diff --git a/.github/workflows/ci-cd.yml b/.github/workflows/ci-cd.yml index cc06d2b..d1653da 100644 --- a/.github/workflows/ci-cd.yml +++ b/.github/workflows/ci-cd.yml @@ -10,12 +10,12 @@ jobs: # Define job steps steps: - name: Set up Python - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: - python-version: "3.9" + python-version: "3.11" - name: Check-out repository - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Install poetry uses: snok/install-poetry@v1 @@ -23,16 +23,30 @@ jobs: - name: Install package run: poetry install + - name: Linter + uses: psf/black@stable + - name: Test with pytest run: poetry run pytest tests/ --cov=sysloss --cov-report=xml - name: Use Codecov to track coverage - uses: codecov/codecov-action@v3 + uses: codecov/codecov-action@v4.0.1 with: - files: ./coverage.xml # coverage report + token: ${{ secrets.CODECOV_TOKEN }} + slug: geddy11/sysloss + #files: ./coverage.xml # coverage report (dont enable) + + # - name: Coverage badge + # uses: tj-actions/coverage-badge-py@v2 + + - name: Install Jupyter-book + run: pip install jupyter-book==1.0.0 sphinx-autoapi matplotlib toml scipy rich rustworkx pandas numpy + + - name: Create Sphinx configuration + run: jupyter-book config sphinx docs - name: Build documentation - run: poetry run make html --directory docs/ + run: sphinx-build docs docs/_build/html -b html cd: permissions: @@ -51,12 +65,12 @@ jobs: # Define job steps steps: - name: Set up Python - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: - python-version: "3.9" + python-version: "3.11" - name: Check-out repository - uses: actions/checkout@v3 + uses: actions/checkout@v4 with: fetch-depth: 0 @@ -92,3 +106,5 @@ jobs: if: steps.release.outputs.released == 'true' with: github_token: ${{ secrets.GITHUB_TOKEN }} + + \ No newline at end of file diff --git a/.gitignore b/.gitignore index a1d0557..110e4e8 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,7 @@ +*.json +!tests/data/*.json +work + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] @@ -70,6 +74,9 @@ instance/ # Sphinx documentation docs/_build/ +docs/jupyter_execute +docs/conf.py +docs/nb/*.toml # PyBuilder target/ @@ -140,6 +147,3 @@ dmypy.json # MacOS .DS_Store - -# Various -work diff --git a/.readthedocs.yml b/.readthedocs.yml index ae72745..432eae3 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -9,7 +9,7 @@ version: 2 build: os: ubuntu-22.04 tools: - python: "3.9" + python: "3.11" jobs: post_create_environment: # Install poetry @@ -20,7 +20,10 @@ build: post_install: # Install dependencies with 'docs' dependency group # https://python-poetry.org/docs/managing-dependencies/#dependency-groups - - poetry install + - poetry install --with docs + pre_build: + # Generate the Sphinx configuration for this Jupyter Book so it builds. + - "jupyter-book config sphinx docs/" # Build documentation in the "docs/" directory with Sphinx sphinx: diff --git a/CHANGELOG.md b/CHANGELOG.md index 3f537a1..4dc68c6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,7 +1,2 @@ # Changelog - - -## v0.1.0 (29/02/2024) - -- First release of `sysloss`! \ No newline at end of file diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000..cf76643 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,20 @@ +cff-version: 1.2.0 +title: sysLoss +message: >- + If you use this software, please cite it using the + metadata from this file. +type: software +authors: + - given-names: Geir + family-names: Drange + affiliation: Inventas AS + orcid: 'https://orcid.org/0009-0009-6795-8583' +repository-code: 'https://github.com/geddy11/sysloss' +url: 'https://github.com/geddy11/sysloss' +abstract: sysLoss is a tool for analyzing system power and losses. +keywords: + - system + - power + - loss +license: MIT +version: 1.0.0 diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 6772a21..1067755 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -63,9 +63,11 @@ Ready to contribute? Here's how to set up `sysloss` for local development. Before you submit a pull request, check that it meets these guidelines: -1. The pull request should include additional tests if appropriate. -2. If the pull request adds functionality, the docs should be updated. -3. The pull request should work for all currently supported operating systems and versions of Python. +1. Use Black code formatter (formatting is checked in the CI pipeline). +2. The pull request should include additional tests if appropriate. Code coverage shall be 100%. +3. If the pull request adds functionality, the docs shall be updated. +4. The pull request should work for all currently supported operating systems and versions of Python. +5. Tutorials shall not contain references to or mentions of specific vendors, products or components. ## Code of Conduct diff --git a/README.md b/README.md index b57b404..9f391d0 100644 --- a/README.md +++ b/README.md @@ -1,16 +1,47 @@ -# sysloss -Power analysis of circuits and systems. +![sysLoss logo](docs/sysloss.svg) -## Installation +

+Actions Status +PyPI +Code style: black +Conv. commits +

+ +# sysLoss +sysLoss is a tool for analyzing system power and losses. From the smallest IoT sensor to large industrial installations. The tool is efficient and easy to use, the analysis result provides a detailed report on voltages, currents, power and efficiency for every component defined in the system. Output format is Pandas DataFrame: Create charts, plots and export to Excel and other formats. +## Installation ```bash $ pip install sysloss ``` ## Usage - -- TODO +```python +from sysloss.components import * +from sysloss.system import System + +bts = System("Bluetooth sensor", Source("CR2032", vo=3.0, rs=10)) +bts.add_comp("CR2032", comp=Converter("Buck 1.8V", vo=1.8, eff=0.87)) +bts.add_comp("Buck 1.8V", comp=PLoad("MCU", pwr=13e-3)) +bts.add_comp("CR2032", comp=Converter("Boost 5V", vo=5.0, eff=0.82)) +bts.add_comp("Boost 5V", comp=RLoss("RC filter", rs=6.8)) +bts.add_comp("RC filter", comp=ILoad("Sensor", ii=6e-3)) +bts.tree() +``` +``` +Bluetooth sensor +└── CR2032 + ├── Boost 5V + │ └── RC filter + │ └── Sensor + └── Buck 1.8V + └── MCU +``` +```python +bts.solve() +``` +![result](docs/bts.png) ## Contributing @@ -19,7 +50,3 @@ Interested in contributing? Check out the contributing guidelines. Please note t ## License `sysloss` was created by Geir Drange. It is licensed under the terms of the MIT license. - -## Credits - -`sysloss` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter). diff --git a/docs/Getting started.md b/docs/Getting started.md new file mode 100644 index 0000000..cf716ff --- /dev/null +++ b/docs/Getting started.md @@ -0,0 +1,47 @@ +# Welcome to sysLoss +*sysLoss* is a tool for analyzing system power and losses. From the smallest IoT sensor to large industrial installations. The tool is efficient and easy to use, the analysis result provides a detailed report on voltages, currents, power and efficiency for every component defined in the system. Output format is Pandas DataFrame: Create charts, plots and export to Excel and other formats. + +## Installation +To get started with *sysLoss*, install the python package from PyPI with: +```bash +$ pip install sysloss +``` +Upgrade to the latest release with: +```bash +$ pip install --upgrade sysloss +``` + +## First system model +A simple, battery-powered Bluetooth sensor can be defined as simple as this: +```python +from sysloss.components import * +from sysloss.system import System + +bts = System("Bluetooth sensor", Source("CR2032", vo=3.0, rs=10)) +bts.add_comp("CR2032", comp=Converter("Buck 1.8V", vo=1.8, eff=0.87)) +bts.add_comp("Buck 1.8V", comp=PLoad("MCU", pwr=13e-3)) +bts.add_comp("CR2032", comp=Converter("Boost 5V", vo=5.0, eff=0.82)) +bts.add_comp("Boost 5V", comp=RLoss("RC filter", rs=6.8)) +bts.add_comp("RC filter", comp=ILoad("Sensor", ii=6e-3)) +bts.tree() +``` +``` +Bluetooth sensor +└── CR2032 + ├── Boost 5V + │ └── RC filter + │ └── Sensor + └── Buck 1.8V + └── MCU +``` +```python +bts.solve() +``` +![result](bts.png) + +## Next step +The best way to learn *sysLoss* is to explore the tutorials section. The tutorials are Jupyter Notebooks that can also be found in the GitHub repository under docs/nb. + +```{tableofcontents} +``` + diff --git a/docs/_config.yml b/docs/_config.yml index 2952f8a..4d05c01 100644 --- a/docs/_config.yml +++ b/docs/_config.yml @@ -34,4 +34,24 @@ repository: # See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository html: use_issues_button: true - use_repository_button: true \ No newline at end of file + use_repository_button: true + +# Sphinx autodoc +sphinx: + extra_extensions: + - 'autoapi.extension' +# - 'sphinx.ext.autodoc' + - 'sphinx.ext.napoleon' + - 'sphinx.ext.viewcode' +# - 'sphinx_external_toc' +# - 'sphinx.ext.autosummary' + config: +# autosummary_generate: True + + config: + autoapi_dirs: ["../src"] # location to parse for API reference + autoapi_options: + ['members', 'undoc-members'] + #external_toc_path: "docs/_toc.yml" + #html_theme: "sphinx_rtd_theme" + add_module_names: False diff --git a/docs/_toc.yml b/docs/_toc.yml index d4311d4..138f06d 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -2,8 +2,13 @@ # Learn more at https://jupyterbook.org/customize/toc.html format: jb-book -root: intro +root: Getting started chapters: -- file: markdown -- file: notebooks -- file: markdown-notebooks \ No newline at end of file +- file: autoapi/index +- file: nb/Component files +- file: tutorials + sections: + - file: nb/Bluetooth sensor + - file: nb/Sensor daisy chain + - file: nb/PCIe FPGA + - file: nb/Datacenter rack diff --git a/docs/bts.png b/docs/bts.png new file mode 100644 index 0000000..ef42f48 Binary files /dev/null and b/docs/bts.png differ diff --git a/docs/content.md b/docs/content.md deleted file mode 100644 index 0f6aca7..0000000 --- a/docs/content.md +++ /dev/null @@ -1,5 +0,0 @@ -Content in Jupyter Book -======================= - -There are many ways to write content in Jupyter Book. This short section -covers a few tips for how to do so. diff --git a/docs/intro.md b/docs/intro.md deleted file mode 100644 index 95577c4..0000000 --- a/docs/intro.md +++ /dev/null @@ -1,11 +0,0 @@ -# Welcome to your Jupyter Book - -This is a small sample book to give you a feel for how book content is -structured. -It shows off a few of the major file types, as well as some sample content. -It does not go in-depth into any particular topic - check out [the Jupyter Book documentation](https://jupyterbook.org) for more information. - -Check out the content pages bundled with this sample book to see more. - -```{tableofcontents} -``` \ No newline at end of file diff --git a/docs/logo.png b/docs/logo.png new file mode 100644 index 0000000..8cf7e19 Binary files /dev/null and b/docs/logo.png differ diff --git a/docs/markdown-notebooks.md b/docs/markdown-notebooks.md deleted file mode 100644 index 6d97104..0000000 --- a/docs/markdown-notebooks.md +++ /dev/null @@ -1,54 +0,0 @@ ---- -jupytext: - cell_metadata_filter: -all - formats: md:myst - text_representation: - extension: .md - format_name: myst - format_version: 0.13 - jupytext_version: 1.11.5 -kernelspec: - display_name: Python 3 - language: python - name: python3 ---- - -# Notebooks with MyST Markdown - -Jupyter Book also lets you write text-based notebooks using MyST Markdown. -See [the Notebooks with MyST Markdown documentation](https://jupyterbook.org/file-types/myst-notebooks.html) for more detailed instructions. -This page shows off a notebook written in MyST Markdown. - -## An example cell - -With MyST Markdown, you can define code cells with a directive like so: - -```{code-cell} -print(2 + 2) -``` - -When your book is built, the contents of any `{code-cell}` blocks will be -executed with your default Jupyter kernel, and their outputs will be displayed -in-line with the rest of your content. - -```{seealso} -Jupyter Book uses [Jupytext](https://jupytext.readthedocs.io/en/latest/) to convert text-based files to notebooks, and can support [many other text-based notebook files](https://jupyterbook.org/file-types/jupytext.html). -``` - -## Create a notebook with MyST Markdown - -MyST Markdown notebooks are defined by two things: - -1. YAML metadata that is needed to understand if / how it should convert text files to notebooks (including information about the kernel needed). - See the YAML at the top of this page for example. -2. The presence of `{code-cell}` directives, which will be executed with your book. - -That's all that is needed to get started! - -## Quickly add YAML metadata for MyST Notebooks - -If you have a markdown file and you'd like to quickly add YAML metadata to it, so that Jupyter Book will treat it as a MyST Markdown Notebook, run the following command: - -``` -jupyter-book myst init path/to/markdownfile.md -``` \ No newline at end of file diff --git a/docs/markdown.md b/docs/markdown.md deleted file mode 100644 index deaf054..0000000 --- a/docs/markdown.md +++ /dev/null @@ -1,55 +0,0 @@ -# Markdown Files - -Whether you write your book's content in Jupyter Notebooks (`.ipynb`) or -in regular markdown files (`.md`), you'll write in the same flavor of markdown -called **MyST Markdown**. -This is a simple file to help you get started and show off some syntax. - -## What is MyST? - -MyST stands for "Markedly Structured Text". It -is a slight variation on a flavor of markdown called "CommonMark" markdown, -with small syntax extensions to allow you to write **roles** and **directives** -in the Sphinx ecosystem. - -For more about MyST, see [the MyST Markdown Overview](https://jupyterbook.org/content/myst.html). - -## Sample Roles and Directives - -Roles and directives are two of the most powerful tools in Jupyter Book. They -are kind of like functions, but written in a markup language. They both -serve a similar purpose, but **roles are written in one line**, whereas -**directives span many lines**. They both accept different kinds of inputs, -and what they do with those inputs depends on the specific role or directive -that is being called. - -Here is a "note" directive: - -```{note} -Here is a note -``` - -It will be rendered in a special box when you build your book. - -Here is an inline directive to refer to a document: {doc}`markdown-notebooks`. - - -## Citations - -You can also cite references that are stored in a `bibtex` file. For example, -the following syntax: `` {cite}`holdgraf_evidence_2014` `` will render like -this: {cite}`holdgraf_evidence_2014`. - -Moreover, you can insert a bibliography into your page with this syntax: -The `{bibliography}` directive must be used for all the `{cite}` roles to -render properly. -For example, if the references for your book are stored in `references.bib`, -then the bibliography is inserted with: - -```{bibliography} -``` - -## Learn more - -This is just a simple starter to get you started. -You can learn a lot more at [jupyterbook.org](https://jupyterbook.org). \ No newline at end of file diff --git a/docs/nb/Bluetooth sensor.ipynb b/docs/nb/Bluetooth sensor.ipynb new file mode 100644 index 0000000..a2c2c72 --- /dev/null +++ b/docs/nb/Bluetooth sensor.ipynb @@ -0,0 +1,2306 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a1ca17a0-128e-46b7-b65d-011116b44d28", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Bluetooth Sensor\n", + "The system to be analyzed in this tutorial is a very simple one: A battery operated Bluetooth sensor. \n", + "The system consists of the following components:\n", + " * A CR2032 3V lithium battery\n", + " * A MCU operating at 1.8V\n", + " * A sensor that needs 5V to operate\n", + "\n", + "To power the MCU and sensor from the 3V battery two converters are needed: \n", + " * A boost converter that outputs 5V. The 5V is filtered with an RC filter to reduce switching noise.\n", + " * A buck converter that outputs 1.8V\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0ddf2bf4-d388-4195-aaaa-c0a4969b02b0", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# This cell can be removed, it is only used for running the notebook during Sphinx documentation build.\n", + "import sys, os\n", + "if os.getcwd().replace('\\\\', '/').endswith(\"/docs/nb\"):\n", + " sys.path.insert(0, os.path.abspath(os.path.join(\"../../src\")))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3555e267-3e0a-4c54-8611-f498f616146e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from sysloss.components import *\n", + "from sysloss.system import System" + ] + }, + { + "cell_type": "markdown", + "id": "3e9ec23a-db71-4f10-acac-81928ed90913", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Define the system\n", + "A sysloss system is defined using the System class. Components are added to the system, always starting with a Source component (in this case the battery). Each component must have a unique name. \n", + "\n", + "Parameters for voltage, current, power and resistance are always in basic units of electricity (Volt, Ampere, Watt and Ohm).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "04d7771f-09e0-42b5-936f-d5481aebe7f3", + "metadata": {}, + "outputs": [], + "source": [ + "bts = System(\"Bluetooth sensor\", Source(\"CR2032\", vo=3.0, rs=10))\n", + "bts.add_comp(\"CR2032\", comp=Converter(\"Buck 1.8V\", vo=1.8, eff=0.87))\n", + "bts.add_comp(\"Buck 1.8V\", comp=PLoad(\"MCU\", pwr=13e-3))\n", + "bts.add_comp(\"CR2032\", comp=Converter(\"Boost 5V\", vo=5.0, eff=0.82, iis=3e-6))\n", + "bts.add_comp(\"Boost 5V\", comp=RLoss(\"RC filter\", rs=6.8))\n", + "bts.add_comp(\"RC filter\", comp=ILoad(\"Sensor\", ii=6e-3))" + ] + }, + { + "cell_type": "markdown", + "id": "1c895bcd-ec85-4fff-ba8b-80d810e7469a", + "metadata": {}, + "source": [ + "That's it - for the initial system! sysloss has two functions for summarizing the system:\n", + " * *.tree()* that displays the power tree structure\n", + " * *.params()* that lists all system parameters in a Pandas frame" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "672b2393-2e1c-4aab-b688-140db460aa7c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Bluetooth sensor\n",
+       "└── CR2032\n",
+       "    ├── Boost 5V\n",
+       "    │   └── RC filter\n",
+       "    │       └── Sensor\n",
+       "    └── Buck 1.8V\n",
+       "        └── MCU\n",
+       "
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ComponentTypeParentVin (V)Vout (V)Iin (A)Iout (A)Power (W)Loss (W)Efficiency (%)Warnings
0CR2032SOURCE3.02.8170880.0182910.0182910.0548740.00334693.902943
1Boost 5VCONVERTERCR20322.8170885.00.0129870.0060.0365850.00658582.0
2RC filterSLOSSBoost 5V5.04.95920.0060.0060.030.00024599.184
3SensorLOADRC filter4.95920.00.0060.00.0297550.0100.0
4Buck 1.8VCONVERTERCR20322.8170881.80.0053040.0072220.0149430.00194387.0
5MCULOADBuck 1.8V1.80.00.0072220.00.0130.0100.0
6System total0.0548740.01211877.915822
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" + ], + "text/plain": [ + " Component Type Parent Vin (V) Vout (V) Iin (A) Iout (A) \\\n", + "0 CR2032 SOURCE 3.0 2.817088 0.018291 0.018291 \n", + "1 Boost 5V CONVERTER CR2032 2.817088 5.0 0.012987 0.006 \n", + "2 RC filter SLOSS Boost 5V 5.0 4.9592 0.006 0.006 \n", + "3 Sensor LOAD RC filter 4.9592 0.0 0.006 0.0 \n", + "4 Buck 1.8V CONVERTER CR2032 2.817088 1.8 0.005304 0.007222 \n", + "5 MCU LOAD Buck 1.8V 1.8 0.0 0.007222 0.0 \n", + "6 System total \n", + "\n", + " Power (W) Loss (W) Efficiency (%) Warnings \n", + "0 0.054874 0.003346 93.902943 \n", + "1 0.036585 0.006585 82.0 \n", + "2 0.03 0.000245 99.184 \n", + "3 0.029755 0.0 100.0 \n", + "4 0.014943 0.001943 87.0 \n", + "5 0.013 0.0 100.0 \n", + "6 0.054874 0.012118 77.915822 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bts.solve()" + ] + }, + { + "cell_type": "markdown", + "id": "358a3f65-dd89-484a-b3fd-767919d135f1", + "metadata": {}, + "source": [ + "## Load phases\n", + "Very few systems operate with constant power. Most systems will have two or more operating modes, where different parts of the system is shut-down or operating at lower or higher power. Our Bluetooth sensor will acquire and transmit sensor data once per hour and stay in sleep mode otherwise. \n", + "\n", + "sysloss captures these operating modes with *load phases*. We define three phases:\n", + " * \"sleep\": the 5V boost converter is shut-down (and the sensor with it). MCU is sleeping.\n", + " * \"acquire\": the 5V boost converter is enabled, MCU is acquiring sensor data.\n", + " * \"transmit\": the MCU uses higher power to transmit sensor data, while the 5V boost converter is shut-down.\n", + "\n", + "Load phases in sysloss are defined in a dict with name and duration (seconds) of each phase. The duration values are used to calculate time-averaged power numbers. We start by defining the load phases on system level.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e3cc5eb3-f7f4-4afe-8a99-0cd243462379", + "metadata": {}, + "outputs": [], + "source": [ + "bts_phases = {\"sleep\": 3600, \"acquire\": 2.5, \"transmit\": 2e-3}\n", + "bts.set_sys_phases(bts_phases)" + ] + }, + { + "cell_type": "markdown", + "id": "0ff22d97-2f3c-475f-ac32-844786ec6122", + "metadata": {}, + "source": [ + "To define which load phases a converter is active in, we set the converter parameter *active_phases*, which is a list of phases. \n", + "```{tip}\n", + "When a converter is shut-down, the output voltage is 0.0, and all components connected to it will be off too. There is no need to define load phases for those components.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "764d696e-1ca0-4c6f-8d17-f1d358efc2e8", + "metadata": {}, + "outputs": [], + "source": [ + "bts.set_comp_phases(\"Boost 5V\", phase_conf=[\"acquire\"])" + ] + }, + { + "cell_type": "markdown", + "id": "7eb80e85-ab97-4328-97c8-94505f73b8d6", + "metadata": {}, + "source": [ + "Next, we set the load phases for the MCU, which has different power for each phase. For load components the load phase power consumption is defined as a dict." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "db9dd7fe-b5e4-4c8e-8a6c-bc9fa66ca524", + "metadata": {}, + "outputs": [], + "source": [ + "mcu_pwr = {\"sleep\": 12e-6, \"acquire\": 15e-3, \"transmit\": 35e-3}\n", + "bts.set_comp_phases(\"MCU\", phase_conf=mcu_pwr)" + ] + }, + { + "cell_type": "markdown", + "id": "9b20170b-11e5-432a-84f2-e54f5e9598db", + "metadata": {}, + "source": [ + "The *.phases()* function returns a DataFrame with the load phases summarized:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6c720555-c251-4608-b559-30385dd3ef2c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComponentTypeParentActive phasers (Ohm)ii (A)pwr (W)
0CR2032SOURCENone
1Boost 5VCONVERTERCR2032acquire
2RC filterSLOSSBoost 5VNone
3SensorLOADRC filterN/A0.006
4Buck 1.8VCONVERTERCR2032N/A
5MCULOADBuck 1.8Vsleep0.000012
6MCULOADBuck 1.8Vacquire0.015
7MCULOADBuck 1.8Vtransmit0.035
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" + ], + "text/plain": [ + " Component Type Parent Active phase rs (Ohm) ii (A) pwr (W)\n", + "0 CR2032 SOURCE None \n", + "1 Boost 5V CONVERTER CR2032 acquire \n", + "2 RC filter SLOSS Boost 5V None \n", + "3 Sensor LOAD RC filter N/A 0.006 \n", + "4 Buck 1.8V CONVERTER CR2032 N/A \n", + "5 MCU LOAD Buck 1.8V sleep 0.000012\n", + "6 MCU LOAD Buck 1.8V acquire 0.015\n", + "7 MCU LOAD Buck 1.8V transmit 0.035" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bts.phases()" + ] + }, + { + "cell_type": "markdown", + "id": "cf77fff3-ee6c-4899-bc79-13b869b531e7", + "metadata": {}, + "source": [ + "Analyzing again:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f6118236-22a6-4f94-92a1-b0fe8a1d2b79", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComponentTypeParentPhaseVin (V)Vout (V)Iin (A)Iout (A)Power (W)Loss (W)Efficiency (%)Warnings
0CR2032SOURCEsleep3.02.9999240.0000080.0000080.0000230.099.997467
1Boost 5VCONVERTERCR2032sleep2.9999240.00.0000030.00.0000090.0000090.0
2RC filterSLOSSBoost 5Vsleep0.00.00.00.00.00.0100.0
3SensorLOADRC filtersleep0.00.00.00.00.00.0100.0
4Buck 1.8VCONVERTERCR2032sleep2.9999241.80.0000050.0000070.0000140.00000287.0
5MCULOADBuck 1.8Vsleep1.80.00.0000070.00.0000120.0100.0
6System totalsleep0.0000230.00001152.646258
7CR2032SOURCEacquire3.02.8083320.0191670.0191670.05750.00367493.611075
8Boost 5VCONVERTERCR2032acquire2.8083325.00.0130270.0060.0365850.00658582.0
9RC filterSLOSSBoost 5Vacquire5.04.95920.0060.0060.030.00024599.184
10SensorLOADRC filteracquire4.95920.00.0060.00.0297550.0100.0
11Buck 1.8VCONVERTERCR2032acquire2.8083321.80.0061390.0083330.0172410.00224187.0
12MCULOADBuck 1.8Vacquire1.80.00.0083330.00.0150.0100.0
13System totalacquire0.05750.01274577.834565
14CR2032SOURCEtransmit3.02.859270.0140730.0140730.0422190.0019895.309007
15Boost 5VCONVERTERCR2032transmit2.859270.00.0000030.00.0000090.0000090.0
16RC filterSLOSSBoost 5Vtransmit0.00.00.00.00.00.0100.0
17SensorLOADRC filtertransmit0.00.00.00.00.00.0100.0
18Buck 1.8VCONVERTERCR2032transmit2.859271.80.014070.0194440.040230.0052387.0
19MCULOADBuck 1.8Vtransmit1.80.00.0194440.00.0350.0100.0
20System totaltransmit0.0422190.00721982.901156
21System average0.0000630.0000252.663754
