{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import HTML, display, Image\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Advanced evaluation\n",
    "\n",
    "In this tutorial we evaluate probesets in finer detail: besides the summary values for each metric (see basic evaluation tutorial) we can get e.g. per gene and per cell type information of each evaluation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages and setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "import spapros as sp\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scanpy==1.10.0 anndata==0.10.6 umap==0.5.5 numpy==1.26.4 scipy==1.12.0 pandas==1.5.3 scikit-learn==1.4.1.post1 statsmodels==0.14.1 igraph==0.11.4 pynndescent==0.5.11\n",
      "spapros==0.1.5\n"
     ]
    }
   ],
   "source": [
    "sc.settings.verbosity = 1\n",
    "sc.logging.print_header()\n",
    "print(f\"spapros=={sp.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 2638 × 1838\n",
       "    obs: 'celltype'\n",
       "    var: 'gene_ids', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n",
       "    uns: 'log1p', 'hvg'\n",
       "    obsm: 'X_umap'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata = sc.datasets.pbmc3k()\n",
    "adata_tmp = sc.datasets.pbmc3k_processed()\n",
    "\n",
    "# Get infos from the processed dataset\n",
    "adata = adata[adata_tmp.obs_names, adata_tmp.var_names]\n",
    "adata.obs['celltype'] = adata_tmp.obs['louvain']\n",
    "adata.obsm['X_umap'] = adata_tmp.obsm['X_umap']\n",
    "del adata_tmp\n",
    "\n",
    "# Preprocess counts and get highly variable genes\n",
    "sc.pp.normalize_total(adata)\n",
    "sc.pp.log1p(adata)\n",
    "sc.pp.highly_variable_genes(adata, flavor=\"cell_ranger\", n_top_genes=1000)\n",
    "\n",
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Set up the ProbesetEvaluator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": []
   },
   "source": [
    "As described in the basic evaluation tutorial Spapros provides a set of evaluation metrics that measure the performance of a gene set.\n",
    "\n",
    "By default the `ProbesetEvaluator` sets the argument `scheme=\"quick\"`, which means that only the metrics that are quickly calculated are included, which are:\n",
    "\n",
    ">- neighborhood similarity (knn)\n",
    ">- cell type (forest) classification\n",
    ">- marker correlation (if marker list given: `marker_list=\"../path/to/marker_list.csv\"`)\n",
    ">- gene redundancy (correlation)\n",
    "\n",
    "Through setting `scheme=\"full\"`, additionally the following metric is calculated:\n",
    ">- clustering similarity (nmi)\n",
    "\n",
    "\n",
    "Alternatively, you can specify `scheme=\"custom\"` and `metrics=custom_list` where `custom_list` is a list of the metrics of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize a ProbesetEvaluator\n",
    "evaluator = sp.ev.ProbesetEvaluator(\n",
    "    adata,\n",
    "    scheme=\"full\",\n",
    "    marker_list=\"../../data/pbmc3k_marker_list.csv\",\n",
    "    verbosity=2,\n",
    "    results_dir=None\n",
    ")\n",
    "\n",
    "# The pbmc3k_marker_list.csv includes the following data:\n",
    "# pd.DataFrame(\n",
    "#     data={\n",
    "#         \"CD4 T cells\"       :[\"IL7R\", None],\n",
    "#         \"CD14+ Monocytes\"   :[\"CD14\", \"LYZ\"],\n",
    "#         \"B cells\"           :[\"MS4A1\", None],\n",
    "#         \"CD8 T cells\"       :[\"CD8A\", None],\n",
    "#         \"NK cells\"          :[\"GNLY\", \"NKG7\"],\n",
    "#         \"FCGR3A+ Monocytes\" :[\"FCGR3A\", \"MS4A7\"],\n",
    "#         \"Dendritic Cells\"   :[\"FCER1A\", \"CST3\"],\n",
    "#         \"Megakaryocytes\"    :[\"NAPA-AS1\", \"PPBP\"],\n",
    "#     },   \n",
    "# ).to_csv(\"../../data/pbmc3k_marker_list.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.  Run evaluation methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "hide-cell"
    ]
   },
   "source": [
    "The central method to run evaluations is `ProbesetEvaluator.evaluate_probeset()`, which needs to be invoked once for each probe set.\n",
    "All results are stored as class variables.\n",
    "The `set_id` has to be specified in each iteration. Otherwise, the results will be overwritten."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   ],
   "source": [
    "# select reference probesets with basic selection methods\n",
    "selections = sp.se.select_reference_probesets(adata, n=20)\n",
    "\n",
    "# Take Spapros genes from basic selection tutorial\n",
    "genes = [\n",
    "    'PF4', 'HLA-DPB1', 'FCGR3A', 'GZMB', 'CCL5', 'S100A8', 'IL32', 'HLA-DQA1', 'NKG7', 'AIF1', 'CD79A', 'LTB', 'TYROBP',\n",
    "    'HLA-DMA', 'GZMK', 'HLA-DRB1', 'FCN1', 'S100A11', 'GNLY', 'GZMH'\n",
    "]\n",
    "\n",
    "# we add it to the dictionary of probesets\n",
    "selections[\"spapros\"] = pd.DataFrame({\"selection\": adata.var_names.isin(genes)}, index=adata.var_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
     ]
    },
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       "<span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">than </span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">40</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> cells: [</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">'Dendritic cells'</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">, </span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">'Megakaryocytes'</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">]</span>\n",
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   "source": [
    "# now start the evaluation for each of the collected probesets\n",
    "for probeset_name, probeset_df in selections.items():\n",
    "    gene_list = probeset_df.index[probeset_df[\"selection\"]].to_list()\n",
    "    evaluator.evaluate_probeset(gene_list, set_id=probeset_name)"
   ]
  },
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   "source": [
    "There are maximally four steps to calculate for each metric:\n",
    "1. shared computations: this step is only needed once for all probe sets (e.g. clusterings on the reference data that includes all genes)\n",
    "2. pre computations: this step is run for each probe set, but it's independent of the shared results of step 1\n",
    "3. main computations: this step is run for each probe set and depends on the shared results of step 1 (if there was a step 1 for a given metric) and potentially the pre results of step 2\n",
    "4. summary computations: extract the final metric value as a summary statistic\n",
    "\n",
    "In the above progress bars you can see that the shared computations take longer for the first probe set. For the following probe sets, the shared computations are reused.\n",
    "\n",
    "Not all metrics include all 4 steps. E.g. forest classification simply has a main computation step, as it doesn't require any shared computations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster_similarity nmi_5_20</th>\n",
       "      <th>cluster_similarity nmi_21_60</th>\n",
       "      <th>knn_overlap mean_overlap_AUC</th>\n",
       "      <th>forest_clfs accuracy</th>\n",
       "      <th>forest_clfs perct acc &gt; 0.8</th>\n",
       "      <th>gene_corr 1 - mean</th>\n",
       "      <th>gene_corr perct max &lt; 0.8</th>\n",
       "      <th>marker_corr per marker</th>\n",
       "      <th>marker_corr per celltype</th>\n",
       "      <th>marker_corr per marker mean &gt; 0.025</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PCA</th>\n",
       "      <td>0.683315</td>\n",
       "      <td>0.533974</td>\n",
       "      <td>0.293958</td>\n",
       "      <td>0.887955</td>\n",
       "      <td>0.897735</td>\n",
       "      <td>0.820372</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.454319</td>\n",
       "      <td>0.555655</td>\n",
       "      <td>0.573841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spapros</th>\n",
       "      <td>0.721423</td>\n",
       "      <td>0.553337</td>\n",
       "      <td>0.167580</td>\n",
       "      <td>0.923666</td>\n",
       "      <td>0.990460</td>\n",
       "      <td>0.772479</td>\n",
       "      <td>0.993647</td>\n",
       "      <td>0.703430</td>\n",
       "      <td>0.902092</td>\n",
       "      <td>0.902092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HVG</th>\n",
       "      <td>0.495991</td>\n",
       "      <td>0.408694</td>\n",
       "      <td>0.056358</td>\n",
       "      <td>0.819195</td>\n",
       "      <td>0.720651</td>\n",
       "      <td>0.840755</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.591106</td>\n",
       "      <td>0.766929</td>\n",
       "      <td>0.753666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random (seed=0)</th>\n",
       "      <td>0.243881</td>\n",
       "      <td>0.255338</td>\n",
       "      <td>0.023879</td>\n",
       "      <td>0.575532</td>\n",
       "      <td>0.007778</td>\n",
       "      <td>0.976067</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.229733</td>\n",
       "      <td>0.290810</td>\n",
       "      <td>0.290023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>0.712277</td>\n",
       "      <td>0.545842</td>\n",
       "      <td>0.160027</td>\n",
       "      <td>0.916620</td>\n",
       "      <td>0.955688</td>\n",
       "      <td>0.757868</td>\n",
       "      <td>0.792178</td>\n",
       "      <td>0.672017</td>\n",
       "      <td>0.863489</td>\n",
       "      <td>0.863489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 cluster_similarity nmi_5_20  cluster_similarity nmi_21_60  \\\n",
       "PCA                                 0.683315                      0.533974   \n",
       "spapros                             0.721423                      0.553337   \n",
       "HVG                                 0.495991                      0.408694   \n",
       "random (seed=0)                     0.243881                      0.255338   \n",
       "DE                                  0.712277                      0.545842   \n",
       "\n",
       "                 knn_overlap mean_overlap_AUC  forest_clfs accuracy  \\\n",
       "PCA                                  0.293958              0.887955   \n",
       "spapros                              0.167580              0.923666   \n",
       "HVG                                  0.056358              0.819195   \n",
       "random (seed=0)                      0.023879              0.575532   \n",
       "DE                                   0.160027              0.916620   \n",
       "\n",
       "                 forest_clfs perct acc > 0.8  gene_corr 1 - mean  \\\n",
       "PCA                                 0.897735            0.820372   \n",
       "spapros                             0.990460            0.772479   \n",
       "HVG                                 0.720651            0.840755   \n",
       "random (seed=0)                     0.007778            0.976067   \n",
       "DE                                  0.955688            0.757868   \n",
       "\n",
       "                 gene_corr perct max < 0.8  marker_corr per marker  \\\n",
       "PCA                               1.000000                0.454319   \n",
       "spapros                           0.993647                0.703430   \n",
       "HVG                               0.900000                0.591106   \n",
       "random (seed=0)                   1.000000                0.229733   \n",
       "DE                                0.792178                0.672017   \n",
       "\n",
       "                 marker_corr per celltype  marker_corr per marker mean > 0.025  \n",
       "PCA                              0.555655                             0.573841  \n",
       "spapros                          0.902092                             0.902092  \n",
       "HVG                              0.766929                             0.753666  \n",
       "random (seed=0)                  0.290810                             0.290023  \n",
       "DE                               0.863489                             0.863489  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluator.summary_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explanations of the metrics:\n",
    "\n",
    "**Variation recovery metrics**:\n",
    "- *cluster_similarity nmi_5_20*: Assess the similarity of clustering the dataset on all genes vs just the selected gene set. Clusterings are calculated with different leiden resolutions to genertate clusterings of n = 5 to 20 clusters. The NMI is calculated between the clusterings of the full dataset and the gene set. Finally the average NMI over the different n's is calculated. This metric measures how well the clustering structure of the dataset is recovered with a gene subset. As n is chosen to be between 5 and 20 this metric assess clustering structure on a coarse level.\n",
    "- *cluster_similarity nmi_21_60*: Same as above but with n = 21 to 60 clusters. This metric assesses clustering structure on a finer level.\n",
    "- *knn_overlap mean_overlap_AUC*: Similar to cluster_similarity this metric assesses how well the dataset's structure is recovered with a gene subset. It looks at even finer variation recovery than cluster_similarity nmi_21_60 as it assesses the overlap of each cell's k nearest neighbors of the full dataset knn graph and the gene set knn graph. \n",
    "\n",
    "**Cell type classification metrics**:\n",
    "- *forest_clfs accuracy*: This metric assesses how well a random forest classifier can predict cell types based on the gene set. The metric is the mean accuracy of the classifier over all cell types.\n",
    "- *forest_clfs perct acc > 0.8*: This metric assesses how many cell types can be predicted with an accuracy of at least 0.8. More specifically the transformation function is not a sharp step function at 0.8 but instead at step function with a linear increase from 0 to 1 between 0.75 and 0.85 (i.e. smoothed thresholding). The metric gives an idea about how many cell types can be identified with high confidence with the given gene set.\n",
    "\n",
    "**Gene redundancy metrics**:\n",
    "- *gene_corr 1 - mean*: Mean over maximum correlations of each gene with all other genes. This metric assesses how redundant the gene set is. Note that the actual value is 1 - the mean correlations which means the higher the value the less redundant the gene set is.\n",
    "- *gene_corr perct max < 0.8*: Percentage of genes that have a maximum correlation of less than 0.8 with all other genes. This metric gives an idea about how many genes show unique expression profiles in the gene set. The reasoning behind introducing this metric with the 0.8 threshold is that genes that are only \"somewhat correlated\" (e.g. r=0.5) could still encode for different information and thus be useful in a gene set, while genes that are highly correlated (e.g. r=0.9) are likely redundant. Note however, that redundancy is not necessarily bad, as it can be used to increase robustness of the gene set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Visualize the evaluation results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#evaluator.summary_statistics(set_ids=selections.keys())\n",
    "evaluator.plot_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I this plot, each row summarizes the evaluation metrics of a given gene set.\n",
    "The metrics evaluating the variability recovery are shown in the green columns (1-3).\n",
    "The clustering similarity (coarse level clustering: 5-20, fine level clustering: 21-60) is evaluated by the normalized mutual information (NMI) of the clustering of the selected probeset and the clustering on the full gene set. High scores means that the cells build similar cluster on the gene subset as on the full gene set.\n",
    "\n",
    "\n",
    "Both NMI values are highest for the pca based selection, which indicates that this method recovers the variability best in this comparison.\n",
    "\n",
    "Even more fine grained variation recovery is measured with the third column (knn_overlap). It measures the neighbors overlap of the knn graphs of the full gene set and the respective probeset. \n",
    "\n",
    "The subsequent purple columns (4-8) indicate the performance of each selection method with respect to cell type identification.\n",
    "Column 4 contains the mean classification accuracy of a random forest prediction over cell types. This quantifies the overall classification success while column 5 provides an estimate for the percentage of reliably captured cell types, i.e. the average of a smoothed thresholding of classification performance around 0.8 for each cell type.\n",
    "The other 3 purple columns (6-8) derive from the comparison of the correlation within the selected probeset with the correlation within the marker list.\n",
    "\n",
    "The last two, blue columns (9, 10) evaluate the correlation within a selected probeset. If genes with highly correlated expression are selected, the information content could also be provided by just one of them.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluations beyond summary metrics\n",
    "\n",
    "The summary metrics are useful to get an overview of the performance of the gene sets. However, to understand the performance in more detail, we can look into more specific evaluation scores that were calculated on the run and still accessible in the `ProbesetEvaluator` object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### cell type identification\n",
    "\n",
    "To see and compare how well each cell type can be classified with different gene sets we plot the cell type classification confusion matrices. The confusion matrix shows how many cells of a given cell type were classified as another cell type. The diagonal of the matrix shows the correct classification rate. The better the classification the more the diagonal is filled with high values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false,
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x900 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluator.plot_confusion_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can oberserve that CD8 T cells are the most difficult to classify across gene sets. The highest classification accuracy for all cell types in this example is achieved with the Spapros selection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gene redundancy\n",
    "\n",
