docs/_tutorials/spapros_tutorial_advanced_evaluation.ipynb

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   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import os\n",
    "os.chdir('/big/st/strasserl/spapros/docs')\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "pycharm": {
     "name": "#%% md\n"
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   "source": [
    "# Advanced evaluation\n",
    "\n",
    "\n",
    "## ! This tutorial will be updated the next days !\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.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Content**  \n",
    "\n",
    "0. [Import packages and setup](#0.-Import-packages-and-setup)  \n",
    "1. [Load dataset](#1.-Load-dataset)  \n",
    "2. [Set up the ProbesetEvaluator](#2.-Set-up-the-ProbesetEvaluator)\n",
    "3. [Run evaluation methods](#3.-Run-evaluation-methods)\n",
    "4. [Visualize the evaluation results](#4.-Visualize-the-evaluation-results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Import packages and setup\n",
    "\n",
    "To run the notebook locally, create a conda environment using this [yaml file](../requirements_dev.yaml):\n",
    "\n",
    "    conda create -f environment.yaml\n",
    "\n",
    "Then add the conda environment as ipython kernel:\n",
    "\n",
    "    python -m ipykernel install --user --name spapros --display-name Python (spapros)\n",
    "\n"
   ]
  },
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   "execution_count": 2,
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   "source": [
    "import spapros\n",
    "from spapros import se, ev, pl\n",
    "from IPython.display import clear_output\n",
    "import scanpy as sc\n",
    "import pandas as pd\n",
    "import os\n",
    "import yaml\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scanpy==1.8.2 anndata==0.8.0 umap==0.5.2 numpy==1.21.5 scipy==1.7.3 pandas==1.3.5 scikit-learn==1.0.2 statsmodels==0.13.2 python-igraph==0.9.9 pynndescent==0.5.6\n",
      "spapros==0.1.0\n"
     ]
    }
   ],
   "source": [
    "sc.settings.verbosity = 0\n",
    "sc.logging.print_header()\n",
    "print(f\"spapros=={spapros.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 2638 × 1838\n",
       "    obs: 'size_factors', 'celltype'\n",
       "    var: 'gene_ids', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n",
       "    uns: 'hvg', 'log1p'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata = sc.datasets.pbmc3k()\n",
    "adata_tmp = sc.datasets.pbmc3k_processed()\n",
    "adata = adata[adata_tmp.obs_names, adata_tmp.var_names]\n",
    "adata_raw = adata.copy()\n",
    "sc.pp.normalize_total(adata, target_sum=1e4, key_added=\"size_factors\")\n",
    "sc.pp.highly_variable_genes(adata, flavor=\"cell_ranger\", n_top_genes=1000)\n",
    "adata.X = adata_raw.X\n",
    "sc.pp.log1p(adata)\n",
    "adata.obs['celltype'] = adata_tmp.obs['louvain']\n",
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Set up the ProbesetEvaluator"
   ]
  },
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   "source": [
    "The quality of a selected probeset is not easy to describe. A useful probeset represents both the biological importance and the variability inherent in the total gene set. Spapros provides a set of evaluation metrics that measure the performance in these categories.\n",
    "\n",
    "By default, `metric_scheme=\"quick\"`, which means that only the faster metrics are calculated, which are:\n",
    "\n",
    ">- knn\n",
    ">- forest classification\n",
    ">- marker correlation (if marker list given: `marker_list=\"../path/to/marker_list.csv\"`)\n",
    ">- gene correlation\n",
    "\n",
    "Through setting scheme=\"full\", additionally the following metric is calculated:\n",
    ">- 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.\n",
    "\n",
    "Below, the metrics will be further explained."
   ]
  },
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   "source": [
    "For each evaluation metric, several parameters can be customized. Therefor, the ProbesetEvaluator takes an dictionary containing the parameters as values as `metric_params` argument.\n",
    "Here, we stored a set of paramets in a yaml file:"
   ]
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   "execution_count": 5,
   "metadata": {
    "scrolled": true
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    {
     "data": {
      "text/plain": [
       "{'data': {'name': 'adata_pbmc3k', 'celltype_key': 'celltype'},\n",
       " 'metrics': {'cluster_similarity': {'ns': [5, 21],\n",
       "   'AUC_borders': [[7, 14], [15, 20]]},\n",
       "  'knn_overlap': {'ks': [5, 10, 15, 20, 25, 30]},\n",
       "  'forest_clfs': {'ct_key': 'celltype', 'threshold': 0.8},\n",
       "  'marker_corr': {'per_celltype': True,\n",
       "   'per_marker': True,\n",