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" + ], + "text/plain": [ + " Component Type Parent Phase Vin (V) Vout (V) \\\n", + "0 CR2032 SOURCE sleep 3.0 2.999924 \n", + "1 Boost 5V CONVERTER CR2032 sleep 2.999924 0.0 \n", + "2 RC filter SLOSS Boost 5V sleep 0.0 0.0 \n", + "3 Sensor LOAD RC filter sleep 0.0 0.0 \n", + "4 Buck 1.8V CONVERTER CR2032 sleep 2.999924 1.8 \n", + "5 MCU LOAD Buck 1.8V sleep 1.8 0.0 \n", + "6 System total sleep \n", + "7 CR2032 SOURCE acquire 3.0 2.808332 \n", + "8 Boost 5V CONVERTER CR2032 acquire 2.808332 5.0 \n", + "9 RC filter SLOSS Boost 5V acquire 5.0 4.9592 \n", + "10 Sensor LOAD RC filter acquire 4.9592 0.0 \n", + "11 Buck 1.8V CONVERTER CR2032 acquire 2.808332 1.8 \n", + "12 MCU LOAD Buck 1.8V acquire 1.8 0.0 \n", + "13 System total acquire \n", + "14 CR2032 SOURCE transmit 3.0 2.85927 \n", + "15 Boost 5V CONVERTER CR2032 transmit 2.85927 0.0 \n", + "16 RC filter SLOSS Boost 5V transmit 0.0 0.0 \n", + "17 Sensor LOAD RC filter transmit 0.0 0.0 \n", + "18 Buck 1.8V CONVERTER CR2032 transmit 2.85927 1.8 \n", + "19 MCU LOAD Buck 1.8V transmit 1.8 0.0 \n", + "20 System total transmit \n", + "21 System average \n", + "\n", + " Iin (A) Iout (A) Power (W) Loss (W) Efficiency (%) Warnings \n", + "0 0.000008 0.000008 0.000023 0.0 99.997467 \n", + "1 0.000003 0.0 0.000009 0.000009 0.0 \n", + "2 0.0 0.0 0.0 0.0 100.0 \n", + "3 0.0 0.0 0.0 0.0 100.0 \n", + "4 0.000005 0.000007 0.000014 0.000002 87.0 \n", + "5 0.000007 0.0 0.000012 0.0 100.0 \n", + "6 0.000023 0.000011 52.646258 \n", + "7 0.019167 0.019167 0.0575 0.003674 93.611075 \n", + "8 0.013027 0.006 0.036585 0.006585 82.0 \n", + "9 0.006 0.006 0.03 0.000245 99.184 \n", + "10 0.006 0.0 0.029755 0.0 100.0 \n", + "11 0.006139 0.008333 0.017241 0.002241 87.0 \n", + "12 0.008333 0.0 0.015 0.0 100.0 \n", + "13 0.0575 0.012745 77.834565 \n", + "14 0.014073 0.014073 0.042219 0.00198 95.309007 \n", + "15 0.000003 0.0 0.000009 0.000009 0.0 \n", + "16 0.0 0.0 0.0 0.0 100.0 \n", + "17 0.0 0.0 0.0 0.0 100.0 \n", + "18 0.01407 0.019444 0.04023 0.00523 87.0 \n", + "19 0.019444 0.0 0.035 0.0 100.0 \n", + "20 0.042219 0.007219 82.901156 \n", + "21 0.000063 0.00002 52.663754 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bts.solve()" + ] + }, + { + "cell_type": "markdown", + "id": "75115a12-6843-4a41-8d78-1a9d12e16a2a", + "metadata": {}, + "source": [ + "The result table now has a new column **Phase**, results for each phase and a system average row." + ] + }, + { + "cell_type": "markdown", + "id": "0d128d07-2d9e-4848-a5d1-3fa9a59ad157", + "metadata": {}, + "source": [ + "## Parameter interpolation\n", + "The converters in the system have been defined with constant efficiency. Converter efficiency is a function of many factors, including output current and input voltage. sysloss supports interpolation of converter efficiency. This means that we can take data points from the converter data sheet (or measurement data from a lab test) and provide these data points to sysloss as an interpolated parameter. sysloss will then connect the dots with 1D or 2D linear interpolation. \n", + "\n", + "Let us define the efficiency curve for the 1.8V buck converter. The data points are entered in a dict with a specific format:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b896f244-16c1-4811-9609-321992fe7594", + "metadata": {}, + "outputs": [], + "source": [ + "buck_eff = {\"vi\": [3.0], \"io\": [1e-6, 1e-5, 1e-4, 1e-3, 1e-2], \"eff\": [[0.72, 0.89, 0.92, 0.925, 0.93]]}\n", + "bts.change_comp(\"Buck 1.8V\", comp=Converter(\"Buck 1.8V\", vo=1.8, eff=buck_eff))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6545492c-949f-4ede-8405-50f46f50d2c8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComponentTypeParentvo (V)vdrop (V)rs (Ohm)eff (%)iq (A)ii (A)iis (A)pwr (W)pwrs (W)
0CR2032SOURCE3.010
1Boost 5VCONVERTERCR20325.00.820.00.000003
2RC filterSLOSSBoost 5V6.8
3SensorLOADRC filter0.0060.0
4Buck 1.8VCONVERTERCR20321.8interp0.00.0
5MCULOADBuck 1.8V0.0130.0
\n", + "
" + ], + "text/plain": [ + " Component Type Parent vo (V) vdrop (V) rs (Ohm) eff (%) iq (A) \\\n", + "0 CR2032 SOURCE 3.0 10 \n", + "1 Boost 5V CONVERTER CR2032 5.0 0.82 0.0 \n", + "2 RC filter SLOSS Boost 5V 6.8 \n", + "3 Sensor LOAD RC filter \n", + "4 Buck 1.8V CONVERTER CR2032 1.8 interp 0.0 \n", + "5 MCU LOAD Buck 1.8V \n", + "\n", + " ii (A) iis (A) pwr (W) pwrs (W) \n", + "0 \n", + "1 0.000003 \n", + "2 \n", + "3 0.006 0.0 \n", + "4 0.0 \n", + "5 0.013 0.0 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bts.params()" + ] + }, + { + "cell_type": "markdown", + "id": "44e6c1ea-a4d1-49d1-bf4b-cf51099324a6", + "metadata": {}, + "source": [ + "If we look in the parameter table now, the entry for \"Buck 1.8V\" efficiency is *interp*, which means an interpolated parameter. We can also plot the interpolation curve with the *.plot_interp()* method:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6ef5c7f4-e0d7-4c76-8380-ca5ce67fdc33", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bts.plot_interp(\"Buck 1.8V\");" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7973970c-9aaf-445b-9eae-289e141e6e7b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComponentTypeParentPhaseVin (V)Vout (V)Iin (A)Iout (A)Power (W)Loss (W)Efficiency (%)Warnings
0CR2032SOURCEsleep3.02.9999220.0000080.0000080.0000240.099.997388
1Boost 5VCONVERTERCR2032sleep2.9999220.00.0000030.00.0000090.0000090.0
2RC filterSLOSSBoost 5Vsleep0.00.00.00.00.00.0100.0
3SensorLOADRC filtersleep0.00.00.00.00.00.0100.0
4Buck 1.8VCONVERTERCR2032sleep2.9999221.80.0000050.0000070.0000150.00000382.703704
5MCULOADBuck 1.8Vsleep1.80.00.0000070.00.0000120.0100.0
6System totalsleep0.0000240.00001251.041607
7CR2032SOURCEacquire3.02.8125150.0187480.0187480.0562450.00351593.750503
8Boost 5VCONVERTERCR2032acquire2.8125155.00.0130080.0060.0365850.00658582.0
9RC filterSLOSSBoost 5Vacquire5.04.95920.0060.0060.030.00024599.184
10SensorLOADRC filteracquire4.95920.00.0060.00.0297550.0100.0
11Buck 1.8VCONVERTERCR2032acquire2.8125151.80.005740.0083330.0161450.00114592.907407
12MCULOADBuck 1.8Vacquire1.80.00.0083330.00.0150.0100.0
13System totalacquire0.0562450.0114979.571099
14CR2032SOURCEtransmit3.02.8687840.0131220.0131220.0393650.00172295.626137
15Boost 5VCONVERTERCR2032transmit2.8687840.00.0000030.00.0000090.0000090.0
16RC filterSLOSSBoost 5Vtransmit0.00.00.00.00.00.0100.0
17SensorLOADRC filtertransmit0.00.00.00.00.00.0100.0
18Buck 1.8VCONVERTERCR2032transmit2.8687841.80.0131190.0194440.0376340.00263493.0
19MCULOADBuck 1.8Vtransmit1.80.00.0194440.00.0350.0100.0
20System totaltransmit0.0393650.00436588.911973
21System average0.0000630.00001951.061426
\n", + "
" + ], + "text/plain": [ + " Component Type Parent Phase Vin (V) Vout (V) \\\n", + "0 CR2032 SOURCE sleep 3.0 2.999922 \n", + "1 Boost 5V CONVERTER CR2032 sleep 2.999922 0.0 \n", + "2 RC filter SLOSS Boost 5V sleep 0.0 0.0 \n", + "3 Sensor LOAD RC filter sleep 0.0 0.0 \n", + "4 Buck 1.8V CONVERTER CR2032 sleep 2.999922 1.8 \n", + "5 MCU LOAD Buck 1.8V sleep 1.8 0.0 \n", + "6 System total sleep \n", + "7 CR2032 SOURCE acquire 3.0 2.812515 \n", + "8 Boost 5V CONVERTER CR2032 acquire 2.812515 5.0 \n", + "9 RC filter SLOSS Boost 5V acquire 5.0 4.9592 \n", + "10 Sensor LOAD RC filter acquire 4.9592 0.0 \n", + "11 Buck 1.8V CONVERTER CR2032 acquire 2.812515 1.8 \n", + "12 MCU LOAD Buck 1.8V acquire 1.8 0.0 \n", + "13 System total acquire \n", + "14 CR2032 SOURCE transmit 3.0 2.868784 \n", + "15 Boost 5V CONVERTER CR2032 transmit 2.868784 0.0 \n", + "16 RC filter SLOSS Boost 5V transmit 0.0 0.0 \n", + "17 Sensor LOAD RC filter transmit 0.0 0.0 \n", + "18 Buck 1.8V CONVERTER CR2032 transmit 2.868784 1.8 \n", + "19 MCU LOAD Buck 1.8V transmit 1.8 0.0 \n", + "20 System total transmit \n", + "21 System average \n", + "\n", + " Iin (A) Iout (A) Power (W) Loss (W) Efficiency (%) Warnings \n", + "0 0.000008 0.000008 0.000024 0.0 99.997388 \n", + "1 0.000003 0.0 0.000009 0.000009 0.0 \n", + "2 0.0 0.0 0.0 0.0 100.0 \n", + "3 0.0 0.0 0.0 0.0 100.0 \n", + "4 0.000005 0.000007 0.000015 0.000003 82.703704 \n", + "5 0.000007 0.0 0.000012 0.0 100.0 \n", + "6 0.000024 0.000012 51.041607 \n", + "7 0.018748 0.018748 0.056245 0.003515 93.750503 \n", + "8 0.013008 0.006 0.036585 0.006585 82.0 \n", + "9 0.006 0.006 0.03 0.000245 99.184 \n", + "10 0.006 0.0 0.029755 0.0 100.0 \n", + "11 0.00574 0.008333 0.016145 0.001145 92.907407 \n", + "12 0.008333 0.0 0.015 0.0 100.0 \n", + "13 0.056245 0.01149 79.571099 \n", + "14 0.013122 0.013122 0.039365 0.001722 95.626137 \n", + "15 0.000003 0.0 0.000009 0.000009 0.0 \n", + "16 0.0 0.0 0.0 0.0 100.0 \n", + "17 0.0 0.0 0.0 0.0 100.0 \n", + "18 0.013119 0.019444 0.037634 0.002634 93.0 \n", + "19 0.019444 0.0 0.035 0.0 100.0 \n", + "20 0.039365 0.004365 88.911973 \n", + "21 0.000063 0.000019 51.061426 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bts.solve()" + ] + }, + { + "cell_type": "markdown", + "id": "d34c792a-a8e9-4ab7-814a-874536099e32", + "metadata": {}, + "source": [ + "The effect is not very large in this system, but the \"sleep\" efficiency is about 1.5% lower." + ] + }, + { + "cell_type": "markdown", + "id": "86a70daf-f7ef-4add-8352-d25423363fdc", + "metadata": {}, + "source": [ + "## Saving and loading system from file\n", + "A system can be saved to .json format with the *.save()* method. Restore the system from file with the *.from_file()* method." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f87bbabf-680b-485f-9824-20ecc46f0837", + "metadata": {}, + "outputs": [], + "source": [ + "bts.save(\"bts.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "7dd5f072-f309-4347-aba8-40e5bbec3a8a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComponentTypeParentPhaseVin (V)Vout (V)Iin (A)Iout (A)Power (W)Loss (W)Efficiency (%)Warnings
0CR2032SOURCEsleep3.02.9999220.0000080.0000080.0000240.099.997388
1Buck 1.8VCONVERTERCR2032sleep2.9999221.80.0000050.0000070.0000150.00000382.703704
2MCULOADBuck 1.8Vsleep1.80.00.0000070.00.0000120.0100.0
3Boost 5VCONVERTERCR2032sleep2.9999220.00.0000030.00.0000090.0000090.0
4RC filterSLOSSBoost 5Vsleep0.00.00.00.00.00.0100.0
5SensorLOADRC filtersleep0.00.00.00.00.00.0100.0
6System totalsleep0.0000240.00001251.041607
7CR2032SOURCEacquire3.02.8125150.0187480.0187480.0562450.00351593.750503
8Buck 1.8VCONVERTERCR2032acquire2.8125151.80.005740.0083330.0161450.00114592.907407
9MCULOADBuck 1.8Vacquire1.80.00.0083330.00.0150.0100.0
10Boost 5VCONVERTERCR2032acquire2.8125155.00.0130080.0060.0365850.00658582.0
11RC filterSLOSSBoost 5Vacquire5.04.95920.0060.0060.030.00024599.184
12SensorLOADRC filteracquire4.95920.00.0060.00.0297550.0100.0
13System totalacquire0.0562450.0114979.571099
14CR2032SOURCEtransmit3.02.8687840.0131220.0131220.0393650.00172295.626137
15Buck 1.8VCONVERTERCR2032transmit2.8687841.80.0131190.0194440.0376340.00263493.0
16MCULOADBuck 1.8Vtransmit1.80.00.0194440.00.0350.0100.0
17Boost 5VCONVERTERCR2032transmit2.8687840.00.0000030.00.0000090.0000090.0
18RC filterSLOSSBoost 5Vtransmit0.00.00.00.00.00.0100.0
19SensorLOADRC filtertransmit0.00.00.00.00.00.0100.0
20System totaltransmit0.0393650.00436588.911973
21System average0.0000630.00001951.061426
\n", + "
" + ], + "text/plain": [ + " Component Type Parent Phase Vin (V) Vout (V) \\\n", + "0 CR2032 SOURCE sleep 3.0 2.999922 \n", + "1 Buck 1.8V CONVERTER CR2032 sleep 2.999922 1.8 \n", + "2 MCU LOAD Buck 1.8V sleep 1.8 0.0 \n", + "3 Boost 5V CONVERTER CR2032 sleep 2.999922 0.0 \n", + "4 RC filter SLOSS Boost 5V sleep 0.0 0.0 \n", + "5 Sensor LOAD RC filter sleep 0.0 0.0 \n", + "6 System total sleep \n", + "7 CR2032 SOURCE acquire 3.0 2.812515 \n", + "8 Buck 1.8V CONVERTER CR2032 acquire 2.812515 1.8 \n", + "9 MCU LOAD Buck 1.8V acquire 1.8 0.0 \n", + "10 Boost 5V CONVERTER CR2032 acquire 2.812515 5.0 \n", + "11 RC filter SLOSS Boost 5V acquire 5.0 4.9592 \n", + "12 Sensor LOAD RC filter acquire 4.9592 0.0 \n", + "13 System total acquire \n", + "14 CR2032 SOURCE transmit 3.0 2.868784 \n", + "15 Buck 1.8V CONVERTER CR2032 transmit 2.868784 1.8 \n", + "16 MCU LOAD Buck 1.8V transmit 1.8 0.0 \n", + "17 Boost 5V CONVERTER CR2032 transmit 2.868784 0.0 \n", + "18 RC filter SLOSS Boost 5V transmit 0.0 0.0 \n", + "19 Sensor LOAD RC filter transmit 0.0 0.0 \n", + "20 System total transmit \n", + "21 System average \n", + "\n", + " Iin (A) Iout (A) Power (W) Loss (W) Efficiency (%) Warnings \n", + "0 0.000008 0.000008 0.000024 0.0 99.997388 \n", + "1 0.000005 0.000007 0.000015 0.000003 82.703704 \n", + "2 0.000007 0.0 0.000012 0.0 100.0 \n", + "3 0.000003 0.0 0.000009 0.000009 0.0 \n", + "4 0.0 0.0 0.0 0.0 100.0 \n", + "5 0.0 0.0 0.0 0.0 100.0 \n", + "6 0.000024 0.000012 51.041607 \n", + "7 0.018748 0.018748 0.056245 0.003515 93.750503 \n", + "8 0.00574 0.008333 0.016145 0.001145 92.907407 \n", + "9 0.008333 0.0 0.015 0.0 100.0 \n", + "10 0.013008 0.006 0.036585 0.006585 82.0 \n", + "11 0.006 0.006 0.03 0.000245 99.184 \n", + "12 0.006 0.0 0.029755 0.0 100.0 \n", + "13 0.056245 0.01149 79.571099 \n", + "14 0.013122 0.013122 0.039365 0.001722 95.626137 \n", + "15 0.013119 0.019444 0.037634 0.002634 93.0 \n", + "16 0.019444 0.0 0.035 0.0 100.0 \n", + "17 0.000003 0.0 0.000009 0.000009 0.0 \n", + "18 0.0 0.0 0.0 0.0 100.0 \n", + "19 0.0 0.0 0.0 0.0 100.0 \n", + "20 0.039365 0.004365 88.911973 \n", + "21 0.000063 0.000019 51.061426 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bts2 = System.from_file(\"bts.json\")\n", + "bts2.solve()" + ] + }, + { + "cell_type": "markdown", + "id": "60a5ce3c-c0b4-4598-97f3-1b4680450519", + "metadata": {}, + "source": [ + "## Summary\n", + "This notebook demonstrates the basic use of sysLoss, including load phases, component parameter interpolation and saving and loading system from file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7a886bf9-2298-4195-afba-32d810886127", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/nb/Component files.ipynb b/docs/nb/Component files.ipynb new file mode 100644 index 0000000..3c00cb7 --- /dev/null +++ b/docs/nb/Component files.ipynb @@ -0,0 +1,2185 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a1ca17a0-128e-46b7-b65d-011116b44d28", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Component parameter files\n", + "Component parameters can be loaded from file (.toml format) rather than specifiying them in code when building a system. This enables a component library to be created from .toml files. This notebook demonstrates the component parameter files for each of the component types in `sysLoss`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0ddf2bf4-d388-4195-aaaa-c0a4969b02b0", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# This cell can be removed, it is only used for running the notebook during Sphinx documentation build.\n", + "import sys, os\n", + "if os.getcwd().replace('\\\\', '/').endswith(\"/docs/nb\"):\n", + " sys.path.insert(0, os.path.abspath(os.path.join(\"../../src\")))" + ] + }, + { + "cell_type": "markdown", + "id": "85e3dec7-a1a4-4564-bceb-c6db7c6a5f27", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Import packages:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3555e267-3e0a-4c54-8611-f498f616146e", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from sysloss.components import *\n", + "from sysloss.system import System\n", + "import toml" + ] + }, + { + "cell_type": "markdown", + "id": "3e9ec23a-db71-4f10-acac-81928ed90913", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## About the .toml format\n", + "The general format of a component parameter file consists of two tables:\n", + " * \\[\\] \n", + " * component specific parameters\n", + " * \\[limits] (optional)\n", + " * limits for input and output voltages and currents\n", + "\n", + "`sysLoss` will silently ignore surplus parameters in the .toml file. Parameter files for each of the component types are detailed below.\n", + "```{tip}\n", + "TOML is picky on array values - a mix of integer and floating point values are not allowed. Always use floats! \n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "70d957e1-d4b0-4b3f-8c65-9d0b13187f6b", + "metadata": {}, + "source": [ + "## Source" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7c385138-f993-4794-95d2-d83c70a9bf05", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComponentTypeParentDomainvo (V)vdrop (V)rs (Ohm)eff (%)iq (A)ii (A)iis (A)pwr (W)pwrs (W)vi limits (V)vo limits (V)ii limits (A)io limits (A)
0My sourceSOURCEMy source3.30.007[0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1.0]
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" + ], + "text/plain": [ + " Component Type Parent Domain vo (V) vdrop (V) rs (Ohm) eff (%) \\\n", + "0 My source SOURCE My source 3.3 0.007 \n", + "\n", + " iq (A) ii (A) iis (A) pwr (W) pwrs (W) vi limits (V) vo limits (V) \\\n", + "0 [0.0, 1000000.0] [0.0, 1000000.0] \n", + "\n", + " ii limits (A) io limits (A) \n", + "0 [0.0, 1000000.0] [0.0, 1.0] " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "source_params = \"\"\"[source]\n", + "vo = 3.3 # output voltage (mandatory)\n", + "rs = 0.007 # series resistance (optional)\n", + "\n", + "[limits] # optional, any combination of below parameters can be specificed\n", + "vi = [ 0.0, 1000000.0] \n", + "vo = [ 0.0, 1000000.0]\n", + "ii = [ 0.0, 1000000.0]\n", + "io = [ 0.0, 1.0]\n", + "\"\"\"\n", + "\n", + "with open(\"source.toml\", \"w\") as f:\n", + " f.write(source_params)\n", + "\n", + "sys = System(\"Test toml\", Source.from_file(\"My source\", fname=\"source.toml\"))" + ] + }, + { + "cell_type": "markdown", + "id": "956aadeb-17e8-4099-983b-c460ca6b0fd8", + "metadata": {}, + "source": [ + "## Converter" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "68ea0008-3f53-4638-9c01-6f1cc9a2df0d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComponentTypeParentDomainvo (V)vdrop (V)rs (Ohm)eff (%)iq (A)ii (A)iis (A)pwr (W)pwrs (W)vi limits (V)vo limits (V)ii limits (A)io limits (A)
0My sourceSOURCEMy source3.30.007[0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1.0]
1Buck 2.5VCONVERTERMy sourceMy source2.50.870.0000010.0[2.7, 16.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1.0]
\n", + "
" + ], + "text/plain": [ + " Component Type Parent Domain vo (V) vdrop (V) rs (Ohm) \\\n", + "0 My source SOURCE My source 3.3 0.007 \n", + "1 Buck 2.5V CONVERTER My source My source 2.5 \n", + "\n", + " eff (%) iq (A) ii (A) iis (A) pwr (W) pwrs (W) vi limits (V) \\\n", + "0 [0.0, 1000000.0] \n", + "1 0.87 0.000001 0.0 [2.7, 16.0] \n", + "\n", + " vo limits (V) ii limits (A) io limits (A) \n", + "0 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1.0] \n", + "1 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1.0] " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conv_params = \"\"\"[converter]\n", + "vo = 2.5 # output voltage (mandatory)\n", + "eff = 0.87 # efficiency (mandatory)\n", + "# efficiency can optionally be specified as 1D interpolation data\n", + "#eff = {\"vi\": [3.3], \"io\": [0.1, 0.5, 0.9], \"eff\": [[0.55, 0.78, 0.92]]}\n", + "# or as 2D interpolation data\n", + "#eff = {\"vi\": [3.3, 5.0, 12], \"io\": [0.1, 0.5, 0.9], \"eff\": [[0.55, 0.78, 0.92], [0.5, 0.74, 0.83], [0.4, 0.6, 0.766]]}\n", + "iq = 1.2e-6 # Quiescent (no-load) current (optional)\n", + "iis = 0.33e-6 # Sleep (shut-down) current (optional)\n", + "\n", + "[limits] # optional, any combination of below parameters can be specificed\n", + "vi = [ 2.7, 16.0] \n", + "vo = [ 0.0, 1000000.0]\n", + "ii = [ 0.0, 1000000.0]\n", + "io = [ 0.0, 1.0]\n", + "\"\"\"\n", + "\n", + "with open(\"converter.toml\", \"w\") as f:\n", + " f.write(conv_params)\n", + "\n", + "sys.add_comp(\"My source\", comp=Converter.from_file(\"Buck 2.5V\", fname=\"converter.toml\"))" + ] + }, + { + "cell_type": "markdown", + "id": "51be684b-e43e-436d-ac86-653a9d4f306f", + "metadata": {}, + "source": [ + "## LinReg" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d819a10e-e224-43b8-adfd-e0c11da23215", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComponentTypeParentDomainvo (V)vdrop (V)rs (Ohm)eff (%)iq (A)ii (A)iis (A)pwr (W)pwrs (W)vi limits (V)vo limits (V)ii limits (A)io limits (A)
0My sourceSOURCEMy source3.30.007[0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1.0]
1LDO 1.8VLINREGMy sourceMy source1.80.250.0000010.0[2.7, 24.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 0.25]
2Buck 2.5VCONVERTERMy sourceMy source2.50.870.0000010.0[2.7, 16.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1.0]
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" + ], + "text/plain": [ + " Component Type Parent Domain vo (V) vdrop (V) rs (Ohm) \\\n", + "0 My source SOURCE My source 3.3 0.007 \n", + "1 LDO 1.8V LINREG My source My source 1.8 0.25 \n", + "2 Buck 2.5V CONVERTER My source My source 2.5 \n", + "\n", + " eff (%) iq (A) ii (A) iis (A) pwr (W) pwrs (W) vi limits (V) \\\n", + "0 [0.0, 1000000.0] \n", + "1 0.000001 0.0 [2.7, 24.0] \n", + "2 0.87 0.000001 0.0 [2.7, 16.0] \n", + "\n", + " vo limits (V) ii limits (A) io limits (A) \n", + "0 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1.0] \n", + "1 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 0.25] \n", + "2 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1.0] " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ldo_params = \"\"\"[linreg]\n", + "vo = 1.8 # output voltage (mandatory)\n", + "vdrop = 0.25 # dropout voltage (optional)\n", + "iq = 1.2e-6 # Quiescent (no-load) current (optional)\n", + "iis = 0.33e-6 # Sleep (shut-down) current (optional)\n", + "\n", + "[limits] # optional, any combination of below parameters can be specificed\n", + "vi = [ 2.7, 24.0] \n", + "vo = [ 0.0, 1000000.0]\n", + "ii = [ 0.0, 1000000.0]\n", + "io = [ 0.0, 0.25]\n", + "\"\"\"\n", + "\n", + "with open(\"linreg.toml\", \"w\") as f:\n", + " f.write(ldo_params)\n", + "\n", + "sys.add_comp(\"My source\", comp=LinReg.from_file(\"LDO 1.8V\", fname=\"linreg.toml\"))" + ] + }, + { + "cell_type": "markdown", + "id": "9f2e4e25-004b-4a36-af2a-1444cb8d05e6", + "metadata": {}, + "source": [ + "## RLoss" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c7fe5b8a-9feb-4200-b769-a9fdbdf8acfe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComponentTypeParentDomainvo (V)vdrop (V)rs (Ohm)eff (%)iq (A)ii (A)iis (A)pwr (W)pwrs (W)vi limits (V)vo limits (V)ii limits (A)io limits (A)
0My sourceSOURCEMy source3.30.007[0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1.0]
1LDO 1.8VLINREGMy sourceMy source1.80.250.0000010.0[2.7, 24.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 0.25]
2Buck 2.5VCONVERTERMy sourceMy source2.50.870.0000010.0[2.7, 16.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1.0]
3FilterSLOSSBuck 2.5VMy source7.5[0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0]
\n", + "
" + ], + "text/plain": [ + " Component Type Parent Domain vo (V) vdrop (V) rs (Ohm) \\\n", + "0 My source SOURCE My source 3.3 0.007 \n", + "1 LDO 1.8V LINREG My source My source 1.8 0.25 \n", + "2 Buck 2.5V CONVERTER My source My source 2.5 \n", + "3 Filter SLOSS Buck 2.5V My source 7.5 \n", + "\n", + " eff (%) iq (A) ii (A) iis (A) pwr (W) pwrs (W) vi limits (V) \\\n", + "0 [0.0, 1000000.0] \n", + "1 0.000001 0.0 [2.7, 24.0] \n", + "2 0.87 0.000001 0.0 [2.7, 16.0] \n", + "3 [0.0, 1000000.0] \n", + "\n", + " vo limits (V) ii limits (A) io limits (A) \n", + "0 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1.0] \n", + "1 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 0.25] \n", + "2 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1.0] \n", + "3 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1000000.0] " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rloss_params = \"\"\"[rloss]\n", + "rs = 7.5 # series resistance (mandatory)\n", + "\n", + "[limits] # optional, any combination of below parameters can be specificed\n", + "vi = [ 0.0, 1000000.0] \n", + "vo = [ 0.0, 1000000.0]\n", + "ii = [ 0.0, 1000000.0]\n", + "io = [ 0.0, 1000000.0]\n", + "\"\"\"\n", + "\n", + "with open(\"rloss.toml\", \"w\") as f:\n", + " f.write(rloss_params)\n", + "\n", + "sys.add_comp(\"Buck 2.5V\", comp=RLoss.from_file(\"Filter\", fname=\"rloss.toml\"))" + ] + }, + { + "cell_type": "markdown", + "id": "e596f2d8-1697-4587-a3df-8087e905b302", + "metadata": {}, + "source": [ + "## VLoss" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "35743730-2beb-4f61-8d55-91f0f5861060", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComponentTypeParentDomainvo (V)vdrop (V)rs (Ohm)eff (%)iq (A)ii (A)iis (A)pwr (W)pwrs (W)vi limits (V)vo limits (V)ii limits (A)io limits (A)
0My sourceSOURCEMy source3.30.007[0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1.0]
1LDO 1.8VLINREGMy sourceMy source1.80.250.0000010.0[2.7, 24.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 0.25]
2Buck 2.5VCONVERTERMy sourceMy source2.50.870.0000010.0[2.7, 16.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1.0]
3Voltage dropSLOSSBuck 2.5VMy source0.33[0.0, 200.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 0.75]
4FilterSLOSSBuck 2.5VMy source7.5[0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0]
\n", + "
" + ], + "text/plain": [ + " Component Type Parent Domain vo (V) vdrop (V) rs (Ohm) \\\n", + "0 My source SOURCE My source 3.3 0.007 \n", + "1 LDO 1.8V LINREG My source My source 1.8 0.25 \n", + "2 Buck 2.5V CONVERTER My source My source 2.5 \n", + "3 Voltage drop SLOSS Buck 2.5V My source 0.33 \n", + "4 Filter SLOSS Buck 2.5V My source 7.5 \n", + "\n", + " eff (%) iq (A) ii (A) iis (A) pwr (W) pwrs (W) vi limits (V) \\\n", + "0 [0.0, 1000000.0] \n", + "1 0.000001 0.0 [2.7, 24.0] \n", + "2 0.87 0.000001 0.0 [2.7, 16.0] \n", + "3 [0.0, 200.0] \n", + "4 [0.0, 1000000.0] \n", + "\n", + " vo limits (V) ii limits (A) io limits (A) \n", + "0 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1.0] \n", + "1 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 0.25] \n", + "2 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1.0] \n", + "3 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 0.75] \n", + "4 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1000000.0] " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vloss_params = \"\"\"[vloss]\n", + "vdrop = 0.33 # voltage drop (mandatory)\n", + "# voltage drop can optionally be specified as 1D interpolation data\n", + "#vdrop = {\"vi\": [3.3], \"io\": [0.1, 0.5, 0.9], \"vdrop\": [[0.23, 0.34, 0.477]]}\n", + "# or as 2D interpolation data\n", + "#vdrop = {\"vi\": [3.3, 5.0, 12], \"io\": [0.1, 0.5, 0.9], \"vdrop\": [[0.23, 0.34, 0.477], [0.27, 0.39, 0.51], [0.3, 0.41, 0.57]]}\n", + "\n", + "[limits] # optional, any combination of below parameters can be specificed\n", + "vi = [ 0.0, 200.0] \n", + "vo = [ 0.0, 1000000.0]\n", + "ii = [ 0.0, 1000000.0]\n", + "io = [ 0.0, 0.75]\n", + "\"\"\"\n", + "\n", + "with open(\"vloss.toml\", \"w\") as f:\n", + " f.write(vloss_params)\n", + "\n", + "sys.add_comp(\"Buck 2.5V\", comp=VLoss.from_file(\"Voltage drop\", fname=\"vloss.toml\"))" + ] + }, + { + "cell_type": "markdown", + "id": "66a6c661-ee20-4edc-b2a2-46c040414ba3", + "metadata": {}, + "source": [ + "## PLoad" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "437cfd1c-ea62-4b13-aae8-a71085d5c464", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComponentTypeParentDomainvo (V)vdrop (V)rs (Ohm)eff (%)iq (A)ii (A)iis (A)pwr (W)pwrs (W)vi limits (V)vo limits (V)ii limits (A)io limits (A)
0My sourceSOURCEMy source3.30.007[0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1.0]
1LDO 1.8VLINREGMy sourceMy source1.80.250.0000010.0[2.7, 24.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 0.25]
2MCULOADLDO 1.8VMy source0.50.001[0.0, 24.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0]
3Buck 2.5VCONVERTERMy sourceMy source2.50.870.0000010.0[2.7, 16.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1.0]
