notebook.ipynb

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   "source": [
    "# Supplementary Code: Image-Based & Machine Learning-Guided Multiplexed Serology Test for SARS-CoV-2 \n",
    "\n",
    "*Vilja Pietiäinen, Minttu Polso, Ede Migh, Christian Guckelsberger,  Maria Harmati, Akos Diosdi, Laura Turunen, Antti Hassinen, Swapnil Potdar,  Annika Koponen, Edina Gyukity Sebestyen, Ferenc Kovacs, Andras Kriston, Reka Hollandi, Katalin Burian, Gabriella Terhes, Adam Visnyovszky, Eszter Fodor, Zsombor Lacza, Anu Kantele, Pekka Kolehmainen, Laura Kakkola, Tomas Strandin, Lev Levanov, Olli Kallioniemi, Lajos Kemeny, Ilkka Julkunen, Olli Vapalahti, Krisztina Buzas, Lassi Paavolainen, Peter Horvath and Jussi Hepojoki*\n",
    "\n",
    "## Code Overview\n",
    "\n",
    "This Python Jupyter Notebook captures most of the analysis pipeline as described in the main article and supplementary text. It comprises analysis steps 6 and 7 to generate per-cell and per-well predictions of antibody positivity on automated high-content fluorescence microscopy images, obtained from high-throughput serological tests. Moreover, it contains evaluation scripts for comparing our predictions against ELISA and visual ground truth data. \n",
    "\n",
    "The analysis is exemplified on SARS-CoV-2 antigens N, Spike (S), Membrane (M) and RBD (R), as well as antibodies IgG, IgA and IgM. The notebook allows to *reproduce all results* from the article and supplementary.\n",
    "\n",
    "The code is separated into 4 parts:\n",
    "\n",
    "1. **Cross-Validation for Hyperparameter Tuning**: Yields optimal classifier hyperparameters that maximises a specificity x sensitivity trade-off on 5-class per-cell predictions. A train-validation split is applied across plates to warrant good prediction performance on previously unseen plates.\n",
    "2. **Model Training**: Uses the cross-validation output or pre-determined hyperparameters to re-train the classifier over the entire training data set.\n",
    "3. **Predictions**: Generates per-cell predictions and per-well positive ratio estimates with this classifier on a dataset that is distinct from the training data.\n",
    "4. **Evaluation**: Compares the predicted per-well positivity ratios against visual ground truth data from expert virologists, and ELISA on the same samples.\n",
    "\n",
    "The data used is the result of analysis step 5, i.e. image-pre-processing, nucleus segmentation and feature extraction. It has been anonymized. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n",
    "Libraries used in multiple parts of this notebook - do not modify."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import csv\n",
    "import random\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from IPython.display import display, HTML\n",
    "from IPython.lib import kernel\n",
    "\n",
    "import construct_data, training, predict, evaluate, plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Settings\n",
    "\n",
    "Set the variables below to run this notebook for any combination of antibody, protein, classifier and normalisation scheme. \n",
    "\n",
    "Small amounts of randomness in the classifier training can yield different optimal hyperparameters in the cross-validation. To ensure successful reproduction of the results as documented in the article, set the \"use_original_hyperparameters\" flag to True.\n",
    "\n",
    "**Note**: Once a setting has been changed, all cells below must be re-run to ensure consistency of the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Notebook settings\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# Check id for output location\n",
    "connection_file_path = kernel.get_connection_file()\n",
    "connection_file = os.path.basename(connection_file_path)\n",
    "kernel_id = connection_file.split('-', 1)[1].split('.')[0]\n",
    "\n",
    "# Fixed settings\n",
    "from fixed_settings import path_data_raw, path_data_auxiliary, path_output, file_classes, file_controls, file_metadata, file_original_hyperparameters, file_elisa, file_visual_gt, dilution_antibody\n",
    "path_tmp = path_output / kernel_id\n",
    "if not os.path.exists(path_tmp):\n",
    "    os.mkdir(path_tmp)\n",
    "\n",
    "# Virus protein\n",
    "protein = \"S\" \n",
    "assert protein in [\"N\", \"S\", \"R\", \"M\"], \"Incorrect protein setting. Please choose from the available options.\"\n",
    "\n",
    "# Antibody\n",
    "antibody = \"IgG\"\n",
    "assert antibody in [\"IgM\",\"IgG\",\"IgA\"], \"Incorrect antibody setting. Please choose from the available options.\"\n",
    "\n",
    "# Normalisation method\n",
    "normalisation_type = \"NegOnly\"\n",
    "assert normalisation_type in [\"PosNeg\",\"NegOnly\",\"PosNegBal\"], \"Incorrect antibody setting. Please choose from the available options.\"\n",
    "\n",
    "# Classifier (ANN, SVM, RF)\n",
    "classifier_type = \"ANN\"\n",
    "assert classifier_type in [\"ANN\",\"SVM\",\"RF\"], \"Incorrect classifier setting. Please choose from the available options.\"\n",
    "\n",
    "# Original classifier hyperparameters flag \n",
    "# True: use the hyperparameters documented in the article. \n",
    "# False: use hyperparameters determiend in the cross-validation in this notebook.\n",
    "use_original_hyperparameters = True\n",
    "assert use_original_hyperparameters in [True, False], \"Incorrect setting for use_original_hyperparameters flag. Please use a Boolean value.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Global Data\n",
    "\n",
    "Load data used throughout the different parts of this notebook: a mapping from class indices to human-readable labels, a list of control well identifiers and metadata with per-well/plate information on a well's content, dilution and whether it has successfully transferred, the latter being determined automatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes:\n",
      "{1: 'positive', 2: 'negative', 3: 'atypical', 4: 'small bright', 5: 'trash'}\n",
      "\n",
      "Controls:\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Content</th>\n",
       "      <th>Type</th>\n",
       "      <th>Dilution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>POS1_25</td>\n",
       "      <td>positive</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>POS2_25</td>\n",
       "      <td>positive</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>POS1_100</td>\n",
       "      <td>positive</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>POS2_100</td>\n",
       "      <td>positive</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NEG1_25</td>\n",
       "      <td>negative</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NEG2_25</td>\n",
       "      <td>negative</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NEG3_25</td>\n",
       "      <td>negative</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NEG1_100</td>\n",
       "      <td>negative</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NEG2_100</td>\n",
       "      <td>negative</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NEG3_100</td>\n",
       "      <td>negative</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metadata:\n"
     ]
    },
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     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Destination Plate Barcode</th>\n",
       "      <th>well</th>\n",
       "      <th>Sample Name</th>\n",
       "      <th>Sample</th>\n",
       "      <th>Content</th>\n",
       "      <th>Dilution</th>\n",
       "      <th>Transferred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>M_1_1</td>\n",
       "      <td>A1</td>\n",
       "      <td>EMPTY</td>\n",
       "      <td>0</td>\n",
       "      <td>EMPTY</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M_1_1</td>\n",
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       "      <td>M_1_1</td>\n",
       "      <td>C1</td>\n",
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       "    <tr>\n",
       "      <td>M_1_1</td>\n",
       "      <td>D1</td>\n",
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       "      <td>25</td>\n",
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       "      <td>M_1_1</td>\n",
       "      <td>E1</td>\n",
       "      <td>S2020_004</td>\n",
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       "      <td>M_1_1</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <td>M_1_1</td>\n",
       "      <td>G1</td>\n",
       "      <td>S2020_005</td>\n",
       "      <td>1</td>\n",
       "      <td>Sample3_100</td>\n",
       "      <td>100</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M_1_1</td>\n",
       "      <td>H1</td>\n",
       "      <td>S2020_007</td>\n",
       "      <td>1</td>\n",
       "      <td>Sample4_25</td>\n",
       "      <td>25</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M_1_1</td>\n",
       "      <td>I1</td>\n",
       "      <td>S2020_007</td>\n",
       "      <td>1</td>\n",
       "      <td>Sample4_100</td>\n",
       "      <td>100</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M_1_1</td>\n",
       "      <td>J1</td>\n",
       "      <td>NEG3</td>\n",
       "      <td>0</td>\n",
       "      <td>Secondaries only</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
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       "  </tbody>\n",
       "</table>"
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   ],
   "source": [
    "# Classes in the data and label code\n",
    "classes = dict()\n",
    "reader = csv.reader(open(path_data_auxiliary/file_classes, 'r'))\n",
    "for k, v in reader:\n",
    "    classes[int(k)] = v\n",
    "print(\"Classes:\\n{}\\n\".format(classes))\n",
    "\n",
    "# Controls: well name, type (positive/negative), dilution\n",
    "df_controls = pd.read_csv(path_data_auxiliary/file_controls)\n",
    "print(\"Controls:\")\n",
    "display(HTML(df_controls.to_html(index=False)))\n",
    "\n",
    "# Metadata\n",
    "df_meta = pd.read_csv(path_data_auxiliary/file_metadata)\n",
    "df_meta = df_meta[['Destination Plate Barcode', 'Dest Well', 'Sample Name', 'Sample', 'Content', 'Dilution', 'Transferred']]\n",
    "df_meta.rename(columns={'Dest Well':'well'}, inplace=True)\n",
    "print(\"Metadata:\")\n",
    "display(HTML(df_meta.head(10).to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Cross-Validation for Hyperparameter Tuning\n",
    "We perform a four-fold leave-one-out cross-validation on the level of plates to identify optimal classifier hyperparameters that yield good predictions also on previously unseen plates.\n",
    "\n",
    "\n",
    "### Create cross-validation data\n",
    "Each of the four cross-validation folds constructed here comprises training data of three plates, and used the remaining plate as validation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading plate S_1_5\n",
      "\n",
      "Labelled samples for antibody IgG for dilution 100:\n",
      "positive: 245\n",
      "negative: 524\n",
      "atypical: 463\n",
      "small bright: 122\n",
      "trash: 127\n",
      "\n",
      "Normalising for dilution 100\n",
      "Only 4 of 6 positive control wells were transferred -- still sufficient. Continuing normalisation.\n",
      "Normalisation based on negative controls only\n",
      "\n",
      "Reading plate S_1_3\n",
      "\n",
      "Labelled samples for antibody IgG for dilution 100:\n",
      "positive: 178\n",
      "negative: 366\n",
      "atypical: 329\n",
      "small bright: 105\n",
      "trash: 102\n",
      "\n",
      "Normalising for dilution 100\n",
      "Only 2 of 6 positive control wells were transferred -- still sufficient. Continuing normalisation.\n",
      "Only 3 of 10 negative control wells were transferred -- still sufficient. Continuing normalisation.\n",
      "Normalisation based on negative controls only\n",
      "\n",
      "Reading plate S_1_1\n",
      "\n",
      "Labelled samples for antibody IgG for dilution 100:\n",
      "positive: 373\n",
      "negative: 836\n",
      "atypical: 166\n",
      "small bright: 160\n",
      "trash: 140\n",
      "\n",
      "Normalising for dilution 100\n",
      "Only 7 of 10 negative control wells were transferred -- still sufficient. Continuing normalisation.\n",
      "Normalisation based on negative controls only\n",
      "\n",
      "Reading plate S_2_4\n",
      "\n",
      "Labelled samples for antibody IgG for dilution 100:\n",
      "positive: 286\n",
      "negative: 302\n",
      "atypical: 142\n",
      "small bright: 95\n",
      "trash: 145\n",
      "\n",
      "Normalising for dilution 100\n",
      "Normalisation based on negative controls only\n",
      "\n",
      "Exporting cv data for dilution 100 and antibody IgG\n",
      "Leaving out plate S_1_5. Size of train and validation sets: 3725, 1481\n",
      "Leaving out plate S_1_3. Size of train and validation sets: 4126, 1080\n",
      "Leaving out plate S_1_1. Size of train and validation sets: 3531, 1675\n",
      "Leaving out plate S_2_4. Size of train and validation sets: 4236, 970\n"
     ]
    }
   ],
   "source": [
    "construct_data.construct_data_cross_validation(path_data_raw/\"data_{}_cv/\".format(protein), path_tmp/\"data_{}_cv/\".format(protein), classes, df_controls, df_meta, normalisation_type, antibody, dilution_antibody)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example file: leave_S_1_1_out_train_on_S_1_5_and_S_1_3_and_S_2_4_ab_IgG_and_DAPI_dil_100.hdf5\n",
      "\n",
      "Features:\n"
     ]
    },
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       "      <th>DONUT.HARALICK.TEXTURE-4.Alexa.IgG.0.3</th>\n",
       "      <th>DONUT.HARALICK.TEXTURE-4.DAPI.0.3</th>\n",
       "      <th>DONUT.HARALICK.TEXTURE-5.Alexa.IgG.0.3</th>\n",
       "      <th>DONUT.HARALICK.TEXTURE-5.DAPI.0.3</th>\n",
       "      <th>DONUT.HARALICK.TEXTURE-6.Alexa.IgG.0.3</th>\n",
       "      <th>DONUT.HARALICK.TEXTURE-6.DAPI.0.3</th>\n",
       "      <th>DONUT.HARALICK.TEXTURE-7.Alexa.IgG.0.3</th>\n",
       "      <th>DONUT.HARALICK.TEXTURE-7.DAPI.0.3</th>\n",
       "      <th>DONUT.HARALICK.TEXTURE-8.Alexa.IgG.0.3</th>\n",
       "      <th>DONUT.HARALICK.TEXTURE-8.DAPI.0.3</th>\n",
       "      <th>DONUT.HARALICK.TEXTURE-9.Alexa.IgG.0.3</th>\n",
       "      <th>DONUT.HARALICK.TEXTURE-9.DAPI.0.3</th>\n",
       "      <th>DONUT.INTEGRATED.INTENSITY.Alexa.IgG</th>\n",
       "      <th>DONUT.INTEGRATED.INTENSITY.DAPI</th>\n",
       "      <th>DONUT.INTENSITY-MAX.Alexa.IgG</th>\n",
       "      <th>DONUT.INTENSITY-MAX.DAPI</th>\n",
       "      <th>DONUT.INTENSITY-MEAN.Alexa.IgG</th>\n",
       "      <th>DONUT.INTENSITY-MEAN.DAPI</th>\n",
       "      <th>DONUT.INTENSITY-MEDIAN.Alexa.IgG</th>\n",
       "      <th>DONUT.INTENSITY-MEDIAN.DAPI</th>\n",
       "      <th>DONUT.INTENSITY-MIN.Alexa.IgG</th>\n",
       "      <th>DONUT.INTENSITY-MIN.DAPI</th>\n",
       "      <th>DONUT.INTENSITY-STDDEV.Alexa.IgG</th>\n",
       "      <th>DONUT.INTENSITY-STDDEV.DAPI</th>\n",
       "      <th>DONUT.NUMBER.OF.COMPONENTS</th>\n",
       "      <th>DONUT.PERIMETER</th>\n",
       "      <th>DONUT.REAL.DIAMETER</th>\n",
       "      <th>DONUT.REAL.PERIMETER</th>\n",
       "      <th>DONUT.SOLIDITY</th>\n",
       "      <th>DONUT.VOLUMETRY</th>\n",
       "      <th>NUCLEUS.AREA</th>\n",
       "      <th>NUCLEUS.ELLIPSE-ECCENTRICITY</th>\n",
       "      <th>NUCLEUS.ELLIPSE-MAJOR.AXIS.LENGTH</th>\n",
       "      <th>NUCLEUS.ELLIPSE-MINOR.AXIS.LENGTH</th>\n",
       "      <th>NUCLEUS.ENCLOSING.CIRCLE.RADIUS</th>\n",
       "      <th>NUCLEUS.EXTENT</th>\n",
       "      <th>NUCLEUS.FORM.FACTOR</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-1.Alexa.IgG.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-1.DAPI.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-10.Alexa.IgG.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-10.DAPI.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-11.Alexa.IgG.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-11.DAPI.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-12.Alexa.IgG.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-12.DAPI.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-2.Alexa.IgG.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-2.DAPI.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-3.Alexa.IgG.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-3.DAPI.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-4.Alexa.IgG.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-4.DAPI.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-5.Alexa.IgG.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-5.DAPI.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-6.Alexa.IgG.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-6.DAPI.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-7.Alexa.IgG.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-7.DAPI.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-8.Alexa.IgG.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-8.DAPI.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-9.Alexa.IgG.0.3</th>\n",
       "      <th>NUCLEUS.HARALICK.TEXTURE-9.DAPI.0.3</th>\n",
       "      <th>NUCLEUS.INTEGRATED.INTENSITY.Alexa.IgG</th>\n",
       "      <th>NUCLEUS.INTEGRATED.INTENSITY.DAPI</th>\n",
       "      <th>NUCLEUS.INTENSITY-MAX.Alexa.IgG</th>\n",
       "      <th>NUCLEUS.INTENSITY-MAX.DAPI</th>\n",
       "      <th>NUCLEUS.INTENSITY-MEAN.Alexa.IgG</th>\n",
       "      <th>NUCLEUS.INTENSITY-MEAN.DAPI</th>\n",
       "      <th>NUCLEUS.INTENSITY-MEDIAN.Alexa.IgG</th>\n",
       "      <th>NUCLEUS.INTENSITY-MEDIAN.DAPI</th>\n",
       "      <th>NUCLEUS.INTENSITY-MIN.Alexa.IgG</th>\n",
       "      <th>NUCLEUS.INTENSITY-MIN.DAPI</th>\n",
       "      <th>NUCLEUS.INTENSITY-STDDEV.Alexa.IgG</th>\n",
       "      <th>NUCLEUS.INTENSITY-STDDEV.DAPI</th>\n",
       "      <th>NUCLEUS.PERIMETER</th>\n",
       "      <th>NUCLEUS.REAL.DIAMETER</th>\n",
       "      <th>NUCLEUS.REAL.PERIMETER</th>\n",
       "      <th>NUCLEUS.SOLIDITY</th>\n",
       "      <th>NUCLEUS.VOLUMETRY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>-2.040941</td>\n",
       "      <td>-0.531862</td>\n",
       "      <td>-1.924443</td>\n",
       "      <td>-0.444304</td>\n",
       "      <td>-1.384312</td>\n",
       "      <td>0.039557</td>\n",
       "      <td>-1.257202</td>\n",
       "      <td>-0.292082</td>\n",
       "      <td>-0.010608</td>\n",
       "      <td>0.331813</td>\n",
       "      <td>0.127710</td>\n",
       "      <td>0.281892</td>\n",
       "      <td>0.796886</td>\n",
       "      <td>0.406461</td>\n",
       "      <td>0.703477</td>\n",
       "      <td>-0.607741</td>\n",
       "      <td>0.206292</td>\n",
       "      <td>0.542901</td>\n",
       "      <td>0.129853</td>\n",
       "      <td>0.949020</td>\n",
