{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IMZMLprocess\n",
    "============="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "['../', '/mnt/f/dev/git/pyIMS/examples', '/usr/lib/python38.zip', '/usr/lib/python3.8', '/usr/lib/python3.8/lib-dynload', '', '/home/mjoppich/.local/lib/python3.8/site-packages', '/home/mjoppich/.local/lib/python3.8/site-packages/pIMZ-1.0-py3.8-linux-x86_64.egg', '/home/mjoppich/.local/lib/python3.8/site-packages/progressbar-2.5-py3.8.egg', '/usr/lib/python3/dist-packages', '/usr/local/lib/python3.8/dist-packages', '/usr/local/lib/python3.8/dist-packages/mpld3-0.3.1.dev1-py3.8.egg', '/usr/local/lib/python3.8/dist-packages/IPython/extensions', '/home/mjoppich/.ipython', '../']\n",
      "../pIMZ/__init__.py\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "import os,sys\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import progressbar\n",
    "import dill as pickle\n",
    "from datetime import datetime\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import logging\n",
    "\n",
    "sys.path = [\"../\"] + sys.path\n",
    "\n",
    "print(sys.path)\n",
    "\n",
    "%autoreload 2\n",
    "from pIMZ.regions import SpectraRegion, ProteinWeights\n",
    "%autoreload 2\n",
    "from pIMZ.imzml import IMZMLExtract\n",
    "%autoreload 2\n",
    "from pIMZ.comparative import CombinedSpectra\n",
    "\n",
    "import pIMZ\n",
    "\n",
    "print(pIMZ.__file__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load an imzML file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening regions file for /mnt/d/dev/data/msi/slideD/181114_AT1_Slide_D_Proteins.imzML\n",
      "0 356 400 215 273\n",
      "1 436 478 632 687\n",
      "2 1572 1612 608 666\n",
      "3 1149 1197 142 205\n",
      "4 618 666 211 266\n",
      "5 633 684 630 688\n",
      "6 1357 1400 628 686\n"
     ]
    }
   ],
   "source": [
    "imze = IMZMLExtract(\"/mnt/d/dev/data/msi/slideD/181114_AT1_Slide_D_Proteins.imzML\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explore which regions are there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 ((356, 400, 215, 273), 2655)\n",
      "1 ((436, 478, 632, 687), 2408)\n",
      "2 ((1572, 1612, 608, 666), 2419)\n",
      "3 ((1149, 1197, 142, 205), 3136)\n",
      "4 ((618, 666, 211, 266), 2744)\n",
      "5 ((633, 684, 630, 688), 3068)\n",
      "6 ((1357, 1400, 628, 686), 2596)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 674.791x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{0: ((356, 400, 215, 273), 2655),\n",
       " 1: ((436, 478, 632, 687), 2408),\n",
       " 2: ((1572, 1612, 608, 666), 2419),\n",
       " 3: ((1149, 1197, 142, 205), 3136),\n",
       " 4: ((618, 666, 211, 266), 2744),\n",
       " 5: ((633, 684, 630, 688), 3068),\n",
       " 6: ((1357, 1400, 628, 686), 2596)}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imze.list_regions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start with prozessing the first region , region 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spectra0_orig = imze.get_region_array(0, makeNullLine=True)\n",
    "spectra0_intra = imze.normalize_region_array(spectra0_orig, normalize=\"intra_median\")\n",
    "spectra0 = imze.normalize_region_array(spectra0_intra, normalize=\"inter_median\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spectra1_orig = imze.get_region_array(1, makeNullLine=True)\n",
    "spectra1_intra = imze.normalize_region_array(spectra1_orig, normalize=\"intra_median\")\n",
    "spectra1 = imze.normalize_region_array(spectra1_intra, normalize=\"inter_median\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imze.plot_fcs(spectra0_orig, [(5,30),(10,30),(20,30),(25,30),(35,30),(40,30)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imze.plot_fcs(spectra0_intra, [(5,30),(10,30),(20,30),(25,30),(35,30),(40,30)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imze.plot_fcs(spectra0, [(5,30),(10,30),(20,30),(25,30),(35,30),(40,30)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imze.plot_fcs(spectra1_orig, [(5,30),(10,30),(20,30),(25,30),(35,30),(40,30)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imze.plot_fcs(spectra1_intra, [(5,30),(10,30),(20,30),(25,30),(35,30),(40,30)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imze.plot_fcs(spectra1, [(5,30),(10,30),(20,30),(25,30),(35,30),(40,30)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now ensure that the normalized spectra are indeed comparable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where are the highest peaks? This may give a hint on whether or not a normalization by max intensity would also have worked. => here: most probably yes!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imze.list_highest_peaks(spectra0, counter=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the norm of each spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imze.plot_tnc(spectra0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that the norm of the spectra differs. However, given that intensities should be comparable, and that there was sample everywhere => just continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec = SpectraRegion(spectra0, imze.mzValues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.calculate_similarity(mode=\"spectra_log\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are various (unsupervised) clustering techniques. Like UMAP+HDBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.segment(method=\"UMAP_DBSCAN\", number_of_regions=15, min_cluster_size=9, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.redo_hdbscan_on_dimred(number_of_regions=15, min_cluster_size=9, num_samples=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(spec.dimred_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams['figure.figsize'] = (10,6)\n",
    "spec.vis_umap(legend=True)\n",
    "mpl.rcParams['figure.figsize'] = (6,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.plot_segments()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.filter_clusters(method='remove_singleton')\n",
    "spec.filter_clusters(method='merge_background')\n",
    "spec.filter_clusters(method='remove_islands')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.plot_segments()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which may or may not work well - more robustly, and maybe faster is a classic clustering of the similarity scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.segment(method=\"WARD\", number_of_regions=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.plot_segments()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.filter_clusters(method='remove_singleton')\n",
    "spec.filter_clusters(method='merge_background')\n",
    "spec.filter_clusters(method='remove_islands')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.plot_segments()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A manual curation of the segmentation is still possible!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.segmented[0:6,] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.plot_segments()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consensus Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.consensus_spectra()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.consensus_similarity()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.plot_consensus_similarity()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.plot_consensus_similarity(mode=\"spectra\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All clusters hare a 95% similarity in median. Cluster 0 (background) is the most heterogeneous one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#spec.plot_inter_consensus_similarity()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Differential Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the stand-alone features of pyIMS is the integration with differential expression analysis. Here several key-features are presented.\n",
    "\n",
    "First, single masses are looked at:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.mass_heatmap(14954, min_cut_off=0.0025)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mass with m/z-value 14954 apparently is most active within the aorta structure (center of the image). Is this specific to a specific cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfobj = spec.mass_dabest(14954)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DABEST-Plot also clearly reveals that in contrast to the background, cluster 8 intensity values for this mass are quite higher than for all other clusters.\n",
    "\n",
    "Abviously this mass is most intense in Cluster 8, but also cluster 14. We can now take a look at this mass by setting cluster 14 as reference cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.mass_dabest(14954, background=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not knowing where this cluster 8 is, we can highlight it specifically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.plot_segments(highlight=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or together with cluster 14, where this mass is also prevalent.\n",
    "\n",
    "On a sidenote: background is set to 0, other regions == 1, and the target region is set to 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.plot_segments(highlight=[8,11])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just for the sake of clarity: we now remove all differential expression results!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.clear_de_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Protein m/z to name\n",
    "\n",
    "For any combination of sequencing results with these IMS analyses, knowing which protein was measured is of interest.\n",
    "\n",
    "Using a previously performed LC-MS/MS experiment, which delivers detected proteins together with the masses, allows an easy translation of m/z values to protein name.\n",
    "This work is done in the ProteinWeights object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 10:19:23,279  ProteinWeights  INFO: Loaded a total of 5371 proteins with 5341 masses\n"
     ]
    }
   ],
   "source": [
    "pw = ProteinWeights(\"protein_weights.tsv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 10:43:14,176  ProteinWeights  INFO:          Number of total proteins: 5371\n",
      "2020-10-01 10:43:14,176  ProteinWeights  INFO:          Number of total proteins: 5371\n",
      "2020-10-01 10:43:14,177  ProteinWeights  INFO:            Number of total masses: 5341\n",
      "2020-10-01 10:43:14,177  ProteinWeights  INFO:            Number of total masses: 5341\n",
      "2020-10-01 10:43:14,178  ProteinWeights  INFO: Number of proteins with collision: 1485\n",
      "2020-10-01 10:43:14,178  ProteinWeights  INFO: Number of proteins with collision: 1485\n",
      "2020-10-01 10:43:14,180  ProteinWeights  INFO:         Mean Number of Collidings: 1.2552188552188552\n",
      "2020-10-01 10:43:14,180  ProteinWeights  INFO:         Mean Number of Collidings: 1.2552188552188552\n",
      "2020-10-01 10:43:14,181  ProteinWeights  INFO:       Median Number of Collidings: 1.2552188552188552\n",
      "2020-10-01 10:43:14,181  ProteinWeights  INFO:       Median Number of Collidings: 1.2552188552188552\n"
     ]
    }
   ],
   "source": [
    "pw.print_collisions(print_proteins=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 10:43:02,510  ProteinWeights  INFO:          Number of total proteins: 5371\n",
      "2020-10-01 10:43:02,510  ProteinWeights  INFO:          Number of total proteins: 5371\n",
      "2020-10-01 10:43:02,511  ProteinWeights  INFO:            Number of total masses: 5341\n",
      "2020-10-01 10:43:02,511  ProteinWeights  INFO:            Number of total masses: 5341\n",
      "2020-10-01 10:43:02,512  ProteinWeights  INFO: Number of proteins with collision: 1119\n",
      "2020-10-01 10:43:02,512  ProteinWeights  INFO: Number of proteins with collision: 1119\n",
      "2020-10-01 10:43:02,513  ProteinWeights  INFO:         Mean Number of Collidings: 1.2100089365504916\n",
      "2020-10-01 10:43:02,513  ProteinWeights  INFO:         Mean Number of Collidings: 1.2100089365504916\n",
      "2020-10-01 10:43:02,515  ProteinWeights  INFO:       Median Number of Collidings: 1.2100089365504916\n",
      "2020-10-01 10:43:02,515  ProteinWeights  INFO:       Median Number of Collidings: 1.2100089365504916\n"
     ]
    }
   ],
   "source": [
    "pw.print_collisions(maxdist=1.0, print_proteins=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 10:54:39,539  ProteinWeights  INFO: Loaded a total of 7283 proteins with 10191 masses\n",
      "2020-10-01 10:54:39,539  ProteinWeights  INFO: Loaded a total of 7283 proteins with 10191 masses\n",
      "2020-10-01 10:54:39,539  ProteinWeights  INFO: Loaded a total of 7283 proteins with 10191 masses\n"
     ]
    }
   ],
   "source": [
    "pw_theo = ProteinWeights(\"protein_weights.theo.tsv\", max_mass=30010)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 10:54:45,850  ProteinWeights  INFO:          Number of total proteins: 7283\n",
      "2020-10-01 10:54:45,850  ProteinWeights  INFO:          Number of total proteins: 7283\n",
      "2020-10-01 10:54:45,850  ProteinWeights  INFO:          Number of total proteins: 7283\n",
      "2020-10-01 10:54:45,851  ProteinWeights  INFO:            Number of total masses: 10191\n",
      "2020-10-01 10:54:45,851  ProteinWeights  INFO:            Number of total masses: 10191\n",
      "2020-10-01 10:54:45,851  ProteinWeights  INFO:            Number of total masses: 10191\n",
      "2020-10-01 10:54:45,852  ProteinWeights  INFO: Number of proteins with collision: 6316\n",
      "2020-10-01 10:54:45,852  ProteinWeights  INFO: Number of proteins with collision: 6316\n",
      "2020-10-01 10:54:45,852  ProteinWeights  INFO: Number of proteins with collision: 6316\n",
      "2020-10-01 10:54:45,855  ProteinWeights  INFO:         Mean Number of Collidings: 2.8738125395820138\n",
      "2020-10-01 10:54:45,855  ProteinWeights  INFO:         Mean Number of Collidings: 2.8738125395820138\n",
      "2020-10-01 10:54:45,855  ProteinWeights  INFO:         Mean Number of Collidings: 2.8738125395820138\n",
      "2020-10-01 10:54:45,858  ProteinWeights  INFO:       Median Number of Collidings: 2.0\n",
      "2020-10-01 10:54:45,858  ProteinWeights  INFO:       Median Number of Collidings: 2.0\n",
      "2020-10-01 10:54:45,858  ProteinWeights  INFO:       Median Number of Collidings: 2.0\n",
      "2020-10-01 10:54:45,860  ProteinWeights  INFO: Proteins with collision: [('Mbp', 30), ('Crem', 25), ('Homer1', 18), ('Clta', 18), ('Eif4e2', 18), ('U2af1l4', 16), ('Tspan32', 16), ('Tpd52', 16), ('Asph', 16), ('Hmga1', 15)]\n",
      "2020-10-01 10:54:45,860  ProteinWeights  INFO: Proteins with collision: [('Mbp', 30), ('Crem', 25), ('Homer1', 18), ('Clta', 18), ('Eif4e2', 18), ('U2af1l4', 16), ('Tspan32', 16), ('Tpd52', 16), ('Asph', 16), ('Hmga1', 15)]\n",
      "2020-10-01 10:54:45,860  ProteinWeights  INFO: Proteins with collision: [('Mbp', 30), ('Crem', 25), ('Homer1', 18), ('Clta', 18), ('Eif4e2', 18), ('U2af1l4', 16), ('Tspan32', 16), ('Tpd52', 16), ('Asph', 16), ('Hmga1', 15)]\n"
     ]
    }
   ],
   "source": [
    "pw_theo.print_collisions(print_proteins=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 10:54:54,221  ProteinWeights  INFO:          Number of total proteins: 7283\n",
      "2020-10-01 10:54:54,221  ProteinWeights  INFO:          Number of total proteins: 7283\n",
      "2020-10-01 10:54:54,221  ProteinWeights  INFO:          Number of total proteins: 7283\n",
      "2020-10-01 10:54:54,223  ProteinWeights  INFO:            Number of total masses: 10191\n",
      "2020-10-01 10:54:54,223  ProteinWeights  INFO:            Number of total masses: 10191\n",
      "2020-10-01 10:54:54,223  ProteinWeights  INFO:            Number of total masses: 10191\n",
      "2020-10-01 10:54:54,224  ProteinWeights  INFO: Number of proteins with collision: 4881\n",
      "2020-10-01 10:54:54,224  ProteinWeights  INFO: Number of proteins with collision: 4881\n",
      "2020-10-01 10:54:54,224  ProteinWeights  INFO: Number of proteins with collision: 4881\n",
      "2020-10-01 10:54:54,226  ProteinWeights  INFO:         Mean Number of Collidings: 1.9096496619545176\n",
      "2020-10-01 10:54:54,226  ProteinWeights  INFO:         Mean Number of Collidings: 1.9096496619545176\n",
      "2020-10-01 10:54:54,226  ProteinWeights  INFO:         Mean Number of Collidings: 1.9096496619545176\n",
      "2020-10-01 10:54:54,228  ProteinWeights  INFO:       Median Number of Collidings: 2.0\n",
      "2020-10-01 10:54:54,228  ProteinWeights  INFO:       Median Number of Collidings: 2.0\n",
      "2020-10-01 10:54:54,228  ProteinWeights  INFO:       Median Number of Collidings: 2.0\n",
      "2020-10-01 10:54:54,230  ProteinWeights  INFO: Proteins with collision: [('Mbp', 19), ('Spcs2', 11), ('Tpd52', 11), ('Erh', 10), ('Crem', 10), ('Vps25', 10), ('Ifi27', 10), ('Nudt7', 10), ('Clta', 10), ('Eif4e2', 10)]\n",
      "2020-10-01 10:54:54,230  ProteinWeights  INFO: Proteins with collision: [('Mbp', 19), ('Spcs2', 11), ('Tpd52', 11), ('Erh', 10), ('Crem', 10), ('Vps25', 10), ('Ifi27', 10), ('Nudt7', 10), ('Clta', 10), ('Eif4e2', 10)]\n",
      "2020-10-01 10:54:54,230  ProteinWeights  INFO: Proteins with collision: [('Mbp', 19), ('Spcs2', 11), ('Tpd52', 11), ('Erh', 10), ('Crem', 10), ('Vps25', 10), ('Ifi27', 10), ('Nudt7', 10), ('Clta', 10), ('Eif4e2', 10)]\n"
     ]
    }
   ],
   "source": [
    "pw_theo.print_collisions(maxdist=1.0, print_proteins=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the m/z->protein object we can now find all marker masses for the 15 detected regions\n",
    "\n",
    "For example we can also try to find out, which protein corresponds to mass 14954 ! (it's Ifitm3 ...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Ifitm3', 14954.0)]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pw.get_protein_from_mass(14954)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Tnfrsf12a', 14952.97469999999), ('Ifitm3', 14954.185999999994)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pw_theo.get_protein_from_mass(14954)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2257.5839,\n",
       " 8927.188999999998,\n",
       " 30240.013400000025,\n",
       " 33037.03710000002,\n",
       " 40233.347700000035,\n",
       " 44032.78440000003,\n",
       " 44451.46180000004,\n",
       " 45477.38780000004,\n",
       " 48313.85780000006,\n",
       " 50982.752300000066,\n",
       " 51110.88150000007,\n",
       " 52189.072400000085,\n",
       " 55078.22360000007,\n",
       " 57618.98890000011,\n",
       " 57747.11810000011,\n",
       " 57875.24730000012,\n",
       " 102224.47280000069,\n",