4Voltage dropSLOSSBuck 2.5VMy source0.33[0.0, 200.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 0.75]
5FilterSLOSSBuck 2.5VMy source7.5[0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0]
\n", + "
" + ], + "text/plain": [ + " Component Type Parent Domain vo (V) vdrop (V) rs (Ohm) \\\n", + "0 My source SOURCE My source 3.3 0.007 \n", + "1 LDO 1.8V LINREG My source My source 1.8 0.25 \n", + "2 MCU LOAD LDO 1.8V My source \n", + "3 Buck 2.5V CONVERTER My source My source 2.5 \n", + "4 Voltage drop SLOSS Buck 2.5V My source 0.33 \n", + "5 Filter SLOSS Buck 2.5V My source 7.5 \n", + "\n", + " eff (%) iq (A) ii (A) iis (A) pwr (W) pwrs (W) vi limits (V) \\\n", + "0 [0.0, 1000000.0] \n", + "1 0.000001 0.0 [2.7, 24.0] \n", + "2 0.5 0.001 [0.0, 24.0] \n", + "3 0.87 0.000001 0.0 [2.7, 16.0] \n", + "4 [0.0, 200.0] \n", + "5 [0.0, 1000000.0] \n", + "\n", + " vo limits (V) ii limits (A) io limits (A) \n", + "0 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1.0] \n", + "1 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 0.25] \n", + "2 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1000000.0] \n", + "3 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1.0] \n", + "4 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 0.75] \n", + "5 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1000000.0] " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pload_params = \"\"\"[pload]\n", + "pwr = 0.5 # load power (W) (mandatory)\n", + "pwrs = 1e-3 # sleep mode load power (optional)\n", + "\n", + "[limits] # optional, any combination of below parameters can be specificed\n", + "vi = [ 0.0, 24.0] \n", + "vo = [ 0.0, 1000000.0]\n", + "ii = [ 0.0, 1000000.0]\n", + "io = [ 0.0, 1000000.0]\n", + "\"\"\"\n", + "\n", + "with open(\"pload.toml\", \"w\") as f:\n", + " f.write(pload_params)\n", + "\n", + "sys.add_comp(\"LDO 1.8V\", comp=PLoad.from_file(\"MCU\", fname=\"pload.toml\"))" + ] + }, + { + "cell_type": "markdown", + "id": "431bcf8b-1007-4340-bb97-6edcbf18315e", + "metadata": {}, + "source": [ + "## ILoad" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0b1464be-18d2-44ba-ae98-08e368c33b34", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComponentTypeParentDomainvo (V)vdrop (V)rs (Ohm)eff (%)iq (A)ii (A)iis (A)pwr (W)pwrs (W)vi limits (V)vo limits (V)ii limits (A)io limits (A)
0My sourceSOURCEMy source3.30.007[0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1.0]
1LDO 1.8VLINREGMy sourceMy source1.80.250.0000010.0[2.7, 24.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 0.25]
2MCULOADLDO 1.8VMy source0.50.001[0.0, 24.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0]
3Buck 2.5VCONVERTERMy sourceMy source2.50.870.0000010.0[2.7, 16.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1.0]
4Voltage dropSLOSSBuck 2.5VMy source0.33[0.0, 200.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 0.75]
5FilterSLOSSBuck 2.5VMy source7.5[0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0]
6LoadLOADFilterMy source0.1250.00001[0.0, 24.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0]
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" + ], + "text/plain": [ + " Component Type Parent Domain vo (V) vdrop (V) rs (Ohm) \\\n", + "0 My source SOURCE My source 3.3 0.007 \n", + "1 LDO 1.8V LINREG My source My source 1.8 0.25 \n", + "2 MCU LOAD LDO 1.8V My source \n", + "3 Buck 2.5V CONVERTER My source My source 2.5 \n", + "4 Voltage drop SLOSS Buck 2.5V My source 0.33 \n", + "5 Filter SLOSS Buck 2.5V My source 7.5 \n", + "6 Load LOAD Filter My source \n", + "\n", + " eff (%) iq (A) ii (A) iis (A) pwr (W) pwrs (W) vi limits (V) \\\n", + "0 [0.0, 1000000.0] \n", + "1 0.000001 0.0 [2.7, 24.0] \n", + "2 0.5 0.001 [0.0, 24.0] \n", + "3 0.87 0.000001 0.0 [2.7, 16.0] \n", + "4 [0.0, 200.0] \n", + "5 [0.0, 1000000.0] \n", + "6 0.125 0.00001 [0.0, 24.0] \n", + "\n", + " vo limits (V) ii limits (A) io limits (A) \n", + "0 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1.0] \n", + "1 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 0.25] \n", + "2 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1000000.0] \n", + "3 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1.0] \n", + "4 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 0.75] \n", + "5 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1000000.0] \n", + "6 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1000000.0] " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iload_params = \"\"\"[iload]\n", + "ii = 0.125 # load current (A) (mandatory)\n", + "iis = 1e-5 # sleep mode load current (optional)\n", + "\n", + "[limits] # optional, any combination of below parameters can be specificed\n", + "vi = [ 0.0, 24.0] \n", + "vo = [ 0.0, 1000000.0]\n", + "ii = [ 0.0, 1000000.0]\n", + "io = [ 0.0, 1000000.0]\n", + "\"\"\"\n", + "\n", + "with open(\"iload.toml\", \"w\") as f:\n", + " f.write(iload_params)\n", + "\n", + "sys.add_comp(\"Filter\", comp=ILoad.from_file(\"Load\", fname=\"iload.toml\"))" + ] + }, + { + "cell_type": "markdown", + "id": "80e89778-0347-437e-b75e-5048334df5af", + "metadata": {}, + "source": [ + "## RLoad" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f2eb9fd3-4a3c-4b00-ab1c-ceffd8d9524f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComponentTypeParentDomainvo (V)vdrop (V)rs (Ohm)eff (%)iq (A)ii (A)iis (A)pwr (W)pwrs (W)vi limits (V)vo limits (V)ii limits (A)io limits (A)
0My sourceSOURCEMy source3.30.007[0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1.0]
1LDO 1.8VLINREGMy sourceMy source1.80.250.0000010.0[2.7, 24.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 0.25]
2MCULOADLDO 1.8VMy source0.50.001[0.0, 24.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0]
3Buck 2.5VCONVERTERMy sourceMy source2.50.870.0000010.0[2.7, 16.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1.0]
4Voltage dropSLOSSBuck 2.5VMy source0.33[0.0, 200.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 0.75]
5ResistorLOADVoltage dropMy source12.0[0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0]
6FilterSLOSSBuck 2.5VMy source7.5[0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0]
7LoadLOADFilterMy source0.1250.00001[0.0, 24.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0]
\n", + "
" + ], + "text/plain": [ + " Component Type Parent Domain vo (V) vdrop (V) rs (Ohm) \\\n", + "0 My source SOURCE My source 3.3 0.007 \n", + "1 LDO 1.8V LINREG My source My source 1.8 0.25 \n", + "2 MCU LOAD LDO 1.8V My source \n", + "3 Buck 2.5V CONVERTER My source My source 2.5 \n", + "4 Voltage drop SLOSS Buck 2.5V My source 0.33 \n", + "5 Resistor LOAD Voltage drop My source 12.0 \n", + "6 Filter SLOSS Buck 2.5V My source 7.5 \n", + "7 Load LOAD Filter My source \n", + "\n", + " eff (%) iq (A) ii (A) iis (A) pwr (W) pwrs (W) vi limits (V) \\\n", + "0 [0.0, 1000000.0] \n", + "1 0.000001 0.0 [2.7, 24.0] \n", + "2 0.5 0.001 [0.0, 24.0] \n", + "3 0.87 0.000001 0.0 [2.7, 16.0] \n", + "4 [0.0, 200.0] \n", + "5 [0.0, 1000000.0] \n", + "6 [0.0, 1000000.0] \n", + "7 0.125 0.00001 [0.0, 24.0] \n", + "\n", + " vo limits (V) ii limits (A) io limits (A) \n", + "0 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1.0] \n", + "1 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 0.25] \n", + "2 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1000000.0] \n", + "3 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1.0] \n", + "4 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 0.75] \n", + "5 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1000000.0] \n", + "6 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1000000.0] \n", + "7 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1000000.0] " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rload_params = \"\"\"[rload]\n", + "rs = 12.0 # series resistance (mandatory)\n", + "\n", + "[limits] # optional, any combination of below parameters can be specificed\n", + "vi = [ 0.0, 1000000.0] \n", + "vo = [ 0.0, 1000000.0]\n", + "ii = [ 0.0, 1000000.0]\n", + "io = [ 0.0, 1000000.0]\n", + "\"\"\"\n", + "\n", + "with open(\"rload.toml\", \"w\") as f:\n", + " f.write(rload_params)\n", + "\n", + "sys.add_comp(\"Voltage drop\", comp=RLoad.from_file(\"Resistor\", fname=\"rload.toml\"))" + ] + }, + { + "cell_type": "markdown", + "id": "22a41bdc-9b80-4d5c-b403-e9488d68b93a", + "metadata": {}, + "source": [ + "## Summary\n", + "All component parameters can be loaded from .toml files." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d67b1920-0f7a-46a8-8492-9437df9ebc54", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComponentTypeParentDomainvo (V)vdrop (V)rs (Ohm)eff (%)iq (A)ii (A)iis (A)pwr (W)pwrs (W)vi limits (V)vo limits (V)ii limits (A)io limits (A)
0My sourceSOURCEMy source3.30.007[0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1.0]
1LDO 1.8VLINREGMy sourceMy source1.80.250.0000010.0[2.7, 24.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 0.25]
2MCULOADLDO 1.8VMy source0.50.001[0.0, 24.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0]
3Buck 2.5VCONVERTERMy sourceMy source2.50.870.0000010.0[2.7, 16.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1.0]
4Voltage dropSLOSSBuck 2.5VMy source0.33[0.0, 200.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 0.75]
5ResistorLOADVoltage dropMy source12.0[0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0]
6FilterSLOSSBuck 2.5VMy source7.5[0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0]
7LoadLOADFilterMy source0.1250.00001[0.0, 24.0][0.0, 1000000.0][0.0, 1000000.0][0.0, 1000000.0]
\n", + "
" + ], + "text/plain": [ + " Component Type Parent Domain vo (V) vdrop (V) rs (Ohm) \\\n", + "0 My source SOURCE My source 3.3 0.007 \n", + "1 LDO 1.8V LINREG My source My source 1.8 0.25 \n", + "2 MCU LOAD LDO 1.8V My source \n", + "3 Buck 2.5V CONVERTER My source My source 2.5 \n", + "4 Voltage drop SLOSS Buck 2.5V My source 0.33 \n", + "5 Resistor LOAD Voltage drop My source 12.0 \n", + "6 Filter SLOSS Buck 2.5V My source 7.5 \n", + "7 Load LOAD Filter My source \n", + "\n", + " eff (%) iq (A) ii (A) iis (A) pwr (W) pwrs (W) vi limits (V) \\\n", + "0 [0.0, 1000000.0] \n", + "1 0.000001 0.0 [2.7, 24.0] \n", + "2 0.5 0.001 [0.0, 24.0] \n", + "3 0.87 0.000001 0.0 [2.7, 16.0] \n", + "4 [0.0, 200.0] \n", + "5 [0.0, 1000000.0] \n", + "6 [0.0, 1000000.0] \n", + "7 0.125 0.00001 [0.0, 24.0] \n", + "\n", + " vo limits (V) ii limits (A) io limits (A) \n", + "0 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1.0] \n", + "1 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 0.25] \n", + "2 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1000000.0] \n", + "3 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1.0] \n", + "4 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 0.75] \n", + "5 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1000000.0] \n", + "6 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1000000.0] \n", + "7 [0.0, 1000000.0] [0.0, 1000000.0] [0.0, 1000000.0] " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sys.params(limits=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "dbc87a3b-ab82-4c95-a536-51309b62590d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Test source\n",
+       "└── My source\n",
+       "    ├── LDO 1.8V\n",
+       "    │   └── MCU\n",
+       "    └── Buck 2.5V\n",
+       "        ├── Voltage drop\n",
+       "        │   └── Resistor\n",
+       "        └── Filter\n",
+       "            └── Load\n",
+       "
\n" + ], + "text/plain": [ + "Test source\n", + "└── My source\n", + " ├── LDO 1.8V\n", + " │ └── MCU\n", + " └── Buck 2.5V\n", + " ├── Voltage drop\n", + " │ └── Resistor\n", + " └── Filter\n", + " └── Load\n" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sys.tree()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "233467a3-1a5c-4d7f-8619-fb6683547e07", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComponentTypeParentDomainVin (V)Vout (V)Iin (A)Iout (A)Power (W)Loss (W)Efficiency (%)Warnings
0My sourceSOURCEMy source3.33.2961890.5443980.5443981.7965140.00207599.884522
1LDO 1.8VLINREGMy sourceMy source3.2961891.80.2777780.2777780.9156080.41560854.608516io
2MCULOADLDO 1.8VMy source1.80.00.2777780.00.50.0100.0
3Buck 2.5VCONVERTERMy sourceMy source3.2961892.50.266620.3058330.8788310.11424887.0
4Voltage dropSLOSSBuck 2.5VMy source2.52.170.1808330.1808330.4520830.05967586.8
5ResistorLOADVoltage dropMy source2.170.00.1808330.00.3924080.0100.0
6FilterSLOSSBuck 2.5VMy source2.51.56250.1250.1250.31250.11718862.5
7LoadLOADFilterMy source1.56250.00.1250.00.1953120.0100.0
8System total1.7965140.70879360.546185Yes
\n", + "
" + ], + "text/plain": [ + " Component Type Parent Domain Vin (V) Vout (V) \\\n", + "0 My source SOURCE My source 3.3 3.296189 \n", + "1 LDO 1.8V LINREG My source My source 3.296189 1.8 \n", + "2 MCU LOAD LDO 1.8V My source 1.8 0.0 \n", + "3 Buck 2.5V CONVERTER My source My source 3.296189 2.5 \n", + "4 Voltage drop SLOSS Buck 2.5V My source 2.5 2.17 \n", + "5 Resistor LOAD Voltage drop My source 2.17 0.0 \n", + "6 Filter SLOSS Buck 2.5V My source 2.5 1.5625 \n", + "7 Load LOAD Filter My source 1.5625 0.0 \n", + "8 System total \n", + "\n", + " Iin (A) Iout (A) Power (W) Loss (W) Efficiency (%) Warnings \n", + "0 0.544398 0.544398 1.796514 0.002075 99.884522 \n", + "1 0.277778 0.277778 0.915608 0.415608 54.608516 io \n", + "2 0.277778 0.0 0.5 0.0 100.0 \n", + "3 0.26662 0.305833 0.878831 0.114248 87.0 \n", + "4 0.180833 0.180833 0.452083 0.059675 86.8 \n", + "5 0.180833 0.0 0.392408 0.0 100.0 \n", + "6 0.125 0.125 0.3125 0.117188 62.5 \n", + "7 0.125 0.0 0.195312 0.0 100.0 \n", + "8 1.796514 0.708793 60.546185 Yes " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sys.solve()" + ] + } + ], + "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": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/nb/Datacenter rack.ipynb b/docs/nb/Datacenter rack.ipynb new file mode 100644 index 0000000..35184cd --- /dev/null +++ b/docs/nb/Datacenter rack.ipynb @@ -0,0 +1,430 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a1ca17a0-128e-46b7-b65d-011116b44d28", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Datacenter Compute Rack\n", + "In this tutorial we will calculate the yearly energy consumption of a 19in server rack. \n", + "\n", + "Configurations:\n", + " * 16 2U servers with 230VAC input\n", + " * Server power (maximum): 850W\n", + " * PDU distribution resistance: 2.5mOhm\n", + " * Three different versions of 80plus certified PSUs: Bronze, Gold and Titanium\n", + "\n", + "The rack is operated as follows during a year:\n", + " * 1 day shutdown for maintenance\n", + " * 75% of active time operating at 100% power\n", + " * 20% of active time operating at 50% power\n", + " * 5% of active time operating at 5% of power" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0ddf2bf4-d388-4195-aaaa-c0a4969b02b0", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# This cell can be removed, it is only used for running the notebook during Sphinx documentation build.\n", + "import sys, os\n", + "if os.getcwd().replace('\\\\', '/').endswith(\"/docs/nb\"):\n", + " sys.path.insert(0, os.path.abspath(os.path.join(\"../../src\")))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3555e267-3e0a-4c54-8611-f498f616146e", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from sysloss.components import *\n", + "from sysloss.system import System\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "3e9ec23a-db71-4f10-acac-81928ed90913", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## System definition\n", + "The function *create_rack()* below is used to create a system with the PSU efficiency parameter as input.\n", + "\n", + "The following load phases are defined as well:\n", + " * \"Service\"\n", + " * \"Full load\"\n", + " * \"Half load\"\n", + " * \"Idle\"\n", + "\n", + "```{note}\n", + "sysLoss treats AC and DC voltages the same. This is valid when the rms AC voltage is used on single phase (power factor 1).\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "47140e30-4db4-4d4d-93ea-3e72769df775", + "metadata": {}, + "outputs": [], + "source": [ + "DAY_SECS = 24*60*60 # seconds in a day\n", + "rack_phases = {\"Service\": DAY_SECS, \"Full load\": 364*DAY_SECS*0.75, \"Half load\": 364*DAY_SECS*0.75, \"Idle\": 364*DAY_SECS*0.05}\n", + "\n", + "def create_rack(psu_efficiency):\n", + " sys = System(\"Compute rack\", source=Source(\"230VAC\", vo=230.0))\n", + " sys.set_sys_phases(rack_phases)\n", + " for i in range(16):\n", + " idx = \"[{}]\".format(i+1)\n", + " sys.add_comp(\"230VAC\", comp=RLoss(\"PDU resistance\"+idx, rs=2.5e-3))\n", + " sys.add_comp(\"PDU resistance\"+idx, comp=Converter(\"PSU\"+idx, vo=12.0, eff=psu_efficiency))\n", + " sys.set_comp_phases(\"PSU\"+idx, [\"Full load\", \"Half load\", \"Idle\"])\n", + " sys.add_comp(\"PSU\"+idx, comp=PLoad(\"Blade\"+idx, pwr=850.0))\n", + " sys.set_comp_phases(\"Blade\"+idx, {\"Full load\": 850.0, \"Half load\": 425.0, \"Idle\": 42.5})\n", + " return sys" + ] + }, + { + "cell_type": "markdown", + "id": "a6eb2a0c-07ee-43b0-8569-4d94976635a4", + "metadata": {}, + "source": [ + "Define efficiency for the three different PSU ratings:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8937c0ea-f451-408a-ae43-074bb6c113ad", + "metadata": {}, + "outputs": [], + "source": [ + "bronze_eff = {\"vi\": [230.0], \"io\":[3.55, 7.1, 14.2, 35.5, 71.0], \"eff\":[[.67, .79, .85, .88, .85]]}\n", + "gold_eff = {\"vi\": [230.0], \"io\":[3.55, 7.1, 14.2, 35.5, 71.0], \"eff\":[[.79, .86, .90, .92, .89]]}\n", + "titanium_eff = {\"vi\": [230.0], \"io\":[3.55, 7.1, 14.2, 35.5, 71.0], \"eff\":[[.84, .90, .94, .96, .91]]}" + ] + }, + { + "cell_type": "markdown", + "id": "326d0203-ed99-4f25-acfc-acdb9f499213", + "metadata": {}, + "source": [ + "## Analysis\n", + "Analysis is straight forward - run *solve()* with each of the three PSU ratings.\n", + "\n", + "```{tip}\n", + "Set the *energy* parameter in *.solve()* to True - the results table will then contain a new column with the 24h energy consumption.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9b2ba15e-8f1d-47d2-8a75-aee3f9597128", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComponentTypeParentRatingPhaseVin (V)Vout (V)Iin (A)Iout (A)Power (W)Loss (W)Efficiency (%)24h energy (Wh)Warnings
598PDU resistance[1]SLOSS230VACTitaniumIdle230.0229.999450.219980.2199850.5953590.00012199.99976139.101297
599PSU[1]CONVERTERPDU resistance[1]TitaniumIdle229.9994512.00.219983.54166750.5952388.09523884.039.101203
600Blade[1]LOADPSU[1]TitaniumIdle12.00.03.5416670.042.50.0100.032.845011
601System totalTitaniumIdle809.525745129.52574583.999799625.620746
602System averageTitanium10664.913806789.51396393.210233255957.931346
\n", + "
" + ], + "text/plain": [ + " Component Type Parent Rating Phase \\\n", + "598 PDU resistance[1] SLOSS 230VAC Titanium Idle \n", + "599 PSU[1] CONVERTER PDU resistance[1] Titanium Idle \n", + "600 Blade[1] LOAD PSU[1] Titanium Idle \n", + "601 System total Titanium Idle \n", + "602 System average Titanium \n", + "\n", + " Vin (V) Vout (V) Iin (A) Iout (A) Power (W) Loss (W) \\\n", + "598 230.0 229.99945 0.21998 0.21998 50.595359 0.000121 \n", + "599 229.99945 12.0 0.21998 3.541667 50.595238 8.095238 \n", + "600 12.0 0.0 3.541667 0.0 42.5 0.0 \n", + "601 809.525745 129.525745 \n", + "602 10664.913806 789.513963 \n", + "\n", + " Efficiency (%) 24h energy (Wh) Warnings \n", + "598 99.999761 39.101297 \n", + "599 84.0 39.101203 \n", + "600 100.0 32.845011 \n", + "601 83.999799 625.620746 \n", + "602 93.210233 255957.931346 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raitings = {\"Bronze\": bronze_eff, \"Gold\": gold_eff, \"Titanium\": titanium_eff}\n", + "\n", + "res = []\n", + "for r in raitings.keys():\n", + " rack = create_rack(raitings[r])\n", + " res += [rack.solve(tags={\"Rating\": r}, energy=True)]\n", + "df = pd.concat(res, ignore_index=True)\n", + "df.tail()" + ] + }, + { + "cell_type": "markdown", + "id": "e07b2ecb-97ad-4620-9419-7fb94ba9ceec", + "metadata": {}, + "source": [ + "Since we are interested in the yearly power consumption, a new column is created for this:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4e53290e-d990-4af0-b463-4ac357e083d0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\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", + "
ComponentRatingPower (W)Loss (W)Efficiency (%)Annual power (kWh)
System averageBronze11492.9749201617.57508085.894183100678.460297
System averageGold10978.2752981102.87545690.14660396169.691606
System averageTitanium10664.913806789.51396393.21023393424.644941
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"Annual power (kWh)\"] = df[\"24h energy (Wh)\"] * 365 / 1000\n", + "df[df.Component == \"System average\"][[\"Component\", \"Rating\", \"Power (W)\", \"Loss (W)\", \"Efficiency (%)\", \"Annual power (kWh)\"]].style.hide(axis='index')" + ] + }, + { + "cell_type": "markdown", + "id": "606a90fd-ad74-4228-8502-37cba6010b75", + "metadata": {}, + "source": [ + "The power savings from using a Titanium certified PSU over a Gold certified PSU is 7263kWh per year. Is there an economic gain to use higher rated PSUs? That depends on the lifetime of the rack, the energy prices and the cost difference between e.g. a Bronze PSU and Titanium PSU." + ] + }, + { + "cell_type": "markdown", + "id": "99692134-a950-46fd-bb29-60f7e6f5cf8a", + "metadata": {}, + "source": [ + "## Summary\n", + "This tutorial demonstrates how system energy consumption can be analyzed with sysLoss by enabling the *energy* parameter in *solve()*." + ] + } + ], + "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": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/nb/PCIe FPGA.ipynb b/docs/nb/PCIe FPGA.ipynb new file mode 100644 index 0000000..f20689e --- /dev/null +++ b/docs/nb/PCIe FPGA.ipynb @@ -0,0 +1,1475 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a1ca17a0-128e-46b7-b65d-011116b44d28", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# PCIe FPGA Data Aquisition Board\n", + "The topic in this notebook is the analysis of a high-speed data acquisition PCIe board based around a large FPGA (Field Programmable Gate Array). A number of high-speed ADCs, each with its own power delivery system, is connected to the FPGA with JESD204B links. Data is pre-processed in the FPGA before being sent across the PCIe bus. The board can only draw power from the PCIe connector (no extra ATX connectors), and the task at hand is to determine how many ADC channels can be added to the board within the power limits of the PCIe specification (75W).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0ddf2bf4-d388-4195-aaaa-c0a4969b02b0", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# This cell can be removed, it is only used for running the notebook during Sphinx documentation build.\n", + "import sys, os\n", + "if os.getcwd().replace('\\\\', '/').endswith(\"/docs/nb\"):\n", + " sys.path.insert(0, os.path.abspath(os.path.join(\"../../src\")))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3555e267-3e0a-4c54-8611-f498f616146e", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from sysloss.components import *\n", + "from sysloss.system import System\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "3e9ec23a-db71-4f10-acac-81928ed90913", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## System definition\n", + "Each ADC channel will use the following resources:\n", + " * Two JESD204B lanes to the FPGA\n", + " * About 7% of FPGA logic for pre-processing\n", + " * Four power rails: 1.8V and 3x 1.2V (converted from 12V):\n", + "\n", + "![ADC power](PCIeADC.png)\n", + "\n", + "The FPGA has 32 transceivers, 8 are used for PCIe while the rest are dedicated to JESD204 lanes. This sets the upper limit for the number of ADC channels to 12. The FPGA itself has 4 power rails: 1.8V (AUX), 1.2V (AVTT), 0.9V (AVCC) and 0.85V (VCCINT). Except for the high-speed transceivers, there are practically no I/O used, so I/O bank power is left out of the analysis.\n", + "\n", + "Power consumption of the ADC is collected from the data sheet, and FPGA power is estimated using the FPGA power tools. FPGA power consumption is summarized in the table below:\n", + "\n", + "| Voltage | ADC (per channel) Power (W) | System control & PCIe Power (W)| Static FPGA power (W) |\n", + "|--------:|------:|-----:|------:|\n", + "| VCCINT | 1.58|1.254| 1.67|\n", + "| AVCC | 0.051| 0.45| 0.57|\n", + "| AVTT | 0.22| 0.76| 0.031|\n", + "| VAUX | | | 1.35|\n", + "\n", + "FPGA and ADC's will be powered from the 12V input, while board monitoring and test signal generators will be powered from the 3.3V input. \n", + "\n", + "```{tip}\n", + "Use *limits* on components like Converters (input voltage range, output current) and LinRegs (output voltage and current) to get warnings if component voltages or currents are out of spec. \n", + "```\n", + "\n", + "Buck converter efficiency is defined as interpolation data. The subsystems are defined in functions for easy manipulation of key system parameters and system architecture." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "47140e30-4db4-4d4d-93ea-3e72769df775", + "metadata": {}, + "outputs": [], + "source": [ + "eff_2v3 = {\"vi\":[12], \"io\":[1e-3, 1, 2, 3, 4, 5], \"eff\":[[0.45, 0.95, 0.94, 0.925, 0.9, 0.88]]}\n", + "eff_1v7 = {\"vi\":[12], \"io\":[1e-3, 1, 2, 3, 4, 5], \"eff\":[[0.33, 0.92, 0.92, 0.91, 0.9, 0.875]]}\n", + "eff_0v85 = {\"vi\":[12], \"io\":[1, 2, 5, 10, 20, 30], \"eff\":[[0.48, 0.7, 0.86, 0.9, 0.87, 0.84]]} \n", + "eff_1v8 = {\"vi\":[12], \"io\":[.1, .25, .5, 1.0, 1.5, 2.0], \"eff\":[[0.63, 0.82, 0.86, 0.9, 0.9, 0.885]]}\n", + "eff_0v9 = {\"vi\":[12], \"io\":[.1, .25, .5, 1.0, 1.5, 2.0], \"eff\":[[0.54, 0.75, 0.82, 0.85, 0.84, 0.83]]}\n", + "eff_1v2 = {\"vi\":[12], \"io\":[.01, .1, .5, 1.0, 2.0, 4.0], \"eff\":[[0.72, 0.78, 0.84, 0.88, 0.87, 0.8]]}\n", + "\n", + "def adc_subsystem(sys, channel, src):\n", + " idx = \"[{}]\".format(channel+1)\n", + " sys.add_comp(src, comp=Converter(\"ADC\"+idx+\" buck 2.3V\", vo=2.3, eff=eff_2v3))\n", + " sys.add_comp(\"ADC\"+idx+\" buck 2.3V\", comp=RLoss(\"ADC\"+idx+\" ferrit1\", rs=0.087))\n", + " sys.add_comp(\"ADC\"+idx+\" ferrit1\", comp=LinReg(\"ADC\"+idx+\" LDO 1.8V\", vo=1.8, iq=0.5, vdrop=0.15, limits={\"vo\":[1.8, 1.8]}))\n", + " sys.add_comp(\"ADC\"+idx+\" LDO 1.8V\", comp=ILoad(\"ADC\"+idx+\" AVVD18\", ii=0.5))\n", + " sys.add_comp(src, comp=Converter(\"ADC\"+idx+\" buck 1.7V\", vo=1.7, eff=eff_1v7))\n", + " sys.add_comp(\"ADC\"+idx+\" buck 1.7V\", comp=RLoss(\"ADC\"+idx+\" ferrit2\", rs=0.103))\n", + " sys.add_comp(\"ADC\"+idx+\" ferrit2\", comp=LinReg(\"ADC\"+idx+\" LDO[1] 1.2V\", vo=1.2, iq=0.23, vdrop=0.15, limits={\"vo\":[1.2, 1.2]}))\n", + " sys.add_comp(\"ADC\"+idx+\" LDO[1] 1.2V\", comp=ILoad(\"ADC\"+idx+\" AVVD12\", ii=0.74))\n", + " sys.add_comp(\"ADC\"+idx+\" ferrit2\", comp=LinReg(\"ADC\"+idx+\" LDO[2] 1.2V\", vo=1.2, iq=0.23, vdrop=0.15, limits={\"vo\":[1.2, 1.2]}))\n", + " sys.add_comp(\"ADC\"+idx+\" LDO[2] 1.2V\", comp=ILoad(\"ADC\"+idx+\" CLKVDD\", ii=0.086))\n", + " sys.add_comp(\"ADC\"+idx+\" ferrit2\", comp=LinReg(\"ADC\"+idx+\" LDO[3] 1.2V\", vo=1.2, iq=0.23, vdrop=0.15, limits={\"vo\":[1.2, 1.2]}))\n", + " sys.add_comp(\"ADC\"+idx+\" LDO[3] 1.2V\", comp=ILoad(\"ADC\"+idx+\" DVDD\", ii=1.41))\n", + " return sys\n", + "\n", + "def fpga_subsystem(sys, channels, src):\n", + " sys.add_comp(src, comp=Converter(\"FPGA VCCINT\", vo=0.85, eff=eff_0v85, limits={\"io\":[0.0, 30.0]}))\n", + " sys.add_comp(\"FPGA VCCINT\", comp=PLoad(\"FPGA INT static\", pwr=1.67))\n", + " sys.add_comp(\"FPGA VCCINT\", comp=PLoad(\"FPGA INT dynamic\", pwr=1.25+channels*1.58))\n", + " # VCCAUX\n", + " sys.add_comp(src, comp=Converter(\"FPGA VCCAUX\", vo=1.8, eff=eff_1v8, limits={\"io\":[0.0, 2.0]}))\n", + " sys.add_comp(\"FPGA VCCAUX\", comp=PLoad(\"FPGA AUX static\", pwr=1.35))\n", + " # AVCC\n", + " sys.add_comp(src, comp=Converter(\"FPGA AVCC\", vo=0.9, eff=eff_0v9, limits={\"io\":[0.0, 2.0]}))\n", + " sys.add_comp(\"FPGA AVCC\", comp=PLoad(\"FPGA AVCC static\", pwr=0.57))\n", + " sys.add_comp(\"FPGA AVCC\", comp=PLoad(\"FPGA AVCC dynamic\", pwr=0.45+channels*0.051))\n", + " # AVTT\n", + " sys.add_comp(src, comp=Converter(\"FPGA AVTT\", vo=1.2, eff=eff_1v2, limits={\"io\":[0.0, 4.0]}))\n", + " sys.add_comp(\"FPGA AVTT\", comp=PLoad(\"FPGA AVTT static\", pwr=0.031))\n", + " sys.add_comp(\"FPGA AVTT\", comp=PLoad(\"FPGA AVTT dynamic\", pwr=0.76+channels*0.22))\n", + " sys.add_comp(src, comp=ILoad(\"Board fan\", ii=0.06))\n", + " return sys\n", + "\n", + "def PCIe_system(channels):\n", + " # power inputs 12V and 3.3V with current limits set to PCIe spec.\n", + " sys = System(\"PCIe FPGA board\", source=Source(\"12V\", vo=12.0, limits={\"io\":[0.0, 5.5]}))\n", + " sys.add_source(Source(\"3.3V\", vo=3.3, limits={\"io\":[0.0, 3.0]}))\n", + " # 3.3V subsystem\n", + " sys.add_comp(\"3.3V\", comp=Converter(\"Buck 2.5V\", vo=2.5, eff=eff_2v3))\n", + " sys.add_comp(\"Buck 2.5V\", comp=PLoad(\"Board monitor\", pwr=0.35))\n", + " sys.add_comp(\"Buck 2.5V\", comp=PLoad(\"Signal generators\", pwr=1.55))\n", + " # FPGA subsystem\n", + " sys = fpga_subsystem(sys, channels, \"12V\")\n", + " # ADC channels\n", + " for i in range(channels):\n", + " sys = adc_subsystem(sys, i, \"12V\")\n", + " return sys" + ] + }, + { + "cell_type": "markdown", + "id": "93e6215c-b07d-4707-9a13-a15bd43a9635", + "metadata": {}, + "source": [ + "## Analysis\n", + "We start by looking at the power tree and power consumption for a one channel board:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5a22a5b7-951f-4254-8ef6-a014de81d855", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