       "      <td>0.762243</td>\n",
       "      <td>-0.637954</td>\n",
       "      <td>0.122870</td>\n",
       "      <td>1.367794</td>\n",
       "      <td>-0.426233</td>\n",
       "      <td>-0.036512</td>\n",
       "      <td>-0.158962</td>\n",
       "      <td>-1.141819</td>\n",
       "      <td>0.612655</td>\n",
       "      <td>1.909250</td>\n",
       "      <td>-0.312776</td>\n",
       "      <td>0.302532</td>\n",
       "      <td>0.302584</td>\n",
       "      <td>0.605258</td>\n",
       "      <td>-0.301136</td>\n",
       "      <td>-0.227820</td>\n",
       "      <td>0.216861</td>\n",
       "      <td>1.092369</td>\n",
       "      <td>-0.022064</td>\n",
       "      <td>0.605258</td>\n",
       "      <td>-0.301136</td>\n",
       "      <td>-0.022671</td>\n",
       "      <td>-0.180359</td>\n",
       "      <td>0.068430</td>\n",
       "      <td>-0.192261</td>\n",
       "      <td>-0.321980</td>\n",
       "      <td>-0.443417</td>\n",
       "      <td>-1.166977</td>\n",
       "      <td>-0.376207</td>\n",
       "      <td>-2.133615</td>\n",
       "      <td>-0.774946</td>\n",
       "      <td>-2.017542</td>\n",
       "      <td>-0.798233</td>\n",
       "      <td>-1.362501</td>\n",
       "      <td>0.039557</td>\n",
       "      <td>-1.311758</td>\n",
       "      <td>-0.614652</td>\n",
       "      <td>-0.200775</td>\n",
       "      <td>0.291178</td>\n",
       "      <td>0.129852</td>\n",
       "      <td>0.139071</td>\n",
       "      <td>0.489947</td>\n",
       "      <td>0.703477</td>\n",
       "      <td>-0.110909</td>\n",
       "      <td>0.142403</td>\n",
       "      <td>0.053903</td>\n",
       "      <td>0.059536</td>\n",
       "      <td>-0.004198</td>\n",
       "      <td>1.659932</td>\n",
       "      <td>0.684240</td>\n",
       "      <td>-0.531745</td>\n",
       "      <td>-0.752994</td>\n",
       "      <td>-0.308174</td>\n",
       "      <td>-0.500449</td>\n",
       "      <td>0.337617</td>\n",
       "      <td>0.309228</td>\n",
       "      <td>-0.724139</td>\n",
       "      <td>-0.386070</td>\n",
       "      <td>0.118364</td>\n",
       "      <td>-0.383458</td>\n",
       "      <td>0.359260</td>\n",
       "      <td>0.359252</td>\n",
       "      <td>-0.298172</td>\n",
       "      <td>-0.530209</td>\n",
       "      <td>-0.468012</td>\n",
       "      <td>-0.370139</td>\n",
       "      <td>0.279284</td>\n",
       "      <td>-0.248810</td>\n",
       "      <td>-0.298172</td>\n",
       "      <td>-0.530209</td>\n",
       "      <td>0.382154</td>\n",
       "      <td>0.352293</td>\n",
       "      <td>0.434401</td>\n",
       "      <td>0.390288</td>\n",
       "      <td>-0.061998</td>\n",
       "      <td>-0.208169</td>\n",
       "      <td>-1.049576</td>\n",
       "      <td>-0.203105</td>\n",
       "      <td>-1.402221</td>\n",
       "      <td>-0.301332</td>\n",
       "      <td>-1.375445</td>\n",
       "      <td>-0.359481</td>\n",
       "      <td>-1.411839</td>\n",
       "      <td>-0.261320</td>\n",
       "      <td>-0.941372</td>\n",
       "      <td>-0.126066</td>\n",
       "      <td>0.079959</td>\n",
       "      <td>-0.004198</td>\n",
       "      <td>0.009582</td>\n",
       "      <td>0.635054</td>\n",
       "      <td>-0.110909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1.193856</td>\n",
       "      <td>-0.101084</td>\n",
       "      <td>1.048213</td>\n",
       "      <td>0.966896</td>\n",
       "      <td>1.010765</td>\n",
       "      <td>0.219107</td>\n",
       "      <td>0.733229</td>\n",
       "      <td>0.319364</td>\n",
       "      <td>1.350177</td>\n",
       "      <td>2.016387</td>\n",
       "      <td>0.699539</td>\n",
       "      <td>-1.156622</td>\n",
       "      <td>-1.237982</td>\n",
       "      <td>-0.486184</td>\n",
       "      <td>0.369703</td>\n",
       "      <td>1.535312</td>\n",
       "      <td>0.245752</td>\n",
       "      <td>0.670478</td>\n",
       "      <td>0.670467</td>\n",
       "      <td>1.599527</td>\n",
       "      <td>1.046290</td>\n",
       "      <td>0.956456</td>\n",
       "      <td>1.246504</td>\n",
       "      <td>1.364789</td>\n",
       "      <td>0.971082</td>\n",
       "      <td>1.599527</td>\n",
       "      <td>1.046290</td>\n",
       "      <td>-1.076836</td>\n",
       "      <td>-1.234356</td>\n",
       "      <td>-0.997138</td>\n",
       "      <td>-1.247852</td>\n",
       "      <td>2.768641</td>\n",
       "      <td>0.412200</td>\n",
       "      <td>0.928258</td>\n",
       "      <td>0.309544</td>\n",
       "      <td>3.027545</td>\n",
       "      <td>0.381167</td>\n",
       "      <td>2.881808</td>\n",
       "      <td>-0.108766</td>\n",
       "      <td>0.790291</td>\n",
       "      <td>0.295750</td>\n",
       "      <td>2.675490</td>\n",
       "      <td>0.210423</td>\n",
       "      <td>-0.010608</td>\n",
       "      <td>0.967347</td>\n",
       "      <td>1.010765</td>\n",
       "      <td>1.025585</td>\n",
       "      <td>0.511152</td>\n",
       "      <td>1.193856</td>\n",
       "      <td>1.182700</td>\n",
       "      <td>-0.074367</td>\n",
       "      <td>1.089405</td>\n",
       "      <td>0.967304</td>\n",
       "      <td>1.009863</td>\n",
       "      <td>-0.325282</td>\n",
       "      <td>0.575113</td>\n",
       "      <td>0.629381</td>\n",
       "      <td>1.672409</td>\n",
       "      <td>2.030007</td>\n",
       "      <td>-0.171357</td>\n",
       "      <td>-1.162084</td>\n",
       "      <td>-1.267443</td>\n",
       "      <td>-0.261739</td>\n",
       "      <td>0.913476</td>\n",
       "      <td>1.642933</td>\n",
       "      <td>-0.576080</td>\n",
       "      <td>0.520059</td>\n",
       "      <td>0.520140</td>\n",
       "      <td>2.328419</td>\n",
       "      <td>1.082532</td>\n",
       "      <td>1.023629</td>\n",
       "      <td>1.301849</td>\n",
       "      <td>1.798395</td>\n",
       "      <td>0.945295</td>\n",
       "      <td>2.328419</td>\n",
       "      <td>1.082532</td>\n",
       "      <td>-1.087673</td>\n",
       "      <td>-1.285268</td>\n",
       "      <td>-1.028740</td>\n",
       "      <td>-1.305496</td>\n",
       "      <td>2.343311</td>\n",
       "      <td>1.300669</td>\n",
       "      <td>1.136696</td>\n",
       "      <td>1.007970</td>\n",
       "      <td>4.331106</td>\n",
       "      <td>0.441810</td>\n",
       "      <td>4.355155</td>\n",
       "      <td>-0.013505</td>\n",
       "      <td>0.722645</td>\n",
       "      <td>0.295750</td>\n",
       "      <td>2.473964</td>\n",
       "      <td>0.821454</td>\n",
       "      <td>-0.200775</td>\n",
       "      <td>0.996734</td>\n",
       "      <td>1.009863</td>\n",
       "      <td>0.819377</td>\n",
       "      <td>0.429528</td>\n",
       "      <td>1.182700</td>\n",
       "      <td>1.108507</td>\n",
       "      <td>-0.115436</td>\n",
       "      <td>0.917689</td>\n",
       "      <td>0.938821</td>\n",
       "      <td>0.962998</td>\n",
       "      <td>0.098417</td>\n",
       "      <td>0.494827</td>\n",
       "      <td>1.704492</td>\n",
       "      <td>1.479049</td>\n",
       "      <td>0.844066</td>\n",
       "      <td>0.335998</td>\n",
       "      <td>-1.127737</td>\n",
       "      <td>-1.181719</td>\n",
       "      <td>1.142401</td>\n",
       "      <td>1.190310</td>\n",
       "      <td>1.277030</td>\n",
       "      <td>-0.184086</td>\n",
       "      <td>0.536951</td>\n",
       "      <td>0.536976</td>\n",
       "      <td>0.014101</td>\n",
       "      <td>0.522610</td>\n",
       "      <td>1.223366</td>\n",
       "      <td>1.312906</td>\n",
       "      <td>0.176641</td>\n",
       "      <td>0.709355</td>\n",
       "      <td>0.014101</td>\n",
       "      <td>0.522610</td>\n",
       "      <td>-1.277089</td>\n",
       "      <td>-1.179873</td>\n",
       "      <td>-1.349258</td>\n",
       "      <td>-1.240911</td>\n",
       "      <td>0.321136</td>\n",
       "      <td>0.100071</td>\n",
       "      <td>1.071453</td>\n",
       "      <td>0.314392</td>\n",
       "      <td>0.182395</td>\n",
       "      <td>0.108527</td>\n",
       "      <td>-0.043451</td>\n",
       "      <td>0.077325</td>\n",
       "      <td>-0.059334</td>\n",
       "      <td>0.450967</td>\n",
       "      <td>1.242458</td>\n",
       "      <td>-0.086966</td>\n",
       "      <td>0.932386</td>\n",
       "      <td>0.962998</td>\n",
       "      <td>0.949325</td>\n",
       "      <td>0.377078</td>\n",
       "      <td>1.108507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-0.200042</td>\n",
       "      <td>-0.517340</td>\n",
       "      <td>-0.307919</td>\n",
       "      <td>0.208509</td>\n",
       "      <td>-0.097149</td>\n",
       "      <td>-1.477604</td>\n",
       "      <td>-0.328907</td>\n",
       "      <td>0.141470</td>\n",
       "      <td>0.129139</td>\n",
       "      <td>0.064372</td>\n",
       "      <td>-0.211778</td>\n",
       "      <td>-0.232679</td>\n",
       "      <td>-0.266929</td>\n",
       "      <td>0.226997</td>\n",
       "      <td>0.286497</td>\n",
       "      <td>0.706029</td>\n",
       "      <td>-0.544947</td>\n",
       "      <td>0.322986</td>\n",
       "      <td>0.322975</td>\n",
       "      <td>0.484804</td>\n",
       "      <td>0.016525</td>\n",
       "      <td>0.277279</td>\n",
       "      <td>0.289195</td>\n",
       "      <td>0.853751</td>\n",
       "      <td>0.317569</td>\n",
       "      <td>0.484804</td>\n",
       "      <td>0.016525</td>\n",
       "      <td>-0.217422</td>\n",
       "      <td>-0.249039</td>\n",
       "      <td>-0.225239</td>\n",
       "      <td>-0.253618</td>\n",
       "      <td>-0.091808</td>\n",
       "      <td>-0.279752</td>\n",
       "      <td>-0.280445</td>\n",
       "      <td>-0.329083</td>\n",
       "      <td>-0.159089</td>\n",
       "      <td>0.112195</td>\n",
       "      <td>-0.009711</td>\n",
       "      <td>-0.008989</td>\n",
       "      <td>-1.279385</td>\n",
       "      <td>0.262672</td>\n",
       "      <td>-0.189494</td>\n",
       "      <td>-0.148291</td>\n",
       "      <td>-0.015288</td>\n",
       "      <td>-0.264670</td>\n",
       "      <td>-0.097149</td>\n",
       "      <td>0.018351</td>\n",
       "      <td>0.313004</td>\n",
       "      <td>-0.200042</td>\n",
       "      <td>-0.421865</td>\n",
       "      <td>-0.500052</td>\n",
       "      <td>-0.172659</td>\n",
       "      <td>0.204766</td>\n",
       "      <td>-0.091706</td>\n",
       "      <td>-1.345294</td>\n",
       "      <td>0.128493</td>\n",
       "      <td>0.409772</td>\n",
       "      <td>0.063550</td>\n",
       "      <td>0.100690</td>\n",
       "      <td>0.845645</td>\n",
       "      <td>-0.254246</td>\n",
       "      <td>-0.228173</td>\n",
       "      <td>0.480909</td>\n",
       "      <td>-0.169997</td>\n",
       "      <td>0.909586</td>\n",
       "      <td>1.753621</td>\n",
       "      <td>0.303543</td>\n",
       "      <td>0.303522</td>\n",
       "      <td>0.120388</td>\n",
       "      <td>0.140287</td>\n",
       "      <td>0.318661</td>\n",
       "      <td>0.210259</td>\n",
       "      <td>0.445431</td>\n",
       "      <td>0.449692</td>\n",
       "      <td>0.120388</td>\n",
       "      <td>0.140287</td>\n",
       "      <td>-0.267226</td>\n",
       "      <td>-0.227155</td>\n",
       "      <td>-0.300601</td>\n",
       "      <td>-0.214941</td>\n",
       "      <td>-0.036227</td>\n",
       "      <td>-0.435534</td>\n",
       "      <td>-0.214697</td>\n",
       "      <td>-0.881875</td>\n",
       "      <td>-0.195514</td>\n",
       "      <td>-0.159431</td>\n",
       "      <td>-0.042051</td>\n",
       "      <td>-0.160959</td>\n",
       "      <td>-1.266879</td>\n",
       "      <td>0.262076</td>\n",
       "      <td>0.040251</td>\n",
       "      <td>-0.361370</td>\n",
       "      <td>-0.251289</td>\n",
       "      <td>0.053600</td>\n",
       "      <td>-0.091706</td>\n",
       "      <td>-0.140195</td>\n",
       "      <td>0.395269</td>\n",
       "      <td>-0.421865</td>\n",
       "      <td>0.275031</td>\n",
       "      <td>-0.639562</td>\n",
       "      <td>0.092158</td>\n",
       "      <td>0.500074</td>\n",
       "      <td>0.111149</td>\n",
       "      <td>-0.056720</td>\n",
       "      <td>0.532637</td>\n",
       "      <td>-0.087321</td>\n",
       "      <td>0.136383</td>\n",
       "      <td>0.201104</td>\n",
       "      <td>-0.226461</td>\n",
       "      <td>-0.248396</td>\n",
       "      <td>-0.298973</td>\n",
       "      <td>-0.036869</td>\n",
       "      <td>0.479107</td>\n",
       "      <td>0.407743</td>\n",
       "      <td>-0.661646</td>\n",
       "      <td>0.341373</td>\n",
       "      <td>0.341379</td>\n",
       "      <td>0.090925</td>\n",
       "      <td>-0.215720</td>\n",
       "      <td>0.169203</td>\n",
       "      <td>0.330334</td>\n",
       "      <td>0.478993</td>\n",
       "      <td>0.018052</td>\n",
       "      <td>0.090925</td>\n",
       "      <td>-0.215720</td>\n",
       "      <td>-0.225956</td>\n",
       "      <td>-0.266838</td>\n",
       "      <td>-0.197305</td>\n",
       "      <td>-0.262176</td>\n",
       "      <td>-0.035656</td>\n",
       "      <td>-0.285621</td>\n",
       "      <td>-0.239294</td>\n",
       "      <td>-0.283782</td>\n",
       "      <td>-0.212011</td>\n",
       "      <td>-0.245076</td>\n",
       "      <td>-0.193560</td>\n",
       "      <td>-0.269564</td>\n",
       "      <td>-0.135895</td>\n",
       "      <td>0.897858</td>\n",
       "      <td>-0.310437</td>\n",
       "      <td>-0.374604</td>\n",
       "      <td>0.231024</td>\n",
       "      <td>0.111149</td>\n",
       "      <td>0.281766</td>\n",
       "      <td>0.243252</td>\n",
       "      <td>0.275031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Label</th>\n",
       "      <th>Label (human-readable)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>atypical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load and print some random lines from cross-validation data\n",
    "example_file = [fn for fn in os.listdir(path_tmp/\"data_{}_cv/\".format(protein)) if (fn.endswith(\"hdf5\") and (antibody in fn))][0]\n",
    "print(\"Example file: {}\\n\".format(example_file))\n",
    "\n",
    "example_file_path = Path(path_tmp/\"data_{}_cv/\".format(protein))/example_file\n",
    "df_X_train = pd.read_hdf(example_file_path, key='X_train')\n",
    "df_y_train = pd.read_hdf(example_file_path, key='y_train')\n",
    "df_y_train[\"Label (human-readable)\"] = df_y_train.replace({\"Label\":classes})\n",
    "rnd_ixs = random.sample(range(0, df_X_train.shape[0]), 5)\n",
    "\n",
    "print(\"Features:\")\n",
    "display(HTML(df_X_train.take(rnd_ixs).to_html(index=False)))\n",
    "\n",
    "print(\"Labels:\")\n",
    "display(HTML(df_y_train.take(rnd_ixs).to_html(index=False)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform cross-validation \n",
    "\n",
    "Each score (sensitivity, specificity, accuracy) is recorded twice:\n",
    "- default: Calculated over all classes via \"micro averaging\", taking into account the total true positives, false negatives and false positives.\n",
    "- \"pos\"-suffix: Calculated over the \"positive\" class only to determine how good the model is at disciminating \"positive\" cells from all other classes.\n",
    "\n",
    "**Note**: Running this code can take up to 30 minutes (fastest for SVM, slowest for ANN). To run this demonstration within a reasonable time, the parameter grid for all classifier types has been downsized. To warrant reproduction, it still includes the hyperparameter configuration identified as optimal in the article."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading cross-validation data features and labels from data_out/cb182265-a18c-436b-9303-7e224b120fee/data_S_cv\n",
      "\n",
      "Training and validating ANN on fold leave_S_1_1_out_train_on_S_1_5_and_S_1_3_and_S_2_4_ab_IgG_and_DAPI_dil_100.hdf5\n",
      "Train/validation split: 3531 / 1675\n",
      "Tuning hyper-parameters for specsens\n",
      "Best parameter set for specsens:\n",
      "{'hidden_layer_sizes': 32, 'alpha': 0.05}\n",
      "\n",
      "Scores on validation set:\n",
      "specificity:\n",
      "0.986 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.986 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.985 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.985 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.985 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.984 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.984 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.984 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.984 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.984 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.984 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.984 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.983 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.983 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "0.983 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.982 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "specificity pos:\n",
      "0.993 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.993 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.993 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.993 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.993 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.992 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.992 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "0.992 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.992 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.992 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.992 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.992 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.992 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.992 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.991 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.990 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "sensitivity:\n",
      "0.945 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.944 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.941 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.940 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.939 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.938 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.937 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.936 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.936 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.936 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.934 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.934 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.933 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.932 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "0.931 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.930 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "sensitivity pos:\n",