       " 139017.76520000066}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:33,879  SpectraRegion  INFO: Processing Mass 19494.35879999999 with best existing mass 19493.942202125156\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19494.35879999999 [('Ccl27a', 19494.35879999999)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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rT+ZzYeZdHKVUdQ5rYH3Jjo/nBO7/5O9v4PZv5Njh1suOJzumxil90MeZljM2sobXMt/JDfm1LWTvuGca2ZWf5f5nf8jnStkVrPe6qf9jGx0f1YmB8XdT5w8HVRnbV3qGYRguYzldPOC7hX9JRNaMhZqdhmGMDF8S0bGdLv4kVa0UkRwAL4vIRn9mhD34J8PrACAmIX1vbRiGMWYI/8JAI82nV+n/WysiT8JXIe0NZ509NTISs4q0PzYww8e2sK7QPp19oVx/ofIv8htF97MfXR27/aH8s6zhpbBMgpTtTp2AaD5Y1Vezhucey4581qE6nDoEE47leM+e+9g3K2IG60ITn2bfNe/HnBNOjub+TP4Z616eFP717C3k8UyoYQ0wfbWTjSeGfcO6ClnX2nU81zCJbQ7sb8o/ed20ZRy7WnE5x+1mPeVW5+KuxNfz2Pak8+Bv/jJrgpMn81i3PMz57jZ9g30YZ/6B45RrTudjUzePha2Yb7JGBqfGRPoalrX741gfrZvH/U2q4GORuor3r/VO1uw885zYZiffoHvh1JPp6L2V3L7rVRLVFdifT5STHAYKhL3Lyn5PySKSKCLJu/8HcDaAfWY4NQxjbLM7IiOY12gxkiu9XABP+jMcRAH4h6q+EJJeGYZxyDJmCwP5i3wfEcK+GIZxiOPLpzf2/fSCpj9JUXtSwJ8o2qmvmbLFyTfn4cGbnM+6SR9Y95j4VB3Zs/7GdRneW8Y51iL6WPdoncQa4YQlfLe+5V72e4t+idef+Dz7DUbexDpT+f3sd5i+ktf3ZrBmFnE4x/L2J7DOFNHFmt6Wy7k/ucsdX7F+Fmuqz+bxy1nDOfDcGh65K9lPMLIj4HvWfBj7AO76POuV0R287dhm1mcnvsh28xTWwKIW8rHPfozHsutVjq1tOJvHJnM565W7zmIN76IbXiH7sTvO5PVPYL+//Ps+IHvTrZw/z5vOfnlTl/C5FtXCPqT1R/BDvrr5vL2EOv58QgX7Ke48mzXVyC4e706nrm7mx+7xCLQf2TsCUQ8Y1VvXYDA/PcMwQoZP0xujt7eGYRh7YzyEoRmGYQDwXen1e8PbZeWgTnoxsf0omRzwDeu5n3WVlOt2kt32NvtGZcex5lTlXEXXHc91ErwncE6xpq+yriNnsF9c5p3sO7b9O3N4+0tZV9EIp3ZoPy+PSOD2Cpby9qs+w75krm6TWMG6T9VJrNnFNrMf4rwjuNZq+8Osc235Mm8/eT2fnFUns6YY3cr7V384x7fmvx04fSZev5mWlS6ZQXbOM6yv9k92fBa7OXY060PW5KpS+diKU6O4aTbb7sVG7rPsN+jNYg3ttcW87w13OhoYS3jwzONY2Lh6PhkTpnMNj9409pl0fUKjZ3DscvKbfO40zOFjN+kHXB+l/gH24ex3akh3TOD96cjj7cc1jTCJ3gBGM9oiGOxKzzCMkHEoPL0Nb8XRMIxDDq9GBPUaChFZIiK1IrLXoAcRuVxE1orIRyKyXESCcqGzSc8wjJAR4oiMBwEs3Mfy7QBOUdW5AG6BP9x1KA6un15zNBqfDehY3ou5joHeV0R2ThdrXB92cR3ZGC6r4Av8G8CmO3jin/pP1shqIzjHW/RKrluLb7BfXnM/+0K5NTk68ljzykrm/IDtBayhpW1l3aoriw9HzXHcXhR3H71OrO2H77FvXNJx/Js27X72s4vewfGnLQt4/JNfXM/9O4n9BqsXBHzpGh/mZS1HsUaU8zjnj+soZD0ybU0N2U3z2YewcAnns9vwOz42cUnsFxe1jo/V+p+zPhxX6Whklay5xRbwWKU8zT6Su47nY5Oync/VpDd5eeKtrMGtLWc9F9WsKYpye50FbC9/m0XMqEJuri/J6U85nwsF/+HvXs2CgEjqiRnZ7WmoND1VfUNESvaxfPkA810AhYOtOxDT9AzDCBm+dPFBT3pZIrJ6gL3Yn6Bkf7gGwPPBrGiTnmEYoUOH5bJSH4oSEyJyGnyT3knBrD+kprc3MVFEMkTkZRHZ4v9rifIMwzjoSURF5HAA9wFYpKoNQ60PBHel9yCA/wXwlwHv3QjgVVW9TURu9Ns/HLqHgHeAlBL1spPvbRfrPjU3cGxqWgKLWtWbOEeaOCnGZv6QdSDPXNaB8u5cTnbLxVznwLuZD0zBataN6uc6NSma3Px8Tg2QVexn6OanczVC9wFXP7vpIZqbQ9HLTm3YYt6+vMOapU5nDbDuCN5gbyL7KaZu5fEvuTvgm1d9GfuJSR9vu+Zi1qCaP8XHtj/OqQM7mT+f+nEu2VPvd+qTeLnvUXVcF7ZrCsfqtju5D7uO43x/MU6sbvk1vO+Jq8lEV7bjp+fUR3E1vMxlHFvccCqf+zuu4P4lreH+RLfz8qhOxw+vgLefXMnj1TiXHR27cgLj7R3h/d/Bir0VkWIATwC4UlU3D7X+bobcvUHExEUATvX//xCAZQhm0jMMY0wzTE1vn4jIw/DNM1kiUgHgpwCiAUBV7wVwE4BMAH/0p7gLqiLj/s7puaq6u9z9Lvhy6xmGYYRs0lPVS4dYfi2Aa4fb7ogfZKiqigyeYHpgjYzoFJP+DGMss9tPL5zZ30mvRkTyVbVaRPIB1A624sAaGakxOVr8twExkLGsiXmWODnWvshtNX6K/chwAs+10S2sY3SfyL5jXZn8VCkhgfPrtU3kz8s0jp+M+xf3LzWZ2+vK4M9XXMYiXfYzHE/pdeqLFj7C8ands9ntqOxc1nUSdvHJ1e/kv8v8iHWoyCzWtTpmsB3XwO1lL+NY6ObjWJdqPjmg45X8leOcM6ey3hrRw2OXvIN3vrWETEx6jDXp9qmsQbk+jwm1LOi2nMB+ch5HDy1+nmNdN13Lx2bBXNaDP36CfURdvbVo0Xayq3tKyE57h9dvPpOPjZs7Mqqc/Ri7j3cE3OWsB7ecxu1N+w77YFZfyJql96wmshOfDejrEew+OjwU6A/z1FL727ulAK7y/38VgKdD0x3DMA5ldmt6h3SNjEHExNsAPCoi1wAoB3DRgeykYRiHDof87e0+xMQzQtwXwzAOccayprdfeONi0D0roAvtPIt1nbhWjgeMuIAffLRNYt0m40MnJ1gzL4/fyrpQ8xSO56w7mjWyJJalELOC4zf70ti3rHkq60rZ77MfX/8qFpLS13J/orrZT7H8Kvabc/3+Mp3Q4KRqR3zxOhpnDetWO7/MOe7ia3j9Hvc5UyTvn+u/lbsy4FvWNYvHtjObV26Yy1+Ekmd5LLPvWUV2zXULyG5jF0tEt3B76vQ1cwOPTfxO1mdbZvPYp2zic6lsGY9VYgSfWzVODY6qVq5pccK175H97p+OItvTy/2NrOXvQm+247TZwBrflH9wEedNx7L+23XkRG6/h491qpM/MGFzwK9xa9tIRD1AbdIzDGM8YUlEDcMYN6iOAU3PMAwjeAQeb3i7rBzUSU8jgb6UgJYR4+gyEy7jnGNbf8K6y8zfs+/RtqtZx4jo48HeejUHikSzrIOEal6/y6kNmlzG69cexbpK6jbWeRpnsS6TWM3LPYm83PU1687m9Sfc/i7Z9Y7O1TSd2yv4N/vVlV7L45O6lfcve1kF2Rnrebzh5f70x/HxapoeiB91a6Vm3seOaZmF7OO35QbOb1eQfCz3tYx1pZQdvO3oFtZPo7dzrK23jf3aKr7BuRUz13H7HidOujOHzw1X78x+jce+7kz2C1y7hLfX8Gn2U4xyNLzJj3F/j/4TC7gPvzef7J1f4hod+U9x+zu/yLG8WuNkPhH+6rcWBzTZvsdY6x4upukZhjFuCGXs7YHCJj3DMEKH+nS9cMYmPcMwQoo9vR2AJ07QND2wyeQdrBk1f+5wsif8h3WXXZ9mXajkSY4fLP5zGdlvP34k2fGfqie79UOOPfXEOTnKupzYVg7PhHsVn1rGvlVd6ayjlF7Guk/hq7x+Qp3jK3YLa3hZH3H/Gmc7HehnXWfy4y1ku5qi69e37QL23Zr8c05RlvUvHm/vrJI9/0fVsU9gxzms0Ukdx4ZOWMb73pHHp6KbfDfCKcuavZFramz6PhdMmfJYJ9lF93EsbffR7PiXuYLDx1uO4Dq7faew5lY3mfPhRVax7fka9y/pFfZjzF/ONThKL+Vzo+JRTgIcOYfHr3se7594+OQsWcznUswGrvtbdbETiztAxhtJ6KzCND3DMMYVFpFhGMY4w+sN70lvf2tk/ExEKkXkA//rMwe2m4ZhHAqo+m5vg3mNFvtbIwMA7lDV3wxnYyqAZ4Cs1Fbs1DVgmQI9qewv1OGUCo1v4HjHl1bPJTuFXbmQ/v84Frb1s7zcrbFReyLrThFdLDQVPsl+g1tuYz+3SXdye80znVqoy0t5hUwn1riI/QyrT+YORrWyJqc97JvVPoljh+vncv8zNrIfX2IVn4iRGY5zWoSTL/DUQPtFD7CfXN08PnYFb/FYxi37iOyuC+eR7dYXaStku/VL3PdJT/HJE7mJfRY33sWaX8YbrG92FHD+v8wVdWS3THbqseTxsSh+mU+2mibW8LI/cpbP53MhppCdSHs8vDzhPT53vY4rnRunXXUi+5T2nscaZtoGXj9yQPfcWi3DJdxvb4e80lPVNwA0HoS+GIYxBlAN7jUUe7vLdJaLiPxeREpFZK2IHLW39VxGEi9yg39DS/ZVAlJErhOR1SKy2tPZMdhqhmGMEUJ4e/sggIX7WH4OgGn+13UA7gmm0f2d9O4BMAXAPADVAH472IqqulhVj1HVYyITEgdbzTCMMYAiuAkvmEkviLvMRQD+oj7eBZDmL1+xT/br6a2q7nFCEpE/A3gm6A8P2NdIdj36hN9bW4nzUeeSuCOPNaq09UNsejnHMybPYj+47IfXkt03n2tsdDixshrDwsqEB9lXq20irz/lx5wzDpkZZDbOzybbrVUQX8nt9SfxgNQumk72wFqmABDnlEJOW1FJdtThfL40nVxCduom9sVL3RZwnms/gTUztyZvzA72kWxbyD6ZyeWsR0av5zjsvlkcq9s0gzWu+sPZT61zIR+76Fi+y4js4WMV18SOgJ5NrLdG9LOmN+NW9mHc/Afun9dpr97j1IPhzWNiJn+3t/TwV7O3kzW6nDXcft08Xn/is3ysmmewD2bKNtZA648IjN9IS1wcxICMCQAGircV/veq9766j/2a9HYXBfKbFwDY6z23YRjjDAU0eJeVLBEZWDZ9sb+Q2AFlf2tknCoi8+Cb1MsAfPUA9tEwjEOIYbij1AdTnHsfVAIYWCKx0P/ePtnfGhn3B98vwzDGEwcx4cBS+B6oPgLgOAAtA+5AB+WgRmSIl7We+Hr2derKZjHBW8B1FDJfZV2jiyUwJFVye6kb2Pep7qus4bl+gRvvOozstA9Ys8v+wIl37OT+uVpI9ZmsuzTM4XhUV5MreJPtlO28vZpjWbfKW+E4IjrkvsLxpL3FHE/atIAdH93+189zf7HZLzK2OeDQFdPEmlxUF4tWFZ/nmsWFT/EPck8Jx0FLKm8rYjn79aULa4Iuke/w2JSfx3VzY9r52FRcwgJqWgGfK25uxM7juZ6Jt4711mOO5hoWG8q55kYshzGjeinXtEhwayK/wJrf5i+xT+jJC3h83pjGsbVJq/jg1h3NmmjuysC558Y5D4dQxt4OcpcZDQCqei+A5wB8BkApgE4AXwqmXQtDMwwjdCg++URyf5savBLj7uUK4PrhtmuTnmEYIcXy6RmGMb6wSW/AxroUmesCWkt8KftuVZ/NfmLedtbUmmZxexnreHQ7L+L8cX3Psu7Rm8KX3eLkk0vcytvLXcmaYNXJHMuakVrA23dqvc785vtke53Y2LqvHk92UinX/ZVOXr9gGetUnnjub9RmjjetuYB1pM483v/CV9l3rX0i6zzTfs2+ap46jkeNSA6MR9/RXLOhpcQdS9YntZn9yGI3OzUrcjjIp/4rXCMiqdIRnpw7qvh2Hqusjx2/ucuc/lSzXtoxwanJsYXPlYbDnP17lzW/0g2Oz+RUp17KVO5PZAufO8Uv83j0ZrFj/9SH+dht/Q9/ORKmc3s55/O5EfEka6xdA+Ret77x8JDhuKyMCnalZxhG6FBLImoYxnjDbm8Nwxhf2JXeHvrjBfVzA4tTZh4AACAASURBVA5I+S+V0fLcd1hT6spjXSfWiR3VCP5Jyf8F707DHF4/rZR1lIQb2Fes6a+sc6DfSbDn/ILVz3Vib//DAadVX+dMN/nLWHPMeeA9sjffN5vsGTezLlV9MmuU2e85ulQH21Hd3GFX19JIPjnT3mPNTrvZD7H7PNbVIrsC7fWm8thHd/C2y87lY9t3JWuARS+QiaRN7MiW9Seuo6sLuK5sVz77cLp+ebEslyLzEdbwor/G+QB3vck+jH0JPFYdxTyWsQ3sB9c6lfc/fhcvz36BNbu+FMdnNcupSzuRP5++hZfP+gFHgq7+C4/P9hr2gyyo4KR5DbMC7Y009tau9AzDGF/YpGcYxrhheAkHRoVgamQUicjrIrJeRNaJyLf872eIyMsissX/d9BEooZhjCM0yNcoEcyVXj+A76nqeyKSDGCNiLwM4GoAr6rqbSJyI4AbAfxwXw1F9AEJuwLaRft/HUfL6+fxHFzyb9bIti9iX6WWLNYl+uM4Z1jLTB7ZSU+zjtK2mOss9OY6dRhmsF9eP8tSKFnM8ZXbr2edKprd/FB/FMeTtlzMmt/k+1hDqz+Ra2Tk/n452WW3cnxoURRrginbHU0umwM6WyfxDsWl8/L47ezbVXc4ny5xDYF4047T2G9s6o2sX3blsEY26UcryW67hH0WscvJv3cxL09bxnVcO+ZxLOx9V/8v2V9feznZnRGsobWtYZ/L9B187kR38Prpa/lcjfsvrnOb/AzXyJB+bq8nnceyeSrH7qZu4+1NfJrHo+Yk1ujeLOcaGMmdvL2Icj7WvU7ct1sfZkSEuctKMDUyqlX1Pf//bQA2wJeobxGAh/yrPQTgcweqk4ZhHDqIBvcaLYal6YlICYAjAawAkDsgjcsuALmDfOY6+PLXIybR7oANY0wzyreuwRD0w2kRSQLwOIBvqyrFEPmzHex1VwfWyIiKtRoZhjG2Ed/tbTCvUSKoKz0RiYZvwvu7qj7hf7tmd9p4fzGO2sFb8BHZ40XqloD2053LOkNUBw/ElkvZlyp1Ey/vbWM/ubyLy8iO+DvnKNt5Bud4y3+bNcGEWvaLayvk9ROreF7f9CPWkTLX8vLUrU6+Pccvrnk2a2hRDVw0pG8uj0/L5axrufn4YndwzrX22VzXoXGmU0fhIdbFGs4oITsph/PvTXya249oDoiW/feyz2PLF1ivnfAia1JS6PjBxfPYbP8/HEsaX+P8pibzD2jucnbEu/Kta/nzG9mPr62Ij316GTffyKkVMeN3XLOj+SQ+t/r/xmMdFb1vzSxpJx/rpHLHr/F81qe70518g057GY/zePS51xfO8NWc7Mb+BsZ/ZLG3n9xWuBHM01uBL1PyBlX93YBFSwFc5f//KgBPh757hmEccniDfI0SwczpJwK4EsBHIvKB/73/AXAbgEdF5BoA5QAuOjBdNAzjkCGESUQPFMHUyHgLgwfTnRHa7hiGcagzmk9mg+Hgxt4mRqDmuIDvW8YGzhcX08rdiZzPvl5tWazLTL16HdkbpnDdhKmbWFOL7mCNbsdn+O5+2l8dP7mjWHObcR/Hg8Y3cXxnw2wn9tf5SZj6B/YTnPEH1rkqz2O/wT6WdZD3Fuegy3iB40U3/oRzuKWv59+qzI9Zx/IUsE4U7fh2edPZr7B1Fu9v2rsBP8rSO1lvjKvhsY3q5M8mbGVNKaWc9dQmR++c9A/2g6s/if3gmjl1IKYsZs2s5/9yvZiYbj4X8u7ksenOZI2u8r/YD647i8eqN53v15K28/57+NSFN4pFt7Rt/F1IqOZjl1jD49WZw+13ZbLdx4cOsU7J7KhO1sNTtgf6X+PUjhk2YT7pjTS02DAM44AgIgtFZJOIlPoDINzlxf5osfdFZK2IfCaYdm3SMwwjpITCOVlEIgHcDeAcALMBXCois53VfgLgUVU9EsAlAP4YTP8O6u1tZLcifXPgNqbqU3yLkVzGI9FczWFgKRu5uzv+m+sEF/2bb1G2fcHZPeeJ0cybNpPd+Bm+Ryp81UkBfkUG2bENfAvSlc+3IOlv8T1N5WlOunrl/Ytt4P3P2MS3w51FfL/bN5NvZ/M4+xJSX+P985ZwOv6ICk4lldLIYX+Nx/P6PWnc/6SKgJtKTFMxLYvmqDR05vKxiH1+K9mtJ3JInTeGx8Kzhd1rcAL7wk/7/XayN/6whOzUp9j9xxvH+7LzfG4+6QQeG++T7L4z+W7uf+fR7MISV8NjWTvfud90LjdqjuZzJWUHn3u9SfyBnnTuf8F/eMArzuDbZ4/j3tTn3I7nnByQDyJW8Hk3bELzIGM+gFJV3QYA/tq2iwCsH7glBOqSpgKoCqZhy7JiGEboUAzHHSVLRFYPsBer6mL//xMADAz+roCvoPdAfgbgJRH5JoBEAGcGs1Gb9AzDCCnDeHpbr6rHDL3aoFwK4EFV/a2ILADwVxGZo6r7nHZN0zMMI7SEJrVUJYCBqcwL/e8N5BoAjwKAqr4DIA5AFobgoF7peeIETdMDrghZa1kDaziM0+ukfex0z5EKYtmDBI0zeP3s1TzhJ5ezWwCcH4SMD7jB3hzWRfLf5o935HF/26c4oUdOlcLipU469hjuryeB3TTqj+AwvLz/sIsLGtmlp/RO1uCSn+DlOxayZpm6jTXCnlQe4JRyJ1TrZdbVtt4ScFOZek8ZLeuvZHml/SJ2aYnMziY7qZq3Fdu679/jtonc19gFrKkl7uDP9zqSWl8yH6u4Om6vvpTdeaauYz+O+oUcgtidyZ9PjeWxzf/3DrI7D+Nj1fd11uR6n+bSAK1TyUQcn0qIamEXnbyV7JKSdRNrnhue5nNh2jGBBtdH8rEYNqFxWVkFYJqITIJvsrsEwGXOOjvg8xV+UERmwTfpOSPzSez21jCMkBGqtFGq2i8iNwB4EUAkgCWquk5EbgawWlWXAvgegD+LyHfgm2qv9ic/2Sc26RmGEVpCFIamqs8BeM5576YB/6+HL0x2WNikZxhGaAnziIwhJz0RKQLwF/iShCp8j5XvEpGfAfgKAvfQ/+OfmQfFEwO0FwdGJHMdawdR3ayRxdez5hZfx/5Dkd38eY1mHSeii5e3l7BG5y3i9EWxTW5ZPw5LK/80+xVOe4g1trnXcljYllfYl3LrZayxxtfyL2LeOxxmFsWbR9VZrIMVvOqk1/89r7/9Z8eSPeW3G8guu573v/gF3n5/MmuMEse+ZLmrA+PVOp/LZ8bVcRhX0wzua1sRp9bPW86alkbytiNTWJSb/ICjkc3msLT0Lc43zzFrj+JTv30in2uxjdzfxjmsr7olIQtf4HPB1Wvh4XMrpoXD7iJe4QS7rp/ilL+wVLX1Cj4XNnyXxyex1CkN8AD7dHacwifXKx8FzoW2rlcwEkKaev4AMJIaGQBwh6r+5sB1zzCMQ4pRTgUfDMFkWakGUO3/v01EdtfIMAzD+CRhPukNy0/PqZEBADf4A32XDFYCUkSuE5HVIrLa09Gxt1UMwxhLjIESkAA+WSNDRO4BcAt83b8FwG8BfNn9nD+sZDEA5M7O0EWnr9izbOVy1pz6WTZBRx7PybHNrKOUn8MfmPIvjnfszWANKu1t1oGqPldCdvpXeDkuYU1w8lNOicjZHIvb8EfW7KKdeJwIz76fam2+ijXHM+avJbv0p6wReuPZF6ujiONL0zj0FjuuYw2vO491pr4U1ixj13EJSE8D5yfqTQroeP1OuveKs1hTynvTKaG4hjWqtjk8dikreNtlN8zhvudwe9mryURCLR+78i/yvs74VinZ1RfPJPvEL60h++ObOG1Z+flO6YI07n/+O6zZtc/j1Fotjt9dXzr3r3AqV1/Ynuv49RWzJpe4ns9197vUlcP9nfAUnzttEwJ6el3XyJ6+hvvtbVBXenurkaGqNarq8Yd8/Bm+AGHDMIywZr9rZPiLAe3mAgAfh757hmEccoyB29vBamRcKiLz4Ot+GYCvHpAeGoZx6KBjwGVlHzUy9umTtzfaaxLx9u8Cd8F92dzs5PtZU6v+LPt+deawDlH0CvvtlV7Gmlj2Kt5+5fUcn5nAbnWo+HcJ2Z5reHn3LI5vTFmxb7/Cpum8PLaB20uuYB0nfwlfLFdMc0pYXs6Ha+qjPH49yU56+CXvkp0Rz5qfTOLxrTmRNcrMngKye4/i/mSsCvimaRwfG28Ux456eTF2fJ7z4UU4Kdzid7EfWvFS1hO3/oQ1QxU+9ts/zzcxye/z+pVfZA2vO5MvPS7IYJHwxVOOIjtrJfc3qYo1vO2XcnuT/s7LW6Zxf2b9soLsjb9kP8dpp3IJyqgIPtd2vj+JbFdX603hNxpn8rnZkxFoz8NdGz5hrulZRIZhGCFDEP4PMmzSMwwjtNikZxjGuGEsRGSEEo0EetICWkvB85xzrfJzXGfBLRHZl+yUWJzD4kP6Oh5tt45AxjrWQdyygZP/zr5jm2/iGhYTH2IdJKrNcbaOcGpI7HDy6/Xz9iO3ck7EDX9gP7q01bx/Ra84utBU1rEaT+bxyl7D7X2CCi6rmL6F24uuYBGy14lvrfp0QHeK6uB9zV3ezNsSHpu+BPZbSy1jv7r+JBYBq0/ivk29hfvWVchjFdPg+DAeyXrsjFvayK4/njXE/3MfP5dL4tXhuYC3z5kLgZzHWB/d6SQyd31ON93OGl7aG+x3VzaBvxte55s7dVEZv+H4mLadwJpfzXGsecpAH9KRTlo26RmGMZ445J/eGoZhDAu70jMMY9wwyo7HwXCQa2QALdMD1775r/Dm00rZWcvr5MeLbeLl4mGNLfvvH5K968vzyE6sdvLx9fL2+7O4rkHWcxyL6onl6/bGmawz9bJM9YmD74njN7LWco6z9FWs82Tfs5zs7vM40i/lYfbDSy4/guxdJ7GvnHh4+zkreXvueKOHNcSGw1gnK/hdII46ItEJ9pxcSGZXPo9V05FOLsQobjtzHceWFj3LGlr1aVzDonUKH5sZizm/XW8e67Nts/nzdSdyf6KTed8PK2I/uo9eYUE48nBW9fJfY5/TxCrWQ+M3sZ666TbWFHsXcnvFv+bxKb2MNb+eWzk2t/J61jiTnLDyy8/5D9kvVwf8FmsSRlb3NtwfZFg1NMMwQkuIwtBEZKGIbBKRUhG5cZB1LhKR9SKyTkT+EUz37PbWMIyQEoorPRGJBHA3gLPgK/S9SkSW+uti7F5nGoAfAThRVZtEJGfvrTF2pWcYRmgJzZXefAClqrpNVXsBPAJgkbPOVwDcrapNAKCqtQiCYGpkxAF4A0Csf/3HVPWn/nqUjwDIBLAGwJX+zg2OAhEDpJNdp7GOkbqdtYT4razjdJew71M/h5JCe3nzvSzjILqFdaKIXtZJKk926syuYL+3uA3sV+eN4ljU1kjWGN0Dm/0hx9p25PL6vU7d2cjDWDdqKeHDVfeTE8iesIxrsybU8Pa80dy+9Dn59BK5P1Vf59qu0ZyuEFEFAZ2q4r94LGIbeecnX7eJ7MTLWT/ddAPH+SZV8bGpup7t4l+y5tVRyDUi+nK4/cpTWAPzRnP/Esp43zt5d/DBW6y/ItbxCW3l9jvm8v60F/Kx2/4FXp71Cm9fvNxeo+NymVjm1Omdy5/vS2GN0xPL1zd/e/EUsvPeHbB+nRMoPQxCVQISvuzsA5MqVgA4zllnOgCIyNvwlYn8maq+MFTDwVzp9QA4XVWPADAPwEIROR7A7fDVyJgKoAm+auOGYYx3gr/Sy9qdVd3/um6YW4oCMA3AqQAuha8Gbto+P4HgsqwogN2/8dH+lwI4HYGK4w8B+BmAe4bZacMwxhjDuNKrV9VjBllWCWBgGqBC/3sDqQCwQlX7AGwXkc3wTYJOfiUm2MzJkf5cerUAXgawFUCzqu6+Wa3AIMWCrEaGYYwzQqPprQIwTUQmiUgMgEsALHXWeQq+qzyISBZ8t7vbhmo4qKe3quoBMM9/6fgkgJlDfGTgZ/fUyEjMKtL0jwNaRPom1qC2XsQiXcJEtturHc0snjW38h+zH1tfEo/spq+wr1hcNe++Gz4T++F2siuu4N32shsfurN4e26+P9dPLqKf96f4Pta9Wk/lQgqLrmXfqlWXcM2MzhK+sk+sZA0zqp5FuV2n88MujWKdaMq9fP5s+RbHb2pKYDwTdjk1MD7k/HcZN/CxXv95HstYXh01F3FfJ9/Gyz0J7Ic2/fgy7ttiPjg5a1inuvC2F8n+/Yen8QZauf0pf2sie9N/s/5b/Agfy+4Mtlt56FDwOl9vRPayvhpXw+f2ts/z9lJL+VyK4uFFVx4fy9Rtju+dOprp5wN6eN97I4wjC4Gmp6r9InIDgBfh0+uWqOo6EbkZwGpVXepfdraIrAfgAfADVW0YvFUfw3JZUdVmEXkdwAIAaSIS5b/a29ulp2EY440QZllR1efgJCtW1ZsG/K8Avut/BU0wNTKyd4uDIhIPn9/MBgCvA7jQv9pVAJ4ezoYNwxijjIEaGfkAHvI7C0YAeFRVn/FfUj4iIrcCeB++4kGGYYxzDvksK6q6Fr4C3+772zDMso8aCfQnygCbLzSTypwLzzLWqGa81Up2/Tx2xMt9ljWohjNKyI5zfMd6UvnoeB1Nq/M49lNLqub101ZVk11zOvteeWJ4f3ae45QaUSeW9yzOmZbyJn/+zR8sILv1RNZlupyaI8XPsS4mTv4/dfL/Zb/HyxtOLyF72t1ci7ZmYeDhmqtZ7TqNA5Gbfsdx0HllvK1dJ7DeGrGa/ewaf865DvueZ7+89hXcV8933Vhc9uu747WFZMdXsQZXtIo1tZ8s5QinL/3zerKj21g/devipmzkr1o9l9FF3gq2NZI/X/AWa37Jazl2t/4kPveip/F3pTyH9fHiJzjWuHnugHNpiPrMQxHusbcWhmYYRuiwLCuGYYw7bNIzDGO8YNXQHLzRQGd+YER2xcV/YvlAin7DtUd7T2MhpM+p89ozg3WN/jhenrB8M9m13zyM7LTN+45XTPoXCy8bf8uhgCnbeHs1x/LwTnqcdaLqBexLljqFdRhvB8dfuhpcK7vxYcrf2Nlt2yXpZKdtYo00c73jx1fHhSAyHL++7Vex5jjx3wHftZxlvG8bvs/56twvQtskPvZtszluevI/+APV8RynXbiGNcGUch7L6hNZo9t0De97bD0f28z1rHHVzOf2vnHXDWTnb+X1dx3H+zPzBx+RXf7tuWR/5/PsZ3tvDcfSpzrjlbCD97f2ND7XY1v43JWVrKnGJnKDbdfxuZL0ZuB4RfSMTNOzKz3DMMYVouE969mkZxhG6NAx4LJiGIYxLML7Qu/gTnoRfUBCdUAvSClnXSTu3yvJrr+W/dJSdnD8YFopfz5mA9cxyKljTUt7+fNufrikHVwbdfvnON4xN5LdEhOqWBdqPoLbP/GwLWRv3MlJ0dQJJe54OZfswhXsi1V3EsfKuvGmTWtYc0vfyGdf2gbW7CodX7pFd3J/l93C+foKX2NdqWVW4PNuHdbZtzg+fedw3yLcMgx9PJbbLuPLhew3HE3qJj54Hi8Hn+pHWWw79U16nQRETdN5B7qzef3kdu5f4nb2+6v6FOd6LP8Wa3gxThngOx4/n2wnNSTinuHvQuciPveiul2fU+5fxyTnu5XJ57Y8w/3tLQ60556Xw8UeZBiGMb6wSc8wjHFDCBMOHCiCSTgQJyIrReRDf8Whn/vff1BEtovIB/7XvKHaMgxjHDAGEg7sThffLiLRAN4Skef9y36gqo8NZ4M6YJpN3MDxlBvvOp7siCz2I0v7M/sPxe1kXaV/KvsuVS9gTa5jDsdz5j3Puk39Ebx+9vt8ZFI+4Loj3kjW4DLXk4n3dnC+u1h2u0NvGm9/4qmsg/V8yLVSM/+6huzKOE46m/MvrpMLcfytnNqyhR529Pt336fIznuc29MTB/9d64/lbZV+nTW8qHZeHsPyIjLeZyEpqpPt2hOcfHPv8NjHcro79M9jvz+J5rGOy2a/wuhNHMs78/ccV93xJ8cHs4frzKZxKkR0s5siCl7Yxf1pZw2y48gisjf/6Viyc97i8ch6kzO59U1gjQ7KKmHTHI5tbpnB57bmBsbD1T+Hw5hwTt5HunjDMIxPIN7wnh72K128qu4OTfiFiKwVkTtEJHYfTRiGMR4I9tZ2FOfFoCY9VfWo6jz4MiTPF5E58BXZnQngWAAZAH64t88OrJHR32U1MgxjrCPe4F6jxf6mi1+oqr/xv90jIg8A+P4gn9lTIyM5rVDTtgYctFqOYl2m+EXWbVqc+MyK0/nnIbGSLy5j2py6ASwJInGds34rO4s1zWDNK4K7g11nssbWdAx//sy5G8jesZHr1ia8xdvPXMs60eaYQrKzivg3qf+ao8lOrOUORs6aRnb3BNapGubw9gufdDTETPbbkyjn9HDq5NaeHxjgqXc4NXSTONehx9Ez2wv5WOWs4W9BbBP7mX1qHvsQvtXLeml3AX8+Mo77k/s073vLJO5Q0zzeXmQP68Ot7DaHXKemcGcWa25pW3l52cV87riUPMIaYuI21gwTalmj1CjeXlcu71/aFvbL6yhwfE5XuyWqA+3VN4zt2Nv9TRe/UUTy/e8JgM8B+PhAdtQwjEOD3QW/h3oN2Y7IQhHZJCKlInLjPtb7goioiAxWTpIYSbr410QkG74HNh8A+FowGzQMYwyjAEKQcMA/39wN30VWBYBVIrJUVdc76yUD+BaAFZ9sZe+MJF386cFuxDCM8UOI9Lr5AEr9ZSkgIo8AWATAcQzDLQBuB/CDYBs+qBEZ/fGC+jkB3azfyfE18SYuTJ4Xx7rLtp/w3Ns0h0c3d7mTX8+Jr1Rnbzvy+I3im9kvreJHHHuasdHR0JazBvhq1xyys9awelB3shP7W8OfT97COk36lTt4exG8v1vfnki25xonvvQprt2atZZ909Dj6Drq1Ag59yiyu9K5f9GbA+uXfof9zqb+ln0o2yexj2RHDrfVH8dj1fhV7lvZKo5bjurivubNZJ/PuuWsifXH8rnWzxIXDpvJ+mbjMh5bN5dhv5NrMWM973/NcewXl76Jz52az/H+1Z3Mml/WWj5XYhtYoG45kuOwe5O4PwlPfEB2WiH7wLZP4HOvrSTwf9/a/df0humnlyUiA5NmLvY/AwCACQAGHpQKAJTAUkSOAlCkqs+KSHhOeoZhjHFUh3N7W6+qQelwLiISAeB3AK4e7mdt0jMMI6SEKCKjEsDAMJVC/3u7SQYwB8Ay37NU5AFYKiLnqyqnXHewSc8wjNASmklvFYBpIjIJvsnuEgCX7dmEaguAPfnDRGQZgO8PNeEBozDpDczVpY7DzOb7+Uo35SPWpLI/YF2k6tTB2waAkkc5H13jfK6z0Fbs6CDncs6ylHLWyGKuZ1+q+ib2a0tcyb5pZ3/7TbL/voZrahS8xb5h9XNZZ6l8ieNXU7c5muJ01l76HmOdp30y2C7m8Zy6hbfXl8z7G/8UO6e1fIc1zrgjA3UWEp7l3IW9mXxqdWbxWBc8XUa2drJfWUIdxwU3fI3rhyQ8xmPfs541sTiWEJFQ6/hkzuJ9b7qbx7orm/ubtpm/ycn/fJfsjhd4sHNv5u1HtbAm15HHwbmJl1SRXfUea5ITn+f+pr3Lsbfll3P/m7/IuSjdfId9iXzuFL4e6N+utpHNWqG40lPVfhG5AcCL8DkRLlHVdSJyM4DVqrp03y0Mjl3pGYYROhRAiGJvVfU5AM857900yLqnBtuuTXqGYYQUq5FhGMb4wqqhBdAEL7xHBhKp5T/AfngdeU7sax8PXmsxi3YpXMYW6R9wLc+NP2bdZ2Yx+721LCshu3kqD0dPhuPb9fcJbLPrGMSJL334Y9YoY6p5/6rZdQp9xaz75D3PGlxyKdeFaJnM+9dwPGuExU+zbtMymfdPe9lXbMYveEBlcgnZXkczjXg5kMMtspfHquZo7vvcczeS/XHqTLI7i1iv1Bi+XEhyfAhrTuH1kzY5RZMddnya992bwJ9vK+LlHidnUOZ69nEsu4U1s8nf54R+4mG/PVSyvqwRrOlF/4I10dwM3v+KUzkOPTuNY4Mz1/Gx749nTTKyl9vLfJ+Tf3QVBvwKXa19uBzy+fQMwzCCZpTTRgWDTXqGYYQMX0RGeM96QV/I+hOJvi8iz/jtSSKywp8B4Z8iEjNUG4ZhjAO8Qb5GieFc6X0LwAYAu5O03Q7gDlV9RETuBXANgHv21YB0RCBqRcCXzePkPEuqYl2i/BIemckP8S9I3eEsolUs5DoBkx5iHWbrGRxPmVzF7cU38Pba+1nE6nN8v9I28OebzmIdJ20Z6zDNs3l9bzLvb1IKa3rVZ/D2a45jP0BX88xazocz8V3W6Loyp5NdcQX7whXev47stsPZ769jEh+v9A8Dv5kZ6xy9cQoP1vonWMPL+ZCPTdcO1uRuvXUJ2bd/8Uqyd57J68edWk92z+tc93byE87YnsTBt22Hsb6Z9yqP5Y6zWeSb8AYfu9IrWJOb9hD3x9PKfoYTXuJYYa3gGhodl3Mcd8FbPF4x77JG2nMiC8yJy0vJrrqcx7/uCNaDf3Tpo3v+//nnnSK9w2RMXOmJSCGAzwK4z28LgNMB7C4K9BB8OfUMwxjPqPr89IJ5jRLBXundCeC/4Yt3A4BMAM2quvvnrgK+rAiGYYxzwv3pbTCZk88FUKuqa4Zad5DP76mR4bEaGYYx9tmdaWWo1ygRzJXeiQDOF5HPAIiDT9O7C0CaiET5r/bcDAh7GFgjIyGnSKM7Aju763iec92cZYnrWLeJaudiqW5dhZgyRyfpZh0naSprWukbeblbuq5hNus+qVudOgyOb9qMH7OvVs7DW8le9h7rLhceyb8jHzRxjYytO1nDi+zh8Ynf5eS/43BNbLhlCtn5y7i/+W87P0I5rIM1zObTo+Rp1r0aZgU0x8rTuK+TlvKx2v451vjKP8vHNtsJE7/+79eRncQl3umJAAAAB41JREFUMdBdxH3pa+H8dRGZvK+Vp/GxFJbkMOF51k8Tn+DcjumZrBd3HD+J7P401jt3nstx3r2X89j2Jzn1XNp5+ZRfsb664Q4+d2dv4mSRtUfyc0TvcazhdU1mTXDy38jE6+cEzs02D9cjGRYa/hEZQ17pqeqPVLVQVUvgy3TwmqpeDuB1ABf6V7sKwNMHrJeGYRw6hPmV3kh8r38I4LsiUgqfxnd/aLpkGMYhTZjXvR1uCchlAJb5/98GXx57wzCMPYS7y8pBjcjwJCjVF53oZMRqLXZiX9n1CU0zWRdqL3TqJMRwTrWdZzr545awL1TtKbx+zhXlZKfc7+Sz+xcLT9v+xkJTTAvrMpt/69Q6PY1PhlcXc/BtG8tESNvOtieW9ze2xYlNnsm6UmIOa3axLU5hiHfXkrntZo4nLVnKvmWRDazTRRcH4j8bjuB8dc2VrLFlv8dCjzeK98V94lf4GmtQrRPZT06iub2UZPaRbJ7A7UdvYp/OvlTeYMpa9qtrvPRYstudGsSxjfz5md/jsYxw9NGOmezz2Oac605oMXqPYh/KWb9i37nSr/O5GeO41rm5JaOr+btQebJT8+PWgKbXU/0S9hsF4LFJzzCMcYJA7UrPMIxxhk16hmGMK2zSCxDZKUj/ILDJxhlOnv6/bCK74oszyPZe3ED2xGtYyGg+lesUFL/EulDPRPa16kvi7W/8qIjs4kbWyCKmseiW8y+OrUWEm6ONhZXZv+R8fo2LWadq3sI6UGItN7/rQt6fiOWs0WW/w9uL8LDvXNVJvL9TSnl/Jj7D8bM9mayDJdSzxpdSFvCVaz3DqfdxJJ9akd2sicW0cl+6clijm/Ebx+1zIh/brCzWF78x5T9k37ziXG4/j48NUlmD3PBdp2ZFmZOPjkt4IMLx89v0/+aSfcFJXF9k5S9Y3838iDXIrRfxueSN4bHv/RTbEU7J4iinf+2zeIW0NazpXfK1l8n+R9NZe/7vX7H/dW996eL3/+MDEZGF8PkERwK4T1Vvc5Z/F8C1APoB1AH4sqqWf6IhhxGmCzQMw2BENajXPtsQiQRwN4BzAMwGcKmIOC7qeB/AMap6OHx5AH4VTP9s0jMMI7SExjl5PoBSVd2mqr0AHgGwiDejr6vq7kvmd+GLDBsS0/QMwwgdqoA36PvbLBEZ6Ae22B+2CvgSmOwcsKwCANdQZa4B8HwwGz2ok15Uhwc57wZ0oaqf8uCUJbCG15vm+Ha9yZrXji+xXfQM18ioOIc1vMI/fkh22wWcsyx3OWsZnji+EC69krcXV8/rJ5zJmmPP+xx/2XgS/xC1v8rtT17DscDNk1nzS3qLNbyc1axrYeVHZNbewHVq+9IdXavZ8cOLZE1Qs1hHajuCdamOvMD6fZ2ODpTK24qrd2pYLGA91lvF+mPpN1nDS6jk9nu7WaP66/XnkZ14JI/d/AvZj+5bea+Q/aVffofspjlOjeFM1lP7P+JjsfCE98le/yWOle1cwMc6cQe3P+NPzrlTkEJ22Xk8ft54/nxvOh+77QvvI3tB/hfIvn8d+2TKgoCeq0+PUJQL/uP1qnrM0KvtGxG5AsAxAE4JZn270jMMI6SEyE+vEsDAJ4t7TWoiImcC+DGAU1S1x12+N0zTMwwjtIRG01sFYJq/LEUMfMlOKIZLRI4E8CcA56tq7V7a2Ct2pWcYRuhQhCQrsqr2i8gNAF6Ez2VliaquE5GbAaxW1aUAfg0gCcC/fMncsUNVzx+qbdGD6EgoInUAygFkAagfYvXRJJz7F859A6x/IyFc+jZRVbOHXu2TpMbl6QnFVwW17gtbfrUmFJrecDm4xb79Aykiq0djZ4MlnPsXzn0DrH8jIZz7NiwsIsMwjHGDAvCEd+pkm/QMwwghCqhNentj8dCrjCrh3L9w7htg/RsJ4dy34Anz29uD+iDDMIyxTWpMrp6Qd2lQ676w866x/yDDMIxxQJhfSNmkZxhGaLFJzzCMcYMq4PEMvd4oYpOeYRihxa70DMMYV9ikZxjG+EFDEnt7ILFJzzCM0KGAmnOyYRjjCrvSMwxjXGGanmEY4wZzWTEMY7yhwRcGGhVs0jMMI4QElQp+VLFJzzCM0BGidPEHEisMZBhGaFFvcK8hEJGFIrJJREpF5Ma9LI8VkX/6l68QkZJgumeTnmEYIUMBqFeDeu0LEYkEcDeAcwDMBnCpiMx2VrsGQJOqTgVwB4Dbg+mjTXqGYYQO1VBd6c0HUKqq21S1F8AjABY56ywC8JD//8cAnCH+smj7wjQ9wzBCiobGZWUCgJ0D7AoAxw22jr9kZAuATAxRUc4mPcMwQkYbml58RR/LCnL1OBFZPcBerKoHPGW+TXqGYYQMVV0YoqYqARQNsAv97+1tnQoRiQKQCqBhqIZN0zMMIxxZBWCaiEwSkRgAlwBY6qyzFMDuyuIXAnhNgyj6Y1d6hmGEHX6N7gYALwKIBLBEVdeJyM0AVqvqUgD3A/iriJQCaIRvYhwSq4ZmGMa4wm5vDcMYV9ikZxjGuMImPcMwxhU26RmGMa6wSc8wjHGFTXqGYYwrbNIzDGNcYZOeYRjjiv8Pr7hY10UYSGkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:33,996  SpectraRegion  INFO: Processing Mass 10099.632999999985 with best existing mass 10099.587949181696\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10099.632999999985 [('Ccl27a', 10099.632999999985), ('Chchd7', 10101.449999999995)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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EOmeLSKuIrPd/vjdSu2PNp1ft/1kvIs/CVyFthbNOoEZGYbEOJAW0jpg21pB23j6d7KR/8P6inVjYjktZp5jyd9awmhazzpGykw/31FM5gPLtTVx9zRPvxNI6ZflmPscaY8XlrNvsaWK/wFwn3tKbwHZmCQtJ4uH+Nt/OOlnPJvbr88Y5ZQ2dWNv899humcXtx1Vx+8kxnKOuP9Xpz42B4z+jiPPjbV/PeqdG8Tf7nst4MKe/yHrpQBIfS1w7L0/fwn5q2VdwKbemFtZ3Y7L5Czd9DTv+9aewm4V7rsuv4Gsr4xU+nqYz+VrWLreaGh9P61FOtbOP2I9w/l1cA6PxRK5v4vr/9jiV/Ype5nyCbuW7lLXcQO3tJ+z7feDxt3CwKBBOl5UBAF9X1XUikgpgrYi8OkSZ2bdV9fJQGz3oOz0RSfZ3BCKSDOBCAJuG38owjInMYERGKJ8R21KtUdV1/t/bAWzFEBUXR8tY7vTyADwrIoPt/FlVXx5+E8MwJjqjKAyUIyJrguwH/E+G+yEiMwAsBrB6iMWniMgGAHsAfGOkshVjqZFRhv2SKxmGMZnx5dML+SVFQyi+vSKSAuAZAHeoapuzeB2A6araISKXAvgbgLluG8Ec3nx6sYq+3ID/lSeZdZL4Jh6s7E2s2ZV/jtub9/U9ZFfcwvGUue9xe0mf4vU//CvrTqmOb9aeC1hHWvhfThX7T/L+PPFOHYRmFob2nMPtz5hbR3b8fPZN6/5f1nVatrGGJ87Z60vn/SfVOnV72RUOxX/h42lZxk8OTQtYm0ms5/an/CZwfO8u5oIm8QVOTV3HB1Gdor4DidzX+++9l+ybNn2G13+bNb3a11jDi3P+77SSNby9y1jvnTqDXU1rSthHMaGOx2LK23zuOovZ8S5hMQfj9mRxLDAK+Nre/lOud5LqxIG7scddc9npcsaTvLxxCWt4Gdud+isc+gtPQmDAxpovIJwJB0QkFr4J70+q+ld3efAkqKovishvRCRnON9h89MzDCNs+DS98PjpiU87ewjAVlW9+wDr5AOoU1UVkaXwvadoHGrdQWzSMwwjrIQxDO00ADcC+EhE1vv/9l0A0wBAVX8H4FoAnxeRAQDdAK5XVR2qsUFs0jMMI2woBAPe8LisqOpKYPgZVFXvA3DfaNo9vPn0egXJZYGYwrZZTn42LtmAjiLW/BI28m1z7RWzyJ6yjnUOz9f4LreyjGN1MZc1u9x3eDgKv+bUmf00a4CdxSwCzruPNcPd17DOlF7GOlLhYq6JUf8N9lPsPM+JJ93KX2CNi9mOa3bqNFRx/7JW1ZBd8uVismNbeX8zz6kgu+f/cc66pgWB85O7gcc+vo794upOZT2y6BWuO9s+l0Wmy/7+72TnfsDH1nMcH1tytZO70Mnfl+HUJxHnH3OPsB/c/Ic5ULv6PO6/Jjj1SY7lc9nR6dRf4bBrHPVDx8fzE47foJMfT/jSwYKfc/8aTmQNr2GJc7PjaKiZm/hcx7YHlrv7Gi1WI8MwjEnDKN/ejgs26RmGEVYsy4phGJMGq5HhENupyF8diFHcdRFrdplbnBoZy3j7BE63t1+NiJKbWReZ/SOOfU1d6uYYY91p72LWaXZ96Viy+47lPGPeRu5/8zLWvDIuZA3Nve3f8NeFZPd8nI9fnWKuewvZju7kb9TeXBZj6tN5f3UXsW6VstHJ35fnjP8TrDHeeM9LZD9+1yX7fk+o4LhdiOMj6dSdLfsuj/Wcb9eSnbyLNb6Y3XvJjm9lH8am+c7dhWP2pTl1YWc6cc7xbHcXslNjbAePTclnWeOLX+/ECju5Ct2bn613OH50XG4F2Vs4lnf3eXyt1S/j7dMrWFNNL3NyK17EGuNAoqPpdQz7wnNUmKZnGMakwZcu3iY9wzAmCxo+l5VDxYiKo4g8LCL1IrIp6G9ZIvKqiOzw/8wcrg3DMCYHR0IS0VDu9B6Bz/nvsaC/fRvAa6p6l4h8229/a6SG+lKFtIkpa1l36HHqxOa9x9vXnse6Rdts1l2mvsG6RP2JrPHlbuCCnqU3sM4x+0nW7HZdwvGd0TGsmWmfW4PDqVPwMjtntc92HKAcP79Zf+Pj6/wP9v1qWst+hgWrWDgaSODxq76c/RATyvh422fz9jOe4/54nHjYv/zgIu7frMDxJ8xjvdAbw2Pjntucp3lsd1/NNSYS9/K59BzLy7uncPsznmQfyaaTuU6smx8voYH7k76aNcbGo3l5WgWPjevnlvfqbrLblrC+m7yb9ePoKtYoT3mlkuyHVp9BdkoJ78+tyVz5b9y/gW7+145J5Fjftqm8fMobgeOXMcp7kf54O+KdnqquAOCUMsaVAB71//4ogI+HuV+GYRyBDGp64cind6g4WE0vT1UHX03WwpdbzzAMI+Lv9Mb8IsOf3eCAN8T+gkG3AUBMukl/hjGRmch+enUiUqCqNSJSAKD+QCsG18hIT8jX2Y8F8pA1n8gaVe577OtVexb7IuW/zt1N2c26RtU5rOFFsaSF3gzePnk3v2WqW8rbT32bG6jr4oDI/G2s0bVN4/a9Z3J8acJa9u3qyWUdpi/dqavwKz7+7FT+bkneyH6A1Vex75o4dZi8cbx9TJtTFyLJqfvg5NPrLuDjzf0g0F7rDCdu+XnWuGq+zLn65jzOPpb9yY5fXjePTfMCVmKi+dSj/FOsoQ0kO7GmW3n91EpentjA+mZcG+/P6yTo0ytZ8ZF/4e1TLmXHu6ovcr685BrWKFfeyv8LMdfweLp1dF0f1zhHY7zsmvfJfvv+k8h2j6fx4oDmN/C2k1hyNCgwEOERGQfbu+cB3Oz//WYAz4WnO4ZhHMlMCE1PRB4HcDZ8+eyrAHwfwF0AnhKRWwFUArjuUHbSMIwjhyP+8VZVbzjAovPC3BfDMI5wJrKmd1D0ZsWi4rqA/1RvNmsHsd2s67TOYw0pxolv7E9l36rpf2OdZfttHHubsZP7E5xDDACaF7Eu07OU/fryn3Bqt57O/Ynp5OVnFZaTvePXHGur0by+6xfn+m92TnXq2p7N+fA6ixzNrsOJB20ePta29hTneFh2gybz+egPqk0b08Ntld