixdupSlS5cWb3DlxMcff0xUVBRQPDO7IHMp4507dwgNDS2W8YUQQgh9kWRXGfdarXp8deFfRtRugIlBxfhm1NDMgqqzv8WiejVMrm6GlJN8fvexrdDTgPOZt1ieLek02N2TDBtnOrh5Yf+/QtjyQVuUhDYuHpzqN4wrMVHanz3xn3RU/JJmzM/ng9nQubc20S/KvqpVq0r9JyFAu0uigYEBdnZ2xXKOunXr8ueffxIUFFQs4wshhBD6IsmuMubyo4dsv32d8fWbA2Bjoib4pTcwriCJriwqlQrzjr1Jv7qZHzxHMLp3OwwMVGTEPcLQypY7j5KYuXE/C1PX5Zl0yogMeWr767Xq6xx7mJqKCaW3vpkoP5o4utDEseiXrpQHBihMu3yRm0mJbAm5wis1fPUdkiiA+Ph4rl27pr0fEhLCmTNnsLOzo3LlylL/SQhg2LBh/P777zg7Oxfb8tysul1//vnnU2fmCSGEEGWJJLvKkPDEeBr9uoqkjHQa2jvRyb0KQJEmuvKT/Cktdl2NoiMwoGNzTDzrkHj5PJdHDMDl1fepMnwcTRo8gJOQnJbBk9WOVOrM3YYSN07L8xxZ/bKcjAyn64E9TLGqxpgivBYhsiSlp5Gq0WBj8mz1jso7A2B8dW8emFhp6xWKsuPUqVN06NBBe3/cuHFA5of7VatWSf0nIQAjo8y36R4eHsV2jqwNBq5fv85nn33GtGnT5HdICCFEuSDJrjLE1dyS12vV40bcI7ytKz39AQVQ2OSPPoVEJwPgam0KQPS+39EkxhO2Yg7xZ47RrN9IACITUrMluwwdKmM9YXOBi8zvuHOd6LQ0frHyYpRGI79AosgtDfqX+Wf/YUHLDrxWq56+wynV3vaS2ZVlVfv27VEUJdfjUv9JiMwadJB9Z9KiVKdOHe3/jxo1ipSUFMzNS897PSGEEKKwSs1n9YCAAGbMmKHT9vgW4oqiMGPGDL755hvtt7xfffWVdvo1QEpKChMmTGD9+vXab3mXL19erN+IFacMjYaF50/yRq362P2vZs+iVp0wNjDIdTp7YXcJLGzyR5/MjTOL8KdlZCad3N+ZgtqjKrc/+5DY4/sxuX0JfMwwz2VnxsJcyyeN22CXHEef7TMwMnjjWcIXIhtFUdh66woPU5IwkB3lhBCiwlIUhT///BMABweHYjuPubk51atX5/r16/Ts2VMSXUIIIcqNUpPsgsy6AXv27NHef3wa9fz581m4cCGrVq2iZs2azJo1C39/fy5fvqxd1jB27Fi2bdvGhg0bsLe3Z/z48fTs2ZPAwMAyOSX7zUN/supKEMHRD1jVvgcAJnlcx7PuEliaEln50cnbDk7ArqsP6fO/0B16DcaiTkOuT3oNJeo24IFq3yYU77qoimCHSpVKxTtVqhKnpGvb7iUm4Gxu8cxjC6F5eId9jZvwa4QrL5obkh56Sed4aUs461tGZAiKohAY84hfwsOYXasORgYGpXLZtRBCFMSjR4/YvHkzULzJLshcynj9+nWCgoJ0lhcLIYQQZVmpSnYZGRlptxx/nKIoLF68mClTptC/f2YyZ/Xq1Tg7O7Nu3TpGjhxJTEwM33//PT/99JO2OO2aNWvw9PRkz549dO3atUSvpSiMrNOQrTev0snNK1/9K9ougdXtzYkD5u6/SbydNwPqO2NsaMAjp+p82f8rmmyZxgAeELnxG4wq18X++QFFev6MyBA2hN5lxLkzrG/clB7OLtp2IQrq8WT180Buv6XPsrtoefH4sutUVPTw6kWUoZqWf/9A56R72foJIURZk5KSgouLCxEREbi7uxfruXx9ffntt984e/YsYWFhuLm5Fev5hBBCiJJQqpJdV69exc3NDbVaTYsWLZgzZw7VqlUjJCSEiIgIunTpou2rVqvx8/Pj6NGjjBw5ksDAQNLS0nT6uLm5UbduXY4ePZprsislJYWUlBTt/bi4uOK7wKfI0Gi4EfeIGjaZ20u3dHbn1uB3sC5goeqKtktgy8o2DNkYzNjtV3C2NOHy/URMDFX0GPkx7B2HZaNW2HV9ocjO9/gH7W2OzUmy8mLLvnU89+DfHPsJkR+BkWF4AxYVJFn9LJ5cdv3mpQvcSUrCs81UrGwz6xnKLDghRFnm4uKCj48PERERODk5Feu5sorUf//991y8eJG///67WM8nhBBClIRSk+xq0aIFP/74IzVr1uTevXvMmjWL1q1bExwcrK3b9WSBTmdnZ27dugVAREQEJiYmVKpUKVufrMfnZO7cudlqhenDg+REBuz5jaCoB5x98XVczTNLqhc00VURzWtmzMgWldhzLYqElCS8apvSvZYDZjF3SAQ8xs1B9b/ln5qUZKJ2bca+56BCb+P9+AftVRoN7e/e5g3P3jo1luSDtiiIS48e0urIQRq6dWSfnSc2FShZXViP/37Nk+dLCFEOZRWoL+5k1+P1b8PCwor1XEIIIURJKTXJru7du2v/v169erRq1Yrq1auzevVqWrZsCZAtOaEoylMTFk/rM3nyZO2W5wChoaH4+PgU5hKeiYWRMQ+Tk0hMT+N81H1tskvk7vEZVi7AkMeOKQf/WwZmYPpfPa07X3zMg82reHRgB1WmL8PI2rZQ5876oG0EvOOp+/OSrtFgWAT1wUTFcT7qPmaGhjhlpGBhVGpeloUQQuhRSSW7atWqhYGBARqNhsOHDxfruYQQQoiSUmo/VVlYWFCvXj2uXr1K3759gczZW66urto+kZGR2tleLi4upKamEh0drTO7KzIyktatW+d6HrVajVr93+yp2NjYIr6S3GkURTsbyMzImE3+fdEoCrVs7UsshrKsMDtImnn7oDI2IebgDi4OaU+1uT9g4du4SOLRKApTTx7i3wf32N7tRYwk4SXy6aVqtWlNMg9/eF/foZRpkUkJ3ImPo4lj9tqPQghRlnzxxRclluxSq9XUqFGDy5cvc/HixTK7i7kQQgjxuFL7aTwlJYWLFy/i6upK1apVcXFxYffu3drjqampHDx4UJvIatKkCcbGxjp9wsPDCQoKyjPZpS9hCXF03L6eNVeDtW01bOwk0VVAhg6VMXKvnevtyaWETgPepNb3f2Li7kVq2G0uv9GdyA3foCjKM8dyPTaaJUGB7Lwbwl93bjzzeKJicVab4pku9bgKa9fdEFx+WsbQ/dv1HYoQQjyzCxcuaP+/uHdjBLSrGoKDg5/SUwghhCgbSk2ya8KECRw8eJCQkBD++ecfXnzxRWJjYxk2bBgqlYqxY8cyZ84ctmzZQlBQEK+99hrm5uYMHjwYABsbG9544w3Gjx/P3r17OX36NEOGDKFevXra3RlLkx+vBnMw/A4T/zlAcnp6kY6dERlCeuilbDfZJTCThU8j6qw5gG2HnijpadxZ8BEnRw9h5+kQrj8sfLKhho0dazr05Mf2Pejp5V2EEYvyKvB+BJcfPdR3GOVCCyc3jA0MMTcyJjY15ekPEEKIUmzgwIEA2NraYlQCy9uz6nZ9++23LF++vNjPJ4QQQhS3UrOM8e7duwwaNIgHDx7g6OhIy5YtOX78OF5eXgBMnDiRpKQk3n33XaKjo2nRogW7du3CyspKO8aiRYswMjJiwIABJCUl0alTJ1atWoXh/4qTlyYf1m9OaEIc79dtgmkRvYl5vIZVfvpVZEZWNlSbv5qtn32G2+aFJJw6xGuGx4kwc6STtx3LeteitpPF0wd6Qr+qNYshWlEeaRSFNw/9yfmo+6zv1Jt+/1tNnVtSWpLVebMxURM65F0cTOX1TQhR9mUluNzc3ErkfFkzuy5cuMBvv/3Gu+++WyLnFUIIIYpLqUl2bdiwIc/jKpWKgIAAAgICcu1jamrK0qVLWbp0aRFH9+zuxsey4uIZZjZth4FKhaGBAUvb+BfpOQpTw6oi+/zQLSbFNGPC4K94sboZJ57z52BINLP2hdDu6xMcG9UCb4fCf3BOTE9j9N+7mVC/OXUqFf8SBFG2xKam4GlhzY3YR3Rwq4wq/gEgyepnIYkuIUR5UVL1urJkzexSq9WMGDGiRM4phBBCFKdSk+wqz5LT02n12xruJsRhpzZlXP3mefbPeHC70AkrSWTlz/34VKbtus6E57z4/Pn/lrkOaeSKX+QJDiz8nAXOn7Di7S6FPseE4/v54fJ5jkeGce6F4bJDo9Bhqzbl924vEJYQl5mkMZVkdVHRKArpGg0mpXBWrxBC5MfOnTsBsLOzK5HzZe3ImJKSQqtWrUrknEIIIURxkmRXCTA1MmJqo9b838Uz9PaqkWffjAe3iV3Q/6ljWk/YLB96n8G6MxGoVCo+8vPSaVfS03m0fAY+D2/itfJ17jjPx6PvYFT/2zWzIGY0acup+xEsbNVREl0iV24W/y3Flt/pZzfvzHEWnT/F7GbteKN2A32HI4QQBaYoCj/99BMA1tbWJXJOtVqNt7c3V65c4cKFCyW2fFIIIYQoLpLsKiEj6jTg9Vr1njrTIGtWh/nATzF0qprteEZkCIkbp+U5+0M83Z2YZLxsTbG3MNFpVxkZUXP5Fs5MfBOLy4FEzh5N2om9VJ68ECNr2wKdw9HMnH/6Di1UokyUX/Fpqay4cJp3fBphYWzy9AeIAknVZHAvKYG/7oRIsksIUSalpqbi6urK3bt38fT0LLHz+vr6cuXKFfbt20ft2rXx8PAosXMLIYQQRU2mm5QQlUpVoCU1hk5VMXKvne2WUwJMFJyjhQmhsSnEp2TfCVPt7sXZUd+yvPrLYGhE9O6tXBjUltiThwp8nscTXeGJ8ay7diGP3qIiWHT+JB/+c4Duf/6i71DKpVdr1GXX8wNY27GXvkMRQohCUavVNG7cGKBEE05ZRernzp3LN998U2LnFUIIIYqDJLtEhfRyA2eS0jL4+vjdbMeS0jL48p9wIruPoPYPf6GuXJ20e2FcfbcfSTcuFep895MSab7lR4bs28b2W9eeNXxRhvnYOlDVyoZRvo31HUq55GVlg79HVanXJYQo07IK1Ds6OpbYObOK1BsYGJCcnFxi5xVCCCGKgyxjFBWSVyUz3m/tyUd/XSMmOZ33WnvibGnCkZuPmLLzOjeiklj9ki8WHtbUWbOfu4umAmBWrXahzudgakaPytU5GH6bOpXsi/JSRBnzQrVa9PLyxkjquAkhhMjFgweZO/SWZLIra2aXlZUV8+bNK7HzCiGEEMVBkl2iwlrQoyZmxoYsPHKb2ftvYmigIkOjUMPBnJ3DG9HEI7MorKG5JV5TFqNkZGgfm3o/nEcHduD44vB81eRSqVQsbdOZxPR0LGPvkR56L/e+suNeuSezjopXUnoaKy6cYV/YLbZ06S+JRSFEmbJ582Zu3LgBlGyyK2tHxpiYGCIiInB1dS2xcwshhBBFTZJdosIyNFAxp5s3Hz7nxZ9XHhKbnE4tR3P8qlbCwCB7Akv1vwSFotFwM2AUcf8cIObQn1SZvgxjB5enns/YwBDL2FDtbpuXjK2okp6AqaLJ1ld22yx/Pv33b5o6uNDNs5psWlDMjA0MmX36GA9Tkvg74i5+bvK7JIQoO27evIlGk/newMHBocTOa2pqSvXq1bl69SrBwcGS7BJCCFGmSbJLVHiVzI0Z3PDpySotlQrb57oTf/o4scf2caxvK9a1GUtyk86MaOFOay/bXB+atYvmga4fMvTGHXo5u/Jjw8ba5Ifstlk+nY+6z/RTR1CAoBeH42tXct/UV0RGBgZ81LAFJoaG1LaVZcNCiLKlU6dOQOas8EqVKpXouX19fbl69SqjR49m2bJl2liEEEKIskaSXaVURmRIgdpFyVGpVCR1GcL4ICveOjKfWnE3eWvvDA5dO8jzx4fyansflvSqmefsHUs7N1Ku3ea+ypA0F2/MjYxL8ApESXMzt2R8/ebcS0qQRFcJmdCghb5DEEKIQjH439JrBwcH7f+XFF9fX7Zu3cqlS5cIDAyUZJcQQogyS5JdpYxKbQ5A4sZp+eonSp6iKPT98SyxVp40XrMHy1+/5N6aZTx36wB/RZ1jWMpH/J+TBW+3zH278A4Ojuzr+TItnd0wNpD6TeWdvakZn7fsgKIo+g5FCCFEKZdVnL4klzBmySpSX716dfr06VPi5xdCCCGKiiS7ShlDh8pYT9ic5zI2KWCuX/uvR3MmPJ79bzWmtrsdvB9ApY49uTlzNOr0DJo1b8jCI7cZ0dw9x9pfWdq5eurcf5ichE1xBy/0Smp1layUjHT2h91GURS6V66u73CEECJf9uzZA4CNTcm/K/D19QUgKiqKmjVrlvj5hRBCiKIiya5SSBJZpduea1G4Wavxq/ZfHQ2Luk2ps+YAaZHhDIy1YO2PZ7n1MIFKwYew8ev+1CTH4vMnCQj8mz3NWyEfycuP7beu8cuNy8xs2hYvK0lllrS1Vy/wxqE/aeroIskuIUSZsWrVKgAM9bBzb9aOjNHR0bIjoxBCiDJN9mMXooAyFAVjA1W2BJaBiRq1RxXURpm/Vombv+X6hCFcGzOA1Ii7uY6XrtGw9eZVYlJT+O1eeLHGLkqORlGY+M8BfrwaxP9dPKPvcCqkHpWr42FhRTNHVzI02Xc9FUKI0ihr+aI+Ek1ZOzICrFy5ktjY2BKPQQghhCgKkuwSooBaVbbh1qNkTofm/AZwc3AkbtZq7CxMUJmoiT26l+CBrbm/aSXK/z5wZ0SGkB56ifTQSxB+hU316vF1vQZMsTEpyUsRxchApWJDp970qFydjxq21Hc4FZKzuQW3B7/D8rZdMCzhIs9CCFFYzz33HIDelhFm1e2aMmUKgYGBeolBCCGEeFayjFGIAupZ24EqlUwZueUSfw1vhJ35fzsp/nn5AT+cCuOTjlVx7dSOSm39uTnzfRLOneD2Z+OJadoCe9vsGxAYAgOBpKwGEzNSMzIw0cMSBlF06ts7sb3bi/oOo0KTOmlCiLImq0C9o6N+du/19fXlt99+w87OjrS0NL3EIIQQQjwrSXYJUUBGhgZsHlKfzt+fptr8vxnc0AV3azUHbkSz51oUveo4MKl9FQBMq9Sk1rd/cP+X7whd9ikxp/4h0cYSj3cmYd0q5+28M4xNeefiBSKTTvGrfz+MZEZKmRKWEEeaRiM1ukqZm3ExOJqaYWEssyeFEKWbPndjhP9mdvn4+NClSxe9xCCEEEI8K0l2CVEIjdytOTumBV8du8sv5+8Rm5xOLUcLfhroy8v1nTEy/C9BpTI0xOnlkdi07cqt2WOJP30Ms4YdMHKvnePYFx5G8tPVYNI0Gv6OuIufm2xYUFZoFIVhB/7gRGQ4Gzr1lqLopcSLu7fwa8gV1nXsxSBvH32HI4QQuTpz5gyHDx8G9DuzCyA4OBhFUWSGrBBCiDJJkl3FJOPBbZSUxFyPq9TmsutiGedhY8rcbt7M7eadr/5qjyrUWL6FpMvnMfP2ITE1g33Xo0g/exTPlm1o7GWPSqWivr0T6zv2RqVCEl1lzKOUZBLS00hXFKpZ2+o7HPE/NWzsMFCpuBITpe9QhBAiT+Hh4aSkpAD6m9n1+I6M9+7dw8XFRS9xCCGEEM9Ckl3FIOPBbWIX9H9qP+sJmyXhVcGoVCrMatVj3oGbzDt4E6d7V1h98mNCzFz5ou1oxr/7Mk08rOlXVbcobdY3q5JELd3sTM041OsVzjy8Ry1be32HI/7ng3pNmVC/OfamZvoORQgh8tS8eXOMjY1JS0vTW7LLzMyMqlWrcv36dZo2bcrFixexsrLSSyxCCCFEYUmyqxhkJSPMB36KoVPVbMczIkNI3Dgtz6SFKL+m7brO7P03Gd3ak5GGcaRdsaPqo1DG7/qIPUF/YPLZQur5/Lf87VFKMi/u2cr7lb1ot3b0U8eXJKp+GRkY0NSx5LeLF7lzMrPQdwhCCJEvJiYm2qLw+lrGCFC3bl2uX79OaGgoly9fpmnTpnqLRQghhCgMSXYVI0OnqrnWZRIVU1hsCvMO3mJG52p80rkaUIv0du0I+3o29zf9QOeww8S+2Z7I96fi+OIbqIyMWHT+JHtDbxH8IIJAlQH2A2ZIErWUmXziIB4WVrzr00hqm5RyUn9GCFGaZRWnNzMzw9zcXG9x+Pj48Ntvv9GzZ09q15b3skIIIcoeSXYJUYLWn4nA2FDFmLb/zbwysral8qTPse81mFNT38fydjB3Fkwmev8f1FzxG1Mbt+Z2fCzvOTtgdnGNJFFLmRORYXx25jgATRycaWaYIUtNS6GIxHg+OLaP81H3Of/icEl4CSFKpb179wJga2ur1ziyitTHxMRgaWmp11iEEEKIwpBklxAlKDwuhcq2ptiYZv/Vs/BphDJvM3NmLODjuz9TqVNvVCoVxipDVrbvQXroJeL0ELPIW1NHV75s3ZmbcTE0M8yQen2llK2JKdtvXyc+LZXABxGy1FQIUSqtWrUKACMj/b5F9/HJ3LlWdmQUQghRVkmyS4gS5Gql5vajZGKS03NMeAXdT+a3yl34v/kfYO/8X62O2OP7SLl6CiPgWkI8U3dvYaXf81iZqEswepETA5WK0XWbAJAeegmQen2lkamREV+37YK3tS2NHWRnMSFE6WRjYwPobyfGLLVr10alUhEVFcXSpUt5//339RqPEEIIUVAG+g5AiIpkUEMX0jIUFh+5ne1YXEo6Xx69Q18fRxzcXFAZGgKgSU7i1tzxRPywEA3wwrEj/BpyhQ+O7Svh6MXjTj+4R7pGk+OxrKWmT95ySoCJkjOkhi8tnd0xkBkKQohSqmPHjgDUqVNHr3GYmZlRuXLmDORx48Zpi+YLIYQQZYUku4pRRmQI6aGXst0yIkP0HZrQEzdrNR+1r0LAnhuM/u0SFyMTiE1O5/cL9/H7v0Ai4lKY4V9N5zEqI2OcXh6JgZk5BsC031fRIj2N6bUbkJKec7JFFK9rMdG0+30t7bet42Fykr7DEUI8g7S0NO7cucPly5eJiorSdziigssqUK/PnRiz1K9fH8is3xUTE6PnaIQQQoiCkWWMxUClztw9J3HjtHz1ExXLTP9qWKsN+ezgLZYdu6ttb+ZhzcERTfB11i0EqzIywnnQ21jX8yV5zQc0Sonim1+WcWvrauZ6Ps+Nqi3oV9eJQe4peZ4348FtKZxeRK7GRGGgUmFkYICtLCUtU648imLD9YvUsKnEIG8ffYcj9CQ+Pp61a9eyfv16Tpw4QUrKf6+fHh4edOnShREjRtCsWTM9Rll6fP3113z99dfcvHkTyEx+fPLJJ3Tv3h3I3OV0xowZfPPNN0RHR9OiRQu++uorbZFzgJSUFCZMmMD69etJSkqiU6dOLF++HA8PD31cUqmVlezS9zJGgLp167Jt2zZat25dKuIRQgghCkKSXcXA0KEy1hM2S2JB5EilUvGhXxXea+3JgRvRxCanU8vRgoZuVnk+zsTFi2TAqa6Ttm02f7M74zrLb9aky4lj2JJzEjXjwW0pnF6EuleuzrkXh2OoUmFoIBNky5K/7t5geuARnnP1lGRXBbVo0SJmz55NlSpV6N27Nx999BHu7u6YmZkRFRVFUFAQhw8fxt/fn5YtW7J06VJq1Kih77D1ysPDg88++wxvb28AVq9eTZ8+fTh9+jS+vr7Mnz+fhQsXsmrVKmrWrMmsWbPw9/fn8uXLWFll/m0bO3Ys27ZtY8OGDdjb2zN+/Hh69uxJYGAghv9btl/RxcbGsmHDBgDs7Oz0HI1ukXohhBCirJFkVzGRhIF4GjNjQ7rXyv83pWk27nQx+4gmjkYs6FqF+F2/EnP7Oh9UaUFYSjLtKg3hfc+aTMzhZy8r8SqF04tOFSsbfYcgCqGPVw32hd6mf9Wa+g5F6MnRo0fZv38/9erVy/F48+bNGT58OCtWrOD777/n4MGDFT7Z1atXL537s2fP5uuvv+b48eP4+PiwePFipkyZQv/+mV+qrF69GmdnZ9atW8fIkSOJiYnh+++/56effqJz584ArFmzBk9PT/bs2UPXrl1L/JpKowcPHhAfHw+As7OznqNBOzMvODgYjUaDgXy5I4QQogyRZJcQZcSvQZGcSrJl7UutsHS0wPKdhjgrCn9G3eeLcydxvVeJRkuHcPvBcNyHj8XQ3DLbGFmF00XBxaWm8OqBP/i0aTvq2uVdSyW3unxSr0//vKxs2Nr16bMcRfn1yy+/5KufWq3m3XffLeZoyp6MjAx++eUXEhISaNWqFSEhIURERNClSxdtH7VajZ+fH0ePHmXkyJEEBgaSlpam08fNzY26dety9OjRXJNdKSkpOktMY2Nji+/CSgEXFxc8PDy4e/duqVg2WKtWLQCioqJo0aIFJ0+e1HNEQgghRP5JskuIMuJseDze9mbUdLTQtqlUKurbO7G6Qw+OLJ6PWcoj7q9ayKkD23lu6HvY9xyk3dVRPJuppw6z9eZVLkY/JPilN3Jcvij1+oQoHzQaDXfv3tXuRifg/PnztGrViuTkZCwtLdmyZQs+Pj4cPXoUyD4TydnZmVu3bgEQERGBiYkJlSpVytYnIiIi13POnTuXGTNmFPGVlF7m5uYkJmbOsC4NBerNzc3x9PTkzp07BAcHoygKKtnNVgghRBkhyS4hygi1kQExyelkaBQMDbK/2bzX8VWWnUunqdU5FtVpwKRfvueVjd/iOW42Zq663xDHJKcTn5KOo4UJJkayLCE/Pm7Yihuxj/iwQYtc63RJvb6yIy41hb/uhtCjcnXMjYz1HY7Qk5UrV7Jx40Zu3bqFtbU17dq144MPPsDIyIiqVauSkZGh7xBLjVq1anHmzBkePXrEr7/+yrBhwzh48KD2+JNJkPwkRp7WZ/LkyYwbN057PzY2Fk9Pz0JeQemXnp5OdHQ0UDoK1EPmjox37tzho48+0ncoQgghRIEU+lOubNUtRMnqVceB+wlp/HHpQbZjiqLwfWA4jxp2JKrvEDIMDEiwsiHpynmuvN2b0KUBAPwbGkvX7//FNuAAHnOP4DzrEOO2XyE2Ob2Er6bscTa3YFu3F3nONe8PWoYOlTFyr53rTRJd+pfx4DZNfvmWAXt+Y+e5I6SHXtK5ZTy4re8Qy7X09HSmTp1K1apVMTMzo1q1asycORONRqPtoygKAQEBuLm5YWZmRvv27Yu0SHZGRgZ9+vTh7bffxszMjN69e9OgQQM2bdpEnTp1+Ouvv4rsXOWFiYkJ3t7eNG3alLlz59KgQQOWLFmCi4sLQLYZWpGRkdrZXi4uLqSmpmoTOTn1yYlarcba2lrnVp4dPXoURVGA0lGgHtDWtouIiJBZXUIIIcqUAs3skq26hdCfZh7WdKhWiTd/vcBGdT3aV6uESqUiITWDT/fe4M/LD9kwqC4D6regf/U6+L84nIhv53N/8yoMbewgBt7ecgmVWy2+6V8Hd2s1B0Oi+b9/QgkJjuSHPM6d8eB2hZytFJYQx76w2wyp4avvUEQRydqZtIN9QzRmLjz6YwlxCXez9ZOdSYvPvHnzWLFiBatXr8bX15dTp07x+uuvY2Njw5gxYwDytbvfs1i0aBH//PMPZ86coU6dOtp2jUbDwoUL/5+9+w6L4voaOP7dpXfpRVHEgiLYSyyx914Se/dnjC0aS4wpaoxdY080GlvsRsWoMSo27MSGolhRRBDEgnQWWOb9g5dNVkBBgaHcz/Pso8zenTmz4pYz957DZ5999sHHKOokSUKlUlG2bFkcHBzw9vamRo0aACQlJeHj48P8+fMBqFWrFnp6enh7e9OzZ08AwsLCuHnzJgsWLJDtHAqarVu3AmlJPl3dgrH44r9F6gVBEAShMMn2O6lo1S0I8lIoFPzRz5NOm67TfO1VKtkaU9LCkEtPoohNUrOgXXl6VUu7wt6hdDkASk9ZiGWPIcy8d53RZ6/RxymBETUiSAw4gWXzTrSuqstnzhb8uPNqlsdNTw68S1FKDqhfBBMRHUm9s6cITUzEODqCzg6OmvuLanKvOEhP2i5s3g0je9cMMxVEZ9K8d+HCBbp06UKHDh0AcHFxYfv27Vy+fBlIS6K8q7vfh9q4cSMLFy7USnQBKJVKJk2ahCRJTJky5YOPU1R88803tGvXDmdnZ2JiYtixYwenTp3i8OHDKBQKxo8fz5w5c6hQoQIVKlRgzpw5GBsb07dvXwAsLCwYNmwYEydOxNraGisrKyZNmoSnp6emO6MAhoaGAAVqBpu7uzsAV65cYdeuXZpkpSAIgiAUdNlOdmW3VfeqVatYv369aNUtCHnA2kSfs5/XxvvBK3b7PyNapWZcw9IMre2Ei5VRpo+ZFhHG0pBgjjs0xjtkLeoQ0ANi7+wBwApY8v9jVToGGV4U0r/0G/f6ER27shn2X9SSA+nJPQOgjXUNThvZUdZrBjEpcVrjilJyrzgydignOpPmspiYGK1ueQYGBhgYGGQY16hRI1avXs29e/eoWLEi169f5+zZsyxduhQgW939PlRgYCAfffRRlvdPnjyZyZMnf/Bxiopnz54xYMAAwsLCsLCwoGrVqhw+fJhWrVoB8NVXX5GQkMCoUaOIjIykXr16HD16VGsW3pIlS9DV1aVnz54kJCTQokULNm7ciI5ooqLRuHFjli9fXqA+P1eqlPY6GRcXx7Jly0SySxAEQSg0sp3syk6rbkmSiIiIEK26BSEPKZUK2lS0pk1F62yNH1TRg3W3b5GS4oH56EFEntjPi90bUMdGAWBUrjKvmvWlj78Zh3Xtyeojto5d2WKRHFAnpiW1THr9yEqbMsSp1VjoDfj3/iKW3CvuJEniRWICtkaiQ+aHSp8Bkm769OnMmDEjw7gpU6YQFRVFpUqV0NHRQa1WM3v2bPr06QP8W/vpbd39PpSJiQnPnz/PMqng5+fH8uXLWb/+bQu8i49169a99X6FQsGMGTMy/fdOZ2hoyIoVK1ixYkUuR1d0vHiRVpOzIHRiTGdsbEypUqUICQnBzc1N7nAEQRAEIdveqyCA6F4kCIVHdRt7xpZqy+rQMHCqhN0gd8y7juDllp+J2L6aKL/r6Phd50urahg8Lg82teUOOVfktM6YOjWVCRdPEBv1goX8m9wzzIdYBXnceBlBt6NeKBUK7vUaLoovf6CAgABKliyp+TmzWV0AO3fuZMuWLWzbto0qVarg5+fH+PHjcXJyYtCgQZpx79PdL7uaNGnC6tWradCgQYb7wsPD6d27N/fv3xfJLiFfPX/+HCg4nRjT1ahRg5CQEGrVqiV3KIIgCIKQbTnqxpif3Yvmzp2rqQORLjvdmVQqFWPHjsXGxgYTExM6d+5MSEjG4sOCUJz0qurIq/hk/vCP4LUqkfrH9nGoeUc89l3B9tNhpCh1qf/qOkaPbmRrf+ndogqq9KWIMSv6Z3mLXtRdq+veP8/DWHnrKuufPOaKQcHogiXkrbJmFoTExfAkLpqQuBi5wyn0zMzMtLrmZZXsmjx5Ml9//TW9e/fG09OTAQMG8OWXXzJ37lyAbHX3+1DTp09nz549DBo0iJs3b5KYmMjTp0/59ddfqVOnToGaWfO+RNfswmfVqlUABaY4fbr0WZuiSL0gCIJQmOTo3TS/uhddunSJNWvWULVqVa3t2enONH78eA4cOMCOHTuwtrZm4sSJdOzYkStXroi6EEKxVdXRjC7utnzudZsu4an4vYxgsf8lOrTpz8xyQzj8UXW2mV/Bukt/zWNib/yDvp2TJiP+6FUC8y8E8If/M2KS1FS0MeazuiX5vHTBm8n5PnXG6tuXZEWDllgnvKb2w3cv2xYKPzN9A4537E11aztM9fTlDqfYiI+PR6nUvtamo6NDamoqQLa6+32oqlWrcujQIYYOHcqWLVs023V1dRk3bhxjx46lTJkyuXKs/CS6ZhdekiQRFhYGFKxljKDdkTEuLg4TExOZIxIEQRCEd8tRsis/uhfFxsbSr18/1q5dy6xZszTbs9OdKSoqinXr1rF582ZNd58tW7bg7OzMsWPHaNOmzQfFJgiF2eZeVei7/SZbTj3HxM4ZlaoELgvOo6tUsKJ/E6pV/wTl/3/hl1KSCZo2kqSIp9h3+QRjoP9Of8JMyjK+UWlKmhvg8+g1Xx9+wA27KE2B+8zkdDlhbnpXnTG/qCjK2SRiaZC2WHFUlZqkhN5BzPEp+tQRjwD4CCAilpQ3tgt5p1OnTsyePZvSpUtTpUoVrl27xuLFixk6dChAtrr75YYmTZpw//59/vnnHx49eoS5uTn169fHysqKuLg4pk+fnmvHyg+ia3bhJkkStWrV4vLly7i4uMgdjpb0mV2nT5/mhx9+YMGCBTJHJAiCIAjvlqNkV350Lxo9ejQdOnSgZcuWWsmu7HRnunLlCsnJyVpjnJyc8PDw4Pz585kmu1QqldaVz5gY8TVXKJrMDHQ5MLg6V0Ki2X0zgujEFNxqGtO/hiPHwwNx3b6HeXWbMLxydVJev0TPzglVyCNeHf4D43ql+N+r0/Se2AUzSwsAPqtXijH1SzF+7f4sj5m+nPBd5OhseMzInsEXzlDv4UMOt+uJvpj5WSwoDNIK0cfv/D5b44Tct2LFCr7//ntGjRpFREQETk5OjBgxgmnTpmnGZKe73/sKDg6mdOm01xulUslHH32U4bONiYmJJtkVGhqqVYusoBJdsws3pVKpmd3o6OgoczTa/nuR29/fX8ZIBEEQBCH7cpTsyuvuRTt27ODq1atcunQpw33Z6c4UHh6Ovr4+lpaWGca8Wfsj3dy5c/nhhx/eK15BKIxqlTKnVilzrW2/37vJK1UiT+NjAdCzcaDir/uJvniCByu+B5JoHHyUJ4PqYtO5H5YtuqDQN6COLnxWNgXuQ7I6NcMLyvssJ8wvjupElICuQolKnZIh2ZXVDB8x86dw07EpjfmkvVq/c389C2dNcBBd7B0ZWrpMns42FNJqey1dupSlS5dmOSY73f3eV506dejcuTPDhw+nbt26mY6Jiopi165dLFu2jBEjRjB27NhcjyO3ia7ZhV96N8aCVqDe2NiYMmXK8PjxYyZNmiR3OIIgCIKQLTlKduVl96InT54wbtw4jh49iqFh1v3P3qc709vGTJ06lQkTJmh+Dg0NzdA+XRCKOq/W3dl4z58+5f69evs4NpqEStU40m0uI69MxM7DLu2OB/uJffDvbK70eVvhSTpkTGeleddyQjlUSYriVIOPqepWGz3lv4kuMfOn6HszkXX3eRSHIp6Rom/EZ/XFcvei7vbt28yZM4e2bduip6dH7dq1cXJywtDQkMjISAICArh16xa1a9dm4cKFtGvXTu6Qc0x0zS58njx5orkwW9CSXQCenp48fvyYO3fu0KJFC7nDEQRBEIR3ylGya/r06dSvXx+FQsHkyZMpX748r1694sCBA8yaNQsXFxfu37//XoFcuXKFiIgIrbbGarWa06dPs3LlSu7evQukJdX+O737v92ZHBwcSEpKIjIyUmt2V0RERKYJOkhbCvnfjlHR0dHvFb8gFGa6SiX/q1RNa9uXF45z4PEDWltW50/jqZwZ6Eb0ucPE37uJ02dfa8adO36Gkf76HLUt2MWco5NUjDx7lB9qNcLl/7dVM7dAV6k9oyuzmT9vEjN/ipZPXN1ITlXT1UUs6SoOrKysWLRoEbNmzeLQoUOcOXOGoKAgEhISsLGxoV+/frRp0wYPDw+5Q80xtVpN9+7dOXz4MO3bt6dz585ERkaye/du1qxZw4oVK+QOUcjC4cOHSUpKAgpegXpIK1J/8OBBAgIC5A5FEARBELIlR8muvOxe1KJFiwx1AIYMGUKlSpWYMmUKrq6u7+zOVKtWLfT09PD29qZnz54AhIWFcfPmTVFMUxByQKVOQZLS/j6gcgX6Xg7kWKID7ftpL19IjnyB/i/T+VnHAJ29r1B/OgwdU/NM9gh+T2PSOjmq1LjZGtOvugOmeXwe/11yOPb6VbaFPOFWRCgXKr49USUSWcWLq3kJvq2Z+QURoegyNDSke/fumqY3RUF+dc0Wcl96vS6lUompaV6/O+Zc+qqH/fv306dPHxo1aiRzRIIgCILwdjlKdkHedS8yMzPLcBXVxMQEa2trzfZ3dWeysLBg2LBhTJw4EWtra6ysrJg0aRKenp6a7oyCILybgY4u+9p050FUJOXMS/CLy0uG7QlgaFN96peyoUPpcqhSUlnldR5XPQtKJTzj6c8/8mzzCux6j8Cu9wh0zUto9jfx4D2WPwrB1kQPe1N9Vl0M4atD99naRI8mb4njfTs5KgyMiVLqEbJ7DqXUCQBM1TEgwL4h80OOkegXqRknCIJQVORH12whb9SuXRtIW6XwrvIccqhSpQoAISEhHD9+XCS7BEEQhAIvx8kuyF73oryQne5MS5YsQVdXl549e5KQkECLFi3YuHEjOqLTmiDkWHmLtOXAe/pXpeWGi8zxPwO3UqmrW4vAED1exhvz4/d/0ij5GmHrF6N6fJ+wNfN5tvVn7HoOx6ZlWv2jf55Esa13XT71tENXR0l4jIpvDj9gxrHLnMzi2B/SyXHzyyg+d+1OF3tHttZM+wJhBpyXJBSKoYBYiihoS5UkLjwL5cDjB/xQuxEGOu/19igIssqPrtlC3kgvTl8QlzACVKr0b93N9MSXIAiCIBRk2f40/99W3dmRG626T506pfVzdrozGRoasmLFClGXQhBykZ2pPqf+V4fhJ2I5Ex6KndKaxrVNGV7HiX2hN5kRZ8WwVfsoe/0i4et+IuFBAM+2/Ey0Rz0cgG+audCpuoNmfw5mBvzWw52hT+9CWObHzEknx5uvnmNtaISjcdrSD08rW1SpqTxMUaN0ckNZAK+SCwVPz2N/8jQ+lqZOpWnr7Cp3OIKQY3ndNVvIO8+fPwcKZnF6SPvdKlu2LI8ePSqwCTlBEARB+K9sJ7uKaqtuQRCyx8rIiD0d2pMqSVrJo40n/bn9+iXNnEpTvVU3LFt04dGpv7j/5CH3nkN/oEWJGJ5vmo9pjY/Qs7bXPHZwGRWEwcu4JOwzOSa8u5Pj+Fs3+DnoEd/XbMDM2h8DUNPGnhufDMXD0qZALgcRCh6lQsHAih48iY3GxtBI7nAE4b3kZddsIW8tW7YMSOsgXlBVqVKFR48ecevWLZo0eVsRAkEQBEGQX7aTXcWhVbcgCO/230SXJEnMqduYI08e0dwprTmFQqnkhFNphj8IoELqQ/oDqj+moQsk3v6DxP/sK73/Y5zCgOxITlVzKPghLUuWIf0RH5Ww4lfFYyJV/+5ZoVDgaSWuPAs5M7eu+PImFG552TVbyFuPHz8G0CrNUdC4u7tz8OBBLl68yKBBgzAxMZE7JEEQBEHIkjK7A9NbdT99+pRVq1ZRsWJFXrx4ofnQ1K9fP65cucK5c+dEoksQigmFQkFXl4qs+rgNJQwMNdsjVYmY6elTyao0dZlEdMtveRFjRwuzBoxLciXEN4TIBEcO2fXBs8QIvnl8l0PBgZrHRyTE0eGfC/Ry0C6A23j/Nroe3YtX0L9f1ro6OPK0/2hWNGyV9ycsCIJQgKV3zT579izVqlXDxMQEZ2dnvvjiC/r06cO2bdsK9Myh4ix9Nl7FihVljiRr6bW6Nm/ejI+Pj8zRCIIgCMLb5bgCb1Fs1S0IQu6aXK0e4z1r8yJeRaWrvkyJsOGL79dybf8WAq0cGHfmOEnnzlHp3Dnc2/Rk58Oy1La1p33pcgAkqdUcfR6BvpGd1n7bObsSFBNFojoFSGs6Yaijg6mR6Koo5J6IhDiCY6OpbesodyhCPgsMDGTFihU8fvwYtVqt2b5//34Zo8qZvOqaLeSt9EZKZcqUkTmSrLm7u2v+HhaWRcFNQRAEQSggRLspQRDyhJ5SB0dTY37r4U7v7f48iIzh84ofk0A8h7/cS+y+9XQIPkaX4Pu0+3Qwjf9/GWRqkgorQyM2VKtJytFfiFN15vdzwfzhH8FrFXxs8zGuus5I0jOZz1AoirxDHtHm0C4qWlhxp9dwucMR8lnXrl0ZM2YMvXr1QqnM9uT3AkeurtnC+0svUF+Qi79XrlxZ8/fOnTvLGIkgCIIgvJtIdgmCkKc+rWqPjYkec04Gsfr4KwAsDCUG9f8Gt48WUyMiCBOPWgBIajUBvT/GuFJVerRpT0rcE3pt8+d4vB3t3azxcDDF52EkLX67yvQqyYyT88SEIqmenRN6Sh1M9fR5rUrUWp4rFH0mJiaMGDFC7jDeixxds4XcERUVxaNHj4CC240R0v5/uLq68vDhQ27evEmzZs3kDkkQBEEQsiSSXYIg5Llm5axoVs6KV/HJxKhScDAzwED3/2dN2FlrxsX6XUQV/ABV8APiLhykVL1StH95hl+HDqWkVVrSQapdgv0Byew4cU2OUxGKOHN9A0L7j8LGUCyNLY6mTp3KlClTaNmyJQYG/zbOaNy4sYxRZY/oml14BQQEEBISAhTsmV0Anp6ePHz4kBs3bohklyAIglCgiWSXIAj5xspYDytjvSzvN6vVkMpbfYjYsZqoU/sAGGDwD2z9h5j/jGv+/zcASc8or8IViimR6Cq+jhw5wqlTp3jw4IFmGaNCoSgUyS7RNbtoKMgzuwA8PDz4888/WbJkCf369Svw8QqCIAjFl0h2CYJQoBi7eeIy/Wemuw9A/6+1dIs8jzo6EgCXaSswqugJwNmgSIYdeMxxXTtcZIxXKLpSJYnkVDUGOuKtsrjw8fHh1q1bKBQKuUPJsfSu2bNmzeLQoUOcOXOGoKAgEhISsLGxoV+/frRp0wYPDw+5QxXe8N8OjNbW1m8ZKb+qVasC8PjxYwICAgpFIlgQBEEonnLtE7yvry+BgYH07duXV69eER8fT6lSpXJr94IgFDOJptZsqjSEqRN+IfLoXmKunMW06SeaL6HKLT9SJzgSdXRjsBKzu4TctfC6Lz/duMSMWg353L2G3OEI+aRu3boEBgZSvnx5uUN5b6JrduGTXpze3NwcfX19maN5O0/PtAtO+vr6BbpzpCAIgiDkSrJrxowZXL16lTt37tC3b18SEhLo3bs3Z8+ezY3dC4JQDDUqU4IFPo+5GqGidsc+WHfso7lPHReD/r7VTE5OIKrvdh616IJN90GYVv8ow4yMuCQ1x+6/JCZJjZuNMbVLmRfKWRtC/lJLEs8S4jgS8kgku4qRa9euUaVKFSpVqoSBgQGSJKFQKPjnn3/kDk0owtKTXXZ2djJH8m4VKlTAwMAAlUpFSkqK3OEIgiAIQpZyJdm1b98+rl27Rs2aNQEoWbIkMTEx73iUIAhC1tpXsqGctRHD9wRwZFhN7Ez/vdq9/84rdpbry+dRpzELu8+rv3fx6u9dGLpUwKbrQKw79kbHwoo5J4NYePoxUYn/fiCv7mjKmu6VqeNsIcdpCYVE//Lu1LKxp4lj9rvbCYXfn3/+mWGbSI4LeW3p0qUAmjpxBZmuri7u7u5cu3YNf39/ypUrJ3dIgiAIgpCpXEl2pXcsSv9A+Pr1a/HhUBCED6KjVOA1oBotf7uK64JzfOppR0lzA049jOTc4yg+bTeAhr3mkXTHj+dem4g86kVi0H1Cln6POi6GFWU+Yd6pIL5sVJox9UvhZG6Az6PXfH80kOZrr3J+VB08HUzlPk2hgCplak4pU3O5wxDyyYABA9i8eTOffPJJpp9fxMwuIS/dv38fACOjwrEk38PDg2vXrnHgwAG6du0qdziCIAiCkKlcSXaNHDmSXr168eLFC2bNmsXOnTuZMmVKbuxaEIRizNPBlBvj6rHaN5Q//J9xQqXGzdaYP/p50r2KHUqlAj2PWph41ML5y1m8OrKXF39uIbnZJyzc+JgfW7nyhdkT4g8eRqdTH9pUtKWRSwlqLvdluncgewdUk/sUBUEoABYsWADA7t27ZY4kd3z99dd8//33mJiYyB2KkA2NGjXC398fNzc3uUPJlvSC+uvXr+enn36iRIkS8gYkCIIgCJnIlWRXv379qFevHsePH0eSJHbs2EGVKlVyY9eCIBRz9mYGTG/pyvSWrm8dp2Nqjm2Pwdj2GMxCnyAMdBR80bA0z776mujzxwj9ZRYlGrfFpusAvqhfgS8OPiAyPhlLY718OhOhsFGpU/jl1jWOhDzizzbdRVfGIqR3795MmzYNd3d3ABwdHQGKTMHtU6dOsWnTJmbNmsXQoUPFbPsCTlc37bXF1fXt73MFRZ06dQDQ09MjLCxMJLsEQRCEAumDP7mnpqZSp04d/Pz8qFy5cm7EJAiC8EHCY5JwLmGIuaEuSa26khL1ivhbV3l98iCvTx6krrUDw80bEvHQGUuPwnElXch/ekodFt74h7D4WE4+Daatc+H4Iiq8265duzh16hQnTpzQJLz+S5IkYmJiMDcvnEtZL168yJYtW/jmm29YuXIlS5cupUmTJnKHJWQhvUC9ra2tzJFkT3pHRrVajYuLi7zBCIIgCEIWPrgSplKppG7duty6dSs34hEEQfhgTuYGBL9O5HVCMjad+lJ50zHcd5zFrs8IdCwsUb4MZ/ijPSTPH/XW/UiSRNCrBPzDY7WK3AvFg1Kh4Ovq9VjWoAXVrQt+lzQhZ6pVq0azZs0y/fwSERGBpaWlDFHlnv79+3P37l26dOlChw4d6N69Ow8fPpQ7LOENycnJBAQEAGBjYyNzNNnj6OiIlZUVqamp3L59W+5wBEEQBCFTudL25Z9//qFGjRp4eHhQt25d6tSpQ926dXNj14IgCDnWt7oDyWqJxWeCNduMyrvjPHEuLnuvs6z+ZIJK18K++0DN/SkxUTz56RsSHqR96TgQ8Jw6K/+h7IJzVF16EftZpxm86xZh0ap8Px9BPl941OYLj9o4GItmBkWJQqFg48aNNG/enGbNmnHz5s0MYyRJkiGy3GVkZMSMGTO4e/cuJiYmeHh4MGXKFG7evIlarZY7PIG0WV03btwACs/MLoVCoZnd5e/vL3M0giAIgpC5HC1jfLPGRbrMWnULgiDIxdHcgG+bufDD8Uc8j0tidH1nTSfHGcce8ti6PlO//wIbW2PNY14d3k3E9tVEbF9NvIsnGwzrY1e/PXv6V03r5PgwkiVng/F5FMn5kXVwNDeQ8QwFQfgQkiSho6PD1q1b6devH82bN+fEiRN4eHhoxhTmOlcqlYpz585x584d7t69y927d7lz5w4qlYpFixaxcOFCDAwMcHd358qVK3KHW6ypVCr09PRITk7G3t5e7nCyrVy5cvj4+PD9998zaNAgucMRBEEQhAxylOzKqsZFekHXwl7jQhCEomN6S1fMDXWZeyqI1b6hmu31S1vg81ktKttpdykzrlCFEs078drnb4yD/PkWf5RBW7B80QWbLgOo16Qefao7UGflP0zzDmRtj4x1foSiSaVO4UToYxLVarqVrSh3OEIuUiqVbN26lf79+9O8eXOOHz+umbFSmDVr1gw/Pz+qVq1KxYoV+fjjjxk2bBgVK1akYsWKJCYm4ufnp5lRJMjHxcVFM4uwsMzsAqhevToAT5484dWrV1hZWckbkCAIgiC8IccF6tNrXJw4cSJDx8WIiAicnJzE1HhBEGSnUCiY8HEZRtd3xudhJNGqFNxsTfB0yHw5mmn1jzCt/hG/eV/nzIYNfJl4HnXwA14e2M7Lv3bieeA6pe1LMqZ+KeadCmJpJzdM9HXy+awEOXg9ukefEweoVMJKJLuKiP/O2lIqlWzZskUr4VWYZthk5uXLl5w/f16TkHiTkZERzZo1o1mzZvkbmJDB69evSUlJqwlZmJJd6R0ZLS0tMTIykjkaQRAEQcgoRzW7ikuNC0EQig4DXSWtK1rziad9lomu/7qtMuJizZ5U2+OL27q/se7UF6t2PdG3LwlAI5cS1A85z9PQZ3kdulBAtCtdDhczC5o5lUGlFo0KioI3P6ukJ7xatWpFixYt8PPzkyewXHL37t0sE11CwZLeidHU1BRDQ0OZo8m+9AvekZGRxMXFyRyNIAiCIGSUo2TXf2tctGjRgubNm2dIeBXmGheCIAgWhrpExCaRpJYwrVYPl+krcZnxs+b+Z3fvMtd/CTED6hC8YAqJT0R3s6LOQt+Ah71H8Euj1hjo5HhCtFAA/fXXX1hYWGhtS094tW7dmh49esgUmVDcrFq1CgADg8JVB9LMzIyyZcsCoki9IAiCUDC9VzfG9BoXLVu2pHnz5uJNThCEIqNnVXteJ6aw+WqYZlt6Ej9FnYqX7z3CrcoiJSbwfNdabnWvw4OJ/Ym5el7MbC3CxIWcoqVdu3aZJheUSiWbN2+mS5cuMkQlFEd37twBCl+yC9DU792+fbvMkQiCIAhCRjlexqh54P9fAU1PeIkip4IgFAWV7EzoV92BsfvvsvpiCAnJaTUIH7yIp/f2m+xJLoXZL0eo8IsX5g1bgSQR5XOIe5915M7AFiQ+foA6VWJ/wHMG7rxJt9+v8/Xf93nwIl7mMxNyw6Po17xWJcodhpAHrl69SlJSkubzzYULF+QOSSgG6tevD0D58uVljiTn7OzsANi8ebPMkQiCIAhCRjlexqj14CJW40IQBAHgtx6V6VnVnpH77mD742lKzz1DhUXnOfkwkl19PWla3hrzuk2osGwn7n9cwKbbIBQGhqhCg3htbM1Hv1yiy+/XuREWg0qdytpLT6n403nmnHwk96kJH2DQyb9w3fErux7ekTsUIQ/UqVOHoKAgIO3iXt26deUNSCgWdHXTlkaXK1dO5khyrmnTpkDarLTERHERQBAEQShYclR85G01LgYMGCBqXAiCUCQY6umwqWcVprUoy56bEcSo1LjZGtPDww4jPe0OjEZl3Sjz7RKcRn1Lwv1bdNwTyJPXiZwdUQvrmb0wdq+J5YhxLLwez7dHAnGxNKJvdQeZzkz4EJUtrVEqFDyMfi13KEIeEMuQBTmkF6gvTJ0Y06V3ZExJSUFfX1/maARBEARBW46SXe3atct0e3qNiwEDBrBjx45cCUwQBEFu5ayN+aqJS7bG6lnacNm+GmeCLnNwcHWqRt7mnv9l4vwv8/LgdkYP/4rrFeoy/1QQfarZixpQhdBnlaoxvFI1rA2N5A5FEIQi4tq1awCUKFFC3kDeQ4UKFTAwMCAuLo6goCBcXV3lDkkQBEEQNN6rQH2mOxI1LgRBKOYO3nmBvak+7SpaY1qjPhV+3otx5eqkxsUQsvR7pnp9jsnNs4RGq+QOVXgPVoZGItElCEKukSSJ06dPA2BoaChzNDmnq6tL5cqVAUTtXkEQBKHAybVkF4gaF4IgFG9JagkzAx2USgUKhQLzek2ptOkYZb5bhq6VLXrhj1jhN4eX3wwkRSyFK9RSxZI3oRiZO3cuderUwczMDDs7O7p27crdu3e1xkiSxIwZM3BycsLIyIimTZty69YtrTEqlYqxY8diY2ODiYkJnTt3JiQkJD9PpUBJSEjAyCgtgV62bFmZo3k/9vb2AEyZMkXmSARBEARBW64muwRBEIqzGk5mPHiZwJ2IOM02hVKJTdcBeOy9RMBHfUhR6KAb+QwdEzMZIxXeV6QqkT7H9+OybRVJarXc4QhCvvDx8WH06NFcvHgRb29vUlJSaN26NXFx/77WLViwgMWLF7Ny5UouXbqEg4MDrVq1IiYmRjNm/PjxeHl5sWPHDs6ePUtsbCwdO3ZEXUz/LxkbG2NpaQlAyZIlZY7m/aTP7Hr8+LHMkQhFXXR0NGvXrqV3795UqlQJW1tbSpUqRb169fjiiy/w9vYutq8lgiBkTiGJiqxaQkJCcHZ25smTJ5QqVUrucARBKERUKamUnncWTwdT9g+shrH+v8XsLzx+TcvfrvJtxVTG17XH2M0TgNTEeF6f+hvL1t1QKNOuP4REJbLv1nNiVCm42ZrQsZIN+rri2kRBkCpJOG35mWcJcRxt35NWpQrnbIzcVFTeN5VKJXfu3KFixYpyh1LgPX/+HDs7O3x8fGjcuDGSJOHk5MT48eM1M3xUKhX29vbMnz+fESNGEBUVha2tLZs3b6ZXr14APH36FGdnZw4dOkSbNm3eedzo6GgsLCyIiorC3Nw8T88xP0iShKGhIUlJSTx69AgXFxe5Q8qxv//+m/bt21OqVCmCg4NFPUoh10VHRzNv3jyWL1+ulWDPTOnSpZk6dSpDhw4VTRMEgaL3vplTOSpQLwiCIGTNQFfJzj4edNjoR+XFFxha24mS5gacehjJLv9n1HO2YHyvGlpJsPBNywlbu4CIHatxmDCXrwNN+O3SU3QUYGagy8v4ZBzM9FnXw532lWxkPDsBQKlQ8EujVjgZm1LXzknucARBFlFRUQBYWVkB8OjRI8LDw2ndurVmjIGBAU2aNOH8+fOMGDGCK1eukJycrDXGyckJDw8Pzp8/n2myS6VSoVL9W+MwOjo6r05JFjExMSQlJQGFsxsjoClfEhISovlSJQi55eTJk/Tv35+nT58CUKlSJfr06UP9+vVxcHBApVLx4MEDTp48yR9//EFwcDAjR45kxYoVrFmzhoYNG8p8BoIgyElMFRAEQchFTctZcWlMXVqUs2Th6ccM33ubf0KimdumPEeHaSe6AHTMLFAamRB38wqBQ1vjuOFbljQswfPvm/BiWhP8x39ETSczum6+zrmg1/KclKCle1k3PrIviVLMYChSpk+fjo2NSCi/iyRJTJgwgUaNGuHh4QFAeHg48G/9pnT29vaa+8LDw9HX19cs28tszJvmzp2LhYWF5ubs7JzbpyOr33//HQA9PT1MTExkjub9WFtba/5dRJF6IbdIksRPP/1EixYtePr0KeXLl2ffvn0EBAQwbdo0WrVqhaenJ7Vr16Z37978+uuvPH36lOXLl2Nra0tAQACNGzdm5syZYmmjIBRjItklCIKQy9ztTVn/aRViZzZDPacF9yY1YGLjMhjp6WQYa993JFX2/oNeix4AdHx6ikYLuhH/xypSk5PwcDBl38BqeNib8sPxh/l9KsIb1C+CSQm9k+VN/SJY7hCF9zR9+nTNTCUha2PGjOHGjRts3749w31vLmGTJOmdy9reNmbq1KlERUVpbk+ePHn/wAug27dvAxT65VZubm7Av8k7QfgQkiQxefJkJk2ahCRJDB06FD8/P7p06fLW1xNDQ0PGjh3LnTt3GDhwIKmpqUyfPp3u3bsTHx+fj2cgCEJBIZYxCoIg5CGl8t2zf/RtHdnfeioHE2qzLnInibf9CF02naSwEEp/NR89HSVj6pdi2J7bPI9Nwta0cH8xKqzUL4KJXtQdgEe6JvxhWhpbtYohMdpJSPNJe9GxKS1HiIKQp8aOHcv+/fs5ffq0Vn02BwcHIG32lqOjo2Z7RESEZraXg4MDSUlJREZGas3uioiIoEGDBpkez8DAAAMDg7w4lQKhWrVqAIV+xlr6rLSDBw/KHIlQ2EmSxNSpU/npp58AWLRoERMmTMhRLTgrKys2bdpEy5YtGT58OPv376dVq1YcOHBAXNAQhGJGzOwSBEEoACITUnhdxhP3TccoM20F+o7O2Pcbpbm/rFVae/rXiSlyhVjsSaq0K8PGvX7kVufvmWflwbqyTTEbuwWzsVsw7vWj1jhBKCokSWLMmDHs3buXEydOULasdmOGsmXL4uDggLe3t2ZbUlISPj4+mkRWrVq10NPT0xoTFhbGzZs3s0x2FXU6Ommzfd98PgubJk2ayB2CUEQsX76c+fPnA7Bq1SomTpz43k0PBgwYwLFjxyhRogTnz5+nYcOGhISE5Ga4giAUcCLZJQiCUAC4Whvx8FUCEXHJ2HTuh4fXFQxKltHcH3j4AKZKNY5mYlaX3HTsytKpWiO6lKnApFqNUDq5oVuyEjp2hfsLqyBkZfTo0WzZsoVt27ZhZmZGeHg44eHhJCQkAGnLF8ePH8+cOXPw8vLi5s2bDB48GGNjY/r27QuAhYUFw4YNY+LEiRw/fpxr167Rv39/PD09admypZynJ5vnz58Dhbc4fbouXboAEBkZSXJysszRCIXV4cOHmTBhAgDz58/n888//+B9NmrUiLNnz1KqVCnu3LlDixYtePbs2QfvVxCEwkEkuwRBEAqAvtUc0FUqmHHsYVoNG91/V5k//PtPamz4kh03Z6EfFSFjlEK6EgaG7GvTnSFuVUWh+vcQGhpK//79sba2xtjYmOrVq3PlyhXN/ZIkMWPGDJycnDAyMqJp06bcunVLxoiLt1WrVhEVFUXTpk1xdHTU3Hbu3KkZ89VXXzF+/HhGjRpF7dq1CQ0N5ejRo5iZmWnGLFmyhK5du9KzZ08aNmyIsbExBw4c0MxwKm7Sf+cLewdDFxcXzM3NUalU3L17V+5whELo8ePH9OnTh9TUVIYNG8bkyZNzbd9VqlTh3LlzlC5dmnv37tG6dWsiIyNzbf+CIBRcBSbZtWrVKqpWrYq5uTnm5ubUr1+fv//+W3N/dj74qlQqxo4di42NDSYmJnTu3FlMVxUEoVCwNNZjcYeKrPYNpdOm6/x99wU3wmJYfi6Y8YeDiNUzweHpLW73b0bM1fNyhysI7y0yMpKGDRuip6fH33//TUBAAD/99BMlSpTQjFmwYAGLFy9m5cqVXLp0CQcHB1q1akVMTIx8gRdjkiRlehs8eLBmjEKhYMaMGYSFhZGYmIiPj4+mW2M6Q0NDVqxYwcuXL4mPj+fAgQOFvl7Vhzh8+DAAKSmFe3m6UqnU1B/z9fWVORqhsElOTqZPnz68fv2aunXr8ssvv7z30sWslC5dmuPHj+Pg4MCNGzdo164dsbGxuXoMQRAKngKT7CpVqhTz5s3j8uXLXL58mebNm9OlSxdNQis7H3zHjx+Pl5cXO3bs4OzZs8TGxtKxY0fRclYQhELh849KsbOvJ49eJdB+gx/Vlvky8a/7GNVrTul13hhVqELKq+fcG9mFZ9tWIUmS3CEXe3HJSWx7EEBA5Au5Qyk05s+fj7OzMxs2bKBu3bq4uLjQokULypUrB6QlVpYuXcq3335L9+7d8fDwYNOmTcTHx7Nt2zaZoxeE3JNefL9MmTLvGFnwpZ9LemFxQciuhQsXcuHCBSwsLNi5c2eedSctX7483t7eWFlZ4evrS58+fcR3REEo4gpMN8ZOnTpp/Tx79mxWrVrFxYsXcXd31/rgC7Bp0ybs7e3Ztm0bI0aMICoqinXr1rF582ZN7YctW7bg7OzMsWPHaNOmTabHValUqFQqzc/ZuWosSRIpKSniBVIQhGzT0dFBV1f3nVcre1a151NPO25HxBGjUuNqZaTpvpi64QiPZ43n1eHdhCz+lrhbVynz3VJ0jEzy4xSETIw6683v928ytkotFruUevcDirCYmBiio6M1P2fVSW///v20adOGTz/9FB8fH0qWLMmoUaMYPnw4AI8ePSI8PJzWrVtr7atJkyacP3+eESNG5P3JCEI+cHFx4eXLl1SpUkXuUD5Y5cqVOXbsmFhRIeTI3bt3mTlzJgArVqzAxcUlT4/n4eHBoUOHaNq0KQcPHuSrr74SCVpBKMIKTLLrv9RqNX/88QdxcXHUr18/Wx98r1y5QnJystYYJycnPDw8OH/+fJbJrrlz5/LDDz9kO7akpCTCwsKIjxfdtgRByBljY2McHR3fedVSoVDgbm+aYbvS0BiXH3/FxKMWT5Z8T+SRPVi27IJls455FbLwDr3LVeJseAhlzQp3zZ3c4O7urvXz9OnTmTFjRoZxDx8+ZNWqVUyYMIFvvvmGf/75hy+++AIDAwMGDhxIeHg4APb29lqPs7e35/Hjx3kWvyDkt4iItBqMhb1APcAnn3zCihUr0NPTS6s7KWoZCu+QmprK8OHDUalUtG3blv79++fLcevVq8emTZvo1asXixcvxs3Njc8++yxfji0IQv4qUMkuf39/6tevT2JiIqampnh5eeHu7s7582n1ad72wTc8PBx9fX0sLS0zjEn/4JyZqVOnajp/QFrR3Dc/sKdLTU3l0aNH6Ojo4OTkhL6+vngzFwThnSRJIikpiefPn/Po0SMqVKiAUvl+q8gVCgV2vUdg5FaVmH9OiUSXDNQRjzR/b6GUuPNxYxQKhdb24iggIICSJUtqfs5sVhekvZfWrl2bOXPmAFCjRg1u3brFqlWrGDhwoGbcm++v4gu0UJRIklRkujEC1K1bF11dXV69ekVoaCilShXvma7Cu61Zs4YzZ85gYmLC6tWr8/X1vXPnzgwePJiNGzcyatQoXF1dadmyJX369OHQoUMsWrRIM9s4ISGBX375hWrVqtGkSRP09PTyLU5BED5MgUp2ubm54efnx+vXr9mzZw+DBg3Cx8dHc//7fPB915g3l1n8dwnGm5KSkkhNTcXZ2RljY+N3nY4gCIKGkZERenp6PH78mKSkJAwNDT9of2Y16mNWo77m5+RXz3l5YDv2A8YQEZdMjEqNk7kBxvrFs8tZXlAYpL3ux+/8PlvjihszMzPMzc3fOc7R0THDRaXKlSuzZ88eABwcHIC0i1iOjo6aMRERERkueglCYXX48GESExOBopHsMjQ0pHLlyvj7+3Pt2jWR7BLeKiwsjK+++gqAOXPm5HvdujNnzrBx40aMjIxISEjgk08+4eLFiyQnJxMdHa313TAgIIBJkyZhZmbGy5cvNdvFBRhBKPgKVLJLX1+f8uXLA1C7dm0uXbrEsmXLmDJlCvD2D74ODg4kJSURGRmpNbsrIiKCBg0a5Gqc7zsjQxCE4i2vXjuk1FQeTh1G7JWzHN13iDGunxOjZ4qJvg4Dajgws1U5Td0v4f3p2JTGfNJeJFXGZeySJHE56jWVrezQsSktQ3SFR8OGDbl7967Wtnv37mm+7JQtWxYHBwe8vb2pUaMGkHaxycfHh/nz5+d7vIKQF65fvw6kvS+YmmZctl4YlS5dGn9/fzZs2JChFq8g/Nf3339PTEwMdevWZfTo0Xl6rKNHj7Jy5Uq6du3K0KFDAWjUqBHlypXD09OTZ8+eceHCBTp27MiePXuYM2eO5qILgK6uLp988gkODg5as7q6d++Ovr4+M2fOxM3NLU/PQRCE91OgszaSJKFSqbQ++KZL/+CbnsiqVasWenp6WmPCwsK4efNmrie7BEEQChKFUsn96h1JVOpR5ck/HAmYzqlWRkz6uDR/+EfQcPVlnscmyR1mkaBjUxrdkpUy3HrcDKDBudPsixb1HN/lyy+/5OLFi8yZM4cHDx6wbds21qxZo/nCo1AoGD9+PHPmzMHLy4ubN28yePBgjI2N6du3r8zRC0LuSP9ybGdnV2Rmh6Svejh37pzMkQgF2Y0bN1i/fj0AS5YsQUcnb2egX7p0iQMHDvDbb79pthkZGfHgwQO8vLz4888/KVOmDIGBgUyaNAlXV1etWcrVqlXjjz/+YMWKFZptT58+5c8//2TXrl15Hr8gCO+vwCS7vvnmG86cOUNQUBD+/v58++23nDp1in79+mXrg6+FhQXDhg1j4sSJHD9+nGvXrtG/f388PT013RkFQRCKohhVCn3DK7C73yr0HUuj//wJZtN68qVpEL6j6/AqPpnpxx7KHWaRVs/OEUMdXcLiY+UOpcCrU6cOXl5ebN++HQ8PD3788UeWLl1Kv379NGO++uorxo8fz6hRo6hduzahoaEcPXoUMzMzGSMXhNwjSRKQNpOxqGjfvj2QdkFaEDIjSRKTJk1CkiQ+/fTTXJ+QoFar2bRpE3fu3NFsGzVqFFOmTGHdunWZPsbW1pb9+/djYmLCsWPHmDRp0juP4+TkxJUrV1i8eLFmVRLA+vXrOXPmzIefiCAIuUMqIIYOHSqVKVNG0tfXl2xtbaUWLVpIR48e1dyfmpoqTZ8+XXJwcJAMDAykxo0bS/7+/lr7SEhIkMaMGSNZWVlJRkZGUseOHaXg4OAcxfHkyRMJkJ48eZLhvoSEBCkgIEBKSEh4v5MUBKFYy6vXkDW+IZLO1GPSk9cJUvLrV9K90d2ly7UspSv1HaTX549J044+kEy/PyHFqVJy9bjCv16rEqUoVaLcYcjibe+bgpCboqKiJECKioqSO5QP9vPPP0uA1LVrV7lDyTWxsbGSUqmUAOnp06dyhyMUQIcOHZIASV9fXwoMDMz1/Y8bN+69/1/t3btXAiRA+u2333L8+FevXknm5uYSIB0/fjzHjxeEvFCU3jffR4GZ2bVu3TqCgoJQqVRERERw7NgxWrVqpblfoVAwY8YMwsLCSExMxMfHBw8PD619GBoasmLFCl6+fEl8fDwHDhzA2dk5v09FEAQhX919Hkc5KyNKWRiia2FJuSXbKdG0A1KSiicLp9K0jBmxSWqeRqvkDrXIstA3wFw/8+6DgiAIb/rnn38AKFGihLyB5CITExMqVaoEwJUrV2SORiho1Go1kydPBmDs2LG4urrm+jE+++wzrK2tadCggWb2ZHZ169aNmTNnAjBy5Mj3Wo7bu3dv6tWrR9OmTXP8WEEQcl+BSXYJhUfTpk0ZP358kT+mIBQWZga6PI9LIiklFQClnj5l567D9pOhVFi+i9A49f+PE3Ul8sOrxAS5QxAEoYDbv38/ACkpKTJHkrtq164NwMmTJ2WORChotm/fzq1bt7C0tOTbb7/94P1JksTq1av55ZdfNNvc3d0JCQlh8uTJ71UL77vvvuPTTz8lOTmZ7t27ExwcnO3HWlpa8uuvv3L69GlNQ6LU1FTGjx/PvXv3chyLIAgfTiS7ZHIjLIbd/s84eu8lqv//gprXBg8ejEKhYN68eVrb9+3bl6M3hL179/Ljjz/mdni5Ki+TY4cOHUKhUGR569mzZ54cVxCy0sPDjsiEFLZfD9dsU+rpU/rrReg5lWHVxRAaly2BtSSSMHlJkiQ+9d6H/eaV3HgZIXc4giAUYAYGaTNBXVxc5A0kl718+RKA3bt3yxyJUJCkpKTwww8/ADB58mQsLS0/eJ9eXl6MHDmSyZMnExISotluaGj43vtUKBRs2LCB6tWrExERQZcuXYiLi8vRPvT1/+1+vWrVKpYtW0bDhg1zvB9BED6cSHbls2uh0Xz08z9UW+bLp1v9abP+Gs5zz7D0bHCOp9u+D0NDQ+bPn09kZOR778PKyqpYFwlu1qwZYWFhWreQkBBatWqFjY0N33//vdwhCsWMh4Mpn3jaMWrfHTZdeaqZ4fXkdSIDdt3iYnAUP5g/xL9TNaIviqvteUWhUJAqSaRIqRwLDZI7HEEQCrD0JFfNmjXlDSSX1alTB4CICJHwF/61detWHjx4gI2NDWPGjMmVfXbt2pWOHTsye/ZsHB0dc2WfkLYc988//8TW1hY/Pz+GDBny3t/ROnToQPPmzZkzZw4mJia5FqMgCNkjkl356NazWJquuYIqJZV9A6ryaloT/Md/RHcPO748eI9ZJx7leQwtW7bEwcGBuXPnZjlGpVLxxRdfYGdnh6GhIY0aNeLSpUua+9+cNbV79248PT0xMjLC2tqali1bEhcXx++//461tTUqlXadoB49ejBw4MAsjx8XF8fAgQMxNTXF0dGRn376KcOYw4cP06hRI0qUKIG1tTUdO3YkMDAQSJvB5uPjw7JlyzSzrYKCgt75uOwyMjLCwcFBc7O1tWXSpElcu3aNEydO4OnpmaP9CUJu2PRpFTpUsmHwHwE4zD6N26LzuMw/y4Hbz9nSy4PS1/8mNS6GBxP7iYRXHvqhdiMCPh3GhKp15Q5FEIQCLDw8bSauvb29zJHkrlGjRqFQKEhMTCQsLEzucIQCIDk5WbMiZPLkye99wTw6OpqZM2eiVqeVZlAqlezfv5/x48ejo5O7ZRpKly7N3r170dPT448//mDWrFnvtR8XFxe8vb353//+p9kWGBjIxYsXcytUQRDeQiS78tH3RwOxNzPg9Oe16VLFDktjPTwcTFndrTLfNHPhxxOPiIjN23bNOjo6zJkzhxUrVmhN+f2vr776ij179rBp0yauXr1K+fLladOmDa9evcowNiwsjD59+jB06FBu377NqVOn6N69u6alsFqt1tSlAHjx4gUHDx5kyJAhWcY4efJkTp48iZeXF0ePHuXUqVMZCp3GxcUxYcIELl26xPHjx1EqlXTr1o3U1FSWLVtG/fr1GT58uGbmVXqjgrc97n2o1Wr69++Pt7c3x48fF4kuQTbG+jrs6leVm19+xLiGpelU2YbV3SoT+s3H9KnugMuPv2LRuB2SKjEt4eV7Su6QiyQPK1sqW9rIHYYgCAWYJEk8e/YMAAcHB5mjyV22trZUrlwZEEXqhTSbN28mMDAQW1tbRo8e/V77SE1NpWnTpkyfPp0FCxZotr9PXa7satSoEatWrQJg2rRpbN269b32o1QqNXEmJSXRu3dvGjVqxPbt23MtVkEQMieSXfnkVXwyfwY8Z1xDZ8wMdDPcP/HjMigVCrb7hWfy6NzVrVs3qlevzvTp0zPcFxcXx6pVq1i4cCHt2rXD3d2dtWvXYmRkxLp16zKMDwsLIyUlhe7du+Pi4oKnpyejRo3C1NQUIyMj+vbty4YNGzTjt27dSqlSpbLsUhIbG8u6detYtGgRrVq1wtPTk02bNmmu4qTr0aMH3bt3p0KFClSvXp1169bh7+9PQEAAFhYW6OvrY2xsrJl9lX7F522Pyym1Ws2AAQM0ia6qVavmeB+CkNuq2JsyvaUrizpUZHjdkprXG6WePq7zN/yb8JrQVyS88lh+LE0XBKHwOXbsGAkJaTUUi9rMLvi3SL1IdgkpKSmaWVFTpkx576V8SqWSL7/8Emdn53ztdDhs2DAmTpwIwJAhQzh27NgH7S8pKYny5ctjaWlJgwYNciNEQRDeQiS78klEbBKpElR1MM30fitjPZwtDAiNVmV6f26bP38+mzZtypDkCQwMJDk5mYYNG2q26enpUbduXW7fvp1hP9WqVaNFixZ4enry6aefsnbtWq16YMOHD+fo0aOEhoYCsGHDBk2h/MwEBgaSlJRE/fr1NdusrKxwc3PLMK5v3764urpibm5O2bJlAd7ZNeV9H/em9ETX0aNHOX78ONWqVXvrWEEoCDQJr4/bioRXHopPSWbihRNU2vUb8SnJcocjCEIBc/36dSDtC3xRrONjapr2WfePP/6QORJBbrt27eLRo0fY2toycuTIHD02Pj5eaxXKgAEDuH37ttZ3hPywYMECevfurenQ6Ofn9977MjU1Zdu2bVy9epUyZcpoticmJuZCpIIgvEkku/KJrYkeSgXcepZ5J47XCcmERKlwMNPP9P7c1rhxY9q0acM333yjtT19JsKbyShJkjJNUOno6ODt7c3ff/+Nu7s7K1aswM3NjUeP0uqP1ahRg2rVqvH7779z9epV/P39GTx4cJZxZXcmRKdOnXj58iVr167F19cXX19fIO2KSV487r/SE11Hjhzh2LFjmSa6goKCqFatGsOHD6dGjRqoVCo2bNhA3bp1qVq1KtOmTcv28QQhN72Z8Hp98qDcIRU5Rjq67A26x72oV+wPui93OIIgFDDly5cHiuasLvg32fXgwQOZIxHkJEkS8+fPB2DcuHEYGxtn+7HBwcE0bNiQjh07amZBArIkh5VKJRs3bqRZs2bExMTQtm1b7ty58977UygUmvIqkDYD0tXVlb///js3whUE4T9EsiufWJvo06GSDcvOBROXlHGmz7JzT0hJlehTLf9qN8ybN48DBw5w/vx5zbby5cujr6/P2bNnNduSk5O5fPmypgbDmxQKBQ0bNuSHH37g2rVr6Ovr4+Xlpbn/f//7Hxs2bGD9+vW0bNlS6wX+TeXLl0dPT0+rcGNkZCT37t3T/Pzy5Utu377Nd999R4sWLahcuXKG7pL6+voZZlRl53HvolarGThwoCbRVb169SzH3rp1i7Fjx3Ljxg0CAwM5dOgQFy5cwM/Pj2vXrnHhwoUcHVsQcotS3wDX+Rtwnjwf56/+rX3xLEbFb/+EsuTMYw7deYE6VSzDex8KhYJ5dZuwr3V3upd1e/cDBEEoVlJSUgA0s8uLmr59+6JQKFCpVKJIfTF2+PBhbty4gampKaNGjcrx40NDQ3n69CkPHz7Mg+hyxsDAAC8vL6pVq8azZ89o3rw59+/nzsWsBQsWEBYWxurVq0X5A0HIZRmLRwl55sdW5Wi4+jLN115hZqtyNClbgpAoFSsvPGHZuSd808wFR3ODfIvH09OTfv36sWLFCs02ExMTRo4cyeTJk7GysqJ06dIsWLCA+Ph4hg0blmEfvr6+HD9+nNatW2NnZ4evry/Pnz/XSoz169ePSZMmsXbtWn7//fe3xmRqasqwYcOYPHky1tbW2Nvb8+2336JU/puXtbS0xNramjVr1uDo6EhwcDBff/211n5cXFzw9fUlKCgIU1NTrKyssvW4t0lNTWXgwIHs27eP3bt34+joqOmmlM7W1lZTH6xixYqaOl7Hjx/nwoUL1KpVC0irTRYYGJjvU7EFIZ1S3wC7XsMBSFGnMvngbY4cu8hd0zIY6ekQl6SmdAlD1n/iTovyVjJHW/j0Kpf5xQFBEISiWpw+XbVq1XB3d+fWrVtcvnyZTp06yR2SIIP0WV0jRozA0tIyR48tXbo0Bw4cwNHRkdKlS+dFeDlmYWHBsWPHaN68Of7+/jRr1gwfHx/KlSv3Qfv9/fffqVSpEl9++WWeFtwXhOJIzOzKR9WczDgxvCaqlFTarr+G0fcnqbDoPJuuhDG3bXlmtf6wF8v38eOPP2a4ijBv3jx69OjBgAEDqFmzJg8ePODIkSOZvlGZm5tz+vRp2rdvT8WKFfnuu+/46aefaNeundaYHj16YGpqSteuXd8Z08KFC2ncuDGdO3emZcuWNGrUSJMkgrTpxDt27ODKlSt4eHjw5ZdfsnDhQq19TJo0CR0dHdzd3bG1tSU4ODhbj9u4cWOWbzSXLl1i27ZtxMfH0759exwdHTPcYmJiNOP/O11bkiQ+++wz/Pz88PPz48GDB/Tv3/+dz4Ug5Icv9t3Cdu3XbLz8HcHtFcTObMalMXWpaGNM+w3X8A2OkjtEQRCEIiN99nqJEiXkDSQP1a1bF0BTLkIoXi5evIiPjw96enp8+eWX7xyvUqkYNWoUly5d0myrV69egUl0pbOxseHYsWO4u7sTGhpK48aNuXHjxgft08DAgB9++EHr9WD16tUftFRSEIQ0CknMl9QSEhKCs7MzT548oVSpUlr3JSYm8ujRI8qWLYuhoeF7H0OSJC6FRHPvRTzmBrq0LG+Fsb7Oh4ZeoLVq1YrKlSuzfPlyuUN5qxkzZnDq1ClOnTr1QfsJCgrik08+4fLlywDcvHmTXr16cfbsWSwtLQkJCcHIyAhra+tciFooLHLrNSQ3PXwZT+UFPhwMW41VwBkUBkaUX7IN87pNUKWkUnflP5S0MODQkBpyh1roxCYnsfV+AFdfhPNr47Zyh5Nn3va+KQi5KTo6GgsLC6KiojA3N5c7nPdWokQJoqKiGDJkCOvXr5c7nDyxcuVKxo4dS506dfjnn3/kDkfIZ926dWPfvn0MHTo0027ub/rmm2+YO3cupUuX5u7duwXmM1JWwsPDadmyJbdu3cLc3BwvLy+aN2+eK/s+dOgQHTp0wMzMjFu3br21/IsgvEtRed98X2JmlwwUCgV1nS3oX8ORzu62RTrR9erVK3bs2MGJEycYPXq03OG805EjR1iwYMG7B+aQh4cHU6ZMoWnTpnh6etKzZ0/i4jJvViAI+WnHjWcYGBpS/5ftWDRqjaRK4MGXfYn1u4iBrpKxDZw5fO8lL+Oy38RBSBOfkszoc0dZc+c6d16/lDscQRAKCH39tGZEFSpUkDmSvBMUFATAtWvXRFfqYub27dvs27cPhULB5MmTs/WYr7/+mvr167NmzZoCn+iCtCXIZ8+epXHjxkRHR9O2bVs2bdqUK/uuVasWjRs3ZuDAgbIkuhITE4mKiuLFixc8e/aMmJgYUlNT8z0OQcgNomaXkKdq1qxJZGQk8+fPx82t4Bdqzq2i8S4uLppZXekGDhzIwIEDc2X/gpBbXsYl42RugKmpMa4LNhE4eSDR57x5MLEfldYfoZy1FZIErxJSsDbJn26xRYWdkQljq9SipIkpNoZGcocjCEIB4ejoyPPnz6lZs6bcoeSZtm3bsnjxYlJSUrh9+zYeHh5yhyTkk/QSIV26dKFSpUpZjrtz547mfnNzc86dO1eoalaVKFGCI0eOMHDgQP744w8GDx6Mj48PK1as+KCukfb29hw7dkyrzExCQgIqleqDlz6npKQQFBTEvXv3uHv3Lnfv3uXx48c8f/5cc/tv98v/MjY2xsTEBHt7e8qUKUOZMmUoXbo05cqVo3r16ri6umrVWBaEgkAku4Q8lX5lTxCEgqmMpSGPXiXwPDYJW1MDys3fwN0RnYm/dZX7X/TEb/hvGOgqcTATia73saRBC7lDEAShgHn69CkATk5OMkeSd1q0aEHTpk05efIkvr6+ItlVTISEhLBlyxYApkyZkukYSZKYNm0as2fPZs+ePXTr1g2gUCW60hkaGrJjxw6qVKnCDz/8wIYNG/D19WXr1q1v7dj+Lnp6epq/S5LEyJEjOXfuHF5eXjn6vxQXF8fFixc5c+YMZ86c4eLFi8THx79XTPHx8cTHx/P8+XNu3ryZ4X5TU1OqVatGjRo1aNSoEU2aNCmyTTiEwkMkuwRBEIqxvtUd+OrvB8w++YglHSuiNDSm/JLt3BnShuTn4RzzPkfPBs0wMxBvF4IgCB9KpVLx4sULoGgnuxQKBfXq1ePkyZNcvHgx047eQtGzdOlSkpOTadKkCR999FGmYxQKBVFRUUiShJ+fnybZVVgplUqmT59O48aN6du3LwEBAdSqVYuRI0cyc+ZMrKw+rKP1ixcvOHnyJCEhITx79uytya4XL15w9uxZzp49y5kzZ7h69SopKSlaYwwNDalQoQJubm64ubnh6uqKnZ0dtra22NraYm1tjaGhIXp6eigUChISEoiLiyM2NpaYmBjCwsJ4/PgxwcHBPH78mDt37nDz5k1iY2M5d+4c586dY+XKlQC4ubnRtGlTWrZsSZs2bTAzM/ug50IQckp8exEEQSjGbEz0md+2POMP3iMkSsXo+qUoaW7M1WHL2X72DtctKnJRhk6xRUmqJOETFsyT2BgGVhSzGwShONu+fTuQ9gX5Q78EF3TpyY707pNC0RYZGcmvv/4KZD2rK93ixYtp3749bdsWneYtzZo1w8/Pjy+++IJdu3bx888/s3PnTiZOnMhnn3323v/fbW1tuXLlCqdOnaJFC+3Z4o8fP9bM2jpz5gy3b9/O8HhnZ2c+/vhjPv74Yxo1aoS7u3uOlhsaGxtjbGyMra0tAFWrVs0wJiUlhbt37+Ln58elS5c4ffo0fn5+mqWSv/76K3p6ejRr1oxOnTrRqVMnypQpk8NnQhByTnRjfEN+dGMUBKF4KsivIb9fecrME48IfJlWq0GpgA6VbFjasSKu1sakREWiY16iUC4zkNupp8E0O7idEvoGhA8Yg4FO0brOJLoxCvmlKHSV+vLLL1m6dCnGxsZFvlHNli1bGDBgAECh/jcTsmf27Nl89913VK1aFT8/P63PCz4+Puzbt4/FixcXi88RJ0+eZMyYMQQEBABgZGTEwIED6dmzJ40aNdI0qciJxMRETTLp5MmTHD58ONP6Wu7u7prE1scffyxbUikyMpIzZ85w8uRJ/vrrL+7fv691f/369enbty89e/bEzs5OlhiLg6LwvvkhitYnbkEQBOG9DKzlRP8ajviFxRCjUlPe2oiSFmkJufi7/jwY3xv7fiOx7z9G5kgLn48dSuFhaUN9+5LEJCcVuWSXIAjZl16Q29XVVeZI8p6lpaXm75cuXcowK0UoOhISEli2bBkAX331lVZCKzw8nLZt25KYmEj16tUZNGiQXGHmm/RZXtu2bWPp0qX4+fnx66+/8uuvv2JmZkaLFi2oWrUqlStXxtXVFVNTU01R+6ioKKKionj69CmBgYE8ePAAPz8//P39MyxJBKhXr54msdWwYUNsbGzy+3QzZWlpSefOnencuTNLlizh7t277N+/n/3793Pu3DkuXLjAhQsXGD9+PC1btmTw4MF069YNAwMDuUMXihDxiVsQBEEAQKlUULNkxqs+MVfPkfw8jJCl09B3KIVly675H1whpqNUcuOTocXiarYgCG+XlJQE8NYudUVFgwYNaNCgAefPn8fX11cku4qwDRs28Pz5c1xcXOjVq5fWfQ4ODsyfP58LFy7Qs2dPmSLMf3p6egwaNIiBAwdy+vRpNmzYwKFDh3j+/Dn79u1j3759Od6nnZ0dderUoVy5cpw4cYLffvuNevXq5X7wecDNzY3JkyczefJknj59yq5du9i2bRuXLl3iyJEjHDlyBFtbW4YMGcJnn31GuXKihIbw4USySxAEQXgru94jUIUE8XznGh5NG4mejQOm1TMvPCtkTiS6BEGA4tGJMZ2lpSU9e/bk/Pnzom5XEZaSksKiRYsAmDhxIrq6ujx48AAzMzPs7e0BGDt2LGPHji2W74UKhYImTZrQpEkTUlNTuXz5MqdPn+b27dvcuXOH4OBgEhISiI+PJzU1FQsLCywsLLCzs6N8+fKUL1+eypUrU6dOHZydnTXPoSRJWs/nn3/+ibOzMzVr1pTrVLPNycmJ8ePHM378eO7fv8/mzZtZt24dT58+ZcGCBSxYsIC2bdsyZcoUmjRpUix/b4TcIZJdgiAIwlspFAqcJ8wmKTyEKJ9DPJjYj0rrDmPoUkHu0AqdJ7HRvEhMoIaNvdyhCIIgg0uXLgEUmKVGeS191snFixczfDkXioY//viDR48eYWNjw9ChQzl27BiffPIJNWrUwNvbG11dXfHv/v+USiV169albt26H7yv/z6nISEhDBw4kPj4eE6ePEmjRo0+eP/5pUKFCsycOZNp06Zx8OBBfv31V44cOcLhw4c5fPgwH330EdOmTaNt27bi90jIsey3YhAEQRCKLYWODq6z12DiUQt1VCT3x/Uk+WWE3GEVKtsfBFBm2yq+OH9M7lAEQZDJ6dOnAdDR0ZE5kvxRqlQpdHR0eP78OQ8fPpQ7HCGXSZLEvHnzAPjiiy8wNjamVKlSqNVqkpKSiIqKkjnC4sHQ0JA2bdpQq1YtTRfUwkZXV5euXbvy999/c//+fUaOHImBgQEXL16kffv2tGnTBn9/f7nDFAoZkewSBEEQskVpaEy5JdsxKFWWpNDHhK6cKXdIhUpjR2eUCgW6CiUqdcYis4IgFG1qtRo9PT0grWNacXD//n3UajUAZ8+elTkaIbcdOnSIGzduYGJiwpgxaQ1sKlWqhI+PDydPnsTa2lrmCIsHGxsbdu3apZlJB5CamsqSJUuIiYmRObqcK1euHL/88guPHz9m0qRJ6Ovr4+3tTfXq1RkzZgyxsbFyhygUEiLZJeSrly9fYmdnR1BQkNyhCEXQJ598wuLFi+UOo0jTs7Sh/LKdWLbpgfOkuXKHU6g4JEQS3KIN3jVroBP+gJTQO1o39YtguUMUBCEP6ejooK+vD0DFihVljiZ/1KpVi5IlSwJw6tQpeYMRct2cOXOAtLpdz54902yvWbOm5nddyD9mZmaav//2229MmDCBevXqaRLOhY29vT0LFy7k9u3bfPLJJ6SmpvLzzz/j6emJr6+v3OEJhYBIdhUjgwcPRqFQaG7W1ta0bduWGzduaI3p2rVrhseeOnUKhULB69evtfaR2W3w4MFZxjB37lw6deqEi4uL1rY6depgZmaGnZ0dXbt25e7duxke+8svv1C2bFkMDQ2pVasWZ86cydE+snuczLzt2JD2Jv/dd99RtmxZjIyMcHV1ZebMmaSmpr51v6tWraJq1aqYm5tjbm5O/fr1+fvvv3N8/DedPn2aTp064eTkhEKhyLTjS3bGvCmnz+HcuXNRKBSMHz/+nfuGt59ndp6radOmMXv2bKKjo7N1vP86dOjQW3+vi1MHoXcxLFMe19lr0TExe/dgAQD1i2CiF3XHaM0wYlb0z/QWvai7SHgJQhEWExPD69evAXB2dpY3mHxibm7OmjVrADGzq6g5c+YM58+fR6FQoFKp2LRpk9whCf9RtmxZypUrx4gRIwr9smlXV1f++OMPjh07RpkyZQgKCuLjjz9m9erVcocmFHAi2VXMtG3blrCwMMLCwjh+/Di6urp07NgxR/tIf3xYWBhLly7F3Nxca9uyZcsyfVxCQgLr1q3jf//7n9Z2Hx8fRo8ezcWLF/H29iYlJYXWrVsTFxenGbNz507Gjx/Pt99+y7Vr1/j4449p164dwcHB2d5HdsZk5l3HBpg/fz6rV69m5cqV3L59mwULFrBw4UJWrFjx1n2XKlWKefPmcfnyZS5fvkzz5s3p0qULt27dytHx3xQXF0e1atVYuXLlB415U06ew0uXLrFmzRqqVq2arX2/6zyz81xVrVoVFxcXtm7dmu1zStesWTOt3+OwsDBCQkJo1aoVNjY2fP/99zneZ3EgSRLhG5cRvvntv+vFnaSKB8C414+Yjd2C/qhNJI9Yj9nYLZiN3YJxrx+1xgmCUPQ8efIEgBIlSmjNwCjqGjZsiEKh4MGDB5pulELhFRcXh0qlYu7ctNndffv2Zd26dZpZXkLB0KpVK27evMno0aM1265cucKAAQMK7QqbFi1acP36dXr06EFycjIjR45k8uTJSJIkd2hCQSUJWp48eSIB0pMnTzLcl5CQIAUEBEgJCQkyRPbhBg0aJHXp0kVr2+nTpyVAioiIyHKMJEnSyZMnJUCKjIzU2r5hwwbJwsIiW8ffs2ePZGNj885xEREREiD5+PhottWtW1f6/PPPtcZVqlRJ+vrrr7O9j/cZk91jd+jQQRo6dKjWmO7du0v9+/d/674zY2lpKf322285Ov7bAJKXl9cHj8lMVs9hTEyMVKFCBcnb21tq0qSJNG7cuHfu633O883nSpIkacaMGdLHH3+c/ZPIQkpKitS7d2/JxsZGunHjxgfvT5IK/2tIZqJ8T0mXa1lKl2tZSiEHd0tPoxKlpBS13GEVOMkht6VXU2pJySG3pXW3r0slNiyRxp71zvT+wuZt75uCkJuioqIkQIqKipI7lPcyatQoCZAcHR3lDiXfVa9eXQKkHTt2yB2K8AF27dolOTg4SJ9//rkESEqlUnrw4IHcYQnZ1Lp1awmQBg4cKHcoHyQ1NVWaNWuWBEiANGrUKCk1NVXusAqkwv6++aHEzK5cok6Iy/KWqkrM/tjEhGyNzQ2xsbFs3bqV8uXL50sBydOnT1O7du13jkvv3GJlZQVAUlISV65coXXr1lrjWrduzfnz57O1j/cdk91jN2rUiOPHj3Pv3j0Arl+/ztmzZ2nfvn2W+36TWq1mx44dxMXFUb9+/RwdP69s3LjxrW1+s3oOR48eTYcOHWjZsmW29pvT88zsuUpXt25d/vnnH1Qq1dtP7i3UajX9+/fH29ub48eP4+np+d77KurM6jQmud0gAIJnjKT95DXYzzrNpL/uERmfLHN0BZODsQmvk1ScDQ8RVyMFoRhJn4lsYmIicyT56+7du9y5cwdImyEuZM+zZ894+vQpyckF571UqVQSHh7Otm3bAOjZsyflypWTOSohu2bPnk3btm2ZMWOGZtvz588L3UwvhULBt99+y/r161EoFPzyyy9MnjxZ7rCEAkhX7gCKCr+Ps669YN6wFRWW7dT8fKOVG6mJmS9VMa3ZELc1BzQ/3+xUnZTXLzOMq3X51XvFefDgQUxNTYG0aciOjo4cPHgQpVKZ6Zh0uVHYMCgoCCcnp7eOkSSJCRMm0KhRIzw8PAB48eIFarUae3t7rbH29vaEh4dnax/vMyYnx54yZQpRUVFUqlQJHR0d1Go1s2fPpk+fPm89XwB/f3/q169PYmIipqameHl5abo05fTcc5uFhQVubm6Z3pfVc7hjxw6uXr3KpUuXsr3f7J7n256rdCVLlkSlUhEeHk6ZMmVydL6Q9rs+YMAATaIru8swi6t9t57TK6Udvzjfo8aTC6y5s5i/G6zhp39COXLvJadH1MbSWE/uMAuUVqVcONGxN40dnN+aTBYEoWjx8PDAx8eHmjVryh1KvipTpgwpKWkdaE+cOCFzNAXPqVOn2LRpE9WqVdOqcerh4cGLFy+4ePEi9erVA+DGjRt4eXnRsGHDLC8o5panT5+yYcMGKlWqRI8ePQDo3r07ixYt0iQWvv766zyNQchdtWvXzlDvdsGCBSxZsoRZs2YVun/PIUOGADB06FB++uknXF1dGTVqlMxRCQWJmNlVzDRr1gw/Pz/8/Pzw9fWldevWtGvXjsePH2c6Jv3222+/ffCxExISMDQ0fOuYMWPGcOPGDbZv357hvje/FEqSlOkXxbft421jtm7diqmpqeb23+Lo7zr2zp072bJlC9u2bePq1ats2rSJRYsWaYp1vm3fbm5u+Pn5cfHiRUaOHMmgQYMICAh4r3PPbd26ddNcjX1TZs/hkydPGDduHFu2bHnrv3VW+33XeWbnuTIyMgIgPj7ntY/SE11Hjx7l+PHjVKtWLctxAsQlqRmyO4AuHg4M3LITE8/aKOOi6LZnMhf6uBASpWL6sYdyh1ng6Cl1aOZUBh2leAsWhOIkNjYWIMv3lqLK0NCQkydPAmmzvF6+zHgRtzgLCAhg48aNHDt2TGu7np4eSqUSR0dHzbbjx48zY8YMVq1apTV29uzZrF+/XtMA4X1IkqT1+Wbfvn189913zJgxQzMLWaFQcPv2bSRJon379sXud7mokSSJR48eoVartS7uqlQqkpKSZIws+4YMGcLs2bMBGDt27DubeAnFi5jZlUuqn3mS5X0KpXYHjKreWXevUyi0v/x4HPD7oLjeZGJiQvny5TU/16pVCwsLC9auXcusWbMyHQMQEhLywce2sbEhMjIyy/vHjh3L/v37OX36NKVKldJ6nI6OToaZTBERERlmAmW1j+yM6dy5s+bKGaTNENLR0cnWsSdPnszXX39N7969AfD09OTx48fMnTuXQYMGZbrvdPr6+prnu3bt2ly6dIlly5bx66+/5ujc81NWz+GVK1eIiIigVq1amm1qtZrTp0+zcuVKVCpVph1hsnueb3uu0r16lTbr0dbWNkfnlJ7oOnLkSKaJrqCgILp06ULdunXx9fXl0qVLbNu2jVWrVpGYmEjXrl2ZOXNmjo5Z2O28Hk6MKoVF7Sugb2JEucXbuDu0LaonDzGePYzRvX9mxT9Pmd+uPEZ6hbsTUF6RJImY5CSM5Q5EEIQ8l16gvnTp0jJHkv8aNWpE5cqVuX37NmfPnqVLly5yhySLixcvMnnyZBYtWqT5XNi2bVu+/vprmjdvrjX26dOnGZa6u7u7M2jQIBo2bKjZlpCQwPTp01Gr1bRq1YoSJUoAaV2mfX19ad68OU2aNAHS3nOuX79OQkKCVhmIOXPmsGLFChYvXqxZlfDpp5+yc+dOhg8fTmpqKjo6OgQGBrJx40YAvv3221x9boT8p1Ao2L17Nzdu3NAq2bFx40amTZvG999/z5gxY2SMMHumTp3K7du32bJlC3369OHatWs5/h4gFE3isnIu0TEyyfKmNDDM/lhDo2yNzS0KhQKlUklCQsK7B3+gGjVqZJiFA2lvvGPGjGHv3r2cOHGCsmXLat2vr69PrVq18Pb21tru7e1NgwYNsrWP7IwxMzOjfPnympuRkVG2jg1ps4iUb8zS0NHRITU1Nct9Z0WSJE29qeweP7+86zls0aIF/v7+WrMCa9euTb9+/fDz88uy9fH7nud/n6t0N2/epFSpUtjY2GT7vNRqNQMHDuTIkSMcO3aM6tWrZzru1q1bjB07lhs3bhAYGMihQ4e4cOECfn5+XLt2jQsXLmT7mEVBQEQc5a2NKWOZ9vusZ2lD+eW70LW2w6ZLf5q72ROtUhMa9f7104qyM2FP8Ny9nuGnD8sdiiC80+nTp+nUqRNOTk4oFAr27dundb8kScyYMQMnJyeMjIxo2rSpVrdcSJstMHbsWGxsbDAxMaFz5865cjGtMFCpVJrnI6uLcUVd48aNgbTfpeJqyZIlnD17lunTp2u2ubq6MnfuXFq1apVhvEKh0Jrh3qZNGzZu3Mjw4cM12xITExk3bhydO3fW+t06dOgQM2fO5MiRI5ptsbGx1KhRgwYNGmg+o0LaxcXw8HCtfxtbW1t8fHzo37+/5vPbzJkzUavVtG3bVpbPoULeqFq1qtbvmZeXFxEREVr14tRqNS9evJAjvHdSKBSsWrWKSpUqERoayogRI0RNVAEQM7uKnfRaRgCRkZGsXLmS2NhYOnXqlOfHbtOmDVOnTiUyMhJLS0vN9tGjR7Nt2zb+/PNPzMzMNPFZWFhokkITJkxgwIAB1K5dm/r167NmzRqCg4P5/PPPs72P7IzJzLuODdCpUydmz55N6dKlqVKlCteuXWPx4sUMHTr0rc/JN998Q7t27XB2diYmJoYdO3Zw6tQpDh/+98tvdo7/ptjYWB48eKD5+dGjR/j5+WFlZaW5opydMV5eXkydOlWz5PBdz6GZmVmGGmgmJiZYW1trbX9zv9k5z+w8VwBnzpzJUOj+bVJTUxk4cCD79u1j9+7dODo6Zphhln51qGLFippp3sePH+fChQuaWWyxsbEEBgZmKJhflJno6/AyPpkUdSq6OmnJXkNnVzz2XkLHxIxnfuGacUIadcQjzd9NoqO4FfmC0JgoIkvoiDdkoUCLi4ujWrVqDBkyRFO/578WLFjA4sWL2bhxIxUrVmTWrFm0atWKu3fvYmZmBsD48eM5cOAAO3bswNramokTJ9KxY0euXLmS5cWQoiIoKIhnz54B4OycdZ3XokqlUmmWL546dUreYGS0fPlyrK2tc3VWlKWlJT/99FOG7U2aNCE5OZmPP/5Ysy0+Ph5HR0dMTExQqVSaz7+DBw+mW7duWqsQ3nT37l22bNkCUOxmshc3Bw4cYP/+/TRr1kyz7cSJE7Rv354BAwawfv16GaPLnKmpKTt27KB27dp4eXmxe/duPv30U7nDEuSWf40fC4e3tVBPSEiQAgICpISEBBki+3CDBg3StGgFJDMzM6lOnTrS7t27tcZ06dIlw2NPnjwpAVJkZKTW9g0bNkgWFhbZjuGjjz6SVq9erbXtvzH997ZhwwatcT///LNUpkwZSV9fX6pZs6bk4+OTo31k9ziZeduxJUmSoqOjpXHjxkmlS5eWDA0NJVdXV+nbb7+VVCrVW/c7dOhQzX5tbW2lFi1aSEePHs3x8d+U/u/15m3QoEE5GrNhwwbpvy8T7/McNmnSRBo3bpzWtjf3m53zzM5zlZCQIJmbm0sXLlzI1vEkSZIuXryY5Xml3yIjI6VHjx5JtWrV0jxu2bJl0g8//JDleWemsL+GvOlaaLTEFG9ph19YhvtSU1OlJqsvSS1+Oia9OChazac8fyy9mlIrw23tzK7So6kfaX5Oef5Y7lBz7G3vm0LRBEheXl6an1NTUyUHBwdp3rx5mm2JiYmShYWF5j3/9evXkp6enrRjx7+vB6GhoZJSqZQOHz6c6XESExOlqKgozS39d60wtlA/ffq05j0lMTFR7nDyXWpqqmRjYyMBkkKhKJT/hu9j//790rJly+QOI1f06dNHAqROnTrJHYogg++++04CpJEjR2pt//3336Xg4GCZospo2rRpEiDZ2tpm+N5aHEVFRRXa983cIJJdbyjKya6C4K+//pIqV64sqdVquUMRiqCVK1dKrVq1yrB9+vTpUpMmTT5o328mu/z9/SV3d3fp1atXkiSlvXa8ePHirfsoiq8hnTZek8ynnZB23wiXUtSpkiRJ0rMYlfS/3bck44n7pYsda0qXa1lKLw5slzlS+aU8fywlh9zO8lYYE12SJJJdxdGbya7AwEAJkK5evao1rnPnztLAgQMlSZKk48ePS4DmNTNd1apVpWnTpmV6nOnTp2d6AaIwfmi/fPmyBEgODg5yhyKbn376SZPw2rdvn9zh5KnU1FTpxIkTkkKhkBQKRaE/35s3b0oKhUICpGvXrskdjiCTgIAA6cGDB5qfHz58KAGSrq6uFBMTI2Nk/1KpVFKlSpUkQPryyy/lDkd2xT3ZJWp2Cfmqffv2jBgxgtDQULlDEYogPT09VqxYkWH7kSNHWLBgQa4ey8PDgylTptC0aVM8PT3p2bMncXFxuXqMwmBrbw8auZTgk63+OM89Q83lvjjPPcOWa+Es71WTki07AhD04xdE+56SN1iZ6diURrdkpSxviRYOcocoCO8lfen3m41T7O3tNfeFh4ejr6+vVcbgzTFvmjp1KlFRUZpbeoH3wig4OBgonsXp002YMIGePXsCaNWRKipSU1M5duwYgwYNonTp0jRv3hwpbWIBXbt2xc7Ojl69erFjx44M9UYLuqlTpyJJEj169MiyrqlQ9FWuXJly5cppfn716hWNGjWiYcOGmJqaarYPGzaM7t27c+XKlXyPUV9fn6VLlwKwYsUKbt++ne8xCAWHKBEi5Ltx48bJHYJQRH322WeZbs+NwvEuLi5cvnxZa9vAgQMZOHDgB++7MDMz0OWvITW49CSKP/wjiFGlMLCmIwNqOGBtoo9UazpJz0KJPLqXwK8G4vbb3xhXqCJ32AVKREIcI88cxff5UwJ7j8BAR7w1C4XTfwscQ1rR+je3veltYwwMDDAwMMi1+OT0+PFjoHgnuyCtfusvv/zC0aNH5Q4lVx06dIipU6dy48YNre0WFhYAREVF8fz5c3bt2sWuXbuwsbFhwoQJjB07VitJUBAdP36cAwcOoKurq+ncLggAtWrV4syZM1qF7JOTk9m9ezfR0dFMmTJFs/3hw4fcv3+fxo0bv7VWcm5o06YNnTp14sCBA3z77bfs3bs3T48nFFxiZpcgCILwweo4W7CgfQVWdavM+EalsTbRB0ChVOIy42dMazYkNS6WB+N6kvRMzOz8L0sDQ3yfPyU0LpYToY/lDkcQcszBIW1W4psztCIiIjSzvRwcHEhKSiIyMjLLMUXZ8uXLgbRZB8VZ9erVUSqVBAYGEhgYKHc4H+zly5f06dOHDh06cOPGDYyMjKhevTpHjhwhJiaG169f8/r1a2JjYzl37hzffPMNJUuW5MWLF3zzzTdUqlSJv/76S+7TyJJarWbChAkAjBw5kkqVKskckVAQ6enpaf6uo6PDsWPHmDNnDrVr19Zs//3332nbtm2G5l1SHnVNnDdvHgqFAi8vLy5dupQnxxAKvgKT7Jo7dy516tTBzMwMOzs7unbtyt27d7XGSKKttSAIQqGj1Deg3KLNGLq6kRwRxv0veqKOjZY7rAJDT6nDusbtuPnJUNqVLvfuBwhCAVO2bFkcHBzw9vbWbEtKSsLHx4cGDRoAaTMA9PT0tMaEhYVx8+ZNzZiiLCwsDBAzuwYOHEhqaipQ+Jcy+vn5UatWLXbs2IGOjg6jRo1CX18fPz8/7ty5ozVjy8TEhAYNGjB79myCgoL4/fffKVu2LKGhoXTs2JH+/fvz4sULGc8mc+vXr+fGjRtYWloyffp0ucMRCgGlUkmdOnWYOnWqVpddIyMjSpUqRcuWLTXbIiMjKVOmDEOGDCEpKSlX43B3d6d///4AfP/997m6b6HwKDDJLh8fH0aPHs3Fixfx9vYmJSWF1q1ba9XASW9rvXLlSi5duoSDgwOtWrUiJiZGM2b8+PF4eXmxY8cOzp49S2xsLB07dkStVstxWoIgCAKga16CCsv/QM/GgdTEeFJev5Q7pAKljbMrVaxs5Q5DELIUGxuLn58ffn5+ADx69Ag/Pz+Cg4NRKBSMHz+eOXPm4OXlxc2bNxk8eDDGxsb07dsXSFvONWzYMCZOnMjx48e5du0a/fv3x9PTU+vLT1GVPvutOCT23qZhw4ZYW1sDFOqljEeOHKFhw4Y8fvyYcuXKcfHiRX7++WdWrlxJ8+bNsyyrAKCrq8uAAQO4efMmkyZNQqlUsnXrVqpXr46vr28+nsXbRUdH89133wEwbdo0zb+bILyPKVOmEBwczKBBgzTbTpw4wZMnT/D19dWa9bpv3z5Onz6ttTzyfcyYMQMdHR2OHDnC1atXP2hfQiElY3H8t4qIiJAAycfHR5Kk/GtrHRAQILoxCoKQJ8RriCTF378lJb14JncYBVpCcrLcIeSI6MZYPJw8eTLTzoiDBg2SJCntc9r06dMlBwcHycDAQGrcuLHk7++vtY+EhARpzJgxkpWVlWRkZCR17NgxRy3rC2tXKbVaLRkYGEiAFBgYKHc4skpJSdF0pjQzM5OSkpLkDinHvLy8JD09PQmQWrVqlaHDaGpqao725+vrK7m5uUmApK+vL/3666+5Ge57GzdunARIFSpUkFQqldzhCEVQQkKC5O3tLf3555+abampqVKZMmUkQDp48OAHH6NPnz4SIPXp0+eD91UYFdb3zdxSYGZ2vSkqKgoAKysrIO0KYnh4OK1bt9aMMTAwoEmTJpw/fx6AK1eukJycrDXGyckJDw8PzZg3zZ07FwsLC83N3d09r05JEASh2DMq746etZ3mZ1XIIxmjKXhmXD6L09afufI88+50giCXpk2bajrL/fe2ceNGIK04/YwZMwgLCyMxMREfHx88PDy09mFoaMiKFSt4+fIl8fHxHDhwAGdnZxnOJn+FhYWhUqnQ0dEpFuf7Njo6OtSoUQMbGxtiYmK4ePGi3CHlyN9//03Pnj1JTk6mV69eHDx4kEuXLpGQkKAZ866mDG+qW7cu//zzD926dSMpKYkRI0YwYsSID57V8iHOnTunqTO3fPnyYl9rTsgbhoaGtGzZks6dO2u2JSQk0LBhQ0qVKkXTpk012/fv38+ECRN48OBBjo4xefJkAHbt2qVpFCIUHwUy2SVJEhMmTKBRo0aaD0r51dY6ICAgt09HEARByMTLg9u52aMedzevYdnZYGYdf8gfN56hSkmVOzTZBEa/JlKVyO/3b8odSr6ZO3euZhlcOikbNToFobA4dOgQAI6OjlqFnIsrpVJJq1atgMJVt+vKlSv06NFDk+jaunUrly9fpn379nz00Ue8fv36vfdtbm7Onj17NEW116xZQ4cOHTQX//NTQkICQ4cORZIkBg8eTNu2bfM9BqH4MjY2ZuvWrQQHB2NiYqLZvmjRIpYsWcLmzZtztL8aNWrQokUL1Go1S5Ysye1whQKuQCa7xowZw40bN9i+fXuG+/KirbW5ubnmZmZm9v6BC4IgCNkW/yQI1ClELZvKgd93sPJCCD23+VN63lkO3n4ud3iymFrjI/a26sbij5rLHUq+uHTpEmvWrKFq1apa27NTo1MQCgsvLy8g5zN+iqrNmzdz8uRJoPAku8LCwujSpQsJCQm0adOGzZs3o6OjQ2xsLDY2NlSpUgULC4sPOoZCoWDKlCn8+eefGBsb4+3tTYMGDQgKCsqdk8imH374gXv37uHg4MDixYvz9diCkO7N18uvv/6aDh06aNXDCwsLy9b/j/TZXb/99luGjsBC0Vbgkl1jx45l//79nDx5klKlSmm2i7bWgiAIRcv3Vp04WLIZOkgsCFhOYDdTAibU5yNnc7ptvsGZR8XvA4m7pQ3dylZER1ng3p5zXWxsLP369WPt2rVaM7IlSWLp0qV8++23dO/eHQ8PDzZt2kR8fDzbtm2TMWJB+DDlyoluqwD6+vqaz/NXrlzh2bNnMkf0dgkJCXTt2pXQ0FAqVarEzp07NTP0Wrduza1bt1i9enWuJTM7derEmTNncHJyIiAggHr16uVb4fqLFy+ycOFCAFavXp1htYwgyKV9+/YcPHiQkiVLarZ9/fXXVK5cmd9///2tj23dujVVq1YlLi6OVatW5XWoQgFSYD5NS5LEmDFj2Lt3LydOnKBs2bJa94u21oIgCEXHvedxbLoWjuOURZjXb46UGM+D8b0pG/+U3f2rUs3RlB9PFO96XpIkkZxaeDoJx8TEEB0drbmpVKq3jh89ejQdOnTI0IkvOzU6BaEwSa8/265dO5kjKRhat27Nn3/+SfXq1ZEkiQMHDsgdUpYkSeKzzz7jn3/+wcrKigMHDmSYwWVtbY25uXmuHrdmzZr4+vpSrVo1IiIiaNq0Kbt3787VY7zp5cuX9OzZk9TUVPr06UOXLl3y9HiC8CGSk5MJDQ0lKSmJChUqvHWsQqFg0qRJAKxcuVLWenhC/iowya7Ro0ezZcsWtm3bhpmZGeHh4YSHh2sKPoq21oIgCEXHrhvPsDDUZWCd0rjO24CRW1VSIl9wd0QnUh7fY0x9Z7zvv+J5bJLcocriUHAg1fds4Kcbl+QOJdvc3d21Gr7MnTs3y7E7duzg6tWrmY7JTo1OQShMAgMDAXB1dZU5koLB0tKSzp0706NHDwD+/PNPmSPK2vbt29myZQs6Ojrs3r2b8uXLExMTQ6tWrThz5kyeHrtUqVKcOXOGDh06kJiYyKeffsr8+fORJCnXj5WSkkK/fv148uQJ5cuXF7NfhAIvfYLLhQsXqF+/vmZ7Vv8/evXqhZ2dHWFhYQU6wS7krgKT7Fq1ahVRUVE0bdoUR0dHzW3nzp2aMV999RXjx49n1KhR1K5dm9DQUI4ePapVZ2vJkiV07dqVnj170rBhQ4yNjTlw4AA6OjpynJYgCIKQiciEFBzM9DHU00HHxIwKK3djVNGDlJcRRHp7UcbSEICoxBSZI5XHi8QEbrx6zvq7N/Lki01eCAgI0Gr4MnXq1EzHPXnyhHHjxrFlyxYMDQ2z3N/71OgUhIImNTWVu3fvAmIZ45u6du0KgLe3N7GxsfIGk4mQkBBGjx4NwLRp02jWrBkAs2bN4tixYwwePDjPZ4iYmZmxb98+xo4dC6Qt2xo+fHiuHleSJMaNG8eRI0cwMjJi9+7dH1x/TBDyg0KhoG7dupqfw8LCqF+/PteuXcswVl9fn2HDhgGIZG4xUmCSXZm1s07vApJOtLUuGJo2barVNauoHlMQhLxTztqIh68SeBaTttRNz9KGiqv+pNT4mTh+9jUXgqMw0lPiYFY82533LleZnz5qhm/XgYUmwWNmZqbV8MXAwCDTcVeuXCEiIoJatWqhq6uLrq4uPj4+LF++HF1dXc2MrrfV6BSEwsLLy0tTS9bNzU3maAqO2NhYzp49i5mZGSqVqsAVqk9NTWXIkCG8fv2aunXr8s0332jumzJlCqNHj+bXX3/Nl+6aurq6LF++nGXLlqFUKlm3bh3t2rXj1atXH7xvSZKYNm0av/zyCwqFgm3btlGtWrVciFoQ8t+UKVPw9fXVdBN902effYZCoeDYsWPcv39fhgiF/FZgkl1C3hs8eDAKhYJ58+Zpbd+3b1+Ovkzt3buXH3/8MbfDy1V5mRw7dOgQCoUiy1vPnj3z5LiCUJT0qeaArlLB9GMPNR9IdC0sse8/hqfRKlacf0L/KpboPLknc6Ty0NfRYULVulgaZD3zqbBq0aIF/v7++Pn5aW61a9emX79++Pn54erq+s4anYJQWJw9exYAIyMjjI2NZY6m4FAoFHzxxReaDqt5XY8qp37++WeOHTuGkZERmzdvRldXV3OflZUVK1euzPcSKV988QV//vknJiYmHD9+HE9PT44dO/be+0tNTWXq1KnMmjULgGXLlmlm2wlCYbRixQo+/fRTvLy8Mv1u6+Lioqmd+Ouvv+Z3eIIMRLIrn6lfBJMSeifLm/pFcJ4e39DQkPnz539Q21UrKyutpaPFTbNmzQgLC9O6hYSE0KpVK2xsbPj+++/lDlEQCjxLYz2WdqzIr76hdNjox8Hbz7kWGs2SM4+p9/MljEjhizNzuTOsLbF+F+UOV3Ypqalyh5BrzMzM8PDw0LqZmJhgbW2Nh4dHtmp0CkJhkV6nq2bNmjJHUrCYmJgwevRoPvvsMwAOHDhAfHy8zFGluX37Nl999RUACxcupGLFigAFYqllx44dOXv2LBUrVuTp06e0atWKL7/8kri4uBztJyoqil69ejF//nwAfvrpJ81SSUEorCwsLNi1axcuLi5Zjhk5ciQAGzZsIDExMZ8iE+Qikl35SP0imOhF3YlZ0T/LW/Si7nma8GrZsiUODg5vLRysUqn44osvsLOzw9DQkEaNGnHp0r9Fkt+cNbV79248PT0xMjLC2tqali1bEhcXx++//461tXWGjlw9evRg4MCBWR4/Li6OgQMHYmpqiqOjIz/99FOGMYcPH6ZRo0aUKFECa2trOnbsqCkAO3jwYHx8fFi2bJlmtlVQUNA7H5ddRkZGODg4aG62trZMmjSJa9euceLECTw9PXO0P0Eorj6rV4rd/TwJiVLRadN1aq74hymHH9C4bAl8hlVHX60iNS6W+2M/JebKObnDlcXD6Nf0OvYn7f/+Q+5Q8lV2anQKQmGQ/hmjXr16MkdS8CxZsoTVq1dTtmxZ4uLi+Ouvv+QOieTkZAYMGEBiYiKtW7dm1KhRAMTHx+Pp6cmwYcM+6IJxbqhevTpXr17l888/B2Dp0qW4ubmxbt06kpLe3tRFkiT2799P1apV2b17N3p6emzcuJEJEybkR+iCkK+uXr3KlClTtJY0tmvXjtKlS/Pq1Sv++KN4fbYqjkSyKx9JqrQrVsa9fsRs7JYMN+NeP2qNyws6OjrMmTOHFStWEBISkumYr776ij179rBp0yauXr1K+fLladOmTaa1AcLCwujTpw9Dhw7l9u3bnDp1iu7duyNJEp9++ilqtZr9+/drxr948YKDBw8yZMiQLGOcPHkyJ0+exMvLi6NHj3Lq1CmuXLmiNSYuLo4JEyZw6dIljh8/jlKppFu3bqSmprJs2TLq16/P8OHDNTOv0uu2ve1x70OtVtO/f3+8vb01U8oFQci+Hp72XB9XjzsT63NpTF3CvvmYbX08cXawovzS7ZjVa0pqQhz3v+hJtO8pucPNd7pKJXse3cU7NIj7UR9en6WgOnXqFEuXLtX8nJ0anYJQGNy+fRuAypUryxxJwfTf8g//bUoll1mzZnHlyhUsLS1Zv369ZinU4cOHCQoKwtvbO8t6hPnJxMSEVatWcfDgQVxcXAgNDeV///sfLi4uTJ48mRMnTvD69WskSUKtVnPnzh1++eUX6tSpQ5cuXQgODsbV1ZVTp04xaNAguU9HEHLdixcvaNy4MQsWLGDz5s2a7To6OpoZpatXr5YrPCG/SIKWJ0+eSID05MmTDPclJCRIAQEBUkJCwnvtOznktvRqSi0pOeT2e93/oQYNGiR16dJFkiRJ+uijj6ShQ4dKkiRJXl5eUvqvQmxsrKSnpydt3bpV87ikpCTJyclJWrBggSRJktSkSRNp3LhxkiRJ0pUrVyRACgoKyvSYI0eOlNq1a6f5eenSpZKrq6uUmpqa6fiYmBhJX19f2rFjh2bby5cvJSMjI80xMxMRESEBkr+/f4YY3+bNx+VESkqK1KdPH8na2lry8/PTui80NFTq27dvjvcpFG0f+hpSHKkTE6R7X/SULteylK7Ud5Ben/OWO6R8t/rWNenag+tScsjtLG8pzx/LFt/b3jcFITdFRUVJgBQVFSV3KNkSFxcnmZubS4Dk4+MjdzgFUnJysrRx40YJcgoCHAAA2VNJREFUkAwMDKRXr17JFouvr6+ko6MjAdL27dsz3H/u3Dnp6NGjMkT2dgkJCdKiRYskR0dHCdC66enpZdhmZGQkTZkyRYqJiZE7dEHIU/Pnz5e6dOmS4XXl6dOnmv/rN2/elCm6/FHY3jdzm5jZVUzNnz+fTZs2ERAQoLU9MDCQ5ORkGjZsqNmmp6dH3bp1NVcn/6tatWq0aNECT09PPv30U9auXas1vXv48OEcPXqU0NBQIG19dHqh/MwEBgaSlJRE/fr1NdusrKwydDAKDAykb9++uLq6Ym5uTtmyZQEIDn77EtD3fdyb1Go1AwYM4OjRoxw/fjxD5xonJye2bt2ao30KgpCR0sCQcgt/x6JJe6QkFYET+/P69GG5w8pX/7OzoszaobIugRcEIef8/PyIjo4GoEqVKjJHUzD169ePwYMHY29vj0qlkm12V3x8PAMGDECtVtO7d2969+6dYUyDBg1o1aqVDNG9naGhIRMnTuTRo0d4eXnRp08fSpUqBaQty4S0EhyNGjXip59+IigoiHnz5mFqaipn2IKQ5yZNmoSXlxeWlpZa2x0dHencuTMAa9eulSM0IZ+IZFcx1bhxY9q0aaPVShnQrGl+MxklSVKmCSodHR28vb35+++/cXd3Z8WKFbi5ufHo0SMAatSoQbVq1fj999+5evUq/v7+DB48OMu4pEzaxGamU6dOvHz5krVr1+Lr64uvry/AO2sVvO/j/is90XXkyBGOHTuWaYvmoKAgateuTVBQENWqVWPw4MG4u7szcuRI9u3bR7169ahSpYpoeysI2aDUN6Dc/A2UaNEZha4euhaW735QEfLfJfAmYzbLsgReEIScCw8PB9KSEdbW1jJHUzA1b94cS0tLatSoAcDGjRtlieOrr77i3r17lCxZkp9//lmz/c6dO0RFRckSU04ZGBjQtWtXtm3bxpMnT4iLiyM4OJjnz58THR3NmTNnmDBhAnZ2dnKHKgj5QqlUan1//W9JnvSljL///jsJCQn5HpuQP0SyqxibN28eBw4c4Pz585pt5cuXR19fX9MqG9KuCl2+fDnLehMKhYKGDRvyww8/cO3aNfT19fHy8tLc/7///Y8NGzawfv16WrZsqamflZny5cujp6fHxYv/dl+LjIzk3r17mp9fvnzJ7du3+e6772jRogWVK1fOUCxUX18ftVqttS07j3sXtVrNwIEDNYmu6tWrv/Mxt2/fZurUqfj7+3Pq1CnOnTuHr68vY8eOZeXKlTk6viAUVwpdPVxn/0alDUcwrVb8Cj2rUPJjZCJuZ3yItXFBt2QlzU3Hrqzc4QmCkIn0WV2iOH3WBg0aREREBBs3bkRHRwdfX99MVxLkpaNHj2oSXBs2bMDKygqAlJQUPv30UypWrKj1WbmwMDY2xtnZGRsbG3R1deUORxBkk5SUxOTJkylbtqymaVmrVq0oU6YMkZGR7NmzR94AhTwjkl3FmKenJ/369WPFihWabSYmJowcOZLJkydz+PBhAgICGD58OPHx8QwbNizDPnx9fZkzZw6XL18mODiYvXv38vz5c63EWL9+/QgNDWXt2rUMHTr0rTGZmpoybNgwJk+ezPHjxzUt55XKf39VLS0tsba2Zs2aNTx48IATJ05k6CLj4uKCr68vQUFBvHjxgtTU1Gw97m1SU1MZOHAg+/btY8uWLTg6OhIeHq51ezPBBuDm5oabmxs6OjpUrlyZli1bAlC1alXNC64gCO+m0NXFqLy75uf4Ozd4/tdOLodEczLwFU9eF90W0vqksu9ZGEExUWy5f0vucARByIarV68CULNmTZkjKbgMDQ3R1dXF3t6eDh06APDrr7/m2/FfvXqlaZo0ZswYrWWKoaGhJCcno1arqVSpUr7FJAhC7tLV1cXX15fo6GhNmRkdHR3+97//AbBmzRo5wxPykEjzy0Ad8ShH2/PSjz/+yK5du7S2zZs3j9TUVAYMGEBMTAy1a9fmyJEjGdY7A5ibm3P69GmWLl1KdHQ0ZcqU4aeffqJdu3ZaY3r06MFff/1F165d3xnTwoULiY2NpXPnzpiZmTFx4kStKeRKpZIdO3bwxRdf4OHhgZubG8uXL6dp06aaMZMmTWLQoEG4u7uTkJDAo0ePcHFxeefjNm7cyJAhQzJdTnnp0iW2bdsGQPv27TONPTIykhIlSmht+2/XHqVSqflZqVRmmhwTBOHdksJD8B/RBWVcNHMqD2dfyZYoFNDezYZlnSpSztpY7hBzlQKYW6kKKgt7urlUlDscQRDeQZIkzSz19CV6wtsNHTqU/fv3s2HDBmbNmpUvNaVGjx7N06dPcXNzY/78+Vr3lSlThhs3bnD79m3NbC9BEAofpVLJ+vXrefjwIa1bt9ZsHzJkCDNmzODMmTPcvn1bdM0tgkSyKx8pDNK+fMXv/D5b43JbZnUQypQpQ2Ki9mwIQ0NDli9fzvLlyzPdz6lTpzR/r1y5MocPv7tYdFhYGP369ctWu2ZTU1M2b96s1SZ28uTJWmNatmyZobj+fxNUFStW5MKFCxn2/a7HBQUF0aRJk0zjqlevXrZrigmCkLdW3k3mmeVH9Iw7wne31zCrWWkuenZhzskgGq66zIVRdShrZSR3mLmqnZ09uiXF7AJBKAyCg4O5dOkSkDaTW8ja8+fP6dq1Kzdv3qRcuXIEBgayZcsWPv/88zw97vbt29mxYwc6Ojps3rwZY+OMn7/19fUzrc0qCELhUr58ecqXL6+1rWTJknTo0IH9+/ezdu1aFi9eLFN0Ql4RyxjzkY5Nacwn7c1QXPi/N/NJe9GxKS13qLnm1atX7NixgxMnTjB69Gi5w3mnI0eOsGDBArnDEAThLV7EJTH16EOSh83Art8oABJ/+Y5WJ5dzYZgnejoKpnsHyhxl3pIkSSTfBaEAO3nyJJBW11R0Ynw7Gxsbnjx5QnR0NG3btgVg6dKlpKam5tkxnzx5wqhRae8f3333HXXq1NHcd/36df744w/xGisIRVR8fDw+Pj6AdqH6NyeACIWfmNmVz4pSIis7atasSWRkJPPnz8fNzU3ucN4ps9lg78PFxYXLly8DaP4E2L17t+bvH330EQcPHsyV4wlCcbL1WjgKhYJvmpfFuuOP6BgZE/bbIp7vWovR1XN83W0mE2484+eulTAzKDpvc+lL3feEPWXug3t8X8GNLg6OsiyBFwTh7dJnslerVk0UB38HhULBli1bKFu2LBYWFmzdupW7d++yd+9ePvnkk1w/nlqtpl+/frx+/Zo6derw7bffau6TJInRo0dz7tw5fvzxR7777rtcP74gCPIJCQmhQYMGvHjxgrt379K2bVtKlSpFSEgIXl5e9OnTR+4QhVwkZnYJeSooKIioqCgmTZokdyiCIBQRj18nUtbSEBsTfRQKBU6ff0P5ZTvRtbQh4UEANW/sI0ktER6TJHeoueK/S+BjVvTn4vHfuR4dxYJzh4hZ0V+zND6vlsALgpBz6cXp69evL3MkhUPjxo1xdnbG3NycsWPHAjBnzpw8mV01e/Zszpw5g5mZGdu3b0dPT09zX0pKCi1btsTa2ppBgwbl+rEFQZBXyZIlcXFxwdbWlpCQEFGovogTl5oEQRCEQsXGRI/QaBXxSWqM9XUAsGjYCvftZwj7bSFHG32O4q8grIyKxltc+hJ4SRUPwPSUFIwD7zPBtT1memldchUGxsVu5rAgFGTps7pFcfqcGzduHIsXL+batWvs27ePbt265dq+z507xw8//ADAL7/8Qrly5bTu19PTY8aMGXz11VeZ1vASBKFwUygUbNu2DSsrK83/8aFDhzJz5kxOnTpFQEAA7u7u79iLUFiImV2CIAhCodK7qj2xSWrW/hOqtV3Pxh77SQtYfvkF7SpaY2Wky6Ppo4i5ck6mSHOPjk1pdEtWQrdkJUqU8WB2825Yu3hqtolElyAUHPfu3eP06dMANGzYUOZoCo979+7xv//9j6lTpzJu3DgApkyZQnJycq7sPyIigj59+mg6jvfv3z/LsSLRJQhFV6lSpbT+jzs7O9OlSxcA/o+9+w6PouwaOPybremBJCSkEELvNfQivUoXRVEEURSxvnZfK34qYm9YeEVsqGABBSki0nvvHQIJpJDeNlvn+2NhISSQAEk25dzXlQv2mWdnzs5smTnzlI8++shdYYlSIMkuIYQQFUrdQC8md4zgycVHeePfk6TkWFBVlS2xGQyevZMjybm80rcuyb9/Q+pfP3PkweGc/eJNVJvN3aGXin2p59wdghDiEj/88AMOhwOdTidT2V+D7OxsZs2axXfffcfkyZMJDg7m6NGjfP755ze8brPZzMiRI4mNjaVBgwbMmDEj3/Ljx49z6623cuLEiRvelhCi4li2bBkLFy7kP//5D+AcqD45OdnNUYmSIskuIYQQFc7HQxvyWJdavLbiBDVeX4P3yyvpOGMrJ1JNLJ7Qmg61/AkYfBuBQ8eCw0H8V+9y+P6bMZ897e7QS4yqqty7ejEtfv2aRaeOuTscIcR5ubnOLsctW7ZEURQ3R1NxtGnThv/+978sW7aM8PBwV3fDF154gVOnTl33elVV5f7772fDhg1Uq1aNhQsX4uvrm6/O008/za+//sqjjz56Q69BCFFxzJ07l4EDBzJ58mRat25N27ZtycvL48svv3R3aKKESLJLCCFEhaPTanhvSEPinu/OV6OaMG1AfZbc05pjT3eld/0AALRePkS98il13vgfGm9fcvZs5cAd3Uld9hsA8Zlmvtwcx7trTrHwwDls9tKb5r40KIpCkIcXCrAjOdHd4Qghzjt48CAA48aNc3MkFYuiKLzxxhv06NEDjUbDpEmT6NKlC9nZ2UyaNAmH4/q+o6dPn853332HVqtl3rx5hc4O/vrrrzNo0CDeeeedG30ZQogKYvjw4TRq1IgxY8YAuFp3zZgxA4ulckxyVNUpamlMc1KBxcXFUatWLWJjY4mIiMi3LC8vj5MnT1KnTh08PDzcFKEQoqKS7xD3MZ89zckXJ5GzZysAu26ayGSPQaiAt15DptlOhL+RWbc0pX/DQPcGew2yrRYOpqXQPjjUbTFc7XdTiJKUmZmJv78/GRkZ+Pn5uTucQtntdgICAsjMzGT79u20bdvW3SFVaEeOHKFVq1bk5eUxdepUXn755Wt6/owZM3j44YcB+OSTT1z/F0IIAIvFgsFgcP0/KiqK+Ph4vv/++6uO61dRVITfzdIkLbuEEEJUesawSBrN/IvQ+57CqjPynqUxbwyoR/JLN5ExtRc7HulA02Bvhn67i02nM9wdbrH56A1uTXQJIfKbM2cOmZmZ+Pr60qpVK3eHUyElJyczffp03nzzTRo2bOgaX+uVV17h559/LtY6VFVl+vTpruTWs88+y0MPPVSgXk5OTskFLoSocC4kui78/8L3xAcffIC0Car4JNklhBCiSlB0Oky3Ps6Qzp8wZewAnukRRTVPPTn7d9A6xJOF41vTJNibqf9UzAGKU/JMfLZ/h7vDEKJKmz59OgDh4eFotVo3R1Mx7dy5k+eee47p06eTnZ3NxIkTXbMz3nnnncyaNeuqz8/MzGTChAk899xzADzzzDNMmzatwPhpBw8epFatWrzzzjvX3UVSCFE5JCQkcMcdd9C8eXM8PT3ZsWMHy5Ytc3dY4gZJsksIIUSV8fPuRKx+gdzbPgyAnIO7OHzvIPaNiCb9t1k82q4GS4+kkJJTscZqyLFaaP7LLB5av5y/Th93dzhCVFmJic7x82699VY3R1Jx9enTh2HDhvHJJ59gNBoBeO+997jvvvtwOBzcd999jB07tsCg9Varle+++46WLVvy3XffodFo+Pjjj5k+fXqhEwV89dVXpKWlsXr1aplIQIgqbvr06fz888/897//5cEHHwTg1VdfldZdFZzO3QGIqiUlJYUmTZqwZcsWoqKi3B2OqGRGjx5Nly5deOKJJ9wdiiinUnKthPoZ8dQ7W1xYzpxC618dS0Icse88Swu/AO6p0Z/Uc80I9K443QO99QbGNWjGktgTBBhlPDgh3OHEiROkpKSg0WhkVr8boNFo+OOPP/KVabVaZs6cSXh4OK+//jo//fQTP//8M506daJOnTqkp6ezfv16MjKc3dCjoqL49ttvuemmm664nXfeeYdmzZrRq1cvSXYJUcW98sorHDt2jKlTpxIeHs7nn3/O5s2bWbp0KYMGDXJ3eOI6ScuuKmTChAkoiuL6CwwMZODAgezZsydfnREjRhR47qpVq1AUhfT09HzrKOxvwoQJV4xh2rRpDB06NF+ia9q0abRv3x5fX1+Cg4MZMWIEhw8fLvDczz77zDWwd3R0NGvXrr2mdRR3O4W52rYBbDYbL774InXq1MHT05O6devy2muvFdks/vPPP6dly5b4+fnh5+dH586dWbJkyTVv/3Jr1qxh6NChhIWFoSgKCxYsuK46l7vWfXih28Djjz9e5Lrh6q+zOPvq5Zdf5o033iAzM7NY27vU4sWLr/q+vu222655naL8qVPdg5OpJs5lO1tuVe87nBZ/7CTyuXcxhEWiyUzloeM/kz2+E3GfTMWefeX3UrrJSlxGHhZb+ej+MrVdN3bcMoHOIeHuDkWIKulCgqZHjx4EBQW5OZrKR1EUXn31VTZu3EifPn1QVZWNGzfy448/snjxYjIyMggODmb69Ons27fvqokucCbVJk6cSJ06dcroFQghyqtq1aqxcOFC2rZtS0hIiGvsrmeeeQa73e7m6MT1kmRXFTNw4EDi4+OJj49nxYoV6HQ6hgwZck3ruPD8+Ph4PvzwQ/z8/PKVffTRR4U+z2QyMWvWLO6777585atXr+ahhx5i06ZNLF++HJvNRv/+/fMNGjp37lwef/xxXnjhBXbu3En37t0ZNGgQp0+fLvY6ilOnMEVtG5xNX7/44gs+/fRTDh48yNtvv80777zDJ598ctV1R0RE8NZbb7Ft2za2bdtG7969GT58OPv377+m7V8uJyeHVq1a8emnn95Qnctdyz7cunUrM2fOpGXLlsVad1Gvszj7qmXLlkRFRTFnzpxiv6YLevXqle99HB8fT1xcHP369SMoKIiXXnrpmtcpyp+xrWuiURT+79+TrqbpGg9PaoyeSI3v1vN+u/+QElQHR04WKX/OQdEVbAC9+kQa/b7aQfWpq6k1bR0hr6/hPwsPk5prLeuXk4+nTo9eI2MECeEOFovFNZZUYTcNxbVTVZXFixczYsQIrNaL36/t27fnn3/+4fjx4/z000+8/fbbfPnll2zYsIGzZ8/yzDPP4O3tXeg6bTYbX331FTabraxehhCiAho/fjzVqlVj3759fP311+4OR1wvVeQTGxurAmpsbGyBZSaTST1w4IBqMpncENmNGz9+vDp8+PB8ZWvWrFEBNSkp6Yp1VFVVV65cqQJqWlpavvLZs2er/v7+xdr+b7/9pgYFBRVZLykpSQXU1atXu8o6dOigTp48OV+9xo0bq88991yx13E9dYq77ZtvvlmdOHFivjqjRo1S77rrrquuuzDVq1dXv/rqq2va/tUA6vz582+4TmGutA+zsrLUBg0aqMuXL1d79OihPvbYY0Wu63pe5+X7SlVV9dVXX1W7d+9e/BdxBTabTb399tvVoKAgdc+ePTe8PlWt+N8hlcXH606pPLtcHfHtLnX5kWT1QGK2+tnGWLX2tLVq6Our1ZiUHDVt1WI1+a+5ruc47HY19uNX1QVL1qna5/9R232yWf1qS5y6+NA59dnFR1T/V1aqTd/boKbkWNz4ys7H6nCoPxzZp96/ekmZbO9qv5tClKSMjAwVUDMyMtwdSgHTpk1TAVWj0ajnzp1zdziVQk5OjhoSEqIC6owZM0pkna+//roKqP3791cdDkeJrFMIUbnMmzdP9fPzU4cMGaICao0aNdTk5GR3h3VdyvPvZlmQll1VWHZ2NnPmzKF+/foEBgaW+vbWrFlDu3btiqx3YbyFgIAAwHm3dPv27fTv3z9fvf79+7Nhw4ZireN66xR32926dWPFihUcOXIEgN27d7Nu3ToGDx58xXVfzm638/PPP5OTk0Pnzp2vaful5ZtvvrnqOBZX2ocPPfQQN998M3379i3Weq/1dRa2ry7o0KEDW7ZswWw2X/3FXYXdbueuu+5i+fLlrFixghYtWlz3ukT580jXSH4Y04z9STn0m7WTpu9v5OE/DtEmzJf1D7andoAX1XoMInDwxa6rGWuWkvjtR0S8OJQfjn/EipsU7m0fzqBGQbw1qAGbH2pPfJaZV8vBTI4nstKZsGoxMw/t5u+4k+4OR4gqYd68eQA0adJEujCWEC8vL9544w2efvpp7rjjjhJZZ9OmTfH19eXOO++UcbqEEIXKysoiMzOTjIwMmjZtyrlz53jqqafcHZa4DjJAfQnJsTrHf/HS6V0/nha7HavDjk6jwajVFajrqdOjOV/X6rBjsdvRKho8dEXXvd6uKosWLcLHx8e57pwcQkNDWbRoERqNptA6F5REX+WYmBjCwsKuWkdVVZ544gm6detG8+bNAUhOTsZutxMSEpKvbkhICAkJCcVax/XUuZZtP/vss2RkZNC4cWO0Wi12u5033nijWCdne/fupXPnzuTl5eHj48P8+fNp2rTpdb32kubv70+jRo0KXXalffjzzz+zY8cOtm7dWuz1Fvd1Xm1fXRAeHo7ZbCYhIYHatWtf0+sF53t93LhxrkTXhW6YZ8+e5emnn76uLpKi/LmzTSh3tKrJ3oRssix26gV4EupnvGJ9Y0QU6a374rdrBQ2Or+PoxAH4tr+JkLsfwa99DxrV8ObBThF8uiGW6YPquwbAd4d6ftV5vk0nPLU6eoTWclscQlQVKSkprvErp02b5uZoKpd77723RNc3cuRIOnToUOT5qBCi6rrnnnvw8vJi9OjRbN26la5du/LNN99w6623XlNDBuF+0rKrhPjM/gCf2R+QnGdylb2zezM+sz/g4fXL89UN/v5TfGZ/wOlLBj6esX8HPrM/4N41+QfcjvrpC3xmf8DBtGRX2TeH9153nL169WLXrl3s2rWLzZs3079/fwYNGpRv+uZL61z4++qrr657mxeYTCY8PK4+S9jDDz/Mnj17+Omnnwosu/wOnKqqhd6Vu9o6rlZnzpw5+Pj4uP4uHRy9qG3PnTuXH374gR9//JEdO3bw7bff8u677/Ltt98Wue5GjRqxa9cuNm3axIMPPsj48eM5cODAdb32kjZy5EgOHTpU6LLC9mFsbCyPPfYYP/zww1WP9ZXWW9TrLM6+8vT0BCA3N7foF3iZC4muv//+mxUrVtCqVSvXsrCwMEl0VTIajUKrMF+6RVW7aqILwLN+UxaPeJ1nBn1G4JA7QKsja+sajj1yK7v7NcCWlUGfegFkmu2czbxyq8Jss43PNsbSe+Z22n+6hbt+3seaE2klPrX1a+2683ybzvlutAghSscbb7xBbm4ubdq0ueZxUMW1SUlJuebnZGZmkpx88Tw6PDxcWnUJIa5IURRuv/12dDodnTt35pFHHgHg7rvvJi4uzs3RiWshZ8FVjLe3N/Xr13c9jo6Oxt/fn//973+8/vrrhdYBSuSDHRQURFpa2hWXP/LII/z555+sWbOGiIiIfM/TarUFWjIlJSUVaAl0pXUUp86wYcPo2LGj63F4eDharbZY23766ad57rnnuP322wFo0aIFp06dYtq0aYwfP77QdV9gMBhc+7tdu3Zs3bqVjz76iC+//PKaXntZutI+3L59O0lJSURHR7vK7HY7a9as4dNPP8VsNqPVFmzxUtzXebV9dUFqaioANWrUuKbXdCHRtWzZsgKJLnC2TBw9ejTbtm0jJiaG4cOH06ZNG7Zs2UKPHj0YMGAA06ZNIzs7mwULFtCgQYNr2r4o/7wNWvbqaxL+wieEPvAciT/MIHXpL+iDaqLz9SfhuPP9a/3iZeJ8vPHr0gef1p3Q6A0AnEjJpe+snZxKMzGoURD1g7xYfSKNObsSmNIpgk+HNyqVCzDbuVPkmrLwukLiSzF6oQ2KLPHtClEVrF692jXRy1tvvSVJlFKSnp7Oww8/zNKlS9m6dWuxZ1DMyclh5MiRxMXFsXTpUpl5UQhxzUJDQ/H39yclJYVbbrmFf//994qTYIjyRZJdJST7nv8Azm6MFzzdqiOPt2iHTpO/AV3SuIcBZ9fECx5q1pZJjVuhVfLXjbljcoG6ExqV3PhBiqKg0WgwmUxFV75Bbdq04YcffihQrqoqjzzyCPPnz2fVqlUFTkQMBgPR0dEsX76ckSNHusqXL1/O8OHDi7WO4tTx9fXF19e3wPOK2jY4WxFpLjvOWq0Wh8Nx1XUXRlVV13hTxXntZamofdinTx/27s3f8vCee+6hcePGPPvss4UmuuD6X+el++qCffv2ERERcU1jptjtdu6++26WLVvGP//8Q+vWrYt8zsGDB5k3bx7169enefPm+Pj4sHnzZtesnFealVRUXLc0D+b//j3Jr3uTuKN1LSKffotaT7yBNTkBh0Pli01x9Az3IPfnH8kx55H4/SdovHzwbdcdv859uPdwIFpjEEee6kK9QC/A+R7+cvMZHlxwiOY1fXiwU+FJeoDD53J4d80pft2bRJbFTqMgLx7oGM4DHSMw6gpvqH301AEenD8TD9XB94lXHufP76nfJeElxDXKzc1lyJAhWK1WWrRoQb9+/dwdUqVlNBo5fPgw6enpbNy4sdhJq/T0dE6cOEFycjKpqamS7BJCXJOTJ0/y6quvYjab8fb2ZsuWLYwZM4bff/8dg8Hg7vBEESTZVUK89QXf7AatFkMhF/eF1dVrtIWOw3WlutfrwlhGAGlpaXz66adkZ2czdOjQ615ncQ0YMIDnn3+etLQ0qlev7ip/6KGH+PHHH/njjz/w9fV1xefv7+/qkvbEE08wbtw42rVrR+fOnZk5cyanT59m8uTJxV5HceoUpqhtAwwdOpQ33niDyMhImjVrxs6dO3n//feZOHHiVffJf//7XwYNGkStWrXIysri559/ZtWqVSxduvSatn+57Oxsjh075np88uRJdu3aRUBAAJGRkcWuM3/+fJ5//nlXl8Oi9qGvr2+BMdC8vb0JDAzMV375eovzOouzrwDWrl1bYKD7q3E4HNx9990sWLCAX3/9ldDQ0AItzAprJdaoUSPXuGNNmjRxDcbfsmVLlixZUqC+qPhahfkyrEkQD8w/iFajMKpZDXRaLWleQfz394OsO5XOX2ObENXgEzI2riBz4wpsqefIWLOEjDVLeBew9byFeoFdXetUFIXJnSJYdSKND9ad5oEO4Wg0BVuGrDmRxuBvdlHNQ8fDXWoR5mdk9Yk0nvzrKPP3n2PxPa0LHScs15TNas8QNBotiTd/QX3vy8ZjTDpJ7tyXUM3X3u1XiKruQmtegE8++URadZUiT09P5s+fz5EjR+jdu3exnxceHs6///5LYmJivlbnQghRHHXq1OHXX39l+/bt9OvXjz59+vDXX38xdOhQfvvttwLjXItypuwngCzfrjaFuslkUg8cOKCaTCY3RHbjxo8frwKuP19fX7V9+/bqr7/+mq/O8OHDCzx35cqVKqCmpaXlK589e7bq7+9f7Bg6deqkfvHFF/nKLo3p0r/Zs2fnqzdjxgy1du3aqsFgUNu2bauuXr36mtZR3O0U5mrbVlVVzczMVB977DE1MjJS9fDwUOvWrau+8MILqtlsvup6J06c6FpvjRo11D59+qh///33NW//cheO1+V/48ePv6Y6s2fPVi/9mriefdijRw/1sccey1d2+XqL8zqLs69MJpPq5+enbty4sVjbU1VV3bRp0xVf14W/tLQ09eTJk2p0dLSqqmq+/6uqqt5yyy3qypUrVVVV1Y0bN6o333xzoduq6N8hQlUz86zqkNk7VZ5drob832q15QcbVf1//1E9X1yhzt56Jl9dh92u5hzcpZ796l11yaie6uZ2gWr8d5+4lueeOKTuG91RPfbUOHXl1OfVgXe/rB7bvFm15WbnW0+e1a7WfH212uvLbWq22ZZv2dqTaarHCyvUF5YeLTRea9xB9dP/G6X+s3Wt+uD8g2rQa6tU4wsr1JYfbFRnbDit5p7ar6Y+G61a4w5e9z652u+mECWpPE2h/tVXX6mKoqiA+n//93/uDqdKOnbsmPr666+rZ8+edZUdOnRInTRpkjpr1iw3RiaEqKyWLVumenp6qoDatGlTdc+ePe4O6arK0++mO0iy6zKVOdlVHvz1119qkyZNVLvd7u5QRCX06aefqv369StQ/sorr6g9evS4oXVLsktcaltshvrckqPqQwsOqh+vO6Wm5liuWv/pv46oTV9boloz011lqcsXqNuiqxf6t+fmFmrKkl9UVVXVn3bFq8anFqn7D8eoDoejwLofXnBQDf6/1arFVvB71Rp3UE19Nlrt9MJ3aujrq9XnlhxVP153Sr3l+92q9vl/1Ps++V2SXaLMzZgxQ42KilKNRqPatm1bdc2aNcV6Xnk4aU9KSlIHDRrkuhny0EMPFfq5FKXL4XCovXr1UgH1kUcecZXPmTNHBdTg4GA1NzfXjREKISqroUOHun4D9Hq9+uSTT6rJycnuDqtQ5eF3052kG6MoU4MHD+bo0aOcOXOGWrVquTscUcno9Xo++eSTAuXLli2TMbREiYqO8CM6wq/Y9bvU9uedNTr2ZCq0PT98n2/7m6j/8TzyYo6y7N/NEHeMlrZE7OkpWBLiUAzOGSK3xWUy3HoY09hx7PavjkdUQzwi66IPDEFXPYjRFg/mJXsTm55H3fNjgV1gtTvHDWwc7M3yB7qQYslBqyg80jWS1SfSeGrWwpLZIUIU09y5c3n88cf57LPP6Nq1K19++SWDBg3iwIEDri705VFSUhI//vgjTz/9NDabDYAnn3ySd955R7ovusnEiRMxm83cc889rrLhw4czduxYJk+efNUhKoQQ4nqoqkqLFi1YsmQJHTp0YMOGDbz33nt8+umn9O7dm/vvv5+bbrqJgIAAd4cqAEVVS3i+8wouLi6OWrVqERsbW2A2v7y8PE6ePEmdOnXw8PBwU4RCiIpKvkOqLpvdQcP3NlLdU8eyiW0I8r44HuMf+5MYPWcv/9e/Hs/1jMKWnkJezFE8ohqiqxbAf5ceI/GXr5iy539whZ/sJ1s+xQ8fPEGEvwepy+dz+q2n0FcPwuTtTb3QTOL1rVlVqwFPAf1Cwvlj+F0AvDFnCVP2voTnlO/wiGx6Xa/tar+bQlyuY8eOtG3bls8//9xV1qRJE0aMGMG0adOu+tzMzEz8/f3JyMjAz6/4yearOXv2LDt37uTUqVN4eXkRFBSEyWTizJkzzJs3j+TkZDw8PNi3bx8XTpm1Wi2vvfYa//3vf0skBiGEEBXLmTNnCAsLY+nSpbz44ovs2LEj3/K6devSqlUrkpOTCQ4OZuDAga4xjjMyMsjJySE0NJSwsDDXJGcxMTEEBgbSqFGjfLPR34jS+N2sSKRllxBCCFHKdFoNv9/Vkn6zdlD37fWMaRlCuJ+RlSfSWHMynVHNavBUd2erFl21QHxaB7qeO7hxEN1r9OXWrx/mJmM6eScPYz5zClvaOaypyWzffxKfiNqE+zlbgtlSkrBnpGHPSMPoa4DQCOzrFhGo88Nyyz2kZKaTY7XgrTfQo0512AuJ2WZqu2XPiKrEYrGwfft2nnvuuXzl/fv3Z8OGgrOFms3mfDPuZmZmlnhM77zzDh9++GGx6kZHR3P77bczadIk/P39SzwWIYQQFUN4eDgAgwYNYuDAgdx///388MMPeHl5kZqayokTJzhx4oSr/m+//VbsdT/77LO89dZbJR5zVVSukl1r1qzhnXfeYfv27cTHxzN//nxGjBjhWq6qKlOnTmXmzJmkpaXRsWNHZsyYQbNmzVx1zGYzTz31FD/99BMmk4k+ffrw2Wefyd1mIYQQbtU6zJfdj3Xi801x/LI3kSyznUZBXvx0R3NubRGCtpBZGAG61vanc6Q/9y48yfxxLekwwDmzqdnm4K1VMbxqP8H3I5u5ulIFDh2Lb4ce2FKTWbh8JREZf1Bj8Ai65NlZEHeInjffhjHpBDZASTkFgF6rKZN9IKq25ORk7HZ7gTvWISEhBWbBBedsh1OnTi3VmIKCgqhWrRo5OTlUr16d2rVrYzQaCQoKIjY2lho1ajBmzBgGDBhAaGhoqcYihBCi4lEUhf/973988cUXmEwmrFYrO3bsYPPmzcyZM4fs7GyaNm1Kbm4uJpOJU6dOkZqaio+PD76+vjgcDmw2G0lJSXh5eckMjyWoXHVjXLJkCevXr6dt27bccsstBZJd06dP54033uCbb76hYcOGvP7666xZs4bDhw/j6+scBOXBBx9k4cKFfPPNNwQGBvLkk0+SmprK9u3b0WoLTst+OenGKIQoLfIdIq5XQpaZQV/vZFd8Nu0i/AjzNbDxdAbncqxM7VuXl/vWLfR5m3bupdHcewpddinfJ39DV+P62nZJN0ZRXGfPniU8PJwNGzbQuXNnV/kbb7zB999/z6FDh/LVL6xlV61atapsdwwhhBDiWkg3xnJk0KBBDBo0qNBlqqry4Ycf8sILLzBq1CgAvv32W0JCQvjxxx954IEHyMjIYNasWXz//ff07dsXgB9++IFatWrxzz//MGDAgDJ7LUIIIURJqelrZMvDHVh4MNnVKmxcm1AmdQincbD3FZ/XsXVzbln9KllZmXwwpCGNg513C612B9/vjGfGxjheHtiMW64z0SXEtQgKCkKr1RZoxZWUlFTo+CRGoxGj0VhW4QkhhBCiEilXya6rOXnyJAkJCfTv399VZjQa6dGjBxs2bOCBBx5g+/btWK3WfHXCwsJo3rw5GzZsKDTZdfldw6ysrNJ9IUIIIcR10Gs1jGoezKjmwcV+jqIofDqxHwO/3kmLuel0jlQJ8zOy/lQ6CVkqL/XuxqgehbcKE6KkGQwGoqOjWb58OSNHjnSVL1++nOHDh7sxMiGEEEJUNhUm2XXhLmBh4zycOnXKVcdgMFC9evUCdQobCwKubzwIh8NxTfWFEALku0O4R5ifkW0Pd2DBgXP8sjeRzDwbt7UI4f6O4TQLkXEhRNl64oknGDduHO3ataNz587MnDmT06dPM3nyZHeHJoQQQohKpMIkuy64MADvBaqqFii73NXqPP/88zzxxBOux2fOnKFp08KnXzcYDGg0Gs6ePUuNGjUwGAxFblsIIVRVxWKxcO7cOTQaDQaDwd0hiSrGoNNwW8sQbmtZMlNZC3G9xowZQ0pKCq+99hrx8fE0b96cxYsXU7u2dKUVQgghRMmpMMmumjVrAs7WW5fOhnPpOA81a9bEYrGQlpaWr3VXUlISXbp0KXS9l48HcbVprTUaDXXq1CE+Pp6zZ8/e0OsRQlQ9Xl5eREZGotHIzHdCiKprypQpTJkyxd1hCCGEEKISqzDJrjp16lCzZk2WL19OmzZtALBYLKxevZrp06cDEB0djV6vZ/ny5dx2220AxMfHs2/fPt5+++0SicNgMBAZGYnNZsNut5fIOoUQlZ9Wq0Wn00lrUCGEEEIIIYQoZeUq2ZWdnc2xY8dcj0+ePMmuXbsICAggMjKSxx9/nDfffJMGDRrQoEED3nzzTby8vBg7diwA/v7+3HvvvTz55JMEBgYSEBDAU089RYsWLVyzM5YERVHQ6/Xo9foSW6cQQgghhBBCCCGEuHHlKtm1bds2evXq5Xp8YSyt8ePH88033/DMM89gMpmYMmUKaWlpdOzYkb///htfX1/Xcz744AN0Oh233XYbJpOJPn368M0336DVasv89QghhBBCCCGEEEKIsqWoqqq6O4jyJC4ujlq1ahEbG0tERIS7wxFCCCHKNfndFGUlMzMTf39/MjIy8PPzc3c4QgghRLlW1X83ZZRkIYQQQgghhBBCCFFplKtujOWBw+EAnAPbCyGEEOLqLvxeXvj9FKK0XOiMcLWZs4UQQgjhdOH3sqp25pNk12USExMB6NChg5sjEUIIISqOxMREIiMj3R2GqMSysrIAqFWrlpsjEUIIISqOrKws/P393R1GmZMxuy5js9nYuXMnISEhaDQXe3n27NmTVatWFahfWPnlZVlZWTRt2pQDBw7kG0y/LF0p/rJaT3GfV1S9qy2XY3Rj65FjVDQ5RquuWibHqGoeI4fDQWJiIm3atEGnk3toovQ4HA7Onj2Lr68viqK4ytu3b8/WrVuva53FfW5R9a62vLBl11qWmZnpGhuvtMddkf1ZsmR/lizZnyVL9mfJKm/7U1VVsrKyCAsLy5fbqCrkrPQyOp2O9u3bFyg3GAyFDrxbWPnlZReaD4aHh7ttYLgrxV9W6ynu84qqd7XlcoxubD1yjIomx0iOUUk9r7IdI2nRJcqCRqMp9L2v1Wqv+/1c3OcWVe9qywtbdr1lfn5+pf79KvuzZMn+LFmyP0uW7M+SVR73Z1Vs0XVB1UvvXaeHHnqo2OVXqutOJRXT9a6nuM8rqt7VlssxurH1yDEqmhyj64unLMkxur54hKjIbuR97q7P7I2UlTbZnyVL9mfJkv1ZsmR/lqzyvj+rGunGWAaq+pSfFYEco/JPjlH5J8eo/JNjJETFJJ/dkiX7s2TJ/ixZsj9LluzPqktadpUBo9HIK6+8gtFodHco4grkGJV/cozKPzlG5Z8cIyEqJvnslizZnyVL9mfJkv1ZsmR/Vl3SsksIIYQQQgghhBBCVBrSsksIIYQQQgghhBBCVBqS7BJCCCGEEEIIIYQQlYYku4QQQgghhBBCCCFEpSHJLiGEEEIIIYQQQghRaUiySwghhBBCCCGEEEJUGpLsKgcWLVpEo0aNaNCgAV999ZW7wxGFGDlyJNWrV2f06NHuDkUUIjY2lp49e9K0aVNatmzJL7/84u6QxGWysrJo3749rVu3pkWLFvzvf/9zd0jiCnJzc6lduzZPPfWUu0MRQtwA+SyXDPn9Kllyzlby5Drlxsi1eOWlqKqqujuIqsxms9G0aVNWrlyJn58fbdu2ZfPmzQQEBLg7NHGJlStXkp2dzbfffsuvv/7q7nDEZeLj40lMTKR169YkJSXRtm1bDh8+jLe3t7tDE+fZ7XbMZjNeXl7k5ubSvHlztm7dSmBgoLtDE5d54YUXOHr0KJGRkbz77rvuDkcIcZ3ks1wy5PerZMk5W8mT65TrJ9filZu07HKzLVu20KxZM8LDw/H19WXw4MEsW7bM3WGJy/Tq1QtfX193hyGuIDQ0lNatWwMQHBxMQEAAqamp7g1K5KPVavHy8gIgLy8Pu92O3Gspf44ePcqhQ4cYPHiwu0MRQtwA+SyXHPn9Kllyzlby5Drl+sm1eOUmya4btGbNGoYOHUpYWBiKorBgwYICdT777DPq1KmDh4cH0dHRrF271rXs7NmzhIeHux5HRERw5syZsgi9yrjRYyRKX0keo23btuFwOKhVq1YpR121lMQxSk9Pp1WrVkRERPDMM88QFBRURtFXDSVxjJ566immTZtWRhELUTWVxXlJVfosl8X+rEq/X2V53lwVztnkOqR0ybW4uBpJdt2gnJwcWrVqxaefflro8rlz5/L444/zwgsvsHPnTrp3786gQYM4ffo0QKF3hhRFKdWYq5obPUai9JXUMUpJSeHuu+9m5syZZRF2lVISx6hatWrs3r2bkydP8uOPP5KYmFhW4VcJN3qM/vjjDxo2bEjDhg3LMmwhqpyS+D6Njo6mefPmBf7Onj1b5T7Lpb0/oWr9fpXF/oSqc85WVvuzqpJrcXFVqigxgDp//vx8ZR06dFAnT56cr6xx48bqc889p6qqqq5fv14dMWKEa9mjjz6qzpkzp9Rjraqu5xhdsHLlSvWWW24p7RCrvOs9Rnl5eWr37t3V7777rizCrNJu5HN0weTJk9V58+aVVohV3vUco+eee06NiIhQa9eurQYGBqp+fn7q1KlTyypkIaqkkvg+vVxV/iyXxv68XFX6/Sqt/VlVz9lK8/0p1ylyLS4KkpZdpchisbB9+3b69++fr7x///5s2LABgA4dOrBv3z7OnDlDVlYWixcvZsCAAe4It0oqzjES7lWcY6SqKhMmTKB3796MGzfOHWFWacU5RomJiWRmZgKQmZnJmjVraNSoUZnHWlUV5xhNmzaN2NhYYmJiePfdd5k0aRIvv/yyO8IVosoqifMS+SxfVBL7U36/LiqJ/SnnbBfJdUjpkmtxoXN3AJVZcnIydrudkJCQfOUhISEkJCQAoNPpeO+99+jVqxcOh4NnnnlGZncpQ8U5RgADBgxgx44d5OTkEBERwfz582nfvn1Zh1slFecYrV+/nrlz59KyZUtXX/3vv/+eFi1alHW4VVJxjlFcXBz33nsvqqqiqioPP/wwLVu2dEe4VVJxv+uEEO4ln9WSVRL7U36/LiqJ/SnnbBeV1OddrlMKJ9fiQpJdZeDyfr+qquYrGzZsGMOGDSvrsMQlijpGMiuH+13tGHXr1g2Hw+GOsMQlrnaMoqOj2bVrlxuiEpcq6rvuggkTJpRRREKIwhT3s1oU+Sw73cj+lN+vgm5kf8o5W0E3+nmX65Srk2vxqku6MZaioKAgtFptgcx8UlJSgQyzcA85RuWfHKPyT45R+SfHSIiKQT6rJUv2Z8mS/VmyZH+WLtm/QpJdpchgMBAdHc3y5cvzlS9fvpwuXbq4KSpxKTlG5Z8co/JPjlH5J8dIiIpBPqslS/ZnyZL9WbJkf5Yu2b9CujHeoOzsbI4dO+Z6fPLkSXbt2kVAQACRkZE88cQTjBs3jnbt2tG5c2dmzpzJ6dOnmTx5shujrlrkGJV/cozKPzlG5Z8cIyEqBvmslizZnyVL9mfJkv1ZumT/iqsq+wkgK5eVK1eqQIG/8ePHu+rMmDFDrV27tmowGNS2bduqq1evdl/AVZAco/JPjlH5J8eo/JNjJETFIJ/VkiX7s2TJ/ixZsj9Ll+xfcTWKqqpqSSfQhBBCCCGEEEIIIYRwBxmzSwghhBBCCCGEEEJUGpLsEkIIIYQQQgghhBCVhiS7hBBCCCGEEEIIIUSlIckuIYQQQgghhBBCCFFpSLJLCCGEEEIIIYQQQlQakuwSQgghhBBCCCGEEJWGJLuEEEIIIYQQQgghRKUhyS4hhBBCCCGEEEIIUWlIsksIIYQQQgghhBBCVBqS7BJCCCGEEEIIIYQQlYYku4QQQgghhBBCCCFEpSHJLiFEmZoxYwZRUVHodDqefvrpAstTUlIIDg4mJiamRLc7evRo3n///RJdpxBCCCGEuPbzNzkvE0KUNkVVVdXdQQghqoZ9+/bRpk0bFixYQNu2bfH398fLyytfnaeeeoq0tDRmzZoFwIQJE0hPT2fBggX56q1atYpevXqRlpZGtWrVitz2nj176NWrFydPnsTPz6+kXpIQQgghRJV3+flbUeS8TAhR2qRllxCizPz5559ER0dz8803ExoaWiDRZTKZmDVrFvfdd1+Jb7tly5ZERUUxZ86cEl+3EEIIIURVdT3nb3JeJoQobZLsEkKUiXr16vHCCy+wefNmFEVh3LhxBeosWbIEnU5H586dr3n9MTExKIpS4K9nz56uOsOGDeOnn366kZchhBBCCFGpDRs2rNBzKkVR+PPPPwvUv9L526+//kqLFi3w9PQkMDCQvn37kpOTk287cl4mhCgtkuwSQpSJjRs3UrduXd555x3i4+P57LPPCtRZs2YN7dq1u67116pVi/j4eNffzp07CQwM5KabbnLV6dChA1u2bMFsNl/36xBCCCGEqMxmz55NfHw8R48eBWDx4sWu86vBgwcXqF/Y+Vt8fDx33HEHEydO5ODBg6xatYpRo0Zx6Qg6cl4mhChNOncHIISoGnx8fIiJiaFbt27UrFmz0DoxMTGEhYUVKF+0aBE+Pj75yux2e77HWq3Wtd68vDxGjBhB586defXVV111wsPDMZvNJCQkULt27Rt8RUIIIYQQlU9gYCDgvFGpKArdunXD19f3ivULO3+Lj4/HZrMxatQo1zlXixYt8tWR8zIhRGmSZJcQokzs2bMHKHiicymTyYSHh0eB8l69evH555/nK9u8eTN33XVXoeu59957ycrKYvny5Wg0Fxuwenp6ApCbm3vN8QshhBBCVCV79uwhKirqqokuKPz8rVWrVvTp04cWLVowYMAA+vfvz+jRo6levbqrjpyXCSFKk3RjFEKUiV27dlG/fn28vb2vWCcoKIi0tLQC5d7e3tSvXz/fX3h4eKHreP3111m6dCl//vlngZOz1NRUAGrUqHEDr0QIIYQQovLbs2cPLVu2LLJeYedvWq2W5cuXs2TJEpo2bconn3xCo0aNOHnypKuOnJcJIUqTJLuEEGVi165dtGrV6qp12rRpw4EDB657G7/99huvvfYa8+bNo169egWW79u3j4iICIKCgq57G0IIIYQQVUFMTAyNGjUqst6Vzt8URaFr165MnTqVnTt3YjAYmD9/vmu5nJcJIUqTJLuEEGVi165dtG7d+qp1BgwYwP79+wtt3VWUffv2cffdd/Pss8/SrFkzEhISSEhIcN01BFi7di39+/e/5nULIYQQQlQ1DoeDU6dOERcXl29g+csVdv62efNm3nzzTbZt28bp06f5/fffOXfuHE2aNHHVkfMyIURpkmSXEKLUORwO9u7dW2TLrhYtWtCuXTvmzZt3zdvYtm0bubm5vP7664SGhrr+Ro0aBTgHrZ8/fz6TJk26rtcghBBCCFGVPProo6xfv57GjRtfNdlV2Pmbn58fa9asYfDgwTRs2JAXX3yR9957j0GDBgFyXiaEKH2KerVvLiGEKGOLFy/mqaeeYt++ffkGl79RM2bM4I8//uDvv/8usXUKIYQQQohrP3+T8zIhRGmT2RiFEOXK4MGDOXr0KGfOnKFWrVoltl69Xs8nn3xSYusTQgghhBBO13r+JudlQojSJi27hBBCCCGEEEIIIUSlIWN2CSGEEEIIIYQQQohKQ5JdQgghhBBCCCGEEKLSkGSXEEIIIYQQQgghhKg0JNklhBBCCCGEEEIIISoNSXYJIYQQQgghhBBCiEpDkl1CCCGEEEIIIYQQotKQZJcQQgghhBBCCCGEqDR07g6gvLHZbOzcuZOQkBA0GskFCiGEEEIIIYQQomJxOBwkJibSpk0bdLqql/qpeq+4CDt37qRDhw7uDkMIIYQQQgghhBDihmzZsoX27du7O4wyJ8muy4SEhADON0RoaKiboxFCCCGEEEIIIYS4NvHx8XTo0MGV46hqJNl1mQtdF0NDQ4mIiHBzNEIIIYQQQgghhBDXp6oOz1Q1X7UQQgghhBBCCCGEqJQk2SWEEEIIIYQQQgghKg3pxiiEEEIIIYQQQohyy263Y7Va3R1GuaPX69Fqte4Oo1ySZJcQQgghhBBCCCHKHVVVSUhIID093d2hlFvVqlWjZs2aKIri7lDKFUl2CSGEEEIIIYQQoty5kOgKDg7Gy8tLEjqXUFWV3NxckpKSAOcke+IiSXYJIYQQQgghhBCiXLHb7a5EV2BgoLvDKZc8PT0BSEpKIjg4WLo0XkIGqBdCCCGEEEIIIUS5cmGMLi8vLzdHUr5d2D8ypll+kuwSQgghhBBCCCFEuSRdF69O9k/hJNklhBBCCCGEEEIIISoNSXYJIYQQQgghhBBCiEpDkl1CCCGEEEIIIYQQotKQZJcQQgghhBBCCCFEKdiwYQOKojBw4EB3h1Kl6NwdgBBCCCGEEEIIIURpUFUV246T2PacQtFo0HWoh65JRJlt/+uvv+aOO+7gt99+4/Tp00RGRpbZtqsySXYJIYQQQgghhBCi0rGfOkfOc3OwH01A8fVAdajwyRJ0bevg/eYdaIL8SnX7OTk5zJ07lxUrVpCWlsY333zDyy+/XKrbFE7SjVEIIYQQQgghhBCViiMtm6zJ/0O1OfD5/D78/32FaitexvvdcdjjUsiaMgs1z1qqMcydO5eaNWvSoUMH7rzzTmbPno2qqqW6TeEkyS4hhBBCCCGEEEJUKuZfN6FmmfD9/D707eujKAqKTouhZzN8P56I40QiluW7SzWGWbNmceeddwIwYsQIkpKSWLFiRaluUzhJsksIIYQQQgghhBCVimXZbgz9WhbaVVFbvya6DvWxLCu9ZNfhw4fZsGEDY8eOBcDHx4fhw4fz9ddfF6hrt9tLLY6qqtIlu6ZNm0b79u3x9fUlODiYESNGcPjwYXeHJYQQQgghhBBCiDKiZpnQhFW/4nJNWHXUrLxS2/6sWbNo3749DRs2dJXdeeed/P7776SlpRETE0OrVq2YNGkSbdq0wWw2M3v2bDp06EDLli3LfGyvypZLqXTJrtWrV/PQQw+xadMmli9fjs1mo3///uTk5Lg7NCGEEEIIIYQQQpQBTXggtr2nC12mqiq2PafRhAeUyrZtNhvfffedq1XXBQMGDMDX15c5c+YAsH//fh555BH27NnD8ePHWbx4MRs3bmTXrl3s3LmTjRs3lkp8halsuZRKNxvj0qVL8z2ePXs2wcHBbN++nZtuuslNUQkhhBBCCCGEEKKsGEe2J/e137BuO46+Xb18yyxLd+E4kYjXU0NLZduLFi0iMTGR5s2bs2/fvnzLunfvzqxZsxgyZAgNGzakZcuWAKxYsYKNGzcSHR0NQHZ2NsePH6dz586lEuPlKlsupdIluy6XkZEBQEBA4Rlbs9mM2Wx2Pc7KyiqTuIQQQgghhBBCCFE6DIPaYFmyi+zHvsE4pguG3s3BZseydBfm3zdjGNQGXft6Ra/oOsyaNQuAfv36XbFOamoqXl5erseqqnL//feXePfFrKwsMjMzXY+NRiNGo7HI5xWVSynvKl03xkupqsoTTzxBt27daN68eaF1pk2bhr+/v+uvadOmZRylEEIIIYQQQgghSpKi0+Lz/niMt3XGMn8zWRNmkHXfF1hW7sdjcn+8Xr0VRVFKZdsLFy5EVdWr/l2eROrduzdz584lLS0NgLi4OFJSUm44lqZNm+bLeUybNq3I5xQnl1LeVeqWXQ8//DB79uxh3bp1V6zz/PPP88QTT7genzlzRhJeQgghhBBCCCFEBad46PF6bDCeD/TFHnMOFAVt3WAUfflLhTRv3pxnn32Wnj174nA48PX15eeffyYwMPCG1nvgwAHCw8Ndj4vTqqs4uZTyrvwd4RLyyCOP8Oeff7JmzRoiIiKuWO/yJnyXNu8TQgghhBBCCCFExaZ4GNA1Di+6YhmKiopi27Zt+cruvvtu7r777hLdjq+vL35+fsWuX9xcSnlX6ZJdqqryyCOPMH/+fFatWkWdOnXcHZIQQgghhBBCCCFEuVXZcimVLtn10EMP8eOPP/LHH3/g6+tLQkICAP7+/nh6ero5OiGEEEIIIYQQQojypbLlUirdAPWff/45GRkZ9OzZk9DQUNff3Llz3R2aEEIIIYQQQgghRLlT2XIpla5ll6qq7g5BCFFBHc1IJctquWodX72BBv4Vc/pdIYQQQgghhChMZculVLpklxBCXI+jGak0nPu/YtU9MmaSJLyEEEIIIYQQopySZJcQQoCrRdcPvYbQpHrh0/seTEvhrpWLimz9JYQQQgghhBDCfSTZJYQQl2hSPZC2QTVxqCp7UpJoFhCEXqN1d1hCCCHKQFHd2aUruxBCCFExSLJLCCEK8ebOjby0bS0daoSyeuhYPHTydSmEEJVZcbuzS1d2IYQQovyTqzchhCjEpMateGnbWm4KrSWJLiGEqAKK6s4uXdmFEEKIikOu4IQQVZ7ZbuPbI/vylYV4eXP89geo7ePnKqtsM5QIIYQo6EJ3diGEEEJUXBp3ByCEEO62JSmej/dtB+BkVoarvK5fNbQa59ekqqq8uG2t6/9CCCEqvy8O7GTRqWPkSGsuIYQQ12nDhg0oisLAgQPdHUqVIi27hBBVktluw6h1fgV2D63FbXUbMe/EYXKtVnYkJxSovy4hjqWxJwGIycogukZomcYrhBCibJntNv6z8V/y7Db2jZ7oKk8z55FtteCjN7gxOiGEEBXF119/zR133MFvv/3G6dOniYyMdHdIVYIkuyqJomYPAplBSAiAXJuVqdvX88uJQ+wZPdF1sfJ6+5uYd+Iwd6/6q8h1tAwMLu0whRBClLFzptx8j7OsFu5q0JR9qck0rR7EzpREAL45speBi3/htXbdeL5NZ3eEKoQQopjcPctuTk4Oc+fOZcWKFaSlpfHNN9/w8ssvl9r2xEWS7KoEijt7EMgMQkJoUPjlxCFOZmUw7/ghJjZuCUAD/wCOjJl0TUnjxNwcUs0mmlQPKvW4hRBClJ4si5nbV/wJwJr4WNoG1STIw4v/3TSoQN1TWZnYVAe1ff1dZWdyshj770L6htfmxTZdUBSlzGIXQghRuPIwy+7cuXOpWbMmHTp04M477+Sll17ipZdekt+JMiDJrkqgqNmDQGYQElXb4fQUGvoHoCgKHjodM7sPJM9u4+bIevnqXcuPXJ7Nxoi/f+dAWjILBoyiV1jtkg5bCCFEGfE1GBke1YBvj+wjxNP7qnU/7NKH6gYPAj08XWUrz55mTXwsOVYrL7Xt6ir/7cRhQry86VAjFINWW2rxCyGEKKg8zLI7a9Ys7rzzTgBGjBjBAw88wIoVK+jbty9nz57l6aefZs6cOaW2/apMkl2ViMweJER+qqry0PrlfHFgJ38OuIUhtesD0Dci6obXnWuzotdo0Go0hHv53vD6hBBClJ0si5n/27GBx1u0I8zb+R0+uUlrvj2yD4eqFjp248G0FNf/6/hVy7esV1gkM7sPwPuScbxUVWXK+r9JMuWyZuhYuofWAiDHasGo1aHTXH2eKHd3vRFCiMrCXdfJhw8fZsOGDcyePRsAHx8fhg8fztdff03fvn0JCwuTRFcpkmRXJffCljV46XRE1whxdyhClDlFUfDVG1CBjYlnXcmukhDg4cnym8dwJCONhtXkYkMIISqS8asWMz/mCDHZGczrOwLA1VLrrpWLrvpc30IGpg/39mVSk9b5yrKsFnqE1mJLUjwdgi9OavLxvu28uWsTz7XqyAttuxS6jfLQ9UYIIcSNmTVrFu3bt6dhw4ausjvvvJNbbrmFtLQ0MjIyGD16NNu2bSMmJobhw4fTpk0btmzZQo8ePRgwYADTpk0jOzubBQsW0KBBAze+mopHkl2VmM3h4KN928ixWZnbZ5irfHdKEntSkugeWouoS8abEKIy+DvuJE2qBVLLxw+AF9t0ZkRUAzqHhJf4toxaHS0Carge70s9xyvb1jGrxyCqGT1KfHtCCCFKxsttu3AgLZl7G7V0lRVn7MZraU3lZzAyr+8IVFXNNzbL1nMJZFst+BmMrrIsi5lxK/+iR2gtHm0eXS663gghhLh+NpuN7777jueeey5f+YABA/D19WXOnDkMGTIk37KDBw8yb9486tevT/PmzfHx8WHz5s188cUXfPrpp3z00Udl+RIqPEl2VWJWh5032t/E9uQE6l7S3P6nYweYvnsz9zVumW/g1Y2JZ2gVGIyXTu+GaIW4ca9sW8trOzYwpm5jfu47HHCOw1Iaia7LOVSVO1b8yb60ZKobPfiqR8FBjYUQQpQ9i93O+3u3Euzh5ZqUpHVQCAduuw/NZQMEl0YrqcsHIf613wj2pCQR6uXjKluXEMcfp46yJzWJ/7Rs7yrPsVkxaLQ0qx4kgxkLISotVVWx7z6FZdkuHOm5aMKqYxzWDkIq7lAhixYtIjExkebNm7Nv3758y7p3786sWbMKJLsaNWpEo0aNAGjSpAl9+/YFoGXLlixZsqRsAq9EJNlViXnq9DzWoh1AvrEnIn386BQcRo/QSFdZfG42Xf74AaNWS+r4x1wJL5PNiodWJydYokIYGdWQ6bs3E+btg0NVC1zElCaNovB9ryE8selfpnfsWWbbFUIIcXU/HjvA81tW428wMiyqPkEeXgBl+htxKY2i0Doo//ASTasH8XbHnhg0+Qexf3PnRk5lZ/JH/1EMi5LuK0KIykfNs5Dz/I9Y1x5CE1YdTVh1LFuOYf52NTzQG7rVcneI12XWrFkA9OvX74p1UlNT8z02Gi+2+NVoNK7HGo0Gu91eClFWbpLsqkQuHTj1asumNGvLlGZt8y0/nZ1JqJcPNTw887XsGr/qL9YnnOGTrn0ZVadRseIoakBVkEFVxY1TVZUfjx3ApjoY37AF4LxTf3rsgwQXMZNWaWkdFMK/Q+7IV3YwLZkm1YPcEo8QQggY16AZ804c4vZ6TQg0ehb9BDeo7evP0606FigP9fIhxZznGtwe4FhGGudMuWUZnhBClJrcN+Zj3XYC77fvQt+zKYpGg2q2Yv5pPRm/rEdtE3zD27jSdfLVrp9v1MKFC4usExMTU2rbF5LsqhQuDJRa1ICql9a9XMfgMM7cOYUMizlf+dZzCZzNzSbgkpPDTYlneGLTv9xcq16BgVWLO6AqlM2gqpJ4q7x+P3mEu1YuoprByOBa9ajh6bxT765EV2GWxp7g5qW/8p8W7Xi7Yy+3tSIQQoiqQlVVvj2yjwUxR/m9/0g0ioJWo2HxoFvdHdp1mdGtHy0DgvPN3PjgumWsSzjjxqiEEKJk2M+mYlm6C69nh2Po3dxVrhj1eEzoSW5yOmpOHqqqXtf6i3udfKVrZFGxSbKrEijOgKpQdFJHUZQCg2rvv/Vetp1LoH2Ni1O1rk2IY2PiWUIuSyr8d8tqrA4HcOUBVaHsBlUtb4k3ceMuHeR3RFQDutWMYGBEnXL7A7Un5RwOVSU1Lw9JcwkhROlLNOXwyIZ/yLZamHv8IHfUb+rukG7YpYmuTIuZbKsV2/nzLSGEqMis6w+DRsFwc1tUuwPzj+uwrj6Ax+R+6NvVw9CnBaQlgtkKntfeMrekJx4paVFRUWzbtq3A/wF+/fVX1/87derEokVFN2wR+Umyq5IorQ+ol07PTaH5+0nfUa8JoV4+hJxvSQOQY7Xw9u7N2M9n3ZtUD6RtUE12JCdwIjOdNkEh1POrDuDKzF9vhr64iprJCMp2NiNpZVa44uwXT62Of86cYnHscf4aeKvrTv2aoWPL9Xhyz7TuSKvAGvQKq12u4xRCiIrs0jEaa3r5ML1DD3JsVkbXLd7wC+VZYV1sPu3al+VxMTy/dY2r7O1dm6luNHJPo5b5kmNCCFGuma1g0GFPyiB36i/Y95wGIPvhr/F+9VaU+jUgDbiBy8aqdm1VEVmtVhISEsjNzaVGjRoEBJTMMSuVZFdpBSvKhwgfP+5q0CxfmdXhYGp0N9YnnmFJ7AlX+VeH9vD5gZ38X7vuvHi+y+OFrpKdFvyA6d4nXSdln+3fwaLTx7mzflPuPL9+m8PBp/u346c3Mq5hM/TnB249Z8rFZLcRaPTAu4hWPRcSb+4krcwKdy37xVunJ8dmZf7JI9xy/gKmIiSQBtSqm+/xy9vWMrhWXTqVwQyRQghR2S05fZz/bPyX+f1HusZHvHxc0oroWrrexGVn8vL2tZjtdqJ8/ekXUacsQhRCiBumqR8KJgtZd3wEFht4G9E1Cce27QSOhHRsmdkQpAW9tuiViQolOzubOXPm8NNPP7FlyxbM5ovDKUVERNC/f3/uv/9+2rdvf5W1XF2JJbvKIlhRflUzevBC2y7sSE7Il+wK9/KhY3AoUb7+rrIcmxVwNsu/9O7jrpQklsSeoMslSYBMi5n/bPwXgHENLybY3t69mXf3bOHJlu15t1NvwJkYa/bLLPwMBlbcfLur7obEM/x64jDda0YwKLKeq3zRqWPE5WTlex1ZFjMWhwMfvR6jtuRyweWtlVl5cbX9km214KM3uPbLo82jqe3jz4gKPBvVnKP7+b8dG3h39xaO3X4/Yd4VdzplIYQoDz47sJPDGalM3b6en/sOd3c4JeZaut6Y7Tamd+jJ2oRY+oZHuZYnmXKo4eFVIW4MCSGqJjUjx/kfiw1d2zp4Tb0NTYg/1pX70TQMJfP57+Gh7ig6SXZVJh988AFvvPEGUVFRDBs2jOeee47w8HA8PT1JTU1l3759rF27ln79+tGpUyc++eQTGjS49mvAErmaL6tgRcXzQtsuBQaxD/PyAWBB/5H5yu9r3JIuIeG0Dsw/48bY+k0x2WyuVl3g7LJg1Grx01+cnjXLauFIhnP6Vo9LElXbziXw7ZF9WFq2dyW7bA4HQ5f9ViDeD/Zu45Xt67i/cSu+vGmgq7zh3JkoKKwccrsrQfFnzFHmHDtA77DaPNC0tavuR3u3oSgwrkFzqp8fAy0x1/lF7q3Xu72VWXl0aes7h6oydfs63t+7jc0jxrmSYKPrNqrw+254VAOG125Ap5AwSXQJUYZUVcV+IA77wTOg16Lv1BBNiH/RTxTljs3hwKGqGLTOc4JPu/ajSbVAXo7u6ubISl5xW3kbtToea9GOx1q0c5VZ7HY6Lfie2j7+fNNzMLV95f0uhCh/DP1aYp63EfuhM9jiUrAs3I6mViD2fbGYX/sVpWEIio8H9nOZqGEGFK10064MNmzYwMqVK2nRokWhyzt06MDEiRP54osvmDVrFqtXr3ZfsqusghWVw4U7jDUuGfMLoENwGB2Cw/KVBXh4Mqf30ALreK9zb97r3BvHJeN+eev0rB46lmyrxXUSDNA6MJhqzaO5qebFscesDjsdaoSSYjZxPDPdVW622wHnWGUXOFSVoxlpQP5BYvelJTPvxCF89QYeoLWr/IWta8ixWRkSWd+V7FoZ7+x//tn+nYyIauiqu/rsaTqFhJVoK7KKTqMo7Ek9R7bVwvdH93NrJRhz5QIfvYHf+4/MN1h9psWMTqPJ954TQpQce8w5cl6ei/1AHGgUcKig1WAY2Bqv50ageJbPCS5EQdvPJXD/2qUMjazPq+26AVDb15+3O/Vyc2Tlz5Zz8ZzJycZksxHkce2DOgshRGlwnMvENPMfvJ4YguJpQNFo8P1qMo7TyeR9t5q871ZDnhUlwAePWzujjulI0tFjqEouDus5NLWCUKRLY4X3yy+/FKue0WhkypQp172dErnCLqtgRcVR2ICqxVl2rTSXNM03aLUFBtMHuCm0Fo+3yN991lOnZ/PIu9mRnED079+6yt/ocBOvtevmGmgfQAG2jRxPrs3qSl4B9I+IwldvoHG1/Hdeb6/XhGyrhWqGi63OLiQyQrwuJvi2JsXTb/FcWgeG8Pfg267xlVcudtWB3eFAez6Z+H6n3txVvxmj6jRkZ0qim6MrWZe+Z20OB2NW/EFynok/B9xC6PlWj0KIkuFIyiDrgZkofp74fDgBXeeGYLJg/msHpk+X4kjNxufje6Sbl5sVNVHJhe56xzPT2JGcyJmcbJ5p3VFuElxFt5oRHL39fo5lpOUb2/SjvdsYEdVAWnoJIcqUqqpYl+0m9+0/UDNNKEYdXk8NA5wNIbS1a+D90mi8XhjlHLvLqEdRFPLy8lC8PVA1CmqeFXtMEtrIIBSjfP9fylFJZuhNTk5m8+bN2O122rdvT2ho6A2tT5qTiBJV3AFVL61b3mg1Gi69X6AoCtE1Cnafa1cjlHY1Cn4Av+oxqEDZsNr1mbp9Pf+5JOmWbsnDR2+glrcv/pckxqqiKeuWMzSyvqsbSh2/atTxq+beoMrAicx0tiYlkGuzcjYnW5JdQpSwvB/Xgc2O75f3owk4//ny8cBjTBc0YdXJ+c+32LYdR9++vnsDrcKKO1HJkTGTuLVuY05nZzKuQXNJdBVDpI8fkT5+rsfrE+J4fOMK/rt1DbFjHyRAWnwJIcqAIy2b3GkLsP67DwBtk3CMozoWWlfRaMDj4jWiwWBA62EgETMBDgMGsw1OxKMJqYbGq3xeS5YlVVWxWCycO3cOjUaDwVBx98lvv/3GvffeS8OGDbFarRw+fJgZM2Zwzz33XPc6SzXZVdKZOVH+FWdAVbh4l7YslFUrs2vVL6IOO0dNoJrRI1+rAlslycxfi23nEjiSnspDzdoSWIVOvhtWC2DzyHEcSU8tNKEqhLgxlsU7MQxpi+LvhW1XDNaNR1CCfDEOaoO+W2M0dYKxLN4pyS43utJEJUmmHF7bvoGY7Azic3PIslpQFIWnWhV+gSSK5qs30Csskvp+1fMlui5tWS2EECXJsnIfuW/OR03LAa0Gj0l98JjQs9gDzms0GurUqUN8fDwJ2dnO9VhtkHAGxd9LhiI4z8vLi8jISDQV6Ls8OzsbH5+LN/qnTp3Kli1baNjQOeTPX3/9xaRJk8pnsqs0MnOiYriQxFJVFfuhM6gZuWhCq6OtXaNM4yiPrcwKS66lmE35lk1Z9zcLB46uUq18RtdpxO31m3AqO4NT2Rn5lrkzIVkW6vlVp55fddfjmKwM5p88wuMt2knXKiFugMNsRU3NxrYvloxBb6KmZruWmT5ditcTQ9DWCnSeOAu3u3SiEoB0cx7Hs9JJN+e5MarKpWVgMCtuvh2Lw+4qS8zNodMf3/NY82gebhadb2xSIYS4EXk/rcP0nvM6TFMvBO+pt6FrHH7N6zEYDERGRmKz2bDlmsn9eDG2TUdRAnzw/eQeFI+qnfDSarXodLoKd90QHR3N22+/zfDhzpmUdTodSUlJrmRXYmLiDbdUK7FkV1lk5kTFYVmxF9OMZThOJ7vKdK2j8HxyCLomEWUSQ3lqZXYtibftyYlsSDjDLZVoYPZLJebm8H87NvBup16u/fLrycP8evLwVZ9XXru9lqQ8m42hS39lX1oyMdkZjG/Y/Kr1y7KFpBAVgaqqrpM9NcmZOLfvPgWA4uuBrnMj7EfO4og5h1KzGvaj8ei7NkbNs4BB5+w+IdwmOS+XIA/n2JbVjB782HsoWVYLI/+e7+bIKg9FUfJNivP5gZ3EZGXw47GDPNa83VWeKYQQ18bQryV5X6/EOKI9HpP6ohiuP/WgKAp6vR69vx6PF0aT99nf6Pu2QFfNr+gni3Jp2bJlTJkyhW+++YYZM2bw0UcfMWbMGOx2OzabDY1GwzfffHND2yixZFdZZOZExWBevJPcl+ei79YYr+dHoAkPwL4/jrzZK8m6fya+/3vgurL616O8JAKKm3hLzstlb2pypU10OVSV/ovnsif1HBpF4eOufctNQrI88NDpeLBpG17fuYGP923n433bi3zOkTGTqsS+EeJKHClZWNccxLJyP4q3EZ9pYwHQ1gpCExGAIz4drxdGYhjcFkWnRVVVbDtOYj+djCM+HcOwdpi+WI51zUGMt3XGOCQaxcejiK2Kkjb3+EE+3b+TpYNupfv5yWb6hEexIznBzZFVbi+07Uy4tw+NqwW6EsU2h4PlcScZWKtuhWspIIRwHzXHjOXfvRiHOhPnmiA//Oc/XeK/qYpGg+fDA/OV2XbFoG0UJt0aK5CoqCgWL17Mjz/+SI8ePXjsscc4duwYx44dw26307hxYzw8buy9U2LJrrLIzInyTzVbMb230Dml+/+NcZ0kacMC0HdrTNbEzzB9tBjfzye5OdKyV9yERP+Iuq7/51gtPLt5Na+26+q6212RaRSFdzr14qlNK3mwaWug/CQky4spzdrSKjCYbn/OcY1hY7LZ8NTl/7o+mJbCXSsXFZkoFKIysselYF21H+uqA9h2n4ILM+gadah5FleXBt/vHyHrns8wfboMNdeCvnsT1Fwz1lX7Mc/dgGFYO7SNw7A++wOO+HRM7y7E9PnfGIdGY7ytC9rIIDe+yqplf1oKuTYrc44dcCW7ROnTa7RMatI6X9n3R/cxcfUSbo6sx6KBo4Hiz5gphLiyyvw5sm47Tu5rv+I4m4bi64mhZzOAMrl5ZNt9iqyHvkJbryY+H4xHE+hb6tsUJWfs2LEMGjSIp556ip49ezJz5kxat25dIususWRXWWTmRPlnXXMQNSMXj/v7oigK9mMJ2PadxtC/FYqXEePdPch9aS72M6lowyvml3lZenzjCr46tIet5+LZNGJchbzDmmTKIdGUS4sA55ht/SPqsHNUbRkM9youJLaaVA/EYrczavl8vukxmEGR9dwcmRDulzP1FywL87d61DYJR9+zGYZezeCS6cg1vp74znyA3Hf+xPTBX5jeXQiA4u+Fx3198Li3N4pGg9/P/8G8eAfmnzfgOHUO888bMP+8AV2XRnjc1R19BxnAvjSoF5KUwLOtOjIqqiH3NGrhxogEQKbFgpdOT6+wSODaZsysqBfqQpS2yvo5UvMsmD5divnnDQBowqqj+JfxDXqNguJhwH4gjqx7PsPn43vQRgWXbQziuixZsoQDBw7QqlUrZs2axapVqxg7diyDBw/mtddew9PzxiYuK/EB6kszMyfKP0dCOngZcCSkk/vun9g2HAHAPG8jPu+Pd43X5UhMl2RXMTzSLJoNiWd4p1OvCpno2pOSxKAlv6DXaNgxaoJr9qfCEl2OjFysy/fgSMxACfDG0K8VmiC5M/PB3m0kmXL56tAeSXaJKkW1O7DvPY1l5T487umFppo3ANq6IaDVoGsT5Uxw9WyGpma1K65HE+CDz7SxOJKzsB+LB50WXfNIFI+LSTHF24jHrZ0x3tIR25ZjmH/egHX9YWwbDmOtHSTJrhJmddh5ddt6sqwWJjRyjk14OjuT1kHB7ExJzFe3sk9SUh491qIdY+o1xt9gBC7OmNkpOIxnW3ck0if/GDnS0liIol1p5tkLKuLnyLbnFDmv/uIao9kwqiNejw1G8TaWaRy6FpH4zp5C9qOzccSlkDXxc+d1Z+uoMo1DXJtnnnmGb7/9ll69evHZZ58xYcIEXnrpJXbu3Mlrr71G69at+fDDDxk0aNB1b6NEk12lnZkT5Z/9bCrkWsie8pWzQKOApwH7kXhMny5F37+ls7h61Zlp8Ea0DAxmzy0T8yWH9qQkUdvX33USWp7V9vXHU6fDoNGSas7LN9X5pfJ+WIvp82Vgc6Cp4YcjNRvTR0vwGNcdjwf7V+lBo7/vNYTmAUH8p0V7V1mGxYzV4XBjVEIUj/1kEvYj8WDQomtXD43v1c8DVIsN25ZjWFbtd7YUPj+DorZBKMYh0QAYhrfDMDTalfwqLk2Qb5EJdEWjQd+pIfpODbHHJmOetxHjbZ1dy227YrD8s0e6ON6gTYlneXPXRgD6hNcGip7ApSpMUlKe1CxkRuhNSWf5/eQRvus1xA0RCVE5XD7zbEWV991qTJ8uBYeKEuyH94u3oO/ivjGHtZFB+M5+kOz/fIt9XyxZU77Ce+ptGPq1dFtM4uq+/vprli1bRnR0NKmpqXTq1ImXXnoJg8HA66+/zh133MEDDzxQPpJdZZGZExWA43yXBK0G4y0dMY7tBloNpg/+wuPJoeQ+Nwdt43A0UTXcG2cFcmmiK8mUw6Alv+Cp07F00G3U969+w+t3ZOSipuWgVPdGUwLNjk02K546Z4sJf4ORJYNuJdTLB58rXKiYF2zB9OFfGO/oiseEnmgCfXFk5mKeu4G8L/8Box7P+/rccFwVlUGr5aW2XfOVPb7hH1aePe2miIQomj02mdzXf8e2/cTFQg89xtGd8Hx4IIpOm79+XAqmGcuwbjgMOWZXueLrgb5bk3yJJY1f2XSP0NYKwuvJofnK8uasxbpyv7OLY9dGeNzeFV3H+lU6IX89uofW4tXorjSrHsTwqAZFTlRSkcexqUx6hNbi1ehursdWhx2tIu99Ia6VxW7ni4M7ic/NYVqHHu4O57poomqAQ8UwuA2eTw0ts9/mq8ZU3QffLyaR8+LPWFcdIOf5H1GqeaFvL62zyyMvLy9OnjxJdHQ0sbGxBYa8atasGevWrbuhbZRYsqssMnOifHEkpJM3dwOG3s3RtXCO6+AxvgeOuBRsm4+BVoOi06KpWQ2Pe3tj+r9fse08ic9H92DbdBRdpwYVsmueOyWZctFrNBg0Wmp6XVurhsvZjydi+uJvrGsOgt0BGgV99yZ4Ptgfbf3ru+O0PiGOO1b8yWfd+jOktvOH5WoXKKrNjul/KzAMapPvolLj54XnpL6ouRbyvl+Dxx3dyrxJdHmVaTGzNPYkCaYcd4ciRKEcSRlk3T8TxdOA97Sx6Ls0Qs0yYf5jK3lfr0RNycbz8cGoGbnOLomA4mHA+s9eUFWUIF8MPZuh79kMXbu6BRJj7mQc3QlsdqzrDmNbf5js9YfRRAZhHNPFOYujfE8VKttq4bXt6/lvm85UMzpPZl+5JGkiiayK4f3OvanrV831+LXt61mbEMcTLdtf+UlCiAJeXLaYd+IOcJ/Jn7wjGgxD2rrGMHxr1yZmdO1PDU/3J48updrs2E8moWsQCoDhpqZovn/YNURNeaF4GPCefhem9xfhSMpA17Zu0U8SbjFt2jTuvvtuHn30UXJzc/n2229LfBslluwqi8ycKB9sh89i/mEtlr93g92BIy4Fn3fGAc5ZF30+vRfzt6sxzV6J+af1oNeC1Y5Sww/vt+/CfjoZ0zt/YhjcBq8Xb0ExlPjQcZVW84Aa7Bg1gTRLXr6WUlaHHb2m+BeEtkNnyHpgJppAXzyfHIq2fgiOE0nk/bSezImf4fv5JHTNrn02rF9OHCI2J4tpuzZxc2S9QpOZanYetiNnUTNN2PbFoiZmgE5L7vQ/ULNMGAa2Rt+tMQAeY7pg/n4N1g2Hq2Qz5CuNVTO37zDmHT/EjAM7XWXLYk9Q37869fxuvLWfEDci7/s1YLHh+/0jrm6DircRw81tscecw7JkJ5alO9G1q+eamVcT5Ivn08PQNQ1H2zSi3LaU0ndsgL5jA1cXR/Of23Cc/02z/LUDv+8edneI5dKt/yxgaexJYnOy+KnPMHeHI0pAujmPT/bvIMNiZlAtuZgUoiiO88ms3PcWcc+64/w60p8BJ02Y5i7D9NkyYh92dpn//eQRvul5s+t5i04d43hmOoNq1aVhNffcGLAfTyTn1Xk44lLwm/cEmhrOcfvKW6LrAkWrwfOpoWB3oGid5xOq1eZ87CFd4suLO++8k4EDB3LixAkaNGhAtWrVSnwbJZZlKIvMnHAfVVWxbTxC3g9rsW055irXtauLcXj+O3qKouAxoSfGWztjXXsQR0YumrDq6Ds3RNFpMf+6CbQaLIt34jibhve746557JWqLMDDM9/YV7+eOMSr29fzS9/hNKlevDFkcqctQBsRiO/MBy62RGhbF8PNbcl6YCa5b87H94dHCiSrVIsNNdOEmmVydns8f9zssclYFu/klawcPFV/Hj1kIeuPL1CzTKiZuXg+OADjCOf7xHYwjuwHv8q3XsvCba7/a+qFuJJdqt75A+U4P25PVXFhbJqixrC5UDfTYmbcyr9It+Tx75A76FazfJ58lJTKPHV3RaeqKpa/dmAY0QFNkC+2/bFY1x3Cumo/9qMJl1QENc+KarO7Wm55XDI2Vnl3oYuj5+T+rlkcDYPbuJarZiu2HSev2MXRfiwB2/5Y0GrQd6iPJti/LMMvc69Gd+NweioPNm1TdGVRIVQzerBj1AR+OnaAfhFRPLdlNeC8oNdIq30hXE5lZbg+H+A8D6730u0c7d0crU6LIz2HvC+WY/xhA4zw4+FmbfHSXZxAZdbhPSyIOYrVYeepah0B55Ahq+Nj6RQc5motWxpUuwPznLWYPv/b2XDBzxP7qXOuZFd5pigKnD+/UFWV3Nd+w346GZ8PxqMJkLGjy4vAwEACAwtO2FBSSizZVRaZuWvx2Wef8c477xAfH0+zZs348MMP6d69u1tjqshynvzO2d0NnCfnfVvgcVf3q2b0FW8jhoGtC5QbR3dCExFA9rNzsO2KIWv8DHw+nIC2jkwRe63sDgcvbl3L4YxUfjh6gDc63FTkc2xHzmLfH4v3e3ejeBtR8yxY1x3GkZqNProung/0I/ux2Vj+3IZl0XYcmSZngivTBGaraz1ez41gR/da/H7yCG9oIsj73woAnrkQ2yXbdKRdTFZpqvugiQxC8fMEnRb7rhh0nRuiaxqB4uuJrk2Uq675G+fJgenjxdj3x2IY3AZd+/quuzSVVQP/gCLHsIGLSZ3Y7EzaBoVwMiuDjsGhZRSle1TWqbsrC8fZNNRME9p6zu6Jef9bgXXdIedCrQZd2zo4kjLQRATi+9E9boy0ZFw6i6NrzErA8vducqf+WqCLo/1sKrmv/oJtx8mLK9FqMAxohddzI1C8Kkc3yCRTDscz0+kcEg5Ax+AwDo+ZdE0tkEX5U1hr40GRdV3leXYb7ed/y4SGLXioWVtJegkBHM9M5+fjB9GfH98u5s72+LUNgfRzrjrqvR05k3UGyObuhs3zPb9PWG3Mdjs9QiNdZZuT4hm05Bdqefty+s4prvI0cx7VDMZiDRNzNCOVjLRMrCsPYN10BDXHjCbEH0PPpuja18eRnInus3+I2hIHgK5rI7xfvKVCJLou54hPw7rhMGpGLln3fIbPx/egrS3jR19NaedSTp8+TWRkZNEVzztz5gzh4eHXvJ0SSXZdCLa4mbnrDba45s6dy+OPP85nn31G165d+fLLLxk0aBAHDhy4pp1alTmyTCgeehS98y2i69gA67bjGEd0wHhHV7ShN9ZVSt+pIX6zp5D9n29xnEkl657P8H7rTvSdGpRE+FWGVqNh1dA7eHfPFl5t17XoJ4BremBd2zpYVh/A9O6fOOLTAVCeG4FhSFvAOWC0bWdMwRUoCoqvBwk2M70X/Uye3Uar1t6MvKUjGj9PFF9PFD8vFD9P158mpNrFmOvXxP/3p1yPsyZ9gXouE49xN6H4XLw7pVpszsGqdRow27As3oll8U6UQF8MA1thGNQGbaOwSjvu27Ukamr5+LF08G2k5plcF5OqqnLnvwsZVKsudzZoVmkuOirj1N0VmZpnxbbrJNYNR7BuOoLjRBL4emA/5mzFpe/dHDz06Ls1Rt+tMYqPBxnDpqOtVXp38dxB0Wjgkhy8mp0H3kZXF0fTZ8sw9G/FoZ0HyfbU4TF1CLq2dcBqx7LuEOZ5G9C+lEbIC7fSMKBi75v9qefo89dc7KqD3bfcQ5i3syurJLqun2q2OltGqiraeiFlnhQtbmvjv2Nj2JGcSHxuDuMaNCvVFidClFdWh53jmek0rub8Lu8dXptX2nal2c5EbuMY96Ttg9/3FXyic2JavNPN2JLiwGJDqebFlIjGPNSkTb4bvZkWM/X8qhF92ayOPRf+SJIpl/n9R9Ip5MrX2gVuHEZf+E8GxG+EP52z5dIatp72oMV9N2MY3q7CnnNrwwLw/fpBsh+d7bzunPg5Pu/dja51lLtDK5fKIpfSvn17hg0bxqRJk+jQoUOhdTIyMpg3bx4fffQRDzzwAI888sg1b0dRL4yGdwNCQkLKJNji6tixI23btuXzzz93lTVp0oQRI0Ywbdq0qz43Li6OWrVqERMTQ0RE5e4KVBjH2TQsczdg+XMbHk8NxXCzM/Gh5lnBakMpYtr4a95eWg6mZ+dg330K9Fp8fnsiX2JEXDtVVXl1x3omNGxOHd9qBZZbNxwm+/FvMETXw36+dYFSww9t81roh0ajiQgg89b38Zp6Kxq9/nzy6vyfryf4GF3dcqbv3sye1HN80bU/vobr6wNvOxpP9gMzUQJ88Li9K9pGYdhjkjD/vAH7qXN4fzAejYcB65Jd2P7Zi5qR63yiTovvkuedLcREAQtPHWPkPwvw1Oo4ctt9hBYyjXxFtCM5kQ5/fM+W4eNoGxRyzcvFjXMkpmP9dz+2TUex7zwJZtvFhRoFTecG2Paexu+nx9AE5b8DbP5jK7lv/I7vdw+ja1x6N73KAzXHjHXxTiy/bMRxKpnjfhra3150d8WDo++lQQnMtOsuZruNrn/+iE11MK/PMBpKC8vrptrs5M1agfnXzRd/+7yNGIdG4zllQJmOPXM0I63I7uP1/Krx5cFdRPr4cXNkPdcyVVUr7EWyENfiSEYqI5bPJ9dm48Doifm6I5o+W8rB9XtQXxhO3sx/cRyJdy1TfIwYHx+M8vKvtJn5KHkf/IV9y/GLK1YU53m4vxdKdW+8vpyEoihY7HaUDUdQ03Mx+RkJP76cPNXO6ZvHExociKLR8NPxg8w8tJs76zXlvsbO8W+3n0ug458/8MVuaPPQCJRAX9embLtjML3/F8eqaXmgtzebuw4nunHlaIzgSM0m98nvcBw4AwYdnlNvdd6Uq8Ti4uKIiooiNja22LmNG8mlFFdqaipvvvkmX3/9NXq9nnbt2hEWFoaHhwdpaWkcOHCA/fv3065dO1588cXrnuSwRJJdZRVscVgsFry8vPjll18YOXKkq/yxxx5j165drF69Ol99s9mM2XxxmvMzZ87QtGlT5s2bR40aVad5o2dsBjVWn6TankSU810x0lvV5NS41qW+bcXmIOKXfZjCfEnuUafUt1fZ/ZGeyIdJMVTX6phTpzWel91Nr7H8GCFLj6JVFFSNQlLPKJL61MNhdLbiy5u7Htumo3hPvxPFoM/33AOmbGrqjQSc//G+8PVxoyex9rOpWP7Yim33KTj/jaRtGo5xWDu0dS4mLBSbA9/DyVTfcRZVgdN3tXYtC1twgLxQX9Jb1sThqaeqMzsc/JbubF0zNiDMVZ5us1JNV3H3z5G8HB44vY+PI5py2JzNHlMWdweEU9/DO9/yLyOb09BDxgIsCRqTFcWhYvd2Xlj7704g6vtdruUWfyNZjYLIahhEdsNArBYrudPmg17n/Aw3r4WaY8a2/hCWZbvRdaiP5z293PRq3MCh4nM0hTMLtjBiZCAfHlDwHNjMuUxV8T6ZRl6QFweXbOa9zoEV8r2barNQXat3/Racs5rx0+oxltOJBioC1eEg76sV2HbGoO/VHH37eqDVYNt5Ess/e9BGBeP52OByNVtpYXblZjI7JY5nQuoSbpCWXqJyszgc3B2zG7Pq4K3wxjS65LvcsvEI1edso45vEFqrA7tRS2K/+tg9dSh2lbMp57D8vRufd+4icv4hvE+lo82xoDPZ8m3D5qVn/2t9XI/rfrkV36POrsQmLewL1NI+yY6qgM3HwD0PRLE48xx3VA/lueMKhtRcdqo5jG1g5b40A+NDaqPxMWL30GP3cf7OW37aQMLJRMaPDquQv0lXozHbiPxxD/77k1AVODOiKSldK2/Pr3PnznHbbbdx4MCBfD3rjEYjRmPBVsLXmku5UXl5eSxevJi1a9cSExODyWQiKCiINm3aMGDAAJo3v7FkZIl0YwwICODdd9/l9ddfLzTYO++8s0SCLY7k5GTsdjshIfnv6IeEhJCQkFCg/rRp05g6dWqpx1Ve+R5IInh1DD7HU11lWQ0DSepRh+yGZdOVQtVpiL29Rb4yfXoedg8dDg+ZqfFadfauxt8ePvT2DSyQ6AIwZFnQKgrptjxOdwjB3qM2GqMOR64Z64p9WP/dj3FkhwKJruWZyUxPOEFrL1+mhzdGqygldqdWGxaA54MDcGSbUDNMKD4eaPwLTrms6jRkNgsms1kwXJKnN6TkUmPdaQDC5x8ks2kN0tqGkdW4Bqqual5sGTWafEkugDOWPCae2kNf3yAeD4lyjR9REVgcDtLsF8eM0ygwKzmOPNXBf4IvJsm352YAzgusynRyVqYcKl6xGfgeTsb3SDJepzNI7FuPxP71AchuEEhWw0CyGgaR2TgIc4gPXPJdoPEy4PnUUMw/rCNv1r8X12vUoe/bEuOIdmX9itxLo5DdKIiT5nQgkGrRUdQ+/940JOfS5Ov9AGwP1vMeELQ2hmo1AsgN98MS6OV8s5djyzOT+SDxJI8GRzHQ33mTsIa+cow95k72A3HYtp/E4/6+6KMvznaojQxC2zQC07sLsW4+hqFrIzdGeXWqqvLpuVMcN+fya1oCj4VEuTukSi3Okkeuw37F5V4aLRGScCxRiVYzK7NSuP38+ZZBo+H1sIaE6o14a/Nfw1QLqE59jwCwOsiqW53Y21tgDXCe69oTM7D+uhJdpwYoHgZi77hkBnK7A12u1Zn4yrGiseU/xrm1q6FqFXQ5zjptM5wtMBUVFLvK2IAwmnn60MDoTbVd+/E9loqpjh4a+PCDr5k3Z2xBp0J2neocf7ADaBS0HeqSeuA4lZHDqCNmfBvCFxwkYHMs5uCqca7YtGnTfI9feeUVXn311QL1rjWXcqM8PDwYNWoUo0aNKvF1QwkOUA+lH+y1KDCL3BWaUD///PM88cQTrscXWnZ16NChQnZjVHPMzgH4Mk1oIgLQtat31cG8c+Z9jf14qnPQ+f4tMYzthl/DUNzZuUTNziNn0pegUfB6dxyaGxwfrCoa5uiFTtGgKAqOc5mczcpECfEn3NsXtXU7rBuPEHQyEd9v18C+NWiC/XEkZYDNgccjd+ExuX+Bz0tgWjIf/nmKOiE16dClc76m2e7mSM/BmumJdclOOJFEtT2JzlaKfp7o+rXAcEsn14DZVdmH+7ZhiVGx+3nT+6Ye7g6n2P48dYy7Vi2iY40wpnfoAaf30aFtNM8EVcNXb2BYi4vJk/f+ng+AqbqfayBNu8PBe3u30jkknC7BYWgrcWsTR5YJy5Kd2I8loHjo0fdohq5tnSIT06rFhnXZbmfXxC3HnBNSXCICLxpeOjDpgN5FBzNqCPaYc9iPxoNBhz66br5x+aoaNXILoNK0fzfaBoXw/NY1JOtyGNGuBt13pqA7f/0SsO0stVNiATDc0xOPyf2cz8814zidjKZuCIqh/NwIWrtrE6aE4+w1wOvdukl3tRKSvWgOjjbt8X3sbhRFcbZ8NlvRdagP3SFrdy7q0Tz8nivfky8ty2rFqzvW83HnPvgZSjYJao9LwbbpKKrdjq5JBNoWkVX2/Xc0I41ev84qsl5F7yZdnmRazET9/CWZVgvDojswIMJ54+2Kn8jukLM3A+vmY/gqetpY/dEo1bDuPIllwXaUOk3xfeuBa5+hvpBBw1WrDTUjFzXHzO21a3D7+XJLTiCO44l4nToG2BiSokNfsxpqei4+Sbksys2kV4tm9PWtBV67AWjTpk2lHBZCvak7jpPnaFO3ck+QFhfnnGCgsJZdV1PcXEp5V37OlkpIUFAQWq22QOYxKSmpQIYSCjbhy8zMBECr1aLVlu+m4ZdSVZW82avI+3YV5Jidd9lVFU14AF7Pj0TfqQGO9BzMv27CeH5KeADPCb2wNgrH4/auaELKx9Tn9nNZqBkm1JQscu79wjmAYPPK27y0NGi1WlSbHfPcjWTOXM4dw/w4FmTg9/4j6VYzAt0g51hs3rd1w7JsF47kLDTnB36/dIydLIsZ3/Mnpy2CQtgxagIN/QPK3ZedNtAP/T29UCf0xH4kHsuSnViW7kJNzsL62xYMbeuhbei866Y6HK4xx6qaJ1t1pHNIOAEenq7vN5PNyk/HDnJ3w+boysF+2ZJ0lt9PHmFY7fp0qem84dA8sAa5Nhsx2Rk4zr/1tFoNU9sXPMEbXa8Ri2KP0ys80vUa96Yl899ta/HVG0gb/5gr2XUoPYUaHl4EelSOcd8sy/eQ89qvYLGhbRCKIyMH09xNaFvVxufdcWiqXxy3TbXYcJxNQxvlbImj6iH7w8XOgdUBxccDXft66Ls0QtepwXVPSqKtVxPq1Sy6YhWQ270R2A9BYjrakDCMWi2zk05Q456ODBjuBx/+BsDZfo1osy8Tx9FE9I3CXO9j6/44ch7+GnRatHWD0TYKQ9soDF3jcLQNQlG8rz2R4MjMxbJwO7a9p0GjQd+hPoaBrYocC+rSk97/tulMLR8/xjVoVqkTyWVNSczA0KI2mqw8TO8vwrJkFwCGQa3xenYExhaRmOdvKffnqvWqBfB976H5yh7b8A9RPv482jz6ut4zanYeOa//hnXFPmfLR60Gi8WGtlEY3m/cjjaqcl+8FibX4ezqVtTkLbkOW7l/z5Rnl373Vff04t7GLdmenEi4j1+B/epIzsL00WI8HxvkOrf2+/Q+bLtPkTd7JeZ3F4Gqovh64Dm0PR739r72RNeVaLXgUfA3wfOWTgDU3bgL9i7j6U7dqPaSs+y3E4d5758FfBR/hN3pUej8vc+vSlN53zMNLs5gbj+ZRO4Hf+E99dZ850sV3YVj5+vri59f0TNpXmsupbyrdMkug8FAdHQ0y5cvz9fPdPny5QwfPtyNkZWuvP+tIG/mPxjv7IbHHd1QQvyx74vF9PnfZD82G/1NTbBuOAJmK1hseE4ZAIC+Q330Heq7Ofr8tPVr4vftQ2T/5xvsRxPIun8m3q/eiqF/K3eHVmHYdp8i96352I8mkOqpkI2DPJuVEE9nc+mjGakXB5odcOmgk7mQnIuqqiyIOcoXB3exYfhdrpkBG1Ur37OEKYqCrlEYukZheD4yCNvW41j+3o2+RxNXnbxvV2NdfQDDoDYY+rcs8IOmWm1YluzC/MdWHAnpaPy9nHVHtEdTwhM0uMOFBNIFH+/bznNbVjP3xEGWDR5T5vGk5JnyJZv+d2g3Xx3ag8luc8XawD+AvaMn0qx6EDtTEgFcU91fTne+a2bzgItjLmoUhVvrNsJDq8t3YTV57TJWx8fyU++h3F7f2bzboaooFH8cunyfpUL46g3XNLPm9bLtiiHnxZ/R922B139uRhPkh6qq2DYdJeeVeWQ98S3eL9+KbfNRrBuPYNt+Ak2gL34LnkZRFBSdFuOtnZytfDs1RNu8VrkfC6iiUFWV2/75g9/tRwDIff138m5JZ2L7OqRHZnDz9lRyv1uOrl99IIk7/eJpdnMQu0e8cukEj6jpuSh+nqiZJuxH4rEfiYeF250LFQXvN253/U46skxgtaMJuPIJu3XjEbKfmwNmG7pWtVGtdnLf+B3TF3/j8+GEQicQcKgq7+7ewpqEWP4ccAsaRUGr0TChUYtCtiBuiK8ntt2nyBz9vnNwekUBBSxLdqHv2hjHmVSUQrr7l3ebk87y8T7n+7Z7aATtaoQW8Yz8VIeD7Ce+xXb4LF4vjMQwsDUYdNg2HyP3g7/IemAmft8/gia4fNzALWtNqgfSNkhuMJQ0VVVZHHuCV7atY0H/kUT4OJMGb3XoiV6jKXDOYFm+h9y3FjhbV+VZ8HlnnGuZrlVtfD6cgJpjRjWZUap5l/nvrbZ+COwF07ersDesh7Z2DYbUrse9jVoSmZRH6FdbSb+3E3CwTONyF1VVyXlpLvZDZ8i653O8/m8MtnWHsCzZiSMtB02IP8Zh7TDe0um6bixVJJUtl1Lpkl0ATzzxBOPGjaNdu3Z07tyZmTNncvr0aSZPnuzu0EqFIz2HvG9W4XFPLzwfGpBvmeJtBLsD60rnmCDahqFoK8AMWJqa1fD96kFyXvwJ69pD5Pz3J+ynzuFxX59y16qoPHGk52D6ZAmWP7YBoPh5UueRQWy6uRX701Np4B9QcLrhIsw+vJc3O1ScLm8XKFoN+k4N0HfKP4OMddlu7McSMO2LxfT+IvSdG2IY3Ab9TU1AhezHZmPbGYOuc0OMQ6Oxx6Zg+mwZ5t824/vFJDQ1q7nnBZWSIA9PAo2e3Fm/WZlu1+qwc9OfP7I56Synxj5IrfMnjrfUaYTJZnN1B7jgQvLKV+9scXLXykVXXf+FegAtA4OZ13dEvuWqqrqSVK0vaZ7/R8xRHl6/nLsaNGN6x55X3UZxP0tHxkwq9YRX3jer0NYLwfu1Ma6u64ribPGgbRqBbd0hsm59P99zVLMVNT0H5XzC1/OhgaUaY1WSmmci4HwSV1EUtIqC4/zsG9omEZjeX0Q1u4PXwDm73h1d8bytFSz8Ab1GQ5SvP1r9xVO0V7etIyTSm1v/eprqaXnYD591/dkOn0VNykQTGeSqb1myC9Pbf6DU8EPXKAxt4zBXSzBNaHUcp5PJfup79O3r4fXSLWjOz8Rlj0sh578/kf3obPx+eaLA2IkxWRm8un0dJruNhaeOMTyqcszQVS5l5+E44UzuaxvUxOvFW8DmwPLvXrStapPzf7/icV+fIlZS/nSoEcqX3QdwKivzmhNdALYNR7DtOInPjHvRd7z4/tN3bohvozAyR79H3k/r8XpscEmGXaHYHQ7mHDvAXQ2aoZFz5hIzfdcmticn8OauTXzWrT8Ahstbc6XnkDv9D6zL9wCgbRSG5wP9Cl2f4m10W+LEdS2l05J52wfouzRCE1qN9/ecxnboDPq+LTAOawd/HiQ2O7PSJ1AVRcH79TFkPzobR1wK2RM/B6MOw81t0dYKwn74DKbP/8ayZCc+X9xf6LjClUllyqVUymTXmDFjSElJ4bXXXiM+Pp7mzZuzePFiateu7e7QSoX1n71gd2C8sxvgvIjLfmgWti3H8tXzev12DANaVZhkkeJtxPvduzF9vBjznHXkffkPAJ6T+ro5svLJduQs2Q9+5Zqe3DCsHZ6PDHS1XGof7DypvHCBX9PTmzc63ETrwILN/S80d3+udUfeaH9TGb2CsuEz414sy/dgWbwT+4E4rOsOYV13CLyNaGpWwxGfhu//HkDXOsr1HHtcCtkP/o+cl+fiO/MB9wVfCu5t3IrRdRvnSw79ExfDV4d282aHHtT1q1Zk6yW4egumTIuZpbEnSDXnMblpGwD0Gq2ztzWwMfGMK9k1sFZdBtaqW+h6wNnK68iYSTfcmkpRFLaPmkByXi6Bxosty9YnxnE2N5sMizlf/bv+XUhD/wAeaR5NdaNz3KkLMRTVbaSofXejVLMV6/rDeD41tMAYjdaV+7GtO+R8oFHQtauHvnND9J0boqkXUmF+DyqKbKuFW5bPZ018HLF3PkiQh/OEeGq7btxarzGjly/g1F3t8birO46Yc85kZINQFE89h863Vvx3yO3U8r7Y1SDHauGt3Zsw2+30CK1FUFgQ2rAAMro2wEunw0ejxZGajeJ38X2snssARUE9l4n1XKbzO+48xc8TXcf6KD5GvN+6E+wOVLsDRatBGxGIz/t3kzFkOpaF2/G4K39X4bp+1ZjRrR92VWVY7fLVKryyMQyNxnQwDjwNGCf0RNsoDBQFfZaJ7ClfoQnwQd+nBdlPfofno4PQ1q4YM4grisL9TVrnK0vNM3HvmiW81aFHkS3ILUt3oW0Yiq5DfVRVxTJ/C2qmCeO4m9AE+GC4uS2WpbuqdLLr5W3reHPXRhadPua60XMqK8O9QVVAZ3KyCPb0On++ovBep978cuIQ/23TudD6ljUHyH3jd9SUbNBq8JjYC4+JvVD05fdyO/bp/hj3JmHdeAT1SAqaSH/0dw9H1zqKA+nO36RxK/9ifn8DfcKj3BtsKdNGBeM7ewoZI94BkwUcKvqODTD0dk6w5zG+J1kPzMT07kK8/6/se0KUpcqUSym/n74bNGXKFKZMmeLuMMqEIy0bpZqXq5+3oihoagXCjpMYBrVG16URuc//iDYyqMJd2ChaDV7/GYK2dg3yvlmFcWQHd4dUbmnrBKME+qAJ9sPruZHoWl39CynBlMOvJw4zsVFLVFXlswM7AXioWVtXnVvrNq5w75miaAJ98bi9Kx63d8Uek4Rl8U5nM+X4dBwnk/CY1MeV6LKfTEJbJxhtRCCeTwwh5+kfsB05i65h2NU3UsH4XzJosKqqPLdlNduTE6jp5c1DzdoWuyXghRZMqqpidThcdzz3pp5jzIo/qW704L7GrVxjg33ebQDBnl6Eel3b2Agl2UrqQjLigv9r151htRu4EloAp7MzmXPsAFpF4YmW7V3l2885xzOo6eXt1rueqsXmHKMx0Afb7lNowgNc4zLq+zQHrYJ101G0jcPwef0Ot8VZWWVazK6Bt330BpLzTOTZbaw8e5pb6zYGnF3AL7SwKNAqMf99KUI8vante7ELlk1VeTW6GzuSE2hySSLgte3rmXV4D2+2v4kpl3xvg7OVnseEXtiPxmM7dOZiS7ATSaiZJmy7T2MY0BrFQ0/uewsx/74FbZ1gNLWDnLP91a+JZdkuGNqGVw5tY1LjVtQ/P6j1PY1aIkqebX8sap7VNfOi8ZaO6JrXwvTJUnJf+Jnc1351dmXMs6JtVgvvN24nb9a/WFcfwLr5KF5PDsUwon2F/M1+dssqFsQc5VRWJttHjb/qa3Ck56CJCHTVsW45hvWfvajZeXg+PBBNRCBqek5ZhV4uNaoWgIdWx8iohoDzd/3/dmwAYPXZ05W+lU5J+GDPVl7Yuoa3O/bk4ebRgPOm8YUbx5czL9lJ7ktzAdDUCcZ76m3ompbfic4u3OAct36ps8DVuD8ZYtY4/86zOuz5bgpWZo5zmWCyoG0Sjv3gGXKenYPjPzfjMbYb2vo18binF6ZPl+L5n5uvOkxAZeDOXMrx48f55JNPOHXqFHb7xdlH//zzz2teV5kkuzZv3szx48cZO3Ysqamp5ObmVsiZDssrTbA/aloOjsQM1yDznpP64DmpD5ogP8x/bAVFQRNc9KB05ZVxVEcMN7dFMV6cAdCRnlNyAzlWQGp2Hnk/r8djfA8UvQ5Fr8P344koQb7F6vs/pm5jPu7qbCW3JPYED69fjl6joU94xcvaXy9tVDCeUwbgMbkf5l82YXrnTwx9nGPP2I7Gk3XHR+j7tcT7hVHouzcBnRbbrlOVLtl1KUVR+Oqmgby2YwMvtu3C6WznpB1fdhtAi8AaGAsZpPTSFkxfHtjFm7s2cn/jVrzQtgsAHYPD6BwSRpeQcHJtVldioFUhrQrdzVOn56bQWvnKfHR6Pu3ajzM5Wfhc0gLu5+POsSz+iDnquuNpdzhIt5jLdNB7xduIEuxH3i+bsO85hbZxOL5fTEIx6tG3q4e2fk3Mv29xdkkQJeZ0dibjV/7F8cw0Ttwx2ZXE/axbf2p4eFHXr1q++tfbKtHfYOS51p0K1N2cdJYMi5kanhcTtgm52XxxYBc3R9ajfXAoutZR+VqpqlYbjlPJZD30Fcd9wJqcQE7CWew+DjiX4Pxz9oLHx6oy45X3+aapkVVnT7NxxDjs246j5pjR1q6BJiKgxFss2A6dwbYrBsD13q3s1Fwzps//xjx3A5pgf/zm/QfFy4ii0aBrEoHvZ/dhOxqPbetxcKjoWtdG26wWiqLgObk/joR0bFuPk/vG71jXHXJ2S61g50Yvte3KmZxsXm7bpchknVLdG9uOk67WiPpujbH+s9fZlbtJOPaDcVV+Fu+7GzanT3htwr2dNz0yLGZMducA9oW1QhYFeeh0mOw2/jlzypXsuhpDz2bkRQah79EUz8n98l2vlEfF+T0CMGq0ZFot+YZ7qMzs++NAo+DzvwcwffAXlt82Y121H+NtnVF0WvQ9m2L68C/sR+LRdJJu/KVlxIgRPPzww4wZMwbNDU58U+rJrldffZUdO3Zw6NAhxo4di8lk4vbbb2fdunWlvekqw9CnBbnvLcQ061+8nh/hbNl1ftYPNddM3g9r0XVpmG+WvYro0h8Oy9Jd5Eybj8+0sei7NHJjVGVPVVWsy3aT+8Ei1JRsFJ0Wjwk9Aa5pPKlnWnck2NN5QjyoVl3G1m9K+xo1aeQf4BoEvKpQNBq0Dc5fVDmc4+rYD58FrQbr8j1k7o/Fa+ptoKpQ8W6aX7PWQSH83t85KOWFZNfi2ONM272Jmd0H0O/8eFrHMtJYHHucNoEXT4IUxfmc5WdiXMkunUbDhuHjqKgCPDzztXi8oM751jdtLjkJ3Jx0lu4Lf2RQrbq81q5bmcWoCQvAvv2E8/81/JzvVZyDOZs+WQI4u0WJG5Nrs+Klc/4WhXh6sTf1HKlmE9uTE+gY7EyCX/i3MCXZKnH10LFsS06g6SWtvRafPsHUHetZHHuCLSPvdpWb7TaMWudNEW39mpxsHkxb9sPv+6EB0KDw84NFKyxs6BzAS227oFEUcr5fi23DYedCjYImLABNZBDa2kFoa9dwtiy6joGWHQnp5Lz4szPRZdCBqmKy2tF1qI/3/41xjSlW2VjXHSL3rQU4EtIB0LWJQrXZC/zM6BqEomtQsEWJJsQfnxn3Yv5xHaYZy7CuPkDmvli8Xxldoc6NIn38WDzo1nxlv504zImsdJ5o0R6tRuOcYXreRucYtHlWLAu3YxzRHuOQaOxH4zHPWUfOK/PA7rjiGEmVmclmBfJP3pJoutjC7YXWnRm36i/XeR/A05tWogBPteqQr7yyKc5EMicy06nh6eVq9TapcStqeftyc2S9Qp+jmiyYF2zBOKYLikaD4mnA78dHi5zJtjy5nt+jk5npTFy9hK96DKSeXyVMKms14FBRHCpez41A1ygMfd8WF3/X8pyfM3Qy83Bp8vb25oEHSmbYmFJPdi1YsICdO3fStq3zQiE8PJysrKzS3myVovh44PXoYOesH5m5eNzRDU1oNWx7TpP39b84kjLwfrPydF1RVRXzou2QYyb78W/wfHIoHmO6uDusMmE/mUTu9AXYtp2/qI0MQlsCzaQVReGHXkMqZPeHkqJrEgHeRizLduFZfyDGIdFoo4LJeeEnHGdSyX5gpvOuetsrjydVmW1KiifRlJOvZdOgJb9wLDONdy4ZyH1EVAMivH3pFRbphijL1pRmbZl1eG++FmpbzsXjUFV8dPnv6r61axONqwUwIKIOnrqSu+Or5prJeXku9vOtYdBqUKp7Y910FDU9B/OCrdj3x+H18i2VairtsrYrOZEp6/9Gr9GyeuhYAIxaHXN6D6FJ9SAifcr+ZpJWoymQWIvy9eeWOg3pdEm5Q1Wp89OXNPCvzpxeQ4jw8cMyoDnErGW2bxNa9rs4PECm2cz2P9bgu+YoD/T2pub7EzgQGu6axVQbVQM1NRv76XOQa8ERl4IjLgXbhsMoPh4YbunoWlfuewtxnMtEGxnk/K2KqoEmMgiNX/6uw2p2HlmT/wd2O97vjkPfzdn107pyP7nvLSRrylf4fftQhbqILIojJYvc9xZh/Xs3AJqw6ng9PxJ954bXvC5Fo8HjrpvQta9Pzos/4ziZRPajs/GaehvGmwsm6SuC1DwTk9ctIznPhK/ewL15/uRO/wPHceeNOKWaN7lv/Ib9yFkMQ6IxDGuHdfVBHHEpoNdiGNTGza+gbKmqyhs7NwLFn7zlbE4WH+3bhtXh4ObIepU22XUtkzJ1Dglj/bC7nDN7azQMucK4hLZdMeS8+ovz/aYoeNzeFaBSfUddyZT1f7Mq/jRT1v3tlhm8S5uuY33QKFgW78Q4uhPGUR3zLTf/tQPFxwNd81pXWIMoCc8//zzPPvssffv2xWi8ONTKTTdd+zjSpZ7suhDghYvo9PT0Kn1BXVqMozuBh568L5eTde/nrnJtq9r4fnl/oXcEKypFUfB5fzy50+ZjWbgd0zt/4og5h+eTQ8p86t6yopos5M36l7wf1oLNDkYdHhN74zHuJhRDyXyMq/rnUvE0YBzVkbwf1qJtWRtD9ybomtfCb86jZD/7A7bNzoF1TB/+hffU2yp9X/3L/dZvBPG52XQOuTib6/Co+uxITsRDd/E9GOzpzeAr3AmtCh5v0Z5RdRqRZ7ORbXPeSU435/HC1jU4VJVTYx8k0seZ7LrQ2uZ6ORLSyX7iW+xH4kGvxevZETgS0zH/vgXLb5sB0HWoj88nEwvMSiqKdunxCfLwZHNSPBoU4nOzXePMDbjKZAru0Du8Nr0v64q+OyWJ+Nxscm1WQrycF7S6NnUgZi1nl21n4IYkqvdswVlzLiOTtxKvdzBzYCuwHEMx6l2JLgCvJ4b8f3v3HeZUmfZx/HvSk6n0MkNHytC7KEgRaRYQUVFBXSuudV0su+5a1raL5dW1rQWsqIAK2EARERCQJkVAqvTep2Qm9bx/BAJDhymZyfw+18UFk5yccyfMk5Pc537uB4h8uTb3ZBHasJvwhl2ENu6OXA0/4jwSmLWK8IZdBI6K0UhNwNqoOkmv3gyAb/zcyMIgH9+Drd7haYuOi5pjrVOZzGtexj9pEc7+8dGzM7wrk8yr/w8zMxcsBs5rO+G+/SIMd8G+KNsaVif5w7vJ/e+3BKb/HllhuJQq53Txn/ZdeX/5Iq7+ZAXZEyNJQSPFg/vu3tgvbo3vg+n4Pp2Jb0wkyYPdCm4H5Prx/nscCS9cj1HA6S+lxcbsTObt2o7dYuHNzr1O2CLgyGnS1TyJjO85gImb/qDLERenZm3fTL3kctH3itLudBeSSbI7OLdydfzh0AnPy6YvQO7/JuP7aAaYJkaVFKx1y8b0vkNGXNCHO37+ntfOj8/qSWu1cth7NMf7ykQsNStibx9JeB6aVeP7+Gdc13cpE4nNWPruu+/46aefWLNmTXQao2EYJTPZdccdd3D11Veze/dunnrqKUaPHs1DDz1U1Ictk5yXtMHRpxWhpZsws3KxpJfHWrvk9cQpDIbDhufRgVhrVSL31Un4xs4mtGUPic9ci5HoOvUOShnvs+PwfxtpIG/v1Aj3A5dhTSu86TAS4b6jJ+H1u8j5y/vkZaRjy0gntGkPwTlrMCqnYO7PITh7Ff5Ji3BdW3xT1EoCt83GwIPNtg95rkM3DMPg193bYxRVyXSoyufQ6xIIh7kzozXrsvbnqwC6e+YP/LRtI/9u34UBdc58ylHO42MIrdqGUT6RxOeHYGseSXK4brkQ84AXw2GLy/fDM3U6U1iOnM4xdesGHvjlJ1pWqMw7XfoAkJ6YzCfdL6VztRpnvKBCrLWqWIU/Bt3OygN7sVsiF4QOJaUeOc+D848sbnp2HCkWg2ZXVsKaZMd+fiOYuuaE+zQMA6NicqQ9QpvjJ/w8f72E0LqdhDYeToiZOzMx9+dg7js8vco/aTG47GQNehlL1RQsNStha1oDR68WWOtXxdbxnLhKdlkqJWNrV5/w5j14/jEgUlVcSAyXHc+D/TD/3Cs69k3TJDBtOfYLGpea5I9hGAzJTaL/y2shxxdZhXJAO57qlMwNTeti82aS1S8Ds29DQut2QjCEpUYFwruz8D7xGSkH9tIiMxejlPUuO1u1klJYMOBG5u7cesx5+kQMw6BvzXr5Lk75QkGunvIle/JymXzx1ZxfNX76KzcuV4HWFauSE/DzwpJ55IaCPNu+S/T+b3oPpHO1E1frBJdvJufxMYT/2AlE2gJ4/nppmTvHVk9IYkKvK/Ld9uOWDbSvXC1f5X9plvDIALLvf5/sP7+DtWF1LLUqEVqxhfDG3dh7tsB1e49Yhxj3pk2bxrJlywqlEKPIk13XXXcdHTp0YMqUKZimyaeffkqTJk1O/UA5K4bVcspV+OKFYRi4buyKpWZFcv45muCsVWTe9EZkukMBr5CWNK6buhNcugn3PX2wd8kolMF/ZF+HM7kvnhkOGwkvDCHw8wr8E+YT/G0jRooHz+NX4rioOeFNe/B9PgfnoLIxbfZUyno14CEnGi+Hbq/k9kQXgzjSD1vWsy7rQLQHFMCm7EwmbfqDvjXrRZsLn4jnH1fgfepzPI9difWIhsyG1YJRxioPT+R0p7Asu/JmMspVBMBmWFiwezvrsvbzeqee0VVFr6pXeitl6iSnUueIhvnmwZ5u1TwJXPHIEFKfTgIDPgz4sRgGazP3FfiY9vMaHtM3yvT6IlVgvmD0tvD+7EivRNOMrIq7bT/BOavJG/Ej1gbVMBJcmFm5BY4nVsxAEN+on3Fc0ia6SmrCP68Al73IqtGP/ALunzAf71OfR/qfPX4llsopJ3lkyWFrUB2Lx4mldmU8D/XjE9t+/vPTN7y2ZgnZgaPrBYEVB/++LAEIsMrwcQ5lI9kFkYssBZ1Ovd2bQ1pCIiYmbSsdrrI0TbPUnu9DZjjfz9tzc3hswc/YDAu3NmoRvT3BfuL2Ar7P5+AdPgFCYYwKiXgeGYDjgowii7k0mbNzK30mjqVhanl+vGTQMStcl0ZGgpPE128hMGsl/m8XYu7Nwta8Jo5/DMDWqk6pHQulSfv27Vm7di316x9/KvGZKNJkVzgcpl27dixatIjGjUvvh0Qp2Rzdm2Kplkr2/R/guKBxqU90mf4geR9Ox8zKxXPfxUCkT0ryZ/cXylXZQ/0aTtXX4chtyxLDYsFxQcZxP8hY61fF81C/6M9mnh/vfybguq1HvmSDlA2nO5ZONI4WXfEnftiynq7VDk8hGbd+FffOmkK36jX58ZLDvRYPJSdCyzZhaxrZ3ppegaT/3Vag5xDvTjWF5f2VS/nvsgW8+fsiXj4vkpDsVDWdtzr34vI6DaKJrnhz6MP6V72uoHbS4eRHOWfRVikYHie2Rmn5brNWL49Zw0Li09cQ3rib0LqdBKb/TmDWykjlYoonWrVomibk+EpNNUXwt43kPPU54bU7CK7cSuKzkX5vxRq/xQCnneDcNWRe8zKev18eXXW4JAnvysQ3bi6uW7pHGn4nOEl6+3Ys1cthWCx0y06mT4261E1O5bVlv55yWtqhsW/6g4XW7qEk8YWCDP7xax5q2YG2lQqnVUmtpBRm9xvClpysfFP5Lv3uc+onp/JIq/Pyrf5a0r39+yL++svUfLfVSy5H3xp1ub5BU+okpbBwT94p92PNiLxn2S9qjuehfqVutdOiFDZNyjtd1EtOpbyz+FahLmqG1YKjc2McnZW/iIWFCxfStGlTGjZsiNPpjCbc586de8b7KtJ3f4vFQvv27Vm2bJmquaRI2RqnkzzqHozUwydhMxAs9GXRC8r0B/F/vxj/N78S3pONpXIyzkvaRFf6CMxZHWnCunE3GAbOy9pG+wEU1vSD011u+OipPXKs3Fcm4f9qAYGfluH550Ac3ZvGOqQio0rAY53OWDrZOEp2OI+ZvljJ5eHcytW57IjGuL5QkIafvk2HHUGGf76V6s/deNyV1s50ul5ZcmgKi2mamIDlYLJn/PrVAEzc9AcvH9zWMAxubdwyJnEWt5JwhdpxWVu8j48ltG4n9jZ1sbWsjfPy9oT355D7v8n4P/sFR7+2AIRWbSPrT69jP78hjr6tsJ/fqEQmMswcH7mvTcI39pdIb5/UBOwx+tLkvKwttua1yPnnp4R+30LOQ6MIXNoGz7DLMBKcp95BETODIXyfziT3rR/A68dSvRzOSyIrx1rTDyez0hOT+ab3QObv3sZry36lcbkKVHC6Gb12Bfc3b4ftqM9IpmmSO+JHApOXkDTyDgxP7J9rYXpm4Ww+W7eSmTu28Meg2/P1ziwIwzBIP6JCbMGu7XyzcS12i4X7m5fcqcQTN65l4qZ1DGvRPlrhVsntOe458ZujVv48mhkKE1q+GVuzyIUlW+N0kj+5t8z15zodHauksWDADSTaHdHzatg0MSgZ5xcpnSZMmFBo+yryTwhz586lVatWNGjQAI/HU6DMnMjJHNkw3PQFyLrjHewd6uO89ULCyzbj//ZXwvtysFRNxXlpW6z1ivekFc70kn3XyMgJtMM52NvXI7RqGzn/+BTrpzOxVE4h8ONSAIwKSXj+cjGWOkXTc62sfuEtbM5rOxFcuonQsk3kPPgRwSvPxX3fxRjOwlttL9ZUCXhyhT2WrqmfwTX1M6KVXADTV65iQ04muZYwiUGD8N5sAKZt3UiKw0mLCpVZk7nvtKbrrbr61jIz/sNHvIYAT/86i7dWLOaljhdyeZ3Iqnc902vz5K+zeKtzr1iEKICjd0v83/xK9r3v4rquM/YezSBs4v9uMf4J87B3bhxtth6cvQr8QQJTlxGYugwjyYX9wmY4erfE1rpOiehJ5Z+2HO/wCZg7DgDguLg17r9cHNNqEGvtSiSNvIO8t34g771p+L9aQHDhehKeGhTTVcUC89fiHT4h2gfJ2rQG1vpVT7i9YRhYjcj/sWma3DJ9Ij9s2cC8XdsYe1H/fNua2Xn4xs7G3J1FzhOfkfDva+Pqy/f9zduzZO8ubm/cstASXcfTumIVvu97FUv37s43TfLzP1ZybpXqp5xuXxTCpsmGrAP5pmY/s+gXft6+mablK3LbwYsVPdJq817Xvtz407enve/Qxt2RfpjLt5D0wZ3YGkRWtlWi68SqH/U78Nj8Gaw6sI+RXfqQUAY/F0rB1apVeC2ZijzZdWRmbu/evZQvXzY+ZEtsBaYtJ7RkA6ElG/BNmIe5MxNLtVQsNSriX7gO30czcF59XmQFx2L6cOx9ehzhLXtJ+uAubBmRpp9mMIT3/77GP3o2ITZFVma6qiPuoT1LzTSNssyaVp6kEUPJff17fB9Mwzf2F4KL1pPw7LVxsziEKgFj49CXsuCKLbR6cALfmtnsrOgm+bWbsbeNNBT+y+wfWbhnB6O6X0qj1Mhrf7pTe+JNIByKNl/fk5dLr2/HsDZzH9/1vSq6TU4wwMbsTD5YvTSa7DrULy3ZEV9VH6frVP3mioNhs5L4fzeS+9ok8j75mbwRP0ZuT3RFViocelH0PO28oQu2jg3wT1qE/7tFmDsz8Y+fh3/8PIwqKSS9dgvW2pWKLfaj+SbMw/vk5wBY0srj+dvlJWYlVMNuw31nb2wdG+B9dAzhrXsxA8FTP7AIhHcewPvStwS+P7jKYmoC7nv64Lik9Rl9Jht8ThM252TxTPtjV+iyJLlJHD6YrNveIjDlN3zvT8N1Y9fCegoxl+JwMq7ngCI/jmEYXJReh4vS60Rv25qTxXVTvwLgt4E3Feu5f23mPs4d/yGBcJjd198Trei7rn4GTctVpHn5w+M/0e6g2cGfT/VeZ4ZN8j6dSe4rk8AXgAQn4W374WCyS07PxuxMhi+eiz8cYlC9xtFzrcjpGDJkCB9++CHt2rXLd3GixE5jBEhNTWXUqFGMGDGCxYsXEwzG5sQqZYujZwtMrx/v019EEl01KpD41m1YK6VEmsV+PofcF77GUim5WD78hLfvJzB1KZ6H+kcTXRC58hiYuCjygwGJr92MvV3Bm/FJ8TFsVjz39MHerh45j44mtHo7mYNfIfHf12HvdHqrIpV0SmLFhv+nZZHKz7wA59WuROJTN2CtEWmiHgiHqJmYzOrMffRIq8XmnCwAtnqzeX/VUm5s2Ixr68eugW5RTan0BgO4rbboh6DXlv3KEwtmMvicDF7seCEQ6Tu1Yv+eaHLrkBsaNKVHWm3aVTpx5UhZUdB+c4XNcNnx/PVS3EN7Elq9DQBrw+rH9OA0DANbw+rYGlbHfVdvggvX4Z+4kMCUpeALYkk//DsVmL8Wa3oFLFVTi+U5ADh6NCdv5FTsFzbDfduFJXJ5envruiR9ci/BOWuwtzqcwDDzAhiu4qlKznnkE4IL10cu8F1xLq47LsKSfGa9oAzD4IYGzbj+nKb5vhQdWdFpa14Lz7BL8f57PLmvf4e1UVqJST6ejd15Xubt3EafI1ZQjIX9fh/tKlXDNE3qJx/uV3rkRYfCOAcs3L2Dj1Yvo0FKeW7PaAlA7cQUQqZJ2DRZk7mPRqmRiztDM1qd8Dhw6vc6y9PjyZ25ASCymMOjA4v1vSNe1ExM5sdLBvHj1g1KdMkZGz58OACfffZZ9LaCFksVWbLrxx9/ZOTIkXzxxRckJSXRqVMnFi1aVFSHEzmGvVNDMACHnfCmPWT/6Q0SX7oRa/2quAadT3jDbnI/mo5j4LlgmuALYvoC4D/4ty+ItVH16JS04O+bCa3YGr3P9AeijzF9Qdy3dI+ucuT7egH+8fOi95n7IqtN5b42Ce+LX5P01m3YmtTAkpqAe9hlmLsyyX1lImZufFZdlAX2jg1I/uRech4dQ2jlVqwNCqdhrJRNweWbyXngIzBNbB3OIeHf12JJOtz81W6xMr7XgOiXi0PJrqlbNzJ5y3paVawSTXZlB/x0/eoTAPJChy84zdu5jS3eLJqXr0zdI6aDFNTproB4simVYdMkK+An5WDFlWmatPj8XZbt282aq2+LTl9xWa3syvPy295d0cdaDIMve11BjcQk9vsPNx9umFqBhqnHVr2VRQXtN1dUjAQntpa1T29bqwV723rY29bDfLAfoQ27oqsbmuEwOY+NwdxxAFur2jh6t8LeoxmWlMJtrh1avxPf+Hm47+2LYRgYCU6SR/+l2JJGZ8uS5MbR43CT+tD6XWTd/ibuu3rjuKRNkUz3O3JFP/ddffD+91s8D1x2zIIFZ+rIWBfv2cmgKV/mu99xRQeCyzfj/3I+OY98QtIHd2FNK30XcELhMNdO+YrJW9bzynk9uKtpm5jFklGuItMvvZbMgD/6+vtDIZp9NpIeabX4U8NmtBv3wSn3c+Q5wDRNftu7i7rJqSQeTFAt2L2dF3+bR8cq1aPJLqvFwqx+g6mXnBpNrJ3MOSnl+b3Llex4aizm7iyszWpiSfEQXLEFc2cm1mY1ca/YRu3t+8Blx31vX5xXdCgRU6JLq/OrpnN+1cMX9r3BAJ+sWc5NDZvH1VRiKXzVqkW+OxVmsVShJrs2b97Me++9x7vvvsuOHTvo168fn332Gb169eL3339n/PjxhXk4kZMKzF4FYZPEd27H+8inhDfuJnPQS6RMegRLxSQc/driGzubA10fP+E+kr8YhrXmwUqKH5eR9+7UE27rHNA+muwK78okuGj9MduYmZHl003f4WWznX1bEd5xgNxXJoJ5zEOkFLFUTCbx1ZsIb9qTb3n38Pb9ukIoZ8TaOA1H/3YYNgvuv14a/RJ/tKM/7N/aqDk90mrRvfrhfgdbc7KjSQ3XEStsvbNiMW+tWMzjbc7nsTadgMOJseqeRD7v2T+6/7WZ+8gJBKidlHLKKX+nWgHx6CmVu/O8JNoc0b4zY9b+zk3TJtK9ei2+7H0FEPlCazEMwqbJ8v17osmuS2vVZ27/62lSvmK+Y3RPizz/X3dvjx7zeMrq4goQXxWbhtMe7a0DYO73Yk2vQHBnJsGF6yNVRM99if28Bjj6tMLeuXGBElJmIEjeez+RN3IqBEJYa1fG2b9dJJYSnug6Ht/oWZh7svE+8RmBn1fi+fvlhZYYDO84gPelb7DWqYz7tsiKp7YWtUh6Z2iBvvgeb+wOnfEdazP357vNMAw8D/UjtGY7oeWbyXngQ5Lev7PELWB0KmFMGqaWZ+aOLXStXvPUDyhihmFEL0YAfLNxLasO7CXT7+O6cyIXWs5kWn3Xrz9h+rZNfNajP1fUjSzA0iu9Drc0ak7v9Lr5Ht/oDC5amOEw1R6dQDW/hcS37otWR5vhML4xs8l9PjId09qiFgmPXxm9XwqHaZrcNn0So9YsZ8HuHbzeqWesQzqlU1Ulglp3FJWiKJYqtHf6vn37MnXqVLp3786//vUv+vfvT0LC4WacyuRKsfMFwRKZ8pD07p/JeegjgvP/wMyLvIEZyUctkWsY4LRFKrmcNgyHPVLxdZC1XpVIk1ynHcNpA0fk70PbH9kg39GtCdaaFaP3hTNz8T40CtfQi3D0bYWlQv5mjv7JS8BmjWmjWCkchsWCtdbhnhH+n5aR8/DHuO/ujfPaTnovlBMK78vGsNswEl2RL2gP98ewntnV5VpJKVx+1AqPtZNSGNujH1f+MOGY2ztUrkbDIz6wbcnJYsHu7ayyO/Il0oYvmnPcxNhN074lzZPE8+d2w3rwSnim3wccXgHxEF8oyHZvTr4vP52/HMXP2zczue/V9EivDUAVdwI5wQArD+zNF+9H3S6hostNFffhzxaV3QlUdp+48XdJm64nxcNSPpGkN28jvOMA/u8W4Z+0iNCqbQSm/05g+u84rz4PzwOXnfDxgV9WkffxTIK//gEm2FrXwTXofOznNyS4eAM5T38ebapuO68htg6lu/2Ae9ilGJWSyXtzMoEpv5H52wYSnriqQG0VzEAQ36ifyR3xI+T6CbgdOK85P1qherbnwrMZ04bTTuLwwWTe/AbOKzuWukQXRC5svHL+RTzQokO+RvEnYppmZCXQYqpQurxOA6Zecg17fbnRiyqNy1VgxrbNXF6nQTTmPXm5/HfpgsMxHtSyQmXm79rOFm9W9LYaicm8fUGfAsUVnLOG8NodJL0zFGuNipEVeQ94saQm4Bp0PoEFfxD6bSOJb96G5QQXlaRgOlVN54t1q7iqbslv7XG6lelQthb8KUpFXSxVaO/2kyZN4tprr+W+++6jbdu2hbVbkbNmbVANwmakL0XHBiS+fguhNduxVIqccAMzV4LFIHn0fVjSKoDdetIPX47eLXH0bnl6x65d+ZgG5YEuGfhGz8LRtQlG9cNXfoPLNpH3zhQcvVockwST0i8w43cIhsj9v28IzluL57GBWMolnvqBUqaE1mwn+/73sdSuROKLN2DYrGec6DoRh9V63GmKf2vVkb+16pjvtuqeRL7qdQXZR13VdFitVHS5ST9i1aUtOVmM/WMlyXYH/3fehdHbD32RGbliCa07RZJd07dtovvXn3BOSjlGdb80um1lV6R6ZG3mfnocvK195Wr8ftUt+XrBADQtf+aNx0vqdD0pHpYqKbiu74Lr+i6E1u7AP2kh/kmLcFzUPLpNcOkm/N8vxtGnJdZGafjen0buq5OwNk6LVCIZBv7JS8i+912sTdIJLd8SSSKUT8Tz10ux9yz9U3MMqwX3Td2wn3sOOf+IVMJn3/EOzsGdcf+5F4bjzL4uBOasxjv8S8IbItOLrc1r4XmoX76p2GfrTMf0nrxcKrjcWKqmkvL5sFJXebffl0eKwxn9HTtVoiu4bBN5H80gMG05+INY61fFOfDcg5XCRZvMOVRxdqiidsmendw3ewp/nzedLdf9mVSnC5fVyqjVywHYlJNFm0qRaUuPtT6f4R264rQWbiIyMHcNlmqpWFvUInzAi/fZcYRWbSP543swXA6cfVuRM3UZ7M2GIyrypXAYhsHQjFYMqNMg34UpbzAQXSCmJDlVZTrE/4I/xak4iqUK7R1l5syZjBw5ku7du1OtWjWuu+46rr32WurXL91Xu6T0sjaribVBNXJfmYg1Ix1Liic6zSG0dW+kiWyXDKx1imc5Yc8/ryD7jrfJvPZl7J0aYalTmdDq7QRnr8LaJP2kV5ml9PL84wpsjdPxvvg1gZ9XkHntf0l48uroinoigZ9XkP3IJ5DjA4sFc282xll+6C7odL0kh5NLah173n7l/It45fyL8l2JL+d08VLHC/GHQ/m2PXCwsquc8/CKsvWTUwmZJnvy8giEw9Hb/3t+D97r2pekI6bDuG32M5qmcipKZAlEqrPdd/bGdUfPSCX3Qb6v5uP/fA6+j3/GUi2V8Lb9OK48F8+D/aIftJ2DO5M54HlCyzYD4LisLe57+xZ6D7BYs2WkkzzqHrwvfo1/3Fx8H83AUjUV16Dzo9uE1u0kMGc1hMLYmtbE2rxm9HUK78rE+8JXBH74DQCjfGJklcW+rQq1wuh0x/T49asYMvUbxlx4GX1q1suX6ArvzyG8fX+Be4YVpSy/j/O//Ii2Favxv849cZ8iOeCf8hs5f/8ES3oF3Lf1wEh2E5i9Cu/wCQRmrSRh+OAiT3gdyWG10q16TeompZJ68HyQYHcwNKMlry77lfJHnCPKuwqeCD2uUBhsVoJz15Dz+FjMXZlgtRBcuB57xwaHq/zC6iNSlI5MdO3w5tBh/Af8OaM1D7RoXyIvFhxdmS5FoziKpQot2dWxY0c6duzIyy+/zKeffsrIkSN54oknaNeuHddddx1NmjQprEOJnBbDMEj419Vk3f4WmVf9H87+7bDUrEhoxRb8Xy3ASPHgebBfscVjSU0g6d0/4//6V3zf/kroh9+wVErG848BOHq3jDbCl/hiGAbOgedibVGLnL99THj9LrLveAfXLd1x3dy9WD94Sslimia+T2aS+9I3EDaxta5DwvDBWFJPPC3vRIprut6RH0oruxO4t9mxH06eO7cbbb54n941Dq/0Vs2TyObr/kx1TyIL9+yI3p6WoGpWKV5HJ10c3ZpiZuYSmL6c8Lb9APjH/kJoxVYcvVviHNAew27D88RVZN/2JrZWdUh4dGAMIi8ehttBwiMDsHdqhH/8XJwDzwWIVMU8NobAzyvAYQOLAXkBrI3SSHh6ENZalTB9AQLTf4+ssnhVR1y3X1Qo1Vxna9Tq5WQH/HyxflW+FQxDm/eQfecIzFw/yR/ejaVKyazo+WnbJlbs38t+n49Mv/+kya7wAS85j43B3r0pCU9eHf1s4RzQIXJB5a8f4Bs7G9c1nYorfBqlVuDHS67BF8rfXPpPDZvx6rJfo43oi5K1cRq+j38m+84RAFhqVSLhqauxNY40UPdP+Q1L1VSMSqeeGiqF4+M1y9mQncnIlUv4c5NWxfJ7cDbeXL6IH7asZ2hGSy5Mqx3rcOJScRRLFfqkdY/Hw0033cRNN93EypUrGTFiBM888ww7duwokZlbiW/W+lVJ+uAufB9OJ+/TmZDjwyiXgOOKDrgGdy726WSGy4Fz4LnRD49SdtjOqUbyh3fjfe5L/F/OJ+/tKdha1cHeXtWvZZEZDOH9zwT84+YC4OjXNtKj6yx7yZTE6XpHfjEzDEOJLSmR7Oeeg/3cczCz8zhw5YtgMTB3ZRL6bSO+XZk4r4ycr+3Na+EY2JHgrJUxjrh4OLpk4OgSaTRuBkNk3TWC8B87cD94Gc7+7cFmIThnDTnPjiNr6Nskf3Q31vQKeP5+OdYG1fItGBArH194KRcsr8EdGa3y3W6pmISR4CS8ZS/ZD35E0tu3n/FUzeJwaa36/HDx1bitNqp4Tn4RxP/1AgiF8TxwGYbNGlkN/IAXS+UU7J0aYb+wGb6xv+AcdH6xfx8r7KmJpyu4ait57/0U/dnRr23k9XFFkiv+acvxf7swMlW3kNoGyKnd16wtHpudLtVrRBNdxdkUPtPvw4ToAgtrM/dxzZSv8AYDfNDt4uh2P2/fzGfrVtKmUlUlu4pIcRRLFem7T8OGDRk+fDjPPvssX331FSNHjizKw4kclzWtPJ6H++N+qB/4g+CwKfEqMWG4HSQ8OhB7+/oEV21ToqsM8/7rM/zfLgTDwH1vH5zXdS7w+5Km64mcPSPRhaV8ItYG1XDf2Qv/90swnPb8lWC+AJTBatzA1GWEf98CQN7bU7BUTcXWoDq+8fMwt+4Dpw3f2Nm4h/bEeUmbGEd7mN1i5e6mh+MxTZO5u7bRoXJ1EoYPJuv6Vwkt24R3+AQS/nFFDCM9sW5HrKx7MqEVWyMtOw4ulpTzxGcEJi/B0b8d7jt7Ye/UiMD3iyEvAO6SWUlT2PLe+J7w2h0YyW7MQIjAlKV4bVYslZIJzltLcMEf2Ls3xXld8VW7SeTi1+0ZLaM/F0VT+GA4zO/797ApO5O+R1R13j59Em+tWMzwDl15oEUHAMo5XMzbtQ2A3GAguu019RvTplKV0x6DcvaKsliqWFLtVquV/v37079//+I4nMhxGYYBmiooJcDRix2Ed2XiGz0L1209SuTVZSl8zms7EZizGs8jA3BckBHrcIpEQfuHiRQ3+3kNyRszC89fL8V1bf4vwGaun8CU33D0axej6GLH/90iLA2qYZgmodXbybn/g8hURn8QrBasDdPwT1yEe2jPWId6Us8snM0/5s/guQ5dGdaiAwlPX0P2Pe/iHz8PW0Y6zgEdYh0im7IzGfbLVF49/yIquc+gJ5zdGun7eJD71gsJfL8Y/7i5BKb8hq3DOZE7bMVXwRTrc4Dnb5eTmzgJ918uBn+QvLGzCUxZipnrw1qrEglPDcJ+UXNVdcXYsn27ATCA1zpdRIfKx1aFnqwp/IJd25m+bRPNK1SKVmDt8+XR/LORGEDOTfdHK82rHqyQ3JqTHX18eZeb8T0HUC85ldzQ4WRX35r16Iv66xa3wi6W0rcqEZEYMk2TnEdHE5y3lsDsVSQ8cw3WWme+6pyUfOH9OdF+XLZGaaRMeKjUrQx2Ooqrf5hIYXNe0YG80TPJfmgUCU9eHa2SCe/PIefRMZj+YHRaY1li7vdiq1cVzz+vIPeN7/F9OB38QWwta+N+qB/BWavIe3dqrMM8pZyDVRuWg5UC9o4NcP25J3mvfYd3+JdY61fF1jy2VRx/mvYtU7ZsIDcY5Mvep19tZm1QDf+X8wku34wtIx1rncokvTMU7/AJhFZtIzB5CXichJZvwdaiaJ9jrM4B/u8WE1yxBc+9fQGwVE4h4cmro/d77u4Dd/cp1GNKwVU92Ly+VcUq3NKoBXbLiatnX1gylwN+P59f1D86PXbc+lU8vXA2Qxsf7q1V0eWmdlIKlV0e9vrySDuY7LqvWTv+2rw9yUcsigPQr3YkGXxoJdGTJWR1wa54FFaxlJJdIiIxZBgGrus6k7N6O6GVW8kc/Aqeh/vjvLh1dBszO4/w7kyMJDeWCup7VBr5vpiD96VvSHr15uiXqXhMdEHJ7B8mcjosVVNJfOF6sod9yIGLn8XWth4YBsH5a8FmIfH567FWL3u/t5bq5Qgu2wx2K557++K4sBnhfdnYOzXCMIzI1Mbq5WId5ik9074LfWrUpXO1GtHbXDd2JbR8M4Gpy8j970QS3749pq0uXu54IbdMn8TL5114Wtubpolv7C/kvjIRgOy/vEfiKzdha1AdW8vaJL5+C9nDPiC0aAN4fWTd/AauP3XDfWevInsOxX0OCGflkjt8Av6Ji4BIElNtIkoPhzWS3PrveT2iia6PVi/jP4t+oXeNujx3brfotuPXr8YbDLIu60B01eZzK1dnYJ2GtKtULbqdYRisu2boMcc6cpXo4zndRO2R20rJpmSXiEiM2Ts1Ivnje8j552iCC/7A+9gYgnPX4LqxK3nvTsU/eQkEQgDY2tXDdVsP7K3qnGKvhc8MBAlM+53w5j0YiS7s3Zoo+XYKZihM7kvf4PtkJgD+yUtiXjlQHJTIktLK3q4+KV8+hP+r+QQX/AEmuG+/CEe/tme1Umo8cPRrh//bhfgnLsLZtxW2poeTRcHlmwlMW477/ktiGOHpOzLRFQiHGLFiCbc+diWWCkm47ugZ856uTcpXYla/wacVR3hPFjn/+ozgzMiiCdZWtQnvzCTr2v9izUjHSPUQXLQefEHcf7mY0B878E+Yj7V5zSJ+FsV3Dggc/MwU3r4frBZcN3XD1rr4Px9Jwblth9MS/lCIpft2Uz0h/0JitzVuSaOUClRwHl7l9ZJa9bmkVuEkN08nUQu6YFeaKNklIlICWCqnkPj6LeS9O5W8t37A/82v+CcuhNQE3EN7Ym1Wg/DWffjGzCZ76NskvnA99k6Nii0+/0/L8D4zDnNvNkaKBzPHB89/hfPKc3Hf2ze6zHlZFd6fE0kCuhxY6lXBMAzM7Dyy//5JdPU215974fpT15jGKSKnZknx4Bp8AQy+INahlAi21nVw9GmF94mxhJZtwtGnFYbLjn/qUvI+moE1Ix1nKexlduNP3/LxmuUsaLSdtx/uH7M4Fu3egcdmp0Fq5Mvz6SS6/NOX433yc8x9OeCw4b6nD86rOkIoTGDqMvzTloM/iOu6zjj7t8dSJQUA1+ALsNapfHg/k5dglEvA3rZ09SYyA0Fy/zcZ3wfTwTSxpFcg4V9XlYmLSWVBrxp1mNjnShofrN46ZMg5TWhdsWqRHltJrPiiZJeISAlhWC24b7kQW5u6ZN81AoJhkkfegTX94Mm+daS5fc4DH5Lz5OekfPNwsSSZAvPWkPPQKOydG+G+szfWOpUJZ+Xi/3wOuW98D2ETzwOXFXkcJVF4Vybel74hMGUpBCPVd5ZalXAMaI9/wjzCf+wEp52Ef12F48JmMY5WROTMGYaB5/ErsdSqiG/0LHyjZ0XucNlxXtIG9919SuW07Etr1mP8+tUMqN0g3+2+L+aAy4Gzb6sij2F3npd+33/Bfl8eE/tcyXlV00/5GO9/J+L7YBoA1nOqkvDkIKz1DyYALBYcPVvg6NniuI89MtEV3peN95kvMLPysPdsgefevtGkWEmX/Zf3Cf6yGgBH/3Z47r8Ew+M8xaOktEhLSCItQTMHpOCU7BIRKWGsNStCIITzpu7RRJfpDxKYthxsFmznNiAwYwV5I6dia1cPw2bFek616JeNcKYXM8cXSYTZLGCz5v/3GU7TyP3fZKxNapDwn8HRVYssSW5cN3YFi0Hua9/hur5LqfmQXFjCu7PIuvkNTH8Q9929sbWrh7k3m7yPZpD3f98AYFRKJvHF67E1PvUXGBGRkurQxRjX9V0IrdoKwTDW+lUxEk/eA6ckG1Q/g+5ptajsPjw91T9tOd5nxoHThrVuZWyN0oo0hmA4TM3EZBwWKxnlKp7WY2wNq+MDnIM74/5zr7NfxdlmxdG7Jb7P5xD4fjEHZvyO++buOK/rhGEv2V8RnQPPJbRiK55/DMDRtUmswxGREqpkv5OJiJRB4a37IGzi7HG4Esj36Uxy/zsx33Z5b/0Ab/0AQPLY+6NXbH2jfiZvxI8n3H/Sh3dFky95n86MrKJ1KCFmPZQci/ztvr0HocUbSHj6GgKzVuIfNxccNqz1qmBrVQfHxa3JfXMy/h+W4Lquc2G/FCVa3ogpmF4/yR/djaVqavR2a7t6ZA18kfCmPSS+fCO2Bscuoy0iUhoZDhu2pkXf86m4HJno2pOXy9+MDTzRpQGuaavIGfYhSR/dXaS92qp6EvnxkkFs8+aQeoLm2WYoTHjL3siFMMDRqwXWelUOV3OdJUuSG89D/XH0a4d3+JeElmwg99VJ+L6cj+eBy7B3bHDqnRST8O5MQht3Y29dFwBH1ybY29Yr1clWyU8rIEpRULJLRKSEOVSKH955AGv9qpjBEMEVW7G1roMZDIEvSGjlVowKSRgJzsj0OecRb+cWI/JzMAyh8LEHOFidBZGVHs092ZF/HyeW8L6cyC7TyhFcvIHA9N8BCPzwW2QDe2Qapf/HpTgubJYv6RPPTH8Q3ze/4rqmE0bFJIKrt2GpkoIl2YPFaiXxf7eSefnzBH9ZrWSXiEgJZ5omV/0wgR+3bmDXhfV4b11Fwht3k/P3T0j8758KvWVApt9HsiNyrrdbrNRMTD7udqGte/E+OobQpt0kf3IflvKRht0FTXQdydYojaQRQ/F/8yu5r0wkvHE32fe+S/IXww63UYgh/49L8T79BQDJn96HpVLktVKiKz5oBUQpSkp2iYiUMJa6lbHUrYxvzGxsHRtg2KwkPnNN9P68j6aTu2Z7pKKo0rEfkN23X4T79osAMMMHE17BMGYgCMEwRvLhVWycAzpgv6BxJDEWDEWSaYf+HQpjrV0JrBaCSzdh73AOxt8vx8z1E1y2meCvf2DuzgIgtHgDZu7h1WuCizcQPpCDrWVtLMmeonqpip1pmoQ37yEwexV4/fh/XEreqBmQF8DRuyWeJ6/GMAysVVKx1q5EaMveWIcsIiKnYBgG/+nQhSFTv+bZ87qR2DBI5o2vEZy7htzXvsNzb99CO9aK/Xs4f8JHPNGmE3c2aX3c1gKmaeKfuAjvf8ZDjg88DkJrtmNpXzirzh3NMAycl7TB0bUJuW/9AKaZL9FlhsMYFstJ9lD4zBwf3he+wv/lfACsDatj5p18lTwpfbQCohQlJbtEREoYwzBw39aDnIc/xvvseNy3XYilYjJmnh/fhPnkvvodzgHtj5voOmZfFgtYLGAHw33sFTFL+cToleITsXdrgm/UDBy9W+Ic0CF6ezgcxvvwxwR+XoGjT0sstStF78sbPYvA94vBMLDWj0x5tLWug61VHSwVSk/TUTM7L3r12DRNMvs/R/iIBFZ43c7IPxKchHdnEt64G2utSpiBIOGdmdjjKNEnIhLP2laqxtKBN2O1WCAVEh4bSPbDH+P7cDq2xmknbPp+pt5ftZS9vjw+W7eSoRmtsB2V7ApnevH+e0LkHApYm9ck4V9XF0uVlZHownP/JZjm4Vrv0B87yB72Ie57+2K/oPEZ9/08G8HFG8h5dHTkfGsYuG7oguv2HiW+l5icHSWxpKjoHUNEpARy9GiO+fdcvC9+jX/CPCzVyxHemw1eP47+7XD/9dJii8V9V2+ybnqDrBtewzXkAmwtaxPevp+8MbMIzlqF59GBOC9rm+8x1rTyhGpVIrxhF6HV2wmt3o5vzGwgUrmW/PG9xbKS5JkIH/AS+n0zwWWbCS3bRHD5ZgynnZQJDwKRJKQlrTzhXZlYG1TD3JuNGQiS+PJNWOtXyXfV2z9pEeb+HBy9CufLkYiIFD3rEe/jq1tX44ZbqvPOmB002nmg0I7xTLsLqJGQxBV1GmI7qloqMH8tOY+NwdxxAKwWXLdeiOvGrsV+vjwyoZU3cmpkSudfP8B2XkM8wy6N9g8rbKZpkvfWD5G+o2ETS7VUPE9cFe3VJSJyJgzzyNS9sHnzZmrUqMGmTZtIT9fqWSISW+GsXALfLSa0ZS+WFA/2i5pjTSv+K2ChTbvJffnbSM+ucOS0Ya1fFdftPXB0a3rCx4X3ZBFcuJ7gr38QXLiO0JodWBtVJ/nDu6PbZP/t40jT49aR6i9LeoWTXjk2gyH8X/+K74s5hDbswkhw4riwGc5rzsda/cxfm9w3vsf/3WLCm4/TANVqIXXKo9HqrvDuLIwUN4bdRnD5ZrJu+R+2lrVx39sXW8PqmF4fvi/nk/vKROxdmuSbfioiIqWDaZp0+epjZmzfTL9yaYy/cnCxHDfnibH4v1qAJb0CCU9dXSIWAzC9PvJGTiXvoxmRHqF2K67BF+C6qdtxK8YLyvvv8fg++wVH31Z4Huyn3lwiBVDWcxtKdh2lrP9CiIicTHh3JuGt+zASXVjqVD7j6QzhA17M3VlY61UBwMzzs7/L4/ka6RuVkrG1qoO9dR1sbetF+oYdZPqDZA/7gODs1dg7NcTWui7hXZn4v/kVQmESX70ZW9Ma+Y5pBkOE1mwntGwzwWWbCK3aRtLIO6LLtec8/UVklUnAUqMC1ox0bBnp2JrUwNqw+kk/zAfmriHn8TGYOzMxUjyRvmXBEI6LW+N5uD+G035Gr4+IiJQM27zZ/GXWFF7v1JPyrkivSzPPD3YbhvXM+lf9uGUDkzb9wTPtuxxTzWWaZvRcambnkfvuVNw3d48uVlNShNbvwvv8lwR/WQ2AUSUFzwOX4ejapED7NU0TvP7IgjtEXuPAL6sLvF8RUW5Dya6jlPVfCBGR4mQGggQX/EFwwTqCC9cRXLYJAqHo/fbuTUkcHrmibpom3mfH4f9yPokv/wl7h3MO7yc7j6x7RhLecYCUCQ8SXLKBwA+/EVy+mdCqbeAP5jtu0nt3RpNiwZVbMfflYM1IO6tm+mYwRODnFYTW7cRwO7B3ycBardzZvBwiIlJChTbvYdvDH1Dh/Ca47+h52o/L9Puo/+lb7Mrz8p/2XXmwZaT3pWma+Mb+QnDBHyT8+9pi6YVVUKZpEpi2nNwXviK8bT/u+/riGnzBWe8vvDcb75OfYeb6SXz9lmJvgi8S78p6bkM9u0REJGYMuw37uQ2wn9sAADMvQHDpxkjia+F67OceTmiF1u/C/8VcsFnJ+/hnQr9vwdqiFuaebILLN+O65UJy7nmXwLTlhDfvjfYIAzCS3VgbRyq2rE3S81WL2RpWL9hzsFkjV6B1FVpEJG59Ne9X/tQ+jw++nU6vxmmnXXmU7HDyRqeevLJsAXc3bQ1EpsTn/OszgrNWAhCYtrxUVDIZhoGjaxPs5zbA98UcnFd1jN4XWrcTS+WUaIUWHFzBeNs+yA1gqZaar1rNP+N3vE9+jrk3G+xWQiu2Yssoe1/GRaToKNklIiIlhuGyY29bD3vbesfcF1q6MfKPYIjgzJUEZ67Md7+tQTUsaeUJLt2Eo1cLnHs6RZNbp+oDJiIiciKmaTLCfYD9LgufNnDQ6bExWN+/E2vtyqf1+CvqNmRAnQYYhoF/2vJIkmd/DjhsuO/pg71LRhE/g8JluOy4ru0U/dkMBMke9iFmTh7ue/vi6N2SwI9LyRs5ldDKrZGN3A4cfVvhurEree/9hP/zOUCk/6fnyauxnVMtFk9FROKYkl0iIlIq2NvVB8D1l4sxDCM67dFSPglrRjpGWjlMXwDDZsHWKA1bo7QYRywiIvHAMAzG9ujPS0vmctuCJZCznuxhH5L83p0nbKA+Zct62leqRpLjYDVTXoCcF7+O9oi0NqhGwpODoj0sS7PwjgMQDmPuzsL7z9HkvTmZ8Oa92Do2IOG5wVjKJRKYu4a8UTPwfzk/2q7AeV0n3H/upf6WIlIklOwSEZFSwaiSgqVWJUJLNpL4n+vgiKvKAIE5qzF3Z2E7OCVSRESksLhsNh5ufR7hZ5qROeQVwut3Mfa5D6l35yUYlvyVw8v27uaW6ROpkZjM7H5DqOT2kP3QqMi0RcPAObgz7jt6RhdKKe2s6RVIHv0X8j6aQd6IKYQ37wUDLDUrYGtbD0uSG2vzmvi/X0x4/S7wOEh8/nrs7evHOnQRiWPx8Q4rIiJxzzAMXEM6433qC/I+moFz0HkYNisAoTXb8T75eWQlxdZ1YhypiIjEK0uFJBKHD+avb77Hf9P2wvgPTrjt2sz97PXlUsntwX3rheSs24Hn0YHRSuV4YjhsuG/qhun14ftgGoRN/KNnE/h+Cckf3o2laioJT19DziOfEN6TrXO1iBS5uFryYv369dx8883UqVMHt9tNvXr1eOyxx/D7/bEOTURECoGjXzucQy4g96VvONBvODn/+JSs294kc9BL4HaQ+NwQ9eYSEZEiZW1Sg21tIiv6XhFM5qfv/Uz9PDPyZ3wWMzZV4uU6bQDICQYAsDWrSfK4B+Iy0XUkc38O1kZpJL5+C5Y6lbE2qI5RJQWILAjjvqs3ZOViZubGOFIRKYjSkHuJq8quFStWEA6HefPNN6lfvz5Lly7l1ltvJScnh+effz7W4YmISAEZhoHn3r44erXA98Vcwht2YSS48DxxFY4ezdT3Q0REipxhGDx0cW/GjvuAu77aQrseHXBc2gavy4r9l7Xkvf49wXl/wIAkQpv2QMWqkccdrEaOZ0aii/CuTGxt6pL8yb2Ymbn5LkKFdxwAi4HhdsQwShEpqNKQe4mrZFfv3r3p3bt39Oe6deuycuVK3njjjRLzgouISMHZGqVh+/vlsQ5DRETKqNCG3QC4buiCZ9BFfPvdDC7eMItJWyvTPjsPnAcTW66ydRHG0aM5vo9mEJi6LHIRqnxi9D4zEMT3xRzsnRop2SVSypWG3EtcJbuO58CBA5QvX/6E9/t8Pnw+X/TnrKys4ghLRERERERKqcC05ZAE9q5NyH3/Jz74bTo0cPL1ro20t1qw1KsM5GGtlBzrUIuVtUk69k6NyHnyM0xfAEfP5hh2G6ENu8h96RvCG3eT8M8rYh2mSJmSlZVFZmZm9Gen04nT6Sz045wq91Lc4qpn19HWrl3LK6+8wtChQ0+4zbPPPktKSkr0T0ZGRjFGKCIiIiIipY25NxsAw2bB1rwW/dYHefwXL49v95A04g4c3ZtFtguZsQyz2BmGQcIz12BvXx/vY2M4cNFTHLj032Re8QLB3zaR+NwQbE1rxjpMkTIlIyMjX87j2WefLfRjnE7upbiVimTX448/jmEYJ/0zf/78fI/ZunUrvXv35sorr+SWW2454b7/9re/ceDAgeif5cuXF/XTERERERGRUsxIdgNghk3sreow8K+DeeiCbpT/8B5sTWsQ2rYvsp217C2aYnicJD43hOQxf8F5fRccvVuR8NQgUr55GHunRrEOT6TMWb58eb6cx9/+9rcTbluUuZfiViqmMd51110MGjTopNvUrl07+u+tW7fSrVs3OnbsyFtvvXXSxx1dwndkeZ+IiIiIiMjR7Oc1hN83EZz/B/Sthr1jA+wdGwAQ3pdNcNrv0LNs9es6mrVuFdx1q8Q6DJEyLykpieTk05tSXZS5l+JWKpJdFStWpGLFiqe17ZYtW+jWrRtt2rTh3XffxWIpFcVrIiIiIiJSSlgbVYffYfHHPxDenYm9SwaGy0FoyQZyP53JypSyV9ElIqVfPOVeSkWy63Rt3bqVrl27UrNmTZ5//nl27doVva9q1aoxjExEREREROJFsiMyM+T2C1yQtwS+W3L4zg5w6GtWkl2rDopI/CkNuZe4SnZ9//33rFmzhjVr1pCenp7vPtMsW80hRURERESkaJyTUp5VV99KVsBPeF8Owd82QCCMpVYlrPUqYxgGSXYH56SUnJXJREQKS2nIvRhmSYmkhNi8eTM1atRg06ZNx/yniYiIiIiIiIiUdGU9t1GyJlWKiIiIiIiIiIgUgJJdIiIiIiIiIiISN5TsEhERERERERGRuKFkl4iIiIiIiIiIxA0lu0REREREREREJG4o2SUiIiIiIiIiInFDyS4REREREREREYkbSnaJiIiIiIiIiEjcULJLRERERERERETihpJdIiIiIiIiIiISN5TsEhERERERERGRuKFkl4iIiIiIiIiIxA0lu0REREREREREJG4o2SUiIiIiIiIiInFDyS4REREREREREYkbSnaJiIiIiIiIiEjcsMU6gJImHA4DsG3bthhHIiIiIiIiIiJy5g7lNA7lOMoaJbuOsmPHDgDat28f40hERERERERERM7ejh07qFmzZqzDKHaGaZpmrIMoSYLBIAsXLqRKlSpYLKee5dm1a1d++umnszrWmT72dLfPysoiIyOD5cuXk5SUdFaxlRUF+f8rbrGMtaiPXZj7L4x9ne0+impMg8b16SpNYxpiF6/GdNE9TufqwleaxnW8nqsLe98F3Z/GdOmmMV0yjl2SztX6Tl00wuEwO3bsoFWrVthsZa/Oqew941Ow2Wy0a9futLd3OBykp6ef1bHO9LGnu31mZiYAaWlpJCcnn1VsZUVB/v+KWyxjLepjF+b+C2NfZ7uPohrToHF9ukrTmIbYxasxXXSP07m68JWmcR2v5+rC3ndB96cxXbppTJeMY5ekc7W+UxedsljRdYga1BfQnXfeWWyPLcix5PhK02say1iL+tiFuf/C2NfZ7kNjOvZK22saq3g1povucaXtd7A0KE2vabyeqwt73wXdn8Z06VaaXtN4HdOFvf9YjemzeWxp+v2TgtE0xjiUmZlJSkoKBw4cKNVZaBE5TONaJL5oTIvEF41pkfiiMV36qbIrDjmdTh577DGcTmesQxGRQqJxLRJfNKZF4ovGtEh80Zgu/VTZJSIiIiIiIiIicUOVXSIiIiIiIiIiEjeU7BIRERERERERkbihZJeIiIiIiIiIiMQNJbtERERERERERCRuKNklIiIiIiIiIiJxQ8muMujrr7+mYcOGnHPOObzzzjuxDkdECujyyy+nXLlyDBw4MNahiEgBbdq0ia5du5KRkUHz5s0ZO3ZsrEMSkQLKysqiXbt2tGzZkmbNmvH222/HOiQRKQRer5datWoxbNiwWIcix2GYpmnGOggpPsFgkIyMDKZOnUpycjKtW7dmzpw5lC9fPtahichZmjp1KtnZ2bz//vt89tlnsQ5HRApg27Zt7Nixg5YtW7Jz505at27NypUrSUhIiHVoInKWQqEQPp8Pj8eD1+uladOmzJs3jwoVKsQ6NBEpgEceeYTVq1dTs2ZNnn/++ViHI0dRZVcZM3fuXJo0aUJaWhpJSUn07duX7777LtZhiUgBdOvWjaSkpFiHISKFoFq1arRs2RKAypUrU758efbu3RvboESkQKxWKx6PB4C8vDxCoRCqNxAp3VavXs2KFSvo27dvrEORE1Cyq5SZPn06l156KdWrV8cwDMaPH3/MNq+//jp16tTB5XLRpk0bZsyYEb1v69atpKWlRX9OT09ny5YtxRG6iBxHQce0iJQshTmm58+fTzgcpkaNGkUctYicTGGM6/3799OiRQvS09N58MEHqVixYjFFLyJHK4wxPWzYMJ599tliiljOhpJdpUxOTg4tWrTg1VdfPe79o0eP5r777uORRx5h4cKFdO7cmT59+rBx40aA415FMgyjSGMWkRMr6JgWkZKlsMb0nj17uP7663nrrbeKI2wROYnCGNepqaksXryYdevW8fHHH7Njx47iCl9EjlLQMT1hwgQaNGhAgwYNijNsOVOmlFqAOW7cuHy3tW/f3hw6dGi+2xo1amQ+/PDDpmma5syZM83+/ftH77vnnnvMUaNGFXmsInJqZzOmD5k6dap5xRVXFHWIInIGznZM5+XlmZ07dzY/+OCD4ghTRM5AQc7VhwwdOtQcM2ZMUYUoImfgbMb0ww8/bKanp5u1atUyK1SoYCYnJ5tPPPFEcYUsp0mVXXHE7/ezYMECevbsme/2nj17MmvWLADat2/P0qVL2bJlC1lZWXz77bf06tUrFuGKyCmczpgWkdLjdMa0aZrceOONdO/enSFDhsQiTBE5A6czrnfs2EFmZiYAmZmZTJ8+nYYNGxZ7rCJyaqczpp999lk2bdrE+vXref7557n11lt59NFHYxGunIQt1gFI4dm9ezehUIgqVarku71KlSps374dAJvNxgsvvEC3bt0Ih8M8+OCDWglGpIQ6nTEN0KtXL3799VdycnJIT09n3LhxtGvXrrjDFZFTOJ0xPXPmTEaPHk3z5s2jPUQ+/PBDmjVrVtzhishpOJ1xvXnzZm6++WZM08Q0Te666y6aN28ei3BF5BRO9/O3lHxKdsWho3twmaaZ77bLLruMyy67rLjDEpGzdKoxrRVVRUqXk43pTp06EQ6HYxGWiBTAycZ1mzZtWLRoUQyiEpGzdarP34fceOONxRSRnClNY4wjFStWxGq1HpNx3rlz5zGZaREp+TSmReKLxrRI/NG4FokvGtPxQ8muOOJwOGjTpg2TJ0/Od/vkyZM577zzYhSViJwtjWmR+KIxLRJ/NK5F4ovGdPzQNMZSJjs7mzVr1kR/XrduHYsWLaJ8+fLUrFmT+++/nyFDhtC2bVs6duzIW2+9xcaNGxk6dGgMoxaRE9GYFokvGtMi8UfjWiS+aEyXEbFbCFLOxtSpU03gmD833HBDdJvXXnvNrFWrlulwOMzWrVub06ZNi13AInJSGtMi8UVjWiT+aFyLxBeN6bLBME3TLLbMmoiIiIiIiIiISBFSzy4REREREREREYkbSnaJiIiIiIiIiEjcULJLRERERERERETihpJdIiIiIiIiIiISN5TsEhERERERERGRuKFkl4iIiIiIiIiIxA0lu0REREREREREJG4o2SUiIiIiIiIiInFDyS4REREREREREYkbSnaJiIiIiIiIiEjcULJLRERERERERETihpJdIiIiImfptddeo3bt2thsNh544IFj7t+zZw+VK1dm/fr1hXrcgQMH8uKLLxbqPkVERETihWGaphnrIERERERKm6VLl9KqVSvGjx9P69atSUlJwePx5Ntm2LBh7Nu3jxEjRgBw4403sn//fsaPH59vu59++olu3bqxb98+UlNTT3nsJUuW0K1bN9atW0dycnJhPSURERGRuKDKLhEREZGz8OWXX9KmTRsuvvhiqlWrdkyiKzc3lxEjRnDLLbcU+rGbN29O7dq1GTVqVKHvW0RERKS0U7JLRERE5AzVq1ePRx55hDlz5mAYBkOGDDlmm4kTJ2Kz2ejYseMZ73/9+vUYhnHMn65du0a3ueyyy/jkk08K8jRERERE4pKSXSIiIiJnaPbs2dStW5fnnnuObdu28frrrx+zzfTp02nbtu1Z7b9GjRps27Yt+mfhwoVUqFCBCy64ILpN+/btmTt3Lj6f76yfh4iIiEg8ssU6ABEREZHSJjExkfXr19OpUyeqVq163G3Wr19P9erVj7n966+/JjExMd9toVAo389WqzW637y8PPr370/Hjh15/PHHo9ukpaXh8/nYvn07tWrVKuAzEhEREYkfSnaJiIiInKElS5YA0KxZsxNuk5ubi8vlOub2bt268cYbb+S7bc6cOQwePPi4+7n55pvJyspi8uTJWCyHi/LdbjcAXq/3jOMXERERiWdKdomIiIicoUWLFlG/fn0SEhJOuE3FihXZt2/fMbcnJCRQv379fLdt3rz5uPt46qmnmDRpEnPnziUpKSnffXv37gWgUqVKZxq+iIiISFxTzy4RERGRM7Ro0SJatGhx0m1atWrF8uXLz/oYn3/+Of/6178YM2YM9erVO+b+pUuXkp6eTsWKFc/6GCIiIiLxSMkuERERkTO0aNEiWrZsedJtevXqxbJly45b3XUqS5cu5frrr+ehhx6iSZMmbN++ne3bt0eruQBmzJhBz549z3jfIiIiIvFOyS4RERGRMxAOh/ntt99OWdnVrFkz2rZty5gxY874GPPnz8fr9fLUU09RrVq16J8BAwYAkab148aN49Zbbz2r5yAiIiISzwzTNM1YByEiIiISj7799luGDRvG0qVL8zWXL6jXXnuNCRMm8P333xfaPkVERETihRrUi4iIiBSRvn37snr1arZs2UKNGjUKbb92u51XXnml0PYnIiIiEk9U2SUiIiIiIiIiInFDPbtERERERERERCRuKNklIiIiIiIiIiJxQ8kuERERERERERGJG0p2iYiIiIiIiIhI3FCyS0RERERERERE4oaSXSIiIiIiIiIiEjeU7BIRERERERERkbihZJeIiIiIiIiIiMQNJbtERERERERERCRu/D+LKbJPYOK+EAAAAABJRU5ErkJggg==\n", 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\n", 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ALC0ts/VhZWUFkOcMo4Lo6+tjZWWVbfyixJWXwjyv8O9S2ydlbW2t/d48rmHDhgDcunVLeyy358vMzAw/P79CPV9ZHj58yNixY/nggw/w8PDItU1Rxxo8eDDr16/PdfmwEEIIIZ6eJLuEEEKIMiokJAQAX1/fIj0uMDAQjUbDzJkzC9Xe09OTMWPGsHLlSi5fvlzkOB9XvXp1rKysmD17NitWrODGjRtF7mP79u34+/trEzv5uX79OqGhodSoUUN77OzZswDUrl07R/vatWtrz+clLS2N33//PVufeTl48CDp6emFaluUuHr27ImHhwdvvvkm586dIz4+nkOHDvHhhx/SrVs3qlevXuB4WTIyMkhLS+P27dvMmjWLy5cv8+abbz5RXKUpq87c6tWrC2y7b98+9PT0CvxdiYmJ4cSJEzmer/zGeuONN/D29i6wVl1hxwJo1aoVCQkJHDhwoEh9CiGEEKJwJNklhBBClBFZSYnk5GROnTrFyJEjcXZ2ZtKkSUXqx8nJiYkTJ7Ju3TpOnz5dqMfMmDEDU1NT3n777ScJXcvU1JR169aRlpbG6NGj8fb2xs7Ojn79+rFt27ZC9fHXX39lq2WWl7S0NEaMGIGZmRkTJ07UHs+aXWZjY5PjMTY2NgUWeA8MDOTq1avMmjUr33ZxcXGMGTMGd3d3hg8fXmC8RYnL0tKSv/76i9TUVGrWrIm5uTktW7akUaNGbN68ucCxHte5c2f09fVxdXVl8eLFbNq0iS5dujxRXKVJrVaj0WgKnDW2a9cu1qxZw7hx47C1tc237dixY0lISGDGjBmFGmv79u189913fPnll4WevVbQWAD+/v6oVCr++OOPIvUphBBCiMKRZJcQQghRRkydOhV9fX2MjIyoW7cuZ8+eZdu2bdl2tktPTyctLU17y6tQ+ZQpU7CxsWHq1KmFGtvW1papU6fyww8/8Pfffz/VdXTu3JmwsDB+/PFHJk+eTI0aNfjpp5/o3r17gbNjoqOjSUxMzHVZ2OMURWHEiBH8/vvvfPvtt7i7u+dok9dyufyW0X311Vd88MEHvPnmm/To0SPPdo8ePaJ3796EhoayefNmzMzMtOcKeo4KE1dUVBQ9evQgNjaWdevWcejQIZYtW8bhw4fp3r07aWlp2u/D42NlHX/ckiVLOHr0KFu3bqVDhw7079+fDRs2FPr78jTLDp/GkCFDSEtLY8iQIXm2OXHiBP369aNx48bMmzcv3/5mzpzJunXrWLRoEQEBAQWOFRMTw+jRo5k6dSo1a9YsUuz5jQX/LicNDw8vUr9CCCGEKBxJdgkhhBBlxPjx4zl27BiHDx/m448/JjU1lR49emSbWVO5cmX09fW1t/feey/XviwsLHjnnXfYsWMH+/fvL9T4EyZMwMXFhSlTpjz1tRgbG9OzZ08WLFjAwYMHuXr1Kn5+fnz22Wc5dk58XFJSEpB/TSpFUXjllVdYu3Ytq1evzpGUyprdk9uMpIcPH+Y6gwlg1apVjB49mlGjRrFgwYI8x09OTqZXr14cPnyYn3/+mUaNGmU736ZNm2zPUdasr6LENX/+fE6ePMnu3bsZOHAgLVq04LXXXmPdunXs2rWLdevWAfDNN99kGyu3elY+Pj40aNCA7t27891339GmTRvGjh2rTcI96fdL14KDg2nXrh0+Pj78+uuvGBoa5tl29uzZzJkzhw8++KDQyxFnzJiBvr4+r7/+OtHR0URHRxMfHw9AYmIi0dHRKIryxGMZGRlpf96FEEIIUbz0Cm4ihBBCiNLg5uamLUrfrFkznJycGDRoELNmzWLp0qUAbNu2jeTkZO1jXFxc8uzvtdde49NPP2Xq1Km89tprBY5vbGxMYGAgo0aNYvv27U95Ndl5eHgwatQoJkyYwLlz5/KscZWVeHn48GGu57MSXatWrWLlypUMGjQoR5usWThnzpyhc+fO2c6dOXMm11k6q1at4pVXXmHo0KF8/vnnec5mSk5OpmfPnuzfv5+tW7fSpk2bHG2++OKLbIXH7ezsihzXyZMncXV1zVHov0GDBsC/dba6devGsWPHco01Lw0bNmTHjh3cu3cPR0fHJ/p+6VpwcDBt27bF09OTXbt25Sjk/7jZs2cTGBhIYGBgkZbpnj17lhs3buDk5JTj3NChQ4HMGXiP15YrylhRUVHanw0hhBBCFC9JdgkhhBBl1EsvvcRXX33Fl19+yVtvvYWnpye1atUq9OMNDAyYM2cOL730UqH/qR4+fDiLFi1i2rRpeS6RzE9cXBwqlSrbsr4sFy5cAPJP0BkYGFCpUiWuXbuW45yiKIwcOZJVq1bxxRdf8PLLL+fah6urKw0bNmTt2rVMnjwZjUYDZNYCu3TpEhMmTMjWfvXq1bzyyisMGjSIr776Kt9EV69evdi3bx9btmyhQ4cOubarWrXqU8fl4uLC3r17CQ8Px9XVVXv8zz//BDITo5CZHCyoTtXjFEXh4MGDWFlZaR9X1O+Xrp08eZK2bdvi5ubG7t27sba2zrPt+++/T2BgIO+8806BNdj+a/HixURHR+cYe+LEiQQGBtKyZctsP+dFGev27ds8evQIPz+/IsUkhBBCiMKRZJcQQghRhs2fP59GjRrx/vvv89VXXxX58QMGDODjjz/mt99+K1R7jUbD3Llz6dWrF5D7Dn3Xrl3j+++/z3Hcz8+PxMREOnTowIsvvkjLli1xdnYmKiqK7du3s2LFClq1akXTpk3zjaFVq1a5xvvGG2+wcuVKhg8fTq1atfjrr7+05wwNDfH399fenz9/Pu3ataNv376MGTOGyMhIpk2bRs2aNbMlyTZv3syIESOoW7cuo0eP5ujRo9nG9Pf31y6Pe+GFF/jtt9+YMWMGtra22ca3sLAoVOKisHGNHTuWdevW0a5dO6ZNm4a7uztnz55lzpw5ODo68tJLLxU4Vo8ePahTpw5169bF1taW27dvs3r1ag4ePMhnn32Gnt6/bwMLG1dezp8/z/nz5wGIiIggMTFR+zPi5+eX7Xvz22+/kZCQoJ39dv78eW3bzp07Y2JiAsC3337L8OHD+frrr7W1tC5dukTbtm0B+OCDD7hy5QpXrlzR9l25cmXs7e0B+OSTT3j33Xfp2LEjXbp0yfZ8ATRu3Fj7dW5j1a1bN8/rrVGjBq1atdLeL8pYgPZ869at8xxDCCGEEE9BEUIIIUSJGzp0qGJqaprruZCQEAVQFixYkOv5vn37Knp6esrVq1fzHQNQxo4dm+P4rl27FEABlGPHjmmPz5o1SwGUe/fu5XhM06ZNFUDp0qVLjjHyus2aNUuJiopS5syZozz//POKq6urYmBgoJiamip169ZV5syZoyQmJuZ7DYqiKHv37lUA5ejRo9mOe3p65jm2p6dnrtfduHFjxcjISLGxsVGGDBmi3L17N1uboUOH5ntNISEhhbr2li1bFnhdRYlLURTlxIkTSq9evRQ3NzfF0NBQqVSpkvLKK68oYWFhhRpn/vz5SoMGDRRra2tFo9Eotra2SocOHZRffvnlqeLKTdbPUl4/F4/L73l8/Pu9atUqBVBWrVqV41het8fbtmzZMt+2j8ttrNzs379fAZTNmzdnO16UsRRFUQYPHqzUqlUr37GEEEII8eRUipJLZU0hhBBCCB2qXbs2zZo1Y/ny5boORYhiFRsbi4uLC4sWLWLkyJG6DkcIIYSokGQ3RiGEEEKUOR999BGrV6/m1q1bug5FiGK1aNEiPDw8CrU8VAghhBBPRpJdQgghhChzOnbsyIIFCwgJCdF1KEIUKwsLC1avXp2tZpoQQgghipcsYxRCCCGEEEIIIYQQFYbM7BJCCCGEEEIIIYQQFYYku4QQQgghhBBCCCFEhSHJLiGEEEIIIYQQQghRYUiySwghhBBCCCGEEEJUGJLsEkIIIYQQQgghhBAVhux5/B9paWkEBwfj6OiIWi25QCGEECI/GRkZ3L17F39/f/T05G2FKDkZGRncvn0bc3NzVCqVrsMRQgghyjRFUYiLi8PFxeWZzG3Iu9L/CA4OpmHDhroOQwghhChXjh49SoMGDXQdhqjAbt++jbu7u67DEEIIIcqVmzdv4ubmpuswSp0ku/7D0dERyHzT7uzsrONohBBCiLLtzp07NGzYUPv6KURJMTc3BzLftFtYWOg4GiGEEKJsi42Nxd3dXfv6+ayRZNd/ZE3vc3Z2fiazn0IIIcSTeBanx4vSlbV00cLCQpJdQgghRCE9q0v/5Z2pEEIIIYQQQgghhKgwJNklhBBCCCGEEEIIISoMSXYJIYQQQgghhBBCiApDanYJIYQQQgghhI5lZGSQkpKi6zCEEOWIvr4+Go1G12GUSZLsEkIIIYQQQggdSklJISQkhIyMDF2HIoQoZ6ysrHBycnpmC9HnRZJdJSwxJZ09Vx8Sl5yGr50J9d0s5IdQCFFhpN8PQ0lOzPO8ytAEjZ1HKUYkhBBClC+KonDnzh00Gg3u7u6yu60QolAURSExMZHIyEgAnJ2ddRxR2SLJrhKiKAofHrjBRwdDiX6Upj1e19mMFb2r08DdUofRCSHE00u/H0bsx70LbGcxeYskvIQQQog8pKWlkZiYiIuLCyYmJroORwhRjhgbGwMQGRmJg4ODLGl8jCS7SsiMndeYd+AG45u5M7aJO64WhhwKiWLm7us8/+UJ/nitPrWdzXUdphBCPLGsGV0m/d9H4+Cd43x6ZAiJm2bmO/NLCCGEeNalp6cDYGBgoONIhBDlUVaSPDU1VZJdj5FkVwm4HZvMR4dCmd22Eu+2raQ93rGqHS28rQlY8jezdl/nxyF1dBilEEIUD42DN3qu1XQdhhBCCFGuSakTIcSTkL8duZMF4SVgw8kIDDQqxjfPuWzH1EDDG03d+fnCPaISU3UQnRBCCCGEeFp3795l/vz52lopQgghhCg7JNlVAu7EJeNuaYSlUe4T52o4mpGhwL0E2VpYCFFxXI+NZszhXXx27oSuQxFCiBL3ySefMG3aNBYsWKDrUIQQQgjxH5LsKgEuFoaERT8iOinnzK1HoVc5ExGPRq3C3lTW5Qshyrd4lYa0f7ZJ3387lOXng/nw5F+kZqTrODIhhChZ586dA+DYsWM6jkQIoUuDBw9m7ty5ug5DVEBnzpzBzc2NhIQEXYdSLkmyqwQMrOtEWobCwt/Dsh1PvHiac30bw4IxDHZXsDbR11GEQgjx9C7ExdHGtS0zL10AYJBPDQZW8WNN667oqeTlRQhRsV26dAmAkydPoiiKjqMRonSpVKp8b8OGDcvRzszMjDp16rB69eoC+/fy8kKlUvHXX39lOz5hwgRatWqlvR8YGIhKpeLVV1/N1u7kyZOoVCpu3LgBwI0bN1CpVJw8eTLX8dLT05k3bx7VqlXD2NgYGxsbGjduzKpVq/KN8/Tp02zfvp1x48YBmQXCp06dSq1atTA1NcXFxYUhQ4Zw+/btbI9LTk5m3Lhx2NnZYWpqSvfu3bl165b2/I0bNxgxYgTe3t4YGxtTuXJlZs2aRUrKvyuDTp06xYABA3B3d8fY2Jjq1avz6aef5htvloMHDxIQEICRkRGVKlXi888/z9Fm8eLFVK1aFWNjY9zd3Zk4cSKPHj3Kt9/AwECqVauGqakp1tbWtG3blr///rtI156bQ4cO0a1bN1xcXFCpVPz000852mzZsoUOHTpgZ2eX73P9uAMHDtCjRw+cnZ0xNTWlbt26rFu3Lke/7dq1w97eHgsLC5o0acLOnTsL7DuLoih06tQpR9yFeY5r1apFw4YNWbRoUaHHE/+S/0ZKgJO5ITNae/H+vhBe+/EC5+7GE5WYypGde8lQoMnNP3h93TAivvkfGal5L2W8F5/C18fC+fRwGL9duk96hryREkKUHefj47hiYMGG27eITn6EoUaPdc93o5WLhxTKFEJUaCkpKYSEhAAQExOj/VqIZ8WdO3e0t8WLF2NhYZHt2ONJl1WrVnHnzh1OnTpF//79efnllwuVLDAyMmLq1KmFardy5UouX778xNcTGBjI4sWLef/99zl//jz79+9n5MiRREVF5fu4pUuX0rdvX8zNzQFITEzkxIkTzJw5kxMnTrBlyxYuX75M9+7dsz1uwoQJ/Pjjj2zcuJHDhw8THx9P165dtTtzXrx4kYyMDL744gvOnTvHokWL+Pzzz3n77be1fQQFBWFvb8/atWs5d+4cM2bMYPr06SxdujTfmENCQujcuTMtWrQgODiYt99+mzfeeIMffvhB22bdunVMmzaNWbNmceHCBVauXMmmTZuYPn16vn37+vqydOlSzpw5w+HDh/Hy8qJ9+/bcu3ev0Neem4SEBOrUqZPvtSUkJNCsWTM+/PDDfGN83JEjR6hduzY//PADp0+fZvjw4QwZMoRt27Zp2xw6dIh27drx66+/EhQUROvWrenWrRvBwcGFGmPx4sW5vi8uzHMM8PLLL7N8+fJ8vz8iD4rI5ubNmwqg3Lx586n6ycjIUD45dEOxe++AwtTd2lvv9zYoxwe1U44HWCvHA6yVs30aKjFHD2Z7bGpaujJp2yXF4O09imrabsX4nb0KU3crnvN+V/ZcefBUcQkhRHFJvXVBWTjnBSX0761K6q0LOW6PTvyqPJwaoKTeuqDrUEUJepLXzYMHDypdu3ZVnJ2dFUD58ccftedSUlKUKVOmKDVr1lRMTEwUZ2dnZfDgwUp4eHi2Ph49eqS8/vrriq2trWJiYqJ069btqV+7RdkWExOjAEpMTIyuQ1HOnz+vANrb999/r+uQRDmWlJSknD9/XklKSlIUJfP/iPj4eJ3cMjIyihz/qlWrFEtLy1zP/fdvvKIoio2NjTJp0qR8+/T09FTGjx+vGBgYKNu3b9ceHz9+vNKyZUvt/VmzZil16tRR2rVrp/Tt21d7PDg4WAGUkJAQRVEUJSQkRAGU4ODgXMerU6eOEhgYmG9M/5Wenq5YWVkpv/zyS77tjh49qgBKaGiooiiKEh0drejr6ysbN27UtgkPD1fUarWyY8eOPPv56KOPFG9v73zHGjNmjNK6det820yZMkWpVq1atmOjR49WGjdurL0/duxY5fnnn8/WZtKkSUrz5s3z7fu/sv5u79mzR1GUJ7/2x+X2M/W4gp7rgnTu3Fl5+eWX823j5+enzJ49u8C+Tp48qbi5uSl37twpMG5Fyf05Tk5OVgwNDZW9e/fm+bj//g3JUpZeN3VBZnaVEJVKxaQWntyc1pwdw/35bmAtTr7RiB9mvki9b3bgFfgZetZ2PLpxhSuv9SR07iTtYyf8cplPj9xk5vPe3HvnORLff56jYxvgY2dCl9Un+TssRodXJoR4Vt1JjGfEwV+J/2dGqsrQhGFxIZhveY+4JYO0t4ilQ/n4m0Be3rdV206Ix+X3CW1xfSouREn67wySwn7CL0RhJCYmYmZmppNbYmJiiV1Xeno63333HQ8fPkRfv+ByLl5eXrz66qtMnz6djH/qg+blww8/5IcffnjiGnpOTk7s27cv2wykgpw+fZro6Gjq16+fb7uYmBhUKhVWVlZA5oys1NRU2rdvr23j4uJCzZo1OXLkSL792NjYFDhWQW3+/PPPbGMDdOjQgePHj5Oamllzunnz5gQFBXH06FEArl+/zq+//kqXLl3y7ftxKSkprFixAktLS+rUqQM8+bUXl2HDhmVbBpubgr6HGRkZxMXFZWtz4MCBbMtmIfP3eMCAASxduhQnJ6dCxZfb2AYGBtSpU4fff/+9UH2If+W+XaAoNkb6Gjr42mY7plKrse06AMuWnbm9fC73vl+JcRU/AG48TGLZX7f4pLMPE1t4ah/TwN2SX4bVpdFnRwncc53fhvuX6nUIIZ5tiqLQdcf3nLh/FxUqvmrZCY2dBxaTt6AkZ39j/DAxkXcO7CFdUZjQ/VUa2nnoKGpRVnXq1IlOnTrles7S0pLdu3dnO7ZkyRIaNmxIWFgYHh4exMTEsHLlStasWUPbtm0BWLt2Le7u7uzZs4cOHTqU+DWIZ1tWskuj0ZCens6JE7ILrRB5GTBgABqNhkePHpGeno6NjQ2vvPJKoR77zjvvsGrVKtatW8fgwYPzbFevXj369evHtGnT2Lt3b5FjXLhwIS+88AJOTk7UqFGDpk2b0qNHjzxfqyCz5pJGo8HBwSHPNo8ePWLatGkMHDgQCwsLACIiIjAwMMDa2jpbW0dHRyIiInLt59q1ayxZsoRPPvkkz7H+/PNPvvvuO7Zv357fpRIREYGjo2OOsdPS0rh//z7Ozs68+OKL3Lt3j+bNm6MoCmlpabz22mtMmzYt374BfvnlF1588UUSExNxdnZm9+7d2NnZPfG1FydnZ+d8E6fff/89x44d44svvsizzSeffEJCQgL9+vXTHjMxMaFq1arZkrgTJ07U/hwVRn7Psaura7ZEmigcSXbpkJ65JR5T5mPfeyhG3lUB2Hj6Ls1iLzDIwADwzNbeUE/NuKbuvPLDBe4npGAnuzkKIYpB+v2wHAmrx6kMTdDYefC/pm1548geptZtpD2nySWRVRl4KyoOTzMLannXKImQRRkUFxdHbGys9r6hoSGGhobF0ndRPxWXZJcoaVnF6du0acOuXbtkZpcoViYmJsTHx+ts7OK2aNEi2rZty82bN5k0aRITJ06kSpUqAMydOzfbTobnz5/Hw+Pf9xb29vZMnjyZd999l/79++c7zpw5c6hevTq7du3KNwGVGz8/P86ePUtQUBCHDx/WFkQfNmwYX331Va6PSUpKwtDQMM86pampqbz44otkZGSwbNmyAmNQFCXXvm7fvk3Hjh3p27dvnknCc+fO0aNHD959913atWunPW5mZqb9etCgQdpC9P8dR/lnk42s4wcOHOCDDz5g2bJlNGrUiKtXrzJ+/HicnZ2ZOXMm69atY/To0drH//bbb7Ro0QKA1q1bc/LkSe7fv8+XX35Jv379+Pvvv/N9TvK69uI2b968PM8dOHCAYcOG8eWXX1KjRu7vXzds2EBgYCBbt27Ndj0NGzbk4sWL2vs///wz+/btK/RrQ0HPsbGxcYnOuqyoJNlVBmTN6gKIio7l7bPLCBt7l/hOfXEbPxt9u3+nPVaxzXwBepiYKskuIcRTS78fRuzHvXM9F6fS46aeCX6psVhM3kIzJw+O9RqKuhBvRuY1bFncoYoyzs/PL9v9WbNmERgY+NT9Ften4kIUp6yZXX379mXPnj1EREQQERFR6KUqQuRHpVJhamqq6zCKjZOTE1WqVKFKlSps3rwZf39/6tevj5+fH6+++mq2GTIuLi45Hj9p0iSWLVtWYMKocuXKjBw5kmnTprFy5coix6lWq2nQoAENGjRg4sSJrF27lsGDBzNjxgy8vb1ztLezsyMxMZGUlBQMDLL/X5aamkq/fv0ICQlh37592tcvyPx+pKSkEBUVle11LDIykqZNm2br5/bt27Ru3ZomTZqwYsWKXOM+f/48zz//PCNHjuSdd97Jdu7xHQmzYnBycsrxWhkZGYmenh62tpkrkmbOnMngwYO1iZdatWqRkJDAqFGjmDFjBt27d6dRo38//HR1ddV+bWpqqn2+GzdujI+PDytXrmT69OlFuvbSdPDgQbp168bChQsZMmRIrm02bdrEiBEj2Lx5s3ZWeV727dvHtWvXtB/SZenTpw8tWrTgwIED2mOFeY4fPnxI5cqVi3RNQnZjLHM8zPU5bFMHVCoe/raZs30acnf9cpR/apAcuxWLgUaFk3nxfFouhHi2Zc3oMun/Pubj1mpv90d8Qfuag+lbqQt3NYbadoVJdIln0/nz54mJidHeCtqxqTCK61NxIYpbVrLL39+fqlUzZ+fL7C4hClalShX69OmjfY2wsbHRJkaqVKmCnl7OuRhmZmbMnDmTDz74INsM4ty8++67XL58mY0bNz51rFkf4iQkJOR6vm7dukDm69/jshJdV65cYc+ePdoEUpaAgAD09fWzLdm/c+cOZ8+ezZbwCQ8Pp1WrVtSrV49Vq1ahVuf81/3cuXO0bt2aoUOH8sEHH+Q4//j3NmsmUpMmTXKUC9i1axf169fXLsNLTEzMMZ5Go0FRFBRFwdzcPFvfxsbGuX6PIPO1OTk5uUjXXpoOHDhAly5d+PDDDxk1alSubTZs2MCwYcNYv359oeqWTZs2jdOnT3Py5EntDTJnOa5atUrbrjDPMcDZs2fx95cyRkUlM7vKmBcbV8Zt/ygMOw6g75ElJJ47wa2FM4ja+zOW0/7H//6IoG8tRyyM5KkTQhQfjYM3eq7VtPddUlPQO3kSdXoakRojqjxBn4qisDv8BkvOBrG6VRdsjfJ+IyTKP3Nz82yfXD+t4vpUXIjiFhMTw927dwHw8fHB39+fCxcuEBwcnG99HyFEpjfffJM6depw/PjxAou7Zxk1ahSLFi1iw4YN2WYU/ZejoyOTJk1iwYIFuZ7PWoL8OD8/PwYOHEizZs1o2rQpTk5OhISEMH36dHx9falWrVouPWUusaxXrx6HDx/WJr7S0tJ44YUXOHHiBL/88gvp6enaWVQ2NjYYGBhgaWnJiBEjePPNN7G1tcXGxobJkydTq1Yt7Yyh27dv06pVKzw8PPj444+zFc7PmkGalehq3749kyZN0o6j0Wiwt7fP83v06quvsnTpUiZNmsTIkSP5888/WblyJRs2bNC2yZrl5O/vr13GOHPmTLp3745Go8m134SEBD744AO6d++Os7MzDx48YNmyZdy6dYu+ffsCFOracxMfH8/Vq1e190NCQjh58iQ2NjbaZa8PHz4kLCyM27dvA/8+105OTtrv2fTp0wkPD+fbb78F/k10jR8/nj59+mi/hwYGBtpC8Rs2bGDIkCF8+umnNG7cWNvG2NgYS0tLAI4ePcqQIUPYu3cvrq6u2cZ8nIeHh3aWYGGeY8isDRceHl7gbDKRC11tA1lWPckW6sVt0e+hClN3Ky+sCVYOf/GZcryFu3I8wFr5vZGLUnvaRuX6g0SdxSaEqFhSb11QHk4NUFJvXchx7lpMlBJ+7WSe5wuSkZGh1P3+a4UvPlTeD/qjOMIVZdDTvm6Sy1bcKSkpSs+ePZUaNWookZGROR6TtXX5pk2btMdu375dpK3LRflTVrZQP3r0qAIoTk5OiqIoyoIFCxRAeeGFF3Qalyi/kpKSlPPnzytJSUm6DuWJrFq1SrG0tMz1XG5/4xVFUdq1a6d06tQpzz49PT2VRYsWZTu2fv16BVBatmypPTZr1iylTp062drFxsYqdnZ2CqCEhIQoiqIoISEhCpDrLSQkRFmxYoXSunVrxd7eXjEwMFA8PDyUYcOGKTdu3Mj32j///HOlcePG2vv5jbN//35tu6SkJOX1119XbGxsFGNjY6Vr165KWFiY9vyqVavy7Ofxa8/tvKenZ74xK4qiHDhwQPH391cMDAwULy8vZfny5dnOp6amKoGBgUrlypUVIyMjxd3dXRkzZowSFRWVZ59JSUlKr169FBcXF8XAwEBxdnZWunfvrhw9ejRHu/yuPTf79+/P9VqHDh1a4Pds1qxZ2jZDhw7N9vMzdOjQXB/zeJuWLVsWOHZWfFk/b7n57+9CYZ5jRVGUuXPnKh06dMj3+5PX35Cy8rqpKypF+acanQDg1q1buLu7c/PmTdzc3HQWxzdBt3lvbwjXHybhnBTJ7HOfYWhrR5Ol6/Gxrzhr+IUQupUWfpG4JYMwH7eWSCtX7j9KoratQ67nH5/5VVg/XL/EoYibTKzVAC9zy+IMXZQRT/K6+fgntP7+/ixcuJDWrVtjY2ODi4sLffr00X4q/viOUVmfigO89tpr/PLLL6xevVr7yfCDBw8ICgrK81NnUb7FxsZiaWlJTExMsc4iLKqsOj4tW7bkwIED7N27l7Zt21K5cuVsMw+EKKxHjx4REhKCt7c3RkZGug5HFMGjR4+oWrUqGzdupEmTJroOR1QwycnJ+Pj4sGHDBpo1a5Znu7z+hpSV101dkZpdZdTQABeuTG7K8dcbsu6NjnTYtIuXVq3TJrrSYqKIOby7gF6EEKLwAoP+oO4Pq/jw5F/F1mefSlX5tGlbSXSJbI4fP46/v7+2/sSkSZPw9/fn3Xff5datW/z888/cunWLunXr4uzsrL0dOXJE28eiRYvo2bMn/fr1o1mzZpiYmLBt2zZJdJWAefPm0aBBA8zNzXFwcKBnz545lgINGzYMlUqV7da4ceNsbZKTkxk3bhx2dnaYmprSvXt3bt26VZqXUiyy6nX5+voCaH+Or127RkxMjM7iEkKUPiMjI7799lvu37+v61BEBRQaGsqMGTPyTXSJvEnhpzJMrVYR4JYzA6soCmEfvknU7p+w6zUEt4lz0JiY5dKDEEIUjqIoJKalogAtnHQ3q1U8G1q1akV+E8sLM+ncyMiIJUuWsGTJkuIMTeTi4MGDjB07lgYNGpCWlsaMGTNo374958+fz7ZjXMeOHbMV3v3v7mQTJkxg27ZtbNy4EVtbW9588026du1a7mbj/TfZlVUzJiwsjFOnTvHcc8/pMjwhRClr2VJ2oBYlw9fXV/taI4pOkl3lUUYGBk5uoFJx/8dviT16CO/3lmNWJ+9ijUIIkZ+MezdYXdWHd9ycqZIeT1r4RQDSI0OKpf+Q2GgWnjlGBzdvuno+Sbl7IYSu7NixI9v9VatW4eDgQFBQULbEjqGhYa4FeSGzqPvKlStZs2aNtsju2rVrcXd3Z8+ePXTo0KHkLqCYZc1qy9qFETJnd4WFhREcHCzJLiGEEKIMkGRXOaTSaHAb/x6WzdpzI3AMKeE3uDSyC07DJuI88i3U+gYFdyKEEIDK0ASAxE0zAXAE4vJp96S+uniapedOEHQ/QpJdQpRzWUv1snaqynLgwAEcHBywsrKiZcuWfPDBB9qt7oOCgkhNTaV9+/ba9i4uLtSsWZMjR47kmuxKTk7WblcPmbVHdE1RlBwzuyAz2bV161aCg4N1FZqoAKSUshDiScjfjtxJsqscM6/fHL+NhwlbMI2H2zcS8fUnxB7ZQ6X5qzF09dR1eEKIckBj58FPfT+mu60tlvr6ubZRGZqgsfN4qnHeqBnAqYeRTKxVH0VRUKlUT9WfEEI3FEVh0qRJNG/enJo1a2qPd+rUib59++Lp6UlISAgzZ87k+eefJygoCENDQyIiIjAwMMDa2jpbf46Ojtpt3P9r3rx5zJ49u0Svp6hu375NYmIiGo2GSpUqaY9n1e2SZJd4ElnLeFNSUjA2NtZxNEKI8iYxMREA/Tzeyz+rJNlVzmnMLPCevQyrFh0InTeJ1Kh7xOqZsOrgDTafiSQuOQ1fOxNGNXSlSzU7+QdTCJHN73duMjzobxyNTbn24ihMS2hmqKOJKb90fKFE+hZClJ7XX3+d06dPc/jw4WzH+/fvr/26Zs2a1K9fH09PT7Zv307v3r3z7C+/5Pf06dOZNGmS9n5sbCzu7u5PeQVPJ2sJY6VKlbL9U5GV7Dp//jzJyckYGhrqJD5RPunp6WFiYsK9e/fQ19dHrZY9xIQQBVMUhcTERCIjI7GysipX9S9LQ5lKdoWHhzN16lR+++03kpKS8PX1ZeXKlQQEBACZT+bs2bNZsWIFUVFRNGrUiM8++4waNWpo+0hOTmby5Mls2LCBpKQk2rRpw7Jlywq9HXp5Zd22B2Z1G3H54nX8V17kfmIqPavbUUkviV2RyXT75hSD/J1Y3bcGGrUkvIQQmTIUhepWtjzn7F5iiS4hRMUwbtw4fv75Zw4dOlTg+ypnZ2c8PT25cuUKAE5OTqSkpBAVFZVtdldkZCRNmzbNtQ9DQ8MylzTKbQkjgJubG7a2tjx48ICzZ89q37sKURgqlQpnZ2dCQkIIDQ3VdThCiHLGysoqz5qZz7Iyk+yKioqiWbNmtG7dmt9++w0HBweuXbuGlZWVts1HH33EwoULWb16Nb6+vsyZM4d27dpx6dIlzM3NgYqz08+T0Ng48sLf17E00uPvsQ0wPPA9txbP5K1ZS9nRIoBBm85S19mcN5+TJY5CiEwtXTw488JwEtNSS2W8xLRUVl06w72kRALrNy+VMYUQT0dRFMaNG8ePP/7IgQMH8Pb2LvAxDx484ObNmzg7OwMQEBCAvr4+u3fvpl+/fgDcuXOHs2fP8tFHH5Vo/MUpK9n1eHF6yExW+Pv7s2fPHoKDgyXZJYrMwMAAHx8fUlJSdB2KEKIc0dfXr/B5jidVZpJd8+fPx93dPduW1V5eXtqvFUVh8eLFzJgxQzsd/ptvvsHR0ZH169czevToCrXTz5PYeeUBF+8lcuS1+rhYGHJl5w+kx8VwffJgWgwex5A6vVly5CYTmnvI7C4hhJZGrcbcoHRmTwTfv8vrf+zGQK1htF9dnE3MSmVcIcSTGzt2LOvXr2fr1q2Ym5tra2xZWlpibGxMfHw8gYGB9OnTB2dnZ27cuMHbb7+NnZ0dvXr10rYdMWIEb775Jra2ttjY2DB58mRq1aqlfc9WHmQtY8xtK/jHk11CPAm1Wo2RkZGuwxBCiAqhzCwI//nnn6lfvz59+/bFwcEBf39/vvzyS+35kJAQIiIisu3iY2hoSMuWLTly5AhQ8E4/uUlOTiY2NlZ7i4vLbR+y8uFQSDRuloY09rBEpVLh87/vcBjwKgB31yxh1C9TiL97h7DoRzqOVAiha7+GXeOH65dKffeWZk5uDKzixyeNW2NZSgk2IcTTWb58OTExMbRq1QpnZ2ftbdOmTUBmce0zZ87Qo0cPfH19GTp0KL6+vvz555/amfcAixYtomfPnvTr149mzZphYmLCtm3bytUn0nktY4R/63adOHGiVGMSQgghRE5lZmbX9evXWb58OZMmTeLtt9/m6NGjvPHGGxgaGjJkyBDtp4iOjo7ZHufo6Khd215RdvopLio9fdzfnItZnUbceH8cBhePse76ZVJP2UDrNroOTwihI6kZ6bxxZA/XYqP5vHkHRvvVLdXx1z3frVTHE0I8nYKS4sbGxuzcubPAfoyMjFiyZAlLliwprtBKVUpKCiEhIUDOZYzwb7Lr9OnTpKenl6sknhBCCFHRlJmZXRkZGdSrV4+5c+fi7+/P6NGjGTlyJMuXL8/W7r879hRmC/uCdvqJiYnR3s6fP/90F6JDz3lbcSsmmb/CYrIdt27bg+rf7uO+fSXsUmKImzaA5Ds3dRSlEELX0jIyGFjFj0rmVrzk46frcIQQoly4fv066enpmJqaamuRPc7HxwcTExMSExO1M8CEEEIIoRtlJtnl7OyMn1/2f7qqV69OWFgYgHZ3gf/O0IqMjNTO9np8p5+82vyXoaEhFhYW2tvj0+3Lmw4+tlSzN+GVHy4QHvPvUkVFUdgSZUavWrOJqN8V5+GTMHTW7dbdQgjdMdbT5736LbjUfyRmOtqBUVEU9obfYMj+X0jNSNdJDEIIURSPL2HM7UNUjUZD3bp1AaRulxBCCKFjZSbZ1axZM23RzyyXL1/G0zNz50Bvb2+cnJzYvXu39nxKSgoHDx7Ubln9+E4/WbJ2+slrW+uKRK1W8ePgOsQ8SqPygiO8uP4Mk365TP2lR3lp41n6BHjS4bPVOI+con1McngoiRdP6TBqIYSu6Kl19xKQkpHOS/t+Yc2Vc3x37aLO4hBCiMLKep+a2xLGLFlLGSXZJYQQQuhWmanZNXHiRJo2bcrcuXPp168fR48eZcWKFaxYsQLIXL44YcIE5s6di4+PDz4+PsydOxcTExMGDhwIVJydfp5GNQdTTk9ozMpj4Ww+E8nJO3H42pkwu20lulSzy/ZJZEbyI65PHUbS9Yu4T56HXa+hBS4JFUKUXzEpyUw4sodpdRtT1cpWp7EYavSYUqch12NjaOroqtNYhBCiMPIrTp9Fkl1CCCFE2VBmkl0NGjTgxx9/ZPr06bz33nt4e3uzePFiXnrpJW2bKVOmkJSUxJgxY4iKiqJRo0bs2rUrx04/enp69OvXj6SkJNq0acPq1aufqSKhNib6vNXSi7daeuXbTklNQd/emcSLpwibO4n4k3/jMf1jNMampROoEKJUfXzqb1ZfPkvQ/buc6vOyzpPb412cUGwtIC6CtLicm4ioDE3Q2HnoIDIhhMgpK9lV2JldhakrK4QQQoiSoVJKe9/5Mu7WrVu4u7tz8+ZN3NzcdB1OiVMyMri7Zinhn70HGRkYVa6O2cwv+CXOkrjkdHztTOhW3Q59TZlZ8SqEeEJnHt7jnWOHGOZbi17eec9MKA3p98OI/bh3ge0sJm+RhFcZ96y9bgrdiY2NxdLSkpiYGCwsLEp9fCcnJ+7evcuxY8eoX79+rm2Sk5MxMzMjLS2N0NBQPDzk75cQQgjd0PXrpq6VmZldQjdUajVOQ9/AtGYA16eP4NG1CzwY0YFtNV/jmGcLHiam4mxuwFd9/OhczU7X4QohnkItG3u2duij6zAAUJITATDp/z63zBz5NOQaQ9w8qGtpCUB6ZAiJm2Zq2wkhhC7FxMRw9+5dIP9ljIaGhtSoUYNTp04RHBwsyS4hhBBCR2S6jgDAPKAZX7+0gmDr6pimJ/Fxyn7uz2jO6QmN8Xcxp+eaU/xxI1rXYQohKhiNgzfv3gxnyY3rLIqIRM+1Gnqu1dA4eOs6NCGE0Mpawujk5FTgp+NSt0sIIYTQPUl2CQCuPUhkycUUkmetxXnkW/jMX4VKo6GWkxk/DalDLSczZu+9ruswhRBPYMKRPcwN/pOE1BRdh5KrybUb0sbVk2G+NXUdihBC5KowxemzSLJLCCGE0D1ZxigA2HT6LmYGGoY38sC4+fRs56K+X8lk98oM/CuDe/Ep2JsZ6ChKIURRXYp+wJJzJ8hQFNq6etLQwUXXIeXgb+fIni4v6joMIYTIkyS7hBBCiPJFkl0CgIeJqTiZG2Ksn33XypjDu7i5YCo+hsa08X2NqKQmkuwSohzxsbRhTeuuHIu8UyYTXUIIUR5cunQJyH8nxix16tQB4ObNmzx48ABbW9sSjU0IIYQQOckyRgGAt40xN6KSuBuXnO24aa0GmDdqhSo5iflnFqK/aRFKRoaOohRC5CX9fhhp4Rdz3DJuX6KfsZqPfX10HWKBElJTWHHhJL+EXtV1KEIIkU1RZnZZWFhQpUoVQGZ3CSGEELoiM7sEAAPrOPHWr1eYvTeEz3pURaVSAaBnaY35B2tY8+rr9Ly6lYerF5IechHv95ajMTXXcdRCCMhMdMV+3DvH8Qyyf6JhMXkLGruytTNYemSI9uul164w7eJ56lla0tanbMUphHh2KYpSpGQXZC5lvHr1KsHBwbRt27YkwxNCCCFELiTZJQCwNtHnky6+jPnpIjejHzG2iRuulkYcvB7FgkOhpNUezuDezxO/eAoxB3/l4ssdqLJwHYZumTumpd8PQ0lOzLN/laFJmfsnW4iKIut3z6T/+9pdDPffv8fkC2eZW9WPtiSSuGlmvr+jpU1laAJA4qaZ2mO91QasdmlF7+sniQ++guaxdkIIoSu3b98mISEBjUZDpUqVCvUYf39/Nm/eLDO7hBBCCB2RZJfQeq2xG7Ym+szec51Oq04CoFGr6FXDno87++Bp3YIEv+pcmzyER9cvEn/ybwzdvPOcVfJfZXFWiRAVicbBGz3XagB8dPIkp2Nj2ZGYQgcvbx1HlpPGzgOLyVuyJeDMgdOKop1ZKklyIURZkDWry9vbGwODwtUtzSpSf+LEiRKLSwghhBB5k2SXyKZfbUf61nLgQmQCscnpVLIxxuGxgvSmNetTbc1eovf+jG3XzN3Tsv5ZTew6kw3hRuy+8pCElHQ8rY3oXcOBTrbxJG9+t0zNKhGiotvYpgcLTv3NxFoNIOqmrsPJlSSyhBDlQVayqzDF6bNkJbsuX75MfHw8ZmZmJRKbEEIIIXInBepFDiqVCj9HMxp7WGZLdGUxsHfG4cXR2vtpsVEADNoRxbxLRlSt5U+n1k2ItqpM3/0pTD6aUmqxCyEy2RoZ82GjVjiamOo6lCJTFIX9t0P56cZlXYcihBDanRgLW68LwNHREWdnZxRF4fTp0yUVmhBCCCHyIMku8VQURSF86XsATD3/OZderkySdQir7u5h8+Ca7Bzuz9GbsTqOUohnh6Ioug7hqf0Qconnf9nI+CN7SZPdX4UQOlbU4vRZsmZ3Sd0uIYQQovRJsks8FZVKxa2AbgC4Rt1g48zhfHXxNO3dvNGoVLTzsaVnDXsA0tLln1YhStqrv+9k8L5fuBj9QNehPLGuHlXwNLOgs3slEtJSdR2OEOIZlzWzqyjLGAHq1asHSLJLCCGE0AWp2SWeWpBZFWoDhi6e1Lx8hj629uibW2LYvD0Atd1NUc5ARHwyXjqNVIiKLTo1lW+unCU5PZ3Xa9bTdThPzEhPj6svjkZPLZ/HCCF0KyUlhZCQEEBmdgkhhBDliSS7xFNT/7NzmuespaiXL2L6wd9QDv7GrXsRxLw0hsFXjvGyTS0m6ThOISo686hb7G/cjO13I6iXEkNaeOYS4vTIEB1HVnSS6BJClAUhISGkp6djamqKi4tLkR6blew6e/Ysqamp6Ovrl0SIQgghhMiFJLvEU2vqaUnSCTUh125Qa9I73PN05cHWNSQc+YX9tesSm57GISMHZpvKj5sQJUFlaAJA4qaZVAOqAfG78m5XnoTFx3LqQSTdPKvoOhQhxDPo8eL0qn8+3CssLy8vrKysiI6O5vz589SpU6ckQhRCCCFELiT7IJ5abU97Grk8T51ze3jv94VYZqTi1sgNgJd2zMHY1I1WSZGYmY7VcaRCVEwaOw8sJm9BSU7Ms43K0ASNnUcpRvX0Tt6/S8CP32Cmp8+tl8ZgbmCo65CEEM+YJy1OD5l1TevWrcuBAwcIDg6WZJcQQghRiiTZJZ7awRSF04bWXNC3JehhIxq7WuNgpk9weBx34lMY5OOEpeYaSXfuYmbnweGIWzS0d8ZAo9F16EJUGK9fuICLqRmv+fljZ1T+ZnDlpratAz4W1riamnPvUZIku4QQpe5pkl2QuZQxK9k1bNiwYoxMCCGEEPmRZJd4ah3cK3Go20DuJCTwKMaCzWciuZqchm/1Srzf0JXqDy5wafQsVBo9Lr/1IYPvR9LOzYst7XphpCc/gkI8rdC4GFZcPEWGotC3UrUKk+xSq1Qc6zVEklxCCJ150p0Ys0iReiGEEEI3JNMgikULZ3ft10MCshdwTbetiWWz9sQc+o0Hqz9Fr3M/rAwM0ZcC1EIUCycTU9a07srxe3eoZmWr63CKlSS6hBC6VBwzuwBOnjxJRkYGannvI4QQQpQKecUVT+yvu+FEJT8qsJ3GxIzKH6/Bceh4GoffYNUPq3h35w+QnJSjraIoZGQoJRGuEBWWoUaPgVX8WNikja5DKTFJaansCw/VdRhCiGdIbGwsERERwJMnu6pVq4aRkRFxcXFcv369OMMTQgghRD5kZpd4Ig8fJdF95xbUKhV7u/Snho19vu1VajVu42Zh7O2Las4EEvZt4/LtMCovXMeXNy7grzbht3MP2XPlIYlpGXhYGdG7hgP9ajtgZGpe7gprCyGKz/1HiVTb9BXRKY8IHfgarqbmug5JCPEMyJrV5ejoiKWl5RP1oaenR61atTh27BjBwcFUqSI7ywohhBClQZJd4olEJCVga2SEWqXCx9Km0I+z7ToAQzdvrk0eTOLFUyz8ZQ3T06BKShz7w3czSUnPbBgN/AEpf0AKYDF5iyS8hMjFhCN7aOzgQm/vqhV20wc7IxNq2NhxIy6G67HRkuwSQpSKp13CmMXf31+b7Orbt29xhCaEEEKIAkiySzwRP2s7TvZ5mdsJ8UX+B9usbmOqfbOHB79soEvj1ny2/xc8sMNq9NcY6f/b15mIOD7ZcpAl6RtRkhOL+xKEKPfOPLzHp2eDWKo6QZizOy4VOAm0sU13HIxM0Ei9GyFEKXna4vRZsup2nThx4qljEkIIIUThSLJLPDFDjR7eFlZP9lhXT1xGTyN432F+D99NSq+PSTz2F6YvDEelUgHg7wq1Lj+AU5CaniE/rEL8h6OxCbPqNeP+o6QKnegCcDYx03UIQohnTHHO7ILMHRkVRdG+zxFCCCFEyZGPyEWRLD5zjM3XL6IoxVNE/uK9RCwzUtHfuJCb89/iytuv8Mq+Xzh5/y4ALbytAYiISy6W8YSoSByMTQms35ylzdvpOpRSoygKpx5E6joMIcQzoLiSXbVq1UKtVhMZGcmdO3eKIzQhhBBCFECSXaLQLkTdZ8rfB+i3Zyu/R9wqlj71NZmfbprWbQIaPRbdj2Dl1XN02r6JxLRU4pMza3gZaORHVYhnXVpGBo1/WkPdH1ZJwksIUaIURdEmu552GaOJiQnVqlUDMmd3CSGEEKLkSQZBFJq3uRXT6zamj7cvLZzciqXPFt5WAJzzbobvZ1sYePMa/nduMv3gdpQr5/j5wj0AHMwMimU8ISoCRVGYeewQJ+5H6DqUUqWnVuNlbomRRo+TD+7qOhwhRAV2584d4uPjUavVVKpU6an7e3wpoxBCCCFKniS7RKEZ6ekxu34LNrftWWz1Jmo6ZtbheX9vCKftatLgy1/59uxxmp0+xtnhnUgOOgQg9S2EeMzhiFvMCf6T5lvXEZfybC3x/ahRK269NIahvrV0HYoQogLLmtXl7e2NgcHTf+AmyS4hhBCidEnNb1GgR2lpGGo02oRTcSaesvpqYPyQMZ9vxdfOBNfmb9A4YwU1Hl5kWPw++tdoycdxsdQutlEzpd8Py3eXR5WhCRo7j2IeVYinZ2NoxIDK1bEyNMLcwFDX4ZQqT3NLXYcghHgGFNdOjFkk2SWEEEKULkl2iQINPbCdxLRUljVvj7uZRbH2rTI0AWDaw9VMA7j/zwkfADfG2tdnt4kzL508wamqDVAXU6It/X4YsR/3LrCdxeQtkvASZU4NG3vWt+lebBtFlFd3EuNxNDYttr8LovQcOnSIBQsWEBQUxJ07d/jxxx/p2bOn9ryiKMyePZsVK1YQFRVFo0aN+Oyzz6hRo4a2TXJyMpMnT2bDhg0kJSXRpk0bli1bhptb8SyzF8+24ipOnyUr2RUSEkJ0dDRWVlbF0q8QQgghcifJLqGV20ynqwnx/BhyiQzgbsR13KvULdYxNXYeWEzekucMq8UpKTw4Fcwnz3Um7q99GFeujoGDy1OPmzWeSf/30Th45zifHhlC4qaZ+c78EkLXnuXlvaMP7eDrS2fY1rEPHd2fvp6OKF0JCQnUqVOHl19+mT59+uQ4/9FHH7Fw4UJWr16Nr68vc+bMoV27dly6dAlzc3MAJkyYwLZt29i4cSO2tra8+eabdO3alaCgIDQaTWlfkqhgsmZ2FVeyy9raGi8vL27cuMHJkydp1apVsfQrhBBCiNxJsksAec90cgQO6pvzl5Edlb/6jvQSmOmUX38OwE7v2iRcOMmlyUPQM7ek0sdrMKsZUDxjO3ij51qtWPoSoqSlZ2Sw7HwwA6pUx87IRNfh6JSJnj5pSgb7b4dJsqsc6tSpE506dcr1nKIoLF68mBkzZtC7d+br0jfffIOjoyPr169n9OjRxMTEsHLlStasWUPbtm0BWLt2Le7u7uzZs4cOHTqU2rWIiqm4dmJ8nL+/Pzdu3CA4OFiSXUIIIUQJkwL1Asg+08l83NpstwavLmdM5+HZ2pU2PQtrDF09uZKaQqOf13Hslw3FPsb12GjmBf/JjbiYYu9biOLw683rvHFkD3V/WEV6Roauw9GpSbUbcLLPy8xv1ErXoYh/xMXFERsbq70lJz/Z5gkhISFERETQvn177TFDQ0NatmzJkSNHAAgKCiI1NTVbGxcXF2rWrKltI4rPvHnzaNCgAebm5jg4ONCzZ0/tzKcsiqIQGBiIi4sLxsbGtGrVinPnzmVrk5yczLhx47Czs8PU1JTu3btz69at0ryUQklNTeX69etA8c3sAqnbJYQQQpQmSXaJbLJmOl00tuWOpQt6rtXQc62W61K/0mTo6km1r3ewsMuLnLdzZMJf+wlf/gFKRgYpaRlsOhVBnzWnaL/yBOO2XuRMRHyBfV7RNyMxPU17f+2Vc7x97BCv/r6zwMem3w8jLfxinrf0+2FPdb1C5MZQoyHAzomBlf3QqJ/tP98uj6Kp8eih/A6WIX5+flhaWmpv8+bNe6J+IiIiAHB0dMx23NHRUXsuIiICAwMDrK2t82wjis/BgwcZO3Ysf/31F7t37yYtLY327duTkJCgbZO19HTp0qUcO3YMJycn2rVrR1xcnLbNhAkT+PHHH9m4cSOHDx8mPj6erl27kp6erovLytP169dJT0/HxMQEV1fXYutXkl1CCCFE6ZFljCKHcw/v0WzrWjKAzW17lJklQhozC7aMmsLIb5cwfv92IpKTiL5ykaEuIwi6n0YTD0tcLQ354WwkS/+8xdutvZjTvnKudY0mnz/L/9w68klYKBM9agJQ29aeNq6eDPb5twByQloaQ3b+QB/vqgys4oeeWi3F7YXOtHfzpr2bNyll7B/D0vbf38Fk1KhQMCB7wX75HSxd58+fz5YYMDR8up1C//u3W1GUAuvUFaaNKLodO3Zku79q1SocHBwICgriueeeq3BLTx8vTl+cP09Zya4LFy6QlJSEsbFxsfUthBBCiOwk2SVyqGZlS00be9IyMgiwc9J1ONk4mlnw85gZ3Hf3JmzuJNKO7aRbNRNWjxlJNQczANLS7VgbHMGS/X9QxyCafq3ra3ety3rT6mNqiqJScSn+3xlgPb186emVuVwhLfwiAL9ERvBz6FXOPLynTYIpyYkogKkUtxc6YvCMF99+fNn18gSFeVcv80G16gxz9wTkd1BXzM3NsbB4+h17nZwyX3ciIiJwdnbWHo+MjNTO9nJyciIlJYWoqKhss7siIyNp2rTpU8cg8hcTk7nc38bGBih46eno0aMLXHqaW7IrOTk523LY2NjYkrqkbIp7J8YsLi4u2Nvbc+/ePc6ePUuDBg2KtX8hhBBC/EuSXQJFUfgp4jat/7mvUav5sX0vbAyN0SujS6Xsug0kJFXNtTNrWGdrR68fJhGX9u8/tn3+ubETttkt4YNrV3i/fgvauWUmp4a6eVD3twXUb/CWNrH1uPTIEAAaW9nwXv3m2BgaaxNliqLQ3uV5at6JZq6vKy6m5iV9ueIZ9ygtjZ23QujiUbnM/k7qgsbBm5R7MUSmJPNjVCyvNJbNJioCb29vnJyc2L17t3YmTEpKCgcPHmT+/PkABAQEoK+vz+7du+nXrx8Ad+7c4ezZs3z00Uc6i/1ZoCgKkyZNonnz5tSsmTkzOr+lp6Ghodo2RV16Om/ePGbPnl3cl1CgrHpkxVmcHjI/cPP392fXrl0EBwdLsksIIYQoQZLsEryw+ye23LjMPIsqTP7nmIOxqU5jKow/0m340qY2Vwws+LzF63xatRoJ505gVrcxAMdOnKLaHwv4KTSUvyPv8MnpY9pkl7GJOX6psSRumpnvGF7Wdsz0yb4M6lRsLEFGtpy7fYv/6RuUzMUJ8Zjvrl9k6IHtNHN05XCPQboOp0wZUa02lSws6eHlo+tQRBHEx8dz9epV7f2QkBBOnjyJjY0NHh4eTJgwgblz5+Lj44OPjw9z587FxMSEgQMHAmBpacmIESN48803sbW1xcbGhsmTJ1OrVi3tEjlRMl5//XVOnz7N4cOHc5wr7qWn06dPZ9KkSdr7sbGxuLu7P0HURVNSM7uAbMkuIYQQQpScMpPsCgwMzPHp3eOf9imKwuzZs1mxYgVRUVE0atSIzz77jBo1/q2vlJyczOTJk9mwYQNJSUm0adOGZcuW4ebmVqrXUt60cfXk17CraFC0M5r+K6/jupSeAd9F/M4XLcfzcZtehM8ZT/ivm9k/+HVeemksGXaZS5qGu3jg4ODMhJr1tY/V2HlgMXlLvsucVIYmudb7qW1hwS+393OzwwQsDP6tSXM3MQFHk7KfJBTlz6P0NOyMjOniUVnXoZQ5tkbGvFBJZnSVN8ePH6d169ba+1kJjaFDh7J69WqmTJlCUlISY8aM0b7m79q1C3Pzf2fSLlq0CD09Pfr166d9zV+9ejWaZ3yZb0kaN24cP//8M4cOHcr23qqklp4aGho+de23J1HSyS6AEydOFHvfQgghhPhXmUl2AdSoUYM9e/Zo7z/+hjVrl5/Vq1fj6+vLnDlzaNeuHZcuXdK++Z0wYQLbtm1j48aN2Nra8uabb9K1a1eCgoKemTe/6ffDCkzghBlYkKZk4GOZWWvjVT9/OpgZYbN8U4EznVSGJsUa79Oo4WiKS/ojxjhVRl+lRt/OmdmtOrPbwJALy+bg79QUP6CenRXNPPxyPP5JC1erVSqaPrqPuYcXkJmIfefY73xy5ii/d3sJ/6e4JiFyM6p6XYb61iQtI0PXoQhRLFq1aqWtpZgblUpFYGAggYGBebYxMjJiyZIlLFmypAQiFI9TFIVx48bx448/cuDAAby9s9errEhLT2NjY7lz5w5Qssmu06dPk5aWhp5emXorLoQQQlQYZeoVVk9PT/vp4OMq2i4/JaUwuwTuMnZihFtratra80f3QWjUatQqFZU9/Uh/wplOutKykjWPgHn7b7C0am1cx77D69vWc/bKOSqdD6be7zuhrgn6mpKtcaRSqQiNjyE5PZ0fQi7h71a2ivqLisFQo4fhs5GzfyL/O3ucrTeusqpVZ1x0HYwQFczYsWNZv349W7duxdzcXDvr3tLSEmPjzJqWFWXp6ZUrVwBwcHDAysqq2PuvUqUKZmZmxMfHc+nSpWwrFIQQQghRfMpUsuvKlSu4uLhgaGhIo0aNmDt3LpUqVSqxXX4g504/cXFxJXeBJezxHcry2iWwxvdz0agy/3GOSnmEndG/M7XKUiKrMPQ1ah4BV+4n4LPgCEMDnHGxbkYXlTEBYVuw14sCTIj5Yze2/Up2mdOiJm3o412VXt6+uRa8F6Iw/jszMyY1lVuPkqhhnrnDXVlLOOva48urN10I5kjUQ7ad/oNXjHUYlBAV0PLly4HMGXmPW7VqFcOGDQOoMEtPs4rTl8SsLgC1Wk2dOnX4448/CA4OlmSXEEIIUULKTLKrUaNGfPvtt/j6+nL37l3mzJlD06ZNOXfuXInt8gO62+mnJGkcvNFzzUzuZCgKx+7doZFD5lwH1/QkDjd9jhpV66MuoGhsebF+QE0+uWLM6qA7xD5Ko6q9Mw1m/0CtrdOBcO6uWYJlh0HoWVoX2FdRPP6PtjXQzQDSwi+WyfpmouzLbWbmCosqvG3nz9DYayy6n1nfxWLylmc+4ZW1nPrxZdfDTd3oqjHiue2/kJielK2dEOLp5LfkNEtFWXqaVa+ruHdifJy/v7822TVokGw6IoQQQpSEMpPs6tSpk/brWrVq0aRJEypXrsw333xD48aZu+sV9y4/kHOnn/DwcPz8ctZ3Ko/iUpLpvON7/rp7m+O9h5L12WF1c/MKk+gCcE6OYFF9bxbVd8h2PH3IKBI3z8JlzDvFmujK7R/txyWp1Gwyr8Q4A5leIgovt5mZ986fRXPjOg2a9sLEuBeJm2bmu9T4WZHbBhND/9NGZsEJIZ5ESRanz5JVt0t2ZBRCCCFKTplJdv2XqakptWrV4sqVK/Ts2RMo/l1+IOdOP7GxscV8Jbpjpm+AnZExRnp6XIuNooaBriMqXgUlnbKYN2il/Tr27wNozC0x9XvyMvL57eSYrii0PnyQU7ExWDyIZoS95xOPI55Nj8/MXOhajUnxsVgbGqGJvK7jyMoWSWQJIUpC1jLGkp7ZBZnJrsJ8cCuEEEKIoiuzya7k5GQuXLhAixYtKtQuPyUtg8yEix6ZM+E+b96BR+lpeJpbVrhaUvklnbI8Prsj+VYI16e9TEbyIzzfXoht1wFPNXZu9IAhfnF8cvoYTiZmT9y/EFnczDLrdaXpOI7y4FFaGgfvhBGXmsILlUq2Tp8QouJRFKVUZnbVqFEDfX19oqOjCQ0NxcvLq8TGEkIIIZ5VZSbZNXnyZLp164aHhweRkZHMmTOH2NhYhg4dWqF2+SlJ0ampDHJsRsDli3zgVh0ARxNTHUdVsooyu0PPyhazuo2J+X0nNwLHknD+JO6T5qDS0y/WmN6oWZ8R1epgaWBYcGMhcnEnMR4DtQZbI1kKWxQ7b4XQc9cWKplb0ce7qsyWEEIUSUREBPHx8ajVaipVqlRi4xgYGFCzZk2Cg4MJDg6WZJcQQghRAtS6DiDLrVu3GDBgAFWrVqV3794YGBjw119/4emZuQxsypQpTJgwgTFjxlC/fn3Cw8Nz3eWnZ8+e9OvXj2bNmmFiYsK2bdvK1C4/JWnf/XvsNHVh0fVrhCeU310lS4rGzILKn6zDeeQUAO599yXnRvVg8S9BNFx6lOqfHKHHNyfZfvF+oYrx5kVPrZZEl3gq7xw7hMf65ay6dFrXoZQrbVw9qWxhRRtXTx6ly1w4IUTRZC1h9Pb2zlbioiRkrVQ4ceJEiY4jhBBCPKvKzMyujRs35nu+ouzyU5J6O7vwzsMzdGneC8focNKis5+XXQJBpVbjMnoaJtXqcG3mqySf/ouql/rTrPcHqKv6czAkiq6rTzLY34lVfWugUT/dzJB94aG8f+IIP3XoLQkwUSjpisL5qAckpqVSzcpW1+GUK2b6Blx9cbSuwxBClFOlsYQxS7169fj6668l2SWEEEKUkDKT7BJFl5aRwf/OHudVP39M9PRRGZowKfoi/DKP/OZ1ZRV2f5ZZtOjIW60XMPH3ObjE3mJi2nE8uvZHURQ2nrrLoE1nqetizqQWT15gPi0jg1cP7+RKTBRzg/9kfqNWxXcBouK6d4ND9evzd3QUDdLitLX2JFkthBAlqzSTXQEBAQAEBQVJkXohhBCiBEiyqxwbsv8XNly7QPD9SNY837XIBdufZTuvPGB/sg1zPtuO075vcB75FpA5g3BAXSd2Xn7A//64yfhmHk88u0tPreaLFh3YfP0SM/ybFGf4ogL67+6iNSDXpLUkqwvnZnwstkbGmBRzTT4hRMVVGjsxZqlduzZqtZq7d+9y584dXFxcSnxMIYQQ4lkiya4yKP1+WKESVqOr1+W3m9fp7lVFe04SWYVzKCQaN0tDmlRzQ1X9He1xJT2d0LkTGdi4H99EPyIs+hHeNk9eJLy1iyetXZ58dph4dmjsPAgftQofAz00eXzCL8nqwum1aws/3bjC5rY9ZFdGIUShlebMLhMTE/z8/Dh79ixBQUGS7BJCCCGKmSS7ypj0+2HEftw7z/MP1QbYZKRgMXkLLV08uDHwNakF9YRySyfcXfsZD7auxebX7+hTeQiK0rRYxwyLj8XDzKJY+xQVQ2JaKq0P78fSwJCdnftR2cJa1yGVW97mlqhVKi5GP9R1KEKIciI1NZXr168DpZPsgsyljFnJrm7dupXKmEIIIcSzoszsxigyZc3oMun/Pubj1mpvhmO/ZUbrKbT0eYF7akNtO0l0PZnnvK24GZPMX2Ex2Y7bdh+IRbN2qFJTmH7xKzLmjyEtLiaPXgovJT2d4Qd+xWfjCs49vPfU/YmK59zD+ygKZCgKnmaWug6nXHurTiMiB4/jnXrFm6wWQlRcISEhpKWlYWJigqura6mM+XjdLiGEEEIUL0l2lVEaB2/0XKtpb2mOldkXHUN4air7TBx1HV6518HHlqr2JrzywwXCYx5pj+tZ2XJ86CIW+w4hQ6NHzL6fufBSSxLOPt0bUX21mvuPkkjNSGdPeOjThi8qoAYOzoQOfJWf2vdGTy1/mp+Gs4kZtkZPvvxYCPHsyVrC6OPjg7qU/gZLsksIIYQoOfIfVTlhYWDIj+178VP9RvSPD9N1OOWeWq3ip8F1iH6URuUFRxiw4QyTt1+mwdKjDPzuPPR4hapf/YqBqycpt8O4OKITD37Z+MTjqVQqljVvx+HuLzG+Vv1ivBJRkZjqG1Db1kHXYQghxDOnNIvTZ6lTpw5qtZo7d+5w586dUhtXCCGEeBZIsqsMuxT9gOP3/n3zU93aji6OTjqMqGKp5mDK6fGNeL9dJa4+SGLbhfu4WBjyy7C6fNuvBha16uO37iDWbXug0tPHpHqdpxrPzcyCpk5uxRS9qCgURZGlrSXgWmwUA/b+TMdfv9N1KEKIcqA0i9NnMTU1pVq1zE00ZHaXEEIIUbykQH0ZdS85mc6HvudOYjy/dHyB511lR7+SYGtqwFstvXirpVeu5zVmFnjP+5rk0CsYef37Bjgl8jYGDi6kpmdw+EY0ccnpVLU3oaq9aYFjpt8P40FcNOvCb/K6VyVU/9l5T3bce7b8evM6XXd8T79K1djUtoeuw6kwTPT02XjtAgB3ExNwNCn4d1MI8ezSRbILMpcynj9/nhMnTtC1a9dSHVsIIYSoyCTZVUYZqtX4WlmjoFDTxk7X4TzTVCpVtkRX/Km/ufxqD8LaDGesUTtux6drz7X0tmJpj2rUdDLLta/0+2FEfvICDdw7Ea5nguX+L+mVcCtHO4vJWyTh9Yw4ef8uapVKduksZs4mZnzatA0Bdk7YSf0uIUQBdLGMETKTXWvWrJGZXUIIIUQxk2RXGWWhr8+2Di8QkRiPg7HMSChLYg7vRklNwX3H5yx3/wvX2Z/j5u3JwZBo3tt7nee+OM6fYxrkOstLSU7ESMngZQ9vtsQm4tt0CubWNtrz6ZEhJG6aqd1tU1R8M+o15cUq1THXN9B1KBXOGzWlPp4QomBxcXHamlk+Pj6lOrYUqRdCCCFKhiS7ypjzcbG4k5n00ABOQFrMbe359MgQXYUm/qE35C1mHU/jnUtf4XrzJHqTumAUuIx+zdvR3seGekuOMmv3dTYOrJVnH9PrNOQdF18MNfIrKKCyhbWuQxBCiGdW1hJGe3t7rK1L9+9x3bp1UalUhIeHc/fuXRwdZcdtIYQQojjIf9plyKpLpxlxaD+zLH15Y9PMfNuqDE1KKSrxX+tPRrDH9TlWThvAg/dGk3TpNFcn9Meuz8u4vRHIG03deevXK0QnpWJlrJ9rHwZqNXqPJbouRz/E18om17aiYrqdEIeRRg8bWWJXos48vMfWG1fo5F6JAHvZ4EMIkVNWsqu0lzACmJmZUa1aNS5cuEBQUBCdO3cu9RiEEEKIikiSXWXImYf3UICUpgMxr1Ilz3ZSwFy3wmOT8bQywrFqVexX7ST8f7OI3LiC+z+swqx2Q+pWa0dahkJkfEqeya7H7b4VQuffvmdirfrMcZN/xp8V044eZEvIZZY3b89g35q6DqfCWnDqb9ZcOUdMSrIku4QQudJVcfos9erVk2SXEEIIUcwk2VWGLGzShnauXnR0z7lDnyg7HM0MuBXziLjkNMwNDXGf/CFWrbrwcOcWbDr34+Lf4ahVYGtScKIL4OSDSNKUDMIT45Fn/dmQlpHBhegHJKSlUt3aVtfhVGh9vKsSm5JCYwcXXYcihCijsorT6yrZFRAQwLp16zhx4oROxhdCCCEqIkl26Vh8agqmevra5FYnj8o6jkgU5MU6TkzbcZXP/rzFtFZeAJjXb4F5/RYkpqTzvyM36eNtxL1x3dAbOQXLJm3y7e+tOo2oaW3H866eqCKulsIVCF3TU6s52nMIf0fepr69s67DqdB6ePnQw6t0C04LIcoXXS5jBClSL4QQQpQEta4DeJYlpaXSdvtGRhz8jZT0dF2HIwrJ3cqISS08eXvnVab9doWb0Y9Iz1DYd/Uhbb86QWhUEtMe/EbC6WNcHdeXG++/QXp8bLY+0iNDSAu/qL2106Siibiq3YBg4bWrXI5+qIvLE6VEpVLR2NFV12EIIcQzTVEUnS9j9Pf3R6VScfPmTe7du6eTGIQQQoiKRmZ26dDBOzc5di+CyzFRzKzXFG8LK12HJAppfscqmOqr+eT3MOYfDNUe93MwZc8r9ajj0Ijb+ulEblzBg61rif1rH55vL8Lsn0+NE/PZgGCjmSdTL57jo5DrXOo/ElspYF6hXIi6j6+lDRq1fNZQmuJSkjn98B7NnNx0HYoQogyJiIggLi4OtVpN5cq6mV1vbm6Or68vly5dIigoiI4dO+okDiGEEKIikWSXDnV0r8S2Dn0w1dOXRFc5o1arCGxXmTef82Tn5QfEPkqjqr0pTT0ttUtS3Sd/iFWbHoS+N47km9e5Or4/tt0G4vLaN6j1NHn23VNR883xv+jqUUUSXRVMfGoKzX5eh52RMbs698fL3FLXIT0TQuNi8Nm0Ao1KzYOhb2CiV7h6ekKIii9rVpeXlxeGhoY6i6NevXqS7BJCCCGKkSS7dEBRFG1CpLPU6CrXzA31eKGWY97n/Zvgt+EQ4cs+IHLD5zzYth61kTGmr3/AyuO32XwmkrjkNHztTBjV0JUu1exwVqk46FwJA/W/CbHk9DQM1BrZuKCcO/vw3+Up7qbmOozk2eJhZoGziRkGag034mLws7bTdUhCiDJC10sYswQEBLBhwwap2yWEEEIUE0l2lZD0+2EoyYk5ju+5F8mCa1fY2OQ57F2kaPKzQG1kgvukD7B+vhvhS98jofcbNP30b+4nptLTzx5XC0MOhETR7ZtTDPZ3YlXfGhhq/v3VTM/IoN+erdgZGfN5iw7oq/OeFSbKtsaOroQNfI2QuBhZxliKVCoVJ3oPk5mSQogcsnZi1FVx+ixZReplR0YhhBCieEiyqwSk3w8j9uPeOY4no+YVj06E65nwwdq5fDJ8Nho7Dx1EKHTBrG5jfFZsp8aiP7E00uOvMfXJ+OJdLBo/z8evd2TjqbsM2nSWui7mTGrhqX3ckbvh/BJ2DX21mrGurtQ2znuZhcrQRH6myjgzfQNq2djrOoxnjiS6hBC5adCgAYMGDaJFixY6jcPf3x+A0NBQHjx4gK2trU7jEUIIIco7SXaVgKwZXSb930fj4K09bg78EhfL/LPBTLt+LteZX6Ji23nlARfvJfLnmAaYndzPtc0rubd5JZYtO9N74hwG+zvzvz9uMr6ZBxp15pLFFs7u/NyhDwlRd/D+agRxBYxhMXmLJLzKmAxF4WL0A1k+VwYoikK6oqAnM+uEEED//v3p37+/rsPA0tISHx8frly5QlBQEO3bt9d1SEIIIUS5JsmuEqRx8EbPtVq2Y3WBNeYWxB3/UicxCd06FBKNu6UhjdwtUBxa4zh4HHfXLyPm4K/E/rmX4Z2Gsym2CWHRj/C2+XcmShePyqRpUokjM4l6x9yJyJRk6llaadukR4aQuGmmJFHLoJ9Dr9Br14+8VMWPtc9303U4z6wvL5xk/qm/GVWtLlPqNtJ1OEIIkU1AQIAku4QQQohiIh9tl4K7iQlEJMbrOgxRxqiNjHEbPxu/Db9j3rAlSkoypluX8/2RiSQf2IqiKLk+LsHajR4ng3n+ryMcUIzQc62Gnmu1bLMIhe6k3w8jLfxitltwyHlUgJuSSvr9MF2H+MxKycjgWmw0O25d13UoQgiRQ7169QCkSL0QQghRDGRmVylwNDElPCGOR2lpGOnJt/xZ9py3FR8euMHfN2Np7GEJgHGlavh8toXoA9sJnjMVp5g7pK75GKVbb1T6Brn242hsyt2kRKpYWpdm+KIAedXrmwB00zPD6sZWYnenyFJTHent7YuLiRltXT0LbiyEEKUsq0i9JLuEEEKIpyeZl1Liamqu6xBEGdDBx5Zq9iaM+P48u0b442pppD33m3V9XglYwLeGf9CuYyvU/yS6lLRU0hP+nRlooa/P9k4vEBoXi5e5pfb4jsi7NEBVehcjcsirXh9kLmGWpaa65WxiRi9vX12HIYQQucqa2XXjxg0pUi+EEEI8JUl2CVGK1GoVPw6uQ9uvTlB5wRF6+tnjamHIgZAoToTHMTjAg159O2mL0wPc+/5rbn+5ANeho7S/sOHRKWw9G0tc8kOq2pvi4ZhOt2N/4ePWnmNpaVjmPrwoJVn1+sIT4jDR08fa0KjgBwkhdCI1NZWIiAgSExOxt7fHxsZG1yGJZ5SVlRWVK1fm2rVrBAcH07ZtW12HJIQQQpRbkuwqQemRIUU6Lp4N1RxMOT2hMSuPhbP5TCQn78RR1c6E99pVpnNVW1SqfxNdiqIQtfdn0mMeErFqIW6N3Phs3Q4mRtXESE+NtbE+t2OTsbCOx9pOn3rJDzGVpbJlQnxqCv32bOX0w3usaNGBAVX8dB2SAB6lpfHtlbMcunOTb1t3Ra2S2ZDPovj4eNatW8eGDRs4evQoycnJ2nNubm60b9+eUaNG0aBBAx1GKZ5FAQEBXLt2jaCgIEl2CSGEEE9B/isuASpDEwASN80sVDvx7LEx0eetll681dIr33YqlQrf5Vu59/3X3F/zEQAdDy2knmdDar4yDnMHe8KikvjfkUTCrqUzh1Pax0YnP+L9E0eYVrcx9sYmpN8Py3f5nMrQROpIFSNDjQYLAwMURaGOrYOuwxH/0KhVTPn7ADEpyYyrGUAjBxddhyRK2aJFi/jggw/w8vKie/fuTJs2DVdXV4yNjXn48CFnz57l999/p127djRu3JglS5bg4+Oj67DFMyIgIIDvvvtO6nYJIYQQT0mSXSVAY+eBxeQtklgQxUKlp4fDi6OIqVIbvn8dh5oOOHADNr1JHGANzHq8/T9J1PdO/MGiM8f5K/I2h5o9l2vh9P+SwunFR1+tYXPbnlyLjcbP2k7X4Yh/6Ks1jK8ZgL5ag4uJma7DETpw5MgR9u/fT61atXI937BhQ4YPH87nn3/OypUrOXjwoCS7RKmRHRmFEEKI4iHJrhIiCQNR3DbFOPC9wVT2t9HjwbqlJF29QKW5X2HoXgmAXy7c48094fxp5IQD0MPTh/23w5gV0EybeDXu9x4aB+9sSyVBCqcXl6PRUfxpXonx/9w30zeQWV1l0Oz6LXQdgtChzZs3F6qdoaEhY8aMKeFohMguK9l1/fp1oqKisLaWXZeFEEKIJyHJLiHKiehHaSRZuGLdsilWz/Uk8XwwpjXqac87rNlA9dvpRCd0xsHMgJYuHgT1HoZapSIt/CIAW9INWX3qNAubPE9NG3tdXUqFFBIbTbu//iDRPoBKl0/RLZc2Uq9PiPIhIyODW7du4eEhH1yJ0mVjY4O3tzchISGcOHGCNm3a6DokIYQQolySZJcQ5UQVWxOuP0ziTmwyzhaG2RJdj8Kuob9lGbMz0kkes5uHr07Duk0P1Gq1tk0GMPvyBS4nJDB2z1/UM/bF196UgXWdMNXB9VQ0XuaWvOJdhTOnDxKw60fidqbl2Vbq9eleekYGf0XexlCjob69s67DETqyatUqNm3aRGhoKBYWFrRo0YKJEyeip6eHt7c36enpug5RPIMCAgIk2SWEEEI8JXXBTXKXmprKzZs3uXTpEg8fPizOmIQQuXixjiMGGhXv7r6GoijZzt0ztGZV9UE8MrIgJfQKIdNHcGFQa6J/36ltqwaaJjhBrB1nL5qy88pDxv18CeePdrPqzE0dXFH5pygK6RkZQOZmAota9+DXgeNxeX015uPW5nqTumhlw/xTf9P853XMC/5L16EIHUhPT6dHjx68+uqrGBsb0717d+rUqcP3339P9erV2bFjh65DLHMOHTpEt27dcHFxQaVS8dNPP2U7P2zYMFQqVbZb48aNs7VJTk5m3Lhx2NnZYWpqSvfu3bl161YpXkX5EBAQAEjdLiGEEOJpFGlml2zVLYTuWBnr87/uVXnlhwuERT9iTGM3XC2NOHA9ikWHw9Cr3oe3P5yBettK7q77jKTLZ7g2cQCmtRvgMWYyABfCkljbpzP9ajuir1FzOzaZRt9vYFToXZLNvHhVx9dYnqRnZDDhz70kpKaysmUnVCoVapUKIwcvXYcmCpB+P4znDcBaXx+bjGTtMt8ssoFIyUpLSyMwMJB169YRERGBs7Mzw4YN45133tHORlUUhdmzZ7NixQqioqJo1KgRn332GTVq1CiWGBYtWsTff//NyZMnqV69uvZ4RkYGCxcuZNSoUcUyTkWSkJBAnTp1ePnll+nTp0+ubTp27MiqVau09w0MDLKdnzBhAtu2bWPjxo3Y2try5ptv0rVrV4KCgtBoNCUaf3kiyS4hhBDi6RU62SVbdQuheyMauGJros97e0PoueY0AAYaFf1qO/Jhxyq4WhrBqKk49BtJxLf/I3LTlzy6fomwNAMcgbdbe9Hd/98lW7amGqo6GBEeDo2S72uPK4qSo4i9yC7ofgTLzgejKAqvVKtNUyc3XYckCiH9fhixH/fGB7iECr1LCnEHcraTGXglZ/78+Xz++ed888031KhRg+PHj/Pyyy9jaWnJ+PGZ2zt89NFHLFy4kNWrV+Pr68ucOXNo164dly5dwtzc/KljWL16NQsWLMiW6AJQq9VMnjwZRVGYOnXqU49TkXTq1IlOnTrl28bQ0BAnJ6dcz8XExLBy5UrWrFlD27ZtAVi7di3u7u7s2bOHDh06FHvM5VVWkfqrV68SExODpaWljiMSQgghyp9CJ7sKu1X38uXL+frrr2WrbiFKSM8aDvTws+f6wyRiH6XhZW2MtYl+tjZ6Vja4vRGI48BXSbxyjnW3UxkAtPOxIXzpe1i364VJ1VoYavTY0/VFVuzYTZWQzTxISMERmHHsEFdioggMaEYNG3vS74flu1NjRZsJU5jrbejgwYoWHTDTN5BEVzmS9bya9X8fjYN3jvOyM2nJ+/PPP+nRowddunQBwMvLiw0bNnD8+HEgM9m+ePFiZsyYQe/evQH45ptvcHR0ZP369YwePfqpY7h27VqOJXaPe+utt3jrrbeeepxnzYEDB3BwcMDKyoqWLVvywQcf4OCQuSNtUFAQqamptG/fXtvexcWFmjVrcuTIkVyTXcnJydlWEcTGxpb8RZQBtra2eHp6EhoayokTJ2jdurWuQxJCCCHKnUInuwqzVbeiKERGRspW3UKUMJVKRWXbgouc69s5YWnnROKmnQAk/7mdhz8s4+EPy7Bo1Arb7oMw8qxCQ1U0AAmp6SSkpvDZuRPEpqYwxLcm1TKSiP24d4FjVZSZMFkzf3JzW2OEiZKOVUYqFpO3MKJanVKOThQXjYM3eq7VAIhKfoS1oZGOIyr/4uLisiUjDA0NMTQ0zNGuefPmfP7551y+fBlfX19OnTrF4cOHWbx4MQAhISFERERkS4oYGhrSsmVLjhw5UizJLlNTU+7du5fnh3InT57kf//7H19//fVTj/Ws6NSpE3379sXT05OQkBBmzpzJ888/T1BQEIaGhkRERGBgYIC1tXW2xzk6OhIREZFrn/PmzWP27NmlEX6ZExAQQGhoKEFBQZLsEkIIIZ7AExWoX7VqFR07dqR69eo0atSIyZMnEx4eTmRkJN7eOT8pfxLz5s1DpVIxYcIE7TFFUQgMDMTFxQVjY2NatWrFuXPnsj1Oip8KkZObvQ0ASvB3uDVyw62RGxZcJfXnQOKWDMJz/3wAHGysMNU34EiPQUyt04iuHpW1M1z+7DiFkwMX5yi4btL//cy+K8hMmKzrMOn/frbrvDlsGR2r9mVwzZdIUqkrzPU+yzIUhVbb1mP37f+4Fhul63DKPT8/PywtLbW3efPm5dpu6tSpDBgwgGrVqqGvr4+/vz8TJkxgwIABANrEh6OjY7bH5ZcUKaqWLVvy+eef53ouIiKCF198kW+++aZYxnpW9O/fny5dulCzZk26devGb7/9xuXLl9m+fXu+j8tv2fz06dOJiYnR3m7efHY2U8mq23XixAkdRyKEEEKUT0UqUJ+enk7v3r3ZsWMHnTt3pnv37kRFRfH999+zYsUKlixZUixBHTt2jBUrVlC7du1sxwtTw0OKnwqRU9dm/jTbP4WOXsa8374KKbeu82DrWmKPHgQlczfB+7aVaW5gCkANG3s+bNRK+/h0YFL4PS5dDuEVz6bUNvPE196UtlVsKKu/VU+79PLxmT8Aqof3iMs4wn1FRZTaAIdijVbogvqfTQUyFIU/IsKpbGFd8INEns6fP4+rq6v2fm6zugA2bdrE2rVrWb9+PTVq1ODkyZNMmDABFxcXhg4dqm333wRIcdYSnDVrFk2aNEGlUvHWW29RpUoVHj58yLZt25gzZw5eXl5cuXKlWMbSldTUVCIiIkhMTMTe3h4bG5tSHd/Z2RlPT0/t99HJyYmUlBSioqKyze6KjIykadOmufaR1+zAZ4EUqRdCCCGeTpGSXaWxe1F8fDwvvfQSX375JXPmzNEeL0wNDyl+KkTuzAz1ePuF5xi48SwHH8XxWmN/XEc05ljtMyRvWUGHsL3Y2qShZ22nfczj/1gmqvSwTjWE9GS+OpiIofoqyWkZeNjqs7GFIdXyGlhH8luK+Lj8ll5ejo/nzLULPO/iib2xCbVs7NnVuR+eCfcxOLMq18eI8mdxkzZYGxrhbmah61DKPXNzcywsCv4+vvXWW0ybNo0XX3wRgFq1ahEaGsq8efMYOnSotsB51k6NWSIjI3PM9npStWvX5tdff2X48OGsXbtWe1xPT4/x48czbtw4PD09i2Ws0lSWds1+8OABN2/e1D6HAQEB6Ovrs3v3bvr16wfAnTt3OHv2LB999FGJx1PeZBWpv3z5MrGxsYX63RJCCCHEv4qU7CqN3YvGjh1Lly5daNu2bbZkV2FqeBRH8dO4uLinil+Isqp/HSfsTQ2Ysy+EYZvPA2BuqGHo4HepVG8BZvH3UKkzVzZnPErk4vBO2HR6AZsmTTFX0ki+ZMA7rbvxxmBP7Ez1OXozlq47fqDN8Sh+NLShjS4v7j8eX4pYUBHyhNQUgu7fJSYlmW6eVbRtBgQf43RsLD+270VPL18AGjq4kBYei/yVqDhq28ocvdKWmJiIWp29ioJGoyEjI3OWqbe3N05OTuzevRt/f38AUlJSOHjwIPPnzy+2OFq2bMmVK1c4evQoISEhWFhY0KRJE2xsbEhISGDWrFnFNlZpKOlds+Pj47l69ar2fkhICCdPnsTGxgYbGxsCAwPp06cPzs7O3Lhxg7fffhs7Ozt69eoFgKWlJSNGjODNN9/E1tYWGxsbJk+eTK1atbQfUIp/2dvb4+7uzs2bNwkODqZly5a6DkkIIYQoV4qU7Crp3Ys2btzIiRMnOHbsWI5z+dXwCA0N1baR4qdC5O35KjY8X8WG+wkpxCWn42RugLF+1kLEf5cfPfj1O5IunyH88hnub7DBtbYVb9Y156X2//5jVMXBgBT9GJKUNKwyUkv5Sgrnv0sRQ2KjOX4/gpqm9jj9c+zE/bu03LYed1PzbMmuptY2mBiZoad6otKGooxLjwwp0nFRfLp168YHH3yAh4cHNWrUIDg4mIULFzJ8+HAAbb3OuXPn4uPjg4+PD3PnzsXExISBAwc+9fhhYWF4eGTO6FSr1TRu3DjHextTU1Ntsis8PDzb8syyqqR3zT5+/Hi2QumTJk0CYOjQoSxfvpwzZ87w7bffEh0djbOzM61bt2bTpk3aMhOQmZDT09OjX79+JCUl0aZNG1avXi1lJvIQEBDAzZs3CQoKkmSXEEIIUURFSnaV5O5FN2/eZPz48ezatQsjo7x3xXqSGh4FFT/NesMGmW9q/fz8ihC5EOWPnakBdqZ5n7ftOgCVRo+Ib5egPAwFrKizcgJhIX/gOOh1DN28sDUy5sbA13h/+2/4hmwmJikVW+DTM8fRV6sZXrU2Rnp6T107qzhN+fsA34dc4qPqNXjln2O1bOzxMLOgrq0DqRnpZP2lWFKzTrZEmagYVIaZu5gmbpqpPXbawIqVFpVxTk9iWtT5bO1E8VuyZAkzZ85kzJgxREZG4uLiwujRo3n33Xe1baZMmUJSUhJjxowhKiqKRo0asWvXrmyJkyfVoEEDunfvzsiRI2nYsGGubWJiYvjuu+/49NNPGT16NOPGjXvqcUtaSe+a3apVKxRFyfP8zp07C+zDyMiIJUuWFFuN14ouICCAn376Sep2CSGEEE+gSMmurN2LciskmrV70ZUrV54o2RUUFERkZKS2ICdkFsQ/dOgQS5cu5dKlS9px8qrhURzFTx/fNl2IZ5XawBC7HoOw7TaQb+d/iFvMFvQNFGJ2riVm9waqfLoRfWt7zIEhpmkAxCSnoUl+xLvHfyc2NQU3U3O6mBk+de2sp/H7g/s855yB5p8lU40cnLkRH4Olnr62jZWhEaEDX9PeTyv2KERZorHzwGLylmwJ2Oi7Eaw5/jdexibMeWk6aiPTUkvAPovMzc1ZvHgxixcvzrONSqUiMDCQwMDAYh//woULzJ07l44dO6Kvr0/9+vVxcXHByMiIqKgozp8/z7lz56hfvz4LFiygU6dOxR5DSVu1ahWbNm0iNDQUCwsLWrRowcSJE9HT08Pb25v09HRdhygKQYrUCyGEEE+uSMmukty9qE2bNpw5cybbsZdffplq1aoxdepUKlWqVGANDyl+KkTxUqnVZNR7HvZvwaHmv7WNHq2dyKN/vs4q4WxrZYmhRo+5DVvya9g1unpWIeN2ZpL6Wre38XL1wUpfP1v/j9fOKm5TbP356q8/WKQyZEKtzGLMk+s0YnKdRqSFXyyw7pYsc6u4/pvIautQiTcepdHZvRIaV2/UxbTjnyibbGxs+Pjjj5kzZw6//vorv//+Ozdu3CApKQk7OzteeuklOnToQM2aNXUdapGV1q7ZonRkJbsuX75MXFxcscxsFEIIIZ4VRUp2leTuRebm5jneWJqammJra6s9XlANDyl+KkTx69a8Hk0PvcXLtayY1MITJS0V1T8zox6E3yJi6hCS9E1x/3kDxn1eZmyNeoytkbmLVMY/twHXbxN6PgTD+z48ijPF186EUQ1dGeVRcrud+aXEoAYePEoq0uNyW+aWXztR/pnqG/BpU3mNeNYYGRnRu3dv7Q7PFUFp7JotSo+DgwNubm7cunWLkydP0qJFC12HJIQQQpQbRUp2gW53LypMDQ8pfipE8bIzNWBkx8a8uf0KJzJSGdvEDVcjQw5cj2Lr5quMSTXFLu4BESs/IWL1p1g/3w2H/iMxrdMIgAiNEeExyaTpKbxR14dKVmYcDInmzR0XOOWYwCfFFGdEYjzxqalUscxcwjwk7jotWr1IXRcH0sIvZmub3+ys3Ja5/Vdp1hkTQojCKo1ds0XpqlevHrdu3SIoKEiSXUIIIUQRqJT8qo0+g27duqXd6tnNzU3X4QhRZqw6fpv39l7nRlTmAkaVCjr52rKoUyXsz+4nctOXxAf/qW1vXLU2rq9PJWPrTN50fZtxL7bA395ee77Zlg2cuBPO9/f20GHk4qcqBr8vPJQ+u3/Ex9KGIz0GoXp4S6e1wkT5dCXmIb+GXeflqrWwMDAs+AECkNfNssTY2JjTp08XaZfF8iQ2NhZLS0tiYmKwsLDQdTil4r333mPWrFkMGjSINWvW6DocIYQQ5ciz+Lr5uELP7Hp8q+7CKC9bdQshCufl+i4MqefMifBYYpPT8bEzwcPqn51TnXpi3bYniZfOELlpBQ93/kDyzescidGjMTCtpRc+1lbavmJTkjkbE8Ej/XQc0h+Rmp6BHhCfmoKJnr62ZlJhd3KsZmWDAqQrGdx7lIizzM4ST6DLju+5EhOFh5kFvbx9dR2OEEVWkrtmC92QIvVCCCHEkyl0squibtUthCg8jVpFA3fLPM+bVK2F17tLcHtjNokXT7E3NJXGgLeNMRdHdMLAyQ2H/qMw92/CzZfG8Nm+3VS5vpm78cl4ATOP/c7W0CssaNSanhbGec7OygBOGNpQP/khFpO34GLnwaFuA/GztkPvn50XJZEliqqnpw8nHtzF9D8bKQhRXpTkrtlCN7KSXRcvXiQ+Ph4zMzMdRySEEEKUD4VOdj0LW3ULIYqHnpUNFo1bo39rNwBJZw6TFnaWtLCz3Di6A0NXL6xad6WlZebsT321GkVR+Dn0KiFxMRjr6WlnZWn6zsbAwVubxEpIS6P7sb/48+ED9t3aRdN/2tW2dcglEiEK76PGrXUdghBPpSR3zRa64eTkhIuLC7dv3+bUqVM0a9ZM1yEJIYQQ5YK6sA2ztuq+ffs2y5cvx9fXl/v372vfNL300ksEBQXxxx9/SKJLCAFA0yrOAKQf/AK3Rm7am71bGvpXfqL68c8AsIy/j0ql4kzf4ax/vhsd3Ly1fXybrKbywf18FZOEnms1LD1r4mhpi5FGw1V92YZdCCGyZO2affjwYerUqYOpqSnu7u688cYbDBgwgPXr1yOlWssfWcoohBBCFF2Rd2OsiFt1CyFKRv06Neh3aDYPo2NY2NWHGo7mZCTG8+DwbkJ//QnT+6FkpGXg1TUdABM9fQZU8QMg7Z8+foq4w+3EeG7GJBF0KxYvayM+a9aeOK9L2K7cqKMrExVZcnoad5MS8TB79gp5PuuuXbvGkiVLCA0NJT09XXv8559/1mFURaPLXbNFyahXrx7btm2TZJcQQghRBEVOdgkhRGGpVCqWvNyWTl8HU/e7GOq7KbhaGHLkVlPuVarPoo6J9In+G4sm/2fvvsOiuL4Gjn+X3pEiIMWuWBB7iw27xl4Te4uxxRJjN7EkxpbEEo0mGluiRhONxq5YQI0dxd6FYEGxIL0u8/7hj31FiqDIIJzP8+yj3Llz58wOsMvZO+c21u0T/Ov3xN69g32DF7eUjbIpzc379szcHs7MpFMY6mvo7OHA91X01TotkYftCrpN1/3/UNnekSNte6gdjshh7du357PPPuOjjz5CTy/Tk99zHT09PWrVqkWtWrVStJubm0uy6z0kM7uEEEKIrJNklxDinXKyNObUZzXYfvUJf118RHhsIj0rF2JgDRfKOpgD7XR9lcQEQv5aQeLTR0Qe/hvXmq74rl5DlZq9WNyzLC7WxvjeCeX7I0H0vX2XjeqdlsijytnYE5WYQGBEGHHaRIz15WUyPzE3N2fQoEFqh/FGZNXsvCs52XX16lWioqIwNzdXOSIhhBAi95N38UKId85QX4+OHg509HhNEXl9A0r+8DuPN68m8t/tAPQO2k7CrZ3YPGqPQ5f+VKlXja6ejvRacDcHIhf5TVFLay527k85G3v0NBq1wxE5bOLEiYwfP54mTZpgbGysa69fv76KUWWOrJqddzk7O+Pk5MTDhw85f/58mqttCiGEECIlSXYJIXINjUaDuUc1zD2qsXHrh7icmIJJsaIQEEjk4b8xNErC2MYCR2BwyUS4CDHxiUiZepGdPGwLqh2CUMnevXvx8fHh1q1butsYNRrNe5HsklWz87aqVauyc+dOzp49K8kuIYQQIhMk2SWEyJVuJpjTDLB3SAQH1xeNEWeIWNQTgKb/6/fw4kUsipRD8x7X1xFC5A6+vr5cvnwZzXs4qy951ewZM2awa9cujhw5QmBgIDExMdjb29OjRw+aN2+Oh4eH2qGKN5Cc7JK6XUIIIUTmZFuy6+TJk9y+fZvu3bvz7NkzoqOjcXV1za7hhRD5ja0bDY3Gc2xAOYwMUhejPzlnGhbnDpK4fxiXV36Pffte2LXuhqG9owrBirxm1rnj/HXnOsvqN6dawUJqhyNySI0aNbh9+zYlS5ZUO5Q3Jqtm501VqlQBpEi9EEIIkVnZkuyaNm0aZ8+e5dq1a3Tv3p2YmBg+/vhjjh49mh3DCyHyoa6ejnzlbcPvD60ZVDNl4jxRm8T3tm1oUsyID/7zIe5eAPcXf839pTMpUL8F9u16YfVBY+KTYPOlEDZdfEREnBb3gmYMrO5CRWe58VFk7PTjh5x7+ohdQXck2ZWPnDt3jvLly1OmTBmMjY1RFAWNRsOpU6fUDk3kc8lF6q9cuUJMTAympqYqRySEEELkbtmS7Nq6dSvnzp3Tferk4uJCREREdgwthMinShc0p0+VQozYdp34xCT6VXPGwtiAayFRjN99k13xTnw+bSGehYwI9d7Kk62/EXXhNM8P7SD6xkVs1xyjxSp/Lj2Kok4Ra5ytjNly+TE/Hb/HRK+ifNu8xHt5q5LIGcM9qtCxWGmauxZTOxSRg/75559UbfJ7QuQGLi4uODg4EBISwvnz56lVq5baIQkhhBC5WrYku5JXLEp+Q/j8+XN5cyiEeGvLOpbFQE/DqB03GLf7FgVMDXgYEU9Bc0M29/SkfnEbAOzb9sC+bQ9ibl3hyT9rMXIpQtt1F3kem4jf4EoU+GU8Ns06YN65GfOPP2DCnluUsjejXzVnlc9Q5FYNnYuoHYLIQb169eL333+nc+fOab5/kZldQm0ajYaqVauye/du/Pz8JNklhBBCvEa2JLuGDBnCRx99xJMnT5gxYwYbN25k/Pjx2TG0ECIfMzLQ49fO5fiqcTG2XH5MeGwiZRzMaVeuIMYGqQvSm5Ysh9sXMzl8J5RTy/zY278yxW4d4Y73FkK9t2Do6EzvTv25WKwq3x/+j75VC0liXgjB3LlzAdi0aZPKkWSPCRMm8NVXX2Fubq52KCIbvZzsEkIIIUTGsiXZ1aNHD2rWrMmBAwdQFIUNGzZQvnz57BhaCCEoYmPKqLqFM91/z42nFLI0omkpW+ItquDYZyRPt60j4dEDHiyZwSgDQ3YWrE3ABxMoLp+Oi3SExcexK+g2oXGxDC1fRe1wRDb6+OOPmTJlCuXKlQOgUKEXddmKFMkbM/p8fHxYs2YNM2bMoH///pLUzyOS63adPXtW5UiEEEKI3C/11IgsSkpKonr16pQtW5bPPvuM4cOHS6JLCKGqxCQFU0N9NBoNxs6FcR0+lQo7LlB0+lLMylVBk5hA6+DDhH72IbGBN9QOV+RSl549pvvB7Xx55gjapCS1wxHZ6M8//6RRo0ZcuXIlze2KohAeHp7DUWWfEydO8N133zF9+nSqVKmCr6+v2iGJbJBcG/fy5cvExsaqHI0QQgiRu711sktPT48aNWpw+fLl7IhHCCHeWnVXK+48i+HSw0hdm56xCXatPqLsb/vZ3v9XDro2wKJGA0yKltb1CT24nfiQB7qvbzyOYuahACbuucXqMw+Iitfm6HkIddV0cKaukyuflqlIdGKC2uGIbFaxYkUaNmyY5vuXkJAQbGxsVIgq+/Ts2ZPr16/Trl07WrVqRceOHblz547aYYm34Obmhr29PYmJiVy4cEHtcIQQQohc7a2TXfCicGvlypXx8PCgRo0aVK9enRo1amTH0EIIkWXtyxfExcqYIVuvERGXmGLbodvPmP2gAJHDvqf04s269oTQJwR8+SkX21Tk5ri+jPtuLe7fH2OOTyAbLzyi/+YruM48wl8XHuX06QiVGOjpcaRtD2bX9MLSyFjtcEQ20mg0rF69mkaNGtGwYUMuXbqUqo+iKCpElr1MTU2ZNm0a169fx9zcHA8PD8aPH8+lS5fQaiV5/75JLlIPSN0uIYQQ4jWyVLPr1RoXydJaqlsIIdRiqK/HXz0q0GLlOUp+d4zeVQrhYmWMz51Qtl99TOOStkxqWAyN3v/n+xOfP8XcoyqRZ48RfnAbH7GNti6lKd5rME6tuxIUrWH8nlt8/MdFbM0MaVzSVsUzFEK8DUVR0NfXZ926dfTo0YNGjRpx8OBBPDw8dH3e5zpXcXFx/Pvvv1y7do3r169z/fp1rl27RlxcHN9//z3fffcdxsbGlCtXTpIm75mqVauyd+9euW5CCCHEa2RpZld6NS6KFClCkSJFKFy4MDY2NrqvhRBCLbWLFODsiJp85OnI72eDmbz3FkHPY1nSvgw7+lRKtZqjaTF33JftwOSnffzt0pgkQxNM798gePZoLnzoge2NE6z/2IMabtbMOBig0lkJNSiKwvmnIYTGSY2cvEZPT49169bRpEkTGjVqxMWLF9UOKVs0bNiQ1q1b89tvvxEaGkq9evX47rvvOHv2LJGRkTx9+pRdu3bRu3dvtUMVWSQzu4QQQojMyfJqjMk1Lg4ePJiqEH1ISAjOzs4yNV4IkSuUsDPjx7bu/NjWPdP7bI6wZUnlYUwa/jORu/8g5K8VJDwJwbRkOfT1NAyt5UrvPy8TEhmPg4XRO4xe5BZt9m5mZ9BtVjZoST93T7XDEdng5Vlbenp6rF27lp49e9KoUSMOHDiAo6OjitG9vadPn3Ls2DEqVaqU5nZTU1MaNmxIw4YNczYw8daSk12XLl0iLi4OY2O5xVoIIYRIS5ZmduWXGhdCiPwrLDYRBwsjzO3scOz5GR5/n6HMyj0Y2r/449fV2phplxfz+I+fSYqNUTlakROq2Tthqm/Ao5hotUMR2eTV9yrJCa+mTZvSuHFj/P391Qksm1y/fj3dRJd4vxUuXBhbW1sSExPzzExEIYQQ4l3IUrLr5RoXjRs3plGjRqkSXu9zjQshhChd0Izbz2K4H/biljWNvj5m7hV02y/4HKZ18GFif5nGpQ7VCPnzV5Li49QKV+SAzz2r86zPSCZUqqV2KCKb7Ny5E2tr6xRtyQmvZs2a0alTJ5UiEyJjUqReCCGEyJw3Wo0xr9a4EEKIjys6YWKgx6S9t0lKSjn7I/BZDLPuWXK45XgMHV1IeBzM3bnjuNShGo//Xo2SmJCiv6IoJGqTcjJ88Q5YGxljYpDlu/5FLtayZcs0b//S09Pj999/p127dipEJUTmVK9eHYDjx4+rHIkQQgiRe2X5Nkbdjv/7BDQ54XXhwoVsD04IIXKatYkBS9uX4fdzwTRc7sfG8w85GvicGQfuUOOnU5ibmtB93Cg8tpzBbfx3GBYsRMKj+wTNHM2lTjWIDbzJxYeR9N54CfMphzCcfJCS3/3LXN9AouOlnuH7Lklu1c+zzp49S3x8vO79jSQSRG5Vv359AHx9fVWORAghhMi9snwbY4qd81iNCyGEAOhVpRA7+lQiXqvw8R+XqPfzGWb6BNK2bEGODamGo6UxekbGOHQZgMdWP1y/mImBnQMkJXEs1pKaP53i6H9hTG5YjJWdy1G3SAG+2nebpivOEiUJr/fSjefPaL5rIzW2/KZ2KOIdqV69OoGBgcCLD/dq1KihbkBCpKNOnTro6+sTGBjIf//9p3Y4QgghRK6UpfsyMqpx0atXL6lxIYTIMz4sY8+HZey5FxZLRJwWV2tjLI1T/8rUMzbBsdtgCnboTXjALRr/dZ26RQuwtXs5Hkzsi22rj+jbpT1Da7vScJkf3xy4w+yWpVQ4I/E27E1M2X//P5IUhbuR4bhZWKkdkshmssCOeF9YWFhQrVo1Tp48ia+vL71791Y7JCGEECLXydLMLqlxIYTIb1ytTSjrYJ5moutleiZm7IwtyOOoBBa1dSfaezNhR/cRMHEAt0Z0oZJhBINquvLr6QfEJUodr/eNrYkpv3m14mrXT3A1t1Q7HCFEPufl5QWAj4+PqnEIIYQQudUbFahPcyCpcSGEyOf8gyMpaWeKe0FzbBq1odDAcWiMjAk/fpDLH31A+3v7eRoVz73/rfQo3i89SpWnTAE7WXVYCKE6SXYJIYQQGcu2ZBdIjQshRP5mrK8hPE5LUpKCvoUVzoMmUG69L+ae1UmKisT81y/56dwMDJ8+UDtUIYQQ77Hkul0BAQEEBQWpHY4QQgiR62RrsksIIfKz1mULEhIZz85rT3RtJkVL4758Fy6fzyDewJiazy4Su2CsilGKt3H04T1GHz/AsYf31A5FCJGPWVpaUrVqVUBWZRRCCCHSIskuIYTIJjXdrGhQrACf/H2Vw3dCdQWvY7Two10zPqoxl+hytXEbOzvdMaLitSw7eY+mv56l1k+n6PPnZf4NfJ5DZyBeZ/X1i8y/eIZNAdfVDkUIkc/JrYxCCCFE+rK0GqMQQoj0aTQa/urhSes1/jRY5oeHozku1iacDAojLC6RWR3rUc+rV4p9HvwyG4MCdhTsMoD/nsfRZMVZAp7F0KyUHeUdLTgcEMpvZ4MZ/oEbC9uUlnpRKutc3B0FhZZuxdUORQiRz3l5eTF37lxJdgkhhBBpkGSXEEJko4IWRhwbUp29N57y18VHhMcmMrS2K59Ud6GYrWmKvtE3LxO84ntISiJ0/z98VmQAipEDV0fXpnRBcwCSkhR+PnmPYf9cp5yDOYNruapxWuJ/WrgVp4UkuvKkqVOnYm9vr3