      "0.981 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.981 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.973 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.973 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.973 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.971 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.962 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.960 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.960 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.957 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.957 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.952 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.952 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.952 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "0.946 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.922 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "specsens:\n",
      "0.932 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.931 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.928 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.926 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.925 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.923 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.922 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.921 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.921 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.921 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.919 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.919 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.918 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.916 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "0.915 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.913 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "specsens pos:\n",
      "0.973 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.973 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.966 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.966 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.966 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.964 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.954 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.953 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.950 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.949 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.949 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.944 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.944 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "0.944 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.938 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.914 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "accuracy:\n",
      "0.945 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.944 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.941 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.940 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.939 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.938 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.937 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.936 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.936 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.936 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.934 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.934 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.933 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.932 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "0.931 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.930 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "\n",
      "Training and validating ANN on fold leave_S_1_5_out_train_on_S_1_3_and_S_1_1_and_S_2_4_ab_IgG_and_DAPI_dil_100.hdf5\n",
      "Train/validation split: 3725 / 1481\n",
      "Tuning hyper-parameters for specsens\n",
      "Best parameter set for specsens:\n",
      "{'hidden_layer_sizes': (128, 64), 'alpha': 0.1}\n",
      "\n",
      "Scores on validation set:\n",
      "specificity:\n",
      "0.986 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.986 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.986 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.986 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.986 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.985 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.985 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.985 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.985 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.985 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.984 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.984 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.984 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.984 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.984 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.984 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "specificity pos:\n",
      "0.999 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.999 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.998 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.998 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.998 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.998 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.998 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.998 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.998 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.998 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.998 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.998 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.998 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.998 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.998 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.997 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "sensitivity:\n",
      "0.945 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.945 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.945 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.943 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.943 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.941 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.941 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.941 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.941 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.939 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.938 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.938 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.937 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.937 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.937 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.935 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "sensitivity pos:\n",
      "0.886 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.886 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "0.873 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.861 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.861 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.861 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.857 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.857 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.857 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.857 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.853 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.853 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.853 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.849 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.845 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.841 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "specsens:\n",
      "0.932 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.932 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.932 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.929 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.929 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.927 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.927 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.927 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.927 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.925 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.923 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.923 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.922 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.922 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.922 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.919 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "specsens pos:\n",
      "0.884 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.883 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "0.871 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.860 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.860 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.859 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.856 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.855 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.855 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.855 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.852 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.852 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.852 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.848 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.844 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.839 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "accuracy:\n",
      "0.945 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.945 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.945 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.943 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.943 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.941 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.941 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.941 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.941 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.939 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.938 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.938 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.937 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.937 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.937 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.935 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "\n",
      "Training and validating ANN on fold leave_S_2_4_out_train_on_S_1_5_and_S_1_3_and_S_1_1_ab_IgG_and_DAPI_dil_100.hdf5\n",
      "Train/validation split: 4236 / 970\n",
      "Tuning hyper-parameters for specsens\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter set for specsens:\n",
      "{'hidden_layer_sizes': 64, 'alpha': 0.1}\n",
      "\n",
      "Scores on validation set:\n",
      "specificity:\n",
      "0.973 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.973 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.973 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.973 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.973 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.973 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.972 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.972 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.972 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.972 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.971 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.971 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.970 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.970 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.968 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.968 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "specificity pos:\n",
      "0.990 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.985 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.984 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.984 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "0.982 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.981 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.981 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.980 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.978 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.977 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.975 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.975 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.974 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.974 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.972 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.968 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "sensitivity:\n",
      "0.893 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.893 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.893 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.892 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.892 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.891 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.888 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.888 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.887 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.887 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.884 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.882 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.881 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.879 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.872 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.871 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "sensitivity pos:\n",
      "0.969 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.962 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.962 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.962 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.958 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.955 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.948 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.948 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.944 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.944 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.937 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.937 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.934 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.927 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.916 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.906 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "specsens:\n",
      "0.869 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.869 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.869 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.868 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.868 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.866 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.863 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.863 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.861 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.861 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.858 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.857 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.855 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.853 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.844 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.843 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "specsens pos:\n",
      "0.942 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.938 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.936 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.936 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.934 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.932 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.930 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.928 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.927 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.923 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.919 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.917 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.914 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.912 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.903 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.891 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "accuracy:\n",
      "0.893 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.893 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.893 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.892 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.892 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.891 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.888 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.888 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.887 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.887 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.884 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.882 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.881 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.879 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.872 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.871 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "\n",
      "Training and validating ANN on fold leave_S_1_3_out_train_on_S_1_5_and_S_1_1_and_S_2_4_ab_IgG_and_DAPI_dil_100.hdf5\n",
      "Train/validation split: 4126 / 1080\n",
      "Tuning hyper-parameters for specsens\n",
      "Best parameter set for specsens:\n",
      "{'hidden_layer_sizes': 32, 'alpha': 1.0}\n",
      "\n",
      "Scores on validation set:\n",
      "specificity:\n",