3CfYPwue4qYh/L7lwnNnY664lR/U7NjVks6mW/xRpXch2fS49TE7i9mNtP3uPopTv4WuqbytdmTQnrrYmr+CVd2gzWp4sfZFGx+jMLyN57Ef8rJmzh8Uip4v7tXebkE9zFx/OPf7KGN+VqrulRU8/68pTMQH6+upgxaHoA1CY9wzAmE5ZwwDCMSYPqBND0DMMwQkfg8Ua2y8rhnfSiAE9SQJs46hH2Y4v+Jcea5j3A+dz6nbqt7hdK52zWXTSRtYmmeawB5q1lP7yOs1jDy/kb++313calND0VrOvEOhraqie4FmzsLNZlonl3iHZ1KycHXKeTT6+rgJcXLeb409a/su9a8yI+3sz1fPpdP73UCzjHnTzItWD7g4Ynvo31vpwPOY559wV8bhKf48Dqhv88lezsTdxefDNrdM3zWMMruJn10+o/zyTbzafn6rnl17ImJknc36K/8ljNu499JDuO4aCksh9w/9JfmE82zmLNb943WKNsXcwFY1yfycwNjt9in5uL0dEA13H/ohyfzbYdAT9BT8fYpgXT9AzDmDRYPj3DMCYX6tP1Ihmb9AzDCCv29jYYLxAVlIOu7BPs25T0NGtkMQn8lRHF6eYwkMS6xkCCUyd2HdstC1knqryUt9dWJ57RyY/X9SRrWtM/zbpOzF+4/xVOjrPocs4h58aHehN4/ahux8+ule3iVx3drIvjWzObub2C11kXyvkC62A7l3Md4agH+HjbHN+2zJKARlh3MuulfdeynbGCj3XHL08mO7rLjYXlk13p1HiYvpw1sP4vs99Zwjw+9vRy51xO4Us/a517bE4srpP/ruhJDjf/8Desn6a/wLG1HYV8LWb/geO4e6exfqxRvP6sB9jJdPenZpPdfWob2VEl3L53Oo+Xp4ePt+iZwLnc03bwfnoK0/QMw5hURH5ERmS/WzYM44jD65WQPiMhIsUi8oaIbBGRzSLy1SHWERH5pYiUishGEVkyUrsHWyPjByJSLSLr/Z9LRzwCwzAmPKq+x9tQPiEwAODrqroQwDIAXxSRhc46lwCY6//cBuC3IzV6sDUyAOAeVf1ZCNvvIza1H/mnV++za1eyBuVlGWg/O2Wvk7+umLuf2MhaRPZHXBcg28k5Vn4F60RJFaxzdBSwzpTQ4uR4e5F1HBzH5ty7OH6zcTFrej3Z/J1T9HfWibrmct2J5rm8/s5ruP/eFPbDS3qLx6v6TF6/YRX7sqU08/FWX8G6VsZq3n+UJ7B+lONzmJ/DPpd7nLje3LfYD82teVF+O/cl9R1eXr+Ex7Lwj7vI9h7NGl/rDMdH83X2Qay6kuuZxFWzD2nVFby85lG2Wxwfz7SNrA/nr+blNafx8hm/2EZ23eePITvzLTKRtZ3P9ek3bSD7A8fvsP6L7AfZepSj20UFnesxPp2G6/HWn529xv97u4hsBVAIYEvQalcCeExVFcB7IpIxmOvzQO0ebI0MwzCMIVEN7QNfuro1QZ/bDtSmiMwAsBjAamdRIYDgjLVV/r8dkLG8yPiSiNwEYA18t6DNQ60UnC4+fkrqUKsYhjGBGMXb2wZVPXGklUQkBcAzAO5Q1baR1h+Jg32R8VsAswEsgu/28+cHWlFVH1DVE1X1xNiMpAOtZhjGBEARmp4X6sQoIrHwTXh/UtW/DrFKNYDgPGZF/r8dkIO601PVfcm5ROT3AF4IZbv+jljUvBu488wsYV2hdbZb95b90HZexzpO/ruOxufmYPOwbuJ18tcl1bCddTHHriZ8l32tSm7h4N+UMjKhTmH3Sx9fRfbdqy8gO3cF60y909hvsSvXqZOQyzqXW8dh2vFVZPfFce3XzO1ObdR4Pn4n5R3U8eU6818/IDs4Z1vmFu5b+6u87/h0Jxb0dNak5v/SSd73MptVF/MXZjTLtdh5x1FkZ23m/bka3ravcE2KeA6rhrRzgZEYx4+wYwavn7KZr7WufCe2tYU1zPxVrPGV/I59JAv+4viUfob98hIauP0nXj2d7OwbuX/utZmx1Tn376wPWtkZ3FESroAMEREADwHYqqp3H2C15+F76nwCwMkAWofT84CDnPQcofAqAJuGW98wjEmCAhqCO0qInAbgRgAficjgrPxdANMAQFV/B+BFAJcCKAXQBeCWkRo92BoZZ4vIIvgm9QoAt4/uWAzDmKiEKyJDVVdihHfJ/re2XxxNuwdbI+Oh0ezEMIzJgyUcCCK+2YOZzwS8XxpOYA3LrVNb8VUnNrWL/cb6nBoZSXW8fk86a17NZ3P8Ydoq1ggbOlizS17ovG32sujVMYt1l5QyFk5e/OQpZM926txWXObkQLu2nezOVTlkD8xgrSV9JfvdldZwrGzCCawjedncz7cudwOfgNQSHt8XW7juQmxQTZBOx2XRu4SPJWo9j2VyqdOZEcI93VhYtz6J69OZVs56cF8RX2sz/8btaRTbW37MNT6Sd/ANR+467nDNmc4BOGbjCWx3TGcNMOMNp75JN/enyyk5EtPlxFFv4B22zeTxSXZqbDSc6rR/Z+Ba7fst5zocDRZ7axjG5EKxf3bfCMMmPcMwwoo93hqGMbmwSS9AT75gx7cDOlTes46f3jzWIaY+xrpH5cf4trkni+34Vjcnm1NX90XHby/W0Qzf5jq5jcfx9mklrKO0z3JifTezJtZ4IutI6eWsKeauIxNdVazhJTl1HKa/wNt3FbOQ1VnBGl9fGm+f/z73N76J+1tzihOLXMvbd53A+y/+VUDTjN5WScu2/mQe2Qv/dzfZVVexSLX9czz283/ITpBV/8axtJlOXVhc3khm9Vk8ljEs8e1X8ze9lJdPcWJdu9mtD6mf4+Np+SfXcyl8k/38BpL4X60rj/vf6sRVywCvn72W94+r2bGwZzkfb3cha3aZ252axk3cfnKQO6+rrY8OCafLyiHB7vQMwwgfai8yDMOYbNjjrWEYkwu709tHfD0w+96ADlT5DRZaYjayL1d3Nndvoavz3ODUCcjhwR5IYA2udSHrHFlFnDOtu5M1LU8ba4Ax3U7s7h7WSfacwQeC3psAACAASURBVMsT69juS2MNrulKPn7ZxrG+7Yu4TkT2Q1vJ9s7iOhMpnFJuv1ja2mVOTrt17Cvn+jk2LmL7qLu5P+2zAv1N28hCUEKto2Et4Fjc/Hvf5bbu42PZ8c053Lc9zrl1cldE/4NzD/YXObG3H3DfPTt5LDrzub+u/to+g306S7YWkZ3o3N10FPP6tac5Y/lH1vw0is+9x3Fj7JjGx9/fwj6lSXypIud9vvb7bmLNc853+fj3nhykP4/1Ts3u9AzDmFTYpGcYxqQhvAkHDgmh1MgYsjiHiGSJyKsissP/M3OktgzDmARoiJ9xIpQ7vcHiHOtEJBXAWhF5FcBnALymqneJyLcBfBvAt4ZtKCkK9ScGdLucJzl2tf5EHon6s1gnynmHdZKsrU5NiO1cY6J1CetIA8l8uKl/Z9+wgXmsufUfyzpQRzF/g6Xsds6c8PKpL3Jar52f4f7M/i9H14nj/TX+lMfHrRXrhvukO/GhKdW8/cBu1nlcDXTKbzgT98DneH9Ru3h8934ibd/vnjguENJTyOemeS6PrXx+Ptk5z/L3r+vHVryc9de6ZXzusrZzIHH3FNZnm+exSJbQ5MSiLnVyM87m7RO47C1m/r6C93cc+x1WncPHO/M5Ho+4n3GDcfc6dW+d/HeNBY7PaQz3d+pKzkcoa7aQvf30RWRHH8v/C8G+eTJmTe8Iv9NT1RpVXef/vR3AYHGOKwE86l/tUQAfP1SdNAzjyEE0tM94MSpNzynOkReUSLQWQN4BttlXIyM2xZ6ADWNCM86PrqEQco2M4Ypz+BP5DXmowTUyYhKTh1rFMIwJg/geb0P5jBMh3ekdoDhH3WDaeBEpAFB/4Bb87XiA+LaANrF3Mc+58U2OJrWLdZHSW7nWaFQ/rx+1ZNjKb8jewn56CXXsJ1dYzppaZgnHeyaV8iHWn8H564pe5EqZ3kz2vUot5/5UfIJvjnM3cP+S7uPvkfiTWOhx40n70thuSuX1l125kezK73Bdib23LSU7upf337+QfdPSS4LWdeI1Y9NZY0u5jP3Eou/jWNHdF7FmNfePfHDlV7OG1zed228+ji/lo77AfoBl/825DbunOLGuSXzutYnHrnA5a3A77mF9Nu01vlYL3+QBqbzYcbyr5u295/LimE7u3/zfcH7C1vl8sttn8PZ113GRsWlOnHvLLG7f1TjHxJF+pzdMcY7nAdzs//1mAM+Fv3uGYRxxeEP8jBOh3OkdqDjHXQCeEpFbAVQCuO7QdNEwjCOGiZBEdITiHOeFtzuGYRzpjOeb2VA4rBEZUR5FQnPAvyhhr6ODvOz4gZ3m1HzYy3NvdI9Ti5RTmqHoNdZpvLH8NN82h2N9M9ZzjrL+JF6/Y34W2Tkb2DfKzZ8XNcD9c3UvV1xI+ZDr1vZP5+P3xvHpSnRibXszeXzaF3H86OsbFpCdcTSPf9syrsGRvI79IhsXsO9ad5AvXf5q1iOTV7Ce2dPHL7HSevjcxHSy5lV7qlOfxPlHmv64E1uayse+93Os4WmRc2yv8bHlruex7UvhHdadwRpkfzefzPg2Xn8gifvn6tVpx7BG17yD/fQydvDzX+3prC/3nk3vEjH9x7x+0wJe380dGef0N608MD7RvWN89ozwSS/kt7eGYRgTAYu9NQwjrNjjbRDeGEFXbmCXSXv5Nrp7Jj8edk51HmedkoWFL7Ebgbt9RxE/MrWwhwYSncflzgLOCR7f7KQUX19Hdumt7HLiiXMeGUr5RtoNlWpYzI+XzWdMI7u9yEnxvaCV7O4WduMo/BXnFM895WiyG48Z/nE2+5/8+Np0oeMTU82PhBnbAsfbNp0vpdguHouMHbyv2mX8ODdtOT+KV17CuZI88dxeVD9fO50FfGxpu5x06Q/x42blZ3l/qY/x8pQ23j5m5SayvTHsEtLCmbD2e8SL4d2hs5evzd48J+3ZfezfVP75udze++yysutSbj97E4epNX6KQx7TnmX5obMocO69m8f4AHikv8gwDMMIGcW4uqOEgml6hmGElXDF3orIwyJSLyKbDrD8bBFpFZH1/s/3Qumf3ekZhhFewqfpPQLgPgCPDbPO26p6+WgaPbyaXoYHPR8PpAjK+Q27MdQuZZ3Dk+CUMHyP3RwqrmMNrmcK6xhwQqGmLOf26y/k5fP/m3WPXR/jFOTbvswuJCmVjiZY6LgtOKHGCWXsEjPlfQ6r27uI28vZyM8Jmd9j3cebyG4L9bcs4fY/4OUFT3LZwt501okaFjvPJQP8IBA1lYUpnR80Xi/wWHXl87HkruDUUP2prOn1ZLEmd9SvuK+NZ3EIXFSvc64d4hudcpzHOOUt13H/Go7l7WM7+Vy2XLvY2QPvf/rfHReTpXw8USzHoqef//Xm38fnqulR1mt7G3js++tY80yay+Pb2e64T33AGmDT0U651KDSBp6xzgphmvRUdYU/yUlYscdbwzDCRqiPtv7H2xwRWRP0ue0gdnmKiGwQkZdE5OiRV7fHW8Mwwk3ob28bVPXEkVc7IOsATFfVDhG5FMDfAMwdYRu70zMMI8wcpnTxqtqmqh3+318EECsiOSNsNvKdnogUwyck5sHX1QdU9Rci8gMA/wZg0Fnuu/4dHxBvXzQ6dgW0BT2efaMyt7NO0p3Dc3L5J9hO3c7tT3uZdZXd57GGN5DI30B5jsanwn5p6WXcXvrZ7BdYo6wpTlvu6kxs77mEU1/1clQbktgNEB6WhdCfxTrYjJ+VkF32Gqds7810U6qzb1ZvNh/f9Je4v70ZPD415/Ly5saAbhQ1l69ibw7rr7uvmkp2Tz7rk+0tfCn2ZHH6dU88n7v6T7KmVfA2H0vF5azh5b/Hy3vT+FqK7uP+X/2fr5L92zfPJ3vmfC4F4H2erwWN4fY8zrXX08g+j7uu4LC7gv9xHPsKHb++dG7PU8ManptmrPg1Dpncu5gF5+4gl1Md4/OfW3r0UCEi+QDqVFVFZCl8N3GNI2w2phoZAHCPqv7soHttGMbEIoyp4EXkcQBnw6f9VQH4PoBYAFDV3wG4FsDnRWQAQDeA6/0JjYcllCwrNQBq/L+3i8hgjQzDMIz9Cd/b2xtGWH4ffC4to2JUmp5TIwMAviQiG/1OhEMWwBCR2wbfzng6OoZaxTCMicQEKAEJYP8aGSLyWwA/gq/7PwLwcwCfdbdT1QcAPAAAyTnFmrsmMM/WL2NdJ66NNb6cjRyvmfJxdnZq3cJ+bgn1rIPEN7JO0j7TjY11/OzmsBDS46RqSv0V+6LlObpJ6wwW4VxfL6+TMdxN9z7lA/5DwyLW8BqOYx2odC2/oc8pddp7nVNVVX2cfd0K3+LxT9zNvmJJm/hLKm07pyuqviCgGRY/x2nBqi9hjavwH7Vk909lvXHX+Xzu0ytYP+xJ5+XFr/DYdjrp3zO5AiJ6Mnl5ZyGf28zt3N79G84ke+azPFZ1J/FYdl3O/U0p5/YLX+FSArtjWNCNa+X977qQNcu4Fm5vgC+N/UsHZHB75Xc463/E7cU1B34XPtRRE+kJB0K60xuqRoaq1qmqR1W9AH4PYOlwbRiGYUQCB10jw18MaJCrAAwZH2cYxiRjAjzeHqhGxg0isgi+7lcAuP2Q9NAwjCMHPXwuKwfLWGpkDOuTNxSeBKBlXsCe8yTHR3bl8Y3n7gtYuIh5le2O+Y74EMUa3vQXmsnuz2JNrPwKFtkK3+LmOli2QVcea3aZJXx2Ez7Gjna1lawBHvW598muvJNTmrdPZ9+ylmP4+Nz4zJ4c1oV6HL+/ugucl+zOWWw4hk9/0ldZw+v5H/ati+lm3Sr/3YCQ1HgyxyW3z+J1yz/NJQ/7U3jskmq4c03zuW+9WXxr0FvDyztOYP1XG1kTi81n0Sv7Wb6W6k9wyonu5nPRuJCX3/wvy8l+bMfJZCetZsG39CZ+zxfHlyZ6chyNzYnV7Uvn409oGD7XZGw7L3/zhvvJXip8jzL9J4H/xV3tw8c1j0iEa3oWhmYYRtgQRP6LDJv0DMMILzbpGYYxaQhjRMah4rBOerHtisK3AtpBTDPrMF1Hs+Y26xH2M9tzGYtst562guxH0peRve0o1viSK/lws9c7+e8SnJoUXayL6AmsqSW/wRpf+/+yrpXm+I613cD9i+3g9t14ybRt3N/uaXw8bvxq/F72ZfPGcvtZTmxzzam8vHQt1+jIT2bdrfIq7l9SRUD38sQ7y6q5L52zWL+d93s+9zWn88Gn1vC+czZy38XD527KOt5/TzYv33OV058CPjfx7EaHXmf7PnZRxG9WcsnnnNXcfmwn9z+21dlfC7evUXwuWufz8RYdxX6QLcvZRzV/JYuE2+5gzfLEFZ8ne+6POHdk2fWBa7f3d3wso8YmPcMwJhNH/NtbwzCMUWF3eoZhTBrG2fE4FA7rpDeQIGhcGPCNizqKHcsSGvm+2NXwetjtDQ+t5PjIMxZvI7virvlkV5/D7Res5Fjd0lt4OBbeyX539bXcn/gfVZBd+e4MsrM28/668lnXyV/FulZcNesyTcvYt61pAWuIee84vm61nMOuP42PJ2EvL1duHilrWVNtmePEw25yasNWB3Sn6vMcDczDGlV8Hfel/CrWJzO38LGkVvDY1J3E+d8yypy47RLOddibyTWJvd28//SLOR9e1XaOFY7KYcc3bymPTWI1t+fWc3FzQfZlObkZuawt4pv5ePJW8/HvOYs1PDmTrxV53sm/183ju184bTVf2955QceXMLbnU3uRYRjG5MImPcMwJhN2p2cYxuTiSJ/0RCQBwAoA8f71n1bV74vITABPAMgGsBbAjarad+CWgLi2ARS+FPA3qvuZ48f2Ltf0iGFZAz3Tufn817j726azjtO2hJdnbOGz0TqHdZoFsyrJrjt/OnfAiV0tWTWD7DmPsq604xb225v9/bVkl/8frlMb082xrlGOEBPfxP1P/9/3yG7/JPsBurrS3uP5eOOc/HtJtcNrOam7WDcquyrgp7fwzgpa1rWIff46HL+4xhN4X1mr2Q+t4XQWHN2xSPmwmv8gTu7DDaxZLdjKeuj2/2LHuymrHQ2yjeOyu/hUIq2CNb/Kf2W/urm37yQ7+ewFZJ/943fIfvrJs8i+6jo+t6u+xpnb2hu5/zUXOcfvnNuERtZjO85hvTvj5cDymtaDrxcmR4BzcihH1wvgXFU9HsAiABeLyDIAP4WvRsYcAM0Abj103TQM44ghwlNLjTjpqY/B9Bux/o8COBfA0/6/Pwrg44ekh4ZhHFGMotj3uBBq5uRofy69egCvAtgJoEVVBx86qnCAYkHBNTL6BrqGWsUwjIlEhN/phfQiQ1U9ABaJSAaAZwHMH2GT4G331chIKCrW0s8ExJHjMnbQutUV7LdXewELOSlbWGfp+EQL2QU/YV+u7A/Xk918NdeFdeMdcRvnUOu7ws1xxmcqg90Csfc0Fn6K3uT+R01nP79kR5Ya4O7vVyh+ygrWvbb+/iSypdfJuebW0U3k5fmrWFeL7mW7bbrjixbvaIKtgQ42nj+TljUdzZ2f9RTHLXsSONa28jr2Qyt4l/XDnmzW5OouZs2w6XTWe4/6BW+/+xKuyVH8OMcC77qQTMQ38rGrE47al+oEG4O/0AeWzCG7rZjbe3I767l9eTz229pZn+6cysffUczj2zuPBfDYnXyuvHG8fsYO1iSb5wUuPq9Tb3nURLimN6q3t6raIiJvADgFQIaIxPjv9ooAVA+/tWEYE56J8CJDRHL9d3gQkUQAFwDYCuAN+IrtAsDNAJ47VJ00DOMIYgI83hYAeFREouGbJJ9S1RdEZAuAJ0TkxwA+hK94kGEYk5wjPsuKqm6Er8C3+/cyjLLsY1Q/kLQnoC3sfOIoWt55PE//US3cvcI3WRcqOYZ1i44iFiNSPayrDMQ7dRCcUgC157GO4nHq1E75Wwkvb2gku+VGrnnh1pHtdwrlxjsaoeuX2HQM2z3T2Der8GW+UW+d5cbG8tXnjeHjbzya15/xLGuk7cWssWY+sors7m+euu/3tJ3c+c6pLFD2TuFzNeWDdrLb5qSQ3ZXPg59czRpU3UmsvyZtY42t+ny2i1/hc1F2Lcemxjc6Glk2j11iLY91+xLWDLWV+1v+MV7/0avvI/t7Zezs0PUsj1f5lrlkt7Ecjbk/5uKDTVcsJFs+7dRrKefA9Yovc3uz7g6cj91tEzv29uC9EA3DMFxCfbQNYWIUkYdFpF5EhiwvKz5+KSKlIrJRRJYMtZ6LTXqGYYSX8Gl6jwC4eJjllwCY6//cBuC3oTRqk55hGGFjsBpaOJyTVXUFgKZhVrkSwGP+AIr34PMoKRhmfQCHO+GAAtF9gaPN2uzUyChg3Sfa69SQyGCd5vR57Of3QQWLYN4YrhPgTvHR17LfW5RT4yK+jc/Mtp9zLK52zyY7wand2jqHdZToY7iYafGP2besdQHrTLkfcn+rz2LdaM4Du8nuzi4me+9i7k+OUxOk6A0e/x03soY3+2nW3Ro/y5pl8T8a9v0uTXxsefHsqx5Xxfnfumfz2MQ5tVZ701hv7Cpw6tg69UWytrFPZNVVbFcu5fUTV7Jd+ArHTW+/nfvnddzyJIrHMu9tvrhqz+Lj+T+fv43sWT9kJ881U9iHs6uQ289dy3bNv/C13rKY/Q5Rw3V207c6dYQz2d7zn4Hz0/+1MYpyh0/TKwQQ/E8wGCRRM/TqPizLimEYYUU05FkvR0TWBNkP+IMZDik26RmGET50VC4rDap64hj2Vg0g+PEmpCAJ0/QMwwgvh885+XkAN/nf4i4D0Kqqwz7aAof5Ti+my4ucDzv22fUnsYblcXLzT13J9qk/e5/sP684lWydyrpG1mae06c6Oc4q/8R+fFGObtN0HJ+ZtLXsG9aVz8vdbziNdvzw3ub4T1H2i0vZzb5orbN5f6m7uP3Gs1gHyizh7dvm8AFlvcLHv+17s8gufJ0PIKqT20ur4PYqrwzkP8zdyMfmiXdqCHewH1rSpj1kezPZhzEhljW9utO4/dwNTlz2R7Vkx57EmmJ8LY9l60LePrGBNbzTTt5Cdk0X77/5cWfs3+f9153GNTcqrmONr65qBtnTVrAm2ubUbG6byePZOZ37f9KCMrIHvLz+TWewj+U3n7mR7N7qwPh7+8dW9zZcfnoi8jiAs+F7DK4C8H34sjxBVX8H4EUAlwIohS/4+ZZQ2rXHW8MwwkuYJj1VvWGE5Qrgi6Nt1yY9wzDCxwRJOJAgIu+LyAYR2SwiP/T//RERKReR9f7PokPfXcMwIp4JkHBgMF18h4jEAlgpIi/5l31TVZ8eZluiPzUK1ecEtIquaaxz5K7mOTjlze1kf3jlDLIXgHWh2vvYz6/5KK65sfcd1vCyOnnkG49l3y3NZD+6lD0c2+uJZ+0jqZbb23sBa2IDyexn1+3Epya+zb5b6VGO5vgWO+7t/B/2m2ufxrpVxjbuj2cv+6LFN3J8Z7ITK9yX5+hK07n/Ra8F/Pg0xtHwtrMPYfmXOQVj8Ss8djXfZD0253duXDUfS8Fr7PfXeTTHTaeWkwlvDG+ftIsv/cZjeflj01eQveD+L5Cd5uQu3HUN+8RqDB9PZi77PGYmsY9kbx73v/5EvhZnPs/rV2bwud7xxDyy3Zx4X1/CGqR7t5NQEzgf4rj8jYZB5+RIJpSEAwpgqHTxhmEY+yHeyJ4eDipdvKqu9i/6v/5A33tExE0laxjGZCOMCQcOFSFNeqrqUdVF8Dn/LRWRYwB8B7608ScByALwraG2Da6RMdDVGaZuG4YRqYg3tM+49U9DDxnxbSDyPQBdqvqzoL+dDeAbqnr5cNumZBbponO+us+uu4Fzkg3UcKxsTLtTo8KxE89ijap5s1M3t8vR6Jx4ydQlnA9Pn2Nfrb5U3j5zB/tGJb9fQXb57azBpewe3o+vP5nbT6lhjbPqMrbj6lioyV3HDcY3c/96s1i9cPPtJex1zr1Tk6NxCbevsWznrA6033wen8vCx7mvKZs5v1t/AecG7E9jvbA3nfvacDx3zpPAfZ/3A/ara7zyaLLrT+OxTN/ijI3jtxeXycfTX88a46xnWPhqPJo1trRd3J7HqVHRPo2Pryd7+P/DPscHNda5Ftz6J8m7+X6mYwYff/FyJ5b31EB/qu67Bz1Vu52rITRSsor1uPPvCGndVX/5xtoxRmQcFAebLn7bYDYDERH4yj8OmfPKMIzJRaSXgBxLuvjXRSQXvvuD9QA+dwj7aRjGkYACGOXT4+FmLOnizz0kPTIM44jmiK+REVaiBAMJgSfqnKdZJ/na//0z2d9/+F/ITmjgb5CcO1inaLqdly+7mJ+4V7zHOk/PCtYAO05k3WTBLzgectsXOEdZylzW8NKXcX6++lkcr5m4nV9we1gGQnQfqw3Zjt9izvoOsqN38/40nf3qmhZy/Kdb8yP3pkpefi7HaqfsYbklppt1quozA7rStEdZoyq/ls9F1u187E2OJpX1Pl+Kbj2P3A/5XLuaWPv5C8huZhMZG7n9fg71RVQ3j/XM+3l/nhT22fTG8fptc/g/3ZMw/L9Wx0xuP6mKjyeKd4ccp/97zudzkf4Rj2dSPfdnIJHb98bx/tOCQnej2L10VEwIPz3DMIyQUT3yH28NwzBGg93pGYYxubBJL0B/uhd1lwcEg7h41iW+sfI6sqdU8+g1LGZ7+i0cKxq3PJ/skhaueZFSwTrMgFNCo+B11j3KfsA61Mz7ub/9KU7/klhDi8pkXSX1DNbg+p/j/jUv5PY0h4WdptNZt5n2xDSyO7/IGmT6b1ij7Mzn011Sw/3N/AzXAElsZt3HG8v7Hzg6oDHG/JM1uKRyjiv2bGL9NGqmo4E5NYnjW3gsak/j5Wlcghi5d3A+ub4HWW9tuIBjV73tfCyJNTw227/CgmtChVskg82ClY4f4Puc/HD7T/n4c19yY4u5vSgn/rVlNl+7RS85155TFze6jzuY7PwvTf8Gx7W/szFQg9qzfGyzlt3pGYYxeVAAER57a5OeYRhhxVxWDMOYXNjb2wCJcf04tjiQA2/PH7hGA6axDlF/BgsbeStYc/uom/PBeRZ2kV3bwH5yMzdwPOXuc1mncevEoiyFzM6vc6xv1qc5djfpZd5/6cMLye5+hTW0/gtYk0z8gJ3HprzsxE8WsA7VOtOp/Xo3++l15fN4DSQ68att7LjXcSn7AcY/waJn7RnO9h2B7Ssv477lbOSv+46pfCypZWxnlPG5TipnfbJlPsdFZ21nZ7Km/zeD7O4Fzrl0zBnP8z9m1DeqyPb8nPPbFf0Xx/aeksH1Rh7byWHnpbdxDeKsV3j/mZv43LfOcXw667l/rUezZphSzee2N8fRX538hol7eQC2/54dGaPODfrfiDZNzzAMIzTGOW1UKNikZxhG2PBFZET2rBdy3Vt/ItEPReQFvz1TRFaLSKmIPCkicSO1YRjGJMAb4mecGM2d3lcBbAUwKDz9FMA9qvqEiPwOwK0AfjtcA549cWj/YcAhqeEaPvL597Jm1nICa2DNR7m1VJ32a9i3KsrDOkZ3Du+v6A3HD24ha3ztji9Z3ANZZJd+i/3svE650IJnePv+JCceci1reFlO7GtVOvsdppdy+53T+Bu1O5+/d5KdWu9plexn2MKhyOhp4vFzc8CduIyd4+ruDGiycc18MvYuYT3U9Yl0cw3G/+MDsiu/wzWNM7c4fn1O7GvlxxzRzqlREVfBx5a4i/XYkjrWDNNm8L/GexUzyN64hvXavHLWc/tT2A9v72k89kl7eUC8ccPfHSXu5v7Un8XX7vxf8f6ll4+/7R7W/Go38/9W0oZAf6O6Qr4XGpIJcacnIkUALgPwoN8WAOcCGCwK9Ch8OfUMw5jMqPr89EL5jBOh3undC+A/AAy+HswG0KKqg19fVQAKh9rQMIzJRaS/vQ0lc/LlAOpVde3B7CC4RkZ/v9XIMIwJz2CmlZE+40Qod3qnAbhCRC4FkACfpvcLABkiEuO/2ysCUD3Uxqr6AIAHACBpboG2/Hug/mfOM6yjaDLrLgNOPGZmCes6TQt4zo5vYDuzhHWMzpvY96s4h2NhG57i2qxH/YQ1rJLvHkV25jzWhWL+xJqfmxOu9xMtZOf/guNTq1JYw+vP4v53FLs55xzNsYWPv8/JGZdw//tkZ03lurlNS3h/yTWsG1Xdw36RaeUNgb4t4HOZ/wbrs5VXs4a09yTue/bf2U+tazprYNOf47HbfRnHsj540f1k//Brt5Ld5viA9hawT2NSMmtiHcV8LcbG8tgUvtLE68/l/mdtYz/C3N+uI7v6W6xZRnNoMPKWc67D7f/Ofn+Zaxyfzfl8sts/yX6AMc+z/uw5lse3M6h571heSWr4IjJE5GL45ppoAA+q6l3O8s8A+B8E5p77VPXBkdod8U5PVb+jqkWqOgPA9QBeV9VPA3gDwLX+1W4G8Fxoh2IYxoQmDHd6/vIUvwZwCYCFAG4QkYVDrPqkqi7yf0ac8IBRuKwMwbcAfE1ESuHT+B4aQ1uGYUwUwlP3dimAUlUtU9U+AE8AuDIc3RuVc7KqvgngTf/vZf6OGYZh7CNMLiuFAHYH2VUATh5ivWtE5EwAJQD+XVV3D7EOcXgjMtpi4F0e0GJ6pvLixuNZl/Cmsq/RlLdYx4hv5u27l7GvWO0x7DgXu5Y1tz0beH+9S7i9ukdYN0pcwTfG3u2sYzU6Oc2yNvHJT/wz13qtOpd1ppgOtmWAT0/BKtbYOqbyeLgaXg93DyV/OIHsjA94f7Pm1JLd/HXWtaKfdjTYpIBfY8sc7mt7EWt4bn689DLe956b2Glw9pNOTeR09nub9jRLyF9Zdj3vr4DP/axP7CC7qp3PhWcla17XXreS7Kf+eRrZ5Zz6kWpMAEDLXB673tv54ohxCqYWvs3H23YSJ9iTfh6vlpP5Woh38v31l7LGmHA+69lRnSzcxe0IjK+w4iqx5gAABVhJREFU3Dc6FIAn5EkvR0TWBNkP+N8BhMrfATyuqr0icjt8rnMjFiyzMDTDMMKGQEdzp9cwTLHvagDBb2/2e1mqqsFvEh8E8N+h7HRsrteGYRgu4XFZ+QDAXH+4axx8L1GfD15BRAqCzCvgixgbEbvTMwwjvIRB01PVARH5EoDl8LmsPKyqm0XkTgBrVPV5AF8RkSsADABoAvCZUNo+rJNebNsACl4P+HZ5E1mT2n4r+63F1fJyL5vIuox1nfhLOXY1601ub00U15SQD1kHGZjLvlp9b7Gml1Hm+P1NcfwE57Nu0qSsqyTvYV0mfYdT5+AEbr/oNV4e3c3LezNYl2mbz2LMgu9XkN140WyyM7e2k11eyEE1Ub3c3ziWwbDzk4Hjy3+P993u+BS2zeZjydjBem18i5Orz4mtbTiWNbLCp1h/jH2T+57YxM5ipX9jH8P2Y1gTm7OSHeXWrGCBN3e6k3+vn+2eTOdaaOLlU37PF29/Co/X3uOcuGeWMJG51akj/Az3f+e1fC3ENzl+iYW8f6nja/87n3pq3+8/fNoRy0eDImzJBFT1RQAvOn/7XtDv3wHwndG2a3d6hmGElUhPOGCTnmEY4cUmPcMwJg2qgDeyKwMd1kmvLyMGu64I6GTpZTw4ha/zN0T1hax7RG3n7lZWO5rbrez418zhmPAex+0nb+PY28QPOfY1azvvP7GKEyZUn8c544ofYnvP6bx/GeD9p+528vkdy7pLTwbrRC23sAYXs5x1oOKXeX+1V7OG51K7jONPEzhcFvn3vsvrf5XjRaesCZy/upPYLy5zK5/b9J2sR8a1cGyqJ4HPbescFrUK3mKdydvGY9Hn6I2tx/H+pJvHvnAqx87Wn8jnPnMba46p5az57bjN8aFc7mh2SU4d3w0VZJffzTWGM1fy+LXP5vFL2OtonMc5195r3N/qM7k/0aU8nv2pPB53rgvU+NjTxTV7R01kz3l2p2cYRngxTc8wjMmFTXqGYUwaFOOaFTkURA/jrCwiewFUAsgB0DDC6uNJJPcvkvsGWP/GQqT0bbqq5o682v6kJ+TrqdNuDmndl3f899phwtAOGYf1Tm9wIEVkzXgcbKhEcv8iuW+A9W8sRHLfRoU93hqGMWlQAJ7Ifn1rk55hGGFEAbVJbyhGkzNrPIjk/kVy3wDr31iI5L6FToQ/3h7WFxmGYUxs0uPy9NT8G0Ja9+Xdv5j4LzIMw5gERPiNlE16hmGEF5v0DMOYNKgCHs/I640jNukZhhFe7E7PMIxJhU16hmFMHjTiY29t0jMMI3wooOacbBjGpMLu9AzDmFSYpmcYxqTBXFYMw5hsqBUGMgxj8qD2eGsYxiTiCEgXHzXyKoZhGKNAvaF9RkBELhaR7SJSKiLfHmJ5vIg86V++WkRmhNI9m/QMwwgbCkC9GtJnOEQkGsCvAVwCYCGAG0RkobParQCaVXUOgHsA/DSUPtqkZxhG+FAN153eUgClqlqmqn0AngBwpbPOlQAe9f/+NIDzREQwAqbpGYYRVjQ8LiuFAHYH2VUATj7QOqo6ICKtALIxQkU5m/QMwwgb7Whe/k99OifE1RNEZE2Q/YCqHvKU+TbpGYYRNlT14jA1VQ2gOMgu8v9tqHWqRCQGQDqAxpEaNk3PMIxI5AMAc0VkpojEAbgewPPOOs8DGKwsfi2A1zWEoj92p2cYRsTh1+i+BGA5gGgAD6vqZhG5E8AaVX0ewEMA/igipQCa4JsYR8SqoRmGMamwx1vDMCYVNukZhjGpsEnPMIxJhU16hmFMKmzSMwxjUmGTnmEYkwqb9AzDmFTYpGcYxqTi/wPk0AiHQ2hg7gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:34,132  SpectraRegion  INFO: Processing Mass 14651.815799999984 with best existing mass 14651.133720567635\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14651.815799999984 [('Ccl27a', 14651.815799999984), ('Vbp1', 14652.91139999998)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:34,479  SpectraRegion  INFO: Processing Mass 12579.54569999998 with best existing mass 12579.09381867344\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12579.54569999998 [('Ccl19', 12579.54569999998)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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E9qTWXuoss7cGSuy4cVob5F419n3WqLyx3Ik9n7JGlVvCfnuazBXFdl3NdTXSNnIFsrJbnboUz7A2kvMv1qkiatlum8nxo66u03gtx59GOuUYU9ewzpP6hhMf6sTPehs5Z9+mh9k3bfwrvP3uk1kXS9zO8bbj/8i6VtvTHJsc9xxrko4XJaoX5pE9/dpNe/8XR8/0ZvMPX8sEPlcpb3Fw8Kb/mUF29G5uW/5Szje48zTOb1j0Mp/rXVO4PWM2cV/sLnLq6fyBNcO4T9gPsnsq+/Vtvomv1d0T2I9ywo9Y/3UrG9YezceX+49qskuv476Or+XjyfmY219zohMH/yZfG7ucOPwJf2HNMrI6cG1GtDtOlINAgVHrWrPfQ/j+ptY2DOPgZU8ESiifkcZQ7gz3K7W2YRgHN2Eu9jRsDKUGyn6l1jYM4+DFl89w5N31hcKwutbENvZg4vMB/67aY1hHcuM9O77OGlxyBes6O49m36zc5azLRNazLlJ0bS3Z3lmTyXbzFaZuZl0nqp01TnFikXElu1tm3cm+X90ZrPtsv/RwstNKePuxTnysdDvxtFP59OW/yZokyjmn3rZvci3i6L9y+z1FrIF2JfEvfOQrvPnqrwT6K3sF9/WOE7nvcj5mTa/6Kxx7G+d4qiZVclt2HsNx3Blr+WLpzOD5Lfnc9qgOvlYSanj9uC3cgLrzOH9gxkru2+k/YQ1To1gnczMC7jiO25f/FuvBdaeyRtg9ho+/+Kcc29w4z6kps52XT3uVY5n1y3ytwcvnvvasgA+u5xW+bgdLuB6BRWQBgAsB7FTVw3uZPxu+qnh7EoP+VVXv9s+bC+BB+IrVPaaq9wy0P/MzNAwjbPg0w7A9Jj8B4CEAT/azzPuqemHwBBGJBPB7AGcDqASwXEQWqeq6/nY2Oh/uDcMYsXghIX0Gwu+m1zDggvtyPIBSVS1T1S4AzwK4ZKCVbDA0DCNsKASensiQPmHiJBH5VEReF5E92ks+gG1By1T6p/XL8D4mq5LOtptDhZG9knWPuAbWCCPu4HjU1Mf5+KLf5DoV3lgOKO2czZrZrmLWkSLbnXjaz1hz0zbOqbfzcq4xkvIg60K7HE2veTz/9ngSnfyHHzsBr058a/p4judtd0oJ7zyFffuyS7eS7WqyOctYtyr9CscXj2HZCbtYRsOUhwKx4TXnc+xw7sMfk916EftIppVxY2LeWE72jv/kuO60Ur4WmibwuUvfyH6Eha+wBthwFOvBSY2s+dWexnHi7Tl85xIxk/s2wuP4kDpx2y1Xss/ruPncmT2T2Ce08TyOzS78M+t23lP42u1K5vY1Hcv9GdXBfpsZj7HfY/11rMdr0KU6VMlvENElmSIS/KWd7/dLDpVVAMaraouInA/gbwAmD7BOn5hmaBhG2Bjk2+S6oWS6UtXmoP9fE5GHRSQTQBWA4F+bAv+0frHB0DCMsDJcWWtEJBfADlVVETkePtmvHkATgMkiUgzfIHgVgK8MtD0bDA3DCBvhrIEiIs8AmA3f43QlgLsARAOAqj4K4HIA3xQRD4B2AFepqgLwiMgtABbD51qzQFXX9rILYlgHQ42OQEd2IKZy0tNcd7jsy6zL6AQnx9xyjg3Ob+L523/EOpNbV6Lglx+QHfdl1nU8cXwSqy9iUTPnA25vxh9Zh6n9BuswWY/y/JpHuY5GTqHzoiyCf1G94ziHXeaTq8huvJJz5HWO4fbX3MjzWwtYk+1ZzZ4GySdy+7sdXaroddZMO6YGcuJ1pTgamxNn7YarVl/Pml1qtrNvdkFFYjn7KSa+vY1sGZvDtlMHGVezhlj+Efdt9kq+lnzfoaD2vch9X3ErP915Y1mjc/XZ9b+cyvv7kLff7fiUtmfw/OZCvpjzlnBce/bDXNPEpf4m59r8kK/lHacGffeGTzPsF1WdN8D8h+Bzvelt3msAXhvM/uzO0DCMsOFL+28RKIZhHOqohNNtZlgZUOkUkQUislNE1gRNSxeRJSJS4v9riQoNwzjok7s+gX1DYm4D8Jaq3iMit/ntWwfakCdeUDsr4B9WtJbrUER0sS9Y/HusO2WsZZ2pJ4bH8vYs9v2aeh/rKD2Hs19g/eF8QrzxvH7WYRzL7FnLdTGaHF+t3KX1vL/j2Dcsf4lTt7iGhTFvJWt4ec+wpljRwrpY25u8PY+TgDC1lDVC8TjLz2HfP9dXb8v1bi0M3kHekoDfZ9R01uDavsAaWfLrnN0tdRU7STacwHHbhQ9+SvbmBezkOPEe9tPbeQz7SGYtZ00s+jGeX+DU5O5O5K9C6la+1jb9L9dhnvI99mndeSP3ZfZyjtVOe5d9ZEu/O5HsjOV8N5W+ituvsTy/8mKuYZK+LoPsxM/ZZ7U7gc/91sv5u5ZSFjjX4pRfGSyj9TF5wDvDPkJiLgGw0P//QgCXhrldhmGMQvZohodSPsMcVd2TmrcGvtyGhmEYI3KgC4Uhv0DxOzz2WVswuAZKVIpJi4ZxMBNOP8PhZn8Hwx0ikqeq1SKSB2BnXwsG10BJjczUwoc+3zuv+Sz2zYpjyQ1Zq9i3LKqOdZj13+cb0qJX2Lmr+aQisutmsu4y4Veryd5wP6dMSzmfNcfS+04gO2E7n/TtZ2aS7WHJc5942e1n8fK7vnok2RHXO7WDv8q60CQn3rV7Fscub7mQY6Vzl7EYFNXG/SUrWLNMmsl+kbnvs47VUxYodJ39ENcMcf3aEp0az+65m/Ik68ct57LeOvmOHWTX/s6pYf0U/x43zkwjuzOFFaGIbl5/+yl8beg49juc9gs+du9RrIlGc/PRle7EqR/DGmFqCS+fVsoaZfMMbn/KRo4jd/XhK+7jJPOLZvC10v0FJ7Z6Jevfwfkao1pdn8tBoIBnlBaE2t9WLwJwrf//a+FLsGgYxiHOQa0Z9hEScw+A50XkBgDlAK78dzbSMIzRw0gc6EJhwMGwn5CYM8PcFsMwRjmHoma4X3SMjUfJ9wO1L9I2cKcl1nB8Zsm3WdeZ8AT7RmUtd57ye1jrSHjpI7K9V3L9qu4T2O8w+19OvOjZ7Ds27b4KstsPc3LgZXJ3NuTy8XWns4g4ZiPnK8x/mXWxsuvYl674do6t3uXkzKvh0Gzkve9ohO1s1x7Jvn3JWRzLHN3GOlxEA2u2NdcH+id3Cdf9zXic8xk2Xc41O2IauW+aJzh1jpdxxqXq8zguPeX3fK5rZ/G1UPAr7iucOJPM8vPYx3PC31ifFo8Txx3P12Lpd/hcT7udY6Wbj+Ncm1Hs1oinf/Ybss9663tkFz3P34VT/u8Tbs9M1iQfTKLM95iUxvpvRw33b+LxrEd3LQvM1/KhRZCoDYaGYRjhS9Qw3NhgaBhG2FA9iDVDwzCM0BF4e0ana82wDoZRbUBmkPTRnsm/IO1ZrFVM/ebnZO+6mHWf9Bc5frX6RvbTy23k2rzFD7AO1Jnl1JlgGQYR3bx8yX3sF5gYzzpT6nyuFVz4ButaHdlckyWunjXDzgkcr1v8CPvuYTqXd0h6fhlvbxKLhol/Yd2s5rs8P38h+ylWPcEaaN4Vm3n+NziHX2RnkKbYw3216WFHb32E/eQSK7nvt53DmtbuQtZLx77PfY1ln5GZkMt+jSd/yn37xj2s1479F88vmcfzJ3+X+7bm+9x3dx37PNs/4YjUtJV8LXexmyXm3fXfZOc59Xe6k9h+46enk+2dx9+dCS+ynlv6I/bhjeQSK8j7NWugbWMDdk/U0O7sTDM0DOOQx/IZGoZhAID6dMPRiA2GhmGEFXubHAI90UB7dkBcTapkX6rqU7kTe9pY6Ej7Bwd0tpzDscTJzvYiStj3q+QO1hATq3h/OctZl2opZB0p82+sc6WuY91JYzm+tGQe62BRrY7O8xw7n3lTHdEyjeNHN9zOmuS073K86fgX2U+x8m/Tye5cwz/Zm//ANV66drKmmX4m+2WmbmENtPLMwLlsz3H8ADeSidpjOda2xamZPf4n7BPadA3HRTdM577sOJU1vN9843Gy//fLl5OdHMN9HdXAwcRTnnRqbJ/bfwXLB37HQVeJfGowppSvjcozWKNrPILbU/Akz49p4Gsp+T7249w2n/XjyAanRkwVXzu7juNclQ2H8bXdmR64Nr3/xH6jGL2a4eh87WMYxggltLjkUHTF3rLsO/OvFpHPRORzEflARGYFzdvqn77aKVTfJ/aYbBhGWOnpCdud4RPYN8t+MFsAnK6qjSJyHnzZsYJTS52hqnW9r7ov+1sD5SciUuUfdVeLyPmh7tAwjIMXVd9jciifgbfVa5b94PkfqOqe3GrLABT0tWwo7G8NFAB4QFV/O5idRbf2IOfjgA4Y8RHf/W4/i+tMRDg1S7xrNpDdfiPnbIv+M8cuI541uFgnHjbzc9Zl6g9jXSrnLY6P9Wzl2OSjPmHfukXPnUp24WLWjXYXsC4U0cq6UY9zfFvuZF1s3PNOnrksPt7WqdwfBTdtIXv9vXx8E+Y7OQH/yb575beyr+C4f7DOlrMskFQv9kbuq9an2GcxfS1rWtnLuG823csa4cT/5prT5T/lvhh/F/tQ3v/BVWRHdrJGFlXOdYa9dZw8091+cjnrq215bGcv53Pf5tRZ3nEM68uTfsXXeuVNrHe3Z/L2PPGs6W1aV0z2I3ctIPsHC28ge9wS7m9PPMdip2zl/tl6VGAo6OGmD5pBuNZkOo+w8/35T/eHGwC8HmQrgDf9iaf/EMp2Q8las1REivazgYZhHGIMwrWmTlX7f1MVAiJyBnyDYfDdyKmqWiUi2QCWiMgG/51mnwzlBcotfvFyQX+lQkXkZhFZISIrurtb+1rMMIyDhHA9JoeCiMwE8BiAS1R17+2+qlb5/+4E8BKA43vfQoD9HQwfATARwJEAqgHc19eCqjpfVY9V1WOjoxP7WswwjIMARWgDYTgGQxEpBPBXAF9V1U1B0xNFJHnP/wDOAdDrG+lg9uttsqrudWgTkT8CeDWk9SIFnWMCgkT0qRxrnPe2k0dtC/sJRk5i3ST9wk1ku3U32o5lZ7a8f7HfYvMELiTRNpZP0JarWY8tfoqbt/hRzlmXtZV1sNojWXzJ/IxrjtSexrpacjHHPhe84/ghxnP/NB/By6cs5Zot1VewL1rBK+yHGfUW+/Zt/BNrhEXPcXsrf8Drj7s8EL/b3sk/vC1OTeokJxa5J5P13LH/ctqWx3WB3RojpfdzLsfESv5dz7uf49b3icv+B2uGWat5/5FdrOF1pLO+6onn/UV28LNh8i62a77CPq5Zn7BmV3alc+1H8f6nfoPr9dx59fVkj61g/TuqnMsSjcnh/m4u5vMRH+RzG8GX8aAJVwBKH1n2owFAVR8FcCeADAAPiwgAePyP3TkAXvJPiwLwtKq+sc8OHPZrMNxTDMpvXoYQRl3DMA4BFNAwudb0k2V/z/wbAdzYy/QyALP2XaN/9rcGymwRORK+H4GtAL4+2B0bhnFwMlojUPa3BsrjvUwzDMOwRA2hIB5FXF1A2yg/n1+oFN/Lfm4RyewbtWtWNtmJZez3l/GpE1tc5NR9WMUaZOO3nbrLv+P2NhezprjlGtYQ00pY1+lKYd0nZznrQrsLWafJ+Ivje/Yk5/Br38zxpd4U1rUmPMd+h5LAulDee6yLbb2U/RAbp7CONv0XHP+66eus2025kfuv9JdBGq1zMzDpz7zvuuPYJ7L+SP7GpDr1cKrvczTD9bz9yLGs/7bE8rmKmMVx2d1O7LDHiQOvvJD7NqGUz9X4F9lPsXUq67Vu7sumSbx+3IUcN16+jnNXxtWwBpmymfun9gauIZO6hfXcinM4tnrSGj6euJ18LSZUOXH8zQF9envj/tdNHs2xyRaOZxhG+FAANhgahmHYY7JhGIYPGwwHxhsXgcapAR1v4h9Zg+pw6hi7vldZH7KuknAix3dWnc4a4fiXOWHFjvPZT7F4Hse/Nl7LfoqRXXxWPQlsp31SS/b62zlnX/Y7rBtFOb5o3sMnkJ3wEsejRqQ7+R1j+PijdzVz+3J4/+15jo7GMhFaJ7NDWccE1hTT3JyEl3FdjaK/B3Smdqe+S+MsDkrqvJTr9CYu5fnZK7mGR8xux0FfWJNrb2Y9eex7vP3SedwXEV4nl+O3uC/HP5otUX4AACAASURBVOPUcR7Hy6//oePTmcvtzb2URc2MLq5BXZHPendPPmt4SWu4/+IbWLeruYj9CDOv5PpAqQV87VZczz6mha/wd6GnZCvZ3u7AtaDKbRscEjbXmuHG7gwNwwgfai9QDMMwfNhjsmEYBrCPn9UoYXjrJu/qROYbQbV4nXyDUW+vJDtlJvvBdbIMhMZprLEVPe34EZ7EscNNLEki4VKOp21k1zTkLOefuEm/YRGtp8Wpo3Ed1xmOiOPjq/0q52vcfhrrYvn3co6+yEzW8KqumUq2dLCulPAw+/Y1PzKR7PZc1t0imvn0V57BF3HBW6wp1pzEulb26wG/xI5MjgOPYDc44F3WCHM/5r7bdTf7DUYs5L6hGs0AWvjUou5oLkwcvZuPJWMdC6atR/D+Esq4bxI/47jwMSXskxq12+m7O5zck4u5TnT6OtaPd7fwtZH5PMdS11/BcfsRn7Gj5NZfsEaYvJVMZK7hE1B/DF9LsVP4y+SNDvRXzxKuGT1o7M7QMAwDNhgahmGEM1HDcBNKDZRxIvKOiKwTkbUi8l3/9HQRWSIiJf6/fSZ4NQzjEEJD/IwwQrkz9AD4oaqu8idMXCkiSwB8DcBbqnqPiNwG4DYAt/a7pegoaG5Au+hZW9LPwkBKOes83hjHF6yYx/KqSzi2N/9l1hBT17AOpbHsx/i9X3JW8EfrLiI7YQbrYpGfsUZYeg/7liWVc/ui2vkKcHW1TQs4A3rBK9y+/Nc5R51G8+kre4F9y6LjeH9JW7n/xv6Da+3UnM7xwzHvso5VVMV+mltuDGiS7UWsL8aXs0YW20gmulN4ftff2A8vpZE7J34z66EtY/PYHsfHVvhT1l93fos1vbwb+Nh3n8o+n5EdrCHG1bKG2FbAenVyBS+/7Q4yEfM2Xws9zjev60QWtMc8wT6wep2jEVZx/+w8ivszmS99pJU4CSGXcR6AlitOQNgYpa41A94Zqmq1qq7y/78bwHoA+QAuAbDQv9hCAJf+uxppGMboQTS0z0hjUJqhvzDUUQA+ApATlOC1Br7ssr2tczOAmwEgLjq1t0UMwzhYGKGPwKEQcg0UEUkC8BcA31NVigNT1T67ILgGSkxUQm+LGIZx0CC+x+RQPiOMkO4MRSQavoHwKVX9q3/yjj3p/0UkD8DOvrfgw5MQibqjA/5Nu77MOdomz+fau/E7OB6z/AIeTKOnsy+XvMV3nl3jHD+9M3j98S+xbvSnBy4ke+wfWXfquJD9EptuPILsjE/592DMMyvI3nIX1xgZs551pvyHOb9h1ylcN6M7l33NuhP49OV+yLHKJddw/O7EF1j32n4ma4RufO/GB5y6yYv5+BK3B+z8d9nnMWYr50bsceoUt8/hvst5bh3ZksRtb5/G+Q1Tynl/SdX85ZJjOW69g0OLseE+1pez3+T1k98tJVvbue/io6aQHSe8fuSz3P7i7/PxLfuANcLuJNaHK3/GGmHRj1lDrPs6z8/9iL8rDdPZJ3TMJr7WtjzB53bCwoAGGeEZ4q3dwXpnKL6qKo8DWK+q9wfNWgTgWv//1wJ4OfzNMwxj1NET4mcA/GWId4pIrzWWxMf/LyKl/rLFRwfNu9bv6VIiItf2tr5LKI/JpwD4KoA5IrLa/zkfwD0AzhaREgBn+W3DMA5l9iR3Dc9j8hMA5vYz/zwAk/2fm+ErYQwRSYevVtMJ8NVLvisU179QaqD8E30HG5450PqGYRxahOtNsaou9b+07YtLADzpf2exTETS/JLdbABLVLUBAPyugHMBPNPf/oY1AiXCo0ioDfgONsxhX6nWw/iFdMUVfC+d+S5vL+OXW8nuaWVfqrqbWVfJ+oR1po3fYI0x959Ordu/cbBy4rN8Iz1mA7c/9vXlZDd/mWv7ulpK6lMcAypOXeiKc9h3zJvDvnxu2rniv3C8a8Yn/BvWme7Exx7DOlPrVta5Jv8n11Uu+R37ok39Y8B5sGMs65llP2RNLuPTQrJdP7pax48u2vHJjNnNPqcVl/K1kZTBscZJPyUT419jPTWiia+VnbNZkyz7A9e76WxmDS6mmusoZ37uxE5/hfXsDQv4Wpq0mvMhdqU79WuWObkvZ7MPa+PJfPIz1nB/5D7DGiWyWD+PKefjrT0y8H/32iG+3Ah9MMwUkWBhfb6qzh/EnvIBBHtUVvqn9TW9XywczzCMA0Wdv+j7iCBk1xrDMIxQGEan6yoAwY8gBf5pfU3vl2G9M+xOENQeFdjlxIf5Vn/7afzoMe2XXF6xZg6HbDVdwu4ZTZfxo0/2Qn60it/B7hHZH3J43i4nvC/mTdZcu520/81X8qNZ1GQO+RInzb6bdr/7HP5RrCvm4y9exO3dNZHT+Kc9yY+a2/4/3r/7uNKRyadbnUfR6pPZvWPyJg7vc/GkBtrTmcrrTn2YPa2qz+XHsvZL2E0p+x8cP1Z1GT9WxzrlK4te4LZ0pXJZVdm2iey6izn9Wfa7/Jia8Rj3ZVwTSwIJ21lS0Eg+mdE7+DE8+SUnHm4m9+WOE1iicUM10xd+THbZL7m/Jl/L7XXxHs/fjY4cvnYSqp3H8Pgw+v0Nnw/hIgC3iMiz8L0s2eV39VsM4JdBL03OAXD7QBuzx2TDMMKHIiS3mVAQkWfgexmSKSKV8L0hjgYAVX0UwGsAzgdQCqANwHX+eQ0i8jMAe0T8u/e8TOkPGwwNwwgrYXybPG+A+Qrg233MWwBgwWD2Z4OhYRjhZZRGoAyzaw0QvzPQUx1OecmObL6/lm7WiZKrHNHNoeAhPpz66axj7R7HriNuSJfrztE0gdf3JLAWkvgS61QZqzjkbP23WRcSD68f8w6nUdp9PYdo5bzPOlXCTtYUqx3Xn/F38pPAjpNZ80zfwBrt2Ad5/3XXcXhk00x2xyh8nfu/9NpA/0y5kd2EXM2qeSKf2+yHWBPjMwHENbBry46buC+wmvu+4C3Wi7uddGsJddz2jd8aS/aElzgNvltmYPMVrLlNfqqF7M4CXr/sNu57ieDtpXCFC4zZyPpz91lcIiKqja+d0v9lt61J3+P+bziMr/XMT9jVJ+4Vdr3xnBkIz9vadmiG49mdoWEYYWOkpucKBRsMDcMILyMwI00o2GBoGEZ4OVjvDEVkHIAn4UveqvCFzDwoIj8BcBOAWv+id6jqa/1vDOgJKkkYV8saVuHr3Jy6U1nXyVjFmtiWK1jT8saxxpe1is9K42T2I0zfwDpSVDvbBQ9x2vv2M1kHqzqN2xvhYZ0osbx/n/bmL3KI1YR72fetejbnncpexTpV16e8v57POOVYwhT2lYupdXS1k/h4YptY19t+Jvff9PtYE532UCCEbOsLvK383zs+ky9zKGHj1zj8zsPRaMhYyxphs6MRpmzltrbn8QaqzuDtpX/G5yKxiu9eKuZyejdX383+mPdXMZfbM+7n3PeTO48kuzWf29cTydurPYp9XrucPMjKl/a+GuH13J+Zz7EerNOKyG77Il8bSYsDiWEi2hx9dpBImFxrhpuh1EABgAdU9bf/vuYZhjGqOJg1Q39q/2r//7tFZE8NFMMwjH0ZpYPhoGKTnRoogC8U5jN/EsZe84WJyM0iskJEVnjaW3tbxDCMg4mDuFQogH1roIjIIwB+Bt9h/QzAfQCud9fzp+SZDwDxeeO0O0gaaZjOOk32yxxPGuFlXzHsqCOzO5HT1ud+xGJFYgX7bsXVcwqriC5efusF7EuWmc26T9rnrFl6Lk515rPm1zidfx8KX+f21M90dCova5ZNR3GKsAgv60oZa3j5+ptYNxqzgbWfHadyfzUe5qR9+oB1soh2trddxvHF6esD3oG5aRxHvvUC1nun/KGG7Kyt3PbtF7JfYXQ1lyAofoJjnT2VHHfvlgJN3UAm0tfyD3FUHeuvEfP53Hgv4vkVt7AmmlzB3+aSB9nvzy0Dm7WKbXVuQ8ZsZP3ck8giYcJ2jlOXZE6ZlvUKlymoeopju6P/zhpn1odcu9VzVCB2Wj9h/9/BMlofk0O6M+ytBoqq7lBVr6r2APgjfBllDcMwRiX7XQPFn1F2D5cB6LVOgWEYhxgH8WPynhoon4vIav+0OwDME5Ej4TusrQC+/m9poWEYowc9iF1r+qmB0r9PYW/bEsAb5G6V8biTj+9HrPu05XGvTv6AdZzSqx8h+9xbufxh+0VsR7c4foWObpRUwZphdCvvPzh/HwBE7mZdp+Fo1ghTS8iEJ5G7O7mCI3LPf4b7o+LPF5CdvoF1o4j3PiG79TvcfxH/Yl+znBrWYHOX8PG1TWa/xvx3+cGhLZsvg/g3A36YsoVLFkyp3kh28xmcz8/ND5hcyX2x/bw8spO287lLbmRNMama56csKydbu9jP0W1P4mwucdBzOscGxzbyrczuQu6LSc+x5hi5mk9+14kcR96ax36YrWNZz649izXE6XewXr3tJsev802en/oEa4rbr+Rrp/E0vpYnPhx0LQz1rm0E3vWFgkWgGIYRNgSj9wWKDYaGYYQXGwwNwzjkOZgjUMK6sw72jXNrduR+zLpORwU3r+wnHMt7wfGscUWxaxtac1jTyylhnalmThbv/wX2c6y5YgrZca9yrHL8Kdz+3Y5bZCyH8iJzSSXZFddMJHv+QtYIx89fS/bm/55BdkYu+7aN2cj91zmX+2t3AfdnVxrrXq3jWHebfg/X8djxTT7A5icC+ReLH2D9sXU2a3Ipq9lPcOs81gQXXPc7su++4CqyG47hOHS3LGzSFvbxbJvJfovdSXwtVM3hb+zk7bO4fV/n+RMfYH1529msyXVksW9e5+W8vdoTuH8KX+W+rpvJGmLBX/lctc7ki7vgMXbeaD2Nc2EmlXD+wvjV3H9JldyezoyArVHDVip0RGF3hoZhhJXR+jbZSoUahhFewuRnKCJzRWSjiJSKyG29zH9ARFb7P5tEpClonjdo3qJQmm13hoZhhI8wOVSLSCSA3wM4G0AlgOUiskhV99YrUNXvBy3/n/DlTdhDu6pyPO0ADOtgqAJ4YwJ6xLh/sA7TNIVjb7vZRGwjaxlufGpEIq8Qs3sc2VXnssY4ZiMHkG74Metc0+/ZQnb1Nzn2N/6MWrLTfsGxxhrB7W06jX3xxv6Wc+C5lD7DulP8x7y9jqvZtyz+Qa57cdcjj5F92x3fILvnAo5PTV3CscuIZd+3SQuqyS65MaD7RVWxX1/bMdyWXZexRpj/Dvvl3fUz9gkt+T23ZcynfOw7v816bcJOx4e0w6k5srSM7MS/cpz79v/ic5vAJVpQ9iXenhZy++Vz7quM11h/juzkayuuzs0ZyJph8mqO5UYH+x32FLKGWPElPv6sd7n/Yuu5/XWzuD8n3Bo4YFE+tsESphcoxwMoVdUyAPDXRr4EwLo+lp8HXynR/cYekw3DCC/heUzOBxD8Bq8SfaQOFJHxAIoBvB00Oc6fLWuZiFwaSrPtMdkwjLAyiDvDTBFZEWTP92e5GixXAXhRVYNvj8erapWITADwtoh8rqqb+9uIDYaGYYSX0AfDOlU9to95VQCCda4C/7TeuApOMXlVrfL/LRORd+HTE4c2GIpIHIClAGL9y7+oqneJSDGAZwFkAFgJ4Kuq2tX3loCeKKAtJ/BknlrKvRa7i3WPRCceNbaWtYzWSzlrWHcCP/Wn/oVjd9OzWDMs/TrnfJv2c84JV/sE18LN/RFrdF0b2Ndsy6Xsazb2fW5/2ofsZ7jpXtapil/m4+vp4ePJXu7Ev77POtPOY1m3+tURp5AdM5vbU93AGmt0AZ+P6nNZ50uudOJ/g2S4rf9RRPPGvs9tjfjnarIbXmUfzug/s89kxkrWtNIXOHHbd7FmePRX2Sdzy53sd+etZX3Xewb7YHq565D/Lvst9kQ7cdp38vzW7Byyz32bz/VTJZwLMuXnvL/uFO77nWfwE2H9MeyvEjGGv2rj/8zti3+Hr/2eDtYoG526y5seC4xJnXdzXw+GMJYKXQ5gsn+cqYJvwPvKPvsTmQZgDIAPg6aNAdCmqp0ikglfsplfD7TDUDTDTgBzVHUWgCMBzBWREwHcC18NlEkAGgHcEMK2DMM42AmDZqiqHgC3AFgMYD2A51V1rYjcLSIXBy16FYBnVTV4i9MBrBCRTwG8A+Ce4LfQfRFK1hoFsOe1b7T/owDmIDBSLwTwEwCPuOsbhnFoEa5wPH+1zdecaXc69k96We8DAEe40wci1EzXkf5chjsBLIHv2bvJP3oD/b/p2VsDxWs1UAzj4OcgTu4K/1uaI0UkDcBLAKYNsErwuntroMROyNeWYwN51ZpqWbOK4JR2SF7Buot2sq9V16/YT7D9zWyyo89jP73EJawrdWXx+L3pR5O4Ac6NdevFrGNlrOMGu3Uv2rI4Hnb7vaxZTrqedZ22s2aSPeWbXMije+YEspumsS9f9vJmsjfcz7HMUx9zfNu6uJZv0Suc8651LGugFefz6uNfDRx/bDMfa/n5nC9v8lbu6/S7edubvsF9mfEB66FuneXiFznwu/w91iB74vlc7XqNz23GLaz/5nezPtyRxX2TsJL9KJNv5VjflBrW5hfvPJ3siKn8Vas6k0xkr2Q9Nq6eNcHodu6vHSey3TSJ72sq5jnn/n4+9xNe4u8SvIHRqbF+iCPVCBzoQmFQb5NVtUlE3gFwEoA0EYny3x3296bHMIxDhVGctSaUGihZ/jtCiEg8fOEx6+ETJi/3L3YtgJf/XY00DGMUcRA/JucBWOiPFYyA763OqyKyDsCzIvJzAJ/AVzTKMIxDnNGatSaUt8mfgQOg90wvwyDLg0Y1RyD9rYAWk1LWfwxkxdVFZKeVOjng1rMuNPVlflJvPop9uyJS2C9w6mO8f13BOeIqXuAXUt3jeH8Jv2VNb1zz4WTHbthOdnIl59jDNNYARfnnUpJZE9x5LMc+n/Mf7A/25kInvnYr767qdNbhps7nnHdlX+Lauj28OOKr+Pgbg8Jtu7lrkbKZj2Xrf3AuxLHvsz4ZWc87c/Xj7gTed9U5rNllrWZNLPFz7vtdr/H+2yexJhe7lM997c3sh6iRRWQnfcZx2nXncm7KtCf53BS8xedu93l8bXWk8UOaRrDjY/Kzy8juTuBznbaZ+zOmmTVPXcP1cLxzOIfBtrMC/d+5ZWhRuqP1MdkiUAzDCB8j9BE4FGwwNAwjvNhgaBjGoY5VxwsRjQB6gqSQqtnsZ5i5hoWi5HJWYqtOZ90oawXbFV9iX7b895z40gz2JYOj0dXfwDpM+gs8vzOV99dyMUupjVPY1y4tq6jf/UW1si7Uncjrd03ieNculvSw7nz2q2z5LetGOa+yL1rKX1Zxc7pZN5voZd+0ntXsaNlxEUvEP7z/z3v//92NX6Z5u8fxvmOaue8il7HPZ2Esa2hVs7lvovlUIqmKr40dx7FGlpbG+mz2Q5w7suJOjm1OyeFzOfZ1zifYcDz3dVw514dpKeDcmdGXn0D29i/w8U9+igMQIrvYL3PbWazbTf+Q8xe6sdqeOZwPMvNDrjmz64ucDyGhmv0M0zYENMMaN9XiYLHB0DAMY98XgaMFGwwNwwgfehC71hiGYQyK0XljOLyDYXR9O7KfDvLn6uZg3roXOb9g6m+d2NuPWUeK8HCvp23mn6T6w3n9hiN4+YhcFkeiNjl+hKvZrxFwYo05/BSTv9N/TZNd13AOue4k7v4Op45x2ges+4z/gHPylTw5newsRyNsT2fdKXou+5Z1JfP8pilsp8xkDTV5G+tMj15yYWDdE1mzy1zFPowRtVyzet2jHIed5eRmPHfuCrI//SlrerENrHemredrqXQeOz6m5PCxFL7J9Xd6ovncVp/DPqpteU5NlVO4L8e/zLHSXVmsh5990nqyV6/k9dsz+dyP+wfr5+t/xe2Zfitf65uu5PZP/QOfj4bpzrnd5MT5pwY0yx7e1KCxFyiGYRiA3RkahmEc7Ika4kTkYxH5VETWishP/dOfEJEtQYWaB1Wj1DCMg5SDOFHDnrT/LSISDeCfIvK6f95/q+qLoe5M42KgUwK6YPtY1lWSHuIeai7i5rmxsnGNrJskvPQR287+m37NulHM57xE1mrWnXZN5B26+5+y0NGdHB2p5BrWONNXO7V/T2RxJp3TG0Kb2blOPdw+TxevX38Ba6BjFrPvWnMh92f6etaNar7A2xv/q0/JjpjI8b31xwbig9X5WXV9FLfc7fhw8qlCVLujyUXxsST/kP36qp8pInvXFNbIpj7Ay3ePzyI7so77truI6wxHdnJ7slfxtVZyHfdlyuccK901hkxEfYtjl/Uwnp+znH1Ed49n/Te2lI9v4w+4BveMn3KN76Y/8bXduZn1c08qbz9hR+D43Lycg+GgdrruJ+2/YRjGPkjP6Bwe9ivtv6ru+V3/hYh8JiIPiEhsP5swDONQINRH5BE4XoY0GKqqV1WPhC+j9fEicjiA2+FL/38cgHQAt/a2bnANlG5P/ym7DMMY/UhPaJ8BtyMyV0Q2ikipiNzWy/yviUht0HuLG4PmXSsiJf7PtSG1WwcZOiMid8JXk/S3QdNmA/gvVb2wzxUBpEi6niCB4g/e2ZwzrieGx+Ztc1ikm/xLjmdtmcOlWFxNLK6eezyugf0G3dv5irm8/tSfbyLbM5XjT3ecwJpnxlr2fetKYQ1uVxHbOStZF2vNY40xtYTjV3ccx75zSdV8PK5mWnUbx9/GNvDxNh7O/TP5z/xjFbWDfQPLruXjz18aaH90g1PTupgDqeNf/pjs+ptYQ8z4I8fatn6JY3urzua2J5Xyucpw/AzLL3JigReyPto8gfXUnmheviOd7dajWdMb9wzvf+wdXHN7zV/ZB9SNpXZ9SnPf55osW7/IGmThG6xxuj60kd2OD62r+4ljOoNR9QWBa7f6zt+jc0uls0ZoJKWP05lnfS+kZT984b9W9lVE3p9MehN8mfUr4aujPC+45KeIfA3Asap6i7NuOoAVAI6F7x50JYBjVLWxv/bsb9r/DSKS558mAC4FsKbvrRiGcaiwp5D8QJ8BOB5AqaqWqWoXgGcBXBJiE86FT85r8A+ASwDMHWiloaT9f1tEsuD7zVkN4BshNtQwjIMVxT7ZmfohU0SCQ43m+6tpAr7Sw8EuAZUA+HHBx5dE5DT47iK/r6rb+li311LGwQwl7f+cgdY1DOPQYxCJGur6ekwOkVcAPKOqnSLydQALAez3uDSsESje9EQ0nxeIz+1McfITPsq6UV48589TL2tkbRmswaVu4XjO9nSeXzmHD9ebyNsrepnXbzqba/HWnMy/eJGZrJPlv8p1ixvmcjxp/lscr9v0C9axUn7OGmTNiawRJldxeztTWeVImskaancit3f8U+x7l7vIEZZiWbPsnMC+ea7u1DkmKAfeCeyn15nB+578XirZrka47UWuH9PexMea8SHrx7G7+RtXcR73RcFibqsnmddP2erotflOneSdfC2kr+drpy2H7aavsWNh5Nm8f/faKXzdiXuvYzkrsoM1w4iSCrJ77uRcl3F/4v174vhk1c1kO3G7czKbg/rHu19yIYCw+hlWAQgWqfcpR6yqwQHhjwH4ddC6s5113x1oh0Or/GIYhhGMauif/lkOYLKIFItIDICrACwKXmDPews/F8NXwhgAFgM4R0TGiMgYAOf4p/WLxSYbhhFWwnFnqKoeEbkFvkEsEsACVV0rIncDWKGqiwB8R0QuBuAB0ADga/51G0TkZ/ANqABwt6o27LMTBxsMDcMIL2FyqFbV1wC85ky7M+j/2+Hzd+5t3QUAFgxmf8M6GPZEA615gSfzxGrWfepuZt8zbyxrF8lZrKN0Jzn5B59lP0TvXNahpj6yg+dnsCa38VusmWV8wNuPq2VVIaaE4z/X/8CNRearQqN5/da3ua5G7eXcHxrJdnw9tydjNWuQW7/Iul3Rjzm/4tbb2e9w3BLWOGtOZt/AsU9xXegx2ayhRnYF2ide1mcn/Ig1wdrrndyIlaxXdmzn9YtfZU2tcRIfe8pKPvbkv7JPaPkdrDcn1Di5G2fwpT/xefYj3HY267edxazvTnmI/f5KbmINb+KPV5LNc4G282aRveNSjl2OYhdTtH5hKtnZ/8PnbuPXefnp91SSnfIM1xRvv/g4smOaA/1fO8TYiIM2NtkwDCNkFMAojU22wdAwjLBiNVAMwzCAwThdjyiGdTCMaepGwaJAPVpvKedgK/8J60r5S1mn0Rj2FctbyroRJnENFdcvrvE49vuL6uCfsKLn+CTGvsW6T/nTrNukLWDftOgWp6ZJluPrdRTHk+Y6Oewi3uOEhqX3c82UtDUcK7z5KvYtm/RACdmSy0pV1mdOzZmjWDMds4nnd80s4u0513jCss17/++JnkTzosZzHHNbjqN3fsZ+fnnvs0YX3cxx3i2FfO7btnFfyliOCyj6G788bJ3AemjyNj6YzZdzrPLUx7mmScv93J6yL/K15MYCe07ihIXNRZzUqWWcU++mhK/FlBLWBLtT+VqruID14WNnbSR79bdZ3838hOtI75rAGmrRE2V7/y9p5u/dYDHN0DAMY4Sm5woFGwwNwwgbvgiU0TkahhyB4k/w+omIvOq3i0XkI3+usef8XuKGYRzq9IT4GWEM5s7wu/CFu+wRX+4F8ICqPisijwK4AcAj/W2gMyMaZdcEtJb8pax5dWZzPGj5eawTTfyvzWQ3fdX1XWOtw43PrOX0iUgpc3LSvbSV7J3XHEN28bc4Z92Oy1gny36SNb+6r7CO5frWSRdfEV1z2fdrzHpuf8WFrBPFsKyEjQ+wLjT1bl6gM5V9+Rpm8f4bTmXfvsTPWadqz+Ff/IYrAolAOpxciVOr0siO4FOLxhmsV6aWuLkUWQ+ObWSNMLqFN+hJ4GMrv8jxuXyY6xY3ncP6r8Zy+zd9jdcf/2vWDItb2RGw8EG+Nt+byH6Dkeu4L4ufZZ9XVw+vu8fxwa1mo/e/PQAACYZJREFUn9apD3P/7P47x5F7vu5okH9h/TvqAv4y1D8e6F/Pd4ZWOPmgvjMUkQIAF8AXDL0nh+EcAHuKQS2EL6ehYRiHMqo+P8NQPiOMUO8M/xfAjwDs+TnPANCkqnt+nkPKF2YYxsHPaH2bHEqm6wsB7FTVlQMt28f6e2ugeJ1HC8MwDkLCk7Vm2AnlzvAUABeLyPkA4uDTDB8EkCYiUf67w31yje3Bn7l2PgAkZI/TxO2BTohuYF+zGXezb1ePUzfY1Vwz/7mdbM+WcrI7/pNjcZN4NiI7+IRsv7SI7K45rMv0/JltTzxrelLMvnWui4E6fo9lX2LfNm+SU5Pk/9gP0RvHWk5MnRNE+jvOebfrgiPIbs/k3z43dtqTwLpWF7vmoXAJa547g/wUp73Ofn0bvsUrT3uUfSQ1itvScDhriDqNNbL2PO6b7iSn3s0rXGMlt5tzhkqqo1FuZD1191VkIvshPje1s9hPMP9tPjcf/I1jjVPquG93TWS7K5/zO5Zdyu8fcyJryY5ocXJznsvrd83iG434z3l5PXYG2VGtrA93PBekOTYMwclER28EyoB3hqp6u6oWqGoRfDnF3lbVqwG8A+By/2LXAnj539ZKwzBGD6P0znAoyV1vBfADESmFT0N8PDxNMgxjVDNK6yYP6n5YVd+FP322qpbBV8HKMAxjL6PVtWZYI1CialuROX/ZXtut2Vz9DfYbdGuiVP+ANUD3rVX6Ova1yvkd5/OTozheNLKONcDWw/PIjlrHOlFEGus0Y9/luhV1J2Rye/7E7a+8g9s/+c7VZG/8NWt8Vaexbjb2n6xTyRaWaRsu5fV3OnU3Mpez3Z3AIubY59iPcuPtE8iuO4J1rYSawPYialkTlG7uq403sWY3+dtc4zljI88vuZNzUV5zxvtkP5nE18qkZvbpbM/kS1sjOE676nSen7jE8ek8x6lD7GG79hg+vuDYXgDQbtZXo8+eTPYuJ1Z56p/4Wiy/kNs7+R7WRDf/nH1SJ93DfpDb53Cs96ab+dxN/y1rprEfBPIfRrXzdTYoFIDXBkPDMA5xBGp3hoZhGABG5MuRULDqeIZhhJcwvU0WkbkistGf/+C2Xub/QETWichnIvKWiIwPmucVkdX+zyJ33V735+p2/07SpmXr7Mcu32tXLuDY3rQS1irqD2Nfr5jd3NaWcTyWFz7NjoSVXxpPdksRO0Adcxzn//v0Pc4B5/olxjXy+kkvsO7l5vDrSWbdRjqc/IyJ7NdXdwzH82a/w36U3m2sEbb9nfe3rZw1y9x32dcsoYZ1rKZJrCM1fYH9Pgue43hZT0Lfv51dTj2a9DUtZLcUcV+0ZfG2Uio41tiNPY5ZyXrm9q+xppixhvs26m2OEfCewbG4Hel8bKkruK81ifVadLImJx7209v0LQ7AGruU5zcX8kNY5xzW7KL/yX6ZbXlOneU3ef9dqU4dZ6c/xSnLnPGYo7//kPXr4LjzygcfQOe2bftVPDk1YayeOPWmkJZ9c/XdK/sqIi8ikQA2ATgbvgi35QDmqeq6oGXOAPCRqraJyDcBzFbVL/vntahqUi+b7hO7MzQMI6yIakifATgeQKmqlqlqF4BnAVwSvICqvqOqeyIPlsEX/LHf2GBoGEZ4Cf0xOXNPqK7/c3PQVvIBbAuyB8p/cAOA14PsOP82l4lISElk7AWKYRjhQxXoCTker66vx+TBICLXADgWwOlBk8erapWITADwtoh8rqqbe9+Cj2EdDNs7Y/BJaUDHy3b6rLmYNTTXjzC1lOMvY3azprjpO1wDJXEbmSh4i3dY855Tq3Y6yyQ9LCuhaRJrcHFzON9hS6ajQ33OsdYbvst1kif/p+NrF8F+kFvn8Q9h/nuc/zH5xjre3jjWlTwJfHo9idz+7A/YT1KUtx/ZwduLe5V93XZ8J6A7ZX3Kem/NKew3mP/4GrKTmlkzq/2m42P6CO+rJ4qPJf8V1k9bZ3Dfbn+BfS6jP+Rr69R5q8j+19OsKUY4ZUCi27Rf262BkrSa27e7gK/Nsb9nvbYjnUW+zpM57tyTwNd68lq+thq+yMefVMXt2/IMx06jnOdPebR67/91tc7BDJbwxCZXAQgWxXvNfyAiZwH4HwCnq+res6aqVf6/ZSLyLoCjAPQ7GNpjsmEYYSVMmuFyAJP9GfVj4MuLQG+FReQoAH8AcLGq7gyaPkZEYv3/Z8KXbGYdBsAekw3DCC9h8FBRVY+I3AJgMYBIAAtUda2I3A1ghaouAvAbAEkAXvDlm0aFql4MYDqAP4hID3w3fPcEv4XuCxsMDcMIH4qwZbFW1dcAvOZMuzPo/7P6WO8DAEf0Nq8/htXPUERqAZQDyARQN8DiB5KR3L6R3DbA2jcURkrbxqtq1sCL7UtqXK6eXHhtSMu+UfLrPv0MDwTDeme4p4NFZMVI6gSXkdy+kdw2wNo3FEZy2wbFKA3Hs8dkwzDChwLwjs5U1zYYGoYRRhRQGwwHw/wDtN9QGcntG8ltA6x9Q2Ekty10Rulj8rC+QDEM4+AmNSZHT86dF9Kyb2x78NB9gWIYxiHAKL3BssHQMIzwYoOhYRiHPKqA1zvwciMQGwwNwwgvdmdoGIYBGwwNwzAADVts8nBjg6FhGOFDATWna8MwDNidoWEYBgDTDA3DMMy1xjAMw4+GXhBqRGGDoWEYYUTtMdkwDCOcaf+HG6uOZxhGeNGe0D4DICJzRWSjiJSKyG29zI8Vkef88z8SkaKgebf7p28UkXNDabYNhoZhhA0FoD0a0qc/RCQSwO8BnAdgBoB5IjLDWewGAI2qOgnAAwDu9a87A77SoocBmAvgYf/2+sUGQ8MwwodquO4MjwdQqqplqtoF4FkAlzjLXAJgof//FwGcKb6aoZcAeFZVO1V1C4BS//b6xTRDwzDCiobHtSYfwLYguxLACX0t46+zvAtAhn/6Mmfd/IF2aIOhYRhhYzcaF/9DX8wMcfE4EVkRZM9X1QNW+sAGQ8Mwwoaqzg3TpqoAjAuyC/zTelumUkSiAKQCqA9x3X0wzdAwjJHIcgCTRaRYRGLgeyGyyFlmEYA9FesvB/C2+oo6LQJwlf9tczGAyQA+HmiHdmdoGMaIw68B3gJgMYBIAAtUda2I3A1ghaouAvA4gP8TkVIADfANmPAv9zyAdQA8AL6tqgMKmVYdzzAMA/aYbBiGAcAGQ8MwDAA2GBqGYQCwwdAwDAOADYaGYRgAbDA0DMMAYIOhYRgGABsMDcMwAAD/D2hWsvqlqA7BAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:34,601  SpectraRegion  INFO: Processing Mass 14557.82079999998 with best existing mass 14557.567461341532\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14557.82079999998 [('Ccl21a', 14557.82079999998)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:34,721  SpectraRegion  INFO: Processing Mass 15271.877899999985 with best existing mass 15271.387471243903\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15271.877899999985 [('Il11', 15271.877899999985)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:34,839  SpectraRegion  INFO: Processing Mass 13065.960899999984 with best existing mass 13066.543846577173\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13065.960899999984 [('Il7', 13065.960899999984)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:34,959  SpectraRegion  INFO: Processing Mass 4892.7843 with best existing mass 4893.078363213031\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4892.7843 [('Il2ra', 4892.7843)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:35,070  SpectraRegion  INFO: Processing Mass 12904.570899999993 with best existing mass 12905.066592751478\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12904.570899999993 [('Il15ra', 12904.570899999993), ('Cav2', 12905.530399999985)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:35,189  SpectraRegion  INFO: Processing Mass 18122.08549999999 with best existing mass 18122.14011121341\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18122.08549999999 [('Il15ra', 18122.08549999999), ('Msh3', 18122.3698)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:35,304  SpectraRegion  INFO: Processing Mass 21365.61989999999 with best existing mass 21365.26738664723\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21365.61989999999 [('Il15ra', 21365.61989999999), ('Epo', 21364.868400000014)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:35,416  SpectraRegion  INFO: Processing Mass 11161.56199999999 with best existing mass 11162.017731361968\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11161.56199999999 [('Il15ra', 11161.56199999999), ('Rbis', 11162.921699999992)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:35,523  SpectraRegion  INFO: Processing Mass 21687.485200000006 with best existing mass 21688.22189429862\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21687.485200000006 [('Il3ra', 21687.485200000006)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:35,643  SpectraRegion  INFO: Processing Mass 26612.208100000018 with best existing mass 26612.523569375648\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26612.208100000018 [('Il22ra2', 26612.208100000018)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:35,759  SpectraRegion  INFO: Processing Mass 17490.081799999993 with best existing mass 17489.81329483055\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17490.081799999993 [('Il17a', 17490.081799999993)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:35,893  SpectraRegion  INFO: Processing Mass 18279.695999999993 with best existing mass 18279.089965399136\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18279.695999999993 [('Il1rn', 18279.695999999993)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:36,017  SpectraRegion  INFO: Processing Mass 5903.754799999999 with best existing mass 5904.197616140279\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5903.754799999999 [('Il1rn', 