       "   'per_celltype_min_mean': None,\n",
       "   'per_marker_min_mean': 0.025},\n",
       "  'gene_corr': {'threshold': 0.8}}}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading metric parameters from file\n",
    "param_yaml = \"../tutorials/data/pbmc3k_parameters.yml\"\n",
    "with open(param_yaml, \"r\") as file:\n",
    "    custom_params = yaml.load(file, Loader=yaml.FullLoader)\n",
    "custom_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize an ProbesetEvaluator\n",
    "evaluator = ev.ProbesetEvaluator(adata,\n",
    "#                                  metrics_params=custom_params,\n",
    "                                 scheme=\"full\",\n",
    "                                 marker_list=\"../data/pbmc3k_marker_list.csv\",\n",
    "                                 verbosity=2,\n",
    "                                 results_dir=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.  Run evaluation methods"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "The principal method for evaluation 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."
   ]
  },
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       "Output()"
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     "ename": "KeyError",
     "evalue": "'highly_variable'",
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     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/spapros/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/spapros/lib/python3.8/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/spapros/lib/python3.8/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'highly_variable'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-265f07701492>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# select reference probesets with basic selection methods\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mselections\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect_reference_probesets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbosity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# highly variable genes were only needed for the selection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0madata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"highly_variable\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/spapros/lib/python3.8/site-packages/spapros/selection/selection_procedure.py\u001b[0m in \u001b[0;36mselect_reference_probesets\u001b[0;34m(adata, n, genes_key, seeds, verbosity, save_dir, reference_selections)\u001b[0m\n\u001b[1;32m   1573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1574\u001b[0m                 reference_probesets[f\"ref_{selection_name}\"] = reference_methods[selection_name](\n\u001b[0;32m-> 1575\u001b[0;31m                     \u001b[0madata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mgenes_key\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mreference_selections\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselection_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1576\u001b[0m                 )\n\u001b[1;32m   1577\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/spapros/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3456\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3457\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3458\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3459\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/spapros/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'highly_variable'"
     ]
    }
   ],
   "source": [
    "# select reference probesets with basic selection methods\n",
    "selections = se.select_reference_probesets(adata, n=50, verbosity=2)\n",
    "\n",
    "# highly variable genes were only needed for the selection\n",
    "del adata.var[\"highly_variable\"]\n",
    "\n",
    "# this is the probeset selected in the standard selection section of the spapros_tutorial_advanced_selection\n",
    "# genes = ['LST1', 'HLA-DPB1', 'HLA-DQA1', 'IL32', 'GZMB', 'CCL5', 'S100A8',\n",
    "#        'GNLY', 'NKG7', 'CD79A', 'TYROBP', 'HLA-DPA1', 'AIF1', 'SAT1', 'LTB',\n",
    "#        'UBXN1', 'CTSW', 'FCER1A', 'FCN1', 'LGALS2', 'IRF8', 'IGFBP7', 'FERMT3',\n",
    "#        'CST3', 'CST7', 'GZMK', 'FGFBP2', 'LYAR', 'VAMP5', 'ATP5A1', 'HLA-DQB1',\n",
    "#        'MS4A6A', 'PSME1', 'ATP5H', 'LAMTOR1', 'PPP1R2', 'CD79B', 'KLRG1',\n",
    "#        'LINC00926', 'TIGIT', 'CCT7', 'PDIA3', 'GIMAP7', 'HLA-DRB1', 'CAPZB',\n",
    "#        'ARF6', 'UBE2L6', 'IGLL5', 'PNRC1', 'OAS1']\n",
    "\n",
    "genes = ['LST1', 'HLA-DPB1', 'HLA-DQA1', 'IL32', 'GZMB', 'CCL5', 'S100A8',\n",
    "       'GNLY', 'NKG7', 'CD79A', 'TYROBP', 'HLA-DPA1', 'AIF1', 'SAT1',\n",
    "       'LTB', 'UBXN1', 'CTSW', 'FCER1A', 'FCN1', 'LGALS2', 'IRF8',\n",
    "       'IGFBP7', 'FERMT3', 'CST3', 'CST7', 'GZMK', 'FGFBP2', 'LYAR',\n",