YYQmRanTp10NPT486dO9y9exc3Nze1QxJCCCFyDbmNUQghspm+noYPy9izqkt5NveqyLfNS6ZKdAGYliiL2xez0DM1J/LcMaZtG8JvpicpZff/ffX0NAyt7cZHno7MOxpEUpKSk6ciRL4xdepUbG1t1Q4j1zp8+DBt2rTB2dkZjUbD1q1bU2xXFIVp06bh7OyMqakpXl5eXL58OUWfuLg4hg8fjr29Pebm5rRt25Z796T+3ZuysrKSul1CCCFEOiTZJYQQKtHo6eHw0UDK/XGEkGJVMUmKx2TNDG582prYoNsp+vao5MTNJ9HcDYtVKVrxsgdREey5e0ftMITIMVFRUVSsWJHFixenuX3u3LnMmzePxYsXc/r0aZycnGjatCkRERG6PqNGjWLLli1s2LCBo0ePEhkZSevWrdFqtTl1GnmO1O0SQggh0ibJLiGEUJmxa1EO9VnMsipD0DOzINL/BNcHtiIp7v8TW0YGL35da2Vml6q0T4K4duMMLuuW0GHvZsKDLpF4/5ruoX0SpHaIQrwTLVu2ZMaMGXTs2DHVNkVRWLBgAZMnT6Zjx454eHiwZs0aoqOjWb9+PQBhYWGsWLGCH374gSZNmlC5cmXWrl3LxYsX2b9/f06fTp4hyS4hhBAibVKzSwghcoHaRW3oaNuQQQu6Y/nrl9g0bY+esYlu+9+XQnCxMqZwAZMMRhHvkvZJEOHfd8QBKOz2IQW1sdxcNoziiVEp+lmN+Rt9+8LqBCmECgICAnj48CHNmjXTtRkbG9OgQQOOHTvGoEGD8PPzIyEhIUUfZ2dnPDw8OHbsGM2bN081blxcHHFxcbqvw8PD3+2JvIfq1q2Lnp4et2/f5t69e7i6Sl1HIYQQAnLRzK6lS5fi6emJlZUVVlZW1K5dm927d+u2Sy0IIURe1qasPUVtTBh0LAq77zZi3763btve/cfZcvwqQ2u7YqCfa35t5ztKXDQA5h99w+U2H3GiU38qDvkFy+FrsRy+FrOPvknRT4j84uHDhwA4OjqmaHd0dNRte/jwIUZGRtjY2KTb51WzZs3C2tpa95AC7KlZWVlRpUoVQOp2CSGEEC/LNX81ubq6Mnv2bM6cOcOZM2do1KgR7dq10yW0pBaEECIvM9DX4++engQ8i6HEd8cYuvUaMw7codOCvSRN7ck6vykMLxKvdpgC0HcohkXh8hi4lEnx0HcopnZoQqhKo9Gk+FpRlFRtr8qoz8SJEwkLC9M97t69m22x5iVyK6MQQgiRWq5JdrVp04YPP/yQ0qVLU7p0ab799lssLCw4ceKE1IIQQuQLlV2sOD+yJkNrubL/1jMWH7+HJi6GApbm2IY/4PYnLYk4e0ztMMVLtElJxCYmqh3Ge+f+/fv07NkTOzs7zMzMqFSpEn5+frrtmZnNLXIPJycngFQztEJCQnSzvZycnIiPjyc0NDTdPq8yNjbWzfhPfojUGjRoAEiySwghhHhZrqzZpdVq+euvv4iKiqJ27drvrBYEpK4H8fJMsfQoikJiYqLMGBNCZDs7Y5ji5coUr/+vu5LQpwZ354wh5sZlbnw9AuehX2JTv0WWxtXX18fAwOC1syxE5v1w4RSz/U8wwqMqX1Wpo3Y4743Q0FDq1KlDw4YN2b17Nw4ODty+fZsCBQro+iTP5l69ejWlS5dmxowZNG3alOvXr2Npaale8CJNxYoVw8nJCW9vbypXrgxAfHw8vr6+zJkzB4CqVatiaGiIt7c3Xbt2BSA4OJhLly4xd+5c1WLPC5Lrdt26dYv79+/j4uKidkhCCCGE6nJVsuvixYvUrl2b2NhYLCws2LJlC+XKlePYsRczGdKqBfHff/8Bb1YLAl7Ug5g+fXqmY4yPjyc4OJjoaKnJIoTIQQO/xPD5U5TYGEKApxf80be0ztIQZmZmFCpUCCMjo3cTYz7jYGLGk9gYvO8FSrIrC+bMmYObmxurVq3StRUtWlT3/1dncwOsWbMGR0dH1q9fz6BBg3I6ZAFERkZy69Yt3dcBAQH4+/tja2tL4cKFGTVqFDNnzqRUqVKUKlWKmTNnYmZmRvfu3QGwtrZmwIABfPHFF9jZ2WFra8uYMWOoUKECTZo0Ueu08oQCBQpQuXJl/Pz88PX11T3nQgghRH6Wq5Jd7u7u+Pv78/z5czZv3kyfPn1SFNvM7loQ8KIexOjRo3Vf379/n3LlyqXZNykpiYCAAPT19XF2dsbIyEhmSQghcoyiFCPhaQiJz58CYFjACkMbu0zspxAfH8/jx48JCAigVKlS6OnlmrvY31sdipVmr5k5jZ2LqB1KrhAREZFitTxjY2OMjY1T9du2bRvNmzenS5cu+Pr64uLiwtChQxk4cCCQuZX9RM47c+YMDRs21H2d/N6pT58+rF69mnHjxhETE8PQoUMJDQ2lZs2a7Nu3L8VMvPnz52NgYEDXrl2JiYmhcePGrF69Gn19/Rw/n7zGy8sLPz8/fHx8JNklhBBCkMuSXUZGRpQsWRKAatWqcfr0aRYuXMj48eOBF7O3ChUqpOufXi2Il2d3hYSE8MEHH6R7zFffjGe0rHV8fDxJSUm4ublhZmb2ZicphBBvwdS1CAnm5iSGPsXEwQnNK38kJmqTCItLRJsEJgZ6WBrro9FoMDU1xdDQkP/++4/4+HhMTExUOoP3nzYkAAAToJEGlOAbJL7Unl+9+kHR1KlTmTZtWqp+d+7cYenSpYwePZpJkyZx6tQpRowYgbGxMb17985wZb/k2dwi53l5eaEoSrrbNRoN06ZNS/OaJzMxMWHRokUsWrToHUSYvzVo0IAffvhB6nYJIYQQ/5Orkl2vUhSFuLi4XFcLQmZECCHUZGhjj0EBO93MUkVRULSJPIjSEhIZT9JLf48aG+hR1MYES2MD+d31ljTGLz7kiN74VZrbFUDzUr/85sqVKylqBaU1qwtezJKuVq0aM2fOBKBy5cpcvnyZpUuX0rt3b12/N5nNLUR+Va9ePTQaDTdv3uTBgwc4OzurHZIQQgihqlyT7Jo0aRItW7bEzc2NiIgINmzYgI+PD3v27EGj0UgtCCGEeMnLf/QnPgsh9nEIYaaOOBawwsHCCAM9DVHxWu6Fx3HjSTRlCpojNwq9HX37wliN+RslLmXNxt/vBbEo4A7jSpaiS9FS6NsXVilCdVlaWmZqtbxChQqlmgVWtmxZNm/eDKRc2S+92dxCiJSS63adPXsWX19funXrpnZIQgghhKpyzcf8jx49olevXri7u9O4cWNOnjzJnj17aNq0KQDjxo1j1KhRDB06lGrVqnH//v00a0G0b9+erl27UqdOHczMzNi+fbvUghBC5FlKUhIJz0PRS0qkaHQwjppYDPX10Gg0WBgbUNrODBMDPR6Ex71+MPFa+vaFMXApk+JxU2PMufAw/nj6PN8murKiTp06XL9+PUXbjRs3KFLkRe2zl2dzJ0uezZ1RWQIh8jsvLy8AuZVRCCGEIBfN7FqxYkWG26UWhBBCpKbR0yPcvgh6j4IwT4wm7l4giqMzBrYF0Wg06OlpcDA34r/nsRQyzTW/8vOU/u6euJhb8lGJMmqH8l74/PPP+eCDD5g5cyZdu3bl1KlTLFu2jGXLlgFkaja3ECK1Bg0aMG/evBSLOwkhhBD5lfzlI4QQ77kERUOYlTOllDASQx8T/+g+SfFxGDm5otFoMDZ4MYlXm5R+cWnx5kpa21DS2ub1HQUA1atXZ8uWLUycOJGvv/6aYsWKsWDBAnr06KHrk5mV/YQQKSXX7bp+/TrBwcEpbgMWIj9ITEwkNDSUZ8+e8ezZM54+fZrh/6Ojo6lUqRL16tWjfv36lC1bVuqbCpGHSLJLZJmXlxeVKlViwYIFefqYQrwvjPQ1xGkV9JycMTIyIv7RAxJDn4CiYFTIjegELRoNGOjLGziRO7Ru3ZrWrVunuz0zs7mFECnZ2NhQqVIlzp07h6+vLx9//LHaIQnxziiKwrp161iyZAkPHz7k2bNnhIWFZXmcq1ev8scffwBgZ2enS3zVr1+fihUrYmAgfy4L8b6Sn16VXAiO4MaTaKyMDWhQ3EY38+Jd6tu3L2vWrGHWrFlMmDBB175161Y6dOiQ4ZLiL/v7778xNDR8V2Fmi3eZHNu1axetWrVKd3uXLl34888/s/24QqTH1syQe+FxPIqMx9XOAY2hEXH3A9EzNkGbpBASGY+NqSH6erKS3bu0714Aq65fZHLl2njYFlQ7HCFEPtSgQQPOnTuHj4+PJLtEnnXnzh2GDBnCvn370txubW2Nra2tbtGy5MfLX9vZ2aGvr8+pU6c4fPgwx44d4+nTp2zdupWtW7cCLxZeqVOnDvXr16devXpUr1493ZWGhRC5jyS7cti5++EM2XqNk3fDdW0FzQ2Z1LAYI+u4vfNl1U1MTJgzZw6DBg3CxubNbruxtbXN5qjeLw0bNiQ4ODhFm1arpV+/fpw7d46vvvpKpchEfmWor4ezlTH3w+KI1yo4mFtgWNSd8CR9gh9Hk6SAs5URJCaoHWqetvTKObYG3sTN3JK5tRqqHY4QIh/y8vJiwYIFUrdL5EmJiYnMnz+fqVOnEhMTg7GxMZMmTaJp06a6JJaNjU2WZmO1aNECeLEQytmzZzl8+DCHDx/m6NGjhIWFsWfPHvbs2QO8+DuqZs2auuRXjRo1sLa2fifnKoR4e3JPSw66/CgSr2V+xCUmsbWXJ8+mNODiqFp09HDg8x03mHEw4J3H0KRJE5ycnJg1a1a6feLi4hgxYgQODg6YmJhQt25dTp8+rdvu5eXFqFGjdF9v2rSJChUqYGpqip2dHU2aNCEqKorffvsNOzs74uJSrgLXqVMnevfune7xo6Ki6N27NxYWFhQqVIgffvghVZ89e/ZQt25dChQogJ2dHa1bt+b27dvAixlsvr6+LFy4EI1Gg0ajITAw8LX7ZZapqSlOTk66R8GCBRkzZgznzp3j4MGDVKhQIUvjCZEdClkaU6SACZFxiVx7HM3FpwkEhMZipK/B3c4Yg8isT+0XWTOobCVGeFSle8lyaocihMinkut2Xbt2jYcPH6odjhDZ5syZM1SvXl1X09HLy4sLFy4wZcoUateujbu7OwULFnzj2w6NjIyoVasW48aNY8eOHTx9+pRz586xcOFCOnXqRMGCBYmNjcXX15dvvvmGZs2aUaBAAdzd3enZsycLFy7k33//JTo6OpvPXAjxpiTZlYO+2ncbR0tjDg+uRrvyDtiYGeLhZMHPHcoyqWFRvjkYQEhk/DuNQV9fn5kzZ7Jo0SLu3buXZp9x48axefNm1qxZw9mzZylZsiTNmzfn2bNnqfoGBwfTrVs3+vfvz9WrV/Hx8aFjx44oikKXLl3QarVs27ZN1//Jkyfs2LGDfv36pRvj2LFjOXToEFu2bGHfvn34+Pjg5+eXok9UVBSjR4/m9OnTHDhwAD09PTp06EBSUhILFy6kdu3aDBw4kODgYIKDg3Fzc3vtfm9Cq9XSs2dPvL29OXDggCS6hKoKWhhRwcmCMgXNKGVvSgUnc0rZmcL9AOIe/EfC89Q/wyL7tHArzsIPmlDJ3lHtUIQQ+ZStrS0VK1YEkNldIk+IjIzk888/p2bNmvj7+2NjY8PKlSs5ePAgpUuXfmfH1dfXp1KlSowYMYJNmzbx6NEjrl27xrJly+jZsydFixYF4MaNG6xbt45Ro0ZRt25drKysqFixIgMGDODnn3/Gz8+P+Ph3+/edECJtchtjDnkWncA/Vx7zY1t3LI1TP+1f1CvCD0eC+MP/ISPrFn6nsXTo0IFKlSoxdepUVqxYkWJbVFQUS5cuZfXq1bRs2RKA5cuX4+3tzYoVKxg7dmyK/sHBwSQmJtKxY0eKFCkCkCLh0717d1atWkWXLl0AWLduHa6urnh5eaUZW2RkJCtWrOC3336jadOmAKxZswZXV9cU/Tp16pTi6xUrVuDg4MCVK1fw8PDAyMgIMzMznJycsrRfVmi1Wnr16qVLdHl6emZpfyHeBY1Gg8Urv2P0LaxIio0m4clDtHFvltgVQgjxfmjQoAH+/v74+vry0UcfqR2OEG9s165dDBkyhKCgIODF3xXz58/HwcEhx2PRaDS4u7vj7u7OwIEDAXj8+DFnzpzhzJkznD59mtOnT/Pw4UMuXLjAhQsXWLlyJfBi1ljFihWpXr061atXp2rVqpQqVQoTE5McPw8h8hNJduWQkMh4khTwdLJIc7utmSFu1sbcD49Lc3t2mzNnDo0aNeKLL75I0X779m0SEhKoU6eOrs3Q0JAaNWpw9erVVONUrFiRxo0bU6FCBZo3b06zZs3o3Lmzrh7YwIEDqV69Ovfv38fFxYVVq1bRt2/fdGuT3b59m/j4eGrXrq1rs7W1xd3dPVW/r776ihMnTvDkyRPdzKygoKAMk1Zvut+rkhNd+/bt48CBA7pPUdPrq6+vn+mxhchuhgWdIElL3JMQtGHPCDu2H5NG6a+EJ97OrbBQ1t68zGjP6lgZSSFbIUTO8vLyYuHChfj4+KgdihBv5OHDh4waNYqNGzcCULRoUZYuXaqrr5VbFCxYkJYtW+omCCiKwoMHD3SJr+QkWGhoqK4tmUajwdXVlZIlS1KiRAlKlCiR4v9WVlZqnZYQeYYku3JIQXND9DRw+VEU9YqlLgz/PCaBe2FxOFka5Ug89evXp3nz5kyaNIm+ffvq2pNXZHw1GaUoSpoJKn19fby9vTl27Bj79u1j0aJFTJ48mZMnT1KsWDEqV65MxYoV+e2332jevDkXL15k+/bt6caV2RUh27Rpg5ubG8uXL8fZ2ZmkpCQ8PDxeO034Tfd7WXKia+/evekmugIDA2nXrh01atTg5MmTnD59mvXr17N06VJiY2Np3749X3/9daaPKcTb0Gg0GDq6YBAfDyFPuLdgCiZGRljXbaZ2aHmOoii02buJa8+fUcTSin7uMuNTCJGz6tevj0aj4erVqzx69AhHR7m1WrwfFEVh5cqVjBkzhufPn6Onp8fnn3/O9OnTMTc3Vzu819JoNLi4uODi4kL79u2BF+d0586dFLO/zp07R0REBHfv3uXu3bscOnQo1VgFCxZMkQBL/rdUqVLY29vn8JkJ8X6SZFcOsTM3olUZexb+G0SvKoUwN0o502fhv3dJTFLoVtEpnRGy3+zZs6lUqVKK+91LliyJkZERR48epXv37gAkJCRw5syZFEXpX6bRaKhTpw516tRhypQpFClShC1btjB69GgAPvnkE+bPn8/9+/dp0qSJrn5WWkqWLImhoSEnTpygcOEXt3OGhoZy48YNGjRoAMDTp0+5evUqv/zyC/Xq1QPg6NGjKcYxMjJCq9WmaMvMfq+j1Wrp3bs3e/fuZf/+/VSqVCndvpcvX+b3339n+fLlXLlyhV27dnH8+HE0Gg3t2rXj+PHjKWawCfEuaTQaDAsWQu/+A9BquT2uD6V+/AvLanUBSEpS2HvzKX9deEREvBZ3ezM+qe5CUVtTlSN/v2g0GvqWroBv8F2KWMgKTUKInGdra4unpyfnz5/H19eXrl27qh2SEK91/fp1Bg0apKs1V6VKFZYvX06VKlVUjuztaDQa3Wyt5NuKFUXh8ePH3L59m1u3bnH79u0U/3/8+LHuceLEiVRj1q1bl/79+9OlSxcsLNK+a0gIIcmuHPVN0xLU+fkMjZb78XXTEjQoVoB7YXEsPn6Xhf/eZVLDohSyyrlbXipUqECPHj1YtGiRrs3c3JwhQ4YwduxYbG1tKVy4MHPnziU6OpoBAwakGuPkyZMcOHCAZs2a4eDgwMmTJ3n8+DFly5bV9enRowdjxoxh+fLl/PbbbxnGZGFhwYABAxg7dix2dnY4OjoyefJk9PT+fy0FGxsb7OzsWLZsGYUKFSIoKIgJEyakGKdo0aKcPHmSwMBALCwsdEsRv26/jCQlJdG7d2+2bt3Kpk2bKFSoUKqVjgoWLKi7ZbF06dK6Ol4HDhzg+PHjVK1aFXhRm+z27duS7BI5SqPRYGBti1H1esSdOoSh/YuaF0+i4mm92p+Td8Mp72iOs5Uxi4/fY5ZPILNalGRcg6LqBv6eGVexJuMr1VI7DCFEPtagQQNJdon3QkJCArNnz2bGjBnEx8djZmbGN998w4gRI954ZcXcTqPR4ODggIODQ5p/C4SHh6dKgCX/e+/ePY4ePcrRo0cZPnw4Xbt2pX///tSpUyfdMjFC5Fd58zdILlXR2ZKDA6vw6d9XabHynK69gIkBs1qUZHyDIjke0zfffMOff/6Zom327NkkJSXRq1cvIiIiqFatGnv37tXV4XqZlZUVhw8fZsGCBYSHh1OkSBF++OEH3b3ryX06derEzp07dVN6M/Ldd98RGRlJ27ZtsbS05IsvviAsLEy3XU9Pjw0bNjBixAg8PDxwd3fnxx9/TFH0fsyYMfTp04dy5coRExNDQEAARYsWfe1+q1evpl+/fmneTpl8KyLAhx9+mGbsoaGhFChQAAAzMzNdu6IofPrpp0yZMuW15y/EO6XR4Db6WwyiIzBycn2xcuq6i9x5FoPPp1WpX6wAGo2GqHgtMw4GMH73LYoUMOGjHJx1+r6TN5tCCLV5eXnx448/St0ukaslJCTQpUsX/vnnHwBatGjB0qVLdSsd5ldWVlZUrlyZypUrp9r24MEDfvvtN1auXMnNmzdZtWoVq1atolSpUvTv35/evXvj7OysQtRC5D4aJbNFkvKJe/fu4ebmxt27d1OtABgbG0tAQADFihV7q9UzFEXh9L1wbjyJxsrYgCYlbTEzytsFzJs2bUrZsmX58ccf1Q4lQ9OmTcPHx+et3xwGBgbSuXNnzpw5A8ClS5f46KOPOHr0KDY2Nty7dw9TU1Ps7OyyIWohMiet32Eng8IYPmMl0z76gA8b10zRX1EUWq32Jzg8jrMjakoSJ4uiEuLZffcOnYq55+nnLqPXTSGyU3h4ONbW1oSFhUnx5td4+vSprq7Po0ePVFm9ToiMJH+wvn79eoyNjVm5ciXdunXL06+X2UlRFP79919WrVrFxo0biYqKAl5MCmjRogX9+/enTZs2GBnlTD1okTvl99dNvdd3EdlNo9FQw82anpUL0bZcwTyd6Hr27BkbNmzg4MGDDBs2TO1wXmvv3r3MnTs328f18PBg/PjxeHl5UaFCBbp27ap7URJCTSd27ORH/1m4/jCAuAdBKbZpNBr6V3PGPziSBzm0UmxekZCkpcSGZXTZ/w8nQx6oHY4QIp+xs7OjQoUKALoaSELkFoqiMHToUNavX4+BgQGbNm2ie/fukujKAo1GQ926dVmxYgUPHz5k5cqV1K1bl6SkJHbt2kXnzp1xcXHh888/58KFC2qHK4QqJNkl3qkqVaowaNAg5syZg7u7u9rhvNbx48epUaPGW49TtGhR3ayuZL179+b8+fNcvHiRY8eO6QrwC6GmxwVL8siiEAkhD7gxpD3xj4NTbC9g8uJu99jEJDXCe28Z6unT1KUoJawK8CwuVu1whBD5UHKZBkl2idxEURTGjRvHL7/8gkajYe3atbRu3VrtsN5rFhYW9OvXjyNHjnD9+nUmTpyIs7MzT548YcGCBVSsWJFq1aqxZMkSYmPlPYnIPyTZJd6pwMBAwsLCGDNmjNqhCCHSULakG59UnIzGqTDx9wO5Oawjic+f6rbvvv4UOzNDXK3f/Nbt/GpJ3abc/OhTPixcQu1QhBD5UHKyS+p2idzk22+/5fvvvwdg+fLluhUKRfYoXbo0M2fO5L///mPnzp106tQJQ0ND/Pz8GDZsGF5eXqkW2BIir5JklxBC5GOdPBzQ2Doyt8ksDAoWIvbOdW5+1hltZDin74bxy6n7fFLdGWMDebnIKksjY7klQwihmvr16wNw+fJlHj9+rHI0QsDChQv56quvAJg/f36aK72L7GFgYMCHH37Ipk2bePDgAfPnz8fW1paTJ09So0YNzp8/r3aIQrxz8teLEELkYyaG+qz/2IN/nhjzWdWviDO3Ifraebb17YLX0pN4OlnwVePiaof5XlMUhRvPn6kdhhAin7G3t8fDwwOAw4cPqxyNyO9WrlzJqFGjAJg+fbru/+Lds7e3Z9SoUZw4cYLSpUtz9+5d6tSpw/bt29UOTYh3SpJdQgiRzzUpZcfJYdUpWsGDIZ4TidY3JijJlCmNirL/kyqY5+FFNN6153GxlP3zV8r/tYLHMdFqhyOEyGfkVkaRG/z5558MHDgQgC+++EI3u0vkrFKlSnHixAmaNGlCVFQU7dq14/vvv0dRFLVDE+KdkGSXEEIIPAtZ8vtHHvj/OIBK6w4xYtPfjG9SOk+vFpsTChibYGFohKGeHmefSI0MIUTOkmSXUNuuXbvo0aMHSUlJDBw4kO+++05u8VeRjY0Nu3btYvDgwSiKwtixY/nkk0+Ij49XOzQhsp0ku4QQQuhoNBosS5ZBo/fi5UFJSiL6mtR1eBu/N2zFo16f0dxNbgcVQuSs5Lpdly5d4smTJypHI/IbHx8fOnXqRGJiIt26dWPp0qWS6MoFDA0NWbJkCQsXLkRPT4+VK1fSrFkznj59+vqdhXiPSLJLCCFEmpLi47gzsT/X+jUn4uwxtcN5b5W1scfSyFjtMIQQ+VDBggUpX748IHW7RM46deoUbdq0ITY2ljZt2rBmzRr09WW2eG6h0WgYMWIEO3bswNLSEl9fX2rWrMm1a9fUDk2IbCPJLiGEEGnSGBhCUhJKQjy3x/QkNvCm2iG99+K0iWqHIITIZ+RWRpHTLl68SIsWLYiMjKRRo0b8+eefGBoaqh2WSEPLli05fvw4RYsW5fbt29SqVQtvb2+1wxIiW0iySwghRJo0enoU++ZnzD2qog1/zs2RH5EQKrfBvIn7URG02bOJMn/+ijYpSe1whBD5iCS7RE66efMmTZs2JTQ0lFq1avHPP/9gYmKidlgiA+XLl+fUqVPUqVOHsLAwWrZsyZIlS9QOS4i3JskuIYQQ6dIzMaPEvPUYuRQh/n4gt0d3Jyk2Ru2w3jv2Jqb8++g+gRFhnHocrHY4Qoh8JLlu18WLF6Vul3ingoKCaNKkCY8ePaJixYrs2rULCwsLtcMSmVCwYEEOHDhA79690Wq1DBs2jBEjRpCYKDPSxftLkl0iX3n69CkODg4EBgaqHYrIgzp37sy8efPUDiPbGdoWpNTCjehbFSDq4hkCpgxGkdlJWWKsb8CqBh9ytesn1HZ0UTscIUQ+4uDgQLly5QA4cuSIytGIvOrRo0c0adKEoKAg3N3d2bdvHzY2NmqHJbLA2NiY1atXM2vWLAAWLVpE69atCQsLUzkyId6MJLvyCY1Gk+Gjb9++9O3bV/e1gYEBhQsXZsiQIYSGhr52/OR9Z8+enaJ969atKVZdyUq/9u3bp3u8kJAQBg0aROHChTE2NsbJyYnmzZtz/PjxDOOcNWsWbdq0oWjRoinaqlevjqWlJQ4ODrRv357r16+n2nfJkiUUK1YMExMTqlatmuINY2bGyOxx0pLRsQESExP58ssvKVasGKamphQvXpyvv/6apNckJJYuXYqnpydWVlZYWVlRu3Ztdu/eneXjv+rw4cO0adMGZ2dnNBoNW7dufaM+r3rXz/PLY2g0GkaNGpWlcadMmcK3335LeHh4lo4HL5bmzuhntGvXrlkeMzuZFC1Nie/XojE0IuzoPmJuXFI1nvdRu6KlKFPATu0whBD5kNzKKN6lZ8+e0axZM27evEmRIkXw9vbGwcFB7bDEG9BoNEyYMIHNmzdjZmbG3r17qV27Nnfu3FE7NCGyTJJd+URwcLDusWDBAqysrFK0LVy4EIAWLVoQHBxMYGAgv/76K9u3b2fo0KGZOoaJiQlz5sx5bXIss/0y0qlTJ86fP8+aNWu4ceMG27Ztw8vLi2fPnqW7T0xMDCtWrOCTTz5J0e7r68uwYcM4ceIE3t7eJCYm0qxZM6KionR9Nm7cyKhRo5g8eTLnzp2jXr16tGzZkqCgoEyPkZk+aXndsQHmzJnDzz//zOLFi7l69Spz587lu+++Y9GiRRmO7erqyuzZszlz5gxnzpyhUaNGtGvXjsuXL2fp+K+KioqiYsWKLF68+K36vOpdPs/JTp8+zbJly/D09MzysT09PSlatCjr1q3L9Dkla9iwYYqfyeDgYO7du0fTpk2xt7fnq6++yvKY2c2yygcU/XoppZdswayM5+t3EEIIkStIsku8K+Hh4bRs2ZILFy7g5OTE/v37cXNzUzss8ZY6duzIkSNHcHZ25urVq9SoUYPTp0+rHZYQWaOIFO7evasAyt27d1Nti4mJUa5cuaLExMSoEFn2WbVqlWJtbZ2qvU+fPkq7du1StI0ePVqxtbV97Zh9+vRRWrdurZQpU0YZO3asrn3Lli3Ky99mWen3aizJQkNDFUDx8fF5bVwv27x5s2Jvb//afiEhIQqg+Pr66tpq1KihDB48OEW/MmXKKBMmTMj0GG/SJ7PHbtWqldK/f/8UfTp27Kj07Nkzw7HTYmNjo/z6669ZOn5GAGXLli1v3Sct2fk8K4qiREREKKVKlVK8vb2VBg0aKCNHjszyuNOmTVPq1auX6XNIT2JiovLxxx8r9vb2yoULF956PEV5N7/DkhITFUVRlERtknLiv+eK940nSuCz6GwbPy9JfPyfEnjrrDJy31/Kx9t/VxLuXU3xSHz8n9ohvpGMXjeFyE5hYWEKoISFhakdynvn4cOHCqBoNBrl6dOnaocj8oioqCilXr16CqDY2dkpFy9eVDskkc3u37+vVK1aVQEUe3t75ebNm2qHJLIgv79uysyubKKNiUr3kRQXm/m+rxR+Tq9fTrhz5w579uzJ9FLB+vr6zJw5k0WLFnHv3r237pceCwsLLCws2Lp1K3FxcZne7/Dhw1SrVu21/ZLvS7e1tQUgPj4ePz8/mjVrlqJfs2bNOHbsWKbGeNM+mT123bp1OXDgADdu3ADg/PnzHD16lA8//DDdsV+l1WrZsGEDUVFR1K5dO0vHf1dWr16d4vbWV73p85zeuMOGDaNVq1Y0adLktbGld+waNWpw6tSpLH1vvkqr1dKzZ0+8vb05cOAAFSpUeOOx3qXoG5e48tEHrP97PyXm/kutJadpuuIcRef8S8uV57j5JFrtEHMN7ZMgwr/vyPNVI1kYcJuN9+9xdclAIhb11D3Cv++I9kn6MyaFEOJNOTo6UrZsWRRF4fDhw2qHI/KAuLg4OnTowJEjR7CysmLv3r14eHioHZbIZs7Ozvj4+FC1alWePHlCixYtCAkJUTssITLFQO0A8gr/eulP17Wq05RSCzfqvr7Q1J2k2LT/CLSoUgf3Zdt1X19qU4nE509T9at6Jv3b9d7Gjh07sLCwQKvVEhv7IkmXlYLbHTp0oFKlSkydOpUVK1a8db+0GBgYsHr1agYOHMjPP/9MlSpVaNCgAR9//HGqW89eFhgYiLOzc4ZjK4rC6NGjqVu3ru4F+8mTJ2i1WhwdHVP0dXR05OHDh5ka4036ZOXY48ePJywsjDJlyqCvr49Wq+Xbb7+lW7duGZ4vvFidqXbt2sTGxmJhYcGWLVt0hWyzeu7ZzdraGnd39zS3vc3znNa4GzZs4OzZs5maop3RsV1cXIiLi+Phw4cUKVLktWO9SqvV0qtXL12iK6PvabUFL5tDbOBNbH4YRPPey+j9cTWcrYzxvRPKt4cCqLP0NCeGVqe4nZnaoapOiXvxO79Mp4l8+TyOqtYFKPlhWwz1XnzmpA0JIHrjV7p+QgiR3by8vLh69So+Pj4Z1kUV4nUSEhLo2rUr+/btw9zcnN27d1O1alW1wxLviIWFBTt37qR27drcvn2b1q1bc+jQIczNzdUOTYgMycwukULDhg3x9/fn5MmTDB8+nObNmzN8+HDd9nXr1ulmVllYWKRZqHzOnDmsWbOGK1euZHiszPZLS6dOnXjw4AHbtm2jefPm+Pj4UKVKFVavXp3uPjExMZiYmGQ47meffcaFCxf4448/Um17dSaQoihpzg7KaIyM+mT03L7u2Bs3bmTt2rWsX7+es2fPsmbNGr7//nvWrFnz2rHd3d3x9/fnxIkTDBkyhD59+qS6Jpk99+zWoUMHrl27lua2N32e0xr37t27jBw5krVr1772e+R1xzY1NQUgOjrrSYvkRNe+ffs4cOAAFStWTLdfbmDxxTzuWLjhEBfKyENTqWWvRzFbU/pWc+b40OqYGOgxdb8UNH2ZvkMxvmnYnvZVvDB1K4eBSxkMXMqg71BM7dCEEHmc1O0S2SH5vcq2bdswNjZm27ZtfPDBB2qHJd4xR0dH9uzZg52dHadPn6Zr164kJiaqHZYQGZKZXdmk0pG76W7T6Omn+NrTO/2V4TSalPlHj+3+bxVXVpmbm1OyZEkAfvzxRxo2bMj06dP55ptvAGjbti01a9bU9XdxcUk1Rv369WnevDmTJk2ib9++6R4rs/3SY2JiQtOmTWnatClTpkzhk08+YerUqemOZW9vn2FR/OHDh7Nt2zYOHz6Mq6triv309fVTzWQKCQlJNeMpvTEy0yet51ZfXz9Txx47diwTJkzg448/BqBChQr8999/zJo1iz59+mR43YyMjHTXvFq1apw+fZqFCxfyyy+/ZOncc9LbPM9p8fPzIyQkJMWnklqtlsOHD7N48WLi4uLQ19fP1LjJiyQULFgwS+eU/OZx7969aSa6AgMDadeuHTVq1ODkyZOcPn2a9evXs3TpUmJjY2nfvj1ff/11lo75tjbcjmFe1Un8c3EqMbeucGdif0rO/wONgSH25kaMqFOYyXtv8VO7MliZyMuNEEKoqX79+gBcuHCBZ8+eZVgCQIi0JCUlMXDgQDZu3IihoSF///03jRo1UjsskUNKly7Njh07aNSoEbt27WLw4MEsX748Rz4AF+JNyMyubKJvap7uQ8/YJPN9TUwz1TenTJ06le+//54HDx4AYGlpScmSJXWP5Fksr5o9ezbbt29/bV2nzPbLjHLlymW44l7lypXTnEWmKAqfffYZf//9NwcPHqRYsZQzLIyMjKhatSre3t4p2r29vXWfZL1ujMz0Seu5zcyx4cUsIj29lD/O+vr6JCUlpTt2ehRF0dWbyuzxc0p2PM9pady4MRcvXsTf31/3qFatGj169MDf3x99ff1Mj3vp0iVcXV2xt7fP9HlptVp69+7N3r172b9/P5UqVUqz3+XLlxk+fDgXLlzg9u3b7Nq1i+PHj+Pv78+5c+c4fvx4po+ZHYKex2Lm7EaphRvQMzEj/PhBgmaPQVEUAKq6WBKvVXgUGZ+jcb0P4rVafrniz+Aje9UORQiRTzg5OVGmTBkURUlzZr4QGVEUhREjRrBq1Sr09PT4448/slQbVuQNtWrVYsOGDejp6bFixYoc/6BViKyQj9pFhry8vChfvjwzZ85k8eLFmd6vQoUK9OjRg0WLFr1xv7CwMPz9/VO02draYm5uTpcuXejfvz+enp5YWlpy5swZ5s6dS7t27dI9VvPmzZk4cSKhoaHY2Njo2ocNG8b69ev5559/sLS01M1isra21iWFRo8eTa9evahWrRq1a9dm2bJlBAUFMXjw4EyPkZk+aXndsQHatGnDt99+S+HChSlfvjznzp1j3rx59O/fP91xASZNmkTLli1xc3MjIiKCDRs24OPjw549e7J0/FdFRkZy69Yt3dcBAQH4+/tja2tL4cKFM91ny5YtTJw4UXfLYXY9z6+Oa2lpmar2lrm5OXZ2drr2zF6/I0eOpCron5GkpCR69+7N1q1b2bRpE4UKFUo1ky55lljp0qV1NbwOHDjA8ePHdbPRIiMjuX37tm5xgZxgb27I/fA4KFGTYjN/5faYnjzZ+jtm5SpTsGNfbj6JRqMBW1N5qXnVf5FhDP13H0mKwgB3TyqrHZAQIl/w8vLi2rVrbNu2LcP3TEK8TFEUJkyYwE8//YRGo2HNmjV06tRJ7bCEStq2bcuSJUsYPHgw06ZNw9XVlQEDBqgdlhCp5ejaj++BjJZQj4mJUa5cuaLExMSoEFn2WbVqlWJtbZ2qvU+fPkq7du1Sta9bt04xMjJSgoKC0h0zrX0DAwMVY2Nj5eVvs6z0A1I9+vTpo8TGxioTJkxQqlSpolhbWytmZmaKu7u78uWXXyrR0dEZnnutWrWUn3/+OUVbWscBlFWrVqXo99NPPylFihRRjIyMlCpVqii+vr5ZGiOzx0lLRsdWFEUJDw9XRo4cqRQuXFgxMTFRihcvrkyePFmJi4vLcNz+/fvrxi1YsKDSuHFjZd++fVk+/qsOHTqU7vXLSp9Vq1al+L7Iruf51XHT0qBBA2XkyJFZGjcmJkaxsrJSjh8/nmKsjI534sSJdMdOfoSGhioBAQFK1apVdfstXLhQmT59eobn8Krs/h0W8DRa0UzwVuYf+U9RFEV59OevyvUh7ZXEyHAlJj5R8Zh3TGm16ly2HOt9l3DvqvJsfFUl4d5VXdvY4weVhRdPKzEJCWluf19k9LopRHbK70uoZ4cjR44ogGJqaqo8e/ZM7XDEe+Lrr7/WvSf55Zdf1A5H5BJffvmlAij6+vrKzp071Q5HpCG/v25KsusV+SHZlZ/t3LlTKVu2rKLVatUOReRBixcvVpo2bZqqferUqUqDBg3eauxXk10XL15UypUrp/tj5e7du8qTJ08yHONd/A4btvWqoj9xvzLd+7YSEhGnaBMSlGOBoUrDX84oJpMPKKeCnmfbsd5nycms2LO7lIR7V1M9Ys/ukmSXeK9NnTo1VaLe0dFRtz0pKUmZOnWqUqhQIcXExERp0KCBcunSpSwdI7+/ac8OSUlJSoUKFRRAmT9/vtrhiPfADz/8oPuZnjdvntrhiFwkKSlJ6du3rwIoZmZmyqlTp9QOSbwiv79uyr0lIl/58MMPuXnzJvfv38fNzU3tcEQeY2homOYtuXv37mXhwoXZeiwPDw/Gjx+Pl5cXSUlJWFpasmHDBuzs7LL1OK+zsI07pob6zPQJZOr+Oxjpa4jXKhS3MeGA7SnKPDYBN1mlSWNsBkD0xq8y1U+I91H58uXZv3+/7uvkhT0A5s6dy7x581i9ejWlS5dmxowZNG3alOvXr2NpaalGuPmSRqNh2LBhDB48mCVLljBixIhUNT+FSPbzzz/zxRdfAPDNN9/w+eefqxyRyE00Gg3Lli0jODiYvXv30qpVK44fP06JEiXUDk0IADSK8r9KwgKAe/fu4ebmxt27d1OttBYbG0tAQADFihXDxMQknRGEECJ3epe/w55GxbPj2hMi4rS4FzSj0pXdBH0zAn0LK0ov34lZqfLZerz3kfZJEEpcdKr246HPmH7jGlPKV6S+e9U09szdMnrdFPnHtGnT2Lp1a6pam/Ci3o+zszOjRo1i/PjxAMTFxeHo6MicOXMYNGhQpo4RHh6OtbU1YWFhWFlZZWf4+UpkZCQuLi6Eh4ezd+/eLNWZFPnHb7/9Rp8+fQCYMGECM2fOlFX3RJoiIiLw8vLi7NmzlCxZkmPHjmV5VXLxbuT3181c81HOrFmzqF69OpaWljg4ONC+fXuuX7+eoo+iKEybNg1nZ2dMTU3x8vLi8uXLKfrExcUxfPhw7O3tMTc3p23btty7dy8nT0UIIfIdO3Mj+lR15rMP3Ghayg675p2wqFQLbWQ4t4Z3Ie5BkNohqk7fvjAGLmVSPX57FsaBJ4+Zdee22iEK8VZu3ryJs7MzxYoV4+OPP+bOnTvAiwVIHj58mCKpYmxsTIMGDTJcjTkuLo7w8PAUD/H2LCwsdEmMJUuWqByNyI3++usv+vXrB8CIESMk0SUyZGlpyc6dOylatCi3bt2idevWREVFqR2WELkn2eXr68uwYcM4ceIE3t7eJCYm0qxZsxQ/KMlT4BcvXszp06dxcnKiadOmRERE6PqMGjWKLVu2sGHDBo4ePUpkZCStW7dGq9WqcVpCCJEv6ZmYUmLeekxKlCXhyUNuftaZhNAnaoeVK02u/AEDy1Rkad3maocixBurWbMmv/32G3v37mX58uU8fPiQDz74gKdPn+pWmHV0dEyxj6OjY6rVZ182a9YsrK2tdQ8pP5B9hg4dCsD27dsJCpIPI8T/27FjB927dycpKYkBAwYwf/58SXSJ13JycmLPnj3Y2dlx6tQpPv74YxITE9UOS+RzuSbZtWfPHvr27Uv58uWpWLEiq1atIigoCD8/P+DFrK4FCxYwefJkOnbsiIeHB2vWrCE6Opr169cDEBYWxooVK/jhhx9o0qQJlStXZu3atVy8eDFFDQkhhBDvnoFVAUot3oRRITfigm5xa+THaKMj1Q4r1ylqac2y+i0oammtdihCvLGWLVvSqVMnKlSoQJMmTdi5cycAa9as0fV59Q9mRVEy/CN64sSJhIWF6R537959N8HnQ2XKlKFRo0YkJSXxyy+/qB2OyCX2799P586dSUxMpHv37vzyyy9S001kmru7O9u3b8fExIQdO3YwdOhQpGKSUFOu/e0VFhYGgK2tLZC5KfB+fn4kJCSk6OPs7IyHh0e60+RfnSL/8iwxIYQQb8eoYCFKLdqEvrUt0VfOcmdcH5SkJLXDytXkjaHIC8zNzalQoQI3b97EyckJINUsrpCQkFSzvV5mbGyMlZVViofIPsOGDQNg+fLlxMXFqRyNUJOiKGzevJl27doRFxdH+/btWb16dYpFJoTIjNq1a7Nhwwb09PRYvnw5M2bMUDskkY/lymSXoiiMHj2aunXr4uHhAZCpKfAPHz7EyMgIGxubdPu86tUp8uXKlcvu0xFCiHzNpGgpSi3ciJ65BQW8WqGRT4nT9CQ2mtHHD9By91+S8BLvvbi4OK5evUqhQoUoVqwYTk5OeHt767bHx8fj6+vLBx/Iaq1qadu2LS4uLjx+/JjNmzerHY5Qyc2bN/nwww/p3Lkz0dHRtGjRgg0bNmBoaKh2aOI91a5dO3766ScApkyZwqpVq1SOSORXufIvjs8++4wLFy7wxx9/pNqW1Snwr+vz6hT5K1euvHngQggh0mTuUZUK/5yjYOf+KdoVReH4f88ZveMGAzdf4fvD//E4Ml6lKNUVm5jIT5fPsfdeACdDHqgdjhBZMmbMGHx9fQkICODkyZN07tyZ8PBw+vTpg0ajYdSoUcycOZMtW7Zw6dIl+vbti5mZGd27d1c79HzLwMBAtxJm8h+mIv+IiYlhypQpeHh4sGfPHoyMjPjyyy/ZsmULxsbGaocn3nODBw9m0qRJAAwcOFB3a7sQOSnXJbuGDx/Otm3bOHToUIolzDMzBd7JyYn4+HhCQ0PT7fOqV6fIW1paZufpCCGE+B+DAna6/yc+f8a9f/6g+cpzfLD0DH9dfMT54Ei+3Hcb11lH+OVk/ltF19XCiu9qebGnZRdqOjirHY4QWXLv3j26deuGu7s7HTt2xMjIiBMnTlCkSBEAxo0bx6hRoxg6dCjVqlXj/v377Nu3T953qeyTTz7BwMCAY8eO4e/vr3Y4Iofs2LGD8uXL88033xAfH0+zZs24dOkS33zzDSYmJmqHJ/KIGTNm0Lt3b7RaLZ07d+bw4cNqhyTymVyT7FIUhc8++4y///6bgwcPUqxYsRTbMzMFvmrVqhgaGqboExwczKVLl2SavBBC5BLaqAiuD/yQR98Mw+bYVrb28uS/8XU59VkN7k+sS/9qzgzeco3tVx6rHWqOG+FRjeZuxfPVylezZs3SzfxJpigK06ZNw9nZGVNTU7y8vLh8+bJ6QYrX2rBhAw8ePCA+Pp779++zefPmFKUhNBoN06ZNIzg4mNjYWHx9fXWlKoR6ChUqRKdOnQBYsmSJytGIdy0wMJB27drRpk0bAgICcHV1ZdOmTezZs4dSpUqpHZ7IYzQaDb/++itt2rQhNjaW1q1bc+bMGbXDEvlIrkl2DRs2jLVr17J+/XosLS15+PAhDx8+JCYmBiBTU+Ctra0ZMGAAX3zxBQcOHODcuXP07NlTtzKQEEII9embWxJbqSEAYy8uxSvUHz29F8kdO3MjlrQvQ8PiNsz0CVQxSvUlJGnzfO2u06dPs2zZMjw9PVO0z507l3nz5rF48WJOnz6Nk5MTTZs2lUVkhHgHkgvVr1u3jufPn6sbjHgn4uLi+PbbbylXrhzbtm3DwMCAcePGcfXqVTp16pSvPmAROcvQ0JA///yThg0bEhERQYsWLaRskMgxuSbZtXTpUsLCwvDy8qJQoUK6x8aNG3V9MjMFfv78+bRv356uXbtSp04dzMzM2L59u6wmIoQQuci2av3xdvNCk6Tl9vh+RF44pdum0Wj4tKYLJ4LCCA7PnyuErbt5GfeNy9l7L0DtUN6ZyMhIevTowfLly1MsLKMoCgsWLGDy5Ml07NgRDw8P1qxZQ3R0NOvXr1cxYiHypuQFoaKjo1mzZo3a4Yhstm/fPipUqMCXX35JTEwMXl5enD9/njlz5mBhYaF2eCIfMDEx4Z9//qFGjRo8ffqUJk2acOfOHbXDEvmAgdoBJMvMp9fJU+CnTZuWbh8TExMWLVrEokWLsjE6IYQQ2SkqUeGv2qPo5GZA+LH93Br1Me6/7sK0eBkAHMyNAIhO0KoZpmrOPnlEQEQYiy+fpYVbcbXDyZSIiAjCw8N1XxsbG2dY5HjYsGG0atWKJk2apFiaPCAggIcPH9KsWbMUYzVo0IBjx47pCmoLIbKHRqNh2LBhDBkyhCVLljB8+HD0ZNXcDGm1WsLCwnj27BnPnj3j6dOnqf4fFRVFqVKlqFSpEhUrVsTJySlHZ1Ddu3ePzz//nE2bNgEvahv/8MMPdOvWTWZyiRxnaWnJ7t27adCgAZcuXaJp06YcOXIEZ2epUSrenVyT7BLvDy8vLypVqsSCBQvy9DGFEO9OeUcL5h2Nx2D8Uswnf0zUJT9uDu+M+7KdGLsUwfvWM6xNDHCxyp8rQo2rWBNncwuGlKusdiiZ9nJ9JoCpU6em++HUhg0bOHv2LKdPn061LXkhmlcXlnF0dOS///7LnmCFECn06NGDcePGcePGDQ4cOEDTpk3VDkl1ly9fZuXKlTx69ChFMuvZs2eEhoZm+TZzBwcHKlasqEt+VapUCXd3dwwMsvfPsYSEBBYsWMD06dOJiopCX1+f4cOHM336dKysrLL1WEJkha2tLfv27aNevXrcvn2bpk2b4uvri729vdqhiTxKkl35SN++fVmzZg2zZs1iwoQJuvatW7fSoUOHTL9o//333xgaGr6rMLPFu0yO7dq1i1atWqW7vUuXLvz555/Zflwh8pKPPB0ZveMGXxy8x7p5f3Dn01YkxcehZ2zMpYeRLD1xj35VC2FimD9vQXc0M+cLzxpqh5ElV65cwcXFRfd1erO67t69y8iRI9m3b1+Gq369OvNAURSZjSDEO2JpaUmfPn1YvHgxS5YsydfJroiICKZPn87ChQtJTEzMsK+FhQV2dnbY2tpia2ub4v/GxsZcu3YNf39/rl+/TkhICN7e3ikW0jI2NsbDwyNFEqxixYpYW1vr+iQkJBAeHq6bPfu6x/Hjx7l27RoAderU4aeffqJixYrv5skSIosKFSrE/v37qVu3LleuXKFly5YcOHBAErHinZBkVw7TPglCiYtOd7vG2Ax9+8Lv7PgmJibMmTOHQYMGpaiRkhW2trbZHNX7pWHDhgQHB6do02q19OvXj3PnzvHVV1+pFJkQ7w8zI31WdylH53UXqfU0hmGfLMXZSMuK42GsOH2V4ramTG3yfty+lxMiE+KxMDRSO4wMWVpaZurNqp+fHyEhIVStWlXXptVqOXz4MIsXL+b69evAixlehQoV0vUJCQlJNdtLCJF9hg4dyuLFi9m2bRtBQUEULvzu3o/mRoqisHHjRr744gsePHgAQOvWrfHy8kqVyLKzs8PGxgYjo8z9Xo6Ojuby5cv4+/vj7+/P+fPnOX/+PJGRkfj5+eHn55eiv7OzM4mJiYSHhxMbG5vlcylYsCBz586ld+/eckuqyHWKFi3K/v37qVevHmfOnKFNmzbs2bMHU1NTtUMTeYwku3KQ9kkQ4d93fG0/qzF/v7OEV5MmTbh16xazZs1i7ty5afaJi4tj7NixbNiwgfDwcKpVq8b8+fOpXr06kHrW1KZNm5g+fTq3bt3CzMyMypUr888//7B582Y+//xzHjx4kOIT/k6dOmFubs5vv/2W5vGjoqIYMmQIf//9N5aWlowZMyZVnz179jBjxgwuXbqEvr4+tWvXZuHChZQoUYK+ffvi6+uLr68vCxcuBF7UgClatGiG+2WWqalpil/GWq2Wnj17cu7cOQ4ePEiFChUyPZYQ+Vm78g74DqrKrEOBDPZ9gqKAvXk0w2q78lniOZTjj6FRG7XDVFVA+HOG/ruPkJhoznTokydmNjVu3JiLFy+maOvXrx9lypRh/PjxFC9eHCcnJ7y9valc+cVtnPHx8fj6+jJnzhw1QhYiXyhbtiwNGzbk0KFDLFu2LEUtvbzu8uXLDB8+nEOHDgFQokQJfvzxRz788MNsGd/MzIzq1avr3ksDJCUlERAQkCIB5u/vz927d3XJtpeZmppiZWX12oe9vT0dO3bM9x9Oi9ytTJky7N27l4YNG3L48GE6d+7Mli1bMp1AFiIzJNmVg5JndJl99A36DsVSbdeGBBC98asMZ369LX19fWbOnEn37t0ZMWIErq6uqfqMGzeOzZs3s2bNGooUKcLcuXNp3rw5t27dSvXCGRwcTLdu3Zg7dy4dOnQgIiKCI0eOoCgKXbp0YcSIEWzbto0uXboA8OTJE3bs2MGePXvSjXHs2LEcOnSILVu24OTkxKRJk/Dz86NSpUq6PlFRUYwePZoKFSoQFRXFlClT6NChA/7+/ixcuJAbN27g4eHB119/Dbz4hOt1+73JJ1/JiS5vb29JdAnxBj4oUoDtfSsRHa8lKl6LrZkhsdf8udbvM1CSKDzhewp27Kt2mKqxNDLi6MP7xCQmcO7pI6rYO6kd0luztLTEw8MjRZu5uTl2dna69lGjRjFz5kxKlSpFqVKlmDlzJmZmZnTv3l2NkIXIN4YNG8ahQ4dYvnw5X331VYaLTOQFERERfP311yxYsIDExERMTEyYNGkSY8eOzfA26+ygp6dHiRIlKFGiBJ06ddK1P3v2jJs3b6ZIbllaWub6EiJCZFWVKlXYuXMnzZo1Y9euXfTu3Zt169ahr58/S1iI7CfJLhXoOxTDwKWMasfv0KEDlSpVYurUqaxYsSLFtqioKJYuXcrq1atp2bIlAMuXL8fb25sVK1YwduzYFP2Dg4NJTEykY8eOFClSBCBFwqd79+6sWrVKl+xat24drq6ueHl5pRlbZGQkK1as4LffftPVi1izZk2qpNzLbwoAVqxYgYODA1euXMHDwwMjIyPMzMxwcnLK0n5ZodVq6dWrF97e3hw4cABPT0/dtgcPHjB27FjWrVuXpTGFyK/MjPQxM3rx5sasdAXs23bnyZbfCJo5msRnj3EaMCZPzGrKKnsTM9Z4fYinrQMlrd/s1vP30bhx44iJiWHo0KGEhoZSs2ZN9u3bh6WlpdqhCZGntW3bFmdnZx48eMDmzZvzbII5rVsW27Vrx/z58ylWLPUH0jnJ1taWmjVrqhqDEDmlbt26/P3337Rt25aNGzdiaWnJsmXL8uV7PpH95CbufGrOnDmsWbOGK1eupGi/ffs2CQkJ1KlTR9dmaGhIjRo1uHr1aqpxKlasSOPGjalQoQJdunRh+fLlhIaG6rYPHDiQffv2cf/+fQBWrVpF37590/0Fdvv2beLj46ldu7auzdbWFnd391T9unfvTvHixbGystK9MQkKCsrwvN90v1clJ7r27dvHgQMHUhX+dHZ2lkSXEG9IY2BA4UnzcRrwBQAPfp7F3bnjUbRalSNTR8di7nk+0eXj45NiQRGNRsO0adMIDg4mNjYWX1/fLH8gIYTIOkNDQwYNGgTAkiVLVI7m3bhy5QqNGzemW7duPHjwgOLFi7Njxw62bt2qeqJLiPyoRYsWrF+/Hj09PX799VfGjh2b5dVOhUiLJLvyqfr169O8eXMmTZqUoj35F0tmV8HS19fH29ub3bt3U65cORYtWoS7uzsBAQEAVK5cmYoVK/Lbb79x9uxZLl68SN++fdONK7O/2Nq0acPTp09Zvnw5J0+e5OTJk8CLui7vYr+XJSe69u7dy/79+9Nc4SYwMJBq1aoRGBhIxYoV6du3L+XKlWPIkCFs3bqVmjVrUr58eW7evJnp4wqRn2g0GlyGTMZtzGzQaHj8168EfDmQpPg4tUNT1eOYaJLkDaAQ4h0aOHAgBgYG/Pvvv5w/f17tcLJNREQEY8eOpWLFihw6dAgTExOmT5/O5cuXM1xlWwjx7nXu3Jnly5cD8MMPP/Dtt9+qHJHICyTZlY/Nnj2b7du3c+zYMV1byZIlMTIy4ujRo7q2hIQEzpw5Q9myZdMcR6PRUKdOHaZPn865c+cwMjJiy5Ytuu2ffPIJq1atYuXKlTRp0gQ3N7d0YypZsiSGhoacOHFC1xYaGsqNGzd0Xz99+pSrV6/y5Zdf0rhxY8qWLZtiNhmAkZER2ldmgWRmv9fRarX07t1bl+h6uY5Yeq5evcrEiRO5ePEiPj4+/Pvvv5w8eZLhw4ezePHiLB1fiPzG4eNPKfbtcjQGhoR6b+XZro1qh6QK7ZMgvvb5h6Lrl7DxzEES719L8dA+ydrsVCGESE+hQoXo2PHFgkp5YXaXoihs2LCBMmXK8P3335OYmEjbtm25cuUKU6ZMeee1uYQQmdO/f3/mz58PwFdffcWPP/6ockTifSc1u/KxChUq0KNHDxYtWqRrMzc3Z8iQIYwdOxZbW1sKFy7M3LlziY6OZsCAAanGOHnyJAcOHKBZs2Y4ODhw8uRJHj9+nCIx1qNHD8aMGcPy5cvTXYExmYWFBQMGDGDs2LHY2dnh6OjI5MmTUxSPt7Gxwc7OjmXLllGoUCGCgoKYMGFCinGKFi3KyZMnCQwMxMLCAltb20ztl5GkpCR69+7N1q1b2bRpE4UKFeLhw4cp+hQsWDBVUUV3d3fdbZhly5alSZMmAHh6erJ79+5MH1+I/Mq2WUcMrG15fngPdu16pdgWm6DFN+A5EXGJuBc0p4KThUpRvjvJK/lG2ZQj2qY8Ww9vpsXjU6n6vcuVfIUQ+cuwYcP4888/Wbt2LXPmzKFAgQJqh/RGAgMD6d+/v26VxeLFi/Pjjz/KTC4hcqlRo0YRFhbGtGnTGDlyJNbW1vTp00ftsMR7SpJdKtCGBGSp/V365ptv+PPPP1O0zZ49m6SkJHr16kVERATVqlVj79692NikrhljZWXF4cOHWbBgAeHh4RQpUoQffvhBV9w+uU+nTp3YuXMn7du3f21M3333HZGRkbRt2xZLS0u++OILwsLCdNv19PTYsGEDI0aMwMPDA3d3d3788ccURe/HjBlDnz59KFeuHDExMQQEBFC0aNHX7rd69Wr69euX5u2Up0+fZv369QDpLkUdGhqa6g3hyysZ6enp6b7W09NLNftMCJE2q5peWNX00n2tjYlmyd7zTL8Qz9PoBF17rcLW/Ny+DBWd804h8+QVekc36soH+hZ86NA2xW3lObGSrxAif6lXrx7ly5fn8uXL/Pbbb4wYMULtkLLs7NmzfPjhhzx69AgTExMmTpzIuHHjZCaXELnclClTeP78OQsWLKB///5otVr69++vdljiPSTJrhykMTYDIHrjV5nql91Wr16dqq1IkSLExsamaDMxMeHHH39Md+qoj4+P7v9ly5Zlz549rz12cHAwPXr0yNQS1hYWFvz+++/8/vvvurZXV4Fs0qRJquL6LyeoSpcuzfHjx1ON/br9AgMDadCgQZpx1axZU4olCpELKImJ7B7Uk9K3/BncZyHd2jfE2dIY34BQpnrfocEyP44PrU5ZB3O1Q81Wds4laafiSr5CiPxDo9EwbNgwhg4dypIlSxg+fPh7tTravn376NSpE5GRkXh6erJlyxaKFy+udlhCiEzQaDTMmzePiIgIVqxYwYABAzhz5gwLFizAyMhI7fDEe0SSXTlI374wVmP+zvDTd42xWZ66DeXZs2fs27ePgwcPvhf1qfbu3cvChQvVDkMIkYEHwY+JDAqgVPxzOq3/jMLV1mNZtQ7tyzvQqIQtVRadZNr+O2zsXkHtUN+ZeK2Wm2HPKG9bUO1QhBB5VM+ePRk/fjzXr1/nwIEDujIMud3vv/9O//79SUxMpGHDhmzZsgVra2u1wxJCZIFGo2HZsmUUKVKEqVOnsnTpUs6fP89ff/2Fs7Oz2uGJ94QUqM9h+vaFMXApk+4jLyW6AKpUqcKgQYOYM2eOrm5Vbnb8+HFq1Kjx1uMULVqUM2fO6P5NtmnTJt1tk7Vq1WLHjh1vfSwh8ps/AuIZUetrTCrVJikqgpvDOxN6cDsAViYGjPjAjb8vhRAWm6hypO/GrbBQKmxaSZOdG4lMyPxKskIIkRWWlpb07t0beD8K1SuKwpw5c+jduzeJiYl069aN3bt3S6JLiPeUnp4eX331Fdu3b8fa2ppjx45RtWpV/v33X7VDE+8JSXaJdyowMJCwsDDGjBmjdihCiDziQXgcdgXtKLt4EwW8WqHEx3FnQj/uLZxCUmw0FZwsSExSCInMm4kgNwtLtEoSSShcDX2qdjhCiDxsyJAhAPzzzz/cvXtX5WjSp9VqGTFihG7hoS+++IK1a9dmqnyGECJ3a9WqFadPn6Z8+fI8fPiQhg0bsmTJEikvI15Lkl1CCCHeK06WRgQ9jyUSQ4rPXoV9p36QlMSj3xdzZ/KnXAmJQl9PQ0FzQ7VDfSeM9Q3Y0qwjNz/6lOoOhdQORwiRh5UvXx4vLy+SkpJYtmyZ2uGkKTY2lo8++khXLmPevHl8//33KVbyFkK830qVKsWJEyfo2rUrCQkJDBs2jP79+6eqPS3Ey+RVQAghxHuleyUn4rQKi47dRWNgQJGJP1Bi/h8YFXLDutdIfvz3Lu3K2lPANG8lu7QhASTev0bi/WuUjXmK2eMXX6uxkq8QIv8YNmwYAMuXLyc+PnfNmA0NDaVZs2Zs3rwZIyMjNmzYwOeff652WEKId8DCwoINGzbw3Xffoaenx+rVq6lbty5BQUFqhyZyKSlQL4QQ4r3iam3CmHqF+XLfbZ5ExTOsthsutZpwb/Ye+h+6y4PwODb39OTxplUYOblgXbeZ2iG/ldet5HvS2A5DYxu83tFKvkKI/K1du3Y4Ozvz4MEDNm/eTLdu3dQOCYCgoCBatmzJlStXsLa2ZuvWrbq6qEKIvEmj0TBmzBgqV67MRx99hJ+fH1WrVmXjxo00atRI7fBELiPJLiGEEO+dWS1KYmlswHeH/2PBv/9fR6ZSIQsOfVqVkrEPuPLDRJSEeGxbdsHti5kYFLBTMeI3l9FKvqvu/senF/ypbG3DaVtXFaITQuR1hoaGfPrpp0ybNo0lS5bkimTXxYsXadmyJffv38fFxYXdu3dToULeXYFXCJFS48aN8fPzo0OHDpw7d46mTZsyd+5cRo8ejUajUTs8kUvIbYxCCCHeOxqNhsmNinF/Uj229vLk94/Kc2pYdc6OqEk1VyuMnFxx+Ggg6OnxbPdfXO5Sm2feW1IUMw14FsO8I/8xff8dNpx/SGyCVsUzylh6K/m2r1gPG2MTqhZyI0abN1efFEKob+DAgRgYGHD06FH8/f1VjcXHx4e6dety//59ypUrx/HjxyXRJUQ+VKRIEf7991969+5NUlISY8aMoXv37kRFRakdmsglJNklhBDivWVupE+78g70rFyI6m7Wuk/z9EzMcB31DWVW7sWkeBkSQ58QMHEAd8b2JjL4Pv3/ukyJ7/7ly323+eXkPbr9cQm32UfZduWxymeUNQVNzbj98SCW12+JhaGR2uEIIfIoZ2dnOnToAEDz5s3ZtWuXKnFs3LiR5s2bEx4eTr169Th69Chubm6qxCKEUJ+pqSmrV69m0aJFGBgYsGHDBmrXrs3t27fVDk3kApLsEkIIkWeZe1Sl7DofCg0cB/oGPPfZydmPG7DpbBCL2rrz+KsGPJhcn2tf1KZOkQJ0WnuBw3dC1Q47S2yMTdQOQQiRD3z33Xd4eHgQEhJCq1atGD58ODExMTl2/AULFvDxxx8THx9Pp06d2LdvHzY2Njl2fCFE7qTRaPjss884ePAgjo6OXLx4kWrVqrF79261QxMqk2SXEEKIPE3P0AjnQRMot84HvVKerCzUgh86eDKsthvmRvoAuBc0Z1OPClRytuTrg+/n6oZPY2MY8e9+boe/X8k6IcT7oUiRIpw+fZqRI0cCsHjxYqpVq8b58+ff6XGTb09KXmVx+PDhbNy4ERMTSfQLIf5fvXr18PPzo1atWjx//pxWrVoxYMAAAgLez/d14u1JsksIIUS+YFqyHDv7LWN76Q70rlIIgMiLp3m0filJcbEY6OsxrJYrB24940lUvMrRZt3gI3tZdNmPsSd81A5FCJFHmZiYsGDBAnbv3o2joyNXrlyhRo0azJ8/n6SkpGw9VlJSEps2baJKlSr88MMPAMydO5eFCxeir6+frccSQuQNLi4u+Pj4MHjwYBRFYeXKlZQuXZpBgwYRFBSkdngih0myS+QrT58+xcHBgcDAQLVDEXlQ586dmTdvntphiAw8i4eC1qYYG+iRFB/Hf9+M4N68yVz40IN7P06jaMKLml1hse9fsfdpVetQxd6R4R5V1A5FCJHHtWjRgosXL9KmTRvi4+MZPXo0LVq04MGDB289tlarZcOGDXh6etKlSxfOnz+PhYUFa9euZezYsbLSmhAiQ8bGxixdupRjx47RtGlTEhMTWbZsGSVLlmTo0KHcu3dP7RBFDpFkVz6h0WgyfPTt25e+ffvqvjYwMKBw4cIMGTKE0NDX3xKTvO/s2bNTtG/dujXFm5Ks9Gvfvn26xwsJCWHQoEEULlwYY2NjnJycaN68OcePH88wzlmzZtGmTRuKFi2aoq169epYWlri4OBA+/btuX79eqp9lyxZQrFixTAxMaFq1aocOXIkS2Nk9jhpyejYAImJiXz55ZcUK1YMU1NTihcvztdff/3aT1mXLl2Kp6cnVlZWWFlZUbt27TTvb3/d8V91+PBh2rRpg7OzMxqNhq1bt75Rn1e96+f55TE0Gg2jRo3K0rhTpkzh22+/JTw8PEvHA9i1a1eGP6Ndu3bN8pgitZJ2ptx+GsPDiDg0BoY4fDwIQ0cXtGHPePTbj1iMaMSi87OxuOCLos14dcaQyHjuPI0mJpes4ljetiBnOvShoXMRtUMRQuQDBQsW5J9//uHnn3/G1NQUb29vPD09M/V6npbExETWrl1L+fLl6datG5cvX8ba2popU6bw33//0aNHj+w9ASFEnla7dm327dvHkSNHaNSoEQkJCSxdupQSJUowYsSIbEnOi9xNkl35RHBwsO6xYMECrKysUrQtXLgQePFJXXBwMIGBgfz6669s376doUOHZuoYJiYmzJkz57XJscz2y0inTp04f/48a9as4caNG2zbtg0vLy+ePXuW7j4xMTGsWLGCTz75JEW7r68vw4YN48SJE3h7e5OYmEizZs1SLFu7ceNGRo0axeTJkzl37hz16tWjZcuWuumwmRkjM33S8rpjA8yZM4eff/6ZxYsXc/XqVebOnct3333HokWLMhzb1dWV2bNnc+bMGc6cOUOjRo1o164dly9fztLxXxUVFUXFihVZvHjxW/V51bt8npOdPn2aZcuW4enpmeVje3p6UrRoUdatW5fpc0rWsGHDFD+TwcHB3Lt3j6ZNm2Jvb89XX32V5TFFat0rOWGkr2GK9x3QaCjYsS8V/jlHiR/WYVStARpFofbjs9wb24PAacPSHGP/zac0+OUMjjMOU+K7Yzh8c5hhW6/lilsfX/7QQFEUFSMRQuQHGo2GQYMGcfbsWSpXrszTp0/p0KEDn376aaZfdxMSEli1ahVlypShV69eXL9+HRsbG77++msCAwOZPn06tra27/hMhBB5Vd26dTlw4ACHDh2ifv36xMfHs2jRIkqUKMHnn3/Ow4cP1Q5RvCuKSOHu3bsKoNy9ezfVtpiYGOXKlStKTEyMCpFln1WrVinW1tap2vv06aO0a9cuRdvo0aMVW1vb147Zp08fpXXr1kqZMmWUsWPH6tq3bNmivPxtlpV+r8aSLDQ0VAEUHx+f18b1ss2bNyv29vav7RcSEqIAiq+vr66tRo0ayuDBg1P0K1OmjDJhwoRMj/EmfTJ77FatWin9+/dP0adjx45Kz549Mxw7LTY2Nsqvv/6apeNnBFC2bNny1n3Skp3Ps6IoSkREhFKqVCnF29tbadCggTJy5Mgsjztt2jSlXr16mT6H9CQmJioff/yxYm9vr1y4cOGtx1OUvPM77G0tP3lPYby30uxXP2XrpUfK6bthyne+gUqhGb5KrUkblauzJyjnvIoqzw5u1+0T/zREibhwSll/9oGiN8Fbqf3TKeU3vwfK/ptPla/23lJsp/so7t//qzyOjFPxzF7QJiUpv9+4pFTetEp5Hhf7zo+X0eumENkpLCxMAZSwsDC1QxFpiIuLU8aNG6doNBoFUEqXLq2cPn06w/7Lli1TihYtqgAKoNjZ2SmzZs2SayyEeCeSkpKUAwcOKHXq1NH93jE1NVXGjBmjhISEqB1etsvvr5sys0uk686dO+zZswdDQ8NM9dfX12fmzJksWrQow3uhM9svPRYWFlhYWLB161bi4uIyvd/hw4epVq3aa/uFhYUB6D5FjI+Px8/Pj2bNmqXo16xZM44dO5apMd60T2aPnfyJxY0bNwA4f/48R48e5cMPP0x37Fcl18iIioqidu3aWTr+u7J69eoMa3O86fOc3rjDhg2jVatWNGnS5LWxpXfsGjVqcOrUqSx9b75Kq9XSs2dPvL29OXDgABUqVHjjsURqn9Rw4e+enoRExtP+9wtUX3yKyXtv0bikLZvGtaXM+Fl47r5MgXotdPs83ryK6/2awxetmaF3hsN9y9GrSiEal7Tl62YlODm0Oo+jEpjqfUfFM3tBqyTx7bljnHv6iIUXz6gdjhAinzAyMmLOnDns378fFxcXbty4Qe3atZk9ezbal24Lj4uLY+nSpZQsWZJPP/2UwMBAHBwc+O677wgMDGTChAlYWVmpeCZCiLxKo9HQqFEjjhw5wt69e6lZsyYxMTF8//33FCtWjIkTJ/L06VO1wxTZRJJd2SQqIZ6ohPgUt43Ea7VEJcQTp01Ms2/SS30Tkl70jU3MXN93ZceOHVhYWGBqakqJEiW4cuUK48ePz/T+HTp0oFKlSkydOjVb+qXFwMCA1atXs2bNGgoUKECdOnWYNGkSFy5cyHC/wMBAnJ2dM+yjKAqjR4+mbt26eHh4APDkyRO0Wi2Ojo4p+jo6OqY57TWtMd6kT1aOPX78eLp160aZMmUwNDSkcuXKjBo1im7dumV4vgAXL17EwsICY2NjBg8ezJYtWyhXrtwbnXt2s7a2xt3dPc1tb/M8pzXuhg0bOHv2LLNmzXptXBkd28XFhbi4uDd+frRaLb169dIlupJvp3zw4IHUK8lGHTwcODuiJjfHfMDZ4TV4OLk+v3/kgYv1i6Xs9UzM0BgY6PonxUSTZGBE6fAAmu2dy6VWHtydN5nYwJsAlLQ3Y1gtV347G6x6DS9DPX0WftCEWTUaMK5iTVVjEULkP40aNeLChQt06tSJxMREJk6cSOPGjblx4wY//vgjJUqUYOjQody9excnJyfmz59PQEAAY8aMwcLCQu3whRD5gEajoVmzZhw/fpxdu3ZRrVo1oqKimD17NkWLFmXy5MncunVL7TDFW5JkVzaxWDUfi1XzeRIbo2v77vxJLFbN57N/vVP0dfh9MRar5hMU+f9FrH+6fBaLVfMZcDhlcfCif/yMxar5XA19omtbff3iOzqLF3WD/P39OXnyJMOHD6d58+YMHz5ct33dunW6mVUWFhZpFiqfM2cOa9as4cqVKxkeK7P90tKpUycePHjAtm3baN68OT4+PlSpUoXVq1enu09MTAwmJiYZjvvZZ59x4cIF/vjjj1TbXp0JpChKmrODMhojoz4ZPbevO/bGjRtZu3Yt69ev5+zZs6xZs4bvv/+eNWvWvHZsd3d3/P39OXHiBEOGDKFPnz6prklmzz27dejQgWvXrqW57U2f57TGvXv3LiNHjmTt2rWv/R553bFNTU0BiI6Ofu04r0pOdO3bt48DBw5QsWJF3TZnZ+c3qgUm0qfRaChpb0ZlFytszDKeweo6YhpbPt/G+kr9MHIpgjYijJD1S7ncuSbXBrQEwKu4DZHxWh6Epz+rLzw2kUX/BtHglzNU+fEk3f64yKHbz7K9vlYz12JMqFQLk5cSdkIIkVNsbW3566+/WLlyJebm5vj6+uLu7s7IkSO5f/8+rq6uLFq0iDt37jBq1CjMzMzUDlkIkQ9pNBpatmzJqVOn2L59O5UrVyYyMpKZM2dSqlQp3N3dGT16NAcPHiQ+Xv3arCJrJNklUjA3N6dkyZJ4enry448/EhcXx/Tp03Xb27Zti7+/v+6R1m2B9evXp3nz5kyaNCnDY2W2X3pMTExo2rQpU6ZM4dixY/Tt2zfDmWL29vYZFsUfPnw427Zt49ChQ7i6uqbYT19fP9VMnZCQkFQzntIbIzN90npuM3vssWPHMmHCBD7++GMqVKhAr169+Pzzz3WzlDK6bkZGRpQsWZJq1aoxa9YsKlasqFuwICvnnpPe5nlOi5+fHyEhIVStWhUDAwMMDAzw9fXlxx9/xMDAIMXtF68bN3mRhIIFC2bpnJITXXv37mX//v0pEl3wYmZi8nULDAykYsWK9O3bl3LlyjFkyBC2bt1KzZo1KV++PDdv3szSsUXmGNnasbpwW9w3nabkwo1Y120G+voY/2/1wwcRcWiUJCIHNeLOhH6E/PkrMbeuoPxvVdRbT6LxXHCC0TtvUsDEgJpuVpwPjqTR8rMM2XotSwmvrPZ99tIHMUIIkRM0Gg39+vXD39+fGjVqAFC4cGF+/vlnbt26xWeffab7gEgIIdSk0Who3bo1fn5+bNmyhcaNG2NoaMiNGzeYP38+jRs3xt7eni5durB69WoePXqkdsgiE+Qj32wS2e9zAMwM/n92wNiKNRlVoRoGeilziiG9PgPA9KW+w8pXYWCZiuhrUvYN7DY4Vd++7jlXv2fq1Km0bNmSIUOG4OzsjKWlJZaWlq/db/bs2VSqVInSpUtnS7/MKFeuXIbLXVeuXJm1a9emalcUheHDh7NlyxZ8fHwoVqxYiu1GRkZUrVoVb29vOnTooGv39vamXbt2mRojM33Se25fd2x4MYtI75XvM319fZL+90d2Zq9bcpzJ9aYyc+45KTue57Q0btyYixdTzpjs168fZcqUYfz48ejr62d63EuXLuHq6oq9vX2mz0ur1dK7d29doqtSpUqv3efq1av8+eeflCxZEg8PDywsLDh58qRuVc7khKXIPp0qODL9QAAbLz6mV52mWNdpijY6Em1UBElJCj+fuEdX6+ckBt4gNPAGofv/AUDf2gaLirX4OdKFYo5VOTSmLcVsX/yBpygKv55+wKd/X6WCkwXDarule/yrIVH8cPg/Nl0KISIuEfeC5nxaw4XBNV0wMdRPc5+7keEMPLyHe1ER+Hfql+r1SAgh3rWSJUvy77//4u/vj6enJ0ZGRmqHJIQQadJoNLRv35727dsTHh6Ot7c3O3fuZOfOnYSEhLBp0yY2bdqERqOhevXqtGrVitatW1O5cuUcuetFZI0ku7KJuWHqF24jfX2M9FP/AZJWX0M9fQz1Mt83p3h5eVG+fHlmzpzJ4sWLM71fhQoV6NGjB4sWLXrjfmFhYfj7+6dos7W1xdzcnC5dutC/f388PT2xtLTkzJkzzJ07N8METPPmzZk4cSKhoaHY2Njo2ocNG8b69ev5559/sLS01M1isra21n3iOHr0aHr16kW1atWoXbs2y5YtIygoiMGDB2d6jMz0Scvrjg3Qpk0bvv32WwoXLkz58uU5d+4c8+bNo3///umOCzBp0iRatmyJm5sbERERbNiwAR8fH/bs2ZOl478qMjIyxX3uAQEB+Pv7Y2trS+HChTPdZ8uWLUycOFF3y2F2Pc+vjmtpaZmq9pa5uTl2dna69sxevyNHjqQq6J+RpKQkevfuzdatW9m0aROFChVKNZMurVli7u7uurpjZcuW1RXV9/T0ZPfu3an6i7dXwcmCjuULMmTrNTQa+MjTEUMzC4ITDJj412WOBYWxt1ddSrfYQeTZY0Sc/ZeoC6fRhoUSdng33YDOnYwpZvsRAAlPHhFx7hjdXYtxzN2MBUeDGFLTFT291G+WfO+E0mq1PzamBoz4wA1nK2N8A0IZu+smWy+HsLt/ZUzTSHiZGxhy+nEwEQnxnAp5wAdOGc9yFEKId8HAwCBTiwQJIURuYWVlRadOnejUqRNJSUn4+fmxY8cOdu7ciZ+fH6dOneLUqVNMnTqVQoUK0apVK91CV1J/MJfIyaUf3wcZLaEeExOjXLlyRYmJiVEhsuyzatUqxdraOlV7nz59lHbt2qVqX7dunWJkZKQEBQWlO2Za+wYGBirGxsbKy99mWenH/5aDffnRp08fJTY2VpkwYYJSpUoVxdraWjEzM1Pc3d2VL7/8UomOjs7w3GvVqqX8/PPPKdrSOg6grFq1KkW/n376SSlSpIhiZGSkVKlSRfH19c3SGJk9TloyOraiKEp4eLgycuRIpXDhwoqJiYlSvHhxZfLkyUpcXFyG4/bv3183bsGCBZXGjRsr+/bty/LxX3Xo0KF0r19W+qxatSrF90V2Pc+vjpuWBg0aKCNHjszSuDExMYqVlZVy/PjxFGNldLwTJ06kO3byIzQ0VAkICFCqVq2qKIqS4v+KoiidOnVSDh06pCiKohw/flxp1apVmsfKK7/D1BQZl6h0+v28wnhvxXa6j1Lm+38V/Yn7FYuvDiprzz5I1T8pIV6JvHhaWTHpK2Vpq2ZK2Okjum3PDm5XzlS10T0O1HRV/Ls1UG5P6KfcWzRdibp+UVEURYlN0CqO3/gqjZadUaLiElOMfzQgVDH98oAyac/NdGPefvG4cuD0UeWbtbuU+lPXKtUmrVE6zf1TWb/rkBL932Ul8fF/b/WcZPS6KUR2yu9LqAshhMgd7t+/r/z6669K+/btFXNz8xTv242MjJT69esrU6dOVXx8fJTY2FjV4szvr5saRcnmqrjvuXv37uHm5sbdu3dT1eOJjY0lICCAYsWKZaqItch9du3axZgxY7h06VKq2/6EeFs//fQT//zzD/v27UvRPm3aNHx8fPDx8XnjsQMDA+ncuTNnzpxJ8X+Azp0789lnn+Hl5cWJEyeYMWMGO3bsSDWG/A7LPhcfRrLp4iMi4rS4FzSjW0UnrEzSnyw9btdN/rr4iIDxdXVtYf96E7xyHnH3Akl8mrr2Q/HZK7Fp0p4//B/y0+Lf+DFkI5ZFi2PsUhSjQm4YFLDDwMqGxZciWfnInGtTW2JkkPL3mvZJEOHfd3zt+ViN+Rt9+8JZeAb+X0avm0Jkp/DwcKytrQkLC8PKykrtcIQQQgji4uLw9fVl586d7Nixgzt37qTYbmJiwgcffEDDhg1p2LAh1atXz7HbufP766bcxijylQ8//JCbN29y//593NzSr40jxJswNDRM85bcvXv3Sg2tPKaCkwUVnDI/Rb1OEWu+O/wffvfCqer64s2G9f/qfgEMWu/H5QtX2dbCloT7gcTdC8S09Iv6jH73w6nKY5QHAYQ/CEg1dkvAu+I47oU1pLidGaEHtnFv4RQMrG0xKmCGrTkcCXWlkntJnloVwLR8FUoWLs7Z++Es2HqYn5I2oMRlffVQId7UkiVL+O677wgODqZ8+fIsWLCAevXqqR2WEEIIkWXGxsY0a9aMZs2asWDBAm7evMmhQ4c4dOgQPj4+PHr0iIMHD3Lw4EEAzMzMqFu3ri75lbxAlsh+8qyKfGfkyJFqhyDyqE8//TTN9uPHj7/12EWLFtXN5Hr5/wCbNm3S/b9WrVppzuoS6mpVxp7itqYM/Psqe/tXpqDF/3+i9/elEFZces7MlnWxbVA01b5G+npsd67P15+2IeFBIHH3/yP+4T0Sw0JJDHtG2OMnPDWyxvh/s7oSHj8k/kEQ8Q+CSLQ0wramK8VunGDt88d83aAlDe4Esrf2h9RwgfI3n8JFSNQmyRsCkSM2btzIqFGjWLJkCXXq1OGXX36hZcuWXLlyRVevUQghhHgfaTQaSpcuTenSpRk0aBCKonD16tUUya+nT5+yb98+3Z0glpaW1KtXT5f8qlSpEvpp1P0WWSfvbYUQQoh3zEBfjy29PGm64hzF5v5L1woOuFibcOj2M/79L4wuFRwYXTftP/RblbFnlo8px6zL0bJG/VTb26z2R/95LM5WxgDYNO+IWblKaMOfs+PgEVyjd2PXrhe14/XQ6hsQZWBIZEI8FoZGNChmAxfhUWQcRd7pMyDEC/PmzWPAgAF88sknACxYsIC9e/eydOlSZs2apXJ0QgghRPbRaDSUK1eOcuXKMWzYMJKSkrh06ZIu+eXr68vz58/ZtWsXu3btAqB3796sWbNG5cjzhlxVtOjw4cO0adMGZ2dnNBoNW7duTbFdURSmTZuGs7MzpqameHl5cfnyo/NcYAAAjq1JREFU5RR94uLiGD58OPb29pibm9O2bVvu3buXg2chhBBCpOZZyJLzI2sypl5hTt4NZ43fA0wN9fmzewU2dKuAgX7aL8kfFLHmgyLWDNh0hZNBYbr22AQt07xvs+PaE8Y1KKJb8trQxh4LzxpY123G47IvkmMFO/en4bg5+HXuz5Gun2Dxv5V+YxKSXuyTzrGFyE7x8fH4+fmlWrG2WbNmHDt2LFX/uLg4wsPDUzyEEEKI95Wenh6enp6MHDmSrVu38uTJE/z8/Pj+++9p1aoVlpaW1K5dW+0w84xcNbMrKiqKihUr0q9fPzp16pRq+9y5c5k3bx6rV6+mdOnSzJgxg6ZNm3L9+nUsLS0BGDVqFNu3b2fDhg3Y2dnxxRdf0Lp1a/z8/GQ6oBBCCFU5WRozrWkJpjUtkel9NBoNf/f0pOUqf2otOU1VF0ucrYw5HhTGk6gEvm5anB6VC6W57wdFrMEPTgaFUdcVPO0cUmzfce0J1YCC5jlTKFXkb0+ePEGr1eLo6Jii3dHRkYcPH6bqP2vWLKZPn55T4QkhhBA5Sl9fnypVqlClShW++OILEhMTSUxMVDusPCNXfZTbsmVLZsyYQceOqVeOUhSFBQsWMHnyZDp27IiHhwdr1qwhOjqa9evXAxAWFsaKFSv44YcfaNKkCZUrV2bt2rVcvHiR/fv35/TpCCGEENnC0dKYU8Oq83dPT0rbm6Eo0KdKIa5/UZuvGhdPd7/Kzi8+CJp+4A5n7///rJj4xCRmHgrAJyAUQDcrTIic8Or3m6IoaX4PTpw4kbCwMN3j7t27ORWiEEIIkeMMDAxkxfRslKtmdmUkICCAhw8fppj6bmxsTIMGDTh27BiDBg3Cz8+PhISEFH2cnZ3x8PDg2LFjNG/ePNW4cXFxxMXF6b6OiIh4tycihBBCvAEDfT06eDjQwcPh9Z3/JzmBUMHgKf0WbcHTyYKC5kb4B0fwNDqB8eUUuPKuIhYiJXt7e/T19VPN4goJCUk12wtevM8zNjbOqfCEEEIIkYe8N8mu5DdGaU19/++//3R9jIyMsLGxSdUnrenx8GZT5JOSkrLUXwghcgP53ZX/aIzNAPgqbA1fAbz6UnglZT8h3iUjIyOqVq2Kt7c3HTp00LV7e3vTrl07FSMTQgghRF7z3iS7kmV26ntm+0ycOJHRo0frvr5//z7lypVLs6+RkRF6eno8ePCAggULYmRkJLd+CCFyPUVRiI+P5/Hjx+jp6WFkJPWZ8gt9+8JYjfkbJS463T4aYzP07dNeCVKI7DZ69Gh69epFtWrVqF27NsuWLSMoKIjBgwerHZoQQggh8pD3Jtnl5OQEvJi9VajQ/xfifXnqu5OTE/Hx8YSGhqaY3RUSEsIHH3yQ5rivTpHPaKUfPT09ihUrRnBwMA8ePHir8xFCiJxmZmZG4cKF0dPLVeUaxTsmiSyRm3z00Uc8ffqUr7/+muDgYDw8PNi1axdFihRROzQhhBBC5CHvTbKrWLFiODk54e3tTeXKlYEXS1j7+voyZ84cAKpWrYqhoSHe3t507doVgODgYC5dusTcuXOzJQ4jIyMKFy5MYmIiWq02W8YUQoh3TV9fHwMDA5mNKoRQ3dChQxk6dKjaYQghhBAiD8tVya7IyEhu3bql+zogIAB/f39sbW0pXLgwo0aNYubMmZQqVYpSpUoxc+ZMzMzM6N69OwDW1tYMGDCAL774Ajs7O2xtbRkzZgwVKlSgSZMm2RanRqPB0NAQQ0PDbBtTCCGEEEIIIYQQQry9XJXsOnPmDA0bNtR9nVxLq0+fPqxevZpx48YRExPD0KFDCQ0NpWbNmuzbtw9LS0vdPvPnz8fAwICuXbsSExND48aNWb16Nfr6+jl+PkIIIYQQQgghhBAiZ2kURVHUDiI3uXfvHm5ubty9exdXV1e1wxFCCCFyNXndFDklPDwca2trwsLCsLKyUjscIYQQIlfL76+bUqVYCCGEEEIIIYQQQuQZueo2xtwgKSkJeFHYXgghhBAZS369TH79FOJdSb4ZIaOVs4UQQgjxQvLrZX69mU+SXa949OgRADVq1FA5EiGEEOL98ejRIwoXLqx2GCIPi4iIAMDNzU3lSIQQQoj3R0REBNbW1mqHkeOkZtcrEhMTOXfuHI6Ojujp/f9dnl5eXvj4+KTqn1b7q20RERGUK1eOK1eupCimn5PSiz+nxsnsfq/rl9F2uUZvN45co9eTa+STYZtco/x5jZKSknj06BGVK1fGwEA+QxPvTlJSEg8ePMDS0hKNRqNrr169OqdPn07VPzPt4eHhuppzatUzSS/OnBors/u8rl9G2+Uavd1Yco1eT66RXKPs2icvXSNFUYiIiMDZ2TlFbiO/kHelrzAwMKB69eqp2o2MjNIsvJtW+6ttydMHXVxcVPvlkl78OTVOZvd7Xb+Mtss1ertx5Bq9nlwjuUbZtV9eu0Yyo0vkBD09vTS/9/X19dP8fs5Ku5WVlWq/t9KLM6fGyuw+r+uX0Xa5Rm83llyj15NrJNcou/bJa9coP87oSpb/0ntvaNiwYZluT6+vmrIrpjcdJ7P7va5fRtvlGr3dOHKNXk+u0ZvFk5PkGr1ZPEK8z7LyM5FRu1qyM543GSu3/d7KSkw5Ra7Rm8eUU+QavXlMOUWu0ZvHJN6M3MaYA/L7kp/vA7lGuZ9co9xPrlHuJ9dIiJTkZyL3k2uU+8k1yv3kGuV+co2yn8zsygHGxsZMnToVY2NjtUMR6ZBrlPvJNcr95BrlfnKNhEhJfiZyP7lGuZ9co9xPrlHuJ9co+8nMLiGEEEIIIYQQQgiRZ8jMLiGEEEIIIYQQQgiRZ0iySwghhBBCCCGEEELkGZLsEkIIIYQQQgghhBB5hiS7hBBCCCGEEEIIIUSeIckuIYQQQgghhBBCCJFnSLIrF9ixYwfu7u6UKlWKX3/9Ve1wRBo6dOiAjY0NnTt3VjsUkYa7d+/i5eVFuXLl8PT05K+//lI7JPGKiIgIqlevTqVKlahQoQLLly9XOySRjujoaIoUKcKYMWPUDkWIXEl+RnIvea3J/eQ92/tB/vbJfSRnkHUaRVEUtYPIzxITEylXrhyHDh3CysqKKlWqcPLkSWxtbdUOTbzk0KFDREZGsmbNGjZt2qR2OOIVwcHBPHr0iEqVKhESEkKVKlW4fv065ubmaocm/ker1RIXF4eZ2f+xd9/hUZRrG8Dvmdme3SQkoQcIvdcAgoKChaYCon4WBFFEUewHFY/do3KOvYGKgogigg0RRUUEUUAQpPcWQksghCTby8x8f2xYEpKQti2b+3ddUXb33Zln592ZfffZt5jgcDjQqVMn/P3330hOTo50aHSOJ554Anv37kXTpk3x6quvRjocoqjDcyR68bMm+rHNVjPwu090Yc6gatizK8LWrVuHjh07onHjxrBYLBg2bBh+/vnnSIdF5xg4cCAsFkukw6AyNGzYEN26dQMA1KtXD0lJScjNzY1sUFSMJEkwmUwAAJfLBVmWwd9aos/evXuxa9cuDBs2LNKhEEUlniPRjZ810Y9ttpqB332iC3MGVcNkVzWtXLkSV199NRo1agRBELBw4cISZaZPn47mzZvDYDAgPT0df/zxR+CxY8eOoXHjxoHbqampOHr0aDhCrzWqW0cUesGso/Xr10NRFDRp0iTEUdcuwaijvLw8dO3aFampqXj00UeRkpISpuhrh2DU0eTJkzF16tQwRUwUXOH4vOc5Uj3hqCN+1lRPONvNbLNVDb/b1DzMGUQGk13VZLfb0bVrV7z77rulPj5//nw8+OCDeOKJJ7Bx40b0798fQ4cORWZmJgCU+muTIAghjbm2qW4dUegFq45OnTqFsWPHYsaMGeEIu1YJRh0lJiZi8+bNOHjwID7//HNkZ2eHK/xaobp19N1336FNmzZo06ZNOMMmCppgXKfS09PRqVOnEn/Hjh3jORIEoa4jgJ811RWOOgLYZquOcNURBQ9zBhGiUtAAUL/99tti9/Xu3VudOHFisfvatWunTpkyRVVVVV21apU6cuTIwGP333+/Onfu3JDHWltVpY7OWL58uXrttdeGOsRar6p15HK51P79+6tz5swJR5i1WnXOozMmTpyoLliwIFQh1npVqaMpU6aoqamparNmzdTk5GQ1Pj5efe6558IVMlFQBeM6dS6eI8EVijo6Fz9rqidUdcQ2W/CE8jzid5/QYM4gfNizK4Q8Hg82bNiAQYMGFbt/0KBBWL16NQCgd+/e2LZtG44ePQqr1Yoff/wRgwcPjkS4tVJF6ogiqyJ1pKoqxo0bh0svvRRjxoyJRJi1WkXqKDs7GwUFBQCAgoICrFy5Em3btg17rLVVRepo6tSpOHz4MDIyMvDqq69iwoQJePrppyMRLlHQBePznudIaAWjjvhZE1rBqCO22UKL321qHuYMQkcT6QBiWU5ODmRZRv369YvdX79+fWRlZQEANBoNXnvtNQwcOBCKouDRRx/lijFhVJE6AoDBgwfjn3/+gd1uR2pqKr799lv06tUr3OHWShWpo1WrVmH+/Pno0qVLYAz8p59+is6dO4c73FqpInV05MgRjB8/HqqqQlVV3HvvvejSpUskwq2VKnqtI4pVPAeiXzDqiJ81oRWMOmKbLbSCda3jd5/wYc4gdJjsCoNzx9OqqlrsvuHDh2P48OHhDouKKK+OuNpF5J2vjvr16wdFUSIRFhVxvjpKT0/Hpk2bIhAVFVXete6McePGhSkiovCq6DlQHp4joVOdOuJnTXhUp47YZguP6l7r+N0n/JgzCD4OYwyhlJQUSJJUIot+4sSJEplbigzWUfRjHUU/1lH0Yx1RbcdzIPqxjqIf6yj6sY5qHtZZ6DDZFUI6nQ7p6elYunRpsfuXLl2KCy+8MEJRUVGso+jHOop+rKPoxzqi2o7nQPRjHUU/1lH0Yx3VPKyz0OEwxmqy2WzYt29f4PbBgwexadMmJCUloWnTpnj44YcxZswY9OzZE3379sWMGTOQmZmJiRMnRjDq2oV1FP1YR9GPdRT9WEdU2/EciH6so+jHOop+rKOah3UWIeFfADK2LF++XAVQ4u/WW28NlJk2bZrarFkzVafTqT169FB///33yAVcC7GOoh/rKPqxjqIf64hqO54D0Y91FP1YR9GPdVTzsM4iQ1BVVQ12Ao2IiIiIiIiIiCgSOGcXERERERERERHFDCa7iIiIiIiIiIgoZjDZRUREREREREREMYPJLiIiIiIiIiIiihlMdhERERERERERUcxgsouIiIiIiIiIiGIGk11ERERERERERBQzmOwiIiIiIiIiIqKYwWQXERERERERERHFDCa7iIiIiIiIiIgoZjDZRUREREREREREMYPJLiIKq2nTpiEtLQ0ajQaPPPJIicdPnTqFevXqISMjI6j7ve666/D6668HdZtEREREVPn2G9tlRBRqgqqqaqSDIKLaYdu2bejevTsWLlyIHj16ICEhASaTqViZyZMn4/Tp05g5cyYAYNy4ccjLy8PChQuLlVuxYgUGDhyI06dPIzExsdx9b9myBQMHDsTBgwcRHx8frJdEREREVOud234rD9tlRBRq7NlFRGGzaNEipKen48orr0TDhg1LJLqcTidmzpyJO+64I+j77tKlC9LS0jB37tygb5uIiIiotqpK+43tMiIKNSa7iCgsWrZsiSeeeAJr166FIAgYM2ZMiTJLliyBRqNB3759K739jIwMCIJQ4m/AgAGBMsOHD8e8efOq8zKIiIiIYtrw4cNLbVMJgoBFixaVKF9W++2rr75C586dYTQakZycjMsvvxx2u73YftguI6JQYbKLiMJizZo1aNGiBV555RUcP34c06dPL1Fm5cqV6NmzZ5W236RJExw/fjzwt3HjRiQnJ+Piiy8OlOnduzfWrVsHt9td5ddBREREFMs+/vhjHD9+HHv37gUA/Pjjj4H21bBhw0qUL639dvz4cdx00024/fbbsXPnTqxYsQKjRo1C0Rl02C4jolDSRDoAIqodzGYzMjIy0K9fPzRo0KDUMhkZGWjUqFGJ+xcvXgyz2VzsPlmWi92WJCmwXZfLhZEjR6Jv37549tlnA2UaN24Mt9uNrKwsNGvWrJqviIiIiCj2JCcnA/D/UCkIAvr16weLxVJm+dLab8ePH4fP58OoUaMCba7OnTsXK8N2GRGFEpNdRBQWW7ZsAVCyoVOU0+mEwWAocf/AgQPx3nvvFbtv7dq1uOWWW0rdzvjx42G1WrF06VKI4tkOrEajEQDgcDgqHT8RERFRbbJlyxakpaWdN9EFlN5+69q1Ky677DJ07twZgwcPxqBBg3DdddehTp06gTJslxFRKHEYIxGFxaZNm9CqVSvExcWVWSYlJQWnT58ucX9cXBxatWpV7K9x48albuOFF17ATz/9hEWLFpVonOXm5gIA6tatW41XQkRERBT7tmzZgi5dupRbrrT2myRJWLp0KZYsWYIOHTrgnXfeQdu2bXHw4MFAGbbLiCiUmOwiorDYtGkTunbtet4y3bt3x44dO6q8j6+//hrPP/88FixYgJYtW5Z4fNu2bUhNTUVKSkqV90FERERUG2RkZKBt27blliur/SYIAi666CI899xz2LhxI3Q6Hb799tvA42yXEVEoMdlFRGGxadMmdOvW7bxlBg8ejO3bt5fau6s827Ztw9ixY/HYY4+hY8eOyMrKQlZWVuBXQwD4448/MGjQoEpvm4iIiKi2URQFhw4dwpEjR4pNLH+u0tpva9euxUsvvYT169cjMzMT33zzDU6ePIn27dsHyrBdRkShxGQXEYWcoijYunVruT27OnfujJ49e2LBggWV3sf69evhcDjwwgsvoGHDhoG/UaNGAfBPWv/tt99iwoQJVXoNRERERLXJ/fffj1WrVqFdu3bnTXaV1n6Lj4/HypUrMWzYMLRp0wZPPvkkXnvtNQwdOhQA22VEFHqCer4rFxFRmP3444+YPHkytm3bVmxy+eqaNm0avvvuO/zyyy9B2yYRERERVb79xnYZEYUaV2MkoqgybNgw7N27F0ePHkWTJk2Ctl2tVot33nknaNsjIiIiIr/Ktt/YLiOiUGPPLiIiIiIiIiIiihmcs4uIiIiIiIiIiGIGk11ERERERERERBQzmOwiIiIiIiIiIqKYwWQXERERERERERHFDCa7iIiIiIiIiIgoZjDZRUREREREREREMYPJLiIiIiIiIiIiihmaSAcQbXw+HzZu3Ij69etDFJkLJCIiIiIiIqKaRVEUZGdno3v37tBoal/qp/a94nJs3LgRvXv3jnQYRERERERERETVsm7dOvTq1SvSYYQdk13nqF+/PgD/G6Jhw4YRjoaIiIiIiIiIqHKOHz+O3r17B3IctQ2TXec4M3SxYcOGSE1NjXA0RERERERERERVU1unZ6qdr5qIiIiIiIiIiGISk11ERERERERERBQzOIyRiIiIiIiIiKKWLMvwer2RDiPqaLVaSJIU6TCiEpNdRERERERERBR1VFVFVlYW8vLyIh1K1EpMTESDBg0gCEKkQ4kqTHYRERERERERUdQ5k+iqV68eTCYTEzpFqKoKh8OBEydOAPAvskdnMdlFRERERERERFFFluVAois5OTnS4UQlo9EIADhx4gTq1avHIY1FcIJ6IiIiIiIiIooqZ+boMplMEY4kup05PpzTrDgmu4iIiIiIiIgoKnHo4vnx+JSOyS4iIiIiIiIiIooZnLOLiIiIiIhqDFVW4P1zF7yrdwM+GZoOTaAb0g1CnD7SoRERUZRgsouIiIiIiGoE+WgubA/OhnLwBMTm9SAYdfB8vwHOd5Yg7qWboL2wbaRDJCKiKMBhjEREREREFPVUtxe2e2cCXhmWTyYh4cuHET/nXiQsegyabmmwPfIp5H1ZkQ6TiKiY1atXQxAEDBkyJNKh1CpMdhERERERUdTz/LoVyuFTML8+FpqOTQL3iw0SEfe/0RDrmOH6/M8IRkhE0UhVVXg3HIDz4+VwffI7fDuPhHX/s2bNwk033YTly5cjMzMzrPuuzTiMkYiIiIiIop53xXZouqVBalEfqqLANeNXyIdPwfTIcIiJcdBd1QPuL/+KdJhEFEXkQydhnzIX8t4sCBYDVEUF3lkCTY/miHvpJogp8SHdv91ux/z587Fs2TKcPn0as2fPxtNPPx3SfZIfe3YREREREVHUU11eCHXioKoqnK8vhuuj3+D9eTOsd82AkmOFkBgH1eWJdJhEFCWU0zZYJ34I1afA/N4dSPjtGSQuexpxr46BfOQUrPfMhOryhjSG+fPno0GDBujduzdGjx6Njz/+GKqqhnSf5MdkFxERERERRT2pVQP4/jkI57Sf4f5iNQBASDBBEARAK8G3di+klg0iHCURRQv3V39BtTphee8OaHu1giAIEDQSdAM6wvL27VAOZMOzdHNIY5g5cyZGjx4NABg5ciROnDiBZcuWhXSf5MdkFxERERERRT39Nb2h5jvgnr0CAGB8dAQsn0yCedp4yFsz4f1zN/SjLohskEQUNTw/b4buii6lDlWUWjWApncreH4OXbJr9+7dWL16NW6++WYAgNlsxogRIzBr1qwSZWVZDlkctVXMJbumTp2KXr16wWKxoF69ehg5ciR2794d6bCIiIiIiKgaVLsr8G+xUR0Ieg3knUfhfPMH2P41B9pL2kOFCvngiQhGSUTRQrU6ITaqU+bjYqM6UK2uMh+vrpkzZ6JXr15o06ZN4L7Ro0fjm2++wenTp5GRkYGuXbtiwoQJ6N69O9xuNz7++GP07t0bXbp0CfvcXrGWS4m5ZNfvv/+OSZMm4a+//sLSpUvh8/kwaNAg2O32SIdGRERERERVpGmfCuODw6C9rBPE+glw/Odr2B//HL5th2G8fyi0g7rC+cI3sN75AXx7jkU6XCKKMLFxMnxbS1/9UFVV+LZkQmycFJJ9+3w+zJkzJ9Cr64zBgwfDYrFg7ty5AIDt27fjvvvuw5YtW7B//378+OOPWLNmDTZt2oSNGzdizZo1IYmvNLGWS4m51Rh/+umnYrc//vhj1KtXDxs2bMDFF18coaiIiIiIiKgqVFX1z8sFwHDL2fa86nBD9coQ4o0QBAFKnh1S+8aQdx6F7a4ZML8zHppOTSIVdkTIh07C+8cuqG4vpDYNob2wLQQp5vo3EFWI/ppecDz/Nbzr90Pbs2Wxxzw/bYJyIBumyVeHZN+LFy9GdnY2OnXqhG3bthV7rH///pg5cyauuuoqtGnTBl26dAEALFu2DGvWrEF6ejoAwGazYf/+/ejbt29IYjxXrOVSYi7Zda78/HwAQFJS6Rlbt9sNt9sduG21WsMSFxERERERnZ/3731wfbwC5pdvgWA2FHtMMOkhFLktJsbB8t4EWO//GPKWQ7BO+gjmN26FtkeL8AYdAarDDfvzX8H761ZAr4Vg0ELNd0BsmIi4/9wITbe0SIdIFHa6od3hWbIJtgdmQ3/DhdBd2gnwyfD8tAnub9ZCN7Q7NL1alr+hKpg5cyYA4IorriizTG5uLkwmU+C2qqq48847gz580Wq1oqCgIHBbr9dDr9eX+7zycinRLqbT/Kqq4uGHH0a/fv3QqVOnUstMnToVCQkJgb8OHTqEOUoiIiIiIjqXb2smbA/PgW/dPrgKJ6Uvj2A2wPLu7f4vsHY3bPd9DO9fe0IbaISpqgrblM/hXb0bpqeuReJvTyNx2dOwzLkXYoNEWO+bBXl/dqTDJAo7QSPB/Pqt0P9fX3i+XQvruGmw3vE+PMu3wzBxEEzPXh/oNRps33//PVRVPe/fuUmkSy+9FPPnz8fp06cBAEeOHMGpU6eqHUuHDh2K5TymTp1a7nMqkkuJdjHds+vee+/Fli1b8Oeff5ZZ5vHHH8fDDz8cuH306FEmvIiIiIiIIsi35xhs988CnB5oLmgNw52XV/i5gkkP8xvjYHvsM/hW7YbtoU8QP/8hSE1TQhhx5MibD8G3ejfiXrkFuoFnv5RqOqTC/PbtKLjhDbhmr0Dcf26IYJREkSEYtDA9MAzGuy6HnHESEARILepB0EZfKqRTp0547LHHMGDAACiKAovFgi+++ALJycnV2u6OHTvQuHHjwO2K9OqqSC4l2kVfDQfJfffdh0WLFmHlypVITU0ts9y5XfiKdu8jIiIiIqLwkg+dhO3eWVCtLkhdm8H86hgIusp9bREMWphfHQP7E19Aal4vZhNdAOD5eTPERnWgvaQDVEWBvOc4NO38X2wFow66a3rDNeNXmJ65DoJGinC0RJEhGHSB8yJapKWlYf369cXuGzt2LMaOHRvU/VgsFsTHx1e4fEVzKdEu5pJdqqrivvvuw7fffosVK1agefPmkQ6JiIiIiIgqQD5+GtZ7PoKaa4PUthHMb46DYNRVaVuCVoO4qTcD4tlhSqqsxNyE7arVCbFBIpRjp+F4/iv4th1G/Nz7ITWvBwCQGtYBvDLg8QFMdhFRGWItlxJbV3oAkyZNwmeffYbPP/8cFosFWVlZyMrKgtPpjHRoRERERERUBlVVYX9iHtTsfIhpdWF+93aIFmO1tilIYmBOHtXlge2ej+CavzoY4UYNoVEd+LYdRsFNb8H3z0FAEiEfODtHl3fzIQh14oAqJg2JqHaItVxKzCW73nvvPeTn52PAgAFo2LBh4G/+/PmRDo2IiIiIiMogCALinrwWUtdmsEy7A2Idc1C37/lpE3wbDsD5yiK4Pvk9qNuOFPnIKfjW7fP32nJ6oElvgfh5D0B3WWcAgPO9X+D5+i/oruoRsom4iSg2xFouJSaHMRIRERERUc0jtagPy0cTQ5KY0Y3oBeV4Hlwzf4PznSVQnW4Y7rqixiaB3N+sheONHwCnB9CIgE8B6sRBOZoL1eaC59etcBeuYunbdhiqywPBwN5dRFS6WMulxFzPLiIiIiIiqhlUlxe2h2bDu25f4L5QJZ8EQYDx7kEw3jsEAOD66Dc43/yhxn7BUwuc/t5cPZrD8uW/YHryWii7jsE2aSast7wD97w/oenXDjDqIG/MgO3hOVBd3kiHTUQUFjHXs4uIiIiIiKKf6vXBPmUuvH/ugm/bYSQseqzKk9FXhmHcAMCghfPV7+Ge+ydUlxemx0ZAEKO7H4CqKFBz7RBTLAAA/S39ITZIhHZQFwiiCE2TZOiGp0M5cAKqxwexSTJEixG+TRmw3jcLvnX7YPvXHJhfGwvBoI3wqyEiCq3ovqITEREREVHMUWUF9qcXwPvnLkCvRdzLt4Ql0XWG4caLYHryWkAQ4P11K5SsvLDtuyrko7mw3fMRrPd8CNXt750laCTohnQrlqQTRBFSqwbQdEgNTO6v6ZYG89u3AUYdfGv3wjZ5TmAbRESxij27iIiIiIgobFRVheOlb+BdugXQSDC/cgu03cO/xL1+ZC8IRh3EJsmQGiWFff8VoSoK3F+vhfPtJf65uQxayLuOQdO1WaW2o+3eHOa3xsF2/8fw/bUX3uXboRvSLTRBExFFASa7iIiIiIgoLFRVhfONH+D5bj0gCoh74UZoL2wbsXh0g7sWu+3bfQxSWl0I+sgP85OP5cLx/FfwrT8AAND0aA7T09dBSk2u0va0PVrA/NZtkLdmMtFFRDGPyS4iIiIiIgoL78+b4f78TwCA6alrobu8c4QjOsu3NRPWSR9B06mpf16rMA6rLEpVVXi+XgvHWz8GenMZ7x0C/f/1rfa8Ytr0FtCmtzi7L6cHkEQIOn4tJKLYwjm7iIiIiIgoLLSXd4Z2cFcYJ18N/dU9Ix1OMarHB6iAb90+WO+dCdXmilAgKjzLtvpXWuyehvgvHoThxouCPoG+6nDDdv8s2KfMher1BXXbRHTW6tWrIQgChgwZEulQahUmu4iIiIiIKCwEjYS4F26E4caLIh1KCdr0FrBMvwOC2QB58yFY7/kISp49LPtWVdWfbIN/knnTU9fCOPlqmD+4s8rDFssj78uCb8cReFfuhP3xz6H65JDsh6i2mzVrFm666SYsX74cmZmZkQ6n1mCyi4iIiIiIQsbzy2Y4/rcQqqIAAARBiHBEZdN0bgrz+xMgJMZB3nEE1rtmQDllDek+5eOnYZs0E47Xvg/cJzVKCklvrqI0XZrB/NpYQKeBd8UOJrwoJu3Nz8U/OVll/u3Nzw3p/u12O+bPn48HH3wQl156KWbPnh3S/dFZHJxNREREREQh4f1zF+xPzQdkBVKH1KgbulgaTbvGsMy409+za382rBM+gGXGXRBTLEHdj6qq8HyzDo63fgAcHmDLIRjvuAxi3fig7ud8tH3awPzqGNj+NQfe5dthf+ILxL14IwSNFLYYiEJlb34u2sz/sNxye26YgNYJoVmRdf78+WjQoAF69+6N0aNH46mnnsJTTz0V1Un/WMGeXUREREREFHTe9fthe/QzQFagG9INuit7RDqkCpNa1Iflw7sgNkiE1DQFQoIxqNsP9Oaa+i3g8EDTLQ3x8x4Ia6LrDO2FbWF+ZQygleBdthX2p+azhxfFBKvXAwD4bOBV2DDq1hJ/nw28qli5UJg5cyZGjx4NABg5ciROnDiBZcuWAQCOHTsWeIyCjz27iIiIiIgoqHzbMmF7+BPA44P24vYwPXt9SIfkhYLUJAWWmXdDSDBB0Abna1OJ3lx6LYyTBkN/44URPT7afu0Q9/ItsD/yGXzr9kHJygvZXGFE4da+TjJ6pDQI+353796N1atX4+OPPwYAmM1mjBgxArNmzcLll1+ORo0aYe7cuWGPq7aoWZ84REREREQU1eR9WbDd/7G/x1LvVoibenONHRYn1k+AYNAC8CeqnNN/hm/v8SpvT813wDn9Z8DhgdS1GeLnPQDDzf2iIhGo698e5lfHwPzeHUx0EQXBzJkz0atXL7Rp0yZw3+jRo/HNN9/g9OnTyMjIQM+e/qHdGRkZ6Nq1K8aNG4cOHTrg7rvvxsKFC3HBBRegY8eO2Lt3b6ReRo3Fnl1ERERERFFIOW2D57v18P69H1BVaLo0hf6aCyDWT4h0aGVSXV5YH/gYaoETUuemML86BoJeG+mwgsI9fzVcs5bD/dVfML9zOzQdm1ToeaqqBubnERPjYHp8JJQTBdDfcCEEKfJJrqK0/doVuy0fPAGxaUrUxUkU7Xw+H+bMmYMpU6YUu3/w4MGwWCyYO3currrqqmKP7dy5EwsWLECrVq3QqVMnmM1mrF27Fu+//z7effddvPXWW+F8CTUer1pERERERFHGu34/8ke+AueHv0IwaCGYDXDNW4X8a16BZ9nWSIdXJsGghemR4ZA6NYH5rXEQTPpIhxQ0uit7QOrSFGqBE9Z7PoJvU0bgMdXmgpJrC6w4eYaSlQfbvbPg+W3b2e1c3sXfmyvKE0jejQdRMPZdOJ7/CqqslP8EIgpYvHgxsrOz0alTJ2zbti3wt2vXLvTv3x8zZ84s8Zy2bduibdu2kCQJ7du3x+WXXw4A6NKlCzIyMsL8Cmo+9uwiIiIiIooiSk4BbP+aA02npoh78UaIiXEAANXuhv3Fb2B/4gtIafUgtawf4UhLpxvQEdqL20fF0LxgEi1GWN4dD9vDn8C3/gCsk2bCcNsAeNfsgbz5EABAqJ8A/agLoL+lP7w/boTjzR8AuxvK4Rz/MalBwznVXBvg8cHzwz+AKMD01LUxV6dEoXImmXXFFVeUWSY3N7fYbb3+7I8DoigGbouiCFnmohGVxWQXEREREVEUcX+zDlBUxP33ZoiWs6sACnF6xD13PfI3HoRr/mrE/fuaCEZ5llLggOM/X8P44JWQGicBQMwmRQSTHuY3b4Pt0c/gW70brveXQmxRH6ZnroNgNsC7ajdcH/4K99w/oBY4AQBSl2aIe+a6GpXoAgDdZZ2BF1XYn/gCnu83AIIA05OjYrZuKXbtPH2qUvcHw/fff19uGfbWCi0mu4iIiIiIooj3r73QXtweosUI1eWB7dG50A3oAP2oCyBoNdD2awfviu2Qx/SH2LBORJMoqsMN2wOzIW/NhJKVB8ucewPzU8UqwaCFcfJVsI7aDQBQjp6Ctm8bCMkWqHkOeH7a5E90aUQY7x0K/U0XRf2QxbLoLu8CKCrsT34Bz6L1/h5e/76GCS+qESxaHQDgluWLK1SOYguTXURERERE0URWIOg0UGUF9qfmw7d6N/RXpwceVnNtUHNtKLjmVUAjQUxNgtQ0BWKzupCapUDbpw3EBolBD8u36yh8f+2FKivQdGoCqWsz2P81B/LWTAjxRsQ9c33MJ7rO8P6wETDpoO3fHtoL20JMiYdvayYcL34DABASTIBZD8Mt/SMcafXpBnX1J7yeng/Pwr/9PbweH8mEF0W91glJ2HPDBFi9njLLWLQ6tE5ICmNUZ6WlpWH9+vUl/g0AX331VeDfffr0weLF50/YUUlMdhERERERRRFNpybw/LoFztcXw7t8O6CR/MkT+Ff28+08ApgNgFcG3F4oGSehZJwEsBMAEPfqGOgKk13e9fvh+e5vfyLsTEKsaQoEY8V7MiinrLA/MQ++9QeAOD0EjQRXvgMw6QCHBzDpYH77dkitGgT7UEQt+UA2NF3TEPfCjYEEn6ZzU+iv7wuxcRJg0sH50rdQZaXG9uoqSjekG1RVheOZBVBzrICsAEx2UQ0QqUQWVZzX60VWVhYcDgfq1q2LpKTg1FlIkl2hCpZqDuWUFe4v18Dz02ao+XaIDetAN6IX9CN6QTDExvLTRERERKGgv64P3AtWwz1/NQAg7rn/g7Z3K6iqCtes5VBPFMD8/gRoejSHeqIA8qGTkDNzoBT+v+jE9b4tmfAs2VRiH0L9BEhNU2B8YBg07RoDAFSXF9CIxYZFql4fbPfNgnLKhrhXboG2f3uoAmC/72P41u3zx/fM9dB0ahLCIxJ9BIMOyrHTJXqymR4bAQBwzloO6DWAGDs93fRDu0NMtkDTPQ2Cln0miKjqbDYb5s6di3nz5mHdunVwu92Bx1JTUzFo0CDceeed6NWrV5X3EbSrVDiCpZpBzsyBdeIMqDYXdIO7QUpNhm9bJpyvL4bnp02wvDseQlzsLENNREREFEzyoZOA6v+3kBgH+dBJOGf+Bu9v2yDvPgbDXZdD27Ol//EGiRAbJEJ7QetSt6Xt0xqCKPgTYodyoGTmQM2zQ83Ohy87v1hiy71gNZzTfyk2LFK1OiHvOY64aeOh7d0KgiDA9dEyf6JLEgGNCHnXMeCyziE/LtFEO7AjPEs2wrf9MDQdiyf6VI8PnkXroRvYKeaGdWp7twr8W1VVeJdvh3Zgx5h7nUQUOm+88QZefPFFpKWlYfjw4ZgyZQoaN24Mo9GI3NxcbNu2DX/88QeuuOIK9OnTB++88w5aty79M+58gpLsClewFP1UVYX9iXkQjHrEz7kXYkp84DHf9sOw3vMRnO8sgWnKyMgFSURERBSlfNsOw/7EFwAA7SUdoGolfw8vVYWmaxqM9w2Ftk/F29GaDqnQdEgtdp+S74CSmQP50EmITZID98uHTwE+ucSwSACwT5oJy+f3Q9OmEfTXXgDvyp0w3NIfvo0H4fllM4yTBlfvhdcw2ovbQ2rVALZHP0Pcc/8HTXoLCIIAJSsPjpe/g5KdB/1/b450mCHlfH0x3PNWQX/TRTA+fBUTXkRUIatXr8by5cvRuXPpP5L07t0bt99+O95//33MnDkTv//+e+SSXeEKlqKfvCUT8s6jML9zO8SUeKiKAtXqgphggqZjExhu6Q/XJ7/DeO8QCGZDpMOlKKIqCuQtmVBO5ENIMkPTvXlMzHFBRERUGUKcHmKyGWJaPcT9b3RIVloUE0wQOzeFpnPTYvebHh8Jw+0DoRzyJ8KUzBx4fvwHqk8B3F5ITVL8z69jhuXjeyBIIpTjeVALHEGPMdoJGgnmd26H7aFPYJv4IcTGSRDMBsh7jwNGHcyvjAkMD41VUkv/HG3ueasAUYDxwSuZ8CKicn355ZcVKqfX63HPPfdUeT9BSXaFK1iKfr5tmYBWA9+BbLi/+gu+jQehFjhhuHsQDLcPhG5gJ7g++BXyvixouqVFOlyKEt7Vu+F49XsomTmB+8QGiTDeOwS6Id0iFxhRDePbfhjur/6CvD8bgkEL7SUdoLs6HWK8KdKhEVEFSc3r+RNJBl1IEl3nI4gipIZ1IDWsE+g9pjrc8G3KgOXz+4tNan/mBynflkMQU5NL3V6sE+vGwzJnEnzr9sP7506oHhn6URdAN6RbrZiyQz+yF6CqcLz4Ddxz/wREEcb7hzLhRUGnKEqkQ4hqsXJ8cnJysHbtWsiyjF69eqFhw4bV2h5nFqRqURUFyuFTkJrV9d8hiYDPB9cbPxQr53rvF6g2F7SXdfLfoWGPHfLz/rUHtoc+gaZnS5ieGAVN20aQM07CNed32J/8AqqiQj+se6TDJIp6zvd+gWvmbxAb1YGmdyuoeXY43/kJrjkrYZk2PuyrpKkuLzy/boG85zgEvQba/u0hdW4akS9BqqpC3nwIvp1HIGg10PRtA6kxF8+h6KF6fJD3HIOmk7+nlZhsiXBEZ+lH9ILn+w3w/rwZ+pG9iz3m25QB7x87YXp0RISiizxBFKHt07pSQ0tjif6a3oCiwjH1W7g/XQkIAoz3DWHCi4JCp9NBFEUcO3YMdevWhU6n43urCFVV4fF4cPLkSYiiCJ2u4qvsRpuvv/4a48ePR5s2beD1erF7925MmzYNt912W5W3KaiqqgYxxmKCnZkLhyNHjqBJkyY4fPgwUlNTy39CLaMqCpQDJ+Bdvx++DQfg++cgVKsTib894++6ffAECq5/HWLrBtAP7gZNz5bwbcqA801/8kvToznkAyeQ8OPjEHTMtdZ2qqqi4MY3ISaZYX7n9uKrP6kq7E98Ad+GA0j4YUrYf90mqkk8S7fA/vjnMN47BPoxFwd6XCgn8mF7cDZUqxPx30wO2+pZ3jV7/MnqfId/gmubC+opKzQ9miPuf6Mh1jGHJQ4AkPdnw/7kPMh7s/wro/kUQFGhvbwz4p68tlb0vqDopioK7E/Nh/fXrTA9ez30Q6PrBx5VVeF46Vt4Fv4N3fB06Ib1gKDXwLN8O9zzV0PTIRXmd8ezXVfLub5cA+f/vgMAGG4fCOM9/jnclDw74PJCSLGwLUdV4vF4cPz4cTgctW+4dEWZTCY0bNiwRLIrmnMbNpsNZvPZ9mCXLl3w1VdfoU2bNgCAH374ARMmTMCxY8eqvI+QfSqFIjNHkeP9aw/c3/4N34YDUPPsxR80aCEfPAFN56aQmteDtl87+LYdhia9BTSdmviXorYY4J6zEr5NGTBMuIwNIgIAyLuOQtmfHWgQnaHkO6CessIw7hJYf9kM75o90PVvH6EoiaKfa+4f0FzQGoZxA4rdL9ZLQNx/bkTBDW/A+9t26AZ3DXksvt3HYPvXHH9vzUeuhtQkBaqiwLd6D+zPfwXbQ5/AMutuCGLoe/gqWXmw3jUDYooF5mnjoenVEvD44FmyCY43f4Bt8hyYp40PSyxEZXG99wu8P28GJBFiUvgSwRUlCAJMj4+E1DQFrnl/wvPdev/9ZgP0114A492D2K4jGK7vCygqnK8vhtSiPrxr9sD18XL4/jkIABASTNCN6AXj7QPDPm+v6vLA89NmeH7bCjg8ENPqQj/qghILN1B00ul0aNq0KXw+H2RZjnQ4UUeSJGg0mhrX4y09PR0vv/wyRozw9wzWaDQ4ceJEINmVnZ1d7Z5qQevZFY7MXDhEc/azopQCB1SbC2KSBYJBW6nnqqoKJeMkfBsOQHNRW0gN6wAA3F+vhWPqt/5CBi00XdOgSW8Bbc8WkDqkFvulRsl3wHb/x5C3H4bUpRmkxknw7TgC5dBJ6IZ2h+nZ6wFRALwyG0e1jOr2Qs44CXl/NpQD2fCu2wd5xxEAgHnGndD2aAEAcH+/Ho7nvgKMOsDpgaZnS+iu6gFNh1SIzepy4nqiIlSnB3n9n4bpmeugv7onPCu2w/G/76DmOyDoNYBe6/+3QQexSTIMYy+B7nL/gjJyxgm45qyEoNcCek3h/7UQCv+t6ZYWGP6o2lyQ92UBBq3/s0WvhVD4B70m8Dlge2Ie5B1HED//oRLXeO/6/bBN/BDmt2+D9sK2IT82jte+h2fJJsR/9TDExLjisazeDdv9H8P87vhaO/yIIs/9zVo4XvK3r86cw9FMLVypUfXJkNLqQjDU3CEzFBpyZg58mzPgeP5rSJ2bQj+qN8Q6ZnjX7YX723WQmtaF5YM7w9arVj6WC9ukmVCO5ELTqyXEZAt8Gw9CycqDfszFnGOMYlo05zYyMjJwzz33QK/XY9q0adi/fz9uvPFGyLIMn88HURQxe/ZsDBs2rMr7CFqmIRyZOTo/35ZDcH60DL7Ve/x3GHXQDe0O452XQUyJL/U5qqpCOXQSvvUH4N1wAL5/DkA9ZfM//bERkK7vCwDQ9G0Nw92DoE1vAalj6nmHwogJJlhmToR3xXZ4lmyCcvw0NB1SoXt8ZGBZZucHS+Fbvx/mN8ZxVcYIUj0+eFfvhppjhZBigfbCtkFJQKpeH5RDORBSLIEvmO4fN8Lx7AJAKT2/rmSeAgqTXWqBM5DoAgDf+v3wrd/vL2jUwfLueGi6NvOXdXoAnYYJMKq9zpxThckmsY4Z6skCAP5zHFaX/99eJ+QdR6DanGefmpUHz6L1ZW7a+NCVgWSXvD8b1jveL7OsYeIVMNw+EN7l26G/vg8Kbn7rbPKscJ5GFQAMWjhnLQ8ku5SsPNj/87V/IwIAQfD/3/8f6C7tGJgnSDltg+M/X/vLFC0P//+1fdv4548BoNrdcH/1F8TGSXC8ssj/ZUYQoElvDt3wntD0bQOxRX14ftrIZBdFhHfVbjjODPuacFnUJ7oA/wqE4Z7/j2oWId4Ix38XQnd1Ogx3Xgbv7zuhvSod2ovaQndlD1jHvw/X7BUwThpc/saqSVUU2P81B1BUxH/5EKS0ev77ZQXueX/C+eaPkJrXg3549J97RLEmLS0NP/74Iz7//HNccskleOCBB7Bv3z7s27cPsiyjXbt2MBiqlycIWrLr559/xj333IPZs2dj2rRpeOutt3DDDTeUyMxRaHhX7YbtX3MgtagH0xOjIDZMhG9LJtxfroHvrz2wzLwbYt3iCS/f7mOw3f8x1FPW4hvTa6Dp0qzY5KhSoyQYx19a4XgEjQTd5V2gu7xLiceUkwVwz1sF1eaCdeKHML9zW1jnbyE/9+INcL69BGquzd/TTlEh1Inzz/kzoleFtqH6ZChHTkHen+3/O+D/v5KZA8gKTE9fF2hAiA0S/PuIN0JqWR9ii/qQWtSH67OVEBvUge7q9MB2DaP7Q3fDhXBM/hTeTQehG9YDyp7j8O0+Bjg9EFPPTizt+ng5XJ//CaltI2jaN4bUrjE07VMhplW9B5iqqpC3H4GSdRpCYhw03dI4zwRFLRUqhIaJ8P62Dfqh3aHp2gzmt26D2CwF8MmQM3Ngf3gO9KP7Q9urZbEvqmJqMgyTBgNuL1S3D3B5obr9f3B7zy4+AgCSADE1OfCY//++wMOCXuufD8vjA4w6KBkny445/+y8G6rDDd/avWWWlVrUO1vW5YV35c4yywp14nCmr4Di8QJefy+UorF4lmyEZ/E/MD15LaTUJKh5nAOEws+3+xhsj88FZAW6K3vAcOflkQ6JKCg8P/wDKCoMd1wK2z0zoWTmQMnKg6Z7mn8y/14t4fpyDbRX9oAmzf8Zo7q9kPcc9/94IQmAKAKi4B9iLgoQEkyBIb6qrEDNsfrbrqLgXxxLEPxtPkEAtFLgh1vfuv2Q92bBPOPOQKIL8K8karjlYvg2H4Lrsz+guzo9rL27VJcXvvX7/SNxmqZAat+Yvcuo1rr55psxdOhQTJ48GQMGDMCMGTPQrVu3oGw7aMmucGTmqHSq1wf7819C26c14l4dE/hSru3TBrqr01Ew9l3YHvkUUmoypDYNYRh7CQBAapzkn39Lp4GmS1P/HFs9W0LTsUlIhxeKdeNhfn8CbPfNgrzrKKwTPoBl2h0Q6yeEbJ9UnOenTXA8+yV0Q7rBcPtAiM3rQTl0Eq5Zy/29JiQR+qvOJp9UWYFyLBfy/mz/+6jwy7Jv/X7Y7p1V+k7i9FDtrsBNTaemSPjp3xCSLcU+0KXUZNgemg3bQ7NhvP1SSG0bQT50Eq5Pfof3z13+yXoLY1Fl/+qfRROx8r4swOWFvPkQ5M2Hzu7foIXUthHMr40tMXzpfLzr98PxyiIo+7MD9wn1E2C8e1CxY0IUaaqqwrtiOxyvfg81xwrv8Ty4f/gH+it7QHuRv9eUanfDMXUhhMQ4GO++osSQIyk1GcbbBlZof5pOTZGw8JHiMSgK4JGhur0QdBoIOg3ERnWgHMmF+YM7zybFZP+S2KrLC8fUb6Hp0TywDbFeAkzP33DmRfn/Av8GpBb1z5aNN8H0xKjAYygyE4OqqsXLGvX+L0hNkqEb1BVQVagFTrjm/Qnfpgx4ft8O3+5j0F3coUKvnyiYPEs2Ag4PNL1awvTkKH7RpZghZ5yE1LohpEZJ0I/sBefbS+D+dKV/pcYiPF+ugeaR4QD8i6lYb5te5jb1N1wIU2FZ9bQN+VdOLbOsbnhPxD19nX8ff/h/HLFN/PBsAk2ngZBkhphigVA/AcqBbP8Ih7rx8K7fD7GO2T+Zfrwx6Oelqqpwf/YHXLNXFPvRR2rbCKZ/XwNNxyZB3R9RtFuyZAl27NiBrl27YubMmVixYgVuvvlmDBs2DM8//zyMRmO1th/0jEYoM3NUOu/vO6CesvnHnBcmujw/bYL3z13wbjgAnLZDPm2HvO0wpCOnAskuwWyAZfY9kFrU9/8iH0aado1h+XAirJM+gpJxEtY73oN5+h2QmqSENY7aSJUVON5ZAu1lnWH6zw2BD3IprR5Mz/0fFLcXjtcXQzlZAOXgCcgHTkA+mB3owWG4bSCMhckuqUV9f1KpRX1ILQv/CntsCfUTijUSBJ0GQinDabUXtUXca2PhfG1xsSFSQr14mJ6/AfphZ1elEiQRUlrdYs+Pe2UMlMwc+HYehbzzCOSdRwM9wOR9WRDiz14k7f/5CvLBk/4eYO1L9gDzbjwI232zoOncFKZ3x0PqmArl8Cm4Pl0Jx7NfAj4F+pEV6/VGFEry0Vw4X1kE75+7AABiwzoQW9aH45kFcH+5Btq+baDm2eH5eTNUWYHlrdtCMreOIIqAQSw2P6R+1AVwzvgVhhsvgia9ReB+VVXhfHsJ4JWLJdgEs6HYeX7e/cXpA8MUyy1r0EJ/cz+4Zi2H9unrAokw3checM9bBcGog5qdD92InlC9vrCtVEkEAMYHhkFskAjdlT343qOYIhh1UE5ZoSoKDGMvgWAxwr14Q+FKuAqUXBvU7PziP3KLIsTGSYCiQFVU//B8RSn8vwrBWOTzS1H9w/bPPH4usUiCqvCHFv92/BObqx4fVJsLSmaOf9GSwnKq2+tPip2h00BMNkNIiYeYYoGmVysY/q9v4GHf7mP+x+uYKzySwPXhMrhm/Ar99X2hv6EvxHoJ8G3MgPODpbBO/BCWjyZC07ZRhbZFVNM9+uij+OSTTzBw4EBMnz4d48aNw1NPPYWNGzfi+eefR7du3fDmm29i6NChVd5H0CaoB4pn5i6//HKsWLECkyZNClpmLhyieRK3sjg/Wgb3gjVI/OXJwH22B2cHvgRBIwI+BbqRvaAb1j0wCXg0ULLyYL3nIyiZORCSzTC/Ox6a1g0jHVZMOzNBtGX2JGg6NYG8LwtKVh60/doB8P8KZn/ok5JP1GsgpdXzD7e4uR8A/5dXqGpQVjJTFQW+jRlQTuRDrGOGpmeLKg8dVGUl0G1e27dN4P78a16BcvhU8cIGLaQ2jaDp3ATeTRkQJNE/cWqRLx+qqsLx/FfwrtyJhB8er/TCD0TBonp9cH32B1wf/Qa4vYBGgmHMxTCMHwjotfD+vgPur9dC3p8FQa+FdkBH6K/vA6lRUvkbD1aMLg+s98yEvPc49CN7QXtRO6hWJ9zf/Q3fX3thfHAYDLdcHJ5YbC4UjH8Pao4VhlsvgbZ/e6h2NzyLN8D99VroRvWG6eGrUDD2Xegu6QDDHVwtmEJH9clnh1sRxSjfpgxY73gfcW/cWmIlbVVWYB37LoRkCyxv3xaU/alFkmJQ1EDvLQBw/7wZjifmwfz+BEhNUwBFheryQDllg5pjhfunjZD3HEfCoseg5tlhvftDqDlW/9yx59BdnY64Z67379PlRV6/p/wPiAKEOoU9xVIs/sRYjxbQX9kj8FwlKw+qqqLgmldhuPUSGO8eVPw1OD0oGPsupNRkmN+4NSjHhQiI7txGSkoKfv75Z6SnpyM3Nxd9+vTBnj17Ao9v374dd911F/78888q7yNoLbpwZOaodIJRB9Xugur0BH750F3ZA1K7RtCkt4BqdcH+6Gcw3HJxiV4xkSY2SITlw7v8Qxr3HIe8+xiTXSGmnrYDAMTGdeD8YClcH6+AdmDHQLJLk+4fXiR1bgrtRW0htWoAqUV9iI2TSjTQz0z4HAyCKEKbHpxErCCJkJrXg9S8XrH7zW/cCt+Owh5gu47Ct6uwB9iWQ1ALHFAyTvqHAms1sL/4DQSDFrqr06Fp0wiG2wfC8/0GeP/cWepcdEShpsoKrOOmQ97tX9VYk94Cpikji73PdQM6QjegY6RCBAAIBh0s08bDNfM3uBf+Dfe8VQAAqUMq4v43GrrLOocvFrMBlg/uhPPNH+B87xd/zzIAQrIZxkmDoR97MTw/b4ayPxuu/dnwLN+OuKeuhaZLs7DFSLWDqqpwvPgNVJsLcf+5gasYUsySujaDpmcLf4/4p66Ftn97CJIIJTsfjrd+hLwvC+bJVwdtf/55vUp/THdpRzjrxcM5/RdY3r4tsCiWlFYP3lW74VuzF4a7LocgiRCSLUhY8DAA/xxiyikr1BwrlBwrlFPWYqNPVKsTQrIF6mmbP4F2ygr5lBXYXVhAUQPJLtXlQf5V/w081/PrVvi2ZkJMtkBslgL9DRdCtBhhuOkiOKYuhHLaxrmMqVYwmUw4ePAg0tPTcfjw4RJTXnXs2LFaiS4giD27wpGZC4dozn6WRT6Wi4IRr8D4yPBi3WsBf+PK/tAnkI+fRvwXD0btnBBKgQO+NXuhG9w10qHEPN+WQ7De/p5/Xp1jpwEAuhE9YXryWgiCEPhFzvzBnUFLPkWrMz3AfDuPQs44Afes5Yhf+AjE+gnIu/gZwOv/FV43tBsME69AwfVvwHjvYBhu6hfp0KmWcs74Fe4v18D44JXQDesetdf0M1SvD8rJAgg6LcQUS/lPCCElzw55fzYEreSfDLhI703Pb9vg+N9C/2rEggD9jRfCeM/g4kNniKrB+dEyuN5fCogCzNPvgLZny0iHRBQySoED9imfw7duX2BlbvngCUCvQdzT14X1R0Pftkz//LKSCN3QbhBT/HNz+f7aC+3F7RH38i1VH0ngk6Hm2aHknEmMFUDJsUJq3RC6S/zzQSpZeci/5hV/m7IUmt6tYJl+R6D9HT//IUgt65dalqiyojm3MXfuXEyYMAGJiYlwOBz45JNPMGLEiKDuI2g9u8KRmaPSSY2SoLuqB5xv/gBBp/F/AdJpoJy2wTVjGbx/7kLcf2+O6i9FYrypWKJLybVB3nk0MMkyBYfq8sCzbBsAQDl2GkKdOJgeGQ7tFV0gCAJUWYFz5m8QmyRD0z0tssGGQdEeYPL+bLhnLYd8IBti3XiYnrwW3j92wvvrVnh+3AjPL1sAn8xf4ylsVEWB59u/IbVtCE2npgAAw62XQH/jhRDjTRGOrmIErSasQyjPR0yMg1hGAl93aSdo0lvA+eYP8Hy/Ae55q+D9fQdMT4yC9oLWYY6UYo37h3/8iS4ApkdHMNFFMU+MN8E8bTzk7YfhWb4dcHqgv64PdEO6BXpXhYumU1NYPr8f7vmr4V22DarTA7FZXZievd4fTzVW2xY0UuGcXiXnpD1DbJCIxNUvwDV7BVwf/Iq4l26C6vRAPVkA9/cbYJw0GAAg78/2D4lMZq8uqh1Gjx6NIUOG4MCBA2jdujUSExODvo+g9ewKR2YuHKI5+3k+e3JO4uS0H+FdvQeCxQAkmKBm5wEQoB/dD/orusCi1aF1Qui/dOzNz4XV6zlvmfPFotpcsN75AeT92TA9dW21VsCrbizBFOlY5EMnYXtgNpQjp7A/XoRNJ0Bs1QCGa3pDTE2GcjQX7sUbIO86CtNDV6FO7za14ricoaoqNt35NmxGDUz/vgaCxt8n3nfgBNxfrIK8/QgAwKzVoutzY0Pe6y1ajku0xVKReGIhFt/uY3D8dyHkrZmQ2jSEZc695TbIa8NxCUcs3jV74HjxGyhZedD0bQPz27cF7ceimnxcGEvVYvGeWbXYJ0M/5mKYHhgWsViqg7EwFsZSPUquDf/c+D94RvSA4Qb/SBxVViFIAlSXF7an5iNep0PX/42v1CriVRVNbTvGEjo1NbcRLEGdoP7UqVMhzcxVxvTp0/HKK6/g+PHj6NixI958803079+/3OfVxDfE3vxctJn/YfkFAey5YUJIT85gxKL6ZDhe/Aae7zcAAIyTr4bhxosiEkuwREMsqsuLgpvfwj6NFz0vr9jkuLXhuFQllt1XjUWbRqGbW66mHpdQx1KZeGpqLKrdDef7v8A9f7V/ot04PYx3D4L++r7nndQ61o9LuGM5Uw/6my4K9ExTfXK1egDEwnFhLJWLRT6QDevt70G1uaC9ogviXryxwgu6xPJxYSyMhbGUbf0vMro8fmNIf1SNprYdYwmtquY2qppLqajMzEw0bdq0wuWPHj2Kxo0bV3o/QRnGeCbY5ORkJCcnl1u+qsFW1Pz58/Hggw9i+vTpuOiii/DBBx9g6NCh2LFjR6UOak1xJvv82cCr0L5O6cd/5+lTuGX54nIz1dEQi6CRYHrqWghmA9zzVsH56vdQbS4Yxl9aqV/XY+24VIV3/X5oujf3T7xp0ML82lhAcgE/zcNnA69Cu8QkyIdygNN2INEEKa0uBEGI+eNyvlhmp/ZE2vwNULPyA48JiUbor+2Lfd3qY8yKH2DT+d+HqqrC8eyX/gn+L+kQtN4f0XhcoiGWisRTU2NRVRXeZdvgeP17qCcKAADaK7rA9PBVEOuWPTwiFLFUVyzEIsTpYfpX8QmUHf/7DqrNBdMjwyEmVX6YSSwcF8ZSiXNaVmB7bC5UmwtSl2aIe/b6Sq1cHKvHhbEwltoeyyx9a7T4fgdU69kVH8Xm9XBgUBvclrMFVqsdtrs/hOGOy/zffUKwems0te0YS/QJRy6lV69eGD58OCZMmIDevXuXWiY/Px8LFizAW2+9hbvuugv33XdfpfcTlGRXuIKtqNdffx3jx4/HHXfcAQB488038fPPP+O9997D1KlTK7QNWZYhy6VPJBhtZFkBALSJr4Oudc6utigrClyyD6IgBMrIsgKb2wVFVaGXNNAUNrzOlBUEASaNNrANl88HWVWgkyRoRancsja3u1gsiqrC6fMCAOK0umLx2txuOD0e6CT/ds8tq3tgKGDWo2Dmb7B/tBRxBXZY7h/mn1tKVeEoLGvSaANJBo8sw6vI0IpSYD+tLYloZU4oteyZeGVZCdS3vfDiZdRoIZ6zXY0oQi+dPW1KK+tVZHhkGZIgwqDRFHvNTUxmdEpIhlR43IuWLVpHVrcLqqrCIGkCZX2KAndhfRqLHHenzwvlnLKeU1bkv7UYyq/bkHjfMOhv8veMczaKhz3HGaijbkn1ICemlKjPonXk9nqr9D4pre7PLXtmP62K1FHRsm7ZV6KOitb9uWV9igKtKAXeU+W9T4rW55lYmndthb6X94e8+RCUrHz44g1Qe6RBq9dCOn0qULbA5YT3j53AD//A88M/kLo0hXjPICidGher+8q8TwDA4fOWOI9Kq/uideTxeiv8PintvD9f2TP7aWlJQCtzwnnP+zN1FIz3SWn1qahqiWNzbtkz8fp8MgpczjLrvqrvk6L16fXJgVjaxyeV+Z6yud3w+nznrXvv6t049cRcqAJgTK0D8yMjoOnTGh5FhsflLPV9UvQaUfQa08aSWKI+z62jqrxPzq3PssqWVkelnffeInVU1jWiKu+TovVZ9POxXXydEmV9hXVoc7vh8/nKrHvl2Gl4Fq2HXVCQv2Efku4fBu2QbhAEocKfD0XrqK2lTolrxLl1dL5rREU/H8oqW1odlXbee3w+FLic1XqflNeOKFpHbS11SpT1Fqmjou2ysq4RVXmfnDnvi9ZR+/ikEmXPraPy6l7375HwvvMzjK+MhktQ4XU5K9yOKK2OitbnmVjdXn8dVed9Ulo7omjZonXUxpJYou49Pp//tbg9xeqouu+T87Uhm5jM6JCQVKLsuXVUXt1Xpx1RWh2dqU+TRhuI1eX1oqASdV/aeV9e2aJ11NqSWKLu3d7COvIUr6OqvE/Ka0cUraOOCcklytrdnmJ1VNnvGpX5fCjve8mZWJ0efx1Vpm1Q2XbEmX11GtIHba65Ar5/DsLg8ELTrC407RpDc+IY8P0WKP3bAgt3wDXjV3jX74fy9DUQUywVbkOeW5+V+V5S2nlf9Ltjdd8npbUjzq2j0soWbU+5vJ5KfdeoTDuitO+ORcsWjaXod8doVpUYg5FLKc/OnTvx0ksvYciQIdBqtejZsycaNWoEg8GA06dPY8eOHdi+fTt69uyJV155BUOHDq3SfoKS7ApXsBXh8XiwYcMGTJkypdj9gwYNwurVq0uUd7vdcBeeZABgtVoBAOvWrcPBgwdDFmcw7XHZAQAbN26E3XB2jPd6ez4eOboLLfUmPFq/RaDMhJOHsNlpxTMNW2GAxZ+13uq04v7DO9BYq8dnzbsFtjHl6G6stefhsfotMCTB/0G+z2XHhMxtSJG0+LJlj0DZZ4/txe+23GKxHPG4MCZjM+JECYtb9SwW7yU/fIG7UprgxqRGAICTXg/+7+BGSBDwa5vCpGlbHd6/qRHmxznx6IZ/MO6pbGQPbg2b7MPV+/3DHJe27gWN4L+Yvn8yE/NPH8cNdRri0sLXtmHjRlxweDsA4PuW6TAXNjRm5xzBJ7lHSxy7y/esgwwVC5p3R93CC9wXucfwQc5hDI5PwZQGZyeWvWrfetgVGZ+mdUWqzj/h5rd5WXj7xCFcYk7Cs41al3jNHzbthFaF+/op/yT+l30AF8Ql4vbk1EAs1xzfi6NeN95u0gGdjf4VzFZYT+G54/vQ1WjBm006BGK449BW7Hc78ErjduhpikfixuPY8fcejB5oRLfhFny+fTey//BfmO/N3I7tLlux17zJUYCHjuxEM50Rs9O6lIj33w1a4op4/3LLu1w23J25HfU1OnzRonsghieP7sEq+2n8q15zXJVYDwBw0O3A7Ye2IkHSYGHLs/OuvXB8H5ZZT2FS3aboYvT3WFm2fh36HNsDgyBiSetegbKvZh3ADwUni8Wb5/PimgP/AACWt7kgUPbdE4fwdV4WRic1wh0pTQAATkXGsH3rAQA/tuoJY+EH3kc5hzE39xiuTWyAe+s1K/Gav23RA4kaLWABPsvZjZnzfsCV8XUxPLF+IJaLD++AS1Xw6+Bm6PbbYWBLJt5991M8caEJg7QJeLx5u0BsI/dvQL7sw6xmndFc759YfHHeCbx24iAuiquDFxq3CZS98cBGZPs8xV7z0oIcvJS1H+mmeLya2r5EvG+ktkc3k/9Y/mnLxVPH9qKjwYx3m3YMbHfioW3Y7bbjpUZt0NdcB0Dxa8RHzToHyj54eEfgGtFI639ff7N2NfpmHyzzGlE03vKuEffXa4ZrEhsAQKnXCAD4b9Z+/FyQU+o1oui+AODN7IP4Lv8Ebk1qjAsLX9vqfzbggiM7AZR9jZhY1//rlE9VcMXevwGUfo0YkVAPD9ZvHojtzDXiv43aBmKZ6cgvcY0oWkflXSOgqLjxljrI1gMfNuqAVt4s4I+sYteI/zY+u2DHLQc3FbtGFN1XWdeIosdtje00/n1sD9rq4/B+s06BsmeuEf9p1Br9zP7u+aVdIwBg8pGd2OAoKPUacW4dFb1GtCm8b8m6v9Ana995rxHX1fEPF87yunHTwU1lXiPGJ6filmR/j/Gi14gPmnYKxPK/gpwS14itDmvguJV3jTDedwH6OHb56/a/X0M3/3ccubYDPpZzMfPUEVwZXxeTG5wddjJ0799wqQrmNe+GBlp9sTq6zJKMJxu2CpQ9c40oetzKu0a817Qj2hn8PcxKu0YAwLiMLTjkcZZ6jTi3jopeI5I1hZ99a1bhrZMZ571GVLcd8WLjs+fRI7nHSlwj/rD62xVDflyAH4vU/fmuEcXaESh+jRiX4v+sLa0dUbSOyrpGFD1u5V0jFjTvjrpj2gFb/6lSO+LcOrp+/z/Ikb34sGknKIXP/2DVCnySe7TcawRQwXZEnP8LXtFrxMOFr23jxo247URGiWvE93nZAIAbfvkGnxep+/NdI6rajihaR6VdI3SFyYAzx628a0R12xHn1tHAPWv9ddiiB04Ufpa/vPJXfJd/otxrBAB8dfo4pp3MLPMaUVY7Ymzha9u4cSOuPranxDXi01P+9u5dvy7C9KZnr/nnu0ZUtR1RtI5Ku0bUK7y+nDluVfmuUZl2RNF9nXuNOBPrEyt+wu+23HKvEUDV2xHDEuoFYumbuf3sNeKkFTh5AO+cyAAAPNnciek3dUHq19uBfw4ibeEsFOiE8tsRKH6NKO27xplrRNE6KusacU/dZoF4b8o+cN5rRHXbEUXrqLRrRNF4K/tdozLtiN5xiQCAPzasxwVH/Z/3Ra8RRWM593t3tDp50v89ymq1oqCgIHC/Xq+HXq8vUb6yuZSqSkpKwquvvooXXngBP/74I/744w9kZGTA6XQiJSUFo0ePxuDBg9GpU6fyN3YeQUl2hSvYisjJyYEsy6hfv/iSrfXr10dWVlaJ8lOnTsVzzz0X8rio6lyNLEC+E7JBg9xeNWMetUjQ2D1I++IfJOw8id1N/Ke2u54Z2RdxJbFQOjmgBXb2bo4GS/cBdn+jKn7nCaSu9+HY8HZQ9EFb9JZiTNyBXIhJPigGDSAKkE1aQPYCmuAPWaDqc6YmAHv8/1YkAfE7T6LtK3/CeH0joGYsjkmhpqqo//M+oEWR6XCjeCVsIopOeemN4GiagLRPNwGqCoDXEaqeDh06FLv9zDPP4Nlnny1RrrK5lOoyGAwYNWoURo0aFfRtA0GeoD4aHDt2DI0bN8bq1avRt2/fwP0vvvgiPv30U+zatatY+XN7dh09ehQdOnRARkZGjZmg/p+cbPT+7lOsGzEGPVLOvjGLdi3emZcbKNM+MSlkwxg35GThkh++CMRSWpfRM/H+fuWN6FW3YYWHH2g8CvRx/l80VFWF3euFIJTdtXjb6Rz0/u5TrB1+C9ol+n9ZOLfs3yePF4sXCM0wxqKvuU+9RqV2Ld6RdypQR+0SkyrVrdz9x06oLyyEZHMDWgnSbQOg3tQXkk5Touw/OdnFXnNp9Vk03gvqNQrZMMatuf46+mv4LWhfWEfn1v36k8XfU6Eaxlj0NV9Yv3GpZbefPltHbRPqlKh714FsWD/4Geofu2Fq0QBxcyZBEMVKD2PceE4dlVb3Zb2ngj2McUvuSfT+7lOsGT4aHRKTK3Teh3IY45rsoyXO2aJlK3LeB2sY4868XPRZ9BnWjRiDTnVSzvue6lu/MURBgHLKBtvbP8D+6xZIKhA/dgAME68I1H1Fhh+UVrbovnqk1C/3vA/lMMa/ThwrUUeVOe+DOYxxd/7pwDnbsU5yibIbTmbhgkWfnfe8L63u9Zmn4X7pW8jbDsObZIL+s3uhTYw77+dD0TpKT2lQYojSplMnih23UA5jXFtKHZ3vvA/lMMZdRdonHRKTS5RdfzILfQrr6KIGZ9tloRjGWFb75EzZc+vo3PeJ+7M/4H7nJ9gtepjn3Y+4lIQqtyPWnSjZPilan5sL62j11aPRsU5ySIcxlteG/PvkcfRdNBcrr7wJFzZoXGK7wRzGWLSOetdrWKLsuXUUymGM52tDmjRabDx1Ar2/+xSrrr4ZneqkhHQYY3ltyHUnjuPC7+di5VU34cL6Z+soFMMYy2tDbj51Ehf/MC9w3EI5jPHcNuS5Zc/E+udVN6NzUkpIhzGW14Zce+IYLvr+c/x51c3oU9/fW1V1e5H33VpoR/SCSaeDWDiVi09VqjWMsSJtyNLO+1AMY6zs95LSzvtz676q7YjS2pDnDmMs63t3tDpy5AjS0tKwY8eOYnOml9Wzq7K5lGgXc10OUlJSIElSiczjiRMnSmQogZIVfaZ7nyRJkKSqr7oUTlLhxIWSJBaLWZIk6LTaEmXMekMp2zhbtqi4Uo7B+cqaC4/lmVgkAFqN5pzn+2Mx6/Uw6s5eQEorCwCmMzEUCduzfBvEL1bD/NpYCIazzzFKEozn7EejkRBvMOJcxlLiBYB4qfSyJe8tvawkSTBoz73v7GsueuyKli1aR5Yy6khfynE3SxKMaQ1R4PRC6twUcU9dC6lF6Rdfc2l1VEp9Fo1Xf068FX6foPT6jCv2HvXvR1tGHZnKqqPzvU/OUVrZ0uqz6GvWaM7/npIksdR441o3Qtyrt8G32T/8RFN4rCxeCe5FG6AZ2QuCTircRsn3CQBYyqijc+v+fO+pst4n5yqrPs2l1JFOo4FFo4N39W54D5+CYDFCe3F7xCXGVeg9BVTufVJafUqFr7fovs4tW5HzvtRzuYLvE+Dsea/VnKlLEUad7vzvKUGE59t1cL77E2BzIU4QoL++D4y3DoBQGL+ljDoq631S1r7OvcaXdd4H831StGxpdVSZ87667xPgbH1KtvzAPk26ko06TWEdnu+8L7bdM9f8NkboZt4N94LViKubAF29whUbVRVQVAiSWOLzoWgdxZ3TwIyXjKXWUUXq3v+c0uuzrLKl1VFZ531pdVSZ90l57Yii19Vzjwtw9jwz6/XF2jhlXSOq8j45G1fp7ZMzZc89bkXfJ55ft8D9zk8AgJTxl8PQoPjkxpVtR5RWR5ZS6kivLb2OKvM+Kas+9RVsQ+oKj2OcXlesjqr9PsH525AGbck25LnHrTJtg8q2I8prQ56J1aDVllpHFW1DVqRseW1IvbawjnTF6ygY75PKtiHj9LpAOUmSKv1do0RZlH3el/e95EysRl0ZdVSZtkE5ZctrQxoKX69Rpz1bRyYJKTddEiijuryw3TsT+hsvQvzlnUtso6LtiIq0ISvy3TEY7YjKfi8p7bw/V1XbEeW1IYuWOfd7d7QKfH5YLIiPL3/Bo8rmUqJdzCW7dDod0tPTsXTpUlxzzTWB+5cuXYoRI0ZEMLLQ21k4eXZlHwuFUMaiOj1w/Hch1FM2WO+aAfM7t593ZaxYPC6qT4a87TA03dIAAFKrBrB8NBFSh9QKr9oSi8clGIIRi6Zrs2K3XfNWwTXtZ7g//R2GuwdDN6RrhVbliqbjsnXtVlg/+xtqrh0waAGPD5ixCNohXXHwirblbyDIynr94T4uZ/apnLJBOX4a0GshNa8HQSMGYvEdPAHrw19B3nEEACC1bwzT49dA0yH4vYej7bhU5v5QCkUsgiTCcFO/Yvd5l22Fa85KxD19HaRWDcIWS1UxltJVNhbf5kOwP70AAKD/v77Qj+5XarlwxBJKjKV0jKV0jKV01YnFPX81fJsy/H/X9YHxoSsh6EvJjlYzlorGE0yMJTrEWi4l5pJdAPDwww9jzJgx6NmzJ/r27YsZM2YgMzMTEydOjHRoIWEpzHDfsnxxhcvW5FgEow7mt2+H7b5ZkHcfg3XCB7BMGw+xQWLYY6moYMYi78uC/fmvIO8+Bssnk6Bp5++SqulcsaVgY/W4RHMsYoNECHXjoRzPg+Pp+XB/thLGe4dA07dNoKt7uGKprDPbH3f0H2CgBODcX4X2Ab/vC0ssRfdR3rGJaCxbi9/U/7DZn+iK08M4aQj0114Q9KXEa8RxifFYVEWBc/ovUDJzUHDLOzDcNgCG2wdCKOxRUVuPS6zHImfmwPbwJ4DHB+3F7WH819WlXtfDEUuoMBbGwliiIxb96H5QrU64Zq+A+6u/4NucgbipN0NKqxeSWMqLJxgYS/SJpVxKzM3Zdcb06dPx8ssv4/jx4+jUqRPeeOMNXHzxxeU+78iRI2jSpAkOHz5cY+bsAoC9+bmwFo7nL4tFq0PrhKSYiUU+dBLWez6Cmp0PsUEizNPvgNQ0JSKxVER1Y1G9PrhmLYdr1nJAViCYDTA9ez10AzqWWj6UsQRTbYlFdXng/mI1XLNXQLW5AACani1gvG8oNB2bhDWWytp433RYZR/inr4OwjmTp7sWrYfnq7/QcNqdaNu8YgnX6irv2ITruCjZ+dg4aRpsBgn64T0hdUyFmu+A59ct8K3aA911F6Du//VDS68Gzum/wHjPIIgp5Xchr6poOS61ORblZAEc/10I7+87AABii/qIe/o6aDo1CXss5WEs1Y9FybPDett0KIdPQeqQCssHd0IwBu8LUE09LoyFsTCW0MbiXbMH9qfnQz3t721vmjIS+qvSy31eZWKpTDzVxVhCp6q5jarmUqJNzCa7qqqmJrtqKyUrD9Z7PoKSmQMhyQzzu7dD06ZRpMMKOt+2w7A//xWUA/7lvbWXdIBpykiIdUP3xZlCQ8mz+3+Rm78a8MrQXtoJ5pdviXRYZVKy85F/5VQYH74KhpvPDs3x/LQJACA2TIR14ocw3j+0xJCuWGd/8Rt4V+5A/Nz7A0ksOTMHjpe/g5KVB+XwKSQsnsLztJZRVRXeX7fC8fJ3/i8iogD9zf1gnHgFBIMOqs0F95KNkLcfBiQR2r5toB3QEYIm+uf+oOJUuxu2xz+HcjAblo8nQUyxRDokIqollJwC2J+aD9/f+wEAxvuGwnDrJeU8K/opOVaoBQ4IdeMhWkqfN4sqribmNvbv34933nkHhw4dgizLgfsXLVpU6W2FJdm1du1a7N+/HzfffDNyc3PhcDii9mDXxDdEbaecsvqHNO45Dv2Yi2F6YFikQwoq5/u/+HtzKSqEJDNMjw6H9rLOQRkmQZEjHz8N1wdLYbhtIKRmdQH438sAICaf/cKkKgp8/xyEciQXQoIR2r5tIBhC03Va3pcFOeMk5MM5UDJzoBw+BfngCaj5DoipSUhY+GigbP51r0HJOBm4LSSYoOnRHFLL+pDaNoJuYKeQxBgtVLcXeZc9D/3N/aAf1BXy0Vz4thyCe94q/3xmWgkQRRgnXAbDuAGRDpciQMmzw/naYniWbAQAmN+fAHhl2KbMBVxeSB1SAbcX8p7jEFOTYX5rXOBaQDWH6pOh5lhLTKVARBRqqqzA9fFyuBesQfyn90GsnxDpkKrMtykDzg+WBpJ30EjQXtoJxnsGQUpNPv+TqUw1MbfRuXNn3HvvvejSpQvEInMcX3DBBZXeVsiTXc8++yz++ecf7Nq1C3v27MHRo0dxww034M8//wzlbqusJr4hCFCsTri//AuGcZdUaOLvmsQ1fzWcryyCblh3GB++CmJiXKRDohCxP/clPMu2wnDLxTCM7g/f1kw4/rsQypGzk2EKFgMM4y+FfnT/Sic8VZcXypFThcmsU1DdXhjvvDzweP4Nb0DZn13qc4V4IxKWPQ2hcNlr58vfwbf7OOT9WYDdXays1KYh4j9/IHDb8eYPEIw6fyKsRX2ITVOq3YtFtbng+WUz5EMnIZj00F3WucwJwau1H1WFesrmP25Hc6Ht2RJi/QQoJ/KRP2xqqc/R9GkN02MjYH9srn+F1MevKbUc1Q7eP3fBt/kQdEO7oWDMO9D2agXjlJGQCpMjvr3HYf/3PMDlQfyCh4M6DI5Cw/v3Pmh6tuSPTkQUFVSbC4L57IqJvk0ZkLo2qzHXKO+q3bD9aw6k1g2gv+FCSKnJ8G0/DPe8VVA9PlhmToTUJKX8DVEJNTG30adPH/z1119B2VbIJ6hfuHAhNm7ciB49egAAGjduDKvVGurdUi0jWoww3j4wcFv1yfBtzYS2e/MIRlU1itUJNTs/8MVdf30fSG0a1sjXQhWnen2QM04CDg9cM371f8DbXZB6tIDl2eshdW4K5fhpuOf+CeebPwI+pdQeQ6pPLpZIcn60DL71+yEfPgU1O794YaMOhgmXBRpDmg6pkI06SE1SIDZNLvx/CpwfLYOyPxtqgRNCggmCIMD02EioquofyvfTJsT95wYox05DPpBdrIeDKitwL1jj7+10hkaC2CwFUov60PZqCf2oyv1S4/lpE+wvfQO4fRBTk6Hm2eH6cBm0l3VG3HPXV6vnm2/3MXgWb4ByNBfykVwoR3MBtzfweNxLN0E3qKu/USkK/h6XFgPE1GSIjZOgu7wLtJd1ArwylOx8aPszOV3bafu1g7ZfO9hf+BpCYhyMD10J232zYHpgGLT92kHTuiHMb9yKgmtehefnTdCP7B3pkOk8XAvWwPnyd9CNugCmx0fWmC+TRBS7iia6PCu2wz75U2gv7QTTU9dG/VBA1SfD/uLX0PRuBfPrYwNtWE23NOiu7AHruOlwvr4Y5jfGRTZQCpvHH38cjz32GC6//HLo9frA/VWZMyzkya4zAZ5pDOTl5bFhQCGlygrsT82Hd9lWmJ6+LjBho+ry+H/5iDdB0EVuIVLfnmPw/LgR6ikbhHrx0F+VDqm5fxUVz+874PjvQgg6DeK/eBCCUQdBFJnoqgUErQaWWXfDu2wrnNN+hnLY35tLzcqDklMASRQgpSbD9NgIqBoRzhm/QmhUB+rJAv9ww8wcKIdzoNpcSPjtmcB1Vt5xBL71B87ux2yA2DQFYpNk/4IOPhkoXC0u7pnrS43N9MAwWG9/D9bbpsMwbgA03dKgZOXB/cUqeFfuhOnf15Q9bNErw3jPIMj7syEfOAH5YDbg8EDZn+3vRSYrgWSXqqqw3v4exEZ1ILWof7YnWOOkwOqF3jV7YH9qPnRDusF47xCI9ROgen3w/LwZjv8uhP2ZL2H+3+hiIaiqCjXfAeVIbqCHlnLklD+hdTQXpn9dDd2l/viV46f9QxGLEgWI9RP8cZgKP9NMemj6t4e8PwsJXzxYIsHmXrQear4DuqHdzlftVIt4f98B/checH32B5SDJ2B7cDY06S2g6dEcmq7NIHVrVliGya5o5Vm5A85X/XOGiPUT2J4loqijnrIBGgne37bBuvMI4l66ucIrtkeCd9VuqCcKYCxMZqlOT6CHs5gYB8Otl8Dx0rdQTuRDrFdzh2lSxf38889YsWIF9u3bFxjGKAhCdCa77r77btxwww3IycnBCy+8gPnz5+Oxxx4L9W6plhOMOkBR4Xj2S8gZJ6EczYX3t22ArAAGLXRDu8M4/tKwzrGh+mQ4XvgansX/QEi2QGqaDHnNHrg/+R26q3tCdbnhXbrVH3/TFCjZ+ZDSOH9LbSIIAnSXd4HYqgGs170OmA1QjpyCfcrnME6+GoYbLwIA/0IFHh8c/55X6nbUXBuEwnm/9Nf1gfbSTpCa+ntpnemZVRlSs7qwzJwIx+s/wPGfr4HC0e9is7qIe/Em6AZ3Lfs1GbQw3HL2w0lVVf/k7fuz/b3Ampydh0HJyoO8NRPy1kx4i25Er4HUrC50w3rAu2I7pC5NYXz2usDrELQa6IZ0g3LKBtc7S+B8/xdoL+sMTeuGAPxJBvvkT8uMUcnMOfta2zSE/pb+EBsnQSrsrSU2TISgLflxaZx4hT8JOGkmTPcNhdS1GdQCJ9zfrIXrg1+huzq90suBU+xS3V4I8SaYxl8KwWyAe+4f8G04AN+Gs8lo2agr1tCnyFBVFfLmQ/Cu988do+nRHNBr/cNNFRW6kb1gKNKbnIgoWuivvQBS+8awP/45lKO5sN7xPoz3DIZ+TP+om+pFybHCu3wboJXgfO17+HYcgeG2gTCOvzRQRtO9OaCqkI/mMtlVS/z+++/Yvn17UH5QCnmya/To0bjggguwbNkyqKqKL774Ah07dgz1bqkWEyQRpidH+b9MfP4n3LNXQIg3wnDvEEhpdSHvOgr3V3/B++cuWD68K2yTHjrfWQLPkk0wPTkKuqvSIWgkKG4vHC9+A8/36/2FJBH6W/rDOOFyCAZtWOKiKFTgBABY3r0d3tV74Pl+fbElpaUW9eFbuw9CigWabmklhh0Kdc4OndNe2DYoIUlp9WB5+zZ/QurIKQhmA6S2jSr9QSQIAqSGdSA1rANtv3bFHhMT4xD3xq3+RFhhMkzOOAG4fZD3HIfSLRe+TRkw/ecG4LQDeSNehtQsBarNBSUrD1D8STjXR79BMOoDyS6xsX95aKFe/NkEVtFkVpGkstQoCaYHr6zQa9G0bgjLu+Nhf3YBrHe8D+g0gFf2n8fXXgDjQxXbDtUOUquG8P61F4bChVT0I3vBu24f5C2H4Nt8CMqx04AoFEt02Z+aD9XhhqZLM3/vr3aNY/qzQfX6oGSeAgQEZW6/qpCP5sI+ZS7knUchJJgAAK73lwKSCMiKf06+KRy+SETRS9MhFfFz7/dPNbF0C5zvLIF3w37EPft/EJPMEY1NybPD8fIiyNsy/Z97hXz/HAQAyHuOB+5TZQVKdh4AQIjTg2qH3r17Y//+/WjVqlW1txXSZJeiKOjVqxc2bdqE9u3bh3JXRMUIogjDg8Pg+fEfqHkOqAVOqDkF0N7SH7r+7aG/5gJYx78HxyuLYHnrtpDHoxQ44P7qLxhuHxgYoqK6PHBM+RzeP3f5C4kCzB/cCW23tJDHQ9FNqBsPAJAzT8F45+Uw3D6w2Jc+3XV94J63Csb7hkJ/ZY+wxiY2SAxZj0jBqIOuf3ug/9nPC1VWoBzLhbw/G9CIcC9YA7FuvH84ZOFKdgF6DaCoEOslQGxUJ3C31KI+Elf9B4I++EkCTddmiP/6X/CtPwD5QDYEkx7afu0i3pik6KO/rg8cT8+HZ9lW/4IKzepCalYX6nV94Jr2M1yzVyDuqWsD5VVFgfePnVBtLnh/3+G/UyNBatsImi5NoenZErpLOkTo1QSX6pPhmrUc7q/+gpprA+C/DhpuuBD6MRcHhjGHPA6bC7Z7PgIAmN8dD03vllCsLlhHvw01Kw/QiDA9MSoiSTgiosoQzAbEvXQTPBe0guOV7+FbvQe+zRlhWS1bVVWo2fnwbcuEb2smxDrmwDyzgtng/0xzewFBgNgsBcqhHGgu6QDTPYMCP0Aq2fmw/esTQJIgNqsLqfAHTIp9GzduRKdOndC2bVvo9XqoqgpBELBu3bpKbyukyS5RFNG7d29s376dvbko7OSNGVDzHND9X194FqyBe+6fgKzANHk4xBQLdEO6wTXzNzg/XQnRYgBkFaqsALIMyAp0I3oFJnX0rtkD34YDhY+f/VNlBfDJMN51RSAB4PllMzxLNhUpK0PJsQJuH7y/7zi7apxeCwgCoJWgv7YP3F+sApyeCB4xihZSwzrQ9GoJ92crobu0U7GeHKqqwv3ZH0CcHrqBsX9dFSQRUpMUSE1SoLo8gFEH34YDMNxxGeK/mQz50EmIFiPExklQvTIKRr4Mw7gB0F3Rpdg2EMIvy4IoQtu7FbS9q/8LFMUu3ZCu8P65C/bHP4dnYCdoL+kAuL3w/PAPfJsyYHxwGHSXdyn2HPPbt8FX2PPLtyUT6ikr5O2H/X/7soolu9yL1kNq1QBSm4YVTsYoOVa4F66Db2smBFGE5oJW0F3ZI6wTGquyAvtjc+FdtRv6Ub2hvbwzoKjw/LwZzuk/Q96fBdPzN4SlJ5X7+/VQsvIQ//W/Ar2+5U0ZUE/kQ0i2QM23w7tsK6RbKj9vCBFRuAmCAP3I3tB0bgbPyh2lJrqUXBu8y7dDtTohpiZBe3GHKs1t7NuU4f+82poJ37bDUE8WBB4TW9Q7m+zSSDBNGQGxXgI0HZtAMBvgeH0x3PNXw9utGfQNEgGTHq5PVkDedQwAYLh3CHvT1iLfffdd0LYV8mGM69atQ/fu3dGmTRuYTKZqZeaIKkM5eAKQRJgeGQ5Nh1Q4/vcdtAPOJgeUk/6V6Vxv/Vjq87UXdwAKG/y+fw7ANXtFmfvS33hRINklHz4F7x87Sy0n7zkO1eYC4P8AMv37GqhWJ4TEOLi/WAXV7S31eVT7GO8fCuuED2C98wMYbh8ITeFqjK7PV8H7y2b/MBpT7erSLRh00A3rDveCNdBd2QNS0xT/JPvwf2F2PrMAMOqgG1T2HGJEkSKIIuL+cwM8PZrDNX81vMv8czRqejRH3Bu3+ns1nlNe06UZNF2aAbcUznd3/DTkLZnwbT4UWLEXKBwW8vxX/ht6LTQdU/3DHrs0g6ZzU4iJJVcF9azYDvsT8wAI0PZqCdXjg/ONH+D6cBnMb44L24TG3t+2wfv7jhLHQNuzJbTpLWB/8gvohnav1JBsVVEAt8//meryQnV5obo8EOvGQyycz1DJKYB39R6oLi/g9j/u/mYdxIaJcM1eAd2lnaC9sC10l3SA8MY4CHUtcH28Ap5fthSbh5CIKNpJLevD2LJ+4LaSUwDHf7+DkGT2T6WiqBDi9P5Vt+vEwTRlJHSXdS51W6qqQilc6Efbp03gfvsLX0PJOFlkpyKk1g2h6dwEUudmxbahv7pnsdvG+4cCPhnOt5fA+f5SiMkWKMdPB1a9dn+5Bvqr0iGmWIJwNCjaNWvWrPxCFRTyZFfRzFxubi6SkpJCvUsiP4PW3/sq3wH9VenQXtQWYp2zQ4uERP+/pU5N/PdrCnt/SCIESSw2b4qmaxr0N10EaKSzvUSKlD3TeAb8y8yLyebCx/3l5ew8uN5eAv34SyE2PzthtVg3HqgbD/cP//hjKfJBRLWbpn0qLO/fCcfL3xWbXF1skAjTs9cXm8OrNjFOvAK+v/fDeus06K/vA016CygnCuD++i/I2w4j7vkbOK8DRS1BEqG/rg90117g78krihWeg0sQBEiNkiA1SoJuSLdij6lWJzQXtYW8NRNqgRO+fw4G5j8BAMNtA2CcNMRfVlEg7z8B++OfQ3txB5ieHBXoyaWcLIBtylzYHpyN+K//VWqSLBhUt9c/xYDTA9dnKwO90Ty/bYPq8voTdY2ToB3cFeKHv8LxyiJoL2jtX1U5kJzyQnV7/T05L/b3cPOu2QPb5DmA21fqfo2PDIfhhgsBAPKhnLMJwqKxAfAcyYXYOCmQYNNe5P+/2CAR8q6jITgiRETh45i6MDA8Xju0m3/US4IJcsYJOKf/Avvjn0N4+zZo+7SBanfDt+MwfFsPQ956CL6th6Hm2QGDFokrng30JNb2bw+leT1oOjWF1KUpNO0bl1ipuiyCRoLp0REwjL0Enl82Q8l3+Ec5XNgGtvs+hnLoJGz/mgPLB3fG9LyVtd2YMWPw6aefolevXsV68kXtMEYASExMxNy5czFz5kxs3rwZPl/pDRCiYNNe1BbQSnB/vda/8mKd4nPoqHl2CCkWWD6aWO6QD22/diUm0y6Lpm0jaNo2KnG/97dt8C7fDsNNFxW7X8mxwjXjV2j6tIbUJKVC+6DaQdOpCeLn3At5X5Z/UvgEEzRdmoVt/ppoJNYxwzJzIlwzfoXr8z+Bj34D4O8dY37ndmgvaB3hCInKJwgCEMSemVKTFFjeug2qokA5lFM47NE/nEQ5eAJikc8WeedR/4IKAKTmdSHvOAKhU1MIcXqIdeMR9/ItKLj6f/B8tx66kb38v64X9oxSnYXJJqf/39qBHSE18v+I6V2/H+5v1gGFCSnV5Sks5/933JPXBj5HPcu2wfH0/GKvwXbfrMC/Tc/fAKlxEgRBgJiaDN+q3XAfPlXqa1dPnB0qA41YMtGl00DQawCDDoL27Ge9mGyG5qK2EAy6wOO+v/ZC9Xihv74vtD1alNiXb/MhiGFa1IaIKFT0t14SSHZ5l2yCK8kM471DIDZNQdzUm2G7+0M4318K7+874P56bWABoACtBKl1w8LvUv55Zk0PDKt2XGKDRBjGXlLsPvObt8I6bjrk7Ydhf+5LxL14Y9StKknB8fLLLwMAvvrq7A9R1e0sFbJk12+//YZZs2bhm2++gcViQb9+/bBp06ZQ7Y6oBLGOGfrr+sA141eICSbohveEoNNAtbvhmvsHPIvWwzj56rBNNBv39HWw3jkDBTe+Cf01vSE1rwd5bxbcC9cBGgmmKSPDEgfVPFKrBsWGLNV2Yh0zTI+NhPGBYVByrBBMek4ITwT/0EepeT1IzetBP7IXAEDJdxT7nPNtzfSvGgr/yqUA/ENFjDrA5YV5+h3QXtQW3lW7AKMOzpfLnjtDbJIcSHYpWXnw/rK5zLJnhvADgGDU+ntKG7RQnR5Ar4WUmuRPSBl1EIusKAuXF0K9eOhH9PL3EtBr/AkqgxYwaCG1OTtpsaZzM8R//xgEg9ZfVqcp88cBKa1eiQVqPL/vgP1fcyCl1YWma/FhFJ6VOyBvOYS4/40u8zUSEdUE8s6jgEaEbnhPeL5ZB/fcP+FdsQNqgQPx8x+C/oYLYX9srn84u6JCbJgIqVNTaDr7/6S2jao0r1dVSE1SEPfyLbBNmgnv0i1wt20UmP+LYkvDhv7P82B2lgrqu/TIkSOYPXs2Pv74Y2RnZ2PEiBH46quvMHjwYOzcuRMLFy4M5u6IymV8YBhUuxuO/y6E871fIDasAznzJODywjDhMugLhzOEg9SiPiyfTIJr9gq4Pl0JuLxAnB76K3vAMG4AxHoJYYuFKBYIBl1gEmkiKp2YYCp2W39dHzg//BWajk0gxpvg23LIv/y73e0v4PRAMOqgnLJCjDdCSLFAMPqTUChMMp25XXQIv6ZTExgfvqqwXGGZM0kpo67YCqnaAR1R568X/bubtRyumctgfus2/9D+IuTDOf7J+/91NQz/17fc1yoYtJAa1im3XFm0/dtBe3ln2P89D75R+6G9ogsEUYRn6Ra4v14L7YAOxeb+JCKqidQCB4QEE+L+PQraC9vC8fxXUI7mAvD/ICI29v+IobmoLQxjLylxbQ43bXoLmP59DdxfrCoxjJ9iRyg6SwmqqqrlFyvfsGHDsHz5clx66aW4+eabMXLkSMTFnf1lbvv27ejSpQtkWQ7G7kLmyJEjaNKkCQ4fPozU1NRIh0NBImecgOfHjVBO2yE2SIT+yh6BCeUjQZUV/3wtRl2tHpJGREThZ3toNpQTBbB8dh8EQYCSY4XqcPsTUyY9Cq55BbrBXWGaPDzksSh5dhTc/BaEOANMU0ZC06M5oKrwrdsPx9RvAUFA/Nz7wzYXn+qT4Zq9Au4v10A9ZQMACEn+nuKG2weGrTc4EVGouH/cCMczCxC/8BFIjZOg5BTAt+EgxGYpkFo1gGfh33C8/B0SfnoiqnrOq14fBG14epTFimjPbZTWWWr06NGBzlJdu3atVv4oaO+Wn376CTfffDMefPBB9OzZs/wnEIWRlFYPxnsGRzqMAEESAbMh0mEQEVEtpL++L2z3fwz3F6tguKlf4QpXFqiqCudr30PNc0B/bZ+wxCImxsHy3gTYHv0MtrtmQKgTBygq1HwHpA6piPvf6LAuOiFoJBjvuAyGWy+BcijHH2OzFH7BIqKYobu0I5yv6OF6fylMz/8fxJR46Ab7V7JWChxwffYHtBe3j6pEF4Bi12HPiu2Q2jQMDKWnmqdoZ6nnn3++RGepopPUV1XQPrlXrVqFWbNm4dJLL0XDhg0xevRo3HzzzWjVqlWwdkFERERE1aS9sC30Yy6G87XF8Py6FbqBnQCfDM9PmyDvy4JpykhIRVYODjWpWV3Ef/EgfOv3w7cxAxAATc+W0HRLC0pjtyoErYZzJRJRTBIMOhgnD4fjmQVQTttguPEiiA0S4duaCdec36FanTDeNzTSYZbJvXgDHM9+CbFlfcTPvBsCOxDUSOHoLBW0YYxnOBwOfPHFF5g1axbWrFmDXr16YfTo0ejYsSOuuOIKDmMkIiIiijBVVf0rbc1fDd+WQ4AoQtu7FfQ394M2veRKhEREFFs8K7bD9cFSyHuz/HcIArT92sL44JWQmtWNbHDnoWTno+DWd6HmWKG5sC3Mr4/lEPMyRHNuY82aNZg1axbmz59famepYEyDFfRkV1G7d+/GzJkz8emnnyI7OxuCIDDZRURERERERBRhqqpCyTgJ1eqE2LBOxCejryjfjiOwTvgAcHuhv+kimP51daRDiko1IbcRys5SIU12nSHLMr7//nvMmjULixYtCvXuqqUmvCGIiIiIiIiIaivPsq2wPzYXAGCaMhL668Iz12RNUtNyG8HuLBWWZeAkScLIkSOjPtFFRERERERERNFNd1lnGCb5FyBzvLII3r/2Rjgiqq62bdvi5ZdfxpEjR/DNN9/gyiuvrNb2wpLsIiIiIiIiIiIKFsO4AdBd2QOQFfg2Hox0OBQkweosxXWUiYiIiIiIiKhGEQQBpidGQXtJB+gu7RTpcCjKsGcXEREREREREdU4gk5TLNGlen1QPb4IRkTRgskuIiIiIiIiIqrRlDw7bJNmwvHSNwjDOnwU5ZjsIiIiIiIiIqIaTd57HL7Nh+BZ/A/cn/we6XAowpjsIiIiIiIiIqIaTdurFYyTrwYAON/9CZ7ftkU4IookJruIiIiIiIiIqMYzXN8X+hsuBADYn5oP384jEY6IIoXJLiIiIiIiIiKKCcaHroTmwjaA2wvbw3OgnMiPdEgUAUx2EREREREREVFMEDQSzC/dDLFFfagnC2B/en6kQ6IIYLKLiIiIiIiIiGKGYDbA/MatkDo3hemxkZEOhyJAE+kAiIiIiIiIiIiCSWqcBMusuyEIQqRDoQhgzy4iIiIiIiIiijlFE13e9fvhXrIxgtFQOLFnFxERERERERHFLN+OI7BNmgkIAsT6CdD2aBHpkCjE2LOLiIiIiIiIiGKW1K4RtAM6Aj4Z9kc+g3w4J9IhUYgx2UVEREREREREMUsQRcQ9dz2kDqlQ8x2wPfQJFKsz0mFRCDHZRUREREREREQxTTDoYH59LIT6CVAyTsI+5XOoPjnSYVGIMNlFRERERERERDFPTImH+fVbAaMOvrV74XhlEVRVjXRYFAJMdhERERERERFRraBp2whx/7kBEATA4QFkJdIhUQhwNUYiIiIiIiIiqjV0AzpCnHU3pE5NIAhCpMOhEIipnl0ZGRkYP348mjdvDqPRiJYtW+KZZ56Bx+OJdGhEREREREREFCU0nZsGEl2qokA5kR/hiGqOmpB7iameXbt27YKiKPjggw/QqlUrbNu2DRMmTIDdbserr74a6fCIiIiIiIiIKIqoTg/sT30BefcxWD65F2KSOdIhRb2akHsR1Bifje2VV17Be++9hwMHDlSo/JEjR9CkSRMcPnwYqampIY6OiIiIiIiIiCJFybPDett0KIdPQerSFKbJw+FZthXqaTvE+gnQXZUOqXFSpMOstHDnNiqbewm1mOrZVZr8/HwkJZX9xnS73XC73YHbVqs1HGERERERERERUYSJiXEwvzEO1tumQd6SCevYd4E6cZAaJcH72za4PvoNhnGXwHDP4Bo5v5fVakVBQUHgtl6vh16vD/p+ysu9hFtMzdl1rv379+Odd97BxIkTyywzdepUJCQkBP46dOgQxgiJiIiIiIiIKJKktLrQpLcI3DZc3wfxn0xCwpJ/wzDxCrg+XgH3vFURjLDqOnToUCznMXXq1KDvoyK5l3CrEcmuZ599FoIgnPdv/fr1xZ5z7NgxDBkyBNdffz3uuOOOMrf9+OOPIz8/P/C3Y8eOUL8cIiIiIiIiIooSSq4N3lW7oRng7/zimrEMnl82QzDqYBx/KXQje8H1ye9QfXKEI628HTt2FMt5PP7442WWDWXuJdxqxDDGe++9FzfeeON5y6SlpQX+fezYMQwcOBB9+/bFjBkzzvu8c7vwFe3eR0RERERERESxzbt6N+CVEffktXA1ToJ77p9w/HchtBe2hWA2QH9Nb3gW/g1522FouqVFOtxKsVgsiI+Pr1DZUOZewq1GJLtSUlKQkpJSobJHjx7FwIEDkZ6ejo8//hiiWCM6rxERERERERFRJLi8gCRCSDDBeP8wqFYX9KN6QzAbAABC4QqNqssTyShDLpZyLzUi2VVRx44dw4ABA9C0aVO8+uqrOHnyZOCxBg0aRDAyIiIiIiIiIopGYsv6gKzAt+EAtD1bIu7p64o97lu7FxAESGn1IhRhdKkJuZeYSnb98ssv2LdvH/bt21diaU1VVSMUFRERERERERFFK023NIgt6sH59hJo3psAIe7sVEdKTgFcs5ZD268txAaJkQsyitSE3IugRkskUeLIkSNo0qQJDh8+XKLSiIiIiIiIiCj2+HYcgfXuDyEmmKAfdQHEtLqQdx6F+5u1gFaC5aOJkBolRTrMCqvtuY2Y6tlFRERERERERFRZmg6piP9kElwzl8P5wVLAKwMmHfRX9oDhtoEQ6yVEOkSqBCa7iIiIiIiIiKjWk9LqIe4/N8D01LVQ7S4IFiMEjRTpsKgKmOwiIiIiIiIiIiok6DQQdOZIh0HVEF1rQxIREREREREREVUDk11ERERERERERBQzmOwiIiIiIiIiIqKYwWQXERERERERERHFDCa7iIiIiIiIiIgoZjDZRUREREREREREMYPJLiIiIiIiIiIiihlMdhERERERERERUcxgsouIiIiIiIiIiGIGk11ERERERERERBQzmOwiIiIiIiIiIqKYwWQXERERERERERHFDCa7iIiIiIiIiIgoZmgiHUC0URQFAHD8+PEIR0JEREREREREVHlnchpnchy1DZNd58jOzgYA9O7dO8KREBERERERERFVXXZ2Npo2bRrpMMJOUFVVjXQQ0cTn82Hjxo2oX78+RLH8UZ4DBgzAihUrqrSvyj63ouWtVis6dOiAHTt2wGKxVCm22qI69RdukYw11PsO5vaDsa2qbiNU5zTA87qiatI5DUQuXp7ToXseP6uDryad17H6WR3sbVd3ezynazae09Gx72j6rOZ36tBQFAXZ2dno3r07NJra18+p9r3icmg0GvTq1avC5XU6HVJTU6u0r8o+t6LlCwoKAACNGzdGfHx8lWKrLapTf+EWyVhDve9gbj8Y26rqNkJ1TgM8ryuqJp3TQOTi5Tkduufxszr4atJ5Hauf1cHednW3x3O6ZuM5HR37jqbPan6nDp3a2KPrDE5QX02TJk0K23Orsy8qXU06ppGMNdT7Dub2g7Gtqm6D53Tk1bRjGql4eU6H7nk17T1YE9SkYxqrn9XB3nZ1t8dzumarScc0Vs/pYG8/Uud0VZ5bk95/VD0cxhiDCgoKkJCQgPz8/BqdhSais3heE8UWntNEsYXnNFFs4Tld87FnVwzS6/V45plnoNfrIx0KEQUJz2ui2MJzmii28Jwmii08p2s+9uwiIiIiIiIiIqKYwZ5dREREREREREQUM5jsIiIiIiIiIiKimMFkFxERERERERERxQwmu4iIiIiIiIiIKGYw2UVERERERERERDGDya5aaPHixWjbti1at26Njz76KNLhEFE1XXPNNahTpw6uu+66SIdCRNV0+PBhDBgwAB06dECXLl3w5ZdfRjokIqomq9WKXr16oVu3bujcuTM+/PDDSIdEREHgcDjQrFkzTJ48OdKhUCkEVVXVSAdB4ePz+dChQwcsX74c8fHx6NGjB9auXYukpKRIh0ZEVbR8+XLYbDZ88skn+OqrryIdDhFVw/Hjx5GdnY1u3brhxIkT6NGjB3bv3o24uLhIh0ZEVSTLMtxuN0wmExwOBzp16oS///4bycnJkQ6NiKrhiSeewN69e9G0aVO8+uqrkQ6HzsGeXbXMunXr0LFjRzRu3BgWiwXDhg3Dzz//HOmwiKgaBg4cCIvFEukwiCgIGjZsiG7dugEA6tWrh6SkJOTm5kY2KCKqFkmSYDKZAAAulwuyLIP9DYhqtr1792LXrl0YNmxYpEOhMjDZVcOsXLkSV199NRo1agRBELBw4cISZaZPn47mzZvDYDAgPT0df/zxR+CxY8eOoXHjxoHbqampOHr0aDhCJ6JSVPecJqLoEsxzev369VAUBU2aNAlx1ER0PsE4r/Py8tC1a1ekpqbi0UcfRUpKSpiiJ6JzBeOcnjx5MqZOnRqmiKkqmOyqYex2O7p27Yp333231Mfnz5+PBx98EE888QQ2btyI/v37Y+jQocjMzASAUn9FEgQhpDETUdmqe04TUXQJ1jl96tQpjB07FjNmzAhH2ER0HsE4rxMTE7F582YcPHgQn3/+ObKzs8MVPhGdo7rn9HfffYc2bdqgTZs24QybKkulGguA+u233xa7r3fv3urEiROL3deuXTt1ypQpqqqq6qpVq9SRI0cGHrv//vvVuXPnhjxWIipfVc7pM5YvX65ee+21oQ6RiCqhque0y+VS+/fvr86ZMyccYRJRJVTns/qMiRMnqgsWLAhViERUCVU5p6dMmaKmpqaqzZo1U5OTk9X4+Hj1ueeeC1fIVEHs2RVDPB4PNmzYgEGDBhW7f9CgQVi9ejUAoHfv3ti2bRuOHj0Kq9WKH3/8EYMHD45EuERUjoqc00RUc1TknFZVFePGjcOll16KMWPGRCJMIqqEipzX2dnZKCgoAAAUFBRg5cqVaNu2bdhjJaLyVeScnjp1Kg4fPoyMjAy8+uqrmDBhAp5++ulIhEvnoYl0ABQ8OTk5kGUZ9evXL3Z//fr1kZWVBQDQaDR47bXXMHDgQCiKgkcffZQrwRBFqYqc0wAwePBg/PPPP7Db7UhNTcW3336LXr16hTtcIipHRc7pVatWYf78+ejSpUtgDpFPP/0UnTt3Dne4RFQBFTmvjxw5gvHjx0NVVaiqinvvvRddunSJRLhEVI6Ktr8p+jHZFYPOnYNLVdVi9w0fPhzDhw8Pd1hEVEXlndNcUZWoZjnfOd2vXz8oihKJsIioGs53Xqenp2PTpk0RiIqIqqq89vcZ48aNC1NEVFkcxhhDUlJSIElSiYzziRMnSmSmiSj68Zwmii08p4liD89rotjCczp2MNkVQ3Q6HdLT07F06dJi9y9duhQXXnhhhKIioqriOU0UW3hOE8UentdEsYXndOzgMMYaxmazYd++fYHbBw8exKZNm5CUlISmTZvi4YcfxpgxY9CzZ0/07dsXM2bMQGZmJiZOnBjBqImoLDyniWILz2mi2MPzmii28JyuJSK3ECRVxfLly1UAJf5uvfXWQJlp06apzZo1U3U6ndqjRw/1999/j1zARHRePKeJYgvPaaLYw/OaKLbwnK4dBFVV1bBl1oiIiIiIiIiIiEKIc3YREREREREREVHMYLKLiIiIiIiIiIhiBpNdREREREREREQUM5jsIiIiIiIiIiKimMFkFxERERERERERxQwmu4iIiIiIiIiIKGYw2UVERERERERERDGDyS4iIiIiIiIiIooZTHYREREREREREVHMYLKLiIiIiIiIiIhiBpNdREREREREREQUM5jsIiIiIqqiadOmIS0tDRqNBo888kiJx0+dOoV69eohIyMjqPu97rrr8Prrrwd1m0RERESxQlBVVY10EEREREQ1zbZt29C9e3csXLgQPXr0QEJCAkwmU7EykydPxunTpzFz5kwAwLhx45CXl4eFCxcWK7dixQoMHDgQp0+fRmJiYrn73rJlCwYOHIiDBw8iPj4+WC+JiIiIKCawZxcRERFRFSxatAjp6em48sor0bBhwxKJLqfTiZkzZ+KOO+4I+r67dOmCtLQ0zJ07N+jbJiIiIqrpmOwiIiIiqqSWLVviiSeewNq1ayEIAsaMGVOizJIlS6DRaNC3b99Kbz8jIwOCIJT4GzBgQKDM8OHDMW/evOq8DCIiIqKYxGQXERERUSWtWbMGLVq0wCuvvILjx49j+vTpJcqsXLkSPXv2rNL2mzRpguPHjwf+Nm7ciOTkZFx88cWBMr1798a6devgdrur/DqIiIiIYpEm0gEQERER1TRmsxkZGRno168fGjRoUGqZjIwMNGrUqMT9ixcvhtlsLnafLMvFbkuSFNiuy+XCyJEj0bdvXzz77LOBMo0bN4bb7UZWVhaaNWtWzVdEREREFDuY7CIiIiKqpC1btgAAOnfuXGYZp9MJg8FQ4v6BAwfivffeK3bf2rVrccstt5S6nfHjx8NqtWLp0qUQxbOd8o1GIwDA4XBUOn4iIiKiWMZkFxEREVElbdq0Ca1atUJcXFyZZVJSUnD69OkS98fFxaFVq1bF7jty5Eip23jhhRfw008/Yd26dbBYLMUey83NBQDUrVu3suETERERxTTO2UVERERUSZs2bULXrl3PW6Z79+7YsWNHlffx9ddf4/nnn8eCBQvQsmXLEo9v27YNqampSElJqfI+iIiIiGIRk11ERERElbRp0yZ069btvGUGDx6M7du3l9q7qzzbtm3D2LFj8dhjj6Fjx47IyspCVlZWoDcXAPzxxx8YNGhQpbdNREREFOuY7CIiIiKqBEVRsHXr1nJ7dnXu3Bk9e/bEggULKr2P9evXw+Fw4IUXXkDDhg0Df6NGjQLgn7T+22+/xYQJE6r0GoiIiIhimaCqqhrpIIiIiIhi0Y8//ojJkydj27ZtxSaXr65p06bhu+++wy+//BK0bRIRERHFCk5QT0RERBQiw4YNw969e3H06FE0adIkaNvVarV45513grY9IiIioljCnl1ERERERERERBQzOGcX0f+3YwcyAAAAAIP8re/xFUYAAADAhuwCAAAAYEN2AQAAALAhuwAAAADYkF0AAAAAbMguAAAAADZkFwAAAAAbsgsAAACADdkFAAAAwEaNu6fUBd5dmwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data: DataSet\n", - "for data in project_ex1.get_data_sets():\n", - " drt: DRTResult\n", - " for drt in project_ex1.get_drts(data): # Get the DRT analysis results for a specific data set.\n", - " fig, axes = mpl.plot_drt(drt, data)\n", - " \n", - " # The raw data can be obtained here as well.\n", - " f: ndarray = drt.get_frequency()\n", - " Z: ndarray = drt.get_impedance()\n", - " \n", - " tau: ndarray = drt.get_tau()\n", - " gamma: ndarray = drt.get_gamma()\n", - " \n", - " # Non-empty array only for BHT method results.\n", - " imaginary_gamma: ndarray = drt.get_gamma(imaginary=True)\n", - " \n", - " # Non-empty arrays only for TR-RBF method results where credible intervals were calculated.\n", - " mean: ndarray\n", - " lower_bound: ndarray\n", - " upper_bound: ndarray\n", - " tau, mean, lower_bound, upper_bound = drt.get_drt_credible_intervals()" - ] - }, - { - "cell_type": "markdown", - "id": "e7c13101-ed88-42ec-8565-c401086f0ec9", - "metadata": {}, - "source": [ - "##### Circuit fit results\n", - "\n", - "Circuit fit results can be plotted in the same way as Kramers-Kronig test results." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "3bcf6798-51c6-4aec-8578-443da34f6546", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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t1q1bN9a9e3c2ZMgQnckuxoqSyCNHjixXu4QQQgjRRLcxEkIIIa9Zo0aNAADPnz/XKD9w4AASExMxaNCgCrWjUCiwcOFC+Pj4YPbs2TqPcXBwQKtWrbhte3t7dOjQAWvXrtWo9/z5c+zbt6/CsQBAcHAwACAqKoorO3nyJIRCIaZOnQoAOH36NLcvMjJS47higwYNwvHjx/Hw4cMy49i8eTOUSiX69eunUW5nZ6dV18nJCS4uLnjy5AlXlpiYiOjoaAwaNAhCoZArb9GiBby9vbFv375S2xQIBAgICNBoU1/dgIAACAQCrbq6lCcufapVqwY+nw+BQFBmXUOvFwDw+RX/uHj+/HkkJSVh2LBhGuV9+/aFqalpmee1ceNG8Hg8DB8+vMy+rKysIBKJtMqbNGkCAHj69GmZbdja2mpc/9dp69atOHnyJFavXl1qvUGDBmH79u3Iycl5I3EQQgghHwJKdhFCCCGvWVxcHADA29tbo/zQoUOws7ODr69vhdq5dOkS0tPT0bNnz3LNmxQUFIR///0XmZmZXFl4eDgUCoVW4snQWAAgMDAQfD6fS2IBRQmtRo0awd7eHgEBARqJsMjISAgEArRu3VorPsYYDh8+XGYchw4dQoMGDWBpaVlm3UePHuHx48eoU6cOV3bz5k0AQN26dbXq161bl9uvj1KpxOnTpzXa1OfkyZNQqVQG1a1IXIwxKJVKZGRkYOfOndi8eTOmTJlS4WSNruv1qvSdl0gkgo+PT6nXOysrC7t370a7du3g6empsa94nrvNmzeXGUNERASEQqHW3yMAqNVqKJVKvHjxAqtXr8bRo0cxY8YMg/s6deoUzMzMIBKJ4Ovri59//llrfjAASElJwaRJk7BgwQK4uLiUGm9QUBDy8vI0/nYIIYQQUj6U7CKEEEJekUqlglKpRG5uLo4ePYoff/wRbdq0QY8ePTTqnTt3Dg0bNqxwOwkJCQCg9cW/LA0bNoRardaY4PvcuXOQSqXw8fHReUxxEkAul+PatWsYOXIkHB0dERoaytWxtrZG3bp1Nb6UR0VFITAwEEBRMuzlRFhUVBQCAgJgbm6u0ZednR2cnZ3x77//lnku58+fL/UaFlMqlRgxYgRMTU0xefJkrjwtLY2LvSRra2tuvz5z587FgwcPMGfOnFLr5eTkYOzYsXB1dTVoVFJF4lq4cCFEIhGsra3Rv39/TJo0CfPmzSuzL130Xa9X9SrX+88//4RMJsOIESO09hWPYCtr1Fl4eDj++OMPjB8/HjY2Nlr7x44dC5FIBDs7O0yePBnLly/HqFGjDOqra9euWLZsGQ4dOoTdu3ejYcOGmDp1KoYOHaqzn1q1aulc0KGkBg0agMfjGfT3QAghhBDd3sw4bUIIIeQD0qxZM43t2rVr4++//9YaYfPs2TM0btz4ldspr+Jb1hITEzViqVatmt4RYjNmzNAY4WJmZobIyEh4eHho1AsODsbSpUvx7NkzSCQS3Lx5E4sXLwZQlOz6+eefkZWVhaysLMTFxeHTTz/VG+PL8emSmZmJ/Px8nbfgvYwxhhEjRuD06dPYs2cPXF1dteroO+/SRsxt2LABP/30E6ZMmYKePXvqrVdQUIA+ffrg8ePHiIiI0Fh1T6VSgTHGbfP5fI0kSnniGjp0KNq3b4/09HRERERg8eLFyMrKwooVKwAUXYeSo4x0PZcMuV6vqiLXe+PGjbCxsUHv3r219g0ePBiDBw8utc8rV67g008/RbNmzTB//nyddWbOnIkvvvgCKSkpOHjwIMaNG4e8vDzuNtzS+lq1apXGds+ePWFlZYWVK1ciNDQUDRo0AADs2bMHBw8exNWrVw0akSkSiWBpaVnm3wMhhBBC9KORXYQQQsgr+v333xEdHY2IiAiMGjUKsbGx6N+/v1Y9mUwGIyOjCrfj5uYG4L9bCg1V3KdMJjM4lokTJyI6OhpnzpzBkiVLoFAo0LNnT62ROC/P2xUVFQWBQICWLVsCADd32OnTp/XO1/VyjC/Hp0vx/tLiZozhiy++wNatW7F582atpFTx6B5dI4rS09N1jkACgLCwMIwaNQpffvkll8zTRS6Xo3fv3jhz5gwOHDiApk2bauxv164dRCIR9yge9VWRuBwcHNCoUSN07NgRCxYswPfff4+VK1fi6tWrAIAtW7Zo9KVrPquyrterquj1vn79Oi5duoTPP/8cEomk3P1evXoVHTp0QM2aNXH48GG9bbi5uaFRo0b46KOPsGbNGnz55Zf4+uuv8eLFi3L3CQCff/45AHCjKHNzcxESEoLx48fDyckJmZmZyMzMRGFhIYCiBG5eXp5WO4b8PRBCCCFEPxrZRQghhLyi2rVrcxO4BwcHQ6VSYcOGDdi9ezc++eQTrp6trS3S09Mr3E6jRo1gbW2Nv//+G/Pnzzd43q7iPm1tbTViuXLlit5jXFxcuFhatmwJBwcHfP7555gzZw5WrlzJ1WvTpg0EAgGioqIgkUjQsGFDbiSTubk56tevj8jISKSnp0MoFHKJMF0xlhw1VlJx4kTfNSxO3ISFhWHjxo1c4uFlfn5+AIAbN27go48+0th348YNbv/LwsLC8MUXX2DIkCFYu3at3usul8vRq1cvREZG4u+//0a7du206vz2228aE48X/5tUJK6Siidiv3fvHho0aIDu3bsjOjpab31Drter8vf3B1B0Di/PVadUKnHnzh2dSWGgaFQXAHzxxRfl7vPq1ato37493N3dER4eDgsLC4OPbdKkCdauXYtHjx6hWrVq5e67eNRe8Wi91NRUPH/+HD///DN+/vlnrfpWVlbo2bMn9u/fr1GekZGh8fdKCCGEkPKhkV2EEELIa7Zo0SJYWVnh22+/hVqt5sp9fHwMWnFQXzsikQgzZszAnTt38MMPP+g8JiUlRWuun0ePHgGARrLBx8cHaWlpyMrKMiiWgQMHIigoCOvXr8fjx4+5cgsLCzRo0IAb2RUUFKRxXPG8XVFRUWjSpInGLX3FlEolnjx5UubE/WKxGNWrV9d5DRljGDlyJMLCwvDbb79prf5XzNnZGU2aNMHWrVs1bvE7f/487t69iz59+mjU37x5M7744gt8/vnn2LBhQ6mJrt69eyMiIgJ79uxBp06ddNarVasWGjVqxD2KE3zljUuX4tFzXl5eAIqSgy/3VZy8BAy/Xq+qadOmcHR01Jrcfffu3cjNzdV5XnK5HFu3bkWTJk0MSvK9LCYmBu3bt4eLiwuOHTsGKyurch0fGRkJPp+P6tWrl+u4Yr///juA/25JdnBwQGRkpNajU6dOMDIyQmRkJH788UeNNp49e4aCggKDF7IghBBCiA6MEEIIIRUSFhbGALDo6GitfYsWLWIA2B9//MGVff/990woFLK8vLwKt6NWq9nQoUMZANa1a1e2bds2durUKXbw4EE2bdo0ZmFhwX799VeNNsaPH89sbGyYWq3myk6dOsUAsKNHj2rUjYuLYwDY4sWLtWK5cOECA8BGjBihUT5t2jQGgPF4PHbo0CGNfX///Tfj8XgMAJs1a5ZWm4wxdvnyZQaAHThwQOf+lw0fPpw5OjpqlY8bN44BYMOHD2fnzp3TeFy5ckWjbmRkJBMKhax3797s2LFjbNu2bczV1ZX5+fmxgoICrt6uXbsYn89nDRs2ZP/++69Wuy/X7datG3eOJevdunWrzPMqT1zffvstGzVqFNu2bRuLiopi+/fvZ6NHj2YCgYD17dvXoL7Kc73i4+PZX3/9xf766y/WuXNnBoDb1vWcLemPP/5gANiXX37JIiMj2bp165ilpSXr0KGDzvo7duxgANi6dev0trllyxYmEAjYli1buLI7d+4wGxsbZm1tzQ4ePKh1XikpKVzdkSNHsilTprCdO3eyqKgotnv3btavXz8GgE2bNq3MvrZt28Y+/vhjtmnTJnbixAm2Z88e9tlnnzEAbOjQoWVekyFDhjATExOd+/bs2cMAsOvXr5fZDiGEEEJ0o2QXIYQQUkGlJalkMhlzc3NjNWvWZEqlkjHG2IMHDxiPx2O7du16pXYYK0oide3alVWrVo0JhUJmZWXFgoOD2dq1a5lcLufqqdVq5u7uzsaPH6/RrkqlYh4eHmzs2LEa5aUluxhjrG/fvkwoFLIHDx5wZYcPH2YAmEAgYFlZWRr109PTGZ/PZwDYsWPHdLY5e/ZsZmtrq5HQ0efEiRMMALt48aJGubu7OwOg8+Hu7q7VTnh4OGvWrBkzMjJi1tbWbPDgwez58+cadYYMGaK3TQAsLi6Oq1tavcDAwDLPqzxxHThwgLVv357Z29szoVDITE1NWZMmTdjy5cuZQqEwqJ/yXK/i56eux5AhQwzqb/v27axu3bpMLBYzBwcHNmHCBJaTk6OzbocOHZiJiQnLzs7W215xTGFhYQbFWbLupk2bWOvWrZmtrS0TCoXM0tKSBQYGaiSnS+vr3LlzrF27dszBwYGJRCJmbGzMGjduzFavXs1UKlWZ16O0ZNegQYOYv79/mW0QQgghRD8eYy8tCUQIIYSQN6p79+5QKpX4559/3kp/J06cQMeOHXHr1i34+Pho7Pv555/x008/ITExEVKp9K3EU5JKpYKXlxcGDBiAn376yaBj6tati5YtW2LNmjVvODpC3q7s7Gw4OTlh6dKlGDlyZGWHQwghhLyzKNlFCCGEvEU3b95EgwYNcPbsWTRu3PiN9xccHAwvLy+sX79ea19BQQFq166NkJAQTJ069Y3HosuWLVswdepU3L9/H5aWlgYdc+TIEfTu3Rv379+Hi4vLmw2QkLfou+++w86dO3H9+nUIhbSOFCGEEFJRNEE9IYQQ8hb5+fkhLCwMycnJb7yvjIwMBAYG6h0xZWRkhD/++AMSieSNx6KPWq3Gtm3bDE50AUDnzp2xePFixMXFvbnACKkE5ubm2Lx5MyW6CCGEkFdEI7sIIYQQQgghhBBCyHuDRnYRQgghhBBCCCGEkPcGJbsIIYQQQgghhBBCyHuDkl2EEEIIIYQQQggh5L1ByS5CCCGEEEIIIYQQ8t6gZBchhBBCCCGEEEIIeW9QsosQQgghhBBCCCGEvDco2UUIIYQQQgghhBBC3huU7CKEEEIIIYQQQggh7w1KdhFCCCGEfIBycnLQuHFj1K9fH/7+/li/fn1lh0QIIYQQ8lrwGGOssoMghBBCCCFvl0qlglwuh7GxMfLz8+Hn54fo6GjY2NhUdmiEEEIIIa+ERnYRQgghhHyABAIBjI2NAQAFBQVQqVSg30AJIYQQ8j6gZBchhBBCyDvo1KlT6N69O5ycnMDj8bB//36tOqtXr4anpyeMjIwQEBCA06dPa+zPzMxEvXr14OLigunTp8PW1vYtRU8IIYQQ8uZQsosQQggh5B2Ul5eHevXqYeXKlTr379y5E5MmTcKsWbNw9epVtG7dGl26dEFCQgJXx9LSEteuXUNcXBy2b9+O58+fv63wCSGEEELeGJqzqxRqtRrPnj2DmZkZeDxeZYdDCCGEkBIYY8jJyYGTkxP4/A/3Nzwej4d9+/ahV69eXFnTpk3RsGFDrFmzhiurXbs2evXqhfnz52u1MWbMGLRt2xZ9+/bV2YdcLodcLue2lUolYmNj4erq+kFfe0IIIaQqUqvVeP78ORo0aAChUFjZ4bx1H94Zl8OzZ8/g6upa2WEQQgghpAxPnjyBi4tLZYdRZRQWFuLy5cv46quvNMo7duyIs2fPAgCeP38OqVQKc3NzZGdn49SpUxgzZozeNufPn4/vvvvujcZNCCGEkNfr4sWLaNy4cWWH8dZRsqsUZmZmAIo+QJubm1dyNIQQQggpKTs7G66urtx7NimSmpoKlUoFe3t7jXJ7e3skJycDAJ4+fYoRI0aAMQbGGMaNG4e6devqbfPrr79GaGgot/3kyRP4+fnh4sWLcHR0fDMnQgghhJAKSUpKQpMmTbQ+C3woKNlViuJbF83NzSnZRQghhFRhNN2AbiWvC2OMKwsICEBMTIzBbUkkEkgkEm7bwsICAODo6Eij6gghhJAq6kOdauDDPGtCCCGEkPeYra0tBAIBN4qrWEpKygf7Cy8hhBBCPhyU7CKEEEIIec+IxWIEBATg2LFjGuXHjh1DixYtKikqQgghhJC3g25jJIQQQgh5B+Xm5uLBgwfcdlxcHGJiYmBtbQ03NzeEhoZi0KBBaNSoEZo3b45169YhISEBo0ePrsSoCSGEEELePEp2EUIqTKVSQaFQVHYYhJD3mEgkgkAgqOwwqqRLly4hODiY2y6ePH7IkCHYvHkz+vXrh7S0NHz//fdISkqCn58fDh8+DHd397cWI71PEELeR/TeREjVR8kuQki5McaQnJyMzMzMyg6FEPIBsLS0hIODA01CX0JQUBAYY6XWGTt2LMaOHfuWIvoPvU8QQt539N5ESNVGyS5CSLkVf4Gxs7ODsbExvckTQt4Ixhjy8/ORkpICoGjVP/JuoPcJQsj7it6biqhSE8Dk+aXW4UmMIbB1e0sREaKJkl2EkHJRqVTcFxgbG5vKDocQ8p6TSqUAilYRtLOzo9tG3gH0PkEIed996O9NqtQEZC/pY1Bd86l7KeFFKgUluwgh5VI894qxsXElR0II+VAUv94oFIoP7gvFu4jeJwghH4IP+b2peESXcb8fILDz1FlHlRKH/J2zyxz9RcibQskuQkiF0C0phJC3hV5v3k3070YIeZ/RaxwgsPOE0NmnssMgRCd+ZQdACCGEEEIIIYQQQsjrQskuQggpJw8PD/z6669vrb+oqCjweDxa1YwQUiWsWrUKvr6+CAoKquxQCCGEEEJ0omQXIaTSZBUoEZ8uQ16h6o33NXToUPB4PCxYsECjfP/+/eUehh4dHY0vv/zydYb32gUFBWHSpEmVHQYh5D0UEhKC27dvIyoqqrJDIYQQQgjRiZJdhJC37vLTbPTYEgPr76Lguehf2H5/EsP+uoWEzII32q+RkREWLlyIjIyMV2qnWrVqNPEyIYSQUlXGjw5V6YeOqKgoeHh4vLXjCCFvDmNMY3v67Zv41KEV9ic/q6SICCkbJbsIIW/VqUcZaLX2Eh6mybCypw/CRzTA7HaeCL+fjmarLuJR2ptbsaV9+/ZwcHDA/PnzS623Z88e1KlTBxKJBB4eHvj555819pe8jXHu3Llwc3ODRCKBk5MTJkyYAAD4/vvv4e/vr9V+QEAAvv32W739Hz58GN7e3pBKpQgODkZ8fLzG/rS0NPTv3x8uLi4wNjaGv78//vzzT27/0KFDcfLkSSxbtgw8Hg88Hg/x8fFQqVQYMWIEPD09IZVKUatWLSxbtqzUa0EIIZUlR67E2vNPMWL3bYzaG4u9N1OgVKnfeL+vayTw3r178cMPP7zu8F6rt50cO3z4MPe+pOvx6aefvrVYCCFlS5HlYczpo2j7vx0a5U8LZDhu7IiUtGdQJt6BMvEOZE9u49bdaG5blRJXSVETUoRWYySEvDVqNcOw3bfR1NUcR4Y3gJGoaJnmDjVtMKKRE5qvuYRJ/7uHA0Pqv5H+BQIB5s2bhwEDBmDChAlwcXHRqnP58mV8+umnmDt3Lvr164ezZ89i7NixsLGxwdChQ7Xq7969G0uXLsWOHTtQp04dJCcn49q1awCA4cOH47vvvkN0dDQaN24MALh+/TquXr2Kv/76S2eMT548QZ8+fTB69GiMGTMGly5dwpQpUzTqFBQUICAgADNmzIC5uTkOHTqEQYMGoXr16mjatCmWLVuGe/fuwc/PD99//z2AotFoarUaLi4u2LVrF2xtbXH27Fl8+eWXcHR0pC8YhJAq5dj9NHy67Qay5UoEOJsjX6HCuouJ8LY1xqGh9eFl+2ZH1xaPBB41ahSsrKwq1Ia1tfVrjurdFxwcjKSkJI0ylUqFYcOG4erVq5g9ezbS0tIqKTpCSEl5CgU23b2BQrUK19NSUNfGDgAwxccPra7/jeYJh5ET8SsAYL+JC760a4qpGbcxPTOWa4MnobshSOWgkV2EkLfm+IN0PEqXYWGXmlyiq5i9mQRfB3ngf3dS8TTrzd3O2Lt3b9SvXx9z5szRuf+XX35Bu3btMHv2bHh7e2Po0KEYN24cFi9erLN+QkICHBwc0L59e7i5uaFJkyYYOXIkAMDFxQWdOnVCWFgYVz8sLAyBgYGoXr26zvbWrFmD6tWrY+nSpahVqxYGDhyolWRzdnbG1KlTUb9+fVSvXh3jx49Hp06duASahYUFxGIxjI2N4eDgAAcHBwgEAohEInz33Xdo3LgxPD09ubZ37dpV3stICCFvTGxKHnpsuYbm7haIn9EKF8c1wc3JzXF5fBMAQMdNV5H/hud6NGQksFwux4QJE2BnZwcjIyO0atUK0dHR3P6So6Z2794Nf39/SKVS2NjYoH379sjLy8Pvv/8OGxsbyOVyjfY//vhjDB48WG//eXl5GDx4MExNTeHo6Kg1ChkAjhw5glatWsHS0hI2Njbo1q0bHj58CED/KOCyjnsVUqmUe19ycHBAtWrVMHXqVFy9ehURERE6R0MTQt4u9Uu3LHqaW2Jly/aI7NYf/tbVuPKmNRsgZNQv8B/zG8zGb4XZ+K2IbTEISh4faNybKzOfuhcCW7fKOA1CKNlFCHl77rzIg5GQjyau5jr3B1e3AmPAvRdv7lZGAFi4cCG2bNmC27dva+2LjY1Fy5YtNcpatmyJ+/fvQ6XS/nLVt29fyGQyVK9eHSNHjsS+ffugVCq5/SNHjsSff/6JgoICKBQKbNu2DcOHD9cbW2xsLJo1a6Zxq0zz5s016qhUKvz000+oW7cubGxsYGpqivDwcCQkJJR57mvXrkWjRo1QrVo1mJqaYv369QYdRwghb8uvZxJgYyzCns/rwtXSiCtv6GyOg0PqIT5Dhp3Xn7/RGIpHAq9YsQJPnz7VWWf69OnYs2cPtmzZgitXrsDLywudOnVCenq6Vt2kpCT0798fw4cPR2xsLKKiotCnTx8wxtC3b1+oVCocOHCAq5+amor//e9/GDZsmN4Yp02bhsjISOzbtw/h4eGIiorC5cuXNerk5eUhNDQU0dHROHHiBPh8Pnr37g21Wo1ly5ahefPmGDlyJJKSkpCUlARXV9cyj3tdVCoVPv/8cxw7dgwnTpygRBchVUB0ShLq7t6ER9mZXNnI2vUR5OSmdRu3wNYNQmcf7rGwbR/83bEP5gT24Moo0UUqEyW7CCFvjalYALlKjQyZUuf+pJyiX7VNJQKd+1+XNm3aoFOnTpg5c6bWPsaY1pt5yUk5X+bq6oq7d+9i1apVkEqlGDt2LNq0aQOFQgEA6N69OyQSCfbt24eDBw9CLpfj448/1tteaX0V+/nnn7F06VJMnz4dERERiImJQadOnVBYWFjqcbt27cLkyZMxfPhwhIeHIyYmBsOGDSvzOEIIeZv23UrB4IaOkIq03wu8q5kgyNMK+26lvPE4ShsJnJeXhzVr1mDx4sXo0qULfH19sX79ekilUmzcuFGrflJSEpRKJfr06QMPDw/4+/tj7NixMDU1hVQqxYABAzRGAW/btg0uLi4ICgrSGVtubi42btyIJUuWoEOHDvD398eWLVu0fpT5+OOP0adPH9SsWRP169fHxo0bcePGDdy+fVvvKOCyjnsdVCoVBg0axCW66tat+1raJYRUHGMME84ex62MVHwTfarcx/N4PPTwqAkjoZBrb8TJwzgQf/91h0qIQSjZRQh5a7rVrgYhn4ffLuj+lXzN+adwtzRCgLPukV+v04IFC3Dw4EGcPXtWo9zX1xdnzpzRKDt79iy8vb25LwElSaVS9OjRA8uXL0dUVBTOnTuHGzduAACEQiGGDBmCsLAwhIWF4bPPPit1JUdfX1+cP39eo6zk9unTp9GzZ098/vnnqFevHqpXr4779zU/SIjFYq0vPadPn0aLFi0wduxYNGjQAF5eXq/lthRCCHmd8hVq2JqI9O63NRFBpnjzE9UD+kcCP3z4EAqFQmMksEgkQpMmTRAbG1uyGdSrVw/t2rWDv78/+vbti/Xr12usDDxy5EiEh4cjMTERQNEt78UT5evy8OFDFBYWaoz8tba2Rq1atbTqDRgwANWrV4e5uTk8PT0BoMwRvRU9zhDFia7w8HCcOHEC9erVe+U2CSGvjsfjYV/H3hhRqy5+a93pldvb+TAWm+7ewGcnDiApP/c1REhI+VCyixDy1tiZijG2mQu+PfYIK88+gUxRlIxJz1dg+uH7+PPac8xu5wkB3/DVrirK398fAwcOxIoVKzTKp0yZghMnTuCHH37AvXv3sGXLFqxcuRJTp07V2c7mzZuxceNG3Lx5E48ePcIff/wBqVQKd3d3rs4XX3yBiIgI/PPPP6XewggAo0ePxsOHDxEaGoq7d+9i+/bt2Lx5s0YdLy8vHDt2DGfPnkVsbCxGjRqF5ORkjToeHh64cOEC4uPjkZqaCrVaDS8vL1y6dAlHjx7FvXv3MHv2bI35ZQghpCrwdzBF+H3tWwEBQK5UI+pRBvzsTd5KLPpGAhePwtU1ElhXgkogEODYsWP4559/4OvrixUrVqBWrVqIiytaraxBgwaoV68efv/9d1y5cgU3btzQuShKyf7L0r17d6SlpWH9+vW4cOECLly4AABljuit6HFlKU50HT16FMePH6dEFyGVSJWaAGXiHSiexnIrKNpmPMVaL09IX8QVraiYWvEE9yfVffCJZy2sb9MZjsamrzFyQgxDqzESQt6qJR/VRL5CjfEH7uKb8IdwsZDgYZoMKsawsIsXRjR2fmux/PDDD1qTszds2BC7du3Ct99+ix9++AGOjo74/vvv9X7psLS0xIIFCxAaGgqVSgV/f38cPHgQNjY2XJ2aNWuiRYsWSEtLQ9OmTUuNyc3NDXv27MHkyZOxevVqNGnSBPPmzdNIks2ePRtxcXHo1KkTjI2N8eWXX6JXr17Iysri6kydOhVDhgyBr68vZDIZ4uLiMHr0aMTExKBfv37g8Xjo378/xo4di3/++acCV48QQt6MMU2dMeSv29h/KwW96thx5YwxfHf8EV7kKTCqqfZqum/KggULUL9+fXh7e3NlXl5eEIvFOHPmDAYMGAAAUCgUuHTpksak9C/j8Xho2bIlWrZsiW+//Rbu7u7Yt28fQkNDART9MLJ06VIkJiaiffv23PxZunh5eUEkEuH8+fNwcyuaEycjIwP37t1DYGAgACAtLQ2xsbH47bff0Lp1awDQGrmsaxSwIcdVhEqlwuDBg7lEV/369V+5TUJIxahSE5C9pA+y+CJ86tAKM9NvIbBA9+3hFZ1kXsjnY1f7nnpHqBLyplGyixDyVgkFfKzrUxvT27hje0wy0vIVGBZghEENHWFnKn5j/ZYcHQUA7u7uKCjQXvnx448/LnVereLVqgCgV69e6NWrV6l9M8bw/PlzjBo1yqBYu3Xrhm7dummUvTxJsbW1Nfbv319qG97e3jh37pxWefHtlC8rbbUxQgh52wY2cMT/7qTi463X0dffHj19qyFfocIfV5JwMi4Ti7p4wcfu7YzsAnSPBDYxMcGYMWMwbdo0WFtbw83NDYsWLUJ+fj5GjBih1caFCxdw4sQJdOzYEXZ2drhw4QJevHiB2rVrc3UGDhyIqVOnYv369fj9999LjcnU1BQjRozAtGnTYGNjA3t7e8yaNQt8/n83bVhZWcHGxgbr1q2Do6MjEhIS8NVXX2m08/IoYFNTU1hbWxt0XHmp1WoMHjwY+/fvx+7du+Ho6Kg1IrlatWp6pwsghLxeTF60GNTyZl8gOjkVoTW64GZQO4hfeg1RpcQhf+dsrm5FvJzoylUUYumNaHxVvxlEfPpbJ28eJbsIIZXCy9YY37avXtlhvHEpKSn4448/kJiYWOqqWoQQQooI+Dz82d8fq889wcpzT7mVF1t7WGL/oLro+dJor7dF10jgBQsWQK1WY9CgQcjJyUGjRo1w9OhRWFlZaR1vbm6OU6dO4ddff0V2djbc3d3x888/o0uXLhp1Pv74Yxw6dKjMH1EAYPHixcjNzUWPHj1gZmaGKVOmaIzw5fP52LFjByZMmAA/Pz/UqlULy5cv15j0XtcoYA8PjzKP27x5M4YNG2bw7ZTR0dHYvn07AOCjjz7SWScjIwOWlpYGtUcIeT2+r98MufFPMMa3AYxt7d9YP4wxdDq8C2efJyK9oABLW7R7Y30RUoySXYQQ8gbZ29vD1tYW69at0/kFiBBCiDYBn4fxLd0wroUrsuUqCPk8mIjfzkgAQ0cCGxkZYfny5Vi+fLnOdqKiorj/r127No4cOVJm30lJSRg4cCAkEkmZdU1NTfHHH3/gjz/+4MqmTZumUad9+/Zak+u/nKDSNwq4rOPi4+O52yUN0bRpU4MTY4SQt8dIIMC6Np3feD88Hg8z6jXFhLPH0c29xhvvjxCAkl2EEPJG0Yd7QgipOB6PBwuj9//janp6OsLDwxEREYGVK1dWdjhlOnr0KJYtW1bZYRBCKiCnUI4jSc/Q/i3328OjJjq5ekIieP9f00nVQM80QgghhBBisFWrVmHVqlWvvDIf+U/Dhg2RkZGBhQsXolatWpUdTpl0jQYjhLwbvr54CqtuX8FkKz8sest9v5zoykl+BKmq9PcRnsS4QpPjEwJQsosQQgghhJRDSEgIQkJC8PTp01JXDCSGe3nhk/eFh4eH3pUp38RxhJCyMcZgLhZDyOOhtZ7VF9+Gf25dwIiTh7HsxSW0lz0vtW5FV4MkhF92FUIIIYQQQggxHCW7CKl6eDwe5jUJxL3gDgiSVV6y6+izJ0gSGmO190cwG79V58O43w8A8EqrQZLyWbNmDerWrQtzc3OYm5ujefPm+Oeff7j9jDHMnTsXTk5OkEqlCAoKwq1btyox4tLRyC5CCCGEEEIIIeQD4SqVIgeAKiVOb53S9r2qOd61YHL5b0zvNANCZ5831g8pHxcXFyxYsABeXl4AgC1btqBnz564evUq6tSpg0WLFuGXX37B5s2b4e3tjR9//BEdOnTA3bt3YWZmVsnRa6NkFyGEEEIIIYQQ8p56lpeDby+dwXeNWsHZxAw8iTEAIH/n7DKPLa77OpkJRZiaGQsTIaUj3oacnBxkZ2dz2xKJROeqv927d9fY/umnn7BmzRqcP38evr6++PXXXzFr1iz06dMHQFEyzN7eHtu3b8eoUaPe7ElUAD27CCGEEEIIIYSQ99TM6FPYcu8m4nOzcLzrZxDYusF86t4ybxF8WxPEx2akoraV7Rvv50Pl6+ursT1nzhzMnTu31GNUKhX++usv5OXloXnz5oiLi0NycjI6duzI1ZFIJAgMDMTZs2erZLKL5uwihJASIiIi4OPjA7Va/drb/uSTT/DLL7/o3Ddo0CDMmzfvtfepS+PGjbF3716D6m7cuFHjjY2Q10Uul8PNzQ2XL1+u7FAIIYSQ99a4Og3RysEF8xq34coEtm4QOvuU+njTiS4VY+gTvg++f23E+eeJb7SvD9nt27eRlZXFPb7++mu9dW/cuAFTU1NIJBKMHj0a+/btg6+vL5KTkwEA9vb2GvXt7e25fVVNlUx2zZ8/H40bN4aZmRns7OzQq1cv3L17V6OOIZOjyeVyjB8/Hra2tjAxMUGPHj3w9OnTt3kqhJAqYujQoeDxeODxeBAKhXBzc8OYMWOQkZGhVXf69OmYNWsW+Pyil8jNmzdzx/J4PNjb26N79+5arzmFhYVYtGgR6tWrB2NjY9ja2qJly5YICwuDQqEAAHz77bf46aefNIYSA8D169dx6NAhjB8/nisLCgri+hSLxahRowa+/vpryOVyAMCRI0fA4/G03mAcHBy0Vkh7+vQpeDwewsPDAQCzZ8/GV199VWZCTy6X49tvv8Xs2f8Nc1+/fj1at24NKysrWFlZoX379rh48aLWsatXr4anpyeMjIwQEBCA06dPc/sUCgVmzJgBf39/mJiYwMnJCYMHD8azZ8+4Ounp6Rg/fjxq1aoFY2NjuLm5YcKECcjKyio1ZgBISEhA9+7dYWJiAltbW0yYMAGFhf8tb3337l0EBwfD3t4eRkZGqF69Or755hvu30kfQ8791KlT6N69O5ycnMDj8bB///4y4wWAkydPIiAggItn7dq1Gvtffj68/OjataveNuPj4zFixAh4enpCKpWiRo0amDNnjsa1SEtLQ+fOneHk5ASJRAJXV1eMGzdO6zla0ty5c7VicXBw0Ft/1KhR4PF4+PXXX7kyiUSCqVOnYsaMGWVcHUIIIYRUVKNqjjjVfQCa2DlVdigaBDweLMUSCHg8nE95VvYBpELMzMy4SefNzc113sJYrFatWoiJicH58+cxZswYDBkyBLdv3+b283g8jfqMMa2yqqJKJrtOnjyJkJAQnD9/HseOHYNSqUTHjh2Rl5fH1SmeHG3lypWIjo6Gg4MDOnTogJycHK7OpEmTsG/fPuzYsQNnzpxBbm4uunXrBpVKVRmnRQipZJ07d0ZSUhLi4+OxYcMGHDx4EGPHjtWoc/bsWdy/fx99+/bVKDc3N0dSUhKePXuGQ4cOIS8vD127duWSBoWFhejUqRMWLFiAL7/8EmfPnsXFixcREhKCFStWcImxunXrwsPDA9u2bdNof+XKlejbt6/W5I4jR45EUlISHjx4gEWLFmHVqlXcsONWrVpBKBQiKiqKqx8bG4uCggJkZ2fjwYMHXHlkZCREIhFatmwJAOjatSuysrJw9OjRUq/Znj17YGpqitatW3NlUVFR6N+/PyIjI3Hu3Dm4ubmhY8eOSEz87xe5nTt3YtKkSZg1axauXr2K1q1bo0uXLkhISAAA5Ofn48qVK5g9ezauXLmCvXv34t69e+jRowfXxrNnz/Ds2TMsWbIEN27cwObNm3HkyBGMGDGi1JhVKhW6du2KvLw8nDlzBjt27MCePXswZcoUro5IJMLgwYMRHh6Ou3fv4tdff8X69esxZ86cUts25Nzz8vJQr149rFy5stS2XhYXF4ePPvoIrVu3xtWrVzFz5kxMmDABe/bs4ers3bsXSUlJ3OPmzZsQCARaz9WX3blzB2q1Gr/99htu3bqFpUuXYu3atZg5cyZXh8/no2fPnjhw4ADu3buHzZs34/jx4xg9enSZcdepU0cjphs3buist3//fly4cAFOTtofsgcOHIjTp08jNja2zP4IIYQQUkSVmgBl4p1SH6rUBK5+VUxIqFLiMNfVEVdbB2OctZlm7G9wgnyin1gshpeXFxo1aoT58+ejXr16WLZsGfeDZskf2VNSUrRGe1UZ7B2QkpLCALCTJ08yxhhTq9XMwcGBLViwgKtTUFDALCws2Nq1axljjGVmZjKRSMR27NjB1UlMTGR8Pp8dOXLEoH6zsrIYAJaVlfUaz4aQd5tMJmO3b99mMplMa58yP1fvQ1UgM7yuLL/MuuU1ZMgQ1rNnT42y0NBQZm1trVE2fvx49sknn2iUhYWFMQsLC42yAwcOMADs+vXrjDHGFi5cyPh8Prty5YpW34WFhSw397+Y586dy1q3bs1tq1QqZmlpyf73v/9pHBcYGMgmTpyoUdanTx/WsGFDbrt58+Zs1KhR3Pbq1atZ165d2UcffcTWr1/PlQ8fPpy1bNlSo62hQ4eyQYMGacX7su7du7OpU6eWWkepVDIzMzO2ZcsWrqxJkyZs9OjRGvV8fHzYV199pbedixcvMgDs8ePHeuvs2rWLicViplAo9NY5fPgw4/P5LDExkSv7888/mUQiKfX1fPLkyaxVq1Z69+ui69xfBoDt27evzHamT5/OfHx8NMpGjRrFmjVrpveYpUuXMjMzM43nliEWLVrEPD09S62zbNky5uLiUmqdOXPmsHr16pXZ39OnT5mzszO7efMmc3d3Z0uXLtWqExQUxGbPnq23jdJed+i9uvI8efKEAWBPnjzRKC/t34sQQt4Xlflap3zxmKXPCCj1kTojgLVbOpH9ev4YK1Dq/9xUGQyJv/ihfKH/cyEpnb736fJo27YtGzJkCJeDWbhwIbdPLpdr5GCqmndigvriW1asra0BwKDJ0S5fvgyFQqFRx8nJCX5+fjh79iw6deqk1Y9cLuduDwJQ5i0chBBNMa1d9e4zb9kBNZft5Lavd6gFdYHuSTFNG7ZErXUHue2b3etDmZmmUSfgUvorxfro0SMcOXIEIpFIo/zUqVPo379/qcdmZmZi+/btAMAdv23bNrRv3x4NGjTQqi8SiTT6adKkCebPnw+5XA6JRILr168jMzMTjRo1KrXfa9eu4d9//4WHhwdXFhwcjN27d3PbkZGRCAoKglqtRmRkJL744guufODAgRrtNWnSBIsWLSq1z9OnT2sdV1J+fj4UCgX3Gl1YWIjLly/jq6++0qjXsWNHnD17Vm87WVlZ4PF4sLS0LLWOubk5hKWs3nPu3Dn4+flpjCLq1KkT5HI5Ll++jODgYK1jHjx4gCNHjnCryxiq5LlX1Llz57TmRevUqRM2btwIhUKh9TwFiuZS++yzz2BiYsKVbd68GcOGDQNjTG9fWVlZpcb77Nkz7N27F4GBgRrlPB4PYWFhGDp0KFd2//597vbHpk2bYt68eahevTq3X61WY9CgQZg2bRrq1Kmjt88mTZpo3OZKCCGEEP2KJ5Y37vcDBHaeOuvsvRuDEw8eI/rWNXxeryUkgqrz1V/fBPkv5HIky+XwNzcH8PYmyCdFZs6ciS5dusDV1RU5OTnYsWMHoqKiuKlTJk2ahHnz5qFmzZqoWbMm5s2bB2NjYwwYMKCyQ9ep6jzj9WCMITQ0FK1atYKfnx8AlDo52uPHj7k6YrEYVlZWWnX0TaA2f/58fPfdd6/7FAghVcT//vc/mJqaQqVSoaCgAAC0JouPj4/XeatVVlYWTE1NwRhDfn7RG3OPHj3g4+MDoOhLf1BQkEFxODs7Qy6XIzk5Ge7u7oiPj4dAIICdnZ1W3dWrV2PDhg1QKBQoLCwEn8/HqlWruP1BQUGYN28ekpKS4OjoiJMnT2LatGlQq9VYtmwZAODJkyeIi4vTSvI4OzsjISEBarWam5/sZZmZmcjMzNR5PV721VdfwdnZGe3btwcApKamQqVSlWsCy4KCAnz11VcYMGAAzP//A05JaWlp+OGHH8pc7SU5OVmrbysrK4jFYq3+W7RogStXrkAul+PLL7/E999/X2rbJZU894rSFbO9vT2USiVSU1Ph6Oiose/ixYu4efMmNm7cqFFuYWGBWrVq6e3n4cOHWLFiBX7++Wetff3798fff/8NmUyG7t27Y8OGDRr7a9WqBQsLC267adOm+P333+Ht7Y3nz5/jxx9/RIsWLXDr1i3Y2NgAABYuXAihUIgJEyaUev7Ozs6Ij48vtQ4hhBBCNAnsPCF09tG5r7tajV/O7YUgaChsjKRvObKylUxihT+NQ++ow6hpYYWrfYZWydsu33fPnz/HoEGDkJSUBAsLC9StWxdHjhxBhw4dABTNayyTyTB27FhkZGSgadOmCA8P15qGpaqo8smucePG4fr16zhz5ozWvopMjlZana+//hqhoaHcdnZ2ttYkz4QQ/eqffqJ3H48v0Niue+yunpoAj6eZePE7GPNKcRULDg7GmjVrkJ+fjw0bNuDevXsaE8IDgEwmg5GRkdaxZmZmuHLlCpRKJU6ePInFixdrTCBuyOtPMam06ANHcdJMJpNBIpHoPH7gwIGYNWsWsrOzsXDhQpibm+Pjjz/m9rds2RJisRhRUVGoV68eZDIZGjZsCMYYsrOzcf/+fZw7dw4SiQQtWrTQikOtVkMul3MxlbwWAHRej2KLFi3Cn3/+iaioKK16hr5GKxQKfPbZZ1Cr1Vi9erXOfrKzs9G1a1f4+vpqzKvVpUsXbkSQu7s7Nzearn509b9z507k5OTg2rVrmDZtGpYsWYLp06cjISFBY5nmmTNnasxzVda5V4Su66XvXDZu3Ag/Pz80adJEo7x3797o3bu3zvafPXuGzp07o2/fvtyIv5ctXboUc+bMwd27dzFz5kyEhoZq/HvcuXNHo36XLl24//f390fz5s1Ro0YNbNmyBaGhobh8+TKWLVuGK1eulPm3IZVKub8HQt4VaWlpqF27Ni5evKgx4vZ1+eSTT9CiRQuNz6Zvo19D49DlbcdGPizleS6+LCoqCkOHDi33jyoVPa6qEPH5GJrzCGYe1cuuXAU0snWAgMeDkMdHiiwf9sYmZR9EXquSP6KWxOPxMHfuXG7+4KquSk5QX2z8+PE4cOAAIiMj4eLiwpUbMjmag4MDCgsLtVZaK20CNYlEorFKgb7RBYQQ3QRSE70PvsTI8Lolfn3SVaciTExM4OXlhbp162L58uWQy+VaozltbW11rtDI5/Ph5eUFHx8fjBo1CoMGDUK/fv24/d7e3gZPsJ2eXnQLZrVq1bg+8/PzNVbIK2ZhYQEvLy80bNgQW7duxcmTJzXeiIyNjdGkSRNERkYiMjISrVq1gkAggFAoRIsWLbjy5s2bayVk0tPTYWxsrDPRBQA2Njbg8Xg6rwcALFmyBPPmzUN4eDjq1q3Lldva2kIgEBg0gaVCocCnn36KuLg4HDt2TOfrbk5ODjp37gxTU1Ps27dP45a+DRs2ICYmBjExMTh8+DCAotf/kn1nZGRAoVBo9e/q6gpfX1/0798fCxYswNy5c6FSqeDk5MS1GxMTozVZu75zryhdMaekpEAoFHKjpIrl5+djx44dOhNW+jx79gzBwcFo3rw51q1bpzcGHx8f9OzZE7/99hvWrFmDpKQkg/swMTGBv78/7t+/D6DoFtiUlBS4ublBKBRCKBTi8ePHmDJlitaX4PT0dO7vgZDKZujqvfPnz0f37t01ns+GHpucnIzx48ejevXq3Cqo3bt3x4kTJ7g6+lbvrUi/a9euhZmZGZRKJXdMbm4uRCKRxgIkQNHfLo/Hw71790qNQ5eSsRmywjpQ+uq9hrZjaF+6lNX/mjVrULduXe77QfPmzfHPP/+UeS3Kiqeiq/eWFbOHh4fO1XtDQkJeOeaKXIvyrt47f/587papl5XnuViWw4cP67xGxY9PP/30lfuoLKVNZVCVWRtJcbnPEET3HkyJLvJaVMlkF2MM48aNw969exEREQFPT837kD09PeHg4IBjx45xZYWFhTh58iQ3ciEgIAAikUijTvHqVSVHNxBCPkxz5szBkiVL8OzZf0sdN2jQQGN5XX0mT56Ma9euYd++fQCAAQMG4Pjx47h69apWXaVSqbGa7M2bN+Hi4gJbW1sAQP369QGgzH5FIhFmzpyJb775RmMUTHBwMKKiohAVFaVxK2VgYCBXrmueqps3b6Jhw4Z6+xOLxfD19dUZ1+LFi/HDDz/gyJEjWnONicViBAQEaLz+AsCxY8c0Xn+LE13379/H8ePHtZI6QNGIro4dO0IsFuPAgQNaCTtnZ2d4eXnBy8sL7u7uAIDmzZvj5s2bGoma8PBwSCQSBAQE6D1fxhgUCgUYYxAKhVy7Xl5eGnNclXbuFdW8eXOt6xUeHo5GjRppzde1a9cuyOVyfP755wa1nZiYiKCgIDRs2BBhYWE6b1ktqfiD8svzWJZFLpcjNjaWu+Vy0KBBuH79ukbS0MnJCdOmTdNaBfTmzZs657sjpLKUtXqvTCbDxo0bdSadyzo2Pj4eAQEBiIiIwKJFi3Djxg0cOXIEwcHBGskIXav3VrTf4OBg5Obm4tKlS1z906dPw8HBAdHR0RrvKVFRUXBycoK3t7feOHTRFZshK6yXtXqvoe0YUkcXQ/p3cXHBggULcOnSJVy6dAlt27ZFz549udHEuhgST0VW7zUk5ujoaI2VcovfX0pbvdfQmCtyLQDDV++Njo7GunXrdP6QZOhz0RDBwcEa8SQlJeHp06fo0KEDbG1tMXv27Ffuo7L8/fg+Ag9uR2Tqi8oOpdxqWljT7Yvk9amESfHLNGbMGGZhYcGioqJYUlIS98jP/2+FtgULFjALCwu2d+9eduPGDda/f3/m6OjIsrOzuTqjR49mLi4u7Pjx4+zKlSusbdu2rF69ekypVBoUB63wRIi2d3WVLV2rMTLGWEBAAAsJCeG2ly9fzgICAjTq6FqNkbGi1Rz9/f2ZWq1mBQUFrHXr1szKyoqtXLmSxcTEsIcPH7KdO3eyhg0bsqtXr2rEMnz4cI22GjZsyFasWKFRpms1RrlczhwdHdnixYu5soiICAaAmZqasvPnz3PlZ86cYWZmZgwAO3XqlFb8gYGB7Pvvv9cqL3mOH3/8sUbZwoULmVgsZrt379Z4jc7JyeHq7Nixg4lEIrZx40Z2+/ZtNmnSJGZiYsLi4+MZY4wpFArWo0cP5uLiwmJiYjTakcvljDHGsrOzWdOmTZm/vz978OCBRp3SXseVSiXz8/Nj7dq1Y1euXGHHjx9nLi4ubNy4cVydrVu3sp07d7Lbt2+zhw8fsl27djFnZ2c2cODAUq+HIeeek5PDrl69yq5evcoAsF9++YVdvXq11FUmHz16xIyNjdnkyZPZ7du32caNG5lIJGK7d+/WqtuqVSvWr18/ne3s3buX1apVi9tOTExkXl5erG3btuzp06caMRc7dOgQ27RpE7tx4waLi4tjhw4dYnXq1NFavbNWrVps79693PaUKVNYVFQUe/ToETt//jzr1q0bMzMz4/6NddG3GqO7uzv7/fff9R5HqzFWTRVZjfFtrtpbkZV7GTNs9d49e/YwW1vbCh3bpUsX5uzsrHMl1YyMDI3tkqv3vkq/Tk5ObP78+dz29OnTWUhICPP19WXHjh3jytu2bav1WlgyDl30xfaykiusM1ax1Xt1tVOROhXtnzHGrKys2IYNG0qtU554YODqvYyVP+aJEyeyGjVqMLVabXC8hsRcrKxrYejqvTk5OaxmzZrs2LFjOj8DMWbYc7GkyMhI5u7uXmodpVLJPvvsM2Zra8uttF3WcZX5mVjxNJalzwhgiqexWvta7v+D4bcFbMbxPXrrVHUKlYr9HXev3M9Zoul1rMb4LquSyS4AOh9hYWFcHbVazebMmcMcHByYRCJhbdq0YTdu3NBoRyaTsXHjxjFra2smlUpZt27dWEJCgsFx0AdoQrS9b8mubdu2MbFYzL02pKenM6lUyu7cucPV0Zfsevz4MRMKhWznzp2MMcYKCgrY/Pnzmb+/PzMyMmLW1tasZcuWbPPmzUyhKFryWSaTMXNzc3bu3DmNttauXcuaNWumUabvg95PP/3EqlWrxiVYZDIZk0gkzNTUlOuHsaLEmLGxMZNKpVwCqdjTp0+ZSCQq880vNjaWSaVSlpmZyZW5u7vrfI2eM2eOxrGrVq1i7u7uTCwWs4YNG2p8WI6Li9P7Wh8ZGckYK/qQqa9OXFxcqXE/fvyYde3alUmlUmZtbc3GjRvHCgoKuP07duxgDRs2ZKampszExIT5+vqyefPmlfm8NuTc9cU9ZMiQUtuOiopiDRo0YGKxmHl4eLA1a9Zo1bl79y4DwMLDw3W2ERYWxl7+Hat4W9ejWEREBGvevDmzsLBgRkZGrGbNmmzGjBlaX7pLvg/369ePOTo6MpFIxJycnFifPn3YrVu3Sj1HXcmus2fPMktLS40ftEqiZFfVsnLlSla7dm1Wo0aNcie7LgVY6X3cm/CpRt0rLZ311r0zsptG3Zh2XjrrVUTJ94uHDx8yX19fZm9vz5VNnDiRde7cudzHpqWlMR6Px+bNm2dQLIcPH2YSiYR7/apov4wxNmDAANaxY0duu3Hjxuyvv/5iY8aMYTNnzmSMFb1vSKVSrcRFyTh00Rfby+7fv88AcJ/Z5XI5EwgEGol0xhibMGECa9OmjcHtVLRORfpXKpXszz//ZGKxuMzXvPLEY2iyq7wxy+VyZmNjw3766SeN8pLvFxWJWd+1KNn2nDlzmLGxMXN0dGQeHh6sX79+7OHDh1rtDR48mE2aNIkxpv8zkCHPxZLKSloVJ7psbGzYtWvXDD6uqia7nuRksclnj7NnD2PeyWSXSq1m9XdvYvhtAYtM1P9DISkbJbuIXvQBmhBt72qyqzymTZvGvvzyyzfS9sqVK1mHDh20ymUyGXNzc2Nnz559I/2WNHXqVDZy5EiD6vbt29fgL2aElNcnn3yi9SWsJEp2VU0VGdn1riS7BAIBMzExYUZGRlyC+JdffuHq9OzZU2uEriHHXrhwgQHQSlToc+3aNQaAGzFZ0X4ZY2zdunXMxMSEKRQKlp2dzYRCIXv+/DnbsWMHa9GiBWOMsZMnTzIAWomIknHooi+2Ymq1mnXv3p21atWKK0tMTGQA2L///qtR96effmLe3t4Gt1OROuXt//r168zExIQJBAJmYWHBDh06VGrb5Y3H0GRXea/Zzp07mUAgYImJiRrlJUcClyfmsq5FybYPHz7Mdu/eza5fv86N2rK3t2epqalcnT///JP5+flxrxv6kl2GPBdLKi1ppVQqWf/+/ZmNjQ2LiYkx+DjGqkayq+DKYaZ4GqvzUXDl8DuZ7GKMsTGnjzKbzcvY9vuGJ5SJtg892VXlV2MkhJC3bdasWVi1ahVUKhUEAkHZB5SDSCTCihUrtMqNjIzw+++/IzU19bX2p4+dnR2mTp1qUN3FixfjwIEDbzgi8iGSy+WoV68eJk+eXNmhkLekqq/aW6ys1Xv1rdxb1rGslFVWddG1em9F+i3en5eXh+joaGRkZMDb2xt2dnYIDAzEoEGDkJeXh6ioKLi5uaF6dc3V20rGoUtpsQGvb4X10toprc62bdswatQobvuff/5BjRo1DO6/Vq1aiImJQWZmJvbs2YMhQ4bg5MmTuHr1qla7JSf9NyTm8jL0mm3cuBFdunSBk5OTRnlpq/cCpces71oUr2Jcsu2yVu998uQJJk6ciPDw8DJXNzbkuWgolUqFQYMGITw8HCdOnEC9evVeuc23hScxBgDk7/xvbjEGQNdfTXHdd8kPjVpjSbNgGAtFZVcmRA9KdhFCSAkWFhaYOXPmG2n7yy+/1LsvMDDwjfSpy7Rp0wyu6+7urvGFiZDXRSKR4JtvvqnsMMhbVJ7VdN9UXUMUr94LAMuXL0dwcDC+++47/PDDDwD0r9xb1rE1a9YEj8dDbGwsevXqVWYculbvrUi/AODl5QUXFxdERkYiIyODe89xcHCAp6cn/v33X0RGRqJt27ZlxqFLabEVr7B+6tQpjRXWy7N6b2ntGFKnR48eaNq0Kbft7OwMgUBgcP9isZi7vo0aNUJ0dDSWLVuGJUuWaLVb3pjLozzX7PHjxzh+/Dj27t1brj7Kilnftfjtt98Mar/k6r2XL19GSkqKxiIyKpUKp06dwsqVKyGXy7kfHw15LhqiONF19OjRdy7RBQACWzeYT90LJv8v6Tck5jLMBEJM96oJN2lRgosnMYbA1q2ywqwwGyPdK4UTUh5VcjVGQgghhBDyYWGMIUumQFK2HMk5chQoVJUdEqfk6r2Grtxb8lhra2t06tQJq1at0rlKYGZmpsZ2ydV7K9pvsdJW7z169CjOnz+vd/Xel+PQRVdsrIwV1g1dvbesdgypY2ZmprHKrlQqNbh/XRhjkMvlOts1NOaKKE/MYWFhsLOzQ9euXQ1qu6IxF18LQ5Vcvbddu3a4ceOGxuq9jRo1wsCBAxETE6Mxyt6Q52JZVCoVBg8ejKNHj+L48ePcqtjvGoGtG4TOPhA6++CZhRP+THyK3xLiUWDzX/m7mOgq6XpaCtILZJUdBnkHUbKLEEIIIYRUqly5Ejef5+F+mgzPcwvxLFuOm8/z8CAtHyo1q+zwEBQUhDp16mDevHkAgE6dOuHWrVt6RzKVduzq1auhUqnQpEkT7NmzB/fv30dsbCyWL1+O5s2baxx7+vRpdOzYkdt+lX6BomTXmTNnEBMTozGaODAwEOvXr0dBQYHOZFfJOHTRFVtISAi2bt2K7du3w8zMDMnJyUhOToZM9t8X19DQUGzYsAGbNm1CbGwsJk+ejISEBIwePbpc7RhSRxdD+p85cyZOnz6N+Ph43LhxA7NmzUJUVBQGDhyot11D4snNzeWSOwAQFxeHmJgYJCQkvHLMarUaYWFhGDJkCIRC7Zt59u3bBx8fn3LHbMi1KNn21KlTcfLkScTFxeHChQv45JNPkJ2djSFDhgAoSkT6+flpPExMTGBjYwM/Pz+NGA15LpZGrVZj8ODB2L9/P7Zu3QpHR0fuPIsfKlXVSbQbytXEDBHd+uO7gFbwtap4IrCqmXT2OOrtCcPq21crOxTyLqqcqcLeDTTpLSHaiifjLG3lNEIIeZ3y8/NpgvoqqCIT1OsiK1Syy0+zWOzzXJYrL1pRVqVWsxe5cnblaRa7m5L7VpefN3T13mbNmrG1a9dW6Nhnz56xkJAQbsVaZ2dn1qNHD241Wsb0r977Kv0Wr4Tr4+OjUa/437JGjRpabeiLQ5eSscGAFdYZK331XkPbMbQvXcrqf/jw4dz+atWqsXbt2uldFbc88VR09V5DYj569CgDwO7evavzeF2rMRoSsyHXomTbFVm9V9cE9bqei4asKvnyRPPnz5/Xe57Fj+LViKvyBPUfim33bzH+uoVs3JnS/96Ibh/6BPU8xljl/1xWRWVnZ8PCwgJZWVkwNzev7HAIqRJUKhXu3bsHOzs72NjYVHY4hJAPQFpaGlJSUuDt7a21aAS9V1eep0+fwtXVFU+ePNGY16egoABxcXHw9PQsc7JpAIhPlyFbrkQde1MI+JrTK2fKFHiQJkMtW2OYGZU+1axSpUZqvgIFCjX4fMDSSAQzicDgyeDL6/Dhw5g6dSpu3rwJPv/13yyxatUq/P333wgPD3+r/Roahy5vOzbyYdH1XJw7dy53a64+UVFRGDp0KOLj48vVX1nHlfe1jpSfQq1CYl4uPMwsKjuUd5K+9+kPBU1QTwgpF4FAAEtLS6SkpAAAjI2N39gXCULIh40xhvz8fKSkpMDS0vK1r45KKh9jDOkyBRzMJFyii6nVKEx5BoHUGOZSU0iEfKTLFKUmu9LyChGfWQAAMBYJoFQzpOQqYCoWoIaNFCLB60+8fPTRR7h//z4SExPh6ur62tvXt3rvm+7X0Dh0eduxkQ+Lrufi0aNHsWzZskqKqPKp1Gp0PLwL3d1rYFTt+pC+Z6sXivgCSnSRCqNkFyGk3BwcHACAS3gRQsibZGlpyb3ukPcLA6BmgFjw348m6gIZlOkvoPz/balNDSjVRckqplIBfL7GjyzZBUrEZRTAxlgEFwsJRAI+GGPIkavwKF2Gh2ky1Kr2Zn6YmThx4mtvs1hpq/e+yX7LE4cubzM28mHR9Vw8d+5cJURSdfz9+D4inj3GtbQUjKpdv7LDeaOyCuXIVyrgaGxa2aGQdwQluwgh5cbj8eDo6Ag7OzsoFIrKDocQ8h4TiUQ0ous9xufxIBLwkFeogq1JURlPIIDIuhpUsjxArUauigdbo6JklzwxHuoCGQTGpuAbm0BgbIrkHDVMxAJ4WBlxCS0ejwdzIyE8raW4n5qPXLmqzNsgCSHvJw8PD0yaNOmtHfc2dXatjrWtOkEN9t6N6nrZxjvXMOHsCQzwqo31bbpUdjjkHUHv+oSQChMIBPQllBBCyCupZiJCck4h7EzFkIoE4EuMIHYomlskKbsAyuxC2BqLwBiDuiAfTKmEMjsDyC5a9c+BJwCMjKEUWkBkXU2jbXOJAGIBDxkFSkp2EfKBep+TXcZCEUb51q/sMN44bwtr5CsVuJ72AmrGwKcpVIgB6F2fEEIIIYRUGjtTCTJkStx9kQ97UzEsjIRQqhlS8xVIz1fA0UwMI1HRDytSrzpQy/Khys+FOj8XKlkeBGoVIMuBig+NZJcyKx18IymEfB7UtB4TIYS8s1o5uOB8r0FoUs2R5gomBqNkFyGEEEIIqTRCPg+1bI3xNFuOpBw5ErPlAACJkA83SyNUM/nv1hwenw+BiSkEJkVztjC1GncT02DNK4S1mTFXT61UQJ74GADgzBcCUhMoeBYQmJiBLxJrxaBmDC/yCpGaV7Sao4DPg5VUCHvT/xJthBBSVeQUyjEk6jCG1/LHR2413vuRTjweD03tnCo7DPKOoWQXIYQQQgipVEIBHx5WUrhYGEGuVIPPA4yE/DJ/wefx+TCzMMPTnEKYSI3/+2CrUkFgYgZlfh6EaiWQl4XCvCwAAF8sgcjWAUJLawCAWs1wP61oXi9LqRDVTMRQqNRIzVcgLV+BmrbGMJPQR2ZCSNWx9cFt7Iu/h9sZqejqVqOyw3mr1IwhU14AayNpZYdCqjh65yaEEEIIIVWCkM+DUFy+kVQOphLkFKhw90U+bIxFMJcIoVDzkWrkAJlAhepSNUxUMqjycqEuyIe6UK5x/POMHEiz0+Fsaw0TcymXYHMwk+BBWj4epcvg72D63o+cIIS8Ozq5eGJK3caobWnzQd3Wd/xpPEadPoqGtvb4q0Ovyg6HVHGU7CKEEEIIIe8sAZ+HmrbGeJ5biBd5hXiRV7RKsLmREN6WRv8/KssKAMBUKqjycyGQFi39qGYMhdmZsJVnAIkZkCULwDcxg8DEHAJTM7hZGuHW8zxkypSwNn5/VzojhLxbqptbYkmztpUdxltnayTFo5xMZCvkKFAqYSSkdAbRj54dhBBCCCHknSbg8+BkLoGjmRgqNQOPx4OArz3agScQQGhmwW0XqtTI54nBTC3Al+WBqZRQZWdClZ0JAOBLpDAxskOeQgVrlJ7sYowhq0AJuUoNIY8HC6kQQj7/tZ4nIYR8yOrb2mNfx97o4OxBiS5SJnqGEEIIIYSQ9wKPx4NQYPgtPXzwkCc0hsLGBpZGQqgL8qHKzYYqNwdqWT7UCjkUUiGKU1aKzDRAzSAwM9eY6D5TpkBCZgEKVQw8HsAYwM8E7M3EcDKTfFC3GRFC3pwXsnz8dPUsxvg2QC1Lm8oOp1L08vCu7BDIO4KSXYQQQgghxGCrVq3CqlWrUFhYWNmhvDKRgAepiI+0PAWspCIIpCZFtzhWcwRTKpGTk4vCPMDCqOgjszItBWp5AZAM8I2kEJhaQC4xxcMcBnMjIbxsJDAWC6BQqfE8txBJ2YUAA5wtjCr5TAkh74M/7t/EspuXce75M1zoPbiyw6l0asZoPkWiF42tJoQQQgghBgsJCcHt27cRFRVV2aG8Mh6PB3tTMTILlHiWLYeaMW5fvhqIKxDAVCyAiVgAxhiEFtbgG5sAPB7UBTIoUpPBT3yAmrmP4a7KhPH/T64vEvDhYmEEJ3MxknMLoVCpK+sUCSHvkQBbB/R0r4lRtetXdiiV6tzzRHQ6vBMT/j1e2aGQKoySXYQQQggh5INlYyyCk7kYz7LluJ6Uiwep+YhNyUNsSj6EfB5q2BSt0Mjj8SCytYfUwxvGNf0gcXIHz8wCah4fArUSTKXk2mSMIbBVSyyYNQ0AkCFT6uueq58rVyI+Q4Z7L/LwKF2GLJkC7KXkmyGCgoIwadKkcl+Dt8HHxwcbNmyo7DAIeacFOrlhf6c+GO5Tt7JDqVT5SgXCn8Zj24NbKFSpKjscUkXRbYyEEEIIIaRKUaUmgMnz9e7nSYwhsHV7LX3xeDw4mRvBSipCap4CE0d/gX07t+Hb737EnNkzuVtk9u/fj969e4MxBp5QCKGlNeTGFrjHy4WPKYPQSMK1qS7Ix9ZFP0EkEkKc+xSMmUMltAZfItWav4sxhscZBUjNV0Ai5MNYxIdMocL9fAXMJAJ42RjrnGz/dQgKCkL9+vXx66+/vpH2i8lkMjx48AD16tV7o/0QQj4MwU7uWNAkEH2r+0AsEFR2OKSKomQXIYQQQgipMlSpCche0qfMeuZT9762hBcASEUCuFoKYG4khJGREZYvXYJJ48fCyspK7zFiAQ8MPOSLpDAx+m/CejAGWycnqPPzAZUcyH6BguwX4IklEJpZQGhpA76kaB6vpJxCpOYr4GFlBBtjEXg8HhhjyJGr8DAtH/EZMtSwMX5t51kZbt68CcYY/Pz8KjsUQt5JWYVy7HgQi/5etWEulpR9wHuOz+NhRv1mlR0GqeLoNkZCCCGEEFJlFI/oMu73A8zGb9V6GPf7QaPem9C+fXs4ODhg/vz5euvI5XJMmTwJnRrUgIuNOVq2aoXo6GgAgMDYFF2GfokJy9Yj2dgOAjML7Dt2Ao0/6gkL9xqo5uSE9u3bIycnF2FhYehQ1xNmQsaN+uLxeBg2sB/mTxuLDJkScqX2nF95eXkYPHgwTE1N4ejoiJ9//lmrzpEjR9CqVStYWlrCxsYG3bp1w8OHDwEAQ4cOxcmTJ7Fs2TLuNs34+PgyjyuPmJgYtG3bFq1atYJarYabmxuWLl1a7nYI+dDteBCL0WeOot2hHZUdCiHvDEp2EUIIIYSQKkdg5wmhs4/WQ2Dn+eb7Fggwb948rFixAk+fPtVZZ/r06dizZw82bgrD9iOnYevsjo6dOiEp5QWyC5SQKdTIVzGYV6uGDKEUw6Z+jWHDhiHm1AlERkahT58+yCtU4tN2bcCUCuzZvBHK7EwwtQqpqan43//+hy9HDAcAZBdoz/k1bdo0REZGYt++fQgPD0dUVBQuX76sUScvLw+hoaGIjo7GiRMnwOfz0bt3b6jVaixbtgzNmzfHyJEjkZSUhKSkJLi6upZ5nKEePnyIwMBAtG3bFj169ECfPn0wZcoUhIaG4tKlSwa3QwgBLCUS1La0wYAavpUdSpVyJzMN484cw8/XL1Z2KKQKotsYCSGEEEIIKaF3796oX78+5syZg40bN2rsy8vLw5o1a7B582b06t4N+QoVanmvQtuGvliwfC0GjZ4INWOwkAhRzUSMK3eToFQq0XfAQLi7uwMA6tati+wCJUQioO9HnbFl23b0bN0c4POxecceuDg5IbhNa8SkyKCG5kT1ubm52LhxI37//Xd06NABALBlyxa4uLhArlQjv1AFqYiPjz/+WOO4jRs3ws7ODrdv34afnx/EYjGMjY3h4OCgUa+s4wwxevRo9OnTB9988w2aNm2Kvn37IjQ0FAsXLsTp06fRqFEjg9ohhAD9atTGp9V9oGS0suvLYlKfY9XtK3A1McMkv0YQ8GksD/kPPRsIIYQQQgjRYeHChdiyZQtu376tUf7w4UMoFAq0bNkSAGAsEsDPyRLNmzVB+pOHqG1nAhOxAEaioo/a9erVQ7t27eDv74++ffti/fr1yMjIgFTER7yJM/p/OQ4nzp5HUloGoFbj9x07MaBbF8ji74MxwEQk0Oq/sLAQzZs3BwDkyJVIVojhWt0LmQVK3E7Jw+2UPMTcvosBAwagevXqMDc3h6dn0ai4hISEUs/74cOHFTquWHJyMiIiIjB69GioVCrcuHEDDRo0AJ/Ph1AohFgsLrsRQogGHo8HEZ8mYy+mSk1ANzHDUBc3rPatA+Wzu1Am3tF4qFINe80i7yca2UUIIYQQQogObdq0QadOnTBz5kwMHTqUK2esaKRVyZUVeQDEQgFMxJpfSAUCAY4dO4azZ88iPDwcK1aswKxZs3DhwgVYWzhA4NMA/nXrYdfp8+gQHIhb9x9g9/o1yBFIYSziw0QsAGMMhYmPwTc1g1qp4NrOLlDiflo+TEQCSAR82BqLUNNWiuScQnTv1ROe7m5Yv349nJycoFar4efnh8LCwlLPu3v37nB1dS33ccXOnz8PtVqN+vXr486dO5DJZKhfvz6ePHmC1NRULklICCldvlKBc88TEezkzq0MSzQXMvnl/8v0zeL4uhczIe8OSnYRQgghhBCix4IFC1C/fn14e3tzZV5eXhCLxThz5gwGDBgAAFAoFLh06RImTZqksx0ej4eWLVuiZcuW+Pbbb+Hu7o59+/ZhwsRJyFeo0LnvQKzfuAYPHj9Bq6C2yKvRGDIw1LKWgsfjQZWbDWV2BpCdASdWCJFIhDMnjqFB574wEQtQTSDHwwf30TY4CBZGIihysxB3/y6+XbQMbdu2BY/Hw5kzZzRiEovFUKlUGmVpaWmIjY3Fb7/9htatWwOA1nFlKU6KFRQUICYmBi4uLrCxscHSpUvh6+uL+vXrl6s9Qj5Uex7dxeCoQ2jr5I4T3T6r7HCqjJcXMtE3j6MqJQ75O2e/0cVMSNVGyS5CCCGEEEL08Pf3x8CBA7FixQquzMTEBGPGjMG0adNgbW0NNzc3LFq0CPn5+RgxYoRWGxcuXMCJEyfQsWNH2NnZ4cKFC3jx4gVq164NoYAPn2omGDZ4EH79YTa2bt6EH5f9hmomItiZiiEWFN0KyZMYQVTNEarsTJgCGNynJ76a9Q1WMyXsnN0wfcUq8F+ar8ba2hrWNjbY9UcY6tV0R8bzZ/jqq6804vLw8MCFCxcQHx8PU1NTWFtbw8rKCjY2Nli3bh0cHR2RkJCgdVxZmjVrBqFQiO+//x65ubmoUaMGVq9ejaVLlyIyMrJcbRHyIUuTy2AuEiPIybWyQ6mSihcyeSHLx46HsahtaYP2Lh6VHRapIijZRQghhBBCqhxVSly5yt+kH374Abt27dIoW7BgAdRqNQYNGoScnBw0atQIR48ehZWVldbx5ubmOHXqFH799VdkZ2fD3d0dP//8M7p06QIAEPB58Hauhr6ffIxDhw5h4rD+kEgkGm3wRWKIqzkA1RyglsuwaP585E0KRb9xk2BqbILQ0MnIzs0FAKgVheDxBfjzzz8xOmQ8mgfUR61atbB8+XIEBQVxbU6dOhVDhgyBr68vZDIZ4uLi4OHhgR07dmDChAnw8/PTedzmzZsxbNgw7nbOktzc3LBp0ybMmDEDSUlJEAqFyM/Px+HDh9GkSZOK/BMQ8kGa5N8Yo2rXh6IcK6F+iJbfvIQfr55DV7calOwinCqZ7Dp16hQWL16My5cvIykpCfv27UOvXr24/UOHDsWWLVs0jmnatCnOnz/PbcvlckydOhV//vknZDIZ2rVrh9WrV8PFxeVtnQYhhBBCCCknnsQYAJC/c7ZB9V63zZs3a5W5u7ujoKBAo8zIyAjLly/H8uXLdbYTFRXF/X/t2rVx5MiRMvtOSkrCwIEDtRJdJfElUlh7emHdrr2Y+TwTnhIlTOzsMWPmLACAPPExlNmZaFWrBo4cjYC9vQ0sjIvafDlB5e3tjXPnzmm13759e61J+V8+Lj4+HoGBgaXGOGjQIAwaNAjW1tbYtGmTxmd5QojhpEIRpJUdRBU3sGYdHHkah49cq1d2KKQKqZLJrry8PNSrVw/Dhg3TWvq4WOfOnREWFsZtl1zVZdKkSTh48CB27NgBGxsbTJkyBd26dcPly5chENAqFoQQQgghVZHA1g3mU/eWOs8KT2L8Xk04nJ6ejvDwcERERGDlypUGH2cqFoCJJHguNEbxVzzGGNSFBQBTg+VmwRlZQMILyE0tILCwgsDEDDy+5oLsSpUaL/IUSMtXQKFmEAt4sDEWoZqJGAK+9qTYR48exbJly8qM7+nTp8jIyIC/v7/B50QIKfo7TszLgYupeWWH8k7wsbRBdO8hlR0GqWKqZLKrS5cu3LBufSQSCRwcHHTuy8rKwsaNG/HHH3+gffv2AICtW7fC1dUVx48fR6dOnV57zIQQQggh5PV4nxJZhmjYsCEyMjKwcOFC1KpVy+DjeDweXCyM8ChdhriMAjiaiSEVCcBz8UJGRg5UOZmwUeWBpyyEMjsDyuwM8KUmkHr+N9l+oVKNu6n5KFSpYS0VwUjEh6xQhcRsOdLyFfC2NYZIoJkc0zUaTJcbN27AxMQE1avTaAtCyuPf54loc2Abenl4Y0+HXlorvxJCylYlk12GiIqKgp2dHSwtLREYGIiffvoJdnZ2AIDLly9DoVCgY8eOXH0nJyf4+fnh7NmzepNdcrkccrmc287Ozn6zJ0EIIYQQQj548fHxFT7W2lgENWN4miVHer4CPB7AGCDkC+Bk7wQTExHUBflQZWVAmZ0JwUsjRZhajfSnTyAWmqCmvRWMRP/d/SBTqHD3RT4SMgtQw6Zit4x26dIFuf8/jxghxHBnkxPBAFhJJJToKgelWo0TifGobWULp8oOhlS6dzLZ1aVLF/Tt2xfu7u6Ii4vD7Nmz0bZtW1y+fBkSiQTJyckQi8VaE4Ta29sjOTlZb7vz58/Hd99996bDJ4QQQggh5LWxNRHD2liErAIlFCoGIZ8HSyMh+P9/C6JAagKB1AQie2eA/TfRdUFWJszy02GGdLCCFyi0sITQ3Ap8iRGkIgGczCVIyCxAoUrNrQpJCHnzptdvir7Va0H3EhCkWMkFSwZdvYQdzxLxTc1a+May9LkPyfvvnUx29evXj/t/Pz8/NGrUCO7u7jh06BD69Omj9zjGWKmZ8a+//hqhoaHcdnZ2NlxdaZlXQgghhBBStfF5PFhJRaXW4fF4AO+l0VtMALnIFObKfKgLC6B+kQzFi2TwjYwhtLCCpaklEgDkF6ogllKyi5C3ydPcsrJDqLL0LWTS1sQV4bYNgAu7kZ95R6Mu+fC8k8mukhwdHeHu7o779+8DABwcHFBYWIiMjAyN0V0pKSlo0aKF3nYkEkmZq98QQgghhBDyPmBGUiRK7VHN3hjIz4YyKwOqvByoC/JRWJAPgdQMAMBD2T8aE0JeD6VaDSGfksul0beQyUC1Gp8DEP3/9XvfFjMh5fNe/BWlpaXhyZMncHR0BAAEBARAJBLh2LFjXJ2kpCTcvHmz1GQXIYQQQgghHwozSdHv3mlyFYQW1jByqwFpzToQO7hCaGWLdCUffB5gIhGgMPExCp48gjIrA0yt0mqLMYbsAiXS8xXIlSvBGN2ARUh5Pc7JgsMfKzHuzDGo6W+oVAJbNwidfTQexq6+kLr6ctuU6Cqf+fPno3HjxjAzM4OdnR169eqFu3fvatQZOnQoeDyexqNZs2aVFHHpquTIrtzcXDx48IDbjouLQ0xMDKytrWFtbY25c+fi448/hqOjI+Lj4zFz5kzY2tqid+/eAAALCwuMGDECU6ZMgY2NDaytrTF16lT4+/tzqzMSQgghhBDyIRML+LA2FiExSw6pUABzIyH4QhF4VjbIKlAiKV2GaiZiCBiDPCcLYGqocrLA4/MhMLOAwNwKAhMzpMmUSMyWQ6H678u5VMSHm4URzIyq5NcNQqqkvx7dQZpchtuZqeDTSMpX8ig7Ey4mZhALBGVXJgCAkydPIiQkBI0bN4ZSqcSsWbPQsWNH3L59GyYmJly9zp07IywsjNsWi8WVEW6ZquS7z6VLlxAcHMxtF8+jNWTIEKxZswY3btzA77//jszMTDg6OiI4OBg7d+6EmZkZd8zSpUshFArx6aefQiaToV27dti8eTME9GQnhBBCCCEEAOBuaQSFSo17qfkwEQtgJOQjX6GCTKGGhZEQLhZFq8FJq3tDmZUJZXYGWKEcyqyMolFefAFyRJYws7SFvZkEEiEf+YUqPMuR415aPrxtjbkRZISQ/6hSE7RuwxtvYw7/ps0h5vGhTLxDt+FV0CfH9mFP3D383bEPenjUrOxw3hlHjhzR2A4LC4OdnR0uX76MNm3acOUSiQQODg5vO7xyq5LvPEFBQaUOfT569GiZbRgZGWHFihVYsWLF6wyNEEIIIeSDtmrVKqxatQqFhYWVHQp5DQR8HrxtjZFVoERqvgJypRpGQj5cLCQwlwi5ebr4EinEdlKIqjlAXZAPVVYGlNmZgFIBY4kQdtZS8Hg8MJUKJiiEt40x7qbm42mWHLXtquRXDkIqjSo1AdlLdC+s1uT//5vz//81n7qXEl7l5G5qAR6AmLQUSnYByMnJQXZ2Nrdt6FzlWVlZAABra2uN8qioKNjZ2cHS0hKBgYH46aefYGdn93qDfg14jG6o1ys7OxsWFhbIysqCubl5ZYdDCCGEkBLovbryPH36FK6urnjy5AlcXFy48oKCAsTFxcHT0xNGRkaVGCF509LzC/E8JQOeDpYwMir64qTITEPhswTwJUZQGFsgTiGBt6MlpCK6u4K8X17ltU6ZeAc5Kz6Hcb8fILDz1FlHlRKH/J2zYTZ+K4TOPq8j5A9GUn4uAMDR2LSSI6lcxe/TJc2ZMwdz584t9VjGGHr27ImMjAycPn2aK9+5cydMTU3h7u6OuLg4zJ49G0qlEpcvX65yi/3RzyyEEEIIIYSQclOoGGQiKZfoAgCmUAA8PtTyAgjkBfACoEpIhcLKGgJzS/CFosoLmJAqRmDnCaGzD/KVCnQ6vAsfe3pjrG9DmmfqFX3oSa6Sbt++DWdnZ27bkKTUuHHjcP36dZw5c0ajvF+/ftz/+/n5oVGjRnB3d8ehQ4fQp4/u0YqVhZJdhBBCCCGEkHITCfhQM0CuVEMiLFrkXVzNASLralDmZEKekQ6eLA88eT4Kk/OB54kw9vYDT6D5FUShUuNFngIZMgWUagaJgI9qJiJYG4u42ygJeZ/ti7uHM8lPkZiXgwl+jSo7nPeKUq2GkM+v7DAqlZmZWblGv48fPx4HDhzAqVOnNEZu6+Lo6Ah3d3fcv3//VcN87T7sf3VCCCGEEEIqIC0tDXZ2doiPj38j7X/yySf45Zdf3nq/hsYBABZGQgj4PCTnyDVie/zkCQQW1nhq4oREa0+IHVzAlxpDYGyqkegqfJEMWWYGYp/nIjlHDmORANVMRODzgLiMAtxPk0FNM66Q/1fac7E8fHx8sGHDhtcQ0evTxbU6VrXsgNkNW9AqjK9JgVKJIZGH4Lh1JTLkBZUdzjuBMYZx48Zh7969iIiIgKen7ltsX5aWloYnT57A0dHxLURYPpTsIoQQQggh5P8NHToUPB4PPB4PQqEQbm5uGDNmDDIyMjTqzZ8/H927d4eHh0e5j01OTsb48eNRvXp1SCQSuLq6onv37jhx4gRX59tvv8VPP/2kMalwRftdu3YtzMzMoFQquWNyc3MhEonQunVrjfZPnz4NHo+He/fulRoHUDS5vbO5BC/yFIhLl+H7H39Ct27dYOPogvup+Vj5yyJ81rUdrN2rw71pa3w2YQru3r0LAFArFFCkJkP9LB7H1y5En+Z1UNvJCt2DW+L5nSvwtjVGjlyJZ9lyzJ8/H40bN4aZmRns7OzQq1cvrp2Xr0tZdfRZvXo1N+9SQECAxvw0ALBmzRrUrVsX5ubmMDc3R/PmzfHPP/+U2qYh8Zw6dQrdu3eHk5MTeDwe9u/fb1C8ZcXs4eHBPR9efoSEhLxyzBW5FnPnztWKpbSV3ObPnw8ej4dJkyZplJf2XDSUTCbDgwcPUK9evQq38SZYG0kxtk5DDKtVt7JDeW8YCYW4mvYcqQUy/O/xg8oO550QEhKCrVu3Yvv27TAzM0NycjKSk5Mhk8kAFL1vTJ06FefOnUN8fDyioqLQvXt32Nraonfv3pUcvTZKdhFCCCGEEPKSzp07IykpCfHx8diwYQMOHjyIsWPHcvtlMhk2btyIL774otzHxsfHIyAgABEREVi0aBFu3LiBI0eOIDg4WCMZUbduXXh4eGDbtm2v3G9wcDByc3Nx6dIlrv7p06fh4OCA6Oho5Ofnc+VRUVFwcnKCt7e33jheZmcqhruVEVIychAWtgmteg7A3Rf5UKgY7lw5jwnjx+H8+fM4duwYlCoVOnbsiLy8PAAAs7DBriPH8dX8RZj2xTD8+9c2NPP3RZfOnZH+5BHsTMRIzVMgKuokQkJC/mtHqdRoBwBOniy7ji47d+7EpEmTMGvWLFy9ehWtW7dGly5dkJCQwNVxcXHBggULcOnSJVy6dAlt27ZFz549cevWLb3tGhJPXl4e6tWrh5UrV5YaY3ljjo6ORlJSEvc4duwYAKBv376ltmtIzBW5FgBQp04djZhu3Lihs150dDTWrVuHunW1kz5lPRcNcfPmTTDG4OfnV+E2yLtjcdMgnOkxEANr1qnsUN4Ja9asQVZWFoKCguDo6Mg9du7cCQAQCAS4ceMGevbsCW9vbwwZMgTe3t44d+4czMzMKjl6HRjRKysriwFgWVlZlR0KIYQQQnSg9+rK8+TJEwaAPXnyRKNcJpOx27dvM5lMpnVMbqGc5RbKmVqt5srkSiXLLZSzAqVCZ13VS3ULVUV1ZQrD6lbEkCFDWM+ePTXKQkNDmbW1Nbe9Z88eZmtrW6Fju3TpwpydnVlubq7W8RkZGRrbc+fOZa1bt34t/To5ObH58+dz29OnT2chISHM19eXHTt2jCtv27YtGzhwYKlx6PLX7t3MxtaWpebJWXaBQuPfuFhKSgoDwE6ePMkYYywxq4D5NQhgo0aMYAWJj1nenWss99YV5l3dg02bNJHlFChY9JMslidXltqOLobUYYyxJk2asNGjR2uU+fj4sK+++qrU46ysrNiGDRtKrVOeeACwffv2GdRWeWOeOHEiq1Gjhs5/k1eJuVhZ12LOnDmsXr16ZfaXk5PDatasyY4dO8YCAwPZxIkTteoY8lzU5erVqyw4OJiJxWIGgNna2rJffvml3O28rLTXurIonsay9BkBTHb5EBt7ZCf7+3IkkyXcYoqnsdyj4Mphlj4jgCmexr5SnOTDpe99+kNBI7sIIYQQQshbYRq2FKZhS5FaIOPKFl+7ANOwpRj37zGNunZ/rIRp2FIk5P53y9KqW1dgGrYUI05p3jbl8edamIYtRWxGKle2+a7ukSPl9ejRIxw5cgQi0X+rCJ46dQqNGpU9iXTJY9PT03HkyBGEhITAxMREq76lpaXGdpMmTXDx4kXI5fJX6hcAgoKCEBkZyW1HRkYiKCgIgYGBXHlhYSHOnTuH4ODgUuPQ5czp02jcqBFsjMUwkwh1TiyflZUFALC2tgYAKAoLEXs9Bh0/+ggSJzdIa/pB4lodHYKCcOHyFXCzdWWlQhZ3F4q0FKgVCq12dDGkTmFhIS5fvoyOHTtqlHfs2BFnz57VeYxKpcKOHTuQl5eH5s2b6227IvEYorwxFxYWYuvWrRg+fLjGv8nmzZvLnPy/rJj1XQtdbd+/fx9OTk7w9PTEZ599hkePHmm1FxISgq5du6J9+/Z6YzLkuVjSw4cPERgYiLZt26JHjx7o06cPpkyZgtDQUI3Rjm8TT2IMADj5969Y/TgOAy7+ixerhyFnxefcI3/nbI26hJDyodUYCSGEEEIIecn//vc/mJqaQqVSoaCgaGLjlyfGjo+Ph5OTU7mPffDgARhj8PHxMSgOZ2dnyOVyJCcnw93dvcL9AkXJrsmTJ0OpVEImk+Hq1ato06YNVCoVli9fDgA4f/48ZDKZVrKrZBy6lBYbUDTxcWhoKFq1asXdQlaYkwGVSgUTK1sAAI/Ph9DMAk41auLYmbNIz1dAJOCBl5sFtSwfhbJ8sOeJmDRxKlo2a4o6/38dX17NUaVmkAj5mDRxkkZfuqSmpkKlUsHe3l6j3N7eHsnJyRplN27cQPPmzVFQUABTU1Ps27cPvr6+etsu69wrqjwxA8D+/fuRmZmJoUOHapRbWFigVq1aFYq5rGtRsu2mTZvi999/h7e3N54/f44ff/wRLVq0wK1bt2BjYwMA2LFjB65cuYLo6OhSz9+Q52JJo0ePRp8+ffDNN9+gadOm6Nu3L0JDQ7Fw4UKcPn3aoATy6yawdYP51L2onvECE+IfgQ8eHLtv0arHkxhDYOv21uN7n6QW5GNd7DU8zM7AxsCPKjsc8hZRsosQQgghhLwVucMmAwCMhf+NOJpWrykm+TfSWho+ZdA4AID0pbohdRpipE89CHiadeP7j9aqO7SWf4XjDA4Oxpo1a5Cfn48NGzbg3r17GD9+PLdfJpPByMio3Mey/19ZsKwRNcWkUikAcHNqVbTf4v15eXmIjo5GRkYGvL29YWdnh8DAQAwaNAh5eXmIioqCm5sbqlevXmocupQWGwCMGzcO169fx5kzZ7gyY7EAAJCSWwiZQgWpqGhbrVZDDeBFngLOFhJIqnlClZ0JZVYGJs78Bjdux+LYH5uQf/8mYGKO+8JqUDMGK6kIIgEP0ydPwLXrN/DX4eNgjIHH42Hbtm0YNWoU1/c///yDGjVqAND+9yg+5mW1atVCTEwMMjMzsWfPHgwZMgQnT57E1atXtdotOem/rnN/VYbEDAAbN25Ely5dtBKRvXv3LnVC6dJi1nctihNeJdvu0qUL9//+/v5o3rw5atSogS1btiA0NBRPnjzBxIkTER4eXupzCDDsufiy5ORkRERE4OzZs1CpVLhx4wbmzZsHPp8PoVAIsVhsUDtvgsDWDTVt3bCsZkClxfAhkKtU+Cb6FBiAuQGt4GpqXtkhkbeEkl3kvccYg1yphkTIN/jDJSGEEEJePxOR9hdLsUAAsUBgUF0RXwAR3/C6FWViYgIvLy8AwPLlyxEcHIzvvvsOP/zwAwDA1tZWa4VFQ46tWbMmeDweYmNj0atXrzLjSE9PBwBUq1btlfoFAC8vL7i4uCAyMhIZGRkIDAwEADg4OMDT0xP//vsvIiMj0bZt2zLj0KW02MaPH48DBw7g1KlTcHFx4cqrVasGgUCA9BcpuPU8D+ZGQkgEPNx5/AxmVtVgYyyCg6kYPB4PfOtqCJ3zPf45cxbHD+yHq5U51AUy5BWqIJbwUNPWBCIBHyGjR+H0sX9w6OgJqMzt8Ty3EA5mEvTo0QNNmzbl+nZ2doZAIIBAINAaEZWSkqI1ckosFnPXt1GjRoiOjsayZcuwZMkSrXYNOfeKsrW1NTjmx48f4/jx49i7d2+5+igrZn3X4rfffjOofRMTE/j7++P+/fsAgMuXLyMlJQUBAf8lfVQqFU6dOoWVK1dCLpdD8P+vEYY8F192/vx5qNVq1K9fH3fu3IFMJkP9+vXx5MkTpKamomXLlga1Q95dziZmCK3bGL6WtrAUSyo7HPIW0Zxd5L31KC0fo/bGwnxOFKSzI+Hw02l8feQB0vIKKzs0QgghhLxD5syZgyVLluDZs2cAgAYNGuD27dvlPtba2hqdOnXCqlWrdK4SmJmZqbF98+ZNuLi4wNbW9pX6LRYcHIyoqChERUUhKCiIKw8MDMTRo0dx/vx5rVsYdcWhi67YGGMYN24c9u7di4iICHh6emrsF4vFCAgIwJ3o03C3MgJjDHmFKpw/FYmg1i3hYWUEHo9Xop1I1ApoAml1HyhcauK52AruVlII+TyEjBmDffv24dD6VfA1F8NJUIAXuXIwxmBmZgYvLy/uIZVKuf6LVyssduzYMbRo0aLU68sYg1wu19muIedeUeWJOSwsDHZ2dujatatBbVc05uJrYSi5XI7Y2Fg4OjoCANq1a4cbN24gJiaGezRq1AgDBw5ETEwMl+gCDHsuvqywsOhzf0FBAWJiYuDi4gIbGxv89ttv8PX1Rf369Q2O+3XbfPcG7mWmV1r/H5IlzdpiuE9dmFGy64NCyS7yXop5loNGKy/iYOwLhLZ2w++f1sFnde2x+twTNF9zCc9zDH9DJoQQQsiHLSgoCHXq1MG8efMAAJ06dcKtW7f0jmQq7djVq1dDpVKhSZMm2LNnD+7fv4/Y2FgsX75ca8Lz06dPa0xE/ir9AkXJrjNnziAmJoYb2QUUJbvWr1+PgoICncmuknHooiu2kJAQbN26Fdu3b4eZmRmSk5ORnJwMmey/BQpCQ0OxceNGHNy5FerUBKxfMBvJiU8wcdxYbkS+vnZe5CsAsRFMxAKEhIRg259/IuyXxTCVSpH48D4K7l2DfXIsChIToMzNBmNqrbhDQ0OxYcMGbNq0CbGxsZg8eTISEhIwevRors7MmTNx+vRpxMfH48aNG5g1axaioqIwcOBAvdfDkHPPzc3lkjsAEBcXh5iYGCQkJJR6rQ2JWa1WIywsDEOGDIFQqH0zz759+7TmjjMkZkOuRcm2p06dipMnTyIuLg4XLlzAJ598guzsbAwZMgQAYGZmBj8/P42HiYkJbGxstOYLM+S5+LJmzZpBKBTi+++/x6lTp1CjRg2sXr0aS5cuRVhYmMHtvG7P8nIw4tQ/qLVrPZ68tAgHIeQ1etvLP75LaDnzd5NarWZ1fjnLGiw7zzLyCzX2PUjNYw4/nmSfbb9eSdERQgh5nei9uvLoW9JcJpOx27dvM5lMVkmRvZohQ4awnj17apVv27aNicVilpCQwBhjrFmzZmzt2rUVOvbZs2csJCSEubu7M7FYzJydnVmPHj1YZGQkd4xMJmPm5ubs3LlzGm29Sr9xcXEMAPPx8dGoV/xvWaNGDa029MWhS8nYAOh8hIWFaRy3atUq7lo0bNiQnTx5UmO/vnZ+XrWOXXuWzdRqtd46a3+cy3JvXWG5t64wRVaGzriXLl/BnF3dmEgsZj7+9diWff+wlFw5U6vVjDHGhg8fzsVXrVo11q5dOxYeHl7qtTDk3CMjI3XWGTJkSJnXuqxrdvToUQaA3b17V+fxYWFhrORXQUNiNuRalGy7X79+zNHRkYlEIubk5MT69OnDbt26Ver5BQYGsokTJ2qU6Xou6jqPkn7//Xfm6OjIADChUMgaN27MoqKiSj3GEK/yWhebkco+OryLtTmw7ZXjIIbJVxSyvx7Gsj2P7lR2KG+NvvfpDwWPMcat6ks0ZWdnw8LCAllZWTA3p4ns3hWnHmUgcN1lnPiiIdp6aS+VvOxMAqYevo+nX7eCvdmbHcqaI1di57XnuJ+WDwsjIfr626OmLS0fTAghrwu9V1eep0+fwtXVFU+ePNGY16egoABxcXH4P/buOr7K+gvg+Of2XXcXYwGD0d0lEioiIRaKBQq22D9UFMFAxEAFAxQL7A4w6O5mjLGxsbHu7ebz+2NwZdIwdhfn/Xrx0vvkuavnuec53/ONjo4+a7Pp+uyXX35h8uTJ7Ny5E7W65gdLzJkzh++//54//vijVs97rnGcSm3HVlRpISm3guYBrrgbTq5eOphXjq2ijEiNCXtZMcamzVAd6+VmycvGbjZhMnhwoFyFVqPGz02HTq2muNJKYaUVb6OWpn4uqKXna51wqp/FZ5991jE092x8fX358MMPz6lf3rmoib91Vrv9pMk5xKWxYN8Obl32C4k+/uwYfbuzw6kVp7tONxbSoF40ONsySzBo1fSL8Tnl+iHN/Hjgp/3szi67pMmuT7ZkMvG7vZSZbUT7upBdauap35MZ2y6Y90a2wKCVC5sQQghRXw0dOpSkpCQyMjKIiIio8ePrdDrefPPNWj/vucZxKrUdm6dBi1Gr5lBBJXH+ro57K0VRyCu3kF9hJdLbC4O7/qTZCq2FedhNlWjIJU6tQefpjVbljcbVnUB3PUWVFg7kVpB9rMG9cL5T/Sz+/vvvvP7662fdNz09nYKCAlq1uvBZWi8FSXTVnuFN4pix1ZcrImOw2G0XNYmJqB8k2SUaHINWjcVmp9Rsw8OgRbFasOQeRbFaMIRHU1hpBcD12zmk2UqwV5ajWC0oFjN2iwXsNgxhUURMftFxzMwPXsVWVoLG1R2Nuwdab390/oFofQPQ+QWh9aqeWPtlby43L97FTW2DeWFQLBHeRiosNj7enMn9P+5HrVKx4NqWtfp1EUIIIUTNuv/++y/ZscePH++U855PHKdSm7GpVCpi/VzYn1vOzqxSvFy06NQqSs02Kix2/N10BLjpHNsepygK+qBwSvPzUZUVobHbsBXmYSvMQ6XRoPHyxSs4HB9XKzllFoKOzQh5JhabnWKTDUVRcNVpcNXLB+madqqfxTVr1pzTvjt27MDNzY2mTZvWdFjnbV32EVr6+ON+illkxaXjbTCy99o7zvq7LBoOSXaJBkOx2yn483t679/P0zvXsvGmF/AtzsKanw2Kgnv77jSb9xMfbDhCsIce49LPyMk9espjucRXf+qT+8OnmDMOnXJbXUAIrX/d5Xid/cU8/l6Zxq0BMbwzqAN676qyZhedhgldwrErMOn7vUwZEE2MnwxpFEIIIYS4UEadhhZB7uSVmSmosGKy2jFq1UR4GfEwaE75wValUqFx96DArKXc4EczdwVrcRG2kkIUqwVsNgC8jVryyy1YiovQeXg4hkCeyK4oHC6sJLfcwonNYdz0GqJ9jBh1kvSqC4YMGUJpaamzw8BkszL01y8x2WysHT6WRN8AZ4fUqEiiq3GRZJeodxSbjYoDuyndvh5sVgKvm1C1QqXi8MuPYS3I5cpj21qP76TRYlcUZq1IZd76DF4ZGkdg0O3YLSY0Lu6odDpUOn3Vf1VqNJ7e1c4ZMOpWLLlHsZeVYCstxlKQhzXvKJb8HAxhUdW2zVj4FtcdTQdgx48PowsMwSW2BS4xCbi16sQtPYfyyC9JfL0zm0f7NLlUXyYhhBBCiEZBq1YR5GE47/YUKhXYFdC4eaBx80AJDsNeUeZIatkVBaPNhCUjHYtajcbdE62HNxp3T1QaDYqikJJfQWGllVBPA/6uOjRqFcWVVtKLTOzLKSch0A29tK4Qx6SWFONndKHMYiHB28/Z4TRKiqKwsyAXT52eKA8vZ4cjLiFJdok6T7FaKNu5ieJ1/1C6bR1lOzdhL696MqMLCnMku1QqFb6DR2IrKUYd2oS303R8n++KITiMwJAgtmaVkfNzEg/2jOThXpGoek8+5xiCx957+viOPf1z6H01K/5eQ0+yUOUewZKdiSU7k+LVf+Laoj0J/a/Cz1VHcaWV/N++xtgkDpf4RFQyZl8IIYQQotZ4GbXklFkoNVlxN2irKr5c3YF/+365axVUegOK2YStuBBbcSEc287q6kFxpZ5oP3d8XXWO43q76HDTa9h1tIysUjOR3g13ogZxfuK9fdl37Z1klJWgkXt/p3h47V+8tmMjD7fuxMyu/Z0djriEJNklnCYpt5z8cgtRPsYzNv7cN/4qyravr7ZM7eaBe6uOuCV2QLHbHYmiiIdnOLaZoSgMSyviky1Z5JaZuaWDF7d1DCUh0K1G34dKU708ven9z9CtZDnPDWzKwx18qUjeQ8WBPVQc2IUhPJqU/AoOF1XS3B1SHroLbDa03n54du2HZ7cBeHbth84vsEZjFEIIIYQQ1XkZqxrcpxRUEuvngsuxIYd2u8KREhMlJhtBfj64GAOwV1ZgKynEVlKE3VSJrawEVVkJ7h5h+LhUfaRS7HZQqVCpVOg0avzddOSUWc452aUcGwcpQ60aNpVKRbi7zB7sLL2CI5izawvlVuvZNxb1miS7RK37dV8uTy85yMb0YgDUKriyuT8vtlTw3vYXxWv/Iu6tr1Ebqm4M3Ft3ojL1AJ6de+PRoRfubbtgjG52UpLpv1QqFd2jvOke5X2p31I1XkYtY1oH8drKNK5vE0xEmy64t+kCgM2ucOMXO/EyaLkqQkd25z6Ubl+PtTCP/N++Iv+3rwBwbd6GgDF34n/VDbUauxBCCFFTlBMbGAlRB6lUKuL8XUnKLWfX0TLc9Rq0xxrcW+0K4V4GvF2qKrY0Lq5oXFwhMLQq2VVSRElhERjdHMkpS/YRrKXFaN290Hh4YdTosNkV7IqC+gwJrBKTlaMlZopMVhSlqt9XoLsOXxedJL7qsPP9G5ddUUaA0VW+p052RWQM2Tffi5deZllt6CTZJWrV4u1Hue7zHfSJ9uHbsa1pYisk9YdFWBd+R3lhCuXHtivZsByvnpcDEDL+McLum1qvhvnNGBzLsoMFdHprPff1iKBXE28OF5mYs+Ywa9OK+Pz6VnhFBuH15lcoVgulOzZSvPpPitf8SfnebZTv3Ya97N8mmtaSIsxH0qqGO/7nAmmzK2jUctEUQghRN+h0VcmB8vJyXFxcnByNEGdm0KppEeRGQYWVwgoLdgX83XT4u+pO21xebTCiNhgp1XpRXmFBURRUKhW2shIUswlLfjaW/GyMag1hWjfspXZUbqducJ9TZia1oBIXnZowTwNqlYrCSisp+ZWUuNmI8jZKcqSOKi+v+uRy/G/e2Vz+y2LKrRY+7z+MDgHBlzI0cQZ6jQb9WYomRMMgyS5RayotNiZ9v5dRiYF82NbGkTkTKNmwnPBjT0Wsai0HIzowcOz1uCV2cOx3vHdCfRLqaWD1xE5M+SOZ5/9ModJqB6BXE29+v60dl8X925BSpdXh0a4bHu26ETbpf1jysile8xeeXfs5tilY8i1p0x/CEBmD3xXXofQbwRv7rCzYlMnRUjP+bjpuaR/CQ72iCPWUpxRC1KYDueV8vDmTI8Umgj30jG0fQrOAmh0uLUR9otFo8Pb2Jjs7GwBXV6lkEHWfmxrc3E54sGqzUGmznHEfD7WNbJOJzHwFXzc9BEeiVJRhLyvBWl4GVisGaxHFqUWodQaMUTHV9jdb7aTmluPjoiXUU4dKpQAKnu5qCtSQUVSGEaujukzUDYqiUF5eTnZ2Nt7e3mjOIXGSUVbCgaICLHY70dIUvc4oNptw0+qkf1oDpVKkxvy0iouL8fLyoqioCE9PGVd9sRZvzWTMF7vY+3A3IovT2D2mBwDu7brhO3g0P/p04o4lmaQ/0atBJWxKTFYyikx4GrUX/L4y33+FzPmvoZgqAbCjYmNAGyp7j8S7zxB2FViZvykTo1bN8gkdiPFzrcm3IIQ4Bbtd4eGf9zN71WF8XLTE+btyIK+C/HILd3UJ462rm0vVZS2Qa7XzpKenExERweHDhwkPD6+2TlEUsrKyKCwsdE5wQtSSvHILpSYbnkYN7noNapWKcouNokorersFH7UVxVSJ2mBE6+VTtZOiYMnLxqzWUYqWAG8P1Ke4XhwtMaOgnLG3rXAeb29vgoODzzmZX2oxsz47k/7/mcldOMcdy37lkwO7+HnwKAaENXF2OJfEma7TjYFUdolLSlEUyrav5+inb2MsUeEbcmtVxUNAApFPzMKzW38MoZEAdM4qRfkjk9SCigaV7PIwaGkeeHG/aiF3PELg9XdR+PdP/D1vHtFHttE5Zyt8vRXN0te4+fvNPNwrit5zN3L7V7v5Z0LHmgleCHFa0/9O4fXVh5k5NI6J3cJx0WkwWe3MW5fOAz/tx9dVxwuDYp0dphBOoVKpCAkJITAwEIvlzNUxQtRnkXaFt9ccZuHqLIpNVQ2vVSroG+3DM5c1I9jDgKIoKBYz6mM9gsp2b+HQi/ejAjwAk6sr7q0649GhB+7tu6Pz8Qfgnw0ZvLkmnc33dTljDPtzyvh0axZr04qwKdAuxJ2b2oXQJtTjUr71Rk2n051TRdeJ3HV6SXTVIWqVCpPNxt9H0hpssquxk2SXuCQURaFo+W9kzZ9F2c5NAIRodeA+nPxyC76uOgJGjqu2z6GCCgB8pFT7lDRuHhR1u5rRKwP4bKI7vVP+JO+nz3GNS0Tj7kkI8MKgWJ6c9zM7s5qRGCw3OEJcKuVmG6+uSOP+7hE83PvfG1eDVs29PSLJLDHzxqrDPNanCZ5GudSKxkuj0Zz3B0Ih6ptHBsQzqVcMq1ILqbTYaR3iTpTPf/rVndC/TtcskaYPPsfXC74gNnUdHkcPU5Kyj5IfFgIQ8dgrBI6+naxKOFoJRuPpZ3P8YlsWYxftItBdz+hWgeg0ar7Zmc1bG7KZfWU89/eMvCTvWZy7s01QIJzjkTaduTexA4nHksui4ZE7cFGjFEWhaOUfZM57ifI9WwFQ6Q34Db0W7dW3U/pFDnPWHGbKgKbV9rPbFV5fdZh2oR40C5AheKezNbOqaf1lPdsTMLgroeMfx1ZS6Fg/yLuSqHWPUjRhLnl3TcZ34DWotPJrLsR/bckoZn9uOV5GLX2b+py2CfHpLE8poLDSyoQupy4Jn9AljBn/HGLpgXxGJAbWRMhC1Blz5sxhzpw5mM1mZ4ciRJ3hqtcw8ISerGeicfPA57Krsbh34LLvdrN3uAeu25ZRtGoJ5bu34JbQFovNzidbsrhHs5cDD7+HV7f+eHbthyE82nGc5Lxybl68i+vaBPHByBbotVV9h14aHMvjvx3ggZ/20ynCs9ZnJhfVXf/nD5RazEzr1Jt2/kHODkccE+fl6+wQxCV2wZ+CLRYLWVlZlJeXExAQgK+v/LAIyFn8PodfeQwAtYsbgWPuJPCGu9H5BgBw/+Eknl16EBUwqVsEPq46kvPKeXrJQf5MzueHm9tIE9szMBy7iSk2WQlw16PSaNB6/3tjlbdrK2a1DteMJA5NmcCRd2cQfPN9+F15HWrD6Z8KCtFYbEwv5u7v9rIxvdixzN9Nx5N9m/BAz8hz/vtTZrYBEOB26krUQHc9AOUW20VGLETdM2nSJCZNmuToBSKEuDA3tQvm+T8PMnqdiq9vuo+ECY9jyT1KidGbOxfvIq2wkmFsp2jZLxQt+wUAfVgTR+Lr/fxQ3PUa5o1IcCS6ANRqFS8OjuW73Tm8ufqwJLucqNhs4rtDSZjtNmZ07uPscIRoVM4r2VVaWsqnn37K559/zvr16zGZTI514eHhXH755YwfP55OnTrVeKCi7lKsFlTaqg98voNHkTX/NXyHXkvQ2HscPQeOmzG4qn/Nc3+l8NxfKXgbteSUVQ1r/GRMIlcmBNR6/PVJrybeuOs1fLjxyCl7AX1uaM3svvNYG72X0q/mYc44RNqMh8h872UCb7ybgFG3oXGRWeJE47Qjq5R+8zYRH+DKj7e0oVe0D+lFlby5+jAP/ZxEqdl2UtXp6bQMqpoldumBfMa0OXn68CVJ+VXbBcrvmxBCiFPzMGj57bZ2DJ2/ldiZq+kZ5Y2bXsOylF3YFfj8+kQSjMEUxcZQvPZvSretw5xxiJyvPiTnqw+5RqWh4O6vcTlFdbJarWJkYiCfbMl0wjsTx3nqDWwfdSu/HU6htZ9Uetc1ZRYzz21eze/pKay9eixGGRHToJzzbIyvvfYaL7zwAk2aNGHYsGF07tyZsLAwXFxcyM/PZ+fOnaxYsYJvv/2Wrl278uabbxIXF3ep47+kZIanM7PkHiVjzvOYMw8T9853jooIu8WMWqc/475HS0x8syuH/HILTX1dGN4y4JQXanGyx39N4tUVabw3IoGx7UPQqFXY7QqLth9l3Je7mNAlnDeGNcNeWU7udwvJWvgmlqNHULu60+qn7Wg9vZ39FkQDUWKy8t76DOZvPMKREjPB7npubh/ChC5hdWKadMVuB0VBdaxf0Ji5y6k8uJd5V8VgxI7dYkKxmFHMZr7ems7MnABWvnA9ge56zEczKPz7538PdrziS6VCrdfjltiRIX+Wk1VqZvnNzXFJ3Y1Kr0etN1KqMjDim4Pg6s6K+3qetVosq8TENzuzq/4e+rlyjfw9PC9yrXaexj7LkxA1pdxs44ttWfy6Pw+LTaFzhCe3dwwl6D+zMNrKSijZtIritX9TvPZv0ooq+fTOz/j0ukQAUqbchb2iDI+OPfHo2Itn9mn5YvtR0p7odcbzpxVWMnddOssOFqBSqegd7c2ELuFEesuoANFw2XLTsFWW0fSvP8iorOTrDp0ZFhziWK8yuKLxr9897xr7dfqck12jR4/m6aefplWrVmfczmQy8cEHH6DX67njjjsuKKjly5fzyiuvsGnTJjIzM/n2228ZPny4Y72iKEydOpV58+ZRUFBAly5dmDNnDi1btqwWx+TJk/n888+pqKhgwIABvP322+f1TW6MN9CKorAipZCVqYWogH4xvnSJ8Kz2Yc1uNpH92TtkfjgLe3lVD6nmC5bgltjBSVE3Llabndu+2s3CLVlEehtpGeTG3pxyUvIrGNUqkE/HJFYrZbdbzOT/shhbWQlBN9ztWF644ne8ug2Qnl6N2JFiE6sOFQLQPcqLMK9zv6nNLTPTb94m9uWWMyoxkNYhHuw+WsriHdlEeRv5Z3wHQmpoVlVFUbAV5WPJPYq1MB9jdDy6Y09HS7evJ+fLD7AW5mEtKsRWVoK9vNTx3+hp8/AdPIrcMjOj7pvFq9tePu15XmlxJ90nTOK+HpEUb1hO0t3DT7tt2H3PUjTkdnrP3UTzgv3MWvboKbdTaXWE3PEIIXdMBsCclU7ay4+h9fFD4+3Hb1kKP2QoFBo8sXn4stvujdrLh7eubs4NbU+uGBMna4zX6rqisd9EC+Fsz3y/ldc2F5DxZC/cVDa29Y/BXlnuWF9o8CK7SQcuG3ElHp17Y4w4uXr5u13ZXPf5TgwaFVc0rxqR8fPeXMw2hS+uT+TqllKNJBoeW24axTNHAPCFexQuipWB5Vm4KtXbT3hO/qZeJ7wa+3X6nD/lfvnll+e0ncFgYOLEiRccEEBZWRlt2rTh1ltvZeTIkSetf/nll5k1axYLFiwgPj6eadOmMXDgQPbt24eHR9UMdA888AA//vgjX3zxBX5+fjz88MNceeWVbNq0SWYFOo3kvHJGf7qDLUdK8HHRYlfgyd+T6RrpxeIbWhHhbaR061pSp91P5aEkAFxbtidi8gxJdNUirUbNx2MSubd7BB9tziSjyMSgOF9u6RB6UmISQK3T43/1TdWWlWxcQfKD12OIjCX0rsfxGXiN9EprRAorLEz8bi+Ld2Rjs1c979CoVYxuFcg7w5ufU1XW3d/t5Wipma33daHFsSF9AE8PKKfvvE3c/vVufrm13VmPo9hsWHIy0Xh4oXGr+vtdunUtRz+fS/bhw5RnHcGtNBed3erYJ3r6+/heXnWDYsnLJv/X01+fbGVVCfnsUjMlWlfsodG4ubqg0ukd/9R6PSqtHqsSwpHiquH5Oh9/fAYOPzleux3FbMYYGUNwgBvrJnZi7ud5HNoYjtZuxmi34KGYMVjKQVGqhnmfUOlqyc+haPmvjtftj/07znDtRKZHjOGmRTvxriwgftHz6INC0QWGog8MRR8cVvX/weGOKeyFEEI0Trf3ac4rG1czdtEuPhmdQPy731OycQVF65dTuHkN3qYivPf9RdqMv/Do3If4t7917GvOySRV5cWYz3ZwdYsAPhzVAndD1UfDUpOVcV/uZsznO9n1YFdi/GTyqAvx0ta1ZJWXMbFlO2mGXscopqqksOuY57kzMPqk9bbsFMoXTXFsJ+qnGi3psNvtpKenExl5cdnPIUOGMGTIkFOuUxSF2bNn89RTTzFiRNWHnY8++oigoCA+++wzJkyYQFFRER988AELFy7ksssuA+CTTz4hIiKCpUuXMmjQoIuKryHKL7fQ/73NGLVqltzejgGxvigK/J6Ux8Tv9jLk3dV8z28Ufv0BKApavyDC73sW3yGjUanVZz+BqHGdIrzoFOF1QftaC/PQevthSjtAypN3kP35u0Q8/G/S0mS1k1ViwlWnIcD9zENSRf1SabEx6MMtJOWWM/vKeMa0rpoVaPGOo0z5I5lBH25h2fgOZ5ydML2osmpa86ubV0t0AcT6uzJ9UAy3fLmbpNxy4vyrbpDNOZmUbl6N6Uga5iOpx/6bhjnzMIrVQtMXP8TnsuFVMebnUvjn9+iBE3/6CnUelLt442eB47eMrvGtCLt/KlpvP7SePmjcPdG4uaN2dUfj5oHm2LDdQHc9W3xbsPm27085g2J+uYWfpq/gpWNDRlxiW9B0xodn/Xo28XVhxqRrME+4msJKK15GLQatGsVux15Rhq20GPUJffL0weFEPjGLwqNZvLd0B7187TTTV1ZVpuXnEhARzidjEskus/DBT+t4csVvpz6xSkXIHY8QOuFxoGpoS/Hav8nxCOXnIjcK0dMswI0RLQPOe6ZJIYQQ9UOkt5Evb2zNqE+2E/7Saq5uEYDe43J+CG1PkcedfNTWSq+S3ZRsXIlnt/6O/Sx52ewY0pISnzCe9kjg9i7XoMvzRgmJQKVS4W7QsnBMSyJmrGTOmnRmXRnvxHdZP1ntdl7fuYnM8lJ6h0RIsquO0gRGow1r7uwwxCVyQcmu+fPns2jRIlJTU/H09KRXr148+OCDaLVaoqOjsdku3exTKSkpZGVlcfnllzuWGQwG+vTpw+rVq5kwYQKbNm3CYrFU2yY0NJTExERWr1592mSXyWSq1nS/uLj4lNs1RO+tz+BoqZmkyd2JODY+X6WCIc38+eP29rSeuZzMnX/hoij4DbuR8Aeel95P9ZjPZcPx7DaAo5++zdGFb1G2YyN7xw3EY9BoPm45lreTrBRUVFXS9GzizVP9mjC4mf9Zjirqg8+3HWVDejHrJ3WmY/i/Q74mdYugS4QXnees54ttRxnXMfS0x9iYXoxdgWta/juhhGK3Y85KpzI1ib4H9vHY3uVs/9tE3OgrACjbvoGUp+489QE1WqyFBY6Xc3O82dN8HDf1b0uP9s2qKpv8AtmRa2bw/C20ynLnj2PbGsKiCB5771nft7+bniua+TN7ZRo3tQvBTV89AfTayjTsisJ1bS5sSnC9Vu2YgRFApVZXJduOVasdp/MNIGDkOL5em85r6ft4/H+98XU9uZLu/u4R3Lorman3vohvRS7moxmYjx7Bkn0Ec1YG9sryajOxFu3fTcpj4wDoDeS6+JHkEs7z3pH07dOZXlcNxhgZc0HvTQghRN11RXN/9j7cjXfWpvNncgE2u8K1rQKZ1C2C5oFuwFUw/rFq+1Qk7QSNBo+CDAYXZJDx3FIyAF1QKO5tuxEwYhweHXowIjGQpQfyzxrDofwK5q7PYE1qIRq1istifbm9U1i162JjowLe6z2YLw/u5Qq5/tZ5RWYTXx/cR1ZFGU+26+bscBoNi8VCVlYW5eXlBAQE4Otbs0nh80p22Ww2RowYwW+//cbQoUMZNmwYBQUFfPXVV8ybN48333yzRoM7laysLACCgqp/IAkKCiI1NdWxjV6vx8fH56Rtju9/KjNmzGDq1Kk1HHH9sGj7UUYmBjoSXQB2UyUqnZ44f1cGJgTzrusDvD0oHM8ufZ0XqKgxGjcPQsc/hv81N3NkzjTyfvqckt+/pO2qtdzxyJdcFufH0VIz89ZnMHTBVj4Y2YJbz5AAEfXDR5uOcHmcX7VE13Edwz25PM6PBZuOnDHZpTk25LUseS+HvptHedIuKg/tRzFVOrYZDeQmtwKqkl0uMQm4temCIawJhtAI9KFRGEKj0IdFoQ8IcfSOq7DYmLXPxrjr7mLwf54ktwk18PpVzRjz2Q52ZpWSGFy9quxspl0eQ493N9Jv3iamDmxK7xNmY5yzJp1nBkSf1Az4UimosOBl1J4y0QVVFWN5Bh9K+11OYmT1Ck5FUbDm56A6YRjjjL8O0sorjnhbLrrSAvwr8vCvyIP8bXDwRzYrJrrf8xAApoxUcr58H9fmbXBp1gpjZKyjib8QQoj6J8rHhReHnPvEYJ5d+9P2r4Pc+PSHDDEn0aVkH2V7tmE5eoSC37/Gq3vVyBi9RkVgQRpZCzfg0a4brs3bOGZgP+6LbVncvHgXrjoNg+L9MFvtPPdnCi/+c4jvb25D35jGWdGkUau5IjJGEl31xMHiQm5f/itGjZZ7W7bHxdkBNWClpaV8+umnfP7556xfv75asVF4eDiXX34548ePp1OnThd9rvNKdr322musW7eOrVu3kpCQ4Fhut9uZNWsW48ePv+iAztV/+wspinLWnkNn2+aJJ57goYcecrwuLi4mIiLi4gKtJwoqLESdkOiqOLCbg0/did8VYwi++T6ivI0sK4jFs0tXJ0YpLgV9QAhNnp3DovBBBC16kXYT7mPUFVVJBkVRuKFNEBO+3cvE7/YyLMEfP7fG+5SuIcgsMXNVwumr9BKD3PhxTy5Q1U+rMmUf5Xu3U3FgF+VJu/AdPIoeA0Zj1Kr5accRev70uWNflU6PISKadPdwfirx5OHevR3rjE3iaP7Bryed77+2ZZaSX245bXP2a1oGYNCq+Ss5/7yTXW1CPfjzjvZM+HYPQ+dvdSz3ddXx8pBYJveOOq/jXYymvi7klVvYn1NGfIDbSevXpBaiVlHt7/JxKpXK0aAfYE92GTNzAnj/xW/o2ikMa0kRlSn7qDy4l7LkvSxbtoHlBX50P7Z96fb1HP1kjmN/tdEVl/iWuDZrjWuz1nh2H4A+UBLbNe1SP70UQojzoXHzwLXrZTyd3IGDj/YAUzllOzZSumUNHp16YbXZ+X53Do9VbCfj9beBquuFW+tOuLftinu7bhz0b87YRbu4vk0Q71yT4Kiaziszc93nOxn28Tb2T+5OcC09SBLiQrX1C+TqqDg6BQRjO7f5+8QFeO2113jhhRdo0qQJw4YN4/HHHycsLAwXFxfy8/PZuXMnK1asYODAgXTt2pU333yTuLhzT+T/13kluxYsWMArr7xSLdEFoFarmTx5Moqi8Nhjj51m75oRHFz1ASgrK4uQkH+nBs3OznZUewUHB2M2mykoKKhW3ZWdnU337t05HYPBgMHQOP8Yx/i5sjq1EEVRyPnqQ9JnT0ExVZK96D0CRt/B6rQiYnwlx91QWW12ZmZ6cdO9H3LLVc0cy/O+/4SCv37kuXteYOEWhY83Z/Jgr9pLCIiaF+KhZ+fRstOuP3jwMBN2fs6+O6ZRvnd7tVmdoKrnVJOrbuC2jqFMWZ/KDyPupU23TrjEJGAIjeKvQ8WM+ngbIxMDadK95WnOcnr2YzcYWvWpH0yoVSrUKrBf4H1Il0gvttzXhY3pxSTlVeBp0DAg1heXWu5rdXWLAPzddPzvj2S+uL4V6hPeb365hZeXp3Jlc/9zmtFy8faj+Lhouald1TVR6+GFe+vOuLfujD9g63OED77azYtlZvzd9BgjogkYfQfl+7ZRsX8X9spyyrZvoGz7BgBiZn3mSHZVHtpPRcp+3Ft1otLDj3fXpjN/UybpRZUEuusZ2y6ESd3C8Zck+CnV5tNLIYQ4X/d0j2DB5kwe+SWJmUPj8OzcB8/OfbDbFR76eT8ZxSZ6dG2Hh3UIpdvWYisqoGT9MkrWLwPAptbQrd9MPhjVD52mql+lSq3Gz03P4htaETZjBe+tz2DKgJNngWzIPjuwm0JTJTfEtsDbcO4zXQvnUalUfDdohOO19Qzbigu3evVq/v77b1q1anXK9Z07d+a2227j3Xff5YMPPmDZsmW1l+xKTk6ma9fTV/Y88sgjPPLIIxcczLmIjo4mODiYJUuW0K5d1UxfZrOZZcuW8dJLLwHQoUMHdDodS5Ys4dprrwUgMzOTnTt38vLLp592vjG7o1Moty3cyOp7bsK4rqr6wrP7ZTR5dg7fHihhc0YJzw2UMtyGKq/cQm6Zhb4xvo7qR7vFzJG5L2LJyaRk00oeSxjN/szbAEl2OZuiKGzLLOVQQQW+Ljp6NPFGc5rk0H+N6xDKbV/vZsP2A8Tm7qF89xb0oZEEjBjHxvRi/kgt48ld31J6LOmkdnXHtVkrXOJb4RrXArfEjgC8ekUchwoq6LevF+12eNA6p5Jd2ZvZmF5Mv6Y+zBl+Yc0+WwW7467X8M2ubNqEepy0/rf9eVRY7PSIurDJGaDqhuZiJnioCUadhrevbs6Yz3cw4P3N3Nc9gmhfF9amFTFzRSqFFVZeGXpuF/fCCgvBHgYM2lNPFhJ5rDqsuNKKv5set8SOju+jYrNhOpxM+d7tlO/bTvm+Hbg2b+3YN//3b8h8r+q6mesWiM0jjttadsS9XQ8267x5ZXkqCzYd4Z/xHYjykQciJ6rtp5dCCHG+2od58sZVzbj3h338tDeX0a2qqoYXbz9Kcn4Fbw1rRrtuETBiJIrdTuXBvZRuXUvJljWUbl2DOSeH7j06otNUXX8Oz3yCkk0r8WjXDfe23bg+zIslSflnTXaVmKx8uiWLDenF6DUqBsf7cUVzf7Sa+jcJlqIoPLdpFfuK8tGq1YxPaOvskISoM7788vQzqJ/IYDAwceLEiz7feSW73NzcyMnJOe3N2NatW3njjTf48MOzz2B1JqWlpRw4cMDxOiUlha1bt+Lr60tkZCQPPPAA06dPJy4ujri4OKZPn46rqys33HADAF5eXtx+++08/PDD+Pn54evry+TJk2nVqpVjdkZR3TCfcr7a/gzG/BRsai3KzY9xdOitzF6ezdz1GVzbOogh8X5nP5Col1yPlZ1nlZody9Q6PfHvfEfay49Ssn4ZV29dSPHhZRQ3eR3Pzn2cFWqjtya1kHt+2MfmjBLHskhvI9Muj2Fs+5BT7qMoCpXJeyjdtp5eW9fyy9oVqJdkcvDYekPrriwKHsDTS5JpGR1MYOwjuIVH4dqiHcaouFP2czLqNPx4S1t+25/Hgk1HSMorJ9LLwP/6tebKhIBzTr79l4dBy20dQ3l1RRoD4/zo2cTbsS6tsJL7f9xHlwhPpyaqasro1kF4GDRMWXKQEZ9sB0CtgqsSAnh5SOwphzeeSqy/K2+tSSerxHTKoSKrDhXiqlOfcp1Ko8HYJB5jk3h8B486ab3WyxeX2BaUH9iDf1k2A8uyIWsV/Pk6Xb18eeKtXxjwYw63LN7FPxM6nudXoGGr7aeXQghxIe7pHkHHcE/eWJXGgk2ZAPRt6sPCMYl0PaFnpEqtxiW2BS6xLQgYdRuKotDy6R8YZfy3cql0y2oqk/dQmbyHnK8+ZCKQ6x5MSmZf3Nt2wf/qsSfN4v7bvlyu+3wnJSYr7cM8KTPbeHddBs0DXPl5XFua+rnWytehplgVOxNbtuOzA7u5Pibh7DsIp7Jlp1R7bVcU1hTkoyrM5NRXb1GfqBTl3Aeljhw5Ejc3Nz7++OOT1mVlZdG3b1+SkpIuejbGf/75h379+p20/JZbbmHBggUoisLUqVOZO3cuBQUFdOnShTlz5pCYmOjYtrKykkceeYTPPvuMiooKBgwYwNtvv31ePbiKi4vx8vKiqKgIT8+Tmzk3FLayEnZe0xFrfg6V7r480WYyK1xiAQhy1zOpWzhP9G1SL5+uiHM35MMtHC01s35Sp2rfa0VRWPLBfJQPp+FvLgTAZ9BIIh58Hp3/qfsqiUtjXVoRfedtom2oB//rH03ncE8O5lcwa2Uai7cfZe41zRnfJRx7ZQWmzDRcoquGpCqKwvaB8VgL8xzHUlBxwD2SnZ4xbPVO4PfwvlzbOog5VzfD2+XUTdNrS7nZxtD5W1h+qJDB8X6O9/nljmyC3PX8Pb4D0Q1sWPWB3HLyKyxEehvPu7dJQbmFsBkruLFtMPNGJFTrTZlWWEnHN9cxvGUg80Zc2E13ZrGJ5tP+4I0EE5fbDlGyZTVl29aj0mpp82cyX+7KZcxnO9ji9gfuhUfw7NoPz679MIRHn7WXZk1oLNfquig9PZ2IiAgOHz5MeHi4s8MRQtSyEQu3cSCvgm33d0GlUmHJz6F061pKt6yheMsayvbuQIMdAH1IBK1+3ObYt/CfX0jT+tDt11L6xQXwzvDmjomyNhwu4oYvdqIAOx/oirGWWw2cD1tuGoqp/LTrVQZXNP6RtRiROBe23DSKZ444afmbXvE849eGAeWZfJm1Es/J39Tr719jv06fV7Jr+/btdOvWjVGjRvHII48QGxtLfn4+P/74I9OmTaNJkyasXr36opNddUVjuoHO/mIe+b99SdOXP8LuE8TenHJUQEKgG/rTDI0RDcuKlAL6vbeZkYmBzBwaR4S3EZtd4btd2dzx9R56BqqZU/oTOV99CIpC8/l/4JbYwdlhNyp95m6k1Gxj1V0dq9342W02nnjnB4rW/s397mlUbF+H1suXVr/sdCQbUv43HkteNu5tOuPWpgvurTpx1G5gdWohAN2jvAk9h/5QtcVstfPJlkw+2HiElPwK/Fx13NguhPGdw047g2Fj9v76DO78Zg8D43yZ2DWcUE8DfycXMHtVGkatmjUTO11wg+Bf9uZyxYKtpD7e0zEkUrFaMKWnYGwSj8lqx/i/v1i15UEMeRmO/fRhUXh2qUp8eXTqjdbj0lTj1edrtd1uJz09ncjI+nkj3dhvooVo7P46kM+A9zcz7fIYnuzXxHHPYbMrTPxuL5+vTWbdAA2eBzejNroScuuDACh2O9sGxGArKaJc60Jgh654duiBe7tuuLVoh9pgZE92GS1mreGj0S24uUPdnDTldAmT/6rvCZOG6lSJyv2lpXRbtYwRwaG816k72oD63b6lsV+nzyvZBbBs2TJuu+02Dh065Fim1Wq5//77uffee4mKisJut9d0nE5Rn2+gz0ax27EW5qHzDah6rShgs540nbBoXL7acZTbv9pNqdlG8wA3csstZJeaGRzvxxc3tMLLqKVsz1ZKN60k6KZ7HPtZCnLR+Zx+hj9xana7wj8HC9iTU4abTsMVzf0JcD91o++DeeXEvLKaL65PZEybqoq6wn9+Jv/3byjZsLxa1RaAzj+YFl+sROstM741Ft/tyua5P1PYcqRqiKtBq2ZM6yBeHBx7To3uT+eP/XkM+nALex/uRrNTDK0sqrTi/czfLO5kp2fJborX/k3ZtnUoVotjm8KAaGZd+z7uBi2jEgMZ3jLA0ePlYtWHa/X8+fNZtGgRqampeHp60qtXLx588EG0Wi2hoaH19iFhY7+JFkLA1KUHeXbpQdqEuDMyMRCzTeHzbVmk5Ffw/sgW3Nrx5ESVtTCflGfu5si6VbhbqycbVDo9ASPHETH5RXq/uxF/Nx3fjG1TW2/nvFgz9lLy5k24jnkeTWA0S3OyAejvH4BapcKWnUL5oil43PsJ2rAL62Uqap/JZsWgOa9uT3VWY79On/d3sU+fPiQlJbF+/XpSUlLw9PSkW7du+Pr6UlZWxjPPPHMp4hQ1yF5ZQcrTd1GRvJfmH/yK1vtYU3JJdDV6o1oFMSjej8+3ZrHraBlueg0jEgPpGP7vB0i3hLa4ndBss+RgEntv6ktKh2GkDrmboR2a0jLI3QnR1y+rUwu59cvd7M8tR6dRYbEpGLRq7u4SxitD404aNpyRmUuf7A209mvnWFayaSUFS74FqhrJr/Rojn+3fgy/cQTGJvG1MoRM1B3DWwZydYsAUvIrKDHZiPIx1siQ1G5RXrjrNSzcnMm0QbEnrV+4ORONRk33gb0I8RpEyK0PYisvpXTzapZ++R3KlhVs82iBTqPmUEEF1362g1bB7vx+W7uLSsLVBzabjREjRvDbb78xdOhQhg0bRkFBAV999RXz5s3jzTffdHaIQghxUZ65rCk9orx4Y/VhXl91GI1axWWxvnx+XeJp+2tqvX2Je30RbZ9awuutYZiSQunWNZRsWYs17yga96r7zgA3HbaSIvbeNgiPDr3w6NQL99adURurtzLYfbSUuesy2JpZgkGr5qrm/tzcIRQvY+0kLDSB0WjDmvO/devYlJvF2z0v5+4W7c6+o6iTGkqiqy6yWq18/PHHKIrCzTffjE53afMP5/ydTEtLc5TZq9VqunbtetLMjG5ubo5kV0ZGBmFhYTUYqqgJ1uJCDjx4PWXb1qHS6SnfuxXPrv2dHZaoQzwMWsZ3ObfM/9KkPL6Z9ja3myuIWbMIr81/8ED8Lbj3HcbH1yXiYZCLxalsO1LCwPc30y7Ugw9GdaRHlBf55RbeXZfBs0sPUmaxM3dYLGU7N1G87m+K1y3DdecmXrXbSFndkoRrhwHgc9lwNO6eeHbpR0FES+6fuY4FQ1rgEl03y/3FpadSqWq8ma+HQcuELmG8vDyVNiEejGoV6EikLk3K48nfD3B9myDCvP5tUqxxdecX99bc4KLm6SkP81SvcIzGqsTWpvRirv54G8MXbmPtxE4NOin72muvsW7dOrZu3UpCwr890+x2O7NmzWL8+PFOjE4IIWrGZXF+XBZ3/hNZtQjx4rsKPXeMG0jgdeNRFAVTegpqg5FKi41lKYU85bqfsu0bKNu+gaz5s1Dp9Li17oxnp154dOzFewX+PPT7IQLd9VwW60tBhYUHf05ixj+H+P329rQKrp0HsGabja6BoRwuK2Z002a1ck5xaWWVl+Ki1eGlb9gP5mrTk08+SY8ePbBarTz++OO8+uqrl/R85/xJtFOnTgwbNow777yTzp07n3KboqIiFi9ezOuvv86ECRO49957ayxQcfEsBbkkTRpJxf4daDy8iJn5CR4dejg7LFFPbc4o5sqPttG3z80YbhgEc6fgn5bMiztmsz7rHybkP8ynDwxt0B9kT2dNaiFz1qSzKaMYvUbN0Ob+3N013NHv6Lk/DxLuZeSP29s7ZsL0c9PzVP9oQnOTKJx3H5tf2wcVpdWOe9QzjB82pzBwpB2dRo17my64t+kCwFM/7MNNp+GaxMDafbOiUZg+KJZDBZVc+9kOWgS60SbEnX255WzOKKFfUx/eGV59eIaiKLy87BBDm/kxdWBMtXUdwj1ZMLoFAz/YwvKUQvo09anNt1KrFixYwCuvvFIt0QVVDw0nT56Moig89thjTopOCCGc6+4uYdzxzR5+2pPDlQkBqFQqjBFNURSFx387QH6FhSHXX45/MzdKNqygZONyLNmZlG5aSemmlcAMliVM4NEbbuP5gTHo1FWzRh4urGTYR1sZOn8LSZO710qDe71Gw1s9BzK7+wC0aul3XN/dv3opb+3azOvdBnCP9CiuMXa7nfLyctRqda20vjrnZNeePXuYPn06gwcPRqfT0bFjR0JDQzEajRQUFLB792527dpFx44deeWVVxgyZMiljFucgclqJ7fMjJdRi/uxyhpL7lH2TxxO5cF9aH0DiH/7W1xiWzg5UlGfzfjnEE18jHx/cxsMWjX2vn3J+uh1sua/RuecrbT5/DbWqx+my/2POjvUWjXtz4NMWXKQGD8XrmzuT4nJxjtr03lr9WF+uKUNHcM9+X5PLrOuiENXmk/++mXoQyIcSavhLQM5mLsJAI2XL56de+PZpS8eXfpRUu7GBx9s4eCCrTx7WVPHLIWvrkhj3voMZg6Nk2o6cUnotWq+vLEVfycX8OHGI6QXVRLj68LUy5oypJk/GnX1pHZWiZmtmaU82S/6lMcbEOtLmKeBX/blNuhkV3Jy8klV8Cd65JFHeOSRR2oxIiGEqDtu6RDKT3tzGb5wO2NaBzEswZ9Ss42PN2eyPKWQmUPjaB4XBXFR+F91Q1Xl1+GDjsTX4RV/Y23ZjRcHx6JSqche/D7ZX8zFq/sAPm3Zk/Z/qVm0/Si31GKDe0l0NQxN3L2wKwo7C3KdHUqDMmPGDBYtWoTVauWll1665Oc7509Fvr6+zJw5k2nTpvHLL7+wYsUKDh06REVFBf7+/tx4440MGjSIxMTESxmvOIOjJSae/yuFjzdnUmKyoVbBVQkBTGltxPDcTZjSktEFhBD/zrcYm8Q7O1xRj5msdr7blcNLQ2IxHJutU20wEjr+MXwHjyLtxcmwfhmb0wvocor9Uwsq+G5XzrFG+K4Ma1FzzaovhVKTlU+3ZrE8pRCA3tHe3Ng22JFMPu6XvblMWXKQ5wY25al+0aiPJQBmXxXPyE+2c+389SztZmPSvsV02ZvE9rS9APhecZ0j2eWd0JpF7e/A2K47UycMQ3XCTVNf4KdxbZn0/V56vLPRsdzXVcfrV8Vzb/eIS/dFEI2eSqWif6wv/WPPPumB2Vb1tM7dcOqn6SqVCneDBrO1YUxoczpubm7k5OQQFxd3yvVbt27ljTfe4MMPP6zlyIQQwvk0ahWLb2jFW2vSmbPmMJ9tzQKgVxNvfri5DVe1CKi2vUqlwhgZgzEyBt9rbiHuySW80bu5YxRB8Zq/MKUlk52WDMzjb42ejEPtyLluDN59hqDzDzplHEm55cxbn8Guo6W46TUMbxHAqFZBjnvcs9lSVIifRyFNPb0v+Gsh6pZb4hMZ3iSOaPme1iidTsdNN91Ua+c77xIAo9HIiBEjGDHi7NOsitpzpNhEj3c2UGq2cX+PSLpGenGooIK316Qz8uM9LK404xIcTvy732MIP/WTdiHOVbnZhtWuEHFCj57jjJExxM35homPv46pbV/uPLa8bOcmrHoXHtoOCzYdQadR42HQkFtmIcRDz4ejWjC4WfUZHfdklzF/4xFSCyvxd9VxQ9tgukd51erQyNWphVz98Tbyyy10jaxqtPrFtiz+90cy39/chu5R3o5tZ69Ko0uEJ//rH10tRje1nVnrplK8dS3W3yzcfMLxXeJb4RL9b2+HYrOdOUGDeTqhabVE13GD4v3Y/3B3lqUUkJJfgZ+rjkHxfrVSoi/EuQrzNBDsoeenPbkMaXbyTK37csrYl1POlP4N+3rUp08f3n33Xbp3737SuqysLK677jqSkpIk2SWEaLS0GjUP9Izk/h4RFFZY0WvVuOnPfk9jVxTsqKvd/0Q//y7FG5ZTvHopRauXwtEjRKesI23GOg7PfJw2S5PQuHlUO87M5ak8+msSPi46ejXx5kixiZsW7eL5v1L4/bZ2RPm4/PfUJ3lo20ZWrlzGh23aMTY80rHclp1yHl8JUZf4Gl3wNZ79ey/qNhnv0kBM/nk/JqudTfd2cfQFArijUxhXLjBwh9tU1t7VHkNY5BmOIsS58TJqCXDTsSq1kNGtT35KVmKysdDQhscDq2bTUWw2Dj1/H+UH9xEW1p+3JzzOjf1b427QsjOrlEd+SeLqj7exbEJHukZ6oSgKk39OYtbKNALcdLQO8WD94SLeXpvOsAR/vrihFS41kNxJya/g213ZlJpsNA904+oWAdWe4mUUVTJ0/lZaB7uzcExLxw1PakEFYxft4or5W9n1UDdCj80otzylkFc7uZD3/UIsudmE3DEZALVOj7okH4PdQpF7AEeiO/GToTmznryV0MjqE3m8vOwQJpvC2PYhp41brVbRL8aXfjGn3UQIp9Jq1EzoHMaLy1K5tnVQtaGK5WYbk77fR6C7npENvMfcM888Q7du3VCpVDzyyCPExsaSn5/Pjz/+yLRp02jSpAlJSUnODlMIIZxOpVLh43ruM7PpNGrah3nw3a4cbu9UdS+lcffEp9+V+PS7ksziSvpO+YIXfA7RMmUlGnfPaomuQ8/dy0GtH7OzYnl0SBeevaypI3G2I6uUqz/exlUfbWPrfV0clfonxWxwxYQa49EDaF2C6PTD85TYKk65nai/bHY7mkYyPHXGjBl888037N27FxcXF7p3785LL71Es2b/PphXFIWpU6cyb948CgoK6NKlC3PmzKFly5ZOjPzUVIqiKM4Ooq4qLi7Gy8uLoqIiPD09nR3OaeWWmQmdvoKXBsfyYK8oAGylxZTt2oxnl75sPVJCuzfW8d3Y1lzdsmF/sBC154nfDjBnzWHWTepMQqCbY7miKDz66wFeW5lG6mM9CPMyYi0uZMeUSdhX/QqA2sWNwDF3EnjD3eh8AzBb7XR8az0RXgZ+vrUdry5PZfIvScwcGsc93SOqeoLZFb7Zlc3Ni3dxbasgFlx74X9QTVY7d327h482Z2LUqvEyaskqMRPorueDkQlcmVBVNv/0H8m8tjKNw0/0xNul+g1YQbmFyBdX8mhHH+71zKB43T9s+vkXIsszAVDpDbT966BjeuzSrWuZsDSLsoAmvHF1c7rMWY+LTsNT/ZowIMaXrFIz76xN5+PNmTw3sClTBjS94PcnRF1QabFx5Ufb+OdgAcNbBNAvxofMYhMLNmVSWGnl53Fta6RfV12/Vi9btozbbruNQ4cOOZZptVruv/9+7r33XqKiomqlSeulkJ6eTkREBIcPHyY8/Nxm8RVCiJqyYOMRbv1qNx+NbsHNJ/TlqrTYuPazHfydXEDa4z3xcdVhN5tQH5tVz5KbxfbB//YudmvVEd8h1+IzcDg6n6pq5DWphXR/ZyO/3Nr2lBXKx9ly01BM5Rw1VRJkOHnEg8rgisZfig3qI0VRuHfVUhYf3Mva4WPr5TDV871ODx48mOuuu45OnTphtVp56qmn2LFjB7t378bNrerz3ksvvcQLL7zAggULiI+PZ9q0aSxfvpx9+/bh4eFxxuOnpaURGXnuvw8ZGRmEhYWdfcPTkMquBiA5rwKLTaF/TFUfFbvZxIGHb6J06xqip82j7cBr8HPVsTu7jKvrXsJV1FOP9Ynipz059HhnA/d1j+DyeD/yyizMW5/BT3tzmTk0jrBjwxy1nt58e8Vz/E0v5hZ+TcWuTWQtmM3Rz+cSMOIWgm6+l0ndwrn7u73klJqYuSKVOzuH8XDvKMf51GoVo1oFkVls4oGf9jNtUAzhx45vsdn5YXcOi7YfpbDSSqyfK3d0CqV92Kk/+N7+1W6+2pnNW8OacUuHUNz0GvZkl/HYr0lcs3A7/4zvQI8m3vy4J4eRiYEnJboAfFx1vHb0C9o8/zXJStUH1UjAplLj2aojnl36YLeYHcmusph2fPnlSl7s4kOkt5FVd3Xkvh/3c+c3ezj+yCHcy8Dbw5tzV5cL/6MuRF1h1Gn4ZVxb3tuQwdx1GXy/Owd3vYbRrYN4qGckzU9Ikjdkffr0ISkpifXr15OSkoKnpyfdunXD19eXsrIynnnmGWeHKIQQ9dItHUJYeaiQW77czXsbjjC0mR8FFVY+2ZJJQYWVb8a2dlSLHU90wbGHro/P5vt58+lasIOyHRsp27GRw68+iVf3AQTdOImuHXoQ5+/Kz3tPPRz/uOOJLLlza3hUKhV7CvPIqSzn20P7ebh1Z2eHdMn99ttv1V7Pnz+fwMBANm3aRO/evVEUhdmzZ/PUU0852lp99NFHBAUF8dlnnzFhwoQzHr9Tp04MGzaMO++8k86dT/31LCoqYvHixbz++utMmDCBe++994LfjyS7GoDj49qzy8wodjupz91L6aaVqN3cMUY0pdRkpdRsw/0cxr8Lca68XXQsm9CR//2RzKsr0pj6Z1VfglbB7nx+fSLXtQmutn1BhYXCJm1IeGACRct+JfPDVynfvYXsz9/Fs2s/mgS0R1FgVWoRWSVmbu946plzxnUM5YGf9vPL3lzGdwknp9TMkPlb2JRRQqdwT6J8jPywO4d31qbzYM9IXr0irlr/rD3ZZXy6NYv3RyY4yt4BEgLd+Pqm1nSZs4Hn/jzI77e3p9Jqx09no3TrWoo3LKdkwwqin3sHfXDVkxG7XwhqxY4hKg7Pzn1ICm/P1VtceXRwIv/r/2+D+hKTlbGLd+GiVTPu2JPHpn6u/DSuLWmFlezLKcNdr6FTuCfaOtyoX4jzpdeqmdQtgkndGt/kCSc+vVSr1XTt2vWkmRnd3Nwcya6LfXophBCNjUql4r2RCQxp5sfba9OZuSINo1bNsBYB3N8jstrIgxNp3DxwGXId924M5dsrguiWtoL8376ifM9Wilb8jne/K/Ho2BN3/ZknUtlXmEe4mwduOv2leovCyZ5u353H2nahf2jU2Teuw0pKSiguLna8NhgMGAyGM+xRpaioCKiarBAgJSWFrKwsLr/88mrH6tOnD6tXrz5rsmvPnj1Mnz6dwYMHo9Pp6NixI6GhoRiNRgoKCti9eze7du2iY8eOvPLKKwwZMuRC3q6DJLsagBaBbsT7u/Lu2nRa/D6H/N++Ao2WmJc+wrV5G95afRiLzc6w/8xoIsTF8nXV8fbw5rw8JJZDBZW46NQ09XU5ZQP5pr4uvLfhCHnlFvz7DsWrzxBK1v1NwV8/4dn9MtYsPYibXoPPHwu45VAaXgUhcKwh/Inc9Rp0GjWVx24+rv9iJ4eLTKyZ2MnRQN5qszNnTToP/LSfWD8XJp7wQXvx9qP4uGi5qd3JPbF0GjX3tfZgwcffkVTwNS8tW07gon3ss1sd2xRvWO6Y/nquW1fa3dmb9yf0B6oqux4PPsiUJQf5eEsmVzTzp8Rk5eud2dgV+OGWNvj+px9FpLexWp89IUTDUNtPL4UQojFSqVSMbBXEyFannmnxdHxddDTxMfJ9lprhN04k6MaJVKTsI/+3r/C9/BpSCyrYllnC0xXLSLrvGQJG3IpXr0GoNFXFA4qicN2fP5BSUsQ3A6+hf1j9ToaIU+sT2jCGoLZo0aLa62eeeYZnn332jPsoisJDDz1Ez549SUxMBKom1wEICqr++xYUFERqaupZ4/D19WXmzJlMmzaNX375hRUrVnDo0CEqKirw9/fnxhtvZNCgQY7zXSxJdjUAarWK//WP5uvX3iBrz1wAmkx5HZdOffho0xEe+SWJm9uHnNNsIkJcCHeDlsRg9zNuM7Z9CE/8nszUpQd5Y1gzVCoVnl3749m1P4cLK3l7bTo3t/TB4913ubekkLLbP2N3s9Z4dR+AR6feuLfujNrowt/JBZisdtqEeLA5o5g/D+Tz9U2tHYkuqGqOfX/PSDZmFDNzRRp3dQl3VFkVVFgI8TCg16gw52RSsW8HhvBojE3iAIjO2Mqr22dSvP3fknSzuy9BXXvh0akPnp37ADBrRRqbS7S8dn27au/zfwOaclmcH3PWHOaPpDz0mqrKlru6hktSS4hGpLafXgohhDh3arWKiV3DefL3ZMa0DmJwM39copsRdvdTVFpsTPxuO556DTEbv6M4ZS/Fq//EEB5N4I0T8b/qevIUKLGYqbRZaeMnPZFF3bZ79+5q1ePnUtV1zz33sH37dlauXHnSuv8WNiiKcspih9MxGo2MGDHCMRTyUqmRZFdycjJvvvkmqamp2Gw2x/IffvihJg4vzsE1mjSa7/8AgA9ir2XtwaakT19BTpmF69oE8c7w5k6OUDR2/m56XhkSy30/7ie1sJK7u4YT4mHgr+R8Xl2RhqtOw//6N0Gne5rln3xKxOEtVOzbTsW+7WTNfw2VVofHFdfziPf1tAh0o1eUJ+9+/CNxdjNDQ1TYK8tR6auSSYrZBMDN7UP4ZEsW+w5lErDjL0xHUhmyeSedkvez5bsclPJSAELGP0bo+McAWOMaR4x7BO0H9MO7XRfeLgzi6R02+sf4cm14EBxSWPzDJv5KLuDxvk3ofYoG210jvaol34QQjU9tP70UQghxfh7oGcnylEKuWLCVK5v7MzDOj+xSMx9tziS71Mz3N7chzuUTcr/5iNzvF2JKT+HwS49w5N0ZBI6+jV2j7yBJpcJHb2RJUh77c8vx0Gu4ork/fm4ytLGhKLOYeX/vdv7JTOObgdecV1KnrvDw8DivSXzuvfdefvjhB5YvX16tsX1wcFWbmqysLEJC/h0lk52dfVK1V11QI7MxtmrVinvuuYfWrVujPmFazi5dulzsoZ2qrs/wdKLDrz5J9ufv4tL7Cn4e9hzJBZX4uui4vm0wrc5ScSNEbfpiWxbP/5nC7uwyAHQaFaNbBfHykFhHQ/ujJSaGvv4XMQdXM4pkItM2oyvM5uv4Ebzb7Cb+vKM9iYbyajPp/FfgDXeTPvIRur+zka1jQrDe2evkjdRqjE3i8L96LEE3TuRwYSUd31rP8BYBzB2RAFQ9qfhqRzazV6WxOrVq3Hr3KC8e6BHJ6NZ174+6EI1NfbpWNzQyG6MQor6z2OzM33iEd9dlsOtoKa46DSMSA3mwZ2S1UQu28lLyfvyco5+9jTmjariWV+8hpE96k9u/3sPB/Ap0GhUWm4JRq+a+HhFMHxSLRl3/EiOiugqrhcCFb1FqMbNq2I10D64/17vzvU4risK9997Lt99+yz///ENcXNxJ60NDQ3nwwQd59NFHATCbzQQGBvLSSy+dtWdXbauRyi43N7c698Yam/CHXsAltgU+A4fTwlWSW6Luuq5NMGNaB7E3p5xSk5Wmvi4nPf0K8jDwx4MDeHVFHPdsyCAnwEyMLZ+rWoWw4cqOxPi5UpGSiS0gHGveUQx2y0nnUcwmvt+dg5dRS2xsJEe6X4Y+KAxDZFOWVXjzxHYbiW0SuLNHUyo8DSxcnsqsFam4aNU8e1lTx3FUKhWjWwcxunUQFltVnzCdNJEXQgghhKj3dBo147uEM77LmRMBGld3AsfcScCo29i79DuUT9+hYMgdDJm/la6RXnwyNJQOIW4UGrx5e81hnv8rhQqLnTeGNauldyIuFRetjifadsVTp6eZt5+zw7mkJk2axGeffcb333+Ph4eHo0eXl5cXLi5VfZkfeOABpk+fTlxcHHFxcUyfPh1XV1duuOEGJ0d/shqp7Pr+++9ZvXo1l112WbXxn717977YQztVfXhafL7jY4WobxRFodJqx6hVn/SzbrMrNHt1NX4GNb/emIAHZlQaDSq9gY1HTQyYv50JncN49cr4k4771Y6jPP9nCtuzqoYy6jUqxrQO4sUhcYR6nn0cuxCibqgP1+qGSiq7hBCNjV1RiF80D4Nag19pMwqLdWy4pzPZb0wh99uPCbr5XoJumsTsDbk8+msSBx/tIX2ThdOc73X6dHmF+fPnM27cOKDqs9nUqVOZO3cuBQUFdOnShTlz5tTJtgw1kuyaOHEi//zzDwkJCY5hjCqVisWLF190gM5U12+g8//4hsK/fiTyqdloPaQ/kGicNqUXM/CDzWjUKm5uF0KUj5FVqUV8szObLhGe/H57e9z0mlPuqygKB/IqKDFZifZxwec/MyUKIeq+un6tbsgk2SWEaGz2FubR8ZuP0KjUFCcl8N7wRG7vGML+u4ZRunk1ALqAEPzGP0GLzcE8OSCWx/o2cW7QotFq7NfpGhnGuGzZMnbt2iUVRrXIlJFK6gsPYi8rwTWhHcG33OfskIRwig7hnmy+twuzV6Xx2bYsCiusxPi5MHNoHOO7hOGiO3WiC6qS8nH+rrUYrRBCCCGEqK+ae/uRcdMkfjhwiJv3HSbWzwWVWk383B8pWPItGW89h/lIGlkv3MeH3k05EDQZJNnVIFjsNpakHyKttJi7WrQ7+w7irDZv3syKFSvQ6/X06NGD1q1b1+jxayTZ1blzZ5KTk4mNja2Jw4mzUKwWUp66A3tZCW6tOxN040RnhySEUzXxdWH2Vc2YfZX0RRBCCCGEEJeOl97A1U1j0GnS2XykhL4xvqhUKnwvH4F3n6FkL36PIx+8SpPCgzRZMJGckEoCRo5zdtjiIm3MyeKK377CXafn5vhEXLUyIuRizJ49m4ceeghvb2+0Wi25ubm0bNmSBQsW0KFDhxo5R410Wd6yZQstW7akTZs2dO7cmU6dOtG5c+eaOLSgaqjVP8n5PP1HMv/7/QDLpz9D2c5NaNw9iX7hPVTaGslZCiGEEEIIIYQ4hSKzyfH/nkYto1sF8fqqw+SVmR3L1QYjwWPv5au7F/NdxEDU7l549xnsjHBFDesaGErXwFBujW9FufXkybHE2X344Yds3rwZk8nE9OnTefHFF8nLyyM7O5vU1FSuvvpq+vbty8qVK2vkfDWSJfn+++9PWiZDGmtGakEF1yzczpYjJQS560koTGLYP3MBUN3zIoaQCCdHKIQQQoj/io6OvqB7oQceeID77qvbrQnmzJnDnDlzMJvNZ99YCCEagAJTJVGfvcNlYU1Y0HconnoDzw1sSte3N9DtnY082bcJfZv6kFFsYs6aw3y+rZhXHnyZVq090Hr7Oo6T+f5MfAePxBAe7cR3Iy6ESqVizfCxzg6jXnvllVc4cOAAAHa7nQ0bNvDaa6/Rvn172rZty7Rp0wgLC2Py5MmsXbv2os93UcmusWPHsnDhQkaNGnXKG7r169dfzOEbvVKTlQHvb8auwNI72tM33IW9Yx+jEoW1Tfry3N5QtpeYCPKQmeOEEEKIumTBggUXtF+TJk1qNI5LYdKkSUyaNMnR+FYIIRq6P9JTKLGYSS4uwEOnByDGz5VVd3Xk/h/3c+tXux3bRnkbeX9kArd3Cqt2jKJVSzjy7nSyPnqd8Aefx/+aW1CpVNjtCn8fLCA5rxwfFx1DmvnhbpCRO6Lh2bNnD5WVlezYsYNevXqhVqtZvHgxU6ZMobKykvDwcGJiYtiyZQs//vgjiYmJREdfeGL4on6LXn75ZQC++uqrizmMOI1Pt2aRkl/B3oe7E+fvSsXBvdhKi9H6BTLi9bd5bO4e3l2XwTOXNXV2qEIIIYQ4QZ8+fZwdghBCiBoyJiaBVr4B5JsqqxV5xAe48ett7TiUX0FSXjmeBi0dwz3RqE8uBDFGN8O9fQ9KN68ibfpDFP7zC6k3PsuEZQUczK9wbOdh0PBE3yY83reJjJaqow4UFVBsMdHeP9jZodQ7RqORTp060aNHD9q0acOiRYuw2+3s2bOHbdu2sXz5cv755x/GjRtHQUEB7u7uFBcXX9C5zivZdd111/H000/TokULAEJCQgCIioq6oJOLM1u8/SiD4/0cs8W5NG1Oi8WrMaUl4xYWzHWt81m0/agku4QQQog66L/3TUIIIeqvFj7+p13XxNeFJr4uZ9zfEBpJ/Lvfk/35u2TMeZ7i1UvRrF/DVf0nM+buW+ga6UVaYSWvrzrMk78nY7EpPC2f8+qc+fu2c9uyX+kXGslfV17v7HDqrVdffZW+ffty8OBB7rrrLtq0aUNkZCSbN28mNDSU9PR00tPT2blz5wWf47wa1C9evJj+/fuze/fuU65XFOWCs27iZEWVVsK9jNWWaT28cGvZHoBwLwNFlVZnhCaEEEKIszjbfZPdbpf7JiGEqOMqrTX3eUulVhN040QSPvmbw/5xeFrLGPvHVKJ+fRuVSkWUjwuzroznib5NmPHPoWrN70Xd0D80Cq1KjUGjwWK3OTuceqtt27Zs2rSJ1NRUunbtitFoxNvbmzfffJOXXnoJgPDwcAYPvvAJHs57NsY2bdrQr18/du3addK67OxsfHx8LjgYUV28vysrDhWS9+uX5P30OYqiVFu/PKWQ+GNVX0IIIYSoe85035STkyP3TUIIUYdtzMkk7NM5PLNxRY0eN9snilGtp5I/+DZQqXBNaFdt/YM9I7HaFb7amV2j5xUXL8rDi+yb7+XXIdeiU2ucHU69FhMTw5IlS8jIyOCbb77hiy++4MCBA9x44401cvzzSnapVCoWLFhA//796dev3ylLyv6bkBEX7s7OYRxJz+TAi49y6NlJFPz+tWPdL3tz+ftgAeM7h53hCEIIIYRwFrlvEkKI+u2TpF3kmyo5WFxUo8fNLjVjU2vxv/tpWixejXefIY511uJCAtz1+LnqyC6Vyq66yMdgPPtG4pxlZGQwZMgQRo8eXaMT35xXsktRFDQaDZ9++ikDBgygf//+J924SRO9mtO3qQ9vFX2PtqyI3ICmbIjqxZ8H8pn43V6GL9zGsAR/rm0d5OwwhRBCCHEKct8khBD126td+/Pd5SN4vG2XGj1u2LFWNVszS3GJbuZYbs5KZ9fIzux7dyY5paaTWtqIuqXMYqbAVOnsMOq9Tp06cejQoRo/7nkPYwRQq9V8+umnXHbZZfTv358dO3bUdFwCqNi/k4TtPwEwO3E8gz/awWXvb+bbXdlM6R/NVze1PuVMH0IIIYSoO+S+SQgh6ieNWs3VTeJo6RtQo8cN9TQwON6PmctTKTX92xOs4M8fsBbkUvr+dJ7b8w4jmnnV6HlFzXlj50aCFr7FzG3rnR1KvXepqtzPexijY0e1mk8++cRx47Z9+/YaD66xy3jzWVAUfAZew08v3UnS5O7se7gbaY/3ZMqApug0F5SrFEIIIUQtkPsmIYSon0w26yUfZv7i4FjSCivp9e5Gvt5xlCPFJg72vIE/+j+AVaVmcMY/ZN4/Ekt+ziWNQ1yYEFd3yqwWVh/NcHYo4jTOexhjtZ2P3bgNHDiQAQMGsHXr1pqM7YyeffZZVCpVtX/BwcHVYn322WcJDQ3FxcWFvn37nrI5bF1VvPYvitf+jUqrI2zSFDRqFbH+rsQHuEmSSwghhKgH6tJ9kxBCiHM3Y8ta2nw9n98OH7xk52gT6sGyCR1w1WsY9ekOwqavoPs7G5nr25/D989F4+FF2fb17L15ABUHTj2rr3CeqyJjWTHsRv668jpnhyJOQ3s+G//88894eVUvpTx+4zZ27FhGjhxZo8GdTcuWLVm6dKnjtUbz72wIL7/8MrNmzWLBggXEx8czbdo0Bg4cyL59+/Dw8KjVOM9VWmElW4+UoFdD+OvPAhAw+jYM4U2cGpcQQgghzl9du28SQghxdnZF4aOknRwqKaLYbLqk52of5smquzuxJ7uMg/kVeBu1dI30QqNWUdmrDQcevB5TWjL77hhK/Ls/4Nq89SWNR5w7o1ZLz+BwZ4chzuC8kl1Dhgw55XK1Ws3ChQsZO3YsX3zxRY0Edi60Wm21aq7jFEVh9uzZPPXUU4wYMQKAjz76iKCgID777DMmTJhwyuOZTCZMpn//oBUXF1+awP/jSLGJid/t5Yc9ORx/CNzT71qesLvRctxDtRKDEEIIIWrWudw3ff7557UclRBCiDNRq1RsHjGOj/fvZOQJzeMvpYRANxIC3aotM0bF0nz+HyQ/fCN2swlDRHStxCLOjS03DcVUDlTlH+yA5oT2BSqDKxr/SCdFJ+A8k12nsnnzZhITE9Hr9XzyySfcf//9NRHXOUlKSiI0NBSDwUCXLl2YPn06TZs2JSUlhaysLC6//HLHtgaDgT59+rB69erTJrtmzJjB1KlTayt8APLKzPSeu5FKi5251yRwZXN/ik1WFmxqwtXLE7h+6VE+HlOzDRGFEEII4VzHm9bX5n2TEEKIc+NjMHJ/q47ODgOtlw9xb32NvbICjVvdHJ3UGNly0yieWVVU87FHNHO84plcuIfRpWnVtvOc/I0kvJzoopNdnTp1Ys+ePcTHx6NSqejcuXNNxHVWXbp04eOPPyY+Pp6jR48ybdo0unfvzq5du8jKygIgKCio2j5BQUGkpqae9phPPPEEDz30byVVcXExERERl+YNHDN71WGOlpjZ/kBXon1dUBSFEE8DMwbH0jzAlXFf7uae7hF0jpCZOIQQQoj6Ijo6ulqD+nP1wAMPcN99912CiIQQQpxNpdWKUXvRH5FrlNrogtro4nidOf81AEJufdBZITV6xyu6XMc8T36hiaSkfXyfOIzbOnUFwJadQvmiKY7thHNc9G/ypZ6l4nROHBrQqlUrunXrRkxMDB999BFdu1b9kP33JlNRlDPeeBoMBgwGw6UJ+DTmbzzC2PYhVYkuu519d16BR/seBN9yHze1C+GZJQeZv/GIJLuEEEKIemTBggUXtF+TJk1qNA4hhBDnxq4odP7uY5p7+zKra3/C3T2dHdJJynZt5sic5wFQTBWETHjigh6siJqhCYzm9rhQokNjGBkdj1Zfu7mEhuKZZ57B39+/xo9bt9LWF8HNzY1WrVqRlJTE8OHDAcjKyiIkJMSxTXZ29knVXs6kKAoZxSbahrgDULjsF8q2raPiwG6Cxt6DVq2iVbA76UWXtjGiEEIIIWpWnz59nB2CEEKI87AhJ5Md+TmklhTh0btuJi3cWrYn7L5nyXjjWTLfn4lisxM68SmOlppJLzLh76qjia/LWY8jak6kuyfjmrVydhj12jPPPHNJjttgkl0mk4k9e/bQq1cvoqOjCQ4OZsmSJbRr1w4As9nMsmXLeOmll5wc6b9UKhVB7nr2ZJcBcPSj1wEIHDMerac3iqKwL7ec3k28nRilEEIIIYQQQjQ8JzYZ7wBs6tWXPaUluOWkYKVuNhkPvvk+VFod6bOeImv+LL7elsEDviNQqKrw6hHlxbTLY+gb4+vkSIVwrnqb7Jo8eTJXXXUVkZGRZGdnM23aNIqLi7nllltQqVQ88MADTJ8+nbi4OOLi4pg+fTqurq7ccMMNzg69mrHtQ/hgQwYP+OdStnMTKp2ewDF3AvDNzmyScst5f0SCk6MUQgghhBBCiIbjxCbjx0Uf+1dywrK62GQ86Ia7ySqzYZ37ND02L+Kn/nqC73uOA/mVzF6ZxsAPtvD9zW0Y2rzmh4aJU/v+UBLv793G1I49ae3sYARQj5Nd6enpXH/99eTm5hIQEEDXrl1Zu3YtUVFRADz66KNUVFQwceJECgoK6NKlC3/88QceHnVrFouHekby2dYsfnr5JboCXoNGUWT0Zv6yQzy95CDDEvzpFe3t7DCFEEIIIYQQosE4XtHlcu1zlPtG4KnTVVtf15uMP6zqTkKniYzb8DZBfy0kdsRw2nftx4iWAVz98TYmfLuHlEd7oNWonR1qo/D5gd38lJZMjKc3M6PCnR2OoB4nu7744oszrlepVDz77LM8++yztRPQBVAUhbTCSh6IU9P529UADMptT/Lzy9FrVIzrEMrrV8VL00EhhBBCCCGEuAR+UVy4/Z8/+V/77jzcurOzwzknB/PK+fNAPuPGTyJyYCz2inI8u/YDQKtR8/zlMXR4cz1/JOVLddclZMtOcfz/nYF+NFHFcZOfV7XlwnnqbbKrvtt9tJSbF+9iU0YJDyR9jFqxs863FSEtWzGlcxhDmvkT6K53dphCCCGEEEII0WAtOpJBodlEfmWls0M5ZwfzKwDoHulFQLtx1dYpVivtwzwxaNUk59XNqrT6TmVwBaB80RTHsvbH/gGU/2c74RwXney6VNNENmRphZX0nbeJIHc9v9zalt6aMDK/CcPVry2bDpXgszObm9uHnP1AQgghhBBCCCEu2MftOnBNQnsGhkc7O5Rz5uNSNeQytbCSpn7/JlSsJUUcuH8MustGYbI2wdtFd7pDiIug8Y/Ec/I3ZxziWhcnN2hsaiTZJc7PK8sOAfDP+A74uekBf2IfnUEs4LHjKKM+3cGq1CJ6yiyMQgghhBBCCHHJaFQqroutXxOCtQv1IM7flddXHaZvUx9H25v8nxdRtn09yo4NXN3qXq5K6OPkSBuu0yWyduTn8OHe7TzVvhtSEuRc0q2ulimKwidbsri9U9ixRFd117QMJMbPhYWbM50QnRBCCCGEEEI0fKnl5didHcQFUqtVPDewKd/vzuHOr/eQVlg1BFNz1TgOdR2NSlF4aucc1NtWODnSxmfcPz8ze+dGPk3a7exQGj3p2VXLbHaFwkorcX4umNIPceTd6fgPvxmPjj2Bqj9ccX6u5JSZnRypEEIIIYQQQjQ8ZpuNgWtX4R52GYvLSmnu7IAuwHVtgik12Xj45/3M33SEQHc9eeUW1J6j+Lx1MZHbfyf50XE0m/cjrs3bODvcRuOuhLb8np5Ce/8gZ4fS6Emyq5ZpNWqCPfRsOVLCoPVfkf/bV1iL8h3JLovNzrbMEq5tLb8cQgghhBBCCFHTtudnk2cxU6ExElR8FGvG3pO2qQ8z6t3ROYzr2gTx9c5sDhVUEuCmY1SrIPwNfThw37WUbFhO0v1jaP7h7xjCopwdbqNwZ0Jb7kxo6+wwBJLscorbOoby1qpUbl3/OQD+V491rHt/wxEyS8zc2iHUWeEJIYQQQgghRIPVMSCEpEHD2DzvHvjqJ0rOsG1dn1HP3aDlllN8dox55WP23XkFFUm7SJkynmYf/Obo7SVEYyDJrlqWWlCBCmhdsBclNxOL0Z3CxD6kHSnhw41HeHPNYe7qEkabUA9nhyqEEEIIIYQQDVJgWBwD732vwc6op3H3JPb1RaT8bzxRT86SRFctKzBV8kXyHm6JT8RVK7NiOoMku2qJ3a4w+ZckZq9Kw12v4amc1QD86tOR52ZvBMDPVcdzlzXlyX71Z9pbIYQQQgghhKgPsivKOFJWSttj/ZTqayLrXOkDQ4mf+6MkumqZoij0+P4T9hTm4anTc2NcS2eH1CjJbIy15NmlB5m9Ko2XBsdy5LFuDMpZB4D7ZdcAMLFrGOlP9OR/A5qiVssfIyGEEEIIIYSoSVM2rKD9Nwt4Zds6Z4dSa05MdBWvX0bGnGlOjKZxUKlUXBeTQCvfAKnqciKp7KoFJSYrr61M49HeUTzSpwlFq5ZgK8pH6xvA5EnXc/DHJL7ckc2sK5s5O1QhhBBCCCGEaHDsikKp1YICdAsKc3Y4tc58NIMD949BsZjRB4URMOpWZ4fUoD3RritT2neXqjonksquWvD7/jxKzTYmdosAQLFYMETF4TPgalRaLXd3DSenzMKKlAInRyqEEEIIIYQQDY9apeLT/lex59o76Bkc7uxwap0+KIyQOx4BIO2VRynesNzJETVsOrVGEl1OJsmuWlBqsgEQ7K4HwLvvUFp+tZbwB54DIMTDULWd2eacAIUQQgghhBCiEWju7efsEJwm+LaH8B0yGmw2Dj56C5WpB5wdUoNnVxT+zDiEzW53diiNjiS7akHzQDcA/jn4b+WWSqVCbTAC8Pex5c0D3Go/OCGEEEIIIYRooEw2KzO2rKHYbHJ2KE6nUqmI+t/ruLXuhK2kiAMPXs/a3Wnc9e0ehn20lTu/3s2KlAIURXF2qA2Coih0+vYjLvt5EX+kpzg7nEZHkl21oEuEJ21C3PnfH8mUmqzV1hWUW5i69CC9o70dSTEhhBBCCCGEEBdv9o6NPLlhOX1+/EySOIDaYCTmlYXogsMxpSWz+YGxLN17FEWpKsLoPXcTIz/ZjskqlUgXS6VS0Ss4HG+9gczyMmeH0+hIg/paoFKpeG9EAv3f20z7N9dzT7dwEgLd2J5ZypurD1NitvHlja2cHaYQQgghhBBCNCjt/IOI8fTmwVadpIfSMTq/QH4a+SK93r2dZvFN2PNgV3RGI3a7wlc7s7l58S4e+mk/c4Y3d3ao9d7T7XswvXMfmZXRCSTZVUs6RXixZmInpv55kId+TsJmV9BrVIxuFcQzlzUlzt/V2SEKIYQQQgghRINyeXg0O0fdjkGjcXYodUZxpZUZKS6o7/uEx27o70gCqtUqrm0dRHJeOVP/TGHqwKb4u+mdHG395mt0cXYIjZYku2pRYrA7X97YmuJKK3nlFvzddHgY5FsghBBCCCGEEJeKUSufuU604lAhpWYbowZ1dyS6FLsdS/YR9MHh3NoxlCd/T+av5AKubR3k5GgbjuTiAqLcvdCqpZtUbZCvshN4GrVE+7pIoksIIYQQQgghaliZxUyfHz/jh0NJzg6lTjIf68flaaz6PGorLSZ58lj23jYIS142nsc+p0rfrppzw58/EPvFPH49fNDZoTQakuwSQgghhBBCCNFgvLlrM8szD3Pf6qVUWq1n36GRaRvqgUoFP+/NdSwzpR3Akp3JwSdu56edmQC0D/NwVogNTqibOypgc26Ws0M5o+XLl3PVVVcRGhqKSqXiu+++q7Z+3LhxqFSqav+6du3qnGDPQkqLhBBCCCGEEEI0GPcndiCvsoLeIREyhPEUon1dGNrMn2eWJNM/xocoH09iZi5kzy2XUbp5FXtn/o8+fe6hZZC7s0NtMB5u3Zl7WnagiYeXs0M5o7KyMtq0acOtt97KyJEjT7nN4MGDmT9/vuO1Xl83+7rJb74QQgghhBBCiAbDRavjla79nB1GnfbuNc3pPXcTrWav5eb2IbQOdqfkiqfou/gJrtj/A9deMxDo6OwwG4wQV+clDktKSiguLna8NhgMGAyGU247ZMgQhgwZcsbjGQwGgoODazTGS0GGMQohhBBCCCGEqPf2FuY5O4R6I9zLyPpJnbivewTf7sphwrd7ecWcwO7e4wAon/0o5Um7nBtkA1VqMWO1114/tBYtWuDl5eX4N2PGjIs63j///ENgYCDx8fHceeedZGdn11CkNUsqu4QQQgghhBBC1Gtbco/S6duPGBEdz8J+V2LQyEfds/F30zNtUCzTBsWiKAoqlQrF1oMD96dSvPZvUp66gxafr0Sl0Tg71Abj6Y0rmL1jIx/1vYJrouNr5Zy7d+8mLCzM8fp0VV3nYsiQIYwePZqoqChSUlKYMmUK/fv3Z9OmTRd13EtB/gIIIYQQQgghhKh3bLlpKKZyANakHgLAXl6CJusAVkBlcEXjH+m8AOsRlUpV9V+Nhuhp75H86M2E3zdVEl01zGK3U2Ix83Nacq0luzw8PPD09KyRY40ZM8bx/4mJiXTs2JGoqCh+/vlnRowYUSPnqCmS7BJCCCGEEEIIUa/YctMonvnvh+vrgQS9N2EpFZSsft2x3HPyN5LwOk9ab1+azfvJ2WE0SBNbtKN/aCSXhTVxdig1IiQkhKioKJKSkpwdykkk2SWEEEIIIYQQol45XtHlOuZ5NIHRAPQ6Yb0tO4XyRVMc24kLV753G5b8HLy6X+bsUOq9CHdPItxrpsqqLsjLy+Pw4cOEhIQ4O5STSLJLCCGEEEIIIUS9k6vW8+yRfF5t1otgJ85215CV7tjA/ruuRqXVkfDxnxijYp0dUoNhVxRsih2duu4MFS0tLeXAgQOO1ykpKWzduhVfX198fX159tlnGTlyJCEhIRw6dIgnn3wSf39/rrnmGidGfWoyG6MQQgghhBBCiHrnUf/2fJaRzvV//ujsUBos1+ZtcEtoi72shORHxmIrL3V2SA3CFwd2k7D4febu3ursUKrZuHEj7dq1o127dgA89NBDtGvXjqeffhqNRsOOHTu4+uqriY+P55ZbbiE+Pp41a9bg4eHh5MhPJpVdQgghhBDinM2ZM4c5c+ZgNpudHYoQopF7tGAXR8Jb83r3Ac4OpcFS6/Q0ffFD9tzUn8qD+0h97l6iZ3zoaGgvLkxuZQX7i/L5OGkX9yR2cHY4Dn379kVRlNOu//3332sxmovT4Cu73n77baKjozEajXTo0IEVK1Y4OyQhhBBCiHpr0qRJ7N69m3/++cfZoQghGrnmlhJWdu9Fa79AZ4fSoOn8g2n60nxUWh0FS7/n6MI3nR1SvXdLfCJzegzkzyvGnH1jcUEadLJr0aJFPPDAAzz11FNs2bKFXr16MWTIENLS0pwdmhBCCCGEEEKI85RVXsqeglzHa6kwqh3ubboQ/vB0ADLeeo7i9cucHFH95qE3MLFlezz0BmeH0mA16GTXrFmzuP3227njjjtISEhg9uzZRERE8M477zg7NCGEEEIIIYQQ58Fit3Ht0u/p/N1Cfss+ClTNumjN2HvSP1t2ipOjbXgCRt2G35XXg91O3o+fOzucBuVMQwfFhWmwPbvMZjObNm3i8ccfr7b88ssvZ/Xq1afcx2QyYTKZHK+Li4svaYxCCCGEEEIIIc5NqcWCVq1GBcR4+wFQvmjKGfdRGVxrIbLGQaVSEfn4TFxbtCNg5K0AFFZY+HZXDkdLzYR5GrimZQDuhgabZqgxttw0FFM5GwoLeCFpH83dPXgxoaVjvcrgisY/0okR1n8N9qcwNzcXm81GUFBQteVBQUFkZWWdcp8ZM2YwderU2ghPCCGEEEIIIcR58DEY+WPoGHYX5JLgF4ht8jcopvLTbi8Jg5qnNroQeO0dKIrCy8sOMXVJMpVWO96uegoqLNzzvYaXhsRxV9dwZ4daZ9ly0yieOQKAVNcQfg7uyerMNB5Z+iJ6/q3w8pz8jfz8XoQGm+w67r9juBVFOe247ieeeIKHHnrI8bq4uJiIiIhLGp8QQgghhBBCiNPLr6zA1+gCgFatdjSkl0SA87y+6jBTftrNx4WLaN+3J3E33klqQQUv/H2Iu7/bi4tOzS0dQp0dZp10PEHrOuZ5rglowlP793JDWAR+7tcCVUNzyxdNOWMiV5xdg012+fv7o9FoTqriys7OPqna6ziDwYDBIA3ihBBCCCGEEKIu2JGfQ68fPmVqh57cl9hBGtLXARUWG8/9eZAX3XYT+/ePlOz4jdLWbYlq1Ym51zSnqNLKlD+SualdCBq1fL9ORxMYjTasOdPCE5wdSoPUYBvU6/V6OnTowJIlS6otX7JkCd27d3dSVEIIIYQQQgghztXi5D0UmU18dygJmzTxrhN+359HQYWVKybdjXf/q1CsFg4+Og5LXjYqlYqHekZyuMjEqkOFzg5VNGINtrIL4KGHHmLs2LF07NiRbt26MW/ePNLS0rjrrrucHZoQQgghhBBCiLN4rmMvIt09GRndDK26wdZq1Cv55RYAmvq6oHrmLfYe2k/lwX0cfPxW4t/5jmjfqiGn+RUWZ4YpGrkGnewaM2YMeXl5PPfcc2RmZpKYmMgvv/xCVFSUs0MTQgghhBBCiEbt+Ix0/1Vus+Ki1qA2uqHxj+TOhLa1H5w4rePJrHWHi+nRxJuYVxay5+YBlG5Zw+FZ/2PblZOBqmSYEM7SoJNdABMnTmTixInODkMIIYQQQgghxDEnzkh3onKVhlHBvYizlDArdxM+MiNdndMn2odYPxeeXXqQX25tizEqlujn55L80A3kLH6PJRkedIy7nNYhHs4OVTRiDT7ZJYQQQgghhBCibjlxRjpNYLRj+YbcHNavW80e9yAeKNyLt8xIV+eo1SreHt6cKxZspffcTTzcK5KEZj3JHTYJ428fs9XsxpvDmjk7TNHISbJLCCGEEEIIIYRTHJ+R7rjLw5rziZsf4ZWFRCctcmJk4kwGxvmx9Pb2PPH7AUZ/ugMAldKLq6/tyaxR3egY7unkCOs+W3bKeS0X50eSXUIIIYQQQgghnCavsgKdWo2n3gDA9bEtsGbspcTJcYkz693Uh1V3dyI5r5yjpWbCPA1E+fzbp8t0JA19YAgqrc6JUdY9KoMrAOWLppzTduLCSLJLCCGEEEIIIYRTHCovZ9iqT/EzuvDbkNG46fTODkmcpxg/V2L8qidmilYvJeXJO/C78joiJr/opMjqJo1/JJ6Tvznl5AzHqQyu0qvuIkmySwghhBBCCCHEeTvdbIrHncsH9gqbjczyUootJjLLy4j1kmRXQ6CYTdhKi8n+Yh6uzdvid+V1zg6pTpFE1qUnyS4hhBBCCCGEEOfldLMp/pfnWWZTTPDw4OfBo4h09yTcXfo8NRTefa8g5I7JZL4/k9QZD2GMaY5bQltnhyUaEUl2CSGEEEIIIYQ4L6ebTfE4W3YK5YumnFT5ZVcUXty6lsFGNdHHtuscGA1FpViLjlTbX9RvIeMfp3zfDopW/M7BR26m+cK/0Pn4Ozss0UhIsksIIYQQQgghxAX572yKZ/PCltU8vXElH7q5s0ylAWnS3WCp1GqaPPcue2+5DFNaMgcfu5W4OV+jlr5sohZIsksIIYQQQgjhFGfr+QTSqLmhmdiiPZ8m7ebRNl0IHnSVNOlu4LQeXsTMXMjeWy+ndPMqchbNI+ime5wdlmgEJNklhBBCCCGEqHXn2vMJzt73SdRdVrudv9NTGBheNdTRz+jCjtG3oVNrnByZqC0uTZvT9IUPKFz2MwFjxjs7HNFISLJLCCGEEEIIUevO1vMJTt/3SdQPFSo1Q9asZH1hAX9eeR39QqMAJNHVCHn1HIhXz4HODkM0IpLsEkIIIYQQQlyQmhiGeL49n0T94aLYSfTwZG95OYUmk7PDEXWEYrNx5J0X8B16LbawWH7em0teuYUoHxcGxfmi1aidHaJoACTZJYQQQgghhDhvMgxRwMmzJi7Nyaa9lzdehRkAzGyRyNO9hhLp7umM8EQddGTui2QtmE3Kj19xffvnSVfc0KpVWO0KYZ4G3rq6GcNbBjo7TFHPSbJLCCGEEEIIcd7qwjDEs1WWSYPzS+f4LInlJ8ym+IxvK970bs4txcm8lrsZAA83T7wl0SVOEHTDXaR89wUueem8u+91Wr77LU0Cvdh6pIRnliQz8pPt/DSuLUOa+Ts7VFGPSbJLCCGEEEIIccGcNQzxXCvLpKrs9C4mWajxj8Rz8jfV9h+el8s761bj3eZy3BMeRG10k6+9OEm50Yu7W05m3rqnCE7bivLW4yhT36ZtqAffjG3DwPc38+RvBxgc74dKpXJ2uKKekmSXEEIIIYQQot45W2XZuVSVNebKsItNFm7NPcoLWzbTNzSSSS3bA9A/DNJi2xLi6l7j8YqG44c9Oew0hBE49T0K/3cz+b8sQh8cTtjEp9CoVTzcO4orF2xl19EyEoPlZ0lcGEl2CSGEEEIIIWpccnEB+3Ky8dN50OHYsnKrhVnbN1ButTA1tKonjy07he+yjrAkJ4cB/gGMCAl1HOPxTavBpyX/s1rxPrZsV34OuwvzaGoqoin/VpaVWy24aLTnXAlSE5VhF5ssc+b+F5ssXJt9hK9S9rEhJ5O7EtqiUVc1FZdElzibnFIzrjo1MQOHkFs2i9Rp95P14avog8IIGDmOZv5VQ2RzysxOjlTUZ5LsEkIIIYQQQpyTw6XF7CvMJ8rDk+PpkcMVFVz91YeUWSwkXz/Bse0r29Yzd89WJrtHOpJdVrudKRtXAPBU1LVAVc+nlb6tmOfdHO323xmYv71qW1S82nQU+LTgUa3BcdxvDyUxZeMKbouIYuYJsQV8/CYmm5W9195JrJcPAMuMgfy5awe9TTC66b9DLTPLS/GuKAUuPNlzsckyZ+9/3LkMQ92Vn8OC/TsZEBbF4IimAIyLb8X2/BwmtmjnSHQJcS4ivI2UW+zsyymj2fCxmI+mk/XxW+j8gwDYlFFctZ2X0ZlhinpOkl1CCCGEEEKIakrMJubv38Hh0hJe6drPsfz5zat5b+82nu3Qg6eCq5pH++h07MjPAaoqt1y1OgBiPL1p5eFJYG6lY39XrY7xzdvgqtWh9gt39HwakpuNV34+XXy64RFQVfFlsdt5aN8ezCo1nkH/JqPC3NzpFRxOc/d/K4isdjvlVgsA/kYXx/L1Rj/eOnSQSqN7tWRXwuL3KTabWKtzp/2xZM+67COszEqnU0AIvUMizvo1utjKKGfvfyZ2RcGuKI7Xiw/uZeb29ezIz3Eku4xaLW/3vPy8jy3Elc398XfTMXXpQT69LpGQ8Y/jO3QMxoimVFhsvLQsld7R3sQeq/AS4kJIsksIIYQQQohGbGn6Ib45tJ/eweFcF9vCsfz+1X8C8FS7bngbqiosmnn7kuDth4dO79jOJf8wv3Xpho9OjzozCeuxKp8H/b24r3kTyrclO7bVqtXM7T3435MfqzgaFNacQf+JSwu8GtGC/7q1WWtubdYaa8ZeSo4t06hUlN76IIVmE176f6vAulbm8lhMHJ2PJWgAyixmyq0WFCDY+m8i7vfDKTyzaSV3NG9dLdnVetmfeLqu5+uB1xDm5gFASnEh6QX5BKkNNL3IBv0X2+C/picIeGjNn3x2YA9ftG1Hm2PLrotJYGd+LrfEJ9bYeUTjZdRpmH1lPDct2kWxycbDvSKJ8Qtl485sXvg7haLUFOZeLz9r4uJIsksIIYQQQggnOVvPJThz36Xz2d9it7Fw/y5WH83gzR6X4XKsAmtt9hHe2b2FYrPJkezy0Bu4rVkrAl3csCp2x7Eebt2Zh1t3dpwbqoYhdj62vuIMMVxKKpUKN50etxOScAC9KnMY2rwF2rBmjmVuOj2Vt08mM2UbrvO+dCxv6ePP9TEJ9AwOdywrUWnZU1oKpaV4nnDsj5N28uymVYz1TeTDE8437p+fCTC68lS7bhyvOzPZbKjs9jo31K/QYuFtr3hyd+3gjbATh3iWcbSijCU52Y5kV4KPP19ffo1zAhUN0o3tQjBq1Tz1RzL939vsWD7KPZfHtj2NMcULy3s/o/MLdGKUoj6TZJcQQgghhBBOcK49l+DUfZfOtL8JNZuNvlhQ0bsyB8/J36D1i+Cx9f+QW1nB+IQ2dA4MxZabRj89PNw0lr4+nlgz9jqOMTe2qhpKVZoLxpOTbRr/SMcwxDO51DMa2rJTzms5gFqlIshgdFSGAYxs2oyRTZtV286o2FjTozdH3fzxOKFizKDREuniQlR+mWNZsdnER/t3AvBMhx6O5S8nJzHt9595uFUnXj42JFRRFJ7cuwt3r3jut1rxOrZtTkU55VYL/kYX/j3buVMUhTxTBYUmE5EnDEP8+uA+vjm0n6siYxwJTbui8IxfG1SHDvKcqdJRvfdw607c0bw1XW1lmP+4gCCEOEcjWwUxIjGQTRkl5JaZifJxIVZdwt51LpjSkkm6dxTxc39E6+F19oMJ8R+S7BJCCCGEEMIJztZzCc7cd+nE/VUBTbArCtpj1UPfHE7jju1b6OzmSu8dH6GYylGpVIxv3hY7CgFGV0eyrAVwfLBgyUlnqXK6JueXMol1NserxcoXTTmn7S6EDoWO3j5ow+KqLX+8bVcmB3hT8ubH/54HmNW1P9mV5bjr9FiPLU+vrMSuKI5eZgAlFjOvJB8AvzY8cMLkkbN3bGT61jXcl9iBV6OqKsysdjvhC99Cr1azfdRtjqTUrOQDvBF5Bbfu38vzxyqzzHYbAR+/CUD25UMcH/Z25Ofw2YHdGDUaR7LLV6/nxuIUElp0w5K5H+uxyrW2x/ax5aUhc+GJS02lUtEx3POEJW7EzfmGfXdeQcX+nRy4fwxxc75G4+LmtBhF/STJLiGEEEIIIZzoYnsuPZNfwYc7ljKn50BHE/bLvUIJTNpHtJcPygnbvtC5t+P/rRlZwIUn25ztXCrLzqWq7EIqw07FQ2/gwdadTlo+J7E10/pciV6tcSyzKwoPRMeQvX0prpp/P5LZFDsGjaZa3zGzYudoRVUFmfaEoZAlNisZWldyzSbHMoNGi4tGi1atpsRqxefY8sER0bjrdLT3D3ZsqzK48mbuRli+EZafPtF5qYegCvFfxsgY4t76mv3jr6Rs+3oOPnIzMbM+Q62/kHpH0VhJsksIIYQQQoh6QFEUtuVl809mGvcndnQsr7Tbyaks5+8jaY5kV4S7J1k33YPtyD5KVp75uDXd4Lw2XUxlWU1Vhp0tWaZVqwlxda+2zttg5JUWiZT8+WK1/aeFBzMt/ErsiuJYblBr2DbyVsx2W7XqsNsjouj39xwiW99Tbfhp/uVD0KrVVYnKY8u6BoXRNSisWgw1lSwU4lJwjWtJ7BuLSZo4guK1f5Py1J00nfEhKq2kMMS5kZ8UIYQQQggh6oEyq4Uu3y3EbLcxKDya4wPr7oyMYmRiF7oEhlbbXqVSnXwQ4XCxyZ6LTZad6/5aoxutT9GkO8LbDy9zAXz3/Gmrss50fnDuMFQhzsa9VSdiZi7kwAPXYcnJxG6qQKP1cHZYop6QZJcQQgghhBB1zL7CPF7etg6lvIhXjy1z1+m5MjIGm6Jgtv87Q2Izdw+0IRHOCbSeu5hkz8Umy5y9vxD1gWeXvsS+sRi3hLZo3CTRJc6dJLuEEEIIIYRwMovdRoXViuexnjQVVisf7tuBUa1mqkrD8Y94Xw0c7qjYsmbkOylacdzFJpKcvb8Q9YFnp97VXheu+B2vbgNkSKM4I/npEEIIIYQQwone27WRZ5b8ym0RUUxrXjVTXktF4YnYePporLgcsDm2laGJQojG7Ognc0ifPQWfQSOJfOZtfksu5IttRymsBBh1CwAAzxRJREFUtBLj68KdncNIDHY/+4FEgyfJLiGEEEIIIWpRemkxvkYXDMd6KenXfUVOUDf+2rWGkiXTHds9csI+Z+q7dKZZA89lRsGL3V8IIWqLITwaNFoKfv+apftzmRg9gVZh3kR6G1m84yhvrD7MI72jeGlIrDwcaOQk2SWEEEIIIUQtGb/8N97fu40Ffa/g5vhEPCd/w7XlJQTk5jAwIBCd+vaT9jld36VzbXB+4rY1ub8QQtQ2775DiXlpAfsfvYUOKctY5a2i03OfoDa6YrHZeX3VYR75JYloXxfu7hru7HCFE9XLZFeTJk1ITU2ttuyxxx7jxRdfdLxOS0tj0qRJ/PXXX7i4uHDDDTcwc+ZM9Hp9bYcrhBBCCCEaqd0FuSR4+zkqDCLdPVGAbXnZQFXPJQ9g2AW0XjqXBuVw+mTZxe4vhBDOkBrfiwdbP8rs3a+h3fIPSfeMIua1z9F5eDG5dxRbj5TwyvJUJnQOQ62W6q7zsXz5cl555RU2bdpEZmYm3377LcOHD3esVxSFqVOnMm/ePAoKCujSpQtz5syhZcuWzgv6NOplsgvgueee484773S8dnf/d1yuzWbjiiuuICAggJUrV5KXl8ctt9yCoii8+eabzghXCCGEEEI0QLbctFMmixRFYeDa1SzLz2Vl34H0iG8PwISEttwY24JoT+8aOb80OBdCNDa/7s9jR1hH4iZ+zaGHrqd061r2TxhG8wV/oNYbuKVDCJ9uzWJvThktgqR/1/koKyujTZs23HrrrYwcOfKk9S+//DKzZs1iwYIFxMfHM23aNAYOHMi+ffvw8Khbs2XW22SXh4cHwcHBp1z3xx9/sHv3bg4fPkxoaCgAr776KuPGjeOFF17A09OzNkMVQgghhBANkC03jeKZIxyv07SuRFr/TXyFBnRC5x7J2i9fpOuEmWj8IwlwcSXARYYECiHEhTJb7bjoNHi370b8vJ9IuncUfkNHoz42m62HoSrNYbYpzgyzXhoyZAhDhgw55TpFUZg9ezZPPfUUI0ZUXfs++ugjgoKC+Oyzz5gwYUJthnpWamcHcKFeeukl/Pz8aNu2LS+88AJms9mxbs2aNSQmJjoSXQCDBg3CZDKxadOm0x7TZDJRXFxc7Z8QQgghhBCncryiyzbqWf7P3n3HR1FtARz/zfb0ShLSCB1C711ApaqA2AvFgvJQRBGxvGcv2Lug2BARwQZYqNKbFOmEToAEEkJ63zrvj4WFUNM3Cef7+fDRvTM7e3Zmsztz5t5zB7Z9iPZ1biRv1Ff4jJ2Bz9gZvDZ4FIfaNOf+nMNXHCoohBCieNpF+pKSa2FTQhaejZrTbPY6Qu991LX8z93J+Bi1NAyWGwsAOTk5RXIcZrO5VNuJj48nOTmZvn37utqMRiM9e/Zk3bp15RVuuamWya5x48Yxa9Ysli9fzqOPPsqHH37ImDFjXMuTk5MJDQ0t8pyAgAAMBgPJycmX3O6kSZPw8/Nz/YuKiqqw9yCEEEIIIaovi93u+n//2g1QjB4oisJmrSe6iCboIppQr0Fbakc0dGOUQghR8/RvFETdQA/G/r6PrEIbOv9A17J/9yUS++69vGzchZdB68Yoq47Y2NgieY5JkyaVajtncinn51pCQ0Mvm2dxlyozjPGll17i5Zdfvuw6mzZton379jzxxBOutpYtWxIQEMCtt97q6u0FXHSaUVVVLzv96LPPPsv48eNdj7OzsyXhJYQQQgghXFIL83l83VLWJCcS16Onq/2LHv2pZfIgzFPqwwghREXSahRm39WcPl9vpcl76xjZLpw6/ibWHs3ENHcKo7MO0fi3F0kKKSTsgQmXzQFcDeLi4oiIiHA9NhqNZdre+fvzSnkWd6kyya5HH32UO++887LrxMTEXLS9c+fOABw8eJCgoCDCwsLYsGFDkXUyMjKwWq0XZCHPZTQay3zghRBCCCFEzeWrN7L0+FGSC/JYlnqKbqfbWwTWcmtcQghxNekQ5ce/YzvywZpjTN14nMwCK/WDPBn10JMEbg0kfeZnnPh8EoVHDlDnfx+iMV29Qxp9fHzKpW75mZrpycnJ1K5d29WekpJy2TyLu1SZZFdwcDDBwcGleu7WrVsBXDu8S5cuvP766yQlJbnaFi9ejNFopF27duUTsBBCCCGEqNFyLGa+2LONLaknmXndIAAMWi2f9+hHhJc3rS1Z5Lg5RiGEuFrVD/Lk08FN+HRwk6K9i3q/ined+hx7+ynSF/5CwaE91Hv7O0xR9dwbcDVXt25dwsLCWLJkCW3atAHAYrGwcuVK3nrrLTdHd6Eqk+wqrvXr1/PPP//Qu3dv/Pz82LRpE0888QSDBg0iOto5dXLfvn2JjY1l2LBhvPPOO6SnpzNhwgRGjRolMzEKIYQQQohiybZaeG7TKqwOB4+3aE/HEOfkR4NjnHW4bMez3BmeEEKI084fRlfrlpGYYhpy+Nn7KTiwm73DrqXBh7Pwbt3ZTRFWD7m5uRw8eND1OD4+nm3bthEYGEh0dDSPP/44b7zxBg0bNqRhw4a88cYbeHp6cvfdd7sx6ourdskuo9HI7NmzefnllzGbzdSpU4dRo0YxceJE1zparZa//vqLMWPG0K1bNzw8PLj77rt599133Ri5EEIIIYSoiuypx1DN+WzLymJbdiYjo+oAEAo8W78h0T7+tAoKufTzU+JLtUwIIUTF8WnXjaYzlnP4mfuwJCdilJ5dV7R582Z69+7tenympvmIESOYNm0aEydOpKCggDFjxpCRkUGnTp1YvHgxPj4+7gr5khRVVVV3B1FVZWdn4+fnR1ZWlvQIE0IIIaog+a12n8TERKKiokhISCAyMtLd4ZSaPfUY2e8OZbfBjx6RfTE67GxP+IsQe9Gp2X0n/IY2OPqizy2Oiz1fCCFExXNYLViSEjBF1wecBdVtmWlo/IJYuD+NX3eeJMdip3GwJw90iKBuoIebIy4fNeV3urSqXc8uIYQQQgghysrmcHAkJ4sYcz4A7Yc8SddDx4ny8EB33Sf4eDqLGdtT4smf/Tzq6fXOpQ2OxnfCbxdddi7F6CmJLiGEcBON3uBKdAGkz/+Jo28/zdft/8NkfTuahXoR7mvk0/WJTFpxhLcGNGTCNXXcGLEoD5LsEkIIIYQQV5W4jFRuWvgrNtXB3h49AdCF1mNF637oNdoSbUuSWEIIUX2oqkr6gp9R87K5f+VbDO8xhHYPfoDOx488i51Xlx7mqfkHqONv4raWVW+GQVF8GncHIIQQQgghRGWq6+NHns1KntXKntyz8ymWNNElhBCielEUhVPjP2dq3VtRFQ261XOJu7MbmasX4WXQMql/A/o1CuLNlUeQik/VmyS7hBBCCCFEjZVpLuSVf9dyx9/zXG0eOj1/9r+Fo3ePpqWvnxujE0IIUdn+3J/J763vpdGXf2GMrIv15AkOPXEXh599AFtGKve3D2fL8RyScyzuDlWUgSS7hBBCCCFEjZVlMfPqlnX8dHgvm08ludrb16qNl97gxsiEEEK4g9nmwNeow7d1J2JnrSZ02FjQaMj4ey7mY4fwNzmrPRXaHG6OVJSFJLuEEEIIIUSNcTQni58P73U9ruPjxwvtuvLjtTfRJkjqrwghxNWudbgP+1PzOZCaj8bkSeS4l2ny3d9EjnsZ79admb8vlWAvPSGWDHeHKspACtQLIYQQQohqzZ56DNWcz56cHNqtXo5WUehKIaFGEwDPRUddsZC8PSW+RO1CCCGqp1tbhDD+r/2M/X0fc4e1xKTX4tW0NV5NW7PhWBZfbjzOfxvDvqHtCOx3CxGPPI8++OI3S3Yl5zJ3dwr5VgctwrwZ2jwEo076FFUFkuwSQgghhBDVVt7JeCwf3AZABNAy/Fq8HTZOfPs4ntZc13q+E367aMJLMXoCkD/7+cu+zpn1hBBCVG8eei0z72zOoO+20+yDf3igQzi1fYysOJzBrO3JdIj0ZbjyLyctZtL+mEnG0t8JGz6WkLtGo/XyASDHbGPY7N3MizuFn0mHv0nH0cxCannpmX57M/o3DnbzuxSS7BJCCCGEENXOqYJ8ntqwnPXH41mNgt8dr6ANqcsSmxUfnd61nj0lnvzZz6Oa8y+6HW1wNL4TfrvkcnAmuq7UM0wIIUT10adhEBse6cA7q47y6tJ4Cm0O6gV68Grf+oztGoWHvgP+LduR8O6z5O/ewonPJ5EyayphI8cRfMv93P7jPtYdzWTGHc24vWUoeq2GfafyGP/nfgZP387a/3SgfaSvu9/mVU2SXUIIIYQQotrx0utZkHCYlIJ8VnqEcHNIXXQRTQgoxbYkkSWEEFeflrV9+P6O5ky/XcXmUNFriw4/9G7RgSbfLiZjyRxOfPEm5mOHSPzwBY798j2LGr/Kr8Nac3PzENf6jWt5MWdYK1p/vIE3lsfz27BWlf2WxDlkMKkQQgghhKjydqef4q1t/7gee+r0fNGjH+u6XcP1BSfdGJkQQojqTFGUCxJdrmUaDYH9bqHZT+up8/zHGMIi2dmgF5H+HgyOrYXqcGDLznStb9BpeLhjBL/vSaXAaq+kdyAuRpJdQgghhBCiSkspyKPNb9N4ZuNKNqaccLUPiWlEB//S9OUSQgghik/R6QgefC/NftvE+rZ3EOFnQqNRyFq7hJ03tCDh/f9iPn4UgCh/E3aHSr5Fkl3uJMMYhRBCCCFElXMyP49QTy8AQjy8uKdBLDlWC34Go5sjE0IIcbXSGIzUqx3AzD3xZBZYyVz+J46CPFJmTiHlx8/x69aHw1F9CfGMwd9Df+UNigojyS4hhBBCCFFlZFvMDFv+J0uPHyX+rtHU8nDOgvjVNQPQamRQghBCCPca2S6cF5Yc5qW/D/P+/z4i4PrBnJzxGTkbV5K1ZjG9WUxH/9qkTHuAsJGPo1zit0tVVZYeTOfPvamYbQ7ahPtwd+swvI2SpikPsheFEEIIIYRb2VOPuWZD9FBVjmemUmCzsmjnOu6MiCzWbIj2lPgStQshhBClUdvXyHs3NGTcH/vZdyqfhzu1pNbEb1m7fisZc6bRP3E53plJZK6cT+37x7ue57CY0ZzunZyUbWbQ9O1sTswmJsCEn0nH1I3HmbjgID/c2ZwbmgS76+3VGJLsEkIIIYQQbmNPPUb2u0OLtL1j8MdTtdPo8M/knG7znfDbRRNeitHZ8yt/9vOXfZ0z6wkhhBBl9Vi3aGr7GHl9eTw3f78DAG+DluF3P0uznp/Amj/R+fi71rdlZ7JrUGt8u1yLb+9BDNnhR5JFx7JRbelVLwBFUTiWWcjYeXsZ+v121o/pQNsIXze9u5pBkl1CCCGEEMJtzvTo8rzjVbQhdQHocc5ye0o8+bOfd613Pm1wNL4TfrvkcqBYPcOEEEKIkritZSi3tgjhWGYh+VYH0f4mvAxa58Kb7i6ybva6v7HnZpOxZC4ZS+bygUaPvm0P6u4cjC2gP/rAWkT7m/j5npY0+2A97646ysy7WrjhXdUckuwSQgghhBBupw2piy6iSemeK4ksIYQQbqAoCnUCPK64XkC/WzDFNCR90a/s+2MO/pnHYfMyjm5eBopC/Xem49/rBgw6Dfe3D+flpfGoqoqiKJXwLmomSXYJIYQQQgghhBBCVBBFUfBs0grPJq14PPAWPJIP8XbAYTJXzid/3w68mrd3rRvqbcBsc2B3qOi0kuwqLZnSRgghhBBCCCGEEKISxIZ6My83AJ9hj9P0+2W0XBCHPjjUtfzvg+k0CvZEp5V0TVnI3hNCCCGEEEIIIYSoBA92jCDPYueFJYdRVRV9UIhr2arDGfyyK4XRnSLcGGHNIMMYhRBCCCGEEEIIISpBtL+JD29sxGN/7GfL8Wzubx+On0nH/H1pfLcliR4x/ozpEuXuMKs9SXYJIYQQQgi3s6fEl6hdCCGEqK7GdosmJsCDt1cdZcTPcQBE+Br5X+8YnuoZg1Eng/DKSpJdQgghhBDCbRSjJwD5s58v1npCCCFETXBTbC1uiq1FZoEVs81BsJcBrUYK0pcXSXYJIYQQQgi30QZH4zvhN1Rz/iXXUYyeaIOjKzEqIYQQonL4e+jdHUKNJMkuIYQQQgjhVpLIEkIIIUR5koGgQgghhBBCCCGEEKLGkGSXEEIIIcRVKCEhgV69ehEbG0vLli35+eef3R2SEEIIIUS5kGGMQgghhBBXIZ1Ox4cffkjr1q1JSUmhbdu2DBw4EC8vL3eHJoQQQghRJpLsEkIIIYS4CtWuXZvatWsDEBISQmBgIOnp6ZLsEkIIIUS1J8MYhRBCCCGqoVWrVnHTTTcRHh6OoijMnTv3gnUmT55M3bp1MZlMtGvXjtWrV190W5s3b8bhcBAVFVXBUQshhBBCVDzp2XUZqqoCkJ2d7eZIhBBCCHExZ36jz/xmX03y8vJo1aoV9913H7fccssFy2fPns3jjz/O5MmT6datG1988QUDBgwgLi6O6Oizsx+mpaUxfPhwvvrqq8u+ntlsxmw2ux5nZWUBkJSUVE7vSAghhBDl5czvs8PhcHMk7qGoV+PZYTElJibKHU4hhBCiGkhISCAyMtLdYbiNoijMmTOHIUOGuNo6depE27ZtmTJliqutadOmDBkyhEmTJgHOBFafPn0YNWoUw4YNu+xrvPTSS7z88ssVEr8QQgghKsbGjRvp0KGDu8OodNKz6zLCw8NJSEjAx8cHRVFK9Nzs7GyioqJISEjA19e3giK8Osi+LF+yP8uP7MvyI/uy/Fxt+1JVVXJycggPD3d3KFWKxWLh33//5ZlnninS3rdvX9atWwc4993IkSO59tprr5joAnj22WcZP36867HNZmPPnj1ERUWh0ZytjNGrVy9WrFhxwfOL256Tk0NsbCxxcXH4+PhcMa6Kcql4K3t7JXlecda93DolXVZVjx1Uv+NX1mN3ueXV7W8Pyvf4XQ1/e1B1jl91+9srzrrV9W/P4XBw8uRJ2rRpU27brE4k2XUZGo2mzHeJfX19r4qLjcog+7J8yf4sP7Ivy4/sy/JzNe1LPz8/d4dQ5aSmpmK32wkNDS3SHhoaSnJyMgBr165l9uzZtGzZ0lXv6/vvv6dFixYX3abRaMRoNBZp69at2wXrGQyGi54/Fbf9zNDUiIgIt36GLxVvZW+vJM8rzrqXW6eky6rqsYPqd/zKeuwut7y6/e1B+R6/q+FvD6rO8atuf3vFWbc6/+2dW7bgaiPJLiGEEEKIGur8numqqrraunfvXiF1PB555JFyaXe38o6rtNsryfOKs+7l1inpsqp67KD6Hb+yHrvLLa9uf3tQvrHJ317lqm5/e8VZ92r626tJpGZXBcnOzsbPz4+srCy33xmp7mRfli/Zn+VH9mX5kX1ZfmRfXp3Or9llsVjw9PTk559/5uabb3atN27cOLZt28bKlSvdFOmVyWe4+pJjV73J8ave5PhVX3LsKobmyquI0jAajbz44osXdPcXJSf7snzJ/iw/si/Lj+zL8iP7UoBziES7du1YsmRJkfYlS5bQtWtXN0VVPPIZrr7k2FVvcvyqNzl+1Zccu4ohPbuEEEIIIaqh3NxcDh48CECbNm14//336d27N4GBgURHRzN79myGDRvG559/TpcuXZg6dSpffvklu3fvpk6dOm6OXgghhBCi4kiySwghhBCiGlqxYgW9e/e+oH3EiBFMmzYNgMmTJ/P222+TlJRE8+bN+eCDD7jmmmsqOVIhhBBCiMolyS4hhBBCCCGEEEIIUWNIzS4hhBBCCCGEEEIIUWNIsksIIYQQQgghhBBC1BiS7BJCCCGEEEIIIYQQNYYku0pg1apV3HTTTYSHh6MoCnPnzi2yXFVVXnrpJcLDw/Hw8KBXr17s3r27yDpms5mxY8cSHByMl5cXgwYNIjExsRLfRdUwadIkOnTogI+PDyEhIQwZMoR9+/YVWUf2Z/FMmTKFli1b4uvri6+vL126dGHBggWu5bIfS2/SpEkoisLjjz/uapP9WXwvvfQSiqIU+RcWFuZaLvuyZI4fP869995LUFAQnp6etG7dmn///de1XPanuBr8+eefNG7cmIYNG/LVV1+5OxxRQjfffDMBAQHceuut7g5FlEBCQgK9evUiNjaWli1b8vPPP7s7JFECOTk5dOjQgdatW9OiRQu+/PJLd4ckSig/P586deowYcIEd4dSrUiyqwTy8vJo1aoVn3766UWXv/3227z//vt8+umnbNq0ibCwMPr06UNOTo5rnccff5w5c+Ywa9Ys1qxZQ25uLjfeeCN2u72y3kaVsHLlSh555BH++ecflixZgs1mo2/fvuTl5bnWkf1ZPJGRkbz55pts3ryZzZs3c+211zJ48GDXRa7sx9LZtGkTU6dOpWXLlkXaZX+WTLNmzUhKSnL927lzp2uZ7Mviy8jIoFu3buj1ehYsWEBcXBzvvfce/v7+rnVkf4qazmazMX78eJYtW8aWLVt46623SE9Pd3dYogQee+wxpk+f7u4wRAnpdDo+/PBD4uLi+Pvvv3niiSeKnLOLqs3T05OVK1eybds2NmzYwKRJk0hLS3N3WKIEXn/9dTp16uTuMKofVZQKoM6ZM8f12OFwqGFhYeqbb77paissLFT9/PzUzz//XFVVVc3MzFT1er06a9Ys1zrHjx9XNRqNunDhwkqLvSpKSUlRAXXlypWqqsr+LKuAgAD1q6++kv1YSjk5OWrDhg3VJUuWqD179lTHjRunqqp8LkvqxRdfVFu1anXRZbIvS+bpp59Wu3fvfsnlsj/F1WDt2rXqkCFDXI8fe+wxdebMmW6MSJTG8uXL1VtuucXdYYgyaNGihXrs2DF3hyFKIS0tTY2OjlZPnTrl7lBEMe3fv18dOnSo+u2336pPPvmku8OpVqRnVzmJj48nOTmZvn37utqMRiM9e/Zk3bp1APz7779YrdYi64SHh9O8eXPXOlerrKwsAAIDAwHZn6Vlt9uZNWsWeXl5dOnSRfZjKT3yyCPccMMNXH/99UXaZX+W3IEDBwgPD6du3brceeedHD58GJB9WVK///477du357bbbiMkJIQ2bdoUGYYg+1NUB1cqBwEwefJk6tati8lkol27dqxevdq17MSJE0RERLgeR0ZGcvz48coIXVD24yfcpzyP3ebNm3E4HERFRVVw1OKM8jh+mZmZtGrVisjISCZOnEhwcHAlRX91K49jN2HCBCZNmlRJEdcskuwqJ8nJyQCEhoYWaQ8NDXUtS05OxmAwEBAQcMl1rkaqqjJ+/Hi6d+9O8+bNAdmfJbVz5068vb0xGo2MHj2aOXPmEBsbK/uxFGbNmsWWLVsu+qMi+7NkOnXqxPTp01m0aBFffvklycnJdO3albS0NNmXJXT48GGmTJlCw4YNWbRoEaNHjy4yHEj2p6gOrlQOYvbs2Tz++OP897//ZevWrfTo0YMBAwZw7NgxwHm+cD5FUSo0ZnFWWY+fcJ/yOnZpaWkMHz6cqVOnVkbY4rTyOH7+/v5s376d+Ph4Zs6cycmTJysr/KtaWY/dvHnzaNSoEY0aNarMsGsMnbsDqGnOP+lSVfWKJ2LFWacme/TRR9mxYwdr1qy5YJnsz+Jp3Lgx27ZtIzMzk19//ZURI0awcuVK13LZj8WTkJDAuHHjWLx4MSaT6ZLryf4sngEDBrj+v0WLFnTp0oX69evz3Xff0blzZ0D2ZXE5HA7at2/PG2+8AUCbNm3YvXs3U6ZMYfjw4a71ZH+KqmzAgAFFvhfO9/777/PAAw/w4IMPAvDhhx+yaNEipkyZwqRJk4iIiCjSkysxMVFqmFSish4/4T7lcezMZjM333wzzz77LF27dq2UuIVTef7thYaG0rJlS1atWsVtt91WoXGLsh+7f/75h1mzZvHzzz+Tm5uL1WrF19eXF154obLeQrUmPbvKyZkZxs6/O56SkuK60x4WFobFYiEjI+OS61xtxo4dy++//87y5cuJjIx0tcv+LBmDwUCDBg1o3749kyZNolWrVnz00UeyH0vo33//JSUlhXbt2qHT6dDpdKxcuZKPP/4YnU7n2h+yP0vHy8uLFi1acODAAflsllDt2rWJjY0t0ta0aVPXnT/Zn6K6s1gs/Pvvv0WG2QL07dvXNcy2Y8eO7Nq1i+PHj5OTk8P8+fPp16+fO8IV5ynO8RNVU3GOnaqqjBw5kmuvvZZhw4a5I0xxCcU5fidPniQ7OxuA7OxsVq1aRePGjSs9VlFUcY7dpEmTSEhI4MiRI7z77ruMGjVKEl0lIMmuclK3bl3CwsJYsmSJq81isbBy5UrX3Y927dqh1+uLrJOUlMSuXbuuujskqqry6KOP8ttvv7Fs2TLq1q1bZLnsz7JRVRWz2Sz7sYSuu+46du7cybZt21z/2rdvzz333MO2bduoV6+e7M8yMJvN7Nmzh9q1a8tns4S6devGvn37irTt37+fOnXqAPKdKaq/1NRU7Hb7ZYfi6nQ63nvvPXr37k2bNm146qmnCAoKcke44jzFOX4A/fr147bbbmP+/PlERkayadOmyg5VnKc4x27t2rXMnj2buXPn0rp1a1q3bl1kdmXhPsU5fomJiVxzzTW0atWK7t278+ijj14w27iofMX93hSlJ8MYSyA3N5eDBw+6HsfHx7Nt2zYCAwOJjo7m8ccf54033qBhw4Y0bNiQN954A09PT+6++24A/Pz8eOCBB3jyyScJCgoiMDCQCRMm0KJFiwsKYdd0jzzyCDNnzmTevHn4+Pi4/qD9/Pzw8PBAURTZn8X03HPPMWDAAKKiosjJyWHWrFmsWLGChQsXyn4sIR8fH1fduDO8vLwICgpytcv+LL4JEyZw0003ER0dTUpKCq+99hrZ2dmMGDFCPpsl9MQTT9C1a1feeOMNbr/9djZu3MjUqVNddVNkf4qa4kpDcQcNGsSgQYMqOyxRTFc6fosWLarskEQxXe7Yde/eHYfD4Y6wRDFd7vi1a9eObdu2uSEqURzFLUExcuTISoqo5pBkVwls3ryZ3r17ux6PHz8egBEjRjBt2jQmTpxIQUEBY8aMISMjg06dOrF48WJ8fHxcz/nggw/Q6XTcfvvtFBQUcN111zFt2jS0Wm2lvx93mjJlCgC9evUq0v7tt9+6/pBlfxbPyZMnGTZsGElJSfj5+dGyZUsWLlxInz59ANmP5U32Z/ElJiZy1113kZqaSq1atejcuTP//POPqzeS7Mvi69ChA3PmzOHZZ5/llVdeoW7dunz44Yfcc889rnVkf4rqLDg4GK1We9mhuKLqkuNXfcmxq97k+FVfcuwqnqJebGobIYQQQgghKoiiKMyZM4chQ4a42jp16kS7du2YPHmyqy02NpbBgwdLgfMqRo5f9SXHrnqT41d9ybGrfNKzSwghhBBCVLgrlYMYP348w4YNo3379nTp0oWpU6dy7NgxRo8e7caoxRly/KovOXbVmxy/6kuOnZupQgghhBBCVLDly5erwAX/RowY4Vrns88+U+vUqaMaDAa1bdu26sqVK90XsChCjl/1JceuepPjV33JsXMvGcYohBBCCCGEEEIIIWoMjbsDEEIIIYQQQgghhBCivEiySwghhBBCCCGEEELUGJLsEkIIIYQQQgghhBA1hiS7hBBCCCGEEEIIIUSNIckuIYQQQgghhBBCCFFjSLJLCCGEEEIIIYQQQtQYkuwSQlzV0tLSCAkJ4ciRIyV+7q233sr7779f/kEJIYQQQgghhCg1SXYJIaqs+fPnoyjKJf/dfvvtZX6NSZMmcdNNNxETE1OkfceOHQwdOpSgoCBMJhPNmjXjnXfewWazudZ54YUXeP3118nOzi5zHEIIIYQQQgghyocku4QQVVbv3r1JSkoq8i8xMZE+ffoQHBzM888/X6btFxQU8PXXX/Pggw8WaV+5ciWdO3fGw8ODefPmsX37diZOnMi7777L0KFDcTgcALRs2ZKYmBh++OGHMsUhhBBCCCGEEKL8KKqqqu4OQgghisNut3Pvvffy999/s2zZMlq0aFGm7f322288/PDDnDp1qshrNGzYkK5duzJjxowi68fFxdG6dWumTJnCAw88AMDLL7/M0qVLWbVqVZliEUIIIYQQQghRPqRnlxCiWjiT6FqyZAlLly4tc6ILYNWqVbRv375I28aNG4mPj+epp566YP3Y2FgGDhzI7NmzXW0dO3Zk48aNmM3mMscjhBBCCCGEEKLsJNklhKjy7HY7w4YNcyW6WrZsWS7bPXLkCOHh4UXa4uPjAWjYsOFFn9OoUSOOHj3qehwREYHZbCY5OblcYhJCCCGEKIvPPvuMmJgYdDrdRW/elWVynsuRiXuEEFWJJLuEEFXamUTX4sWLWbp0Ka1atbrkeiVVUFCAyWQq0ubr6wtAenr6RZ+TkZHhWgfAw8MDgPz8/BK/vhBCCCFEedq1axePP/44n332GQkJCbz88ssXrHP+5DwjR45kyJAhF6y3YsUKFEUhMzOzWK8tE/cIIaoSSXYJIaqsM4muRYsW8ffff1+Q6Dpy5AitWrVi1KhRtGnTBrPZzLfffkvHjh1p2bIlL7zwwmW3HxwcTEZGRpG2Ll26oNfr+eOPPy4az+LFi+nevbur7UxSrFatWqV9m0IIIYQQ5eL333+nXbt23HDDDdSuXRtPT88iyy81OU95kIl7hBBViSS7hBBVkt1uZ/jw4a5EV+vWrS+63u7duxk7diw7duzg0KFDzJ8/n/Xr17Nt2za2bt3K+vXrL/kabdq0IS4urkhbUFAQjz32GK+99honTpwosuyDDz4gLS2NJ554wtW2a9cuIiMjCQ4OLv2bFUIIIYQoo/r16/Pf//6XDRs2oCgKw4YNu2CdBQsWoNPp6NKlS4m3f+TIERRFueBfr169XOsMGjSIH3/8sSxvQwghyoUku4QQVY7D4WD48OHMnTuXGTNmULt2bZKTk4v8OzNssVGjRq4aXkuXLmX9+vW0a9eOtm3bsmfPHg4dOnTJ1+nXrx+7d+8u0rsrNzeXxx57jLp169K7d2+2bNkCwDvvvMNzzz3HJ598gsFgcL3+6tWr6du3b0XtCiGEEEKIYlm/fj316tXjnXfeISkpicmTJ1+wzsUm5ymuqKgokpKSXP+2bt1KUFAQ11xzjWsdmbhHCFFV6NwdgBBCnG/Tpk3MnDkTgIEDB150nTMJqnO756uqykMPPXTF4YtntGjRgvbt2/PTTz/x8MMPA/Duu+8WqW/x8ccfM23aNCZOnAjA/fffDzgL2YeFhTFnzhwWLVpUwncohBBCCFG+vL29OXLkCN27dycsLOyi61xsch6AP//8E29v7yJt59dD1Wq1ru0WFhYyZMgQunTpwksvveRa59yJe+rUqVPGdySEEKUnPbuEEFVOp06dUFX1sv/8/f0veN61117L7NmzXYmwxMRE0tLSLvtazz//PB999BEOhwOAl156qcjrTJs2DeCC14+JieHrr7+mU6dOdO7cuVzfvxBCCCFESe3YsQNw3sy7lItNzgPQu3dvtm3bVuTfV199dcntPPDAA+Tk5DBz5kw0mrOXlDJxjxCiqpCeXUKIGqN58+Y8/fTT9OrVC4fDgY+PD7NmzSIoKOiSzxk4cCAHDhzg+PHjREVFlej19Ho9n3zySVnDFkIIIYQos23bttGgQQO8vLwuuc7FJucB8PLyokGDBkXaEhMTL7qN1157jYULF7Jx40Z8fHyKLJOJe4QQVYUku4QQ1VZMTAybN28u0jZ8+HCGDx9eou2MGzeuVK//0EMPlep5QgghhBDlbdu2bRfMXH2+Nm3aMGPGjFK/xq+//sorr7zCggULqF+//gXLZeIeIURVIcMYhRBCCCGEEKKa27Zt2yVnrz7jYpPzFNeuXbsYPnw4Tz/9NM2aNXNNGnSmNxfIxD1CiKpDkl1CCCGEEEIIUY05HA527tx5xZ5d507OU1KbN28mPz+f1157jdq1a7v+DR06FHAWrZ8zZw6jRo0q1XsQQojypKiqqro7CCGEEEIIIYQQFW/+/PlMmDCBXbt2FSkuX1afffYZ8+bNY/HixeW2TSGEKC2p2SWEEEIIIYQQV4myTM5zOTJxjxCiKpGeXUIIIYQQQgghhBCixpCaXUIIIYQQQgghhBCixpBklxBCCCGEEEIIIYSoMSTZJYQQQgghhBBCCCFqDEl2CSGEEEIIIYQQQogaQ5JdQgghhBBCCCGEEKLGkGSXEEIIIYQQQgghhKgxJNklhBBCCCGEEEIIIWoMSXYJIYQQQgghhBBCiBpDkl1CCCGEEEIIIYQQosaoscmuSZMm0aFDB3x8fAgJCWHIkCHs27fP3WEJIYQQQgghhBBCVCk1LYdSY5NdK1eu5JFHHuGff/5hyZIl2Gw2+vbtS15enrtDE0IIIYQQQgghhKgyaloORVFVVXV3EJXh1KlThISEsHLlSq655hp3hyOEEEIIIYQQQghRJVX3HIrO3QFUlqysLAACAwMvuY7ZbMZsNrse22w29uzZQ1RUFBpNje0EJ4QQQgghhBBCiBrE4XBw7NgxYmNj0enOpn6MRiNGo/GKzy9ODqUquyp6dqmqyuDBg8nIyGD16tWXXO+ll17i5ZdfrsTIhBBCCCGEEEIIISrHiy++yEsvvXTZdYqbQ6nKropk1yOPPMJff/3FmjVriIyMvOR65/fsSkhIoHnz5mzcuJHatWtXRqhCCCGEEEIIIYQQZZKUlETHjh3ZtWsXUVFRrvbi9Owqbg6lKqvxwxjHjh3L77//zqpVq654kM4/6H5+fgDUrl272h5gIYQQQgghhBBCXJ38/Pzw9fUt9volyaFUZTU22aWqKmPHjmXOnDmsWLGCunXrujskIYQQQgghhBBCiCqnpuVQamyy65FHHmHmzJnMmzcPHx8fkpOTAWdW08PDw83RCSGEEEIIIYQQQlQNNS2HUmNrdimKctH2b7/9lpEjRxZrG4mJiURFRZGQkFCtu+8JIYQQQgghhBDi6lHSfEZ55FCqkhrbs6uG5vCEEEIIIYQQQgghylVNy6Fo3B2AEEIIIYQQQgghhBDlRZJdQgghhBBCCCGEEKLGkGSXEEIIIYQQQgghhKgxJNklhBBCCCGEEEIIIWoMSXYJIYQQQgghhBBCiBpDkl1CCCGEEEIIIYQQosaQZJcQQgghhBBCCCGEqDEk2SWEEEIIIYQQQgghagxJdgkhhBBCCCGEEEKIGkOSXUIIIYQQQgghhBCixpBklxBCCCGEEEIIIYSoMSTZJYQQQgghhBBCCCFqDEl2CSGEEEIIIYQQQogaQ5JdQgghhBBCCCGEEKLGkGSXEEIIIYQQQgghhKgxJNklhBBCCCGEEEIIIWoMSXYJIYQQQgghhBBCiBpDkl1CCCGEEEIIIYQQosaQZJcQQgghhBBCCCGEqDEk2SWEEEIIIYQQQgghagxJdgkhhBBCCCGEEEKIGkOSXUIIIYQQQgghhBCixpBklxBCCCGEEEIIIYSoMSTZJYQQQgghhBBCCCFqDEl2CSGEEEIIIYQQQogao0Ynu1atWsVNN91EeHg4iqIwd+5cd4ckhBBCCDdQzVbMf20h94lp5Iz6nLxXfsG265i7wxJCCCGEqDJqUg6lRie78vLyaNWqFZ9++qm7QxFCCCGEmzhOZZM97FPyX/wJNd+CJiwA27+HyRk5mfx3fkdVVXeHKIQQQlz1HKnZ2HYdw56Q6u5Qrlo1KYeic3cAFWnAgAEMGDDA3WEIIaqhA1np5Fgtl1zuozfQ0C+wEiMSQpSGqqrkTpyBmleIz4/j0DWs7Wx3ODD/soGCt+ehqVML0+1d3BypEEIIcXWyx6dQ8PF8rGv2wekbUNrYSDz+0xd9l0Zuju7qUpNyKDU62VVSZrMZs9nsepyTk+PGaGoW1WbHkZQBGg2a2v4omhrdqVBUcwey0mk0+8srrrf/jlGS8BKiirPvOIZ95zG8P7rPlegCUDQaTLd3wb7zKOYfVmO8tZP8NgkhhBCVzB6fQs4DU1D8vfB87ma0zSJxHE/H/ONacsd9i9eb92C4trm7w6zWcnJyyM7Odj02Go0YjUY3RlQ5JNl1jkmTJvHyyy+7O4waRbXaKPxuJeaf/0FNcyYPNVFBGO/ujvHWziiK4uYIhbjQmR5dM3rfSNOAoAuW78lI497lf16255cQomqwrNsLPibsJzNh3T70XRsXWW4Y0AbLgm04jqejjQp2T5BCCCHEVSr/gz9RArzxmTYGjY+Hs7FROPprYsl75gfy35yLvkcTFL2kLkorNja2yOMXX3yRl156yT3BVCL5xJzj2WefZfz48a7Hx48fv+CDIYpPtdnJmzgD6/oDGId0QN+rGdjsmBdspeCteTiOnMLzqUHuDlOIS2oaEETb4DB3hyGEKCZVVXEkpmHfnYhtdwK2uETsuxLA7qDgjTnoezdzJbvU3EIsC7eh1Dmd4LI73Bi5EEIIcfVxJGdiW7cfz+dvQePjgWqzY127D12rOmj8vfAY3ZfsOz7AumoPhutauDvcaisuLo6IiAjX46uhVxdIsquI87vzndvVT5ScZeE2rKv34v3xfUXupOu7N6GwdQwFb83D0LcVulZ13BilEMWTnJ+Lv8GESSdfm0JUFY5T2TjSc9E1Dnc22Oxk3/EhWGwXrKuNjUDXIhpw1uvK+98srGv2ookOBn9PNBEyJFk4Sc1GIYSoHPakDAB0Leug2h3kPTsT6/LdKKF++M4ch7Z+KHgZcRxPd3Ok1ZuPjw++vr7uDqPSyVWbqDDmORvRdW7oSnTZDyaDpwFteCDGWzph/mEN5jkbJNklqizH6QKZB7LS6fPXbNoFh/HT9YPdHJUQ1YsjLQfr8t04svLRRgSi79UMxaQv+XZyCrDHJWLbnYg9ztlrS03JRtswDN8fHwdA0evQNY9CtdjQNYtCGxuJtkk4uU98BygYBndwrqfRoO/eBOu6fTiOpaIE+6Bm5KGE+JXjOxfVkdRsFEKIynNm2KL9eDqFP6zGunw3APr29dH4eeLIyIUCC4qvpzvDFNVUjU525ebmcvDgQdfj+Ph4tm3bRmBgINHR0W6M7OrgOJaK8c5uqA4HhV8vo/CLv8HDgM/Uh9A1jUTXJgb70VPuDlOIC2w+lQzAd/t30b5WbRLzckjKz2NH+ilOFea7OTohqgfV7qDg04WYf1wLgOJtQs3MQ/HzxPOpQRj6t770c212FJ3W9TjnsW+wrdt/4YoaBRSlyPreXzx0QT1I77fvJfeRr8m++R0M/VujCfXHti0eHCroNKipOWQP+xTv94ahay7nB1czqdkohBCVR1M/FE39UPLfnod6IgM0Cp4v3Oo6RzDPXgdaDbqeTd0b6FWkJuVQanSya/PmzfTu3dv1+Ew9rhEjRjBt2jQ3RVXxqkr3e8XTiHoqm/xJc7HM2ehsLLCQ+/g0fL4dgyMlG8XLVOFxiOpFNVuxLt+NPT4FxcOAvncztHVqVWoMx3KdQ5iXJB7BYrfTO7wOf/S/hVaBIYR6enEiP7dS4xGiOir4dCHmH1Zjeuh6jLd3QePriT0hlYIpS8h7fjaKtwl99yaoNjv2wyfP9tranYAjORO/Jc+jaJ2zI2r8nHd0NRGBaGMjnb22mkWiaxKB4mEo8roXm/hE1yQC3x8eo3D2OqxLdqDmFaKJCsbzf0PRtokhb+IPOA6dJOehqXj+dyjGG9pW/A4SVZrUbBRCiIqnKAq6phFY/twCgOk/fTHe2A5HZh6FP6+n8KtlKKF+FLz9O14v3laqnuGiZGpSDqVGJ7t69eqFenoY0tWiKnW/11/fAsuvG/B85XYsC7fhMXYAljkbsB9IJnf0lzhOZOD5v1sqNAZRvVhWxZH/8i+oWfkooX6oOQUUfLoQfb9WeD1/a6X9wN0c05DXt65nYqtO7Mpw9j4MNnlwPD+H4/k57MlIq5Q4hKiuHGk5mH9ci+mh6/F48DpXuzYqGK/X7iA3M4+8SXPQhPlj33sCzNYLt3EsFW3dEAA8HumPx5M3ofH3KnVMmjB/PMcNhHEDL1jm+80Y8l6YjXVlHAWTF2Ho3QzF8+oo3iouLddqwVt/Npk6aet69mdJ3RhRdVWVG95CFJd100FXogujjsLPFmH+YQ1qTgFoFPT9WmH9eyfWJTvIOZ6O93vD0dS6+mpPVaaalEOp0cmuq1FV6X7vyMzDdEdXLHM3UfjVMrzeG4ahY0P0PZuSc+8nOE5kgIcBQ/9WFRqHuDJVVbFtPIj5l3+wHzqJYtKj7xmL8ZZOaIIr78fEtu0IeU/NQN+9CR7jBqKNDkY1W7Es2Er+u3+QZ/8J7zfvqZDX3pGWwqRt/zCt10CMWh2+BudF7gOrFlz2eUsSj8idfyEuwrp8N6gqhgFtsK7fj23HUWw7j+H10u1ogn0w3tGVvCenYz+Z5XyClxFdbKSr15YuNhIl9Gz9LE2Yf4XGq3gZ8XrnXgq/XIq+Z6wkugS/HN7Ht/t2snbwvdTz9Qfgq707OJyTWWS9IzlZrElOpEdYJHV8pOabcJ+qdMNbiOLSta2H4ZZOKJ5GPB6+Hsvy3ThOZKDx80R/bXM0gd5Yh3Yib+IM7HGJZA//FO/3hqOLjXR36KIakGRXDXVu9/ssixlfveGiQzvKm6qqmL9bSeG0Ffh88x+8Jz9I3sQZ5I35moIwf1SrHTUjD4x6vN8fgWIyXHmjosKoqkrBO79j/mk92gZh6K9pipqZR+GM1Zh/Wo/3J/dX2o9JwVdL0TYIw+ute1y1dxSjHuOQjqDXkf/iT9gPJqNtUL7JJYvdzg0LfyExL4d6Pv683vEaGvoFsv+OUZdMCn+861++27+L5SeOMbFVp0r52xKiOnCkZGHdcADz75tBVcke8g6cc3fQFpeA4ZpYNOEBAJhGXYehXys00cEoGo27wgacRes9Hu5TpM2yYjfaeqFoo4PdFJVwl7lHDpBckMe3+3bwaodrUFWV/7XtwrwjB5h39Gwtkz+OHuSxdX/TP6ouCwbc7mo/kJVOPR9/tG7+XIurR1W54S1ESShaDZ7PDHH+v6JgHNDmgnX07erh890j5I7/DsfhFHJGfY7Xi7dh6CudJsTlSbKrhlNVlTozpwDw79ARrvaE3GzybTaa+AcSbCqf2S3UQgt5r/yKdfF2ACxLduDxcB98f30S69p92HYcRdFo0HVqgLZtXTSVfAIoXbsvZPlrC+af1uP5zBDnXZXTSRuPJ24k9/Fp5I7/Dr95E1GMpRs+qKoqmG2oBWbUPDNqgQXyLSghvmhrOy92Hak5mH/9B9s/B9B1bED+m3NR8y1gtqLrUB/jLZ0w9GtFwQd/OT9T5ZzsMmi1TO3Rj493/8sTLdu72i/3Wfi250A6h4RzX+MWkugSVy210IJtdyLayCA0p3thWTceJP/lX4qsp4kMQtcyGl3LOugaRwBg33kMNEql9yAtCdvuBPKe+xHFqMNr0j3oOzd0d0iiEn3Q5Vr2ZKYxrrnzd0FRFO5r3JJWQSFFkl0BRhOdQmrTu/bZmaXNdhstf/kWvUbDntsfJMLLB3D+JspvhqhoUm9OVHW2vccx/7oBz6cHo+i0xfpe1EYG4fvNGHL/+yO2tfvIe+5HHBnOkURCXIoku2q4DHMhWRYzAOGe3q7//+vYYb7cu52HmrTii2v6u9Z/YfNqor19uat+U7z0xe91ZT+RTt6E77HvTwKtxjnT1i2dziaYmgU6/52RngI4E0wx+9KxHzmF6fYu5fCOL066dl+ceeYa9D2aYry1M+CcAU3NLkDNN2Ma2Yu8J6dj+Xsnxhva4jiZhWVlHOSbUfOdiSs13+xMTBVYMNzUDsN1LQCw7Uog55GvoMDinO3sPKbRfVx1fBwZuRR+udT5vI0Hi6xnXRmH/XAKXs/djBLkjZpbWC7v+1huNtkWM80DnYXvB0TXp39UvWJfhCiKwujYoneecixmfAwy9EnUTKqqop7Mwrb9qGtIon3fCbA78Bh/I6a7uwOga1UHXesYtM0iMc/ZiK5DA7zfubdIry1Hdj6F369C361JlU10AWhC/dE2Cce+4xi5j32DxxM3YLyzmyQraqgjOVnsSEsh0tuZmEotLOCa2lFsTTtZZL3zazbe27AZ9zZsVqTtcHYmBo0Go1ZHuKe3q/2ZjStZfuIYT7fqxC31GhcrLrlRJ0pDVVVe27qOhr4B3Nkg1t3hCOFiP3qK3LHfoGbkoQn0xuM/fYv9XMXbhPf7Iyj4ZAGWuZvQd2xQgZGKmkCSXTVcoMmD/PvHcyw3Gw/d2d45Bq2WGB8/GvmfPUFKLyzg1S3rALir/tnpXT+P28qChMPc0yCW289ptzscaDUarJsPkff0D86i4oHeeL11D/o2dYudYNr0cw71M2xoArww9GlZHm/7AtK1+0JqbiH2/UkY770GKFoc/gwlxA/blsMYb2iL/XgaBW/Pu+T2tC3P3tVGr4U8c9EVPAwongYUD2ORejiaAG8MA9tgWbwdbbMo9F0bo3gawGyj8Of1mO7uhiM9F8fRVJTB7SmrTSlJDFjwM34GI5uHjiDA6JwRtCwXsFPitvLqlnWsuuluGvgFlDlGIcrKfvQUloXbUDPz0IQFYLihTakTS/YjKeSM+Qo1JfuCZUot3yIJbW1UMD5fjQZA1yyKvP/OInfMVxjv7IYmPAD7zgQKZ6xCzS7A4yKF4qsSTbAPPp8/RP6kOVj++JeC9/7EfjAZz6eHoBjk9Kkmic/OpNPc78mymJne+wYA7l3+52Wf43OZG4JNA4JJHzGOhLycIr8ty08cY9OpJMwOu6stMTebFzavoVd4NMMbNS+yHblRJ0qt0Eo7i5FH1i0nwtObHuHR7o5ICBwns8h95GvUjDy0TSIwDbumxNtQtBo8H78B0z09ihSqVy02+W0WF5BPxFXAQ6ensX/RBM/9jVvwabeitUlsqoOxzdqRWphfpFfX2uTj/H70IF1CI1xtmeZCQr7/hLp6L1Z+nojR6kDbNIKTLw3iVLAPMQ57sRNM5v4t4Met5L0wGyXIG33beuX59ouoKl27q8Sd2tMn4KrDQeEPqyn4cP7Z2jqnE1OgutbT1PJFf30LFA8DiqfRudzDeDqBZUB7Tm0vbd0QfH+b4FzmaQST/pL1eDTBPni9cgeKlxHL4h0YXrkDbYTzvRvv7QFaDfmT5oJWwb73BLlPfIfH2P5o64WW6m038AvAx2DA32gk12pxJbtKy2K3M3XPNpLyc5l5MI4X2nUr0/aEKAvVZif/zblY5m5C8fVAE+qHPeFfCiYvwjTqOkwPXHvRxK4jIxfbjmPYdhzFvuMo2hZ18HxsAACasADUtFzQatA2qu0cjtiyDtqW0WjC/C+ZKDb0bYXiaaTg8yXkTfje2ahRzk5CUadWhe2H8qIYdHi+cCvaBmEUfDQfy7zN2I+cwvudYWgCva+8AVEt1PHxo0dYJPE5WXQLjbhszUYo3m+0VqMh5ryC9T9fP5i1J49zXfjZm0OrkhP5dv9OdmekFkl2zT2y39UbX27UiZIomL6SzHlxzGiv5UhjI9M//ZEOba5B7RXj7tDEVcyRmUfO2K9xJGeiiQ7G++P7ULxLfw5+bqLLuukgeS/9jPdb96BrXrbE7pWu0UB61FYEq9VKcnIy+fn51KpVi8DA8tm/FZrsqqigxZWd383+Su0AIR5efNzt+gvaH23eli6h4XQ9J9l1KDsTq8NBtsaBV4s6zindnxvKY/8s5rulu3i1fQ8GRjuTVnV8fFl5IoEGfgHcGF3fdWG0M/0UAOlD26JPMmNdEcexZ6ez4LneeIT680CTs0UHZx2MY1taCoPqNKBrmDOpkpyfyzMbV2LQaJl6zlDMd7dvYEFCPKOatHR13U4rLADgtiVzOXTXaNe60/btZGHCYdoEn02cqKrK9rQUwjy9CPXwKvchK1XlTq3iZUTbOBzzF0twJGUCYBjaCc+Jg1B0WuxHTpF963voTicftVHBxZ4NUTHoSlzQ2TTqeqz/HCBn5GcY7+yGvkN9HGk5mH/ZgG3DATweG0DBlMVgtWNduxfD4A54PHx9sXqrWOx2DFpn0fsAo4m/b7iDCE8fTLqyfwUatFoWDridWYf28FjzdmXenhBlUfDRfCx//IvH04MxDmqPYtSj5hZSOH0lhZ8vQePvhfHWzqgOB5Y5m1xDEh3HUotsRy20uv5fMenx+XYM2rohKB4lm1RE370Jum6NcSSkoWbnowkLQBPsUy7vtbIoioLpnh5o64aQ99yP2LcfxfL7Zkwje7k7NFEGjtM3dzSKgkZRXD26SlLCoaTq+PhdMGNji8BaPNe6i2v4JDjPQx5atYhThc6e1mdu1KUXFqDTaFwzBgtxLtXs/N7OXbMH0+1d+bRrA65LjueOhFMUvDWPwqQOULb7e0KUippvJvfxaTgOp6CE+OL92QPldsNIVVUKv1yKejKLnIem4vXCrRj6ty7Vtop7jQbSo7Y85Obm8sMPP/Djjz+yceNGzOazo4IiIyPp27cvDz30EB06dCj1a5R7sqsyghaXdqZbfVm635+vU0g4nULCXY8dqdm08gvi6N3/IaUgD+9bg5w9dxQFh6riodXR8JyhXMdysxn/zzJCPDw5OWysq/3rvTsA2JSWzI2v3UXOmC9JSjzO2H3/EHrEo0iya+6RA8w+vJdwL29XsivPZuW7/bvw1huKJLv2ZKax7MRRro84e+f0zCCbwzlZRQrErjt5nNmH9+J/Tu+eHKuFNr9NAyD3vidcJ77f79/FshNHGRLTiMExzkLFqqqyPyudME/vYs94WZWGVBrv6kb+Sz8DYHp8IKZ7ejiPY0aus6ddLV8M17eo8DgANIHe+Hw1moLPFlH4zTIKpywGQNs4HK937sXQuzn6a5pS8OlCrCvisMzZiGXBVkzDrsF07zUoXhc/+f/3VDK3/z2Pyd370C/Kmbir71u+Qw1DPb0Y1+LsEEtVVbE6HK4EmxCVwZGRi/mXfzA93AfTbUVrIOra1kX772EKvl2OYUgHFJ2Wgm+WoZ7Mcq2jqRfi6rWlO3dYMpRpVlZFUWrEbIb6ro3xmfYI5l//wTi85EMvRNVRYLMycsV8GvsH8kr7HkDFJrkup0VgLVp0LNrLMcdqoUtoOBtSTnCy4GxpgSlxW3l5y1qeatmJ1zvKZ1AUZVm6E4BhN/kzpWcMt9SrzwPUhwFQ2DgC6/eL4JaqWydR1Fx5/52FfVcCip8nPp8+4JqkqjwoioL3ByPJ+98srKv3kPe/WdjjUzA9fH2JZ3m+0jUaSI/a8vLBBx/w+uuvExMTw6BBg3jmmWeIiIjAw8OD9PR0du3axerVq+nTpw+dO3fmk08+oWHDkk8UVK7JrsoKWlxaQ7/Acul+fym2bUfIfXoGhutbEv3UIKK9i/5oTu99I9/1ugG7qrLjdBF6naLhtnqN8dYVPZGs5+vP6uREAgxGFJMe7/dHEDBmMjcdLiTQpEXNM7sSGAOj61Pb05vWQWd7YAWbPHmzY088zuudM6pJK66PiKFVUIirzc/gfO2vzkmKgbOwbGxAEAFGE1/s2QY4i/qHenhhttuKnPyuSk5g2v5d1PP1dyW7sq0Wmvz0FQD594931UWbeTCO1UkJDKrTgAHR9QFnAiQxLwfr6Vod7hxSeSbhZ7ihLfZ9JzD/uBbLvM2oJ7NQM/KwLN+N4mHA+9P7K3X8uybIB68XbsVz/I04kjPBw4AmPMCVRNTGhOD97nBs246Q/9F87DuPUfjlUsy/bcD7nWEXXKADfLtvJ4dzMnlh8xr6Rtat8OLSdoeDR9cu4VhuNnP7DUWvkYSXqBzWNXvBasc4tKPz8YYDFHz4F/aDJ51DlL2MkGfGvuc4uhbRGG/pDBarc0hi8yg0vuUzM29Npo2pheeTN7keq2Yrlj+3YLi5Q4lPqoX7LEqM56fDezFotNzfuOUFww3dzddgZF6/W/j3VBLt50x3tR/MzsDqcBTpBQbOm4eGWC3NAoJlAoWrmGVZHHSFU1Yzc4/uJ+pUIYqPB9oQX6zX1uO9AzL0WriH8e5u2OIS8X5vWKnLkFyO4mXE691hFHy2CPP0lRR+vQz74ZPOMikl7JEOVafsTU22bt06li9fTosWF+9U0bFjR+6//34+//xzvv76a1auXOn+ZFdlBS0ur6K6VJp/3UD+O7+DzY5ty2HUfHORQuNnKIqC7pyTrQZ+Afx0/ZAL1nuseTu+27+LnqeLZmr8vWjy9v1Mv38K+q7RYDz78RzeqPkFhVv9DEaebt35gu12Do2g8zlDLgFXwqFNcGiRE8FrakdxTe0otqQmu9rq+PiRPOxR7A5HkW3cUa8p9Xz86Vk7ytWWVljg6tF17gQAy44f5et9O4jw8nElu7IsZqJnTrkg3p8O7SHAaKJPZN0LllUE2/aj5H80H+/3h6Px98Jj/I3oe8Zi/nUD1g0HUIx6TPf1wnhzRzRB7hlupHib0Da49I+MrnUMPt/8B+vSXRR8thA1Kx9NzMXr/7zbuTdeej3Ptu5cKRcB+7LS+W7/LgrtNlaeSOD6yJgKf00hAOfMqDotip8ntr3HyX1yOpwejqiJCETbMAzrijjUfGePa4/7e7sz3GpPVVXyX/8Ny/ytWP/Zj9fLt1/0N1FUPUNiGvFa+x50D4uscomuc53/m/Vtrxt4pnVnapmKJqYnx23lj6MHOXr3fyozPFHFeB3PBJznbTMOxDGDuKIrRDgT8l7nnK8KURn0HRrgN28iiqniPnuKVoPnYwPQ1gsh//XfsC7fTc4DU/CZ+nCpaoPl26xM/GcFt9RrRO/wC2+mi7L5+eefi7We0WhkzJgxpX6dck12VVbQonKpVhv5b/+OZc5GAPTXt8DrxdtKlSm/Em1kEL7TH0UJ9auQxERJaplpz7tLf31kzAWJi3q+/mTd9wSFNluR9qF1GxHh5V0kMXaqMB+9RoNJqyvS825RYjzT9+9mxU130S2s9EOFisOycBt5r/wCFhsFUxbj9ezNKIqCvn199O3rV+hrlzdFUTBc3wJ9z6bYD6e4eqUcyspgxvS5/Lfnteha1sGk0/FWp16VFldsQDC/9BlCrtUiiS5RqbR1Q8Bmx7piN/lv/w6FVnSdGuL18u1ogn0wz92IdeWealEYvjpQFAVd+/pYluxwnlQnTsHrveFow6WGR1W07PhRuoSGu25M/bdtVzdHVDrnTzgE0D0sks4h4UXOm3r/8SN1ffx4pX13Ir1l6NrVoIHDwHZLY6y3dCD32ZmoSZmgVfCcMIiT9QN46PffeCIv4KKfIXH1qehC7IUzVqHv1sR5bgIVmug6l/HGdmijgsmdMN154/wSZU6uZNLW9XwWt4U/jh1k/x2jMGplXr/KkJqayoYNG7Db7XTo0IHatWuXaXty1MRlOVJzyH16BvbtR0FR8HikH8YRPUuUiCppsXxNmL/r/1W7A9v6/ei7NylR3OeriFpm5zq/0PnA6PoMjC6aPGroF4j5gQmsSk6g1x8/utrNdjtBJg9XXbGKoKoqhV8tpfCLvwHQX9MUz3EDK/AVK4+i16Fr7Kwpl2kupNMv35JmtFJr0tcMj26MxyP9Kr1e0PnH3u5wXJA8FaK86drXQ4kKIu+lnyHPjKZeCF5v3o3GxwNHWg6F365A361xke9YUTbGQe3R1qlF7sTvsR9IJmf4Z3i9cy/6NpXTU1cUz+TdW3h07RJuq9eEH68bhKaaDfW70nnUR12vKzLk5kBWOiuSjrEmWcP7Xa51tR/KzsDPYCTYJEOWaxJVVVmRdIxOfVsR/cdOjBoPCneloQR54/XKXejb18e6NZ7t0zPwf/emK29QVDg1txDL4u3Yj5xC8TJiuK7FZUc0lLeKLsRe+OMaCj6cT+G3K/D99Uk0/l6lCbPUdK3q4Pv9WJSAsxONqXYHirb45+JPt+7M6uRE/tumiyS6Ksmvv/7KAw88QKNGjbBarezbt4/PPvuM++67r9TbrJQjV94ZOlE5VLuDnP98iSM+BcXbhNdrd5Yo6VTWBJNqd5D3zA9Yl+/G839DMQ7pWPzgz1PRtcyKS1GUC97vFz36kWUxE+5VMUMGVYuN/Fd/xbJgKwDGe3vgMXZAib7wqwt/o4nHG7Xmz+07uCYpC+uBnVhX7MZ4SydMo65DE1D59SqyLWZuXjyHW+s15j+xbSr99cXVQ9Fo8H7xNnLGfAl6LfprW2DfeQzznuOYf14PKnhMkAud8qZrVQff7x4ld8L32PceJ3f0l3g+MwTjzaX/zRLlq1lAMDqNhgCjCYeqVptkV2nPo2J8/Fh2453EZaQVmYDn6Q0rmHPkAJ9378uopq3LPV7hHt/t38V9K+dzZ2x9PptvofDbFQB4PH4DutYxWP7eQf6b8zA2CMPy+2Z0repQ6G3Aoap4u2lihquZZeE28t74Dcw2NFFBqBl5FH65FH3vZqWuMVVSFVmI3Tx/KwXvOb+zjHd3r/RE1xlFOk84HORNnIG2bgimMX0vWmPzYFYG3x/Y7XrsrTew4qa7KyPUq1Zubi7e3mevzV5++WU2btxIo0aNAPjrr78YNWpU1U52VUSGTlQORavB45F+FExehPc7w0o89KWsCSZFq0FbLxTr8t3kT5qLJti3TD28qur0sF56Q5FC+HlWa7lt25GZR96E77FtOwJaDZ5PD8Y4tFO5bb8qOFWQj0ZRCDJ5APBc995M7NoTbf9T5H+8ANu6fZh/Wo/5ry2YRvTCdG+PSi26P/NgHMtOHOXf1GRur9fEFacQFUHXOgafaY9Q8OVSzN8ux2x3gFGPoX8rPEZdL726KogmzB+frx4m7+VfsC7ZQcGnC9H3bua2k3xRVM/waLbdch9N/YOqVQH30p5H6TVaeofXKVJnRlVVUgrycahqkV5gu9JPMfvQHobWbUyb4PIvHC0qXkpBPjpFQ+uoSHQNTjlHYwD57/9J/ptzIc+MtlMDyMzHujKOdSnJPNTfj44RkUzrdYN7g7/KWDccIO/52c7f5EcHoAn1Q7XZsSzeQf6kOeS9MBvvd4ZVWjzlXYjdumYv+S87yxoZ7+qGqYrUBrVtPIh1ZRzWlXHY41PwevWOIjU2TxXk0/a3aa7v2kv1pr3SMlEy7dq14+2332bw4MEA6HQ6UlJSXMmukydPYjCULflb7ld8lZGhExVHtdlxJKajPV3s29CrGfruTVB0pZtRrqwJJtPoPjiSM7H8tYXcZ37AZ+rD6GIrtq5VZbnYl+WOtBTGr19Wrq/jOJXt7Jn35j3oO9esCSE2pSQxdMkcmgcG82e/W9FqNGgUBYNWCw3C8Pn4PqwbD1Lw8QLse49jWbgV0/DKna794aatScjNYWjdRpLoEhXG/NcWtA3C0DUOR9coHJ93hqHmmVFzClD8vSqtVsbVTDEZ8HrjLgobhKFrVUcSXW60PzOdR9YuZkbvmwj1dB6H2IDKHc5eXsrrRp2iKKwadA9Hc7KKzKQ9+9AeXtu6nj2ZafzS52ZX+8WG31+pxk9l9JAXF5rYuhODYhpQT+eBWb8DdFo8n7oJR1qus5dvt8boGoVjP3SS3CemkZ2Rzb4cB5nxhaR0yiPEQ76rKkvh18vQtojC8+XbXb2LFJ0W40Bnz//8F2ZjP5hcqUMaP9ixicXH45nUoSety5Dwtm6NJ/fpGWB3YBjYBo8nbqgyNxf0nRvh+eod5L/6K9aVceQ8MAWv90egrR0AQC0PTx5u2prVyQlsSEm6Ym9aKH3pG3HWokWLGDNmDNOmTeOzzz7jo48+4o477sBut2Oz2dBoNEybNq1Mr1Huya7KyNCJiuHIzCPvmR+wHzqJ7/djXT0ASpvoKg+KouD5v6E4UnOwbThA7uPT8PnmP2gjq29xzeIOS/Ash/HhGn8vvD8aCQ61Qqb6dTeDVktaYQHx2VmkFOZT2/PCYYr6jg3QTX8E6+IdKIHers+zarZi2xKPrnPDCv0xVhSF1zsWTbCpqlplTgCqq4ourFqdWNfvJ/+VX8Cgw3fGWFcvXMXLiFLKwqyidBRFweOBa4u0WTceRBPsUyO/g6siVVUZvuJPNqQkMXbdkovOBn01q3Pe7JNdQiO4pW4j7qjf1NWWaS6k4eyp9ImI4ZueAzHpdMWu8VOa+j6i7JqcLjqvnzIK+8FkdA0vLBmjrR+Kz7RH6D5hOl8sT6JXUg5+AXEwpENlh3tVcqTnYtsSj8cLt2LfnYh10yHsexLRNonAdEdXDH1bUvDeH1iW7sSjApJdpwrymXVoD2mFBQyKaeBqX3L8CAsT4rkhqr4r2ZVtMZdo+KL9YDJ5T3wHZhv6Hk3wfOHWiw4VdCfjgDZoI4PInTAd+4Fkfnl6Mr3H3UZIO2eO4vUO16DTaDiUnSHnl5UkJiaG+fPnM3PmTHr27Mm4ceM4ePAgBw8exG6306RJE0ymks+kea5yT3ZVRoZOlD/bvhPkTZiOIykTPA3Yj56qMsNdFL0O77fuIeehL7DvTyL3sW/x+eY/1fau+eWGJThUlen7d3Ff4xY0KeVdaPPcTQAYT5+8aGNCSh9sFXRuoqhVUAjzB9xG2+BQfA2XvqhXNBoM/VsXaTP/tJ6Cj+aj61Afj8cGoGtaOT0G47MzuWvZ73zTc2C17WngbhVdWLU08bjrxMh+MJncZ35w3km9tjmaSp6MQVye/UgKuU99D4DXa3di6NH0Cs8QZaUoCtN73cgT65fySdc+7g6nyrvYhDqLEuNJLSxge3qKawKeM99xn3S9nq5hERdsp7T1fUTpWB12nt6wgvEtOhSZbVNRlIsmus7QBHrjM2UUd77yC9ZD28l/7VfsR0/hMbZ/lUtO1DRqvhmAgrfnQeHZkiXW5bsxz1iF8a7u4OeBmmcu82sdy81mTXIijf0CaVfLmTjLtBTy2Lq/MWq1DIg+O4nKQ01bcUN0ffpFnm17Zctavt67o9ivp4T6OXujKeA16R63dpS4HF2LaHy/e5QXPvqa1+vaGPLLT8xKvRFjv9bOUSFU3bI3Ndndd9/NgAEDmDBhAr169WLq1Km0bt26XLZd7smuysjQifJlWbSdvFd+AbMVTVQQ3u8OR1u/at2BVrxNeH90H9kjP8NxMhP7wWQ07etf+YlV1OW+SNvXKt0EDqrDQcEnCzF/vwq0GrTNIi97wlMd7ctM48FVC/mu1w3U8/UHoFd4dKm2pZqtoNdi23SInGGfYujfGtOYvmjDzx4bVVWx7ziGPSEVxduEvnNDFFPZeqY++c9yNqQkMXr1IlbedLf08CqFiiysWlLuTLw50nLIfWIa5JnRta2L53+HyuepilH8vdA1Dse2JZ688dNxnDejsWqzg80ORr0cuzKwOxzszUyjWaCzV2Mj/0D+GnCbm6MqP6qqYtt2BNvGg2B3oGtZB12XRhU20cytdRuzdtA9ZJ/z/amqzjmjx677mxU33kXPUv72ivLxwY7NfLBzM3OPHGBvx8HY5mzCY3RfFO8rX2MpRj1er91JYXQwhV8uZeXhgxzdtZXRLdtVQuQ1n6qqOA6nYN10ENu/hzGN6ImueTSaYF8w6jhksJMX5oW2STiamBCs6/ajHk+Hv9aAxUatSC+aleD1MsyF/Hsqmesi6rh+R97etoHP4rYwrnk7V7KrgW8At9VrTPOAWljsDtfzh8Q0KrI9m8PB0uNHybQUP+mm8fHA+9P7weao8mUTNGH+DH7kNt7580ci81QID3B3SFe1BQsWEBcXR6tWrfj6669ZsWIFd999NwMHDuSVV17Bw6NsJWAqrEpzRWboRPlQ7Q4KPluEefpKAHRdG+H12p1ofKvmlNSaWr74fHI/am4hupZ1rvyEGuDXw/vYl5XOc226XHY9tcBC3guzsS53ziJieuDaSh3vX16u1ENm3Lq/WZN8nLFrl5T5QsbjweswDGxD4ZTFWBZsw7JwG5alOzHe0RXTfb1xHDlF3hu/4Th00vUcxceEaUSvIherJfXlNf3RazR80OVaubgto0sVVt2bmYZNdRRpszsc5FgteOh05TqFtLsSb2qhldwnnb1xNVFBeL19b6VOvCCKR+PvhffkB8l/53csv26g4NOF2A8lY7ilM+Yf12JdsRvsDjRh/hhu7ojpnu5lTqhfbfKsFu5c+jsrkxJYO/heWgSWbDKdqs6RnEnuxBnY4xJRArxAp6Xwm+Wuv/uKuKml1WjoGla0t3PW6QtfT52OjiFnX3Pavp2sSDpGl9Dwco9DXFr/qLrMPbqfh5q0xvzW79i2xKPmmfF6sXjnRoqi4PFwH3ZEeXBD4lq0G5bRoXaEKzEiik9VVRyJadg2HcK66RC2fw+jpue6lmsb1UbXPBrFpOfYjc3pEHT09JJUcKRCZ4CzvfPI3Mi+zJbU+fsA+utbFBnJYrHbyTAXumoRWux2as/4FLPdzuE7H6bu6RvBPWpHsulUEnV9/F3PVRTFNax7S2oycOli61/06Mesg3v4YNdmV9uSxHi8dHrXd4MjOx/rqj0Yb3QmSavyb5fVYWdvZrrr96FDZDQH7xlN7aQ8dI3OfndJmZHKNXHiRL777jt69+7N5MmTGTlyJM8//zxbt27llVdeoXXr1nz44YcMGDCg1K9RIWfGFZ2hE+XD/MNqV6LLOKInHmP6VdhdwvJyfs0TR2Yeip9njfxi2pZ6klv/ngtAt9CIS95FdZzKJnf8d9j3HAe9Fs8XbsU4oE0lRlo+ittDpm9kDF/3LP2X3rm04YF4vXonxnt6UPDRfGybDmGesRr7oZPYtsSjbVwb78kPomsdgyMlC/PsdRR8uhC10ILH6L6les0gkwezrx9cLvELZw2KX+P3MTr27Gf+pc1rmH14b5H1juVmU2/WF3jp9OTeP97V/tQ/y/klfh9Pt+rk2kamuZC7l/2BSavj1z5DXN8v844cYGNKEtdF1OHaCGfC3epwJtWO5+dwR/2m6CphGIjqcJD30k/YdyWg+Hrg/eHIajus+2qg6LR4PXszuoa1nUmv08l1JSLQOXQoyAfb5kMUfrMM69p9+Hz2QKVMPV9T6DVacq1WrA4Hh7Mza1SySy20kPPI12C24v3p/eg6NgBFwb47kfw3fiN3zFf4zhyHppbvlTdWRv5GZ4+hBQNuw0N3tufGT4f3siDhMP7nlBIw222sTkqke1ikaxhkRVDtDqxr92LbftQ5fK99fXQd618Vw/FaBoWwZtC9WOZvpWBLPBj1mB66vsTb6TigG7f8nYqvwUAjvwAKp69E17EBuiYXDlUVZ6lWG4re+dm27z1OzrBPi65g1KNrHYO+Q3305wxft97ZCZYc5Yt/rDTv0Bxd0wgcmflY/t6J42AyR0d1ZWTaTjJ3xBP85lzUj+bjcXsXTPf24Ke0Y4xcOZ++EXX5vf8tgLN2bcvAENLNBSQX5LmSXXfUb1qkDt/5ils/+My6ZruNh1Yv4khOFrOvG8RtEfXJfeI77NuPoqblYhrRsyS7r1KdyMth0KLfiM/JZOet9xPu5QNAlI8fnFPD0LYnkfw35+H1xl1oI5y98B3Z+ahpuSh+nmgCL6wLLMrmm2++YdGiRbRr14709HQ6d+7M888/j8Fg4LXXXuOuu+7i4YcfrlrJrsrI0InyYby9C5aVcaeLIrZydzglZtuTSO7j0zANuwbTvZU7w15laB0cyrjm7dBpNHQLu3g9Kdv+E+Q+8R3qySwUfy+83x2GrnVM5QZaTi7WQ+ZUQT67M1LpFR7t6h0zqWNPwi5SiL4sdE0i8J78ILb1+yn4bBFqgQVNZCA+k0eBqoJOgzYyCM8nb0LxNlH47QqMt3VBE+RT5tdelZTAhzs3M/Pamyr0oqAmyrdZ6TLvew5lZ6LXaHigifN7rJaHJxFe3hzPO3tntdBuA8B0Xq+u5Pw8juRkkWc7Wz8j22phQcJhjFptkUT6woTDfL5nGwatxpXsKjj9vKc3rOSJFmeL/P4Wv49/Tp7ggSYty/ldAxabs/aHTovXO8NcBelF1Wa8tTNKbX/yxk0DFQy9m7t+u4wD2mC8uRM5o6dS+M1yPB7p595gqxGDVsuvfYZwOCez1GUAqirLwu04jqXi+9MTaOuerb+pax6F9+QHyRr8Nuaf1+MxpvI+L97nzUD2dKtOtAisRceQ2ny0618A1iYfp8/82dTz8efQXQ9XSBz2g8nkPvU9joQ0NGH+qA4HhdNWoKkfivd7w6v1REaX41BVNGd+l3ILKfxoPgAeo65zzSxXEoqiMPO6m9BrtFj+3kHexwvApMfr9bsw9Iwtz9DdzpFTgGX+VuyHT6KY9Bh6N0fbqk6xbpg7UnOwbT6EdfMhbJsPo2tfD6//ORNO2oa1UQK80NYNcSZcO9RH1yzqor2tNT7ODh/NYhvSdNZWyPsHAF3bupheGoFvAx/4bSdvntzLmpEBTF2cQ8/vVmKevY7QO1piNto5kJ1eZJurbrq7xOePl6sffK4z9UYzzYVcH1GHRQnx3BBRl9ynf8C+/SiKjwld10aX3Ya7BZs8UVFRgf1ZGa5k17lUVSV/0lzscYnkjPgMjydvxLoiztX7GkDXpREeo/ugaxZVye+g5vL09CQ+Pp527dqRkJBwQamrZs2asWbNmjK9RrlfWVVGhk5cmXXNXgpnrcW29QgooGtXD9Nd3VE8DGhbRKFoNCgmAz5fja62vaJsW4+gpuVS8OF8NCF+1TJhdyUfdLnussfHtm4/6sksNDG18P5wZI04uTszNO1YbjYDF/xChrmQNYPuueQQsfKiKAr6ro3RNKpN9oBJzvpHJj15r/+GPS4Rz/8ORRcbifGubhR+txLLkh2Y7uxWptfMt1m57e+5pBTk89b2f3ixXfdyejdXB0+dnmENmzF9/+4iCeFPuvXhvsYtaPfbd662Jv5BmB+YgPl00uuMVzv04NHmbYk85+QnwGDk254DsZ83FPLaiDoYtFo6h1w4XKddcCg65Wxvgo93/cvKpAR8DUYGRtcr83s9l2Iy4P3+COxxiVfNkO6aQj2VDQro+7bCY8zZ3qGqqqJrHoVxcAfMczdievj6KlvgtyqYdTCOdHMhY5q1BSDQ5EGgqeaNGrD8vQNdxwZo64ag2h2Yv1+FmluIaXQfNP5eGPq1xrJkR6Umu87XMzyanuHRrmFRAGnmAsI9vel+3o26gQt+ppbJk5fbdyfmvFkhS8KRlkPOI1+hCfLBZ9oj6JpHueqa5b/6q6vHW3HqV1UnORYzXefNYHRsG0Y3bY158mLU9Fw0MbUw3lP68we9xvldo+/cCF3nhiRvOwgTvscxbgDGe3pU2+uEc1kWbyfv1V/BYkPbIAxHZh7mH9aga1sXr3eGofErWsJFVVWsy3c7hyZuPoQjPqXIcpvm7D5RdFr8FjxXou9sj5E98X90KPEJx3klfjsZqo15nRvC6b+jPC89pwyw474OXDsnAXtcIrHf/8vGIAOx1zdFzTejeDp7U5b2RmlJaoj6G018ec0Acs2F8OpcbGv3gVHPI482oVnuER63BeOpqzq1ulIL8wkyeqAoCgatltnXDcZDpyPiIokucF4DeL8zjNwnp2Pfe5z852ej+Hvh8cQN6JpEYD9yCvOsteQ89AXeH9+Pvl35ntddrSZNmsTw4cN57LHHyM/P57vvvrvyk0qo3JNdlZGhE5dXMPVvCqf+jbZ5FB7/6QMOFfPi7eQ++jUAptF98HjwOoBq/QNmvKsbjhPpmGetI+/Fn1CCfdC3rVlfPuceH1VV+fPYIW6Mru9qN47oCToNhsEdXHeLaoooLx+6hIZzMCsDP4ORXFslzfCUmQ+qirZeCGpuIdalO1GzC8j5z5f4fPYguuZRaIJ9UNNyyvxSnjo9s64bxOTdW5nYqlM5BH91ODcJ9ULbbjzeogN+l5mNE3Cd8JyZbeeMGB+/Cy66fAxGRjZuccE2bqvXhNvqNSnSdmYW0KnX9C/y9/rE6ZiGN2pOamE+ADvTT/Hu9o081LR1qSZWcJzMQgnxRVEUFJ1WEl3VkH1/Epq6oXi/fperTc0tJPuej9F3a4xSyxc1I885bCK09MmAmmxdciJ3LfsDjaLQrlYYnS6SfK4p1NxCV48u64rdFHy6EABHajaeL96GJsS3XGZuK4lL1fg5t/22ek24tW7jIj1mT+bnsSDhMADvdO7lav/3VDIZ5sISDXk0/7oBNd+C948PuIYWKYqCvk1dvD+5n+yh72L+awumO7qW9O1VaV/u3c6ujFTe27GR4Wogtl+cvYI8nxniGlJXFoUmLU/dHsEvzTNYNTON2h/Ox340Fc+nB1fr5Lv138Pk/W8Whn6t8Bh3g/MczuHAtm4/eS/9TN5T3+P13nAcR06ha+H8bVYUhYLJi3AcOcXpBrSNw9G1r4e+fX10beoWeY3S7B/FpMcrohbfLd+LAmSfUxB+eMPmvNi2G22CQzEN0WFbt5+CL/+mwa4EbGv3w7gbSr0/SktVVTQfLcK8cBtoNcS9OICZR1ajTT/GLXUb0di/atxw//XwPh5avZC3O/Vy9fhv4HflXo+aUD98vnqYrJvecv4OZ+bhSMpAe1sXdK1jMAxoTe7Yb8h/7Vd8f33yqhguXdHuuece+vfvz+HDh2nYsCH+/v7l/hrlnuyqjAyduDTbtiMUTv0b05h+eNzfG3CeLFm3HcGx9wQA9viUGlGAT1EUPJ64EcfJLKzLd5P35HR8vv7PBXW9agJVVRm5Yj7TD+ziDW00z9w1FMXTiKIoNW4I55kZnxRF4bteN6BVFLz0hiJ3jSuSEuQNGgX7gWR0Levg++uT5D39A7Yt8eSO/Rqvt+7BkZKNUqt8LkR7h9ehd7gkLYrDoao8t3ElG1Kc32WXuui60rLKMDimIYNjGgK4kl3zjhxgzpED6DXaEie77MdSyblvMvrrWuA5cVC1vvC4qhn1qLkFqA6H60TZumYvjuPpmH9a71ot/9MFGPu3Qdehvkw8cJ4uoRE80Lgl/kYj7S8yQUVNoo0Kxrb9KKrDgf7a5mjqheI4fBLLn1vQ1PLFtuc4mqjKucAsbo2fM+spilJkyGOA0cTfN9zBjrRThHicrTH40a7NfH9gN8+27swbHZ11f849D7gYy987OdavCXZHLmpyNuZf/wGjHuPg9ihGhfxr6+G1Zhttaliya1zz9njo9DTw9YdJS0FVMQxojb6cZifXKho2pSaTprGz6oH23PHpZixzNuI4no7Xm3dX2QmsrqRw2gq0jcPxfOn2s7WJzTbQadC1r4f1751kXfcKGHT4L3/RlTg03tgOx6ls57DENnUv6P1VHkI9vXizY09iA4IxaM7+rp8/AY++W2N0XRth23AAHKrrHEC12Cj4Ygmm27qgCfMv9/jOVfjVUtfvlNfLt9OlTytmHPTjUHZmkUTXkZysMvXcLKtD2Zmkmwv5/sBu7m/cskTXu46UbNSMPPS9m2FdvhvzD2uwHzmF95v3oHgY8BjTj5wHP8e2Jb7c/u6udkFBQQQFVdzvWLmfQVVGhk5cWuFP69HUqYXpvl4A2I+eInfC987utwYdiocepQZNca5oNXi9eic5Y77CvuMouY99i8+3YyqlWGtlUhSFTv61mKmCbuVe8vb+5Jx9rYYcR3AmMsB557Ld6borvlforVMRNAHe6Ls3oXDmGgz9W6MJ8Mb7w5HkPPqN8zP2hDOBb+hbAbWYgG/27iDTUsj4lh0rZPvV2d7MND7ctRmz3Q4Uv7BqZShO4u2Wuo0J8/TmngZn66Ak5+dyz7I/GdGoOcMaNrvo37QjK5/cx6ehZuVj33cCbA6QZFe1ZOgZi/n7VdjWH0DfrTEA+t7N8P5wJJZlu7D8tQXsDqwLtmFdsA28jHhPuht918buDbwSXG423gxzIeGe3jQNCEZRFKZe0/9s3aIaTH9NUywLtmKZuwnj0E74/fQE5nmbyH/1Vwq/XQGA50tlm5m4uIpT4+dMfZ+LMWi1XBcRw3URMUXaQz28CPf05vpz2nekn6L//J8YEtOQKT0uHKJ5SDHTLiQBzgxV1wMOYE6c8/HpTjf7s9JLNFSrqtNqNPzn9EQqjldup+DzJXg83Kfctm/Qavnh2ptIzM3h+sgYLPViyXvuR2wbD2LbEo+hV7Nye63Kouabsa3fj+ezN6NoNVhW7Mb841ps24+CzX52RYeKJsQPx8ksV0kQ08he5R7Pmb+f+JwsVzLr6dadi/VcRVHQdy5aH8vy+2bM363E/MMaDIPbYxrZq1S124r1+l7O0VoeE27C0L81APc0LPqZSM7PpfnPX9MtLILZ1w12TWxR0WwOh2uCoCdbdsDfaGRkoxYlvk5yHEsFwHPCIGx9W5H30k/ObZy+6aRtVQc0Co6jqSDJrjI5duwY0dHFv/F7/PhxIiJKPnFGuSa7zgRd3AxdaYMuicmTJ/POO++QlJREs2bN+PDDD+nRo0eFvqY72eMS0feKdf1x2w+dxBGfghLii/fbw7D89a+zjlcNopj0eL8/nJz7p+A4lkrBZwvxeul2d4dVruwn0rl70hrap2XRsFCL4b42NSrRBbAxJQmAL/ZsZ0xsW1oGhVzhGRXHY0w/sh+YQs6ozzE9dD261jF4jO1P3lPfo2bmg4cBNTMPynkGvI0pJ3hg1QIAOoaEX1Dv5GoXGxDMtJ4DsTocdA4NL3Zh1YpUkhmNOobUvuDEcMaB3Sw7cZQCu5XhjZpf8BzVaiPvqe9xHEtFU9sf7/eHo5iqTl0MUTLaVnXQtYkh7+Wf8XrjLnTt6qGcnrXL+s9+sDswje6DmpaLZfku1NScIr2Vrf8cwJGei75Hkxo1dL24s/Huv2MUDf0Cr4pEl23XMfLfmocS6kf+G3OwbT2CYWAbNBGBaJtFYd+d4FyxEnv+VcT36Tude/N2p16o57QtSYwnuSCPhLyi5QK+iNtGXV8/NFF+QDZfZ4dSb8kB0CjgcE4m4/XucLZNnsdDLexX/I2oLvZkpNLYP6jI514T5IPXf4eW+2s18Q+iyeleOoYeTdF8PbraJroAVLNzKK0S6Dxfsx9IwvavczitEuqHvkN9rFvinZM+vHF3hcfz/o5NADy9YQU3xzQq83eZtmFtdO3qYfv3MJZfN2CZtxnDTe2cSa+I8v17Nd3dHV37eugaXXro+JrkRCwOO1kW8xXLS5QHs93G/zatZlvaSRYNvAONoqDVaHioaetSbe9MnT/HqSwMfVqiiQpCGxnk6hGopuY4e9Z5Vf7N+MpW0TmUDh06MGjQIEaNGkXHjhe/wZ+VlcVPP/3ERx99xMMPP8zYsWNL/Drl+gtZWUEX1+zZs3n88ceZPHky3bp144svvmDAgAHExcWVKJNYrei1qPlnf9wN1zZHff4W9N2aoAn2wfzTOtDXvB4BGn8vvD++j8Kpf+M5cbC7wylXtp3HyH1yOmp6Lo2CffD+dAS6ppFYHXYcqopRWzOGuHQOdf54Pty0NTbVccGwxcoclqZtEIbPFw+R/+Zc8iZ8f3ZBgBdKiC9qWi72+BS0MeWbkOtQqzZPtuyATtHQLVSm/QbYlX6KAKPJVVT0zgZVa3aoks5odL476zfFbLe7Li4ArA47vf74kQFR9Xjoj2Pot8Q7e/h8MLJcZgAV7qMoCl5v30vu+Onkjv4STZ1aaIK8se05DhYbHhMHY7q9CwAeT92E/WBykaEphTNXY1u3H3RadB3rY+jdHH3P2Go/JfrFZuM9Y/mJYzz1z3JU4ERebo3qqXMplhW7yfvvLDBb0TYJR39rZyxzNmJZsNW5QoAX2iYR2Pcep/DzJRiubV6thzYrisK5l/xjm7ejXa2wIsO68m1Wxq3/G7PdzqzubSBlK/WWHKBhtoPgl+/EcSAJXaeGOPalk7c/A1rUjB7+J/Pz6DpvBk38g5jbdyhBCVnoGldOnboMcyGvp+/llVvPXuA6UrOx/RuPoV/VnhRKNVuxLN3lHOLqbcK28RCG3s0xDu4AGg2Gvi3RRAahZuRhGTip0mbYe6x5O34/epD7GrVgW9rJC5aX9FxX16oOPl88hHXLYQq/WoZt40Hnd8XvmzHc0BbP524u03eDbdsRNPVDXTdXLpfoAri1XhPaBoeRZ7O6bsrbHA4+3LmJUU1bl3sCLDEvh8lxW8m3Wfn7+BH6Rta98pMuQ9s8CiXUD/PsdeiaR6NrUvRc3PzTOjDp0Xdvcokt1AyVkUPZs2cPb7zxBv3790ev19O+fXvCw8MxmUxkZGQQFxfH7t27ad++Pe+8806pJzdU1DMD48tBeno6b7zxBt98880Vg/7f//5X4TMydurUibZt2zJlyhRXW9OmTRkyZAiTJk264vMTExOJioriyJEjREZWjx4W+R/+ieWvrfj98TSKqejwHTW3kMwbJ2G6qxseD/e9xBZEVWJdsoOCV5yzx2ga1cbz3WFoQv1Izs/jzmV/0NgvgC8u0sW/ukg3F+Cl02PU6jiQlUHTX76+4nP23PoADYtRaLK82A8mYz+WiuJlRNe2LmqeGcfeE+g6N6yQ16sJ9fTKy4qkY9y8ZC4NfP1ZfsOdF0x5X1PNO3KAW5bOIwQdu748hU6jwfP94RX2mROVT3U4nBcly3ah5lvQ1q2F8ab2aEIuX+fE/PUyrEt2Fp0ZTKOgbR2D/rrmGG4t3nCYqmZL6kk6zvuejYOH0Tb4wrqbb23fwH83r77k8prE8ss/FL73JzhU51T3b9yJ4mlEtTtwHE8HhwNNRCBoNJg/XYThzq5oroLJDJLz8/jf5tXszUrng+hWdNm8gOW/ZvNT1yDmRGoY6R/Ns1sKsSzaTtwtLegZdLxGfF4WJx7hjmW/09A3gNXR3TD/52t0PZviMenus/WnKoCqqnT7YyYbTyUxpmlrPu56PWqhlbzRX+LYcxzDfb0wPnRdlSvSbT9yCuvcTVj/2oKaXQCAEhGALTUbn6kPF0leqA4H+a/9hmXJdvz+eAZNOffWP8Nst7luTFf0ua5t+1HMXy/DvuEguq6N8PxgRIm34drWzmPkP/oNmqggPD+9v9T756u9Oxi9djGN/QLZect95d4zd8aB3fgZjNxUp0G5bM88dyP5b8zBeFc3TCN7oQnwRs0tpPDn9RR+vgTT/b2q1XV0YmIiMTExJCQkFDufUdYcSkkUFhYyf/58Vq9ezZEjRygoKCA4OJg2bdrQr18/mje/cNRDSZRrsuuMig66OCwWC56envz888/cfPPNrvZx48axbds2Vq5cecFzzGYzZvPZmTCOHz9ObGwsP/30E7Vq1arwmMuD/VQW+S//grZxOKb7eqHxdmbiHdkFFH69DPuRFLxeug1NQPW+C3xFqkrIssMoVgcn+1fPC0SN2UaTN1ejzzGTFVuLY/e0wmF0/lj+m5/FxMS9eGi0fFWnBWH66tedNsVqZuLxvcQYPHm+dgO0ikKipZB8h/2Sz/HUaIk0VK2pxPWZhYCK1b/8hxQ5VJWv0xLp5R1IQ1PFnIRVZUnWQsYc2020wYNXwxvhW0N6MV5JgcPOmqRkghfuZ+QeM4lDY0nrGs3TiXsJ0xu5NzCCWldJ4k9cnDElF7+dJ/HbeRLPxGwAchoEcnj02V71uqxCbH5V6/vyUvYX5vHwsV18Ed2c+kZPfkw/wRD/ULxP/82fu7xRTf0udKjUXrCfkOXxAKR1iiRxaCyUJKHhUJ1D+mq4M5+Hmf/aeCHaxqFgI0/+dYyeaXYM1zXneKMQ7s8/wheRsTTyrP69YU9ZLeTbrPT5dCseJ3NJ7RzF8Vsrfljh5rwsPk45wnO169PE5A0OlbCFBwhd5hwGmNkyjGN3tUB194gRVcV/WxJB6xPwPpzharb4m0jrFEl6q1CyvvobR1IG+m6N0TaNQM0pxLpmD474UxhH9sTQpWLqIh425/Pc8X2MD61LRy9/gEo51/U8monDoKWwtvPzr8sqpPb8/aRcVw9zyIXXgPbkTNRT2SgeBjR1Q/BIyaP+5I3oCqxkNw7myH1tUXWlS2xuzMvks1NHuckvhFsDapfpfZ20mvkw5QhjakUTZai4ofyWJdsxz90MqooS4IWalQ92B/rrmmMc2qnKJXkv59SpU9x+++3ExcUVKR9lNBoxGi+8fixNDqUqq5ArB5PJxNChQxk6tPzHkhdXamoqdrud0NCid3RCQ0NJTr74rG6TJk3i5ZdfrozwKoy2lh8e/+lLwRdLyHt6JtrG4aCq2PedQDHo8BjTt+YnugCvI5nUXnAAAKu/ifTOldM9uaRUmx1HRh6KXovi51mkR4/DqCP+vjb47zhJ0sBGRU5g23n68WRoXVp4+FTLRBfAcWshxy1m8hx20m0WaumNVS6RdSX69Hzqf74JNAoHx3TE5lu+8c/OSGJm+gn+zEzh1fCGmDQXP6GsiknA8lBbb+KjqFhq643olepzYlFWHhotfSIi8Olq4GR4BmldozlqLmBjfhYaYETQ2Ttz5/cEvNJJNNTcz8vVxBziTcp13qRcVx99ej5+u1KwBJ498ddlm4l9bQWFYT5ktQglq0UohWHecLEJEPIKsa3bj237EVSLHU14AIaesWjruqdu4scpR/g9K4VdBTlMimh81fR0Df9jL7VWHwUgqX9DUq6rd9HjdSl+25IIWR7P4Yc7YPesmXX9FKu9SGIl5+bWfKozcOxUOvWGxaIJ8kZjV4n8YjX09aDWqqPQv+JvsFe0WnoDtdYex+NkLjYvPckDK+cmbnsvP76JaYHuzO+vRiF5YCPMwZ5E/rob/x3J6DMKOHJfW2y+bjwXVRSCVx3FKyELVYHs2BDSOkeR0zjYde7sOf4GLAu3YV2zD+vy3QBom0TgMW4gutiKG70zJ/MkJ20Wvks7TgdPPxRFqZTf3/w6/kUehyyPJ/DfEwRsOUFmqzBOXt8Ac5g39sQ0zLPWYj9w9trYFOBLXV0QukI7eXX8OTq8dakTXQAdvfxp5+mHek5lvn2FeXyacoSHa0XT3MOn2OcuU1OP8U9eJmaHg/ejmpY6pisx9GmFrksjbBsP4cjIQ+NrQte+frW+ho6NLVoG5MUXX+Sll166YL3S5FCqshp/m/z8k6TLDRF69tlnGT9+vOvxmZ5dHTt2rDbDGAHoAY47B2P5fbOzGL0CupsHYLixfYVMm1sl9YDCAk8s3ywnas4eGnVpW6XGV6sFFgq/XYZ57mZnoXNA2ygc462d0UYGoWtf7+zK98DFOuZW92kWegD1jjWlZWAtor2rZ20NR3Imed/uRE3KoMX03XhOebBca+d4pZ5k6rzvyXbYGJe457LrVvbwzoqQZ7Xw8JrFPNGiPe3OmXL7qnX6j7wh0NXhIDSpEbvSUxncor1rlQdXLSStsIAX2nTFW2+gdzGGR0DN+LyIc5xXqtK6eg8FGg0eSTl4JOUQtvggmqggdL2boe8ViyY2EkVRsB9MJmfsN6hZ+ei7t0Lx98S28SCOTzdjuq8XHv+pnGHyXqkn4dgu2rRpQytVZe2iXxnf+Rquqdv4guXVfVjapdhD6pO//RuMT9xA44FtKEk/E7XQQu7bH6KmZNNqziE8PxqJYqxZCS/r0p0UfrIQzw9H4uXtuOTnIcdi4f6UBMg7ScDWJDp16oBhUPtLbLXqmnFgN62DQmgeWAvHyUxyn18GgPf4QXTt19YtMZ0qyCfY5IHSQ8HWO56Cp3/AKyGL5lO34vneMLQNKv53W7XasK2Iw/LnFjxfvQPF15nkt1oDcBw+if6m9viF+nHRW9x9rkO12VEz81FMelcx8orUyd6VF7esZWLLjgRU0syEF2OvVQ+zZjm2VXsI2JZMwPaTaDvWx7L9CNrwSEwfDUPXqg72/Sco+N9PUGBBCfIm7MtHqF0B145vL/qVXYW5/GOA61s2L/a5y/KBd/LWjg182OVaGvhWwjlMxVZcqhSJiYkAF+3ZdTklyaFUZTU22RUcHIxWq70gA5mSknJBpvKM87vzZWc7hwZotVq02upV+FMb5Iv+vmvhPndH4j6e/+kLKVlY/txCwX9noZv6cIXevSkutdBKzrhp2PedwOPmjui7NXaOBZ+9DvOkuaDT4PPtGHRNix9rXEYq7+/YxOc9+rmm3q2Klh4/QovAWoR4OIehDK7b6ArPqNq0EUH4fD6KnIe+wHHkFAVjv8X781HlVvdBe3r4ysWKNp+xJyONe5f/Sb7DVu2+p8734oZ1zDq8l42nktl7x4PoL9GTraZScwvJe/lnPMYNdE19foZWq6V/dH36R5+d6jrPamH24b0U2G38r21X8h02AKb3uoFmgcEXfY2a9HkRl6bt1RzD4v9hXb0H67JdzlkcE9KwTF+FZfoqPF+9A8P1Lch98nv0wb54/zAOTS3nTQfV7sA8YzUFnyzA0CgCQ9+KL0R95rtOq9XQITiM+LsexuecQsbnLq9Jn1vVZncVj9bGRqH//enSXYB7eeDz8X1kPzAF+9YjFL70C14VXNOpMlnX7aPghZ/BZsf25xa09ziTPfuzM1yfjTMmbljB0jxn4W8VKHzrd/R1Q9G1jqnkqEvvQFY6D69djN2h8u/QEdT7cDEUWNC1jsF0Uzu3DKH6/cgB7l+5gBfbdWNs83ZoOzRAN+0Rch+fhuNYKoVvzsPnm/9U2MWwPSEV85xNWP7YjJrhvElsW7Qd053dAND2Keb3lFYLoRVbBmB/ZjqN/J0TaXhotbzduXeFvl5xaJtFY3h/BLZ9Jyj8ehnWZbuwbziIFtDXD8PUrzXkmSmYugwKLOBpRM0tRGvQo1TAd+5XPQfwypZ1PNu6M2lmZ221qT36Uc/Xv0hS8J+TJ0jMy6FZQDD3Lv8TX5ORBQNvL/d4arIzv5k+Pj74+l65c0FpcihVWc34FbwIg8FAu3btWLJkSZH2JUuW0LVrVzdFJSqToih4/u8WZ1HnQiu5j0/DdiQFy+Lt5H/4FwWfLcS65TAVULbussyz12GPS8Tn81F4PnkT+s6NUPy9cBw8/aVic+DIzC/29gptNq7/azZf79vBG1vXV1DUZfdb/D76z/+ZgQt+IcdivvITqgltRCA+U0ahBPlgP5hM7qPf4MgpKNfXaBoQRNvgMBr7BTL70F4a+QXSNjiMtsFhl0yCVUcvte9O7/BoZlx749WX6LLZyX3mB6zLd5P31PeoDscVn+OlN7B56Aje7NiTDrXO1sFYezKRh1YtIjE3x/U5qYmfF3F5Gj9PjDe2w/v9EfgveR6vSXej79MSxceEvmtjrEt34UjORN+7GQVT/8a6fj+q1Yai1WAa0RNd54YU/rCmwuM8npfDI2uc52p7MtLYkprMgewMtqQmu/5V5my8lcW27wTZt76PbcdRV1tZeppoG4Th/d5w0GuxLttFwbt/VPr5TUWwbjlM7lMzwGZH36clHo/2x+d0zcJ7l/9Ju9++K/Jv6fGz+zOgQ0Ow2cmdOANHcqab3kHJeWh1DIiqx7UR0TTZk4512S7QavB8ZojbagUl5OWQZi7gx0NxOE5/rrTRwfh8Owb99S3weu3Ock90qVYblr93kDPmK7Jvfhfz9JWoGXkotXwxPXgthl4VX7espGYejCP256/4cOcmd4dyUbrG4Xi/fS/eXzzkatPU8kVRFBwZuTgy81ACvfH+9D6wObAs2VEhcYR7+fB5j37U8Tk7ucbq5ERuXjyHHWmnaBschtXh4JG1S3h3x0a01bBHUXVV03IoNbZnF8D48eMZNmwY7du3p0uXLkydOpVjx44xevRod4cmKomi0+L91r3kjPoc+/4kcu74AOwqmqgg1EIrhd+uQNssCu93h7nualc0828bMPRthSbUH+vWeOy7Eyn4ZAHYHWibRWKPP4V9Szx0KV6vJ5NOxyddr2dy3FZGx7au2ODLoHlALfyNRhr7B7pmpakptNHB+Ex5kJyHp2Lfe5zcsd/g8+kD5d5F/va/5zE/4TBJ+blM731juW7bXY7kZBFz+mTHz2Bk6Q3lf8Jc1amqSv47v2P75wCY9Hi+cGuxL2hiA4KJDSjai2thQjxHc7PJtlpcbWa7zXWBIq4+ipcRQ5+WGPq0dPUmsm4+hLZRbazrD2DfcRTLnI1g0KEJ80cT5o9qsWHfnYAj34zG09nLSnU4yv1ie/TqRfyTcgJwJi8ux6eGTMxgXb+f3KdnQL6Fgs8W4f35qHL53tO3r4/XK3eQ99yPmH9ejxLii8d97u9VUlq2uERyn/gOzFb03Zvg9eodKFoNDf0C2X/HKHLO+Y4r8jyHgwCjiQYGb3IOT8G6P4ncJ6fj8/XoC2Yqr4oivX2Z03co+TYrytajaCIC0fduVinDBC9lTGwbTFot9zZsVmQ2PY2fJ95v3lNkXeumg+ja1HX1WiwtNauAvP/OArsDFAVdl0YYh3ZE371JmbddUQ5nZ2JXVbamplTpYV9nzk89X78TfQdnsRRtVDC+X/8HR3Y+ukbhKEHeOE5mVlpM29NSyLFaCPVwDpvsWKs2Q2MaEebpRVQ1LXdSXdWkHErNuuI8zx133EFaWhqvvPIKSUlJNG/enPnz51OnTh13hyYqkeJlxGPiYHJHfY4mIhDvd4ejrReKqqrYNh4k75VfyHn0a3xnjEXRF/9PQlVVyDODh8E1VMC26xi2XQmoWfmoWfk4Tv/3zD+vTx/AcTwd3QPXUjhrLebvzs5ooe/bCq8XbiV3wnTsR0+V6D3eUq8xQ+s2qrI/qgCN/APZdPMIor19y33a4apAWy8U78kPkjv6S9Q8M2qhtdyTXf9r25VtaSnc37ilqy3fZi3X16hMH+/azJPrl/PT9YO5+fSQ1qr8Ga4o5h/XYvl1AygKXq/dWaIhzBfzdc8B7M5IZeg5w4R/jd/Pf1Yv4rZ6Vad24dXqQFb6JS/SwZnQaegXWGGv77pIdKig1eAxug/WZbuwLN+NmpaD41gqjmOpZ9c/J0eaO+Yr7EdOoakd4EqKOf/fD214YKkuxr/o0Y+RK+bzdHRz9B8twpGQhhIegOJpwBGfAnodHg/3IbBb0wrdL5XF/Ptm8l//DewOdO3r4fXOsHL93jP0aYkjNZuC9/6k8LNF6FrWQd+u3pWfWMXYD58kd+w3kGdG164eXm/eUyTBUdzPwqlXb2bIrz/wyQkt1xRaq0Wy6wxPnR46NMB39hPg5psViqLwQJMrDxW0rIoj78nv0XVpiPcbd4Nei2XpLuz7nRNl6a+JRdss8sJ6QDY71lV7sMcl4vFofwA0wT4Yh3ZE8fHAMKQD2vCq//f/3zZdaB4YzKA6Dav0+Ywm4HS5DbtapNbsme91R1a+sxddJRZk/7rnAFILC+gXWRdwfuZmXz8YnUbDltTqVxi9OnN3DuXQoUN88sknHD16FLv97MQFv//+e4m3VanJrg0bNnDo0CHuvvtu0tPTyc/Pr/DC72PGjGHMmDEV+hqi6rMu3AYB3vjOeAzl9B1qRVHQd2qI93vDybn3E8zzNqNvHYMjKx9di2gUg/PPw7J0J9Y1e50Jq+yCIgks7A585z7lqq9jXRFH4bQVl4xDzSkAow7HqWw0of5oooJQ/DwxXNcC473OatSOU9mlqi127o/qmuREmgcE4+/GYpg2h4Mn/1nGA41b0jLIOatXzDndlWsiXcPaeE95EE2wb7kWqj+jS2gE8XeNRn9Oz4q5R5yzjn60czPfVaPeXqqqsi8zHZvqYPmJY65k19XGsiqOgg/+AsBj3IByGZYRYDTxWPOiBZkXJ8aTbbUUSTSrqsq/qcm0Cw6r0iflNcmBrHQazf7yiuvtv2NUhSd2dC3rYPnjXzQRgXg+MwSPiYNwJGXiSMrAkZxJ4bQVqNkF4Hk2OeBIykRNzcGemoN957Ei21Nq+eK/4DnX44JPF6LmFp6TEPNHU9sfJciHXLvVVZMr3MuHRf1uJfvuj6DAgtc7D6Fr6TyhdqTmkP/u71hf/gufb6KhGie7VFWlcOrfFH65FADDgNbOXpwluMlWXKa7uuM4lQOqiq5NTLlv///t3Xd8U/X6B/DPyW7STYFCB4Wyy6YUkCFFhgJCBRSkgAgyvBfHVa7j+pOhXvW6rl43WEAQFQRERYbIkL0pUGYZtbTQVuhOs3N+f4QGQoHOjKaf9+uVF+3JyTlPQ05y8pzn+3xdQfe/9RALSmzV9x88BkFVtYb7r6YlIyVQgpda+GGPh0/S9I/dm6GRy/FK557wkd34e6v6tzuLKIr4+mwKGmt8Meh6YsLOKgIKGcy7z6Lw0Q9hLTYARTpIIkNs/WkXbrUled9OhCRQA8vlXBjXHIDhp4MQrxUBABTDYyGNtFUsq19McPFfVzmiKOK786fwSLPWkEkkEAQBCVGefz4jaRAAWWwzGL7dCcXA9mXehwzf7wJEW/LcZTEJAu6PcEzMe3IfYm/nzhxKQkICZs6ciTFjxkBSzdeAy5Jdc+fOxeHDh3H69GmMGzcOOp0OY8eOxc6dzu8HQWTcfBzK4bH2RJf1WhGKn1oIsciWvAIA3dtrUNpp6eYEluV0Joy/HLrjtsXCG/2ZpK0bQz6gPQR/NSQBaggBagiBtn8lAWpImzaAon97GH8+CP8V/4DqkZ4O2zIduQjr+Wwonqr69B9Jp49i+o6NGBIRjTWDR7qtiuq1w7vwv5RDWH3xLM6Omepw4ubNZC0bO/xu2nvWVs5fQzNjKW5pFHq+MB8AEK7xsy8TRRFWUYTUg08SBEHAR/cMQN9GEXikjlYbmc9ctg3REEUoHoqDMtF5c6wuvHcInmjdEYVGAxacPgoAOPhXFuLWLEH74PpIHvW4V1ZceprSiq47TTpROoHA3Sq/aori/o7QfbIBJfN+sCUTfFWQhgVDGhYMw9pDsKb9BfUrIx0SoX6L/mZPhlmv5MOaXZocK4BQzzHBb9x0DNbM3DL7/S1KiSfj1fhp5Dj0aWSbM033+W+wXsiB5p1ESG96D5WE+EHzxlgUjvkQ+m92wPetcU56NpxLNFtQ8u/V9nMJ1eP9oPrbYKcmmX2eur9WJ7HVr4+B7sNf4fP0EAiau88adjdf9hkMhVSKf3fra38+rFn5kIQG1lCkNeN0/jV8lHIQIoCBYVHo+vFOyLo0hXJkd4+baGDRmeOYsn09GvpokPLwZISobiQRFf1iIPlqOoqfWgTrlXxALoX6rXFQDuwA0WKFaedp24iKKV9A0jjINnz/etWaUM/X4Vy9NnhuzxZ8mHIQmzPT8FXfB2rVMefz5GAUzZiP4qcXwefJQZC2j4SYU2gbebJ0O1ST451y4ZaoPBqNBtOnT6+Rbbks2bVmzRocOXIEXbrYZlAJCwtDUVGRq3ZPdZxYYrgx05TBhKJp82G9zVBB4XqCCkazfZmsR0uofJQ3klcBPteTVxrb7zddcVMM6ADFgLtfBVFO7AvjpE9RPGsp1LMehLRJfYhWK8y7z0L72kpI24ZDVsF+XbfTqV5DyCQSBCqVMFktbuuP9Y/23bDh0kX8q3OPOpPoupXh54MoeX0V5L1bQfPO+Cpfwb9bc+bRTVthTVoqhja5MUvfxoyLeGrX73i5Uw9Mbu26q3LluVRciK/PpuCVzj0hCAJkEgnGRLdxd1huIwnxgzS6IQSNCuoXR9TYSfKdXi9qmQx/FhXYfz9TkAuNTI6YoBCHRNcPF06jR4PG7JHhRKWTTriToFLA970JKHpmEQqG/weKQR0hBKph2psKS8olKEbEQpHQzeExkmBf25efmIhyt6+aHA9rRq4tMZZ1PUGWU4hvWsiQJwe+PJVsT3YZVtgmV9G+sMwWW5AG0uiGUCbEQX5fOygf7Ardl5s8ugfOXQkCxGI9IBGgfjEBylHdXbDLm6o4DSaUvL8Wqgl9II24/UytnkA0me2fkxI/H2heHV3tbfoplFjcb6ht+2YLdB+tw6WNB9D8s7+5tQfWrVoFBGPlwATszb6Mnifyod10DKY/TkJ+TytIwzyrovHR5m3w8YlDGBPdBkGKsiMIZG3CIe0YCfPus4DJgpI5KyBYRSgGd4Ti3rYwj4yDYdE2+3m4rHsLKB+Kg7xfW4/txXUnfRtF4NOTh9G9QeNa994k69gEvh89jpI3f0TR5M8BQbAlHtUKqKYPgGpKf5fGc7dzXW+cpITu7OWXX8aLL76IAQMGQKm8kfzu27dvpbflsm/BpYGWvhHk5+fXujcFqr2kTerDfOgCMLYXBKUcvu9PhDXjmi2xpVGgaPpXUAxsD80LI8o8Vt61WY32u5C1aATfDx6D9pXvUTjqfVtpt1YP8VoxZJ2jbEmRalzF61o/FMmjHkergGCXH2NGi8VeeRSkVGFvwoQ6XS0iaRQIKKQw7TgN7b++s00FX4kTuZtnnCpP/ZuurM4/dRTnCvOQkle53m/OpDUZcc9P3yBDWwQ/uQLPtI8t/0FeTlLPD35fTAOuNwyvrsq8XvzkCoxvEYOHolog/6bZUf/SleDRzT/DKopIG/ckIpnwchqjxYJ/7NmMIpMR/+zYHe2D67s8BlmnKPh//ywMK/bAtO0ERIMJ0uah0Lw/EfK+bar1GaIc0a3MMtFswffZefjobDL+ea+tcbooihA0Stg7EumMEPO0MB+8APPBC5B+Gw7lyO6AyXK9z1jt+0wRpBJoXh8Lc0o65LHR5T+ghuk+XAfj6n0w70uF38InIannV/6DXEws1qPoyQVQPNAZqnG9nbafHZfTMWqEGvP+9zX+8dpMSAI1TttXZQiCgJFNW+Gh+k1QMPp9AIDqsXs9LtEFAD4yOQ489Ngdh5iVXsBVPR4Py+lMmLafgvaV7yAJC4asXQRUk/vDsHQHJE3rw/fdCfaRFLXRQ01bInXMNIdZBWsTeVxz+K9+HuaDF2z9Ev1UkPdqXa1qysqq7LkLeb+NGzdi27ZtOHfunH0YoyAInp3sevLJJzFmzBhcvXoVb7zxBpYvX44XX3zRVbunOk45sjtK3v0ZpoPnIY+NhjSqPqRRti8WukVbgbxiqEY6/0prKXn3Fgj49SVb084zmRAUcsj7tIa0fWSNJKhaBzqeOGhNRmic/AGRWpCLIetX4v0e8Rge1QIA6nSiCwDk3ZrD972JKH7ua5i2noB29gr7bFIVUd6MU6VubWi9NH4oFp9tgmGRN75UnS/MwxuHd+P5DnFo54Yv1Rq5Ai907I4Fp48i4frroy4SLVaYD12APM42+5GtMrRmKh+r8nrRyBUO7w1/6UvQKzQcJWaTQ6Jr4eljUMtkGB7VwtY0mSrFZLVgf84VHLqpya5CKsWGSxdxoSgfj7dq77bYpI2CoH5mCPDMEKdsXxRF7M7ORK/QcAgyKTRhIfhX2AD7/YIgQPlQHPTLdiBg3b8AixXWK3kw7TgFw6p9kPeLgWnvWUiaNgBEEebj6ZC2i/D4C6aWC9kw/HIIPk/bhjYJKrlbEl0AoJrSH6bdZ2DNzEXxs4vh9+U0jxoqJuqMKHpmESynMqHPLoBiaBdInNBfS5BJsXVES2hPH8ZvfkY88dI38P/kCbdWE+XqddDI5fYqfN383yFeLYIkvB5Uj/VzW1zluTnRZRVFXNPrUP/6LHowWQCTBZKwYKim9LfNOG4wQ9bOVskp8VFA2qkJJEG+tS7RVWwy4l/7t+O12N72vri1NdFVSpBIbOck189LXK2q57rkvf744w+cOHGiRj7nXZbsSkxMRPfu3bF582aIoojvv/8eMTHVb8RLVBGKEbEwbklB8dOLoBwRC/m9bSGWGGH89TBMf5yEakp/l5ezC0o5lEM6A0M6O20fJqsFz+/Zij+upGNPwgSnfkn9/OQRnCvMw/8d3IGhkdEe3S/KleQ9W0Lzn0Ro//kNTL8dRYlCamtKXMHnpyof7Bq5An+P6eKw7MPjB7H4bAqydSVY98DDld5mVYiiCL3FbB/G+lS7rpjauiNUMq+eCPiudB+vh+GbHVDNGAifJ+6r8e1X90SwbVAI/nhwHPTmG0O5LVYrXjmwHVk6LX4aNNKezKY7u7Vv3rr0C0j4bTWa3FIpN7drL2SWFCO+sXfOEi2KIp7e/Ts+OXEYX/W9/46zuSkT4qBfuBX6TzbA58URkAWoIWsdBtXj8TDtPgv9l5vg84+hMG07Ce1LyyBtHQblmJ62oZc11A+xJpkOnod21lJbg/56flCNd14/voqQhPjB9+PJKJr8OSynMlH84jL4/vcxjxgyJhrNKP7nUliO/gnBVwXfjyc7JdFV6q0+AxANFYZ+twnWoovQfbAW6ttU9bvKtB0bcDz3KpbED0XXfMCwfDcAQP3P4R7XmP52rpQUY+LWtbiq12FvwgRb0k4hgyQ0EObDF6Ac2gXqZ4faZjC/TiwxwHIqE3InVvA5S+KWX/Dzn+dwrjDPZedSdQGTWHSzuLg4nD9/Hs2bVz8B65JvHFarFd26dUNycjLatPG+/iwWiwUmk8ndYXgUuVwOqdT9J1GlBLkMvv99DPrF22BYvR+GH/YCAKTNQ6Ge+zAUQ7uUs4Xa6apeh+UXTiFHV4L16Rcwqlkrp+3rne7xkAkSzOoYx0TXLRR92wJvjYP25W9hXHsYkMug/tdDLq1MmNAiBtk6LWa0uZFc1ZqMWHnxDMZGt6nx3m4mqwUzd27C2YI8bBzyiH14a11OdBlW7YPhmx0A4PFXs2/+f9JbzJjSugM2XLroMFPSstQT2JWdiamtO6JzSMMy20gtyK2TV2rfP7YfH6ccwsudemJ6204AgH6NI9HAR40WAUH4s7jQvu6Elu0cHlv6fM07tAtL44fBX+E51TdVIQgCfKQyCAD0N00ffitJwwCoXxiBkrd+hPl0JpQPxkLwU8G04zSMvx219fQZ3cM2Q5hCBsvpTJTMWwndh+ugfCgOytE9PKbhuHHjUWjnrgBMFkg7NIFimGecX0gjQ+D74SQUzZgP856zKHl9FdRzH3ZrhZxotkD7yne2JuUqOXw/ehyyVo3Lf2A1CIKAqX37wDgnGNpZS2FYsQdbmyhx/8ODXH7ukqPTYldWJv7Sl0AlSFDy9o+AxQp5/3aQ93Le+VpNEkURR67moMRswuGr2ejZMAyCIEDxUBz0C7dAOaoHZG3D7a8zURShW7AZ0BlvO9TZ083r2hspuVcxt2svd4dC5LWOHDmCdu3aoVWrVlAqlfZ+nfv376/0tgTx5lS7E02bNg3PPPNMrarmysjIQEREBC5duoTw8PAy94uiiKysLOTn57s+uFogMDAQoaGeN6W9aLbAmpUPQS6D0MDf4+KraduvXEK+Qe+Uaozkq9noWK+B1z+HNcW48Si0r34PZWIf+9AWd/ok5RCe2v07+oSGY/vwxBrd9tn8XMT++DWKTUZsGPJI2enJ6xjT3lQUP7MIsFidVtXlan1+XoadWRl4t3s/zOpoGwZeekKSWpCLlssXVGg7Z8dMrZUJL4vVih1ZGdhy+U/M7tLLPqzn9cO7MPvgTjzcrBVWDEiwry+KIo5cy0bX1V/fcTbGRaeP45OThxGh8cMZL5nFVhRF7Mu5jB4Nw8pd17T3LPRLtsO8/xwAQBIWDOXoHlCOvcfeuNyar4VxzQEYVu6FNSvf9kCJAPm9baF5fazbqmFEUYRhyXbbkC0A8v7toHltjMdV55h2nkbx80ts70WT4+Hzt8FuiUO0WlEybyWMvx4G5FL4fjgJ8u6urRrVfbUZSVv+wFP9NBjduBm+GzLqjn2onKXAaMDWy39iWJEKRVM+B5RyBPzwnMckbytiS+afaKzxdWihIeqMKHpyASzns6EcGQd5r9YQC3Uw/HQA5j1n4fPsEKjGV77/jjvcOjGGyWqBXOI5F/SJ7qa8fIYn+vPPP2+7vEmTylfBu+wS+/79+9G5c2e0bNkSarW6Whk6T1Ga6GrQoAHUarXbv7h6ClEUUVJSgpycHABAo0aN3ByRI0Em9fiqiprUt1H5s2ZVxWcnDmPmrk14O64fXujkun5ntZlicEdIoupD2rKRR7xfaORyhGl8MfamGRFFUcSfxYWIqmYPipaBwVgxYARMVmudT3RZLmSj+MVvAIvV1nzZxTMcOcurXe7BstSTDjNqbr2cjmd2/44hkbYKsDsldQDb7Erjt64tt/rLU4iiiFyDHvVUPvZlIzf9iDyDHg9ENEPP68mcxOYxiA0JLfPeKwhChRvxvtsj3iHRlaPTooGPZzTSLk++QY8vTiXjhY7dIREECIJQoUQXAMh7tIS8R0uIehNgtgAaZZn3SkmgBqpJ/aAc38fW12vFHpgPnIf1WpFDYkmsoYkfKkK0WKF77xcYfrDNKql8tBd8nh1arclmnEXeuzXUr4yE7sNfqzXzc3WZd5+1JbqkEmjeGufyRBdg62UWdC0dcvyFqJAQSN3wuRygUCIhyvb/4LdkJqx/Xq1ViS4A6B9W9guo4KOA32dPQPfVFhh/OgDDsp0AAGnrMGjeHlfuzOWe4nT+NUzc+iuW9R9mvyjDRBeRc1UlqXUnLkt2/fTTT/afc3NzERxc+67i3sxisdgTXfXq1Z3ESUX5+Ni+DOTk5KBBgwYeNaSxLiswGvDcns14LbYPwjTVm41JZzFDBPBncUHtnQ7eDW4eoiEaTDD+fhxKNw2jfbxVByQ2j4F4Yx40/J6ZhsHrVmB8ixgsiR/msH55w9KO5/6FxmpfDLye3Lp5yFtdZc0tRvGziwGtAbJOUVC/OsprjpVB4U3LJDK/O38SKXlX0SIgCADQJqgeuoSEosBoQIAbh+SV99otbzjl9iuXMHbzz2ji6489CRMAAFKJBI9Gt0Gh0ejQD7GZfyCa+QfedjsVacR7ayw/XjyLCVvX4u24ezGzXdc7Ps4TWKxWDFy3HAf/ykKR0Yh/x1WtcqMiEzcIMikU8e2giG8Hy/lsiLobz6k1X4vCsR9CMagjlI/0dPoFLkvqFRh+3A8IAnz+MdSpswnWBOXwWMj7tnHrTITy3q1tCcFgXyj6uWfUhyAISHzxcXTM/QsxQSEue28+nX8N2SVa3Ns40mG5rHUY0LpiiWFPlVqQiyVnU/BabB8IaiXUTz8AnxkDYc0pgKCUQwjxq1WfgU/v+h0H/rqCp3f/jvUPPOLucIi82oQJE7B06VJ069bN4X2iOkVSLkt2BQYGYtmyZUhKSsLRo0dhvqn5bW1U2qNLrXZeE83arvS5MZlMTHZ5iCl/rMOqi2dxobAAW4aNrdYJx/Md4tAhuD4GhEXVqhMXTyFarCh+bgnM+1JhvZLntmFtiluOzV1ZmRABBCpUDsvP5l9DqxVfVWib+xMmoFsD5/Zd8UTWnAIYNx6FNbcYkvr+UAzuCNPO07BezoMkvB40702AoPDunmXvdo9HzwZhUEql+DEtFQCQXlyI5t9/icTmbbGo31D7utuvXAJgq1oqZbFaAaBGe+dUdEhl6XDK3VkZ+DEtFfdHNMV9YVEAgCi/AFwpKUaRyQid2WSvuvq096BKx1PZIZvLz5+C1mzClRJt+Su7mVQiwTPtYvHCvm14JLq16/Yb7dgzzvjbMYhXi2D4dicM3+2CvFcrKMfcA1n35hWeHKQyZK3DoJ4zGoJcBsUA982sWRk3J7rMqVcgFush7+z8KtybK+7c3bi/VOkMxdasfOhW78X73f0wvU0nNFTXfDLQKoqYtn0DdmRl4JNeAzFdHmobcdCsbN/D2qbQaED3NUuRZ9CjmX8gHm9lq94SFLJaO6JiafwwPLtnMz7uNaD8lYmoWt555x0AwMqVK+3Lqlsk5fSz7i1btmDhwoVYvXo1/Pz80Lt3byQnJzt7ty7DL/l3xufG8/ynez+kFuThne79Kv3/ozeb8VHKQTzXoZu9hHtgHR+eVh2CVAJ5jxYw70uF/otNEBQyqCbe6+6wMDe2Nx6Jbu1QhXOxMB8Dfl0OAFjabyjaBoeUedxVvQ7jNv+CawYdDNY7N6L2RqIoQv/pRuiXbgdkUkgaBti+NP1vPVRP9If69TGQtQl3axWFqwQqVZjcugMOX82yL/s9Iw0mqxVnC/Ic1v3q9FEAwOn8XNwfEQ0A2J51Cfet/R5xDRphb8JE+7pvHtmDS8WFmNamk70Zfr5Bj6PXctDAR402QWVfk6VKq6huN6TSKorYlJGGfx3Ybl9vxYXT+CjlEAqNBnuyK9LXH9sfHIdu9Ru5fJKFb+8bjqGR0Xi42Y3k0TW9DmqZzCN7eo1vEYMRTZrDz42VfMrR3SFtHAT9it0w7z4L087TMO08DUlkCJSP9IRyeCwEdfXis6RfBaxWSKMa2Pb5gPNmVnYm85nLKJr2pW2Y7VcznDoztWHlXhg3JMP3w0kQfFXlP8CFRL0JhVM+x1vhBrwj88HyC6dwZOTjZS4IVZfRYkFMUAiOXMvBsLCmKPn7UljOZUHz77G1ZmjfnfgrlHipYw+sv3QBA66/d9ZGuXodgq8PV2+o1uC7+4a7OSKiuqG09VFNFkk5pZlARkYG3njjDURHR2P48OEQRRErV67E5cuXMW/ePGfskogqINo/CEdGPY5uDSrXR00URTz8+xq8tP8PPLnjNydFV/eoJvSF6klbZYjuf+uh/26nmyOyaRsU4jDM9YtTybikLbLdFxyCLiGh6BISis71Gtp/HhTeFEvjbVU7ag/8Au5M+qQt0C/eBtXU+xC48RUErJ4F/7UvQTWhL/RfbIJYUAJpVH13h+k2k1t3wJlHpmJ2l3sclrcLsj0nDXxuVEjn6vUQAcgEx9OTNWmp+OJUMjKuvw4B4PDVbPRb+x1GbVrjsG7ill8Q88NXWJ9+3mH5zqwMHLv2l/012yG4AR767Uf868B2h/USolri8ZbtMeKWST36NIpwy2yiEkHAhJbtHPY9dfsGdFi5CPtyLrs8nlvtzc7EsA0rob1paKY7E10AIEgkkPduDb//TYb/quehHHsPoFHCmn4Vug/XQSypXp848/F0FE3+HMVPLYT1amH5D/Bg0ib1IW0eCrFYj6KnF95o+l/DDOuPoOQ/P8GcnAbDuiNO2Ud1CCo5fKbeh4dTjYgstOB5RXiNJ7oA20y3n/cZjAtjp6PhhpOwnLkMQaOErKt3DPuf1TEOvw8dgwhff3eHUiUrL5xG1HdfYHNmmrtDIapztmzZgvHjx6NRo0aYN28eoqKiUN25FGv8rG3IkCHYunUr+vfvj9deew0JCQnQaG5czWa1D5F7SW46Bi8VFyLfaED74Lt/ERcEAU+27Yxd2ZkY17yts0OsU3ym9AdMZui/2gLd+2shyGVQju7h7rAczO3aCzJBwJvJe+3LtCYjAhd/hE1Dx6Df9b4jzhjy4enEEgP0S7dDmdjbPhTVsPYQDCv3wve9ibDma6FfuBXKUd3ts8nVRS0Dg9Ey0LEM/YVO3bH8wmmHGbxGRLVA1viZMN1SHTgzpgsuFOaj7U2VWYIAtAoIRnP/IId1UwvycDLvGsy3nCB9cSoZ27MuYVIr2zAzmUSCFgFByDXoUHLTVcN+jSPtr2lPlF2ixb6cy8jRlbg9sWywmPHI7z/hkrYI8w7twjs94t0az+1Im9SHetZw+Dw5GIZ1hyHmFEISciOZr/tkA6TtIiDv08ahobw1rxiGNbaZ40SzBbK24VCO7gFL2l/QvvIdYDBD0igMQO0+rxVUcvh+8BiKnvgC1os5KHp6IfwWzIAkoObadBi3nUDJ3B8AUbRV1j3sWZ9xpZQJcWibmoU9P+yGj2I3LDGdnFbpVk9rRsHntouHPn+/H5IgX6fsx9UkgmB7c4ZtCPmfRQX2Kqlbldcr0dVEUcSKC6dRZDJiWepJe2UvETlPRkYGFi9ejEWLFiE7OxsjRozAypUrMXjwYJw6dQpr1qyp1vZr/Mx7w4YNGDduHJ599lnExsbW9OaJqIYcvpqF+9f9ALVMhkMjJznMMHY7QyKjcfHRGW5tMu2tVNMHQjRaYFjyB0reXgOo5FAO85xG1D4yOUY1a+WQ7FqSegJm0YpJ237F2THTnHIFvDYw7U0FtAYoH7kH1rxiGNcehu7TjYDZAuPaQ1A+3APGH/fDfCQN8rjm7g7X48kkktsmTSe2bFdmWXzjJjg9ZmqZ5V/3G4IsnbZMEn9kVMsywxiXxg9DhrYQPdZ8U83IXaehWoOTjzyB7VcuOfyNFwrz0dQvwKUXFZVSGVYMGIF3j+7HnK69XLbfqhA0Sqge7umwzHIhG/rF2wAAksZBUI7uAcWIbrCm/YXiZxdBNJohv6cVJD4KGDcehWH5bpTO5yHr1Qq+b42r9nBITyAJUMPv48konPwZrBdyoH1+CXw/meIwu2VVmfafg/blb22z0Q7tAp9ZD3r0hW+ffwyF5WIOzPvPofi5ryFZOB1jD2zCS516oE81ZrfemZWBJWdT8HbcvQhW+aDkv78CWgOkMRFQJHSrwb/AM1S0z2dpr0RPIAgClsYPQ6+G4ZgZ456Jg4jqElcUSdV4smvXrl1YuHAh+vfvj0aNGiExMRHjxo1D8+Y8yfc0u3fvRq9evTB48GBs2LDB3eGQizXzC0SAQgm5RIK9OZlopHa8qng2PxcfHD+AT3sNtDcbZ6LLOQRBgM9T9wMmM4y/HIK0iecPeevZoDH+1aknElu0rbOJLgCwFukAALoP1sK08zRgsTVYl9/XHsqJfSEW2u4XtXq3xehOp/KuVem+6mgTFHLbHl6vdOmJLiGOVRphGj9k6zy/8futAhRKPNjkxnlVprYInVYtQp/QcCzr/yAClc7th1RoNMD/+udBj4ZhWDXoIafuz1kEfx8oJ94L408HYL2cB93/1kP35SbAKkIaHQrfTyZDEqiBaLVC99E6GJbZhprL+7aB5p3x9kbr3kASGgi//01G0RNfwJycBu2r30PzdqJDtVtlmY/9ieLnlwAmC+TxMbbZaJ0wQUBNEmRSaN58FEWPfQprZi5e//RrrGukR/K1HJwfO71KQ5mtoognd2xESt5VqGUyvCuLgmnjUUAiQP3SiGo9x56q2Gyy//xIs1Z4sZNjNd+pvGsYv3XtXWemrUl3mpFXFEUkX8tB30YRaBEQDKVUhmfas1iDyBVcUSRV48munj17omfPnvjoo4/w/fffY+HChZg3bx66deuGxMRExMS4Z3phT2U5lwXD+iMQc4shCQ2EYlhXSMNcc4Vj4cKFePTRR7Fq1Sqkp6cjMtJzh21QzQtUqvBln0G479flGLZh1R3Xi1uz1KOuvHkrQRDg89wwKB/tBWljz32uxRIjdF9tRtNfDmFWVj6EwO0oeaAzVON6O6kLpGczfGv78mv64yQAQNo2HMrhsVAkdIMgkcB06IJteaTnJzBrkp9cAQAYv3VthdelqtuTnQmDxYJrBp1Tn09RFPFW8l58eSoZO4cn1tq+PKUkIf5QP/0AfKbdB+OGozCs2A3L2SsAAMvpTFhSLkHSuzX0i/+wJ7qEIA1EieBVia5S0uah0Lw/EcUzkyBqDYDRDPhU7fUkmi3Qzv0B0Bkh69ECmn8/WmueM0mgBr4fPIbCxz/FP1NFpHeLxrNdelS5Z59EEPB5n8F49cAOvNq+O0omzQcAKEf3gKxNeE2G7nFej+2NVzrf49ZqvsrOyEtEruGKIimnNRBRq9WYPHkyJk+ejDNnziApKQlvvvkmsrOzPbp82VVEswUlb6yCce1hCMG+kDQOgmnbCei/2gLlxL7wmXm/U58nrVaL5cuXY/PmzcjLy8PixYsxe/Zsp+2PPFPp1f/SWcp0ZjN8rp/MnS/Mw3+S9+HQ1WyXXXmr6wRBcEh0mU9lwHq1CIo+bdwYlaPieT9Afzofivs7QtYmHJb0qzD+fBDG9UdgeXeUu8NzKrFYD+OWFCge6GTvv6W4rx30aX9BCFTD97+PQdY2wmF9/VebIe3QBNLo2j+tfGW0CAjG2TFTy33vcGXPljtVkjmrwsyVRjdrjZigENt7yPXKGaso4mxBrkNPtOrSmk1YcjYF6cWF+DHtLJ5u5x0VEIJKAWVCNyhGxKLo8c9gvZIHCAJk3WyzhCpHd4fp92NQju8L65U86Jf84eaInUfetRn8vpgGaZswCIqqf00QZFL4vjcBuvm/QzPn4Wptyx2k0Q3h99kTkEaHYsUtCb8io6HSkzD0Dg3H1gcfhWi2wPBQNxhW7YPP3wbXZMgeaUhktP37zKIzx1BgNOChqJb2+7UmE07lXUWw0sdpfT/vNiPv5yeP4KvTxxzWIyLXcEWRlEs+eVq1aoV33nkHb731Fn755RcsXLjQFbv1aLoPf4Vxw1Go/28kFMO6QpBJIeqN0H+3C/pPN0ISpIFqfF+n7X/58uUIDQ1FXFwcEhMT8eqrr+LVV19lIrKOahNUDydyr+LxP9Yh5eEpaB1YD11CQhHtH4Suq792d3h1kiUtB0UzFgBGM4T3J0LWowXMhy/CmpkLIUANeY8WEFSur4g5Az3Unz96U1IuCtYRMdC+uRqpX28EWrk8JKcSRRHmwxdtCb3NxwG9CYKfCop4Ww8p1fi+kMU1R/FTi6CdvQKqsb0gadoAltQrMHy/G2K+Fr4Lprv5r3APT7lCXtEqs9peYXbr0M0vTyXj6V2/Y07XXvi/W2bCrCpfuQKbho7BuvQLmN62U41s05MIggDBzweyYF9o3hwHQWnrWSXxV8NvyUwIMqkt0WWt3uxQnk7WsYn9Z1EUYT2fXeFG7aLVah+qKG3WEL5vJzolRleQtXMc8WAt0iFTMKHPz8vwZNvOZYbm3UoURWjNJvje9N4iyKRQje8L5dhetabSrab89/hBHM/9C+2C6iNYZbvYevhqNp7dsxmx9UNx4KHH7OuO3vQjTufn4uNeAxDf2PZ6TC3IxZenktHULxB/v6mn1pGr2TBYzGgVWA9BdxnC3SaoXpmh7PP73I/eoeGYtG1dTf6pRFQJziyScullFqlUioSEBCQkJLhytx7Hmq+FYdU+qKYNgDIhzr5cUCng83g8rJfzbLN7jbnHabN3JSUlITHRdgKSkJCA6dOnY/PmzRgwYIBT9keezSqKWHjmOJRSGQqNBneHQwAk4fUg79kSpt+Po/i5JbahM3/dmOJe8PeBanJ/KBN7uyRJrSm29d+Y3l0G7P2l7ArdAcA2o11tTxoAgDUrH4a1h2D85RCsmbn25ZIox+GIgloJeaem8EuaAf0Xm1Dyzk+2L8JSCeT9YuAzYyCkTRu4Ony6SUWqzDxtVrCacCDnCsyitdq9FvVmM1Ly/kJs/UYAgAhff69MdJWSdYiEfsl2wGwBcKNBe2liwrj1BGQdmtzh0d5FtFqh+++vMKzYA9/3J0Leu/Vd17deLULxM4vg88wQr5qQQxRF6JO2wLBiD1bO6Ys/iwux8Mxx/D2mi0Mi61Y/pp3F33Zuwkf33Icx0W0gmi3211FdS3QBwLDIaMQEhSDC1w/a6z29LKIVQUoVghSOSarUgjycyLsKs9XqsOz9YwfQJaShQ7Lrmd2/Y0dWBn4YMAKjm9leo7uzMvDA+h/QoV4DfHTPffZ1/5O8F2lFBfik10BIJRIIglDujORE5Do1XSRVu2qKvYRp1xnAZIFyZNxt71eO6m6bvet4OuRdmtX4/s+cOYPdu3dj0aJFAABfX1+MGDECCxcuLJPsslgskNbh5tN1hUQQsHzAcMzctQnHc/9C3PWG9OQ+gkwKzRtjUXS1CJbkNIjXiuDzwnAoR3aH9UoeDN/uhO7DXwGzBapJ/aq0D1EUIRbrIRaUAKIIacSNyhDdgt8h5hbDml8CsaAEoZdzcaCwAMUama2vyysjb6ybtAVQyGDaeAzBo++p9UkDy5U8FA5/BxCvV29olFAM6gjlg10hbR952+SirGVj+H7wGKz5Woj5JRCCNZD4q10cOd1JbX9NVsXCfkMwrnlbxDe+UZ2SVlQAf7kCweXMvluqyGjA8I2rsf+vK9g8dAx6NAxzVrgeQ/lQHPSLt0H779XQvPaI/aKjKIowLNsJy/F0aD6Y6OYoXUQQbJNsWKwofmkZ/L6YBlm7289IaC0oQfHMJFjOZaHknZ/g//2z3pPQMZph2nYSYm4xpnxyGIoX++PB5q3umugCbEPksnVapOT+hYcyROj+tx7ql0ZA3s17EoGV8WbcvfafD1/NAgD0axyJ5zqU/T60NH4Yrup16FTvxsWiJr7+eKFjd4T6OA53DFVrEOUXgBDVjc/cAqMBhSYjtCaTw7q/pp/HjqwMKKVSfHgPL/ATeaqaKpJisssd9CZAECD43/5kUxKoubGeEyQlJaFbt25o2fLGmPnExESMGjUKeXl5KCgowIgRIxAXF4d9+/bhwIED+Pbbb/H5559Dr9cjISEBr732mlNiI/dp4KPBigEJ7g6DbiLIpBAtVsBXBRTroftkA2StwyDr0ATqFxMApRy6rzZDMao7BB8FxOuJKWuBFmJBia3qqHsL+/aKX/gG1mtFEAts65V+iQEAWddm8Ptymn1dw4o9EPMcZ6mLBoBCI6S+RvhfHwogGs3IX3MCMFlsK32xHfmrkyFr3wSy9pGQxTaDrLXnfkEWRRGWM5dhSb0C5YO2/kPSRkGQtg2HoJJD8WBXKO5rD6GCTZolgRog0Dl9R4gqa0B4lP1nqygiccsvOFeYhxX3jcC9jSPvOENZKaVECqVUCqkgQG+xuCBi95OE+EPz+lho//UdCka8A8XgThBUcpj+OAnL2StQTeoHRd+27g7TJQRBgPrVUbDmFsO85yyKn10M36QZQJEO5oO2yTdknZtCEt0QxU8vguVcFoQQP/h+OMl7El0ABKUcvu9NQOFjn8CSmoXHvj0Dzdtd7ff/lJaKULUG8ltmmnw9tg861muAh8OioZu+FNYr+TDvP1dnk12V0aFe2YromOD6+E/3fmWW3+7ctV/jSJwdMxXW60NJS/2tbWcMi2yOoZE1X0xARJ6HyS43kEY3BEQR5gPnHb6IljLtTQUEARInDH0xm81YsmQJXnrpJYflgwcPhp+fH5YtW4Zhw4bhxIkTWLp0KRYsWICTJ09i3bp12LNnDwRBwIgRI7Bnzx707NmzxuMjohssGddgPZ4O9euPwPjLYZj3n0PJOz/Db/40CGollIm9YfhmBwoG/9s2a9YtZF2bObzHmI9cLJPAAgCo5MAtX0yUD/cELFYIgWoIARoIagW0c1ZAER8D9azhN1YURahnj7ZVox6+CEgEiDmFMG0+DtPm45DHx8D33QnXVxVh2noCsrbhkIQG1shzVFXWfC2M64/A+PNBWFKzAKUM8n4xkPjZLkL4fTkNgkpezlaIao+skmLkGfQoMZvRzD+wwjOUJY+aBIkgqVNDfRT920H6zVPQL98N429HAbMVsrbh8Hn6Ach7tCx/A15EkEnh+59EFE2fD8upTBQ9+iFgtACa68NjP9kAqJVAiQFCgBp+nz4BaXjNTYrgKSShgfB9ZwKKps+HaUsK9Elb4DN1AH44fwqPbP75ro99/9gBHNAWokWjQKim9HdRxJ7BXROD+Mjk9ore0ioyABjbvG4kqonIhskuN5B2bAJp81BblUZMBATfG+PUrVcLoV+4BbJ7WkLaKKjG97127VpkZ2ejXbt2SElJcbivT58+SEpKwrBhw9CyZUt06NABALB582bs2bMHXbvarmIVFxfj/PnzTHZ5EW+epaw2E3OLAQCyFo2heL8dip9eaKsKVdu+ZEjrBwACbiS6rleMCgFqCIHqMjMAqmcNB6SS6wksNSQBtn9LmzDfzGda2fJ+y/g+0C/YDFmnKCgejIUglQAKGST+aphPZ0LxQCeo/zUS5pMZsBxPh/nYnw5fDK2ZudC+8I0t1Ab+N6q/OkRC2rrqM39Zs/JhvVoIIdjXYTbLW4lmC8x7U2H4+SBM209d78kDQCGD/N4YQGsArie7mOgib9NY44cjoybh6LUcRPj6278Azu3SC0OaNINUsFWlXCwqwLFrOWgZEIzxW9fCIoroWK/uJLpK3Tpcuy4T1Epo3hyHwoffB4wWSMLrwW/JTEAlQ/HU+bCcuAQIgPrtcV4986ysYxOoX05AyeuroP/yd0ibhyK7vq3Pafug+pjfdzCkEgH7sq+gZ8PGEAQBJ85exMSU7SiWC/CZNdwtE8u4Q12ZGISIPBuTXW4gCALUcx9G8YwFKHz0QyhH9YCkSQgsZy7DsGofIJdC/eIIp+w7KSkJADBw4MA7rpObmwu1+sa4d1EUMW3aNMyePdspMZH78GTEswn1/QEA5tQrUDYPhe9Hj8O0/5z9fmtWPiACqqcfgHJENwh+KvssWLejGNyxWvGoJveH9Uo+St5YDX3SFkhbNobl0lVYL+RAFtcc6n+NhOCjgLxrM8i7lh0iIBaUQNqqMSznshyqvwAAcil8nhkC1dhetnVFsdzG++aTGdB9sgHmm54TWZem8Pn7/Q6ziZUy/LAHuvdvvNalbcKgGB4LxeCO7K9FdYJSKivTk/GNI3vw66UL2DR0DLQmIwavW4Greh3evc1wIaq7THvOAlYRgr8PrJm5MB++AGtmri3RpZABFiusZ64AXj5ETzmiGyznsmD4bhe0s1egx0LbDIKf9xmIHg3D8PnJI3hq9+8Y06w1vrtvOLQ/rAba2D6bFPfWnaoiT5wYhBd2ieoer012/fvf/8avv/6K5ORkKBQK5OfnuzskB7LWYfBb/HfoF22Fbv7vtsoMtQLKoV2gejwekgYBTtnvL7/cZha1W6SlpTn83r9/f4wZMwZPPfUUgoKCkJGRAR8fH9Sr531l6nWNJ56M0A3SRkGQdYuGYel2KOLbQVAroegXA8CWDNIt3ApolFCN7mGv9nImQSqBZvZoKEd1h+Gng7Bm5UPWJhyK5x6ELC76rok2AJDFRMB/2dMQdUaYT1yCJeUSzMf+hPl4OsQ8LSTXk3sAYN5/DtrXVtoqv9pHQtahCaStGturv8zH01E0YwGkTUKgnvcIpC0bwXohB/plO1A0Yz40746HeK0YkoaBkPewDeVUDOwA/eI/oBjUAYrhsZC1aOS8J4uollDJpAj10cBfroC/XIHE5m2xO/syOod4b4UOVZ5xYzLkvVtDNaU/rFn5UPSLgWi2wJJ6BYoBHWD49TCMvx2Fanwfd4fqdD7PDIEl/SoU8TGQBNl6NPrIbNXAWpMRCokUvULDYdp4FJaTmUAbfygn9HVnyG7hKeeOvLBL5Dwen3NxdwDOYjQa8fDDD6Nnz572aiZPI42qD828R6B+ZSTEEgMEX5VHNvRs164dXnzxRfTr1w9WqxV+fn74/vvvmezyEp5yMkK35/PUAyia9iWKpn0J1ZT+kLWPhPVyLvTf7oLpt6NQv5TgkkTXzWQxEZDF3H5GrooQfBSQx0ZDHhsNwJa4s2bmQhLka1/HfDwdYnYBTNnHYfr9RvWXtHUYZO0jYNp7DtLmoQ69tcTmoRCCNdD+3/fQ/uNrQARkcc3tyS5JiD8C1r1sG35JRACAlQMS0D64vr2S8oOe90FnNuFMQa6bIyNPIhbpIGkTZnvvv/7+L8ik0Mx9BABgOnwRllOZ7gzRZQSZ1NaEXxCAm/pBAcCsjt3xUNOWiPINgH7FD/bl0gb+t26GXIQXdomcx9NzLl6b7Jo3bx4AYPHixe4NpAIEhazKvWqcISoqCgcPHnRYNnHiREycWEem2ibyILK24fD7YipK3vkZ2ueX2JcLDQOgnjPaPoNgbSYIQpmGxqrEPpB1ioL5eDosx9Jtya98LSzH02E5ng4Ati8bKjlM+1Jh2psK07YTsF66MRxBaBgAefcWDkMimegiclTfR43GGj/77xJBgIYVDnQLaVgwzMfS73i/5fifkITVnWTBrcPsdct2QnvNAnPKJYRIJTD0aAHllP7wuScCSN/hpiipFBNZRM7h6TkXz8mweACDwQCDwWD/vaioyI3REBHZyNpFwn/JTJhTr8CamQvB3weyDk08shK0pty2+ivjGszH0mHcmAzz7rOQtrNVFxjXH4Fx7WHbA9UKKO5rD+Mvh6CaMRAqL0gGEhG5myIhDtrnl8C47YR9OH0p064zMB9Jg+bNR90UnfuIBhMAwLTuCEwWJRQPdIZoNMO4PtnWh3fOEDdHSERky2sUFhbaf1cqlVAqXTsyxB2Y7LrJW2+9Zc9OEhF5GlmLRkAd7TMlCAKkESGQRoRA0sAfxbvPwnolD5JADWSdmgKCAFnXZlD0bwdrTiGMvxyCJIBN54mIaoK8T2vI42OgfelbmEd3h2JQR0AiwLjpGAwr9kDeuzXk97V3d5guZ7mQAwA4GyQFCo1Qd2sE87ksyD97FIYlf+Dk0t+Avj5ujpKI6rq2bR0nyJgzZw7mzp3rnmBcqFYlu+bOnVtuMurAgQOIja3alfyXX34Zzz33nP33zMzMMi8MIiJyL1nnphDq+8Pw7S7IXh8DZUI3KBO62e/Xf7cTQoAa8u4t3BglUe3AGcqoIgSJBJq3xkGftAWGlXth+H63bXmAGqrxfaCaNqBODhNXrD8GRAHT+9sa1eP0Ztu/204AkQAibYkuNj8nInc6efIkwsLC7L/frarL2TkXV6pVya6ZM2di7Nixd10nKiqqytu/tZzv5lI/IiLyDIJMCp/pA1HyxioIvkqoJsVD0jAA1quF0C/dDuOqffB5fhgEpdzdoRJ5LM5QRpVV+t6rejwelgvZgChC2qxhnX6vjdqfgWNRnWEc2hHa11fDetFW6aWc2BfKwR1R8tF6aAr0aDGNPaOIyH38/Pzg71+xiTKcnXNxpVqV7AoJCUFISIi7wyAiIjdTJnSDqDdC99lGGFbtgxCghlioA2RS+My8H8qxvdwdIpFH4wxlVFWCQgZZ67DyV6wLBKC5qIRPWASs8yah6MkFkDQMgO+YARBkUhTrZRCN3ttfk4i8jzflXGpVsqsy0tPTkZubi/T0dFgsFiQnJwMAmjdvDl9f37s/mIiIPJ5qbC8oH4yFcctxWHMKIannB/l97SDxY38UoopgIouoemRdmsG46RhU0wZA0jAAAatn2e+zFulg2nMWqsfudWOERETO4+k5F69Nds2ePRtff/21/ffOnTsDALZu3Yp+/fq5KSoiIqpJgkYJJWdcJCIiN1A92gtFT3wB3Ye/wuepB+yzJIs6I0pmrwAAKB+Kc2eIRERO4+k5F0EURdHdQXiqjIwMRERE4NKlSwgPD3e4T6/X4+LFi2jatClUKpWbIvRsfI6IiIiIyJvpv98F3Xu/QGgYAEXfNhANZpi2pkA0WuD7znjIe7Vyd4hEVEfdLZ9RF3htZRcREREREZEzqcb2gqxzUxh+2APT4YsQpBIoEuKgfLgHpI05VJiIyF2Y7CIiIiIiIqoiWavGkP3fKHeHQUREN5G4OwByn927d0MQBNx///3uDoWIiIiIiIiIqEawsstNUgty7zrdN+D8Kb8XLlyIRx99FKtWrUJ6ejoiIyOdti8iIiIiIiIiIldgsssNUgty0XL5ggqte3bMVKckvLRaLZYvX47NmzcjLy8PixcvxuzZs2t8P0RERERERERErsRklxuUVnR9Ez8MbYLq3XadU3nXMH7r2nKrv6pq+fLlCA0NRVxcHBITE/Hqq6/i1VdfhSAITtkfEREREREREZErsGeXG7UJqocuIaG3vd0pCVZTkpKSkJiYCABISEhATk4ONm/eDAC4fPmy/T4iIiIiIiIiotqEya466MyZM9i9ezfGjRsHAPD19cWIESOwcOFCAEDjxo2xbNkyd4ZIRERERERERFQlTHbVQUlJSejWrRtatmxpX5aYmIjVq1cjLy8PaWlpiI2NBQCkpaWhY8eOmDRpEtq2bYsnn3wSa9asQffu3RETE4PU1FR3/RlERERERERERGUw2VXHmM1mLFmyxF7VVWrw4MHw8/O7bUXXqVOn8PLLL+P48ePYtm0bdu3ahX379uGpp57CJ5984qrQiYiIiIiIiIjKxQb1dczatWuRnZ2Ndu3aISUlxeG+Pn36ICkpCcOGDXNY3qpVK7Rq1QoA0KZNGwwYMAAA0KFDB6xfv941gRMRERERERERVQCTXXVMUlISAGDgwIF3XCc3N9fhd6VSaf9ZIpHYf5dIJLBYLE6IkoiIiIiIiIioapjscqNTedeqdF91/PLLL+Wuk5aW5pR9ExERERERERE5G5NdbuAnVwAAxm9dW+F1iYiIiIiIiIiofIIoiqK7g/BUGRkZiIiIwKVLlxAeHu5wn16vx8WLF9G0aVOoVKpKbzu1IBdFJuNd1/GTK9AiILjS2/YU1X2OiIiIiIiIiKjy7pbPqAtY2eUmtTmJRURERERERETkqSTuDoCIiIiIiIiIiKimMNlFREREREREREReg8kuIiIiIiIiIiLyGkx2VRP7+98ZnxsiIiIiIiIicjUmu6pILpcDAEpKStwciecqfW5KnysiIiIiIiIiImfjbIxVJJVKERgYiJycHACAWq2GIAhujsoziKKIkpIS5OTkIDAwEFKp1N0hEREREREREVEdwWRXNYSGhgKAPeFFjgIDA+3PERERERERERGRKzDZVQ2CIKBRo0Zo0KABTCaTu8PxKHK5nBVdRERERERERORyTHbVAKlUysQOEREREREREZEH8MoG9WlpaZgyZQqaNm0KHx8fREdHY86cOTAaje4OjYiIiIiIiIio1qoNORevrOw6ffo0rFYrvvzySzRv3hwpKSmYOnUqtFot3nvvPXeHR0RERERERERUK9WGnIsgiqLo7iBc4d1338Xnn3+OCxcuVPgxGRkZiIiIwKVLlxAeHu7E6IiIiIiIiIiIaoar8xlVybk4k1dWdt1OQUEBgoOD77qOwWCAwWBweAwAXLlyxamxERERERERERHVlNI8RkFBAfz9/e3LlUollEplje+vIjkXlxLrgHPnzon+/v7iggUL7rrenDlzRAC88cYbb7zxxhtvvPHGG2+88cYbb153mzNnjttyLq5Uq4Yxzp07F/PmzbvrOgcOHEBsbKz998uXL+Pee+/Fvffei6+++uquj721sstsNuPUqVOIiIiARFJ+L/9+/fph27Zt5a5XE4+r6GOKiorQtm1bnDx5En5+fpWOrS6p6v+fO7grVmfvtya3XxPbctUxXZn1eUxXDI9nz9i3Jx3T1Xm8s45pHs8Vx2Pa/fuu6W2765jmebdn4DHt/v160md0dbbB8+47s1qtSE9PR9u2bSGT3RjUd7fKLmfnXFypVg1jnDlzJsaOHXvXdaKiouw/X758GfHx8ejZsyfmz59f7vZv95/eq1evCsenUCiqNBa2Ko+r6GMKCwsBAGFhYQ6li1RWVf//3MFdsTp7vzW5/ZrYlquO6cqsz2O6Yng8e8a+PemYrs7jnXVM83iuOB7T7t93TW/bXcc0z7s9A49p9+/Xkz6jq7MNnnffXWRkZKXWd3bOxZVqVbIrJCQEISEhFVo3MzMT8fHx6Nq1KxYtWlShyqzq+vvf/+6yx1V1X3Rntek5dVeszt5vTW6/JrblqmO6Nr32aova9Jy6M9a6dExX5/E8pt2vNj2n3npM1/S23XVM87zbM9Sm55Tn3a7ZFs+7PYOn51wqo1YNY6yo0jK6yMhILFmyBFKp1H5faGioGyNzvcLCQgQEBJRpSkdEtROPaSLvweOZyLvwmCbyLjym76w25FxqVWVXRf322284d+4czp07V6ZE0Qtze3elVCoxZ84cp8y2QESux2OayHvweCbyLjymibwLj+k7qw05F6+s7CIiIiIiIiIiorrJswZVEhERERERERERVQOTXURERERERERE5DWY7CIiIiIiIiIiIq/BZBcREREREREREXkNJruIiIiIiIiIiMhrMNlVh61duxatWrVCixYt8NVXX7k7HCKqpoceeghBQUEYPXq0u0Mhomq6dOkS+vXrh7Zt26JDhw744Ycf3B0SEVVRUVERunXrhk6dOqF9+/ZYsGCBu0MiohpQUlKCJk2aYNasWe4OhW5DEEVRdHcQ5Hpmsxlt27bF1q1b4e/vjy5dumDfvn0IDg52d2hEVEVbt25FcXExvv76a6xcudLd4RBRNVy5cgXZ2dno1KkTcnJy0KVLF5w5cwYajcbdoRFRJVksFhgMBqjVapSUlKBdu3Y4cOAA6tWr5+7QiKgaXnnlFaSmpiIyMhLvvfeeu8OhW7Cyq47av38/YmJiEBYWBj8/PwwZMgQbN250d1hEVA3x8fHw8/NzdxhEVAMaNWqETp06AQAaNGiA4OBg5ObmujcoIqoSqVQKtVoNANDr9bBYLGC9AVHtlpqaitOnT2PIkCHuDoXugMmuWmr79u148MEH0bhxYwiCgDVr1pRZ57PPPkPTpk2hUqnQtWtX7Nixw37f5cuXERYWZv89PDwcmZmZrgidiG6jusc0EXmWmjymDx48CKvVioiICCdHTUS3UxPHc35+Pjp27Ijw8HC88MILCAkJcVH0RHSrmjimZ82ahbfeestFEVNVMNlVS2m1WnTs2BGffPLJbe9fvnw5nn32Wbzyyis4cuQI+vTpgwceeADp6ekAcNurSYIgODVmIrqz6h7TRORZauqYvnbtGiZOnIj58+e7Imwiuo2aOJ4DAwNx9OhRXLx4Ed9++y2ys7NdFT4R3aK6x/RPP/2Eli1bomXLlq4MmypLpFoPgPjjjz86LIuLixNnzJjhsKx169biSy+9JIqiKO7atUtMSEiw3/f000+Ly5Ytc3qsRFS+qhzTpbZu3SqOGjXK2SESUSVU9ZjW6/Vinz59xCVLlrgiTCKqgOp8RpeaMWOGuGLFCmeFSESVUJVj+qWXXhLDw8PFJk2aiPXq1RP9/f3FefPmuSpkqiBWdnkho9GIQ4cOYdCgQQ7LBw0ahN27dwMA4uLikJKSgszMTBQVFWHdunUYPHiwO8IlonJU5JgmotqjIse0KIqYNGkS+vfvjwkTJrgjTCKqgIocz9nZ2SgsLAQAFBYWYvv27WjVqpXLYyWi8lXkmH7rrbdw6dIlpKWl4b333sPUqVMxe/Zsd4RLdyFzdwBU865evQqLxYKGDRs6LG/YsCGysrIAADKZDO+//z7i4+NhtVrxwgsvcEYYIg9VkWMaAAYPHozDhw9Dq9UiPDwcP/74I7p16+bqcImoHBU5pnft2oXly5ejQ4cO9l4iS5cuRfv27V0dLhHdRUWO54yMDEyZMgWiKEIURcycORMdOnRwR7hEVI6KnneT52Oyy4vd2oNLFEWHZcOHD8fw4cNdHRYRVVF5xzRnVCWqXe52TPfu3RtWq9UdYRFRFdzteO7atSuSk5PdEBURVVV5592lJk2a5KKIqLI4jNELhYSEQCqVlsk85+TklMlQE5Hn4zFN5F14TBN5Dx7PRN6Fx7T3YLLLCykUCnTt2hWbNm1yWL5p0ybcc889boqKiKqKxzSRd+ExTeQ9eDwTeRce096DwxhrqeLiYpw7d87++8WLF5GcnIzg4GBERkbiueeew4QJExAbG4uePXti/vz5SE9Px4wZM9wYNRHdCY9pIu/CY5rIe/B4JvIuPKbrCPdNBEnVsXXrVhFAmdtjjz1mX+fTTz8VmzRpIioUCrFLly7iH3/84b6AieiueEwTeRce00Teg8czkXfhMV03CKIoii7LrBERERERERERETkRe3YREREREREREZHXYLKLiIiIiIiIiIi8BpNdRERERERERETkNZjsIiIiIiIiIiIir8FkFxEREREREREReQ0mu4iIiIiIiIiIyGsw2UVERERERERERF6DyS4iIiIiIiIiIvIaTHYREREREREREZHXYLKLiIiIiIiIiIi8BpNdRERERERERETkNZjsIiIiIqqGTz/9FFFRUZDJZPjnP/9Z5v5r166hQYMGSEtLq9H9jh49Gh988EGNbpOIiIjIGwiiKIruDoKIiIioNkpJSUHnzp2xZs0adOnSBQEBAVCr1Q7rzJo1C3l5eUhKSgIATJo0Cfn5+VizZo3Detu2bUN8fDzy8vIQGBhY7r6PHTuG+Ph4XLx4Ef7+/jX1JxERERHVeqzsIiIiIqqin3/+GV27dsXQoUPRqFGjMokunU6HpKQkPPHEEzW+7w4dOiAqKgrLli2r8W0TERER1WZMdhERERFVQXR0NF555RXs27cPgiBgwoQJZdZZv349ZDIZevbsWentp6WlQRCEMrd+/frZ1xk+fDi+++676vwZRERERF6HyS4iIiKiKtizZw+aNWuGd999F1euXMFnn31WZp3t27cjNja2StuPiIjAlStX7LcjR46gXr166Nu3r32duLg47N+/HwaDocp/BxEREZG3kbk7ACIiIqLayNfXF2lpaejduzdCQ0Nvu05aWhoaN25cZvnatWvh6+vrsMxisTj8LpVK7dvV6/VISEhAz549MXfuXPs6YWFhMBgMyMrKQpMmTar5FxERERF5Bya7iIiIiKrg2LFjAID27dvfcR2dTgeVSlVmeXx8PD7//HOHZfv27cP48eNvu50pU6agqKgImzZtgkRyozDfx8cHAFBSUlLp+ImIiIi8FZNdRERERFWQnJyM5s2bQ6PR3HGdkJAQ5OXllVmu0WjQvHlzh2UZGRm33cYbb7yBDRs2YP/+/fDz83O4Lzc3FwBQv379yoZPRERE5LXYs4uIiIioCpKTk9GxY8e7rtO5c2ecPHmyyvtYtWoVXnvtNaxYsQLR0dFl7k9JSUF4eDhCQkKqvA8iIiIib8NkFxEREVEVJCcno1OnTnddZ/DgwThx4sRtq7vKk5KSgokTJ+LFF19ETEwMsrKykJWVZa/mAoAdO3Zg0KBBld42ERERkTdjsouIiIiokqxWK44fP15uZVf79u0RGxuLFStWVHofBw8eRElJCd544w00atTIfhs5ciQAW9P6H3/8EVOnTq3S30BERETkrQRRFEV3B0FERETkrdatW4dZs2YhJSXFobl8dX366af46aef8Ntvv9XYNomIiIi8ARvUExERETnRkCFDkJqaiszMTERERNTYduVyOT7++OMa2x4RERGRt2BlFxEREREREREReQ327CIiIiIiIiIiIq/BZBcREREREREREXkNJruIiIiIiIiIiMhrMNlFREREREREREReg8kuIiIiIiIiIiLyGkx2ERERERERERGR12Cyi4iIiIiIiIiIvAaTXURERERERERE5DWY7CIiIiIiIiIiIq/x/2UzgWTpZplhAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data: DataSet\n", - "for data in project_ex1.get_data_sets():\n", - " fit: FitResult\n", - " for fit in project_ex1.get_fits(data): # Get the fit results for a specific data set.\n", - " fig, axes = mpl.plot_fit(fit, data)\n", - " \n", - " # The raw data and plot-specific data can also be obtained with FitResult objects.\n", - " re: ndarray\n", - " im: ndarray\n", - " real, imag = fit.get_nyquist_data(num_per_decade=25)" - ] - }, - { - "cell_type": "markdown", - "id": "4718d88f-54e7-4a1a-a120-650060e23e94", - "metadata": {}, - "source": [ - "##### Simulation results\n", - "\n", - "Simulations can also be accessed and plotted easily:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "44eea7ec-8faf-4d88-978e-c96263af8905", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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REREBOzs7eHl5YebMmRg1ahSCgoLQsWNHfPnll4iNjcWkSZNqNCffoySVuvY3gYjqJhYviIgMnCiKSExMxP3796WOQgbE1tYWLi4udWJQzrNnz6Jbt27a5w9mAnnllVewefNmDB8+HKmpqfjoo4+QkJCAgIAA7N+/H97e3jWSj+9RMgR16W8CEdVNLF4QERm4Bx+KnJycYG5uzgvTOk4UReTm5iI5ORkA6sTUnSEhIRBF8bFtJk+ejMmTJ9dQIl18j5KU6uLfBCKqm1i8ICIyYGq1WvuhyN7eXuo4ZCDMzMwAlAxK6eTkxO7iEuJ7lAwB/yYQUV3AATuJiAzYg/vnzc3NJU5ChubB7wTHWJAW36NkKPg3gYhqOxYviIiMALuh08P4O2FY+P+DpMbfQSKq7Vi8ICIiIiIiIiKDxuIFERERERERERk0Fi+IiKhahISEQBAECIKAiIiICm/ftWtXfP/993rPdfHiRXh4eCAnJ6fUuqtXr8LFxQVZWVl6P+7D9u3bh1atWkGj0egs9/Hx0f7cOPUmERERUQkWL4iIqNqMHz8eCQkJCAgIQExMjPZDuSAIsLGxQYcOHfDzzz+X2m7fvn1ITEzECy+8oF323w/1ZmZmaNy4MT799NNSU2heuHABzz33HJydnWFqagp/f3+MHz8e165dAwAEBgaiXbt2WLZsWanjzps3D1OmTIGVlRUAICwsTCezvb09unfvjuPHj2u36dChA1577TWd/axduxaCIGDjxo06y8eNG4dOnToBAAYMGABBEEoVaM6cOYNdu3Y98WdLpA9VKTKmpqbCyckJMTEx1ZJt2LBhWLp0aY0ft7w5Ro8erf3Z/fjjj+XeX1hYGHx8fCqco7LbERHVFixeEBFRtTE3N4eLiwsUin9n5v7999+RkJCAU6dOoV27dnj22WcRGRmps92KFSswZswYyGS6p6mPPvoICQkJuHz5MmbPno133nkHX375pXb9vn370KFDBxQUFGDr1q24fPkytmzZAhsbG7z33nvadmPGjMHatWuhVqu1y+Lj4/HTTz9hzJgxpV7H1atXkZCQgLCwMDg6OuLpp59GcnIyAKBbt24IDQ3VaR8WFgZPT88yl3fr1k0nx8qVK3XaODo6ws7OruwfKFE1+G+REdD9UK5QKODl5YXXXnsN6enpOtstWrQIAwcO1PlAXd5tExMTMW3aNNSvXx8qlQqenp4YOHAg/vjjD22b999/HwsWLEBmZmaVj7tu3TpYWVmhuLhYu012djaUSiWeeuopnf0fPXoUgiBoC56PyrF8+XIkJCSU50f8RPv379cplD78eP755/VyHCIiY8biBRGRkVLn5TzyoSnIL3/b/LxytdUXe3t7uLi4oHHjxliwYAGKiop0PuTfu3cPv//+OwYNGlRqWysrK7i4uMDHxwevvvoqmjdvjoMHDwIAcnNzMWbMGPTv3x8//fQTevbsCV9fX7Rv3x6fffYZ1q9fr91Pnz59kJqaisOHD2uX7dy5Ey1atICHh0ep4zo5OcHFxQWBgYF49913kZGRgVOnTgEoKV48KG48cPjwYbz99tsICwvTLouLi8OtW7d0iheDBg3C6dOncevWrUr8JIn0o6wiY9++fZGQkICYmBh89dVX+PnnnzF58mTt+ry8PGzcuBGvvvpqqf09aduYmBi0adMGf/75J5YsWYKLFy/iwIED6NatG6ZMmaJt17x5c/j4+GDr1q1VPm63bt2QnZ2Ns2fPatsfPXoULi4uOHPmDHJzc7XLw8LC4ObmBn9//0fmAAAbGxu4uLg8+QdcDt26dUNCQoLOIz4+Hr169YKDg4NO8ZWIqK5SPLkJEREZooinPB+5zrpzL/gt36F9/nevRtDk55bZ1rJ1ZzT68t9bNyIHtkTx/dRS7dqcTatC2tKKioqwYcMGAIBSqdQuP3bsGMzNzdGkSZNHbiuKIg4fPozLly/Dz88PAPDbb7/h3r17mDNnTpnb2Nraav9tYmKCFi1a4OjRo+jevTsA4MiRIwgKCnps5tzcXGzatEknc+fOnaFUKhEWFoYXX3wRUVFRyMvLw9ixYzF37lxcv34dfn5+CA0NhYmJifa2EQDw9vaGk5MTjh49ivr16z/22GR8Hlf0E2RyyFSm5WsryCAzNXtsW7mZRSVTlk2lUmk/mHt4eGD48OHYvHmzdv2vv/4KhUKBjh07VnjbyZMnQxAEnD59GhYW/+Zu1qwZxo4dq7OvQYMGYdu2bdpbsyp73EaNGsHNzQ1hYWHo0KEDgJIixeDBgxEaGooTJ06gZ8+e2uX/LTKWlUPfzMzMYGb2n//HajVeeuklXLhwAX/++ScCAwN1iqFERHURixdERFSjOnXqBJlMhry8PGg0Gvj4+Oh0iY6JiYGzs3OpW0YAYO7cuXj33XdRWFiIoqIimJqa4vXXXwcAXL9+HQDQuHHjcuVwd3fXuWf+wbfBZXnQGyM3NxeiKKJNmzbo0aMHAMDCwgJt27bVFi/CwsLQpUsXqFQqdO7cGWFhYfDz80NYWBjat28Pc3Pzx+ag2qMmC4z6Li7+161bt3DgwAGdImN5in1lbZuWloYDBw5gwYIFOoWLB/5bZASAdu3aYdGiRSgoKIBKpar0cYGS8T1CQ0Px1ltvAQBCQ0MxZ84caDQahIaGomfPnigsLMTJkydL3c71cI7q9KBwcejQIW3hgoiIWLwgIjJaLY/GPXKdIJPrPG9+6Oqj2wq6RYKAnyOqlOtJduzYgcaNG+PatWuYMWMG1q1bpzPGQ15eHkxNTcvc9s0338To0aORkpKCefPmoXv37tqeDA8P3PkkZmZmOl3FH3fco0ePwsLCAhcuXMDcuXOxefNmnQ9F3bp1w//+9z8AJd/ahoSEAACCg4MRFhaG8ePHIywsDC+//PITcxAZgn379sHS0hJqtRr5+SW3of130MqYmBi4ublVeNsbN25AFMUKFRkLCgqQmJgIb2/vSh8XKClevPHGGyguLkZeXh4uXLiArl27Qq1WY8WKFQCA8PBw5OXllep58XCO6qJWqzFq1CgcOnQIf/zxB5o3b15txyLSp2KNBjNP/on9cTcxIyAIUwPK/jKAqCpYvCAiMlIV6SZeXW0rw9PTE35+fvDz84OlpSWeffZZREVFwcnJCQDg4OBQanC/BxwcHNCwYUM0bNgQu3btQsOGDdGhQwf07NlTe3/6lStXyuxS/rC0tDQ0aNBAZ9+POq6vry9sbW3h7++P/Px8PPPMM4iMjNR+A9utWzcsWLAAd+7cweHDhzF79mwAJcWLlStXIjY2FtHR0aU+ED3I4ejo+MS8VPNWr16N1atXo7CwsFLbG2uBESj5nV67di1yc3Px1Vdf4dq1a5g2bZp2/eOKfY/b9kGRURCEcuV4cCvFgwJfZY/7YH1OTg7OnDmD9PR0+Pv7w8nJCcHBwRg1ahRycnIQFhYGLy+vUrdxPZyjOjwoXBw8eBB//PEHWrRoUW3HItKH7KJCWCpNAAAKmQwnk+/gTk42enr4SBuMai0O2ElERJIJDg5GQEAAFixYoF3WqlUrJCYmPrKQ8EC9evUwbdo0zJ49G6Ioonfv3nBwcMCSJUvKbH///n2d55GRkWjVqpXOcaOiop6YedSoUdBoNFizZo12WadOnaBSqbBmzRrk5eVpbz8JCgpCRkYG1q9fD1NTU+299g/k5+fj5s2bOjnIcEyZMgVRUVGVHmtAbmbxyMd/x7t4Ytv/jHfxqLb6ZmFhgYYNG6J58+ZYsWIFCgoKMH/+fO36xxX7Hretn58fBEHA5cuXy5UjLa3kdpgHBb7KHhcAGjZsCA8PD4SGhiI0NBTBwcEAABcXF/j6+uL48eMIDQ3VjoPzuBz69qBw8dtvv+H3339n4YIM3tmUBLT4YROKNRrtso+DnsLG4H5oZPNvb8qr91Mr3DOS6FFYvCAiIknNmjUL69evx507dwCUFBEcHR1x/PjxJ247ZcoUXL16Fbt27YKFhQW++uor/PLLLxg0aBB+//13xMTE4OzZs5gzZw4mTZqk3S4mJgZ37tzRDtAHlMxAcvLkSZ3pU8sik8kwY8YMfPLJJ9pvYc3MzNC+fXusXLkSnTt3hlxe8q26UqlEx44dsXLlSm2B47/Cw8OhUqnK1VOESEoffPABPvvsM9y9exdA+Yt9D29rZ2eHPn36YPXq1cjJKT3waFlFRg8PDzg4OFTpuA9069YNYWFhOrd3ASWF1N9++w3h4eFl9pB6OIc+qdVqvPzyy9rCRcuWLfV+DCJ9Ums0eDn0F9zKuo/9sTe1y/t41seIhk21PatuZKSjze5vMPi33bj/0CxoRJXB4gUREUlqwIAB8PHx0fa+kMvlGDt2bKlpCcvi6OiIUaNG4cMPP4RGo8HgwYNx4sQJKJVKjBgxAo0bN8aLL76IjIwMfPzxx9rttm3bht69e+vcu96/f38olUr8/vvvTzzu2LFjUVRUhFWrVmmXdevWDVlZWTofiICSD0VZWVllfiDatm0bRo4cWWoQTyJDExISgmbNmmHhwoUASop9ly5demIPqbK2XbNmDdRqNdq1a4ddu3bh+vXruHz5MlasWFGqkHf06FH07t1b+7wqxwVK3qfHjh1DRESEtucFUPI+3bBhA/Lz88t8rz6cQ180Gg1efvll/Pjjj/juu+/g6uqKxMREnceTCqpENU0uk2H9U33Q2dkdXVxKTy/+wLl7iSjSaHCvIBdJepxyneowkaokIyNDBCBmZGRIHYWIaqG8vDwxKipKzMvLkzpKhQUHB4vTp0+v1LaJiYmivb29GBMTo99Qoijm5+eLnp6e4rFjx0qtW716tdi7d2+9H7MsycnJop2dnXjr1q1S60JDQ0UAYnp6+iO3f9zvBs9N+hUXFycCEOPi4kqtM+b3qCiW/T595ZVXxMGDB5dqu3XrVtHExESMjY0VRVEUO3ToIK5bt65S2969e1ecMmWK6O3tLZqYmIju7u7ioEGDxNDQUO02eXl5orW1tXjy5EmdfVXluNHR0SIAsXHjxjrtHvw/btCgQal9PCrHAwDEPXv2iKIoips2bRKfdHkdGhoqent7i6IoiuHh4SKAxz4e/B3473ZlMfbfRTI+Go3miW1uZ2WUqx1V3OPOTbUVe14QEVG1WbNmDSwtLXHx4sUKbefs7IyNGzciNjZW75lu376NefPmoXPnzqXWTZgwAV27dkVWVpbej/uw6OhorFmzBr6+vjrLmzVrhn79+lX78YkeZfPmzfjxxx9LLR8xYgQKCgrg6VkyBex7772H5cuXQ/Ofe97Lu62rqytWrVqFmJgYFBQUID4+Hnv37tXpubRx40a0b9++1FgxVTmuj48PRFEsNeaGh4cHRFHEjRs3Su3jUTnKEhMTo9Oj40nat28PURQf+3h4+lgiqSTl5iAl799Ba8sz8K6XpXW5B+glehLONkJERNVi69atyMvLAwB4eXlVePvBgwfrOxIAwN/fXzszycMUCgXmzZtXLcd9WLt27dCuXbtSy/fv34+ioiIAgLW1dY1kobptzZo1+Oqrr3Dy5EkEBgaWe7v+/fvj+vXruHPnjrY4oE9KpRIrV66s8eOWN8ekSZPw3Xff6Sz77bffsHz58mrPRFTTNKKIV8J+wV+pydjRczC6ulbsvVes0eDLyxHwtLTGQO+G1ZSSajsWL4iIqFq4u7tLHcEo/XccDqLqVtUi4/Tp0/UdSWvChAmSHLe8OT766CPttMiurq4AgJMnT9ZYLqKadC8/F3E5mbhfWACHh2ZAKo/Vl85jxsk/4GNlg17uPjBV8GMoVRx/a4iIjIDIacboIfydIH1gkbHynJyc4OTkVOHtfHx8MGPGjBrbjkgfnMwscPaZV3A6OQFN61V81p1XGzfHpmsXMb5xCyhkHLmAKofFCyIiA6ZUKgEAubm5MDOr+DcdVHs9mKb1we8IERkHFi/IWJkplAh2q3gPLQCwUJrgwtDRHP+CqoTFCyIiAyaXy2Fra4vk5GQAgLm5OU/8dZwoisjNzUVycjJsbW0hl8uljkRERLWURhRxPDEeXVw8qnz9wesXqioWL4iIDJyLiwsAaAsYRABga2ur/d0gIiKqDr/fiUGf/TvR0dkNxwe9pJcCxNmUBMwKD8Wazr3QzM5RDymprmDxgojIwAmCAFdXVzg5OWlnoaC6TalUsscFERFVu5isDFgolGjr6Kq3nhMfnz+JIwlx+PrqRXzesbte9kl1A4sXRERGQi6X8wMrERER1ZgJTVrihQZNUKBW622fU5u1RgNrW4xuVP6pmYkAFi+IiIiIiIjoEaxNVHrdX08PH/T08NHrPqlu4Dw1REREREREpFWgLkZsdqbUMYh0sHhBREREREREWv+7dRW+29bhtaO/VdsxLqffw7zTR5BRWFBtx6DahbeNEBERERERkda5lERoRBFuFpbVsn9RFPHsoR9x+X4qGljbYmzj5tVyHKpdWLwgIiIiIiIirWWdemBCkxZwNDOvlv0LgoCxjQJxJCEe9a1tq+UYVPvwthEiIiKiOiokJASCIEAQBERERFRo29TUVDg5OSEmJqZasg0bNgxLly6t8eOWN8fo0aO1P7sff/yx0vtv3LgxvvrqqyokJKoeTeo5wMG0eooXADC7RXv81PdZhLh5VdsxqHZh8YKIiIioDhs/fjwSEhIQEBAAQPdDuUKhgJeXF1577TWkp6frbLdo0SIMHDgQPj4+2mXl3TYxMRHTpk1D/fr1oVKp4OnpiYEDB+KPP/7Qtnn//fexYMECZGbqDhpYmeOuW7cOVlZWKC4u1m6TnZ0NpVKJp556Smf/R48ehSAIuHbt2mNzLF++HAkJCeX5ET9SXl4ebty4gRYtWlRpP0T6ohFFaERR6hhEZWLxgoiIiKgOMzc3h4uLCxSKf+8m7tu3LxISEhATE4OvvvoKP//8MyZPnqxdn5eXh40bN+LVV18ttb8nbRsTE4M2bdrgzz//xJIlS3Dx4kUcOHAA3bp1w5QpU7TtmjdvDh8fH2zdurXKx+3WrRuys7Nx9uxZbfujR4/CxcUFZ86cQW5urnZ5WFgY3Nzc4O/v/8gcAGBjYwMXF5cn/4AfIzIyEqIoagtHRFI7nBALt+9WY054aI0dM6uwADtuXobIogk9AYsXRERERKRDpVLBxcUFHh4e6N27N4YPH46DBw9q1//6669QKBTo2LFjhbedPHkyBEHA6dOnMWzYMPj7+6NZs2aYOXMmwsPDdfY1aNAgbNu2rcrHbdSoEdzc3BAWFqZtHxYWhsGDB6NBgwY4ceKEzvJu3bo9NkdVRUREoHv37ujSpQs0Gg28vLywbNkyve2fqLL2x95CUl4OUvJzn9xYD4o0atTfvh4v/PETTqdUrScT1X4sXhARERFVg5yiQuQUFep8m1ioViOnqBAF6uIy2/63u3aRpqRtfvGT21anW7du4cCBA1AqldplR44cQVBQUIW3TUtLw4EDBzBlyhRYWFiUam9ra6vzvF27djh9+jQKCgqqdFygZHyP0NB/v00ODQ1FSEgIgoODtcsLCwtx8uTJUsWLh3NUxc2bNxEcHIzu3btj0KBBGDp0KGbNmoWZM2fq9AwhksKCtl3x+9PDMT3gye8zfVDK5Ojj4YtGNna4X5BfI8ck48XiBREREVEZVq9ejaZNmyIkJKRS21tuWgbLTctwLz9Pu+zTv07BctMyTD1+SKet05ZVsNy0DLHZ/46rsPrSeVhuWoZxR37VaeuzbR0sNy3D5fR7lcpVHvv27YOlpSXMzMzQoEEDREVFYe7cudr1MTExcHNzq/C2N27cgCiKaNy4cblyuLu7o6CgAImJiVU6LlBSvDh+/DiKi4uRlZWFCxcuoGvXrggODtb2yAgPD0deXl6p4sXDOapi0qRJGDp0KN59913ExsaiY8eOmDNnDmxtbXH06NEq75+oKkzkcvRw90FLB+caO+a6p/rg8vOvoo9n/Ro7JhknFi+IiIiIyjBlyhRERUXp3GpQV3Tr1g0RERE4deoUpk2bhj59+mDatGna9Xl5eTA1Na3wtg96oQiCUK4cZmZmAKAdk6Kyx32wPicnB2fOnMHRo0fh7+8PJycnBAcH48yZM8jJyUFYWBi8vLxQv77uh6iHc1RWYmIi/vzzT0yaNAlqtRoXL15Eq1atIJPJoFAoYGJiUqX9ExkjS6VJuf8mUOWtXbsWzZs3h7W1NaytrdGxY0f8+uu/xXFRFPHhhx/Czc0NZmZmCAkJwaVLlyRMXBqLF0RERETVIHvMG8ge8wYcTM20y95s0R7ZY97Aqs69dNomj5qK7DFvwMvSWrtsSrPWyB7zBjZ27afTNubFScge8waa1HOotuwWFhZo2LAhmjdvjhUrVqCgoADz58/XrndwcCg1g0h5tvXz84MgCLh8+XK5cqSlpQEAHB0dq3RcAGjYsCE8PDwQGhqK0NBQBAcHAwBcXFzg6+uL48ePIzQ0FN27d39ijsoKDw+HRqNBy5YtceXKFeTl5aFly5aIi4vDvXv30Llz5yrtn6gqphw7iM//Po3U//QWq0miKCJNomPXBR4eHvjkk09w9uxZnD17Ft27d8fgwYO1BYolS5Zg6dKlWLVqFc6cOQMXFxf06tULWVlZEif/F4sXRERERNXAQmkCi4e+UTSRy2GhNIFKriizrew/bZWykramiie3rW4ffPABPvvsM9y9excA0KpVK0RFRVV4Wzs7O/Tp0werV69GTk5Oqbb379/XeR4ZGQkPDw84ODhU6bgPdOvWDWFhYQgLC9O5HSg4OBi//fYbwsPDS90yUlaOyiosLAQA5OfnIyIiAh4eHrC3t8f69evRtGlTtGzZskr7J6qsxNxsrI26gNnhoch/aEyemnA6+S58tq1Dj1+21/ixjV1WVhYyMzO1j0eNzTNw4ED0798f/v7+8Pf3x4IFC2BpaYnw8HCIoogvvvgC8+bNw9ChQxEQEIBvvvkGubm5+P7772v4FT0aixdERERE9FghISFo1qwZFi5cCADo06cPLl269MheEI/bds2aNVCr1WjXrh127dqF69ev4/Lly1ixYkWpWUSOHj2K3r17a59X5bhASfHi2LFjiIiI0Pa8AEqKFxs2bEB+fn6ZxYuHc1RWhw4doFAo8NFHH+HIkSNo0KAB1qxZg2XLlmHTpk1V3j9RZZnKFVjeqSemNG0NdwurGj++r5Ut4rIzceV+GgfurKCmTZvCxsZG+1i0aNETt1Gr1di+fTtycnLQsWNHREdHIzExUefvnEqlQnBwsM5sTFJj8YKIiIiInmjmzJnYsGED4uLiEBgYiKCgIOzcubPC2/r6+uL8+fPo1q0bZs2ahYCAAPTq1Qt//PEH1q5dq90mPz8fe/bswfjx47XLqnJcoKR4kZeXh4YNG8LZ+d8BCYODg5GVlYUGDRrA09NTZx9l5XiUzZs3P/befS8vL3z99dfYtm0bvvzySxw/fhybN2/G/v370a5du3K9JqLqYKsyxbSANljVpdeTG1cDRzNzHBk0EqmvvA5bVdnj2lDZoqKikJGRoX28/fbbj2x78eJFWFpaQqVSYdKkSdizZw+aNm2qHYz4v38XHzzXx0DF+iKIYg3Ns1VLZWZmwsbGBhkZGbC2tn7yBkRERNWM5yb9io+Ph6enJ+Li4uDh4aGzLj8/H9HR0fD19X3kQJKGLCQkBC1btsQXX3xR4W3379+P2bNnIzIyEjKZ/r8PW716Nfbu3YuDBw/W6HHLm+MBQRCwZ88eDBkyBB9++KH2tpQnsbOzw9dff40hQ4boJaex/y4SUcU87tz0KIWFhYiNjcX9+/exa9cufPXVVzh8+DDu37+Pzp074+7du3B1ddW2Hz9+POLi4nDgwIHqehkVwp4XRERERHXYmjVrYGlpiYsXL1Zou/79+2PixIm4c+dOteRSKpVYuXJljR+3vDkmTZoES0tLnWW//fYblixZ8sR9xsfHIz09HYGBgXrLSVRZf6UmY3/sTeQX1/xYF1SzTExM0LBhQwQFBWHRokVo0aIFli9fDhcXFwAo1csiOTm5VG8MKSme3ISIiIiIaqOtW7ciL69kdH8vL68Kbz99+nR9R9KaMGGCJMctb46PPvoIs2fPBgDtN5UnT54s1z4vXrwICwuLUlOyEklh1aVz+OrK33g9oA2Wd+opaZaNV/7C7uhr+LhtV7RyMJwPzbWVKIooKCiAr68vXFxccOjQIbRq1QpASS+Nw4cPY/HixRKn/BeLF0RERER1lLu7u9QRjJaTkxOcnJwqtW2/fv2QnZ2t50REleNiZgF3C0sM8GogdRTsi72J/XG30MnZncULPXvnnXfQr18/eHp6IisrC9u3b0dYWBgOHDgAQRAwY8YMLFy4EH5+fvDz88PChQthbm6OESNGSB1di8ULIiIiIiKiOur/2nbFR0FPwRAGQny1cQt0dHLHEB8/qaPUOklJSRg1ahQSEhJgY2OD5s2b48CBA+jVq2SQ1jlz5iAvLw+TJ09Geno62rdvj4MHD8LKquZnn3kUFi+IiIiIiIjqMEEQ8Oh5cmrO014N8LQB9ACpjTZu3PjY9YIg4MMPP8SHH35YM4EqgQN2EhERERER1UHpBflSRyAqNxYviIiIiKqIM8+T1Pg7SBWVXVQIp29XosnODcgoLJA6jlahWo3Qu7exJ/qa1FHIwPC2ESIiIqJKUiqVAIDc3FyYmZlJnIbqstzcXAD//k4SPUlEajLUogb5ajVsTFRSx9E6dCcGAw78AG9Lawzx8YMgGMINLWQIWLwgIiIiqiS5XA5bW1skJycDAMzNzXmhTTVKFEXk5uYiOTkZtra2kMvlUkciI9HFxQOpr0xHTFaG1FF0hLh6wsvSGl1dPZGnLoa5ggU5KsHiBREREVEVuLi4AIC2gEEkBVtbW+3vIlF51VOZop7KVOoYOiyUJoh5cRILwVQKixdEREREVSAIAlxdXeHk5ISioiKp41AdpFQq2eOCahUWLqgsLF4QERER6YFcLucHSCIyCkcT4rAm6gKG+PhheIMmUsd5pPSCfFgrTSCXcZ4J4mwjREREREREdcqvcbew/eZl7I+9JXWUR+r601bYfbMcEam8JY9KGG3xYu3atWjevDmsra1hbW2Njh074tdff9WuF0URH374Idzc3GBmZoaQkBBcunRJZx8FBQWYNm0aHBwcYGFhgUGDBiE+Pr6mXwoREREREVGNeda3ET5o3RkvNjTcXhdWShMAwF8sXtA/jLZ44eHhgU8++QRnz57F2bNn0b17dwwePFhboFiyZAmWLl2KVatW4cyZM3BxcUGvXr2QlZWl3ceMGTOwZ88ebN++HceOHUN2djYGDBgAtVot1csiIiIiIiKqVm0cXfBhUBf09awvdZRH+qJTDySNmoqxjZtLHYUMhCCKoih1CH2xs7PDp59+irFjx8LNzQ0zZszA3LlzAZT0snB2dsbixYsxceJEZGRkwNHREVu2bMHw4cMBAHfv3oWnpyf279+PPn36lOuYmZmZsLGxQUZGBqytravttREREZUXz036FR8fD09PT8TFxcHDw0PqOERERHXy3GS0PS/+S61WY/v27cjJyUHHjh0RHR2NxMRE9O7dW9tGpVIhODgYJ06cAACcO3cORUVFOm3c3NwQEBCgbVOWgoICZGZm6jyIiIiIiIiMwdGEOJxJTkCxRiN1FKIKMerixcWLF2FpaQmVSoVJkyZhz549aNq0KRITEwEAzs7OOu2dnZ216xITE2FiYoJ69eo9sk1ZFi1aBBsbG+3D09NTz6+KiIiIiIioerx1+jDa/fgttlyPlDrKE/18+wZGh/2CfbdvSB2FDIBRFy8aNWqEiIgIhIeH47XXXsMrr7yCqKgo7fqH5wcWRfGJcwY/qc3bb7+NjIwM7SMuLq5qL4KIiIiIiKgGiKIIN3NL2JqoEOLqJXWcJwq7G4tvrkXil9ibUkchA6CQOkBVmJiYoGHDhgCAoKAgnDlzBsuXL9eOc5GYmAhXV1dt++TkZG1vDBcXFxQWFiI9PV2n90VycjI6der0yGOqVCqoVKrqeDlERERERETVRhAE/K/XEKg1Gsie8KWuIRji4wdTuQL9vAx3YFGqOUbd8+JhoiiioKAAvr6+cHFxwaFDh7TrCgsLcfjwYW1hok2bNlAqlTptEhISEBkZ+djiBREREdUNq1evRtOmTRESEiJ1FCIivZLLZE/skW4InnL1xIJ2XdHFpW4MSEmPZ7Q9L9555x3069cPnp6eyMrKwvbt2xEWFoYDBw5AEATMmDEDCxcuhJ+fH/z8/LBw4UKYm5tjxIgRAAAbGxuMGzcOs2bNgr29Pezs7DB79mwEBgaiZ8+eEr86IiIiktqUKVMwZcoU7YjuRETGrkBdDJXcaD8CUh1ntL+5SUlJGDVqFBISEmBjY4PmzZvjwIED6NWrFwBgzpw5yMvLw+TJk5Geno727dvj4MGDsLKy0u5j2bJlUCgUeP7555GXl4cePXpg8+bNkMvlUr0sIiIiIiIivcstLoLTtyvRwt4Jv/Z7DtYmxnErvFqjwcW0FOSri9HB2V3qOCQhQRRFUeoQxiwzMxM2NjbIyMiAtbW11HGIiIh4btKzBz0v4uLi4OHBrstEZJwO341FyL5t8LSwwu0RrxnFbSMAsPHKX3j1yAEEu3oibOAIqeMYjLp4bjLanhdERERERERUPl1dPXHrhYmIy8kymsIFAHRydoeV0gT1VKblmj2Sai8WL4iIiIiIiGo5QRDga20LX2tbqaNUSGNbe6S/Mh1yWa2aa4IqgcULIiIiIiIiMkiCIEDO3haEWjZVKhEREREREemKzryP6Sd+x65bV6WOUiUaDtdYp7F4QUREREREVIsdTYzHishz+CLyrNRRKiUuOxPBP3+PhtvXg/NN1F28bYSIiIiIiKgWa2Jrj9cD2sDPup7UUSrF0dQc4Ul3UahR42bmfTS0Mc7XQVXD4gUREREREVEt1tbJFW2dXKWOUWmmCgV29hyMRrZ2aGBkA46S/rB4QURERERERAZtsI+f1BFIYhzzgoiIiIiIqJZKys3Bzcx0jhVBRo/FCyIiIqIalHfrCjJO/M4PEkRUIzZfu4iG27/EmMP7pY5SJRpRxA+3rmDuqTDkFhdJHYckwOIFERERUQ1K3PQFbrz+PDKP/y51FCKqA9IK8qGUyRBo5yh1lCqRCQJeP/EHlvx1ChH3kqSOQxLgmBdERERENUjp6AIz/wBYd+yuXZZ55ghMvRvCxMlNwmREVBstbh+C+W26oFjUSB2lykY0bILsoiLYmKikjkISYPGCiIiIqAZ5vP4hxKnvQ5CVdIDVFBUi+t2JKL6fCru+w+AyairMGjaVOCUR1Samitrxse+zDt2f3IhqLd42QkRERFTDHhQuAKA4LQWm3g0AdTHSftmOqBe64Pq0Ycg8fZjjYhAREf2DxQsiIiIiCZk4u6PRl/vQePMh1Os1BJDJkHnyT1yf/AyuvNIT2RfPSB2RiIzU/50/jgEHfsCBuFtSR9EbURRxJycL+cXFUkehGsbiBREREZEBsAhog/qLvkbAnnNwfH48BJUZcqMuQGZiKnU0IjJSB+Ki8UvsTdzLz5M6it50+HELPLauwcnkO1JHoRrG4gURERGRAVG5e8NrzmIE7vsL3h+sgnmjQO26u+s/QfKODdAU5EuYkIiMxfJOPbC8Uw+EuHpKHUVvPC2tIBME3Mq8L3UUqmGCyJspqyQzMxM2NjbIyMiAtbW11HGIiIh4btKz+Ph4eHp6Ii4uDh4eHpLlKEy6g8ghbSAWFUJh7wyXUVPh8OxoyM0sJMtERFTT7uZkwVZlCnOFUuookjKUc1NNYs8LIiIiIiOgsLWHx8wFUDq7ozg1CfFfvIfIQa2Q+M0KqPNypI5HRFQj3Cys6nzhoq5i8YKIiIjICMhUpnB6bhwCfjwH73eXw8TdB8Xp93Bn5YeIHNQKWeeOSx2RiAzInuhrOBQfjeyiQqmjEOkFixdERERERkSmNIHDkFEI2HUaPh+ugcrDF5r8PJjWbyR1NCIyILPDQ9F7/06cTKp9A1tuvPIXhh3ag6MJcVJHoRqkkDoAEREREVWcoFDAfsALsOs7DLnXI6Gs56BdF/3BZFgGBsF+8EuQKU0kTElEUijSqNHB2Q2CALRzcpM6jt79cec2dkVfQyt7ZzxViwYjpcdj8YKIiIjIiAkKBSyatNQ+zzp/Amm/bEfaL9uRuGUl3MbPhV2/5yDI5dKFJKIapZTJsbX7QKljVJuX/JqhtYMz+njWlzoK1SDeNkJERERUi1gEtIHnm4uhsHdC4Z3biPlwMqJe6Iz7Yb+Ak8wRUW3Q36sBZrdoj0A7R6mjUA1i8YKIiIioFpGZqOA0fDwCfjwH96kfQG5ti/zoa7g5exSujuuLwlp4/zsR6SpUq6WOQKR3LF4QERER1UJyMwu4jJ6OwJ8i4DJ2FmSm5ihKTYGC31QS1XoNtq9Hk50bcD0jTeoo1SarsABHEuJwIyNd6ihUQ1i8ICIiIqrF5JbWcJ88DwE/nkX9BRu0A3iKxUW4s+ojFCbflTghEelTYm424nOycC0jHe4WVlLHqTbTTvyO4J+/x5brkVJHoRrC4gURERFRGVavXo2mTZsiJCRE6ih6oXRwgUVAG+3zlN3fIHHzF4h8pi3urF0AdU6WhOmISF9czC2RNGoq/nz6BZgrlFLHqTZBDi7wtLCCiYyDEdcVgsiRm6okMzMTNjY2yMjIgLW1tdRxiIiIeG7Ss/j4eHh6eiIuLg4eHh5Sx9GbnMhziFv2LnL+OgUAUNg5wm3CW3AYMgqCghPSEZFh04giZIIgdQzJ1NZz0+Ow5wURERFRHWQR0AaNvtqP+ku+gcqrAYrTUhD7ySxEjQxGZnio1PGIiB6rLhcu6ioWL4iIiIjqKEEQUK/7QDTbeQKesz+B3KYe8m9eRuLmLzitKpEREkUR00/8jjWXziO7qFDqOER6xeIFERERUR0nKJRwemECAnafhdOLE+HxxscQ/vlWU52dieKsDIkTElF53M3NxorIc3j9xO+Q14GeCd9ei0TLXZvw/tmjUkehGsAbGomIiIgIAKCwqQfPWYt0lt1dvwhpv/4At8nz4DB4FAQ5B8cjMlQyQcC8Vh2Rmp8Hs1o8WOcDOcVF+Cs1GW7mllJHoRrA4gURERERlUlTVIiss8dQfD8VsQtnImXXZnjOXgSrVh2ljkZEZXA1t8THbbtKHaPGPO3VAHt6P4M2Di5SR6EawNtGiIiIiKhMMqUJmmz5Ex6zFkJuaY28q3/j2vincWveeBSmJEgdj4jqOC9Lawzx8YenJWfWqgtYvCAiIiKiRxIUSji/OAnN9pyFwzOvAIKA9N924dKz7ZFx/JDU8YjoH6Io4mJaCoo1GqmjEFULFi+IiIiI6ImU9RzgPW8Zmmz5ExaBQYAgwLxxC6ljEdE/4nOy0PyHr2H3zXIUadRSx6kxtzLvY9PVv/F7fIzUUaiasXhBREREROVm3rgFGm08gMabD0Jp76RdnvTdahQm35UwGVHdFpOVAWulCepb20IpqzsD6/7v1hWMPfwrvrryl9RRqJqxeEFEREREFSLIZDDzbaR9nnHsEOK/eA+XhnVA0nerIBYXSZiOqG56ytUT6aNn4FD/4VJHqVHtndzQzc0LbRw5aGdtx+IFEREREVWJ0tkNFs3bQpObjfgv3kfUyBBkXTgpdSyiOkcmCHA0M5c6Ro0KcfPCnwNexJst2ksdhaoZixdEREREVCXmfs3Q6Ktf4f3ucsht7JB/8zKujX8aMfOnovh+mtTxiIioFmDxgoiIiIiqTJDJ4DBkFAJ2nYbDMy8DAFJ//h7Xpw2DKIoSpyOq3WKzMxH88/d461SY1FEkoxFFZBcVSh2DqhGLF0RERESkNwpbO3jP+wKNNv4Ks4ZN4Tp+DgRBkDoWUa12JjkBRxLicLCOzrixPioCNpu/wMyTf0odhaqRQuoARERERFT7WLZojyZbD0OQ/zvrwb2ftqLw7m24jJkJmcpUwnREtUtHZzdsCu4PlbzuzDLyX3ampsguKkRkeorUUagasXhBRERERNXiv4WL4ox0xC97F+qsDKQd/BHe73wOq6CnJExHVHu4WVhhdKNAqWNIppe7DyKHjUUjW3upo1A14m0jRERERFTt5Na28J73BZQOLiiIvYFrkwYjZv4UDuhJRFVmqzJFMztHKGT8eFub8f8uEREREVU7QRBQr+dgNPshHI7DxgKCgNSft+HScx2QdmAXB/UkqqR7+bnYHX0VMVkZUkchqlYsXhARERFRjZFbWsPrrc/QaOOvMK3fGMXp9xD93gTkR1+VOhqRUTqaEI9nD/2IZw7uljqKpM7fS8Q7pw9j45W/pI5C1YRjXhARERFRjbNs3g5NtoYh6ZsVUOdmw6x+Y6kjERklQQBa2juhvZOb1FEkdeFeEhZFhKOHuzfGNW4hdRyqBixeEBEREZEkZEoTuL46W2dZfsx13F40C15zFsOsQROJkhEZjyE+/hji41/nb73q5OyOCY1boLOLh9RRqJrwthEiIiIiMhjxX7yH7HPHcHlkCO5+uRiaokKpIxEZBUEQpI4gqSb1HLC+a1+87B8gdRSqJixeEBEREZHB8Hr7c9g81RdicRESvlyMyyNDkBN5VupYRAaprve2oLqFxQsiIiIiMhgmzu5osHQrfBd+BUU9B+TfuoIrY/ogbuk8aPJzpY5HZFB+i4+G59Y1mHb8kNRRDEZibjZuZd6XOgZVAxYviIiIiMigCIIAu95D0ex/4bDrPxwQRSR/vxYpe76VOhqRQTl/LwnxOVm4l58ndRSDsObSebh+txqzw0OljkLVgAN2EhEREZFBUtjawfejtbDrPRQpuzbB6blxUkciMijTmrVGV1dPmMn5sQ4AmtSzhwAgm2Pl1Er8LSciIiIig2bTpRdsuvTSPtcUFeLmrJfgNHw8bDr3esyWRLWblYkKXTi7hlZnZw9kjXkDFkoTqaNQNeBtI0RERERkVFJ2bEDmid9xY/pwxHw4BcW8v52IAJjI5Sxc1GIsXhARERGRUXEcNgZOI14DBAGp+7bh0nMdcT9sv9SxiGrUpbQUfPrXKZxIjJc6ClGNYPGCiIiIiIyKzNQcnjMXoNHGX2Hq44fi1CTcnP0Sbr3zKorvp0odj6hGHIyPwZxTYfj079NSRzEoh+/G4vnff8SHZ49JHYX0jMULIiIiIjJKls3bocnWw3AZPQOQy5F+cDduL5wpdSyiGuFnUw/P1W+E3h6+UkcxKMn5ufjfrav4Ne6W1FEMyqJFi9C2bVtYWVnByckJQ4YMwdWrV3XajB49GoIg6Dw6dOggUeLSOGAnERERERktmcoU7lPfh233gYhbMgcer38odSSiGjHAuyEGeDeUOobB6ejkhk/bh6C1g4vUUQzK4cOHMWXKFLRt2xbFxcWYN28eevfujaioKFhYWGjb9e3bF5s2bdI+NzExnDFEjLbnRXkqR6Io4sMPP4SbmxvMzMwQEhKCS5cu6bQpKCjAtGnT4ODgAAsLCwwaNAjx8bxvjIiIiMiYWDRthUabDkL1n2+h765biLTfdkEURQmTEVFN8rC0xuwW7dHd3VvqKDUiKysLmZmZ2kdBQUGZ7Q4cOIDRo0ejWbNmaNGiBTZt2oTY2FicO3dOp51KpYKLi4v2YWdnVxMvo1yMtnjxoHIUHh6OQ4cOobi4GL1790ZOTo62zZIlS7B06VKsWrUKZ86cgYuLC3r16oWsrCxtmxkzZmDPnj3Yvn07jh07huzsbAwYMABqtVqKl0VERERElSQIgvbf2X+fRsJXnyF63njcmvMKiu4lSZiMSL+yiwqRU1QodQwyAE2bNoWNjY32sWjRonJtl5GRAQClihNhYWFwcnKCv78/xo8fj+TkZL1nrixBrCWl6JSUFDg5OeHw4cPo2rUrRFGEm5sbZsyYgblz5wIo6WXh7OyMxYsXY+LEicjIyICjoyO2bNmC4cOHAwDu3r0LT09P7N+/H3369HnicTMzM2FjY4OMjAxYW1tX62skIiIqD56b9GP16tVYvXo1CgsLcfPmTcTFxcHDw0PqWFROmqJCJH69FAlfLwXUxZDb1IPXm4tRr8+zOkUOImO0LuoCphw/hLGNArGhaz+p4xicrMIC/JWWAhOZDO2c3KSOUy3i4+Ph6emJqKgouLu7a5erVCqoVKrHbiuKIgYPHoz09HQcPXpUu3zHjh2wtLSEt7c3oqOj8d5776G4uBjnzp174j5rgtH2vHjYw5Wj6OhoJCYmonfv3to2KpUKwcHBOHHiBADg3LlzKCoq0mnj5uaGgIAAbZuHFRQU6HTLyczMrK6XRERERBKaMmUKoqKiEBYWJnUUqgSZ0gRuE99Ck2//gJl/INQZ6Yh+dwJuzh7FXhhk9K5lpEEjinAwNZc6ikHadO0invppKxZFhEsdpdpZWVnB2tpa+yhPkWHq1Kn4+++/sW3bNp3lw4cPx9NPP42AgAAMHDgQv/76K65du4ZffvmluuJXSK0oXoiiiJkzZ6JLly4ICAgAACQmJgIAnJ2dddo6Oztr1yUmJsLExAT16tV7ZJuHLVq0SKdbjqenp75fDhERERHpiXmjQDT59ne4TXobgkKJjMP7cfXV/hCLi6WORlRpSzv2wN2XpmB6QBupoxik5nZO8LSwgiOLO6VMmzYNP/30E0JDQ5/Ym9DV1RXe3t64fv16DaV7vFox28iDytGxY6Xn8n24W6Aoik/sKvi4Nm+//TZmzvx3Cq7MzEwWMIiIiIgMmKBQwvXVN2ET3B+350+F04uTIChqxWUw1WGu5pZSRzBYwa6eiB05WeoYBkUURUybNg179uxBWFgYfH2fPMVuamoq4uLi4OrqWgMJn8zoe148qnLk4lIyNc7DPSiSk5O1vTFcXFxQWFiI9PT0R7Z5mEql0umWw3uJiYiIiIyDuV8zNN58CHb9n9cuyzh2CKn7tnNGEqJahOPalDZlyhR89913+P7772FlZYXExEQkJiYiLy8PAJCdnY3Zs2fj5MmTiImJQVhYGAYOHAgHBwc888wzEqcvYbTFC1EUMXXqVOzevRt//vlnqcqRr68vXFxccOjQIe2ywsJCHD58GJ06dQIAtGnTBkqlUqdNQkICIiMjtW2IiIiIqPYQFArtB5vizPuI+b/XEfPhZNx840UUJt+VOB3Rk+2PvYmJRw7g59s3pI5CRmTt2rXIyMhASEgIXF1dtY8dO3YAAORyOS5evIjBgwfD398fr7zyCvz9/XHy5ElYWVlJnL6E0faXmzJlCr7//nvs3btXWzkCABsbG5iZmUEQBMyYMQMLFy6En58f/Pz8sHDhQpibm2PEiBHatuPGjcOsWbNgb28POzs7zJ49G4GBgejZs6eUL4+IiIiIqpnc3BJOL0xAwpeLkXHsIKKe7wSPWQthP+BFfnNLBuu3+Gh8eeUvmCuUGOjdUOo4Bmvnzcv4JOIUgl09saxTD6njSO5JvcvMzMzw22+/1VCayjHa4sXatWsBACEhITrLN23ahNGjRwMA5syZg7y8PEyePBnp6elo3749Dh48qFM5WrZsGRQKBZ5//nnk5eWhR48e2Lx5M+RyeU29FCIiIiKSgKBQwHXMG7Dt2hcx86chN+o8bs+fivRDP8J73jKYOLs/eSdENewZH39YKJTo4e4tdRSDViyKuJCaBHOOb1NrCCJv8KuSzMxM2NjYICMjg+NfEBGRQeC5Sb/i4+Ph6emJuLi4J47MTsZLLC5G0tY1uLt+EcTCAsgsrNDsh3CYOBrGQHVEVDGJudkIT76LFnZO8LW2lTqO3tXFcxPLUERERERU5wkKBVxeeR02Xfvg9vypUHk1ZOGCyIi5mFtiiI+/1DFIj1i8ICIiIiL6h5lvIzTaeACawnztssKUBGQcOwiHIS9zLAyS1O2sDGQVFaKxrT0UMqOde4GoUvgbT0RERET0H4JcDrmZBYCSQe5iF85C7II3cH3yMyiIj5E2HNVp6y9HIPCHrzH1+KEnNybcyryPzVcv4lB8tNRRSA9YvCAiIiIiegyrdl0hqMyQdeYIol7oguTtX0LUaKSORXVQkUYDS6UJWtg7SR3FKOyJuYYxh/fjy8t/SR2F9IDFCyIiIiKiRxAEAc4vTkKzHcdg2aYLNPm5iPvsLVwd/zTyb9+QOh7VMZ926IaM0TMwrlFzqaMYhbaOrujm5oU2ji5SRyE9YPGCiIiIiOgJVB6+8F/7I7ze+hwyc0vk/HUKUSO6IvPMEamjUR0jEwSYyOVSxzAKXV098eeAF/FWyw5SRyE9YPGCiIiIiKgcBJkMjsPGoNnOE7Du0A1KBxdYBLSROhYRUZ3A4gURERERUQWYuHig4cof0Hjj/n8H9tRokLJrMzSFBRKno9pqReRZ9Ni3Hd/fiJI6itERRRF5xUVSx6AqYvGCiIiIiKiCBEGA0uHf++hTdn6F2EUzcfmlEGRfPCNhMqqtjiTE4c+7t3E3J0vqKEZlReRZ2G7+Am+dPix1FKoiFi+IiIiIiKpI6ewGhZ0j8m9dxdWxfRH3+dtQ52ZLHYtqkfltumBj134Y4N1Q6ihGxcZEhcyiQlxKvyd1FKoiFi+IiIiIiKqoXrcBaLbzJOyefgEQRSRvW4+o4Z2RcfIPqaNRLdHMzhFjGzdHY1t7qaMYlQFeDXFx2Fj82u85qaNQFbF4QURERESkBwpbO/jOX4OGK/8HE1dPFCbE4ca055Dw1adSRyOqs+xNzRBg5wiljDO0GDsWL4iIiIiI9MimYw803XEcTi9OhKA0gU2XPlJHIiMXnnQHe2OuI5G3IlEdxuIFEREREZGeyc0t4TlrEQL2nod54+ba5an7d6Lgzm0Jk5ExWnc5AkMO7sa6qAipoxilk0l38O6ZI9gTfU3qKFQFLF4QEREREVUTEyc37b/zbkQhZv5URA3vjMQtKyEWF0uYjIyJr5UNWtg7oa2Tq9RRjNKfd25jwYWT2M3ihVFj8YKIiIiIqAbITM1g2bIDNPm5uLP8A1x+uQdyLp2XOhYZgQ/adEHEs2PwtFcDqaMYpa6unhjfuAUGePPnZ8xYvCAiIiIiqgEqD1/4r9sL73eXQ25ti7xrF3FldC/EfvoW1NmZUscjqrWecvXEl137YniDJlJHoSpg8YKIiIiIqIYIggCHIaPQ7IdTsOv3HCCKSNnxJS6/0ou3kVCZ1BqN1BGIDAKLF0RERERENUxp5wjf/1sPv1W7oPLwheMzL0NQKKSORQbo9RO/w/v7tdh89aLUUYxecl4OkvNypI5BlcTiBRERERGRRKw7dEPT7cfg9MJE7bKss0eRuPkLaIoKJUxGhuJCahJiszNhIpdLHcWovXHiDzhvWYUVkeekjkKVxPIuEREREZGEZKZm2n9rCgtwe+EsFMTeQOr+HfCa+xms2nSWMB1J7dd+zyPiXhKa1nOQOopRq29tCwFAcl6u1FGoktjzgoiIiIjIQAhKE7iOmwVFPQfk37qKaxMHIvq9iSi6lyR1NJKIjYkKwW5ecDQzlzqKURvtH4CsMW/gy659pY5ClcTiBRERERGRgRAEAfZPD0ezH07BYehoQBCQ9uv/EPlsOyRtW8dBPYkqycpEBQulidQxqApYvCAiIiIiMjAKm3rwfmcpGm8+BPOmraDJyUL85+8gMzxU6mhUg7bfiMJnf53C5fR7UkchkhzHvCAiIiIiMlAWzVqj8aaDuLd3C7LOHoN1557adWJxEQSFUsJ0VN02Xv0bv9+5DRsTFZpwzIsq23HzMvZEX8Nz9Rvj2fqNpI5DFcSeF0REREREBkyQy+E4dDTqL/wKgiAAAIoz0hE5tC0Sv1nBWUlqsYHeDTHUxx8dnd2ljlIrnE1JxI5bVxCWECt1FKoE9rwgIiIiIjIy937cgsK7sbiz8kPc2/sdPGctgE3nXlLHIj17PSAIrwcESR2j1hjs4wdHU3MEu3lKHYUqgcULIiIiIiIj4zxqKhR2Drizcj4KYm/gxvThsO7cC55vfAxTHz+p4xEZpC4uHuji4iF1DKok3jZCRERERGRkBJkMDgNHIGD3GTiPmgpBoUTm8UO4NLwz4pe/D1EUpY5IVXQ3Jwt5xUVSxyAyGCxeEBEREREZKbmlNTymf4SmO47D5qk+gLoY6uws7dgYZLwmHP0NVpuW4bvrl6SOUqtkFhbgRGI84rIzpY5CFcTiBRERERGRkTP1boiGy7ah4cr/we21d7TL82OuI+P4IQmTUWXF52RBLYpoYG0rdZRa5dUjv6LzT1ux4+ZlqaNQBXHMCyIiIiKiWsKmYw+d53HL3kXm8UOw7tANHjP+D2YNm0qUjCrqwtDRSMjNhoOpudRRapVAO0ecSLojdQyqBPa8ICIiIiKqhcTiYpj5+peMhxEeiqgRXRHzf6+jMPmu1NGoHARBgJuFFUzkcqmj1CrzWnVC/MgpmN2ivdRRqIJYvCAiIiIiqoUEhQIeM/4PzX4Ih22PQYBGg9S93yHymba4s3YB1Lznn+ogGceDMVosXhARERER1WIqD180WLwZjb4+AIsW7SEW5CFx4+dI3bdN6mj0CO+cPozXjv6Gv1OTpY5CZDBYvCAiIiIiqgMsm7dDo6/2o8FnW2DdsTscho7WritMugNRo5EuHOnYdvMy1l2OQGpBntRRaqUF508gaPc3+DHmmtRRqAJYvCAiIiIqw+rVq9G0aVOEhIRIHYVIbwRBgG3I0/Bb+QNkJioAJWNjXJs8FJdfCkHGsUMQRVHilHWbKIpY3C4Yc1u0Ryt7Z6nj1Eox2Rk4dy8R51ISpY5CFSCI/OtUJZmZmbCxsUFGRgasra2ljkNERMRzk57Fx8fD09MTcXFx8PDwkDoOkd7lXovEtQkDtGNgWLbsALfJ78KqdSeJkxFVj1PJd3EnJwvtHF3hYWmc58m6eG5izwsiIiIiojrM3D8AAT+eh/OoaRBUpsiOCMe1CQNwbcpQ5Fw6L3U8Ir1r7+SGob6NjLZwUVexeEFEREREVMcpbO3gMX0+AvacheOwsYBcgaxTYbjySk/kXvlL6nh1yvHEeFxOvwc1xyAh0sHiBRERERERAQBMnNzg9dZnCNh9BvYDX4RV264wa9Rcu774fpqE6eqGcYd/RdP/bcTB+Gipo9Rql9JS8O21SMRkZUgdhcqJxQsiIiIiItKhcveGzwer4bfyBwiCAAAozkhH5JDWuDl3NPJuREmcsHYq1mjgaGYOc4USrR1cpI5Tq71x8k+8EvYLfr8TI3UUKieF1AGIiIiIiMgwCYp/Py5knvwT6pws3P/jJ9z/4yfYdh8I13GzYd4oUMKEtYtCJsPRQSOh1mggl/F75urUxcUDhRo1bP+ZdYcMH98RRERERET0RHZ9n0XTbUdh22MQIAi4/+fPuDwyGDfeGMGBPfWMhYvq936bzggbOALD6jeWOgqVE98VRERERERULmYNm6LB4s1ouv0Y6vV5FhAEZBw9gKvj+qEoLUXqeERUi7F4QUREREREFWLWoAnqL9iAZv8Lh93TL8B+0Ego7Ry163MunYcoihImNE7d921Dr1+2Iyr9ntRR6gxRFKHh76pR4JgXRERERERUKaY+fvCdv0anUJF7/RKuvNITZv6BcBk9HfV6DIYgl0uY0jjkFxfjSEIc1KIIK6WJ1HHqhJdD9+Gn2zews+dg9PbwlToOPQF7XhARERERUZU8mJEEAPJvXobMzAJ51y4i+p1XETm0LVJ++Bqa/DwJExo+pUyGU0NextfB/eBhYSV1nDohr7gYGYUF+Ds1WeooVA6CyP5cVZKZmQkbGxtkZGTA2tpa6jh1WvH9NGT/fRpFyXehyc+FJj8fmsICaAryIBbkwzbkaVh36AYAKEyMR/wX70FuZQO5pU3Jf61soPjnuamPH1QePtK+ICKiSuK5Sb/i4+Ph6emJuLg4eHh4SB2HyCgU309D8o4vkbxzA9QZ6QAAhZ0jnIZPgPPI1yAzNZc4IREQmZYCjSiisa09TIysd1BdPDfxthEyGurcbOTdiELBndsoiI9G4Z3bqNdnKGw69gAA5F75Czdnjnjk9iYuHtriRdG9RKT/vveRbV3GzYL7a/NK2qalIObDyTD19oOpjz9Mffxg6uMHhZ2jzrcMRERERFRCYWsHt4lvwfnlaUjd+x2SvluNwsR4JO/cAOdRU6WORwQACPjPOC1k+Fi8IINWkBCH5O3rkX0hHLlX/wLUap31Jq6e2uKFyrM+zPwDYeLqCbm5BWQqUwgqM8hMVJCZmsKiRXvtdkpnd3i+uRjqrAyoszNQnJXxz78zoc7KgMrNW9s2P/oaMk/8gcwTf+gcW25lA7MGTeA4fDzsej1TjT8FIiIiIuMkN7OA0wsT4ThsLNIO/QixuAgyExUAQFSrEbd0HuyfHg6Lpq0kTiq9NZfOw9PSGt3dvGDBMS+ISmHxggxGwd1YZF84CaWjC6zbBQMAxKJCJG9do22jdHSFyqsBVO7eUHn4wqrtU9p1KndvNP3+cLmOZeLoCqfh48vVVuVVH17zliE/5rr2UXj3NtRZGciOCIdd/+e1bfNjb+Lenm9g1aYLLFt2gNyS3bWJiIiIBIUS9v2e01l2/8ivSNnxJVJ2fAnL1p3gPGIybLr2hSCre8Py5RcXY8bJP1Ck0SDmxUksXtSgn2Ku4+y9RIxv3AKevHY3aCxekKQK4mOQdmgP0g/uRt71SwAA2x6DtMULlWd9OI2cDPPGLWDVqiNMXGr+fi4TR1c4PvOKzjJNQT4K4m4h53KENisAZIaHImnLKiRtWQXIZDBv1By2wf1Rr+dgmPr41XR0IiIiIoNl6uMHu/7DkfbbLmSfP4Hs8ydKrv1GvAb7AS9AbmYhdcQak1VUiBENm+JGRjq8+AG6Rn184STOpCSguZ0jixcGjgN2VhEHRas4URSRvG0d0g/uRk7kuX9XyBWwaNICNsH94TrmDekCVkHW+RNI/WU7ss+fQEHcLZ11pg2aoP7Cr2DWoIlE6YioruC5Sb/q4qBoRDWpMPkuUnZ+hZRdm6DOygAAyK1t0XTbUZg4u0ucjmq7RRdO4mbmfYxr3Bwdjej3rS6em9jzgmqEOi9HWz0XBAHph/aUFC5kMlgFPQW73kNh230gFNa20gatIqvWnWDVuhOAkhNx5sk/kf7HT8g8FYaCuFs6PUeyI8KhqOcAU++GUsUlIiIikpyJkxvcp74Pl7EzkbpvO5K/Xwu5dT0ondy0bQqT78LkP8+J9OXtVh2ljkDlVOniRVFRERITE5GbmwtHR0fY2dnpMxfVEvlxt5C8dQ3SDvyAgL0XoLCpBwBwfmkqiu4loV7PwVDaO0mcsnqYOLnBYfBLcBj8Eooz7yP3yl+Q/2fO7tglc5F37SKs2gXD8dkxsA3uB0GhlDAxERERkXTk5pZwev5VOD47BkVpydpZ3YqzMnDp2fYw82sG5xGvwTbkaQiK2vUdbEZhAWz+GciUiMpWoXd9dnY2tm7dim3btuH06dMoKCjQrvPw8EDv3r0xYcIEtG3bVu9BybjkRJ5F4rcrcT90H/DPnUmZp0Jh13soAKBej0FSxqtxCmtbnbExNAX5UDo4I+96JLJOH0bW6cNQOrrCYcgoODzzMr9ZICIiojpLkMth4uiqfZ7z12mIRYXI+fs0bv19GiaunnAaPh72g0dBYWUjYVL9yCsugsM3K+BhaYXzQ0ejnspU6kh10r38XNiYqKCUyaWOQo9Q7qF8ly1bBh8fH2zYsAHdu3fH7t27ERERgatXr+LkyZP44IMPUFxcjF69eqFv3764fv16deYmAyRqNLh/5ACujn8aV0b3xv0/fwZEEdade8F/3U+ox+lEtWQqU/it2ImAnyLgMmYmFHaOKEpJQMKGJbg4sAUSNi2TOiIRERGRQbDp0guB+/6C66uzobC1R2FCHOK/eB8X+wcgdslcFN1LlDpilVy+n4piUYPc4iLYsveFJFrt2gTHb1fiwr0kqaPQY5R7wM7nnnsO77//PgIDAx/brqCgABs3boSJiQleffVVvYQ0ZBwU7V+FSXdwcVBLQK2GoFDCrt8wOL80lQNUloOmqBD3//wZKbs2Ifv8CTT8YjtsuvTWrhMUSm3XSSKiJ+G5Sb/q4qBoRIZKk5+HtAM/IGnbOuTfvAzI5Qjce0GSGen06X5BPm5nZ6JFLb2d2tD1+mU7fr9zG993H4gXGzaVOk651MVzE2cbqaK6foFYmBivc7KI/WQ2ZOaWcHphAm99qKS8W1dg6u0HQV7SZe3u+k9w/8gBuI6dCdtuA+rk3OdEVDF1/dykb3XxApHI0ImiiKzTh5F77SJcRk3TLo//4j2Y+QfCrvczHEuMyi0uOxP1VKawVJpIHaXc6uK5Sa/FC41Gg/j4eHh5eelrlwavrl4gFibfxZ2VHyHt4G403RoGMyOpUBobUa3GxUEtUZR0BwBgWr8RXMfOQr3eQ1nEIEloigoBjQayf+7HVedkIffyX9Dk50GTnwuxuBhicdE/j2KYNw6ERUAQgJIB19J+2f7PngRAJoOgVEKmVEFQKmHq4w/zRiW9+0S1Gvkx1yC3sILMzAJyC0tehFZAXT03VZe6eIFIZIzyY67h0rAOAAClsxuchk+A49DRkFvy7yDVPnXx3FSpYXo3bdqEHTt24Pbt27C2tsZTTz2FN954AwqFAr6+vlCr1frOWcqRI0fw6aef4ty5c0hISMCePXswZMgQ7XpRFDF//nx8+eWXSE9PR/v27bF69Wo0a9ZM26agoACzZ8/Gtm3bkJeXhx49emDNmjV15n9+ZWjy85D03Sokbl4OTX4uACDzFIsX1UWQy9F0axiStq1Dyo4NyL91FdHvTkDityvgPu0DWHfozttJqNJEtRrF6SkoTE6A0sFZ21sqP+YaEjcvR1H6PaizMqDOug91ViaKszIgFuSVTGc3ekZJ29ibuDbp0QPwuoyZ+W/xIi0FcZ+9/ci2Ti9MgHmjT0ra3k9F1PDOOusFExXkVrZQ2NRDvV5D4DZ+Tsnr0GiQ/P0aKOydoXRwhtLeCUp7Z8itbfn+ICKqQxR2TnB7bR6Sd25AUdJd3FnxIRI2fgaHwaPgPOI1g7y1JKeoEBOP/oYgRxdMa9YGcn45RfRIFSpeqNVqDB06FAcOHED//v0xaNAgpKen44cffsCXX36JlStXVlfOUnJyctCiRQuMGTMGzz77bKn1S5YswdKlS7F582b4+/vj448/Rq9evXD16lVYWZVMVzljxgz8/PPP2L59O+zt7TFr1iwMGDAA586dg1zOUWYflh66D/Gfv4PCxHgAgEXzdvCctRAWzVpLnKx2U9jaw/21eXAZNQ3JOzYgactK5F2LxI1pz8HttXlwHTdL6ohkoESNRttDJz/2JlL+9xUKE++gKCUBhSkJKLqXBPxTbHafPl/b7Vadk43UfdseuV91Xo7233JLa5j6+kNmag6ZyhSC0gSCQlEyTotCCdP6jf5ta2GJer2H/jMDkQhRo4FYVASxuBBiYSFUXg21bTWFBZDb2EGTlwOxsGRmK7GwAMWpSSWP9FRt2+KMNMR/8X6pnILSBCbO7rDrNwxuE0uKJg+6Gas868PE2V17exYRERk/hbUtXMfNgvOoqSXjYny3Gvm3riD5+7VI3rEBfit2wrp9iNQxdUSkJmPrjSj8eTcWMwI5Y6OUlkScQkRqEr7o1ANOZhZSxzFqRUVFSExMRG5uLhwdHWFnZ6eX/VaoeLFs2TKcOnUKERERaNLk30EYNRoNli5digkTJuglVHn069cP/fr1K3OdKIr44osvMG/ePAwdWjI15zfffANnZ2d8//33mDhxIjIyMrBx40Zs2bIFPXv2BAB899138PT0xO+//44+ffqUue+CggKdKWIzMzP1/MoM0+0FM3Bvz7cAAKWzOzxe/7Dk1gV+q1lj5JbWcB03C47PjkHC15/j3o9bYNdnqNSxSGLFmfdREHsD+bdvoiDuJvJjb6Eg9iYK7sTA9dU34TxyMgBAnZ2J5G3rS+9AJoPS3hmC/N/TgcrdB+5T34fC1h5ym3pQWNlAbmkDudU/DwsrbVtTz/po9r/wcmVVOrig/sKvytVW5eqJln/cAACIxUVQ5+ZAnZMFdeZ9FGekQfnfAc3Uatj1HYai1GQUpSahKDUZ6ox0iEWFKIiPhjo769+fV/o9XJ9S8r4RFEqYuPtA5eEDU09fqLz9YNm8nfbWFaq86rpoISIqD5mJCg6DRsJ+4AhknvwDSVtWIe/6JVi2aKdtU5x5HwprW+lC/sPN3BIfBXWBDLymltrGq3/jWkYaxjQKRC8PX6njGJ3s7Gxs3boV27Ztw+nTp3U+M3t4eKB3796YMGEC2ratfJGuQsWLzZs349NPP9UpXACATCbD7NmzIYoi5s6dW+kw+hIdHY3ExET07t1bu0ylUiE4OBgnTpzAxIkTce7cORQVFem0cXNzQ0BAAE6cOPHI4sWiRYswf/78an8NhsbUxx+QyeDyynS4jpsFmam51JHqLIWtHTxnLoDbhLk693DGfjIbCls7uIyewf8/tVDx/TTk3bwMRT17mNVvDADIiTyLK6N7P3KbwoRY7b9VnvXhPGoaTJzdoXR2g4mjK5ROrlDaOUFQ6J4KHvweGQpBoYTC2rbkItPVs9R6pYMzfD/+UmeZprAARfeSUJgYD0U9e+1ydXYmVN5+KLx7u6S4cfs6Cm5fx4MytOPwCfB68xNt2/jl78OsYTOYNwqEWcOmvG/6MWriooWIqCIEQYBNp56w6dQTRWkp2usjUaPB1XF9obR3huurb8KyTWfJvpDztbbFe607P7khVbvXmrZEvlqN+gZQ1DI2y5Ytw4IFC+Dj44NBgwbhrbfegru7O8zMzJCWlobIyEgcPXoUvXr1QocOHbBy5Ur4+flV+DgVKl7cvHkTHTp0eOT6N998E2+++WaFQ+hbYmLJXM/Ozs46y52dnXH79m1tGxMTE9SrV69Umwfbl+Xtt9/GzJkztc8zMzPh6Vn6YtrYaQoLUJScAJWHDwDA6cVJsGrbFeb+AdIGI63/fojKuxGFlB++BgCk7tsOjxn/B9seg9gzxgiJGg0KYm8g91ok8q5f+ue/kShKTgAAOL04EZ6zFgEAVN4lf/SVjq5QeTWAqVd9qDzrw9SrAVQevjBx99buV2FlA4/pdafwKjNRQeXmBZWb7gDSpl4NELDrFES1GkUpCciPu4WCfx75MTdg2fzfD9b5Mde0Pc4eMHH3gbl/AMwbBcLmqb7spfGPmrpoISKqLKWdo/bfedcvoSAuGvnR15B19igsW3aA66tvwqp9CK+d6jDetlN5J06cQGhoKAIDy74uateuHcaOHYt169Zh48aNOHz4cPUXLywsLJCSkvLIA0VERGDFihX4+uuvKxykOjz8x0cUxSf+QXpSG5VKBZVKpZd8hirvRhSi350ATWEBmmwNg9zMAoJMxsKFATNt0AT1F29G/LJ3UZgYj1tvjYFV267wfPMT7bf0ZHhKChU3IRYXaQe9LUpN0o6U/jATNy/IzC21zxVWNmh5NA5y3pdZYYJcDhMXj5LB29p2LbONwtYBLmNnIe96JHKvRaIo6Q4K78Sg8E4M7ofug8zMQlu8KEyMR/rve2ERGATzJi0hM6nd54mH1dRFCxGRPpg3CkTAj+eQ+M0K3Nu7BdkR4bg+9VlYBAbBbfK7sH7EeUHf8oqLcDEtBc3tnGCqqNQ8CkQG4X//+1+52qlUKkyePLnSx6nQuyQ4OBjr1q1Dp06dSq1LTEzECy+8gOvXr0tevHBxcdFmcnV11S5PTk7W9sZwcXFBYWEh0tPTdXpfJCcnl/n66orUX3bg9oIZEAsLoKjngILbN2DeuIXUsegJBEFAvR6DYNO5JxK/WY7Eb1Yg68wRRL3YFS4vT+OtPgai+H4qciLPISfyLLIvnkXupfNQZ2fC5qk+aLisZIBME0dXqDx8S24P8QuAuX8AzPyawaxBkzJvWWDhovqoPHzgPnme9nnx/TTkXo9E3tWLyL36Nyxb/3uuyDp3HPFfvAegZFYUi6atYNGiPSz/eShs6pXaf21SUxctRET6YuLiAa+5S+Ay9g0kfbsSKbu/Qc7Fs7j+2hA0+mo/LFs+ure5vpxJSUTwz9/D18oGt16cVO3HoyfLLipEZFoKghxdoeDMLwanQsWLDz74AB07doQgCHjzzTfRsGFDpKWl4eeff8bHH38MHx8fXL9+vbqylpuvry9cXFxw6NAhtGrVCgBQWFiIw4cPY/HixQCANm3aQKlU4tChQ3j++ecBAAkJCYiMjMSSJUskyy4VUa3GndX/h6RvVwAArDv3gs/7K3UHxSODJzM1h9vEt2E/YATiPn8HGUd+xb0938L5paksXkhIFEVcHtEVedcvlVonqMx0BssEgGZ7zrLbqgFS2NrBum3XMr+RU9o7wqZrP+T8fRrF91ORHRGO7IhwJAGAIKDh8h2w6VQyOHR5egHWRhqNBvHx8fDy8npyYyKiGmLi6ArPWQvh8sp0JGxahrzrl2DRor12vTo3G/L/9HrUp5S8XDiYmqGlvfOTG1O1E0URbt+tRlZRIaKeG4cm9RykjkQPqVDxonnz5ti/fz/Gjh2L77777t+dKBSYPn06pk2bBm9v78fsQX+ys7Nx48YN7fPo6GhERETAzs4OXl5emDFjBhYuXAg/Pz/4+flh4cKFMDc3x4gRIwAANjY2GDduHGbNmgV7e3vY2dlh9uzZCAwM1M4+UleoszMR/e4EZBw7CABwGTsLbpPe1k6xSMZH5e6Nhku34n7YfogatfZbX1EUUXw/FUr+MdY7Ua1G3vVIZJ07juwLJ6HOzoT/ur0ASnrGPOg1ofL2g2VgECwC2sAiIAhmDZtAUCh19lUXP9gaO+sO3WHdoTtEUURB7E1k/3UK2X+dQs5fp5B/+wbMm7TUtk3c/AXuh+6DdfsQWHfoDovmbSFTmkgXXs82bdqEHTt24Pbt27C2tsZTTz2FN954AwqFAr6+vlD/Mz0vEZEhUTo4w+vNTyCq1drzsDo7E5HPtke9bk/DdeJber9+erZ+Iwz19UdOcZFe90uVIwgCmtazR2x2FpLzctGkdneaNEqCKIpiRTfSaDQ4ffo0oqOjYW1tjY4dO8LOzg45OTn47LPP8MEHH1RHVh1hYWHo1q1bqeWvvPIKNm/eDFEUMX/+fKxfvx7p6elo3749Vq9ejYCAf8dtyM/Px5tvvonvv/8eeXl56NGjB9asWVOhATgzMzNhY2ODjIwMWFsb5yj0MR9NQ+pPWyGoTOHz3krY9X1W6khUTdIO7cHtj6fDffJ7cBw2FoJcLnUko5Z3IwqZ4X/qFCy0BAEt/ripnYYt//YNKGzta/3tA1Ra8f00KGz/nSr02mtDkHXmiPa5zNwSVm26wGHwSNiGPK2XY0pxblKr1Rg6dCgOHDiA/v37w9/fH+np6Th48CDS0tKwcuVKjB071iiLF/Hx8fD09ERcXBw8PDykjkNENSR13zbEfDgFwD9T1r86G47DJ9SqgjPpyisugtlDXyoZqrp4bip38SI2NrZCXT3v3LkDd3f3SgczFrWheFGUloKbs1+C56xFsGjWWuo4VI1uvvky7ofuAwCYN2sN73eWcbaEchJFEQW3b0Dl1UDbKyn6vUlI+3Wnto3MwhKWLTrAqnVnWLbuCIumrUtNQ0pUmJKArFOHkXkqFJnhoShOvwcAcHttHlzHzdLLMaQ4N3322Wf47LPPEBoaqjOlukajwdKlSzFv3jwUFxezeEFERiXr7DHELZ2HvGsXAZRMPe45axFsuvSSOBnVdYZ0biouLsa3334LURTx8ssvQ6msngJQuYsXzs7OGDRoEMaPH4927dqV2SYjIwM7d+7E8uXLMXHiREybNk2vYQ2RsRYvChPjS0bZ/0ddvQe7rhE1GtzbvRnxK+dDk5MFyOVwfnESXCfMrbb7OY1ZUWoyss4cKfmQefowipLuosm2ozD3awYASDu4G2n7d8IqqAss23SBuV8AixVUIaJGg7xrkcgM/xM2XfvqbXYgKc5NAQEBmDt3LkaNGlXm+k8//RRz586FRqOpkTz6ZEgXiERU80S1Gqn7tuHOmo9RnJoMAKjX6xn4/t/6Sp/3jyTEYc6pUPT3bID323TWZ1yqIwzp3DRnzhx07twZxcXFOHHiBD7//PNqOU65322XL1/GwoUL0bdvXyiVSgQFBcHNzQ2mpqZIT09HVFQULl26hKCgIHz66afo169ftQSmqksP3Yfod16F55zFcHzmFQC8x76uEGQyOA4bC5vgfoj77G3c/+MnJH23GmmH9sDnvRWw7tBd6oiSy4+5jns/bUVmeKj2W5YHBBMV8qOvaosXdr2Hwq73UCliUi0hyGQwb9wc5o2bSx2lym7evIkOHR49Ov+bb76JN998swYTERHphyCXw2HwS6jXczASNixB0vdrIVOZVukLi+OJ8TiVnACvMmYSI+moNRpMPPob/k5LwcH+z8NWZSp1JKOg0WiQm5sLmUxWrV9SlHtERjs7O3z22We4e/cu1q5dC39/f9y7d087u8jIkSNx7tw5HD9+nIULA5Z2YBduvTUGYlEhss4eQyWGPKFawMTRFQ0Wb0bD5Ttg4u6DoqS7gLzu9RgQRRH5MddRkBCnXVaYGI+kb1doCxdm/oFwHjUNfqt2oeWfN1msIHoECwsLpKSkPHJ9REQExo4dW4OJiIj0S25hBY8Z/4cm3/4JjxkfaZcXJsYj//aNx2xZ2ii/Zvi++0C81rSVvmNSFchlMhyMj8aZlARE/nNbJz3ZokWLoFarUVhYqJ3dszpUasBO+pcx3TZyb+93uP3xdEAUYff0C/B5fyUHbCRo8vOQcew31Os5RLss8/RhWDRtpZ0hozZRZ2ci6+xRZJz4A5kn/0BhQhycRk6G5xsfAyj5ecQufhPW7UNg1S4YSjtHiRMTVZwU56Znn30WFhYW+Pbbb0utS0xMREhICK5fv84xL4ioVhFFETdnjkDWmaPweONjOAx9hT2ajdyWa5EwUyjQzc0b9qZmUsd5pIqemxYtWoTdu3fjypUrMDMzQ6dOnbB48WI0atRI2+bBpBdffvmlzqQXzZo1q86XUm6cC7OOSNnzDW7/3+uAKMJh6Gj4fLCKhQsCAMhMzXQKF4XJd3Fz5khEPtsOKXu+gVhcLF04PdEUFiBh0zJcnTAAET0a4ubsUbi3ezMKE+IgKE2gyc3RtpWZmsHng1Ww6zuMhQuiCvjggw+wa9cuvPLKK4iMjER+fj7u3r2L9evXo23btnB05PuJiGofsSAfmoJ8aPJzEbtoJm7OHIGif8bFIOM0yj8Aw+o3NujCRWUcPnwYU6ZMQXh4OA4dOoTi4mL07t0bOTn/XgcvWbIES5cuxapVq3DmzBm4uLigV69eyMrKkjD5v9jzooqMoedFeug+3Jo7GtBo4PTiRHjMXMiKMD1SzuUIRM8bj4LYmwAAlVcDuE18C/V6PaOdZcPQFaYkoCAuGlatOwEoqSJf7B+AopQEACWvybpDd1h37A6roC6Qm1lIGZdI76Q6Nx0+fBhjx45FTEyMdplCocD06dMxbdo0eHt7c8BOIqp1RI0GydvW4c7q/4NYWABFPQd4v7cCtl37ltn+wr0kRKXfQxcXD3hb2dRwWqotqnpuSklJgZOTEw4fPoyuXbtCFEW4ublhxowZmDt3LgCgoKAAzs7OWLx4MSZOnKjvl1Bhde8m9zoo9/JfgEYDhyGjWLigJ7Jo0hJNdxxHyv82IvHrpSiIvYnoeeORuGkZnEdNg12foRAMbP5rdXYmsiPCkXkqDJmnwpB/6woUtvZofvAqBJkMgiDAZfQMQBBg06kHVB6+UkcmqpWCg4Nx/fp1nD59GtHR0bC2tkbHjh1hZ2eHnJwcfPDBB1JHJCLSO0Emg/PIybBuH4Lodycg70YUbs4cAeeXX4f7lPdK9Xb+/kYUPvv7NCY1aYm1T/WRKDU9ikYUEZmWgotpKXixYVPIDPyzU1ZWFjIzM7XPVSoVVCrVE7fLyMgAUDK2JQBER0cjMTERvXv31tlXcHAwTpw48djiRWxsLLy8vMqd+c6dO3B3dy93+weM42tUqhL3yfNQ/9Nv4fXW5yxcULnIlCZwHvEaAvaeh9uktyGzsELejSjcXvgGirMypI6nde/Hb3FlTG9E9GiAGzNeQPK2dci/dQUQBJi4eaE47d/BA52Gj4fT86+ycEFUDWJjY7X/lslk6NChA1588UU8/fTT2osiCwsLbfHizp07kuQkIqpOZg2bovG3f8BpxGsAgPQ/9kKdU7q7vbelNdo7ueIpV8+ajkjloBFFtN3zLV4K3YcYA7rufZSmTZvCxsZG+1i0aNETtxFFETNnzkSXLl0QEBAAoGR8KgBwdnbWaevs7Kxd9yht27bF+PHjcfr06Ue2ycjIwIYNGxAQEIDdu3c/MWNZ2POiliq6lwi5lS1k/0zvU6/bAIkTkTGSW1jB9dU34fj8eKTs2gSxMB/Keg7a9bFL5sKyRTtYd+oJRTV1exRFEQXx0ciJPIecyHNwm/S29lgFd2KRc/EsAEDlWR9WQV1KBtoM6gqFrV215CGi0tq2bYtBgwZh/PjxaNeuXZltMjIysHPnTixfvhwTJ07EtGnTajglEVH1k5mo4DlzASwCg2Dq7QeFtW2pNlMD2mBqQJuaD0flopDJ0NnFHcUaDbKLCqWO80RRUVE6vRjK0+ti6tSp+Pvvv3Hs2LFS6x7+slsUxSd+AX758mUsXLgQffv2hVKpRFBQENzc3GBqaor09HRERUXh0qVLCAoKwqefflrp2Uk55kUVGeKYF+rcbFwZ0wcKW3s0/Py7WjljBEkv9+pFXB4ZXPJELodlYFtYtQ+BRdNWMG/SstKDXRYmxiPz9GHkXY9E3vUo5F67CHXmfe36hl9sh02Xku5sudcikXv1b1gFPQUVv70g0qrpc1NaWhoWLlyIr7/++okXLe+++67RTanOMS+IqKru7f0OgokK9v2ekzoK1RKVPTdNmzYNP/74I44cOQJf3397JN+6dQsNGjTA+fPn0arVv1P4Dh48GLa2tvjmm2+euO/8/Hzs378fR48eRUxMDPLy8uDg4IBWrVqhT58+2l4elaWXnhc3b97EypUrcfv2bZ0p0H766Sd97J4qQBRF3P6/15F/8zIU9s5Q52SxeEHVQlHPHi5jZuJ+2D7kR19DdkQ4siPCtes95yyB0/OvAgDybl3BvT3fQmZqDgDQFOZDLCxAceZ9FKelwGXMG7BuV1IIyY4Ix+2PdL+RFZQmMG8UCIuANlA6umqXm/sHwNy/an8Eiajq7Ozs8Nlnn+Hjjz8u86Jl5MiRerloISIyRnk3ohC7aBbE4iLcT4iD7+gZkBnJIOhUe4iiiGnTpmHPnj0ICwvTKVwAgK+vL1xcXHDo0CFt8aKwsBCHDx/G4sWLy3UMU1NTDB06FEOHDtV7fkBPxYshQ4Zg6tSpGD58ON+IEkvashLph34E5Ao0WLwJJs4VHwiFqDxMnNzgPuVduE95FwV3Y5F54ndk/3UKOVERKIi9odPzoiD2JpK3rXvkvur1GAT8U7ww9W0Eq3bBMGvQBGb+ATBr2BRmDZtCpjSp9tdERFVT3RctRETGyNS3EZxGvIakb1fgjUvncfzLxfg85GmMbtxc6mj0BOW5ZcJYTJkyBd9//z327t0LKysr7TgWNjY2MDMzgyAImDFjBhYuXAg/Pz/4+flh4cKFMDc3x4gRIyROX0IvxQsLCwuDmDqlrss8FYY7qz4CAHjOXgTLlh0kTkR1hcrNC47DxsJx2FgAgDonS2dkbZWHL1xGz4AmPxcQBAhKFWQqFeQWVlDYOcGyeVttW/NGgfBfs6fGXwMRERFRdRDkcni8/iGUji64Gn0NaTIZinZ+Bc1bn0Jm8uTxCajm5RUXoe+v/8OltHuIHfkazA1spr3KWLt2LQAgJCREZ/mmTZswevRoAMCcOXOQl5eHyZMnIz09He3bt8fBgwdhZWVVw2nLppcxL/bu3YsTJ06gZ8+eOgOEdO3ataq7NniGMuZFYWI8okaGQJ2RBvuBI+D9/spaUyUkIqKKMZRzU23BMS+ISF8SDvyAQ6v+D96pSXBt1RENlnzDW7wNlMuWVUjKy8GpIaPQzslN6jil1MVzk156Xvz2228ICwvDjRs3tLeNCIJQJ4oXhuL2ghlQZ6TBvElLeL31GQsXREREREQGxrXvMAyp54Cbb76MrNOHkbJ7M1xefl3qWFSGLd2ehqOpOZr+Z6Y9kpZeiheHDx/GpUuX+IFZQh7TP8LtnCz4zF+rnR6ViIiIiIgMi3X7EPiv/wn3w36B8yhOG22oenn4PrkR1Si9FC/atWuHmzdvomHDhvrYHVWCWcOmaLTxAAtIREREREQGaMH5E9BAxCi/APg0aQmLJi2168TiIojFRdqZ2YiM2fnz53H06FGYmJigc+fOaN5cP4PT6qV4ceHCBTRr1gyNGzeGSqXSjsp6+vRpfeyeHkGTn4f8mGswb9wCAFi4ICIiIiIyQKIoYuWl80jKy0EPN2/4WNlo12kKCxD9zqtQ52aj4bJt7EVtIArUxdh3+yau3E/FO6068rNWOX3xxReYOXMmbG1toVAocO/ePTRr1gybN29GmzZtqrRvvRQv9u7dW2oZ/+dWv7sbliDpu1XwnLkQTsPHSx2HiIjqKF9f30qd92fMmIHXX+e93kRU+6lFEe+06oDTyQlo5eCss64g9iYyT4VBk5eDm3NeQYPPtnCKeAMgisDwP/ZCLYoY3SgQ7haGMeOGIfr666/RsmVLNGvWDAsXLsQnn3yCN998E4IgIC4uDuvXr0dISAh+/fVXdOnSpdLHqVLxYtSoUdiyZQuGDRtW5kULe15Un9zrl5D03SpArYaJs7vUcYiIqA7bvHlzpbbz8fHRaw4iIkOlkMnwekBQmevMGjZFwy+24/rrzyPz+CHEvP8afBdsgPDPRAgkDVOFAs/6NoKlUolijUbqOAbt008/xY0bNwAAGo0GZ86cwbJly9C6dWu0bNkSH3/8Mdzd3TF79myEh4dX+jhVKl4sWbIEAPDDDz9UZTdUQaJajdgFMwC1GrbdBsA2pL/UkYiIqA4LDg6WOgIRkVGzatMZDT/fghszXkT6oT0w9W4At0nvSB2rztvRc7DUEYzC5cuXkZ+fj4sXL+Kpp56CTCbDzp078d577yE/Px8eHh5o0KABLly4gJ9//hkBAQHw9a34gKgVKue98MILiIqK0j53dXUFAHh7e5f5oOqR8sPXyIk8B5mFJTzf/ETqOERERKWuEYiI6F+nk+/ifkH+Y9tYd+gOr3eWAgASvvoMqb/sqIloRHphamqKtm3bonPnzmjRogXCw8ORlZWFv//+G4sWLYK/vz+KioowevRoNGjQANbW1hU+RoWKFzt37kT37t0feXEiiiIyMzMrHILKryj9Hu6uXQAAcJ/6AUyc3CRORERE9ORrBI1GY3TXCKtXr0bTpk0REhIidRQiMmJFGjVCft6Get8sx83M9Me2dRg0Es6vTAcAxC6Zg+LM+zWQkJ7kSYUn+tfnn3+OJUuW4NVXX8X58+fh7++PgQMHwtLSEm5ubkhNTUVsbCx27txZ4X1X+EaqFi1aoFu3brh06VKpdcnJyahXr16FQ1D5JXy5GOrsTJg1ag7HoaOljkNERKT1uGuElJQUo7tGmDJlCqKiohAWFiZ1FCIyYndysuFuYQU7lSl8rWyf2N59yntwGDoafqt+gML6ye2p+twvyIf7d6vh8O0K5BUXSR3HKLRs2RLnzp3D7du30aFDB5iamsLW1hYrV67E4sWLAQAeHh7o27dvhfddoeKFIAjYvHkzunfvjm7duiEyMrJUG1EUKxyCyk/l7gOZhRU8Zy6AIJdLHYeIiAgArxGIiB7Fx8oG11+YgOgXJ0FWjpmZBJkM3u8shWVg2xpIR49jY6JCoUYNtSjiWsbje83Qvxo0aIBDhw7hzp072L17N7Zv344bN25g5MiRVdpvhYoXoihCLpdj69at6NGjB7p3717q4oRTpFYv55emoPkvF2HVprPUUYiIiLR4jUBE9HjWJqpKbZd79SLiPnuLBWAJCIKA0AEvImP0DLSwd5I6jtG5c+cO+vXrh+eeew6enp5V3l+l5t+RyWTYunUrevbsie7du+PixYtVDkLlJ7es+OAmRERENYHXCERE+qPOzsS1iQORvP1LJH+/Vuo4dVKAnWOlC091Xdu2bRETE6O3/VX4thHthjIZvvvuO+3Fyd9//623UKRLFEXcXjQLGccPseJKREQGidcIRESl3cq8D/8dX2LS0d8qdR0vt7SG25T3AAB3Vs5HTuQ5fUckqjb6/uxa4dtGdDb+5+KkV69e6NGjByIiIvSZjf6RGf4n7u3ahJtzXkFxWorUcYiIiErhNQIRUWnHEuNxPSMdf6UmV/rWOcdhY2HbYxDE4iLcemccZyCpYTlFhVgScQpjwn6Bhl8kS6pCxYtffvkFNjY2ujv45+Kkd+/eePbZZ/UajkouBhO+LBmV1XHYWCh5rxURERkgXiMQEZU22McP+/oOw/utO1V6H4IgwOe9FTBx90Hh3Vjc/mgae2PXIBO5HO+dPYrN1yIRk5UhdZw6rULFi379+kGlKn2/j0wmw5YtWzB48GC9BaMS2eeOI+fiWQgqU7i8/LrUcYiIiMpUnmsEXmwTUV1jY6LC014N0M+rQZX2I7e0Rv1FGyEolLgf9gtSdnypp4T0JEqZHG8EBmFh264wVyikjlOnVWrAzv86f/48CgsLtd+unDx5Uh+56B+Jm5cBABwGjWSvCyIiMkoPBvEMDw+XOgoRkdGyaNoKHjP+DwCQeSqMBeEa9En7ELzdqiNczC2ljlKnVbl01LZtW1y+fBn+/v4QBAHt2rXTRy4CkHvlb2SGhwJyOZxfmip1HCIiojL5+vpW6l7uGTNm4PXX2auQiGqnC/eScDLpDrq7e6Oxrb1e9uk4fDwU9o6o12Mwp5+mOqfKxQtW/KpP8vb1AIB6PQZD5e4tcRoiIqKybd68uVLb+fj46DUHEZEh+d+tK1gUEY4x/oH4OqS/XvYpCALsej2jl31RxeQUFeLK/TS0cXSROkqdxZt2DJh1x+7Iu3kZziMmSR2FiIjokYKDg6WOQERkcBrb2qOXuw96evhUy/7VeTm4u+Zj2A96CeZ+zarlGFQis7AA9b5ZDo0oIv2V6bBVmUodySh88MEHcHBw0Nv+WLwwYHZ9nkW93kPZJYyIiIiIyMi87B+Al/0Dqm3/d5Z/gJQfvkb2hXA03nwQgkJZbceq66xNVPC0sEKBWo3Y7EwWL8rpgw8+0Ov+qjxgJ1UvFi6IiIiIiOhhrq++Cbm1LXKv/IXEb1ZIHafW+3vYWCSMmormnERBMixeGKCs8yeQvH09ijPvSx2FiIiIiIgqKDkvB0UadbUeQ+ngDM/ZnwAAEjYs+f/27js86ipt4/g9aZNCCimkkNCLRqqgSFEICoKKIoqd4iKrK4qIrq76rqIrZtfCshZYEVdsCLqKDaRYaAoISBSISpGYhCQEAqmkz+/9w2UkEhBIOTOZ7+e65rqYM2dm7nkoCU/OOT+V7kpt0PfzdCF+x14OHI2L5oULyp0/WxlPP6Cc/zxjOgoAAACAU3TbmuUKfWWm5jdwQyF8+GiFnn+xrKpKpT12p6zqhm2YACbRvHAxlYcOKH/NMklSxGXXG04DAAAA4FR9n39ApdVVat0spEHfx2azqdWDM+TdLESHU7fowKLXGvT9PFlxZYUmrv5E/T94o8FX1aB2dW5e1PcJop7u4NL/StVVCkzsqYAOiabjAAAAADhF20ffou2jJ+icqNgGfy+/qFjF/ekhSVLOqzNlVVU1+Ht6okAfXy3c/YO+2rdXP+YfNB3HI9X5aiP1fYKopzv4yX8lSRGXXGs4CQAAAIDT4WWzKbF54/2AN+qqm1V5IEctrp0omw8XlGwIXjabnuqTpHB/f8UHBZuO45H4k+1CytJ363DqN5K3t5oPGWk6DgAAAAA3YPPxUctJfzUdo8m7NbGH6QgejTMvXMihFe9LkkLOuUC+XIIHAAAAcDvXffqB/rz+C+UcLjaWofjbDbIcDmPvDzQEmhcupCr/gOTto+ZDrjQdBQAAAMApyiop0sKfftAz330tu7eZRe5p0ybpxwnDlbd4gZH3b8qqHQ5tO7hfC3alyrIs03E8DttGXEjCPcmKveU+2fz8TEcBAAAAcIqCff30etJl2lVwSM3t/kYy+LfrLEna+9yjap50mbwb+IonnqTCUa3u774ih2VpYFwrxQY2Mx3Jo7DywsX4hDaXd0CQ6RgAAAAATlGwn103dTxL03oPMJahxfW3yd66o6oO7lfWi8nGcjRFAT6+OjcqVgNi4nWovMx0HI9D8wIAAAAAmggvXz+1+vMvTYvct19WeeYew4malnUjx2jN5Tc26tVk8AuaFwAAAABQR7mlJXprV6qyDR7UeUTIeYMV0u9CqbpKe2dNNx0HqBc0LwAAAACgjpZm7NENn3+kkcveMx1FktRy0sOSpEPL31PJ9ylmwzRBHNjZ+GheAAAAAEAd2b291TMiWhe2bG06iiQpsHNXhQ8fLb/YBFUX5puO02RUVFfrwo8XKPK1Z5XPuReNiquNAAAAAEAdXdv+TF3b/kyX+ol8wj3J8goMkpef3XSUJsPP21s/FeXrYHmZUvJyNSiulelIHoPmBQAAAADUE5vNZjqCk09YuOkITdJL5w9TpH8Ah3Y2MpoXAAAAAFAHxZUVCvLxdanGxdGsqiod+OhN+UZEK+yCYabjuL2L4tuYjuCROPMCAAAAAOrg9rXLFffGC1qwK9V0lFrlvjNX6dPvVuaMh+SorDAdBzgtNC8AAAAAoA7W52Ypp7REkf6BpqPUKvKKm+QTHqXyzD068P7rpuO4Pcuy9EHaTj2yaY1KaAY1GpoXAAAAAFAH3131B6287Hr1j2lpOkqtvAObKfaWP0uScv4zQw6uklEnNptNt61Zpse++UrfHdxvOo7HoHkBAAAAAHXg7+OjgXGtFODjazrKcUWOHCPf6DhV7s/WgQ/fNB3H7Y1ud4bGd+qiIBf+PW9qaF4AAAAAQBPn5WdXzLgpkqScef+Uo6LcbCA392z/i/TKoEvVLaKF6Sgeg+YFAAAAAJwGy7J0/Wcf6h8p61XkBs2AyCtukm9UrCr3ZSnvw/mm4wCnhOaFpFmzZqlt27by9/dXr169tGbNGtORAAAAALi4HwsOasHu7/XI5rXy9fI2Hed3edn9FTPuLgWfO1ABnbuYjtMk5JWVqsrhMB3DI3h882LhwoWaMmWKHnroIW3ZskXnn3++hg8frvT0dNPRAAAAALiwcLu/Zva9UH/udq78fXxMxzkpUddOVKdZi9Ss6zmmo7i9nu++osjXntVWDu1sFB7fvJgxY4YmTJigW265RWeeeaZmzpyphIQEzZ4923Q0AAAAAC6sRUCQ7uraW3875wLTUU6azWYzHaHJCLcHSJJ+zM8znMQzuEd7sIFUVFRo8+bN+stf/lJjfOjQofrqq69qfU55ebnKy3/dz1ZYWNigGQEAAACgvlUe3K99rz+vgE5dFDF8tOk4bmnuBcMU4R+gED+76SgewaNXXhw4cEDV1dWKjo6uMR4dHa2cnJxan5OcnKzQ0FDnLSEhoTGiAgAAAHAhOwsO6pP03TpcVWk6ymk5uORt7Xv9OWX9O1mWm34G09qGhNG4aEQe3bw44rdLpyzLOu5yqgceeEAFBQXOW0ZGRmNEBAAAAOBCXt2xTZcs/a8mrl5qOsppibxqvHyaR6pib5oOLv2v6TjA7/Lo5kVkZKS8vb2PWWWRm5t7zGqMI+x2u0JCQmrcAAAAAHiWYF8/JQQFa2h8G9NRTot3QJCib5wkScp57VlZXDHjtMza/o1u/Pwj7So4ZDpKk+fRzQs/Pz/16tVLK1asqDG+YsUK9evXz1AqAAAAAK7u/h7n6ecb/qSbOpxlOsppi7r6ZnkFBavspx9V8OWK338CjjF/V6rm70rVhtws01GaPI9uXkjS1KlTNXfuXP3nP//R999/r7vvvlvp6em67bbbTEcDAAAA4MJsNpu8vdz3v1TezUIUddXNkqR9rz1rOI17+kPnbnq89/nqGVn7yn3UH4++2ogkXXvttcrLy9Njjz2m7OxsdenSRUuWLFHr1q1NRwMAAADggvLLyxRm9zcdo160uP5W5c6freIt61T83ddq1u1c05Hcyh/O6GY6gsfw+OaFJN1+++26/fbbTccAAAAA4OIclqVOC19SVECAFg8brTbBoaYj1YlfVKwiR43/5dct4syGAU6A5gUAAAAAnKQf8vOUV16qsuoqtQxqZjpOvWh13z9MR3BrpVWV+jYvV51CwxXuH2A6TpNF8wIAAAAATlJi80jtH3OnUvPz5OvlbToOXMDgjxdofW6WFl54ua5pf6bpOE2W+54uAwAAAAAGhPsHaEBMvOkY9a5460btvn+8KvZnm47iVnpGRqtFQKCKKitMR2nSWHkBAAAAAFDmzIdV8u0G2ePbKv7OR0zHcRv/7DtYL/QfIpvNZjpKk8bKCwAAAAA4CR//vEvXffqBPkzbaTpKg4gZO1mStP+//1F1caHhNO7D7u1D46IR0LwAAAAAgJOwKG2HFv70g1ZlZ5iO0iBCz79Y/m07yVFSpAMfvGE6DlADzQsAAAAAOAl/Suypv57dT9e0P8N0lAZh8/JSixv+JEnKXThHVnW14UTuY+bWjTp30WtasCvVdJQmi+YFAAAAAJyE3lGxeqz3+erTIs50lAYTMfwaeYeGqyIrXfmrlpiO4zbSigq0cX+21uVmmY7SZNG8AAAAAABIkrz8AxR11c2SpNy3/m04jfsY07GLFlx4ue7pdq7pKE0WzQsAAAAA+B2ztn+jbw7kyLIs01EaXNToP8jeuqPCki6T5XCYjuMWekXF6Nr2Z6pVsxDTUZosLpUKAAAAACewt6RIk75cIZukrJsmKSawmelIDcovKlZn/Xc9V9CAS6F5AQAAAAAnUFxZoVFtOqm4qqLJNy6OoHFx6tKKCvRlTqbahYSpb3RL03GaHJoXAAAAAHACncMi9O7QKz1iy8jRHJUVOrR8kRzlpYoaNd50HJc3O3WLnvx2g249swfNiwZA8wIAAAAAToKnrUYo/HKF0h75k7xDwxVxybXy8g8wHcml9Ytuqb7RceoU2tx0lCaJ5gUAAAAAHMeewnw1t/srzO5vOkqjCz1/mPziWqkiK115n7ytqCvHmY7k0q5o01FXtOloOkaTxdVGAAAAAOA47l73uaJee06v7thqOkqjs3l7q8U1EyX9ctlUT9s2A9dC8wIAAAAAauGwLGWWFKnKcqhHRLTpOEZEjhwjr8BmKvvpRxVtXG06jluwLEuHqypNx2hyaF4AAAAAQC28bDZtGjVOO66dqG7hUabjGOHdLEQRl14nScpd+JLhNK7v1R1bFfXac7pj7QrTUZocmhcAAAAAcAIdQ8M97rDOo0VdM0GSVLBmqcqz0g2ncW3N/fyVV16qlLxc01GaHA7sBAAAAIDfcFiWbPK8K4zUJqBtZwX3GSQ5HHIcLjYdx6UNimuljVeOVVcPXanTkGheAAAAAMBvfLo3TRNXL9UtZ3TTX8/ubzqOcR1mzJeXB15x5VSF+NnVOyrWdIwmiW0jAAAAAPAbH6btUnpxoTKLi0xHcQk0Ltzb6tWrNWLECMXFxclms+n999+v8fj48eNls9lq3M477zwzYY+DlRcAAAAA8BtPnTdIQ+LbqE1wqOkoLqXywD4d+uJjRV39B7bUHMeO/IN6a3eqmvn66Z5u55qOI0kqKSlR9+7ddfPNN+uqq66qdc6wYcP0yiuvOO/7+fk1VryTQvMCAAAAAH4jwMdXV7TpaDqGS3GUlWr71X1UXVyowI5nqVkP1/rJvKv4qShf0zZ/qU6h4S7TvBg+fLiGDx9+wjl2u10xMTGNlOjUsW0EAAAAAPC7vPwDFHbh5ZK4bOqJnBMVqxs7JGpyl16yLKtB36uoqEiFhYXOW3l5+Wm/1sqVK9WiRQt16tRJEydOVG6ua10xheYFAAAAAPxPRXW1Riz9r+b+8K0qqqtNx3E5La6ZKEk69PlHqtifbTiNa4rwD9Abg0do0llnN/jWmsTERIWGhjpvycnJp/U6w4cP15tvvqnPP/9czzzzjDZu3KjBgwfXqRlS39g2AgAAAAD/syRjtz5O363NB/bp5k5dTcdxOYGdu6pZj/NUnLJeB96bp7hbHzAdyaOlpqaqZcuWzvt2u/20Xufaa691/rpLly7q3bu3WrdurcWLF2vUqFF1zlkfWHkBAAAAAP/Tp0Wc/nHuIN3fvY+8vfjvUm2irv1l9cX+916Vo7LCcBrXdbCsVJsaeHVKcHCwQkJCnLfTbV78VmxsrFq3bq2dO3fWy+vVB/42AgAAAMD/xAY20309+uiurr1NR3FZzZMuk29UrKrycpX/+Uem47ikrQf3K+K1ZzV0ydsNfu5FQ8jLy1NGRoZiY2NNR3GieQEAAAAAOGk2H19FXjlWNru/KvbtNR3HJZ0RFq4Abx9F+gcot/Sw6TgqLi5WSkqKUlJSJEl79uxRSkqK0tPTVVxcrHvvvVfr1q1TWlqaVq5cqREjRigyMlJXXnml2eBH4cwLAAAAAJD0ZMoG9WkRq/NjE+TVwActursW192qFtdMlE9YuOkoLsnXy1s5Y+5QiF/9bOOoq02bNikpKcl5f+rUqZKkcePGafbs2dq6datee+015efnKzY2VklJSVq4cKGCg4NNRT4GzQsAAAAAHu/nogLd//VK2SSl3/AnxTcLMR3JpfmEhJmO4PJcpXEhSYMGDTrh9pVly5Y1YprTQ/MCAACgFi+88IJeeOEFVVRwGB3gCRyWpQmdu+lgeRmNi1N0+IfvZI9vI2/qhgbEmRcAAAC1mDRpklJTU7Vy5UrTUQA0grYhYZo7cLjeG+o6e/zdQdqjd+j7mwbpwEfzTUdxOWVVVRr3xWIlvj1XJVyVpc5oXgAAAAAATkvQWWdLkva/8x9ZDofhNK7F38dHn2Wl6fv8PG0+sM90HLdH8wIAAACAR/t878/KLC40HcMthQ8fLa+gZipP36WijatNx3E5T/dJ0pJhV6tnRAvTUdwezQsAAAAAHqvSUa0bPv9IrebP1lc5mabjuB3voGBFXHq9JGn/Oy8bTuN6ruuQqOGt2ivYhQ7vdFc0LwAAAAB4rNzSwzozLEItAoJ0TotY03HcUtTVN0uS8ld/ogoaQGggNC8AAAAAeKyWQcH6YsT1Sh09Qb5e3qbjuKWAdmcouPf5ksOh/e/OMx3H5aQc2KdZ279RzuFi01HcGs0LAAAAAB4v3D/AdAS3FjV6giSp4MsVsizLcBrXcsvqpZr05Qqtzs4wHcWt+ZgOAAAAAAAmbMzNVtfwKPn78N+iugobeInaPfmqwi4YJpvNZjqOSxka30aR/gEK4dyLOuFvKQAAAACPU1xZoQsXL5Dd21vrR45R+5DmpiO5NZuPj5oPHmE6hkt64tyBpiM0CWwbAQAAAOBxdhYcUpifXeH2ALUNDjMdp0mxqqpUVVRgOgaaGJoXAAAAADxOz8ho/XT9bVo6fLS82OZQb/JXLtHWK3oqc+ZfTUdxOZWOah2uqjQdw23RvAAAAADgkXy8vNQ2JMx0jCbFJyxclfv26uDSd1VVmG86jsv48/ovFPLKTL38w3emo7gtmhcAAAAAPMruwkNcEaOBBHXvo4COZ8kqL1Xeh2+ajuMyQvz8VFZdpZS8faajuC2aFwAAAAA8Rs7hYp359lyds+g1FVaUm47T5NhsNudlU/f/9z+yHA7DiVzDhM7d9OM1EzX3guGmo7gtmhcAAAAAPMaG3Gx52Wyye3tz6coGEj58tLybhag8c48K139uOo5LiAsKVqewcC4jWwc0LwAAAAB4jCvadFTmjbdrzvkXm47SZHkHBClixPWSpP3vvGw4DZoKmhcAAAAAPEqkf6DOCo8yHaNJi7r6l60jBWuXq2LfXsNp0BT4mA4AAAAAAA2torpa2YeL1To41HQUj+DfuoNaTp6m4F4D5Bfd0nQcNAGsvAAAAADQ5L2xc7s6LJij+zesNB3FY8SMnaygs842HQNNBM0LAAAAAE3e+twsVVkOtQgINB3FI3HVEdQV20YAAAAANHlzLhimCWd0U5fmkaajeJTKAznKevHvKt39gzq//AlX28BpY+UFAAAAAI/Qp0Wcgnz9TMfwKDZfu/KWvKOS775Wccp603HgxmheAAAAAGiydhUcUmlVpekYHssntLkihl8tSdq/cI7hNHBnNC8AAAAANEmWZWn0p++r7Vsv6sucTNNxPFbUtX+UJB364mMum4rTRvMCAAAAQJO0t6RIh8rLVFJVqTPCIkzH8ViBHc9Ss7P7SdXV2v/uPNNx4KZoXgAAAABokuKbhWjndX/UF5ddpwj/ANNxPFqLayZKkg4selWO8jLDaeCOaF4AAAAAaLJ8vbzVOyrWdAyPFzboUvlGx6nq0AEdWvG+6ThwQ1wqFQAAAECTkl9eptXZGRrRugOX5nQRNh8fxd48VZUHchTcZ6DpOHBDbrnyYvr06erXr58CAwMVFhZW65z09HSNGDFCQUFBioyM1OTJk1VRUVFjztatWzVw4EAFBASoZcuWeuyxx2RZViN8AgAAAAAN5W/ffKUrlr+n29cuNx0FR4m6+g+Ku+1B+bESBqfBLVdeVFRUaPTo0erbt69efvnlYx6vrq7WpZdeqqioKK1du1Z5eXkaN26cLMvSc889J0kqLCzUkCFDlJSUpI0bN2rHjh0aP368goKCdM899zT2RwIAAABQT4J9/WT39tYVbTqajgKgnrhl8+LRRx+VJM2bN6/Wx5cvX67U1FRlZGQoLi5OkvTMM89o/Pjxmj59ukJCQvTmm2+qrKxM8+bNk91uV5cuXbRjxw7NmDFDU6dOZXkZAAAA4Kam9R6gWxN7KDawmekoqEXB2hXa99Zstf6/f8kem2A6DtyEW24b+T3r1q1Tly5dnI0LSbr44otVXl6uzZs3O+cMHDhQdru9xpysrCylpaUd97XLy8tVWFhY4wYAAADAtdC4cF373nxBRRtWav/bL5mOAjfSJJsXOTk5io6OrjHWvHlz+fn5KScn57hzjtw/Mqc2ycnJCg0Ndd4SEugUAgAAAKZVORy6Z93nSisqMB0Fv6PFDX+SJB1Y9JqqDxcbTgN34TLNi2nTpslms53wtmnTppN+vdq2fViWVWP8t3OOHNZ5oi0jDzzwgAoKCpy3jIyMk84EAAAAoGE8t22zZmzdqPM/fFOVjmrTcXACof2HyN6qg6qLC5X34XzTceAmXObMizvuuEPXXXfdCee0adPmpF4rJiZGGzZsqDF26NAhVVZWOldXxMTEHLPCIjc3V5KOWZFxNLvdXmOrCQAAAADzRrTuoHf2/KA/dO4mXy9v03FwAjYvL7W4/lZl/OPPyl3woqJGT5DNm98znJjLNC8iIyMVGRlZL6/Vt29fTZ8+XdnZ2YqN/eUyPMuXL5fdblevXr2ccx588EFVVFTIz8/POScuLu6kmyQAAAAAXEOH0OZaM+JGeXHwvluIuOw6Zc2ervLMPSpYs0xhgy4xHQkuzmW2jZyK9PR0paSkKD09XdXV1UpJSVFKSoqKi3/ZLzV06FAlJiZqzJgx2rJliz777DPde++9mjhxokJCQiRJN9xwg+x2u8aPH69t27Zp0aJFeuKJJ7jSCAAAAOBG8svLnL/29vLie3k34R0QpMgrx0mS9s2fbTgN3IFbNi8efvhh9ezZU4888oiKi4vVs2dP9ezZ03kmhre3txYvXix/f3/1799f11xzjUaOHKmnn37a+RqhoaFasWKFMjMz1bt3b91+++2aOnWqpk6daupjAQAAADgF6/ftVcL82Xpu22bn+XVwHy2uuUWBiT0VefkN/P7hd9ks/pTUSWFhoUJDQ1VQUOBc1QEAgEl8bapfmZmZSkhIUEZGhuLj403HAXCU29Ys04vfp+jGDol6Y/AI03GARuOJX5tc5swLAAAAADgVswYMVa/IaI1ud4bpKAAaGM0LAAAAAG7Jy2bTxDN7mI6BOqouLdGB91+XVVWpmDF3mo4DF+WWZ14AAAAA8Eyb9+foLxtWqtrhMB0F9aR485fKfOZBZc99SlVFBabjwEXRvAAAAADgFkoqK3TF8nf1j283KDllvek4qCch/S6Sf7sz5Cgp1oF3XzEdBy6K5gUAAAAAtxDk66dnzhus3lExurNLL9NxUE9sXl6KGTtZkrTvrX/LcdTlb4EjaF4AAAAAcBvXtj9TG0aOVaif3XQU1KPwYVfJN7qlqvJylbd4gek4cEE0LwAAAAC4tNd3bFNxZYXzvpfNZjANGoLNx1fRN94uSdr3+vOyqqsNJ4KroXkBAAAAwGX9I2W9xq5crPM/fFNlVVWm46ABRY4cI++QMJVn/KT8lYtNx4GLoXkBAAAAwGUNT2in5nZ/3dghUf4+PqbjoAF5BzZTi2v/qLCky2RPaGc6DlwMf/sBAAAAuKxuES304zUTFRUQaDoKGkHsH++XjW1BqAUrLwAAAAC4jEpHtSatXa4d+QedYzQuPAeNCxwPzQsAAAB4tNKqSlU6fj0cML24UK/v2KYP03bWmLc4fbde3bFVaUUFzrGK6mpllRTVeD7qJnnLes1K3aJLlr6jCg5t9Fjl2Rn6+YmpKtm22XQUuAiaFwAAAGjyDldV6sucTC3P3FNjvPd7ryrwPzO0KivDObbt4H6NXblYj2xeW2PuU99u0PiVS/R1bpZz7Nu8XLV8c5bav/Vijbnzftyqv6esV+qhAw3waZq2SWedrU6h4Xq230Xy8/Y2HQeGZL/0pA68N09ZLz1pOgpcBM0LAAAANCmVjmqt37dXB8tKnWOfpP+kAR++qb9sWFVjbnO7XZKUV/7r3FbNQjQ0vo36RbesMbdPizgNS2ir+KBg59ihijJ52WzHbGv4z4/f6YGvV+m7vFzn2I78gxq25G09vGlN3T9kE1LtcGhlVrrzfoR/gLaN/oMuadXeYCqYFnPz3ZK3twq/XMHqC0jiwE4AAAA0MRctXqjV2Rl6bdClGtOpiySpZ2S04gKbqXNYeI25rw66VEG+fgrx9XOOdQmP0rJLrj3mdf/RZ9AxY0Pj26rylj+rpLKixviVbTqpTXCouoZHOce+PZirZZl7lF9Rpsd6n+8cv/PLFSqprNSUrr3VLaLFaX1md1VWVaVLlr6jlVnp+vTS6zS4ZWtJkq8XKy48nX9CO4UPG62Dixcoe+5T6jBzgelIMIzmBQAAANxSXlmpHtm0Vt/k5ejLy29yHvTXJypW2w7uV0FFuXNuu5Aw7b1p0jGvEXfUKorT5WWzKdjPXmPs7m7nHDPv3KhYzTn/YjU7qlFiWZYW7v5B+8sO69bEHs7xr3Oz9Nau7zU0vo2GN+EVCP4+PmoXHKavfbJ18KjVL4AkxU64Rwc/eVsFa5erJHWLghJ7mo4Eg9g2AgAAALdgWVaNrSBBPr6at2Or1u3L0uYDOc7xR3r11/6xk3VHl14mYh5X6+BQTTyzh67vkOgcsyS9eP7F+r+efdU9/NdVF8sy92jmtk16fef2Gq8x78et2pCb5bYHhFY5HHoxNUX7Dpc4x57tf5G+GTVeV7c7w2AyuCL/Vu0VPmy0JCn7pacMp4FpNC8AAADg8lZmpavDgjm68YuPnGP+Pj566rwkfXTxVerS/NftGUG+fvJyk8stetlsurJtJ/3tnAvk7/ProujzYxJ0x1ln68q2nZxj+eVlunnVEp33/us6WFbmHP+5qEA5h4sbNffpGrdysW5bu0yPfvOlcyzQx1edfrOdBzgidsI9kpeXCtYs1eEfvjUdBwaxbQQAAAAux7IslVZXKdDHV5KU0CxYPxXl62B5qYoqyp3bNP7URJeRD4prpUFxrWqMHSov02Wt2mt/2WFFBwY5xx/ZvFav7timf5w7SPf16CPpl0Mwy6qrFHTUFpXGZlmWNuRm6YywCIXZ/SVJt57ZQ8sy9qhL80hjueBe/Ft3UOSV4+QTEia/mHjTcWAQzQsAAAC4lE/Sd+ue9V9oWEJbzeh7oSSpfUhzLR52tQbGJhj9D7lJbUPC9NGwq48Zzy8vl03SWeG/NgS+PZircxa9pn7RLbXm8hud44UV5Qr29XOeD9KQbluzTHN++FbP9bvIuYXngtgEpd/4J2dTCjgZrR94xnQEuACaFwAAAHApNptN3+fnqaiyQk/2SZKP1y87nbl0Zu3ev3iUCivK5XfUFTq2HTwgh2XJ37vmVTuGLF6oH/Lz9O6QK3VRfBtJUvbhYm07uF/tQ5qrXUjYSb1ntcMh7//9vliWpbu++kwb92fr88uuU8D/GhMdQ5vL39tHB8pqHsRJ4wJ1YVlWozTf4Ho48wIAAADGHCwr1cOb1mjh7u+dY0Pj22ruBcO0ffQEZ+MCJxbiZ69xZsbYTl2UddMkPd9/iHPMsiztLDykwsoKxR617eTTzDQNXfK2/rhmaY3XHPDBG+q0cI5SDuxzjr2+Y5sCX35GY1cudo7ZbDYtStuh9blZWrcvyzl+c+duyr5pkqb1HlCvnxWeqzhlvX685RIVp6w3HQUGsPICAAAAxryyY6v+9s1Xah8SpqvadpaPl5e8bDZNOKO76WhuLzawmWIDmznv22w2Zd80STsLDtU4INPXy0tnNY/UGaERNZ7/U1GBsn9zEKiXzabS6irtLjxUY/z/evZTsJ+fekT8esWUCP+A+vw4gPI+fksl325Q5nOPqvPcJazA8DA2y7Is0yHcWWFhoUJDQ1VQUKCQkBDTcQAA4GtTPcvMzFRCQoIyMjIUH89hcXVVUV2tgopyRQUESpJKKit0+bL3dHtiT13ZtpPbXCXEE2w9uF8FFeXqGdHCec5IUUW58srLFOZndx7CCTSWitwsbbuyt6zyMrWfMV9hFwwzHckYT/zaxDo8AAAANIqvcjLV5b8va8KqT5xjQb5++uyy63RVu840LlxM1/AoDYiJr3FAarCfXW2CQ2lcwAi/FnGKvv42SdLe5x+VVV1tOBEaE80LAAAANIoI/wD9VJivDfuzlFtaYjoOADcUPe4ueYc2V9lPPypv8QLTcdCIaF4AAACgQRRWlGtlVrrzfuewCC0aOkq7rv2jWgQEneCZAFA7n+BQxd48VZKU9WKyHL+5mg2aLpoXAAAAqHd7CvN1xttzNWLZu9pbUuQcH9G6g4L97AaTAXB3UaMnyC8mXpX7spS3eKHpOGgkXG0EAAAA9a51cKjaBIdof2mpcg6XqGVQsOlIAJoIL7u/4u95QtXFhYq45FrTcdBIaF4AAACgzhyWpXf3/Kir2v5y8KaXzaa3L7xCkf6B8vfhW04A9at50mWmI6CRsW0EAAAAdWJZlkYue0/XfPqBXtj+jXM8vlkIjQsADa66tEQVuVmmY6CB0bwAAABAndhsNg1LaKtAH1/Zvb1NxwHgQYq2rNP2q/so7ZHbZVmW6ThoQLTCAQAAcMpyDherrLpabYJDJUm3JfbUiNYdlNAsxHAyAJ7Er0WcqvIPqmjfauV/9qGaX3SF6UhoIKy8AAAAwClZk52hrv/9j6799ANVOqolSV42G40LAI3O3rK1YsZOliRl/PP/VF1aYjgRGgrNCwAAAJyS1sGhqnQ4VO6o1v7Sw6bjAPBwMePukl9cK1Xu26ucV2aajoMGQvMCAAAAv6u4ssL561bNQvT5Zddpw8gxiuMSqAAM8/IPUPzdj0uS9r3+nMoyfjKcCA2B5gUAAABO6LUd29R6/mylHNjnHDs7MkZ2b45PA+AawgZdqpDzkmRVVihzxkOm46AB0LwAAADAcVmWpf/+9KMOlpdpVuoW03EAoFY2m00J9/5dNh9fefkHyFFeZjoS6hnNCwAAAByXzWbTvEGX6OnzkjR7wFDTcQDguPzbdNRZ736tdsn/kZfd33Qcl7J69WqNGDFCcXFxstlsev/992s8blmWpk2bpri4OAUEBGjQoEHavn27mbDHQfMCAAAANXyYtlMzvvvaeT/cP0D3dDtX3l586wjAtdlbtjYdwSWVlJSoe/fuev7552t9/Mknn9SMGTP0/PPPa+PGjYqJidGQIUNUVFTUyEmPj42KAAAAcNpyYJ+uWP6ebJLOiYrV+bEJpiMBwCmrPJCj9H/cp8grxih0wBDTcRpMUVGRCgsLnfftdrvsdvsx84YPH67hw4fX+hqWZWnmzJl66KGHNGrUKEnSq6++qujoaM2fP1+33nprw4Q/RbTPAQAA4NQzMlq3ntlDd5zVS31axJmOAwCnJXfBHOV/8bF+Tr5b1cWFv/8EN5WYmKjQ0FDnLTk5+ZRfY8+ePcrJydHQob9uDbTb7Ro4cKC++uqr+oxbJ6y8AAAA8HA7Cw6qVbMQ59VDZg0YKi+bzXAqADh9sbfcq0OffqDyzD3K/NfDav3QTNORGkRqaqpatmzpvF/bqovfk5OTI0mKjo6uMR4dHa2ff/65bgHrESsvAAAAPNjSjJ/U+71Xdfva5bIsS5JoXABwe17+gWr912clSQcWvabCDSvNBmogwcHBCgkJcd5Op3lxhO03//ZblnXMmEk0LwAAADxccVWldhYcUml1lekoAFBvgnv1V9ToWyRJPz9+V5PePlIXMTExkn5dgXFEbm7uMasxTKJ5AQAA4MGGJbTTsuHX6NNLr1Ogj6/pOABQr1re+bD8WrZWRXaGfp4+xbnCDL9q27atYmJitGLFCudYRUWFVq1apX79+hlMVhPNCwAAAA9yoOywxn7xsfLKSp1jF8W3kZ+3t8FUANAwvAObqe3jcyRvHx3+4TtVFxw0HcmI4uJipaSkKCUlRdIvh3SmpKQoPT1dNptNU6ZM0RNPPKFFixZp27ZtGj9+vAIDA3XDDTeYDX4UDuwEAADwINd99qE+2/uziiortGjoKNNxAKDBNet6jjo884aa9ewr76Bg03GM2LRpk5KSkpz3p06dKkkaN26c5s2bp/vuu0+lpaW6/fbbdejQIfXp00fLly9XcLDr1MtmsW6mTgoLCxUaGqqCggKFhISYjgMAAF+b6llmZqYSEhKUkZGh+Ph403HqbOvB/RrzxceaP3iEEptHmo4DADgNTe1r08lg2wgAAEATd/QWka7hUfpm1HgaFwA8kmVZyl3won6efrfpKDhFNC8AAACaKMuy9LdvvlTnhS9pR/6v+7y5FCoAT1W6K1UZMx7SgUWvKu/jBabj4BTQvAAAAGiiyqur9fHPu5VXXqqP0neZjgMAxgV2PEtxf7xfkvRz8j0q+T7FbCCcNJoXAAAATZS/j48+Hna1Xh10qe7pdq7pOADgEmJunqqQfhfJKi/V7qk3qiI3y3QknASaFwAAAE1IbmmJFqfvdt6PCgjU2E5dDCYCANdi8/ZWuyfmyr9dZ1Xuz9buqTequrTEdCz8DpoXAAAATcS+wyXq/8GbGrnsPX2amWY6DgC4LO9mIerwzwXyCYvQ4R++VdrDfxIX4nRtNC8AAACaiKiAQPWOilF8s2AlNAs2HQcAXJq9ZWu1f/p12ewBCu59vmwcZuzSfEwHAAAAQP3wstn06qBLlV9RphYBQabjAIDLa9bjPHX9cIt8I1qYjoLfwcoLAAAAN/benh/16Oa1zvt+3t40LgDgFBzduKg8dEAF6z4zmAbHw8oLAAAAN/Vjfp5Gf/qBHJalsyNjNKJ1B9ORAMBtVeUf1I5bR6g8/Se1f/oNhQ4YYjoSjsLKCwAAADfVOSxCj/YaoIlndNclCe1MxwEAt+YdHKqA9omyqiq1+76xKvx6lelIOArNCwAAADdiWZYqqqud9x/q2Vcvnn+xvL34tg4A6sLm7a22f/u3QgdeIquiXLun3qjilPWmY+F/3O6rXFpamiZMmKC2bdsqICBA7du31yOPPKKKiooa89LT0zVixAgFBQUpMjJSkydPPmbO1q1bNXDgQAUEBKhly5Z67LHHuDwOAABwWVUOh25Z/Ymu++xDVTkckiSbzcYJ+QBQT2w+vmqX/LJC+g6Wo+ywdt51rYq3bjQdC3LDMy9++OEHORwOvfjii+rQoYO2bdumiRMnqqSkRE8//bQkqbq6WpdeeqmioqK0du1a5eXlady4cbIsS88995wkqbCwUEOGDFFSUpI2btyoHTt2aPz48QoKCtI999xj8iMCAADUatvB/XpjZ6qqLIfW7dur82MTTEcCgCbHy8+u9k+9pp2Tr1XxN19q55+uVIeZbym49/mmo3k0t2teDBs2TMOGDXPeb9eunX788UfNnj3b2bxYvny5UlNTlZGRobi4OEnSM888o/Hjx2v69OkKCQnRm2++qbKyMs2bN092u11dunTRjh07NGPGDE2dOpWfYAAAAJfTIzJaCy+6XJJoXABAA/LyD1SHmW/pp/vHqyxtp+wciGyc220bqU1BQYHCw8Od99etW6cuXbo4GxeSdPHFF6u8vFybN292zhk4cKDsdnuNOVlZWUpLSzvue5WXl6uwsLDGDQAAoKHkl5dpf+lh5/2RbTppZJtOBhMBgGfwDmym9jPmq/NLi+UXFWs6jsdz++bF7t279dxzz+m2225zjuXk5Cg6OrrGvObNm8vPz085OTnHnXPk/pE5tUlOTlZoaKjzlpDATz0AAEDDyC0t0aCP39LQJQuVX15mOg4AeBwvXz/5xcQ77x9c+q4y//WwrP+dO4TG4zLNi2nTpjkPnDrebdOmTTWek5WVpWHDhmn06NG65ZZbajxW27YPy7JqjP92zpHDOk+0ZeSBBx5QQUGB85aRkXHKnxUAAOBkFFSUK+dwibIPlyj7cLHpOADg0SpyMpX26CTte/157b7nJlUVFZiO5FFc5syLO+64Q9ddd90J57Rp08b566ysLCUlJalv376aM2dOjXkxMTHasGFDjbFDhw6psrLSuboiJibmmBUWubm5knTMioyj2e32GltNAAAAGkrH0HCtuPRa2b281Sks/PefAABoMH4x8Wr9f//Sz9OnqGDNUv0w9kK1f+o1BXRINB3NI7hM8yIyMlKRkZEnNXfv3r1KSkpSr1699Morr8jrN9c179u3r6ZPn67s7GzFxv6yN2n58uWy2+3q1auXc86DDz6oiooK+fn5OefExcXVaJIAAAA0ptRDB1TlcKhbRAtJUtfwKMOJAABHRFx6rfzbddZP941TecZP+mH8ULX+v38pfNhVpqM1eS6zbeRkZWVladCgQUpISNDTTz+t/fv3Kycnp8YqiqFDhyoxMVFjxozRli1b9Nlnn+nee+/VxIkTFRISIkm64YYbZLfbNX78eG3btk2LFi3SE088wZVGAACAMdsO7tfAj+ZryJKF2pF/0HQcAEAtgs7soTNf/1zBfQbJUXZYe/5vojL/9bDpWE2ey6y8OFnLly/Xrl27tGvXLsXHx9d47MiZFd7e3lq8eLFuv/129e/fXwEBAbrhhhucl1KVpNDQUK1YsUKTJk1S79691bx5c02dOlVTp05t1M8DAABwRHxQsOKDguXj5aUI/wDTcQAAx+ETFqGOz76jrH8nK+eVGfIODjMdqcmzWUf+x4/TUlhYqNDQUBUUFDhXdQAAYBJfm+pXZmamEhISlJGRccwPThrCgbLD8vXyVqgfZ2wBgDso2rRWzXqcJ5vPL2sDKnIy5RsZLZuPb4O9Z2N/bXIFbrdtBAAAoCn5JH23Pkjb6bwf6R9I4wIA3Ehw7wHOxoWjolw7J1+j78cNUemuVMPJmhaaFwAAAIZ8nZulK5a/p2s+/UAbc7NNxwEA1FHZTz+qMm+fSn/8Tqk3DlTGMw+qurjQdKwmgeYFAACAIWdHxujy1h00sk1H9YhsYToOAKCOAs/oprMWfqmwpMuk6mrlvvVvbRt1jg588IYsh8N0PLdG8wIAAMAQHy8vzR98ud4cPEK+Xt6m4wAA6oFvZIzaP/WaOj7/X/m36aiqg/v1898m64fxQ1RVmG86ntuieQEAANCIZm3/Rslb1jnv+3l7y8eLb8kAoKkJOW+wEhesVfyUv8krqJm8m4XIOzjUdCy35XaXSgUAAHBX2YeLdd+GlSqpqlS/6JYaGNfKdCQAQAOy+fgq+qZJCh8+Wo7yMtlsNtOR3BbNCwAAgEYSG9hMH158lVZlp+uC2ATTcQAAjcQ3gnON6ormBQAAQCMa3LK1BrdsbToGAABuhQ2WAAAAAADApdG8AAAAAAAALo3mBQAAAAAAcGk0LwAAAAAAgEujeQEAAAAAAFwazQsAAAAAAODSaF4AAAAAAACXRvMCAAAAAAC4NJoXAAAAAADApdG8AAAAAAAALo3mBQAAAAAAcGk0LwAAAAAAgEujeQEAAAAAAFwazQsAAAAAAODSaF4AAIAmLSMjQ4MGDVJiYqK6deumd955x3QkAABwinxMBwAAAGhIPj4+mjlzpnr06KHc3FydffbZuuSSSxQUFGQ6GgAAOEk0LwAAQJMWGxur2NhYSVKLFi0UHh6ugwcP0rwAAMCNsG0EAAC4tNWrV2vEiBGKi4uTzWbT+++/f8ycWbNmqW3btvL391evXr20Zs2aWl9r06ZNcjgcSkhIaODUAACgPrHyoo4sy5IkFRYWGk4CAMAvjnxNOvI1yt2VlJSoe/fuuvnmm3XVVVcd8/jChQs1ZcoUzZo1S/3799eLL76o4cOHKzU1Va1atXLOy8vL09ixYzV37twTvl95ebnKy8ud9wsKCiRJ2dnZ9fSJAAComyNfkxwOh+EkjcdmNZXvbAzJzMzkpzcAAJeUkZGh+Ph40zHqlc1m06JFizRy5EjnWJ8+fXT22Wdr9uzZzrEzzzxTI0eOVHJysqRfGhJDhgzRxIkTNWbMmBO+x7Rp0/Too482SH4AAOrT119/rXPOOcd0jEbByos6iouLU0ZGhoKDg2Wz2U7qOYWFhUpISFBGRoZCQkIaOKH7oT7HR21OjPqcGPU5saZUH8uyVFRUpLi4ONNRGlxFRYU2b96sv/zlLzXGhw4dqq+++krSL/UYP368Bg8e/LuNC0l64IEHNHXqVOf9qqoqff/990pISJCX1687bgcNGqSVK1fWeO7vjRUVFSkxMVGpqakKDg4+hU9aN7XlaujXONn5J5p3qo9R/1Of35j1l8z8HlD/X8fc9e8A9T+Ww+HQvn371LNnzzq9jjuheVFHXl5ep/1TrZCQELf/BrkhUZ/jozYnRn1OjPqcWFOpT2hoqOkIjeLAgQOqrq5WdHR0jfHo6Gjl5ORIkr788kstXLhQ3bp1c56X8frrr6tr1661vqbdbpfdbq8x1r9//2Pm+fn5HfM9wO+NHdnS07Jly0b9c1ZbroZ+jZOdf6J5p/oY9T/1+Y1Zf8nM7wH1N1v/42VryOd7Sv2P3hrpCWheAAAAt/fb1Y+WZTnHBgwY0CB7gidNmnTaY42tPjKc6muc7PwTzTvVx6j/qc+n/g3zGtS/prrmoP6QOPPCiMLCQoWGhqqgoKBJ/HSvvlGf46M2J0Z9Toz6nBj1cQ+/PfOioqJCgYGBeuedd3TllVc65911111KSUnRqlWrDCWtHX/OzKL+5vF7YBb1N4v61w2XSjXAbrfrkUceOWZJKn5BfY6P2pwY9Tkx6nNi1Mc9+fn5qVevXlqxYkWN8RUrVqhfv36GUh0ff87Mov7m8XtgFvU3i/rXDSsvAACASysuLtauXbskST179tSMGTOUlJSk8PBwtWrVSgsXLtSYMWP073//W3379tWcOXP00ksvafv27WrdurXh9AAAoD7QvAAAAC5t5cqVSkpKOmZ83LhxmjdvniRp1qxZevLJJ5Wdna0uXbron//8py644IJGTgoAABoKzQsAAAAAAODSOPMCAAAAAAC4NJoXAAAAAADApdG8AAAAAAAALo3mRT1ZvXq1RowYobi4ONlsNr3//vs1HrcsS9OmTVNcXJwCAgI0aNAgbd++vcac8vJy3XnnnYqMjFRQUJAuv/xyZWZmNuKnaBjJyck655xzFBwcrBYtWmjkyJH68ccfa8zx5PrMnj1b3bp1U0hIiEJCQtS3b1998sknzsc9uTa1SU5Ols1m05QpU5xjnlyjadOmyWaz1bjFxMQ4H/fk2kjS3r17ddNNNykiIkKBgYHq0aOHNm/e7Hzc0+sD1/Pxxx+rc+fO6tixo+bOnWs6jse58sor1bx5c1199dWmo3icjIwMDRo0SImJierWrZveeecd05E8SlFRkc455xz16NFDXbt21UsvvWQ6kkc6fPiwWrdurXvvvdd0FJdE86KelJSUqHv37nr++edrffzJJ5/UjBkz9Pzzz2vjxo2KiYnRkCFDVFRU5JwzZcoULVq0SAsWLNDatWtVXFysyy67TNXV1Y31MRrEqlWrNGnSJK1fv14rVqxQVVWVhg4dqpKSEuccT65PfHy8/v73v2vTpk3atGmTBg8erCuuuML5HyhPrs1vbdy4UXPmzFG3bt1qjHt6jc466yxlZ2c7b1u3bnU+5sm1OXTokPr37y9fX1998sknSk1N1TPPPKOwsDDnHE+uD1xPVVWVpk6dqs8//1zffPON/vGPf+jgwYOmY3mUyZMn67XXXjMdwyP5+Pho5syZSk1N1aeffqq77767xveKaFiBgYFatWqVUlJStGHDBiUnJysvL890LI8zffp09enTx3QM12Wh3kmyFi1a5LzvcDismJgY6+9//7tzrKyszAoNDbX+/e9/W5ZlWfn5+Zavr6+1YMEC55y9e/daXl5e1tKlSxste2PIzc21JFmrVq2yLIv61KZ58+bW3Llzqc1RioqKrI4dO1orVqywBg4caN11112WZfHn55FHHrG6d+9e62OeXpv777/fGjBgwHEf9/T6wPV8+eWX1siRI533J0+ebM2fP99gIs/0xRdfWFdddZXpGB6va9euVnp6uukYHikvL89q1aqVtX//ftNRPMqOHTusUaNGWa+88op1zz33mI7jklh50Qj27NmjnJwcDR061Dlmt9s1cOBAffXVV5KkzZs3q7KyssacuLg4denSxTmnqSgoKJAkhYeHS6I+R6uurtaCBQtUUlKivn37UpujTJo0SZdeeqkuuuiiGuPUSNq5c6fi4uLUtm1bXXfddfrpp58kUZsPP/xQvXv31ujRo9WiRQv17NmzxjJYT68P6t/vbSGVpFmzZqlt27by9/dXr169tGbNGudjWVlZatmypfN+fHy89u7d2xjRm4S61h91U5/137RpkxwOhxISEho4ddNRH/XPz89X9+7dFR8fr/vuu0+RkZGNlN791Uf97733XiUnJzdSYvdE86IR5OTkSJKio6NrjEdHRzsfy8nJkZ+fn5o3b37cOU2BZVmaOnWqBgwYoC5dukiiPpK0detWNWvWTHa7XbfddpsWLVqkxMREavM/CxYs0DfffFPrP+ieXqM+ffrotdde07Jly/TSSy8pJydH/fr1U15ensfX5qefftLs2bPVsWNHLVu2TLfddluNJeGeXh/Uv9/bQrpw4UJNmTJFDz30kLZs2aLzzz9fw4cPV3p6uqRfvkb+ls1ma9DMTUld64+6qa/65+XlaezYsZozZ05jxG4y6qP+YWFh+vbbb7Vnzx7Nnz9f+/bta6z4bq+u9f/ggw/UqVMnderUqTFjux0f0wE8yW+/AbEs63e/KTmZOe7kjjvu0Hfffae1a9ce85gn16dz585KSUlRfn6+3n33XY0bN06rVq1yPu7JtcnIyNBdd92l5cuXy9/f/7jzPLVGw4cPd/66a9eu6tu3r9q3b69XX31V5513niTPrY3D4VDv3r31xBNPSJJ69uyp7du3a/bs2Ro7dqxznqfWB/Vv+PDhNf5O/taMGTM0YcIE3XLLLZKkmTNnatmyZZo9e7aSk5PVsmXLGistMjMz2ft8Cupaf9RNfdS/vLxcV155pR544AH169evUXI3FfX55z86OlrdunXT6tWrNXr06AbN3VTUtf7r16/XggUL9M4776i4uFiVlZUKCQnRww8/3FgfwS2w8qIRHDn5/7c/pcvNzXX+xC8mJkYVFRU6dOjQcee4uzvvvFMffvihvvjiC8XHxzvHqY/k5+enDh06qHfv3kpOTlb37t31r3/9i9rol2X7ubm56tWrl3x8fOTj46NVq1bp2WeflY+Pj/MzenKNjhYUFKSuXbtq586dHv/nJzY2VomJiTXGzjzzTOdPOTy9PmhcFRUV2rx5c40tSJI0dOhQ5xakc889V9u2bdPevXtVVFSkJUuW6OKLLzYRt8k5mfqj4ZxM/S3L0vjx4zV48GCNGTPGRMwm62Tqv2/fPhUWFkqSCgsLtXr1anXu3LnRszZFJ1P/5ORkZWRkKC0tTU8//bQmTpxI46IWNC8aQdu2bRUTE6MVK1Y4xyoqKrRq1SpnV7lXr17y9fWtMSc7O1vbtm1z+86zZVm644479N577+nzzz9X27Ztazzu6fWpjWVZKi8vpzaSLrzwQm3dulUpKSnOW+/evXXjjTcqJSVF7dq18/gaHa28vFzff/+9YmNjPf7PT//+/Y+5LPOOHTvUunVrSfzbg8Z14MABVVdXn3Cbko+Pj5555hklJSWpZ8+e+vOf/6yIiAgTcZuck6m/JF188cUaPXq0lixZovj4eG3cuLGxozZJJ1P/L7/8UgsXLtT777+vHj16qEePHjWunoXTdzL1z8zM1AUXXKDu3btrwIABuuOOO465uhtOz8n++4Pfx7aRelJcXKxdu3Y57+/Zs0cpKSkKDw9Xq1atNGXKFD3xxBPq2LGjOnbsqCeeeEKBgYG64YYbJEmhoaGaMGGC7rnnHkVERCg8PFz33nuvunbteswBhe5m0qRJmj9/vj744AMFBwc7/5KGhoYqICBANpvNo+vz4IMPavjw4UpISFBRUZEWLFiglStXaunSpR5fG0kKDg52no9yRFBQkCIiIpzjnlyje++9VyNGjFCrVq2Um5urxx9/XIWFhRo3bpzH//m5++671a9fPz3xxBO65ppr9PXXX2vOnDnOfdSeXh+Y8XvblC6//HJdfvnljR3LY/xe/ZctW9bYkTzKieo/YMAAORwOE7E8xonq36tXL6WkpBhI5TlOdpvq+PHjGymR+6F5UU82bdqkpKQk5/2pU6dKksaNG6d58+bpvvvuU2lpqW6//XYdOnRIffr00fLlyxUcHOx8zj//+U/5+PjommuuUWlpqS688ELNmzdP3t7ejf556tPs2bMlSYMGDaox/sorrzj/cnpyffbt26cxY8YoOztboaGh6tatm5YuXaohQ4ZI8uzanCxPrlFmZqauv/56HThwQFFRUTrvvPO0fv165+oCT67NOeeco0WLFumBBx7QY489prZt22rmzJm68cYbnXM8uT5oXJGRkfL29j7hNiU0HOpvFvU3i/qbRf3rj82q7WhrAAAAnDabzaZFixZp5MiRzrE+ffqoV69emjVrlnMsMTFRV1xxBQdG1jPqbxb1N4v6m0X9Gw4rLwAAAOrB720hnTp1qsaMGaPevXurb9++mjNnjtLT03XbbbcZTN10UH+zqL9Z1N8s6t9ILAAAANTZF198YUk65jZu3DjnnBdeeMFq3bq15efnZ5199tnWqlWrzAVuYqi/WdTfLOpvFvVvHGwbAQAAAAAALo1LpQIAAAAAAJdG8wIAAAAAALg0mhcAAAAAAMCl0bwAAAAAAAAujeYFAAAAAABwaTQvAAAAAACAS6N5AcBl5eXlqUWLFkpLSzvl51599dWaMWNG/YcCAAAA0OhoXgA4LUuWLJHNZjvu7ZprrqnzeyQnJ2vEiBFq06ZNjfHvvvtOo0aNUkREhPz9/XXWWWfpqaeeUlVVlXPOww8/rOnTp6uwsLDOOQAAAACYRfMCwGlJSkpSdnZ2jVtmZqaGDBmiyMhI/fWvf63T65eWlurll1/WLbfcUmN81apVOu+88xQQEKAPPvhA3377re677z49/fTTGjVqlBwOhySpW7duatOmjd5888065QAAAABgns2yLMt0CADur7q6WjfddJM+/fRTff755+ratWudXu+9997Trbfeqv3799d4j44dO6pfv3564403asxPTU1Vjx49NHv2bE2YMEGS9Oijj+qzzz7T6tWr65QFAAAAgFmsvABQZ0caFytWrNBnn31W58aFJK1evVq9e/euMfb1119rz549+vOf/3zM/MTERF1yySVauHChc+zcc8/V119/rfLy8jrnAQAAAGAOzQsAdVJdXa0xY8Y4GxfdunWrl9dNS0tTXFxcjbE9e/ZIkjp27Fjrczp16qSff/7Zeb9ly5YqLy9XTk5OvWQCAADSCy+8oDZt2sjHx6fWHyjU5cDtE+EwbsCz0bwAcNqONC6WL1+uzz77TN27dz/uvFNVWloqf3//GmMhISGSpIMHD9b6nEOHDjnnSFJAQIAk6fDhw6f8/gAA4Fjbtm3TlClT9MILLygjI0OPPvroMXN+e+D2+PHjNXLkyGPmrVy5UjabTfn5+Sf13hzGDXg2mhcATsuRxsWyZcv06aefHtO4SEtLU/fu3TVx4kT17NlT5eXleuWVV3TuueeqW7duevjhh0/4+pGRkTp06FCNsb59+8rX11cfffRRrXmWL1+uAQMGOMeONDmioqJO92MCAICjfPjhh+rVq5cuvfRSxcbGKjAwsMbjxztwuz5wGDfg2WheADhl1dXVGjt2rLNx0aNHj1rnbd++XXfeeae+++477d69W0uWLNG6deuUkpKiLVu2aN26dcd9j549eyo1NbXGWEREhCZPnqzHH39cWVlZNR775z//qby8PN19993OsW3btik+Pl6RkZGn/2EBAIAkqX379nrooYe0YcMG2Ww2jRkz5pg5n3zyiXx8fNS3b99Tfv20tLRaL78+aNAg55zLL79cb731Vl0+BgA3RfMCwClxOBwaO3as3n//fb3xxhuKjY1VTk5OjduRbSKdOnVynoHx2Wefad26derVq5fOPvtsff/999q9e/dx3+fiiy/W9u3ba6y+KC4u1uTJk9W2bVslJSXpm2++kSQ99dRTevDBB/Xcc8/Jz8/P+f5r1qzR0KFDG6oUAAB4lHXr1qldu3Z66qmnlJ2drVmzZh0zp7YDt09WQkJCjUuwb9myRREREbrggguccziMG/BcPqYDAHAvGzdu1Pz58yVJl1xySa1zjjQcjl5KalmW/vjHP/7udpEjunbtqt69e+vtt9/WrbfeKkl6+umna+ytffbZZzVv3jzdd999kqQ//OEPkn452DMmJkaLFi3SsmXLTvETAgCA2jRr1kxpaWkaMGCAYmJiap1T24HbkvTxxx+rWbNmNcZ+eyaWt7e383XLyso0cuRI9e3bV9OmTXPOOfow7tatW9fxEwFwJ6y8AHBK+vTpI8uyTngLCws75nmDBw/WwoULnY2NzMxM5eXlnfC9/vrXv+pf//qXHA6HJGnatGk13mfevHmSdMz7t2nTRi+//LL69Omj8847r14/PwAAnuq7776TpBNeEr22A7clKSkpSSkpKTVuc+fOPe7rTJgwQUVFRZo/f768vH79LwuHcQOei5UXABpFly5ddP/992vQoEFyOBwKDg7WggULFBERcdznXHLJJdq5c6f27t2rhISEU3o/X19fPffcc3WNDQAA/iclJUUdOnRQUFDQcefUduC2JAUFBalDhw41xjIzM2t9jccff1xLly7V119/reDg4BqPcRg34LloXgBoEG3atNGmTZtqjI0dO1Zjx449pde56667Tuv9//jHP57W8wAAQO1SUlKOe1n0I3r27Kk33njjtN/j3Xff1WOPPaZPPvlE7du3P+ZxDuMGPBfbRgAAAAD8rpSUlONeYeyI2g7cPlnbtm3T2LFjdf/99+uss85yHgR+ZLWFxGHcgCejeQEAAADghBwOh7Zu3fq7Ky+OPnD7VG3atEmHDx/W448/rtjYWOdt1KhRkn45xHPRokWaOHHiaX0GAO7NZlmWZToEAAAAgKZhyZIluvfee7Vt27Yah23W1QsvvKAPPvhAy5cvr7fXBOA+OPMCAAAAQL2py4HbJ8Jh3IBnY+UFAAAAAABwaZx5AQAAAAAAXBrNCwAAAAAA4NJoXgAAAAAAAJdG8wIAAAAAALg0mhcAAAAAAMCl0bwAAAAAAAAujeYFAAAAAABwaTQvAAAAAACAS6N5AQAAAAAAXNr/A1oaNomxHNPNAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sim: SimulationResult\n", - "for sim in project_ex1.get_simulations():\n", - " fig, axes = mpl.plot_circuit(sim.circuit, sim.get_frequency(num_per_decade=20))" - ] - }, - { - "cell_type": "markdown", - "id": "f3ae21e2-3b66-4e87-9b25-bab54c8aca2e", - "metadata": {}, - "source": [ - "##### Composed plots\n", - "\n", - "Plots composed in the GUI program (`Plotting` tab) for the purposes of, e.g., comparing different results can be plotted as follows using the provided function:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "62e7a538-45a5-48e9-b323-ceaf29e50f01", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "settings: PlotSettings\n", - "for settings in project_ex1.get_plots():\n", - " if \"template\" in settings.get_label().lower():\n", - " # Skipping the plot that was used simply as a template for defining the styles\n", - " # (markers, colors, etc.) so that the styles could be copied to other plots.\n", - " continue\n", - " fig, axis = mpl.plot(settings, project_ex1)" - ] - }, - { - "cell_type": "markdown", - "id": "9fb64782-44ef-4cf4-a0d6-76835d68a0d7", - "metadata": {}, - "source": [ - "##### Custom composed plots\n", - "\n", - "The dictionary `deareis.mpl.MPL_MARKERS` can be used to look up matplotlib's equivalent string representation for similar markers.\n", - "If you are using a plotting library that is not currently supported, then you can compare the return value of `series.get_marker()` against _DearPyGui_'s `mvPlotMarker_*` constants and figure out the appropriate value for your plotting library of choice." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d42b0a1d-2d21-42d5-b7ed-5d14047b4131", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Dgoay9xrF2LgSfiAQINxbA4BR9YGvDhrKoXpvwS2g9hqGLq0xid/2jwsJ9TrxmO34QmNQI1MwxnfAntGDyI690Juar7CvBfzU//IFPTZ8jeKqQt99NLroFLQG+TkjhBBCCCGOTrtN4t1uNw8++CATJkwIPqkoLS3FZDIRGRnZpGx8fDylpaUt1vPMM8/w5JNPHvd4hTiVVFZWUJS3BaPZStee/QGoqtqD/vnzSQASWrjG6szHFJlIILUn9sL1VJhjifJU4reEEVAMBPxelJh0AhUFhPY7F8Vsa7zOasX8wJdonrrGBNxoafzTYELRNV3Ww2g0kvnED83urWka+L1oXheKJTR4vHr8VALleaiOMjRHBbqGKgwuB0ZvPTqDkXB7DNC4TV5MQ+PPEIu/AepLoSwHNgOLwAHYb30HQ3ovAoEAK779gIiqnUTv+pmY+koc8T0p6HcD4Vl9CVv2DiZPBZqmoShKs1iFEEIIIYQ4mHaZxPt8Pq666ipUVeW11147ZPmD/bL80EMPcffddwffO51OUlNlayshoLF3ffeufMryNuEu2kJIyUZia3YS6q8jFfDrjKjp36CzxxAVFcMexYBR8wev9xss+MPiUCKTMMelo4/LIOzWafi3LUf/wyuoxRUYDAY0Tx2moVdg7jaS2jf+gn3ElU2GsevsMUDMUbdDURQwmlGM5uAxvV5PhwFnHdb1BoOBij88ha9gPUplAUZnGRZXNWZfPQbNjwLoopIBcKyZQ5dl/w72/Pt0RrSGKkx5SymvLkFxOgjz+SSJF0IIIYQQR6XdJfE+n48rrriCvLw85s+f32S+QEJCAl6vl+rq6ia98eXl5ZxxRsurXZvNZsxmc4vnhDjduFwuqirLSU5JB8Dn92F9/Wo6EWixvEH1ESjejM4+HJ1OR9ikN9FV7EQfm4EuNgNdSESL1xk7D8GeNZj6Gffi27QIgMCOX/GZrMelXcfKaDTSeei5MPTcZudcLhfuqiKU0CgAqlbPwaQPITRQj0LjUP2I2iIiaougeDkASlxHdDodqqpStWs70cmZKAbjiWySEEIIIYRop9pVEr8vgd+2bRsLFiwgOjq6yfn+/ftjNBqZO3cuV1xxBQAlJSXk5OQwderUtghZiJOaw1nD9vXLce1YTUTRahKc+ajGUPy3vYkhMQuzyUxlWArxtQUAqIqeQFQK5vReWNK6o0/ORp+QFazPmtELMnod1r0VRcH2hwdwbP0FY7czUWvKcM9787i083iyWq1YkzsF33f887N4V36NY8EMyjwartA4rP56IupLCfHVEtAZMe1N2Hfvyifs9QlUo9BgicSV2JWQnmOI7zcGvSWkrZokhBBCCCFOYidVEl9XV8f27duD7/Py8li7di1RUVEkJSUxfvx4Vq9ezf/+9z8CgUBwnntUVBQmk4nw8HBuvPFG7rnnHqKjo4mKiuLee++lZ8+ejB07tq2aJcRJ56evp2Pf+D1Jjp10oOkGFaG+Wjybl2FIbEzOO936CoEdy9EnZaOP79iqPca68DjC7/+qsRdbUfBvW45v68/oolJa7R4nmmIwYR4yHi11CIG500nPW4DR46QqoTc70keRXb8FpTofgPLCrYTSuPBeiLuKkLyfIO8nHF//A7cpjEDWcFL+9A8Zdi+EEEIIIYJOqi3mFi5cyOjRo5sdv+6663jiiSfIzMxs8boFCxYwatQooHHBu/vuu48PP/wQl8vFmDFjeO211w57nrtsMSdONbt25bF7w88MPvcqdHsXglvz0q1klP7apJym6NASsrB0Goi511gMqT3aItxTxqZNmyguLiY9JYmkyg00/DgNfUM1miUUY3RKcIs5h6OG3SvnQu5Cwsq3EOqtCc6n90SkkPDglwAUFuykYdX/SBp8Pvbf9PwLIYQQQoj270jy0JMqiT8ZSBIv2jtVVdmxbSOlaxYQs20+CfW7AXD1vpikq/8OwKY1y4j/7B6IzcTW7UwMHQdgSO2B0sI2aeLoOBwONE0LboGp+b1ULfwI3YpPMCRmEXbDv1q8zu12U7BmId51c0jvfxb2gRcC8NMH/0e3DR8B4NWbccZlYx0ynuSB5zRbpV8IIYQQQrQvksQfA0niRXugBfyg0zcZZp2ft43diz4leecCIvbuf/5b/ogkYh/8en8dXhfKSbqQ3KlMCzSu3q/oj2w206+fvETauk8xqd4mxwOKjprIjsRfei9hnfq3WpxCCCGEEOLEOZI8VLpvhGhH1LoqGr77FzVPjKJh2ac0uBqC55xV5XTfPKtJAq8BpPTAev6dRN3wYpO6JIFvG4recMQJPMDAKycTP2UZrptnUNT1ApyWKDRAr6lEV21DV7IlWHZX3la8blcrRi2EEEIIIU4W0hP/O9ITL05Gal0VDQum413+OUW2JCr1dlLrd+GJTqfrHW+iKAper5fd/3c1Ec7d6DsOwNJzDMZuZ6ILO/r91cXJzVlTye4fZ2LdtZb0659BF5FAIBCg6PGxhPjrqI7IIDBwPB1GXopRtrATQgghhDhpyXD6YyBJvDiZqHVVuBfPoPTXHyiwJBPrqSC5oahJmbBbp2FI791Y3lmBYrKiWELbIlxxEigp2YXu31dg0nzBYwF07InJRjf8T3QaNDa4wKEQQgghhDg5SBJ/DCSJFyeDfcm75+f/ssHWiW6OTehRm5TRAH2HAYSccxuG9MPbm12cHrxuFwU/zsS45hvsdcX8doO6utAEUh6c1apbBQohhBBCiGNzJHnoSbVPvBAC9hTlofvPdSjeBhq6jMFc7WySwDeEJWH21ODqMBx19CTC09PbMFpxMjJZrGRdcBNccBPe2hoKf5iGeeNsQtxVGEMjgwl83s6tVG1dSdeRl2Cz2to4aiGEEEIIcTgkiRfiJKCqKptzVuJY/DFZu5dSlTaI6JJ1mLcvRU0eTm1IAlpaH0KH/JFip0rqtw9R6/Hjq6khXZJ4cRCmsAg6XX4PXH4PdUU7CFMCwXNl86bTZeccGha+xPak/oSe+1ei4lIIDw9vsvPBb/ny1uBZ/D6m3udg6nPuiWqGEEIIIYTYS5J4IdqQz+dj7U+zsSybQYpzB4l7j4cXr8F+7xfUL/mQzr98hqapKPZwIlM6YtpeAEBkZCQJvWQYvTh8ockdm7wP0SuN0zJQSS3+Fd65ngpLLOt7jWfg+ROwWvbvYODLW4N73pv4d/wKgGKPlSReCCFEmwkEAhQWFpKWloZer2/rcIQ4oSSJF6INuN1uVn3/AUlrPqLT7/Z0Vy1hhAy6BJ0lBPtFd+EcOJ6iWS8Rt+pLqld9RUiHM9FrAUIjIg7YWyrE4eh549P49txK8XevY9s8H4PqI8ZdQcyK19mzZiaJ936CVlUUTN71CVmE/Ok53AumtXXoQgghTnMVFRXs2LEDs9lMUlJSW4cjxAklSbwQbcBgNJC6+gPCfM79B+M6Yhs5EVPvcSgGU/DwngYfRVnnUZY+goRdPxGzYyH6gJdAINBCzUIcmT0+PdqZN1F/xnVUL/ucmO3zCPU6sPkbqH73bgylm9HiO7Fr5F30OPtK9AYD7oXvtnXYQgghTnNlZWUAlJeXSxIvTjuSxAtxAhQVFZK34GMGXfwXTPYoDHoD1UOuI3TJKxi6nIH1zIkYOvRvsWe9srISnU5H5z6DiBl7PlvWroD1PxCXNgjZSE4cC03T2LFjB263GwBTxjASx0yk7v07MTt246urobD3tQRSOpP17aPsXPEBlYOvp4OmJ6SNYxdCCHF6UVWVTZs24fM1bqFaXV2N1WqlsrKSNWvWAGAwGOjWrZsMrxenPEnihTiOdu3Ko/D7t8jeOZfuaBTv2UjG5PcA6H3Bdagjzkdnjz1oHVlZWZhMJmy2xtXDewwcRmWHbMLDw497/OLUpigKgwYNYuPGjVRWVpKWlkZ8QgLVkbEojgICegNZXbqwY2sOADGucmIWTqVBZ2GlLoZe1VVERka1cSuEEEKcLhoaGnA6neh0OmJjY8nMzCQvL4+KigpUVSUsLKytQxTihJB94n9H9okXraGwYAe7v3+TLvnz0bH/r5hqDiXq4W9RzNKPKU4emqaxbNkyrFYrEREReDweHBsW07VqJWr+GnTxHQlEJKFt+xmd6g9e51UMbOt8Ef2vvrvJInhCCCHE8aCqKjt27KCgoID09HSysrLYvn07+fn5pKWl0alTJ3Q6XVuHKcRRkX3ihWgjHq+Hle/9k+wds+n62+TdEkbIqOswDxkvCbw46bjdblwuFy6Xi6qqqsaDUR1xj7yEqLpduOe9ibplCfqETihxHfBsmI9B82PS/HTe9i3GumvBktq2jRBCCHHK0+l0dOjQgd27d+N0Olm1ahWKoqDT6ejYsaMk8OK0Id/pQrQio8FIXNn6YO97wBqB9cK7iXr4OyyjrkexyCx2cfLZs2cPABEREQwdOjS4QFBFRQXGjgMIm/QmoTe9gc5qJ7B+DgbNjy6lG6rJhmINQx+RAEBdfR3L3niIbVs2tFlbhBBCtB7N68K9eAaO5/6Ib8fKtg4HgKqqKgKBANXV1TQ4q6mq3IOqqlRWVrZ1aEKcMNITL8QxcLldrP7hI7oNPovIxAx0Oh2G8U/i//IJws6cgHngJShGc1uHKcRBRURE0KVLF5KTk9HpdHTr1o2YmJgmCy0aOw7A2HEAvh0r8SyZibH3OExdzyRQuRvFYARg3Q8f0i1/Lrw7l+WJgwkf+xeyu/dtq2YJIUS7omkavs1L0RocmPtf2LaxeF14fvkM96L30VxOUAPUFeYS2XFAm8YFjQvamTwOOletxrx5Pq4zrifX0omqqiri4uLaOjwhTghJ4oU4CqqqsnLR/4hb8G+6eWtoWPcxEX+fjaIoZGb3RXvgS9nDXbQbYWFhzRYDOtAvQvuS+X0MydnBrztY1eDXnUuWw4zl/BrfH/s5t9KlW5/WDVoIIU4R+5J397w3CRTlgjmkzZL43yfvpgEXYxl1A47nLqGkuIQITWvT32/UmlISN3xG7OpvUMw2NEUh0qwwZMgQWZFenFYkiRfiCG3bkoNr1tNk1WwLHrN4nKjlO9HHdwSQBF6cluJHXI7b68C94gsUNYAGdCpbBe//hV9SRtB/0lSMRmNbhymEECeF3yfvhoy+GHuPw7f5pxNzfzUAgKLTNw6b/+ljXItm4PV6ULNHonQdSb3RQkPuWnyWOAKuOmpra7Hb7WxdvwLvro2omoqmqqhqAE3T0FDQNIU+467AEBoJwPpfF+Mq3oqq06MpBlB0jb8n6QwoOh19R12E2dQ4anH71hycFSUoej06nQGdXo/BZEbnc8PWpURt+Aaj2YL17EnoBl5C3UtXAxASIusNidOLJPFCHKbq6kpyP3uR7B0/sC9F11Aw9LuQ8HNuRRcuQ7jE6U0XFo3tkgcwD78a1+xX8W34EQANiKrdhUEnD7eEECcP1bkHxWxDMdtO6H1bSt5D//I6ho4D8Cz9AF/uUjRPA5q7rvHlqUcXnYouJAKAwJ5CfJt/QvO5wecm4GnA11CLr6EWzesiauyfgyOmcj7/N7HrZqGoPhQ1gKKpjS80FMB2yYMYs4fjfOVaCgNWUt1OzACb5jS+gH3pcY2zkM2bN2Oz2fB8/zIdqnMP2Ea3zU/ouX8FwLvsk8bRWQcqq1ZgPvdWACoWzCQ7b94Byzb0u5SYP9yJYg5hyfvP0qO2gtp571C58AO8egsegxWv0YbPaCPlmn+QkNC4xsuGVT/hyFuPzhyCzmzDGGLHHBKOLdSOLSyC6KiYVnvIrAX8qHsKgh07QhwPksQLcZi2/fd5uu6cG3yvpvYi/NKHMCRmtWFUQpx89DFphF7zLP7CHFzfvYQ/fy1RYaGgaxzqWFy8i51zZpB94V+IiZGHX0KIEyuwZxfu+dPwrv0e8/AJ2M6/87jcR21wolbtRmtwoDU4UF1OAqU78G1ciFZXiT6pSzB5921cgPPZi1HrqtD8HmoeP7NJXSETnsHU62wAcj58hrTiX5vdb18KqlaOg71JvH7zIkze2gPGqAX8KJZQ9IlZaLuLm54L/tn4ADbEHk5pIEBpaSl6SzR+xQCK0qS0snfnapNh/3pASnwnOEgSr/jdwa91YTEHLAdgwx/c5cfgaWyXET9Gv58Qfz149pf1blkMCVcB4MhdRvecTw5Yb+GN79MxqxsAv8z5L6Z13+I12/FbwyE0CiUkCpM9GktYJJnZvQkNab5QsRbw413zHe7501CrirDf/xX6qOSDtkeIoyVJvBAHEQgEgnOsOl06Gc9LS9FbQgj/44MYu42UYfNCHIQhrQehk97Ct+FHdLHpwb8vBV/+m+6FC3Bv/ZalPcfT84IbCLdHtG2wQohTgqqq/Prrr3Ts2JGYmKYJYWBPYWPyvuZ7lNAoFEsomqf+yOqvKcW/ayNqXSVabSVqbSVafU1jkt7gIOTShzFk9AHAt2EeDbOmHLAuY48xGDsNBGDLD++RUlMCwO9/swigA2X/hlK6+uoW6/MqRlzGUOzpvYLHajqPoaF8K6o5BMyhYAlDZw1Fbw3FYAmj++DRKAYTYTe+SlLOUup+/gx93q9YYlJQhk4gxxeBqkGv2fdj7TYcn8+HyWSi+58eIzo6+rA+s/5X3YV2+e0Q8KH5POD37v3Tg+b3oguNCpYddOkteLOy0Dz1aO46fFt/IbB7E2gqSkQ85vj0YNle503E+8oPB7yvdc/O4NchSZ0gp/FrFR0BnR4VHQoqBtVPWPkW2JvE+ysK6FKZc8B6S//8Hpkds9Hr9Sz77gPsa2bhMoXhVhUCGijWDIz2MKK2biK9dxRWq/WwPichjsRJlcQvXryY5557jlWrVlFSUsKsWbO45JJLguc1TePJJ5/kzTffpLq6msGDB/Pqq6/SvXv3YBmPx8O9997LRx99hMvlYsyYMbz22mukpKS0QYtEe1VfX8+6T18gtngNHe6did5sIyomnsDdn6Czx6AYTG0dohDtgqIomHqNbXIsxdLYZ2NRPXRf9wG1G79kQ98J9D//WvllRwhxTKqrq6mtraWoqIiwHYvw/PI5mt+LVlfVuMq6To8SFo1iC0et3IV/+wpUVy1qRT5qTRlqTWnjy1nemKDXVRJy+RP7E/Mtyw6amKvOiuDXuSsXkawzomkaei0Q3H4WwKWzwJzX8BesxTrmZvyq1qQet2LGaYmkwRqFOzSeoT3H7D857nbyPA1YQiOwhUUQGhaO3R5BpKn57yZDL7/tsD+72B7Docdw/Plrcf34Fv6v/knXqDTyk0egaSoVFeX4QqPIyMg47AR+H0VvAL0BxXTwn/GKJRTzwD8E31vH3oza4MTz00e4l36Ie96baLVVWEZOJCSlC96wGMx9zsXU74LGhyn11agNNWj1NRhSugXr6dWrP7VLw9EaHOhQ0alqk/taKrYGv04dcDZazqf4zGH4jDa8OhN+dKhqAJ3fi8nnZuHChQweOAD/ro0k1+Y3bUTN3j+/fIiiqOl06twDgBULvkbduACfPQF9ZCKW6CTC45KJS0ghLLTp4rJCHMpJlcTX19fTu3dvbrjhBi677LJm56dOncoLL7zA9OnT6dy5M0899RRnn302W7ZsCa6sPHnyZL755hs+/vhjoqOjueeee7jwwgtZtWqVrFp5mlNVFY/Hc8gkYe3yhUR++w+6ep0AlHz+HCkTHgdAH5V03OMU4lSmaRpR9jC8e98HFB0h/nq6//oWRRu+pGb07QwYeX6bxiiEaF9UVcXv9wNQWloKQGVlJQ31Ragl29Ghgt6ILjoFzCHgdaGPzUQt3YHmacC3bjYNX/6/A9fvKAt+nbenirCwBPzavqTOg8VXj0nz4TTaCe+wf/cOv6sOg+prUleNKYJaawwNofH0GX4engXTqH3tenQdz6J4yBXY6sswL/+EpCfmkHiAeHr2H3aUn9ThMWT0IezGV/Hnr6Xsi/+j44YPGmOvcaCFaFRUVNCx44mb762z2bGePQnzsKuDybxn+WeYB49HCfhQzLZDTm3Ux6QR8fcf0XweVGcFWu0eVEd548Ma557gQxqA5DALtZqKye3A5Hbw+yXzGlZ8SpQ+EvcvL5Dg8eGwp+GOzsBtCsHjC6A2OLDUlhLmrycqJiF4nXfXJrrvXtIsNj+wyxBK4Ma3yMhsbMf2rRtx7ikhKjGdhMRULBbL0X584hR1UiXx5513Huedd16L5zRN46WXXuKRRx7h0ksvBeC9994jPj6eDz/8kEmTJuFwOJg2bRozZsxg7NjGnp+ZM2eSmprKvHnzOOecc05YW8TJZ9euXezYsYMRI0a0uHhJVdUednzwJJ2Kfg4e84XEED9Qvm+EaC2KohAy/u+Y+p5Pw1fPQnkeAF6dkWh3BaWlO9o4QiHE0fJ6vfz666/06NGD8PDwE3bf5b/8gre6lIDejGowEx8fj3vDfLT1M/f3fgd8qJW7g9cYu48GNPTJ2egik1AiElBDonEbbbg0A25/ANVTj6pp9MwaEryuLvdn4mtLW4wjoDMSMIeyb+C74cxr2eGqJyQqjoiYeKKi44g0mZtcY+59Nr4N88j48W3U7+ajhEWjaZ7mlbcBJbUnub2uI8pVQgdnLh3OuJiG0lrq6urweDyYzeZDV9KKWkrm8dTTfALCgSlGM/roFIg+8AhdfUIW4Q9/j1pdjFpdQqCqCE95AVp1KThK8TorySj5EVVvInbsbTD7JcKdhb8JVA9qAEOH/ph3r4WoxpwkdsC55EanotaUoK8pwVZbSnhDGeHeGkL9dRCxf3RD6S/f0H3TZwDUo7DLGktNWAqeiBSU6FR6jP6jTEM7zZ1USfzB5OXlUVpayrhx44LHzGYzI0eOZNmyZUyaNIlVq1bh8/malElKSqJHjx4sW7asxSTe4/Hg8ez/Yel0Oo9vQ0SbKSsrQ1VVKioqSEra36OuaRqrf5pN4ndP0UltXFxFVXQYR0wk4uybUIzy9FOI1mbsOAD7HR/iWfoBrh/fwuTzoCk6enfIDJbJz9uG2WIlMVGmQwnRHlRUVOByuSgpKTluSbxaX0OgZBuB0m0ESrcTKN1Ol7KdKD43O3tchX3wH8jMzKTGtRtl/W+GqBst6JO6oMV2oE5nRrf1FzBa0IUnsGLxd2TW1mCtKcUK/Ha8noqCV2dm328C7sxB5IbEoEYkoo9MIiQmkYjYZGLjE+lgaTrSr8+gkYdsT3WNg+2eSPrf8SGBjfNx/fg2iunErpZ/IDqdjj59+hARMQqDoTFlGJQWoKqqClMLQ/dPWFy/Sea9q7/FmD28VetXdDoUeyw6eyyk98br9bJy8WLYu8SCzu8mOzYN08YfCMx9FZc9Db/JRpR+74Mif2Ne4d+5CmOX/aMmOkXbif/8XfRxHdAlZaKPG4g+vgNeewIVdW7SwiP2x2CPozAii5jaImyBBmJc5cS4yqF8NQDqmRcHy/705TsY81fiic7AGJ9JeFIHElM7EBEeGSyjaZqs43SKaTdJ/L7hUfHx8U2Ox8fHU1BQECxjMpmIjIxsVmbf9b/3zDPP8OSTTx6HiEVbCwQCVFRUoKoqqqridDpRFIWioqLGAp46rDnf4139PxK8Hqx7E3hfQheiJzyNPi6j7YIX4jSgGIxYRl2Psdc4XN88hz9vDSHZQ4HGv781nz5BgiOPJb2uot9FN8o+wEKchPx+P4FA437jZWWNw87Ly8vJyMhAURR0Oh1GoxHNXYdaV4U+Ju2w6tX8XgJlO9GFRKCLaByS7Nu8lLrpk5uVVQAUPTbNQ2VlJampqZQboijveDEGi5VA0SasATdRJbuILliHWdHh1nRYohODnbjWgJsAOqqscTjsyXjCk1GiU7DGptL9N9tjDr9s0lF/Vi0pKirC6XRSXeMgpvc5GHueDQHfoS88ARRFabY4oF6vJzY2to0iakpns2MZfvVxv4/JZKJXr17k5uYSCATo0W8QsbEXULLjchzz3iZu188oLj2m4VdhGnY1gYL11M+4F8voP2PsckawnkD5TrT6Gvx5qyFvdZN7RFhC8V14N+YBjcn5GRdcA+degWayUVNTRVlxIc6yAnx7dqM4yhj6mwTdULiGrNIVULoCNjYe04A8UySV9lRSExMxbfoR+13/JWCPb9MHMKL1tJskfp/fP0U6nCdLByvz0EMPcffddwffO51OUlNTjz1Q0eZqa2vJydm/uqjRaCQjI4Odm9ZTvfoz4nb9hKoGMClQnn0+xi2zsZ37VyLOuBJFpztIzUKI1qSPSiL0uhdRa0rRhTUOJ6ytcxLqrcWseumx9n3Kcv9HxYhbGDD6D03WN/HvzsW/YwXmERPl760QJ5imaSxdujQ4Hx0gISGB0tJSli5dCjT2Wg5QdqP+8imaGiDyyUUt1qNW7sa/K4fArpzGP4u3QMCP5exbsI75S2NdsRmNf0anoI/vhD4xC29EMmVehUqHA48tCZ3Xy9KlS9HlfEPf0p9ajLvWEIbPYse8N1nOOO8m6tQbiU9IJuYEJDiBQKCxzarKnj17gMYHIBEREY3t0xuPYIC4OBHi4uKw2+0sW7aMyspKdDodVe4AlV0vpsOV9xP45RPcSz/C/dMnGLs0Pow29R6HPqFTsA5j9nDCbp9JoDwPtXwngbI8AuU7USt3o7nrUCz7t63zbV9B/Xt3oYtMwpSYRUZiZ/SJWej7DGyc/vGbf+9CRl3HurwBGBwlmCrziXQUEOOuIMJbjanajakuDwJ+tLpKVn3yEvHlOeyJ7IAnthPmpCxiUrNISesoyX07026S+ISExqewpaWlJCbuX+qjvLw82DufkJCA1+ulurq6SW98eXk5Z5xxBi0xm80nfE6PODEiIiKCT04VRaFP1yyMa7/G+Mun5JsS8Iem0+Uvz+F5exKd4mOwXPUjilG+F4RoK/t62wBCSjai1RejGszUKRYiPVVEzptCzsrPMF90P1TsInHnXNStywAwdj8LfYw8gBXiRFIUhezsbHJzc1FVlY4dO5KWlkZMTAxbc9YSlbeYxN0/EfB70cdlEihrXPNCU9VgEhKo3E3tq9ehNTia12+1g7Z/FfEyd4CiMQ/i2bMLY8UOovK+IsZVTiwQCyw542FsNjupqalsLUyhxJlEVVgKWmwmWskWIpI7kdR1IBGfPIBiNaHVVKAoepJT0pvd+3hxOBz8+mvTPd7j4uIoKSmhpKRxizmbzXbA31tF2/F6vaiqSklJCUVFReh0usbRnhY7tvPuwDJiIu4lM/As+7TxAl3TNEsxWjAkZ2NIzm5yXPN7UfcUooTvH22sVu5q/LO6GLW6GN+m3zz8MtkIveaZ4FB9XQA0SyyDz5+ArmoXrvlv48hZwp7ITvg6DKLPkDHU/nsCAOHVO4n0VBJZWgmlv8KGxiodip7i8E70vH8Gur1/N71eryT2J7F2k8RnZmaSkJDA3Llz6du3L9D4zbVo0SKeffZZAPr374/RaGTu3LlcccUVAJSUlJCTk8PUqVPbLHbRduLi4jDjZ/esf+Gb9zMF+ihsmo6utVsAcO3eFFyARhJ4IU4eusgk9Gk9oXADdjzU25NQ6qtIq9mKOuMm8kIz8epdhJxxFZ5lH7d1uEKcthISErDb7axcuZLq6mpiw0MILJlB13VfY9D8mAf9EdOgS/Es+5hAyTYcL1yBIb0XIZc9CjQ+vNO8btAb0Sd1wZDWE29MJiV+I1V7yunW/4/BOeo7l35N9zXvNYuhyhLDHnsGJqOB/v37ExkZSYcOk9my5TzCNY2ePXvi+vFt3AveQV+xHlK7EzrpbfyblzbpKT0RwsLCSE5OpqioCJPJRNeuXYmMjMRqtVJQUIDBYKBTpxMbkzg85eXlAFgslsaRnTt34na7KS8vJz09HV1oZDCZ9+etRhd7eA+HFIOp2fehZfgETH3Pb1z3oWTr/nUgynaCtwHd3oRfVVXUdd/RZ/PX1C59BsXnQrGEEXnGeBJH34DOZiewdwFZgLS7ZlBUsJ2qwq0ESrdh3bOD+JodhPjrATWYwAPkvnAdFm8dVTFdUBO7EJbWjdSOXYmMiDrGT1K0hpMqia+rq2P79u3B93l5eaxdu5aoqCjS0tKYPHkyU6ZMISsri6ysLKZMmYLNZmPChL1Pl8LDufHGG7nnnnuIjo4mKiqKe++9l549ewZXqxenF7XBQeC1a0h017Imfhi9y35GT+NTfXdMB+JSs6lr4xiFEM3p4zsQdsvbeJZ9imv2q4Q4i9EUHSWWeDw6C6QNYGvmEAYm2SSJF6KNmUwmVHcdlu1zafh8IZaAB2dUZyJSO+LPX4vnl/+C1rjInFq+E/9vxoo76+soGX0n1dWV6Mq2EbVmIXENH5EAJACFyVlEDjoTgJD07uQV9qAhugP6hE6Ep3QiKT2LjuGR/H7DM6PRSI8ePYLvLcOuwrP0QwIlWwm94V/oDEZMPUYf3w+mBTqdLpi45+Tk4HK5sNvtVFdXY7Va6dev3yG3whVtw2q1kpqaSqdOnYJrA2zfvr3ZiF5daCSmnmOO+X66kAh0HQdg7Lh/20It4KeucDNb9rjQKjfi9/ux79kGgOJzNZZx1+JZMgPP0g/QxWVgPX9y8PrQkFC6dOsD3foEj6mqSnl5KaF1+0fDeL1eEh35GDUfcYUlULgQljee22qNpzz9DIZf/8gxt1EcvZMqiV+5ciWjR+//gbpvrvp1113H9OnTuf/++3G5XNx2221UV1czePBg5syZE9wjHuDFF1/EYDBwxRVX4HK5GDNmDNOnT5c94k9TiiWM0pShuEq20q+scW6cBhSmjybpj3ejjz7QLqxCiLam6PSYeozGm7uIwI6VKJpKoruMqphuVHUdjcvhYNWarWj2bJwLf+TM86/AZjs5VnUW4nSyp6yE1Jz/ElW+gUBoDNv6TyZ9xZuwdjOBvWWUsBhcDXU4+1+BvfcY9q1dv/nXBXSeO7XZnujltkSqorOIsO1f0LLvkLNgyFlHFaNiCcV6/h34tv+KoXPbD1Xft9p7QUEB27ZtC259K/uBn7ySk5ObvDcajXTt2vWExqDoDQQiUyjNW42qqpjNZkwjb0FdOR1dwRp8RhteSwQWfwN6Vw1qeT660P1TjBu+fQn/jl/Rp3TDkNINfXJX9AmdSEhIAvbv3GQymTA9+B27dubizM9FKd1M1J6txNcXEesqo7KhJlhWVVXW/t+faYhMx5DWg/hOvUhN74hBf1KlmaccRdM07dDFTh9Op5Pw8HAcDgd2u72twxHHyOV2UfH0BYT5GrcO9OhMGA0GXD0uIPys6wiLScDx/y7C1O98rONubeNohRC/51nxJQ1fPIVqsLAnoQ9RpWupG/U3kkb8kZKSEnK/fYshu2ajopDb6QK6X3YHkZEy1E+I402tq8K3ZRm+3MV4t/yMpgbQdR8NOfNQbBE4QxLx6Iy4bFF46p3EVG0jwlsDQO7wyZxx4Z8AyNu5FdeHD1AT3RktqQvhaV1J7dj1lN8De9OmTRQXF2M2m4mNjWX37sY97PdNBxDiYOrq6sjJyaGhoYH+/fsTHh7O+jmfYV//FeF7ctHFpmMaegX6mHT0EfE4X7icsFvepuG7fxMoXN+0MoMJfXI2hrReWM/5K4rBeMD71tbVUrB9E2arjawuPQHYVZhH6GuXNynn0lspjuqCO6k7Mb1Gkd29b6t/BqeiI8lD5RGJOKVZLVZqknoTVrAEV4chxF9yL55f/otuxSwCubNxDZ+AFvC2dZhCiAMwD7oEXXQK7h/fIm7nLzSEJeJxuxq3rNI04nR+tod0oFP9Trpv/x+1Lyxi87BbGXT2ZTICS4hWFqjIx5szH1/uEgK7coJD5BVACYnEfs6tMO4WNn33LkmbviGMpv1EAXSUhqWj+80aNJkdOsOjs05kM04KgUCA2NhYunbtislkIj4+ntzcXHy+k2N7OXFyCw0NpXPnzqxevZri4mKKi4tx2hJg3H2kRBlwz3sL99fPNSbzvc/df92EKfh3bSRQlIt/9yYCu3PR3LUECtajVpdiPf/OYFn34hmgN2JI740+MQtFbyAsNIwefQY3iSUyOpatFz9DQ0EOluKNJFVtxhpw0bFiLVSsbdz1bm8SX1tXS+6KBaR27Ud8fFKTOfjiyEhP/O9IT3z75/F6qKuqIDohBQCfz0fDjtWEZ+//oaM6ynEveg/Pilng92I560bpiRfiJOfd/itlXzxPaNUOXPZkajqNJm7dp+gDXmqzRuIoLSClNh+A/Khu2C99iI6dTuxQRyFOJVqgces4Ze+wWNe8t3DP+0/wvMsaiUsxYXPXsKPLhQy/7iEA6uvrcf1zNFWWaMrNsQSMVuxhdhIKfyHx6SUnviFCnII2b94cHMGh1+sJBALo9XrOPPNM9Ho9/t2bcM97C9/mxr9zYbe8jSGjT5M6NE1D3VOIv3AD+L2YB18aPO546my0+prGgkYzhpTuGDL7YcjsiyG9F4qp5bUb/AE/uwp2ULZ9Pf6C9UQOupDuvQfjL1jP6u9nkpU/H4BKSwwl0d3Qd+hPYtcBpKV3Ou0fvh9JHipJ/O9IEt++lZUWU/efv2B3VxFy92fYYlMOWl51lONZ/gXGHmdhSOp8gqIUQhyNqqoqVq9eTWjVDtJ2LcJasZWAzoBe3ZtoxGawM3EgCRv/hzXgYnPqSIb+9f/aOGoh2hdNDeDPW4Nvwzy8OfOxXfIQph6jcblcbHrzPpLLVmNWm49g2xHblwH3vBV8X11dRWRkFIE9u3AveAfvmu9ApyfyqWUnsjlCnLKWLVuG1+ula9euhIeHs3HjRqqrq+nXrx9RUfunlfl3b8KXMx/LmL+gGA9vzQXN78W98D38hRsIFG5Ac9c2OW/I6EPYLW/vL+91o5harttfsB7XvDfxb/uFLVE9MXucpDTsxqAFmpRzGsOpuehxeu9dyPJ0JEn8MZAkvv3KXbWIqM8exKQ1DkWrH3AFKePvb+OohBCtpbCwkB07dtC1a1cSEhJwbFhM7Q+vYavaic4WjlZfDYqOQL9L2Lqnhg6X3kVCfONyWW63G5PJJEP3hGiBpqr4C9bhWz8Xz4Yfoa4yeM7U/yJCLn8cVVXJe/I8ojyVBNBRYs/AEdcVQ1rPvQtZdTjoQlaBPbtQHWVNVtoWQhy9qqoqbDZbcDFETdOoqKggKioquHBia9BUFbUiH3/BOvw7V+PLW42573lYz/1b43l3HTX/HIs+Iauxp75Df4wd+hEo2xlM3nXxHbCOuRnFEkLdO7fj+cs7LFu9FkPNLuJr80mrzMWiunFM+oiMzCwAfl34P/ybFkLmAJK6DiA1rcNB/w3XvG4C5TsP2hZdWAy68LhW+2xamyTxx0CS+PZp9ddvk7HsDRQaV5/3jbqZuHNuQlGUQ10qhGgnNE0jEAg0+eVEVVVUTwM6LYDrm+fxrvkeAF1sBvY7ZgZ7HZa98RAGdw3J4x8kOeXw9u4Vor3ZsWMHcXFxTXbtgb2jzn75DGP30RhSmk4xqS4twP/GDRjczmb11RtCSHx8LnqjCYCVi7/HFGKnQ3ZvQkNCj19DhBAnLU3TIOAPLoDn27acuml//V2pxt/IlZBILKNuwDzsKoqKiylf8QOZK95g08iH8duiMRgMeL1eFEWhtqaMHr0GkdkhE4Blr99P14L5wRqrzNGUJg/AmDWYDj2HEBPTNBlv+PLZxu0sD0KxhRPx9x+P+TM4Xk7IwnY+n4/S0lIaGhqIjY1tMmxDiBPFH/Cz4e0HycxbCIBPbyLkhpeJ6tS/bQMTQrQ6RVGa9S7odDp01sZkIuTKf2LsNY6GWVMwdh4aTOBLS4vpWLgQk+rD+9pVLO07kYF/uBGzydzsHkK0V/X19eTl5eF2u+nevTuwd/2XhdMb138J+EBTqVOM2BxFmLqNBCBvxuNk/i6BrzFGUJrQCy2tD3Ea7JulOuDM805kk4QQJyFFUeA3K9gbswYT/tC3+PPW4Pnlc/z5a2DvopZafTWK0YSi02G329njdwNgtVhI79EDnU7Hzp07qa6uxh6ZQHhEeLDe6FHXsHFjByyFq0jZs5EoTyVRO2fDztkEZuuoffRHwkIbH1iqqoqx+0g8v/wX6/l3YujUdPE9fC5q3/4rxu6jOVUcURJfV1fHBx98wEcffcSKFSvweDzBcykpKYwbN46bb76ZgQMHtnqgQrQk5z/3klG4FICGkFjib38PQ8TJO0xGCHF8mbqOwJDxKYp+/y8YsQY/pRc9QuEv39CpbBXdV73Dzs2z8Z9/Pz37D2vDaIVoPeXl5QBUVFTgry7Fu/h9PCtm4bDGUNb1D1jyfiVu8UeYF06n3mDG+OhsFEso9cm9KHNVUpnYG0OH/qR06Ud6UiqZMvVECHGYdOHxmPqciz6lGw1fTMG/cyVKeBy68AT0exNqu91OR2MdfiB54fMY9ozG1G0kjsoGwiOj6dmzZ3BqAECXrr3p0rU30Dglblvuampyf8Fe+CuqYqBv6P4RR6teugVF06hPHEb0hhV0HTahySJ57sUzIeDDMvqGE/OBnACHPZz+xRdf5OmnnyYjI4OLL76YQYMGkZycjNVqpaqqipycHJYsWcKsWbMYMmQIL7/8MllZWcc7/lYnw+nbl5L8LRj+cy0NST1Ju+VVFKP0rAkh9tPUALX/uZlAwTpMg8eTa+9M1OI3iPRUAZCbfhZdr3pQ9pYX7VJubi5OZ2MvusvlIlRzE7bxW2pryqgzhJLkKiHWU9HsOk9sR2KvnYo+Nj24orUQQrQW385VuOe9hX/nSvSJnbGMvRljt5HUf/IYvrU/NCkb0JvwJfciatD5mHqdfcBV73/L6/ViMjVO86mrr8P91FnoNTV4vtocRXHKYJLHTCQjJRXH1D9g7HomIZc92roNbWXHZU785Zdfzt///nd69ux50HIej4dp06ZhMpn4y1/+cvhRnyQkiT/5OasrCIuICc53dzsqMNtjZP67EKIZzeem4Zv/w7uicR9qXUQiXHAPG1b/THbuLJzmSGLv+yw4JE+I9mTnzp1s2boFV3UxWfX5pOTNQzWYWBk3nEG7Zjcp69GZqIvuSPSlDxKZ2b2NIhZCnE5+n8wbOg3Cs2Qm+V3HY28oJqxsI0a3o7GwTk/4o3PR2RrzL7XBiWINO+Tv96qqUrS7gF0blqHf/jNpZWuwqI2jxcuvfZv0PTm4fngZ+71foI9KPq7tPVaysN0xkCT+5Lbl5++J/upxHD3Op+OfnmjrcIQQ7YRv+woaPvsnak0JAKZBf6Q0ayz1Xj+99g6pV1WVgvztZHaQ7SbFySsQCLBzey5lG5dhyVtB2p4cjJqf7WFZdKrdRqDrGFaE9MRWtoEeJT+h7zqS0KGXUf/Zk5h7nR1cUVoIIU6U3ybzACV/eI6sAcNRFIW8X+bi3bSItGg7YZc9HLzG+doNaHVVGHuOwdRrHPqkLofVYVe7aSm5s17GndidoVffRf3/XdYueuGhDZN4VVXZvXs3aWlprVXlCSdJ/Mlrw//eIXnpayiAT2ck+rG56K2yOq4Q4vBonnpcP7yC5+fG1WuV0GjCbn4DfVzjSrjLf/ySTnOfJjf7D/S69Hbs9vDgdf68NRi6DJMRP6LNlJaVsPOLF0kuXond13QhOr+ix6AF8IbEoPe5UHxulL4XEj7mz+ijG3ueHM9dgqnnWEnihRBtxrdzNd7tv2Id8xd0v5nG4/P5MBr3r2Wjueuoefpc8LmDx3TRqZh6nY2p9zj0CZ0OeA9N06h940ZQVUw9x+D64ZV20QsPJ2B1+nfffZdPPvmEgoIC7HY7I0aM4K677sJgMJCZmUkgEDiqwIU4kHUznyI150sUwGO0Yf/b+5LACyGOiGIOwfaHBzD2PJuGWVNQDCZ00anB896iLejQ6L75SyqfX0jumbfSU+fEu3QmWoODsNtnYkjObsMWiPZI0zScTid2u/2wHwI1uBrYtqGxx6r3oDMBCAkJpXPhQvSailtnxmO2E+auQqcFMGgBAnoTDTGdyc88m9hdv5C48Uec677DPOiPWEZdf7yaJ4QQh83YoR/GDv2aH/9NAg+gWEKJeGwuvi3L8K6fi2/zEtTKXbgXvIN7wTuYh4zHdsmDLd5DURSsY2+mbtrf2GbLxDr0JiLbQQJ/pI4oiQ8EAlx66aX88MMPnH/++Vx88cVUV1fz2Wef8eabb/Lyyy8frzjFaUpVVTa+dR9peYsAqLPFknDPRxhDIto2MCFEu2Xs0A/7nR+i1lai6Bv/GdT8XgZ2TGd7t5fQ/fB/JNbtImLeM2wM64g9eQBx236EgL+NIxftUXV1NatXr6ZPnz7ExMS0WCYQCJC3YzOlG3/GkrectIoc0jQf+ZFdYW8SHxYaRs7Ie0nPX4g1fwUWV+OCdWpsB3ZF9yJs0MVkZvcgqraWnLAoXF3PpodvJ54lHzRuMSeEEO2IYrJi6jkGU88xaJ4GfLmLGxP6LcvQp/cKlgtUl+DbMA9TvwvQhTYuUmvoNBgy+1GePBiTxUJHTQs+RA3sKcQ9/x1QFEIuf7xN2tYajiiJf/HFF1m+fDlr166la9euweOqqvLCCy9w8803t3qA4vSlqiq5L99KSskqAGqiOpJ21wz0RlMbRyaEaO8Ugwl9ZGLwvXvhe7jn/YdkWwRen4+1kb3p6silU+0OfHUFrI/oiWxGJ47Gvq3fysrKWkzil05/mpSdC4j21hD9m+MV1njqYzrh3bkaQ0IndDY7Q8+9As9KCw1F6zH1Pgfz4EvRJXfF5vFgtTau6BweHs6QIUPw+/2YzaOwDL0C98+f4ln6IbqIhBPRZCGEaFWK2Yapz7mY+pyL6qpF+c0+9b51s3H98AquH17B2PVMzAP/gCFrCK4xk9Hyi/F4/TidTkJ9Dtzzp+Fd8z1oKoq9fW9JfURJ/PTp03nuueeaJPAAOp2Oe++9F03TeOCBB1o1QHF60/kb58JUJfahw+1vosi+tUKIVqb5PAQq8gEFGmowAX3CDbgvepGt82bQufgXQn11bRylaC80TaO4uBi/v3HkRnl5OXq9npKSYvYU78RXspXs0VeSnNI4vFNXW0G4twaX3kphfF/UjoNJzepJaul64ld+Rf2b32C98B4sw68GwNT7HEw9zkKx7J9Sti+B30ev1we3jVMsoVhH/xnr6D+fiOYLIcRxpbM23U1GF5OOPrU7gV0b8W1cgG/jArxmOzWJ/YnKGonH46Zi+n0EStbgM4WiDruBCEMAz+rv2qgFreOIFrazWq2sX7++Xe7/frhkYbuTSyAQYMfcmWSdc60sKCWEOC48a76n4ZPHQG/EGdOFsPJNKJoKegOmIZdTHNWFmG+eIOy26RjSerDqpzkkd+pJQnzioSsXp51AIMCSJUvw+XzU19Wgq84nvnorGZU5mFQfAEvPeJixnRPwL3yH7eVV6AePp8vIS9Ht3oDn11n4chZAoLEsRguWkddhHXtTG7ZKCCFOboHS7dT+9F98a3/A4KtvPKjoQVPxW+wUp52Ju8tZ9Oo3AH6agWfFl0Q8fHIl8sdtYbuQkBAqKioOmMSvXbuWf//737zzzjtHUq0QQV6Ph7x37yfzmicwhUWi1+vpfO51bR2WEOIU5XK52KzGENL/T0TkfkdY2UbqYrNR/G5Cq/Pw/vQRoSl9g+WLigpJ/vYJVEXHT4NuZND5f2q2II84NVRVVWGxWLDZbEd0nV6vx+SrIm3J60R6q5uc22OOoTAym6zC+biX/YI+sTMZDYVYo0NxvzYRtTxvfz3J2ZgHXoKpz7lNet2FEEI0p0/oRMRlD+E9/w7K3rmXkF2/ougNKKNvYr0/noSUNAZkZ6PX63G1dbCt4IjGJo8cOZI33nijxXOlpaVcddVVvPfee60SmDj9+Hweip4bT1z+T1Q8Px5NVds6JCHEKU6n0+Hy+CiM7MHmEQ9Qe8afCfNUEVKdT21EJi5bHPVZo4Pl1XoHxRGdsAbcdPv5VTY/dw25OavbsAXieFBVlfXr17Nt27aDlnO73WxY9RNLPvg/tuSuCx63hUcT6a3GrTOzKXYAK/v8hc1D/orZEkK/0qUkag6s599J2G3vAqDo9I1bHZpDMA2+jLDbZ2K/fSbmIeMlgRdCiCNgsobgi++MpjMQ0BTKCnegaAEsFktwmtGp4Ih64h9//HGGDh2Koijcd999dOrUiaqqKr755hueeuopMjIyDvkPnhAt8Xnd7H72ciLqS9CAuh7nyfx3IcRxZzabGTRoEFu2bKGkpARD/4uJOP8v7PjyNcJyvsHoriE0fwH7Nk6NWvMpYTgp7nYBIVsXk+LcCTNvZmmnC+g+/k4iI6LatD2idVRVVeH3+6msrMTv92MwNP66pKoqBfnbKM75GePO5aSVryNF9ZICbNTp6NK1NwBdeg5io/o8lU4PETV5dNk5j7CafBpC4qHLCLTSLbi++xeGzkOD97RddA+K1Y5isrYUkhBCiMMQCAQoiO6DMiydjNJfiNk2j6gdC6guPQst+f5T5sHoESXxvXr14rvvvuPPf/4zM2fO3F+JwcCdd97J7bffTnp6eqsHKU4tXq8Xk2n/CvM+r5uiZ8cTUV+KBpT3n0D25Xe3XYBCiNOKwWAgIyODkpISKioqKC4uJpAymIrEfvQzVOBe0DhFTNM0fNtWoNVVkli1GyKT2G3sQEr5Orpv/5aql5ZjffBLLBZLG7dIHI2ysjKqqqqAxnmJRqMRn8/Hxo0bMZlMOJ01xHz3OFGeKiJ+c12VOZqSlEHYswYGj1mtViJjU/EWL6Hz6rcB8FsjsNWXwZYyNECx2lErCoLX6MLjT0ArhRDi1Obz+VAUhYzu/Ug//4/4a8op/+ZlojfPwfH/lmAePgHN3f4Xqz2iJB4ah9Rv27aNFStWkJeXh91uZ+jQoURFRVFfX8/jjx+//fb8fj9PPPEEH3zwAaWlpSQmJnL99dfz6KOPotvba6tpGk8++SRvvvkm1dXVDB48mFdffZXu3bsft7jE4duzZw/r1q3jjDPOwGq1EvD72D31imACXzbgGrqOv6utwxRCnGb2bQO2Z88eLBYLbnfjzhi+IeOw97+IQPFm9Kk9CL9vFp5f/ot78Qy06mJSKCZgi6QuAOVdxtHxNwm8qqrBf5t+S/O58Sz/gkDpdmyXPiqjjo6Bpmk4HA4iIiKOuS6Hw0FRUREBNYCvoRprTQEBdDjNQ/B4PGiaRrwWwK0zsyuuN74Og0nueQaZ6R3p2ML/w6ioKGyJscH3BlcNmqJAej9Ch12BMXs4yIKtQgjRqiwWCyNHjgwuiG2MjCf52qcIOO7Es/h93Aung99z+mwxV1hYSFpaGtA4h3DIkCEMGTKkSZmQkJBgEl9UVERycnIrhgrPPvssb7zxBu+99x7du3dn5cqV3HDDDYSHh3PnnXcCMHXqVF544QWmT59O586deeqppzj77LPZsmULYWFhh7iDON5KS0vRNI2ysjIyMjLY8eJ1xNYVowGl/SfQTRJ4IUQbqK6uxmg00q1bN6Kjo9mxYwcFBQXU1NQQmpKCIa1nY0GzDcvI6zAPubwxmV/yAfq6KsKB2IT9Q+k3rluOZ87rWM+7g649+gGNyXv+l69i3/Q9OlcNALaL7wMZPn3UKioqWL9+PQMGDDjqRF5VVQoLdlCx+WdCtv9Mxp4NWFQPANXmKIriOpFRuJhomxHHDW8QmZhK4u9GW2iahlqRj3fjQnQ2O+bBlxEZGYk2YCyO2c+ji89EFxqNL28V7FqHb3s6+pRu6MKiWwpJCCHEMWhpRyt9eCy2i+7BMvI63Iva/xpuh73FXHx8PBdffDE33XQTgwYNarGMw+Hg008/5V//+heTJk3i9ttvb9VgL7zwQuLj45k2bVrw2GWXXYbNZmPGjBlomkZSUhKTJ08O7lfv8XiIj4/n2WefZdKkSYe8h2wx17o0TaO6uhpVVdE0jZycHFRVJQIXGSXLKNy5mfSGQkq6Xkj3655o63CFEKcpl8vVuKr4b6b61NXVYbVaD7oQjub34l03B++vXxFy3QvB/Ws3PnMlSY4dAGxOGU50Zk/i1nyKWl9NdVJ/Erv1xzP3DSL+seSo50CrqkpFRQVxcXGn7RacGzZsoKysjLS0NDp37nxUdax48RayylY2OVZtiqQwthcYLXQr+BGj5gejhch/Lg2W0dx1+PPX4tu+At/mpah7CgHQxWYQfs9nwXKqqzb4faF5GvaP5HDXYep7Pt6VX2O74knM/S44qviFEEKcGo7LFnO5ublMmTKFc889F6PRyIABA0hKSsJisVBdXc2mTZvYuHEjAwYM4LnnnuO888475ob83vDhw3njjTfYunUrnTt3Zt26dSxdupSXXnoJgLy8PEpLSxk3blzwGrPZzMiRI1m2bFmLSbzH48Hj8QTfO53OVo/7dOZyuVi9ev/KzWZXFVl7fsW0dRF+o43wiAwc3jCyL7uzDaMUQpzurNbmiXRo6KEXv1EMJsz9L8Tc/8LgMU3TSLDowAEakL17KerupeRE9aam0xVYYtKxG6sw0Pgz0mq0HFUSXlpayqZNm+jXrx9RUafHgnqqqlJcXIzf7wcapz/o9XpKS0uDD2CMRiNJSUlNPlOXy8W2jatwbFlO2O41dLnjreD/c29cJzwV6ymM640vcxDOgJHMol/oWbwUVW+irMNZhIWGYt+0fz/hug8ewLdxIaiB/cHpjRg6DcTUbRSaqganSexL4AGU34/kWDyj8bju1FkxWQghxPF32El8VFQUzz//PE899RTfffcdS5YsIT8/H5fLRUxMDNdccw3nnHMOPXr0OG7BPvDAAzgcDrL37vEXCAR4+umnufrqq4HGX2igcdTAb8XHx1NQUNCsPoBnnnmGJ5988rjFfLqz2Wz07t2b7SuXELt9LlHFqyizJqJ0Hktx8ghiagtILc85bXuRhBCnIDWAJSUbb8VOlL1Jng7oXrUOqtaxKX4whbEd6AD88ssvZGRl06FDhyO+zb55/OXl5e0miQ8EAvh8vqNe/M/v97Nt2zYCgQCKomAymcjKymLr5lyql/yX+PyFOJL7EzvxUfK3b6Z8089Y8leStieHVM1H6t56tm1cRa8BwwHoceGNWC77G3EeJ865bxFY9TWa3oSh38V47fFYtq/FsvVX+M3ARcVkAzWALjoVQ8cBGDsNxth5yGGvevzbZN6Xuxhj1zOP6vMQQghxejrihe0sFguXXnopl1566fGI56A++eQTZs6cyYcffkj37t1Zu3YtkydPJikpieuuuy5Y7vcJoaZpB0wSH3roIe6+e/9K6E6nk9TU1BbLiiOnOvdgW/Q62au+xWe0UpQ4gJTiFWhbiigJ70JWVhbuX9s6SiGEaD2K3oAhsy/e1d+COYSG0AQMjhJM/gYAUtQa6vf2AofZ7SSGmQ5WXVAgECAvL49AoPHBQGVlJUajkcqCrRQtfx/Ljp+o+8M/Ses34qR9MJqXl0fZjk0M6tsLY1zGEV9vMpkYNGgQOTk51NXV0blTRyKKV9Ft2RvgLEWn0xNqCbBy9id0XfovYn9z7R5LLGUpAzF3GkRW1v7FbiPCI/Es/xzHV1NB0aGERKJzO1FXzcIABGetG/b/f7KcdSPWsyehi0g4mo8hSDHbMPU595jqEEIIcfo54iS+Ld133308+OCDXHXVVQD07NmTgoICnnnmGa677joSEhr/Md23cv0+5eXlzXrn9zGbzZjN5uMf/GnK8+uXeFd+jWa1sz1uCNkFcwCosCXiC0tEVdU2jlAIIVqfecDFGDoMwL3wXVj5NQGDlYqkgYSE2AjtOoI9Oxvny5O/HPdXk3HYorBlD8OcNRBDRl/0kYnN6gwEAhQXF+P1etHr9UQaVTJKFqGt+w4NBZ3qo2bXVlL7Dj/mJF7TNPw7VqJW7sI8uHUe2gdqyuDH18guWEbt+iSi7vviqOoJCQmhT6+e/PzJK2xe818Un4sEj5+tWVdwpm8rer2exG6Dqf8lhN0J/VDSepGSkkmaGVKrS1Crt6B+tRBnVREhf3oWfUwa7BvOHvCh1O1p/NocgiGlG/qUbmh11XjXzQ7GoI9OOdaPQwghhDhq7SqJb2hoaLZdj16vDyaCmZmZJCQkMHfuXPr27Qs07km+aNEinn322RMerwDLyOvQhcWwa/Y7ZBfMQQGcpnBKR9yNLhDA6axBdlQWQpyK9FFJhFz6CJZRN1D8+fPE7FyK32ilVBeGTtf48Dhy6zwAzA1VBFZ/Q8PqbwDQRSSgT++NZdjVGNIap6mZTCYGDx7M5l+XYln7BbHFv6Iz26jvdxl5xmS6//wCXTp3aXFbu8O1L3l3z3sTf/6axvsOvOSot8Hz+/1U5G3GsPIz9DmzCVcMuG2xGN31FBcXA41rDxzOQrJ19XXkLP0O/5afiN+TS29vVZPz3pLlBKhDZwsnPaMTgcfnEfPLp7i+fRGAhhbqDJTtRB+ThnngJShhsbjnv02gKBd0RszDrsYyfAI6mx33Tx/D+jlH9RkIIYQQra1dJfEXXXQRTz/9NGlpaXTv3p01a9bwwgsv8Oc//xloHEY/efJkpkyZQlZWFllZWUyZMgWbzcaECRPaOPrTk2Iw4o7vQkR9KQrg1pnRBXz0K/qB0o5jMSkyCkIIcYoLj2dbxwsxJAyhQ9lyErd+T+OSdxDzt3fZvGAWCes/J9xbE7xErSlFrSltsmK5Z/V3uOe+TlpNGQFFT33mUFw9LqTOFI6usjEhPthK+gejaRq+Hb9S+dWLWCq2oU/uiqnfhXhX/++om606yqn5/g1M679D1RkpzhiNs9NZZJQtR928kE2bNgGNa+707dYZ1VGGWu9Aa6jBX1tF1e7tKC4nERYTllHX4zOEkrXgOXS0vKlOcl1+Y1vcdeh0OnQ6HWpoJABKSAS6yCR0kYnBP/WRSehT96/jY8oehil7GGrtHtyLZuBZMgPPTx9hGT5B9nMXQghxUmlXSfzLL7/MY489xm233UZ5eTlJSUlMmjSJv//978Ey999/Py6Xi9tuu43q6moGDx7MnDlzZI/4NuJ1VuJ+8yZMqPgVA8rN7xJXvgn3/GnEbJyPPr4jgUNXI4QQ7VYgEMBms5HedQSJiVfgryqi8puXCZRuJ94exZDxt+L/402sX76AwPLP6VC2Eh1QHtGR8OSuAHjy1uL47CmMqhcAvaYSumMJoTuWEK0z4rWEN71n6XYCFfnowmJQLKHBFyZbsFddbXCiNjgIFG7As+xjArs3EQhLpnToJLKGnYsvZ/5B26WpKvi9aD43+D1oXhe68AQ0dx3uhe/iWf45ekVPILErNUoIIZ5qUvP/h1a2HdXrxObcTWinfnTt2pXKH99Bv/CtJvWH7P3TCxh7jiEiezhLE4dgd+wmrWEXAIrVjmKPRRcWjVtvw1CcA7/Zss/U4yxM3UahmG2H/f9LFxaD7cK7sIyciHvRDNyL3wefB4wybkwIIcTJ4bD3iT9dyD7xraumsoy6l67G5nNSfdW/6NhnGACa34d39be4509Dra0g4u8/ophDDlGbEEKc+srLS9m27DuiOvWha49+AGxZ+zORH0+m2hyJougw6/WE6FX0dXuabHMWesO/MXY5A9fc/+D+8a3mlSsKmGyE3fwfat+6Bc1dh0LjuAC/MQSPLQY0FVttEYqmogBhd3yEISkLAPdPH+Oe9yaazwN+T7Pqw255G/fC9/BtXoISFoNWu+fA7TzzDjqfN5H1z04grWZbs/M+RU+tLY7E4Zdi7nEW+th0AALlebgXvY93zfcolhAsZ07EPPRyFHMItW/chC4ykZAr/3H4H/gh7OuZ1xpqCLlCdrMRQghxfByXfeKFOBoR0fGYHvqGok0ryNqbwEPjMHvzoEsw9bsA1VkuCbwQQuwVF5dA3CV/bnJsT+EW4ggQ59mfFJda4qlMHIolOZsYm4Xwha8Ez+nsMejTe6HVVaG569DcdRDwN26T5qlHMZrRhcWguuvw680YAh6MvnqMjvpm8Tj2lBC9N4lHU9FczuZB6/RgtKD5vVgvugfFEkrdunn4TOF47Qm4VVDctYR6HaBpmHQ69hhj6Ay4bTEEanZQYs/EkdAdY1oPEjr1IiU1k7gWpgfo4zIJufxxrGdPwr3wvcYHFotnYDlzYuOogFa2r2deCCGEOFm0Sk/8jh07ePnllykoKAhufQPw9ddfH2vVJ5z0xLeO6pwlRHQ/9hWShRBCNNpVmEfh2kWYt/9EesV6DNr+f2+LzphE8rL/EHrDv9ni0lNbvIOwxEziUzoQHR2LTqdD83mCCb0uMolARR6l79yHPuDB0rAHX2Qqtr7n4g5NYNeOLVidu4nftYyQh37AFB4DgFpfg1Zf3Ziw643UNrjZU11JbXU5fYaMCS6qt+yNh+iaP/eAbdkTkkLB0L8xcOBAXO4GwkLthIQc3cNctaYU98L38Pz6JQR8mPqe36o98UIIIcSJcMJ74i+55BL+9re/ceWVVx7Tqrji1FDy05eYv3mKgvBUku/5CKNJ5hEKIcSxSk3LJDUtE7ie2rpadm5agzP3J0wlm8hc9nawXM3q2XTfuv8heqneisMSRYMlCq8tkuwJjxJpMGJI7Ex5Qi+cTgfu5JHYqgvQrVxKICQCpy0JmzGSSMVARFgUAD//8DG6rT9h9Dixuh1EuCqwqB5igBigrseP2O2Nc/NVa+MvH7XGMPaEpuA0hIHPhd2gkGTREVtXhpqaitlsJjy86Xz+I6WLSMB2yQNYRl2H+6ePMXTof0z1CSGEECe7VkniQ0JCmDRpUmtUJdq5usLNmL6ZggLoXLUENDC2dVBCCHGKCQsNo/egM2HQmQD4C9bhXfMD+tTu6JN2saXuDMIdBcQ2lGANuLDWF0F9EVSCybR/V5AGn4/eZcug7DeV/2a0vMOwf1HYQOl2sot/bhZLtTkKpy2O+PraYBKffcGNcPEk0iKjSNt3fXk+7vnT8K6bjRKdQpcuXVrt84C9yfwFk1u1TiGEEOJk1CrD6b/66iuWLVvG2LFjMZv3/3Jw5plnHmvVJ5wMpz96gQYnFVMuxOxvwK8Y0O74L3GJqW0dlhBCnLY8Xi/lpUXU1uyhwVGJr7aKoefv33J12Tfvo+QuRENBr/pQFAU/OlRFQVEDpAaqSH34axRFYdP6X3GW5GMMDccSGk5EdAIxcQmYTUe2VWigogDN68KQnN3azRVCCCHarSPJQ1slib/ttttYuHAhXbt2DQ6nVxSFTz/99FirPuEkiT86mqZROPUK7NV5aEDFZc/RZeDotg5LCCHEIezZvoGcLdvIijAStn4WfgzsHvY3fF4P/Xr1RJEpUUIIIcRxd8LnxC9atIiNGzfKImanscJP/x/26jwACvpeQ19J4IUQol2I6dST/stn4l/xCwFPPWE3/JteXXqhaZr8uy6EEEKchFplFbpBgwaxY8eO1qhKtEO15bsIW/M5ACVR2fS+/M42jkgIIcSRsI25CTz16FN7YOg8FEASeCGEEOIk1So98WvWrKF79+5kZ2djNpuDT+9XrFjRGtWLk1xYXCobBt+Ife0XpN32huxQIIQQ7Yw+oRO2K57EkNJdknchhBDiJNcqc+ILCgqaV6wopKWltVD65CZz4o9eIBBAr9e3dRhCCCGEEEII0a6csDnxEydOZMaMGYwfP77FJ/fSE39qK/rhbWwd+hLZuXFPXknghRBCCCGEEOL4OqYkfurUqQB89tlnrRKMaD+cW3/FuvANtIWQd+kUMgeNa+uQhBBCCCGEEOKUd0RJ/FVXXcXf//53unXrBkBiYiIA6enprR+ZOGlp7jpc79+NCXDrzUR1GdTWIQkhhBBCCCHEaeGIViD79NNPOeuss9i0aVOL5zVNw+l0tkpg4uRV+PY9mPwuNKDmj1MID49o65CEEEIIIYQQ4rRwxMuI9+7dm9GjR7Nx48Zm58rLy4mMjGyVwMTJqfyXb7DvXgVAftY5dB0wso0jEkIIIYQQQojTxxEl8YqiMH36dM466yxGjx5NTk5OszKtsNi9OEn56qpQvn4aAIc5ip7XPtG2AQkhhBBCCCHEaeaIknhN09Dr9XzwwQeMGTOGs846q1kiL/vLnrry3nsMg+pHRcF4/SsYjca2DkkIIYQQQgghTitHPJweQKfT8cEHHzB27FjOOussNmzY0NpxiZNQ3DX/oDS6C7sHTCQ5s3NbhyOEEEIIIYQQp50jHk4fvFCnY+bMmcFEfv369a0enGh7brebnJwc/PUOzGu+Itmk0fPsq9o6LCGEEEIIIYQ4LR3RFnO/n+++L5H/05/+xJgxY5g5c2arBifaXnH+dtTZL+JwbEfnawBNI1BVhC48rq1DE0IIIYQQQojTzhH1xH/77beEh4c3rWBvIj9u3Dguu+yyVg1OtB3NXYdrwTvw4d2kVqzDG/DDVVPbOiwhhBBCCCGEOK0dUU/8eeed1+JxnU7HjBkzmDhxIh9//HGrBHYgRUVFPPDAA3z//fe4XC46d+7MtGnT6N+/P9A4WuDJJ5/kzTffpLq6msGDB/Pqq6/SvXv34xrXqcLtqKLwy38TuX0+is9FqKIHoMEQSkFxFV2AzZtz6Z7eG53uqJZUEEIIIYQ4aQQCAXw+X1uHIYQ4DZhMplbJoY4oiW/J6tWr6dGjByaTiZkzZ3LnnXcec1AHUl1dzbBhwxg9ejTff/89cXFx7Nixg4iIiGCZqVOn8sILLzB9+nQ6d+7MU089xdlnn82WLVsICws7brGdCvy7cnC/cwcxnnoqkgbhaXCQWrUJDagYcz/+mgoAbDab7EIghBBCiHZN0zRKS0upqalp61CEEKcJnU5HZmYmJpPpmOo55iR+4MCB5Obm0rlzZxRFYdCgQcda5QE9++yzpKam8u677waPZWRkBL/WNI2XXnqJRx55hEsvvRSA9957j/j4eD788EMmTZp03GI7FSjmEJSQCBSXE4PqJrZqEwC7Op+HLSKWOmclAGlp6ZLECyGEEKJd25fAx8XFSQeFEOK4U1WV4uJiSkpKSEtLO6afOcecxP9+sbvj6euvv+acc87h8ssvZ9GiRSQnJ3Pbbbdx0003AZCXl0dpaSnjxo0LXmM2mxk5ciTLli1rMYn3eDx4PJ7ge6fTefwbcpLSx2Viv+tTPGt/wDTrORSgQW+lISYbpaEB5B83IYQQQpwCAoFAMIGPjo5u63CEEKeJ2NhYiouL8fv9GI3Go66nXU1q3rlzJ6+//jpZWVnMnj2bW265hTvuuIP3338faHyiChAfH9/kuvj4+OC533vmmWcIDw8PvlJTU49vI05yit5A8e58Qv31aEC5LZnsZS8S/9NrmKsL2zo8IYQQQohjtm8OvM1ma+NIhBCnk33D6AOBwDHV066SeFVV6devH1OmTKFv375MmjSJm266iddff71Jud8PTdA07YDDFR566CEcDkfwtWvXruMWf3vhj+2IW2emyN6RjFtewXbFk9h9NXTI+aitQxNCCCGEaDUyhF4IcSK11s+cYx5OfyIlJibSrVu3Jse6du3K559/DkBCQgLQ2COfmJgYLFNeXt6sd34fs9mM2Ww+ThG3T53POI/6Pmdi9XqIiIiC6Asw9T4H9+rv8W/8EX1cZluHKIQQQgghhBCnpXbVEz9s2DC2bNnS5NjWrVtJT08HIDMzk4SEBObOnRs87/V6WbRoEWecccYJjbU90lQ1+HWILaQxgd9L0RuwDryIsOtfQhcS0QbRCSGEEEIIIYRoV0n8XXfdxS+//MKUKVPYvn07H374IW+++SZ//etfgcbhCZMnT2bKlCnMmjWLnJwcrr/+emw2GxMmTGjj6E9+Bc/8kZ0vXIunquX1A4QQQgghRPswf/58srOzUX/TSdNaxo8fzwsvvNDiuYkTJzJlypRWv2dLBg4cyBdffHFYZadNm9Zk8WshWovH4yEtLY1Vq1adsHu2qyR+4MCBzJo1i48++ogePXrwz3/+k5deeolrrrkmWOb+++9n8uTJ3HbbbQwYMICioiLmzJkje8Qfwo4FnxJeW0Rk+SaKtq5r63CEEEIIIcTvXH/99SiKgqIoGAwG0tLSuPXWW6murm5W9v777+eRRx5Bp2v8dX/69OnBaxVFIT4+nosuuoiNGzc2uc7r9TJ16lR69+6NzWYjJiaGYcOG8e677wYXBPz73//O008/3WxXp/Xr1/Ptt99y++23B4+NGjUqeE+TyUTHjh156KGHgrtD/fDDDyiK0mwR6oSEhGYLTu/evRtFUZgzZw4Ajz32GA8++OAhH1R4PB7+/ve/89hjjwWPvfXWW4wYMYLIyEgiIyMZO3YsK1asaHbta6+9RmZmJhaLhf79+7NkyZLgOZ/PxwMPPEDPnj0JCQkhKSmJa6+9luLi4mCZqqoqbr/9drp06YLNZiMtLY077rgDh8Nx0JgBCgsLueiiiwgJCSEmJoY77rgDr9cbPL9lyxZGjx5NfHw8FouFDh068Oijjwb/Px3I4bR98eLFXHTRRSQlJaEoCl9++eUh4wVYtGgR/fv3D8bzxhtvNDn/2++H374uuOCCA9aZn5/PjTfeSGZmJlarlY4dO/L44483+SwqKys599xzSUpKwmw2k5qayt/+9rdD7jz2xBNPNItl3xTtlkyaNAlFUXjppZeCx8xmM/feey8PPPDAIT6d1nPMSfzjjz9OTExMa8RyWC688EI2bNiA2+0mNzc3uL3cPoqi8MQTT1BSUoLb7WbRokX06NHjhMXXHgUCAULmvQxAlT2VDkPOaeOIhBBCCCFES84991xKSkrIz8/n7bff5ptvvuG2225rUmbZsmVs27aNyy+/vMlxu91OSUkJxcXFfPvtt9TX13PBBRcEkyGv18s555zD//t//4+bb76ZZcuWsWLFCv7617/y8ssvBxP+Xr16kZGRwQcffNCk/ldeeYXLL7+8WefZTTfdRElJCdu3b2fq1Km8+uqrPPHEEwAMHz4cg8HAwoULg+Vzc3Nxu904nU62b98ePL5gwQKMRiPDhg0D4IILLsDhcDB79uyDfmaff/45oaGhjBgxInhs4cKFXH311SxYsICff/6ZtLQ0xo0bR1FRUbDMJ598wuTJk3nkkUdYs2YNI0aM4LzzzqOwsHHHpoaGBlavXs1jjz3G6tWr+eKLL9i6dSsXX3xxsI7i4mKKi4t5/vnn2bBhA9OnT+eHH37gxhtvPGjMgUCACy64gPr6epYuXcrHH3/M559/zj333BMsYzQaufbaa5kzZw5btmzhpZde4q233uLxxx8/aN2H0/b6+np69+7NK6+8ctC6fisvL4/zzz+fESNGsGbNGh5++GHuuOOO4PplAF988QUlJSXBV05ODnq9vtn36m9t3rwZVVX5z3/+w8aNG3nxxRd54403ePjhh4NldDodf/jDH/j666/ZunUr06dPZ968edxyyy2HjLt79+5NYtqwYUOL5b788kuWL19OUlJSs3PXXHMNS5YsITc395D3axWaaMLhcGiA5nA42jqUEyb3k+e0qgf6a5UP9Ncq8nLbOhwhhBBCiOPK5XJpmzZt0lwuV5PjDX71gC+3Xz3ssq7DLHukrrvuOu0Pf/hDk2N33323FhUV1eTY7bffro0fP77JsXfffVcLDw9vcuzrr7/WAG39+vWapmnas88+q+l0Om316tXN7u31erW6urrg+yeeeEIbMWJE8H0gENAiIiK0//3vf02uGzlypHbnnXc2OXbppZdq/fr1C74fOnSoNmnSpOD71157Tbvgggu0888/X3vrrbeCx//85z9rw4YNa1LX9ddfr02cOLFZvL910UUXaffee+9By/j9fi0sLEx77733gscGDRqk3XLLLU3KZWdnaw8++OAB61mxYoUGaAUFBQcs8+mnn2omk0nz+XwHLPPdd99pOp1OKyoqCh776KOPNLPZfNA85a677tKGDx9+wPMtaantvwVos2bNOmQ9999/v5adnd3k2KRJk7QhQ4Yc8JoXX3xRCwsLa/K9dTimTp2qZWZmHrTMv/71Ly0lJeWgZR5//HGtd+/eh7zf7t27teTkZC0nJ0dLT0/XXnzxxWZlRo0apT322GMHredAP3s07cjy0HY1nF60Pk9DHdFr/gtARWIfYjKy2zgiIYQQQoi2MWJBzQFf96+va1L27EUHLnvHmqZlL1rqaLHcsdq5cyc//PADRqOxyfHFixczYMCAg15bU1PDhx9+CBC8/oMPPmDs2LH07du3WXmj0UhISEjw/aBBg1ixYkVwWPz69eupqak55H3XrVvHTz/91CTm0aNHs2DBguD7BQsWMGrUKEaOHNns+OjRo5vUN2jQoCZD3FuyZMmSQ8bV0NCAz+cjKqpxYWev18uqVauazaMfN24cy5YtO2A9DocDRVGIiIg4aBm73Y7BcOCNwn7++Wd69OjRpNf3nHPOwePxHHDu9fbt2/nhhx8YOXLkAettye/bfrR+/vnnZp/XOeecw8qVKw84xH/atGlcddVVTb639k39OBiHw3HQeIuLi/niiy+afRaKojB9+vQmx7Zt20ZSUhKZmZlcddVV7Ny5s8l5VVWZOHEi9913H927dz/gPQ/ne7G1SBJ/mts583EMWgAVheRrn2nrcIQQQgghxEH873//IzQ0NDg3eNOmTc3m4ubn57c45NfhcBAaGkpISAiRkZF8/PHHXHzxxWRnN3bibNu2Lfj1oSQnJ+PxeIJz2fPz89Hr9cTFxTUr+9prrxEaGorZbKZPnz5UVFRw3333Bc+PGjWKrVu3UlJSAjTOqx45ciQjR44MDrPftWsXeXl5zZL45ORkCgsLDzgvvqamhpqamhY/j9968MEHSU5OZuzYsQDs2bOHQCDQbJvq+Pj4ZvP393G73Tz44INMmDABu93eYpnKykr++c9/MmnSpIPGU1pa2uzekZGRmEymZvc/44wzsFgsZGVlMWLECP7xj38ctO7f+33bj1ZLMcfHx+P3+9mzZ0+z8itWrCAnJ4e//OUvTY6Hh4fTpUuXA95nx44dvPzyyy0Olb/66qux2WwkJydjt9t5++23m5zv0qUL4eHhwfeDBw/m/fffZ/bs2bz11luUlpZyxhlnUFlZGSzz7LPPYjAYuOOOOw7a/uTkZPLz8w9aprW0q33iRetye9zE7mx8WlSeNYaukbFtHJEQQgghRNtZMjrigOd+3/M1d+SBy/6+D/Gb4eEtljsao0eP5vXXX6ehoYG3336brVu3NllIDsDlcmGxWJpdGxYWxurVq/H7/SxatIjnnnuuycJjmqYdsgd0H6vVCjT24u67p9lsbvH6a665hkceeQSn08mzzz6L3W7nsssuC54fNmwYJpOJhQsX0rt3b1wuF/369UPTNJxOJ9u2bePnn3/GbDY32zbaarWiqioejycY0+8/C6DFz2OfqVOn8tFHH7Fw4cJm5X7fngN9Rj6fj6uuugpVVXnttddavI/T6eSCCy6gW7duTeatn3feecEe3PT09ODaAy3dp6X7f/LJJ9TW1rJu3Truu+8+nn/+ee6//34KCwvp1q1bsNzDDz/cZB75odp+NFr6vA7UlmnTptGjRw8GDRrU5Pgf//hH/vjHP7ZYf3FxMeeeey6XX355s+Qf4MUXX+Txxx9ny5YtPPzww9x9991N/n9s3ry5Sfnzzjsv+HXPnj0ZOnQoHTt25L333uPuu+9m1apV/Otf/2L16tWH/LthtVqDfx+ON0niT2MWs4XSa9/A/d2LdJzw97YORwghhBCiTVn1h5fAHs+yhxISEkKnTp0A+Pe//83o0aN58skn+ec//xksExMT0+KK9TqdLnhtdnY2paWlXHnllSxevBiAzp07H/bCXFVVVQDExsYG79nQ0IDX68VkMjUpGx4eHrzvzJkz6d69O9OmTQsu7maz2Rg0aBALFiygqqqK4cOHo9frgcZe5n0LsA0dOrRZollVVYXNZmsxgQeIjo5GUZQWPw+A559/nilTpjBv3jx69eoVPB4TE4Ner2/W611eXt6st9nn83HFFVeQl5fH/PnzW+yFr62t5dxzzyU0NJRZs2Y1mU7w9ttvBx827DuekJDA8uXLm9RRXV2Nz+drdv99q/h369aNQCDAzTffzD333ENSUhJr164Nlvv98PMDtf1oJSQktPh5GQwGoqOjmxxvaGjg448/PqJRA8XFxYwePZqhQ4fy5ptvHjCGhIQEsrOziY6OZsSIETz22GMkJiYe1j1CQkLo2bMn27ZtAxqnYpSXl5OWlhYsEwgEuOeee3jppZea9LxXVVUF/z4cbzKc/jSX0a0f2ffOwGS1tXUoQgghhBDiCD3++OM8//zzTbY169u3L5s2bTrktXfddRfr1q1j1qxZAEyYMIF58+axZs2aZmX9fj/19fXB9zk5OaSkpAR3qerTpw/AIe9rNBp5+OGHefTRR5v0Wo4ePZqFCxeycOFCRo0aFTy+b0j9woULmw2l3xdHv379Dng/k8lEt27dWozrueee45///Cc//PBDsznzJpOJ/v37M3fu3CbH586d22Q0wL4Eftu2bcybN69ZsgqNPfDjxo3DZDLx9ddfN3sQkZycTKdOnejUqRPp6ekADB06lJycnOAUA4A5c+ZgNpvp37//AduraRo+nw9N0zAYDMF6O3Xq1CSJP1jbj9bQoUObfV5z5sxhwIABzdZt+PTTT/F4PPzpT386rLqLiooYNWoU/fr149133w1unXgw+0YB7Fu34XB4PB5yc3ODSf/EiRNZv349a9euDb6SkpK47777mu2KkJOT0+J6EseDJPGnKUfR9uA3thBCCCGEaJ9GjRpF9+7dmTJlSvDYOeecw9KlSw95rd1u5y9/+QuPP/44mqYxefJkhg0bxpgxY3j11VdZt24dO3fu5NNPP2Xw4MHB3klo7KH87SJmsbGx9OvX77DuO2HCBBRFaTLMefTo0Wzbtq3ZwmwjR47kf//7H/n5+S0m8b+PoyUtfR5Tp07l0Ucf5Z133iEjI4PS0lJKS0upq9u/KOHdd9/N22+/zTvvvENubi533XUXhYWFwbnYfr+f8ePHs3LlSj744AMCgUCwnn3b9tXW1jJu3Djq6+uZNm0aTqczWCYQCBww5nHjxtGtWzcmTpzImjVr+PHHH7n33nu56aabgj39H3zwAZ9++im5ubns3LmT//73vzz00ENceeWVB10073DaXldXF0xaoXH7uLVr1wa312vJLbfcQkFBAXfffTe5ubm88847TJs2jXvvvbdZ2WnTpnHJJZe0+NBj1qxZTdZmKC4uZtSoUaSmpvL8889TUVERjHmf7777jnfffZecnBzy8/P57rvvuPXWWxk2bBgZGRnBctnZ2cGHVgD33nsvixYtIi8vj+XLlzN+/HicTifXXXcd0DiSo0ePHk1eRqORhISEZvP2D+d7sdUccv3608zpsMVcya6dWuUD/bVdj5+tNVTsautwhBBCCCFOqINt83Qya2mLOU3TtA8++EAzmUxaYWGhpmmaVlVVpVmtVm3z5s3BMi1tMadpmlZQUKAZDAbtk08+0TRN09xut/bMM89oPXv21CwWixYVFaUNGzZMmz59enBLNJfLpdntdu3nn39uUtcbb7zRbDuxlraY0zRNe/rpp7XY2FittrY2WKfZbNZCQ0ObbL3m8Xg0m82mWa1WzePxNKlj9+7dmtFo1HbtOvjvs7m5uZrVatVqamqCx9LT0zWg2evxxx9vcu2rr76qpaenayaTSevXr5+2aNGi4Lm8vLwW6wC0BQsWaJqmaQsWLDhgmby8vIPGXVBQoF1wwQWa1WrVoqKitL/97W+a2+0Onv/444+1fv36aaGhoVpISIjWrVs3bcqUKYf8vj6cth8o7uuuu+6gdS9cuFDr27evZjKZtIyMDO31119vVmbLli0aoM2ZM6fFOt59913tt2nqvvctvfaZP3++NnToUC08PFyzWCxaVlaW9sADD2jV1dVN6ga0d999N/j+yiuv1BITEzWj0aglJSVpl156qbZx48aDtrGlLeaWLVumRUREaA0NDQe9trW2mFP2Nkbs5XQ6CQ8PD279cCrKff5aEvZswqczEf3kQvRG06EvEkIIIYQ4RbjdbvLy8sjMzGyVxbxORvfffz8Oh4P//Oc/rV73q6++yldffcWcOXOaHHe73XTp0oWPP/6YoUOHtvp9f+++++7D4XAccH70b11xxRX07duXhx566LjHJU4/l19+OX379m22cODvHexnz5HkoTKc/jRSWFjI1s3rid/TOCfImdoPz/f/buOohBBCCCFEa3vkkUdIT08/6JDto2U0Gnn55ZebHbdYLLz//vstbid2PMTFxTVZ0O9gnnvuOUJDQ49zROJ05PF46N27N3fdddcJu6f0xP/OqdoT7/V6WbJkCdG/vkt6zWb8ih6DFgCjhch/HnrukhBCCCHEqeJ06IkXQpx8pCdeHJGK3flYts0nraZxb8S6hO6YR17bxlEJIYQQQgghhDgSsk/8KWzHjh1Ul+4mfMdCIrbNw2aMRQECio7KAdfhK/iFE7OToRBCCCGEEEKI1iBJ/CnMkr+c9AWvo1N9VGWOJGnnYgCKEwdS4/Jh9fnaOEIhhBBCCCGEEEdChtOfwqKNAfQBDwGjFa81EtPdX1DQ+TwqO4whJiaGjIz0tg5RCCGEEEIIIcQRkCT+FGY5cyLhd32KJ6EbiTmf43/tWkLDolBNNhITE9HrZSCGEEIIIYQQQrQnksWd4rSoFDaljsGWMJSU3UuJWvUhYWY79bUXEx4T0dbhCSGEEEIIIYQ4ApLEn+IqKyvpuewFjJof9ey/Ybjodpz/e4WIX2fiUhQwmNo6RCGEEEIIIYQQh0mG05/iCtcvxaq6MWh+wiJjsGd0J/XWVzD/dSamPudg6NCvrUMUQgghhBCtbP78+WRnZ6OqaqvXPX78eF544YUWz02cOJEpU6a0+j1bMnDgQL744ovDKjtt2jTGjRt3nCMSpyOPx0NaWhqrVq06YfeUJP4UpqoqkcveAcBnDMHaaywAer2ekNQuhFz5T8Ju+HdbhiiEEEII0a4ESrfT8PVzNHw19YAv19z/oAX8rX7v66+/HkVRUBQFg8FAWloat956K9XV1c3K3n///f+/vfsOi+Ja/wD+XcouSxVFKaKIDcQONgQFEpUkRpOYqFET5WosscUWEzUq6o3YTWJN1IsxFjRREo25igVQA5YoKMWCChbK1ShNytLO7w9/zHVdFlEpd+X7eZ55HuacM2femT3w8O7MnMGcOXOgp/f43/2tW7dK28pkMlhbW6Nfv36Ii4tT266goADLli1D+/btYWxsDCsrK3h4eCAwMBCF//9mo3nz5uHrr79GVlaW2raXLl3CwYMHMWnSJKnM29tb2qdcLkezZs0wa9YsqFQqAMChQ4cgk8mQlpam1peNjQ0aNWqkVnb37l3IZDKEhIQAAObOnYsvv/zymV9UqFQqzJs3D3PnzpXKNm3ahB49esDS0hKWlpbo1asXzp49q7Ht+vXr4ejoCCMjI7i5ueHkyZNSXWFhIb744gu0bdsWJiYmsLOzw/Dhw5GSkiK1efjwISZNmgQnJycYGxujcePGmDx5MjIzM8uNGQBu376Nfv36wcTEBFZWVpg8eTIKCgqk+qtXr8LHxwfW1tYwMjJC06ZN8dVXX0mfkzYVOfYTJ06gX79+sLOzg0wmw6+//vrMeAEgPDwcbm5uUjwbN25Uq39yPDy59O3bV2ufSUlJGDVqFBwdHaFUKtGsWTPMnz9f7Vw86cGDB7C3t4dMJkNGRsYzYy7vMwaAR48eYeLEibC3t4dSqUSrVq2wYcMGqV6hUGDGjBn44osvnrmvyqLTSXxAQABkMhmmTJkilQkh4O/vDzs7OyiVSnh7e2v8caotrsSeR728/wAADLt9ABknsiMiIiJ6KUW3LkIVsRuFl0+iKDFKY1FdOIj88G1AcdW8yveNN95AamoqkpKSsHnzZhw4cADjx49XaxMREYGEhAQMHDhQrdzc3BypqalISUnBwYMHkZOTg759+0rJUEFBAXx9fbFkyRKMGTMGEREROHv2LCZMmIA1a9ZI/1O3a9cOTZo0wY4dO9T6X7t2LQYOHAgzMzO18tGjRyM1NRXXr1/HsmXLsG7dOvj7+wMAPD09YWBggLCwMKn95cuXkZ+fj6ysLFy/fl0qDw0NhaGhITw8PAAAffv2RWZmJg4fPlzuOdu7dy9MTU3Ro0cPqSwsLAxDhgxBaGgoIiMj0bhxY/Tp0wfJyclSm927d2PKlCmYM2cOoqKi0KNHD7z55pu4ffs2ACA3NxcXLlzA3LlzceHCBezbtw/Xrl1D//79pT5SUlKQkpKCFStWICYmBlu3bsWhQ4cwatSocmMuLi5G3759kZOTg1OnTiEoKAh79+7F9OnTpTaGhoYYPnw4QkJCcPXqVXzzzTfYtGkT5s+fX27fFTn2nJwctG/fHmvXri23ryclJibirbfeQo8ePRAVFYXZs2dj8uTJ2Lt3r9Rm3759SE1NlZbY2Fjo6+trjNUnXblyBSUlJfj+++8RFxeH1atXY+PGjZg9e3aZ7UeNGoV27dpVKOZnfcYAMHXqVBw6dAjbt2/H5cuXMXXqVEyaNAm//fab1GbYsGE4efIkLl++XKH9vjSho86ePSuaNGki2rVrJz777DOpfMmSJcLMzEzs3btXxMTEiMGDBwtbW1uRlZVVoX4zMzMFAJGZmVlFkVefCys/EQ+/cBN/f9lZFGc/qOlwiIiIiP4n5OXlifj4eJGXl/fc25aockX6ot7i0d5/atQV52aJ9PleIufAysoIU8OIESPEO++8o1Y2bdo0UbduXbWySZMmiQ8++ECtLDAwUFhYWKiV7d+/XwAQly5dEkIIsXTpUqGnpycuXLigse+CggLx6NEjad3f31/06NFDWi8uLhZ16tQRv//+u9p2Xl5eav+rCyHEgAEDhKurq7Tu7u4uxo4dK62vX79e9O3bV7z11lti06ZNUvnIkSOFh4eHWl9+fn7i448/1oj3Sf369RMzZswot01RUZEwMzMTP/74o1TWpUsXMW7cOLV2zs7O4ssvv9Taz9mzZwUAcevWLa1t9uzZI+RyuSgsLNTa5o8//hB6enoiOTlZKtu1a5dQKBTl5ilTp04Vnp6eWuvLUtaxPwmACA4OfmY/M2fOFM7OzmplY8eOFd26ddO6zerVq4WZmZna2KqIZcuWCUdHR43y9evXCy8vL3Hs2DEBQKSnp5fbT0U+49atW4uFCxeqtXF1dRVfffWVWpm3t7eYO3duufsr72/P8+ShOnkl/tGjRxg2bBg2bdoES0tLqVwIgW+++QZz5szBgAED0KZNG/z444/Izc3Fzp07azDi6peScgeN70UBAIqad4eead0ajoiIiIhI98nkShj1HI6Cv/ajOD1VrU715y6IokIYeQ2vllhu3ryJQ4cOwdDQUK38xIkT6NSpU7nbZmRkSP8fl26/Y8cO9OrVCx07dtRob2hoCBMTE2m9S5cuOHv2rHRb/KVLl5CRkfHM/V68eBF//vmnWsw+Pj4IDQ2V1kNDQ+Ht7Q0vLy+Nch8fH7X+unTponH789NOnjz5zLhyc3NRWFiIunUf/89cUFCA8+fPazxH36dPH0RERGjtJzMzEzKZDHXq1Cm3jbm5OQwMtN8lGxkZiTZt2sDOzk4q8/X1hUql0vrs9fXr13Ho0CF4eXlp7bcsTx/7i4qMjNQ4X76+vvjrr7+03uK/ZcsWfPjhh2pjq/TRj/JkZmZqxBsfH4+FCxdi27Zt0mMkT5PJZNi6dSuAin/Gnp6e2L9/P5KTkyGEQGhoKK5duwZfX1+17SoyFiuLTibxEyZMQN++fdGrVy+18sTERKSlpal9EAqFAl5eXlp/2VQqFbKystSWV0FS+M/Sh1uv18gajYWIiIjoVaLo9j5kxhbID/2XVFaSlw3VqZ1QdHsfemZWVbbv33//HaamptKzwfHx8RrP4iYlJaklf6UyMzNhamoKExMTWFpaIigoCP3794ezszMAICEhQfr5WRo2bAiVSiU9y56UlAR9fX00aNBAo+369ethamoKhUKBDh064P79+/j888+lem9vb1y7dg2pqY+/FAkPD4eXlxe8vLyk2+zv3LmDxMREjSS+YcOGuH37ttbn4jMyMpCRkVHm+XjSl19+iYYNG0r5xd9//43i4mJYW1urtbO2ttZ4fr9Ufn4+vvzySwwdOhTm5uZltnnw4AEWLVqEsWPHlhtPWlqaxr4tLS0hl8s19t+9e3cYGRmhRYsW6NGjBxYuXFhu3097+thfVFkxW1tbo6ioCH///bdG+7NnzyI2NhaffPKJWrmFhQWcnJy07ufGjRtYs2YNxo0bJ5WpVCoMGTIEy5cvR+PGjbVu6+TkBAsLCwAV/4y/++47uLi4wN7eHnK5HG+88QbWr18PT09Pte0aNmyIpKQkrfuuTDqXxAcFBeHChQsICAjQqCs92c/zyxYQEAALCwtpeXoCDV3V6f2JuOP5KR61fRv6jdvWdDhERERErwy1q/EPH09iVl1X4X18fBAdHY0zZ85g0qRJ8PX1VZtIDgDy8vJgZGSksa2ZmRmio6Nx/vx5bNy4Ec2aNVObeEwI8cwroKWUSiWAx1dxS/epUCjK3H7YsGGIjo5GZGQkBg0ahJEjR+L999+X6j08PCCXyxEWFob4+Hjk5eXB1dUVbm5uyMrKQkJCAkJDQ6FQKNC9e3eNOEpKSqQ7Ap6Wl5cHAGWej1LLli3Drl27sG/fPo12Tx+PtnNUWFiIDz/8ECUlJVi/fn2Z+8nKykLfvn3h4uKi9tz6m2++CVNTU5iamqJ169Za961t/7t378aFCxewc+dOHDx4ECtWrADweGK80n5NTU3LfGtAecf+Iso6X9qOZcuWLWjTpg26dOmiVv7ee+/hypUrZfafkpKCN954AwMHDlRL/mfNmoVWrVrho48+Kje+K1eu4L333ntmzE+Wfffddzh9+jT279+P8+fPY+XKlRg/fjyOHj2qtp1SqZR+H6qaTs10dufOHXz22WcICQkpd5BV9JcNePyBT5s2TVrPysp6JRJ5uVyOdm+XP2EGEREREb0YRbf3kX9iG/LDAqF8c3K1XIUHABMTEzRv3hzA4+TCx8cHCxYswKJFi6Q2VlZWZc5Yr6enJ23r7OyMtLQ0DB48GCdOnAAAtGzZssITcz18+BAAUL9+fWmfubm5KCgogFwuV2trYWEh7Xf79u1o3bo1tmzZIk3uZmxsjC5duiA0NBQPHz6Ep6cn9PX1ATy+ylw6AZu7u7tGDvDw4UMYGxtLXyo8rV69epDJZGWeDwBYsWIFFi9ejKNHj6pNhmZlZQV9fX2NC4H37t3TuGBYWFiIQYMGITExEcePHy/zKnx2djbeeOMNmJqaIjg4WO1xgs2bN0tfNpSW29jY4MyZM2p9pKeno7CwUGP/pbmLi4sLiouLMWbMGEyfPh12dnaIjo6W2j19+7m2Y39RNjY2ZZ4vAwMD1KtXT608NzcXQUFBz3XXQEpKCnx8fODu7o4ffvhBre748eOIiYnBL7/8AuC/Xx5YWVlhzpw5WLBggUZ/FfmM8/LyMHv2bAQHB0sz6Ldr1w7R0dFYsWKF2t0LDx8+lH4fqppOXYk/f/487t27Bzc3NxgYGMDAwADh4eH47rvvYGBgIJ3sivyylVIoFDA3N1dbdN3du3dx9erVmg6DiIiI6JX15NX4vN9XVeuz8E+aP38+VqxYofZas44dOyI+Pv6Z206dOhUXL15EcHAwAGDo0KE4evQooqKiNNoWFRUhJydHWo+NjYW9vT2srB5/adGhQwcAeOZ+DQ0NMXv2bHz11VdqVy19fHwQFhaGsLAweHt7S+Wlt9SHhYVp3EpfGoerq6vW/cnlcri4uJQZ1/Lly7Fo0SIcOnRI45l5uVwONzc3HDlyRK38yJEjancDlCbwCQkJOHr0qEayCjy+SNinTx/I5XLs379f44uIhg0bonnz5mjevDkcHBwAAO7u7oiNjZUeMQCAkJAQKBQKuLm5aT1eIQQKCwshhICBgYHUb/PmzdWS+PKO/UW5u7trnK+QkBB06tRJY96GPXv2QKVSPfPKeank5GR4e3vD1dUVgYGBGs+87927FxcvXkR0dDSio6OxefNmAI/nQ5gwYUKZfVbkMy4sLERhYaHG/vT19TUe4YiNjS1zPokq8cyp7/6HZGVliZiYGLWlU6dO4qOPPhIxMTGipKRE2NjYiKVLl0rbqFQqYWFhITZu3Fihfej67PTXr8WJu7M9Rfw/3xfZx7aIjFUDy5w9lYiIiKi2epnZ6Z9UOlP9wy/cqmxG+ieVNTu9EEK4ubmJCRMmSOvfffedcHNzU2tT1uz0Qjye3b5t27aipKRE5Ofnix49eghLS0uxdu1aER0dLW7cuCF2794tXF1dRVRUlFosI0eOVOvL1dVVrFmzRq2srNnpVSqVsLW1FcuXL5fKjh8/LgAIU1NTcfr0aan81KlTwszMTAAQJ06c0Ijfy8tLY+bwso7x/fffVytbunSpkMvl4pdffhGpqanSkp2dLbUJCgoShoaGYsuWLSI+Pl5MmTJFmJiYiKSkJCGEEIWFhaJ///7C3t5eREdHq/WjUqmEEI/zl65du4q2bduK69evq7UpKirSGnNRUZFo06aNeP3118WFCxfE0aNHhb29vZg4caLUZvv27WL37t0iPj5e3LhxQ+zZs0c0bNhQDBs2rNzzUZFjz87OFlFRUSIqKkoAEKtWrRJRUVHlzrp/8+ZNYWxsLKZOnSri4+PFli1bhKGhofjll1802np6eorBgweX2c++ffuEk5OTtJ6cnCyaN28uXnvtNXH37l21mLUJDQ0tc3Z6JycnsW/fPmn9WZ+xEI/HWOvWrUVoaKi4efOmCAwMFEZGRmL9+vVqfTs4OIht27ZpjUmIypudXqeS+LI8/YdhyZIlwsLCQuzbt0/ExMSIIUOG1KpXzJ34Ya54+IXbf5evPETW92OfvSERERFRLVFZSbwQQuT9GSTS53uL4qz7lRBZ+bQl8Tt27BByuVzcvn1bCCHEw4cPhVKpFFeuXJHaaEvib926JQwMDMTu3buFEELk5+eLgIAA0bZtW2FkZCTq1q0rPDw8xNatW6VXouXl5Qlzc3MRGRmp1tfGjRs1XidWVhIvhBBff/21qF+/vpQ45uXlCYVCIUxNTdVevaZSqYSxsbFQKpVSYlzq7t27wtDQUNy5c0fLGXvs8uXLQqlUioyMDKnMwcFBANBY5s+fr7btunXrhIODg5DL5cLV1VWEh4dLdYmJiWX2AUCEhoYKIf6bTJa1JCYmlhv3rVu3RN++fYVSqRR169YVEydOFPn5+VJ9UFCQcHV1FaampsLExES4uLiIxYsXP3NcV+TYtcU9YsSIcvsOCwsTHTt2FHK5XDRp0kRs2LBBo83Vq1cFABESElJmH4GBgeLJa82l62Ut2mhL4gGIwMBAtbLyPmMhhEhNTRV+fn7Czs5OGBkZCScnJ7Fy5UpRUlIitYmIiBB16tQRubm5WmMSovKSeNn/H4zO8vb2RocOHfDNN98AeHwLyYIFC/D9998jPT0dXbt2xbp169CmTZsK9ZeVlQULCwvp1Q+6ICUlBVcux8MkJRoO8XuhEIUo0FfgRsdRsL4bAUNVNnL6+6NVq1Y1HSoRERFRjcvPz0diYiIcHR0rZTIvUZAPmfzl+6lMM2fORGZmJr7//vtK73vdunX47bffEBISolaen58PJycnBAUFwd3dvdL3+7TPP/8cmZmZGs9Hl2XQoEHo2LEjZs2aVeVxUe0zcOBAdOzYEbNnzy63XXl/e54nD9WpZ+LLEhYWJiXwwONJ7fz9/ZGamor8/HyEh4dXOIHXRaKkGOZ3z8MlcjVUyTFQiMfvYFR0/xAmLdwg8PicaJsTgIiIiIhezv9aAg8Ac+bMgYODA4qLiyu9b0NDQ6xZs0aj3MjICNu2bSvzdWJVoUGDBmoT+pVn+fLlMDU1reKIqDZSqVRo3749pk6dWm371Pkr8ZVN167EZ28ej6LrZ6HfuB1upj+CQ/ZNAIDp9H04cSkBLa78AguDYliMffY3lERERES1QWVfiSciqgheiScAgGFrb8iMTHH3wUMpgX9UxwGRl5Ogr68PPT096Mn4MRMREREREb0KmN3pOCP3QTD/4gDu13f5b6G+HOZ6RSgqKoJKpQJvtiAiIiIiIno1MIl/BegpzWDT4wOkmDVBvqEpTB+lwOHQXLT5z0kYFOagRJQ8uxMiIiIiIiL6n2dQ0wFQ5XBq7YqSVnugp6eHkrxsqP4MguzUDijyH0HfolNNh0dERERERESVgEn8K0RP7/GNFXpKMyh7jYbC40OoTv8M/br2NRwZERERERERVQYm8TouNy8X8dsWoJ6DExq/NhT6T7ziRE9pBqXPyBqMjoiIiIiIiCoTk3gdF38uHM0SjwGJx5Bf8BAm/WfUdEhERERERERURTixnY4rij4k/axo37sGIyEiIiIiXdGkSRN888031ba/sLAwyGQyZGRkVNs+iV5VTOJ1RPGDu8hY/CYy/L2lJXmhL5qlngYAFMoMkP3Dp8jeMrGGIyUiIiKqPXJzc5GYmFgtr/T18/ODTCbDkiVL1Mp//fVXyGSy5+rr3LlzGDNmTGWGV+m8vb0xZcqUmg6D6H8Ok3gdoWdSByjIg14dGxi9NgoKn1E479gPBqIYAGAgioDiAhTd+KtmAyUiIiKqRW7fvo0bN24gJyenWvZnZGSEpUuXIj09/aX6qV+/PoyNjSspKiKqTkzidYTMyBQKz6Eo/vsO5B3fRH5zT1jfifxvvdwI+jbNgef7EpaIiIiIXpAQAvfv3wcA/Oc//6mWffbq1Qs2NjYICAgot93evXvRunVrKBQKNGnSBCtXrlSrf/p2en9/fzRu3BgKhQJ2dnaYPHkyAGDhwoVo27atRv9ubm6YN2+e1v3/8ccfaNmyJZRKJXx8fJCUlKRW/+DBAwwZMgT29vYwNjZG27ZtsWvXLqnez88P4eHh+PbbbyGTySCTyZCUlITi4mKMGjUKjo6OUCqVcHJywrffflvuuSB61XBiOx2i33UgVGd/R8aPM5B1/y6aqbIAACpTa4iSYhg79QTuJ9VskERERESvsNzcXFy5cgUlJSUQQkClUsHY2Bh3796Vro6bmZnBycmpSvavr6+PxYsXY+jQoZg8eTLs7TVfJXz+/HkMGjQI/v7+GDx4MCIiIjB+/HjUq1cPfn5+Gu1/+eUXrF69GkFBQWjdujXS0tJw8eJFAMDIkSOxYMECnDt3Dp07dwYAXLp0CVFRUfj555/LjPHOnTsYMGAAxo0bh08//RR//fUXpk+frtYmPz8fbm5u+OKLL2Bubo6DBw/i448/RtOmTdG1a1d8++23uHbtGtq0aYOFCxcCeHz3QElJCezt7bFnzx5YWVkhIiICY8aMga2tLQYNGvQyp5ZIZzCJ1yE3Dm9H/Zy/UZLzNxIbeqPV3TDolxQgzd4df9t3g9Xd02hc9Y9jEREREdVaenp6yMvLQ15eHuRyORo3bgwbGxskJiZKV+VNTU2rNIb33nsPHTp0wPz587FlyxaN+lWrVuH111/H3LlzAQAtW7ZEfHw8li9fXmYSf/v2bdjY2KBXr14wNDRE48aN0aVLFwCAvb09fH19ERgYKCXxgYGB8PLyQtOmTcuMb8OGDWjatClWr14NmUwGJycnxMTEYOnSpVKbhg0bYsaM/75VadKkSTh06BB+/vlndO3aFRYWFpDL5TA2NoaNjY3UTl9fHwsWLJDWHR0dERERgT179jCJp1qDt9PrENv69SATxRAyfTjq58Jy1u942NUPjyyaAAAs69TBc85pQkRERETPwcjICF27doW1tTUKCgpgbm4Oc3NzFBUVQV9fH61bt4azs3OVx7F06VL8+OOPiI+P16i7fPkyPDw81Mo8PDyQkJCA4uJijfYDBw5EXl4emjZtitGjRyM4OBhFRUVS/ejRo7Fr1y7k5+ejsLAQO3bswMiRI7XGdvnyZXTr1k1tsj13d3e1NsXFxfj666/Rrl071KtXD6ampggJCcHt27efeewbN25Ep06dUL9+fZiammLTpk0V2o7oVcEkXofU8RoKs+nByLZpC7OkSOSuGABFwgkUKOugdevWat9SEhEREVHVMDAwgIODAwAgLS0Nf/31FwoKCmBsbAxbW9tqiaFnz57w9fXF7NmzNeqEEBqz1Zc3e36jRo1w9epVrFu3DkqlEuPHj0fPnj1RWFgIAOjXrx8UCgWCg4Nx4MABqFQqvP/++1r7q8hM/StXrsTq1asxc+ZMHD9+HNHR0fD19UVBQUG52+3ZswdTp07FyJEjERISgujoaPzjH/945nZErxLeTq9jDOvaIqXDEKTYe6D+3dOwSj6LtqeWoiR/AEQdy5oOj4iIiKhWuHfvHgDg77//hpGREfLz8wEAeXl5UCqV1RLDkiVL0KFDB7Rs2VKt3MXFBadOnVIri4iIQMuWLaGvr19mX0qlEv3790f//v0xYcIEODs7IyYmBq6urjAwMMCIESMQGBgIhUKBDz/8sNyZ7V1cXPDrr7+qlZ0+fVpt/eTJk3jnnXfw0UcfAQBKSkqQkJCAVq1aSW3kcrnGnQMnT55E9+7dMX78eKnsxo0bWmMhehUxidcxjx49Qn6+CjBpgEKFOW52Gw/zO3+h/tndyCspAvT5kRIRERFVtfT0dMjlcrRu3RqWlpa4fv06bt++jfT09GpL4tu2bYthw4ZhzZo1auXTp09H586dsWjRIgwePBiRkZFYu3Yt1q9fX2Y/W7duRXFxMbp27QpjY2P89NNPUCqV0t0GAPDJJ59ICfaff/5Zblzjxo3DypUrMW3aNIwdOxbnz5/H1q1b1do0b94ce/fuRUREBCwtLbFq1SqkpaWpJfFNmjTBmTNnkJSUBFNTU9StWxfNmzfHtm3bcPjwYTg6OuKnn37CuXPn4Ojo+Dynjkin8XZ6HVNSUgJzc3O4tWsNRwsDdOjuAz3fyUh9ZxkU7oNg2KpnTYdIRERE9Mpr06YN3N3dUa9ePejp6aFly5bo1q0brK2tqzWORYsWady+7urqij179iAoKAht2rTBvHnzsHDhwjIntQOAOnXqYNOmTfDw8EC7du1w7NgxHDhwAPXq1ZPatGjRAt27d4eTkxO6du1abkyNGzfG3r17ceDAAbRv3x4bN27E4sWL1drMnTsXrq6u8PX1hbe3N2xsbPDuu++qtZkxYwb09fXh4uKC+vXr4/bt2xg3bhwGDBiAwYMHo2vXrnjw4IHaVXmi2kAmKvLQSi2SlZUFCwsLZGZmwtzcvKbDISIiIqJKlp+fj8TERDg6OsLIyKimw9EJQgg4Oztj7NixmDZtWk2HQ6STyvvb8zx5KO+9JiIiIiIire7du4effvoJycnJ+Mc//lHT4RDVejp1O31AQAA6d+4MMzMzNGjQAO+++y6uXr2q1kYIAX9/f9jZ2UGpVMLb2xtxcXE1FDERERERkW6ztrbGkiVL8MMPP8DSkhMpE9U0nUriw8PDMWHCBJw+fRpHjhxBUVER+vTpg5ycHKnNsmXLsGrVKqxduxbnzp2DjY0Nevfujezs7BqMnIiIiIhINwkhcP/+fQwdOrSmQyEi6Njt9IcOHVJbDwwMRIMGDXD+/Hn07NkTQgh88803mDNnDgYMGAAA+PHHH2FtbY2dO3di7NixGn2qVCqoVCppPSsrq2oPgoiIiIiIiOgF6dSV+KdlZmYCAOrWrQsASExMRFpaGvr06SO1USgU8PLyQkRERJl9BAQEwMLCQloaNWpU9YETERERERERvQCdTeKFEJg2bRo8PT3Rpk0bAEBaWhoAaLzaw9raWqp72qxZs5CZmSktd+7cqdrAiYiIiIiIiF6QTt1O/6SJEyfi0qVLOHXqlEadTCZTWxdCaJSVUigUUCgUVRIjERERERERUWXSySvxkyZNwv79+xEaGgp7e3up3MbGBgA0rrrfu3dP4+o8ERERERERka7RqSReCIGJEydi3759OH78OBwdHdXqHR0dYWNjgyNHjkhlBQUFCA8PR/fu3as7XCIiIiIiIqJKpVNJ/IQJE7B9+3bs3LkTZmZmSEtLQ1paGvLy8gA8vo1+ypQpWLx4MYKDgxEbGws/Pz8YGxvzlRhEREREVGscP34czs7OKCkpqfS+P/jgA6xatarS+61qDx48QIMGDZCUlFTTodAraMaMGZg8eXK17EunkvgNGzYgMzMT3t7esLW1lZbdu3dLbWbOnIkpU6Zg/Pjx6NSpE5KTkxESEgIzM7MajJyIiIiIXnVCCBRe/RO5+5dDFBVWev9+fn6QyWSQyWQwMDBA48aN8emnnyI9PV2j7cyZMzFnzhzo6T3+d3/r1q3StjKZDNbW1ujXrx/i4uLUtisoKMCyZcvQvn17GBsbw8rKCh4eHggMDERh4eNjmjdvHr7++mu1VzPn5+fDz88Pbdu2hYGBAd59912NmMLCwtRiKF2uXLmi1i4rKwtz5syBs7MzjIyMYGNjg169emHfvn0QQgAAvL29MWXKlOc6fwEBAejXrx+aNGkCALh48SKGDBmCRo0aQalUolWrVvj22281touJiYGXlxeUSiUaNmyIhQsXSnEAwL59+9C7d2/Ur18f5ubmcHd3x+HDh9X62LRpE3r06AFLS0tYWlqiV69eOHv27DNjFkLA398fdnZ2UCqV8Pb21vjMxo4di2bNmkGpVKJ+/fp45513NM7p0ypy7BX5TMuSnp6Ojz/+WHr718cff4yMjAyp/umx+ORy7949rf0GBASgc+fOMDMzQ4MGDfDuu+/i6tWram38/f3h7OwMExMT6TyfOXOm3HivXr0KHx8fWFtbw8jICE2bNsVXX30ljXegYmN35syZCAwMRGJiYoXO08vQqYntnvxl0UYmk8Hf3x/+/v5VHxARERER1XpCCBRdi0De0R9QfOdxgqXo+j70rZtW+r7eeOMNBAYGoqioCPHx8Rg5ciQyMjKwa9cuqU1ERAQSEhIwcOBAtW3Nzc1x9epVCCGQnJyMmTNnom/fvrh27RrkcjkKCgrg6+uLixcvYtGiRfDw8IC5uTlOnz6NFStWoGPHjujQoQPatWuHJk2aYMeOHfj0008BAMXFxVAqlZg8eTL27t1b7jFcvXoV5ubm0nr9+vWlnzMyMuDp6YnMzEz885//ROfOnWFgYIDw8HDMnDkTr732GurUqfPc5y0vLw9btmzBH3/8IZWdP38e9evXx/bt29GoUSNERERgzJgx0NfXx8SJEwE8/kKhd+/e8PHxwblz53Dt2jX4+fnBxMQE06dPBwCcOHECvXv3xuLFi1GnTh0EBgaiX79+OHPmDDp27AjgcRI4ZMgQdO/eHUZGRli2bBn69OmDuLg4NGzYUGvcy5Ytw6pVq7B161a0bNkS//znP9G7d29cvXpVukjp5uaGYcOGoXHjxnj48CH8/f3Rp08fJCYmQl9fv8x+K3Lsz/OZPmno0KG4e/cuDh06BAAYM2YMPv74Yxw4cAAAMHjwYLzxxhtq2/j5+SE/Px8NGjTQ2m94eDgmTJiAzp07o6ioCHPmzEGfPn0QHx8PExMTAEDLli2xdu1aNG3aFHl5eVi9ejX69OmD69evq42zJxkaGmL48OFwdXVFnTp1cPHiRYwePRolJSVYvHixWtvyxm6DBg3Qp08fbNy4EUuXLq3w+XohgtRkZmYKACIzM7OmQyEiIiKiKpCXlyfi4+NFXl7eS/VTUlIiCq6cEplrh4uHX7iJzPX/ELnHNouHX7iJorQblRTtf40YMUK88847amXTpk0TdevWVSubNGmS+OCDD9TKAgMDhYWFhVrZ/v37BQBx6dIlIYQQS5cuFXp6euLChQsa+y4oKBCPHj2S1v39/UWPHj0qHKcQQoSGhgoAIj09XcsRCvHpp58KExMTkZycrFGXnZ0tCgsLhRBCeHl5ic8++0xrP0/bu3evsLKyema78ePHCx8fH2l9/fr1wsLCQuTn50tlAQEBws7OTpSUlGjtx8XFRSxYsEBrfVFRkTAzMxM//vij1jYlJSXCxsZGLFmyRCrLz88XFhYWYuPGjVq3u3jxogAgrl+/rrVNWZ4+9idp+0yfFh8fLwCI06dPS2WRkZECgLhy5UqZ29y7d08YGhqKbdu2PVe89+7dEwBEeHi41jalud3Ro0efq++pU6cKT09Pab0iY1cIIbZu3SoaNWqktb68vz3Pk4fq1O30REREREQ1Tfz/bfPZ6/3wKPAzQE8fpqPWwmzcFhg261Rtcdy8eROHDh2CoaGhWvmJEyfQqVP5cWRkZGDnzp0AIG2/Y8cO9OrVS7p6/CRDQ0PpaicAdOnSBWfPnoVKpXruuDt27AhbW1u8/vrrCA0NlcpLSkoQFBSEYcOGwc7OTmM7U1NTGBi82I3EFTknAJCZmYm6detK65GRkfDy8lJ7JbWvry9SUlK0PltfUlKC7OxstX6elpubi8LCwnLbJCYmIi0tDX369JHKFAoFvLy8EBERUeY2OTk5CAwMhKOjIxo1aqS177I8fewvIjIyEhYWFujatatU1q1bN1hYWGiNedu2bTA2NsYHH3ygVi6TybB169Zy4wWgNeaCggL88MMPsLCwQPv27aVyPz8/eHt7a+33+vXrOHToELy8vDTqtI3dUl26dMGdO3dw69Ytrf1XBp26nZ6IiIiIqCYV37+FnD3zUHwnDvoO7WA6ai0MmneFTCarlv3//vvvMDU1RXFxMfLz8wFAY5K5pKSkMpPgzMxMmJqaQgiB3NxcAED//v3h7OwMAEhISCg3uXlSw4YNoVKpkJaWBgcHhwptY2trix9++AFubm5QqVT46aef8PrrryMsLAw9e/bE33//jfT0dCmeyqTtnDwpMjISe/bswcGDB6WytLQ06Rn6UqWvrk5LS9N4WxYArFy5Ejk5ORg0aJDWfX355Zdo2LAhevXqpbVN6Wuzn35VtrW1tUaSuH79esycORM5OTlwdnbGkSNHIJfLtfb9tLKO/UWkpaWVeUt8gwYNNF4DXupf//oXhg4dCqVSqVbu5OQECwuLMrcRQmDatGnw9PREmzZt1Op+//13fPjhh8jNzYWtrS2OHDkCKysrqd7W1rbMCR+7d++OCxcuQKVSYcyYMVi4cKHaNuWN3VKlj0YkJSVV+PfiRTCJJyIiIiKqoOK0BBTfvQzIlZC394VBk47VlsADgI+PDzZs2IDc3Fxs3rwZ165dw6RJk9Ta5OXlwcjISGNbMzMzXLhwAUVFRQgPD8fy5cuxceNGqV4IUeFjKU24Sr8MqAgnJyc4OTlJ6+7u7rhz5w5WrFiBnj17SvNfVcX51HZOSsXFxeGdd97BvHnz0Lt3b7W6p+MpL85du3bB398fv/32m9bnu5ctW4Zdu3YhLCxMimnHjh0YO3as1Obf//639Dx7Wft/umzYsGHo3bs3UlNTsWLFCgwaNAh//vknjIyM8Oabb+LkyZMAAAcHB42J8co79hdR1nnRNrYiIyMRHx+Pbdu2adSVNznfxIkTcenSJZw6dUqjzsfHB9HR0fj777+xadMmDBo0CGfOnJE+j4CAgDL73L17N7Kzs3Hx4kV8/vnnWLFiBWbOnAng2WO31Iv8XrwIJvFERERERBUkb9sL+tNaIP/4FuQdWIn8sB9h5OMHRad3IDNUPLuDl2RiYoLmzZsDAL777jv4+PhgwYIFWLRokdTGysqqzBnr9fT0pG2dnZ2RlpaGwYMH48SJEwAeTwp2+fLlCsXx8OFDANA6WVhFdevWDdu3b5f6srS0rHAMz0PbOQGA+Ph4vPbaaxg9ejS++uortTobGxuNK8ilM6g/fYV89+7dGDVqFH7++WetV9hXrFiBxYsX4+jRo2jXrp1U3r9/f7Vb0Bs2bIjU1FQAj69u29raqu3/6X2XzgTfokULdOvWDZaWlggODsaQIUOwefNm6ZXcTz96Ud6xvwgbGxv85z//0Si/f/++RswAsHnzZnTo0AFubm4V3sekSZOwf/9+nDhxAvb29hr1pb8jzZs3R7du3dCiRQts2bIFs2bNKrff0scPXFxcUFxcjDFjxmD69OlaJwd8cuyWqqzfi2fhM/FERERERM9Bv74DTAYvhPm0n2HYrBPy9q9A5vL3kB+5B6KwoFpjmT9/PlasWIGUlBSprGPHjoiPj3/mtlOnTsXFixcRHBwM4PGs4kePHkVUVJRG26KiIuTk5EjrsbGxsLe3V7tN+UVERUVJCaqenh4GDx6MHTt2qB1PqZycHBQVFb3QfrSdk7i4OPj4+GDEiBH4+uuvNerd3d1x4sQJFBT893MNCQmBnZ2d2m32u3btgp+fH3bu3Im+ffuWGcPy5cuxaNEiHDp0SOP5fDMzMynxbN68OZRKJRwdHWFjY4MjR45I7QoKChAeHo7u3buXe7xCCGm+goYNG0r9PnmL97OO/UW4u7sjMzNT7fV5Z86cQWZmpkbMjx49wp49ezBq1KgK9S2EwMSJE7Fv3z4cP368zEcZtG33vHM3CCFQWFhY7tvRnhy7pWJjY2FoaIjWrVs/1/6eF5N4IiIiIqIXUFYy/2jbtGqNwdvbG61bt1Z7FZavr2+Ztxk/zdzcHJ988gnmz58PIQSmTJkCDw8PvP7661i3bh0uXryImzdvYs+ePejatSsSEhKkbU+ePKk24Rrw+KpudHQ0Hj58iMzMTERHRyM6Olqq/+abb/Drr78iISEBcXFxmDVrFvbu3Su90gwAFi9ejEaNGqFr167Ytm0b4uPjkZCQgH/961/o0KEDHj16JLW9f/++tI/SRdtz176+voiLi1O7Gl+axPbu3RvTpk1DWloa0tLScP/+fanN0KFDoVAo4Ofnh9jYWAQHB2Px4sWYNm2adHv4rl27MHz4cKxcuRLdunWT+imdeA14fAv9V199hX/9619o0qSJ1ObJ43maTCbDlClTsHjxYgQHByM2NhZ+fn4wNjbG0KFDATye3DAgIADnz5/H7du3ERkZiUGDBkGpVOKtt97S2ndFjr0in+nTWrVqhTfeeAOjR4/G6dOncfr0aYwePRpvv/222u3owOM7F4qKijBs2LAy+3J2dpa+YAKACRMmYPv27di5cyfMzMykmEvvMsjJycHs2bNx+vRp3Lp1CxcuXMAnn3yCu3fvqr1ucdasWRg+fLi0vmPHDuzZsweXL1/GzZs38fPPP2PWrFkYPHiwNJFiRcYu8Pj3okePHhrP91e6Z85fX8vwFXNEREREr7bKesXc04ruJYlHQXNF+oLXRHH2w0rtWwjtr/nasWOHkMvl4vbt20IIIR4+fCiUSqXaK73KesWcEELcunVLGBgYiN27dwshHr/CLCAgQLRt21YYGRmJunXrCg8PD7F161bp9W55eXnC3NxcREZGqvXl4OAgAGgspZYuXSqaNWsmjIyMhKWlpfD09BQHDx7UiCkjI0N8+eWXokWLFkIulwtra2vRq1cvERwcLL3WzcvLq8x9zZ8/X+v569atm9qr2ebPn19mHw4ODmrbXbp0SfTo0UMoFAphY2Mj/P391V4vpy2WESNGPPPclBevEI9fMzd//nxhY2MjFAqF6Nmzp4iJiZHqk5OTxZtvvikaNGggDA0Nhb29vRg6dKjW17k977E/6zMty4MHD8SwYcOEmZmZMDMzE8OGDSvz1Wzu7u5i6NChWvsBIAIDA9XWy1pK2+Tl5Yn33ntP2NnZCblcLmxtbUX//v3F2bNn1fodMWKE8PLyktaDgoKEq6urMDU1FSYmJsLFxUUsXrxY7e9DRcduy5Ytxa5du7QeU2W9Yk72/yeE/l9WVhYsLCyQmZkJc3Pzmg6HiIiIiCpZfn4+EhMT4ejoWO5kZ7ps5syZyMzMxPfff1/pfa9btw6//fYbQkJCKr3vqvTHH39gxowZiI2NhZ4eb0imynXw4EF8/vnnuHTpktZXIZb3t+d58lCOXiIiIiKiV8ycOXPg4OCA4uLiSu/b0NAQa9asqfR+q9pbb72FsWPHIjk5uaZDoVdQTk4OAgMDtSbwlYlX4p/CK/FEREREr7bacCWeiP738Eo8ERERERERUS3DJJ6IiIiIiIhIRzCJJyIiIqJaqaSkpKZDIKJapLKeZK/6p+6JiIiIiP6HyOVy6OnpISUlBfXr14dcLpfe+U1EVBWEELh//z5kMhkMDQ1fqi8m8URERERUq+jp6cHR0RGpqalISUmp6XCIqJaQyWSwt7eHvr7+S/XDJJ6IiIiIah25XI7GjRujqKioSl7DRkT0NENDw5dO4AEm8URERERUS5Xe1vqyt7YSEVUnTmxHREREREREpCOYxBMRERERERHpCCbxRERERERERDqCz8Q/pfTdfVlZWTUcCREREREREdUGpflnRd4lzyT+KdnZ2QCARo0a1XAkREREREREVJtkZ2fDwsKi3DYyUZFUvxYpKSlBSkoKzMzMIJPJqmWfWVlZaNSoEe7cuQNzc/Nq2SfVbhxzVBM47qi6ccxRdeOYo+rGMffqEEIgOzsbdnZ20NMr/6l3Xol/ip6eHuzt7Wtk3+bm5vzlo2rFMUc1geOOqhvHHFU3jjmqbhxzr4ZnXYEvxYntiIiIiIiIiHQEk3giIiIiIiIiHcEk/n+AQqHA/PnzoVAoajoUqiU45qgmcNxRdeOYo+rGMUfVjWOuduLEdkREREREREQ6glfiiYiIiIiIiHQEk3giIiIiIiIiHcEknoiIiIiIiEhHMIknIiIiIiIi0hFM4omIiIiIiIh0BJP4KnLixAn069cPdnZ2kMlk+PXXX9XqhRDw9/eHnZ0dlEolvL29ERcXp9ZGpVJh0qRJsLKygomJCfr374+7d+9W41GQLgkICEDnzp1hZmaGBg0a4N1338XVq1fV2nDcUWXasGED2rVrB3Nzc5ibm8Pd3R3//ve/pXqON6pqAQEBkMlkmDJlilTGcUeVzd/fHzKZTG2xsbGR6jnmqCokJyfjo48+Qr169WBsbIwOHTrg/PnzUj3HXe3GJL6K5OTkoH379li7dm2Z9cuWLcOqVauwdu1anDt3DjY2Nujduzeys7OlNlOmTEFwcDCCgoJw6tQpPHr0CG+//TaKi4ur6zBIh4SHh2PChAk4ffo0jhw5gqKiIvTp0wc5OTlSG447qkz29vZYsmQJ/vrrL/z111947bXX8M4770j/RHC8UVU6d+4cfvjhB7Rr106tnOOOqkLr1q2RmpoqLTExMVIdxxxVtvT0dHh4eMDQ0BD//ve/ER8fj5UrV6JOnTpSG467Wk5QlQMggoODpfWSkhJhY2MjlixZIpXl5+cLCwsLsXHjRiGEEBkZGcLQ0FAEBQVJbZKTk4Wenp44dOhQtcVOuuvevXsCgAgPDxdCcNxR9bC0tBSbN2/meKMqlZ2dLVq0aCGOHDkivLy8xGeffSaE4N85qhrz588X7du3L7OOY46qwhdffCE8PT211nPcEa/E14DExESkpaWhT58+UplCoYCXlxciIiIAAOfPn0dhYaFaGzs7O7Rp00ZqQ1SezMxMAEDdunUBcNxR1SouLkZQUBBycnLg7u7O8UZVasKECejbty969eqlVs5xR1UlISEBdnZ2cHR0xIcffoibN28C4JijqrF//3506tQJAwcORIMGDdCxY0ds2rRJque4IybxNSAtLQ0AYG1trVZubW0t1aWlpUEul8PS0lJrGyJthBCYNm0aPD090aZNGwAcd1Q1YmJiYGpqCoVCgXHjxiE4OBguLi4cb1RlgoKCcOHCBQQEBGjUcdxRVejatSu2bduGw4cPY9OmTUhLS0P37t3x4MEDjjmqEjdv3sSGDRvQokULHD58GOPGjcPkyZOxbds2APxbR4BBTQdQm8lkMrV1IYRG2dMq0oZo4sSJuHTpEk6dOqVRx3FHlcnJyQnR0dHIyMjA3r17MWLECISHh0v1HG9Ume7cuYPPPvsMISEhMDIy0tqO444q05tvvin93LZtW7i7u6NZs2b48ccf0a1bNwAcc1S5SkpK0KlTJyxevBgA0LFjR8TFxWHDhg0YPny41I7jrvbilfgaUDqj6dPfgt27d0/6Rs3GxgYFBQVIT0/X2oaoLJMmTcL+/fsRGhoKe3t7qZzjjqqCXC5H8+bN0alTJwQEBKB9+/b49ttvOd6oSpw/fx737t2Dm5sbDAwMYGBggPDwcHz33XcwMDCQxg3HHVUlExMTtG3bFgkJCfxbR1XC1tYWLi4uamWtWrXC7du3AfB/OmISXyMcHR1hY2ODI0eOSGUFBQUIDw9H9+7dAQBubm4wNDRUa5OamorY2FipDdGThBCYOHEi9u3bh+PHj8PR0VGtnuOOqoMQAiqViuONqsTrr7+OmJgYREdHS0unTp0wbNgwREdHo2nTphx3VOVUKhUuX74MW1tb/q2jKuHh4aHxmuBr167BwcEBAP+nI3B2+qqSnZ0toqKiRFRUlAAgVq1aJaKiosStW7eEEEIsWbJEWFhYiH379omYmBgxZMgQYWtrK7KysqQ+xo0bJ+zt7cXRo0fFhQsXxGuvvSbat28vioqKauqw6H/Yp59+KiwsLERYWJhITU2VltzcXKkNxx1VplmzZokTJ06IxMREcenSJTF79myhp6cnQkJChBAcb1Q9npydXgiOO6p806dPF2FhYeLmzZvi9OnT4u233xZmZmYiKSlJCMExR5Xv7NmzwsDAQHz99dciISFB7NixQxgbG4vt27dLbTjuajcm8VUkNDRUANBYRowYIYR4/GqI+fPnCxsbG6FQKETPnj1FTEyMWh95eXli4sSJom7dukKpVIq3335b3L59uwaOhnRBWeMNgAgMDJTacNxRZRo5cqRwcHAQcrlc1K9fX7z++utSAi8ExxtVj6eTeI47qmyDBw8Wtra2wtDQUNjZ2YkBAwaIuLg4qZ5jjqrCgQMHRJs2bYRCoRDOzs7ihx9+UKvnuKvdZEIIUTP3ABARERERERHR8+Az8UREREREREQ6gkk8ERERERERkY5gEk9ERERERESkI5jEExEREREREekIJvFEREREREREOoJJPBEREREREZGOYBJPREREREREpCOYxBMRERERERHpCCbxRERERERERDqCSTwRERFVmQcPHqBBgwZISkp67m0/+OADrFq1qvKDIiIi0mFM4omIiGqpP/74AzKZTOsyaNCgl95HQEAA+vXrhyZNmqiVX7p0CQMGDEC9evVgZGSE1q1bY/ny5SgqKpLazJs3D19//TWysrJeOg4iIqJXBZN4IiKiWsrHxwepqalqy927d9G7d29YWVlh7ty5L9V/Xl4etmzZgk8++UStPDw8HN26dYNSqcRvv/2GixcvYubMmVixYgUGDBiAkpISAEC7du3QpEkT7Nix46XiICIiepXIhBCipoMgIiKimldcXIyPPvoIR48exfHjx9G2bduX6m/fvn0YO3Ys7t+/r7aPFi1aoHv37ti+fbta+/j4eHTo0AEbNmzAqFGjAAALFizAsWPHcOLEiZeKhYiI6FXBK/FEREQkJfBHjhzBsWPHXjqBB4ATJ06gU6dOamVnz55FYmIiPv/8c432Li4ueOutt7B7926prEuXLjh79ixUKtVLx0NERPQqYBJPRERUyxUXF+Pjjz+WEvh27dpVSr9JSUmws7NTK0tMTAQAtGjRosxtWrZsiVu3bknrDRs2hEqlQlpaWqXEREREpOuYxBMREdVipQl8SEgIjh07hvbt22tt97zy8vJgZGSkVmZubg4AePjwYZnbpKenS20AQKlUAgByc3Ofe/9ERESvIibxREREtVRpAn/48GEcPXpUI4FPSkpC+/btMXr0aHTs2BEqlQqBgYHo0qUL2rVrh3nz5pXbv5WVFdLT09XK3N3dYWhoiAMHDpQZT0hICDw9PaWy0mS/fv36L3qYRERErxQm8URERLVQcXExhg8fLiXwHTp0KLNdXFwcJk2ahEuXLuHGjRv4448/EBkZiejoaERFRSEyMlLrPjp27Ij4+Hi1snr16mHy5Mn45z//iZSUFLW61atX48GDB5g6dapUFhsbC3t7e1hZWb34wRIREb1CmMQTERHVMiUlJRg+fDh+/fVXbN++Hba2tkhLS1NbSm+fb9mypfSM/LFjxxAZGQk3Nze4urri8uXLuHHjhtb9+Pr6Ii4uTu1q/KNHjzB58mQ4OjrCx8cHFy5cAAAsX74cs2fPxpo1ayCXy6X9nzx5En369KmqU0FERKRz+Io5IiKiWubMmTPo1q1buW3S09ORkZGBDz74AH/99RcA4LvvvkNGRsYzb6N/kru7O/z8/DB27FgAgL+/PxYsWCDVjxgxAlu3boVMJlPbLjExETY2NrC2tsbhw4efGS8REVFtwSSeiIiIypSUlKSWxMfGxmLw4ME4deoULC0tcffuXSiVStSrV09rH3/88QdmzJiB2NhY6Ok93w2A69atw2+//YaQkJCXOg4iIqJXiUFNB0BERES6oU2bNvjiiy/g7e2NkpISmJmZISgoqNwk/q233kJCQgKSk5PRqFGj59qfoaEh1qxZ87JhExERvVJ4JZ6IiIiIiIhIR3BiOyIiIiIiIiIdwSSeiIiIiIiISEcwiSciIiIiIiLSEUziiYiIiIiIiHQEk3giIiIiIiIiHcEknoiIiIiIiEhHMIknIiIiIiIi0hFM4omIiIiIiIh0BJN4IiIiIiIiIh3BJJ6IiIiIiIhIRzCJJyIiIiIiItIR/wfcz7nP2u37tAAAAABJRU5ErkJggg==\n", 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" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Prepare the figure that will be used to create a custom Nyquist plot.