5903.754799999999)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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90Uo+3uBaFi09DeupPD2fg9bz5oMDFs0046KXUBHJE5GCqb8B3AfgQLo6ZhjG0mPKAyCVT7pZyJ1ZFYCnRGTqON9zzm1NS68Mw1iyXIKEJimxkBwAJwFsmrOiYRgrhng8s6VrZzY/pp1o7C2OMV/Qzs/940X87N15Cz/Ml+9XWaKv5/qrfsSaV1aIDbmGrquiss4tOLaOtw+s5/ZLjnJ/C4/z9lAtFRF8mTUyr8qJHFzFustoFfen+q3ZczV6xlmXch6+qBq3sr9k6wfZjqz5CTYXDBdXUHlMZTyfrjmGz4sXxn3VOTtjd7JGVtDGgxHcyJoSVIityqdZI4sofU/bKI5Vq5yq/XztNDyt8iOs4va0HVvMx+2d28z7T9zFmlrxoTwqR1VOgPPmTt3c9G7i/uep/Ba9Vyu7vBGVYb2J2ws3sQ1jzqmkRrjQtSidj5AiUgzgXwFchfhV8NvOuTcvVNfszAzDSBtxzSytj5mPANjqnPtkIqVl7kwVbTEzDCOtpMudSUSKANwB4PMA4JybADAxU31bzAzDSBsOgoh+Jp+ZchHZNa38aMKUa4oWAOcA/JuIbAKwG8AXE7k0z+OyLmZZnhjyS5JBssbKuflIQOkOBcrO6VXWvLpvYN2kRNlN9V7PmlxwNfencSvbHnkH+fhjDexPN8FFtN/B/dc6TfF7XO7ewv3z9ylbqGPKP0/9H9S5hcfHH+T6zqPiuj/TRuW+W1nEa370GJXbf50dFnXOgsHVMxsglRzlzvpC7CtYHFUalpr7EaVplbzdReUiH9fvuof1vJpnW6k8upGTCIxWKj2zXo2dejKqfYOvhXAZ71/zo+NU7vswX1wSUxpVCZeHV7Nmt/5f2A7t3Ad0fDT+/Q6s56etvG4+Xv96vrbC1Twf9c8qG81pOWm1X+h8mYd1f+8cwSm8AK4D8AfOuR0i8giALwP4HxeqvDjvUA3DWJZMvc1M5ZMCbQDanHM7EuUnEF/cLogtZoZhpJWYy0rpMxfOuS4ArSKyLvHVBwEcmqm+aWaGYaSNS5AD4A8AfDfxJvMkgP8wU8XLu5gNe5D1atJ+KHAnB9wKdrIjeu129t3s38h2TlpT0rY62vao5g3WDSZK2P8u2MK2OtnDrEOEm7nBpnq2yxr6d9akzn2YdZfyl5Xv6I18/OxBno66X7COktfDol0kh/93C/Sx2NHx0XoqT7C7JPKubqSyf5jHzx9izUxrmNFpMbj61/NY1v2U+z5SxxqXvt71sXtv5ZwB+e089jU/OUvlsY1cP6eVY7P1byzn7SrmPlQMr9EqPp9QnbKjCzdRWecc8A+rnKy5KudrP2tWx36LNbL6V1WO0krWFMcq+Hghlc+i7CBfWyMDSnO8RdskJsvap3e+pDMihnNuL4CUgr7anZlhGGkjHjZ7pXgAGIaxfHHzMs1IK3OqcCLyTRHpEZED074rFZFtInIs8W/JbMcwDGNlkOnBGb8F4B8BfHvad18G8JJz7qsi8uVE+UtzHSiW5zB8fVJHckP8cJ5Vp3wPP8AakX4BUqp8I7XdVxGbUSHQxYkgw7VsqxMu5wEeL1HH28vHO3eMNbIYyzKIjfP/UL03sw4S6GTbJR3zSyZUbsQHeXvZTqWb1LCuomO5Zw/w/l03c/2ik7xD3wY+f507cnJazoOoX+lppeyLKDFtg8c2fn3X8FwEW9TFLqxhDbWw3udR+mmoljUo/xC3H+XDoahVx9jn7QWtvL+2o9O+o2EdT+3khCpz/e4beS5Gqnjsh5t5PLQmln+a7dCG1rK+rGPhecb4eNPzhi4kBwCweI+Zc96ZJSLH9quvPwbgscTfjwH4eJr7ZRjGEmRKM1tK8cyqnHOdib+7EI9tZhiGsXRfADjnnIi4mbZPzwHgKSueqZphGMuAS2BnljIXu5h1i0iNc65TRGoA9MxUkXIANNc7N01H8ncrTYrd3TCwkdfIoqO83TPOukHji1w+8VvKNmicNbicXtY9yg6yrnH2Xta0svtVrkalu0RzuL/5R1Wey9s57n24jXWdwXXqfE+yYVjdC3w+w8q/UOtC3rDS4JQWUrGb45sF17DOVfs661pjyvYqOi2m10gN90X7mca8XPYG2Qavcgfrn5EC1pB0vC499qMsEaH4mMpvUM5jd57f6Sqe67wevpbOy4l6Peu9BUoDm8zXP2g+fk4vH7/oFPcne4CvzWg27z+s7MpCtXytTKrxCCihKL+d2/ONJPvTMTLjvcncOCCyxFLNPQPgocTfDwF4Oj3dMQxjKZPRmpmIfB/AXYiH62gD8GcAvgrgcRH5AoAzAD6d9p4ZhrEkydjHTOfcZ2fY9ME098UwjCXOUtTMLgrxOPgKkrHeo3mskwyBdRH/oLJd4s3n2S7JBOsAFdtZd8lvZ52m51o+oI7jVHScj1/QyhVCtTx8Y7/E/oCFe1ijO1fCuR/Ljiu7utu4Pa0zjVawKhBq4v1juazDFLzHOst4KR9/aBX3r/n7HP+s+4Ocs0C/5iloS45HjtITBzaw3VjFDhZtnOj4XtyXwiOcN9MfUrHr9vNYh1XOz76NSgPrnN1XsvAsa1T5h1gG7voQ+37WvqY1LZVDQOU81b6nWZO8fWgV7x/ewHNZuEP5Ea/j9qteV9fGzXyt1/5fKp6X93O4PjlekbcXthi5lbCYGYax/LkU1v2pYIuZYRhpw7kM1swMwzBSRxCNLbEkwBeDjGQhe1fSAKbsEGtQOR0c36zjz5WG9HP2Zx+tYk1M6w6NT3RSOVrKxjfeMeVvp3MNNvDxBm5RMfw5pBbcIbb10XZgk9VsxxbqZx2k9hUVx30Dbw/0cf8Kj3H/snn4zjMsC/Qpu7QQtxcrZN1puiYGAL3XKDszf1JnCQzOPpadd7FNXaWycSt86QiVIxubqVz2HBsZunp2OhlqYY2s/kXW6E5/gvXKVY+yYVjvh1qoHLyf/W4DAzyW3hEeu96rdU5XHjudZ7P1AR6fyu28Pa+dNcJottJvj/FPd7QSvP0XPJfdf8zx5Yq/xXM5XJ883swm8KlhmplhGEsei2dmGMbywMV1s8XAFjPDMNLKinibKTHAO83dr+tmbt5/DS/p9Z/voPL4dawDaDuz3A7lfDjMMZ4iTaybaP85HZNrtIFteYr2sEanfSF1XPYoV0fL97j+RBHrLkMtbHs0WsvnU/U22w6138HjUb6Pt3fcztubnmTbqdE1rEEOXsWBAErf4tyV+WWci7LrnuT45JxmDabkvdmDYo3Us51T4RCLPvLWASqH79lM5Zwj3LeCdhX7Llv71ar2NzdQOa+bNa78DhX77SaezFADa1paz5zM076TrG82PMfX1iinSCC/V+B8P9twOZebn+FYfV1b2M924l2e6+xerj9wRfK3uBDXSgfTzAzDWBak1wNARE4DGAYQBRCZLWmwLWaGYaSVWCztd2Z3O+d656p0sTkA/lxE2kVkb+LzkYX21jCMpY9z8cfMVD7p5mJzAADA15xzfzuv1gRw06SE/FbeHPSxZnPmv6jn/H6lkfWqmFRrWKeoH589AK7Ou1l0mnUM76jOMsPt9944uy4kRdzA+HFl26NiUpUeUe2HeXpGalm3adjG/olZx3lAs7ZcSeWhTZykwKfszIoPsqHaqd9gW6vG53n7eGHSrk7rlzrPZE4Pj9VIlcqPcA33rfGnrPd5x7ivrpA1ofyX2U5NSjgHa92PWC+MVatYchvYRjC/bZzK5Qd4bgbX8NzohETaRlET6OHj57bx+Q1sYA1wuJF//JW71Xh4ub3R61kTi/Urm8zVSrM8nZwfD3dt3szjMbNcRHZNKz+aiH84HQfgxUQA2P93ge3vk0rUjO0i0pxq7wzDWNnMwzSjdzYNLMFtzrl2EakEsE1EjiTykpzHQvwOfl9E9iUeQ2dMNSciD4vILhHZFRkbmamaYRjLhHQ+Zjrn2hP/9gB4CsBNM9W92MXsnwGsBrAZQCeAv5ulM486525wzt3gzcmbqZphGMsAh9QWslQWMxHJE5GCqb8B3AfgwEz1L+ptpnOue1qDXwfwbEr7CRCb1qK2y8rjcFqo2sGa0OB69q30B1k3GGni+9vBK3jxHK3mtVvbBrXfwcLH2u9wTK3BjazDIJ91FOln26Y1teeo3F3DuR5Lj7Bt05kHVcyrfI7BX/hDPn64gnWPaON67l4r61SjKg5+QOk8rZ9l26nmb7F4cuajfP6Fp5LjN17MfS84y3MTCfB2HYM/h9MjQNtd9igbQ4DLdc/wWA1v5vhjeaf4Worm8Fj6xtRYqRykYXV+Wr/VNoVVu7g/4VLWSycLuP1InsohG9X5G7j9wdU8eaISgTZ/XfnVXsX1c3tUbL5pvpkLTUieRgeAKgBPSTz2nRfA95xzW2eqfFGL2VQyk0TxE5hltTQMYwXhAJcm0wzn3EkAm1Ktf7E5AO4Skc2IL8KnAfzOxXTWMIzlR8Z6AMyQA+Abl6AvhmEsA1aEo3ksx2H4qqTtVc4Jfs7PivAoDGxkW5soyww49Un+H6Dp33n/SC6XsweUb6SKy162T+kUYdYV/MOsq3i6VPJG9Tql+2nWyKrfYDutc9ezbVPhYRWXvlXFQ8vTceRVns232M6s88EmKuf0q5hcYeX7+SwLP51b+ITqfs6+n903JHWe4pOskY2VqJylyrew8DjHMzt7v7LzUrHial9mUc35WNiJ1PAL9dx2trPSDF7BmltI23Ht4rn3DfP+WZNKY6vii3NgLWtgpYeUL+Q6bt+v4r+FS3n8xsu4vcatOl4a158o4p921ds83hpvODl/3vGLX43MN9MwjOWBQ/xN3yJgi5lhGGllRTxmGoaxAlgJi5lkOfhyk5rZ2GreXnycu6Njkfdv5NvXlsdV3skbWfOpfY11ipFq1inCFdxA6VHWIbrv5CBTOs5T7RusE03mcgXnUTGxbmU7LRWiH/5hpfll8/l2b+HtVW/y9rZPNVM55xw3oNsbalG6yg720MiKsu7TdTOXx4uT/Ym0c1+qXmqncugqjoXWu4n10OqdylFWzf1kKc9d1qTyZVw3u0F27jm2CQwM8v6lB/jc2+7ludJzX7WTbfBy+mbPCTDcxDaBZXvZ7m28is9PP6n5gyrPaCPrtb03cvv5J3huA928f3CV8s08Oc0uLraQ1UjSZpoxX+zOzDCM9OHsBYBhGMuFlfCYaRjGSmAF3JmJOAQCSV2q5DnWOQbX8iA0P8nBJb1jHO8sVM+6gUfJLuNlvD2m7NS0MKFzL8a8vL34ODdw5gG2darcwYfX++e3s64R6GeNrm8Da1Lan7HgBB9fYiyC5XVxebyI93fK9ErHcxtcq2KIKXsj7Y9Y/m5Sh+q7kscuv4njheV0siY1UcCa2Vi5ygeRzSKVL8Sal6eXfR8rXuim8uRq9s30drON38BNHOvuvc+z32/z06yJnfp1HsuemHLGVAR6eaw8E1xuvV/5uZ7muSvZw9d+54c4R0JBG4/H2m9zf4Mqh2zHHfxb82ozPElqaNEjC0zia3dmhmEsC2wxMwxjyZNGR/P5kkoOgAYReUVEDonIQRH5YuL7UhHZJiLHEv/OGKDRMIwVhEvxk2ZSuTOLAPgj59yeRKC03SKyDcDnAbzknPuqiHwZwJcBfGm2A0nQA8+25JpXvp19CcO/wrkMT32a48Lnn1G+lSrG1GiN8jXcqXIhqryYlXtYNPKMcP0Tn2JdZ7JA+ZJqHzZlGOdUXKj+DfxF4Wllm/Um6zqdd7GuUnSKdRLdfuu9fPySw9x+TM12cI3SaQ5yf2LcHPpv5vHJiiR1ssYfnKFt3b/EfqmibNZ8yhex6Aif+1gta1jeELc91shj4yvWNoQ8V2Pr2M6t8tVOKoeLOd9B/LJP0tzAsemGdnJ951F5Lsf4/LyjPNY1b/L5jFar/rbwvUHpYdbEwuWsUfZdw+cfVTaKOT3cn6F1VETlnqTfrWdi9twWc7JIphlz3pk55zqdc3sSfw8DOAygDsDHADyWqPYYgI9fqk4ahrF0EJfaJ93MSzNLJDa5FsAOAFXTAjR2IR4V8kL7PAzgYQDw5duTqGEsay7RI2QqpPwOVkTyAfwYwB8658gXwzk34ylYDgDDWElI/DEzlU+aSenOTER8iC9k33XOPV8JmiYAAB81SURBVJn4unsqfLaI1ADomfkIcaK5DoObklqBd6yethcfZ51C+8P1bZy9u6ufYOOZc5t58cwOKt2gRcWc2s8aWtl+vT5z2TfMHQzVK7u0YypHQQ1rWjnKX7D7FtaBPEp3iSldJvSfWWfK3c62XaEG3r/iHdZCsvu5P9lD3F8dv23jX7AtF6LJ7R0f49hpExyeDJPXsJ1ZzXfZTkvnVyjZN0DlSKGywcv1zFoufIs1vMm7m6ncfTfboWkbxIH13D/fd1gjK1Rzl3uGfS17bimlcvudfO1WvMP91XZoE4W83acSm+W1c2y5UJ32XeX62UGey+ZneP/p8d2ih5emnVkqbzMF8ciyh51zfz9t0zMAHkr8/RCAp9PfPcMwlhyxFD8pICIeEXlHROZMmpTKndmtAH4TwH4R2Zv47k8BfBXA4yLyBQBnAHw6te4ZhrFsSX9wxi8i/tKxcK6KqeQAeB0zO1t9cH79MgxjuZOuN5UiUg/gAQB/DeC/zVX/8vpmTgr8PckmBznNI7Iis2s4Fe8qOydlD6NjWhUoX8j+dXy6WcqOKriG7cr0s38kV1SZt08WKE1thPuXFeXziwZ03HbVvPLtrHmRNatwCes+Y+u4/fqX+QTHS1TuxA6ur8ej51oWknJrWeMcWpP8u/k51ivb71B5LR9jO6rRSp33UemDfp4rzyjrmYV7Oafp8CZ+md72mVVUrniXNaL+dayJ6WtlvJDnRmtq+trTml7fDXy8qtf5eCO1XC55j6/tgStUXs+gulY28fbKPbz/ObVd+/lKhPs/XR/Vce/mTfo0s38A8CcACuaqCFx8RnPDMIyFUi4iu6Z9Hp7aICIfBdDjnNud6sHMN9MwjLQyj8fMXufcDTNsuxXAgyLyEQABAIUi8h3n3OdmOtjlfcyMAv7B5O2uPmkd0ma0QvkDKeVOu8T41WNdVLkvjVXz9lVPchgZnX6s9zoVYqdNpf8q5fYLTnL/xouVucApPl5YbS89yo8m3Tdye+0fZfOA4Bqu3/I0Pyf6e9Wj310cQilHhVIeuZ/rh/s4tHLB7f1Ujh5JupsNrOO6+e08NmPlfK7F76kQ3Xvfo3Lwwc1U9gf5XHO6+lSZHyOzJvgx8twmfgzUj53hMvVYlsNjU/skT27Hr/JjbPYg71/zChUhKhR1zatsVjOo0yqqLIYeFXFodDU/dndfx+fT+L94Lls/zBpG6S6+9tGoNJOFkIYXAM65rwD4CgCIyF0A/ni2hQywOzPDMNKJQ8pmF+nGFjPDMNJKuv0unXOvAnh1rnq2mBmGkV5WQnDGmB8YaUxqH1rjab+DhYH8VhVSRz2KD6xTr8/9XL9sn9IpfsHlE59knaf5OdYhxou4PyM3sc6Qv4P3H2cPFkTyuMP69ftYBess5TtZk+q9WqW6U+efNcFf9CsXHM8ECy/6lbt2ebm6poPK/jrWqX7xNtvSTG9du3LltynNrFKZIjzFMcAjN27gsgphU3SSx2b0OnafmixgTa7oANfv28jhpEZUyJ2h1bO/2D/9+VWzbo95ZzflqH0tROXuLSps9hn+LeR1qMlWxdqtyrTFsVlSj9Jba95kTU2H4c7vnDbXC12MVsJiZhjG8uZShfdJBVvMDMNIL5Y30zCMZUGm3pmJSAOAbyMefNEBeNQ594iI/DmA/wRgKp7wnzrnnp/tWFn+KHLrk9rB4Br2HdWpzyaV5uQL8ShVvsM6Q+uHef9IgHUFnXpt1dOsIwxcwbZI9Y+zbdHEXnYf6mFTqPPszqrfYpEqVMvDrcNkOw/3t+k5Disz0sC6yKQKEzNSx+3ntWvdhbcXH2fRbNfutVTW7k1FJ2fWlfwqvFJBK4d51vqd28galHZXKnteheH+VY7zHBjk9gaV5lVwjEUrrzKr6rqb9cCal3m7dq/S7k2D9/C1U/QM65UThTz2xz/Ddly5LE+i9+rZf4o55/TccvvBFtZvRzgCPUqOade4LiqPNyYDp+pzny8Ldoe6SBaSAwAAvuac+9tL1z3DMJYUmayZJUJjdyb+HhaRqRwAhmEY55OpwRmno3IAAMDvi8g+EfnmTKnmROThKUfSSFCnUTYMY9mRwanmAJyfA0BE/hnAXya69ZcA/g7Ab+v9nHOPAngUAAJ1DW7yYFInm+SIMig5wg/bfQ+wLhAJsQ7SP85rcdXrrAsMqnRa5e/y8Vt/j0WhyFkVQudzXD7zPOsSOgSQ9nUMF3P/KnZy2Jqh9Srmj5K4PCqVnD5eNptSoUTrKq38n0dE5WDI7uPxzT/D/oGlh1lTiwb4+NkDye3jJcqwSp1L4SnlC1nJ+mSohi/F3Hrua2CA584b5r7Uvq40un7WG4tO8mRVvzZM5bMPsJHgRIlKhXeMiqh9XIfY4e05fdzf4ZCeOz5+bh9reF03sx4a8/GAesb5fHxj3J5EuL2BK1iznLyWU+9V7JumWS7wZeRiPWamdGd2oRwAzrlu51zUORcD8HUAN126bhqGYczORecASCQxmeITAA6kv3uGYSw5Mvgxc6YcAJ8Vkc2Jbp0G8Dvp755hGEsKl8GmGbPkAJjVpuyCx/I7TDYmtY2GJ7j58SK+USz/CQsRw028PU/FzOq/ktvTOkfvJt6/+BnWZXQMq+BWFvXcjXw8bYelyeviCqONrEmFS7i9soPK7m0dn39+Jx/Po3SSjju0byaXS4/M0WE1y7l72NZr6I4WKnuHknM51MJt5Z1mDStczRrPwFrWnOqeY7sndPdSMbaWDaeiuawBtd/FeqYvyPXrtvHxxhpZrwxX8liW71H6ZzmXexpY02p8gQ3ZssKsN9aOcP/6N3L/B8r4txBYz6n28n7I/dV2bJP5s/82Ctp57gfW8PjrNIYLIlNNMwzDMFJFkMF2ZoZhGPPCFjPDMJY8mewBkE5kQpB9KqkDjZbzWWvhsH8Tlyv2cIWxCuWPd4aPF1xDRdS/pOKm38Q6T06PsutSceELz3D7XXeoHAGVHNceR1gnyu5jHcmv0od1fYB1FR0Tq+qlHiqHruT0ao3Ps+2U9xzbWsUK+PjRfD7/+q/zC+meT7EIma80QJlM2kYFBnksPAPcl2yfjj3HJ9d7K5+Ld4zjbWUPqrbVD6Z6B89t35WsSbV+hOOZha7kuSjcy/0JKRtIr7L3jqkY/eOl/EUkoFLZneYDVL/J/R2rZn00+xe8/5mPsqZVtZPHe3C1iud2ku3WtEbmH1a/vXQuQCthMTMMY/mTsW8zDcMw5oXdmRmGseRJo0GsiAQAbAeQjfha9YRz7s9mqn95NbMI4J9mPjPyAOsq4fZ8KvuGWSfQdmZFJ5X/W4OyIzuiYlKV8uk2/bCdyi7AusfJz7LO4hnj/lRvpyKCTWwLNJnLusVQC9u1FZ1kW6SSY6wLFezhoFejG9mfzhPm43t7eTyP/Q7n2Vzzbc41GctmnWXiOhYZq7ayndnYBo7nljWatIvzjrENXaSSxyKax5qNT8U/80xwueT1s1Tuv7ORyvkqXhqc8qVUsdciOSrnaRnPdbaKjxZgM6/zfqCTBXwtaN9JHf8ssoGvbY0Ozho4x+Xiw1zOmlTne4qvhYkCbt83quzOzrJmF50e+y9zfDPHAdzjnAslXCpfF5GfOufeulBluzMzDCO9pGkxc845AFPRXH2Jz4xHn1cIIMMwjLmYSmoy1wdA+VR4sMTn4fOOJeJJuFH2ANjmnNuh60xhd2aGYaSX1O/Mep1zN8x6KOeiADaLSDGAp0TkKufcBYNapJID4IIinIi0APgBgDIAuwH8pnNuYuYjAS4/hvCWZA6Aqu+wHVbPtcq/rINHJcqmNxgr5Yd7HSe96CT7y/Vcz+3FvKwBTeQrTexN1rB8w6xxeYPsS5nbxZpY64dYl8nppiLyDqkvlO7T8THWibROUvXj96jc+5ErqFzAKQxw7uYyPp5y1YywGRqq9vP5OZUbsvvu5PhVvsY2cJFy1oj616kcpMqOK7Cfyx2faKZyXhdrQmOVPLa5XayhjZWxHjhWyXPrVSaBY5yiFFEVn2yiSOWf2MV6bcERFtlGb+exnlQ2gwXtfD5aY+tfz5PhUb+snE62W+u5QcWiO8rj4R3mA7Tey/k3ojnJ85vcffGi2aVKNeecGxSRVwDcjxki9KTymDklwm0CsBnA/SJyM4C/QTwHwBoAAwC+kJ5uG4axpElTCCARqUjckUFEcgDcC+DITPXnXMxcnAuJcPcAeCLx/WMAPj539wzDWO7MQzObixoAr4jIPgBvI66ZPTtT5ZQ0MxHxIP4ouQbAPwE4AWDQOTf1oNKGGZKcJES9hwHAW150oSqGYSwn0vc2cx/iOUdSIqXFTItwANbPo0Pv5wDIrWpwOa8ntZTW+5QmNcSjEFK5/2pfU8/9D/H+/kOsiY0Xcbn0yOz+e9oXMpLLX4xV8nCVHGZhxati9pceVHZuxaxFtP4qC0f1P+WYWwEVRz7Qz+cbXcV2ZEXHWEdxfr7xlgj3Z+AK1mVye1QOho9wEoWypw9S2TPdLi2mfFhExaxXmk/jVtYzw5WsqZW8xzuMF/PYFx1kjarzTtaoCltVfocctf8p1j877lDbj3J/m37C+RvG6lgT7LybbRJ1zoJQPc+FU+HbhtYq31Uvz1XjC6xfThbyePlVTtlgE28v/8lpbt/Lmlnl7qSG1zG6wNVoKXgATBPhbgFQLCLexN1ZPYD22fc2DGPZs4hRM1LJAXAhEe4wgFcAfDJR7SEAT1+qThqGsYTI4BwANQAeS+hmWQAed849KyKHAPxARP4KwDuIJz0xDGOFk7FRM2YS4ZxzJzHP9HKx/BjCtyf9B/3vse6gNab+jay7dG5Rdlt7lX9d3+zx0YJNrIFlKfe+0kOs0/Rew8cP9Cpf0UYVh71A5ULs4u3a/69wH+sgXUr3Ca7i/q19LITZiJSwBjaZz9PrHWXbJqdmv3AvCzlnP8XvdIqDHB8tazI5wJ0fZpu9QD8Pvn9Y+dE2q/wG7Tz2wUbWfHJ7WQM7+RnOc1l6iI/feQvbmekco2338HZ9rQypnKuRXM5xnaX0R+1b6tQzz8hGvthGa/laLHtH+aYeZUO44CrWf7WNYM/N6tpr5Q4E71pL5Yq9M+eDWOhj4ooIzmgYxjLnEj1CpoItZoZhpBdbzAzDWOqsmOxMXk8UlUVJ3acrizWz4UaVN3MfazzBJtY58jpVToBK3n+kVmlUJ7g/ed1KQ8qaXePS/oGhWu5PDrsnIqp8HXWuw1C9ynOpNLzqt1Sc96uLub0+1j203VxuF+8fLub+epU9Uf8trHvVb1VC01VsXnj63qSOU/wet5Ud5LEK9LA+GK5kzWyomcdCxwvzhrnvuZ3ctf4NPPcNL/Fgtt3NY+Mf4voV7/BYDq3in0ZBG2/Xc681stLdHDturJydP4tOqzyWa7k93yhfPCU72Y/31K/zXJXuVSuIWlHCxdzB0Voe3+ZHku6OWSGeq3mzEhYzwzCWP+IWZzWzxcwwjPThMtg0wzAMY16slMfM2PRg5y3sS1j0JOso/VdqXYJH6Ry7JsKVsk5S/VO25Rn4JNvuDIX59PN3sU4xWqdth7g/HiUt9N7NtlJVL3D7JW/PrnvUv8L+ito/w3+SA8NPrGIdpmonj6evg/0X+27l9rS/ZNF+1nkGrlXxz1Q8Nd80szNtZ9W/js89ci2XG17kfAW5J3kwx5pYH+y6ifdvfJFt7jq3sP461MIaWclh1fdRvn3oX8/XQmgNa1rhMt4eyeXjVe/g4x37PPtqSkzZpY1rvVX78arJV76u5ftVfolVfLxwGR8ve0D5yqpLbej+je//HX3xRSyEFfECwDCMFYAtZoZhLHky3NE8ICI7ReRdETkoIn+R+P5bInJKRPYmPpsvfXcNw8h4MtjR/IK56xLb/rtz7olZ9iUiIz6ce7vq/XLZIT6j3s2zx+Dvu4q7q/NYjmXz9qFVvFaHg2zLVPkq6zCFp1lIOF3LGt5wMxWR28ntV/6MjxeqU+2XsmaV261zHHD9UD3rPmjhAG/jRdx+Fofogn8Va4A6Dn7hadZdRptZpxpXdnE5yvd1es4Fj4rlVvcS63W9f81tyVY+t4kqjmE/uFoFl1MMN7GvYm63sqkrU3ZVVXwuRbexfhkd4bHK38HxvrJu5fOJHeGx6tvImlXFO9yfogMcD22ikvNFTBTxtesP8rU/eH0llUeVTaXOUVBxHZ9f764qKhereG2khy5gocloo9n55q4zDGNlo192XC5Syps5S+66vxaRfSLyNRHJnuUQhmGsBFJ9xLwE611Ki5lzLuqc24x4RNmbROQqAF9BPHz2jQBKAXzpQvuKyMNTST6jIyMXqmIYxjJCYql95jyOSIOIvCIihxJ6/Rdnq3+xYbPvd879beLrcRH5NwB/PMM+7+cAyF5V52Krk7rU5FnWKareVrkEi3itLTjLI9D1QdYVsoJ8Ojqmv/iUr6OKWRX1s0a2+gm2ZTp7H+s6Oo+n1jFq3mS7rxOf4uM3Pc/91xM8VjG7r6j272v7VS4X7uL2snTuxV7+whPi8lALJ6DpvI37U3w4+bd3nM99aD3vW/Ff2XF1dC1v71Z2ZDqvZdNPhqh89qO8f+EpHrzgOr6WKt5S+uVzrCEJTy0iN7Ed3Hi7ipm/X8cz4/Z1vLHjn+N4aN4wj2XUz8erfU3lDFC3HdkDXH9EpRMaerWa21OpMM/dyOPTuDXZ/wW7I6XvrisC4I+cc3tEpADAbhHZ5pw7dKHKFxs2+4iI1CS+E8TTzF0wMadhGCuLdKWac851Ouf2JP4eRjxc/wWzwAELC5v9sohUIP4CYy+A303hWIZhLGccgEvgaC4izYhHvN4xU52FhM2+ZwF9MwxjmTIPR/NyEdk1rfxoQpbi44nkA/gxgD90zgX19ikuqwdAjn8SG+qSceYPXNPEFfaxrU5wNW/OO8sP/iW7lO/lZhYqlIwDF+LTzenm47mPc97KrDd5/0Af+/+Fy3j/8Dr2LzxbzJpgoEfF6Bpmw7CuLWx7VHiGr4rcDj5+7zUqLnwPn7AnrOK5neH2xsrZ1mvkSu5v5R7WDMsO8fgNT4vTn9fKNnrDLXys0Eb2VYz5ZvcV9KscquNVfK7al7FYxcyfzOO56rmdr42CoyofhNK4ah/ha7HjDi5PFHL7WRN8PjpWXv0rPPaDq9XYqxyx/Ru4f9Nt+oDzcybkqWv5nMpfMVmixuuQ0hBLkvvHPEpgmwfztDPrdc7dMOvx4ratPwbwXefck7PVNXcmwzDSh3Npe8xM6PHfAHDYOff3c9VPyTTDMAwjVdL1AgDArQB+E8A909wmPzJTZbszMwwjvaRJ/3fOvY74k2tKXNbFzEEontlN1x2j7fs7OcZ8bjvvX/Eu22113sI6SvWrrGv0PchCTKxD5ZVUtkWVX+XjjShdo6BN5SRYo+LI/4wNz4ZW8zxoYXSySMXcem/2OPO9V3P/Vz3B/oIjt6u8om+xndngGnU+7ZFZy72bWMPTdmqVP08G4m9/gIPL6XwJWiObyOOx8ym7Mq3vnf4E1/cqGTgS4MkM9KtflJfLwxv5ZHKP89iEGnjsio/Nfj7ZA7x9oojnbqiFf2qV7/D55Sm906mfsFM61ulfZk2t+Ahvb3iMf1v9H2YBWsdLG1qT3F/bT86XjPXNNAzDSBkHYJF8M20xMwwjrVgOAMMwlgcrITvT2uwhPHvFT98vX7/707R9soAHwT/IOsDgWtaMyj/Molroe6zbVP2IdQ8dn2uc3eXQuUVpZldyToE1X2ddJLuP6w9s5P5H8pWOov7HCnXz8IfLVcytk7x/NJt1mHM3cEyt3DeVrdFm7k/ZAZVntETFjVft53WoXJhDXO65MxmfTfsO6pwA44XcVvYwH6tsL4tgPTfzuTU9w3pe1wdm14XHi3l7/hHWxGLcHURzuL9j5cpmT8Vr84d0jH2+VkZVvLKmH3N8sa4PcnyyaLaa+1Mqj2czd7joqMq58AHW4PpvZBtObx8f36c0x+xpKVKz+LKbN6aZGYax9LlE4X1SwRYzwzDSRtwDIIODMwLvB2h8R0SeTZRbRGSHiBwXkR+KiH+uYxiGsQKIpfhJM/O5M/si4iE4pgI7/Q2ArznnfiAi/wLgCwD+ebYDHAiVYd1rv/V+eWKANa28IeUrqXSNyK/0U7njLaWRKVuf0Qpld3Y9b/eEeC0veo/br3uEfSHb7+UYWjVvKl/M+9hAp/o1Pl7/lUq3GOX/wUJ5vL3vau5/4Umu37OFzyf3DE/nRAXrLiU7OKZY7208frldfPzAoIovp3SvwrNJWy2J8r7dN/Lclh1iTWe4TvvJ8tiNVivfzXGuX3RcjcVtfK6+Pq6frfRXrZFljat8DnvYprFP+a2OVvG1M9zAvqBRPn303si+qVW/4Pa1hnfuWpXT9SzXn1D675rHeK7a7uT+lrzHq0dM2901Js9H65/zJaPvzESkHsADAP41URYA9wCYSmbyGOIxzQzDWMk4F7czS+WTZlK9M/sHAH8CYMrMugzAoHNu6r/DNswSNM0wjJVDJufN/CiAHufc7otpgHIABC0HgGEse6YiZ8z1STOp3JndCuDBhLd6AHHN7BEAxSLiTdyd1QNov9DO03MABOobnDuZtL/JDc4e06r4JOssZ3rY/65A6SAjlSw86JhSQ0O83RdSOorySRurV/HFTvHxzm1S/nvv8QQFW/j/iuo3+XxCSjcq28/H77p59v9rCg/z/r4RFZd+nP33Tv8a3zyX72edKdDLGuDQarajC2hNsip5/IIzvG/ZQT7X7OffpnLwd2+h8mSB8jVUGk/JTw9TufU/Xkll7yCPhdbEqraxHViojt9XTfdNBID2O/ncI7naroyKKD+gzreP2xtu5uN1beHjle9RvwUeTvR+gOcq9wyPV/vtym/3Ox1UjlRyDoPjn1Gx8KbZ0Wn9bl64xfMAmPPOzDn3FedcvXOuGcCvAXjZOfcbAF4B8MlEtYcAPH3JemkYxtJhke7MFvLe4ksA/puIHEdcQ/tGerpkGMaSZpHyZs431dyrAF5N/H0SwE3p75JhGEuZxTLNuLzxzDxApDD5QO0d5YfzkXple+NnXSD3BB9Pa2xZD/ZRefTpMq6g3Pl8nBoRhWdVPK+reHhGm1SgeA/rJLknub8elk0w1KJyQ46p3Ic1PB7aH3CkVtmpcYh+BNdwWdt+lR5Uml4jn58o0TA7yOLHRIH2D0wOYKSQ9+27ks91YgtrZGv+5SyVRzaxzdvgWn5oKNnFc6nt1nR7WtPq26AMvxQBNsE7b260DtR7HX+R08NjOdTE/RnawHqjRHkuIyx5IdDH7RfcyTaW4X2c9zOmTNbbHuTx1HpwkdJ3p9v1LehtpAMQXQGLmWEYyxuBWxl3ZoZhrABsMTMMY1mwEhYzzxhQvD+phWz5AtvhvnBsA5XHJ1lI8A0ruzAlg1T+TxYGTn+Mt/uCs+sU3TewJlR6mHWRwjPaH4/LIy2sqRUdVJpbHU9y7XbWfUZqWGcpOMX9i/l4/2gOn0/tduWfOMLls/fxgJUe5PMbXMvtR5UOU7GP4+YH1yTt/vLb2DBK5zPI7udznWyqoLJE+NwatrGBdd8trBFNFPC5F55WseMKlN/taW7fE+ZzP3sfn6w/OHu8tFX/zoJo1M/txXxK721T8dwG+HwHVSy8rLDyFf03NV5lylezSOWbUDHJCk8q0U+dXl53cnsHu6XOD4e0OZGLyDcBTBntXzVXfUs1ZxhGWhHnUvqkwLcA3J9qu/aYaRhGeknTY6ZzbruINKda3xYzwzDSh3NALOXnzHIR2TWt/GjC/fGiuKyLWTTHYWBz8mF+68+vpe1Sy7pLodKMhhtVnPZaHrSOLJU78RzvP8HhyFD3KosDwVUsoulciJFcFRde5ZHUWkHwCt4/r1XnwWSdRvtWaitprYv4h5RdnsrlePxzPL2Fh3j7JLvnoegk61xj5UrneYP9I0c/cfX7f2tfxqZ/PEDlzs+x5JHTp3wv32il8kQLx8jP6eO+5bfx/tGAmptJFSN/PWtYta8MUbl6J49Vr4olV/MGa2Td17P+WLWLr12dI2BwHRXhstRc9nM5wCaT8EzwtVS9nft/7qZSKhed4v72XsP9rdk+SOVwVXL+ZKHheVLXzHqdczcsrLEkdmdmGEZaMTszwzCWB5kcadYwDCMlpjKapyHSrIh8H8CbANaJSJuIfGHW+u4yrqIicg7AGQDlAHovW8PzJ5P7l8l9A6x/CyFT+tbknKuYu9r5FAWq3ZbGh1Kqu/XY/969ZDWzqQESkV3pPIl0k8n9y+S+Ada/hZDJfZsXppkZhrHkcQCiixNq1hYzwzDSiAPcylrMLtow7jKRyf3L5L4B1r+FkMl9S51Fesy8rC8ADMNY3hT5q9yW6s+mVHdr6yNL9wWAYRgrAHsBYBjGssAWM8MwljzOAdHo3PUuAbaYGYaRXuzOzDCMZYEtZoZhLH1S87u8FNhiZhhG+nCAW2FGs4ZhLFfszswwjGWBaWaGYSx5zDTDMIzlgks9oUlascXMMIw04uwx0zCMZcBU2OxFwBYzwzDSi5lmGIax1HEAnN2ZGYax5HErL9KsYRjLFLdIphkWadYwjLQhIlsRT5mXCr3OufvT1rYtZoZhLAcso7lhGMsCW8wMw1gW2GJmGMaywBYzwzCWBbaYGYaxLPj/IhA5a+A70NMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:36,130  SpectraRegion  INFO: Processing Mass 13626.99059999998 with best existing mass 13626.43226872047\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13626.99059999998 [('Cxcl17', 13626.99059999998), ('Wfdc10', 13626.576499999985)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:36,247  SpectraRegion  INFO: Processing Mass 18202.5728 with best existing mass 18202.1241715196\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18202.5728 [('Il17rd', 18202.5728)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:36,359  SpectraRegion  INFO: Processing Mass 23928.884999999987 with best existing mass 23929.28471608513\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23928.884999999987 [('Ilrun', 23928.884999999987), ('Stradb', 23930.18550000001)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:36,468  SpectraRegion  INFO: Processing Mass 24900.095199999996 with best existing mass 24899.657372252623\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24900.095199999996 [('Ilrun', 24900.095199999996)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:36,579  SpectraRegion  INFO: Processing Mass 29990.628199999992 with best existing mass 29989.96370079532\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29990.628199999992 [('Il33', 29990.628199999992), ('Gnas', 29991.278499999975)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:36,693  SpectraRegion  INFO: Processing Mass 23789.840400000016 with best existing mass 23790.444460459297\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23789.840400000016 [('Il6', 23789.840400000016)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:36,811  SpectraRegion  INFO: Processing Mass 24383.67730000004 with best existing mass 24383.53381329573\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24383.67730000004 [('Il6', 24383.67730000004), ('Trappc4', 24384.7599)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:36,928  SpectraRegion  INFO: Processing Mass 19399.816999999995 with best existing mass 19400.375942899052\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19399.816999999995 [('Il2', 19399.816999999995)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:37,040  SpectraRegion  INFO: Processing Mass 16810.60699999999 with best existing mass 16810.70334883464\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16810.60699999999 [('Il21', 16810.60699999999)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:37,159  SpectraRegion  INFO: Processing Mass 8872.763199999998 with best existing mass 8872.66264674908\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8872.763199999998 [('Il18', 8872.763199999998), ('Ss18l2', 8873.036699999993)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:37,264  SpectraRegion  INFO: Processing Mass 22149.901800000007 with best existing mass 22150.016657575838\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22149.901800000007 [('Il18', 22149.901800000007), ('Bhlha15', 22149.542699999965)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:37,373  SpectraRegion  INFO: Processing Mass 5272.061099999999 with best existing mass 5271.870799757418\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5272.061099999999 [('Il4', 5272.061099999999)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:37,674  SpectraRegion  INFO: Processing Mass 14107.345999999974 with best existing mass 14107.845763770905\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14107.345999999974 [('Il13', 14107.345999999974), ('Tnfrsf18', 