    "We can look into the coexpression patterns of genes by looking at the correlation matrix of the selected genes. Ideally we find a few correlative modules. If there is a correlative module that's very large compared to the others, then a high capacity of the gene set is wasted on redundant information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rVVVVdv7551d0e3Q1rzrwwAMrypk8eXKPH0Lpal/75z//ORs8ePB6x+1cGC5fvjybMWNGxde/uro6+9a3vtXjdW9qasoOPfTQXt++3/72tyu6bQEAAKASTkm6mbjkkkvi8ssvj4iIAQMGxGGHHRbjxo2LPn36xIsvvhj333//etetrq6OXXfdNXbdddcYOXJkDBgwINra2mLBggXx0EMPxaOPPlpa9qqrroqGhob44Q9/2KvtmzlzZlx22WUREbH99tvHYYcdFjvttFO0tbXFY489Frfeemu0tbVFRMTzzz8fn/nMZ+L222/v9nRJ8+bNi+nTp8drr71W+ltNTU1Mnz49dt9996itrY1nnnkm/vSnP8XKlSvjf//v/x0TJkzo1XZfe+21cdxxx8Xq1atLf2tsbIwZM2bE2LFjo7W1NWbPnh1/+ctfSss888wz8b73vS8eeOCBGDNmTEU5WZbFcccdF/fee29EREycODEOPvjgGDZsWLz55pvxpz/9qfTda1mWxemnnx4HHHBADBo0KA477LB49dVXo7q6Ovbff//Yd999o7GxMV5++eW48cYbY/HixRERsWTJkvjEJz4Rjz76aPTt27dXt0N33nrrrXj88cdLl/fdd9+K1mtpaYkPfvCD8fLLL5f+1tDQEAcddFBMmDAhBg4cGK2trfHWW2/FU089FY888kgsW7asx3FXr14dxxxzTNx4442lv1VVVcXee+8de++9d2y33XbR0tISzz77bNx+++2xfPnyiIi49957Y9q0afHggw9G//79N9n4s2bNiqOOOqq0XkREfX19HHHEETFx4sRYvXp1PPHEE3HbbbfFqlWr4pxzzqnolJ6bi6effrrs8o477ljxup2Xffrpp2Py5Mk9rtf5MXnXXXfFJz/5yYpzATY3qfaha/3jH/+I8847L1paWqK6ujoOPPDAmDRpUmyzzTbxwgsvxM033xxLly6NiDWna//MZz4TDQ0N8ZGPfKSi8Wtra2P33XePXXbZJYYPHx4NDQ3R0tISr776ajzwwAPx7LPPRsSaOc9FF10UQ4YMiS996Uu9uIUiTj755LjnnnsiYs3cc8aMGTF69OhYunRp3HffffHwww+Xlv3b3/4WRxxxRNx9991RV1dX0fhz5syJL37xi7F06dKoqqqKfffdNyZPnhyDBg2KBQsWxF//+tey5VtaWuLQQw+NBx98sOzve+21V0ydOjUGDRoUr732WvzpT3+KefPmRUREe3t7fP3rX4+mpqb4zne+s95tOeOMM+L2228vXe7Tp0/sv//+sccee8TQoUOjqqoqlixZEs8991zpVKUAAACw0W3iwrKwOh9h2KdPnywiss997nPZ0qVL11m+81GBS5YsyQ499NDsmmuuyRYvXtxt1uzZs7MDDjigV0f2dDzCsLa2Nquqqsr69OmTXXLJJV0eoTh79uxs9OjRZRm33nprtxlHHXXUOp8OnzNnzjrLLVy4MPvwhz+cRcQ6p47s7gjDl156KRs4cGDZ8qecckq2bNmydZZ99tlns8mTJ69zRFlbW9t6x+/4Sfja2tosYs2RoL/73e/WWbatrS376le/Wjb+Jz/5yeyDH/xgFhHZe97zni5PW/XWW29lhx12WNl6P/3pT9e7TRvi1ltvLRv/17/+dUXr/fKXvyxb76STTurysbtWa2trdvvtt2f//M//nD300EPrXe7cc88tG/fII4/MnnnmmS6Xfeutt7IzzjijbPkTTzyx2+1OOf6qVauyPffcs2z5o446qsvT9M6dO7d0RGrnx3VPRxj26dMn6c8vf/nL9WZ3PA1dTU1Ntnr16m63taPOj5mLLrqoovWWL19edjToWWedVXEmwOZoY+9DOx9huHa/sttuu2WPPvroOssvX748O+WUU8rWGTp0aLenDH/ssceyY445Jrvuuuuy5cuXd3v97r777myXXXYpmyf1dHR4x3nV2u2vqqrKZs6c2eWp0v/yl7+sc5aKb3zjG+sdv/MRhmvn3nvssUf2j3/8o8t1Os55v/jFL5atP3LkyNKpxTtavXp1dumll5adkryqqiq76aabusx48cUXy8bdd999s+eff77b2+rRRx/N/vVf/7VXpwUHAACAnigMN5HOhWFEZP/6r/+aLK+5uTmbNGlSKeu4447rdvmOheHan//6r//qdp177723bPnjjz9+vcv++c9/Llt2t9126/aNslWrVnV5CqjuCsPjjz++bNnPfe5z3W7/smXL1il7ujtFV8c3tiIiq6uryx588MFuMzoWt9XV1VnEmu/Ie/PNN9e7zsKFC8tOb7n2e302ls7fSdddmdfRpz/96dI6EyZM6FVxtD6PPPJIWTF08sknZ+3t7T2ud84555Tdrs8+++wmGf/KK68suy2nT5/e7fdBLl++PNtrr73WeVz3VBh2Xn5j/3SX3/F7pLr6btXu/P73vy/LOffccyted+TIkaX1DjnkkF7lAmxuNvY+tHNhGLHmlPILFizodr2TTz65bJ3Pf/7z72g7Olq8eHHZKdd7es3vPK+KiOw73/lOt+s8+eSTZd9PXVdXl7322mtdLtu5MIyIbPz48d3OwdZ67rnnSvO2iMgGDx683g8brdW5FB43blyXc46f//znZXOMF198scftAQAAgBSqg83CmDFj4t///d+Tjd+3b9/42te+Vrp88803R5ZlFa9/zDHH9HgKwAMOOKDs1IHdnUb1xz/+cdnlK664IhobG9e7fE1NTVx55ZWxzTbbVLS9CxcujN/+9rely2PGjInvfe973a4zYMCA+PnPf152GtXenLr1rLPO6vF0nqeeemrp9/b29oiI+NGPfhSDBw9e7zrDhg0rO0XY3//+97JTrL5TL774YtnlUaNGVbTeggULSr+/973vjerqd/5y8t3vfrf0uBw7dmxcfvnl3Z7Wdq0LL7wwRo8eHRFrbtcrr7xyk4zf8XFdV1cXV155ZdTW1q533IaGhvjpT3/aY/7mZMWKFaXfe3sq1c7LNzU1VbzuyJEjS793fswCbGlS7EM7++EPfxjbbbddt8v84Ac/iB122KF0+eqrr46VK1dulPwhQ4bEWWedVbrc8VTglZg0aVJ85Stf6XaZXXbZJS644ILS5dbW1vj5z39eccbll1/e7Rys43Jr520RERdffHGPp8k//vjj40Mf+lDp8nPPPRd//OMf11mu42Nh2LBhsdNOO1Wy6QAAALDRKQw3EyeddNJG/V66ruy+++6l35cuXRpPPfVUxet+7nOfq2i5gw46qPT7c889F6tWrVpnmdbW1rI3TPbaa6848MADexx79OjR8dGPfrSi7bjllluipaWldPmzn/1sRWXj3nvvHdOnTy9dfvjhh2P+/PkVZZ5++uk9LtP5+9p22mmnmDFjRo/r7b///qXfV65cuVELk87fgzNs2LCK1mtoaCj9/uijj5a9kbYhVq5cGb/73e9Kl0877bSKnxN9+/YtK1XvuOOO3Md/+eWX4x//+Efp8lFHHRVjx47tcex99tknpkyZUtF2rJWtOTo82c9nPvOZ9Wa//fbbpd97+5rVefmOY/Vk++23L/1e6XMSYHO1sfehnY0ZM6asrFqfAQMGxEknnVS6vHTp0rLv0nunOs49n3zyyV59UOTMM8+sqEg99dRTy+Z4v//97ysaf+zYsXH44YdXtOz1119f+n3w4MFx4oknVrRe58Kz4zhrdXwsLFy40PcTAgAAsMkoDDcTBx988Aav++abb8ZPfvKT+NSnPhV77rln7LDDDtG/f/+oqakp+9ltt93K1ps3b15F4/fp06fiQmPMmDGl37Msi2XLlq2zzKxZs8rKvKOPPrqisSOioje/ItY9uvHDH/5wxRmdS8n77ruvx3VGjBgR7373u3tcrvPRe1OmTKnoCLfO6y1ZsqTHdSrV8c27vn37VnyUw3777Vf6/emnn46TTz453nrrrQ3ejvvuu6+sYO5YPldi/Pjxpd8fffTRdY6gTT3+Aw88UHY5xeN6c9DxKMHW1tZerdvxed95rJ50fDO4tbW1yw8jAGwpNvY+tLMPfOADFc0vItbdB/3tb3/rcZ1XX301Lr300vjnf/7n2G233WL77bePfv36rTP3POqoo0rrtLe396oM++AHP1jRcgMGDCibR8+ePbuiD6RMmzatovEXLFgQc+fOLV0+4ogjKv7AzNSpU8s+iNXVnLLjYyHLsvjIRz4Sc+bMqWh8AAAA2JhqNvUGsMbEiRN7vc7KlSvjwgsvjEsvvbTXb9xHRMVvTA0ePLjiU4H279+/7HJTU1Nsu+22ZX/rfGTjnnvuWdHYEWuORqzEM888U7ZNHcuenkyaNKns8tNPP93jOh1Pl9idzrfPhq7Xm0/o96RjiVNXV1fxeieddFJ8+9vfjqVLl0ZExC9+8Yv49a9/HUcccUS8//3vjwMOOCD22GOP6NOnT0XjdTw6LyI26Ki7tdra2mLZsmUxcODA3MbP43G9OWhoaCidrq43RwhGRDQ3N68zVqU6vznb3Nzc7eleATZnG3sf2llv9kG77757VFdXl45yfPLJJ9e77Jtvvhlf/epX46qrrtqgoyIrnXuOGDEihg4dWvG4e+21V9x8880RsWYfPWfOnNhjjz26XafSuXfHOWXEmlPI9sakSZPiT3/6U2msLMvKytzJkyfH+973vtKH3R588MHYeeedY8qUKTFjxoyYOnVq7LvvvtGvX79e5QIAAEBvKQw3E4MGDerV8itWrIgjjzwy7rnnng3O7Pzm/fp0Lqu60/nT7F19T2LnN4t6+n6djjqelrA7HTO22267Xn030PDhw9c71vpU+iZO59tnQ9frzfdP9qRjEdOb4nnYsGHxu9/9Lj72sY+V3vBsbm6O66+/vnTKrYaGhpg6dWrMmDEjPv7xj5d9T1JnixYtKrv8Tr+ncenSpWWFXurx83hcbw4aGhpi4cKFEbHmdajzG5/dWb58+TpjVarz0YmVfogBYHO0sfehnfVmv7LNNttEY2Nj6ewF65v3LFiwIA455JBendK+s0rnnr3dL3ZevpK5W6Vz785jdZ4n9qTj8qtXr47ly5ev873dv/3tb+Pwww8vfUgty7L461//Gn/9618jYs13ee+zzz5x2GGHxcc//vGyU70CAADAxuKUpJuJ3h4p85WvfKWsLGxsbIwzzjgjfve738Xjjz8eb775Zrz99ttl30vW8XRKERu3dOqNzkfH9aaQrHTZjsVEb8aPWLfE6FxybG06Xt+WlpZeHTFw6KGHxuOPPx6nnXZaDBgwYJ1/b2pqiltuuSW++MUvxo477hgnn3xyLF68uMuxNuZpViNineuRevw8Htebg45Hxba1tcXrr79e8bqdT4Pc+VS73Vl7VGPEmpK7psbnXYAt28bch3bW2/1Kx+XXdxaDk046qaws3G677eIrX/lK3HDDDfHUU0/FkiVLorm5uWzu2fk7fyude76T7e/uOnRU6dy78zwwxbxy9OjR8fDDD8fFF1/c5b6xra0tHnjggbjoootijz32iEMPPTRmz57dq+0AAACAnigMt0CvvfZaXHnllaXLu+66azz11FPxox/9KD760Y/GbrvtFoMHD17n+8E25mks34nOb5ysWLGi4nUrXbbjG2+9GT9i3dupqzfxtiadT4u69uixSo0aNSp+/OMfx4IFC+Lmm2+Or33ta3HwwQevcwRYW1tb/PznP4/3vve98corr6wzTuejLZ966qmyNx17+9Px+zTzGD+Px/Vanb8jamP/XH311evN7nwKt5dffrni7e68bG9OxbxgwYLS75Weyhdgc7ex9qGd9Xa/0nH5ro7+fvDBB+Omm24qXT7ooINizpw5cckll8TRRx8dEydOjIEDB65z+ugNnXu+k+2P6N0R7D3pPA9MNa/s169ffO1rX4uXXnopHnjggfjOd74TH/7wh7s8Netf/vKXmDx5ctxyyy292hYAAADojkM0tkA33XRT2ekUr7jiihgxYkSP63V8w31TGjx4cNnl3hRUlV6HjhkLFy7s1WkTOx8x1Xl7tzadi6958+b1+nRbEWtOaTZjxoyYMWNGRESsWrUq7r333rj22mvj6quvLn2i/pVXXokTTzwxbrvttrL1O78h1vkUou9U6vG7elzvuOOOFa3b2+fmOz2dak+6O8p0l112Kbv8yCOPxOTJkysat/P3SPamMJw/f37p986PWYAt3Tvdh3bWm7lVc3NzLFu2rHS5q3nPDTfcUPq9qqoqfvnLX65zWs2ubOjcs7cfXuqcszHnbp3H6s2R9Z2X79OnT48fRKuuro7JkyeX9q1ZlsXjjz8e119/ffz0pz8tffimubk5PvWpT8Xzzz9fdop0AAAA2FCOMNwCPfPMM6Xf+/fvHwceeGBF6z300EOpNqlXOhcOjz76aMXrVrrszjvvXPp9xYoV8eyzz1ac8U5KjS1R5+/BmTNnzkYZt7a2Ng4++OC4/PLL48knnyw7xdbtt98eL774Ytnyu+66a9nlhx9+eKNsR17j5/G43hx0fr257777Klrv7bffjlmzZpUu77HHHhV/f9Ty5cvjtddeK1sXYGvW231oZx1fb3vy2GOPlX1QpPP+MqJ87jlx4sSKP7ixoXPP+fPn9+qDPR33ozU1NTF+/PgNyu1KxzllxJoPyvRGx+V33nnnij/AtlZVVVXsvvvucf7558ezzz4bRxxxROnfFi9eHL///e97NR4AAACsj8JwC9Txu9gGDhxY8RsP//3f/51oi3pnr732Kjtl1R//+MeK1/2f//mfipY74IADyi5ff/31FWf84Q9/6Hasrc3ee+9d9hh67LHHNnrGqFGj4txzzy37W+c3Mw855JCorv7/vyRdd911G3UbUo+///77l11O8bhe652cSrWSn8985jPrzd57773LTgl6ww03RHNzc4/bfN1118WqVatKlz/84Q9XfH0fe+yxsu+92meffSpeF2BLV8k+tLObbrqp4u8L7LwP6uqo8Y5zz0qP3lu1atU72tfeeOONFS23fPnysu9K3GOPPdY5pes7sf3228e73/3u0uU//elP0dLSUtG69957b9nRku90Ttm3b9/4j//4j7K/bUkfOgIAAGDzpjDcAnU8ldEbb7wRK1eu7HGdm2++eaMfUbWh6urq4qijjipdfuSRR+Lee+/tcb158+ZV/CnqGTNmlJWSV1xxRbz99ts9rvfII4/EX/7yl9Llfffdd6v/vrSBAwfGnnvuWbr897//PUlOxzfbIiJaW1vLLg8ePLh0KraIiDvvvLPsDcB3KvX4O+64Y7z3ve8tXf7jH/8YL7zwQo/rPfzwwxU9/jcXVVVV8bGPfax0ecmSJfHrX/+6x/V+/OMfl13+p3/6p4ozOx+hMm3atIrXBdga9LQP7Wzu3LllpxFdn6amprjqqqtKlwcOHBiHHnroOst1nHu+9NJLPY4bEfGzn/2s16fv7Ojyyy+vqPT86U9/WjbH++hHP7rBmevT8UMub731VvziF7+oaL3O5d5HPvKRd7wtvX0sAAAAQKUUhlug3XbbrfT7qlWr4v/+3//b7fIvvfRSnHLKKYm3qndOP/30ssuf/exnS9/P05XVq1fHaaedVlE5GhExbNiwOO6440qXX3zxxTjnnHO6XaepqSn+5V/+pey0XGeddVZFeVu6jkXafffdV3Yk2Pr0tli8++67yy53dTqzCy64oOzy//pf/6vXp0h94YUX1lvUpR6/4+O6tbU1TjvttGhra1vvWCtWrIhTTz214qNANhdf+cpXygr5c889NxYvXrze5a+++uqy+//DH/5wr04r2rHY3X333ctOzQewJUqxD+3si1/8YrzxxhvdLnP22WfHq6++Wrp8/PHHR79+/dZZruPcc/78+XHTTTd1O+6sWbN6nHf15OGHH16ncOvsmWeeiW9+85uly7W1tfEv//Iv7yi3K5/73OfKzlJw3nnn9Th/uOaaa8rOWjF+/Pg48sgj11nu0Ucf7VXptyGPBQAAAKiEwnALdNRRR0VdXV3p8tlnn73eI+9uvfXWOOigg2L+/PnRv3//vDaxR4cffnjZUYaPPfZYHHHEEV0WMYsWLYpjjz02brrpprKSoicXXnhhDBw4sHT58ssvj9NPPz2amprWWfb555+Pww8/vOx7ZqZMmVJWOm7NOn7ifeXKlRV9L92xxx4be+65Z1x22WUxb9689S7X3t4eV155ZXz/+98v/W306NFdnlZyv/32iy9/+culywsWLIh99903rrjiim5Pe9nc3BzXX399fPzjH48JEybE7Nmzu1wu9fgnnnhiWRF22223xUc+8pGy05Gt9dJLL8WRRx4Z//jHP3r1uN4cjBo1Ks4888zS5YULF8YhhxzS5XdqXXPNNWUfWKitrY1vfetbFWetWrUq7rzzztLlY445ZkM2GWCzkmIf2lHfvn1j7ty5ceihh3Z5qvEVK1bEZz/72fjJT35S+tvQoUPj61//epfjdX7tPeGEE+Kee+7pctnf/OY3MX369Ghqatrguefa/eI555wTF110UZcfvrn77rvjsMMOi2XLlpX+9rWvfS2GDx++QZndGTt2bHzhC18oXX7rrbdi+vTpXZ6poL29PS677LI48cQTy/7+wx/+sMuvEbj00ktjp512iq9//es9nl707rvvLhu3qqrKfhEAAICNpmZTbwC9N3z48Pjc5z4Xl156aUREvP322/Gxj30s3vve98ZBBx0UAwcOjEWLFsVdd90VTzzxRERE9OnTJ37wgx/Eaaedtik3vcxPfvKT2G+//eK1116LiIj7778/Jk6cGIceemi85z3viZqampgzZ07cfPPNpSML/+M//qOsqOjOjjvuGD/72c/iuOOOi9WrV5cyf/3rX8eRRx4ZY8eOjdbW1pg9e3b85S9/KXszavvtt4//+q//ij59+mzka7152m+//WL8+PGlT8v//ve/r+i0j7Nnz44vfOELcdZZZ8XYsWNj0qRJMXLkyBg4cGC0tLTEyy+/HHfeeWfMnz+/bL0f/vCHZZ/U7+iSSy6JF154ofS9R0uXLo0zzjgjzj333DjwwANj/PjxMXDgwFi5cmUsXrw4Hn/88Zg9e3ZF36OXevza2tr45S9/GQceeGCpmL7xxhtjzJgxMWPGjNh5551j9erV8cQTT8Stt94aq1atiqqqqvj+978fn//85yva/s3FxRdfHA8++GDpDePHHnssJkyYEB/4wAdi4sSJ0dTUFHffffc65er/+T//J3bfffeKc+64446y78761Kc+tVG2H2BTS7EPXeviiy+O8847Lx577LHYa6+94qCDDopJkybFNttsE3Pnzo2bbrqp7LW1uro6rrzyyhg2bFiX402aNCk+8pGPlI6YW7RoURx00EExderUmDx5cvTr1y9ef/31uO2222Lu3LkREdGvX7+4+OKLN+hsDfvvv38MGzYsrr322vj6178eP/rRj+LII4+MkSNHxrJly+L+++9f5yjNyZMnx7/927/1OqtS3/nOd+K+++6LBx98MCLWnCp/+vTpMWnSpJgyZUoMGjQoXn/99bjlllvilVdeKVv3nHPO6fLowrVef/31uOiii+Kiiy6K4cOHx6RJk2Ls2LExaNCg0r//7W9/W2ef+oUvfCEmTJiwca8oAAAAxZWxSVxwwQVZRJR+equlpSU79NBDy8ZY309tbW129dVXZ3Pnzi37+y9+8Yv1jn/CCSeUlttpp50q3q5f/OIXZRlz587tdvknn3wy22GHHSq6HmeeeWaWZVnZ3y644IIet+kPf/hD1q9fv4oyIiKbMGFC9vzzz/c47rRp00rrTJs2rYJbJ9ug7c+yLLvjjjvK1rvjjjsqzqvUxRdfXBp/5MiR2erVq7tdfqeddqr4Nl37U1dXl/3sZz/rcVtWr16dXXDBBVmfPn16nRER2U033bRJx7/77ruzxsbGHseprq7Ovvvd7/bqubk5eeONN7KDDz64otuspqYm+973vtfrjFNOOaU0xpQpUxJcC4D8bex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      "text/plain": [
       "<Figure size 1850x1250 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluator.plot_coexpression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### clustering similarity\n",
    "\n",
    "For the clustering similarity we can look at the similarity (nmi) between the gene set and the full gene set for different total numbers of cluster. This can give us an idea if a gene set is for example more capable to recover coarse clustering structure than fine clustering structure. For larger datasets such behavior could be expected e.g. for DE selections (coarse clustering focused) and PCA based selections (fine clustering focused). Note that this fine level analysis is mainly useful when comparing gene sets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HYF7RCO+GXQvBWX7S5YuIiIiIiEjrYZimabZ0EA21f/9+Jk6cSFpaGgDBwcG43W4qK71DDY4YMYIlS5YQFRXVqHIrKyu54oor+Oyzz3zrQkNDcTgcvuSExWLh73//O3fddVejyrbb7URERFBUVER4eHijjm1LPvr7OjL3FrE42MlGfyd7Yu/FryQdZr0FAy5q6fBERERE6tVRntdERJpKm6mR4HK5mD59OmlpaXTq1InFixdTWlpKWVkZ7777LmFhYWzYsIHrrruu0WU//vjjviTCr371Kw4dOkRxcTHl5eWsXbuWs846C4/Hw9133826deua+tLaheSq5g2jgoMAg/WhE7wb1LxBRERERESkXWkziYTXXnuNLVu2ADB//nymTp0KeGsKzJo1ixdffBGAhQsXsmTJkkaV/frrrwMwYcIEnnvuOZKTk31ljxo1is8//5zQ0FBM0+TDDz9sqktqV6o7XIwrA0z4b/4w74ZdX6p5g4iIiIiISDvSphIJAJMmTeKMM86otf2qq66iR48ewJHEQENlZmYCMHr06Dq3R0RE0LdvXwBKSkoaVXZHkdgzHKufBXeZi0GGH0uKO1MRkgzOUtizuKXDExERERERkSbSJhIJZWVlrFixAoBp06bVuY9hGJx//vkAfP31140qv2fPngD1NlsoKipi9+7dQP3Jho7O5mdl6ETvSBDnVvpjMw1WBZ7t3ajmDSIiIiIiIu1Gm0gk7NixA4/HA8DgwYPr3a96W1ZWFgUFBQ0u/5e//CUA3333HXfccQfp6ekAmKbJ+vXrueiiiygpKeGMM844oT4YOorRF3YnJDIAW7mH0yptvJA31Lth9yJwlLVscCIiIiIiItIk2kQiISMjw7dc3X9BXY7edvQxx3PHHXdw7733YrFY+M9//kPnzp0JCwsjMDCQUaNGsXfvXu6//36WLFmC1Wo9ZlmVlZXY7fYaU0fhH2hj3OW9ATi90o8dZd0pDU4GZxnsaVwtEREREREREWmd2kQiobi42LccHBxc735Hbzv6mOOxWCw88cQTvPLKK4SGhgLevhCqh36sqKigqKiI0tLS45b1xBNPEBER4Zu6dOnS4Djag96j4uncPwqrCZPL/fnedpZ3g5o3iIiIiIiItAttIpHQ3PLy8pgyZQo33XQTZ5xxBsuXL6ewsJDMzEw++ugj4uLieP755xk7dqyv2UN9HnjgAYqKinzTwYMHT9FVtA6GYTD+qr4YVoPeLisfZZ7p3bDna3AcPxEjIiIiIiIirVubSCSEhYX5lsvK6m9rf/S2o485nhtvvJHvvvuOCRMm8NVXXzFu3DgiIiJITExkxowZLF++nNjYWFJSUrj//vuPWVZAQADh4eE1po4mKjGEEVO9NTF6lHamIKCHmjeIiIiIiIi0E20ikZCUlORbPlaNgKO3HX3MsezYsYOFCxcCcPfdd2MYRq194uPjueGGGwD46KOPME2zQWV3ZKOmdccIthLpsbCgaLZ3pZo3iIiIiIiItHltIpEwYMAALBZvqFu3bq13v+ptiYmJREdHN6js7du3+5Z79epV7359+vQBvLUecnJyGlR2R+YfaOPMmd6OF+2FgyhyJcLur6GypIUjExERERERkZPRJhIJwcHBjBs3DoBFixbVuY9pmnz11VcAnHvuuQ0uuzpBAbB///5698vOzvYtV3fIKMc27Mwk7BFWrFj4tvTXmM5y2PNVS4clIiIiIiIiJ6FNJBLA248BwNKlS1m9enWt7R988AEpKSkAvmYIDTFy5Ejf8vPPP1/nPqWlpbz++usADB06lJCQkAaX35EZhkH/C7rixiSjfBBplWPUvEFERERERKSNa1OJhCFDhmCaJjNnzmTJkiUAeDwePvjgA2699VYApk2bxpQpU2ocO3fuXAzDwDAM0tLSamzr1q0b06dPB+Czzz7j+uuvZ9++fZimidPpZOXKlUycONGXpLj77rub+Urbl2lndmVTsAeAH+y34Nz1PVQ2fGhOERERERERaV3aTCLBZrOxYMECunfvTnp6OlOnTiUkJISQkBCuvPJK7HY7I0aM4K233mp02a+88gqjRo0C4M0336R3796Ehob6mlSsXbsWgD/84Q+Nqu0gEOhnJXZsHHbDQ7EnnvVFF8JuNW8QERERERFpq9pMIgGge/fubN68mT/96U8MHjwYwzDw8/Nj1KhR/P3vf2fVqlVERUU1utzY2FhWrVrFSy+9xHnnnUdCQgJOpxObzUbPnj257rrr+OGHH3jqqaea4arav0tHd+HbICcA60tnUPjTNy0ckYiIiIiIiJwow9RYhs3KbrcTERFBUVER4eHhLR1Oi/B4TM7+f0uZml5KvCuErgEbuOjJWzCCIlo6NBERERE9r4mINFKbqpEgbZPFYnDJiCQ+DbJgwcWByhGkfrW0pcMSERERERGRE6BEgpwSl41MptAK7uAdAPyw1MBZ6W7hqERERERERKSxlEiQU6J3fBhDkiP4wj+QUEsOJZVhfP2/TXjcnpYOTURERERERBpBiQQ5ZS4dkcwOkhkW/TZWKknbWsjSN3ZietRNh4iIiIiISFuhRIKcMhcPS8JqsfCp2ZXzIp/GwM3OVVmsmL8X9fkpIiIiIiLSNiiRIKdMXFgAZ/eJ5W33FOLCUpgc8SwAm5YcZP1X+1s4OhEREREREWkIJRLklJoxIplK/HmFS+gf9B1nxX8EwKpPUti6LL2FoxMREREREZHjUSJBTqlzByYSGmDjueLxOILiGGZ5g1HDiwD4/p1d7F2X08IRioiIiIiIyLEokSCnVJC/lXMGJlCJP8virgFgbPmfGHRWIpiw+JVtHNie38JRioiIiIiISH2USJBTbmK/OAD+XXQWhMRjFB1gfP+19B4Vj8dt8uULW8hKKWrhKEVERETkVJg7dy6GYdSaAgMD6dy5MxdffDHvv//+cTvn/vLLL7ntttsYNGgQ0dHR+Pn5ERMTw2mnncbvfvc7Vq9e3aB47rvvPl8M1157bVNcoki7Y5jqLr9Z2e12IiIiKCoqIjw8vKXDaRUOlzoY9dhiPCZsPm8P4d8/ApFdcf9yLV+8uIOD2wsICLEx4+6RxCSFtnS4IiIi0s7pea1lzZ07l0cffRSAhIQE3/qioiIqKip8n6dNm8bHH39MQEBAjeN3797Ntddey9q1a33rrFar7zt1u92+9ZMmTeL9998nNja2zlhcLhddunQhKysLgMDAQDIzM4mMjDzp6xRpT1QjQU65qBB/hneJBOCrwGkQEg+FB7Bue5/zbxtMQo9wKktdfPavjdjzyls2WBERERE5ZbKysnxTaWkpW7du5ZxzzgG8NQ4eeuihGvv/9NNPjB07lrVr1xISEsIDDzzApk2bcDqd5Ofn43A42LZtG3/9619JSEhg6dKlHDp0qN7zf/HFF2RlZTFo0CAmT55MRUUFb7/9drNes0hbpESCtIiJ/eIB+GZvMYy707ty2d/w9zO56NfDiE4KobTIwYJ/baTM7mjBSEVERESkJVgsFgYNGsSCBQvo3bs3AC+++CIulwuA/Px8LrvsMgoLC0lKSmL16tU8/vjjDB06FMMwfGUMHDiQBx98kJSUFG677Tbftrq8/PLLAFx//fXccMMNNdaJyBFKJEiLqO4nYcXefBwjboKQOCjcD5veJTDEj+m/GU5YTCBFueUsfXNnywYrIiIiIi0mMDCQK664AoDi4mJ27vQ+Gz711FO+2gXvvPMOgwYNOmY5wcHBvPjiiwwZMqTO7ZmZmSxcuBCLxcJ1113HzJkzCQkJYf369WzatKkJr0ik7VMiQVrE4KQIYkP9Kal0sS6j8kithB/+Dm4noVEBXHjHUAyLQdrmPA7tLGjZgEVERESkxXTu3Nm3bLfbcblcvPjiiwBMmTKF8ePHN7gsi6XuV6DXXnsNt9vNlClTSE5OJjQ0lJkzZwKqlSDyc0okSIuwWAzG9/HWSvhudw6MvhmCY+FwGmx+D4CYpFAGj08GYPmHe/F41C+oiIiISEeUlpbmW46Ojmbt2rUUFXlH+ZoxY0aTnOOVV14B8DVpALjxxhsBeOutt6isrGyS84i0B0okSIuZUNW84ftdueAfclRfCX8Ht7ft22kX9SAg2Eb+oRJ2/pjZUqGKiIiINJppmpQ5XO1qaokB3+x2O2+99RbgTSL07duXbdu2+baPGDHipM+xbNky9uzZQ1hYGJdddplv/aRJk+jSpQsFBQV88sknJ30ekfbC1tIBSMc1vk8cFgN2ZhWTWVROpzFzYMW/4HCqt1bCiGsJDPVj9AXdWfHhXlZ9mkLvUfH4B+qfrYiIiLR+5U43A//0VUuH0aS2//k8gv1PzbNYYWEh69at47777iMjIwOAO++8E4vFQn5+vm+/6Ojokz5XddOFmTNnEhwc7FtvGAbXX389jz/+OC+//DKzZs066XOJtAeqkSAt5uhhIL/z1Ur4rXfjsr/5aiUMmdiZiLggyu0O1n+1v4WiFREREZHmZhiGb4qKimLq1KmsW7cOgOuuu44//vGPTX5Ou93Ohx9+CNRs1lCtunnDkiVLOHDgQJOfX6Qt0k+70qIm9otn/YFCvtuVw9WndYUxtxyplbDlfRh+DVabhTNn9ubLF7aw8ZuDDDo7mbDowJYOXUREROSYgvysbP/zeS0dRpMK8rM2a/kJCQm+5YCAAGJjYxkxYgTXXnstkyZN8m2LiYnxLRcUnFyn3O+++y5lZWV07dqViRMn1tret29fTj/9dFatWsW8efN45JFHTup8Iu2BEgnSoib2i+Mfi3d7h4F0efD3D4EzfwvfPALfPwVDrgSrjR7DYknqE0nGnkJ+/Hgf58459vA+IiIiIi3NMIxT1gygvcjKymrQfkcP9bhhwwbOPPPMEz5ndbOGAwcO1DuiQ7V58+bxpz/9CcMwTvh8Iu2BmjZIi6oxDOT+w96VY26B4JiqWgkfAN4b8VlX9AED9vyUTVZKUQtGLSIiIiItafTo0URERADw8ccfn3A5W7duZc2aNQ3ef//+/XzzzTcnfD6R9kKJBGlRtYaBBAgIhTN/411e9pSvr4S4rmH0P6MTACs+3NMivQaLiIiISMuz2WzcdtttgLfvgmXLljX4WI/H41uuro0wcuRIiouLjzldeumlwJFhIkU6MiUSpMXVGAay2phbISgaClLgi7sgaysAp1/cE1uAlawUO3vX5rREuCIiIiLSCtx7770kJSUBcPXVV9cYErIu5eXl/OpXv2LLli0AOBwO3nzzTQCuvPJKQkNDjzlVj9jw8ccfn3S/DCJtnRIJ0uKOHgYyo7DcuzIgFMbf411e/zq8MA7+cwYhW55j5HhvNbaVH+/F5XC3UNQiIiIi0pJiY2OZP38+4eHhZGRkMHbsWB588EG2bt3qq7lqmiY7d+7kqaeeolevXjz//PO+bZ9++il5eXmAN5FwPNOnTycoKIjKykreeuut5rswkTZAiQRpcUcPA/n97qNqJZz+K5j1JvS/CKz+kLMdljzK8M3nEOpvp6Sgkk2LdrdM0CIiIiLS4qpHUxg5ciSlpaU88cQTDBkyBH9/f2JiYvD392fAgAHcd999ZGZmct5559GlSxfgSLOGUaNG0aNHj+OeKyQkhGnTpgFq3iCiRIK0ChP7xQPw3a6jmisYBgyYDle9BffshunPQPez8TOcnB7k/R//ui9TKH31JtjyITgrWiByEREREWlJAwYMYN26dXz++efMmTOH/v37Exoait1uJzw8nDFjxnDXXXexbt06Fi1aRExMDAcPHmTx4sVAw2ojVKved+PGjaxfv75ZrkekLTBM9VjXrOx2OxERERQVFREeHt7S4bRamw8VcvGzKwgNsLH+4XPwtx0jx1WUjrnlQz78KIqcss4MDFrMpIj/QL8L4Op3Tl3QIiIi0i7oeU1EpHFUI0FahTqHgaxPRDLGWXdy1q8uBmB7+VTyXD1g10IoPHgKohUREREREem4lEiQVqHOYSCPo1PvSHqPigcMljvuxDSBnV80X5AiIiIiIiKiRIK0HtXDQH63M/c4ex5xxoxeWGwG6fZupFWOhh2fNVd4IiIiIiIighIJ0opUDwO5K/uoYSCPIzw2iOFTvD3vLrPfTnHqbihpeCJCREREREREGkeJBGk16h0G8jhGnd+dyIRgSjyxLMh/mLKNi5opQhEREREREVEiQVqVOoeBPA7/IBsX3zmc0GAHhe7OfPaxP5XlruYKUUREREREpENTIkFalYlV/SSs2JuPw+Vp8HFh0YFccltXgixF5JXGsfDZ9bgc7uYKU0REREREpMNSIkFalUYNA/kzkf0HMr3na/gbpWTsK2HR/7bidjc8GSEiIiIiIiLHp0SCtConMgzk0eJGjubCqL9itbjYvyWfJa/uwPSYjS7H7fJQZnc0+jgREREREZH2TokEaXVOZBhInwHTSfLfwfnR/8BiMdjzUzY/vLcb02xYMsHpcLNpyUHeeOhHXr1/BWlb8hofg4iIiIiISDtma+kARH7u58NAJkUGNfzgxKEQ2ZXuhT8y9dxyvv4qkC3fpxMQ4sfYi3vWe1hluYut3x9i05KDlBc7fetXfLiXrgOjsViVcxMREREREQHVSJBW6ESHgQTAMGDAxQD08XzChKv7AbB2YRobvzlQa/fyEgerPt3H6w+uZNUnKZQXOwmPDWT8VX0JDPWjMLuM7SsyT+p6RERERERE2hMlEqRVOpFhIH0GTPfOdy1i8JlxnH6ptybCig/3smNlBgClhZUs/2APrz+4knVf7sdR7iIqMZipswdy7aOnM2RiZ8Zc2AOANZ+n4qjQcJIiIiIiIiKgpg3SSk3sF8c/Fu/2DQPpb2tEzqvzaRCaACXZkLaMkedNoaLUxcbFB1j6xk72by0gdXMuHpe334S4rmGMmtaNnsPiMCyGr5hBZyex+duDFOWWs/Gbg5x2UY+mvkwREREREZE2RzUSpFU6mWEgsVig/4Xe5R2fYRgGZ17WiwHjOmGasG99Dh6XSafeEVz0m2Fc8cBoeo2Ir5FEALDaLJx+aS8ANiw+QGlRZVNcmoiIiIiISJumRIK0SkcPA/nSDylUutyNK6C6ecPOL8DjxjAMJl7bn6GTO9NzRBwz7h7BZfeMotugGAzDqLeYXiPjSOgRjqvSzU9fpJ3g1YiIiIiIiLQfSiRIq3X12K7YLAZLduZw/ctrKCxzNPzg7mdDYASU5sLB1YA3OXH2lX2ZdvsQkvpENagYb22G3gBsX57B4azSRl+HiIiIiIhIe6JEgrRaY7pH8+rs0wgLsLEmtYDLnl/JwYKyhh1s9YN+F3iXd3x2UnEk9Ymkx7BYTI/Jjx/vO6myRERERERE2jolEqRVO6tPLB/88gw6RQSSklvKjP+sYNPBwoYdXN28YcdnYJonFccZM3phWAxSN+WRsbeB5xcRERGR45o7dy6GYdSYLBYL4eHhdO7cmTPPPJM77riDDz/8EIej/hqqaWlptco51vTqq6+euosUaWeUSJBWr39iOJ/cMY6BncLJK3Fw1X9XsXh79vEP7DUZ/IKh6CBkbjypGKISQxg4rhMAK+fvxTzJxISIiIiI1JaQkEBCQgLx8fEYhkFGRgY//vgj//nPf7jiiitISkrihRdeOG454eHhvrLqm4KCgk7BFYm0T0okSJuQEB7I+784gwl94yh3urn9jbW8/mPasQ/yC4I+53iXT7J5A8CYi3pgC7CSnWonZUPuSZcnIiIiIjVlZWX5pqKiIpxOJ5s3b+bpp5+mR48e5Ofn88tf/pJrr732mD/s/Otf/6pRVl3TrFmzTuGVibQvSiRImxEaYOOlG0dz1ZgueEz406fb+OsX2/F4jlE7YMDF3nkTJBJCIgIYMbULAD9+vA+323PSZYqIiIhI/axWK0OGDOH3v/89W7du5aqrrgLg7bff5sknn2zh6EQ6LiUSpE3xs1p44rIh/OG8fgD874dUfv3Oeiqc9QwP2edcsPpD3m7I3XXS5x9+TleCwv0pyi1n+w8ZJ12eiIiIiDRMcHAwr732GiNGjADgySefpKCgoIWjEumYlEiQNscwDO6Y1Jt/XTUcP6vBwi1ZXPvSagpK6+h8JzAcek70Lu9YcNLn9g+0cdpFPQD