\n", - "fig = Figure(figsize=(12,3))\n", - "axis = fig.gca()\n", - "\n", - "# Get the settings for the composed plot that contains the series (data sets, fit results, etc.) that we wish to plot.\n", - "settings = list(filter(lambda s: s.get_label() == \"Noisy\", project_ex1.get_plots()))[0]\n", - "\n", - "# Each data set, fit result, etc. can be represented as a PlotSeries object that contains the required data and the style (color, marker, etc.).\n", - "plot_type: PlotType = settings.get_type()\n", - "assert plot_type == PlotType.NYQUIST\n", - "series: PlotSeries\n", - "for series in project_ex1.get_plot_series(settings):\n", - " # Figure out if the series should be included in the figure legend.\n", - " label: Optional[str] = None\n", - " if series.has_legend():\n", - " label = series.get_label()\n", - " \n", - " # Figure out the color and marker.\n", - " color: Tuple[float, float, float, float] = series.get_color()\n", - " marker: Optional[str] = mpl.MPL_MARKERS.get(series.get_marker())\n", - " \n", - " # Determine whether or not the series should be plotted using markers, a line, or both.\n", - " # Use the plotting functions provided by DearEIS.\n", - " if series.has_line():\n", - " mpl.plot_nyquist(\n", - " series,\n", - " color=color,\n", - " marker=marker,\n", - " line=True,\n", - " label=label if marker is None else \"\",\n", - " fig=fig,\n", - " axis=axis,\n", - " num_per_decade=50,\n", - " )\n", - " if marker is not None:\n", - " mpl.plot_nyquist(\n", - " series,\n", - " color=color,\n", - " marker=marker,\n", - " line=False,\n", - " label=label,\n", - " fig=fig,\n", - " axis=axis,\n", - " num_per_decade=-1,\n", - " )\n", - " elif marker is not None:\n", - " mpl.plot_nyquist(\n", - " series,\n", - " color=color,\n", - " marker=marker,\n", - " line=False,\n", - " label=label,\n", - " fig=fig,\n", - " axis=axis,\n", - " num_per_decade=-1,\n", - " )\n", - " \n", - "# Add the figure title and legend.\n", - "fig.suptitle(settings.get_label())\n", - "axis.legend()\n", - "fig" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "ccefd62b-c930-4972-a441-79238d63669e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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dep4PPc8PKLNarFTc9RnlWZto2ra7v1yPB7emR9MZ0EU0BKBkyZe03fYVAGWhSVRe+BDJrTuQvWIxVcs/x2Qvw+jxHPf3khBCCCGEEMdS74J4l8vFVVddRWZmJvPnz/ePwgPEx8fjdDopKSkJGI0vLCykd+/etbZnNpsxm821vifE34kv8dx+HPZKmjY/sr1b3FcPk6xcFFfsJv5631Rya7OuuLfMwqDceAp2Y0hKRafTkXjfNLyleejjmvuPDw+PgJEPBpxLH9eMkOsnUfnNSzhXfQOAc9W3UF502q/zz9Dr9cSntCA+pUVAea8r74Ur78XtcqHpfKuTKvZuwWWOJsxVTlVUM0IaJBMWFoZ52y+0yVuGFw1DQgt0Oh0ej4fM3+aR3KId5uiEurg0IYQQQghRz9SrIP5wAL9r1y7S09OJjo4OeL9Lly4YjUbmzp3LVVddBfiSSm3evJkJEybURZeFOGt5vV6UUv5kbavmfUOr+f8HplA8Y2agj0rAaDRSGNOapAOb0B/I9B/bqdcFuJPi0IXHowuL8ZfrY1PQx6acdB+sQ+/GtXUBps7D8Zbm41j59aF36tcIteGoNfWNr38e946lVM19l4bZC6n+KoetaSPw6E04dSbcljBCNd89z87cSfS3j1MFeEc+irW37/eW2+1Gr9fLSL0QQgghhKjhrAriKyoqyMjI8L/OzMxk/fr1REVFkZCQwBVXXMHatWv54Ycf8Hg8/nXuUVFRmEwmwsPDufXWW3nooYeIjo4mKiqKhx9+mHbt2vmz1QshYPH0iTTa8T8cXS6j5aX3AhDXvB3e+RDkLMeduRZ9lG9kOO2OV/Hk7UCf0DqgDUNy27/cD11QOOFjv0cz+mbDeAr+hWvXKnTR9XdJi6ZpGFv3pTK0CfsXzyJhzzwaLnyNkIjGZHW8gRbuXCjyPRCxFRUQqunRKw/GpFR/G9v+fSPxpbtxtDyPpJvlAaQQQgghhDjirAriV69ezcCBRzI3H16rfuONN/LMM88we7YvuVXHjh0DjktPT2fAgAEAvPbaaxgMBq666irsdjsXXHAB06ZN+1NbQwlR37lcLrZv/o2yzM30ufRf/infwSV7CXPZcP/2GWrkaDSdnkYpzbFf/hQGtx1Dk87+NnQhkeha9DxtfTwcwINvmr0+rtlpO9eZVFJaSkWDVFTvkQRVZuP+6U1arv0ApTdCvG+5QcceA/B0XYatKA99dDwATqeTuNIMDMqD8hzJjp+xczPOhR8R0eF8Gna5EE1/Vv36FkIIIYQQZ4imlFJ13Ymzic1mIzw8nLKysoD19kLUR8XFB1EThqIDPH2uJ2bE/QDs3rmJsJkPYWrcntArnkQXFF63HT0HVVX5stkHBQUBvgSbtvW/wtJPMDRoTPBVx94VI2/fbopW/UTLbgMxNfLNeFg29Xna7PgOgPDxP6ILjwN8D2qO3iJPCCGEEELUP38kDpWhHCHqIa/tAJo1LGAUe8e2DZTMeR+zpuh417/RjBaiomLYHtGMuNLdaFlr/XWbtWyHevIXWXN9Gh0O3g/TNI3wToOg04mX9jRMbkbD5HsDysyxSZRnRWIIDifyUAB/8GAhnn9fgsMSRoPrXsTSvOupuwAhhBBCCHFWkiBeiHrEU7Ab+68f4No0DzXobow9/kFoSCgAXreLVnkrAKjetgxre982aS3veRv3njUYmvcIaEsC+Pqly8U3w8U3B5TtXreYlsqFyV6E3uP0l29fu4S4hGQi408+yaAQQgghhKgfJIgXoh7wFOym4pd38GxdgD4ing0R7Wk9710ObF1Bq3vfAaBFakey4toSaduH5qzwH6sLicLU/sK66ro4jboMvISsIAvGzN+IaNYN8E2vD/t6PMpjJ6f9cBKvPfa0fSGEEEIIUf9IEC/EWezwyPvujG3EaU7y21xG88tGY3zvYczKRYOc1XgKs9A3aIxBb6DZ6LfAaEHTSSLHvwODwUDzPsOhz3B/WVHxQbw6A5oHIpKO7Gu/ftk8DAW7aDb0JqxWa110VwghhBBCnAISxAtxFjp62vzO0Ba0rNpPdlwXDiZ2J+xgMU08xVRGNSaiaXs4KmDXzMF12GtxNoiPawjPp1OyYzXWpOb+cnP6ZOLL91Ky5Qcsj/8gyymEEEIIIeopXV13QAgRKHvnJmyvX4dr41zKWl6IK9SXxCypcD0hJh379u3D4KjAGZdKTrur0cfU3z3VxekT2aoruuAIADweD56IRLxoWFp09wfw27esY/XHL5Kfn1OHPRVCCCGEEH+EjMQLcZZwOp1se/VmGpXuoLzVIEJzNxC2Yy4hcV0pjWqBoUUv2qSmkZWTj9frpbq6Gp3sEClOgl6vp+3db+CxV6B5XP5yW/pHtNi/BMeuOainfgGDmcLCQmJiYtDray7JUF4Pzg1zcCz6BFO3S7D0vvpMXoYQQgghhECCeCHqlNPpxGQyAWAymTAcmhtjzv6N8Md+pHrVtyT+OgW9vRRvWSIhyo7L5UIDYmNjiW3duu46L+odvTUk4HVIbCLeHB1abBM0o4WysjJW/baSkKJttL3oJmJjfbNADgfv1fM/wHsgG3R6PAW76+IShBBCCADcbje7du2iefPmGI3Guu6OEGeUBPFC1AG73c6amf8mZfc8wq77P8Lb9AQg6prnUN89R3inYWg6PUF9R+FscwHZs98hIXsRZf++kpikLui9LoJDQk5wFiGOL/XqR1CX3o23shSAwsJCdPvX0jHrByp3/4LnkS9xZ6zyB+/G1v0Ivvp5qr79v7rtuBBCiL+9goICcnJyCA0NJSkpqa67I8QZJUG8EHXAbDaTlLWQEHcF5d9NJLzN1wA0TG4G93wUUPdAcSkHkntxMLEb8YXridk1B72zErfHWxddF+eY7LxCqqurodBGYWEhoTqFAvTKS/HrN2Ioz8fbsi+7Woyg4+BRGCyWuu6yEEIIQWFhof+/EsSLvxsJ4oU4A7ZtXkPJiu/odvUYjKFR6HQ6qgbdR+icCUS27o7yuNH0tf84lpaWYjKZSEvrRETEhWTsuJiKtT+T0KwfoWf4OsS5RSlFQUEB5eXlaJpGaGgozS65E9cHazCX52MLbUhh71F4rEG0/vUlctfOJGX8rLruthBCiL8hj8fDunXrcLvdAFRWVhISEkJxcTErVqwAfFuvduzYEYNBQhxxbpPvcCFOs8rKSiI+vY94r4Oc6UU0vustAFL7jUD1vBDNePyRzbS0NIxGo3+9V6vUtpQnpxAUFHTa+y7ObZqm0bVrV3bu3ElOTg4RERFENYinKKUjbP6F4KoDpIZrZBVmARDuLEVzO/zH5+Xtp2FDGf0QQghx+ul0OoxGI6Wlpeh0OpKSkkhJSSE7O5ucnBy8Xi8xMTHodLL5ljj3yXe5EKdB5p6d/v8PDg4mr8UFAIRW5KM8bv97JwrgAYKCgmokbAkNDa01e7gQf5Rer6dNmzZYrVZsNhvbtm0jv+MotvR6EEurnrh+/i+JO+egSz0f43k3+rety6xSmP57KSveuB+Px1O3FyGEEOKcp2ka7du3p1WrViilUEphObTESylFy5Yt6dChgwTx4m9BRuKFOIUcTgeb3xhNkwMbyOl/O4nDbgeg003P4t45BEPzHsecNi9EXamsrMRut2O32ykrK0MpBcGxlHcbTdyg26iePwXn+p/R9m+iOiwK3E4qig+gAc3yVqHZbRASWdeXIYQQ4jRRSqFpWl13A03TaNiwIbt27cJms7F06VL/Lj8JCQlnRR+FOBPkUZUQp5DZZCbYWYEGGFfORLl9e3JrmoaxVR8J4MVZ6eDBgwDExcXRr18/mjRpAkBRURH62BSCr36OsAe/xNisG/bv/40nP4O0cD0qPB4tuR26QwF8SUkxaybdwtqlc/F6JfGiEELUd96yQqpmT6T0yT44ty6q6+4Avr9ZXq8Xm82G1VmGrfggSikOHDhQ110T4oyRiEKIv6C4+CBbfppKxwuvIjQuBYDwf76E58cJRJx3LUjQLuqBBg0aEBQURExMDJqm0axZM2JiYnwj8occDuYt59+KY+lnGFv3JaRFT5Td5q+z9eePSD24kYO/FqB6DASZ0iiEECdNuZ04fvsOZbdhPf/WOu2Lt6yQ6oUf4Vg1y7f0z+2kLGcPsann1Wm/AGw2GyEVuTQvWI4+8zeSz/sXW8ytsdlsNGzYsK67J8QZIRGGEH+S1+ulYPK/aFuxn9LsRYQ+/j1waJu4O9+t494JcfKsVitWqzWgLDw8vNa6+tgUgi4d53+thUT5/z81OgSACJzolAcw4PF42LR6Me269pM8DkIIUYvDwXt1+lSUrRDMwXUWxP8+eLecfyvmXldR+twFFBTkE1PH0+rd+7cSv/RdYncsRRebgtdoIcSko0+fPjKVXvytSBAvxB/g9Xr9CVN0Oh2Oblej0v+N1VWB13YQXVhMHfdQiLoTMfB6HCYjuqgkNKMZgDULf6DFnOdZu7QzXca8IwmHhBDikIDgvfwgpg5D0CzBONb979Sfy+tFO+r3r7eiGOW0owuJQjNZ8ZYVcuCX96javBCr5iX8/Fux9L4am9PNjrmfo7fEg72CsrIyIiIi2Ljkf2gZK4hs0pak/lcCUFR0gN2zJ2PUvHS4/B50YbEArJ7/LWrPasLb9KJln+EA2GxlbPhxKprBRN8rR/v7tWnNUsrz9hDTrAMt27QHwOl0smH+17B3I033zMUQm0LwqBeoTO7CwQ/uJdLtJeZ3CYCFONdJEC/ESdqbvZsDX71ESNOOtLrsXgDaX3g1rsYpmJp0QjNZT9CCEOc2zWjBMuCmgDJL3lYUEKWqZJRECHFWce/fhi68AbrQ6DN63tqCd8v5t6KLTaF64UcBdV27V6MqSzA06+bfHcSduxPn2h/RRTbE0mcUACWlxVR+9gTGigKiRz2HITkNgA3TniFp+494Y5sQ+9AXAGzd+Bsxn92LUbkJvuHf6Bs0wfafURwwxJDoLMERHIMt7WK2bN5OUeF+ui97B4BiRxJbtmzBbDajLfmUpqXbcGUuhkNBvK2shBY7fLMSXdvaY+7xD99FbJpL87yVeLLS4VAQX1Fho+2G6QBUx4dj6XcdAJW/zaZN1q9Uro9HPf49npxtFM6ZSvOd6QCYzv8XQYNuQ9PpWfnR/9G2JINtGfFEH5oh4HA6yH1hOE6DlcSHPiMk2DdDbPXin6nesRxLq1507TfUf383rFqEKSiEZi3b+hPk/VVeezmevZswtOwlf/fEaSNDIkKcpPxfP6LpgQ3ErvwYd1EO4BuNN7fqLQG8EMfQokEUGhDbtJ3/w0zmnh2seON+9mRsq9vOCSHOSUfn86iNa88ayt+7k/I3r6d6wbTT1g9vaT7OzfNx7V595Nzbl1D67AXYv3sFQ2Jrwh74guBRz+PO2UrpYz1wrPnBXzcnZy9Fn4yj8tPxeA9k+cu3z3gex5IZlC7/xl+WvWMjwdmrMBVloypL/OXG3C3oUHgcdn+ZptNjVG68aKAUuoh4TB2H4dIbUIBLZ0LvdWGz2ah2uSkzhmPXWQiLbuDfp70oLIVKUxiumCb+dkPDIiiKaEq1JRwt6MiSLH3TzrgMVrzBR5ZfWa3BlIUmHqpwZEzR0MDXnqW6FFVZQvnkm3HvWU25McxX1WRB0/mWZum9TgDa7F8CXt9Wp1WVlUQ4S2lQlYdu+0J/u/bMDaRl/IjasQR1qK7L5aLRNw8SP/12quyV/rpLv5vKrmcvYsmXkwO+nktnT2PF3K+prDxS9+gkrl57OfZ572F7ZQQVU+/De3AvQpwuMhIvxHE4nU7/k9nWVz6M7d/LsUQ0QPO46rhnQtQP1gvvwNiyF7rYxv6you//Q6u839j+i5GmzSfVXeeEEOccj8fD0qVLadmyJfHx8QHvufasoXree7j3rEHfsCVaaDTK7Thue4cfCBx+COkp2I1r5wp0EXGY2g3y17O98U88B/cRNnoq+rimvvPtXEHVNy9gbN0XY7OuAORsX4vF48EM6JNS0cU0AmDP/z4kXnmx26uwHGrzwP49pFSXUq23Emo4Mkrs9fiC0LL4thy+wpDIGA5a47CHNKBtYht/3YouV7CjeD/xnS7wlzVq0orcm6diCQohOtkXNAdf8SRtD+zDuXAa2tof0KbcQtdeo9gW3Zw9Ax+n09zxmFNSqaysJCgoiHbXjyU0NDTgXsXENCBm3Bc17mGnEbfCiMA1/pGRUUQ8MhPlqAoYCOly8U2423UCjwtdSBRBlz+B7tf38ZbkokUmoFmPnLPHyFuwTfoBjGb/7j8hIaEUt+yHaedidOVHstVHt+gEW7+kecbP4H0adHqqHdUUW+MIcpYRkb0a2l/ou79lhcTYC8l3HAnWnU4nqcve9P1/t4FoVRpBQUEsm/U+TdbOICOpFx0KVqLcLozNu7Mmv4Sgtctpe16DGjlnhDgVzqogftGiRUycOJE1a9aQl5fHrFmzuPTSS/3vK6V49tlnee+99ygpKaFHjx689dZbpKWl+es4HA4efvhhPvvsM+x2OxdccAGTJ08mKSmpDq5I1FcHDhSQPeNZIqryaTb2CzS9gbDQMEIe/RrNGirTo4T4Awwp7QNeNwq1Qh40jQ7zl5VXlOOothMT0+BMd08IcQ4pKirC6XSSl5dH+LafcCyajvK4we3wjdZqOjBZ8ZTkgaMS15aFeAfegnvvJjRrKMYWPf1t2f4zCs+BbMLu/xT9oRFid/Ym7D++hrF1X38Qb7fbcZaXoHdU4q0s5XAKz21LfyJBZ6RCsxByqKw0oQMp3o8pMkUTPfcdnBvnYh10G1Um3+/D7Mb9abXrJwCi4huxLWUg3pgmNExK9fcr+IonKfJ6SEhI8Zc1b9kWnv6xxv3oPuTqGmXBwcG0aNWuRrkxNhnjFU9iGXgz1elTcf76Ls3NIeQk9UXhJS8vD29ENA0aNKgRwP8ZmslaYyajZrJgbNHD/9rcdQSmTsNwrv2B6vkfUjXrZVwZq7BecBv6+OZoodGYul165BqMRqIvvBVP+4Hoj3qY0apVGpUNW6I8bjSDb/18aEgoutZdcK77Ca0k11+3zYDLYeuXpG39GuW+H81gwulyktWwG+aqYmIL97Jm70G6tU/Fm59BkKcKr60IU5cRWPrfiP3AXtq/fzukb6a6+2B/EL/8p08J2vgDlWlD6T3iBv/5sjJ3ER0bT2jIX7+n4u/jrJpOX1lZSYcOHXjzzTdrfX/ChAm8+uqrvPnmm/z222/Ex8dz4YUXUl5e7q8zZswYZs2axcyZM1myZAkVFRVcfPHFeA49tRR/Xx6Ph9LS0pOqW1yYQ9P8VUTb9lL441v+cl1QmATwQvwFSilCk1qAwUx03yv85Rt+nIbztX+w4pcv67B3Qoi/6ujpxWeKw+HAZrNhs9nIy8sDoLi4mIqyMtwOOzir0IIjMXUejj6pDbrgSMzdLwWlQKfHtXM5lZ+Op3rJZwHtKo8bPC685UX+su1791IWloQt4kgAvWPDCvS2AvJCGmFo1NZf7lYKo9dFXnIvf1l0fCN2JPYhv80wQu/8AF1YDJUzxhFq0Kga+SRpiUdmDzRKaUrvuyYGJH4DaNa8Dc1btj1tQZ8+OongK54k7KGvKW+QRlLG/9CUoqrKjlKKgoKCEy5ZOJU0vQFzt0sJe/gbgv7xBJ7927D9ZxQVM8aC017jc5khuS3mriMxNGxx5JqiEgm7/1PCHwycKWDqfhnWEQ9jaH7kwUGEEdAb0AVHoB2aARESHELL+Bgalu3GvuZ/NNwzD/ebo2iWvwZneALtGiViHf4AurAYnC4XW8PasCcqjfDwCH+7nsJMGpXuxFNxZLmDw+kg/N1rcL8wkJKSI99nm9etYNmPM8jYueVU3EJxDjqrRuKHDRvGsGHDan1PKcV//vMfHn/8cS6//HIAPvroI+Li4vj000+54447KCsrY8qUKXzyyScMGuR7Ojp9+nSSk5OZN28eQ4YMqdGuw+HA4Tgylcpms9WoI84N2dnZ7Nmzh379+mE2m2u873A6MJt85a3SOrOlxWAalGUS0334me6qEOcsTdOwDr4Lc+9R6EIiAd+H/qjdi7B67BiCZCRCiPqqurqa5cuX06FDB6Kiok58wCmyfvkivEX7sAc3wGuwkJSURNVv36Pf8jkacDChK9HXvUBwdAxlr17lm5ptMAMKQ1IbdDEpkNQWb3Rjf5sZOzdjdxpwxXagU+OO/nLX3k2E2/azK6wpCYfKwhskUmaKoDK4gT/oAwgddg8FXi+tk5v6y5IbNSH53v8eqXPrW7iz1mP49X3cs5/HbTSD7iz5eB7RkIwWI4luPZRmVbtp3nUkzv0HKSkpwel01vpZ6nQ6HMybOl/sH5lX1RWg+/PblxqbdMLYpFNAmaFhCyKeX4qqKgN8MciBAwcgqAG6pPY4SvJJ2LsUr96Eof9NmOZPRmXY/dn/Q4OCSLVtQxeTgntLOqZ2vqUMjc8fRXazziQlNvOfq6S4CIcxDJPHSVx4pL+8dMOvpG2fxRbbdTRv6Ztx7HK5WPv2A7jCE+ly9RisFpmm/3d2lvyWOLHMzEzy8/MZPHiwv8xsNtO/f3+WLVvGHXfcwZo1a3C5XAF1EhISaNu2LcuWLas1iH/55Zd59tlnz8g1iLpVWFgIwIEDBwKWV7gqivltzjekrP0Y6zX/R1RabwBSb3kRlArYkkUIcWocDuABsNtIcBWhgHbRQf7ibZvXoNcb/dsMCSHOboWFhXg8HvLy8k5LEK/cTjwFe1COSoxNu/jLW654A0pz2dnpVqK7XEjTpk0pq+4MWz7HozMSk7sa3bS7qLzwTso7jMBRYSNm0zwwB6OFRrP2h6m0zN3MjrTL6X6oTb3BRFJZBuXGUP96awBX6iA2x7UmPvFIYN6kaUt4bh6Nf9ffVm06nPCaCgsL2b7XRp+bXkft24T91/dRRyWhq0t6vZ6ePXsSFBTk3x60c4NGVFRUnPEA/mhHB/OubYsxpNRcGvCXz6HTo4X4voddLhebNm1CWdtA6zbolIeQxmnoVs7EOH8yFWFJVMS0JNzjQa8/8kDBezAb1JGZKfEGD8E/Pok+sTXc68vMHx+fgOeB6Tg0Y8AWrIbE1uyo6EtQypGZHfn5+2mZu4LqfDNm01h/+eJPXyUycxmVPa+jxwWX+c7t9eJwOrBarCilUOVFsg3yOabeBPH5+fkAxMXFBZTHxcWRnZ3tr2MymYiMjKxR5/Dxvzd+/HgefPBB/2ubzUZycvKp7LqoIy6Xi5ycHLxeL16vl4qKCvR6Pfv27cPpdKKz5ROy6Xs82xaRpDcR5K6i+qunUa3/h6Y3+KZnydR5IU47TW/A1HEI7uyNmFr5HqK5XC7Uty8QW7GP34Y9S7f+MiNGiLNRVVUVLpcv2evhz1oHDx6krMw3iqnX6wkODsZbmo+3NL/GqOexeEvzcedsx5DYGl2Eb4q5O2MVFdPGoItrSvgDR6ZFGxNb4fa6CTbpOXjwILGxsWRUQlHTS4jv0J+UX55F2W1kzHqDRvb9eIyheF3l6GJ9U+JdQYeCtdIjidAaJjRi8/AXiIhLxuv1+gOsXkNrrjH/K3Jzc3E6nRw8eJC4xh0JvfWtEx90BoWEhAS81jTtlKyHPxU0vQFT24Gn/Twmk4muXbuyefNmHA4H7dp1IDZ2CPnth7Jv3kc0zFpISOYCqr96Bsv5RxL4WS8Zi6FJZ//rw9nqNaMloP2qL5/BnbkWw7X/h6m9byZxj35D8Lbv6JslckhIaDjbBzyK11FBw6MCfnPhLhLLs9jhcfvL8vNzML9+OXtCkkmMCkft3UTYA59Tog8lJCQMiyWwD6L+qTdB/GG/X/eiDu0LeTzHq2M2m+v0aaI4fex2O7t370YphU6nIyQkhJSUFPZuWIF75VSi89fhtYRi0muUpA3FmLGQqMvGBjxxF0KcfpolhKBLxqLcTv/Pn73ajlkH5YYQWnc5r9bjlFK4ti7EnbES68UPyc+uEGeYUoqVK1cG5B1q1KgRe/fu5bfffgPAZC+hg30r3vU/gd5A5PNLa7bjqMRzcB+GxNb+ssovn8W9+zeCLn8cc3ff6KI+vjlaUDi6sAb+z3Z2u51tyefhCG6BJ6oVXrvdd+4tP9EjbxFrg8NofcFt2H94lZTL7qHiu4mUGCMx6vUEVVcAkPaP+zFbxtLHemQmkMViCdhP/FTes6qqKpRSeL1eiouLAcjLyyM4ONh3z0ymU7ZnuTg1wsPD6d69O4sXL+bAgQM4HA5KSsoobXIeaaMewr3uB6rTp+Fc/4s/mauxSSd0odH+NkztB2FoNg9lLw9o+/BrXfRRs0QzVlE5/VH0yW0Ju3saAJERUXTr0AldWGzA8bEjH2LTnm20aufbAUEpRf7GRTRB4fW40SpLUYfOk/H96zTPXc6GQY/R44JLAd/f29KSIuLiEgJmA4izW735xHN4m5D8/HwaNmzoLy8sLPSPzsfHx+N0OikpKQkYjS8sLKR3795ntsOizoWFhfmfnDqdTlrFhWFa8h4NNi6ixBSBIyaN1ne/SdV/ryElKgzL4z9K0joh6tDRa0nNGUuIsu0DUxAhhiM/l0s+ew1zYhuCivaSkDUfCjJ89ftciz5GZlEJcSZpmkaHDh3YsmULTqeTFi1akJycTFRUFDt+W0SD3b8SnbsaZQ3F0KQz7sy1vj26XQ40sy9g9hzci+3f/wCjhYhnFvj3ADc07oiqLAW90X++3fkHKGx7NcGJLel46O+1y+Wk+c++ZZHL+z+HxWyhRYsWbCpowXZHCU5jCObul1G9YBrGrNXEXPUUwZ+NQ5/QGk/udjS9icjIM7d+v6ysjNWrVweUJSYmkpOTw8GDBwGwWq306dPnjPVJnJzKykq8Xi+5ubnk5h7JZl/t9hLS6yrMXS/Bsfo7qtOn+d4w1hzt1gVHQHBEQFnYmM/w2ssDMvWrqjIwBfl3RTisYur9KFshoaOn+ZMoVh7IRVdZTnhYOK5dK7DPe4/G2RspT+5MUIeLCWnRFttrVwEQVFGAXnkJiTkSS2VsW0fS5/exKaIFHcYdSe5YWJhPZGQ0RqMRcfapN0F8kyZNiI+PZ+7cuXTq5JuK5XQ6WbhwIa+88goAXbp0wWg0MnfuXK66yvfNmpeXx+bNm5kwYUKd9V3UnfDwcNonx5A/61XUnHVUW8LYG5VG+4JlqEpw7tvqrysBvBBnD2Pz7hjbXYAhuS2axTedc9umNaRtmIF3g0a+NR5neCRhg++ies7bddxbIf6+oqKi6NGjBytXrqS4uJhQbxXO9Km02ZmOZg3FOuRuzL2uxLn2R9x7VlP67PmYe15B0LB7AdBFJYLRii4oDGU7gBYRj9vjZqMWiyuyPV3TjuzFXrB5KWmrP2Brk8HQYwAAYWHhrEjsg9McTmhoCJ07dSE4OJjk5DvYs+dCojweNKMZy4CbsP/wKrqs9Rha9PAllNu/DV34md3WMjw8nGbNmrFnzx6MRiPt2rUjPDwck8lEZmYmFoslYOtkcfY4nFspIiKCpk2bsmvXLsrLyzlw4AAhISG+77NDwbwnPwN99Mlvb62zBi5RMHe/DFPXS8BV7S9TTjuawYjSNPQNGgO+nZe0zXNJ2/U/Kte9j666HH1yGqE3/5ew2MboIxr61uYf0nbspxQVHaBZaLi/rKq4AJdmoCrkSGAPUPj+PXgrcyke9R9S2/uyRdir7eh1epkpchY4q4L4iooKMjIy/K8zMzNZv349UVFRNGrUiDFjxvDSSy/RokULWrRowUsvvURQUBDXXnst4PvFeOutt/LQQw8RHR1NVFQUDz/8MO3atfNnqxd/L97KUlzv3kSMx0VBch9ymg/Frek4UL6HsLgUIpJaUX7iZoQQZ5guNIaQ617xJeRRCueWBUT98g6F5hjyQ5IxN+5GcXwbejS0+KYJnsHtjoQQNSnbQYI3zETl/IZRb6E0pjWR4RGYuowI3AvcUYknb6f/ZUFhAXu734YWGkW3Q2vfdZqO+IVvEuaysS/7Yv+e5hHNOrLtwAUYmnUNOHfPo7K9H6bT6WjevLn/9eHReG9xDsFX+UbuDUltahx3ummaRpMmTQgLC2PdunWUlZWh1+spKioiODiYrl27ysjnWSoiIgKz2UxycjI6nY5u3bqRlZVFWFhYQD3NaMaQ/NcfxGg6HZiPLPHQTFbCH/2OsgP5rNvq+xlyu91El+3zVXBUUNjrdiqTuhCBlciJl6GZgwi5+fUjbXicxMQ0CBi46nHBZTj7Dad15ZFPxE6nk7DqIsxeJ7ENj6zLX7/ge5otfI2dLYbR95an/OVH540QZ8ZZFcSvXr2agQOPJKg4nHDuxhtvZNq0aTz66KPY7XZGjx5NSUkJPXr0YM6cOQEJNl577TUMBgNXXXUVdrudCy64gGnTpgVkixR/H1pQOHtaXwrZa2iybxnxMZHsju3O3p730q59B3RB4SdsQwhRd7yFe6ic+SSevJ3oDBYauKvRRyZT0aI7FQcPsuy3zUQGJZP1v+84/7KbzpqES0L8nRzM3kXi9m+JOrAFb2gs2T3uImH5u3BgK+7MNZja+QZS9lmTcLfqR1L/qzn8k7pv+1paLPkvWVGpcCiBpU6nY2/r4eD10DjoSGK1tp16Qqeef6qPmtFM0GXjcWdvxHDUlnF15XAegaysLDIyMjAYfB/J5fPq2atBg8BZGzqdjqZNmx6j9umjt4ZQXl6Oy+XCarWiDXsINnyNtuEnolZPx3lgL7o2vX1LUYwWtKNG+at+eBXXpl+xDrsXc7dLAd9DcF8ehqPW75tMJD4zj7y8fSTFHkkq7inYjVG54Kg23R43mS+MpCSsES1ueumMLk/5OzurgvgBAwYcdzRF0zSeeeYZnnnmmWPWsVgsvPHGG7zxxhunoYeivql2VJOw/XusnmqKI5oQvXMpTTf8D2fr8wlp16KuuyeEOAFP/m48+Rkoo5XiuI6EFmeg738zbdu2pbCwkI3f/Eqjqn3ostMxmW6v6+4K8bfjylyHdcptGK2R6PvfDCu+oMmCVyiOak5ZbEtaNmzpr+vWdDRZ9ykZ8a1oEJ8IQHyzNDLiuuJIDNwmrN91D53yvppS+2NK7V+j3OPx+DPsnyn5+fnodDosFguxsbHs27cPr9dLQUFBjV2WhDiawWCgU6dO7Nq1i7KyMoIiY7FeOo5N8b0I3bOY2IJN6Moy0AbdhaFZN6pdHpyhcVR7FNXFhXj0FnTmMFS1b6q+pyiHyq+ew9CoHUHD7gk4V0xMPNXVR6b0d77yfg4cuJomBqO/fP++TMIMGubqAxhNJn/5uvRvoWAPwe0H0jL15Hal+DswmUynZNaCpmQOYgCbzUZ4eDhlZWU1pseI+mnDJy+SvGUWrq7/IHbE/ThXfk31wo9RdhumriNxbUnH3OMfWAffVdddFULUwnNwL9Xzp+Bc9z9cxmDK0obTeORd7MsroOqn/+Io2Y/pgttp13sI4JvW53Q6ZQsdIU4xd/YGHGt/xNC4I+ZOFwGgXNWUPncBRKcQfttk0HRs+elDklZPp8wYRkz3iwkacAOuLQtYvvB/WC0WrH1G0b5rvzq+mkNZvPPzKS0tPePnPvzQwGDwbWnr9Xpxu90YDAaZlixOyuGHT4e/Xw5PaTca9KjqCnBWATowWcBRhRYajaY3ojxu0Ol90/UB5ajyJdIzmAKy6XvtNgA0U9Bxd39RSuF2u1BeDybzkaUzDlsRRo8TlyUMszX4UB89OO1V6I0mjKa/5+5gOp2OJk2a1JpX4I/EoWfVSLwQp4LNVobTVkRMkm+KU7vrxuMsugFLrC9zteW86zH3vALHiq98wXxlKSBJ7YQ4W+ljGhF81bOYBtzM3pkvE73uc4q3/oSrUU8a5qxApzwEWY5scbVm0U+ELXqX6mGP0KFb7dvTCSFOzFtlQ7ME+zPGu7M24Fz5Dd6SPH8Qv/TT10hzOdiS3J++h7Jut7j4Tg6un0VRcAKGtb/gWvU1+gZN6VC2k8ixK+vqcmo4HMA3aNCAoKAgSXAr6pXq6mrcbt/e8Jqm+WczBwcH+167XXgrS1DV5aBC0EclBuanOER5vahDCfR0h9bgK6XwFGaC8qKPSkIz+R6KK5cD5XKgmSwBO8rU2j97AzyOaoxBoRiNRlR1BfZKG+ZgcOtM6CLiUUphNptxOqrRG4zn/HKSw7sb5OXl0ahRo7/0O0eCeHFOycrYhnHanRi9ThyPzsYcEeubrhYbuPWUZrL6g3nn+l8wNO9WRz0WQpysMl0ImalXktt4IE3zlxO7aw7gG0nQJ/u22vF6vRhXfkaDqjy2ZG8FCeKF+FMqP38S58a5hNz6FsamXbDb7exa8QsNNT26rpf76+mifNPiKSvwl1ksFuKf/ZUkvQFvlQ3H0s+oXvJprVtu1RWPx+MP4KOjo098gBBnGbfbjV6vx2q1otfrqa6uxuVyYTQaD+VYsEBIKMrtQlWXowVHHCdoDAp4pZQXFZOActrRhYb7j/M6bHirDqIRjj4k8Uh9txP0xoD2LRaLLzmt3Ya3LBfcDpQxGKUPQm8KgkN1zWYzFO9D73Xjjm6E9dCo/bkqNjaW3Nxc3G73X0piKUG8OKfo8WBxV6FDUbLmf8RfcMNx62smK+bul56Zzgkh/hKHw+HbfqnLECIjR1G5fydF379OUME23967gKa8NGnTla1V3eg28mb/sSWlxYQEh0rWZyFqoRyVuLM2YGzVG4CMnVvQ7d5CpMeNe89ajE27YLVaMTtsGJSHbJeJ9oeObdv/Euh/CX3DAhPFGg5Nv9UFhWG98A7Mfa5BVRSdycs6rsPT2YOCgk5QU4izk9VqRafT+QNnq9Va63przWBEC/ljyeY0TYdWy572GIy+6fXmI4G28rjx5GeAzoA+vjmaTnckeLcdBLcDzRKCLrIhVuXFe3AvhDSkyuH7GXQ4HRxe220yHXnQV1FWhK6qDG9wJCFhJ84ToTwulNN+/OvSG2udjXAmHZ5G7/F4JIgX4rDk5m3ZNfxxYixG4rsNr+vuCCFOocTERBISEvwfWIKTWhJ815sopfxl1Qum4V02g9TYFEwG3z7UXq+XXdOewFp1kKArn6FZi9Q6uwYhThelFFu3biUxMZGIiIiA99w523Es+RRTl4sxNu8e8F7WtrWEfjIazeshYvyP6MIbUF5cQGPbXg4ENaTloNuOtHPJE1RENqBt0pEtp8LCTm6XF11QGASdfbmGZAq9qK9qm3p+uqej64IjITgwoFZuB2g60OtB0/BWlfmDd/RGdJEJ6IIjcDgcuKoqsAB2u90XUGsaTocTQuJwKC8Wl+vINTgqMXmqsXvc/nN5vV6qyoowWIIxW6wBP79e20FUZckJLkCPIaHVqbodf8qp+p3zp4N4l8tFfn4+VVVVxMbGEhUl2wmIM89ebSdj8j1Edh1G0nlXANCi36V12ykhxGlT2x+/o8uMrfvi2vQr5v43+NfxFhbmE1+0DYu7Goe5bp/AC3G6lJeXk5eXh8fj8Qfx7pztVM97D9e2RQB4QmM4WGYj2qgwtb8QgIP/e5twrxuHNRJvWQG68AY0adOJzbuuIqhpp4D9n9M69KiTaxNCnL105mC0hJbgcaMqivGWFfiCep0ePC44NCvHYDDgPTTmrtfpMB+aBVNdXY3H48GgNwSMTJsj46m2V2K2HJkt46iuwlJ5AE9VMSQc2fnC4/GgC4rAU1mCLqIhmvV3DwyVB0/BHrSgiNN0F868PxTEV1RUMGPGDD777DNWrVqFw+Hwv5eUlMTgwYO5/fbb6dZN1heLM2PX+w+TXLgB708bcbTpgfl3a9+FEH8vhsTWhN77ie/DwyEx7jLKh4wmU4ugc6Mm/vLMPTtplNLsnE+kI/4eCgsLASgqKsK5dwvO9Cm4ti1CF9OIoKueZe/8zwhe8gVWbzWV1jCMqQPQDEYcTXqyWyl0Pa8ivpFvm7eI8Ej6Xv9oXV6OEKIe0TQdGEwQFIbmcviy3WsamjkY7VAuDL1ej0nzhfH6yiIIDkXTNF8AbzD49rw/+qG80YTRaKpxnmpTCEpnwHxUXUdhNpryoMxhmCqK0f9u/b+3zLeU5+js+/XdSe9h8dprr9G4cWPef/99zj//fL755hvWr1/Pjh07WL58OU8//TRut5sLL7yQoUOHsmvXrtPZbyEASB71JNUGKxVpQzFFJ574ACHEOU/TG/x/vJXbSeWXz8APr5DqOZJ4Ky9vP5YPbmHDq7dSWnaC6XdCnKXWr1/PwoULWbhwIdnZ2TRQNhqv+ZC1n7zApv35ZES1w9vvZsydhxPjrcCt6fGi4Urq4MtYDfS57Fa6PvQBnfsMruOrEafa/Pnzad26NV6v95S3fcUVV/Dqq6/W+t7111/PSy+9dMrPWZtu3brxzTffnFTdKVOmMHiwfJ+fTpreiD4qAX1cMzRLKMpRhadwD96KYtRR34devQm73U5lZSWgMNryUeUHA+rUxmINIqRBI0JjEvxlHo8Ho8eB0evCGBLhS6Bnt2GvqqDaXoXyuPFWFKMFRx53q7y/wuFw0KhRI9asWXNa2q/NSQfxy5YtIz09ndWrV/PUU08xdOhQ2rVrR/PmzenevTu33HILU6dOpaCggJEjR7Jw4cLT2W/xN1aSv8///5Gx8TR4+ldSrn/ev9+lEEL4aTpMHQajhcdh6nKxvzgvcztKA6XpCAs9uTW9QpxtYmNjsZUVU7x7LY22zyL515cIc5dREtOG1uU7aV68Cd2KmQDoNHB3vZzQZxfT4NbX0P3BRFfi7HDTTTehaRqapmEwGGjUqBF33XUXJSU1H0Y++uijPP744/7lENOmTfMfq2kacXFxjBgxgi1btgQc53Q6mTBhAh06dCAoKIiYmBj69OnD1KlT/QkBn3rqKV588UVsNlvAsRs3buTHH3/k3nvv9ZcNGDDAf06TyUSzZs0YP368f0bvzz//jKZp5OfnB7QVHx9PcnLgDMv9+/ejaRpz5swB4Mknn2TcuHEnfFDhcDh46qmnePLJJ/1l77//Pv369SMyMpLIyEgGDRrEqlWrahw7efJkmjRpgsVioUuXLixevNj/nsvlYuzYsbRr147g4GASEhK44YYbyM3N9dcpLi7m3nvvpVWrVgQFBdGoUSPuu+8+ysrKjttngL179zJixAiCg4OJiYnhvvvuw+l0+t/fsWMHAwcOJC4uDovFQtOmTXniiSf8X6djOZlrX7RoESNGjPDnovn2229P2F+AhQsX0rVnL4ITmtLyvIt479Nv8JYW4CnIAOXlgitvIiiuEaGhoYSFhREeHoGpYTMuvvxKf8Z6AOVy+oP6rKwsbr31Vpo0aYLVaqVZs2Y8/fTTOJ1O9Ho9uoYtcUU1wmgNQbOEcCBrF82bt8AaFEzJ/kzg+KPwx/sag282+j333ENSUhJWq5U2bdrw9ttv+983m808/PDDjB079qTu0alw0lHPl19+Sbt27U5Yz2w2M3r0aP71r3/9pY4JUZuN309B/ecydn/+f/4yvfH4+1QKIf6+NL0B6/m3Ev7w1/4M9gBpxkp0N75J/Kin/R9wXS4XG1cvPi2jVkKcKkd/gE9MTKTJphkM2DkDe4lvpomKSMAQnsDa1Gtwh8VjSj0PdSgxVEOrHpP57NnmTfw5Q4cOJS8vj6ysLD744AO+//57Ro8eHVBn2bJl7Nq1iyuvvDKgPCwsjLy8PHJzc/nxxx+prKxk+PDh/u8rp9PJkCFD+L//+z9uv/12li1bxqpVq7j77rt54403/AF/+/btady4MTNmzAho/8033+TKK68kNDQ0oPy2224jLy+PjIwMJkyYwFtvvcUzzzwDQN++fTEYDCxYsMBff9u2bVRXV2Oz2cjIyPCXp6enYzQa6dOnDwDDhw+nrKyMX3755bj37OuvvyYkJIR+/fr5yxYsWMA111xDeno6y5cvp1GjRgwePJicnBx/nc8//5wxY8bw+OOPs27dOvr168ewYcPYu3cvAFVVVaxdu5Ynn3yStWvX8s0337Bz505GjhzpbyM3N5fc3FwmTZrEpk2bmDZtGj///DO33nrrcfvs8XgYPnw4lZWVLFmyhJkzZ/L111/z0EMP+esYjUZuuOEG5syZw44dO/jPf/7D+++/z9NPP33ctk/m2isrK+nQoQNvvvnmcds6WmZmJhdddBH9+vVj3bp1PPbY44wZ/xTfLt2IZg5BVdn48r3/kLk7w39f1q7+Db1ezxVXXBE4Bb54P568nXirK9i+fTter5d3332XLVu28Nprr/HOO+/w2GOPAb6p+tagEAB0obHc/sBY2qal+dqpLDnuKPyJvsYADzzwAD///DPTp09n27ZtPPDAA9x777189913/jrXXXcdixcvZtu2bSd9v/4SdQp5PB6VnZ19Kps848rKyhSgysrK6rorohbb/u9qVTy2i8p9coDyul113R0hRD3kylqvisd1VcVP9FGe0gJ/+dLZH6nisV3UkncfC6jvKclT1cu/lN85ok7t35+t1ky4QW1/9mLl8Xj85YumT1K7nxqsisd2UfteukzlPzdMFY/tooreu1s592311yudcImq+t8bddH1s5Ldbldbt25Vdrs9oNzrqPL983qPlLmcvjKXo/a6R309vG6Xr8xZfVJ1/6gbb7xRXXLJJQFlDz74oIqKigoou/fee9UVV1wRUDZ16lQVHh4eUDZ79mwFqI0bNyqllHrllVeUTqdTa9eurXFup9OpKioq/K+feeYZ1a9fP/9rj8ejIiIi1A8//BBwXP/+/dX9998fUHb55Zerzp07+1/36tVL3XHHHf7XkydPVsOHD1cXXXSRev/99/3lt9xyi+rTp09AWzfddJO6/vrra/T3aCNGjFAPP/zwceu43W4VGhqqPvroI39Z9+7d1Z133hlQr3Xr1mrcuHHHbGfVqlUKOG5M9MUXXyiTyaRcrmN/D/z0009Kp9OpnJwcf9lnn32mzGbzceOUBx54QPXt2/eY79emtms/GqBmzZp1wnYeffRR1bp164CyO+64Q/Xs2VMppZTX6VCeipKAn6/XXntNhYaGBnxveT1u5crdqVz7tgb8nHgdVcpTZVNer0dNmDBBNWnSpEYfJk+erM7r3VPN+fITBagDW1Ye92ftZL7GaWlp6rnnnguo07lzZ/XEE08ElA0YMEA9+eSTxzyXUsf+3aPUH4tD/9T846lTpzJ06FDatGlDjx49ePjhh8nJyeHAgQM0adLkxA0I8Sc1GfMR+U37E/PoN6dtXYsQ4tymBYVjaNwJU9vz0YU38Jd7nXZcmgF9Skff69J8qr79P8omXkrVt/+HJ3dHHfVY1GdKKQoKCv7QDI/8gjyW//w561cu8JdFRcWSWLKTBlV5FKRPx1vqm3rc6ZI7yOt4HU5zOI5GXdnW5yEy067GU5hJxZvXU/HRg7hztp/qyzpnlT7Vj9Kn+qEqS/1l1Ys+pvSpflR9NyGw7vMXUvpUP//XAsCx/Atf3a+fD6hb9soIX90Dmf4y55rv/3J/9+zZw88//1xjv+lFixbRtWvX4x5bWlrKp59+CuA/fsaMGQwaNIhOnTrVqG80GgkOPrI/ePfu3QMSXW/cuJHS0tITnnfDhg0sXbo0oM8DBw4kPT3d/zo9PZ0BAwbQv3//GuUDBw4MaK979+41pj//3uLFi0/Yr6qqKlwul3/HLafTyZo1a2qsox88eDDLli07ZjtlZWVomlZjq8ff1wkLC8NgOPZn2eXLl9O2bVsSEo6s/x4yZAgOh+OYa68zMjL4+eef6d+//zHbrc3vr/3PWr58eY37NWTIEFavXo3L5UIzmtD9LunclClTGDVqVMD31kcff4IxoSX6uKYBn/e95UV4i/ahbAcpKyur0d+tW7fy3HPP8fG0j9Ap3wwkV3BMQBuapjFt2jTg5L/Gffv2Zfbs2eTk5KCUIj09nZ07dzJkyJCA407me/FU+UNBvMfj4ZJLLuHOO+/EarUycuRIOnTowFdffUWbNm34+eefT1c/xd+Uy+1ix6w3/WtizGYzbW7/N8aQyBMcKYQQtdPHNibk9ncJuuwxf5mqrqCztRrnrR/QpVsff/CesX0jma0OraVXqo56LOqzgwcPsmnTJg4cOHDMOuUV5QFrWHcv/x+tF0zEseJLf5nVaiXvkpfxNOuJZe7rOFb6knnZ7XYc4Um4bv2QpteOp0ev3nhaDyBr0JMEXf0cngNZlL/xT7zFuTXOK+qnH374gZCQEP/a4K1bt9ZYi5uVlRUQ/B1WVlZGSEgIwcHBREZGMnPmTEaOHEnr1q0B2LVrl///TyQxMRGHw+Ffy56VlYVer6dBgwY16k6ePJmQkBDMZjMdO3bkwIEDPPLII/73BwwYwM6dO8nLywN866r79+9P//79/dPs9+3bR2ZmZo0gPjExkb179x7zQVlpaSmlpaW13o+jjRs3jsTERAYNGgT4fnY9Hg9xcXEB9eLi4mqs3z+surqacePGce211xIWFlZrnaKiIp5//nnuuOOO4/YnPz+/xrkjIyMxmUw1zt+7d28sFgstWrSgX79+PPfcc8dt+/d+f+1/Vm19jouLw+12c/DgwRr1V61axebNm2ssww4PD6dVq1ZoRnPgAQYj6AzsyT3AG2+8wZ133olyOfCU5lNdXsY111zDxIkTSWnREky+rencOiPqqL/frVq2IBQHnoN7T/pr/Prrr5OamkpSUhImk4mhQ4cyefJk+vbtG3BcYmIiWVlZJ32//oo/NJT52muvsXLlStavX0+bNm385V6vl1dffZXbb7/9lHdQ/H15PB6yJl5Dg7Is9mStpumYqbXuES2EEH+UpmlgOrI22D7nHRzLZhK85kfK7WXoLCEYL7gdtWoeTbZ+y8aItvSpw/6K+uvw1m+FhYU1PigCLH/rIVrsX0z2Vf+lbadeADRs24s9GUvwNOqIc/3Pvu3gTBY69hiA0+qm6mAm2qEkTTExMfTv39+/VaLVaqVr1654vV7flk7tB+PcOAfHkk/Rxzc/Q1ddf0U8d2gUzXjk94PlvBuw9L02YOtKgIgn5/r+x3Ak0DD3ugpz98t8+2QfJXzs9zXqmrqM+FN9HDhwIG+//TZVVVV88MEH7Ny5MyCRHPge7lgsNfMfhIaGsnbtWtxuNwsXLmTixIm88847/veVUif9WctqtQK+UdzD5zSbzbUef9111/H4449js9l45ZVXCAsL4x//+If//T59+mAymViwYAEdOnTAbrfTuXNnlFLYbDZ27drF8uXLMZvN9O7du0Y/vF4vDofD36ff3wug1vtx2IQJE/jss89YsGBBjXq/v55j3SOXy8WoUaPwer1Mnjy51vPYbDaGDx9OampqwLr1YcOG+UdwU1JS/LkHajtPbef//PPPKS8vZ8OGDTzyyCNMmjSJRx99lL1795Kamuqv99hjj/nXkZ/Mtf8Ztd2vY13LlClTaNu2Ld27dw8ov+yyy7jssstq1NeHx5FT4WbYyAFceeWV/Otf/8JrO4CqKGbcC6/Spk0brr32WrxeLx5ruP/8brcbnduBKj/I5l+/BU2Hcjk4HAqf6Gv8+uuvs2LFCmbPnk1KSgqLFi1i9OjRNGzYMODBh9Vq9f88nG5/KIifNm0aEydODAjgAXQ6HQ8//DBKqTOalU+c2/R6PfboJqiyLLyRSRLACyFOC6+9HG+pb/TH66wit+mFJA6/E31ENOXZuZRX5JJcue8ErQjho5Riz549uN2+qZwHDhzAaDSSl5dL3vZVaGV5NB14DU2aNEFTXjwK9MpLWdZmOBTEN23eBh7+ENubN1C5citBVz6D+dDuCsa2AwlvOxDtUECpaZo/gD/s6DJNb8Dc6SLMnS46U7egXtNMNYNAzWAEjCdXV2+AWpb7HbPunxAcHEzz5r4HMq+//joDBw7k2Wef5fnnj0zhj4mJqTVjvU6n8x/bunVr8vPzufrqq1m0aBEALVu2POnEXMXFxYBvl4TD56yqqsLpdGIyBSYdDg8P9593+vTppKWlMWXKFH9yt6CgILp37056ejrFxcX07dvX/z3cu3dvfwK2Xr161Qg0i4uLCQoKqjWAB4iOjkbTtFrvB8CkSZN46aWXmDdvHu3bt/eXx8TEoNfra4x61/ZAzuVycdVVV5GZmcn8+fNrHYUvLy9n6NChhISEMGvWrIDlBB988IH/YcPh8vj4eFauXBnQRklJCS6Xq8b5D2fxT01NxePxcPvtt/PQQw+RkJDA+vXr/fV+P/38WNf+Z8XHx9d6vwwGA9HRgdnhq6qqmDlz5h+aNZCbm8v5559Pr169eO+993yF5mA0q4MFy1axacsWvvrqK+DIw4OmTZsy9r67ePbBu/DqjBAWjwEP3srSk/oa2+12HnvsMWbNmsXw4cMBX2LH9evXM2nSpIAgvri42P/zcLr9oen0u3fvpmfPnsd8/5FHHpGsvuKUavuvV6gc9SotbnqhrrsihDhHuXcux7V1IeiN7G83ioKU/hSWVaDPWEpHo42Qy58g0nVkK6BlP80gY+fmOuyxOJt5vV7y8vLYk7mbjIydBAUF0bZtW0wmE93WvE3XjG/ZnbETx2+zsf37H0Qf2I5t+GP0Hn49zo3zAqZ9GlP7o4UHfljXdHp/AC8EwNNPP82kSZMCtjXr1KkTW7duPeGxDzzwABs2bGDWrFkAXHvttcybN49169bVqOt2uw/t6+2zefNmkpKSiImJAaBjx44AJzyv0Wjkscce44knnggYtRw4cCALFixgwYIFDBgwwF9+eEr9ggULakylP9yPzp07H/N8JpOJ1NTUWvs1ceJEnn/+eX7++ecaa+ZNJhNdunRh7ty5AeVz584NmA1wOIDftWsX8+bNqxGsgm8EfvDgwZhMJmbPnl3jQURiYiLNmzenefPmpKSkANCrVy82b97sX2IAMGfOHMxmM126dDnm9SqlcLlcKKUwGAz+dps3bx4QxB/v2v+sXr161bhfc+bMoWvXrjXyNnzxxRc4HA7++c9/nlTbOTk5DBgwgM6dOzN16lT/zjI6cxD66CS+njWLtWvXsmzZMpalz+XdCb6HA+lff8xdN/8ThzUad3gChpAIf5sn8zV2uVy4XC7/+Q7T6/U14t7NmzfXmk/idPhDQXxwcPBx13StX7+eW2655S93Svx9VZWXsvP123FV+fYd1el0JHc8r457JYQ4V1VWVrKmMoTsHndSFZpAo7XTSF33HuUb5lM8axKuzfMpX/M/f/3dGdtoufi/RHx4C3uzd9dhz8Xplp+fX2MP7JOh1+vRbPvouvBZzBnpNGjQgOjoaJo1a87qhv35rdFgWm2cRvWsF9DHNSXeUUCc1UDZxEup/HQcnr2b/G1Z+l1H+NjZ/lF4IWozYMAA0tLSeOmll/xlQ4YMYcmSJSc8NiwsjH/96188/fTTKKUYM2YMffr04YILLuCtt95iw4YN7Nmzhy+++IIePXqwa9cu/7GLFy8OSAgWGxtL586dT+q81157LZqmBUw7HzhwILt27aqRmK1///788MMPZGVl1RrE/74ftantfkyYMIEnnniCDz/8kMaNG5Ofn09+fj4VFRX+Og8++CAffPABH374oX9rsb1793LnnXcCvgcbV1xxBatXr2bGjBl4PB5/O4e37SsvL2fw4MFUVlYyZcoUbDabv47H4zlmnwcPHkxqairXX38969at49dff+Xhhx/mtttu84/0z5gxgy+++IJt27axZ88evvzyS8aPH8/VV1993KR5J3PtFRUVrF+/3j+Sn5mZyfr16wO2Xvu9O++8k+zsbB588EG2bdvGhx9+yJQpU3j44Ydr1J0yZQqXXnpprQ89Zs2aFZCbITc3lwEDBpCcnMykSZM4cOCAv8+HNWvWjA4dOtCtWzc6tmpGk0aJALTp2oewlDbog8OxBgWRmprKrO9/9B93oq9xWFgY/fv355FHHmHBggVkZmYybdo0Pv744xpT/k/me/GUOWH++qNcfvnlx9zCIS8vT7Vq1UrpdLo/0uRZR7aYqzsej0dlP32hKh7bRWU/OzRg+wkhhDgdXC6XWrVqlZo7d65akJ6u9i38RpW8fr0qHttF7X9+uMr6v6tU5rKfVfHYLsqVvUkd2L1ZLX/zAbXszQfruuviNHK73erXX3+tdZut31s5/zu1dPLDKnPPTn/ZprXLVPHYLmrlS/9U69atU1s2bVC7vnlT5T3j+xtnmzpG2Zd9obwuhyoe20VVr/lBVXz+tCp9ZaRyblt8Oi9NHHK8bZ7OZrVtMaeUUjNmzFAmk0nt3btXKaVUcXGxslqtavv27f46tW0xp5RS2dnZymAwqM8//1wppVR1dbV6+eWXVbt27ZTFYlFRUVGqT58+atq0af4t0ex2uwoLC1PLly8PaOudd97xbyd2WG1bzCml1IsvvqhiY2NVeXm5v02z2axCQkICtl5zOBwqKChIWa1W5XAEbvO3f/9+ZTQa1b59+45xx3y2bdumrFarKi0t9ZelpKQooMa/p59+OuDYt956S6WkpCiTyaQ6d+6sFi5c6H8vMzOz1jYAlZ6erpRSKj09/Zh1MjMzj9vv7OxsNXz4cGW1WlVUVJS65557VHX1ke0LZ86cqTp37qxCQkJUcHCwSk1NVS+99NIJv69P5tqP1e8bb7zxuG0vWLBAderUSZlMJtW4cWP19ttv16izY8cOBag5c+bU2sbUqVPV0WHq4de1/auNvaRQzf3iIwWovJ0blK2kyH9PADVl8uvKlXvkd/bxvsZK+eLcm266SSUkJCiLxaJatWql/v3vfwfEKsuWLVMRERGqqqrquPfnVG0xpx26mJOyceNGevXqxRVXXMEjjzxC8+bNKS4u5vvvv+eFF16gcePGLFu27LhPlc52NpuN8PBw/9YP4sza/MOHNFzyNqWDHqTZoGvqujtCiL8Br9dLZmYmmZmZtGzZkuTkZLb99DFhG78juGwvuphGeA/uJXT0NKrTp+DOXI/58iewtr8AgCp7Fes+eYlGw24lOVm2WT0XFBQUsGnTJjRN47zzzvNPA62yV7EvcyetUjv666787920zFvJ1l6j6XOJbzai0+lkz55d7N+XS3TeGhpmzsdsL6YkNo2wgTegn/U0eFyEPfIttldGEHTVs5hS+4MpCE33p3b/FX9QdXU1mZmZNGnS5JQk8zobPfroo5SVlfHuu++e8rbfeustvvvuO+bMmRNQXl1dTatWrZg5cya9evU65ef9vUceeYSysrIj66OP46qrrqJTp06MHz/+tPdL1B2lFOXl5aAUZo8dnb3Ml3/EFIwpKh7NYMJrO4C3shRDwxan7LxXXnklnTp1qpE48PeO97vnj8ShfyijRvv27fnpp5+45ZZbmD59+pFGDAbuv/9+7r33Xv86DiFqo5SisrKSkJCQWt9ve/Et2PpeQrOImlNrhBDidNDpdMTGxpKZmUlBQQHZ2dnoI1tQ3PcBusfqqP71fV9FvR5PUQ7KUYmpYTP/8Wu/fY+0PT+T99EWPOO/qpFkTNQP+/bt82+BVFVVhcViobq6mnXr1mE0GimvKKP1T+OI0DTsT83HavEl0dJ3HsGWvDYktjuyf4HJZEKvNxFUto/GW31Jlmy9b2VPUAuSg5NoGN8cVV2Ot6zAf4xmqf3vohB/1uOPP85bb72Fx+M55b+XjEYjb7zxRo1yi8XCxx9/XOt2YqdDgwYNap2qXZuJEycye/bs09wjUdeUUuh0OkwmEyZTOMrbAJftALrKUjz5u9GCwk/5OR0OBx06dOCBBx445W0fyx8aiT/M6/WyatUqMjMzCQsLo1evXkRFRVFZWcmkSZMCtkw4ldxuN8888wwzZswgPz+fhg0bctNNN/HEE0/4kw0opXj22Wd57733KCkpoUePHrz11lukpaWd1DlkJP70ys/PZ/PmzfTs2ZOQkBCcVRXse/NfxF77PGFJp+5pmBBC/BEZGRlkZWWhaRphYWGUlfkS2XXv3p3Q0FCU7QC68AYorwfPvi0YUo5k8S346mWqtiyios/NtBt0FeD7O+l2u2tkaPZ6vXjLD+JcPB1PfgYht7whScr+Aq/X688i/Fd3MMnKyiIjIwMAd7WNkKxl2MMTCGraA7vdjk7T0WjxRLyaDtMN/6Fxk+P/zaqoqMCxYxm6Lx/zbTemvKim3dD3u4nQRq18HyQ9Lkqf6E3QVc9i7jz8L/Vf/DF/h5F4IcQRyutFVZbgLS8Crxv0xlM6En+yzvhI/N69e2nUqBHgG7Xo2bNnjUz1wcHB/gA+JyeHxMTEk23+pLzyyiu88847fPTRR6SlpbF69WpuvvlmwsPDuf/++wFfooZXX32VadOm0bJlS1544QUuvPBCduzYQWho6Cntj/jjCgp8ow6FhYWEhISw77V/ElW+H/vbNxH87AL0hppbuAghxOlWXl6O1Wqlbdu2hIWFsX//fnbt2oXNZiMsLAwtvAHgywx+dACvHFWYNv2MyVFJUnKSv3z9ivkEz/svFeffQ5e+QwDwlh9k75eTCNu9EM3j8lV0O6GWrafEySkoKGDLli0YjcZakyOdDLvdzs7Nv9GoeVs6duzIli1bqDxQTI/9C8gpa4wtJIZWexcQagB1/wzCwyNqZCkGcO/bgmP1bAyJbTB3v5SQkBCCO56P7ddE9I3aYWzameoln+H96G4qW/XGesHt6BNa/tVbIIQQ4iRoOh1aaDRacCSqsvbtBuuTkw7iu3XrxsiRI7ntttvo3r17rXXKysr44osv+O9//8sdd9zBvffee8o6CrB8+XIuueQS/x59jRs35rPPPmP16tWAbxT+P//5D48//jiXX345AB999BFxcXF8+umn3HHHHae0P+LEvF4v+fn5eL1elFIUFRWh0+ko3bKUg/97EbvLg1vTU9z9n8RJAC+EqCPt2rUL2Fs7OTmZuLi4Glvi1GCyEnLDv3Ft+hVDiyMPtq3p79CgKo/S3b/h7dCF6oUf41jxFcHoKGg8gMZpname/cpf6rPb7SYnJ4fk5ORag8q/g8LCQv9//2wQv+3Nu2hStJmdgx6jx6DLiYmJweGo5rcmFxPqKqPNytdRegNKZyAy0rc9k1IK78FsdOFx/v2/3fu34Fz5NZ6UDpi7Xwr49gEPe/gb/2wLU9dLcG2ah/3XDyiffBOGFj3+4h0QQgjxRxwO5uu7kw7it23bxksvvcTQoUMxGo107dqVhIQELBYLJSUlbN26lS1bttC1a1cmTpzIsGHDTnln+/btyzvvvMPOnTtp2bIlGzZsYMmSJfznP/8BfFsf5OfnB6T2N5vN9O/fn2XLltUaxDscDhwOh//1n9lORhyb0+lk27Zt/n1vw8r30TR3Cfp9G6gKiccUnYIn5wAtLxhVxz0VQvyd1bYVz++nwtdG0zSMzbpibHZkj12lFPFmBeXQxL6fsldGgt5IVqsRZOmiCWnQhLCqQoKA0pISQqONx90K6Fjy8vLYtWsXwcHB/n2az3Uej4fMzEzcbjcARUVFGI1GCgoKfNPpvR6MJhNNmzWvMb2+pKSYbTNfJqJoN63Gf+l/YFPVqAsHqwpRHg9er5eK3etpsesXIg5spdoaTWbqlUSYdURu/s7fVsV7d+DOXEvw9ZMwpQ0AwJTaH09+Bqa0wC2wjl4uoen0mDoMwdhukD+YB9AM5lN+r4QQQpy7TvpTQ1RUFJMmTeKFF17gp59+YvHixWRlZWG324mJieG6665jyJAhtG3b9rR1duzYsZSVldG6dWv0ej0ej4cXX3yRa67xZTE/vFdgXFxcwHFxcXFkZ2fX2ubLL7/Ms88+e9r6/HdnsVjo2rUruxfNJmb7T4QW7aIwOBHaX8H+Bl1oWJmNOWflX17LKIQQZw2vB3PzbjiK9qHfv5nc5H4UNupLyIYvGFC6jV0NupDfoCVNgXXr19O4RWuaNm36h09z9Ch0TEwMylGJZg4+xRdzarlcLux2+5/OOeP1esnNzcXpdKLT6QgNDaVZs2bs2bYJz+JPaJC9iLKUXpTGjGfP5lUYTFY6dD8PgJCQUFL2ryTIU0Xm7m00b+n7vNL9sjswGO6mSd5OyqY9QMudS3GFNMB0+ZMQ14rYr17EbMsJ6Ic+rinufZvxlh7Zo1gXHkfwZcfPSnzY0cG8Z+8m9Empf+p+CCGE+Hv6w4/+LRYLl19+uX+6+pn0+eefM336dD799FPS0tJYv349Y8aMISEhgRtvvNFf7/cBoVLqmEHi+PHjefDBB/2vbTYbycnJp+cC/oY8B7LRffcKTTJWURUST3ZiL1JylqM2fkVev2Y0a9aUquV13UshhDh1NL0BQ8veODfMQeeqJsSgOOB2EOXyJctLaNAAm9U3BTsqMpJYTzHKm3LCBHdut5vt27f7t3EtKSnBYrFg27WGnF9fJShnPQeHP0vzvhedtQ9Gd+/eTemO1XRql4a5aac/fLzRaKRnz55s2bKFoqIiGkaHY10/i8SFn6K5qtHpNeJCzWxe/gutF05id0wHOBTEG41Gci98lJCYhjRv0srfpslkwv7rB1TPfQcMRki9gJhrX0BnMBLktMPBHaC8cNRouWXQHViHj0Ez/rWEaJpOj6Fxx7/UhhBCiL+fPz5/rw498sgjjBs3jlGjfFOv27VrR3Z2Ni+//DI33ngj8fHxAP7M9YcdzlxbG7PZjNks09hOF9e2RbgzVqEsYeQ0H8ZBazyx+espDE3BZYrA5XLVdReFEOKUM6Weh3HsbKqXzkRb8intdy+muEEazta9sKSdT8HaJQA4t87FvX02B8xhxD7zqz/4PtbDZ5vNRlVVFXq9niSjnbgds2HXMpxm38i2s+zgcR9cnyzlceNc/zPeon1YLrzzlDwUcOVsx/K//6NVwWYqtyVgHvfntnoymUy0a9mUHZ9+iWXRUn4LakpLl51VaTcwoHwNOk0juV1PctY2xtGgBZ78DNAb0Mc2ptuAi1FeD+WTb6ayaD9hj36HzhqKvmELMAWBswpd3jZUWQFEJ6GZrARf/RzunG04ln/l74MuJPIv3w8hhBDiz6pXQXxVVVWN5D16vR6v1wtAkyZNiI+PZ+7cuXTq5HvC73Q6WbhwIa+88tcSCIk/x9zvn+jjW1A8+zVarJ9Ko+imqBHjcKgotMpKbGU2JC+zEOJcpFlCsF7wLyx9RpH51WuEb5+DrnAzBw/kYzT79qkN37cGAGdoA3+g7HK5sH80Bs3twDriYQyJrQHfuv0ePXqwe9kvGFd+RsTBbehiGlHS7072esNpt/QVmjdv/peS3B0O3qvnf4C3aD/gG3XmTwbxDoeDgo1LMa/+AlP2asxBMVRENsHssLFnzx4AwsLCTmpNf2lZCZt++gh93jZal24j3uXgYFIPqj0mzKVOmmX9isddhC4kiqSkxiQ9/hXVi6dj+88oTB2HETzqecA3+u21HUDZbXgLM9GltMeU2h/dHe9SvWg6rl0rsP37H5g6XYTl/FsxdRyKt7L0T98DIYQQ4lSrV0H8iBEjePHFF2nUqBFpaWmsW7eOV199lVtuuQXwTaMfM2YML730Ei1atKBFixa89NJLBAUFce2119Zx7/+eNE2jxOGmKKUnUX1uIGjtV3i+fZrU5LbY2l9KkDkIVdedFEKI08hjsJDZsDdaTCdalW8lZtOP6JyVADR+8GM2r1tO27QjU8vXLfiO5hkr0QDNdGS6tn3BR1Snf0iso5LqoBgO9LiFykbdUY4qDOW+7XJ0fzLQVB43jnU/Ufa/yZgqD2JMG4ixTX8cS2b86et252yn8n9vEZqxnOqgGLLbjcLdoh/J2emorfP9QXxsbCxRRg+e/Aw0aziGRm0pKSkmc9ta4jd9S6jmJvjq59BrJtps/BS98j24L4tpg3bhPbQ06ti87yJa/voCOGwoV7W/D7rQGDRrGOgDP+4EX/MSOmsouphG/jJDYhtCrnkR5arGsXIW1Qun4Vz3E6ZOF6FZQv70fRCirsyfP5/Ro0ezdevWU76DxRVXXEHv3r0DlqQedv3119OmTRsee+zkckT8Fd26dWP8+PEntcx3ypQpfP7558yZM+e090v8vTgcDlq0aMGsWbPo0qXLGTlnvdqT5o033uCKK65g9OjRtGnThocffpg77riD559/3l/n0UcfZcyYMYwePZquXbuSk5PDnDlzZI/4OlJ5YD/apw/RcM0MbPu3Ezr6Q0JueRM0jeAfX4C5r9d1F4UQ4rRSShEdHU3H7r1JvPJRoh77EXfv66lM7orREkKXvkMxRx5Z8uXduhAN2N/4PHTRvhwtVTtWcHDuFHBU4klMY0vPB9gb2hJbeQWNfhxPy0WBs83c2RuoXjwdd/bGgHJvWQGqugKlFJ4D2Th3raTqf69T9soI7F89R2VQHHv7jcHc+2qoZY2+8npQjiqU0x54vr2bcO1agXI58BTsoeLjhyh/459o+zfhbTeUXeeNpSypG61T0zBvn4fRXoKpqohGjRrRokUL9s39hIqp91O9eDoAWxd+S5PZ4zDsXYt7z2q85UWEhkWwtelQ9oU0RgER7lKaNwijcZPm9DvvfAz9boDIBDTjkSVyxg5DiHh6PsFXPh3QX2OTTujjm6MZau5AoBktWPpeQ/ij32G9aAyunctwLP3sxF9o8bfhObgX+y+Tsf/y1jH/VS+egfK4T/m5b7rpJjRNQ9M0DAYDjRo14q677qKkpOa+148++iiPP/64P4CfNm2a/1hN04iLi2PEiBFs2bIl4Din08mECRPo0KEDQUFBxMTE0KdPH6ZOnepfBvnUU0/x4osv1tjVaePGjfz4448B20wPGDDAf06TyUSzZs0YP368f3eon3/+GU3T/AmqD4uPj6+Rp2r//v1omuYPxJ988knGjRvnn5V7LA6Hg6eeeoonn3zSX/b+++/Tr18/IiMjiYyMZNCgQaxatarGsZMnT6ZJkyZYLBa6dOnC4sWL/e+5XC7Gjh1Lu3btCA4OJiEhgRtuuIHc3Fx/neLiYu69915atWpFUFAQjRo14r777qOsrOy4fQbYu3cvI0aM8O9Cct999+F0Ov3v79ixg4EDBxIXF4fFYqFp06Y88cQTJ1yuejLXvmjRIkaMGEFCQgKapvHtt9+esL8ACxcupEuXLv7+vPPOOwHvH/39cPS/w1uI1yYrK4tbb72VJk2aYLVaadasGU8//XTAvThaUVERSUlJaJpGaWnpCft8vK8xQEVFBffccw9JSUlYrVbatGnD22+/7X/fbDbz8MMPM3bs2BOe61SpVyPxoaGh/Oc///FvKVcbTdN45plneOaZZ85Yv8RxBEdgC44nvCKPkK4X+7ZjatkTQ4seuHetxD7vXTyOqoAPXUIIcS4xGo107NjR/1qzhBA78v5j1u9wxwS2rEwnqXUnf7K7zAo3CZ4qCswNiDmQRVzwMsL7/5OEuGjKf3bUaMO1YxnV86dg7nUVhpT2gO9hQtkrI8HrIXz8T9hevw7lqkYDFFAZ1og9Ha4H5aVg5vMEleeiAZ6yQgyRvpwz1Qs/pvqXtzB1HUnwFU/5z1c+5R5wVBL2yLfYf34D17bFaKExqPKDmHHTtn0H1q5dy759+4h1VKNHERsWRMuWLdnx3AgaVOXhikzGHOnLZxPVrD05WxqjIuJp1edidIfKz7vtObz2ChwrvsSxeDplEy/F3O1SLANuInrQTZRnLA1INvdX1vIfDubNPS7DsXIWqrJmkCT+nly7VlCd/iFaSFStyQ29FUXg9WLuflmNWSCnwtChQ5k6dSput5utW7dyyy23UFpaymefHXnYtGzZMnbt2sWVV14ZcGxYWBg7duxAKUVOTg6PPvoow4cPZ+fOnZhMJpxOJ0OGDGHDhg08//zz9OnTh7CwMFasWMGkSZPo1KkTHTt2pH379jRu3JgZM2Zw1113+dt/8803ufLKK2sMnt12220899xzOJ1OfvvtN26++WbAt0tU3759MRgMLFiwwJ/3atu2bVRXV2O328nIyKB58+YApKenYzQa6dOnDwDDhw/ntttu45dffjnu9tZff/01ISEh9OvXz1+2YMECrrnmGnr37o3FYmHChAkMHjyYLVu2kJiYCPiSao8ZM4bJkyfTp08f3n33XYYNG8bWrVtp1KgRVVVVrF27lieffJIOHTpQUlLCmDFjGDlyJKtXrwYgNzeX3NxcJk2aRGpqKtnZ2dx5553k5uby1Vdf1dpf8G2pOXz4cGJjY1myZAlFRUXceOONKKV44403AN/flxtuuIHOnTsTERHBhg0buO222/B6vbz00kvHbPtkrr2yspIOHTpw8803849//OOYbR0tMzOTiy66iNtuu43p06ezdOlSRo8eTWxsrL+Nb775JiD4LioqokOHDjW+V4+2fft2vF4v7777Ls2bN2fz5s3cdtttVFZWMmnSpBr1b731Vtq3b09OTk4trQU60dcY4IEHHiA9PZ3p06fTuHFj5syZw+jRo0lISOCSSy4B4LrrruORRx5h27ZttGnT5qTu11+iRICysjIFqLKysrruyjnD7Xar/bu31fqe1+tVXqf9DPdICCHql2X/m6kKx3VXS956WJXOfEoVj+uuSp6/UC367/1q8ScT1d7lP6risV2Uc/tSpZRSjg1zVPmn41X1qm/9bXgddlX8WE9VPLaL8lZXqrI3rlfFY7uoA+N7qeKxXdT210erjatXqnnz5qq5c+eqonHdVPHYLqowY5O/DfviT1Xx2C6q/NPxAf0re/MGVfrqVcpdmKU8FSWq7Mc31K5nLlJ5j/dVtmkPql0bVquvP/9IHRjXTW19+iKV/+RAtfDXucrj8ahlbz6g9jx5gVq9+Oc/dE+81ZXKnj5VlTx7vip+rIeqnPWyKn31SlUx88m/cKfF34Xdbldbt25Vdvsf/wzitZerkmcGqsrvJtR8r7pClTx7vqqc9X+nops13HjjjeqSSy4JKHvwwQdVVFRUQNm9996rrrjiioCyqVOnqvDw8ICy2bNnK0Bt3LhRKaXUK6+8onQ6nVq7dm2NczudTlVRUeF//cwzz6h+/fr5X3s8HhUREaF++OGHgOP69++v7r///oCyyy+/XHXu3Nn/ulevXuqOO+7wv548ebIaPny4uuiii9T777/vL7/llltUnz59Atq66aab1PXXX1+jv0cbMWKEevjhh49bx+12q9DQUPXRRx/5y7p3767uvPPOgHqtW7dW48aNO2Y7q1atUoDKzs4+Zp0vvvhCmUwm5XK5jlnnp59+UjqdTuXk5PjLPvvsM2U2m48bpzzwwAOqb9++x3y/NrVd+9EANWvWrBO28+ijj6rWrVsHlN1xxx2qZ8+exzzmtddeU6GhoQHfWydjwoQJqkmTJjXKJ0+erPr3769+/fVXBaiSkpLjtnMyX+O0tDT13HPPBdTp3LmzeuKJJwLKBgwYoJ588vh/g473u+ePxKGnZDr97t27GTNmDJdddhkjR470/xN/XwfXzEEp32p3vV5PYtPWtdbTNO0vb9EjhBDnul5Dr8by+Dya/eNBwq9+lrCHv6a6RT/a5i4hbfNM9IUZ/rprl81l5eZNZKdegrnbJf5yzWQh8sXlRDy/FExWgq99GXtIPFURvumqUbZMWmgH6dyxIyZnBblNLgAgMrGZvw1zj8uJeG4xwVf7lrFVVFawdulctrW6hPAHPkcfm4IuOIJtGTuItheQ0XQw7t2/Efn5vSQUbUWvvFi8Djx6I04vlJWV0f6m50l5Zg5d+g75Q/dEMwdhGXAT4WO/x3rhnTg3zsVbsOdP32MhTpZmCcHc7zocq2bhLSsMeM+x/EuUowrLgBuPcfSptWfPHn7++WeMRmNA+aJFi+jatetxjy0tLeXTTz8F8B8/Y8YMBg0a5E8QfTSj0UhwcLD/dffu3Vm1apV/WvzGjRspLS094Xk3bNjA0qVLA/o8cOBA0tPT/a/T09MZMGAA/fv3r1E+cODAgPa6d+9eY/rz7y1evPiE/aqqqsLlchEVFQX4lhWsWbOGwYMHB9QbPHgwy5YtO2Y7ZWVlaJpGRETEceuEhYVhMBx7psby5ctp27YtCQkJ/rIhQ4bgcDhYs2ZNrcdkZGTw888/079//2O2W5vfX/uftXz58hr3a8iQIaxevfqYU/ynTJnCqFGjAr63Di/9OJ6ysrIa/d26dSvPPfccH3/88THzQGiaxrRp04CT/xr37duX2bNnk5OTg1KK9PR0du7cyZAhgX+3TuZ78VQ5JXN8Lr30Uu655x6uvvrqU544Q9Q/e398l9DF75M1732SH/wUw+/+sAghhPjjQkNCCQ3xTVHVRycRcvEDbLIm48zeSPsVhxLQaRpVWxfTNuMnthgt0KEHAGW2Ugpfu5ZKSxRpD07FqGnooxLZm9ANe3Eero630TpvGYYvnsIWlkRlaGvcOkgE9CbfcqclX79L+I5fqex6JT0H+6Y9lpeV0OT78Th1RjwDL0Gv903/d0YmU52/Di0qkfCrZ3Pwf+/QcsMPHAxuSIzVhOZ20qJFC4KDgzGZaq5L/yMOB/PmXlfhWD0bQ9IZmMYo/vYsva/GsXgG1QumEXTJowAoRyXViz7B3O1SdBHxp+3cP/zwAyEhIXg8HqqrfYkcX3311YA6WVlZAcHfYWVlZYSEhKCUoqqqCoCRI0fSurVvsGXXrl0MGDDgpPqRmJiIw+EgPz+flJQUsrKy0Ov1NGjQoEbdyZMn88EHH+ByuXA6neh0Ot566y3/+wMGDOCll14iLy+Phg0bsnDhQh555BG8Xi///e9/Adi3bx+ZmZk1gvjExET27t2L1+utNQ4pLS2ltLS01vtxtHHjxpGYmMigQYMAOHjwIB6Pp8Y21XFxcTXW7x9WXV3NuHHjuPbaawkLC6u1TlFREc8//zx33HHHcfuTn59f49yRkZGYTKYa5+/duzdr167F4XBw++2389xzzx237d/7/bX/WbX1OS4uDrfbzcGDBwO2AAdYtWoVmzdvZsqUKQHl4eHhtGrV6pjn2b17N2+88Qb//ve//WUOh4NrrrmGiRMn0qhRI3/y1N9r1aoV4eG+HWJO9mv8+uuvc9ttt5GUlITBYECn0/HBBx/Qt2/fgOMSExPJyso6Zr9PpVMSxAcHB5/wG1H8fZTv204o4HE5QSdb8gghxOkQEhxCr5E3AeAp2o9r0zwMjTtiPVDOFoOZiBZHMuSWFh8kxl6IxVUVMPrldlbT8eBqlkQ0Zk+nmzEVZxG8ZzE9c+bh0X73Ybi8iCTbHjYXHUnYFBMTx+bINlSHNCDU6SDIGgRA1yvvxWR8iIaHgvoGV4zDO+xOrItnUL3sc7TIhqSkpJzS+6GZg7D0GXVK2xTiWA6Pxlf/+gGWATehC29wxkbhBw4cyNtvv01VVRUffPABO3fuDEgkB2C327FYas50DA0NZe3atbjdbhYuXMjEiRMDEo8ppU46l4TV6tsk+PDDALvdjtlsrvX46667jscffxybzcYrr7xCWFhYwDrrPn36YDKZWLBgAR06dMBut9O5c2eUUthsNnbt2sXy5csxm8307t27Rj+8Xi8Oh8Pfp9/fC6DW+3HYhAkT+Oyzz1iwYEGNer+/nmPdI5fLxahRo/B6vUyePLnW89hsNoYPH05qaipPP30k4eawYcP8I7gpKSn+ZIO1nae283/++eeUl5ezYcMGHnnkESZNmsSjjz7K3r17SU1N9dd77LHHauwacLxr/zNqu1/HupYpU6bQtm1bunfvHlB+2WWXcdlll9Xafm5uLkOHDuXKK6/kX//6l798/PjxtGnThn/+85/H7d/27dtPqs9Hl73++uusWLGC2bNnk5KSwqJFixg9ejQNGzYMePBhtVr9Pw+n2ykJ4sePH8/YsWMZNGgQZvORBGXnnXfeqWhe1DNpd77Gxh+nkdL3EgynIaGLEEKIQProJPQDbgKgS5/B0CdwamCD+ET23vABTnslR+d6NrXqxQaHHVdoPB63m+iGSeR6+1JgjMDgttNJ5fv3R0887x/sa9uP5omN/ccbTSY6jf2kRn+slpofpHXBEViH3o35vH/C77LbC1EfHT0abx169xkZhQff4NnhRG+vv/46AwcO5Nlnnw3YrSkmJqbWjPU6nc5/bOvWrcnPz+fqq69m0aJFALRs2ZJt27adVD+Ki4sB3zaRh89ZVVWF0+msMcMmPDzcf97p06eTlpbGlClTuPXWWwEICgqie/fupKenU1xcTN++ff0ze3r37k16ejrLly+nV69eNQLN4uJigoKCag3gAaKjo31bHtdyPwAmTZrESy+9xLx582jfvr2/PCYmBr1eX2PUu7CwsMbIrcvl4qqrriIzM5P58+fXOgpfXl7O0KFDCQkJYdasWQEPVD/44AP/w4bD5fHx8axcuTKgjZKSElwuV43zH87in5qaisfj4fbbb+ehhx4iISGB9evX++v9fvr5sa79z4qPj6/1fhkMBqKjowPKq6qqmDlz5h+aNZCbm8vAgQPp1asX7733XsB78+fPZ9OmTf5kgYcfHsTExPD444/z7LPP1mjvZL7Gdrudxx57jFmzZvkz6Ldv357169czadKkgCC+uLjY//Nwup2SCOuXX35hwYIFZGRk+KexaJomQfzfyOFtVLRDQXv74TfVYW+EEEIczWqx0iq1Y43y7gNHUtysDdt/W0Jzw0HMK2YRHxZHwbB7cDqdRLZt66/bpGkraHrs6Y0nSxcUDkHhf7kdIera0aPxwBldC3+0p59+mmHDhnHXXXf5p4x36tSJrVu3nvDYBx54gFdffZVZs2Zx2WWXce211/LYY4+xbt26Guvi3W43DofDv3Z58+bNJCUlERMTA+DfhWPr1q0BO3L8ntFo5LHHHmP8+PFcc801BAX5ZvAMHDiQmTNnUlJSEjClv3///ixYsIDly5f7s9ofbfPmzXTu3PmY5zOZTKSmprJ169Yaa58nTpzICy+8wC+//FJjzbzJZKJLly7MnTs3YFR47ty5/ozkcCSA37VrF+np6TWCVfCNwA8ZMgSz2czs2bNrPIg4nBH+aL169eLFF1/0LzEAmDNnDmaz+bh7kSulcLlcKKUwGAz+hye/d7xr/7N69erF999/H1A2Z84cunbtWiNvwxdffIHD4TjhyPlhOTk5DBw4kC5dujB16tQaSye+/vpr/4MQgN9++41bbrmFxYsX06xZs983B5zc19jlcuFyuWqcT6/X19jacPPmzbXmkzgtTpj67iSkpqYqr9d7Kpqqc5Kd/s/Z894Dav/Tg1Rlwd667ooQQog/qOLL51TxYz1U8dguypW1vq67I8Rp91ey0x/tcKb64rFdTltG+qPVlp1eKaW6dOmi7r77bv/r119/XXXp0iWgTm3Z6ZXyZbdv166d8nq9qrq6WvXr109FRkaqN998U61fv17t3r1bff7556pz585q3bp1AX255ZZbAtrq3LmzeuONNwLKastO73A4VMOGDdXEiRP9ZfPnz1eACgkJUStWrPCXL1myRIWGhipALVq0qEb/+/fvXyNzeG3X+I9//COg7JVXXlEmk0l99dVXKi8vz/+vvLzcX2fmzJnKaDSqKVOmqK1bt6oxY8ao4OBglZWVpZRSyuVyqZEjR6qkpCS1fv36gHYcDodSSimbzaZ69Oih2rVrpzIyMgLquN3uY/bZ7Xartm3bqgsuuECtXbtWzZs3TyUlJal77rnHX2f69Onq888/V1u3blW7d+9WX3zxhUpMTFTXXXfdce/HyVx7eXm5WrdunVq3bp0C1KuvvqrWrVt33Kz7e/bsUUFBQeqBBx5QW7duVVOmTFFGo1F99dVXNer27dtXXX311bW2880336hWrVr5X+fk5KjmzZur888/X+3fvz+gz8eSnp5ea3b6Vq1aqW+++cb/+kRfY6V832NpaWkqPT1d7dmzR02dOlVZLBY1efLkgLZTUlLUxx9/fMw+KXXqstOfkiD+pptuUrt27ToVTdU5CeL/OFvBXlU0tosqHttFbfjgsbrujhBCiD/IfXCfKh7fXdk+uPvElYU4B5yqIF4ppewLpqniJ/ooT8mxA4pT5VhB/IwZM5TJZFJ79/oGU4qLi5XValXbt2/31zlWEJ+dna0MBoP6/PPPlVJKVVdXq5dfflm1a9dOWSwWFRUVpfr06aOmTZvm3xLNbrersLAwtXz58oC23nnnnRrbidUWxCul1IsvvqhiY2P9gaPdbldms1mFhIQEbL3mcDhUUFCQslqt/sD4sP379yuj0aj27dt3jDvms23bNmW1WlVpaam/LCUlRQE1/j399NMBx7711lsqJSVFmUwm1blzZ7Vw4UL/e5mZmbW2Aaj09HSl1JFgsrZ/mZmZx+13dna2+v/27jwuqur/H/hr2IZhG1F2UEFFEVdcEtwQFWxTq59paSotarllZpTZR9AK3FvU9KMWZqJmXyU1TcFM0MAlERUwpAQXZD6owIDsy/n9wYf7cYBBUBaR1/PxmEfOueee+753DhNv7rnnPPfcc0KhUIjWrVuL2bNni4KCAmn7rl27RJ8+fYSJiYkwNjYWrq6uIjAw8IH9ujbnri3uqVOn1tj28ePHhZubmzAwMBCOjo5iw4YNVeokJiYKACIsLKzaNoKDg8X995or3lf30kZbEg9ABAcHa5TV9BkLIURaWprw9fUVdnZ2wtDQUHTp0kWsXr1a4yZ2VFSUaNWqlcjLy9MakxD1l8TL/nsyj6R37964fPkyXFxcIJfLpckAzpw586hNN7rs7GwolUpp6QeqncsnDkB28gd0XBBSZbgMERE9/or/PgtdKyfomFk0dShEDa6goADJyclwcnJ65Mm8hBAQ+dnlj4o8Rvz8/KBWq/Hvf/+73ttev3499u3bh7CwMI3ygoICdOnSBbt27YKHh0e9H7eyDz74AGq1usrz0dUZP3483NzcsHDhwgaPi1qel19+GW5ublUmDqyspu+euuSh9fJM/L59+6qU1XZmS3oydB0yGhgyuqnDICKih6TfqX9Th0DULMlkMsgeswQeABYtWoT169ejtLRUmiSuvujr62Pt2rVVyg0NDbFt2zbcuXOnXo+njZWVFRYsWFCruitXrsT+/fsbOCJqiQoLC9GrVy+89957jXbMR7oTP3nyZPzwww/o379/tUk778Q/2a7vXQ3TnsNh3qmRJnAgIiIiqgf1eSeeiKi2Hos78StWrAAAaSp/ajkyzh2B6ZmdEGd24tqkdWjfw72pQyIiIiIiInri1SmJf+WVV7B48WK4uroCgLTcQfv27es/Mnqs3VO0gYGuIYp19GHt3LupwyEiIiIiImoRdB5c5X92796N4cOHa113UgiB7OzsegmMHm/tXPvBbNER6M34lsPQiIiIiIiIGkmdkngA6NWrF7y8vBAfH19lW3p6OszNzeslMHo8lebfk/5tZGQMW4cOTRgNERERERFRy1KnJF4mk2Hr1q0YPnw4vLy8EBcXV6VOPaxYR4+p/Ds3kbl0JP75zg+itKSpwyEiIiIiImpx6pTECyGgq6uLkJAQjBgxAsOHD6+SyHNpuSfXtR+XQVeUwCwpAjm59x68AxEREREREdWrOg+nBwAdHR2EhIRg5MiRGD58OC5dulTfcdFjyOmt1bjhNAzpXu/BzKxVU4dDRERERETU4tR5OL20o44Otm/fLiXyFy9erPfgqOnl5ubizz//ROHt6yg5tBrtVDFw6Tu4qcMiIiIiokfg6OiIL7/8stGOd/z4cchkMmRlZTXaMYmeVHUeTq+x838TeW9vb4wYMQKxsbH1GRs9BlR/xUJxdA3urR6HophDEPnZKMu+3dRhERERET0WCgoKcOPGjUY5lq+vL2QyGZYtW6ZR/vPPP9f5kdazZ89i+vTp9RlevRs2bBjmzZvX1GEQPXbqlMQfPHgQSqVSs4H/JvI+Pj74f//v/9VrcNR0SjPTkLs3EPg5AHZZV1CoK4fu1LVNHRYRERHRY+X69etITExEbm5uoxzP0NAQy5cvR2Zm5iO1Y2lpCSMjo3qKiogaU52S+GeeeQZyubxqIzo6+OGHHzB27Nh6C0yb1NRUvPbaa2jTpg2MjIzQu3dvnDt3TtouhEBAQADs7OygUCgwbNiwapfDo+rlqVKQtG42sla8gNzzR3DbrD0EgCzDNoiNuwwAuHjxAsrKypo2UCIiIqImJoRAeno6AEj/bWgjR46EjY0NgoKCaqy3Z88edOvWDXK5HI6Ojli9erXG9srD6QMCAtCuXTvI5XLY2dlh7ty5AIClS5eiR48eVdrv27cvFi9erPX4hw4dQufOnaFQKODl5YWUlBSN7Xfv3sWrr74KBwcHGBkZoUePHti5c6e03dfXFxEREfjqq68gk8kgk8mQkpKC0tJSvPnmm3BycoJCoUCXLl3w1Vdf1XgtiJ40eo/aQExMDLp37w4DAwNs374d7777bn3EVa3MzEwMGjQIXl5e+PXXX2FlZYV//vkHrVq1kuqsWLECa9aswdatW9G5c2d89tln8Pb2RmJiIkxNTRsstidB8d9nURg8B20EkNrRGxntB6ODSzdk3buNwrwS6KReBQBYWVlBR+eh5kQkIiIiatby8/ORlJSEsrIylJWVoaCgAIaGhrhx4wbUajUAwMTEBJ06dWqQ4+vq6iIwMBATJ07E3Llz4eDgUKXOuXPnMH78eAQEBGDChAmIiorCzJkz0aZNG/j6+lap/3//93/44osvsGvXLnTr1g0qlQoXLlwAALzxxhtYsmQJzp49i/79+wMALl68iPPnz+Onn36qNsYbN27gpZdewttvv4133nkHf/75J95//32NOgUFBejbty8+/PBDmJmZ4eDBg5g8eTI6dOiAAQMG4KuvvsKVK1fQvXt3LF26FED56IGysjI4ODhg9+7dsLCwQFRUFKZPnw5bW1uMHz/+US4tUbPxyEl8//79cfnyZXTu3BkymQxPPfVUfcRVreXLl6Nt27YIDg6WyhwdHaV/CyHw5ZdfYtGiRXjppZcAAN9//z2sra2xY8cOzJgxo0qbhYWFKCwslN5nZ2c3WPyPO13LdtBr1xMlyTGwy05CwR0rFBZ0hFPnXoiLi4OuXnl3sbGxbeJIiYiIiJpOVlYWioqKYGBgADs7O9jZ2SE5ORl37twBAOjpPfKv2DV68cUX0bt3b/j7++Pbb7+tsn3NmjUYMWIE/vWvfwEAOnfujISEBKxcubLaJP769euwsbHByJEjoa+vj3bt2km/0zs4OGDUqFEIDg6Wkvjg4GB4enqiQ4cO1ca3YcMGdOjQAV988QVkMhm6dOmCS5cuYfny5VIde3t7LFiwQHo/Z84cHD58GD/99BMGDBgApVIJAwMDGBkZwcbGRqqnq6uLJUuWSO+dnJwQFRWF3bt3M4mnFuORb6dWnuyuIe3fvx/9+vXDyy+/DCsrK7i5uWHz5s3S9uTkZKhUKvj4+Ehlcrkcnp6eiIqKqrbNoKAgKJVK6dW2bdsGP4/HlY7SGqYzNkHx+jqos9WwiA+F8f99gPO7v8G9nByUlZY2dYhERERETUqhUMDd3R0WFhYoKipCmzZtpFGhOjo6cHFxQbdu3Ro8juXLl+P7779HQkJClW2XL1/GoEGDNMoGDRqEpKQklFbz+9zLL7+M/Px8dOjQAdOmTUNoaChKSkqk7dOmTcPOnTtRUFCA4uJihISE4I033tAa2+XLl+Hu7q4x2Z6Hh4dGndLSUnz++efo2bMn2rRpAxMTE4SFheH69esPPPeNGzeiX79+sLS0hImJCTZv3lyr/YieFM1qTPTVq1exYcMGODs748iRI3j77bcxd+5cbNu2DQCgUqkAANbW1hr7WVtbS9sqW7hwIdRqtfRqrNlFH2c3/gyHecEdKEtykCNToOOF7+EYsQKm6VX/J0FERETU0hgYGKBjx44AgP/85z84f/48CgoKYGJiAgcHhzrPFP8whg4dilGjRuHjjz+usk0IUSWGmm68tW3bFomJiVi/fj0UCgVmzpyJoUOHori4GAAwevRoyOVyhIaG4sCBAygsLKxxQuva3ORbvXo1vvjiC/j5+eHYsWOIjY3FqFGjUFRUVON+u3fvxnvvvYc33ngDYWFhiI2Nxeuvv/7A/YieJA071qeelZWVoV+/fggMDAQAuLm5IT4+Hhs2bMCUKVOketV9aWn7MpXL5dVO1teSGfR5HllXTiDDtC1c3vkaBqrLwOFvYJR0sKlDIyIiInos3D+hnYGBgZREVjwj3xiWLVuG3r17o3Pnzhrlrq6uOHnypEZZVFQUOnfuDF1d3WrbUigUGDNmDMaMGYNZs2bBxcUFly5dQp8+faCnp4epU6ciODgYcrkcr7zySo0z27u6uuLnn3/WKDt16pTG+xMnTmDs2LF47bXXAJT/np+UlISuXbtKdQwMDKqMHDhx4gQGDhyImTNnSmX//POP1liInkTNKom3tbWFq6urRlnXrl2xZ88eAJCel1GpVLC1/d9z2+np6VXuzpN27bu6oXTxr7ArKSn/A0fHfjCf9R2K/vkTxRfDoWfX+cGNEBERET3BMjMzoaenB1dXV7Rp0waJiYm4desWMjIyYGdn1ygx9OjRA5MmTcLatZrLAL///vvo378/Pv30U0yYMAHR0dFYt24dvvnmm2rb2bp1K0pLSzFgwAAYGRnhhx9+gEKhQPv27aU6b731lpRg//HHHzXG9fbbb2P16tWYP38+ZsyYgXPnzmHr1q0adTp16oQ9e/YgKioK5ubmWLNmDVQqlUYS7+joiNOnTyMlJQUmJiZo3bo1OnXqhG3btuHIkSNwcnLCDz/8gLNnz8LJyakul46oWWtWw+kHDRqExMREjbIrV65IXzBOTk6wsbFBeHi4tL2oqAgREREYOHBgo8baHInS/z37pKurW2WEgkHHfjB+cSFkcuPGDo2IiIjoseLi4gIPDw9YWVlBV1cXrq6u6N+/P6ysrBo1jk8//bTK8PU+ffpg9+7d2LVrF7p3747Fixdj6dKl1U5qBwCtWrXC5s2bMWjQIPTs2RO//fYbDhw4gDZt2kh1nJ2dMXDgQHTp0gUDBgyoMaZ27dphz549OHDgAHr16oWNGzdKI2kr/Otf/0KfPn0watQoDBs2DDY2NnjhhRc06ixYsEC6tpaWlrh+/TrefvttvPTSS5gwYQIGDBiAu3fvatyVJ2oJZOIRZ6bT0dHBX3/9VWUYT0M4e/YsBg4ciCVLlmD8+PE4c+YMpk2bhk2bNmHSpEkAyif5CAoKQnBwMJydnREYGIjjx4/Xeom57OxsKJVKqNVqmJmZNfQpPTaEELi5ZBSKlbaw910BuTlHLhAREdGTqaCgAMnJyXBycmq0oe/NnRACLi4umDFjBubPn9/U4RA1SzV999QlD21Ww+n79++P0NBQLFy4EEuXLoWTkxO+/PJLKYEHAD8/P+Tn52PmzJnIzMzEgAEDEBYWxjXiH+DKsd2wKsiAKMhAemoK2jKJJyIiIiKUP5r6ww8/IDU1Fa+//npTh0PU4j1yEu/v7w8LC4v6iKVWnn/+eTz//PNat8tkMgQEBCAgIKDRYnoSdBj6EhLu3oJexjV0617zECkiIiIiajmsra1hYWGBTZs2wdzcvKnDIWrx6iWJp+ZPX18fvca/19RhEBEREdFj5hGfviWietasJraj+ld4T43S/JymDoOIiIiIiIhqgUl8C5f83QfIXDoSKYe2NHUoRERERERE9ABM4luwgsICtEm7AF1RioLsu00dDhERERERET0Ak/gWzFBuCNm8vbjZwQvO47hUCBERERER0eOuWS0xR/XPwtoeFtNXNnUYREREREREVAu8E99CZaYkcKZRIiIiIiKiZoZJfAt0/coFiI1TkLpkFIqybzd1OERERERUz44dOwYXFxeUlZXVe9vjxo3DmjVr6r3dhnb37l1YWVkhJSWlqUOhJ9CCBQswd+7cRjkWk/gWKOPkT5AB0C0pgL5Jm6YOh4iIiIhqwdfXFzKZDDKZDHp6emjXrh3eeecdZGZmVqnr5+eHRYsWQUen/Nf9rVu3SvvKZDJYW1tj9OjRiI+P19ivqKgIK1asQK9evWBkZAQLCwsMGjQIwcHBKC4uBgAsXrwYn3/+ObKzs6X9CgoK4Ovrix49ekBPTw8vvPBClZiOHz+uEUPF66+//tKol52djUWLFsHFxQWGhoawsbHByJEjsXfvXmkk6bBhwzBv3rw6Xb+goCCMHj0ajo6OAIALFy7g1VdfRdu2baFQKNC1a1d89dVXVfa7dOkSPD09oVAoYG9vj6VLl2qMaN27dy+8vb1haWkJMzMzeHh44MiRIxptbN68GUOGDIG5uTnMzc0xcuRInDlz5oExCyEQEBAAOzs7KBQKDBs2rMpnNmPGDHTs2BEKhQKWlpYYO3ZslWtaWW3OvTafaXUyMzMxefJkKJVKKJVKTJ48GVlZWdL2yn3x/ld6errWdoOCgtC/f3+YmprCysoKL7zwAhITEzXqBAQEwMXFBcbGxtJ1Pn36dI3xJiYmwsvLC9bW1jA0NESHDh3wySefSP0dqF3f9fPzQ3BwMJKTk2t1nR4Fk/gWJCkpCXfu3EHvNz6D6pl/Ia+DO3JDPmzqsIiIiIiolp5++mmkpaUhJSUFW7ZswYEDBzBz5kyNOlFRUUhKSsLLL7+sUW5mZoa0tDTcunULBw8eRG5uLp577jkUFRUBKE/gR40ahWXLlmH69OmIiorCmTNnMGvWLKxdu1ZKHnv27AlHR0eEhIRIbZeWlkKhUGDu3LkYOXJkjeeQmJiItLQ06eXs7Cxty8rKwsCBA7Ft2zYsXLgQMTExiIyMxIQJE+Dn5we1Wv1Q1y0/Px/ffvst3nrrLans3LlzsLS0xPbt2xEfH49FixZh4cKFWLdunVQnOzsb3t7esLOzw9mzZ7F27VqsWrVKYyRCZGQkvL29cejQIZw7dw5eXl4YPXo0zp8/L9U5fvw4Xn31Vfz++++Ijo5Gu3bt4OPjg9TU1BrjXrFiBdasWYN169bh7NmzsLGxgbe3N3JycqQ6ffv2RXBwMC5fvowjR45ACAEfHx+UlpZqbbc2516Xz/R+EydORGxsLA4fPozDhw8jNjYWkydPlrZPmDBB4/NPS0vDqFGj4OnpCSsrK63tRkREYNasWTh16hTCw8NRUlICHx8f5ObmSnU6d+6MdevW4dKlSzh58iQcHR3h4+OD27e1jz7W19fHlClTEBYWhsTERHz55ZfYvHkz/P39q9Stqe9aWVnBx8cHGzdurPW1emiCNKjVagFAqNXqpg6lXuXn54vw8HARG3FY3NvzmchY+JTI+LCvyPhkUFOHRkRERNSo8vPzRUJCgsjPz9coLyvMK3+Vlf2vrLiovKy4sPq6paX/KyspLi8rKqhV3bqaOnWqGDt2rEbZ/PnzRevWrTXK5syZI8aNG6dRFhwcLJRKpUbZ/v37BQBx8eJFIYQQy5cvFzo6OiImJqbKsYuKisS9e/ek9wEBAWLIkCG1jlMIIX7//XcBQGRmZmo5QyHeeecdYWxsLFJTU6tsy8nJEcXF5dfN09NTvPvuu1rbqWzPnj3CwsLigfVmzpwpvLy8pPfffPONUCqVoqDgf59pUFCQsLOz0+gnlbm6uoolS5Zo3V5SUiJMTU3F999/r7VOWVmZsLGxEcuWLZPKCgoKhFKpFBs3btS634ULFwQA8ffff2utU53K534/bZ9pZQkJCQKAOHXqlFQWHR0tAIi//vqr2n3S09OFvr6+2LZtW53iTU9PFwBERESE1joVud3Ro0fr1PZ7770nBg8eLL2vTd8VQoitW7eKtm3bat2u7bvn/lhrk4fyTnwLcfvvOLRO+BnWvwagKP44FE/PgeGomQ/ekYiIiKiFyFo8BFmLh0DkZkllBZHbkLV4CPL2rdCs+6k3shYPQVmWSiorjN5dXnfPpxp11ctHl9e9/b9htkXnDjxyvFevXsXhw4ehr6+vUR4ZGYl+/frVuG9WVhZ27NgBANL+ISEhGDlyJNzc3KrU19fXh7GxsfT+qaeewpkzZ1BYWFjnuN3c3GBra4sRI0bg999/l8rLysqwa9cuTJo0CXZ2dlX2MzExgZ7ewy2uVZtrAgBqtRqtW7eW3kdHR8PT0xNyuVwqGzVqFG7duqX12fqysjLk5ORotFNZXl4eiouLa6yTnJwMlUoFHx8fqUwul8PT0xNRUVHV7pObm4vg4GA4OTmhbdu2WtuuTuVzfxjR0dFQKpUYMGCAVObu7g6lUqk15m3btsHIyAjjxo3TKJfJZNi6dWuN8QLQGnNRURE2bdoEpVKJXr16SeW+vr4YNmyY1nb//vtvHD58GJ6enlW2aeu7FZ566incuHED165d09p+feASc0+wy5cvI+taIqz+OQrzm2eQpbCFgSjGdaP2UKMdLG5EwoYz1BMRERE1G7/88gtMTExQWlqKgoICAKgyyVxKSkq1SbBarYaJiQmEEMjLywMAjBkzBi4uLgDKH72sKbm5n729PQoLC6FSqdC+ffta7WNra4tNmzahb9++KCwsxA8//IARI0bg+PHjGDp0KO7cuYPMzEwpnvqk7ZrcLzo6Grt378bBgwelMpVKJT1DX8Ha2lra5uTkVKWd1atXIzc3F+PHj9d6rI8++gj29vY1DlNXqVQax7v/+JWTxG+++QZ+fn7Izc2Fi4sLwsPDYWBgoLXtyqo794ehUqmqHRJvZWUlnU9l3333HSZOnAiFQqFR3qVLFyiVymr3EUJg/vz5GDx4MLp3766x7ZdffsErr7yCvLw82NraIjw8HBYWFtJ2W1vbaid8HDhwIGJiYlBYWIjp06dj6dKlGvvU1Hcr2NvbAyjvb7X9uXgYTOKfYG2unYR1xGaU6Clwu+c4tIk7ABkAYWSOoqIi6OjoQiaTNXWYRERERI+FVktPlP9D31AqMxw6BYaDJwI6upp1/xVe/g+9/92hlXuMh/ypFwGZ5mBX5YcHqtQ16Dv6oWL08vLChg0bkJeXhy1btuDKlSuYM2eORp38/HwYGhpW2dfU1BQxMTEoKSlBREQEVq5cqfH8rhCi1r8bViRcFX8MqI0uXbqgS5cu0nsPDw/cuHEDq1atwtChQ6XJ4hri91Nt16RCfHw8xo4di8WLF8Pb21tjW+V4aopz586dCAgIwL59+7Q+371ixQrs3LkTx48fl2IKCQnBjBkzpDq//vordHV1tR6/ctmkSZPg7e2NtLQ0rFq1CuPHj8cff/wBQ0NDPPPMMzhxorxvt2/fvsrEeDWd+8Oo7rpo61vR0dFISEjAtm3bqmyraXK+2bNn4+LFizh58mSVbV5eXoiNjcWdO3ewefNmjB8/HqdPn5Y+j6CgoGrb/PHHH5GTk4MLFy7ggw8+wKpVq+Dn5wfgwX23wsP8XDwMJvFPMKWlLfL05dAtLQZy7sD8/f/DXxH7kGVoCzs7O7STt0Nh4oPbISIiImoJZAaKqmV6+gD0a1dXVw/Qrfrrtda6D8HY2BidOnUCAHz99dfw8vLCkiVL8Omn/xvCb2FhUe2M9To6OtK+Li4uUKlUmDBhAiIjIwGUTwp2+fLlWsWRkZEBALC0tHyo86jg7u6O7du3S22Zm5vXOoa60HZNACAhIQHDhw/HtGnT8Mknn2hss7GxqXIHuWIG9cp3yH/88Ue8+eab+Omnn7TeYV+1ahUCAwNx9OhR9OzZUyofM2aMxhB0e3t7pKWlASi/u21ra6tx/MrHrpgJ3tnZGe7u7jA3N0doaCheffVVbNmyBfn5+QBQ5dGLms79YdjY2OA///lPlfLbt29XiRkAtmzZgt69e6Nv3761PsacOXOwf/9+REZGwsHBocr2ip+RTp06wd3dHc7Ozvj222+xcOHCGtutePzA1dUVpaWlmD59Ot5//33pjymV3d93K9TXz8WD8Jn4J5i87/NQfngA2c4j0ObaH8hd/SLMMlMgE6UwNzeHjowfPxEREVFz5u/vj1WrVuHWrVtSmZubGxISEh6473vvvYcLFy4gNDQUQPms4kePHtWYVb1CSUmJxizgcXFxcHBw0Bim/DDOnz8vJag6OjqYMGECQkJCNM6nQm5uLkpKSh7qONquSXx8PLy8vDB16lR8/vnnVbZ7eHggMjJSmsEfAMLCwmBnZ6cxzH7nzp3w9fXFjh078Nxzz1Ubw8qVK/Hpp5/i8OHDVZ7PNzU1lRLPTp06QaFQwMnJCTY2NggPD5fqFRUVISIiAgMHDqzxfIUQ0nwF9vb2Urv3D/F+0Lk/DA8PD6jVao3l806fPg21Wl0l5nv37mH37t148803a9W2EAKzZ8/G3r17cezYsWofZdC2X13nbhBCoLi4WGMpwcru77sV4uLioK+vj27dutXpeHXFLO4JV2pgjETzXrjk8T7Unbyg/CcC3f9YgcKwbyByq/9rJBERERE1D8OGDUO3bt0QGBgolY0aNaraYcaVmZmZ4a233oK/vz+EEJg3bx4GDRqEESNGYP369bhw4QKuXr2K3bt3Y8CAAUhKSpL2PXHihMaEa0D5Xd3Y2FhkZGRArVYjNjYWsbGx0vYvv/wSP//8M5KSkhAfH4+FCxdiz549mD17tlQnMDAQbdu2xYABA7Bt2zYkJCQgKSkJ3333HXr37o179+5JdW/fvi0do+Kl7bnrUaNGIT4+XuNufEUS6+3tjfnz50OlUkGlUmksRzZx4kTI5XL4+voiLi4OoaGhCAwMxPz586Xh4Tt37sSUKVOwevVquLu7S+3cvxzeihUr8Mknn+C7776Do6OjVOf+86lMJpNh3rx5CAwMRGhoKOLi4uDr6wsjIyNMnDgRQPnkhkFBQTh37hyuX7+O6OhojB8/HgqFAs8++6zWtmtz7rX5TCvr2rUrnn76aUybNg2nTp3CqVOnMG3aNDz//PMaw9GB8pELJSUlmDRpUrVtubi4SH9gAoBZs2Zh+/bt2LFjB0xNTaWYK0YZ5Obm4uOPP8apU6dw7do1xMTE4K233sLNmzc1lltcuHAhpkyZIr0PCQnB7t27cfnyZVy9ehU//fQTFi5ciAkTJkgTKdam7wLlPxdDhgyp8nx/vXvg/PUtzJO2xNytW6lC9ZG7SP9ogLh94ieRd1cl/gn+RNz+eCCXmCMiIqIWqaZlnh5n2pb5CgkJEQYGBuL69etCCCEyMjKEQqHQWNKruiXmhBDi2rVrQk9PT/z4449CiPIlzIKCgkSPHj2EoaGhaN26tRg0aJDYunWrtLxbfn6+MDMzE9HR0RpttW/fXgCo8qqwfPly0bFjR2FoaCjMzc3F4MGDxcGDB6vElJWVJT766CPh7OwsDAwMhLW1tRg5cqQIDQ2VlnXz9PSs9lj+/v5ar5+7u7vG0mz+/v7VttG+fXuN/S5evCiGDBki5HK5sLGxEQEBARrLy2mLZerUqQ+8NjXFK0T5MnP+/v7CxsZGyOVyMXToUHHp0iVpe2pqqnjmmWeElZWV0NfXFw4ODmLixIlal3Or67k/6DOtzt27d8WkSZOEqampMDU1FZMmTap2aTYPDw8xceJEre0AEMHBwRrvq3tV1MnPzxcvvviisLOzEwYGBsLW1laMGTNGnDlzRqPdqVOnCk9PT+n9rl27RJ8+fYSJiYkwNjYWrq6uIjAwUOP7obZ9t3PnzmLnzp1az6m+lpiT/feC0H9lZ2dDqVRCrVbDzMysqcN5ZJdiTsJ+9zzIABhN/RLyroMhhEBJTgZKonehLDcLxi8tauowiYiIiBpNQUEBkpOT4eTkVONkZ82Zn58f1Go1/v3vf9d72+vXr8e+ffsQFhZW7203pEOHDmHBggWIi4uDjg4HJFP9OnjwID744ANcvHhR61KINX331CUP5cR2T7gefQbjjt0+3Dt/GK26lD+HIpPJoG/WBvqjZjVxdERERETUEBYtWoT169ejtLRU68RcD0tfXx9r166t1zYbw7PPPoukpCSkpqbWeQ11ogfJzc1FcHCw1gS+PjXrP0EFBQVJz4pUEEIgICAAdnZ2UCgUGDZsWJVlFFoaCxt7OD7zJmT8iyMRERFRi6BUKvHxxx/XewIPANOnT6/yfHNz8e677zKBpwYxfvx4jRUGGlKzzerOnj2LTZs2aSzNAJRPGrFmzRqsW7cOZ8+ehY2NDby9vZGTk9NEkTadrIz0pg6BiIiIiIiI6lGzTOLv3buHSZMmYfPmzTA3N5fKhRD48ssvsWjRIrz00kvo3r07vv/+e+Tl5WHHjh1NGHHjS756BaUrnsPNAG8UXbvU1OEQERERERFRPWiWSfysWbPw3HPPYeTIkRrlycnJUKlUGstdyOVyeHp6Iioqqtq2CgsLkZ2drfF6EqT9eQQ6EDAuyISuvIGXOCAiIiIiIqJG0ewmttu1axdiYmJw9uzZKtsq1oW0trbWKLe2tsa1a9eqbS8oKAhLliyp/0Cb2MDxc5DUoQeM0hNhbtOpqcMhIiIiIiKietCs7sTfuHED7777LrZv317jciAymUzjvRCiSlmFhQsXQq1WS68bN27Ua8xNybnfMNg/O6OpwyAiIiIiIqJ60qzuxJ87dw7p6eno27evVFZaWorIyEisW7cOiYmJAMrvyNva2kp10tPTq9ydryCXyyGXyxs28EZUVlaGlJQU3Lt3Dz179oQoKQIAyPQMmjgyIiIiIiIielTNKokfMWIELl3SnKTt9ddfh4uLCz788EN06NABNjY2CA8Ph5ubGwCgqKgIERERWL58eVOE3OgS42Og3DUPhUY2yE4egLILh6HXsT9MJi1r6tCIiIiIiIjoETWr4fSmpqbo3r27xsvY2Bht2rRB9+7dpTXjAwMDERoairi4OPj6+sLIyAgTJ05s6vAbjBACRUVFKCoqwp0/9kBRWgCbnBSUnPo/CJkMpfcyUFZW1tRhEhEREVEjOXbsGFxcXBrkd8Bx48ZhzZo19d5uQ7t79y6srKyQkpLS1KHQE2jBggWYO3duoxyrWSXxteHn54d58+Zh5syZ6NevH1JTUxEWFgZTU9OmDq3B3Lx5EyePhSNx21J0uHkKV42dkKF0xKWBC3DX1BFZWVmIj49v6jCJiIiInmhCCBQn/oG8/SshSorrvX1fX1/IZDLIZDLo6emhXbt2eOedd5CZmVmlrp+fHxYtWgQdnfJf97du3SrtK5PJYG1tjdGjR1f5HbGoqAgrVqxAr169YGRkBAsLCwwaNAjBwcEoLi4/p8WLF+Pzzz/XWNWpoKAAvr6+6NGjB/T09PDCCy9Uien48eMaMVS8/vrrL4162dnZWLRoEVxcXGBoaAgbGxuMHDkSe/fuhRACADBs2DDMmzevTtcvKCgIo0ePhqOjIwDgwoULePXVV9G2bVsoFAp07doVX331VZX9Ll26BE9PTygUCtjb22Pp0qVSHACwd+9eeHt7w9LSEmZmZvDw8MCRI0c02ti8eTOGDBkCc3NzmJubY+TIkThz5swDYxZCICAgAHZ2dlAoFBg2bFiVz2zGjBno2LEjFAoFLC0tMXbs2CrXtLLanHttPtPqZGZmYvLkyVAqlVAqlZg8eTKysrKk7ZX74v2v9PR0re0GBQWhf//+MDU1hZWVFV544QXpceoKAQEBcHFxgbGxsXSdT58+XWO8iYmJ8PLygrW1NQwNDdGhQwd88sknUn8Hatd3/fz8EBwcjOTk5Fpdp0fRrIbTV+f48eMa72UyGQICAhAQENAk8TQ2UZgH8yth6Bn1A2TF+chu7wHn52dDbtkWuYmJwD9HoKurK31ZEREREVH9EkKg5EoU8o9uQumN8gRLPuD/Qde6Q70f6+mnn0ZwcDBKSkqQkJCAN954A1lZWdi5c6dUJyoqCklJSXj55Zc19jUzM0NiYiKEEEhNTYWfnx+ee+45XLlyBQYGBigqKsKoUaNw4cIFfPrppxg0aBDMzMxw6tQprFq1Cm5ubujduzd69uwJR0dHhISE4J133gFQPk+VQqHA3LlzsWfPnhrPITExEWZmZtJ7S0tL6d9ZWVkYPHgw1Go1PvvsM/Tv3x96enqIiIiAn58fhg8fjlatWtX5uuXn5+Pbb7/FoUOHpLJz587B0tIS27dvR9u2bREVFYXp06dDV1cXs2fPBlD+BwVvb294eXnh7NmzuHLlCnx9fWFsbIz3338fABAZGQlvb28EBgaiVatWCA4OxujRo3H69GnpEd/jx4/j1VdfxcCBA2FoaIgVK1bAx8cH8fHxsLe31xr3ihUrsGbNGmzduhWdO3fGZ599Bm9vbyQmJko3Kfv27YtJkyahXbt2yMjIQEBAAHx8fJCcnAxdXd1q263NudflM73fxIkTcfPmTRw+fBgAMH36dEyePBkHDhwAAEyYMAFPP/20xj6+vr4oKCiAlZWV1nYjIiIwa9Ys9O/fHyUlJVi0aBF8fHyQkJAAY2NjAEDnzp2xbt06dOjQAfn5+fjiiy/g4+ODv//+W6Of3U9fXx9TpkxBnz590KpVK1y4cAHTpk1DWVkZAgMDNerW1HetrKzg4+ODjRs3Nvyj3II0qNVqAUCo1eqmDqVW1Gsni4wP+4rsTTPEP+ejRXh4uLhz544oKCgQR48eFdfXvyPUG6c3dZhEREREj438/HyRkJAg8vPzH6mdsrIyUfTXSaFeN0VkfNhXqL95XeT9tkVkfNhXlKj+qado/2fq1Kli7NixGmXz588XrVu31iibM2eOGDdunEZZcHCwUCqVGmX79+8XAMTFixeFEEIsX75c6OjoiJiYmCrHLioqEvfu3ZPeBwQEiCFDhtQ6TiGE+P333wUAkZmZqeUMhXjnnXeEsbGxSE1NrbItJydHFBcXCyGE8PT0FO+++67Wdirbs2ePsLCweGC9mTNnCi8vL+n9N998I5RKpSgoKJDKgoKChJ2dnSgrK9Pajqurq1iyZInW7SUlJcLU1FR8//33WuuUlZUJGxsbsWzZMqmsoKBAKJVKsXHjRq37XbhwQQAQf//9t9Y61al87vfT9plWlpCQIACIU6dOSWXR0dECgPjrr7+q3Sc9PV3o6+uLbdu21Sne9PR0AUBERERorVOR2x09erRObb/33nti8ODB0vva9F0hhNi6dato27at1u01fffUJQ994obTtzSGw16HTmt7XE1ToWjfp2gduwOJ5/5AVFSUNHxK2/J6RERERFR34r/D5nO+8cW94HcBHV2YvLkOpm9/C/2O/RotjqtXr+Lw4cPQ19fXKI+MjES/fjXHkZWVhR07dgCAtH9ISAhGjhwp3T2+n76+vnS3EwCeeuopnDlzBoWFhXWO283NDba2thgxYgR+//13qbysrAy7du3CpEmTYGdnV2U/ExMT6Ok93EDi2lwTAFCr1WjdurX0Pjo6Gp6enhqrWY0aNQq3bt3S+mx9WVkZcnJyNNqpLC8vD8XFxTXWSU5Ohkqlgo+Pj1Qml8vh6emJqKioavfJzc1FcHAwnJyc0LZtW61tV6fyuT+M6OhoKJVKDBgwQCpzd3eHUqnUGvO2bdtgZGSEcePGaZTLZDJs3bq1xngBaI25qKgImzZtglKpRK9evaRyX19fDBs2TGu7f//9Nw4fPgxPT88q27T13QpPPfUUbty4gWvXrmltvz40++H0LZ1Bdy/odx0C9aZP0OPaUSD/PyiIyUL2wDdxo0iOoqIiCC3DaIiIiIiobkpvX0Pu7sUovREP3fY9YfLmOuh1GtBoN01++eUXmJiYoLS0FAUFBQBQZZK5lJSUapNgtVoNExMTCCGQl5cHABgzZgxcXFwAAElJSTUmN/ezt7dHYWEhVCoV2rdvX6t9bG1tsWnTJvTt2xeFhYX44YcfMGLECBw/fhxDhw7FnTt3kJmZKcVTn7Rdk/tFR0dj9+7dOHjwoFSmUqmqPJZasXS1SqWCk5NTlXZWr16N3NxcjB8/XuuxPvroI9jb22PkyJFa66hUKo3j3X/8ykniN998Az8/P+Tm5sLFxQXh4eEwMKj9EtPVnfvDUKlU1Q6Jt7Kyks6nsu+++w4TJ06EQqHQKO/SpQuUSmW1+wghMH/+fAwePBjdu3fX2PbLL7/glVdeQV5eHmxtbREeHg4LCwtpu62tbbUTPg4cOBAxMTEoLCzE9OnTsXTpUo19auq7FSoejUhJSan1z8XDYBL/BJDp6sF5zNv4+8dMWOenwUxWDMND/mjT0QOFhWqUGbZq6hCJiIiIngilqiSU3rwMGChg0GsU9BzdGnXUo5eXFzZs2IC8vDxs2bIFV65cwZw5czTq5Ofnw9DQsMq+pqamiImJQUlJCSIiIrBy5Ups3LhR2i6EqPW5VCRcFX8MqI0uXbqgS5cu0nsPDw/cuHEDq1atwtChQ6XJ4hriemq7JhXi4+MxduxYLF68GN7e3hrbKsdTU5w7d+5EQEAA9u3bp/X57hUrVmDnzp04fvy4FFNISAhmzJgh1fn111+l59mrO37lskmTJsHb2xtpaWlYtWoVxo8fjz/++AOGhoZ45plncOLECQBA+/btq0yMV9O5P4zqrou2vhUdHY2EhARs27atyraaJuebPXs2Ll68iJMnT1bZ5uXlhdjYWNy5cwebN2/G+PHjcfr0aenzCAoKqrbNH3/8ETk5Obhw4QI++OADrFq1Cn5+fgAe3HcrPMzPxcNgEv+EsLV3hO38fwMARGkJis7/ioJjW6CbmQrd1v2bODoiIiKiJ4NBj5HQne+MgmPfIv/AahQc/x6GXr6Q9xsLmb78wQ08ImNjY3Tq1AkA8PXXX8PLywtLlizBp59+KtWxsLCodsZ6HR0daV8XFxeoVCpMmDABkZGRAMonBbt8+XKt4sjIyAAArZOF1Za7uzu2b98utWVubl7rGOpC2zUBgISEBAwfPhzTpk3DJ598orHNxsamyh3kihnUK98h//HHH/Hmm2/ip59+0nqHfdWqVQgMDMTRo0fRs2dPqXzMmDEaQ9Dt7e2RlpYGoPzutq2trcbxKx+7YiZ4Z2dnuLu7w9zcHKGhoXj11VexZcsW5OfnA0CVRy9qOveHYWNjg//85z9Vym/fvl0lZgDYsmULevfujb59+9b6GHPmzMH+/fsRGRkJBweHKtsrfkY6deoEd3d3ODs749tvv8XChQtrbLfi8QNXV1eUlpZi+vTpeP/997VODnh/361QXz8XD8Jn4p9AMl09yPuNhtn7e2A0YSkMvd5o6pCIiIiInhi6lu1hPGEpzOb/BP2O/ZC/fxXUK19EQfRuiOKiRo3F398fq1atwq1bt6QyNzc3JCQkPHDf9957DxcuXEBoaCiA8lnFjx49ivPnz1epW1JSgtzcXOl9XFwcHBwcNIYpP4zz589LCaqOjg4mTJiAkJAQjfOpkJubi5KSkoc6jrZrEh8fDy8vL0ydOhWff/55le0eHh6IjIxEUdH/PtewsDDY2dlpDLPfuXMnfH19sWPHDjz33HPVxrBy5Up8+umnOHz4cJXn801NTaXEs1OnTlAoFHBycoKNjQ3Cw8OlekVFRYiIiMDAgQNrPF8hhDRfgb29vdTu/UO8H3TuD8PDwwNqtVpj+bzTp09DrVZXifnevXvYvXs33nzzzVq1LYTA7NmzsXfvXhw7dqzaRxm07VfXuRuEECguLtZYSrCy+/tuhbi4OOjr66Nbt251Ol5dMYlv5rKz1Ti3YT5Sfv0WpUUFGttkunqQuz0L/U68E09ERERU36pL5u9tm9+oMQwbNgzdunXTWApr1KhR1Q4zrszMzAxvvfUW/P39IYTAvHnzMGjQIIwYMQLr16/HhQsXcPXqVezevRsDBgxAUlKStO+JEyc0JlwDyu/qxsbGIiMjA2q1GrGxsYiNjZW2f/nll/j555+RlJSE+Ph4LFy4EHv27JGWNAOAwMBAtG3bFgMGDMC2bduQkJCApKQkfPfdd+jduzfu3bsn1b19+7Z0jIqXtueuR40ahfj4eI278RVJrLe3N+bPnw+VSgWVSoXbt29LdSZOnAi5XA5fX1/ExcUhNDQUgYGBmD9/vjQ8fOfOnZgyZQpWr14Nd3d3qZ2KideA8iH0n3zyCb777js4OjpKde4/n8pkMhnmzZuHwMBAhIaGIi4uDr6+vjAyMsLEiRMBlE9uGBQUhHPnzuH69euIjo7G+PHjoVAo8Oyzz2ptuzbnXpvPtLKuXbvi6aefxrRp03Dq1CmcOnUK06ZNw/PPP68xHB0oH7lQUlKCSZMmVduWi4uL9AcmAJg1axa2b9+OHTt2wNTUVIq5YpRBbm4uPv74Y5w6dQrXrl1DTEwM3nrrLdy8eVNjucWFCxdiypQp0vuQkBDs3r0bly9fxtWrV/HTTz9h4cKFmDBhgjSRYm36LlD+czFkyJAqz/fXuwfOX9/CNLcl5k6F7xEZH/YVGR/2FXm/rm3qcIiIiIgee/W1xFxlJekp4t6uf4nMJcNFaU5GvbYthPZlvkJCQoSBgYG4fv26EEKIjIwMoVAoNJb0qm6JOSGEuHbtmtDT0xM//vijEKJ8CbOgoCDRo0cPYWhoKFq3bi0GDRoktm7dKi3vlp+fL8zMzER0dLRGW+3btxcAqrwqLF++XHTs2FEYGhoKc3NzMXjwYHHw4MEqMWVlZYmPPvpIODs7CwMDA2FtbS1GjhwpQkNDpWXdPD09qz2Wv7+/1uvn7u6usTSbv79/tW20b99eY7+LFy+KIUOGCLlcLmxsbERAQIDG8nLaYpk6deoDr01N8QpRvsycv7+/sLGxEXK5XAwdOlRcunRJ2p6amiqeeeYZYWVlJfT19YWDg4OYOHGi1uXc6nruD/pMq3P37l0xadIkYWpqKkxNTcWkSZOqXZrNw8NDTJw4UWs7AERwcLDG++peFXXy8/PFiy++KOzs7ISBgYGwtbUVY8aMEWfOnNFod+rUqcLT01N6v2vXLtGnTx9hYmIijI2NhaurqwgMDNT4fqht3+3cubPYuXOn1nOqryXmZP+9IPRf2dnZUCqVUKvVMDMza+pwNIjiAqBUcwjRlSvxkB1ajVZ5/0HrN9dCr31PLXsTEREREQAUFBQgOTkZTk5ONU521pz5+flBrVbj3//+d723vX79euzbtw9hYWH13nZDOnToEBYsWIC4uDhpKWai+nLw4EF88MEHuHjxotalEGv67qlLHsqJ7ZqJ0tspyP7yFY0kvlTXAJalxZCh/O8wORvegK5NJ5jN29VUYRIRERHRY2DRokVYv349SktLtU7M9bD09fWxdu3aem2zMTz77LNISkpCampqnddQJ3qQ3NxcBAcHa03g6xPvxFfyuN6JF8UFUK8YCx0zSxh6TkWZAM7cBdqVpqP138dQeu1CeUUdPZgHnmraYImIiIgeYy3hTjwRPX7q6048x5E0EzJ9Qxh6TkVpWhJ07bsiV2EBEfszELEFZVlpgL4hdNv3AhpvmVIiIiIiIiJqZBxO34yU9XwWuWd+Rcl386DOVqN7cSZ0ANxTtEOpfiuYOPaDzs34pg6TiIiIiIiIGgjvxDcjNw9sgFH6XxA5d3Gt/TDkGJhDAEhv7Yqk3m/gxn/ugg9HEBEREdVOWVlZU4dARC1IfT3JzjvxzYht1z4oSDwC3eI8dLmXBNt53yP91H7kFplBJgOsLC0hS3pwO0REREQtmYGBAXR0dHDr1i1YWlrCwMBAWvObiKghCCFw+/ZtyGQy6OvrP1JbTOKbEdM+o2DUdRD+2REI8+QTUK96EcLYGqLnZPTs2Qtm/2Qhr6mDJCIiInrM6ejowMnJCWlpabh161ZTh0NELYRMJoODg8MjrxjBJL6Z0TE0hsr5adyyeQpt0s7B+loEukWtQklmDEot7Zo6PCIiIqJmwcDAAO3atUNJSQlKS0ubOhwiagH09fXrZclHJvHNjFqtRlFREXTlRpDnZ+Cm+9vQT7sM279PoDDhHqDLj5SIiIioNiqGtT7q0FYiosbEjK+Z0dPTg5WVFTo6toVO2T8wcPdBatZgZOS8jHZ3YlGWfbupQyQiIiIiIqIGIhP1NUXeEyI7OxtKpRJqtRpmZmZNHQ4RERERERE94eqSh3KJOSIiIiIiIqJmgsPpK6kYmJCdnd3EkRAREREREVFLUJF/1magPJP4SnJycgAAbdu2beJIiIiIiIiIqCXJycmBUqmssQ6fia+krKwMt27dgqmpKWQyWaMcMzs7G23btsWNGzf4HD41CvY5agrsd9TY2OeosbHPUWNjn3tyCCGQk5MDOzs76OjU/NQ778RXoqOjAwcHhyY5tpmZGX/4qFGxz1FTYL+jxsY+R42NfY4aG/vck+FBd+ArcGI7IiIiIiIiomaCSTwRERERERFRM8Ek/jEgl8vh7+8PuVze1KFQC8E+R02B/Y4aG/scNTb2OWps7HMtEye2IyIiIiIiImomeCeeiIiIiIiIqJlgEk9ERERERETUTDCJJyIiIiIiImommMQTERERERERNRNM4omIiIiIiIiaCSbxDSQyMhKjR4+GnZ0dZDIZfv75Z43tQggEBATAzs4OCoUCw4YNQ3x8vEadwsJCzJkzBxYWFjA2NsaYMWNw8+bNRjwLak6CgoLQv39/mJqawsrKCi+88AISExM16rDfUX3asGEDevbsCTMzM5iZmcHDwwO//vqrtJ39jRpaUFAQZDIZ5s2bJ5Wx31F9CwgIgEwm03jZ2NhI29nnqCGkpqbitddeQ5s2bWBkZITevXvj3Llz0nb2u5aNSXwDyc3NRa9evbBu3bpqt69YsQJr1qzBunXrcPbsWdjY2MDb2xs5OTlSnXnz5iE0NBS7du3CyZMnce/ePTz//PMoLS1trNOgZiQiIgKzZs3CqVOnEB4ejpKSEvj4+CA3N1eqw35H9cnBwQHLli3Dn3/+iT///BPDhw/H2LFjpV8i2N+oIZ09exabNm1Cz549NcrZ76ghdOvWDWlpadLr0qVL0jb2OapvmZmZGDRoEPT19fHrr78iISEBq1evRqtWraQ67HctnKAGB0CEhoZK78vKyoSNjY1YtmyZVFZQUCCUSqXYuHGjEEKIrKwsoa+vL3bt2iXVSU1NFTo6OuLw4cONFjs1X+np6QKAiIiIEEKw31HjMDc3F1u2bGF/owaVk5MjnJ2dRXh4uPD09BTvvvuuEILfc9Qw/P39Ra9evardxj5HDeHDDz8UgwcP1rqd/Y54J74JJCcnQ6VSwcfHRyqTy+Xw9PREVFQUAODcuXMoLi7WqGNnZ4fu3btLdYhqolarAQCtW7cGwH5HDau0tBS7du1Cbm4uPDw82N+oQc2aNQvPPfccRo4cqVHOfkcNJSkpCXZ2dnBycsIrr7yCq1evAmCfo4axf/9+9OvXDy+//DKsrKzg5uaGzZs3S9vZ74hJfBNQqVQAAGtra41ya2traZtKpYKBgQHMzc211iHSRgiB+fPnY/DgwejevTsA9jtqGJcuXYKJiQnkcjnefvtthIaGwtXVlf2NGsyuXbsQExODoKCgKtvY76ghDBgwANu2bcORI0ewefNmqFQqDBw4EHfv3mWfowZx9epVbNiwAc7Ozjhy5AjefvttzJ07F9u2bQPA7zoC9Jo6gJZMJpNpvBdCVCmrrDZ1iGbPno2LFy/i5MmTVbax31F96tKlC2JjY5GVlYU9e/Zg6tSpiIiIkLazv1F9unHjBt59912EhYXB0NBQaz32O6pPzzzzjPTvHj16wMPDAx07dsT3338Pd3d3AOxzVL/KysrQr18/BAYGAgDc3NwQHx+PDRs2YMqUKVI99ruWi3fim0DFjKaV/wqWnp4u/UXNxsYGRUVFyMzM1FqHqDpz5szB/v378fvvv8PBwUEqZ7+jhmBgYIBOnTqhX79+CAoKQq9evfDVV1+xv1GDOHfuHNLT09G3b1/o6elBT08PERER+Prrr6Gnpyf1G/Y7akjGxsbo0aMHkpKS+F1HDcLW1haurq4aZV27dsX169cB8Hc6YhLfJJycnGBjY4Pw8HCprKioCBERERg4cCAAoG/fvtDX19eok5aWhri4OKkO0f2EEJg9ezb27t2LY8eOwcnJSWM7+x01BiEECgsL2d+oQYwYMQKXLl1CbGys9OrXrx8mTZqE2NhYdOjQgf2OGlxhYSEuX74MW1tbftdRgxg0aFCVZYKvXLmC9u3bA+DvdATOTt9QcnJyxPnz58X58+cFALFmzRpx/vx5ce3aNSGEEMuWLRNKpVLs3btXXLp0Sbz66qvC1tZWZGdnS228/fbbwsHBQRw9elTExMSI4cOHi169eomSkpKmOi16jL3zzjtCqVSK48ePi7S0NOmVl5cn1WG/o/q0cOFCERkZKZKTk8XFixfFxx9/LHR0dERYWJgQgv2NGsf9s9MLwX5H9e/9998Xx48fF1evXhWnTp0Szz//vDA1NRUpKSlCCPY5qn9nzpwRenp64vPPPxdJSUkiJCREGBkZie3bt0t12O9aNibxDeT3338XAKq8pk6dKoQoXxrC399f2NjYCLlcLoYOHSouXbqk0UZ+fr6YPXu2aN26tVAoFOL5558X169fb4Kzoeaguv4GQAQHB0t12O+oPr3xxhuiffv2wsDAQFhaWooRI0ZICbwQ7G/UOCon8ex3VN8mTJggbG1thb6+vrCzsxMvvfSSiI+Pl7azz1FDOHDggOjevbuQy+XCxcVFbNq0SWM7+13LJhNCiKYZA0BEREREREREdcFn4omIiIiIiIiaCSbxRERERERERM0Ek3giIiIiIiKiZoJJPBEREREREVEzwSSeiIiIiIiIqJlgEk9ERERERETUTDCJJyIiIiIiImommMQTERERERERNRNM4omIiIiIiIiaCSbxRERE1GDu3r0LKysrpKSk1HnfcePGYc2aNfUfFBERUTPGJJ6IiKiFOnToEGQymdbX+PHjH/kYQUFBGD16NBwdHTXKL168iJdeeglt2rSBoaEhunXrhpUrV6KkpESqs3jxYnz++efIzs5+5DiIiIieFEziiYiIWigvLy+kpaVpvG7evAlvb29YWFjgX//61yO1n5+fj2+//RZvvfWWRnlERATc3d2hUCiwb98+XLhwAX5+fli1ahVeeukllJWVAQB69uwJR0dHhISEPFIcRERETxKZEEI0dRBERETU9EpLS/Haa6/h6NGjOHbsGHr06PFI7e3duxczZszA7du3NY7h7OyMgQMHYvv27Rr1ExIS0Lt3b2zYsAFvvvkmAGDJkiX47bffEBkZ+UixEBERPSl4J56IiIikBD48PBy//fbbIyfwABAZGYl+/fpplJ05cwbJycn44IMPqtR3dXXFs88+ix9//FEqe+qpp3DmzBkUFhY+cjxERERPAibxRERELVxpaSkmT54sJfA9e/asl3ZTUlJgZ2enUZacnAwAcHZ2rnafzp0749q1a9J7e3t7FBYWQqVS1UtMREREzR2TeCIiohasIoEPCwvDb7/9hl69emmtV1f5+fkwNDTUKDMzMwMAZGRkVLtPZmamVAcAFAoFACAvL6/OxyciInoSMYknIiJqoSoS+CNHjuDo0aNVEviUlBT06tUL06ZNg5ubGwoLCxEcHIynnnoKPXv2xOLFi2ts38LCApmZmRplHh4e0NfXx4EDB6qNJywsDIMHD5bKKpJ9S0vLhz1NIiKiJwqTeCIiohaotLQUU6ZMkRL43r17V1svPj4ec+bMwcWLF/HPP//g0KFDiI6ORmxsLM6fP4/o6Gitx3Bzc0NCQoJGWZs2bTB37lx89tlnuHXrlsa2L774Anfv3sV7770nlcXFxcHBwQEWFhYPf7JERERPECbxRERELUxZWRmmTJmCn3/+Gdu3b4etrS1UKpXGq2L4fOfOnaVn5H/77TdER0ejb9++6NOnDy5fvox//vlH63FGjRqF+Ph4jbvx9+7dw9y5c+Hk5AQvLy/ExMQAAFauXImPP/4Ya9euhYGBgXT8EydOwMfHp6EuBRERUbPDJeaIiIhamNOnT8Pd3b3GOpmZmcjKysK4cePw559/AgC+/vprZGVlPXAY/f08PDzg6+uLGTNmAAACAgKwZMkSafvUqVOxdetWyGQyjf2Sk5NhY2MDa2trHDly5IHxEhERtRRM4omIiKhaKSkpGkl8XFwcJkyYgJMnT8Lc3Bw3b96EQqFAmzZttLZx6NAhLFiwAHFxcdDRqdsAwPXr12Pfvn0ICwt7pPMgIiJ6kug1dQBERETUPHTv3h0ffvghhg0bhrKyMpiammLXrl01JvHPPvsskpKSkJqairZt29bpePr6+li7du2jhk1ERPRE4Z14IiIiIiIiomaCE9sRERERERERNRNM4omIiIiIiIiaCSbxRERERERERM0Ek3giIiIiIiKiZoJJPBEREREREVEzwSSeiIiIiIiIqJlgEk9ERERERETUTDCJJyIiIiIiImommMQTERERERERNRNM4omIiIiIiIiaCSbxRERERERERM3E/wf2OXOvOFbL0AAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Alternatively, use matplotlib directly instead of the plotting functions available\n", - "# through the API of DearEIS.\n", - "fig = Figure(figsize=(12,3))\n", - "axis = fig.gca()\n", - "\n", - "for series in project_ex1.get_plot_series(settings):\n", - " # Figure out if the series should be included in the figure legend.\n", - " label: Optional[str] = None\n", - " if series.has_legend():\n", - " label = series.get_label()\n", - " \n", - " # Figure out the color and marker.\n", - " color: Tuple[float, float, float, float] = series.get_color()\n", - " marker: Optional[str] = mpl.MPL_MARKERS.get(series.get_marker())\n", - " \n", - " # Determine whether or not the series should be plotted using markers, a line, or both.\n", - " if series.has_line():\n", - " axis.plot(\n", - " *series.get_nyquist_data(num_per_decade=50),\n", - " label=label if marker is not None else None,\n", - " color=color,\n", - " linestyle=\":\",\n", - " )\n", - " if marker is not None:\n", - " axis.scatter(\n", - " *series.get_nyquist_data(),\n", - " label=label,\n", - " marker=marker,\n", - " edgecolor=color,\n", - " facecolor=\"None\",\n", - " )\n", - " elif marker is not None:\n", - " axis.scatter(\n", - " *series.get_nyquist_data(),\n", - " label=label,\n", - " marker=marker,\n", - " edgecolor=color,\n", - " facecolor=\"None\",\n", - " )\n", - "\n", - "# Set the correct aspect ratio and axis labels for a Nyquist plot.\n", - "axis.set_aspect(\"equal\")\n", - "axis.set_xlabel(r\"$Z_{\\rm re}\\ (\\Omega)$\")\n", - "axis.set_ylabel(r\"$-Z_{\\rm im}\\ (\\Omega)$\")\n", - "\n", - "# Show the figure.\n", - "fig.suptitle(settings.get_label())\n", - "axis.legend()\n", - "fig" - ] - }, - { - "cell_type": "markdown", - "id": "8ed6c530-cce2-454f-b1ab-1d60eacc7c69", - "metadata": {}, - "source": [ - "#### Generating tables\n", - "\n", - "`FitResult` and `SimulationResult` objects have a `to_dataframe` method that returns a `pandas.DataFrame` object, which can be used to generate circuit element parameter tables in various formats." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "9bd82d0c-2bc9-406b-a4ae-1dd8f570a28b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "| Element | Parameter | Value | Std. err. (%) | Fixed |\n", - "|:----------|:------------|---------:|:----------------|:--------|\n", - "| R_0 | R | 100 | - | No |\n", - "| R_1 | R | 200 | - | No |\n", - "| C_2 | C | 8e-07 | - | No |\n", - "| R_3 | R | 500 | - | No |\n", - "| W_4 | Y | 0.0004 | - | No |\n", - "\n", - "% R(RC)(RW) (2022-03-21 07:23:52) - LaTeX\n", - "\\begin{tabular}{llrll}\n", - "\\toprule\n", - "Element & Parameter & Value & Std. err. (%) & Fixed \\\\\n", - "\\midrule\n", - "R_0 & R & 100.000884 & - & No \\\\\n", - "R_1 & R & 199.998169 & - & No \\\\\n", - "C_2 & C & 0.000001 & - & No \\\\\n", - "R_3 & R & 500.009498 & - & No \\\\\n", - "W_4 & Y & 0.000400 & - & No \\\\\n", - "\\bottomrule\n", - "\\end{tabular}\n", - "\n", - "\n", - "\n", - "\n", - "| Element | Parameter | Value |\n", - "|:----------|:------------|-----------:|\n", - "| R_0 | R | 99.6 |\n", - "| R_1 | R | 200 |\n", - "| C_2 | C | 7.96e-07 |\n", - "| R_3 | R | 497 |\n", - "| W_4 | Y | 0.000399 |\n", - "\n", - "% R(RC)(RW) (2022-03-21 07:24:19) - LaTeX\n", - "\\begin{tabular}{llr}\n", - "\\toprule\n", - "Element & Parameter & Value \\\\\n", - "\\midrule\n", - "R_0 & R & 99.632760 \\\\\n", - "R_1 & R & 200.029000 \\\\\n", - "C_2 & C & 0.000001 \\\\\n", - "R_3 & R & 497.180800 \\\\\n", - "W_4 & Y & 0.000399 \\\\\n", - "\\bottomrule\n", - "\\end{tabular}\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "data: DataSet = project_ex1.get_data_sets()[0]\n", - "fit: FitResult = project_ex1.get_fits(data)[0]\n", - "print(f\"\")\n", - "print(fit.to_dataframe().to_markdown(index=False, floatfmt=\".3g\"))\n", - "print(f\"\\n% {fit.get_label()} - LaTeX\")\n", - "print(fit.to_dataframe().style.hide(axis=\"index\").to_latex(hrules=True))\n", - "print(\"\\n\")\n", - "\n", - "sim: SimulationResult = project_ex1.get_simulations()[0]\n", - "print(f\"\")\n", - "print(sim.to_dataframe().to_markdown(index=False, floatfmt=\".3g\"))\n", - "print(f\"\\n% {sim.get_label()} - LaTeX\")\n", - "print(sim.to_dataframe().style.hide(axis=\"index\").to_latex(hrules=True))\n", - "print(\"\\n\")" - ] - }, - { - "cell_type": "markdown", - "id": "49c92ecb-1f21-49f4-b062-a0e20ade0836", - "metadata": {}, - "source": [ - "#### Generating circuit diagrams and equations\n", - "\n", - "Each `FitResult` and `SimulationResult` object contains a `pyimpspec.Circuit` object that can be used to output SymPy expressions, LaTeX math equations, and LaTeX code for drawing circuit diagrams." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "1f8ce616-f093-4578-948c-66555ac8e4cf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# SymPy expression\n", - "R_0 + 1/(2*I*pi*C_2*f + 1/R_1) + 1/(sqrt(2)*sqrt(pi)*Y_4*sqrt(I*f) + 1/R_3)\n", - "\n", - "% LaTeX math equation\n", - "Z = R_{0} + \\frac{1}{2 i \\pi C_{2} f + \\frac{1}{R_{1}}} + \\frac{1}{\\sqrt{2} \\sqrt{\\pi} Y_{4} \\sqrt{i f} + \\frac{1}{R_{3}}}\n", - "\n" - ] - }, - { - "data": { - "text/markdown": [ - "Rendered $\\LaTeX$ math equation" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/latex": [ - "$\\displaystyle Z = R_{0} + \\frac{1}{2 i \\pi C_{2} f + \\frac{1}{R_{1}}} + \\frac{1}{\\sqrt{2} \\sqrt{\\pi} Y_{4} \\sqrt{i f} + \\frac{1}{R_{3}}}$" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "% LaTeX circuit diagram\n", - "\\begin{circuitikz}\n", - "\t\\draw (0,0) node[above]{WE} to[short, o-] (1,0);\n", - "\t\\draw (1.0,0.0) to[R=$R_{\\rm 0}$] (3.0,0.0);\n", - "\t\\draw (3.0,-0.0) to[R=$R_{\\rm 1}$] (5.0,-0.0);\n", - "\t\\draw (3.0,-1.5) to[capacitor=$C_{\\rm 2}$] (5.0,-1.5);\n", - "\t\\draw (3.0,-0.0) to[short] (3.0,-1.5);\n", - "\t\\draw (5.0,-0.0) to[short] (5.0,-1.5);\n", - "\t\\draw (5.0,-0.0) to[R=$R_{\\rm 3}$] (7.0,-0.0);\n", - "\t\\draw (5.0,-1.5) to[generic=$W_{\\rm 4}$] (7.0,-1.5);\n", - "\t\\draw (5.0,-0.0) to[short] (5.0,-1.5);\n", - "\t\\draw (7.0,-0.0) to[short] (7.0,-1.5);\n", - "\t\\draw (7.0,0) to[short, -o] (8.0,0) node[above]{CE+RE};\n", - "\\end{circuitikz}\n" - ] - } - ], - "source": [ - "data: DataSet = project_ex1.get_data_sets()[0]\n", - "fit: FitResult = project_ex1.get_fits(data)[0]\n", - "print(\"# SymPy expression\")\n", - "print(fit.circuit.to_sympy())\n", - "print(\"\\n% LaTeX math equation\")\n", - "print(fit.circuit.to_latex())\n", - "print()\n", - "display(Markdown(\"Rendered $\\LaTeX$ math equation\"))\n", - "display(Math(fit.circuit.to_latex()))\n", - "print(\"\\n% LaTeX circuit diagram\")\n", - "print(fit.circuit.to_circuitikz())" - ] - }, - { - "cell_type": "markdown", - "id": "9a37b935-fd9f-49fe-be20-cbd7c2a3d5b9", - "metadata": {}, - "source": [ - "Circuit diagrams can also be drawn by generating a `schemdraw.Drawing` object:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "a2734281-f0a4-4a42-9401-15ef1002b9e0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Il8sVs8fevXuDlu3Cdazq8p9vJBRsBKMWLHFEOrKK5Wv3PSorK9mmMX5o0cG73e6YxlhVVRW0FBpJsgpWCg02KlSrb3QZSWk11u9938OIXC6XsFqt/jbp0aOHePrppxWLDRQXF+sdpiZ0TY59+/b1N/Drr7/u3z579mxFgrRYLIZ61PcpKlYP34hDiPAda6iOPlQZMFRHH6oMWFNToyh3meXR0Ns0Vv/35e2mxUM90gtVWg1WUvUJVVoNlTzDjVaHDBmi6euXJEn3/q2+vm7gwIHC6/UKIYTiO+FvfetbAe2UCHRNjt26dfM3cGlpqX/7jh07dO9M9Xxs27ZN0U6hyoChOt1QZcBg5T+fUMlBvWKMWR5sU3M8xo0bp2ibUMkq1L5QpdVgJVWfUKNV9So8De0xZ84cf1vU1tb6t0e7rKJZ6Joc5R3NU0895S9XqFcX0fvTU32fnu12u2jatGnMHs2aNRNjxowJaKf6Otb6yn8+oUYywcp/PqFGVvLE4XQ6Y/ba5debbRr7NtVq5BjLa9WtW7eAmeqhklWoEmio0WG4CTv1nVeejG02W0xfu1H7O3lcHTt2FN98843wer1i8eLF/u3p6ekBbZgIdE2OJ0+eVDR+amqq4j4lAOLcuXN6hmgI9XWskUz0CFYGjOS7s2Cjp3CJ41743ogWiyVm5wyFbWq889YnWGk1VEnVJ1gSjOT7yPpGpFre6hHvNo1GkyZNFP+327Vrp+ijFyxYoHeImtB9turUqVPrHcZHuv5lQxCsDBjJLQLByoChyn8+wZKElrMu9egc2KbGOm99giWrSGayBiuthiqp+gQbrUaSjO+FkZPjhx9+WO93zd27d0/YW+50T45CCLFq1SqRnp4uJEkSkiSJjIwMsXr1ar3DMhR1JxrpiCPYiCZU+c8n2MiqU6dOmn1w0aNzYJsa67z1CZasIhnFBRslRnIPpBCBo8QpU6aETcb3wsjJUQghPv30U5GTk+MvraekpIgXXnghYROjEAZJjhSeumOdOXNmxCMOecctTwjhVnqRj6Kee+45TVd60aNzYJsa67yhyEurJSUlEY/i5MmwqKgo4tVz1CNTrVfPMXpybIiYHE1E3rHK7z0Kt+qKvAwoP66+8p+PvNOXH6fFjex6dQ5sU+OcNxR5spK3W7hRnLy0Kj8u3Lqr8tGq/AOMFiVVIZgcjYjJ0UTqm/IfbsSh/uHdcOU/H/XISqvynxD6dQ5sU+OcNxR5spI/wo3i1KXVSEqqPvLRopYlVSGYHI2IydFEgnWskY441NPFw5X/fNQ/9qtF+U8I/ToHtqlxzhuOeqGESEdx6h9QDldS9VH/gLJWJVUhmByNiL/naCJOpxOdOnVSbCsqKoro2IEDByr+7tOnT0THTZw4UfF3RkZGQv1gL9vUPAoLCxV/P/TQQ2F/BBkAnn/+ecXfzZs3D/sjyADwyiuvKP62WCyYPHlyBJFSQtA7O1N01GXASEcc6jJguPKfj3pkpdXtNXp+cmabGuO84ahLq5GO4tSl1UhKqj7y0qqWv9TBkaPxMDmajMvl8r/RO3fuHNWxrVq1EsCdVU2i0atXL/8bV6vfI9Szc2CbGuO8kcjOzhbAndWpopkYU1BQ4J9cc+LEiYiP++Uvf+lPjmvXrr2bkCPC5Gg8TI4mdOTIETFr1ixx9erVqI4rLy8XM2fOFGfOnInquMrKSlFSUiL2798f1XHR0LtzYJvqf95IVFRUiFmzZkX0naFcTU2NmDNnjti1a1fU/+aKFSs0TYxC6P//nwJJQgihSb2WKApWqxVerxcWiwUej0fvcBKCVm3KaxV7bFPj4YQcIiIiFSZHIiIiFSZHIiIiFSZHIiIiFSZHIiIiFSZHIiIiFSZHIiIiFSZHIiIiFSZHIiIiFSZHIiIiFSZHIiIiFSZHIiIiFSZHIiIiFSZHIiIiFSZHIiIiFSZHIiIiFSZHIiIiFSZHIiIiFSZHojDKysrw6KOPokmTJrBYLJAkCY0aNUKXLl2wefNmvcNrcEpKSiBJEn72s58F7Ovfvz8kSUKHDh0C9v3617+GJEl4/PHH4xAlmZ1N7wCIjOzVV1/FvHnzIIRAdnY2Hn74Ydjtdnz++ec4efIkRo0aBbfbDZuNb6V4ue+++wAA165dU2y/desW9uzZAwC4fft2wHGLFy8GAKxYsULjCCkR8B1NVI/Fixdj7ty5cDgc2LlzJ/Lz8xX7jx49ikmTJjExxlmbNm0AADdu3FBsnz59OrxeLxwOB2pqahT7Dhw4gAsXLuCBBx5Aly5d4hUqmRjLqkRBnD9/HjNmzIAkSdizZ09AYgSAnJwcHD16NP7BNXBpaWkAgJs3byq2v/vuu0hJSUF2djZqa2sV+1588UUAwNy5c+MTJJkekyNREM8++yy8Xi9Gjx6NXr166R0OyfiSY2VlpX/bypUrcfv2bUyePBnJyclwu93+fTdu3MD+/fuRkpKCoqKiuMdL5sTkSBTEJ598AgB48803dY6E1NLT0wEAVVVV/m3z58+HzWbD3Llz0bRpU3i9Xv++adOmQQiBn/zkJ3GPlcyLyZFI5fDhw3C73WjVqhUyMzP1DodULJY73ZbL5QIAHDp0COXl5SgoKIDdbkdKSgqEEP7nr1u3DlarFfPmzdMlXjInJkcilcOHDwMA2rVrF9Hzb968iY4dO8Jms0GSJDRu3Bi/+MUvtAyxwbNaraiurgYAPP/88wD+Nws1NTUVAHD58mX89re/hcvlwtChQ2G32wPO869//QsWiwXJyclxipzMgsmRSMX3fVVdXV1Ez6+urka7du2we/duuN1uzJw5E4sWLcJf/vIXLcNs0Bo1aoSamhr/94nf+c53cP/99wP4X3K8ePEiFixYAKD+2zeGDRuGlJSU+ARNpsLkSKTy6KOPArgzYzUSrVu3xu7du5Gfnw+bzYZXX30VjRs3xpYtW7QMs0Gz2+1wu9144YUXIITAkiVL/PtatmwJANi1axfKy8vx4IMPBi2PL1u2DBUVFSgsLIxb3GQeTI5EKl27dkVqaioqKyvrLY+WlJTgrbfeCrrv1KlTcLlc6Nevn5ZhNmhOpxMejwfr169H69atMWjQIP++Vq1aAQDeeOMNAMDChQsDjr958yZmzJiB1atX+7/DJJLj/wqiIDZs2ACLxYJFixahffv2KCwsxIQJE9C7d284nU7MmzcPHTt2DDiuuroaeXl5yMrK4m0DGkpKSoLX60V1dTWmT5+u2OdLjteuXUOLFi0wYsSIgONHjhyJrl274sknn4xLvGQ+XNqDKIjHHnsMX3zxBSZOnIjjx4/jww8/BAA4HA7cf//9KCoqQt++fRXH1NXVoXPnzgDAxQE05ptA43A48POf/1yxz7eCDgBMnTo14Ni//e1v2LdvH8rLy7UNkkxNEvI5z0Q6sVqt8Hq9sFgs8Hg8eocTNa/Xi06dOqGiogJnzpzxf++lJ63a1OzXqri4GL///e8hSRIA+G/7kCQJly9fRuvWreMek9nbNBExOZIhmL1zePDBB3H+/HmcPn064ltAtMbkGNyVK1fw1Vdf+f+eP38+duzYgdLSUvTs2VOX7yDN3qaJiGVVonu0b98+lJWVAfjf0mYAMGjQIOzYsUOvsKgeLVu2VIzsW7ZsCYvFwmUCSYEjRzIEfnKOPY4czYNtajycrUpERKTC5EhERKTC5EhERKTC5EhERKTC5EgUxrRp05CcnIyXXnrprvZTeNOmTYPD4YDVajX8w+Fw8Fo3AJytSoZg5Nl6DocDtbW1sNvtqKmpiXq/Xsw0W9XXhmYR62tt5P//DRXvcyQKw/fTVfX9hFW4/RSevO2MvBC41+sFwGvdEDA5EpFhGH3k5BvhUeIz7kc0IiIinTA5EhERqTA5EhERqTA5EhERqTA5EhERqTA5EhERqXARADIE36+yA8a7z00+dT/Y28WosfviliQpprcfaPF6w7WxUWh1rbW6VnT3mBzJEOSdjpGFS45GFcu3udav18hdUjyutZFff0PCRQDIEHr06IHDhw9DkiTDJZtoPskbceTYo0ePmJ5Xi2tlxtGSFiPHWF8runscORKFEW7dS66Lee/M0oZmiZPunXE+5hIRERkEkyMREZEKkyMREZEKkyMREZEKkyMREZEKkyMREZEKkyMREZEKFwEgIsPwer2wWq16h1EvMy5WQHeHyZEoDJvNhtraWthswd8u4fZTeL42BMyRgHitEx/LqkRhTJ06FUlJSZg6depd7afwpk6dCrvdDovFYviH3W7ntW4AuHwcERGRCkeOREREKkyOREREKkyOREREKkyOREREKkyOREREKkyOREREKkyOREREKkyOREREKkyOREREKkyOREREKkyOREREKv8HV7SWvoOnym0AAAAASUVORK5CYII=\n", - "image/svg+xml": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2022-11-28T11:01:58.703408\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.6.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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go about batch processing (potentially a large amount of) experimental data into a `Project` object that can be used in the GUI program." - ] - }, - { - "cell_type": "markdown", - "id": "19213cfd-e774-43d5-bf1e-d03bbfeeefc7", - "metadata": {}, - "source": [ - "#### Creating a new project\n", - "\n", - "First we need to create a new project." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "0e99302f-185b-4f68-9f18-e37f6880f3fd", - "metadata": {}, - "outputs": [], - "source": [ - "project_ex2: Project = Project()" - ] - }, - { - "cell_type": "markdown", - "id": "98f0bc8e-ba08-4782-8e5c-440f675027b4", - "metadata": {}, - "source": [ - "#### Parsing data files\n", - "\n", - "Now we can iterate over data files and parse them.\n", - "In this case we are only going to load `.csv` files." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "56572f31-6c21-4f47-8038-05d0098a891f", - "metadata": {}, - "outputs": [], - "source": [ - "from os import walk\n", - "\n", - "f: str\n", - "files: List[str]\n", - "for _, _, files in walk(\".\"):\n", - " files = list(filter(lambda f: f.endswith(\".csv\"), files))\n", - " break\n", - "\n", - "data_sets: List[DataSet] = []\n", - "for f in files:\n", - " data_sets.extend(deareis.parse_data(f)) # parse_data returns a list since some file formats may contain multiple spectra." - ] - }, - { - "cell_type": "markdown", - "id": "9a055c31-203d-4158-8844-25fc5540c83b", - "metadata": {}, - "source": [ - "#### Adding data sets to the project\n", - "\n", - "The parsed data sets are added to the project." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "b6ee130f-c1d4-431c-8721-ee13ee6504d4", - "metadata": {}, - "outputs": [], - "source": [ - "data: DataSet\n", - "for data in data_sets:\n", - " project_ex2.add_data_set(data)" - ] - }, - { - "cell_type": "markdown", - "id": "6e3ccdb3-7b52-41c8-8575-bfec282579d8", - "metadata": {}, - "source": [ - "#### Performing analyses\n", - "\n", - "We can also take the opportunity to perform, e.g., Kramers-Kronig tests and DRT analyses." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "ff6c1f8a-fe9f-4d88-9253-4c1127d31d7c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "| Score | Real (%) | Imaginary (%) |\n", - "|:------------------------|-----------:|----------------:|\n", - "| Mean | 99.9 | 99.3 |\n", - "| Residuals, 1 sigma | 6.9 | 86.2 |\n", - "| Residuals, 2 sigma | 17.2 | 96.6 |\n", - "| Residuals, 3 sigma | 89.7 | 100.0 |\n", - "| Hellinger distance | 14.0 | 36.9 |\n", - "| Jensen-Shannon distance | 18.4 | 48.1 |\n" - ] - }, - { - "data": { - "image/png": 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\n", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for data in project_ex2.get_data_sets():\n", - " test: TestResult = deareis.perform_test(data, TestSettings(\n", - " test=deareis.Test.COMPLEX,\n", - " mode=deareis.TestMode.AUTO,\n", - " num_RC=data.get_num_points(),\n", - " mu_criterion=0.85,\n", - " add_capacitance=True,\n", - " # The rest are not necessary/relevant for this type of test,\n", - " # but these would be always be included when using the GUI\n", - " add_inductance=True,\n", - " method=deareis.CNLSMethod.LEASTSQ,\n", - " max_nfev=1000,\n", - " ))\n", - " project_ex2.add_test(data, test)\n", - "\n", - " drt: DRTResult = deareis.calculate_drt(data, DRTSettings(\n", - " method=deareis.DRTMethod.BHT,\n", - " rbf_type=deareis.RBFType.GAUSSIAN,\n", - " rbf_shape=deareis.RBFShape.FWHM,\n", - " shape_coeff=0.5,\n", - " derivative_order=1,\n", - " num_samples=2000,\n", - " num_attempts=5,\n", - " maximum_symmetry=0.5,\n", - " # The rest are not necessary/relevant for this method,\n", - " # but these would be always be included when using the GUI\n", - " mode=deareis.DRTMode.COMPLEX,\n", - " lambda_value=-1.0,\n", - " inductance=False,\n", - " credible_intervals=False,\n", - " circuit=None,\n", - " W=0.15,\n", - " num_per_decade=50,\n", - " ))\n", - " project_ex2.add_drt(data, drt)\n", - " \n", - " # Plot/print the results for inspection\n", - " _, _ = mpl.plot_fit(test, data, label=\"KK test\")\n", - " _, _ = mpl.plot_drt(drt, data, label=\"BHT\")\n", - " print(drt.get_score_dataframe().to_markdown(index=False, floatfmt=\".1f\"))" - ] - }, - { - "cell_type": "markdown", - "id": "58f03f5f-aa7b-4366-9920-070751c0d52f", - "metadata": {}, - "source": [ - "#### Saving the project\n", - "\n", - "Finally, the project is written to disk so that it can be opened in the GUI program." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "d04831f2-acc4-46e6-b56b-507946378dc4", - "metadata": {}, - "outputs": [], - "source": [ - "project_ex2.save(\"./example-2.json\")" - ] - }, - { - "cell_type": "markdown", - "id": "f767b6bd-d860-4f4a-903d-a8704c2a0a48", - "metadata": {}, - "source": [ - "### Example 3 - An _ad hoc_ parser\n", - "\n", - "This example will demonstrate how to implement an _ad hoc_ parser for a data format that is not currently supported by the `parse_data` function that was used in the previous example.\n", - "Note that new parsers can be contributed to the [pyimpspec package](https://github.com/vyrjana/pyimpspec)." - ] - }, - { - "cell_type": "markdown", - "id": "53c195ad-dcb6-4cf1-aec1-d0424605c584", - "metadata": {}, - "source": [ - "#### Turning JSON into DataSet\n", - "\n", - "We will use the `json` module, which is included in the Python standard library, to implement a parser that takes a file containing JavaScript Object Notation (JSON) and returns a `DataSet` object.\n", - "The new data set will then be added to a new project." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "92564350-74fe-47a0-aa2e-7cdea043c24d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cmath import rect\n", - "from json import load\n", - "from math import radians\n", - "from numpy import array\n", - "from os.path import basename, exists, splitext\n", - "from typing import IO\n", - "\n", - "\n", - "def parse_json(path: str) -> DataSet:\n", - " assert exists(path), \"Invalid path to data file!\"\n", - " fp: IO\n", - " with open(path, \"r\") as fp:\n", - " json: dict = load(fp)\n", - " assert (\n", - " \"frequency\" in json and type(json[\"frequency\"]) is list\n", - " and \"magnitude\" in json and type(json[\"magnitude\"]) is list\n", - " and \"phase\" in json and type(json[\"phase\"]) is list\n", - " ), \"Invalid data structure!\"\n", - " frequency: ndarray = array(json[\"frequency\"]) # Frequency (in hertz) of the excitation signal.\n", - " magnitude: ndarray = array(json[\"magnitude\"]) # Magnitude (or modulus) of the complex impedance in ohms.\n", - " phase: ndarray = array(json[\"phase\"]) # Argument of the complex impedance in degrees.\n", - " # Turn the polar coordinate representation into a Cartesian coordinate representation\n", - " # (1D numpy.array of complex numbers).\n", - " impedance: ndarray = array(list(map(lambda z: rect(z[0], radians(z[1])), zip(magnitude, phase))))\n", - " return DataSet(\n", - " frequency, # 1D numpy.array of frequencies in hertz\n", - " impedance, # 1D numpy.array of complex impedances\n", - " path=path, # Optional\n", - " label=splitext(basename(path))[0], # Optional\n", - " )\n", - "\n", - "\n", - "project_ex3: Project = Project()\n", - "project_ex3.add_data_set(parse_json(\"example-data.json\"))\n", - "for data in project_ex3.get_data_sets():\n", - " fig, axes = mpl.plot_data(data)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/post-build.py b/post-build.py new file mode 100644 index 0000000..8458f80 --- /dev/null +++ b/post-build.py @@ -0,0 +1,74 @@ +from os import ( + makedirs, + remove, + walk, +) +from os.path import ( + basename, + exists, + isdir, + join, + splitext, +) +from shutil import ( + copy, + copytree, + rmtree, +) +from typing import List + + +def copy_html(src: str, dst: str): + if exists(dst): + rmtree(dst) + files: List[str] = [] + for _, _, files in walk(src): + break + assert len(files) > 0 + files = [ + _ + for _ in files + if not _.startswith(".") + and splitext(_)[1] + in ( + ".html", + ".js", + ".py", + ".png", + ".pdf", + ) + ] + dirs: List[str] = ["_images", "_static"] + if not isdir(dst): + makedirs(dst) + name: str + for name in files: + copy(join(src, name), join(dst, name)) + for name in dirs: + copytree(join(src, name), join(dst, name)) + + +def copy_pdf(src: str, dst: str, version_path: str): + version: str = "" + with open(version_path, "r") as fp: + version = fp.read().strip().replace(".", "-") + assert version != "" + name: str + ext: str + name, ext = splitext(basename(src)) + dst = join(dst, f"{name} - {version}{ext}") + if exists(dst): + remove(dst) + copy(src, dst) + + +if __name__ == "__main__": + copy_html( + src="./docs/build/html", + dst="./dist/html", + ) + copy_pdf( + src="./docs/build/latex/latex/deareis.pdf", + dst="./dist", + version_path="./version.txt", + ) diff --git a/pre-build.py b/pre-build.py new file mode 100644 index 0000000..43f8654 --- /dev/null +++ b/pre-build.py @@ -0,0 +1,85 @@ +from typing import ( + List, + IO, +) +from os import ( + makedirs, + walk, +) +from os.path import ( + basename, + exists, + isfile, + isdir, + join, +) +from shutil import rmtree + + +def update_file(src: str, dst: str): + if not isfile(src): + return + src_contents: str = "" + fp: IO + with open(src, "r") as fp: + src_contents = fp.read() + if isfile(dst): + with open(dst, "r") as fp: + if fp.read() == src_contents: + return + with open(dst, "w") as fp: + fp.write(src_contents) + + +def copy_additional_files(files): + src_dir: str = "." + dst_dir: str = join(".", "src", "deareis") + licenses_dir: str = join(dst_dir, "LICENSES") + if not isdir(licenses_dir): + makedirs(licenses_dir) + path: str + for path in files: + update_file(join(src_dir, path), join(dst_dir, path)) + + +if __name__ == "__main__": + data_files: List[str] = [ + "CHANGELOG.md", + "CONTRIBUTORS", + "COPYRIGHT", + "LICENSE", + "README.md", + ] + files: List[str] + for _, _, files in walk("LICENSES"): + data_files.extend(map(lambda _: join("LICENSES", _), files)) + break + assert all(map(lambda _: isfile(_), data_files)) + copy_additional_files(data_files) + # The changelog bundled with the package will also be updated when running this script. + update_file( + "CHANGELOG.md", + join("src", "deareis", "gui", "changelog", "CHANGELOG.md"), + ) + # The licenses bundled with the package will also be updated when running this script. + update_file( + "LICENSE", + join("src", "deareis", "gui", "licenses", "LICENSE-DearEIS.txt"), + ) + list( + map( + lambda _: update_file( + _, + join("src", "deareis", "gui", "licenses", basename(_)), + ), + filter(lambda _: basename(_).startswith("LICENSE-"), data_files), + ) + ) + # Remove old dist files + dist_output: str = "./dist" + if exists(dist_output): + rmtree(dist_output) + # Remove old documentation files to force a rebuild + docs_output: str = "./docs/build" + if exists(docs_output): + rmtree(docs_output) diff --git a/requirements.txt b/requirements.txt index 191adf2..c018e0e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,3 @@ dearpygui==1.8.0 -pyimpspec~=3.2 -requests~=2.28 -xdg~=5.1 \ No newline at end of file +pyimpspec~=4.0 +requests~=2.28 \ No newline at end of file diff --git a/setup.py b/setup.py index 115ad6e..d515124 100644 --- a/setup.py +++ b/setup.py @@ -1,28 +1,5 @@ -from setuptools import ( - setup, - find_packages, -) -from os import walk -from os.path import ( - basename, - exists, - join, -) - - -def update_file(src: str, dst: str): - if not exists(src): - return - src_contents = "" - with open(src, "r") as fp: - src_contents = fp.read() - if exists(dst): - with open(dst, "r") as fp: - if fp.read() == src_contents: - return - with open(dst, "w") as fp: - fp.write(src_contents) - +from setuptools import setup, find_packages +from os.path import exists, join entry_points = { "gui_scripts": [ @@ -35,44 +12,28 @@ def update_file(src: str, dst: str): dependencies = [ "dearpygui==1.8.0", # Used to implement the GUI. - "pyimpspec~=3.2", # Used for parsing, fitting, and analyzing impedance spectra. + "pyimpspec~=4.0", # Used for parsing, fitting, and analyzing impedance spectra. "requests~=2.28", # Used to check package status on PyPI. - "xdg~=5.1", # Used to figure out where to place config, state, etc. files. ] dev_dependencies = [ - "flake8", - "setuptools", - "build", + "build~=0.10", + "flake8~=6.0", + "setuptools~=67.2", + "sphinx~=5.3", + "sphinx-rtd-theme~=1.2", ] optional_dependencies = { - "cvxpy": "cvxpy~=1.2", # Used in the DRT calculations (TR-RBF method) + "cvxopt": "cvxopt~=1.3", # Used in the DRT calculations (TR-RBF method) "kvxopt": "kvxopt~=1.3", # Fork of cvxopt that may provide wheels for additional platforms + "cvxpy": "cvxpy~=1.3", # Used in the DRT calculations (TR-RBF method) "dev": dev_dependencies, } -licenses = [] -for _, _, files in walk("LICENSES"): - licenses.extend( - list( - map( - lambda _: join("LICENSES", _), - filter(lambda _: _.startswith("LICENSE-"), files), - ) - ) - ) - -data_files = [ - "COPYRIGHT", - "CONTRIBUTORS", - "LICENSES/README.md", - "src/deareis/gui/changelog/CHANGELOG.md", -] + licenses - # The version number defined below is propagated to /src/deareis/version.py # when running this script. -version = "3.4.3" +version = "4.0.0" if __name__ == "__main__": with open("requirements.txt", "w") as fp: @@ -83,30 +44,12 @@ def update_file(src: str, dst: str): fp.write(version) assert version.strip != "" copyright_notice = "" - with open("COPYRIGHT") as fp: - copyright_notice = fp.read().strip() - assert copyright_notice.strip() != "" + if exists("COPYRIGHT"): + with open("COPYRIGHT") as fp: + copyright_notice = fp.read().strip() + assert copyright_notice.strip() != "" with open(join("src", "deareis", "version.py"), "w") as fp: fp.write(f'{copyright_notice}\n\nPACKAGE_VERSION: str = "{version}"') - # The changelog bundled with the package will also be updated when running this script. - update_file( - join("CHANGELOG.md"), - join("src", "deareis", "gui", "changelog", "CHANGELOG.md"), - ) - # The licenses bundled with the package will also be updated when running this script. - update_file( - join("LICENSE"), - join("src", "deareis", "gui", "licenses", "LICENSE-DearEIS.txt"), - ) - list( - map( - lambda _: update_file( - _, - join("src", "deareis", "gui", "licenses", basename(_)), - ), - licenses, - ) - ) setup( name="deareis", version=version, @@ -114,7 +57,6 @@ def update_file(src: str, dst: str): packages=find_packages(where="src"), package_dir={"": "src"}, include_package_data=True, - data_files=data_files, url="https://vyrjana.github.io/DearEIS", project_urls={ "Documentation": "https://vyrjana.github.io/DearEIS/api/", diff --git a/src/deareis/__init__.py b/src/deareis/__init__.py index 1bd00ae..64d3ea9 100644 --- a/src/deareis/__init__.py +++ b/src/deareis/__init__.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -21,42 +21,19 @@ _dpg.create_context() +from pyimpspec import ( + get_default_num_procs, + set_default_num_procs, +) from deareis.data import Project from deareis.api.data import ( DataSet, - # - exceptions - UnsupportedFileFormat, # - functions parse_data, ) -from deareis.api.circuit import ( - Circuit, - CircuitBuilder, - # - connections - Connection, - Parallel, - Series, - # - elements - Element, - Capacitor, - ConstantPhaseElement, - DeLevieFiniteLength, - Gerischer, - HavriliakNegami, - HavriliakNegamiAlternative, - Inductor, - ModifiedInductor, - Resistor, - Warburg, - WarburgOpen, - WarburgShort, - # - exceptions - ParsingError, - UnexpectedCharacter, - # - functions - get_elements, - parse_cdc, -) +from deareis.api.circuit import * +from deareis.typing import * +from deareis.exceptions import * from deareis.api.kramers_kronig import ( TestResult, TestSettings, @@ -74,8 +51,6 @@ # - enums CNLSMethod, Weight, - # - exceptions - FittingError, # - functions fit_circuit, ) @@ -99,9 +74,17 @@ DRTMode, RBFShape, RBFType, - # - exceptions - DRTError, # - functions calculate_drt, ) +from deareis.api.zhit import ( + ZHITResult, + ZHITSettings, + # - enums + ZHITInterpolation, + ZHITSmoothing, + ZHITWindow, + # - functions + perform_zhit, +) from deareis.api.plot import mpl # matplotlib-based plotting diff --git a/src/deareis/__main__.py b/src/deareis/__main__.py new file mode 100644 index 0000000..bec2cac --- /dev/null +++ b/src/deareis/__main__.py @@ -0,0 +1,23 @@ +# DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). +# Copyright 2023 DearEIS developers +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +# +# The licenses of DearEIS' dependencies and/or sources of portions of code are included in +# the LICENSES folder. + +from deareis.program import main + +if __name__ == "__main__": + main() diff --git a/src/deareis/api/__init__.py b/src/deareis/api/__init__.py index 5178fe8..889fda1 100644 --- a/src/deareis/api/__init__.py +++ b/src/deareis/api/__init__.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by diff --git a/src/deareis/api/_utility.py b/src/deareis/api/_utility.py index 70a5e9a..1dd396f 100644 --- a/src/deareis/api/_utility.py +++ b/src/deareis/api/_utility.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by diff --git a/src/deareis/api/circuit.py b/src/deareis/api/circuit/__init__.py similarity index 64% rename from src/deareis/api/circuit.py rename to src/deareis/api/circuit/__init__.py index edbdcf0..ccc2dec 100644 --- a/src/deareis/api/circuit.py +++ b/src/deareis/api/circuit/__init__.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -17,35 +17,23 @@ # The licenses of DearEIS' dependencies and/or sources of portions of code are included in # the LICENSES folder. -from pyimpspec.circuit import ( - # Circuits - Circuit, - CircuitBuilder, - # Connections +from pyimpspec.circuit.base import ( Connection, - Parallel, - Series, - # Elements + Container, Element, - Capacitor, - ConstantPhaseElement, - DeLevieFiniteLength, - Gerischer, - HavriliakNegami, - HavriliakNegamiAlternative, - Inductor, - ModifiedInductor, - Resistor, - Warburg, - WarburgOpen, - WarburgShort, - # Functions - get_elements, - parse_cdc, ) -from pyimpspec.circuit.tokenizer import ( - UnexpectedCharacter, +from pyimpspec.circuit.connections import * +from .elements import * +from pyimpspec.circuit.registry import ( + ContainerDefinition, + ElementDefinition, + ParameterDefinition, + SubcircuitDefinition, + get_elements, + register_element, ) -from pyimpspec.circuit.parser import ( - ParsingError, +from pyimpspec import ( + Circuit, + CircuitBuilder, + parse_cdc, ) diff --git a/src/deareis/api/circuit/elements.py b/src/deareis/api/circuit/elements.py new file mode 100644 index 0000000..41cf577 --- /dev/null +++ b/src/deareis/api/circuit/elements.py @@ -0,0 +1,20 @@ +# DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). +# Copyright 2023 DearEIS developers +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +# +# The licenses of DearEIS' dependencies and/or sources of portions of code are included in +# the LICENSES folder. + +from pyimpspec.circuit.elements import * diff --git a/src/deareis/api/data.py b/src/deareis/api/data.py index 18e1f2d..fbec870 100644 --- a/src/deareis/api/data.py +++ b/src/deareis/api/data.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -19,19 +19,14 @@ from typing import List, Optional import pyimpspec -from pyimpspec.data import ( - UnsupportedFileFormat, -) from deareis.api._utility import _copy_docstring -from deareis.data import ( - DataSet, -) +from deareis.data import DataSet @_copy_docstring(pyimpspec.parse_data) def parse_data( path: str, - file_format: Optional[str] = None, + file_format: str = "", **kwargs, ) -> List[DataSet]: return list( diff --git a/src/deareis/api/drt.py b/src/deareis/api/drt.py index 81bb508..f7b9282 100644 --- a/src/deareis/api/drt.py +++ b/src/deareis/api/drt.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -19,11 +19,8 @@ from uuid import uuid4 as _uuid4 from time import time as _time +from numpy import array import pyimpspec as _pyimpspec -from pyimpspec import ( - DRTError, -) -from pyimpspec.analysis.drt.bht import _get_default_num_procs from deareis.data import DataSet from deareis.data.drt import ( DRTResult, @@ -44,7 +41,7 @@ def calculate_drt( data: DataSet, settings: DRTSettings, - num_procs: int = -1, + num_procs: int = 0, ) -> DRTResult: """ Wrapper for the `pyimpspec.calculate_drt` function. @@ -53,7 +50,7 @@ def calculate_drt( References: - - Kulikovsky, A., 2020, Phys. Chem. Chem. Phys., 22, 19131-19138 (https://doi.org/10.1039/D0CP02094J) + - Kulikovsky, A., 2021, J. Electrochem. Soc., 168, 044512 (https://doi.org/10.1149/1945-7111/abf508) - Wan, T. H., Saccoccio, M., Chen, C., and Ciucci, F., 2015, Electrochim. Acta, 184, 483-499 (https://doi.org/10.1016/j.electacta.2015.09.097). - Ciucci, F. and Chen, C., 2015, Electrochim. Acta, 167, 439-454 (https://doi.org/10.1016/j.electacta.2015.03.123) - Effat, M. B. and Ciucci, F., 2017, Electrochim. Acta, 247, 1117-1129 (https://doi.org/10.1016/j.electacta.2017.07.050) @@ -70,14 +67,12 @@ def calculate_drt( settings: DRTSettings The settings to use. - num_procs: int = -1 + num_procs: int, optional The maximum number of processes to use. - A value below one results in using the total number of CPU cores present. + A value less than 1 will result in an attempt to automatically figure out a suitable value. """ - if settings.method == DRTMethod.M_RQ_FIT: - assert settings.circuit is not None, "A (fitted) circuit has not been provided!" - if num_procs < 1: - num_procs = _get_default_num_procs() + if settings.method == DRTMethod.MRQ_FIT: + assert settings.fit is not None, "A fitted circuit has not been provided!" result: _pyimpspec.DRTResult = _pyimpspec.calculate_drt( data=data, method=_drt_method_to_value[settings.method], @@ -92,27 +87,33 @@ def calculate_drt( num_samples=settings.num_samples, num_attempts=settings.num_attempts, maximum_symmetry=settings.maximum_symmetry, - circuit=settings.circuit, - W=settings.W, - num_per_decade=settings.num_per_decade, + circuit=settings.fit.circuit if settings.method == DRTMethod.MRQ_FIT else None, + fit=settings.fit if settings.method == DRTMethod.MRQ_FIT else None, + gaussian_width=settings.gaussian_width, + timeout=settings.timeout, num_procs=num_procs, ) + real_gammas: _pyimpspec.Gammas = result.real_gammas if hasattr(result, "real_gammas") else result.gammas + imaginary_gammas: _pyimpspec.Gammas = result.imaginary_gammas if hasattr(result, "imaginary_gammas") else array([]) return DRTResult( - _uuid4().hex, - _time(), - result.tau, - result.gamma, - result.frequency, - result.impedance, - result.real_residual, - result.imaginary_residual, - result.mean_gamma, - result.lower_bound, - result.upper_bound, - result.imaginary_gamma, - result.scores, - result.chisqr, - result.lambda_value, - data.get_mask().copy(), - settings, + uuid=_uuid4().hex, + timestamp=_time(), + time_constants=result.time_constants, + real_gammas=real_gammas, + imaginary_gammas=imaginary_gammas, + frequencies=result.frequencies, + impedances=result.impedances, + residuals=result.residuals, + mean_gammas=result.mean_gammas if hasattr(result, "mean_gammas") else array([]), + lower_bounds=result.lower_bounds + if hasattr(result, "lower_bounds") + else array([]), + upper_bounds=result.upper_bounds + if hasattr(result, "upper_bounds") + else array([]), + scores=result.scores if hasattr(result, "scores") else {}, + pseudo_chisqr=result.pseudo_chisqr, + lambda_value=result.lambda_value if hasattr(result, "lambda_value") else -1.0, + mask=data.get_mask().copy(), + settings=settings, ) diff --git a/src/deareis/api/fitting.py b/src/deareis/api/fitting.py index 40d0aa3..9ef8f89 100644 --- a/src/deareis/api/fitting.py +++ b/src/deareis/api/fitting.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -18,22 +18,22 @@ # the LICENSES folder. from time import time as _time -from typing import Optional +from typing import ( + Dict, + Optional, +) from uuid import uuid4 as _uuid4 from numpy import ( integer as _integer, issubdtype as _issubdtype, ) import pyimpspec as _pyimpspec -from pyimpspec import ( - Circuit, - FittedParameter, - FittingError, -) +from pyimpspec import Circuit from deareis.data import ( DataSet, FitResult, FitSettings, + FittedParameter, ) from deareis.enums import ( CNLSMethod, @@ -48,7 +48,7 @@ def fit_circuit( data: DataSet, settings: FitSettings, - num_procs: int = -1, + num_procs: int = 0, ) -> FitResult: """ Wrapper for the `pyimpspec.fit_circuit` function. @@ -63,8 +63,9 @@ def fit_circuit( settings: FitSettings The settings that determine the circuit and how the fit is performed. - num_procs: int = -1 + num_procs: int, optional The maximum number of parallel processes to use when method is `CNLSMethod.AUTO` and/or weight is `Weight.AUTO`. + A value less than 1 will result in an attempt to automatically figure out a suitable value. Returns ------- @@ -86,24 +87,34 @@ def fit_circuit( weight: Optional[Weight] = _value_to_weight.get(result.weight) assert method is not None assert weight is not None + parameters: Dict[str, Dict[str, FittedParameter]] = {} + for element_symbol in result.parameters: + parameters[element_symbol] = {} + for parameter_symbol, param in result.parameters[element_symbol].items(): + parameters[element_symbol][parameter_symbol] = FittedParameter( + value=param.value, + stderr=param.stderr, + fixed=param.fixed, + unit=param.unit, + ) return FitResult( - _uuid4().hex, - _time(), - result.circuit, - result.parameters, - result.frequency, - result.impedance, - result.real_residual, - result.imaginary_residual, - data.get_mask(), - result.minimizer_result.chisqr, - result.minimizer_result.redchi, - result.minimizer_result.aic, - result.minimizer_result.bic, - result.minimizer_result.ndata, - result.minimizer_result.nfree, - result.minimizer_result.nfev, - method, - weight, - settings, + uuid=_uuid4().hex, + timestamp=_time(), + circuit=result.circuit, + parameters=parameters, + frequencies=result.frequencies, + impedances=result.impedances, + residuals=result.residuals, + mask=data.get_mask(), + pseudo_chisqr=result.pseudo_chisqr, + chisqr=result.minimizer_result.chisqr, + red_chisqr=result.minimizer_result.redchi, + aic=result.minimizer_result.aic, + bic=result.minimizer_result.bic, + ndata=result.minimizer_result.ndata, + nfree=result.minimizer_result.nfree, + nfev=result.minimizer_result.nfev, + method=method, + weight=weight, + settings=settings, ) diff --git a/src/deareis/api/kramers_kronig.py b/src/deareis/api/kramers_kronig.py index 22876f9..1f63daa 100644 --- a/src/deareis/api/kramers_kronig.py +++ b/src/deareis/api/kramers_kronig.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -45,15 +45,15 @@ def perform_test( data: DataSet, settings: TestSettings, - num_procs: int = -1, + num_procs: int = 0, ) -> TestResult: """ Wrapper for the `pyimpspec.perform_test` function. Performs a linear Kramers-Kronig test as described by Boukamp (1995). The results can be used to check the validity of an impedance spectrum before performing equivalent circuit fitting. - If the number of (RC) circuits is less than two, then a suitable number of (RC) circuits is determined using the procedure described by Schönleber et al. (2014) based on a criterion for the calculated mu-value (zero to one). - A mu-value of one represents underfitting and a mu-value of zero represents overfitting. + If the number of (RC) circuits is less than two, then a suitable number of (RC) circuits is determined using the procedure described by Schönleber et al. (2014) based on a criterion for the calculated |mu| (0.0 to 1.0). + A |mu| of 1.0 represents underfitting and a |mu| of 0.0 represents overfitting. References: @@ -69,9 +69,9 @@ def perform_test( The settings that determine how the test is performed. Note that `Test.EXPLORATORY` is not supported by this function. - num_procs: int = -1 + num_procs: int, optional The maximum number of parallel processes to use when performing a test. - A value less than one results in using the number of cores returned by multiprocessing.cpu_count. + A value less than 1 will result in an attempt to automatically figure out a suitable value. Applies only to the `TestMode.CNLS` test. Returns @@ -96,25 +96,24 @@ def perform_test( num_procs=num_procs, ) return TestResult( - _uuid4().hex, - _time(), - result.circuit, - result.num_RC, - result.mu, - result.pseudo_chisqr, - result.frequency, - result.impedance, - result.real_residual, - result.imaginary_residual, - data.get_mask().copy(), - settings, + uuid=_uuid4().hex, + timestamp=_time(), + circuit=result.circuit, + num_RC=result.num_RC, + mu=result.mu, + pseudo_chisqr=result.pseudo_chisqr, + frequencies=result.frequencies, + impedances=result.impedances, + residuals=result.residuals, + mask=data.get_mask().copy(), + settings=settings, ) def perform_exploratory_tests( data: DataSet, settings: TestSettings, - num_procs: int = -1, + num_procs: int = 0, ) -> List[TestResult]: """ Wrapper for the `pyimpspec.perform_exploratory_tests` function. @@ -130,7 +129,7 @@ def perform_exploratory_tests( The settings that determine how the test is performed. Note that only `Test.EXPLORATORY` is supported by this function. - num_procs: int = -1 + num_procs: int, optional See perform_test for details. Returns @@ -140,6 +139,7 @@ def perform_exploratory_tests( assert ( settings.mode == TestMode.EXPLORATORY ), "Use deareis.perform_test to perform the test!" + assert settings.method != CNLSMethod.AUTO num_RCs: List[int] = list(range(1, settings.num_RC + 1)) assert len(num_RCs) > 0, "Invalid settings!" results: List[_pyimpspec.TestResult] = _pyimpspec.perform_exploratory_tests( @@ -158,18 +158,17 @@ def perform_exploratory_tests( return list( map( lambda _: TestResult( - _uuid4().hex, - time, - _.circuit, - _.num_RC, - _.mu, - _.pseudo_chisqr, - _.frequency, - _.impedance, - _.real_residual, - _.imaginary_residual, - mask.copy(), - settings, + uuid=_uuid4().hex, + timestamp=time, + circuit=_.circuit, + num_RC=_.num_RC, + mu=_.mu, + pseudo_chisqr=_.pseudo_chisqr, + frequencies=_.frequencies, + impedances=_.impedances, + residuals=_.residuals, + mask=mask.copy(), + settings=settings, ), results, ) diff --git a/src/deareis/api/plot/__init__.py b/src/deareis/api/plot/__init__.py index 5178fe8..889fda1 100644 --- a/src/deareis/api/plot/__init__.py +++ b/src/deareis/api/plot/__init__.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by diff --git a/src/deareis/api/plot/mpl/__init__.py b/src/deareis/api/plot/mpl/__init__.py index c71e952..a637ea8 100644 --- a/src/deareis/api/plot/mpl/__init__.py +++ b/src/deareis/api/plot/mpl/__init__.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by diff --git a/src/deareis/api/plot/mpl/mpl.py b/src/deareis/api/plot/mpl/mpl.py index f43a90e..92f1bf9 100644 --- a/src/deareis/api/plot/mpl/mpl.py +++ b/src/deareis/api/plot/mpl/mpl.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -71,13 +71,13 @@ def plot( project: Project, x_limits: Optional[Tuple[Optional[float], Optional[float]]] = None, y_limits: Optional[Tuple[Optional[float], Optional[float]]] = None, - show_title: bool = True, - show_legend: Optional[bool] = None, + title: bool = True, + legend: Optional[bool] = None, legend_loc: Union[int, str] = 0, - show_grid: bool = False, + grid: bool = False, tight_layout: bool = False, - fig: Optional[Figure] = None, - axis: Optional[Axes] = None, + figure: Optional[Figure] = None, + axes: List[Axes] = None, num_per_decade: int = 100, ) -> Tuple[Figure, Axes]: """ @@ -91,35 +91,39 @@ def plot( project: Project The project that the plot is a part of. - x_limits: Optional[Tuple[Optional[float], Optional[float]]] = None + x_limits: Optional[Tuple[Optional[float], Optional[float]]], optional The lower and upper limits of the x-axis. - y_limits: Optional[Tuple[Optional[float], Optional[float]]] = None + y_limits: Optional[Tuple[Optional[float], Optional[float]]], optional The lower and upper limits of the y-axis. - show_title: bool = True + title: bool, optional Whether or not to include the title in the figure. - show_legend: Optional[bool] = None + legend: Optional[bool], optional Whether or not to include a legend in the figure. - legend_loc: Union[int, str] = 0 + legend_loc: Union[int, str], optional The position of the legend in the figure. See matplotlib's documentation for valid values. - show_grid: bool = False + grid: bool, optional Whether or not to include a grid in the figure. - tight_layout: bool = False + tight_layout: bool, optional Whether or not to apply a tight layout that the sizes of the reduces margins. - fig: Optional[Figure] = None + figure: Optional[|Figure|], optional The matplotlib.figure.Figure instance to use when plotting the data. - axis: Optional[Axes] = None + axes: List[Axes], optional The matplotlib.axes.Axes instance to use when plotting the data. - num_per_decade: int = 100 + num_per_decade: int, optional If any circuit fits, circuit simulations, or Kramers-Kronig test results are included in the plot, then this parameter can be used to change how many points are used to draw the line (i.e. how smooth or angular the line looks). + + Returns + ------- + Tuple[|Figure|, List[|Axes|]] """ assert type(settings) is PlotSettings, settings assert type(project) is Project, project @@ -147,14 +151,20 @@ def plot( ) ) ), y_limits - assert type(show_title) is bool, show_title - assert type(show_legend) is bool or show_legend is None, show_legend + assert type(title) is bool, title + assert type(legend) is bool or legend is None, legend assert issubdtype(type(legend_loc), integer) or type(legend_loc) is str, legend_loc - assert type(show_grid) is bool, show_grid + assert type(grid) is bool, grid assert type(tight_layout) is bool, tight_layout - assert type(fig) is Figure or fig is None - if fig is None: - fig, axis = plt.subplots() + assert type(figure) is Figure or figure is None + axis: Axes + if figure is None: + assert axes is None + figure, axis = plt.subplots() + axes = [axis] + else: + assert len(axes) > 0 + axis = axes[0] assert axis is not None assert ( issubdtype(type(num_per_decade), integer) and num_per_decade >= 1 @@ -169,12 +179,13 @@ def plot( Union[DataSet, TestResult, DRTResult, FitResult, SimulationResult] ] series = settings.find_series( - uuid, - project.get_data_sets(), - project.get_all_tests(), - project.get_all_drts(), - project.get_all_fits(), - project.get_simulations(), + uuid=uuid, + data_sets=project.get_data_sets(), + tests=project.get_all_tests(), + zhits=project.get_all_zhits(), + drts=project.get_all_drts(), + fits=project.get_all_fits(), + simulations=project.get_simulations(), ) if series is None: continue @@ -192,8 +203,8 @@ def plot( label: Optional[str] = settings.get_series_label(uuid) or series.get_label() if label.strip() == "" and label != "": # type: ignore label = "" - if label is not None and show_legend is None: - show_legend = True + if label is not None and legend is None: + legend = True color: List[float] = list( map(lambda _: _ / 255.0, settings.get_series_color(uuid)) ) @@ -205,144 +216,145 @@ def plot( if has_line: mpl.plot_nyquist( series, - color=color, - marker=marker, + colors={"impedance": color}, + markers={"impedance": marker}, line=True, label=label, legend=False, - fig=fig, - axis=axis, + figure=figure, + axes=[axis], num_per_decade=num_per_decade, adjust_axes=i == num_series - 1, ) if marker is not None: mpl.plot_nyquist( series, - color=color, - marker=marker, + colors={"impedance": color}, + markers={"impedance": marker}, line=False, label="" if has_line else label, legend=False, - fig=fig, - axis=axis, + figure=figure, + axes=[axis], num_per_decade=-1, adjust_axes=i == num_series - 1, ) elif plot_type == PlotType.BODE_MAGNITUDE: if has_line: - mpl.plot_impedance_magnitude( + mpl.plot_magnitude( series, - color=color, - marker=marker, + colors={"magnitude": color}, + markers={"magnitude": marker}, line=True, label=label, legend=False, - fig=fig, - axis=axis, + figure=figure, + axes=[axis], num_per_decade=num_per_decade, adjust_axes=i == num_series - 1, ) if marker is not None: - mpl.plot_impedance_magnitude( + mpl.plot_magnitude( series, - color=color, - marker=marker, + colors={"magnitude": color}, + markers={"magnitude": marker}, line=False, label="" if has_line else label, legend=False, - fig=fig, - axis=axis, + figure=figure, + axes=[axis], num_per_decade=-1, adjust_axes=i == num_series - 1, ) elif plot_type == PlotType.BODE_PHASE: if has_line: - mpl.plot_impedance_phase( + mpl.plot_phase( series, - color=color, - marker=marker, + colors={"phase": color}, + markers={"phase": marker}, line=True, label=label, legend=False, - fig=fig, - axis=axis, + figure=figure, + axes=[axis], num_per_decade=num_per_decade, adjust_axes=i == num_series - 1, ) if marker is not None: - mpl.plot_impedance_phase( + mpl.plot_phase( series, - color=color, - marker=marker, + colors={"phase": color}, + markers={"phase": marker}, line=False, label="" if has_line else label, legend=False, - fig=fig, - axis=axis, + figure=figure, + axes=[axis], num_per_decade=-1, adjust_axes=i == num_series - 1, ) elif plot_type == PlotType.DRT: + num_lines: int = len(axis.lines) mpl.plot_gamma( series, - color=color, + colors={"gamma": color}, label=label, legend=False, - fig=fig, - axis=axis, + figure=figure, + axes=[axis], adjust_axes=i == num_series - 1, ) elif plot_type == PlotType.IMPEDANCE_REAL: if has_line: - mpl.plot_real_impedance( + mpl.plot_real( series, - color=color, - marker=marker, + colors={"real": color}, + markers={"real": marker}, line=True, label=label, legend=False, - fig=fig, - axis=axis, + figure=figure, + axes=[axis], num_per_decade=num_per_decade, adjust_axes=i == num_series - 1, ) if marker is not None: - mpl.plot_real_impedance( + mpl.plot_real( series, - color=color, - marker=marker, + colors={"real": color}, + markers={"real": marker}, line=False, label="" if has_line else label, legend=False, - fig=fig, - axis=axis, + figure=figure, + axes=[axis], num_per_decade=-1, adjust_axes=i == num_series - 1, ) elif plot_type == PlotType.IMPEDANCE_IMAGINARY: if has_line: - mpl.plot_imaginary_impedance( + mpl.plot_imaginary( series, - color=color, - marker=marker, + colors={"imaginary": color}, + markers={"imaginary": marker}, line=True, label=label, legend=False, - fig=fig, - axis=axis, + figure=figure, + axes=[axis], num_per_decade=num_per_decade, adjust_axes=i == num_series - 1, ) if marker is not None: - mpl.plot_imaginary_impedance( + mpl.plot_imaginary( series, - color=color, - marker=marker, + colors={"imaginary": color}, + markers={"imaginary": marker}, line=False, label="" if has_line else label, legend=False, - fig=fig, - axis=axis, + figure=figure, + axes=[axis], num_per_decade=-1, adjust_axes=i == num_series - 1, ) @@ -352,14 +364,14 @@ def plot( axis.set_xlim(x_limits) if y_limits is not None: axis.set_ylim(y_limits) - if show_title and settings.get_label() != "": - fig.suptitle(settings.get_label()) - if show_legend: + if title and settings.get_label() != "": + figure.suptitle(settings.get_label()) + if legend: axis.legend(loc=legend_loc) - axis.grid(visible=show_grid) + axis.grid(visible=grid) if tight_layout: - fig.tight_layout() + figure.tight_layout() return ( - fig, - axis, + figure, + axes, ) diff --git a/src/deareis/api/plotting.py b/src/deareis/api/plotting.py index a9ae5aa..960ad57 100644 --- a/src/deareis/api/plotting.py +++ b/src/deareis/api/plotting.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by diff --git a/src/deareis/api/simulation.py b/src/deareis/api/simulation.py index 0fce292..c48a9a9 100644 --- a/src/deareis/api/simulation.py +++ b/src/deareis/api/simulation.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by diff --git a/src/deareis/api/zhit.py b/src/deareis/api/zhit.py new file mode 100644 index 0000000..8064075 --- /dev/null +++ b/src/deareis/api/zhit.py @@ -0,0 +1,109 @@ +# DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). +# Copyright 2023 DearEIS developers +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +# +# The licenses of DearEIS' dependencies and/or sources of portions of code are included in +# the LICENSES folder. + +from time import time as _time +from uuid import uuid4 as _uuid4 +from numpy import ( + integer as _integer, + issubdtype as _issubdtype, +) +import pyimpspec as _pyimpspec +from deareis.data import ( + DataSet, + ZHITResult, + ZHITSettings, +) +from deareis.enums import ( + ZHITSmoothing, + ZHITInterpolation, + ZHITWindow, + zhit_smoothing_to_value as _zhit_smoothing_to_value, + zhit_interpolation_to_value as _zhit_interpolation_to_value, + zhit_window_to_value as _zhit_window_to_value, +) + + +def perform_zhit( + data: DataSet, + settings: ZHITSettings, + num_procs: int = 0, +) -> ZHITResult: + """ + Wrapper for the `pyimpspec.perform_zhit` function. + + Performs a reconstruction of the modulus data of an impedance spectrum based on the phase data of that impedance spectrum using the Z-HIT algorithm described by Ehm et al. (2000). + The results can be used to, e.g., check the validity of an impedance spectrum by detecting non-steady state issues like drift at low frequencies. + See the references below for more information about the algorithm and its applications. + The algorithm involves an offset adjustment of the reconstructed modulus data, which is done by fitting the reconstructed modulus data to the experimental modulus data in a frequency range that is unaffected (or minimally affected) by artifacts. + This frequency range is typically around 1 Hz to 1000 Hz, which is why the default window function is a "boxcar" window that is centered around :math:`\log{f} = 1.5` and has a width of 3.0. + Multiple window functions are supported and a custom array of weights can also be used. + + References: + + - W. Ehm, H. Göhr, R. Kaus, B. Röseler, and C.A. Schiller, 2000, Acta Chimica Hungarica, 137 (2-3), 145-157. + - W. Ehm, R. Kaus, C.A. Schiller, and W. Strunz, 2001, in "New Trends in Electrochemical Impedance Spectroscopy and Electrochemical Noise Analysis". + - C.A. Schiller, F. Richter, E. Gülzow, and N. Wagner, 2001, 3, 374-378 (https://doi.org/10.1039/B007678N) + + + Parameters + ---------- + data: DataSet + The data to be tested. + + settings: ZHITSettings + The settings that determine how the Z-HIT computation is performed. + + num_procs: int, optional + The maximum number of parallel processes to use when performing the computations. + A value less than 1 will result in an attempt to automatically figure out a suitable value. + Applies only when there are multiple possible options for smoothing, interpolation, or window function. + + Returns + ------- + ZHITResult + """ + assert isinstance(data, _pyimpspec.DataSet), data + assert isinstance(settings, ZHITSettings), settings + assert _issubdtype(type(num_procs), _integer), num_procs + result: _pyimpspec.ZHITResult = _pyimpspec.perform_zhit( + data=data, + smoothing=_zhit_smoothing_to_value[settings.smoothing], + interpolation=_zhit_interpolation_to_value[settings.interpolation], + window=_zhit_window_to_value[settings.window], + num_points=settings.num_points, + polynomial_order=settings.polynomial_order, + num_iterations=settings.num_iterations, + center=settings.window_center, + width=settings.window_width, + weights=None, + num_procs=num_procs, + ) + return ZHITResult( + uuid=_uuid4().hex, + timestamp=_time(), + frequencies=result.frequencies, + impedances=result.impedances, + residuals=result.residuals, + pseudo_chisqr=result.pseudo_chisqr, + mask=data.get_mask().copy(), + smoothing=result.smoothing, + interpolation=result.interpolation, + window=result.window, + settings=settings, + ) diff --git a/src/deareis/arguments.py b/src/deareis/arguments.py index 7b116a0..9f9d9d4 100644 --- a/src/deareis/arguments.py +++ b/src/deareis/arguments.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -34,7 +34,7 @@ def parse() -> Namespace: DearEIS ({PACKAGE_VERSION}) A GUI program for analyzing, simulating, and visualizing impedance spectra. -Copyright (C) 2022 DearEIS developers +Copyright (C) 2023 DearEIS developers This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by diff --git a/src/deareis/config/__init__.py b/src/deareis/config/__init__.py index d66ea6b..d5ecba0 100644 --- a/src/deareis/config/__init__.py +++ b/src/deareis/config/__init__.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -50,6 +50,7 @@ FitSettings, SimulationSettings, TestSettings, + ZHITSettings, ) from deareis.enums import ( Action, @@ -64,6 +65,7 @@ DEFAULT_MARKERS, DEFAULT_COLORS, DEFAULT_TEST_SETTINGS, + DEFAULT_ZHIT_SETTINGS, DEFAULT_FIT_SETTINGS, DEFAULT_SIMULATION_SETTINGS, DEFAULT_DRT_SETTINGS, @@ -75,7 +77,9 @@ def _parse_v4(dictionary: dict) -> dict: - # TODO: Update when VERSION is incremented to 4 + # TODO: Update when VERSION is incremented + if "num_procs" not in dictionary: + dictionary["num_procs"] = 0 return dictionary @@ -107,48 +111,46 @@ def _parse_v3(dictionary: dict) -> dict: del dictionary["export_extension"] del dictionary["export_experimental_clear_registry"] del dictionary["export_experimental_disable_previews"] - return _parse_v4(dictionary) + return dictionary def _parse_v2(dictionary: dict) -> dict: - return _parse_v3( - { - "version": 3, - "auto_backup_interval": dictionary["auto_backup_interval"], - "num_per_decade_in_simulated_lines": dictionary[ - "num_per_decade_in_simulated_lines" - ], - "markers": dictionary["markers"], - "colors": dictionary["colors"], - "default_test_settings": dictionary["default_test_settings"], - "default_fit_settings": dictionary["default_fit_settings"], - "default_drt_settings": dictionary.get( - "default_drt_settings", - DEFAULT_DRT_SETTINGS.to_dict(), - ), - "default_simulation_settings": dictionary["default_simulation_settings"], - "export_units": dictionary.get("export_units", 1), - "export_width": dictionary.get("export_width", 10.0), - "export_height": dictionary.get("export_height", 6.0), - "export_dpi": dictionary.get("export_dpi", 100), - "export_preview": dictionary.get("export_preview", 4), - "export_title": dictionary.get("export_title", True), - "export_legend": dictionary.get("export_legend", True), - "export_legend_location": dictionary.get("export_legend_location", 0), - "export_grid": dictionary.get("export_grid", False), - "export_tight": dictionary.get("export_tight", False), - "export_num_per_decade": dictionary.get("export_num_per_decade", 100), - "export_extension": dictionary.get("export_extension", 4), - "export_experimental_clear_registry": dictionary.get( - "export_experimental_clear_registry", - True, - ), - "export_experimental_disable_previews": dictionary.get( - "export_experimental_disable_previews", - False, - ), - } - ) + return { + "version": 3, + "auto_backup_interval": dictionary["auto_backup_interval"], + "num_per_decade_in_simulated_lines": dictionary[ + "num_per_decade_in_simulated_lines" + ], + "markers": dictionary["markers"], + "colors": dictionary["colors"], + "default_test_settings": dictionary["default_test_settings"], + "default_fit_settings": dictionary["default_fit_settings"], + "default_drt_settings": dictionary.get( + "default_drt_settings", + DEFAULT_DRT_SETTINGS.to_dict(), + ), + "default_simulation_settings": dictionary["default_simulation_settings"], + "export_units": dictionary.get("export_units", 1), + "export_width": dictionary.get("export_width", 10.0), + "export_height": dictionary.get("export_height", 6.0), + "export_dpi": dictionary.get("export_dpi", 100), + "export_preview": dictionary.get("export_preview", 4), + "export_title": dictionary.get("export_title", True), + "export_legend": dictionary.get("export_legend", True), + "export_legend_location": dictionary.get("export_legend_location", 0), + "export_grid": dictionary.get("export_grid", False), + "export_tight": dictionary.get("export_tight", False), + "export_num_per_decade": dictionary.get("export_num_per_decade", 100), + "export_extension": dictionary.get("export_extension", 4), + "export_experimental_clear_registry": dictionary.get( + "export_experimental_clear_registry", + True, + ), + "export_experimental_disable_previews": dictionary.get( + "export_experimental_disable_previews", + False, + ), + } def _parse_v1(dictionary: dict) -> dict: @@ -175,21 +177,19 @@ def _parse_v1(dictionary: dict) -> dict: if old in markers: markers[new] = markers[old] del markers[old] - return _parse_v2( - { - "version": 2, - "auto_backup_interval": dictionary.get("auto_backup_interval", 10), - "num_per_decade_in_simulated_lines": dictionary[ - "num_per_decade_in_simulated_lines" - ], - "markers": dictionary["markers"], - "colors": dictionary["colors"], - "default_test_settings": dictionary["default_test_settings"], - "default_fit_settings": dictionary["default_fit_settings"], - "default_drt_settings": DEFAULT_DRT_SETTINGS.to_dict(), - "default_simulation_settings": dictionary["default_simulation_settings"], - } - ) + return { + "version": 2, + "auto_backup_interval": dictionary.get("auto_backup_interval", 10), + "num_per_decade_in_simulated_lines": dictionary[ + "num_per_decade_in_simulated_lines" + ], + "markers": dictionary["markers"], + "colors": dictionary["colors"], + "default_test_settings": dictionary["default_test_settings"], + "default_fit_settings": dictionary["default_fit_settings"], + "default_drt_settings": DEFAULT_DRT_SETTINGS.to_dict(), + "default_simulation_settings": dictionary["default_simulation_settings"], + } class Config: @@ -199,6 +199,7 @@ def __init__(self): self.auto_backup_interval: int = None # type: ignore self.num_per_decade_in_simulated_lines: int = None # type: ignore self.default_test_settings: TestSettings = None # type: ignore + self.default_zhit_settings: ZHITSettings = None # type: ignore self.default_fit_settings: FitSettings = None # type: ignore self.default_drt_settings: DRTSettings = None # type: ignore self.default_simulation_settings: SimulationSettings = None # type: ignore @@ -206,6 +207,7 @@ def __init__(self): self.markers: Dict[str, int] = None # type: ignore self.colors: Dict[str, List[float]] = None # type: ignore self.keybindings: List[Keybinding] = None # type: ignore + self.user_defined_elements_path: str = None # type: ignore self.from_dict(self.default_settings()) if not exists(self.config_path): self.save() @@ -228,9 +230,11 @@ def __init__(self): def default_settings(self) -> dict: return { "version": VERSION, + "num_procs": 0, "auto_backup_interval": 10, "num_per_decade_in_simulated_lines": 100, "default_test_settings": DEFAULT_TEST_SETTINGS.to_dict(), + "default_zhit_settings": DEFAULT_ZHIT_SETTINGS.to_dict(), "default_fit_settings": DEFAULT_FIT_SETTINGS.to_dict(), "default_drt_settings": DEFAULT_DRT_SETTINGS.to_dict(), "default_plot_export_settings": DEFAULT_PLOT_EXPORT_SETTINGS.to_dict(), @@ -238,6 +242,7 @@ def default_settings(self) -> dict: "colors": DEFAULT_COLORS, "markers": DEFAULT_MARKERS, "keybindings": list(map(lambda _: _.to_dict(), DEFAULT_KEYBINDINGS)), + "user_defined_elements_path": "", } def to_dict(self) -> dict: @@ -245,9 +250,11 @@ def to_dict(self) -> dict: dump_json( # This is done to get new instances in memory { "version": VERSION, + "num_procs": self.num_procs, "auto_backup_interval": self.auto_backup_interval, "num_per_decade_in_simulated_lines": self.num_per_decade_in_simulated_lines, "default_test_settings": self.default_test_settings.to_dict(), + "default_zhit_settings": self.default_zhit_settings.to_dict(), "default_fit_settings": self.default_fit_settings.to_dict(), "default_drt_settings": self.default_drt_settings.to_dict(), "default_simulation_settings": self.default_simulation_settings.to_dict(), @@ -255,6 +262,7 @@ def to_dict(self) -> dict: "colors": self.colors, "markers": self.markers, "keybindings": list(map(lambda _: _.to_dict(), self.keybindings)), + "user_defined_elements_path": self.user_defined_elements_path, } ) ) @@ -278,6 +286,7 @@ def save(self): fp.write(new_config) def from_dict(self, settings: dict): + self.num_procs = settings["num_procs"] self.auto_backup_interval = settings["auto_backup_interval"] self.num_per_decade_in_simulated_lines = settings[ "num_per_decade_in_simulated_lines" @@ -288,6 +297,12 @@ def from_dict(self, settings: dict): DEFAULT_TEST_SETTINGS.to_dict(), ) ) + self.default_zhit_settings = ZHITSettings.from_dict( + settings.get( + "default_zhit_settings", + DEFAULT_ZHIT_SETTINGS.to_dict(), + ) + ) self.default_fit_settings = FitSettings.from_dict( settings.get( "default_fit_settings", @@ -360,7 +375,11 @@ def from_dict(self, settings: dict): for key, theme in color_themes.items(): themes.update_plot_series_theme_color(theme, self.colors[key]) self.keybindings = list(map(Keybinding.from_dict, settings["keybindings"])) - self.validate_keybindings(self.keybindings) + try: + self.validate_keybindings(self.keybindings) + except AssertionError: + print(format_exc()) + self.user_defined_elements_path = settings["user_defined_elements_path"] def check_type(self, user: Any, default: Any, key: str): assert type(user) == type(default), (user, default, key) @@ -401,7 +420,13 @@ def load(self): 4: _parse_v4, } assert version in parsers, f"{version=} not in {parsers.keys()=}" - dictionary = self.merge_dicts(parsers[version](dictionary), self.to_dict()) + v: int + p: Callable + for v, p in parsers.items(): + if v < version: + continue + dictionary = p(dictionary) + dictionary = self.merge_dicts(dictionary, self.to_dict()) self.from_dict(dictionary) def validate_keybindings(self, keybindings: List[Keybinding]): @@ -429,7 +454,7 @@ def validate_keybindings(self, keybindings: List[Keybinding]): "The same keybinding has been applied to multiple actions in the same context or in overlapping contexts:\n- " + "\n- ".join( map(repr, filter(lambda _: str(_) == string, keybindings)) - ) + ) + "\n\nYou should modify one or more of the keybindings to resolve the situation. Alternatively, reset the keybindings." ) actions: List[Action] = list(map(lambda _: _.action, keybindings)) action: Action diff --git a/src/deareis/config/defaults.py b/src/deareis/config/defaults.py index 13ce372..9608d22 100644 --- a/src/deareis/config/defaults.py +++ b/src/deareis/config/defaults.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -35,6 +35,9 @@ RBFType, Test, Weight, + ZHITInterpolation, + ZHITSmoothing, + ZHITWindow, ) from deareis.keybindings import Keybinding from deareis.data.plotting import PlotExportSettings @@ -43,398 +46,469 @@ FitSettings, SimulationSettings, TestSettings, + ZHITSettings, ) DEFAULT_KEYBINDINGS: List[Keybinding] = [ Keybinding( - dpg.mvKey_N, - False, - True, - False, - Action.NEW_PROJECT, + key=dpg.mvKey_N, + mod_alt=False, + mod_ctrl=True, + mod_shift=False, + action=Action.NEW_PROJECT, ), Keybinding( - dpg.mvKey_O, - False, - True, - False, - Action.LOAD_PROJECT, + key=dpg.mvKey_O, + mod_alt=False, + mod_ctrl=True, + mod_shift=False, + action=Action.LOAD_PROJECT, ), Keybinding( - dpg.mvKey_Q, - False, - True, - False, - Action.EXIT, + key=dpg.mvKey_Q, + mod_alt=False, + mod_ctrl=True, + mod_shift=False, + action=Action.EXIT, ), Keybinding( - dpg.mvKey_Next, - False, - False, - True, - Action.NEXT_PROGRAM_TAB, + key=dpg.mvKey_Next, + mod_alt=False, + mod_ctrl=False, + mod_shift=True, + action=Action.NEXT_PROGRAM_TAB, ), Keybinding( - dpg.mvKey_Prior, - False, - False, - True, - Action.PREVIOUS_PROGRAM_TAB, + key=dpg.mvKey_Prior, + mod_alt=False, + mod_ctrl=False, + mod_shift=True, + action=Action.PREVIOUS_PROGRAM_TAB, ), Keybinding( - dpg.mvKey_F1, - False, - False, - False, - Action.SHOW_SETTINGS_APPEARANCE, + key=dpg.mvKey_F1, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.SHOW_SETTINGS_APPEARANCE, ), Keybinding( - dpg.mvKey_F2, - False, - False, - False, - Action.SHOW_SETTINGS_DEFAULTS, + key=dpg.mvKey_F2, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.SHOW_SETTINGS_DEFAULTS, ), Keybinding( - dpg.mvKey_F3, - False, - False, - False, - Action.SHOW_SETTINGS_KEYBINDINGS, + key=dpg.mvKey_F3, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.SHOW_SETTINGS_KEYBINDINGS, ), Keybinding( - dpg.mvKey_F11, - False, - False, - False, - Action.SHOW_HELP_LICENSES, + key=dpg.mvKey_F11, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.SHOW_HELP_LICENSES, ), Keybinding( - dpg.mvKey_F12, - False, - False, - False, - Action.SHOW_HELP_ABOUT, + key=dpg.mvKey_F12, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.SHOW_HELP_ABOUT, ), Keybinding( - dpg.mvKey_P, - False, - True, - False, - Action.SHOW_COMMAND_PALETTE, + key=dpg.mvKey_P, + mod_alt=False, + mod_ctrl=True, + mod_shift=False, + action=Action.SHOW_COMMAND_PALETTE, ), Keybinding( - dpg.mvKey_S, - False, - True, - False, - Action.SAVE_PROJECT, + key=dpg.mvKey_S, + mod_alt=False, + mod_ctrl=True, + mod_shift=False, + action=Action.SAVE_PROJECT, ), Keybinding( - dpg.mvKey_S, - False, - True, - True, - Action.SAVE_PROJECT_AS, + key=dpg.mvKey_S, + mod_alt=False, + mod_ctrl=True, + mod_shift=True, + action=Action.SAVE_PROJECT_AS, ), Keybinding( - dpg.mvKey_W, - False, - True, - False, - Action.CLOSE_PROJECT, + key=dpg.mvKey_W, + mod_alt=False, + mod_ctrl=True, + mod_shift=False, + action=Action.CLOSE_PROJECT, ), Keybinding( - dpg.mvKey_Z, - False, - True, - False, - Action.UNDO, + key=dpg.mvKey_Z, + mod_alt=False, + mod_ctrl=True, + mod_shift=False, + action=Action.UNDO, ), Keybinding( - dpg.mvKey_Y if dpg.get_platform() == dpg.mvPlatform_Windows else dpg.mvKey_Z, - False, - True, - False if dpg.get_platform() == dpg.mvPlatform_Windows else True, - Action.REDO, + key=dpg.mvKey_Y if dpg.get_platform() == dpg.mvPlatform_Windows else dpg.mvKey_Z, + mod_alt=False, + mod_ctrl=True, + mod_shift=False if dpg.get_platform() == dpg.mvPlatform_Windows else True, + action=Action.REDO, ), Keybinding( - dpg.mvKey_Next, - False, - True, - False, - Action.NEXT_PROJECT_TAB, + key=dpg.mvKey_Next, + mod_alt=False, + mod_ctrl=True, + mod_shift=False, + action=Action.NEXT_PROJECT_TAB, ), Keybinding( - dpg.mvKey_Prior, - False, - True, - False, - Action.PREVIOUS_PROJECT_TAB, + key=dpg.mvKey_Prior, + mod_alt=False, + mod_ctrl=True, + mod_shift=False, + action=Action.PREVIOUS_PROJECT_TAB, ), Keybinding( - dpg.mvKey_D, - True, - True, - False, - Action.SELECT_DATA_SETS_TAB, + key=dpg.mvKey_D, + mod_alt=True, + mod_ctrl=True, + mod_shift=False, + action=Action.SELECT_DATA_SETS_TAB, ), Keybinding( - dpg.mvKey_F, - True, - True, - False, - Action.SELECT_FITTING_TAB, + key=dpg.mvKey_F, + mod_alt=True, + mod_ctrl=True, + mod_shift=False, + action=Action.SELECT_FITTING_TAB, ), Keybinding( - dpg.mvKey_K, - True, - True, - False, - Action.SELECT_KRAMERS_KRONIG_TAB, + key=dpg.mvKey_K, + mod_alt=True, + mod_ctrl=True, + mod_shift=False, + action=Action.SELECT_KRAMERS_KRONIG_TAB, ), Keybinding( - dpg.mvKey_O, - True, - True, - False, - Action.SELECT_OVERVIEW_TAB, + key=dpg.mvKey_Z, + mod_alt=True, + mod_ctrl=True, + mod_shift=False, + action=Action.SELECT_ZHIT_TAB, ), Keybinding( - dpg.mvKey_P, - True, - True, - False, - Action.SELECT_PLOTTING_TAB, + key=dpg.mvKey_T, + mod_alt=True, + mod_ctrl=True, + mod_shift=False, + action=Action.SELECT_DRT_TAB, ), Keybinding( - dpg.mvKey_S, - True, - True, - False, - Action.SELECT_SIMULATION_TAB, + key=dpg.mvKey_Home, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.SELECT_HOME_TAB, ), Keybinding( - dpg.mvKey_Return, - False if dpg.get_platform() == dpg.mvPlatform_Windows else True, - True if dpg.get_platform() == dpg.mvPlatform_Windows else False, - False, - Action.PERFORM_ACTION, + key=dpg.mvKey_O, + mod_alt=True, + mod_ctrl=True, + mod_shift=False, + action=Action.SELECT_OVERVIEW_TAB, ), Keybinding( - dpg.mvKey_Delete, - True, - False, - False, - Action.DELETE_RESULT, + key=dpg.mvKey_P, + mod_alt=True, + mod_ctrl=True, + mod_shift=False, + action=Action.SELECT_PLOTTING_TAB, ), Keybinding( - dpg.mvKey_Next, - False, - False, - False, - Action.NEXT_PRIMARY_RESULT, + key=dpg.mvKey_S, + mod_alt=True, + mod_ctrl=True, + mod_shift=False, + action=Action.SELECT_SIMULATION_TAB, ), Keybinding( - dpg.mvKey_Prior, - False, - False, - False, - Action.PREVIOUS_PRIMARY_RESULT, + key=dpg.mvKey_Return, + mod_alt=False if dpg.get_platform() == dpg.mvPlatform_Windows else True, + mod_ctrl=True if dpg.get_platform() == dpg.mvPlatform_Windows else False, + mod_shift=False, + action=Action.PERFORM_ACTION, ), Keybinding( - dpg.mvKey_Next, - True, - False, - False, - Action.NEXT_SECONDARY_RESULT, + key=dpg.mvKey_Return, + mod_alt=False if dpg.get_platform() == dpg.mvPlatform_Windows else True, + mod_ctrl=True if dpg.get_platform() == dpg.mvPlatform_Windows else False, + mod_shift=True, + action=Action.BATCH_PERFORM_ACTION, ), Keybinding( - dpg.mvKey_Prior, - True, - False, - False, - Action.PREVIOUS_SECONDARY_RESULT, + key=dpg.mvKey_Delete, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.DELETE_RESULT, ), Keybinding( - dpg.mvKey_A, - True, - False, - False, - Action.APPLY_SETTINGS, + key=dpg.mvKey_Next, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.NEXT_PRIMARY_RESULT, ), Keybinding( - dpg.mvKey_M, - True, - False, - False, - Action.APPLY_MASK, + key=dpg.mvKey_Prior, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.PREVIOUS_PRIMARY_RESULT, ), Keybinding( - dpg.mvKey_N, - True, - False, - False, - Action.SHOW_ENLARGED_NYQUIST, + key=dpg.mvKey_Next, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.NEXT_SECONDARY_RESULT, ), Keybinding( - dpg.mvKey_D, - True, - False, - False, - Action.SHOW_ENLARGED_DRT, + key=dpg.mvKey_Prior, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.PREVIOUS_SECONDARY_RESULT, ), Keybinding( - dpg.mvKey_I, - True, - False, - False, - Action.SHOW_ENLARGED_IMPEDANCE, + key=dpg.mvKey_A, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.APPLY_SETTINGS, ), Keybinding( - dpg.mvKey_B, - True, - False, - False, - Action.SHOW_ENLARGED_BODE, + key=dpg.mvKey_M, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.APPLY_MASK, ), Keybinding( - dpg.mvKey_R, - True, - False, - False, - Action.SHOW_ENLARGED_RESIDUALS, + key=dpg.mvKey_N, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.SHOW_ENLARGED_NYQUIST, ), Keybinding( - dpg.mvKey_E, - True, - False, - False, - Action.SHOW_CIRCUIT_EDITOR, + key=dpg.mvKey_D, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.SHOW_ENLARGED_DRT, ), Keybinding( - dpg.mvKey_D, - True, - False, - True, - Action.COPY_DRT_DATA, + key=dpg.mvKey_R, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.SHOW_ENLARGED_IMPEDANCE, ), Keybinding( - dpg.mvKey_I, - True, - False, - True, - Action.COPY_IMPEDANCE_DATA, + key=dpg.mvKey_B, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.SHOW_ENLARGED_BODE, ), Keybinding( - dpg.mvKey_N, - True, - False, - True, - Action.COPY_NYQUIST_DATA, + key=dpg.mvKey_E, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.SHOW_CIRCUIT_EDITOR, ), Keybinding( - dpg.mvKey_B, - True, - False, - True, - Action.COPY_BODE_DATA, + key=dpg.mvKey_D, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.COPY_DRT_DATA, ), Keybinding( - dpg.mvKey_R, - True, - False, - True, - Action.COPY_RESIDUALS_DATA, + key=dpg.mvKey_I, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.COPY_IMPEDANCE_DATA, ), Keybinding( - dpg.mvKey_C, - True, - False, - False, - Action.COPY_OUTPUT, + key=dpg.mvKey_N, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.COPY_NYQUIST_DATA, ), Keybinding( - dpg.mvKey_A, - True, - False, - False, - Action.AVERAGE_DATA_SETS, + key=dpg.mvKey_B, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.COPY_BODE_DATA, ), Keybinding( - dpg.mvKey_T, - True, - False, - False, - Action.TOGGLE_DATA_POINTS, + key=dpg.mvKey_R, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.COPY_RESIDUALS_DATA, ), Keybinding( - dpg.mvKey_C, - True, - False, - False, - Action.COPY_DATA_SET_MASK, + key=dpg.mvKey_C, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.COPY_OUTPUT, ), Keybinding( - dpg.mvKey_S, - True, - False, - False, - Action.SUBTRACT_IMPEDANCE, + key=dpg.mvKey_A, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.AVERAGE_DATA_SETS, ), Keybinding( - dpg.mvKey_A, - True, - False, - False, - Action.SELECT_ALL_PLOT_SERIES, + key=dpg.mvKey_T, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.TOGGLE_DATA_POINTS, ), Keybinding( - dpg.mvKey_A, - True, - False, - True, - Action.UNSELECT_ALL_PLOT_SERIES, + key=dpg.mvKey_C, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.COPY_DATA_SET_MASK, ), Keybinding( - dpg.mvKey_C, - True, - False, - False, - Action.COPY_PLOT_APPEARANCE, + key=dpg.mvKey_S, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.SUBTRACT_IMPEDANCE, ), Keybinding( - dpg.mvKey_C, - True, - False, - True, - Action.COPY_PLOT_DATA, + key=dpg.mvKey_I, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.INTERPOLATE_POINTS, ), Keybinding( - dpg.mvKey_E, - True, - False, - False, - Action.EXPAND_COLLAPSE_SIDEBAR, + key=dpg.mvKey_A, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.SELECT_ALL_PLOT_SERIES, ), Keybinding( - dpg.mvKey_P, - True, - False, - False, - Action.EXPORT_PLOT, + key=dpg.mvKey_A, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.UNSELECT_ALL_PLOT_SERIES, ), Keybinding( - dpg.mvKey_L, - True, - False, - False, - Action.LOAD_SIMULATION_AS_DATA_SET, + key=dpg.mvKey_C, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.COPY_PLOT_APPEARANCE, + ), + Keybinding( + key=dpg.mvKey_C, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.COPY_PLOT_DATA, + ), + Keybinding( + key=dpg.mvKey_E, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.EXPAND_COLLAPSE_SIDEBAR, + ), + Keybinding( + key=dpg.mvKey_P, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.EXPORT_PLOT, + ), + Keybinding( + key=dpg.mvKey_P, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.ADJUST_PARAMETERS, + ), + Keybinding( + key=dpg.mvKey_L, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.LOAD_SIMULATION_AS_DATA_SET, + ), + Keybinding( + key=dpg.mvKey_L, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.LOAD_ZHIT_AS_DATA_SET, + ), + Keybinding( + key=dpg.mvKey_W, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.PREVIEW_ZHIT_WEIGHTS, + ), + Keybinding( + key=dpg.mvKey_D, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.DUPLICATE_PLOT, + ), + Keybinding( + key=dpg.mvKey_Next, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.NEXT_PLOT_TAB, + ), + Keybinding( + key=dpg.mvKey_Prior, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.PREVIOUS_PLOT_TAB, ), ] # TODO: Replace string keys with int keys (e.g. themes.nyquist.data) and use strings only in the -# config file +# config file? DEFAULT_MARKERS: Dict[str, int] = { "bode_magnitude_data": dpg.mvPlotMarker_Circle, "bode_magnitude_simulation": dpg.mvPlotMarker_Cross, @@ -454,17 +528,17 @@ DEFAULT_COLORS: Dict[str, List[float]] = { "residuals_real": [ - 238.0, - 51.0, - 119.0, - 190.0, - ], - "residuals_imaginary": [ 0.0, 153.0, 136.0, 190.0, ], + "residuals_imaginary": [ + 238.0, + 51.0, + 119.0, + 190.0, + ], "nyquist_data": [ 51.0, 187.0, @@ -532,27 +606,27 @@ 190.0, ], "impedance_real_data": [ - 51.0, - 187.0, 238.0, + 119.0, + 51.0, 190.0, ], "impedance_real_simulation": [ - 238.0, - 51.0, - 119.0, + 0.0, + 153.0, + 136.0, 190.0, ], "impedance_imaginary_data": [ - 238.0, - 119.0, 51.0, + 187.0, + 238.0, 190.0, ], "impedance_imaginary_simulation": [ - 0.0, - 153.0, - 136.0, + 238.0, + 51.0, + 119.0, 190.0, ], "drt_real_gamma": [ @@ -616,11 +690,12 @@ shape_coeff=0.5, inductance=False, credible_intervals=False, + timeout=60, num_samples=2000, num_attempts=10, maximum_symmetry=0.5, - circuit=None, - W=0.15, + fit=None, + gaussian_width=0.15, num_per_decade=100, ) @@ -640,3 +715,15 @@ clear_registry=True, disable_preview=False, ) + + +DEFAULT_ZHIT_SETTINGS: ZHITSettings = ZHITSettings( + smoothing=ZHITSmoothing.LOWESS, + num_points=5, + polynomial_order=2, + num_iterations=3, + interpolation=ZHITInterpolation.AKIMA, + window=ZHITWindow.HANN, + window_center=1.5, + window_width=3.0, +) diff --git a/src/deareis/data/__init__.py b/src/deareis/data/__init__.py index cc03a74..837d658 100644 --- a/src/deareis/data/__init__.py +++ b/src/deareis/data/__init__.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -26,6 +26,7 @@ from .fitting import ( FitResult, FitSettings, + FittedParameter, ) from .simulation import ( SimulationResult, @@ -39,3 +40,7 @@ DRTResult, DRTSettings, ) +from .zhit import ( + ZHITResult, + ZHITSettings, +) diff --git a/src/deareis/data/data_sets.py b/src/deareis/data/data_sets.py index a2d1054..2c67a24 100644 --- a/src/deareis/data/data_sets.py +++ b/src/deareis/data/data_sets.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -17,10 +17,6 @@ # The licenses of DearEIS' dependencies and/or sources of portions of code are included in # the LICENSES folder. -from typing import ( - Dict, - Optional, -) from numpy import allclose import pyimpspec @@ -32,26 +28,29 @@ class DataSet(pyimpspec.DataSet): Parameters ---------- - frequency: ndarray + frequencies: Frequencies A 1-dimensional array of frequencies in hertz. - impedance: ndarray + impedances: ComplexImpedances A 1-dimensional array of complex impedances in ohms. - mask: Dict[int, bool] = {} + mask: Dict[int, bool], optional A mapping of integer indices to boolean values where a value of True means that the data point is to be omitted. - path: str = "" + path: str, optional The path to the file that has been parsed to generate this DataSet instance. - label: str = "" + label: str, optional The label assigned to this DataSet instance. - uuid: str = "" + uuid: str, optional The universivally unique identifier assigned to this DataSet instance. If empty, then one will be automatically assigned. """ + def __hash__(self) -> int: + return int(self.uuid, 16) + def __eq__(self, other) -> bool: # This is implemented because gui/data_sets.py checks if the newly selected DataSet is the # same as the current DataSet (if there even is one) and then decides whether to clear the @@ -79,10 +78,12 @@ def __eq__(self, other) -> bool: other.get_num_points(masked=None), ) assert allclose( - self.get_frequency(masked=None), other.get_frequency(masked=None) + self.get_frequencies(masked=None), + other.get_frequencies(masked=None), ) assert allclose( - self.get_impedance(masked=None), other.get_impedance(masked=None) + self.get_impedances(masked=None), + other.get_impedances(masked=None), ) except AssertionError: return False @@ -116,10 +117,6 @@ def from_dict(Class, dictionary: dict) -> "DataSet": Create an instance from a dictionary. """ # This is implemented to deal with the effects of the modified to_dict method. - mask: Optional[Dict[str, bool]] = dictionary.get("mask") - if type(mask) is dict and len(mask) < len(dictionary["frequency"]): - i: int - for i in range(0, len(dictionary["frequency"])): - if mask.get(str(i)) is not True: - mask[str(i)] = False + if any(map(lambda _: type(_) is str, dictionary["mask"].keys())): + dictionary["mask"] = {int(k): v for k, v in dictionary["mask"].items()} return Class(**Class._parse(dictionary)) diff --git a/src/deareis/data/drt.py b/src/deareis/data/drt.py index 01820e6..a59782f 100644 --- a/src/deareis/data/drt.py +++ b/src/deareis/data/drt.