    "       'VAMP5', 'ATP5A1', 'HLA-DQB1', 'MS4A6A', 'PSME1', 'ATP5H',\n",
    "       'LAMTOR1', 'PPP1R2', 'CD79B', 'KLRG1', 'LINC00926', 'TIGIT',\n",
    "       'CCT7', 'PDIA3', 'GIMAP7', 'HLA-DRB1', 'CAPZB', 'ARF6', 'UBE2L6',\n",
    "       'IGLL5', 'PNRC1', 'OAS1']\n",
    "\n",
    "# we add it to the dictionary of probesets\n",
    "selections[\"spapros_selection\"] = pd.DataFrame({\"selection\": [True if g in genes else False for g in adata.var_names]}, index=adata.var_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bcd00147ba6f40f6addf16108af31b2a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spapros_selection\n"
     ]
    },
    {
     "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: #808000; text-decoration-color: #808000; font-weight: bold\">The following cell types are not included in forest classifications since they have fewer </span>\n",
       "<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",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1;33mThe following cell types are not included in forest classifications since they have fewer \u001b[0m\n",
       "\u001b[1;33mthan \u001b[0m\u001b[1;33m40\u001b[0m\u001b[1;33m cells: \u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m'Dendritic cells'\u001b[0m\u001b[1;33m, \u001b[0m\u001b[1;33m'Megakaryocytes'\u001b[0m\u001b[1;33m]\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now start the evaluation for each of the collected probesets\n",
    "for probeset_name, probeset_df in selections.items():\n",
    "    \n",
    "    print(probeset_name)\n",
    "    \n",
    "    gene_list = probeset_df.index[probeset_df[\"selection\"]].to_list()\n",
    "    evaluator.evaluate_probeset(gene_list, set_id=probeset_name)\n",
    "    \n",
    "    clear_output(wait=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Visualize the evaluation results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "mpl.rcParams['figure.dpi'] = 60\n",
    "plt.rcParams[\"font.size\"] = 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>spapros_selection</th>\n",
       "      <td>0.734799</td>\n",
       "      <td>0.57498</td>\n",
       "      <td>0.191625</td>\n",
       "      <td>0.925486</td>\n",
       "      <td>0.98626</td>\n",
       "      <td>0.849805</td>\n",
       "      <td>0.841053</td>\n",
       "      <td>0.683124</td>\n",
       "      <td>0.739218</td>\n",
       "      <td>0.776473</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   cluster_similarity nmi_5_20  cluster_similarity nmi_21_60  \\\n",
       "spapros_selection                     0.734799                       0.57498   \n",
       "\n",
       "                   knn_overlap mean_overlap_AUC  forest_clfs accuracy  \\\n",
       "spapros_selection                      0.191625              0.925486   \n",
       "\n",
       "                   forest_clfs perct acc > 0.8  gene_corr 1 - mean  \\\n",
       "spapros_selection                      0.98626            0.849805   \n",
       "\n",
       "                   gene_corr perct max < 0.8  marker_corr per marker  \\\n",
       "spapros_selection                   0.841053                0.683124   \n",
       "\n",
       "                   marker_corr per celltype  \\\n",
       "spapros_selection                  0.739218   \n",
       "\n",
       "                   marker_corr per marker mean > 0.025  \n",
       "spapros_selection                             0.776473  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluator.summary_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false,
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 660x300 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluator.summary_statistics(set_ids=selections.keys())\n",
    "evaluator.plot_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
        "hide-cell"
    ]
   },
   "source": [
    "I this plot, each row summarizes the evaluation metrics of one probeset.\n",
    "The metrics evaluating the variability recovery are shown in the green columns (1-3).\n",
    "In the first column, you can see the NMI of coarse clusterings of the selected probe set and the full gene set.\n",
    "Here, for both gene sets clusters with between 5 and 20 clusters were created.\n",
    "The second column shows the NMI of clusterings from 21 to 60 clusters and thus represents the performance of finer variability recovery.\n",
    "As the clusterings should picture the heterogeinity of the expression data, a variability recovering subset would would cluster similarly.\n",
    "Both NMI values are highest for the pca based selection, which indicates that this method recovers the variability best.\n",
    "\n",
    "The third column reveals the graph overlap of the knn graphs of the full gene set and the respective probeset. If this value is high, it means that fine variations within the full gene set are captured by the 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": "code",