46YtUHOWuky5TRERERBrG39+fBx98EAC73c4nn3zSsgGJdFBKJEibdcnwZF6/eSzhgTbW7T/MZf9ZQVpeae0djx69oQkMGNeJyIRgyoudbFh8oEnKFBEREZGGOf/887FarQB8//33LRyNSMekRIK0aWf0imH+L88kOTKItPwyLnt+Jev2H665U78LwLBA5iY4vP+kz2m1WjhjRi8ANi4+QGlh5UmXKSIiIu2QaYKjtH1NrWDkqtDQUHr27AnAvn376tznzjvvJDEx8ZiTiJw4W0sHIHKy+iSE8fEdZzLn1bVsSS/imv+t4l9XDef8wd7hGgmJhW7jIO0H2Pk5nHHHSZ+zx7BYOvWKIHNfEWs+S2HS9QNOukwRERFpZ5xl8HhSS0fRtB7MAP+Qlo6C6OhogHr7SLDb7djt9lMZkkiHohoJ0i7EhwXy7m2nM6V/PJUuD798az0vL089skMTN28wDIMzZ/b2Frkyk/z0kiYpV0RERERO3rx58zBN85iTiJw41UiQdiMkwMaL149i7mfbeHPVAf7y+XYOHS7joQsHYu1/IXx5LxxYBcXZEJZw0udL7BlBrxFx7NuQy+fPbuKiXw8jJjm0Ca5ERERE2gW/YO8v+O2JX3BLRwAcqYkQExPTwpGIdExKJEi7YrNa+Mslg+kcFcyTX+5k3oo0MgrL+b9ZIwhKHgXp62DXFzD65iY531lX9iE/o5TC7DLm/20d5906mG6DdEMTERERwDBaRTOA9qakpISUlBQAevXq1cLRiHRMatog7Y5hGPxiQi/+ffUI/K0WvtqWzdX/W0Vpz2neHbYvaLKOgkKjApl57yiS+0birHDzxXOb2bosvUnKFhEREZHaFi1ahNvtBmDixIktG4xIB6VEgrRb04cl8eYtY4kI8mPjwUJuW1vV+WLKUni6H7w9C757EnYt8jZ3OEGBIX5M/+1w+p+eiOkx+f7tXaz4cA8ej9reiYiIiDQlh8PB448/DkBERASXXnppywYk0kGpaYO0a6f1iOajX53JTfPWsKIAPg8cz4XGcoySbNi9yDtVC0uCpBGQNNw77zQcQuMadB6rzcLkGwcQER/M6gUpbPzmIEW55Zxz8yD8ArzjHGOa3iqOIiIiItJo5eXlzJkzhw0bNgDwwAMPEBkZ2bJBiXRQSiRIu9crLpSPfjmOW177iV8f+gX3WW7mrsGVXNMln+C8LZCxAXJ3QXEG7Mrw9qFQLSQO4gdA/MAj87j+EBhe6zyGYTD6gu6ER/vx7Zu7SN2Uxydzv+CCgQsJsW+E/L3QcwJc+braS4qIiIg0gMfjYfv27Xz99dc8++yzpKZ6R+W6/vrruffee1s4OpGOyzA19kmzstvtREREUFRURHh47ZdPOXXKHW7+8OEmPt+cCUBEkB+/m9qH607vhp+rDLKqkgoZGyBzI+TtAer5zyOiS1ViYQBE9wR7BuTs8CYkCvaRWdGbhYfvp8KMINSSy0VRjxHjd8B7bLdxcM17EBB2Sq5bREREjk3Pay1r7ty5PProowAkJBwZWauyshK73Y7H4/Gti42N5bHHHuP222+vVU5aWho9evQAIDw8nKCgoGOed9asWfzrX/9qiksQ6XBUI0E6jCB/K89eM5KrT8vjL59vZ2dWMY9+tp03Vu3noQsHMKnf6RjdzjhygKPUmxjI2QE526umHVCcCUUHvdOer+s8V6fQdC5PepPPU2+gsDSO+cX/x/nTPXRdezPsXwFvzoRrP6yzZoOIiIhIR5Wd7e23yjAMQkJCSExMpGvXrowYMYIpU6Ywffp0/P39j1uO3W7Hbrcfc5+ioqImiVmkI1KNhGamDHfr5PaYvPfTQZ7+ehf5pQ4Azu4Ty8MXDaRvwnFqCpQVQO7OI4mFglQIT/I2eYjv752HJ4NhUFHqZNGLW0jfXYhhMejR14b14DIMdyVGcDRG70kYfv4YFgPDMDAMMCwG0YnBDDgrCatV/aGKiIg0Nz2viYg0jhIJzUw3ptbNXuHkuaV7mbc8DYfbg8WAa8d2465z+hIdcvxsd0O4XR6+e3MnO1dlNeq46KQQJl3Xn8SeEU0Sh4iIiNRNz2siIo2jREIz042pbdifX8oTC3eyaJv3ZT8s0MadU/owe1wPrJaTH2nBNE32b8mnKK8cTPAUZWKunYfpqMAM7YQ5/FpMWzCmx8Tt9LDjx0wqSpxgwODxyZx+aS8CgtQSSUREpDnoeU1EpHGUSGhmujG1LatS8vnzZ9vZnultUze5fzzPXD2C0IBmeInP2QmvTYfSHO9oEDd8CqHxAJSXOFg5fy87f/QmNkIi/Bl/VT96jmjYcJQiIiLScHpeExFpHCUSmpluTG2P22Py/tqDzF2wjUqXh/6JYbx80xiSI4/d8+8JydvjTSYUZ0JsX7jxMwhL9G0+tLOA797aRVFuOQA9hsUy/qq+hEYFQmUJOMt8yQcRERE5MXpeExFpHCUSmpluTG3XxoOF3PLaWvJKKokLC+ClG0YzrEtk058ofx+8djHYD0F0L28yISLZt9nlcLN2YQobvj6IxwN+VienJyxisOd1LLhg6FVw7mMQqtoKIiIiJ0LPayIijaNEQjPTjaltO3S4jFteW8vOrGIC/Sz888rhTBvSqelPdDgNXp0ORQcgqjvMfBkKD0D6Ou+UsZH88ji+s/+SLGd/AOL9djMp/D/E+u2HwEg451EYcQNYNNKDiIhIY+h5TUSkcZRIaGa6MbV9xRVOfvPOBr7blQvAH87rx68m9sIwTr4TxhoKD8JrF3mTCnUJCMdMGsm2ygv5cWsvHA7vcJE9w7cy1Po2nfx2YHQdCxf9ExIGNW1sIiIi7Zie10REGkeJhGamG1P74HJ7eOyLHby6Mg2Ay0d15vEZQ/C3NfGv/0Xp8MYMKEiBxMGQPAqSR3vnMb19tQ1KCyv54f3d7Fuf6zs0zi+VYcGf0jt4NdYzb4cJ94F/SNPGJyIi0g7peU1EpHGUSGhmujG1L6+tTOPRz7bhMWFsj2heuG4UUSH+TXsSj9s72Y5fbn56CZu/PciuNdm4nR4AgiyHGRy8iMEJ2wi++E/Q7/ymjU9ERKSd0fOaiEjjKJHQzHRjan+W7srhN29voKTSRY/YEF65aQw9Ylv2l//yEgfbfshg63eHKC1yAGDBSd/AHxg6yE7clffV6MBRREREjtDzmohI45xQIuH1119vkpPfcMMNTVJOa6YbU/u0M8vOnFfXkl5YTkSQHw9fNJCLhyU1fVOHRnK7PaSsz2XTN2lk7y/1re8UsJPep3Wmy5QpRCYEN33/DiIiIm2YntdERBrnhBIJFovlpF9EDMPA5XKdVBltgW5M7VdOcQW3vr6OTQcLAegUEcics3pw1WldCQ2wtWxwQFZqEZsXbmPf1lI8ptW3PjQqgM4DounSP4rO/aMJDm/iphkiIiJtjJ7XREQa54QTCSd9YsPA7XY3+rji4mKefvpp5s+fT2pqKlarlb59+3LVVVfxm9/8Bn//k3spysrK4rnnnmPhwoWkpqZSXl5OfHw8AwYMYOLEidx99934+fk1uDzdmNq3Cqebl5enMm9FGnkllQCEBdq4/vRu3DSuO/FhgS0cIZQUlLPrrTc5uKeUTMcAPNT89xuTHErnAVF0GRBNUu9I/AKs9ZQkIiLSPul5TUSkcU4okbB///4mOXm3bt0afd6JEyeSlpYGQHBwMG63m8pK7wvciBEjWLJkCVFRUScUz3vvvcdtt92G3W4HIDAwEH9/f99ngMOHDxMZGdngMnVj6hgqnG4+2ZDOf5elkJLnbVLgb7Uwc1Qyt5zdk15xoS0cIfDjf3AumkuWoz8HQy/joHkGeYdKa+xisRr0GBbL6At6ENu5FcQsIiJyCuh5TUSkcdpMZ4sul4uRI0eyZcsWOnXqxOuvv87UqVPxeDx88MEH3HrrrRQXF3PBBRfwxRdfNLr8Dz74gKuuugqPx8Ntt93GnXfeycCBAwFvLYiNGzfy8ccf85e//IWQkIZ3rKcbU8fi8Zgs3pHNi9/vY/2BQgAMA84ZkMDtE3oxqtuJJbmazNaP4OPbwe2ALqdTPv0NDh0wObSjgIM7DlNcUOHbteeIOMZc2J3YzmEtGLCIiEjz0/OaiEjjtJlEwssvv8wtt9wCwMqVKznjjDNqbH/nnXe45pprAPjmm2+YMmVKg8vOzMxk0KBBHD58mKeffprf//73TRa3bkwd19q0Al74PoVvdmT71o3pHsXt43sxuX88FksLdXiYthzeuQYqiyC2H1z3IUR2xTRN8tNLWbcojb3rcqDq/wxKKIiISHun5zURkcZpM4mE8ePH88MPPzBp0iS+/fbbWttN06RXr16kpqZyww038NprrzW47AceeIAnn3ySESNGsG7duibt0V43JtmbU8z/lqXy8YZ0HG4PAL3jQ7ltfE8uGZ5EgK0F+iTI3g5vXQ72dAhN9CYTEof4NhdklLJ2YSp7jk4oDI9j9IXdieuihIKIiLQvel4TEWmclh2rroHKyspYsWIFANOmTatzH8MwOP/88wH4+uuvG1V+9XCW1113nYbFkybXOz6M/3f5UH64bxK/mNCLsAAbe3NKuPfDzYx/aikvfL8Pe4Xz1AaVMBDmLIb4gVCSBa9Mg5TvfJujk0I495bBXP3wWPqMjgcDUjbm8v5ff2Lh85vJPVh8auMVERGRdmvu3LkYhtGg5/C0tDTfvq+++ioA55xzDoZhMHjw4Aafs6ysjPDwcAzD4Le//W2d+3g8Hj7++GNuvvlmBg4cSExMDH5+fkRFRTF48GCuv/563nrrrRr9qYl0FCc0Rl3Pnj1P+sSGYbBv374G7btjxw48Hu8vucf6H0T1tqysLAoKCoiOjj5u2ampqWRkZAAwatQotmzZwhNPPMHSpUspKCggLi6OcePG8dvf/pZx48Y1KN62oNxVzkd7PuL0TqfTK7JXS4fTISSEB3L/tP7cMakX76w5wCvL08iyV/Dklzt59tu9XDu2K7PH9SAx4hSN9BCRDLO/hHevhf3L4c3L4dLnYegVvl2iEwM59+pOjD7DYO2SPPZsd5G6KY/UTXl07+5k+KWjSeoXrQSciIiItJg5c+bwzTffsG3bNtasWcNpp5123GM++OADiouLfcf/3OrVq7nxxhvZtWuXb53VaiUiIoLS0lK2bdvGtm3bePPNNwkPD2fu3LncddddTXdRIq3cCSUSqjOBJ9MqojEvHtUv+gDJycn17nf0toyMjAYlEnbv3u1bXrFiBY8++igOh4OgoCACAwNJT0/n/fff54MPPuDRRx/l4YcfbnDcrdnTa5/mvV3vYbPYuGXILdw65Fb8rSc3dKY0TFigH7eN78VNZ/ZgwaYMXvx+H3tySnhxWQqvrEjl0uHJzDm7B/0TT0HVyqBIuL6qA8ZtH8NHt8Cq56CiCMoKvHNMooFzgdGxnVlbcgV7Ks4iLc2PtP/bREy8wbDz+9FnTAI2Pw0dKSIiIqfWjBkziI6OpqCggHnz5jUokTBv3jwARo4cybBhw2ps++STT5g1axYOh4OYmBh+97vfMWPGDAYOHOh7h8nJyeGHH37gjTfe4LPPPuO9995TIkE6lBNKJFTr0aMHl19+OcHBwU0VT52qs4XAMc919LajjzmWw4cP+5Yffvhhunbtyv/+9z8mT56MxWJh+/bt3HHHHXz33Xf86U9/YtCgQVx22WX1lldZWekbjhJolVWdMkoymL9nPgAuj4sXNr3AV2lf8eiZjzIifkQLR9dx+NssXD6qM5eNSGbprhxeXJbCmtQCPlh3iA/WHaJfQhgXDu3EhUM7Ne/wkbYAmPkKhHWCVf+BjA219wmIgOAoooNjODdoK2PcRWzaEsKu0jPJzwnk29d38uP8PQya0IXB45MJiQxovnhFREREjhIQEMC1117Lv//9b959913++c9/EhhYfw3Pffv2sWzZMqB2bYSdO3dy3XXX4XA4GDp0KF9++SVJSUm1yoiPj2fmzJnMnDmTbdu28dJLLzXtRYm0cieUSIiKiuLw4cOkpaXxwgsvcMUVVzB79uw2WfW/uskEeDtsnD9/PiNHjvStGzhwIJ999hl9+vQhKyuLRx999JiJhCeeeIJHH320WWM+Wf/d/F9cHhdjO43lyr5X8vjqx0ktSuWGL29gVr9Z3DnyTsL81aHeqWKxGEwZkMCUAQmsP3CY/y3zjvSwK7uYXYuL+cfi3fRPDGP6sCQuHNKJ7rENH360EUHA+U/AwEuhNBeCoyE4BoKiISgKrDX/VxEFTCzN4/RFT7L9xzy2lE2jpDSOtQvTWP/VfnqPimfo5C4kdD9+rQq3y0N5sZOKUqfv72FYwLAYVcs/m1sN/AOsGC016oWIiIi0OnPmzOHf//43hYWFzJ8/n2uvvbbefefNm4dpmgQGBvpGfav20EMPUVpaSkhICB9//HGdSYSfGzRoEP/85z9P+hpE2pITSiRkZmby8ccfM2/ePL755hteeeUV5s2bR9++fbnpppu44YYb6NSpU5MFGRZ25KW2rKys3v2O3nb0MQ0te8qUKTWSCNVCQ0O54447ePjhh9m8eTPZ2dkkJCTUWd4DDzxQY/hIu91Oly5dGhTLqXDQfpBP9n4CwK+H/5rh8cMZ22ks/1j3Dz7a8xHv7XqPpQeX8sexf2Ry18ktG2wHNLJrFM9fN4qicieLt2fz+eYMlu/JY2dWMTuzdvG3r3YxKCmcC4d24qIhSXSNaeLaQF3HNnzfkFgCZ/6dkWdsZPiXD5Cyx2Rz6UVkOgeye002u9dkk9gzgn6nJ+J2eigvcVBe7KS8uGpe9dlR7mp0mFY/C+ExgUTEBxMRG0R4XBAR8UFExAYRFhuI1dom+pEVERGRJjJs2DBGjRrFunXrmDdvXr2JBI/H4+to/bLLLiMyMtK3LTMzk48++giA66+/vkn6hRNpr04okeDv78+sWbOYNWsW6enpzJs3j1dffZVdu3bx4IMP8tBDD3Heeedx8803M336dPz8/E4qyKMzgenp6QwdOrTO/dLT0+s85liO7ldhwIAB9e43cOBA3/L+/fvrTSQEBAQQENB6q3W/sPkF3KabccnjGB4/HICIgAgePfNRLuhxAY/++CgHiw9y59I7OafbOTw49kFig2JbNugOKCLIj8tHdebyUZ0pLHPw9bZsPtucwcp9+WzLsLMtw85Ti3YxvEskj106mMHJES0XbNJwLDcvpPfW+fT++mFyCgLZXHoReyrHk5VSRFZK0XGLsFgMAkJsYBiYHhPTY+KpMQfTc6RPFrfTw+GsMg5n1U4sGgaExQQSHhtEfLcwBpyZRGRC8za/EhERkZY3Z84c1q1bx7fffsv+/fvp1q1brX0WL17MwYMHffsfbenSpb4+4C6++OLmD1ikDTupPhLA+yL+0EMP8dBDD7Fs2TJefvll5s+fz8KFC/nyyy+Jjo7m2muvZfbs2bU6MmmoAQMGYLFY8Hg8bN26td4hILdu3QpAYmJigzpaBG+CwGq14na7j7nf0R1LttUe6lOLUvk85XPAWxvh58Z2GstHF3/E85ue57Vtr7F4/2JWZa7intH3MKP3jDZ73W1dZLA/V47pwpVjulBQ6uCrbVl8sTmTlfvy2HiwkMueX8kj0wdyzWldW+47MgwYcjn0m0b88n8ydcUznOF4g63l55MdOImAhC4ExcUSFOZHUJi/dx7q7/scEGQ7blMF0zQxTfC4PZQWOijKLcOeW07RUZM9txyX04M9rwJ7XgWHdh5m/VcHSOoTyaCzk+g5Ik4dQoqIyCljmiblrvKWDqNJBdmCmvV5IzEx8Zjbj/XMfs0113D33XdTXl7OvHnzmDt3bq19XnnlFcDb19ukSZNqbNu+fbtvefjw4Q0PWqQDOulEwtHGjx/P+PHjee6553j33XeZN28eP/74I//+97/597//zaOPPspDDz3U6HKDg4MZN24cP/zwA4sWLeIPf/hDrX1M0+Srr74C4Nxzz21w2YGBgYwfP56lS5eyY8eOever/h+LYRh07969cRfQSryw6QU8poeJnScyOLbuYTQDbYHcNeouzu9+PnN/nMv2/O08svIR1mSt4cmznzzFEcvPRYf4c/VpXbn6tK7kFFfw4Edb+WZHNn/8eCs/pRbw1xlDCAlo0v+sG8c/BCY/BCOuI+Trhxi74x3gHcgHLAOg00wYdBnE1D/6Sn28Y0aDxWIlIi6IiLigWvuYpkmZ3UFRTjmFOWWkbsxl/9Z8MvYUkrGnkIAQG/1P78TAs5KI7tQMfU2IiIgcpdxVzti3G9FssA1Yfc1qgv2ar6Zfdnb2CR8bERHBzJkzefPNN3nttdd45JFHaiQ9Dh8+zKeffgrA7NmzayVE8vPzfcv1/Si5d+9ezjrrrDq3ffTRR5x55pknHL9IW9IsDYlDQ0O55ZZbWL58Oc8884yvacPJjGBw4403At4qR6tXr661/YMPPiAlJQWAG264oVFlz549G4AlS5awfv36WttLSkr4z3/+A8DYsWOJi4trVPmtwd7De/ky9UsA7hhxx3H3HxAzgLcueIt7Rt+DzbDxRcoXrM6s/XeXlhMfFsj/bhjFA9P6Y7UYfLIxg0ueW8Ge7IaNWNKsorrDrDfhhgXQ/yKw+kPuDlj6GPx7JLw4AVY8A4UHm/S0hmEQEhFAUp9IBo5L4sI7hnH9X89kzEU9CI0KoLLUxaYlB3nn0dV89Pd17FqVictx7NpIIiIicup4ayDWP6Wmph7z+JtvvhnwDlf/7bff1tj21ltvUVlZicVi4aabbjqh+FwuF9nZ2XVODofjhMoUaYua5afL1NRU5s2bx+uvv87BgwcxTRObzXZSv+TfeOON/Otf/2LLli3MnDmT1157jSlTpuDxeJg/fz633norANOmTWPKlCk1jp07d65vJIXU1NRacVx77bU8++yzrFmzhpkzZ/LSSy8xadIkLBYLO3bs4I477iArKwuLxcJf//rXE76GlvSfTf/BxOScbufQP7p/g46xWWzcOOhGDhUf4t1d7/KPdf/gnQvfwWKoI7vWwjAMbp/QixFdo/j12+vZm1PCxc+u4InLhnDpiMb/6t/kek7wTuWFsPML2DofUr6DzI3eafHD0OV0GDwTBl0KofFNHkJYdCCnXdSD0Rd058C2fLb9kMH+rflk7i0ic28RP7y/h35jExl0djLRSaqlICIiTSfIFsTqa9rXDzFBtto1AluTiRMn0rNnT1JSUnjllVdqvBdUN2s499xz6+wMPSYmxrdcUFBQZ+fx/fv3r9HkOS0tjR49ejTlJYi0CU2WSCgvL+eDDz5g3rx5/PDDD76s4cCBA5k9ezbXX3898fEn/pJgs9lYsGABkyZNIi0tjalTpxIcHIzH46GiogKAESNG8NZbbzW6bIvFwqeffsqUKVPYvn27r2w/Pz+Kirwdxfn5+fHcc88xeXLbG8lgV8EuFu9fjIHBL4f9stHH/2LYL1iwbwHb87fzVdpXTOtRdx8V0nJO6xHNwjvP5s53N7Bibz6/e28jq1MLeGT6QAJbQ58AQZEw4lrvVJoH2z+FrR/B/hVwcJV3+vIPYFjBYgPLz+e2oz77QadhMOI66DHBO3RlA1gsBt2HxNJ9SCwlhyvZ+WMG25ZnUFJQyealh9i89BBJfSIZPCGZnsPjsNqUMBMRkZNjGEazNgOQ2gzD4Oabb+ahhx7i448/pqioiIiICDZt2sSGDRuAI7UWfu7oztU3btzYpKPQibQ3J/2kvHLlSm699VYSExOZPXs233//PWFhYdx2222sXr2arVu3cvfdd59UEqFa9+7d2bx5M3/6058YPHgwhmHg5+fHqFGj+Pvf/86qVauIioo6obITExNZv349f//73xkzZgx+fn6Ul5fTvXt3br75ZtavX++r9dDWPLfxOQDO734+faL6NPr4mKAYZg/2Nv94Zv0zON3OJo1PmkZsaACv3zyW307pg2HAO2sOMPP5lezPL23p0GoKiYUxc2D2F/D77XDeE5A82rvNdIO7EpxlUGmH8sNQmgvFmVB0EA6nQf4e2PohvHEp/GsoLH3cu74RQqMCGH1BD65/7Ewu+s0wegyLxTAgY08hX7+0jdceWMGqT/Zhz29fHWSJiIh0BDfddBNWq5Xy8nLeeecd4EhthJiYGC655JI6j5s0aZKv34QFCxacmmBF2ijDPLpuTgNlZmby+uuv8+qrr7J7925M08QwDCZPnszs2bO57LLLCAwMbI542xy73U5ERARFRUWEh4ef8vNvy9vGVV9chcWw8PElH9Mz4sTGwy1zlnHhxxeSV57H/afdz7UD6h6bV1qHZbtz+d17GykodRAWaOPvVwzjvEHH7gW5xZUXgqsCPK6qyX3UcvVnNzhKYOfnsOUDqDhqaMke42HE9TBgOvg1vtplcUEF21dksH15BmVFVW0cDeg2OIbB45PpOigGy3FGlmgM0zRxVrqx+lmwWlX7QUSkJbX081pHd3Qz5OO9mhzdlGDevHn19nVw4YUXsnDhQsaMGcPy5ctJSkoiPz+f3/3ud/zzn/+st/zLL7+c+fPnExISwpYtW47bbOHoeJYuXcrEiROPub9Ie3FCTRu6dOnia7rQvXt3Zs+ezY033kjXrl2bOj45SdW1ES7sceEJJxEAgv2C+eWwX/KXVX/hxU0vcnGviwnzD2uqMKWJje8bxxe/PYtfv72BdfsPc/sb60iODKJ7bDDdYkLoERNCt5hgesSG0CU6uPU0f2ioXpPg3Me8/S5seANSvofUZd4pIAIGX+ZNKiSP9A5N2QBh0YGMnd6T0Rd0J21THluXpXNo52H2b8ln/5Z8wmICGTiuE8ERAZgeE4/bxOMxvctV86PXuyo9OCpcVZMbZ9XcUX7ks2mCX6CVHkNj6T0qnq4DY7D6KakgIiJysubMmcPChQv56aefeOKJJ3wjMtTXrKHaY489xqJFiygtLeXSSy/lyy+/JCkp6VSELNKmnFCNBIvF4hsGcfz48Sc0lqxhGLz88suNPq6tackM98acjVz/5fVYDSsLLl1A1/CTS/S4PC5mfDqDNHsatw65ld+O/G0TRSrNxen28NSinby0PJX6/ks3DEiKCKJbTDDdY0M4rXs05wxMaNlhJBur8ABsfAc2vuldrhaacNQUDyFxP1uO934OjKyzr4XC7DK2Lktn54+ZVJa5mv0y/AOt9BgWR+/R8XQZEK1+GkREThHVSGhZzVEjwel0kpycTG5uLhaLBY/Hw5gxY1izZs1x4/nkk0+YNWsWDoeDmJgYfve73zFjxgwGDhzoe++x2+2sXLmS5557js8//xxQjQTpWE4qkXCiqptCuN3tf9i1lrwx3fr1razKXMWM3jP487g/N0mZSw4s4XdLf0egNZDPZ3xOQkhCk5Qrzaug1EFKbglp+WWk5ZWSll815ZVRUln7BTnIz8o5AxO4ZHgSZ/eJw7+tvNB6PJD2A2x4E3Ys8DaVaIiAcBh5A5z+K4ioPdqFy+Fm77ocUjbmYnpMDIuBxWJgWKvmR382vHM/fyv+QVb8A234B1rxq5r7B9nwD7ThF+jdlp9ewt61Oexdn0NpYeWRkIJt9BgWS+9RCXTuH6WkgohIM1IioWU1RyIB4J577uHpp5/2fX7hhRe4/fbbGxTTqlWruOmmm9i1a5dvndVqJTIyEqfTWWNY+7CwMO69917uueceNe+WDuOEEgkTJ048qURCtaVLl550Ga1dS92Y1matZfZXs7EZNj6/7HOSQ5tmKEDTNLnhyxvYmLuRmX1mMvfMuU1SrrQM0zTJL3VUJRfK2JNdzKJtWezPL/PtExnsx4VDOnHJ8GRGd4tq0n4CmlWFHfL3ejtrLMmB0hzvvCTnyLqSbKgoPHKMxQZDroAzfwMJg05puKbHJCuliL3rvEkFXz8NeJMK3YbEEN0phPDYIMJjg4iIDSIgxNYk/y8WEenolEhoWc2VSNixY4dvJIagoCCysrIa9f16PB4++eQTPvvsM1atWkV2djbFxcWEhobSqVMnRo4cybnnnsvMmTMJCdEQ0tKxnFAiQRquJW5Mpmly81c3szZ7LVf0vYI/nfGnJi2/usmExbDw0cUf0SuyV5OWLy3LNE02HSri043pfLYpk7ySI7+SJ0cGMX1YEpcMT6J/Ylj7eIl1OSDlO1j5jLc2Q7Xe58C430L3sxvcz0JTMT0mmfuK2Ls2m70bcim3O+rczz/QSnhc0JHkQpw3wZDYOwI//1bQ74WISBuhRIKISOMokdDMWuLGtDpzNbd8fQt+Fj8WXraQxJCm763/zm/v5NuD3zKxy0T+PfnfTV6+tA4ut4dVKQV8sjGdRVuzajSD6Bkbwll9YjmzVyxn9IwhItivBSNtIunrYMUz3mYRpse7LmkEjLsTBlwMllP0cu7xeGtKlB/GU3qYzL2HST9kYHclYC9wYM8tp7So7uQCgC3ASo8hMfQenUDXQdHYTrAzzfJiBxl7CsGALgOi8Q9sQ/1miIg0ghIJIiKNo0RCMzvVN6ajmx5c0/8aHhj7QLOcJ6Uohcs+vQy36ebV819lVMKoZjmPtB4VTjdLd+bw6cYMvt2Zg8Pt8W2zGDA4OYJxvWMZ1yuW0d2jWsdIECcqfx/8+BxsfOtIPwtR3eH0OyB5FITGeTtrbMwwk6YJ5Yeh6CAUHYLCg97lkhzv+vKCqvlh71CY1PG/Zv8w6H8hDLkcV+ezsRe6seeVe6fcCoryysk7VExJwZFaJI3pwNFR7iJjTyGHdh3m0K7D5B8q8W2z2ix0GRBFj+Fx9BgWS1Cof8Ov/QRV96cjItLclEgQEWmcE0okLFu2rElOPn78+CYppzU71Tem5enL+eU3vyTAGsCXl31JXHBcs53rzz/+mQ92f8DQuKG8Oe1NPfB3IPYKJyv35rNyXx4r9uaxL7e0xnZ/m4VRXaMY1zuGM3rFMigpvG0mFkrzYM1/Yc3/vC/6P+cfVpVUiD+SXAiJh5BYqLRXJQsOHUkeOEpql3Es/qEQFOWdyvLBnn5kW1AUDLwEBs+EbuN8tSVM0yQnrZg967LZty6HksM1O3DsOTyO3qPiSe4fhek2yUwpIn2nN3GQs78Y01PzlhCTHILL6aEop9y3zjAgqU8kPYbH0XN4HGHRTdexlOkxObT7MNt/yCBlYy7hsUEMn9qFfqcnnnDNChGR41EiQUSkcVpk1AbwDv/ocjX/cGot7VTemEzT5JovrmFr/lZuGHgDfxjzh2Y9X25ZLhd+fCHlrnL+MfEfnNPtnGY9n7ReWUUVrNibx4p9eazcm0+WveZoCTaLQd+EMIZ2jmBI5wiGJkfSLzGs7YwG4SiFDW/BlvfBnuntuNFdf9OCYwqJg4guENEZIrt6h58Mjoag6CNJg+rJdtSv/h4PHPoJtn4I2z72dhhZLTQRBs3wJhU6j/b16VDdgeOedTnsW5dDmb1mB44uhwe360jNEoCIuCCS+0fRuV8UyX2jCA73xzRNCjJLSdmQS8rGXPIO1kyIxHcL89VUiE4MwTiBDjlLiyrZ+WMm25dnYM+rPdpGULg/Qyd1ZvD4ZAJD2kEzGhFpVZRIEBFpnBNOJJz0iTX8Y5MzTZOv9n/Fq1tf5dkpzxIbFNus5wN4dsOzvLj5RbqFd+PjSz7Gz6IH/I7ONE325Zb6aiv8lHaYgtLaL93+Vgv9O4UxODmCockRDE6OICkyiMggv9Y/MoRpQkWRt8ZC6c9GgijN8a4PCDsqYdAFIrp6h5ZsTHOI+rhd3o4ht8739udQUXRkW2RX6DEeOo+B5NEQPwAsVjwek8w9hd6kwvocKkqcAIRE+NO5fzSd+0eR3C+qdu0Cj8ebmDgqeWzPKydlozepkLmvqEYrDP8gG/HdwojvHk5C1RQSGVDnZXg8Jge3F7B9eQZpm/PwVNWG8A+00ve0RPqdnkhWShGblhz01aywBVgZdFYSw6Z0adKaECLSsSmRICLSOCeUSNi/f3+jT5Sbm8tf/vIXPv/8c1+7VyUS2r5SZykXfHQBBRUFPDT2IWb1n9XSIUkrY5omGUUVbDlUyOZDRWxJL2LzoSKKyp117m+1GESH+BMT4k9saAAxoUfNQ7zzkAAb/jYL/lYLATaLd7nq89HLHaK5jasS9n3rTSrsXAjOms1M8A/1dhjZeUzVNBpPUCw5+4sJCLYRmRCMYZpQnOHtG6JgHxSkQH5K1XIqhMbD2b+HEdeDtWaysMzuIHVTLikbcknfU4jbWbOGA3iTFfHdw0noEU58t3BCowLYuy6H7SsyavTnkNgzgoFnJdF7VDx+AUeaMbjdHvb+lM2GxQfIT/den8Vi0HtMPCPO6UZs59Am/IOKSEfU3p/XRESaWrN3tlhWVsbTTz/N008/TXFxMaZp0r9/fx5//HEuvfTS5jx1q9ARbkzv7HyHx1c/TnRgNAsvW0iIn8bRlWMzTZNDh8vZfKiIzemFbDlUxI5MO4fL6k4unKjIYD96xobQKy6UnnGh9IzzLneNDm47zSoaw1EKqcu8TSAO/QTp6+vulyGym7fTSLfDmzQoSAVXee39fi6qO0x8EIZcXucIFm63h4L0UnL228lOs5OTZqcgo5Rj3WUCgm30GxPLwIHlxFhSIW8P5O2G/D3euAIjIb4/xPXHjBvAgeLebFhrI32P3VdG14HR9B2bSGCoHwHBNgKCbAQEe5eP1bmkiEi1jvC8JiLSlJotkeB2u3nhhRd47LHHyMnJwTRNOnfuzNy5c7npppuapHlEW9ARbkxOj5NLP7mUA8UH+OWwX/Kr4b9q6ZCkjXK4PBwuc5BXUkl+iYP80kryih3klVZ9Lqkkv9RBmcNNpcuNw+U5Mrk9ON0N+9+Z1WLQNTrYm2SID6VPfCjjeseSFNkEzQ5aE48bcnfCobVVyYW13s91jQhhsXkTDDG9ILpX1bwHRPWAvUtg2d+8zTYA4gbA5D9C/4tqNHmoi7PSTe6BYm9yIdVOTko+9sNukmLyGRT9Ez3Nr7CVpDX60nICz2RDyQz25fXCpP4YbP4Wb2IhxK8qwWDDP7gq0VD1OeDozyE2/INsBAb74Rdo7Ri1WkSkQzyviYg0pWZJJLz77rs8/PDDpKSkYJomUVFR3H///fzmN78hMLBjtWntKDemr9K+4p7v78FqWBnbaSzndT+PyV0mExkY2dKhSQfi8Zg43B4qq5ILucWV7MstISW3lJS8Et9ymaPuZlV9E0KZ0DeOCX3jGdMjigBbOxwloKLIW1MhcyPYgqoSBj29fStYa/dxUulyY5oQaFbA6hdhxf8d6ZMhaSRMeRh6Tqo/oVB0CFK+q5q+h9IcPKYFi/GzJhDBMRDTB2Krppg+3tjKCiB3B+TsPDKvTmgARa4ENpddSL6rO5VGJJWWaCrdwTgcJ58AMAy8SYejajhU13jwDz5S+yE0OpCI2CDC4wI1soRIG9VRntdERJpKkyYSvv76ax544AE2btyIaZoEBQXxm9/8hvvvv5/IyMimOk2b0lFuTKZpcu+ye1mUtsi3zmbYGNtpLOd2P1dJBWk1TNMk216dYChhX24pmw4VsulgIUePfBjkZ+WMXjFViYU4usd2rCY7JZUuXluZxv9+SAHgTxcNZMaIZIyKIvjxWfjxP0f6Y+h+Nkx+GLqOhfJCb0eQ1cmD/L01C/YLhq6nQ+IQiO17JHkQHN3w4MoKIGfHkcRCzg5IXwuuI6M9eLDhSDidys6TcSScQUVIHxyVJpVlrqrJSWW5q8ZnR7mLiqplj+vEbo2hUQGExwYRERdERHzQUcvBBATZTqjM5mJ6TCpKnZTZHZQVOyi3OwiJDCCpT6RqYkiH01Ge10REmkqTJBLWrl3L/fffz9KlSzFNE6vVyuzZs5k7dy5JSUlNEWeb1dFuTGlFaXy9/2u+TvuaXYd3+dYrqSCtXWGZg+V78/h+Vy7f784lp7iyxvZuMcGM6x1LsJ8Vh7tmk4qj586qOYaBzWJgtRhYDQOb1bvsW2cxsFosBNoshAf5ERZoIzzQ72fLVfNAP0IDbVhPwWgWpZUuXvsxjf8tS6nVZ8WEvnH8dcZgOkcFQ0kuLP8H/PTSkaEwY3p7+1wwj6ptYFi8/TH0nOidOo8BW92jOJwUZznsX+nteHLvEm+S4WiBEd7z95oCvad4R9M4BpfDXSvRUFnmwlF+ZLmyzEVFqZPiggrsueU4Ko7dgbDN34JfgNU32fytNT77BVixBVix+f2so1CjxgwMo1YFkCOfa+5sGN5BRirLXJRXJQzK7N6kQXmJE9NT+xEgJjmE4VO70mdMgvqYkA6joz2viYicrJNKJOzdu5cHH3yQ+fPnU13MjBkzePzxx+nXr1+TBdmWdeQb07GSCmd3Ppu/jPsLEQERLRihSN1M02RnVjHf787l+125rN1f0OD+F5pTiL+VsEBvoiE00OZbDg+0ERrg/RwR5Mfg5AiGJEc0qkPJMoeLN37cz4vLUnzDdfaIDeG3U3qTUVjBv5bsweHyEOJv5b5p/blubDfvMJ1Fh+D7p2DDm2BWvUjH9j2SOOg2DoIiG32tpmmyK7uYiCA/OkWcQN8VRelVSYVvvDUjKgprbo/rX5VUmOyN8SSH5TRN76/7RTnlFOWWY88r9y0X5ZVTbq89BGprERjiR3CEP4EhfuQcKMZV6f0egyP8GTqpM4POTiYwREP7thdldgd5h4oxDIO4rmGt7rs1TZPyYif2vHLK7A56Do87JeftyM9rIiIn4oQSCVlZWTz66KO88sorOJ3eX6wmTJjAk08+ydixY5s8yLZMNyavupIKV/e/mgfHPtjCkYkcX0mlix/35bNu/2FMTAKOGmbSz/qzoSer5gAuj4nbY1bNPbg94PZ4fOudbpMKp5viChf2Cif2cif2ChfFRy3by51UumoPqXg8gX4WRnaN4rQe0ZzWI5oRXaII8q/dfr/c4eat1ft54ft95JV4X3a7xQTz28l9uGR4Ejar91r25ZZw//zN/JR2GIDR3aJ4cuZQesdXDb1YkAJZW721DyKST+TPDMDBgjI+Wp/ORxsOsT+/DIDuMcGc0SuG03vGcEbPGOLDG9nXjsft7Rdi3xJvbYX0tTVrTdgCoduZ0HuqN7kQ1692nw8Vdig6CIUHq+b7vcv2DAgIO9IxZXTPquXuNZITjgoXFSVOnJXueieXw42zwo2regjNqtuz7yZtmuB2Yroqvc04XJXgrKj6XD1V+JZNl8P72fTgH+xHcJdeBPcZRVBiIsFh/gSH+xMY5ofVeiThVFHqZPvyDDZ/e5DSIu+/B1uAlQFndmLY5C5ExLVQh6TOcu93t/Nz79+h3zTv9xWgoT/r4/GYFGaXkXeomPxDJeQdLCH3UEmtpFZYTCDxXcOI6xZGXNcw4ruGExjavMkF02NSUljpTbTllHkTb7lHJmdV7R6L1eD2f0/0Ji2bmZ7XREQa54QSCSEhIVRUVGCaJsOGDeOJJ57g/PPPb4742jzdmGpbdmgZdyy5Az+LH19e9iUJIQktHZJIq1bp8iYbiitclFQnGipclFR6l4uPWs4trmTd/sO1miX4WQ2Gdo70JRYGJ0WwYFMGz3+3j7wSbzOOLtFB/HZyH2aMSPYlEI7m8Zi8tXo/T365k1KHG3+rhTun9uG28T3xq2P/hiqucLJwSybz16ezJrXAtz7Qz4LD5eHnte97xYVwRq8YzugZy9ie0cSGNrKpRFkBpH7vfTHd9y3Y02tuD0/21lJwlFQlDg4c6WCyMcI6HZVc6A5B0d4Ehml6a2+YHm+Sw/RUTW7vNleFN8byAu+8rADK8r2f3U1Qs6H72TD8Ghhwcb0v4m6Xhz1rs9m4+CD56d4hRA0Deo6IY/iZISRG2yEkFkLiweZ/8jHVxVnurVGy7RPYvaj2UKa2QOg1GQZMh77nN66fjSbkdnoozCmjILOUw1llFGaVYrFavC/l3cKI7RKGX0DTd8Jperx9flT3b1FW7KCsyEFBZil5B4spyCg9kpQ6mgGR8cF4PCb23LqHfQ2NDiC+azhxXcOITAj2nc/jMTFNE4/bxPSYmCa+ZY/HxOVw43J4cDrcR5Yrq5fdOKs+lxyuOHY/JAaERgYQERfE+bcPOSW1JvS8JiLSOCeUSLBYjrTf7NKlywkN5WgYBvv27Wv0cW2Nbky1mabJ7K9msy57nWoliDQDj8dkX24Jq1MLWJNawOrUfLLtlfXu3zkqiN9M7s1lIzs3KCGQXljOHz/ewne7cgEY0Cmcp2YOZUjnhjdVcntMlu/NY/66Q3y1LctX68Iw4KzesVw2MpnzBiXi8pj8lFrAj/vyWZWaz7YMOz+/a/VNCGVUt2j6JYTSNzGMvglhDU8umCbk7vK+sO5bAmkrwF3P3yooGiK7QEQX7ygXEV28tS8qiqAgFQ6nemtmFKRB5QkkHhrIbfGnwi+SclsEpdYIii3hFBnhFBJKgRlGvieEHFcI2a4QMh3BHHbauDJyJ1f4Ladz4dojBfkFe5MJw66CHuPB8rOXXY8HsyCFQz9tZ+MqBweyj7yoR9v2E2QpwooTmw2sflas/jZsAX5YAwKwBgZhCwrCGhSKNTgUW0g41tBwrP5+2PwsWP0s2PwsWGzeudXmnSxmJdYDP2DZ/QWWfV9hcRZhwe2tIBLeGQZdCoaBZ/sXOApyqfQE4zCDqTTDcMSNxpEwlsqoITg8wbgcHiw2A6vVwGK1YLEa3nNUfbbaaq+va131vobFoDi/gsNZpRzOLPPOs7y/ph/rScowIKpTSNWv/uHe5ELnUGx11BByOz2Ul1T1Y1HspLy6T4viqg4xi52+zxXFTjx19HFxNFuAldjkEGI7hxHbJZSYzqHEJIX6EhuVZU7v8KwHisk9UEzu/mKK6kkuNDWL1SAsJpCIuGBvh6RVU3hcEOGxp34EFD2viYg0zgknEk76xIaB233sjqnaA92Y6rYmcw1zvp6jWgkip4BpmhwsKGd1aj5rUgtYk1bA/vwykiOD+PXk3swc2blR/SlUl/nJxnT+/Nl2Dpc5sVoMrhrThaTI41d9