      "0.948 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.947 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.946 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.946 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.946 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.946 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.945 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.943 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.942 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.942 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.942 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.941 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.941 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.941 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.938 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.937 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "specificity pos:\n",
      "0.847 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "0.844 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.844 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.841 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.838 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.833 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.833 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.831 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.830 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.830 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.824 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.823 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.819 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.817 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.814 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.814 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "sensitivity:\n",
      "0.794 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.787 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.783 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.783 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.783 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.782 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.779 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.771 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.769 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.767 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.767 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.765 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.764 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.763 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.750 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.747 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "sensitivity pos:\n",
      "1.000 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "1.000 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "1.000 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "1.000 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "1.000 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "1.000 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.994 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.994 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.994 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.989 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.989 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.989 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.989 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.983 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.916 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.910 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "specsens:\n",
      "0.753 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.745 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.741 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.741 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.741 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.740 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.736 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.727 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.725 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.722 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.722 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.720 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.719 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.718 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.703 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.700 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "specsens pos:\n",
      "0.834 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.833 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.830 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.829 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.828 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.827 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.824 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.822 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.821 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.819 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.818 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.817 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.814 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.809 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.773 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.771 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "accuracy:\n",
      "0.794 for {'alpha': 1.0, 'hidden_layer_sizes': 32}\n",
      "0.787 for {'alpha': 1.0, 'hidden_layer_sizes': 64}\n",
      "0.783 for {'alpha': 1.0, 'hidden_layer_sizes': (128, 64)}\n",
      "0.783 for {'alpha': 0.05, 'hidden_layer_sizes': 32}\n",
      "0.783 for {'alpha': 0.001, 'hidden_layer_sizes': 32}\n",
      "0.782 for {'alpha': 0.1, 'hidden_layer_sizes': 32}\n",
      "0.779 for {'alpha': 1.0, 'hidden_layer_sizes': (32, 32)}\n",
      "0.771 for {'alpha': 0.1, 'hidden_layer_sizes': 64}\n",
      "0.769 for {'alpha': 0.1, 'hidden_layer_sizes': (128, 64)}\n",
      "0.767 for {'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "0.767 for {'alpha': 0.05, 'hidden_layer_sizes': (32, 32)}\n",
      "0.765 for {'alpha': 0.001, 'hidden_layer_sizes': (32, 32)}\n",
      "0.764 for {'alpha': 0.001, 'hidden_layer_sizes': 64}\n",
      "0.763 for {'alpha': 0.05, 'hidden_layer_sizes': 64}\n",
      "0.750 for {'alpha': 0.05, 'hidden_layer_sizes': (128, 64)}\n",
      "0.747 for {'alpha': 0.001, 'hidden_layer_sizes': (128, 64)}\n",
      "\n",
      "Model with best mean scores on specsens: {'alpha': 0.1, 'hidden_layer_sizes': 32}\n"
     ]
    }
   ],
   "source": [
    "# Run cross-validation and print scores for different hyperparameter configurations\n",
    "hp_best_cv, cm = training.train_cv(classifier_type, path_tmp/\"data_{}_cv/\".format(protein), antibody, classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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+RSmVCewGLMDlwHVKqU3Apj/cw6SUarmImqZpmgeKKo0iPySfcv9yyv3LEacQVRqFt8ObNclr8K31JSU7hVL/0jbdL6gqiJCqEEoCS8iKzDLeoyQKa61RrbgjbgfVvtUM3zQcaeVxUVJOElW+VRQGF2KttdJndx82J2wmIT+Baks1G5I24PByEFscS1Ju23p10MkTlKunFAP8r8kKEacCZwNzReQh4B3gWuATETlRKfX1H++jlHIcrpg1TdP+jjpTHatSVuE0ORm6ZSgBtgBWpa5iR9wOAEoDSwmoDmBtz7XUexvPoBwmB6uTV5O+Ox0fe8sVZtYlr6Pcv5yUfSkk5Cewrfs29kXtQ5TQf2d/csJy8HZ4szV+a7N2W+K30DOnJ+EV4QzePrjxeHZYNjYfG/H58axIX0FCfgLBVcGsSVlDUFUQ4RVtW4at0yaoJssWveF6nQBMBR7FKHy4FLgCOAujAOIXjEq+FglK0zSts6ix1OA0ORGnEFwdjJfywt/mb/Sm/MoBqPSrpNKvsrGNEkVhSCGOvQ6cOBufLfnV+iEIVb5VAARXBWNSJoKqjQq8KmtV4z1qfWqp9Wm+pFppYCm1Rc2P1XvVk9E9g7S9adSaa3GYHARXBhNWaSx8XeFX0XUTVENiarqmnuvZ03BgPMZ8pweVUi+JyPdAMnAnMBKjQOI/bghb0zStXQTYAjDbzdR717Oy10qstVZyQ3MBiC2OZVDm/rUHigOKWZm2slmZeY1PDSv6rQDg+DXHY3aYCakMoSi4iE2JmwgrD6MgxCjdD6k0VqsZtaH5XMRlQ4wNCxvKzJvK7GYURsSUxFDnXYc4he3dt2OpM+bF+dnavrZmpyuScM1t8hORBQAikgo8C3yGsTpEH+CfIuKvlMpQSn2hlDoOOAM4zW2Ba5qmtQOT08SgjEGElYdR5VtFXmgefrV+pO1NI6Yk5s9v0Ir0XenEFsXiEAfZ4dl4KS/i8+NJ3XdoK5s0FEak7TVWf/Gx+9B3d1/qTfWU+ZcRnxdPZFlkm+/XqXpQItJNKZWF8dwpWURWAL2AB5RSlSLyBsaEr9OBu1yFEzUASqnPXPcw6WdOmqZ1ZsHVwQzJGPKn14VVhnHyyuY7UlvrrC2OWewW+u3qR1v9sX2DwJpATlrdfJfhuOI44orj2nzvpjpND0pERgN3ikigUmoH8G+MYb0CpdR/AFxLFb0OfIpRRv6oiDRbb0UnJ03TtM6h0yQoIB94TClVISJmYB0wB9gsIt+IiBUak9RrwDdAGeC5e05omqZpB9RpEpRSaotSKkNE4oD/An2UUo9iJKl6jDLyhgL9S4BFSqnbXc+s3L7fk6ZpmnZoOkWC+sMSRAFABDBNRMa6JuNeg7Eg7HoRWQjcD5Q3NPDUXXQ1TdO0A/P4BOUqanCKSJKIjFBKbQPuwCiGuEhETnIlqXOBj4AsIN61qoTHfz5N0zStdR79C1xEvFyJZgDwMzBaREKUUlsxJuQ6gEtE5GSlVJlS6h9KqVtca+uZ9Np6mqZpnZdHJyhXzyke+By4Vyn1b6BcRHyUUhuAuzCKIGaLyPA/tNXVepqmaZ1YZ5gHdRSwUin1lKtk/HXAS0QqMFYmfwxjOaNf3Rijpmma1s48LkE1WWOvwWYgVkQ+xyiQ2At8AswCBiqlPgY2utp66WE9TdO0rsGjElTDKg8iEo2RjAKVUmtE5BqMnlSpUup917UXA9Gu7xvW59PJSdM0rYvwmATlSjIOEekPvA3sA/qJyKfAbUqp38QQADwPxAKvgC4j1zRN64o8pkjCNaE2FOMZ03xgIsYK5EcDT7su64UxxykRGKKUsjfsoqtpmqZ1LR6RoJqs9BCBUTq+UClV7VpzbxRwgohc5SovXwAc12Sbdl2tp2ma1gWJO0fHmjxzsiilakUkHHgTeFcptaDh2ZKI/AdjUdh/NWnr7oIIPayoaZrWPlpdjs5tz6CaPHPqB9wvIlVAKVAC9BKR8Uqpz12XpwBFTdvrgghN07SuzS09qIbej4h0A34DnsHokcQBVwI/AplAX4xiiV7AYKWU/bAHeyCVOV27BxUQywUrH3V3FB3mrSE3A+Dc+I6bI+k4XunnQtled4fRsYLjoTLH3VF0nIBYAMZNHOfmQDrO0sVLwZN6UK7klAhMAN5SSt0H4KrQqwLGAe8CaRiLvk5tKIjQz5w0TdOODO4sM48DHgdWiki0UioPIzm9C4wAtjbsggt6J1xN07Qjjduq+JRSKzB2xI3CqNLzd81n+hUIxKjoa3q9Tk6apmlHELdO1FVK/SoiF2GUjvcXkRXAmRgbEP7uztg0TdM093L7ShJKqeUicimwDBgArAGGucrL9bCepmnaEcojJuoqpX4ATgKSMVYuV67jOjlpmqYdodzeg2qglFohIlcBT4uIL8Zk3Tp3x6Vpmqa5h0f0oBoopZYD1wM3AhY3h6Npmqa5kcf0oBoopb4UkRVKqWp3x6Jpmqa5j0f1oBro5KRpmqZ5ZILSNE3TNJ2gNE3TNI+kE5SmaZrmkTyuSELTNE37e26cdSPpfdKJjIikvr6eLdu2sOCVBezas6vxmlHHjmL6+dOJi42juLiYjz//mHfeb7m6//Uzr+f0U08HYO4Dc1nxy4pm548ffTx33HIHAO8vfp9nFzzbbp9D96A0TdO6mPHjxlNZVck3331DdU01Rw89mgfmPoDZbAagT1of/jHnH0RFRLH8++WYTCZmXDKD0085vdl9jhl6DKefejp2e+s7HUWERzD76tkHPP936QSlaZrWxVx747XccOsNzH9qPnP+MQeAyIhIesT3AOC8s8/Dy8uL1xe9zrz585g3f55x/JzzGu8RHBTMTdfdxBdffkFhUWGr7zPnhjkUFRfxw4ofOuRz6ASlaZrWxWRkZjR+7+1tPMlxOBwUlxQDkNwzGYBtGduMr9uNrzHRMfj7+wPGMGFNTQ3PvPBMq+8xeeJk0vum89CjD1FX3zGL/uhnUJqmaV2Ur68vt1x/CwDvffReY4IKDQkFwGazNfsKEBYSxqiRozhm2DHcfPvN1NTUtLhvYkIil110Ga+9+Ro7du7osPh1gtI0TeuCgoOCuf/u+0lLTeOzJZ+x4JUFjedKSkuIjorG19cXAKvV2niuuLSYE8ecSFVVFVPPnQpASEgIAOdPOR+LxUK3uG54e3vTv19/+qX3IznR6JGNOHoEdXV1vPTaS+3yGXSC0jRN62KiIqN48J4Hie8ez8J3FvLy6y83O5+5M5PoqGh69+rN+o3rSUtNAyAvP4+qqipEhKCgIIYPG96sXVpqGt27dQfAy8uLo4ce3ex8bEwsfdL6tNvn0AlK0zSti5n/8HwiwiPIy8/DYrFw9RVXA/DN8m/YmrGVt99/m+HDhnPh1AtJ7JHIoAGDAPjfe/8DaCysaPDaC68REx3TrMz89YWvN56/5fpbGHfSuHYvM9cJStM0rYuJCI8AIDoqmskTJzcez9yRydaMrWzavIkHH3mQC6deyPGjjqektIQXX32RTz7/xF0ht0pcewNqh6oy57D8w23eksG1N9/ByjXriIwIZ959dzFp4viOf+OAWC5Y+ehfajouciCjw9OJt0awongLz+1e0nju+PCjmBgzjBCzP1srs3hu9xJK66sA6BsQz6S44ST5RVNlt3H9hgXN7hvhE8RViaeQ4h9LUV0Fr+z5ig0Ve/5SjG8NuRkA58aWExP/rs2Z+5j9wIus3LSDyNAg/n3zdCaNNYZCFrz7FQ+/+CG5haUcO7g3L953DXFRYe0eA4BX+rlQtrdD7v3f51/hlbfeYf2mrZx/9kReeeYxAN58+wOuuvH2xuucTic1NTZ+//YThgzs3/6BBMdDZU7737cVbvlZDIgFYNzEcR37Pm60dPFSAGntnC4z92B2u50zp17KGaeOpXjPJp5/4mEunDGLbRmZ7g7toErqK/kw5xeWF21odrxPQHfO63Ycj2Z+xIy1T1FQV8bspP0TA2ud9Swv3MBb+5a3et/ZSaezuzqfK9c8zf+yfuD6nhMI9La2eq272O0OJl03j9PHDKHwx5d5du5VXHT7k2zblc23v27kzicW8sGTt1K44mWSukUx7dbH3R3yXxIXE82dt1zHZRdOaXZ82pRJVGZtafzv6UceoGdiAoMHHOWmSNtHZ/1Z7Ox0gvJgW7ZtJzs3lxtnXYnJZOLEMcdx7PBhvL7oPXeHdlC/lW7n97LtVNhtzY4PCk7ml5JtZNmKcCgn7+f8TJ/AeKJ8ggHIrM7lh+LN5NeWtbhnjCWURL8o3s1eQb2y81tpBntrCjk6JPWwfKa22rIzi+z8Ym646HRMJi9OPKYfIwem8cbH3/Hp8pWcM2446Snx+Ji9ufPqs/nu981k7sl1d9iHbPLE8Zx1ximEh4Ue9LpXF77LRVPPRqTVP5A7jc76s9jZ6QTVySil2LBpi7vD+Muk2ffGq3hrxJ+2624NJ7+2DJuzvvHYnpoCurehrbsppdiQsdf1fdPjxtcN2ztmGM7ddu/Zx3crfuGiqWe7O5QO0dl/FjuDIy5BSZM/5cTD/6xLS00mKjKCefOfpr6+nqVffcvyH36mupWJc53BuvKdDA9LI94agVm8mRw7HKdS+HiZ/7Str5eZakdts2PVjlp829D2cEpLjCMqPJhHXl5Mfb2dpT+u5bvfN1Ftq+OU4wbyzpKfWLd1NzW2Ou579l1EhGpb7Z/fuBN6bdF7jBpxNEmJCe4O5W/raj+LncURlaBExKSUUiISLCK+QJjruEcmKrPZzIcLX+LTJV8RkzyQR598jimTJ9A9Ltbdof0lGyr28G72Cm7sOZEnjrqCgrpybM46iusr/rStzVmP1WRpdsxqsjTrUXkCs9mb9x+fw2ffrSLu+Ct57NVPOPeUEXSPDmPsiP7MvfZczr3xUXqOu5bEuEgC/X3pHh3u7rA7xGuL3uPi889xdxjtoqv9LHYWR0yZuYh4KaUcItIfeB4oAiJE5N9KqffdHN4B9e/Xl+Vf7A9v5EkTuHjalIO08GzLCtawrGANYDxXOitmOPtqWl+Isql9NUVEWYLx9TI3JqUEayQrijd3aLx/Rf+0Hnzzyj2Nr4+bdifTzxwDwMzzT2Xm+acCsG1XNg88/z79UuLdEmdH+vHn38jOzeOcM09zdyjtpqv9LHYGR0wPSinlFJFuwEfAW8DlwIvAuyIywK3BHcS6DZuw2WxUV1fzyOPPkJOXzyUe/kPhhWAWE14IXuLV+L1ZTHT3NXoL4eZAruhxMkvyV1HlGroTwCwmTOIFruuN7yG3toTd1flMjh2JWUwMDUkhwRrBr6UZB4jCfdZt3Y2tto7qmloefXkxOYUlXHLW8dhq69iQsQelFHtyCrl67vNcN208ocEB7g75kNntdmw2Gw6HA4fDgc1ma7blwqsL3+XsCeMJDOx8n+1AOuPPYmd3RPSgXL0nJ9Af2KCUekJETMAFwJtKqbUiEqKUKnVvpC29vvBdFry2kPr6ekaNPIZlHy3CYrH8eUM3mhQ7nLPjRja+HhXel/eyV/B5/ipmJZ1OlCUEm7OO5YUbeDv7x8bregd05660/cv9vzr4BjZV7OX+bW8D8OTOT7k68VReGHgthXUVPL7jYyrsnvcM4I2Pv+PF97+mvt7OcUP6sOSFu7D4mCktr+LCW58gc18egX6+XHLWCdw7e6q7w/1L7p/3BPf8e37j6zfe/oC7b7uBubffhM1m4+0PPuW919tvRQFP4Ek/i5W+lWR0y6AsoAynOAmrCCNtbxrWOiu5obnsiN2BzceGQmGtsxJfEE98wZ/31Kst1fzc52ccJgcB1QGM2Dyi2XmnOPml9y9U+lUCcPya4zE7zFT7VLMxaSPl1nKCqoPot6sf1jpjCkhmbCYFwQUcs+WYxsKoturSE3VFRFzPnAKVUhUichIwG7gM+ArYrJS6QESCgP8D5iul8tt088M0Uddt/sZE3c6gIyfqeoqOnKjrMQ7jRF23aGWibr2pnp/6/kStTy0RpRF4KS/yQ/Pxr/FnxKYR7I7eTXFgMdY6KzazjcIQYwh9yNYhhFUeeFK4Eye/p/1OuX85SlSrCWpbt23sid6DEuPXX0OCWttzLUVBRUSXRJMbmktEeQQDdgyg2hM2CjIAACAASURBVKean9J/YujWoQRXB7f6vkfsRF1XcgoBlorISGAP0AfYAPyulLrAdelTQArw5w9DNE3T3KjUv5Ran1p8a30ZlDmIATsGEFAdQJW1ivyQfBLzEhm8fTB99vRhUOYgAqqNYdYay8FHG3bG7qTCr4IeeT1aPV8cUMzu6N0k5SS1OFflW0VoRSjpu9MJqwyjytdYHWZr/FZiimMOmJz+TJdOUC4mYDuQqpTKAO4AgoE1InKZiLwKDACmuZ5TeWRFn6ZpGoCXMn5t13vXU+1Tjc1so9ZsPMetsBoVsWV+ZWzpvoXVyaup9KvEv8afyNLIA96zzK+MnbE76bWvF/42/xbn673q2Zi4keiSaOKK4lqc97f5UxJYwvqk9RQHFONv86cguIDSgFJS9/31yfRd7hmUq5Tc0fBaKVUkImuA+0XkI6XUeyJiA8YA/hi9qsuVUnYR8VZK2Q9wa03TNLcLrQglpDKE0oBSfjzqx2bn6szGzrZV1ir2RruGdxWEl4fj7Wz9171DHGxI2kBYeRjxBfFkh2e3uGZLwhaUKPrs6YPd1PJXZOq+VOqS6sgPzieoOojk7GTWpKwhJSuFwpBCdkftBiAhP4FuRd3a/Fm7RIISkcuAHUqpb12l5MlAslJqKYBS6lERGQ3cKCL3KqU+FZHPXYUTDfcw6eSkaZqn88KLIVuHkBeWR5VvFb51vpQElJAbnouP3QeAuKI4YotiqbHUsC5pHXui92C2m+mZ27PF/cr8y6j2rcZsN7M6eTU2H9cuuxYbq5NXMyhzELnhufjZ/NiQuAGnV+OvTdYnrSd1XyqBtkCGbR3WeDwzNhNvuzehlaH81Pcn+uzpg0KxqccmQipD8K9t2Utr/bN2DTFA0xmbpwGfiMgTIjLDdexToOngabOhvKa9Lk3TNI8mEFscS0p2ClGlURQFFQEQVh6G3cvuukTwq/UjqDoIgGrfasAohqiyVFFlqUKxv9arLKCMwpDCxgo9u8neWGDR0L4wpJDioOLGY0XBRdR7N58sX+NTw67oXfTe25tKayUIhFSGEFoZCoJxrI06dQ+qoUpPKfUv1+u+QIRS6kkR+RaYBMwUkfHAh8B5wE/AMzohaZrWWa1KXYXZbsbb4U1hUCH15noiSiMIqwzjx/QfsdZa91fxBRtJJrzMmINY61PLin7GpoPHrzmesMowTl55cuO9s8Oz2Zi4sVkVX9PzNT41/HDUD43tzY7my41tid9CTEkMIVUhVDiNZ2KbemxqPO9X69fmz9kpe1AixuxN1bJG/kTgWxEZp5RaDzwAjAIKgJGAGTgaTdO0TiygJoDSgFJywnPwwovE3ET67zD22worN6rossKzKA0oJag6iPSd6cSWdPyyTAVBrsKILKMwIrAmkJR9KVRbqqm2VJOyL4XAmsA2369T9qBc1XZW4FzgdYwS8SuUUreJSATwkYhMVEotAyqBq0QkBlgOdN2JL5qmHRF67+1N7729Wz3XZ28fOMj0N2udtVmP6I/iiuJardRrS/vI8khOWHtCs2NJeUkk5bUsTW+LTtmDcjkNuAl4CFiFUUqOUmou8DCwWETGNlyslMpVSi1sqNZzQ7yapmnaIei0v6hd5eIpwIPAR0qpF5qcu9s1nekDETlfKfXJH9rqaj1N0zQP1yl7UCLS8FQuF3gOCBSRG12LwQJGkgKeBG5xQ4iapmna39SpelCtTMJ9FXhVRGZiLPzqFJE3XJNzpyml7tArQ2iapnVOnSZBNSQnVyn53a7VIIqVUjcqpZ4WEYWRpKJEZBjgKyILG5YvaqXiT9M0TfNgnWaIz5Wc0oHvMIb2fgNOEJG3XeefAV7A+Ex5wEk6OWmapnVenakHFQA8BjyplLrHdewy4BwRWaaUOlkp9ZqImJVS9a7zem09TdO0Tsqje1BNnx8ppSqBp4GXRMQkIquBdUBv4CQRWeG6rr5JG52cNE3TOimPTVBNNhuMEpEpAEqpj4B9wG3ALqXUJUAJsADY49olV9M0TesCPHKIr0lBRCxwJzBMRGqUUh+7kpYDcLhWk3gYqFJKTXW1bdjeXdM0TevEPLIH5UpOA4CPAF8gHpglIue7LtkBRAM/AwOBG6Gx16WTk6ZpWhfgqT0oP+B54GOl1H2uCbj/BM4TkXKl1DsisgroBvzoSmi6IELTNK0L8cgeFOAEyoEvAJRSWcA/MLZq/5eInKWUylRKfedKTnqzQU3TtC7GIxJUw/YZDZRSNozYbmlyrBBj5XIncLqIDG9yTu/tpGma1sWIu+ewNimIiAfigCil1MciMhRjIditSqlZrmufwqjaGwZsU0rNdlvgoCf/apqmtY9Wl6Rza4JqUkreH/gMWIGxoeA3wJuADzAXCANygBilVKqITAZuB45XSlW5I3ZVuLVLJyiJSENl/ebuMDqMdBsGwAUrH3VzJB3nrSE3Q+kud4fRsUISoTLX3VF0nIAYAMZNHOfmQDrO0sVL4QAJyq1FEq7kFAA8AzyilJovIlEYSxktVUotFJHPgbOAUuB7V9NBGAmrvrX7apqmaZ2f2xJUkzXy/AAT8Ljr1AfA/1zJKVUpleE6hmvS7pXA9cAopVSdO2LXNE3TOt5hL5JostqDxfW1DMgCLhaR34FMpVTDfKf7ReTEJs2TMeY/jVJKrT0sAWuapmlucVh7UK5ek0NEjgLmiUgxxrBdHnADUKCUush17asYRRPLG9orpX4SkdWuKj9N0zStCztsCaphCSIRiQM+x5iI2x0YgtGb2omxnt43GAkrBRjRZJ6TAxpL0DVN07Qu7rAlKFdySgTGA68ppe51DfdNA0YBduB94FugFliiV4jQNE07ch3uIok44ClguYiEu7ZmfwOjxHAccCEwu6GXpFeI0DRNO3Id1iIJpdQKYASQCIwWET/X4q6vYzxrysfoPTVcr1eI0DRNO0Id9jJzpdQvInIJxvbsIiKfK6VqROSFhq3Z9ZYZmqZpmlvmQSmllovIDOBZwCoi7yqlmvacdHLSNE07wrltsVil1HKMCbcTmiYnTdM0TQP3L3W0VESWuTMGTdM0zTO5fbsN13p8rS4UqGmaph253J6gwEhS7o5B0zRN8yweueW7pmma1n4S4hO44uIr6NO7D2ZvM2vWreHpF54mvyCfk088mTk3zGnR5tqbriVjewapyalMv2A6yUnJBAcFU1JawvIflvPqG69Sb+/YDSV0gtI0TevC/P39eejeh4gIj+DnX3+m3l7PqJGjiIuN46rrrmq8buXqlezeu7vxdWlpKQCJPRI5Kv0o1q5fS01NDaOPHc2UyVPw8vLi+Zee79DYdYLSNE3rwtL7pBMRHkFuXi7/vP+fADwz/xmSeyZz7PBjG6/7evnXLPu6Zc3axs0bmXbZNKqrqwHIzslm+vnTGTxwcIfH7hHPoDRN07SOUVdnbJsXGBhITHQMEeERhIeFA9AzqWfjdTNnzOTjdz7mxadf5KwJZzUez87JbkxOAN7eRr+msLCww2PXPShN07QubN2GdWzYtIF+ffvx2guvNTsXFhpGdk42W7ZtYcfOHQQFBTHi6BHMnDGTuro6PlvyWbPr+/bpy+SJk6mtreWVN17p8Nh1gtI0TevCnE4nc/4xhzHHjSEhPoGCggKOSj+KE48/kdKyUr785ku+/ObLxusvu+gypp4zleNGHNcsQQ0bMoy7brsLheKeB+5h+47tHR67TlCapmldnIjw9fKvAQgOCubiCy8GYPXa1cTFxpGdk92iTdPZPycdfxI3X3cz1dXV3HXfXWzeuvmwxK0TlKZpWhf34D0PUl5RTlVVFcMGDyMkOISff/uZtevXMu+BeQQGBrItYxsBAQGMOHoEQGNCGzJwCHNumIOXlxdbM7YyZtQYxowaA8CzC57t0Lh1kYSHmX7NHOLSRxGcNIS0Y05hwevvNJ6rrq5h5q33EJk2nJCeQxkz4UI3Rvr3LPpoKX2Pn0JA6mhSjp3E97+sBuCrH36lz5hz8U8ZxYnnXsPufTlujrRtInyCuDVlEi8MuJan+1/NJfEn4oUQYwnlpuQzebb/NTw/YCb/l3I2sZbQZm2jfIK5JfksXhw4m+cGzOT8bqPd9Cnapra2lstn30aPo44lML4fA0edxufLvgXg599Wc/KkCwlLGkhkyhDOvWQmObn57g24nezavZfTzp5GaHxvYpL7M+vmO7DbO8d2dTt37aRf336MPWEsDqeDRe8u4r6H7gPgq2+/oq6ujuNGHMfgAYPZsWsH8+bP46tvvwIgLCwMLy8jVQwbMozJEyc3/tfRRC/i8Neowq0d8g+3cUsGKUk9sFh82JKxgxPOvIhPFj7LkAH9mH7NHOx2B088eCdhocGs2bCZIQP6dUQYSEQaKuu3Drn3su9+YcacB1j09AMcPSidnDyjGshi8SHl2Em8MO8fTBg7irvmPccPv67hp49favcYpNswAC5Y+Wi73O/WlEmU1Vfz0p4v8TNZuL3XOXxTsJ7tVTnEWyP4rXQ7Nkcdk+OGc0xoGrdsfBkAk3jxSPqlLMtfw1eFa3EqRYxvKHtr/n6F1FtDbobSXX/7Pn9UVVXNvCef55ILziGhexyfLf2G82dcz/ofv2Dz1u1UVlVxyomj8fb2Ztatd5Odm8cX777a7nEAEJIIlbkdc+8/OO3saURFRPDs4/+mtKyckyeex4xLpnHdNVd03JsGxAAwbuK4jnsPN1u6eCkYm9a2cMQN8YlIvFJqr7vjOJD03qmN34sYY8eZO/fi7+fH4i++Zu+65QQFBgB0WHLqaHMffZ67bric4UOOAqBbbBQAz7/xAem9enLuGWON626eQeRR49iyfRe9UxLdFW6bRPoEszR/DfXKQZm9mnVlu+huDWdJwWoyq/f/Av0sbxWTYkcQYPKl0mFjTHg6JXWVfJa/svGa9khOHcnf34+5/3dD4+szTj2JpITurFyznrMnjm927awZFzHmjKmHO8QOsXPXHmZdeRm+vr7E+Ppy6tgT2Lh5q7vD6tKOqCE+EekHvCAi090dy8HMvPUe/BMG0mfEacRGR3La2NH8umodPeLjuPvfTxKZNpz+oyfw3sdL3B3qIXM4HPy+bjMFRaWkHjuZ+KFnMOsf86ipsbFx2w76992foP39rCQndmPj1h1ujLhtPs9fxYiwNHzEm1BzAAOCk1hbvqvFdX0CulNSX0mlwwZAin8cBXXl3JoymecGzOTOXlOI9404zNH/PXn5BWzL3El6714tzn234tdmf3R1ZjdcO4NF731IdXU1Wdk5fL7sa04de4K7w+rSjqgEBdiA9cBZIuKxf9Y9/fDdlO9cyXcfv8mk00/GYvFhX3YuGzZnEBwUQNb673jywbu4ZNbtbN6W6e5wD0leQTH19Xbe++wrvnv/eVYvfYM1G7dy/xMvUVlVTbCrd9ggODCAiqrqA9zNc2yp3Ed33wheHDSbp/pfxY6qXH4vbV6GG2YO4JKEE3lj7/L9x3wCGBGWxpL8Vcxc9yxrynZwc8qZmKRz/GjW19cz7cobuXjq2fTuldzs3LoNm7n34SeYd8/tboqufY0eOZyNm7cSFNeL7mmDGTpoAGdNGP/nDbW/rHP8FLQTpdR24GlgC3C+Jycpk8nEccOHsC8nl2deXojV6ovZbObOm67Bx8eHMccezQnHHc3Sb350d6iHxOprAWDWpVOIjY4gIiyEG2dcwOdfryDA34/yyqpm15dXVBHo7+eOUNtMgNtSzubX0gwuXf0EV655Cn9v32bFDoHeVv4v9RyWFazlp5ItjcfrnHa2VmaxtnwXDuXkk7zfCfC20s033A2f5NA4nU6mX3UTPmYz/513T7Nz23fsYvyUS3n8wX8yauTRboqw/TidTk6dfAGTJ55GVV4mhbs2UlJaxm133e/u0Lq0IypBASildgIvARvw8CQFYLc7yNy1l/5901qc64zbaIWGBNE9NqpZ7A3fp/fqybpNGY3Hq6pryNy9j/S0ni3u40kCTFYiLUEszV+NXTmodNj4rnAjA4OTAPA3Wbg99RxWlWXyUe4vzdrurSmATlinpJTi8tm3kVdQyHuvPYPZbG48t3vPPsaedSF33TKb6VM7vtLrcCguLmHP3ixmXXkZFouF8PAwLr3wPD5b+pW7Q+vSjogEJdJ8vEQplQm8gpGkLvCUJJVfUMSiDz6lsrIKh8PBkq+/Z9EHn3LS6BGMHjGUhG6xPPj489jtdn78ZRXf/PALp5x4nLvDPmSXTJnAf19+m/zCYkpKy5m/4C1OH3sck8Yfz4atmbz36dfYbLXc+58F9O+T6vEFEhWOGvJrSxkbORAvBD+ThVHhfdlTU4DVy4f/Sz2bbZVZLMr6vkXbH4o2kxIQS7/ABARhfNRgKuw1ZNmK3PBJ2u6am+5k87btfLxwAVarb+PxrOxcTjxzGrNmXMTVl01zY4TtKyIinKTEBJ5Z8Cp2u53S0jJefett+vfr6+7QurQuX2YuIiallENEegETgSjgGaXUThHpAVwJ9APeUkr9r6337Ygy84LCYs697DrWbtyK0+mkR3wcs2dMZ8b0KYBRgj7jxjtZt2kbPbrHcf8dNzDp9JPbOwygY8vM6+vtXH/3oyz8cAm+Fh/OPWMsD/9jNr6+Fr78/ldm3zmP3ftyOWZQOi//558kxse1ewztXWbewxrJ9PgT6GGNxIliY/keXtn7NQOCErkmaTw2Rz1Nu0pzNr5CUX0FAMNCUji/22iCzH7sqs7n5T1ftUuC6qgy89179pE4YBQWi0/jwqEAzz32ANt37mbuQ/Px/8OwbOW+je0eB3BYy8zXrNvADbf9k7UbNmHy8uLEMcfx5CMPEB0V2XFveoSXmR80QYlIm8ZWlFIeWWYlIuLaUr4/8CXwLRAE9AVmKqU+EZEEYAZwAjBXKfXlAW/YREfNg/IUHZmgPEF7JyhP1FEJyqMcxgTlFgdIUBXWCjK6ZVDuX47Dy4G11kp8QTzxBfEA1PjUkNEtg+KgYhxeDix1FlKzUokujW71bb7v9z02i63F8dCKUIZuGwqAQrErZhdZ4VnYfGx4O7yJLIskfXc6CsW27tvIDs/G5DSRmpVKbHEsAFWWKn7u+zPDtgwjqCaoxXv8nXlQ2zH+7BOaj5T/8bXpT+7jFq7kFAUsAO5XSj0BICLlwFMiYlZKfSAiLwP7gK/dGK6maVqbrEleg81iI6gqCH+bPzlhOWxJ2IJ/jT8BNQH8lvYbtT61BFcGE1ATgM3HRo2l5oD3iyuKw27avypGfmg+Nh8b1lpr47HNCZvJiszCp96H2OJYnOJsvGdhcCF7ovcQURaBzWxjU49NhJeF4+PwYUvCFuIK41pNTn/moAlKKdX47EZELgXGAnOB3UAP4J+Axz0lFBEvpZTT9dIMLFNKPSEiFuAn4GWgFviviFiUUouA51ppq2ma5lGcOLH5GL2d9F3pBNgCqPStpMK/ghpLDcVBxdT61BJbGEu/3W2bzJ+cs3+KQJ13Hfsi9wGQkJ8AGL2grIgszPVmRmwagY/dp1n7Kl+j+jZ9ZzolgSWsS15HjaWGEp8SKqwV9N/R/y991kNZSeI+IFUp1ZCGM0TkKmAbRsGBR2jyzCkRCFZKrRWRh12n5wPblFLXi8iJwDnAKcCihvY6OWma5sm88CIhP4E90XvYmLgRf5s/FX4VBFQHEFUaxeoUY13LWp9alvdfjhJFeFk4aXvT8HH4/MndYV/EPpxeTsLKwwisCQSgJLAEBHzsPqxKXUWVpQp/mz+99vUirDIMf5s/AOt7rqfWXIuX0wufeh/WJa8jNSsVs8N8sLc8yGc9tGsT/3CsBx40vOd65uRwPXP6FThFRAKVUmUiYgZigYblFy7A6A1e7p5oNU3T/pqo0ih8a30p9y8nJzwHUUJUaRTeDm/qvesBKA0oJaIsArPdTG54LpsSN/3pfZ3iZG+ksRJcQ+8JaLxnlbUKb7s34RXhVPhXsCZlDTazjYiyCOLz4in3K8dustN3d1/2Re7DUmchpDKEVSmr+CH9Bzb22Ijdq+0L7B5KD+o/wNeu5zV7gXjgEtdxtxERb6WUHZo9c1qEUfDwdMN1Sql6EVkNzBORi4EE4GqllFMP62ma1lnUmepYlbIKp8nJ0C1DCbAFsCp1FTviduBj98FsN3orcYVx9NnbhzK/Mn7t8yuFQYU4ceJ1kH5JbmgudT51WG1WIsr2L7nVcE9vhzeDMwbjhRcr+q6gylpFcWAxccVx9N7Xm977egPGkOCmHpsYtnUYGxM3YnKaGJg5kN97/Y6l3kJKdkqbPmube1BKqXnApUA0Rrl2DHCZUurhgzbsQK75S5f/YZ5TT6CgITlJ89msLwKXAR8AvZRSdteQoE5OmqZ1CjWWGpwmJ+IUgquDMTvMjUNsVb5VjcNyf2RSJsRVLFdlqaLKUoVTmv/q2xO1BzB6T9KksC6wuvV7ApicLQfRtsZvJa4ojqDqICr8KgisDiTAFoC1zkqFX0WbP+shrWaulPoC+OJQ2nQwO7DqD72gesBPRJJcq0YIoEQkDuOZ1OKGxg3Pq9wTuqZp2qELsAVgtpup965nZa+VWGut5IYapfYhlSEEVgeSFZFFdkQ2Ti8nZf5lAMQWxTYmnRX9VgAwfNPw/c+ZAkqo8K/A2+5NXFHzuYfB1cGEVIRQGljK6tTVmJwmqqxVWOoshJWHNbs2LySPcr9yjtpp7Fbgb/MnOyKbOnMd5X7lzYYO/0ybe1AiYhGRB0Rkh4iUuY6NE5FZbX63dqaUelcp9ZuIpAJXiIgfsAujcu9a1/Bfw58ITwDX/qG9Tk6apnUqJqeJQRmDCCsPo8q3irzQPPxq/Ujbm0ZMSQz+tf4M3D4Qf5s/uWG5OLwcJOYkkrrv4KvKN/SeuhV2w9vZsu/Sf0d/ooujKfcvpySghIiyCIZsG4LZub8AwiEOtnXfRkpWSmNhRJ/dfbDUWcgPySe8PJyknKQ2f9ZDfQbVDZgGfO46ttF1/L+HcJ+O0B94FPB1lZNPx5iYGyMiFUAw0As4340xapqmtYvg6mCGZAw54PmI8ggiyg+8bcvJK1uuQDNgx4CDvqfFbqH/zoOXi5uUiVEbRrWIdcTmEQdtdyCHkqAmASlKqSoRY+BSKZUlIt3+0jv/DX8cmlNKveea4/Sga/LtoyIyApgChGMUdVzkeubUWFShaZqmea5DSVB1f7xeRCKBw7qqpetZU8PaescDEcArSqm3RKQW+I8rgc5XSj30h7YmnZw0TdM6h0OZB/UO8KqIJAGISCzG0N6ig7ZqRw2FECKSgjHP6URgArBcRKYppd4D5gCzgNtFpNnud/qZk6ZpWudxKAnqDmAnxo60IUAGkA3c2wFxtcqVnKKAG4G7lFJTlVIjMMrH54jIKNeK5A8CQ4Cqg9xO0zRN82CHMg+qTil1o1IqAGMuVKDrdW3HhWdomOfkes50O8YqEI2xu4byfsUolEAptUApdbZr4m7n29VP0zRNO6Qy8+KG75VSBcq1T4eI5HdEYE25ek4JGM/AFmEs+HquiPRpctlrQLZrSaOmbbv0thiapmld1aEM8bVY7c+VDDp8LT4RMQF3Aw8qpX7B6CntBO4XkYaaxtkYZeb1HR2Ppmma1vH+tIpPRL7H2PvJV0S++8Pp7sCKjgjM9d6iDA4R+QUYD6CU+sqVtK4BvhKRxYANOLNpu46KS9M0Tet4bSkzX4CxXNAwjGKEBgrIowM2+WuSmJommdeB/xORfyql7lVKLRWRaqAGowR+nlKqVkR8lFJ17R2Tpmmadnj9aYJSSr0KICI/K6W2dEQQTVcTb5hIKyLRwEKMsvFc16Tg/wPOFJFuSqkspdQPIhKIMSH3FhGZr5Ra2xExapqmaYfXoTyDmikiI5seEJGRIjL/7wbhKoLwFZHBruTUD2NpogxgHjBfRM4FNgF9XOca2n4OvAkEAVeLyJ/vyKVpmqZ5vENJUOcDv//h2EqMku+/xVUKPgt4SUQuBNZgLKt0FXAr8BnwGHAuxnOve0UkvKG9UupLjEnD9+nhPU3TtK7hUBKUauV60yHeo/UbG8+a3gY2AC8BjyulXnad+931/fEYK5X/APTF6Ek1VBKilPpGKZX9d2PRNE3TPMOhJJfvMcq6GybNemFsmf793wnAVY2HUmoPUIvRexogIse6zptdz6gylVIvK6UmAx//P3t3Ht5UlT5w/HuSNN33vXSDUgq0gOyIoDg4IDKDgvuC64A6OiIKOuK+KzrqzLiNgoOgIiqouI2iLPJDUEG2FkoLpbTQfU/TpmmT8/vjpqGVAkXbJi3n8zx9SO6W9+SGvDn3ngWtwy6qWbmiKErPJNrbGlsIEQt8DkQDh9CmTC8E/iylPPybXtwxKrmjQUQEWlPxSmAeMA74u5Ty/xzbRkopix2Po9EaUFwipSz7La/dAVQzdkVRlI7R5og/7R7NXEp5WAgxDBiNdh8oH/jpt06X3mJU8sHAJ0ApkAj8HW0uJxuwUAgxH7gZbYr5SY7dLwKSHdu4Rm2Ry166S/hFganQ1VF0Hv9oAGTFARcH0nlESBJXbfuHq8PoVO8Nvxtqe/Dn1E/7nE6aNukkG3Zf36z+5rjrTnXKdzvaMEO/m6PlXm+0yQ9fklI+J4S4FXgUbfbb79Cmb/8vcISjyQmgBjhfSlnZEbEoiqIo7ueECUoIsVdK2dwYIZ/jXNaSUrZ/kvmjxxbAROC/juQkgJlolw2vQ2uAsRitdV6FI6F5SCkbpZTvnurrKYqiKN3LyWpQs1o8vqYjX9gx0vjHaPeyAL4HDkkprxRCfITWQTdfSvkeOC8JqgYRiqIop4kTJqjmBgqOxxs6+sWllOVAuRBiIFArpbzSsSoXrYPu+y22/U33uhRFUZTu6WSX+No1GaGU8qHfGUcTMFkI8SCQitZYYqzjsp5ezYSrKIpy+jnZJb64Fo+9gIuBnznazHwUsPL3BiGlzBJC3ABcAVQDbqAsVAAAIABJREFU4x3JSaeSk6IoyunpZJf4bmh+LIR4H7hSSrmyxbIZaMMP/W5SyreFEMubhypqHjS2I46tKIqidD+nMpLEFLT+Si2tBi7oqGBaJCehkpOiKMrp7VQS1H7gtl8tuxXo8J6OarJBRVEU5VQ66v4F+FgIcQ9ax9leaI0bZnRGYIqiKMrp7VSGOtouhEgGxgAxaB1qN6u+SYqiKEpn+M1TZUgpvweMQgjfDoxHURRFUYBTSFBCiEFAFvAm2hBEAOegzd+kKIqiKB3qVO5BvQY8JKVcJoRoHqR1A1rCUhRFUdzchLMnsGDeAgBWrV7F64tex9/Pn5tvupnhZwzHP8CfqqoqNm3exKIli2hsaiQyIpJli5Ydc6w33nqDjz75qFPjPZUElQq843gsAaSUZiGEd4dHpSiKonSosNAw/nbL32hqasJgOPrVf/NNNzNp4iRKS0tZ890axp81nunTplNrrmXZ8qOJ6VDeIbbt2OZ8nn0gu9NjPpUElQsMB7Y2LxBCjEJrfq4oiqK4sfl3zqe8opzc3FwmnD3BuTw6SptzasWqFaz+YjV19XVcOv1SoiKjWu2/L3sfry96vStDPqUE9SDwhRDidbTGEfcBt9B6xHNFURTFzcyYNoPUgancMe8OZkxr3TPok88+oX+//lw+43J6J/Rm/Fnjqa6p5tPPP2213fix45kwfgJV1VX88OMPLFm2hLr6uk6Nu92NJKSUnwPnA+Fo954SgBlSyuNPh9hFhBB+Qgijq+NQFEVxN4nxidx47Y0sfXcpOQdzjlmfmZXJnsw9hIeHM/X8qQT4B7Bz904Ki4/OVFxQWMCmLZtY9/06fLx9uOhPF3HHX+/o9NjbVYMSQujRWvANlFL+tXNDaj/HJIchaLPy/lMI8ZGUssHFYSmKoriNcWPHYTAYGJw2mLTUNJISkwA4c9SZWK1Wzhh0Bv1T+rP47cV8/NnHzL5hNtOmTkNKyZMLn6S4pJjrb77eebz1G9fz9KNPM3bMWIQQdObAP+1KUFJKmxDChjaiudskAMeQSOVCiA3A/UCDEOIzlaQURVE0Qgh0Oh2jRoxqtTw6KpoBKQNIiE8AtJqU1Wola38WAPGx2lyyEeERlFeUY7O1nlhC2jt/RLpTuQf1EvCBEOIp4DAtpn+XUh5bb+xkQogEYIGU8mYp5XwhhBl4yrFOJSlFURRg2fJlrVrjzZszj0kTJzmbmT/1yFOMGDaCubfPZfvO7YwdMxaA9L3pAEw+bzLn//F8dmfsprGxkbPGnAVoNanOHjb1VBLUy45///ir5RLQd0w4pyQc+KMQYqmU8lop5SPaFT+VpBRFUdrr+X8+z03X3cSwIcP448Q/UlVVxSeff8JbS7UxGHbs2kH/lP4MGzIMHx8fSstK+fSLT1n+4fJOj02cLAMKIXyAB4BBwC/A01JKS6dHdhJCCB0wEngVyJRSXu1Y/ghwFbAA6LwkVVvU6fVbv6ikVs/r6y38ddb1/Pv5Jzv7pcEvCkyFJ9/uFL38n7dY8t4H7M7I5MpLLmLJ6y8BYLVaueqm29i6fSeH8g6z7ouPmDB+bIe/vpO/1rRWVnT4YPw0NDTw1/kP892GH6iorCapdzxPPTiPKeedw7sffsotdz/o3NZut1Nfb+Hn7z5h+BlpHRqHCEniqm3/6NBjRnkG8czA6/ipMotXc79ioF8c18afS6iHP3YkmabDLMlfS2VjLQCjg/sxJWI4CT7hHDAX8UTWBx0az3vD74bajv+ctmVvZja33b2AbTt2ER4WynOPP8j0aVM690X9tM/ppGmTOvd1XOib1d8AiLbWtacV3yvAn4G9aDPqPtdhkf0OUko78BPaFCD9hRDvOpY/AryHVpOaKoTwclmQv1Nt0QHnX9H+XXh7e3HpRX9ydVi/S0x0FA/Mn8ONM684Zt24MaN4581/ExUZ4YLIOk5Tk424XtGs/+w9qnK38/iCuVx+4x3k5h3m6ksvxJS3y/n3ysJH6ZMYx7Ahqa4Ou12uj59IjrnI+fywpZxnslcya+cr3LbrPxQ1VHFj/HnO9eYmC1+VbGN10U+uCLfDNDU1ceEVN/Cn88+jIm8Pb/xrIdfMup2s7I7/gaMc1Z4EdT4wSUp5D9qkhS79hnTUnABnI4mf0ZLUgF8lqWXAG0CP+Omx8tMviAgPY/xZY1wdyu8yY9oFXPSnKYQGB7dabjQaufO2WYw7czR6vSuuGHccX18fHrl3Donxseh0Ov40+Q/0Tohl2470Y7ZdumIVMy+fjuPytFs7MziFuqYGMkx5zmU1TXVUNZqdz+3STqRnkPN5uimPHyuzqLSa6c4ys/ZTUFTE3Ntno9fr+cM54zhrzEiWvb/y5Dsrv1l77kH5SikLAaSU+UKIwE6O6biap4F3NC8fDNQB+6WUW4QQtwKvCyHelVJeLaV8XAhhBTJcFW9Hevu9D7j2yku7xReZ0lpxSRlZBw6S2j+51fJD+Uf4/oefWfyvZ1wUWft564xcEjOWJ7M+5NywQa3WhXr488zAa/HWe2LHzqJDa1wUZdeSUpK+J9PVYfRo7UlQBiHEuRy9Rvjr50gp13ZGcC0JIXSO5KQDNjlevx9a/6cPpZQ/CiFuAf7taCDxZynls50dV1c4lJfPhv/bzOJXXnB1KMopamxs5Jqb7+LaK2bQv1/re4pLV3zM+DNH0DshzkXRtd+lMWexviydCse9pZbKG03M2vkKvnov/hA2iAJLhQsi7FwpyUlEhIfx3EuvMvf22az7fhMb/m8L557difdJlXZd4itBm1JjseOv/FfPF3VadC1IKe2OmtP3QJ6UcgxwGXAFcJcQYoiU8kdgLhAghOjVFXF1hWXvf8S4M0fROzHe1aEop8But3PtrfMwGj14+dmHj1m/bMXHXHu5+09IneAdTlpAPF+WbDvhdmabhe/LM7g76UJ0bd/z7rY8PDz4ZPlbfPH1d0QlncE//v0fLpvxZ2Jjol0dWo920hqUlDKxC+Jor3FAupTyFsfzSwBvIA64QwjxbynlJiHEZHdoadhRlr73EX+/63ZXh6GcAiklf7njPopLy/ji/cV4eHi0Wr/px20UFJVwybTzXRRh+w3wjyPMGMi/B80GwEvngU4IenmHcv/ed1ptqxc6Aj188dZ7Yrb1mP+CAAxOG8iG/61yPh878c9cd/VlLoyo5zuVflBdznFZz95i0U4cHYSFEP8EzpRSJgoh5qE1hS8VQmT+OjkJIYTs7B5lneSHLT9zpLCQS6f/2dWhdIimpiaampqw2W3YbDYsFgsGgwGDwUBDQ4Oz45/VasViseDp6dkt77vdOu8h9mbtZ82qpXh7H9uQ9O33V3Hxnybj7+/nguhOzdrSXWyuOHqvZWrkSMI9A3gr71tGBvXlcH05RQ2V+Bm8uSZ2Agfrip3JSSAwCB16IRAIPIQeu5TYsB/v5dzWrvQ99OvbB7vdzqtvvk1hcQnXqwTVqdw2QQkh9I4hlgQQD1iklMVCiM2OOajigOsdmzcCbwMvtlVzklJKIYQncK6U8n9CiD5oye3drinNb/f2ex8wY9oF3eKLrD2eWPgSjz5z9F7aOytW8vDf7+KRBfNIGT6eQ3mHAZg8/SoADu7+kcRucI+mpUP5R3hjyXI8PY1EDzzTufz1fzzO1ZdeiMXSwIeffMlHS15xYZTtZ5VNWJuanM8b7FYa7U2YmuoJ9vDn6tgJBBh8sNit7DXl8+KBo6Ngjw8dyC2JR2uJbw+7kw1l6fzn0NddWoaOsGz5RyxaupzGxkbGjx3Nmk/fx9PT0yWxmLxNZPfKpsa3BpvOhneDN3GlccSVxmEXdrJ6ZVEaXEqDoQGPJg9Ca0JJOZyCh82jzeOdbJ8D0QfIiWl7wKBxu8fhZfUiKzaLgtAC9HY9yUeSia7QLn+aPc1sGbiFkZkjCagPOKVynrSjris5GkRsB2qBROAvUsqvHC0J9wBrgGxgDlrCOW6nBCHEAmAEWgOL+4A7pZTvHG/7k+qCjrou1Ukddd1GJ3bUdRed0VHX3XRlR12XOE5H3Y1pG7F4WggwB+Br8aUwpBAEDN83nEr/SnJicjA0GYisjKQssIwGYwPR5dGk5bbdGbw5AR1vn7KAMsoDyp3bm73MlAeWY7AZOHvX2VT4V7Cj7w7CqsOweFio86pj/K7xGG1GtiVvw8fiw4D8AW2+9u/tqNulHCOnN5uPNnrFNOBF4HMhxJVSymrgcrQpP0YDU06UnBxeQWuW/hzaCBPvOF7P7d4DRVGU47Fjx2LULhSl5qaSlpuGf50/APWe9dR71gPQq6wXA/MGkliUqK0z1h/3mCfbJ6wmjJTDKc4/vV37mo4pi0Fv12P20vq5pR5MpU9hH+w6O/We9RQHFWPyNtG3oO9vKqtbXeL71WW95ukcl0kpy4HnhRA1wDuOW0rvCyGmoF3BO+FwRo7jVgshDqG1ArQLIS6TUn7wq3tciqIobk2HjviSePIi88hIzMDX4ovJx4RfnR8RVRH4WnwpCSrhSNgRmvRNlAWWobPpSCxOPO4xY0tj271PvbGe0qBShBTEl2gti30tvgDs7rObBo8GdHYdxkYju5J2kXwk+biXFk/GbRKUo0GEzVGj2YlW5YsBlgBrAaSUbwgh7MB7QohGKeUJu3ELIYxSSivaYLY2KeX9Qgh/4GngEkei+9Cx7dlSyu87rYCKoigdJKIqgpKgEmp8a6jxrUHYBRFVERhsBnwtvoTWhFISXMKR8CMABNcE41d//PvYp7JPXkQeUkgiKiPwtnoDEFYdRlxxHIWhhejtegYeGsjh8MN4Wj0Jqg3il76/UOdZR3BtMCn5KRjs7Us9bpGgHK3smmsyjwDrgX8AU4G/CyGKpJQLAaSUi4QQjWj3oNo61lQgVUq5UEppFUIMAx4UQvgBr0spVwohnkBr9Xep437WmUCkEGJjd23tpyjK6cGqt/JL31+w6+2MyByBn8WPX5J/IScmB2OTkUq/SkqCS4gtiaXf4X7kReSxP3Y/u/rsYnTm6DaPuTd+b7v2adI1URBaAOCsPYHWWrP/4f70P9wf0BpG7EnYw8h9I8lIzEBv13PGgTPY2m8rno2e7b7k5/L7Ly2bgDuai88A3pVS5gJL0e5DzXasA0BK+baUcm9bx0KbYXe+EOI2x+KP0Aa63Qa8KoS4U0pZBDwG7EMbCDcJmK6Sk6Io7q7esx673o6wCwLrAvGweTgvsZm9zNR6a6N9BNQFoJd6As2BznXNzJ5mzJ5m7EKrF7RnH4CC0AKaDE34m/0Jrm09nmZL++L2EVMeQ0BdACYfE/51/vhZ/PC2emPyMbW7rC6tQf1qbL0hgCdax9sbgS1SSpMQ4jPAjjbOnlVK+a/jHc/RnPwDtGbnTwshhgOLpZRPOl5vO/CsY5rilxxTc3gC9Y59DVLKpuMdX1EUxdX8LH54NHnQaGhkW79teDd4UxSsjTAfVBuEXdgxe5vZ32s/1b7VVARUONc1+yHtBwDG7BmDf70/QbVBJ91HIsmL0AYKTihJOG58xUHF1PjUMOigNmajr8WXgrACrB5WanxqWtW8TsZlCcrRcKHl2HofAu8DlWjTZDwgpXxCSmkWQnwJ/AXYdZJjCillgxBiJdo9rOeAA8CTAFLKFY7Xe0II4Qu8JKU0O/bVqeSkKIq709v1DM0eyv5e+zH5mKjxqcGnwYfYsliiKqMIqw5DJ3WUBpZSEFqAR5MH0eXRJB9OPu4x+x3ud9J9ygLLqPeqx2g1ElkR2eZxbMJGVmwWfY/0dTaMGHBoAHsS9lASVEJoTSi9C3u3u6wu7QflqDltB/ZJKS93LDMAtwPDgGwp5ePN257oElzzeiFEKGCUUhYKIa4C/gM8JKV8scW2N6BNG3LJb76sp/pBdW+qH1SPcLr2g+pJTtQPytWNJCYC+S2S032ADa1/UzZwphBinpTy+ZMkp+bm6RHAo4BZCPG0lPI9RxJ8wlFD+geAlPK/wH8d+3bbYZAURVF6MlcnqCJgtBDiGbSkNBhYDQwE0oFVwDFjovyqYUVz8/Q04H5gANrQSGYhxOtSyneFEBItSflJKR9teSyVnBRFUdyTSxOUlDJdCHE78Ae0UcqvBBBCfAiUNfdRaoMOrabVPA1HDFrT9EeAu9DG6DvHcaxXHDUpb7R7W6rGpCiK0g24ugaFlPID4IPm545JB8cBC9raXggxCbhbCHE+2j00OxAM/CKlfNmx2dNCiHLgTsc+r0gpFwsh/uu4T6WSlKIoiptzeT+oZkKIOCHE02i1oKlSyuzjbLofmOtIMM1Jxhc4SwgxonkjKeUbQClwPnCDECKwedJDlZwURVHcn9skKLSZe9cBZ0kpfznBdgellHscl/X+J4Twl1L+BLyJNv17/xbb7gV+RuuMGwfqnpOiKEp34TYJSkrZIKX85iRTZugcl+jigVi0pomfO+4vPYHWsGKtEOJZIcQ3wFApZfNUtLM7uwyKoihKx3GbBHUyjuRkF0IkARuBUOBWwAT8D6iVUt4MPIw2OOzPwATH7vuA/C4PWlEURfnNXN5Ior0cySkOmAz8S0r5FYAQ4k7gJeAbIcQUKeWbLfcTQswHLgSe7+qYFUVRlN+uW9SghMYDWAi8DNQ4lhuklPvRWutVANuFEF6OdYFCiOuBy4DJbQ0uqyiKorgvt0xQjtEfWs6u6yGlbESrBX0K/A2geaBZR5L6O/Ax2kCxOGbd/Rw4X0q5rYuLoCiKovxObneJT2iz5PoLIT6XUtY5Roh4UghRhVZLehmYJYT4DjivRb+mTOBexzEMUsomKWWZywqiKIqi/C7uWINqAF4AzhNC9AG+AbYCWUAY8DbaLLvFaM3Mj+nXpEYlVxRF6f7cLkFJKdcCV6FNldE8eeHjjjmdbga+BO4D/gWEo828qyiKovQwLp1u40SEEOeg3W/ajzayRLHj3tQI4J/ADUAdUCCltLkgRPd84xRFUbqfNqfbcLsaVDMp5Qa0YYrCgXOFEL6OS3lbAT/AT0qZ7xjJXH+iYymKoijdj9s1kmhJSrlFCHEtsAgYJITYDEwDrMCOFtt1fQ2qroe3v/AJg+o8V0fReQId007Xlbo2js7kE96zywfgE96jJ2V8b/jdwGkxYWGb3DpBgVaTcsyA+z9gDLAZOLO55uSiy3uKoihKJ3PbS3wtSSn/D23A12LgQSllo6MpuUpOiqIoPZTb16CaSSnXCSHWt+j3pJqSK4qi9GDdogbVTE02qCiKcvroVgkK1HxOiqIop4tul6AURVGU04NKUIqiKIpbUglKURRFcUsqQSmKoihuSSUoRVEUxS2pBKUoiqK4JZWgFEVRFLekEpSiKIrilrrNUEeKoijKb/fck88xZNCQVstyD+Uy+2+zGTpkKDOvnElyUjKenp7s3L2T+ffPd25nNBq57+776J/Sn9CQUABm/mUmxSXFnRqzSlCKoiinkVWrVzkfV1RUABAbE4unpye5ebmkJKccs4/BYCC5bzJZ2VmcOfrMLotVJShFUZTTyOuLXj9m2WdffcZnX33G9GnT20xQdXV1XHPTNfj6+vLx8o+7IkxAJShFUZTTysr3ViIQZB/IZvHbi8nan+XqkI5LNZJQFEU5DdTX17Plpy1s2LiB4tJihg4ZylOPPkVwULCrQzsuVYNSFEU5DTz0xEPOxwaDgbdee4uoyCiGDB7C+u/Xuy6wE1A1KEVRlB7O0+hJSEhIm+uk3X1nMFI1KEVRlB4uKCiIxa8tZueunRSXFDOg/wCiIqOoqKxg+87tpA5IZcqkKcTHxQMQFxvHvDnzyD+cz4qVKwCYN2ceBsPRlDH7htnUW+p54603qDHVdErcKkEpiqL0cDWmGr5d+y1nDD6DwWmDMdeZ2bR5E0veWUKNqYaY6BgmTZzk3D4kOIRJEyexc/dOZ4JquR5g/FnjAVi2fJlKUKeriopKbvrrnXzz3XrCQkN4+tEHuOryi10d1m/28htLWPLeR+zes48rL57Gktf+4Vy3aOlynnnxNYpKShk3ZiRvvfwcMdGRLoy2Y1xz4618t34j5ro6oiIjuGfu7fzl+mtcHVaH6klljPIM4pmB1/FTZRav5n4FwKTwoVwQORw/gxdFlkqW5a9nn/mIc59E7whmxp1Lb58IGuyNfFr0I/8r2e6qIhyjvr6el1556bjr16xdw5q1a054jEnTJp1wfWdQCcrN3Tb3XoxGD4oPZrBjVzpTL76KIYNSSR3Y39Wh/SYxUZE8MO9vfL32e+rrLc7l6zduZsFjz7Hus/dJTkpkzt8f5cqb/saGLz9wYbQd4755c1j82kt4enqSuS+bCedfxNAhgxg+dMjJd+4melIZr4+fSI65yPk8ySeKK3qN5/Gs9zlYV8J5YUOYmzSNW3e9jkTir/fm3uSLeefwOn6szMYgdIQY/V1Ygp5DNZJwY2azmZWffs7jD96Hn58f48aOYdoF57Nseff90p4xbQoX/WkyoSFBrZZ//vV3XHrRBaQO6IfRaOTB+Xfw/Q8/cuDgIRdF2nFSB/bH09MTACEEQggO5OS6NqgO1lPKeGZwCnVNDWSY8pzLwj0DOWIp42BdCQAbKzII8PAh0OADwAWRw9lVk8umikyapA2LvZECS4VL4u9pTpsEJYTQCyH0ro7jVGRlH8BgMNAvOcm5bMigVDL27nNhVJ1HSnnM4/Q9PaOsf73zHnzCEug/dCzRUZFcMHmiq0PqcN29jN46I5fEjOWdw+tbLd9ZfRAdOpJ8ohAIzgkdRG5dCVVNZgD6+kZjbrLwSMqVvDb4VuYlXUSoh6pBdYQen6CEEHFCCA8ppU1KaRNC6IQQlwghLnF1bCdTazYT4O/XallgYACm2loXRdR5zj9vAh98/AW70vdSX2/hsYX/RAhBXX29q0PrEK++tBBTcQ4b16xmxoVTnbWNnqS7l/HSmLNYX5ZORWPr/1/1dis/VWXxcP8rWDrsTi6OOZNFh75xrg8x+jE+dCBL89dyx+43KLFWc3ufqV0dfo/UoxOUEMIDeB74qcXiLOBOYLEQYqUQoq9LgmsHP19fakyt/7PU1Jjw9/M7zh7d13kTxvHofXO5+NpbSBx8Fonxsfj7+xEbE+3q0DqMXq9n3NgxHD5SwGtvLnF1OJ2iu5YxwTuctIB4vizZdsy6CaGDOCc0jXsy3ubaX17klYNfMr/vdII8fAGw2pvYWrWfnLpiGqWNVQWbSfHrhbfO2NXF6HF6dIKSUjYCjwJHhBDrHLWm1VLKcUAfIAJYKITo58o4j6dfchJNTU1k7z/gXLZzdwapA44dzLEnuG3WdWT/soHi7G1cPG0KTU1NpA3seWVtarJ1y/szp6K7lXGAfxxhxkD+PWg2rw6+hamRIxgVnMyTA64h0SecX6pzKGqoRAK7anKpbDTTzzcGgLz6Mlp2dXXfbq/dT49OUABSyj3A3UAN8G+gzLG8HPgTWpJ6QggxwGVBHoevry8zLpzKQ48/i9lsZtPmH/n0i6+YeeVlrg7tN2tqasJisWCz2bHZbFgsFuey9D37kFKSl3+E2XPuY84tNxIcFOjqkH+XkpJS3v/wY2pra7HZbHy9Zi3LP/yYieeOd3VoHaYnlHFt6S7mpi/ivj1LuW/PUr4t3cX26oM8k72SA+Yihgb2JsKofRbT/BOI9grmcH05ABvK0xkZ1JcE73D06JgePYZM02Hq7VZXFqlH6LHNzIUQeimlDUBKuU8IMQ94BbgWeMqxvFoIcQGwCVgghLjRUetyG6++uJAbb51DROJAQkOCee2l57ptE3OAJ577N48+e7Q/xjsffMzD997JnbfeyFV/uYMDuYfw9/Pjhqsv5fH773ZhpB1DCMFri5Zwy5z52O12EuLieGnh40yber6rQ+swPaGMVtmEtanJ+bzBbqXR3oSpqZ6NFXuI9AzigZTL8NV7UWE1sfjQGgoatJZ6e0z5rDjyf8zvOx1PnQf7ao/w8sEvOzXeWq9asntlU+1XjV3YCTGFkJKfgrfVm4LQAjISM47ZZ9TeUQTWtf2Dr9y/nJyYHGp8arDr7ASbghmRNcK53iZs7O69m2q/aqweWuIdt3sc3lZv5/o9CXsoDSrFs9GT/nn9CTVpExtW+FWwo+8OxmaMxavR65TKKVq2nOophBAGKWWTEEIHPAh8LqXcJoRIBl4HBHC+lNLq2N4fCJVS5rb7RerKet4b15JPGFTnnXy77ipQG9KFulLXxtGZfMJ7dvkAfMK5ats/Tr5dN/XecO1HWstOso36RjYP3EyDsYGwqjB0UkdJcAm+9b6cuedMCkMLyUjMIKQmBL/6o/erE4oTjpsg8sPzORJ2BCEFNb41xySoRl0jW1K34FfnR1lQGdA6QeWF57Evfh+RFZFU+1Zj19k5e9fZSCRbBm4hpjyGxOLENl/7m9XfgPadfIwed4lPCCFaJKftQH+gHEBKmQ3cDDQBXwohPB3LTaeUnBRFUVykyreKBmMDXg1eDD0wlCE5Q/Cr88PsbaYkqMS5XXRFNCmHU5x/J6q9xJXGMWbvGKIqotpc72H3YPzu8aTlprW53uxlRm/TM/jgYOJK47B6WGk0NJIXof3IjS+O/01l7XGX+OTRKuFSIENKeRWAo7Wej5RylxDiRuAjx9+fXROpoijKqdNJrV7RaGikzliHTupo8GgAwORtwseqdSDeF7ePvfF78bJ6EVcaR3zJb0sS7eFr8cWmt7Gjzw5MviaMjUbswk5OTA5DDgxB9xvrQj0uQYGzeXkj8KHj8WIgEUgQQqyXUl4nhLgaUHcxFUXpVoJNwQTVBlHlV8WmQZtarbN6WPFp8CHAHIB/nT+NhkZKg0rZF7cPnV1HbFlsp8TUq6wX1b7VlAaVYmw0kpaXRnZsNmHVYRhsBn7u9zNWDyth1WH0O9wP0fYVvWP0iATVskEEaM3LhRCFwHLgKyAauAYYBswWQvhIKQ+0fTRFURT3pUPH8H3DKQ4d54wAAAAgAElEQVQpxuxlxsvqRaVfJUWhRRibjERXRBNTEePcPjsmm9zoXEqCSjotQemlnkG5g5zPK/wqKA0sZWzGWH7q/xOhNaH0O9yPn1N+xtfi2+44un2CatEgQgBjAC8p5Top5QIhxBdAMZDr2OYKwMelASuKovxeQrvHBGA1WNkfsx+AkJoQ6j3r8Wk4/tecHTv1ntoILT4NPu2uzbSXHTuZ8Zn0LuytXX40NhBQF0BgXSAGmwGTt6ndx+rWCUoIoWvRIGIT4A34CyEqgPOklJsc2w10dNK9E/iDlLLOdVEriqL8Pr8k/4JHkwcGm4GygDIaPRoJqwojpDaErf220qhvJKAugCZ9E6VBWkvO5oTWYGzgh7QfAJiwYwIeNg8qfSs5EnYEs7c2vqDZy0x6Qjq+Fl96F/cGID0hHak72ng5KzYLvU1Pv8P9MNqOjpqRH5EPaK0GBQKPJg9yI3Op8K+g0aMRX4tvu8vZrVvxSSntjprTd8A+KeUZUsokYCDwsRCiuUlKEjAJOFdKucNF4SqKonQIv3o/qvyqKAwtRIeOxKJEBucMBiCqPEpreh5UQnlAOX71fqQeTHUmqLbUe9VTGFZIja828aDVw0phWCHlgeXObQrDCikKOToNSUlwCYVhhdj0zrsrNBgayInJISU/BR06BIK0g2kIKSgPKCeqIopeZb3aXc5u2Q+q+bKe43Es8Bow3VGbWgQMAuxoDSWukFIWCCECpJQdN+2j6gfVval+UD3DadgPqqfpUf2gWvRzMgghlgNm4C7HsieBkVLK0cBzwDjgfUcjis6Zk1hRFEXpFN3qHtSvWuvdByClrAQqhRDeaDWnexzrE4CLgR0tW/gpiqIo3YNbJyhHbUk6Huub53MCVgOVwJMttqt3NI54SAhxLlqDiDQp5UFXxa8oiqL8dm59ia9FcpoJGB3J6Ta0uK8Gkh2bNs+U+y5aa74BwGgp5f6ujVhRFEXpKG5dgwIQQsQDLwCjgfOBTVLKC4QQr6JNOrhTSpkDIKVcA6wRQhibB4JVFEVRuie3T1BSyjwhxES0mlEhMNux/K9CCC9gqxBihJQyp0XrPreaMkNRFEU5dW59ia+FSuAttMTzjKNWhZTyRmAFsF8IEd/c9Fx2x7bziqIoSituX4MCkFLmA3OEECnAp4BeCPGMlLIAeBOIAk5tJixFURTFrXWLBNXMMTPuJcCHgKcQwg5MA0ZIKQtdG52iKIrSkbrLJT4nKWU6Wv+mRsAf+JNKToqiKD1Pt6pBNZNS7hFC3Ik2VJNqEKEoitIDdcsEBdDcIEJRFEXpmbrdJT5FURTl9KASlKIoiuKWuuV0G25CvXGKoigdo83pNrrtPShX68nzs4A2R0tPLqNjDpoeX8aeXD7o+WVs/pymjUhzcSSdJ31r+nHXqUt8iqIoiltSCUpRFEVxSypBKYqiKG5JJShFURTFLakEpSiKorgllaAURVEUt6QSlKIoiuKWVIJSFEVR3JJKUIqiKIpbUglKURRFcUsqQSmKoihuSSUoRVEUxS2pBKUoiqK4JZWgFEVRFLekptvoQtOnTWfyeZNJiEtAr9ezbPkyli1f5lw//qzxzLxyJjHRMVRUVPDZV5/x4aoPW62/4pIriI2JxWazkZuXy9L3lrJj1w4Arr/mekaPHE1URBQAObk5vLX0LTL2ZnRJ+ebePpfUAamEh4XT2NhIZlYmi5YsIjcvt11ljIqMYtb1s0jum0xIcAg1php+2voTi5YsotZcC0BkRCSzb5zNoIGD8PHxobS8lG/Xfsu7K97tkjKe6Bz2SezDzTfdTEpyCj4+PhQVF3HtrGtb7R8ZEcmts25l6OCh2O12tm7fyqtvvEplVSUAQgiuueIazv/j+QQGBpKfn89by97i520/d0n5TmTC2RNYMG8BAKtWr+L1Ra/j7+fPzTfdzPAzhuMf4E9VVRWbNm9i0ZJFNDY1kpyUzMyrZpLUO4nAgEAqqyrZ8H8bePudt2lsanRxiY567snnGDJoSKtluYdymf232QwdMpSZV84kOSkZT09Pdu7eyfz757d5nCcfeZKRw0YCcMucW8g5mNPpsbflv//5LyOHj2y1LPtANtMvn841V1zD1ClTiY+Nx8PDg4O5B3lt0Wus/369c9uU5BTuvfteBqcNxmKx8O26b1n44kLq6uqc20ycMJFZN8yib1JfGpsayd6fze1zb6fGVNNh5VA1qC6UnJSMyWSitKz0mHUDUgZw//z7iQiLYMPGDej1emZdP4upk6cCEB0VzYJ5C0hOSuaXHb+QtT+LtIFpPPbgY3h5egHwh3P+AMDGHzZSUlrCoNRBPPHwE4SEhHRJ+aZMmkKtuZZ136+jrr6OUSNG8eQjT+Lh4dGuMkaERzB65GjyD+ez9vu1+Hj7cMHkC5j7t7nO15g3Zx7jx46nxlTD2g1rCQsJ47qrr2PSxK6ZE+hE5zAiPILg4GAOHDzQ5r5CCB5/6HHGjh5Lxt4M9ufs55xx5/Dwgoed21w24zJmXjkTm83Gho0biIuN47EHHiMhLqHTytQeYaFh/O2Wv9HU1NRq+c033cykiZOw2+2s+W4NXl5eTJ82nSsuvQKAxIREBqUOIvtANht/2EhIcAiXzbiMG669wRXFOKlVq1c5/75d9y0AsTGxeHp6tvqh1ZZpU6cxdPDQLoiy/Za9t8z599mXnwEw8dyJ+Pv5s+77dWTtzyJ1YCovLnyR/in9AfDx8eGNV95g1IhRfL/pe44UHuGS6ZfwyP2POI87ZfIU/vn8P0num8y679fxzbff4Ofrh5eXV4fGr2pQXWjhiwsBeGTBI0RFRrVad/nFl6PT6Vj2/jJWfrKSMwafwcInFnL5JZfzxddfEBkRiV6vp7qmmkeffhQvLy9Wf7AaL08vgoKCKCou4vFnHif7QDYAXl5erHh7Bb4+vgxIGcCmzZs6vXy3zb3N+fqREZEsW7SM8LBwEuIS2J+z/6RlPFxwmGtnXUtFZQUAu9N3M//O+Qw7Y5jzNWKiYgB4879v8uPWH/H18WX8WeOJjIjs9PLBic/hlp+3sOXnLYwdPZZBqYOO2ffMUWeSGJ9