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -21,6 +21,7 @@ from typing import ( Callable, Dict, + List, Tuple, Optional, ) @@ -28,15 +29,28 @@ angle, array, floating, + full, + isnan, issubdtype, - ndarray, + nan, ) from scipy.signal import find_peaks from pandas import DataFrame +from pyimpspec.analysis.utility import _calculate_pseudo_chisqr from pyimpspec import ( - Circuit, - parse_cdc, + ComplexImpedances, + ComplexResiduals, + Frequencies, + Gamma, + Gammas, + Impedances, + Indices, + Phases, + Residuals, + TimeConstant, + TimeConstants, ) +from deareis.data.fitting import FitResult from deareis.enums import ( DRTMethod, DRTMode, @@ -51,14 +65,30 @@ rbf_shape_to_label, rbf_type_to_label, ) -from deareis.utility import format_timestamp +from deareis.utility import ( + format_timestamp, + rename_dict_entry, +) +from deareis.data import DataSet + + +VERSION: int = 3 -VERSION: int = 2 +def _parse_settings_v3(dictionary: dict) -> dict: + if "fit" not in dictionary: + dictionary["fit"] = None + if "circuit" in dictionary: + del dictionary["circuit"] + if "timeout" not in dictionary: + dictionary["timeout"] = 60 + return dictionary def _parse_settings_v2(dictionary: dict) -> dict: - # TODO: Update implementation once VERSION is incremented + rename_dict_entry(dictionary, "W", "gaussian_width") + dictionary["fit"] = None + del dictionary["circuit"] return dictionary @@ -66,7 +96,7 @@ def _parse_settings_v1(dictionary: dict) -> dict: dictionary["circuit"] = "" dictionary["W"] = 0.15 dictionary["num_per_decade"] = 100 - return _parse_settings_v2(dictionary) + return dictionary @dataclass(frozen=True) @@ -105,11 +135,15 @@ class DRTSettings: inductance: bool Whether or not to include an inductive term in the calculations. - TR-RBF methods only. + TR-RBF method only. credible_intervals: bool Whether or not to calculate Bayesian credible intervals. - TR-RBF methods only. + TR-RBF method only. + + timeout: int + The number of seconds to wait for the calculation of credible intervals to complete. + TR-RBF method only. num_samples: int The number of samples to use when calculating: @@ -126,15 +160,13 @@ class DRTSettings: Smaller values provide stricter conditions. BHT and TR-RBF methods only. - circuit: Optional[Circuit] - A circuit that contains one or more "(RQ)" or "(RC)" elements connected in series. + fit: Optional[FitResult] + The FitResult for a circuit that contains one or more "(RQ)" or "(RC)" elements connected in series. An optional series resistance may also be included. For example, a circuit with a CDC representation of "R(RQ)(RQ)(RC)" would be a valid circuit. - It is highly recommended that the provided circuit has already been fitted. - However, if all of the various parameters of the provided circuit are at their default values, then an attempt will be made to fit the circuit to the data. m(RQ)fit method only. - W: float + gaussian_width: float The width of the Gaussian curve that is used to approximate the DRT of an "(RC)" element. m(RQ)fit method only. @@ -152,11 +184,12 @@ class DRTSettings: shape_coeff: float inductance: bool credible_intervals: bool + timeout: int num_samples: int num_attempts: int maximum_symmetry: float - circuit: Optional[Circuit] - W: float + fit: Optional[FitResult] + gaussian_width: float num_per_decade: int def __repr__(self) -> str: @@ -174,11 +207,12 @@ def to_dict(self) -> dict: "shape_coeff": self.shape_coeff, "inductance": self.inductance, "credible_intervals": self.credible_intervals, + "timeout": self.timeout, "num_samples": self.num_samples, "num_attempts": self.num_attempts, "maximum_symmetry": self.maximum_symmetry, - "circuit": self.circuit.to_string(12) if self.circuit is not None else "", - "W": self.W, + "fit": self.fit.to_dict(session=False) if self.fit is not None else None, + "gaussian_width": self.gaussian_width, "num_per_decade": self.num_per_decade, } @@ -187,31 +221,71 @@ def from_dict(Class, dictionary: dict) -> "DRTSettings": assert type(dictionary) is dict assert "version" in dictionary version: int = dictionary["version"] + del dictionary["version"] assert version <= VERSION, f"{version=} > {VERSION=}" parsers: Dict[int, Callable] = { 1: _parse_settings_v1, 2: _parse_settings_v2, + 3: _parse_settings_v3, } assert version in parsers, f"{version=} not in {parsers.keys()=}" - del dictionary["version"] - dictionary = parsers[version](dictionary) + v: int + p: Callable + for v, p in parsers.items(): + if v < version: + continue + dictionary = p(dictionary) + assert "method" in dictionary + assert "mode" in dictionary + assert "lambda_value" in dictionary + assert "rbf_type" in dictionary + assert "derivative_order" in dictionary + assert "rbf_shape" in dictionary + assert "shape_coeff" in dictionary + assert "inductance" in dictionary + assert "credible_intervals" in dictionary + assert "timeout" in dictionary + assert "num_samples" in dictionary + assert "num_attempts" in dictionary + assert "maximum_symmetry" in dictionary + assert "fit" in dictionary + assert "gaussian_width" in dictionary + assert "num_per_decade" in dictionary dictionary["method"] = DRTMethod(dictionary["method"]) dictionary["mode"] = DRTMode(dictionary["mode"]) dictionary["rbf_type"] = RBFType(dictionary["rbf_type"]) dictionary["rbf_shape"] = RBFShape(dictionary["rbf_shape"]) - dictionary["circuit"] = ( - parse_cdc(dictionary["circuit"]) if dictionary["circuit"] != "" else None - ) + if dictionary["fit"] is not None: + dictionary["fit"] = FitResult.from_dict(dictionary["fit"]) return Class(**dictionary) +def _parse_result_v3(dictionary: dict) -> dict: + if "pseudo_chisqr" not in dictionary: + dictionary["pseudo_chisqr"] = nan + if "chisqr" in dictionary: + del dictionary["chisqr"] + return dictionary + + def _parse_result_v2(dictionary: dict) -> dict: - # TODO: Update implementation once VERSION is incremented + rename_dict_entry(dictionary, "tau", "time_constants") + rename_dict_entry(dictionary, "gamma", "real_gammas") + rename_dict_entry(dictionary, "imaginary_gamma", "imaginary_gammas") + rename_dict_entry(dictionary, "real_impedance", "real_impedances") + rename_dict_entry(dictionary, "imaginary_impedance", "imaginary_impedances") + rename_dict_entry(dictionary, "frequency", "frequencies") + rename_dict_entry(dictionary, "real_residual", "real_residuals") + rename_dict_entry(dictionary, "imaginary_residual", "imaginary_residuals") + rename_dict_entry(dictionary, "mean_gamma", "mean_gammas") + rename_dict_entry(dictionary, "lower_bound", "lower_bounds") + rename_dict_entry(dictionary, "upper_bound", "upper_bounds") + dictionary["chisqr"] = nan return dictionary def _parse_result_v1(dictionary: dict) -> dict: - return _parse_result_v2(dictionary) + return dictionary @dataclass @@ -227,47 +301,43 @@ class DRTResult: timestamp: float The Unix time (in seconds) for when the test was performed. - tau: ndarray + time_constants: TimeConstants The time constants (in seconds). - gamma: ndarray - The corresponding gamma(tau) values (in ohms). - These are the gamma(tau) for the real part when the BHT method has been used. + real_gammas: Gammas + The corresponding gamma values (in ohms). + + imaginary_gammas: Gammas + The gamma values calculated based the imaginary part of the impedance data. + Only non-empty when the TR-RBF method has been used. - frequency: ndarray + frequencies: Frequencies The frequencies of the analyzed data set. - impedance: ndarray + impedances: ComplexImpedances The modeled impedances. - real_residual: ndarray - The residuals for the real parts of the modeled and experimental impedances. + residuals: ComplexResiduals + The residuals for the real and imaginary parts of the modeled impedances. - imaginary_residual: ndarray - The residuals for the imaginary parts of the modeled and experimental impedances. - - mean_gamma: ndarray + mean_gammas: Gammas The mean values for gamma(tau). Only non-empty when the TR-RBF method has been used and the Bayesian credible intervals have been calculated. - lower_bound: ndarray + lower_bounds: Gammas The lower bound for the gamma(tau) values. Only non-empty when the TR-RBF method has been used and the Bayesian credible intervals have been calculated. - upper_bound: ndarray + upper_bounds: Gammas The upper bound for the gamma(tau) values. Only non-empty when the TR-RBF method has been used and the Bayesian credible intervals have been calculated. - imaginary_gamma: ndarray - These are the gamma(tau) for the imaginary part when the BHT method has been used. - Only non-empty when the BHT method has been used. - scores: Dict[str, complex] The scores calculated for the analyzed data set. Only non-empty when the BHT method has been used. - chisqr: float - The chi-square goodness of fit value for the modeled impedance. + pseudo_chisqr: float + The calculated |pseudo chi-squared| (eq. 14 in Boukamp, 1995). lambda_value: float The regularization parameter used as part of the Tikhonov regularization. @@ -282,18 +352,17 @@ class DRTResult: uuid: str timestamp: float - tau: ndarray - gamma: ndarray - frequency: ndarray - impedance: ndarray - real_residual: ndarray - imaginary_residual: ndarray - mean_gamma: ndarray - lower_bound: ndarray - upper_bound: ndarray - imaginary_gamma: ndarray + time_constants: TimeConstants + real_gammas: Gammas + imaginary_gammas: Gammas + frequencies: Frequencies + impedances: ComplexImpedances + residuals: ComplexResiduals + mean_gammas: Gammas + lower_bounds: Gammas + upper_bounds: Gammas scores: Dict[str, complex] - chisqr: float + pseudo_chisqr: float lambda_value: float mask: Dict[int, bool] settings: DRTSettings @@ -302,50 +371,99 @@ def __repr__(self) -> str: return f"DRTResult ({self.get_label()}, {hex(id(self))})" @classmethod - def from_dict(Class, dictionary: dict) -> "DRTResult": + def from_dict( + Class, dictionary: dict, data: Optional[DataSet] = None + ) -> "DRTResult": """ Create an instance from a dictionary. + + Parameters + ---------- + dictionary: dict + The dictionary to turn into a DRTResult object. + + data: Optional[DataSet], optional + The DataSet object that this result is for. + + Returns + ------- + DRTResult """ - assert type(dictionary) is dict + assert isinstance(dictionary, dict), dictionary + assert data is None or isinstance(data, DataSet), data assert "version" in dictionary version: int = dictionary["version"] + del dictionary["version"] assert version <= VERSION, f"{version=} > {VERSION=}" parsers: Dict[int, Callable] = { 1: _parse_result_v1, 2: _parse_result_v2, + 3: _parse_result_v3, } assert version in parsers, f"{version=} not in {parsers.keys()=}" - dictionary = parsers[version](dictionary) - dictionary["tau"] = array(dictionary["tau"]) - dictionary["gamma"] = array(dictionary["gamma"]) - dictionary["frequency"] = array(dictionary["frequency"]) - dictionary["real_residual"] = array(dictionary["real_residual"]) - dictionary["imaginary_residual"] = array(dictionary["imaginary_residual"]) - dictionary["mean_gamma"] = array(dictionary["mean_gamma"]) - dictionary["lower_bound"] = array(dictionary["lower_bound"]) - dictionary["upper_bound"] = array(dictionary["upper_bound"]) - dictionary["imaginary_gamma"] = array(dictionary["imaginary_gamma"]) + v: int + p: Callable + for v, p in parsers.items(): + if v < version: + continue + dictionary = p(dictionary) + assert "uuid" in dictionary + assert "timestamp" in dictionary + assert "time_constants" in dictionary + assert "real_gammas" in dictionary + assert "imaginary_gammas" in dictionary + assert "real_impedances" in dictionary + assert "imaginary_impedances" in dictionary + assert "frequencies" in dictionary + assert "real_residuals" in dictionary + assert "imaginary_residuals" in dictionary + assert "mean_gammas" in dictionary + assert "lower_bounds" in dictionary + assert "upper_bounds" in dictionary + assert "real_scores" in dictionary + assert "imaginary_scores" in dictionary + assert "pseudo_chisqr" in dictionary + assert "lambda_value" in dictionary + assert "mask" in dictionary + assert "settings" in dictionary + dictionary["time_constants"] = array(dictionary["time_constants"]) + dictionary["real_gammas"] = array(dictionary["real_gammas"]) + dictionary["imaginary_gammas"] = array(dictionary["imaginary_gammas"]) + dictionary["frequencies"] = array(dictionary["frequencies"]) + dictionary["mean_gammas"] = array(dictionary["mean_gammas"]) + dictionary["lower_bounds"] = array(dictionary["lower_bounds"]) + dictionary["upper_bounds"] = array(dictionary["upper_bounds"]) dictionary["settings"] = DRTSettings.from_dict(dictionary["settings"]) - del dictionary["version"] mask: Dict[str, bool] = dictionary["mask"] - key: str - for key in list(mask.keys()): - flag: bool = mask[key] - del mask[key] - mask[int(key)] = flag - dictionary["impedance"] = array( + dictionary["mask"] = { + i: mask.get(str(i), False) for i in range(0, len(dictionary["frequencies"])) + } + dictionary["impedances"] = array( + list( + map( + lambda _: complex(*_), + zip( + dictionary["real_impedances"], + dictionary["imaginary_impedances"], + ), + ) + ) + ) + del dictionary["real_impedances"] + del dictionary["imaginary_impedances"] + dictionary["residuals"] = array( list( map( lambda _: complex(*_), zip( - dictionary["real_impedance"], - dictionary["imaginary_impedance"], + dictionary["real_residuals"], + dictionary["imaginary_residuals"], ), ) ) ) - del dictionary["real_impedance"] - del dictionary["imaginary_impedance"] + del dictionary["real_residuals"] + del dictionary["imaginary_residuals"] dictionary["scores"] = { k: complex( dictionary["real_scores"][k], @@ -355,30 +473,39 @@ def from_dict(Class, dictionary: dict) -> "DRTResult": } del dictionary["real_scores"] del dictionary["imaginary_scores"] + if isnan(dictionary["pseudo_chisqr"]): + dictionary["pseudo_chisqr"] = _calculate_pseudo_chisqr( + Z_exp=data.get_impedances(), + Z_fit=dictionary["impedances"], + ) return Class(**dictionary) def to_dict(self) -> dict: """ Return a dictionary that can be used to recreate an instance. + + Returns + ------- + dict """ dictionary: dict = { "version": VERSION, "uuid": self.uuid, "timestamp": self.timestamp, - "tau": list(self.tau), - "gamma": list(self.gamma), - "real_impedance": list(self.impedance.real), - "imaginary_impedance": list(self.impedance.imag), - "frequency": list(self.frequency), - "real_residual": list(self.real_residual), - "imaginary_residual": list(self.imaginary_residual), - "mean_gamma": list(self.mean_gamma), - "lower_bound": list(self.lower_bound), - "upper_bound": list(self.upper_bound), - "imaginary_gamma": list(self.imaginary_gamma), + "time_constants": list(self.time_constants), + "real_gammas": list(self.real_gammas), + "imaginary_gammas": list(self.imaginary_gammas), + "real_impedances": list(self.impedances.real), + "imaginary_impedances": list(self.impedances.imag), + "frequencies": list(self.frequencies), + "real_residuals": list(self.residuals.real), + "imaginary_residuals": list(self.residuals.imag), + "mean_gammas": list(self.mean_gammas), + "lower_bounds": list(self.lower_bounds), + "upper_bounds": list(self.upper_bounds), "real_scores": {k: v.real for k, v in self.scores.items()}, "imaginary_scores": {k: v.imag for k, v in self.scores.items()}, - "chisqr": self.chisqr, + "pseudo_chisqr": self.pseudo_chisqr, "lambda_value": self.lambda_value, "mask": {k: True for k, v in self.mask.items() if v is True}, "settings": self.settings.to_dict(), @@ -388,221 +515,250 @@ def to_dict(self) -> dict: def get_label(self) -> str: """ Generate a label for the result. + + Returns + ------- + str """ method: str = drt_method_to_label[self.settings.method] timestamp: str = format_timestamp(self.timestamp) return f"{method} ({timestamp})" - def get_frequency(self) -> ndarray: + def get_frequencies(self) -> Frequencies: """ Get the frequencies (in hertz) of the data set. Returns ------- - ndarray + Frequencies """ - return self.frequency + return self.frequencies - def get_impedance(self) -> ndarray: + def get_impedances(self) -> ComplexImpedances: """ Get the complex impedance of the model. Returns ------- - ndarray + ComplexImpedances """ - return self.impedance + return self.impedances - def get_tau(self) -> ndarray: + def get_time_constants(self) -> TimeConstants: """ Get the time constants. Returns ------- - ndarray + TimeConstants """ - return self.tau + return self.time_constants - def get_gamma(self, imaginary: bool = False) -> ndarray: + def get_gammas(self) -> Tuple[Gammas, Gammas]: """ Get the gamma values. - Parameters - ---------- - imaginary: bool = False - Get the imaginary gamma (non-empty only when using the BHT method). - Returns ------- - ndarray + Tuple[Gammas, Gammas] """ - if imaginary is True: - return self.imaginary_gamma - return self.gamma + return ( + self.real_gammas, + self.imaginary_gammas, + ) - def to_dataframe( + def to_peaks_dataframe( self, threshold: float = 0.0, - imaginary: bool = False, - latex_labels: bool = False, - include_frequency: bool = False, + columns: Optional[List[str]] = None, ) -> DataFrame: """ Get the peaks as a pandas.DataFrame object that can be used to generate, e.g., a Markdown table. Parameters ---------- - threshold: float = 0.0 - The threshold for the peaks (0.0 to 1.0 relative to the highest peak). - - imaginary: bool = False - Use the imaginary gamma (non-empty only when using the BHT method). - - latex_labels: bool = False - Whether or not to use LaTeX macros in the labels. + threshold: float, optional + The minimum peak height threshold (relative to the height of the tallest peak) for a peak to be included. - include_frequency: bool = False - Whether or not to also include a column with the frequencies corresponding to the time constants. + columns: Optional[List[str]], optional + The labels to use as the column headers for real time constants, real gammas, imaginary time constants, and imaginary gammas. Returns ------- DataFrame """ - tau: ndarray - gamma: ndarray - tau, gamma = self.get_peaks(threshold=threshold, imaginary=imaginary) - f: ndarray = 1 / tau - dictionary: dict = {} - dictionary["tau (s)" if not latex_labels else r"$\tau$ (s)"] = tau - if include_frequency is True: - dictionary["f (Hz)" if not latex_labels else r"$f$ (Hz)"] = f - dictionary[ - "gamma (ohms)" if not latex_labels else r"$\gamma\ (\Omega)$" - ] = gamma + if columns is None: + if self.settings.method == DRTMethod.BHT: + columns = [ + "tau, real (s)", + "gamma, real (ohm)", + "tau, imag. (s)", + "gamma, imag. (ohm)", + ] + else: + columns = [ + "tau (s)", + "gamma (ohm)", + ] + assert isinstance(columns, list) + if self.settings.method == DRTMethod.BHT: + assert len(columns) >= 4 + else: + assert len(columns) >= 2 + real_taus: TimeConstants + real_gammas: Gammas + imag_taus: TimeConstants + imag_gammas: Gammas + (real_taus, real_gammas, imag_taus, imag_gammas) = self.get_peaks( + threshold=threshold + ) + if self.settings.method != DRTMethod.BHT: + dictionary: dict = { + columns[0]: real_taus, + columns[1]: real_gammas, + } + else: + dictionary: dict = { + columns[0]: real_taus, + columns[1]: real_gammas, + columns[2]: imag_taus, + columns[3]: imag_gammas, + } return DataFrame.from_dict(dictionary) def get_peaks( self, threshold: float = 0.0, - imaginary: bool = False, - ) -> Tuple[ndarray, ndarray]: + ) -> Tuple[TimeConstants, Gammas, TimeConstants, Gammas]: """ Get the time constants (in seconds) and gamma (in ohms) of peaks with magnitudes greater than the threshold. The threshold and the magnitudes are all relative to the magnitude of the highest peak. Parameters ---------- - threshold: float = 0.0 + threshold: float, optional The threshold for the relative magnitude (0.0 to 1.0). - imaginary: bool = False - Use the imaginary gamma (non-empty only when using the BHT method). - Returns ------- - Tuple[ndarray, ndarray] - """ - assert ( - issubdtype(type(threshold), floating) and 0.0 <= threshold <= 1.0 - ), threshold - assert type(imaginary) is bool, imaginary - gamma: ndarray = self.gamma if not imaginary else self.imaginary_gamma - assert type(gamma) is ndarray, gamma - if not gamma.any(): - return ( - array([]), - array([]), - ) - indices: ndarray - indices, _ = find_peaks(gamma) - if not indices.any(): - return ( - array([]), - array([]), - ) - max_g: float = max(gamma) - if max_g == 0.0: - return ( - array([]), - array([]), - ) - indices = array( - list( - filter( - lambda _: gamma[_] / max_g > threshold and gamma[_] > 0.0, indices + Tuple[TimeConstants, Gammas, TimeConstants, Gammas] + """ + assert issubdtype(type(threshold), floating), type(threshold) + assert 0.0 <= threshold <= 1.0, threshold + + def filter_indices(gammas: Gammas) -> Indices: + max_g: Gamma = max(gammas) + indices: Indices = find_peaks(gammas)[0] + return array( + list( + filter( + lambda _: gammas[_] / max_g > threshold and gammas[_] > 0.0, + indices, + ), ) ) - ) - if indices.any(): - return ( - self.tau[indices], - gamma[indices], - ) + + real_indices: Indices = filter_indices(self.real_gammas) + real_taus: TimeConstants + real_gammas: Gammas + if real_indices.size > 0: + real_taus = self.time_constants[real_indices] + real_gammas = self.real_gammas[real_indices] + else: + real_taus = array([]) + real_gammas = array([]) + imag_indices: Indices + if self.imaginary_gammas.size > 0: + imag_indices = filter_indices(self.imaginary_gammas) + imag_taus: TimeConstants + imag_gammas: Gammas + if imag_indices.size > 0: + imag_taus = self.time_constants[imag_indices] + imag_gammas = self.imaginary_gammas[imag_indices] + else: + imag_taus = array([]) + imag_gammas = array([]) + else: + imag_taus = array([]) + imag_gammas = array([]) + if real_taus.size != imag_taus.size: + + def pad( + t: TimeConstants, + g: Gammas, + w: int, + ) -> Tuple[TimeConstants, Gammas]: + tmp_taus: TimeConstants = full(w, nan, dtype=TimeConstant) + tmp_gammas: Gammas = full(w, nan, dtype=Gamma) + tmp_taus[: t.size] = t + tmp_gammas[: g.size] = g + return ( + tmp_taus, + tmp_gammas, + ) + + max_size: int = max(real_taus.size, imag_taus.size) + real_taus, real_gammas = pad(real_taus, real_gammas, max_size) + imag_taus, imag_gammas = pad(imag_taus, imag_gammas, max_size) return ( - array([]), - array([]), + real_taus, + real_gammas, + imag_taus, + imag_gammas, ) - def get_nyquist_data(self) -> Tuple[ndarray, ndarray]: + def get_nyquist_data(self) -> Tuple[Impedances, Impedances]: """ Get the data necessary to plot this DataSet as a Nyquist plot: the real and the negative imaginary parts of the impedances. Returns ------- - Tuple[ndarray, ndarray] + Tuple[Impedances, Impedances] """ return ( - self.impedance.real, - -self.impedance.imag, + self.impedances.real, + -self.impedances.imag, ) - def get_bode_data(self) -> Tuple[ndarray, ndarray, ndarray]: + def get_bode_data(self) -> Tuple[Frequencies, Impedances, Phases]: """ Get the data necessary to plot this DataSet as a Bode plot: the frequencies, the absolute magnitudes of the impedances, and the negative phase angles/shifts of the impedances in degrees. Returns ------- - Tuple[ndarray, ndarray, ndarray] + Tuple[Frequencies, Impedances, Phases] """ return ( - self.frequency, - abs(self.impedance), - -angle(self.impedance, deg=True), + self.frequencies, + abs(self.impedances), + -angle(self.impedances, deg=True), ) - def get_drt_data(self, imaginary: bool = False) -> Tuple[ndarray, ndarray]: + def get_drt_data(self) -> Tuple[TimeConstants, Gammas, Gammas]: """ Get the data necessary to plot this DRTResult as a DRT plot: the time constants and the corresponding gamma values. - Parameters - ---------- - imaginary: bool = False - Get the imaginary gamma (non-empty only when using the BHT method). - Returns ------- - Tuple[ndarray, ndarray] + Tuple[TimeConstants, Gammas, Gammas] """ - gamma: ndarray = self.gamma if not imaginary else self.imaginary_gamma - if not gamma.any(): - return ( - array([]), - array([]), - ) return ( - self.tau, - gamma, + self.time_constants, + self.real_gammas, + self.imaginary_gammas, ) - def get_drt_credible_intervals(self) -> Tuple[ndarray, ndarray, ndarray, ndarray]: + def get_drt_credible_intervals_data( + self, + ) -> Tuple[TimeConstants, Gammas, Gammas, Gammas]: """ Get the data necessary to plot the Bayesian credible intervals for this DRTResult: the time constants, the mean gamma values, the lower bound gamma values, and the upper bound gamma values. Returns ------- - Tuple[ndarray, ndarray, ndarray, ndarray] + Tuple[TimeConstants, Gammas, Gammas, Gammas] """ - if not self.mean_gamma.any(): + if not self.mean_gammas.any(): return ( array([]), array([]), @@ -610,31 +766,32 @@ def get_drt_credible_intervals(self) -> Tuple[ndarray, ndarray, ndarray, ndarray array([]), ) return ( - self.tau, - self.mean_gamma, - self.lower_bound, - self.upper_bound, + self.time_constants, + self.mean_gammas, + self.lower_bounds, + self.upper_bounds, ) - def get_residual_data(self) -> Tuple[ndarray, ndarray, ndarray]: + def get_residuals_data(self) -> Tuple[Frequencies, Residuals, Residuals]: """ - Get the data necessary to plot the relative residuals for this DRTResult: the frequencies, the relative residuals for the real parts of the impedances in percents, and the relative residuals for the imaginary parts of the impedances in percents. + Get the data necessary to plot the relative residuals for this DRTResult: the frequencies and the relative residuals for the real and imaginary parts of the impedances in percents. Returns ------- - Tuple[ndarray, ndarray, ndarray] + Tuple[Frequencies, Residuals, Residuals] """ return ( - self.frequency, - self.real_residual * 100, - self.imaginary_residual * 100, + self.frequencies, + self.residuals.real * 100, + self.residuals.imag * 100, ) def get_scores(self) -> Dict[str, complex]: """ - Get the scores (BHT method) for the data set. + Get the scores for the data set. The scores are represented as complex values where the real and imaginary parts have magnitudes ranging from 0.0 to 1.0. A consistent impedance spectrum should score high. + BHT method only. Returns ------- @@ -642,32 +799,52 @@ def get_scores(self) -> Dict[str, complex]: """ return self.scores - def get_score_dataframe(self, latex_labels: bool = False) -> Optional[DataFrame]: + def to_scores_dataframe( + self, + columns: Optional[List[str]] = None, + rows: Optional[List[str]] = None, + ) -> Optional[DataFrame]: """ - Get the scores (BHT) method for the data set as a pandas.DataFrame object that can be used to generate, e.g., a Markdown table. + Get the scores for the data set as a pandas.DataFrame object that can be used to generate, e.g., a Markdown table. + BHT method only. Parameters ---------- - latex_labels: bool = False - Whether or not to use LaTeX macros in the labels. + columns: Optional[List[str]], optional + The labels for the column headers. + + rows: Optional[List[str]], optional + The labels for the rows. Returns ------- - Optional[DataFrame] + Optional[pandas.DataFrame] """ - if not self.scores: + if self.settings.method != DRTMethod.BHT: return None + if columns is None: + columns = [ + "Score", + "Real (%)", + "Imag. (%)", + ] + assert isinstance(columns, list), columns + assert len(columns) == 3 + if rows is None: + rows = [ + "Mean", + "Residuals, 1 sigma", + "Residuals, 2 sigma", + "Residuals, 3 sigma", + "Hellinger distance", + "Jensen-Shannon distance", + ] + assert isinstance(rows, list), rows + assert len(rows) == 6 return DataFrame.from_dict( { - "Score": [ - "Mean" if not latex_labels else r"$s_\mu$", - "Residuals, 1 sigma" if not latex_labels else r"$s_{1\sigma}$", - "Residuals, 2 sigma" if not latex_labels else r"$s_{2\sigma}$", - "Residuals, 3 sigma" if not latex_labels else r"$s_{3\sigma}$", - "Hellinger distance" if not latex_labels else r"$s_{\rm HD}$", - "Jensen-Shannon distance" if not latex_labels else r"$s_{\rm JSD}$", - ], - ("Real (%)" if not latex_labels else r"Real (\%)"): [ + columns[0]: rows, + columns[1]: [ self.scores["mean"].real * 100, self.scores["residuals_1sigma"].real * 100, self.scores["residuals_2sigma"].real * 100, @@ -675,7 +852,7 @@ def get_score_dataframe(self, latex_labels: bool = False) -> Optional[DataFrame] self.scores["hellinger_distance"].real * 100, self.scores["jensen_shannon_distance"].real * 100, ], - ("Imaginary (%)" if not latex_labels else r"Imaginary (\%)"): [ + columns[2]: [ self.scores["mean"].imag * 100, self.scores["residuals_1sigma"].imag * 100, self.scores["residuals_2sigma"].imag * 100, diff --git a/src/deareis/data/fitting.py b/src/deareis/data/fitting.py index a7dddce..3325d12 100644 --- a/src/deareis/data/fitting.py +++ b/src/deareis/data/fitting.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -17,12 +17,12 @@ # The licenses of DearEIS' dependencies and/or sources of portions of code are included in # the LICENSES folder. -from collections import OrderedDict from dataclasses import dataclass from typing import ( Callable, Dict, List, + Optional, Tuple, Union, ) @@ -30,35 +30,112 @@ angle, array, integer, + isnan, issubdtype, - ndarray, + log10 as log, + nan, ) from pandas import DataFrame import pyimpspec +from pyimpspec.analysis.utility import _calculate_pseudo_chisqr from pyimpspec import ( Circuit, + ComplexImpedances, + ComplexResiduals, Element, - FittedParameter, + Frequencies, + Impedances, + Phases, + Residuals, ) -from pyimpspec.analysis.fitting import _interpolate +from pyimpspec.analysis.utility import _interpolate from deareis.enums import ( CNLSMethod, Weight, + cnls_method_to_label, + weight_to_label, ) -from deareis.utility import format_timestamp +from deareis.utility import ( + format_timestamp, + rename_dict_entry, +) +from deareis.data import DataSet + + +VERSION: int = 2 + + +def _parse_fitted_parameter_v2(dictionary: dict) -> dict: + return dictionary + + +def _parse_fitted_parameter_v1(dictionary: dict) -> dict: + stderr: Optional[float] = dictionary.get("stderr") + dictionary["stderr"] = stderr if stderr is not None else nan + dictionary["unit"] = "" + return dictionary -VERSION: int = 1 +class FittedParameter(pyimpspec.FittedParameter): + @classmethod + def from_dict(Class, dictionary: dict) -> "FittedParameter": + """ + Create a FittedParameter object from a dictionary. + + Parameters + ---------- + dictionary: dict + The dictionary to turn into a FittedParameter object. + + Returns + ------- + FittedParameter + """ + assert isinstance(dictionary, dict), dictionary + assert "version" in dictionary + version: int = dictionary["version"] + del dictionary["version"] + assert version <= VERSION, f"{version=} > {VERSION=}" + parsers: Dict[int, Callable] = { + 1: _parse_fitted_parameter_v1, + 2: _parse_fitted_parameter_v2, + } + assert version in parsers, f"{version=} not in {parsers.keys()=}" + v: int + p: Callable + for v, p in parsers.items(): + if v < version: + continue + dictionary = p(dictionary) + assert "value" in dictionary + assert "stderr" in dictionary + assert "fixed" in dictionary + assert "unit" in dictionary + return Class(**dictionary) + + def to_dict(self) -> dict: + """ + Generate a dictionary that can be used to recreate this object. + + Returns + ------- + dict + """ + return { + "version": VERSION, + "value": self.value, + "stderr": self.stderr, + "fixed": self.fixed, + "unit": self.unit, + } + + +def _parse_settings_v2(dictionary: dict) -> dict: + return dictionary def _parse_settings_v1(dictionary: dict) -> dict: - assert type(dictionary) is dict - return { - "cdc": dictionary["cdc"], - "method": CNLSMethod(dictionary["method"]), - "weight": Weight(dictionary["weight"]), - "max_nfev": dictionary["max_nfev"], - } + return dictionary @dataclass(frozen=True) @@ -93,20 +170,47 @@ def __repr__(self) -> str: def from_dict(Class, dictionary: dict) -> "FitSettings": """ Create an instance from a dictionary. + + Parameters + ---------- + dictionary: dict + The dictionary to turn into a FitSettings object. + + Returns + ------- + FitSettings """ assert type(dictionary) is dict assert "version" in dictionary version: int = dictionary["version"] + del dictionary["version"] assert version <= VERSION, f"{version=} > {VERSION=}" parsers: Dict[int, Callable] = { 1: _parse_settings_v1, + 2: _parse_settings_v2, } assert version in parsers, f"{version=} not in {parsers.keys()=}" - return Class(**parsers[version](dictionary)) + v: int + p: Callable + for v, p in parsers.items(): + if v < version: + continue + dictionary = p(dictionary) + assert "cdc" in dictionary + assert "method" in dictionary + assert "weight" in dictionary + assert "max_nfev" in dictionary + dictionary["method"] = CNLSMethod(dictionary["method"]) + dictionary["weight"] = Weight(dictionary["weight"]) + return Class(**dictionary) def to_dict(self) -> dict: """ Return a dictionary that can be used to recreate an instance. + + Returns + ------- + dict """ return { "version": VERSION, @@ -117,45 +221,32 @@ def to_dict(self) -> dict: } +def _parse_result_v2(dictionary: dict) -> dict: + if "pseudo_chisqr" not in dictionary: + dictionary["pseudo_chisqr"] = nan + return dictionary + + def _parse_result_v1(dictionary: dict) -> dict: - assert type(dictionary) is dict - return { - "uuid": dictionary["uuid"], - "timestamp": dictionary["timestamp"], - "circuit": pyimpspec.parse_cdc(dictionary["circuit"]), - "parameters": { - element_label: { - parameter_label: FittedParameter.from_dict(param) - for parameter_label, param in parameters.items() - } - for element_label, parameters in dictionary["parameters"].items() - }, - "frequency": array(dictionary["frequency"]), - "impedance": array( - list( - map( - lambda _: complex(*_), - zip( - dictionary["real_impedance"], - dictionary["imaginary_impedance"], - ), - ) - ) - ), - "mask": {int(k): v for k, v in dictionary.get("mask", {}).items()}, - "real_residual": array(dictionary["real_residual"]), - "imaginary_residual": array(dictionary["imaginary_residual"]), - "chisqr": dictionary["chisqr"], - "red_chisqr": dictionary["red_chisqr"], - "aic": dictionary["aic"], - "bic": dictionary["bic"], - "ndata": dictionary["ndata"], - "nfree": dictionary["nfree"], - "nfev": dictionary["nfev"], - "method": dictionary["method"], - "weight": dictionary["weight"], - "settings": FitSettings.from_dict(dictionary["settings"]), - } + rename_dict_entry(dictionary, "frequency", "frequencies") + if "real_impedance" in dictionary: + rename_dict_entry(dictionary, "real_impedance", "real_impedances") + if "imaginary_impedance" in dictionary: + rename_dict_entry(dictionary, "imaginary_impedance", "imaginary_impedances") + rename_dict_entry(dictionary, "real_residual", "real_residuals") + rename_dict_entry(dictionary, "imaginary_residual", "imaginary_residuals") + old_parameters: dict = dictionary["parameters"] + new_parameters: dict = {} + counts: Dict[str, int] = {} + key: str + for key in sorted(old_parameters.keys()): + name: str = key.split("_", 1)[0] + if name not in counts: + counts[name] = 0 + counts[name] += 1 + new_parameters[f"{name}_{counts[name]}"] = old_parameters[key] + dictionary["parameters"] = new_parameters + return dictionary @dataclass @@ -177,26 +268,26 @@ class FitResult: parameters: Dict[str, Dict[str, FittedParameter]] The mapping to the mappings of the final, fitted values of the element parameters. - frequency: ndarray + frequencies: Frequencies The frequencies used to perform the fit. - impedance: ndarray + impedances: ComplexImpedances The complex impedances of the fitted circuit at each of the frequencies. - real_residual: ndarray - The residuals of the real part of the complex impedances. - - imaginary_residual: ndarray - The residuals of the imaginary part of the complex impedances. + residuals: ComplexResiduals + The residuals of the real and imaginary parts of the fit. mask: Dict[int, bool] The mask that was applied to the DataSet that the circuit was fitted to. + pseudo_chisqr: float + The calculated |pseudo chi-squared| (eq. 14 in Boukamp, 1995). + chisqr: float - The chi-squared value calculated for the result. + The |chi-squared| calculated for the result. red_chisqr: float - The reduced chi-squared value calculated for the result. + The reduced |chi-squared| calculated for the result. aic: float The calculated Akaike information criterion. @@ -227,11 +318,11 @@ class FitResult: timestamp: float circuit: Circuit parameters: Dict[str, Dict[str, FittedParameter]] - frequency: ndarray - impedance: ndarray - real_residual: ndarray - imaginary_residual: ndarray + frequencies: Frequencies + impedances: ComplexImpedances + residuals: ComplexResiduals mask: Dict[int, bool] + pseudo_chisqr: float chisqr: float red_chisqr: float aic: float @@ -244,54 +335,143 @@ class FitResult: settings: FitSettings def __post_init__(self): - self._cached_frequency: Dict[int, ndarray] = {} - self._cached_impedance: Dict[int, ndarray] = {} + self._cached_frequencies: Dict[int, Frequencies] = {} + self._cached_impedances: Dict[int, ComplexImpedances] = {} def __repr__(self) -> str: return f"FitResult ({self.get_label()}, {hex(id(self))})" - # TODO: Refactor @classmethod - def from_dict(Class, dictionary: dict) -> "FitResult": + def from_dict( + Class, + dictionary: dict, + data: Optional[DataSet] = None, + ) -> "FitResult": """ Create an instance from a dictionary. + + Parameters + ---------- + dictionary: dict + The dictionary to turn into a FitResult object. + + data: Optional[DataSet], optional + The DataSet object that this result is for. + + Returns + ------- + FitResult """ - assert type(dictionary) is dict + assert isinstance(dictionary, dict), dictionary + assert data is None or isinstance(data, DataSet), data assert "version" in dictionary version: int = dictionary["version"] + del dictionary["version"] assert version <= VERSION, f"{version=} > {VERSION=}" parsers: Dict[int, Callable] = { 1: _parse_result_v1, + 2: _parse_result_v2, } assert version in parsers, f"{version=} not in {parsers.keys()=}" - mask: Dict[str, bool] = dictionary["mask"] - if len(mask) < len(dictionary["frequency"]): - i: int - for i in range(0, len(dictionary["frequency"])): - if mask.get(str(i)) is not True: - mask[str(i)] = False + v: int + p: Callable + for v, p in parsers.items(): + if v < version: + continue + dictionary = p(dictionary) + assert "uuid" in dictionary + assert "timestamp" in dictionary + assert "circuit" in dictionary + assert "parameters" in dictionary + assert "frequencies" in dictionary + assert "real_residuals" in dictionary + assert "imaginary_residuals" in dictionary + assert "mask" in dictionary + assert "pseudo_chisqr" in dictionary + assert "chisqr" in dictionary + assert "red_chisqr" in dictionary + assert "aic" in dictionary + assert "bic" in dictionary + assert "ndata" in dictionary + assert "nfree" in dictionary + assert "nfev" in dictionary + assert "method" in dictionary + assert "weight" in dictionary + assert "settings" in dictionary + dictionary["circuit"] = pyimpspec.parse_cdc(dictionary["circuit"]) + dictionary["parameters"] = { + element_label: { + parameter_label: FittedParameter.from_dict(param) + for parameter_label, param in parameters.items() + } + for element_label, parameters in dictionary["parameters"].items() + } + dictionary["frequencies"] = array(dictionary["frequencies"]) if ( - "real_impedance" not in dictionary - or "imaginary_impedance" not in dictionary + "real_impedances" not in dictionary + or "imaginary_impedances" not in dictionary ): - Z: ndarray = pyimpspec.parse_cdc(dictionary["circuit"]).impedances( - dictionary["frequency"] + dictionary["impedances"] = dictionary["circuit"].get_impedances( + dictionary["frequencies"] + ) + else: + dictionary["impedances"] = array( + list( + map( + lambda _: complex(*_), + zip( + dictionary["real_impedances"], + dictionary["imaginary_impedances"], + ), + ) + ) + ) + del dictionary["real_impedances"] + del dictionary["imaginary_impedances"] + mask: Dict[str, bool] = dictionary["mask"] + dictionary["mask"] = { + i: mask.get(str(i), False) for i in range(0, len(dictionary["frequencies"])) + } + dictionary["residuals"] = array( + list( + map( + lambda _: complex(*_), + zip( + dictionary["real_residuals"], + dictionary["imaginary_residuals"], + ), + ) + ) + ) + del dictionary["real_residuals"] + del dictionary["imaginary_residuals"] + dictionary["settings"] = FitSettings.from_dict(dictionary["settings"]) + if isnan(dictionary["pseudo_chisqr"]): + dictionary["pseudo_chisqr"] = _calculate_pseudo_chisqr( + Z_exp=data.get_impedances(), + Z_fit=dictionary["impedances"], ) - dictionary["real_impedance"] = list(Z.real) - dictionary["imaginary_impedance"] = list(Z.imag) - dictionary = parsers[version](dictionary) return Class(**dictionary) def to_dict(self, session: bool) -> dict: """ Return a dictionary that can be used to recreate an instance. + + Parameters + ---------- + session: bool + If False, then a minimal dictionary is generated to reduce file size. + + Returns + ------- + dict """ assert type(session) is bool, session dictionary: dict = { "version": VERSION, "uuid": self.uuid, "timestamp": self.timestamp, - "circuit": self.circuit.to_string(12), + "circuit": self.circuit.serialize(), "parameters": { element_label: { parameter_label: param.to_dict() @@ -299,10 +479,11 @@ def to_dict(self, session: bool) -> dict: } for element_label, parameters in self.parameters.items() }, - "frequency": list(self.frequency), + "frequencies": list(self.frequencies), "mask": {k: True for k, v in self.mask.items() if v is True}, - "real_residual": list(self.real_residual), - "imaginary_residual": list(self.imaginary_residual), + "real_residuals": list(self.residuals.real), + "imaginary_residuals": list(self.residuals.imag), + "pseudo_chisqr": self.pseudo_chisqr, "chisqr": self.chisqr, "red_chisqr": self.red_chisqr, "aic": self.aic, @@ -317,37 +498,90 @@ def to_dict(self, session: bool) -> dict: if session: dictionary.update( { - "real_impedance": list(self.impedance.real), - "imaginary_impedance": list(self.impedance.imag), + "real_impedances": list(self.impedances.real), + "imaginary_impedances": list(self.impedances.imag), } ) return dictionary - def to_dataframe(self) -> DataFrame: + def to_statistics_dataframe(self) -> DataFrame: + """ + Get the statistics related to the fit as a pandas.DataFrame object. + + Returns + ------- + DataFrame + """ + statistics: Dict[str, Union[int, float, str]] = { + "Log pseudo chi-squared": log(self.pseudo_chisqr), + "Log chi-squared": log(self.chisqr), + "Log chi-squared (reduced)": log(self.red_chisqr), + "Akaike info. criterion": self.aic, + "Bayesian info. criterion": self.bic, + "Degrees of freedom": self.nfree, + "Number of data points": self.ndata, + "Number of function evaluations": self.nfev, + "Method": cnls_method_to_label[self.method], + "Weight": weight_to_label[self.weight], + } + return DataFrame.from_dict( + { + "Label": list(statistics.keys()), + "Value": list(statistics.values()), + } + ) + + def to_parameters_dataframe(self, running: bool = False) -> DataFrame: """ Get a `pandas.DataFrame` instance containing a table of fitted element parameters. + + Parameters + ---------- + running: bool, optional + Whether or not to use running counts as the lower indices of elements. + + Returns + ------- + pandas.DataFrame """ + assert isinstance(running, bool), running element_labels: List[str] = [] parameter_labels: List[str] = [] fitted_values: List[float] = [] stderr_values: List[Union[float, str]] = [] fixed: List[str] = [] + units: List[str] = [] + internal_identifiers: Dict[ + Element, int + ] = self.circuit.generate_element_identifiers(running=True) + external_identifiers: Dict[ + Element, int + ] = self.circuit.generate_element_identifiers(running=False) element_label: str - parameters: Union[ - Dict[str, FittedParameter], Dict[int, OrderedDict[str, float]] - ] + parameters: Union[Dict[str, FittedParameter], Dict[int, Dict[str, float]]] element: Element - for element in sorted( - self.circuit.get_elements(flattened=True), - key=lambda _: _.get_identifier(), + ident: int + for (element, ident) in sorted( + internal_identifiers.items(), + key=lambda _: _[1], ): - element_label = element.get_label() + element_label = self.circuit.get_element_name( + element, + identifiers=external_identifiers, + ) parameters = self.parameters[element_label] parameter_label: str param: FittedParameter for parameter_label in sorted(parameters.keys()): param = parameters[parameter_label] - element_labels.append(element_label) + element_labels.append( + self.circuit.get_element_name( + element, + identifiers=external_identifiers + if running is False + else internal_identifiers, + ) + ) parameter_labels.append(parameter_label) fitted_values.append(param.value) stderr_values.append( @@ -356,12 +590,14 @@ def to_dataframe(self) -> DataFrame: else "-" ) fixed.append("Yes" if param.fixed else "No") + units.append(param.unit) return DataFrame.from_dict( { "Element": element_labels, "Parameter": parameter_labels, "Value": fitted_values, "Std. err. (%)": stderr_values, + "Unit": units, "Fixed": fixed, } ) @@ -369,108 +605,130 @@ def to_dataframe(self) -> DataFrame: def get_label(self) -> str: """ Generate a label for the result. + + Returns + ------- + str """ - cdc: str = self.settings.cdc - while "{" in cdc: - i: int = cdc.find("{") - j: int = cdc.find("}") - cdc = cdc.replace(cdc[i : j + 1], "") + cdc: str = self.circuit.to_string() if cdc.startswith("[") and cdc.endswith("]"): cdc = cdc[1:-1] timestamp: str = format_timestamp(self.timestamp) return f"{cdc} ({timestamp})" - def get_frequency(self, num_per_decade: int = -1) -> ndarray: + def get_frequencies(self, num_per_decade: int = -1) -> Frequencies: """ Get an array of frequencies within the range of frequencies in the data set. Parameters ---------- - num_per_decade: int = -1 + num_per_decade: int, optional If the value is greater than zero, then logarithmically distributed frequencies will be generated within the range of fitted frequencies. + + Returns + ------- + Frequencies """ assert issubdtype(type(num_per_decade), integer), num_per_decade if num_per_decade > 0: - if num_per_decade not in self._cached_frequency: - self._cached_frequency.clear() - self._cached_frequency[num_per_decade] = _interpolate( - self.frequency, num_per_decade + if num_per_decade not in self._cached_frequencies: + self._cached_frequencies.clear() + self._cached_frequencies[num_per_decade] = _interpolate( + self.frequencies, num_per_decade ) - return self._cached_frequency[num_per_decade] - return self.frequency + return self._cached_frequencies[num_per_decade] + return self.frequencies - def get_impedance(self, num_per_decade: int = -1) -> ndarray: + def get_impedances(self, num_per_decade: int = -1) -> ComplexImpedances: """ Get the complex impedances produced by the fitted circuit within the range of frequencies in the data set. Parameters ---------- - num_per_decade: int = -1 + num_per_decade: int, optional If the value is greater than zero, then logarithmically distributed frequencies will be generated within the range of fitted frequencies and used to calculate the impedance produced by the fitted circuit. + + Returns + ------- + ComplexImpedances """ assert issubdtype(type(num_per_decade), integer), num_per_decade if num_per_decade > 0: - if num_per_decade not in self._cached_impedance: - self._cached_impedance.clear() - self._cached_impedance[num_per_decade] = self.circuit.impedances( - self.get_frequency(num_per_decade) + if num_per_decade not in self._cached_impedances: + self._cached_impedances.clear() + self._cached_impedances[num_per_decade] = self.circuit.get_impedances( + self.get_frequencies(num_per_decade) ) - return self._cached_impedance[num_per_decade] - return self.impedance + return self._cached_impedances[num_per_decade] + return self.impedances - def get_nyquist_data(self, num_per_decade: int = -1) -> Tuple[ndarray, ndarray]: + def get_nyquist_data( + self, num_per_decade: int = -1 + ) -> Tuple[Impedances, Impedances]: """ - Get the data required to plot the results as a Nyquist plot (-Z\" vs Z'). + Get the data required to plot the results as a Nyquist plot (-Im(Z) vs Re(Z)). Parameters ---------- - num_per_decade: int = -1 + num_per_decade: int, optional If the value is greater than zero, then logarithmically distributed frequencies will be generated within the range of frequencies in the data set and used to calculate the impedance produced by the fitted circuit. + + Returns + ------- + Tuple[Impedances, Impedances] """ assert issubdtype(type(num_per_decade), integer), num_per_decade if num_per_decade > 0: - Z: ndarray = self.get_impedance(num_per_decade) + Z: ComplexImpedances = self.get_impedances(num_per_decade) return ( Z.real, -Z.imag, ) return ( - self.impedance.real, - -self.impedance.imag, + self.impedances.real, + -self.impedances.imag, ) def get_bode_data( self, num_per_decade: int = -1 - ) -> Tuple[ndarray, ndarray, ndarray]: + ) -> Tuple[Frequencies, Impedances, Phases]: """ - Get the data required to plot the results as a Bode plot (|Z| and phi vs f). + Get the data required to plot the results as a Bode plot (Mod(Z) and -Phase(Z) vs f). Parameters ---------- - num_per_decade: int = -1 + num_per_decade: int, optional If the value is greater than zero, then logarithmically distributed frequencies will be generated within the range of frequencies in the data set and used to calculate the impedance produced by the fitted circuit. + + Returns + ------- + Tuple[Frequencies, Impedancesy, Phases] """ assert issubdtype(type(num_per_decade), integer), num_per_decade if num_per_decade > 0: - freq: ndarray = self.get_frequency(num_per_decade) - Z: ndarray = self.get_impedance(num_per_decade) + f: Frequencies = self.get_frequencies(num_per_decade) + Z: ComplexImpedances = self.get_impedances(num_per_decade) return ( - freq, + f, abs(Z), -angle(Z, deg=True), ) return ( - self.frequency, - abs(self.impedance), - -angle(self.impedance, deg=True), + self.frequencies, + abs(self.impedances), + -angle(self.impedances, deg=True), ) - def get_residual_data(self) -> Tuple[ndarray, ndarray, ndarray]: + def get_residuals_data(self) -> Tuple[Frequencies, Residuals, Residuals]: """ Get the data required to plot the residuals (real and imaginary vs f). + + Returns + ------- + Tuple[Frequencies, Residuals, Residuals] """ return ( - self.frequency, - self.real_residual * 100, - self.imaginary_residual * 100, + self.frequencies, + self.residuals.real * 100, + self.residuals.imag * 100, ) diff --git a/src/deareis/data/kramers_kronig.py b/src/deareis/data/kramers_kronig.py index 4427f6d..6d6ac44 100644 --- a/src/deareis/data/kramers_kronig.py +++ b/src/deareis/data/kramers_kronig.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -21,7 +21,9 @@ from typing import ( Callable, Dict, + Optional, Tuple, + Union, ) from numpy import ( angle, @@ -30,35 +32,45 @@ integer, issubdtype, log10 as log, - ndarray, + nan, ) +from pandas import DataFrame import pyimpspec -from pyimpspec import Circuit -from deareis.data.data_sets import DataSet -from pyimpspec.analysis.fitting import _interpolate +from pyimpspec import ( + Capacitor, + Circuit, + ComplexImpedances, + ComplexResiduals, + Frequencies, + Impedances, + Inductor, + Phases, + Residuals, + Resistor, + Series, +) +from pyimpspec.analysis.utility import _interpolate from deareis.enums import ( CNLSMethod, TestMode, Test, ) -from deareis.utility import format_timestamp +from deareis.utility import ( + format_timestamp, + rename_dict_entry, +) +from deareis.data import DataSet + +VERSION: int = 2 -VERSION: int = 1 + +def _parse_settings_v2(dictionary: dict) -> dict: + return dictionary def _parse_settings_v1(dictionary: dict) -> dict: - assert type(dictionary) is dict - return { - "test": Test(dictionary["test"]), - "mode": TestMode(dictionary["mode"]), - "num_RC": dictionary["num_RC"], - "mu_criterion": dictionary["mu_criterion"], - "add_capacitance": dictionary["add_capacitance"], - "add_inductance": dictionary["add_inductance"], - "method": CNLSMethod(dictionary["method"]), - "max_nfev": dictionary["max_nfev"], - } + return dictionary @dataclass(frozen=True) @@ -124,13 +136,27 @@ def from_dict(Class, dictionary: dict) -> "TestSettings": ) parsers: Dict[int, Callable] = { 1: _parse_settings_v1, + 2: _parse_settings_v2, } - assert version in parsers, ( - version, - parsers, - ) assert version in parsers, f"{version=} not in {parsers.keys()=}" - return Class(**parsers[version](dictionary)) + v: int + p: Callable + for v, p in parsers.items(): + if v < version: + continue + dictionary = p(dictionary) + assert "test" in dictionary + assert "mode" in dictionary + assert "num_RC" in dictionary + assert "mu_criterion" in dictionary + assert "add_capacitance" in dictionary + assert "add_inductance" in dictionary + assert "method" in dictionary + assert "max_nfev" in dictionary + dictionary["test"] = Test(dictionary["test"]) + dictionary["mode"] = TestMode(dictionary["mode"]) + dictionary["method"] = CNLSMethod(dictionary["method"]) + return Class(**dictionary) def to_dict(self) -> dict: """ @@ -149,32 +175,24 @@ def to_dict(self) -> dict: } +def _parse_result_v2(dictionary: dict) -> dict: + if "chisqr" in dictionary: + dictionary["pseudo_chisqr"] = dictionary["chisqr"] + del dictionary["chisqr"] + if "pseudo_chisqr" not in dictionary: + dictionary["pseudo_chisqr"] = nan + return dictionary + + def _parse_result_v1(dictionary: dict) -> dict: - assert type(dictionary) is dict - return { - "uuid": dictionary["uuid"], - "timestamp": dictionary["timestamp"], - "circuit": pyimpspec.parse_cdc(dictionary["circuit"]), - "num_RC": dictionary["num_RC"], - "mu": dictionary["mu"], - "pseudo_chisqr": dictionary["pseudo_chisqr"], - "frequency": array(dictionary["frequency"]), - "real_residual": array(dictionary["real_residual"]), - "imaginary_residual": array(dictionary["imaginary_residual"]), - "mask": {int(k): v for k, v in dictionary.get("mask", {}).items()}, - "impedance": array( - list( - map( - lambda _: complex(*_), - zip( - dictionary["real_impedance"], - dictionary["imaginary_impedance"], - ), - ) - ) - ), - "settings": TestSettings.from_dict(dictionary["settings"]), - } + rename_dict_entry(dictionary, "frequency", "frequencies") + if "real_impedance" in dictionary: + rename_dict_entry(dictionary, "real_impedance", "real_impedances") + if "imaginary_impedance" in dictionary: + rename_dict_entry(dictionary, "imaginary_impedance", "imaginary_impedances") + rename_dict_entry(dictionary, "real_residual", "real_residuals") + rename_dict_entry(dictionary, "imaginary_residual", "imaginary_residuals") + return dictionary @dataclass @@ -197,22 +215,19 @@ class TestResult: The final number of parallel RC circuits connected in series. mu: float - The mu-value that was calculated for the result. + The |mu| that was calculated for the result (eq. 21 in Schönleber et al., 2014). pseudo_chisqr: float - The pseudo chi-squared value calculated according to eq. N in Boukamp (1995). + The calculated |pseudo chi-squared| (eq. 14 in Boukamp, 1995). - frequency: ndarray + frequencies: Frequencies The frequencies used to perform the test. - impedance: ndarray + impedances: ComplexImpedances The complex impedances of the fitted circuit at each of the frequencies. - real_residual: ndarray - The residuals of the real part of the complex impedances. - - imaginary_residual: ndarray - The residuals of the imaginary part of the complex impedances. + residuals: ComplexResiduals + The residuals of the real and the imaginary parts of the fit. mask: Dict[int, bool] The mask that was applied to the DataSet that was tested. @@ -227,73 +242,139 @@ class TestResult: num_RC: int mu: float pseudo_chisqr: float - frequency: ndarray - impedance: ndarray - real_residual: ndarray - imaginary_residual: ndarray + frequencies: Frequencies + impedances: ComplexImpedances + residuals: ComplexResiduals mask: Dict[int, bool] settings: TestSettings def __post_init__(self): - self._cached_frequency: Dict[int, ndarray] = {} - self._cached_impedance: Dict[int, ndarray] = {} + self._cached_frequencies: Dict[int, Frequencies] = {} + self._cached_impedances: Dict[int, ComplexImpedances] = {} def __repr__(self) -> str: return f"TestResult ({self.get_label()}, {hex(id(self))})" @classmethod - def from_dict(Class, dictionary: dict) -> "TestResult": + def from_dict(Class, dictionary: dict, data: Optional[DataSet] = None) -> "TestResult": """ Create an instance from a dictionary. + + Parameters + ---------- + dictionary: dict + The dictionary to turn into a TestResult object. + + data: Optional[DataSet], optional + The DataSet object that this result is for. + + Returns + ------- + TestResult """ - assert type(dictionary) is dict + assert isinstance(dictionary, dict), dict + assert data is None or isinstance(data, DataSet), data assert "version" in dictionary version: int = dictionary["version"] + del dictionary["version"] assert version <= VERSION, f"{version=} > {VERSION=}" parsers: Dict[int, Callable] = { 1: _parse_result_v1, + 2: _parse_result_v2, } assert version in parsers, f"{version=} not in {parsers.keys()=}" + v: int + p: Callable + for v, p in parsers.items(): + if v < version: + continue + dictionary = p(dictionary) + + assert "uuid" in dictionary + assert "timestamp" in dictionary + assert "circuit" in dictionary + assert "num_RC" in dictionary + assert "mu" in dictionary + assert "pseudo_chisqr" in dictionary + assert "frequencies" in dictionary + assert "real_residuals" in dictionary + assert "imaginary_residuals" in dictionary + assert "settings" in dictionary + dictionary["circuit"] = pyimpspec.parse_cdc(dictionary["circuit"]) + dictionary["frequencies"] = array(dictionary["frequencies"]) + dictionary["settings"] = TestSettings.from_dict(dictionary["settings"]) mask: Dict[str, bool] = dictionary["mask"] - if len(mask) < len(dictionary["frequency"]): - i: int - for i in range(0, len(dictionary["frequency"])): - if mask.get(str(i)) is not True: - mask[str(i)] = False + dictionary["mask"] = { + i: mask.get(str(i), False) for i in range(0, len(dictionary["frequencies"])) + } if ( - "real_impedance" not in dictionary - or "imaginary_impedance" not in dictionary + "real_impedances" not in dictionary + or "imaginary_impedances" not in dictionary ): - Z: ndarray = pyimpspec.parse_cdc(dictionary["circuit"]).impedances( - dictionary["frequency"] + dictionary["impedances"] = dictionary["circuit"].get_impedances( + dictionary["frequencies"] + ) + else: + dictionary["impedances"] = array( + list( + map( + lambda _: complex(*_), + zip( + dictionary["real_impedances"], + dictionary["imaginary_impedances"], + ), + ) + ) + ) + del dictionary["real_impedances"] + del dictionary["imaginary_impedances"] + dictionary["residuals"] = array( + list( + map( + lambda _: complex(*_), + zip( + dictionary["real_residuals"], + dictionary["imaginary_residuals"], + ), + ) ) - dictionary["real_impedance"] = list(Z.real) - dictionary["imaginary_impedance"] = list(Z.imag) - return Class(**parsers[version](dictionary)) + ) + del dictionary["real_residuals"] + del dictionary["imaginary_residuals"] + return Class(**dictionary) def to_dict(self, session: bool) -> dict: """ Return a dictionary that can be used to recreate an instance. + + Parameters + ---------- + session: bool + If False, then a minimal dictionary will be generated to reduce file size. + + Returns + ------- + dict """ dictionary: dict = { "version": VERSION, "uuid": self.uuid, "timestamp": self.timestamp, - "circuit": self.circuit.to_string(12), + "circuit": self.circuit.serialize(), "num_RC": self.num_RC, "mu": self.mu, "pseudo_chisqr": self.pseudo_chisqr, - "frequency": list(self.frequency), - "real_residual": list(self.real_residual), - "imaginary_residual": list(self.imaginary_residual), + "frequencies": list(self.frequencies), + "real_residuals": list(self.residuals.real), + "imaginary_residuals": list(self.residuals.imag), "mask": {k: True for k, v in self.mask.items() if v is True}, "settings": self.settings.to_dict(), } if session: dictionary.update( { - "real_impedance": list(self.impedance.real), - "imaginary_impedance": list(self.impedance.imag), + "real_impedances": list(self.impedances.real), + "imaginary_impedances": list(self.impedances.imag), } ) return dictionary @@ -301,119 +382,148 @@ def to_dict(self, session: bool) -> dict: def get_label(self) -> str: """ Generate a label for the result. + + Returns + ------- + str """ - circuit: str = f"R(RC){self.num_RC}" + label: str = f"#(RC)={self.num_RC}" if self.settings.add_capacitance: - circuit += "C" - if self.settings.add_inductance: - circuit += "L" + label += ", C" + if self.settings.add_inductance: + label += "+L" + elif self.settings.add_inductance: + label += ", L" timestamp: str = format_timestamp(self.timestamp) - return f"{circuit} ({timestamp})" + return f"{label} ({timestamp})" - def get_frequency(self, num_per_decade: int = -1) -> ndarray: + def get_frequencies(self, num_per_decade: int = -1) -> Frequencies: """ Get an array of frequencies within the range of tested frequencies. Parameters ---------- - num_per_decade: int = -1 + num_per_decade: int, optional If the value is greater than zero, then logarithmically distributed frequencies will be generated within the range of tested frequencies. + + Returns + ------- + Frequencies """ assert issubdtype(type(num_per_decade), integer), num_per_decade if num_per_decade > 0: - if num_per_decade not in self._cached_frequency: - self._cached_frequency.clear() - self._cached_frequency[num_per_decade] = _interpolate( - self.frequency, num_per_decade + if num_per_decade not in self._cached_frequencies: + self._cached_frequencies.clear() + self._cached_frequencies[num_per_decade] = _interpolate( + self.frequencies, num_per_decade ) - return self._cached_frequency[num_per_decade] - return self.frequency + return self._cached_frequencies[num_per_decade] + return self.frequencies - def get_impedance(self, num_per_decade: int = -1) -> ndarray: + def get_impedances(self, num_per_decade: int = -1) -> ComplexImpedances: """ Get the complex impedances produced by the fitted circuit within the range of tested frequencies. Parameters ---------- - num_per_decade: int = -1 + num_per_decade: int, optional If the value is greater than zero, then logarithmically distributed frequencies will be generated within the range of tested frequencies and used to calculate the impedance produced by the fitted circuit. + + Returns + ------- + Frequencies """ assert issubdtype(type(num_per_decade), integer), num_per_decade if num_per_decade > 0: - if num_per_decade not in self._cached_impedance: - self._cached_impedance.clear() - self._cached_impedance[num_per_decade] = self.circuit.impedances( - self.get_frequency(num_per_decade) + if num_per_decade not in self._cached_impedances: + self._cached_impedances.clear() + self._cached_impedances[num_per_decade] = self.circuit.get_impedances( + self.get_frequencies(num_per_decade) ) - return self._cached_impedance[num_per_decade] - return self.impedance + return self._cached_impedances[num_per_decade] + return self.impedances - def get_nyquist_data(self, num_per_decade: int = -1) -> Tuple[ndarray, ndarray]: + def get_nyquist_data( + self, num_per_decade: int = -1 + ) -> Tuple[Impedances, Impedances]: """ - Get the data required to plot the results as a Nyquist plot (-Z\" vs Z'). + Get the data required to plot the results as a Nyquist plot (-Im(Z) vs Re(Z)). Parameters ---------- - num_per_decade: int = -1 + num_per_decade: int, optional If the value is greater than zero, then logarithmically distributed frequencies will be generated within the range of tested frequencies and used to calculate the impedance produced by the fitted circuit. + + Returns + ------- + Tuple[Impedances, Impedances] """ assert issubdtype(type(num_per_decade), integer), num_per_decade if num_per_decade > 0: - Z: ndarray = self.get_impedance(num_per_decade) + Z: ComplexImpedances = self.get_impedances(num_per_decade) return ( Z.real, -Z.imag, ) return ( - self.impedance.real, - -self.impedance.imag, + self.impedances.real, + -self.impedances.imag, ) def get_bode_data( - self, num_per_decade: int = -1 - ) -> Tuple[ndarray, ndarray, ndarray]: + self, + num_per_decade: int = -1, + ) -> Tuple[Frequencies, Impedances, Phases]: """ - Get the data required to plot the results as a Bode plot (|Z| and phi vs f). + Get the data required to plot the results as a Bode plot (Mode(Z) and -Phase(Z) vs f). Parameters ---------- - num_per_decade: int = -1 + num_per_decade: int, optional If the value is greater than zero, then logarithmically distributed frequencies will be generated within the range of tested frequencies and used to calculate the impedance produced by the fitted circuit. + + Returns + ------- + Tuple[Frequencies, Impedances, Phases] """ assert issubdtype(type(num_per_decade), integer), num_per_decade if num_per_decade > 0: - freq: ndarray = self.get_frequency(num_per_decade) - Z: ndarray = self.get_impedance(num_per_decade) + f: Frequencies = self.get_frequencies(num_per_decade) + Z: ComplexImpedances = self.get_impedances(num_per_decade) return ( - freq, + f, abs(Z), -angle(Z, deg=True), ) return ( - self.frequency, - abs(self.impedance), - -angle(self.impedance, deg=True), + self.frequencies, + abs(self.impedances), + -angle(self.impedances, deg=True), ) - def get_residual_data(self) -> Tuple[ndarray, ndarray, ndarray]: + def get_residuals_data(self) -> Tuple[Frequencies, Residuals, Residuals]: """ Get the data required to plot the residuals (real and imaginary vs f). + + Returns + ------- + Tuple[Frequencies, Residuals, Residuals] """ return ( - self.frequency, - self.real_residual * 100, - self.imaginary_residual * 100, + self.frequencies, + self.residuals.real * 100, + self.residuals.imag * 100, ) def calculate_score(self, mu_criterion: float) -> float: """ - Calculate a score based on the provided mu-criterion and the statistics of the result. - A result with a mu-value greater than or equal to the mu-criterion will get a score of -numpy.inf. + Calculate a score based on the provided |mu|-criterion and the statistics of the result. + A result with a |mu| greater than or equal to the |mu|-criterion will get a score of -numpy.inf. Parameters ---------- mu_criterion: float - The mu-criterion to apply. + The |mu|-criterion to apply. See perform_test for details. Returns @@ -425,3 +535,71 @@ def calculate_score(self, mu_criterion: float) -> float: if self.mu >= mu_criterion else -log(self.pseudo_chisqr) / (abs(mu_criterion - self.mu) ** 0.75) ) + + def get_series_resistance(self) -> float: + """ + Get the value of the series resistance. + + Returns + ------- + float + """ + series: Series = self.circuit.get_elements(flattened=False)[0] + assert isinstance(series, Series) + for elem_con in series.get_elements(flattened=False): + if isinstance(elem_con, Resistor): + return elem_con.get_value("R") + return nan + + def get_series_capacitance(self) -> float: + """ + Get the value of the series capacitance (or numpy.nan if not included in the circuit). + + Returns + ------- + float + """ + series: Series = self.circuit.get_elements(flattened=False)[0] + assert isinstance(series, Series) + for elem_con in series.get_elements(flattened=False): + if isinstance(elem_con, Capacitor): + return elem_con.get_value("C") + return nan + + def get_series_inductance(self) -> float: + """ + Get the value of the series inductance (or numpy.nan if not included in the circuit). + + Returns + ------- + float + """ + series: Series = self.circuit.get_elements(flattened=False)[0] + assert isinstance(series, Series) + for elem_con in series.get_elements(flattened=False): + if isinstance(elem_con, Inductor): + return elem_con.get_value("L") + return nan + + def to_statistics_dataframe(self) -> DataFrame: + """ + Get the statistics related to the test as a pandas.DataFrame object. + + Returns + ------- + DataFrame + """ + statistics: Dict[str, Union[int, float, str]] = { + "Log pseudo chi-squared": log(self.pseudo_chisqr), + "Mu": self.mu, + "Number of parallel RC elements": self.num_RC, + "Series resistance (ohm)": self.get_series_resistance(), + "Series capacitance (F)": self.get_series_capacitance(), + "Series inductance (H)": self.get_series_inductance(), + } + return DataFrame.from_dict( + { + "Label": list(statistics.keys()), + "Value": list(statistics.values()), + } + ) diff --git a/src/deareis/data/plotting.py b/src/deareis/data/plotting.py index c42460a..a6c73b4 100644 --- a/src/deareis/data/plotting.py +++ b/src/deareis/data/plotting.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -18,6 +18,7 @@ # the LICENSES folder. from dataclasses import dataclass +from inspect import signature from typing import ( Callable, Dict, @@ -31,10 +32,18 @@ array, integer, issubdtype, - ndarray, +) +from pyimpspec.typing import ( + ComplexImpedances, + Frequencies, + Gammas, + Impedances, + Phases, + TimeConstants, ) from deareis.data import DataSet from deareis.data.kramers_kronig import TestResult +from deareis.data.zhit import ZHITResult from deareis.data.drt import DRTResult from deareis.data.fitting import FitResult from deareis.data.simulation import SimulationResult @@ -59,7 +68,7 @@ class PlotSeries: A class that represents the data used to plot an item/series. """ - data: Union[DataSet, TestResult, DRTResult, FitResult, SimulationResult] + data: Union[DataSet, TestResult, ZHITResult, DRTResult, FitResult, SimulationResult] label: str color: Tuple[float, float, float, float] marker: int @@ -72,48 +81,56 @@ def __repr__(self) -> str: def get_label(self) -> str: return self.label - def get_frequency(self, num_per_decade: int = -1) -> ndarray: - if type(self.data) is DataSet or type(self.data) is DRTResult: - return self.data.get_frequency() - return self.data.get_frequency(num_per_decade=num_per_decade) + def get_frequencies(self, num_per_decade: int = -1) -> Frequencies: + if "num_per_decade" not in signature(self.data.get_frequencies).parameters: + return self.data.get_frequencies() + return self.data.get_frequencies(num_per_decade=num_per_decade) - def get_impedance(self, num_per_decade: int = -1) -> ndarray: - if type(self.data) is DataSet or type(self.data) is DRTResult: - return self.data.get_impedance() - return self.data.get_impedance(num_per_decade=num_per_decade) + def get_impedances(self, num_per_decade: int = -1) -> ComplexImpedances: + if "num_per_decade" not in signature(self.data.get_impedances).parameters: + return self.data.get_impedances() + return self.data.get_impedances(num_per_decade=num_per_decade) - def get_nyquist_data(self, num_per_decade: int = -1) -> Tuple[ndarray, ndarray]: - if type(self.data) is DataSet or type(self.data) is DRTResult: + def get_nyquist_data( + self, num_per_decade: int = -1 + ) -> Tuple[Impedances, Impedances]: + if "num_per_decade" not in signature(self.data.get_nyquist_data).parameters: return self.data.get_nyquist_data() return self.data.get_nyquist_data(num_per_decade=num_per_decade) def get_bode_data( self, num_per_decade: int = -1, - ) -> Tuple[ndarray, ndarray, ndarray]: - if type(self.data) is DataSet or type(self.data) is DRTResult: + ) -> Tuple[Frequencies, Impedances, Phases]: + if "num_per_decade" not in signature(self.data.get_bode_data).parameters: return self.data.get_bode_data() return self.data.get_bode_data(num_per_decade=num_per_decade) - def get_tau(self) -> ndarray: + def get_time_constants(self) -> TimeConstants: if type(self.data) is not DRTResult: return array([]) - return self.data.get_tau() + return self.data.get_time_constants() - def get_gamma(self, imaginary: bool = False) -> ndarray: + def get_gammas(self) -> Tuple[Gammas, Gammas]: if type(self.data) is not DRTResult: - return array([]) - return self.data.get_gamma(imaginary=imaginary) + return ( + array([]), + array([]), + ) + return self.data.get_gammas() - def get_drt_data(self, imaginary: bool = False) -> Tuple[ndarray, ndarray]: - if type(self.data) is not DRTResult: + def get_drt_data(self) -> Tuple[TimeConstants, Gammas, Gammas]: + if not isinstance(self.data, DRTResult): return ( array([]), array([]), + array([]), ) - return self.data.get_drt_data(imaginary=imaginary) + return self.data.get_drt_data() - def get_drt_credible_intervals(self) -> Tuple[ndarray, ndarray, ndarray, ndarray]: + def get_drt_credible_intervals_data( + self, + ) -> Tuple[TimeConstants, Gammas, Gammas, Gammas]: if type(self.data) is not DRTResult: return ( array([]), @@ -121,7 +138,7 @@ def get_drt_credible_intervals(self) -> Tuple[ndarray, ndarray, ndarray, ndarray array([]), array([]), ) - return self.data.get_drt_credible_intervals() + return self.data.get_drt_credible_intervals_data() def get_color(self) -> Tuple[float, float, float, float]: return self.color @@ -325,6 +342,12 @@ def to_dict(self, session: bool) -> dict: "themes": self.themes.copy() if session else {k: -1 for k in self.themes}, } + def get_num_series(self) -> int: + return len(self.series_order) + + def get_series_uuids(self) -> List[str]: + return self.series_order[:] + def get_label(self) -> str: return self.plot_label @@ -391,17 +414,24 @@ def set_series_line(self, uuid: str, state: bool): def add_series( self, - series: Union[DataSet, TestResult, DRTResult, FitResult, SimulationResult], + series: Union[ + DataSet, TestResult, ZHITResult, DRTResult, FitResult, SimulationResult + ], ): # TODO: Refactor so that series is replaced by uuid? # Include the type as another argument to determine whether or not a line should be drawn? - assert ( - type(series) is DataSet - or type(series) is TestResult - or type(series) is DRTResult - or type(series) is FitResult - or type(series) is SimulationResult - ), series + for Class in [ + DataSet, + TestResult, + ZHITResult, + DRTResult, + FitResult, + SimulationResult, + ]: + if isinstance(series, Class): + break + else: + raise NotImplementedError(f"Unsupported series type: '{type(series)}'") uuid: str = series.uuid if uuid in self.series_order: return @@ -430,15 +460,18 @@ def remove_series(self, uuid: str): def find_series( self, uuid: str, - datasets: List[DataSet], + data_sets: List[DataSet], tests: Dict[str, List[TestResult]], + zhits: Dict[str, List[ZHITResult]], drts: Dict[str, List[DRTResult]], fits: Dict[str, List[FitResult]], simulations: List[SimulationResult], - ) -> Optional[Union[DataSet, TestResult, DRTResult, FitResult, SimulationResult]]: + ) -> Optional[ + Union[DataSet, TestResult, ZHITResult, DRTResult, FitResult, SimulationResult] + ]: def find_dataset() -> Optional[DataSet]: data: DataSet - for data in datasets: + for data in data_sets: if data.uuid == uuid: return data return None @@ -450,6 +483,13 @@ def find_test() -> Optional[TestResult]: return test return None + def find_zhit() -> Optional[ZHITResult]: + zhit: ZHITResult + for zhit in [zhit for _ in zhits.values() for zhit in _]: + if zhit.uuid == uuid: + return zhit + return None + def find_drt() -> Optional[DRTResult]: drt: DRTResult for drt in [drt for _ in drts.values() for drt in _]: @@ -477,6 +517,9 @@ def find_simulation() -> Optional[SimulationResult]: test: Optional[TestResult] = find_test() if test is not None: return test + zhit: Optional[ZHITResult] = find_zhit() + if zhit is not None: + return zhit drt: Optional[DRTResult] = find_drt() if drt is not None: return drt diff --git a/src/deareis/data/project.py b/src/deareis/data/project.py index 89882fa..f97fe98 100644 --- a/src/deareis/data/project.py +++ b/src/deareis/data/project.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -43,12 +43,15 @@ ) from uuid import uuid4 from numpy import ( + inf, ndarray, ) +from pyimpspec.circuit.parser import Parser from deareis.data import DataSet from deareis.data.fitting import FitResult from deareis.data.drt import DRTResult from deareis.data.kramers_kronig import TestResult +from deareis.data.zhit import ZHITResult from deareis.data.simulation import SimulationResult from deareis.data.plotting import ( PlotSettings, @@ -57,17 +60,41 @@ from deareis.enums import PlotType -VERSION: int = 4 +VERSION: int = 5 -def _parse_v4(state: dict) -> dict: +def _parse_v5(state: dict) -> dict: # TODO: Update implementation when VERSION is incremented return state +def _parse_v4(state: dict) -> dict: + def update_cdcs(dictionary: dict, tests: bool): + for k, v in dictionary.items(): + if isinstance(v, dict): + update_cdcs(v, tests=tests or k == "tests") + elif isinstance(v, list): + for i in v: + if isinstance(i, dict): + update_cdcs(i, tests or k == "tests") + elif (k == "circuit" or k == "cdc") and isinstance(v, str): + circuit = Parser().process(v, version=0) + if tests is True: + for element in circuit.get_elements(): + keys = element.get_values().keys() + element.set_lower_limits(**{_: -inf for _ in keys}) + element.set_upper_limits(**{_: inf for _ in keys}) + dictionary[k] = circuit.serialize() + + update_cdcs(state, tests=False) + if "zhits" not in state: + state["zhits"] = {} + return state + + def _parse_v3(state: dict) -> dict: state["drts"] = {_["uuid"]: [] for _ in state["data_sets"]} - return _parse_v4(state) + return state def _parse_v2(state: dict) -> dict: @@ -100,22 +127,23 @@ def _parse_v2(state: dict) -> dict: uuid4().hex, ).to_dict(session=False) ) - return _parse_v3(state) + return state def _parse_v1(state: dict) -> dict: state["active_plot_uuid"] = "" state["plots"] = [] - return _parse_v2(state) + return state class Project: """ - A class representing a collection of notes, data sets, test results, fit results, simulation results, and complex plots. + A class representing a collection of notes, data sets, analysis results, simulation results, and complex plots. """ def __init__(self, *args, **kwargs): self._path: str = "" + self._is_new: bool = False self.update(*args, **kwargs) def __repr(self) -> str: @@ -130,14 +158,35 @@ def update(self, *args, **kwargs): self._data_sets: List[DataSet] = list( map(DataSet.from_dict, kwargs.get("data_sets", [])) ) - self._drts: Dict[str, List[DRTResult]] = { - k: list(map(DRTResult.from_dict, v)) - for k, v in kwargs.get("drts", {}).items() - } - self._fits: Dict[str, List[FitResult]] = { - k: list(map(FitResult.from_dict, v)) - for k, v in kwargs.get("fits", {}).items() - } + uuid: str + data_lookup: Dict[str, DataSet] = {_.uuid: _ for _ in self._data_sets} + self._drts: Dict[str, List[DRTResult]] = {} + for uuid, results in kwargs.get("drts", {}).items(): + data = data_lookup[uuid] + self._drts[uuid] = list( + map( + lambda _: DRTResult.from_dict(_, data=data), + results, + ) + ) + self._fits: Dict[str, List[FitResult]] = {} + for uuid, results in kwargs.get("fits", {}).items(): + data = data_lookup[uuid] + self._fits[uuid] = list( + map( + lambda _: FitResult.from_dict(_, data=data), + results, + ) + ) + self._zhits: Dict[str, List[ZHITResult]] = {} + for uuid, results in kwargs.get("zhits", {}).items(): + data = data_lookup[uuid] + self._zhits[uuid] = list( + map( + lambda _: ZHITResult.from_dict(_, data=data), + results, + ) + ) self._label: str = kwargs.get("label", "Project") self._notes: str = kwargs.get("notes", "") path: str = kwargs.get("path", "").strip() @@ -164,15 +213,21 @@ def update(self, *args, **kwargs): map(SimulationResult.from_dict, kwargs.get("simulations", [])) ) self._tests: Dict[str, List[TestResult]] = { - k: list(map(TestResult.from_dict, v)) + k: list(map(lambda _: TestResult.from_dict(_, data=None), v)) for k, v in kwargs.get("tests", {}).items() } + for uuid in data_lookup: + if uuid not in self._drts: + self._drts[uuid] = [] + if uuid not in self._fits: + self._fits[uuid] = [] + if uuid not in self._zhits: + self._zhits[uuid] = [] + if uuid not in self._tests: + self._tests[uuid] = [] @staticmethod - def parse(state: dict) -> dict: - """ - Used when deserializing project files. - """ + def _parse(state: dict) -> dict: assert type(state) is dict, type(state) if "version" in state: version: int = state["version"] @@ -187,17 +242,22 @@ def parse(state: dict) -> dict: 2: _parse_v2, 3: _parse_v3, 4: _parse_v4, + 5: _parse_v5, } assert version in parsers, ( version, parsers, ) - state = parsers[version](state) + for v, p in parsers.items(): + if v < version: + continue + state = p(state) assert type(state["uuid"]) is str # Basic validation assert type(state["data_sets"]) is list assert type(state["fits"]) is dict assert type(state["drts"]) is dict + assert type(state["zhits"]) is dict assert type(state["label"]) is str assert type(state["notes"]) is str assert type(state["plots"]) is list @@ -214,8 +274,12 @@ def from_dict(Class, state: dict) -> "Project": ---------- state: dict A dictionary-based representation of a project state. + + Returns + ------- + Project """ - return Class(**Class.parse(state)) + return Class(**Class._parse(state)) @classmethod def from_file(Class, path: str) -> "Project": @@ -226,6 +290,10 @@ def from_file(Class, path: str) -> "Project": ---------- path: str The path to a file containing a serialized project state. + + Returns + ------- + Project """ assert type(path) is str and exists(path) fp: IO @@ -243,6 +311,10 @@ def from_json(Class, json: str) -> "Project": ---------- json: str A JSON representation of a project state. + + Returns + ------- + Project """ assert type(json) is str return Class.from_dict(parse_json(json)) @@ -253,6 +325,15 @@ def merge(Class, projects: List["Project"]) -> "Project": Create an instance by merging multiple Project instances. All UUIDs are replaced to avoid collisions. The labels of some objects are also replaced to avoid collisions. + + Parameters + ---------- + projects: List[Project] + A list of the Project instances to merge. + + Returns + ------- + Project """ assert type(projects) is list and all( map(lambda _: type(_) is Class, projects) @@ -297,6 +378,7 @@ def replace_uuids(dictionary: dict) -> dict: state["plots"].extend(other["plots"]) state["simulations"].extend(other["simulations"]) state["tests"].update(other["tests"]) + state["zhits"].update(other["zhits"]) state["label"] = other["label"] state["notes"] = state["notes"].strip() # Check for UUID collisions. @@ -306,6 +388,8 @@ def replace_uuids(dictionary: dict) -> dict: uuids.extend(list(map(lambda _: _["uuid"], fits))) for drts in state["drts"].values(): uuids.extend(list(map(lambda _: _["uuid"], drts))) + for zhits in state["zhits"].values(): + uuids.extend(list(map(lambda _: _["uuid"], zhits))) uuids.extend(list(map(lambda _: _["uuid"], state["plots"]))) uuids.extend(list(map(lambda _: _["uuid"], state["simulations"]))) for tests in state["tests"].values(): @@ -350,6 +434,10 @@ def to_dict(self, session: bool) -> dict: ---------- session: bool If true, then data minimization is not performed. + + Returns + ------- + dict """ return { "data_sets": list( @@ -362,6 +450,9 @@ def to_dict(self, session: bool) -> dict: "drts": { k: list(map(lambda _: _.to_dict(), v)) for k, v in self._drts.items() }, + "zhits": { + k: list(map(lambda _: _.to_dict(), v)) for k, v in self._zhits.items() + }, "label": self._label, "notes": self._notes, "plots": list(map(lambda _: _.to_dict(session=session), self._plots)), @@ -377,6 +468,10 @@ def to_dict(self, session: bool) -> dict: def get_label(self) -> str: """ Get the project's label. + + Returns + ------- + str """ return self._label @@ -409,12 +504,20 @@ def get_path(self) -> str: """ Get the project's currrent path. An empty string signifies that no path has been set previously. + + Returns + ------- + str """ return self._path def get_notes(self) -> str: """ Get the project's notes. + + Returns + ------- + str """ return self._notes @@ -436,7 +539,7 @@ def save(self, path: Optional[str] = None): Parameters ---------- - path: Optional[str] = None + path: Optional[str], optional The path to write the project state to. If this is None, then the most recently defined path is used. """ @@ -459,10 +562,15 @@ def save(self, path: Optional[str] = None): fp.write(dump_json(dictionary, sort_keys=True, indent=1)) if exists(tmp_path): remove(tmp_path) + self._is_new = False def get_data_sets(self) -> List[DataSet]: """ Get the project's data sets. + + Returns + ------- + List[DataSet] """ return self._data_sets @@ -487,6 +595,7 @@ def add_data_set(self, data: DataSet): data.set_label(label) self._data_sets.append(data) self._fits[data.uuid] = [] + self._zhits[data.uuid] = [] self._drts[data.uuid] = [] self._tests[data.uuid] = [] self._data_sets.sort(key=lambda _: _.get_label()) @@ -548,34 +657,16 @@ def delete_data_set(self, data: DataSet): del self._fits[data.uuid] del self._drts[data.uuid] del self._tests[data.uuid] + del self._zhits[data.uuid] list(map(lambda _: _.remove_series(data.uuid), self._plots)) - def replace_data_set(self, old: DataSet, new: DataSet): - """ - Replace a data set in the project with another one. - - Parameters - ---------- - old: DataSet - The data set to be replaced. - - new: DataSet - The replacement data set. - """ - assert type(old) is DataSet, old - assert type(new) is DataSet, new - assert old.uuid in list(map(lambda _: _.uuid, self._data_sets)) - assert old.uuid == new.uuid, ( - old.uuid, - new.uuid, - ) - self._data_sets.remove(old) - self._data_sets.append(new) - self._data_sets.sort(key=lambda _: _.get_label()) - def get_all_tests(self) -> Dict[str, List[TestResult]]: """ Get a mapping of data set UUIDs to the corresponding Kramers-Kronig test results of those data sets. + + Returns + ------- + Dict[str, List[TestResult]] """ return self._tests @@ -587,6 +678,10 @@ def get_tests(self, data: DataSet) -> List[TestResult]: ---------- data: DataSet The data set whose tests to get. + + Returns + ------- + List[TestResult] """ assert type(data) is DataSet, data assert data.uuid in list(map(lambda _: _.uuid, self._data_sets)), data @@ -629,9 +724,77 @@ def delete_test(self, data: DataSet, test: TestResult): self._tests[data.uuid].remove(test) list(map(lambda _: _.remove_series(test.uuid), self._plots)) + def get_all_zhits(self) -> Dict[str, List[ZHITResult]]: + """ + Get a mapping of data set UUIDs to the corresponding Z-HIT analysis results. + + Returns + ------- + Dict[str, List[ZHITResult]] + """ + return self._zhits + + def get_zhits(self, data: DataSet) -> List[ZHITResult]: + """ + Get the Z-HIT analysis results associated with a specific data set. + + Parameters + ---------- + data: DataSet + The data set whose tests to get. + + Returns + ------- + List[ZHITResult] + """ + assert type(data) is DataSet, data + assert data.uuid in list(map(lambda _: _.uuid, self._data_sets)), data + return self._zhits[data.uuid] + + def add_zhit(self, data: DataSet, zhit: ZHITResult): + """ + Add the provided Z-HIT analysis result result to the provided data set's list of Z-HIT analysis results. + + Parameters + ---------- + data: DataSet + The data set that was tested. + + zhit: ZHITResult + The result of the analysis. + """ + assert type(data) is DataSet, data + assert data.uuid in list(map(lambda _: _.uuid, self._data_sets)), data + assert type(zhit) is ZHITResult, zhit + assert zhit.uuid not in list(map(lambda _: _.uuid, self._zhits[data.uuid])) + self._zhits[data.uuid].insert(0, zhit) + + def delete_zhit(self, data: DataSet, zhit: ZHITResult): + """ + Delete the provided Z-HIT analysis result from the provided data set's list of Z-HIT analysis results. + + Parameters + ---------- + data: DataSet + The data set associated with the test result. + + zhit: ZHITResult + The analysis result to delete. + """ + assert type(data) is DataSet, data + assert data.uuid in list(map(lambda _: _.uuid, self._data_sets)), data + assert type(zhit) is ZHITResult, zhit + assert zhit in self._zhits[data.uuid], zhit + self._zhits[data.uuid].remove(zhit) + list(map(lambda _: _.remove_series(zhit.uuid), self._plots)) + def get_all_drts(self) -> Dict[str, List[DRTResult]]: """ Get a mapping of data set UUIDs to the corresponding DRT analysis results of those data sets. + + Returns + ------- + Dict[str, List[DRTResult]] """ return self._drts @@ -643,6 +806,10 @@ def get_drts(self, data: DataSet) -> List[DRTResult]: ---------- data: DataSet The data set whose analyses to get. + + Returns + ------- + List[DRTResult] """ assert type(data) is DataSet, data assert data.uuid in list(map(lambda _: _.uuid, self._data_sets)), data @@ -688,6 +855,10 @@ def delete_drt(self, data: DataSet, drt: DRTResult): def get_all_fits(self) -> Dict[str, List[FitResult]]: """ Get a mapping of data set UUIDs to the corresponding list of fit results of those data sets. + + Returns + ------- + Dict[str, List[FitResult]] """ return self._fits @@ -699,6 +870,10 @@ def get_fits(self, data: DataSet) -> List[FitResult]: ---------- data: DataSet The data set whose fits to get. + + Returns + ------- + List[FitResult] """ assert type(data) is DataSet, data assert data.uuid in list(map(lambda _: _.uuid, self._data_sets)), data @@ -744,6 +919,10 @@ def delete_fit(self, data: DataSet, fit: FitResult): def get_simulations(self) -> List[SimulationResult]: """ Get all of the simulation results. + + Returns + ------- + List[SimulationResult] """ return self._simulations @@ -777,6 +956,10 @@ def delete_simulation(self, simulation: SimulationResult): def get_plots(self) -> List[PlotSettings]: """ Get all of the plots. + + Returns + ------- + List[PlotSettings] """ return self._plots @@ -843,10 +1026,15 @@ def get_plot_series( ---------- plot: PlotSettings The plot whose items/series to get. + + Returns + ------- + List[PlotSeries] """ assert type(plot) is PlotSettings, plot data_sets: List[DataSet] = self.get_data_sets() tests: Dict[str, List[TestResult]] = self.get_all_tests() + zhits: Dict[str, List[TestResult]] = self.get_all_zhits() drts: Dict[str, List[DRTResult]] = self.get_all_drts() fits: Dict[str, List[FitResult]] = self.get_all_fits() simulations: List[SimulationResult] = self.get_simulations() @@ -854,9 +1042,24 @@ def get_plot_series( uuid: str for uuid in plot.series_order: series: Optional[ - Union[DataSet, TestResult, DRTResult, FitResult, SimulationResult] + Union[ + DataSet, + TestResult, + ZHITResult, + DRTResult, + FitResult, + SimulationResult, + ] ] - series = plot.find_series(uuid, data_sets, tests, drts, fits, simulations) + series = plot.find_series( + uuid=uuid, + data_sets=data_sets, + tests=tests, + zhits=zhits, + drts=drts, + fits=fits, + simulations=simulations, + ) if series is None: continue label: str = plot.get_series_label(uuid) or series.get_label() diff --git a/src/deareis/data/simulation.py b/src/deareis/data/simulation.py index dafad25..324a119 100644 --- a/src/deareis/data/simulation.py +++ b/src/deareis/data/simulation.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -28,7 +28,6 @@ angle, integer, issubdtype, - ndarray, ) from pandas import DataFrame from pyimpspec import ( @@ -36,7 +35,13 @@ Element, ) import pyimpspec -from pyimpspec.analysis.fitting import _interpolate +from pyimpspec import ( + ComplexImpedances, + Frequencies, + Impedances, + Phases, +) +from pyimpspec.analysis.utility import _interpolate from deareis.utility import format_timestamp @@ -167,12 +172,12 @@ class SimulationResult: settings: SimulationSettings def __post_init__(self): - self._cached_frequency: Dict[int, ndarray] = {} - self._cached_impedance: Dict[int, ndarray] = {} - self._frequency: ndarray = self.get_frequency( + self._cached_frequencies: Dict[int, Frequencies] = {} + self._cached_impedances: Dict[int, ComplexImpedances] = {} + self._frequency: Frequencies = self.get_frequencies( num_per_decade=self.settings.num_per_decade ) - self._impedance: ndarray = self.get_impedance( + self._impedance: ComplexImpedances = self.get_impedances( num_per_decade=self.settings.num_per_decade ) @@ -203,7 +208,7 @@ def to_dict(self) -> dict: "version": VERSION, "uuid": self.uuid, "timestamp": self.timestamp, - "circuit": self.circuit.to_string(12), + "circuit": self.circuit.serialize(), "settings": self.settings.to_dict(), } @@ -214,15 +219,26 @@ def to_dataframe(self) -> DataFrame: element_labels: List[str] = [] parameter_labels: List[str] = [] values: List[float] = [] + internal_identifiers: Dict[int, Element] = { + v: k + for k, v in self.circuit.generate_element_identifiers(running=True).items() + } + external_identifiers: Dict[ + Element, int + ] = self.circuit.generate_element_identifiers(running=False) element: Element - for element in sorted( - self.circuit.get_elements(flattened=True), - key=lambda _: _.get_identifier(), + ident: int + for (ident, element) in sorted( + internal_identifiers.items(), + key=lambda _: _[0], ): - parameters: Dict[str, float] = element.get_parameters() + element_label: str = self.circuit.get_element_name( + element, identifiers=external_identifiers + ) + parameters: Dict[str, float] = element.get_values() parameter_label: str for parameter_label in sorted(parameters.keys()): - element_labels.append(element.get_label()) + element_labels.append(element_label) parameter_labels.append(parameter_label) values.append(parameters[parameter_label]) return DataFrame.from_dict( @@ -237,17 +253,13 @@ def get_label(self) -> str: """ Generate a label for the result. """ - cdc: str = self.settings.cdc - while "{" in cdc: - i: int = cdc.find("{") - j: int = cdc.find("}") - cdc = cdc.replace(cdc[i : j + 1], "") + cdc: str = self.circuit.to_string() if cdc.startswith("[") and cdc.endswith("]"): cdc = cdc[1:-1] timestamp: str = format_timestamp(self.timestamp) return f"{cdc} ({timestamp})" - def get_frequency(self, num_per_decade: int = -1) -> ndarray: + def get_frequencies(self, num_per_decade: int = -1) -> Frequencies: """ Get an array of frequencies within the range of simulated frequencies. @@ -255,19 +267,23 @@ def get_frequency(self, num_per_decade: int = -1) -> ndarray: ---------- num_per_decade: int = -1 If the value is greater than zero, then logarithmically distributed frequencies will be generated within the range of frequencies defined by the minimum and maximum frequencies used to generate the original simulation result. + + Returns + ------- + Frequencies """ assert issubdtype(type(num_per_decade), integer), num_per_decade if num_per_decade > 0: - if num_per_decade not in self._cached_frequency: - self._cached_frequency.clear() - self._cached_frequency[num_per_decade] = _interpolate( + if num_per_decade not in self._cached_frequencies: + self._cached_frequencies.clear() + self._cached_frequencies[num_per_decade] = _interpolate( [self.settings.min_frequency, self.settings.max_frequency], num_per_decade, ) - return self._cached_frequency[num_per_decade] + return self._cached_frequencies[num_per_decade] return self._frequency - def get_impedance(self, num_per_decade: int = -1) -> ndarray: + def get_impedances(self, num_per_decade: int = -1) -> ComplexImpedances: """ Get the complex impedances produced by the simulated circuit within the range of frequencies used to generate the original simulation result. @@ -275,28 +291,37 @@ def get_impedance(self, num_per_decade: int = -1) -> ndarray: ---------- num_per_decade: int = -1 If the value is greater than zero, then logarithmically distributed frequencies will be generated within the range of simulated frequencies and used to calculate the impedance produced by the simulated circuit. + Returns + ------- + ComplexImpedances """ assert issubdtype(type(num_per_decade), integer), num_per_decade if num_per_decade > 0: - if num_per_decade not in self._cached_impedance: - self._cached_impedance.clear() - self._cached_impedance[num_per_decade] = self.circuit.impedances( - self.get_frequency(num_per_decade) + if num_per_decade not in self._cached_impedances: + self._cached_impedances.clear() + self._cached_impedances[num_per_decade] = self.circuit.get_impedances( + self.get_frequencies(num_per_decade) ) - return self._cached_impedance[num_per_decade] + return self._cached_impedances[num_per_decade] return self._impedance - def get_nyquist_data(self, num_per_decade: int = -1) -> Tuple[ndarray, ndarray]: + def get_nyquist_data( + self, num_per_decade: int = -1 + ) -> Tuple[Impedances, Impedances]: """ - Get the data required to plot the results as a Nyquist plot (-Z\" vs Z'). + Get the data required to plot the results as a Nyquist plot (-Im(Z) vs Re(Z)). Parameters ---------- num_per_decade: int = -1 If the value is greater than zero, then logarithmically distributed frequencies will be generated within the range of frequencies and used to calculate the impedance produced by the simulated circuit. + + Returns + ------- + Tuple[Impedances, Impedances] """ assert issubdtype(type(num_per_decade), integer), num_per_decade - Z: ndarray = self.get_impedance(num_per_decade) + Z: ComplexImpedances = self.get_impedances(num_per_decade) return ( Z.real, -Z.imag, @@ -304,20 +329,71 @@ def get_nyquist_data(self, num_per_decade: int = -1) -> Tuple[ndarray, ndarray]: def get_bode_data( self, num_per_decade: int = -1 - ) -> Tuple[ndarray, ndarray, ndarray]: + ) -> Tuple[Frequencies, Impedances, Phases]: """ - Get the data required to plot the results as a Bode plot (|Z| and phi vs f). + Get the data required to plot the results as a Bode plot (Mod(Z) and -Phase(Z) vs f). Parameters ---------- num_per_decade: int = -1 If the value is greater than zero, then logarithmically distributed frequencies will be generated within the range of frequencies and used to calculate the impedance produced by the fitted circuit. + + Returns + ------- + Tuple[Frequencies, Impedances, Phases] """ assert issubdtype(type(num_per_decade), integer), num_per_decade - f: ndarray = self.get_frequency(num_per_decade) - Z: ndarray = self.get_impedance(num_per_decade) + f: Frequencies = self.get_frequencies(num_per_decade) + Z: ComplexImpedances = self.get_impedances(num_per_decade) return ( f, abs(Z), -angle(Z, deg=True), ) + + def to_parameters_dataframe(self) -> DataFrame: + """ + Get a `pandas.DataFrame` instance containing a table of element parameters. + + Returns + ------- + pandas.DataFrame + """ + element_labels: List[str] = [] + parameter_labels: List[str] = [] + values: List[float] = [] + units: List[str] = [] + internal_identifiers: Dict[int, Element] = { + v: k + for k, v in self.circuit.generate_element_identifiers(running=True).items() + } + external_identifiers: Dict[ + Element, int + ] = self.circuit.generate_element_identifiers(running=False) + element_label: str + parameters: Dict[int, Dict[str, float]] + element: Element + ident: int + for (ident, element) in sorted( + internal_identifiers.items(), + key=lambda _: _[0], + ): + element_label = self.circuit.get_element_name( + element, + identifiers=external_identifiers, + ) + parameters = element.get_values() + parameter_label: str + for parameter_label in sorted(parameters.keys()): + element_labels.append(element_label) + parameter_labels.append(parameter_label) + values.append(parameters[parameter_label]) + units.append(element.get_unit(parameter_label)) + return DataFrame.from_dict( + { + "Element": element_labels, + "Parameter": parameter_labels, + "Value": values, + "Unit": units, + } + ) diff --git a/src/deareis/data/zhit.py b/src/deareis/data/zhit.py new file mode 100644 index 0000000..bfd44fe --- /dev/null +++ b/src/deareis/data/zhit.py @@ -0,0 +1,439 @@ +# DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). +# Copyright 2023 DearEIS developers +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +# +# The licenses of DearEIS' dependencies and/or sources of portions of code are included in +# the LICENSES folder. + +from dataclasses import dataclass +from typing import ( + Callable, + Dict, + Optional, + Tuple, + Union, +) +from numpy import ( + angle, + array, + isnan, + log10 as log, +) +from pandas import DataFrame +from pyimpspec.analysis.utility import _calculate_pseudo_chisqr +from pyimpspec import ( + ComplexImpedances, + ComplexResiduals, + Frequencies, + Impedances, + Phases, + Residuals, +) +from deareis.enums import ( + ZHITInterpolation, + ZHITSmoothing, + ZHITWindow, + value_to_zhit_interpolation, + value_to_zhit_smoothing, + value_to_zhit_window, + zhit_interpolation_to_label, + zhit_smoothing_to_label, + zhit_window_to_label, +) +from deareis.utility import format_timestamp +from deareis.data import DataSet + +VERSION: int = 1 + + +def _parse_settings_v1(dictionary: dict) -> dict: + return dictionary + + +@dataclass(frozen=True) +class ZHITSettings: + """ + A class to store the settings used to perform a Z-HIT analysis. + + Parameters + ---------- + smoothing: ZHITSmoothing + The smoothing algorithm to use. + + num_points: int + The number of points to consider when smoothing a point. + + polynomial_order: int + The order of the polynomial to use in the Savitzky-Golay algorithm. + + num_iterations: int + The number of iterations to use in the LOWESS algorithm. + + interpolation: ZHITInterpolation + The spline to use when interpolating the phase data. + + window: ZHITWindow + The window function to use when generating weights for the offset adjustment. + + window_center: float + The center of the window function on the logarithmic frequency scale (e.g., 100 Hz -> 2.0). + + window_width: float + The width of the window function on the logarithmic frequency scale (e.g., 2.0 means 1 decade on each side of the window center). + """ + + smoothing: ZHITSmoothing + num_points: int + polynomial_order: int + num_iterations: int + interpolation: ZHITInterpolation + window: ZHITWindow + window_center: float + window_width: float + + def __repr__(self) -> str: + return f"ZHITSettings ({hex(id(self))})" + + @classmethod + def from_dict(Class, dictionary: dict) -> "ZHITSettings": + """ + Create an instance from a dictionary. + + Parameters + ---------- + dictionary: dict + The dictionary to turn into a ZHITSettings object. + + Returns + ------- + ZHITSettings + """ + assert type(dictionary) is dict + assert "version" in dictionary + version: int = dictionary["version"] + del dictionary["version"] + assert version <= VERSION, f"{version=} > {VERSION=}" + parsers: Dict[int, Callable] = { + 1: _parse_settings_v1, + } + assert version in parsers, f"{version=} not in {parsers.keys()=}" + v: int + p: Callable + for v, p in parsers.items(): + if v < version: + continue + dictionary = p(dictionary) + assert "smoothing" in dictionary + assert "num_points" in dictionary + assert "polynomial_order" in dictionary + assert "num_iterations" in dictionary + assert "interpolation" in dictionary + assert "window" in dictionary + assert "window_center" in dictionary + assert "window_width" in dictionary + dictionary["smoothing"] = ZHITSmoothing(dictionary["smoothing"]) + dictionary["interpolation"] = ZHITInterpolation(dictionary["interpolation"]) + dictionary["window"] = ZHITWindow(dictionary["window"]) + return Class(**dictionary) + + def to_dict(self) -> dict: + """ + Return a dictionary that can be used to recreate an instance. + + Returns + ------- + dict + """ + return { + "version": VERSION, + "smoothing": self.smoothing, + "num_points": self.num_points, + "polynomial_order": self.polynomial_order, + "num_iterations": self.num_iterations, + "interpolation": self.interpolation, + "window": self.window, + "window_center": self.window_center, + "window_width": self.window_width, + } + + +def _parse_result_v1(dictionary: dict) -> dict: + return dictionary + + +@dataclass +class ZHITResult: + """ + A class containing the result of a Z-HIT analysis. + + Parameters + ---------- + uuid: str + The universally unique identifier assigned to this result. + + timestamp: float + The Unix time (in seconds) for when the test was performed. + + frequencies: Frequencies + The frequencies used to perform the analysis. + + impedances: ComplexImpedances + The reconstructed impedances. + + residuals: ComplexResiduals + The residuals of the reconstructed impedances and the original impedances. + + mask: Dict[int, bool] + The mask that was applied to the original data set. + + pseudo_chisqr: float + The calculated |pseudo chi-squared| (eq. 14 in Boukamp, 1995). + + smoothing: str + The smoothing algorithm that was used (relevant if this setting was set to 'auto'). + + interpolation: str + The spline that was used to interpolate the data (relevant if this setting was set to 'auto'). + window: str + The window function that was used to generate weights for the offset adjustment (relevant if this setting was set to 'auto'). + + settings: ZHITSettings + The settings that were used to perform the analysis. + """ + + uuid: str + timestamp: float + frequencies: Frequencies + impedances: ComplexImpedances + residuals: ComplexResiduals + mask: Dict[int, bool] + pseudo_chisqr: float + smoothing: str + interpolation: str + window: str + settings: ZHITSettings + + def __repr__(self) -> str: + return f"ZHITResult ({hex(id(self))})" + + @classmethod + def from_dict( + Class, + dictionary: dict, + data: Optional[DataSet] = None, + ) -> "ZHITResult": + """ + Create an instance from a dictionary. + + Parameters + ---------- + dictionary: dict + The dictionary to turn into a FitResult object. + + data: Optional[DataSet], optional + The DataSet object that this result is for. + + Returns + ------- + ZHITResult + """ + assert isinstance(dictionary, dict), dictionary + assert data is None or isinstance(data, DataSet), data + assert "version" in dictionary + version: int = dictionary["version"] + del dictionary["version"] + assert version <= VERSION, f"{version=} > {VERSION=}" + parsers: Dict[int, Callable] = { + 1: _parse_result_v1, + } + assert version in parsers, f"{version=} not in {parsers.keys()=}" + v: int + p: Callable + for v, p in parsers.items(): + if v < version: + continue + dictionary = p(dictionary) + assert "uuid" in dictionary + assert "timestamp" in dictionary + assert "frequencies" in dictionary + assert "real_impedances" in dictionary + assert "imaginary_impedances" in dictionary + assert "real_residuals" in dictionary + assert "imaginary_residuals" in dictionary + assert "mask" in dictionary + assert "pseudo_chisqr" in dictionary + assert "smoothing" in dictionary + assert "interpolation" in dictionary + assert "window" in dictionary + assert "settings" in dictionary + dictionary["frequencies"] = array(dictionary["frequencies"]) + dictionary["impedances"] = array( + list( + map( + lambda _: complex(*_), + zip( + dictionary["real_impedances"], + dictionary["imaginary_impedances"], + ), + ) + ) + ) + del dictionary["real_impedances"] + del dictionary["imaginary_impedances"] + mask: Dict[str, bool] = dictionary["mask"] + dictionary["mask"] = { + i: mask.get(str(i), False) for i in range(0, len(dictionary["frequencies"])) + } + dictionary["residuals"] = array( + list( + map( + lambda _: complex(*_), + zip( + dictionary["real_residuals"], + dictionary["imaginary_residuals"], + ), + ) + ) + ) + del dictionary["real_residuals"] + del dictionary["imaginary_residuals"] + if isnan(dictionary["pseudo_chisqr"]): + dictionary["pseudo_chisqr"] = _calculate_pseudo_chisqr( + Z_exp=data.get_impedances(), + Z_fit=dictionary["impedances"], + ) + dictionary["settings"] = ZHITSettings.from_dict(dictionary["settings"]) + return Class(**dictionary) + + def to_dict(self) -> dict: + """ + Return a dictionary that can be used to recreate an instance. + + Returns + ------- + dict + """ + return { + "version": VERSION, + "uuid": self.uuid, + "timestamp": self.timestamp, + "frequencies": list(self.frequencies), + "real_impedances": list(self.impedances.real), + "imaginary_impedances": list(self.impedances.imag), + "real_residuals": list(self.residuals.real), + "imaginary_residuals": list(self.residuals.imag), + "mask": {k: True for k, v in self.mask.items() if v is True}, + "pseudo_chisqr": self.pseudo_chisqr, + "smoothing": self.smoothing, + "interpolation": self.interpolation, + "window": self.window, + "settings": self.settings.to_dict(), + } + + def to_statistics_dataframe(self) -> DataFrame: + """ + Get the statistics related to the modulus reconstruction as a pandas.DataFrame object. + + Returns + ------- + DataFrame + """ + statistics: Dict[str, Union[float, str]] = { + "Log pseudo chi-squared": log(self.pseudo_chisqr), + "Smoothing": zhit_smoothing_to_label[ + value_to_zhit_smoothing[self.smoothing] + ], + "Interpolation": zhit_interpolation_to_label[ + value_to_zhit_interpolation[self.interpolation] + ], + "Window": zhit_window_to_label.get( + value_to_zhit_window.get( + self.window, + self.window, + ), + self.window, + ), + } + return DataFrame.from_dict( + { + "Label": list(statistics.keys()), + "Value": list(statistics.values()), + } + ) + + def get_label(self) -> str: + timestamp: str = format_timestamp(self.timestamp) + return f"Z-HIT ({timestamp})" + + def get_frequencies(self) -> Frequencies: + """ + Get an array of frequencies within the range of frequencies in the data set. + + Returns + ------- + Frequencies + """ + return self.frequencies + + def get_impedances(self) -> ComplexImpedances: + """ + Get the complex impedances produced by the fitted circuit within the range of frequencies in the data set. + + Returns + ------- + ComplexImpedances + """ + return self.impedances + + def get_nyquist_data(self) -> Tuple[Impedances, Impedances]: + """ + Get the data required to plot the results as a Nyquist plot (-Im(Z) vs Re(Z)). + + Returns + ------- + Tuple[Impedances, Impedances] + """ + return ( + self.impedances.real, + -self.impedances.imag, + ) + + def get_bode_data(self) -> Tuple[Frequencies, Impedances, Phases]: + """ + Get the data required to plot the results as a Bode plot (Mod(Z) and -Phase(Z) vs f). + + Returns + ------- + Tuple[Frequencies, Impedancesy, Phases] + """ + return ( + self.frequencies, + abs(self.impedances), + -angle(self.impedances, deg=True), + ) + + def get_residuals_data(self) -> Tuple[Frequencies, Residuals, Residuals]: + """ + Get the data required to plot the residuals (real and imaginary vs f). + + Returns + ------- + Tuple[Frequencies, Residuals, Residuals] + """ + return ( + self.frequencies, + self.residuals.real * 100, + self.residuals.imag * 100, + ) diff --git a/src/deareis/enums.py b/src/deareis/enums.py index 8c2252f..57ef6a2 100644 --- a/src/deareis/enums.py +++ b/src/deareis/enums.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -27,6 +27,7 @@ class Context(IntEnum): OVERVIEW_TAB = auto() DATA_SETS_TAB = auto() KRAMERS_KRONIG_TAB = auto() + ZHIT_TAB = auto() DRT_TAB = auto() FITTING_TAB = auto() SIMULATION_TAB = auto() @@ -34,6 +35,8 @@ class Context(IntEnum): class Action(IntEnum): + CANCEL = auto() + CUSTOM = auto() # Program-level NEW_PROJECT = auto() LOAD_PROJECT = auto() @@ -65,8 +68,12 @@ class Action(IntEnum): SELECT_OVERVIEW_TAB = auto() SELECT_PLOTTING_TAB = auto() SELECT_SIMULATION_TAB = auto() + SELECT_ZHIT_TAB = auto() + NEXT_PLOT_TAB = auto() + PREVIOUS_PLOT_TAB = auto() # Project-level: multiple tabs + BATCH_PERFORM_ACTION = auto() PERFORM_ACTION = auto() # - Load data set # - Perform test @@ -76,6 +83,8 @@ class Action(IntEnum): DELETE_RESULT = auto() # - Data set # - Test result + # - Z-HIT result + # - DRT result # - Fit result # - Simulation result # - Plot @@ -86,11 +95,14 @@ class Action(IntEnum): NEXT_SECONDARY_RESULT = auto() PREVIOUS_SECONDARY_RESULT = auto() # - Test result + # - Z-HIT result + # - DRT result # - Fit result # - Simulation result # - Plot type APPLY_SETTINGS = auto() # - Kramers-Kronig tab + # - Z-HIT tab # - DRT tab # - Fitting tab # - Simulation tab @@ -112,15 +124,21 @@ class Action(IntEnum): COPY_BODE_DATA = auto() COPY_RESIDUALS_DATA = auto() COPY_OUTPUT = auto() + ADJUST_PARAMETERS = auto() # - Fit output # - Simulation output LOAD_SIMULATION_AS_DATA_SET = auto() + LOAD_ZHIT_AS_DATA_SET = auto() # Project-level: data sets tab AVERAGE_DATA_SETS = auto() - TOGGLE_DATA_POINTS = auto() COPY_DATA_SET_MASK = auto() + INTERPOLATE_POINTS = auto() SUBTRACT_IMPEDANCE = auto() + TOGGLE_DATA_POINTS = auto() + + # Project-level: Z-HIT tab + PREVIEW_ZHIT_WEIGHTS = auto() # Project-level: plotting tab SELECT_ALL_PLOT_SERIES = auto() @@ -129,9 +147,12 @@ class Action(IntEnum): COPY_PLOT_DATA = auto() EXPAND_COLLAPSE_SIDEBAR = auto() EXPORT_PLOT = auto() + DUPLICATE_PLOT = auto() action_contexts: Dict[Action, List[Context]] = { + Action.CANCEL: [], + Action.CUSTOM: [], Action.NEW_PROJECT: [Context.PROGRAM], Action.LOAD_PROJECT: [Context.PROGRAM], Action.EXIT: [Context.PROGRAM], @@ -153,6 +174,24 @@ class Action(IntEnum): Action.REDO: [Context.PROJECT], Action.NEXT_PROJECT_TAB: [Context.PROJECT], Action.PREVIOUS_PROJECT_TAB: [Context.PROJECT], + Action.NEXT_PLOT_TAB: [ + Context.DATA_SETS_TAB, + Context.KRAMERS_KRONIG_TAB, + Context.ZHIT_TAB, + Context.DRT_TAB, + Context.FITTING_TAB, + Context.SIMULATION_TAB, + Context.PLOTTING_TAB, + ], + Action.PREVIOUS_PLOT_TAB: [ + Context.DATA_SETS_TAB, + Context.KRAMERS_KRONIG_TAB, + Context.ZHIT_TAB, + Context.DRT_TAB, + Context.FITTING_TAB, + Context.SIMULATION_TAB, + Context.PLOTTING_TAB, + ], Action.SELECT_DATA_SETS_TAB: [Context.PROJECT], Action.SELECT_DRT_TAB: [Context.PROJECT], Action.SELECT_FITTING_TAB: [Context.PROJECT], @@ -160,17 +199,26 @@ class Action(IntEnum): Action.SELECT_OVERVIEW_TAB: [Context.PROJECT], Action.SELECT_PLOTTING_TAB: [Context.PROJECT], Action.SELECT_SIMULATION_TAB: [Context.PROJECT], + Action.SELECT_ZHIT_TAB: [Context.PROJECT], Action.PERFORM_ACTION: [ Context.DATA_SETS_TAB, Context.KRAMERS_KRONIG_TAB, + Context.ZHIT_TAB, Context.DRT_TAB, Context.FITTING_TAB, Context.SIMULATION_TAB, Context.PLOTTING_TAB, ], + Action.BATCH_PERFORM_ACTION: [ + Context.KRAMERS_KRONIG_TAB, + Context.ZHIT_TAB, + Context.DRT_TAB, + Context.FITTING_TAB, + ], Action.DELETE_RESULT: [ Context.DATA_SETS_TAB, Context.KRAMERS_KRONIG_TAB, + Context.ZHIT_TAB, Context.DRT_TAB, Context.FITTING_TAB, Context.SIMULATION_TAB, @@ -179,6 +227,7 @@ class Action(IntEnum): Action.NEXT_PRIMARY_RESULT: [ Context.DATA_SETS_TAB, Context.KRAMERS_KRONIG_TAB, + Context.ZHIT_TAB, Context.DRT_TAB, Context.FITTING_TAB, Context.SIMULATION_TAB, @@ -187,6 +236,7 @@ class Action(IntEnum): Action.PREVIOUS_PRIMARY_RESULT: [ Context.DATA_SETS_TAB, Context.KRAMERS_KRONIG_TAB, + Context.ZHIT_TAB, Context.DRT_TAB, Context.FITTING_TAB, Context.SIMULATION_TAB, @@ -194,6 +244,7 @@ class Action(IntEnum): ], Action.NEXT_SECONDARY_RESULT: [ Context.KRAMERS_KRONIG_TAB, + Context.ZHIT_TAB, Context.DRT_TAB, Context.FITTING_TAB, Context.SIMULATION_TAB, @@ -201,6 +252,7 @@ class Action(IntEnum): ], Action.PREVIOUS_SECONDARY_RESULT: [ Context.KRAMERS_KRONIG_TAB, + Context.ZHIT_TAB, Context.DRT_TAB, Context.FITTING_TAB, Context.SIMULATION_TAB, @@ -208,17 +260,24 @@ class Action(IntEnum): ], Action.APPLY_SETTINGS: [ Context.KRAMERS_KRONIG_TAB, + Context.ZHIT_TAB, Context.DRT_TAB, Context.FITTING_TAB, Context.SIMULATION_TAB, ], Action.APPLY_MASK: [ Context.KRAMERS_KRONIG_TAB, + Context.ZHIT_TAB, Context.DRT_TAB, Context.FITTING_TAB, ], Action.SHOW_ENLARGED_IMPEDANCE: [ + Context.DATA_SETS_TAB, + Context.KRAMERS_KRONIG_TAB, + Context.ZHIT_TAB, Context.DRT_TAB, + Context.FITTING_TAB, + Context.SIMULATION_TAB, ], Action.SHOW_ENLARGED_DRT: [ Context.DRT_TAB, @@ -226,17 +285,22 @@ class Action(IntEnum): Action.SHOW_ENLARGED_NYQUIST: [ Context.DATA_SETS_TAB, Context.KRAMERS_KRONIG_TAB, + Context.ZHIT_TAB, + Context.DRT_TAB, Context.FITTING_TAB, Context.SIMULATION_TAB, ], Action.SHOW_ENLARGED_BODE: [ Context.DATA_SETS_TAB, Context.KRAMERS_KRONIG_TAB, + Context.ZHIT_TAB, + Context.DRT_TAB, Context.FITTING_TAB, Context.SIMULATION_TAB, ], Action.SHOW_ENLARGED_RESIDUALS: [ Context.KRAMERS_KRONIG_TAB, + Context.ZHIT_TAB, Context.DRT_TAB, Context.FITTING_TAB, ], @@ -271,9 +335,11 @@ class Action(IntEnum): Context.SIMULATION_TAB, ], Action.LOAD_SIMULATION_AS_DATA_SET: [Context.SIMULATION_TAB], + Action.LOAD_ZHIT_AS_DATA_SET: [Context.ZHIT_TAB], Action.AVERAGE_DATA_SETS: [Context.DATA_SETS_TAB], Action.TOGGLE_DATA_POINTS: [Context.DATA_SETS_TAB], Action.COPY_DATA_SET_MASK: [Context.DATA_SETS_TAB], + Action.INTERPOLATE_POINTS: [Context.DATA_SETS_TAB], Action.SUBTRACT_IMPEDANCE: [Context.DATA_SETS_TAB], Action.SELECT_ALL_PLOT_SERIES: [Context.PLOTTING_TAB], Action.UNSELECT_ALL_PLOT_SERIES: [Context.PLOTTING_TAB], @@ -281,10 +347,15 @@ class Action(IntEnum): Action.COPY_PLOT_DATA: [Context.PLOTTING_TAB], Action.EXPAND_COLLAPSE_SIDEBAR: [Context.PLOTTING_TAB], Action.EXPORT_PLOT: [Context.PLOTTING_TAB], + Action.PREVIEW_ZHIT_WEIGHTS: [Context.ZHIT_TAB], + Action.DUPLICATE_PLOT: [Context.PLOTTING_TAB], + Action.ADJUST_PARAMETERS: [Context.FITTING_TAB, Context.SIMULATION_TAB], } action_to_string: Dict[Action, str] = { + Action.CANCEL: "cancel", + Action.CUSTOM: "custom", Action.APPLY_MASK: "apply-mask", Action.APPLY_SETTINGS: "apply-settings", Action.AVERAGE_DATA_SETS: "average-data-sets", @@ -303,13 +374,16 @@ class Action(IntEnum): Action.EXIT: "exit-program", Action.EXPAND_COLLAPSE_SIDEBAR: "expand-collapse-sidebar", Action.EXPORT_PLOT: "export-plot", + Action.INTERPOLATE_POINTS: "interpolate-points", Action.LOAD_PROJECT: "load-project", Action.LOAD_SIMULATION_AS_DATA_SET: "load-simulation-as-data-set", + Action.LOAD_ZHIT_AS_DATA_SET: "load-zhit-as-data-set", Action.NEW_PROJECT: "new-project", Action.NEXT_PRIMARY_RESULT: "next-primary-result", Action.NEXT_PROGRAM_TAB: "next-program-tab", Action.NEXT_PROJECT_TAB: "next-project-tab", Action.NEXT_SECONDARY_RESULT: "next-secondary-result", + Action.BATCH_PERFORM_ACTION: "batch-perform-action", Action.PERFORM_ACTION: "perform-action", Action.PREVIOUS_PRIMARY_RESULT: "previous-primary-result", Action.PREVIOUS_PROGRAM_TAB: "previous-program-tab", @@ -324,6 +398,7 @@ class Action(IntEnum): Action.SELECT_FITTING_TAB: "select-fitting-tab", Action.SELECT_HOME_TAB: "select-home-tab", Action.SELECT_KRAMERS_KRONIG_TAB: "select-kramers-kronig-tab", + Action.SELECT_ZHIT_TAB: "select-zhit-tab", Action.SELECT_OVERVIEW_TAB: "select-overview-tab", Action.SELECT_PLOTTING_TAB: "select-plotting-tab", Action.SELECT_SIMULATION_TAB: "select-simulation-tab", @@ -344,6 +419,11 @@ class Action(IntEnum): Action.TOGGLE_DATA_POINTS: "toggle-data-points", Action.UNDO: "undo", Action.UNSELECT_ALL_PLOT_SERIES: "unselect-all-plot-series", + Action.PREVIEW_ZHIT_WEIGHTS: "preview-zhit-weights", + Action.DUPLICATE_PLOT: "duplicate-plot", + Action.ADJUST_PARAMETERS: "adjust-parameters", + Action.NEXT_PLOT_TAB: "next-plot-tab", + Action.PREVIOUS_PLOT_TAB: "previous-plot-tab", } string_to_action: Dict[str, Action] = {v: k for k, v in action_to_string.items()} # Check that there are no duplicate keys @@ -353,6 +433,10 @@ class Action(IntEnum): action_descriptions: Dict[Action, str] = { + Action.CANCEL: """ +Cancel and close the current modal window. +""".strip(), + Action.CUSTOM: "", Action.NEW_PROJECT: """ Create a new project. """.strip(), @@ -427,6 +511,9 @@ class Action(IntEnum): """.strip(), Action.SELECT_KRAMERS_KRONIG_TAB: """ Go to the 'Kramers-Kronig' tab. +""".strip(), + Action.SELECT_ZHIT_TAB: """ +Go to the 'Z-HIT analysis' tab. """.strip(), Action.SELECT_OVERVIEW_TAB: """ Go to the 'Overview' tab. @@ -436,11 +523,19 @@ class Action(IntEnum): """.strip(), Action.SELECT_SIMULATION_TAB: """ Go to the 'Simulation' tab. +""".strip(), + Action.BATCH_PERFORM_ACTION: """ +Batch perform the primary action of the current project tab: +- Kramers-Kronig: perform tests. +- Z-HIT analysis: perform analyses. +- DRT analysis: perform analyses. +- Fitting: perform fits. """.strip(), Action.PERFORM_ACTION: """ Perform the primary action of the current project tab: - Data sets: select files to load. - Kramers-Kronig: perform test. +- Z-HIT analysis: perform analysis. - DRT analysis: perform analysis. - Fitting: perform fit. - Simulation: perform simulation. @@ -450,6 +545,7 @@ class Action(IntEnum): Delete the current result in the current project tab: - Data sets: delete the current data set. - Kramers-Kronig: delete the current test result. +- Z-HIT analysis: delete the current analysis result. - DRT analysis: delete the current analysis result. - Fitting: delete the current fit result. - Simulation: delete the current simulation result. @@ -459,6 +555,7 @@ class Action(IntEnum): Select the next primary result of the current project tab: - Data sets: data set. - Kramers-Kronig: data set. +- Z-HIT analysis: data set. - DRT analysis: data set. - Fitting: data set. - Simulation: data set. @@ -468,6 +565,7 @@ class Action(IntEnum): Select the previous primary result of the current project tab: - Data sets: data set. - Kramers-Kronig: data set. +- Z-HIT analysis: data set. - DRT analysis: data set. - Fitting: data set. - Simulation: data set. @@ -476,22 +574,25 @@ class Action(IntEnum): Action.NEXT_SECONDARY_RESULT: """ Select the next secondary result of the current project tab: - Kramers-Kronig: test result. +- Z-HIT analysis: analysis result. - DRT analysis: analysis result. - Fitting: fit result. - Simulation: simulation result. -- Plotting: plot type. +- Plotting: plot series tab. """.strip(), Action.PREVIOUS_SECONDARY_RESULT: """ Select the previous secondary result of the current project tab: - Kramers-Kronig: test result. +- Z-HIT analysis: analysis result. - DRT analysis: analysis result. - Fitting: fit result. - Simulation: simulation result. -- Plotting: plot type. +- Plotting: plot series tab. """.strip(), Action.APPLY_SETTINGS: """ Apply the settings used in the current secondary result of the current project tab: - Kramers-Kronig: test result. +- Z-HIT analysis: analysis result. - DRT analysis: analysis result. - Fitting: fit result. - Simulation: simulation result. @@ -499,6 +600,7 @@ class Action(IntEnum): Action.APPLY_MASK: """ Apply the mask used in the current secondary result of the current project tab: - Kramers-Kronig: test result. +- Z-HIT analysis: analysis result. - DRT analysis: analysis result. - Fitting: fit result. """.strip(), @@ -546,6 +648,9 @@ class Action(IntEnum): """.strip(), Action.COPY_DATA_SET_MASK: """ Select which data set's mask to copy. +""".strip(), + Action.INTERPOLATE_POINTS: """ +Interpolate one or more data points in the current data set. """.strip(), Action.SUBTRACT_IMPEDANCE: """ Select the impedance to subtract from the current data set. @@ -570,6 +675,24 @@ class Action(IntEnum): """.strip(), Action.LOAD_SIMULATION_AS_DATA_SET: """ Load the current simulation as a data set. +""".strip(), + Action.LOAD_ZHIT_AS_DATA_SET: """ +Load the current Z-HIT analysis result as a data set. +""".strip(), + Action.PREVIEW_ZHIT_WEIGHTS: """ +Preview the weights for the Z-HIT offset adjustment. +""".strip(), + Action.DUPLICATE_PLOT: """ +Duplicate the current plot. +""".strip(), + Action.ADJUST_PARAMETERS: """ +Adjust the (initial) values of circuit parameters prior to fitting or simulation. +""".strip(), + Action.NEXT_PLOT_TAB: """ +Select the next plot type. +""".strip(), + Action.PREVIOUS_PLOT_TAB: """ +Select the previous plot type. """.strip(), } # Check that every action has a description @@ -583,29 +706,15 @@ class CNLSMethod(IntEnum): Iterative methods used during complex non-linear least-squares fitting: - AUTO: try each method - - AMPGO - - BASINHOPPING - BFGS - - BRUTE - CG - - COBYLA - - DIFFERENTIAL_EVOLUTION - - DOGLEG - - DUAL_ANNEALING - - EMCEE - LBFGSB - LEASTSQ - LEAST_SQUARES - NELDER - - NEWTON - POWELL - - SHGO - SLSQP - TNC - - TRUST_CONSTR - - TRUST_EXACT - - TRUST_KRYLOV - - TRUST_NCG """ AUTO = 1 @@ -728,13 +837,16 @@ class FitSimOutput(IntEnum): CDC_EXTENDED = auto() CSV_DATA_TABLE = auto() CSV_PARAMETERS_TABLE = auto() + CSV_STATISTICS_TABLE = auto() JSON_PARAMETERS_TABLE = auto() + JSON_STATISTICS_TABLE = auto() LATEX_DIAGRAM = auto() LATEX_EXPR = auto() LATEX_PARAMETERS_TABLE = auto() + LATEX_STATISTICS_TABLE = auto() MARKDOWN_PARAMETERS_TABLE = auto() + MARKDOWN_STATISTICS_TABLE = auto() SVG_DIAGRAM = auto() - SVG_DIAGRAM_NO_TERMINAL_LABELS = auto() SVG_DIAGRAM_NO_LABELS = auto() SYMPY_EXPR = auto() SYMPY_EXPR_VALUES = auto() @@ -745,13 +857,16 @@ class FitSimOutput(IntEnum): FitSimOutput.CDC_EXTENDED: "CDC - extended", FitSimOutput.CSV_DATA_TABLE: "CSV - impedance table", FitSimOutput.CSV_PARAMETERS_TABLE: "CSV - parameters table", + FitSimOutput.CSV_STATISTICS_TABLE: "CSV - statistics table", FitSimOutput.JSON_PARAMETERS_TABLE: "JSON - parameters table", + FitSimOutput.JSON_STATISTICS_TABLE: "JSON - statistics table", FitSimOutput.LATEX_DIAGRAM: "LaTeX - circuit diagram", FitSimOutput.LATEX_EXPR: "LaTeX - expression", FitSimOutput.LATEX_PARAMETERS_TABLE: "LaTeX - parameters table", + FitSimOutput.LATEX_STATISTICS_TABLE: "LaTeX - statistics table", FitSimOutput.MARKDOWN_PARAMETERS_TABLE: "Markdown - parameters table", + FitSimOutput.MARKDOWN_STATISTICS_TABLE: "Markdown - statistics table", FitSimOutput.SVG_DIAGRAM: "SVG - circuit diagram", - FitSimOutput.SVG_DIAGRAM_NO_TERMINAL_LABELS: "SVG - circuit diagram without terminal labels", FitSimOutput.SVG_DIAGRAM_NO_LABELS: "SVG - circuit diagram without any labels", FitSimOutput.SYMPY_EXPR: "SymPy - expression", FitSimOutput.SYMPY_EXPR_VALUES: "SymPy - expression and values", @@ -768,12 +883,12 @@ class PlotType(IntEnum): """ Types of plots: - - NYQUIST: -Zim vs Zre - - BODE_MAGNITUDE: |Z| vs f - - BODE_PHASE: phi vs f + - NYQUIST: -Im(Z) vs Re(Z) + - BODE_MAGNITUDE: Mod(Z) vs f + - BODE_PHASE: -Phase(Z) vs f - DRT: gamma vs tau - - IMPEDANCE_REAL: Zre vs f - - IMPEDANCE_IMAGINARY: Zim vs f + - IMPEDANCE_REAL: Re(Z) vs f + - IMPEDANCE_IMAGINARY: -Im(Z) vs f """ NYQUIST = 1 @@ -840,9 +955,9 @@ class Weight(IntEnum): Types of weights to use during complex non-linear least squares fitting: - AUTO: try each weight - - BOUKAMP: 1 / (Zre^2 + Zim^2) (eq. 13, Boukamp, 1995) - - MODULUS: 1 / |Z| - - PROPORTIONAL: 1 / Zre^2, 1 / Zim^2 + - BOUKAMP: :math:`1 / ({\\rm Re}(Z)^2 + {\\rm Im}(Z)^2)` (eq. 13, Boukamp, 1995) + - MODULUS: :math:`1 / |Z|` + - PROPORTIONAL: :math:`1 / {\\rm Re}(Z)^2, 1 / {\\rm Im}(Z)^2` - UNITY: 1 """ @@ -887,26 +1002,27 @@ class DRTMethod(IntEnum): - TR_NNLS - TR_RBF - BHT - - M_RQ_FIT + - MRQ_FIT """ + TR_NNLS = 1 TR_RBF = 2 BHT = 3 - M_RQ_FIT = 4 + MRQ_FIT = 4 drt_method_to_label: Dict[DRTMethod, str] = { DRTMethod.BHT: "BHT", DRTMethod.TR_NNLS: "TR-NNLS", DRTMethod.TR_RBF: "TR-RBF", - DRTMethod.M_RQ_FIT: "m(RQ)fit", + DRTMethod.MRQ_FIT: "m(RQ)fit", } label_to_drt_method: Dict[str, DRTMethod] = { v: k for k, v in drt_method_to_label.items() } drt_method_to_value: Dict[DRTMethod, str] = { DRTMethod.BHT: "bht", - DRTMethod.M_RQ_FIT: "m(RQ)fit", + DRTMethod.MRQ_FIT: "mrq-fit", DRTMethod.TR_NNLS: "tr-nnls", DRTMethod.TR_RBF: "tr-rbf", } @@ -926,6 +1042,7 @@ class DRTMode(IntEnum): - REAL - IMAGINARY """ + COMPLEX = 1 REAL = 2 IMAGINARY = 3 @@ -964,6 +1081,7 @@ class RBFType(IntEnum): - INVERSE_QUADRIC - PIECEWISE_LINEAR """ + C0_MATERN = 1 C2_MATERN = 2 C4_MATERN = 3 @@ -1013,6 +1131,7 @@ class RBFShape(IntEnum): - FWHM - FACTOR """ + FWHM = 1 FACTOR = 2 @@ -1067,10 +1186,11 @@ class DRTOutput(IntEnum): class PlotUnits(IntEnum): """ The units of the plot dimensions: - + - INCHES - CENTIMETERS """ + INCHES = 1 CENTIMETERS = 2 @@ -1107,6 +1227,7 @@ class PlotPreviewLimit(IntEnum): - PX8192 - PX16384 """ + NONE = 0 PX256 = 8 PX512 = 9 @@ -1151,6 +1272,7 @@ class PlotLegendLocation(IntEnum): - UPPER_CENTER - CENTER """ + AUTO = 0 UPPER_RIGHT = 1 UPPER_LEFT = 2 @@ -1194,3 +1316,157 @@ class PlotLegendLocation(IntEnum): ".ps", ".svg", ] + + +class ZHITSmoothing(IntEnum): + """ + The algorithm to use when smoothing the phase data: + + - AUTO: try all of the options + - NONE: no smoothing + - LOWESS: `Local Weighted Scatterplot Smoothing `_ + - SAVGOL: `Savitzky-Golay `_ + """ + + AUTO = 1 + NONE = 2 + LOWESS = 3 + SAVGOL = 4 # Savitzky-Golay + + +label_to_zhit_smoothing: Dict[str, ZHITSmoothing] = { + "Auto": ZHITSmoothing.AUTO, + "None": ZHITSmoothing.NONE, + "LOWESS": ZHITSmoothing.LOWESS, + "Savitzky-Golay": ZHITSmoothing.SAVGOL, +} +zhit_smoothing_to_label: Dict[ZHITSmoothing, str] = { + v: k for k, v in label_to_zhit_smoothing.items() +} +zhit_smoothing_to_value: Dict[ZHITSmoothing, str] = { + ZHITSmoothing.AUTO: "auto", + ZHITSmoothing.NONE: "none", + ZHITSmoothing.LOWESS: "lowess", + ZHITSmoothing.SAVGOL: "savgol", +} +value_to_zhit_smoothing: Dict[str, ZHITSmoothing] = { + v: k for k, v in zhit_smoothing_to_value.items() +} + + +class ZHITInterpolation(IntEnum): + """ + The spline to use for interpolating the smoothed phase data: + + - AUTO: try all of the options + - AKIMA: `Akima spline `_ + - CUBIC: `cubic spline `_ + - PCHIP: `Piecewise Cubic Hermite Interpolating Polynomial `_ + """ + + AUTO = 1 + AKIMA = 2 + CUBIC = 3 + PCHIP = 4 + + +label_to_zhit_interpolation: Dict[str, ZHITInterpolation] = { + "Auto": ZHITInterpolation.AUTO, + "Akima": ZHITInterpolation.AKIMA, + "Cubic": ZHITInterpolation.CUBIC, + "PCHIP": ZHITInterpolation.PCHIP, +} +zhit_interpolation_to_label: Dict[ZHITInterpolation, str] = { + v: k for k, v in label_to_zhit_interpolation.items() +} +zhit_interpolation_to_value: Dict[ZHITInterpolation, str] = { + ZHITInterpolation.AUTO: "auto", + ZHITInterpolation.AKIMA: "akima", + ZHITInterpolation.CUBIC: "cubic", + ZHITInterpolation.PCHIP: "pchip", +} +value_to_zhit_interpolation: Dict[str, ZHITInterpolation] = { + v: k for k, v in zhit_interpolation_to_value.items() +} + + +class ZHITWindow(IntEnum): + """ + The window functions to use for determining the weights when adjusting the Mod(Z) offset: + + - AUTO: try all of the options + - BARTHANN + - BARTLETT + - BLACKMAN + - BLACKMANHARRIS + - BOHMAN + - BOXCAR + - COSINE + - FLATTOP + - HAMMING + - HANN + - LANCZOS + - NUTTALL + - PARZEN + - TRIANG + + See `scipy.signal.windows `_ for information about these. + """ + + AUTO = 1 + BARTHANN = 2 + BARTLETT = 3 + BLACKMAN = 4 + BLACKMANHARRIS = 5 + BOHMAN = 6 + BOXCAR = 7 + COSINE = 8 + FLATTOP = 9 + HAMMING = 10 + HANN = 11 + NUTTALL = 12 + PARZEN = 13 + TRIANG = 14 + LANCZOS = 15 + + +label_to_zhit_window: Dict[str, ZHITWindow] = { + "Auto": ZHITWindow.AUTO, + "Barthann": ZHITWindow.BARTHANN, + "Bartlett": ZHITWindow.BARTLETT, + "Blackman": ZHITWindow.BLACKMAN, + "Blackman-Harris": ZHITWindow.BLACKMANHARRIS, + "Bohman": ZHITWindow.BOHMAN, + "Boxcar": ZHITWindow.BOXCAR, + "Cosine": ZHITWindow.COSINE, + "Flat top": ZHITWindow.FLATTOP, + "Hamming": ZHITWindow.HAMMING, + "Hann": ZHITWindow.HANN, + "Lanczos": ZHITWindow.LANCZOS, + "Nuttall": ZHITWindow.NUTTALL, + "Parzen": ZHITWindow.PARZEN, + "Triangular": ZHITWindow.TRIANG, +} +zhit_window_to_label: Dict[ZHITWindow, str] = { + v: k for k, v in label_to_zhit_window.items() +} +zhit_window_to_value: Dict[ZHITWindow, str] = { + ZHITWindow.AUTO: "auto", + ZHITWindow.BARTHANN: "barthann", + ZHITWindow.BARTLETT: "bartlett", + ZHITWindow.BLACKMAN: "blackman", + ZHITWindow.BLACKMANHARRIS: "blackmanharris", + ZHITWindow.BOHMAN: "bohman", + ZHITWindow.BOXCAR: "boxcar", + ZHITWindow.COSINE: "cosine", + ZHITWindow.FLATTOP: "flattop", + ZHITWindow.HAMMING: "hamming", + ZHITWindow.HANN: "hann", + ZHITWindow.LANCZOS: "lanczos", + ZHITWindow.NUTTALL: "nuttall", + ZHITWindow.PARZEN: "parzen", + ZHITWindow.TRIANG: "triang", +} +value_to_zhit_window: Dict[str, ZHITWindow] = { + v: k for k, v in zhit_window_to_value.items() +} diff --git a/src/deareis/exceptions.py b/src/deareis/exceptions.py new file mode 100644 index 0000000..830966e --- /dev/null +++ b/src/deareis/exceptions.py @@ -0,0 +1,20 @@ +# DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). +# Copyright 2023 DearEIS developers +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +# +# The licenses of DearEIS' dependencies and/or sources of portions of code are included in +# the LICENSES folder. + +from pyimpspec.exceptions import * diff --git a/src/deareis/gui/__init__.py b/src/deareis/gui/__init__.py index b6844df..ecdc3a0 100644 --- a/src/deareis/gui/__init__.py +++ b/src/deareis/gui/__init__.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by diff --git a/src/deareis/gui/about.py b/src/deareis/gui/about.py new file mode 100644 index 0000000..b44cdb3 --- /dev/null +++ b/src/deareis/gui/about.py @@ -0,0 +1,102 @@ +# DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). +# Copyright 2023 DearEIS developers +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +# +# The licenses of DearEIS' dependencies and/or sources of portions of code are included in +# the LICENSES folder. + +import webbrowser +from typing import ( + Callable, + Dict, +) +import dearpygui.dearpygui as dpg +from deareis.signals import Signal +from deareis.utility import calculate_window_position_dimensions +from deareis.version import PACKAGE_VERSION +import deareis.signals as signals +import deareis.themes as themes +from deareis.state import STATE +from deareis.enums import Action +from deareis.keybindings import ( + Keybinding, + TemporaryKeybindingHandler, +) + + +class AboutWindow: + def __init__(self): + self.create_window() + self.register_keybindings() + + def register_keybindings(self): + callbacks: Dict[Keybinding, Callable] = {} + # Cancel + kb: Keybinding = Keybinding( + key=dpg.mvKey_Escape, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.CANCEL, + ) + callbacks[kb] = self.close + # Create the handler + self.keybinding_handler: TemporaryKeybindingHandler = ( + TemporaryKeybindingHandler(callbacks=callbacks) + ) + + def create_window(self): + x: int + y: int + w: int + h: int + x, y, w, h = calculate_window_position_dimensions(270, 100) + self.window: int = dpg.generate_uuid() + with dpg.window( + label="About", + modal=True, + pos=(x, y), + width=w, + height=h, + no_resize=True, + on_close=self.close, + tag=self.window, + ): + dpg.add_text(f"DearEIS ({PACKAGE_VERSION})") + url: str + for url in [ + "https://vyrjana.github.io/DearEIS", + "https://github.com/vyrjana/DearEIS", + ]: + dpg.bind_item_theme( + dpg.add_button( + label=url, + callback=lambda s, a, u: webbrowser.open(u), + user_data=url, + width=-1, + ), + themes.url_theme, + ) + signals.emit(Signal.BLOCK_KEYBINDINGS, window=self.window, window_object=self) + + def close(self): + if dpg.does_item_exist(self.window): + dpg.delete_item(self.window) + self.keybinding_handler.delete() + signals.emit(Signal.UNBLOCK_KEYBINDINGS) + + +def show_help_about(*args, **kwargs): + AboutWindow() diff --git a/src/deareis/gui/batch_analysis.py b/src/deareis/gui/batch_analysis.py new file mode 100644 index 0000000..dddd7b7 --- /dev/null +++ b/src/deareis/gui/batch_analysis.py @@ -0,0 +1,293 @@ +# DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). +# Copyright 2023 DearEIS developers +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +# +# The licenses of DearEIS' dependencies and/or sources of portions of code are included in +# the LICENSES folder. + +from dataclasses import dataclass +from typing import ( + Callable, + Dict, + List, + Optional, +) +import dearpygui.dearpygui as dpg +from deareis.data import DataSet +from deareis.utility import ( + calculate_window_position_dimensions, + is_filtered_item_visible, +) +from deareis.signals import Signal +import deareis.signals as signals +import deareis.tooltips as tooltips +from deareis.tooltips import attach_tooltip +from deareis.state import STATE +from deareis.enums import Action +from deareis.keybindings import ( + Keybinding, + TemporaryKeybindingHandler, +) + + +@dataclass +class Entry: + data: DataSet + row: int + checkbox: int + + def __hash__(self) -> int: + return int(self.data.uuid, 16) + + def is_visible(self, filter_string: str) -> bool: + return is_filtered_item_visible(self.row, filter_string) + + def is_ticked(self) -> bool: + return dpg.get_value(self.checkbox) + + def toggle(self, flag: Optional[bool] = None): + dpg.set_value( + self.checkbox, + flag if flag is not None else not dpg.get_value(self.checkbox), + ) + + +class BatchAnalysis: + def __init__(self, data_sets: List[DataSet], callback: Callable): + self.callback: Callable = callback + self.create_window() + self.entries: List[Entry] = [] + self.populate(data_sets) + self.register_keybindings() + + def register_keybindings(self): + callbacks: Dict[Keybinding, Callable] = {} + # Cancel + kb: Keybinding = Keybinding( + key=dpg.mvKey_Escape, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.CANCEL, + ) + callbacks[kb] = self.close + # Accept + for kb in STATE.config.keybindings: + if kb.action is Action.PERFORM_ACTION: + break + else: + kb = Keybinding( + key=dpg.mvKey_Return, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.PERFORM_ACTION, + ) + callbacks[kb] = self.accept + # Select filtered + for kb in STATE.config.keybindings: + if kb.action is Action.SELECT_ALL_PLOT_SERIES: + break + else: + kb = Keybinding( + key=dpg.mvKey_A, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.SELECT_ALL_PLOT_SERIES, + ) + callbacks[kb] = lambda: self.select_unselect(flag=True) + # Unselect filtered + for kb in STATE.config.keybindings: + if kb.action is Action.UNSELECT_ALL_PLOT_SERIES: + break + else: + kb = Keybinding( + key=dpg.mvKey_A, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.UNSELECT_ALL_PLOT_SERIES, + ) + callbacks[kb] = lambda: self.select_unselect(flag=False) + # Focus filter input + kb: Keybinding = Keybinding( + key=dpg.mvKey_F, + mod_alt=False, + mod_ctrl=True, + mod_shift=False, + action=Action.CUSTOM, + ) + callbacks[kb] = self.focus_filter_input + # Create the handler + self.keybinding_handler: TemporaryKeybindingHandler = ( + TemporaryKeybindingHandler(callbacks=callbacks) + ) + + def create_window(self): + x: int + y: int + w: int + h: int + x, y, w, h = calculate_window_position_dimensions(width=500, height=400) + self.window: int = dpg.generate_uuid() + with dpg.window( + label="Batch analysis", + modal=True, + pos=(x, y), + width=w, + height=h, + tag=self.window, + on_close=self.close, + no_resize=True, + ): + with dpg.group(horizontal=True): + self.filter_input: int = dpg.generate_uuid() + dpg.add_input_text( + hint="Filter...", + width=-80, + callback=lambda s, a, u: self.filter_possible_series(a.lower()), + tag=self.filter_input, + ) + attach_tooltip(tooltips.batch_analysis.filter) + self.select_unselect_button: int = dpg.generate_uuid() + dpg.add_button( + label="Select", + width=-1, + callback=self.select_unselect, + tag=self.select_unselect_button, + ) + attach_tooltip(tooltips.batch_analysis.select) + self.table: int = dpg.generate_uuid() + with dpg.table( + borders_outerV=True, + borders_outerH=True, + borders_innerV=True, + borders_innerH=True, + scrollY=True, + freeze_rows=1, + tag=self.table, + height=-24, + ): + dpg.add_table_column( + label=" ?", + width_fixed=True, + ) + attach_tooltip(tooltips.batch_analysis.checkbox) + dpg.add_table_column( + label="Label", + width_fixed=False, + ) + self.accept_button: int = dpg.generate_uuid() + dpg.add_button( + label="Cancel", + callback=self.accept, + width=-1, + tag=self.accept_button, + ) + + def populate(self, data_sets: List[DataSet]): + self.entries.clear() + data: DataSet + for data in data_sets: + label: str = data.get_label() + row: int + with dpg.table_row( + filter_key=label.lower(), + parent=self.table, + ) as row: + checkbox: int = dpg.add_checkbox( + default_value=False, + user_data=data, + callback=lambda s, a, u: self.toggle(), + ) + dpg.add_text(label) + attach_tooltip(label) + self.entries.append( + Entry( + data=data, + row=row, + checkbox=checkbox, + ) + ) + + def select_unselect(self, flag: Optional[bool] = None): + selection: Dict[Entry, bool] = {} + filter_string: str = dpg.get_value(self.filter_input).strip() + for entry in self.entries: + if filter_string != "" and not entry.is_visible(filter_string): + continue + selection[entry] = entry.is_ticked() + if not isinstance(flag, bool): + flag = not all(map(lambda _: _ is True, selection.values())) + for entry in selection: + dpg.set_value(entry.checkbox, flag) + self.toggle() + + def toggle(self, index: int = -1): + if index >= 0: + # This is primarily for use in the GUI tests. + row: int = dpg.get_item_children(self.table, slot=1)[index] + checkbox: int = dpg.get_item_children(row, slot=1)[0] + dpg.set_value(checkbox, not dpg.get_value(checkbox)) + num_data_sets: int = len(self.get_selection()) + dpg.set_item_label( + self.accept_button, + "Cancel" if num_data_sets == 0 else f"Accept ({num_data_sets})", + ) + self.update_select_button_label(dpg.get_value(self.filter_input).strip()) + + def update_select_button_label(self, filter_string: str): + selection: List[Entry] = list( + filter(lambda _: _.is_visible(filter_string), self.entries) + ) + dpg.set_item_label( + self.select_unselect_button, + "Unselect" + if all(map(lambda _: _.is_ticked() is True, selection)) + else "Select", + ) + + def get_selection(self) -> List[Entry]: + selection: List[Entry] = [] + for entry in self.entries: + if entry.is_ticked(): + selection.append(entry) + return selection + + def filter_possible_series(self, filter_string: str): + filter_string = filter_string.strip() + dpg.set_value(self.table, filter_string) + self.update_select_button_label(filter_string) + + def close(self): + dpg.hide_item(self.window) + dpg.delete_item(self.window) + self.keybinding_handler.delete() + signals.emit(Signal.UNBLOCK_KEYBINDINGS) + + def accept(self): + selection: List[Entry] = self.get_selection() + if not selection: + self.close() + return + self.close() + dpg.split_frame(delay=60) + self.callback([_.data for _ in selection]) + + def focus_filter_input(self): + if dpg.is_item_active(self.filter_input): + return + dpg.focus_item(self.filter_input) diff --git a/src/deareis/gui/busy_message.py b/src/deareis/gui/busy_message.py index 423b3f7..c55e48b 100644 --- a/src/deareis/gui/busy_message.py +++ b/src/deareis/gui/busy_message.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by diff --git a/src/deareis/gui/changelog/__init__.py b/src/deareis/gui/changelog/__init__.py index b2b281f..a5b872b 100644 --- a/src/deareis/gui/changelog/__init__.py +++ b/src/deareis/gui/changelog/__init__.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -24,6 +24,8 @@ join, ) from typing import ( + Callable, + Dict, IO, List, ) @@ -31,6 +33,12 @@ from deareis.utility import calculate_window_position_dimensions import deareis.signals as signals import dearpygui.dearpygui as dpg +from deareis.state import STATE +from deareis.enums import Action +from deareis.keybindings import ( + Keybinding, + TemporaryKeybindingHandler, +) def format_changelog(changelog: str, width: int) -> str: @@ -74,47 +82,88 @@ def format_changelog(changelog: str, width: int) -> str: return "\n".join(lines) -def show_changelog(): - changelog_path: str = join(dirname(abspath(__file__)), "CHANGELOG.md") - assert exists(changelog_path), changelog_path - changelog: str = "" - fp: IO - with open(changelog_path, "r") as fp: - changelog = fp.read() +class ChangelogWindow: + def __init__(self): + changelog_path: str = join(dirname(abspath(__file__)), "CHANGELOG.md") + assert exists(changelog_path), changelog_path + versions: List[List[str]] = self.parse_changelog(changelog_path) + self.create_window(versions) + self.register_keybindings() + signals.emit(Signal.BLOCK_KEYBINDINGS, window=self.window, window_object=self) - window: int = dpg.generate_uuid() - key_handler: int = dpg.generate_uuid() + def parse_changelog(self, path: str) -> List[List[str]]: + fp: IO + with open(path, "r") as fp: + lines: List[str] = fp.readlines() + versions: List[List[str]] = [] + while lines: + line: str = lines.pop(0).strip() + if line == "": + continue + elif line.startswith("# "): + tmp: List[str] = [line] + while lines: + line = lines.pop(0).strip() + if line.startswith("# "): + lines.insert(0, line) + break + tmp.append(line) + versions.append(tmp) + else: + raise NotImplementedError(f"Unsupported changelog format: {line}") + return versions - def close_window(): - if dpg.does_item_exist(window): - dpg.delete_item(window) - if dpg.does_item_exist(key_handler): - dpg.delete_item(key_handler) - signals.emit(Signal.UNBLOCK_KEYBINDINGS) - - with dpg.handler_registry(tag=key_handler): - dpg.add_key_release_handler( + def register_keybindings(self): + callbacks: Dict[Keybinding, Callable] = {} + # Cancel + kb: Keybinding = Keybinding( key=dpg.mvKey_Escape, - callback=close_window, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.CANCEL, + ) + callbacks[kb] = self.close + # Create the handler + self.keybinding_handler: TemporaryKeybindingHandler = ( + TemporaryKeybindingHandler(callbacks=callbacks) ) - x: int - y: int - w: int - h: int - x, y, w, h = calculate_window_position_dimensions(640, 540) - with dpg.window( - label="Changelog", - modal=True, - no_resize=True, - pos=( - x, - y, - ), - width=w, - height=h, - on_close=close_window, - tag=window, - ): - dpg.add_text(format_changelog(changelog, w - 40)) - signals.emit(Signal.BLOCK_KEYBINDINGS, window=window, window_object=None) + def create_window(self, versions: List[List[str]]): + self.window: int = dpg.generate_uuid() + x: int + y: int + w: int + h: int + x, y, w, h = calculate_window_position_dimensions(640, 540) + with dpg.window( + label="Changelog", + modal=True, + no_resize=True, + pos=(x, y), + width=w, + height=h, + on_close=self.close, + tag=self.window, + ): + i: int + for i, lines in enumerate(versions): + label: str = lines.pop(0) + assert label.startswith("# "), label + with dpg.collapsing_header( + label=label[2:].strip(), + default_open=i == 0, + ): + changelog: str = "\n".join(lines).strip() + dpg.add_text(format_changelog(changelog, w - 40)) + dpg.add_spacer(height=8) + + def close(self): + if dpg.does_item_exist(self.window): + dpg.delete_item(self.window) + self.keybinding_handler.delete() + signals.emit(Signal.UNBLOCK_KEYBINDINGS) + + +def show_changelog(): + ChangelogWindow() diff --git a/src/deareis/gui/circuit_editor/__init__.py b/src/deareis/gui/circuit_editor/__init__.py index 2e47ae5..2da8843 100644 --- a/src/deareis/gui/circuit_editor/__init__.py +++ b/src/deareis/gui/circuit_editor/__init__.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by diff --git a/src/deareis/gui/circuit_editor/editor.py b/src/deareis/gui/circuit_editor/editor.py index 4f01e26..0e97427 100644 --- a/src/deareis/gui/circuit_editor/editor.py +++ b/src/deareis/gui/circuit_editor/editor.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -17,24 +17,227 @@ # The licenses of DearEIS' dependencies and/or sources of portions of code are included in # the LICENSES folder. -from typing import Callable, Dict, List, Optional, Tuple, Type -from numpy import inf -from pyimpspec import Circuit, Element, ParsingError +from enum import ( + IntEnum, + auto, +) +from typing import ( + Callable, + Dict, + List, + Optional, + Tuple, + Type, +) +from numpy import ( + array, + inf, + isinf, +) +from pyimpspec import ( + Circuit, + Connection, + Container, + Element, + Series, +) import pyimpspec import dearpygui.dearpygui as dpg -from deareis.gui.circuit_editor.parser import Parser, Node +from deareis.signals import Signal +import deareis.signals as signals +from deareis.utility import ( + calculate_window_position_dimensions, + process_cdc, +) +from deareis.gui.circuit_editor.parser import ( + Node, + Parser, +) import deareis.themes as themes -from deareis.tooltips import attach_tooltip, update_tooltip +from deareis.tooltips import ( + attach_tooltip, + update_tooltip, +) import deareis.tooltips as tooltips -from deareis.keybindings import is_alt_down, is_control_down +from deareis.keybindings import ( + is_alt_down, + is_control_down, +) +from deareis.enums import Action +from deareis.keybindings import ( + Keybinding, + TemporaryKeybindingHandler, +) + +# TODO: Keybindings +# - Add dummy +# - Add element +# - Parse CDC +# - Clear + + +class ContainerOption(IntEnum): + DEFAULT = auto() + CUSTOM = auto() + SHORT = auto() + OPEN = auto() + + +CONTAINER_OPTIONS_TO_LABELS: Dict[ContainerOption, str] = { + ContainerOption.DEFAULT: "Default", + ContainerOption.CUSTOM: "Custom", + ContainerOption.SHORT: "Short", + ContainerOption.OPEN: "Open", +} +LABELS_TO_CONTAINER_OPTIONS: Dict[str, ContainerOption] = { + v: k for k, v in CONTAINER_OPTIONS_TO_LABELS.items() +} class CircuitEditor: - def __init__(self, window: int, callback: Callable): + def __init__( + self, + window: int, + callback: Callable, + keybindings: List[Keybinding] = [], + ): assert type(window) is int and dpg.does_item_exist(window), window + self.window: int = window self.parameter_inputs: List[int] = [] self.callback: Callable = callback - self.window: int = window + self.current_node: Optional[Node] = None + self.setup_window() + self.register_keybindings(keybindings) + self.keybinding_handler.block() + + def register_keybindings(self, keybindings: List[Keybinding]): + def delete_callback(): + if not dpg.does_item_exist(self.window): + return + elif not dpg.is_item_shown(self.window): + return + elif not dpg.is_item_hovered(self.node_editor): + return + elif self.has_active_input(): + return + tag: int + link_tags: List[int] = dpg.get_selected_links(self.node_editor) + if link_tags: + for tag in link_tags: + self.delink(-1, tag) + node_tags: List[int] = dpg.get_selected_nodes(self.node_editor) + if node_tags: + for tag in node_tags: + node: Node = self.parser.find_node(tag=tag) + if node == self.parser.we_node or node == self.parser.cere_node: + continue + self.delete_node(node) + elif self.current_node is not None: + if not ( + self.current_node == self.parser.we_node + or self.current_node == self.parser.cere_node + ): + self.delete_node(self.current_node) + + def accept_callback(): + if self.has_active_input(): + return + self.callback(dpg.get_item_user_data(self.accept_button)) + + callbacks: Dict[Keybinding, Callable] = {} + # Cancel + kb: Keybinding = Keybinding( + key=dpg.mvKey_Escape, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.CANCEL, + ) + callbacks[kb] = lambda: self.callback(None) + # Accept + for kb in keybindings: + if kb.action is Action.PERFORM_ACTION: + break + else: + kb = Keybinding( + key=dpg.mvKey_Return, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.PERFORM_ACTION, + ) + callbacks[kb] = accept_callback + # Delete node + for kb in keybindings: + if kb.action is Action.DELETE_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Delete, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.DELETE_RESULT, + ) + callbacks[kb] = delete_callback + # Previous circuit element + for kb in keybindings: + if kb.action is Action.PREVIOUS_PRIMARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Prior, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.PREVIOUS_PRIMARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle_nodes(step=-1) + # Next circuit element + for kb in keybindings: + if kb.action is Action.NEXT_PRIMARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Next, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.NEXT_PRIMARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle_nodes(step=1) + # Previous circuit node + for kb in keybindings: + if kb.action is Action.PREVIOUS_SECONDARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Prior, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.PREVIOUS_SECONDARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle_elements(step=-1) + # Next circuit node + for kb in keybindings: + if kb.action is Action.NEXT_SECONDARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Next, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.NEXT_SECONDARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle_elements(step=1) + # Create the handler + self.keybinding_handler: TemporaryKeybindingHandler = ( + TemporaryKeybindingHandler(callbacks=callbacks) + ) + + def setup_window(self): with dpg.group(horizontal=True, parent=self.window): self.parameter_window: int = dpg.generate_uuid() with dpg.child_window(width=250, tag=self.parameter_window): @@ -57,11 +260,13 @@ def __init__(self, window: int, callback: Callable): dpg.get_value(self.cdc_input) ), ) + attach_tooltip(tooltips.circuit_editor.parse_cdc) dpg.add_button( label="Clear", width=80, callback=lambda s, a, u: self.parse_cdc(""), ) + attach_tooltip(tooltips.circuit_editor.clear) with dpg.group(horizontal=True): dpg.add_text(" Element") attach_tooltip(tooltips.circuit_editor.element_combo) @@ -70,17 +275,22 @@ def __init__(self, window: int, callback: Callable): for _ in pyimpspec.get_elements().values() } items: List[str] = list(elements.keys()) - element_combo: int = dpg.add_combo( - width=-147, items=items, default_value=items[0] + self.element_combo: int = dpg.generate_uuid() + dpg.add_combo( + width=-147, + items=items, + default_value=items[0], + tag=self.element_combo, ) dpg.add_button( label="Add", width=50, callback=lambda s, a, u: self.add_element_node( - u.get(dpg.get_value(element_combo))() + u.get(dpg.get_value(self.element_combo))() ), user_data=elements, ) + attach_tooltip(tooltips.circuit_editor.add_element) dpg.add_button( label="Add dummy", width=80, @@ -165,58 +375,15 @@ def show(self, circuit: Optional[Circuit]): self.update(circuit, update_input=True) self.update_outputs(circuit=circuit) self.update_status("OK" if circuit is not None else "", False) - self.setup_keybindings() + self.keybinding_handler.unblock() dpg.show_item(self.window) def hide(self): - if hasattr(self, "key_handler") and dpg.does_item_exist(self.key_handler): - dpg.delete_item(self.key_handler) + self.keybinding_handler.block() dpg.hide_item(self.window) - def setup_keybindings(self): - def delete_callback(): - if not dpg.does_item_exist(self.window): - return - elif not dpg.is_item_shown(self.window): - return - elif not dpg.is_item_hovered(self.node_editor): - return - elif ( - dpg.is_item_active(self.cdc_input) - or dpg.is_item_active(self.basic_cdc_output_field) - or dpg.is_item_active(self.extended_cdc_output_field) - or dpg.is_item_active(self.status_field) - or any(map(lambda _: dpg.is_item_active(_), self.parameter_inputs)) - ): - return - tag: int - link_tags: List[int] = dpg.get_selected_links(self.node_editor) - if link_tags: - for tag in link_tags: - self.delink(-1, tag) - node_tags: List[int] = dpg.get_selected_nodes(self.node_editor) - if node_tags: - for tag in node_tags: - node: Node = self.parser.find_node(tag=tag) - if node == self.parser.we_node or node == self.parser.cere_node: - continue - self.delete_node(node) - - def accept_callback(): - if not (is_alt_down() or is_control_down()): - return - self.callback(dpg.get_item_user_data(self.accept_button)) - - self.key_handler: int = dpg.generate_uuid() - with dpg.handler_registry(tag=self.key_handler): - dpg.add_key_release_handler(key=dpg.mvKey_Delete, callback=delete_callback) - dpg.add_key_release_handler( - key=dpg.mvKey_Escape, callback=lambda: self.callback(None) - ) - dpg.add_key_release_handler( - key=dpg.mvKey_Return, - callback=accept_callback, - ) + def is_shown(self): + return dpg.is_item_shown(self.window) def update(self, circuit: Optional[Circuit], update_input: bool = False): dpg.hide_item(self.accept_button) @@ -232,22 +399,25 @@ def update(self, circuit: Optional[Circuit], update_input: bool = False): def parse_cdc(self, cdc: str): assert type(cdc) is str, cdc - if cdc == "": - self.clear_parameter_window() - circuit: Optional[Circuit] = None + self.current_node = None + self.clear_parameter_window(add_info=True) + circuit: Optional[Circuit] + msg: str try: - circuit = pyimpspec.parse_cdc(cdc) + circuit, msg = process_cdc(cdc) + except Exception as err: + circuit = None + msg = str(err) + msg = msg or "OK" + if circuit is not None: dpg.bind_item_theme(self.cdc_input, themes.cdc.valid) self.update_outputs(circuit=circuit) - self.update_status("OK", False) - except ParsingError as err: - dpg.bind_item_theme(self.cdc_input, themes.cdc.invalid) - self.update_outputs(stack=[]) - self.update_status(str(err), True) - if circuit is not None: self.parser.circuit_to_nodes(circuit) else: + dpg.bind_item_theme(self.cdc_input, themes.cdc.invalid) + self.update_outputs(stack=[]) self.parser.clear_nodes() + self.update_status(msg, circuit is None) self.update(circuit, update_input=cdc == "") def update_status(self, msg: str, invalid: bool): @@ -289,45 +459,96 @@ def clear_parameter_window(self, add_info: bool = False): The parameters of the element represented by a node can be altered by left-clicking on the label of the node. The parameters that can be modified will then show up in the sidebar to the left. An element's label can also be modified. -Nodes can be deleted by left-clicking on the label of a node and then left-clicking on the 'Delete' button that shows up in the sidebar to the left. Alternatively, you can select a node and press the Delete key. Note that the 'WE' and 'CE+RE' nodes, which represent the terminals of the circuit, cannot be deleted. +Nodes can be deleted by left-clicking on the label of a node and then left-clicking on the 'Delete' button that shows up in the sidebar to the left. Alternatively, you can select a node and use the keyboard shortcut for deleting a result (e.g., Alt+Delete). Note that the 'WE' and 'CE+RE' nodes, which represent the terminals of the circuit, cannot be deleted. You can pan the node editor by holding down the middle mouse button and moving the cursor. """.strip(), - wrap=235, + wrap=220, parent=self.parameter_window, ) - def node_clicked(self, sender: int, app_data): + def replace_equation(self, lines: List[str], prefix: str) -> List[str]: + i: int + line: str + for i, line in enumerate(map(str.strip, lines)): + if line.startswith(prefix): + lines[i] = "See documentation for equation(s)." + break + else: + return lines + i += 1 + if lines[i].strip() == "": + i += 1 + line = lines.pop(i) + while not (line == "Parameters" or line == "Subcircuits"): + if not lines: + break + line = lines.pop(i) + return lines + + def move_equation(self, lines: List[str], prefix: str) -> List[str]: + j: int = -1 + k: int = -1 + i: int + line: str + for i, line in enumerate(map(str.strip, lines)): + if line.startswith(prefix): + # lines[i] = f"Z = {element._equation}" + j = i + elif line == "Parameters" or line == "Subcircuits": + k = i + break + if j < 0: + return lines + equation_lines: List[str] = [ + "Equation", + "--------", + ] + if k == -1: + k = j + 1 + while k > j: + equation_lines.append(lines.pop(j)) + k -= 1 + if lines[j].strip() == "": + lines.pop(j) + for i, line in enumerate(map(str.strip, lines[j:])): + if line == "Parameters" or line == "Subcircuits": + break + for i in range(j, i): + equation_lines.append(lines.pop(j)) + lines.extend(equation_lines) + return lines + + def node_clicked(self, sender: int, app_data: tuple): assert type(sender) is int self.clear_parameter_window() + dpg.clear_selected_nodes(self.node_editor) + dpg.clear_selected_links(self.node_editor) + if self.current_node is not None and dpg.does_item_exist(self.current_node.tag): + self.current_node.set_unselected() node: Node = self.parser.find_node(tag=app_data[1]) + self.current_node = node + node.set_selected() tooltip_text: str if node.id == self.parser.we_node.id or node.id == self.parser.cere_node.id: with dpg.group(parent=self.parameter_window): - dpg.add_text("Electrode(s)") - with dpg.group(horizontal=True): - attach_tooltip( - """ -The working electrode. - """.strip() + dpg.add_text( + ( + "Working electrode" if node.id == self.parser.we_node.id - else """ -The counter and reference electrodes. - """.strip(), - parent=dpg.add_text("?"), - ) - dpg.add_text( - "WE" if node.id == self.parser.we_node.id else "CE+RE" + else "Counter and reference electrodes" ) + + "\n\nOne of the terminals in the circuit.", + wrap=220, + ) return elif node.id < 0: with dpg.group(parent=self.parameter_window): - with dpg.group(horizontal=True): - attach_tooltip( - tooltips.circuit_editor.dummy_node, - parent=dpg.add_text("?"), - ) - dpg.add_text("Dummy node") + dpg.add_text( + "Dummy node\n\nCan be used as a junction to connect, e.g., two parallel connections together in series.", + wrap=220, + ) + return else: dpg.add_button( label="Delete", @@ -338,7 +559,14 @@ def node_clicked(self, sender: int, app_data): element: Element = node.element with dpg.group(parent=self.parameter_window): with dpg.group(horizontal=True): - tooltip_text = element.get_extended_description() + lines: List[str] = element.get_extended_description().split("\n") + prefix: str = ":math:`Z = " + replace_equation: bool = True + if replace_equation is True: + lines = self.replace_equation(lines, prefix) + else: + lines = self.move_equation(lines, prefix) + tooltip_text = "\n".join(lines).strip() attach_tooltip(tooltip_text, parent=dpg.add_text("?")) label = element.get_description() if len(label) > 30: @@ -346,8 +574,8 @@ def node_clicked(self, sender: int, app_data): dpg.add_text(label) with dpg.group(horizontal=True): dpg.add_text("Label") - default_label: str = element.get_default_label() - hint: str = default_label[default_label.find("_") + 1 :] + default_label: str = str(self.parser.element_identifiers[element]) + hint: str = default_label def update_label(sender: int, new_label: str): assert type(sender) is int @@ -365,11 +593,12 @@ def update_label(sender: int, new_label: str): if new_label == "" and not dpg.get_value(sender) == new_label: dpg.set_value(sender, new_label) element.set_label(new_label) - node.set_label(element.get_label()) + if new_label == "": + new_label = str(self.parser.element_identifiers[element]) + node.set_label(f"{element.get_symbol()}_{new_label}") self.validate_nodes() current_label: str = element.get_label() - current_label = current_label[current_label.find("_") + 1 :] self.parameter_inputs.append( dpg.add_input_text( hint=hint, @@ -385,11 +614,17 @@ def update_label(sender: int, new_label: str): dpg.add_text("Parameters") key: str value: float - for key, value in element.get_parameters().items(): - self.node_parameter(element, key, value, 34) + for key, value in element.get_values().items(): + self.node_parameter(element, key, value) + if isinstance(element, Container): + con: Optional[Connection] + for key, con in element.get_subcircuits().items(): + self.node_subcircuit(element, key, con) def delete_node(self, node: Node): assert type(node) is Node, node + if node == self.current_node: + self.current_node = None self.parser.delete_node(node) self.validate_nodes() self.clear_parameter_window() @@ -405,18 +640,216 @@ def add_dummy_node(self): self.parser.add_dummy_node() self.validate_nodes() + def node_subcircuit( + self, + element: Container, + key: str, + initial_value: Optional[Connection], + ): + assert isinstance(element, Container) + assert type(key) is str + assert isinstance(initial_value, Connection) or initial_value is None + items: List[str] = list(CONTAINER_OPTIONS_TO_LABELS.values()) + default_value: Optional[Connection] = element.get_default_subcircuit(key) + combo: int = dpg.generate_uuid() + cdc_input: int = dpg.generate_uuid() + cdc_tooltip: int = dpg.generate_uuid() + edit_button: int = dpg.generate_uuid() + value_group: int = dpg.generate_uuid() + + def choose_option( + sender: int, + new_value: str, + ): + dpg.hide_item(dpg.get_item_parent(cdc_tooltip)) + dpg.bind_item_theme(cdc_input, themes.cdc.normal) + enum: ContainerOption = LABELS_TO_CONTAINER_OPTIONS[new_value] + dpg.set_value(sender, new_value) + if enum == ContainerOption.CUSTOM: # Custom + dpg.show_item(value_group) + dpg.set_value( + cdc_input, + initial_value.to_string() if initial_value is not None else "", + ) + dpg.enable_item(cdc_input) + dpg.enable_item(edit_button) + element.set_subcircuits(key, initial_value) + else: + if enum == ContainerOption.DEFAULT: + if default_value is None: + enum = ContainerOption.OPEN + elif len(default_value) == 0: + enum = ContainerOption.SHORT + else: + dpg.set_value(cdc_input, default_value.to_string()) + dpg.show_item(value_group) + dpg.disable_item(cdc_input) + dpg.disable_item(edit_button) + element.set_subcircuits(key, default_value) + if enum != ContainerOption.DEFAULT: + dpg.hide_item(value_group) + if enum == ContainerOption.SHORT: + element.set_subcircuits(key, Series([])) + elif enum == ContainerOption.OPEN: + element.set_subcircuits(key, None) + dpg.set_value(sender, CONTAINER_OPTIONS_TO_LABELS[enum]) + self.validate_nodes() + + def parse_cdc( + sender: int, + cdc: str, + tooltip: int, + update: bool = False, + ): + circuit: Optional[Circuit] + msg: str + try: + circuit, msg = process_cdc(cdc) + except Exception as err: + circuit = None + msg = str(err) + if circuit is None: + dpg.bind_item_theme(sender, themes.cdc.invalid) + update_tooltip(tooltip, msg) + dpg.show_item(dpg.get_item_parent(tooltip)) + dpg.set_item_user_data(edit_button, default_value) + element.set_subcircuits(key, default_value) + self.validate_nodes() + return + dpg.bind_item_theme(cdc_input, themes.cdc.normal) + dpg.hide_item(dpg.get_item_parent(tooltip)) + connection: Connection = circuit.get_connections(flattened=False)[0] + dpg.set_item_user_data(edit_button, connection) + element.set_subcircuits(key, connection) + if update is True: + dpg.set_value(sender, connection.to_string()) + self.validate_nodes() + + def show_subcircuit_editor(): + circuit: Optional[Circuit] = None + con: Optional[Connection] = dpg.get_item_user_data(edit_button) + if con is not None: + circuit, _ = process_cdc(con.serialize()) + self.hide() + dpg.split_frame(delay=33) + subcircuit_editor: "CircuitEditor" + subcircuit_editor = None # type: ignore + + def callback(circuit: Optional[Circuit]): + subcircuit_editor.hide() + subcircuit_editor.keybinding_handler.delete() + dpg.split_frame(delay=33) + dpg.delete_item(subcircuit_editor.window) + dpg.show_item(self.window) + self.keybinding_handler.unblock() + signals.emit( + Signal.BLOCK_KEYBINDINGS, + window=self.window, + window_object=self, + ) + if circuit is None: + return + parse_cdc( + cdc_input, + circuit.serialize(), + cdc_tooltip, + update=True, + ) + + subcircuit_editor = CircuitEditor( + window=dpg.add_window( + label=f"Subcircuit editor - {key}", + show=False, + modal=True, + on_close=lambda s, a, u: callback(None), + ), + callback=callback, + ) + x: int + y: int + w: int + h: int + x, y, w, h = calculate_window_position_dimensions() + dpg.configure_item( + subcircuit_editor.window, + pos=( + x, + y, + ), + width=w, + height=h, + ) + signals.emit( + Signal.BLOCK_KEYBINDINGS, + window=subcircuit_editor.window, + window_object=subcircuit_editor, + ) + subcircuit_editor.show(circuit) + + label_pad: int = 8 + with dpg.collapsing_header(label=f" {key}", leaf=True): + tooltip: str = "" + description: str = element.get_subcircuit_description(key).strip() + unit: str = element.get_unit(key).strip() + if description != "" or unit != "": + tooltip = ( + description + (f"\n[{key}] = {unit}" if unit != "" else "") + ).strip() + with dpg.group(horizontal=True): + dpg.add_text("Options".rjust(label_pad)) + if tooltip != "": + attach_tooltip(tooltip) + dpg.add_combo( + default_value="", + items=items, + callback=choose_option, + width=-1, + tag=combo, + ) + with dpg.group(horizontal=True, tag=value_group): + dpg.add_text("Circuit".rjust(label_pad)) + if tooltip != "": + attach_tooltip(tooltip) + dpg.add_input_text( + width=-46, + on_enter=True, + callback=parse_cdc, + user_data=cdc_tooltip, + tag=cdc_input, + ) + attach_tooltip("", tag=cdc_tooltip, parent=cdc_input) + dpg.add_button( + label="Edit", + callback=show_subcircuit_editor, + tag=edit_button, + user_data=initial_value, + ) + dpg.add_spacer(height=8) + enum = ContainerOption.DEFAULT + if initial_value is None: + enum = ContainerOption.OPEN + elif len(initial_value) == 0: + enum = ContainerOption.SHORT + elif ( + default_value is None + or initial_value.serialize() != default_value.serialize() + ): + enum = ContainerOption.CUSTOM + choose_option(combo, CONTAINER_OPTIONS_TO_LABELS[enum]) + def node_parameter( - self, element: Element, key: str, initial_value: float, max_padding: int + self, + element: Element, + key: str, + initial_value: float, ): assert isinstance(element, Element) assert type(key) is str assert type(initial_value) is float - assert type(max_padding) is int fixed: bool = element.is_fixed(key) enabled: bool lower_limit: float = element.get_lower_limit(key) upper_limit: float = element.get_upper_limit(key) - current_value: float cv_input_field: int = dpg.generate_uuid() cv_checkbox: int = dpg.generate_uuid() ll_input_field: int = dpg.generate_uuid() @@ -425,8 +858,17 @@ def node_parameter( ul_checkbox: int = dpg.generate_uuid() label_pad: int = 14 with dpg.collapsing_header(label=f" {key}", leaf=True): + tooltip: str = "" + description: str = element.get_value_description(key).strip() + unit: str = element.get_unit(key).strip() + if description != "" or unit != "": + tooltip = ( + description + (f"\n\nUnit: {unit}" if unit != "" else "") + ).strip() with dpg.group(horizontal=True): dpg.add_text("Initial value".rjust(label_pad)) + if tooltip != "": + attach_tooltip(tooltip) dpg.add_input_float( default_value=initial_value, step=0, @@ -436,14 +878,16 @@ def node_parameter( on_enter=True, ) dpg.add_checkbox( + label="F", default_value=fixed, tag=cv_checkbox, ) - dpg.add_text("F") attach_tooltip("Fixed") with dpg.group(horizontal=True): dpg.add_text("Lower limit".rjust(label_pad)) - enabled = lower_limit != -inf + if tooltip != "": + attach_tooltip(tooltip) + enabled = not isinf(lower_limit) dpg.add_input_float( default_value=lower_limit, step=0, @@ -455,14 +899,16 @@ def node_parameter( enabled=enabled, ) dpg.add_checkbox( + label="E", default_value=enabled, tag=ll_checkbox, ) - dpg.add_text("E") attach_tooltip("Enabled") with dpg.group(horizontal=True): dpg.add_text("Upper limit".rjust(label_pad)) - enabled = upper_limit != inf + if tooltip != "": + attach_tooltip(tooltip) + enabled = not isinf(upper_limit) dpg.add_input_float( default_value=upper_limit, step=0, @@ -474,32 +920,32 @@ def node_parameter( enabled=enabled, ) dpg.add_checkbox( + label="E", default_value=enabled, tag=ul_checkbox, ) - dpg.add_text("E") attach_tooltip("Enabled") def reset_parameter(): - element.reset_parameters([key]) - dpg.set_value(cv_input_field, element.get_parameters()[key]) + element.reset_parameter(key) + dpg.set_value(cv_input_field, element.get_value(key)) dpg.set_value(cv_checkbox, element.is_fixed(key)) value = element.get_lower_limit(key) dpg.configure_item( ll_input_field, default_value=value, - readonly=value == -inf, - enabled=value != -inf, + readonly=isinf(value) is True, + enabled=not isinf(value), ) - dpg.set_value(ll_checkbox, value != -inf) + dpg.set_value(ll_checkbox, not isinf(value)) value = element.get_upper_limit(key) dpg.configure_item( ul_input_field, default_value=value, - readonly=value == inf, - enabled=value != inf, + readonly=isinf(value) is True, + enabled=not isinf(value), ) - dpg.set_value(ul_checkbox, value != inf) + dpg.set_value(ul_checkbox, not isinf(value)) dpg.add_button(label="Reset", callback=reset_parameter) dpg.add_spacer(height=8) @@ -515,21 +961,21 @@ def set_lower_limit(sender: int, new_value: float): assert type(sender) is int if not dpg.get_value(ll_checkbox): new_value = -inf - current_value = dpg.get_value(cv_input_field) + current_value: float = dpg.get_value(cv_input_field) if new_value > current_value: new_value = current_value dpg.configure_item(ll_input_field, default_value=new_value) - element.set_lower_limit(key, new_value) + element.set_lower_limits(key, new_value) self.validate_nodes() def toggle_lower_limit(sender: int, state: bool): assert type(sender) is int assert type(state) is bool - new_value: Optional[float] + new_value: float if state: - new_value = element.get_default_lower_limits().get(key) + new_value = element.get_default_lower_limit(key) current_value: float = dpg.get_value(cv_input_field) - if new_value is None or new_value == -inf or new_value > current_value: + if isinf(new_value) or new_value > current_value: new_value = 0.9 * current_value else: new_value = -inf @@ -539,28 +985,28 @@ def toggle_lower_limit(sender: int, state: bool): readonly=not state, enabled=state, ) - element.set_lower_limit(key, new_value) + element.set_lower_limits(key, new_value) self.validate_nodes() def set_upper_limit(sender: int, new_value: float): assert type(sender) is int if not dpg.get_value(ul_checkbox): new_value = inf - current_value = dpg.get_value(cv_input_field) + current_value: float = dpg.get_value(cv_input_field) if new_value < current_value: new_value = current_value dpg.configure_item(ul_input_field, default_value=new_value) - element.set_upper_limit(key, new_value) + element.set_upper_limits(key, new_value) self.validate_nodes() def toggle_upper_limit(sender: int, state: bool): assert type(sender) is int assert type(state) is bool - new_value: Optional[float] + new_value: float if state: - new_value = element.get_default_upper_limits().get(key) + new_value = element.get_default_upper_limit(key) current_value: float = dpg.get_value(cv_input_field) - if new_value is None or new_value == inf or new_value < current_value: + if isinf(new_value) or new_value < current_value: new_value = 1.1 * current_value else: new_value = inf @@ -570,7 +1016,7 @@ def toggle_upper_limit(sender: int, state: bool): readonly=not state, enabled=state, ) - element.set_upper_limit(key, new_value) + element.set_upper_limits(key, new_value) self.validate_nodes() def set_value(sender: int, new_value: float): @@ -579,13 +1025,13 @@ def set_value(sender: int, new_value: float): lower_limit = dpg.get_value(ll_input_field) if lower_limit > new_value: dpg.configure_item(ll_input_field, default_value=new_value) - element.set_lower_limit(key, new_value) + element.set_lower_limits(key, new_value) if dpg.get_value(ul_checkbox): upper_limit = dpg.get_value(ul_input_field) if upper_limit < new_value: dpg.configure_item(ul_input_field, default_value=new_value) - element.set_upper_limit(key, new_value) - element.set_parameters({key: new_value}) + element.set_upper_limits(key, new_value) + element.set_values(**{key: new_value}) self.validate_nodes() def toggle_fixed(sender: int, state: bool): @@ -612,6 +1058,12 @@ def validate_nodes(self): msg: str stack: List[str] circuit, msg, stack = self.parser.generate_circuit() + if circuit is not None: + try: + circuit.get_impedances(array([1e-3, 1e0, 1e3])) + except Exception as err: + circuit = None + msg = str(err) if circuit is None: self.update_outputs(stack=stack) self.update_status(msg, True) @@ -627,3 +1079,31 @@ def link(self, sender: int, attributes: Tuple[int, int]): def delink(self, sender: int, link: int): self.parser.delink(sender, link) self.validate_nodes() + + def cycle_elements(self, step: int): + items: List[str] = dpg.get_item_configuration(self.element_combo)["items"] + index: int = items.index(dpg.get_value(self.element_combo)) + step + dpg.set_value(self.element_combo, items[index % len(items)]) + + def cycle_nodes(self, step: int): + nodes: List[Node] = self.parser.nodes + if len(nodes) < 2: + return + index: int + if self.current_node is not None and self.current_node in nodes: + index = nodes.index(self.current_node) + step + elif step >= 0: + index = 0 + elif step < 0: + index = -1 + node: Node = nodes[index % len(nodes)] + self.node_clicked(self.node_handler, (dpg.mvMouseButton_Left, node.tag)) + + def has_active_input(self) -> bool: + return ( + dpg.is_item_active(self.cdc_input) + or dpg.is_item_active(self.basic_cdc_output_field) + or dpg.is_item_active(self.extended_cdc_output_field) + or dpg.is_item_active(self.status_field) + or any(map(lambda _: dpg.is_item_active(_), self.parameter_inputs)) + ) diff --git a/src/deareis/gui/circuit_editor/parser.py b/src/deareis/gui/circuit_editor/parser.py index bc5c062..4f617e1 100644 --- a/src/deareis/gui/circuit_editor/parser.py +++ b/src/deareis/gui/circuit_editor/parser.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -17,9 +17,24 @@ # The licenses of DearEIS' dependencies and/or sources of portions of code are included in # the LICENSES folder. -from typing import Callable, Dict, List, Optional, Set, Tuple, Union +from typing import ( + Callable, + Dict, + List, + Optional, + Set, + Tuple, + Union, +) import dearpygui.dearpygui as dpg -from pyimpspec import Circuit, Connection, Element, Parallel, Series, ParsingError +from pyimpspec.exceptions import ParsingError +from pyimpspec import ( + Circuit, + Connection, + Element, + Parallel, + Series, +) import pyimpspec import deareis.themes as themes @@ -46,6 +61,8 @@ def __init__( assert isinstance(element, Element) or element is None assert type(input_attribute) is bool assert type(output_attribute) is bool + self.selected: bool = False + self.invalid: bool = False self.tag: int = dpg.generate_uuid() self.id: int = node_id self.label: str = "" @@ -64,6 +81,7 @@ def __init__( attribute_type=dpg.mvNode_Attr_Output, tag=self.output_attribute ) self.set_label(label) + self.set_unselected() def __repr__(self) -> str: return self.label.strip() @@ -116,12 +134,38 @@ def delete_link_from(self, node: "Node") -> int: return link def set_valid(self): - dpg.bind_item_theme(self.tag, themes.circuit_editor.valid_node) + if self.selected is True: + dpg.bind_item_theme(self.tag, themes.circuit_editor.valid_selected_node) + else: + dpg.bind_item_theme(self.tag, themes.circuit_editor.valid_unselected_node) + self.invalid = False def set_invalid(self, msg: str) -> str: - dpg.bind_item_theme(self.tag, themes.circuit_editor.invalid_node) + if self.selected is True: + dpg.bind_item_theme(self.tag, themes.circuit_editor.invalid_selected_node) + else: + dpg.bind_item_theme(self.tag, themes.circuit_editor.invalid_unselected_node) + self.invalid = True + # msg is returned to be used as an assertion message return msg + def set_selected(self): + if self.invalid is True: + dpg.bind_item_theme(self.tag, themes.circuit_editor.invalid_selected_node) + else: + dpg.bind_item_theme(self.tag, themes.circuit_editor.valid_selected_node) + self.selected = True + + def set_unselected(self): + if self.invalid is True: + dpg.bind_item_theme(self.tag, themes.circuit_editor.invalid_unselected_node) + else: + dpg.bind_item_theme(self.tag, themes.circuit_editor.valid_unselected_node) + self.selected = False + + def set_preview(self): + dpg.bind_item_theme(self.tag, themes.circuit_editor.preview_node) + class Parser: def __init__(self, node_editor: int, node_handler: int = -1): @@ -150,11 +194,24 @@ def __init__(self, node_editor: int, node_handler: int = -1): self.elements: Dict[str, Element] = { _.get_description(): _ for _ in pyimpspec.get_elements().values() } + self.blocked_linking: bool = False - def circuit_to_nodes(self, circuit: Circuit): + def circuit_to_nodes(self, circuit: Circuit, y_step: int = -1): assert type(circuit) is Circuit, circuit + assert isinstance(y_step, int), y_step + if y_step >= 0: + self.y_step = y_step input_stack: List[Tuple[str, Union[Element, Connection]]] = circuit.to_stack() assert circuit.to_string() == "".join(map(lambda _: _[0], input_stack)) + self.element_identifiers: Dict[ + Element, int + ] = circuit.generate_element_identifiers(running=False) + self.element_counts: Dict[str, int] = {} + for element in self.element_identifiers: + symbol: str = element.get_symbol() + if symbol not in self.element_counts: + self.element_counts[symbol] = 0 + self.element_counts[symbol] += 1 self.clear_nodes() if circuit.to_string() not in ["[]", "()"]: self.generate_nodes(input_stack) @@ -204,6 +261,12 @@ def find_node( return self.cere_node raise Exception("Node does not exist!") + def block_linking(self): + self.blocked_linking = True + + def unblock_linking(self): + self.blocked_linking = False + def link(self, sender: int, attributes: Tuple[int, int]): assert type(sender) is int assert ( @@ -211,6 +274,8 @@ def link(self, sender: int, attributes: Tuple[int, int]): and len(attributes) == 2 and all(map(lambda _: type(_) is int, attributes)) ) + if self.blocked_linking is True: + return link: int = dpg.add_node_link(*attributes, parent=sender) src: Node = self.find_node(attribute=attributes[0]) dst: Node = self.find_node(attribute=attributes[1]) @@ -221,6 +286,8 @@ def link(self, sender: int, attributes: Tuple[int, int]): def delink(self, sender: int, link: int): assert type(sender) is int assert type(link) is int + if self.blocked_linking is True: + return src: Node = self.find_node(link_to=link) dst: Node = self.find_node(link_from=link) # print(f"Delink: {src.label=}, {dst.label=}, {link=}") @@ -265,16 +332,24 @@ def add_node(self, **kwargs) -> Node: def add_element_node(self, element: Element, **kwargs) -> Node: assert isinstance(element, Element) + name: str = element.get_name() + symbol: str = element.get_symbol() + if name == symbol: + if element not in self.element_identifiers: + if symbol not in self.element_counts: + self.element_counts[symbol] = 0 + self.element_counts[symbol] += 1 + self.element_identifiers[element] = self.element_counts[symbol] + name = f"{name}_{self.element_identifiers[element]}" kwargs["element"] = element if "label" not in kwargs: - kwargs["label"] = element.get_label() + kwargs["label"] = name node_id: int = kwargs.get("node_id", -1) if node_id < 0: node_id = self.next_element() kwargs["node_id"] = node_id - element._assign_identifier(node_id) node: Node = self.add_node(**kwargs) - node.set_label(element.get_label()) + node.set_label(name) return node def add_dummy_node(self, **kwargs) -> Node: @@ -369,9 +444,7 @@ def walk_nodes( assert num_links_out > 0, self.we_node.set_invalid( "WE is not connected to anything!" ) - assert ( - self.cere_node.id not in node.output_links - ), self.we_node.set_invalid( + assert self.cere_node.id not in node.output_links, self.we_node.set_invalid( self.cere_node.set_invalid("WE is shorted to CE+RE!") ) stack.append("[") @@ -463,9 +536,9 @@ def walk_nodes( visited_nodes.add(node.id) if element is not None: assert num_links_out > 0, node.set_invalid( - f"{element.get_label()} is missing a connection!" + f"{node.label.strip()} is missing a connection!" ) - stack.append(node.element.to_string(12)) # type: ignore + stack.append(node.element.serialize()) # type: ignore # stack.append(node.element.to_string()) # DEBUGGING else: assert (num_links_in > 0 and num_links_out > 1) or ( @@ -570,10 +643,7 @@ def process_series_element(this: Element, x: int, y: int) -> Tuple[int, int]: nonlocal symbol_stack node = self.add_element_node( this, - pos=( - x, - y, - ), + pos=(x, y), ) if type(node_stack[-1]) is list: if len(element_stack) > 2 and type(element_stack[-3]) is Parallel: @@ -605,10 +675,7 @@ def process_series_element(this: Element, x: int, y: int) -> Tuple[int, int]: self.link_nodes(other, node) # type: ignore node_stack.append(node) element_stack.pop() - return ( - 1, - 0, - ) + return (1, 0) def process_series_dummy(x: int, y: int) -> Tuple[int, int]: assert type(x) is int @@ -620,23 +687,12 @@ def process_series_dummy(x: int, y: int) -> Tuple[int, int]: and symbol_stack[-2] == ")" and symbol_stack[-1] == "]" ): - node = self.add_dummy_node( - pos=( - x, - y, - ) - ) + node = self.add_dummy_node(pos=(x, y)) for other in node_stack.pop(): # type: ignore self.link_nodes(other, node) node_stack.append(node) - return ( - 1, - 0, - ) - return ( - 0, - 0, - ) + return (1, 0) + return (0, 0) def process_series(this: Series, x: int, y: int) -> Tuple[int, int]: assert type(this) is Series @@ -671,10 +727,7 @@ def process_series(this: Series, x: int, y: int) -> Tuple[int, int]: if len(node_stack) > 1 and type(node_stack[-2]) is list: assert type(node_stack[-1]) is Node node_stack[-2].append(node_stack.pop()) # type: ignore - return ( - max(1, width), - max(1, height), - ) + return (max(1, width), max(1, height)) def process_parallel_element(this: Element, x: int, y: int) -> Tuple[int, int]: assert isinstance(this, Element), type(element) @@ -683,32 +736,21 @@ def process_parallel_element(this: Element, x: int, y: int) -> Tuple[int, int]: nonlocal symbol_stack node = self.add_element_node( this, - pos=( - x, - y, - ), + pos=(x, y), ) assert type(node_stack[-2]) is Node self.link_nodes(node_stack[-2], node) # type: ignore assert type(node_stack[-1]) is list node_stack[-1].append(node) # type: ignore element_stack.pop() - return ( - 1, - 1, - ) + return (1, 1) def process_parallel_dummy(x: int, y: int) -> Tuple[int, int]: assert type(x) is int assert type(y) is int if type(node_stack[-1]) is list: if len(element_stack) > 2 and type(element_stack[-2]) is Series: - node = self.add_dummy_node( - pos=( - x, - y, - ) - ) + node = self.add_dummy_node(pos=(x, y)) if type(node_stack[-1]) is list: if symbol_stack[-2] == "[" and symbol_stack[-1] == "(": assert type(node_stack[-2]) is Node @@ -724,28 +766,14 @@ def process_parallel_dummy(x: int, y: int) -> Tuple[int, int]: symbol_stack[-2] == ")" or (symbol_stack[-2] == "]" and symbol_stack[-3] == ")") ): - node = self.add_dummy_node( - pos=( - x, - y, - ) - ) + node = self.add_dummy_node(pos=(x, y)) for other in node_stack.pop(): # type: ignore self.link_nodes(other, node) node_stack.append(node) else: - return ( - 0, - 0, - ) - return ( - 1, - 0, - ) - return ( - 0, - 0, - ) + return (0, 0) + return (1, 0) + return (0, 0) def process_parallel(this: Parallel, x: int, y: int) -> Tuple[int, int]: assert type(this) is Parallel @@ -788,10 +816,7 @@ def process_parallel(this: Parallel, x: int, y: int) -> Tuple[int, int]: and node_stack[-2] != self.we_node ): node_stack.pop(-2) - return ( - max(1, width + dummy_width), - max(1, height), - ) + return (max(1, width + dummy_width), max(1, height)) input_length: int = len(input_stack) symbol, element = pop_input() diff --git a/src/deareis/gui/circuit_editor/preview.py b/src/deareis/gui/circuit_editor/preview.py index 0b46f1f..a7383e0 100644 --- a/src/deareis/gui/circuit_editor/preview.py +++ b/src/deareis/gui/circuit_editor/preview.