   "execution_count": 16,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "mpl.rcParams['figure.dpi'] = 50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will have a closer look on single evaluation metrics with individual visualizations via the `ProbesetSelector.plot_evaluations` function. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random forest classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x450 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluator.plot_confusion_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gene correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "mpl.rcParams['figure.dpi'] = 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 740x500 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluator.plot_correlation_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "mpl.rcParams['figure.dpi'] = 45"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluator.plot_knn_overlap()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NMI cluster similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ihuNvYHDq4EiHb0y7EZF7VTX/cPXyfvhKMRDdDiHUF/zq/OzDVRIRBfJV9V7385+AT1X1Sffz74AaVb1LRK4BZqrqzMMcswCoB5reexgAfFdVZ4tIGs6tmGGqul1EbgS+rqrTRSQLKAbSVHXPkV1y93e4JrM5CrHDhnHen79H1Qcfsuu1Nyl6qYiyQRPYkj6UPbXRxCZFk52XxNQZ0zn/9POpCdTwxpY3uPGtG/nOCd9h5uCZkb4EYyKqLcmmAzzZ5P1MYLKIfMf9HA0sPYpjfl1Vl+z7ICIv7nuvqmUi8hLO7Zmf47TOHmrtQCLyOpADZKtqzlHE0m1YImsnnrg4ks45m6Rzziavro7qDz+k4o03qPrkY3T0JMqTpvPSw3v4ym3jiU+JY+bgmUzMmcj/vfd/LCpaxI9O+hFx/rhIX4YxPVnT+/cCXKyq69r5nE8AfxKRl4FBwPMAqloiItuBicDrbtk5IpIC9PgWWqSH3/cInpgYEs88kz6//jVD3n2HPpd+iT7rXiGv5AP+8/BiaqsaAOfZsznnziE5Opkr5l7B3E1zKa4uPszRjTEd4EXgR/tGFYpIkogMb4fz7Ful93HgGVWta7ItH3jQHfG9T3w7xNDlWIusg3miokicNo2EU0/Fd+99BHZ8xH8fFmbeOp6oWB9+r5/bJ9zOx9s/5tWCV/n9kt+jqozLHsdVI69iWNqwSF+CMT3R94FfAIvd+2dBnMSyJpwncQfHPYXTtXh9s21PiEg1MMdtie3Cued2Qzhj6IpssEcEaShE0U9/yoodqVQOnsKoU3Op3F1Hxe5aqsrqOH56X/JGZ1BSU8JH2z/i0SWP8supv2RCzoRIh27MUWnrYA9jjoR1LUaQeDzk3H03x/UuJ2Xde2x9fxX1q1eRWrSM/hWf8+5Ty9i9vZKsuCy+MuQrPDL9EX784Y95o+CNSIdujDGdhnUtRpiIkHP3T/D94Q80bHoVb0oK3tQUvNmJ1Be8y3/vLmPGWR4yLvoSI9NH8uTZT3Lz2zezu243lw2/LNLhG2OaEJFZOJOnN3e2qpZ0dDw9hSWyTkBEyLzlloPKJ14FjX/+lPff38zxfzyPrP/9DrkzL2T2ubO55a1b+O/G/zIweSADUwYyIGkAA5IH0CexD36Pv4WzGGPam6redPhaJtwskXVyk6+eyKt1sZRO+g1R//wtVfPm0evefJ45/xk27928//VqwasU7C1ge9V20mLSnKmwhl/KKX1OifQlGGNMu7JE1smJRzjzmpH869efEZp5D7lFH7L5oovp9bOfMnTKFIamDj2gvqpSWlvK2rK15H+cz2NnPnZQHWOM6U4skXUBUbE+vnLbCaz6cAcfLh9B/On5FD3wHKkPPo43IwNfejrejHTSxg4j6eTJZMVlkRWXxT2T7+HWebfyzIxnSI5OjvRlGGNMu7BE1kXEJkQx/tw8xp3dn8LVZazMSGLbzkq0sRFtCKBbGmlYvYfBv/4Wgyb0JuVL53PK+JNZO3gmd7x/B4+d8RhejzfSl2GMMWHXbRJZKKR4PBLpMNqdeIR+o9LpNyr9oG2lWyt5/285FO3Zw9CH/kJawpN845GHWb17NQ8vfphbx9/a4jFVlaWlS4n2RjMifUR7X4IxxoRVt0hkJZV1fPMvn/Gvmybj9/bcR+My+yVy0Q8msvHzUj55IYnc6pXI7Xdw3/0/5co3r6GusY4RaSPIS86jf1J/ymrLmLt5LnM3zyUzNpOd1Tv5+Sk/56ReJ0X6UozpMtxZ618CEoCXjnZNsmM4/0yc5V2mdeR5O5NukciyEmPITY3l7wu2ctXkvEiHE1EiwuDxWfQbmcYLD/jwl9Qg9/yMWXf/gbcK32ZN2RpeL3idgooCYn2xnDfgPJ44+wlyE3NZvXs133nnO9x/6v2Mzx6//5iBUIA5K+ewtWIrd550J7G+2AheoTEdS0R8qtp4iCr7FtZs8xDhNhzTHIFukcgAfnjucL466xMuHNuH5Fh7jioq1seXvjOG53/TSPSeanLvf5TL7s2nsaiIutWrqdu5hlBlBb7dfnyFS6nJKWbo4ME8dPpDfPfd7/LAaQ8wNmssS0uXcu8n93JaURrD94S4ofyb/P7MR23wiGlf+cntth4Z+XvbvB4Z8CVggYj8EPgtcIIb12LgFmAqzsKaySKyBPiRqs5t4zFnAY/hTPwbA7ygqj926+YDw4E4YDCwG/iqqha5Exc/jJNAy4BPmp3nVuAbgAJrgZtVtdRdN+1/gFJgDFCOM5/jz4ARQCFwkao2nfW/SwhrIhORQcAcIAuoBr6pqotaqNcHeBQYCTTizPL882M5d9+0OL4yrg+/f2c9Pz5/5OF36AHik6P50rfH8NLDir/4ZSpOmoQvM5OYESOIGTEcf84QAsXF1Mz/lEBREXVrVpM5YwYPfPnH3PbebUzuNZnNaz/lnvlZJJbswpOURJ+NtVxXdxWPnvsnsuM7w5JRpltqQ7LpCKo6AQ5YWPNG9/PvcJLWXSJyN21YWLOFYyYCZ6lqnYhEAW+LyDmq+rpbdRIwXlV3uxMJfxe4E2eS4BHAKLfe/jnr3BWrbwQmqeoeEfkN8AiwbxqgCcDxqrpFRP4O/BeYoqrFIvIaTqKbdaS/p0gLd4tsFjBHVR8XkbOAZ0RkuDaZmVhEBHgBeFBV/+6WhWVRuFumDeLsB9/nipP6k5dhqxsApObEc84Nx/HaH+GU33+LhKwkouN9xMT7UVUay+up31NPdXk98TEhvIteJv7me3lo4ii2+bdw1UcNZN78JVK++lVQJfq++/jh3xZxS82V5J/7AJlxmXjFi9fjJd4fT7S3Pf6INiYi2mNhzabHjAUeFZETgBDQGxiLu94Y8Jqq7nbffwSc574/A+d7tgFARGbjLMYJcDbw9yarSD8GLGxyzk9UdYv7fhHOxPH71opaiNP663LClshEJBPnL4gZAKr6ppu0xuP8wvaZDoT2JTG3blE4YkiM8fOd6UP45aur+eOVJ4bjkN1CzoBkzrh6BCs/2EF9TSn1NY3UVQcQgfiU6P2vlevKSc6aysnPXU79qy+QVVJK+n8eQRKTWbuomLId1Qy89jby3nme/DlzeKzhLooyPDSGGglqkKqGKibkTGDGwBlM7TOVKG/UYWMLaYhdtbvYWb2TyoZKRqWPIjUm9YA6FQ0VvL3lbT7Y/gHBUJAobxR+j59oXzT9E/szOHUwg1MGkx2XjfOfnGmrYChoj2W0rj0W1mx6zF/gLIo5TlUDbqur6Q3opmuRBTm67+vmy5s0P2Y4zhFx4Qy6H7BTVQNNygrc8qaJbBRQLCJ/w+kD3g7coaqr91UQkWnAtKuvvvqIg/j6hL785ZMCPtm4m8mDDh6i3lP1HZFG3xFph6wTCoZY8lYh/3pwBZNnnkO/S9JZ8v52Vn6wipyByWT2T+TD5zZQWzmcfuf/hBv/8QfSBmaQ8pWLSJh+Og2eEO9te4+XNrzETz/5KaMzR1MdqGZP3R7K6spoCDYgCFHeqP0tt8qGStJj0+kV34t4fzz37LqH5OhkJuZMZGDyQD7Y/gHLS5dzWt/T+PKgLxPriyUQChAIBqhprGFLxRaeX/88G8o3sKduD8PThjMmcwxjMsdwfObxYb2XV9VQRZw/Do8c+cjY0ppSXtjwArG+WC4ecnHEVv8uqSlhQdECFhYtZMHOBeyt38vEXhM5u//ZnNb3NOL91pPRihdxFtb8ppt0koDeqnos65GlAmvd4/UHvgz8vg37vQVc5X6HAlzVZNsbwMMi8pCqlgM306TrsbuKRPb1AWfi9OEuF5HLcPpp9zdpVXUeMC8/P/+eIz241yPcdf5Ifvnqav7zrZPtL/Qj4PF6GHdOfwaOzeTdp9fw4XMbGD6lFxfdPp7EtBgAxp3dn4pdtaxfVMzSklsZ3r8eefkFin7+MxLPOIPJU6Zw5sR7qYoVVu1eRVJ0EmnRaaTGpBLjiyGkIQKhAPXBelSVpKikA/6NVJUtFVtYULSADeUbuHjIxTxw2gNtat3VNdaxumw1S0uW8u/1/+aej+8hMSqRsVljGZs5ljFZY8hLymtzIgqGgqzYvYIPt3/Ih9s+ZEvFFk7IPoH7p95PQlTCYfdXVRaXLObva/7O58Wfc8GgC9hRtYMLXryAy4ZfxqXDLiUhKoHCykI+2PYBH2z/gOKaYhL8CcT740n0JxLnjyPeH7//FeuLPeCVEp1Cv6R+JEYlHnDuyoZKNpZvZEP5BjaWb2R9+Xo2lm8k2hvNhJwJTMyZyM1jbiYlOoWPdnzEGwVvcP/C+zm598ncNemuFhNtUXURjy15jLrGL/6Ij/HFcN/J97Xp99nFtcfCmj8D/iIiV+EMtHirjfs9DhwHrOSLwR5jAVT1dfd+3odunGtxklm3FraFNd2uxU1A2r5WmYisAy5vOuBDRC4GblPVKU3KGnD+utnV9JjHsrDm+Y98wC8vGs3xuSlHtX9Pp6qEQor3EM/l1VQ08PbsVSDCtAtzaPz0fWrmf0rNggX4MjNJmHYaqVdeiS819aB969auo+r990i55JIWt4frGnZU72BJyRKWlCxhaelSimuKGZU+irFZYzku4ziqGqrYvHczm/ZuoqCigIr6CuqCddQ31qMoQ1OHckqfUzilzykMTR3KY0sf452t7/D/pv8/chNzDzjf7trdLCldwpqyNazZvYbVZavJjsvm0uGXcnbe2ftboTurdvLUiqd4e+vb+1tAp/Q5ham5U8lLyqMqUEV1oJrKhkqqA9XUBGqoDlRT3VhNbaCWmsYaahtrqWuso6yujK0VWwlqkH5J/YjzxbFx70ZCoRCDUgYxKGUQQ1KHOO+TBx0yAdc11jFr6Sw+2fkJj57xKBmxGfu3rS1by3ff/S6XDb+MgckD95d7PV6m9J7S0uFaZAtrmvYQ1hWiReRt4B9NBns8BgxtNtgjDlgBnKaqhW69p4B+2iyYY0lkcz4uYH1JJT+bOfoor8a0hYaUpe8UsvSdQk66YCAJqdH4or1QvJ26996h5tX/kv7VC8m4+mokNpaiRRvY9LfXKS0N0ZiZS+76uQy9fDppl34d8bV/B0FNoIYVu1awpHQJy3ctJzkqmQHJA/a/UqOdlmO0N7rV1vzcTXN58PMH+dXUXzEkdQhvb3mbuZvnsql8EyfmnMiItBEMTx/O8NThpMSktBrLrtpd1AZq6ZvUNyzXVVhZSHWgmkEpg46pS/Wfa//J7JWz+f0Zv2dg8kA+2v4R93x8Dz89+adM7j35mOK0RGbaQ7gT2RCc4fcZQA1wg6ouEJH7gB2qOsutdybOsxcenJuf321pmP6xJLLymgbO/N37fPiD04nx283s9laypYLl726joS5IoL6RQH3QeV/bQMPeGoKBIOL1EF9TTPaQNPqfPR5/XBQLXliH7ixkaMlbDPrmJYDSWFJCY0kJwaoqYseOJX7yFPzZWQecT1UJVdfgTWj5no6q0rBpE97UVHxph7432FyotpaGrVsJbNtG7PHH48vMPKjO8tLl3PbebdQH6zkt9zTOG3AeE3Im4PN0yXvlaEMDFW+8Sd2KFaR941o+ql/Fz+b/jAsGXcCrm1/lkemPMCRhALVLluDPzcWXk3NU3fbdPZHZwpqREdZEFm7HksgAvvP3xZwxPIuZJ/QJX1DmqDRs2071/E9JnnEunrgv7r+oqjOl1rMria4swucXQv5YQt5o1OMhtnYX0dvXkEAFqYN7UVXRyN7yIJWBGOqjUujTuImhJ/Ui+YxpxIweTd3q1VS++ioVr72OJyGBUGUlobo6ogcPJmrgALS2zkmUpSU0lu4CjwdPbCyeuFgkJpbG0lI0ECCqb1/8vXtRs3ARSTNmkH79dQcltJpADV6Pt9VHDlSV4O7dNGzeTP3mzTQUbMETH0fciROIHTsGT7SzX6i2ltolS6j+9FOCe/cS1acPfvflSUxEAwFnYuiGBkJVlQSKimgsLiFQXITWN+DPycaXnYO/Vw5ReXlEDRzY5iQT2LGDPc/+k70vvkjsmDFEDx5M+b/+RfqNN7D9jON4avVsfjjhB8S89xm7/t/v8WVm0lhWRnD3bqIGDyJm5Eiy77yzzefr7onMREa3TmQfrt/Fo+9u4O83tPQHkulMgsEQO9aWgwf8UV58UV5EYG9pLWU7q9m9oYSKnXtJSPGT2jeVjMFZRCdEsfy19ZRs3kv/vYvIXvUysf1zSTrvXBLOPgdf7954vR5C1dXUb9xIw+bNSFwc/qwsfFlZ+NLTUUBragjV1hKoqiUqKx1f8hfdcsGKCsr+8lfKn32WpBnnkXD6dKL69XVaJB4PGgrRsHEjNYsXU7tkKYFt2wiWlxMsLydUU4M3NZWoAXlEDxhAVF4ewb0V1CxaRN2KFUQNHgRAw8ZNxBw/mviJJ+HLSCewYwcN27cT2L6dUGUVEhXlvvx4ExLwZWXjy8nGn52NREURKC6mcWcRgeIi6tdvILhrF3ETJxI/eRJRgwYRLCtzk3cpAfdnY0kpjSUlSHQUKV+5iJSvfRV/tvMMcmDHDop+8Qsai4pJvexSyp5+Bl96Opnf/x6SN5ToOB9aX0/9xo0ECreRdO45bf53tkRm2kO3TmShkHLab9/lmesm0S89MsOdTfsrL65h8Ztb2bS4BPEIwUbnv2nxQFSMj6x+iWT2TySrXxK9hiTja9bVHGgIsvQt5z5fdKyPQeOzGDwui4y+zsCIPTtr2LZ8J4XvrSB1z1qyCj8gWFyCLyODYHk5/j59iD3hBGLHjiGqfx7elBS8KSl44uNabaloYyN1q1aBKjGjRoX1/mDjnj3ULFhIzafzaSgowJuR4STvzEznlZ29/70n9ovHlkIhZU9RNak58Xg8QuU777D3pf+Sevll1PYeyaf/2UjR5gpEIK13Apl9E8jsl8iQCW1/fs8SmWkPLSYyEfnyoXZS1ZfaLaImjjWRATz01jqCIeW2s4eFJyjTaTU2BBERPD7Z/8VaU9FA6dZKSrdWULy5guKCCnKHpzF4fBa5w1PZsKiEhXM30/+4DCacn0djQ4iNn5ew4bMS6msCBBpCJKRE02twMln9k1j98U5AOfXSoSRKFd6kpAO6So9Exa5ayotrnCdW3f8Nm///uO96vF4PHp9zTcFAiGAgRGMghNfvISUrjsT0mGNaxqi8uIa356ymrjpAQ10j/UelM2BMBkkZsXz22haKCyqYeH4eQybmEAyE2L29itKtlVTurmPKxW2fDMISmWkPrSWydw+xj6rq9PYL6QvhSGTb9tTw9T/O5/07TsfbA9YrM4fWUNfIluW72fB5CYWryug7Io1JMweSmnPwoJHKsjqiY31ExX7RWlJV1i0oZv6LGxk+uRe9h6RQW9lAbWWAmsoGPF4hPjma+OQo4tyfsYlReH2e/eff+HkJaz4porKsjqz+SYgA4kwdAUCz5+pCQSXUGCIYVFDF6/fi83vw+j00NgQpL66lak8dccnRJGXEEJsYRWyC3/mZ6D/gc1xiFF7/F49UhELKsncKWfJWIZO/MoihE7Opqw5QsGw3Bct2UbazmuNPz2XkKb33X8OxsERm2kOL/RmqenpHB9JeclPjGJgZzwfrS5k2LOvwO5huLSrGx5AJ2QyZkI2GFDnEHzf7HgJvSkQYdlIO/Y9LZ+HLm1nx/vb9CSMhJZpgY4i9pbXs3FBO9d4GavbWU1PZgIgQlxRFbWUD/UelM+FLA+gzJOWQ5z8SGlKqyuup2FVLbWWAuqoGaioDB3yurQpQs9dJtonpMSSmxVBZVkdcUhRfvfNE4pOdwSexCVGMmNKLEVN6hSW27q6t65GJSAFQD9QCUcCjqvqouy0L+CVwOlCBM/fiq/tmw3frXA48A1zYUb1iXUXXHCt8hL52Yl/+uajQEpk5wLEkkZh4P1O/PrTN9YOBENUV9cQmROGPDv/jIOIREtNiWky+zTXUNVJZVkfl7jo8HqHvyDSbAecQwrwe2ddVdYk7JdUyEfkAWA+8B/wTGKKqQRGJBr7VbN/rgbfdn5bImmixr0BEgk1eIfe1732wo4M8VmeNzGbB5jIq6gKHr2xMO/D6PSSlx7ZLEjtSUTE+0nsnkDc6g36j0i2JtUBEVETuEZGFOHMXJorIH0VkgYgsFZHZIhLnLpvyG2CSiCwRkRltOb47A/1aYBhwOVCpqveoatDdXq+qv2sSzyDgeLfuaSJizeUmWmuRrcFZT+wJnMky6zsqoPYQ4/dy6pBM3lldYs+UGdMFjJ4zut0W1lx+9fI2r3XWHuuRufuPxpk0fSnwPZotjtmC63DWbSwRkReAa3HmfjS0fo9slIhMwvnl3Qn8B3hCVVd0ZHDhNGN0L55dVGiJzJgu4EiSTTtqj/XInhWRWpz7ZNer6rrDtYhFxAtcjbtEFk4DY7aI/LL5tH49Vav3yFR1PjBfROKBS3FWL71XVR/rsOjC6JQhGfzw+WVU1gVIjPFHOhxjTOfXHuuRfV1VlzQr+wxn1efWnI8z7d9/miS93jgDQ945xni6hUOOpxWRPsCtwA+Aj3FWKe2SYvxeThmcwTtrbLozY8wRexFnPTI/gIgkicjwMB3770CKiNzttr4QkSgR+b67/XqcFUPy9r2Au91yQ+uDPS4Wkbk4S27XAlNV9SuqejRN6U5jxuhezF2+M9JhGGO6nu/jDItfLCLLgfdx7nEdM1WtAU4FBgDr3eN/DqS5gzrOAp5tttszwIUi0j5rIHUxrXUtPgcsBN7EacL+oNnih7e2f2jhd+rQTO58fjnV9Y3ER/eIJw+MMUdBVaXZ5yrgf1upOxuY3YZj5h1iWzHOAI6WxDYvUNVCwJbzdrX2bX5vh0bRQWL8Xk52uxcvGNM70uEYY4wJg9YS2eIOjaIDzRjdi/8s2X7Eiaykso6sxMM/bGqM6blsPbLIaC2Rfb+FMgUGAn2BFp/qdB/amwNk4TyH9s2WFswUEcVZJXrfw9U/UtW5Rxb60Zk2LJMfv7CcmoZG4qLa1r24o7yWab+dx2+/OoYvW0vOGNMKVb0p0jH0RC0O9lDV05u+gMtwplGJAf7vEMebBcxR1aHAHcAz0vpDElNVdaz76pAkBk734uRB6by7prTN+/y/d9ZzyfhcfvHKanaU17ZjdMYYY47U4Ybfx4vIvcAyoAwYpqoPtlI3E6dJPRtAVd/EefZifDgDDofzj2D0YsGuat5bW8pPzh/Jd84YzK3/XEIoZM8gGmNMZ9Ha8HuviHwLpxWWC4xT1TtVde8hjtUP2KmqTSc0LHDLW/KWO2fZIyKS3EqddjFtWBbLt+/lutkLeW3FThoaQ63Wfeitddw0bRCxUV4un9iPhGgfT3y4qQOjNcYYcyittchW4TwIfTvwMM7zDMfve4XhvP1V9UScFlw0cMBsISIyTUTyCwoKwnCqg8VGeXn7ttP4+oS+/Pvz7Uz99Tv8Yu5qquoPnOB6bVEln28t59IJ/fbFxa8uPp7ZHxWwakdFu8RmjDHmyLS2sGYB+9esPYiq6sAW9skENgFp+1plIrIOuLylAR9N9hsNvKKqB7XcwrGwZlvsqqrnj+9t5J01JTx6xTiG5yQBcONfF3HGiGy+dmLfA+q/s6aYX8xdwzVT8shJiiE7KYZeKTFkJLTHHKfGdB+2sKZpD61NGpx3pAdS1VIRWQBcAzwuImfh3CP7rGk9EckAqlS1TkQ8OMsSRHS4f0ZCND8+fySTB6Vz7Z8XcutZQxmancj6kioevXzcQfWnD8+muj7I8u17WVhQRnFFHQW7ahjXP4U7zxtB37S4CFyFMSYSjnJhzQRgA/CQqr7mbp8GvIqzvMs+xap6TnvF3l2Ee3qLm4A5InI7UANcoaoqIvcBO1R1FnAC8DsRCeEM41/OoSfM7DDTh2fz3E2JfOtvi9m6u5r7LjwOn7fl3tcLxvQ+4Fm0xmCIvy3YyldnfcIl43O5edogmz3EmG6gPRbWdI97Js7I7htV9UV3+1pVHXss8fZEYf2mVdX1wJQWyu9u8v5NYHQ4zxtOualxPHfjZOYu38n5o9u+dp3P6+GqyXl8eUxvHnprPWc/+D63nT2UmWP74AnTcvbG9BSrh49ot/XIRqxZfdglYtxnXfOBLwELROSHwG9x/hCPxulFugWYirOwZrKILOEInolV1bdEJB/4Ec6kxOYoWZOhBVE+z1GvW5YSF0X+l0dxxUn9+Okrq5nzyRbuuWAk4/rZ3J7GtFVbkk1HaK+FNZuYDzzQ5PMwNyHu85Gqfuuogu9BLJG1kyHZicy5dgLvri3h/55byvF9kvnBecPplXzQ/J/GmM6pPRbWbK55d411LR4FS2TtSESYPjybUwZn8pdPCpj56EdcPrE/N5w6kNioFmf5MsZ0Hu2xsGZzE3HGCZhjcMiZPUx4RPk8XD91IHP/dyqlVXWc9eB7/GfJdmyVcmO6jBcJ88KaInI6zn24XxxzdD2ctcg6UHpCND+bOZo1RRX8+IUVrNpRwZ0zRkQ6LGPM4X0fJ+EsdgeCBHGS0JojPM6zIlKLs5bYRuAqVX2jyfbm98iwrsbDs0QWAcNzkvjrdRO57PFP+fNHm7n25AGRDskY00RHL6zpbp9HC4tomsOzrsUIiYvy8dTVJ/LX+Vt4edmOSIdjjDFdlrXIIig9IZrZ10zkssfnk5EQzaSB6QfVUVX21gYoLKslyuehb1psm9dRM8Z0LFtYMzLsGzHC+qXHMet/xnPDXxdxyuAM6htD1DcGqQuEKKmsZ0d5LQnRPnJTYwkEQxTuqUVVyU2NY1h2ImP6pjCmbzLDshMB2FhazYrte1mxYy+9kmO44qT+NsOIMR3EFtaMDPuG6wRG5yYz+9qJbN5VRbTPS7TPQ5TPQ2ZiNL2SY4nyHdgDXNPQSGFZLWuKKlhSWM6/Pitk065qAPLS4xnVO4lRvZNZtXMvZzzwHtecnMeVkyyhmUPbtqeGlTsqGJadSP/0OJqviauqlNcE2Lanlm17ati2p5Y9NQ3cce4xDd4z5pjZN1snMSwnkWE5iW2qGxfl21//wrHODCSBYAiBg+aGvHlaLY+9u4HpD8zjonG5nD4si3H9UlqdQ7K9qSoVtY2UVtVT2xBkUFa8dZV2oGBICQRDNIaUYFApqqjjzVVFvLayiL21Acb2TeXB4nWUVNYzLDuRvIx4Sivr2Lanlp1760iI9tE3LZbc1DhyU2MZnJUQ6UsyxhJZd+FvJTH1SYnl518ZzS2nD+blpTt48M11rC+p5KQB6ZxzXA7njMom2nf0D2eHQkpJZT2Fe2oorqijpj5ITUMjNYEgVXWN7Kqqp7Synl1VDeyqqqeqvpGkGD8ZidFE+zxsLKkiJc7PcX2SGdEridzUWPqkOK+MhOgjmqdSVQ9qRXRGqsrWshqWb9/Ltj217K0NUF4TYG9tAw2Nur9F7vcKwRDsrW1gb22AvbUB6gIh/F7B7/Xg93rwCASCSmMoRGNQCYRCBINKIKQ0BkMEQ3rAekweEfxewefx4PUIqfF+pg/P5v6Lj2dkr6T9v7+6QJBVOyvYuruG7KQYclNj6ZUcE7E/gIw5FEtkPUSflFhuPG0QN542iKr6Rj7asIuXlu7gZy+v4stjenPpxH6H/Ou6rLqBBZt3s7Wshq1lNRSW1VK4p4bSynoyE6LJTYujV1IM8dE+4qK8xEV7yUmO4bg+yWQkRJOREEVGYjSJ0b4Dko2qsmNvHSu372VNUSUfrNvF9vJadpTXUlpZT3Kcn97JsfRKiSEzIZrqhiB7axsor3G+/KsbGqmuD1IfCAIgAslxflLjokiO9RMMKXUB555jYyjE2L4pnD0yh1OGZBDjdxJ4YVkN89aV8snGXfROjuXUoZlMHJC2f/uRqm0IsqGkivUllZRW1lNd30hVfZDq+ka2ltWwtriSrMRojuuTTP+0OHonxzCiVxIpsX78Xg8NwRCBxhANwRAeEZJj/aTE+UmO9RPj99IYdLY1BpWQKn6vB59X8HoEv8d57/c6icrnkaNK7jF+L+P6pdocoaZLaHFhzc6ioxbW7Ml2VdXz78+28Y+FhSTG+Dh5cAZTBqVzYv80ROCt1cW8uHg7y7bt5eTBGeSlx9M3LZa+aXH0TY0jK/HIWk1HQlXZUxNgR7nTrVVaWU98tJfUuChS4vykxEaREOMkzmifBxEhGFIqagPsqXFaMT6Ph2i/hxifFxGYv2k3b64q5rMtexidm8zWshpQOHVoJqcMzmDbnhreX7+LJYXljOqdtL8V4vcIPq+H8poAO/c68eyqrEdxvvSTYnwkxvgoq2lwukwzExiclUCv5BjionwkRPuIj/bRO8VJWkebJLs6W1izcxORazj6CZD3HSMf+JWq1rmf78OZQ/KZcMTY4jktkRlwksbqnZV8vHEXn2zczeLCcgSYNCidr4ztw6lDMw8adNKV1QWCLCwoo39aPP3SD14Itb4xyJKt5eyubnDuKbndd8mxfnolO91sGQnRiEBtIEhlXSOVdQFS46JIt5XCW2WJ7NDasPZZe5//Go49kSmQqqrlYQrrsKxr0QDOBMcjeycxsncS108duL/7qrsOxIjxe5k6JLPV7dE+Lye18FxfS+KifMRF+chOiglXeCbCRCQW+DNwPNCIs1DwpcASYBZwHs4M+D9Q1f+4+zwNDAeigCLgelXdKiJ5h9mv+dpn+cAfgGE4kxU/paq/c+veBVyBs9K0B7hMVVe2cg1DcGYcScBZxPgdVf1fd9ut7vX4gDLgZnc9yebHuAL4DuAH6oDvqepCd9tVwK1ujEHgSrcuwAciEsRZNSAfWKKqD4lIPPAQMNmt9wrOcjhBEZntnmMw0BdYD3xNVWtaur6mwvotJSKDgDlAFlANfFNVFx2i/mzgajo4e5vD83k9dmPfRMyjN73TbgtrfmvW9LasdXYukKaqIwFEJA1IApKBTao6RkRGA++JyHD3Yefvq2qpW/8W4H7gMvd4h9oPOGDts78Dm1X1EhFJBT4VkZXAAuB2IEdVa0UkhkPPzvRt4BVV/UWTa0BELsdZ3Hiym0AuxEnaB6xwLSInA1cB01S1TkTGAi8B/UTkVOA+4BRV3ebG4lfVm0TkRmDqvu/0Zvdo7wIScf5AiALeBK4H/uhuPwE4HSehvYGTHP/IYYT7z+1ZwBxVfVxEzsJZxnu4ttB/KSIXAYEwn98Y0w20Mdm0pyU4E/jOAt4H9q36HASeAlDV5SKyDGcmj5eAy0XkSiAGpwUTanK8Q+0HB659djYw3q27x01sZ+F86a/B+V59E3hVVQsOcQ3zgAdEJNG9hjfd8pnABOAzN8kI0NKonguB44D5TZKRT0SygQuAv6rqNjfOOpzkczhnA7epagioE5GngPP5Ilm9sK8FJiKfAEPacMzwzbUoIpk4/zCzAVT1TZxf0PgW6mbjLO99a7jOb4wx4aKqm4GRwMvAicAqoDfQ0qACFZFTgO8CX1LV44BvcuAEwC3u1+R9VQvbD6jnfvlPwVlROhV4X0RmHOIaXnDrrwSuw0lm4Hwv/1JVx7qvMarar4VDCE7DZGyTV29VLT5ErEeq+e+laTIM0sbGVjj7jvoBO1W1aSurwC1v7nHgDlWtbOlAIjJNRPILCgrCGJ4xxrSNiOTiDIZ7Gac7rwLnS9UHXOPWGYXTRTcfJ7FUAqUi4gVuaXbI1vZryRvATW7dFJx7WW+4LascVf3I7S58AyfJtnYNQ4BSVX0a+B4wXkR8OGurfUtEMtx6PhE5qMGB01q80r3Hhzgmutv+627r426LcePD/V0lH+LabhARj4hEA9e6Zcekw2+CiMj1wFZVfae1Oqo6T1Xz8/LyOi4wY4z5wmjgIxFZCizD6VrcBuwFBrvl/wa+4d4Xew1YDazDSVDN1ylrbb+W/K9bdznwIfAnd82yZODfIrLCPU428NghruESYJm7vtlc4EZVbXSHwT8BvO12cS7DGWhyAFX9APg/95xLcVql33C3vQ/cA7zqbvsIZ4AGwG+AN0Vkyb4k2MTPcAbOLAM+d39XT3KMwjb83u1a3IRzgzTglq0DLm864ENEngFOxWk2AvQHCoGLmg8MseH3xnQvXXn4/b7Rh6qa0hH7mbYL22APVS0VkQU4zed9gz0E+KxZvSuafnaHnh5voxaNMcYcjXCPWrwJmCMit+M0H69QVXWf7N6hqrPCfD5jjOkQ7gjBlI7ar61EZBEHf5dvVdUvt9c5O5uwJjL3gbopLZTffYh9Ov8sr8YY00mpaqsDPnoKe+LVGGNMl2aJzBhjTJdmicwYY0yXZonMGGNMl2aJzBhjTJdmicwYY0yXZonMGGNMl2aJzBhjTJdmicwYY0yXZonMGGNMl2aJzBhjTJdmicwYY0yXZonMGGNMl2aJzBhjTJdmicwYY0yXZonMGGNMlxbWRCYig0TkQxFZJyKLReSgBd9EJF1EFojIUvf1mojkhTMOY4wxPUe4W2SzgDmqOhS4A3hGRJqvAL0XOF1Vx6jqGOB14OEwx2GMMaaHCFsiE5FMYBIwG0BV3wQEGN+0nqo2qmq1u48ASYCGKw5jjDE9SzhbZP2AnaoaaFJW4JYfREQ+AIqBS4BvNds2TUTyCwoKwhieMcaY7ihigz1UdSqQAzwP/LjZtnmqmp+XlxeJ0IwxxnQh4UxkW4FeIuJvUpbnlrdIVUPA48CVYYzDGGNMDxK2RKaqpcAC4BoAETkL5x7ZZ03riUgfEUluUnQpsCxccRhjjOlZfGE+3k3AHBG5HagBrlBVFZH7gB2qOgsYBDwiIh6cRLcB+J8wx2GMMaaHCGsiU9X1wJQWyu9u8v59YGw4z2uMMabnspk9jDHGdGmWyIwxxnRplsiMMcZ0aZbIjDHGdGmWyIwxxnRplsiMMcZ0aeF+jswY052EQlBXDjVlULP74FdjPcz4daSjND2cJTJjzIFCQdj0Lnz+F9g0D2KSIS7decWmQXwGxKVB2kCIz4x0tMZYIjOmR2uogaoiqCqByiIoXglL/wHpg2DclXDR4+CLjnSUxhySJTJjuqvaPbB7I1Rsh4qdzs/KnU7Cqip2kpfXDwk5kJgNiTmQOgCueRlS+0c6emPazBKZMd1BoA42vw8FH0DJKihe5ZRnDIHkXEjsBal50G+yk7AScyA+C7z2FWC6Pvuv2JiuIhiA8q3QWOcMsmish7JNsHYubJ0PfSfCwNNh6LmQNcK5j2VMD2CJzJjOrm4vfDYHFjwOsSkQFQ/eKOfeVVJvOOF/nHtZUXGRjtSYiLBEZkwkVBbB9s+de1UeL4gHxP3p8YKI87lwAaz4F4z4Mlz1ojMIwxhzAEtkxrS3+krYsQS2fwbbFzkJzOuHPuOdFpWqM+RdQ6Duz32f0wbCLfOtm9CYQwhrIhORQcAcIAuoBr6pqoua1TkJ+B2QBgSBT4Fvq2ptOGMxJmJUnWS17FlnAEbNLuh9gpO4TrgKvvQwxKdHOkpjuo1wt8hmAXNU9XEROQt4RkSGq6o2qVMFXKeqa0TEC/wD+AGQH+ZYjGk/FTth5fPO/avYNPdh4VSn1bXsWef98V+HSTc7owVFIh2xMd1W2BKZiGQCk4AZAKr6pogIMB7Y3ypT1ZVN3gdF5FPguHDFYUy7aaiBNa/A0r9DyWo47iJI6uO0uHatdaZxyhgKl/8TMgZHOlpjeoxwtsj6ATtVNdCkrMAtX9TSDiISD1wP3NmsfBow7eqrrw5jeMa0gSqEGp2h7o11ULQMCj6Cgg+dZDXwdJh0CwycZs9gGdNJROz/RBGJBv4NvK6qLzTdpqrzgHn5+fn3RCI208NU7ITP/gyLn3EGZnh94PE7Q9yzhkPeKXD2z6DXGEtexnRC4fy/civQS0T8TVpleW75Adwk9jywGfjeMZ+5ttz5IpryXfDYyjSmBfVVsHMpBGq+KAvUwop/Q+GnMPYKuO51ZxYMY0yXErZEpqqlIrIAuAbYN9hDgM+a1nOT2Is43Y63NBsIcnR80VC4EP79DfjKH22S054u2AhlG6FoOWxb6Mx6sbfQGTkYk/xFPfHA8PPhoj/ZfzPGdGHh7ie5CZgjIrcDNcAVqqoich+wQ1Vn4dwTOwdYDix2xoMwX1VvOuqz+mPha3+BubfB0xfDpc8c+IVluq/GBihZCdsWwY7FTvIq3+qMFMweBbkTYPw1kDHMWuvGdFNhTWSquh6Y0kL53U3ePwo8Gs7zAs69iy89BO/dD7PPhyv+5UyMarqXqlIonO+0sgoXwO71kDnceUZr8Jlwyq2QNsCZHcMY0yN0rzvXIjDth5CQDX842fmLPHOYMyS69zjIHR/pCE1bBQPOQ8Wla5xktWuD8z7U6EyO23cSHP81yBplAzCM6eG65zfAidfCqK/ArvWwa53zWvAnGHWRk+js4dSOp+o8ZxWb2noXX00ZbHjLmc198wfODO7ZxznPZA0+y/mDJKlXx8ZtjOn0umciA2eW8L4TnBfA1FvhuWvg+Rvgwt/bzf2OEgrB2lfgo0ecARcNNRCfASn9ID7TeZi4ssh5eaNg0OkwciZc8AjEJEU6emNMF9B9E1lzMclw+XMw9//gLxfC15+x+e7CpaEailfCni1ON583CrzRUL4F5v/BSVrT74IBpzr1q0qcbdW7nKSWmON0B9sfF8aYo9BzEhm4A0IehE8ehSemw/SfOF2QNjDgyNSWO91/G96Cncugdo9zPzJtgDNrezAAwQanRfXVP0PO6AP3T8x2XsYYEwY9K5GBc39syrch72R495fw/m/gtB843Vk2PNvRUA27N0B16YHrZO3dBqtedAZhDDnb+SPgrPuc+QbtvqMxJkJ6XiLbp/cJcMU/neeP3v0FvPtzpyylP6T2d55DSu7rfEn7oiIdbfuq2OlMhLtpnpPAggFnAceEbKDJWllxaXDidfC1v3b/34kxpsvouYlsn9wT4crn3RGO6517N8WrYO2rUF4IFdvAF+tMXRST5Cwz7493fkbFQVSC+z4eopOce3GxKRCT4iTCY/3Cb6iByp1QsQMaqposuhgExG0xua+aXe4oTXe0Zn2VG5sbY3wGpA+GjCHOz8oiWPw0lKyC0Zc497EyhjgjC40xpouwRLZPxhDn1ZK6vbB3uzOhbKDa6Xrb9wrUOOWVO516teVQV+7cNyovdFo1WcOdh3Yzhzk/0wc7AxsaamDrx7DxXdj0nrOPeNxl7gXqKsDjc4acJ/aG6ERnu8ft6gNnWPu+lYVjU50h6kPOcX7GJDnnaKhyYq0qgt0bnftaK56H6ASYcL3zILE9i2WM6aLs26stYpKPbsorVee+UulaKF3tJKz5s5zuu5hkqK+A3IkwaDpc8pQzek9D7kudxHWsLTp/7BejM7NHOucyxphuxBJZexKBlL7Oa8iZX5SrQlWxs6qw1x+5+IwxphuwRBYJIjYPpDHGhImNNzfGGNOlWSIzxhjTpVkiM8YY06VZIjPGGNOlhTWRicggEflQRNaJyGIRObGVes+LyA4RURFJCWcMxhhjepZwt8hmAXNUdShwB/CMSIuT8P0BGBPmcxtjjOmBwpbIRCQTmATMBlDVNwEBDlqWWVXfVNXScJ3bGGNMzxXOFlk/YKeqBpqUFbjlR0REpolIfkFBQZhCM8YY0111ygeiVXUeME9EcufMmbMtgqHk4STjzioPi+9Y5dG5Y8yje8WX2z5hmJ4snIlsK9BLRPxNWmV5bvlRUdXrwxHY0RKRfFXNj2QMh2LxHbvOHqPFZ8zhha1r0b3ntQC4BkBEzsK5R/ZZuM4RAfMiHcBhzIt0AIcxL9IBtMG8SAdwGPMiHcBhzIt0AMaIqobvYCJDgDlABlAD3KCqC0TkPmCHqs5y670OjAL6ADuArao6OWyBGGOM6THCmsiMMcaYjmYzewAi8hP3Ie6QiMxsUp4hInNFZL2IrBaRcyMUX6qIvOzGuFRE3hCRwZ0sxmdFZJn7IPwCETmjM8XXJM5r3QfxZ3a2+ERknohsFpEl7uvuzhSjiESJyENuHMtF5KXOFJ/puTrlqMUIeBN4BniqWfkvgcWqOkNERgHviMhAVa3u4PgUeEhV3wIQkf8FngCmdaIYb1TVcje+E4C3RSSjE8WHiOQB3wTmNynuNPG5vq+qLzYr6ywx/gKIAoapakhE9q1F1FniMz2UtcgAVZ2vqpta2HQZ8Hu3zkpgKXBeR8bmnrt8XxJzfYwzIhQ6UYxNPjZdTrtTxCciHpzk/x2gvrPFdxgRj1FE4oAbgB+qasiNpaizxGd6NktkrRCRdMCvqjubFBdwFA94t4