ITm4OCx5ZgE6nY8l/ljCw/0AGpw0mfU86l0y/BMB5LktKS7j68qu5dMalPP/P5zu/gMcx/875lFeUk5uby4SzJziXR0dFA7Bi1QpWf7Gauvo6Lp1+qfO9ydibwdU3Xu385V1QWMDMK2e2Oqfu5PVFrx+z7LOvPuOzrz5j+rTppCSntLlfXK84/nL9X3h3xbtcd/V1nR1muz37wrPHLFv4wkL27tsLgE6n44tVXxAXG8eoEaPI3JfJjAtnEBoSyvrv13PXvXfh7e3NxjUbmXzeZP716r84fOQwd/3tLgBuueOWTq3dqxqUm0jqkwRAVnaW9u9+7d+oyCh8fX1J35POnsw9BAYE8vB9D/PIgkcAWLN2DUXFRQDO5ADar3W9QQ9AWVlZl5Sh5esbDNpvH5vN5kw4JytjRUWFc1vAWfNqGf9Hn36EzWZj1g2zmHv7XEaNGEVRcRFr1q7pxJJ1jObyZ+/X3ie73c6BnAPOdRHhEQQGBGKz2ZzvZfN7lNQ7yQURa2ZMm0HqwFSe+cczWButrdZ98tknNDY2cvmMy5nz1zlMPm8y1TXVfPr5p4CWkFpeFmr+XHTVZ/JUrXxvJaveW8Wzjz9Lv7792rWPXq/n3rvuJedgDss/XN7JEZ6aTWs38cO6H1j06iLSBmqz8jYnp2YeBu3/WXFxMaBd6QCctwbq6+s5eOgger2efn37kRCfQHRUNPWWem649gZ++v4nvvz4S2etuSOpBOUmgoOCAbBYLK3+BQgJCqGpqYk1363B0mDhrDPPYtgZw6iorGDLz1uOOZZOp2PenHkYPYys37iefdn7uqYQDl5eXsybMw+AlZ+udCadk5WxpdhesVx/zfXYbDYWLVnkXL5953ZyD+USHxfPlElT8PT05KetP7VKbO6qufz1lnrnMkuDxbkuKCgIgIaGhqPrHe9RcHBwV4XZSmJ8IjdeeyNL313a5v2UzKxM9mTuITw8nKnnTyXAP4Cdu3dSWFx4zLYDBwxkxrQZNDQ0sOSdJV0QffvV19ez5actbNi4geLSYoYOGcpTjz7lPGcnMvOKmcT2iuXZF57Fbrd3QbQnZzabWf/9er5e8zWFhYWMGTWG//z7P4SGhrba7p659xAVFcX2ndudlzRDQ7RtWv6wqK/XPrNhYWHO98Tby5vYXrF8/e3XRIRH8MC9DzhvM3QUdYnPTVRWVRIZEem8huvt7e1cV1FVwchhI5lz2xyKS4q5/p7r8fH24eV/vMz98+/n5rybOZR/CABPoycP3PsAo0eOZsvPW3juxee6tByBAYE88fATpCSn8OXXX7ZKLicrY7N+ffvxxMNP4O/nzwv/foEft/4IaIn3qUeeIiw0jKeff5rNP23m73f/nWlTp2FpsLR6LXfU3BDC2+touZsfV1ZVUlVVBYCnpydCCKSUR9dXVnZxtJpxY8dhMBgYnDaYtNQ0khK1mtyZo87EarVyxqAz6J/Sn8VvL+bjzz5m9g2zmTZ1GlJKnlz4pPM4I4eP5MF7H0QiefTJR9mfs98l5Tmeh554yPnYYDDw1mtvERUZxZDBQ1o1HmjLHyb8AXOdmVtn3dpq+W2zb2PZ8mXORkxd6fa7bnc+NhgMfLHqC3rF9GLUiFF89fVX6HQ6Hl7wMBdfdDHpGencdudt2Gw2AMorygHtXlQzH2/tcVlZWasfgwseWkD6nnQsFgtXXnYl555zLms3rO2wcqgalJtovrHev592o7L5WndxSTFms5mE+ATn84qKCg4fOUyNqQadTkdcbBwA/n7+PPP4M4weOZo1a9fwyJOPdGlLqYjwCF545gVSklNY/uFyXnrlpVbrT1ZGgGFnDGPhEwvx9vLmsWcea3Xpzs/Xj7DQMEC7TGGxWJyXyOJj4zu3cB2guQbSXG6dTkffpL7OdSWlJdTU1KDX60lOSgagX7J2mSkn1zWtwYQQ6HQ6Ro0YxZiRYwgPDwe0e08DUgY4P5eZWZlYrVbnJcmW52PihIk8ev+jWK1W7n3gXrbt2Nb1BTkBT6PncRsSSbts1zHCw8IZM3IMY0aOcS4blDqI8LDwDonxVHh5eh33daVdYjQaeXHhi1x80cX8sOUHbrjlhlYt7zKzMgGc91F9fHzondgbu91O9v5sCgoLMNWa2jx+y1pXR1A1qC50/h/PJ21gmvNLaezosURGRPLDlh/4YNUHjBk5hmuuuIbEhESGDtFaA61YuQKAPZl7sNvtDE4bzP333I+3lzdRkVE0NDQ4vxQee/AxUgekUmOqoba2ltk3zgZg6y9b2frL1k4v30sLXyIsNIzikmI8PT255S+3ALBuwzr2Ze87aRkT4hJ47MHHMHoYydibwZBBQ5xNf999/11qTDXkH84nLjaOhxc8zL6sfZwz/hwA0vekd3r54MTnMP9wPpdfcjkR4RGAVpucN2ce1TXVvPnfN/nhxx/Iy88jMSGRpx99Gg8PDyLCI8jcl8nO3TsB7R7bjTNv5IF7H2B3xm7OGXcONputVXeDrvTrrhDz5sxj0sRJzmbmTz3yFCOGjWDu7XPZvnM7Y8eMBSB9r3Y+hp8xnPl3zken07EvWztfzeesrQYJrhAUFMTi1xazc9dOikuKGdB/AFGRUVRUVrB953ZSB6QyZdIU4uO0pBsXG8e8OfPIP5zPipUrjulK8M3qbwDXNTMPCQnh85Wf8+PPP1JYVMiQQUPoFdOLsrIytvy0hccefIyJEyZisVg4lHeIO269A4DdGbv58usvWfnJSmbfOJuzx53NC8++QGwvrRXj/775H/lH8gF45713uHX2rTz16FPs3L2TKZOm0NTUxOdffd6hZVEJqgulDUxr1Rw6qU8SSX2SKC4pZtnyZTz9/NNcc8U1TBg/gcqqSha/vdh5wvdk7uG5l57j4osuZuSwkUgpydibwTvvv0NJaQlw9NpxgH8A06dNd76O2WzukgTVXLuJjIhkxrQZzuUHcg6wL3sfe/buOWEZAwMDMXoYAUgdkErqgFTnMT5e/TGmWhMPP/kwN117EwMHDOS8c8+jrLyMTz77hI8++ajTywcnPoe15tpW67y9vZk0cRJFxUW8+d83kVLywGMP8NdZf2XIoCFIKdm4aSOvvPmKc58PVn6Ap9GTyedNZsL4CeQdzmPJsiUnbeLsKs//83luuu4mhg0Zxh8n/pGqqio++fwT3lr6FqB9Wep02oWakcNHtuqb4y4JqsZUw7drv+WMwWcwOG0w5jozmzZvYsk7S6gx1RATHdPqvIYEhzBp4iR27t7p/HHlTqqqq1j9xWpGjxzNyOEjqa2t5bt13/Gv1/5FVXWVs8Wrl5cXV152pXO/Tz77hC+//pK6ujpm3TaLe++6l7PPOpuGhgZWfbqqVYvA1xe/joeHBxf++UIm/3Ey+w/s55U3XmF3xu4OLYuQsn1VWKW1SdMm9eg37pvV3zBpWtf0LXKF5l+5Pb2MPbl80PPL2Pw5TRuR5uJIOk/61nQA0dY6dQ9KURRFcUsqQSmKoihuSSUoRVEUxS2ddo0khBA64B7gAJArpXT9KJyKoijKMU6rBCWEEMBWoBEwA/VCiKVSSvdriqMoinKaO90u8UUBm6WUo4GbgbXAX4UQHT+IlKIoivK7nBY1KMdlvfuBUMAXQEqZLYRYifYe/EUI4SWlXOK6KBVFUZSWenyCclzW2w6UA2FAgxDicinlCillrhBiOeANXCKEWCWl/M2zbdV61ZLdK5tqv2rswk6IKYSU/BS8rd4UhBaQkXjsxIGj9o4isC6wzeOV+5eTE5NDjU8Ndp2dYFMwI7JGtNqmIKSAQ5GHqPOqw6PJg15lvehT2AeBwCZs7EnYQ2lQKZ6NnvTP60+oSevMW+FXwY6+OxibMRavxvbN4XKi8jWz6q1sHrgZq9GKocnAuTvPPe7x2lO+/PB88sPzqfesR2/XE2AOIPlIMv71/h1evgq/CraltD0MT2qu1mn4VM9hcxkORR7C4mHB2+pN78LexFTEONd35TkEMHmbyO6VTY1vDTadDe8Gb+JK44grjcMmbORE51AUUoTVw4p3gzdJBUlEVp18OpPjnfsanxoORB/A5GOi0dCIsdFIZGUkfQv6opM6t/+cHog+QE7MsSNCnLPjHIw243E/N4MPDCayKpI6Yx0ZvTOo8a4hoC6AtNw0ZywHog9QGljK6MzRiLa7ArWpPqCe4gHFWIIs2PV2jHVGQg6GEHIoBLvOTmm/Uqp7VdPk2YTRbCQiK4KAwoDjHq89+2T8+djPfsjBEKLTo7Hr7BQMKcAUacLQYCB6dzR+ZX4AmEPN5I3Ko++6vnhYPNpdRujhl/gcyWkq8H9Syj8A5wGbgYuEEJcBSCnzgMXAdb8nOTXqG/kl+RfKgsoIrA0ktCaU0qBStvfdjuRon96QmhDii+Odf56Nnsc9Zp1XHTadDb96vzbXFwUXkdE7A4vRQnR5NAabgZyYHHIjcwE4EnaEotAiwqrDsAs76b3TkUjs2MmMz6RPYZ92/6dvb/n2Juyl0aN94/+drHwVfhVkxmdi9jITURmBd4M35YHl7Eja0eHlA/Bq9Gp1bmLKjiYRb8vRL7dTOYdFwUVkxmfSpGsiqiIKq8FKRu8MygLKnOu76hw225G0g/LAcrwbvImsjMTsZSYzPpMKvwqyYrPIjc5FJ3VEl0dj9bCyq88uqnyrTnrc4537Wu9aKv0rCagLIKIyggaPBg5FHSK7V3aHl7EzPqfNIiojWp13vdS3Wh9YG9hqvU+DNsBqdmw2Jm8TUZVR1PjUkBWrDU1WZ6wjNyqXAXkDTik5AeSPzMccYcZoNhJQGECDXwOFgwsxh5opSi2iLLkMYRcEHQ6iyauJ/OH51AUff5y89u5jqDcQkhPi/PMt9wWgMr6S6thq/Er8kDrJ4WGHkUikkBQOKiQ8K/yUkxP0/BrUQ2iX9r4VQvhJKUuEEE8BDwIXCiE8pZTLpJT5v/eFqnyraDA24NXgxdAD2hhzmwdsptanlpKgEud20RXRxJTHHO8wrTT/qj0UcYga32NzZ3GwNn9LfEk8SYVJmLxNbBm4hdyoXBKKEzB7mdHb9Aw+OJjcyFyyY7NpNDRSEFKg7Vfc/gFWT1a+yKpICkIKKAkqoU9hnzZ/cZ5q+eo9tSH+/ev8GZQ7iFqvWjanbqbB2IAde4eWD8CnwYeUw0cnpMsLz9Ne3+xPsDmYei8tnlM5hwejDgIwIG8AkVWRHKk9wp7EPeRG5RJWE9al5xDAjh2LUZvCIzU3FT+LH7VetZh8TdR71jvjGXBoACG1IfhafMmKy+Jg1EHneW/Lic59UG0Q43eNx8OufUH5NPiQE5NDhb82Kra7f06bxZXEEVJ7/NmpQ2tCSSo8dt4us5eZYFMwqYdSsXpYMXtpAyPvi9tHVEXUCWvfbZFC0uitJdeYnTF4mbxo8GvAEmTB6mOlJlr7vxSzKwbfcl88TZ4UpRVR2reUhJ/bnpm5vfsYzUaiM6KP2b/BvwFdk464X+IoSyqjeGAxNqONqtgqJJLQnNBj9mmPHp2gpJSPCiEigD8AaUKIX6SURUKIx4DngAlCiE+klG0PzXsKdFKrjDYaGqkz1qGTOho8tHl9TN4mfKzar6l9cfvYG78XL6sXcaVxxJf89lG49XbtF1ytdy02YaPapxqAJkMTFqMFX4svNr2NHX12YPI1YWw0Yhd2cmJyGHJgCLpTqECfrHwBdQHsi99HQnECwaaOmbsovCocH4sPJh8T6Ynp2n9sCYlFiejQdWj5fk0iyYvQEtSvz1F7z6EdO2Zv7csooC6g1b8mb+0j15XnEECHjviSePIi88hIzMDX4ovJx4RfnR8RVREciNFGhzf5mAg0B1LrXavF51N73GPWG+tPeO6baxLO90VocyY11zy7y+d0Z9JO7Do7PhYfEosSia5s/UV9KPIQB6MO4tXoRVR5FH2K+qCT2ue0PKCc3b13U+FXQVhNGKWBpVT5VXFW+lmnFAOAkILQnFDKk8opGFKA0WzEEmjBq9qLgMIASlK0H8T1gfV4V3pjCdB+kDQENhz/mHbRrn3qg+vZc8Ee9I16fMt8idwTiUeDB54mT+wGO3kj8rAEWdA36JE6SWm/UuK2xiHkqdUQm/XIBCWEWAEcklLeI6W8zXGf6SVgrhBiq5SyWAgxD9B1RHICCDYFE1QbRJVfFZsGtZ5e3ephxafBhwBzAP51/jQaGikNKmVf3D50dh2xZbG/6TXji+MpDi6mJLiEtcGt52CxeljpVdaLat9qSoNKMTYaSctLIzs2m7DqMAw2Az/3+xmrh5Ww6jD6He53wssMJytfemI6Xg1e9C3o267LQe3hYfMgqiKKg9EHKQzVJsDztngTYtJ+xXZk+X6tLLCMeq96jFYjUZWOqd0lp3QOGw2NSKFdVtLbtETUnJCaDE3YhK1Lz2GziKoISoJKqPGtoca3BmEXRFRFYLAZ6F3Ym8yETLLissiKy3Lu02Bo+8tNIk/p3Ff5VpEXmYfOrqPvEW1EeHf/nAopCDIF4WvxxWK0UB5YTnqfdDyyPQir0QZI9qv3I8AcgF1npySohIMxB5FCklyQTPLhZKz/3955h9lVVf3/853eJxOGJKR3SmgCSpUOAoIVpOkroILiq4LgS5GmgiCoINLLTxGQJhbAAhZEKUoJxRhAQkxCQmYmPZnJzWTK+v2x9p05mRQmYTLn3mF/nuc+k9Nu1rrnnL32Xmvttcetpqm2iZqVNUx4ewIvTXyJifMmsnDQQmYP8bXcRjeNZsSiEb2SqbqhmuVbLSdTlyFTl0GdorqhmoL2ArZ8Y0vm7zifximNNE5p7LqmvbR9vd/Xm2uKVhVRsaiCgvYCmoc2s2zkMlZXrGb8U+Opm1NHpi7TFYMa+dJIGrdtpKqpioL2Av67139pL22nurGaodOH9vpdHHAGSlIJ8AhwsaTlZnapmR0fjNRVwPmSnjGzpg1/08ZRQAG7vr4rjYMbaSlroWx1GUuqltCwRQMl7SXuFkoExt8Y/gaztppF06CmTTZQNZka9p62N411jbQVtTGoeVBXL6+4vZhCK2SHWTt0nb+4ajELahew17/34tltnmWL5Vswee5kntv6OSpXVW5Qjg3pZzKWVi+lamUVL49/mbYidz90FHbw4oQXmTJ7CiXtJRut39z6ucwcPpOalhp2eWMXWspaeG6b53hxwot88F8fpKSjpM/060l29DRqwaiuXvnG3sPi9mJkwmR0FHZAB3QU+KJwRe1FFFphv95D8OSAqROn0lnYyW6v7UbVqiqmTprKzOEzKWkvYdSCUdSsrGFRjS9aV9pWyvSx09d7/1aVrOr1vV9Qs4BXxr8CwM4zdqYm46PJXH9OxzWMY3zD+K7tV8a9QuPgRpoGNVG/vJ665jr2nL5n1/G59XN5dcyrNNU1MentSVSsruD9r3dXcX9zqzcpai+irrmOZ7Z7hm3nbIthTB8znUHNg6hsrdzAHYT24nZm7z4bKzLGPjWWshVlzN59Ngu2XkBRaxGDZw+mbFkZLVu2YBjFq4p5e+e3KWwtXO939uaayX/s7hy0VrYy48AZZAZnaCtto7i1mJEvdt+Xli1aWDFsBRMfn8jMfWZStaCKodOHMmvvWZQ0lzB4zvpdpUkGnIEys9WSHgAywFWSZGbfCUbqETz+dBQ+WbdvkTdiAKuLVjNjuK8aOnj5YDKlmbVcHUk66eyKuVS0VvSqh9FJJ6VtpYxpch/xvC3m0VnQSVlr2dpulRBwHjd/nLs9SlqpWVlD7cpaijqKulxOm6LfoOZBvF3/Ns0VzWu4gkzGwkEL6XirY5P0y7rHKldVUtxRTM3KGtQpOgs7WVW6ipKV3Y1Jn+gXWFG2gsU1i9caGW3sPSyggMpMJc0Vzb7WvBUAAB7tSURBVCyvWE756nKWVboLL5sY0t/3MFOaobOwE3WK2pW1XS6o5ZXLaSlroVOd1K6s7YqLTBvj6zplR60ALaV+X5JZcRu69+Axquljp1PUUcTOb+zMoJZB6/0Nc+05Xd99zz7DmZIM5avL13qm1+XWypRkmDV0Fru+sau7T+VyZeVuLm9+RwPVVtGGFRnqFOVLyynoLKCkuYRMXYbW6lY61UnF0goqlrrM83aeB0Dlwu7vba3yEXHxymIKOgve8Zq20jYK2wtRx9o69dTTZMzffj71/6lHnaK9vJ3ypeVULK2goK2gy33YGwaMgZJ0EdBoZjeb2SpJvwUMuEbSMjO71syOlDTCzHr/C20EUydNpbi9mKKOIhbWLKStuI36pfUMbh7M85Ofp62wjZqVNbQXtrNg0AKg+0VqLWnl6e2fBmD/l/anuKOYJZVLmFc/r6uhbilrYdqYaVSuqmRc4zhWlq3kpQkvUddcR3thuydjGEyaN2mtl+WtIZ4HMqZxDEIUtxcza+gsFlcvpq24jcpVG34pNqTfiEUj1nBNZNNuk+m7mZLMRutX21zLW0PeoqGuwb+jNIMVGMXtxVRm1pS3L/TLMmeoj56GLR62Ro96+pjpG30PxzaMZdr4abw6+lUW1C7oumZcwziAfr+HVauqKG4vpq2ojRcmv0B5a3nX7zuoeRDz6ucxf/B8qjJVNJc3s6xqGUXtRYyf3z2CyOq4x/Q9qM5Uc8gLh3QdW9e9X1S9yNPz5S7SxrrGrmSMZFJKX+nY18/pC5NeoLStlKpMVZeLD4Ohiz31fuZWM1latZRBLYMwWVdS1LDFw9aS7bVRrzFsyTAGtQxiRacb2+ljpncd31AHKEtpcymFqwvpKOlg9p6zKW4pZvkIT3KoWFzBkjFLWDZiGWUrylhVvYrM4AwFbQVs+Z/uVXZnHOBGe/wT4ylfXv6O1zRv2UzjlEYqF1ZS0OYuPoDKBZUUrV7TjCwatwiTUT+zHgwKVxeycOJCWupb6CjtoLRl/VmvPcl7AxVSyXcBJgBbS2oxs7vMLCPpcbzm3jWSSszs+2Y2b3PJUpWp6nLVlLaVMrZhLOPf9hd72KJhzNtyHk2DmjAZVZkqRjeO7mrc1kWmLMP8+vld26uLVzO/fj51K+oY1ziOoo4iijuKaaxrxGTUrKxh3PxxDFk2ZI3vaS1qZebwmez45o5dAeft/7s9r416jUU1ixi2eBgjFr6z73tD+m0K76TfVku2YvVbq5lXP4/GukYKOwsZvHwwE+dNXCPFt6/0A3eBNQz2BntM45oZT5tyD7dashVtc9qYM3QODYMbKF9dzuS5k7tiF/19Dws7C3nfG+9jxogZrKhYwfKK5VS0VjBy4UiGLRnmDXpRG/O3mI9M1C+tZ9K8Sb1qONdHa0lr12o/i2oXeQMfSBqoXH1ORy4cSUNdAw2DG5CJ2uZaxs0f15XRN2TpEDKlGRbWLuyaVzZi4Yi1kmcW1ITEiH97YkR1ppqJcycye6jHoCbOnUh1pvod5SnoKGD0P0fTtE0Tq2pXkanNUNJSQt3sOmrfrqV5y+auDDp1iqqGKoa+OpTSles3DKUtpRu8pnxZORWLKlg5eCUdxR3uSpw5eA2jBz7S6pkYMXLqSOZvP5/mIc3Uzq2lbnbvk1PyesHCUCHiaeBu4HHgFGA08GszuyuccykwH/iDmb3ZV/93XLAwv4kLFg4MBrqO7/UFC/N2BBVGTlOB183sx2HfhcBlwAGS9gFmAScBu2/OkVMkEolE+p68NVDAQcBbZnYsgKTzgFagApgBbAMcChwZjVMkEonkH/lsoBqA3SVdAYwBdgQeBiYDfzOzyyVVmllLmkJGIpFIZNPI21p8ZjYN+F9gEDDNzKaY2blAI5BNvVp/8alIJBKJ5DT5PILCzO4H7s9uS/oisC9wQTg+oBMZIpFIZCCT1wYqi6RRwOnAycARZvZGyiJFIpFI5F2Sty6+HjThaeZ7m9nUtIWJRCKRyLtnQIygzKwVeCxtOSKRSCTSdwyUEVQkEolEBhjRQEUikUgkJ4kGKhKJRCI5STRQkUgkEslJooGKRCKRSE4SDVQkEolEcpK8Xm4jEolEIgOXOIKKRCKRSE4SDVQkEolEcpJooCKRSCSSk0QDFYlEIpGcJBqoSCQSieQk0UBFIpFIJCeJBioSiUQiOUk0UJFIJBLJSaKBikRykLBKdCTyniYaqEgkx5C0PXCrpM+kLcvGIKlKUknackQGDtFARSK5xyrgX8DHJB2XtjDvhJwtgL8Ax0gqTVum/kBSoaTCtOUYyEQDNcCQpLRl6C+Sug4kvc1sBnAD8BpwfK4bKXMWAU8A3wSOGshGStIoScVm1mFmHZIKJB0t6ei0Zetrgm7nSjpG0vv7/f+PxWIHDpIKwwtTB9QCTcAqM+uUJBtANzuhay3QClSa2aKBpKekCcApwPbAPWZ2b8oirYWkMcD5ZnZa2L4EOAE4H3jYzFpTFK/PkVQM3AVMNrP3hX0zgAZgB+BPwDmhk5HXhE7fC0Ab0AJkgJ+Z2X39JUMcQQ0QQsPcIWlH4Cng58CjwDmSRg2URhu8V5fQ9VHgQeB3kj6Rz3pKWuN9NLM3gZ8C04ATcnQktSVwiKSfAZjZJfiz910G4EjKzNqAbwHzJD0eRk0Pmdk+wHhgCHClpMlpytlHDAOeMbPdgdNwF+7p/fkcRgM1QDAzkzQM+AVwi5ntBfwM+CrwvlSF62PCiHAE8Bu8MfwccDvwC0k7pSrcJhJGhJ2SJks6W9KVksaZ2RvALXhM6nhJx6Ysak+mAscDUyTdDe8JIzUdOAtYDvwYWBj2LwKOxI3UpZK2TU3Id0Fw610InANUAoTn8EHgd8DnJZ3UH7JEAzWwqAFeMbNrwvYXgEfN7CFJI4J7Iq9JjDJ2BKaZ2bXAAtytdLeZvSxpUGoCbgI9Rr9PAh/A9XtC0pFmNhu4GXgF+Iqkg1MUdw3MrBN4FvgysM16jNSHJZWlJmQfkUyIMLPXgbPxjsP/JPYvA44AtgXOz7d3Lrj1XgQOAA7EOx7HApjZLOAe/Bk9WlLNZhfIzOInTz90xxBrgSpgb2AuMAH4B3BvOF6GuyXGpC1zH+haHf4eBPwaGBxeqJ+H/TV4ozgkbZk3Ur8heEP/1cS+5cBs4ONhezzuainIAXkLemwXAnvgI6q7E/svxEcYH0lb5nepb1FWb+BiYNewPQn4M+7+KkmcXw2MTVvujdRR+Ajw+rA9BLgWN0qfSpw3CtiiP2SKI6g8JyRE/AIYb2ZPAX/AG4k5Zpb1Fd+KNx5vpSPlu8fMLIyMHpO0FzAH76VOA543sxPCqdcDEwlul1ymR8ypGPijmV0rqVTSVOAnwH3AdZKOM7OZZnazuSswtXdXUlGQQZJ2kjQJ6DSzfwBfArZLjKS+A1wF/Dsted8tYYTbHn7zF4FtgEXQ5fo6DWjH46ClYf8K8xFHPnER8EtgnKQqM2vCO3uLgY9m5+WZ2Vvm7szNTsziGwBI+hXewztK0v7A/+KjpkfwofokYHczawsJBp3pSbvphLk21wB/MrM7JH0Sj7P9H55htB8eb9s16JqzGX2JLMSxQK25a7LWzJZJuhGoM7PjJB0I3AY8YWYnpykzdCWoZA3kU3ivezLwI+ABM5suaXc8NtNoZkelKG6fIukufOR4QtieCFSY2SuSRuIdxQX5rLOk63HX3snAVDNbLWko3sloA84wsxX9JU8cQeUJWf934m9y3s9FQIeknczsr2H7L7hLaCrwgdBgF+WTcVKPSZCh1/YSHoAeZGYPAp8CxgC74qOqXRK65qpxSsacngU+JKk6GKdiYCs8OxE8tnYJngiSOtmRE/A3fJS+B34PjgO+Hp7BfwJnAjUhmSXvCfelDbhLUnHIWvx/wMOS7jCzucCJwOlpyrkpSLpP0pUAZvZl/B27BthVPt+rEY+3fbM/jRNBoPjJ8Q9wMN5IZUe8o/GGa0LYrgF+D1y7ge8oTFuPXup6CrB/YnsCcGiPc36Dx9QKw/Za8ZC09ViHXkU9tocA04HT13HuxbiL8q/ATBLxj7T1CHJ8ELgpsX0TMAs3qrcDO4f9Zeu5Xmnr0Asd13qGcHfXMuBefFLyaOBjeGZbRdoyb6KeJcBngBnABYn92WSIfYHitOSLI6gcJ/RWhwH3WXhygMOAzwK/l3QKYHgW1cHBJbQWZtbRH/L2AcPwnmqWI4BHJF0r6Qth32+BcYlz1qgikWu6hnkjn+sRNxqPu4NuCOckdbgdN9S/wieEtmfT0PtN6ATriHe9jE9WRdKPgD3NbCzwR+CTwHGSysxs1bq+z8wsxNkOC98xXtKJm02BjSSMvjtCjG1PSQcAmNn5+PN4AXCQmc3B3ZsVKYr7rjCz1cADwLn4M3ph2H88sBRPckmtnFNRWv9x5J1JxFCyjcFo3JXyAzzx4WTg83gv7i3gn8BOuHsvr8jqambfDdvbAfVm9mNJfwU+jk8SPBzP3jsWeAa4MdcM0jpox/35nYkYYBtQIZ/r9F/cyJqk4XhM6qHsxdl4VRqCJ2JlwkcMq8ysUdIzksrxjK6TwultwB3A1eszTgnOAnaTNAU4Dzhj82iwcYT7k02IeAooB6olLQYONk9EQtJ28km6ZwAHmtnK9KTeeCRdhMcIbzazVZJ+i3d0r5G0zMyuNbMjJY3oxb3cfKQ9xIyftT+swwWC92I+BTwOXIqX9gFPgDgMH4534vOeUtdhI3Rdp9sKT/ToJLj3gv5V+Hygm8Kxn6Qt/0bqOgk4Fe9xb4H7+r9Pwv2HB9pvSFvWnvcIHzU9BcwDDg/7a8P2T/EafE0Et3MvvrMW73itcR/X9zz0s74K79lPE/ta8I7fsLB9VHjndkpb3k3QbVe8I3E38OnEscG4W7kTODttWc0sGqhc/YRG7Lbw70n4iKkKHzU9CHwva6QS1xxBDsZfeqFrOT7ZUUHX74X9l+DZeYf0OH8YXr2gqL9lfZd6fhJYQZjrhNduawwN9Y34xNZXSNHnn5C1MPHvc/CU9y3wYHkHcHw4tk9ozB8izA3q7XcDl4UG8XYS82xS0jfZSRgJPEx37O823DvxDJ4cMjzsr0n7Pm2kjgX4/Miv4PUdf4h3iJJG6lI8XNCrjsZmlzltAeKnxw2BEeHv+PDiP40HzM8M+4vx+MSDwBVA+Tq+I6+MVGi4XwpGdwXwhcSxbwUjdfB6rs1ZI7Wu+4Ant8wGzkrc53PxNN7zE41ianolDIjwjMJzcDdW9vipwUgdF7bLgNJefG9J8m/4dzVwHXA/cExi/779qG82+agITw6oAyaFfZcBL4d/fwIfXfwtD98xhXfsvsS+Sjxb73bcK3EuPtF/RNryZj8xBpVDSNoXr7f2f2Y2U9L38Ayh183savBilZLuDJccBvxA0pmWqBptuR+TWQMzezDMKbkc+I2Z3Zo4dnHIH/iVpOPN7JEe17b3r7S9Q90FbScD+wP1uMvo55JagasldQLXmNkVPa4tTEuvhNxZt56A4bgb7y8AZnZLkP3nktrM0/3X930fBqaY2ZXmc2p2AS6UVIVnAj4o6VI88eAYeXX6PYGhkv5uoSXdjPom43vnBf2WAEtCjG0HfJ4d+HSGTwIv5ds7hldeecvMjgWQdB6+CkAFnsG3DXAocKSZzUtNyh5EA5VbNAE/NLMVYd7FK8A3gL0lPQ4cYWaZYKR+BpTigevV6Yn87gjzLNrw5QpuBiZJOhO4P/uiBCNVjLuXHln/t+UGicmsE/F5Tn/AG7eTJV1iZndLKsLTlsslXWtmzdnr02r8QqJKNlPwEtz99gPgw8C5khrMLDtf5jZJbXiq/Hq/D49rfENSi5ldj7uU7sXdTTfIK+1fI+nbuOvpKDw+dcjmME6JxKNkAkgB7qJcgo+YsudlQnLERSGT7wxge/OklnyjAdhd0hX4s7gj7sacDPzNzC6XVGlmLWkKuRZpD+HiZ+0P3mO9GU9lBZ8L9Bhe8yvrjvg8Xm0ge03Ozy3poWPWjVTcY//pePD5a4R6X8CJ+aYjPs/peuAriX3n4m6WDybu4YO5oFdSBrwjMA3YI2xX49mjM9jI4DneiToO+C8+sfWbiWPH4vOnzsg+E3iPvsvlthn1/Qwe+yzADePvcPfdx5P/N3AIcCU+9y6vEiLWofOncFde8h7cB5zS8xnIlU/qAsRPuBGJ7CW8V/NgeKEPDvuyRmoa7idvIM/84An9ssZpu/CCZFOTs8e/BPwd780+hvv8C8KxnHuJet7D0ChfjffIv9bjnFuAZ9dxbWp6JRpjATvjGXlv4su2ZM+pDA3cYhIFbd/he7OGphhPapmLl2xKnnN8+L++SSLph82YzYd7HRbgsa8ZwB1h/w1Bv/HruKZkc8mT4n3/IjCfEG/LxU+cqJsDqHstoHGS9jSz/+ABcwH/I+kg88XrjsF7cvOAUdbtnsgrgtxTcMPTADwHHCDp/nD8RjxrsQDPcjvI8mBV4CDjaNx1fi+e9XWM1lwX6GfA2+qxDENaemXjXeE5ehqvw3Yv7tobIemCIF8LYS2g8PedvldmZvL6ifVmdg8ey9kluHAJ33sPnjm2C7AysX+zTUo2n2B7ED7ZvRNP+sDMTsfn2D0vaXzQIxsGaVvHV+Ul8iXrL8fduB82L3ibk8RisSmTiFfshI8WfgjcbGZLJW2PT2gswpda/mOPa1ObwPluCAHyB4GnzexbYd9UvPf+ZzM7JOzLxqeys/tzMiEii7x24C1Ai5l9VdJB+CTWCjwZ4u+S7sMn4h6WoqhrEGJFL+LJONkgehE+F20X4A3zquT0ppOQiO0MwbMwW4DLzWyRvGLEpcB1ZvaDdcnSH8Za0ijclXkw/t5dHQwX8mK9p+HLZczZ3LL0N/KK6/sBb4aOb84SkyRSJhinUXgtvW+b2fXyFS1LzGyavPTIt/CF6laYL2mQvTZvjFOy4TGzZkk3AFNDo/48njF2HPCapKfNbK+scQrX5KxxyuoWGuV/AocDmNmfg35fAv4s6SFgFfDR5HWpCd7NujK8OvBg+hvAnpLONrPvr0veHokH2SzA7XG33ba4S61F0k3mCSKGF/ytynZQsvTX72FmbwFfk7Q17pUolHSFmb2Nj96H4enzAw7zjN/H0pajN0QDlRvsALwQjFMpcCdQIGkFXpn8h3g5o2dTlHGTSbh7huCFYO83s9+Envt5wCwzO0nSlvikyJp8GB0mDFOyUb0Tz3i7yMy+bWaPSVqJz+VaDVxlZq2hA5Ir2ZfryvB6CI8RTsPXCHp0/ZdTgBu0bIdrOJ4BeAnwdXwUuR/4cg7mqfbl+Eq7qRppM3tdXrLoAaBUnj7/EWA3M5ufllwRJ+/iFwOB0DAneRXYStLvgT/htdt+CWyNV4b+t5ldZikvVLcpBENjkrbCq3SfLeko6Ootd+BLhZTj2VKrzey4XIyvJeUJLkeTNFTSXyTtKq9blsGz9bZWWGrCzJ7EDZfh+u+UQ8YJM5uGu/MGAdPMbIqZnYdPEF9oZreFEcdaSDoUX6hPid+nDq89eJ2ZzTezy/Hn+Rjgy5KGmNntwNHhN+z5PvQrQf9P4nGmanwuUDROOUCMQfUzCf/8ULx0UbWZvSTp/fhIaqmZ/TKc+yi+CNxtafc03w0hvnYr8C+8HNMr+KTVeyQdA3wVXzKkk+61q3JSX0llwHZmNjW4seqAT+MlmhbhFRFexZMhzjKzxxPXHoy7+5rw7L6cMVI9kfRFvEOx74aC6CGZoMx8ocLsSPkDeBWU/czs+cS5T+Bus1/ik3SX5dJ9DnE3JV3LkZSxHEglfK986O4Q7Ai8ho+WGvBSI/XZc3DDla3LlrOlfHqpcwVex+zCsD0Cn+P1azyDCDyFfl+6089zUudwb87G5zJ9Gh/pnhyO7YbXSXwLjxk24anyW/T4jgMItdxy8YNXJ788PJe79OY3CX+H427A6rB9DV5cdpvEuTfhqd1P4hNeU9c3fnL7E0dQ/YykOtw/fyPeyx6GB2lfNbNPhaDtl4AP4D3QtnyIx6yPMOJ4GDjfzJ4L++pxn/9g4GIz+3Xi/JzWVZ5G/l18TtCPzeysHsezxvYoPO7yUTN7MpmRmMtsTIaXujNQR+PP8aX4HLDD8HlTl+G/w53A+/Dsxd0lPYm7AL+6GVWJDAByysc/kEn42evxuMs9ZrbSzGbiK5QeIOk0M3sdTxTYx7qXLs/ZBrsnPeNG5mvJFOAjj+y+hXij1YkHyvdIHMtJXUM2HuZpx634KGonSXuH48WhwX7TzH5iZp/ADXO2vlvOGyfwDC8ze2wjjNMEwkgR71itwEs7NZvZabibsBCf67Z/uPx1fKQZiWyQaKA2M9mGDV9aGXymehMeMM5mgi3Fl1zYAjxoa92L2+VsenVP1D3heJSk3bPJEHg17MGSrkucviu+Mu5oIGdWU10XybihpB3wKvJH4Onx35O0j5m1Bd2HJi49D6gMI8YBhXVPj/gQcK2Z/T4YtTNwI/WYvLbbrWZ2tpl907y23TfwNPucr6kYSZ9ooDYjwfhk54TcJ+lu4Nt4CZzJkg63bh/rRHrcD0tpie9NIaHrjngFhbOAH0v6CZ4ddjXwAUkzJP0dX4jwAjwetYekytSE3wDqnteT1es2PIZyFB5DfAK4UtLe8gK+dyYu/xiePJGTo8JNJWTsFeNZl9cBy8P+IjObgRupxcCLwcWLpFpJJ+Gu0Q+Z2aupCB/JK2IMajORcIGMwN0bN+JpxsPx0ipP4TXItsNrlE3Gg9J5M2LqibxCRDbz8Br5vKcGvNjrPcHN+TFgKfB38xI738GXqT/acjSrTdI43ChdY2ZXSfoSPjq6FpiKx5xOwEtQHWrd1S9OBF4xs3+lI3nfkMjOy44kS8yXztgVXyZjgpnt2OPcbfCkkfOzbtswkjQzW5SaMpG8IhqozYiksXhPe4yZnR32VeF++UPxRm5rvAd6R2iwczpJYF0kGqUh+ATPPcP2U8AcMzte0iRLpCuHc0/F67N90MxeTkf6DROM6ufwsjcXhO2n6K68fReecZkBFodOSV4kRPQGSYfjc4MeMbOVwRtwGd7JWIy76r4AbIkXNraeqePKgzJVkdwkuvg2L8OBHwH7JWITLfiaOCsICxGa2e35aJwS8bXS8HcZPor4rKTn8Uyw48OxSyUdmLh8AjCUHDZO0DWZ+Fd47UDwArezzWx3PNj/DVyHhYm44YAwToFWvJLJwWHO02N47O0/eMLPHfhiho3AH9Y1rykap8imEkdQm5kwafEBPFHgYTNrCb3wl4BTzeyfqQq4iSRGTTvgy5UvxrO5dgD2AhZYd9HXO/ClzfdPGmBJZSHLLy+QtB3wAzM7PGx/H1+24ap8ihduLPKVnm8Nn6Fm9o2wvwKf77Q1/nzfAPzVzL6elqyRgUU0UP2ApP3w4PoD+JIGH8XnheyeTyOmLIn42nC8PuAtwEh8RF6KTzSeg09IbsQTQPYcAHO6JuMTrC8GpgBjgb3Cb5G3evWG8Az/Bl8/6cNm1hg6WrvhXoKT8eUy3h7Iv0Okf4kGqp+QtA/wR+Av+OjpgmTgOV3pNp4QXzscX5fq/ODuOxGf01WEu30G4y6iR0NwPe9jEZI+i1ddXwZ8JhjdgoE8gsoS5qvdx9regJfxihovhPPy8pmO5B7RQPUjkvbCV8k930K9vXwl6PIknmZ9tPlaPwX4UtqH4r3pr2RdeAOp0VKiEvlAMLobQ8IbcD+edv8RfM2ovPQGRHKbmCTRj5jZ0/hCaN+RdIKkkne6JlcJuuyJu7n2lVQRRhF34karCR89Zc8fMI1XwjjpvWScAMzsCdyd9zXgTPw+7xlGyIUbvDgS2UjiCCoF5FWtLwcONLMVacvzbgg96lvxJSZ+H6oF9FzAbsC7v95rSDoATy8/Mbiq31MjyUj/EA1USoQRx8q05egLgpG6CS8W+gvzFTsjA5xEJmfOLJkRGVhEAxXpE+QL151iZselLUuk/4jGKbI5iQYq0mfExioSifQlMUki0mdk3T1pyxGJRAYGcQQViUQikZwkjqAikUgkkpNEAxWJRCKRnCQaqEgkEonkJNFARSKRSCQniQYqEskTJDUnPp2SMontE9OWLxLpa2IWXySSh0iaBXzezP60jmOx7FBkQBBHUJFIniNpf0lzJZ0jqQH4iaSTJD3Z4zyTNDH8u1TS9yXNkdQo6SZJ5akoEImsh2igIpGBwTB8/a0xwKm9OP8KYDKwM76g5Ajgos0mXSSyCUQDFYkMDDqBi82s1cwyGzoxVPs4FTjTzBaHivrfxRdijERyhqK0BYhEIn3CguzikL1gS6ACeCFRmUpAXM8pklNEAxWJDAx6Zju14EYIAEnDEscWAhlgipnN6wfZIpFNIrr4IpGBycvAFEk7SyoDLskeCAtI3gpcLWkIgKQRkj6UiqSRyHqIBioSGYCY2X+AbwN/At4AnuxxyjnADOAfkpaH87buVyEjkXcgzoOKRCKRSE4SR1CRSCQSyUmigYpEIpFIThINVCQSiURykmigIpFIJJKTRAMViUQikZwkGqhIJBKJ5CTRQEUikUgkJ4kGKhKJRCI5yf8HezFBARZ0EkwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise confusion matrix for best hyperparameter configuration\n",
    "print(\"Confusion matrix:\")\n",
    "plotting.plot_confusion_matrix(cm, classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Model Training\n",
    "Here, the chosen classifier is re-trained over the entire training data set using a set of previously identified, optimal hyperparameters.\n",
    "\n",
    "### Create Training Data\n",
    "In contrast to the cross-validation procedure, we do not perform a split into train/validation sets; all data is used for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading plate S_1_5\n",