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -17,10 +17,16 @@ # The licenses of DearEIS' dependencies and/or sources of portions of code are included in # the LICENSES folder. -from typing import Optional +from typing import ( + List, + Optional, +) import dearpygui.dearpygui as dpg from pyimpspec import Circuit -from deareis.gui.circuit_editor.parser import Parser +from deareis.gui.circuit_editor.parser import ( + Node, + Parser, +) class CircuitPreview: @@ -39,7 +45,14 @@ def clear(self): dpg.delete_item(self.node_editor, children_only=True) def update(self, circuit: Optional[Circuit]): + self.parser.unblock_linking() self.clear() if circuit is None: return - self.parser.circuit_to_nodes(circuit) + self.parser.circuit_to_nodes(circuit, y_step=50) + self.parser.block_linking() + nodes: List[Node] = [self.parser.we_node, self.parser.cere_node] + nodes.extend(self.parser.nodes) + node: Node + for node in nodes: + node.set_preview() diff --git a/src/deareis/gui/command_palette.py b/src/deareis/gui/command_palette.py index 888a72f..94aadbd 100644 --- a/src/deareis/gui/command_palette.py +++ b/src/deareis/gui/command_palette.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -112,10 +112,7 @@ def show(self, contexts: List[Context], project: Project, tab: ProjectTab): x, y, w, h = calculate_window_position_dimensions(720, h) dpg.configure_item( self.window, - pos=( - x, - y, - ), + pos=(x, y), width=w, height=h, ) diff --git a/src/deareis/gui/data_sets/__init__.py b/src/deareis/gui/data_sets/__init__.py index cc93a10..0755e13 100644 --- a/src/deareis/gui/data_sets/__init__.py +++ b/src/deareis/gui/data_sets/__init__.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -22,6 +22,7 @@ Dict, List, Optional, + Tuple, ) from numpy import ( angle, @@ -31,10 +32,16 @@ import dearpygui.dearpygui as dpg from deareis.signals import Signal import deareis.signals as signals -from deareis.gui.plots import Bode, Nyquist +from deareis.gui.plots import ( + Bode, + Impedance, + Nyquist, + Plot, +) from deareis.utility import ( align_numbers, format_number, + pad_tab_labels, ) from deareis.tooltips import ( attach_tooltip, @@ -71,19 +78,19 @@ def __init__(self): ) attach_tooltip(tooltips.data_sets.frequency) dpg.add_table_column( - label="Z' (ohm)", + label="Re(Z) (ohm)", ) attach_tooltip(tooltips.data_sets.real) dpg.add_table_column( - label='-Z" (ohm)', + label="-Im(Z) (ohm)", ) attach_tooltip(tooltips.data_sets.imaginary) dpg.add_table_column( - label="|Z| (ohm)", + label="Mod(Z) (ohm)", ) attach_tooltip(tooltips.data_sets.magnitude) dpg.add_table_column( - label="-phi (°)", + label="-Phase(Z) (°)", ) attach_tooltip(tooltips.data_sets.phase) @@ -137,14 +144,14 @@ def update(self, data: DataSet): indices: List[str] = list( map(lambda _: str(_ + 1), range(0, data.get_num_points(masked=None))) ) - frequencies: ndarray = data.get_frequency(masked=None) + frequencies: ndarray = data.get_frequencies(masked=None) freqs: List[str] = list( map( lambda _: format_number(_, significants=4), frequencies, ) ) - Z: ndarray = data.get_impedance(masked=None) + Z: ndarray = data.get_impedances(masked=None) reals: List[str] = list( map( lambda _: format_number( @@ -238,218 +245,350 @@ def update(self, data: DataSet): class DataSetsTab: def __init__(self): self.queued_update: Optional[Callable] = None + self.create_tab() + + def create_tab(self): self.tab: int = dpg.generate_uuid() with dpg.tab(label="Data sets", tag=self.tab): with dpg.child_window(border=False, width=-1, height=-1): with dpg.group(horizontal=True): - self.table_window: int = dpg.generate_uuid() - self.table_width: int = 600 - with dpg.child_window( - border=False, - width=self.table_width, - tag=self.table_window, - show=True, - ): - label_pad: int = 8 - with dpg.child_window(border=True, height=82, width=-2): - with dpg.group(horizontal=True): - with dpg.child_window(border=False, width=-72): - # TODO: Split into combo class? - self.data_sets_combo: int = dpg.generate_uuid() - with dpg.group(horizontal=True): - self.visibility_item: int = dpg.generate_uuid() - dpg.add_text( - "Data set".rjust(label_pad), - tag=self.visibility_item, - ) - dpg.add_combo( - callback=lambda s, a, u: signals.emit( - Signal.SELECT_DATA_SET, - data=u.get(a), - ), - user_data={}, - width=-1, - tag=self.data_sets_combo, - ) - self.label_input: int = dpg.generate_uuid() - with dpg.group(horizontal=True): - dpg.add_text("Label".rjust(label_pad)) - dpg.add_input_text( - on_enter=True, - callback=lambda s, a, u: signals.emit( - Signal.RENAME_DATA_SET, - label=a, - data=u, - ), - width=-1, - tag=self.label_input, - ) - self.path_input: int = dpg.generate_uuid() - with dpg.group(horizontal=True): - dpg.add_text("Path".rjust(label_pad)) - dpg.add_input_text( - on_enter=True, - callback=lambda s, a, u: signals.emit( - Signal.MODIFY_DATA_SET_PATH, - path=a, - data=u, - ), - width=-1, - tag=self.path_input, - ) - with dpg.child_window(border=False, width=-1): - self.load_button: int = dpg.generate_uuid() - dpg.add_button( - label="Load", - callback=lambda s, a, u: signals.emit( - Signal.SELECT_DATA_SET_FILES, - ), - width=-1, - tag=self.load_button, - ) - attach_tooltip(tooltips.data_sets.load) - self.delete_button: int = dpg.generate_uuid() - dpg.add_button( - label="Delete", - callback=lambda s, a, u: signals.emit( - Signal.DELETE_DATA_SET, - data=u, - ), - width=-1, - tag=self.delete_button, - ) - attach_tooltip(tooltips.data_sets.delete) - self.average_button: int = dpg.generate_uuid() - dpg.add_button( - label="Average", - callback=lambda s, a, u: signals.emit( - Signal.SELECT_DATA_SETS_TO_AVERAGE, - ), - width=-1, - tag=self.average_button, - ) - attach_tooltip(tooltips.data_sets.average) - with dpg.child_window( - border=False, width=-2, height=-40, show=True - ): - self.data_table = DataTable() - with dpg.child_window(border=True, width=-2): - with dpg.group(horizontal=True): - self.toggle_points_button: int = dpg.generate_uuid() - dpg.add_button( - label="Toggle points", - tag=self.toggle_points_button, - callback=lambda s, a, u: signals.emit( - Signal.SELECT_DATA_POINTS_TO_TOGGLE, - data=u, - ), - ) - attach_tooltip(tooltips.data_sets.toggle) - self.copy_mask_button: int = dpg.generate_uuid() - dpg.add_button( - label="Copy mask", - tag=self.copy_mask_button, - callback=lambda s, a, u: signals.emit( - Signal.SELECT_DATA_SET_MASK_TO_COPY, - data=u, - ), - ) - attach_tooltip(tooltips.data_sets.copy) - self.subtract_impedance_button: int = ( - dpg.generate_uuid() - ) - dpg.add_button( - label="Subtract", - tag=self.subtract_impedance_button, - callback=lambda s, a, u: signals.emit( - Signal.SELECT_IMPEDANCE_TO_SUBTRACT, - data=u, - ), - ) - attach_tooltip(tooltips.data_sets.subtract) - self.enlarge_nyquist_button: int = dpg.generate_uuid() - self.adjust_nyquist_limits_checkbox: int = ( - dpg.generate_uuid() - ) - dpg.add_button( - label="Enlarge Nyquist", - callback=self.show_enlarged_nyquist, - tag=self.enlarge_nyquist_button, - ) - dpg.add_checkbox( - default_value=True, - tag=self.adjust_nyquist_limits_checkbox, - ) - attach_tooltip(tooltips.general.adjust_nyquist_limits) - self.enlarge_bode_button: int = dpg.generate_uuid() - self.adjust_bode_limits_checkbox: int = ( - dpg.generate_uuid() - ) - dpg.add_button( - label="Enlarge Bode", - callback=self.show_enlarged_bode, - tag=self.enlarge_bode_button, - ) - dpg.add_checkbox( - default_value=True, - tag=self.adjust_bode_limits_checkbox, - ) - attach_tooltip(tooltips.general.adjust_bode_limits) - self.plot_window: int = dpg.generate_uuid() - self.plot_width: int = 400 - with dpg.child_window( - border=False, - width=-1, - no_scrollbar=True, - tag=self.plot_window, - show=True, - ): - self.nyquist_plot: Nyquist = Nyquist() - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="Data", - line=False, - theme=themes.nyquist.data, - ) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="Data", - line=True, - theme=themes.nyquist.data, - show_label=False, - ) - self.bode_plot: Bode = Bode() - self.bode_plot.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z|", - "phi", - ), - line=False, - themes=( - themes.bode.magnitude_data, - themes.bode.phase_data, + self.create_sidebar() + self.create_plots() + + def create_sidebar(self): + self.table_window: int = dpg.generate_uuid() + self.table_width: int = 600 + with dpg.child_window( + border=False, + width=self.table_width, + tag=self.table_window, + show=True, + ): + label_pad: int = 8 + with dpg.child_window(border=True, height=82, width=-2): + with dpg.group(horizontal=True): + with dpg.child_window(border=False, width=-72): + # TODO: Split into combo class? + self.data_sets_combo: int = dpg.generate_uuid() + with dpg.group(horizontal=True): + self.visibility_item: int = dpg.generate_uuid() + dpg.add_text( + "Data set".rjust(label_pad), + tag=self.visibility_item, + ) + dpg.add_combo( + callback=lambda s, a, u: signals.emit( + Signal.SELECT_DATA_SET, + data=u.get(a), + ), + user_data={}, + width=-1, + tag=self.data_sets_combo, + ) + self.label_input: int = dpg.generate_uuid() + with dpg.group(horizontal=True): + dpg.add_text("Label".rjust(label_pad)) + dpg.add_input_text( + on_enter=True, + callback=lambda s, a, u: signals.emit( + Signal.RENAME_DATA_SET, + label=a, + data=u, + ), + width=-1, + tag=self.label_input, + ) + self.path_input: int = dpg.generate_uuid() + with dpg.group(horizontal=True): + dpg.add_text("Path".rjust(label_pad)) + dpg.add_input_text( + on_enter=True, + callback=lambda s, a, u: signals.emit( + Signal.MODIFY_DATA_SET_PATH, + path=a, + data=u, + ), + width=-1, + tag=self.path_input, + ) + with dpg.child_window(border=False, width=-1): + self.load_button: int = dpg.generate_uuid() + dpg.add_button( + label="Load", + callback=lambda s, a, u: signals.emit( + Signal.SELECT_DATA_SET_FILES, ), + width=-1, + tag=self.load_button, ) - self.bode_plot.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z|", - "phi", - ), - line=True, - themes=( - themes.bode.magnitude_data, - themes.bode.phase_data, + attach_tooltip(tooltips.data_sets.load) + self.delete_button: int = dpg.generate_uuid() + dpg.add_button( + label="Delete", + callback=lambda s, a, u: signals.emit( + Signal.DELETE_DATA_SET, + data=u, ), - show_labels=False, + width=-1, + tag=self.delete_button, ) + attach_tooltip(tooltips.data_sets.delete) + self.create_process_menu() + self.create_table() + self.create_bottom_bar() + + def create_process_menu(self): + self.process_button: int = dpg.generate_uuid() + dpg.add_button( + label="Process", + width=-1, + tag=self.process_button, + ) + attach_tooltip(tooltips.data_sets.process) + process_popup_dimensions: Tuple[int, int] = ( + 110, + 82, + ) + process_popup: int + with dpg.popup( + parent=self.process_button, + mousebutton=dpg.mvMouseButton_Left, + min_size=process_popup_dimensions, + max_size=process_popup_dimensions, + ) as process_popup: + self.average_button: int = dpg.generate_uuid() + dpg.add_button( + label="Average", + callback=lambda s, a, u: signals.emit( + Signal.SELECT_DATA_SETS_TO_AVERAGE, + popup=process_popup, + ), + width=-1, + tag=self.average_button, + ) + attach_tooltip(tooltips.data_sets.average) + # + self.interpolation_button: int = dpg.generate_uuid() + dpg.add_button( + label="Interpolate", + callback=lambda s, a, u: signals.emit( + Signal.SELECT_POINTS_TO_INTERPOLATE, + data=u, + popup=process_popup, + ), + width=-1, + tag=self.interpolation_button, + ) + attach_tooltip(tooltips.data_sets.interpolate) + # + self.subtract_impedance_button: int = dpg.generate_uuid() + dpg.add_button( + label="Subtract", + callback=lambda s, a, u: signals.emit( + Signal.SELECT_IMPEDANCE_TO_SUBTRACT, + data=u, + popup=process_popup, + ), + width=-1, + tag=self.subtract_impedance_button, + ) + attach_tooltip(tooltips.data_sets.subtract) + + def create_table(self): + with dpg.child_window( + border=False, + width=-2, + height=-40, + show=True, + ): + self.data_table = DataTable() + + def create_bottom_bar(self): + with dpg.child_window(border=True, width=-2): + with dpg.group(horizontal=True): + self.toggle_points_button: int = dpg.generate_uuid() + dpg.add_button( + label="Toggle points", + tag=self.toggle_points_button, + callback=lambda s, a, u: signals.emit( + Signal.SELECT_DATA_POINTS_TO_TOGGLE, + data=u, + ), + ) + attach_tooltip(tooltips.data_sets.toggle) + self.copy_mask_button: int = dpg.generate_uuid() + dpg.add_button( + label="Copy mask", + tag=self.copy_mask_button, + callback=lambda s, a, u: signals.emit( + Signal.SELECT_DATA_SET_MASK_TO_COPY, + data=u, + ), + ) + attach_tooltip(tooltips.data_sets.copy) + self.enlarge_plot_button: int = dpg.generate_uuid() + self.adjust_plot_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_plot, + tag=self.enlarge_plot_button, + ) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + callback=lambda s, a, u: self.toggle_plot_limits_adjustment(a), + tag=self.adjust_plot_limits_checkbox, + ) + self.adjust_plot_limits_tooltip = attach_tooltip( + tooltips.general.adjust_plot_limits, + ) + self.plot_combo: int = dpg.generate_uuid() + dpg.add_combo( + default_value="?", + items=[], + width=-1, + callback=lambda s, a, u: self.select_plot( + sender=s, + label=a, + ), + tag=self.plot_combo, + ) + + def create_plots(self): + self.plot_window: int = dpg.generate_uuid() + self.plot_width: int = 400 + with dpg.child_window( + border=False, + width=-1, + no_scrollbar=True, + tag=self.plot_window, + show=True, + ): + self.plot_tab_bar: int = dpg.generate_uuid() + with dpg.tab_bar( + callback=lambda s, a, u: self.select_plot( + sender=s, + tab=a, + ), + tag=self.plot_tab_bar, + ): + self.create_nyquist_plot() + self.create_bode_plot() + self.create_impedance_plot() + pad_tab_labels(self.plot_tab_bar) + plots: List[Plot] = [ + self.nyquist_plot, + self.bode_plot, + self.impedance_plot, + ] + plot_lookup: Dict[int, Plot] = {} + label_lookup: Dict[int, str] = {} + tab: int + for tab in dpg.get_item_children(self.plot_tab_bar, slot=1): + label_lookup[tab] = dpg.get_item_label(tab) + plot_lookup[tab] = plots.pop(0) + # Tab bar + dpg.set_item_user_data(self.plot_tab_bar, label_lookup) + # Combo + tab_lookup: Dict[str, int] = {v: k for k, v in label_lookup.items()} + labels: List[str] = list(tab_lookup.keys()) + dpg.configure_item( + self.plot_combo, + default_value=labels[0], + items=labels, + user_data=tab_lookup, + ) + # Limits checkbox + dpg.set_item_user_data( + self.adjust_plot_limits_checkbox, {_: True for _ in labels} + ) + # Enlarge button + dpg.set_item_user_data(self.enlarge_plot_button, plot_lookup) + + def create_nyquist_plot(self): + with dpg.tab(label="Nyquist"): + self.nyquist_plot: Nyquist = Nyquist() + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + label="Data", + line=False, + theme=themes.nyquist.data, + ) + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + label="Data", + line=True, + theme=themes.nyquist.data, + show_label=False, + ) + + def create_bode_plot(self): + with dpg.tab(label="Bode"): + self.bode_plot: Bode = Bode() + self.bode_plot.plot( + frequency=array([]), + magnitude=array([]), + phase=array([]), + labels=( + "Mod(Z)", + "Phase(Z)", + ), + line=False, + themes=( + themes.bode.magnitude_data, + themes.bode.phase_data, + ), + ) + self.bode_plot.plot( + frequency=array([]), + magnitude=array([]), + phase=array([]), + labels=( + "Mod(Z)", + "Phase(Z)", + ), + line=True, + themes=( + themes.bode.magnitude_data, + themes.bode.phase_data, + ), + show_labels=False, + ) + + def create_impedance_plot(self): + with dpg.tab(label="Real & Imag."): + self.impedance_plot: Impedance = Impedance() + self.impedance_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + labels=( + "Re(Z)", + "Im(Z)", + ), + line=False, + themes=( + themes.impedance.real_data, + themes.impedance.imaginary_data, + ), + ) + self.impedance_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + labels=( + "Re(Z)", + "Im(Z)", + ), + line=True, + themes=( + themes.impedance.real_data, + themes.impedance.imaginary_data, + ), + show_labels=False, + ) def is_visible(self) -> bool: return dpg.is_item_visible(self.visibility_item) @@ -463,21 +602,58 @@ def resize(self, width: int, height: int): if dpg.is_item_shown(self.plot_window): dpg.hide_item(self.plot_window) dpg.set_item_width(self.table_window, -1) - dpg.set_item_label(self.enlarge_nyquist_button, "Show Nyquist") - dpg.set_item_label(self.enlarge_bode_button, "Show Bode") + dpg.set_item_label(self.enlarge_plot_button, "Show plot") else: if not dpg.is_item_shown(self.plot_window): dpg.show_item(self.plot_window) dpg.set_item_width(self.table_window, self.table_width) dpg.split_frame() - dpg.set_item_label(self.enlarge_nyquist_button, "Enlarge Nyquist") - dpg.set_item_label(self.enlarge_bode_button, "Enlarge Bode") + dpg.set_item_label(self.enlarge_plot_button, "Enlarge plot") if not dpg.is_item_shown(self.plot_window): return - width, height = dpg.get_item_rect_size(self.plot_window) - item: int - for item in dpg.get_item_children(self.plot_window, slot=1): - dpg.set_item_height(item, height / 2) + + def toggle_plot_limits_adjustment(self, flag: bool): + label: str = dpg.get_value(self.plot_combo) + dpg.get_item_user_data(self.adjust_plot_limits_checkbox)[label] = flag + + def select_plot( + self, + sender: int, + tab: int = -1, + label: str = "", + ): + label_lookup: Optional[Dict[int, str]] = dpg.get_item_user_data( + self.plot_tab_bar + ) + tab_lookup: Optional[Dict[str, int]] = dpg.get_item_user_data(self.plot_combo) + limits_lookup: Optional[Dict[str, bool]] = dpg.get_item_user_data( + self.adjust_plot_limits_checkbox + ) + assert label_lookup is not None + assert tab_lookup is not None + assert limits_lookup is not None + # Store the value of the checkbox of the previous plot + old_label: str + if tab > 0: + old_label = dpg.get_value(self.plot_combo) + else: + old_label = label_lookup[dpg.get_value(self.plot_tab_bar)] + limits_lookup[old_label] = dpg.get_value(self.adjust_plot_limits_checkbox) + # Adjust tab bar or combo to show the current plot + if tab > 0: + label = label_lookup[tab] + dpg.set_value(self.plot_combo, label) + if sender <= 0: + dpg.set_value(self.plot_tab_bar, tab) + elif label != "": + tab = tab_lookup[label] + dpg.set_value(self.plot_tab_bar, tab) + if sender <= 0: + dpg.set_value(self.plot_combo, label) + else: + raise NotImplementedError("Unknown means of selecting plot!") + # Update the value of the checkbox to match the current plot + dpg.set_value(self.adjust_plot_limits_checkbox, limits_lookup[label]) def clear(self): dpg.set_value(self.data_sets_combo, "") @@ -487,9 +663,11 @@ def clear(self): dpg.set_item_user_data(self.toggle_points_button, None) dpg.set_item_user_data(self.copy_mask_button, None) dpg.set_item_user_data(self.subtract_impedance_button, None) + dpg.set_item_user_data(self.interpolation_button, None) self.data_table.clear() self.nyquist_plot.clear(delete=False) self.bode_plot.clear(delete=False) + self.impedance_plot.clear(delete=False) def populate_data_sets(self, labels: List[str], lookup: Dict[str, DataSet]): assert type(labels) is list, labels @@ -547,52 +725,137 @@ def select_data_set(self, data: Optional[DataSet]): dpg.set_item_user_data(self.toggle_points_button, data) dpg.set_item_user_data(self.copy_mask_button, data) dpg.set_item_user_data(self.subtract_impedance_button, data) + dpg.set_item_user_data(self.interpolation_button, data) real: ndarray imag: ndarray real, imag = data.get_nyquist_data() - self.nyquist_plot.update( - index=0, - real=real, - imaginary=imag, - ) - self.nyquist_plot.update( - index=1, - real=real, - imaginary=imag, - ) + i: int + for i in range(0, 2): + self.nyquist_plot.update( + index=i, + real=real, + imaginary=imag, + ) freq: ndarray mag: ndarray phase: ndarray freq, mag, phase = data.get_bode_data() - self.bode_plot.update( - index=0, - frequency=freq, - magnitude=mag, - phase=phase, + for i in range(0, 2): + self.bode_plot.update( + index=i, + frequency=freq, + magnitude=mag, + phase=phase, + ) + for i in range(0, 2): + self.impedance_plot.update( + index=i, + frequency=freq, + real=real, + imaginary=imag, + ) + limits_lookup: Optional[Dict[str, bool]] = dpg.get_item_user_data( + self.adjust_plot_limits_checkbox ) - self.bode_plot.update( - index=1, - frequency=freq, - magnitude=mag, - phase=phase, + label: str + flag: bool + for label, flag in limits_lookup.items(): + if flag is True: + self.get_plot(label=label).queue_limits_adjustment() + + def get_plot(self, tab: int = -1, label: str = "") -> Plot: + if tab > 0: + pass + elif label != "": + tab = dpg.get_item_user_data(self.plot_combo)[label] + else: + if dpg.is_item_shown(self.plot_window): + tab = dpg.get_value(self.plot_tab_bar) + else: + label = dpg.get_value(self.plot_combo) + tab = dpg.get_item_user_data(self.plot_combo)[label] + return dpg.get_item_user_data(self.enlarge_plot_button)[tab] + + def should_adjust_limit( + self, + tab: int = -1, + label: str = "", + plot: Optional[Plot] = None, + ) -> bool: + if tab > 0: + label = dpg.get_item_user_data(self.plot_tab_bar)[tab] + elif label != "": + pass + elif plot is not None: + label = dpg.get_item_user_data(self.plot_tab_bar)[ + { + v: k + for k, v in dpg.get_item_user_data(self.enlarge_plot_button).items() + }[plot] + ] + else: + tab = dpg.get_value(self.plot_tab_bar) + label = dpg.get_item_user_data(self.plot_tab_bar)[tab] + return dpg.get_item_user_data(self.adjust_plot_limits_checkbox)[label] + + def next_plot_tab(self): + index: int + if dpg.is_item_shown(self.plot_window): + tabs: List[int] = dpg.get_item_children(self.plot_tab_bar, slot=1) + index = tabs.index(dpg.get_value(self.plot_tab_bar)) + 1 + self.select_plot(sender=-1, tab=tabs[index % len(tabs)]) + else: + tab_lookup: Dict[Plot, int] = { + v: k + for k, v in dpg.get_item_user_data(self.enlarge_plot_button).items() + } + label_lookup: Dict[int, str] = dpg.get_item_user_data(self.plot_tab_bar) + labels: List[str] = list(label_lookup.values()) + index = labels.index(label_lookup[tab_lookup[self.get_plot()]]) + 1 + self.select_plot(sender=-1, label=labels[index % len(labels)]) + + def previous_plot_tab(self): + index: int + if dpg.is_item_shown(self.plot_window): + tabs: List[int] = dpg.get_item_children(self.plot_tab_bar, slot=1) + index = tabs.index(dpg.get_value(self.plot_tab_bar)) - 1 + self.select_plot(sender=-1, tab=tabs[index % len(tabs)]) + else: + tab_lookup: Dict[Plot, int] = { + v: k + for k, v in dpg.get_item_user_data(self.enlarge_plot_button).items() + } + label_lookup: Dict[int, str] = dpg.get_item_user_data(self.plot_tab_bar) + labels: List[str] = list(label_lookup.values()) + index = labels.index(label_lookup[tab_lookup[self.get_plot()]]) - 1 + self.select_plot(sender=-1, label=labels[index % len(labels)]) + + def show_enlarged_plot(self): + signals.emit( + Signal.SHOW_ENLARGED_PLOT, + plot=self.get_plot(), + adjust_limits=dpg.get_value(self.adjust_plot_limits_checkbox), ) - if dpg.get_value(self.adjust_nyquist_limits_checkbox): - self.nyquist_plot.queue_limits_adjustment() - if dpg.get_value(self.adjust_bode_limits_checkbox): - self.bode_plot.queue_limits_adjustment() def show_enlarged_nyquist(self): signals.emit( Signal.SHOW_ENLARGED_PLOT, plot=self.nyquist_plot, - adjust_limits=dpg.get_value(self.adjust_nyquist_limits_checkbox), + adjust_limits=self.should_adjust_limit(plot=self.nyquist_plot), ) def show_enlarged_bode(self): signals.emit( Signal.SHOW_ENLARGED_PLOT, plot=self.bode_plot, - adjust_limits=dpg.get_value(self.adjust_bode_limits_checkbox), + adjust_limits=self.should_adjust_limit(plot=self.bode_plot), + ) + + def show_enlarged_impedance(self): + signals.emit( + Signal.SHOW_ENLARGED_PLOT, + plot=self.impedance_plot, + adjust_limits=self.should_adjust_limit(plot=self.impedance_plot), ) def has_active_input(self) -> bool: diff --git a/src/deareis/gui/data_sets/average_data_sets.py b/src/deareis/gui/data_sets/average_data_sets.py index 2f1047c..ff8de3c 100644 --- a/src/deareis/gui/data_sets/average_data_sets.py +++ b/src/deareis/gui/data_sets/average_data_sets.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -19,6 +19,7 @@ from typing import ( Callable, + Dict, List, Optional, ) @@ -27,7 +28,11 @@ ndarray, ) import dearpygui.dearpygui as dpg -from deareis.gui.plots import Nyquist +from deareis.gui.plots import ( + BodeMagnitude, + BodePhase, + Nyquist, +) from deareis.tooltips import attach_tooltip from deareis.themes import ( PLOT_MARKERS, @@ -35,13 +40,19 @@ create_plot_series_theme, ) import deareis.themes as themes -from deareis.utility import calculate_window_position_dimensions +from deareis.utility import ( + calculate_window_position_dimensions, + is_filtered_item_visible, + pad_tab_labels, +) from deareis.signals import Signal import deareis.signals as signals from deareis.data import DataSet +from deareis.state import STATE +from deareis.enums import Action from deareis.keybindings import ( - is_alt_down, - is_control_down, + Keybinding, + TemporaryKeybindingHandler, ) @@ -59,6 +70,101 @@ def __init__(self, data_sets: List[DataSet], callback: Callable): ) ) self.final_data: Optional[DataSet] = None + self.create_window() + self.register_keybindings() + self.update_preview([]) + + def register_keybindings(self): + callbacks: Dict[Keybinding, Callable] = {} + # Cancel + kb: Keybinding = Keybinding( + key=dpg.mvKey_Escape, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.CANCEL, + ) + callbacks[kb] = self.close + # Accept + for kb in STATE.config.keybindings: + if kb.action is Action.PERFORM_ACTION: + break + else: + kb = Keybinding( + key=dpg.mvKey_Return, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.PERFORM_ACTION, + ) + callbacks[kb] = self.accept + # Select filtered + for kb in STATE.config.keybindings: + if kb.action is Action.SELECT_ALL_PLOT_SERIES: + break + else: + kb = Keybinding( + key=dpg.mvKey_A, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.SELECT_ALL_PLOT_SERIES, + ) + callbacks[kb] = lambda: self.select_all(flag=True) + # Unselect filtered + for kb in STATE.config.keybindings: + if kb.action is Action.UNSELECT_ALL_PLOT_SERIES: + break + else: + kb = Keybinding( + key=dpg.mvKey_A, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.UNSELECT_ALL_PLOT_SERIES, + ) + callbacks[kb] = lambda: self.select_all(flag=False) + # Previous plot tab + for kb in STATE.config.keybindings: + if kb.action is Action.PREVIOUS_PLOT_TAB: + break + else: + kb = Keybinding( + key=dpg.mvKey_Prior, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.PREVIOUS_PLOT_TAB, + ) + callbacks[kb] = lambda: self.cycle_plot_tab(step=-1) + # Next plot tab + for kb in STATE.config.keybindings: + if kb.action is Action.NEXT_PLOT_TAB: + break + else: + kb = Keybinding( + key=dpg.mvKey_Next, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.NEXT_PLOT_TAB, + ) + callbacks[kb] = lambda: self.cycle_plot_tab(step=1) + # Focus filter input + kb = Keybinding( + key=dpg.mvKey_F, + mod_alt=False, + mod_ctrl=True, + mod_shift=False, + action=Action.CUSTOM, + ) + callbacks[kb] = self.focus_filter + # Create the handler + self.keybinding_handler: TemporaryKeybindingHandler = ( + TemporaryKeybindingHandler(callbacks=callbacks) + ) + + def create_window(self): x: int y: int w: int @@ -68,10 +174,7 @@ def __init__(self, data_sets: List[DataSet], callback: Callable): with dpg.window( label="Average of multiple data sets", modal=True, - pos=( - x, - y, - ), + pos=(x, y), width=w, height=h, tag=self.window, @@ -79,19 +182,16 @@ def __init__(self, data_sets: List[DataSet], callback: Callable): ): with dpg.group(horizontal=True): with dpg.group(): - with dpg.child_window(border=False, width=300, height=-24): + with dpg.child_window(border=False, width=300, height=-1): + self.filter_input: int = dpg.generate_uuid() with dpg.group(horizontal=True): - dpg.add_text("Label") - self.label_input: int = dpg.generate_uuid() - self.final_data_series: int = -1 dpg.add_input_text( - hint="REQUIRED", - default_value="Average", + hint="Filter...", width=-1, - tag=self.label_input, - callback=lambda s, a, u: self.update_label(a), + tag=self.filter_input, + callback=lambda s, a, u: self.filter_data_sets(a), ) - self.dataset_table: int = dpg.generate_uuid() + self.data_set_table: int = dpg.generate_uuid() with dpg.table( borders_outerV=True, borders_outerH=True, @@ -100,55 +200,87 @@ def __init__(self, data_sets: List[DataSet], callback: Callable): scrollY=True, freeze_rows=1, width=-1, - height=-1, - tag=self.dataset_table, + height=-48, + tag=self.data_set_table, ): dpg.add_table_column(label="", width_fixed=True) dpg.add_table_column(label="Label", width_fixed=True) data: DataSet for data in self.data_sets: - with dpg.table_row(): + with dpg.table_row(filter_key=data.get_label().lower()): dpg.add_checkbox( callback=lambda: self.update_preview([]) ) label: str = data.get_label() dpg.add_text(label) attach_tooltip(label) - dpg.add_button(label="Accept", callback=self.accept) - self.nyquist_plot: Nyquist = Nyquist( - width=-1, - height=-12, - legend_horizontal=False, - legend_outside=False, - legend_location=dpg.mvPlot_Location_NorthEast, - ) - self.key_handler: int = dpg.generate_uuid() - with dpg.handler_registry(tag=self.key_handler): - dpg.add_key_release_handler( - key=dpg.mvKey_Escape, - callback=self.close, - ) - dpg.add_key_release_handler( - key=dpg.mvKey_Return, - callback=lambda: self.accept(keybinding=True), - ) - self.update_preview([]) + with dpg.group(horizontal=True): + dpg.add_text("Label") + attach_tooltip( + "The label for the new data set that will be generated" + ) + self.label_input: int = dpg.generate_uuid() + self.final_data_series: int = -1 + dpg.add_input_text( + hint="REQUIRED", + default_value="Average", + width=-1, + tag=self.label_input, + callback=lambda s, a, u: self.update_label(a), + ) + with dpg.group(horizontal=True): + button_pad: int = 12 + dpg.add_button( + label="Accept".ljust(button_pad), + callback=self.accept, + ) + dpg.add_button( + label="Select all".ljust(button_pad), + callback=lambda: self.select_all(True), + ) + dpg.add_button( + label="Unselect all".ljust(button_pad), + callback=lambda: self.select_all(False), + width=-1, + ) + with dpg.child_window(border=False, width=-1, height=-1): + self.plot_tab_bar: int = dpg.generate_uuid() + with dpg.tab_bar(tag=self.plot_tab_bar): + with dpg.tab(label="Nyquist"): + self.nyquist_plot: Nyquist = Nyquist( + width=-1, + height=-1, + legend_horizontal=False, + legend_outside=False, + legend_location=dpg.mvPlot_Location_NorthEast, + ) + with dpg.tab(label="Bode - magnitude"): + self.magnitude_plot: BodeMagnitude = BodeMagnitude( + width=-1, + height=-1, + legend_horizontal=False, + legend_outside=False, + legend_location=dpg.mvPlot_Location_NorthEast, + ) + with dpg.tab(label="Bode - phase"): + self.phase_plot: BodePhase = BodePhase( + width=-1, + height=-1, + legend_horizontal=False, + legend_outside=False, + legend_location=dpg.mvPlot_Location_NorthEast, + ) + pad_tab_labels(self.plot_tab_bar) def close(self): dpg.hide_item(self.window) dpg.delete_item(self.window) - dpg.delete_item(self.key_handler) + self.keybinding_handler.delete() signals.emit(Signal.UNBLOCK_KEYBINDINGS) - def accept(self, keybinding: bool = False): + def accept(self): if self.final_data is None: return - if keybinding is True and not ( - is_control_down() - if dpg.get_platform() == dpg.mvPlatform_Windows - else is_alt_down() - ): - return label: str = dpg.get_value(self.label_input).strip() if label == "": return @@ -161,7 +293,7 @@ def get_selection(self) -> List[DataSet]: data_sets: List[DataSet] = [] i: int row: int - for i, row in enumerate(dpg.get_item_children(self.dataset_table, slot=1)): + for i, row in enumerate(dpg.get_item_children(self.data_set_table, slot=1)): column: int for column in dpg.get_item_children(row, slot=1): assert dpg.get_item_type(column).endswith("Checkbox") @@ -170,18 +302,22 @@ def get_selection(self) -> List[DataSet]: indices.append(i) break if data_sets: - frequency: ndarray = data_sets[0].get_frequency(masked=None) - for i, row in enumerate(dpg.get_item_children(self.dataset_table, slot=1)): + frequency: ndarray = data_sets[0].get_frequencies(masked=None) + for i, row in enumerate(dpg.get_item_children(self.data_set_table, slot=1)): if i in indices: continue data: DataSet = self.data_sets[i] if not ( data.get_num_points(masked=None) == frequency.size - and allclose(data.get_frequency(masked=None), frequency) + and allclose( + data.get_frequencies(masked=None), + frequency, + rtol=1e-3, + ) ): dpg.hide_item(dpg.get_item_children(row, slot=1)[0]) else: - for i, row in enumerate(dpg.get_item_children(self.dataset_table, slot=1)): + for i, row in enumerate(dpg.get_item_children(self.data_set_table, slot=1)): dpg.show_item(dpg.get_item_children(row, slot=1)[0]) return data_sets @@ -200,19 +336,43 @@ def update_preview(self, data_sets: List[DataSet]): if len(self.nyquist_plot.get_series()) == 0: from_empty = True self.nyquist_plot.clear() + self.magnitude_plot.clear() + self.phase_plot.clear() selection: List[DataSet] = self.get_selection() i: int data: DataSet + label: str + theme: int real: ndarray imag: ndarray + freq: ndarray + mag: ndarray + phase: ndarray for i, data in enumerate(selection): + label = data.get_label() + theme = self.plot_themes[i % 12] real, imag = data.get_nyquist_data(masked=None) self.nyquist_plot.plot( real=real, imaginary=imag, - label=data.get_label(), + label=label, + line=False, + theme=theme, + ) + freq, mag, phase = data.get_bode_data(masked=None) + self.magnitude_plot.plot( + frequency=freq, + magnitude=mag, + label=label, + line=False, + theme=theme, + ) + self.phase_plot.plot( + frequency=freq, + phase=phase, + label=label, line=False, - theme=self.plot_themes[i % 12], + theme=theme, ) self.final_data_series = -1 if len(selection) > 1: @@ -222,18 +382,37 @@ def update_preview(self, data_sets: List[DataSet]): label=dpg.get_value(self.label_input), ) assert self.final_data is not None + label = self.final_data.get_label() + theme = themes.nyquist.data real, imag = self.final_data.get_nyquist_data(masked=None) self.final_data_series = self.nyquist_plot.plot( real=real, imaginary=imag, - label=self.final_data.get_label(), + label=label, + line=True, + theme=theme, + ) + freq, mag, phase = self.final_data.get_bode_data(masked=None) + self.magnitude_plot.plot( + frequency=freq, + magnitude=mag, + label=label, + line=True, + theme=theme, + ) + self.phase_plot.plot( + frequency=freq, + phase=phase, + label=label, line=True, - theme=themes.nyquist.data, + theme=theme, ) except AssertionError: self.final_data = None if from_empty: self.nyquist_plot.queue_limits_adjustment() + self.magnitude_plot.queue_limits_adjustment() + self.phase_plot.queue_limits_adjustment() def update_label(self, label: str): assert type(label) is str, label @@ -241,9 +420,53 @@ def update_label(self, label: str): self.final_data_series ): return - # TODO: Apply a theme that attracts attention when no label is provided + # TODO: Apply a theme that attracts attention when no label is provided? if label == "": pass else: pass dpg.set_item_label(self.final_data_series, label) + + def filter_data_sets(self, fltr: str): + dpg.set_value(self.data_set_table, fltr) + + def select_all(self, flag: bool): + data_sets: Dict[DataSet, int] = {} + fltr: str = dpg.get_value(self.filter_input) + i: int + row: int + for i, row in enumerate(dpg.get_item_children(self.data_set_table, slot=1)): + if not is_filtered_item_visible(row, fltr): + continue + checkbox: int = dpg.get_item_children(row, slot=1)[0] + if not dpg.is_item_visible(checkbox): + continue + if flag is True: + data_sets[self.data_sets[i]] = checkbox + else: + dpg.set_value(checkbox, False) + if flag is False: + self.update_preview([]) + return + frequencies: Optional[ndarray] = None + data: DataSet + for data, checkbox in data_sets.items(): + if frequencies is None: + frequencies = data.get_frequencies(masked=None) + dpg.set_value(checkbox, True) + self.update_preview([]) + elif data.get_num_points(masked=None) == frequencies.size and allclose( + data.get_frequencies(masked=None), + frequencies, + rtol=1e-3, + ): + dpg.set_value(checkbox, True) + self.update_preview([]) + + def focus_filter(self): + dpg.focus_item(self.filter_input) + + def cycle_plot_tab(self, step: int): + tabs: List[int] = dpg.get_item_children(self.plot_tab_bar, slot=1) + index: int = tabs.index(dpg.get_value(self.plot_tab_bar)) + step + dpg.set_value(self.plot_tab_bar, tabs[index % len(tabs)]) diff --git a/src/deareis/gui/data_sets/copy_mask.py b/src/deareis/gui/data_sets/copy_mask.py index 8bbcb6b..3b63463 100644 --- a/src/deareis/gui/data_sets/copy_mask.py +++ b/src/deareis/gui/data_sets/copy_mask.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -17,20 +17,37 @@ # The licenses of DearEIS' dependencies and/or sources of portions of code are included in # the LICENSES folder. -from typing import Callable, List -from numpy import array, ndarray +from typing import ( + Callable, + Dict, + List, + Tuple, +) +from numpy import ( + array, + ndarray, +) import dearpygui.dearpygui as dpg -from deareis.gui.plots import Nyquist +from deareis.gui.plots import ( + BodeMagnitude, + BodePhase, + Nyquist, +) import deareis.themes as themes -from deareis.utility import calculate_window_position_dimensions +from deareis.utility import ( + calculate_window_position_dimensions, + pad_tab_labels, +) from deareis.signals import Signal import deareis.signals as signals from deareis.data import DataSet from deareis.tooltips import attach_tooltip import deareis.tooltips as tooltips +from deareis.state import STATE +from deareis.enums import Action from deareis.keybindings import ( - is_alt_down, - is_control_down, + Keybinding, + TemporaryKeybindingHandler, ) @@ -47,6 +64,95 @@ def __init__(self, data: DataSet, data_sets: List[DataSet], callback: Callable): ] self.labels: List[str] = list(map(lambda _: _.get_label(), self.data_sets)) self.callback: Callable = callback + self.create_window() + self.register_keybindings() + self.select_source(self.labels[0]) + self.nyquist_plot.queue_limits_adjustment() + self.magnitude_plot.queue_limits_adjustment() + self.phase_plot.queue_limits_adjustment() + + def register_keybindings(self): + callbacks: Dict[Keybinding, Callable] = {} + # Cancel + kb: Keybinding = Keybinding( + key=dpg.mvKey_Escape, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.CANCEL, + ) + callbacks[kb] = self.close + # Accept + for kb in STATE.config.keybindings: + if kb.action is Action.PERFORM_ACTION: + break + else: + kb = Keybinding( + key=dpg.mvKey_Return, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.PERFORM_ACTION, + ) + callbacks[kb] = self.accept + # Previous source + for kb in STATE.config.keybindings: + if kb.action is Action.PREVIOUS_PRIMARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Prior, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.PREVIOUS_PRIMARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle_source(-1) + # Next source + for kb in STATE.config.keybindings: + if kb.action is Action.NEXT_PRIMARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Next, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.NEXT_PRIMARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle_source(1) + # Previous plot tab + for kb in STATE.config.keybindings: + if kb.action is Action.PREVIOUS_PLOT_TAB: + break + else: + kb = Keybinding( + key=dpg.mvKey_Prior, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.PREVIOUS_PLOT_TAB, + ) + callbacks[kb] = lambda: self.cycle_plot_tab(step=-1) + # Next plot tab + for kb in STATE.config.keybindings: + if kb.action is Action.NEXT_PLOT_TAB: + break + else: + kb = Keybinding( + key=dpg.mvKey_Next, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.NEXT_PLOT_TAB, + ) + callbacks[kb] = lambda: self.cycle_plot_tab(step=1) + # Create the handler + self.keybinding_handler: TemporaryKeybindingHandler = ( + TemporaryKeybindingHandler(callbacks=callbacks) + ) + + def create_window(self): x: int y: int w: int @@ -76,48 +182,65 @@ def __init__(self, data: DataSet, data_sets: List[DataSet], callback: Callable): tag=self.combo, callback=lambda s, a, u: self.select_source(a), ) - self.nyquist_plot: Nyquist = Nyquist(width=-1, height=-24) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="Excluded", - theme=themes.nyquist.data, - ) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="Included", - theme=themes.bode.phase_data, - show_label=False, - ) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="Included", - line=True, - theme=themes.bode.phase_data, - ) - dpg.add_button(label="Accept", callback=self.accept) - self.key_handler: int = dpg.generate_uuid() - with dpg.handler_registry(tag=self.key_handler): - dpg.add_key_release_handler( - key=dpg.mvKey_Escape, - callback=self.close, - ) - dpg.add_key_release_handler( - key=dpg.mvKey_Return, - callback=lambda: self.accept(keybinding=True), - ) - dpg.add_key_release_handler( - key=dpg.mvKey_Prior, - callback=lambda: self.cycle_source(-1), + self.create_plots() + dpg.add_button( + label="Accept".ljust(12), + callback=self.accept, ) - dpg.add_key_release_handler( - key=dpg.mvKey_Next, - callback=lambda: self.cycle_source(1), - ) - self.select_source(self.labels[0]) - self.nyquist_plot.queue_limits_adjustment() + + def create_plots(self): + settings: List[dict] = [ + { + "label": "Excluded", + "theme": themes.nyquist.data, + }, + { + "label": "Included", + "theme": themes.bode.phase_data, + "show_label": False, + }, + { + "label": "Included", + "line": True, + "theme": themes.bode.phase_data, + }, + ] + self.plot_tab_bar: int = dpg.generate_uuid() + with dpg.tab_bar(tag=self.plot_tab_bar): + self.create_nyquist_plot(settings) + self.create_magnitude_plot(settings) + self.create_phase_plot(settings) + pad_tab_labels(self.plot_tab_bar) + + def create_nyquist_plot(self, settings: List[dict]): + with dpg.tab(label="Nyquist"): + self.nyquist_plot: Nyquist = Nyquist(width=-1, height=-24) + for kwargs in settings: + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + **kwargs, + ) + + def create_magnitude_plot(self, settings: List[dict]): + with dpg.tab(label="Bode - magnitude"): + self.magnitude_plot: BodeMagnitude = BodeMagnitude(width=-1, height=-24) + for kwargs in settings: + self.magnitude_plot.plot( + frequency=array([]), + magnitude=array([]), + **kwargs, + ) + + def create_phase_plot(self, settings: List[dict]): + with dpg.tab(label="Bode - phase"): + self.phase_plot: BodePhase = BodePhase(width=-1, height=-24) + for kwargs in settings: + self.phase_plot.plot( + frequency=array([]), + phase=array([]), + **kwargs, + ) def cycle_source(self, step: int): assert type(step) is int, step @@ -130,43 +253,71 @@ def select_source(self, label: str): assert type(label) is str, label self.preview_data.set_mask(self.data.get_mask()) self.preview_data.set_mask(self.data_sets[self.labels.index(label)].get_mask()) - self.update_preview() - - def update_preview(self): - real: ndarray - imag: ndarray - real, imag = self.preview_data.get_nyquist_data(masked=True) - self.nyquist_plot.update( - index=0, - real=real, - imaginary=imag, - ) - real, imag = self.preview_data.get_nyquist_data(masked=False) - self.nyquist_plot.update( - index=1, - real=real, - imaginary=imag, - ) - self.nyquist_plot.update( - index=2, - real=real, - imaginary=imag, - ) + self.update_previews() + + def update_previews(self): + self.update_nyquist_plot(self.preview_data) + self.update_magnitude_plot(self.preview_data) + self.update_phase_plot(self.preview_data) + + def update_nyquist_plot(self, data: DataSet): + data: List[Tuple[ndarray, ndarray]] = [ + data.get_nyquist_data(masked=True), + data.get_nyquist_data(masked=False), + data.get_nyquist_data(masked=False), + ] + for i, (real, imag) in enumerate(data): + self.nyquist_plot.update( + index=i, + real=real, + imaginary=imag, + ) + + def update_magnitude_plot(self, data: DataSet): + data: List[Tuple[ndarray, ndarray, ndarray]] = [ + data.get_bode_data(masked=True), + data.get_bode_data(masked=False), + data.get_bode_data(masked=False), + ] + i: int + freq: ndarray + mag: ndarray + for i, (freq, mag, _) in enumerate(data): + self.magnitude_plot.update( + index=i, + frequency=freq, + magnitude=mag, + ) + + def update_phase_plot(self, data: DataSet): + data: List[Tuple[ndarray, ndarray, ndarray]] = [ + data.get_bode_data(masked=True), + data.get_bode_data(masked=False), + data.get_bode_data(masked=False), + ] + i: int + freq: ndarray + phase: ndarray + for i, (freq, _, phase) in enumerate(data): + self.phase_plot.update( + index=i, + frequency=freq, + phase=phase, + ) def close(self): dpg.hide_item(self.window) dpg.delete_item(self.window) - dpg.delete_item(self.key_handler) + self.keybinding_handler.delete() signals.emit(Signal.UNBLOCK_KEYBINDINGS) - def accept(self, keybinding: bool = False): - if keybinding is True and not ( - is_control_down() - if dpg.get_platform() == dpg.mvPlatform_Windows - else is_alt_down() - ): - return + def accept(self): self.callback( self.data_sets[self.labels.index(dpg.get_value(self.combo))].get_mask(), ) self.close() + + def cycle_plot_tab(self, step: int): + tabs: List[int] = dpg.get_item_children(self.plot_tab_bar, slot=1) + index: int = tabs.index(dpg.get_value(self.plot_tab_bar)) + step + dpg.set_value(self.plot_tab_bar, tabs[index % len(tabs)]) diff --git a/src/deareis/gui/data_sets/interpolate_points.py b/src/deareis/gui/data_sets/interpolate_points.py new file mode 100644 index 0000000..96b7f6a --- /dev/null +++ b/src/deareis/gui/data_sets/interpolate_points.py @@ -0,0 +1,766 @@ +# DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). +# Copyright 2023 DearEIS developers +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +# +# The licenses of DearEIS' dependencies and/or sources of portions of code are included in +# the LICENSES folder. + +from cmath import rect +from typing import ( + Callable, + Dict, + List, + Optional, + Tuple, +) +import dearpygui.dearpygui as dpg +from numpy import ( + angle, + array, + empty, + flip, + float64, + isclose, + log10 as log, + ndarray, +) +from numpy.typing import NDArray +from scipy.interpolate import Akima1DInterpolator +from statsmodels.nonparametric.smoothers_lowess import lowess +from pyimpspec import ( + ComplexImpedance, + ComplexImpedances, + Frequencies, + Impedances, +) +from deareis.data import DataSet +from deareis.gui.plots import ( + BodeMagnitude, + BodePhase, + Nyquist, +) +from deareis.signals import Signal +from deareis.utility import ( + align_numbers, + calculate_window_position_dimensions, + format_number, + pad_tab_labels, +) +import deareis.signals as signals +import deareis.themes as themes +from deareis.tooltips import ( + attach_tooltip, + update_tooltip, +) +import deareis.tooltips as tooltips +from deareis.state import STATE +from deareis.enums import Action +from deareis.keybindings import ( + Keybinding, + TemporaryKeybindingHandler, +) + + +class InterpolatePoints: + def __init__( + self, + data: DataSet, + callback: Callable, + ): + assert isinstance(data, DataSet), data + assert callable(callback), callback + self.original_data: DataSet = DataSet.from_dict(data.to_dict()) + self.smoothed_data: DataSet = DataSet.from_dict(data.to_dict()) + self.smoothed_data.set_mask({}) + self.preview_data: DataSet = DataSet.from_dict(data.to_dict()) + self.preview_data.set_mask({}) + self.callback: Callable = callback + self.create_window() + self.register_keybindings() + self.update_smoothing() + self.nyquist_plot.queue_limits_adjustment() + self.magnitude_plot.queue_limits_adjustment() + self.phase_plot.queue_limits_adjustment() + + def register_keybindings(self): + callbacks: Dict[Keybinding, Callable] = {} + # Cancel + kb: Keybinding = Keybinding( + key=dpg.mvKey_Escape, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.CANCEL, + ) + callbacks[kb] = self.close + # Accept + for kb in STATE.config.keybindings: + if kb.action is Action.PERFORM_ACTION: + break + else: + kb = Keybinding( + key=dpg.mvKey_Return, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.PERFORM_ACTION, + ) + callbacks[kb] = self.accept + # Previous plot tab + for kb in STATE.config.keybindings: + if kb.action is Action.PREVIOUS_PLOT_TAB: + break + else: + kb = Keybinding( + key=dpg.mvKey_Prior, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.PREVIOUS_PLOT_TAB, + ) + callbacks[kb] = lambda: self.cycle_plot_tab(step=-1) + # Next plot tab + for kb in STATE.config.keybindings: + if kb.action is Action.NEXT_PLOT_TAB: + break + else: + kb = Keybinding( + key=dpg.mvKey_Next, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.NEXT_PLOT_TAB, + ) + callbacks[kb] = lambda: self.cycle_plot_tab(step=1) + # Create the handler + self.keybinding_handler: TemporaryKeybindingHandler = ( + TemporaryKeybindingHandler(callbacks=callbacks) + ) + + def create_window(self): + x: int + y: int + w: int + h: int + x, y, w, h = calculate_window_position_dimensions() + self.window: int = dpg.generate_uuid() + with dpg.window( + label="Interpolate points", + modal=True, + pos=( + x, + y, + ), + width=w, + height=h, + tag=self.window, + on_close=self.close, + ): + with dpg.group(horizontal=True): + self.table_window: int = dpg.generate_uuid() + with dpg.child_window( + border=False, + width=500, + tag=self.table_window, + ): + self.create_table() + dpg.add_button( + label="Accept".ljust(12), + callback=self.accept, + ) + self.create_plots() + + def create_table(self): + with dpg.group(horizontal=True): + dpg.add_text("Num. points") + attach_tooltip(tooltips.zhit.num_points) + self.num_points_input: int = dpg.generate_uuid() + dpg.add_input_int( + default_value=5, + min_value=2, + min_clamped=True, + max_value=self.original_data.get_num_points(), + max_clamped=True, + step=0, + callback=self.update_smoothing, + on_enter=True, + width=100, + tag=self.num_points_input, + ) + dpg.add_text("Num. iterations") + attach_tooltip(tooltips.zhit.num_iterations) + self.num_iterations_input: int = dpg.generate_uuid() + dpg.add_input_int( + default_value=3, + min_value=1, + min_clamped=True, + max_value=100, + max_clamped=True, + step=0, + callback=self.update_smoothing, + on_enter=True, + width=100, + tag=self.num_iterations_input, + ) + self.smooth_polar_checkbox: int = dpg.generate_uuid() + dpg.add_checkbox( + label="Polar", + default_value=True, + callback=self.update_smoothing, + tag=self.smooth_polar_checkbox, + ) + attach_tooltip(tooltips.data_sets.interpolation_smooth_polar) + self.table = dpg.generate_uuid() + with dpg.table( + borders_outerV=True, + borders_outerH=True, + borders_innerV=True, + borders_innerH=True, + scrollY=True, + freeze_rows=1, + height=-24, + tag=self.table, + ): + dpg.add_table_column( + label="?", + width_fixed=True, + ) + attach_tooltip(tooltips.data_sets.interpolation_toggle) + dpg.add_table_column( + label="Index", + width_fixed=True, + ) + dpg.add_table_column( + label="f (Hz)", + ) + attach_tooltip(tooltips.data_sets.frequency) + dpg.add_table_column( + label="Re(Z) (ohm)", + ) + attach_tooltip(tooltips.data_sets.real) + dpg.add_table_column( + label="-Im(Z) (ohm)", + ) + attach_tooltip(tooltips.data_sets.imaginary) + dpg.add_table_column( + label="Mod(Z) (ohm)", + ) + attach_tooltip(tooltips.data_sets.magnitude) + dpg.add_table_column( + label="-Phase(Z) (°)", + ) + attach_tooltip(tooltips.data_sets.phase) + num_points: int = self.original_data.get_num_points(masked=None) + i: int + for i in range(0, num_points): + with dpg.table_row(parent=self.table): + dpg.add_checkbox( + default_value=False, + callback=lambda s, a, u: self.toggle_point(u, a), + user_data=i, + ) + dpg.add_text("") # Index + dpg.set_item_user_data( + dpg.add_text(""), + attach_tooltip(""), + ) # f + dpg.set_item_user_data( + dpg.add_input_text( + hint="?", + scientific=True, + width=-1, + callback=lambda s, a, u: self.modify_override(u, a), + on_enter=True, + ), + attach_tooltip(""), + ) # Re(Z) + dpg.set_item_user_data( + dpg.add_input_text( + hint="?", + scientific=True, + width=-1, + callback=lambda s, a, u: self.modify_override(u, a), + on_enter=True, + ), + attach_tooltip(""), + ) # -Im(Z) + dpg.set_item_user_data( + dpg.add_text(""), + attach_tooltip(""), + ) # Mod(Z) + dpg.set_item_user_data( + dpg.add_text(""), + attach_tooltip(""), + ) # Phase(Z) + + def create_plots(self): + settings: List[dict] = [ + { + "label": "Before", + "theme": themes.nyquist.data, + "show_label": False, + }, + { + "label": "Before", + "line": True, + "theme": themes.nyquist.data, + }, + { + "label": "Masked", + "theme": themes.residuals.imaginary, + }, + { + "label": "Smoothed", + "line": True, + "theme": themes.residuals.real, + }, + { + "label": "After", + "theme": themes.bode.phase_data, + "show_label": False, + }, + { + "label": "After", + "line": True, + "theme": themes.bode.phase_data, + }, + ] + self.preview_window: int = dpg.generate_uuid() + with dpg.child_window(border=False, tag=self.preview_window): + self.plot_tab_bar: int = dpg.generate_uuid() + with dpg.tab_bar(tag=self.plot_tab_bar): + self.create_nyquist_plot(settings) + self.create_magnitude_plot(settings) + self.create_phase_plot(settings) + pad_tab_labels(self.plot_tab_bar) + + def create_nyquist_plot(self, settings: List[dict]): + with dpg.tab(label="Nyquist"): + self.nyquist_plot: Nyquist = Nyquist(width=-1, height=-1) + for kwargs in settings: + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + **kwargs, + ) + + def create_magnitude_plot(self, settings: List[dict]): + with dpg.tab(label="Bode - magnitude"): + self.magnitude_plot: BodeMagnitude = BodeMagnitude(width=-1, height=-1) + for kwargs in settings: + self.magnitude_plot.plot( + frequency=array([]), + magnitude=array([]), + **kwargs, + ) + + def create_phase_plot(self, settings: List[dict]): + with dpg.tab(label="Bode - phase"): + self.phase_plot: BodePhase = BodePhase(width=-1, height=-1) + for kwargs in settings: + self.phase_plot.plot( + frequency=array([]), + phase=array([]), + **kwargs, + ) + + def close(self): + dpg.hide_item(self.window) + dpg.delete_item(self.window) + self.keybinding_handler.delete() + signals.emit(Signal.UNBLOCK_KEYBINDINGS) + + def accept(self): + mask: Dict[int, bool] = self.get_mask() + mask.update({_: True for _ in self.get_overrides().keys()}) + for modified in mask.values(): + if modified is True: + break + else: + self.close() + return + dictionary: dict = self.preview_data.to_dict() + del dictionary["uuid"] + data: DataSet = DataSet.from_dict(dictionary) + data.set_label(f"{self.preview_data.get_label()} - interpolated") + self.callback(data) + self.close() + + def toggle_point(self, index: int, state: bool): + rows: List[int] = dpg.get_item_children(self.table, slot=1) + inputs: Tuple[int, int] = self.get_inputs(rows[index]) + if state is True: + dpg.disable_item(inputs[0]) + dpg.disable_item(inputs[1]) + else: + dpg.set_value(inputs[0], "") + dpg.set_value(inputs[1], "") + dpg.enable_item(inputs[0]) + dpg.enable_item(inputs[1]) + self.update_table(index=index, state=state) + self.update_plots() + + def modify_override(self, index: int, value: str): + self.update_table() + self.update_plots() + + def get_checkbox(self, row: int) -> int: + return dpg.get_item_children(row, slot=1)[0] + + def get_inputs(self, row: int) -> Tuple[int, int]: + cells: List[int] = dpg.get_item_children(row, slot=1) + return ( + cells[4], + cells[6], + ) + + def get_mask(self) -> Dict[int, bool]: + mask: Dict[int, bool] = {} + rows: List[int] = dpg.get_item_children(self.table, slot=1) + i: int + row: int + for i, row in enumerate(rows): + mask[i] = dpg.get_value(self.get_checkbox(rows[i])) + return mask + + def get_real_input(self, row: int) -> int: + return dpg.get_item_children(row, slot=1)[4] + + def get_imaginary_input(self, row: int) -> int: + return dpg.get_item_children(row, slot=1)[6] + + def get_rows(self) -> List[int]: + return dpg.get_item_children(self.table, slot=1) + + def get_overrides(self) -> Dict[int, ComplexImpedance]: + overrides: Dict[int, ComplexImpedance] = {} + rows: List[int] = self.get_rows() + i: int + row: int + Z: ComplexImpedance + for i, (row, Z) in enumerate( + zip(rows, self.original_data.get_impedances(masked=None)) + ): + re: str = dpg.get_value(self.get_real_input(row)) + im: str = dpg.get_value(self.get_imaginary_input(row)) + if re != "" or im != "": + real: float = float(re) if re != "" else Z.real + imag: float = -float(im) if im != "" else Z.imag + overrides[i] = ComplexImpedance(real + imag * 1j) + return overrides + + def get_impedances(self, mask: Dict[int, bool] = {}) -> ComplexImpedances: + if not mask: + mask = self.get_mask() + overrides: Dict[int, ComplexImpedance] = self.get_overrides() + Z: ComplexImpedances = self.original_data.get_impedances(masked=None) + smooth_Z: ComplexImpedances = self.smoothed_data.get_impedances(masked=None) + results: ComplexImpedances = empty( + Z.shape, + dtype=Z.dtype, + ) + i: int + state: bool + for i, state in mask.items(): + if state is True: + results[i] = smooth_Z[i] + elif i in overrides: + results[i] = overrides[i] + else: + results[i] = Z[i] + return results + + def update_table(self, index: int = -1, state: Optional[bool] = None): + if index >= 0: + assert isinstance(state, bool), state + else: + assert state is None, state + original_f: Frequencies = self.original_data.get_frequencies(masked=None) + original_Z: ComplexImpedances = self.original_data.get_impedances(masked=None) + mask: Dict[int, bool] = self.get_mask() + Z: ComplexImpedances = self.get_impedances(mask) + f: List[str] = list( + map( + lambda _: format_number(_, significants=4), + self.original_data.get_frequencies(masked=None), + ) + ) + re_Z: List[str] = list( + map( + lambda _: format_number( + _.real, + significants=4, + ), + Z, + ) + ) + im_Z: List[str] = list(map(lambda _: format_number(-_.imag, significants=4), Z)) + mod_Z: List[str] = list( + map( + lambda _: format_number( + abs(_), + significants=4, + ), + Z, + ) + ) + phase_Z: List[str] = list( + map( + lambda _: format_number( + -angle(_, deg=True), # type: ignore + significants=4, + exponent=False, + ), + Z, + ) + ) + indices: List[str] = list(map(lambda _: str(_ + 1), range(0, len(Z)))) + indices = align_numbers(indices) + f = align_numbers(f) + re_Z = align_numbers(re_Z) + im_Z = align_numbers(im_Z) + mod_Z = align_numbers(mod_Z) + phase_Z = align_numbers(phase_Z) + fmt: str = "{:.6E}" + + def get_cell_and_tooltip(cells: List[int], index: int) -> Tuple[int, int]: + assert 0 <= index < 7 + if index < 2: + return ( + cells[index], + -1, + ) + index = (index - 2) * 2 + 2 + return ( + cells[index], + dpg.get_item_user_data(cells[index]), + ) + + rows: List[int] = dpg.get_item_children(self.table, slot=1) + i: int + row: int + for i, row in enumerate(rows): + if index >= 0 and i != index: + continue + cells: List[int] = dpg.get_item_children(row, slot=1) + cell: int + tooltip: int + dpg.set_value(cells[0], mask[i]) + dpg.set_value(cells[1], indices[i]) + cell, tooltip = get_cell_and_tooltip(cells, 2) + dpg.set_value(cell, f[i]) + update_tooltip(tooltip, fmt.format(original_f[i])) + cell, tooltip = get_cell_and_tooltip(cells, 3) + dpg.configure_item(cell, hint=re_Z[i]) + if mask[i] is True: + dpg.set_value(cell, format_number(Z[i].real)) + update_tooltip( + tooltip, + fmt.format(original_Z[i].real) + + ( + ( + " -> " + fmt.format(Z[i].real) + if not isclose(original_Z[i], Z[i]) + else "" + ) + ), + ) + cell, tooltip = get_cell_and_tooltip(cells, 4) + dpg.configure_item(cell, hint=im_Z[i]) + if mask[i] is True: + dpg.set_value(cell, format_number(-Z[i].imag)) + update_tooltip( + tooltip, + fmt.format(-original_Z[i].imag) + + ( + ( + " -> " + fmt.format(-Z[i].imag) + if not isclose(original_Z[i], Z[i]) + else "" + ) + ), + ) + cell, tooltip = get_cell_and_tooltip(cells, 5) + dpg.set_value(cell, mod_Z[i]) + update_tooltip( + tooltip, + fmt.format(abs(original_Z[i])) + + ( + ( + " -> " + fmt.format(abs(Z[i])) + if not isclose(original_Z[i], Z[i]) + else "" + ) + ), + ) + cell, tooltip = get_cell_and_tooltip(cells, 6) + dpg.set_value(cell, phase_Z[i]) + update_tooltip( + tooltip, + fmt.format(-angle(original_Z[i], deg=True)) + + ( + ( + " -> " + fmt.format(-angle(Z[i], deg=True)) + if not isclose(original_Z[i], Z[i]) + else "" + ) + ), + ) + dictionary: dict = self.original_data.to_dict() + dictionary.update( + { + "mask": {}, + "real_impedances": list(Z.real), + "imaginary_impedances": list(Z.imag), + } + ) + self.preview_data = DataSet.from_dict(dictionary) + + def update_smoothing(self): + log_f: NDArray[float64] = log(self.original_data.get_frequencies()) + Z: ComplexImpedances = self.original_data.get_impedances() + fraction: float = dpg.get_value(self.num_points_input) / Z.size + num_iterations: int = dpg.get_value(self.num_iterations_input) + smooth_polar_data = dpg.get_value(self.smooth_polar_checkbox) + if smooth_polar_data is True: + smoothed_mod: Impedances = lowess( + abs(Z), + log_f, + frac=fraction, + it=num_iterations, + return_sorted=False, + ) + smoothed_phase = lowess( + angle(Z), + log_f, + frac=fraction, + it=num_iterations, + return_sorted=False, + ) + log_f = flip(log_f) + mod_interpolator = Akima1DInterpolator(log_f, flip(smoothed_mod)) + phase_interpolator = Akima1DInterpolator(log_f, flip(smoothed_phase)) + else: + smoothed_real: Impedances = lowess( + Z.real, + log_f, + frac=fraction, + it=num_iterations, + return_sorted=False, + ) + smoothed_imag: Impedances = lowess( + Z.imag, + log_f, + frac=fraction, + it=num_iterations, + return_sorted=False, + ) + log_f = flip(log_f) + real_interpolator = Akima1DInterpolator(log_f, flip(smoothed_real)) + imag_interpolator = Akima1DInterpolator(log_f, flip(smoothed_imag)) + log_f = log(self.original_data.get_frequencies(masked=None)) + dictionary: dict = self.smoothed_data.to_dict() + if smooth_polar_data is True: + Z = array( + list( + map( + lambda _: rect(*_), + zip(mod_interpolator(log_f), phase_interpolator(log_f)), + ) + ) + ) + dictionary.update( + { + "real_impedances": Z.real, + "imaginary_impedances": Z.imag, + } + ) + else: + dictionary.update( + { + "real_impedances": list(map(real_interpolator, log_f)), + "imaginary_impedances": list(map(imag_interpolator, log_f)), + } + ) + self.smoothed_data = DataSet.from_dict(dictionary) + self.update_table() + self.update_plots() + + def update_plots(self): + self.update_nyquist(self.original_data, self.smoothed_data, self.preview_data) + self.update_magnitude(self.original_data, self.smoothed_data, self.preview_data) + self.update_phase(self.original_data, self.smoothed_data, self.preview_data) + + def update_nyquist(self, original: DataSet, smoothed: DataSet, preview: DataSet): + data: List[Tuple[ndarray, ndarray]] = [ + original.get_nyquist_data(masked=False), + original.get_nyquist_data(masked=False), + original.get_nyquist_data(masked=True), + smoothed.get_nyquist_data(masked=None), + preview.get_nyquist_data(masked=None), + preview.get_nyquist_data(masked=None), + ] + i: int + real: ndarray + imag: ndarray + for i, (real, imag) in enumerate(data): + self.nyquist_plot.update( + index=i, + real=real, + imaginary=imag, + ) + + def update_magnitude(self, original: DataSet, smoothed: DataSet, preview: DataSet): + data: List[Tuple[ndarray, ndarray, ndarray]] = [ + original.get_bode_data(masked=False), + original.get_bode_data(masked=False), + original.get_bode_data(masked=True), + smoothed.get_bode_data(masked=None), + preview.get_bode_data(masked=None), + preview.get_bode_data(masked=None), + ] + i: int + freq: ndarray + mag: ndarray + for i, (freq, mag, _) in enumerate(data): + self.magnitude_plot.update( + index=i, + frequency=freq, + magnitude=mag, + ) + + def update_phase(self, original: DataSet, smoothed: DataSet, preview: DataSet): + data: List[Tuple[ndarray, ndarray, ndarray]] = [ + original.get_bode_data(masked=False), + original.get_bode_data(masked=False), + original.get_bode_data(masked=True), + smoothed.get_bode_data(masked=None), + preview.get_bode_data(masked=None), + preview.get_bode_data(masked=None), + ] + i: int + freq: ndarray + phase: ndarray + for i, (freq, _, phase) in enumerate(data): + self.phase_plot.update( + index=i, + frequency=freq, + phase=phase, + ) + + def cycle_plot_tab(self, step: int): + tabs: List[int] = dpg.get_item_children(self.plot_tab_bar, slot=1) + index: int = tabs.index(dpg.get_value(self.plot_tab_bar)) + step + dpg.set_value(self.plot_tab_bar, tabs[index % len(tabs)]) diff --git a/src/deareis/gui/data_sets/subtract_impedance.py b/src/deareis/gui/data_sets/subtract_impedance.py index 9ebac02..b639dd7 100644 --- a/src/deareis/gui/data_sets/subtract_impedance.py +++ b/src/deareis/gui/data_sets/subtract_impedance.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -17,25 +17,31 @@ # The licenses of DearEIS' dependencies and/or sources of portions of code are included in # the LICENSES folder. -from pyimpspec import ( - Circuit, - ParsingError, -) +from pyimpspec import Circuit from typing import ( Callable, + Dict, List, Optional, + Tuple, ) from numpy import ( allclose, array, ndarray, ) -import pyimpspec import dearpygui.dearpygui as dpg -from deareis.gui.plots import Nyquist +from deareis.gui.plots import ( + BodeMagnitude, + BodePhase, + Nyquist, +) import deareis.themes as themes -from deareis.utility import calculate_window_position_dimensions +from deareis.utility import ( + calculate_window_position_dimensions, + pad_tab_labels, + process_cdc, +) from deareis.tooltips import attach_tooltip import deareis.tooltips as tooltips from deareis.gui.circuit_editor import CircuitEditor @@ -45,9 +51,11 @@ DataSet, FitResult, ) +from deareis.state import STATE +from deareis.enums import Action from deareis.keybindings import ( - is_alt_down, - is_control_down, + Keybinding, + TemporaryKeybindingHandler, ) @@ -83,18 +91,146 @@ def __init__( for _ in sorted(data_sets, key=lambda _: _.get_label()) if _ != data and data.get_num_points(masked=None) == _.get_num_points(masked=None) - and allclose(data.get_frequency(masked=None), _.get_frequency(masked=None)) + and allclose( + data.get_frequencies(masked=None), _.get_frequencies(masked=None) + ) ] self.data_labels: List[str] = list(map(lambda _: _.get_label(), self.data_sets)) self.fits: List[FitResult] = fits self.fit_labels: List[str] = format_fit_labels(fits) self.preview_data: DataSet = DataSet.from_dict(data.to_dict()) self.callback: Callable = callback + self.create_window() + self.register_keybindings() + self.editing_circuit: bool = False + self.select_option(self.radio_buttons, self.options[0]) + + def register_keybindings(self): + callbacks: Dict[Keybinding, Callable] = {} + # Cancel + kb: Keybinding = Keybinding( + key=dpg.mvKey_Escape, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.CANCEL, + ) + callbacks[kb] = self.close + # Accept + for kb in STATE.config.keybindings: + if kb.action is Action.PERFORM_ACTION: + break + else: + kb = Keybinding( + key=dpg.mvKey_Return, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.PERFORM_ACTION, + ) + callbacks[kb] = self.accept + # Previous option + for kb in STATE.config.keybindings: + if kb.action is Action.PREVIOUS_PRIMARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Prior, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.PREVIOUS_PRIMARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle_options(step=-1) + # Next option + for kb in STATE.config.keybindings: + if kb.action is Action.NEXT_PRIMARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Next, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.NEXT_PRIMARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle_options(step=1) + # Previous fit/data set + for kb in STATE.config.keybindings: + if kb.action is Action.PREVIOUS_SECONDARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Prior, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.PREVIOUS_SECONDARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle_results(step=-1) + # Next fit/data set + for kb in STATE.config.keybindings: + if kb.action is Action.NEXT_SECONDARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Next, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.NEXT_SECONDARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle_results(step=1) + # Previous plot tab + for kb in STATE.config.keybindings: + if kb.action is Action.PREVIOUS_PLOT_TAB: + break + else: + kb = Keybinding( + key=dpg.mvKey_Prior, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.PREVIOUS_PLOT_TAB, + ) + callbacks[kb] = lambda: self.cycle_plot_tab(step=-1) + # Next plot tab + for kb in STATE.config.keybindings: + if kb.action is Action.NEXT_PLOT_TAB: + break + else: + kb = Keybinding( + key=dpg.mvKey_Next, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.NEXT_PLOT_TAB, + ) + callbacks[kb] = lambda: self.cycle_plot_tab(step=1) + # Open circuit editor + for kb in STATE.config.keybindings: + if kb.action is Action.SHOW_CIRCUIT_EDITOR: + break + else: + kb = Keybinding( + key=dpg.mvKey_E, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.SHOW_CIRCUIT_EDITOR, + ) + callbacks[kb] = self.edit_circuit + # Create the handler + self.keybinding_handler: TemporaryKeybindingHandler = ( + TemporaryKeybindingHandler(callbacks=callbacks) + ) + + def create_window(self): self.options: List[str] = [ "Constant:", " Circuit:", " Fit:", - "Spectrum:", + "Data set:", ] self.circuit_editor_window: int = -1 x: int @@ -117,117 +253,7 @@ def __init__( ): self.preview_window: int = dpg.generate_uuid() with dpg.child_window(border=False, tag=self.preview_window): - with dpg.child_window( - width=-1, - height=104, - ): - self.radio_buttons: int = dpg.generate_uuid() - with dpg.group(horizontal=True): - dpg.add_radio_button( - items=self.options, - default_value=self.options[0], - callback=self.select_option, - tag=self.radio_buttons, - ) - with dpg.group(): - self.constant_group: int = dpg.generate_uuid() - with dpg.group(horizontal=True, tag=self.constant_group): - dpg.add_text("Z' = ") - self.constant_real: int = dpg.generate_uuid() - dpg.add_input_float( - label="ohm,", - default_value=0.0, - step=0.0, - format="%.3g", - on_enter=True, - width=100, - tag=self.constant_real, - callback=self.update_preview, - ) - dpg.add_text('-Z" = ') - self.constant_imag: int = dpg.generate_uuid() - dpg.add_input_float( - label="ohm", - default_value=0.0, - step=0.0, - format="%.3g", - on_enter=True, - width=100, - tag=self.constant_imag, - callback=self.update_preview, - ) - self.circuit_group: int = dpg.generate_uuid() - with dpg.group(horizontal=True, tag=self.circuit_group): - self.circuit_cdc: int = dpg.generate_uuid() - dpg.add_input_text( - hint="Input CDC", - on_enter=True, - width=314, - tag=self.circuit_cdc, - callback=self.update_preview, - ) - dpg.add_button( - label="Edit", - callback=self.edit_circuit, - ) - attach_tooltip(tooltips.general.open_circuit_editor) - self.fit_group: int = dpg.generate_uuid() - with dpg.group(horizontal=True, tag=self.fit_group): - self.fit_combo: int = dpg.generate_uuid() - dpg.add_combo( - items=self.fit_labels, - default_value=self.fit_labels[0] - if self.fit_labels - else "", - width=358, - tag=self.fit_combo, - callback=self.update_preview, - ) - self.spectrum_group: int = dpg.generate_uuid() - with dpg.group(horizontal=True, tag=self.spectrum_group): - self.spectrum_combo: int = dpg.generate_uuid() - dpg.add_combo( - items=self.data_labels, - default_value=self.data_labels[0] - if self.data_labels - else "", - width=358, - tag=self.spectrum_combo, - callback=self.update_preview, - ) - self.nyquist_plot: Nyquist = Nyquist(width=-1, height=-24) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="Before", - theme=themes.nyquist.data, - show_label=False, - ) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="Before", - line=True, - theme=themes.nyquist.data, - ) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="After", - theme=themes.bode.phase_data, - show_label=False, - ) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="After", - line=True, - theme=themes.bode.phase_data, - ) - dpg.add_button( - label="Accept", - callback=self.accept, - ) + self.create_preview_window() self.circuit_editor_window = dpg.generate_uuid() with dpg.child_window( border=False, @@ -237,59 +263,194 @@ def __init__( self.circuit_editor: CircuitEditor = CircuitEditor( window=self.circuit_editor_window, callback=self.accept_circuit, + keybindings=STATE.config.keybindings, + ) + + def create_preview_window(self): + with dpg.child_window( + width=-1, + height=104, + ): + self.radio_buttons: int = dpg.generate_uuid() + with dpg.group(horizontal=True): + dpg.add_radio_button( + items=self.options, + default_value=self.options[0], + callback=self.select_option, + tag=self.radio_buttons, + ) + with dpg.group(): + self.constant_group: int = dpg.generate_uuid() + with dpg.group(horizontal=True, tag=self.constant_group): + dpg.add_text("Re(Z) = ") + self.constant_real: int = dpg.generate_uuid() + dpg.add_input_float( + label="ohm,", + default_value=0.0, + step=0.0, + format="%.3g", + on_enter=True, + width=100, + tag=self.constant_real, + callback=self.update_preview, + ) + dpg.add_text("-Im(Z) = ") + self.constant_imag: int = dpg.generate_uuid() + dpg.add_input_float( + label="ohm", + default_value=0.0, + step=0.0, + format="%.3g", + on_enter=True, + width=100, + tag=self.constant_imag, + callback=self.update_preview, + ) + self.circuit_group: int = dpg.generate_uuid() + with dpg.group(horizontal=True, tag=self.circuit_group): + self.circuit_cdc: int = dpg.generate_uuid() + dpg.add_input_text( + hint="Input CDC", + on_enter=True, + width=361, + tag=self.circuit_cdc, + callback=self.update_preview, + ) + self.circuit_editor_button: int = dpg.generate_uuid() + dpg.add_button( + label="Edit", + callback=self.edit_circuit, + tag=self.circuit_editor_button, + ) + attach_tooltip(tooltips.general.open_circuit_editor) + self.fit_group: int = dpg.generate_uuid() + with dpg.group(horizontal=True, tag=self.fit_group): + self.fit_combo: int = dpg.generate_uuid() + dpg.add_combo( + items=self.fit_labels, + default_value=self.fit_labels[0] if self.fit_labels else "", + width=405, + tag=self.fit_combo, + callback=self.update_preview, + ) + self.spectrum_group: int = dpg.generate_uuid() + with dpg.group(horizontal=True, tag=self.spectrum_group): + self.spectrum_combo: int = dpg.generate_uuid() + dpg.add_combo( + items=self.data_labels, + default_value=self.data_labels[0] + if self.data_labels + else "", + width=405, + tag=self.spectrum_combo, + callback=self.update_preview, + ) + self.create_plots() + dpg.add_button( + label="Accept".ljust(12), + callback=self.accept, + ) + + def create_plots(self): + settings: List[dict] = [ + { + "label": "Before", + "theme": themes.nyquist.data, + "show_label": False, + }, + { + "label": "Before", + "line": True, + "theme": themes.nyquist.data, + }, + { + "label": "After", + "theme": themes.bode.phase_data, + "show_label": False, + }, + { + "label": "After", + "line": True, + "theme": themes.bode.phase_data, + }, + ] + self.plot_tab_bar: int = dpg.generate_uuid() + with dpg.tab_bar(tag=self.plot_tab_bar): + self.create_nyquist_plot(settings) + self.create_magnitude_plot(settings) + self.create_phase_plot(settings) + pad_tab_labels(self.plot_tab_bar) + + def create_nyquist_plot(self, settings: List[dict]): + with dpg.tab(label="Nyquist"): + self.nyquist_plot: Nyquist = Nyquist(width=-1, height=-24) + for kwargs in settings: + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + **kwargs, + ) + + def create_magnitude_plot(self, settings: List[dict]): + with dpg.tab(label="Bode - magnitude"): + self.magnitude_plot: BodeMagnitude = BodeMagnitude(width=-1, height=-24) + for kwargs in settings: + self.magnitude_plot.plot( + frequency=array([]), + magnitude=array([]), + **kwargs, + ) + + def create_phase_plot(self, settings: List[dict]): + with dpg.tab(label="Bode - phase"): + self.phase_plot: BodePhase = BodePhase(width=-1, height=-24) + for kwargs in settings: + self.phase_plot.plot( + frequency=array([]), + phase=array([]), + **kwargs, ) - self.key_handler: int = dpg.generate_uuid() - with dpg.handler_registry(tag=self.key_handler): - dpg.add_key_release_handler( - key=dpg.mvKey_Escape, - callback=self.close, - ) - dpg.add_key_release_handler( - key=dpg.mvKey_Return, - callback=lambda: self.accept(keybinding=True), - ) - self.select_option(self.radio_buttons, self.options[0]) def close(self): if not dpg.is_item_visible(self.constant_real): return - self.circuit_editor.hide() + elif self.circuit_editor.is_shown(): + return + elif self.editing_circuit is True: + self.editing_circuit = False + return + self.circuit_editor.keybinding_handler.delete() dpg.hide_item(self.window) dpg.delete_item(self.window) - dpg.delete_item(self.key_handler) + self.keybinding_handler.delete() signals.emit(Signal.UNBLOCK_KEYBINDINGS) - def accept(self, keybinding: bool = False): + def accept(self): if not dpg.is_item_visible(self.constant_real): return - elif ( - self.circuit_editor_window > 0 - and dpg.does_item_exist(self.circuit_editor_window) - and dpg.is_item_shown(self.circuit_editor_window) - ): + elif self.circuit_editor.is_shown(): return - elif keybinding is True and not ( - is_control_down() - if dpg.get_platform() == dpg.mvPlatform_Windows - else is_alt_down() - ): + elif self.editing_circuit is True: + self.editing_circuit = False return self.close() - self.callback(DataSet.from_dict(self.preview_data.to_dict())) + dictionary: dict = self.preview_data.to_dict() + del dictionary["uuid"] + data: DataSet = DataSet.from_dict(dictionary) + data.set_label(f"{self.preview_data.get_label()} - subtracted") + self.callback(data) def select_option(self, sender: int, value: str): - item_type: str - def disable_group(group: int): for item in dpg.get_item_children(group, slot=1): - item_type = dpg.get_item_type(item) + item_type: str = dpg.get_item_type(item) if item_type.endswith("mvText") or item_type.endswith("mvTooltip"): continue dpg.disable_item(item) def enable_group(group: int): for item in dpg.get_item_children(group, slot=1): - item_type = dpg.get_item_type(item) + item_type: str = dpg.get_item_type(item) if item_type.endswith("mvText") or item_type.endswith("mvTooltip"): continue dpg.enable_item(item) @@ -321,8 +482,8 @@ def enable_group(group: int): def update_preview(self): index: int = self.options.index(dpg.get_value(self.radio_buttons)) - f: ndarray = self.data.get_frequency(masked=None) - Z: ndarray = self.data.get_impedance(masked=None) + f: ndarray = self.data.get_frequencies(masked=None) + Z: ndarray = self.data.get_impedances(masked=None) if index == 0: Z_const: complex = complex( dpg.get_value(self.constant_real), @@ -330,87 +491,161 @@ def update_preview(self): ) Z = Z - Z_const elif index == 1: - try: - circuit: Circuit = pyimpspec.parse_cdc(dpg.get_value(self.circuit_cdc)) - except Exception: + cdc: str = dpg.get_value(self.circuit_cdc) + circuit: Optional[Circuit] = dpg.get_item_user_data(self.circuit_cdc) + if circuit is None or circuit.to_string() != cdc: + try: + circuit, _ = process_cdc(cdc) + except Exception: + return + if circuit is None: return - Z = Z - circuit.impedances(f) + Z = Z - circuit.get_impedances(f) elif index == 2: if len(self.fits) > 0: fit: FitResult fit = self.fits[self.fit_labels.index(dpg.get_value(self.fit_combo))] - Z = Z - fit.circuit.impedances(f) + Z = Z - fit.circuit.get_impedances(f) elif index == 3: if len(self.data_sets) > 0: spectrum: DataSet spectrum = self.data_sets[ self.data_labels.index(dpg.get_value(self.spectrum_combo)) ] - Z = Z - spectrum.get_impedance(masked=None) + Z = Z - spectrum.get_impedances(masked=None) else: raise Exception("Unsupported option!") dictionary: dict = self.preview_data.to_dict() dictionary.update( { - "real": list(Z.real), - "imaginary": list(Z.imag), + "real_impedances": list(Z.real), + "imaginary_impedances": list(Z.imag), } ) self.preview_data = DataSet.from_dict(dictionary) - self.update_plot() + self.update_plots() - def update_plot(self): + def update_plots(self): + self.update_nyquist_plot(self.data, self.preview_data) + self.update_magnitude_plot(self.data, self.preview_data) + self.update_phase_plot(self.data, self.preview_data) + + def update_nyquist_plot(self, original: DataSet, preview: DataSet): + data: List[Tuple[ndarray, ndarray]] = [ + original.get_nyquist_data(masked=None), + original.get_nyquist_data(masked=None), + preview.get_nyquist_data(masked=None), + preview.get_nyquist_data(masked=None), + ] + i: int real: ndarray imag: ndarray - real, imag = self.data.get_nyquist_data(masked=None) - self.nyquist_plot.update( - index=0, - real=real, - imaginary=imag, - ) - self.nyquist_plot.update( - index=1, - real=real, - imaginary=imag, - ) - real, imag = self.preview_data.get_nyquist_data(masked=None) - self.nyquist_plot.update( - index=2, - real=real, - imaginary=imag, - ) - self.nyquist_plot.update( - index=3, - real=real, - imaginary=imag, - ) + for i, (real, imag) in enumerate(data): + self.nyquist_plot.update( + index=i, + real=real, + imaginary=imag, + ) self.nyquist_plot.queue_limits_adjustment() + def update_magnitude_plot(self, original: DataSet, preview: DataSet): + data: List[Tuple[ndarray, ndarray, ndarray]] = [ + original.get_bode_data(masked=None), + original.get_bode_data(masked=None), + preview.get_bode_data(masked=None), + preview.get_bode_data(masked=None), + ] + i: int + freq: ndarray + mag: ndarray + for i, (freq, mag, _) in enumerate(data): + self.magnitude_plot.update( + index=i, + frequency=freq, + magnitude=mag, + ) + self.magnitude_plot.queue_limits_adjustment() + + def update_phase_plot(self, original: DataSet, preview: DataSet): + data: List[Tuple[ndarray, ndarray, ndarray]] = [ + original.get_bode_data(masked=None), + original.get_bode_data(masked=None), + preview.get_bode_data(masked=None), + preview.get_bode_data(masked=None), + ] + i: int + freq: ndarray + phase: ndarray + for i, (freq, _, phase) in enumerate(data): + self.phase_plot.update( + index=i, + frequency=freq, + phase=phase, + ) + self.phase_plot.queue_limits_adjustment() + def edit_circuit(self): + if not dpg.is_item_enabled(self.circuit_editor_button): + return + self.editing_circuit = True + self.keybinding_handler.block() dpg.hide_item(self.preview_window) - circuit: Optional[Circuit] = None - try: - circuit = pyimpspec.parse_cdc(dpg.get_value(self.circuit_cdc) or "[]") - except ParsingError: - pass + circuit: Optional[Circuit] + circuit, _ = process_cdc(dpg.get_value(self.circuit_cdc) or "[]") self.circuit_editor.show(circuit) def accept_circuit(self, circuit: Optional[Circuit]): self.circuit_editor.hide() dpg.show_item(self.preview_window) self.update_cdc(circuit) + self.keybinding_handler.unblock() def update_cdc(self, circuit: Optional[Circuit]): if circuit is not None: for element in circuit.get_elements(): element.set_label("") - for param in element.get_parameters(): + for param in element.get_values(): element.set_fixed(param, True) assert dpg.does_item_exist(self.circuit_cdc) - dpg.set_value( + dpg.configure_item( self.circuit_cdc, - circuit.to_string(6) if circuit is not None else "", + default_value=circuit.to_string() if circuit is not None else "", + user_data=circuit, ) dpg.show_item(self.preview_window) dpg.split_frame(delay=33) self.update_preview() + + def cycle_options(self, step: int): + if self.has_active_input(): + return + index: int = self.options.index(dpg.get_value(self.radio_buttons)) + step + dpg.set_value(self.radio_buttons, self.options[index % len(self.options)]) + self.select_option(self.radio_buttons, self.options[index % len(self.options)]) + + def cycle_results(self, step: int): + index: int + if dpg.is_item_enabled(self.fit_combo): + index = self.fit_labels.index(dpg.get_value(self.fit_combo)) + step + dpg.set_value(self.fit_combo, self.fit_labels[index % len(self.fit_labels)]) + self.update_preview() + elif dpg.is_item_enabled(self.spectrum_combo): + index = self.data_labels.index(dpg.get_value(self.spectrum_combo)) + step + dpg.set_value( + self.spectrum_combo, self.data_labels[index % len(self.data_labels)] + ) + self.update_preview() + else: + return + + def cycle_plot_tab(self, step: int): + tabs: List[int] = dpg.get_item_children(self.plot_tab_bar, slot=1) + index: int = tabs.index(dpg.get_value(self.plot_tab_bar)) + step + dpg.set_value(self.plot_tab_bar, tabs[index % len(tabs)]) + + def has_active_input(self) -> bool: + return ( + dpg.is_item_active(self.constant_real) + or dpg.is_item_active(self.constant_imag) + or dpg.is_item_active(self.circuit_cdc) + ) diff --git a/src/deareis/gui/data_sets/toggle_data_points.py b/src/deareis/gui/data_sets/toggle_data_points.py index 1f89a79..b65b48e 100644 --- a/src/deareis/gui/data_sets/toggle_data_points.py +++ b/src/deareis/gui/data_sets/toggle_data_points.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -17,23 +17,40 @@ # The licenses of DearEIS' dependencies and/or sources of portions of code are included in # the LICENSES folder. -from typing import Callable, Dict, List -from numpy import array, ndarray +from typing import ( + Callable, + Dict, + List, + Tuple, +) +from numpy import ( + angle, + array, + ndarray, +) import dearpygui.dearpygui as dpg -from deareis.gui.plots import Nyquist +from deareis.gui.plots import ( + BodeMagnitude, + BodePhase, + Nyquist, + Plot, +) import deareis.themes as themes from deareis.data import DataSet from deareis.utility import ( align_numbers, calculate_window_position_dimensions, format_number, + pad_tab_labels, ) from deareis.signals import Signal import deareis.signals as signals from deareis.tooltips import attach_tooltip +from deareis.state import STATE +from deareis.enums import Action from deareis.keybindings import ( - is_alt_down, - is_control_down, + Keybinding, + TemporaryKeybindingHandler, ) @@ -47,7 +64,149 @@ def __init__(self, data: DataSet, callback: Callable): self.final_mask: Dict[int, bool] = { i: state for i, state in self.original_mask.items() } + self.plot_tabs: Dict[int, Plot] = {} self.labels: List[str] = [] + self.create_labels(data) + self.create_window() + self.register_keybindings() + self.update_items(self.from_combo, self.labels[0]) + self.update_items(self.to_combo, self.labels[-1]) + self.update_previews() + + def register_keybindings(self): + callbacks: Dict[Keybinding, Callable] = {} + # Cancel + kb: Keybinding = Keybinding( + key=dpg.mvKey_Escape, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.CANCEL, + ) + callbacks[kb] = self.close + # Accept + for kb in STATE.config.keybindings: + if kb.action is Action.PERFORM_ACTION: + break + else: + kb = Keybinding( + key=dpg.mvKey_Return, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.PERFORM_ACTION, + ) + callbacks[kb] = self.accept + # Previous 'from' + for kb in STATE.config.keybindings: + if kb.action is Action.PREVIOUS_PRIMARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Prior, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.PREVIOUS_PRIMARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle_from_item(step=-1) + # Next 'from' + for kb in STATE.config.keybindings: + if kb.action is Action.NEXT_PRIMARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Next, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.NEXT_PRIMARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle_from_item(step=1) + # Previous 'to' + for kb in STATE.config.keybindings: + if kb.action is Action.PREVIOUS_SECONDARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Prior, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.PREVIOUS_SECONDARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle_to_item(step=-1) + # Next 'to' + for kb in STATE.config.keybindings: + if kb.action is Action.NEXT_SECONDARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Next, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.NEXT_SECONDARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle_to_item(step=1) + # Previous plot tab + for kb in STATE.config.keybindings: + if kb.action is Action.PREVIOUS_PLOT_TAB: + break + else: + kb = Keybinding( + key=dpg.mvKey_Prior, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.PREVIOUS_PLOT_TAB, + ) + callbacks[kb] = lambda: self.cycle_plot_tab(step=-1) + # Next plot tab + for kb in STATE.config.keybindings: + if kb.action is Action.NEXT_PLOT_TAB: + break + else: + kb = Keybinding( + key=dpg.mvKey_Next, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.NEXT_PLOT_TAB, + ) + callbacks[kb] = lambda: self.cycle_plot_tab(step=1) + # Select all + for kb in STATE.config.keybindings: + if kb.action is Action.SELECT_ALL_PLOT_SERIES: + break + else: + kb = Keybinding( + key=dpg.mvKey_A, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.SELECT_ALL_PLOT_SERIES, + ) + callbacks[kb] = self.include + # Select all + for kb in STATE.config.keybindings: + if kb.action is Action.UNSELECT_ALL_PLOT_SERIES: + break + else: + kb = Keybinding( + key=dpg.mvKey_A, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.UNSELECT_ALL_PLOT_SERIES, + ) + callbacks[kb] = self.exclude + # Create the handler + self.keybinding_handler: TemporaryKeybindingHandler = ( + TemporaryKeybindingHandler(callbacks=callbacks) + ) + + def create_labels(self, data: DataSet): indices: List[str] = align_numbers( list(map(str, range(1, data.get_num_points(masked=None) + 1))) ) @@ -58,13 +217,13 @@ def __init__(self, data: DataSet, callback: Callable): list( map( lambda _: format_number(_, 1, 9), - data.get_frequency(masked=None), + data.get_frequencies(masked=None), ) ) ), ) ) - Z: ndarray = data.get_impedance(masked=None) + Z: ndarray = data.get_impedances(masked=None) real: List[str] = list( map( lambda _: _.ljust(10), @@ -88,9 +247,10 @@ def __init__(self, data: DataSet, callback: Callable): imag, ): self.labels.append( - f"{i}: " + f"f = {f} | " + f"Z'= {re} | " + f'-Z" = {im}' + f"{i}: " + f"f = {f} | " + f"Re(Z) = {re} | " + f"-Im(Z) = {im}" ) + def create_window(self): x: int y: int w: int @@ -130,7 +290,7 @@ def __init__(self, data: DataSet, callback: Callable): with dpg.group(horizontal=True): dpg.add_text(" ?") attach_tooltip( - """Points can also be chosen by drawing rectangle while holding down the middle-mouse button.""".strip() + """Points can also be chosen by drawing a rectangle on a plot while holding down the middle-mouse button.""".strip() ) dpg.add_button( label="Exclude all", @@ -140,99 +300,182 @@ def __init__(self, data: DataSet, callback: Callable): label="Include all", callback=self.include, ) - self.nyquist_plot: Nyquist = Nyquist(width=-1, height=-24) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="Excluded", - theme=themes.nyquist.data, - ) - real, imag = self.preview_data.get_nyquist_data(masked=False) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="Included", - theme=themes.bode.phase_data, - show_label=False, - ) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="Included", - line=True, - theme=themes.bode.phase_data, - ) - dpg.configure_item(self.nyquist_plot._plot, query=True) + self.create_plots() dpg.add_button( - label="Accept", + label="Accept".ljust(12), callback=self.accept, ) - self.key_handler: int = dpg.generate_uuid() - with dpg.handler_registry(tag=self.key_handler): - dpg.add_key_release_handler( - key=dpg.mvKey_Escape, - callback=self.close, + + def create_plots(self): + settings: List[dict] = [ + { + "label": "Excluded", + "theme": themes.nyquist.data, + }, + { + "label": "Included", + "theme": themes.bode.phase_data, + "show_label": False, + }, + { + "label": "Included", + "line": True, + "theme": themes.bode.phase_data, + }, + ] + self.plot_tab_bar: int = dpg.generate_uuid() + with dpg.tab_bar(tag=self.plot_tab_bar): + self.create_nyquist_plot(settings) + self.create_magnitude_plot(settings) + self.create_phase_plot(settings) + pad_tab_labels(self.plot_tab_bar) + + def create_nyquist_plot(self, settings: List[dict]): + tab: int + with dpg.tab(label="Nyquist") as tab: + self.nyquist_plot: Nyquist = Nyquist(width=-1, height=-24) + self.plot_tabs[tab] = self.nyquist_plot + for kwargs in settings: + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + **kwargs, + ) + dpg.configure_item(self.nyquist_plot._plot, query=True) + + def create_magnitude_plot(self, settings: List[dict]): + tab: int + with dpg.tab(label="Bode - magnitude") as tab: + self.magnitude_plot: BodeMagnitude = BodeMagnitude(width=-1, height=-24) + self.plot_tabs[tab] = self.magnitude_plot + for kwargs in settings: + self.magnitude_plot.plot( + frequency=array([]), + magnitude=array([]), + **kwargs, + ) + dpg.configure_item(self.magnitude_plot._plot, query=True) + + def create_phase_plot(self, settings: List[dict]): + tab: int + with dpg.tab(label="Bode - phase") as tab: + self.phase_plot: BodePhase = BodePhase(width=-1, height=-24) + self.plot_tabs[tab] = self.phase_plot + for kwargs in settings: + self.phase_plot.plot( + frequency=array([]), + phase=array([]), + **kwargs, + ) + dpg.configure_item(self.phase_plot._plot, query=True) + + def update_previews(self): + self.update_nyquist_plot(self.preview_data) + self.update_magnitude_plot(self.preview_data) + self.update_phase_plot(self.preview_data) + + def update_nyquist_plot(self, data: DataSet): + data: List[Tuple[ndarray, ndarray]] = [ + data.get_nyquist_data(masked=True), + data.get_nyquist_data(masked=False), + data.get_nyquist_data(masked=False), + ] + for i, (real, imag) in enumerate(data): + self.nyquist_plot.update( + index=i, + real=real, + imaginary=imag, ) - dpg.add_key_release_handler( - key=dpg.mvKey_Return, - callback=lambda: self.accept(keybinding=True), + self.nyquist_plot.queue_limits_adjustment() + + def update_magnitude_plot(self, data: DataSet): + data: List[Tuple[ndarray, ndarray, ndarray]] = [ + data.get_bode_data(masked=True), + data.get_bode_data(masked=False), + data.get_bode_data(masked=False), + ] + i: int + freq: ndarray + mag: ndarray + for i, (freq, mag, _) in enumerate(data): + self.magnitude_plot.update( + index=i, + frequency=freq, + magnitude=mag, ) - self.update_items(self.from_combo, self.labels[0]) - self.update_items(self.to_combo, self.labels[-1]) - self.update_preview() + self.magnitude_plot.queue_limits_adjustment() - def update_preview(self): - real: ndarray - imag: ndarray - real, imag = self.preview_data.get_nyquist_data(masked=True) - self.nyquist_plot.update( - index=0, - real=real, - imaginary=imag, - ) - real, imag = self.preview_data.get_nyquist_data(masked=False) - self.nyquist_plot.update( - index=1, - real=real, - imaginary=imag, - ) - self.nyquist_plot.update( - index=2, - real=real, - imaginary=imag, - ) - self.nyquist_plot.queue_limits_adjustment() + def update_phase_plot(self, data: DataSet): + data: List[Tuple[ndarray, ndarray, ndarray]] = [ + data.get_bode_data(masked=True), + data.get_bode_data(masked=False), + data.get_bode_data(masked=False), + ] + i: int + freq: ndarray + phase: ndarray + for i, (freq, _, phase) in enumerate(data): + self.phase_plot.update( + index=i, + frequency=freq, + phase=phase, + ) + self.phase_plot.queue_limits_adjustment() def close(self): dpg.hide_item(self.window) dpg.delete_item(self.window) - dpg.delete_item(self.key_handler) + self.keybinding_handler.delete() signals.emit(Signal.UNBLOCK_KEYBINDINGS) - def accept(self, keybinding: bool = False): - if keybinding is True and not ( - is_control_down() - if dpg.get_platform() == dpg.mvPlatform_Windows - else is_alt_down() - ): - return - if dpg.is_plot_queried(self.nyquist_plot._plot): - sx, ex, sy, ey = dpg.get_plot_query_area(self.nyquist_plot._plot) - for i, Z in enumerate(self.data.get_impedance(masked=None)): - if Z.real >= sx and Z.real <= ex and -Z.imag >= sy and -Z.imag <= ey: - self.final_mask[i] = not self.final_mask[i] + def accept(self): + plot: Plot = self.plot_tabs[dpg.get_value(self.plot_tab_bar)] + if dpg.is_plot_queried(plot._plot): + sx, ex, sy, ey = dpg.get_plot_query_area(plot._plot) + if plot == self.nyquist_plot: + for i, Z in enumerate(self.data.get_impedances(masked=None)): + if ( + Z.real >= sx + and Z.real <= ex + and -Z.imag >= sy + and -Z.imag <= ey + ): + self.final_mask[i] = not self.final_mask[i] + elif plot == self.magnitude_plot: + for i, (f, Z) in enumerate( + zip( + self.data.get_frequencies(masked=None), + self.data.get_impedances(masked=None), + ) + ): + if f >= sx and f <= ex and abs(Z) >= sy and abs(Z) <= ey: + self.final_mask[i] = not self.final_mask[i] + elif plot == self.phase_plot: + for i, (f, Z) in enumerate( + zip( + self.data.get_frequencies(masked=None), + self.data.get_impedances(masked=None), + ) + ): + if ( + f >= sx + and f <= ex + and -angle(Z, deg=True) >= sy + and -angle(Z, deg=True) <= ey + ): + self.final_mask[i] = not self.final_mask[i] self.callback(self.final_mask) self.close() def exclude(self): self.final_mask = {i: True for i in self.original_mask} self.preview_data.set_mask(self.final_mask) - self.update_preview() + self.update_previews() def include(self): self.final_mask = {i: False for i in self.original_mask} self.preview_data.set_mask(self.final_mask) - self.update_preview() + self.update_previews() def update_items(self, sender: int, label: str): index: int = self.labels.index(label) @@ -255,4 +498,21 @@ def update_items(self, sender: int, label: str): else: self.final_mask[i] = state self.preview_data.set_mask(self.final_mask) - self.update_preview() + self.update_previews() + + def cycle_from_item(self, step: int): + items: List[str] = dpg.get_item_configuration(self.from_combo)["items"] + index: int = items.index(dpg.get_value(self.from_combo)) + step + dpg.set_value(self.from_combo, items[index % len(items)]) + self.update_items(self.from_combo, items[index % len(items)]) + + def cycle_to_item(self, step: int): + items: List[str] = dpg.get_item_configuration(self.to_combo)["items"] + index: int = items.index(dpg.get_value(self.to_combo)) + step + dpg.set_value(self.to_combo, items[index % len(items)]) + self.update_items(self.to_combo, items[index % len(items)]) + + def cycle_plot_tab(self, step: int): + tabs: List[int] = dpg.get_item_children(self.plot_tab_bar, slot=1) + index: int = tabs.index(dpg.get_value(self.plot_tab_bar)) + step + dpg.set_value(self.plot_tab_bar, tabs[index % len(tabs)]) diff --git a/src/deareis/gui/drt.py b/src/deareis/gui/drt.py index 53b1c10..95ff6e8 100644 --- a/src/deareis/gui/drt.py +++ b/src/deareis/gui/drt.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -29,18 +29,16 @@ log10 as log, ndarray, ) -from pyimpspec import ( - Circuit, - DRTError, - parse_cdc, -) -from pyimpspec.analysis.drt.mRQfit import _validate_circuit -from pyimpspec.analysis.fitting import _calculate_residuals +from pyimpspec.exceptions import DRTError +from pyimpspec import ComplexResiduals +from pyimpspec.analysis.drt.mrq_fit import _validate_circuit +from pyimpspec.analysis.utility import _calculate_residuals import dearpygui.dearpygui as dpg from deareis.utility import ( align_numbers, format_number, is_filtered_item_visible, + pad_tab_labels, render_math, ) from deareis.data.drt import ( @@ -65,8 +63,11 @@ rbf_type_to_label, ) from deareis.gui.plots import ( - Impedance, + Bode, DRT, + Impedance, + Nyquist, + Plot, Residuals, ) from deareis.enums import ( @@ -83,6 +84,10 @@ from deareis.data import ( DataSet, ) +from deareis.gui.shared import ( + DataSetsCombo, + ResultsCombo, +) MATH_DRT_WIDTH: int = 350 @@ -240,7 +245,9 @@ def __init__(self, default_settings: DRTSettings, label_pad: int): ) self.lambda_input: int = dpg.generate_uuid() dpg.add_input_float( - default_value=default_settings.lambda_value, + default_value=default_settings.lambda_value + if default_settings.lambda_value > 0.0 + else 1e-3, width=-1, min_value=1e-16, min_clamped=True, @@ -435,6 +442,18 @@ def __init__(self, default_settings: DRTSettings, label_pad: int): callback=lambda s, a, u: self.update_settings(), tag=self.credible_intervals_checkbox, ) + dpg.add_text("Timeout") + attach_tooltip(tooltips.drt.credible_intervals_timeout) + self.timeout_input: int = dpg.generate_uuid() + dpg.add_input_int( + default_value=default_settings.timeout, + min_value=1, + min_clamped=True, + step=0, + width=-1, + on_enter=True, + tag=self.timeout_input, + ) with dpg.group(horizontal=True): dpg.add_text("Number of samples".rjust(label_pad)) attach_tooltip(tooltips.drt.num_samples) @@ -488,18 +507,18 @@ def __init__(self, default_settings: DRTSettings, label_pad: int): tag=self.circuit_combo, ) with dpg.group(horizontal=True): - dpg.add_text("W".rjust(label_pad)) - attach_tooltip(tooltips.drt.W) - self.W_input: int = dpg.generate_uuid() + dpg.add_text("Gaussian width".rjust(label_pad)) + attach_tooltip(tooltips.drt.gaussian_width) + self.gaussian_width_input: int = dpg.generate_uuid() dpg.add_input_float( - default_value=default_settings.W, + default_value=default_settings.gaussian_width, width=-1, min_value=0.0, max_value=1.0, step=0.0, format="%.3g", on_enter=True, - tag=self.W_input, + tag=self.gaussian_width_input, ) with dpg.group(horizontal=True): dpg.add_text("Num. points per decade".rjust(label_pad)) @@ -516,7 +535,7 @@ def __init__(self, default_settings: DRTSettings, label_pad: int): self.update_settings() def update_valid_circuits(self, fits: Dict[str, FitResult]): - lookup: Dict[str, str] = {} + lookup: Dict[str, FitResult] = {} label: str fit: FitResult for label, fit in fits.items(): @@ -524,7 +543,7 @@ def update_valid_circuits(self, fits: Dict[str, FitResult]): _validate_circuit(fit.circuit) except DRTError: continue - lookup[label] = fit.circuit.to_string(12) + lookup[label] = fit if len(lookup) > 0: longest_cdc: int = max( map( @@ -547,12 +566,12 @@ def update_valid_circuits(self, fits: Dict[str, FitResult]): self.update_settings() def get_settings(self) -> DRTSettings: - cdc: str = dpg.get_item_user_data(self.circuit_combo).get( + fit: Optional[FitResult] = dpg.get_item_user_data(self.circuit_combo).get( dpg.get_value(self.circuit_combo), - "", ) + method: DRTMethod = label_to_drt_method[dpg.get_value(self.method_combo)] return DRTSettings( - method=label_to_drt_method[dpg.get_value(self.method_combo)], + method=method, mode=label_to_drt_mode[dpg.get_value(self.mode_combo)], lambda_value=dpg.get_value(self.lambda_input) if not dpg.get_value(self.lambda_checkbox) @@ -565,11 +584,12 @@ def get_settings(self) -> DRTSettings: shape_coeff=dpg.get_value(self.shape_coeff_input), inductance=dpg.get_value(self.inductance_checkbox), credible_intervals=dpg.get_value(self.credible_intervals_checkbox), + timeout=dpg.get_value(self.timeout_input), num_samples=dpg.get_value(self.num_samples_input), num_attempts=dpg.get_value(self.num_attempts_input), maximum_symmetry=dpg.get_value(self.maximum_symmetry_input), - circuit=parse_cdc(cdc) if cdc != "" else None, - W=dpg.get_value(self.W_input), + fit=fit if method is DRTMethod.MRQ_FIT else None, + gaussian_width=dpg.get_value(self.gaussian_width_input), num_per_decade=dpg.get_value(self.num_per_decade_input), ) @@ -578,8 +598,10 @@ def set_settings(self, settings: DRTSettings): self.update_settings() dpg.set_value(self.mode_combo, drt_mode_to_label[settings.mode]) dpg.set_value(self.lambda_checkbox, settings.lambda_value <= 0.0) - if settings.lambda_value > 0.0: - dpg.set_value(self.lambda_input, settings.lambda_value) + dpg.set_value( + self.lambda_input, + settings.lambda_value if settings.lambda_value > 0.0 else 1e-3, + ) dpg.set_value(self.rbf_type_combo, rbf_type_to_label[settings.rbf_type]) dpg.set_value( self.derivative_order_combo, @@ -589,17 +611,17 @@ def set_settings(self, settings: DRTSettings): dpg.set_value(self.shape_coeff_input, settings.shape_coeff) dpg.set_value(self.inductance_checkbox, settings.inductance) dpg.set_value(self.credible_intervals_checkbox, settings.credible_intervals) + dpg.set_value(self.timeout_input, settings.timeout) dpg.set_value(self.num_samples_input, settings.num_samples) dpg.set_value(self.num_attempts_input, settings.num_attempts) dpg.set_value(self.maximum_symmetry_input, settings.maximum_symmetry) labels: List[str] = list(dpg.get_item_user_data(self.circuit_combo).keys()) default_value: str = "" - if settings.circuit is not None: - cdc: str = settings.circuit.to_string(12) + if settings.fit is not None: label: str - circuit: Circuit - for label, circuit in dpg.get_item_user_data(self.circuit_combo).items(): - if circuit == cdc: + fit: FitResult + for label, fit in dpg.get_item_user_data(self.circuit_combo).items(): + if settings.fit.uuid == fit.uuid: default_value = label break if default_value == "" and len(labels) > 0: @@ -608,7 +630,7 @@ def set_settings(self, settings: DRTSettings): self.circuit_combo, default_value=default_value, ) - dpg.set_value(self.W_input, settings.W) + dpg.set_value(self.gaussian_width_input, settings.gaussian_width) dpg.set_value(self.num_per_decade_input, settings.num_per_decade) self.update_settings(settings) @@ -645,7 +667,7 @@ def update_settings(self, settings: Optional[DRTSettings] = None): items=list(label_to_drt_mode.keys()), ) self.show_setting(self.mode_combo) - elif settings.method == DRTMethod.BHT or settings.method == DRTMethod.M_RQ_FIT: + elif settings.method == DRTMethod.BHT or settings.method == DRTMethod.MRQ_FIT: self.hide_setting(self.mode_combo) if settings.method == DRTMethod.TR_RBF or settings.method == DRTMethod.TR_NNLS: self.show_setting(self.lambda_checkbox) @@ -672,14 +694,17 @@ def update_settings(self, settings: Optional[DRTSettings] = None): self.hide_setting(self.inductance_checkbox) if settings.method == DRTMethod.TR_RBF: self.show_setting(self.credible_intervals_checkbox) + self.show_setting(self.timeout_input) else: self.hide_setting(self.credible_intervals_checkbox) + self.hide_setting(self.timeout_input) if settings.method == DRTMethod.BHT: self.show_setting(self.num_samples_input) elif settings.method == DRTMethod.TR_RBF: self.show_setting(self.num_samples_input) if settings.credible_intervals is False: dpg.disable_item(self.num_samples_input) + dpg.disable_item(self.timeout_input) else: self.hide_setting(self.num_samples_input) if settings.method == DRTMethod.BHT: @@ -690,13 +715,14 @@ def update_settings(self, settings: Optional[DRTSettings] = None): self.show_setting(self.maximum_symmetry_input) else: self.hide_setting(self.maximum_symmetry_input) - if settings.method == DRTMethod.M_RQ_FIT: + if settings.method == DRTMethod.MRQ_FIT: self.show_setting(self.circuit_combo) - self.show_setting(self.W_input) + self.show_setting(self.gaussian_width_input) self.show_setting(self.num_per_decade_input) + dpg.enable_item(self.num_per_decade_input) else: self.hide_setting(self.circuit_combo) - self.hide_setting(self.W_input) + self.hide_setting(self.gaussian_width_input) self.hide_setting(self.num_per_decade_input) def has_active_input(self) -> bool: @@ -740,8 +766,8 @@ def __init__(self): ) for label, tooltip, filter_key in [ ( - "log X²", - tooltips.fitting.chisqr, + "log X² (pseudo)", + tooltips.fitting.pseudo_chisqr, ",".join(drt_method_to_label.values()), ), ( @@ -761,7 +787,8 @@ def __init__(self): with dpg.table_row(filter_key=filter_key): dpg.add_text(label.rjust(label_pad)) attach_tooltip(tooltip) - dpg.add_text("") + cell: int = dpg.add_text("") + dpg.set_item_user_data(cell, attach_tooltip("", parent=cell)) dpg.add_spacer(height=8) def clear(self, hide: bool): @@ -777,7 +804,7 @@ def populate(self, drt: DRTResult): filter_key: str = drt_method_to_label[drt.settings.method] dpg.set_value(self._table, filter_key) statistics: List[str] = [ - f"{log(drt.chisqr):.3g}", + f"{log(drt.pseudo_chisqr):.3g}", f"{drt.lambda_value:.3e}", ] visible_items: List[bool] = [] @@ -786,6 +813,7 @@ def populate(self, drt: DRTResult): for i, row in enumerate(dpg.get_item_children(self._table, slot=1)): cell: int = dpg.get_item_children(row, slot=1)[2] dpg.set_value(cell, statistics[i]) + update_tooltip(tag=dpg.get_item_user_data(cell), msg=statistics[i]) visible_items.append(is_filtered_item_visible(row, filter_key)) dpg.set_item_height( self._table, @@ -1046,6 +1074,7 @@ def __init__(self): tooltip_tag: int = dpg.generate_uuid() dpg.add_text("", user_data=tooltip_tag) attach_tooltip("", tag=tooltip_tag) + dpg.add_spacer(height=8) with dpg.group(horizontal=True): self._apply_settings_button: int = dpg.generate_uuid() dpg.add_button( @@ -1055,6 +1084,7 @@ def __init__(self): **u, ), tag=self._apply_settings_button, + width=154, ) attach_tooltip(tooltips.general.apply_settings) self._apply_mask_button: int = dpg.generate_uuid() @@ -1065,6 +1095,7 @@ def __init__(self): **u, ), tag=self._apply_mask_button, + width=-1, ) attach_tooltip(tooltips.general.apply_mask) @@ -1128,381 +1159,444 @@ def populate(self, drt: DRTResult, data: DataSet): ) -class DataSetsCombo: - def __init__(self, label: str, width: int): - self.labels: List[str] = [] - dpg.add_text(label) - self.tag: int = dpg.generate_uuid() - dpg.add_combo( - callback=lambda s, a, u: signals.emit( - Signal.SELECT_DATA_SET, - data=u.get(a), - ), - user_data={}, - width=width, - tag=self.tag, - ) - - def populate(self, labels: List[str], lookup: Dict[str, DataSet]): - self.labels.clear() - self.labels.extend(labels) - label: str = dpg.get_value(self.tag) or "" - if labels and label not in labels: - label = labels[0] - dpg.configure_item( - self.tag, - default_value=label, - items=labels, - user_data=lookup, - ) - - def get(self) -> Optional[DataSet]: - return dpg.get_item_user_data(self.tag).get(dpg.get_value(self.tag)) - - def set(self, label: str): - assert type(label) is str, label - assert label in self.labels, ( - label, - self.labels, - ) - dpg.set_value(self.tag, label) - - def clear(self): - dpg.configure_item( - self.tag, - default_value="", - ) - - -class ResultsCombo: - def __init__(self, label: str, width: int): - self.labels: Dict[str, str] = {} - dpg.add_text(label) - self.tag: int = dpg.generate_uuid() - dpg.add_combo( - callback=lambda s, a, u: signals.emit( - Signal.SELECT_DRT_RESULT, - drt=u[0].get(a), - data=u[1], - ), - user_data=( - {}, - None, - ), - width=width, - tag=self.tag, - ) - - def populate(self, lookup: Dict[str, DRTResult], data: Optional[DataSet]): - self.labels.clear() - labels: List[str] = list(lookup.keys()) - longest_cdc: int = max(list(map(lambda _: len(_[: _.find(" ")]), labels)) + [1]) - old_key: str - for old_key in labels: - drt: DRTResult = lookup[old_key] - del lookup[old_key] - cdc, timestamp = ( - old_key[: old_key.find(" ")], - old_key[old_key.find(" ") + 1 :], - ) - new_key: str = f"{cdc.ljust(longest_cdc)} {timestamp}" - self.labels[old_key] = new_key - lookup[new_key] = drt - labels = list(lookup.keys()) - dpg.configure_item( - self.tag, - default_value=labels[0] if labels else "", - items=labels, - user_data=( - lookup, - data, - ), - ) - - def get(self) -> Optional[DRTResult]: - return dpg.get_item_user_data(self.tag)[0].get(dpg.get_value(self.tag)) - - def set(self, label: str): - assert type(label) is str, label - assert label in self.labels, ( - label, - list(self.labels.keys()), +class DRTResultsCombo(ResultsCombo): + def selection_callback(self, sender: int, app_data: str, user_data: tuple): + signals.emit( + Signal.SELECT_DRT_RESULT, + drt=user_data[0].get(app_data), + data=user_data[1], ) - dpg.set_value(self.tag, self.labels[label]) - def clear(self): - dpg.configure_item( - self.tag, - default_value="", + def adjust_label(self, old: str, longest: int) -> str: + label: str + timestamp: str + label, timestamp = ( + old[: old.find(" ")], + old[old.find(" ") + 1 :], ) - - def get_next_result(self) -> Optional[DRTResult]: - lookup: Dict[str, DRTResult] = dpg.get_item_user_data(self.tag)[0] - if not lookup: - return None - labels: List[str] = list(lookup.keys()) - index: int = labels.index(dpg.get_value(self.tag)) + 1 - return lookup[labels[index % len(labels)]] - - def get_previous_result(self) -> Optional[DRTResult]: - lookup: Dict[str, DRTResult] = dpg.get_item_user_data(self.tag)[0] - if not lookup: - return None - labels: List[str] = list(lookup.keys()) - index: int = labels.index(dpg.get_value(self.tag)) - 1 - return lookup[labels[index % len(labels)]] + return f"{label.ljust(longest)} {timestamp}" class DRTTab: def __init__(self, state): self.state = state self.queued_update: Optional[Callable] = None + self.create_tab(state) + + def create_tab(self, state): label_pad: int = 24 self.tab: int = dpg.generate_uuid() with dpg.tab(label="DRT analysis", tag=self.tab): with dpg.group(horizontal=True): - self.sidebar_width: int = 350 - self.sidebar_window: int = dpg.generate_uuid() - with dpg.child_window( - border=False, - width=self.sidebar_width, - tag=self.sidebar_window, - ): - # Settings - with dpg.child_window( - border=True, - width=-1, - height=290, - ): - self.settings_menu: SettingsMenu = SettingsMenu( - state.config.default_drt_settings, label_pad - ) - with dpg.group(horizontal=True): - self.visibility_item: int = dpg.generate_uuid() - dpg.add_text("?".rjust(label_pad), tag=self.visibility_item) - attach_tooltip(tooltips.drt.perform) - self.perform_drt_button: int = dpg.generate_uuid() - dpg.add_button( - label="Perform analysis", - callback=lambda s, a, u: signals.emit( - Signal.PERFORM_DRT, - data=u, - settings=self.get_settings(), - ), - user_data=None, - width=-1, - tag=self.perform_drt_button, - ) - # Results - with dpg.child_window(width=-1, height=82): - label_pad = 8 - with dpg.group(horizontal=True): - self.data_sets_combo: DataSetsCombo = DataSetsCombo( - label="Data set".rjust(label_pad), - width=-60, - ) - with dpg.group(horizontal=True): - self.results_combo: ResultsCombo = ResultsCombo( - label="Result".rjust(label_pad), - width=-60, - ) - self.delete_button: int = dpg.generate_uuid() - dpg.add_button( - label="Delete", - callback=lambda s, a, u: signals.emit( - Signal.DELETE_DRT_RESULT, - **u, - ), - user_data={}, - width=-1, - tag=self.delete_button, - ) - attach_tooltip(tooltips.drt.delete) - with dpg.group(horizontal=True): - dpg.add_text("Output".rjust(label_pad)) - # TODO: Split into combo class? - self.output_combo: int = dpg.generate_uuid() - dpg.add_combo( - default_value=list(label_to_drt_output.keys())[0], - items=list(label_to_drt_output.keys()), - tag=self.output_combo, - width=-60, - ) - self.copy_output_button: int = dpg.generate_uuid() - dpg.add_button( - label="Copy", - callback=lambda s, a, u: signals.emit( - Signal.COPY_OUTPUT, - output=self.get_active_output(), - **u, - ), - user_data={}, - width=-1, - tag=self.copy_output_button, - ) - attach_tooltip(tooltips.general.copy_output) - # Results/settings tables - with dpg.child_window(width=-1, height=-1): - self.result_group: int = dpg.generate_uuid() - with dpg.group(tag=self.result_group): - with dpg.group(show=False): - self.validity_text: int = dpg.generate_uuid() - dpg.bind_item_theme( - dpg.add_text( - "", - wrap=self.sidebar_width - 24, - tag=self.validity_text, - ), - themes.result.invalid, - ) - dpg.add_spacer(height=8) - self.statistics_table: StatisticsTable = StatisticsTable() - self.scores_table: ScoresTable = ScoresTable() - self.settings_table: SettingsTable = SettingsTable() - # Plots window - self.plot_window: int = dpg.generate_uuid() - with dpg.child_window( - border=False, + self.create_sidebar(state, label_pad) + self.create_plots() + + def create_sidebar(self, state, label_pad: int): + self.sidebar_width: int = 350 + self.sidebar_window: int = dpg.generate_uuid() + with dpg.child_window( + border=False, + width=self.sidebar_width, + tag=self.sidebar_window, + ): + self.create_settings_menu(state, label_pad) + self.create_results_menu() + self.create_results_tables() + + def create_settings_menu(self, state, label_pad: int): + with dpg.child_window( + border=True, + width=-1, + height=290, + ): + self.settings_menu: SettingsMenu = SettingsMenu( + state.config.default_drt_settings, label_pad + ) + with dpg.group(horizontal=True): + self.visibility_item: int = dpg.generate_uuid() + dpg.add_text("?".rjust(label_pad), tag=self.visibility_item) + attach_tooltip(tooltips.drt.perform) + self.perform_drt_button: int = dpg.generate_uuid() + dpg.add_button( + label="Perform", + callback=lambda s, a, u: signals.emit( + Signal.PERFORM_DRT, + data=u, + settings=self.get_settings(), + ), + user_data=None, + width=-70, + tag=self.perform_drt_button, + ) + dpg.add_button( + label="Batch", + callback=lambda s, a, u: signals.emit( + Signal.BATCH_PERFORM_ANALYSIS, + settings=self.get_settings(), + ), width=-1, - height=-1, - tag=self.plot_window, - ): - self.minimum_plot_side: int = 400 - # Gamma (or real gamma if BHT) - self.drt_plot: DRT = DRT( - width=-1, - height=self.minimum_plot_side, - ) - self.drt_plot.plot( - tau=array([]), - gamma=array([]), - label="gamma", - theme=themes.drt.real_gamma, - ) - with dpg.group(horizontal=True): - self.enlarge_drt_button: int = dpg.generate_uuid() - self.adjust_drt_limits_checkbox: int = dpg.generate_uuid() - dpg.add_button( - label="Enlarge DRT", - callback=self.show_enlarged_drt, - tag=self.enlarge_drt_button, - ) - dpg.add_checkbox( - default_value=True, - tag=self.adjust_drt_limits_checkbox, - ) - attach_tooltip(tooltips.general.adjust_drt_limits) - dpg.add_button( - label="Copy as CSV", - callback=lambda s, a, u: signals.emit( - Signal.COPY_PLOT_DATA, - plot=self.drt_plot, - context=Context.DRT_TAB, - ), - ) - attach_tooltip(tooltips.general.copy_plot_data_as_csv) - # Impedance plot - self.impedance_plot: Impedance = Impedance( - width=-1, - height=self.minimum_plot_side, - ) - self.impedance_plot.plot( - frequency=array([]), - real=array([]), - imaginary=array([]), - labels=( - "Z' (d)", - 'Z" (d)', - ), - themes=( - themes.impedance.real_data, - themes.impedance.imaginary_data, - ), - ) - self.impedance_plot.plot( - frequency=array([]), - real=array([]), - imaginary=array([]), - labels=( - "Z' (f)", - 'Z" (f)', - ), - fit=True, - themes=( - themes.impedance.real_simulation, - themes.impedance.imaginary_simulation, - ), - ) - self.impedance_plot.plot( - frequency=array([]), - real=array([]), - imaginary=array([]), - labels=( - "Z' (f)", - 'Z" (f)', - ), - fit=True, - line=True, - themes=( - themes.impedance.real_simulation, - themes.impedance.imaginary_simulation, + ) + + def create_results_menu(self): + with dpg.child_window(width=-1, height=82): + label_pad = 8 + with dpg.group(horizontal=True): + self.data_sets_combo: DataSetsCombo = DataSetsCombo( + label="Data set".rjust(label_pad), + width=-60, + ) + with dpg.group(horizontal=True): + self.results_combo: DRTResultsCombo = DRTResultsCombo( + label="Result".rjust(label_pad), + width=-60, + ) + self.delete_button: int = dpg.generate_uuid() + dpg.add_button( + label="Delete", + callback=lambda s, a, u: signals.emit( + Signal.DELETE_DRT_RESULT, + **u, + ), + user_data={}, + width=-1, + tag=self.delete_button, + ) + attach_tooltip(tooltips.drt.delete) + with dpg.group(horizontal=True): + dpg.add_text("Output".rjust(label_pad)) + # TODO: Split into combo class? + self.output_combo: int = dpg.generate_uuid() + dpg.add_combo( + default_value=list(label_to_drt_output.keys())[0], + items=list(label_to_drt_output.keys()), + tag=self.output_combo, + width=-60, + ) + self.copy_output_button: int = dpg.generate_uuid() + dpg.add_button( + label="Copy", + callback=lambda s, a, u: signals.emit( + Signal.COPY_OUTPUT, + output=self.get_active_output(), + **u, + ), + user_data={}, + width=-1, + tag=self.copy_output_button, + ) + attach_tooltip(tooltips.general.copy_output) + + def create_results_tables(self): + with dpg.child_window(width=-1, height=-1): + self.result_group: int = dpg.generate_uuid() + with dpg.group(tag=self.result_group): + with dpg.group(show=False): + self.validity_text: int = dpg.generate_uuid() + dpg.bind_item_theme( + dpg.add_text( + "", + wrap=self.sidebar_width - 24, + tag=self.validity_text, ), - show_labels=False, - ) - with dpg.group(horizontal=True): - self.enlarge_impedance_button: int = dpg.generate_uuid() - self.adjust_impedance_limits_checkbox: int = dpg.generate_uuid() - dpg.add_button( - label="Enlarge impedance", - callback=self.show_enlarged_impedance, - tag=self.enlarge_impedance_button, - ) - dpg.add_checkbox( - default_value=True, - tag=self.adjust_impedance_limits_checkbox, - ) - attach_tooltip(tooltips.general.adjust_impedance_limits) - dpg.add_button( - label="Copy as CSV", - callback=lambda s, a, u: signals.emit( - Signal.COPY_PLOT_DATA, - plot=self.impedance_plot, - context=Context.DRT_TAB, - ), - ) - attach_tooltip(tooltips.general.copy_plot_data_as_csv) - # Residuals - self.residuals_plot: Residuals = Residuals( - width=-1, - height=300, + themes.result.invalid, ) - self.residuals_plot.plot( - frequency=array([]), - real=array([]), - imaginary=array([]), - ) - with dpg.group(horizontal=True): - self.enlarge_residuals_button: int = dpg.generate_uuid() - self.adjust_residuals_limits_checkbox: int = dpg.generate_uuid() - dpg.add_button( - label="Enlarge residuals", - callback=self.show_enlarged_residuals, - tag=self.enlarge_residuals_button, - ) - dpg.add_checkbox( - default_value=True, - tag=self.adjust_residuals_limits_checkbox, - ) - attach_tooltip(tooltips.general.adjust_residuals_limits) - dpg.add_button( - label="Copy as CSV", - callback=lambda s, a, u: signals.emit( - Signal.COPY_PLOT_DATA, - plot=self.residuals_plot, - context=Context.DRT_TAB, - ), - ) - attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_spacer(height=8) + self.statistics_table: StatisticsTable = StatisticsTable() + self.scores_table: ScoresTable = ScoresTable() + self.settings_table: SettingsTable = SettingsTable() + + def create_plots(self): + self.plot_window: int = dpg.generate_uuid() + with dpg.child_window( + border=False, + width=-1, + height=-1, + tag=self.plot_window, + ): + self.create_gamma_plot() + dpg.add_spacer(height=4) + dpg.add_separator() + dpg.add_spacer(height=4) + self.plot_tab_bar: int = dpg.generate_uuid() + self.minimum_plot_height: int = -24 + with dpg.tab_bar(tag=self.plot_tab_bar): + self.create_nyquist_plot() + self.create_bode_plot() + impedance_tab: int = self.create_impedance_plot() + self.create_residuals_plot() + pad_tab_labels(self.plot_tab_bar) + dpg.set_value(self.plot_tab_bar, impedance_tab) + + def create_gamma_plot(self): + self.drt_plot: DRT = DRT(width=-1, height=400) + self.drt_plot.plot( + tau=array([]), + gamma=array([]), + label="gamma", + theme=themes.drt.real_gamma, + ) + with dpg.group(horizontal=True): + self.enlarge_drt_button: int = dpg.generate_uuid() + self.adjust_drt_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_drt, + tag=self.enlarge_drt_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.drt_plot, + context=Context.DRT_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_drt_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_drt_limits) + + def create_nyquist_plot(self): + with dpg.tab(label="Nyquist"): + self.nyquist_plot: Nyquist = Nyquist( + width=-1, + height=self.minimum_plot_height, + ) + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + label="Data", + line=False, + theme=themes.nyquist.data, + ) + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + label="Fit", + line=False, + fit=True, + theme=themes.nyquist.simulation, + ) + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + label="Fit", + line=True, + fit=True, + theme=themes.nyquist.simulation, + show_label=False, + ) + with dpg.group(horizontal=True): + self.enlarge_nyquist_button: int = dpg.generate_uuid() + self.adjust_nyquist_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_nyquist, + tag=self.enlarge_nyquist_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.nyquist_plot, + context=Context.DRT_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_nyquist_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_nyquist_limits) + + def create_bode_plot(self): + with dpg.tab(label="Bode"): + self.bode_plot: Bode = Bode( + width=-1, + height=self.minimum_plot_height, + ) + self.bode_plot.plot( + frequency=array([]), + magnitude=array([]), + phase=array([]), + labels=( + "Mod(Z), d.", + "Phase(Z), d.", + ), + line=False, + themes=( + themes.bode.magnitude_data, + themes.bode.phase_data, + ), + ) + self.bode_plot.plot( + frequency=array([]), + magnitude=array([]), + phase=array([]), + labels=( + "Mod(Z), f.", + "Phase(Z), f.", + ), + line=False, + fit=True, + themes=( + themes.bode.magnitude_simulation, + themes.bode.phase_simulation, + ), + ) + self.bode_plot.plot( + frequency=array([]), + magnitude=array([]), + phase=array([]), + labels=( + "Mod(Z), f.", + "Phase(Z), f.", + ), + line=True, + fit=True, + themes=( + themes.bode.magnitude_simulation, + themes.bode.phase_simulation, + ), + show_labels=False, + ) + with dpg.group(horizontal=True): + self.enlarge_bode_button: int = dpg.generate_uuid() + self.adjust_bode_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_bode, + tag=self.enlarge_bode_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.bode_plot, + context=Context.DRT_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_bode_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_bode_limits) + + def create_impedance_plot(self) -> int: + tab: int + with dpg.tab(label="Real & Imag.") as tab: + self.impedance_plot: Impedance = Impedance( + width=-1, + height=self.minimum_plot_height, + ) + self.impedance_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + labels=( + "Re(Z), d.", + "Im(Z), d.", + ), + themes=( + themes.impedance.real_data, + themes.impedance.imaginary_data, + ), + ) + self.impedance_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + labels=( + "Re(Z), f.", + "Im(Z), f.", + ), + fit=True, + themes=( + themes.impedance.real_simulation, + themes.impedance.imaginary_simulation, + ), + ) + self.impedance_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + labels=( + "Re(Z), f.", + "Im(Z), f.", + ), + fit=True, + line=True, + themes=( + themes.impedance.real_simulation, + themes.impedance.imaginary_simulation, + ), + show_labels=False, + ) + with dpg.group(horizontal=True): + self.enlarge_impedance_button: int = dpg.generate_uuid() + self.adjust_impedance_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_impedance, + tag=self.enlarge_impedance_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.impedance_plot, + context=Context.DRT_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_impedance_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_impedance_limits) + return tab + + def create_residuals_plot(self): + with dpg.tab(label="Residuals"): + self.residuals_plot: Residuals = Residuals( + width=-1, + height=self.minimum_plot_height, + ) + self.residuals_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + ) + with dpg.group(horizontal=True): + self.enlarge_residuals_button: int = dpg.generate_uuid() + self.adjust_residuals_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_residuals, + tag=self.enlarge_residuals_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.residuals_plot, + context=Context.DRT_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_residuals_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_residuals_limits) def is_visible(self) -> bool: return dpg.is_item_visible(self.visibility_item) @@ -1522,9 +1616,15 @@ def resize(self, width: int, height: int): if not self.is_visible(): return width, height = dpg.get_item_rect_size(self.plot_window) - height = round(height / 2) - 24 * 2 + height = round(height / 2) - 24 * 2 + 1 self.drt_plot.resize(-1, height) - self.impedance_plot.resize(-1, height) + plots: List[Plot] = [ + self.nyquist_plot, + self.bode_plot, + self.impedance_plot, + ] + for plot in plots: + plot.resize(-1, height) def clear(self, hide: bool = True): self.data_sets_combo.clear() @@ -1539,9 +1639,21 @@ def clear(self, hide: bool = True): label="gamma", ) self.drt_plot.delete_series(from_index=1) + self.nyquist_plot.clear(delete=False) + self.bode_plot.clear(delete=False) self.impedance_plot.clear(delete=False) self.residuals_plot.clear(delete=False) + def next_plot_tab(self): + tabs: List[int] = dpg.get_item_children(self.plot_tab_bar, slot=1) + index: int = tabs.index(dpg.get_value(self.plot_tab_bar)) + 1 + dpg.set_value(self.plot_tab_bar, tabs[index % len(tabs)]) + + def previous_plot_tab(self): + tabs: List[int] = dpg.get_item_children(self.plot_tab_bar, slot=1) + index: int = tabs.index(dpg.get_value(self.plot_tab_bar)) - 1 + dpg.set_value(self.plot_tab_bar, tabs[index % len(tabs)]) + def show_enlarged_drt(self): signals.emit( Signal.SHOW_ENLARGED_PLOT, @@ -1549,6 +1661,20 @@ def show_enlarged_drt(self): adjust_limits=dpg.get_value(self.adjust_drt_limits_checkbox), ) + def show_enlarged_nyquist(self): + signals.emit( + Signal.SHOW_ENLARGED_PLOT, + plot=self.nyquist_plot, + adjust_limits=dpg.get_value(self.adjust_nyquist_limits_checkbox), + ) + + def show_enlarged_bode(self): + signals.emit( + Signal.SHOW_ENLARGED_PLOT, + plot=self.bode_plot, + adjust_limits=dpg.get_value(self.adjust_bode_limits_checkbox), + ) + def show_enlarged_impedance(self): signals.emit( Signal.SHOW_ENLARGED_PLOT, @@ -1590,26 +1716,16 @@ def populate_drts(self, lookup: Dict[str, DRTResult], data: Optional[DataSet]): ) def get_next_data_set(self) -> Optional[DataSet]: - lookup: Dict[str, DataSet] = dpg.get_item_user_data(self.data_sets_combo.tag) - if not lookup: - return None - labels: List[str] = list(lookup.keys()) - index: int = labels.index(dpg.get_value(self.data_sets_combo.tag)) + 1 - return lookup[labels[index % len(labels)]] + return self.data_sets_combo.get_next() def get_previous_data_set(self) -> Optional[DataSet]: - lookup: Dict[str, DataSet] = dpg.get_item_user_data(self.data_sets_combo.tag) - if not lookup: - return None - labels: List[str] = list(lookup.keys()) - index: int = labels.index(dpg.get_value(self.data_sets_combo.tag)) - 1 - return lookup[labels[index % len(labels)]] + return self.data_sets_combo.get_previous() def get_next_result(self) -> Optional[DRTResult]: - return self.results_combo.get_next_result() + return self.results_combo.get_next() def get_previous_result(self) -> Optional[DRTResult]: - return self.results_combo.get_previous_result() + return self.results_combo.get_previous() def populate_fits(self, fits: List[FitResult]): assert type(fits) is dict, fits @@ -1622,19 +1738,35 @@ def select_data_set(self, data: Optional[DataSet]): if data is None: return self.data_sets_combo.set(data.get_label()) - f: ndarray = data.get_frequency() - Z: ndarray = data.get_impedance() + real: ndarray + imag: ndarray + real, imag = data.get_nyquist_data() + self.nyquist_plot.update( + index=0, + real=real, + imaginary=imag, + ) + freq: ndarray + mag: ndarray + phase: ndarray + freq, mag, phase = data.get_bode_data() + self.bode_plot.update( + index=0, + frequency=freq, + magnitude=mag, + phase=phase, + ) self.impedance_plot.update( index=0, - frequency=f, - real=Z.real, - imaginary=-Z.imag, + frequency=freq, + real=real, + imaginary=imag, ) def assert_drt_up_to_date(self, drt: DRTResult, data: DataSet): # Check if the number of unmasked points is the same - Z_exp: ndarray = data.get_impedance() - Z_drt: ndarray = drt.get_impedance() + Z_exp: ndarray = data.get_impedances() + Z_drt: ndarray = drt.get_impedances() assert Z_exp.shape == Z_drt.shape, "The number of data points differ!" # Check if the masks are the same mask_exp: Dict[int, bool] = data.get_mask() @@ -1654,13 +1786,11 @@ def assert_drt_up_to_date(self, drt: DRTResult, data: DataSet): ), f"The data set's mask differs at index {i + 1}!" # Check if the frequencies and impedances are the same assert allclose( - drt.get_frequency(), data.get_frequency() + drt.get_frequencies(), data.get_frequencies() ), "The frequencies differ!" - real_residual: ndarray - imaginary_residual: ndarray - real_residual, imaginary_residual = _calculate_residuals(Z_exp, Z_drt) - assert allclose(drt.real_residual, real_residual) and allclose( - drt.imaginary_residual, imaginary_residual + residuals: ComplexResiduals = _calculate_residuals(Z_exp, Z_drt) + assert allclose(drt.residuals.real, residuals.real) and allclose( + drt.residuals.imag, residuals.imag ), "The data set's impedances differ from what they were when the DRT analysis was performed!" def select_drt_result( @@ -1692,10 +1822,14 @@ def select_drt_result( if drt is None or data is None: if dpg.get_value(self.adjust_drt_limits_checkbox): self.drt_plot.queue_limits_adjustment() - if dpg.get_value(self.adjust_residuals_limits_checkbox): - self.residuals_plot.queue_limits_adjustment() + if dpg.get_value(self.adjust_nyquist_limits_checkbox): + self.nyquist_plot.queue_limits_adjustment() + if dpg.get_value(self.adjust_bode_limits_checkbox): + self.bode_plot.queue_limits_adjustment() if dpg.get_value(self.adjust_impedance_limits_checkbox): self.impedance_plot.queue_limits_adjustment() + if dpg.get_value(self.adjust_residuals_limits_checkbox): + self.residuals_plot.queue_limits_adjustment() return self.results_combo.set(drt.get_label()) message: str @@ -1712,12 +1846,13 @@ def select_drt_result( self.scores_table.populate(drt) self.settings_table.populate(drt, data) tau: ndarray - gamma: ndarray - tau, gamma = drt.get_drt_data() + real_gamma: ndarray + imaginary_gamma: ndarray + tau, real_gamma, imaginary_gamma = drt.get_drt_data() self.drt_plot.update( index=0, tau=tau, - gamma=gamma, + gamma=real_gamma, label="real" if drt.settings.method == DRTMethod.BHT else "gamma", ) if ( @@ -1727,10 +1862,10 @@ def select_drt_result( mean: ndarray lower: ndarray upper: ndarray - tau, gamma, lower, upper = drt.get_drt_credible_intervals() + tau, mean, lower, upper = drt.get_drt_credible_intervals_data() self.drt_plot.plot( tau=tau, - mean=gamma, + gamma=mean, label="mean", theme=themes.drt.mean_gamma, ) @@ -1741,37 +1876,67 @@ def select_drt_result( label="3-sigma CI", theme=themes.drt.credible_intervals, ) - if drt.settings.method == DRTMethod.BHT: - tau, gamma = drt.get_drt_data(imaginary=True) + elif drt.settings.method == DRTMethod.BHT: self.drt_plot.plot( tau=tau, - imaginary=gamma, + gamma=imaginary_gamma, label="imag.", theme=themes.drt.imaginary_gamma, ) - f: ndarray = drt.get_frequency() - Z: ndarray = drt.get_impedance() + real: ndarray + imag: ndarray + real, imag = drt.get_nyquist_data() + self.nyquist_plot.update( + index=1, + real=real, + imaginary=imag, + ) + self.nyquist_plot.update( + index=2, + real=real, + imaginary=imag, + ) + freq: ndarray + mag: ndarray + phase: ndarray + freq, mag, phase = drt.get_bode_data() + self.bode_plot.update( + index=1, + frequency=freq, + magnitude=mag, + phase=phase, + ) + self.bode_plot.update( + index=2, + frequency=freq, + magnitude=mag, + phase=phase, + ) self.impedance_plot.update( index=1, - frequency=f, - real=Z.real, - imaginary=-Z.imag, + frequency=freq, + real=real, + imaginary=imag, ) self.impedance_plot.update( index=2, - frequency=f, - real=Z.real, - imaginary=-Z.imag, + frequency=freq, + real=real, + imaginary=imag, ) real: ndarray imaginary: ndarray - f, real, imaginary = drt.get_residual_data() + f, real, imaginary = drt.get_residuals_data() self.residuals_plot.update( index=0, frequency=f, real=real, imaginary=imaginary, ) + if dpg.get_value(self.adjust_nyquist_limits_checkbox): + self.nyquist_plot.queue_limits_adjustment() + if dpg.get_value(self.adjust_bode_limits_checkbox): + self.bode_plot.queue_limits_adjustment() if dpg.get_value(self.adjust_drt_limits_checkbox): self.drt_plot.queue_limits_adjustment() if dpg.get_value(self.adjust_impedance_limits_checkbox): diff --git a/src/deareis/gui/error_message.py b/src/deareis/gui/error_message.py index 976d4ee..6490572 100644 --- a/src/deareis/gui/error_message.py +++ b/src/deareis/gui/error_message.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -17,14 +17,28 @@ # The licenses of DearEIS' dependencies and/or sources of portions of code are included in # the LICENSES folder. +from typing import ( + Callable, + Dict, + Optional, +) import dearpygui.dearpygui as dpg from deareis.utility import calculate_window_position_dimensions from deareis.signals import Signal import deareis.signals as signals +from deareis.enums import Action +from deareis.keybindings import ( + Keybinding, + TemporaryKeybindingHandler, +) class ErrorMessage: def __init__(self): + self.keybinding_handler: Optional[TemporaryKeybindingHandler] = None + self.create_window() + + def create_window(self): self.window: int = dpg.generate_uuid() with dpg.window( label="ERROR", @@ -63,12 +77,7 @@ def show(self, traceback: str, message: str = ""): if not self.is_visible(): dpg.show_item(self.window) dpg.split_frame() - self.key_handler: int = dpg.generate_uuid() - with dpg.handler_registry(tag=self.key_handler): - dpg.add_key_release_handler( - key=dpg.mvKey_Escape, - callback=self.hide, - ) + self.register_keybindings() else: traceback = f"{dpg.get_value(self.traceback_text)}\n\n{traceback}" width: int @@ -78,9 +87,25 @@ def show(self, traceback: str, message: str = ""): dpg.set_value(self.message_text, message) dpg.set_value(self.traceback_text, traceback) + def register_keybindings(self): + callbacks: Dict[Keybinding, Callable] = {} + # Cancel + kb: Keybinding = Keybinding( + key=dpg.mvKey_Escape, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.CANCEL, + ) + callbacks[kb] = self.hide + # Create the handler + self.keybinding_handler: TemporaryKeybindingHandler = ( + TemporaryKeybindingHandler(callbacks=callbacks) + ) + def hide(self): - if dpg.does_item_exist(self.key_handler): - dpg.delete_item(self.key_handler) + if self.keybinding_handler is not None: + self.keybinding_handler.delete() if dpg.is_item_visible(self.window): dpg.hide_item(self.window) signals.emit(Signal.UNBLOCK_KEYBINDINGS) diff --git a/src/deareis/gui/file_dialog.py b/src/deareis/gui/file_dialog.py index 20053f4..f02986e 100644 --- a/src/deareis/gui/file_dialog.py +++ b/src/deareis/gui/file_dialog.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -24,6 +24,7 @@ ) from os.path import ( basename, + dirname, exists, getmtime, getsize, @@ -73,8 +74,10 @@ def __init__( extensions, ) self._callback: Callable = kwargs["callback"] + self._cancel_callback: Optional[Callable] = kwargs.get("cancel_callback", None) self._save: bool = kwargs.get("save", False) self._merge: bool = kwargs.get("merge", False) + self._multiple: bool = kwargs.get("multiple", True) self._window: int = dpg.generate_uuid() x: int y: int @@ -84,27 +87,27 @@ def __init__( with dpg.window( label=label, modal=True, - pos=( - x, - y, - ), + pos=(x, y), width=w, height=h, show=False, - on_close=self.close, + on_close=lambda: self.close(cancel=True), tag=self._window, ): with dpg.group(horizontal=True): if self._save: dpg.add_button(label="N", callback=lambda: self.create_directory()) - attach_tooltip("Create a new directory.") + attach_tooltip("Create a new directory." + "\n\nShortcut: Ctrl+N") dpg.add_button( label="R", callback=lambda: self.reset_path(), ) - attach_tooltip(f"Reset to current working directory: '{self._cwd}'.") + attach_tooltip( + f"Reset to current working directory: '{self._cwd}'." + + "\n\nShortcut: Ctrl+R" + ) dpg.add_button(label="E", callback=lambda: self.edit_path()) - attach_tooltip("Edit the path via input.") + attach_tooltip("Edit the path via input." + "\n\nShortcut: Ctrl+E") self._path_combo: int = dpg.generate_uuid() dpg.add_combo( tag=self._path_combo, @@ -113,6 +116,10 @@ def __init__( u.get(a, self._cwd) ), ) + attach_tooltip( + "Navigate to different parts of the current path." + + "\n\nShortcut: Backspace" + ) self._path_input: int = dpg.generate_uuid() dpg.add_input_text( hint="Path...", @@ -124,14 +131,18 @@ def __init__( ) with dpg.group(horizontal=True): dpg.add_button(label="C", callback=lambda: self.clear_search()) - attach_tooltip("Clear the search input.") + attach_tooltip("Clear the search input." + "\n\nShortcut: Ctrl+C") self._search_input: int = dpg.generate_uuid() dpg.add_input_text( - hint="Search...", + hint="Find...", width=-100 if not self._save else -1, tag=self._search_input, callback=lambda s, a, u: dpg.set_value(self._table, a.lower()), ) + attach_tooltip( + "Search for something based on a substring." + + "\n\nShortcut: Ctrl+F" + ) self._extension_combo: int = dpg.generate_uuid() dpg.add_combo( default_value=default_extension, @@ -141,6 +152,10 @@ def __init__( tag=self._extension_combo, width=-1, ) + attach_tooltip( + "Filter files based on their extension." + + "\n\nShortcut: Page up/down" + ) self._table: int = dpg.generate_uuid() with dpg.table( borders_outerV=True, @@ -177,21 +192,30 @@ def __init__( width=200, ) with dpg.group(horizontal=True): + button_pad: int = 12 if not self._save: dpg.add_button( - label=("Merge" if self._merge else "Load"), + label=("Merge" if self._merge else "Load").ljust(button_pad), callback=lambda: self.load_files(), ) + attach_tooltip("Shortcut: Enter") + if self._multiple: + dpg.add_button( + label="Select all".ljust(button_pad), + callback=lambda: self.select_files(state=True), + ) + attach_tooltip("Shortcut: Ctrl+A") + dpg.add_button( + label="Unselect all".ljust(button_pad), + callback=lambda: self.select_files(state=False), + ) + attach_tooltip("Shortcut: Ctrl+Shift+A") + else: dpg.add_button( - label="Select all", - callback=lambda: self.select_files(state=True), - ) - dpg.add_button( - label="Unselect all", - callback=lambda: self.select_files(state=False), + label="Save".ljust(button_pad), + callback=lambda: self.save_file(), ) - else: - dpg.add_button(label="Save", callback=lambda: self.save_file()) + attach_tooltip("Shortcut: Enter") self._name_input: int = dpg.generate_uuid() dpg.add_input_text( hint="Name...", @@ -224,18 +248,26 @@ def show(self): with dpg.handler_registry(tag=self._key_handler): dpg.add_key_release_handler( key=dpg.mvKey_Escape, - callback=self.close, + callback=lambda: self.close(keybinding=True), ) if self._save: dpg.add_key_release_handler( key=dpg.mvKey_N, callback=lambda: self.create_directory(keybinding=True), ) + dpg.add_key_release_handler( + key=dpg.mvKey_Return, + callback=lambda: self.save_file(keybinding=True), + ) else: dpg.add_key_release_handler( key=dpg.mvKey_A, callback=lambda: self.select_files(keybinding=True), ) + dpg.add_key_release_handler( + key=dpg.mvKey_Return, + callback=lambda: self.load_files(keybinding=True), + ) dpg.add_key_release_handler( key=dpg.mvKey_R, callback=lambda: self.reset_path(keybinding=True), @@ -252,14 +284,35 @@ def show(self): key=dpg.mvKey_C, callback=lambda: self.clear_search(keybinding=True), ) + dpg.add_key_release_handler( + key=dpg.mvKey_Prior, + callback=lambda: self.cycle_extensions(step=-1), + ) + dpg.add_key_release_handler( + key=dpg.mvKey_Next, + callback=lambda: self.cycle_extensions(step=1), + ) + dpg.add_key_release_handler( + key=dpg.mvKey_Clear, + callback=self.go_back_one_folder, + ) dpg.show_item(self._window) if self._save: dpg.focus_item(self._name_input) - signals.emit(Signal.BLOCK_KEYBINDINGS, window=self._window, window_object=None) + signals.emit(Signal.BLOCK_KEYBINDINGS, window=self._window, window_object=self) - def close(self): + def close(self, cancel: bool = False, keybinding: bool = False): + if keybinding is True and ( + not dpg.is_item_visible(self._window) + or dpg.is_item_active(self._search_input) + or dpg.is_item_active(self._path_input) + ): + return self.hide() dpg.delete_item(self._window) + if cancel is True and callable(self._cancel_callback): + dpg.split_frame(delay=33) + self._cancel_callback() def create_directory(self, keybinding: bool = False): assert type(keybinding) is bool, keybinding @@ -271,7 +324,7 @@ def create_directory(self, keybinding: bool = False): y: int w: int h: int - x, y, w, h = calculate_window_position_dimensions(400, 50) + x, y, w, h = calculate_window_position_dimensions(400, 40) key_handler: int = dpg.generate_uuid() window: int = dpg.generate_uuid() name_input: int = dpg.generate_uuid() @@ -309,10 +362,7 @@ def accept(): with dpg.window( label="Create folder", modal=True, - pos=( - x, - y, - ), + pos=(x, y), width=w, height=h, show=False, @@ -320,7 +370,7 @@ def accept(): tag=window, ): dpg.add_input_text(hint="Name...", width=-1, tag=name_input) - dpg.add_button(label="Accept", callback=accept) + dpg.add_button(label="Accept".ljust(10), callback=accept) dpg.show_item(window) dpg.split_frame() @@ -329,7 +379,11 @@ def accept(): def clear_search(self, keybinding: bool = False): assert type(keybinding) is bool, keybinding - if keybinding and not is_control_down(): + if keybinding and ( + not is_control_down() + or dpg.is_item_active(self._search_input) + or dpg.is_item_active(self._path_input) + ): return dpg.set_value(self._table, "") dpg.set_value(self._search_input, "") @@ -342,14 +396,13 @@ def focus_search(self, keybinding: bool = False): def select_files(self, state: Optional[bool] = None, keybinding: bool = False): assert type(state) is bool or state is None, state assert type(keybinding) is bool, keybinding - if keybinding: - if not is_control_down(): - return - if dpg.is_item_focused(self._path_input) or dpg.is_item_focused( - self._search_input - ): - return - state = not is_shift_down() + if keybinding and ( + not is_control_down() + or dpg.is_item_focused(self._path_input) + or dpg.is_item_focused(self._search_input) + ): + return + state = not is_shift_down() assert state is not None filter_key: str = dpg.get_value(self._search_input).lower() files: Dict[int, Optional[str]] = {} @@ -480,11 +533,14 @@ def update_contents_table(self, root: str): row, path, ), + enabled=self._multiple, + show=self._multiple, ) - attach_tooltip( - "Select multiple files to " - + ("merge." if self._merge else "load.") - ) + if self._multiple: + attach_tooltip( + "Select multiple files to " + + ("merge." if self._merge else "load.") + ) if link: dpg.add_text("L") attach_tooltip(f"Link to file: '{path}'") @@ -536,7 +592,13 @@ def click_file(self, path: str): else: dpg.set_value(self._name_input, splitext(basename(path))[0]) - def load_files(self): + def load_files(self, keybinding: bool = False): + if keybinding is True and ( + not dpg.is_item_visible(self._window) + or dpg.is_item_active(self._search_input) + or dpg.is_item_active(self._path_input) + ): + return paths: List[str] = list( filter( lambda _: _ is not None, @@ -552,7 +614,13 @@ def load_files(self): self._callback(paths=paths, merge=self._merge) self.close() - def save_file(self): + def save_file(self, keybinding: bool = False): + if keybinding is True and ( + not dpg.is_item_visible(self._window) + or dpg.is_item_active(self._search_input) + or dpg.is_item_active(self._path_input) + ): + return name: str = dpg.get_value(self._name_input).strip() if name == "": dpg.focus_item(self._name_input) @@ -564,3 +632,24 @@ def save_file(self): path += extension self._callback(path=path) self.close() + + def go_back_one_folder(self): + if ( + not dpg.is_item_visible(self._window) + or dpg.is_item_active(self._search_input) + or dpg.is_item_active(self._path_input) + ): + return + path: str = self.get_current_path() + root: str = dirname(path) + if exists(root): + self.update_current_path(root) + + def cycle_extensions(self, step: int): + combo: int = ( + self._extension_combo if not self._save else self._name_extension_combo + ) + items: List[str] = dpg.get_item_configuration(combo)["items"] + index: int = items.index(dpg.get_value(combo)) + step + dpg.set_value(combo, items[index % len(items)]) + self.update_current_path(self.get_current_path()) diff --git a/src/deareis/gui/fitting.py b/src/deareis/gui/fitting/__init__.py similarity index 53% rename from src/deareis/gui/fitting.py rename to src/deareis/gui/fitting/__init__.py index 756951d..c9d0c62 100644 --- a/src/deareis/gui/fitting.py +++ b/src/deareis/gui/fitting/__init__.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -17,27 +17,32 @@ # The licenses of DearEIS' dependencies and/or sources of portions of code are included in # the LICENSES folder. +from math import isclose +from traceback import format_exc from typing import ( Callable, Dict, List, Optional, Tuple, - Type, ) from numpy import ( allclose, array, + isnan, log10 as log, ndarray, ) from pyimpspec import ( Circuit, + ComplexResiduals, + Connection, + Container, Element, FittedParameter, ) import pyimpspec -from pyimpspec.analysis.fitting import _calculate_residuals +from pyimpspec.analysis.utility import _calculate_residuals import dearpygui.dearpygui as dpg from deareis.signals import Signal import deareis.signals as signals @@ -59,7 +64,9 @@ ) from deareis.gui.plots import ( Bode, + Impedance, Nyquist, + Plot, Residuals, ) import deareis.tooltips as tooltips @@ -70,7 +77,10 @@ from deareis.utility import ( align_numbers, calculate_window_position_dimensions, + find_parent_containers, format_number, + pad_tab_labels, + process_cdc, render_math, ) import deareis.themes as themes @@ -78,6 +88,11 @@ CircuitPreview, CircuitEditor, ) +from deareis.gui.shared import ( + DataSetsCombo, + ResultsCombo, +) +from .parameter_adjustment import ParameterAdjustment MATH_WEIGHT_WIDTH: int = 300 @@ -248,7 +263,7 @@ def get_settings(self) -> FitSettings: if circuit is None or cdc != circuit.to_string(): circuit = self.parse_cdc(cdc, self.cdc_input) return FitSettings( - cdc=circuit.to_string(12) if circuit is not None else "", + cdc=circuit.serialize() if circuit is not None else "", method=label_to_cnls_method.get( dpg.get_value(self.method_combo), CNLSMethod.AUTO ), @@ -266,11 +281,19 @@ def set_settings(self, settings: FitSettings): def parse_cdc(self, cdc: str, sender: int = -1) -> Optional[Circuit]: assert type(cdc) is str, cdc assert type(sender) is int, sender + circuit: Optional[Circuit] + msg: str try: - circuit: Circuit = pyimpspec.parse_cdc(cdc) - except (pyimpspec.ParsingError, pyimpspec.UnexpectedCharacter) as err: + circuit, msg = process_cdc(cdc) + except Exception: + signals.emit( + Signal.SHOW_ERROR_MESSAGE, + traceback=format_exc(), + ) + return None + if circuit is None: dpg.bind_item_theme(self.cdc_input, themes.cdc.invalid) - update_tooltip(self.cdc_tooltip, str(err)) + update_tooltip(self.cdc_tooltip, msg) dpg.show_item(dpg.get_item_parent(self.cdc_tooltip)) dpg.set_item_user_data(self.cdc_input, None) return None @@ -298,11 +321,10 @@ def show_circuit_editor(self): width=w, height=h, ) - circuit: Optional[Circuit] = None - try: - circuit = pyimpspec.parse_cdc(self.get_settings().cdc) - except pyimpspec.ParsingError: - pass + circuit: Optional[Circuit] = self.parse_cdc( + self.get_settings().cdc, + sender=self.cdc_input, + ) signals.emit( Signal.BLOCK_KEYBINDINGS, window=self.circuit_editor.window, @@ -360,6 +382,27 @@ def clear(self, hide: bool): dpg.hide_item(self._header) dpg.delete_item(self._table, children_only=True, slot=1) + def limited_parameter( + self, key: str, value: float, element: Element + ) -> Tuple[str, int]: + lower_limit: float = element.get_lower_limit(key) + upper_limit: float = element.get_upper_limit(key) + if isclose(value, lower_limit): + return ( + "\n\nRestricted by lower limit!", + themes.fitting.limited_parameter, + ) + elif isclose(value, upper_limit): + return ( + "\n\nRestricted by upper limit!", + themes.fitting.limited_parameter, + ) + else: + return ( + "", + -1, + ) + def populate(self, fit: FitResult): dpg.show_item(self._header) column_pads: List[int] = [ @@ -375,41 +418,96 @@ def populate(self, fit: FitResult): dpg.add_text("".ljust(column_pads[2])) dpg.add_text("".ljust(column_pads[3])) return - element_labels: List[str] = [] + element_names: List[str] = [] element_tooltips: List[str] = [] parameter_labels: List[str] = [] + parameter_tooltips: List[str] = [] values: List[str] = [] value_tooltips: List[str] = [] + value_themes: List[int] = [] error_values: List[str] = [] error_tooltips: List[str] = [] - element_label: str + internal_identifiers: Dict[int, Element] = { + v: k + for k, v in fit.circuit.generate_element_identifiers(running=True).items() + } + external_identifiers: Dict[ + Element, int + ] = fit.circuit.generate_element_identifiers(running=False) + parent_containers: Dict[Element, Container] = find_parent_containers( + fit.circuit + ) + element_name: str element_tooltip: str parameter_label: str + parameter_tooltip: str value: str value_tooltip: str error_value: str error_tooltip: str parameters: Dict[str, FittedParameter] - for element in fit.circuit.get_elements(): - Class: Type[Element] = type(element) - element_label = element.get_label() - element_tooltip = Class.get_extended_description() - parameters = fit.parameters[element_label] + element: Element + for (_, element) in sorted(internal_identifiers.items(), key=lambda _: _[0]): + element_name = fit.circuit.get_element_name( + element, + identifiers=external_identifiers, + ) + lines: List[str] = [] + line: str + for line in element.get_extended_description().split("\n"): + if line.strip().startswith(":math:"): + break + lines.append(line) + element_tooltip = "\n".join(lines).strip() + parameters = fit.parameters[element_name] + if element in parent_containers: + parent_name: str = fit.circuit.get_element_name( + parent_containers[element], + identifiers=external_identifiers, + ) + subcircuit_name: str + subcircuit: Optional[Connection] + for subcircuit_name, subcircuit in ( + parent_containers[element].get_subcircuits().items() + ): + if subcircuit is None: + continue + if element in subcircuit: + break + element_name = f"*{element_name}" + element_tooltip = f"*Nested inside {parent_name}'s {subcircuit_name} subcircuit\n\n{element_tooltip}" parameter: FittedParameter for parameter_label in sorted(parameters): parameter = parameters[parameter_label] - element_labels.append(element_label) + element_names.append(element_name) element_tooltips.append(element_tooltip) parameter_labels.append( - parameter_label + (" (fixed)" if parameter.fixed else "") + parameter_label + (" (f)" if parameter.fixed else "") + ) + unit: str = element.get_unit(parameter_label) + parameter_tooltips.append( + ( + f"{element.get_value_description(parameter_label)}\n\n" + f"Unit: {unit}\n" + + ("Fixed parameter" if parameter.fixed else "") + ).strip() ) values.append( f"{format_number(parameter.value, width=9, significants=3)}" ) value_tooltips.append( - f"{format_number(parameter.value, decimals=6).strip()}" + f"{format_number(parameter.value, decimals=6).strip()} {unit}".strip() ) - if parameter.stderr is not None: + value_tooltip_appendix: str + value_theme: int + value_tooltip_appendix, value_theme = self.limited_parameter( + parameter_label, + parameter.value, + element, + ) + value_tooltips[-1] += value_tooltip_appendix + value_themes.append(value_theme) + if not isnan(parameter.stderr): error: float = parameter.get_relative_error() * 100 if error > 100.0: error_value = ">100" @@ -419,9 +517,7 @@ def populate(self, fit: FitResult): error_value = ( f"{format_number(error, exponent=False, significants=3)}" ) - error_tooltip = ( - f"±{format_number(parameter.stderr, decimals=6).strip()}" - ) + error_tooltip = f"±{format_number(parameter.stderr, decimals=6).strip()} {parameter.unit}".strip() else: error_value = "-" if not parameter.fixed: @@ -434,29 +530,35 @@ def populate(self, fit: FitResult): error_values = align_numbers(error_values) num_rows: int = 0 for ( - element_label, + element_name, element_tooltip, parameter_label, + parameter_tooltip, value, value_tooltip, + value_theme, error_value, error_tooltip, ) in zip( - element_labels, + element_names, element_tooltips, parameter_labels, + parameter_tooltips, values, value_tooltips, + value_themes, error_values, error_tooltips, ): with dpg.table_row(parent=self._table): - dpg.add_text(element_label.ljust(column_pads[0])) - if element_tooltip != "": - attach_tooltip(element_tooltip) + dpg.add_text(element_name.ljust(column_pads[0])) + attach_tooltip(element_tooltip) dpg.add_text(parameter_label.ljust(column_pads[1])) - dpg.add_text(value.ljust(column_pads[2])) + attach_tooltip(parameter_tooltip) + value_widget: int = dpg.add_text(value.ljust(column_pads[2])) attach_tooltip(value_tooltip) + if value_theme > 0: + dpg.bind_item_theme(value_widget, value_theme) dpg.add_text(error_value.ljust(column_pads[3])) if error_tooltip != "": attach_tooltip(error_tooltip) @@ -491,6 +593,10 @@ def __init__(self): label: str tooltip: str for (label, tooltip) in [ + ( + "log X² (pseudo)", + tooltips.fitting.pseudo_chisqr, + ), ( "log X²", tooltips.fitting.chisqr, @@ -550,51 +656,55 @@ def populate(self, fit: FitResult): row: int for row in dpg.get_item_children(self._table, slot=1): cells.append(dpg.get_item_children(row, slot=1)[2]) - assert len(cells) == 9, cells + assert len(cells) == 10, cells tag: int value: str for (tag, value) in [ ( cells[0], - f"{log(fit.chisqr):.3f}", + f"{log(fit.pseudo_chisqr):.3f}", ), ( cells[1], - f"{log(fit.red_chisqr):.3f}", + f"{log(fit.chisqr):.3f}", ), ( cells[2], - f"{fit.aic:.3E}", + f"{log(fit.red_chisqr):.3f}", ), ( cells[3], - f"{fit.bic:.3E}", + format_number(fit.aic, decimals=3), ), ( cells[4], - f"{fit.nfree}", + format_number(fit.bic, decimals=3), ), ( cells[5], - f"{fit.ndata}", + f"{fit.nfree}", ), ( cells[6], - f"{fit.nfev}", + f"{fit.ndata}", ), ( cells[7], - cnls_method_to_label.get(fit.method, ""), + f"{fit.nfev}", ), ( cells[8], + cnls_method_to_label.get(fit.method, ""), + ), + ( + cells[9], weight_to_label.get(fit.weight, ""), ), ]: dpg.set_value(tag, value) update_tooltip(dpg.get_item_user_data(tag), value) dpg.show_item(dpg.get_item_parent(dpg.get_item_user_data(tag))) - dpg.set_item_height(self._table, 18 + 23 * 9) + dpg.set_item_height(self._table, 18 + 23 * len(cells)) class SettingsTable: @@ -633,6 +743,7 @@ def __init__(self): tooltip_tag: int = dpg.generate_uuid() dpg.add_text("", user_data=tooltip_tag) attach_tooltip("", tag=tooltip_tag) + dpg.add_spacer(height=8) with dpg.group(horizontal=True): self._apply_settings_button: int = dpg.generate_uuid() dpg.add_button( @@ -642,6 +753,7 @@ def __init__(self): **u, ), tag=self._apply_settings_button, + width=154, ) attach_tooltip(tooltips.general.apply_settings) self._apply_mask_button: int = dpg.generate_uuid() @@ -652,6 +764,7 @@ def __init__(self): **u, ), tag=self._apply_mask_button, + width=-1, ) attach_tooltip(tooltips.general.apply_mask) @@ -719,506 +832,432 @@ def populate(self, fit: FitResult, data: DataSet): ) -class DataSetsCombo: - def __init__(self, label: str, width: int): - self.labels: List[str] = [] - dpg.add_text(label) - self.tag: int = dpg.generate_uuid() - dpg.add_combo( - callback=lambda s, a, u: signals.emit( - Signal.SELECT_DATA_SET, - data=u.get(a), - ), - user_data={}, - width=width, - tag=self.tag, +class FitResultsCombo(ResultsCombo): + def selection_callback(self, sender: int, app_data: str, user_data: tuple): + signals.emit( + Signal.SELECT_FIT_RESULT, + fit=user_data[0].get(app_data), + data=user_data[1], ) - def populate(self, labels: List[str], lookup: Dict[str, DataSet]): - self.labels.clear() - self.labels.extend(labels) - label: str = dpg.get_value(self.tag) or "" - if labels and label not in labels: - label = labels[0] - dpg.configure_item( - self.tag, - default_value=label, - items=labels, - user_data=lookup, + def adjust_label(self, old: str, longest: int) -> str: + cdc: str + timestamp: str + cdc, timestamp = ( + old[: old.find(" ")], + old[old.find(" ") + 1 :], ) + return f"{cdc.ljust(longest)} {timestamp}" - def get(self) -> Optional[DataSet]: - return dpg.get_item_user_data(self.tag).get(dpg.get_value(self.tag)) - def set(self, label: str): - assert type(label) is str, label - assert label in self.labels, ( - label, - self.labels, - ) - dpg.set_value(self.tag, label) - - def clear(self): - dpg.configure_item( - self.tag, - default_value="", - ) +class FittingTab: + def __init__(self, state): + self.state = state + self.queued_update: Optional[Callable] = None + self.create_tab(state) + def create_tab(self, state): + label_pad: int = 24 + self.tab: int = dpg.generate_uuid() + with dpg.tab(label="Fitting", tag=self.tab): + with dpg.group(horizontal=True): + self.create_sidebar(state, label_pad) + self.create_plots() -class ResultsCombo: - def __init__(self, label: str, width: int): - self.labels: Dict[str, str] = {} - dpg.add_text(label) - self.tag: int = dpg.generate_uuid() - dpg.add_combo( - callback=lambda s, a, u: signals.emit( - Signal.SELECT_FIT_RESULT, - fit=u[0].get(a), - data=u[1], - ), - user_data=( - {}, - None, - ), - width=width, - tag=self.tag, - ) + def create_sidebar(self, state, label_pad: int): + self.sidebar_width: int = 350 + self.sidebar_window: int = dpg.generate_uuid() + with dpg.child_window( + border=False, + width=self.sidebar_width, + tag=self.sidebar_window, + ): + self.create_settings_menu(state, label_pad) + self.create_results_menu() + self.create_results_tables() - def populate(self, lookup: Dict[str, FitResult], data: Optional[DataSet]): - self.labels.clear() - labels: List[str] = list(lookup.keys()) - longest_cdc: int = max(list(map(lambda _: len(_[: _.find(" ")]), labels)) + [1]) - old_key: str - for old_key in labels: - fit: FitResult = lookup[old_key] - del lookup[old_key] - cdc, timestamp = ( - old_key[: old_key.find(" ")], - old_key[old_key.find(" ") + 1 :], + def create_settings_menu(self, state, label_pad: int): + with dpg.child_window( + border=True, + width=-1, + height=150, + ): + self.circuit_editor: CircuitEditor = CircuitEditor( + window=dpg.add_window( + label="Circuit editor", + show=False, + modal=True, + on_close=lambda s, a, u: self.accept_circuit(None), + ), + callback=self.accept_circuit, + keybindings=state.config.keybindings, ) - new_key: str = f"{cdc.ljust(longest_cdc)} {timestamp}" - self.labels[old_key] = new_key - lookup[new_key] = fit - labels = list(lookup.keys()) - dpg.configure_item( - self.tag, - default_value=labels[0] if labels else "", - items=labels, - user_data=( - lookup, - data, - ), - ) + self.settings_menu: SettingsMenu = SettingsMenu( + state.config.default_fit_settings, + label_pad, + circuit_editor=self.circuit_editor, + ) + with dpg.group(horizontal=True): + dpg.add_text( + "?".rjust(label_pad), + ) + attach_tooltip(tooltips.fitting.adjust_parameters) + self.parameter_adjustment_button: int = dpg.generate_uuid() + dpg.add_button( + label="Adjust parameters", + callback=self.show_parameter_adjustment, + user_data=None, + width=-1, + tag=self.parameter_adjustment_button, + ) + with dpg.group(horizontal=True): + self.visibility_item: int = dpg.generate_uuid() + dpg.add_text( + "?".rjust(label_pad), + tag=self.visibility_item, + ) + attach_tooltip(tooltips.fitting.perform) + self.perform_fit_button: int = dpg.generate_uuid() + dpg.add_button( + label="Perform", + callback=lambda s, a, u: signals.emit( + Signal.PERFORM_FIT, + data=u, + settings=self.get_settings(), + ), + user_data=None, + width=-70, + tag=self.perform_fit_button, + ) + dpg.add_button( + label="Batch", + callback=lambda s, a, u: signals.emit( + Signal.BATCH_PERFORM_ANALYSIS, + settings=self.get_settings(), + ), + width=-1, + ) - def get(self) -> Optional[FitResult]: - return dpg.get_item_user_data(self.tag)[0].get(dpg.get_value(self.tag)) + def create_results_menu(self): + with dpg.child_window(width=-1, height=82): + label_pad = 8 + with dpg.group(horizontal=True): + self.data_sets_combo: DataSetsCombo = DataSetsCombo( + label="Data set".rjust(label_pad), + width=-60, + ) + with dpg.group(horizontal=True): + self.results_combo: FitResultsCombo = FitResultsCombo( + label="Result".rjust(label_pad), + width=-60, + ) + self.delete_button: int = dpg.generate_uuid() + dpg.add_button( + label="Delete", + callback=lambda s, a, u: signals.emit( + Signal.DELETE_FIT_RESULT, + **u, + ), + user_data={}, + width=-1, + tag=self.delete_button, + ) + attach_tooltip(tooltips.fitting.delete) + with dpg.group(horizontal=True): + dpg.add_text("Output".rjust(label_pad)) + # TODO: Split into combo class? + self.output_combo: int = dpg.generate_uuid() + dpg.add_combo( + items=list(label_to_fit_sim_output.keys()), + default_value=list(label_to_fit_sim_output.keys())[0], + tag=self.output_combo, + width=-60, + ) + self.copy_output_button: int = dpg.generate_uuid() + dpg.add_button( + label="Copy", + callback=lambda s, a, u: signals.emit( + Signal.COPY_OUTPUT, + output=self.get_active_output(), + **u, + ), + user_data={}, + width=-1, + tag=self.copy_output_button, + ) + attach_tooltip(tooltips.general.copy_output) - def set(self, label: str): - assert type(label) is str, label - assert label in self.labels, ( - label, - list(self.labels.keys()), - ) - dpg.set_value(self.tag, self.labels[label]) + def create_results_tables(self): + with dpg.child_window(width=-1, height=-1): + self.result_group: int = dpg.generate_uuid() + with dpg.group(tag=self.result_group): + with dpg.group(show=False): + self.validity_text: int = dpg.generate_uuid() + dpg.bind_item_theme( + dpg.add_text( + "", + wrap=self.sidebar_width - 24, + tag=self.validity_text, + ), + themes.result.invalid, + ) + dpg.add_spacer(height=8) + self.parameters_table: ParametersTable = ParametersTable() + dpg.add_spacer(height=8) + self.statistics_table: StatisticsTable = StatisticsTable() + dpg.add_spacer(height=8) + self.settings_table: SettingsTable = SettingsTable() - def clear(self): - dpg.configure_item( - self.tag, - default_value="", - ) + def create_plots(self): + self.plot_window: int = dpg.generate_uuid() + with dpg.child_window( + border=False, + width=-1, + height=-1, + tag=self.plot_window, + ): + self.circuit_preview_height: int = 250 + with dpg.child_window( + border=False, + width=-1, + height=self.circuit_preview_height, + ): + dpg.add_text("Fitted circuit") + self.circuit_preview: CircuitPreview = CircuitPreview() + self.plot_tab_bar: int = dpg.generate_uuid() + with dpg.tab_bar(tag=self.plot_tab_bar): + self.create_nyquist_plot() + self.create_bode_plot() + self.create_impedance_plot() + self.create_residuals_plot() + pad_tab_labels(self.plot_tab_bar) - def get_next_result(self) -> Optional[FitResult]: - lookup: Dict[str, FitResult] = dpg.get_item_user_data(self.tag)[0] - if not lookup: - return None - labels: List[str] = list(lookup.keys()) - index: int = labels.index(dpg.get_value(self.tag)) + 1 - return lookup[labels[index % len(labels)]] + def create_nyquist_plot(self): + with dpg.tab(label="Nyquist"): + self.nyquist_plot: Nyquist = Nyquist(width=-1, height=-1) + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + label="Data", + line=False, + theme=themes.nyquist.data, + ) + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + label="Fit", + line=False, + fit=True, + theme=themes.nyquist.simulation, + ) + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + label="Fit", + line=True, + fit=True, + theme=themes.nyquist.simulation, + show_label=False, + ) + with dpg.group(horizontal=True): + self.enlarge_nyquist_button: int = dpg.generate_uuid() + self.adjust_nyquist_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_nyquist, + tag=self.enlarge_nyquist_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.nyquist_plot, + context=Context.FITTING_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_nyquist_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_nyquist_limits) - def get_previous_result(self) -> Optional[FitResult]: - lookup: Dict[str, FitResult] = dpg.get_item_user_data(self.tag)[0] - if not lookup: - return None - labels: List[str] = list(lookup.keys()) - index: int = labels.index(dpg.get_value(self.tag)) - 1 - return lookup[labels[index % len(labels)]] + def create_bode_plot(self): + with dpg.tab(label="Bode"): + self.bode_plot: Bode = Bode(width=-1, height=-1) + self.bode_plot.plot( + frequency=array([]), + magnitude=array([]), + phase=array([]), + labels=( + "Mod(Z), d.", + "Phase(Z), d.", + ), + line=False, + themes=( + themes.bode.magnitude_data, + themes.bode.phase_data, + ), + ) + self.bode_plot.plot( + frequency=array([]), + magnitude=array([]), + phase=array([]), + labels=( + "Mod(Z), f.", + "Phase(Z), f.", + ), + line=False, + fit=True, + themes=( + themes.bode.magnitude_simulation, + themes.bode.phase_simulation, + ), + ) + self.bode_plot.plot( + frequency=array([]), + magnitude=array([]), + phase=array([]), + labels=( + "Mod(Z), f.", + "Phase(Z), f.", + ), + line=True, + fit=True, + themes=( + themes.bode.magnitude_simulation, + themes.bode.phase_simulation, + ), + show_labels=False, + ) + with dpg.group(horizontal=True): + self.enlarge_bode_button: int = dpg.generate_uuid() + self.adjust_bode_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_bode, + tag=self.enlarge_bode_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.bode_plot, + context=Context.FITTING_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_bode_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_bode_limits) + def create_impedance_plot(self): + with dpg.tab(label="Real & Imag."): + self.impedance_plot: Impedance = Impedance(width=-1, height=-1) + self.impedance_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + labels=( + "Re(Z), d.", + "Im(Z), d.", + ), + line=False, + themes=( + themes.impedance.real_data, + themes.impedance.imaginary_data, + ), + ) + self.impedance_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + labels=( + "Re(Z), f.", + "Im(Z), f.", + ), + line=False, + fit=True, + themes=( + themes.impedance.real_simulation, + themes.impedance.imaginary_simulation, + ), + ) + self.impedance_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + labels=( + "Re(Z), f.", + "Im(Z), f.", + ), + line=True, + fit=True, + themes=( + themes.impedance.real_simulation, + themes.impedance.imaginary_simulation, + ), + show_labels=False, + ) + with dpg.group(horizontal=True): + self.enlarge_impedance_button: int = dpg.generate_uuid() + self.adjust_impedance_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_impedance, + tag=self.enlarge_impedance_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.impedance_plot, + context=Context.FITTING_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_impedance_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_impedance_limits) -class FittingTab: - def __init__(self, state): - self.state = state - self.queued_update: Optional[Callable] = None - label_pad: int = 24 - self.tab: int = dpg.generate_uuid() - with dpg.tab(label="Fitting", tag=self.tab): + def create_residuals_plot(self): + with dpg.tab(label="Residuals"): + self.residuals_plot: Residuals = Residuals(width=-1, height=-1) + self.residuals_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + ) with dpg.group(horizontal=True): - self.sidebar_width: int = 350 - self.sidebar_window: int = dpg.generate_uuid() - with dpg.child_window( - border=False, - width=self.sidebar_width, - tag=self.sidebar_window, - ): - with dpg.child_window( - border=True, - width=-1, - height=128, - ): - self.circuit_editor: CircuitEditor = CircuitEditor( - window=dpg.add_window( - label="Circuit editor", - show=False, - modal=True, - on_close=lambda s, a, u: self.accept_circuit(None), - ), - callback=self.accept_circuit, - ) - self.settings_menu: SettingsMenu = SettingsMenu( - state.config.default_fit_settings, - label_pad, - circuit_editor=self.circuit_editor, - ) - with dpg.group(horizontal=True): - self.visibility_item: int = dpg.generate_uuid() - dpg.add_text( - "?".rjust(label_pad), - tag=self.visibility_item, - ) - attach_tooltip(tooltips.fitting.perform) - self.perform_fit_button: int = dpg.generate_uuid() - dpg.add_button( - label="Perform fit", - callback=lambda s, a, u: signals.emit( - Signal.PERFORM_FIT, - data=u, - settings=self.get_settings(), - ), - user_data=None, - width=-1, - tag=self.perform_fit_button, - ) - # Results - with dpg.child_window(width=-1, height=82): - label_pad = 8 - with dpg.group(horizontal=True): - self.data_sets_combo: DataSetsCombo = DataSetsCombo( - label="Data set".rjust(label_pad), - width=-60, - ) - with dpg.group(horizontal=True): - self.results_combo: ResultsCombo = ResultsCombo( - label="Result".rjust(label_pad), - width=-60, - ) - self.delete_button: int = dpg.generate_uuid() - dpg.add_button( - label="Delete", - callback=lambda s, a, u: signals.emit( - Signal.DELETE_FIT_RESULT, - **u, - ), - user_data={}, - width=-1, - tag=self.delete_button, - ) - attach_tooltip(tooltips.fitting.delete) - with dpg.group(horizontal=True): - dpg.add_text("Output".rjust(label_pad)) - # TODO: Split into combo class? - self.output_combo: int = dpg.generate_uuid() - dpg.add_combo( - items=list(label_to_fit_sim_output.keys()), - default_value=list(label_to_fit_sim_output.keys())[0], - tag=self.output_combo, - width=-60, - ) - self.copy_output_button: int = dpg.generate_uuid() - dpg.add_button( - label="Copy", - callback=lambda s, a, u: signals.emit( - Signal.COPY_OUTPUT, - output=self.get_active_output(), - **u, - ), - user_data={}, - width=-1, - tag=self.copy_output_button, - ) - attach_tooltip(tooltips.general.copy_output) - # - with dpg.child_window(width=-1, height=-1): - self.result_group: int = dpg.generate_uuid() - with dpg.group(tag=self.result_group): - with dpg.group(show=False): - self.validity_text: int = dpg.generate_uuid() - dpg.bind_item_theme( - dpg.add_text( - "", - wrap=self.sidebar_width - 24, - tag=self.validity_text, - ), - themes.result.invalid, - ) - dpg.add_spacer(height=8) - self.parameters_table: ParametersTable = ParametersTable() - dpg.add_spacer(height=8) - self.statistics_table: StatisticsTable = StatisticsTable() - dpg.add_spacer(height=8) - self.settings_table: SettingsTable = SettingsTable() - self.plot_window: int = dpg.generate_uuid() - with dpg.child_window( - border=False, - width=-1, - height=-1, - tag=self.plot_window, - ): - self.circuit_preview_height: int = 250 - with dpg.child_window( - border=False, - width=-1, - height=self.circuit_preview_height, - ): - dpg.add_text("Fitted circuit") - self.circuit_preview: CircuitPreview = CircuitPreview() - self.minimum_plot_side: int = 400 - with dpg.group(horizontal=True): - with dpg.group(): - self.nyquist_plot: Nyquist = Nyquist( - width=self.minimum_plot_side, - height=self.minimum_plot_side, - ) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="Data", - theme=themes.nyquist.data, - ) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="Fit", - fit=True, - theme=themes.nyquist.simulation, - ) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="Fit", - fit=True, - line=True, - theme=themes.nyquist.simulation, - show_label=False, - ) - with dpg.group(horizontal=True): - self.enlarge_nyquist_button: int = dpg.generate_uuid() - self.adjust_nyquist_limits_checkbox: int = ( - dpg.generate_uuid() - ) - dpg.add_button( - label="Enlarge Nyquist", - callback=self.show_enlarged_nyquist, - tag=self.enlarge_nyquist_button, - ) - dpg.add_checkbox( - default_value=True, - tag=self.adjust_nyquist_limits_checkbox, - ) - attach_tooltip(tooltips.general.adjust_nyquist_limits) - dpg.add_button( - label="Copy as CSV", - callback=lambda s, a, u: signals.emit( - Signal.COPY_PLOT_DATA, - plot=self.nyquist_plot, - context=Context.FITTING_TAB, - ), - ) - attach_tooltip(tooltips.general.copy_plot_data_as_csv) - self.horizontal_bode_group: int = dpg.generate_uuid() - with dpg.group(tag=self.horizontal_bode_group): - self.bode_plot_horizontal: Bode = Bode( - width=self.minimum_plot_side, - height=self.minimum_plot_side, - ) - self.bode_plot_horizontal.