PCIe FPGA board\n",
+       "├── 3.3V\n",
+       "│   └── Buck 2.5V\n",
+       "│       ├── Signal generators\n",
+       "│       └── Board monitor\n",
+       "└── 12V\n",
+       "    ├── ADC[1] buck 1.7V\n",
+       "    │   └── ADC[1] ferrit2\n",
+       "    │       ├── ADC[1] LDO[3] 1.2V\n",
+       "    │       │   └── ADC[1] DVDD\n",
+       "    │       ├── ADC[1] LDO[2] 1.2V\n",
+       "    │       │   └── ADC[1] CLKVDD\n",
+       "    │       └── ADC[1] LDO[1] 1.2V\n",
+       "    │           └── ADC[1] AVVD12\n",
+       "    ├── ADC[1] buck 2.3V\n",
+       "    │   └── ADC[1] ferrit1\n",
+       "    │       └── ADC[1] LDO 1.8V\n",
+       "    │           └── ADC[1] AVVD18\n",
+       "    ├── Board fan\n",
+       "    ├── FPGA AVTT\n",
+       "    │   ├── FPGA AVTT dynamic\n",
+       "    │   └── FPGA AVTT static\n",
+       "    ├── FPGA AVCC\n",
+       "    │   ├── FPGA AVCC dynamic\n",
+       "    │   └── FPGA AVCC static\n",
+       "    ├── FPGA VCCAUX\n",
+       "    │   └── FPGA AUX static\n",
+       "    └── FPGA VCCINT\n",
+       "        ├── FPGA INT dynamic\n",
+       "        └── FPGA INT static\n",
+       "
\n" + ], + "text/plain": [ + "PCIe FPGA board\n", + "├── 3.3V\n", + "│ └── Buck 2.5V\n", + "│ ├── Signal generators\n", + "│ └── Board monitor\n", + "└── 12V\n", + " ├── ADC[1] buck 1.7V\n", + " │ └── ADC[1] ferrit2\n", + " │ ├── ADC[1] LDO[3] 1.2V\n", + " │ │ └── ADC[1] DVDD\n", + " │ ├── ADC[1] LDO[2] 1.2V\n", + " │ │ └── ADC[1] CLKVDD\n", + " │ └── ADC[1] LDO[1] 1.2V\n", + " │ └── ADC[1] AVVD12\n", + " ├── ADC[1] buck 2.3V\n", + " │ └── ADC[1] ferrit1\n", + " │ └── ADC[1] LDO 1.8V\n", + " │ └── ADC[1] AVVD18\n", + " ├── Board fan\n", + " ├── FPGA AVTT\n", + " │ ├── FPGA AVTT dynamic\n", + " │ └── FPGA AVTT static\n", + " ├── FPGA AVCC\n", + " │ ├── FPGA AVCC dynamic\n", + " │ └── FPGA AVCC static\n", + " ├── FPGA VCCAUX\n", + " │ └── FPGA AUX static\n", + " └── FPGA VCCINT\n", + " ├── FPGA INT dynamic\n", + " └── FPGA INT static\n" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sys = PCIe_system(1)\n", + "sys.tree()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b5bb665c-d90a-42ab-80b0-8f7cf244700c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComponentTypeParentDomainVin (V)Vout (V)Iin (A)Iout (A)Power (W)Loss (W)Efficiency (%)Warnings
03.3VSOURCE3.3V3.33.30.6937840.6937842.2894880.0100.0
1Buck 2.5VCONVERTER3.3V3.3V3.32.50.6937840.762.2894880.38948882.987988
2Signal generatorsLOADBuck 2.5V3.3V2.50.00.620.01.550.0100.0
3Board monitorLOADBuck 2.5V3.3V2.50.00.140.00.350.0100.0
412VSOURCE12V12.012.01.3074491.30744915.6893920.0100.0
5ADC[1] buck 1.7VCONVERTER12V12V12.01.70.3451972.2364.1423650.34116591.764
6ADC[1] ferrit2SLOSSADC[1] buck 1.7V12V1.71.4696922.2362.2363.80120.51496986.452471
7ADC[1] LDO[3] 1.2VLINREGADC[1] ferrit212V1.4696921.21.411.412.0722660.38026681.649761
8ADC[1] DVDDLOADADC[1] LDO[3] 1.2V12V1.20.01.410.01.6920.0100.0
9ADC[1] LDO[2] 1.2VLINREGADC[1] ferrit212V1.4696921.20.0860.0860.1263940.02319481.649761
10ADC[1] CLKVDDLOADADC[1] LDO[2] 1.2V12V1.20.00.0860.00.10320.0100.0
11ADC[1] LDO[1] 1.2VLINREGADC[1] ferrit212V1.4696921.20.740.741.0875720.19957281.649761
12ADC[1] AVVD12LOADADC[1] LDO[1] 1.2V12V1.20.00.740.00.8880.0100.0
13ADC[1] buck 2.3VCONVERTER12V12V12.02.30.1369540.51.6434450.49344569.974975
14ADC[1] ferrit1SLOSSADC[1] buck 2.3V12V2.32.25650.50.51.150.0217598.108696
15ADC[1] LDO 1.8VLINREGADC[1] ferrit112V2.25651.80.50.51.128250.2282579.769555
16ADC[1] AVVD18LOADADC[1] LDO 1.8V12V1.80.00.50.00.90.0100.0
17Board fanLOAD12V12V12.00.00.060.00.720.0100.0
18FPGA AVTTCONVERTER12V12V12.01.20.0971290.84251.1655520.15455286.74
19FPGA AVTT dynamicLOADFPGA AVTT12V1.20.00.8166670.00.980.0100.0
20FPGA AVTT staticLOADFPGA AVTT12V1.20.00.0258330.00.0310.0100.0
21FPGA AVCCCONVERTER12V12V12.00.90.1054721.191.2656580.19465884.62
22FPGA AVCC dynamicLOADFPGA AVCC12V0.90.00.5566670.00.5010.0100.0
23FPGA AVCC staticLOADFPGA AVCC12V0.90.00.6333330.00.570.0100.0
24FPGA VCCAUXCONVERTER12V12V12.01.80.1278410.751.5340910.18409188.0
25FPGA AUX staticLOADFPGA VCCAUX12V1.80.00.750.01.350.0100.0
26FPGA VCCINTCONVERTER12V12V12.00.850.4348575.2941185.2182810.71828186.235294
27FPGA INT dynamicLOADFPGA VCCINT12V0.850.03.3294120.02.830.0100.0
28FPGA INT staticLOADFPGA VCCINT12V0.850.01.9647060.01.670.0100.0
29Subsystem 3.3V3.32.2894880.38948882.987988
30Subsystem 12V12.015.6893923.45419277.9839
31System total17.978883.8436878.621137
\n", + "
" + ], + "text/plain": [ + " Component Type Parent Domain Vin (V) \\\n", + "0 3.3V SOURCE 3.3V 3.3 \n", + "1 Buck 2.5V CONVERTER 3.3V 3.3V 3.3 \n", + "2 Signal generators LOAD Buck 2.5V 3.3V 2.5 \n", + "3 Board monitor LOAD Buck 2.5V 3.3V 2.5 \n", + "4 12V SOURCE 12V 12.0 \n", + "5 ADC[1] buck 1.7V CONVERTER 12V 12V 12.0 \n", + "6 ADC[1] ferrit2 SLOSS ADC[1] buck 1.7V 12V 1.7 \n", + "7 ADC[1] LDO[3] 1.2V LINREG ADC[1] ferrit2 12V 1.469692 \n", + "8 ADC[1] DVDD LOAD ADC[1] LDO[3] 1.2V 12V 1.2 \n", + "9 ADC[1] LDO[2] 1.2V LINREG ADC[1] ferrit2 12V 1.469692 \n", + "10 ADC[1] CLKVDD LOAD ADC[1] LDO[2] 1.2V 12V 1.2 \n", + "11 ADC[1] LDO[1] 1.2V LINREG ADC[1] ferrit2 12V 1.469692 \n", + "12 ADC[1] AVVD12 LOAD ADC[1] LDO[1] 1.2V 12V 1.2 \n", + "13 ADC[1] buck 2.3V CONVERTER 12V 12V 12.0 \n", + "14 ADC[1] ferrit1 SLOSS ADC[1] buck 2.3V 12V 2.3 \n", + "15 ADC[1] LDO 1.8V LINREG ADC[1] ferrit1 12V 2.2565 \n", + "16 ADC[1] AVVD18 LOAD ADC[1] LDO 1.8V 12V 1.8 \n", + "17 Board fan LOAD 12V 12V 12.0 \n", + "18 FPGA AVTT CONVERTER 12V 12V 12.0 \n", + "19 FPGA AVTT dynamic LOAD FPGA AVTT 12V 1.2 \n", + "20 FPGA AVTT static LOAD FPGA AVTT 12V 1.2 \n", + "21 FPGA AVCC CONVERTER 12V 12V 12.0 \n", + "22 FPGA AVCC dynamic LOAD FPGA AVCC 12V 0.9 \n", + "23 FPGA AVCC static LOAD FPGA AVCC 12V 0.9 \n", + "24 FPGA VCCAUX CONVERTER 12V 12V 12.0 \n", + "25 FPGA AUX static LOAD FPGA VCCAUX 12V 1.8 \n", + "26 FPGA VCCINT CONVERTER 12V 12V 12.0 \n", + "27 FPGA INT dynamic LOAD FPGA VCCINT 12V 0.85 \n", + "28 FPGA INT static LOAD FPGA VCCINT 12V 0.85 \n", + "29 Subsystem 3.3V 3.3 \n", + "30 Subsystem 12V 12.0 \n", + "31 System total \n", + "\n", + " Vout (V) Iin (A) Iout (A) Power (W) Loss (W) Efficiency (%) Warnings \n", + "0 3.3 0.693784 0.693784 2.289488 0.0 100.0 \n", + "1 2.5 0.693784 0.76 2.289488 0.389488 82.987988 \n", + "2 0.0 0.62 0.0 1.55 0.0 100.0 \n", + "3 0.0 0.14 0.0 0.35 0.0 100.0 \n", + "4 12.0 1.307449 1.307449 15.689392 0.0 100.0 \n", + "5 1.7 0.345197 2.236 4.142365 0.341165 91.764 \n", + "6 1.469692 2.236 2.236 3.8012 0.514969 86.452471 \n", + "7 1.2 1.41 1.41 2.072266 0.380266 81.649761 \n", + "8 0.0 1.41 0.0 1.692 0.0 100.0 \n", + "9 1.2 0.086 0.086 0.126394 0.023194 81.649761 \n", + "10 0.0 0.086 0.0 0.1032 0.0 100.0 \n", + "11 1.2 0.74 0.74 1.087572 0.199572 81.649761 \n", + "12 0.0 0.74 0.0 0.888 0.0 100.0 \n", + "13 2.3 0.136954 0.5 1.643445 0.493445 69.974975 \n", + "14 2.2565 0.5 0.5 1.15 0.02175 98.108696 \n", + "15 1.8 0.5 0.5 1.12825 0.22825 79.769555 \n", + "16 0.0 0.5 0.0 0.9 0.0 100.0 \n", + "17 0.0 0.06 0.0 0.72 0.0 100.0 \n", + "18 1.2 0.097129 0.8425 1.165552 0.154552 86.74 \n", + "19 0.0 0.816667 0.0 0.98 0.0 100.0 \n", + "20 0.0 0.025833 0.0 0.031 0.0 100.0 \n", + "21 0.9 0.105472 1.19 1.265658 0.194658 84.62 \n", + "22 0.0 0.556667 0.0 0.501 0.0 100.0 \n", + "23 0.0 0.633333 0.0 0.57 0.0 100.0 \n", + "24 1.8 0.127841 0.75 1.534091 0.184091 88.0 \n", + "25 0.0 0.75 0.0 1.35 0.0 100.0 \n", + "26 0.85 0.434857 5.294118 5.218281 0.718281 86.235294 \n", + "27 0.0 3.329412 0.0 2.83 0.0 100.0 \n", + "28 0.0 1.964706 0.0 1.67 0.0 100.0 \n", + "29 2.289488 0.389488 82.987988 \n", + "30 15.689392 3.454192 77.9839 \n", + "31 17.97888 3.84368 78.621137 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sys.solve()" + ] + }, + { + "cell_type": "markdown", + "id": "d99cac7e-1d5d-42b3-84aa-adef1ef0c252", + "metadata": {}, + "source": [ + "```{note}\n", + "When the system has more than one voltage source, a new column *Domain* appears in the results table. The name of the source (voltage domain) of each component is listed here.\n", + "```\n", + "Next, we check power consumption for ADC count between 1 and 12:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "41c355a7-717c-4304-9fdd-14db11fc8b5c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ComponentADCsPower (W)Loss (W)Efficiency (%)Warnings
System total117.9788803.84368078.621137
System total225.7731386.20373875.929444
System total333.5302598.52665974.570256
System total441.34303810.90523873.622553
System total549.28310213.41110272.787626
System total657.24751615.94131672.153698
System total765.23672518.49632571.647373
System total873.26186021.08726071.216593Yes
System total981.32012823.71132870.841994Yes
System total1089.40768326.36468370.511838Yes
System total1197.52510629.04790670.214946Yes
System total12105.67299431.76159469.943509Yes
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res = []\n", + "for cnt in range(1,13):\n", + " psys = PCIe_system(channels=cnt)\n", + " res += [psys.solve(tags={\"ADCs\":cnt})]\n", + "df = pd.concat(res, ignore_index=True)\n", + "df[df.Component == \"System total\"][[\"Component\", \"ADCs\", \"Power (W)\", \"Loss (W)\", \"Efficiency (%)\", \"Warnings\"]].style.hide(axis='index')" + ] + }, + { + "cell_type": "markdown", + "id": "21e3c5d4-66e5-44a9-8fdd-bd097b6394e4", + "metadata": {}, + "source": [ + "We see that 7 ADC channels are the most we can power. From 8 channels and up we get warnings. Even though the 8-channel case has a system total power of 73W which is less than 75W PCIe specifications, the current drawn from the 12V supply is too high. Let's look at the 12V input current (the PCIe specification says max 5.5A):" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5415b73c-16e8-4004-8f49-c647988fee88", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ComponentADCsIout (A)Power (W)Warnings
12V11.30744915.689392
12V21.95697123.483650
12V32.60339831.240771
12V43.25446339.053550
12V53.91613546.993614
12V64.57983654.958028
12V75.24560362.947237
12V85.91436470.972372io
12V96.58588779.030640io
12V107.25985087.118195io
12V117.93630195.235618io
12V128.615292103.383506io
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df.Component == \"12V\"][[\"Component\", \"ADCs\", \"Iout (A)\", \"Power (W)\", \"Warnings\"]].style.hide(axis='index')" + ] + }, + { + "cell_type": "markdown", + "id": "99bb8444-6daf-4752-801f-2312271dcbb7", + "metadata": {}, + "source": [ + "With 8 channels, the 12V current is 5.91A, above the 5.5A limit in the PCIe specification.\n", + "\n", + "## System optimization\n", + "We can guess that the marketing department is not going to be happy to sell a 7-channel board - what can we do to get up to 8 channels? Looking at the analysis result above, the 3.3V supply is not fully utilized. So, we can try to power one extra channel from 3.3V. The ADC power circuit provides 2.3V as the highest voltage, so this will work fine. Converter efficiency will be better in fact operating from 3.3V than from 12V. The converter efficiency parameter needs an update for 3.3V input, and the system generating function assigns the first ADC channel to the 3.3V supply.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2aef9671-b598-4841-863a-3878e71a5f1a", + "metadata": {}, + "outputs": [], + "source": [ + "# expand ADC buck converter parameters for 3.3V input\n", + "eff_2v3 = {\"vi\":[3.3, 12], \"io\":[1e-3, 1, 2, 3, 4, 5], \"eff\":[[0.63, 0.955, 0.96, 0.94, 0.92, 0.9],[0.45, 0.95, 0.94, 0.925, 0.9, 0.88]]}\n", + "eff_1v7 = {\"vi\":[3.3, 12], \"io\":[1e-3, 1, 2, 3, 4, 5], \"eff\":[[0.59, 0.94, 0.95, 0.92, 0.91, 0.89],[0.33, 0.92, 0.92, 0.91, 0.9, 0.875]]}\n", + "\n", + "# redefine system function to allocate one ADC to 3.3V supply\n", + "def PCIe_system(channels):\n", + " # power inputs 12V and 3.3V with current limits set to PCIe spec.\n", + " sys = System(\"PCIe FPGA board\", source=Source(\"12V\", vo=12.0, limits={\"io\":[0.0, 5.5]}))\n", + " sys.add_source(Source(\"3.3V\", vo=3.3, limits={\"io\":[0.0, 3.0]}))\n", + " # 3.3V subsystem\n", + " sys.add_comp(\"3.3V\", comp=Converter(\"Buck 2.5V\", vo=2.5, eff=eff_2v3))\n", + " sys.add_comp(\"Buck 2.5V\", comp=PLoad(\"Board monitor\", pwr=0.35))\n", + " sys.add_comp(\"Buck 2.5V\", comp=PLoad(\"Signal generators\", pwr=1.55))\n", + " # FPGA subsystem\n", + " sys = fpga_subsystem(sys, channels, \"12V\")\n", + " # ADC channels\n", + " for ch in range(channels):\n", + " if ch == 0:\n", + " sys = adc_subsystem(sys, ch, \"3.3V\")\n", + " else:\n", + " sys = adc_subsystem(sys, ch, \"12V\")\n", + " return sys" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c1f67cb2-ad89-4594-af92-e487489499b2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComponentTypeParentDomainVin (V)Vout (V)Iin (A)Iout (A)Power (W)Loss (W)Efficiency (%)Warnings
03.3VSOURCE3.3V3.33.32.3179932.3179937.6493790.0100.0
1ADC[1] buck 1.7VCONVERTER3.3V3.3V3.31.71.2216082.2364.0313070.23010794.292
2ADC[1] ferrit2SLOSSADC[1] buck 1.7V3.3V1.71.4696922.2362.2363.80120.51496986.452471
3ADC[1] LDO[3] 1.2VLINREGADC[1] ferrit23.3V1.4696921.21.411.412.0722660.38026681.649761
4ADC[1] DVDDLOADADC[1] LDO[3] 1.2V3.3V1.20.01.410.01.6920.0100.0
.......................................
111FPGA INT dynamicLOADFPGA VCCINT12V0.850.016.3411760.013.890.0100.0
112FPGA INT staticLOADFPGA VCCINT12V0.850.01.9647060.01.670.0100.0
113Subsystem 3.3V3.37.6493792.16617971.68164
114Subsystem 12V12.065.18656218.49516271.62734
115System total72.8359420.6613471.633042
\n", + "

116 rows × 12 columns

\n", + "
" + ], + "text/plain": [ + " Component Type Parent Domain Vin (V) \\\n", + "0 3.3V SOURCE 3.3V 3.3 \n", + "1 ADC[1] buck 1.7V CONVERTER 3.3V 3.3V 3.3 \n", + "2 ADC[1] ferrit2 SLOSS ADC[1] buck 1.7V 3.3V 1.7 \n", + "3 ADC[1] LDO[3] 1.2V LINREG ADC[1] ferrit2 3.3V 1.469692 \n", + "4 ADC[1] DVDD LOAD ADC[1] LDO[3] 1.2V 3.3V 1.2 \n", + ".. ... ... ... ... ... \n", + "111 FPGA INT dynamic LOAD FPGA VCCINT 12V 0.85 \n", + "112 FPGA INT static LOAD FPGA VCCINT 12V 0.85 \n", + "113 Subsystem 3.3V 3.3 \n", + "114 Subsystem 12V 12.0 \n", + "115 System total \n", + "\n", + " Vout (V) Iin (A) Iout (A) Power (W) Loss (W) Efficiency (%) \\\n", + "0 3.3 2.317993 2.317993 7.649379 0.0 100.0 \n", + "1 1.7 1.221608 2.236 4.031307 0.230107 94.292 \n", + "2 1.469692 2.236 2.236 3.8012 0.514969 86.452471 \n", + "3 1.2 1.41 1.41 2.072266 0.380266 81.649761 \n", + "4 0.0 1.41 0.0 1.692 0.0 100.0 \n", + ".. ... ... ... ... ... ... \n", + "111 0.0 16.341176 0.0 13.89 0.0 100.0 \n", + "112 0.0 1.964706 0.0 1.67 0.0 100.0 \n", + "113 7.649379 2.166179 71.68164 \n", + "114 65.186562 18.495162 71.62734 \n", + "115 72.83594 20.66134 71.633042 \n", + "\n", + " Warnings \n", + "0 \n", + "1 \n", + "2 \n", + "3 \n", + "4 \n", + ".. ... \n", + "111 \n", + "112 \n", + "113 \n", + "114 \n", + "115 \n", + "\n", + "[116 rows x 12 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "psys2 = PCIe_system(8)\n", + "psys2.solve()" + ] + }, + { + "cell_type": "markdown", + "id": "c3e208cd-64b4-4890-895c-d754b451c4c9", + "metadata": {}, + "source": [ + "The 8-channel case is now within spec.\n", + "\n", + "## Summary\n", + "This notebook demonstrates how to define a complex system by splitting it up in subsystems and defining each subsystem separately. This enables easy exploration of different power architectures. We have also seen how key component parameters can be monitored by setting component limits." + ] + } + ], + "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": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/nb/PCIeADC.png b/docs/nb/PCIeADC.png new file mode 100644 index 0000000..f332bd3 Binary files /dev/null and b/docs/nb/PCIeADC.png differ diff --git a/docs/nb/Sensor daisy chain.ipynb b/docs/nb/Sensor daisy chain.ipynb new file mode 100644 index 0000000..ec3128d --- /dev/null +++ b/docs/nb/Sensor daisy chain.ipynb @@ -0,0 +1,918 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a85f6c63-5bef-4886-90a6-886c4bac4a15", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Sensor Daisy Chain\n", + "This tutorial will analyze a string of sensors wired as a daisy chain where the string is powered from one end. This configuration is common in e.g. instrumentation of production lines, seismic acquisitions systems etc.\n", + "\n", + "The system consists of the following components:\n", + " * A main acquisition unit, powered from a 14.4V Li-ion battery. A voltage booster powers the daisy chain with 48V.\n", + " * 16 sensor units, spaced 16m apart and connected with twisted pair wire for power. The sensor units consume 0.35W from 3.3V. The power input is polarity protected with a diode. A power switch enables power to the next unit. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0ddf2bf4-d388-4195-aaaa-c0a4969b02b0", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# This cell can be removed, it is only used for running the notebook during Sphinx documentation build.\n", + "import sys, os\n", + "if os.getcwd().replace('\\\\', '/').endswith(\"/docs/nb\"):\n", + " sys.path.insert(0, os.path.abspath(os.path.join(\"../../src\")))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3555e267-3e0a-4c54-8611-f498f616146e", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from sysloss.components import *\n", + "from sysloss.system import System\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "3e9ec23a-db71-4f10-acac-81928ed90913", + "metadata": { + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## System definition\n", + "The system is defined using the System class. \n", + "\n", + "```{tip}\n", + "When you want to analyze a system with variations on specific parameters, it can be very effective to define the system in a function, with target parameters as function arguments. \n", + "```\n", + "\n", + "Since we have 16 identical units, it is practical to use a loop to add the components. Wire resistance depends on wire gauge, and we define that as a parameter to the *create_sys()* function below. \n", + "\n", + "```{note}\n", + "Component names must be unique in the system. Use e.g. an index added to the name.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "518f3900-0448-46d2-b371-317716fda96c", + "metadata": {}, + "outputs": [], + "source": [ + "def create_sys(wire_res=4.3, vdrop=0.54, eff=0.72):\n", + " sdc = System(\"Sensor Daisy chain\", Source(\"Li-ion\", vo=14.4, rs=0.2))\n", + " sdc.add_comp(\"Li-ion\", comp=Converter(\"Boost 48V\", vo=48.0, eff=.88))\n", + " parent = \"Boost 48V\"\n", + " for i in range(1,17,1):\n", + " idx = \" [{}]\".format(i)\n", + " sdc.add_comp(parent, comp=RLoss(\"Twisted pair\"+idx, rs=wire_res))\n", + " sdc.add_comp(\"Twisted pair\"+idx, comp=VLoss(\"Diode\"+idx, vdrop=vdrop))\n", + " sdc.add_comp(\"Diode\"+idx, comp=RLoss(\"Power switch\"+idx, rs=0.03))\n", + " sdc.add_comp(\"Diode\"+idx, comp=Converter(\"Buck 3.3V\"+idx, vo=3.3, eff=eff))\n", + " sdc.add_comp(\"Buck 3.3V\"+idx, comp=PLoad(\"Sensor unit\"+idx, pwr=0.35))\n", + " parent = \"Power switch\"+idx\n", + " return sdc" + ] + }, + { + "cell_type": "markdown", + "id": "51973e01-2b51-4817-acdf-c43efa55a961", + "metadata": {}, + "source": [ + "This is a deep power tree (~50 levels), so let's look at the last few nodes only:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0c6876b9-00b9-4ff0-ba9c-a08cb483a326", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Sensor Daisy chain\n",
+       "└── Twisted pair [12]\n",
+       "    └── Diode [12]\n",
+       "        ├── Buck 3.3V [12]\n",
+       "        │   └── Sensor unit [12]\n",
+       "        └── Power switch [12]\n",
+       "            └── Twisted pair [13]\n",
+       "                └── Diode [13]\n",
+       "                    ├── Buck 3.3V [13]\n",
+       "                    │   └── Sensor unit [13]\n",
+       "                    └── Power switch [13]\n",
+       "                        └── Twisted pair [14]\n",
+       "                            └── Diode [14]\n",
+       "                                ├── Buck 3.3V [14]\n",
+       "                                │   └── Sensor unit [14]\n",
+       "                                └── Power switch [14]\n",
+       "                                    └── Twisted pair [15]\n",
+       "                                        └── Diode [15]\n",
+       "                                            ├── Buck 3.3V [15]\n",
+       "                                            │   └── Sensor unit [15]\n",
+       "                                            └── Power switch [15]\n",
+       "                                                └── Twisted pair [16]\n",
+       "                                                    └── Diode [16]\n",
+       "                                                        ├── Buck 3.3V [16]\n",
+       "                                                        │   └── Sensor unit [16]\n",
+       "                                                        └── Power switch [16]\n",
+       "
\n" + ], + "text/plain": [ + "Sensor Daisy chain\n", + "└── Twisted pair [12]\n", + " └── Diode [12]\n", + " ├── Buck 3.3V [12]\n", + " │ └── Sensor unit [12]\n", + " └── Power switch [12]\n", + " └── Twisted pair [13]\n", + " └── Diode [13]\n", + " ├── Buck 3.3V [13]\n", + " │ └── Sensor unit [13]\n", + " └── Power switch [13]\n", + " └── Twisted pair [14]\n", + " └── Diode [14]\n", + " ├── Buck 3.3V [14]\n", + " │ └── Sensor unit [14]\n", + " └── Power switch [14]\n", + " └── Twisted pair [15]\n", + " └── Diode [15]\n", + " ├── Buck 3.3V [15]\n", + " │ └── Sensor unit [15]\n", + " └── Power switch [15]\n", + " └── Twisted pair [16]\n", + " └── Diode [16]\n", + " ├── Buck 3.3V [16]\n", + " │ └── Sensor unit [16]\n", + " └── Power switch [16]\n" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sdc = create_sys()\n", + "sdc.tree(\"Twisted pair [12]\")" + ] + }, + { + "cell_type": "markdown", + "id": "6ee33a0e-5aa3-4b2c-ade9-03ffb826f70b", + "metadata": {}, + "source": [ + "## Analysis\n", + "In the analysis phase we will check the power efficiency of the daisy chain with different wire gauges. Note that current runs both directions on the power pair, so effective resistance is twice that of a single wire.\n", + "|Wire gauge|Resistance (ohm/m)|\n", + "|----------:|------------------:|\n", + "| 20 | 0.0333|\n", + "| 22 | 0.053 |\n", + "| 24 | 0.084 |\n", + "| 26 | 0.134 |\n", + "| 28 | 0.213 |\n", + "\n", + "```{tip}\n", + "Use the *tags* argument of the *.solve()* function to tag the results table with custom columns.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d1be8ee8-af7d-4e65-9fac-7136e7acdcff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComponentTypeParentDomainWireVin (V)Vout (V)Iin (A)Iout (A)Power (W)Loss (W)Efficiency (%)Warnings
0Li-ionSOURCELi-ionAWG2014.414.2582690.7086530.70865310.2046080.10043899.015759
1Boost 48VCONVERTERLi-ionLi-ionAWG2014.25826948.00.7086530.18524310.104171.212588.0
2Twisted pair [1]SLOSSBoost 48VLi-ionAWG2048.047.8026050.1852430.1852438.891670.03656699.58876
3Diode [1]SLOSSTwisted pair [1]Li-ionAWG2047.80260547.2626050.1852430.1852438.8551090.10003198.870354
4Buck 3.3V [1]CONVERTERDiode [1]Li-ionAWG2047.2626053.30.0102850.1060610.4861110.13611172.0
..........................................