14106.149199999985)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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x8stxa5G5jfs23OL0zSqOpQ07Gh1O51jYjpV8rVz9OFDLenb6Y6wJBs5lH868Q9y33s3byE5by9u3rWcf16KH+ProSs7nF/EnrM7kxCctVbHYW8Mw5hdzOV08EL2Ff1xEXp0LNTsNw5ga0SSicztd/IWqWi8ipQCeEJE3VfWZxA1ik+GtAJDuyR5tH4ZhzBlmf2GgqebTq4/9bBaR+xGtkPaMs028Roa3WHUgrv24JRQjTsnHgRL2Tco94MRnNrLukjvEGl94L8e6uvGGvh7WVcp+d4jbs4RzmkUaWAPzLOBY3Y7lHPtb+uMXyS48bzXZ8DuX3y0bWMR+hq5foGTw9Yo4mtyiTY5OVcw6WM85/Nwpby/75Q0s4NjepvOcGh33x/vD08V90/ZN1jsX/L9kovkS9knMO+zU2ChySkb6+Qszq4n99o5+hjW8yu+8QHb3jZyrse0MdpnIOczXuug+jlUNFTnlMp2Sjp79XGmucIj11KFSvpbBCo6N9eZz3LTXKYmJCGuI3tM5n6Ac4/+F/DSn5kcVt6e/ijXTnJfiNTdk0KnNMgkUmLtZVkQkS0RyRn4H8B4kOA0ahjH/GInISOY1U0xlpVeGaFjIyH5+raqPpqRVhmGcsszZwkCxIt9nTbihYRjzhmg+vTlaGOhEGCrLwJHPxHO41fwf9kOTLNY98vbyN4ank3Wjrku4hkN6m1NnocTJF7eDNTssYD/A+o87upBTg0MqWIdKjD0FgJLXWAfyLeSq860rWJcq2nSU7LaL2Neq+AWufeqvZg0RXr4+HTdzPGnBG6zRqeN/5R1m3zH3+moF94c44a69y+K6VKCTNaLBV1mD6l3qxM464Z29Few5F3JqGhfuYT9Afxtf66zDTqzq+1nDy3mY45LTOllDS3+Nx8aA0xeuNp91hDU0DTtFPZzzS6/nvggV8rUNZzr/ihG+Hsc2sB5bsoPHXpozFuQ19svrvoF9fNtXsu4mobifYeQZ14txcszkrWsymJ+eYRgpI6rpzdHbW8MwjNFIVRjaycImPcMwUoZCEIrMbpeVaZ30/H2Ksi1x3W3/37Fu4n5BLPvBEbIbrq8Zd//ZRzhH2WAJ60rB89m3qWsJ6yI5dY7u5PieNbzPOb6TuLrigcNkN13N8Zglr3TyB5xYYH8v241/yzVESrazX2H7GexHWLSD/fT6alhDTG9x/K+c692xnmNt8x/ZTXbmX7GmmfNAvL1DFZyLb9EjnFvwrdv4Wrss+yHrsYec2NXmc9hPrmQ7Nz6Uwf9o2W/xteh4P8ctFz15mD/fxrHAfQs4LrzgTdYQhxcVkY0q9vPzDPNYal/NceGFd7APZ8TJR9i9zMl/18djo6eafUKL9zk+pE7+vLzN7LMqyv8L/t64YOvqrZNlJqMtksFWeoZhpAx7emsYxrzDHmQYhjFvmGSNjBlh2ie9xJx2p93DGpebHy9UybpJxSbW+A7cupBs3cK+WBGn9mp/Ces+FQ/W8ued2N9wE/vJDefUkL3oDtZJWq7kmhSBThZHhkqcGhsLuC5u4Tb2/eq7hnOipe/n9rSdwXUYjl6VT3ZWA+tA6S1kYjjb8e1yNMZwN/uWpf+cfdsOfDT++dN+wW3b+zluu/+oo8Hl8LEOvY81qojfqV+ykO20Lt5+qNDx6/OwpuVxfAyHTmf90uvkFiy4kzW34SvZBzJjv5NCMsC+bd0ruS9KH+OxO+RoeEeu4POpfIYbnPcGa6Q4wvVNwsOs1+qQU3/mAo4jyH2axy5K4pqkBB2fw0limp5hGPOGaLp4m/QMw5gvaOpcVkQkHdGsTQFE56r7VPUbzjYBAL8AcA6ANgAfVtXD4+13QsVRRO4QkWYReSPhb4Ui8oSI7Iv9LBhvH4ZhzA9SnER0CMBGVT0LwFoAV4rI+c42nwbQoapLAXwPwHcn2mkyK707AfwA0dl0hK8AeFJVvyMiX4nZfz/hngSI+OInW/duniurb+fMVLX/jfPPlTnxnIu+zroL1nGdhZa1fHo5R1gX6jif/dx8Q6zB5fSwX1rNrzhWtn8ta4r+fv58WjdrIxkHWbPDMPumubUJSrexLtO5nttb8SfWlcLZrAupj7/TDr+XNcWI0/ulW9nuuYnHV+4B9lVLS/Al6z6T45gL9vC5DOeyfd01L5F93w7WuLJ38bkE17Gm1dfGml16O/dt2xnsF7jgBfbhDKfzaqTjsxvILtnq6JnPU+VBDG5gP77hXCc/3z7+fKSA/RjDadw3+W+RicECp33L2A8w9yjn4+ut5O1LtnFfDRXy9fRn8VhAf0Jsc2Rqjnqpur1VVQUw4izrj70c71hcD+AfY7/fB+AHIiKxz47KhCu9WCbkdufP1yNaSRyxnzdMtB/DMOY+I5pekvn0ikVka8LruJITIuIVke0AmgE8oaovO5tUAqgFAFUNAegC4HiOMyeq6ZWp6khprWOI5tYzDMOYzEqvdaKysaoaBrA2VvT7fhFZrapTSlY8ZS/C2DJyzKWkiNw6MpMHh/vG2swwjDnAycqcrKqdADYDuNJ5qx5ANQCIiA9AHqIPNMbkRFd6TSJSrqqNIlKO6NJzrMa+XSMju7Bag9lx7aHyadY9UMa6UNVTPEl6+9kXqfVTrMNktLOGVvQG25kNnJPN18o6Ues7ecGa48oCTs60zN3s25VWybqL9wDXmR1exfn1eitYZyl8jussDJQ6OdN6+Ph9S9gXzBNkLaa/zIl3dU5n6a/5+jdewjqRm0/w6Hu5fxJ93xou432nH3P85t7J1+rPjRz7mZ7NfTtY7OTXa2MfygJHwwtl8PHKn2cNL5jNQz2czt/3Cx7la994FfdV1kKOEw86+f7Sevnah3K4b5vWOTUp6nj7kufZifLwjZw7cdEfWGHqOZ37Ku8A68P+Q01kDxU5Pq1d3PeSx5rjCaNAKEURGSJSAiCoqp0ikgHg3Tj+QcUmALcAeBHAjQCeGk/PA058pTdyIMR+/uEE92MYxhxikpreRJQD2CwiOwFsQVTTe1BEviUi18W2+TmAIhHZD+BLiD5UHZcJV3oicjeASxEVHesAfAPAdwDcKyKfBnAEwIeSOQPDMOY+KXx6uxPA2aP8/esJvw8C+OBk9jvhpKeqN4/x1uWTOZBhGHMfi7118HT0Ifu38SfOEmDdIzzo1EEYZF2l5x0cL5nZyhpX9lb2oxtcyX5t4Qw+XW8G60a5h514xT7WhaSAdZTIMZYy+8/j4+W0OzqJ4/6U2eT46TlSRMGzfD7d6znWNpTu1LwYYju7jnWyLEdnGyxnnalwD29/7BJ+8h/K4vZlNsR/9/W4dWp526YdrJemdTo1jzN5+0Avv7/vE7eTvebAX5E9UM4Xt+EiPrcwu3iieCePnbDTl6Vbua893VyHtvki1tzyfvUK2b4qHgtZC7nvBvP5/HLTXf2W2xt04ra7FrNfXtVPOfdhw8fZZ7X0R1wHuO+9HJeeGHcd6Rg/9+FEqE16hmHMJyzhgGEY8wZVSzhgGMa8QhCOWBLRt4kUZKHvXXEtIeMYa2ieEOsyTWdwbVCvU+KhcEcH2T3r2Bepp5JPr+Jh9sUKOn516nW+odJY2wjnOXVgM/l4Oc8eJHt4Nes4/hdZdwluYN+vIx/lmhrVj/L5eQec2OA/cW3T+k9xrLLr19d+OmuYmS28v47lju/aK6w5LnjG8eu7NK75ZbH8CE+YNTrXr63qEfZLq38P599z65Wc84+fJ9vnlGZ144grnmU9tmM5+/m59FzPDwnTW/ncI0UsCmY3cL67zk+wz2jhDs4VmftrjjVu+gLXP2lezz6XFfdwvjtdwPrqgi18fQcv4FjgsttZY3Qd1wLt/M/k7Yrr6Z7hqcXemqZnGMa8wfLpGYYxv9DjnBBmHTbpGYaRUuzpbQLegRByd8VjCBsvc3ScetZJSl/g2E/pZV+p+htYUyvaxRqhWyc2ks2+TvL8drIHnPxx3qXsFxgOsG+Uv4v9CrWG8+8NFjm+V4vY7zBwkHWtjMX8vtSz71ggwN3VdQXXrKj6Hddh0DyuHet3arN2LnN86wKOb905jr/WeawrFe6O626BTu679pXsg7n0hn1kv7aG+y53Jx+qYzlf64xmbtvXvvArsr/504+RPZzHbW8/iz9f9STbnmHn3M/j9lc/xLGv4TzW+Grfze3NbGL9t3s9a35hR5Os/APrzerUrdU07nt1ci9G/NyXoYu4zm/aawf48738v9F6Xjy3ZajhxDMfK0zTMwxjXmERGYZhzDMikdk96Z1ojYx/FJF6Edkee119cptpGMapgGr09jaZ10xxojUyAOB7qvqvkzlYMMeHpovjOdkqHuHanU3vYg0t+znOBdh2FdeJrbyP/eIiRezrVHcla1gL7+H9DVzDtUzDadwRvtd5/wPvYg3N78Sbelu6yM7I5Ms7UMPtGyrg9/MPsEa478t8vln1bqwt61D7bmOdrPx59nXL38f7z2h3dK/TuT1lW1j3abyAda76dycc389tq97EGt+bjy0j282H5/q9ta3ithT9nOuh/FvvR8geWsP7q7+EP7/4D3wuvZUsqhVuY81usID1y5bzndpXzhPKZXf3kt1Tw/px6Qvsc9m9isdC17k89nMeZ5/OSAnXVA4HeOxlNLHe7Y7FYx9mn9DSX+4gu+RIPJB6fzePk8lyyt/equozIlJz8ptiGMZcYLa7rEwlXuQ2EdkZu/0dswRkYrr40IClizeMuc5sv7090UnvxwBOQ7QWZSOAfxtrQ1W9XVXPVdVzfRlZY21mGMYcQJHchDfbNb3jUNW3E/CLyP8F8GAyn/P3hlH6fFzbGFjGfnoZbRzzF17GfmsZTv68SCkvMDvWcA60sldZmxg6jXOgdSxlTav8BU5iJjns5xbM5I7y7GW/uNbrzyC76HH2jfIXcvuOrefz71rC8aHFO/l6+Pv5/Bsu5O+s5T9lv7729Xy+x9azb1nxTr4PqXiGV+LyIus+1f3s+3Xk6viXWM4h9u1q4nRtSONQ1ONia93cgEW7WeNr+Tz7uS34PV/bzqVcc6Pma5w/bvgKLrqV3sbH3/MFJ/b1Kb42btx33pusmUUy3Hx43HeDlTyW3P95dwpofx/HUef/kmN30zN4rEgF5yscWuzUM3Ha330t96U/IU478hxrt5MlVXe3IlKN6LOEsthub1fV7zvbXIpouYpDsT/9XlW/Nd5+T2jSGykKFDPfB2BKJdkMw5gjKKCpc1kJAfhbVd0mIjkAXhWRJ1R1t7Pds6p6bbI7PdEaGZeKyFpEZ9/DAD6b7AENw5jbpOrWNbawaoz93iMiexAt7u1OepPiRGtk/HwqBzUMY+5yMp7exjxIzgbw8ihvbxCRHQAaAHxZVXeNt69pjcgIp3vRsyKua+XuYaGn82L2q+upYh2kYC8LE/XvYk3P45ScyH+Tt5c3OEdZRcdi3t/lvL+qTex7lV3P++u6kn2fAt2sE8HR8PqdOrWuTpTWyaMlfzfnr3vrE1xzo2gHf6Me/QDH/mZcyLHL2sM6UGgv+5Ltv5k1P72Fhbnyzawh+nvix4+kOfnzstkeLmWNLnI+57ur/mc+l8Pv5XNd9x5WUN64qpzb+iqZOPgvrAFWPOPULP7zHrJPf4L93Pb+ZC3Zy/9yK9meBayhtbx3CdkFb7Ke7NbdFWdiGMzna5t3iAdH81/z+ZQ/znVtIzncd+E0x4fUiS3OqnX8+nrifoxu/eTJMMnY22IRSbywt8fqZBMikg3gdwD+u6o6xbKxDcAiVe2NBUk8AGCZu49ELAzNMIzUoTj+Kc3YtKrqueNtICJ+RCe8/1LV3x93uIRJUFUfFpEfiUixqra6244wu/M6G4ZxyqGa3GsiREQQldL2qOq/j7HNgth2EJF1iM5pbaNtO4Kt9AzDSC2p0/TeCeDjAF4XkZE8cP8AYCEAqOpPANwI4PMiEgIwAOAm1fGn1OnV9NKArpq4P1feNo6HzG5g3WUol32/XB2kf4HzB4/jW/Uga3Lt72Odpn01L8OX3MvxkdLHukfaTvbNSitkjc6tqSGDrMu45+NqeIMl3J6uFaxrrfjBMbLf+izrWunO91vfi+yrFSrj69vlKB+BVm5f2assknYs5RuDvoXx/fU6dW4lxOeSdgHYnuIAACAASURBVIyHWt6TnC+ubQ1vP1zIbT32ZdbMhp26tkNO3dtAO7dVnZHedznHUbt9s+rbHBfe8knW1Iq28lhJ6+bzHyjl2N6BIidW1vFJLXqdfST7FvL5FW9nDbThCtYUS7bzWM3cUUt2oIp9Qr3t/L8RyeXjnTiSMpcVVX0Ox7swutv8ANHcAEljKz3DMFKHWhJRwzDmG7M84YBNeoZhpBhb6b2NvyeMyifjuthwNfvFDeazrlJ0HxdO6Lp2DdmL/8A6Rm81+yr1rWQdI9DFOlHFM85X0j6OpUUht6/pRq4tuuDxBrJD+zj/XutnWQcKOBpeZivrOr5BRzMrZzuwjDW6ojVcY2NNUSPZ25oryc66n3PE5dSxZjdQxMOhbRVrlCU7WKM89+Y33/796RecWNE3eeBnNY2v17p+ZOp1cgV+nDWy6kfZ70/C3Pby51kj61jBmlVfObcvvY2PFy5gzbHksUNkd13ANYr9/U5fOnHSgwXc/tynuWZIx3s4d6Lr81l/Gbff73irDedzX/ncuPSVfD55B/l6JdYUiRycolOHrfQMw5hX2KRnGMa8IbUJB04KydTIqBaRzSKyW0R2icgXY38vFJEnRGRf7OeYiUQNw5hHaJKvGSKZld6o6V0AfBLAk6r6HRH5CoCvAPj78XYUSfOgf1E8B1vWkxz/WNTGGtTApRzb2nIOf4Pkv8GaXmEtO6r1ncGxqKEMnuNzn3yTbLeWaKiOfbXK7uPj9Z/HdQv8FTzvJ+YOBIC3/oL9+kq38PHy9vP+B0s5r9lwHndX5xbOl/dUjpNDzfGV81zh5Mt7mJO6tjt1JkpeZZ2qYynrUoe2xftHnFKp3exWh4FSbnvJdtbkQpncN/mVLFqFnue47FZOXYicWm5744V8bj52c0PRHtbMsg9yLsXDN3Bflb/Accv+Xv58ej37vQ1WsYZW+jT7WLZcx/pw6VNc9xZevqCV3c6awlmu+Np47Lg1PQr2ciywr4MvyHA+x4lPiVnusjLhSk9VG1V1W+z3HgAj6V2uB3BXbLO7ANxwshppGMapg2hyr5liUpqek96lLCGR6DFEs5uO9plbAdwKAIGM/NE2MQxjrjDDt67JkPSz6fHSu8Ri3UY9VaqREbAaGYYxt5Ho7W0yrxkiqZXeGOldmkbSxotIOYDmsfcQxTsYRva+uJ+eFLNO01fNOkjExxdm2X9ytpiBhaxDBFr5dFxfqcwjjnOTnzWqSCfH1vbduJ7s7EOs22S8sJf3F3Dqwn6Eg1urNrOOVX8T+8l5HuDP5xxiDa7zdM4vWLOJ27vvb/h8wl1sZz/j1PxgEzmH+Ho3Xck6UNYu9oNc9FD8e65xA3+25Fwn39t/sv7o+q9K2IlDHma/s8yLuO8z7mLNqmsxa2AV/8I1MrCOfTxD2Xxt1Mff/wVvsp6ZXs+aX98SHntt5zoa2m4eK2jlurqFdx7m4y/l3I5hx+fT18CaYNuHzya7uJ5zU3o5rB0S4vNpWe/4bNYnjMWprtRO9ZXeOOldNgG4Jfb7LYgW5zAMY74TSfI1QySz0hsrvct3ANwrIp8GcATAh05OEw3DOGWYXBLRGSGZGhnjpXe5PLXNMQzjVGcmn8wmw7RGZKjPg2BhPIZw8HR+mjtQ4NxtO1Otr4/zy2Xu4lhTN5+d5yjLjE3v49qoAcf3qeA51nkCHazBNZ/Hxy9v4HjItstreH/7nHx0t7LOk/cIn78bb+o5wrpY6CzWPOv+HzJRtIk1t6F8voCBLkfXcRJ1Z9bx9c/ZxvvLrWWNNJjgW+fmvyvMYD+wPWc7sbj1PPTS27lt+b/nh16dy/jcO65l0Uo6eP9HvnkB2Qte4b5oW+mMlSDrqWE+dQxns/7s7+e+Kn6R46DRxHbvJeyXl/081+1FC/uYyrkcy1x/CY+9iqdZz225mH1SxSnX0u3UVM49wnHUaS/FfWY9A6zlTppZPulZunjDMOYVFntrGEZKsdvbBMIBD7oXx+8bip/kVE7DGzldT1YjL8G7F/HtZ3oD3wLVXsNhWAt/x6mfyu7lGsFNH+Iwt0gr32IEz+WwuKFCvoXSbL69LdzGbgnt73Buif7It7OFu51QICf9UGhpBdlpvTyaenbxLc9x6Zk83N6BEqcs4CBvX/UndntoW8tuGXXX8j1T1t54e1f8iM99IJ9vt3wbuS0rbuIQwDf+uILsoUJu2/IfcZhW41XcN6UvsTvS/o856eid29nCvSxdBLP42mTV8+3zUBF/vmM5/+tkHnPcp5wyDf3F7FKTk873zz3n8PlkbNpCdtUhlmIkm8d+oJv9j3xOqqv0Zg5T6z7N2f7MuPSjO/lWf9Kk6EGGiFQD+AWigQ+KaInI7zvbCIDvA7gaQD+AT45EkI2FrfQMw0gdilS6o4wa96+qiauXqxCtc7sMwHoAP479HBPT9AzDSCmpir0dJ+4/kesB/EKjvAQgPxYsMSY26RmGkVpOQmopJ+4/kUoAiaXf6nD8xEhM6+2thIG03vjad+AM1qy8Qb4Sbiql7GOsw3SsZc2s+iEOVRpaxO/DsQuddDuRMzlsLPOh7WSH095Bdu8ZrCFmPPAKt++jnC7eO8Bah3eYNcGW8/i+QHP4/AO1TmotzjiOwXwn3XyVsz8n/dPK/8VlAocXc6iYm+oq4me3h9Lf7nr79yOf51xPkXdw2FbWo9y3rz/IGl4oh99f9DBrag1XV/H2Thj3/v/BmlvFPXzu3iHWI4eddPW5BzjkD07YljeL+6Jol5Nqv9RxgQmz3prhuOQgxGPZExx/FpBMHiuhI9x33jN5cdNTxe3NqOPj5e7n/hkoj19Q9U1Rk0t+QisWka0J9u2qeru70Xhx/yeCaXqGYaSMSaaNalXVc8fbYIy4/0TqAVQn2FWxv42J3d4ahpFaUpRlZZy4/0Q2AfiERDkfQFdCyrtRsZWeYRipJXV+emPF/S8EAFX9CYCHEXVX2Y+oy8qnJtrphJPeWL4yIvKPAD4DYCTe5h9U9eFx9xVReAfGfp6d/xprcurn5nn6WYPzLub0OGhmP7uB1azhpTll9dKf2UV2ZJB1JI3w9hFHE8t7zgklWulogg38bebv49GQd4D99JovYP+oikdYJ8pxUlu9dQvrPEWv8sJd/Xy8jAY+ge51rJMNO75qGa2sAwW6nNFcEc8b27+ENS60sf5XXsvvd67ivo0U8fsHP+mUaHQutcc5XPFDjt9bNZ/Lgmc5dX/GFg7xa7mew8RKXuSx6OvlvuhezOdX/CzfUUVyuW8yn+Vypn1Xsj7sHeL/i4HrzyM77Heux3LO2Sshp7xoC+/v8A38v7DwUdb0Mg/HpTLP8NR8TiRFLisTxP2PbKMA/noy+51KjQwA+J6q/utkDmgYxhxmhlPBJ0MyWVYaATTGfu8RkdF8ZQzDMKLM8klvUg8yRvGVuU1EdorIHWOVgBSRW0Vkq4hsDQ73jbaJYRhziTlQAhLA8b4yIvJjAP8T0eb/TwD/BuAv3M/F/G5uB4C8jHLNPJwQ39nIqZ/6L1hOduuZTrzkHtaYhvJYo2pfybpMxe845baGWKML97Om5qtZSHbLJbyg9TgpzcXP7cMQC02Fb7JGeOQq3r7rNNZ9Kv/EYkigk/e3/8PsnFbyCssdiz/L6etDdzvxrE5dpoFC/s7r5sxbaM7j/S/axNfv0IfifopFLzv6YRufS/Pn2OdPD3NsLHqcoZjL5555zPHhzOW2daxgu2wrj5VjF/J3ck49H9+Na+47jbf3OjpXkRNn3buaY42zDnHqJ93A6erdf/rj0uXn89jObOLzST/E+vXBj7HPa2YT72/ht9mnt/Uz68gufZnbOxVm++1tUiu90XxlVLVJVcOqGgHwfwGsG28fhmEYs4ETrpHhxLe9D8AbqW+eYRinHHPg9nYsX5mbRWQtos0/DOCzJ6WFhmGcOmjqXFZOFlOpkTGuT96o+/J5ECyK61LDp7EGl3mUw+oC1ayrZO/jfG9Ztex4HV69hOzOCzk/X/6zh8mWczhedNgpCxhiVyws+O1bZGsRt29gCftCZb7OvlvL61jDO/DxErKPne8svD2sARa84epYvHnz7tPI9i7ir9PqPzm+cqc5+QlXsC9bqJbjRxd8bT/ZfT+L918w240rdjSw/2Q9cqCItx/O4XPvWsv6oasRdjoa3sJH+Nzcf7xAl3NtnZXGcT6cDezHduwi7tvSJ3gshFeyuiMDTjr73bx9lqMfq481vPwgX3sJO3HUjg9r6TbW/PpLeH++mmqys47x+apIwu+YGrNc07OIDMMwUoZg9j/IsEnPMIzUYpOeYRjzhrkQkZFKIj4PBkvj8aXZB1jDGyznvP1lT3F8pPSwc3PwLNawPM9x/ru0KzhrjVvTYriA4zX9T/Hnyzo4lnb4zBre//ZD3L6F7AhX/37e3tWZ3JKPkTTH163KiY98mnWxAscPcP/n3Bxx3L09C1kj7DyLdbDs51ijvOTG18ne0cy+YIWH48evv5ivZVeYjxXiS4/yF9lHsu0MFlC9ndz2tlWsyS1+gP3+Dl/r1Gw8TrNzcxHyBuktfC0jr7PPY3HeWm6fE2cdSuf9h4scP8SDTl+7JSKvYD++zmXclxXPcNy1q+mJU5OjaDv/b7VtYD/CnKN8vk0b4jU+QrVOkPlksUnPMIz5xCn/9NYwDGNS2ErPMIx5www7HifDNE96SjGGMsy+RWmPbeXNS9iPDV7Wddx4xc5PcE2KgtdZ15BBrqObXsvxhlLGx6u7jH2zKh9iv8DB81hTRMStO8tvu75mbkDMYAnrQsE+9tWqu9wZTUVOHdsGzsfXtZy3L3yd919Qztcn734+3p+r2Y+xYLdbRze+/2C2Eztaw9c6rYF9ApvWscjXV8nXJlzIeqOv3jm3JazhhRewRrX8//Dxg7n8+YYL2c7bzbkasZz7Vh0/OXcsFW7imsqhNewzKsLXztXw/H3sN1f9B46tbT+X67HkHHV0N2doeZw4cFfD663m8/cNJPxfTvH21B5kGIYxv7BJzzCM+cRsX+lZYSDDMFJLihIOxPJ0NovIqMlMRORSEekSke2x19eTaV4yNTLSATwDIBDb/j5V/YaILAbwGwBFAF4F8HFVHR57T4AnpAi0xTeRXva1GryW4xczHmO/OQ2xTuHPYF2n+wr2Y+tfkEd2VgP7TvkGnZoVz7MvlFtnoP18rkvgYUkSeW9wbHBOPl9efw/rNoUvcD7BA5/imhUl27l9A4VOnYRO1mV6qhxfsfPZz6+3izW74F7WLLs+xrqW/zC3v/08vv5Z++I6Xc4RegsDA6zhRZyRlnmMr233ar6Yea/xueXv52N31bAf4NLbeX+hHD7+sNMXJTucWNVFfG0yn2M/PS1ljW/YiQtPC3Pfdtfw2MyRs8jOqmWf03C6c4FaOV9f3n9x3LPnrJVk+xu579zYX5+H1zcFu50OS4gj39/rDOxJMMkSkBNxJ4AfIFqjZyyeVdVrJ7PTZFZ6QwA2qupZANYCuDJWau27iNbIWAqgA8CnJ3NgwzDmKCla6anqMwDaJ9xwkkw46WmUkSWQP/ZSABsB3Bf7+10Abkh14wzDOPUYWe1N9AJQPFJKIva69QQOt0FEdojIIyJyxsSbJ/kgQ0S8iN7CLgXwQwAHAHSq6sg6uA5jFAuKncitABAI5I22iWEYc4nkb29bVfXciTcbk20AFqlqr4hcDeABAMsm+Exyk56qhgGsFZF8APcDWDHBRxI/+3aNjKyiau1dGNc6ClqcWqVV7HuUfg4fprea4zPz/sQ5ymru4/c713AsbDiNNa9MRwdxY3NzDrHmKC9x7dKuj6zn9i3jSd3N0dZTyTpUx3LW8II5rEt5gjx6CvaxrtVT7eTDa3dG28OsYfZySjVESliC9fm4vcEqft/j4/b1r4pfP+3nobToQW7LsXV87m69Dunnvh8oc2pWVPL+ax7mvqm9nPsuncvWHhebmtbDtlsnt/9Grtey+De8v4x9HDs7vJD96Aof50K9jR/i/8XSH75AdvAarnPb8lEe+wV7uS8CzXz+aOA49f7LeNHTsZSvf8WfnZu8UELfeqaYUG+ant6qanfC7w+LyI9EpFhVW8f73KSe3qpqJ4DNADYAyBeRkZFYBaB+zA8ahjE/SPLWNhUPO0RkQaycBURkHaLzWdv4n0ru6W0JgKCqdopIBoB3I/oQYzOAGxF9gnsLgD+cePMNw5gzpGilJyJ3A7gUUe2vDsA3EH2mAFX9CaLzz+dFJARgAMBNqjrh0ZO5vS0HcFdM1/MAuFdVHxSR3QB+IyLfBvAaosWDDMOY56Qqy4qq3jzB+z9A1KVlUiRTI2MnogW+3b8fxCTLPnpCivS2uA+QBvjw2U7efm8r+5lFFrNuU/tp9lXKauSrHfGxNpF3gHWQust4f9XfZr9A/yBrbprn1C1wvlMy7+faokNXsU6Tv4+P79/JdXmP3Laa7A4uIQJfH+teQSdlmyc4vl204RjZ/cOOxljHmmTFEpZGml5nP0VvgjuXcNeh29EPl/yKjx0q5WsZyuC+8Dt1aAv3sP4aDrAyU/N79mwIFvP+Aof5XMLFfPwMR18O/Kuj337wHWT7eliUTKvn+iJHP8UaXvEO1uRaP8tx4m7uyNrLS/n9/81x6a2fcT7fwn6DmW/xXd5gPu9vuJD1b034X4kcmVrMwmyPyLAwNMMwUodlWTEMY95hk55hGPMFq4Y2Ggl5xfqWsK7iGXJiTU8rIjv3ENdVyK5l7cHXybpP+ztY53BraNS0LCU7dAHHR4a37CG79RPnkF26ieMh9/9/rLMs+R3nq/McqCN739fYl6p0CwtjWXVOPKWT0+3wdVxTJNDu5Mu7uoHs+tfKyU5vdba/iHWgcISvb9kW7p+21fHPl7/A8Zota1kv3PN33Je+dh56Xqfvs46x7XdiRWu/yIJn9eN87fzOWAgdPkp2cCn3ZSiT9dKGr/P7C17h/Xu27CJbM1lDrHyS48C9baxPpz3G7Yn4+HoU7Hb001XsN1jyImuIHReyiNpXzn1X/qNXyfYsrSFb+uJ6s2fwxGNvAdhKzzCM+YXrCD7bsEnPMIzUoVYYyDCM+cbsXuhN86Qn7DuX+cgOervvGq4tmrOT8831L+P4xnAG6xa5deyrVXi/Exm3muMZw9kcu9q7kH2XPBXsnliw39HY0jnnW0Yza2SRNKc26ULW1Kofd+IpW9mPb6CCdaH2FayTFe9gnWko1/Gv+h7X/IhcwaOx+qesS/UcYL/H1jNZ5xrewF/hufvi5xvM4mOrE74pAW5r9lE+F0/IiY3t5mN1vIc1rZr7WS89eg37GOYdYL+7PB/7QDZewH1XtpWdGku3kQl1S8GezddqoNCpOdHP56tebp+3i/XmcAdrdEU7ObdjKI/HZjCPx27OPewjmnsun68OcX49hLh9A0vjYyXSOrVpwR5kGIYxv7BJzzCMeUNqMyefFCaMNxGRdBF5JZaob5eIfDP29ztF5FBCfvq1E+3LMIx5QIoyJ58sklnpjaSL7xURP4DnROSR2Hv/Q1XvG+ezhGcwhKy98RhIXcl1BzIf4PhCqa4gO/2J18ju/LCTf3CINbLIGYvJVidPmGeA/ZFy7t1Ctq+SNbhwI8ePDmzkeb78Wa6j6/o7tZ7HNSkK7nyRbFnGtVIjfvbDq/gz77/1bPZzzKnj43Usc3QzpybI3m+u4uPluP5ZrPtUPMbCVuOl8ffF8emrfoI1t73LHQ3tZX6/fTUHEjdcxMda9AhrUkOlrHEt/mUt2a2XcNx0zxK+lgV7nUeMjgaZ0czHazmL/fCyd7PPaLpTw1kGeCweupn97hYf4n893cA+oprmaqTcwO6F/PmyfI4FllrWw92e7TrT8YF9OF57x9Pv1ACeBHPCOTmWqmW0dPGGYRjHIZHZPT0klU5BRLwish1AM4AnVHXkUdE/ichOEfmeiATG2YVhGPOBZG9tZ3BeTGrSU9Wwqq5FNEPyOhFZDeCriKaNPw9AIYC/H+2zInLrSOGP4fDAaJsYhjGHkEhyr5liUk9vY9mTNwO4UlX/NfbnIRH5TwBfHuMzb9fIyC6q1s5z4nm98v+0j7c93/EtambdJ3ThmWSnt7PmFGriugWR0xaQndbAdWnh1CqNrGFfMG3gHGzdH2ANMW8vty+yfTfZQxs5fnOogHUZdWJ9+4ocv78mJ5Z4DWt4A6VOvsCDPJKGWLZBdi1v33mGE6/ayhrgkt86scP9rFO1rYn7TZa9yNf24AdZYypk+RKeQ4fILu5kP7aCO/n99k9xXHPxFvZrc+ubFD/JsboN19eQnd7B18rfzarXQBn3RfmvuN50ZBnHurr6bfgtrpFRuo07Q7Icn9CtHOfd9nEeO8XbWM8tvZv/d+BofprLGqa3nxccbs1mnJZwPm+lYUrM7rvbpJ7elsQKAiEhXfybIlIe+5sgWv5x1CrkhmHML6arRsaJksztbTmAzSKyE8AWRDW9BwH8l4i8DuB1AMUAvn3ymmkYximBAlBN7jUBInKHiDSLyKgLKonyv0Vkf+zZwjtG285lKuniNyZzAMMw5hcp1OvuRLQGxi/GeP8qROvcLgOwHsCPYz/HZVojMiSoyGiOxziGWzl/mzh2+HzW8HzPcd0C/2rW4JDB8ZbeXazrBFcuJNsT5N7xOr5NkQqO9e2t5IVxegfrSBnlrCEOOXUcKh/n81Mv6zCSz1qKDDk53Jz41Mo/s07Tv4B1qACHIiOjlc+3e5Dbt/QOPv9jG7muQlYzf77y6bjGd+gDrOHlHOZjF/2MRb2IExvaV81xxjldHHvazS6dUA/HrqZ3cduCmewTueAODqY9rmjWKs6tmN3DfnrD7+D33bEIJ5+et5g1vIy6PrLbN7AP6mAh+xX2VXL7In7WPLGO7dKXWOOURh5r/Rdy/sHsA6wRakKcuBs3PRlS6aenqs+ISM04m1wP4Bcxt7qXRCRfRMpVtXG8/VoYmmEYqSPJW9cYxSKSGJFwe+zBZ7JUAkj0Sq+L/c0mPcMwpo9JrPRaVfXciTdLLTbpGYaRWqbvyWw9gETfoarY38ZlRic9n6OBDaxhXWM4l+MvfQX8PMXfy85GvtPYd2q4kHUWCXNveBw/QPjZTy2SwXb5c1znwNvHfmtDK1in6VrCn89y4jW1gONNM99iv0B4WHPL5/R3COeyBthXxtcr/wDniAul8/5KXmXxZv9fsIa38FHWtY6tZ8208rvxHG6L+ll/bVvDGp2cw/VAmtazz6HrNxdaytey/Hnu675yHrp5W7geiPbxtQ6u5/x3/t1cryTi6KvBbNZHe6v4WueFHEc3R0+WLB57wyXsl9d1GvdF0S7Wb8t//RbZoRWsR6uPP9/m1IMp4jB29Jfw9Qo4PpnenrhP6FTDyKbRHWUTgNtE5DeIPsDomkjPA2ylZxhGKlEAKYq9FZG7AVyKqPZXB+AbiMb+Q1V/AuBhAFcD2A+gH8CnktmvTXqGYaSUVLmsqOrNE7yvAP56svu1Sc8wjNRi1dDiePoGkfZSPMZQqzhfnavh5TzINTR6ruFY1cwDrMkNVbOvWOuZrMuUvMaxrJEc1l2armDfJzc/3UAht6/oV1xLNFLJutaCO7n9KGHfrYFK1r3aVnL7K55jXzW85PiGXcIaZ+mPXiDbu5yd24Yr+fx8L7BIGPGxQ3soi8+38ru8/74b436gQadeSenz7Cc2UO3GDZOJvIOOT6KTny7rTY7tbVtTSXa4mPd/5FOs71Y+zX3fejX73ZU86dShXcT1RTqXOfrq2axR9i7ivgy0st7bU8ka2sLvvMLtueU8snMLuK88294kO7SBj593iM+v7RxH47uPx46nmP0YIzkJ7ZcpOOphDuTTMwzDSJoZThuVDDbpGYaRMqIRGbN71ksqnx7wdiLR10TkwZi9WERejgX73iMiU8xHYxjGnCCS5GuGmMxK74sA9gAYEU++C+B7qvobEfkJgE8jGvA7JuG8DPRcvuZtu6eKNaOyLRyf2P1e1vAS8/gDgDgaWSiD95d7hHUir5PzTJ38dwuUfbkO3si6Ss3XOH506ErWYdKfcRzplrBvVegN1mU8y1nYqn7EiUXuZL/Ato+dT3bRZtahhi51NDnH98zf6ehaH+fti3c48a5LWKeKXL+O7Kz74n56/e/nOO/wbvYzy2znGhFFuTVkBx5lx7Lmz/G55h1i/bXqUQ4sDhWwH9yif2a9FatZwwv0sMbWu5Y1wkAb+yjWbOJYVZfMIzx2O1c5PpitPBbhdfTreh6bkUz2+wufz/VM/K3shxjKd7ZnE5LNfRk6wjVFWj4Xz1cYPOYW+Z0cc2KlJyJVAK4B8LOYLQA2AhgpCnQXojn1DMOYz6hG/fSSec0Qya70/gPA3wEY+foqAtCpqiNfTyOBvoZhzHNm+9PbZDInXwugWVVfnWjbMT7/do2M4FDvxB8wDOPUJkVJRE8Wyaz03gngOhG5GkA6opre9wHki4gvttobM9A3sUZGXnq55r4ejy/tWM46j7zAfm15p7MOc+QLrPEteIV1l0A728EcJ77wgBOWl8e+XcOOLlK6jdVWz5kryO6p4suX6frhVXCdgsAR1nkyXuf4z5BTV9eNTS7cxjpWn6NDZR5mv8XmC9gXy9/H1yP/AF+v1rXcXjceNqPRqbOwOn49MutYYxp29M7OUr5WxU+xHqmF7FfmZ4kMGbVOnPQxrofiH+Jz7bmaaxIPFPL3e/E2p/5HHecSDJ7O19bbxH6CHe9kP8AmliCx7Besx3qGOA4aOXyt21dy31Tt4ONJHo/NntNZb85sZL227DknmWIOa3rdG7nB2Y1xzdETnMKEpDNb9CcZJlzpqepXVbVKVWsA3ATgKVX9KIDNAG6MbXYLgD+ctFYahnHqMMtXekm7rIzC3wP4kojsR1Tj+3lqmmQYxinNLK97O9kSkE8DeDr2+0EA68bb3jCM+cdsd1mZmAPQ7wAABr5JREFU3ogMj0Az4j7MNb90alhsYM0Ou7j2acVzrIm5S+SBMtY9shydSXzsfxRqYd+rtD2ssaW5j9VLWTcqdPLj1X6AdZ6iXRx/KdUcaxzed5hsVwc75ug8C55nnUidGMlQrqNJvsC6znAJ6zq+F9mvMNfLdSt8A05d4AweLt4jTfHfizhuOOMt1uxCl7Ee2nQF+zCm9fG1znViSVvWO9f+jr1kt97INSBK73qN7MGPcJxyvxMrmxHgvvE8t53sYSfOOdDJfnUVz/LYUj/fRIXS2Y9wuGYJ2eVP81iMdHNf++t4LPhyWe91c+A1XejUCHmc8w3m3rOFbKyLx/JOVdND2CY9wzDmCQK1lZ5hGPMMm/QMw5hX2KQXJ5LmQf/CBF1uIWt0faWsixT4OB+c51nWaXo/yPGeET9rXN5Gx1fJ6QzvGawDSZvji7WRdZfhbN5/yd3sV7hAl/HhnNhXN0+ZJ9fxi2tk5+3Kg6wJRo6w5phdwbpOuIH9/IYv4JxraXWO71cB63Dpr7GGOngOn7+/22lPe3x/B77EfbX8R+zT5x1k562Cvewj2F/OuQ/T9rNPpXdRDdmJufwAoPRl1sSO/QXHFZffvYdsyWSNTbPYrv+bC/jzz7Jfn9e5Fmk7OK46fDHr0/5X+P2sItbcwsfYT1CW1ZCNAb5emQe5zm3zRZz/r+xu1mv73slj/eiXeews2hTXb48bt5NBMaPJBJJhKi4rhmEYxyGqSb0m3I/IlSKyN5bJ6SujvP9JEWkRke2x118m0z67vTUMI7Wk4PZWRLwAfgjg3YjG9m8RkU2qutvZ9B5VvW0y+7aVnmEYqUMViESSe43POgD7VfWgqg4D+A2A61PRxGld6Ukwgoy6eFClW7e1fQU3J+0o14Ft/fgGsoufOMgHCPD+NIN1IgmyZtiwkWNlK+9nTS1/Ox+/d4VTV6Cf/fQ8W1k36rjpHLKzMp3ao07t0n5H4zyurq+j6SHI73vyOR7Tu3kbb79qOZnqaIqRHPbzS9/hxMeW8fkH3xn368uuZR3I1cgy6tnvbKCazzXQxT6BXRcsIjvvVy+RXfcPrLmF0jmOunin46OZzmNhaBlrWmn1rHdW/pE1RXFiZ3vP4rq8gcw1ZLu6WO8V/H7OGxw7HLyY3/c9yfk9fEtqyI4cOMyfv5JzM4bWsB6b8QwvkE6vZ5/SuvfE+zb02tRqZExC0ysWkcREirfHYvWBaNamxKR/dYjWtnX5gIhcDOAtAH+jqrWjbEPY7a1hGCllEn56rap67hQO9UcAd6vqkIh8FtG8nhsn+pDd3hqGkVpSk3CgHkDicvS4TE6q2qaqI4+1fwaAb63GwCY9wzBShyJVmZO3AFgWq8WThmiGp02JG4hIYuzgdYiWs5gQ0Wl0JBSRFgBHABQDaJ1g85lkNrdvNrcNsPZNhdnStkWqWjLxZseTl75AL1h4S1LbPrrvX14d7/Y2lsPzPwB4Adyhqv8kIt8CsFVVN4nIPyM62YUAtAP4vKq+Odb+RphWTW/kQorI1iney59UZnP7ZnPbAGvfVJjNbZsUKVpIqerDAB52/vb1hN+/CuCrk92vPcgwDCN1KIDw7A7JsEnPMIwUooDapDcat0+8yYwym9s3m9sGWPumwmxuW/LM8oQD0/ogwzCMuU1eWplesODmpLZ9tPb74z7IOFnY7a1hGKllli+kbNIzDCO12KRnGMa8QRUIhyfebgaxSc8wjNRiKz3DMOYVNukZhjF/SCqudkaxSc8wjNShgJpzsmEY8wpb6RmGMa8wTc8wjHmDuawYhjHf0ImL/swoNukZhpFCkkoFP6PYpGcYRuoYSRc/i7EaGYZhpBaNJPeaABG5UkT2ish+EfnKKO8HROSe2Psvi0hNMs2zSc8wjJShADSiSb3GQ0S8AH4I4CoAqwDcLCKrnM0+DaBDVZcC+B6A7ybTRpv0DMNIHaqpWumtA7BfVQ+q6jCA3wC43tnmekRr3QLAfQAuF5EJK5WbpmcYRkrR1LisVAKoTbDrAKwfaxtVDYlIF4AiTFBRziY9wzBSRg86HvuT3lec5ObpIrI1wb5dVU96ynyb9AzDSBmqemWKdlUPoDrBror9bbRt6kTEByAPQNtEOzZNzzCM2cgWAMtEZLGIpAG4CcAmZ5tNAEYqi98I4ClNouiPrfQMw5h1xDS62wA8BsAL4A5V3SUi3wKwVVU3Afg5gF+KyH4A7YhOjBNi1dAMw5hX2O2tYRjzCpv0DMOYV9ikZxjGvMImPcMw5hU26RmGMa+wSc8wjHmFTXqGYcwrbNIzDGNe8f8DteDe3d80noMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:37,783  SpectraRegion  INFO: Processing Mass 15409.923999999985 with best existing mass 15410.227726869734\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15409.923999999985 [('Il5', 15409.923999999985)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:37,890  SpectraRegion  INFO: Processing Mass 18540.14679999999 with best existing mass 18540.17001130423\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18540.14679999999 [('Il3', 18540.14679999999)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:38,012  SpectraRegion  INFO: Processing Mass 6986.472600000002 with best existing mass 6986.246130093767\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6986.472600000002 [('Cxcl14', 6986.472600000002)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:38,138  SpectraRegion  INFO: Processing Mass 22348.43089999998 with best existing mass 22347.713108521315\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22348.43089999998 [('Il17d', 22348.43089999998), ('Fgf7', 22346.767500000016)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:38,248  SpectraRegion  INFO: Processing Mass 20308.54879999999 with best existing mass 20308.874137320254\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20308.54879999999 [('Il17b', 20308.54879999999), ('Il17c', 20308.787899999992)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:38,353  SpectraRegion  INFO: Processing Mass 24178.823800000006 with best existing mass 24178.29169628363\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24178.823800000006 [('Il12a', 24178.823800000006), ('Ankrd55', 24177.100800000022)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26204.00270000001 [('Prss2', 26203.25329999999), ('Il12a', 26204.00270000001)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:38,454  SpectraRegion  INFO: Processing Mass 26204.00270000001 with best existing mass 26203.548468564775\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26895.941099999996"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:38,561  SpectraRegion  INFO: Processing Mass 26895.941099999996 with best existing mass 26896.240613480608\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [('Cxcl16', 26895.941099999996)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:38,675  SpectraRegion  INFO: Processing Mass 16325.812299999983 with best existing mass 16326.271587357554\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16325.812299999983 [('Fam183b', 16327.27959999999), ('Ccl2', 