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z| (d)", - "phi (d)", - ), - themes=( - themes.bode.magnitude_data, - themes.bode.phase_data, - ), - ) - self.bode_plot_horizontal.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z| (f)", - "phi (f)", - ), - fit=True, - themes=( - themes.bode.magnitude_simulation, - themes.bode.phase_simulation, - ), - ) - self.bode_plot_horizontal.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z| (f)", - "phi (f)", - ), - fit=True, - line=True, - themes=( - themes.bode.magnitude_simulation, - themes.bode.phase_simulation, - ), - show_labels=False, - ) - with dpg.group(horizontal=True): - self.enlarge_bode_horizontal_button: int = ( - dpg.generate_uuid() - ) - self.adjust_bode_limits_horizontal_checkbox: int = ( - dpg.generate_uuid() - ) - dpg.add_button( - label="Enlarge Bode", - callback=self.show_enlarged_bode, - tag=self.enlarge_bode_horizontal_button, - ) - dpg.add_checkbox( - default_value=True, - tag=self.adjust_bode_limits_horizontal_checkbox, - ) - attach_tooltip(tooltips.general.adjust_bode_limits) - dpg.add_button( - label="Copy as CSV", - callback=lambda s, a, u: signals.emit( - Signal.COPY_PLOT_DATA, - plot=self.bode_plot_horizontal, - context=Context.FITTING_TAB, - ), - ) - attach_tooltip(tooltips.general.copy_plot_data_as_csv) - self.vertical_bode_group: int = dpg.generate_uuid() - with dpg.group(tag=self.vertical_bode_group): - self.bode_plot_vertical: Bode = Bode( - width=-1, - height=self.minimum_plot_side, - ) - self.bode_plot_vertical.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z| (d)", - "phi (d)", - ), - themes=( - themes.bode.magnitude_data, - themes.bode.phase_data, - ), - ) - self.bode_plot_vertical.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z| (f)", - "phi (f)", - ), - fit=True, - themes=( - themes.bode.magnitude_simulation, - themes.bode.phase_simulation, - ), - ) - self.bode_plot_vertical.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z| (f)", - "phi (f)", - ), - fit=True, - line=True, - themes=( - themes.bode.magnitude_simulation, - themes.bode.phase_simulation, - ), - show_labels=False, - ) - with dpg.group(horizontal=True): - self.enlarge_bode_vertical_button: int = dpg.generate_uuid() - self.adjust_bode_limits_vertical_checkbox: int = ( - dpg.generate_uuid() - ) - dpg.add_button( - label="Enlarge Bode", - callback=lambda s, a, u: signals.emit( - Signal.SHOW_ENLARGED_PLOT, - plot=self.bode_plot_vertical, - adjust_limits=dpg.get_value( - self.adjust_bode_limits_horizontal_checkbox - ), - ), - tag=self.enlarge_bode_vertical_button, - ) - dpg.add_checkbox( - default_value=True, - source=self.adjust_bode_limits_horizontal_checkbox, - tag=self.adjust_bode_limits_vertical_checkbox, - ) - attach_tooltip(tooltips.general.adjust_bode_limits) - dpg.add_button( - label="Copy as CSV", - callback=lambda s, a, u: signals.emit( - Signal.COPY_PLOT_DATA, - plot=self.bode_plot_vertical, - context=Context.FITTING_TAB, - ), - ) - attach_tooltip(tooltips.general.copy_plot_data_as_csv) - self.residuals_plot: Residuals = Residuals( - width=-1, - height=300, - ) - self.residuals_plot.plot( - frequency=array([]), - real=array([]), - imaginary=array([]), - ) - with dpg.group(horizontal=True): - self.enlarge_residuals_button: int = dpg.generate_uuid() - self.adjust_residuals_limits_checkbox: int = dpg.generate_uuid() - dpg.add_button( - label="Enlarge residuals", - callback=self.show_enlarged_residuals, - tag=self.enlarge_residuals_button, - ) - dpg.add_checkbox( - default_value=True, - tag=self.adjust_residuals_limits_checkbox, - ) - attach_tooltip(tooltips.general.adjust_residuals_limits) - dpg.add_button( - label="Copy as CSV", - callback=lambda s, a, u: signals.emit( - Signal.COPY_PLOT_DATA, - plot=self.residuals_plot, - context=Context.FITTING_TAB, - ), - ) - attach_tooltip(tooltips.general.copy_plot_data_as_csv) + self.enlarge_residuals_button: int = dpg.generate_uuid() + self.adjust_residuals_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_residuals, + tag=self.enlarge_residuals_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.residuals_plot, + context=Context.FITTING_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_residuals_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_residuals_limits) def is_visible(self) -> bool: return dpg.is_item_visible(self.visibility_item) @@ -1234,21 +1273,25 @@ def resize(self, width: int, height: int): assert type(height) is int and height > 0 if not self.is_visible(): return - if width < (self.sidebar_width + self.minimum_plot_side * 2): - if dpg.is_item_shown(self.horizontal_bode_group): - dpg.hide_item(self.horizontal_bode_group) - dpg.show_item(self.vertical_bode_group) - self.nyquist_plot.resize(-1, self.minimum_plot_side) - else: - if dpg.is_item_shown(self.vertical_bode_group): - dpg.show_item(self.horizontal_bode_group) - dpg.hide_item(self.vertical_bode_group) - dpg.split_frame() - width, height = dpg.get_item_rect_size(self.plot_window) - width = round((width - 24) / 2) - height = height - 300 - 24 * 2 - 2 - self.nyquist_plot.resize(width, height) - self.bode_plot_horizontal.resize(width, height) + height -= self.circuit_preview_height + 24 * 5 + 12 + plots: List[Plot] = [ + self.nyquist_plot, + self.bode_plot, + self.impedance_plot, + self.residuals_plot, + ] + for plot in plots: + plot.resize(-1, height) + + def next_plot_tab(self): + tabs: List[int] = dpg.get_item_children(self.plot_tab_bar, slot=1) + index: int = tabs.index(dpg.get_value(self.plot_tab_bar)) + 1 + dpg.set_value(self.plot_tab_bar, tabs[index % len(tabs)]) + + def previous_plot_tab(self): + tabs: List[int] = dpg.get_item_children(self.plot_tab_bar, slot=1) + index: int = tabs.index(dpg.get_value(self.plot_tab_bar)) - 1 + dpg.set_value(self.plot_tab_bar, tabs[index % len(tabs)]) def clear(self, hide: bool = True): self.data_sets_combo.clear() @@ -1258,8 +1301,8 @@ def clear(self, hide: bool = True): self.settings_table.clear(hide=hide) self.circuit_preview.clear() self.nyquist_plot.clear(delete=False) - self.bode_plot_horizontal.clear(delete=False) - self.bode_plot_vertical.clear(delete=False) + self.bode_plot.clear(delete=False) + self.impedance_plot.clear(delete=False) self.residuals_plot.clear(delete=False) def populate_data_sets(self, labels: List[str], lookup: Dict[str, DataSet]): @@ -1289,26 +1332,16 @@ def populate_fits(self, lookup: Dict[str, FitResult], data: Optional[DataSet]): ) def get_next_data_set(self) -> Optional[DataSet]: - lookup: Dict[str, DataSet] = dpg.get_item_user_data(self.data_sets_combo.tag) - if not lookup: - return None - labels: List[str] = list(lookup.keys()) - index: int = labels.index(dpg.get_value(self.data_sets_combo.tag)) + 1 - return lookup[labels[index % len(labels)]] + return self.data_sets_combo.get_next() def get_previous_data_set(self) -> Optional[DataSet]: - lookup: Dict[str, DataSet] = dpg.get_item_user_data(self.data_sets_combo.tag) - if not lookup: - return None - labels: List[str] = list(lookup.keys()) - index: int = labels.index(dpg.get_value(self.data_sets_combo.tag)) - 1 - return lookup[labels[index % len(labels)]] + return self.data_sets_combo.get_previous() def get_next_result(self) -> Optional[FitResult]: - return self.results_combo.get_next_result() + return self.results_combo.get_next() def get_previous_result(self) -> Optional[FitResult]: - return self.results_combo.get_previous_result() + return self.results_combo.get_previous() def select_data_set(self, data: Optional[DataSet]): assert type(data) is DataSet or data is None, data @@ -1329,23 +1362,23 @@ def select_data_set(self, data: Optional[DataSet]): mag: ndarray phase: ndarray freq, mag, phase = data.get_bode_data() - self.bode_plot_horizontal.update( + self.bode_plot.update( index=0, frequency=freq, magnitude=mag, phase=phase, ) - self.bode_plot_vertical.update( + self.impedance_plot.update( index=0, frequency=freq, - magnitude=mag, - phase=phase, + real=real, + imaginary=imag, ) def assert_fit_up_to_date(self, fit: FitResult, data: DataSet): # Check if the number of unmasked points is the same - Z_exp: ndarray = data.get_impedance() - Z_fit: ndarray = fit.get_impedance() + Z_exp: ndarray = data.get_impedances() + Z_fit: ndarray = fit.get_impedances() assert Z_exp.shape == Z_fit.shape, "The number of data points differ!" # Check if the masks are the same mask_exp: Dict[int, bool] = data.get_mask() @@ -1365,14 +1398,13 @@ def assert_fit_up_to_date(self, fit: FitResult, data: DataSet): ), f"The data set's mask differs at index {i + 1}!" # Check if the frequencies and impedances are the same assert allclose( - fit.get_frequency(), data.get_frequency() + fit.get_frequencies(), + data.get_frequencies(), ), "The frequencies differ!" - real_residual: ndarray - imaginary_residual: ndarray - real_residual, imaginary_residual = _calculate_residuals(Z_exp, Z_fit) - assert allclose(fit.real_residual, real_residual) and allclose( - fit.imaginary_residual, imaginary_residual - ), "The data set's impedances differ from what they were when the fit was performed!" + residuals: ComplexResiduals = _calculate_residuals(Z_exp, Z_fit) + assert allclose(fit.residuals.real, residuals.real) and allclose( + fit.residuals.imag, residuals.imag + ), "Either the data set's impedances differ from what they were when the fit was performed or some aspect of the circuit's implementation has changed!" def select_fit_result(self, fit: Optional[FitResult], data: Optional[DataSet]): assert type(fit) is FitResult or fit is None, fit @@ -1399,21 +1431,21 @@ def select_fit_result(self, fit: Optional[FitResult], data: Optional[DataSet]): if fit is None or data is None: if dpg.get_value(self.adjust_nyquist_limits_checkbox): self.nyquist_plot.queue_limits_adjustment() - if dpg.get_value(self.adjust_bode_limits_horizontal_checkbox): - self.bode_plot_horizontal.queue_limits_adjustment() - self.bode_plot_vertical.queue_limits_adjustment() + if dpg.get_value(self.adjust_bode_limits_checkbox): + self.bode_plot.queue_limits_adjustment() + if dpg.get_value(self.adjust_impedance_limits_checkbox): + self.impedance_plot.queue_limits_adjustment() if dpg.get_value(self.adjust_residuals_limits_checkbox): self.residuals_plot.queue_limits_adjustment() return self.results_combo.set(fit.get_label()) - message: str try: self.assert_fit_up_to_date(fit, data) dpg.hide_item(dpg.get_item_parent(self.validity_text)) - except AssertionError as message: + except AssertionError as err: dpg.set_value( self.validity_text, - f"Fit result is not valid for the current state of the data set!\n\n{message}", + f"Fit result is not valid for the current state of the data set!\n\n{str(err)}", ) dpg.show_item(dpg.get_item_parent(self.validity_text)) self.parameters_table.populate(fit) @@ -1423,55 +1455,51 @@ def select_fit_result(self, fit: Optional[FitResult], data: Optional[DataSet]): real: ndarray imag: ndarray real, imag = fit.get_nyquist_data() + freq: ndarray + mag: ndarray + phase: ndarray + freq, mag, phase = fit.get_bode_data() self.nyquist_plot.update( index=1, real=real, imaginary=imag, ) - real, imag = fit.get_nyquist_data( - num_per_decade=self.state.config.num_per_decade_in_simulated_lines - ) - self.nyquist_plot.update( - index=2, - real=real, - imaginary=imag, - ) - freq: ndarray - mag: ndarray - phase: ndarray - freq, mag, phase = fit.get_bode_data() - self.bode_plot_horizontal.update( + self.bode_plot.update( index=1, frequency=freq, magnitude=mag, phase=phase, ) + self.impedance_plot.update( + index=1, + frequency=freq, + real=real, + imaginary=imag, + ) + real, imag = fit.get_nyquist_data( + num_per_decade=self.state.config.num_per_decade_in_simulated_lines + ) freq, mag, phase = fit.get_bode_data( num_per_decade=self.state.config.num_per_decade_in_simulated_lines ) - self.bode_plot_horizontal.update( + self.nyquist_plot.update( index=2, - frequency=freq, - magnitude=mag, - phase=phase, + real=real, + imaginary=imag, ) - freq, mag, phase = fit.get_bode_data() - self.bode_plot_vertical.update( - index=1, + self.bode_plot.update( + index=2, frequency=freq, magnitude=mag, phase=phase, ) - freq, mag, phase = fit.get_bode_data( - num_per_decade=self.state.config.num_per_decade_in_simulated_lines - ) - self.bode_plot_vertical.update( + self.impedance_plot.update( index=2, frequency=freq, - magnitude=mag, - phase=phase, + real=real, + imaginary=imag, ) - freq, real, imag = fit.get_residual_data() + freq, real, imag = fit.get_residuals_data() self.residuals_plot.update( index=0, frequency=freq, @@ -1480,9 +1508,10 @@ def select_fit_result(self, fit: Optional[FitResult], data: Optional[DataSet]): ) if dpg.get_value(self.adjust_nyquist_limits_checkbox): self.nyquist_plot.queue_limits_adjustment() - if dpg.get_value(self.adjust_bode_limits_horizontal_checkbox): - self.bode_plot_horizontal.queue_limits_adjustment() - self.bode_plot_vertical.queue_limits_adjustment() + if dpg.get_value(self.adjust_bode_limits_checkbox): + self.bode_plot.queue_limits_adjustment() + if dpg.get_value(self.adjust_impedance_limits_checkbox): + self.impedance_plot.queue_limits_adjustment() if dpg.get_value(self.adjust_residuals_limits_checkbox): self.residuals_plot.queue_limits_adjustment() @@ -1494,7 +1523,7 @@ def accept_circuit(self, circuit: Optional[Circuit]): self.circuit_editor.hide() if circuit is None: return - self.settings_menu.parse_cdc(circuit.to_string(12)) + self.settings_menu.parse_cdc(circuit.serialize()) def show_enlarged_nyquist(self): signals.emit( @@ -1506,8 +1535,15 @@ def show_enlarged_nyquist(self): def show_enlarged_bode(self): signals.emit( Signal.SHOW_ENLARGED_PLOT, - plot=self.bode_plot_horizontal, - adjust_limits=dpg.get_value(self.adjust_bode_limits_horizontal_checkbox), + plot=self.bode_plot, + adjust_limits=dpg.get_value(self.adjust_bode_limits_checkbox), + ) + + def show_enlarged_impedance(self): + signals.emit( + Signal.SHOW_ENLARGED_PLOT, + plot=self.impedance_plot, + adjust_limits=dpg.get_value(self.adjust_impedance_limits_checkbox), ) def show_enlarged_residuals(self): @@ -1517,6 +1553,29 @@ def show_enlarged_residuals(self): adjust_limits=dpg.get_value(self.adjust_residuals_limits_checkbox), ) + def show_parameter_adjustment(self): + data: Optional[DataSet] = dpg.get_item_user_data(self.perform_fit_button) + if data is None: + return + circuit: Optional[Circuit] + circuit, _ = process_cdc(self.get_settings().cdc) + if circuit is None or len(circuit.get_elements()) == 0: + return + window: ParameterAdjustment = ParameterAdjustment( + data=data, + circuit=circuit, + callback=self.accept_parameters, + keybindings=self.state.config.keybindings, + ) + signals.emit( + Signal.BLOCK_KEYBINDINGS, + window=window.window, + window_object=window, + ) + + def accept_parameters(self, circuit: Circuit): + self.settings_menu.parse_cdc(circuit.serialize()) + def get_active_output(self) -> Optional[FitSimOutput]: return label_to_fit_sim_output.get(dpg.get_value(self.output_combo)) diff --git a/src/deareis/gui/fitting/parameter_adjustment.py b/src/deareis/gui/fitting/parameter_adjustment.py new file mode 100644 index 0000000..bb4eec0 --- /dev/null +++ b/src/deareis/gui/fitting/parameter_adjustment.py @@ -0,0 +1,830 @@ +# DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). +# Copyright 2023 DearEIS developers +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +# +# The licenses of DearEIS' dependencies and/or sources of portions of code are included in +# the LICENSES folder. + +from dataclasses import dataclass +from traceback import format_exc +from typing import ( + Callable, + Dict, + List, +) +import dearpygui.dearpygui as dpg +from numpy import ( + angle, + array, + inf, + isinf, + isnan, + ndarray, +) +from pyimpspec import ( + Circuit, + ComplexImpedances, + Element, + Frequencies, +) +from pyimpspec.exceptions import ( + InfiniteImpedance, + NotANumberImpedance, +) +from pyimpspec.analysis.utility import _interpolate +from deareis.data import DataSet +from deareis.gui.plots import ( + Bode, + Nyquist, + Impedance, +) +import deareis.themes as themes +from deareis.signals import Signal +import deareis.signals as signals +from deareis.utility import ( + calculate_window_position_dimensions, + pad_tab_labels, +) +from deareis.tooltips import attach_tooltip +from deareis.enums import Action +from deareis.keybindings import ( + Keybinding, + TemporaryKeybindingHandler, +) + + +@dataclass +class ParameterSettings: + element: Element + key: str + tooltip: str + value_input: int + fixed_checkbox: int + value_slider: int + min_value_input: int + max_value_input: int + lower_limit_input: int + lower_limit_checkbox: int + upper_limit_input: int + upper_limit_checkbox: int + callback: Callable + + def reset(self): + self.element.reset_parameter(self.key) + if self.upper_limit_input > 0: + upper: float = self.get_upper_limit() + upper_enabled: bool = not isinf(upper) + dpg.set_value(self.upper_limit_checkbox, upper_enabled) + dpg.configure_item( + self.upper_limit_input, + default_value=upper, + enabled=upper_enabled, + readonly=not upper_enabled, + ) + if self.lower_limit_input > 0: + lower: float = self.get_lower_limit() + lower_enabled: bool = not isinf(lower) + dpg.set_value(self.lower_limit_checkbox, lower_enabled) + dpg.configure_item( + self.lower_limit_input, + default_value=lower, + enabled=lower_enabled, + readonly=not lower_enabled, + ) + maximum: float = self.get_max_value() + dpg.set_value(self.max_value_input, maximum) + minimum: float = self.get_min_value() + dpg.set_value(self.min_value_input, minimum) + value: float = self.get_value() + dpg.configure_item( + self.value_slider, + default_value=value, + min_value=minimum, + max_value=maximum, + ) + dpg.set_value(self.value_input, value) + self.callback() + + def get_value(self, default: bool = False) -> float: + if default: + return self.element.get_default_value(self.key) + return self.element.get_value(self.key) + + def set_value(self, sender: int, value: float): + minimum: float = dpg.get_value(self.min_value_input) + maximum: float = dpg.get_value(self.max_value_input) + if value < minimum: + value = minimum + elif value > maximum: + value = maximum + dpg.set_value(self.value_input, value) + dpg.set_value(self.value_slider, value) + self.element.set_values(self.key, value) + self.callback() + + def get_min_value(self, default: bool = False) -> float: + value: float = self.get_value(default=default) + minimum: float = value / 10 + lower: float = self.get_lower_limit(default=default) + if self.lower_limit_input > 0 and minimum < lower: + minimum = lower + return minimum + + def set_min_value(self, sender: int, value: float): + lower: float = self.get_lower_limit() + maximum: float = dpg.get_value(self.max_value_input) + if value < lower: + value = lower + dpg.set_value(self.min_value_input, value) + elif value >= maximum: + value = self.get_min_value() + dpg.set_value(self.min_value_input, value) + elif sender != self.min_value_input: + dpg.set_value(self.min_value_input, value) + if self.get_value() < value: + self.set_value(-1, value) + dpg.configure_item(self.value_slider, min_value=value) + + def get_max_value(self, default: bool = False) -> float: + value: float = self.get_value(default=default) + maximum: float = value * 2 + upper: float = self.get_upper_limit(default=default) + if self.upper_limit_input > 0 and maximum > upper: + maximum = upper + return maximum + + def set_max_value(self, sender: int, value: float): + upper: float = self.get_upper_limit() + minimum: float = dpg.get_value(self.min_value_input) + if value > upper: + value = upper + dpg.set_value(self.max_value_input, value) + elif value <= minimum: + value = self.get_max_value() + dpg.set_value(self.max_value_input, value) + elif sender != self.max_value_input: + dpg.set_value(self.max_value_input, value) + if self.get_value() > value: + self.set_value(-1, value) + dpg.configure_item(self.value_slider, max_value=value) + + def is_fixed(self, default: bool = False) -> bool: + if default: + return self.element.is_fixed_by_default(self.key) + return self.element.is_fixed(self.key) + + def set_fixed(self, sender: int, value: bool): + if sender != self.fixed_checkbox: + dpg.set_value(self.fixed_checkbox, value) + self.element.set_fixed(self.key, value) + + def get_lower_limit(self, default: bool = False) -> float: + if default: + return self.element.get_default_lower_limit(self.key) + return self.element.get_lower_limit(self.key) + + def set_lower_limit(self, sender: int, value: float): + maximum: float = dpg.get_value(self.max_value_input) + if value >= maximum: + value = dpg.get_value(self.min_value_input) + dpg.set_value(self.lower_limit_input, value) + elif sender != self.lower_limit_input: + dpg.set_value(self.lower_limit_input, value) + dpg.set_value(self.lower_limit_checkbox, not isinf(value)) + if not isinf(value): + minimum: float = dpg.get_value(self.min_value_input) + if minimum < value: + self.set_min_value(-1, value) + self.element.set_lower_limits(self.key, value) + + def toggle_lower_limit(self, sender: int, value: bool): + if sender != self.lower_limit_checkbox: + dpg.set_value(self.lower_limit_checkbox, value) + limit: float = -inf if not value else self.get_lower_limit(default=True) + if value is True and isinf(limit): + limit = dpg.get_value(self.min_value_input) + dpg.configure_item( + self.lower_limit_input, + default_value=limit, + enabled=value, + readonly=not value, + ) + if not isinf(limit): + minimum: float = dpg.get_value(self.min_value_input) + if minimum < limit: + self.set_min_value(-1, limit) + self.element.set_lower_limits(self.key, limit) + + def get_upper_limit(self, default: bool = False) -> float: + if default: + return self.element.get_default_upper_limit(self.key) + return self.element.get_upper_limit(self.key) + + def set_upper_limit(self, sender: int, value: float): + minimum: float = dpg.get_value(self.min_value_input) + if value <= minimum: + value = dpg.get_value(self.max_value_input) + dpg.set_value(self.upper_limit_input, value) + elif sender != self.upper_limit_input: + dpg.set_value(self.upper_limit_input, value) + dpg.set_value(self.upper_limit_checkbox, not isinf(value)) + if not isinf(value): + maximum: float = dpg.get_value(self.max_value_input) + if maximum > value: + self.set_max_value(-1, value) + self.element.set_upper_limits(self.key, value) + + def toggle_upper_limit(self, sender: int, value: bool): + if sender != self.upper_limit_checkbox: + dpg.set_value(self.upper_limit_checkbox, value) + limit: float = inf if value is False else self.get_upper_limit(default=True) + if value is True and isinf(limit): + limit = dpg.get_value(self.max_value_input) + dpg.configure_item( + self.upper_limit_input, + default_value=limit, + enabled=value, + readonly=not value, + ) + if not isinf(limit): + maximum: float = dpg.get_value(self.max_value_input) + if maximum > limit: + self.set_max_value(-1, limit) + self.element.set_upper_limits(self.key, limit) + + +class ParameterAdjustment: + def __init__( + self, + data: DataSet, + circuit: Circuit, + callback: Callable, + hide_data: bool = False, + keybindings: List[Keybinding] = [], + ): + assert isinstance(data, DataSet) + assert isinstance(circuit, Circuit) + assert callable(callback) + assert isinstance(hide_data, bool) + assert isinstance(keybindings, list) + self.hide_data: bool = hide_data + self.data: DataSet = data + self.marker_frequencies: Frequencies = data.get_frequencies() + self.line_frequencies: Frequencies = _interpolate( + self.marker_frequencies, + num_per_decade=20, + ) + self.circuit: Circuit = circuit + self.identifiers: Dict[Element, int] = circuit.generate_element_identifiers( + running=False + ) + self.callback: Callable = callback + self.input_widgets: List[int] = [] + self.create_window() + self.register_keybindings(keybindings) + self.nyquist_plot.queue_limits_adjustment() + self.bode_plot.queue_limits_adjustment() + self.impedance_plot.queue_limits_adjustment() + dpg.split_frame(delay=67) + self.update() + + def register_keybindings(self, keybindings: List[Keybinding]): + callbacks: Dict[Keybinding, Callable] = {} + # Cancel + kb: Keybinding = Keybinding( + key=dpg.mvKey_Escape, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.CANCEL, + ) + callbacks[kb] = lambda: self.close(keybinding=True) + # Accept + for kb in keybindings: + if kb.action is Action.PERFORM_ACTION: + break + else: + kb = Keybinding( + key=dpg.mvKey_Return, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.PERFORM_ACTION, + ) + callbacks[kb] = lambda: self.accept(keybinding=True) + # Previous plot tab + for kb in keybindings: + if kb.action is Action.PREVIOUS_PLOT_TAB: + break + else: + kb = Keybinding( + key=dpg.mvKey_Prior, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.PREVIOUS_PLOT_TAB, + ) + callbacks[kb] = lambda: self.cycle_plot_tab(step=-1) + # Next plot tab + for kb in keybindings: + if kb.action is Action.NEXT_PLOT_TAB: + break + else: + kb = Keybinding( + key=dpg.mvKey_Next, + mod_alt=True, + mod_ctrl=False, + mod_shift=True, + action=Action.NEXT_PLOT_TAB, + ) + callbacks[kb] = lambda: self.cycle_plot_tab(step=1) + # Create the handler + self.keybinding_handler: TemporaryKeybindingHandler = ( + TemporaryKeybindingHandler(callbacks=callbacks) + ) + + def create_window(self): + x: int + y: int + w: int + h: int + x, y, w, h = calculate_window_position_dimensions() + self.window: int = dpg.generate_uuid() + with dpg.window( + label="Adjust parameters", + modal=True, + pos=(x, y), + width=w, + height=h, + tag=self.window, + on_close=self.close, + ): + with dpg.group(horizontal=True): + with dpg.group(): + self.create_parameter_widgets() + dpg.add_button( + label="Accept".ljust(12), + callback=self.accept, + ) + self.create_plots() + + def create_plots(self): + self.plot_tab_bar: int = dpg.generate_uuid() + with dpg.tab_bar(tag=self.plot_tab_bar): + self.create_nyquist_plot() + self.create_bode_plot() + self.create_impedance_plot() + pad_tab_labels(self.plot_tab_bar) + + def create_nyquist_plot(self): + with dpg.tab(label="Nyquist"): + self.nyquist_plot: Nyquist = Nyquist(width=-1, height=-1) + if self.hide_data is False: + real: ndarray + imag: ndarray + real, imag = self.data.get_nyquist_data() + self.nyquist_plot.plot( + real=real, + imaginary=imag, + label="Data", + theme=themes.nyquist.data, + ) + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + label="Sim.", + show_label=False, + line=False, + simulation=True, + theme=themes.nyquist.simulation, + ) + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + label="Sim.", + line=True, + simulation=True, + theme=themes.nyquist.simulation, + ) + + def create_bode_plot(self): + with dpg.tab(label="Bode"): + self.bode_plot: Bode = Bode(width=-1, height=-1) + if self.hide_data is False: + freq: ndarray + mag: ndarray + phase: ndarray + freq, mag, phase = self.data.get_bode_data() + self.bode_plot.plot( + frequency=freq, + magnitude=mag, + phase=phase, + labels=( + "Mod(Z), d.", + "Phase(Z), d.", + ), + line=False, + themes=( + themes.bode.magnitude_data, + themes.bode.phase_data, + ), + ) + self.bode_plot.plot( + frequency=array([]), + magnitude=array([]), + phase=array([]), + labels=( + "Mod(Z), s.", + "Phase(Z), s.", + ), + line=False, + fit=True, + themes=( + themes.bode.magnitude_simulation, + themes.bode.phase_simulation, + ), + show_labels=False, + ) + self.bode_plot.plot( + frequency=array([]), + magnitude=array([]), + phase=array([]), + labels=( + "Mod(Z), s.", + "Phase(Z), s.", + ), + line=True, + fit=True, + themes=( + themes.bode.magnitude_simulation, + themes.bode.phase_simulation, + ), + ) + + def create_impedance_plot(self): + with dpg.tab(label="Real & Imag."): + self.impedance_plot: Impedance = Impedance(width=-1, height=-1) + if self.hide_data is False: + f: ndarray = self.data.get_frequencies() + Z: ndarray = self.data.get_impedances() + self.impedance_plot.plot( + frequency=f, + real=Z.real, + imaginary=-Z.imag, + labels=( + "Re(Z), d.", + "Im(Z), d.", + ), + line=False, + themes=( + themes.impedance.real_data, + themes.impedance.imaginary_data, + ), + ) + self.impedance_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + labels=( + "Re(Z), s.", + "Im(Z), s.", + ), + line=False, + fit=True, + themes=( + themes.impedance.real_simulation, + themes.impedance.imaginary_simulation, + ), + show_labels=False, + ) + self.impedance_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + labels=( + "Re(Z), s.", + "Im(Z), s.", + ), + line=True, + fit=True, + themes=( + themes.impedance.real_simulation, + themes.impedance.imaginary_simulation, + ), + ) + + def parameter_heading( + self, + element: Element, + key: str, + window_width: int, + slider_width: int, + label_pad: int, + ): + settings: ParameterSettings = ParameterSettings( + element=element, + key=key, + tooltip=f"Description: {element.get_value_description(key)}\n\nUnit: {element.get_unit(key)}", + value_input=dpg.generate_uuid(), + fixed_checkbox=dpg.generate_uuid(), + value_slider=dpg.generate_uuid(), + min_value_input=dpg.generate_uuid(), + max_value_input=dpg.generate_uuid(), + lower_limit_input=dpg.generate_uuid(), + lower_limit_checkbox=dpg.generate_uuid(), + upper_limit_input=dpg.generate_uuid(), + upper_limit_checkbox=dpg.generate_uuid(), + callback=self.update, + ) + name: str = self.circuit.get_element_name(element, self.identifiers) + with dpg.collapsing_header(label=f"{name} - {key}", open_on_arrow=False): + self.parameter_value_input( + settings=settings, + slider_width=slider_width, + label_pad=label_pad, + ) + self.parameter_min_max_value_inputs( + settings=settings, + window_width=window_width, + label_pad=label_pad, + ) + self.parameter_limit_inputs( + settings=settings, + slider_width=slider_width, + label_pad=label_pad, + ) + dpg.add_button(label="Reset", callback=settings.reset) + dpg.add_spacer(height=8) + self.input_widgets.extend( + [ + _ + for _ in ( + settings.value_input, + settings.min_value_input, + settings.max_value_input, + settings.lower_limit_input, + settings.upper_limit_input, + ) + if _ > 0 + ] + ) + + def parameter_value_input( + self, + settings: ParameterSettings, + slider_width: int, + label_pad: int, + ): + value: float = settings.get_value() + with dpg.group(horizontal=True): + dpg.add_text("Value".rjust(label_pad)) + attach_tooltip(settings.tooltip) + dpg.add_input_float( + default_value=value, + step=0, + format="%.4g", + on_enter=True, + callback=settings.set_value, + width=slider_width, + tag=settings.value_input, + ) + dpg.add_checkbox( + label="F", + default_value=settings.is_fixed(), + callback=settings.set_fixed, + tag=settings.fixed_checkbox, + ) + attach_tooltip("Whether or not the value is fixed during fitting") + with dpg.group(horizontal=True): + dpg.add_text("".rjust(label_pad)) + dpg.add_slider_float( + default_value=value, + format="%.4g", + no_input=True, + min_value=settings.get_min_value(), + max_value=settings.get_max_value(), + callback=settings.set_value, + width=slider_width, + tag=settings.value_slider, + ) + + def parameter_min_max_value_inputs( + self, + settings: ParameterSettings, + window_width: int, + label_pad: int, + ): + min_max_width: int = (window_width - 162) / 2 - 8 + with dpg.group(horizontal=True): + dpg.add_text("Min.".rjust(label_pad)) + min_tooltip: str = "The minimum value of the value slider." + attach_tooltip(min_tooltip) + dpg.add_input_float( + default_value=settings.get_min_value(), + format="%.4g", + callback=settings.set_min_value, + on_enter=True, + step=0, + width=min_max_width, + tag=settings.min_value_input, + ) + attach_tooltip(min_tooltip) + dpg.add_input_float( + label="Max.", + default_value=settings.get_max_value(), + format="%.4g", + callback=settings.set_max_value, + on_enter=True, + step=0, + width=min_max_width, + tag=settings.max_value_input, + ) + attach_tooltip("The maximum value of the value slider.") + + def parameter_limit_inputs( + self, + settings: ParameterSettings, + slider_width: int, + label_pad: int, + ): + lower: float = settings.get_lower_limit() + upper: float = settings.get_upper_limit() + lower_enabled: bool = not isinf(lower) + upper_enabled: bool = not isinf(upper) + with dpg.group(horizontal=True): + dpg.add_text("Lower limit".rjust(label_pad)) + attach_tooltip(settings.tooltip) + dpg.add_input_float( + default_value=lower, + step=0, + format="%.4g", + width=slider_width, + tag=settings.lower_limit_input, + on_enter=True, + readonly=not lower_enabled, + enabled=lower_enabled, + callback=settings.set_lower_limit, + ) + dpg.add_checkbox( + label="E", + default_value=lower_enabled, + tag=settings.lower_limit_checkbox, + callback=settings.toggle_lower_limit, + ) + attach_tooltip("Enabled") + with dpg.group(horizontal=True): + dpg.add_text("Upper limit".rjust(label_pad)) + attach_tooltip(settings.tooltip) + dpg.add_input_float( + default_value=upper, + step=0, + format="%.4g", + width=slider_width, + tag=settings.upper_limit_input, + on_enter=True, + readonly=not upper_enabled, + enabled=upper_enabled, + callback=settings.set_upper_limit, + ) + dpg.add_checkbox( + label="E", + default_value=upper_enabled, + tag=settings.upper_limit_checkbox, + callback=settings.toggle_upper_limit, + ) + attach_tooltip("Enabled") + + def create_parameter_widgets(self): + label_pad: int = 12 + slider_width: int = 230 + window_width: int = 400 + with dpg.child_window(width=window_width, height=-24): + element: Element + for element in self.identifiers: + key: str + for key in element.get_values().keys(): + self.parameter_heading( + element=element, + key=key, + window_width=window_width, + slider_width=slider_width, + label_pad=label_pad, + ) + + def update(self): + marker_real: ndarray + marker_imag: ndarray + marker_mag: ndarray + marker_phase: ndarray + line_real: ndarray + line_imag: ndarray + line_mag: ndarray + line_phase: ndarray + try: + Z: ComplexImpedances = self.circuit.get_impedances(self.marker_frequencies) + if isinf(Z).any(): + raise InfiniteImpedance() + elif isnan(Z).any(): + raise NotANumberImpedance() + marker_real = Z.real + marker_imag = -Z.imag + marker_mag = abs(Z) + marker_phase = -angle(Z, deg=True) + Z = self.circuit.get_impedances(self.line_frequencies) + if isinf(Z).any(): + raise InfiniteImpedance() + elif isnan(Z).any(): + raise NotANumberImpedance() + line_real = Z.real + line_imag = -Z.imag + line_mag = abs(Z) + line_phase = -angle(Z, deg=True) + except (InfiniteImpedance, NotANumberImpedance): + marker_real = array([]) + marker_imag = array([]) + line_real = array([]) + line_imag = array([]) + except Exception: + dpg.split_frame(delay=60) + self.close() + dpg.split_frame(delay=60) + signals.emit( + Signal.SHOW_ERROR_MESSAGE, + traceback=format_exc(), + message=""" +Encountered exception while calculating impedances. + """.strip(), + ) + return + self.nyquist_plot.update( + index=0 if self.hide_data is True else 1, + real=marker_real, + imaginary=marker_imag, + ) + self.nyquist_plot.update( + index=1 if self.hide_data is True else 2, + real=line_real, + imaginary=line_imag, + ) + self.bode_plot.update( + index=0 if self.hide_data is True else 1, + frequency=self.marker_frequencies, + magnitude=marker_mag, + phase=marker_phase, + ) + self.bode_plot.update( + index=1 if self.hide_data is True else 2, + frequency=self.line_frequencies, + magnitude=line_mag, + phase=line_phase, + ) + self.impedance_plot.update( + index=0 if self.hide_data is True else 1, + frequency=self.marker_frequencies, + real=marker_real, + imaginary=marker_imag, + ) + self.impedance_plot.update( + index=1 if self.hide_data is True else 2, + frequency=self.line_frequencies, + real=line_real, + imaginary=line_imag, + ) + + def close(self, keybinding: bool = False): + if keybinding is True and self.has_active_input(): + return + dpg.hide_item(self.window) + dpg.delete_item(self.window) + self.keybinding_handler.delete() + signals.emit(Signal.UNBLOCK_KEYBINDINGS) + + def accept(self, keybinding: bool = False): + if keybinding is True and self.has_active_input(): + return + self.callback(self.circuit) + self.close() + + def has_active_input(self) -> bool: + widget: int + for widget in self.input_widgets: + if dpg.is_item_active(widget): + return True + return False + + def cycle_plot_tab(self, step: int): + tabs: List[int] = dpg.get_item_children(self.plot_tab_bar, slot=1) + index: int = tabs.index(dpg.get_value(self.plot_tab_bar)) + step + dpg.set_value(self.plot_tab_bar, tabs[index % len(tabs)]) diff --git a/src/deareis/gui/kramers_kronig/__init__.py b/src/deareis/gui/kramers_kronig/__init__.py index 6362941..c42ed65 100644 --- a/src/deareis/gui/kramers_kronig/__init__.py +++ b/src/deareis/gui/kramers_kronig/__init__.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -17,34 +17,70 @@ # The licenses of DearEIS' dependencies and/or sources of portions of code are included in # the LICENSES folder. -from typing import Callable, Dict, List, Optional -from numpy import allclose, array, log10 as log, ndarray -from pyimpspec.analysis.kramers_kronig import _calculate_residuals +from typing import ( + Callable, + Dict, + List, + Optional, +) +from numpy import ( + allclose, + array, + isnan, + log10 as log, + ndarray, +) +from pyimpspec.analysis.utility import _calculate_residuals +from pyimpspec import ComplexResiduals import dearpygui.dearpygui as dpg from deareis.signals import Signal import deareis.signals as signals from deareis.enums import ( - Context, CNLSMethod, - TestMode, + Context, Test, + TestMode, + cnls_method_to_label, label_to_cnls_method, - label_to_test_mode, label_to_test, - cnls_method_to_label, + label_to_test_mode, test_mode_to_label, test_to_label, ) -from deareis.data import TestResult, TestSettings, DataSet -from deareis.gui.plots import Bode, Nyquist, Residuals +from deareis.utility import ( + format_number, + pad_tab_labels, +) +from deareis.data import ( + DataSet, + TestResult, + TestSettings, +) +from deareis.gui.plots import ( + Bode, + Impedance, + Nyquist, + Residuals, +) import deareis.tooltips as tooltips -from deareis.tooltips import attach_tooltip, update_tooltip +from deareis.tooltips import ( + attach_tooltip, + update_tooltip, +) import deareis.themes as themes +from deareis.gui.shared import ( + DataSetsCombo, + ResultsCombo, +) class SettingsMenu: def __init__( - self, default_settings: TestSettings, label_pad: int, limited: bool = False + self, + default_settings: TestSettings, + label_pad: int, + limited: bool = False, + **kwargs, ): with dpg.group(horizontal=True): dpg.add_text("Test".rjust(label_pad)) @@ -119,12 +155,14 @@ def __init__( dpg.add_text("Fitting method".rjust(label_pad)) attach_tooltip(tooltips.kramers_kronig.method) self.method_combo: int = dpg.generate_uuid() + fitting_methods: List[str] = list(label_to_cnls_method.keys()) + fitting_methods.remove("Auto") dpg.add_combo( default_value=cnls_method_to_label.get( default_settings.method, - list(label_to_cnls_method.keys())[0], + fitting_methods[0], ), - items=list(label_to_cnls_method.keys()), + items=fitting_methods, width=-1, tag=self.method_combo, ) @@ -221,6 +259,7 @@ def has_active_input(self) -> bool: return dpg.is_item_active(self.max_nfev_input) +# TODO: AbstractStatisticsTable class StatisticsTable: def __init__(self): label_pad: int = 23 @@ -251,6 +290,9 @@ def __init__(self): ("log X² (pseudo)", tooltips.kramers_kronig.pseudo_chisqr), ("µ", tooltips.kramers_kronig.mu), ("Number of RC elements", tooltips.kramers_kronig.num_RC), + ("Series resistance", tooltips.kramers_kronig.series_resistance), + ("Series capacitance", tooltips.kramers_kronig.series_capacitance), + ("Series inductance", tooltips.kramers_kronig.series_inductance), ]: with dpg.table_row(parent=self._table): dpg.add_text(label.rjust(label_pad)) @@ -270,32 +312,40 @@ def clear(self, hide: bool): def populate(self, test: TestResult): dpg.show_item(self._header) + rows: List[int] = dpg.get_item_children(self._table, slot=1) cells: List[int] = [] row: int - for row in dpg.get_item_children(self._table, slot=1): + for row in rows: cells.append(dpg.get_item_children(row, slot=1)[2]) - assert len(cells) == 3, cells - tag: int + assert len(cells) == 6, cells + R: float = test.get_series_resistance() + C: float = test.get_series_capacitance() + L: float = test.get_series_inductance() + values: List[str] = [ + f"{log(test.pseudo_chisqr):.3f}", + f"{test.mu:.3f}", + f"{test.num_RC}", + format_number(R, significants=3).strip() if not isnan(R) else "", + format_number(C, significants=3).strip() if not isnan(C) else "", + format_number(L, significants=3).strip() if not isnan(L) else "", + ] + num_rows: int = 0 + cell: int value: str - for (tag, value) in [ - ( - cells[0], - f"{log(test.pseudo_chisqr):.3f}", - ), - ( - cells[1], - f"{test.mu:.3f}", - ), - ( - cells[2], - f"{test.num_RC}", - ), - ]: - dpg.set_value(tag, value) - update_tooltip(dpg.get_item_user_data(tag), value) - dpg.show_item(dpg.get_item_parent(dpg.get_item_user_data(tag))) + for row, cell, value in zip(rows, cells, values): + if value == "": + dpg.hide_item(row) + continue + else: + dpg.show_item(row) + num_rows += 1 + dpg.set_value(cell, value) + update_tooltip(dpg.get_item_user_data(cell), value) + dpg.show_item(dpg.get_item_parent(dpg.get_item_user_data(cell))) + dpg.set_item_height(self._table, 18 + 23 * max(1, num_rows)) +# TODO: AbstractSettingsTable class SettingsTable: def __init__(self): label_pad: int = 23 @@ -336,6 +386,7 @@ def __init__(self): tooltip_tag: int = dpg.generate_uuid() dpg.add_text("", user_data=tooltip_tag) attach_tooltip("", tag=tooltip_tag) + dpg.add_spacer(height=8) with dpg.group(horizontal=True): self._apply_settings_button: int = dpg.generate_uuid() dpg.add_button( @@ -345,6 +396,7 @@ def __init__(self): **u, ), tag=self._apply_settings_button, + width=154, ) attach_tooltip(tooltips.general.apply_settings) self._apply_mask_button: int = dpg.generate_uuid() @@ -355,6 +407,7 @@ def __init__(self): **u, ), tag=self._apply_mask_button, + width=-1, ) attach_tooltip(tooltips.general.apply_mask) @@ -468,480 +521,371 @@ def populate(self, test: TestResult, data: DataSet, num_RC_labels: List[str]): ) -class DataSetsCombo: - def __init__(self, label: str, width: int): - self.labels: List[str] = [] - dpg.add_text(label) - self.tag: int = dpg.generate_uuid() - dpg.add_combo( - callback=lambda s, a, u: signals.emit( - Signal.SELECT_DATA_SET, - data=u.get(a), - ), - user_data={}, - width=width, - tag=self.tag, - ) - - def populate(self, labels: List[str], lookup: Dict[str, DataSet]): - self.labels.clear() - self.labels.extend(labels) - label: str = dpg.get_value(self.tag) or "" - if labels and label not in labels: - label = labels[0] - dpg.configure_item( - self.tag, - default_value=label, - items=labels, - user_data=lookup, - ) - - def get(self) -> Optional[DataSet]: - return dpg.get_item_user_data(self.tag).get(dpg.get_value(self.tag)) - - def set(self, label: str): - assert type(label) is str, label - assert label in self.labels, ( - label, - self.labels, - ) - dpg.set_value(self.tag, label) - - def get_next(self) -> Optional[DataSet]: - lookup: Dict[str, DataSet] = dpg.get_item_user_data(self.tag) - if not lookup: - return None - labels: List[str] = list(lookup.keys()) - index: int = labels.index(dpg.get_value(self.tag)) + 1 - return lookup[labels[index % len(labels)]] - - def get_previous(self) -> Optional[DataSet]: - lookup: Dict[str, DataSet] = dpg.get_item_user_data(self.tag) - if not lookup: - return None - labels: List[str] = list(lookup.keys()) - index: int = labels.index(dpg.get_value(self.tag)) - 1 - return lookup[labels[index % len(labels)]] - - def clear(self): - dpg.configure_item( - self.tag, - default_value="", - ) - - -class ResultsCombo: - def __init__(self, label: str, width: int): - self.labels: List[str] = [] - dpg.add_text(label) - self.tag: int = dpg.generate_uuid() - dpg.add_combo( - callback=lambda s, a, u: signals.emit( - Signal.SELECT_TEST_RESULT, - test=u[0].get(a), - data=u[1], - ), - user_data=( - {}, - None, - ), - width=width, - tag=self.tag, - ) - - def populate(self, lookup: Dict[str, TestResult], data: Optional[DataSet]): - self.labels.clear() - labels: List[str] = list(lookup.keys()) - self.labels.extend(labels) - dpg.configure_item( - self.tag, - items=labels, - default_value=labels[0] if labels else "", - user_data=( - lookup, - data, - ), +class TestResultsCombo(ResultsCombo): + def selection_callback(self, sender: int, app_data: str, user_data: tuple): + signals.emit( + Signal.SELECT_TEST_RESULT, + test=user_data[0].get(app_data), + data=user_data[1], ) - def get(self) -> Optional[TestResult]: - return dpg.get_item_user_data(self.tag)[0].get(dpg.get_value(self.tag)) - - def set(self, label: str): - assert type(label) is str, label - assert label in self.labels, ( - label, - self.labels, + def adjust_label(self, old: str, longest: int) -> str: + label: str + timestamp: str + label, timestamp = ( + old[: old.find(" (")], + old[old.find(" (") + 1 :], ) - dpg.set_value(self.tag, label) - - def clear(self): - dpg.configure_item( - self.tag, - default_value="", - ) - - def get_next(self) -> Optional[TestResult]: - lookup: Dict[str, TestResult] = dpg.get_item_user_data(self.tag)[0] - if not lookup: - return None - labels: List[str] = list(lookup.keys()) - index: int = labels.index(dpg.get_value(self.tag)) + 1 - return lookup[labels[index % len(labels)]] - - def get_previous(self) -> Optional[TestResult]: - lookup: Dict[str, TestResult] = dpg.get_item_user_data(self.tag)[0] - if not lookup: - return None - labels: List[str] = list(lookup.keys()) - index: int = labels.index(dpg.get_value(self.tag)) - 1 - return lookup[labels[index % len(labels)]] + return f"{label.ljust(longest)} {timestamp}" class KramersKronigTab: def __init__(self, state): self.state = state self.queued_update: Optional[Callable] = None + self.create_tab(state) + self.set_settings(self.state.config.default_test_settings) + + def create_tab(self, state): self.tab: int = dpg.generate_uuid() label_pad: int = 24 with dpg.tab(label="Kramers-Kronig", tag=self.tab): with dpg.child_window(border=False): with dpg.group(horizontal=True): - self.sidebar_window: int = dpg.generate_uuid() - self.sidebar_width: int = 350 - with dpg.child_window( - border=False, - width=self.sidebar_width, - tag=self.sidebar_window, - ): - # TODO: Split into a separate class? - with dpg.child_window(width=-1, height=220): - self.settings_menu: SettingsMenu = SettingsMenu( - state.config.default_test_settings, label_pad - ) - with dpg.group(horizontal=True): - self.visibility_item: int = dpg.generate_uuid() - dpg.add_text( - "?".rjust(label_pad), - tag=self.visibility_item, - ) - attach_tooltip(tooltips.kramers_kronig.perform) - self.perform_test_button: int = dpg.generate_uuid() - dpg.add_button( - label="Perform test", - callback=lambda s, a, u: signals.emit( - Signal.PERFORM_TEST, - data=u, - settings=self.get_settings(), - ), - user_data=None, - width=-1, - tag=self.perform_test_button, - ) - with dpg.child_window(width=-1, height=58): - label_pad = 8 - with dpg.group(horizontal=True): - self.data_sets_combo: DataSetsCombo = DataSetsCombo( - label="Data set".rjust(label_pad), - width=-60, - ) - with dpg.group(horizontal=True): - self.results_combo: ResultsCombo = ResultsCombo( - label="Result".rjust(label_pad), - width=-60, - ) - self.delete_button: int = dpg.generate_uuid() - dpg.add_button( - label="Delete", - callback=lambda s, a, u: signals.emit( - Signal.DELETE_TEST_RESULT, - **u, - ), - width=-1, - tag=self.delete_button, - ) - attach_tooltip(tooltips.kramers_kronig.delete) - with dpg.child_window(width=-1, height=-1): - with dpg.group(show=False): - self.validity_text: int = dpg.generate_uuid() - dpg.bind_item_theme( - dpg.add_text( - "", - wrap=self.sidebar_width - 24, - tag=self.validity_text, - ), - themes.result.invalid, - ) - dpg.add_spacer(height=8) - self.statistics_table: StatisticsTable = StatisticsTable() - dpg.add_spacer(height=8) - self.settings_table: SettingsTable = SettingsTable() - self.plot_window: int = dpg.generate_uuid() - with dpg.child_window(border=False, tag=self.plot_window): - with dpg.group(): - # Residuals - self.residuals_plot: Residuals = Residuals( - width=-1, - height=300, - ) - self.residuals_plot.plot( - frequency=array([]), - real=array([]), - imaginary=array([]), - ) - with dpg.group(horizontal=True): - self.enlarge_residuals_button: int = dpg.generate_uuid() - self.adjust_residuals_limits_checkbox: int = ( - dpg.generate_uuid() - ) - dpg.add_button( - label="Enlarge residuals", - callback=self.show_enlarged_residuals, - tag=self.enlarge_residuals_button, - ) - dpg.add_checkbox( - default_value=True, - tag=self.adjust_residuals_limits_checkbox, - ) - attach_tooltip(tooltips.general.adjust_residuals_limits) - dpg.add_button( - label="Copy as CSV", - callback=lambda s, a, u: signals.emit( - Signal.COPY_PLOT_DATA, - plot=self.residuals_plot, - context=Context.KRAMERS_KRONIG_TAB, - ), - ) - attach_tooltip(tooltips.general.copy_plot_data_as_csv) - # Nyquist and Bode - self.minimum_plot_side: int = 400 - with dpg.group(horizontal=True): - with dpg.group(): - self.nyquist_plot: Nyquist = Nyquist( - width=self.minimum_plot_side, - height=self.minimum_plot_side, - ) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="Data", - line=False, - theme=themes.nyquist.data, - ) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="Fit", - line=False, - fit=True, - theme=themes.nyquist.simulation, - ) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="Fit", - line=True, - fit=True, - theme=themes.nyquist.simulation, - show_label=False, - ) - with dpg.group(horizontal=True): - self.enlarge_nyquist_button: int = ( - dpg.generate_uuid() - ) - self.adjust_nyquist_limits_checkbox: int = ( - dpg.generate_uuid() - ) - dpg.add_button( - label="Enlarge Nyquist", - callback=self.show_enlarged_nyquist, - tag=self.enlarge_nyquist_button, - ) - dpg.add_checkbox( - default_value=True, - tag=self.adjust_nyquist_limits_checkbox, - ) - attach_tooltip( - tooltips.general.adjust_nyquist_limits - ) - dpg.add_button( - label="Copy as CSV", - callback=lambda s, a, u: signals.emit( - Signal.COPY_PLOT_DATA, - plot=self.nyquist_plot, - context=Context.KRAMERS_KRONIG_TAB, - ), - ) - attach_tooltip( - tooltips.general.copy_plot_data_as_csv - ) - self.horizontal_bode_group: int = dpg.generate_uuid() - with dpg.group(tag=self.horizontal_bode_group): - self.bode_plot_horizontal: Bode = Bode( - width=self.minimum_plot_side, - height=self.minimum_plot_side, - ) - self.bode_plot_horizontal.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z| (d)", - "phi (d)", - ), - line=False, - themes=( - themes.bode.magnitude_data, - themes.bode.phase_data, - ), - ) - self.bode_plot_horizontal.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z| (f)", - "phi (f)", - ), - line=False, - fit=True, - themes=( - themes.bode.magnitude_simulation, - themes.bode.phase_simulation, - ), - ) - self.bode_plot_horizontal.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z| (f)", - "phi (f)", - ), - line=True, - fit=True, - themes=( - themes.bode.magnitude_simulation, - themes.bode.phase_simulation, - ), - show_labels=False, - ) - with dpg.group(horizontal=True): - self.enlarge_bode_horizontal_button: int = ( - dpg.generate_uuid() - ) - self.adjust_bode_limits_horizontal_checkbox: int = ( - dpg.generate_uuid() - ) - dpg.add_button( - label="Enlarge Bode", - callback=self.show_enlarged_bode, - tag=self.enlarge_bode_horizontal_button, - ) - dpg.add_checkbox( - default_value=True, - tag=self.adjust_bode_limits_horizontal_checkbox, - ) - attach_tooltip( - tooltips.general.adjust_bode_limits - ) - dpg.add_button( - label="Copy as CSV", - callback=lambda s, a, u: signals.emit( - Signal.COPY_PLOT_DATA, - plot=self.bode_plot_horizontal, - context=Context.KRAMERS_KRONIG_TAB, - ), - ) - attach_tooltip( - tooltips.general.copy_plot_data_as_csv - ) - self.vertical_bode_group: int = dpg.generate_uuid() - with dpg.group(tag=self.vertical_bode_group): - self.bode_plot_vertical: Bode = Bode( - width=-1, - height=self.minimum_plot_side, - ) - self.bode_plot_vertical.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z| (d)", - "phi (d)", - ), - line=False, - themes=( - themes.bode.magnitude_data, - themes.bode.phase_data, - ), - ) - self.bode_plot_vertical.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z| (f)", - "phi (f)", - ), - line=False, - fit=True, - themes=( - themes.bode.magnitude_simulation, - themes.bode.phase_simulation, - ), - ) - self.bode_plot_vertical.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z| (f)", - "phi (f)", - ), - line=True, - fit=True, - themes=( - themes.bode.magnitude_simulation, - themes.bode.phase_simulation, - ), - show_labels=False, - ) - with dpg.group(horizontal=True): - self.enlarge_bode_vertical_button: int = ( - dpg.generate_uuid() - ) - self.adjust_bode_limits_vertical_checkbox: int = ( - dpg.generate_uuid() - ) - dpg.add_button( - label="Enlarge Bode", - callback=lambda s, a, u: signals.emit( - Signal.SHOW_ENLARGED_PLOT, - plot=self.bode_plot_vertical, - adjust_limits=dpg.get_value( - self.adjust_bode_limits_horizontal_checkbox - ), - ), - tag=self.enlarge_bode_vertical_button, - ) - dpg.add_checkbox( - default_value=True, - source=self.adjust_bode_limits_horizontal_checkbox, - tag=self.adjust_bode_limits_vertical_checkbox, - ) - attach_tooltip(tooltips.general.adjust_bode_limits) - dpg.add_button( - label="Copy as CSV", - callback=lambda s, a, u: signals.emit( - Signal.COPY_PLOT_DATA, - plot=self.bode_plot_vertical, - context=Context.KRAMERS_KRONIG_TAB, - ), - ) - attach_tooltip( - tooltips.general.copy_plot_data_as_csv - ) - self.set_settings(self.state.config.default_test_settings) + self.create_sidebar(state, label_pad) + self.create_plots() + + def create_sidebar(self, state, label_pad: int): + self.sidebar_window: int = dpg.generate_uuid() + self.sidebar_width: int = 350 + with dpg.child_window( + border=False, + width=self.sidebar_width, + tag=self.sidebar_window, + ): + # TODO: Split into a separate class? + with dpg.child_window(width=-1, height=220): + self.settings_menu: SettingsMenu = SettingsMenu( + state.config.default_test_settings, + label_pad, + ) + with dpg.group(horizontal=True): + self.visibility_item: int = dpg.generate_uuid() + dpg.add_text( + "?".rjust(label_pad), + tag=self.visibility_item, + ) + attach_tooltip(tooltips.kramers_kronig.perform) + self.perform_test_button: int = dpg.generate_uuid() + dpg.add_button( + label="Perform", + callback=lambda s, a, u: signals.emit( + Signal.PERFORM_TEST, + data=u, + settings=self.get_settings(), + ), + user_data=None, + width=-70, + tag=self.perform_test_button, + ) + dpg.add_button( + label="Batch", + callback=lambda s, a, u: signals.emit( + Signal.BATCH_PERFORM_ANALYSIS, + settings=self.get_settings(), + ), + width=-1, + ) + with dpg.child_window(width=-1, height=58): + label_pad = 8 + with dpg.group(horizontal=True): + self.data_sets_combo: DataSetsCombo = DataSetsCombo( + label="Data set".rjust(label_pad), + width=-60, + ) + with dpg.group(horizontal=True): + self.results_combo: TestResultsCombo = TestResultsCombo( + label="Result".rjust(label_pad), + width=-60, + ) + self.delete_button: int = dpg.generate_uuid() + dpg.add_button( + label="Delete", + callback=lambda s, a, u: signals.emit( + Signal.DELETE_TEST_RESULT, + **u, + ), + width=-1, + tag=self.delete_button, + ) + attach_tooltip(tooltips.kramers_kronig.delete) + with dpg.child_window(width=-1, height=-1): + with dpg.group(show=False): + self.validity_text: int = dpg.generate_uuid() + dpg.bind_item_theme( + dpg.add_text( + "", + wrap=self.sidebar_width - 24, + tag=self.validity_text, + ), + themes.result.invalid, + ) + dpg.add_spacer(height=8) + self.statistics_table: StatisticsTable = StatisticsTable() + dpg.add_spacer(height=8) + self.settings_table: SettingsTable = SettingsTable() + + def create_plots(self): + self.plot_window: int = dpg.generate_uuid() + with dpg.child_window(border=False, tag=self.plot_window): + self.create_residuals_plot() + dpg.add_spacer(height=4) + dpg.add_separator() + dpg.add_spacer(height=4) + self.plot_tab_bar: int = dpg.generate_uuid() + with dpg.tab_bar(tag=self.plot_tab_bar): + self.create_nyquist_plot() + self.create_bode_plot() + self.create_impedance_plot() + pad_tab_labels(self.plot_tab_bar) + + def create_residuals_plot(self): + self.residuals_plot: Residuals = Residuals( + width=-1, + height=300, + ) + self.residuals_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + ) + with dpg.group(horizontal=True): + self.enlarge_residuals_button: int = dpg.generate_uuid() + self.adjust_residuals_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_residuals, + tag=self.enlarge_residuals_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.residuals_plot, + context=Context.KRAMERS_KRONIG_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_residuals_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_residuals_limits) + + def create_nyquist_plot(self): + with dpg.tab(label="Nyquist"): + self.nyquist_plot: Nyquist = Nyquist( + width=-1, + height=-24, + ) + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + label="Data", + line=False, + theme=themes.nyquist.data, + ) + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + label="Fit", + line=False, + fit=True, + theme=themes.nyquist.simulation, + ) + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + label="Fit", + line=True, + fit=True, + theme=themes.nyquist.simulation, + show_label=False, + ) + with dpg.group(horizontal=True): + self.enlarge_nyquist_button: int = dpg.generate_uuid() + self.adjust_nyquist_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_nyquist, + tag=self.enlarge_nyquist_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.nyquist_plot, + context=Context.KRAMERS_KRONIG_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_nyquist_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_nyquist_limits) + + def create_bode_plot(self): + with dpg.tab(label="Bode"): + self.bode_plot: Bode = Bode( + width=-1, + height=-24, + ) + self.bode_plot.plot( + frequency=array([]), + magnitude=array([]), + phase=array([]), + labels=( + "Mod(Z), d.", + "Phase(Z), d.", + ), + line=False, + themes=( + themes.bode.magnitude_data, + themes.bode.phase_data, + ), + ) + self.bode_plot.plot( + frequency=array([]), + magnitude=array([]), + phase=array([]), + labels=( + "Mod(Z), f.", + "Phase(Z), f.", + ), + line=False, + fit=True, + themes=( + themes.bode.magnitude_simulation, + themes.bode.phase_simulation, + ), + ) + self.bode_plot.plot( + frequency=array([]), + magnitude=array([]), + phase=array([]), + labels=( + "Mod(Z), f.", + "Phase(Z), f.", + ), + line=True, + fit=True, + themes=( + themes.bode.magnitude_simulation, + themes.bode.phase_simulation, + ), + show_labels=False, + ) + with dpg.group(horizontal=True): + self.enlarge_bode_button: int = dpg.generate_uuid() + self.adjust_bode_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_bode, + tag=self.enlarge_bode_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.bode_plot, + context=Context.KRAMERS_KRONIG_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_bode_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_bode_limits) + + def create_impedance_plot(self): + with dpg.tab(label="Real & Imag."): + self.impedance_plot: Impedance = Impedance( + width=-1, + height=-24, + ) + self.impedance_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + labels=( + "Re(Z), d.", + "Im(Z), d.", + ), + line=False, + themes=( + themes.impedance.real_data, + themes.impedance.imaginary_data, + ), + ) + self.impedance_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + labels=( + "Re(Z), f.", + "Im(Z), f.", + ), + line=False, + fit=True, + themes=( + themes.impedance.real_simulation, + themes.impedance.imaginary_simulation, + ), + ) + self.impedance_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + labels=( + "Re(Z), f.", + "Im(Z), f.", + ), + line=True, + fit=True, + themes=( + themes.impedance.real_simulation, + themes.impedance.imaginary_simulation, + ), + show_labels=False, + ) + with dpg.group(horizontal=True): + self.enlarge_impedance_button: int = dpg.generate_uuid() + self.adjust_impedance_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_impedance, + tag=self.enlarge_impedance_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.impedance_plot, + context=Context.KRAMERS_KRONIG_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_impedance_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_impedance_limits) def is_visible(self) -> bool: return dpg.is_item_visible(self.visibility_item) @@ -949,23 +893,7 @@ def is_visible(self) -> bool: def resize(self, width: int, height: int): assert type(width) is int and width > 0 assert type(height) is int and height > 0 - if not self.is_visible(): - return - if width < (self.sidebar_width + self.minimum_plot_side * 2): - if dpg.is_item_shown(self.horizontal_bode_group): - dpg.hide_item(self.horizontal_bode_group) - dpg.show_item(self.vertical_bode_group) - self.nyquist_plot.resize(-1, self.minimum_plot_side) - else: - if dpg.is_item_shown(self.vertical_bode_group): - dpg.show_item(self.horizontal_bode_group) - dpg.hide_item(self.vertical_bode_group) - dpg.split_frame() - width, height = dpg.get_item_rect_size(self.plot_window) - width = round((width - 24) / 2) + 7 - height = height - 300 - 24 * 2 - 2 - self.nyquist_plot.resize(width, height) - self.bode_plot_horizontal.resize(width, height) + return def clear(self, hide: bool = True): self.data_sets_combo.clear() @@ -975,8 +903,8 @@ def clear(self, hide: bool = True): dpg.set_item_user_data(self.perform_test_button, None) self.residuals_plot.clear(delete=False) self.nyquist_plot.clear(delete=False) - self.bode_plot_horizontal.clear(delete=False) - self.bode_plot_vertical.clear(delete=False) + self.bode_plot.clear(delete=False) + self.impedance_plot.clear(delete=False) def get_settings(self) -> TestSettings: return self.settings_menu.get_settings() @@ -1037,23 +965,23 @@ def select_data_set(self, data: Optional[DataSet]): mag: ndarray phase: ndarray freq, mag, phase = data.get_bode_data() - self.bode_plot_horizontal.update( + self.bode_plot.update( index=0, frequency=freq, magnitude=mag, phase=phase, ) - self.bode_plot_vertical.update( + self.impedance_plot.update( index=0, frequency=freq, - magnitude=mag, - phase=phase, + real=real, + imaginary=imag, ) def assert_test_up_to_date(self, test: TestResult, data: DataSet): # Check if the number of unmasked points is the same - Z_exp: ndarray = data.get_impedance() - Z_test: ndarray = test.get_impedance() + Z_exp: ndarray = data.get_impedances() + Z_test: ndarray = test.get_impedances() assert Z_exp.shape == Z_test.shape, "The number of data points differ!" # Check if the masks are the same mask_exp: Dict[int, bool] = data.get_mask() @@ -1073,13 +1001,12 @@ def assert_test_up_to_date(self, test: TestResult, data: DataSet): ), f"The data set's mask differs at index {i + 1}!" # Check if the frequencies and impedances are the same assert allclose( - test.get_frequency(), data.get_frequency() + test.get_frequencies(), + data.get_frequencies(), ), "The frequencies differ!" - real_residual: ndarray - imaginary_residual: ndarray - real_residual, imaginary_residual = _calculate_residuals(Z_exp, Z_test) - assert allclose(test.real_residual, real_residual) and allclose( - test.imaginary_residual, imaginary_residual + residuals: ComplexResiduals = _calculate_residuals(Z_exp, Z_test) + assert allclose(test.residuals.real, residuals.real) and allclose( + test.residuals.imag, residuals.imag ), "The data set's impedances differ from what they were when the test was performed!" def select_test_result(self, test: Optional[TestResult], data: Optional[DataSet]): @@ -1102,9 +1029,10 @@ def select_test_result(self, test: Optional[TestResult], data: Optional[DataSet] self.residuals_plot.queue_limits_adjustment() if dpg.get_value(self.adjust_nyquist_limits_checkbox): self.nyquist_plot.queue_limits_adjustment() - if dpg.get_value(self.adjust_bode_limits_horizontal_checkbox): - self.bode_plot_horizontal.queue_limits_adjustment() - self.bode_plot_vertical.queue_limits_adjustment() + if dpg.get_value(self.adjust_bode_limits_checkbox): + self.bode_plot.queue_limits_adjustment() + if dpg.get_value(self.adjust_impedance_limits_checkbox): + self.impedance_plot.queue_limits_adjustment() return self.results_combo.set(test.get_label()) message: str @@ -1126,7 +1054,7 @@ def select_test_result(self, test: Optional[TestResult], data: Optional[DataSet] freq: ndarray real: ndarray imag: ndarray - freq, real, imag = test.get_residual_data() + freq, real, imag = test.get_residuals_data() self.residuals_plot.update( index=0, frequency=freq, @@ -1150,7 +1078,7 @@ def select_test_result(self, test: Optional[TestResult], data: Optional[DataSet] mag: ndarray phase: ndarray freq, mag, phase = test.get_bode_data() - self.bode_plot_horizontal.update( + self.bode_plot.update( index=1, frequency=freq, magnitude=mag, @@ -1159,35 +1087,50 @@ def select_test_result(self, test: Optional[TestResult], data: Optional[DataSet] freq, mag, phase = test.get_bode_data( num_per_decade=self.state.config.num_per_decade_in_simulated_lines ) - self.bode_plot_horizontal.update( + self.bode_plot.update( index=2, frequency=freq, magnitude=mag, phase=phase, ) - freq, mag, phase = test.get_bode_data() - self.bode_plot_vertical.update( + freq = test.get_frequencies() + Z: ndarray = test.get_impedances() + self.impedance_plot.update( index=1, frequency=freq, - magnitude=mag, - phase=phase, + real=Z.real, + imaginary=-Z.imag, ) - freq, mag, phase = test.get_bode_data( + freq = test.get_frequencies( num_per_decade=self.state.config.num_per_decade_in_simulated_lines ) - self.bode_plot_vertical.update( + Z: ndarray = test.get_impedances( + num_per_decade=self.state.config.num_per_decade_in_simulated_lines + ) + self.impedance_plot.update( index=2, frequency=freq, - magnitude=mag, - phase=phase, + real=Z.real, + imaginary=-Z.imag, ) if dpg.get_value(self.adjust_residuals_limits_checkbox): self.residuals_plot.queue_limits_adjustment() if dpg.get_value(self.adjust_nyquist_limits_checkbox): self.nyquist_plot.queue_limits_adjustment() - if dpg.get_value(self.adjust_bode_limits_horizontal_checkbox): - self.bode_plot_horizontal.queue_limits_adjustment() - self.bode_plot_vertical.queue_limits_adjustment() + if dpg.get_value(self.adjust_bode_limits_checkbox): + self.bode_plot.queue_limits_adjustment() + if dpg.get_value(self.adjust_impedance_limits_checkbox): + self.impedance_plot.queue_limits_adjustment() + + def next_plot_tab(self): + tabs: List[int] = dpg.get_item_children(self.plot_tab_bar, slot=1) + index: int = tabs.index(dpg.get_value(self.plot_tab_bar)) + 1 + dpg.set_value(self.plot_tab_bar, tabs[index % len(tabs)]) + + def previous_plot_tab(self): + tabs: List[int] = dpg.get_item_children(self.plot_tab_bar, slot=1) + index: int = tabs.index(dpg.get_value(self.plot_tab_bar)) - 1 + dpg.set_value(self.plot_tab_bar, tabs[index % len(tabs)]) def show_enlarged_nyquist(self): signals.emit( @@ -1199,8 +1142,15 @@ def show_enlarged_nyquist(self): def show_enlarged_bode(self): signals.emit( Signal.SHOW_ENLARGED_PLOT, - plot=self.bode_plot_horizontal, - adjust_limits=dpg.get_value(self.adjust_bode_limits_horizontal_checkbox), + plot=self.bode_plot, + adjust_limits=dpg.get_value(self.adjust_bode_limits_checkbox), + ) + + def show_enlarged_impedance(self): + signals.emit( + Signal.SHOW_ENLARGED_PLOT, + plot=self.impedance_plot, + adjust_limits=dpg.get_value(self.adjust_impedance_limits_checkbox), ) def show_enlarged_residuals(self): diff --git a/src/deareis/gui/kramers_kronig/exploratory_results.py b/src/deareis/gui/kramers_kronig/exploratory_results.py index 48e0d23..0c19c9a 100644 --- a/src/deareis/gui/kramers_kronig/exploratory_results.py +++ b/src/deareis/gui/kramers_kronig/exploratory_results.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -47,9 +47,11 @@ Nyquist, Residuals, ) +from deareis.state import STATE +from deareis.enums import Action from deareis.keybindings import ( - is_alt_down, - is_control_down, + Keybinding, + TemporaryKeybindingHandler, ) @@ -79,9 +81,8 @@ def __init__( self.residuals_plot: Optional[Residuals] = None self.nyquist_plot: Optional[Nyquist] = None self.bode_plot: Optional[Bode] = None - self.key_handler: int = dpg.generate_uuid() self._assemble() - self._setup_keybindings() + self.register_keybindings() self.data: DataSet = data self.results: List[TestResult] = results self.settings: TestSettings = settings @@ -91,10 +92,12 @@ def __init__( results.sort(key=lambda _: _.num_RC) default_label: str = "" self.label_to_result: Dict[str, TestResult] = {} + max_num_RC_length: int = len(str(max(self.num_RCs))) result: TestResult for result in results: label: str = ( - f"{result.num_RC}: µ = {result.mu:.3f}, " + str(result.num_RC).rjust(max_num_RC_length) + + f": µ = {result.mu:.3f}, " + f"log X² (ps.) = {log(result.pseudo_chisqr):.3f}" ) if result == default_result: @@ -122,31 +125,66 @@ def __init__( self.plot(default_label) signals.register(Signal.VIEWPORT_RESIZED, self.resize) - def _setup_keybindings(self): - with dpg.handler_registry(tag=self.key_handler): - dpg.add_key_release_handler( - key=dpg.mvKey_Escape, - callback=self.close, - ) - dpg.add_key_release_handler( + def register_keybindings(self): + callbacks: Dict[Keybinding, Callable] = {} + # Cancel + kb: Keybinding = Keybinding( + key=dpg.mvKey_Escape, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.CANCEL, + ) + callbacks[kb] = self.close + # Accept + for kb in STATE.config.keybindings: + if kb.action is Action.PERFORM_ACTION: + break + else: + kb = Keybinding( key=dpg.mvKey_Return, - callback=lambda: self.accept( - dpg.get_item_user_data(self.accept_button), - keybinding=True, - ), + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.PERFORM_ACTION, ) - dpg.add_key_release_handler( + callbacks[kb] = lambda: self.accept( + dpg.get_item_user_data(self.accept_button), + ) + # Previous result + for kb in STATE.config.keybindings: + if kb.action is Action.PREVIOUS_PRIMARY_RESULT: + break + else: + kb = Keybinding( key=dpg.mvKey_Prior, - callback=lambda: self.plot( - self.labels[(self.result_index - 1) % len(self.labels)] - ), + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.PREVIOUS_PRIMARY_RESULT, ) - dpg.add_key_release_handler( + callbacks[kb] = lambda: self.plot( + self.labels[(self.result_index - 1) % len(self.labels)] + ) + # Next result + for kb in STATE.config.keybindings: + if kb.action is Action.NEXT_PRIMARY_RESULT: + break + else: + kb = Keybinding( key=dpg.mvKey_Next, - callback=lambda: self.plot( - self.labels[(self.result_index + 1) % len(self.labels)] - ), + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.NEXT_PRIMARY_RESULT, ) + callbacks[kb] = lambda: self.plot( + self.labels[(self.result_index + 1) % len(self.labels)] + ) + # Create the handler + self.keybinding_handler: TemporaryKeybindingHandler = ( + TemporaryKeybindingHandler(callbacks=callbacks) + ) def _assemble(self): x: int @@ -220,8 +258,8 @@ def _assemble(self): magnitude=array([]), phase=array([]), labels=( - "|Z| (d)", - "phi (d)", + "Mod(Z), d.", + "Phase(Z), d.", ), themes=( themes.bode.magnitude_data, @@ -232,7 +270,7 @@ def _assemble(self): frequency=array([]), magnitude=array([]), phase=array([]), - labels=("|Z| (f)", "phi (f)"), + labels=("Mod(Z), f.", "Phase(Z), f."), show_labels=False, line=True, themes=( @@ -244,7 +282,7 @@ def _assemble(self): frequency=array([]), magnitude=array([]), phase=array([]), - labels=("|Z| (f)", "phi (f)"), + labels=("Mod(Z), f.", "Phase(Z), f."), themes=( themes.bode.magnitude_simulation, themes.bode.phase_simulation, @@ -279,7 +317,7 @@ def plot(self, label: str): freq: ndarray real: ndarray imag: ndarray - freq, real, imag = result.get_residual_data() + freq, real, imag = result.get_residuals_data() self.residuals_plot.update( index=0, frequency=freq, @@ -373,16 +411,10 @@ def resize(self, width: int, height: int): def close(self): dpg.hide_item(self.window) dpg.delete_item(self.window) - dpg.delete_item(self.key_handler) + self.keybinding_handler.delete() signals.emit(Signal.UNBLOCK_KEYBINDINGS) signals.unregister(Signal.VIEWPORT_RESIZED, self.resize) - def accept(self, result: TestResult, keybinding: bool = False): - if keybinding is True and not ( - is_control_down() - if dpg.get_platform() == dpg.mvPlatform_Windows - else is_alt_down() - ): - return + def accept(self, result: TestResult): self.callback(self.data, result, self.settings) self.close() diff --git a/src/deareis/gui/licenses/__init__.py b/src/deareis/gui/licenses/__init__.py index 7abc29d..8c38669 100644 --- a/src/deareis/gui/licenses/__init__.py +++ b/src/deareis/gui/licenses/__init__.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -21,9 +21,20 @@ from deareis.utility import calculate_window_position_dimensions from os import walk from os.path import abspath, exists, dirname, join -from typing import Dict, IO, List +from typing import ( + Callable, + Dict, + IO, + List, +) from deareis.signals import Signal import deareis.signals as signals +from deareis.state import STATE +from deareis.enums import Action +from deareis.keybindings import ( + Keybinding, + TemporaryKeybindingHandler, +) def read_file(path: str) -> str: @@ -54,61 +65,150 @@ def get_licenses(root: str) -> Dict[str, str]: return licenses -def show_license_window(): - licenses: Dict[str, str] = get_licenses(dirname(abspath(__file__))) - x: int - y: int - w: int - h: int - x, y, w, h = calculate_window_position_dimensions(640, 540) - window: int = dpg.generate_uuid() - key_handler: int = dpg.generate_uuid() - - def close_window(): - if dpg.does_item_exist(window): - dpg.delete_item(window) - if dpg.does_item_exist(key_handler): - dpg.delete_item(key_handler) - signals.emit(Signal.UNBLOCK_KEYBINDINGS) +class LicensesWindow: + def __init__(self): + self.licenses: Dict[str, str] = get_licenses(dirname(abspath(__file__))) + self.create_window() + self.register_keybindings() - with dpg.handler_registry(tag=key_handler): - dpg.add_key_release_handler( + def register_keybindings(self): + callbacks: Dict[Keybinding, Callable] = {} + # Cancel + kb: Keybinding = Keybinding( key=dpg.mvKey_Escape, - callback=close_window, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.CANCEL, + ) + callbacks[kb] = self.close + # Previous tab + for kb in STATE.config.keybindings: + if kb.action is Action.PREVIOUS_PROJECT_TAB: + break + else: + kb = Keybinding( + key=dpg.mvKey_Prior, + mod_alt=False, + mod_ctrl=True, + mod_shift=False, + action=Action.PREVIOUS_PROJECT_TAB, + ) + callbacks[kb] = lambda: self.cycle_tabs(step=-1) + # Next tab + for kb in STATE.config.keybindings: + if kb.action is Action.NEXT_PROJECT_TAB: + break + else: + kb = Keybinding( + key=dpg.mvKey_Next, + mod_alt=False, + mod_ctrl=True, + mod_shift=False, + action=Action.NEXT_PROJECT_TAB, + ) + callbacks[kb] = lambda: self.cycle_tabs(step=1) + # Previous license + for kb in STATE.config.keybindings: + if kb.action is Action.PREVIOUS_PRIMARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Prior, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.PREVIOUS_PRIMARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle_licenses(step=-1) + # Next license + for kb in STATE.config.keybindings: + if kb.action is Action.NEXT_PRIMARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Next, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.NEXT_PRIMARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle_licenses(step=1) + # Create the handler + self.keybinding_handler: TemporaryKeybindingHandler = ( + TemporaryKeybindingHandler(callbacks=callbacks) + ) + + def create_window(self): + x: int + y: int + w: int + h: int + x, y, w, h = calculate_window_position_dimensions(640, 540) + self.window: int = dpg.generate_uuid() + with dpg.window( + label="Licenses", + modal=True, + pos=( + x, + y, + ), + width=w, + height=h, + no_move=False, + no_resize=True, + on_close=self.close, + tag=self.window, + ): + self.tab_bar: int = dpg.generate_uuid() + with dpg.tab_bar(tag=self.tab_bar): + with dpg.tab(label="DearEIS"): + with dpg.child_window(border=False): + dpg.add_text(self.licenses["DearEIS"], wrap=w) + del self.licenses["DearEIS"] + with dpg.tab(label="Dependencies"): + self.text_widget: int = dpg.generate_uuid() + items: List[str] = list(sorted(self.licenses.keys())) + self.license_combo: int = dpg.generate_uuid() + dpg.add_combo( + items=items, + default_value=items[0], + width=-1, + callback=self.show_dependency_license, + tag=self.license_combo, + ) + with dpg.child_window(border=False): + dpg.add_text( + self.licenses[items[0]], + wrap=w, + tag=self.text_widget, + ) + signals.emit( + Signal.BLOCK_KEYBINDINGS, + window=self.window, + window_object=self, ) - with dpg.window( - label="Licenses", - modal=True, - pos=( - x, - y, - ), - width=w, - height=h, - no_move=False, - no_resize=True, - on_close=close_window, - tag=window, - ): - with dpg.tab_bar(): - with dpg.tab(label="DearEIS"): - with dpg.child_window(border=False): - dpg.add_text(licenses["DearEIS"], wrap=w) - del licenses["DearEIS"] - with dpg.tab(label="Dependencies"): - text_widget: int = dpg.generate_uuid() + def show_dependency_license(self, sender: int, label: str): + dpg.set_value(self.text_widget, self.licenses[label]) - def show_dependency_license(sender: int, label: str): - dpg.set_value(text_widget, licenses[label]) + def cycle_tabs(self, step: int): + tabs: List[int] = dpg.get_item_children(self.tab_bar, slot=1) + index: int = tabs.index(dpg.get_value(self.tab_bar)) + step + dpg.set_value(self.tab_bar, tabs[index % len(tabs)]) - items: List[str] = list(sorted(licenses.keys())) - dpg.add_combo( - items=items, - default_value=items[0], - width=-1, - callback=show_dependency_license, - ) - with dpg.child_window(border=False): - dpg.add_text(licenses[items[0]], wrap=w, tag=text_widget) - signals.emit(Signal.BLOCK_KEYBINDINGS, window=window, window_object=None) + def cycle_licenses(self, step: int): + labels: List[str] = list(self.licenses.keys()) + index: int = labels.index(dpg.get_value(self.license_combo)) + step + dpg.set_value(self.license_combo, labels[index % len(labels)]) + self.show_dependency_license(self.license_combo, labels[index % len(labels)]) + + def close(self): + if dpg.does_item_exist(self.window): + dpg.delete_item(self.window) + self.keybinding_handler.delete() + signals.emit(Signal.UNBLOCK_KEYBINDINGS) + + +def show_license_window(): + LicensesWindow() diff --git a/src/deareis/gui/overview.py b/src/deareis/gui/overview.py index 9864fda..cd546dc 100644 --- a/src/deareis/gui/overview.py +++ b/src/deareis/gui/overview.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -44,7 +44,8 @@ def __init__(self): multiline=True, tab_input=True, callback=lambda s, a, u: signals.emit( - Signal.MODIFY_PROJECT_NOTES, timers=u + Signal.MODIFY_PROJECT_NOTES, + timers=u, ), user_data=[], width=-1, diff --git a/src/deareis/gui/plots/__init__.py b/src/deareis/gui/plots/__init__.py index edb8bca..e8b5bd7 100644 --- a/src/deareis/gui/plots/__init__.py +++ b/src/deareis/gui/plots/__init__.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -48,7 +48,7 @@ def __init__(self, original: Plot, adjust_limits: bool): Nyquist: "Nyquist", Residuals: "Residuals", DRT: "Distribution of relaxation times", - Impedance: "Complex impedance", + Impedance: "Real and imaginary impedance", ImpedanceImaginary: "Imaginary impedance", ImpedanceReal: "Real impedance", } @@ -62,10 +62,7 @@ def __init__(self, original: Plot, adjust_limits: bool): with dpg.window( label=labels.get(type(original), "Unknown plot type"), modal=True, - pos=( - x, - y, - ), + pos=(x, y), width=w, height=h, on_close=self.close, diff --git a/src/deareis/gui/plots/base.py b/src/deareis/gui/plots/base.py index 106b1bf..7d50558 100644 --- a/src/deareis/gui/plots/base.py +++ b/src/deareis/gui/plots/base.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by diff --git a/src/deareis/gui/plots/bode.py b/src/deareis/gui/plots/bode.py index 06e463a..f193265 100644 --- a/src/deareis/gui/plots/bode.py +++ b/src/deareis/gui/plots/bode.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -60,13 +60,13 @@ def __init__(self, width: int = -1, height: int = -1, *args, **kwargs): ) self._y_axis_1: int = dpg.add_plot_axis( dpg.mvYAxis, - label="|Z| (ohm)", + label="Mod(Z) (ohm)", log_scale=True, no_gridlines=True, ) self._y_axis_2: int = dpg.add_plot_axis( dpg.mvYAxis, - label="-phi (°)", + label="-Phase(Z) (°)", no_gridlines=True, ) dpg.bind_item_theme(self._plot, themes.plot) @@ -289,7 +289,7 @@ def __init__(self, width: int = -1, height: int = -1, *args, **kwargs): ) self._y_axis: int = dpg.add_plot_axis( dpg.mvYAxis, - label="|Z| (ohm)", + label="Mod(Z) (ohm)", log_scale=True, no_gridlines=True, ) @@ -310,6 +310,26 @@ def clear(self, *args, **kwargs): dpg.delete_item(self._y_axis, children_only=True) self._series.clear() + def update(self, index: int, *args, **kwargs): + assert type(index) is int and index >= 0, index + assert len(self._series) > index, ( + index, + len(self._series), + ) + assert len(args) == 0, args + freq: ndarray = kwargs["frequency"] + mag: ndarray = kwargs["magnitude"] + assert type(freq) is ndarray, freq + assert type(mag) is ndarray, mag + i: int + series: int + for i, series in enumerate(dpg.get_item_children(self._y_axis, slot=1)): + if i != index: + continue + self._series[index].update(kwargs) + dpg.set_value(series, [list(freq), list(mag)]) + break + def plot(self, *args, **kwargs) -> int: assert len(args) == 0, args freq: ndarray = kwargs["frequency"] @@ -432,7 +452,7 @@ def __init__(self, width: int = -1, height: int = -1, *args, **kwargs): ) self._y_axis: int = dpg.add_plot_axis( dpg.mvYAxis, - label="-phi (°)", + label="-Phase(Z) (°)", no_gridlines=True, ) dpg.bind_item_theme(self._plot, themes.plot) @@ -452,6 +472,26 @@ def clear(self, *args, **kwargs): dpg.delete_item(self._y_axis, children_only=True) self._series.clear() + def update(self, index: int, *args, **kwargs): + assert type(index) is int and index >= 0, index + assert len(self._series) > index, ( + index, + len(self._series), + ) + assert len(args) == 0, args + freq: ndarray = kwargs["frequency"] + phase: ndarray = kwargs["phase"] + assert type(freq) is ndarray, freq + assert type(phase) is ndarray, phase + i: int + series: int + for i, series in enumerate(dpg.get_item_children(self._y_axis, slot=1)): + if i != index: + continue + self._series[index].update(kwargs) + dpg.set_value(series, [list(freq), list(phase)]) + break + def plot(self, *args, **kwargs) -> int: assert len(args) == 0, args freq: ndarray = kwargs["frequency"] diff --git a/src/deareis/gui/plots/drt.py b/src/deareis/gui/plots/drt.py index d2b3d26..05ae66e 100644 --- a/src/deareis/gui/plots/drt.py +++ b/src/deareis/gui/plots/drt.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -141,13 +141,7 @@ def update(self, index: int, *args, **kwargs): def plot(self, *args, **kwargs) -> int: assert len(args) == 0, args tau: ndarray = kwargs["tau"] - gamma: Optional[ndarray] = kwargs.get( - "gamma", - kwargs.get( - "imaginary", - kwargs.get("mean"), - ), - ) + gamma: Optional[ndarray] = kwargs.get("gamma") lower: Optional[ndarray] = kwargs.get("lower") upper: Optional[ndarray] = kwargs.get("upper") label: str = kwargs.get("label", "") diff --git a/src/deareis/gui/plots/image.py b/src/deareis/gui/plots/image.py index 5821169..69af485 100644 --- a/src/deareis/gui/plots/image.py +++ b/src/deareis/gui/plots/image.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by diff --git a/src/deareis/gui/plots/impedance.py b/src/deareis/gui/plots/impedance.py index 728de13..548b6a3 100644 --- a/src/deareis/gui/plots/impedance.py +++ b/src/deareis/gui/plots/impedance.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -28,7 +28,6 @@ array, ceil, floor, - isclose, log10 as log, ndarray, ) @@ -61,12 +60,12 @@ def __init__(self, width: int = -1, height: int = -1, *args, **kwargs): ) self._y_axis_1: int = dpg.add_plot_axis( dpg.mvYAxis, - label="Z' (ohm)", + label="Re(Z) (ohm)", no_gridlines=True, ) self._y_axis_2: int = dpg.add_plot_axis( dpg.mvYAxis, - label='-Z" (ohm)', + label="-Im(Z) (ohm)", no_gridlines=True, ) dpg.bind_item_theme(self._plot, themes.plot) @@ -224,15 +223,20 @@ def adjust_limits(self): x_min = 10 ** (floor(log(x_min) / dx) * dx - dx) x_max = 10 ** (ceil(log(x_max) / dx) * dx + dx) dy: float = abs(y1_max - y1_min) * 0.05 - if isclose(dy, 0): - dy = 0.05 * y1_max if not isclose(abs(y1_max), 0) else 5 - y1_min = y1_min - dy - y1_max = y1_max + dy + n: int = 1 + if dy < 1.0: + y1_min = floor(y1_min / n) * n - n + y1_max = ceil(y1_max / n) * n + n + else: + y1_min = y1_min - dy + y1_max = y1_max + dy dy = abs(y2_max - y2_min) * 0.05 - if isclose(dy, 0): - dy = 0.05 * y2_max if not isclose(abs(y2_max), 0) else 5 - y2_min = y2_min - dy - y2_max = y2_max + dy + if dy < 1.0: + y2_min = floor(y2_min / n) * n - n + y2_max = ceil(y2_max / n) * n + n + else: + y2_min = y2_min - dy + y2_max = y2_max + dy dpg.split_frame() dpg.set_axis_limits(self._x_axis, ymin=x_min, ymax=x_max) dpg.set_axis_limits(self._y_axis_1, ymin=y1_min, ymax=y1_max) @@ -415,8 +419,13 @@ def adjust_limits(self): x_min = 10 ** (floor(log(x_min) / dx) * dx - dx) x_max = 10 ** (ceil(log(x_max) / dx) * dx + dx) dy: float = abs(y_max - y_min) * 0.05 - y_min = y_min - dy - y_max = y_max + dy + n: int = 1 + if dy < 1.0: + y_min = floor(y_min / n) * n - n + y_max = ceil(y_max / n) * n + n + else: + y_min = y_min - dy + y_max = y_max + dy dpg.split_frame() dpg.set_axis_limits(self._x_axis, ymin=x_min, ymax=x_max) dpg.set_axis_limits(self._y_axis, ymin=y_min, ymax=y_max) @@ -455,7 +464,7 @@ def __init__( super().__init__( width=width, height=height, - y_axis_label="Z' (ohm)", + y_axis_label="Re(Z) (ohm)", *args, **kwargs, ) @@ -472,7 +481,7 @@ def __init__( super().__init__( width=width, height=height, - y_axis_label='-Z" (ohm)', + y_axis_label="-Im(Z) (ohm)", *args, **kwargs, ) diff --git a/src/deareis/gui/plots/mu_xps.py b/src/deareis/gui/plots/mu_xps.py index 4b987c3..e4ad95b 100644 --- a/src/deareis/gui/plots/mu_xps.py +++ b/src/deareis/gui/plots/mu_xps.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by diff --git a/src/deareis/gui/plots/nyquist.py b/src/deareis/gui/plots/nyquist.py index 2311c70..5c2efe2 100644 --- a/src/deareis/gui/plots/nyquist.py +++ b/src/deareis/gui/plots/nyquist.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -44,12 +44,12 @@ def __init__(self, width: int = -1, height: int = -1, *args, **kwargs): ) self._x_axis: int = dpg.add_plot_axis( dpg.mvXAxis, - label="Z' (ohm)", + label="Re(Z) (ohm)", no_gridlines=True, ) self._y_axis: int = dpg.add_plot_axis( dpg.mvYAxis, - label='-Z" (ohm)', + label='-Im(Z) (ohm)', no_gridlines=True, ) dpg.bind_item_theme(self._plot, themes.plot) diff --git a/src/deareis/gui/plots/residuals.py b/src/deareis/gui/plots/residuals.py index 8e4931e..f0e5700 100644 --- a/src/deareis/gui/plots/residuals.py +++ b/src/deareis/gui/plots/residuals.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -58,11 +58,11 @@ def __init__(self, width: int = -1, height: int = -1, *args, **kwargs): ) self._y_axis_1: int = dpg.add_plot_axis( dpg.mvYAxis, - label="Z' error (%)", + label="Re(Z) residual (%)", ) self._y_axis_2: int = dpg.add_plot_axis( dpg.mvYAxis, - label='Z" error (%)', + label="Im(Z) residual (%)", ) dpg.bind_item_theme(self._plot, themes.plot) dpg.bind_item_handler_registry(self._plot, self._item_handler) @@ -147,7 +147,7 @@ def plot(self, *args, **kwargs): dpg.add_scatter_series( x=x, y=y, - label="Z'", + label="Re(Z)", user_data=( freq, real, @@ -175,7 +175,7 @@ def plot(self, *args, **kwargs): dpg.add_scatter_series( x=x, y=y, - label='Z"', + label="Im(Z)", parent=self._y_axis_2, ), themes.residuals.imaginary, diff --git a/src/deareis/gui/plots/zhit_weights.py b/src/deareis/gui/plots/zhit_weights.py new file mode 100644 index 0000000..6c88aef --- /dev/null +++ b/src/deareis/gui/plots/zhit_weights.py @@ -0,0 +1,284 @@ +# DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). +# Copyright 2023 DearEIS developers +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +# +# The licenses of DearEIS' dependencies and/or sources of portions of code are included in +# the LICENSES folder. + +from typing import ( + Callable, + List, + Optional, + Tuple, +) +import dearpygui.dearpygui as dpg +from numpy import ( + array, + ceil, + floor, + log10 as log, + ndarray, +) +import deareis.themes as themes +from deareis.gui.plots.base import Plot + + +class ZHITWeights(Plot): + def __init__(self, width: int = -1, height: int = -1, *args, **kwargs): + assert type(width) is int, width + assert type(height) is int, height + super().__init__() + with dpg.plot( + anti_aliased=True, + crosshairs=True, + width=width, + height=height, + tag=self._plot, + ): + dpg.add_plot_legend( + horizontal=True, + location=dpg.mvPlot_Location_North, + outside=kwargs.get("legend_outside", True), + ) + self._x_axis: int = dpg.add_plot_axis( + dpg.mvXAxis, + label="f (Hz)", + log_scale=True, + no_gridlines=True, + ) + self._y_axis_1: int = dpg.add_plot_axis( + dpg.mvYAxis, + label="Mod(Z) (ohm)", + log_scale=True, + no_gridlines=True, + ) + self._y_axis_2: int = dpg.add_plot_axis( + dpg.mvYAxis, + label="Weight", + no_gridlines=True, + ) + dpg.bind_item_theme(self._plot, themes.plot) + dpg.bind_item_handler_registry(self._plot, self._item_handler) + + @classmethod + def duplicate(Class, original: Plot, *args, **kwargs) -> Plot: + copy: Plot = Class(*args, **kwargs) + for kwargs in original.get_series(): + copy.plot(**kwargs) + return copy + + def is_blank(self) -> bool: + return ( + len(dpg.get_item_children(self._y_axis_1, slot=1)) == 0 + and len(dpg.get_item_children(self._y_axis_2, slot=1)) == 0 + ) + + def clear(self, *args, **kwargs): + delete: bool = kwargs.get("delete", True) + if delete: + dpg.delete_item(self._y_axis_1, children_only=True) + dpg.delete_item(self._y_axis_2, children_only=True) + self._series.clear() + else: + i: int + series_1: int + series_2: int + for i, (series_1, series_2) in enumerate( + zip( + dpg.get_item_children(self._y_axis_1, slot=1), + dpg.get_item_children(self._y_axis_2, slot=1), + ) + ): + self._series[i]["frequency"] = array([]) + self._series[i]["magnitude"] = array([]) + self._series[i]["weight"] = array([]) + dpg.set_value(series_1, [[], []]) + dpg.set_value(series_2, [[], []]) + + def update(self, index: int, *args, **kwargs): + assert type(index) is int and index >= 0, index + assert len(self._series) > index, ( + index, + len(self._series), + ) + assert len(args) == 0, args + freq: ndarray = kwargs["frequency"] + mag: ndarray = kwargs["magnitude"] + weight: ndarray = kwargs["weight"] + assert type(freq) is ndarray, freq + assert type(mag) is ndarray, mag + assert type(weight) is ndarray, weight + i: int + series_1: int + series_2: int + for i, (series_1, series_2) in enumerate( + zip( + dpg.get_item_children(self._y_axis_1, slot=1), + dpg.get_item_children(self._y_axis_2, slot=1), + ) + ): + if i != index: + continue + self._series[index].update(kwargs) + dpg.set_value( + series_1, + [list(freq) if mag.size > 0 else [], list(mag) if mag.size > 0 else []], + ) + dpg.set_value( + series_2, + [ + list(freq) if weight.size > 0 else [], + list(weight) if weight.size > 0 else [], + ], + ) + break + + def plot(self, *args, **kwargs) -> Tuple[int, int]: + assert len(args) == 0, args + freq: ndarray = kwargs["frequency"] + mag: ndarray = kwargs["magnitude"] + weight: ndarray = kwargs["weight"] + labels: Tuple[str, str] = kwargs["labels"] + sim: bool = kwargs.get("simulation", False) + line: bool = kwargs.get("line", False) + show_labels: bool = kwargs.get("show_labels", True) + themes: Optional[Tuple[int, int]] = kwargs.get("themes") + assert type(freq) is ndarray, freq + assert type(mag) is ndarray, mag + assert type(weight) is ndarray, weight + assert type(labels) is tuple and len(labels) == 2, labels + assert type(sim) is bool, sim + assert type(line) is bool, line + assert (type(themes) is tuple and len(themes) == 2) or themes is None, themes + assert type(show_labels) is bool, show_labels + self._series.append(kwargs) + func: Callable = dpg.add_scatter_series if not line else dpg.add_line_series + x: list = list(freq) + tag_mag: int = func( + x=x if mag.size > 0 else [], + y=list(mag) if mag.size > 0 else [], + label=labels[0] if show_labels and labels[0] != "" else None, + parent=self._y_axis_1, + ) + tag_weight: int = func( + x=x if weight.size > 0 else [], + y=list(weight) if weight.size > 0 else [], + label=labels[1] if show_labels and labels[1] != "" else None, + parent=self._y_axis_2, + ) + if themes is not None: + dpg.bind_item_theme(tag_mag, themes[0]) + dpg.bind_item_theme(tag_weight, themes[1]) + return ( + tag_mag, + tag_weight, + ) + + def plot_window(self, *args, **kwargs) -> int: + center: float = kwargs["center"] + width: float = kwargs["width"] + label: str = kwargs.get("label") + theme: int = kwargs.get("theme") + tag: int = dpg.add_shade_series( + x=[10 ** (center - width / 2), 10 ** (center + width / 2)], + y1=[0.0] * 2, + y2=[1.0] * 2, + label=label if label is not None and label != "" else None, + parent=self._y_axis_2, + ) + if theme is not None: + dpg.bind_item_theme(tag, theme) + return tag + + def adjust_limits(self): + if not self.is_visible(): + self.queue_limits_adjustment() + return + elif self.are_limits_adjusted(): + return + else: + self.limits_adjusted() + x_min: Optional[float] = None + x_max: Optional[float] = None + y1_min: Optional[float] = None + y1_max: Optional[float] = None + y2_min: Optional[float] = None + y2_max: Optional[float] = None + for kwargs in self._series: + freq: ndarray = kwargs["frequency"] + mag: ndarray = kwargs["magnitude"] + weight: ndarray = kwargs["weight"] + if freq.size > 0: + if x_min is None or min(freq) < x_min: + x_min = min(freq) + if x_max is None or max(freq) > x_max: + x_max = max(freq) + if mag.size > 0: + if y1_min is None or min(mag) < y1_min: + y1_min = min(mag) + if y1_max is None or max(mag) > y1_max: + y1_max = max(mag) + if weight.size > 0: + if y2_min is None or min(weight) < y2_min: + y2_min = min(weight) + if y2_max is None or max(weight) > y2_max: + y2_max = max(weight) + if x_min is None: + x_min = 0.0 + x_max = 1.0 + y1_min = 0.0 + y1_max = 1.0 + else: + dx: float = 0.1 + x_min = 10 ** (floor(log(x_min) / dx) * dx - dx) + x_max = 10 ** (ceil(log(x_max) / dx) * dx + dx) + dy: float = 0.1 + y1_min = 10 ** (floor(log(y1_min) / dy) * dy - dy) + y1_max = 10 ** (ceil(log(y1_max) / dy) * dy + dy) + if log(y1_max) - log(y1_min) < 1.0: + y1_min = 10 ** floor(log(y1_min)) + y1_max = 10 ** ceil(log(y1_max)) + y2_min = -0.1 + y2_max = 1.1 + dpg.split_frame() + dpg.set_axis_limits(self._x_axis, ymin=x_min, ymax=x_max) + dpg.set_axis_limits(self._y_axis_1, ymin=y1_min, ymax=y1_max) + dpg.set_axis_limits(self._y_axis_2, ymin=y2_min, ymax=y2_max) + dpg.split_frame() + dpg.set_axis_limits_auto(self._x_axis) + dpg.set_axis_limits_auto(self._y_axis_1) + dpg.set_axis_limits_auto(self._y_axis_2) + + def copy_limits(self, other: Plot): + src: int + dst: int + for src, dst in zip( + [ + other._x_axis, + other._y_axis_1, + other._y_axis_2, + ], + [ + self._x_axis, + self._y_axis_1, + self._y_axis_2, + ], + ): + limits: List[float] = dpg.get_axis_limits(src) + dpg.set_axis_limits(dst, *limits) + dpg.split_frame() + dpg.set_axis_limits_auto(self._x_axis) + dpg.set_axis_limits_auto(self._y_axis_1) + dpg.set_axis_limits_auto(self._y_axis_2) diff --git a/src/deareis/gui/plotting/__init__.py b/src/deareis/gui/plotting/__init__.py index 3a2bb97..c10f73d 100644 --- a/src/deareis/gui/plotting/__init__.py +++ b/src/deareis/gui/plotting/__init__.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -20,6 +20,7 @@ from inspect import signature from itertools import chain from typing import ( + Any, Callable, Dict, List, @@ -28,6 +29,7 @@ Union, ) from numpy import ndarray +from pyimpspec import ComplexImpedance import dearpygui.dearpygui as dpg import deareis.themes as themes from deareis.tooltips import attach_tooltip @@ -46,6 +48,7 @@ PlotSettings, SimulationResult, TestResult, + ZHITResult, ) from deareis.gui.plots import ( BodeMagnitude, @@ -58,7 +61,10 @@ from deareis.gui.plots.base import Plot from deareis.signals import Signal import deareis.signals as signals -from deareis.utility import is_filtered_item_visible +from deareis.utility import ( + is_filtered_item_visible, + pad_tab_labels, +) TABLE_HEADER_HEIGHT: int = 18 @@ -146,7 +152,9 @@ def populate(self, data_sets: List[DataSet], settings: PlotSettings) -> bool: dpg.add_checkbox( default_value=data.uuid in settings.series_order, callback=lambda s, a, u: signals.emit( - Signal.TOGGLE_PLOT_SERIES, enabled=a, **u + Signal.TOGGLE_PLOT_SERIES, + enabled=a, + **u, ), user_data={ "data_sets": [data], @@ -224,7 +232,11 @@ class TestResultGroup: def __init__(self, parent: int): self.header: int = dpg.generate_uuid() with dpg.collapsing_header( - label="PLACEHOLDER", show=False, tag=self.header, parent=parent, indent=8 + label="PLACEHOLDER", + show=False, + tag=self.header, + parent=parent, + indent=8, ): with dpg.group(horizontal=True, indent=8): self.select_all_button: int = dpg.generate_uuid() @@ -273,7 +285,10 @@ def clear(self): self.active_hash = "" def populate( - self, tests: List[TestResult], data: DataSet, settings: PlotSettings + self, + tests: List[TestResult], + data: DataSet, + settings: PlotSettings, ) -> bool: assert type(tests) is list, tests assert type(data) is DataSet, data @@ -508,11 +523,310 @@ def filter(self, string: str, collapse: bool) -> List[TestResult]: return tests +class ZHITResultGroup: + def __init__(self, parent: int): + self.header: int = dpg.generate_uuid() + with dpg.collapsing_header( + label="PLACEHOLDER", + show=False, + tag=self.header, + parent=parent, + indent=8, + ): + with dpg.group(horizontal=True, indent=8): + self.select_all_button: int = dpg.generate_uuid() + dpg.add_button( + label="Select all", + callback=lambda s, a, u: signals.emit( + Signal.TOGGLE_PLOT_SERIES, **u + ), + user_data={}, + tag=self.select_all_button, + ) + self.unselect_all_button: int = dpg.generate_uuid() + dpg.add_button( + label="Unselect all", + callback=lambda s, a, u: signals.emit( + Signal.TOGGLE_PLOT_SERIES, **u + ), + user_data={}, + tag=self.unselect_all_button, + ) + with dpg.group(indent=8): + self.table: int = dpg.generate_uuid() + with dpg.table( + borders_outerV=True, + borders_outerH=True, + borders_innerV=True, + borders_innerH=True, + scrollY=True, + freeze_rows=1, + width=-1, + height=TABLE_HEADER_HEIGHT + TABLE_ROW_HEIGHT, + tag=self.table, + ): + dpg.add_table_column(label="", width_fixed=True) + attach_tooltip(tooltips.plotting.zhit_checkbox) + dpg.add_table_column(label="Label", width_fixed=True) + dpg.add_spacer(height=8) + self.zhit_hash: str = "" + self.active_hash: str = "" + + def clear(self): + dpg.delete_item(self.table, children_only=True, slot=1) + dpg.configure_item(self.table, height=TABLE_HEADER_HEIGHT + TABLE_ROW_HEIGHT) + dpg.hide_item(self.header) + self.zhit_hash = "" + self.active_hash = "" + + def populate( + self, + zhits: List[ZHITResult], + data: DataSet, + settings: PlotSettings, + ) -> bool: + assert type(zhits) is list, zhits + assert type(data) is DataSet, data + assert type(settings) is PlotSettings, settings + zhit_hash: str = ",".join([_.uuid for _ in zhits]) + if zhit_hash == self.zhit_hash: + return False + self.clear() + self.zhit_hash = zhit_hash + dpg.set_item_label(self.header, data.get_label()) + zhit: ZHITResult + for zhit in zhits: + with dpg.table_row( + filter_key=f"{data.get_label().lower()} {zhit.get_label().lower()}", + parent=self.table, + ): + dpg.add_checkbox( + default_value=zhit.uuid in settings.series_order, + callback=lambda s, a, u: signals.emit( + Signal.TOGGLE_PLOT_SERIES, enabled=a, **u + ), + user_data={ + "zhits": [zhit], + "settings": settings, + }, + ) + dpg.add_text(zhit.get_label()) + attach_tooltip(zhit.get_label()) + dpg.configure_item( + self.table, + height=TABLE_HEADER_HEIGHT + TABLE_ROW_HEIGHT * max(1, len(zhits)), + ) + dpg.show_item(self.header) + return True + + def update( + self, + active_hash: str, + zhits: List[ZHITResult], + data: DataSet, + settings: PlotSettings, + ): + assert type(active_hash) is str, active_hash + assert type(zhits) is list, zhits + assert type(data) is DataSet, data + assert type(settings) is PlotSettings, settings + if active_hash == self.active_hash: + return + self.active_hash = active_hash + zhit: ZHITResult + row: int + for (zhit, row) in zip(zhits, dpg.get_item_children(self.table, slot=1)): + cells: List[int] = dpg.get_item_children(row, slot=1) + dpg.configure_item( + cells[0], + default_value=zhit.uuid in settings.series_order, + user_data={ + "zhits": [zhit], + "settings": settings, + }, + ) + + def filter(self, string: str, collapse: bool) -> List[ZHITResult]: + assert type(string) is str, string + stripped_string: str = string.strip() + dpg.set_value(self.table, string) + zhits: List[ZHITResult] = [] + row: int + for row in dpg.get_item_children(self.table, slot=1): + filter_key: str = dpg.get_item_filter_key(row) + subset: List[DataSet] = dpg.get_item_user_data( + dpg.get_item_children(row, slot=1)[0] + ).get("zhits", []) + if is_filtered_item_visible(row, stripped_string): + zhits.extend(subset) + dpg.configure_item( + self.table, height=TABLE_HEADER_HEIGHT + TABLE_ROW_HEIGHT * len(zhits) + ) + if zhits: + dpg.show_item(self.header) + if collapse: + dpg.set_value(self.header, not string == "") + else: + if collapse: + dpg.set_value(self.header, False) + dpg.hide_item(self.header) + dpg.get_item_user_data(self.select_all_button).update({"zhits": zhits}) + dpg.get_item_user_data(self.unselect_all_button).update({"zhits": zhits}) + return zhits + + +class ZHITsGroup: + def __init__(self): + self.groups: Dict[str, ZHITResultGroup] = {} + self.header: int = dpg.generate_uuid() + with dpg.collapsing_header( + label="Z-HIT analysis results", + tag=self.header, + ): + self.button_group: int = dpg.generate_uuid() + with dpg.group(horizontal=True, indent=8, tag=self.button_group): + dpg.add_button( + label="Expand all", + callback=lambda s, a, u: self.expand_subheaders(True), + ) + dpg.add_button( + label="Collapse all", + callback=lambda s, a, u: self.expand_subheaders(False), + ) + self.select_all_button: int = dpg.generate_uuid() + dpg.add_button( + label="Select all", + callback=lambda s, a, u: signals.emit( + Signal.TOGGLE_PLOT_SERIES, **u + ), + user_data={}, + tag=self.select_all_button, + ) + self.unselect_all_button: int = dpg.generate_uuid() + dpg.add_button( + label="Unselect all", + callback=lambda s, a, u: signals.emit( + Signal.TOGGLE_PLOT_SERIES, **u + ), + user_data={}, + tag=self.unselect_all_button, + ) + dpg.add_spacer(height=8) + self.data_hash: str = "" + + def expand_subheaders(self, state: bool): + assert type(state) is bool + subheader: int + for subheader in dpg.get_item_children(self.header, slot=1): + if "::mvCollapsingHeader" not in dpg.get_item_type(subheader): + continue + dpg.set_value(subheader, state) + + def clear(self): + group: ZHITResultGroup + for group in self.groups.values(): + group.clear() + dpg.hide_item(self.header) + self.data_hash = "" + + def populate( + self, + zhits: Dict[str, List[ZHITResult]], + data_sets: List[DataSet], + settings: PlotSettings, + ): + assert type(zhits) is dict, zhits + assert type(data_sets) is list, data_sets + assert type(settings) is PlotSettings, settings + if not data_sets: + self.clear() + dpg.configure_item( + self.select_all_button, + user_data={}, + ) + dpg.configure_item( + self.unselect_all_button, + user_data={}, + ) + return + data_hash: str = ",".join([_.uuid for _ in data_sets]) + if data_hash != self.data_hash: + self.clear() + self.data_hash = data_hash + active_hash: str = ",".join(sorted(settings.series_order)) + f",{settings.uuid}" + all_zhits: List[ZHITResult] = [] + data: DataSet + for data in data_sets: + if not zhits[data.uuid]: + if data.uuid in self.groups: + self.groups[data.uuid].clear() + continue + if data.uuid not in self.groups: + self.groups[data.uuid] = ZHITResultGroup(self.header) + group: ZHITResultGroup = self.groups[data.uuid] + if not group.populate(zhits[data.uuid], data, settings): + group.update(active_hash, zhits[data.uuid], data, settings) + dpg.get_item_user_data(group.select_all_button).update( + { + "enabled": True, + "zhits": zhits, + "settings": settings, + } + ) + dpg.get_item_user_data(group.unselect_all_button).update( + { + "enabled": False, + "zhits": zhits, + "settings": settings, + } + ) + all_zhits.extend(zhits[data.uuid]) + if all_zhits: + dpg.show_item(self.header) + dpg.get_item_user_data(self.select_all_button).update( + { + "enabled": True, + "zhits": all_zhits, + "settings": settings, + } + ) + dpg.get_item_user_data(self.unselect_all_button).update( + { + "enabled": False, + "zhits": all_zhits, + "settings": settings, + } + ) + + def filter(self, string: str, collapse: bool) -> List[ZHITResult]: + assert type(string) is str, string + zhits: List[ZHITResult] = [] + group: ZHITResultGroup + for group in self.groups.values(): + zhits.extend(group.filter(string, collapse)) + if zhits: + dpg.show_item(self.header) + if collapse: + dpg.set_value(self.header, not string == "") + else: + if collapse: + dpg.set_value(self.header, False) + dpg.hide_item(self.header) + dpg.get_item_user_data(self.select_all_button).update({"zhits": zhits}) + dpg.get_item_user_data(self.unselect_all_button).update({"zhits": zhits}) + dpg.set_item_label(self.header, f"Z-HIT analysis results ({len(zhits)})") + return zhits + + class DRTResultGroup: def __init__(self, parent: int): self.header: int = dpg.generate_uuid() with dpg.collapsing_header( - label="PLACEHOLDER", show=False, tag=self.header, parent=parent, indent=8 + label="PLACEHOLDER", + show=False, + tag=self.header, + parent=parent, + indent=8, ): with dpg.group(horizontal=True, indent=8): self.select_all_button: int = dpg.generate_uuid() @@ -803,7 +1117,11 @@ class FitResultGroup: def __init__(self, parent: int): self.header: int = dpg.generate_uuid() with dpg.collapsing_header( - label="PLACEHOLDER", show=False, tag=self.header, parent=parent, indent=8 + label="PLACEHOLDER", + show=False, + tag=self.header, + parent=parent, + indent=8, ): with dpg.group(horizontal=True, indent=8): self.select_all_button: int = dpg.generate_uuid() @@ -1279,10 +1597,38 @@ def clear(self): dpg.delete_item(self.table, children_only=True, slot=1) dpg.configure_item(self.table, height=TABLE_HEADER_HEIGHT + TABLE_ROW_HEIGHT) + def find_parent_data( + self, + series: Any, + data_sets: List[DataSet], + tests: Dict[str, List[TestResult]], + zhits: Dict[str, List[ZHITResult]], + drts: Dict[str, List[DRTResult]], + fits: Dict[str, List[FitResult]], + ) -> Optional[DataSet]: + if not hasattr(series, "uuid"): + return None + all_results: dict = { + TestResult: tests, + ZHITResult: zhits, + DRTResult: drts, + FitResult: fits, + }.get(type(series), {}) + uuid: str + results: Any + for uuid, results in all_results.items(): + if series in results: + data: DataSet + for data in data_sets: + if data.uuid == uuid: + return data + return None + def populate( self, data_sets: List[DataSet], tests: Dict[str, List[TestResult]], + zhits: Dict[str, List[ZHITResult]], drts: Dict[str, List[DRTResult]], fits: Dict[str, List[FitResult]], simulations: List[SimulationResult], @@ -1290,13 +1636,16 @@ def populate( ): assert type(data_sets) is list, data_sets assert type(tests) is dict, tests + assert type(zhits) is dict, zhits assert type(drts) is dict, drts assert type(fits) is dict, fits assert type(simulations) is list, simulations assert type(settings) is PlotSettings, settings self.clear() series: List[ - Union[DataSet, TestResult, DRTResult, FitResult, SimulationResult] + Union[ + DataSet, TestResult, ZHITResult, DRTResult, FitResult, SimulationResult + ] ] = [] series.extend(filter(lambda _: _.uuid in settings.series_order, data_sets)) series.extend( @@ -1305,6 +1654,12 @@ def populate( list(chain(*list(tests.values()))), ) ) + series.extend( + filter( + lambda _: _.uuid in settings.series_order, + list(chain(*list(zhits.values()))), + ) + ) series.extend( filter( lambda _: _.uuid in settings.series_order, @@ -1322,23 +1677,27 @@ def populate( ser: Union[DataSet, TestResult, DRTResult, FitResult, SimulationResult] types: dict = { DataSet: ( - "D", + "Data", "Data set", ), TestResult: ( "KK", "Kramers-Kronig test", ), + ZHITResult: ( + "Z-HIT", + "Z-HIT analysis", + ), DRTResult: ( "DRT", "DRT analysis", ), FitResult: ( - "F", "Fit", + "Circuit fit", ), SimulationResult: ( - "S", + "Sim.", "Simulation", ), } @@ -1348,7 +1707,7 @@ def populate( type_tooltip: str type_label, type_tooltip = types[type(ser)] with dpg.table_row(parent=self.table): - dpg.add_text(type_label.ljust(4)) + dpg.add_text(type_label.ljust(5)) attach_tooltip(type_tooltip) dpg.add_input_text( hint=ser.get_label(), @@ -1366,7 +1725,23 @@ def populate( "series": ser, }, ) - attach_tooltip(ser.get_label()) + if isinstance(ser, DataSet): + attach_tooltip(ser.get_label()) + else: + parent_data: Optional[DataSet] = self.find_parent_data( + series=ser, + data_sets=data_sets, + tests=tests, + zhits=zhits, + drts=drts, + fits=fits, + ) + if parent_data is not None: + attach_tooltip( + f"{ser.get_label()}\n\n{parent_data.get_label()}" + ) + else: + attach_tooltip(ser.get_label()) dpg.add_button( label="Edit", width=-1, @@ -1443,7 +1818,7 @@ def __init__(self, state): self.plotted_uuid: str = "" self.plot_types: Dict[PlotType, Plot] = {} label_pad: int = 5 - sidebar_width: int = 400 + sidebar_width: int = 420 self.tab: int = dpg.generate_uuid() with dpg.tab(label="Plotting", tag=self.tab): with dpg.child_window(border=False): @@ -1453,11 +1828,12 @@ def __init__(self, state): width=sidebar_width, border=False, tag=self.sidebar_window ): with dpg.child_window(height=82): + combo_width: int = -80 with dpg.group(horizontal=True): dpg.add_text("Plot".rjust(label_pad)) self.plot_combo: int = dpg.generate_uuid() dpg.add_combo( - width=-64, + width=combo_width, callback=lambda s, a, u: signals.emit( Signal.SELECT_PLOT_SETTINGS, settings=u.get(a), @@ -1479,7 +1855,7 @@ def __init__(self, state): dpg.add_text("Label".rjust(label_pad)) self.label_input: int = dpg.generate_uuid() dpg.add_input_text( - width=-64, + width=combo_width, on_enter=True, callback=lambda s, a, u: signals.emit( Signal.RENAME_PLOT_SETTINGS, @@ -1490,17 +1866,17 @@ def __init__(self, state): ), tag=self.label_input, ) - self.delete_button: int = dpg.generate_uuid() + self.duplicate_button: int = dpg.generate_uuid() dpg.add_button( - label="Delete", + label="Duplicate", callback=lambda s, a, u: signals.emit( - Signal.DELETE_PLOT_SETTINGS, settings=u + Signal.DUPLICATE_PLOT_SETTINGS, + settings=u, ), user_data=None, width=-1, - tag=self.delete_button, + tag=self.duplicate_button, ) - attach_tooltip(tooltips.plotting.remove) with dpg.group(horizontal=True): dpg.add_text("Type".rjust(label_pad)) self.type_combo: int = dpg.generate_uuid() @@ -1512,7 +1888,7 @@ def __init__(self, state): [_ for _ in PlotType], ) ), - width=-64, + width=combo_width, tag=self.type_combo, callback=lambda s, a, u: signals.emit( Signal.SELECT_PLOT_TYPE, @@ -1523,20 +1899,32 @@ def __init__(self, state): ), user_data=PlotType.NYQUIST, ) + self.delete_button: int = dpg.generate_uuid() + dpg.add_button( + label="Delete", + callback=lambda s, a, u: signals.emit( + Signal.DELETE_PLOT_SETTINGS, settings=u + ), + user_data=None, + width=-1, + tag=self.delete_button, + ) + attach_tooltip(tooltips.plotting.remove) with dpg.child_window(width=sidebar_width, height=-40): - with dpg.tab_bar(): + self.series_tab_bar: int = dpg.generate_uuid() + with dpg.tab_bar(tag=self.series_tab_bar): with dpg.tab(label="Available"): with dpg.group(horizontal=True): - dpg.add_text("Filter") - attach_tooltip(tooltips.plotting.filter) self.filter_input: int = dpg.generate_uuid() dpg.add_input_text( + hint="Filter...", width=-1, callback=lambda s, a, u: self.filter_possible_series( a ), tag=self.filter_input, ) + attach_tooltip(tooltips.plotting.filter) with dpg.child_window( border=False, width=-1, height=-1 ): @@ -1550,6 +1938,9 @@ def __init__(self, state): self.possible_tests: TestsGroup = ( TestsGroup() ) + self.possible_zhits: ZHITsGroup = ( + ZHITsGroup() + ) self.possible_drts: DRTsGroup = DRTsGroup() self.possible_fits: FitsGroup = FitsGroup() self.possible_simulations: SimulationsGroup = ( @@ -1557,6 +1948,7 @@ def __init__(self, state): ) with dpg.tab(label="Active"): self.active_series: ActiveSeries = ActiveSeries() + pad_tab_labels(self.series_tab_bar) with dpg.child_window(width=sidebar_width, height=-1): with dpg.group(horizontal=True): self.select_all_button: int = dpg.generate_uuid() @@ -1597,12 +1989,13 @@ def __init__(self, state): attach_tooltip(tooltips.plotting.copy_appearance) self.export_button: int = dpg.generate_uuid() dpg.add_button( - label="Export", + label="Export plot", callback=lambda s, a, u: signals.emit( Signal.EXPORT_PLOT, **u ), user_data={}, tag=self.export_button, + width=-1, ) attach_tooltip(tooltips.plotting.export_plot) with dpg.child_window(border=False, width=-1, height=-1): @@ -1671,12 +2064,6 @@ def __init__(self, state): tooltips.plotting.collapse_expand_sidebar ) self.visibility_item: int = dpg.generate_uuid() - self.adjust_limits_checkbox: int = dpg.generate_uuid() - dpg.add_checkbox( - default_value=True, - tag=self.adjust_limits_checkbox, - ) - attach_tooltip(tooltips.general.adjust_limits) dpg.add_button( label="Copy as CSV", callback=lambda s, a, u: signals.emit( @@ -1689,6 +2076,13 @@ def __init__(self, state): tag=self.visibility_item, ) attach_tooltip(tooltips.general.copy_plot_data_as_csv) + self.adjust_limits_checkbox: int = dpg.generate_uuid() + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_limits) def resize(self, width: int, height: int): assert type(width) is int and width > 0 @@ -1728,6 +2122,7 @@ def plot_series( self, data_sets: List[DataSet], tests: Dict[str, List[TestResult]], + zhits: Dict[str, List[ZHITResult]], drts: Dict[str, List[DRTResult]], fits: Dict[str, List[FitResult]], simulations: List[SimulationResult], @@ -1736,6 +2131,7 @@ def plot_series( ): assert type(data_sets) is list, data_sets assert type(tests) is dict, tests + assert type(zhits) is dict, zhits assert type(drts) is dict, drts assert type(fits) is dict, fits assert type(simulations) is list, simulations @@ -1776,7 +2172,13 @@ def plot_series( Union[DataSet, TestResult, DRTResult, FitResult, SimulationResult] ] series = settings.find_series( - uuid, data_sets, tests, drts, fits, simulations + uuid=uuid, + data_sets=data_sets, + tests=tests, + zhits=zhits, + drts=drts, + fits=fits, + simulations=simulations, ) if series is None: settings.series_order.remove(uuid) @@ -1803,7 +2205,8 @@ def plot_series( mag: ndarray phase: ndarray tau: ndarray - gamma: ndarray + real_gamma: ndarray + imaginary_gamma: ndarray if plot_type == PlotType.NYQUIST: if settings.get_series_marker(uuid) >= 0: real, imag = series.get_nyquist_data() @@ -1955,19 +2358,18 @@ def plot_series( if type(series) is not DRTResult: continue if series.settings.method == DRTMethod.BHT: - tau, gamma = series.get_drt_data() + tau, real_gamma, imaginary_gamma = series.get_drt_data() self.series_tags[series.uuid] = plot.plot( tau=tau, - gamma=gamma, + gamma=real_gamma, label=f"{label}, real" if label is not None else label, line=True, theme=theme, show_label=show_label, ) - tau, gamma = series.get_drt_data(imaginary=True) self.series_tags[f"{series.uuid}_imaginary"] = plot.plot( tau=tau, - gamma=gamma, + gamma=imaginary_gamma, label=f"{label}, imag." if label is not None else label, line=True, theme=theme, @@ -1977,7 +2379,7 @@ def plot_series( series.settings.method == DRTMethod.TR_RBF and series.settings.credible_intervals is True ): - tau, mean, lower, upper = series.get_drt_credible_intervals() + tau, mean, lower, upper = series.get_drt_credible_intervals_data() alt_color: List[float] = settings.get_series_color(uuid).copy() alt_color[-1] = themes.get_plot_series_theme_color( themes.drt.credible_intervals @@ -2001,20 +2403,20 @@ def plot_series( theme=theme, show_label=show_label, ) - tau, gamma = series.get_drt_data() + tau, real_gamma, imaginary_gamma = series.get_drt_data() self.series_tags[series.uuid] = plot.plot( tau=tau, - gamma=gamma, + gamma=real_gamma, label=label, line=True, theme=theme, show_label=show_label, ) else: - tau, gamma = series.get_drt_data() + tau, real_gamma, imaginary_gamma = series.get_drt_data() self.series_tags[series.uuid] = plot.plot( tau=tau, - gamma=gamma, + gamma=real_gamma, label=label, line=True, theme=theme, @@ -2022,8 +2424,8 @@ def plot_series( ) elif plot_type == PlotType.IMPEDANCE_REAL: if settings.get_series_marker(uuid) >= 0: - freq = series.get_frequency() - real = series.get_impedance().real + freq = series.get_frequencies() + real = series.get_impedances().real self.series_tags[series.uuid] = plot.plot( x=freq, y=real, @@ -2035,14 +2437,14 @@ def plot_series( if ( (is_simulation or is_fit) and "num_per_decade" - in signature(series.get_frequency).parameters + in signature(series.get_frequencies).parameters and "num_per_decade" - in signature(series.get_impedance).parameters + in signature(series.get_impedances).parameters ): - freq = series.get_frequency( + freq = series.get_frequencies( num_per_decade=self.state.config.num_per_decade_in_simulated_lines ) - real = series.get_impedance( + real = series.get_impedances( num_per_decade=self.state.config.num_per_decade_in_simulated_lines ).real if settings.get_series_line(uuid): @@ -2058,19 +2460,19 @@ def plot_series( if ( (is_simulation or is_fit) and "num_per_decade" - in signature(series.get_frequency).parameters + in signature(series.get_frequencies).parameters and "num_per_decade" - in signature(series.get_impedance).parameters + in signature(series.get_impedances).parameters ): - freq = series.get_frequency( + freq = series.get_frequencies( num_per_decade=self.state.config.num_per_decade_in_simulated_lines ) - real = series.get_impedance( + real = series.get_impedances( num_per_decade=self.state.config.num_per_decade_in_simulated_lines ).real else: - freq = series.get_frequency() - real = series.get_impedance().real + freq = series.get_frequencies() + real = series.get_impedances().real self.series_tags[series.uuid] = plot.plot( x=freq, y=real, @@ -2081,8 +2483,8 @@ def plot_series( ) elif plot_type == PlotType.IMPEDANCE_IMAGINARY: if settings.get_series_marker(uuid) >= 0: - freq = series.get_frequency() - imag = -series.get_impedance().imag + freq = series.get_frequencies() + imag = -series.get_impedances().imag self.series_tags[series.uuid] = plot.plot( x=freq, y=imag, @@ -2094,14 +2496,14 @@ def plot_series( if ( (is_simulation or is_fit) and "num_per_decade" - in signature(series.get_frequency).parameters + in signature(series.get_frequencies).parameters and "num_per_decade" - in signature(series.get_impedance).parameters + in signature(series.get_impedances).parameters ): - freq = series.get_frequency( + freq = series.get_frequencies( num_per_decade=self.state.config.num_per_decade_in_simulated_lines ) - imag = -series.get_impedance( + imag = -series.get_impedances( num_per_decade=self.state.config.num_per_decade_in_simulated_lines ).imag if settings.get_series_line(uuid): @@ -2117,19 +2519,19 @@ def plot_series( if ( (is_simulation or is_fit) and "num_per_decade" - in signature(series.get_frequency).parameters + in signature(series.get_frequencies).parameters and "num_per_decade" - in signature(series.get_impedance).parameters + in signature(series.get_impedances).parameters ): - freq = series.get_frequency( + freq = series.get_frequencies( num_per_decade=self.state.config.num_per_decade_in_simulated_lines ) - imag = -series.get_impedance( + imag = -series.get_impedances( num_per_decade=self.state.config.num_per_decade_in_simulated_lines ).imag else: - freq = series.get_frequency() - imag = -series.get_impedance().imag + freq = series.get_frequencies() + imag = -series.get_impedances().imag self.series_tags[series.uuid] = plot.plot( x=freq, y=imag, @@ -2211,6 +2613,7 @@ def select_plot( settings: PlotSettings, data_sets: List[DataSet], tests: Dict[str, List[TestResult]], + zhits: Dict[str, List[ZHITResult]], drts: Dict[str, List[DRTResult]], fits: Dict[str, List[FitResult]], simulations: List[SimulationResult], @@ -2220,18 +2623,21 @@ def select_plot( assert type(settings) is PlotSettings, settings assert type(data_sets) is list, data_sets assert type(tests) is dict, tests + assert type(zhits) is dict, zhits assert type(drts) is dict, drts assert type(fits) is dict, fits assert type(simulations) is list, simulations assert type(adjust_limits) is bool, adjust_limits assert type(plot_only) is bool, plot_only dpg.set_item_user_data(self.delete_button, settings) + dpg.set_item_user_data(self.duplicate_button, settings) dpg.set_item_user_data(self.export_button, {"settings": settings}) if not self.is_visible(): self.queued_update = lambda: self.select_plot( settings, data_sets, tests, + zhits, drts, fits, simulations, @@ -2246,6 +2652,7 @@ def select_plot( self.populate_possible_series( data_sets, tests, + zhits, drts, fits, simulations, @@ -2254,6 +2661,7 @@ def select_plot( self.active_series.populate( data_sets, tests, + zhits, drts, fits, simulations, @@ -2264,6 +2672,7 @@ def select_plot( self.plot_series( data_sets, tests, + zhits, drts, fits, simulations, @@ -2275,6 +2684,7 @@ def populate_possible_series( self, data_sets: List[DataSet], tests: Dict[str, List[TestResult]], + zhits: Dict[str, List[ZHITResult]], drts: Dict[str, List[DRTResult]], fits: Dict[str, List[FitResult]], simulations: List[SimulationResult], @@ -2282,12 +2692,14 @@ def populate_possible_series( ): assert type(data_sets) is list, data_sets assert type(tests) is dict, tests + assert type(zhits) is dict, zhits assert type(drts) is dict, drts assert type(fits) is dict, fits assert type(simulations) is list, simulations assert type(settings) is PlotSettings, settings self.populate_data_sets(data_sets, settings) self.populate_tests(tests, data_sets, settings) + self.populate_zhits(zhits, data_sets, settings) self.populate_drts(drts, data_sets, settings) self.populate_fits(fits, data_sets, settings) self.populate_simulations(simulations, settings) @@ -2360,6 +2772,32 @@ def populate_tests( } ) + def populate_zhits( + self, + zhits: Dict[str, List[ZHITResult]], + data_sets: List[DataSet], + settings: PlotSettings, + ): + assert type(zhits) is dict, zhits + assert type(data_sets) is list, data_sets + assert type(settings) is PlotSettings, settings + self.possible_zhits.populate(zhits, data_sets, settings) + user_data: List[ZHITResult] = self.possible_zhits.filter( + self.get_filter_string(), False + ) + dpg.get_item_user_data(self.select_all_button).update( + { + "zhits": user_data, + "settings": settings, + } + ) + dpg.get_item_user_data(self.unselect_all_button).update( + { + "zhits": user_data, + "settings": settings, + } + ) + def populate_drts( self, drts: Dict[str, List[DRTResult]], @@ -2467,6 +2905,7 @@ def filter_possible_series( ) -> Tuple[ List[DataSet], List[TestResult], + List[ZHITResult], List[DRTResult], List[FitResult], List[SimulationResult], @@ -2474,6 +2913,7 @@ def filter_possible_series( string = string.lower() data_sets: List[DataSet] = self.possible_data_sets.filter(string, True) tests: List[TestResult] = self.possible_tests.filter(string, True) + zhits: List[ZHITResult] = self.possible_zhits.filter(string, True) drts: List[DRTResult] = self.possible_drts.filter(string, True) fits: List[FitResult] = self.possible_fits.filter(string, True) simulations: List[SimulationResult] = self.possible_simulations.filter( @@ -2483,6 +2923,7 @@ def filter_possible_series( { "data_sets": data_sets, "tests": tests, + "zhits": zhits, "drts": drts, "fits": fits, "simulations": simulations, @@ -2492,6 +2933,7 @@ def filter_possible_series( { "data_sets": data_sets, "tests": tests, + "zhits": zhits, "drts": drts, "fits": fits, "simulations": simulations, @@ -2500,6 +2942,7 @@ def filter_possible_series( return ( data_sets, tests, + zhits, drts, fits, simulations, @@ -2511,3 +2954,13 @@ def has_active_input(self) -> bool: or dpg.is_item_active(self.filter_input) or self.active_series.has_active_input() ) + + def next_series_tab(self): + tabs: List[int] = dpg.get_item_children(self.series_tab_bar, slot=1) + index: int = tabs.index(dpg.get_value(self.series_tab_bar)) + 1 + dpg.set_value(self.series_tab_bar, tabs[index % len(tabs)]) + + def previous_series_tab(self): + tabs: List[int] = dpg.get_item_children(self.series_tab_bar, slot=1) + index: int = tabs.index(dpg.get_value(self.series_tab_bar)) - 1 + dpg.set_value(self.series_tab_bar, tabs[index % len(tabs)]) diff --git a/src/deareis/gui/plotting/copy_appearance.py b/src/deareis/gui/plotting/copy_appearance.py index 7931b34..1cf43dc 100644 --- a/src/deareis/gui/plotting/copy_appearance.py +++ b/src/deareis/gui/plotting/copy_appearance.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -35,15 +35,18 @@ PlotSettings, SimulationResult, TestResult, + ZHITResult, ) from deareis.utility import calculate_window_position_dimensions from deareis.tooltips import attach_tooltip import deareis.tooltips as tooltips from deareis.data import Project import deareis.themes as themes +from deareis.state import STATE +from deareis.enums import Action from deareis.keybindings import ( - is_alt_down, - is_control_down, + Keybinding, + TemporaryKeybindingHandler, ) @@ -61,6 +64,7 @@ def __init__( FitResult, SimulationResult, TestResult, + ZHITResult, ], series assert type(settings) is PlotSettings, settings assert type(marker_lookup) is dict, marker_lookup @@ -176,9 +180,71 @@ def __init__(self, settings: PlotSettings, project: Project): } self.data_sets: List[DataSet] = project.get_data_sets() self.tests: Dict[str, List[TestResult]] = project.get_all_tests() + self.zhits: Dict[str, List[ZHITResult]] = project.get_all_zhits() self.drts: Dict[str, List[DRTResult]] = project.get_all_drts() self.fits: Dict[str, List[FitResult]] = project.get_all_fits() self.simulations: List[SimulationResult] = project.get_simulations() + self.create_window() + self.register_keybindings() + signals.emit(Signal.BLOCK_KEYBINDINGS, window=self.window, window_object=self) + self.change_source() + + def register_keybindings(self): + callbacks: Dict[Keybinding, Callable] = {} + # Cancel + kb: Keybinding = Keybinding( + key=dpg.mvKey_Escape, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.CANCEL, + ) + callbacks[kb] = self.close + # Accept + for kb in STATE.config.keybindings: + if kb.action is Action.PERFORM_ACTION: + break + else: + kb = Keybinding( + key=dpg.mvKey_Return, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.PERFORM_ACTION, + ) + callbacks[kb] = self.accept + # Previous source + for kb in STATE.config.keybindings: + if kb.action is Action.PREVIOUS_PRIMARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Prior, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.PREVIOUS_PRIMARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle_source(-1) + # Next source + for kb in STATE.config.keybindings: + if kb.action is Action.NEXT_PRIMARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Next, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.NEXT_PRIMARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle_source(1) + # Create the handler + self.keybinding_handler: TemporaryKeybindingHandler = ( + TemporaryKeybindingHandler(callbacks=callbacks) + ) + + def create_window(self): x: int y: int w: int @@ -189,10 +255,7 @@ def __init__(self, settings: PlotSettings, project: Project): label="Copy appearance settings", modal=True, no_resize=True, - pos=( - x, - y, - ), + pos=(x, y), width=w, height=h, on_close=self.close, @@ -228,24 +291,25 @@ def __init__(self, settings: PlotSettings, project: Project): dpg.add_table_column(label="Marker", width_fixed=True) dpg.add_table_column(label="Line", width_fixed=True) uuid: str - for uuid in settings.series_order: + for uuid in self.settings.series_order: series: Optional[ Union[ DataSet, TestResult, FitResult, SimulationResult ] ] - series = settings.find_series( - uuid, - self.data_sets, - self.tests, - self.drts, - self.fits, - self.simulations, + series = self.settings.find_series( + uuid=uuid, + data_sets=self.data_sets, + tests=self.tests, + zhits=self.zhits, + drts=self.drts, + fits=self.fits, + simulations=self.simulations, ) assert series is not None SeriesBefore( series, - settings, + self.settings, self.marker_lookup, self.toggle_series, ) @@ -270,7 +334,7 @@ def __init__(self, settings: PlotSettings, project: Project): dpg.add_table_column(label="Line", width_fixed=True) with dpg.group(horizontal=True): dpg.add_button( - label="Accept", + label="Accept".ljust(12), callback=self.accept, ) dpg.add_spacer(width=354) @@ -306,39 +370,13 @@ def __init__(self, settings: PlotSettings, project: Project): callback=lambda s, a, u: self.change_source(), ) attach_tooltip(tooltips.plotting.copy_appearance_lines) - self.key_handler: int = dpg.generate_uuid() - with dpg.handler_registry(tag=self.key_handler): - dpg.add_key_release_handler( - key=dpg.mvKey_Escape, - callback=self.close, - ) - dpg.add_key_release_handler( - key=dpg.mvKey_Return, - callback=lambda: self.accept(keybinding=True), - ) - dpg.add_key_release_handler( - key=dpg.mvKey_Prior, - callback=lambda: self.cycle_source(-1), - ) - dpg.add_key_release_handler( - key=dpg.mvKey_Next, - callback=lambda: self.cycle_source(1), - ) - signals.emit(Signal.BLOCK_KEYBINDINGS, window=self.window, window_object=self) - self.change_source() def close(self): signals.emit(Signal.UNBLOCK_KEYBINDINGS) dpg.delete_item(self.window) - dpg.delete_item(self.key_handler) + self.keybinding_handler.delete() - def accept(self, keybinding: bool = False): - if keybinding is True and not ( - is_control_down() - if dpg.get_platform() == dpg.mvPlatform_Windows - else is_alt_down() - ): - return + def accept(self): dpg.hide_item(self.window) changes: Dict[str, Tuple[str, List[float], int, bool]] = {} row: int @@ -396,12 +434,13 @@ def change_source(self): for uuid in self.settings.series_order: SeriesAfter( self.settings.find_series( - uuid, - self.data_sets, - self.tests, - self.drts, - self.fits, - self.simulations, + uuid=uuid, + data_sets=self.data_sets, + tests=self.tests, + zhits=self.zhits, + drts=self.drts, + fits=self.fits, + simulations=self.simulations, ), source if uuid in source.themes and self.series_checkboxes[uuid] diff --git a/src/deareis/gui/plotting/export.py b/src/deareis/gui/plotting/export.py index 9330b40..8383204 100644 --- a/src/deareis/gui/plotting/export.py +++ b/src/deareis/gui/plotting/export.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -20,6 +20,7 @@ from math import floor from typing import ( Callable, + Dict, List, Optional, Tuple, @@ -61,6 +62,11 @@ label_to_plot_preview_limit, label_to_plot_units, ) +from deareis.enums import Action +from deareis.keybindings import ( + Keybinding, + TemporaryKeybindingHandler, +) class SettingsMenu: @@ -433,7 +439,7 @@ def update_dimensions_output(self, pixel_dimensions: List[str]): class PlotExporter: def __init__(self, config: Config): - self.key_handler: int = -1 + self.keybinding_handler: Optional[TemporaryKeybindingHandler] = None self.settings: Optional[PlotSettings] = None self.project: Optional[Project] = None self.texture_registry: int = dpg.generate_uuid() @@ -468,7 +474,7 @@ def __init__(self, config: Config): with dpg.child_window(border=False): self.save_button: int = dpg.add_button( label="Save as", - callback=lambda s, a, u: self.save(fig=u), + callback=lambda s, a, u: self.save(figure=u), user_data=None, width=-1, ) @@ -477,9 +483,9 @@ def __init__(self, config: Config): def clear(self): settings: PlotExportSettings = self.settings_menu.get_settings() - fig: Optional[Figure] = dpg.get_item_user_data(self.save_button) - if fig is not None: - plt.close(fig) + figure: Optional[Figure] = dpg.get_item_user_data(self.save_button) + if figure is not None: + plt.close(figure) dpg.set_item_user_data(self.save_button, None) if not settings.disable_preview: self.image_plot.clear() @@ -490,28 +496,45 @@ def set_settings(self, settings: PlotExportSettings): self.settings_menu.set_settings(settings) def close(self): - if dpg.does_item_exist(self.key_handler): - dpg.delete_item(self.key_handler) + if self.keybinding_handler is not None: + self.keybinding_handler.delete() dpg.hide_item(self.window) self.clear() signals.emit(Signal.UNBLOCK_KEYBINDINGS) - def initialize_keybindings(self): - self.key_handler = dpg.generate_uuid() - with dpg.handler_registry(tag=self.key_handler): - dpg.add_key_release_handler( - key=dpg.mvKey_Escape, - callback=self.close, - ) - dpg.add_key_release_handler( + def register_keybindings(self): + from deareis.state import STATE + + callbacks: Dict[Keybinding, Callable] = {} + # Cancel + kb: Keybinding = Keybinding( + key=dpg.mvKey_Escape, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.CANCEL, + ) + callbacks[kb] = self.close + # Accept + for kb in STATE.config.keybindings: + if kb.action is Action.PERFORM_ACTION: + break + else: + kb = Keybinding( key=dpg.mvKey_Return, - callback=lambda: self.save( - dpg.get_item_user_data(self.save_button), keybinding=True - ), + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.PERFORM_ACTION, ) + callbacks[kb] = lambda: self.save(dpg.get_item_user_data(self.save_button)) + # Create the handler + self.keybinding_handler: TemporaryKeybindingHandler = ( + TemporaryKeybindingHandler(callbacks=callbacks) + ) def show(self, settings: PlotSettings, project: Project): - self.initialize_keybindings() + self.register_keybindings() self.settings = settings self.project = project dpg.show_item(self.window) @@ -556,7 +579,7 @@ def create_preview_figure( pixel_height = preview_limit width = pixel_width / dpi height = pixel_height / dpi - fig: Figure = plt.figure( + figure: Figure = plt.figure( figsize=( width, height, @@ -564,7 +587,7 @@ def create_preview_figure( dpi=dpi, ) return ( - fig, + figure, pixel_width, pixel_height, ) @@ -580,17 +603,17 @@ def create_final_figure(self, width: float, height: float, dpi: int) -> Figure: format_number(float(pixel_height), decimals=0, exponent=False), ] self.settings_menu.update_dimensions_output(pixel_dimensions) - fig: Figure = plt.figure( + figure: Figure = plt.figure( figsize=( width, height, ), dpi=dpi, ) - return fig + return figure - def plot(self, fig: Figure, export_settings: PlotExportSettings): - assert type(fig) is Figure, type(fig) + def plot(self, figure: Figure, export_settings: PlotExportSettings): + assert type(figure) is Figure, type(figure) assert type(export_settings) is PlotExportSettings, type(export_settings) assert self.settings is not None assert self.project is not None @@ -605,13 +628,13 @@ def plot(self, fig: Figure, export_settings: PlotExportSettings): self.project, x_limits=x_limits, y_limits=y_limits, - show_title=export_settings.show_title, - show_legend=export_settings.show_legend, + title=export_settings.show_title, + legend=export_settings.show_legend, legend_loc=int(export_settings.legend_location), tight_layout=export_settings.has_tight_layout, - show_grid=export_settings.show_grid, - fig=fig, - axis=fig.gca(), + grid=export_settings.show_grid, + figure=figure, + axes=[figure.gca()], num_per_decade=export_settings.num_per_decade, ) @@ -628,15 +651,15 @@ def refresh(self, settings: PlotExportSettings): self.clear() width = width / upi height = height / upi - final_fig: Figure = self.create_final_figure(width, height, dpi) - dpg.set_item_user_data(self.save_button, final_fig) - self.plot(final_fig, settings) + final_figure: Figure = self.create_final_figure(width, height, dpi) + dpg.set_item_user_data(self.save_button, final_figure) + self.plot(final_figure, settings) # Preview if not settings.disable_preview: - preview_fig: Figure + preview_figure: Figure pixel_width: int pixel_height: int - preview_fig, pixel_width, pixel_height = self.create_preview_figure( + preview_figure, pixel_width, pixel_height = self.create_preview_figure( width, height, dpi, @@ -644,8 +667,8 @@ def refresh(self, settings: PlotExportSettings): if settings.preview_limit != PlotPreviewLimit.NONE else 0, ) - canvas: FigureCanvasAgg = FigureCanvasAgg(preview_fig) - self.plot(preview_fig, settings) + canvas: FigureCanvasAgg = FigureCanvasAgg(preview_figure) + self.plot(preview_figure, settings) canvas.draw() tag: int = dpg.add_raw_texture( pixel_width, @@ -665,31 +688,25 @@ def refresh(self, settings: PlotExportSettings): pixel_height, ), ) - plt.close(preview_fig) + plt.close(preview_figure) self.image_plot.queue_limits_adjustment() x_min: float x_max: float y_min: float y_max: float - x_min, x_max = final_fig.gca().get_xlim() - y_min, y_max = final_fig.gca().get_ylim() + x_min, x_max = final_figure.gca().get_xlim() + y_min, y_max = final_figure.gca().get_ylim() self.settings_menu.update_plot_limits(x_min, x_max, y_min, y_max) - def save(self, fig: Optional[Figure], keybinding: bool = False): - if fig is None: - return - elif keybinding is True and not ( - is_control_down() - if dpg.get_platform() == dpg.mvPlatform_Windows - else is_alt_down() - ): + def save(self, figure: Optional[Figure], keybinding: bool = False): + if figure is None: return extension: str = self.settings_menu.get_settings().extension self.close() - dpg.split_frame() + dpg.split_frame(delay=33) signals.emit( Signal.SAVE_PLOT, - figure=fig, + figure=figure, default_extension=extension if extension in PLOT_EXTENSIONS else ".png", extensions=PLOT_EXTENSIONS, ) diff --git a/src/deareis/gui/program.py b/src/deareis/gui/program.py index b244457..a555655 100644 --- a/src/deareis/gui/program.py +++ b/src/deareis/gui/program.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -93,6 +93,8 @@ def update_recent_projects_table(self, paths: List[str]): assert type(paths) is list, paths dpg.delete_item(self.recent_projects_table, children_only=True, slot=1) for path in paths: + if path.strip() == "": + continue with dpg.table_row( parent=self.recent_projects_table, ): @@ -109,11 +111,12 @@ def update_recent_projects_table(self, paths: List[str]): attach_tooltip(path) dpg.add_checkbox( user_data=path, - callback=lambda s, a, u: self.updated_selection(), + callback=lambda s, a, u: self.update_selection(), ) attach_tooltip(tooltips.recent_projects.checkbox) + self.update_selection() - def updated_selection(self): + def update_selection(self): paths: List[str] = self.get_selected_projects() if len(paths) > 1: dpg.set_item_label( @@ -296,11 +299,20 @@ def __init__(self): label="Keybindings", callback=lambda: signals.emit(Signal.SHOW_SETTINGS_KEYBINDINGS), ) + dpg.add_menu_item( + label="User-defined elements", + callback=lambda: signals.emit( + Signal.SHOW_SETTINGS_USER_DEFINED_ELEMENTS + ), + ) + # TODO: Tools? + # - Calculate equivalent capacitance from (RQ) circuit + # - Convert Y to sigma for Warburg elements with dpg.menu(label="Help"): dpg.add_menu_item( - label="Tutorials", + label="Documentation", callback=lambda: webbrowser.open( - "https://vyrjana.github.io/DearEIS/tutorials/" + "https://vyrjana.github.io/DearEIS" ), ) dpg.add_menu_item( diff --git a/src/deareis/gui/project.py b/src/deareis/gui/project.py index 6554986..479be40 100644 --- a/src/deareis/gui/project.py +++ b/src/deareis/gui/project.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -32,6 +32,7 @@ from deareis.gui.overview import OverviewTab from deareis.gui.plotting import PlottingTab from deareis.gui.simulation import SimulationTab +from deareis.gui.zhit import ZHITTab from deareis.signals import Signal import deareis.signals as signals from deareis.enums import ( @@ -50,9 +51,12 @@ SimulationSettings, TestResult, TestSettings, + ZHITResult, + ZHITSettings, ) import deareis.themes as themes from deareis.keybindings import Context +from deareis.utility import pad_tab_labels class ProjectTab: @@ -89,15 +93,18 @@ def select_tab(tab_id: int): self.overview_tab: OverviewTab = OverviewTab() self.data_sets_tab: DataSetsTab = DataSetsTab() self.kramers_kronig_tab: KramersKronigTab = KramersKronigTab(state) + self.zhit_tab: ZHITTab = ZHITTab(state) self.drt_tab: DRTTab = DRTTab(state) self.fitting_tab: FittingTab = FittingTab(state) self.simulation_tab: SimulationTab = SimulationTab(state) self.plotting_tab: PlottingTab = PlottingTab(state) + pad_tab_labels(self.tab_bar) tab_lookup.update( { self.overview_tab.tab: self.overview_tab, self.data_sets_tab.tab: self.data_sets_tab, self.kramers_kronig_tab.tab: self.kramers_kronig_tab, + self.zhit_tab.tab: self.zhit_tab, self.drt_tab.tab: self.drt_tab, self.fitting_tab.tab: self.fitting_tab, self.simulation_tab.tab: self.simulation_tab, @@ -109,6 +116,7 @@ def select_tab(tab_id: int): self.overview_tab.tab: Context.OVERVIEW_TAB, self.data_sets_tab.tab: Context.DATA_SETS_TAB, self.kramers_kronig_tab.tab: Context.KRAMERS_KRONIG_TAB, + self.zhit_tab.tab: Context.ZHIT_TAB, self.drt_tab.tab: Context.DRT_TAB, self.fitting_tab.tab: Context.FITTING_TAB, self.simulation_tab.tab: Context.SIMULATION_TAB, @@ -140,6 +148,7 @@ def resize(self, width: int, height: int): assert type(height) is int and height > 0, height self.data_sets_tab.resize(width, height) self.kramers_kronig_tab.resize(width, height) + self.zhit_tab.resize(width, height) self.drt_tab.resize(width, height) self.fitting_tab.resize(width, height) self.simulation_tab.resize(width, height) @@ -173,7 +182,8 @@ def get_active_context(self) -> Context: return self.context_lookup[dpg.get_value(self.tab_bar)] def get_active_data_set( - self, context: Optional[Context] = None + self, + context: Optional[Context] = None, ) -> Optional[DataSet]: assert type(context) is Context or context is None, context tag: Optional[int] = None @@ -181,18 +191,20 @@ def get_active_data_set( tag = { Context.DATA_SETS_TAB: self.data_sets_tab.delete_button, Context.KRAMERS_KRONIG_TAB: self.kramers_kronig_tab.perform_test_button, + Context.ZHIT_TAB: self.zhit_tab.perform_zhit_button, Context.DRT_TAB: self.drt_tab.perform_drt_button, Context.FITTING_TAB: self.fitting_tab.perform_fit_button, Context.SIMULATION_TAB: self.simulation_tab.perform_sim_button, - }.get(context) + }.get(context, self.data_sets_tab.delete_button) else: tag = { self.data_sets_tab.tab: self.data_sets_tab.delete_button, self.kramers_kronig_tab.tab: self.kramers_kronig_tab.perform_test_button, + self.zhit_tab.tab: self.zhit_tab.perform_zhit_button, self.drt_tab.tab: self.drt_tab.perform_drt_button, self.fitting_tab.tab: self.fitting_tab.perform_fit_button, self.simulation_tab.tab: self.simulation_tab.perform_sim_button, - }.get(dpg.get_value(self.tab_bar)) + }.get(dpg.get_value(self.tab_bar), self.data_sets_tab.delete_button) if tag is None: return None return dpg.get_item_user_data(tag) @@ -200,6 +212,9 @@ def get_active_data_set( def get_active_test(self) -> Optional[TestResult]: return dpg.get_item_user_data(self.kramers_kronig_tab.delete_button).get("test") + def get_active_zhit(self) -> Optional[ZHITResult]: + return dpg.get_item_user_data(self.zhit_tab.delete_button).get("zhit") + def get_active_drt(self) -> Optional[DRTResult]: return dpg.get_item_user_data(self.drt_tab.delete_button).get("drt") @@ -221,6 +236,9 @@ def select_data_sets_tab(self): def select_kramers_kronig_tab(self): dpg.set_value(self.tab_bar, self.kramers_kronig_tab.tab) + def select_zhit_tab(self): + dpg.set_value(self.tab_bar, self.zhit_tab.tab) + def select_drt_tab(self): dpg.set_value(self.tab_bar, self.drt_tab.tab) @@ -235,7 +253,7 @@ def select_plotting_tab(self): def set_label(self, label: str): assert type(label) is str and label != "", label - dpg.set_item_label(self.tab, label) + dpg.set_item_label(self.tab, label.ljust(10)) self.overview_tab.set_label(label) def get_notes(self) -> str: @@ -255,6 +273,7 @@ def populate_data_sets(self, project: Project): labels: List[str] = list(lookup.keys()) self.data_sets_tab.populate_data_sets(labels, lookup) self.kramers_kronig_tab.populate_data_sets(labels, lookup) + self.zhit_tab.populate_data_sets(labels, lookup) self.drt_tab.populate_data_sets(labels, lookup) self.fitting_tab.populate_data_sets(labels, lookup) self.simulation_tab.populate_data_sets(labels, lookup) @@ -269,6 +288,16 @@ def populate_tests(self, project: Project, data: Optional[DataSet]): data, ) + def populate_zhits(self, project: Project, data: Optional[DataSet]): + assert type(project) is Project, project + assert type(data) is DataSet or data is None, data + self.zhit_tab.populate_zhits( + {_.get_label(): _ for _ in project.get_zhits(data)} + if data is not None + else {}, + data, + ) + def populate_drts(self, project: Project, data: Optional[DataSet]): assert type(project) is Project, project assert type(data) is DataSet or data is None, data @@ -310,6 +339,7 @@ def select_data_set(self, data: Optional[DataSet]): assert type(data) is DataSet or data is None, data self.data_sets_tab.select_data_set(data) self.kramers_kronig_tab.select_test_result(None, data) + self.zhit_tab.select_zhit_result(None, data) self.drt_tab.select_drt_result(None, data) self.fitting_tab.select_fit_result(None, data) @@ -318,6 +348,8 @@ def get_next_data_set(self, context: Context) -> Optional[DataSet]: return self.data_sets_tab.get_next_data_set() elif context == Context.KRAMERS_KRONIG_TAB: return self.kramers_kronig_tab.get_next_data_set() + elif context == Context.ZHIT_TAB: + return self.zhit_tab.get_next_data_set() elif context == Context.DRT_TAB: return self.drt_tab.get_next_data_set() elif context == Context.FITTING_TAB: @@ -329,6 +361,8 @@ def get_previous_data_set(self, context: Context) -> Optional[DataSet]: return self.data_sets_tab.get_previous_data_set() elif context == Context.KRAMERS_KRONIG_TAB: return self.kramers_kronig_tab.get_previous_data_set() + elif context == Context.ZHIT_TAB: + return self.zhit_tab.get_previous_data_set() elif context == Context.DRT_TAB: return self.drt_tab.get_previous_data_set() elif context == Context.FITTING_TAB: @@ -347,6 +381,12 @@ def get_next_test_result(self) -> Optional[TestResult]: def get_previous_test_result(self) -> Optional[TestResult]: return self.kramers_kronig_tab.get_previous_result() + def get_next_zhit_result(self) -> Optional[ZHITResult]: + return self.zhit_tab.get_next_result() + + def get_previous_zhit_result(self) -> Optional[ZHITResult]: + return self.zhit_tab.get_previous_result() + def get_next_drt_result(self) -> Optional[DRTResult]: return self.drt_tab.get_next_result() @@ -382,6 +422,11 @@ def select_test_result(self, test: TestResult, data: DataSet): assert type(data) is DataSet, data self.kramers_kronig_tab.select_test_result(test, data) + def select_zhit_result(self, zhit: ZHITResult, data: DataSet): + assert type(zhit) is ZHITResult, zhit + assert type(data) is DataSet, data + self.zhit_tab.select_zhit_result(zhit, data) + def select_drt_result(self, drt: DRTResult, data: DataSet): assert type(drt) is DRTResult, drt assert type(data) is DataSet, data @@ -393,7 +438,9 @@ def select_fit_result(self, fit: FitResult, data: DataSet): self.fitting_tab.select_fit_result(fit, data) def select_simulation_result( - self, simulation: Optional[SimulationResult], data: Optional[DataSet] + self, + simulation: Optional[SimulationResult], + data: Optional[DataSet], ): assert type(simulation) is SimulationResult or simulation is None, simulation assert type(data) is DataSet or data is None, data @@ -404,6 +451,7 @@ def select_plot( settings: PlotSettings, data_sets: List[DataSet], tests: Dict[str, List[TestResult]], + zhits: Dict[str, List[ZHITResult]], drts: Dict[str, List[DRTResult]], fits: Dict[str, List[FitResult]], simulations: List[SimulationResult], @@ -413,20 +461,22 @@ def select_plot( assert type(settings) is PlotSettings, settings assert type(data_sets) is list, data_sets assert type(tests) is dict, tests + assert type(zhits) is dict, zhits assert type(drts) is dict, drts assert type(fits) is dict, fits assert type(simulations) is list, simulations assert type(adjust_limits) is bool, adjust_limits assert type(plot_only) is bool, plot_only self.plotting_tab.select_plot( - settings, - data_sets, - tests, - drts, - fits, - simulations, - adjust_limits, - plot_only, + settings=settings, + data_sets=data_sets, + tests=tests, + zhits=zhits, + drts=drts, + fits=fits, + simulations=simulations, + adjust_limits=adjust_limits, + plot_only=plot_only, ) def select_plot_type(self, plot_type: PlotType): @@ -445,6 +495,13 @@ def set_test_settings(self, settings: TestSettings): assert type(settings) is TestSettings, settings self.kramers_kronig_tab.set_settings(settings) + def get_zhit_settings(self) -> ZHITSettings: + return self.zhit_tab.get_settings() + + def set_zhit_settings(self, settings: ZHITSettings): + assert isinstance(settings, ZHITSettings), settings + self.zhit_tab.set_settings(settings) + def get_fit_settings(self) -> FitSettings: return self.fitting_tab.get_settings() @@ -470,6 +527,8 @@ def show_enlarged_nyquist(self): { self.data_sets_tab.tab: self.data_sets_tab.show_enlarged_nyquist, self.kramers_kronig_tab.tab: self.kramers_kronig_tab.show_enlarged_nyquist, + self.zhit_tab.tab: self.zhit_tab.show_enlarged_nyquist, + self.drt_tab.tab: self.drt_tab.show_enlarged_nyquist, self.fitting_tab.tab: self.fitting_tab.show_enlarged_nyquist, self.simulation_tab.tab: self.simulation_tab.show_enlarged_nyquist, }.get(dpg.get_value(self.tab_bar))() @@ -478,6 +537,8 @@ def show_enlarged_bode(self): { self.data_sets_tab.tab: self.data_sets_tab.show_enlarged_bode, self.kramers_kronig_tab.tab: self.kramers_kronig_tab.show_enlarged_bode, + self.zhit_tab.tab: self.zhit_tab.show_enlarged_bode, + self.drt_tab.tab: self.drt_tab.show_enlarged_bode, self.fitting_tab.tab: self.fitting_tab.show_enlarged_bode, self.simulation_tab.tab: self.simulation_tab.show_enlarged_bode, }.get(dpg.get_value(self.tab_bar))() @@ -485,6 +546,7 @@ def show_enlarged_bode(self): def show_enlarged_residuals(self): { self.kramers_kronig_tab.tab: self.kramers_kronig_tab.show_enlarged_residuals, + self.zhit_tab.tab: self.zhit_tab.show_enlarged_residuals, self.drt_tab.tab: self.drt_tab.show_enlarged_residuals, self.fitting_tab.tab: self.fitting_tab.show_enlarged_residuals, }.get(dpg.get_value(self.tab_bar))() @@ -496,14 +558,40 @@ def show_enlarged_drt(self): def show_enlarged_impedance(self): { + self.data_sets_tab.tab: self.data_sets_tab.show_enlarged_impedance, + self.kramers_kronig_tab.tab: self.kramers_kronig_tab.show_enlarged_impedance, + self.zhit_tab.tab: self.zhit_tab.show_enlarged_impedance, self.drt_tab.tab: self.drt_tab.show_enlarged_impedance, + self.fitting_tab.tab: self.fitting_tab.show_enlarged_impedance, + self.simulation_tab.tab: self.simulation_tab.show_enlarged_impedance, }.get(dpg.get_value(self.tab_bar))() + def next_plot_tab(self, context: Context): + { + Context.DATA_SETS_TAB: self.data_sets_tab, + Context.KRAMERS_KRONIG_TAB: self.kramers_kronig_tab, + Context.ZHIT_TAB: self.zhit_tab, + Context.DRT_TAB: self.drt_tab, + Context.FITTING_TAB: self.fitting_tab, + Context.SIMULATION_TAB: self.simulation_tab, + }[context].next_plot_tab() + + def previous_plot_tab(self, context: Context): + { + Context.DATA_SETS_TAB: self.data_sets_tab, + Context.KRAMERS_KRONIG_TAB: self.kramers_kronig_tab, + Context.ZHIT_TAB: self.zhit_tab, + Context.DRT_TAB: self.drt_tab, + Context.FITTING_TAB: self.fitting_tab, + Context.SIMULATION_TAB: self.simulation_tab, + }[context].previous_plot_tab() + def update_plots( self, settings: PlotSettings, data_sets: List[DataSet], tests: Dict[str, List[TestResult]], + zhits: Dict[str, List[ZHITResult]], drts: Dict[str, List[DRTResult]], fits: Dict[str, List[FitResult]], simulations: List[SimulationResult], @@ -511,12 +599,14 @@ def update_plots( assert type(settings) is PlotSettings, settings assert type(data_sets) is list, data_sets assert type(tests) is dict, tests + assert type(zhits) is dict, zhits assert type(drts) is dict, drts assert type(fits) is dict, fits assert type(simulations) is list, simulations self.plotting_tab.plot_series( data_sets, tests, + zhits, drts, fits, simulations, @@ -545,16 +635,20 @@ def get_nyquist_plot(self, context: Context) -> Optional[Plot]: def get_bode_plot(self, context: Context) -> Optional[Plot]: if context == Context.KRAMERS_KRONIG_TAB: - return self.kramers_kronig_tab.bode_plot_horizontal + return self.kramers_kronig_tab.bode_plot + elif context == Context.ZHIT_TAB: + return self.zhit_tab.bode_plot elif context == Context.FITTING_TAB: - return self.fitting_tab.bode_plot_horizontal + return self.fitting_tab.bode_plot elif context == Context.SIMULATION_TAB: - return self.simulation_tab.bode_plot_horizontal + return self.simulation_tab.bode_plot return None def get_residuals_plot(self, context: Context) -> Optional[Plot]: if context == Context.KRAMERS_KRONIG_TAB: return self.kramers_kronig_tab.residuals_plot + elif context == Context.ZHIT_TAB: + return self.zhit_tab.residuals_plot elif context == Context.DRT_TAB: return self.drt_tab.residuals_plot elif context == Context.FITTING_TAB: @@ -573,6 +667,7 @@ def get_filtered_plot_series( ) -> Tuple[ List[DataSet], List[TestResult], + List[ZHITResult], List[DRTResult], List[FitResult], List[SimulationResult], @@ -581,6 +676,7 @@ def get_filtered_plot_series( return ( self.plotting_tab.possible_data_sets.filter(string, False), self.plotting_tab.possible_tests.filter(string, False), + self.plotting_tab.possible_zhits.filter(string, False), self.plotting_tab.possible_drts.filter(string, False), self.plotting_tab.possible_fits.filter(string, False), self.plotting_tab.possible_simulations.filter(string, False), @@ -596,6 +692,8 @@ def has_active_input(self, context: Optional[Context] = None) -> bool: return self.data_sets_tab.has_active_input() elif context == Context.KRAMERS_KRONIG_TAB: return self.kramers_kronig_tab.has_active_input() + elif context == Context.ZHIT_TAB: + return self.zhit_tab.has_active_input() elif context == Context.DRT_TAB: return self.drt_tab.has_active_input() elif context == Context.FITTING_TAB: diff --git a/src/deareis/gui/settings/__init__.py b/src/deareis/gui/settings/__init__.py index af0f3d3..7e8f36f 100644 --- a/src/deareis/gui/settings/__init__.py +++ b/src/deareis/gui/settings/__init__.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -20,3 +20,7 @@ from .appearance import AppearanceSettings from .defaults import show_defaults_settings_window from .keybindings import KeybindingRemapping +from .user_defined_elements import ( + refresh as refresh_user_defined_elements, + show_user_defined_elements_window, +) diff --git a/src/deareis/gui/settings/appearance.py b/src/deareis/gui/settings/appearance.py index ebf3a7b..6a70824 100644 --- a/src/deareis/gui/settings/appearance.py +++ b/src/deareis/gui/settings/appearance.