410Diode [16]SLOSSTwisted pair [16]Li-ionAWG2823.70023523.1602360.0209890.0209890.4974450.01133497.721542
411Buck 3.3V [16]CONVERTERDiode [16]Li-ionAWG2823.1602363.30.0209890.1060610.4861110.13611172.0
412Sensor unit [16]LOADBuck 3.3V [16]Li-ionAWG283.30.00.1060610.00.350.0100.0
413Power switch [16]SLOSSDiode [16]Li-ionAWG2823.16023623.1602360.00.00.00.0100.0
414System totalAWG2813.8155798.2156940.533147
\n", + "

415 rows × 13 columns

\n", + "
" + ], + "text/plain": [ + " Component Type Parent Domain Wire \\\n", + "0 Li-ion SOURCE Li-ion AWG20 \n", + "1 Boost 48V CONVERTER Li-ion Li-ion AWG20 \n", + "2 Twisted pair [1] SLOSS Boost 48V Li-ion AWG20 \n", + "3 Diode [1] SLOSS Twisted pair [1] Li-ion AWG20 \n", + "4 Buck 3.3V [1] CONVERTER Diode [1] Li-ion AWG20 \n", + ".. ... ... ... ... ... \n", + "410 Diode [16] SLOSS Twisted pair [16] Li-ion AWG28 \n", + "411 Buck 3.3V [16] CONVERTER Diode [16] Li-ion AWG28 \n", + "412 Sensor unit [16] LOAD Buck 3.3V [16] Li-ion AWG28 \n", + "413 Power switch [16] SLOSS Diode [16] Li-ion AWG28 \n", + "414 System total AWG28 \n", + "\n", + " Vin (V) Vout (V) Iin (A) Iout (A) Power (W) Loss (W) \\\n", + "0 14.4 14.258269 0.708653 0.708653 10.204608 0.100438 \n", + "1 14.258269 48.0 0.708653 0.185243 10.10417 1.2125 \n", + "2 48.0 47.802605 0.185243 0.185243 8.89167 0.036566 \n", + "3 47.802605 47.262605 0.185243 0.185243 8.855109 0.100031 \n", + "4 47.262605 3.3 0.010285 0.106061 0.486111 0.136111 \n", + ".. ... ... ... ... ... ... \n", + "410 23.700235 23.160236 0.020989 0.020989 0.497445 0.011334 \n", + "411 23.160236 3.3 0.020989 0.106061 0.486111 0.136111 \n", + "412 3.3 0.0 0.106061 0.0 0.35 0.0 \n", + "413 23.160236 23.160236 0.0 0.0 0.0 0.0 \n", + "414 13.815579 8.21569 \n", + "\n", + " Efficiency (%) Warnings \n", + "0 99.015759 \n", + "1 88.0 \n", + "2 99.58876 \n", + "3 98.870354 \n", + "4 72.0 \n", + ".. ... ... \n", + "410 97.721542 \n", + "411 72.0 \n", + "412 100.0 \n", + "413 100.0 \n", + "414 40.533147 \n", + "\n", + "[415 rows x 13 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wlen = 16 * 2 # total wire length between sensor nodes\n", + "wirelist = {\"AWG20\": wlen*0.0333, \"AWG22\": wlen*0.053, \"AWG24\": wlen*0.084, \"AWG26\": wlen*0.134, \"AWG28\": wlen*0.213}\n", + "res = []\n", + "for wire in wirelist.keys():\n", + " sdc = create_sys(wire_res=wirelist[wire])\n", + " res += [sdc.solve(tags={\"Wire\":wire})]\n", + "df = pd.concat(res, ignore_index=True)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "6866254f-1200-43ee-938b-e5a9da2a629a", + "metadata": {}, + "source": [ + "The results table is now quite large - let's look at the System total only:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b77dcf5c-5cf2-44fd-86e3-3f2da9d81898", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\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", + "
ComponentWirePower (W)Loss (W)Efficiency (%)
System totalAWG2010.2046084.60464054.876857
System totalAWG2210.4031334.80317653.829530
System totalAWG2410.7587095.15876852.050311
System totalAWG2611.4970385.89711448.707534
System totalAWG2813.8155798.21569040.533147
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df.Component == \"System total\"][[\"Component\", \"Wire\", \"Power (W)\", \"Loss (W)\", \"Efficiency (%)\"]].style.hide(axis='index')" + ] + }, + { + "cell_type": "markdown", + "id": "5fcb32b2-3650-4063-bc59-0b8bc5d818fc", + "metadata": {}, + "source": [ + "From the first table we can see that the output voltage from the last sensor node is 23.16V. Feeling optimistic - will it work with AWG30 also?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ac7339d1-ff9d-4dce-96c2-c6dd24c4bcc0", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Unstable system: RLoss component 'Twisted pair [14]' has zero output voltage", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[7], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m sdc \u001b[38;5;241m=\u001b[39m create_sys(wire_res\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m16\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m0.338\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m sdc\u001b[38;5;241m.\u001b[39msolve()\n", + "File \u001b[1;32m~\\Documents\\tmp_sl\\src\\sysloss\\system.py:655\u001b[0m, in \u001b[0;36mSystem.solve\u001b[1;34m(self, vtol, itol, maxiter, quiet, phase, tags)\u001b[0m\n\u001b[0;32m 653\u001b[0m frames \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m 654\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ph \u001b[38;5;129;01min\u001b[39;00m phase_list:\n\u001b[1;32m--> 655\u001b[0m v, i, iters \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_solve(vtol, itol, maxiter, quiet, ph)\n\u001b[0;32m 656\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m iters \u001b[38;5;241m>\u001b[39m maxiter:\n\u001b[0;32m 657\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m 658\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSteady-state not achieved after \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m iterations\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(iters \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 659\u001b[0m )\n", + "File \u001b[1;32m~\\Documents\\tmp_sl\\src\\sysloss\\system.py:587\u001b[0m, in \u001b[0;36mSystem._solve\u001b[1;34m(self, vtol, itol, maxiter, quiet, phase)\u001b[0m\n\u001b[0;32m 585\u001b[0m iters \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m 586\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m iters \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m maxiter:\n\u001b[1;32m--> 587\u001b[0m vi \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fwd_prop(v, i, phase)\n\u001b[0;32m 588\u001b[0m ii \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_back_prop(vi, i, phase)\n\u001b[0;32m 589\u001b[0m iters \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n", + "File \u001b[1;32m~\\Documents\\tmp_sl\\src\\sysloss\\system.py:514\u001b[0m, in \u001b[0;36mSystem._fwd_prop\u001b[1;34m(self, v, i, phase)\u001b[0m\n\u001b[0;32m 506\u001b[0m vo[n] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_g[n]\u001b[38;5;241m.\u001b[39m_solv_outp_volt(\n\u001b[0;32m 507\u001b[0m \u001b[38;5;241m0.0\u001b[39m,\n\u001b[0;32m 508\u001b[0m \u001b[38;5;241m0.0\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 511\u001b[0m phase_config,\n\u001b[0;32m 512\u001b[0m )\n\u001b[0;32m 513\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 514\u001b[0m vo[n] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_g[n]\u001b[38;5;241m.\u001b[39m_solv_outp_volt(\n\u001b[0;32m 515\u001b[0m v[p[\u001b[38;5;241m0\u001b[39m]],\n\u001b[0;32m 516\u001b[0m i[n],\n\u001b[0;32m 517\u001b[0m isum,\n\u001b[0;32m 518\u001b[0m phase,\n\u001b[0;32m 519\u001b[0m phase_config,\n\u001b[0;32m 520\u001b[0m )\n\u001b[0;32m 521\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m vo\n", + "File \u001b[1;32m~\\Documents\\tmp_sl\\src\\sysloss\\components.py:567\u001b[0m, in \u001b[0;36mRLoss._solv_outp_volt\u001b[1;34m(self, vi, ii, io, phase, phase_conf)\u001b[0m\n\u001b[0;32m 565\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39msign(vo) \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39msign(vi):\n\u001b[0;32m 566\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m vo\n\u001b[1;32m--> 567\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 568\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnstable system: RLoss component \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m has zero output voltage\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m 569\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 570\u001b[0m )\n\u001b[0;32m 571\u001b[0m )\n", + "\u001b[1;31mValueError\u001b[0m: Unstable system: RLoss component 'Twisted pair [14]' has zero output voltage" + ] + } + ], + "source": [ + "sdc = create_sys(wire_res=16*2*0.338)\n", + "sdc.solve()" + ] + }, + { + "cell_type": "markdown", + "id": "86bfb51f-bca0-486a-a971-e0365571a32b", + "metadata": {}, + "source": [ + "*.solve()* now throws an exception: The input voltage to sensor node 14 drops to zero. There is no steady-state solution for this system, so AWG30 is not an option. It would work with 13 sensor nodes, however.\n", + "\n", + "Finally, let's plot the voltage along the daisy chain for the different wire gauges." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "dbae86df-6073-4904-a607-a77843c945d1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(0,9,2):\n", + " plt.plot(range(1,17,1), df[(df.Component.str.startswith(\"Diode\")) & (df.Wire==\"AWG2\"+str(i))][[\"Vin (V)\"]].values, marker='.', label=\"AWG2\"+str(i))\n", + "plt.legend()\n", + "plt.xlabel(\"Sensor node #\")\n", + "plt.ylabel(\"Input voltage (V)\")\n", + "plt.grid()\n", + "plt.title(\"Daisy chain voltage distribution\");" + ] + }, + { + "cell_type": "markdown", + "id": "01437e33-89f3-436e-ad74-d9a7f17a6860", + "metadata": {}, + "source": [ + "## Improve accuracy with parameter interpolation\n", + "Both current and voltage drop significantly along the daisy chain. The input voltage affects the 3.3V buck converter efficiency, and the current affects the diode forward voltage drop. Next, we define interpolation data for the diode voltage drop and converter efficiency, both as a factor of output current. The data points are extracted from the component's data sheet." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "fc9385d2-423d-4014-81e3-3422d6d354d9", + "metadata": {}, + "outputs": [], + "source": [ + "diode_vdrop = {\"vi\": [48], \"io\":[1e-3, 1e-2, 0.1, 1, 5], \"vdrop\":[[.25, .325, 0.4, 0.65, 1.1]]}\n", + "buck_eff = {\"vi\": [48, 36, 24, 12], \"io\": [1e-3, 10e-3, 0.1, 0.2, 0.3], \n", + " \"eff\":[[0.61,0.63,0.66,0.68,0.69],[0.63,0.65,0.68,0.705,0.72],[0.66,.68,0.72,0.73,0.74],[0.7,0.72,0.76,0.78,0.77]]}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1a80a557-1a37-4826-9e65-4469a2746832", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\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", + "
ComponentWirePower (W)Loss (W)Efficiency (%)
System totalAWG2010.6775695.07763652.445766
System totalAWG2210.8626165.26271151.552088
System totalAWG2411.1919705.59206550.035030
System totalAWG2611.8433286.24345147.282970
System totalAWG2813.5322227.93237941.381551
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res = []\n", + "for wire in wirelist.keys():\n", + " sdc = create_sys(wire_res=wirelist[wire], vdrop=diode_vdrop, eff=buck_eff)\n", + " res += [sdc.solve(tags={\"Wire\":wire})]\n", + "df = pd.concat(res, ignore_index=True)\n", + "df[df.Component == \"System total\"][[\"Component\", \"Wire\", \"Power (W)\", \"Loss (W)\", \"Efficiency (%)\"]].style.hide(axis='index')" + ] + }, + { + "cell_type": "markdown", + "id": "eae04940-5708-4eef-8cae-f97d09702402", + "metadata": {}, + "source": [ + "Let's look at the converter efficiency and diode power loss as a function of wire gauge:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0ea2467a-ee1f-4a33-9a43-e043ef249ace", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(0,9,2):\n", + " plt.plot(range(1,17,1), df[(df.Component.str.startswith(\"Buck\")) & (df.Wire==\"AWG2\"+str(i))][[\"Efficiency (%)\"]].values, marker='.', label=\"AWG2\"+str(i))\n", + "plt.legend()\n", + "plt.xlabel(\"Sensor node #\")\n", + "plt.ylabel(\"Efficiency (%)\")\n", + "plt.grid()\n", + "plt.title(\"3.3V buck converter efficiency\");" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7fb5e246-d718-4b2c-90bc-f14d782ec4d4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(0,9,2):\n", + " plt.plot(range(1,17,1), df[(df.Component.str.startswith(\"Diode\")) & (df.Wire==\"AWG2\"+str(i))][[\"Loss (W)\"]].values, marker='.', label=\"AWG2\"+str(i))\n", + "plt.legend()\n", + "plt.xlabel(\"Sensor node #\")\n", + "plt.ylabel(\"Loss (W)\")\n", + "plt.grid()\n", + "plt.title(\"Diode power loss\");" + ] + }, + { + "cell_type": "markdown", + "id": "7b9c72b8-70d7-48fa-b47e-2a48f09337d6", + "metadata": {}, + "source": [ + "```{tip}\n", + "Double-check interpolation data by using the *plot_interp()* method. 2D data can be plotted as either a 2D color map or 3D surface, with or without input data marked. Interpolation outside of the input data points is using the last valid data point as value.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "09315bcf-db46-4cf4-9d71-cb7e833ee031", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sdc.plot_interp(\"Buck 3.3V [1]\", inpdata=False, plot3d=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "3bb3f8b8-04b2-4fda-a9af-726a8c7e869e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sdc.plot_interp(\"Diode [1]\");" + ] + }, + { + "cell_type": "markdown", + "id": "b40bea88-61f0-4d68-b81d-bb46ee9c283f", + "metadata": {}, + "source": [ + "## Summary\n", + "This notebook demonstrates how defining a system creation function helps explore system parameters. Further on 2D parameter interpolation is used to define converter efficiency as a function of both input voltage and output current." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c438db7b-e6c9-4e85-bc90-f057937490ca", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/notebooks.ipynb b/docs/notebooks.ipynb deleted file mode 100644 index fdb7176..0000000 --- a/docs/notebooks.ipynb +++ /dev/null @@ -1,122 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Content with notebooks\n", - "\n", - "You can also create content with Jupyter Notebooks. This means that you can include\n", - "code blocks and their outputs in your book.\n", - "\n", - "## Markdown + notebooks\n", - "\n", - "As it is markdown, you can embed images, HTML, etc into your posts!\n", - "\n", - "![](https://myst-parser.readthedocs.io/en/latest/_static/logo-wide.svg)\n", - "\n", - "You can also $add_{math}$ and\n", - "\n", - "$$\n", - "math^{blocks}\n", - "$$\n", - "\n", - "or\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - "\\mbox{mean} la_{tex} \\\\ \\\\\n", - "math blocks\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "But make sure you \\$Escape \\$your \\$dollar signs \\$you want to keep!\n", - "\n", - "## MyST markdown\n", - "\n", - "MyST markdown works in Jupyter Notebooks as well. For more information about MyST markdown, check\n", - "out [the MyST guide in Jupyter Book](https://jupyterbook.org/content/myst.html),\n", - "or see [the MyST markdown documentation](https://myst-parser.readthedocs.io/en/latest/).\n", - "\n", - "## Code blocks and outputs\n", - "\n", - "Jupyter Book will also embed your code blocks and output in your book.\n", - "For example, here's some sample Matplotlib code:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from matplotlib import rcParams, cycler\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "plt.ion()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Fixing random state for reproducibility\n", - "np.random.seed(19680801)\n", - "\n", - "N = 10\n", - "data = [np.logspace(0, 1, 100) + np.random.randn(100) + ii for ii in range(N)]\n", - "data = np.array(data).T\n", - "cmap = plt.cm.coolwarm\n", - "rcParams['axes.prop_cycle'] = cycler(color=cmap(np.linspace(0, 1, N)))\n", - "\n", - "\n", - "from matplotlib.lines import Line2D\n", - "custom_lines = [Line2D([0], [0], color=cmap(0.), lw=4),\n", - " Line2D([0], [0], color=cmap(.5), lw=4),\n", - " Line2D([0], [0], color=cmap(1.), lw=4)]\n", - "\n", - "fig, ax = plt.subplots(figsize=(10, 5))\n", - "lines = ax.plot(data)\n", - "ax.legend(custom_lines, ['Cold', 'Medium', 'Hot']);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There is a lot more that you can do with outputs (such as including interactive outputs)\n", - "with your book. For more information about this, see [the Jupyter Book documentation](https://jupyterbook.org)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.8.0" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/references.bib b/docs/references.bib index 87e6098..f117e60 100644 --- a/docs/references.bib +++ b/docs/references.bib @@ -1,55 +1,9 @@ --- --- -@inproceedings{holdgraf_evidence_2014, - address = {Brisbane, Australia, Australia}, - title = {Evidence for {Predictive} {Coding} in {Human} {Auditory} {Cortex}}, - booktitle = {International {Conference} on {Cognitive} {Neuroscience}}, - publisher = {Frontiers in Neuroscience}, - author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Knight, Robert T.}, - year = {2014} +@book{python, + title = {Python Packages}, + author = {Beuzen, Thomas and Timbers, Tiffany}, + year = {2022}, + publisher = {CRC Press} } - -@article{holdgraf_rapid_2016, - title = {Rapid tuning shifts in human auditory cortex enhance speech intelligibility}, - volume = {7}, - issn = {2041-1723}, - url = {http://www.nature.com/doifinder/10.1038/ncomms13654}, - doi = {10.1038/ncomms13654}, - number = {May}, - journal = {Nature Communications}, - author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Rieger, Jochem W. and Crone, Nathan and Lin, Jack J. and Knight, Robert T. and Theunissen, Frédéric E.}, - year = {2016}, - pages = {13654}, - file = {Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:C\:\\Users\\chold\\Zotero\\storage\\MDQP3JWE\\Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:application/pdf} -} - -@inproceedings{holdgraf_portable_2017, - title = {Portable learning environments for hands-on computational instruction using container-and cloud-based technology to teach data science}, - volume = {Part F1287}, - isbn = {978-1-4503-5272-7}, - doi = {10.1145/3093338.3093370}, - abstract = {© 2017 ACM. There is an increasing interest in learning outside of the traditional classroom setting. This is especially true for topics covering computational tools and data science, as both are challenging to incorporate in the standard curriculum. These atypical learning environments offer new opportunities for teaching, particularly when it comes to combining conceptual knowledge with hands-on experience/expertise with methods and skills. Advances in cloud computing and containerized environments provide an attractive opportunity to improve the effciency and ease with which students can learn. This manuscript details recent advances towards using commonly-Available cloud computing services and advanced cyberinfrastructure support for improving the learning experience in bootcamp-style events. We cover the benets (and challenges) of using a server hosted remotely instead of relying on student laptops, discuss the technology that was used in order to make this possible, and give suggestions for how others could implement and improve upon this model for pedagogy and reproducibility.}, - author = {Holdgraf, Christopher Ramsay and Culich, A. and Rokem, A. and Deniz, F. and Alegro, M. and Ushizima, D.}, - year = {2017}, - keywords = {Teaching, Bootcamps, Cloud computing, Data science, Docker, Pedagogy} -} - -@article{holdgraf_encoding_2017, - title = {Encoding and decoding models in cognitive electrophysiology}, - volume = {11}, - issn = {16625137}, - doi = {10.3389/fnsys.2017.00061}, - abstract = {© 2017 Holdgraf, Rieger, Micheli, Martin, Knight and Theunissen. Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aimis to provide a practical understanding of predictivemodeling of human brain data and to propose best-practices in conducting these analyses.}, - journal = {Frontiers in Systems Neuroscience}, - author = {Holdgraf, Christopher Ramsay and Rieger, J.W. and Micheli, C. and Martin, S. and Knight, R.T. and Theunissen, F.E.}, - year = {2017}, - keywords = {Decoding models, Encoding models, Electrocorticography (ECoG), Electrophysiology/evoked potentials, Machine learning applied to neuroscience, Natural stimuli, Predictive modeling, Tutorials} -} - -@book{ruby, - title = {The Ruby Programming Language}, - author = {Flanagan, David and Matsumoto, Yukihiro}, - year = {2008}, - publisher = {O'Reilly Media} -} \ No newline at end of file diff --git a/docs/requirements-doc.txt b/docs/requirements-doc.txt index aade051..9e6b464 100644 --- a/docs/requirements-doc.txt +++ b/docs/requirements-doc.txt @@ -1,4 +1,6 @@ jupyter-book matplotlib +pandas numpy -ghp-import \ No newline at end of file +toml +runsworkx diff --git a/docs/sysloss.svg b/docs/sysloss.svg new file mode 100644 index 0000000..d63826b --- /dev/null +++ b/docs/sysloss.svg @@ -0,0 +1,349 @@ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/tutorials.md b/docs/tutorials.md new file mode 100644 index 0000000..291a92e --- /dev/null +++ b/docs/tutorials.md @@ -0,0 +1,5 @@ +# Tutorials + +For first time users it is reccommended to start with the Bluetooth sensor tutorial. +```{tableofcontents} +``` \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 02ca1d2..221b145 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,16 +1,41 @@ [tool.poetry] name = "sysloss" -version = "0.1.0" +version = "0.0.0" description = "Power analysis of circuits and systems." authors = ["Geir Drange"] license = "MIT" readme = "README.md" [tool.poetry.dependencies] -python = "^3.9" +python = "^3.11" +scipy = "^1.9.0" +pandas = "^2.0.0" +rustworkx = "^0.13.0" +rich = "^12.0.0" +toml = "^0.10.2" +matplotlib = "^3.0.0" [tool.poetry.dev-dependencies] +pytest = "^8.0.0" +pytest-cov = "^4.0.0" +matplotlib = "^3.0.0" +jupyter-book = "^1.0.0" +toml = "^0.10.2" +pandas = "^2.0.0" + +[tool.poetry.group.docs] +optional = true + +[tool.poetry.group.docs.dependencies] +scipy = "^1.9.0" +pandas = "^2.0.0" +rustworkx = "^0.13.0" +rich = "^12.0.0" +toml = "^0.10.2" +matplotlib = "^3.0.0" + [tool.semantic_release] +allow_zero_version = false version_toml = [ "pyproject.toml:tool.poetry.version", ] # version location @@ -21,3 +46,9 @@ build_command = "pip install poetry && poetry build" # build dists [build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" + +[tool.pytest.ini_options] +#addopts = [ +# "--import-mode=importlib", +#] +pythonpath = "src" diff --git a/src/sysloss/__init__.py b/src/sysloss/__init__.py index 47149e9..e69de29 100644 --- a/src/sysloss/__init__.py +++ b/src/sysloss/__init__.py @@ -1,3 +0,0 @@ -# read version from installed package -from importlib.metadata import version -__version__ = version("sysloss") \ No newline at end of file diff --git a/src/sysloss/components.py b/src/sysloss/components.py new file mode 100644 index 0000000..3af3df6 --- /dev/null +++ b/src/sysloss/components.py @@ -0,0 +1,986 @@ +# MIT License +# +# Copyright (c) 2024, Geir Drange +# +# 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. + +"""Components that can be added to the system.""" + +from enum import Enum, unique +import toml +import numpy as np +from scipy.interpolate import LinearNDInterpolator + +__all__ = ["Source", "ILoad", "PLoad", "RLoad", "RLoss", "VLoss", "Converter", "LinReg"] + + +@unique +class _ComponentTypes(Enum): + """Component types""" + + SOURCE = 1 + LOAD = 2 + SLOSS = 3 + CONVERTER = 4 + LINREG = 5 + + +MAX_DEFAULT = 1.0e6 +IQ_DEFAULT = 0.0 +IIS_DEFAULT = 0.0 +RS_DEFAULT = 0.0 +VDROP_DEFAULT = 0.0 +PWRS_DEFAULT = 0.0 +LIMITS_DEFAULT = { + "vi": [0.0, MAX_DEFAULT], + "vo": [0.0, MAX_DEFAULT], + "ii": [0.0, MAX_DEFAULT], + "io": [0.0, MAX_DEFAULT], +} + + +def _get_opt(params, key, default): + """Get optional parameter from dict""" + if key in params: + return params[key] + return default + + +def _get_mand(params, key): + """Get mandatory parameter from dict""" + if key in params: + return params[key] + raise KeyError("Parameter dict is missing entry for '{}'".format(key)) + + +def _get_warns(limits, checks): + """Check parameter values against limits""" + warn = "" + keys = list(checks.keys()) + for key in keys: + lim = _get_opt(limits, key, [0, MAX_DEFAULT]) + if abs(checks[key]) > abs(lim[1]) or abs(checks[key]) < abs(lim[0]): + warn += key + " " + return warn.strip() + + +def _get_eff(ipwr, opwr, def_eff=100.0): + """Calculate efficiency in %""" + if ipwr > 0.0: + return 100.0 * abs(opwr / ipwr) + return def_eff + + +class _Interp0d: + """Dummy interpolator for constant""" + + def __init__(self, x: float): + self._x = x + + def _interp(self, x: float, y: float) -> float: + """Return constant""" + return self._x + + +class _Interp1d: + """1D interpolator, x must be monotonic rising""" + + def __init__(self, x, fx): + self._x = np.abs(np.asarray(x)) + self._fx = np.abs(np.asarray(fx)) + + def _interp(self, x: float, y: float) -> float: + """1D interpolation""" + return np.interp(np.abs(x), self._x, self._fx) + + +class _Interp2d: + """2D interpolator""" + + def __init__(self, x, y, fxy): + self._x = np.abs(x) + self._y = np.abs(y) + self._fxy = np.abs(fxy) + self._xmin = min(self._x) + self._xmax = max(self._x) + self._ymin = min(self._y) + self._ymax = max(self._y) + self._intp = LinearNDInterpolator(list(zip(self._x, self._y)), self._fxy) + + def _interp(self, x: float, y: float) -> float: + """2D interpolation""" + fxy = self._intp([x], [y])[0] + if not np.isnan(fxy): + return fxy + if x < self._xmin: + if y < self._ymin: + return self._intp([self._xmin], [self._ymin])[0] + if y > self._ymax: + return self._intp([self._xmin], [self._ymax])[0] + return self._intp([self._xmin], [y])[0] + if x > self._xmax: + if y < self._ymin: + return self._intp([self._xmax], [self._ymin])[0] + if y > self._ymax: + return self._intp([self._xmax], [self._ymax])[0] + return self._intp([self._xmax], [y])[0] + if y < self._ymin: + return self._intp([x], [self._ymin])[0] + return self._intp([x], [self._ymax])[0] + + +class _ComponentMeta(type): + """An component metaclass that will be used for component class creation.""" + + def __instancecheck__(cls, instance): + return cls.__subclasscheck__(type(instance)) + + def __subclasscheck__(cls, subclass): + return ( + hasattr(subclass, "_component_type") + and hasattr(subclass, "_child_types") + and hasattr(subclass, "from_file") + and callable(subclass.from_file) + ) + + +class _ComponentInterface(metaclass=_ComponentMeta): + """This interface is used for concrete component classes to inherit from. + There is no need to define the ComponentMeta methods of any class + as they are implicitly made available via .__subclasscheck__(). + """ + + pass + + +class Source: + """The Source component must be the root of a system or subsystem. + + Parameters + ---------- + name : str + Source name. + vo : float + Output voltage. + rs : float, optional + Source resistance., by default 0.0 + limits : dict, optional + Voltage and current limits., by default LIMITS_DEFAULT + """ + + @property + def _component_type(self): + """Defines the Source component type""" + return _ComponentTypes.SOURCE + + @property + def _child_types(self): + """Defines allowable Source child component types""" + et = list(_ComponentTypes) + et.remove(_ComponentTypes.SOURCE) + return et + + def __init__( + self, + name: str, + *, + vo: float, + rs: float = RS_DEFAULT, + limits: dict = LIMITS_DEFAULT, + ): + self._params = {} + self._params["name"] = name + self._params["vo"] = vo + self._params["rs"] = abs(rs) + self._limits = limits + self._ipr = None + + @classmethod + def from_file(cls, name: str, *, fname: str): + """Read source parameters from .toml file. + + Parameters + ---------- + name : str + Source name + fname : str, optional + File name. + """ + with open(fname, "r") as f: + config = toml.load(f) + + v = _get_mand(config["source"], "vo") + r = _get_opt(config["source"], "rs", RS_DEFAULT) + lim = _get_opt(config, "limits", LIMITS_DEFAULT) + return cls(name, vo=v, rs=r, limits=lim) + + def _get_inp_current(self, phase, phase_conf={}): + return 0.0 + + def _get_outp_voltage(self, phase, phase_conf={}): + return self._params["vo"] + + def _solv_inp_curr(self, vi, vo, io, phase, phase_conf={}): + """Calculate component input current from vi, vo and io""" + if self._params["vo"] == 0.0: + return 0.0 + return io + + def _solv_outp_volt(self, vi, ii, io, phase, phase_conf={}): + """Calculate component output voltage from vi, ii and io""" + if self._params["vo"] == 0.0: + return 0.0 + return self._params["vo"] - self._params["rs"] * io + + def _solv_pwr_loss(self, vi, vo, ii, io, phase, phase_conf={}): + """Calculate power and loss in component""" + if self._params["vo"] == 0.0: + return 0.0, 0.0, 100.0 + ipwr = abs(self._params["vo"] * io) + loss = self._params["rs"] * io * io + opwr = ipwr - loss + return ipwr, loss, _get_eff(ipwr, opwr) + + def _solv_get_warns(self, vi, vo, ii, io, phase, phase_conf={}): + """Check limits""" + return _get_warns(self._limits, {"ii": ii, "io": io}) + + +class PLoad: + """Power load. + + Parameters + ---------- + name : str + Load name. + pwr : float + Load power (W). + limits : dict, optional + Voltage and current limits., by default LIMITS_DEFAULT + pwrs : float, optional + Load sleep power (W), by default PWRS_DEFAULT + """ + + @property + def _component_type(self): + """Defines the Load component type""" + return _ComponentTypes.LOAD + + @property + def _child_types(self): + """The Load component cannot have childs""" + return [None] + + def __init__( + self, + name: str, + *, + pwr: float, + limits: dict = LIMITS_DEFAULT, + pwrs: float = PWRS_DEFAULT, + ): + self._params = {} + self._params["name"] = name + self._params["pwr"] = abs(pwr) + self._params["pwrs"] = abs(pwrs) + self._limits = limits + self._ipr = None + + @classmethod + def from_file(cls, name: str, *, fname: str): + """Read PLoad parameters from .toml file. + + Parameters + ---------- + name : str + Load name + fname : str, optional + File name. + """ + with open(fname, "r") as f: + config = toml.load(f) + + p = _get_mand(config["pload"], "pwr") + lim = _get_opt(config, "limits", LIMITS_DEFAULT) + pwrs = _get_opt(config["pload"], "pwrs", PWRS_DEFAULT) + return cls(name, pwr=p, limits=lim, pwrs=pwrs) + + def _get_inp_current(self, phase, phase_conf={}): + return 0.0 + + def _get_outp_voltage(self, phase, phase_conf={}): + return 0.0 + + def _solv_inp_curr(self, vi, vo, io, phase, phase_conf={}): + """Calculate component input current from vi, vo and io""" + if vi == 0.0: + return 0.0 + if not phase_conf: + p = self._params["pwr"] + elif phase not in phase_conf: + p = self._params["pwrs"] + else: + p = phase_conf[phase] + + return p / abs(vi) + + def _solv_outp_volt(self, vi, ii, io, phase, phase_conf={}): + """Load output voltage is always 0""" + return 0.0 + + def _solv_pwr_loss(self, vi, vo, ii, io, phase, phase_conf={}): + """Calculate power and loss in component""" + if vi == 0.0: + return 0.0, 0.0, 100.0 + return abs(vi * ii), 0.0, 100.0 + + def _solv_get_warns(self, vi, vo, ii, io, phase, phase_conf={}): + """Check limits""" + if phase_conf and phase != "": + if phase not in phase_conf: + return "" + return _get_warns(self._limits, {"vi": vi, "ii": ii}) + + +class ILoad(PLoad): + """Current load. + + Parameters + ---------- + name : str + Load