      "\n",
      "Labelled samples for antibody IgG for dilution 100:\n",
      "positive: 245\n",
      "negative: 524\n",
      "atypical: 463\n",
      "small bright: 122\n",
      "trash: 127\n",
      "\n",
      "Normalising for dilution 100\n",
      "Only 4 of 6 positive control wells were transferred -- still sufficient. Continuing normalisation.\n",
      "Normalisation based on negative controls only\n",
      "\n",
      "Reading plate S_1_3\n",
      "\n",
      "Labelled samples for antibody IgG for dilution 100:\n",
      "positive: 178\n",
      "negative: 366\n",
      "atypical: 329\n",
      "small bright: 105\n",
      "trash: 102\n",
      "\n",
      "Normalising for dilution 100\n",
      "Only 2 of 6 positive control wells were transferred -- still sufficient. Continuing normalisation.\n",
      "Only 3 of 10 negative control wells were transferred -- still sufficient. Continuing normalisation.\n",
      "Normalisation based on negative controls only\n",
      "\n",
      "Reading plate S_1_1\n",
      "\n",
      "Labelled samples for antibody IgG for dilution 100:\n",
      "positive: 373\n",
      "negative: 836\n",
      "atypical: 166\n",
      "small bright: 160\n",
      "trash: 140\n",
      "\n",
      "Normalising for dilution 100\n",
      "Only 7 of 10 negative control wells were transferred -- still sufficient. Continuing normalisation.\n",
      "Normalisation based on negative controls only\n",
      "\n",
      "Reading plate S_2_4\n",
      "\n",
      "Labelled samples for antibody IgG for dilution 100:\n",
      "positive: 286\n",
      "negative: 302\n",
      "atypical: 142\n",
      "small bright: 95\n",
      "trash: 145\n",
      "\n",
      "Normalising for dilution 100\n",
      "Normalisation based on negative controls only\n",
      "\n",
      "Exporting training data for dilution 100 and antibody IgG\n",
      "\n"
     ]
    }
   ],
   "source": [
    "construct_data.construct_training_data(path_data_raw/\"data_{}_cv/\".format(protein),  path_tmp/\"data_{}_training/\".format(protein), classes, df_controls, df_meta, normalisation_type, antibody, dilution_antibody)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training data contains 5206 samples.\n",
      "\n",
      "Features:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>CELL.AREA</th>\n",
       "      <th>CELL.ELLIPSE-ECCENTRICITY</th>\n",
       "      <th>CELL.ELLIPSE-MAJOR.AXIS.LENGTH</th>\n",
       "      <th>CELL.ELLIPSE-MINOR.AXIS.LENGTH</th>\n",
       "      <th>CELL.ENCLOSING.CIRCLE.RADIUS</th>\n",
       "      <th>CELL.EXTENT</th>\n",
       "      <th>CELL.FORM.FACTOR</th>\n",
       "      <th>CELL.HARALICK.TEXTURE-1.Alexa.IgG.0.3</th>\n",
       "      <th>CELL.HARALICK.TEXTURE-1.DAPI.0.3</th>\n",
       "      <th>CELL.HARALICK.TEXTURE-10.Alexa.IgG.0.3</th>\n",
       "      <th>CELL.HARALICK.TEXTURE-10.DAPI.0.3</th>\n",
       "      <th>CELL.HARALICK.TEXTURE-11.Alexa.IgG.0.3</th>\n",
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       "      <th>CELL.INTENSITY-MEAN.Alexa.IgG</th>\n",
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       "      <th>CELL.INTENSITY-MIN.DAPI</th>\n",
       "      <th>CELL.INTENSITY-STDDEV.Alexa.IgG</th>\n",
       "      <th>CELL.INTENSITY-STDDEV.DAPI</th>\n",
       "      <th>CELL.NUMBER.OF.COMPONENTS</th>\n",
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       "      <th>CELL.REAL.DIAMETER</th>\n",
       "      <th>CELL.REAL.PERIMETER</th>\n",
       "      <th>CELL.SOLIDITY</th>\n",
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       "      <th>DONUT.AREA</th>\n",
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       "      <th>DONUT.ELLIPSE-MAJOR.AXIS.LENGTH</th>\n",
       "      <th>DONUT.ELLIPSE-MINOR.AXIS.LENGTH</th>\n",
       "      <th>DONUT.ENCLOSING.CIRCLE.RADIUS</th>\n",
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       "      <th>DONUT.INTENSITY-MEDIAN.Alexa.IgG</th>\n",
       "      <th>DONUT.INTENSITY-MEDIAN.DAPI</th>\n",
       "      <th>DONUT.INTENSITY-MIN.Alexa.IgG</th>\n",
       "      <th>DONUT.INTENSITY-MIN.DAPI</th>\n",
       "      <th>DONUT.INTENSITY-STDDEV.Alexa.IgG</th>\n",
       "      <th>DONUT.INTENSITY-STDDEV.DAPI</th>\n",
       "      <th>DONUT.NUMBER.OF.COMPONENTS</th>\n",
       "      <th>DONUT.PERIMETER</th>\n",
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       "      <th>DONUT.SOLIDITY</th>\n",
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       "      <th>NUCLEUS.AREA</th>\n",
       "      <th>NUCLEUS.ELLIPSE-ECCENTRICITY</th>\n",
       "      <th>NUCLEUS.ELLIPSE-MAJOR.AXIS.LENGTH</th>\n",
       "      <th>NUCLEUS.ELLIPSE-MINOR.AXIS.LENGTH</th>\n",
       "      <th>NUCLEUS.ENCLOSING.CIRCLE.RADIUS</th>\n",
       "      <th>NUCLEUS.EXTENT</th>\n",
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       "      <th>NUCLEUS.SOLIDITY</th>\n",
       "      <th>NUCLEUS.VOLUMETRY</th>\n",
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       "      <td>0.382041</td>\n",
       "      <td>-1.281490</td>\n",
       "      <td>0.104785</td>\n",
       "      <td>0.809321</td>\n",
       "      <td>0.128927</td>\n",
       "      <td>0.079495</td>\n",
       "      <td>0.079506</td>\n",
       "      <td>-0.183009</td>\n",
       "      <td>-0.620922</td>\n",
       "      <td>-0.694372</td>\n",
       "      <td>-0.354957</td>\n",
       "      <td>0.349962</td>\n",
       "      <td>-0.365502</td>\n",
       "      <td>-0.183009</td>\n",
       "      <td>-0.620922</td>\n",
       "      <td>0.528046</td>\n",
       "      <td>0.375957</td>\n",
       "      <td>0.612887</td>\n",
       "      <td>0.375366</td>\n",
       "      <td>-0.069812</td>\n",
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       "      <td>-0.483771</td>\n",
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       "      <td>-0.792699</td>\n",
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       "      <td>-0.731010</td>\n",
       "      <td>-0.013592</td>\n",
       "      <td>-0.561294</td>\n",
       "      <td>0.153468</td>\n",
       "      <td>-0.474185</td>\n",
       "      <td>-0.405858</td>\n",
       "      <td>-0.231155</td>\n",
       "      <td>-0.005088</td>\n",
       "      <td>-0.394347</td>\n",
       "      <td>-0.230794</td>\n",
       "      <td>0.440921</td>\n",
       "      <td>0.169400</td>\n",
       "      <td>-0.512330</td>\n",
       "      <td>-2.109734</td>\n",
       "      <td>-0.739316</td>\n",
       "      <td>0.042616</td>\n",
       "      <td>-0.650718</td>\n",
       "      <td>0.769175</td>\n",
       "      <td>0.569797</td>\n",
       "      <td>-0.486401</td>\n",
       "      <td>-0.806256</td>\n",
       "      <td>-0.301063</td>\n",
       "      <td>-0.625305</td>\n",
       "      <td>0.673153</td>\n",
       "      <td>0.623888</td>\n",
       "      <td>-0.833125</td>\n",
       "      <td>-0.711245</td>\n",
       "      <td>0.719859</td>\n",
       "      <td>-0.454896</td>\n",
       "      <td>0.168012</td>\n",
       "      <td>0.167998</td>\n",
       "      <td>-0.458703</td>\n",
       "      <td>-0.579206</td>\n",
       "      <td>-0.776488</td>\n",
       "      <td>-0.665511</td>\n",
       "      <td>-0.102628</td>\n",
       "      <td>-0.505809</td>\n",
       "      <td>-0.458703</td>\n",
       "      <td>-0.579206</td>\n",
       "      <td>0.677363</td>\n",
       "      <td>0.656736</td>\n",
       "      <td>0.730938</td>\n",
       "      <td>0.694672</td>\n",
       "      <td>-0.077030</td>\n",
       "      <td>-0.028218</td>\n",
       "      <td>-0.528191</td>\n",
       "      <td>0.034914</td>\n",
       "      <td>-0.768305</td>\n",
       "      <td>0.085324</td>\n",
       "      <td>-0.748684</td>\n",
       "      <td>0.055702</td>\n",
       "      <td>-0.916711</td>\n",
       "      <td>0.153092</td>\n",
       "      <td>-0.441878</td>\n",
       "      <td>0.179698</td>\n",
       "      <td>-0.371609</td>\n",
       "      <td>-0.650718</td>\n",
       "      <td>-0.400100</td>\n",
       "      <td>0.144706</td>\n",
       "      <td>-0.512330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-1.759573</td>\n",
       "      <td>-1.683885</td>\n",
       "      <td>-2.694062</td>\n",
       "      <td>-1.335927</td>\n",
       "      <td>-2.630149</td>\n",
       "      <td>-0.112617</td>\n",
       "      <td>0.227398</td>\n",
       "      <td>-0.558243</td>\n",
       "      <td>-1.501406</td>\n",
       "      <td>-1.205433</td>\n",
       "      <td>-1.309588</td>\n",
       "      <td>1.749594</td>\n",
       "      <td>1.832375</td>\n",
       "      <td>2.972933</td>\n",
       "      <td>1.258896</td>\n",
       "      <td>-2.435827</td>\n",
       "      <td>-1.548177</td>\n",
       "      <td>-1.689016</td>\n",
       "      <td>-1.688861</td>\n",
       "      <td>-1.155547</td>\n",
       "      <td>-1.239074</td>\n",
       "      <td>-1.545878</td>\n",
       "      <td>-1.814191</td>\n",
       "      <td>-2.239461</td>\n",
       "      <td>-1.990218</td>\n",
       "      <td>-1.155547</td>\n",
       "      <td>-1.239074</td>\n",
       "      <td>1.607644</td>\n",
       "      <td>1.798212</td>\n",
       "      <td>1.511898</td>\n",
       "      <td>1.779902</td>\n",
       "      <td>-0.607107</td>\n",
       "      <td>-0.851948</td>\n",
       "      <td>2.479432</td>\n",
       "      <td>0.213806</td>\n",
       "      <td>-1.969813</td>\n",
       "      <td>-1.001753</td>\n",
       "      <td>-2.130895</td>\n",
       "      <td>-0.559635</td>\n",
       "      <td>-1.238558</td>\n",
       "      <td>-0.844558</td>\n",
       "      <td>-0.308867</td>\n",
       "      <td>-0.202418</td>\n",
       "      <td>-0.021123</td>\n",
       "      <td>-2.197175</td>\n",
       "      <td>-2.630149</td>\n",
       "      <td>-2.157767</td>\n",
       "      <td>-0.083582</td>\n",
       "      <td>-1.759573</td>\n",
       "      <td>-1.483840</td>\n",
       "      <td>-1.688239</td>\n",
       "      <td>-2.631811</td>\n",
       "      <td>-1.349749</td>\n",
       "      <td>-2.615409</td>\n",
       "      <td>1.519351</td>\n",
       "      <td>0.365333</td>\n",
       "      <td>-0.818529</td>\n",
       "      <td>-1.530563</td>\n",
       "      <td>-1.167835</td>\n",
       "      <td>-1.037863</td>\n",
       "      <td>1.731461</td>\n",
       "      <td>1.844876</td>\n",
       "      <td>3.253698</td>\n",
       "      <td>-0.671381</td>\n",
       "      <td>-2.166637</td>\n",
       "      <td>-1.059504</td>\n",
       "      <td>-1.680905</td>\n",
       "      <td>-1.680742</td>\n",
       "      <td>-1.135792</td>\n",
       "      <td>-1.335312</td>\n",
       "      <td>-1.533596</td>\n",
       "      <td>-1.850797</td>\n",
       "      <td>-2.097149</td>\n",
       "      <td>-1.904264</td>\n",
       "      <td>-1.135792</td>\n",
       "      <td>-1.335312</td>\n",
       "      <td>1.636545</td>\n",
       "      <td>1.838067</td>\n",
       "      <td>1.543008</td>\n",
       "      <td>1.832052</td>\n",
       "      <td>-0.025071</td>\n",
       "      <td>-0.724559</td>\n",
       "      <td>3.755786</td>\n",
       "      <td>-0.540884</td>\n",
       "      <td>-1.730999</td>\n",
       "      <td>-0.638448</td>\n",
       "      <td>-2.061716</td>\n",
       "      <td>-0.742395</td>\n",
       "      <td>-1.227584</td>\n",
       "      <td>-0.844570</td>\n",
       "      <td>1.121207</td>\n",
       "      <td>-0.372616</td>\n",
       "      <td>-0.224417</td>\n",
       "      <td>-2.007071</td>\n",
       "      <td>-2.615409</td>\n",
       "      <td>-2.122817</td>\n",
       "      <td>0.319898</td>\n",
       "      <td>-1.483840</td>\n",
       "      <td>-2.066931</td>\n",
       "      <td>1.388311</td>\n",
       "      <td>-0.718400</td>\n",
       "      <td>-2.257296</td>\n",
       "      <td>-2.352397</td>\n",
       "      <td>-2.222310</td>\n",
       "      <td>-1.121884</td>\n",
       "      <td>-0.969952</td>\n",
       "      <td>-1.363539</td>\n",
       "      <td>-1.210756</td>\n",
       "      <td>-1.172249</td>\n",
       "      <td>1.959057</td>\n",
       "      <td>1.988491</td>\n",
       "      <td>1.536950</td>\n",
       "      <td>-1.671143</td>\n",
       "      <td>-2.324162</td>\n",
       "      <td>-1.276871</td>\n",
       "      <td>-1.774060</td>\n",
       "      <td>-1.774174</td>\n",
       "      <td>-0.969748</td>\n",
       "      <td>-0.974252</td>\n",
       "      <td>-1.887395</td>\n",
       "      <td>-2.005525</td>\n",
       "      <td>-2.025338</td>\n",
       "      <td>-1.985051</td>\n",
       "      <td>-0.969748</td>\n",
       "      <td>-0.974252</td>\n",
       "      <td>1.937810</td>\n",
       "      <td>1.970712</td>\n",
       "      <td>1.874723</td>\n",
       "      <td>1.940207</td>\n",
       "      <td>-0.596011</td>\n",
       "      <td>-0.513258</td>\n",
       "      <td>-0.037887</td>\n",
       "      <td>0.259500</td>\n",
       "      <td>-1.045073</td>\n",
       "      <td>0.579233</td>\n",
       "      <td>-1.353450</td>\n",
       "      <td>0.501159</td>\n",
       "      <td>-1.081406</td>\n",
       "      <td>1.647517</td>\n",
       "      <td>0.259735</td>\n",
       "      <td>0.664029</td>\n",
       "      <td>-2.452657</td>\n",
       "      <td>-2.352397</td>\n",
       "      <td>-2.409192</td>\n",
       "      <td>-1.593818</td>\n",
       "      <td>-2.066931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-1.107741</td>\n",
       "      <td>-0.494398</td>\n",
       "      <td>-1.589104</td>\n",
       "      <td>-0.771796</td>\n",
       "      <td>-1.774062</td>\n",
       "      <td>1.060874</td>\n",
       "      <td>1.165445</td>\n",
       "      <td>0.071628</td>\n",
       "      <td>-1.024870</td>\n",
       "      <td>-1.196763</td>\n",
       "      <td>-1.290183</td>\n",
       "      <td>1.232436</td>\n",
       "      <td>1.300793</td>\n",
       "      <td>2.837134</td>\n",
       "      <td>2.184284</td>\n",
       "      <td>-2.144599</td>\n",
       "      <td>-1.682066</td>\n",
       "      <td>-0.705762</td>\n",
       "      <td>-0.705774</td>\n",
       "      <td>-1.129411</td>\n",
       "      <td>-1.193624</td>\n",
       "      <td>-0.962027</td>\n",
       "      <td>-1.215563</td>\n",
       "      <td>-1.982227</td>\n",
       "      <td>-1.737872</td>\n",
       "      <td>-1.129411</td>\n",
       "      <td>-1.193624</td>\n",
       "      <td>1.039339</td>\n",
       "      <td>1.226375</td>\n",
       "      <td>0.938026</td>\n",
       "      <td>1.194992</td>\n",
       "      <td>1.695154</td>\n",
       "      <td>-1.090895</td>\n",
       "      <td>7.533655</td>\n",
       "      <td>-0.794242</td>\n",
       "      <td>-0.586764</td>\n",
       "      <td>-1.632633</td>\n",
       "      <td>-1.623476</td>\n",
       "      <td>-0.541386</td>\n",
       "      <td>-0.091417</td>\n",
       "      <td>-0.286415</td>\n",
       "      <td>3.266520</td>\n",
       "      <td>-1.320446</td>\n",
       "      <td>-0.021123</td>\n",
       "      <td>-1.372044</td>\n",
       "      <td>-1.774062</td>\n",
       "      <td>-1.376790</td>\n",
       "      <td>0.635470</td>\n",
       "      <td>-1.107741</td>\n",
       "      <td>-0.540857</td>\n",
       "      <td>-0.475417</td>\n",
       "      <td>-1.535326</td>\n",
       "      <td>-0.781714</td>\n",
       "      <td>-1.763272</td>\n",
       "      <td>2.811094</td>\n",
       "      <td>0.769458</td>\n",
       "      <td>-0.209395</td>\n",
       "      <td>-1.202114</td>\n",
       "      <td>-1.164004</td>\n",
       "      <td>-1.048955</td>\n",
       "      <td>1.206999</td>\n",
       "      <td>1.312475</td>\n",
       "      <td>3.048178</td>\n",
       "      <td>0.845477</td>\n",
       "      <td>-2.000855</td>\n",
       "      <td>-1.318146</td>\n",
       "      <td>-0.701818</td>\n",
       "      <td>-0.701753</td>\n",
       "      <td>-1.104636</td>\n",
       "      <td>-1.226930</td>\n",
       "      <td>-0.940557</td>\n",
       "      <td>-1.260983</td>\n",
       "      <td>-1.834310</td>\n",
       "      <td>-1.485007</td>\n",
       "      <td>-1.104636</td>\n",
       "      <td>-1.226930</td>\n",
       "      <td>1.074441</td>\n",
       "      <td>1.300021</td>\n",
       "      <td>0.965007</td>\n",
       "      <td>1.283793</td>\n",
       "      <td>8.007280</td>\n",
       "      <td>-0.870914</td>\n",
       "      <td>10.004582</td>\n",
       "      <td>-0.854209</td>\n",
       "      <td>-0.445640</td>\n",
       "      <td>-1.046355</td>\n",
       "      <td>-1.371392</td>\n",
       "      <td>-0.603192</td>\n",
       "      <td>-0.095576</td>\n",
       "      <td>-0.286426</td>\n",
       "      <td>7.103224</td>\n",
       "      <td>-1.152129</td>\n",
       "      <td>-0.224417</td>\n",
       "      <td>-1.573532</td>\n",
       "      <td>-1.763272</td>\n",
       "      <td>-1.410083</td>\n",
       "      <td>0.464011</td>\n",
       "      <td>-0.540857</td>\n",
       "      <td>-2.088741</td>\n",
       "      <td>1.578075</td>\n",
       "      <td>-0.724198</td>\n",
       "      <td>-2.352752</td>\n",
       "      <td>-2.385232</td>\n",
       "      <td>-0.139202</td>\n",
       "      <td>-1.325070</td>\n",
       "      <td>-1.029266</td>\n",
       "      <td>-1.408661</td>\n",
       "      <td>-1.211665</td>\n",
       "      <td>-1.176045</td>\n",
       "      <td>2.022223</td>\n",
       "      <td>2.046680</td>\n",
       "      <td>0.170602</td>\n",
       "      <td>-3.002793</td>\n",
       "      <td>-2.363896</td>\n",
       "      <td>-1.410170</td>\n",
       "      <td>-2.231001</td>\n",
       "      <td>-2.230959</td>\n",
       "      <td>-0.970103</td>\n",
       "      <td>-0.974789</td>\n",
       "      <td>-1.989128</td>\n",
       "      <td>-2.099638</td>\n",
       "      <td>-2.046858</td>\n",
       "      <td>-1.995765</td>\n",
       "      <td>-0.970103</td>\n",
       "      <td>-0.974789</td>\n",
       "      <td>2.016967</td>\n",
       "      <td>2.040281</td>\n",
       "      <td>1.965402</td>\n",
       "      <td>2.013467</td>\n",
       "      <td>1.134846</td>\n",
       "      <td>-0.654305</td>\n",
       "      <td>7.886368</td>\n",
       "      <td>-0.743586</td>\n",
       "      <td>3.734602</td>\n",
       "      <td>-0.673676</td>\n",
       "      <td>2.511954</td>\n",
       "      <td>-0.673353</td>\n",
       "      <td>0.954279</td>\n",
       "      <td>-0.892792</td>\n",
       "      <td>8.366068</td>\n",
       "      <td>-0.314671</td>\n",
       "      <td>-2.452657</td>\n",
       "      <td>-2.385232</td>\n",
       "      <td>-2.454112</td>\n",
       "      <td>-1.221106</td>\n",