16325.812299999983)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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K/of9DnPe4jhwRxwfX9Rn9pBdehtrpGPP5+U9bj43PVfzteO4gccz+m2OK+86la/lvZ/yZ4YeTOytMRK0Oz1jzBUDWPeaQNfVx1tFUYJKqEdkDLZ3BsAbIrLueKjZqSjK4PAmET2+08WfaYwpF5F0ACtEZJvPoXA/vslwIeCt9qQoyvFM6BcGGmw+vXLfv9Ui8jy8FdJWWuvsr5ERl5hjour8OlNkJWtEjha2Y8q5ZkbSVvZz234XV6hKeJv7157Bgz9qJWuIRd9iv7aUzayBVZ3K2497iv3edtzMOkraa2y7C1mHiZ3EmmHtdF4/rpQ1up5Ybr9xPGstUdW8PKKBdabEpbz/sHbW0RJ2Wss3soZZdDH/Go97lsevO96/vHEW64W5d3C+upYvcaxrax5fevFPcv68thtZ0yq5lPefuN2qcVHCx2Zi2fMv410WFSvmsuaW6eb9Ra1kP0DJSid78r3c/55Ubm/sI7u5vQXshzfe8sNbs4PfAUo7X4uND7BfXcyzq8muvpZjlccsYw1RnHytVJ3C575vPRe7ctpAMEDIZ1k54ilZRGJEJG7f/wF8GsAnh99KUZTjmX0RGYF8hovB3OllAHje5xgYBuAJY8xrQemVoijHLMdtYSBfke/p/a6oKMqIwZtP7zgtDHQkJOY04/N3+zPAvDaDdRLPDNbYmlkGgSfMim1tYA2sI4MHuyOLNShHDx9u3svs51d2HmuEyZ9Y+eN6WGdJeoc1x8g6bq/5y6xj2Qn7Yvby/juTuf92XQaH5QcYX8ziS8NE1mlGL2RNse6ufLLT3+HEFF2j+fgj6/kXuzWPNb/Gaf7jzXyAE7y5T+ffQ0cvH3xEA+s+9V9jTWrUu5yLcMeV3HZbHo9NdI2lI2VwrG7pp1lDy3mTx273LXxtTBrF7W17n6/VtPXcflsWt59dytdG+yju3rbn+FofVXb4eFdnJy/f8TDXxU1liQ+uctafS79h1WyutDTRJ/yaqtNwjPZAGc5H10BQPz1FUYKGV9M7Th9vFUVRDkawwtCOFjrpKYoSNAwEvZ7QdlkZ0kmvriYBS/5y4X47o5dzinWnsA6S+SGLWLXTuLtxhTy4Oc+WkO1O5yIWznIO3u2YyvGYo1/k+M7Gk1jHkUbWmVIuZ+1Dbub2Inl3cDbw9p1j2W/P1crHUz3T0iBfYZ3G0WHVXl3A67edz/Ggxb/j/Ye18PGNe4b3X3EWa3wZf2Lfu9QEvw634zped9K9XM+jdRbHujq5pAbcLEeiYg7HprpYfsW4v3Js7dZFHOubtJE1vLzl3GDlKawPx67k3I7NJZyPbvxuvnY6szlXo+1DuecKFvF6xrKPadQqbr83gu+OUj7gi6djHOc6nPwnvpbsa3PLL7n9sFrW8Nqz2O7+od8vsuef7DM5UIYz2iIQ9E5PUZSgoW9vFUUZceiLDEVRRgwDrJExLAzppBfe3ItR//HXD22+lH2NEt4rInvnfVyXYPRi1l0it3IOtD3XcV0GW1pwR7AOE97KK/Scze3FsmyErgKO/yxfyRqk6xxeP/N+dp6q/xLXLmnJ41/EnjjWWVI/Zt+skgtZM8z9zYdkT1jCvl+eTtaxwlq4vfF3c461PT/g0qKO6VaNjdd4fMPb/P2NtOKAy75gaXhdfGwdfCqQvp795mqsuORR73IsacQ/2U54ijW1zDf42mifwPpp2zQem6wMFg07H2e9s+pOPtej77NqNq/i/bk+2kV2xVfYT675dNb4IjezxpcSwZpk1Wy2U2JYQ3U1sV9i1G5eP+1j9iHtSuDzlbjZf66LWyyH0AGimp6iKCMGb7r40J70QvvhW1GUYwvjdVkJ5NMfIvKoiFSLyEETmYjIV0Rko4hsEpH3RSSgsNh+J72DNSwiySKyQkR2+v7VRHmKogQ7iegSAPMPs3wPgLONMdMA/BK+FHb9Ecjj7RIADwD4R5/vFgH4jzHmbhFZ5LNv7W9H7qgwNE/yaxGxu9iPzJ3Dukt4OGsLu6+w8r/Vc3Bu0tQastves/Y304oP3WBpZG+wLtO0iPvX+iT7So1dwr5oLTO4RofM5DoOUTXsV9cTw7pL6kbWXXJ+upPshuVTyO4960Tu3yjeX/dsK8faT9gvUvLYF80dZeWU+z8W3mrOYrt+rn+8xl+1gZbBWIHGs6dxWy72w9s7h8/tmAc5n93We1hPzO/i/HUJxTy2VfPYTy2ujMe24Or1ZFd/h/P3ebhEBrrLOBa38HI+Pkc3/xHnObgmcsIe7t+bix4k+5y1N5O98zbW+MY8wD6hjQV8/I5uq57Jem7P2WXFCqfz+l0Z/v2ZwsE9AAbr8dYYs1JE8g+zvK/j6CoAOYdaty/9Hp0vE3K99fUCAEt9/18K4HOBNKYoyvHNPk1vGPLpXQdgeSArHumLjAxjzL76UJXw5tZTFEUZyISWKiJr+9iLfZnWB4SInAvvpHdmIOsP+u2tMcaIiDnUcqqREZ14qNUURTkOGKCfXu1ga2WLyIkA/grgAmNMXX/rA0c+6VWJSJYxpkJEsgBUH2rFvjUyEpypJm75Jn+HR7PuUnaxlV9vE5mIseI1UzazTmM+4PcpNRexrpG/mHUQ4+TlNq0fsCaYXsfr15zDEoKr1cp/V8zxkyVXFJDtSWXftN71rGuVfMR+d2GW5maEL66kT9ivbs+l/CPTdQH7Cbo7WDMd/zirGNt/zONV8Efub2tuH03U0vB2Pngq2RNuZM2v+Wv8oi1qr1VzYgr7+YXVso9mxJ/ZL618Hi/PfYadLI2Ll3fNO5ns9Ic4rrjmW6yHTvw1+92VXMfnMpxDX9Gcy/pqZBNfG1/86vXcv5N4+5ylPB7he/h4mj/DGmdHOq8/+lHWRKsu5Wsp40U+nspH/H877p2D8NMzQO8QRWSISC6A5wBcZYzZEeh2RzrpLQNwNYC7ff++eIT7URTlOCKYfnoi8iSAc+B9DC4DcDuAcAAwxjwM4OcAUgA85Ctb0RvInWO/k94hGr4bwDMich2AYgCXDfyQFEU5Hgni29sr+ln+dQBfH+h++530DtPwvIE2pijK8c2xEHsrxvanOookRGWZ0/Ov2W/XzGHNLPXpjWQ3LmDfLpveSB7c9ky2Y0stv7N61gBd9axReSLYdym8geMjK+ewZpi4i7ePWs1+dXa8Zea7rJkZJ7dnPuJYWHMG616zHvyI7I+uZr+90jt4f4lPsO5VdwIvz7t7Hdm9Z3B/xaprsfcs9h0bfa9/+11/n8zLlvDvadFXWNNKXMX6pauZ24orZgHX8R5rgt3zWZ8s+qLlNxfBupTDwct7W1njc0TztTH2ETIPyL837wqOe375bX6qStzC12LjXD6ejBetGsncHaopDACt7FKJ/Fc6rOUcG9yZyNsn7mY9ujeKdbfqk/3XRsmf/4jO8tIjmrniJmaakx+6MqB13znv3nWDfZFxJGjsraIoQUUTDiiKMmIwJvQTDuikpyhKEBG4PaGdx2RIJz3jdMCd5Pf9asuyfhEu41jSlgPq3rKd/3I72UnsmgR5/2OyK27k+MrRT3LsbM9YzpdX9DlLwzutiuyyjexXOL6K6zSMesVOyMcaoO3b1mPViu2JZ1+vlb9i3zHh8E4kL7XqIKTzxTfmWfbd3PVzdg4b9w+OXbY119xXOedc9TX+7RPe5L5Efsx+YAkF3NmG2ZwPb8w/efvwanZ8K/4xn7vk7azZ5bzC20c/t5bsnQ+w32Dm+3ztdSazqFY6z6oLu4vtN17gXJCZW7g/cYXsM9k8nn0mw9tYQ+xJ5Ys78zkrH9+lPH41M9mHcsaV7NT67nusz7bl8PGNW8I+pA0T/XHjcvgSvP1i9E5PUZSRwrGQT08nPUVRgoc5MMFOqKGTnqIoQUXf3vbB43KgLcfv75T/dAUtr5rHmlr6etZJ7DqwDRNZ14hsZDGi7jbWgTrTefmW23PJnvAt9r3q+e4Msp1WXgWxQhR3Xc75+aKqWMfJeXEv2UmPs0a2+QnO2RZXyrqP7WeX/wcrOHkMa4oQzn+39Yecwy57uSXeOPhitUMoHZWsCTp6/Mdr+0g2n8mCrJ1PrnU065UlX2M/Nuce1kuz/svbl1/Fdt4j3NleK7Y2qpzHruZkPvZRK604bgdfa+Lhcx9dyXbTWKdlsx486j3ub8mF3N9UvvTgruJw9tTNfG4d77DP5joXX+sZZXx8CRus8PhGzi3pcXIuyCPFQDU9RVFGFKEfkaGTnqIoQcXjCe1J70hrZNwhIuUissH3ufDodlNRlGMBY7yPt4F8hosjrZEBAH80xvx+II05ejyIqvH7qsUvtXyZ/sA6Tncsz8lpG6x4Sis2tCXbaS3n9pM38UC3jOH15WT2bcq3dKLI2zneMeYFrqHRG8s6VWcq+0bt+QrnD9y1gu3uiXx88UVkItnyBdt5O/d34gOsGYa3sEZY8Hdr+6vs37xk7s/FrDnujRhHduNJ/nOZkcXrutayntlQwGOTOLWW7Lpi1sBGv8saWFQJXytJb3ARi0ar5LHHimXN+Q3ny7Mp/gVrYt35rDFOvoP1zOLLWGPLecO6lieyflp6nlX9K9bOTcixs7Gf5pDU5lw+oPRK9tuLrLPqCqdYk8p0Hq/wdj7XfeujDDYd3jH/eNtfcQ5FUZS+hLrLymDm9Ot9NScfPVwJSBFZKCJrRWRtd0/boVZTFOU4IdQfb4900vszgHEAZgCoAHDvoVY0xiw2xswyxsxyhcccajVFUY4DDAKb8EJd0zsAY8z+IFQReQTAy4FsJ509CN9Wvt+u+Rn7cjXPYN1j1Er2JXK0cbxm0zSuQ5u2mJ2ddv2Oc651WK5Irkae85213F7TRNZtIq6yNMP63WTXfp1jZ7vmcPxoby9vn/MY6zTOmzi217zEGmfp+Xy6Ysq4/yaKc7SVzmMdLaqGL7TwBn4O6WK3PoS/yrpc2noen7hyv59kTzRrRsVf5XOV9Da3FbGEHw7GVfL6RZ/l/HWu8Xwscc/z/tJWlJDdNsPyWbSov5bjmLPeZ42tawu31zaFz0XGGu5vyUXso+mOtPz69vLYpy3ncxfWwk9BO6/l9iffwoHle6/i/IUts1lvjl3H4xfWYfXnv1xSYmyR30e2yso7OVBC/On2yO70fMWA9vF5AJ8cal1FUUYQBjAeCejTHwfzHLGWi4j8r4gU+qS2kw62ns2R1sg4R0RmeA8RRQC+GUhjiqIc/wTx0XUJDu45so8LABT4PqfCK7udeoh193OkNTL+1t92iqKMTIL19jYAz5EFAP5hvDUvVolI4r7StIfb75BGZHSnRqD4Or9/UXwRxwcmb2PfrK5U1iWccaxz9ERbNTIWsG/ThL9xTYqmqeybFL+cY1ervjSV7NQNrMlt+7VVB/dVrnvbOoaPJ20Zv7hJKOT8f82Wn2DP06xDpe/mnGdxe1iUbM+y8ueNZQ0urJ3HJ7rSijddwb514mY/vtpTWadzx7AGWTfZf/l4TuKxCrcufHcE96UjhZWVdutc230Pf4c1s3aW2BB2GheRiKzna6nxKtbwGifw9uFt/KfQksf964lhe9ziIm5/Zj7Z0SzPQtxWjY5o3l9LNseRx6fxudn+v7z/58/6A9lX//5G3l++VYO5i48vOp+vNRPuvxaN48idOgYYe5sqIn0THy721ckOlGwAfZNilvm+C51JT1GU4xwDIPBJr1YLAymKcswzhM7J5QD63uLn+L47LKGdzF5RlGMPE+Bn8CwD8FXfW9zTADT1p+cBQ3yn56rtRN5f/P5G7jrW3Hrncg608Gb2hbL99Gou4+47OngOjx7HmlTaBt5ewnj7+jPZV2vKdez7NTaMl79Rxfn2oiq5fVcL+zs5W7j9hH9tIRvTJpJZ8ynOL+hhSRMx5fwY4ey0NLv/cvxo+ac4vrMlz4q1ncGxxAWL+Edz+/c4Vnjcv/zre97jsXQV8rXXa9Vt3Xk1a1h5y1hPjGji/SV/wHHFpZeynlo+39Kw2iwfxUoeK7H+6iKauP3E5zn/XOW8DLJNAtcUzl7B13LlWTy2sRW8/84E1nNjqvlaSbuGj3frvQVkf+UB1vBGP841k9v/kke2cxvry9u+z+Mfs90/Xj0lg7kXCswdJaA9HdxzJBwAjDEPA3gVwIUACnxpXuYAACAASURBVAG0A/haIPvVx1tFUYKHCZ7LyiE8R/ouNwC+O9D96qSnKEpwCfGQDJ30FEUJMsd4aqlgYqIi0D0tf78d3siaVUQl+3rVz2RdpC2bg0Mj2Y0NYx9lDW7L7bz/2L3sZxaVyPub8ABrbuV3s99bk+VHN9qqYdEymk92+s0cm7v3Qc6B1voZrp16zbWvkf33pfO5vVc4p5u0cbyliWAdq+xidmbrmWCtX8i+cWOvZI2x7nL2Jogut7SeVRv3/3f3YzNpUd5jrLk1jbES3MXyWLuj+FK0a/bWfofHPpZPNSbdz9dO+af52nE18e1H7rN88dj66d4zWcPLXsl+f6UXsV5sk3fRHrJ3fJBPdkQdXytJW1l/bT+DHQnjP+Hx6z6Dj7dl9ySyU57n/XezBImMTPYDrGnyH4+di3DA6J2eoigjCp30FEUZMfgSDoQygdTIGC0ib4nIFhHZLCI3+L5PFpEVIrLT9+8hE4kqijKCGDo/vSMikDu9XgA3GWPWi0gcgHUisgLANQD+Y4y5W0QWAVgE4NbD7Ui6exFR4vdnKryOY0nzX2Q/sVorUcz4RWvJbr2EdaTKC9kZLPcF9o0yTvblajmRdZvqk626sou4fXC6PLSn8fB1Xch1Eja+x75Vns+ybjPxNtboXv5kLtlRo/jKcG/eTrZd2zXik1Kyc17j46no5PyDYRdwnYqes/kAI5qs2qlfKyO7ueK0/f+PX23VE1nPfS05nzWqSfdY9UUSWF9szuWxHfUun8twywey9hSrzqyVi7F5PIta5RfztWfXMA7jMGmUzmWhK2UTj03DJL67qbP85MY9z3Vqe0/lfHhl87imRuonfHy2zpaxhH0uoz9k/RhpPB7uWM61mPQ19itM/Id/eV2dNRgDJcRrZPR7p2eMqTDGrPf9vwXAVniDehcAWOpbbSmAzx2tTiqKcuwgJrDPcDEgTc+X5mUmgNUAMvqEfFQCyDjENgsBLASAyLC4g62iKMrxwjA/ugZCwPEmIhIL4N8AfmCMoWcHn2f0QQ+VamQ4ow+2iqIoxw3ifbwN5DNMBHSnJyLh8E54/zTGPOf7umpfwj5f+vjqQ+/BS09cOKrO9WspyZ/wPFl1Kt8JOtt5uTmZfZHiP2FNLGw86xidSaxpuVpZh2kcy4efssmqq9vMfm2eBeyrhZcsP8JS7n/Wem6v2sk6jO1XF7mefbs6k1gT3P1bzglX8LcaskuvYj/A9lHc/oSlDdyfXtb4GnlzuF18YXY9yr53yWv9p7xjHO9L4nksoqp4X+WftnL1seSEmL1W/Y4EK666lPVRu25sXCnvMLaYz2XNTP4BHvMCi3gVczhWNdLyq0t8Zh3ZDXdYcePtPPaFd7D+3JvO19KEa7kub9cFXN8l6z2uoeFx8bXdW8Bx0Xb/M1fx8X/0Jsd5J9zl13fd3x/krdqxfqcnIgJvpuStxpi+mQuXAbja9/+rAbwY/O4pinLM4QnwM0wEcqc3B8BVADaJyAbfdz8BcDeAZ0TkOgDFAC47Ol1UFOWYYWBJRIeFQGpkvIdDB9PNC253FEU51hnON7OBMKQRGQIekNrpdvwhj1bSDtY9Wu9g366qeit29pccTxixdSfZZT8+g+zuJEszDGOdJH4n60LtG7hOQ+4W1kk6k1kn6krg/Yc38fG6txeSXf7cCdz+02QiZSPvr/hSjq01llgx/hnWqepm2jU0eP3mMdy/nLf4+BomsG5WfpE/XrUj3TrWmawxOTnUFg6rtKqzk7dvHst9yX+Bz60nwqoBbGmAriarjm0K9z1+F7cvH3zM+xt3GtlVZ1oa3RI+V8IujAf6Gf6XD7jhOtboqr/D12bz6Tz2ETtYoxNr/OJKuH8Rjda1cgEff8H/sl9fxefG7v+/aRrktBDik55mTlYUZUShsbeKogQVfbztg9sFNPvvojF20Qe0fM+v2SWjNZddOrLv4sfZxIkculQxjx8vI2eyG0XaBn5cdnZbjwQbOV+Ru4ZdQiLO5kcQTzjfKOc9XkQ2wnl43Wn8eFxlPdJELrce71eyC0veMn7E+2Apx+lF1fLx7LmEH4l6Y3m5JPMjYPKb/AjkKuNQJcdYfmR19Nk8rpgWoZdPDTLv53PdeCU/Pqa+zSF0vWWcqn7v9/nayHqYXUbikqaRbYd1hfHT5AFjVXIHn4uE2eyBNfkmS5rYxtJE26WcJizm2dVk772F9x/9b5Ya4vew1hBdy+cicTWPT/PJfC46kvhaPPNba8h+eyn3b+tt+WRH1PqPzzPYWSHEX2To462iKMHDIGguKyIyX0S2i0ihL77fXp7rS4bykYhsFJELA+miTnqKogSVYMTeiogTwIMALgAwBcAVIjLFWu2nAJ4xxswEcDmAhwLpn056iqIEl+CklpoNoNAYs9sY0w3gKXiTnNgt7dO8EgDsRQAMqaYX3maQudof6lV4H+s6Ex9hDan4Tu5eQyVrehnLWYOrmZdLdtM4ntO7rIx/Ex/i1Eq7buA4rHH/4PbS17Husvcs1hDbL+MwrZT17ALTyt2DcfKZT9jBy5vO4PREr/83n2wZw88IyVtYs4zfw5podA23F7eKr5Hd3xxHdkoqH39LHms1o1f4hbKuVHbviXmXU0uV3ciaXOcsFtnCOzgdfPmPWbPKX8bHtv1+K8+X9Uc0YSm7N5Wdy6mlulKs33tre/NUGvc3m/XPyNrDp4u33aO6UvhcZa/gdO+7vsRjHTeF/xaa8zhtWnQVdzhxF/sEvfgh671J3VbJyxq+Nvu6L8lgoyUCf5GRKiJ988UtNsYs9v0/G0BfIbMMwKnW9ncAeENEvgcgBsB5gTSqb28VRQkaA0wbVWuMmdX/aofkCgBLjDH3isjpAB4TkanGmMNO2zrpKYoSXILz9rYcQN/b2xzfd325DsB8ADDGfCAikQBS0U/yE9X0FEUJLsHR9NYAKBCRMSLigvdFxTJrnRL4QmFFZDKASAA16Id+7/REZDSAf8CbJNTA+9x9v4jcAeAbfRr5iTHm1cPuzAM4O/yangnjX4Tti6x8exyJg8wvWb5bEax5Jexi3acjg3NsT1jK6dzFzXfB+T9lX7J6y5cssoFTT9m38QnbeDgz3mTNrPVq1qnGPc7np+w31ul4gX25oip5vMI5Izoiyvn4mr9hhS7F8YDGbmcdKf9F9gMsnc/t26FeFWf695/z8CZa1jOD9dHoSh7rjIctvTWR952zgo+1JZs1qDQ+VQf4BVafxBpezm84ddPO+/ncxu3m/jgtDawnjs9NZBKPXVs698/JEiDG3byKbPtvPjeJU1NlnMU3K42P8rXSfC6nHWu7lc99zDuc07fxDE7FlfwOa7DuKP94D9a5eNCaIABjTK+IXA/gdQBOAI8aYzaLyJ0A1hpjlgG4CcAjIvJDeIf0Gl9uz8MymBoZAPBHY8zvj+SgFEU5DgliKnjfTdSr1nc/7/P/LfBmgRoQgWRZqQBQ4ft/i4jsq5GhKIpyICEehjYgTc+qkQEA1/s8oR89VAlIEVkoImtFZG1PT9vBVlEU5XjiOCgBCeDAGhki8mcAv4S3+78EcC+Aa+3tfH43iwEgctRoU3maX0vIeoc1sr2fYV1k8m85vXnxF9gPrn0K59fpjmMNr2Mi+y5tm8yaYexWnqdz/sy+UfG7WAOL/y1risWbx5LtaOffkJZpnPopnF3H4I7j+Mqo51iHiqrl40tZz75dFedwuvpt32HfseiPuT+Ocj7+7d/kKy+2iNe30xW1p/Pynj7dLV/Isa9pG3jsw63U/w0FfOwtLM8ieYud9ouXR1plCoVPHSrmWBrbBPZBjLOOtWU87y+hiNuvn8wdqLXKh3al8vaT7mdNztaHm8dw+3aqqOa/cWmE1NGs2UWXs0bX9nf2K8xdX0F283S+FmOLWBDuSfCfD2fH4GakUE84ENCd3sFqZBhjqowxbp9PzCPwelAriqKENEdcI8NXDGgfnwfwSfC7pyjKMcdx8Hh7qBoZV4jIDHi7XwTgm0elh4qiHDuY4LisHE0GUyPj8D55B8HV7EHOm/6XGc42dmbqvtrSaTpYF7Jzoo1+g+2oF9gXq8nSUboS+cY26y0uIdl2zmSya6bz8EQ8OYHs/F3c/+iPOalcx1TWIM3pfABF8ezrFVnPw9xwAe9fClmDPOFsTof/0fZ8sruSeX9pG1g4Gvss+zWeeP9Gst8s4+Pt3Mjtj3vMr1ttXWTpo8tYjzV5vLxsPp+LOdP4WIo2cInCyHq+Nag+iTW7OE49iPFLOa7aXVhEdnQVa2DJW/kvtWkMn/uODF5ux4l3p1uxvXlWedBRfLy9MVYctNX/1Kc4ff2OX51Idjqny0OnlU+v7quZZI/7G+ezb5rFPqP1k/zj2bNlkBEVIa7paRiaoihBw66DE4ropKcoSnDRSU9RlBFDECMyjhZDOul5XA60Z/n9gTzhHDDZu5x1ieZZrPHFlbFd+SXW/Mb9h+siNEy26hrks99d2jr2FWvO4+EQbg7RNfyFo5fPrieDdZzwNzeQ3b6As+iMeZs1u9J5nP9u3G/4+KrmsJ9dx/VcAyTsSu7/uGfYr68ngeMtw+tYY3z3T5yuLKWI24/Itq7mPjVAJn2HX97vXcj53JxdvG3uyzyW60s4KW5WA7cduZtjT6s/z2ONPXwt9Vga265v89hPXGzFGd/JGmFbLY912vs8tt0ZvP+K0/hasmN9W60aGq46bq9lDJnouGkG2ZFWGH2Hlc4vvpj12saTWYPsLeYaGzEZHFfdnOf/2xnCfHrDgt7pKYoSVI75t7eKoigDQu/0FEUZMQyz43EgDO2kZ4mc1Z9l3SYu1sr3dgXXwNj2AOs+Se+w7rL7lqlkj32SfcVKPss6UPso1pU62HULqRt5ecME1mHyH2Pfp93XcgDpmE4WaiY9xL5dJQu4wehKbr/mVPZt64ljjdLRyvGXYe3W8nbWDDvHcH69qlncvpzGOlddEfsRhjez5pq0yd/etzexj9/P/8SaXuarrCnVn8k+jHbNh4aJrG92nsHrj/sdx6JWWvVKwsv5WBK2Z5FdczqPbexz3H7245z/rvxWrnmRttIK9j2N/d523Wvl69vFY5e+js9ddyL/KcZt4Ith+69Yv839Hf/tNJzA5yr3ee5exDvst9dzcRHZOY3+2NzSRisQeIDoiwxFUUYWOukpijKSCPU7Pa2RoShKcAlSwgERmS8i20WkUEQWHWKdy0Rki4hsFpEnAuleIDUyIgGsBBDhW/9ZY8ztIjIG3gK8KQDWAbjKV5T3kDhbOhH3lr+4q9vF8ZWJm7iubONnOd5w8j0cT2nCuft7z2fNztbwOtP5XXpXFc/5saVWDjcna2S271XxItZ5XOwWh+0/Zb/B+A/Ylyxht5WvLpX7k/IIF4LY8TD7etn5+sb8i2OJK+ayM1fCHo61dfSyRpn9M8vXIIxzrnUnc//Lz/OP78Onc13bjFoeq865XAOi/gSr3kerbXNXoius/HYnsoYV1mbV77iXfRIzfsnH4gm3/PJut05eIV97STtY3932PdbIJj7I+esqz+PlWS/sInvn9zkX4/jH+NpumcHbu7bytV55Bp+LsE4+/pL/sfL7fY/98vb8iPVm0+fS63rwyB8AB1gC8tD7EXECeBDA+fDWvF0jIst8KeL3rVMA4McA5hhjGkQk/eB7YwK50+sCMNcYMx3ADADzReQ0APfAWyNjPIAGeMuxKYoy0gnOnd5sAIXGmN2+m6mnACyw1vkGgAeNMQ0AYIw5bOnHffQ76Rkv+353w30fA2AugGd93y8F8LlAGlQU5fhm391ef59+yAbQ95V/GQ6szTMBwAQR+a+IrBKR+YH0L6D7WN+t5joA4+G95dwFoNEYs+/d9sE6tG/bhQAWAkCkI/ZgqyiKcjwR+ONtqois7WMv9pWXCJQwAAUAzoG3GPhKEZlmjGnsb6N+Mca4AcwQkUQAzwOY1M8mfbfdXyMjPibbeMb7/a0aJlqxtnnsi5T9Dgs77WPZtyriZtZRsm5hnabmZNZ9uqexb1TvTvbtiqpnTaslx+rfj1jDs3OsFfyDdaHGatb0Oq1w0VYrx1rzJPaPyoxhv7qoVNY8O5J5/9GRVv6/Bu5fcy4vtwvR77qSxzfrv1ascTfvL3mrXyN017KeWP1dHqv0B1njG7c+geyKK08gO7GQ9ceiS7mvcdu4HkqYVYMj+X4eO0c7nxtPImtiHS9wzYu6C3hw8pbztehqtH7AI9ivMPOJzWRvuZ/r1DpZwkP9yXzt2/nx8h8pJLt3PPsFOlv42jZOPpfbvsv7C2MXVvQm+a89Ez5IUS7wzWuNMbMOsawcwOg+do7vu76UAVhtjOkBsEdEdsA7CVrZBpkBvb31zaBvATgdQKKI7PsrOliHFEUZaQT4aBvA4+0aAAUiMkZEXAAuB7DMWucFeO/yICKp8D7u7u5vx4HUyEjz3eFBRKLgfZuyFd7J7wu+1a4G8GK/h6EoyvFPEF5k+KSz6wG8Du9884wxZrOI3Ckil/hWex1AnYhsgXc++pExpu7ge/QTyONtFoClPl3P4Wv8ZV9DT4nIXQA+grd4kKIoI5xgZVkxxrwKqyyFMebnff5vANzo+wRMIDUyNsJb4Nv+fjcGWvaxvQNmzab95qg49t3qjWZNrm4a6zIeXoy02yyRbOM2MuPTppPd4WLNLOtxzgFXcSXH7kbWWfnyWLaBcfCN8vZv2znYWBfqPpt9xZwfsuY46k3e396vc/+zEjnW17WeNbfdX2BfrOx3WRez41s7U1gXy/0XKxT1c/jdVM1FvL/w7X5dLLeB/dq6uCuo/xr78Xm4aSTuZBdPVxPbSev5WuhmSRCNp3MsatZrVv2TyezCVWz5Gox7woplteqj7Poin9vkjYe/Vek6hTW8uE3sN9gyga/F3gi+uLtY4gNSWaMLq+LY450L2a8vbR33z9HO+897nY+3N8q/vP6wrwH6J9QjMjQMTVGU4KFZVhRFGXHopKcoykhBq6HZxETBzPDrVDUzWedwcjq9AzS3vdew5lZ6HvtKZUewr1fULtZ1opezL1bb2exu2Hgy60gpq1h4imiyapWWsi4TV8K6SfVsqy7EX9k3rIolTXRYdWpbxrIiHP4Ua2wtl/H2o97l/kR8sJ33fxYf78Qfsi9ZyW1c5zZxI4s73XGsK7n7nL6KOXwu8v/E565uAZ+b1DXsKLb9myzSRWTy2MW/RCa6z2dNK/Np9llsnMUaV91UPjcTFrLfoJ0vDy5LYyznP5Xbf/Yo2b/45dfI7sjgc9k5g30sI7dbGuEWrlfSlm1pmOls77mOt4/70IoT/94OshOvZZHVuPjadnX6NT5H5+Dy6emdnqIoIwoxoT3r6aSnKErwMFoYSFGUkUZo3+gN7aTXnehA8UV+LSJuj+UHZ/Wm6NFcsjsrrTq4O1mnaU9nR7qGT7Guk/+sVTv1TM43l7KK9xdjtRe7kWN97VqiYWezO2PKTK5zsKMzn+y811jELLqQa6fmv2LVMr2e40cjPVZAzbusq9VczhpowxQe78jv8vhG1nNAaOMpXFfCyeGdpOllL95Ey0q/PY1sVyO3Xf5p9rGccPM6siu+zYKnuK3bh9WsATZM5sUzz99KdvStXL+k7Mes4YVZerJYfm12fr+bnmANb+xarplRfh472qW+zOc2cSOvX/ZLq/7I03y8YS2sMU74djHZnXNYry37E/sJusbztdwdx8eXuK4KwUJfZCiKMrLQSU9RlBFDkDInH00CSTgQKSIfisjHvjz0v/B9v0RE9ojIBt9nxtHvrqIoIU+QamQcLQK509uXLr5VRMIBvCciy33LfmSMefYw2xKOLiCuyG9HNLFuUXEm+xqFFbLv1QGdtfLBJazg2NvYf7EvV91XOf4zeQsLOXut2qldSdxi6XzOYRZRNZrszjzWXdKW5JM9upSX90Tz/nPeYg2vM5mXZ93Aolr5xey3V/QljqeM3sEDZFw83kWXsC7mieBY4AR29ULaS5zTrehbft3I8wLva9Q89oNr//ypZDfHsKbU8iIfS8+73HbzVB6biX/hY93+bfb5rL+ez1XhQtZ7o7mkMjLv4/52/cLW/Cz9uYXHtvArrFFGWYnLGyZZFyvYby7qRV4eXcXH57Dz5Y3h8eqO4/uXhklsj126l2zPdNa7TXSf8XMceb2w48I52ZfJ4GDp4hVFUQ5APKE9PQQ0pYuIU0Q2AKgGsMIYs9q36FcislFE/igiEYfZhaIoI4FAH22HcV4MaNIzxriNMTPgzZA8W0Smwlt6bRKAUwAkA7j1YNuKyEIRWSsia3s72w62iqIoxxHiCewzXAzo7a0xplFE3gIw3xjze9/XXSLydwA3H2Kb/TUyorJGm45Uv3aRvmwPrVt9yjiy43fyvhqm8c9D9pNcS7Tm81N4fTaRt5w1tarZrOEl7GJfpsQ17JdX/AfWGKWIdazUDM6XVzuLY1XjrmQ/wap3c8jO+/Vasuv+xePROI51nI5s1rlit/DNdnSl5ZdXz79x4ublSdtY4+xM5/2ZLPY9G73C77xWV8n6Zt1i9vErWMKaVP1kjiWNv4U1t+pFHKualsA/mM4a1rxyXub2Si7mc5P8kVU3dxbnBmy09N6ct7i/TWN5LEb9jjXAnX9izTJ6E2t0aRv42tvzde5PztNWnHchi4Jlf+JrL+N33B8HX7pwWTnxdvyaNcfE/7Pq/mb6r9WevVbiyoES2k+3R5wufpuIZPm+E3jLP35y6L0oijJSCFKNjKNGII+3WQDeEpGN8BbrWGGMeRnAP0VkE4BNAFIB3HX0uqkoyjGBAWBMYJ9+EJH5IrJdRApFZNFh1rtURIyIHKqyGjGYdPFzA2lAUZSRRTD0Ol9NngfhfbIsA7BGRJYZY7ZY68UBuAHA6gP3cnCGNCIjvMWD7Lf92szkVznWs3AH6zLpf2ddJ8wqHGDX/kz++wdkyzWs01R/jzUr13LWkVzNLIzsvZg1t+R/sIZWfRL/Wrk7eX9Jm/hGum0sLx91Fte8KL2Zf6gcLBth1IesM+25hPfnYJkK1WfzF5Pv4XjP6nM4v2D5Oaxx5t3P8bS901lj7Mjw60p22/n/5rHJvJf125rHOS5YenjsxzzA28v7LPBWLuRzW38qd2DUa9yfvefxX6JdNzeyns9ty2jWzBqtSs/Jszm2eNJdfHytp+eTHb6O+1/wX9Ykq7/OscZd8XztOR2cf9Aer5iXPiLbdRb3r6eIr5XYVayH7/nm+P3/t2PgB0IQ/fRmAyj01eKBiDwFYAGALdZ6vwRwD4AfBbrjI/dCVBRFsQn00db7eJu6z7PD91nYZ0/ZAPpm9CjzfbcfETkJwGhjzCsD6aLG3iqKElQGcKdXa4wJSIc7oA0RB4A/ALhmoNvqnZ6iKMElOM7J5QD6+kHl+L7bRxyAqQDeFpEiAKcBWBbIy4yhvdNr64Cs2rjfXPdTq3+f4u4U38U6y+jfcFKzjizWoMLmWrpIEvtK9X7M8Y7pl7Gm5vgFa4bxRdz+3k+x/1I8yyJoHs/9T6pnHSnxet5f6edYt4mtterSWv1vzmOdaeJfuQZI8QLODwgPb7/tNvYbzH3aqoVgiTk1l7HulryVNVZ3hH//nVZ9j7Ysq0jwHVx/I6Wb9UlpZb01rJc1q4U7Oe73ppdOI3uCFYtb+D0+lvBw3l/LeO5vfDHbzm4+F2HtfH9QczLnLswq53PRmcjXSsO3WGNL2MP9aTyBr5WIGqsObiP7NSbFcn8jU/jclpxx+AApZyfnUuyJ97dvBummFyRNbw2AAhEZA+9kdzmAL+9baIxpgtdrxNumyNsAbjbGrEU/6J2eoijBwwDwmMA+h9uNMb0ArgfwOoCtAJ4xxmwWkTtF5JLBdFE1PUVRgkqwQsyMMa8CeNX67ueHWPecQPerk56iKMFFq6H58STGoH3u7P12TAlrdLlvWDnRijm/W2se/4R0pPLTecbTXOc1KYJ1JI+LdZDKLtbUMiJYF4rdzHUDCnbwcO28Np3slFe4DkJznqUDncR+iPGFfHH0cllcGOvsJBay7lUxlzW8iAbeX8ck1o1ML/enfjL7quW8zrpU9Rkcr7nnEtZQC37vT7jXfuVE7svpvK+iXI6FnfBtll5Kb+L8dWMvYcH0lrWXkh1VZeWPm8waW+4/Wa+sm8KD68nisYot5voj9SdwrGt3ouXnt4evpY4T2Gc0/ZoisnevGEO2LeTH72AhLfN9juMuuoT7k3InJzusuz2fbHcUNxC918qtGMZ2XJF/PJ0cJjxgjvl8eoqiKAEzzGmjAkEnPUVRgoY3IiO0Z72A3976Eol+JCIv++wxIrLaFwz8tIi4+tuHoigjAE+An2FiIHd6N8D76nif0HYPgD8aY54SkYcBXAfgz4fbgXgMnJ3+o5Uu1pwidrKGlrHW0jVuZF+nmHLrFyWdNa6SC3lOD29i2667255h5TRbzbGq7mljyY6st+oa1FgBqPbwWrUHnD3cfuJmzhnXmssaWvVJbPey6xbC2rg/k37FNUKKvsixtpmr2e+uYTr7ehnrJ3H8Exz/uesHfs10/MNch7Xeqh+SmGIdewGPZVs2/xVse5vjfMd/qojs3TE8Fi1j+Nhjy3l/MZVsZ73Px+5oYrtuOmuEsXusurRLOTC6+rusSXp+y3pxZi8LZdUz+R4hrpT7t+tGvnacO/n4trzEGmpuMed+jKxm/dgmYvl6srOS/JprUWOnvfqAOC7u9EQkB8BFAP7qswXAXAD7igIthTennqIoIxkToI/eMNbRCPRO7z4At8Ab+gEAKQAafQ6EwEGCgRVFGZmE+tvbQDInXwyg2hiz7kga6Fsjo6dba2QoynFPkJKIHi0CudObA+ASEbkQQCS8mt79ABJFJMx3t2cHA++nb42MuMQcY5x9tIkwnnOrPmPHA7KO4QnjgWoaTybC29LInvRn1uS2fYs1e33pFAAACA1JREFUK/Fw+8lruIaFpLKfWsNY9vXKeWAD2XWXTef9fcyaZEwF+/HVzGS/xLrprFNFVnP/7DxnvVP5RyT8XRb5uh5ijTFuKY9f43jLd83af/oqLrRg7mNfNsfb/vFpm85+alN/wLn4Vv2bx6byPNYX7Rq7dp3ZbamsEeasYz248lT2c+tO4IOx6872RvBY1Vqxqmn/5fVjKnksS+5gDS9+l1WPpMaqCXICa4Qxe3n9hB3ssxr2L+6fs5Pb70ri4939Va5jO/oN1ii7UllDLLz3FLInPtwnt2XrIJw6zPAW/QmEfu/0jDE/NsbkGGPy4Q36fdMY8xUAbwH4gm+1qwG8eNR6qSjKsUOI3+kNJuHArQBuFJFCeDW+vwWnS4qiHNMEJ7XUUWOgJSDfBvC27/+74U3prCiKsp9Qd1kZ0ogMt0vQnOtvMvJlTnefXs6anF0Dw9HN8ZQ7r+R4xAirxkXVPdx+nFXrs4NDZ1F8KetMydt4f12JlsZ2IouKNadZdXN3st+fq451ltaxPPzRpdw/OwYy/SP+ohKs+3isFGo1y9lXLGcd1yTpTWBNTz74mOzin7JuFf0k5yOUPjJS9IdFtOyT+9mnMqWBNanIFVzTYedvObdi5ioyMeEGfo9mevlayHBy3dmGAh7LnrEct9zRxsc+6UHWR+umc6xw3Ql8Lm3n2upzLc0tka/N+BLur8PK19c0gTW/8DZu4Oy72S9w1XTuT92vuWZIw2Q+vsSdHFee+QFrfFt/6NdnO381iIR6BoBbJz1FUUYIAqN3eoqijDB00lMUZUShk54fd4xB06l+/6X4Un4PUj+Ju+OwNK2EYtZFoitYY4sqZ7+4sD+wTuIq59jeynks6oWx7ANHr5VzbS9rdkUXs6aWYmXnr7d0lZS/sl9fwSTW3CqyWQdqruX9J1jxn5F13L81P3uQ7JPvvp7srv9l37GTkrkW66q7WBfryLP8/Er4/PRM9muUDedxrGzrKCtW1dITi57iQrKTbighu3EO+2w2fJn9ympms+aVv4zPTU88t2/qWcNK2MPb2xpeMx8Owiy/+pgKHvu85ayZ1c5gza32RB67vGUcx9yRxnqpXWNj9ddPIrv7MyzgJm3l/tlx3XZ9ldZc9kMc/7j/2mioH8SkZTCsyQQCQWtkKIoSVMSYgD797kdkvohs92VyWnSQ5TeKyBYR2Sgi/xGRvED6p5OeoijBJQjOySLiBPAggAsATAFwhYhMsVb7CMAsY8yJ8CY/+W0g3dNJT1GU4GEM4PEE9jk8swEUGmN2G2O6ATwFYAE3Zd4yxuzTWFbBGw7bL0Oq6UmXIGK3P/7UY9UitWP2XC38a1B+Ns/RLpZF4Ghi4WXPV7nGRvoa9gPMeJd3UD2HY3OdHdw/Z5eVs62E+5P+LPsduhs5n13XRaxLRS5ijS0jgnWc1m/wgNRMt3KwFfP4PNTIdRhSN/H+XUs5lnZLMseztl7I7YfX8vHZtW1jVvljhRM3cZxzwk6+tMrns37q2cJ9r53LeqbD8vVKWcOaYO35rNeKm/uaf9sHZLvPYU2s8lSOg+6azj6UuX/lsdj7KdbEMlZwqHnFfE4yZOvDiTv4XNaezBpe7VksYCd8xO2V3GrVvFjOmuH3f/Qvsh/7xmfJdkfw+DReYMUS94lF7t41yHuhwDW9VBHpq4Qv9sXqA96sTaV9lpUBYNGZuQ7A8kAa1be3iqIElQH46dUaY2b1v1o/7YlcCWAWgLMDWV8nPUVRgktwXFbKAfR9FDloJicROQ/AbQDONsZ02csPhk56iqIED4NgZUVeA6BARMbAO9ldDuDLfVcQkZkA/gJgvjGmOtAdixlCR0IRqQFQDCAVQG0/qw8nody/UO4boP0bDKHStzxjTFr/qx1IQmSmOSP36oDWfW3nb9cd7vHWl8PzPgBOAI8aY34lIncCWGuMWSYi/wdgGoB9BUJKjDGX9NfukN7p7RtIEVkbjGf5o0Uo9y+U+wZo/wZDKPdtQATpRsoY8yqAV63vft7n/+cdyX718VZRlOBhALhDOyRDJz1FUYKIAYxOegdjcf+rDCuh3L9Q7hug/RsMody3wAnxhAND+iJDUZTjmwRXhjkj84qA1n2t9P7Dvsg4WujjraIowSXEb6R00lMUJbjopKcoyojBGMDt7n+9YUQnPUVRgove6SmKMqLQSU9RlJGDCVbs7VFDJz1FUYKHAYw6JyuKMqLQOz1FUUYUqukpijJiUJcVRVFGGqb/oj/Dik56iqIEkf7LOw43OukpihI8gpcu/qihdW8VRQkuxhPYpx9EZL6IbBeRQhFZdJDlESLytG/5ahHJD6R7OukpihI0DADjMQF9DoeIOAE8COACAFMAXCEiU6zVrgPQYIwZD+CPAO4JpI866SmKEjyMCdad3mwAhcaY3caYbgBPAVhgrbMAwFLf/58FME9EBP2gmp6iKEHFBMdlJRtAaR+7DMCph1rHGNMrIk0AUtBPRTmd9BRFCRotaHj9/8yzqQGuHikia/vYi40xRz1lvk56iqIEDWPM/CDtqhzA6D52ju+7g61TJiJhABIA1PW3Y9X0FEUJRdYAKBCRMSLiAnA5gGXWOssA7Kss/gUAb5oAiv7onZ6iKCGHT6O7HsDrAJwAHjXGbBaROwGsNcYsA/A3AI+JSCGAengnxn7RamiKoowo9PFWUZQRhU56iqKMKHTSUxRlRKGTnqIoIwqd9BRFGVHopKcoyohCJz1FUUYUOukpijKi+H+2vI5CeQnfgwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:38,787  SpectraRegion  INFO: Processing Mass 10892.872099999986 with best existing mass 10893.392019390252\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10892.872099999986 [('Fam216b', 10891.48299999999), ('Ccl11', 10892.872099999986)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:38,898  SpectraRegion  INFO: Processing Mass 11658.704599999988 with best existing mass 11658.522558545646\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11658.704599999988 [('Pate3', 11659.626499999986), ('Ccl12', 11658.704599999988)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:39,010  SpectraRegion  INFO: Processing Mass 11017.193999999998 with best existing mass 11017.140942882841\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11017.193999999998 [('Eif4ebp3', 11018.495599999993), ('Ccl8', 