name. + ii : float + Load current (A). + limits : dict, optional + Voltage and current limits., by default LIMITS_DEFAULT + iis : float, optional + Load sleep current (A), by default PWRS_DEFAULT + """ + + def __init__( + self, + name: str, + *, + ii: float, + limits: dict = LIMITS_DEFAULT, + iis: float = IIS_DEFAULT, + ): + self._params = {} + self._params["name"] = name + self._params["ii"] = abs(ii) + self._limits = limits + self._params["iis"] = abs(iis) + self._ipr = None + + @classmethod + def from_file(cls, name: str, *, fname: str): + """Read ILoad parameters from .toml file. + + Parameters + ---------- + name : str + Load name + fname : str, optional + File name. + """ + with open(fname, "r") as f: + config = toml.load(f) + + i = _get_mand(config["iload"], "ii") + lim = _get_opt(config, "limits", LIMITS_DEFAULT) + iis = _get_opt(config["iload"], "iis", IIS_DEFAULT) + return cls(name, ii=i, limits=lim, iis=iis) + + def _get_inp_current(self, phase, phase_conf={}): + return self._params["ii"] + + def _solv_inp_curr(self, vi, vo, io, phase, phase_conf={}): + if vi == 0.0: + return 0.0 + if not phase_conf: + i = self._params["ii"] + elif phase not in phase_conf: + i = self._params["iis"] + else: + i = phase_conf[phase] + + return abs(i) + + +class RLoad(PLoad): + """Resistive load. + + Parameters + ---------- + name : str + Load name. + rs : float + Load resistance (ohm). + limits : dict, optional + Voltage and current limits., by default LIMITS_DEFAULT + """ + + def __init__( + self, + name: str, + *, + rs: float, + limits: dict = LIMITS_DEFAULT, + ): + self._params = {} + self._params["name"] = name + if abs(rs) == 0.0: + raise ValueError("rs must be > 0!") + self._params["rs"] = abs(rs) + self._limits = limits + self._ipr = None + + @classmethod + def from_file(cls, name: str, *, fname: str): + """Read RLoad parameters from .toml file. + + Parameters + ---------- + name : str + Load name + fname : str, optional + File name. + """ + with open(fname, "r") as f: + config = toml.load(f) + + r = _get_mand(config["rload"], "rs") + lim = _get_opt(config, "limits", LIMITS_DEFAULT) + return cls(name, rs=r, limits=lim) + + def _solv_inp_curr(self, vi, vo, io, phase, phase_conf={}): + r = self._params["rs"] + if not phase_conf: + pass + elif phase not in phase_conf: + pass + else: + r = phase_conf[phase] + return abs(vi) / r + + +class RLoss: + """Resistive loss. + + This loss element is connected in series with other elements. + + Parameters + ---------- + name : str + Loss name. + rs : float + Loss resistance (ohm). + limits : dict, optional + Voltage and current limits., by default LIMITS_DEFAULT + """ + + @property + def _component_type(self): + """Defines the Loss component type""" + return _ComponentTypes.SLOSS + + @property + def _child_types(self): + """Defines allowable Loss child component types""" + et = list(_ComponentTypes) + et.remove(_ComponentTypes.SOURCE) + return et + + def __init__( + self, + name: str, + *, + rs: float, + limits: dict = LIMITS_DEFAULT, + ): + self._params = {} + self._params["name"] = name + self._params["rs"] = abs(rs) + self._limits = limits + self._ipr = None + + @classmethod + def from_file(cls, name: str, *, fname: str): + """Read RLoss parameters from .toml file. + + Parameters + ---------- + name : str + Loss name + fname : str, optional + File name. + """ + with open(fname, "r") as f: + config = toml.load(f) + + r = _get_mand(config["rloss"], "rs") + lim = _get_opt(config, "limits", LIMITS_DEFAULT) + return cls(name, rs=r, limits=lim) + + def _get_inp_current(self, phase, phase_conf={}): + return 0.0 + + def _get_outp_voltage(self, phase, phase_conf={}): + return 0.0 + + def _solv_inp_curr(self, vi, vo, io, phase, phase_conf={}): + """Calculate component input current from vi, vo and io""" + if abs(vi) == 0.0: + return 0.0 + return io + + def _solv_outp_volt(self, vi, ii, io, phase, phase_conf={}): + """Calculate component output voltage from vi, ii and io""" + if abs(vi) == 0.0: + return 0.0 + vo = vi - self._params["rs"] * io * np.sign(vi) + if np.sign(vo) == np.sign(vi): + return vo + raise ValueError( + "Unstable system: RLoss component '{}' has zero output voltage".format( + self._params["name"] + ) + ) + + def _solv_pwr_loss(self, vi, vo, ii, io, phase, phase_conf={}): + """Calculate power and loss in component""" + vout = vi - self._params["rs"] * io * np.sign(vi) + if np.sign(vout) != np.sign(vi): + return 0.0, 0.0, 0.0 + loss = abs(vi - vout) * io + pwr = abs(vi * ii) + return pwr, loss, _get_eff(pwr, pwr - loss, 100.0) + + def _solv_get_warns(self, vi, vo, ii, io, phase, phase_conf={}): + """Check limits""" + return _get_warns(self._limits, {"vi": vi, "vo": vo, "ii": ii, "io": io}) + + +class VLoss: + """Voltage loss. + + This loss element is connected in series with other elements. + + Parameters + ---------- + name : str + Loss name. + vdrop : float | dict + Voltage drop (V), a constant value (float) or interpolation data (dict). + limits : dict, optional + Voltage and current limits., by default LIMITS_DEFAULT + """ + + @property + def _component_type(self): + """Defines the Loss component type""" + return _ComponentTypes.SLOSS + + @property + def _child_types(self): + """Defines allowable Loss child component types""" + et = list(_ComponentTypes) + et.remove(_ComponentTypes.SOURCE) + return et + + def __init__( + self, + name: str, + *, + vdrop: float | dict, + limits: dict = LIMITS_DEFAULT, + ): + self._params = {} + self._params["name"] = name + if isinstance(vdrop, dict): + if not np.all(np.diff(vdrop["io"]) > 0): + raise ValueError("io values must be monotonic increasing") + if len(vdrop["vi"]) == 1: + self._ipr = _Interp1d(vdrop["io"], vdrop["vdrop"][0]) + else: + cur = [] + volt = [] + for v in vdrop["vi"]: + cur += vdrop["io"] + volt += len(vdrop["io"]) * [v] + vd = np.asarray(vdrop["vdrop"]).reshape(1, -1)[0].tolist() + self._ipr = _Interp2d(cur, volt, vd) + self._params["vdrop"] = vdrop + else: + self._params["vdrop"] = abs(vdrop) + self._ipr = _Interp0d(abs(vdrop)) + self._limits = limits + + @classmethod + def from_file(cls, name: str, *, fname: str): + """Read VLoss parameters from .toml file. + + Parameters + ---------- + name : str + Loss name + fname : str, optional + File name. + """ + with open(fname, "r") as f: + config = toml.load(f) + + vd = _get_mand(config["vloss"], "vdrop") + lim = _get_opt(config, "limits", LIMITS_DEFAULT) + return cls(name, vdrop=vd, limits=lim) + + def _get_inp_current(self, phase, phase_conf={}): + return 0.0 + + def _get_outp_voltage(self, phase, phase_conf={}): + return 0.0 + + def _solv_inp_curr(self, vi, vo, io, phase, phase_conf={}): + """Calculate component input current from vi, vo and io""" + if abs(vi) == 0.0: + return 0.0 + return io + + def _solv_outp_volt(self, vi, ii, io, phase, phase_conf={}): + """Calculate component output voltage from vi, ii and io""" + if abs(vi) == 0.0: + return 0.0 + vo = vi - self._ipr._interp(abs(io), abs(vi)) * np.sign(vi) + if np.sign(vo) == np.sign(vi): + return vo + raise ValueError( + "Unstable system: VLoss component '{}' has zero output voltage".format( + self._params["name"] + ) + ) + + def _solv_pwr_loss(self, vi, vo, ii, io, phase, phase_conf={}): + """Calculate power and loss in component""" + vout = vi - self._ipr._interp(abs(io), abs(vi)) * np.sign(vi) + if np.sign(vout) != np.sign(vi): + return 0.0, 0.0, 0.0 + loss = abs(vi - vout) * io + pwr = abs(vi * ii) + return pwr, loss, _get_eff(pwr, pwr - loss, 100.0) + + def _solv_get_warns(self, vi, vo, ii, io, phase, phase_conf={}): + """Check limits""" + return _get_warns(self._limits, {"vi": vi, "vo": vo, "ii": ii, "io": io}) + + +class Converter: + """Voltage converter. + + Parameters + ---------- + name : str + Converter name. + vo : float + Output voltage (V). + eff : float | dict + Converter efficiency, a constant value (float) or interpolation data (dict). + iq : float, optional + Quiescent (no-load) current (A)., by default 0.0 + limits : dict, optional + Voltage and current limits., by default LIMITS_DEFAULT + iis : float, optional + Sleep (shut-down) current (A), by default 0.0 + + Raises + ------ + ValueError + If efficiency is 0.0 or > 1.0. + """ + + @property + def _component_type(self): + """Defines the Converter component type""" + return _ComponentTypes.CONVERTER + + @property + def _child_types(self): + """Defines allowable Converter child component types""" + et = list(_ComponentTypes) + et.remove(_ComponentTypes.SOURCE) + return et + + def __init__( + self, + name: str, + *, + vo: float, + eff: float | dict, + iq: float = IQ_DEFAULT, + limits: dict = LIMITS_DEFAULT, + iis: float = IIS_DEFAULT, + ): + self._params = {} + self._params["name"] = name + self._params["vo"] = vo + if isinstance(eff, dict): + if not np.all(np.diff(eff["io"]) > 0): + raise ValueError("io values must be monotonic increasing") + if np.min(eff["eff"]) <= 0.0: + raise ValueError("Efficiency values must be > 0.0") + if np.max(eff["eff"]) > 1.0: + raise ValueError("Efficiency values must be <= 1.0") + if len(eff["vi"]) == 1: + self._ipr = _Interp1d(eff["io"], eff["eff"][0]) + else: + cur = [] + volt = [] + for v in eff["vi"]: + cur += eff["io"] + volt += len(eff["io"]) * [v] + ef = np.asarray(eff["eff"]).reshape(1, -1)[0].tolist() + self._ipr = _Interp2d(cur, volt, ef) + else: + if not (eff > 0.0): + raise ValueError("Efficiency must be > 0.0") + if not (eff <= 1.0): + raise ValueError("Efficiency must be <= 1.0") + self._ipr = _Interp0d(eff) + self._params["eff"] = eff + self._params["iq"] = abs(iq) + self._params["iis"] = abs(iis) + self._limits = limits + + @classmethod + def from_file(cls, name: str, *, fname: str): + """Read Converter parameters from .toml file. + + Parameters + ---------- + name : str + Converter name + fname : str, optional + File name. + """ + with open(fname, "r") as f: + config = toml.load(f) + + v = _get_mand(config["converter"], "vo") + e = _get_mand(config["converter"], "eff") + iq = _get_opt(config["converter"], "iq", IQ_DEFAULT) + lim = _get_opt(config, "limits", LIMITS_DEFAULT) + iis = _get_opt(config["converter"], "iis", IIS_DEFAULT) + return cls(name, vo=v, eff=e, iq=iq, limits=lim, iis=iis) + + def _get_inp_current(self, phase, phase_conf=[]): + i = self._params["iq"] + if not phase_conf: + pass + elif phase not in phase_conf: + i = self._params["iis"] + return i + + def _get_outp_voltage(self, phase, phase_conf=[]): + v = self._params["vo"] + if not phase_conf: + pass + elif phase not in phase_conf: + v = 0.0 + return v + + def _solv_inp_curr(self, vi, vo, io, phase, phase_conf=[]): + """Calculate component input current from vi, vo and io""" + if abs(vi) == 0.0 or self._params["vo"] == 0.0: + return 0.0 + ve = vi * self._ipr._interp(abs(io), abs(vi)) + if not phase_conf: + pass + elif phase not in phase_conf: + return self._params["iis"] + if io == 0.0: + return self._params["iq"] + return abs(self._params["vo"] * io / ve) + + def _solv_outp_volt(self, vi, ii, io, phase, phase_conf=[]): + """Calculate component output voltage from vi, ii and io""" + v = self._params["vo"] + if vi == 0.0: + v = 0.0 + if phase_conf: + if phase not in phase_conf: + v = 0.0 + return v + + def _solv_pwr_loss(self, vi, vo, ii, io, phase, phase_conf=[]): + """Calculate power and loss in component""" + if io == 0.0: + loss = abs(self._params["iq"] * vi) + else: + loss = abs(ii * vi * (1.0 - self._ipr._interp(abs(io), abs(vi)))) + pwr = abs(vi * ii) + if phase_conf: + if phase not in phase_conf: + loss = abs(self._params["iis"] * vi) + pwr = abs(self._params["iis"] * vi) + return pwr, loss, _get_eff(pwr, pwr - loss, 0.0) + + def _solv_get_warns(self, vi, vo, ii, io, phase, phase_conf=[]): + """Check limits""" + if phase_conf: + if phase not in phase_conf: + return "" + return _get_warns(self._limits, {"vi": vi, "vo": vo, "ii": ii, "io": io}) + + +class LinReg: + """Linear voltage converter. + + Parameters + ---------- + name : str + Converter name. + vo : float + Output voltage (V). + vdrop : float + Dropout voltage (V). + iq : float, optional + Quiescent (no-load) current (A)., by default 0.0 + limits : dict, optional + Voltage and current limits., by default LIMITS_DEFAULT + iis : float, optional + Sleep (shut-down) current (A), by default 0.0 + """ + + @property + def _component_type(self): + """Defines the LinReg component type""" + return _ComponentTypes.LINREG + + @property + def _child_types(self): + """Defines allowable LinReg child component types""" + et = list(_ComponentTypes) + et.remove(_ComponentTypes.SOURCE) + return et + + def __init__( + self, + name: str, + *, + vo: float, + vdrop: float = VDROP_DEFAULT, + iq: float = IQ_DEFAULT, + limits: dict = LIMITS_DEFAULT, + iis: float = IIS_DEFAULT, + ): + self._params = {} + self._params["name"] = name + self._params["vo"] = vo + if not (abs(vdrop) < abs(vo)): + raise ValueError("Voltage drop must be < vo") + self._params["vdrop"] = abs(vdrop) + self._params["iq"] = abs(iq) + self._params["iis"] = abs(iis) + self._limits = limits + self._ipr = None + + @classmethod + def from_file(cls, name: str, *, fname: str): + """Read LinReg parameters from .toml file. + + Parameters + ---------- + name : str + LinReg name + fname : str, optional + File name. + """ + with open(fname, "r") as f: + config = toml.load(f) + + v = _get_mand(config["linreg"], "vo") + vd = _get_opt(config["linreg"], "vdrop", VDROP_DEFAULT) + iq = _get_opt(config["linreg"], "iq", IQ_DEFAULT) + lim = _get_opt(config, "limits", LIMITS_DEFAULT) + iis = _get_opt(config["linreg"], "iis", IIS_DEFAULT) + return cls(name, vo=v, vdrop=vd, iq=iq, limits=lim, iis=iis) + + def _get_inp_current(self, phase, phase_conf=[]): + i = self._params["iq"] + if not phase_conf: + pass + elif phase not in phase_conf: + i = self._params["iis"] + return i + + def _get_outp_voltage(self, phase, phase_conf=[]): + v = self._params["vo"] + if not phase_conf: + pass + elif phase not in phase_conf: + v = 0.0 + return v + + def _solv_inp_curr(self, vi, vo, io, phase, phase_conf=[]): + """Calculate component input current from vi, vo and io""" + if abs(vi) == 0.0: + return 0.0 + if io == 0.0: + i = self._params["iq"] + else: + i = io + if not phase_conf: + pass + elif phase not in phase_conf: + i = self._params["iis"] + return i + + def _solv_outp_volt(self, vi, ii, io, phase, phase_conf=[]): + """Calculate component output voltage from vi, ii and io""" + v = min(abs(self._params["vo"]), max(abs(vi) - self._params["vdrop"], 0.0)) + if not phase_conf: + pass + elif phase not in phase_conf: + v = 0.0 + if self._params["vo"] >= 0.0: + return v + return -v + + def _solv_pwr_loss(self, vi, vo, ii, io, phase, phase_conf=[]): + """Calculate power and loss in component""" + v = min(abs(self._params["vo"]), max(abs(vi) - self._params["vdrop"], 0.0)) + if vi == 0.0 or v == 0.0: + loss = 0.0 + elif io > 0.0: + loss = (abs(vi) - abs(v)) * io + else: + loss = abs(vi) * self._params["iq"] + pwr = abs(vi * ii) + if not phase_conf: + pass + elif phase not in phase_conf: + loss = abs(self._params["iis"] * vi) + pwr = abs(self._params["iis"] * vi) + return pwr, loss, _get_eff(pwr, pwr - loss, 0.0) + + def _solv_get_warns(self, vi, vo, ii, io, phase, phase_conf=[]): + """Check limits""" + if phase_conf: + if phase not in phase_conf: + return "" + return _get_warns(self._limits, {"vi": vi, "vo": vo, "ii": ii, "io": io}) diff --git a/src/sysloss/sysloss.py b/src/sysloss/sysloss.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/sysloss/system.py b/src/sysloss/system.py new file mode 100644 index 0000000..e15e61e --- /dev/null +++ b/src/sysloss/system.py @@ -0,0 +1,1252 @@ +# MIT License +# +# Copyright (c) 2024, Geir Drange +# +# 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. + +""":py:class:`~system.System` is the primary class for power analysis.""" + + +import rustworkx as rx +import numpy as np +from rich.tree import Tree +import json +import pandas as pd +import matplotlib +import matplotlib.pyplot as plt +from matplotlib.ticker import LinearLocator +from scipy.interpolate import LinearNDInterpolator +from sysloss.components import * +from sysloss.components import ( + _ComponentTypes, + _get_opt, + _get_mand, + _get_eff, + _Interp0d, + _Interp1d, + _Interp2d, + RS_DEFAULT, + LIMITS_DEFAULT, +) + + +class System: + """System to be analyzed. The first component of a system must always be a :py:class:`~components.Source`. + + Parameters + ---------- + name : str + System name. + source : Source + Source component. + + Raises + ------ + ValueError + If `source` is not a Source class. + """ + + def __init__(self, name: str, source: Source): + """Class constructor""" + self._g = None + if not isinstance(source, Source): + raise ValueError("First component of system must be a source!") + + self._g = rx.PyDAG(check_cycle=True, multigraph=False, attrs={}) + pidx = self._g.add_node(source) + self._g.attrs["name"] = name + self._g.attrs["phases"] = {} + self._g.attrs["phase_conf"] = {} + self._g.attrs["phase_conf"][source._params["name"]] = {} + self._g.attrs["nodes"] = {} + self._g.attrs["nodes"][source._params["name"]] = pidx + + @classmethod + def from_file(cls, fname: str): + """Load system from .json file. + + The .json file must previously have been saved with :py:meth:`~system.System.save`. + + Parameters + ---------- + fname : str + File name. + + Returns + ------- + System + A new `System` instance. + """ + with open(fname, "r") as f: + sys = json.load(f) + + entires = list(sys.keys()) + sysparams = _get_mand(sys, "system") + sysname = _get_mand(sysparams, "name") + ver = _get_mand(sysparams, "version") + # add sources + for e in range(1, len(entires)): + vo = _get_mand(sys[entires[e]]["params"], "vo") + rs = _get_opt(sys[entires[e]]["params"], "rs", RS_DEFAULT) + lim = _get_opt(sys[entires[e]], "limits", LIMITS_DEFAULT) + if e == 1: + self = cls(sysname, Source(entires[e], vo=vo, rs=rs, limits=lim)) + else: + self.add_source(Source(entires[e], vo=vo, rs=rs, limits=lim)) + # add childs + if sys[entires[e]]["childs"] != {}: + for p in list(sys[entires[e]]["childs"].keys()): + for c in sys[entires[e]]["childs"][p]: + cname = _get_mand(c["params"], "name") + # print(" " + cname) + limits = _get_opt(c, "limits", LIMITS_DEFAULT) + iq = _get_opt(c["params"], "iq", 0.0) + iis = _get_opt(c["params"], "iis", 0.0) + if c["type"] == "CONVERTER": + vo = _get_mand(c["params"], "vo") + eff = _get_mand(c["params"], "eff") + # af = _get_opt(c["params"], "active_phases", []) + self.add_comp( + p, + comp=Converter( + cname, + vo=vo, + eff=eff, + iq=iq, + limits=limits, + iis=iis, + # active_phases=af, + ), + ) + elif c["type"] == "LINREG": + vo = _get_mand(c["params"], "vo") + vdrop = _get_opt(c["params"], "vdrop", 0.0) + af = _get_opt(c["params"], "active_phases", []) + self.add_comp( + p, + comp=LinReg( + cname, + vo=vo, + vdrop=vdrop, + iq=iq, + limits=limits, + iis=iis, + # active_phases=af, + ), + ) + elif c["type"] == "SLOSS": + if "rs" in c["params"]: + rs = _get_mand(c["params"], "rs") + self.add_comp( + p, comp=RLoss(cname, rs=rs, limits=limits) + ) + else: + vdrop = _get_mand(c["params"], "vdrop") + self.add_comp( + p, comp=VLoss(cname, vdrop=vdrop, limits=limits) + ) + elif c["type"] == "LOAD": + # pl = _get_opt(c["params"], "phase_loads", {}) + if "pwr" in c["params"]: + pwr = _get_mand(c["params"], "pwr") + self.add_comp( + p, + comp=PLoad(cname, pwr=pwr, limits=limits), + ) + elif "rs" in c["params"]: + rs = _get_mand(c["params"], "rs") + self.add_comp( + p, comp=RLoad(cname, rs=rs, limits=limits) + ) + else: + ii = _get_mand(c["params"], "ii") + self.add_comp( + p, + comp=ILoad(cname, ii=ii, limits=limits, iis=iis), + ) + phases = _get_opt(sysparams, "phases", {}) + self._g.attrs["phases"] = phases + phase_conf = _get_mand(sysparams, "phase_conf") + self._g.attrs["phase_conf"] = phase_conf + + return self + + def _get_index(self, name: str): + """Get node index from component name""" + if name in self._g.attrs["nodes"]: + return self._g.attrs["nodes"][name] + + return -1 + + def _chk_parent(self, parent: str): + """Check if parent exists""" + if not parent in self._g.attrs["nodes"].keys(): + raise ValueError('Parent name "{}" not found!'.format(parent)) + + return True + + def _chk_name(self, name: str): + """Check if component name is valid""" + # check if name exists + if name in self._g.attrs["nodes"].keys(): + raise ValueError('Component name "{}" is already used!'.format(name)) + + return True + + def _get_childs_tree(self, node: int): + """Get dict of parent/childs""" + childs = list(rx.bfs_successors(self._g, node)) + cdict = {} + for c in childs: + cs = [] + for l in c[1]: + cs += [self._g.attrs["nodes"][l._params["name"]]] + cdict[self._g.attrs["nodes"][c[0]._params["name"]]] = cs + return cdict + + def _get_nodes(self): + """Get list of nodes in system""" + return [n for n in self._g.node_indices()] + + def _get_childs(self): + """Get list of children of each node""" + nodes = self._get_nodes() + cs = list(-np.ones(max(nodes) + 1, dtype=np.int32)) + for n in nodes: + if self._g.out_degree(n) > 0: + ind = [i for i in self._g.successor_indices(n)] + cs[n] = ind + return cs + + def _get_parents(self): + """Get list of parent of each node""" + nodes = self._get_nodes() + ps = list(-np.ones(max(nodes) + 1, dtype=np.int32)) + for n in nodes: + if self._g.in_degree(n) > 0: + ind = [i for i in self._g.predecessor_indices(n)] + ps[n] = ind + return ps + + def _get_sources(self): + """Get list of sources""" + tn = [n for n in rx.topological_sort(self._g)] + return [n for n in tn if isinstance(self._g[n], Source)] + + def _get_topo_sort(self): + """Get nodes topological sorted""" + tps = rx.topological_sort(self._g) + return [n for n in tps] + + def _sys_vars(self): + """Get system variable lists""" + vn = max(self._get_nodes()) + 1 # highest node index + 1 + v = list(np.zeros(vn)) # voltages + i = list(np.zeros(vn)) # currents + return v, i + + def _make_rtree(self, adj, node): + """Create Rich tree""" + tree = Tree(node) + for child in adj.get(node, []): + tree.add(self._make_rtree(adj, child)) + return tree + + def add_comp(self, parent: str, *, comp): + """Add component to system. + + Parameters + ---------- + parent : str + Name of parent component. + comp : component + Component (from :py:mod:`~sysloss.components`). + + Raises + ------ + ValueError + If parent does not allow connection to component. + """ + # check that parent exists + self._chk_parent(parent) + # check that component name is unique + self._chk_name(comp._params["name"]) + # check that parent allows component type as child + pidx = self._get_index(parent) + if not comp._component_type in self._g[pidx]._child_types: + raise ValueError( + "Parent does not allow child of type {}!".format( + comp._component_type.name + ) + ) + cidx = self._g.add_child(pidx, comp, None) + self._g.attrs["nodes"][comp._params["name"]] = cidx + # if not isinstance(phase_config, dict) and not isinstance(phase_config, list): + # raise ValueError("phase_config must be dict or list") + self._g.attrs["phase_conf"][comp._params["name"]] = {} + + def add_source(self, source: Source): + """Add an additional Source to the system. + + Parameters + ---------- + source : Source + Source component. + + Raises + ------ + ValueError + If `source` is not a Source class. + """ + self._chk_name(source._params["name"]) + if not isinstance(source, Source): + raise ValueError("Component must be a source!") + + pidx = self._g.add_node(source) + self._g.attrs["nodes"][source._params["name"]] = pidx + self._g.attrs["phase_conf"][source._params["name"]] = {} + + def change_comp(self, name: str, *, comp): + """Replace component. + + The new component can be of same type (parameter change), or a new type + given that the parent component accepts the connection. + + Parameters + ---------- + name : str + Name of component to be changed. + comp : component + Component (from :py:mod:`~sysloss.components`). + + Raises + ------ + ValueError + If trying to change a `source` component to a different type, or + if the parent does not accept a connection to the new component. + """ + # if component name changes, check that it is unique + if name != comp._params["name"]: + self._chk_name(comp._params["name"]) + + # source can only be changed to source + eidx = self._get_index(name) + if self._g[eidx]._component_type == _ComponentTypes.SOURCE: + if not isinstance(comp, Source): + raise ValueError("Source cannot be changed to other type!") + + # check that parent allows component type as child + parents = self._get_parents() + if parents[eidx] != -1: + if not comp._component_type in self._g[parents[eidx][0]]._child_types: + raise ValueError( + "Parent does not allow child of type {}!".format( + comp._component_type.name + ) + ) + self._g[eidx] = comp + # replace node name in graph dict + del [self._g.attrs["nodes"][name]] + self._g.attrs["nodes"][comp._params["name"]] = eidx + + def del_comp(self, name: str, *, del_childs: bool = True): + """Delete component. + + Deleting the last component (Source) in a system is not allowed. + In a system with multiple sources, a source can only be deleted + together with its child components. + + Parameters + ---------- + name : str + Name of component to delete. + del_childs : bool, optional + Delete all child components as well., by default True + + Raises + ------ + ValueError + If the component does not exist, is the last source or + a source with `del_childs` set to False. + """ + eidx = self._get_index(name) + if eidx == -1: + raise ValueError("Component name does not exist!") + parents = self._get_parents() + if parents[eidx] == -1: # source node + if not del_childs: + raise ValueError("Source must be deleted with its childs") + if len(self._get_sources()) < 2: + raise ValueError("Cannot delete the last source node!") + childs = self._get_childs() + # if not leaf, check if child type is allowed by parent type (not possible?) + # if leaves[eidx] == 0: + # for c in childs[eidx]: + # if not self._g[c]._component_type in self._g[parents[eidx]]._child_types: + # raise ValueError( + # "Parent and child of component are not compatible!" + # ) + # delete childs first if selected + if del_childs: + for c in rx.descendants(self._g, eidx): + del [self._g.attrs["nodes"][self._g[c]._params["name"]]] + del [self._g.attrs["phase_conf"][self._g[c]._params["name"]]] + self._g.remove_node(c) + # delete node + self._g.remove_node(eidx) + del [self._g.attrs["nodes"][name]] + del [self._g.attrs["phase_conf"][name]] + # restore links between new parent and childs, unless deleted + if not del_childs: + if childs[eidx] != -1: + for c in childs[eidx]: + self._g.add_edge(parents[eidx][0], c, None) + + def tree(self, name=""): + """Print the tree structure of the system. + + The tree object is generated with `Rich `_. + + Parameters + ---------- + name : str, optional + Name of component to start with. If not given, print the entire system., by default "" + + Returns + ------- + rich.tree.Tree + System Tree class. + + Raises + ------ + ValueError + If the component name is invalid. + """ + if not name == "": + if not name in self._g.attrs["nodes"].keys(): + raise ValueError("Component name is not valid!") + root = [name] + else: + ridx = self._get_sources() + root = [self._g[n]._params["name"] for n in ridx] + + t = Tree(self._g.attrs["name"]) + for n in root: + adj = rx.bfs_successors(self._g, self._g.attrs["nodes"][n]) + ndict = {} + for i in adj: + c = [] + for j in i[1]: + c += [j._params["name"]] + ndict[i[0]._params["name"]] = c + t.add(self._make_rtree(ndict, n)) + return t + + def _set_phase_lkup(self): + """Make lookup from node # to load phases""" + self._phase_lkup = {} + for c in self._g.attrs["phase_conf"].items(): + self._phase_lkup[self._get_index(c[0])] = c[1] + + def _sys_init(self, phase: str = ""): + """Create vectors of init values for solver""" + v, i = self._sys_vars() + self._set_phase_lkup() + for n in self._get_nodes(): + v[n] = self._g[n]._get_outp_voltage(phase, self._phase_lkup[n]) + i[n] = self._g[n]._get_inp_current(phase, self._phase_lkup[n]) + return v, i + + def _fwd_prop(self, v: float, i: float, phase: str = ""): + """Forward propagation of voltages""" + vo, _ = self._sys_vars() + # update output voltages (per node) + for n in self._topo_nodes: + p = self._parents[n] + phase_config = self._phase_lkup[n] + if self._childs[n] == -1: # leaf + if p == -1: # root + vo[n] = self._g[n]._solv_outp_volt( + 0.0, + 0.0, + 0.0, + phase, + phase_config, + ) + else: + vo[n] = self._g[n]._solv_outp_volt( + v[p[0]], + i[n], + 0.0, + phase, + phase_config, + ) + else: + # add currents into childs + isum = 0 + for c in self._childs[n]: + isum += i[c] + if p == -1: # root + vo[n] = self._g[n]._solv_outp_volt( + 0.0, + 0.0, + isum, + phase, + phase_config, + ) + else: + vo[n] = self._g[n]._solv_outp_volt( + v[p[0]], + i[n], + isum, + phase, + phase_config, + ) + return vo + + def _back_prop(self, v: float, i: float, phase: str = ""): + """Backward propagation of currents""" + _, ii = self._sys_vars() + # update input currents (per node) + for n in self._topo_nodes[::-1]: + p = self._parents[n] + phase_config = self._phase_lkup[n] + if self._childs[n] == -1: # leaf + if p == -1: # root + ii[n] = self._g[n]._solv_inp_curr( + v[n], + 0.0, + 0.0, + phase, + phase_config, + ) + else: + ii[n] = self._g[n]._solv_inp_curr( + v[p[0]], + 0.0, + 0.0, + phase, + phase_config, + ) + else: + isum = 0.0 + for c in self._childs[n]: + isum += i[c] + if p == -1: # root + ii[n] = self._g[n]._solv_inp_curr( + v[n], + v[n], + isum, + phase, + phase_config, + ) + else: + ii[n] = self._g[n]._solv_inp_curr( + v[p[0]], + v[n], + isum, + phase, + phase_config, + ) + + return ii + + def _rel_update(self): + """Update lists with component relationships""" + self._parents = self._get_parents() + self._childs = self._get_childs() + self._topo_nodes = self._get_topo_sort() + + def _get_parent_name(self, node): + """Get parent name of node""" + if self._parents[node] == -1: + return "" + return self._g[self._parents[node][0]]._params["name"] + + def _solve(self, vtol=1e-5, itol=1e-6, maxiter=10000, quiet=True, phase: str = ""): + """Solver""" + v, i = self._sys_init(phase) + iters = 0 + while iters <= maxiter: + vi = self._fwd_prop(v, i, phase) + ii = self._back_prop(vi, i, phase) + iters += 1 + if np.allclose(np.array(v), np.array(vi), rtol=vtol) and np.allclose( + np.array(i), np.array(ii), rtol=itol + ): + if not quiet: + pname = "" + if phase != "": + pname = "'{}': ".format(phase) + print("{}Tolerances met after {} iterations".format(pname, iters)) + break + v, i = vi, ii + return v, i, iters + + def _calc_energy(self, phase, pwr): + """Calculate energy per 24h""" + if phase == "": + return pwr * 24.0 + tot_time = 0.0 + for ph in self._g.attrs["phases"].keys(): + tot_time += self._g.attrs["phases"][ph] + cycles = 24 * 3600.0 / tot_time + return (self._g.attrs["phases"][phase] / 3600.0) * pwr * cycles + + def solve( + self, + *, + vtol: float = 1e-6, + itol: float = 1e-6, + maxiter: int = 10000, + quiet: bool = True, + phase: str = "", + energy: bool = False, + tags: dict = {} + ) -> pd.DataFrame: + """Analyze steady-state of system. + + Parameters + ---------- + vtol : float, optional + Voltage tolerance., by default 1e-6 + itol : float, optional + Current tolerance., by default 1e-6 + maxiter : int, optional + Maximum number of iterations., by default 10000 + quiet : bool, optional + Do not print # of iterations used., by default True + phase : str, optional + Load phase to analyze (all if not specified), by default "" + energy : bool, optional + Show energy consumption per 24h., by default False + tags: dict, optional + Tag-value pairs that will be added to the results table + + Returns + ------- + pd.DataFrame + Analysis result. + + Raises + ------ + ValueError + If the specified phase is not defined. See :py:meth:`~system.System.set_phases`. + RuntimeError + If a steady-state solution has not been found after `maxiter` iterations. + """ + self._rel_update() + phase_list = [""] + if phase != "": + if phase not in list(self._g.attrs["phases"].keys()): + raise ValueError( + "The specified phase '{}' is not defined".format(phase) + ) + phase_list = [phase] + elif len(list(self._g.attrs["phases"].keys())) > 0: + phase_list = list(self._g.attrs["phases"].keys()) + # solve + ppwr, ploss, peff, ptime, pener = [], [], [], [], [] + frames = [] + for ph in phase_list: + v, i, iters = self._solve(vtol, itol, maxiter, quiet, ph) + if iters > maxiter: + raise RuntimeError( + "Steady-state not achieved after {} iterations".format(iters - 1) + ) + # calculate results for each node + names, parent, typ, pwr, loss = [], [], [], [], [] + eff, warn, vsi, iso, vso, isi = [], [], [], [], [], [] + domain, phases, ener, dname = [], [], [], "none" + sources, dwarns = {}, {} + for n in self._topo_nodes: # [vi, vo, ii, io] + phase_config = self._phase_lkup[n] + names += [self._g[n]._params["name"]] + if self._g[n]._component_type.name == "SOURCE": + dname = self._g[n]._params["name"] + domain += [dname] + phases += [ph] + vi = v[n] + vo = v[n] + ii = i[n] + io = i[n] + p = self._parents[n] + + if p == -1: # root + vi = v[n] + self._g[n]._params["rs"] * ii + elif self._childs[n] == -1: # leaf + vi = v[p[0]] + io = 0.0 + else: + io = 0.0 + for c in self._childs[n]: + io += i[c] + vi = v[p[0]] + parent += [self._get_parent_name(n)] + p, l, e = self._g[n]._solv_pwr_loss(vi, vo, ii, io, ph, phase_config) + pwr += [p] + loss += [l] + eff += [e] + typ += [self._g[n]._component_type.name] + ener += [self._calc_energy(ph, p)] + if self._g[n]._component_type.name == "SOURCE": + sources[dname] = vi + dwarns[dname] = 0 + w = self._g[n]._solv_get_warns(vi, vo, ii, io, ph, phase_config) + warn += [w] + if w != "": + dwarns[dname] = 1 + vsi += [vi] + iso += [io] + vso += [v[n]] + isi += [i[n]] + + # subsystems summary + for d in range(len(sources)): + names += ["Subsystem {}".format(list(sources.keys())[d])] + typ += [""] + parent += [""] + domain += [""] + phases += [ph] + vsi += [sources[list(sources.keys())[d]]] + vso += [""] + isi += [""] + iso += [""] + pwr += [""] + loss += [""] + eff += [""] + ener += [""] + if dwarns[list(sources.keys())[d]] > 0: + warn += ["Yes"] + else: + warn += [""] + + # system total + names += ["System total"] + typ += [""] + parent += [""] + domain += [""] + phases += [ph] + vsi += [""] + vso += [""] + isi += [""] + iso += [""] + pwr += [""] + loss += [""] + eff += [""] + ener += [""] + if any(warn): + warn += ["Yes"] + else: + warn += [""] + + # report + res = {} + res["Component"] = names + res["Type"] = typ + res["Parent"] = parent + res["Domain"] = domain + if tags != {}: + for key in tags.keys(): + res[key] = [tags[key]] * len(names) + if ph != "": + res["Phase"] = phases + res["Vin (V)"] = vsi + res["Vout (V)"] = vso + res["Iin (A)"] = isi + res["Iout (A)"] = iso + res["Power (W)"] = pwr + res["Loss (W)"] = loss + res["Efficiency (%)"] = eff + if energy: + res["24h energy (Wh)"] = ener + res["Warnings"] = warn + df = pd.DataFrame(res) + + # update subsystem power/loss/efficiency/energy + for d in range(len(sources)): + src = list(sources.keys())[d] + idx = df[df.Component == "Subsystem {}".format(src)].index[0] + pwr = df[(df.Domain == src) & (df.Type == "SOURCE")][ + "Power (W)" + ].values[0] + df.at[idx, "Power (W)"] = pwr + loss = df[df.Domain == src]["Loss (W)"].sum() + df.at[idx, "Loss (W)"] = loss + df.at[idx, "Efficiency (%)"] = _get_eff(pwr, pwr - loss) + if energy: + df.at[idx, "24h energy (Wh)"] = self._calc_energy(ph, pwr) + + # update system total + pwr = df[(df.Domain == "") & (df["Power (W)"] != "")]["Power (W)"].sum() + idx = df.index[-1] + df.at[idx, "Power (W)"] = pwr + loss = df[(df.Domain == "") & (df["Loss (W)"] != "")]["Loss (W)"].sum() + df.at[idx, "Loss (W)"] = loss + df.at[idx, "Efficiency (%)"] = _get_eff(pwr, pwr - loss) + if energy: + df.at[idx, "24h energy (Wh)"] = self._calc_energy(ph, pwr) + if len(phase_list) > 1: + ploss += [loss] + ppwr += [pwr] + peff += [_get_eff(pwr, pwr - loss)] + ptime += [self._g.attrs["phases"][ph]] + pener += [self._calc_energy(ph, pwr)] + + # if only one subsystem, delete subsystem row and domain column + if len(sources) < 2: + df.drop(len(df) - 2, inplace=True) + df.drop(columns="Domain", inplace=True) + df.reset_index(inplace=True, drop=True) + frames += [df] + + if len(phase_list) > 1: + ttot = np.sum(np.asarray(ptime)) + apwr = np.sum(np.multiply(np.asarray(ppwr), np.asarray(ptime))) / ttot + aloss = np.sum(np.multiply(np.asarray(ploss), np.asarray(ptime))) / ttot + aeff = np.sum(np.multiply(np.asarray(peff), np.asarray(ptime))) / ttot + vals = ["System average", apwr, aloss, aeff] + idxs = ["Component", "Power (W)", "Loss (W)", "Efficiency (%)"] + if energy: + vals += [self._calc_energy("", apwr)] + idxs += ["24h energy (Wh)"] + if tags != {}: + for key in tags.keys(): + idxs += [key] + vals += [tags[key]] + avg = pd.Series(vals, index=idxs) + frames += [avg.to_frame().T] + dff = pd.concat(frames, ignore_index=True) + return dff.replace(np.nan, "") + + def params(self, limits: bool = False) -> pd.DataFrame: + """Return component parameters. + + Parameters + ---------- + limits : bool, optional + Include limits., by default False + + Returns + ------- + pd.DataFrame + System parameters. + """ + self._rel_update() + names, typ, parent, vo, vdrop = [], [], [], [], [] + iq, rs, eff, ii, pwr, iis = [], [], [], [], [], [] + lii, lio, lvi, lvo, pwrs = [], [], [], [], [] + domain, dname = [], "none" + src_cnt = 0 + for n in self._topo_nodes: + names += [self._g[n]._params["name"]] + typ += [self._g[n]._component_type.name] + if self._g[n]._component_type.name == "SOURCE": + dname = self._g[n]._params["name"] + src_cnt += 1 + domain += [dname] + _vo, _vdrop, _iq, _rs, _eff, _ii, _pwr = "", "", "", "", "", "", "" + _iis, _pwrs = "", "" + if self._g[n]._component_type == _ComponentTypes.SOURCE: + _vo = self._g[n]._params["vo"] + _rs = self._g[n]._params["rs"] + elif self._g[n]._component_type == _ComponentTypes.LOAD: + if "pwr" in self._g[n]._params: + _pwr = self._g[n]._params["pwr"] + _pwrs = self._g[n]._params["pwrs"] + elif "rs" in self._g[n]._params: + _rs = self._g[n]._params["rs"] + else: + _ii = self._g[n]._params["ii"] + _iis = self._g[n]._params["iis"] + elif self._g[n]._component_type == _ComponentTypes.CONVERTER: + _vo = self._g[n]._params["vo"] + _iq = self._g[n]._params["iq"] + if isinstance(self._g[n]._ipr, _Interp0d): + _eff = self._g[n]._params["eff"] + else: + _eff = "interp" + _iis = self._g[n]._params["iis"] + elif self._g[n]._component_type == _ComponentTypes.LINREG: + _vo = self._g[n]._params["vo"] + _vdrop = self._g[n]._params["vdrop"] + _iq = self._g[n]._params["iq"] + _iis = self._g[n]._params["iis"] + elif self._g[n]._component_type == _ComponentTypes.SLOSS: + if "rs" in self._g[n]._params: + _rs = self._g[n]._params["rs"] + else: + if isinstance(self._g[n]._ipr, _Interp0d): + _vdrop = self._g[n]._params["vdrop"] + else: + _vdrop = "interp" + vo += [_vo] + vdrop += [_vdrop] + iq += [_iq] + rs += [_rs] + eff += [_eff] + ii += [_ii] + pwr += [_pwr] + iis += [_iis] + pwrs += [_pwrs] + parent += [self._get_parent_name(n)] + if limits: + lii += [_get_opt(self._g[n]._limits, "ii", LIMITS_DEFAULT["ii"])] + lio += [_get_opt(self._g[n]._limits, "io", LIMITS_DEFAULT["io"])] + lvi += [_get_opt(self._g[n]._limits, "vi", LIMITS_DEFAULT["vi"])] + lvo += [_get_opt(self._g[n]._limits, "vo", LIMITS_DEFAULT["vo"])] + # report + res = {} + res["Component"] = names + res["Type"] = typ + res["Parent"] = parent + if src_cnt > 1: + res["Domain"] = domain + res["vo (V)"] = vo + res["vdrop (V)"] = vdrop + res["rs (Ohm)"] = rs + res["eff (%)"] = eff + res["iq (A)"] = iq + res["ii (A)"] = ii + res["iis (A)"] = iis + res["pwr (W)"] = pwr + res["pwrs (W)"] = pwrs + if limits: + res["vi limits (V)"] = lvi + res["vo limits (V)"] = lvo + res["ii limits (A)"] = lii + res["io limits (A)"] = lio + return pd.DataFrame(res) + + def set_sys_phases(self, phases: dict): + """Define system level load phases. + + Parameters + ---------- + phases : dict + A dict defining the system level load phases. + Each entry in the form '"name": duration(s)'. + + Raises + ------ + ValueError + If the dict contains less than two load phases or 'N/A' is used as + a phase name. + """ + if len(list(phases.keys())) < 2: + raise ValueError("There must be at least two phases!") + if "N/A" in list(phases.keys()): + raise ValueError('"N/A" is a reserved name!') + self._g.attrs["phases"] = phases + + def get_sys_phases(self) -> dict: + """Get the system level load phases. + + Returns + ------- + dict + System load phases. Empty dict if no phases have been defined. + """ + return self._g.attrs["phases"] + + def set_comp_phases(self, name: str, phase_conf: dict | list): + """Define component load phases + + Parameters + ---------- + name : str + Component name. + phase_conf : dict | list + Phase configuration. + + Raises + ------ + ValueError + Component does not exist, or phase configuration is invalid. + """ + cidx = self._get_index(name) + if cidx == -1: + raise ValueError("Component name does not exist!") + if not isinstance(phase_conf, dict) and not isinstance(phase_conf, list): + raise ValueError("phase_conf must be a dict or list!") + if isinstance(self._g[cidx], Source): + raise ValueError("Source components does not support load phases!") + if isinstance(self._g[cidx], RLoss) or isinstance(self._g[cidx], VLoss): + raise ValueError("Loss components does not support load phases!") + + self._g.attrs["phase_conf"][name] = phase_conf + + def phases(self) -> pd.DataFrame: + """Return load phases and parameters for all system components. + + Returns + ------- + pd.DataFrame + DataFrame with parameters and load phases. + """ + self._rel_update() + if self._g.attrs["phases"] == {}: + return None + names, typ, parent, phase = [], [], [], [] + rs, ii, pwr = [], [], [] + domain, dname = [], "none" + phase_names = list(self._g.attrs["phases"].keys()) + self._set_phase_lkup() + src_cnt = 0 + for n in self._topo_nodes: + tname = self._g[n]._component_type.name + if tname == "SOURCE": + dname = self._g[n]._params["name"] + src_cnt += 1 + ph_names = [] + if tname == "SOURCE" or tname == "SLOSS": + ph_names += ["N/A"] + elif tname == "CONVERTER" or tname == "LINREG": + if len(self._phase_lkup[n]) > 0: + for p in phase_names: + if p in self._phase_lkup[n]: + ph_names += [p] + if ph_names == []: + ph_names += ["N/A"] + elif tname == "LOAD": + if len(list(self._phase_lkup[n].keys())) > 0: + for p in phase_names: + if p in list(self._phase_lkup[n].keys()): + ph_names += [p] + if ph_names == []: + ph_names += ["N/A"] + + for p in ph_names: + names += [self._g[n]._params["name"]] + typ += [tname] + domain += [dname] + parent += [self._get_parent_name(n)] + phase += [p] + if tname == "SOURCE" or tname == "SLOSS": + rs += [""] + ii += [""] + pwr += [""] + break + if tname == "CONVERTER" or tname == "LINREG": + rs += [""] + ii += [""] + pwr += [""] + if tname == "LOAD": + if "pwr" in self._g[n]._params: + rs += [""] + ii += [""] + if p == "N/A": + pwr += [self._g[n]._params["pwr"]] + else: + pwr += [self._phase_lkup[n][p]] + elif "rs" in self._g[n]._params: + ii += [""] + pwr += [""] + if p == "N/A": + rs += [self._g[n]._params["rs"]] + else: + rs += [self._phase_lkup[n][p]] + else: + rs += [""] + pwr += [""] + if p == "N/A": + ii += [self._g[n]._params["ii"]] + else: + ii += [self._phase_lkup[n][p]] + + # report + res = {} + res["Component"] = names + res["Type"] = typ + res["Parent"] = parent + if src_cnt > 1: + res["Domain"] = domain + res["Active phase"] = phase + res["rs (Ohm)"] = rs + res["ii (A)"] = ii + res["pwr (W)"] = pwr + return pd.DataFrame(res) + + def save(self, fname: str, *, indent: int = 4): + """Save system as a .json file. + + Parameters + ---------- + fname : str + Filename. + indent : int, optional + Indentation to use in .json file, by default 4 + """ + self._rel_update() + sys = { + "system": { + "name": self._g.attrs["name"], + "version": "1.0.0", + "phases": self._g.attrs["phases"], + "phase_conf": self._g.attrs["phase_conf"], + } + } # TODO: version + ridx = self._get_sources() + root = [self._g[n]._params["name"] for n in ridx] + for r in range(len(ridx)): + tree = self._get_childs_tree(ridx[r]) + cdict = {} + if tree != {}: + for e in tree: + childs = [] + for c in tree[e]: + childs += [ + { + "type": self._g[c]._component_type.name, + "params": self._g[c]._params, + "limits": self._g[c]._limits, + } + ] + cdict[self._g[e]._params["name"]] = childs + sys[root[r]] = { + "type": self._g[ridx[r]]._component_type.name, + "params": self._g[ridx[r]]._params, + "limits": self._g[ridx[r]]._limits, + "childs": cdict, + } + + with open(fname, "w") as f: + json.dump(sys, f, indent=indent) + + def plot_interp( + self, + name: str, + *, + cmap: matplotlib.colors.Colormap = "viridis", + inpdata: bool = True, + plot3d: bool = False + ) -> matplotlib.figure.Figure | None: + """Plot 1D or 2D interpolation data. + + If a component has a parameter defined as either 1D or 2D interpolation data, + a figure is returned with linear interpolation shown. 1D data is plotted as an + interpolated line. 2D data can be shown as 2D color map or 3D surface. The + interpolated data is extended to +/-15% outside of input data points. + + Parameters + ---------- + name : str + Name of component. + cmap : matplotlib.colors.Colormap, optional + Colormap to use for 3D-plot., by default "viridis" + inpdata : bool, optional + Show input data (as red dots)., by default True + plot3d : bool, optional + Plot 3D surface (for 2D data), by default False + + Returns + ------- + matplotlib.figure.Figure | None + Interpolated parameter data figure. If the component does not have interpolation + data, `None` is returned. + + Raises + ------ + ValueError + If component name is not found. + """ + if not name in self._g.attrs["nodes"].keys(): + raise ValueError("Component name is not valid!") + n = self._g.attrs["nodes"][name] + if isinstance(self._g[n]._ipr, _Interp1d): + fig = plt.figure() + xmin = max(self._g[n]._ipr._x[0] - 0.15 * max(self._g[n]._ipr._x), 0.0) + xmax = 1.15 * max(self._g[n]._ipr._x) + x = np.linspace(xmin, xmax, num=200) + fx = np.interp(x, self._g[n]._ipr._x, self._g[n]._ipr._fx) + plt.plot(x, fx, "-") + if inpdata: + plt.plot( + self._g[n]._ipr._x, self._g[n]._ipr._fx, ".r", label="input data" + ) + plt.legend(loc="lower right") + plt.xlabel("Output current (A)") + if "vo" in self._g[n]._params: + plt.ylabel("Efficiency") + plt.title( + "{} efficiency for Vo={}V".format(name, self._g[n]._params["vo"]) + ) + else: + plt.ylabel("Voltage drop (V)") + plt.title("{} voltage drop".format(name)) + plt.rc("axes", axisbelow=True) + plt.grid() + return fig + elif isinstance(self._g[n]._ipr, _Interp2d): + xmin = max(min(self._g[n]._ipr._x) - 0.15 * max(self._g[n]._ipr._x), 0.0) + xmax = 1.15 * max(self._g[n]._ipr._x) + X = np.linspace(xmin, xmax, num=100) + ymin = max(min(self._g[n]._ipr._y) - 0.15 * max(self._g[n]._ipr._y), 0.0) + ymax = 1.15 * max(self._g[n]._ipr._y) + Y = np.linspace(ymin, ymax, num=100) + X, Y = np.meshgrid(X, Y) + interp = LinearNDInterpolator( + list(zip(self._g[n]._ipr._x, self._g[n]._ipr._y)), self._g[n]._ipr._fxy + ) + Z = interp(X, Y) + for i in range(len(X)): + for j in range(len(Y)): + if np.isnan(Z[i, j]): + Z[i, j] = self._g[n]._ipr._interp(X[i, j], Y[i, j]) + if not plot3d: + fig = plt.figure() + plt.pcolormesh(X, Y, Z, shading="auto", cmap=cmap) + if inpdata: + plt.plot( + self._g[n]._ipr._x, self._g[n]._ipr._y, ".r", label="input data" + ) + plt.legend(loc="upper left") + plt.colorbar() + plt.xlabel("Output current (A)") + plt.ylabel("Input voltage (V)") + else: + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + surf = ax.plot_surface( + Y, X, Z, cmap=cmap, linewidth=0, antialiased=False + ) + ax.set_zlim(np.nanmin(Z), 1.0) + ax.zaxis.set_major_locator(LinearLocator(10)) + ax.zaxis.set_major_formatter("{x:.02f}") + if inpdata: + plt.plot( + self._g[n]._ipr._y, + self._g[n]._ipr._x, + self._g[n]._ipr._fxy, + ".r", + label="input data", + ) + plt.legend(loc="upper left") + fig.colorbar(surf, shrink=0.5, aspect=10, pad=0.1) + plt.ylabel("Output current (A)") + plt.xlabel("Input voltage (V)") + if "vo" in self._g[n]._params: + plt.title( + "{} efficiency for Vo={}V".format(name, self._g[n]._params["vo"]) + ) + else: + plt.title("{} voltage drop".format(name)) + return fig + else: + print("Component does not have interpolation data") + return None diff --git a/tests/data/System v1.0.0.json b/tests/data/System v1.0.0.json new file mode 100644 index 0000000..85d1ebf --- /dev/null +++ b/tests/data/System v1.0.0.json @@ -0,0 +1,455 @@ +{ + "system": { + "name": "Version 1.0.0 system", + "version": "1.0.0", + "phases": { + "one": 3.0, + "two": 0.24, + "three": 1.25, + "four": 3.3 + }, + "phase_conf": { + "5V": {}, + "Buck 3.3V 0D": {}, + "Buck 3.3V 1D": [ + "one", + "two" + ], + "Buck 3.3V 2D": {}, + "RLoss": {}, + "VLoss 0D": {}, + "VLoss 1D": {}, + "VLoss 2D": {}, + "PLoad 1": { + "one": 0.2, + "three": 0.37, + "four": 0.55 + }, + "ILoad 1": {}, + "RLoad 1": {}, + "ILoad 2": { + "two": 0.25, + "three": 0.44 + }, + "12V": {}, + "RLoss 2": {}, + "LinReg 1": [ + "one", + "four" + ], + "PLoad 2": {} + } + }, + "12V": { + "type": "SOURCE", + "params": { + "name": "12V", + "vo": 12, + "rs": 0.035 + }, + "limits": { + "io": [ + 0.0, + 3.0 + ] + }, + "childs": { + "12V": [ + { + "type": "SLOSS", + "params": { + "name": "RLoss 2", + "rs": 1.77 + }, + "limits": { + "vi": [ + 0.0, + 48.0 + ] + } + } + ], + "RLoss 2": [ + { + "type": "LINREG", + "params": { + "name": "LinReg 1", + "vo": -4.5, + "vdrop": 0.35, + "iq": 0.00034, + "iis": 1.2e-05 + }, + "limits": { + "vi": [ + 0.0, + 8 + ], + "io": [ + 0.0, + 0.66 + ] + } + } + ], + "LinReg 1": [ + { + "type": "LOAD", + "params": { + "name": "PLoad 2", + "pwr": 0.35, + "pwrs": 2e-06 + }, + "limits": { + "vi": [ + 0.0, + 8.0 + ] + } + } + ] + } + }, + "5V": { + "type": "SOURCE", + "params": { + "name": "5V", + "vo": 5.0, + "rs": 0.1 + }, + "limits": { + "vi": [ + 0.0, + 1000000.0 + ], + "vo": [ + 0.0, + 1000000.0 + ], + "ii": [ + 0.0, + 1000000.0 + ], + "io": [ + 0.0, + 1000000.0 + ] + }, + "childs": { + "5V": [ + { + "type": "CONVERTER", + "params": { + "name": "Buck 3.3V 2D", + "vo": 3.1, + "eff": { + "vi": [ + 36, + 20, + 9, + 3.3 + ], + "io": [ + 0.001, + 0.01, + 0.1, + 0.2, + 0.3 + ], + "eff": [ + [ + 0.61, + 0.63, + 0.66, + 0.68, + 0.69 + ], + [ + 0.63, + 0.65, + 0.68, + 0.705, + 0.72 + ], + [ + 0.66, + 0.68, + 0.72, + 0.73, + 0.74 + ], + [ + 0.7, + 0.72, + 0.76, + 0.78, + 0.77 + ] + ] + }, + "iq": 0.001, + "iis": 0.0001 + }, + "limits": { + "vi": [ + 0.0, + 1000000.0 + ], + "vo": [ + 0.0, + 1000000.0 + ], + "ii": [ + 0.0, + 1000000.0 + ], + "io": [ + 0.0, + 1000000.0 + ] + } + }, + { + "type": "CONVERTER", + "params": { + "name": "Buck 3.3V 1D", + "vo": 3.2, + "eff": { + "vi": [ + 3.3 + ], + "io": [ + 0.1, + 0.5, + 0.9 + ], + "eff": [ + [ + 0.55, + 0.78, + 0.92 + ] + ] + }, + "iq": 0.001, + "iis": 0.0001 + }, + "limits": { + "vi": [ + 0.0, + 1000000.0 + ], + "vo": [ + 0.0, + 1000000.0 + ], + "ii": [ + 0.0, + 1000000.0 + ], + "io": [ + 0.0, + 1000000.0 + ] + } + }, + { + "type": "CONVERTER", + "params": { + "name": "Buck 3.3V 0D", + "vo": 3.3, + "eff": 0.88, + "iq": 0.001, + "iis": 0.0001 + }, + "limits": { + "vi": [ + 0.0, + 1000000.0 + ], + "vo": [ + 0.0, + 1000000.0 + ], + "ii": [ + 0.0, + 1000000.0 + ], + "io": [ + 0.0, + 1000000.0 + ] + } + } + ], + "Buck 3.3V 2D": [ + { + "type": "SLOSS", + "params": { + "name": "VLoss 2D", + "vdrop": { + "vi": [ + 24, + 48 + ], + "io": [ + 0.001, + 0.01, + 0.1, + 1, + 5 + ], + "vdrop": [ + [ + 0.2, + 0.3, + 0.39, + 0.6, + 1.0 + ], + [ + 0.25, + 0.325, + 0.4, + 0.65, + 1.1 + ] + ] + } + }, + "limits": { + "ii": [ + 0.0, + 8.0 + ] + } + }, + { + "type": "SLOSS", + "params": { + "name": "VLoss 1D", + "vdrop": { + "vi": [ + 48 + ], + "io": [ + 0.001, + 0.01, + 0.1, + 1, + 5 + ], + "vdrop": [ + [ + 0.25, + 0.325, + 0.4, + 0.65, + 1.1 + ] + ] + } + }, + "limits": { + "ii": [ + 0.0, + 8.0 + ] + } + } + ], + "Buck 3.3V 1D": [ + { + "type": "SLOSS", + "params": { + "name": "VLoss 0D", + "vdrop": 0.27 + }, + "limits": { + "ii": [ + 0.0, + 8.0 + ] + } + } + ], + "Buck 3.3V 0D": [ + { + "type": "SLOSS", + "params": { + "name": "RLoss", + "rs": 1.77 + }, + "limits": { + "vi": [ + 0.0, + 48.0 + ] + } + } + ], + "VLoss 2D": [ + { + "type": "LOAD", + "params": { + "name": "ILoad 2", + "ii": 0.2, + "iis": 0.0 + }, + "limits": { + "vi": [ + 0.0, + 10.0 + ] + } + } + ], + "VLoss 1D": [ + { + "type": "LOAD", + "params": { + "name": "RLoad 1", + "rs": 250 + }, + "limits": { + "vi": [ + 0.0, + 8.0 + ] + } + } + ], + "VLoss 0D": [ + { + "type": "LOAD", + "params": { + "name": "ILoad 1", + "ii": 0.1, + "iis": 1.6e-06 + }, + "limits": { + "vi": [ + 0.0, + 8.0 + ] + } + } + ], + "RLoss": [ + { + "type": "LOAD", + "params": { + "name": "PLoad 1", + "pwr": 0.1, + "pwrs": 1e-06 + }, + "limits": { + "vi": [ + 0.0, + 8.0 + ] + } + } + ] + } + } +} \ No newline at end of file diff --git a/tests/data/converter.toml b/tests/data/converter.toml new file mode 100644 index 0000000..4b7ecb0 --- /dev/null +++ b/tests/data/converter.toml @@ -0,0 +1,10 @@ +[converter] +vo = 5 +eff = 0.87 +iq = 0.0 + +[limits] +vi = [ 0.0, 1000000.0,] +vo = [ 0.0, 1000000.0,] +ii = [ 0.0, 1000000.0,] +io = [ 0.0, 1000000.0,] diff --git a/tests/data/iload.toml b/tests/data/iload.toml new file mode 100644 index 0000000..447b5e8 --- /dev/null +++ b/tests/data/iload.toml @@ -0,0 +1,8 @@ +[iload] +ii = 0.015 + +[limits] +vi = [ 0.0, 1000000.0,] +vo = [ 0.0, 1000000.0,] +ii = [ 0.0, 1000000.0,] +io = [ 0.0, 1000000.0,] diff --git a/tests/data/linreg.toml b/tests/data/linreg.toml new file mode 100644 index 0000000..03a5731 --- /dev/null +++ b/tests/data/linreg.toml @@ -0,0 +1,9 @@ +[linreg] +vo = 2.5 +vdrop = 0.3 + +[limits] +vi = [ 0.0, 1000000.0,] +vo = [ 0.0, 1000000.0,] +ii = [ 0.0, 1000000.0,] +io = [ 0.0, 1000000.0,] diff --git a/tests/data/linreg_bad.toml b/tests/data/linreg_bad.toml new file mode 100644 index 0000000..6ad43ef --- /dev/null +++ b/tests/data/linreg_bad.toml @@ -0,0 +1,2 @@ +[linreg] +vdrop = 0.3 diff --git a/tests/data/pload.toml b/tests/data/pload.toml new file mode 100644 index 0000000..453e154 --- /dev/null +++ b/tests/data/pload.toml @@ -0,0 +1,8 @@ +[pload] +pwr = 27.0e-3 + +[limits] +vi = [ 0.0, 1000000.0,] +vo = [ 0.0, 1000000.0,] +ii = [ 0.0, 1000000.0,] +io = [ 0.0, 1000000.0,] diff --git a/tests/data/rload.toml b/tests/data/rload.toml new file mode 100644 index 0000000..faea7dc --- /dev/null +++ b/tests/data/rload.toml @@ -0,0 +1,8 @@ +[rload] +rs = 200e3 + +[limits] +vi = [ 0.0, 1000000.0,] +vo = [ 0.0, 1000000.0,] +ii = [ 0.0, 1000000.0,] +io = [ 0.0, 1000000.0,] diff --git a/tests/data/rloss.toml b/tests/data/rloss.toml new file mode 100644 index 0000000..e4a879a --- /dev/null +++ b/tests/data/rloss.toml @@ -0,0 +1,8 @@ +[rloss] +rs = 0.030 + +[limits] +vi = [ 0.0, 1000000.0,] +vo = [ 0.0, 1000000.0,] +ii = [ 0.0, 1000000.0,] +io = [ 0.0, 1000000.0,] diff --git a/tests/data/source.toml b/tests/data/source.toml new file mode 100644 index 0000000..115163b --- /dev/null +++ b/tests/data/source.toml @@ -0,0 +1,9 @@ +[source] +vo = 3.0 +rs = 0.007 + +[limits] +vi = [ 0.0, 1000000.0,] +vo = [ 0.0, 1000000.0,] +ii = [ 0.0, 1000000.0,] +io = [ 0.0, 1000000.0,] diff --git a/tests/data/vloss.toml b/tests/data/vloss.toml new file mode 100644 index 0000000..03b161d --- /dev/null +++ b/tests/data/vloss.toml @@ -0,0 +1,8 @@ +[vloss] +vdrop = 1.7 + +[limits] +vi = [ 0.0, 1000000.0,] +vo = [ 0.0, 1000000.0,] +ii = [ 0.0, 1000000.0,] +io = [ 0.0, 1000000.0,] diff --git a/tests/regression/test_json_v1.py b/tests/regression/test_json_v1.py new file mode 100644 index 0000000..281eb5a --- /dev/null +++ b/tests/regression/test_json_v1.py @@ -0,0 +1,56 @@ +# MIT License +# +# Copyright (c) 2024, Geir Drange +# +# 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. + +import pytest +import numpy as np +import pandas as pd + +from sysloss.system import System +from sysloss.components import * + + +def test_case1(): + """Check system saved to json v1 format""" + case1 = System.from_file("tests/data/System v1.0.0.json") + df = case1.solve() + rows = df.shape[0] + assert np.allclose( + df[df["Component"] == "System average"]["Power (W)"][rows - 1], + 1.826334, + rtol=1e-6, + ), "Case1 power" + assert np.allclose( + df[df["Component"] == "System average"]["Loss (W)"][rows - 1], + 0.814912, + rtol=1e-6, + ), "Case1 loss" + assert np.allclose( + df[df["Component"] == "System average"]["Efficiency (%)"][rows - 1], + 54.846542, + rtol=1e-6, + ), "Case1 efficiency" + assert ( + df[df["Component"] == "System total"]["Warnings"][rows - 2] == "Yes" + ), "Case 1 warnings" + dfp = case1.params() + df_bool = dfp == "interp" + assert df_bool.sum().sum() == 4, "Case 1 interp components" diff --git a/tests/test_sysloss.py b/tests/test_sysloss.py deleted file mode 100644 index d181d45..0000000 --- a/tests/test_sysloss.py +++ /dev/null @@ -1 +0,0 @@ -from sysloss import sysloss diff --git a/tests/unit/test_comp_arith.py b/tests/unit/test_comp_arith.py new file mode 100644 index 0000000..0069771 --- /dev/null +++ b/tests/unit/test_comp_arith.py @@ -0,0 +1,455 @@ +# MIT License +# +# Copyright (c) 2024, Geir Drange +# +# 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. + +import pytest +import numpy as np +from sysloss.components import * + +# [vi, vo, io, phase] +INP_CURR_TESTS = [ + [0.0, 0.0, 1.0, ""], + [-0.0, -0.0, 1.0, ""], + [-100, -90, 3.14, "Apple"], + [10, 5, 0.0, ""], + [3.3, 3.0, 1e-3, ""], + [-24, -15, 0.0, "Apple"], +] +# [vi, ii, io, phase] +OUTP_VOLT_TESTS = [ + [0.0, 10, 10, ""], + [-0.0, 0, 0, ""], + [-15, 0.7, 0.7, ""], + [230, 2.1, 2.0, "Apple"], + [4.7, 0.0, 0.0, ""], +] +# [vi, vo, ii, io, phase] +PWR_LOSS_TESTS = [ + [0.0, 0.0, 0.5, 0.6, ""], + [-10.0, -8.5, 0.25, 0.1, ""], + [-200, 0.0, 0.0, 2, ""], + [3.3, 100, 0.1, 1e-3, ""], + [24, 0, 14, 0, "Apple"], + [-0.0, 0.0, 0.0, 0.0, ""], + [48, 48, 5, 4, ""], +] + + +def close(a, b): + """Check if results are close""" + return np.allclose([a], [b]) + + +@pytest.fixture() +def source(name, vo, rs): + """Return a Source object.""" + return Source(name, vo=vo, rs=rs) + + +@pytest.mark.parametrize( + "name, vo, rs", + [("test1", 5.0, 0.0), ("test 2", -12.0, 1.0), ("zero volt", 0.0, 10.0)], +) +def test_source(source, name, vo, rs): + """Test Source object with different parameters""" + assert source._params["name"] == name + + for ict in INP_CURR_TESTS: + ii = 0.0 if (vo == 0.0) else ict[2] + assert close( + source._solv_inp_curr(ict[0], ict[1], ict[2], ict[3]), ii + ), "Check Source input current" + for ovt in OUTP_VOLT_TESTS: + v = 0.0 if (vo == 0.0) else vo - rs * ovt[2] + assert close( + source._solv_outp_volt(ovt[0], ovt[1], ovt[2], ovt[3]), v + ), "Check source output voltage" + for plt in PWR_LOSS_TESTS: + pwr, loss, eff = source._solv_pwr_loss(plt[0], vo, plt[2], plt[3], plt[4]) + epwr = abs(vo * plt[3]) + assert close(pwr, epwr), "Check Source power" + eloss = 0.0 if vo == 0.0 else rs * plt[3] * plt[3] + assert close(loss, eloss), "Check Source loss" + eeff = 100.0 if vo == 0.0 or plt[3] == 0.0 else 100 * (epwr - eloss) / epwr + assert close(eff, eeff), "Check Source efficiency" + + +@pytest.fixture() +def rloss(name, rs): + """Return a RLoss object.""" + return RLoss(name, rs=rs) + + +@pytest.mark.parametrize( + "name, rs", + [("R pos", 25.0), ("R neg", -1.77), ("R zero", 0.0)], +) +def test_rloss(rloss, name, rs): + """Test RLoss object with different parameters""" + assert rloss._params["name"] == name + + for ict in INP_CURR_TESTS: + ii = 0.0 if (ict[0] == 0.0) else ict[2] + assert close( + rloss._solv_inp_curr(ict[0], ict[1], ict[2], ict[3]), ii + ), "Check RLoss input current" + for ovt in OUTP_VOLT_TESTS: + v = 0.0 if (ovt[0] == 0.0) else ovt[0] - abs(rs) * ovt[2] * np.sign(ovt[0]) + if np.sign(v) != np.sign(ovt[0]): + v = 0.0 + if ovt[0] == -15 and name == "R pos": + with pytest.raises(ValueError): + rloss._solv_outp_volt(ovt[0], ovt[1], ovt[2], ovt[3]) + else: + assert close( + rloss._solv_outp_volt(ovt[0], ovt[1], ovt[2], ovt[3]), v + ), "Check Loss output voltage" + for plt in PWR_LOSS_TESTS: + pwr, los, eff = rloss._solv_pwr_loss(plt[0], plt[1], plt[2], plt[3], plt[4]) + vo = plt[0] - (plt[3] * abs(rs)) * np.sign(plt[0]) + valid = True if np.sign(vo) == np.sign(plt[0]) else False + epwr = abs(plt[0] * plt[2]) if valid else 0.0 + assert close(pwr, epwr), "Check Loss power" + eloss = 0.0 if plt[0] == 0.0 or not valid else abs(plt[0] - vo) * plt[3] + assert close(los, eloss), "Check RLoss loss" + ef = 100.0 + if epwr > 0.0: + ef = 100.0 * abs((epwr - eloss) / epwr) + if not valid: + ef = 0.0 + assert close(eff, ef), "Check RLoss efficiency" + + +@pytest.fixture() +def vloss(name, vdrop): + """Return a VLoss object.""" + return VLoss(name, vdrop=vdrop) + + +@pytest.mark.parametrize( + "name, vdrop", + [("V pos", 5.0), ("V neg", -1.77), ("V zero", 0.0)], +) +def test_vloss(vloss, name, vdrop): + """Test VLoss object with different parameters""" + assert vloss._params["name"] == name + + for ict in INP_CURR_TESTS: + ii = 0.0 if (ict[0] == 0.0) else ict[2] + assert close( + vloss._solv_inp_curr(ict[0], ict[1], ict[2], ict[3]), ii + ), "Check VLoss input current" + for ovt in OUTP_VOLT_TESTS: + v = 0.0 if (ovt[0] == 0.0) else