PvAf/pbDGKyIMisgn4N3AxkErnie9W4CNV3T+KtrP9/ly/crvt/iUiwzpRjIOBPcAPRWShiHwkIl/qRPGZHswSWRcjIvcAA4E7Ix1Lc6r6fVUdCFwB/JpO0nUtIsfhJNafRTqWw7hKVYcDxwNzgTciHE9TPpzktEFVJwDX4XTF50UyKGPAElmrVHU3EBCRXk2K8ziGB7yPlYjcBcwAzlPVms4YI4CqvobTGsulc8Q31T3vehEpwJkT9E84XWKdIT4AVHWr+1NV9SkgAaebtjPEuBXnXu3TboxrcLoQB3SS+EwPZons0J4Fvg3g3sQeC7wWiUDcltgFwNmqurfJpojHKCKxIjKgyefJQDqwqTPEp6p/UNVeqpqnqnk4gz1uUNXfd4b43HPHuINj9n2eAQSBws4Qo6ruAl4HznXj6AWMBpZ3hvhMz2bPkeFMswNcD2QClUAdzl/tDcBfgEE4Xyq3qeorEYhvFLAC2AhUucWNqnqiiGRFOkYRSQNeARKBRqAa+ImqvtMZ4mtORObhjAJ9sbPE58bxKs6owBDO/ag73AkFOkuMecCTQBZO6+x+VX2ms8Rnei5LZMYYY7o061o0xhjTpVkiM8YY06VZIjPGGNOlWSIzLXLn/ZvZweeMcueUXCYiTxzBfrNF5HvHcN6xInLp0e5vjImsTvHAqumeRMSrqsEj2OUEYIiqDmuvmFoxFpgJ/ONIdxQRn6o2hjsgY0zbWYusC3Jnb/+hiHwqIgUi8q0m2wpEZGyTz/tbVm7L5U8i8pY7y/pTIjLRrbNJRB5odqpp7jk2iMgfRMTvHidRRP4oziz3S93jxjU5x5Mi8h6wWkSim8XuFZH7RWSF+3pcROJEZDTOxM39xJn5/aDVwd0pm151W2zLROT2Furki8hDTT5fIyIvuu+HuFMrLXXP/Yg7dPw+4HT3vH9x654oIm+LyCK3/Kpmv/97RGQh8LCITBKRz9x6K0XkB234ZzTGhIm1yLqugKqeJCIDgeUi8ldVrWjDfqOB6TjPKq3BmYHjLCAG2CQiT6jqarfuGJxZMcCZLumbwGPAA8CnqnojgIj8DvgRcJdb90TgFFWtbOH81wOT3ToNwN9xnjm7001eD6nq2OY7iYgPeAn4mar+1S3LbMP1NvVt4BVV/YW7f5qqlomzXMpMVZ3plqfgPC91vqpucz8vEpFPVXXtvoO5UzUhIv8BHlDVv+077hHGZYw5BtYi67qeAXBn7S+j7ZO0/kdVa1W1Huch69dVNeAmnTU4D7XuM0dVG1S1AZgNnOmWzwS+57ZAluDMdD64yX7PtZLEAM4GnlLVOncW9Vk4ifRwhgFx+5IYgKqWtmG/puYB14vIL0XkPKC1xD8FZ5qll93rm4fzR9/IJnWebPL+beAut5V2Gs7DzMaYDmItsq6rrsn7IF/8WzYC3ibbYg6zX2vHacm+p+cFuFhV17VSr6qV8kMdM1xavX5VfUFEPsFJyNcBP8FJWs0JsFJVW9q2z/5rVNVHxFlk8gz3mCXA5Ud9BcaYI2Itsu5nPXASgHvfaewxHOsqdyRhFHAVsG9NtBeBHzW5Z5YkIsPbeMw3gGtEJFqcNcJuoG2zvK8FqkXkyn0FrXQtrgdOdO/FRQNfa1J/CFCqqk/jLIUz3u2y3MuBa6h9DAyQJisdi8hIEUloKTARGa6qBar6JJCPM72ZMaaDWCLrfn4M3Cwiy4DbgMXHcKylwAfAKpxEsm9I/PdxuuUWi8hy4H2grYnsCeBT4HNgGVBDG5ZXcUcGXgj8jzjrdS0Drm6h6vM4E+2uxln5e0WTbZcAy9zuwrk4q1o34nQN+t1BIH9R1T3A+cDtbtkq4BGceRBb8i0RWSUii9163z7c9RhjwsfmWjTGGNOlWYvMGGNMl2aJzBhjTJdmicwYY0yXZonMGGNMl2aJzBhjTJf2/wHdybpa+t8O2wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 450x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluator.plot_cluster_similarity()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Marker correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Edit Metadata",
  "kernelspec": {
   "display_name": "Python 3.8.13 ('spapros')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
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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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          "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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          "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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          "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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      "model_module": "@jupyter-widgets/base",
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      "model_module": "@jupyter-widgets/base",
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     "181b9c2aabe547b38062aa81eca693d0": {
      "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\">0:00:12</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[33m0:00:55\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[33m0:00:12\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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          "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"
        }
       ]
      }
     },
     "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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          "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": {
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      "model_name": "LayoutModel",
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     "340b2dbb55154e33900010ff0397ae5e": {
      "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: #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",
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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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         "output_type": "display_data"
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     "35c78e580c4347c6b20b0be5d87d0ba9": {
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     "3c5e24967fa0496b8038ac0b42efa0e0": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
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     "3e00d0a2ecc94e9ca05852024890b67c": {
      "model_module": "@jupyter-widgets/output",
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      "state": {
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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: #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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