zy9x8PnmjBodWvaOD2XmyM5cOiLpmH0iFJU5WZ2az48p+fy4L5+dWcV17hcd4k/fhFD6JoTRJyGMfglh9E0IJTL4OL+eO8th/wpI3+D9hduXMOjc8Gr0pgnlh6EgFU/+PgrTd1GSuRd3eRFOj4HTBJcHHB4Dpwccbu+ydw5lbgt5nlAOm2HeqSpBUGiGUkAY5QRwVNeLjZJMLrdF/sTFLCOq4sCRDeHJMPRKb+2JrC2QuRmyt9aoBZDv7MrGsovZXT4eD6e2Xb3FAhY/K1argdtt+vpxaC38g2xEJQZXTSG4XR5y9heTs99OWVHtWiSGxSC6UwjhsYFUlDp9SYPKMlejzx0QbCMozJ+gMD+Cw/yJTAgmtos3WRERF4RxrGYBzgooyYLibN+88nAueekOcnJt5BaGUeyIwPAPwhIYghEYhiUwDMPPhmEYWCzeBIvFAobV8HYg6mfFFmDxLvtbsflbfB2L2vwt+PlbCYkMIDQ68JQ0WWgoPa+JiDTOCSUSHn300SY5+SOPPNIk5bRmujHVTbUSRFpWUZmzSUaCyCup5NHPtvPZpoxGHxsV7MfFw5KYOaozQ5IjTmjIwYJSB6tT8tmaUcTu7BJ2ZxdzoKCs3l+I48MCqpILofSrSjL0TQglLPDkX4zdHpPUvBK2pBex5ZCdrelFbMsootRx4i+9/lYLIQFWQgK8HWqGVE2hAVZC/G011odW7ffzdTaLhR9T8vlyaxYr9+bh8piAyUhjD7NDV3GOZwWB7roTMtgCIX4gdBoKid6pNKgP2YecuB0uXGUluEuLcJcWe5fLy3CXl+OqqMDtcOCqdOJ2unE7PbhNGy7THzd+3rnph9v0x4V37jFteLDioeHDZNr8LfgH2QjwN/E3iwhwZOLvyMTfKMVmOPBgw2Na8Zg23FjxmH64q9dhq1rvh8fwx4M/bsPvqPVWPB4bHtOC27Tg8RiEBHuIirUS1SmEqC6xRHWJJioxmOBw/3r//ZYWVpJzwJtUyK1KLpQXO+vcF8BiMQgK8yMo3J/gMH9fksD32bfej6Aw/9oja5imt5ZMSc7PkgRZUJJ9ZF6SfWJNdgyLt7PSpJGQNNw7TxgEfo3su6SV0fOaiEjjNMnwj1I/3ZjqtzpzNbd8fYtqJYi0A0t35rB4R/Zxq1oD2KwGZ/eJY1K/+EbXhGiIcoebfbkl7MoqZndOMXuyvcvphfVX2U6KCPQlFfomhNEzLhSrxcDl9uB0m7g8HlxuE+dRn51uk0qXm705JVVJAztldSQNAv0sDOwUTveYEEIDq5IB/nW/9Hu3HVnX1H+fwjIHi7dn8+XWLJbvycPh9hCAg8mWDVwdtJrEQCd5IX05HN4Pe+RAHBE9CQoMJLgqcRFcFXdksB9JEUEN/0XZ4/H28VCSXfWCmwOlOUeWy/K8L6cDL8VMGoXH9PbT4HGbVZMHt8s7t1gNAoL88Auy1ugo0seeATu/gIyN4CgGRylUlnhrV1QWe+eOUm9fFCcrMMJbmyM82dvMpXo5MJw6a40YBqYJpSWQk22hrCKAwE5dCY6PqUoY+BMQbGtcUs2eAWnLIe0HOLDK26+HqxHNE6wBEJYAoYl1zBO9/XdkboKMDd5OS0uyapdhsUHcAAhPgpC4qv4z4iA0/shySBwEx4C1dY0SUU3Pa9IU0tLS6NGjBwCpqal07969ZQNqJg899BB//etfefLJJ7nvvvtaOpxmV/3/5KVLlzJx4sSWDeY4nnzySR544AEeeugh/vKXvzTruZRIaGa6MdXPNE1uWnQT63PWq1aCiDS7kkoXe7K9iYXd2cXsqlrOsjfBC2WVID8rA5PCGZIc4RuGs1dcSJ2dV7Y0e4WTb3fksHBLJt/tzsXRyNFBQvyt9EkIo39iGP2qpv6J4USHNFPni03N7axKLlQlFo5ONFQnHo5eriz2/ppvT/e+vFfamy6W8GToNMw7JQ71zsOTao8eAlWJgxXexEHaciiop7+pgAjvi3xYIoQm1JgXGFFkeyJwBMfj8gvHW0fFW5nBY3o7UTSpmlevw3vf9ivLJqxgC2H5Wwkr2EJ4wRb8HYcbfq1BVU2GYvtATB/vPLYPRPcC/+AT+OM1DT2vSVPoCImEQ4cO0bdvX0JDQ0lNTSUkJKSlQ2p2LZVI+Oijj3jxxRfZsGEDdrudTp06ce655/KHP/yB3r1713lMSUkJPXr0oKysjN27d5OcfOIjaR2PEgnNTDemY1OtBBFpaUXlTvZkF/uaRuzOLvYNPWmzGvhZLdgs3rmf1cBWPbd4h//sEh3EkKqkQXVNhrampNLF97tyScsvpczhorTS7Z073JQ73JRWuihzuCl1uCirdFNQ6sDhrjvxEBcW4E0uJBxJLvRJCCXwJDvPc7g87M8vpcLpISLIj4ggP8ICbS3Xzr7C7n2ptx/yzovSvctF6d7EREOU5defCAiOPZJciOzqrRFQV+LAsHj36X4WdDvLO3xpaEKNl3LTNNmTU8JXW7P4ens2W9KbsjNQk2Ty6Gc5SKxRRCxFxBp24i3FJNqKiTOKiKKIULcdC8dJVkV0gZje3im2j3fe/SywNXJklhOg5zVpCh0hkXDjjTfy+uuv87e//Y177rmnpcM5JU51IsE0TebMmcO8efMAb/+EoaGh2O3eBHZwcDAffPABF1xwQZ3HV9dKmD17Nq+88kqzxalEQjPTjenYVCtBRKTtcbk9pOWXsjOrmF1Zxb75gYKyOve3GNA9JuSomgth9EsMp2t0cK3ES4XTTUpuKXtyitmbU8Ke7BL25paQllda1b/DEYYB4YHepEJksHceflSSIcBqwd9WNVkt+FXN/W0WAmzeRNDPtwVU7e/3s2P9rZYmTVqUO9wcOlxGKOVE2HcSmLcVS9YWbzOC3J3e4UDrYli8tRa6n+UdyrPbGd4mFj/j8ZhsOHiYr7Zl8/W2LNLyj3w3hkFV0xQwMLAY3gdlw/A2yDCMqnVUrTMMDPDtX2Nd1bLbY1JQ6iCvpLLOJj4WPERSQpxRSHcjm55GJj2NDHpaMullZBJplNQ6BqDinjQCQ6NO4C/cOHpek6bQ3hMJ6enpdOvWDavVSmZmJtHRLTPs7ql2qhMJTz31lK/JyCOPPMI999xDaGgou3bt4uabb2blypWEhISwZcsW37+3o+Xl5dGpUycADhw44Ftuag3v0UikGRiGwa+G/4pbvr6FD3d/yJzBc1QrQUSklbNZLfSOD6N3fBgXDT2yvrTS5W02clRyYVd2MQWlDlLySknJK+XLrUfa2Af6WeibEEbv+FDs5U725JRwsKCM+rraCK3qU6Ko3Em5041pemuUFJU7OVDQzBcNDEoKZ9rgRM4f3Ine8Q0cxeMolS433+/K5bPNmXyzPZty55EXbsPoRqh/L8ICLycmzMMg2yEGkEJvdyoJ7gzyQ/uSG3saxfGjCQyLJizAjzA/G2EFBmGBZYQH+uFvs7AqNZ+vt2WzeHs2eSVHRkbxt1o4q08s5w5MYOrAhIYPkXoCSitd5JVUkldSSW6xg9ySSvKKqz9XkltSyfaSSvKKHZRXJR2isHuTC1WJhZ5GBhFGKcMD9VIv0lr873//w+12M3369A6TRDjVDh8+zGOPPQbA7bffzty5c33b+vXrx+eff87AgQPJysriT3/6E2+88UatMmJjYznvvPP44osveOWVV/jjH//YLLGqRkIzU4b7+I6ulXBN/2t4YOwDLR2SiIg0EdM0yS2p9CYVjkow7M4uprKefhkigvzoEx9Kn4RQeseH+ZYTwwN9vwxVutzYy10UlTt8yYSicieFZd55SYULh9uDw+U5Mv/ZstPtofKodc6f7ed01/+I1Cc+1JdUGNAprN4OEp1uD8v35vH5pky+3pZFceWRIR5DA2xUutzHPM/JCAuwMXlAPOcOTGRCvzhCA1rf70ellS5yj0oy5JVUklviILe4ktJKF89cPeKUxKHntZY1ceJEvv/+ex555BH++Mc/8swzz/DOO++wd+9eioqKfL8Eezweli5dyqeffsqaNWs4dOgQOTk5hIWFMXjwYK6++mrmzJmDn1/tTj1/XlsgKCiIxx9/nAULFpCZmUlERASTJk1i7ty59O/fv95Y09PT+fOf/8zChQvJyckhLi6Oc845hz/+8Y/YbLbj1kjIysri6aefZuHChaSlpQHQvXt3LrzwQu6++24SEmr/oPbz2A3D4LHHHuOrr74iJyeH5ORkrr76ah544AFfnwVbt27lySef5Pvvvyc3N5cuXbpw4403ct9999X59zke0zTp2rUrhw4d4v333+eKK66oc7/y8nKee+455s+fz86dOykpKSEyMpK4uDjGjBnDxRdfzMyZM+s8duvWrTzzzDMsXbqU9PR0LBYLPXv2ZPr06dx1113ExsbWG19xcTHPPfccn376Kbt27aK0tJT4+HjGjRvHnXfeyRlnnFHvsYcPH+bxxx/no48+Ij09naioKM466yzuv/9+Ro0adUprJLzyyivMmTMH8H7v3bp1q7XPo48+yty5cwkKCiI3N7fOfirefvttrr32Wrp16+b7d9bUlEhoZroxNUx1Xwn+Fn8WXrZQtRJERNo5t8dkf34pu7K8TRgigv3oHR9Kn/gwYkPrH07xVDJN05dkKKl0sWx3Ll9uzWLF3rwaL//dYoI5f3Ai0wZ3YljnCDwmrE7N57NNmSzamsnhsiPDPSaGB3LR0E5MH5bE0M7eJgmVLg/2CifFFS5KKlwUV7gorvpcvf7odSWVR2/3LlcnZeLDAjh3UALnDkzk9J4xzTIySnuk57WWVZ1IuO+++/jhhx9YuXIlNpuNsLAwCgsL+fbbb5k4cWKNF2qA0NBQrFYrRUVH+v04++yz+eqrrwgKCqpxjqOP/fzzz7n55pvJyckhODgYt9tNZaW3Bk94eDjLli1j2LBhteJcv349U6dO5fBhbwejQUFBWK1WSkpKCA8P53//+x+zZs0C6k4kfP/991x66aUUFhYC+F4AS0u9/apERUWxYMECzjrrrHpjnz9/PnPmzKGwsJDw8HBKS0txu92+a1+yZAlff/01V155JWVlZURERGC326l+5Zs1axbvvvtuQ76WGrZs2cLQod4qaJmZmSQmJtbap7i4mLPPPptNmzYB3prHERERlJSU4HJ5k6j1vdg+9dRTPPDAA3g83v+XBQcH43K5cDgcAHTq1IkvvviCESNqJxc3btzI9OnTOXToEABWq5Xg4GCKi4t9cfz1r3/lgQdq/1iZlpbGxIkT2b9/PwD+/v4EBgZit9vx9/fngw8+4JJLLgFOTSLh6quv5t1332XgwIFs27atzn3WrFnD2LFjAVi0aBHnnXderX0OHTpEly5dANi+fTsDBgxo+mBNaVZFRUUmYBYVFbV0KK2ax+Mxb1h4gzn41cHm46seb+lwRERE6lVY5jA/Wn/QvPW1n8y+f1xodrvvc990xuPfmKMfW1xj3ai/fG0+/MkWc01qvul2e5olpkqn2zxcWtls5bd3el5rWRMmTDABMzQ01AwNDTXnzZtnlpWVmaZpmnl5eWZ+fr5pmqZ58OBB89prrzUXLFjgW2eapllcXGzOmzfPTEpKMgHzrrvuqnWO1NRU3wAlUVFR5rhx48yffvrJNE3TdDqd5uLFi81OnTqZgHn22WfXOt5ut5tdu3Y1AbNr167m119/bXo83v/eVq5caQ4aNMiMjIz0nSM1NbXG8QcOHPBtHzhwoLl8+XLftmXLlpn9+vUzATM6Oto8dOhQvbFHRkaaU6ZMMbdt22aapmmWlZWZzzzzjGm1Wk3AfOihh8yIiAhz1qxZZlpamu/v88c//tFXxuLFixv1/ZimaT777LMmYHbp0qXeff7yl7/4rmH+/PlmRUWFaZqm6Xa7zfT0dPP11183b7311lrHvfTSS77v/69//auZmZlpmqZpulwuc+3atebkyZNNwOzcubNZXFxc49iMjAwzPj7eBMzLLrvMXLt2relwOEzTNM3s7Gzz4YcfNm02mwmYH3/8cY1jXS6XOXr0aN+/iffff990Op2maZrmtm3bzLPPPrvGd7p06dJG/90aa/DgwSZgXnnllfXuU1pa6ovp6aefrne/6v8enn/++eYI1VQioZnpxtRwP2b8aA5+dbA58vWRZlZJVkuHIyIiclwlFU7zi80Z5q/fXm8OfPhLX/Jg6NyvzPs+3GQu35NrOl3ulg5TjqO5ntc8Ho/pLi1tV1P1y3NTqk4kAOaCBQtOuJyffvrJBMyQkBCzvLy8xrajX8b79+/vS1QcbcGCBb59Dh48WGPb//t//88ETH9/f3P79u21js3MzDSjoqLqTST84he/8L2wVr8oH+3gwYNmeHi4CZh33HFHvbEPGjTI94J+tOuvv963zznnnFPn93T22WebgDlnzpxa246nuvyLLrqo3n2mTZtmAubjjzf8R0G73e57WV+0aFGd+zidTnPUqFEmYP7zn/+sse3mm282AfOaa66p9xz/+Mc/TMAcNmxYjfXvvfee72/2zTff1DqutLTU7NWr1zETCd26dfNtb+w0YcKEWuVFR0fXmww7WvXf7O677653nwsvvNAEzBtuuOGYZZ2o1tdYTjqssYljGRk/kvU563ll6yvqK0FERFq9kAAbFwzpxAVDOlHhdPNjSj5Ww1CzAgHALC9n18hRLR1Gk+q3fh1GcPDxdzwBgwYNYvr06Sd8/OjRo4mPjycnJ4eNGzdy+umn17nf3XffXavpA8C0adPw9/fH4XCwZcsWOnfu7NtW3RzgiiuuqLOaeGJiIr/4xS944oknam0zTZP3338fgF/84hd1Ngvo3Lkzv/jFL3jqqad49913efbZZ+uM/a677iIgoHZnqeedd56v473777+/zuZh5513Hj/88AObN2+us+xjycjIACAuLq7efSIjIwFv04eGmj9/PoWFhYwYMaLOKvoANpuNq6++mnXr1vHVV1/xu9/9DoCKigrefvttAN8oB3W54YYb+P3vf8+mTZvIzs729UNR/Z2OGzeOKVOm1DouODiYe++9l9tvv73esuPi4qioqGjQtf5cXR1WVjfHCD7Of2PBwcEUFhb69q9LdZ8S1d9dU1MiQVoNwzD45fBfcuvXt/Lh7g+5efDN6itBRETajEA/K5P6xbd0GCJt1rhx4467j8Ph4JVXXuGjjz5i69at5Ofn+9rRH626vXxdqtuX/5zNZiMuLo709HQKCo4MBVOdWACYPHlyveVOnjy5zkRCamqqr7ypU6fWe/w555zDU089RX5+PqmpqXUO7XfaaafVeezRnTSOGTPmmPtU9/HQGLm5uUDdL7/VLrroIt555x2effZZcnNzmTVrFmedddYxO0lcsWIFADt27KgzwVKtvLwcwNeXAcC6det8L/Hnnntug65j//79vr/D2rVrgeN/p8fy008/Nei8LaH6u6r+7pqaEgnSqqhWgoiIiLQXRlAQ/dava+kwmpRRxy/5TSU+/tiJuJycHKZOnep7qQcIDAwkNjYWq9UKeF+aPB6PrwPDuoSFhdW7zWbzvh45nUc6SS0oKPB1FpicnFzvsUfXYPh53NUaenxOTk6diYT6Yq+OuyH7HH1tDVX9wl5XbYhq11xzDWvWrOHf//437777ru8X/969e3Puuedy8803M2pUzRo61b+WV1RUNOiX/bKyslrHAmRnZzfoOo4+vvp7OZHvtLmEhYVRUFBQI866VG8/1r/l6lo3J1pj4niUSJBWRbUSREREpL0wDKPZmgG0R9XJgPrcddddbNmyhZiYGP72t78xbdq0Wr9id+nShUOHDvlGKZCmERMTAxy/NsP//d//8etf/5oPPviAH374gR9//JG9e/eyd+9e/vOf/3DnnXfyf//3f779q0ecOJHRJKqPBW+NhcDAwEYd3xolJSVRUFBAenp6vfuUlZX5Rv5ISkqqd7/qWjDV311TU+M9aXWqayU4PA5e2fpKre2maZJTlsOazDW8v+t9/vbT37hr6V28u/Nd3TRERERE2iGn08lHH30EwLPPPsvs2bNrJRHcbjd5eXlNfu7o6GhfkuNYL3j1bTu6psWxmlwcve14tTNOteq+EY5u8lGf3r1788ADD7Bw4ULy8/P58ccfufTSSwH417/+xYIFC3z7Vn+HRzdZaKijv/8TOb76b3wi32m1MWPGkJiYeELTZZddVqu8wYMHA7B169Z6z3n0tkGDBtW7X/V3dax+LU6GaiRIq/PzWgn9ovuRUZLBfvt+9tv3k2ZPo9xVXuu4bw58w5a8Lcw9Yy5+Vr8WiFxEREREmkNubq6vivaIESPq3Gf58uXNUo3b39+foUOHsmHDBpYuXcrNN99c537ffvttnet79OhBdHQ0BQUFLFmypM6O/QC++eYbwPsLcl3NGlrSwIEDWbBgASkpKY06zmKxcPrpp/Phhx/Ss2dPDhw4wOLFi7n44osBb78Yr732GuvWrSMzM5NOnTo1uOwxY8b4Osf87LPP6NevX6NiGz16NAcPHmTp0qX17lPfd1otNze3wc0qfq6upMw555zDu+++y44dOzhw4ABdu3attc+iRYsAb9OFs846q97yU1NTAersHLQpqEaCtEpH10p4ZOUjvLj5RRalLWJHwQ7KXeVYDSvdwrsxvvN4rh94PTcPvhmrYWXBvgXctvg2iiqLWvoSRERERKSJhIeH+0Yi2LRpU63tLpeLP/7xj812/lmzZgHwwQcfsGvXrlrbc3JyeOGFF+o81jAM3/EvvvgiWVlZtfbJyMjgxRdfBODqq69uqrCbzPjx4wHv376ysrLOfepbD95mK/7+/oA3uVDtiiuuIDIyEqfTye9///tj1i72eDy+Kv0AISEhXHPNNQD8v//3/zhw4MAxr+HnL+7V38ny5cv57rvvau1fXl7O3/72t2OWmZaWhmmaJzTVdc4ZM2YQFhaGaZo8+eSTtbYXFhb6/p3NnDmTkJCQOuOqrKz0/XcyYcKEY17DiVIiQVolwzC497R76RvVl9EJo5nZZyb3jL6Hf0/+NwsuXcBP1/7E5zM+57kpz3HvmHu5a9RdPDflOUL8QlibvZbrFl7HQfvBlr4MEREREWkCoaGhvlEdfv/73/Ptt9/i8XgAb1XvCy64gLVr19b7YnWyfvnLX9K5c2cqKys5//zzWbJkie+ld/Xq1UydOtUXT10efPBBIiMjKSgoYOrUqaxcudK3bcWKFUydOpXCwkKio6O5//77m+UaTsa4ceOw2Ww4HA42btxY5z5jx47lt7/9Ld99912Nzi4zMjL4zW9+w969ewG44IILfNsiIyN9fSa8++67XHjhhaxevdr3t/R4POzYsYOnn36aQYMG8fnnn9c45+OPP05SUhJ5eXmcccYZvPHGGzWGRMzNzWX+/PnMmDGjVoJm5syZjBw50rc8f/58X78LO3bsYNq0ac024kF9oqKieOihhwB44YUX+POf/+z7W+7evZvp06eTmZlJSEgIf/7zn+stZ8OGDTgcDmw2W4NGQzkhpjSroqIiEzCLiopaOpQOYVfBLnPqB1PNwa8ONs965yxzffb6lg5JREREWjk9r7WsCRMmmID5yCOPHHO/tWvXmiEhISZgAmZAQIAZFhZmAqbNZjNff/11s1u3biZgzps3r8axqampvuNSU1PrPUd9x5umaf70009mZGSkr5zg4GAzNDTUBMywsDDzvffeO+Y5vvvuOzMiIsK3T0hISI3riYyMNJctW1bruIbEvnTpUt8+9Zk3b54JmN26dat3n2O55JJLTMB88MEH69xe/bcDTMMwzMjIyBrXB5h33XVXncc+//zzpr+/f43vNiYmxvTz86tx/Jtvvlnr2O3bt5t9+/b17WOxWMzo6Oha5546dWqtY/ft22d26dKlxnmrvyN/f3/z008/9W1bunTpCf3dGsvj8ZizZ8/2nddqtdb4dxMcHGx+8cUXxyzjgQceMAHz0ksvbbY4VSNB2pW+UX15+4K3GRQziMLKQuZ8NYeFKQtbOiwREREROUmjRo1izZo1XHnllcTGxuLxeAgLC+PKK69k5cqVXH/99c16/tGjR7N582ZuueUWkpOTcblcREREcOONN7J+/XpOO+20Yx4/YcIEduzYwd13382AAQPweDyYpsmAAQO455572LFjB2effXazXsPJuP322wF4++2362yC8O677/Loo48yZcoUevTogcPhwOl00q1bN2bNmsWSJUv4xz/+UWfZv/jFL9i1axf33HMPw4YNIyAggMLCQkJDQxk9ejS/+c1vWLx4cZ3NPgYMGMDmzZt58cUXOffcc4mNjcVut2OaJr179+aKK67gv//9L++//36tY3v27MnGjRv5/e9/T48ePTBNk8DAQC6//HJWrlzp68vhVDIMg1deeYUPP/yQc845h6ioKCoqKujWrRu33normzZtqlGr4+dM0+Ttt98GjnxnzRKnWde/AmkydrudiIgIioqKCA8Pb+lwOowyZxkPLn+QJQeWAPDr4b/mtqG3+drWiYiIiFTT85rI8Xk8Hvr27cu+ffv4/vvvff0mSOuybNkyJkyYQK9evdizZ0+zvf+oRoK0S8F+wTw94WluHHgjAM9ufJaHVjyE0+1s4chERERERNoei8XCX/7yF4A6OwKU1uGJJ54A4LHHHmvWH1FVI6GZKcPd8t7f9T6Pr34ct+lmdMJo/m/S/xERENHSYYmIiEgroec1kYYxTZPTTz+dNWvWsHr16uM255BTa/Xq1Zx++umcdtpprFq1qlkTCaqRIO3elf2urDGiw9VfXM3arLUtHZaIiIiISJtiGAYvvvgijzzyyCkf0UCOLzc3l0ceeYT//ve/zd6kWzUSmpky3K3H7sO7+fWSX5NZmgnAzD4z+f3o3xPur+9FRESkI9PzmohI46hGgnQYfaP68uHFH3JF3ysAmL9nPpd8cglfp31dZ8+zIiIiIiIiUpsSCdKhhPuH86cz/sS88+bRPbw7eeV53P393fx26W/JKs1q6fBERERERERaPSUSpEManTiaDy/+kNuH3o7NYuO7g99x6aeX8s7Od/CYnpYOT0REREREpNVSIkE6rABrAL8e8Wvev+h9hsYNpdRZyuOrH+eGL29g7+G9LR2eiIiIiIhIq6TOFpuZOu9pG9weN+/teo9/rf8XZa4ybBYbNw68kTOTzqRnZE9iAmOavedTERERaRl6XhMRaRwlEpqZbkxtS1ZpFn9d9Ve+O/RdjfVh/mH0jOh5ZIrsSY+IHiSFJGG1WFsmWBEREWkSel4TEWkcJRKamW5MbY9pmnxz4Bs+3fspKUUppJek19tvQoA1gJ4RPbmo50Vc2e9KAm2BpzhaEREROVl6XhMRaRwlEpqZbkxtX6W7krSiNFKLUkkpSvFN+4v24/A4fPvFBcUxZ8gcLu97OQHWgBaMWERERBpDz2siIo2jREIz042p/XJ73KSXpLM6azUvbX6JjNIMAOKD4rll6C3M7DMTf6t/C0cpIiIix6PnNRGRxlEioZnpxtQxON1OPtn3Cf/d/F+ySrMASAhO4NYhtzKjzwwlFERERP5/e3ce3kS1/w/8PdmbtSstLaUg+yYiCChrAUGugrugIiiKyxXEBfxerxcB4SeCiBvgLuIVRRCvV0VFxHIFWbSCG/vSQmkpXdKmSdMmTXJ+f4QMDV1IoTvv1/PMk8mcZc7MdJrJJ2fONGK8XiMiqhkGEuoYP5guLm6vG/859B+89edbyHHmAADiDHH+gEL7G6FWqhu4hURERHQ2Xq8REdUMAwl1jB9MFyeX14V1B9fh3T/fRU6JP6AQb4jH6LajkWROQhtLG7Q2tUakLpKPlSQiImpgvF4jIqoZBhLqGD+YLm6lnlJ8evBTvPvXu8gryauQblQb0drcGknmJCSZk9Da1BptzG3QLrwd9Gp9A7SYiIjo4sPrNSKimmEgoY7xg4kAoMRTgvVH12O/dT+OFR3D8aLjOFl8EgKVn35qhRoD4gdgZJuRSE5MhlFjrOcWExERXTx4vUZEVDMMJNQxfjBRVVxeFzKKMnDM7g8sHCs6Jk+5JblyPo1CgwEJAzCqzSgMTRwKg9rQgK0mIiJqfni9RkRUMwwk1DF+MNH5OFxwGBuObcCG9A1Is6XJyzUKDQYmDMSoNqMwJHEIgwpERES1gNdrREQ1w0BCHeMHE10IIQQOFR7Cd+nfYUP6BqQXpctpWqUWg1sNxm2dbkO/uH4ctJGIiOg88XqNiKhmFA3dACKqmiRJ6BjREVN7TcUXN3yBT8d8iik9piDJnASX14WNxzZiyndTcNMXN+GzQ5+h1FPa0E0mIiIiqrFPPvkEo0ePRmxsLNRqNcLDw9GhQweMHTsWy5YtQ2npmWucu+++G5Ik4e6774YQAm+88Qb69u0Ls9kMs9mMgQMH4qOPPqpyXQUFBXj33Xdx2223oUePHoiMjIROp0NSUhLuuOMO7Nixo8qyc+bMgSRJGDp0KABgzZo1GDJkCCIjI2EwGNC7d28sXboUXq+30vJnt/2dd97BwIEDERUVBUmS8P777wfl37x5M2699VYkJCRAq9UiOjoaw4cPx4oVK6pcBwDs3LkTd955J9q2bQudTgeDwYCkpCQMGTIE8+bNw4kTJ6osSxQSQXXKZrMJAMJmszV0U6gZ8fl8Yl/+PjF/+3xxxYdXiO7vdxfd3+8uBn08SLy26zWRU5zT0E0kIiJqMni91rDuueceAUCejEaj0Ov1QcvS0tLk/JMmTRIAxKRJk8S4ceMEAKFQKERERISQJEkuc8899wifz1dhfbNnz5bzKJVKERERIbRarbxMkiTxyiuvVNrWQNkhQ4aIJ598Us4fEREhFAqFXMeoUaNEaWlphfKBtk+cOFHcfPPNQW1XKBRixYoVct7HHnssqE3h4eFCqVTKy4YNGyaKiooqrOP9998P2g9arVaYzeag/Vl+PUTngz0SiJogSZLQObIznu7/NDbeshFP9H4CLQ0tUeAqwJt/vImR60bin1v+ib35exu6qURERERV2rp1K1asWAGFQoGFCxciPz8fdrsdxcXFyMvLw4YNGzBp0iRoNJoKZT///HOsWbMG8+bNQ0FBAaxWK06dOoWpU6cCAFasWIHXXnutQrn4+HjMnj0bqampcDqdsFqtKCkpwdGjRzF9+nQAwOOPP47du3dX2e7ffvsNixYtwtSpU3Hq1ClYrVYUFBRg3rx5kCQJGzZswFNPPVVl+c8++wz//e9/sXjxYrntNpsNo0aNAgAsXboUL730EgDg/vvvR1ZWFgoKCmCz2fDSSy9BpVLhhx9+wJQpU4LqdTqdmDZtGoQQmDBhAg4fPozS0lLYbDY4HA6kpqZi5syZaNGixTmODFH1OEZCHeM9d1RfPD4Pfjj+Az7c9yF255z54Lu8xeW4s8ud6BbdDRHaCOjV+gZsJRERUeNTV9drQgh43L5aq68xUGkUtTou06JFi/B///d/GDlyJDZs2BBSmbvvvhsrV64EAMyaNQvPPvtshTx33XUXPvzwQ0RGRiIzMxM6nS7kNk2dOhXLli3Dvffei3feeScobc6cOZg7d668jg8++KBC+VmzZmH+/PlQqVQ4duwY4uPjK237q6++imnTplUoX1JSglatWsFqteL222+v9DaN1157DY888ggAIDU1Fb179wYA/Pzzz+jXrx8MBgMKCwuhUqlC3m6imuBfFlEzoVKoMLLNSIxsMxJ/5v6JD/d9iO/Sv8OunF3YlbNLzhemCkOENgIRughE6iLl18C8SWOCUW2EQW2AXq2HQWWQ5xUSOzERERGFyuP24a3p/2voZtSq+18ZArVWWWv1hYeHAwByc3Ph9XqhVIZed1hYGGbMmFFp2jPPPIMPP/wQVqsVGzduxJgxY0Ku99prr8WyZcuwdevWavM988wzlS6fOXMmXnzxRZSUlGDdunWVBgsiIiLwwAMPVFp+48aNsFqtAPyBi8r8/e9/x4IFC3Dy5El89NFHciAhsD/dbjfy8/MRGxtb7TYQnS8GEoiaoR4xPbAwZiEe7/04PjnwCb5O+xq5zly4fW6UeEpQ4ilBVnFWjevVq/QwqP2BhTBVGDRKDbRKbdCrRhG8zKA2IN4Yj0RTIhJNiQjXhvMJE0RERAQAGD58OHQ6HXbv3o1Bgwbh3nvvxbBhw9C2bdtzlu3Tp0+VPUg6dOiAVq1a4cSJE0hNTa0QSDh69CiWL1+OlJQUHDlyBHa7HT5fcO+R6gYkTExMRPv27StNM5vN6N27N7Zu3YrU1NRK81xxxRWV3q4BQC6TmJiIjh07VppHqVRi2LBhWLVqVdA62rVrh86dO2P//v3o168fHnroIYwaNQo9evSoUZCG6FwYSCBqxmINsXjk8kfwyOWPQAgBp8cJa4kVVpcV1hIrClwFsJZaYS21oqDUP+9wO1BcVoxiT7H/tawYPuH/YHV6nHB6nMgtyT3vNhnVRiSaEtHK1EoOLrQytUIbcxvEGeJqa9OJiIganEqjwP2vDGnoZtQqlaZ2eye2a9cO77zzDh588EFs374d27dvBwDExMQgOTkZd9xxB8aOHVvpjxAJCQnV1p2QkIATJ04gJycnaPl//vMf3H777XC5XPIys9kMnU4HSZLgdrtRUFCA4uLiaus+17oBVFh3QHVjFATKnGsdrVq1qrAOpVKJ1atX48Ybb0RaWhr+8Y9/4B//+Af0ej2uuuoq3HTTTZg0aRL0et7qSheGgQSii4QkSXJvgkQkhlxOCIFSbymKy4rhLHPKwYUSTwncPjfcXjdcXhfc3rPmff75IlcRTjhOIMOegRxnDhxlDuyz7sM+674K6xrbbixm9Z8FnSr0+xiJiIgaK0mSavU2gObqzjvvxOjRo7F27VqkpKRg27ZtyMjIwJo1a7BmzRoMGjQIX331Va2MX5Gfn4+7774bLpcLw4YNwzPPPIO+ffsiLCxMzrNp0yaMGDHigtdVnbrsHdCzZ0/s378fX331FTZs2IBt27Zhz549+P777/H9999jwYIFWL9+PXr06FFnbaDmj4EEIqqWJEkIU4UhTBUGhJ07f3VKPaXIdGQiw56BDHsGTthPyPPH7cfxxZEvcKTwCF5Ofpm9E4iIiC4ikZGReOCBB+RxA44cOYJ33nkHCxcuxJYtWzBnzhwsWbIkqExmZma1dQbSy//6//XXX6OoqAgRERH48ssvK/1lPjs7+5ztPZ91hypQprpbK8qnV7YOjUaDm266CTfddBMAfwDl008/xT//+U9kZGRg0qRJ2LVrV4VyRKFiIIGI6o1OpUO78HZoF96uQtrPJ3/GE/97Anvy92DcV+OwZOgS9I7t3QCtJCIioobWrl07LFiwABkZGVi1ahU2btxYIU9qaiocDgeMRmOFtMOHD8tftPv06SMvz8jIAAB06tSpyu7933///Tnbl5GRgSNHjqBdu4rXNHa7Hb/++muFdYcqUObEiRM4ePBgpeMkeL1epKSkAPCPt3AuUVFReOCBB6BUKjFlyhTs3r0b+fn5iIqKqnH7iACAQ7ATUaPQt2VfrL5uNTpFdIK11Ir7NtyHNQfWgE+oJSIiar7Kj1NQmcAtBwpFxa8tJSUlWLx4caXl5s+fD8Df0+Hqq6+Wl1ssFgDAwYMHUVpaWqHcb7/9VunjFiszb968SpcHntigUqlw8803h1RXeVdffbX8Bb+qpza8+eabyMryD5x9++23y8tD3Z9A5fuUKFT86yGiRiPBmIAPRn+Aa9pcA4/wYN6OeZi7fS7cXndDN42IiGqJ1+dFkbsIWY4sHCw4iN05u7HlxBZ8m/YtPj34KVbuWYllvy3Di6kvNnRTqR5MnToVt912G9atWxc0aKDD4cAbb7yBDz74AID/kYxns1gsmDdvHhYsWAC73Q4AyMvLw/Tp07Fy5UoAwKxZs6DTnRl7aeTIkVAoFLBarbjzzjvlWxDcbjfWrFmDkSNHwmQynbPdFosFK1euxPTp05GXlwfA3xPhueeew7PPPgsAePjhhxEfH1/jfRIWFiYHED7++GM8+OCDOHXqFADA6XTi1VdfxaOPPgoAGDdunPzoRwBYvXo1BgwYgDfffBNHjx6Vl3u9XmzYsAH/+Mc/AABXXnklIiIiatw2ogDe2kBEjYperceiwYvQObIzXtn1CtYdWocjhUewZOgSxOhjGrp5RERUjt1tx8GCgzhqOwq72y4/+cdRVu7VHfy+xFMScv3TL58OlYKXq81ZWVkZ1q5di7Vr1wIAjEYjVCoVCgsL5TwDBw7E008/XaHsDTfcgNLSUvzzn//ErFmzYDabUVhYKPdmnDhxIh555JGgMh06dMDMmTOxcOFCfPbZZ/jss89gsVjgdDpRVlaGtm3bYv78+bjzzjurbfdll12Gfv36YdGiRXjttdcQHh6OoqIieL1eAMCIESPw/PPPn/d+mTp1Ko4ePYqXXnoJb775Jt566y2Eh4fDbrfD4/EAAJKTk/H2228HlRNCYNu2bdi2bRsAQKvVwmg0oqCgQH68ZXx8PN57773zbhsRwEACETVCkiTh3h73olNkJzz545P4Lfc3jP9qPF5KfgmXxlza0M0jIrroCCGQXZyN/db92F+wHwesB3DAegAnHNUPBlcdrVILg9oAo9rof9UYg9+rjfLjh6n5mjVrFnr37o2UlBTs27cP2dnZcDgcaNGiBXr27Inbb78dEydOrPIpBx9//DGGDBmC9957DwcOHIDBYECPHj3w0EMP4a677qq0zPPPP49u3bph6dKl+PPPP1FWVob27dvjxhtvxJNPPondu3eH1PaFCxfi8ssvx7Jly/Dnn39Co9GgU6dOuOeee/Dwww9f8JMZlixZgjFjxmDZsmX46aefkJ+fD5PJhMsuuwx33XVXpftl7Nix+OCDD5CSkoJdu3bh5MmTsFqtMJlM6NSpE8aMGYOpU6ciPDz8gtpGJAnegFynioqKYLFYYLPZauWRNUQXm2NFxzD9h+k4YjsCtUKNWf1n4cYONzZ0s4iImhQhBIrcRSh0FaKgtADOMifKfGXw+Dwo85VVOZ9fko8DBf6gQZG7qNK64wxxaB/eHpG6SDkAYNQYgwIC5QMFJrUJBrUBaqW6nvdC1Xi91rTcfffdWLlyJSZNmoT333+/Xtc9Z84czJ07F0OGDMHmzZvrdd1EjUmT65Fgt9vx4osvYt26dUhLS4NSqUTHjh0xfvx4TJs2DRqNptbW9eCDD+LNN98EACQlJSE9Pb3W6iai0CSZk7Dq2lV4astTSMlIwTPbnsHbf74NIQR8wgev8MIrvPK8z+eT35s1ZnSK7IQuUV3QJbILOkd2RoIxAZIkNfRmERGdF5fXBYfbAUfZ6ckd/BoIFBSUFqDAVSDPF7oK4RXeC1q3SlLhkvBL0DmyMzpFdEKnyE7oFNEJ4brw2tk4IiJqMppUIOHYsWMYOnSo/IVer9fD5XIhNTUVqampWLVqFTZt2lQrA4ekpKTgrbfeuuB6iOjCGdQGvJz8Mt78/U0s/305MuwZIZUrLSlFTmYOtmRukZeZNCY5qNA5sjO6RnVFG3MbKBUX1v2QiELj8XngcDtgd9thL7P7X8tP5ZYVuYvO5D09+eBDmCrsnJNWqYVCUkCSJEiQIEkSFFAAEqCAf7lC8o85rZAUkHAmn5z/dLoE/3xgUkrK4HnFmWVKSQm31x20DeW3y+F2yMtKPCVyPeXbGFh3YLmAgLPMCUeZA2W+sgva/wa1AeHacBjUBmgUGqiVaqgUKqgV6qBJpVBBrVTDoDagY0RHdI7sjEssl0CjrL0fbIiIqOlqMoEEj8eDMWPGID09HS1btsQHH3yAESNGwOfzYe3atfLzUCdMmID169df0LqcTiemTJkClUqFnj17IjU1tZa2gojOl0JS4KHLHsJ1l1yHnJIcKCVlpRfw5V/zSvKwz7oP+637sS9/Hw4VHoLdbcfP2T/j5+yf5bpVChVahLVAjD4GLfQt0ELfAjFh/vkYfYycZlQb2ZuBLnpenxeFrsIqv/wHfXl22+EocwQFBZwe5wW3obisuBa2pGmTbyEodxuBUWNEuDYc4dpwROgiEKGN8L+eng/XhUOr1DZ004mIqBloMoGElStX4s8//wQArFu3DldeeSUA//NPx40bB5/PhzvuuANff/01Nm3ahOHDh5/3up5++mkcOXIETz/9NE6cOMFAAlEjkmhORKI5MaS8rUytcFmLy+T3Zd4yHLEdwb78fXKAYb91P0o8JcgqzkJWcVa19elVenSP7o4+cX3QJ7YPLo25lBflFJJcZy5KPaWIN8Y3+t4vXp8XuSW5yHRkIsuRhUxHJk4Wn5Tfnyw+CY/Pc8HrCVOFwaQxwawxw6g2wqQxyZNZY4ZRc2aZWW2GSWOCUWOEUlKixFOCEk8JnB6nPF/iKUFJ2Zl5l9cFASHfBiXgHxIqMC+EgMDptNPzQgj4cPp9+fRy9ci3UZ2eL//eK7wQQkClUMGsMQdtU2XbFaYKA4S/TYH1Vpg/Pdhg+bEH9Cp9o/87IiKi5q3JDLY4ePBgbNmyBcnJyfjhhx8qpAsh0K5dO6SlpWHixInys2NraseOHRgwYADat2+P33//HQ8++CBWrlx53mMkcPAeosbN6/Mix5mDnJIc5DpzkePMQW6J/zXHeXpZSQ7sbnuFshqFBj1b9ESf2D64Iu4KBhZIVuYrw+85v2NL5hZszdyKgwUHAfhHqb/EcgnahbdDu/B2aB/eHu3C2yHBmCB3o69rHp8HOc4cOTCQVZzlfz09ZRdnwyPOHSgwqU1BX4pNGhNMalOFL89BX6TV/i/SRo0RakXjGWiPiNdrREQ10yR6JDidTvz0008AgNGjR1eaR5IkXHPNNXj99dfx3Xffndd6XC4XJk+eDCEE3nrrLeh0uvNuMxE1DUqFEi2NLdHS2LLafCWeEmTaM7ErZxd+yf4FqadSkVeSh1+yf8Ev2b/g9d9fh1qhxqUxl6JPbB+0NLSEUuG//UKlUPlvxVAooZJUQcvVCjW0Si10Kh10Sh20Kq3/VanlL45NTI4zBz9l/oQtmVuwPWs7HGUOOU2CBLVCDZfXhX1Wf4+Y8sJUYWhraYv24e3RytQKCijkX9ADr2degpcHfjkPzAeU/wU+ryRPDhSccp4656B7KoUKLQ0tEW+IR7zRPyUYE/zzhnjE6GOgUjSJSwgiIiKqA03iKmDfvn3w+fxd+7p3715lvkBadnY2rFYrIiMja7SeZ599Fvv27cN9992HIUOGnH+DiajZCVOFoX1Ee7SPaI/bOt0GIQTSi9LloEJqdipyS3Lx66lf8eupX2tlnWqFWg4uGNQGRIdFB43lEBMWEzSvV+trZb0AYHPZcNR2FEcKj6DIXVRlQEQhKeR5rVIrtylSF1njX9hdXpfclT7Tnoms4iyU+cqggAIKhcL/Wm6AO0mS5Fd5GSS5XZWVq7I8FFAqTpc/vexs8hf6cjw+D3ad2oWtmVtxoOBAUFqENgIDEgZgYMJAXBV/FcwaMzIdmThceFiejhQeQZotDSWeEuzN34u9+XtrdqDOk0qhkoMECcYEf9CgXMAgJiyGgSwiIiKqUpMIJGRlnblvOSEhocp85dOysrJqFEjYvXs3Fi1ahNjYWLzwwgvn11D4ezW4XC75fVFR5c9cJqKmTZIktLW0RVtLWzmwcNx+HL9k/4LdObtR5C6C1+d/NKXX54VHeOT3Hp9HXu7yuuDyulDqLYXL44Lb55bXEXieu73MjrySPBwrOlZtm4xqY9DgkOXnywceArdfCCFgLbXKAYOjtqM4WngUR2xHkFeSd0H7RyWpEK2P9g9eGdZCHrgyVh8Li9aCvJI8nLCfCLoPP7ck94LW2dAkSOge3R2DEgZhYMJAdI3qWuHLeGtza7Q2t8aw1sPkZR6fBxn2DBwpPILDhYdxynkqqM6g17MCHGcvr+p9pC4yqFdBdFh0vd1KQURERM1Pkwgk2O1n7k3W66v+xa18Wvky5+LxeDB58mR4PB68+uqrCA8PP692AsCCBQswd+7c8y5PRE2TJElIMichyZyEWzrect71BAUXPKX+AIPXBbvbH0woP25DrjNXHs+hxFPif5a8zYE0W1q167BoLYjWRSO/NB+FrsIq88UZ4tDO0g5RYVFy4CMQCPEJX1BwxOvzosRTghxnDqylVniEB9nF2cguzq7R9utVeiSYEvxfeA3xCFOFBQ1oJyDg9Z1+PT2wXWWD31WYcDqP78xAdtWV8Qpvpb0SAl/My79vF94OAxMGYkDCAETqatYTDvD3DggEpUYkjahxeSIiIqL61iQCCXXt+eefx2+//YbrrrsOt9122wXV9dRTT+Hxxx+X3xcVFSExMbQR5omIlAol9Ap9jW5TEEKguKxYDirkluQGDRxZft7ldcHmssHmsgHwfxFOMCagXXg7XBJ+CdpZ/IMAtrW0hUFtOK9tKPOVIb8kXx6wUh648nT7Cl2FiAqLQoIhQQ4atDK2QrwxHuHacD5ik4iIiKiRaxKBBJPJJM87nVU/f7p8Wvky1dm7dy/mzZsHo9GI5cuXn38jT9NqtdBqOWo7EdUfSZLkkfDbWtpWmU8IgSJ3kdyTIUIXgSRzkv8RdLVIrVAjzhCHOENcrdZLRERERI1DkwgkxMfHy/OZmZm49NJLK82XmZlZaZnqPPzww3C73Zg7dy4iIiLgcDiC0j0e/yOwhBBymlarhVrNx1YRUdMiSRIsWgssWgvaR7Rv6OYQERERURPVJEZa6tKlCxQKf1P/+uuvKvMF0uLi4kIeaDEtzX8v8VNPPQWTyVRhWrVqFQDg+PHj8rJly5ZdyOYQERERERERNVlNIpCg1+sxYMAAAMC3335baR4hBDZs2AAAGDlyZL21jYiIiIiIiOhi0iQCCQAwadIkAEBKSgp27txZIX3t2rU4evQoAGDixIkh15ueng4hRJVTYL1JSUnyskcfffTCN4iIiIiIiIioCWpSgYQePXpACIGbb74ZmzZtAgD4fD6sXbsWU6ZMAQCMHj0aw4cPDyo7Z84cSJIESZKQnp5e300nIiIiIiIiajaaxGCLAKBSqfDFF18gOTkZ6enpGDFiBPR6PXw+H0pLSwEAvXr1ksc0ICIiIiIiIqLa12R6JABAmzZt8Mcff+CZZ55B9+7dIUkS1Go1evfujcWLF2PHjh2IiIho6GYSERERERERNVuSEEI0dCOas6KiIlgsFthsNpjN5oZuDhERERGdhddrREQ106R6JBARERERERFRw2IggYiIiIiIiIhCxkACEREREREREYWMgQQiIiIiIiIiChkDCUREREREREQUMgYSiIiIiIiIiChkDCQQERERERERUcgYSCAiIiIiIiKikDGQQEREREREREQhYyCBiIiIiIiIiELGQAIRERERERERhYyBBCIiIiIiIiIKGQMJRERERERERBQyBhKIiIiIiIiIKGQMJBARERERERFRyBhIICIiIiIiIqKQMZBARERERERERCFjIIGIiIiIiIiIQsZAAhERERERERGFjIEEIiIiIiIiIgoZAwlEREREREREFDIGEoiIiIiIiIgoZAwkEBEREREREVHIGEggIiIiIiIiopAxkEBEREREREREIWMggYiIiIiIiIhCxkACEREREREREYWMgQQiIiIiIiIiChkDCUREREREREQUMgYSiIiIiIiIiChkDCQQERERERERUcgYSCAiIiIiIiKikDGQQEREREREREQhYyCBiIiIiIiIiELGQEIz5C0sbOgmEBERERERUTPFQEIzk7/ifRwZdQ1chw41dFOIiIiIiIioGWIgoRkRZWWwf/cdvDYbjk+5H2UnTzZ0k4iIiIiIiKiZYSChGZHUaiS+vhyadu3gyc7G8fum8DYHIiIiIiIiqlWSEEI0dCOas6KiIlgsFthsNpjN5npZZ9nJk0gffzs8p04hrFcvtH7vXSjCwupl3URERNQ8eB0OuI8da9A26Lp2hSRJdb6ehrheIyJqyhhIqGMN9cHkOnQI6XdOgK+oCMbkZLR67VVIKlW9rZ+IiIiaFiEEXAcPwfHj/1D84xY4d+8GPJ4GbVPnPX9BUirrfD0MJBAR1Qy/WTZT2g4dkPj6chyffC8cKSk4OWcOWs6bVy9RfSIiImoavA4HirdtQ/GWLXD8uAWeU6eC0pVRUZDU6gZqHRERNVYMJDRj+t69kfDiYpx4ZDpsn66DKiYGLaZPb+hmERERUR0SHg+EywWf2w3hdkO4XP73rsD7UpT89VelvQ4knQ76fn1hHDQYxsGDoGndugG3hIiIGisGEpo504gRiJs9G9mzZyP/9TegiolB5B13NHSziIiI6ByEzwef3Q5vQQE8BQXwnp48Viu81tPzBVZ4CwrhtVrhLSiAr7QU8HprtB5NmzYwDB4E46DB0F/RBwqdro62iIiImgsGEi4CEeNugyc3F3lLl+LUvPlQRUbBfM2ohm4WERHRRUMOChQWngkMFNrk4IC3sADewsLTAYNCf77CwhoHBSpQqaDQaCBptZDkVzU0Ca1gGDSIvQ6IiOi8MJBwkYh++O/w5Oai8JNPkDVzJpQRETD069vQzSIiImqShNfrDwjkW+G15p95zcuXAwO1FRSQ9HqoIiKgjIyEMjICqvBy84HlERFQhodDYTAEBw7qYaBCIiK6+DCQcJGQJAlxz8yC15oP+8bvceLhh5H04b+h69y5oZtGRETU6Agh4MnKQumhQ3AdPATX4UPwnMo5EzQoKADO48FXCr1e/tKvDA/3BwHCw6GMCPcHBQJpERFQhkdAGREOhUZTB1tIRER0/vj4xzrW2B4n5HO5cPzee1GS+iuUMdFo8djjULdKgCYhAarYWD4ikoiILjpemw2ugwdRevCgP2hw8CBchw7B53BUX1CSoIyIgCoqEsrIKP9rVPRZQQF/MIBBgcatsV2vERE1dgwk1LHG+MHkLSrCsTsnwHXoUHCCUgl1bCzUCQn+KT5entd17w6l0dAwDSYiIqoFPrcb7iNHKgQNzn7koUylgrZtW2g7doS2Y0eo4+Ohio46EzQID2cAvplojNdrRESNGQMJdayxfjB5cnOR99bbcB85DHdmJjxZJyHKyqrMr7BYEHXfvYicMAGKsLB6bCkREV3sfG43irduRdE338KTlwul2QKlxQKlxQyF2eyfN/vfKy0WKMwWwOeF69ChoKCBOz29ynEKVPEtoevQUQ4aaDt2hLZtG0jsQXBRaKzXa0REjRUDCXWsqXwwCZ8Pntw8lGVmoiwry/96enIdOQJPdjYAQBUTg+i/P4Twm2/mxRUREdUZ4fGgeOdOFH39Newbv4evqKhW6lVYLNB16HA6WHD6tUMHKE2mWqmfmqamcr1GRNRYMJBQx5rDB5PwemH74kvkvfYayrKyAADqxETEPDIN5muvhaRQNHALiYioORA+H0p++w1FX61H0YYN8Obny2mq2FiYR4+GrmsXeIvs8NoK4Ssq8j9CsajIP9kK4bMVwWuzAQA07dpBFwgWnJ5ULVpAkqSG2kRqpJrD9RoRUX1iIKGONacPJp/bjcI1a5H3xhvw5uUBALQdOyLm0UdhTB7KCzMiIqqUEAKirAzC7T4zlXvvczhg37wZRd98A0/WSbmcMjwcpmtGwfy3v0Hfp0+NAtdCCH4uUcia0/UaEVF9YCChjjXHDyaf0wnrB/9G/rvvwme3AwDCLrsMMY89BkO/vg3cOiIiqm2+4mK4Dh+Wxxooy8qqPChQPlhwet5XVgZUMwbP2RQGA0wjRsB87d9guPJKSGp1HW4ZkV9zvF4jIqpLDCTUseb8weQtLET+u+/C+u8PIUpLAQCGAQMQM/0RhF16aQO3joiIakp4vXAfOw7XwQNBgxSWZWQAtXm5oFJB0migUKshaTSQNBrounWD+dprYRwyGAqdrvbWRRSC5ny9RkRUFxhIqGMXwwdT2akc5L3xOgrXfgp4PAAAY3IyYh6ZBl2XLg3cOiIiqozweOA6cgSlf/2Fkr/+Qulfe+A6eBDC5ao0vzI62j/eQIeO0LRJgqQLg6RWQ9L4gwEKjeb0e39goNp5jq1DjczFcL1GRFSbGEioYxfTB5M7IwN5y5bD9sUXgM8HADCNHImYaVOh7dChgVtHRHTxEl4v3Onpp4MGe1D6558o3b9f7k1WnhQWBm379tB27ABdp05nBimMjGyAlhPVj4vpeo2IqDYwkFDHLsYPJtfRNOQtW4air7/2d4WVJJj/9jdEP/wwtJe0bejmERHVK29hIYq3b4fPWQJt+3bQtGsPpdFQa/ULIeBzOODJyfFPubnw5OSgLDCffQquAwfgczorlFUYjdB16wZd924I694dui5doG7dmj0G6KJzMV6vERFdCAYS6tjF/MFUevAg8l5bCvvGjf4FCgUsY8ci+uG/Q5OY2LCNIyKqI0IIuA4ehON/P8Lxv/+hZPduuZdWgCq+pf9X//Yd/K8dOkDb7hIo9PqgenzFxRUCBIH5spwceHL8yyrrWXA2KSwMuq5dEda9G3Tdu0PXrbv/FgUGDYgu6us1IqLzwUBCHeMHE1C6dy9yX30Njs2b/QtUKoTfdBNazHgCyot0nxBR8+IrLkbxzp1wbP4fHD/+CE92dlC6tkN7KKOj4T58BJ7c3CrrUbdqBVWLFvDk58GTmwdRSS+CqijMZqhiYqBqEQN1ixZQtWjhfx8TA027dtC2awdJqTzvbSRqzni9RkRUMwwk1DF+MJ1R8vvvyH1tKYq3bgUAqJNaI3HpUo6fQERNgq+0FF6rFR5rAbwFVnitVpTl5MC5YyecP/8MUe4Rh5JOB0P//jAOGQzj4MFQJyTIad7CQrgOH/ZPhw7L8978/ErXqzCZTgcIWgQHCgLLTs8rwsLqfB8QNVe8XiMiqhkGEuoYP5gqcv7yCzL/7//gyToJhV6Pls8vgHnkyIZuFhFd5LyOYrj270Ppnj0oPXQI3tw8eAoK4LX6gwaVjTFQnrpVKxiHDIFxyGDo+/at8SMMPVYrXIcOw1tghSo6Wu5NUP52ByKqG7xeIyKqGQYS6hg/mCrnsVqR+djjcO7cCQCIeuABxDwyjd1uiaheeIuKULp3H0r37vUHDvbuhTs93T9AbHXUaqgiIqCMjIQqMgLKiEjounaFcegQaC65BJIk1Uv7iah28XqNiKhmGEioY/xgqprweJDzwmJYV64EABgGD0LCCy9AabE0cMuIqDnxFBT4AwZ796J0j/+17PjxSvOq4uL8TzHo3BmquFioIiOhjDgdNIiKgsJoZLCAqBni9RoRUc0wkFDH+MF0brYvv8TJWc9AlJZC3bo1Wi19DbqOHRu6WUQUIuF2o/iXX1B2IhNhPbpD26lTg/Uu8uTnn+llsMf/WpaVVWledUICdF27+gMH3bpC17UrVFFR9dxiImoMeL1GRFQzqoZuAJFlzBho27XDianTUHb8ONLH3474556D+ZpRDd00IqqC12aD48cfYf/hBxRv2QqfwyGnKcxm6C+/HPorroD+ij7Qde0KSXXhHzfC54PXZoM3Lw+e/Hx48vLhycuFNz8friNHUbp3b4WnJQSoW7f2Bw26dj0TNIiIuOA2EREREV2M2COhjjHCHTpPQQEyH38czu07AABR99+PmOmPcNwEokbCffw4HCkpsP+QAmdqKuD1ymnK6Gho27VD6Z9/VhiUUKHXI6xcYCGse3dAkuC12+EttMFXZIO3qAheW5E/UFBkg+/0vMeaD29evj9wkJ8PeDzVN1KSoGnT5kxPg65doevahY+aJaJq8XqNiKhmGEioY/xgqhnh8SBnyUuwvvceAMAwaBCi7p0MdatEqFvGMahAVI+8djtK9+xF8bZtcKT8ANehw0Hp2g4dYBw2DKZhydD16AFJoYDweFC6bz+cv/zin379Fb6iouCKlcqgIERNKS0WKKOjoYqKgio6GsroKGgSEqDr1g3azl2gNBrOu24iujjxeo2IqGYYSKhj/GA6P7av1uPkv/4FUVp6ZqFaDU18PNStW0OT2ArqxNbQtE6EulUiNImtavURaWWnTqF0zx4owsKgMJqgNBmhMJn8k0ZTa+shaiw8BQXyQISBqcKAhEol9FdcAdOwZBiTk6FJTDxnvcLng+vgQTh/Ph1YSE2Ft6BATleYTFCazVBYzP4AgdkCpdkMZbgFCrPZP9hhVBRU0TFQRUdBFRkJiecgEdUyXq8REdUMAwl1jB9M5690/37kLVsG1+EjKDtxAqKsrNr82k6dYB59DczXXANNmzY1Xp/P6YT9++9h+/y/KN6+vcrHwElq9emgghFKowlKiwW67t1h6N8PYb16QREWVuN1C58Ppfv2wbl9O4q37wCED6arr4Zp1CioIiNrXB9dnLw2G0r37IHP7fb//QoAEKfnBYTP518mBODzwpWWJj8C0XPyZKV1qhMSENazJ4zDhsE4aOAFP1VFCAHPqVOQtFooTaZaGTuBiOhC8XqNiKhmGEioY/xgqh3C64Xn1Cm4M06gLOM43Mcz4M44jrKME3BnZMBnswXl13bpAvM118B8zShokpKqrtfng/OXVNg+/xz2DRuC7u3WduoE+HzwOhzw2e1Bg8lVRVKrEdazJ/T9+/sDC5deWuWvp2WZmXBs2yYHD8r/SitTKmHo3x/mv/0NpqtH8D5vCuJzOuH8dRecO3egePsOlO7dW2UALBSapCT/QISBsQW6dIEyPLz2GkxE1Ejxeo2IqGYYSKhj/GCqH578fDg2b0bRN9/6exOUu/9a17UrTIGeCqe7YrvT01H43/+i6L9fBD0aTt26NSzXj4Vl7NgK3baFzwdfcTF8dju8dgd8Dju8djs8ubkoSf0VxTt3VhgxXgoL849e368f9Ff0gScvD8XbtsG5bTvcx44F5VXo9dD36wfDlVdClJWh6JtvUPrXX2cyqNUwDhzoDyoMS4bCULv3gQshAJ8PUCggSVKt1k21Q7jdKPnzTxRv3wHnjh1w/v47cFZPHXVSayhNZkCSTk+ABKnc+zPL1fHxCDsdNNB26QKl0dhAW0ZE1LB4vUZEVDMMJNQxfjDVP09BAezffw/7N9+ieOfO4KBC9+6QVCqU/PabvExhNMI8ejQsN96AsF69zvtLtBACZcePo3jHTv8vxDt2wmu1Vl1AqUTYpZfCcNVVMAy4CmE9ekBSq4OyuI8dQ9E336Bo/ddwHTokL5d0OhiHDIFx8CAInw/C6YQvMBUXw1dcbv70clFWBuHxQHg9QJnHP19uCnwhldRqKMPDoYyI8L8GpqD3FqiiY6CObwllZCQDDwB8paXwWq3wWAvgLSyEQq+HOi4WqpiYCsf1XIQQ8OTmouz4cbiPHYf72DH/rS+//gpx1hMRVC1bwtC/PwxX9oe+X3+oY1vU5mYREV0UeL1GRFQzDCTUMX4wNSyP1Qr7xu9h3/Atinfs9P/iDgAKBQwDByD8hhtgHDYMCp2u1tcthIDr0CE4d/6M4p07UPLrLigjIvyBg6uuhL5v3xr9Auw6dEgOKpzdm6EhSTod1C1b+qeEeKhatoQ6Ph7qlvFQJ8RDGR4OX1ERvIWFZyabLei9p7AQvuJiaFonQdelC3Rdu0DbqXODjL4vhIBwOk8/jtAGb6H/cYRemw0+m80fKLBa4SmwwivPF1T4gi+TJKiio6GKi/MHFmKDX4XXB/fxY2eCBsf9kygpqbQ6ZUQE9P37wdD/Shj694O6dWsGcoiILhCv14iIaoaBhDrGD6bGw5OfD/umTRBlZTBdfTXULZrmL7dCCJTu3Yuir79G6d69UGh1UBgMUOj1/slggMJQbl6vhxQWBkmthqRSQ1KrIKn8E1Sq08tPv1cqIZxOeAJf8gsKgwMABQVnvvzn5MCTm1t3GypJ0LRuDW3XLtB18d+vr+vSGaro6HPuH3g8EGVl8AUCAqeDAT6bDV7b6QBBURG8tkL4gt77X8++XSBkajVUp3tv+IqLUZaTc/51KRRQJyRA07o1NEmtoWnTFvp+faHt0AGSQnF+dRIRUaV4vUZEVDMMJNQxfjBRc+Zzu+HJzkZZ1kmUZWWh7GQWyrKy4Dl5EmWZWSg7eRLC7T5zu0S5WyOC34dD0urgPnrE/xSB/fsrjDcRoIqJgcJs9t+qUW5C+fnaoFb7H0doOf04QosFSosZyvAIKCMjoYyM8D+aMCISqkj/MoXJFNQ7QPh88FqtKMs+BU/OKZRlZ8OTfQqeU9n+ZdnZgCRBndQamtZJZ4IGrVtDHR/PxxwSEdUTXq8REdUMAwl1jB9MdDETQkCUlkLS6Wrc/d5jtaJ03z649u3zBxf27YM7Pb3GTyVQmM3lAgEWKCyn580WOTigMJuhtJwOcJzOK4WF8ZYBIqKLBK/XiIhqhg/wJqI6I0kSpLCw8yqrioyEccAAGAcMkJf5iovhOnwYPpfLf0uGWnP69fQtGoHbNALzWi0kpbK2NoeIiIiIiNAEAwl2ux0vvvgi1q1bh7S0NCiVSnTs2BHjx4/HtGnToDmPrsCZmZn473//i5SUFOzevRuZmZkAgLi4OPTv3x9TpkzBsGHDantTiKiGFAYDwnr2bOhmEBERERFd1JrUrQ3Hjh3D0KFDkZ6eDgDQ6/Xwer1wuVwAgF69emHTpk2IiIgIuc6MjAwkJSWh/G7Q6/UQQqCk3KjpkydPxltvvQVlDX/dZFc5IiIiosaN12tERDXTZIb+9ng8GDNmDNLT09GyZUts3LgRxcXFcDqdWL16NUwmE3bv3o0JEybUqF6v1wshBIYPH46VK1ciMzMTxcXFcDgc2LNnD66//noAwHvvvYc5c+bUwZYRERERERERNR1NpkfCu+++i/vuuw8AsG3bNlx55ZVB6R9//DHuuOMOAMD333+P4cOHh1SvzWbDkSNHcPnll1eaLoTA3/72N3z77bcwGo3Izc2FTqcLud2McBMRERE1brxeIyKqmSbTI2HlypUAgOTk5ApBBAAYP3482rZtCwD44IMPQq7XYrFUGUQA/IPFTZ48GQDgcDiwb9++mjSbiIiIiIiIqFlpEoEEp9OJn376CQAwevToSvNIkoRrrrkGAPDdd9/V6vrL90Dwer21WjcRERERERFRU9IkAgn79u2Dz+cDAHTv3r3KfIG07OxsWK3WWlv/5s2bAQAajQYdO3astXqJiIiIiIiImpom8fjHrKwseT4hIaHKfOXTsrKyEBkZecHrTktLwxtvvAEAGDdu3Dnvm3O5XPJTJAD/PXdEREREREREzUWT6JFgt9vleb1eX2W+8mnly5yvkpIS3HrrrXA6nYiOjsbzzz9/zjILFiyAxWKRp8TExAtuBxEREREREVFj0SQCCQ3B4/HgjjvuwK+//gq1Wo1Vq1YhPj7+nOWeeuop2Gw2ecrIyKiH1hIRERERERHVjyZxa4PJZJLnnU5nlfnKp5UvU1Nerxd33nknPv/8c6hUKnz00UcYOXJkSGW1Wi20Wu15r5uIiIiIiIioMWsSPRLK9wTIzMysMl/5tFB6D1TG6/ViwoQJWLNmDZRKJT788EPccsst51UXERERERERUXPTJAIJXbp0gULhb+pff/1VZb5AWlxc3HkNtBjoibB69Wo5iDBu3LjzazQRERERERFRM9QkAgl6vR4DBgwAAHz77beV5hFCYMOGDQAQ8m0I5Xm9Xtxxxx345JNP5CDC+PHjz7/RRERERERERM1QkwgkAMCkSZMAACkpKdi5c2eF9LVr1+Lo0aMAgIkTJ9ao7kBPhDVr1kClUmHVqlUMIhARERERERFVQhJCiIZuRCg8Hg8uv/xy/Pnnn0hISMDKlSsxfPhw+Hw+rFu3Dvfddx+KioowevRofP3110Fl58yZg7lz5wIA0tLS0KZNGzktMCbC6tWr5YEVb7311lprt81mQ3h4ODIyMmA2m2utXiIiIiKqHUVFRUhMTERhYSEsFktDN4eIqNFrEk9tAACVSoUvvvgCycnJSE9Px4gRI6DX6+Hz+VBaWgoA6NWrF1atWlWjen/66SesXr0aACBJEqZNm4Zp06ZVmf+VV16p0bgJdrsdAJCYmFijdhERERFR/bLb7QwkEBGFoMkEEgCgTZs2+OOPP7B48WJ89tlnSEtLg1qtRrdu3XD77bdj2rRp0Gg0NarT5/PJ82VlZTh16lS1+UtKSmpUf3x8PDIyMmAymSBJUo3K0plfCNijo3HhcWm8eGwaJx6XxonHpfGq72MjhIDdbj/vp34REV1smsytDXRxKioqgsVigc1m40VeI8Lj0njx2DROPC6NE49L48VjQ0TUuDWZwRaJiIiIiIiIqOExkEBEREREREREIWMggRo1rVaL2bNnQ6vVNnRTqBwel8aLx6Zx4nFpnHhcGi8eGyKixo1jJBARERERERFRyNgjgYiIiIiIiIhCxkACEREREREREYWMgQQiIiIiIiIiChkDCUREREREREQUMgYSqN44nU588803mD9/Pm666SYkJSVBkiRIkoQ5c+aEVMepU6fwxBNPoFOnTggLC0NkZCQGDRqEd955Bxw39Pzl5+djxYoVmDBhArp27QqDwQCtVotWrVrhhhtuwH/+859z1mG32zFnzhz06NEDRqMRFosFV1xxBV588UW43e562IrmadeuXZg7dy7Gjh2Lzp07IyoqCmq1GlFRURgwYAD+3//7f7BardXWwfOmfjz//PPy/zRJkqrNy/Ol7rz//vtBx6Gq6fvvv6+yjiNHjuCBBx5A27ZtodPpEBMTg1GjRmHdunX1uCXNV1FRERYuXIirrroKMTEx8udNcnIy5syZg8LCwkrL8bwhImpEBFE9SUlJEQAqnWbPnn3O8qmpqSIqKkouYzQahUqlkt+PGjVKuFyuut+QZqj8fgQgdDqdMBgMQctGjx4tiouLKy2fnp4u2rRpI+fV6/VCq9XK73v16iWsVms9b1Xz8PDDD1c4NiaTKWhZdHS02LZtW6Xled7Uj/379wudThd0XKrC86VurVixQgAQCoVCxMbGVjn9+OOPlZZfv3690Ov18vEwm81CoVDI7++55x7h8/nqeauajx9++EHExsbK+1Oj0Yjw8PCgc2f37t0VyvG8ISJqXNgjgepVREQEhg8fjpkzZ+Ljjz9GXFxcSOVsNhuuu+465Ofno3Pnzvjll19gt9tRXFyMpUuXQq1WY8OGDXj00UfrdgOaKY/Hg759+2L58uU4cuQISkpK4HA4kJaWhnvvvRcA8M033+CBBx6otOyYMWOQnp6Oli1bYuPGjSguLobT6cTq1athMpmwe/duTJgwob43q1no27cvXnjhBWzfvh0FBQUoKSlBUVER7HY7Vq5ciZiYGOTl5eGGG26AzWYLKsvzpn74fD5MnjwZpaWluPLKK6vNy/Ol/iQmJiI7O7vKadCgQRXKpKWl4bbbboPT6cSAAQNw4MAB2Gw22Gw2PPPMMwCAFStW4IUXXqjvzWkWfvrpJ1x77bU4deoUbrrpJvzyyy8oLS1FQUEBiouL8fPPP+Ppp5+GxWIJKsfzhoioEWroSAZdPDweT4VlSUlJIfVI+Ne//iUAiLCwMHH06NEK6c8995wAIJRKpThw4EBtNfmi8cMPP1Sb/sADD8i/+hw/fjwo7Z133pHTKvtV/KOPPpLTv//++1ptNwmxYcMGef9++OGHQWk8b+rHyy+/LACIO++8U8yePbvaHgk8X+peoEdCUlJSjctOmDBBABBxcXGioKCgQvr9998v91Lgr981U1xcLC655BIBQEybNq1GZXneEBE1PuyRQPVGqVSed9kPPvgAADB+/Hi0bdu2Qvq0adNgNBrh9XqxatWq817PxSo5Obna9ECvBABITU0NSlu5cqVcR2W/xpY/ZoHjSLWnf//+8vyJEyeC0nje1L20tDQ8/fTTiIqKwksvvXTO/DxfGq/i4mJ5DISHHnoI4eHhFfI89dRTAPz3+H/++ef12Lqm79///jeOHj2KuLg4LFq0qEZled4QETU+DCRQo3fgwAEcP34cADB69OhK8xiNRrmb6nfffVdvbbtY6HQ6ed7r9crzTqcTP/30E4Cqj40kSbjmmmsA8NjUhS1btsjz7dq1k+d53tSPKVOmoLi4GEuWLEFMTEy1eXm+NG5bt25FSUkJgKqPT5s2bdClSxcAPD41FfiCf+uttwZ9ppwLzxsiosaJgQRq9P766y95vnv37lXmC6Tt3bu3ztt0sdm8ebM836NHD3l+37598Pl8AEI7NtnZ2ed8wgCdm8vlQnp6OpYuXYq77roLANC+fXuMGTNGzsPzpu69/fbb2LRpE0aMGIGJEyeeMz/Pl/qVm5uL3r17w2g0IiwsDJdccgkmTJgQ9P+svJqeM3v27KnV9jZnLpdL7s3Wu3dvHD9+HPfffz8SExOh0WgQGxuLMWPGYP369RXK8rwhImqcGEigRi8rK0ueT0hIqDJfIK2oqAgOh6PO23WxKCwsxIIFCwAAgwYNQqdOneS0mh6bs8tQzeh0OkiSBJ1Oh7Zt22LatGkoKCjAgAEDsGnTJmi1Wjkvz5u6lZmZiZkzZyIsLAxvvvlmSGV4vtQvp9OJXbt2QaPRwOfzIS0tDatWrUJycjImT54Mj8cTlD+wryMiIhAWFlZlvYHjw2MTuvT0dPnxjEePHkX37t3x9ttvIycnBwaDATk5Ofjqq69w3XXXYcqUKUGPpeV5Q0TUODGQQI2e3W6X5/V6fZX5yqeVL0Pnz+fz4a677sLJkyeh0+mwdOnSoHQem/oVFxeH2NhYGAwGeVlycjJefvlltG7dOigvj03deuCBB2Cz2TBnzhxccsklIZXhMakf8fHxmD17Nn7//XeUlpbCarXK3eNHjBgBwP/khcceeyyoXGBfV3dsyqfz2ISuoKBAnp8/fz7UajXWrl0Lh8OBgoICHDt2DLfeeisA4J133gkab4TnDRFR48RAAhFVafr06fjqq68AAMuWLcOll17awC26uKWnpyM7OxsOhwOnTp3C4sWL8dtvv6Fv377yo+mo7n344YdYv349LrvsMjz++OMN3Rw6y8iRIzFnzhxceumlci8dpVKJq666Chs2bMD1118PAFi+fDkOHTrUkE29aARuTQjMv/vuu7jlllugVqsBAK1bt8bq1avRs2dPAMBzzz1XoccIERE1LgwkUKNnMpnkeafTWWW+8mnly9D5mTFjhtwD4aWXXsLkyZMr5OGxaTgtWrTAE088gW+//RaSJGHevHly0Afgsakrp06dwqOPPgqlUom3334bKpUq5LI8Jg1PoVBg8eLFAPxfaL/88ks5LbCvqzs25dN5bEJXfl916NABN9xwQ4U8CoUCM2bMAADk5+fj119/rVCW5w0RUePBQAI1evHx8fJ8ZmZmlfkCaWazGUajsc7b1Zw9+eSTePHFFwEAixcvxqOPPlppvpoem7PL0IXr27cvBg4cCAB466235OU8b+rGP/7xD+Tn5+P+++9H586d4XA4gqbAfeAAKizj+dI4tG/fHtHR0QD89+sHBPZ1QUGB/PSGygSOD49N6MqPX9C5c+cq83Xt2lWeP3bsGACeN0REjRUDCdTolR+lufyo2mcLpJW/EKGamzlzJl544QUAwKJFi/DEE09UmbdLly5QKPz/RkI5NnFxcYiMjKzF1hJw5iL98OHD8jKeN3UjLS0NAPD666/DZDJVmAIDkwKQlz355JMAeL40djU9Z7p161bnbWouIiMjqx0oMaD8IIuSJAHgeUNE1FgxkECNXseOHeWB5L799ttK8xQXF2PLli0A/PfH0vmZMWOG3O130aJFmDlzZrX59Xo9BgwYAKDqYyOEwIYNGwDw2NSVwK+q5bvz8rxpfHi+NA5HjhxBXl4eAKBt27by8oEDB8pPa6jq+Bw7dgz79u0DwONTU4H9Fdh/lSn/GNrAseF5Q0TUODGQQI2eJEnyM9pXr16N9PT0CnmWLVsGh8MBpVKJO++8s55b2DzMmDEj6HaGcwURAiZNmgQASElJwc6dOyukr127Vv6iGziOFBqv1xv0C11lNm3ahJ9//hkAMHToUHk5z5u6sXnzZgghqpxmz54t5w0se/nll+VlPF/q1rnOFyGE/L9NoVDguuuuk9MMBgNuvvlmAP4eJzabrUL5hQsXAvAH7Sq7z5+qds899wDw95z6/PPPK6T7fD45kJ2QkIDLL79cTuN5Q0TUCAmiemS1WkVubq48JSYmCgBi5syZQcvtdntQucLCQhEXFycAiK5du4rU1FQhhBAul0ssX75caDQaAUA89NBDDbFZTd7MmTMFAAFALFmypEZly8rKRI8ePQQAkZCQIL7//nshhBBer1esWbNGmM1mAUCMHj26LprerKWlpYmePXuKN954Qxw5ckT4fD457fjx42LBggXCYDAIACIyMlKcPHkyqDzPm/o3e/Zs+VyqDM+XupWWliauuOKKCueM1+sV27dvF6NGjZKPT2V/90ePHpXPqUGDBomDBw8KIYRwOBxi7ty5QpIkAUAsXLiwXrerubjlllsEABEVFSU+/fRTUVZWJoQQ4tixY+K2226Tj837778fVI7nDRFR48NAAtWrpKQk+UKhumnSpEkVyqampoqoqCg5j8lkEmq1Wn4/cuRIUVpaWv8b1cQdO3ZM3ocKhULExsZWO73wwgsV6khLSxNt2rSR69Hr9UKn08nve/XqJaxWawNsXdOWlpYWdF5oNBoRHR0tf9EJTG3bthW7du2qtA6eN/XrXIEEIXi+1KWzzxmtViuio6OFVqsNWn7PPffIX2LPtn79eqHX6+W8FotFKJXKoLLlg3oUOofDIQYPHhx0fCIiIoKOzezZsysty/OGiKhx4a0N1GT07t0be/bswWOPPYYOHTqgrKwMBoMBAwcOxNtvv41vvvlGfmY4he7s53ufOnWq2snhcFSoo02bNvjjjz/wzDPPoHv37pAkCWq1Gr1798bixYuxY8cORERE1OdmNQvx8fFYu3YtHn74YfTp0wfR0dEoKiqCz+dD69atMWbMGLzzzjvYs2cPevXqVWkdPG8aH54vdSc2NhavvfYa7rjjDnTt2hVmsxmFhYVQq9Xo3LkzJk+ejK1bt+K9996r8tGdf/vb3/DHH39gypQpaNOmDUpLSxEREYGrr74an376Kd577z15IECqGYPBgJSUFLz99tsYPHgwDAYDHA4HEhISMH78ePz000+YM2dOpWV53hARNS6SEOe4oZCIiIiIiIiI6DT2SCAiIiIiIiKikDGQQEREREREREQhYyCBiIiIiIiIiELGQAIRERERERERhYyBBCIiIiIiIiIKGQMJRERERERERBQyBhKIiIiIiIiIKGQMJBARERERERFRyBhIICIiIiIiIqKQMZBARFQL7r77bkiShLvvvruhm9KgvF4vlixZgl69esFgMECSJEiShM8//7xO1xtYz+bNm+t0PUREREQEqBq6AURE1Hw8+uijWLp0KQBAo9EgNjYWAKDT6RqyWfWqsLAQL7/8MgD//ggPD2/Q9hARERHVNgYSiIioVtjtdrz55psAgEWLFmHGjBmQJKmBW1X/CgsLMXfuXAD+nioMJBAREVFzw1sbiIioVuzfvx9lZWUAgIceeuiiDCIQERERXQwYSCAiolrhdDrleaPR2IAtISIiIqK6xEACEdWLoUOHQpIkzJkzB0IIvP322+jXrx/MZjNMJhOuvPJKfPjhh1WWD2UwvfLrqK58fn4+Hn/8cbRr1w5hYWFISkrC1KlTkZubK+c/duwYHnroIbRt2xY6nQ6tW7fGE088Abvdfs5tFULgjTfeQN++fWE2m2E2mzFw4EB89NFH5yybnp6ORx99FN26dYPRaIRer0fnzp0xffp0HD9+vNIy77//PiRJQps2bQAAKSkpuOGGG9CyZUsolcoaDwDp9Xrx3nvvYdiwYYiOjoZWq0VCQgJuvfXWSvd/YP1Dhw6VlwX299nLQ7Vv3z48/PDD6Nq1K0wmE4xGIzp16oTx48dj3bp18Pl8IdWzefNmuR3Vqe7v68SJE3jsscfQrVs3GAwGaLVaxMfHo3fv3njsscfwyy+/yHmHDh2Ktm3byu/btm17zn3hdruxfPlyJCcnIzo6GhqNBnFxcbj++uvxzTffhNTmnJwcPP744+jYsSP0en2F7d2wYQNuuukmtGrVChqNBmazGZdccglGjhyJxYsXw2q1Vrt/iIiIiIIIIqJ6MGTIEAFA/Otf/xLXX3+9ACBUKpUwm80CgDw988wzlZYPpKekpJxzHbNnz66y/MqVK0WrVq0EAGEwGIRGo5HTunTpIgoKCsTPP/8soqKiBABhNpuFSqWS8wwYMEB4PJ4K9U+aNEkAEJMmTRLjxo0TAIRCoRARERFCkiS5/D333CN8Pl+l7f/www+FVquV82q1WhEWFia/N5lMYsOGDRXKrVixQgAQSUlJ4uWXX5bXZ7FYhFqtFpMmTapyn52tsLBQDB06VF6nUqkU4eHhQdswY8aMoDKrV68WsbGxIiIiQs4TGxsrTzfeeGPI6xdCiOeff14oFAq5Lp1OJyIjI4OWFRQUBJWp6u8jJSVFTqtOVeV/++23oO1SKpUVjmn5/XvjjTeK6OhoOS06OrrafZGeni66desm55ckSVgslqBz4sEHH6y2zW+//baIjY2V95XJZAra3rlz5wbVp9frhdFoDFpW3XlFREREdDYGEoioXgS+5EdERAiLxSLef/994XQ6hRBCZGRkiDFjxshfvg8ePFihfG0FEsLDw8Vll10mduzYIYQQwu12i48//ljo9XoBQEydOlUkJSWJYcOGib/++ksIIURJSYl47bXXhFKplL+4nS0QSLBYLEKSJDFv3jxhs9mEEELk5OSIqVOnym145ZVXKpT/7rvvhEKhECqVSjz55JMiLS1N+Hw+4fP5xP79+8Wtt94qBzaOHTsWVDYQSNDpdEKpVIq7775bHD9+XAghhMfjEYcPH65yn53t5ptvFgCERqMRr776qiguLhZCCHHy5EkxefJkeRtef/31CmVD/dJeneXLl8t1jB07VuzevVtOKy4uFt99950YN26cvG8D6iqQMHz4cAFAXH755WL79u1yEMjlcomDBw+KxYsXi0WLFgWVSUtLk+tLS0urcp0Oh0N07txZABBDhw4VmzdvFqWlpUIIf0BnyZIl8hf+l19+uco2G41G0alTJ7Fp0ybh9XqFEEIcOHBACOEPVAQCMI8//rjIzMyUyxcWFootW7aIv//97yI1NbXa/UNERERUHgMJRFQvAl/yAYgffvihQnppaamIj48XAMT8+fMrpNdWICE2Nlbk5eVVSJ81a5acp1u3bvIXuvLuuusuAUAMHz68QlogkABAzJo1q9L2TZgwQQAQkZGRoqSkRF7u9XpFhw4dBADx5ptvVrl9Y8eOFQDE9OnTg5YHAgkAxE033VRl+XPZsWOHXE9V7QgEGqKjo4O2QYgLDyRYrVb51/Tx48dX2XOjMnUVSAj0CNm2bVvIbQk1kPDss88KAGLIkCHC7XZXmuezzz6T93dZWVmlbTabzSIjI6PS8p988okAIDp27Bhy+4mIiIjOhWMkEFG9GjBgAJKTkyss12q1GDVqFADgjz/+qLP1T5kyBVFRURWWB9YNAI8//ji0Wm2VeaprX1hYGGbMmFFp2jPPPAMAsFqt2Lhxo7z8xx9/xKFDhxAdHY377ruvyronTpwIwH+/e1WeeuqpKtPO5ZNPPgEAtGrVqsp2zJs3DwCQl5cXtA214dNPP4XdbodarcaSJUsaxVMfAo9uPHnyZK3X/e677wLw/72p1epK89xwww0wm83Iy8vDr7/+Wmmeu+66C61atao0LdB+u92O4uLiC280ERERETjYIhHVs379+lWZFh8fDwB1OvBb3759K10eGxsrz19xxRXV5ikoKKiy/j59+sBsNlea1qFDB/kLX2pqqrz8p59+AgDYbDbEx8cjLi6u0mnKlCkA/ANBViYsLAyXX355lW07l0CbkpOToVBU/vHQpUsXJCQkVNiG2rBt2zYAQO/evdGyZctarft8XXfddQCASZMm4YknnsD//ve/oKdTnK/MzEz5ON57771VHvOWLVvC4XAAqPq4DxgwoMr19O3bF9HR0Th58iT69euHpUuXYv/+/RBCXPA2EBER0cVL1dANIKKLi8lkqjJNpfL/SyorK6v39QfWHUoej8dTZf2BL9nVpZ84cQI5OTnysqysLAD+7T516lS15QGgpKSk0uVRUVFVBgBCEWjTubahVatWyMzMDNqG2pCdnQ0ASEpKqtV6L8SiRYtw+PBhpKSkYMmSJViyZAmUSiUuu+wyXHvttbj//vvPub8qEzjmgL93RyiqCmC0aNGiyjLh4eH4+OOPcccdd2DPnj2YNm0aAMBisWDw4MG47bbbMG7cuCp7RBARERFVhj0SiIgamNfrBeDvrSH8Y9ecc6qMUqmsz2bXusZwK8PZwsPD8cMPP2DLli148sknMWDAAKhUKvz666949tln0aFDB3z88cc1rjdwzAH/oy5DOeZVPcbzXMd9xIgRSEtLwwcffIBJkyahQ4cOsNls+PLLL3HXXXehV69eyMzMrPE2EBER0cWLgQQiahICX5ZKS0urzGOz2eqrOVU61xeyQHr5X5Hj4uIAVN11vb4E2nTixIlq8wXSq/sl/HzUxX4o39Okqr+dUP5uBg4ciIULF2Lr1q0oLCzEf//7X/To0QMlJSWYPHlySD1JygtsK1A/x91gMOCuu+7C+++/j4MHD+LEiRNYuHAhdDpdUE8FIiIiolAwkEBETUJERAQAICMjo9J0u92Offv21WeTKpWamirf0362w4cPy1/C+/TpIy8P3OOenZ1d6+MO1ESgTSkpKfD5fJXm2b9/vxwMqWosifN11VVXAfDvw9oa3DDwdwNU/bezc+fOGtWp0+kwduxYfPbZZwD8AYqtW7fK6eVvL6mq90ibNm3kWyK+/PLLGq2/NiQkJODJJ5/EE088AQC1PnAmERERNW8MJBBRk9CzZ08AwLp16ypNX7x4MVwuV302qVIlJSVYvHhxpWnz588HAERGRuLqq6+WlycnJ6N9+/YAgMceewxut7vaddTVYJTjx48H4O818c4771SaJ/DkiejoaIwYMaJW13/rrbfCbDbD4/Hgscceq5UBATt27IiwsDAAlf/t+Hw+LFiwoNKyHo+nyoAKALleIDh4UH6wzcLCwirLBwbPfPfdd7F79+4q8wHnf8zPdU4EtuFCxtYgIiKiiw+vHIioSbj99tsB+B99OHv2bBQVFQHwD1T3z3/+E/Pnz5cfddeQLBYL5s2bhwULFsButwPwt3H69OlYuXIlAGDWrFnQ6XRyGZVKhTfeeAMqlQpbt27F4MGDsWnTpqBBJ48ePYo33ngDV1xxBZYvX14nbe/bty9uvvlmAMC0adOwdOlSeYC/7OxsTJkyBWvXrgXgfwxk+W2oDRaLBYsWLQLgfxTljTfeiN9++01OdzqdWL9+Pa6//nr5+J+LWq2Wt+m5557DmjVr5EDNgQMHcOONN1b5OM8TJ06gQ4cOmD9/Pnbv3h00yOYff/yBCRMmAPDfNjBkyBA5LTw8XO5tsGLFiioH53ziiSfQo0cPlJaWIjk5GUuXLkV+fr6cXlhYiG+++QYTJ07EoEGDQtresy1cuBCjR4/Gv//976BbVlwuF9asWYMXXngBAHDttdeeV/1ERER0kRJERPVgyJAhAoCYPXt2lXlmz54tAIghQ4ZUSPN4PCI5OVkAEACEJEkiIiJCSJIkJEkSL7zwQrXrCJRLSUmpdN1paWlynrS0tErzpKSkyHnONmnSJAFATJo0SYwbN04AEEqlUm5joNzEiROF1+uttP7//Oc/wmQyyXnVarWIiooSWq1WXgZAzJ8/P6jcihUrBACRlJRUab01UVhYKO9HAEKlUlXYhhkzZtR4/9TEc889JxQKhVxXWFiYiIyMDFpWUFAQVKa645uRkSHi4+OD9qvZbBYAhMlkEps3b660fPm/icDxjIyMFBqNRl6m0WjE2rVrK6xz3rx5ch6tVisSExNFUlKSGDduXFC+zMxM0b9//6C/6/DwcLl9gal9+/YV1nGuv2khzpxTZ+/L8sezS5cu4uTJk9UeEyIiIqLy2COBiJoEpVKJ9evXY+7cuejcuTM0Gg0kScLIkSOxceNGzJgxo6GbKPv444+xfPly9OrVCx6PBwaDAVdeeSU++OADrFy5sspu5DfccAMOHz6M2bNno2/fvjAajSgsLIRWq0XPnj1x33334T//+Q9mzpxZZ223WCzYtGkT3n33XQwdOhQmkwkOhwNxcXG4+eabkZKSIv+KXVeeeuop/P7775gyZYp8y4fb7UaHDh1w++2347PPPgu6feBcWrVqhZ07d+K+++6TewoYjUZMnDgRu3btCupNUF5CQgK++OILPPbYY+jfvz9atmwJh8MBlUqFrl274uGHH8Zff/2FW265pULZf/7zn3jllVfQp08fqNVqnDhxAseOHZMfcRkQHx+PrVu34uOPP8bYsWPRsmVLOJ1OuN1utGnTBmPGjMHLL7+MH3/8MeTtLe/+++/HW2+9hdtvvx3du3eHXq9HUVERIiIiMGjQILz88svYtWtX0OCPREREROciCVELN6ESERERERER0UWBPRKIiIiIiIiIKGQMJBARERERERFRyBhIICIiIiIiIqKQMZBARERERERERCFjIIGIiIiIiIiIQsZAAhERERERERGFjIEEIiIiIiIiIgoZAwlEREREREREFDIGEoiIiIiIiIgoZAwkEBEREREREVHIGEggIiIiIiIiopAxkEBEREREREREIWMggYiIiIiIiIhCxkACEREREREREYXs/wP+XHlrG91vYAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluator.plot_cluster_similarity()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### neighborhood similarity\n",
    "\n",
    "The detailed evaluation on neighborhood similarity is more of a sanity check than providing additional information. We can plot the overlap for different k's of k nearest neighbors graphs. The summary metric is the mean over all k. The metric should show a very stable increase with k for each gene set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluator.plot_knn_overlap()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Edit Metadata",
  "kernelspec": {
   "display_name": "Python 3.8.13 ('spapros')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
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   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
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   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": [
     "%matplotlib inline\n"
    ]
   }
  },
  "vscode": {
   "interpreter": {
    "hash": "b6b34c492024ffa29961e234fc8481d2351cf3e71b96e865103acdbed47bd476"
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  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "02b477ee815a4045b5c1b90beb5abe6b": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_12d5e42170724f909b6d5843a2541cba",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random genes..................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random genes..................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "0699822ecc724d779f88a951f3747c15": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_bd3ab53f63b148ff896d88d09d9eca25",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:58</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:56</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">52/52</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:58\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:56\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:12\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m52/52\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:12\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
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         "output_type": "display_data"
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      "model_name": "LayoutModel",
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     "0c98817fd6d245179761bb471a833592": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "0d60b3340fbc413382cf2d2745e58a8d": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
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       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random genes..................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random genes..................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "0fb32db1a28e45869a9be2cd5fc76667": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_35c78e580c4347c6b20b0be5d87d0ba9",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random genes..................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random genes..................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
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     "129028ab34704b94b5b7d2da28794759": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:58</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:55</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">52/52</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:47</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:25</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:09</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:47\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:25\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:09\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:49</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:47</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:27</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:25</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:09</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">50/50</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:08</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:49\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:47\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:27\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:25\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:09\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m50/50\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:08\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
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     "2956acb35990433e936b3667f953e4f3": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_5016402c9f344cd484be11942469240d",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:56</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:12</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:56\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:12\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "2af5da5070aa48509b8350ecd7f65d44": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "2b7e10414d454d8681daecaa8efaf89c": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_62ce43a1fc0b4ffeb3adcead922ac3aa",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:26</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:25</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:10</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">50/50</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:09</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:26\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:25\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:10\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m50/50\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:09\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
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     "2dbdabe264ce4504b4d3add28b0adc44": {
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">  0%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m  0%\u001b[0m \u001b[33m0:00:00\u001b[0m\n"
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     "3210c4c5c5b64998ba0ab025f2dae964": {
      "model_module": "@jupyter-widgets/base",
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      "model_module": "@jupyter-widgets/base",
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      "model_name": "LayoutModel",
      "state": {}
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     "340b2dbb55154e33900010ff0397ae5e": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random genes..................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random genes..................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
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      "model_name": "OutputModel",
      "state": {
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     "34bd29298edb450e83ebca3c57a18671": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
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        {
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading pre computations for cluster_similarity.........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading pre computations for knn_overlap................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for cluster_similarity.......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for knn_overlap..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for forest_clfs..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for gene_corr................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for marker_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:02\u001b[0m\n  \u001b[1;2;36mLoading shared computations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading shared computations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading shared computations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:02\u001b[0m\n  \u001b[1;2;36mLoading shared computations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading pre computations for cluster_similarity.........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading pre computations for knn_overlap................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for cluster_similarity.......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for knn_overlap..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for forest_clfs..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for gene_corr................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for marker_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
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     "3c5e24967fa0496b8038ac0b42efa0e0": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
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     "3e00d0a2ecc94e9ca05852024890b67c": {
      "model_module": "@jupyter-widgets/output",
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        {
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:29</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:27</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:40</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">54/54</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:39</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:29\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:27\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:01:40\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m54/54\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:01:39\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "42ea5d0a0f0d469e83a20d22a69bcbe1": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_b1242ec00c4d4956b159afae0992952b",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:26</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:25</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:09</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">50/50</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:08</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:26\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:25\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:09\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m50/50\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:08\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "438b2d4f2806471caa86e807d48640ba": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_07530a7af1804017a99e4755f46a6d9c",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:49</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:47</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:44</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">53/53</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:10</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:49\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:47\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:44\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m53/53\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:10\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:43</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">  0%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:43</span>\n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/5\u001b[0m \u001b[33m0:00:43\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m  0%\u001b[0m \u001b[33m0:00:43\u001b[0m\n"
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      "model_name": "LayoutModel",
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     "4b6cec0d9c064c9a9d35e47167aefddf": {
      "model_module": "@jupyter-widgets/output",
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:58</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:55</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:31</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:28</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:03</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:38</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">50/50</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:37</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:58\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:55\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:31\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:28\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:03\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:01:38\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m50/50\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:01:37\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "4d116d728c604b40a7752df43b107d01": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_172886d16d414cd78e8f63019a7b9104",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:47</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:45</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">52/52</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:47\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:45\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:12\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m52/52\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
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     "4f8238e3c13644d88bdc4e7fbdaa1d39": {
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading pre computations for cluster_similarity.........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading pre computations for knn_overlap................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for cluster_similarity.......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for knn_overlap..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for forest_clfs..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for gene_corr................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for marker_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading shared computations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading shared computations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading shared computations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading shared computations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading pre computations for cluster_similarity.........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading pre computations for knn_overlap................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for cluster_similarity.......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for knn_overlap..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for forest_clfs..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for gene_corr................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for marker_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
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      "model_name": "LayoutModel",
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     "51437f037822490ca616bbdf91d22171": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
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        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:50</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:16</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:14</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">50/50</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:10</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:50\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:16\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:14\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m50/50\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:10\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
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     "51a968390ddf40f2a37714bab5c1d4cc": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
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     "51e6cb250b2545bd8bcad07da9bf7464": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "54f6adbb20d84655b3811fbb257079c6": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_74865221e7f74268b5cc5ed1dccf1894",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">  0%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m  0%\u001b[0m \u001b[33m0:00:00\u001b[0m\n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
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     "55ef5e25db5d4daaaee87b223134b8a1": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_8222c0b44b334e71a36af35fe8062f17",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:29</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:26</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">54/54</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:10</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:29\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:26\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m54/54\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:10\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "56ffb7906a574e3795a7575bbf510f90": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_eb99fd887ec543c6a104275421bf7d6d",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random genes..................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random genes..................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "5763f8ff15d6475ab2f9c3d5ed01b7db": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
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     "5a771b59a3474064bb91de88a6eca34a": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "5c5251c7785345dcb1e6489f9b7ceb73": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "5c9475caf97a4c7fb628d9190f92feb9": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_a86d4295500a43acb7a54f672dc51995",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:29</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:27</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">54/54</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:10</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:29\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:27\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m54/54\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:10\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
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     "5ed87c69ef9d442ca4f8bc4c04f9cc46": {
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading pre computations for cluster_similarity.........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading pre computations for knn_overlap................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for cluster_similarity.......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for knn_overlap..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for forest_clfs..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for gene_corr................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for marker_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading shared computations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading shared computations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading shared computations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading shared computations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading pre computations for cluster_similarity.........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading pre computations for knn_overlap................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for cluster_similarity.......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for knn_overlap..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for forest_clfs..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for gene_corr................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for marker_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
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      "model_name": "LayoutModel",
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     "62b9721958f249828c9ad895fc6ef0ee": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_991b1c31627442a2af621d92105d256b",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:50</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:16</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:14</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">50/50</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:10</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:50\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:16\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:14\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m50/50\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:10\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
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     "62ce43a1fc0b4ffeb3adcead922ac3aa": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "66b048644ff647ea9f7fb991d47dc2c5": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_7588f56beaa94b4da0be109da0711d45",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random genes..................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random genes..................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "66c1cf2d8a5a4bf9a0b32e6b6db1da9f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "671501821e184166bbadecdbe53574e3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "672f31ea18d240c5b3b651dfea95fbc4": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_75601c34ee214b08964350dc42e6d6c4",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random genes..................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random genes..................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:58</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:55</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">52/52</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:58\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:55\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:12\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m52/52\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
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     "69c2bec945654bf2affe3be88a828057": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:43</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">53/53</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:10</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:43\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m53/53\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:10\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:51</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:49</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:30</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:28</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:16</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">54/54</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:15</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:51\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:49\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:30\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:28\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:16\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m54/54\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:15\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:47</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #f92672; text-decoration-color: #f92672\">━━━━</span><span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">╺━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 1/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">1:01:06</span>\n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:47\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;249;38;114m━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 1/5\u001b[0m \u001b[33m1:01:06\u001b[0m\n"
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">  0%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m  0%\u001b[0m \u001b[33m0:00:00\u001b[0m\n"
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     "83c476838e2a46078411ad28b5a41c41": {
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:58</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:55</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:58</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:30</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">52/52</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:29</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:58\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:55\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:01:01\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:58\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:30\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m52/52\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:29\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
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     "89830fe2e51e47dba29217688b35ba46": {
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:58</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:55</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:13</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">52/52</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random genes..................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random genes..................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random genes..................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
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     "afcde7544ae3477fbb91ece0b91a42f8": {
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">52/52</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:12\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m52/52\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
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     "b2104ee30e244301b87759b4b4df8085": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:58</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:55</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">52/52</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:58\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:55\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:12\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m52/52\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
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         "output_type": "display_data"
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      "model_name": "LayoutModel",
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     "b8a3d817ce834edfac7f13605cd602a9": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:58</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:01:51</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:03</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:33</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:30</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:06:16</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">50/50</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:06:15</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:01:58\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:01:51\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:03\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:33\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:30\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:06:16\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m50/50\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:06:15\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
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     "b9fbae8248284f23a8b202d83b1b9b31": {
      "model_module": "@jupyter-widgets/output",
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:29</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:27</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">54/54</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:29\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:27\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:12\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m54/54\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
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        {
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
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         "output_type": "display_data"
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      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
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     "bef13975f46940ec8ff30d846ded267d": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
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       "layout": "IPY_MODEL_51a968390ddf40f2a37714bab5c1d4cc",
       "outputs": [
        {
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:49</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:42</span>\n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:49\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/5\u001b[0m \u001b[33m0:00:42\u001b[0m\n"
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      "model_name": "OutputModel",
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:47</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:44</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">52/52</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:47\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:44\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:12\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m52/52\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
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         "output_type": "display_data"
        }
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     "c82223cabd9d44b7a2e1e70fe9a42cd5": {
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      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
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        {
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random genes..................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random genes..................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
         },
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         "output_type": "display_data"
        }
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     "c8a5dc43ad12410bb446e94d499121b0": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
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       "layout": "IPY_MODEL_8c851465921e4cc78f53e81b93d4241f",
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        {
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:50</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:16</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:14</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">50/50</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:10</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:50\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:16\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:14\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m50/50\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:10\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "caad9dc90de34da48ec0be080cf38b11": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_a1ff591cf4da41ab9573bb763d764a08",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:44</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">53/53</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:10</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:44\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m53/53\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:10\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "cc1c6f8c589248199096fe32bbcd7370": {
      "model_module": "@jupyter-widgets/output",
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      "model_name": "OutputModel",
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        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:26</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:24</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:09</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">50/50</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:08</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:26\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:24\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:09\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m50/50\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:08\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "ce5c15ae17894010af512ccac92a9022": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_66c1cf2d8a5a4bf9a0b32e6b6db1da9f",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:29</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:27</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">54/54</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:29\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:27\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:12\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m54/54\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:50</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:16</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:14</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">50/50</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:50\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:16\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:14\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:12\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m50/50\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "d802f821a7d64b1dbc6997361ab26a18": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_5a771b59a3474064bb91de88a6eca34a",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:26</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:24</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:09</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">50/50</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:08</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:26\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:24\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:09\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m50/50\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:08\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "dc1fc74a14134b6f85049f8ee9df18a1": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_bec4690c295b47bc8dadb12b46ddaeaa",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random genes..................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random genes..................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "dd1939a6f15a415485b910aaeac3e140": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_75882465f554439e9b70db7c426a744d",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random genes..................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random genes..................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