ovt[0] - abs(vdrop) * np.sign(ovt[0]) + if np.sign(v) != np.sign(ovt[0]): + v = 0.0 + if ovt[0] == 4.7 and name == "V pos": + with pytest.raises(ValueError): + vloss._solv_outp_volt(ovt[0], ovt[1], ovt[2], ovt[3]) + else: + assert close( + vloss._solv_outp_volt(ovt[0], ovt[1], ovt[2], ovt[3]), v + ), "Check VLoss output voltage" + for plt in PWR_LOSS_TESTS: + pwr, los, eff = vloss._solv_pwr_loss(plt[0], plt[1], plt[2], plt[3], plt[4]) + vo = plt[0] - abs(vdrop) * np.sign(plt[0]) + valid = True if np.sign(vo) == np.sign(plt[0]) else False + epwr = abs(plt[0] * plt[2]) if valid else 0.0 + assert close(pwr, epwr), "Check VLoss power" + eloss = 0.0 if plt[0] == 0.0 or not valid else abs(plt[0] - vo) * plt[3] + assert close(los, eloss), "Check VLoss loss" + ef = 100.0 + if epwr > 0.0: + ef = 100.0 * abs((epwr - eloss) / epwr) + if not valid: + ef = 0.0 + assert close(eff, ef), "Check VLoss efficiency" + + +@pytest.fixture() +def pload(name, pwr, pwrs, phase_loads): + """Return a PLoad object.""" + return PLoad(name, pwr=pwr, pwrs=pwrs) + + +@pytest.mark.parametrize( + "name, pwr, pwrs, phase_loads", + [ + ("No phase", 0.5, 0.001, {}), + ("On-phase", 0.5, 0.1, {"Apple": 0.78}), + ("Off-phase", 0.65, 0.13, {"Pear": 0.9}), + ], +) +def test_pload(pload, name, pwr, pwrs, phase_loads): + """Test PLoad object with different parameters""" + assert pload._params["name"] == name + + for ict in INP_CURR_TESTS: + if phase_loads == {}: + p = pwr + elif ict[3] in phase_loads: + p = phase_loads[ict[3]] + else: + p = pwrs + if ict[0] == 0.0: + ii = 0.0 + else: + ii = p / abs(ict[0]) + assert close( + pload._solv_inp_curr(ict[0], ict[1], ict[2], ict[3], phase_loads), ii + ), "Check PLoad input current" + for ovt in OUTP_VOLT_TESTS: + assert ( + pload._solv_outp_volt(ovt[0], ovt[1], ovt[2], ovt[3], phase_loads) == 0.0 + ), "Check PLoad output voltage" + for plt in PWR_LOSS_TESTS: + pwr, loss, eff = pload._solv_pwr_loss( + plt[0], plt[1], plt[2], plt[3], plt[4], phase_loads + ) + epwr = 0.0 if plt[0] == 0.0 else abs(plt[0] * plt[2]) + assert close(pwr, epwr), "Check PLoad power" + assert 0.0 == loss, "Check PLoad loss" + assert 100.0 == eff, "Check PLoad efficiency" + + +@pytest.fixture() +def iload(name, ii, iis, phase_loads): + """Return a ILoad object.""" + return ILoad(name, ii=ii, iis=iis) + + +@pytest.mark.parametrize( + "name, ii, iis, phase_loads", + [ + ("No phase", 0.5, 0.001, {}), + ("On-phase", 0.5, 0.1, {"Apple": 0.78}), + ("Off-phase", 0.65, 0.13, {"Pear": 0.9}), + ], +) +def test_iload(iload, name, ii, iis, phase_loads): + """Test ILoad object with different parameters""" + assert iload._params["name"] == name + + for ict in INP_CURR_TESTS: + if phase_loads == {}: + i = ii + elif ict[3] in phase_loads: + i = phase_loads[ict[3]] + else: + i = iis + if ict[0] == 0.0: + i = 0.0 + assert close( + iload._solv_inp_curr(ict[0], ict[1], ict[2], ict[3], phase_loads), i + ), "Check ILoad input current" + for ovt in OUTP_VOLT_TESTS: + assert ( + iload._solv_outp_volt(ovt[0], ovt[1], ovt[2], ovt[3], phase_loads) == 0.0 + ), "Check ILoad output voltage" + for plt in PWR_LOSS_TESTS: + pwr, loss, eff = iload._solv_pwr_loss( + plt[0], plt[1], plt[2], plt[3], plt[4], phase_loads + ) + epwr = 0.0 if plt[0] == 0.0 else abs(plt[0] * plt[2]) + assert close(pwr, epwr), "Check ILoad power" + assert 0.0 == loss, "Check ILoad loss" + assert 100.0 == eff, "Check ILoad efficiency" + + +@pytest.fixture() +def rload(name, rs, phase_loads): + """Return a RLoad object.""" + return RLoad(name, rs=rs) + + +@pytest.mark.parametrize( + "name, rs, phase_loads", + [ + ("No phase", 12, {}), + ("On-phase", 27, {"Apple": 33, "Orange": 47}), + ("Off-phase", 150, {"Pear": 190}), + ], +) +def test_rload(rload, name, rs, phase_loads): + """Test RLoad object with different parameters""" + assert rload._params["name"] == name + + for ict in INP_CURR_TESTS: + if phase_loads == {}: + r = rs + elif ict[3] in phase_loads: + r = phase_loads[ict[3]] + else: + r = rs + if ict[0] == 0.0: + i = 0.0 + else: + i = abs(ict[0]) / r + assert close( + rload._solv_inp_curr(ict[0], ict[1], ict[2], ict[3], phase_loads), i + ), "Check RLoad input current" + for ovt in OUTP_VOLT_TESTS: + assert ( + rload._solv_outp_volt(ovt[0], ovt[1], ovt[2], ovt[3], phase_loads) == 0.0 + ), "Check RLoad output voltage" + for plt in PWR_LOSS_TESTS: + pwr, loss, eff = rload._solv_pwr_loss( + plt[0], plt[1], plt[2], plt[3], plt[4], phase_loads + ) + epwr = 0.0 if plt[0] == 0.0 else abs(plt[0] * plt[2]) + assert close(pwr, epwr), "Check RLoad power" + assert 0.0 == loss, "Check RLoad loss" + assert 100.0 == eff, "Check RLoad efficiency" + + +@pytest.fixture() +def converter(name, vo, eff, iq, iis, active_phases): + """Return a Converter object.""" + return Converter(name, vo=vo, eff=eff, iq=iq, iis=iis) + + +@pytest.mark.parametrize( + "name, vo, eff, iq, iis, active_phases", + [ + ("No phase", 12.0, 0.97, 1e-5, 1e-6, []), + ("On-phase", -15, 0.88, 1.7e-4, 2e-5, ["Apple", "Orange"]), + ("Off-phase", 150, 0.5, 1e-3, 1.2e-4, ["Pear"]), + ("zero volt", 0.0, 0.65, 3e-3, 1e-4, []), + ], +) +def test_converter(converter, name, vo, eff, iq, iis, active_phases): + """Test Converter object with different parameters""" + assert converter._params["name"] == name + + for ict in INP_CURR_TESTS: + if ict[0] == 0.0 or vo == 0.0: + ii = 0.0 + elif active_phases == []: + if ict[2] == 0.0: + ii = iq + else: + ii = abs(vo * ict[2] / (eff * ict[0])) + elif ict[3] in active_phases: + if ict[2] == 0.0: + ii = iq + else: + ii = abs(vo * ict[2] / (eff * ict[0])) + elif ict[3] not in active_phases: + ii = iis + assert close( + converter._solv_inp_curr(ict[0], ict[1], ict[2], ict[3], active_phases), ii + ), "Check Converter input current" + for ovt in OUTP_VOLT_TESTS: + v = vo + if ovt[0] == 0.0 or vo == 0.0: + v = 0.0 + elif active_phases != []: + if ovt[3] not in active_phases: + v = 0.0 + assert close( + converter._solv_outp_volt(ovt[0], ovt[1], ovt[2], ovt[3], active_phases), v + ), "Check Converter output voltage" + for plt in PWR_LOSS_TESTS: + pwr, loss, ef = converter._solv_pwr_loss( + plt[0], vo, plt[2], plt[3], plt[4], active_phases + ) + ipwr = abs(plt[0] * plt[2]) + eloss = abs(iq * plt[0]) if plt[3] == 0.0 else ipwr * (1.0 - eff) + if active_phases != []: + if plt[4] not in active_phases: + eloss = abs(iis * plt[0]) + ipwr = abs(iis * plt[0]) + assert close(pwr, ipwr), "Check Converter power" + assert close(loss, eloss), "Check Converter loss" + if ipwr == 0.0 or vo == 0.0: + eeff = 0.0 + opwr = ipwr - eloss + if ipwr == 0.0: + eeff = 0.0 + else: + eeff = 100.0 * abs(opwr / ipwr) + assert close(ef, eeff), "Check Converter efficiency" + + +@pytest.fixture() +def linreg(name, vo, vdrop, iq, iis, active_phases): + """Return a LinReg object.""" + return LinReg(name, vo=vo, vdrop=vdrop, iq=iq, iis=iis) + + +@pytest.mark.parametrize( + "name, vo, vdrop, iq, iis, active_phases", + [ + ("No phase", 12.0, 1.2, 1e-5, 1e-6, []), + ("On-phase", -15, 0.88, 1.7e-4, 2e-5, ["Apple", "Orange"]), + ("Off-phase", 150, 0.67, 1e-3, 1.2e-4, ["Pear"]), + ], +) +def test_linreg(linreg, name, vo, vdrop, iq, iis, active_phases): + """Test LinReg object with different parameters""" + assert linreg._params["name"] == name + + for ict in INP_CURR_TESTS: + if ict[0] == 0.0 or vo == 0.0: + ii = 0.0 + elif active_phases == []: + ii = ict[2] if ict[2] > 0.0 else iq + elif ict[3] in active_phases: + ii = ict[2] if ict[2] > 0.0 else iq + elif ict[3] not in active_phases: + ii = iis + assert close( + linreg._solv_inp_curr(ict[0], ict[1], ict[2], ict[3], active_phases), ii + ), "Check Linreg input current" + for ovt in OUTP_VOLT_TESTS: + v = min(abs(vo), max(abs(ovt[0]) - vdrop, 0.0)) + if ovt[0] == 0.0 or vo == 0.0: + v = 0.0 + elif active_phases != []: + if ovt[3] not in active_phases: + v = 0.0 + assert close( + linreg._solv_outp_volt(ovt[0], ovt[1], ovt[2], ovt[3], active_phases), + v * np.sign(vo), + ), "Check Linreg output voltage" + for plt in PWR_LOSS_TESTS: + pwr, loss, eff = linreg._solv_pwr_loss( + plt[0], vo, plt[2], plt[3], plt[4], active_phases + ) + ipwr = abs(plt[0] * plt[2]) + v = min( + abs(linreg._params["vo"]), max(abs(plt[0]) - linreg._params["vdrop"], 0.0) + ) + eloss = abs(iq * plt[0]) if plt[3] == 0.0 else (abs(plt[0]) - abs(v)) * plt[3] + if active_phases != []: + if plt[4] not in active_phases: + eloss = abs(iis * plt[0]) + ipwr = abs(iis * plt[0]) + assert close(pwr, ipwr), "Check LinReg power" + assert close(loss, eloss), "Check LinReg loss" + if ipwr == 0.0 or v == 0.0: + eeff = 0.0 + opwr = ipwr - eloss + if ipwr == 0.0: + eeff = 0.0 + else: + eeff = 100.0 * abs(opwr / ipwr) + assert close(eff, eeff), "Check LinReg efficiency" diff --git a/tests/unit/test_components.py b/tests/unit/test_components.py new file mode 100644 index 0000000..5a06ad5 --- /dev/null +++ b/tests/unit/test_components.py @@ -0,0 +1,212 @@ +# MIT License +# +# Copyright (c) 2024, Geir Drange +# +# 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. + + +from sysloss.components import * +from sysloss.components import _ComponentTypes, _ComponentInterface +from sysloss.components import LIMITS_DEFAULT +from sysloss.components import _Interp0d, _Interp1d, _Interp2d +import numpy as np +import pytest + + +def close(a, b): + """Check if results are close""" + return np.allclose([a], [b]) + + +def test_classes(): + """Check informal interface on components""" + assert issubclass(Source, _ComponentInterface), "subclass Source" + assert issubclass(ILoad, _ComponentInterface), "subclass ILoad" + assert issubclass(PLoad, _ComponentInterface), "subclass PLoad" + assert issubclass(RLoss, _ComponentInterface), "subclass SLoss" + assert issubclass(VLoss, _ComponentInterface), "subclass SLoss" + assert issubclass(Converter, _ComponentInterface), "subclass Converter" + assert issubclass(LinReg, _ComponentInterface), "subclass LinReg" + + +def test_source(): + """Check Source component""" + sa = Source("Battery 3V", vo=3.0, rs=7e-3) + assert sa._component_type == _ComponentTypes.SOURCE, "Source component type" + assert _ComponentTypes.SOURCE not in list(sa._child_types), "Source child types" + sb = Source.from_file("Battery 3V", fname="tests/data/source.toml") + assert sa._params == sb._params, "Source parameters from file" + assert sa._limits == sb._limits, "Source limits from file" + assert isinstance(sa, _ComponentInterface), "instance Source" + + +def test_pload(): + """Check PLoad component""" + pa = PLoad("Load 1", pwr=27e-3) + assert pa._component_type == _ComponentTypes.LOAD, "PLoad component type" + assert list(pa._child_types) == [None], "PLoad child types" + pb = PLoad.from_file("Load 1", fname="tests/data/pload.toml") + assert pa._params == pb._params, "PLoad parameters from file" + assert pa._limits == pb._limits, "PLoad limits from file" + assert isinstance(pa, _ComponentInterface), "instance PLoad" + + +def test_iload(): + """Check ILoad component""" + ia = ILoad("Load 1", ii=15e-3) + assert ia._component_type == _ComponentTypes.LOAD, "ILoad component type" + assert list(ia._child_types) == [None], "ILoad child types" + ib = ILoad.from_file("Load 1", fname="tests/data/iload.toml") + assert ia._params == ib._params, "ILoad parameters from file" + assert ia._limits == ib._limits, "ILoad limits from file" + assert isinstance(ia, _ComponentInterface), "instance ILoad" + + +def test_rload(): + """Check RLoad component""" + ra = RLoad("Load 1", rs=200e3) + assert ra._component_type == _ComponentTypes.LOAD, "RLoad component type" + assert list(ra._child_types) == [None], "RLoad child types" + rb = RLoad.from_file("Load 1", fname="tests/data/rload.toml") + assert ra._params == rb._params, "RLoad parameters from file" + assert ra._limits == rb._limits, "RLoad limits from file" + assert isinstance(ra, _ComponentInterface), "instance ILoad" + with pytest.raises(ValueError): + ra = RLoad("Load 1", rs=0.0) + + +def test_rloss(): + """Check RLoss component""" + la = RLoss("RLoss 1", rs=30e-3, limits=LIMITS_DEFAULT) + assert la._component_type == _ComponentTypes.SLOSS, "RLoss component type" + assert _ComponentTypes.SOURCE not in list(la._child_types), "RLoss child types" + lb = RLoss.from_file("RLoss 1", fname="tests/data/rloss.toml") + assert la._params == lb._params, "RLoss parameters from file" + assert la._limits == lb._limits, "RLoss limits from file" + assert isinstance(la, _ComponentInterface), "instance RLoss" + + +def test_vloss(): + """Check VLoss component""" + la = VLoss("VLoss 1", vdrop=1.7, limits=LIMITS_DEFAULT) + assert la._component_type == _ComponentTypes.SLOSS, "VLoss component type" + assert _ComponentTypes.SOURCE not in list(la._child_types), "VLoss child types" + lb = VLoss.from_file("VLoss 1", fname="tests/data/vloss.toml") + assert la._params == lb._params, "VLoss parameters from file" + assert la._limits == lb._limits, "VLoss limits from file" + assert isinstance(la, _ComponentInterface), "instance VLoss" + vdata = {"vi": [4.5], "io": [0.1, 0.4, 0.6, 0.9], "vdrop": [[0.3, 0.4, 0.67, 0.89]]} + lc = VLoss("Conv 1D", vdrop=vdata) + assert close(lc._ipr._interp(0.25, 100), 0.35), "VLoss 1D interpolation" + vdata["io"][-1] = 0.59 + with pytest.raises(ValueError): + lc = VLoss("VLoss 1D interpolation, io non-monotonic", vdrop=vdata) + vdata = { + "vi": [4.5, 12], + "io": [0.1, 0.4, 0.6], + "vdrop": [[0.3, 0.4, 0.67], [0.4, 0.55, 0.78]], + } + lc = VLoss("VLoss 2D", vdrop=vdata) + assert close( + lc._ipr._interp(0.0, 100), vdata["vdrop"][1][0] + ), "VLoss 2D interpolation" + + +def test_converter(): + """Check Converter component""" + ca = Converter("Conv 1", vo=5.0, eff=0.87) + assert ca._component_type == _ComponentTypes.CONVERTER, "Converter component type" + assert _ComponentTypes.SOURCE not in list(ca._child_types), "Converter child types" + with pytest.raises(ValueError): + cb = Converter("Conv 1", vo=5.0, eff=1.000001) + with pytest.raises(ValueError): + cb = Converter("Conv 1", vo=5.0, eff=-0.000001) + with pytest.raises(ValueError): + cb = Converter("Conv 1", vo=5.0, eff=0.0) + cb = Converter.from_file("Conv 1", fname="tests/data/converter.toml") + assert ca._params == cb._params, "Converter parameters from file" + assert ca._limits == cb._limits, "Converter limits from file" + assert isinstance(ca, _ComponentInterface), "instance Converter" + edata = {"vi": [4.5], "io": [0.1, 0.4, 0.6, 0.9], "eff": [[0.3, 0.4, 0.67, 0.89]]} + ca = Converter("Conv 1D", vo=5.0, eff=edata) + assert close(ca._ipr._interp(0.25, 100), 0.35), "Converter 1D interpolation" + edata["eff"][-1] = 1.1 + with pytest.raises(ValueError): + ca = Converter("Conv 1D interpolation, eff > 1.0", vo=5.0, eff=edata) + edata["eff"][-1] = 0.0 + with pytest.raises(ValueError): + ca = Converter("Conv 1D interpolation, eff = 0.0", vo=5.0, eff=edata) + edata["io"][-1] = 0.59 + with pytest.raises(ValueError): + ca = Converter("Conv 1D interpolation, io non-monotonic", vo=5.0, eff=edata) + edata = { + "vi": [4.5, 12], + "io": [0.1, 0.4, 0.6], + "eff": [[0.3, 0.4, 0.67], [0.4, 0.55, 0.78]], + } + ca = Converter("Conv 2D", vo=5.0, eff=edata) + assert close( + ca._ipr._interp(0.0, 100), edata["eff"][1][0] + ), "Converter 2D interpolation" + + +def test_linreg(): + """Check LinReg component""" + la = LinReg("LDO 1", vo=2.5, vdrop=0.3, iq=0.0) + assert la._component_type == _ComponentTypes.LINREG, "LinReg component type" + assert _ComponentTypes.SOURCE not in list(la._child_types), "LinReg child types" + with pytest.raises(ValueError): + lb = LinReg("LDO 2", vo=1.8, vdrop=2.0) + with pytest.raises(KeyError): + lb = LinReg.from_file("LDO 1", fname="tests/data/linreg_bad.toml") + lb = LinReg.from_file("LDO 1", fname="tests/data/linreg.toml") + assert la._params == lb._params, "LinReg parameters from file" + assert la._limits == lb._limits, "LinReg limits from file" + assert isinstance(la, _ComponentInterface), "instance LinReg" + + +def test_interpolators(): + """Check interpolators""" + interp0d = _Interp0d(0.66) + for i in range(10): + rng = np.random.default_rng() + assert interp0d._interp(rng.random(), rng.random()) == 0.66, "0D interpolator" + x = [0.1, 0.5, 0.8, 1.2, 1.7] + fx = [0.1, 0.2, 0.3, 0.5, 0.75] + interp1d = _Interp1d(x, fx) + for i in range(len(x)): + assert close( + interp1d._interp(x[i], 10 * rng.random()), fx[i] + ), "1D interpolator" + assert close(interp1d._interp(0, rng.random()), fx[0]), "1D below smallest x" + assert close(interp1d._interp(100, rng.random()), fx[-1]), "1D above largest x" + x = [0.1, 0.5, 0.9, 0.1, 0.5, 0.9, 0.1, 0.5, 0.9] + y = [3.3, 3.3, 3.3, 5.0, 5.0, 5.0, 12, 12, 12] + fxy = [0.55, 0.78, 0.92, 0.5, 0.74, 0.83, 0.4, 0.6, 0.766] + interp2d = _Interp2d(x, y, fxy) + for i in range(len(x)): + assert close(interp2d._interp(x[i], y[i]), fxy[i]), "2D interpolator" + assert close(interp2d._interp(0.0, 0.0), fxy[0]), "2D interpolator q0" + assert close(interp2d._interp(0.0, 5.0), fxy[3]), "2D interpolator q1" + assert close(interp2d._interp(0.0, 100.0), fxy[6]), "2D interpolator q2" + assert close(interp2d._interp(0.5, 77.0), fxy[7]), "2D interpolator q3" + assert close(interp2d._interp(11, 24.7), fxy[8]), "2D interpolator q4" + assert close(interp2d._interp(1.7, 5), fxy[5]), "2D interpolator q5" + assert close(interp2d._interp(1.7, 0.33), fxy[2]), "2D interpolator q6" + assert close(interp2d._interp(0.5, 2.75), fxy[1]), "2D interpolator q7" diff --git a/tests/unit/test_system.py b/tests/unit/test_system.py new file mode 100644 index 0000000..dfc4735 --- /dev/null +++ b/tests/unit/test_system.py @@ -0,0 +1,369 @@ +# MIT License +# +# Copyright (c) 2024, Geir Drange +# +# 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. + +import pytest +import numpy as np +import rich + +import matplotlib +from sysloss.system import System +from sysloss.components import * + + +def test_case1(): + """Check system consisting of all component types""" + case1 = System("Case1 system", Source("3V coin", vo=3, rs=13e-3)) + case1.add_comp("3V coin", comp=Converter("1.8V buck", vo=1.8, eff=0.87, iq=12e-6)) + case1.add_comp("1.8V buck", comp=PLoad("MCU", pwr=27e-3)) + case1.add_comp("3V coin", comp=Converter("5V boost", vo=5, eff=0.91, iq=42e-6)) + case1.add_comp("5V boost", comp=ILoad("Sensor", ii=15e-3)) + case1.add_comp(parent="5V boost", comp=RLoss("RC filter", rs=33.0)) + case1.add_comp(parent="RC filter", comp=VLoss("Diode", vdrop=0.17)) + case1.add_comp("Diode", comp=LinReg("LDO 2.5V", vo=2.5, vdrop=0.27, iq=150e-6)) + case1.add_comp("LDO 2.5V", comp=PLoad("ADC", pwr=15e-3)) + case1.add_comp("5V boost", comp=RLoad("Res divider", rs=200e3)) + with pytest.raises(RuntimeError): + case1.solve(maxiter=1) + df = case1.solve(quiet=False) + rows = 11 + assert df.shape[0] == rows, "Case1 solution row count" + assert df.shape[1] == 11, "Case1 solution column count" + df = case1.solve(tags={"Battery": "small", "Interval": "fast"}) + assert df.shape[0] == rows, "Case1 solution row count" + assert df.shape[1] == 13, "Case1 solution column count" + assert np.allclose( + df[df["Component"] == "System total"]["Efficiency (%)"][rows - 1], + 79.9011, + rtol=1e-6, + ), "Case1 efficiency" + assert ( + df[df["Component"] == "System total"]["Warnings"][rows - 1] == "" + ), "Case 1 warnings" + case1.save("tests/unit/case1.json") + dfp = case1.params(limits=True) + assert len(dfp) == rows - 1, "Case1 parameters row count" + t = case1.tree() + assert type(t) == rich.tree.Tree, "Case1 tree output" + with pytest.raises(ValueError): + case1.tree("Dummy") + t = case1.tree("5V boost") + assert type(t) == rich.tree.Tree, "Case1 subtree output" + edata = {"vi": [3.6], "io": [0.1, 0.4, 0.6, 0.9], "eff": [[0.3, 0.4, 0.67, 0.89]]} + case1.change_comp( + "1.8V buck", comp=Converter("1.8V buck", vo=1.8, eff=edata, iq=12e-6) + ) + dfp = case1.params(limits=False) + assert ( + dfp[dfp.Component == "1.8V buck"]["eff (%)"].tolist()[0] == "interp" + ), "Case parameters interpolator" + + # reload system from json + case1b = System.from_file("tests/unit/case1.json") + df2 = case1b.solve() + assert len(df2) == rows, "Case1b solution row count" + + assert np.allclose( + df2[df2["Component"] == "System total"]["Efficiency (%)"][rows - 1], + df[df["Component"] == "System total"]["Efficiency (%)"][rows - 1], + rtol=1e-6, + ), "Case1 vs case1b efficiency" + + assert np.allclose( + df2[df2["Component"] == "System total"]["Power (W)"][rows - 1], + df[df["Component"] == "System total"]["Power (W)"][rows - 1], + rtol=1e-6, + ), "Case1 vs case1b power" + + +def test_case2(): + """Check system consisting of only Source""" + case2 = System("Case2 system", Source("12V input", vo=12.0)) + df = case2.solve() + assert len(df) == 2, "Case2 solution row count" + assert ( + df[df["Component"] == "System total"]["Efficiency (%)"][1] == 100.0 + ), "Case2 efficiency" + + +def test_case3(): + """Check system with negative output converter""" + case3 = System("Case3 system", Source("5V USB", vo=5.0)) + case3.add_comp( + "5V USB", + comp=Converter("-12V inverter", vo=-12.0, eff=0.88), + ) + case3.add_comp("-12V inverter", comp=RLoss("Resistor", rs=25.0)) + df = case3.solve() + assert len(df) == 4, "Case3 solution row count" + assert ( + df[df["Component"] == "System total"]["Efficiency (%)"][3] == 100.0 + ), "Case2 efficiency" + + +def test_case4(): + """Converter with zero input voltage""" + case4 = System("Case4 system", Source("0V system", vo=0.0)) + case4.add_comp("0V system", comp=Converter("Buck", vo=3.3, eff=0.50)) + df = case4.solve() + assert len(df) == 3, "Case4 solution row count" + + +def test_case5(): + """LinReg with zero input voltage""" + case5 = System("Case4 system", Source("0V system", vo=0.0)) + case5.add_comp("0V system", comp=LinReg("LDO", vo=-3.3)) + df = case5.solve() + assert len(df) == 3, "Case5 solution row count" + + +def test_case6(): + """Create new system with root as non-Source""" + with pytest.raises(ValueError): + case6 = System("Case6 system", PLoad("Load", pwr=1)) + + +def test_case7(): + """Add component to non-existing component""" + case7 = System("Case7 system", Source("10V system", vo=10.0)) + with pytest.raises(ValueError): + case7.add_comp("5V input", comp=Converter("Buck", vo=2.5, eff=0.75)) + + +def test_case8(): + """Add component with already used name""" + case8 = System("Case8 system", Source("10V system", vo=10.0)) + case8.add_comp("10V system", comp=Converter("Buck", vo=2.5, eff=0.75)) + with pytest.raises(ValueError): + case8.add_comp("10V system", comp=Converter("Buck", vo=2.5, eff=0.75)) + + +def test_case9(): + """Try adding component of wrong type""" + case9 = System("Case9 system", Source("10V system", vo=10.0)) + with pytest.raises(ValueError): + case9.add_comp("10V system", comp=Source("5V", vo=5.0)) + + +def test_case10(): + """Change component""" + case10 = System("Case10 system", Source("24V system", vo=24.0, rs=12e-3)) + case10.add_comp("24V system", comp=Converter("Buck", vo=3.3, eff=0.80)) + case10.change_comp("Buck", comp=LinReg("LDO", vo=3.3)) + with pytest.raises(ValueError): + case10.change_comp("LDO", comp=Source("5V", vo=5.0)) + with pytest.raises(ValueError): + case10.change_comp("24V system", comp=LinReg("LDO2", vo=3.3)) + + +def test_case11(): + """Delete component""" + case11 = System("Case11 system", Source("CR2032", vo=3.0)) + case11.add_comp("CR2032", comp=Converter("1.8V buck", vo=1.8, eff=0.87)) + case11.add_comp("1.8V buck", comp=PLoad("MCU", pwr=27e-3)) + case11.del_comp("1.8V buck", del_childs=False) + dfp = case11.params() + assert len(dfp) == 2, "Case11 parameters row count" + with pytest.raises(ValueError): + case11.del_comp("CR2032") + with pytest.raises(ValueError): + case11.del_comp("Non-existent") + case11.add_comp("CR2032", comp=Converter("1.8V buck", vo=1.8, eff=0.87)) + case11.add_comp("1.8V buck", comp=PLoad("MCU2", pwr=27e-3)) + case11.del_comp("1.8V buck") + dfp = case11.params() + assert len(dfp) == 2, "Case11 parameters row count" + + +def test_case12(): + """Warnings""" + case12 = System( + "Case12 system", + Source( + "6V", + vo=6.0, + limits={ + "ii": [0.0, 0.101], + }, + ), + ) + case12.add_comp("6V", comp=ILoad("Overload", ii=0.1011)) + df = case12.solve() + assert ( + df[df["Component"] == "System total"]["Warnings"][2] == "Yes" + ), "Case 12 warnings" + + +def test_case13(): + """Multi-source""" + case13 = System("Case13 system", Source("3.3V", vo=3.3)) + case13.add_source(Source("12V", vo=12, limits={"ii": [0, 1e-3]})) + case13.add_comp("3.3V", comp=PLoad("MCU", pwr=0.2)) + case13.add_comp("12V", comp=PLoad("Test", pwr=1.5)) + with pytest.raises(ValueError): + case13.add_source(PLoad("Test2", pwr=1.5)) + case13.add_source(Source("3.3V aux", vo=3.3)) + df = case13.solve() + assert len(df) == 9, "Case13 solution row count" + assert df.shape[1] == 12, "Case13 column count" + assert ( + df[df["Component"] == "System total"]["Warnings"][8] == "Yes" + ), "Case 13 total warnings" + assert ( + df[df["Component"] == "Subsystem 12V"]["Warnings"][6] == "Yes" + ), "Case 13 Subsystem 12V warnings" + case13.save("tests/unit/case13.json") + with pytest.raises(ValueError): + case13.del_comp("12V", del_childs=False) + case13.del_comp("12V") + df = case13.solve() + assert len(df) == 6, "Case13 solution row count after delete 12V" + case13.del_comp("3.3V aux") + df = case13.solve() + assert len(df) == 3, "Case13 solution row count after delete 3.3V aux" + with pytest.raises(ValueError): + case13.del_comp("3.3V") + # reload case13 from file + case13b = System.from_file("tests/unit/case13.json") + dff = case13b.solve() + assert len(dff) == 9, "Case13 solution row count" + assert ( + dff[dff["Component"] == "System total"]["Warnings"][8] == "Yes" + ), "Case 13 total warnings" + assert ( + dff[dff["Component"] == "Subsystem 12V"]["Warnings"][6] == "Yes" + ), "Case 13 Subsystem 12V warnings" + assert ( + dff[dff["Component"] == "Subsystem 3.3V"]["Warnings"][5] == "" + ), "Case 13 Subsystem 3.3V warnings" + dfp = case13b.params() + assert dfp.shape[1] == 13, "Case13 parameter column count" + phases = {"sleep": 3600, "active": 127} + case13b.set_sys_phases(phases) + dfp = case13b.phases() + assert dfp.shape[1] == 8, "Case13 phases column count" + + +def test_case14(): + """Zero output source""" + case14 = System("Case14 system", Source("0V", vo=0.0)) + case14.add_comp("0V", comp=PLoad("MCU", pwr=0.2)) + case14.add_comp("0V", comp=ILoad("Test", ii=0.1)) + df = case14.solve() + assert len(df) == 4, "Case14 solution row count" + + +def test_case15(): + """Load phases""" + case15 = System("Case15 system", Source("5V", vo=5.0)) + case15.add_comp("5V", comp=Converter("Buck 3.3", vo=3.3, eff=0.88)) + case15.set_comp_phases("Buck 3.3", phase_conf=["active"]) + case15.add_comp("5V", comp=LinReg("LDO 1.8", vo=1.8)) + case15.set_comp_phases("LDO 1.8", phase_conf=["sleep"]) + case15.add_comp("Buck 3.3", comp=PLoad("MCU", pwr=0.2)) + case15.set_comp_phases("MCU", phase_conf={"sleep": 1e-6, "active": 0.2}) + case15.add_comp("LDO 1.8", comp=ILoad("Sensor", ii=1.7e-3)) + case15.add_comp("LDO 1.8", comp=ILoad("Sensor2", ii=2.7e-3)) + case15.set_comp_phases("Sensor2", phase_conf={"sleep": 2.7e-3}) + case15.add_comp("LDO 1.8", comp=ILoad("Sensor2b", ii=1e-3)) + case15.set_comp_phases("Sensor2b", phase_conf={"active": 1e-3}) + case15.add_comp("LDO 1.8", comp=PLoad("Sensor3", pwr=1.7e-3)) + case15.add_comp("5V", comp=PLoad("Sensor3b", pwr=0.05)) + case15.set_comp_phases("Sensor3b", phase_conf={"active": 0.05}) + case15.add_comp("LDO 1.8", comp=RLoss("Resistor", rs=10e3)) + case15.add_comp("LDO 1.8", comp=RLoad("Sensor4", rs=25e3)) + case15.add_comp("LDO 1.8", comp=RLoad("Sensor5", rs=100e3)) + case15.set_comp_phases("Sensor5", phase_conf={"active": 45e3}) + case15.add_comp("5V", comp=Converter("Buck 3.0", vo=3.0, eff=0.83)) + case15.add_comp("5V", comp=LinReg("LDO 1.5", vo=1.5)) + with pytest.raises(ValueError): + case15.set_comp_phases("Dummy", phase_conf={"active": 45e3}) + with pytest.raises(ValueError): + case15.set_comp_phases("LDO 1.8", phase_conf=123.7) + with pytest.raises(ValueError): + case15.set_sys_phases({"sleep": 1000}) + with pytest.raises(ValueError): + case15.set_sys_phases({"N/A": 100, "rest": 1}) + with pytest.raises(ValueError): + case15.set_comp_phases("5V", {"active": 45e3}) + with pytest.raises(ValueError): + case15.set_comp_phases("Resistor", {"active": 45e3}) + assert case15.phases() == None + phases = {"sleep": 3600, "active": 127} + case15.set_sys_phases(phases) + assert phases == case15.get_sys_phases() + df = case15.phases() + expl = 15 + assert len(df) == expl, "Case15 phase report length" + with pytest.raises(ValueError): + case15.solve(phase="unknown") + df = case15.solve(phase="sleep") + assert len(df) == expl, "Case15 solution row count (one phase)" + df = case15.solve(quiet=False) + assert len(df) == 2 * expl + 1, "Case15 solution row count (all phases)" + df = case15.solve(tags={"Tag1": "one"}) + assert df.shape[0] == 2 * expl + 1, "Case15 tagged solution row count (all phases)" + assert df.shape[1] == 13, "Case15 tagged solution column count (all phases)" + df = case15.solve(tags={"Tag1": "one"}, energy=True) + assert df.shape[0] == 2 * expl + 1, "Case15 tagged solution row count (all phases)" + assert ( + df.shape[1] == 14 + ), "Case15 tagged solution column count with energy (all phases)" + + +def test_case16(): + """Plot interpolation data""" + case16 = System("Case16 system", Source("12V", vo=12.0)) + case16.add_comp("12V", comp=Converter("Buck 5.0", vo=5.0, eff=0.88)) + with pytest.raises(ValueError): + case16.plot_interp("Dummy") + assert case16.plot_interp("Buck 5.0") == None, "Case16 - no interpolation data" + d1 = {"vi": [3.3], "io": [0.1, 0.5, 0.9], "eff": [[0.55, 0.78, 0.92]]} + case16.change_comp("Buck 5.0", comp=Converter("Buck 5.0", vo=5.0, eff=d1)) + assert ( + type(case16.plot_interp("Buck 5.0")) == matplotlib.figure.Figure + ), "Case16 1D figure" + d2 = { + "vi": [-3.3, -5.0, -12], + "io": [0.1, 0.5, 0.9], + "eff": [[0.55, 0.78, 0.92], [0.5, 0.74, 0.83], [0.4, 0.6, 0.766]], + } + case16.add_comp("12V", comp=Converter("Buck 3.3", vo=3.3, eff=d2)) + assert ( + type(case16.plot_interp("Buck 3.3")) == matplotlib.figure.Figure + ), "Case16 2D figure" + assert ( + type(case16.plot_interp("Buck 3.3", plot3d=True)) == matplotlib.figure.Figure + ), "Case16 3D figure" + d1 = {"vi": [3.3], "io": [0.1, 0.5, 0.9], "vdrop": [[0.55, 0.78, 0.92]]} + case16.add_comp("12V", comp=VLoss("Diode 1", vdrop=d1)) + assert ( + type(case16.plot_interp("Diode 1")) == matplotlib.figure.Figure + ), "Case16 Diode 1D figure" + d3 = { + "vi": [1, 2, 3], + "io": [0.1, 0.5, 0.9], + "vdrop": [[0.55, 0.78, 0.92], [0.5, 0.74, 0.83], [0.4, 0.6, 0.766]], + } + case16.add_comp("12V", comp=VLoss("Diode 2", vdrop=d3)) + assert ( + type(case16.plot_interp("Diode 2")) == matplotlib.figure.Figure + ), "Case16 Diode 2D figure"