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -62,6 +62,10 @@ import pyimpspec from deareis.signals import Signal import deareis.signals as signals +from deareis.config.defaults import ( + DEFAULT_COLORS, + DEFAULT_MARKERS, +) # TODO: Refactor color and marker widgets to reduce code duplication @@ -70,6 +74,11 @@ class AppearanceSettings: def __init__(self): + self.create_window() + self.register_keybindings() + signals.emit(Signal.BLOCK_KEYBINDINGS, window=self.window, window_object=self) + + def create_window(self): self.marker_items: List[str] = list(PLOT_MARKERS.keys()) self.marker_label_lookup: Dict[int, str] = { v: k for k, v in PLOT_MARKERS.items() @@ -78,16 +87,14 @@ def __init__(self): self.data: DataSet self.sim_data: DataSet self.smooth_data: DataSet - self.real_residual: ndarray - self.imag_residual: ndarray + self.residuals: ndarray self.noise: ndarray self.drt: DRTResult ( self.data, self.sim_data, self.smooth_data, - self.real_residual, - self.imag_residual, + self.residuals, self.noise, self.drt, ) = self.generate_data() @@ -100,525 +107,543 @@ def __init__(self): with dpg.window( label="Settings - appearance", modal=True, - pos=( - x, - y, - ), + pos=(x, y), width=w, height=h, no_move=False, no_resize=True, - on_close=self.close_window, + on_close=self.close, tag=self.window, ): - with dpg.collapsing_header(label="General", default_open=True): - with dpg.group(horizontal=True): - dpg.add_text( - "Number of points per decade in simulated response".rjust( - self.label_pad - ) + self.create_general_settings() + self.create_bode_settings() + self.create_drt_settings() + self.create_impedance_settings() + self.create_nyquist_settings() + self.create_residuals_settings() + self.create_mu_chi_squared_settings() + # TODO: Settings for Z-HIT weights preview plot? + + def create_general_settings(self): + with dpg.collapsing_header(label="General", default_open=True): + with dpg.group(horizontal=True): + dpg.add_text( + "Number of points per decade in simulated response".rjust( + self.label_pad ) - attach_tooltip( - """ + ) + attach_tooltip( + """ This affects how smooth the lines will look when plotting the impedance response of, e.g., fitted circuits, but it may also affect performance when rendering the graphical user interface. This setting also affects how many points are included when copying plot data as character-separated values (CSV). Changes made to this setting will take effect the next time a plot is redrawn. - """.strip() - ) - dpg.add_slider_int( - default_value=STATE.config.num_per_decade_in_simulated_lines, - min_value=1, - max_value=200, - clamped=True, - callback=self.update_simulated_num_per_decade, - width=-1, - ) - dpg.add_spacer(height=8) - with dpg.collapsing_header(label="Bode plots", default_open=True): - self.bode_plot = Bode(width=-1, height=200) - self.bode_data_mag_color: int = dpg.generate_uuid() - self.bode_data_phase_color: int = dpg.generate_uuid() - self.bode_sim_mag_color: int = dpg.generate_uuid() - self.bode_sim_phase_color: int = dpg.generate_uuid() - self.bode_data_mag_marker: int = dpg.generate_uuid() - self.bode_data_phase_marker: int = dpg.generate_uuid() - self.bode_sim_mag_marker: int = dpg.generate_uuid() - self.bode_sim_phase_marker: int = dpg.generate_uuid() - # Data colors and markers - with dpg.group(horizontal=True): - dpg.add_text("Data - magnitude".rjust(self.label_pad)) - dpg.add_color_edit( - default_value=STATE.config.colors["bode_magnitude_data"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_bode_color, - user_data=themes.bode.magnitude_data, - tag=self.bode_data_mag_color, - ) - dpg.add_combo( - items=self.marker_items, - default_value=self.marker_label_lookup[ - STATE.config.markers["bode_magnitude_data"] - ], - callback=self.update_bode_marker, - user_data=themes.bode.magnitude_data, - tag=self.bode_data_mag_marker, - width=-1, - ) - with dpg.group(horizontal=True): - dpg.add_text("Data - phase".rjust(self.label_pad)) - dpg.add_color_edit( - default_value=STATE.config.colors["bode_phase_data"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_bode_color, - user_data=themes.bode.phase_data, - tag=self.bode_data_phase_color, - ) - dpg.add_combo( - items=self.marker_items, - default_value=self.marker_label_lookup[ - STATE.config.markers["bode_phase_data"] - ], - callback=self.update_bode_marker, - user_data=themes.bode.phase_data, - tag=self.bode_data_phase_marker, - width=-1, - ) - # Sim/fit colors and markers - with dpg.group(horizontal=True): - dpg.add_text("Fit/simulation - magnitude".rjust(self.label_pad)) - dpg.add_color_edit( - default_value=STATE.config.colors["bode_magnitude_simulation"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_bode_color, - user_data=themes.bode.magnitude_simulation, - tag=self.bode_sim_mag_color, - ) - dpg.add_combo( - items=self.marker_items, - default_value=self.marker_label_lookup[ - STATE.config.markers["bode_magnitude_simulation"] - ], - callback=self.update_bode_marker, - user_data=themes.bode.magnitude_simulation, - tag=self.bode_sim_mag_marker, - width=-1, - ) - with dpg.group(horizontal=True): - dpg.add_text("Fit/simulation - phase".rjust(self.label_pad)) - dpg.add_color_edit( - default_value=STATE.config.colors["bode_phase_simulation"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_bode_color, - user_data=themes.bode.phase_simulation, - tag=self.bode_sim_phase_color, - ) - dpg.add_combo( - items=self.marker_items, - default_value=self.marker_label_lookup[ - STATE.config.markers["bode_phase_simulation"] - ], - callback=self.update_bode_marker, - user_data=themes.bode.phase_simulation, - tag=self.bode_sim_phase_marker, - width=-1, - ) - with dpg.group(horizontal=True): - dpg.add_text("".rjust(self.label_pad)) - dpg.add_button( - label="Restore defaults", - callback=self.reset_bode_plot, - width=-1, - ) - self.update_bode_plot(True) - dpg.add_spacer(height=8) - with dpg.collapsing_header(label="DRT plots", default_open=True): - self.drt_plot = DRT(width=-1, height=200) - self.drt_real_gamma_color: int = dpg.generate_uuid() - self.drt_imaginary_gamma_color: int = dpg.generate_uuid() - self.drt_mean_gamma_color: int = dpg.generate_uuid() - self.drt_credible_intervals_color: int = dpg.generate_uuid() - with dpg.group(horizontal=True): - dpg.add_text("Gamma/real".rjust(self.label_pad)) - dpg.add_color_edit( - default_value=STATE.config.colors["drt_real_gamma"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_drt_color, - user_data=themes.drt.real_gamma, - tag=self.drt_real_gamma_color, - ) - with dpg.group(horizontal=True): - dpg.add_text("Imaginary".rjust(self.label_pad)) - dpg.add_color_edit( - default_value=STATE.config.colors["drt_imaginary_gamma"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_drt_color, - user_data=themes.drt.imaginary_gamma, - tag=self.drt_imaginary_gamma_color, - ) - with dpg.group(horizontal=True): - dpg.add_text("Mean and 3-sigma CI".rjust(self.label_pad)) - dpg.add_color_edit( - default_value=STATE.config.colors["drt_mean_gamma"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_drt_color, - user_data=themes.drt.mean_gamma, - tag=self.drt_mean_gamma_color, - ) - dpg.add_color_edit( - default_value=STATE.config.colors["drt_credible_intervals"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_drt_color, - user_data=themes.drt.credible_intervals, - tag=self.drt_credible_intervals_color, - ) - with dpg.group(horizontal=True): - dpg.add_text("".rjust(self.label_pad)) - dpg.add_button( - label="Restore defaults", - callback=self.reset_drt_plot, - width=-1, - ) - self.update_drt_plot(True) - dpg.add_spacer(height=8) - with dpg.collapsing_header(label="Impedance plots", default_open=True): - self.impedance_plot = Impedance(width=-1, height=200) - self.impedance_real_data_color: int = dpg.generate_uuid() - self.impedance_real_simulation_color: int = dpg.generate_uuid() - self.impedance_imaginary_data_color: int = dpg.generate_uuid() - self.impedance_imaginary_simulation_color: int = dpg.generate_uuid() - self.impedance_real_data_marker: int = dpg.generate_uuid() - self.impedance_real_simulation_marker: int = dpg.generate_uuid() - self.impedance_imaginary_data_marker: int = dpg.generate_uuid() - self.impedance_imaginary_simulation_marker: int = dpg.generate_uuid() - # Data colors and markers - with dpg.group(horizontal=True): - dpg.add_text("Data - real".rjust(self.label_pad)) - dpg.add_color_edit( - default_value=STATE.config.colors["impedance_real_data"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_impedance_color, - user_data=themes.impedance.real_data, - tag=self.impedance_real_data_color, - ) - dpg.add_combo( - items=self.marker_items, - default_value=self.marker_label_lookup[ - STATE.config.markers["impedance_real_data"] - ], - callback=self.update_impedance_marker, - user_data=themes.impedance.real_data, - tag=self.impedance_real_data_marker, - width=-1, - ) - with dpg.group(horizontal=True): - dpg.add_text("Data - imaginary".rjust(self.label_pad)) - dpg.add_color_edit( - default_value=STATE.config.colors["impedance_imaginary_data"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_impedance_color, - user_data=themes.impedance.imaginary_data, - tag=self.impedance_imaginary_data_color, - ) - dpg.add_combo( - items=self.marker_items, - default_value=self.marker_label_lookup[ - STATE.config.markers["impedance_imaginary_data"] - ], - callback=self.update_impedance_marker, - user_data=themes.impedance.imaginary_data, - tag=self.impedance_imaginary_data_marker, - width=-1, - ) - # Sim/fit colors and markers - with dpg.group(horizontal=True): - dpg.add_text("Fit/simulation - real".rjust(self.label_pad)) - dpg.add_color_edit( - default_value=STATE.config.colors["impedance_real_simulation"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_impedance_color, - user_data=themes.impedance.real_simulation, - tag=self.impedance_real_simulation_color, - ) - dpg.add_combo( - items=self.marker_items, - default_value=self.marker_label_lookup[ - STATE.config.markers["impedance_real_simulation"] - ], - callback=self.update_impedance_marker, - user_data=themes.impedance.real_simulation, - tag=self.impedance_real_simulation_marker, - width=-1, - ) - with dpg.group(horizontal=True): - dpg.add_text("Fit/simulation - imaginary".rjust(self.label_pad)) - dpg.add_color_edit( - default_value=STATE.config.colors[ - "impedance_imaginary_simulation" - ], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_impedance_color, - user_data=themes.impedance.imaginary_simulation, - tag=self.impedance_imaginary_simulation_color, - ) - dpg.add_combo( - items=self.marker_items, - default_value=self.marker_label_lookup[ - STATE.config.markers["impedance_imaginary_simulation"] - ], - callback=self.update_impedance_marker, - user_data=themes.impedance.imaginary_simulation, - tag=self.impedance_imaginary_simulation_marker, - width=-1, - ) - with dpg.group(horizontal=True): - dpg.add_text("".rjust(self.label_pad)) - dpg.add_button( - label="Restore defaults", - callback=self.reset_impedance_plot, - width=-1, - ) - self.update_impedance_plot(True) - dpg.add_spacer(height=8) - with dpg.collapsing_header(label="Nyquist plots", default_open=True): - self.nyquist_plot = Nyquist(width=-1, height=200) - self.nyquist_data_color: int = dpg.generate_uuid() - self.nyquist_sim_color: int = dpg.generate_uuid() - self.nyquist_data_marker: int = dpg.generate_uuid() - self.nyquist_sim_marker: int = dpg.generate_uuid() - # Data colors and markers - with dpg.group(horizontal=True): - dpg.add_text("Data".rjust(self.label_pad)) - dpg.add_color_edit( - default_value=STATE.config.colors["nyquist_data"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_nyquist_color, - user_data=themes.nyquist.data, - tag=self.nyquist_data_color, - ) - dpg.add_combo( - items=self.marker_items, - default_value=self.marker_label_lookup[ - STATE.config.markers["nyquist_data"] - ], - callback=self.update_nyquist_marker, - user_data=themes.nyquist.data, - tag=self.nyquist_data_marker, - width=-1, - ) - # Sim/fit colors and markers - with dpg.group(horizontal=True): - dpg.add_text("Fit/simulation".rjust(self.label_pad)) - dpg.add_color_edit( - default_value=STATE.config.colors["nyquist_simulation"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_nyquist_color, - user_data=themes.nyquist.simulation, - tag=self.nyquist_sim_color, - ) - dpg.add_combo( - items=self.marker_items, - default_value=self.marker_label_lookup[ - STATE.config.markers["nyquist_simulation"] - ], - callback=self.update_nyquist_marker, - user_data=themes.nyquist.simulation, - tag=self.nyquist_sim_marker, - width=-1, - ) - with dpg.group(horizontal=True): - dpg.add_text("".rjust(self.label_pad)) - dpg.add_button( - label="Restore defaults", - callback=self.reset_nyquist_plot, - width=-1, - ) - self.update_nyquist_plot(True) - dpg.add_spacer(height=8) - with dpg.collapsing_header(label="Residuals plots", default_open=True): - self.residuals_plot = Residuals(width=-1, height=200) - self.residuals_real_color: int = dpg.generate_uuid() - self.residuals_imag_color: int = dpg.generate_uuid() - self.residuals_real_marker: int = dpg.generate_uuid() - self.residuals_imag_marker: int = dpg.generate_uuid() - # Zre color and marker - with dpg.group(horizontal=True): - dpg.add_text("Z' error".rjust(self.label_pad)) - dpg.add_color_edit( - default_value=STATE.config.colors["residuals_real"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_residuals_color, - user_data=themes.residuals.real, - tag=self.residuals_real_color, - ) - dpg.add_combo( - items=self.marker_items, - default_value=self.marker_label_lookup[ - STATE.config.markers["residuals_real"] - ], - callback=self.update_residuals_marker, - user_data=themes.residuals.real, - tag=self.residuals_real_marker, - width=-1, - ) - # Zim color and marker - with dpg.group(horizontal=True): - dpg.add_text('Z" error'.rjust(self.label_pad)) - dpg.add_color_edit( - default_value=STATE.config.colors["residuals_imaginary"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_residuals_color, - user_data=themes.residuals.imaginary, - tag=self.residuals_imag_color, - ) - dpg.add_combo( - items=self.marker_items, - default_value=self.marker_label_lookup[ - STATE.config.markers["residuals_imaginary"] - ], - callback=self.update_residuals_marker, - user_data=themes.residuals.imaginary, - tag=self.residuals_imag_marker, - width=-1, - ) - with dpg.group(horizontal=True): - dpg.add_text("".rjust(self.label_pad)) - dpg.add_button( - label="Restore defaults", - callback=self.reset_residuals_plot, - width=-1, - ) - self.update_residuals_plot(True) - dpg.add_spacer(height=8) - with dpg.collapsing_header(label="µ-X² (pseudo) plots", default_open=True): - self.muxps_plot = MuXps(width=-1, height=200) - self.muxps_mu_criterion_color: int = dpg.generate_uuid() - self.muxps_mu_color: int = dpg.generate_uuid() - self.muxps_mu_highlight_color: int = dpg.generate_uuid() - self.muxps_xps_color: int = dpg.generate_uuid() - self.muxps_xps_highlight_color: int = dpg.generate_uuid() - self.muxps_mu_marker: int = dpg.generate_uuid() - self.muxps_xps_marker: int = dpg.generate_uuid() - # Mu-criterion color - with dpg.group(horizontal=True): - dpg.add_text("µ-criterion".rjust(self.label_pad)) - dpg.add_color_edit( - default_value=STATE.config.colors["mu_Xps_mu_criterion"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_muxps_color, - user_data=themes.mu_Xps.mu_criterion, - tag=self.muxps_mu_criterion_color, - ) - # Mu color and marker - with dpg.group(horizontal=True): - dpg.add_text("µ".rjust(self.label_pad)) - dpg.add_color_edit( - default_value=STATE.config.colors["mu_Xps_mu"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_muxps_color, - user_data=themes.mu_Xps.mu, - tag=self.muxps_mu_color, - ) - dpg.add_color_edit( - default_value=STATE.config.colors["mu_Xps_mu_highlight"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_muxps_color, - user_data=themes.mu_Xps.mu_highlight, - tag=self.muxps_mu_highlight_color, - ) - dpg.add_combo( - items=self.marker_items, - default_value=self.marker_label_lookup[ - STATE.config.markers["mu_Xps_mu"] - ], - callback=self.update_muxps_marker, - user_data=themes.mu_Xps.mu, - tag=self.muxps_mu_marker, - width=-1, - ) - # Xps color and marker - with dpg.group(horizontal=True): - dpg.add_text("X² (pseudo)".rjust(self.label_pad)) - dpg.add_color_edit( - default_value=STATE.config.colors["mu_Xps_Xps"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_muxps_color, - user_data=themes.mu_Xps.Xps, - tag=self.muxps_xps_color, - ) - dpg.add_color_edit( - default_value=STATE.config.colors["mu_Xps_Xps_highlight"], - alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, - no_inputs=True, - alpha_bar=True, - callback=self.update_muxps_color, - user_data=themes.mu_Xps.Xps_highlight, - tag=self.muxps_xps_highlight_color, - ) - dpg.add_combo( - items=self.marker_items, - default_value=self.marker_label_lookup[ - STATE.config.markers["mu_Xps_Xps"] - ], - callback=self.update_muxps_marker, - user_data=themes.mu_Xps.Xps, - tag=self.muxps_xps_marker, - width=-1, - ) - with dpg.group(horizontal=True): - dpg.add_text("".rjust(self.label_pad)) - dpg.add_button( - label="Restore defaults", - callback=self.reset_muxps_plot, - width=-1, - ) - self.update_muxps_plot() + """.strip() + ) + dpg.add_slider_int( + default_value=STATE.config.num_per_decade_in_simulated_lines, + min_value=1, + max_value=200, + clamped=True, + callback=self.update_simulated_num_per_decade, + width=-1, + ) + dpg.add_spacer(height=8) + + def create_bode_settings(self): + with dpg.collapsing_header(label="Bode plots", default_open=True): + self.bode_plot = Bode(width=-1, height=200) + self.bode_data_mag_color: int = dpg.generate_uuid() + self.bode_data_phase_color: int = dpg.generate_uuid() + self.bode_sim_mag_color: int = dpg.generate_uuid() + self.bode_sim_phase_color: int = dpg.generate_uuid() + self.bode_data_mag_marker: int = dpg.generate_uuid() + self.bode_data_phase_marker: int = dpg.generate_uuid() + self.bode_sim_mag_marker: int = dpg.generate_uuid() + self.bode_sim_phase_marker: int = dpg.generate_uuid() + # Data colors and markers + with dpg.group(horizontal=True): + dpg.add_text("Data - magnitude".rjust(self.label_pad)) + dpg.add_color_edit( + default_value=STATE.config.colors["bode_magnitude_data"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_bode_color, + user_data=themes.bode.magnitude_data, + tag=self.bode_data_mag_color, + ) + dpg.add_combo( + items=self.marker_items, + default_value=self.marker_label_lookup[ + STATE.config.markers["bode_magnitude_data"] + ], + callback=self.update_bode_marker, + user_data=themes.bode.magnitude_data, + tag=self.bode_data_mag_marker, + width=-1, + ) + with dpg.group(horizontal=True): + dpg.add_text("Data - phase".rjust(self.label_pad)) + dpg.add_color_edit( + default_value=STATE.config.colors["bode_phase_data"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_bode_color, + user_data=themes.bode.phase_data, + tag=self.bode_data_phase_color, + ) + dpg.add_combo( + items=self.marker_items, + default_value=self.marker_label_lookup[ + STATE.config.markers["bode_phase_data"] + ], + callback=self.update_bode_marker, + user_data=themes.bode.phase_data, + tag=self.bode_data_phase_marker, + width=-1, + ) + # Sim/fit colors and markers + with dpg.group(horizontal=True): + dpg.add_text("Fit/simulation - magnitude".rjust(self.label_pad)) + dpg.add_color_edit( + default_value=STATE.config.colors["bode_magnitude_simulation"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_bode_color, + user_data=themes.bode.magnitude_simulation, + tag=self.bode_sim_mag_color, + ) + dpg.add_combo( + items=self.marker_items, + default_value=self.marker_label_lookup[ + STATE.config.markers["bode_magnitude_simulation"] + ], + callback=self.update_bode_marker, + user_data=themes.bode.magnitude_simulation, + tag=self.bode_sim_mag_marker, + width=-1, + ) + with dpg.group(horizontal=True): + dpg.add_text("Fit/simulation - phase".rjust(self.label_pad)) + dpg.add_color_edit( + default_value=STATE.config.colors["bode_phase_simulation"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_bode_color, + user_data=themes.bode.phase_simulation, + tag=self.bode_sim_phase_color, + ) + dpg.add_combo( + items=self.marker_items, + default_value=self.marker_label_lookup[ + STATE.config.markers["bode_phase_simulation"] + ], + callback=self.update_bode_marker, + user_data=themes.bode.phase_simulation, + tag=self.bode_sim_phase_marker, + width=-1, + ) + with dpg.group(horizontal=True): + dpg.add_text("".rjust(self.label_pad)) + dpg.add_button( + label="Restore defaults", + callback=self.reset_bode_plot, + width=-1, + ) + self.update_bode_plot(True) + dpg.add_spacer(height=8) + + def create_drt_settings(self): + with dpg.collapsing_header(label="DRT plots", default_open=True): + self.drt_plot = DRT(width=-1, height=200) + self.drt_real_gamma_color: int = dpg.generate_uuid() + self.drt_imaginary_gamma_color: int = dpg.generate_uuid() + self.drt_mean_gamma_color: int = dpg.generate_uuid() + self.drt_credible_intervals_color: int = dpg.generate_uuid() + with dpg.group(horizontal=True): + dpg.add_text("Gamma/real".rjust(self.label_pad)) + dpg.add_color_edit( + default_value=STATE.config.colors["drt_real_gamma"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_drt_color, + user_data=themes.drt.real_gamma, + tag=self.drt_real_gamma_color, + ) + with dpg.group(horizontal=True): + dpg.add_text("Imaginary".rjust(self.label_pad)) + dpg.add_color_edit( + default_value=STATE.config.colors["drt_imaginary_gamma"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_drt_color, + user_data=themes.drt.imaginary_gamma, + tag=self.drt_imaginary_gamma_color, + ) + with dpg.group(horizontal=True): + dpg.add_text("Mean and 3-sigma CI".rjust(self.label_pad)) + dpg.add_color_edit( + default_value=STATE.config.colors["drt_mean_gamma"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_drt_color, + user_data=themes.drt.mean_gamma, + tag=self.drt_mean_gamma_color, + ) + dpg.add_color_edit( + default_value=STATE.config.colors["drt_credible_intervals"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_drt_color, + user_data=themes.drt.credible_intervals, + tag=self.drt_credible_intervals_color, + ) + with dpg.group(horizontal=True): + dpg.add_text("".rjust(self.label_pad)) + dpg.add_button( + label="Restore defaults", + callback=self.reset_drt_plot, + width=-1, + ) + self.update_drt_plot(True) + dpg.add_spacer(height=8) + + def create_impedance_settings(self): + with dpg.collapsing_header(label="Impedance plots", default_open=True): + self.impedance_plot = Impedance(width=-1, height=200) + self.impedance_real_data_color: int = dpg.generate_uuid() + self.impedance_real_simulation_color: int = dpg.generate_uuid() + self.impedance_imaginary_data_color: int = dpg.generate_uuid() + self.impedance_imaginary_simulation_color: int = dpg.generate_uuid() + self.impedance_real_data_marker: int = dpg.generate_uuid() + self.impedance_real_simulation_marker: int = dpg.generate_uuid() + self.impedance_imaginary_data_marker: int = dpg.generate_uuid() + self.impedance_imaginary_simulation_marker: int = dpg.generate_uuid() + # Data colors and markers + with dpg.group(horizontal=True): + dpg.add_text("Data - real".rjust(self.label_pad)) + dpg.add_color_edit( + default_value=STATE.config.colors["impedance_real_data"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_impedance_color, + user_data=themes.impedance.real_data, + tag=self.impedance_real_data_color, + ) + dpg.add_combo( + items=self.marker_items, + default_value=self.marker_label_lookup[ + STATE.config.markers["impedance_real_data"] + ], + callback=self.update_impedance_marker, + user_data=themes.impedance.real_data, + tag=self.impedance_real_data_marker, + width=-1, + ) + with dpg.group(horizontal=True): + dpg.add_text("Data - imaginary".rjust(self.label_pad)) + dpg.add_color_edit( + default_value=STATE.config.colors["impedance_imaginary_data"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_impedance_color, + user_data=themes.impedance.imaginary_data, + tag=self.impedance_imaginary_data_color, + ) + dpg.add_combo( + items=self.marker_items, + default_value=self.marker_label_lookup[ + STATE.config.markers["impedance_imaginary_data"] + ], + callback=self.update_impedance_marker, + user_data=themes.impedance.imaginary_data, + tag=self.impedance_imaginary_data_marker, + width=-1, + ) + # Sim/fit colors and markers + with dpg.group(horizontal=True): + dpg.add_text("Fit/simulation - real".rjust(self.label_pad)) + dpg.add_color_edit( + default_value=STATE.config.colors["impedance_real_simulation"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_impedance_color, + user_data=themes.impedance.real_simulation, + tag=self.impedance_real_simulation_color, + ) + dpg.add_combo( + items=self.marker_items, + default_value=self.marker_label_lookup[ + STATE.config.markers["impedance_real_simulation"] + ], + callback=self.update_impedance_marker, + user_data=themes.impedance.real_simulation, + tag=self.impedance_real_simulation_marker, + width=-1, + ) + with dpg.group(horizontal=True): + dpg.add_text("Fit/simulation - imaginary".rjust(self.label_pad)) + dpg.add_color_edit( + default_value=STATE.config.colors["impedance_imaginary_simulation"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_impedance_color, + user_data=themes.impedance.imaginary_simulation, + tag=self.impedance_imaginary_simulation_color, + ) + dpg.add_combo( + items=self.marker_items, + default_value=self.marker_label_lookup[ + STATE.config.markers["impedance_imaginary_simulation"] + ], + callback=self.update_impedance_marker, + user_data=themes.impedance.imaginary_simulation, + tag=self.impedance_imaginary_simulation_marker, + width=-1, + ) + with dpg.group(horizontal=True): + dpg.add_text("".rjust(self.label_pad)) + dpg.add_button( + label="Restore defaults", + callback=self.reset_impedance_plot, + width=-1, + ) + self.update_impedance_plot(True) + dpg.add_spacer(height=8) + + def create_nyquist_settings(self): + with dpg.collapsing_header(label="Nyquist plots", default_open=True): + self.nyquist_plot = Nyquist(width=-1, height=200) + self.nyquist_data_color: int = dpg.generate_uuid() + self.nyquist_sim_color: int = dpg.generate_uuid() + self.nyquist_data_marker: int = dpg.generate_uuid() + self.nyquist_sim_marker: int = dpg.generate_uuid() + # Data colors and markers + with dpg.group(horizontal=True): + dpg.add_text("Data".rjust(self.label_pad)) + dpg.add_color_edit( + default_value=STATE.config.colors["nyquist_data"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_nyquist_color, + user_data=themes.nyquist.data, + tag=self.nyquist_data_color, + ) + dpg.add_combo( + items=self.marker_items, + default_value=self.marker_label_lookup[ + STATE.config.markers["nyquist_data"] + ], + callback=self.update_nyquist_marker, + user_data=themes.nyquist.data, + tag=self.nyquist_data_marker, + width=-1, + ) + # Sim/fit colors and markers + with dpg.group(horizontal=True): + dpg.add_text("Fit/simulation".rjust(self.label_pad)) + dpg.add_color_edit( + default_value=STATE.config.colors["nyquist_simulation"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_nyquist_color, + user_data=themes.nyquist.simulation, + tag=self.nyquist_sim_color, + ) + dpg.add_combo( + items=self.marker_items, + default_value=self.marker_label_lookup[ + STATE.config.markers["nyquist_simulation"] + ], + callback=self.update_nyquist_marker, + user_data=themes.nyquist.simulation, + tag=self.nyquist_sim_marker, + width=-1, + ) + with dpg.group(horizontal=True): + dpg.add_text("".rjust(self.label_pad)) + dpg.add_button( + label="Restore defaults", + callback=self.reset_nyquist_plot, + width=-1, + ) + self.update_nyquist_plot(True) + dpg.add_spacer(height=8) + + def create_residuals_settings(self): + with dpg.collapsing_header(label="Residuals plots", default_open=True): + self.residuals_plot = Residuals(width=-1, height=200) + self.residuals_real_color: int = dpg.generate_uuid() + self.residuals_imag_color: int = dpg.generate_uuid() + self.residuals_real_marker: int = dpg.generate_uuid() + self.residuals_imag_marker: int = dpg.generate_uuid() + # Re(Z) color and marker + with dpg.group(horizontal=True): + dpg.add_text("Re(Z) residual".rjust(self.label_pad)) + dpg.add_color_edit( + default_value=STATE.config.colors["residuals_real"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_residuals_color, + user_data=themes.residuals.real, + tag=self.residuals_real_color, + ) + dpg.add_combo( + items=self.marker_items, + default_value=self.marker_label_lookup[ + STATE.config.markers["residuals_real"] + ], + callback=self.update_residuals_marker, + user_data=themes.residuals.real, + tag=self.residuals_real_marker, + width=-1, + ) + # Im(Z) color and marker + with dpg.group(horizontal=True): + dpg.add_text("Im(Z) residual".rjust(self.label_pad)) + dpg.add_color_edit( + default_value=STATE.config.colors["residuals_imaginary"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_residuals_color, + user_data=themes.residuals.imaginary, + tag=self.residuals_imag_color, + ) + dpg.add_combo( + items=self.marker_items, + default_value=self.marker_label_lookup[ + STATE.config.markers["residuals_imaginary"] + ], + callback=self.update_residuals_marker, + user_data=themes.residuals.imaginary, + tag=self.residuals_imag_marker, + width=-1, + ) + with dpg.group(horizontal=True): + dpg.add_text("".rjust(self.label_pad)) + dpg.add_button( + label="Restore defaults", + callback=self.reset_residuals_plot, + width=-1, + ) + self.update_residuals_plot(True) + dpg.add_spacer(height=8) + + def create_mu_chi_squared_settings(self): + with dpg.collapsing_header(label="µ-X² (pseudo) plots", default_open=True): + self.muxps_plot = MuXps(width=-1, height=200) + self.muxps_mu_criterion_color: int = dpg.generate_uuid() + self.muxps_mu_color: int = dpg.generate_uuid() + self.muxps_mu_highlight_color: int = dpg.generate_uuid() + self.muxps_xps_color: int = dpg.generate_uuid() + self.muxps_xps_highlight_color: int = dpg.generate_uuid() + self.muxps_mu_marker: int = dpg.generate_uuid() + self.muxps_xps_marker: int = dpg.generate_uuid() + # Mu-criterion color + with dpg.group(horizontal=True): + dpg.add_text("µ-criterion".rjust(self.label_pad)) + dpg.add_color_edit( + default_value=STATE.config.colors["mu_Xps_mu_criterion"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_muxps_color, + user_data=themes.mu_Xps.mu_criterion, + tag=self.muxps_mu_criterion_color, + ) + # Mu color and marker + with dpg.group(horizontal=True): + dpg.add_text("µ".rjust(self.label_pad)) + dpg.add_color_edit( + default_value=STATE.config.colors["mu_Xps_mu"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_muxps_color, + user_data=themes.mu_Xps.mu, + tag=self.muxps_mu_color, + ) + dpg.add_color_edit( + default_value=STATE.config.colors["mu_Xps_mu_highlight"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_muxps_color, + user_data=themes.mu_Xps.mu_highlight, + tag=self.muxps_mu_highlight_color, + ) + dpg.add_combo( + items=self.marker_items, + default_value=self.marker_label_lookup[ + STATE.config.markers["mu_Xps_mu"] + ], + callback=self.update_muxps_marker, + user_data=themes.mu_Xps.mu, + tag=self.muxps_mu_marker, + width=-1, + ) + # Xps color and marker + with dpg.group(horizontal=True): + dpg.add_text("X² (pseudo)".rjust(self.label_pad)) + dpg.add_color_edit( + default_value=STATE.config.colors["mu_Xps_Xps"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_muxps_color, + user_data=themes.mu_Xps.Xps, + tag=self.muxps_xps_color, + ) + dpg.add_color_edit( + default_value=STATE.config.colors["mu_Xps_Xps_highlight"], + alpha_preview=dpg.mvColorEdit_AlphaPreviewHalf, + no_inputs=True, + alpha_bar=True, + callback=self.update_muxps_color, + user_data=themes.mu_Xps.Xps_highlight, + tag=self.muxps_xps_highlight_color, + ) + dpg.add_combo( + items=self.marker_items, + default_value=self.marker_label_lookup[ + STATE.config.markers["mu_Xps_Xps"] + ], + callback=self.update_muxps_marker, + user_data=themes.mu_Xps.Xps, + tag=self.muxps_xps_marker, + width=-1, + ) + with dpg.group(horizontal=True): + dpg.add_text("".rjust(self.label_pad)) + dpg.add_button( + label="Restore defaults", + callback=self.reset_muxps_plot, + width=-1, + ) + self.update_muxps_plot() + + def register_keybindings(self): self.key_handler: int = dpg.generate_uuid() with dpg.handler_registry(tag=self.key_handler): dpg.add_key_release_handler( key=dpg.mvKey_Escape, - callback=self.close_window, + callback=self.close, ) - signals.emit(Signal.BLOCK_KEYBINDINGS, window=self.window, window_object=self) - def close_window(self): + def close(self): if dpg.does_item_exist(self.window): dpg.delete_item(self.window) if dpg.does_item_exist(self.key_handler): @@ -648,14 +673,14 @@ def generate_data( abs(Z) * normal(0, sd, 1), abs(Z) * normal(0, sd, 1), ), - data.get_impedance(), + data.get_impedances(), ) ) ) - data.subtract_impedance(-noise) + data.subtract_impedances(-noise) sim_data: DataSet = pyimpspec.simulate_spectrum( circuit, - data.get_frequency(), + data.get_frequencies(), ) smooth_data: DataSet = pyimpspec.simulate_spectrum( circuit, @@ -665,24 +690,39 @@ def generate_data( num=5 * STATE.config.num_per_decade_in_simulated_lines + 1, ), ) - real_residual: ndarray = ( - (data.get_impedance().real - sim_data.get_impedance().real) - / abs(data.get_impedance()) - * 100 - ) - imag_residual: ndarray = ( - (data.get_impedance().imag - sim_data.get_impedance().imag) - / abs(data.get_impedance()) - * 100 + residuals: ndarray = array( + list( + map( + lambda _: complex(*_), + zip( + (data.get_impedances().real - sim_data.get_impedances().real) + / abs(data.get_impedances()) + * 100, + (data.get_impedances().imag - sim_data.get_impedances().imag) + / abs(data.get_impedances()) + * 100, + ), + ) + ) ) - f: ndarray = smooth_data.get_frequency() + f: ndarray = smooth_data.get_frequencies() tau: ndarray = 1 / f gamma: ndarray = array( list( map( - lambda _, a=1.1, b=0.0001, c=0.00001: a - * exp(-((_ - b) ** 2) / (2 * c**2)), - tau, + lambda _: complex(*_), + zip( + map( + lambda _, a=1.1, b=0.0001, c=0.00001: a + * exp(-((_ - b) ** 2) / (2 * c**2)), + tau, + ), + map( + lambda _, a=1.0, b=0.001, c=0.0001: a + * exp(-((_ - b) ** 2) / (2 * c**2)), + tau, + ), + ), ) ) ) @@ -713,33 +753,23 @@ def generate_data( ) ) ) - imaginary_gamma: ndarray = array( - list( - map( - lambda _, a=1.0, b=0.001, c=0.0001: a - * exp(-((_ - b) ** 2) / (2 * c**2)), - tau, - ) - ) - ) drt: DRTResult = DRTResult( - uuid4().hex, - time(), - tau, - gamma, - f, - sim_data.get_impedance(), - real_residual, - imag_residual, - mean_gamma, - lower_bound, - upper_bound, - imaginary_gamma, - {}, - 0.0, - 0.0, - {}, - DRTSettings( + uuid=uuid4().hex, + timestamp=time(), + time_constants=tau, + real_gammas=gamma.real, + imaginary_gammas=gamma.imag, + frequencies=f, + impedances=sim_data.get_impedances(), + residuals=residuals, + mean_gammas=mean_gamma, + lower_bounds=lower_bound, + upper_bounds=upper_bound, + scores={}, + pseudo_chisqr=0.0, + lambda_value=0.0, + mask={}, + settings=DRTSettings( method=DRTMethod.BHT, mode=DRTMode.COMPLEX, lambda_value=0.0, @@ -749,11 +779,12 @@ def generate_data( shape_coeff=0.5, inductance=False, credible_intervals=True, + timeout=60, num_samples=2000, num_attempts=50, maximum_symmetry=0.5, - circuit=None, - W=0.15, + fit=None, + gaussian_width=0.15, num_per_decade=100, ), ) @@ -761,8 +792,7 @@ def generate_data( data, sim_data, smooth_data, - real_residual, - imag_residual, + residuals, noise, drt, ) @@ -773,8 +803,7 @@ def update_simulated_num_per_decade(self, sender: int, value: int): self.data, self.sim_data, self.smooth_data, - self.real_residual, - self.imag_residual, + self.residuals, _, _, ) = self.generate_data(self.noise) @@ -793,8 +822,8 @@ def update_bode_plot(self, adjust_limits: bool = False): magnitude=mag, phase=phase, labels=( - "|Z| (d)", - "phi (d)", + "Mod(Z), d.", + "Phase(Z), d.", ), themes=( themes.bode.magnitude_data, @@ -807,8 +836,8 @@ def update_bode_plot(self, adjust_limits: bool = False): magnitude=mag, phase=phase, labels=( - "|Z| (f)", - "phi (f)", + "Mod(Z), f.", + "Phase(Z), f.", ), line=True, themes=( @@ -822,8 +851,8 @@ def update_bode_plot(self, adjust_limits: bool = False): magnitude=mag, phase=phase, labels=( - "|Z| (f)", - "phi (f)", + "Mod(Z), f.", + "Phase(Z), f.", ), line=False, show_labels=False, @@ -867,39 +896,19 @@ def update_bode_marker(self, sender: int, label: str, theme: int): def reset_bode_plot(self): dpg.set_value( self.bode_data_mag_color, - ( - 51, - 187, - 238, - 190, - ), + DEFAULT_COLORS["bode_magnitude_data"].copy(), ) dpg.set_value( self.bode_data_phase_color, - ( - 238, - 119, - 51, - 190, - ), + DEFAULT_COLORS["bode_phase_data"].copy(), ) dpg.set_value( self.bode_sim_mag_color, - ( - 238, - 51, - 119, - 190, - ), + DEFAULT_COLORS["bode_magnitude_simulation"].copy(), ) dpg.set_value( self.bode_sim_phase_color, - ( - 0, - 153, - 136, - 190, - ), + DEFAULT_COLORS["bode_phase_simulation"].copy(), ) self.update_bode_color( self.bode_data_mag_color, None, themes.bode.magnitude_data @@ -939,24 +948,24 @@ def reset_bode_plot(self): def update_drt_plot(self, adjust_limits: bool = False): self.drt_plot.clear() tau: ndarray - gamma: ndarray - tau, gamma = self.drt.get_drt_data() + real_gamma: ndarray + imaginary_gamma: ndarray + tau, real_gamma, imaginary_gamma = self.drt.get_drt_data() self.drt_plot.plot( tau=tau, - gamma=gamma, + gamma=real_gamma, label="gamma/real", theme=themes.drt.real_gamma, ) - tau, gamma = self.drt.get_drt_data(imaginary=True) self.drt_plot.plot( tau=tau, - gamma=gamma, + gamma=imaginary_gamma, label="imag.", theme=themes.drt.imaginary_gamma, ) lower: ndarray upper: ndarray - tau, gamma, lower, upper = self.drt.get_drt_credible_intervals() + tau, gamma, lower, upper = self.drt.get_drt_credible_intervals_data() self.drt_plot.plot( tau=tau, gamma=gamma, @@ -990,39 +999,19 @@ def update_drt_color(self, sender: int, _, theme: int): def reset_drt_plot(self): dpg.set_value( self.drt_real_gamma_color, - ( - 51.0, - 187.0, - 238.0, - 190.0, - ), + DEFAULT_COLORS["drt_real_gamma"].copy(), ) dpg.set_value( self.drt_imaginary_gamma_color, - ( - 238.0, - 119.0, - 51.0, - 190.0, - ), + DEFAULT_COLORS["drt_imaginary_gamma"].copy(), ) dpg.set_value( self.drt_mean_gamma_color, - ( - 238.0, - 119.0, - 51.0, - 190.0, - ), + DEFAULT_COLORS["drt_mean_gamma"].copy(), ) dpg.set_value( self.drt_credible_intervals_color, - ( - 238.0, - 119.0, - 51.0, - 48.0, - ), + DEFAULT_COLORS["drt_credible_intervals"].copy(), ) self.update_drt_color( self.drt_real_gamma_color, @@ -1047,30 +1036,30 @@ def reset_drt_plot(self): def update_impedance_plot(self, adjust_limits: bool = False): self.impedance_plot.clear() - f: ndarray = self.data.get_frequency() - Z: ndarray = self.data.get_impedance() + f: ndarray = self.data.get_frequencies() + Z: ndarray = self.data.get_impedances() self.impedance_plot.plot( frequency=f, real=Z.real, imaginary=-Z.imag, labels=( - "Z' (d)", - 'Z" (d)', + "Re(Z), d.", + "Im(Z), d.", ), themes=( themes.impedance.real_data, themes.impedance.imaginary_data, ), ) - f = self.sim_data.get_frequency() - Z = self.sim_data.get_impedance() + f = self.sim_data.get_frequencies() + Z = self.sim_data.get_impedances() self.impedance_plot.plot( frequency=f, real=Z.real, imaginary=-Z.imag, labels=( - "Z' (f)", - 'Z" (f)', + "Re(Z), f.", + "Im(Z), f.", ), fit=True, line=False, @@ -1079,15 +1068,15 @@ def update_impedance_plot(self, adjust_limits: bool = False): themes.impedance.imaginary_simulation, ), ) - f = self.smooth_data.get_frequency() - Z = self.smooth_data.get_impedance() + f = self.smooth_data.get_frequencies() + Z = self.smooth_data.get_impedances() self.impedance_plot.plot( frequency=f, real=Z.real, imaginary=-Z.imag, labels=( - "Z' (f)", - 'Z" (f)', + "Re(Z), f.", + "Im(Z), f.", ), fit=True, line=True, @@ -1132,39 +1121,19 @@ def update_impedance_marker(self, sender: int, label: str, theme: int): def reset_impedance_plot(self): dpg.set_value( self.impedance_real_data_color, - ( - 51.0, - 187.0, - 238.0, - 190.0, - ), + DEFAULT_COLORS["impedance_real_data"].copy(), ) dpg.set_value( self.impedance_real_simulation_color, - ( - 238.0, - 51.0, - 119.0, - 190.0, - ), + DEFAULT_COLORS["impedance_real_simulation"].copy(), ) dpg.set_value( self.impedance_imaginary_data_color, - ( - 238.0, - 119.0, - 51.0, - 190.0, - ), + DEFAULT_COLORS["impedance_imaginary_data"].copy(), ) dpg.set_value( self.impedance_imaginary_simulation_color, - ( - 0.0, - 153.0, - 136.0, - 190.0, - ), + DEFAULT_COLORS["impedance_imaginary_simulation"].copy(), ) self.update_impedance_color( self.impedance_real_data_color, @@ -1267,25 +1236,21 @@ def update_nyquist_marker(self, sender: int, label: str, theme: int): def reset_nyquist_plot(self): dpg.set_value( self.nyquist_data_color, - ( - 51, - 187, - 238, - 190, - ), + DEFAULT_COLORS["nyquist_data"].copy(), ) dpg.set_value( self.nyquist_sim_color, - ( - 238, - 51, - 119, - 190, - ), + DEFAULT_COLORS["nyquist_simulation"].copy(), ) - self.update_nyquist_color(self.nyquist_data_color, None, themes.nyquist.data) self.update_nyquist_color( - self.nyquist_sim_color, None, themes.nyquist.simulation + self.nyquist_data_color, + None, + themes.nyquist.data, + ) + self.update_nyquist_color( + self.nyquist_sim_color, + None, + themes.nyquist.simulation, ) dpg.set_value(self.nyquist_data_marker, "Circle") dpg.set_value(self.nyquist_sim_marker, "Cross") @@ -1303,9 +1268,9 @@ def reset_nyquist_plot(self): def update_residuals_plot(self, adjust_limits: bool = False): self.residuals_plot.clear() self.residuals_plot.plot( - frequency=self.data.get_frequency(), - real=self.real_residual, - imaginary=self.imag_residual, + frequency=self.data.get_frequencies(), + real=self.residuals.real, + imaginary=self.residuals.imag, ) if adjust_limits: self.residuals_plot.adjust_limits() @@ -1338,27 +1303,21 @@ def update_residuals_marker(self, sender: int, label: str, theme: int): def reset_residuals_plot(self): dpg.set_value( self.residuals_real_color, - ( - 238, - 51, - 119, - 190, - ), + DEFAULT_COLORS["residuals_real"].copy(), ) dpg.set_value( self.residuals_imag_color, - ( - 0, - 153, - 136, - 190, - ), + DEFAULT_COLORS["residuals_imaginary"].copy(), ) self.update_residuals_color( - self.residuals_real_color, None, themes.residuals.real + self.residuals_real_color, + None, + themes.residuals.real, ) self.update_residuals_color( - self.residuals_imag_color, None, themes.residuals.imaginary + self.residuals_imag_color, + None, + themes.residuals.imaginary, ) dpg.set_value(self.residuals_real_marker, "Circle") dpg.set_value(self.residuals_imag_marker, "Square") @@ -1494,6 +1453,3 @@ def reset_muxps_plot(self): dpg.get_value(self.muxps_xps_marker), themes.mu_Xps.Xps, ) - -# TODO: Settings for circuit diagrams -# - WE and CE+RE labels diff --git a/src/deareis/gui/settings/defaults.py b/src/deareis/gui/settings/defaults.py index e3f142c..2ad9b13 100644 --- a/src/deareis/gui/settings/defaults.py +++ b/src/deareis/gui/settings/defaults.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -21,6 +21,10 @@ Callable, List, ) +from pyimpspec import ( + get_default_num_procs, + set_default_num_procs, +) import dearpygui.dearpygui as dpg from deareis.utility import calculate_window_position_dimensions from deareis.tooltips import attach_tooltip @@ -31,15 +35,18 @@ FitSettings, SimulationSettings, TestSettings, + ZHITSettings, ) from deareis.config import ( DEFAULT_TEST_SETTINGS, + DEFAULT_ZHIT_SETTINGS, DEFAULT_DRT_SETTINGS, DEFAULT_FIT_SETTINGS, DEFAULT_SIMULATION_SETTINGS, DEFAULT_PLOT_EXPORT_SETTINGS, ) from deareis.gui.kramers_kronig import SettingsMenu as TestSettingsMenu +from deareis.gui.zhit import SettingsMenu as ZHITSettingsMenu from deareis.gui.drt import SettingsMenu as DRTSettingsMenu from deareis.gui.fitting import SettingsMenu as FitSettingsMenu from deareis.gui.simulation import SettingsMenu as SimulationSettingsMenu @@ -55,9 +62,11 @@ def section_spacer(): def general_settings(label_pad: int, state): with dpg.collapsing_header(label="General", default_open=True): auto_backup_interval: int = dpg.generate_uuid() + num_procs_input: int = dpg.generate_uuid() def update_auto_backup_interval(value: int): state.config.auto_backup_interval = value + set_default_num_procs(value) with dpg.group(horizontal=True): dpg.add_text("Auto-backup interval".rjust(label_pad)) @@ -73,6 +82,23 @@ def update_auto_backup_interval(value: int): width=-54, tag=auto_backup_interval, ) + + def update_num_procs(value: int): + state.config.num_procs = value + + with dpg.group(horizontal=True): + dpg.add_text("Number of processes".rjust(label_pad)) + attach_tooltip(tooltips.general.num_procs.format(get_default_num_procs())) + dpg.add_input_int( + default_value=state.config.num_procs, + min_value=0, + min_clamped=True, + step=0, + on_enter=True, + callback=lambda s, a, u: update_num_procs(a), + width=-54, + tag=num_procs_input, + ) section_spacer() @@ -101,6 +127,30 @@ def callback(): return callback +def zhit_tab_settings(label_pad: int, state) -> Callable: + with dpg.collapsing_header(label="Z-HIT analysis tab", default_open=True): + settings_menu: ZHITSettingsMenu = ZHITSettingsMenu( + state.config.default_zhit_settings, + label_pad, + ) + with dpg.group(horizontal=True): + dpg.add_text("".rjust(label_pad)) + dpg.add_button( + label="Restore defaults", + callback=lambda s, a, u: settings_menu.set_settings( + DEFAULT_ZHIT_SETTINGS, + ), + ) + section_spacer() + + def callback(): + settings: ZHITSettings = settings_menu.get_settings() + state.config.default_zhit_settings = settings + signals.emit(Signal.APPLY_ZHIT_SETTINGS, settings=settings) + + return callback + + def drt_tab_settings(label_pad: int, state) -> Callable: with dpg.collapsing_header(label="DRT analysis tab", default_open=True): settings_menu: DRTSettingsMenu = DRTSettingsMenu( @@ -198,50 +248,64 @@ def callback(): return callback -def show_defaults_settings_window(state): - x: int - y: int - w: int - h: int - x, y, w, h = calculate_window_position_dimensions(390, 540) - - window: int = dpg.generate_uuid() - key_handler: int = dpg.generate_uuid() - settings_update_callbacks: List[Callable] = [] - - def close_window(): - for callback in settings_update_callbacks: +class DefaultsSettings: + def __init__(self, state): + self.settings_update_callbacks: List[Callable] = [] + self.create_window(state) + self.register_keybindings() + + def register_keybindings(self): + self.key_handler: int = dpg.generate_uuid() + with dpg.handler_registry(tag=self.key_handler): + dpg.add_key_release_handler( + key=dpg.mvKey_Escape, + callback=self.close, + ) + + def create_window(self, state): + x: int + y: int + w: int + h: int + x, y, w, h = calculate_window_position_dimensions(390, 540) + self.window: int = dpg.generate_uuid() + with dpg.window( + label="Settings - defaults", + modal=True, + pos=(x, y), + width=w, + height=h, + no_resize=True, + on_close=self.close, + tag=self.window, + ): + label_pad: int = 24 + general_settings(label_pad, state) + self.settings_update_callbacks.append( + kramers_kronig_tab_settings(label_pad, state) + ) + self.settings_update_callbacks.append(zhit_tab_settings(label_pad, state)) + self.settings_update_callbacks.append(drt_tab_settings(label_pad, state)) + self.settings_update_callbacks.append( + fitting_tab_settings(label_pad, state) + ) + self.settings_update_callbacks.append( + simulation_tab_settings(label_pad, state) + ) + self.settings_update_callbacks.append( + plotting_tab_settings(label_pad, state) + ) + signals.emit(Signal.BLOCK_KEYBINDINGS, window=self.window, window_object=self) + + def close(self): + for callback in self.settings_update_callbacks: callback() - if dpg.does_item_exist(window): - dpg.delete_item(window) - if dpg.does_item_exist(key_handler): - dpg.delete_item(key_handler) + if dpg.does_item_exist(self.window): + dpg.delete_item(self.window) + if dpg.does_item_exist(self.key_handler): + dpg.delete_item(self.key_handler) signals.emit(Signal.UNBLOCK_KEYBINDINGS) - with dpg.handler_registry(tag=key_handler): - dpg.add_key_release_handler( - key=dpg.mvKey_Escape, - callback=close_window, - ) - with dpg.window( - label="Settings - defaults", - modal=True, - pos=( - x, - y, - ), - width=w, - height=h, - no_resize=True, - on_close=close_window, - tag=window, - ): - label_pad: int = 24 - general_settings(label_pad, state) - settings_update_callbacks.append(kramers_kronig_tab_settings(label_pad, state)) - settings_update_callbacks.append(drt_tab_settings(label_pad, state)) - settings_update_callbacks.append(fitting_tab_settings(label_pad, state)) - settings_update_callbacks.append(simulation_tab_settings(label_pad, state)) - settings_update_callbacks.append(plotting_tab_settings(label_pad, state)) - signals.emit(Signal.BLOCK_KEYBINDINGS, window=window, window_object=None) +def show_defaults_settings_window(state): + DefaultsSettings(state) diff --git a/src/deareis/gui/settings/keybindings.py b/src/deareis/gui/settings/keybindings.py index 9ebd325..c5f09b6 100644 --- a/src/deareis/gui/settings/keybindings.py +++ b/src/deareis/gui/settings/keybindings.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -92,6 +92,8 @@ def populate(self): action: Action description: str for action, description in action_descriptions.items(): + if action in (Action.CANCEL, Action.CUSTOM): + continue kb: Optional[Keybinding] = self.find_keybinding(action) filter_key: str = "|".join( [str(kb) if kb else "", description.lower()] @@ -303,7 +305,7 @@ def __init__(self, state): height=h, no_move=False, no_resize=True, - on_close=self.close_window, + on_close=self.close, tag=self.window, ): key_filter_input: int = dpg.generate_uuid() @@ -335,13 +337,14 @@ def __init__(self, state): key_filter_input, description_filter_input, state ) with dpg.group(horizontal=True): - dpg.add_button(label="Clear all", callback=self.clear_all) - dpg.add_button(label="Reset", callback=self.reset) + button_pad: int = 12 + dpg.add_button(label="Clear all".ljust(button_pad), callback=self.clear_all) + dpg.add_button(label="Reset".ljust(button_pad), callback=self.reset) self.key_handler: int = dpg.generate_uuid() with dpg.handler_registry(tag=self.key_handler): dpg.add_key_release_handler( key=dpg.mvKey_Escape, - callback=self.close_window, + callback=self.close, ) signals.emit(Signal.BLOCK_KEYBINDINGS, window=self.window, window_object=self) @@ -355,7 +358,7 @@ def reset(self): self.state.keybinding_handler.register(self.state.config.keybindings) self.table.populate() - def close_window(self): + def close(self): if self.table.is_remapping(): return if dpg.does_item_exist(self.window): diff --git a/src/deareis/gui/settings/user_defined_elements.py b/src/deareis/gui/settings/user_defined_elements.py new file mode 100644 index 0000000..97d0670 --- /dev/null +++ b/src/deareis/gui/settings/user_defined_elements.py @@ -0,0 +1,273 @@ +# DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). +# Copyright 2023 DearEIS developers +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +# +# The licenses of DearEIS' dependencies and/or sources of portions of code are included in +# the LICENSES folder. + +from importlib.machinery import SourceFileLoader +from os import getcwd +from os.path import ( + dirname, + exists, + isdir, +) +from types import ModuleType +from typing import ( + Callable, + Dict, + Optional, + Type, +) +import dearpygui.dearpygui as dpg +import pyimpspec.circuit.registry as registry +from pyimpspec import ( + Element, + get_elements, +) +from deareis.utility import calculate_window_position_dimensions +from deareis.signals import Signal +import deareis.signals as signals +from deareis.tooltips import attach_tooltip +import deareis.themes as themes +from deareis.gui.file_dialog import FileDialog +from deareis.enums import Action +from deareis.keybindings import ( + Keybinding, + TemporaryKeybindingHandler, +) + + +DEFAULT_ELEMENTS: Dict[str, Type[Element]] = get_elements() +USER_DEFINED_ELEMENTS: Dict[str, Type[Element]] = {} + + +def update_path(path: str, path_input: int = -1): + if path_input < 1: + return + assert isinstance(path, str), path + if path == "" or exists(path): + dpg.bind_item_theme(path_input, themes.path.valid) + else: + dpg.bind_item_theme(path_input, themes.path.invalid) + dpg.set_value(path_input, path) + + +def update_table(table: int, elements: Dict[str, Type[Element]]): + if table < 1: + return + dpg.delete_item(table, children_only=True, slot=1) + Class: Type[Element] + for Class in elements.values(): + with dpg.table_row(parent=table): + dpg.add_text(Class.get_description()) + attach_tooltip(Class.get_extended_description()) + + +def refresh( + path: str = "", + path_input: int = -1, + close_window: Optional[Callable] = None, +): + global USER_DEFINED_ELEMENTS + key: str + for key in USER_DEFINED_ELEMENTS: + del registry._ELEMENTS[key] + USER_DEFINED_ELEMENTS.clear() + update_path(path, path_input) + if close_window is not None: + close_window() + dpg.split_frame(delay=33) + if path != "" and exists(path): + signals.emit( + Signal.SHOW_BUSY_MESSAGE, + message="Loading user-defined elements...", + ) + dpg.split_frame(delay=1000) + loader = SourceFileLoader("user_defined_elements", path) + mod = ModuleType(loader.name) + loader.exec_module(mod) + USER_DEFINED_ELEMENTS = { + k: v for k, v in get_elements().items() if k not in DEFAULT_ELEMENTS + } + signals.emit(Signal.HIDE_BUSY_MESSAGE) + if close_window is not None: + signals.emit(Signal.SHOW_SETTINGS_USER_DEFINED_ELEMENTS) + + +def select_script(path_input: int, window: int, close_window: Callable): + dir_path: str = dpg.get_value(path_input) + if dir_path != "": + if not isdir(dir_path): + dir_path = dirname(dir_path) + if dir_path == "" or not exists(dir_path): + dir_path = getcwd() + dpg.hide_item(window) + dpg.split_frame(delay=33) + FileDialog( + cwd=dir_path, + label="Select Python script", + callback=lambda paths, *a, **k: refresh( + paths[0] if len(paths) > 0 else "", + path_input=path_input, + close_window=close_window, + ), + cancel_callback=lambda: dpg.show_item(window), + extensions=[".py"], + multiple=False, + ) + + +class UserDefinedElementsSettings: + def __init__(self, state): + self.config = state.config + self.create_window() + self.register_keybindings() + signals.emit(Signal.BLOCK_KEYBINDINGS, window=self.window, window_object=self) + + def create_window(self): + x: int + y: int + w: int + h: int + x, y, w, h = calculate_window_position_dimensions(600, 540) + self.window: int = dpg.generate_uuid() + with dpg.window( + label="Settings - User-defined elements", + modal=True, + pos=( + x, + y, + ), + width=w, + height=h, + no_resize=True, + on_close=self.close, + tag=self.window, + ): + attach_tooltip( + """ +The definitions for user-defined elements are NOT stored inside of project files. If a project depends on a user-defined element (e.g., the element is used in the circuit of a fit or simulation result), then the script defining the user-defined element must be loaded before opening the project. +""".strip(), + parent=dpg.add_text( + "IMPORTANT! HOVER MOUSE CURSOR OVER THIS PART FOR DETAILS!" + ), + ) + with dpg.group(horizontal=True): + self.path_input: int = dpg.generate_uuid() + dpg.add_input_text( + default_value=self.config.user_defined_elements_path, + hint="Path to Python script/package", + width=-64, + on_enter=True, + callback=lambda s, a, u: update_path(a, s), + tag=self.path_input, + ) + dpg.add_button( + label="Browse", + callback=lambda s, a, u: select_script( + path_input=self.path_input, + window=self.window, + close_window=self.close, + ), + width=-1, + ) + update_path(dpg.get_value(self.path_input), self.path_input) + attach_tooltip( + """ +An absolute path to a Python script/package that when loaded defines new elements using pyimpspec's API. See pyimpspec's API documentation for details and examples. User-defined elements can also be used to implement circuits that cannot be implemented using circuit description codes (CDCs). + +Detected user-defined elements will be listed in the table below this input field. If there are no entries in the table below, then you may need to press the "Refresh" button or the path might be invalid. + """.strip() + ) + table: int = dpg.generate_uuid() + with dpg.child_window( + border=False, + width=-2, + height=-24, + show=True, + ): + with dpg.table( + borders_outerV=True, + borders_outerH=True, + borders_innerV=True, + borders_innerH=True, + scrollY=True, + freeze_rows=1, + tag=table, + ): + dpg.add_table_column( + label="Description", + width_fixed=False, + ) + update_table(table, USER_DEFINED_ELEMENTS) + dpg.add_button( + label="Refresh", + width=-1, + callback=self.refresh, + ) + attach_tooltip( + "Reload the Python script/package that contains the definitions for the user-defined elements." + ) + + def register_keybindings(self): + callbacks: Dict[Keybinding, Callable] = {} + # Cancel + kb: Keybinding = Keybinding( + key=dpg.mvKey_Escape, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.CANCEL, + ) + callbacks[kb] = self.close + # Accept + for kb in self.config.keybindings: + if kb.action is Action.PERFORM_ACTION: + break + else: + kb = Keybinding( + key=dpg.mvKey_Return, + mod_alt=True, + mod_ctrl=False, + mod_shift=False, + action=Action.PERFORM_ACTION, + ) + callbacks[kb] = self.refresh + # Create the handler + self.keybinding_handler: TemporaryKeybindingHandler = ( + TemporaryKeybindingHandler(callbacks=callbacks) + ) + + def close(self): + path: str = dpg.get_value(self.path_input) + if isinstance(path, str) and (path == "" or exists(path)): + self.config.user_defined_elements_path = path + if dpg.does_item_exist(self.window): + dpg.delete_item(self.window) + self.keybinding_handler.delete() + signals.emit(Signal.UNBLOCK_KEYBINDINGS) + + def refresh(self): + signals.emit( + Signal.REFRESH_USER_DEFINED_ELEMENTS, + path=dpg.get_value(self.path_input), + path_input=self.path_input, + close_window=self.close, + ) + + +def show_user_defined_elements_window(state): + UserDefinedElementsSettings(state) diff --git a/src/deareis/gui/shared.py b/src/deareis/gui/shared.py new file mode 100644 index 0000000..837188f --- /dev/null +++ b/src/deareis/gui/shared.py @@ -0,0 +1,170 @@ +# DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). +# Copyright 2023 DearEIS developers +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +# +# The licenses of DearEIS' dependencies and/or sources of portions of code are included in +# the LICENSES folder. + +from typing import ( + Any, + Dict, + List, + Optional, +) +import dearpygui.dearpygui as dpg +from deareis.signals import Signal +import deareis.signals as signals +from deareis.data import DataSet + + +class DataSetsCombo: + def __init__(self, label: str, width: int): + self.labels: List[str] = [] + dpg.add_text(label) + self.tag: int = dpg.generate_uuid() + dpg.add_combo( + callback=lambda s, a, u: signals.emit( + Signal.SELECT_DATA_SET, + data=u.get(a), + ), + user_data={}, + width=width, + tag=self.tag, + ) + + def populate(self, labels: List[str], lookup: Dict[str, DataSet]): + self.labels.clear() + self.labels.extend(labels) + label: str = dpg.get_value(self.tag) or "" + if labels and label not in labels: + label = labels[0] + dpg.configure_item( + self.tag, + default_value=label, + items=labels, + user_data=lookup, + ) + + def get(self) -> Optional[DataSet]: + return dpg.get_item_user_data(self.tag).get(dpg.get_value(self.tag)) + + def set(self, label: str): + assert type(label) is str, label + assert label in self.labels, ( + label, + self.labels, + ) + dpg.set_value(self.tag, label) + + def get_next(self) -> Optional[DataSet]: + lookup: Dict[str, DataSet] = dpg.get_item_user_data(self.tag) + if not lookup: + return None + labels: List[str] = list(lookup.keys()) + index: int = labels.index(dpg.get_value(self.tag)) + 1 + return lookup[labels[index % len(labels)]] + + def get_previous(self) -> Optional[DataSet]: + lookup: Dict[str, DataSet] = dpg.get_item_user_data(self.tag) + if not lookup: + return None + labels: List[str] = list(lookup.keys()) + index: int = labels.index(dpg.get_value(self.tag)) - 1 + return lookup[labels[index % len(labels)]] + + def clear(self): + dpg.configure_item( + self.tag, + default_value="", + ) + + +class ResultsCombo: + def __init__(self, label: str, width: int): + self.labels: Dict[str, str] = {} + dpg.add_text(label) + self.tag: int = dpg.generate_uuid() + dpg.add_combo( + callback=self.selection_callback, + user_data=( + {}, + None, + ), + width=width, + tag=self.tag, + ) + + def selection_callback(self, sender: int, app_data: str, user_data: tuple): + raise NotImplementedError() + + def adjust_label(self, old: str, longest: int) -> str: + raise NotImplementedError() + + def populate(self, lookup: Dict[str, Any], data: Optional[DataSet]): + self.labels.clear() + labels: List[str] = list(lookup.keys()) + longest_label: int = max( + list(map(lambda _: len(_[: _.find(" (")]), labels)) + [1] + ) + old_key: str + for old_key in labels: + result: Any = lookup[old_key] + del lookup[old_key] + new_key = self.adjust_label(old_key, longest_label) + self.labels[old_key] = new_key + lookup[new_key] = result + labels = list(lookup.keys()) + dpg.configure_item( + self.tag, + default_value=labels[0] if labels else "", + items=labels, + user_data=( + lookup, + data, + ), + ) + + def get(self) -> Optional[Any]: + return dpg.get_item_user_data(self.tag)[0].get(dpg.get_value(self.tag)) + + def set(self, label: str): + assert type(label) is str, label + assert label in self.labels, ( + label, + list(self.labels.keys()), + ) + dpg.set_value(self.tag, label) + + def clear(self): + dpg.configure_item( + self.tag, + default_value="", + ) + + def get_next(self) -> Optional[Any]: + lookup: Dict[str, Any] = dpg.get_item_user_data(self.tag)[0] + if not lookup: + return None + labels: List[str] = list(lookup.keys()) + index: int = labels.index(self.labels[dpg.get_value(self.tag)]) + 1 + return lookup[labels[index % len(labels)]] + + def get_previous(self) -> Optional[Any]: + lookup: Dict[str, Any] = dpg.get_item_user_data(self.tag)[0] + if not lookup: + return None + labels: List[str] = list(lookup.keys()) + index: int = labels.index(self.labels[dpg.get_value(self.tag)]) - 1 + return lookup[labels[index % len(labels)]] diff --git a/src/deareis/gui/simulation.py b/src/deareis/gui/simulation.py index 319fa31..adaa259 100644 --- a/src/deareis/gui/simulation.py +++ b/src/deareis/gui/simulation.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -17,13 +17,13 @@ # The licenses of DearEIS' dependencies and/or sources of portions of code are included in # the LICENSES folder. +from traceback import format_exc from typing import ( Callable, Dict, List, Optional, Tuple, - Type, ) from numpy import ( array, @@ -32,9 +32,13 @@ import pyimpspec from pyimpspec import ( Circuit, + ComplexImpedance, + Connection, + Container, Element, - FittedParameter, + Frequencies, ) +from pyimpspec.analysis.utility import _interpolate import dearpygui.dearpygui as dpg from deareis.signals import Signal import deareis.signals as signals @@ -42,7 +46,10 @@ from deareis.utility import ( align_numbers, calculate_window_position_dimensions, + find_parent_containers, format_number, + pad_tab_labels, + process_cdc, ) from deareis.tooltips import ( attach_tooltip, @@ -51,7 +58,9 @@ import deareis.tooltips as tooltips from deareis.gui.plots import ( Bode, + Impedance, Nyquist, + Plot, ) from deareis.enums import ( Context, @@ -67,6 +76,7 @@ SimulationResult, SimulationSettings, ) +from deareis.gui.fitting.parameter_adjustment import ParameterAdjustment class SettingsMenu: @@ -161,7 +171,7 @@ def get_settings(self) -> SimulationSettings: dpg.set_value(self.min_freq_input, min_f) dpg.set_value(self.max_freq_input, max_f) return SimulationSettings( - cdc=circuit.to_string(12) if circuit is not None else "", + cdc=circuit.serialize() if circuit is not None else "", min_frequency=min_f, max_frequency=max_f, num_per_decade=dpg.get_value(self.per_decade_input), @@ -177,11 +187,19 @@ def set_settings(self, settings: SimulationSettings): def parse_cdc(self, cdc: str, sender: int = -1) -> Optional[Circuit]: assert type(cdc) is str, cdc assert type(sender) is int, sender + circuit: Optional[Circuit] + msg: str try: - circuit: Circuit = pyimpspec.parse_cdc(cdc) - except (pyimpspec.ParsingError, pyimpspec.UnexpectedCharacter) as err: + circuit, msg = process_cdc(cdc) + except Exception: + signals.emit( + Signal.SHOW_ERROR_MESSAGE, + traceback=format_exc(), + ) + return None + if circuit is None: dpg.bind_item_theme(self.cdc_input, themes.cdc.invalid) - update_tooltip(self.cdc_tooltip, str(err)) + update_tooltip(self.cdc_tooltip, msg) dpg.show_item(dpg.get_item_parent(self.cdc_tooltip)) dpg.set_item_user_data(self.cdc_input, None) return None @@ -209,11 +227,10 @@ def show_circuit_editor(self): width=w, height=h, ) - circuit: Optional[Circuit] = None - try: - circuit = pyimpspec.parse_cdc(self.get_settings().cdc) - except pyimpspec.ParsingError: - pass + circuit: Optional[Circuit] = self.parse_cdc( + self.get_settings().cdc, + sender=self.cdc_input, + ) signals.emit( Signal.BLOCK_KEYBINDINGS, window=self.circuit_editor.window, @@ -279,47 +296,96 @@ def populate(self, simulation: SimulationResult): dpg.add_text("".ljust(column_pads[1])) dpg.add_text("".ljust(column_pads[2])) return - element_labels: List[str] = [] + element_names: List[str] = [] element_tooltips: List[str] = [] parameter_labels: List[str] = [] + parameter_tooltips: List[str] = [] values: List[str] = [] value_tooltips: List[str] = [] - element_label: str + internal_identifiers: Dict[int, Element] = { + v: k + for k, v in simulation.circuit.generate_element_identifiers( + running=True + ).items() + } + external_identifiers: Dict[ + Element, int + ] = simulation.circuit.generate_element_identifiers(running=False) + parent_containers: Dict[Element, Container] = find_parent_containers( + simulation.circuit + ) + element_name: str element_tooltip: str parameter_label: str + parameter_tooltip: str value: str value_tooltip: str - parameters: Dict[str, FittedParameter] - for element in simulation.circuit.get_elements(): + for (_, element) in sorted(internal_identifiers.items(), key=lambda _: _[0]): + element_name = simulation.circuit.get_element_name( + element, + identifiers=external_identifiers, + ) + lines: List[str] = [] + line: str + for line in element.get_extended_description().split("\n"): + if line.strip().startswith(":math:"): + break + lines.append(line) + element_tooltip = "\n".join(lines).strip() + if element in parent_containers: + parent_name: str = simulation.circuit.get_element_name( + parent_containers[element], + identifiers=external_identifiers, + ) + subcircuit_name: str + subcircuit: Optional[Connection] + for subcircuit_name, subcircuit in ( + parent_containers[element].get_subcircuits().items() + ): + if subcircuit is None: + continue + if element in subcircuit: + break + element_name = f"*{element_name}" + element_tooltip = f"*Nested inside {parent_name}'s {subcircuit_name} subcircuit\n\n{element_tooltip}" float_value: float - for parameter_label, float_value in element.get_parameters().items(): - element_labels.append(element.get_label()) - element_tooltips.append(element.get_extended_description()) + for parameter_label, float_value in element.get_values().items(): + element_names.append(element_name) + element_tooltips.append(element_tooltip) parameter_labels.append(parameter_label) + unit: str = element.get_unit(parameter_label) + parameter_tooltips.append( + ( + f"{element.get_value_description(parameter_label)}\n\n" + f"Unit: {unit}\n" + ).strip() + ) values.append(f"{format_number(float_value, width=9, significants=3)}") value_tooltips.append( - f"{format_number(float_value, decimals=6).strip()}" + f"{format_number(float_value, decimals=6).strip()} {unit}".strip() ) values = align_numbers(values) num_rows: int = 0 for ( - element_label, + element_name, element_tooltip, parameter_label, + parameter_tooltip, value, value_tooltip, ) in zip( - element_labels, + element_names, element_tooltips, parameter_labels, + parameter_tooltips, values, value_tooltips, ): with dpg.table_row(parent=self._table): - dpg.add_text(element_label.ljust(column_pads[0])) - if element_tooltip != "": - attach_tooltip(element_tooltip) + dpg.add_text(element_name.ljust(column_pads[0])) + attach_tooltip(element_tooltip) dpg.add_text(parameter_label.ljust(column_pads[1])) + attach_tooltip(parameter_tooltip) dpg.add_text(value.ljust(column_pads[2])) attach_tooltip(value_tooltip) num_rows += 1 @@ -362,6 +428,7 @@ def __init__(self): tooltip_tag: int = dpg.generate_uuid() dpg.add_text("", user_data=tooltip_tag) attach_tooltip("", tag=tooltip_tag) + dpg.add_spacer(height=8) with dpg.group(horizontal=True): self._apply_button: int = dpg.generate_uuid() dpg.add_button( @@ -371,6 +438,7 @@ def __init__(self): **u, ), tag=self._apply_button, + width=154, ) attach_tooltip(tooltips.general.apply_settings) self._load_as_data_button: int = dpg.generate_uuid() @@ -381,6 +449,7 @@ def __init__(self): **u, ), tag=self._load_as_data_button, + width=-1, ) attach_tooltip(tooltips.simulation.load_as_data_set) @@ -532,6 +601,8 @@ def populate(self, lookup: Dict[str, SimulationResult]): for old_key in labels: simulation: SimulationResult = lookup[old_key] del lookup[old_key] + cdc: str + timestamp: str cdc, timestamp = ( old_key[: old_key.find(" ")], old_key[old_key.find(" ") + 1 :], @@ -556,7 +627,7 @@ def set(self, label: str): label, list(self.labels.keys()), ) - dpg.set_value(self.tag, self.labels[label]) + dpg.set_value(self.tag, label) def clear(self): dpg.configure_item( @@ -569,7 +640,7 @@ def get_next(self) -> Optional[SimulationResult]: if not lookup: return None labels: List[str] = list(lookup.keys()) - index: int = labels.index(dpg.get_value(self.tag)) + 1 + index: int = labels.index(self.labels[dpg.get_value(self.tag)]) + 1 return lookup[labels[index % len(labels)]] def get_previous(self) -> Optional[SimulationResult]: @@ -577,7 +648,7 @@ def get_previous(self) -> Optional[SimulationResult]: if not lookup: return None labels: List[str] = list(lookup.keys()) - index: int = labels.index(dpg.get_value(self.tag)) - 1 + index: int = labels.index(self.labels[dpg.get_value(self.tag)]) - 1 return lookup[labels[index % len(labels)]] @@ -585,342 +656,361 @@ class SimulationTab: def __init__(self, state): self.state = state self.queued_update: Optional[Callable] = None + self.create_tab(state) + self.set_settings(self.state.config.default_simulation_settings) + + def create_tab(self, state): self.tab: int = dpg.generate_uuid() with dpg.tab(label="Simulation", tag=self.tab): self.sidebar_width: int = 350 - settings_height: int = 128 + settings_height: int = 150 label_pad: int = 24 with dpg.group(horizontal=True): - self.sidebar_window: int = dpg.generate_uuid() - with dpg.child_window( - border=False, - width=self.sidebar_width, - tag=self.sidebar_window, - ): - # Settings - with dpg.child_window( - border=True, width=-1, height=settings_height - ): - self.circuit_editor: CircuitEditor = CircuitEditor( - window=dpg.add_window( - label="Circuit editor", - show=False, - modal=True, - on_close=lambda s, a, u: self.accept_circuit(None), - ), - callback=self.accept_circuit, - ) - self.settings_menu: SettingsMenu = SettingsMenu( - state.config.default_simulation_settings, - label_pad, - circuit_editor=self.circuit_editor, - ) - with dpg.group(horizontal=True): - self.visibility_item: int = dpg.generate_uuid() - dpg.add_text("".rjust(label_pad), tag=self.visibility_item) - self.perform_sim_button: int = dpg.generate_uuid() - dpg.add_button( - label="Perform simulation", - callback=lambda s, a, u: signals.emit( - Signal.PERFORM_SIMULATION, - data=u, - settings=self.get_settings(), - ), - user_data=None, - width=-1, - tag=self.perform_sim_button, - ) - with dpg.child_window(width=-1, height=82): - label_pad = 8 - with dpg.group(horizontal=True): - self.data_sets_combo: DataSetsCombo = DataSetsCombo( - label="Data set".rjust(label_pad), - width=-60, - callback=lambda s, a, u: signals.emit( - Signal.SELECT_SIMULATION_RESULT, - simulation=self.results_combo.get(), - data=u.get(a), - ), - ) - with dpg.group(horizontal=True): - self.results_combo: ResultsCombo = ResultsCombo( - label="Result".rjust(label_pad), - width=-60, - callback=lambda s, a, u: signals.emit( - Signal.SELECT_SIMULATION_RESULT, - data=self.data_sets_combo.get(), - simulation=u.get(a), - ), - ) - self.delete_button: int = dpg.generate_uuid() - dpg.add_button( - label="Delete", - callback=lambda s, a, u: signals.emit( - Signal.DELETE_SIMULATION_RESULT, simulation=u - ), - width=-1, - tag=self.delete_button, - ) - attach_tooltip(tooltips.simulation.remove) - with dpg.group(horizontal=True): - dpg.add_text("Output".rjust(label_pad)) - # TODO: Split into combo class? - self.output_combo: int = dpg.generate_uuid() - dpg.add_combo( - items=list(label_to_fit_sim_output.keys()), - default_value=list(label_to_fit_sim_output.keys())[0], - tag=self.output_combo, - width=-60, - ) - self.copy_output_button: int = dpg.generate_uuid() - dpg.add_button( - label="Copy", - callback=lambda s, a, u: signals.emit( - Signal.COPY_OUTPUT, - output=self.get_active_output(), - **u, - ), - user_data={}, - width=-1, - tag=self.copy_output_button, - ) - attach_tooltip(tooltips.general.copy_output) - with dpg.child_window(width=-1, height=-1): - self.result_group: int = dpg.generate_uuid() - with dpg.group(tag=self.result_group): - self.parameters_table: ParametersTable = ParametersTable() - dpg.add_spacer(height=8) - self.settings_table: SettingsTable = SettingsTable() - self.plot_window: int = dpg.generate_uuid() - with dpg.child_window( - border=False, + self.create_sidebar(state, label_pad, settings_height) + self.create_plots() + + def create_sidebar(self, state, label_pad: int, settings_height: int): + self.sidebar_window: int = dpg.generate_uuid() + with dpg.child_window( + border=False, + width=self.sidebar_width, + tag=self.sidebar_window, + ): + self.create_settings_menu(state, label_pad, settings_height) + self.create_results_menu() + self.create_results_tables() + + def create_settings_menu(self, state, label_pad: int, settings_height: int): + with dpg.child_window(border=True, width=-1, height=settings_height): + self.circuit_editor: CircuitEditor = CircuitEditor( + window=dpg.add_window( + label="Circuit editor", + show=False, + modal=True, + on_close=lambda s, a, u: self.accept_circuit(None), + ), + callback=self.accept_circuit, + keybindings=state.config.keybindings, + ) + self.settings_menu: SettingsMenu = SettingsMenu( + state.config.default_simulation_settings, + label_pad, + circuit_editor=self.circuit_editor, + ) + with dpg.group(horizontal=True): + dpg.add_text( + "?".rjust(label_pad), + ) + attach_tooltip(tooltips.simulation.adjust_parameters) + self.parameter_adjustment_button: int = dpg.generate_uuid() + dpg.add_button( + label="Adjust parameters", + callback=self.show_parameter_adjustment, + user_data=None, width=-1, - height=-1, - tag=self.plot_window, - ): - self.minimum_plot_side: int = 400 - with dpg.group(horizontal=True): - self.circuit_preview_height: int = 250 - with dpg.child_window( - border=False, - width=-1, - height=self.circuit_preview_height, - ): - dpg.add_text("Simulated circuit") - self.circuit_preview: CircuitPreview = CircuitPreview() - with dpg.group(horizontal=True): - with dpg.group(): - self.nyquist_plot: Nyquist = Nyquist( - width=self.minimum_plot_side, - height=self.minimum_plot_side, - ) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="Data", - theme=themes.nyquist.data, - ) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="Sim.", - simulation=True, - theme=themes.nyquist.simulation, - ) - self.nyquist_plot.plot( - real=array([]), - imaginary=array([]), - label="Sim.", - simulation=True, - line=True, - theme=themes.nyquist.simulation, - show_label=False, - ) - with dpg.group(horizontal=True): - self.enlarge_nyquist_button: int = dpg.generate_uuid() - self.adjust_nyquist_limits_checkbox: int = ( - dpg.generate_uuid() - ) - dpg.add_button( - label="Enlarge Nyquist", - callback=self.show_enlarged_nyquist, - tag=self.enlarge_nyquist_button, - ) - dpg.add_checkbox( - default_value=True, - tag=self.adjust_nyquist_limits_checkbox, - ) - attach_tooltip(tooltips.general.adjust_nyquist_limits) - dpg.add_button( - label="Copy as CSV", - callback=lambda s, a, u: signals.emit( - Signal.COPY_PLOT_DATA, - plot=self.nyquist_plot, - context=Context.FITTING_TAB, - ), - ) - attach_tooltip(tooltips.general.copy_plot_data_as_csv) - self.horizontal_bode_group: int = dpg.generate_uuid() - with dpg.group(tag=self.horizontal_bode_group): - self.bode_plot_horizontal: Bode = Bode( - width=self.minimum_plot_side, - height=self.minimum_plot_side, - ) - self.bode_plot_horizontal.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z| (d)", - "phi (d)", - ), - themes=( - themes.bode.magnitude_data, - themes.bode.phase_data, - ), - ) - self.bode_plot_horizontal.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z| (s)", - "phi (s)", - ), - simulation=True, - themes=( - themes.bode.magnitude_simulation, - themes.bode.phase_simulation, - ), - ) - self.bode_plot_horizontal.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z| (s)", - "phi (s)", - ), - simulation=True, - line=True, - themes=( - themes.bode.magnitude_simulation, - themes.bode.phase_simulation, - ), - show_labels=False, - ) - with dpg.group(horizontal=True): - self.enlarge_bode_horizontal_button: int = ( - dpg.generate_uuid() - ) - self.adjust_bode_limits_horizontal_checkbox: int = ( - dpg.generate_uuid() - ) - dpg.add_button( - label="Enlarge Bode", - callback=lambda s, a, u: signals.emit( - Signal.SHOW_ENLARGED_PLOT, - plot=self.bode_plot_horizontal, - adjust_limits=dpg.get_value( - self.adjust_bode_limits_horizontal_checkbox - ), - ), - tag=self.enlarge_bode_horizontal_button, - ) - dpg.add_checkbox( - default_value=True, - tag=self.adjust_bode_limits_horizontal_checkbox, - ) - attach_tooltip(tooltips.general.adjust_bode_limits) - dpg.add_button( - label="Copy as CSV", - callback=lambda s, a, u: signals.emit( - Signal.COPY_PLOT_DATA, - plot=self.bode_plot_horizontal, - context=Context.FITTING_TAB, - ), - ) - attach_tooltip(tooltips.general.copy_plot_data_as_csv) - self.vertical_bode_group: int = dpg.generate_uuid() - with dpg.group(tag=self.vertical_bode_group, show=False): - self.bode_plot_vertical: Bode = Bode( - width=self.minimum_plot_side, height=self.minimum_plot_side - ) - self.bode_plot_vertical.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z| (d)", - "phi (d)", - ), - themes=( - themes.bode.magnitude_data, - themes.bode.phase_data, - ), - ) - self.bode_plot_vertical.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z| (s)", - "phi (s)", - ), - simulation=True, - themes=( - themes.bode.magnitude_simulation, - themes.bode.phase_simulation, - ), - ) - self.bode_plot_vertical.plot( - frequency=array([]), - magnitude=array([]), - phase=array([]), - labels=( - "|Z| (s)", - "phi (s)", - ), - simulation=True, - line=True, - themes=( - themes.bode.magnitude_simulation, - themes.bode.phase_simulation, - ), - show_labels=False, - ) - with dpg.group(horizontal=True): - self.enlarge_bode_vertical_button: int = dpg.generate_uuid() - self.adjust_bode_limits_vertical_checkbox: int = ( - dpg.generate_uuid() - ) - dpg.add_button( - label="Enlarge Bode", - callback=lambda s, a, u: signals.emit( - Signal.SHOW_ENLARGED_PLOT, - plot=self.bode_plot_vertical, - adjust_limits=dpg.get_value( - self.adjust_bode_limits_horizontal_checkbox - ), - ), - tag=self.enlarge_bode_vertical_button, - ) - dpg.add_checkbox( - default_value=True, - source=self.adjust_bode_limits_horizontal_checkbox, - tag=self.adjust_bode_limits_vertical_checkbox, - ) - attach_tooltip(tooltips.general.adjust_bode_limits) - dpg.add_button( - label="Copy as CSV", - callback=lambda s, a, u: signals.emit( - Signal.COPY_PLOT_DATA, - plot=self.bode_plot_vertical, - context=Context.FITTING_TAB, - ), - ) - attach_tooltip(tooltips.general.copy_plot_data_as_csv) - self.set_settings(self.state.config.default_simulation_settings) + tag=self.parameter_adjustment_button, + ) + with dpg.group(horizontal=True): + self.visibility_item: int = dpg.generate_uuid() + dpg.add_text("".rjust(label_pad), tag=self.visibility_item) + self.perform_sim_button: int = dpg.generate_uuid() + dpg.add_button( + label="Perform", + callback=lambda s, a, u: signals.emit( + Signal.PERFORM_SIMULATION, + data=u, + settings=self.get_settings(), + ), + user_data=None, + width=-1, + tag=self.perform_sim_button, + ) + + def create_results_menu(self): + with dpg.child_window(width=-1, height=82): + label_pad = 8 + with dpg.group(horizontal=True): + self.data_sets_combo: DataSetsCombo = DataSetsCombo( + label="Data set".rjust(label_pad), + width=-60, + callback=lambda s, a, u: signals.emit( + Signal.SELECT_SIMULATION_RESULT, + simulation=self.results_combo.get(), + data=u.get(a), + ), + ) + with dpg.group(horizontal=True): + self.results_combo: ResultsCombo = ResultsCombo( + label="Result".rjust(label_pad), + width=-60, + callback=lambda s, a, u: signals.emit( + Signal.SELECT_SIMULATION_RESULT, + data=self.data_sets_combo.get(), + simulation=u.get(a), + ), + ) + self.delete_button: int = dpg.generate_uuid() + dpg.add_button( + label="Delete", + callback=lambda s, a, u: signals.emit( + Signal.DELETE_SIMULATION_RESULT, simulation=u + ), + width=-1, + tag=self.delete_button, + ) + attach_tooltip(tooltips.simulation.remove) + with dpg.group(horizontal=True): + dpg.add_text("Output".rjust(label_pad)) + # TODO: Split into combo class? + output_items: List[str] = [ + _ for _ in label_to_fit_sim_output.keys() if "statistics" not in _ + ] + self.output_combo: int = dpg.generate_uuid() + dpg.add_combo( + items=output_items, + default_value=output_items[0], + tag=self.output_combo, + width=-60, + ) + self.copy_output_button: int = dpg.generate_uuid() + dpg.add_button( + label="Copy", + callback=lambda s, a, u: signals.emit( + Signal.COPY_OUTPUT, + output=self.get_active_output(), + **u, + ), + user_data={}, + width=-1, + tag=self.copy_output_button, + ) + attach_tooltip(tooltips.general.copy_output) + + def create_results_tables(self): + with dpg.child_window(width=-1, height=-1): + self.result_group: int = dpg.generate_uuid() + with dpg.group(tag=self.result_group): + self.parameters_table: ParametersTable = ParametersTable() + dpg.add_spacer(height=8) + self.settings_table: SettingsTable = SettingsTable() + + def create_plots(self): + self.plot_window: int = dpg.generate_uuid() + with dpg.child_window( + border=False, + width=-1, + height=-1, + tag=self.plot_window, + ): + self.circuit_preview_height: int = 250 + with dpg.child_window( + border=False, + width=-1, + height=self.circuit_preview_height, + ): + dpg.add_text("Simulated circuit") + self.circuit_preview: CircuitPreview = CircuitPreview() + self.plot_tab_bar: int = dpg.generate_uuid() + with dpg.tab_bar(tag=self.plot_tab_bar): + self.create_nyquist_plot() + self.create_bode_plot() + self.create_impedance_plot() + pad_tab_labels(self.plot_tab_bar) + + def create_nyquist_plot(self): + with dpg.tab(label="Nyquist"): + self.nyquist_plot: Nyquist = Nyquist(width=-1, height=-1) + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + label="Data", + line=False, + theme=themes.nyquist.data, + ) + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + label="Sim.", + line=False, + simulation=True, + theme=themes.nyquist.simulation, + ) + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + label="Sim.", + line=True, + simulation=True, + theme=themes.nyquist.simulation, + show_label=False, + ) + with dpg.group(horizontal=True): + self.enlarge_nyquist_button: int = dpg.generate_uuid() + self.adjust_nyquist_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_nyquist, + tag=self.enlarge_nyquist_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.nyquist_plot, + context=Context.SIMULATION_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_nyquist_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_nyquist_limits) + + def create_bode_plot(self): + with dpg.tab(label="Bode"): + self.bode_plot: Bode = Bode(width=-1, height=-1) + self.bode_plot.plot( + frequency=array([]), + magnitude=array([]), + phase=array([]), + labels=( + "Mod(Z), d.", + "Phase(Z), d.", + ), + line=False, + themes=( + themes.bode.magnitude_data, + themes.bode.phase_data, + ), + ) + self.bode_plot.plot( + frequency=array([]), + magnitude=array([]), + phase=array([]), + labels=( + "Mod(Z), s.", + "Phase(Z), s.", + ), + line=False, + simulation=True, + themes=( + themes.bode.magnitude_simulation, + themes.bode.phase_simulation, + ), + ) + self.bode_plot.plot( + frequency=array([]), + magnitude=array([]), + phase=array([]), + labels=( + "Mod(Z), s.", + "Phase(Z), s.", + ), + line=True, + simulation=True, + themes=( + themes.bode.magnitude_simulation, + themes.bode.phase_simulation, + ), + show_labels=False, + ) + with dpg.group(horizontal=True): + self.enlarge_bode_button: int = dpg.generate_uuid() + self.adjust_bode_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_bode, + tag=self.enlarge_bode_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.bode_plot, + context=Context.SIMULATION_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_bode_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_bode_limits) + + def create_impedance_plot(self): + with dpg.tab(label="Real & Imag."): + self.impedance_plot: Impedance = Impedance(width=-1, height=-1) + self.impedance_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + labels=( + "Re(Z), d.", + "Im(Z), d.", + ), + line=False, + themes=( + themes.impedance.real_data, + themes.impedance.imaginary_data, + ), + ) + self.impedance_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + labels=( + "Re(Z), s.", + "Im(Z), s.", + ), + line=False, + simulation=True, + themes=( + themes.impedance.real_simulation, + themes.impedance.imaginary_simulation, + ), + ) + self.impedance_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + labels=( + "Re(Z), s.", + "Im(Z), s.", + ), + line=True, + simulation=True, + themes=( + themes.impedance.real_simulation, + themes.impedance.imaginary_simulation, + ), + show_labels=False, + ) + with dpg.group(horizontal=True): + self.enlarge_impedance_button: int = dpg.generate_uuid() + self.adjust_impedance_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_impedance, + tag=self.enlarge_impedance_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.impedance_plot, + context=Context.SIMULATION_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_impedance_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_impedance_limits) def is_visible(self) -> bool: return dpg.is_item_visible(self.visibility_item) @@ -936,22 +1026,24 @@ def resize(self, width: int, height: int): assert type(height) is int and height > 0 if not self.is_visible(): return - if width < (self.sidebar_width + self.minimum_plot_side * 2): - if dpg.is_item_shown(self.horizontal_bode_group): - dpg.hide_item(self.horizontal_bode_group) - dpg.show_item(self.vertical_bode_group) - self.nyquist_plot.resize(-1, self.minimum_plot_side) - self.bode_plot_vertical.resize(-1, self.minimum_plot_side) - else: - if dpg.is_item_shown(self.vertical_bode_group): - dpg.show_item(self.horizontal_bode_group) - dpg.hide_item(self.vertical_bode_group) - dpg.split_frame() - width, height = dpg.get_item_rect_size(self.plot_window) - width = round((width - 8) / 2) - height = height - 228 - 24 * 2 - 2 - self.nyquist_plot.resize(width, height) - self.bode_plot_horizontal.resize(width, height) + height -= self.circuit_preview_height + 24 * 5 + 12 + plots: List[Plot] = [ + self.nyquist_plot, + self.bode_plot, + self.impedance_plot, + ] + for plot in plots: + plot.resize(-1, height) + + def next_plot_tab(self): + tabs: List[int] = dpg.get_item_children(self.plot_tab_bar, slot=1) + index: int = tabs.index(dpg.get_value(self.plot_tab_bar)) + 1 + dpg.set_value(self.plot_tab_bar, tabs[index % len(tabs)]) + + def previous_plot_tab(self): + tabs: List[int] = dpg.get_item_children(self.plot_tab_bar, slot=1) + index: int = tabs.index(dpg.get_value(self.plot_tab_bar)) - 1 + dpg.set_value(self.plot_tab_bar, tabs[index % len(tabs)]) def clear(self, hide: bool = True): self.data_sets_combo.clear() @@ -960,8 +1052,8 @@ def clear(self, hide: bool = True): self.settings_table.clear(hide=hide) self.circuit_preview.clear() self.nyquist_plot.clear(delete=False) - self.bode_plot_horizontal.clear(delete=False) - self.bode_plot_vertical.clear(delete=False) + self.bode_plot.clear(delete=False) + self.impedance_plot.clear(delete=False) def populate_data_sets(self, labels: List[str], lookup: Dict[str, DataSet]): assert type(labels) is list, labels @@ -1014,21 +1106,23 @@ def select_data_set(self, data: Optional[DataSet]): mag: ndarray phase: ndarray freq, mag, phase = data.get_bode_data() - self.bode_plot_horizontal.update( + self.bode_plot.update( index=0, frequency=freq, magnitude=mag, phase=phase, ) - self.bode_plot_vertical.update( + self.impedance_plot.update( index=0, frequency=freq, - magnitude=mag, - phase=phase, + real=real, + imaginary=imag, ) def select_simulation_result( - self, simulation: Optional[SimulationResult], data: Optional[DataSet] + self, + simulation: Optional[SimulationResult], + data: Optional[DataSet], ): assert type(simulation) is SimulationResult or simulation is None, simulation assert type(data) is DataSet or data is None, data @@ -1051,9 +1145,10 @@ def select_simulation_result( if simulation is None: if dpg.get_value(self.adjust_nyquist_limits_checkbox): self.nyquist_plot.queue_limits_adjustment() - if dpg.get_value(self.adjust_bode_limits_horizontal_checkbox): - self.bode_plot_horizontal.queue_limits_adjustment() - self.bode_plot_vertical.queue_limits_adjustment() + if dpg.get_value(self.adjust_bode_limits_checkbox): + self.bode_plot.queue_limits_adjustment() + if dpg.get_value(self.adjust_impedance_limits_checkbox): + self.impedance_plot.queue_limits_adjustment() return self.results_combo.set(simulation.get_label()) self.parameters_table.populate(simulation) @@ -1062,59 +1157,56 @@ def select_simulation_result( real: ndarray imag: ndarray real, imag = simulation.get_nyquist_data() + freq: ndarray + mag: ndarray + phase: ndarray + freq, mag, phase = simulation.get_bode_data() self.nyquist_plot.update( index=1, real=real, imaginary=imag, ) - real, imag = simulation.get_nyquist_data( - num_per_decade=self.state.config.num_per_decade_in_simulated_lines - ) - self.nyquist_plot.update( - index=2, - real=real, - imaginary=imag, - ) - freq: ndarray - mag: ndarray - phase: ndarray - freq, mag, phase = simulation.get_bode_data() - self.bode_plot_horizontal.update( + self.bode_plot.update( index=1, frequency=freq, magnitude=mag, phase=phase, ) + self.impedance_plot.update( + index=1, + frequency=freq, + real=real, + imaginary=imag, + ) + real, imag = simulation.get_nyquist_data( + num_per_decade=self.state.config.num_per_decade_in_simulated_lines + ) freq, mag, phase = simulation.get_bode_data( num_per_decade=self.state.config.num_per_decade_in_simulated_lines ) - self.bode_plot_horizontal.update( + self.nyquist_plot.update( index=2, - frequency=freq, - magnitude=mag, - phase=phase, + real=real, + imaginary=imag, ) - freq, mag, phase = simulation.get_bode_data() - self.bode_plot_vertical.update( - index=1, + self.bode_plot.update( + index=2, frequency=freq, magnitude=mag, phase=phase, ) - freq, mag, phase = simulation.get_bode_data( - num_per_decade=self.state.config.num_per_decade_in_simulated_lines - ) - self.bode_plot_vertical.update( + self.impedance_plot.update( index=2, frequency=freq, - magnitude=mag, - phase=phase, + real=real, + imaginary=imag, ) if dpg.get_value(self.adjust_nyquist_limits_checkbox): self.nyquist_plot.queue_limits_adjustment() - if dpg.get_value(self.adjust_bode_limits_horizontal_checkbox): - self.bode_plot_horizontal.queue_limits_adjustment() - self.bode_plot_vertical.queue_limits_adjustment() + if dpg.get_value(self.adjust_bode_limits_checkbox): + self.bode_plot.queue_limits_adjustment() + if dpg.get_value(self.adjust_impedance_limits_checkbox): + self.impedance_plot.queue_limits_adjustment() def show_circuit_editor(self): self.settings_menu.show_circuit_editor() @@ -1124,7 +1216,7 @@ def accept_circuit(self, circuit: Optional[Circuit]): self.circuit_editor.hide() if circuit is None: return - self.settings_menu.parse_cdc(circuit.to_string(12)) + self.settings_menu.parse_cdc(circuit.serialize()) def show_enlarged_nyquist(self): signals.emit( @@ -1136,10 +1228,51 @@ def show_enlarged_nyquist(self): def show_enlarged_bode(self): signals.emit( Signal.SHOW_ENLARGED_PLOT, - plot=self.bode_plot_horizontal, - adjust_limits=dpg.get_value(self.adjust_bode_limits_horizontal_checkbox), + plot=self.bode_plot, + adjust_limits=dpg.get_value(self.adjust_bode_limits_checkbox), + ) + + def show_enlarged_impedance(self): + signals.emit( + Signal.SHOW_ENLARGED_PLOT, + plot=self.impedance_plot, + adjust_limits=dpg.get_value(self.adjust_impedance_limits_checkbox), ) + def show_parameter_adjustment(self): + settings: SimulationSettings = self.get_settings() + circuit: Optional[Circuit] + circuit, _ = process_cdc(self.get_settings().cdc) + if circuit is None or len(circuit.get_elements()) == 0: + return + data: Optional[DataSet] = dpg.get_item_user_data(self.perform_sim_button) + hide_data: bool = False + if data is None: + hide_data = True + f: Frequencies = _interpolate( + [settings.max_frequency, settings.min_frequency], + num_per_decade=settings.num_per_decade, + ) + data = DataSet( + frequencies=f, + impedances=array([0.0 for _ in f], dtype=ComplexImpedance), + ) + window: ParameterAdjustment = ParameterAdjustment( + data=data, + circuit=circuit, + callback=self.accept_parameters, + hide_data=hide_data, + keybindings=self.state.config.keybindings, + ) + signals.emit( + Signal.BLOCK_KEYBINDINGS, + window=window.window, + window_object=window, + ) + + def accept_parameters(self, circuit: Circuit): + self.settings_menu.parse_cdc(circuit.serialize()) + def get_active_output(self) -> Optional[FitSimOutput]: return label_to_fit_sim_output.get(dpg.get_value(self.output_combo)) diff --git a/src/deareis/gui/zhit/__init__.py b/src/deareis/gui/zhit/__init__.py new file mode 100644 index 0000000..cd564d8 --- /dev/null +++ b/src/deareis/gui/zhit/__init__.py @@ -0,0 +1,1173 @@ +# DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). +# Copyright 2023 DearEIS developers +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +# +# The licenses of DearEIS' dependencies and/or sources of portions of code are included in +# the LICENSES folder. + +from typing import ( + Callable, + Dict, + List, + Optional, + Tuple, +) +from numpy import ( + array, + allclose, + log10 as log, + ndarray, +) +from pyimpspec import ( + ComplexImpedances, + ComplexResiduals, +) +from pyimpspec.analysis.utility import _calculate_residuals +import dearpygui.dearpygui as dpg +from deareis.signals import Signal +import deareis.signals as signals +import deareis.tooltips as tooltips +from deareis.tooltips import ( + attach_tooltip, + update_tooltip, +) +from deareis.enums import ( + Context, + ZHITInterpolation, + ZHITSmoothing, + ZHITWindow, + label_to_zhit_interpolation, + label_to_zhit_smoothing, + label_to_zhit_window, + value_to_zhit_interpolation, + value_to_zhit_smoothing, + value_to_zhit_window, + zhit_interpolation_to_label, + zhit_smoothing_to_label, + zhit_window_to_label, +) +from deareis.data import ( + DataSet, + ZHITResult, + ZHITSettings, +) +import deareis.themes as themes +from deareis.gui.plots import ( + Bode, + Impedance, + Nyquist, + Plot, + Residuals, +) +from deareis.gui.shared import ( + DataSetsCombo, + ResultsCombo, +) +from deareis.utility import pad_tab_labels + + +class SettingsMenu: + def __init__( + self, + default_settings: ZHITSettings, + label_pad: int, + ): + with dpg.group(horizontal=True): + dpg.add_text("Smoothing".rjust(label_pad)) + attach_tooltip(tooltips.zhit.smoothing) + self.smoothing_combo: int = dpg.generate_uuid() + smoothing_items: List[str] = list(label_to_zhit_smoothing.keys()) + dpg.add_combo( + items=smoothing_items, + default_value=zhit_smoothing_to_label[default_settings.smoothing], + callback=lambda s, a, u: self.update_settings(), + width=-1, + tag=self.smoothing_combo, + ) + self.num_points_input: int = dpg.generate_uuid() + with dpg.group(horizontal=True): + dpg.add_text("Number of points".rjust(label_pad)) + attach_tooltip(tooltips.zhit.num_points) + dpg.add_input_int( + default_value=default_settings.num_points, + min_value=2, + min_clamped=True, + step=0, + on_enter=True, + width=-1, + tag=self.num_points_input, + ) + self.polynomial_order_input: int = dpg.generate_uuid() + with dpg.group(horizontal=True): + dpg.add_text("Polynomial order".rjust(label_pad)) + attach_tooltip(tooltips.zhit.polynomial_order) + dpg.add_input_int( + default_value=default_settings.polynomial_order, + min_value=1, + min_clamped=True, + step=0, + on_enter=True, + width=-1, + tag=self.polynomial_order_input, + ) + self.num_iterations_input: int = dpg.generate_uuid() + with dpg.group(horizontal=True): + dpg.add_text("Number of iterations".rjust(label_pad)) + attach_tooltip(tooltips.zhit.num_iterations) + dpg.add_input_int( + default_value=default_settings.num_iterations, + min_value=1, + min_clamped=True, + step=0, + on_enter=True, + width=-1, + tag=self.num_iterations_input, + ) + with dpg.group(horizontal=True): + dpg.add_text("Interpolation".rjust(label_pad)) + attach_tooltip(tooltips.zhit.interpolation) + self.interpolation_combo: int = dpg.generate_uuid() + interpolation_items: List[str] = list(label_to_zhit_interpolation.keys()) + dpg.add_combo( + items=interpolation_items, + default_value=zhit_interpolation_to_label[ + default_settings.interpolation + ], + width=-1, + tag=self.interpolation_combo, + ) + with dpg.group(horizontal=True): + dpg.add_text("Window".rjust(label_pad)) + attach_tooltip(tooltips.zhit.window) + self.window_combo: int = dpg.generate_uuid() + window_items: List[str] = list(label_to_zhit_window.keys()) + dpg.add_combo( + items=window_items, + default_value=zhit_window_to_label[default_settings.window], + width=-1, + tag=self.window_combo, + ) + self.window_center_input: int = dpg.generate_uuid() + with dpg.group(horizontal=True): + dpg.add_text("Window center".rjust(label_pad)) + attach_tooltip(tooltips.zhit.window_center) + dpg.add_input_float( + default_value=default_settings.window_center, + step=0.0, + format="%.3g", + on_enter=True, + width=-1, + tag=self.window_center_input, + ) + self.window_width_input: int = dpg.generate_uuid() + with dpg.group(horizontal=True): + dpg.add_text("Window width".rjust(label_pad)) + attach_tooltip(tooltips.zhit.window_width) + dpg.add_input_float( + default_value=default_settings.window_width, + min_value=1e-12, + min_clamped=True, + step=0.0, + format="%.3g", + on_enter=True, + width=-1, + tag=self.window_width_input, + ) + self.set_settings(default_settings) + + def update_settings(self, settings: Optional[ZHITSettings] = None): + if settings is None: + settings = self.get_settings() + if settings.smoothing == ZHITSmoothing.NONE: + dpg.disable_item(self.num_points_input) + else: + dpg.enable_item(self.num_points_input) + if settings.smoothing == ZHITSmoothing.AUTO: + dpg.enable_item(self.polynomial_order_input) + dpg.enable_item(self.num_iterations_input) + elif settings.smoothing == ZHITSmoothing.NONE: + dpg.disable_item(self.polynomial_order_input) + dpg.disable_item(self.num_iterations_input) + elif settings.smoothing == ZHITSmoothing.LOWESS: + dpg.disable_item(self.polynomial_order_input) + dpg.enable_item(self.num_iterations_input) + elif settings.smoothing == ZHITSmoothing.SAVGOL: + dpg.enable_item(self.polynomial_order_input) + dpg.disable_item(self.num_iterations_input) + else: + raise NotImplementedError( + f"Unsupported smoothing type: {settings.smoothing}" + ) + + def get_settings(self) -> ZHITSettings: + smoothing: ZHITSmoothing = label_to_zhit_smoothing[ + dpg.get_value(self.smoothing_combo) + ] + num_points: int = dpg.get_value(self.num_points_input) + polynomial_order: int = dpg.get_value(self.polynomial_order_input) + num_iterations: int = dpg.get_value(self.num_iterations_input) + interpolation: ZHITInterpolation = label_to_zhit_interpolation[ + dpg.get_value(self.interpolation_combo) + ] + window: ZHITWindow = label_to_zhit_window[dpg.get_value(self.window_combo)] + window_center: float = dpg.get_value(self.window_center_input) + window_width: float = dpg.get_value(self.window_width_input) + return ZHITSettings( + smoothing=smoothing, + num_points=num_points, + polynomial_order=polynomial_order, + num_iterations=num_iterations, + interpolation=interpolation, + window=window, + window_center=window_center, + window_width=window_width, + ) + + def set_settings(self, settings: ZHITSettings): + assert isinstance(settings, ZHITSettings), settings + dpg.set_value(self.smoothing_combo, zhit_smoothing_to_label[settings.smoothing]) + if settings.num_points > 1: + dpg.set_value(self.num_points_input, settings.num_points) + if 0 < settings.polynomial_order < settings.num_points: + dpg.set_value(self.polynomial_order_input, settings.polynomial_order) + if settings.num_iterations > 0: + dpg.set_value(self.num_iterations_input, settings.num_iterations) + dpg.set_value( + self.interpolation_combo, + zhit_interpolation_to_label[settings.interpolation], + ) + dpg.set_value(self.window_combo, zhit_window_to_label[settings.window]) + dpg.set_value(self.window_center_input, settings.window_center) + dpg.set_value(self.window_width_input, settings.window_width) + self.update_settings(settings) + + def has_active_input(self) -> bool: + # TODO + return False + + +class ZHITResultsCombo(ResultsCombo): + def selection_callback(self, sender: int, app_data: str, user_data: tuple): + signals.emit( + Signal.SELECT_ZHIT_RESULT, + zhit=user_data[0].get(app_data), + data=user_data[1], + ) + + def adjust_label(self, old: str, longest: int) -> str: + return old + + +# TODO: Move to separate file and re-use in other tabs? +class StatisticsTable: + def __init__(self): + label_pad: int = 23 + self._header: int = dpg.generate_uuid() + with dpg.collapsing_header(label=" Statistics", leaf=True, tag=self._header): + self._table: int = dpg.generate_uuid() + with dpg.table( + borders_outerV=True, + borders_outerH=True, + borders_innerV=True, + borders_innerH=True, + scrollY=True, + freeze_rows=1, + height=18 + 23 * 4, + tag=self._table, + ): + dpg.add_table_column( + label="Label".rjust(label_pad), + width_fixed=True, + ) + dpg.add_table_column( + label="Value", + width_fixed=True, + ) + label: str + tooltip: str + for (label, tooltip) in [ + ( + "log X² (pseudo)", + tooltips.fitting.pseudo_chisqr, + ), + ( + "Smoothing", + tooltips.zhit.smoothing, + ), + ( + "Interpolation", + tooltips.zhit.interpolation, + ), + ( + "Window", + tooltips.zhit.window, + ), + ]: + with dpg.table_row(): + dpg.add_text(label.rjust(label_pad)) + attach_tooltip(tooltip) + tooltip_tag: int = dpg.generate_uuid() + dpg.add_text("", user_data=tooltip_tag) + attach_tooltip("", tag=tooltip_tag) + + def clear(self, hide: bool): + if hide: + dpg.hide_item(self._header) + row: int + for row in dpg.get_item_children(self._table, slot=1): + tag: int = dpg.get_item_children(row, slot=1)[2] + dpg.set_value(tag, "") + dpg.hide_item(dpg.get_item_parent(dpg.get_item_user_data(tag))) + + def populate(self, zhit: ZHITResult): + dpg.show_item(self._header) + cells: List[int] = [] + row: int + for row in dpg.get_item_children(self._table, slot=1): + cells.append(dpg.get_item_children(row, slot=1)[2]) + assert len(cells) == 4, cells + tag: int + value: str + for (tag, value) in [ + ( + cells[0], + f"{log(zhit.pseudo_chisqr):.3f}", + ), + ( + cells[1], + zhit_smoothing_to_label.get( + value_to_zhit_smoothing.get( + zhit.smoothing, + zhit.smoothing, + ), + zhit.smoothing, + ), + ), + ( + cells[2], + zhit_interpolation_to_label.get( + value_to_zhit_interpolation.get( + zhit.interpolation, + zhit.interpolation, + ), + zhit.interpolation, + ), + ), + ( + cells[3], + zhit_window_to_label.get( + value_to_zhit_window.get( + zhit.window, + zhit.window, + ), + zhit.window, + ), + ), + ]: + dpg.set_value(tag, value) + update_tooltip(dpg.get_item_user_data(tag), value) + dpg.show_item(dpg.get_item_parent(dpg.get_item_user_data(tag))) + dpg.set_item_height(self._table, 18 + 23 * len(cells)) + + +# TODO: Move to separate file and re-use in other tabs? +class SettingsTable: + def __init__(self): + label_pad: int = 23 + self._header: int = dpg.generate_uuid() + with dpg.collapsing_header(label=" Settings", leaf=True, tag=self._header): + self._table: int = dpg.generate_uuid() + with dpg.table( + borders_outerV=True, + borders_outerH=True, + borders_innerV=True, + borders_innerH=True, + scrollY=True, + freeze_rows=1, + height=18 + 23, + tag=self._table, + ): + dpg.add_table_column( + label="Label".rjust(label_pad), + width_fixed=True, + ) + dpg.add_table_column( + label="Value", + width_fixed=True, + ) + label: str + for label in [ + "Smoothing", + "Number of points", + "Polynomial order", + "Number of iterations", + "Interpolation", + "Window", + "Center", + "Width", + ]: + with dpg.table_row(): + dpg.add_text(label.rjust(label_pad)) + tooltip_tag: int = dpg.generate_uuid() + dpg.add_text("", user_data=tooltip_tag) + attach_tooltip("", tag=tooltip_tag) + dpg.add_spacer(height=8) + with dpg.group(horizontal=True): + self._apply_settings_button: int = dpg.generate_uuid() + dpg.add_button( + label="Apply settings", + callback=lambda s, a, u: signals.emit( + Signal.APPLY_ZHIT_SETTINGS, + **u, + ), + tag=self._apply_settings_button, + width=154, + ) + attach_tooltip(tooltips.general.apply_settings) + self._apply_mask_button: int = dpg.generate_uuid() + dpg.add_button( + label="Apply mask", + callback=lambda s, a, u: signals.emit( + Signal.APPLY_DATA_SET_MASK, + **u, + ), + tag=self._apply_mask_button, + width=-1, + ) + attach_tooltip(tooltips.general.apply_mask) + with dpg.group(horizontal=True): + self._load_as_data_button: int = dpg.generate_uuid() + dpg.add_button( + label="Load as data set", + callback=lambda s, a, u: signals.emit( + Signal.LOAD_ZHIT_AS_DATA_SET, + **u, + ), + tag=self._load_as_data_button, + width=-1, + ) + attach_tooltip(tooltips.zhit.load_as_data_set) + + def clear(self, hide: bool): + if hide: + dpg.hide_item(self._header) + row: int + for row in dpg.get_item_children(self._table, slot=1): + tag: int = dpg.get_item_children(row, slot=1)[1] + dpg.set_value(tag, "") + dpg.hide_item(dpg.get_item_parent(dpg.get_item_user_data(tag))) + + def populate(self, zhit: ZHITResult, data: DataSet): + dpg.show_item(self._header) + rows: List[int] = [] + cells: List[Tuple[int, int]] = [] + row: int + for row in dpg.get_item_children(self._table, slot=1): + rows.append(row) + cells.append(dpg.get_item_children(row, slot=1)) + assert len(rows) == len(cells) == 8, ( + rows, + cells, + ) + tag: int + value: str + for (row, tag, value) in [ + ( + rows[0], + cells[0][1], + zhit_smoothing_to_label[zhit.settings.smoothing], + ), + ( + rows[1], + cells[1][1], + str(zhit.settings.num_points), + ), + ( + rows[2], + cells[2][1], + str(zhit.settings.polynomial_order), + ), + ( + rows[3], + cells[3][1], + str(zhit.settings.num_iterations), + ), + ( + rows[4], + cells[4][1], + zhit_interpolation_to_label[zhit.settings.interpolation], + ), + ( + rows[5], + cells[5][1], + zhit_window_to_label[zhit.settings.window], + ), + ( + rows[6], + cells[6][1], + f"{zhit.settings.window_center:.3f}", + ), + ( + rows[7], + cells[7][1], + f"{zhit.settings.window_width:.3f}", + ), + ]: + dpg.set_value(tag, value.split("\n")[0]) + update_tooltip(dpg.get_item_user_data(tag), value) + dpg.show_item(dpg.get_item_parent(dpg.get_item_user_data(tag))) + dpg.set_item_height(self._table, 18 + 23 * 8) + dpg.set_item_user_data( + self._apply_settings_button, + {"settings": zhit.settings}, + ) + dpg.set_item_user_data( + self._apply_mask_button, + { + "data": data, + "mask": zhit.mask, + "zhit": zhit, + }, + ) + dpg.set_item_user_data( + self._load_as_data_button, + { + "zhit": zhit, + "data": data, + }, + ) + + +class ZHITTab: + def __init__(self, state): + self.state = state + self.queued_update: Optional[Callable] = None + self.create_tab(state) + + def create_tab(self, state): + self.tab: int = dpg.generate_uuid() + label_pad: int = 24 + with dpg.tab(label="Z-HIT analysis", tag=self.tab): + with dpg.child_window(border=False): + with dpg.group(horizontal=True): + self.create_sidebar(state, label_pad) + self.create_plots() + + def create_sidebar(self, state, label_pad: int): + self.sidebar_window: int = dpg.generate_uuid() + self.sidebar_width: int = 350 + with dpg.child_window( + border=False, + width=self.sidebar_width, + tag=self.sidebar_window, + ): + with dpg.child_window(width=-1, height=242): + self.settings_menu: SettingsMenu = SettingsMenu( + state.config.default_zhit_settings, + label_pad, + ) + with dpg.group(horizontal=True): + dpg.add_text("?".rjust(label_pad)) + attach_tooltip(tooltips.zhit.preview_weights) + dpg.add_button( + label="Preview weights", + callback=lambda s, a, u: signals.emit( + Signal.PREVIEW_ZHIT_WEIGHTS, + settings=self.get_settings(), + ), + width=-1, + ) + with dpg.group(horizontal=True): + self.visibility_item: int = dpg.generate_uuid() + dpg.add_text( + "?".rjust(label_pad), + tag=self.visibility_item, + ) + attach_tooltip(tooltips.zhit.perform) + self.perform_zhit_button: int = dpg.generate_uuid() + dpg.add_button( + label="Perform", + callback=lambda s, a, u: signals.emit( + Signal.PERFORM_ZHIT, + data=u, + settings=self.get_settings(), + ), + user_data=None, + width=-70, + tag=self.perform_zhit_button, + ) + dpg.add_button( + label="Batch", + callback=lambda s, a, u: signals.emit( + Signal.BATCH_PERFORM_ANALYSIS, + settings=self.get_settings(), + ), + width=-1, + ) + with dpg.child_window(width=-1, height=58): + label_pad = 8 + with dpg.group(horizontal=True): + self.data_sets_combo: DataSetsCombo = DataSetsCombo( + label="Data set".rjust(label_pad), + width=-60, + ) + with dpg.group(horizontal=True): + self.results_combo: ZHITResultsCombo = ZHITResultsCombo( + label="Result".rjust(label_pad), + width=-60, + ) + self.delete_button: int = dpg.generate_uuid() + dpg.add_button( + label="Delete", + callback=lambda s, a, u: signals.emit( + Signal.DELETE_ZHIT_RESULT, + **u, + ), + width=-1, + tag=self.delete_button, + ) + attach_tooltip(tooltips.zhit.delete) + with dpg.child_window(width=-1, height=-1): + with dpg.group(show=False): + self.validity_text: int = dpg.generate_uuid() + dpg.bind_item_theme( + dpg.add_text( + "", + wrap=self.sidebar_width - 24, + tag=self.validity_text, + ), + themes.result.invalid, + ) + dpg.add_spacer(height=8) + self.statistics_table: StatisticsTable = StatisticsTable() + dpg.add_spacer(height=8) + self.settings_table: SettingsTable = SettingsTable() + + def create_plots(self): + self.plot_window: int = dpg.generate_uuid() + with dpg.child_window( + border=False, + width=-1, + height=-1, + tag=self.plot_window, + ): + self.plot_tab_bar: int = dpg.generate_uuid() + with dpg.tab_bar(tag=self.plot_tab_bar): + self.create_nyquist_plot() + bode_tab: int = self.create_bode_plot() + self.create_impedance_plot() + pad_tab_labels(self.plot_tab_bar) + dpg.set_value(self.plot_tab_bar, bode_tab) + dpg.add_spacer(height=4) + dpg.add_separator() + dpg.add_spacer(height=4) + self.create_residuals_plot() + + def create_nyquist_plot(self): + with dpg.tab(label="Nyquist"): + self.nyquist_plot: Nyquist = Nyquist(width=-1, height=-24) + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + label="Data", + line=False, + theme=themes.nyquist.data, + ) + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + label="Fit", + line=False, + fit=True, + theme=themes.nyquist.simulation, + ) + self.nyquist_plot.plot( + real=array([]), + imaginary=array([]), + label="Fit", + line=True, + fit=True, + theme=themes.nyquist.simulation, + show_label=False, + ) + with dpg.group(horizontal=True): + self.enlarge_nyquist_button: int = dpg.generate_uuid() + self.adjust_nyquist_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_nyquist, + tag=self.enlarge_nyquist_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.nyquist_plot, + context=Context.ZHIT_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_nyquist_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_nyquist_limits) + + def create_bode_plot(self) -> int: + tab: int + with dpg.tab(label="Bode") as tab: + self.bode_plot: Bode = Bode(width=-1, height=-24) + self.bode_plot.plot( + frequency=array([]), + magnitude=array([]), + phase=array([]), + labels=( + "Mod(Z), d.", + "Phase(Z), d.", + ), + line=False, + themes=( + themes.bode.magnitude_data, + themes.bode.phase_data, + ), + ) + self.bode_plot.plot( + frequency=array([]), + magnitude=array([]), + phase=array([]), + labels=( + "Mod(Z), f.", + "Phase(Z), f.", + ), + line=False, + fit=True, + themes=( + themes.bode.magnitude_simulation, + themes.bode.phase_simulation, + ), + ) + self.bode_plot.plot( + frequency=array([]), + magnitude=array([]), + phase=array([]), + labels=( + "Mod(Z), f.", + "Phase(Z), f.", + ), + line=True, + fit=True, + themes=( + themes.bode.magnitude_simulation, + themes.bode.phase_simulation, + ), + show_labels=False, + ) + with dpg.group(horizontal=True): + self.enlarge_bode_button: int = dpg.generate_uuid() + self.adjust_bode_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_bode, + tag=self.enlarge_bode_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.bode_plot, + context=Context.ZHIT_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_bode_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_bode_limits) + return tab + + def create_impedance_plot(self): + with dpg.tab(label="Real & Imag."): + self.impedance_plot: Impedance = Impedance( + width=-1, + height=-24, + ) + self.impedance_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + labels=( + "Re(Z), d.", + "Im(Z), d.", + ), + line=False, + themes=( + themes.impedance.real_data, + themes.impedance.imaginary_data, + ), + ) + self.impedance_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + labels=( + "Re(Z), f.", + "Im(Z), f.", + ), + line=False, + fit=True, + themes=( + themes.impedance.real_simulation, + themes.impedance.imaginary_simulation, + ), + ) + self.impedance_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + labels=( + "Re(Z), f.", + "Im(Z), f.", + ), + line=True, + fit=True, + themes=( + themes.impedance.real_simulation, + themes.impedance.imaginary_simulation, + ), + show_labels=False, + ) + with dpg.group(horizontal=True): + self.enlarge_impedance_button: int = dpg.generate_uuid() + self.adjust_impedance_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_impedance, + tag=self.enlarge_impedance_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.impedance_plot, + context=Context.ZHIT_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_impedance_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_impedance_limits) + + def create_residuals_plot(self): + self.residuals_plot_height: int = 300 + self.residuals_plot: Residuals = Residuals( + width=-1, + height=self.residuals_plot_height, + ) + self.residuals_plot.plot( + frequency=array([]), + real=array([]), + imaginary=array([]), + ) + with dpg.group(horizontal=True): + self.enlarge_residuals_button: int = dpg.generate_uuid() + self.adjust_residuals_limits_checkbox: int = dpg.generate_uuid() + dpg.add_button( + label="Enlarge plot", + callback=self.show_enlarged_residuals, + tag=self.enlarge_residuals_button, + ) + dpg.add_button( + label="Copy as CSV", + callback=lambda s, a, u: signals.emit( + Signal.COPY_PLOT_DATA, + plot=self.residuals_plot, + context=Context.ZHIT_TAB, + ), + ) + attach_tooltip(tooltips.general.copy_plot_data_as_csv) + dpg.add_checkbox( + label="Adjust limits", + default_value=True, + tag=self.adjust_residuals_limits_checkbox, + ) + attach_tooltip(tooltips.general.adjust_residuals_limits) + + def is_visible(self) -> bool: + return dpg.is_item_visible(self.visibility_item) + + def resize(self, width: int, height: int): + assert type(width) is int and width > 0 + assert type(height) is int and height > 0 + if not self.is_visible(): + return + width, height = dpg.get_item_rect_size(self.plot_window) + tmp: int = round(height / 2) - 24 * 2 + if tmp > 300: + self.residuals_plot.resize(-1, 300) + height -= 348 + 24 * 2 - 2 + else: + height = tmp + self.residuals_plot.resize(-1, height) + plots: List[Plot] = [ + self.nyquist_plot, + self.bode_plot, + self.impedance_plot, + ] + for plot in plots: + plot.resize(-1, height) + + def clear(self, hide: bool = True): + self.data_sets_combo.clear() + self.results_combo.clear() + self.statistics_table.clear(hide=hide) + self.settings_table.clear(hide=hide) + dpg.set_item_user_data(self.perform_zhit_button, None) + self.nyquist_plot.clear(delete=False) + self.bode_plot.clear(delete=False) + self.impedance_plot.clear(delete=False) + self.residuals_plot.clear(delete=False) + + def get_settings(self) -> ZHITSettings: + return self.settings_menu.get_settings() + + def set_settings(self, settings: ZHITSettings): + self.settings_menu.set_settings(settings) + + def populate_data_sets(self, labels: List[str], lookup: Dict[str, DataSet]): + assert type(labels) is list, labels + assert type(lookup) is dict, lookup + self.data_sets_combo.populate(labels, lookup) + + def populate_zhits(self, lookup: Dict[str, ZHITResult], data: Optional[DataSet]): + assert type(lookup) is dict, lookup + assert type(data) is DataSet or data is None, data + self.results_combo.populate(lookup, data) + dpg.hide_item(dpg.get_item_parent(self.validity_text)) + if data is not None and self.results_combo.labels: + signals.emit( + Signal.SELECT_ZHIT_RESULT, + zhit=self.results_combo.get(), + data=data, + ) + else: + self.statistics_table.clear(hide=True) + self.settings_table.clear(hide=True) + self.select_data_set(data) + + def get_next_data_set(self) -> Optional[DataSet]: + return self.data_sets_combo.get_next() + + def get_previous_data_set(self) -> Optional[DataSet]: + return self.data_sets_combo.get_previous() + + def get_next_result(self) -> Optional[ZHITResult]: + return self.results_combo.get_next() + + def get_previous_result(self) -> Optional[ZHITResult]: + return self.results_combo.get_previous() + + def select_data_set(self, data: Optional[DataSet]): + assert type(data) is DataSet or data is None, data + self.clear(hide=data is None) + dpg.set_item_user_data(self.perform_zhit_button, data) + if data is None: + return + self.data_sets_combo.set(data.get_label()) + real: ndarray + imag: ndarray + real, imag = data.get_nyquist_data() + self.nyquist_plot.update( + index=0, + real=real, + imaginary=imag, + ) + freq: ndarray + mag: ndarray + phase: ndarray + freq, mag, phase = data.get_bode_data() + self.bode_plot.update( + index=0, + frequency=freq, + magnitude=mag, + phase=phase, + ) + self.impedance_plot.update( + index=0, + frequency=freq, + real=real, + imaginary=imag, + ) + + def assert_zhit_up_to_date(self, zhit: ZHITResult, data: DataSet): + # Check if the number of unmasked points is the same + Z_exp: ndarray = data.get_impedances() + Z_zhit: ndarray = zhit.get_impedances() + assert Z_exp.shape == Z_zhit.shape, "The number of data points differ!" + # Check if the masks are the same + mask_exp: Dict[int, bool] = data.get_mask() + mask_zhit: Dict[int, bool] = { + k: zhit.mask.get(k, mask_exp.get(k, False)) for k in zhit.mask + } + num_masked_exp: int = list(data.get_mask().values()).count(True) + num_masked_zhit: int = list(zhit.mask.values()).count(True) + assert num_masked_exp == num_masked_zhit, "The masks are different sizes!" + i: int + for i in mask_zhit.keys(): + assert ( + i in mask_exp + ), f"The data set does not have a point at index {i + 1}!" + assert ( + mask_exp[i] == mask_zhit[i] + ), f"The data set's mask differs at index {i + 1}!" + # Check if the frequencies and impedances are the same + assert allclose( + zhit.get_frequencies(), + data.get_frequencies(), + ), "The frequencies differ!" + residuals: ComplexResiduals = _calculate_residuals(Z_exp, Z_zhit) + assert allclose(zhit.residuals.real, residuals.real) and allclose( + zhit.residuals.imag, residuals.imag + ), "The data set's impedances differ from what they were when the zhit was performed!" + + def select_zhit_result(self, zhit: Optional[ZHITResult], data: Optional[DataSet]): + assert type(zhit) is ZHITResult or zhit is None, zhit + assert type(data) is DataSet or data is None, data + dpg.set_item_user_data( + self.delete_button, + { + "zhit": zhit, + "data": data, + }, + ) + if not self.is_visible(): + self.queued_update = lambda: self.select_zhit_result(zhit, data) + return + self.queued_update = None + self.select_data_set(data) + if zhit is None or data is None: + if dpg.get_value(self.adjust_nyquist_limits_checkbox): + self.nyquist_plot.queue_limits_adjustment() + if dpg.get_value(self.adjust_bode_limits_checkbox): + self.bode_plot.queue_limits_adjustment() + if dpg.get_value(self.adjust_impedance_limits_checkbox): + self.impedance_plot.queue_limits_adjustment() + if dpg.get_value(self.adjust_residuals_limits_checkbox): + self.residuals_plot.queue_limits_adjustment() + return + self.results_combo.set(zhit.get_label()) + message: str + try: + self.assert_zhit_up_to_date(zhit, data) + dpg.hide_item(dpg.get_item_parent(self.validity_text)) + except AssertionError as message: + dpg.set_value( + self.validity_text, + f"Z-HIT result is not valid for the current state of the data set!\n\n{message}", + ) + dpg.show_item(dpg.get_item_parent(self.validity_text)) + self.statistics_table.populate(zhit) + self.settings_table.populate( + zhit, + data, + ) + real: ndarray + imag: ndarray + real, imag = zhit.get_nyquist_data() + i: int + for i in range(1, 3): + self.nyquist_plot.update( + index=i, + real=real, + imaginary=imag, + ) + freq: ndarray + mag: ndarray + phase: ndarray + freq, mag, phase = zhit.get_bode_data() + for i in range(1, 3): + self.bode_plot.update( + index=i, + frequency=freq, + magnitude=mag, + phase=phase, + ) + self.impedance_plot.update( + index=i, + frequency=freq, + real=real, + imaginary=imag, + ) + freq, real, imag = zhit.get_residuals_data() + self.residuals_plot.update( + index=0, + frequency=freq, + real=real, + imaginary=imag, + ) + if dpg.get_value(self.adjust_nyquist_limits_checkbox): + self.nyquist_plot.queue_limits_adjustment() + if dpg.get_value(self.adjust_bode_limits_checkbox): + self.bode_plot.queue_limits_adjustment() + if dpg.get_value(self.adjust_impedance_limits_checkbox): + self.impedance_plot.queue_limits_adjustment() + if dpg.get_value(self.adjust_residuals_limits_checkbox): + self.residuals_plot.queue_limits_adjustment() + + def next_plot_tab(self): + tabs: List[int] = dpg.get_item_children(self.plot_tab_bar, slot=1) + index: int = tabs.index(dpg.get_value(self.plot_tab_bar)) + 1 + dpg.set_value(self.plot_tab_bar, tabs[index % len(tabs)]) + + def previous_plot_tab(self): + tabs: List[int] = dpg.get_item_children(self.plot_tab_bar, slot=1) + index: int = tabs.index(dpg.get_value(self.plot_tab_bar)) - 1 + dpg.set_value(self.plot_tab_bar, tabs[index % len(tabs)]) + + def show_enlarged_nyquist(self): + signals.emit( + Signal.SHOW_ENLARGED_PLOT, + plot=self.nyquist_plot, + adjust_limits=dpg.get_value(self.adjust_nyquist_limits_checkbox), + ) + + def show_enlarged_bode(self): + signals.emit( + Signal.SHOW_ENLARGED_PLOT, + plot=self.bode_plot, + adjust_limits=dpg.get_value(self.adjust_bode_limits_checkbox), + ) + + def show_enlarged_impedance(self): + signals.emit( + Signal.SHOW_ENLARGED_PLOT, + plot=self.impedance_plot, + adjust_limits=dpg.get_value(self.adjust_impedance_limits_checkbox), + ) + + def show_enlarged_residuals(self): + signals.emit( + Signal.SHOW_ENLARGED_PLOT, + plot=self.residuals_plot, + adjust_limits=dpg.get_value(self.adjust_residuals_limits_checkbox), + ) + + def has_active_input(self) -> bool: + return self.settings_menu.has_active_input() diff --git a/src/deareis/gui/zhit/weights_preview.py b/src/deareis/gui/zhit/weights_preview.py new file mode 100644 index 0000000..39ee4e0 --- /dev/null +++ b/src/deareis/gui/zhit/weights_preview.py @@ -0,0 +1,261 @@ +# DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). +# Copyright 2023 DearEIS developers +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +# +# The licenses of DearEIS' dependencies and/or sources of portions of code are included in +# the LICENSES folder. + +from typing import ( + Callable, + Dict, + List, + Optional, +) +import dearpygui.dearpygui as dpg +from numpy import ( + array, + float64, + log10 as log, +) +from numpy.typing import NDArray +from pyimpspec.analysis.zhit.weights import ( + _generate_weights, + _initialize_window_functions, +) +from deareis.data import ( + DataSet, + ZHITSettings, +) +from deareis.enums import ( + ZHITWindow, + zhit_window_to_label, + zhit_window_to_value, +) +from deareis.gui.plots.zhit_weights import ZHITWeights +from deareis.utility import calculate_window_position_dimensions +from deareis.signals import Signal +import deareis.signals as signals +import deareis.themes as themes +import deareis.tooltips as tooltips +from deareis.tooltips import attach_tooltip +from deareis.state import STATE +from deareis.enums import Action +from deareis.keybindings import ( + Keybinding, + TemporaryKeybindingHandler, +) + +_initialize_window_functions() + + +class WeightsPreview: + def __init__(self, data: DataSet, settings: ZHITSettings): + assert isinstance(data, DataSet) + assert isinstance(settings, ZHITSettings) + self.data: DataSet = data + self.settings: ZHITSettings = settings + self.weights: Dict[ZHITWindow, NDArray[float64]] = {} + self.checkboxes: Dict[ZHITWindow, int] = {} + self.previous_choice: Optional[ZHITWindow] = None + log_f: NDArray[float64] = log(data.get_frequencies()) + enum: ZHITWindow + value: str + for enum, value in zhit_window_to_value.items(): + if enum == ZHITWindow.AUTO: + continue + self.weights[enum] = _generate_weights( + log_f=log_f, + window=value, + center=settings.window_center, + width=settings.window_width, + ) + self.create_window() + self.register_keybindings() + self.update_preview( + settings.window if settings.window != ZHITWindow.AUTO else ZHITWindow.BOXCAR + ) + + def register_keybindings(self): + callbacks: Dict[Keybinding, Callable] = {} + # Cancel + kb: Keybinding = Keybinding( + key=dpg.mvKey_Escape, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.CANCEL, + ) + callbacks[kb] = self.close + # Previous function + for kb in STATE.config.keybindings: + if kb.action is Action.PREVIOUS_PRIMARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Prior, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.PREVIOUS_PRIMARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle(step=-1) + # Next function + for kb in STATE.config.keybindings: + if kb.action is Action.NEXT_PRIMARY_RESULT: + break + else: + kb = Keybinding( + key=dpg.mvKey_Next, + mod_alt=False, + mod_ctrl=False, + mod_shift=False, + action=Action.NEXT_PRIMARY_RESULT, + ) + callbacks[kb] = lambda: self.cycle(step=1) + # Create the handler + self.keybinding_handler: TemporaryKeybindingHandler = ( + TemporaryKeybindingHandler(callbacks=callbacks) + ) + + def create_window(self): + x: int + y: int + w: int + h: int + x, y, w, h = calculate_window_position_dimensions() + self.window: int = dpg.generate_uuid() + with dpg.window( + label="Preview weights", + modal=True, + pos=(x, y), + width=w, + height=h, + tag=self.window, + on_close=self.close, + ): + with dpg.group(horizontal=True): + self.create_table() + self.create_plot() + + def create_table(self): + self.table: int = dpg.generate_uuid() + with dpg.table( + borders_outerV=True, + borders_outerH=True, + borders_innerV=True, + borders_innerH=True, + scrollY=True, + freeze_rows=1, + width=200, + tag=self.table, + ): + dpg.add_table_column( + label="?", + width_fixed=True, + ) + attach_tooltip(tooltips.zhit.select_window_function) + dpg.add_table_column( + label="Name", + ) + for enum in self.weights: + with dpg.table_row(parent=self.table): + self.checkboxes[enum] = dpg.add_checkbox( + default_value=( + enum == self.settings.window + or ( + self.settings.window == ZHITWindow.AUTO + and enum == ZHITWindow.BOXCAR + ) + ), + callback=lambda s, a, u: self.update_preview( + window=u, + flag=a, + ), + user_data=enum, + ) + dpg.add_text(zhit_window_to_label[enum]) + attach_tooltip(zhit_window_to_label[enum]) + + def create_plot(self): + self.plot: ZHITWeights = ZHITWeights(width=-1, height=-1) + f, mag, _ = self.data.get_bode_data() + self.plot.plot( + frequency=f, + magnitude=mag, + weight=array([]), + labels=("Mod(Z)", ""), + themes=(themes.zhit.magnitude, themes.zhit.weight), + show_labels=True, + ) + self.plot.plot( + frequency=array([]), + magnitude=array([]), + weight=array([]), + labels=("", "Weight"), + themes=(themes.zhit.magnitude, themes.zhit.weight), + show_labels=True, + ) + self.plot.plot_window( + center=self.settings.window_center, + width=self.settings.window_width, + label="Window", + theme=themes.zhit.window, + ) + + def cycle(self, index: Optional[int] = None, step: Optional[int] = None): + i: int + checkbox: int + if index is not None: + i = index + elif step is not None: + for i, checkbox in enumerate(self.checkboxes.values()): + if dpg.get_value(checkbox) is True: + break + else: + return + i += step + else: + return + enums: List[ZHITWindow] = list(self.checkboxes.keys()) + enum: ZHITWindow = enums[i % len(enums)] + dpg.set_value(self.checkboxes[enum], True) + self.update_preview(enum, flag=True) + + def update_preview(self, window: ZHITWindow, flag: bool = True): + enum: ZHITWindow + checkbox: int + if self.previous_choice is None: + self.previous_choice = window + elif flag is True: + for enum, checkbox in self.checkboxes.items(): + if enum != window and dpg.get_value(checkbox) is True: + self.previous_choice = enum + dpg.set_value(checkbox, window == enum) + elif flag is False: + window = self.previous_choice + dpg.set_value(self.checkboxes[window], True) + self.plot.update( + 1, + frequency=self.data.get_frequencies(), + magnitude=array([]), + weight=self.weights[window], + ) + self.plot.queue_limits_adjustment() + + def close(self): + dpg.hide_item(self.window) + dpg.delete_item(self.window) + self.keybinding_handler.delete() + signals.emit(Signal.UNBLOCK_KEYBINDINGS) diff --git a/src/deareis/keybindings/__init__.py b/src/deareis/keybindings/__init__.py index 39b6755..9ed3e15 100644 --- a/src/deareis/keybindings/__init__.py +++ b/src/deareis/keybindings/__init__.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -18,13 +18,33 @@ # the LICENSES folder. from traceback import format_exc -from typing import List, Optional, Set +from typing import ( + Callable, + Dict, + List, + Optional, + Set, +) import dearpygui.dearpygui as dpg import deareis.signals as signals from deareis.signals import Signal -from .keybinding import Keybinding, dpg_to_string -from deareis.enums import Action, Context, action_contexts -from deareis.data import DataSet, TestResult, FitResult, SimulationResult +from .keybinding import ( + Keybinding, + dpg_to_string, +) +from deareis.enums import ( + Action, + Context, + action_contexts, +) +from deareis.data import ( + DRTResult, + DataSet, + FitResult, + SimulationResult, + TestResult, + ZHITResult, +) def is_shift_down() -> bool: @@ -43,6 +63,25 @@ def is_alt_down() -> bool: return dpg.is_key_down(dpg.mvKey_Alt) +def filter_keybindings(key: int, keybindings: List[Keybinding]) -> List[Keybinding]: + filtered_keybindings: List[Keybinding] = [] + is_alt_pressed: bool = is_alt_down() + is_control_pressed: bool = is_control_down() + is_shift_pressed: bool = is_shift_down() + kb: Keybinding + for kb in keybindings: + if key not in kb: + continue + if kb.mod_alt is not is_alt_pressed: + continue + if kb.mod_ctrl is not is_control_pressed: + continue + if kb.mod_shift is not is_shift_pressed: + continue + filtered_keybindings.append(kb) + return filtered_keybindings + + class KeybindingHandler: def __init__(self, keybindings: List[Keybinding], state): self.block_events: bool = False @@ -88,39 +127,10 @@ def process(self, key: int): signals.emit(Signal.UNBLOCK_KEYBINDINGS) else: return - filtered_keybindings: List[Keybinding] = list( - filter(lambda _: key in _, self.keybindings) + filtered_keybindings: List[Keybinding] = filter_keybindings( + key=key, + keybindings=self.keybindings, ) - if not filtered_keybindings: - return - if is_alt_down(): - filtered_keybindings = list( - filter(lambda _: _.mod_alt is True, filtered_keybindings) - ) - else: - filtered_keybindings = list( - filter(lambda _: _.mod_alt is False, filtered_keybindings) - ) - if not filtered_keybindings: - return - if is_control_down(): - filtered_keybindings = list( - filter(lambda _: _.mod_ctrl is True, filtered_keybindings) - ) - else: - filtered_keybindings = list( - filter(lambda _: _.mod_ctrl is False, filtered_keybindings) - ) - if not filtered_keybindings: - return - if is_shift_down(): - filtered_keybindings = list( - filter(lambda _: _.mod_shift is True, filtered_keybindings) - ) - else: - filtered_keybindings = list( - filter(lambda _: _.mod_shift is False, filtered_keybindings) - ) if not filtered_keybindings: return project = self.state.get_active_project() # Optional[Project] @@ -167,7 +177,7 @@ def process(self, key: int): def validate_keybindings(self, keybindings: List[Keybinding]): assert len(set(list(map(str, keybindings)))) == 1, ( "The same keybinding has been applied to multiple actions:\n- " - + "\n- ".join(list(map(str, keybindings))) + + "\n- ".join(list(map(repr, keybindings))) ) def perform_action( @@ -238,6 +248,8 @@ def perform_action( project_tab.select_data_sets_tab() elif action == Action.SELECT_KRAMERS_KRONIG_TAB: project_tab.select_kramers_kronig_tab() + elif action == Action.SELECT_ZHIT_TAB: + project_tab.select_zhit_tab() elif action == Action.SELECT_DRT_TAB: project_tab.select_drt_tab() elif action == Action.SELECT_FITTING_TAB: @@ -247,6 +259,27 @@ def perform_action( elif action == Action.SELECT_PLOTTING_TAB: project_tab.select_plotting_tab() # Project-level: multiple tabs + elif action == Action.BATCH_PERFORM_ACTION: + if context == Context.KRAMERS_KRONIG_TAB: + signals.emit( + Signal.BATCH_PERFORM_ANALYSIS, + settings=project_tab.get_test_settings(), + ) + elif context == Context.ZHIT_TAB: + signals.emit( + Signal.BATCH_PERFORM_ANALYSIS, + settings=project_tab.get_zhit_settings(), + ) + elif context == Context.DRT_TAB: + signals.emit( + Signal.BATCH_PERFORM_ANALYSIS, + settings=project_tab.get_drt_settings(), + ) + elif context == Context.FITTING_TAB: + signals.emit( + Signal.BATCH_PERFORM_ANALYSIS, + settings=project_tab.get_fit_settings(), + ) elif action == Action.PERFORM_ACTION: if context == Context.DATA_SETS_TAB: signals.emit(Signal.SELECT_DATA_SET_FILES) @@ -255,7 +288,13 @@ def perform_action( Signal.PERFORM_TEST, data=project_tab.get_active_data_set(), settings=project_tab.get_test_settings(), - ), + ) + elif context == Context.ZHIT_TAB: + signals.emit( + Signal.PERFORM_ZHIT, + data=project_tab.get_active_data_set(), + settings=project_tab.get_zhit_settings(), + ) elif context == Context.DRT_TAB: signals.emit( Signal.PERFORM_DRT, @@ -267,13 +306,13 @@ def perform_action( Signal.PERFORM_FIT, data=project_tab.get_active_data_set(), settings=project_tab.get_fit_settings(), - ), + ) elif context == Context.SIMULATION_TAB: signals.emit( Signal.PERFORM_SIMULATION, data=project_tab.get_active_data_set(), settings=project_tab.get_simulation_settings(), - ), + ) elif context == Context.PLOTTING_TAB: signals.emit(Signal.NEW_PLOT_SETTINGS) # - Create plot @@ -289,6 +328,12 @@ def perform_action( test=project_tab.get_active_test(), data=project_tab.get_active_data_set(), ) + elif context == Context.ZHIT_TAB: + signals.emit( + Signal.DELETE_ZHIT_RESULT, + zhit=project_tab.get_active_zhit(), + data=project_tab.get_active_data_set(), + ) elif context == Context.DRT_TAB: signals.emit( Signal.DELETE_DRT_RESULT, @@ -317,6 +362,7 @@ def perform_action( if ( context == Context.DATA_SETS_TAB or context == Context.KRAMERS_KRONIG_TAB + or context == Context.ZHIT_TAB or context == Context.DRT_TAB or context == Context.FITTING_TAB ): @@ -340,6 +386,7 @@ def perform_action( if ( context == Context.DATA_SETS_TAB or context == Context.KRAMERS_KRONIG_TAB + or context == Context.ZHIT_TAB or context == Context.DRT_TAB or context == Context.FITTING_TAB ): @@ -366,6 +413,12 @@ def perform_action( test=project_tab.get_next_test_result(), data=project_tab.get_active_data_set(), ) + if context == Context.ZHIT_TAB: + signals.emit( + Signal.SELECT_ZHIT_RESULT, + zhit=project_tab.get_next_zhit_result(), + data=project_tab.get_active_data_set(), + ) elif context == Context.DRT_TAB: signals.emit( Signal.SELECT_DRT_RESULT, @@ -385,11 +438,7 @@ def perform_action( data=project_tab.get_active_data_set(), ) elif context == Context.PLOTTING_TAB: - signals.emit( - Signal.SELECT_PLOT_TYPE, - settings=project_tab.get_active_plot(), - plot_type=project_tab.get_next_plot_type(), - ) + project_tab.plotting_tab.next_series_tab() # - Plot type elif action == Action.PREVIOUS_SECONDARY_RESULT: if context == Context.KRAMERS_KRONIG_TAB: @@ -398,6 +447,12 @@ def perform_action( test=project_tab.get_previous_test_result(), data=project_tab.get_active_data_set(), ) + if context == Context.ZHIT_TAB: + signals.emit( + Signal.SELECT_ZHIT_RESULT, + zhit=project_tab.get_previous_zhit_result(), + data=project_tab.get_active_data_set(), + ) elif context == Context.DRT_TAB: signals.emit( Signal.SELECT_DRT_RESULT, @@ -416,19 +471,54 @@ def perform_action( simulation=project_tab.get_previous_simulation_result(), data=project_tab.get_active_data_set(), ) + elif context == Context.PLOTTING_TAB: + project_tab.plotting_tab.previous_series_tab() + # - Plot type + elif action == Action.NEXT_PLOT_TAB: + if context in ( + Context.DATA_SETS_TAB, + Context.KRAMERS_KRONIG_TAB, + Context.ZHIT_TAB, + Context.DRT_TAB, + Context.FITTING_TAB, + Context.SIMULATION_TAB, + ): + project_tab.next_plot_tab(context) + elif context == Context.PLOTTING_TAB: + signals.emit( + Signal.SELECT_PLOT_TYPE, + settings=project_tab.get_active_plot(), + plot_type=project_tab.get_next_plot_type(), + ) + elif action == Action.PREVIOUS_PLOT_TAB: + if context in ( + Context.DATA_SETS_TAB, + Context.KRAMERS_KRONIG_TAB, + Context.ZHIT_TAB, + Context.DRT_TAB, + Context.FITTING_TAB, + Context.SIMULATION_TAB, + ): + project_tab.previous_plot_tab(context) elif context == Context.PLOTTING_TAB: signals.emit( Signal.SELECT_PLOT_TYPE, settings=project_tab.get_active_plot(), plot_type=project_tab.get_previous_plot_type(), ) - # - Plot type elif action == Action.LOAD_SIMULATION_AS_DATA_SET: if context == Context.SIMULATION_TAB: signals.emit( Signal.LOAD_SIMULATION_AS_DATA_SET, simulation=project_tab.get_active_simulation(), ) + elif action == Action.LOAD_ZHIT_AS_DATA_SET: + if context == Context.ZHIT_TAB: + signals.emit( + Signal.LOAD_ZHIT_AS_DATA_SET, + zhit=project_tab.get_active_zhit(), + data=project_tab.get_active_data_set(), + ) elif action == Action.APPLY_SETTINGS: if context == Context.KRAMERS_KRONIG_TAB: test = project_tab.get_active_test() @@ -436,6 +526,12 @@ def perform_action( Signal.APPLY_TEST_SETTINGS, settings=test.settings if test is not None else None, ) + if context == Context.ZHIT_TAB: + zhit = project_tab.get_active_zhit() + signals.emit( + Signal.APPLY_ZHIT_SETTINGS, + settings=zhit.settings if zhit is not None else None, + ) elif context == Context.DRT_TAB: drt = project_tab.get_active_drt() signals.emit( @@ -467,6 +563,14 @@ def perform_action( test=test, data=project_tab.get_active_data_set(), ) + elif context == Context.ZHIT_TAB: + zhit = project_tab.get_active_zhit() + signals.emit( + Signal.APPLY_DATA_SET_MASK, + mask=zhit.mask if zhit is not None else None, + zhit=zhit, + data=project_tab.get_active_data_set(), + ) elif context == Context.DRT_TAB: drt = project_tab.get_active_drt() signals.emit( @@ -483,6 +587,12 @@ def perform_action( fit=fit, data=project_tab.get_active_data_set(), ) + elif action == Action.PREVIEW_ZHIT_WEIGHTS: + if context == Context.ZHIT_TAB: + signals.emit( + Signal.PREVIEW_ZHIT_WEIGHTS, + settings=project_tab.get_zhit_settings(), + ) elif action == Action.SHOW_ENLARGED_DRT: project_tab.show_enlarged_drt() elif action == Action.SHOW_ENLARGED_IMPEDANCE: @@ -493,11 +603,22 @@ def perform_action( project_tab.show_enlarged_bode() elif action == Action.SHOW_ENLARGED_RESIDUALS: project_tab.show_enlarged_residuals() + elif action == Action.DUPLICATE_PLOT: + if context == Context.PLOTTING_TAB: + signals.emit( + Signal.DUPLICATE_PLOT_SETTINGS, + settings=project_tab.get_active_plot(), + ) elif action == Action.SHOW_CIRCUIT_EDITOR: if context == Context.FITTING_TAB: project_tab.fitting_tab.show_circuit_editor() elif context == Context.SIMULATION_TAB: project_tab.simulation_tab.show_circuit_editor() + elif action == Action.ADJUST_PARAMETERS: + if context == Context.FITTING_TAB: + project_tab.fitting_tab.show_parameter_adjustment() + elif context == Context.SIMULATION_TAB: + project_tab.simulation_tab.show_parameter_adjustment() elif action == Action.COPY_DRT_DATA: signals.emit( Signal.COPY_PLOT_DATA, @@ -534,18 +655,21 @@ def perform_action( Signal.COPY_OUTPUT, output=project_tab.drt_tab.get_active_output(), drt=project_tab.get_active_drt(), + data=project_tab.get_active_data_set(), ) elif context == Context.FITTING_TAB: signals.emit( Signal.COPY_OUTPUT, output=project_tab.get_active_output(context), fit_or_sim=project_tab.get_active_fit(), + data=project_tab.get_active_data_set(), ) elif context == Context.SIMULATION_TAB: signals.emit( Signal.COPY_OUTPUT, output=project_tab.get_active_output(context), fit_or_sim=project_tab.get_active_simulation(), + data=project_tab.get_active_data_set(), ) else: raise Exception(f"Unsupported context: {context=}") @@ -566,6 +690,11 @@ def perform_action( Signal.SELECT_DATA_SET_MASK_TO_COPY, data=data, ) + elif action == Action.INTERPOLATE_POINTS: + signals.emit( + Signal.SELECT_POINTS_TO_INTERPOLATE, + data=data, + ) elif action == Action.SUBTRACT_IMPEDANCE: signals.emit( Signal.SELECT_IMPEDANCE_TO_SUBTRACT, @@ -576,12 +705,16 @@ def perform_action( # Project-level: plotting tab data_sets: List[DataSet] tests: List[TestResult] + zhits: List[ZHITResult] + drts: List[DRTResult] fits: List[FitResult] simulations: List[SimulationResult] if action == Action.SELECT_ALL_PLOT_SERIES: ( data_sets, tests, + zhits, + drts, fits, simulations, ) = project_tab.get_filtered_plot_series() @@ -590,6 +723,8 @@ def perform_action( enabled=True, data_sets=data_sets, tests=tests, + zhits=zhits, + drts=drts, fits=fits, simulations=simulations, settings=settings, @@ -598,6 +733,8 @@ def perform_action( ( data_sets, tests, + zhits, + drts, fits, simulations, ) = project_tab.get_filtered_plot_series() @@ -606,6 +743,8 @@ def perform_action( enabled=False, data_sets=data_sets, tests=tests, + zhits=zhits, + drts=drts, fits=fits, simulations=simulations, settings=settings, @@ -630,3 +769,59 @@ def perform_action( Signal.EXPORT_PLOT, settings=settings, ) + + +class TemporaryKeybindingHandler: + def __init__(self, callbacks: Dict[Keybinding, Callable] = {}): + self._blocked: bool = False + self.callbacks: Dict[Keybinding, Callable] = callbacks + self.key_handler: int = dpg.generate_uuid() + with dpg.handler_registry(tag=self.key_handler): + registered_keys: Set[int] = set() + kb: Keybinding + for kb in callbacks: + if kb.key in registered_keys: + continue + registered_keys.add(kb.key) + dpg.add_key_release_handler( + key=kb.key, + callback=lambda s, a, u: self.process(a), + ) + + def delete(self): + if self.key_handler > 0 and dpg.does_item_exist(self.key_handler): + dpg.delete_item(self.key_handler) + + def block(self): + self._blocked = True + + def unblock(self): + self._blocked = False + + def process(self, key: int): + if self._blocked is True: + return + filtered_keybindings: List[Keybinding] = filter_keybindings( + key=key, + keybindings=list(self.callbacks.keys()), + ) + if not filtered_keybindings: + return + try: + self.validate_keybindings( + {kb: self.callbacks[kb] for kb in filtered_keybindings} + ) + self.callbacks[filtered_keybindings[0]]() + except Exception: + signals.emit(Signal.SHOW_ERROR_MESSAGE, traceback=format_exc()) + + def validate_keybindings(self, callbacks: Dict[Keybinding, Callable]): + assert len(set(list(map(str, callbacks.keys())))) == 1, ( + "The same keybinding has been applied to multiple actions:\n- " + + "\n- ".join(list(map(str, callbacks.keys()))) + ) + assert ( + len(set(callbacks.values())) == 1 + ), "There are multiple possible actions to perform:\n- " + "\n- ".join( + list(map(repr, callbacks.values())) + ) diff --git a/src/deareis/keybindings/keybinding.py b/src/deareis/keybindings/keybinding.py index 77681f5..b619f9b 100644 --- a/src/deareis/keybindings/keybinding.py +++ b/src/deareis/keybindings/keybinding.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -17,6 +17,7 @@ # The licenses of DearEIS' dependencies and/or sources of portions of code are included in # the LICENSES folder. +from hashlib import sha1 from typing import Dict from dataclasses import dataclass import dearpygui.dearpygui as dpg @@ -181,6 +182,12 @@ class Keybinding: mod_shift: bool action: Action + def __post_init__(self): + self._hash: int = int(sha1(repr(self).encode()).hexdigest(), 16) + + def __hash__(self) -> int: + return self._hash + def __contains__(self, key: int) -> bool: assert key in dpg_to_string, key if self.key == key: diff --git a/src/deareis/program/__init__.py b/src/deareis/program/__init__.py index d77e0c2..f9e851f 100644 --- a/src/deareis/program/__init__.py +++ b/src/deareis/program/__init__.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -51,6 +51,7 @@ import webbrowser from pyimpspec import ( Circuit, + Element, ) import pyimpspec from pandas import DataFrame @@ -73,11 +74,13 @@ rename_project, modify_project_notes, ) +from .batch_analysis import select_batch_data_sets from .data_sets import ( apply_data_set_mask, delete_data_set, load_data_set_files, load_simulation_as_data_set, + load_zhit_as_data_set, modify_data_set_path, rename_data_set, select_data_points_to_toggle, @@ -86,6 +89,7 @@ select_data_set_mask_to_copy, select_data_sets_to_average, select_impedance_to_subtract, + select_points_to_interpolate, toggle_data_point, ) from .kramers_kronig import ( @@ -94,6 +98,13 @@ perform_test, select_test_result, ) +from .zhit import ( + apply_zhit_settings, + delete_zhit_result, + perform_zhit, + preview_zhit_weights, + select_zhit_result, +) from .drt import ( apply_drt_settings, delete_drt_result, @@ -115,6 +126,7 @@ from .plotting import ( copy_plot_appearance_settings, delete_plot_settings, + duplicate_plot_settings, export_plot, modify_plot_series_theme, new_plot_settings, @@ -128,6 +140,7 @@ toggle_plot_series, ) from .check_updates import perform_update_check +from deareis.gui.about import show_help_about from deareis.gui.plots import show_modal_plot_window from deareis.gui.changelog import show_changelog from deareis.enums import ( @@ -157,6 +170,10 @@ from deareis.state import STATE from deareis.utility import ( calculate_window_position_dimensions, + format_latex_element, + format_latex_element as format_latex_parameter, + format_latex_unit, + format_latex_value, pad_dataframe_dictionary, ) from deareis.gui.plots import ( @@ -176,6 +193,8 @@ AppearanceSettings, KeybindingRemapping, show_defaults_settings_window, + show_user_defined_elements_window, + refresh_user_defined_elements, ) import deareis.themes as themes from deareis.version import PACKAGE_VERSION @@ -236,10 +255,11 @@ def copy_output(*args, **kwargs): elif output == FitSimOutput.CDC_EXTENDED: clipboard_content = fit_or_sim.circuit.to_string(6) elif output == FitSimOutput.CSV_DATA_TABLE: - Z_fit_or_sim: ndarray = fit_or_sim.get_impedance() + Z_fit_or_sim: ndarray = fit_or_sim.get_impedances() dictionary: dict = {} if type(fit_or_sim) is FitResult: - Z_exp: ndarray = data.get_impedance(masked=None) + assert data is not None + Z_exp: ndarray = data.get_impedances(masked=None) indices: ndarray = array( [ _ @@ -248,17 +268,17 @@ def copy_output(*args, **kwargs): ] ) dictionary = { - "f (Hz)": fit_or_sim.get_frequency(), - "Zre_exp (ohm)": Z_exp[indices].real, - "Zim_exp (ohm)": Z_exp[indices].imag, - "Zre_fit (ohm)": Z_fit_or_sim.real, - "Zim_fit (ohm)": Z_fit_or_sim.imag, + "f (Hz)": fit_or_sim.get_frequencies(), + "Re(Z) (ohm) - Data": Z_exp[indices].real, + "Im(Z) (ohm) - Data": Z_exp[indices].imag, + "Re(Z) (ohm) - Fit": Z_fit_or_sim.real, + "Im(Z) (ohm) - Fit": Z_fit_or_sim.imag, } else: dictionary = { - "f (Hz)": fit_or_sim.get_frequency(), - "Zre_sim (ohm)": Z_fit_or_sim.real, - "Zim_sim (ohm)": Z_fit_or_sim.imag, + "f (Hz)": fit_or_sim.get_frequencies(), + "Re(Z) (ohm) - Sim.": Z_fit_or_sim.real, + "Im(Z) (ohm) - Sim.": Z_fit_or_sim.imag, } if dictionary: dataframe = DataFrame.from_dict(dictionary) @@ -269,7 +289,7 @@ def copy_output(*args, **kwargs): or output == FitSimOutput.LATEX_PARAMETERS_TABLE or output == FitSimOutput.MARKDOWN_PARAMETERS_TABLE ): - dataframe = fit_or_sim.to_dataframe() + dataframe = fit_or_sim.to_parameters_dataframe() if output == FitSimOutput.CSV_PARAMETERS_TABLE: clipboard_content = dataframe.to_csv(index=False) elif output == FitSimOutput.JSON_PARAMETERS_TABLE: @@ -278,8 +298,11 @@ def copy_output(*args, **kwargs): clipboard_content = ( dataframe.style.format( { - "Value": "{:.3g}", - "Std. err. (%)": "{:.3g}", + "Element": format_latex_element, + "Parameter": format_latex_parameter, + "Value": format_latex_value, + "Std. err. (%)": format_latex_value, + "Unit": format_latex_unit, } ) .format_index(axis="columns", escape="latex") @@ -291,18 +314,35 @@ def copy_output(*args, **kwargs): index=False, floatfmt=".3g", ) + elif ( + output == FitSimOutput.CSV_STATISTICS_TABLE + or output == FitSimOutput.JSON_STATISTICS_TABLE + or output == FitSimOutput.LATEX_STATISTICS_TABLE + or output == FitSimOutput.MARKDOWN_STATISTICS_TABLE + ): + dataframe = fit_or_sim.to_statistics_dataframe() + if output == FitSimOutput.CSV_STATISTICS_TABLE: + clipboard_content = dataframe.to_csv(index=False) + elif output == FitSimOutput.JSON_STATISTICS_TABLE: + clipboard_content = dataframe.to_json() + elif output == FitSimOutput.LATEX_STATISTICS_TABLE: + clipboard_content = ( + dataframe.style.format({"Value": format_latex_value}) + .format_index(axis="columns", escape="latex") + .hide(axis="index") + .to_latex(hrules=True) + ) + elif output == FitSimOutput.MARKDOWN_STATISTICS_TABLE: + clipboard_content = dataframe.to_markdown( + index=False, + floatfmt=".3g", + ) elif output == FitSimOutput.LATEX_DIAGRAM: clipboard_content = fit_or_sim.circuit.to_circuitikz() elif output == FitSimOutput.SVG_DIAGRAM: clipboard_content = ( fit_or_sim.circuit.to_drawing().get_imagedata(fmt="svg").decode() ) - elif output == FitSimOutput.SVG_DIAGRAM_NO_TERMINAL_LABELS: - clipboard_content = ( - fit_or_sim.circuit.to_drawing(working_label="", counter_label="") - .get_imagedata(fmt="svg") - .decode() - ) elif output == FitSimOutput.SVG_DIAGRAM_NO_LABELS: clipboard_content = ( fit_or_sim.circuit.to_drawing(hide_labels=True) @@ -325,30 +365,33 @@ def copy_output(*args, **kwargs): if len(symbols) == 0: clipboard_content = str(expr) else: - parameters = fit_or_sim.circuit.get_parameters() + # TODO: Update to work with latest version of pyimpspec + identifiers: Dict[int, Element] = { + v: k + for k, v in fit_or_sim.circuit.generate_element_identifiers( + running=True + ).items() + } lines.append( ", ".join(symbols) + " = sorted(expr.free_symbols, key=str)" ) lines.append("parameters = {") if "f" in symbols: symbols.remove("f") + assert len(symbols) == sum( + map(lambda _: len(_.get_values()), identifiers.values()) + ) sym: str for sym in symbols: assert "_" in sym ident: Union[int, str] - label, ident = sym.split("_") + sym, ident = sym.rsplit("_", 1) value: Optional[float] = None - try: - ident = int(label) - assert ident in parameters - value = parameters[ident][label] - except ValueError: - for element in fit_or_sim.circuit.get_elements(): - if not element.get_label().endswith(f"_{ident}"): - continue - value = element.get_parameters().get(label) + ident = int(ident) + assert ident in identifiers + value = identifiers[ident].get_value(sym) assert value is not None - lines.append(f"\t{sym}: {value:.6E},") + lines.append(f"\t{sym}_{ident}: {value:.6E},") lines.append("}") clipboard_content = "\n".join(lines) else: @@ -365,8 +408,24 @@ def copy_output(*args, **kwargs): or output == DRTOutput.LATEX_SCORES or output == DRTOutput.MARKDOWN_SCORES ): - score_dataframe: Optional[DataFrame] = drt.get_score_dataframe( - latex_labels=output == DRTOutput.LATEX_SCORES + score_dataframe: Optional[DataFrame] = drt.to_scores_dataframe( + columns=None + if output != DRTOutput.LATEX_SCORES + else [ + "Score", + r"Real (\%)", + r"Imag. (\%)", + ], + rows=None + if output != DRTOutput.LATEX_SCORES + else [ + r"$s_\mu$", + r"$s_{1\sigma}$", + r"$s_{2\sigma}$", + r"$s_{3\sigma}$", + r"$s_{\rm HD}$", + r"$s_{\rm JSD}$", + ], ) if score_dataframe is not None: if output == DRTOutput.CSV_SCORES: @@ -377,8 +436,8 @@ def copy_output(*args, **kwargs): clipboard_content = ( score_dataframe.style.format( { - "Real (\%)": "{:.3g}", - "Imaginary (\%)": "{:.3g}", + r"Real (\%)": "{:.3g}", + r"Imaginary (\%)": "{:.3g}", } ) .hide(axis="index") @@ -391,8 +450,7 @@ def copy_output(*args, **kwargs): ) else: raise Exception(f"Unsupported output type: {type(output)}") - if clipboard_content != "": - dpg.set_clipboard_text(clipboard_content) + dpg.set_clipboard_text(clipboard_content) signals.emit(Signal.HIDE_BUSY_MESSAGE) @@ -519,8 +577,8 @@ def copy_plot_data(*args, **kwargs): label += " " key = f"f (Hz) - {label}" dictionary[key] = series["frequency"] - dictionary[f"|Z| (ohm) - {label}"] = series["magnitude"] - dictionary[f"-phi (°) - {label}"] = series["phase"] + dictionary[f"Mod(Z) (ohm) - {label}"] = series["magnitude"] + dictionary[f"-Phase(Z) (°) - {label}"] = series["phase"] elif type(plot) is Nyquist: for series in plot.get_series(): label = "Data" @@ -535,12 +593,12 @@ def copy_plot_data(*args, **kwargs): label += " (line)" else: label += " (scatter)" - key = f"Zre (ohm) - {label}" + key = f"Re(Z) (ohm) - {label}" while key in dictionary: label += " " - key = f"Zre (ohm) - {label}" + key = f"Re(Z) (ohm) - {label}" dictionary[key] = series["real"] - dictionary[f"-Zim (ohm) - {label}"] = series["imaginary"] + dictionary[f"-Im(Z) (ohm) - {label}"] = series["imaginary"] elif type(plot) is BodeMagnitude: for series in plot.get_series(): label = "Data" @@ -560,7 +618,7 @@ def copy_plot_data(*args, **kwargs): label += " " key = f"f (Hz) - {label}" dictionary[key] = series["frequency"] - dictionary[f"|Z| (ohm) - {label}"] = series["magnitude"] + dictionary[f"Mod(Z) (ohm) - {label}"] = series["magnitude"] elif type(plot) is BodePhase: for series in plot.get_series(): label = "Data" @@ -580,41 +638,35 @@ def copy_plot_data(*args, **kwargs): label += " " key = f"f (Hz) - {label}" dictionary[key] = series["frequency"] - dictionary[f"-phi (°) - {label}"] = series["phase"] + dictionary[f"-Phase(Z) (°) - {label}"] = series["phase"] elif type(plot) is Residuals: for series in plot.get_series(): dictionary["f (Hz)"] = series["frequency"] - dictionary["real error (%)"] = series["real"] - dictionary["imaginary error (%)"] = series["imaginary"] + dictionary["real_error (%)"] = series["real"] + dictionary["imag_error (%)"] = series["imaginary"] elif type(plot) is DRT: for series in plot.get_series(): - label = "Data" - if context != Context.PLOTTING_TAB: - if series.get("simulation", False): - label = "Sim." - elif series.get("fit", False): - label = "Fit" + label = series.get("label") + if label == "gamma": + label = "" else: - label = series.get("label") or "" - key = f"tau (s) - {label}" - while key in dictionary: - label += " " - key = f"tau (s) - {label}" - dictionary[key] = series["tau"] - if "gamma" in series: - dictionary[f"gamma (ohm) - {label}"] = series["gamma"] - elif "imaginary" in series: - if f"gamma (ohm) - {label}" in dictionary: - dictionary[f"gamma, real (ohm) - {label}"] = dictionary[ - "gamma (ohm) - {label}" - ] - del dictionary[f"gamma (ohm) - {label}"] - dictionary[f"gamma, imag. (ohm) - {label}"] = series["imaginary"] + label = label.capitalize() + if label == "": + suffix = "" + else: + suffix = f" - {label}" + dictionary[f"tau (s){suffix}"] = series["tau"] + if "imaginary" in series: + if "gamma" in series: + dictionary[f"gamma_real (ohm){suffix}"] = series["gamma"] + dictionary[f"gamma_imag (ohm){suffix}"] = series["imaginary"] elif "mean" in series: - dictionary[f"gamma, mean (ohm) - {label}"] = series["mean"] + dictionary[f"gamma_mean (ohm){suffix}"] = series["mean"] elif "lower" in series and "upper" in series: - dictionary[f"gamma, lower bound (ohm) - {label}"] = series["lower"] - dictionary[f"gamma, upper bound (ohm) - {label}"] = series["lower"] + dictionary[f"gamma_lower (ohm){suffix}"] = series["lower"] + dictionary[f"gamma_upper (ohm){suffix}"] = series["lower"] + elif "gamma" in series: + dictionary[f"gamma (ohm){suffix}"] = series["gamma"] elif type(plot) is Impedance: for series in plot.get_series(): label = "Data" @@ -625,13 +677,9 @@ def copy_plot_data(*args, **kwargs): label = "Fit" else: label = series.get("label") or "" - key = f"f (Hz) - {label}" - while key in dictionary: - label += " " - key = f"f (Hz) - {label}" - dictionary[key] = series["frequency"] - dictionary["Zre (ohm)"] = series["real"] - dictionary["-Zim (ohm)"] = series["imaginary"] + dictionary[f"f (Hz) - {label}"] = series["frequency"] + dictionary[f"Re(Z) (ohm) - {label}"] = series["real"] + dictionary[f"-Im(Z) (ohm) - {label}"] = series["imaginary"] elif type(plot) is ImpedanceReal or type(plot) is ImpedanceImaginary: for series in plot.get_series(): label = "Data" @@ -648,9 +696,9 @@ def copy_plot_data(*args, **kwargs): key = f"f (Hz) - {label}" dictionary[key] = series["x"] if type(plot) is ImpedanceReal: - dictionary[f"Zre (ohm) - {label}"] = series["y"] + dictionary[f"Re(Z) (ohm) - {label}"] = series["y"] else: - dictionary[f"-Zim (ohm) - {label}"] = series["y"] + dictionary[f"-Im(Z) (ohm) - {label}"] = series["y"] padded_dictionary: Optional[dict] = pad_dataframe_dictionary(dictionary) if padded_dictionary is None: dpg.set_clipboard_text("") @@ -660,59 +708,6 @@ def copy_plot_data(*args, **kwargs): ) -def show_help_about(*args, **kwargs): - x: int - y: int - w: int - h: int - x, y, w, h = calculate_window_position_dimensions(270, 100) - window: int = dpg.generate_uuid() - key_handler: int = dpg.generate_uuid() - - def close_window(): - if dpg.does_item_exist(window): - dpg.delete_item(window) - if dpg.does_item_exist(key_handler): - dpg.delete_item(key_handler) - signals.emit(Signal.UNBLOCK_KEYBINDINGS) - - with dpg.handler_registry(tag=key_handler): - dpg.add_key_release_handler( - key=dpg.mvKey_Escape, - callback=close_window, - ) - - with dpg.window( - label="About", - modal=True, - pos=( - x, - y, - ), - width=w, - height=h, - no_resize=True, - on_close=close_window, - tag=window, - ): - dpg.add_text(f"DearEIS ({PACKAGE_VERSION})") - url: str - for url in [ - "https://vyrjana.github.io/DearEIS", - "https://github.com/vyrjana/DearEIS", - ]: - dpg.bind_item_theme( - dpg.add_button( - label=url, - callback=lambda s, a, u: webbrowser.open(u), - user_data=url, - width=-1, - ), - themes.url_theme, - ) - signals.emit(Signal.BLOCK_KEYBINDINGS, window=window, window_object=None) - - def restore_unsaved_project_snapshots(): parsing_errors: Dict[str, str] = {} unsaved_project_snapshots: List[str] = STATE.get_unsaved_project_snapshots() @@ -752,6 +747,52 @@ def restore_unsaved_project_snapshots(): ) +def getting_started_window(): + window: int = dpg.generate_uuid() + + def resize(*args, **kwargs): + x: int + y: int + w: int + h: int + x, y, w, h = calculate_window_position_dimensions(640, 120) + dpg.configure_item( + window, + pos=( + x, + y, + ), + width=w, + height=h, + ) + + registration: int = signals.register(Signal.VIEWPORT_RESIZED, resize) + + def close(): + signals.unregister(Signal.VIEWPORT_RESIZED, resize) + + with dpg.window( + label="Getting started", + modal=False, + no_resize=True, + menubar=False, + autosize=False, + no_collapse=True, + on_close=close, + tag=window, + ): + dpg.add_text( + """ +If this is your first time using DearEIS, then you may wish to have a look at the set of short tutorials available online. The easiest way to find the tutorials is to go to the 'Help' menu and click 'Documentation'. + +A lot of useful information is presented in this program via tooltips that can be viewed by hovering the mouse cursor over labels, buttons, etc. + """.strip(), + wrap=620, + ) + dpg.split_frame() + resize() + + def initialize_program(args: Namespace): assert type(args) is Namespace signals.register(Signal.VIEWPORT_RESIZED, viewport_resized) @@ -788,7 +829,7 @@ def initialize_program(args: Namespace): lambda *a, **k: signals.emit( Signal.BLOCK_KEYBINDINGS, window=STATE.program_window.error_message.window, - window_object=None, + window_object=STATE.program_window.error_message, ), ) # Signals for showing/hiding the modal windows for error messages and for indicating when the @@ -811,6 +852,10 @@ def initialize_program(args: Namespace): signals.register( Signal.SHOW_SETTINGS_KEYBINDINGS, lambda: KeybindingRemapping(STATE) ) + signals.register( + Signal.SHOW_SETTINGS_USER_DEFINED_ELEMENTS, + lambda: show_user_defined_elements_window(state=STATE), + ) # Home tab state signals.register(Signal.SELECT_HOME_TAB, select_home_tab) STATE.set_recent_projects(paths=STATE.get_recent_projects()) @@ -843,6 +888,7 @@ def initialize_program(args: Namespace): signals.register(Signal.SELECT_DATA_POINTS_TO_TOGGLE, select_data_points_to_toggle) signals.register(Signal.SELECT_DATA_SET_MASK_TO_COPY, select_data_set_mask_to_copy) signals.register(Signal.SELECT_IMPEDANCE_TO_SUBTRACT, select_impedance_to_subtract) + signals.register(Signal.SELECT_POINTS_TO_INTERPOLATE, select_points_to_interpolate) signals.register(Signal.TOGGLE_DATA_POINT, toggle_data_point) signals.register(Signal.APPLY_DATA_SET_MASK, apply_data_set_mask) signals.register(Signal.LOAD_SIMULATION_AS_DATA_SET, load_simulation_as_data_set) @@ -851,6 +897,13 @@ def initialize_program(args: Namespace): signals.register(Signal.SELECT_TEST_RESULT, select_test_result) signals.register(Signal.DELETE_TEST_RESULT, delete_test_result) signals.register(Signal.APPLY_TEST_SETTINGS, apply_test_settings) + # Signals for the Z-HIT tab + signals.register(Signal.APPLY_ZHIT_SETTINGS, apply_zhit_settings) + signals.register(Signal.DELETE_ZHIT_RESULT, delete_zhit_result) + signals.register(Signal.PERFORM_ZHIT, perform_zhit) + signals.register(Signal.PREVIEW_ZHIT_WEIGHTS, preview_zhit_weights) + signals.register(Signal.SELECT_ZHIT_RESULT, select_zhit_result) + signals.register(Signal.LOAD_ZHIT_AS_DATA_SET, load_zhit_as_data_set) # Signals for the DRT tab signals.register(Signal.PERFORM_DRT, perform_drt) signals.register(Signal.SELECT_DRT_RESULT, select_drt_result) @@ -872,6 +925,7 @@ def initialize_program(args: Namespace): signals.register(Signal.SELECT_PLOT_SETTINGS, select_plot_settings) signals.register(Signal.SELECT_PLOT_TYPE, select_plot_type) signals.register(Signal.DELETE_PLOT_SETTINGS, delete_plot_settings) + signals.register(Signal.DUPLICATE_PLOT_SETTINGS, duplicate_plot_settings) signals.register(Signal.TOGGLE_PLOT_SERIES, toggle_plot_series) signals.register(Signal.RENAME_PLOT_SETTINGS, rename_plot_settings) signals.register(Signal.RENAME_PLOT_SERIES, rename_plot_series) @@ -885,6 +939,8 @@ def initialize_program(args: Namespace): ) signals.register(Signal.EXPORT_PLOT, export_plot) signals.register(Signal.SAVE_PLOT, save_plot) + # Miscellaneous + signals.register(Signal.BATCH_PERFORM_ANALYSIS, select_batch_data_sets) signals.register(Signal.CHECK_UPDATES, perform_update_check) signals.register(Signal.SHOW_CHANGELOG, show_changelog) dpg.split_frame(delay=100) @@ -892,9 +948,17 @@ def initialize_program(args: Namespace): dpg.get_viewport_width(), dpg.get_viewport_height(), ) - signals.emit(Signal.SHOW_BUSY_MESSAGE, message="Rendering assets...") + signals.emit(Signal.SHOW_BUSY_MESSAGE, message="Rendering assets") signals.emit(Signal.RENDER_MATH) signals.emit(Signal.HIDE_BUSY_MESSAGE) + signals.register( + Signal.REFRESH_USER_DEFINED_ELEMENTS, + refresh_user_defined_elements, + ) + signals.emit( + Signal.REFRESH_USER_DEFINED_ELEMENTS, + path=STATE.config.user_defined_elements_path, + ) # signals.register(Signal., ) if args.data_files: signals.emit(Signal.NEW_PROJECT, data=args.data_files) @@ -902,7 +966,15 @@ def initialize_program(args: Namespace): signals.emit(Signal.LOAD_PROJECT_FILES, paths=args.project_files) restore_unsaved_project_snapshots() signals.emit_backlog() + signals.register(Signal.SHOW_GETTING_STARTED_WINDOW, getting_started_window) STATE.check_version() + try: + STATE.config.validate_keybindings(STATE.config.keybindings) + except AssertionError: + signals.emit( + Signal.SHOW_ERROR_MESSAGE, + traceback=format_exc(), + ) def program_closing(): diff --git a/src/deareis/program/batch_analysis.py b/src/deareis/program/batch_analysis.py new file mode 100644 index 0000000..5c9662e --- /dev/null +++ b/src/deareis/program/batch_analysis.py @@ -0,0 +1,125 @@ +# DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). +# Copyright 2023 DearEIS developers +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . +# +# The licenses of DearEIS' dependencies and/or sources of portions of code are included in +# the LICENSES folder. + +from traceback import format_exc +from typing import ( + List, + Optional, + Tuple, + Union, +) +import dearpygui.dearpygui as dpg +from pyimpspec.exceptions import ( + KramersKronigError, + FittingError, + DRTError, + ZHITError, +) +from deareis.data import ( + DRTSettings, + DataSet, + FitSettings, + Project, + TestSettings, + ZHITSettings, +) +from .kramers_kronig import perform_test +from .zhit import perform_zhit +from .drt import perform_drt +from .fitting import perform_fit +from deareis.gui.batch_analysis import BatchAnalysis +from deareis.signals import Signal +import deareis.signals as signals +from deareis.state import STATE + + +Settings = Union[TestSettings, ZHITSettings, DRTSettings, FitSettings] + + +def batch_perform_analyses(data_sets: List[DataSet], settings: Settings): + errors: List[Tuple[DataSet, str]] = [] + kwargs = { + "settings": settings, + "batch": True, + } + data: DataSet + for data in data_sets: + if isinstance(settings, TestSettings): + try: + perform_test(data=data, **kwargs) + except (FittingError, KramersKronigError): + errors.append((data, format_exc())) + elif isinstance(settings, ZHITSettings): + try: + perform_zhit(data=data, **kwargs) + except ZHITError: + errors.append((data, format_exc())) + elif isinstance(settings, DRTSettings): + try: + perform_drt(data=data, **kwargs) + except DRTError: + errors.append((data, format_exc())) + elif isinstance(settings, FitSettings): + try: + perform_fit(data=data, **kwargs) + except FittingError: + errors.append((data, format_exc())) + dpg.split_frame(delay=60) + signals.emit(Signal.CREATE_PROJECT_SNAPSHOT) + if len(errors) == 0: + return + report: str = "Encountered error(s) while processing the following data sets:\n" + for (data, err) in errors: + report += f"- {data.get_label()}\n" + report += "\n" + err: str + for (data, err) in errors: + label: str = data.get_label() + report += f"\n{label}\n" + report += "-" * len(label) + "\n" + report += f"{err}\n\n" + signals.emit( + Signal.SHOW_ERROR_MESSAGE, + traceback=report, + message=f"Encountered {len(errors)} error(s) during batch analysis.", + ) + + +def select_batch_data_sets(*args, **kwargs): + settings: Optional[Settings] + settings = kwargs.get("settings") + if settings is None: + return + elif type(settings) not in (TestSettings, ZHITSettings, DRTSettings, FitSettings): + raise NotImplementedError(f"Unsupported setting: {type(settings)}") + project: Optional[Project] = STATE.get_active_project() + if project is None: + return + data_sets: List[DataSet] = project.get_data_sets() + if len(data_sets) == 0: + return + batch_window: BatchAnalysis = BatchAnalysis( + data_sets=data_sets, + callback=lambda d: batch_perform_analyses(data_sets=d, settings=settings), + ) + signals.emit( + Signal.BLOCK_KEYBINDINGS, + window=batch_window.window, + window_object=batch_window, + ) diff --git a/src/deareis/program/check_updates.py b/src/deareis/program/check_updates.py index 4064f66..5c01f24 100644 --- a/src/deareis/program/check_updates.py +++ b/src/deareis/program/check_updates.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -75,7 +75,7 @@ def perform_update_check(): message: str = f""" Already up-to-date! - PyPI: {pypi_version} +Available: {pypi_version} Installed: {PACKAGE_VERSION} """.strip() if not is_up_to_date(PACKAGE_VERSION, pypi_version): diff --git a/src/deareis/program/data_sets.py b/src/deareis/program/data_sets.py index d7b7501..16b4a9d 100644 --- a/src/deareis/program/data_sets.py +++ b/src/deareis/program/data_sets.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -24,8 +24,9 @@ List, Optional, ) -from pyimpspec.data.formats import UnsupportedFileFormat -from pyimpspec.data import get_parsers +import dearpygui.dearpygui as dpg +from pyimpspec.exceptions import UnsupportedFileFormat +from pyimpspec import get_parsers import pyimpspec from deareis.data import ( DRTResult, @@ -34,11 +35,13 @@ Project, SimulationResult, TestResult, + ZHITResult, ) from deareis.enums import Context from deareis.gui import ProjectTab from deareis.gui.data_sets.average_data_sets import AverageDataSets from deareis.gui.data_sets.copy_mask import CopyMask +from deareis.gui.data_sets.interpolate_points import InterpolatePoints from deareis.gui.data_sets.subtract_impedance import SubtractImpedance from deareis.gui.data_sets.toggle_data_points import ToggleDataPoints from deareis.gui.file_dialog import FileDialog @@ -58,6 +61,7 @@ def select_data_set(*args, **kwargs): data: Optional[DataSet] = kwargs.get("data") project_tab.select_data_set(data) project_tab.populate_tests(project, data) + project_tab.populate_zhits(project, data) project_tab.populate_drts(project, data) project_tab.populate_fits(project, data) project_tab.populate_simulations(project) @@ -65,12 +69,42 @@ def select_data_set(*args, **kwargs): signals.emit(Signal.HIDE_BUSY_MESSAGE) +def load_zhit_as_data_set(*args, **kwargs): + project: Optional[Project] = STATE.get_active_project() + project_tab: Optional[ProjectTab] = STATE.get_active_project_tab() + if project is None or project_tab is None: + return + zhit: Optional[ZHITResult] = kwargs.get("zhit") + data: Optional[DataSet] = kwargs.get("data") + if zhit is None or data is None: + return + signals.emit( + Signal.SHOW_BUSY_MESSAGE, + message="Loading Z-HIT analysis result as data set", + ) + new_data: DataSet = DataSet( + zhit.get_frequencies(), + zhit.get_impedances(), + mask={}, + label=f"{data.get_label()} - {zhit.get_label()}", + ) + project.add_data_set(new_data) + project_tab.populate_data_sets(project) + signals.emit(Signal.SELECT_DATA_SET, data=new_data) + signals.emit( + Signal.SELECT_PLOT_SETTINGS, + settings=project_tab.get_active_plot(), + ) + signals.emit(Signal.CREATE_PROJECT_SNAPSHOT) + signals.emit(Signal.HIDE_BUSY_MESSAGE) + + def load_simulation_as_data_set(*args, **kwargs): project: Optional[Project] = STATE.get_active_project() project_tab: Optional[ProjectTab] = STATE.get_active_project_tab() if project is None or project_tab is None: return - simulation: SimulationResult = kwargs.get("simulation") + simulation: Optional[SimulationResult] = kwargs.get("simulation") if simulation is None: return signals.emit( @@ -78,8 +112,8 @@ def load_simulation_as_data_set(*args, **kwargs): message="Loading simulation result as data set", ) data: DataSet = DataSet( - simulation.get_frequency(), - simulation.get_impedance(), + simulation.get_frequencies(), + simulation.get_impedances(), mask={}, label=simulation.get_label(), ) @@ -278,16 +312,22 @@ def apply_data_set_mask(*args, **kwargs): test: Optional[TestResult] = kwargs.get("test") drt: Optional[DRTResult] = kwargs.get("drt") fit: Optional[FitResult] = kwargs.get("fit") + zhit: Optional[ZHITResult] = kwargs.get("zhit") if test is not None: signals.emit(Signal.SELECT_TEST_RESULT, data=data, test=test) elif drt is not None: signals.emit(Signal.SELECT_DRT_RESULT, data=data, drt=drt) elif fit is not None: signals.emit(Signal.SELECT_FIT_RESULT, data=data, fit=fit) + elif zhit is not None: + signals.emit(Signal.SELECT_ZHIT_RESULT, data=data, zhit=zhit) signals.emit(Signal.CREATE_PROJECT_SNAPSHOT) def select_data_sets_to_average(*args, **kwargs): + if "popup" in kwargs: + dpg.hide_item(kwargs["popup"]) + dpg.split_frame(delay=33) project: Optional[Project] = STATE.get_active_project() project_tab: Optional[ProjectTab] = STATE.get_active_project_tab() if project is None or project_tab is None: @@ -366,37 +406,68 @@ def select_data_set_mask_to_copy(*args, **kwargs): def select_impedance_to_subtract(*args, **kwargs): + if "popup" in kwargs: + dpg.hide_item(kwargs["popup"]) + dpg.split_frame(delay=33) project: Optional[Project] = STATE.get_active_project() project_tab: Optional[ProjectTab] = STATE.get_active_project_tab() data: Optional[DataSet] = kwargs.get("data") if project is None or project_tab is None or data is None: return - def replace_data(new: DataSet): + def add_data(new: DataSet): assert project is not None assert project_tab is not None - assert data is not None - project.replace_data_set(data, new) + project.add_data_set(new) project_tab.populate_data_sets(project) signals.emit( - Signal.SELECT_PLOT_SETTINGS, settings=project_tab.get_active_plot() + Signal.SELECT_PLOT_SETTINGS, + settings=project_tab.get_active_plot(), ) signals.emit(Signal.SELECT_DATA_SET, data=new) - signals.emit( - Signal.SELECT_SIMULATION_RESULT, - simulation=project_tab.get_active_simulation(), - data=project_tab.get_active_data_set(context=Context.SIMULATION_TAB), - ) signals.emit(Signal.CREATE_PROJECT_SNAPSHOT) subtract_impedance_window: SubtractImpedance = SubtractImpedance( data=data, data_sets=project.get_data_sets(), fits=project.get_fits(data), - callback=replace_data, + callback=add_data, ) signals.emit( Signal.BLOCK_KEYBINDINGS, window=subtract_impedance_window.window, window_object=subtract_impedance_window, ) + + +def select_points_to_interpolate(*args, **kwargs): + if "popup" in kwargs: + dpg.hide_item(kwargs["popup"]) + dpg.split_frame(delay=33) + project: Optional[Project] = STATE.get_active_project() + project_tab: Optional[ProjectTab] = STATE.get_active_project_tab() + data: Optional[DataSet] = kwargs.get("data") + if project is None or project_tab is None or data is None: + return + + def add_data(new: DataSet): + assert project is not None + assert project_tab is not None + project.add_data_set(new) + project_tab.populate_data_sets(project) + signals.emit( + Signal.SELECT_PLOT_SETTINGS, + settings=project_tab.get_active_plot(), + ) + signals.emit(Signal.SELECT_DATA_SET, data=new) + signals.emit(Signal.CREATE_PROJECT_SNAPSHOT) + + interpolate_points: InterpolatePoints = InterpolatePoints( + data=data, + callback=add_data, + ) + signals.emit( + Signal.BLOCK_KEYBINDINGS, + window=interpolate_points.window, + window_object=interpolate_points, + ) diff --git a/src/deareis/program/drt.py b/src/deareis/program/drt.py index 44c84b1..df5e682 100644 --- a/src/deareis/program/drt.py +++ b/src/deareis/program/drt.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -17,11 +17,9 @@ # The licenses of DearEIS' dependencies and/or sources of portions of code are included in # the LICENSES folder. -from multiprocessing import cpu_count from typing import ( Optional, ) -from pyimpspec.analysis.drt.bht import _get_default_num_procs import deareis.api.drt as api from deareis.data import ( DRTResult, @@ -104,14 +102,12 @@ def perform_drt(*args, **kwargs): assert ( data.get_num_points() > 0 ), "There are no data points to use to calculate the distribution of relaxation times!" - num_procs: int = _get_default_num_procs() - if num_procs > 1 and num_procs == cpu_count(): - num_procs -= 1 + batch: bool = kwargs.get("batch", False) signals.emit(Signal.SHOW_BUSY_MESSAGE, message="Performing analysis") drt: DRTResult = api.calculate_drt( data=data, settings=settings, - num_procs=num_procs, + num_procs=STATE.config.num_procs or -1, ) project.add_drt(data=data, drt=drt) project_tab.populate_drts(project, data) @@ -120,5 +116,6 @@ def perform_drt(*args, **kwargs): project.get_data_sets(), project_tab.get_active_plot(), ) - signals.emit(Signal.CREATE_PROJECT_SNAPSHOT) signals.emit(Signal.HIDE_BUSY_MESSAGE) + if batch is False: + signals.emit(Signal.CREATE_PROJECT_SNAPSHOT) diff --git a/src/deareis/program/fitting.py b/src/deareis/program/fitting.py index c580176..1ec25c3 100644 --- a/src/deareis/program/fitting.py +++ b/src/deareis/program/fitting.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -17,10 +17,10 @@ # The licenses of DearEIS' dependencies and/or sources of portions of code are included in # the LICENSES folder. -from multiprocessing import cpu_count from typing import ( Optional, ) +import pyimpspec import deareis.api.fitting as api from deareis.data import ( DataSet, @@ -103,10 +103,16 @@ def perform_fit(*args, **kwargs): if data is None or settings is None: return assert data.get_num_points() > 0, "There are no data points to fit the circuit to!" - # Prevent the GUI from becoming unresponsive or sluggish - num_procs: int = max(2, cpu_count() - 1) + circuit: pyimpspec.Circuit = pyimpspec.parse_cdc(settings.cdc) + if len(circuit.get_elements()) == 0: + return + batch: bool = kwargs.get("batch", False) signals.emit(Signal.SHOW_BUSY_MESSAGE, message="Performing fit") - fit: FitResult = api.fit_circuit(data=data, settings=settings, num_procs=num_procs) + fit: FitResult = api.fit_circuit( + data=data, + settings=settings, + num_procs=STATE.config.num_procs or -1, + ) project.add_fit(data, fit) project_tab.populate_fits(project, data) project_tab.plotting_tab.populate_fits( @@ -114,5 +120,6 @@ def perform_fit(*args, **kwargs): project.get_data_sets(), project_tab.get_active_plot(), ) - signals.emit(Signal.CREATE_PROJECT_SNAPSHOT) signals.emit(Signal.HIDE_BUSY_MESSAGE) + if batch is False: + signals.emit(Signal.CREATE_PROJECT_SNAPSHOT) diff --git a/src/deareis/program/kramers_kronig.py b/src/deareis/program/kramers_kronig.py index 429117e..a3489f1 100644 --- a/src/deareis/program/kramers_kronig.py +++ b/src/deareis/program/kramers_kronig.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -17,7 +17,6 @@ # The licenses of DearEIS' dependencies and/or sources of portions of code are included in # the LICENSES folder. -from multiprocessing import cpu_count from traceback import format_exc from typing import ( List, @@ -27,7 +26,7 @@ array, ndarray, ) -from pyimpspec import FittingError +from pyimpspec.exceptions import FittingError import deareis.api.kramers_kronig as api from deareis.data import ( DataSet, @@ -161,15 +160,14 @@ def perform_test(*args, **kwargs): if data is None or settings is None: return assert data.get_num_points() > 0, "There are no data points to test!" - # Prevent the GUI from becoming unresponsive or sluggish - num_procs: int = max(2, cpu_count() - 1) + batch: bool = kwargs.get("batch", False) if settings.mode == TestMode.AUTO or settings.mode == TestMode.MANUAL: signals.emit(Signal.SHOW_BUSY_MESSAGE, message="Performing test(s)") try: test: TestResult = api.perform_test( data=data, settings=settings, - num_procs=num_procs, + num_procs=STATE.config.num_procs or -1, ) except FittingError: signals.emit(Signal.SHOW_ERROR_MESSAGE, traceback=format_exc()) @@ -185,25 +183,37 @@ def perform_test(*args, **kwargs): project.get_data_sets(), project_tab.get_active_plot(), ) - signals.emit(Signal.CREATE_PROJECT_SNAPSHOT) + if batch is False: + signals.emit(Signal.CREATE_PROJECT_SNAPSHOT) elif settings.mode == TestMode.EXPLORATORY: signals.emit(Signal.SHOW_BUSY_MESSAGE, message="Performing test") try: results: List[TestResult] = api.perform_exploratory_tests( data=data, settings=settings, - num_procs=num_procs, ) except FittingError: signals.emit(Signal.SHOW_ERROR_MESSAGE, traceback=format_exc()) return signals.emit(Signal.HIDE_BUSY_MESSAGE) - num_RCs: ndarray = array(list(range(1, settings.num_RC + 1))) - show_exploratory_results( - data, - results, - settings, - num_RCs, - ) + if batch is False: + num_RCs: ndarray = array(list(range(1, settings.num_RC + 1))) + show_exploratory_results( + data, + results, + settings, + num_RCs, + ) + else: + project.add_test( + data=data, + test=results[0], + ) + project_tab.populate_tests(project, data) + project_tab.plotting_tab.populate_tests( + project.get_all_tests(), + project.get_data_sets(), + project_tab.get_active_plot(), + ) else: raise Exception("Unsupported mode!") diff --git a/src/deareis/program/overview.py b/src/deareis/program/overview.py index 80b65f4..eee5f21 100644 --- a/src/deareis/program/overview.py +++ b/src/deareis/program/overview.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by diff --git a/src/deareis/program/plotting.py b/src/deareis/program/plotting.py index 592aed0..d548a21 100644 --- a/src/deareis/program/plotting.py +++ b/src/deareis/program/plotting.py @@ -1,5 +1,5 @@ # DearEIS is licensed under the GPLv3 or later (https://www.gnu.org/licenses/gpl-3.0.html). -# Copyright 2022 DearEIS developers +# Copyright 2023 DearEIS developers # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by @@ -18,9 +18,11 @@ # the LICENSES folder. from os.path import exists, dirname +from re import search from typing import ( Dict, List, + Match, Optional, Tuple, Union, @@ -38,6 +40,7 @@ Project, SimulationResult, TestResult, + ZHITResult, ) from deareis.gui import ProjectTab from deareis.gui.file_dialog import FileDialog @@ -62,15 +65,15 @@ def new_plot_settings(*args, **kwargs): i += 1 label = f"Plot ({i})" settings: PlotSettings = PlotSettings( - label, - PlotType.NYQUIST, - [], - {}, - {}, - {}, - {}, - {}, - uuid4().hex, + plot_label=label, + plot_type=PlotType.NYQUIST, + series_order=[], + labels={}, + colors={}, + markers={}, + show_lines={}, + themes={}, + uuid=uuid4().hex, ) project.add_plot(settings) project_tab.populate_plots(project) @@ -205,12 +208,13 @@ def update_marker(label: str): marker = themes.PLOT_MARKERS.get(label, -1) settings.set_series_marker(uuid, marker) # type: ignore project_tab.update_plots( - settings, - project.get_data_sets(), - project.get_all_tests(), - project.get_all_drts(), - project.get_all_fits(), - project.get_simulations(), + settings=settings, + data_sets=project.get_data_sets(), + tests=project.get_all_tests(), + zhits=project.get_all_zhits(), + drts=project.get_all_drts(), + fits=project.get_all_fits(), + simulations=project.get_simulations(), ) states[1] = hash_state() @@ -223,12 +227,13 @@ def update_line(state: bool): show_line = state settings.set_series_line(uuid, state) # type: ignore project_tab.update_plots( - settings, - project.get_data_sets(), - project.get_all_tests(), - project.get_all_drts(), - project.get_all_fits(), - project.get_simulations(), + settings=settings, + data_sets=project.get_data_sets(), + tests=project.get_all_tests(), + zhits=project.get_all_zhits(), + drts=project.get_all_drts(), + fits=project.get_all_fits(), + simulations=project.get_simulations(), ) states[1] = hash_state() @@ -304,8 +309,8 @@ def export_plot(*args, **kwargs): def save_plot(*args, **kwargs): - fig = kwargs["figure"] # Optional[matplotlib.figure.Figure] - if fig is None: + figure = kwargs["figure"] # Optional[matplotlib.figure.Figure] + if figure is None: return STATE.close_plot_exporter() @@ -314,7 +319,7 @@ def save(*args, **kwargs): directory: str = dirname(path) if exists(directory): STATE.latest_plot_directory = directory - fig.savefig(path) + figure.savefig(path) FileDialog( cwd=STATE.latest_plot_directory, @@ -384,12 +389,13 @@ def select_plot_settings(*args, **kwargs): if not is_busy_message_visible: signals.emit(Signal.SHOW_BUSY_MESSAGE, message="Updating plots") project_tab.select_plot( - settings, - project.get_data_sets(), - project.get_all_tests(), - project.get_all_drts(), - project.get_all_fits(), - project.get_simulations(), + settings=settings, + data_sets=project.get_data_sets(), + tests=project.get_all_tests(), + zhits=project.get_all_zhits(), + drts=project.get_all_drts(), + fits=project.get_all_fits(), + simulations=project.get_simulations(), adjust_limits=kwargs.get("adjust_limits", True), plot_only=kwargs.get("plot_only", False), ) @@ -427,6 +433,7 @@ def toggle_plot_series(*args, **kwargs): enabled: bool = kwargs.get("enabled", False) data_sets: Optional[List[DataSet]] = kwargs.get("data_sets") tests: Optional[List[TestResult]] = kwargs.get("tests") + zhits: Optional[List[ZHITResult]] = kwargs.get("zhits") drts: Optional[List[DRTResult]] = kwargs.get("drts") fits: Optional[List[FitResult]] = kwargs.get("fits") simulations: Optional[List[SimulationResult]] = kwargs.get("simulations") @@ -440,6 +447,11 @@ def toggle_plot_series(*args, **kwargs): list(map(settings.add_series, tests)) else: list(map(lambda _: settings.remove_series(_.uuid), tests)) + if zhits is not None: + if enabled: + list(map(settings.add_series, zhits)) + else: + list(map(lambda _: settings.remove_series(_.uuid), zhits)) if drts is not None: if enabled: list(map(settings.add_series, drts)) @@ -478,3 +490,31 @@ def delete_plot_settings(*args, **kwargs): signals.emit(Signal.SELECT_PLOT_SETTINGS, settings=plots[0]) signals.emit(Signal.CREATE_PROJECT_SNAPSHOT) signals.emit(Signal.HIDE_BUSY_MESSAGE) + + +def duplicate_plot_settings(*args, **kwargs): + project: Optional[Project] = STATE.get_active_project() + project_tab: Optional[ProjectTab] = STATE.get_active_project_tab() + if project is None or project_tab is None: + return + settings: Optional[PlotSettings] = kwargs.get("settings") + if settings is None: + return + existing_labels: List[str] = list(map(lambda _: _.get_label(), project.get_plots())) + label: str = settings.get_label() + match: Optional[Match] = search(r"^(?P