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     "dd3d6f3554c94c2d8d6989fe8be04216": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "dd44c675f46540c7851df289c4f8a417": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_289c29a8566f4e66bf51785991892793",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:51</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:49</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:16</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:15</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">50/50</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:51\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:49\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:16\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:15\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:12\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m50/50\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
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         "output_type": "display_data"
        }
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      "model_name": "LayoutModel",
      "state": {}
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     "e22010168ae34d86ba9f0bfd063a127d": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading pre computations for cluster_similarity.........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading pre computations for knn_overlap................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for cluster_similarity.......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for knn_overlap..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for forest_clfs..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for gene_corr................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for marker_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading shared computations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading shared computations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading shared computations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading shared computations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading pre computations for cluster_similarity.........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading pre computations for knn_overlap................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for cluster_similarity.......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for knn_overlap..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for forest_clfs..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for gene_corr................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for marker_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">  0%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m  0%\u001b[0m \u001b[33m0:00:00\u001b[0m\n"
         },
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         "output_type": "display_data"
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     "ea368c2ead324e7dacab1eaa46d0f255": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
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        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:57</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:55</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">52/52</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:57\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:55\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:12\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m52/52\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:12\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
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     "ebfee9ed791647e9bdc2ec4c81ab97ac": {
      "model_module": "@jupyter-widgets/output",
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      "model_name": "OutputModel",
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        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading shared computations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading pre computations for cluster_similarity.........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading pre computations for knn_overlap................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for cluster_similarity.......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for knn_overlap..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for forest_clfs..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for gene_corr................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Loading final computations for marker_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading shared computations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading shared computations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading shared computations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading shared computations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading pre computations for cluster_similarity.........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading pre computations for knn_overlap................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for cluster_similarity.......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for knn_overlap..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for forest_clfs..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for gene_corr................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mLoading final computations for marker_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
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     "ec02f6018fa347f384992eade59fcd41": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_349869d81e5e46c7832c3fb9db85ff75",
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        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:47</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:28</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:47\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:28\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:11\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 0/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #3a3a3a; text-decoration-color: #3a3a3a\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">  0%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 0/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m  0%\u001b[0m \u001b[33m0:00:00\u001b[0m\n"
         },
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     "f1c37e6288b841bcb5fcf2649e8128ca": {
      "model_module": "@jupyter-widgets/output",
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:43</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">53/53</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:10</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:43\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m53/53\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:10\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
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     "f31de6bf710646e3957c02acd70494c3": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:44</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:10</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">53/53</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:10</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:44\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:10\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m53/53\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:10\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "f3f895673e794222811037df638e4085": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_e280c3fe24f64897817ff476870f13d8",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random genes..................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random genes..................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "f44f82ae59534d83a26855edfbe24e2b": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_46e4e515c628468f860b3e30d2e14141",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:50</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:15</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:50\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:15\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:11\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "f51bd5129e734ed9b59c1c99f8348e70": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_22e8608af7304df78555bf0670bb4c0a",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random genes..................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random genes..................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
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      "model_module": "@jupyter-widgets/base",
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      "model_name": "LayoutModel",
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     "fbb1a54931674624a949f3b195071e14": {
      "model_module": "@jupyter-widgets/output",
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:50</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:16</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:14</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:12</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">50/50</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:50\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:16\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:14\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:12\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m50/50\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
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         "output_type": "display_data"
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     "fbba4f5eb5394953bc66827bc8de275e": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_8ef07eb72d4b4a9db36643add2fadcbd",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:29</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:27</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">54/54</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:29\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:27\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m54/54\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "fc0555e7a9034d7398cc0ebf1fac7eda": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_8fcc82a207dd4e788dd7af6c336c871d",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:48</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:46</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:26</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:25</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:09</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">50/50</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:08</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:48\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:46\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:26\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:25\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:09\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m50/50\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:08\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "fdfba71459ec43cd8e6ec1f0569915a0": {
      "model_module": "@jupyter-widgets/output",
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      "model_name": "OutputModel",
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          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">SPAPROS PROBESET EVALUATION:                                                                </span> \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Shared metric computations................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:56</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for cluster_similarity.....</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:53</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for knn_overlap............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:01</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for gene_corr..............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing shared compuations for marker_corr............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Probeset specific pre computations........................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:54</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for cluster_similarity........</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:52</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing pre compuations for knn_overlap...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:02</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Final probeset specific computations......................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 5/5</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for cluster_similarity......</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">52/52</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for knn_overlap.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 6/6</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for forest_clfs.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">25/25</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:11</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for gene_corr...............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Computing final compuations for marker_corr.............</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">FINISHED</span>  \n          \n</pre>\n",
          "text/plain": "\u001b[1;30mSPAPROS PROBESET EVALUATION:                                                                \u001b[0m \n\u001b[1;34mShared metric computations................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:56\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for cluster_similarity.....\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:53\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for knn_overlap............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:01\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for gene_corr..............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing shared compuations for marker_corr............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;34mProbeset specific pre computations........................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:54\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for cluster_similarity........\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:52\u001b[0m\n  \u001b[1;2;36mComputing pre compuations for knn_overlap...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:02\u001b[0m\n\u001b[1;34mFinal probeset specific computations......................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 5/5\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for cluster_similarity......\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m52/52\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for knn_overlap.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 6/6\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for forest_clfs.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m25/25\u001b[0m \u001b[33m0:00:11\u001b[0m\n  \u001b[1;2;36mComputing final compuations for gene_corr...............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mComputing final compuations for marker_corr.............\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFINISHED\u001b[0m  \n          \n"
         },
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         "output_type": "display_data"
        }
       ]
      }
     },
     "fe1bc4e868cd4848a7304ce94fdc8f4e": {
      "model_module": "@jupyter-widgets/output",
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      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_e098ea3517b148358acf23e3cb9c13ca",
       "outputs": [
        {
         "data": {
          "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">Reference probeset selection..............................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\"> 4/4</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting HVG genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting random genes..................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting PCA genes.....................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n  <span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf; font-weight: bold\">Selecting DE genes......................................</span> <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━</span>  <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #808000; text-decoration-color: #808000\">0:00:00</span>\n<span style=\"color: #000000; text-decoration-color: #000000; font-weight: bold\">Finished</span>  \n</pre>\n",
          "text/plain": "\u001b[1;34mReference probeset selection..............................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m 4/4\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting HVG genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting random genes..................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting PCA genes.....................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n  \u001b[1;2;36mSelecting DE genes......................................\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m  \u001b[35m100%\u001b[0m \u001b[33m0:00:00\u001b[0m\n\u001b[1;30mFinished\u001b[0m  \n"
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         "output_type": "display_data"
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