       "      <td>-2.088741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0.226937</td>\n",
       "      <td>-1.605095</td>\n",
       "      <td>-0.337078</td>\n",
       "      <td>0.609649</td>\n",
       "      <td>-0.231593</td>\n",
       "      <td>0.119439</td>\n",
       "      <td>0.705657</td>\n",
       "      <td>-0.418386</td>\n",
       "      <td>0.332087</td>\n",
       "      <td>0.306304</td>\n",
       "      <td>-0.270292</td>\n",
       "      <td>-0.179410</td>\n",
       "      <td>-0.223369</td>\n",
       "      <td>-0.358393</td>\n",
       "      <td>0.667724</td>\n",
       "      <td>0.492677</td>\n",
       "      <td>-0.224804</td>\n",
       "      <td>0.353479</td>\n",
       "      <td>0.353508</td>\n",
       "      <td>0.192709</td>\n",
       "      <td>-0.121999</td>\n",
       "      <td>0.123555</td>\n",
       "      <td>0.262054</td>\n",
       "      <td>0.547532</td>\n",
       "      <td>0.160406</td>\n",
       "      <td>0.192709</td>\n",
       "      <td>-0.121999</td>\n",
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       "      <td>-0.253737</td>\n",
       "      <td>-0.089124</td>\n",
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       "      <td>0.717789</td>\n",
       "      <td>0.532405</td>\n",
       "      <td>0.196843</td>\n",
       "      <td>1.335250</td>\n",
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       "      <td>1.928459</td>\n",
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       "      <td>-0.231593</td>\n",
       "      <td>0.174193</td>\n",
       "      <td>0.619126</td>\n",
       "      <td>0.226937</td>\n",
       "      <td>0.472927</td>\n",
       "      <td>-1.607902</td>\n",
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       "      <td>0.609288</td>\n",
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       "      <td>0.571374</td>\n",
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       "      <td>-0.048353</td>\n",
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       "      <td>-0.225211</td>\n",
       "      <td>-0.259469</td>\n",
       "      <td>0.527958</td>\n",
       "      <td>-0.135132</td>\n",
       "      <td>0.154416</td>\n",
       "      <td>0.352993</td>\n",
       "      <td>0.352985</td>\n",
       "      <td>0.190805</td>\n",
       "      <td>-0.095808</td>\n",
       "      <td>0.162775</td>\n",
       "      <td>0.259856</td>\n",
       "      <td>0.544273</td>\n",
       "      <td>0.167684</td>\n",
       "      <td>0.190805</td>\n",
       "      <td>-0.095808</td>\n",
       "      <td>-0.110579</td>\n",
       "      <td>-0.232947</td>\n",
       "      <td>-0.081811</td>\n",
       "      <td>-0.239623</td>\n",
       "      <td>3.765931</td>\n",
       "      <td>0.566802</td>\n",
       "      <td>1.348575</td>\n",
       "      <td>0.450598</td>\n",
       "      <td>2.919378</td>\n",
       "      <td>0.207854</td>\n",
       "      <td>2.770557</td>\n",
       "      <td>0.371231</td>\n",
       "      <td>-1.069629</td>\n",
       "      <td>0.271719</td>\n",
       "      <td>3.352769</td>\n",
       "      <td>0.387267</td>\n",
       "      <td>-0.224417</td>\n",
       "      <td>0.073914</td>\n",
       "      <td>-0.227919</td>\n",
       "      <td>0.005372</td>\n",
       "      <td>0.460735</td>\n",
       "      <td>0.472927</td>\n",
       "      <td>-0.297178</td>\n",
       "      <td>0.168421</td>\n",
       "      <td>-0.053984</td>\n",
       "      <td>-0.110626</td>\n",
       "      <td>-0.157705</td>\n",
       "      <td>-0.342678</td>\n",
       "      <td>0.274896</td>\n",
       "      <td>0.311104</td>\n",
       "      <td>-0.393674</td>\n",
       "      <td>-0.788620</td>\n",
       "      <td>-0.361669</td>\n",
       "      <td>0.232125</td>\n",
       "      <td>0.294424</td>\n",
       "      <td>1.510396</td>\n",
       "      <td>-0.075365</td>\n",
       "      <td>-0.450397</td>\n",
       "      <td>0.512865</td>\n",
       "      <td>0.242375</td>\n",
       "      <td>0.242382</td>\n",
       "      <td>-0.788631</td>\n",
       "      <td>-0.354425</td>\n",
       "      <td>0.044414</td>\n",
       "      <td>-0.281721</td>\n",
       "      <td>-0.993155</td>\n",
       "      <td>0.043691</td>\n",
       "      <td>-0.788631</td>\n",
       "      <td>-0.354425</td>\n",
       "      <td>0.112955</td>\n",
       "      <td>0.307770</td>\n",
       "      <td>0.037242</td>\n",
       "      <td>0.320009</td>\n",
       "      <td>-0.245546</td>\n",
       "      <td>0.263568</td>\n",
       "      <td>0.401462</td>\n",
       "      <td>0.242621</td>\n",
       "      <td>-0.321051</td>\n",
       "      <td>0.631807</td>\n",
       "      <td>-0.531804</td>\n",
       "      <td>0.751845</td>\n",
       "      <td>0.703990</td>\n",
       "      <td>0.076343</td>\n",
       "      <td>0.019246</td>\n",
       "      <td>0.356235</td>\n",
       "      <td>-0.198623</td>\n",
       "      <td>-0.157705</td>\n",
       "      <td>-0.158562</td>\n",
       "      <td>0.203758</td>\n",
       "      <td>-0.297178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Label</th>\n",
       "      <th>Label (human-readable)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>trash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>trash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>trash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load training data\n",
    "filename = [fn for fn in os.listdir(path_tmp/\"data_{}_training/\".format(protein)) if (fn.endswith(\"hdf5\") and (antibody in fn))][0]\n",
    "\n",
    "file_path = Path(path_tmp/\"data_{}_training/\".format(protein))/filename\n",
    "df_X = pd.read_hdf(file_path, key='X')\n",
    "df_y = pd.read_hdf(file_path, key='y')\n",
    "df_y[\"Label (human-readable)\"] = df_y.replace({\"Label\":classes})\n",
    "rnd_ixs = random.sample(range(0, df_X.shape[0]), 5)\n",
    "\n",
    "# Print amount of data\n",
    "print(\"The training data contains {} samples.\\n\".format(df_X.shape[0]))\n",
    "    \n",
    "# Print some random lines from training data\n",
    "print(\"Features:\")\n",
    "display(HTML(df_X.take(rnd_ixs).to_html(index=False)))\n",
    "\n",
    "print(\"Labels:\")\n",
    "display(HTML(df_y.take(rnd_ixs).to_html(index=False)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Model\n",
    "If \"use_original_hyperparameters\" has been set to False, this script will use the optimal hyperparameters as identified in the previous cross-validation (part 2). If set to True, optimal hyperparameters as identified in the article will be used. We recommend using the latter setting to reproduce our original findings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data features and labels\n",
      "Training classifier ANN with hyperparameters {'activation': 'logistic', 'max_iter': 1000000, 'learning_rate_init': 0.0001, 'alpha': 0.1, 'hidden_layer_sizes': (32, 32)}\n",
      "Training completed.\n"
     ]
    }
   ],
   "source": [
    "# Depending on \"use_original_hyperparameters\" flag, use the optimal hyperparameters as determined in the article or in the previous cross-validation\n",
    "df_hp_best_original = pd.read_csv(path_data_auxiliary/file_original_hyperparameters, delimiter=\";\")\n",
    "hp_best_original = eval(df_hp_best_original[(df_hp_best_original.protein == protein) & (df_hp_best_original.antibody == antibody)][classifier_type].iloc[0])\n",
    "hp_best = hp_best_original if use_original_hyperparameters else hp_best_cv \n",
    "assert hp_best is not None, \"Optimal hyperparameters for model training missing. Make sure that either the cross-validation has been rund beforehand, or that 'use_original_hyperparameters' is set True.\"\n",
    "\n",
    "# Train model\n",
    "classifier = training.train(classifier_type, path_tmp/\"data_{}_training/\".format(protein), antibody, hp_best)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3: Predictions \n",
    "The trained model is applied to predict the class of each segmented cell in a specific sample well, over all wells in a plate with the antibody-specific dilution. Our per-well antibody positivity predictions correspond to the number of positively classified cells divided by the total segmented cell count in the well.\n",
    "\n",
    "**Note**: Producing the predictions can take up to 5 minutes per plate, i.e. 20 minutes in total.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading feature data for plate data_raw/data_S_test/S_3_2.hdf5\n",
      "\n",
      "Normalising data for dilution 100\n",
      "Normalisation based on negative controls only\n",
      "\n",
      "Calculating predictions for antibody IgG and dilution 100\n",
      "Calculating per-cell predictions\n",
      "Calculating per-well ratios\n",
      "\n",
      "Exporting per-well prediction results to data_out/cb182265-a18c-436b-9303-7e224b120fee/ratios_S/ratios_per_well_plate_S_3_2_ab_IgG.csv\n",
      "\n",
      "Loading feature data for plate data_raw/data_S_test/S_3_1.hdf5\n",
      "\n",
      "Normalising data for dilution 100\n",
      "Normalisation based on negative controls only\n",
      "\n",
      "Calculating predictions for antibody IgG and dilution 100\n",
      "Calculating per-cell predictions\n",
      "Calculating per-well ratios\n",
      "\n",
      "Exporting per-well prediction results to data_out/cb182265-a18c-436b-9303-7e224b120fee/ratios_S/ratios_per_well_plate_S_3_1_ab_IgG.csv\n",
      "\n",
      "Loading feature data for plate data_raw/data_S_test/S_3_4.hdf5\n",
      "\n",
      "Normalising data for dilution 100\n",
      "Normalisation based on negative controls only\n",
      "\n",
      "Calculating predictions for antibody IgG and dilution 100\n",
      "Calculating per-cell predictions\n",
      "Calculating per-well ratios\n",
      "\n",
      "Exporting per-well prediction results to data_out/cb182265-a18c-436b-9303-7e224b120fee/ratios_S/ratios_per_well_plate_S_3_4_ab_IgG.csv\n",
      "\n",
      "Loading feature data for plate data_raw/data_S_test/S_3_3.hdf5\n",
      "\n",
      "Normalising data for dilution 100\n",
      "Normalisation based on negative controls only\n",
      "\n",
      "Calculating predictions for antibody IgG and dilution 100\n",
      "Calculating per-cell predictions\n",
      "Calculating per-well ratios\n",
      "\n",
      "Exporting per-well prediction results to data_out/cb182265-a18c-436b-9303-7e224b120fee/ratios_S/ratios_per_well_plate_S_3_3_ab_IgG.csv\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predict.predict(classifier, classifier_type, path_data_raw/\"data_{}_test/\".format(protein), path_tmp/\"ratios_{}/\".format(protein), antibody, classes, df_controls, df_meta, normalisation_type, dilution_antibody)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example file: ratios_per_well_plate_S_3_3_ab_IgG.csv\n",
      "\n",
      "Per-well IgG positivity predictions on positive controls:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>well</th>\n",
       "      <th>Cell Count</th>\n",
       "      <th>Ratio positive IgG</th>\n",
       "      <th>Ratio negative IgG</th>\n",
       "      <th>Ratio atypical IgG</th>\n",
       "      <th>Ratio small bright IgG</th>\n",
       "      <th>Ratio trash IgG</th>\n",
       "      <th>Destination Plate Barcode</th>\n",
       "      <th>Sample Name</th>\n",
       "      <th>Sample</th>\n",
       "      <th>Content</th>\n",
       "      <th>Dilution</th>\n",
       "      <th>Transferred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A3</td>\n",
       "      <td>4344.0</td>\n",
       "      <td>0.189917</td>\n",
       "      <td>0.298573</td>\n",
       "      <td>0.317449</td>\n",
       "      <td>0.089779</td>\n",
       "      <td>0.104282</td>\n",
       "      <td>S_3_3</td>\n",
       "      <td>POS1</td>\n",
       "      <td>0</td>\n",
       "      <td>POS1_100</td>\n",
       "      <td>100</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>F9</td>\n",
       "      <td>7122.0</td>\n",
       "      <td>0.165965</td>\n",
       "      <td>0.421792</td>\n",
       "      <td>0.273940</td>\n",
       "      <td>0.060095</td>\n",
       "      <td>0.078208</td>\n",
       "      <td>S_3_3</td>\n",
       "      <td>POS1</td>\n",
       "      <td>0</td>\n",
       "      <td>POS1_100</td>\n",
       "      <td>100</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>J15</td>\n",
       "      <td>6845.0</td>\n",
       "      <td>0.152520</td>\n",
       "      <td>0.482104</td>\n",
       "      <td>0.244412</td>\n",
       "      <td>0.054785</td>\n",
       "      <td>0.066180</td>\n",
       "      <td>S_3_3</td>\n",
       "      <td>POS1</td>\n",
       "      <td>0</td>\n",
       "      <td>POS1_100</td>\n",
       "      <td>100</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>E24</td>\n",
       "      <td>7736.0</td>\n",
       "      <td>0.124612</td>\n",
       "      <td>0.523914</td>\n",
       "      <td>0.199974</td>\n",
       "      <td>0.054421</td>\n",
       "      <td>0.097079</td>\n",
       "      <td>S_3_3</td>\n",
       "      <td>POS1</td>\n",
       "      <td>0</td>\n",
       "      <td>POS1_100</td>\n",
       "      <td>100</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Per-well IgG positivity predictions on negative controls:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>well</th>\n",
       "      <th>Cell Count</th>\n",
       "      <th>Ratio positive IgG</th>\n",
       "      <th>Ratio negative IgG</th>\n",
       "      <th>Ratio atypical IgG</th>\n",
       "      <th>Ratio small bright IgG</th>\n",
       "      <th>Ratio trash IgG</th>\n",
       "      <th>Destination Plate Barcode</th>\n",
       "      <th>Sample Name</th>\n",
       "      <th>Sample</th>\n",
       "      <th>Content</th>\n",
       "      <th>Dilution</th>\n",
       "      <th>Transferred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>B6</td>\n",
       "      <td>6777.0</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.796813</td>\n",
       "      <td>0.048694</td>\n",
       "      <td>0.046186</td>\n",
       "      <td>0.108160</td>\n",
       "      <td>S_3_3</td>\n",
       "      <td>NEG1</td>\n",
       "      <td>0</td>\n",
       "      <td>NEG1_100</td>\n",
       "      <td>100</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>L12</td>\n",
       "      <td>6794.0</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.787754</td>\n",
       "      <td>0.064174</td>\n",
       "      <td>0.037092</td>\n",
       "      <td>0.110833</td>\n",
       "      <td>S_3_3</td>\n",
       "      <td>NEG1</td>\n",
       "      <td>0</td>\n",
       "      <td>NEG1_100</td>\n",
       "      <td>100</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>B16</td>\n",
       "      <td>6888.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.886905</td>\n",
       "      <td>0.013357</td>\n",
       "      <td>0.023519</td>\n",
       "      <td>0.076220</td>\n",
       "      <td>S_3_3</td>\n",
       "      <td>NEG1</td>\n",
       "      <td>0</td>\n",
       "      <td>NEG1_100</td>\n",
       "      <td>100</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>L23</td>\n",
       "      <td>7350.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.897415</td>\n",
       "      <td>0.001224</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.081361</td>\n",
       "      <td>S_3_3</td>\n",
       "      <td>NEG1</td>\n",
       "      <td>0</td>\n",
       "      <td>NEG1_100</td>\n",
       "      <td>100</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print per-well predictions for 1 pos/neg control\n",
    "example_file = [fn for fn in os.listdir(path_tmp/\"ratios_{}/\".format(protein)) if (fn.endswith(\".csv\") and (antibody in fn))][0]\n",
    "print(\"Example file: {}\\n\".format(example_file))\n",
    "df_ratios = pd.read_csv(Path(path_tmp)/\"ratios_{}/\".format(protein)/example_file)\n",
    "\n",
    "ctrl_pos = df_controls[(df_controls.Type==\"positive\") & (df_controls.Dilution==dilution_antibody[antibody])].Content.iloc[0]\n",
    "ctrl_neg = df_controls[(df_controls.Type==\"negative\") & (df_controls.Dilution==dilution_antibody[antibody])].Content.iloc[0]\n",
    "\n",
    "df_ratios_ctrl_pos = df_ratios[df_ratios.Content == ctrl_pos]\n",
    "df_ratios_ctrl_neg = df_ratios[df_ratios.Content == ctrl_neg]\n",
    "\n",
    "print(\"Per-well {} positivity predictions on positive controls:\".format(antibody))\n",
    "display(HTML(df_ratios_ctrl_pos.to_html(index=False)))\n",
    "\n",
    "print(\"Per-well {} positivity predictions on negative controls:\".format(antibody))\n",
    "display(HTML(df_ratios_ctrl_neg.to_html(index=False)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 4: Evaluation\n",
    "### Comparison to ELISA\n",
    "To avoid biasing our correlations by the substantially higher number of negative samples, we draw a random subset of negative samples to match the number of positive samples for the given antibody/antigen combination. We report the correlation mean and standard deviation for 1000 sampling repetitions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading ELISA data from data_auxiliary/elisa.csv\n",
      "Loading per-well predictions and comparing to ELISA\n",
      "\n",
      "Spearman correlation mean=0.806626469197366, std=0.01329187225774173\n",
      "Example scatterplot:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show correlation plots\n",
    "c_spearman_mean, c_spearman_std, data_correlations = evaluate.evaluate_elisa(path_tmp/\"ratios_{}/\".format(protein), path_data_auxiliary/file_elisa, protein, antibody, dilution_antibody)\n",
    "print(\"\\nSpearman correlation mean={}, std={}\".format(c_spearman_mean, c_spearman_std))\n",
    "\n",
    "print(\"Example scatterplot:\")\n",
    "plotting.plot_correlation(data_correlations, protein, antibody)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison to Visual Ground Truth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading visual ground truth data from data_auxiliary/visual_gt.csv\n",
      "Loading per-well predictions and comparing against visual ground truth\n",
      "Calculating ROC curve and AUC (area under the curve)\n",
      "\n",
      "Receiver Operating Characteristic (ROC) curve:\n",
      "Area Under the Curve (AUC): 0.9814814814814815\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "auc, df_scores = evaluate.evaluate_visual_gt(path_tmp/\"ratios_{}/\".format(protein), path_data_auxiliary/file_visual_gt, protein, antibody, dilution_antibody)\n",
    "\n",
    "print(\"\\nReceiver Operating Characteristic (ROC) curve:\")\n",
    "print(\"Area Under the Curve (AUC): {}\".format(auc))\n",
    "plotting.plot_ROC_curve(df_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
  },
  "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}