11017.193999999998)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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8ejXefxtF5AkMV0h71dpnf42M2NkzzL5f+Pyb3O+xL1Hum+yL1HI0529LrGJhxTmP6yi0LE4ku+QJjj8cyGYdI+0PrLFJjFXC8QLWGAfSeLh6clhHaV/M/Zv9F9bo+lYuJXvm79mPruZ09l3Lt/LlpZSz1lKwioWlpovYr656B2t08x7leMu6j3C8bPK9lqNjM1fM6juR29t3lk93c1gyUN46vpbRr3ANiohMvrZtZ7DGNrDLKl9p3RjE1rAenFbK59KbyR88W4+N6ONrFVlaRXZ/KscKD6RwexGDLBp2nMjnG1PBc8nFEhuiW3juzHiN54KZwZpmYrU1V/6b7ciNfIBYLo+CHd/g8S5axRpr2ed9gej9dx+8y4kBQt5l5aCXZBGJ9xbrgIjEA/gogPcP/C5FUaYzIxEZgbymisnc6WUDeGI4zx8iAfzNGPNsUHqlKMoRS7AKAx0qJlMjoxzAMePuqChK2DCcT2/6++kFTHS1B0U3+nzvXOVcR8ixmDWpts+zBpfzAsdbln2J4ytz17NO49jC+ewil3Kdg+qbuc5sFBeZR/ZvORa452L2vc59lDW13Gc5QV/dedw/F0t2yHmLdSBnC+tETcfy+TuGrLoMeayxOSxfssLnrbq+ufxDUleJFS/azVpMy0PsBxjxmv0N7utP0dOsX7Yu5JPtOZn1zPTt3LfYZtaoct7mqVl9Lp975cXsg5m6k88l5+43uacn8/dzx2we27brOB9eyrNWHPM93F7EItb8Undwe42Wm75/DQoASKri8+lP5/NtPJ7PL/dNji2Oe5HnmritGhtNPL5tLm4/5sXNZEd/xXc+DufB++kB0IQDiqKED8Oa3jR9vFUURRmNcAhDUxRFATB8p+fyhLbLymFd9DzRkegv8cVvmtnsO9SfauU8e47f7yqvJDuhijWz+PfqyN5zy/FkD+SwbjR3Fft67b6CdZnOP7AOlLiFL2bH/exLlfcz3u62fLMytrGG113IO+Q8XkZ28wrOFxjdzbqQiWW/OjufoGOA94/dzXUXMtO4jm7HbKsmye+4f51FrBtlfNTX3t6hLOvgbMIqUNWTy2Nl+9V1F/PbnfXct/yXWUOsX8YaYqLD8qGcY2l4C7j9kru5/brvsE9jVyHrv5HsQomcDVbctPXJ6izh8+tdwBpdylvs1+c8iWNlWzr4sxLVzePZvJSvdW/dgT/ae/73RLLz7/d9NqqbJvd4qjUyFEUJG/TXW0VRwg79IUNRlLBBa2RYxBf04oRf+PyDXrzvJNo+FM+DFdnHukXfRUut7dy+GWTNzB1j+bXtZtHLRLHGN+8B1vjaFnEOt6he9l+Kupd1Ini4Q13zuf0Z/2EdpyeXY4XrLp1DdsoePp/OIu5/byb7aiXUc/8G0qzznc9+fZEDPD7ZG7m/FZ9kXSwqzcpvWObTmVL30SZEd3Hbaa9Xk/3BrZz70GnVlU3aze/vnMV2/Ums4RWsYp9MzOQaFu1WuY9jTmEfyy3xXGN47s9YI2ubz8dPX8/BrZ4U9oGM6Of+OVu5vfgaq85uKs/97J+yntqXzU6YnijeP7aar1Xuep5rds3k6Ea+lq3H+fIT2m1PFNX0FEUJG4bTxeuipyhKuGBC32VlXMVRRB4SkUYRed/vb2ki8oKI7Pb+m3qgNhRFCQ+mSxLRPwK4B8Cf/P52E4CXjDG3i8hNXvu74zXUVR+P//xi+X7bEc3b0/eyplR5qRVP+Ax/gyRVWPnqjuX8eqml3H4Py0io+ISluzSzRmf/CJVYxbpMQjnrIi3Hck632L18YXd9kfufYvVvkNMLoi+DL09/OreXuZXHK7qDNcCKa3n/WXdammQXn2DrAj7/JCunXdx5fL6Of/j2b7d8/Lr5UqB1Mce2ilWfI6ad9285lc9lwe3sN1d/JvtI7vsSi3YF/2TNLarT6t8NrG/mWP3fdS3rodGVvD19C09eRw/n64u39NWUTdyf3VfzZEystIp6/JTjzBve4vEreoodBbtnsKYY9TZrnC2Xss+pI5fPr3WR7/+uFzEpQv3xdtw7PW8m5FbrzysBrPb+fzWAi4LcL0VRjkBGNL1Qzqd3sA412caYkfCHegzn1lMURQnaoiciBSKyTkQ+EJHtInL9KPuIiPxaRMpE5D0ROW68dif9Q4YxxojYBfeoU/trZETHq/SnKNOZIPvpuQB82xizxZulfbOIvGCM+cBvnwsAzPG+lgG41/vvmBzsotcgIrnGmDoRyQXQONaO/jUyYgoLTJNfyJ8nhnWPtNWsi6RuZJ1CPKwDRde0kT20gOM/M578gOy+Ly8i25PHvkx9Eew7Vfg8+0b1WzUyWn7G2/vW8cWOr7ViVbdZsbOW75Q7hjU/ZyuPjzva9mPk7Z0zWaN0N/Px2+dZtV+XWZppDW/vP4o108RVHP/Zm+nbv28ej6UznjW5wtv4WI4+1iMbTuWausWP8kNI3bl8bQfZxRG9s/l4O7/PAmn2Uzz2sMw2q25t6ps8F6KsuOfKi63+PsYfgaZLeOwGki1B2SKpksej5WHW8IaOsvIJfoL111j7ExjBc6nwy+yX2Hx7CdnZG33n32DFFU8IA7iCFJHhfZqs8/6/S0RKAeQB8P9grwTwJ2OMAfCWiKSMrE1jtXuwvVsL4Arv/68A8ORBtqMoyjRigppehohs8nuNWUZWRIoBHAtgg7UpD4C/a3y1929jMu6dnog8DOAMbwerAfwAwO0A1ojIVQD2Avj0eO0oihIeTODxttkYc8J4O4lIAoC/A7jBGNM5mb4BASx6xpjLxth09mQPrijK9CLYsbciEoXhBe+vxph/jLJLDQB/LSDf+7cxOawRGY5oNxKKff5WQ5v5h42uYtYpMu+z6tIey5pc1aV8F5v/NHvWVNzA+0eymxnSXuL4xvh61ujExTqKXYcgI44b7N3Ofnptc9kXKmUP6zYRPVYt05WsI3mi2J7xOhfxKP8ka56xVs45u/aprYk661n3sR3p5/6Uz6/yYtYMU8p87cXu4LFcsILryO5ZNpffa8UVd5zOmqDDuja9OZb+WGvpj9Gsb6Zb70/dwJ+D2gu5/odYZSFy1laQ3X08x/IOzGP92S7Mm/pvvjatPBUxZ1UD2eVfZAeIoqd57BOreS60zbHiqu0giJl8fmVrrB8RucwwUsp8c9Ge5xPFBGnRk+FSi6sAlBpjfjXGbmsBXCsij2D4B4yOA+l5gIahKYoSZIIYbXEKgC8C2CYiW71/uwVAIQAYY+4D8DSAFQDKAPQC+K/xGtVFT1GUoGFM8CIyjDGvAwdeQb2/2n59Iu3qoqcoShARuD2aRHQ/0VVu5F/r+/HFOFnHQTv/MNN8Befbi+xnrSGq08q3V8jOW8m7WcNqXWzFklqaV93JPBxFz7KvVfLLrPPsWsq1T2Ov5vjQ7LstXSqbdZneTNbIxMq5Zmt4eOs9MuOXct2G5ArWJBOsmiF1K1jnyfsPO2TVnsaaqiuZ+zeYyv1rn+0Yc1v3mRxMG/MZq17HPr7Ws77AfmRlv2T/0oh+vnZ2nHLJQ1a+uHquMeHK4jcMWBJXlPWbYOWVLHoNHsVj5djHY9O8jK91xKDllzjE/dvxPdZ/Zz3In4WWxawJDibz+11W/RVnq+XH9z2ey4WfYU+Pir8uJjuh1tegiZjcnVqwNL1Dhd7pKYoSNDSfnqIo4YX5UOG7kEMXPUVRgoqmi/fDExuFvoW+WrUxDeyL1HhWDtnOT7IvU8pX2LerZzHXvfVYsakJtbx/6vtWrdRTWNjJf4V9rzpmWZrWMZyzrWQttxfVwO83NXvJjsni2FWcwOeb8b51fnl8/KHLWeOMr7c0ywV8OatWsu9XwVPsjFb2Oc4Jt+AuHu9dP2SNVKqsyexnxs1mPbP9M+xo31XE4nbPDB6LiLPZFmPpm2+wXgnrbqI3m8+laQnn24vsseOguT1nI1878fD+ZZmsd2a8TyaazuZrl/w267cmktvLzWbNcyiJY3mz1rPPacvxaXy8c7i/BZdvIdu9g5ONdD/NCQ6THufz6Zjpu5ju13DQGKimpyhKWKHV0BRFCTM8ntBe9A62RsYPRaRGRLZ6XysObTcVRTkSMGb48TaQ11RxsDUyAOBOY8wvJnIwt1PQNtenvbRfavnNlbOddBfrHB/cwtsL/83tu5y8hjcdw85MaaUcr5hQxxpX5EubyW7/KWtohc+zhuf6PusuUZexX93uHx9Fdkwz9y/a8g1LeauJ7N5s9quzY2Pjq7k/KetbyK79OMeLVl/EOlB0LMf+ehItX7M9rCme9lH2E6y+2tf+7mT2OxtYwH2Ns6Iho1bwucb9lt+f/oEVJ7yTnSrbTuF8c+1zeGyjLBfH+Ea+1k5Lf42w8vsNpViOcBZuq75L2mv8h36WFBHRx3M3+Vv80ZN+Ho+hXB6P1FI+oaF4rnlc92322czewH6Fcbexn2Lt5Xy+cPn653FO7ufXI/7x1hjzqjeXlaIoyriEusvKZOJFrvXmpH/oQCUgReTqkSSBrr6esXZTFGWaEOqPtwe76N0LYBaAJRhO5/zLsXY0xtxvjDnBGHNCZGz8WLspijINMAhswQt1Te9DGGP2O3SJyAMAngrkfeIBInt9976z/8K+Ug0nsObm/ibHT6KChZLBRF6zY5tYp8h7je+z953NvlOz/sw6Su11rIvY+fc6i1jnSbidfctMDmtgc/93Jx//yyx09RRw/7qP4ToKtq+Y55OsIUZ+k8+36WzWuXLX8fn15HN/3TE83ns+bcV3pvD1eflN1ijTjvftb5eGSuQwZTg+yX1xPsAPB3svYQ0v6z+WaGbYpzHtGvaB7Hqaaz7YfnxGrLq3hVZuwG2sj+67xopjXs/XvvU4Sw/u4LnosCSzWQ9wf/39VQEg6vlNZO+++USys17j/ua8wp+N0m+yBuiJYj+8/kwr9rmS++v0q6fS1DvJ2NtJvfvQc1CLnlV442IA7x9of0VRwgQDmBB3WTnYGhlniMgSDC/qlQC+egj7qCjKEcQRH5ExRo2MVYegL4qiTANC/dfbwxqR4Y4Cugt83wItx7Nuk/0G6w5177KOU/QK6yzxH9SSPVjAfn0mkr9xohayY1zXQt5/gMMbUXw7xzMOLV9Idvss1gjTLT+/Xb9nXSaplNuPrbPqPKSyI15CDcdzRv6Ofa0qPsea3M2fW0P2Hau5SF3xv1ik3P1F7v/sv/Hxas5gXWgwha9P2kO+GiaDN7Ae2m+FGffv4LGOWsKaUtrbVj2Sz7IGGLmijOyOFM63V7iRa3KYRO576f9wHPG8ezh/nfSyXfJznpvNS7h/c/7MGmBnCR/P9uPztHGsbfss9qFMeI41yaJf8/v7U/n4O67hyXrG0Vzj+T8xc8g2Lh7v/MdZdCy7xjf33M8c/KqlsbeKooQXBoAueoqihBP6eKsoSnihi56P6LoeFP7ozf22OWUJbR9IYyEkroa71zaPNSyA88Xt/bgVu7vTqhPw/yzHu4FGMrsK2Hfq7p0vkX3119hPLWMra4StV3KsbhSHwiL3jS6y3bezzlNRz0LY4Hr2zUq26uYOZLKv2A+f+xTZiRx+ia+ufoLsO3afR3b5Vzmec+7P2S/Q4+Txb/mK73yTK1lvPfH/sd/ZugdZg8u8902yq29mTbBjO/tkpnE5EnTnsv7Z8WnWyLrm8lhFNlpTXTj2dsf17COZsNfSHEstfbWaL25CDPfH8RrHKVc9xrkY4yzP1qb1PPdiLR/OrAc3kt1ZspTs6htnkT2/geeWe3c52WV/OZbsqEqfvisDk3k8lSPfZUVRFCVgjP6QoShKuKGPt4qihBd6p7cfiXXCMdenbew9k32b7G8IB0tWSKpgP7G4vaypyRDHH9o1JEwV+/U50nj/yD7uwIo1N5KdzLIRqs9h368Zr3KH4+p4eIeSWLN0/IB91wriWRdqPJ6Pt/dSq5ZqJ0+u2Y+wiLfnEk7w8H8/+TzZHXO5facdc7mLHQt3/eposmNrffs7hrjv63/OmtPPf3o/2d91XU22K4HPrWQt+81VXZzFx27k/WOb2Y44jfXTmAKrxsYj7KOYyRIkYjp5//bZ1rUbYh/S1gXcXkwB+2gO7uGxjbdjgx3W+bTw3N15P+vfC37JmmL1+TZElOUAACAASURBVDyXEmo4Vjg+h308s5/i/tad5TtfEzXJWzW901MUJazQRU9RlLDhCEg4EEiNjAIRWSciH4jIdhG53vv3NBF5QUR2e/8dM5GooihhhAnwNUUEcqfnAvBtY8wWEUkEsFlEXgBwJYCXjDG3i8hNAG4C8N0DttQ/AOyq3G/mvsmaU/0y1hkytrGv1d6LuLmBZI4/TCrjb5g+ljmAFYvI7M3mNT91Fx8v6w2u5SodXKcgo4Y1QomxdJIfce1RxxDrQu0ns69Y4mbe7ubmIN1WbK6VE23PdWwnvs7jEdnPmmP2Jp55vRmW79tFrCNFWH5/xU/4dCUTwccyVq6+a1dzIh6HFZvrimcNK6qO/cx6c3kwXLF8roMpfC5R7/N3cM4abq9xWZLVnjVWA9xevzWXHLeyj2fqHewz2nwUX8tZD/NcclTy3EltayO74jarxvFOvjb1H+G5b6zbl+guK99fG8cK59/GscpNb/oJvO5J3qmFuMvKuHd6xpg6Y8wW7/+7AJQCyAOwEsBq726rAVw0eguKooQTYgJ7jdvOKJUYre1niEiHX1XG7wfSvwlpet4CQccC2AAg2y+RaD3s8Ajfe64GcDUAOEXTxSvKtCa4j65/xOiVGP15zRhz4UQaDbhGhogkAPg7gBuMMeQrYowZ81T9a2REI2a0XRRFmTbI8ONtIK9xMMa8CqB13B0nSEB3eiISheEF76/GmH94/9wwkjZeRHIBNI7dwsjRIuBI9fnG9afz4V1xVk41y08v403eP/uKSrIrn+GcZDkb2Ner/L+4PdPH7aVZtVZ7Zlp+fxy+iD3fWk523jp+f/Ie3j9tO4tisS3sS+VsYY2v9lT+kpj3IOtC5i72RWvs5thZcXHsbkwHD2jM65yDbegi9sOzdSFHHsefVv/U953ZvY81shXLt5Id8Wn2a3OncF8j2vhcyr6cx9t5aFDwMmtU0eWcf++DWzmW1mzfTfbgD9lJMeYp9mOrX84fylk3cqxwzyUcS9xxDfe/p4rnlieW7YE1PF5dj3JsbvFTfH5Nx7JPa9Zm1pfNxm1kR+byeFd/dibZqT9jH9NUv1Dnej70xAn8Ti9DRPw9JO83xtw/5t6jc5KIvAugFsCNxpjt470hkHTxguFMyaXGmF/5bVoL4AoAt3v/fXKCnVUUZTriGX8XL83GmBMmcaQtAIqMMd0isgLAPwHMGec9AT3engLgiwDO8hMMV2B4sTtXRHYDOMdrK4oSzowkEQ3C4+24hzKm0xjT7f3/0wCiRCRjnLcFVCPjdYwdTHf2hHqpKMq0J5BfZoNyHJEcAA3GGCMiSzF8E9cyztsOb0TGQEYMyq/2aQuZ73J8Y0Q/r62tVv687E2siTWtKiY7NpJHO7qafZ+ynucfmHtz+Ea3P4Pt6E7WtEwkb/dYMYr7zrdqcrBrGOLrWaOrPZvfn7ydt5f8na/f3pXsLLYylTW5p/59KtmxViyx/eVa8V32wzvuHI61Lf8dJ7Er/D37ikW1+8XettTQtns2bCD72Av+m+yOhTy2SbtYU7PHNoLlWVSdw3po7pucjy77db5WEXm8PfIF1msz/8r1UDp+zPnmXGdzIHRCFc9Fx2rWKJMjuP/1J7HnQu9W1ugKqvmz4Ohnn9GYNn5mtOO4ByyN0eXkix3Rb83Vc/haeuJ818P1cmjE3o5RiTEKAIwx9wH4FIBrRMQFoA/AZ70/qh4QDUNTFCUkGaMSo//2ezDs0jIhdNFTFCWoHK7H24Pl8C56Ahi/2/7Yf75Nm4squcRiTxE/MrQuYBcMdzTfwuf8kd0k6q84huy4Jn5EcJ3Eqal63mA3grb5/IiUGc3bZ/yHzA898uyzUk8NxXN78VYYWfZv2C3CdTL3v38h+xI88roVqmQ9vqZ/aS/ZlS8Xk21PzneeW8D9/Zj1CPckP1Km1Pg9v3t4bBfdw4+zcdajdkohP/snPc1jW3jJPrKrH2F3pKwHudxm8+X8+NldRCY6iwrItsPW2u9ZTHZErPU8bcV5NR3Hc9NjheF1LOLH9+gWPl6S5c5U9QV+vL3leC5VsGYBu6D0XsyPs03H2dILt+8oskol1PDj9bwHfNvbmgP/+XVUQjwMTe/0FEUJHgYTcVmZEnTRUxQlqOjjraIo4YUuej5MJDCY5rv37fwch3ENWaJUdz7bMeyBgoQa1k08R88mO/191mXKvsCnywoh4LBCnTLetXSZDtZdBlJYOLF1nqJ/cmjU3v9lN4Oi/8duCWYxhyLt+BK3P2Mt97/uFO5vwZ/LyL7yanYbWfUTTmc04y3WHF97nVNvxbzPuk/LUTyb687zpTdKSmPNKCmmgez29ewu1N/CY/XDu/9K9s1/uJLsoUI+dtqprMGllvG1drhZf0zay9ujG7m/Tcs4VVO/VY60juVTuJ2WRrmL7Xm/55DBPbdwe5nf5xKRUb18gDvf/yTZEd/k4894iUNSe7M4lZYdwuney+Md087PoDVn+1yGhqrZnWXC6KKnKEq4EGjaqKlEFz1FUYKL/nqrKEpYcaTf6YlIAYaT+GVj+HTuN8bcLSI/BPAVACPC1S3eoN8xiW4zKHnCp4tFvsS+VlWPHUV24jOsQ2StY03K08y6RuvFrPO0rWDdpuBh1siW/YCz0LzyNGuMDcssP70tVuhPMn+jfUhjjOewssR/8PkM5LCm17KY9895mXUXZwvvP/tR1hj3XsWa5gNXcXqmjitY59r7nFVCci3rUGWfY80vup3HI/kd//HgsYnax33r/yjbC27nY9268kqy46ySjv2D3NeGE/hcCh+rJrv7tHyyB5J5/0yW1BBhpYfvnsv9Td7Gcyfnjo1k13yTS152FbNGmGiFdrV+iTW8mA6+1oNL2CfTbZXY7C1jv8ZBay7mP89+kG2Lef/uSzgVVuQrPk1PJulyMtn3H2omUyMDAO40xvzi0HVPUZQjiumg6XlTwtd5/98lIiM1MhRFUT5MiC96AaeLBz5UIwMArhWR97wFPEYtASkiV4vIJhHZNDTUM9ouiqJMJ6ZBCUgAH66RISL3AvgJhrv/EwC/BPAl+33e9M/3A0BCWoEZSPEdsvIu1tBi3uU12E5v3nAOpwBv/QinWpp9H8eKJt7Hpxddxxrgf37Nx28+jTWzgie5P81HcXs5b7Fj31ASb68+m3UUjzXa/Wms4Q2wDISc9fwl0bCUNbbIXtbRbC3F8do7ZA8uOZnsoh9wrG/Df/P2OJZQ0V3IByj4yVv7/x//aiZta7yb05NnbuCxrLgsi2z7B7+kSv5UOPjSQKwMQl1LODY1fTvPne5cq4TiMtb4ok/mNF6zf84+ikN8KbHrLo71LV5rpdI/kzXAjK2c3r38Bu5P0n/Ya9TVwMd3Nlppz9p4rhf+ieOs3bmcS7NxOY/Xgm+w0+vub/hO0I7bnSih/ngb0J3eaDUyjDENxhi3McYD4AEASw/UhqIoSigw7qI3Vo0MbzGgES4GMGptSkVRwoxp8Hg7UiNjm4iM5G66BcBlIrIEw92vBPDV0d+uKErYYKaBy8oBamQc0CdvNNzRQGeRT8uYtYY1q8gm9h3yVLLvVaKTNbCs11nHaT+GNb6EfRxvWfpNFs2yX+evm/x/WenQu9hXK3uTlXL7o6ypzXiVdSSnlUPtMzc8T/a/v3MW2X0dfDlqT2MNz82HQ5T1u9DgMfyHiHWsgfbW83b3tuPIbj+GhbPCf1mxz608PhU/8/maeTjMF1lWund7Bg2kW+UlLT88O1V/45k8NsVPs55a8XGeGwlV/BATa+WIS6jl/nUM8tzpKuL9U0p5boqLr03FZ8lEZDPb+z7K+5saPn6i5deYxBIdKi/i/vRn8vm2LGYN1fbbs4NxO5dyfsH0d339qTt8JSCnBI3IUBQlaAhC/4cMXfQURQkuuugpihI2TIeIjGAS3elG3kt+/kFlVbS9zqppYSJYs8vaxJpU2TWsMZkBK/9dA/s+ZXBJDsy8ZgfZW2o4XjPt71y2r2cG60Tp77HOIm6+2pFW2b0Hn/go9yeBt2e8ymUUO47nsoWxzewL1pfJIl/GP/l8a7OKyU6y6lQ0H826T2w61wypO5V1qJz1PL6uGt/06ZhnxR1Hcttp73Lbkf3ctrOVNa3eGzh2NPVR9gOsPpPPNZ7lX0Ra55q8ZhPZO+/lEo8xtTyX0nby+ey8mv3mohstvbOKr0VPPs+NvgI+v9x1fLymY9g5LrmC359j1WMxDj6/Hp66iLT03qz13N+ufJ7LM56t93uv5RQ5UXTRUxQlnDjif71VFEWZEHqnpyhK2DDFjseBcFgXPU+kA4PpftpIOteE8Fh+aC6riIUrkXWPOZezTrPvexw7Gs0p25D2h/Vk73Fx7G1EHuseSWXcwGAiB2AOJLEuMhTH77fjRbPesXSeNH6/iebza53Puo94eECyN7PG56xm3SwpltsbyOD3V17K/Vt4I7//g1t5/5rTrXhRv9qthc/xuTVfxbGhLVY+t6guHqu+03ksIko5dtR9EmtsOa/y/qnP7CR7xw/ncfv/w1GSMVZccdYW1tzqlvNHY85qPh8YHvveGVa+vq3cXtOx7FcX28h+hi4nT/6kMo7VbZ/HuRjdMQfOTly0lmOJ2xdzPpCUcp6crct8sdCu5skF3+oPGYqihBe66CmKEk7onZ6iKOHFkb7oiYgTwKsAYrz7P26M+YGIlAB4BEA6gM0AvmiMJXTYbXX1IvJlX12MXQ+cSNujElnHmP1jjp2Vfm5+8HT2tSpew7VW917CtVZ3/9rSdZpZF7F1GEdVI9ld32FNKzWRdZ7Um3g4XamsiVV8wqqBsZ51sIYzOcdc8aN1ZIub9/c0coDnjtu4Rsic66yA2PN5vC8/kfPpbYxmjTXlHdZ2+ng4kbTXN141p/G5561iv7YePjV0zuJPRsFzvL3xWNbs5t62m+yGS+aS7ZnFybzn/3gX2Tt+MIfson+zRjiUyNfW2cpzo/os9tkcSrI+2fkcsFr4ILeX+0se68gcHszyS4vIblrO4zfv9xz7W/Z59nPM3sBzo3E5x5knV/Bnp345z0X/hcrW1ifCkVACMpB8egMAzjLGHANgCYDzRWQ5gDswXCNjNoA2AFcdum4qinLEEKTUUt6M7I0iMmraOhnm1yJS5s3gftxo+9mMu+iZYUZuwaK8LwPgLACPe/++GsBFgRxQUZTpzcjd3nivAPgjgPMPsP0CAHO8r6sB3BtIo4FmTo7w5tJrBPACgD0A2o0xI8831RijWBDVyMDAaLsoijKdCNKdnjHmVQCtB9hlJYA/eW/M3gKQYiU3HpWAfsgwxrgBLBGRFABPAJg/zlv830s1MnrOXrZ/24Jf8fl0LWAdYueXWdeYfSPXGo12sy7TdSyfr5tdp5D+Dq/xrUezDuJy8nZPPgtRM7+0h+wddy4kO21fGdl1Fywgu8jKAWfX/W35PvsZ9szneNOmJfbl4u+ZTCvfnxy/iOx957DO9K97TyM74hRuvf1o1jijm/j9/r5iSeX83rhd7Cc2mMBjmbmV+1q/jNsuepY1Mk8J5wZM28F6b9NxVpxwM/u1zbuffS53Xp1M9oUnbyH7lYdZ/yz5Iye4c+Vz/r3G4/l4bfP4/AaW8bVNsPLpzbBqHLfP4fFoOJn7G8tyMzo4nR4K1zbx9qO4vxnb+Nq2LArib5qBa3oZIuLvbHu/d70IlDwA+/zskZuvutF3H2ZCZ2qMaReRdQBOwvCqGum928sHUHPgdyuKMu2Z2A8ZzcaYEw5hb0YlkBoZmd47PIhILIBzAZQCWAfgU97drgDw5KHqpKIoRxCHr0ZGDQD/FNAB3XwFounlAlgnIu8B2AjgBWPMUwC+C+BbIlKGYbeVVRPusqIo0w7xBPYKAmsBXO79FXc5gA5jzAEfbYHAamS8h+EC3/bfyzHBso/iMYjs852tu5R9r2pv5cPMv4mFi6obl5Hdn8ZfF7Me49jRnAHWBNvnsN9Z1gZ+f1wj+zLt+hLrNMX/Yo0ufg+351pQSHbeOvatcgyyBoljuL3i/2NdqfFyHo9Iq3ZB/rOsm+U9xEnlyn7AmmNUF3/H5TxaSnb/ibPITl3N8Zlxt9eTXerwCUnOJvZr2/l91qBidvGx+7N4LFI+IBMtC1mQdVlxzV1zWJOKSWMfz7oI1judrdZcWcQ3BKXfZB/H3CFur38e53bszeZrn/sKXwvpZh/O0htZk8x/ibdH1nEd2qSdPPe65vB4xv+dfTA7n+Fr576D9eemyzmWuWQpBx/3fuAbr1CpeysiDwM4A8PaXzWAH2DYewTGmPswXKdnBYAyAL0A/iuQdjUiQ1GU4BHELCvGmMvG2W4AfH2i7eqipyhKcAnxiAxd9BRFCRpaDc3C5RS0zvcJBmYx+y7FbuP9++azjhLVxaOZstvybVrAvlqdRawj2XUH7JoXjSusGhc1vL3iM6xDFT/GmlfjCRyfmfkOi3BDSRzUWPFpbj8hk52tMu7j9ntyWWwxFfvIfucBrjESF8P9Targ82v9GOecS6hmTbNiJWuiMZZuNJTnaz+mhf3KMl5gTa6fXTAhbivWtY2vTf0F3BdHI8eKzvs9F4GoPZM1r+5CPteheCtf3z2ssXUex/3Jf7yS7F23chGKlO3c3s6beayynub8dWnvsSZZuZI1u/hqtnOfYz2+dSEPYG/WSWQ7V/P5Rsy24rSjrZXobNZ/557o629ryyR/ZdBFT1GUcEJMaK96uugpihI8jBYGUhQl3AjtG73Du+hFNfQg91e+vGJ132ZNL6Xc8mNzsA5i52BrO5rt2BrWZRKreHvqVo717cll36U5X2ZnsfqvHs/9qWVNznyLfZ0GX2OdqGEp59Oz/eryn03h/r7PsbmNp7NO1ZvN45FRzDpTxp/Zz6/9U+znl1TB8ap27diUHayjJe+26jq08fUZTPbpWgX/YI3og+9zrG3uczzV7BoPbXNZI4vfxppgwYPbya58sIDsQUuvLHmKz7VtthWIbZWYsP34yr9cTHb2G3z7MsCXBp4ePr9uq65swT8512N/OufTW/hFnnubctmHc/GZnB+w9GnOJ2iPp7OYNcDodu5P7yfZ5zW2zm+8Dlx+Y1z0hwxFUcILXfQURQkbpkPmZBFxisjbIvKuiGwXkR95//5HEakQka3e15JD311FUUKew5dw4KAI5E5vJF18t4hEAXhdRJ7xbvsfY8zjB3gvkxgH9wm+jM7ZGzj+0OGyaqcew35vqVZ8ZsYjnEV694/YT63peB7ZFiunWNEzrKHVft3S8Cxtw1h2/Fe5/aLkdm7vDNbs9l3Ax8//PTsm7r32KLIjrVKrcfV8vI5F7AvWdT5rlA4OT0XK4zyARf2sC7UcxX6O9jd26zzWTCP8ZKCGsznWNfd5fnNXIX+/Fv6tkvtWy35p+77HfmhDi0vIjnuG9cj+Y3nudBWwHtln6aEtJ3L/5n2DcxsOfpUzHg1+hmNjI9ayZjbjRT6/bpZb0TOPr33h73jutv2RzyftDO7frna+VoUv81yrP4XnmnMv9zchnzXE5sV8LbP7fHq1iTh4UW9aOCd749tGSxevKIryIcQT2svDQaWLN8aMpHj4qbcgx50iEnOAJhRFCQcCfbSdwnUxoEXPGOM2xizBcJK+pSKyGMDNGE4bfyKANAzn1/sQ/jUyBgd7RttFUZRpxGHMp3dQHGy6+PONMb/w/nlARP4A4MYx3rO/RkZcdoFpPsrnL5X9G64FGpHKGlVOPWtMHSdYNT9mcv46dwb7mUU2sV9d+rv89VJzBm/PX8e+XY4B9kvbdw5rjKaT8+UhieMv4xoOHOu79zrW8FI/wvnqou9k3cgxZOlkBdz/5Arub4ul2+z9LmuWsY1WfGoCaznxZ3E+w7yf8Pl3F/l0qKSdPBbdsziWtC+bx2KwhP34Kr/BdV9n3sRzw2HlHvREswZm18CoPo/Hzj5+8nae+jU3sIaXvp3jnmvf5/bmvMQ+mnuuZE2zeC33Z+/H2bFv4BzOdZi80/Kjm2FdmwKem43d3F5co3XtT2INL3UH33DE1/ODWf1yX1z30NZJOuqF9tPtQaeL3zFSdUhEBMPlH0etTakoSngRxBKQh4RA7vRyAawWkQgML5JrjDFPicjLIpKJ4R9stgL42iHsp6IoRwIGwJGecOAA6eLPOiQ9UhTliEYTDvjhiQAG/dyJqqw6r0W3bSJ70Yusi7z4J3Z+ap3POkv2SzzaEQNsx9WxX17rItaFhhJ4OKpXsmZW8iTrKlVf4fK/fVl8vNTtrI1EdfM3YNZm7o9Zz5qmXaug6nzuT6JVazZ9K9cI6U9l3y1b94lptfwUT2PNzvEn9vuTt7gugzPRpxE2LkuibRFWXXdHPmtKET3cF1c264+1/8Nzo/Bhrjvbl2n5KFo1JNLfZ00uoZrbT6jlXIfR+9ivrfwLHEe95LSdZO+u4VyEJY9xXHf9aTw3o/jSIHUH2zFtrEd3ncpOljP+zhpcZzHPrZynOfa5+hLWSGtOZ401yvpNMXOr73g1vQd/pzYt/PQURVECxpgj//FWURRlIuidnqIo4YUuej6iuw1mvObTxRqP4xxnETnsu7X2Rc6Z5j6ONbWU9axzuJx2sCybUY3sSxbTxn51fems+xQ9xTpLTHkTby/l7UMlXNNjz3+zR5DHxXbbSdzf4r9wf+2aGM5m3r/9JB6PrFVc6zQlkX3BKlayhumJ4/Gf+Ri311XI41tx23KyI7t8/cnZaNXX+KSVT+5hPlbjMh7rY2ayQFn1NtcLaTuV54InyvJZ/CKLZuY11vxsPbX2U9zf/l7ef/53uD+72lnD6y3g9nZ8y9LMLB/IGa+yRlf7Ef7oxVdbuRrdrHkOxfF42nHgZV9jn9ULLnib7NfvO5H718P9qz7T1/7Qpsn56emdnqIo4YMBEOKxt7roKYoSVNRlRVGU8EJ/vfUxmGlQ9TWftpG9hnWLvgWsieWu5+3xZZxgbiCHtYeYenY+qj/dqhPwQDfbf2MNMfPFKrJbTmcdqfoc9hMsesby2zuXdat517Ez1u6b2a/Plcm6Uts81tByH+H3e2ZyfOeuRaz5VX+b40fzXuHznftQM9n2Y4gniTW/WKuWrfMNHu/uGb7t/ak8lZx1rEG5Yq0axQvZNmu4pm5kP/fN1msz3+X3N0awT2LRJvbDqz+Rz62v1tLgsnj/9o8Uk+22cggVPseOiO0zeYe2Rdz/9tl8rXLfZI2vx/JTTHuZ51JfplUDw6rp4bLipku/znpudiPnK2w7kT9rM171tdfM02bCqKanKEr4MMVpowJBFz1FUYLGcERGaK96AeXTA/YnEn1HRJ7y2iUiskFEykTkURGJHq8NRVHCAE+AryliInd61wMoBTASZHkHgDuNMY+IyH0ArgJw7wEP1uZAxhM+3zjHEJ951PMce4tzrJoVkbxG23UAqj7JOsVgMn/j9N/AsaT957MOUvdxKz+fpSOdcgbXtNjUyvnwBnNZo+s4h+sa5L3COo4rloc/6fUyspFt1bwo4/jKjHWsEQ5y+Cs8UawT7bqOfdGSdvDxM7axThVpxccOpLEu1eunqUawvAn3fNZXozaz5hWdwxpa7q9ZSGpdzvngXLF8LSJ7rLnTxefafRP77XVX8bnO/epGssv/xnWt6lZw7G7Os3zuM37G1yrim8VkJ1p+d9HP8dx2HM3XrqOE9Wf7Wg4tZD27q4v7E1/B9r5zWLPMeJ81wpTnSslu+IxPA/RM8vkvWHd6InI+gLsBRAB40Bhzu7X9SgA/B1Dj/dM9xpgHx2s30HTx+QA+BuBBry0AzgIwUhRoNYZz6imKEs4YM/wDWSCvA+BNZfdbABcAWAjgMhFZOMqujxpjlnhf4y54QOCPt3cB+A58N6XpANqNMSO3LtUA8kZ7o6Io4UWQkoguBVBmjCk3xgwCeATAymD0L5DMyRcCaDTGbB5v3zHev79GxtCA1shQlGnPSKaV8V4HJg/APj97rBurS7zFyR4XkYJRtn+IQJ7eTwHwCRFZAcCJYU3vbgApIhLpvdvLh++5mvCvkZEcN8Mk7/DlyNtzGftW4RSudTp7VQPZey5nnSe6k99f+E+rpsOf2DfpVc/RZM+6i3OkdZ4+m2x3NOtI7/yNNTwXyzDIfI11lboLWSNz7mZdZSCDdSlPJMeb9qfx8fszWOPLWc/td+exjjSYwv2xNbyefJ54fctZcyx4gPePbeDjlezw6UzSz3pm0zL2gYzoZ31w1k0cB930Ea5/0nIM923+LzmfXtVlxWTH1/H+MffwxUkp4bHoW7mU7NTnWROM7OP26k7na5XwLa7D2zWT/QBtP8Po5Tz32mdy3Hdss6VRdvO1b83guZNUyfcrOXdxTZG2K/iz5Bi0aiafy5pip5+bpO2TOCHMhCIyMkTEX+y837teBMq/ADxsjBkQka9iWGYbN7nxuHd6xpibjTH5xphiAJ8F8LIx5vMA1gH4lHe3KwA8OYHOKooyXQn8Tq/ZGHOC38t/wasB4H/n9qEbK2NMizFm5Jv4QQDWL5+jE7DLyih8F8C3RKQMwxrfqkm0pSjKdCE4dW83ApjjdY2LxvAN11r/HUaKk3n5BIa9S8ZloiUgXwHwivf/5RgWGxVFUfYTDJcVY4xLRK4F8ByGXVYeMsZsF5EfA9hkjFkL4DoR+QQAF4BWAFcG0vbhrZER6cBApk/LcCXyw//cP/APHa1LWRea/RDXGjUt7Kf3sbcqyH7s2+eTHTeLdZLy69iPbsbr7Jvl3Mc1OuJy2Xmq9lQWP5Ir2Fkt48lKshsuZR0lcyuff2cRX46OhVZNiwbWnRpP4OPHNhx4stk1N/JfZg0v7WYe34oFc7h9S3eKTvI1GL+D9dQFv+Fr03Aa65GVX0gne07BPrI9j1hxz5cWk529mce6+hq+du1l7KeW/4qlOV7Dfm/Jf+Uay81H8VinfMAPRbKe89U1fGEZ2XMXxLcnRgAABRFJREFUsE9lxXr2AU0ttWJnY3hutlj6auGTvL3mMtZXHfFc3yS5nMen8kLLT4/lbBSv9flNNrVPwnPYAHAHx0/PGPM0gKetv33f7/83A7h5ou1qGJqiKEFDYEI+DE0XPUVRgosueoqihBW66PlwDLnhrPHFRKa9wzpP2be4O+4B1mni63n/6i9xbdInv8y+UzE/YT+9jlL2bZz/a84vNziDa6dWfSKT7NTdrLGlb2dbrFjiocXFZCfU8v6xNaxhxu/keNShBI4lLrh7C9lNX+Aa7LZ/VftMHs+8daxR1pzN59v3APsp5m5pIbvqQtbhXPE+nclzOftMpj5j1R85l/3yiu9njam5hDW8KMtPbjCRNa2q8/hk417n9nJ+x5pb2e2ca3D2T1mjy/g1i1xNLy8geyiej19/Pdfljeji/jY9whre7BdYL531GNtv/Yb7F7/Hiq0t57k673tWDeeV7EMa085zLaGK+x/dzXO17HLf8QaqJlEjw2BKkwkEgt7pKYoSVFTTUxQlvNBFT1GUsMEYwBPaz7eHddEbSI1Exad9OlnKLh6cOXewTgFhbaFjHvvJzV5lhfu6WMcoreFY3bmrOGfbrqtZsyt4gX2j+rMsja6G+9PwET6es4Z9pfpzuD3HAOtI8x5gzRKR7BvWm8/tt13COd+sUqxIW8i6T86V7WSbXNbk0s9hXamzn3WyykLeP6XM8iss9p3P4F7W8OLr+dy6N/O1a7X6Hm1pYumv8bVtuY37Er2Njxdlvb/rKdbU0MJzS4Z4bLc+yVmLhgp4u+3TWHeSpSk2WDUsbF+3fj5+2bnsR9jzNX5/YpX1/kEeT1cmj2dnEc+t3rO5vzP/xsdvOJH7n7Td9/7GvsnVvVVNT1GUsEI1PUVRwgtd9BRFCRsMxs2KPNWIOYyrsog0AdgLIANA8zi7TyWh3L9Q7hug/ZsModK3ImNM5vi7fZhkZ445ufCKgPZ9dvf/bTbGnDD+nsHlsN7pjQykiGyaipMNlFDuXyj3DdD+TYZQ7tuE0MdbRVHCBgPAHdo/3+qipyhKEDGA0UVvNCaSB38qCOX+hXLfAO3fZAjlvgVOiD/eHtYfMhRFmd4kR2ebk3MuC2jfZ/fdPf1/yFAUJQwI8RspXfQURQkuuugpihI2GAO43ePvN4XooqcoSnDROz1FUcIKXfQURQkfTMjH3uqipyhK8DCAUedkRVHCCr3TUxQlrFBNT1GUsEFdVhRFCTeMFgZSFCV8MPp4qyhKGHEEpIt3jL+LoijKBDCewF7jICLni8hOESkTkZtG2R4jIo96t28QkeJAuqeLnqIoQcMAMB4T0OtAiEgEgN8CuADAQgCXichCa7erALQZY2YDuBPAHYH0URc9RVGChzHButNbCqDMGFNujBkE8AiAldY+KwGs9v7/cQBni8i4lcpV01MUJaiY4Lis5AHY52dXA1g21j7GGJeIdABIxzgV5XTRUxQlaHSh7bkXzeMZAe7uFJFNfvb9xphDnjJfFz1FUYKGMeb8IDVVA6DAz873/m20fapFJBJAMoCW8RpWTU9RlFBkI4A5IlIiItEAPgtgrbXPWgAjlcU/BeBlE0DRH73TUxQl5PBqdNcCeA5ABICHjDHbReTHADYZY9YCWAXgzyJSBqAVwwvjuGg1NEVRwgp9vFUUJazQRU9RlLBCFz1FUcIKXfQURQkrdNFTFCWs0EVPUZSwQhc9RVHCCl30FEUJK/4/lKNF4FD2riMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:39,129  SpectraRegion  INFO: Processing Mass 10754.69929999999 with best existing mass 10754.55176376442\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10754.69929999999 [('Ccl20', 10754.69929999999)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:39,246  SpectraRegion  INFO: Processing Mass 13870.752799999982 with best existing mass 13870.911849278998\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13870.752799999982 [('Ccl9', 13870.752799999982)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:39,354  SpectraRegion  INFO: Processing Mass 10167.716399999996 with best existing mass 10167.498943781287\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10167.716399999996 [('Rad54b', 10169.499699999991), ('Ccl4', 10167.716399999996)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:39,473  SpectraRegion  INFO: Processing Mass 10254.250199999993 with best existing mass 10253.51953694077\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10254.250199999993 [('Cxcl1', 10254.250199999993)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:39,589  SpectraRegion  INFO: Processing Mass 10620.574399999989 with best existing mass 10620.238907778563\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10620.574399999989 [('Gm525', 10620.183199999994), ('Cxcl2', 10620.574399999989)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:39,698  SpectraRegion  INFO: Processing Mass 10788.802299999987 with best existing mass 10789.261827670878\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10788.802299999987 [('Zfp866', 10789.753199999994), ('Cxcl10', 10788.802299999987)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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v1chIyy7TnlnhWgF5L3LOs43Xs8Y167859rX/VNY5+gqcwb2SY2Ez/8h1EwbPZ42v5Sz21eW+zjnR1t/A3rJ532NdRlNYtxoZVwwANT93dBonh1n+Cta1cl7k7XnnsPeq6mbOZ+dWyPJs4Wz93SeyBpfYyr+uefeu4P2VcWzvUBnrUvHrWAPd+r3w56l+mGNjXYad6l8ep9xl8VL2sflyWdOrP5Xb5d9n/TauiDW/9d+qovZRX+W4bpSy5rX1kxxr68afJj/Gx77nX46ndmoDx7IO5LFmGF/CP/it81hvLXRKQiY7PsK4Mu7f7KuXc3+XsCd0MI33H+AmMjayvts9J3yu++sPflpQIGqWFREpB/A7AIWhTd+pqrc66wiAWwGcB6APwKdUdeVo2z3oKVlEUkUkfe/fAD4EYO3o7zIMYyqzNyIjklcE+AD8u6rOA3ACgC+KyDxnnXMBzAq9rgXw67E2OpErvUIAjwYnWsQBuF9Vn57A9gzDmAJEqzCQqjYCaAz93S0i6xEsMzsyFOpCAL9TVQXwhohkiUhx6L37ZSI1MrYCWHSw7zcMY+oRzKcX8UOKPBEZeZ9+Z0gO2wcRqQJwNIA3nUUHqr8d/UnvYPD2DCLtlXAMoQr/Isy6j3WhLd/mOTWuhvPnld7CuoQbi9vpanyb2QcY18c5x/w5HPta+VdqQhN5/SFHpxnK4uFs+STHupbewbG/cGJ7dSHrMsP53J/tV3HOtOEcFp4q/8YaYMWfd1K7ZyFrdsOncv48dLEuFb+bAzYbrnRqhHw7rKsFTmAPpKxgDS1+NuundRflcl9/whpV96XHULv6PtZ/ex19trWIx77sBR6bPR/nu6LCF1i/TXQkycxtfG413MAeSS8PFVrfx5rjzF9znLKvkfdX+AK/v+9i1ggz32bfoK+E9VX3i+tbzsqSq6qVDXOcuXfNFhwIb0//AZdFwjgSDrREEtAgImkA/gLgK6raNZG+AebTMwwjigQ1veg9vRWReAQnvD+q6iP7WWXc9bdj+9myYRhHHH5IRK+xCD2ZvRvAelX9+QFWexzAJyXICQA6R9PzALvSMwwjiigEPjcn3MFzMoBPAHhHRPZqQ98CUAEAqnoHgiVozwNQi6Bl5dNjbfSwTnq+zCS0XBCuN9p8POsuR32bdZCZP+Cr1ObLWPMayHdqRNRzQGHO31m3kEzW4PwzWCPDW+yDS8llHaXtXK6VmtjJ+99zHF8456/g5YE+7p9nMetMBS+x7jNYyfvPfYe9W1nrWOPsreL4Vd921vRSO1kOkaxMavtz+f3oYh9hf77jHSsvCzd2cvi1fwHHrnbM5m1X/4l9b0PH81jkvsrLu4/lfHjpa1jzSl3L51KXs34gjq8shsrZg5lWz+9vXuh8NY7msSu61dF3s7jdcjZrmHkv8LlR//+qeHuv8fa3/wv7CMt+yL5EtwaJ/4OsgXbMYB9g6h7+fAmLWf/19oRjk3WDY+obJ9GKtlDVV4DRNxZ6avvF8WzXrvQMw4ga43x6OynYpGcYRlSxLCuGYUwbrEaGg8enSGkOqxGL5rH3asjHSsWuz7KPrPRW9nJhEWtsrk+v8cr51C5+hh/qJNSzD63pGo6NTehx6hb8fSO1N/6yitoBlsCQtYrjSTf+gfPdJWxmb1dqA2tsBa85dRUCnLOt5Viu/Zq3guv+Sh574fw1HL+540PsAyx/nn2M3njWqQpXOAGpI+pG+BtZY/OkcX67lkWsp6Y/xHqjZ2sdb9vJNZi6jfXLPWey57DgFaeOrJNPL7GNv4iifGwHM/jcqXiGNTb9Afvg6r/Bvr3yv/OxGszkY+Nr3EPt0r9QE+2n8OfN3Mp6cNun+dzMW8HGwp0nc2xz1SOOxprGGp+3jU/WrkXh3JGBbRN7EGF1bw3DmDYE08XbpGcYxnRBo2pZOSSMqTiKyD0i0iQia0f8X46IPCcim0P/Zo+2DcMwpgdTJYnovQBuQzCv1V5uBPCCqv5IRG4Mtb8x1obEp0hoC2stWx+bScuL57DOUPa8o1HNYN2j/hTWibI3cs2IvNVOHdth1gxbT2GNK62BlwvLPtj2Jc5Pl/8U6y45z3NdWuTxb8GM/+PFe7isLTzcffRV8ufrquDD1V3F6yd18PqpCaMf3sLlvENxctwF2nn8oTxezaeXjfib64mkr2UNKfcd3nbfRRxy2XQM//6WvsSaXNJ21rDaFvP2smrZB+h9kVOq7fxP1uDcurf5v+b6JX0X8sFJby6ndsW9XBe3bwnnNkxpcpx0AdZDN9zA5/KcX7EmOlzC+m57jZOPsN3xUFY7NZOr+P3bL+BJpnQpa64Z74RrnHj7XRfg+Ij129sxr/RCmZDbnP++EMB9ob/vA3BRlPtlGMYRyF5NL0r59A4JB6vpFY6Ib9uNYG49wzCMmL/Sm/CDDFVVEfdGMAzVyEjIPNBqhmFMAaayT2/P3uykIlIMoOlAK46skZGaX67tR4W1hBxHgxM/z53D2axjJLaydyqliXWdhA7WgYYzOYbQ48SiJs/lC9S4AdZd1MMHb8Zv+P2azr654Vkc79m0hHWTwmWsMZY/yvGlDefx+1NfYm9XXB/X2S18nfOeDRQ5dYLrOJY3UMG6W8pmrlXbeiKPR85aPh7Jjby/tFfCsdHq4dyBriCavYw/y86fskex8iY+VnHvsD7aeY6TJTydj3V8O9fo7fkY56dza2pIImuOrVezD67gRT42bhyzWyPD41gYU5ZzHDnm8/jMvcXJfuTnc9kzyBv0J/K5uO2TrAlW/9nxJb66jtpzGquoHUjir37jmeFzY/jBCVwLKeCL8YiMg+3d4wCuCv19FYDHotMdwzCOZKaEpicifwJwOoKpnXcB+G8APwLwoIhcA2A7gEsPZScNwzhyOOJvb1X18gMsOjPKfTEM4whnKmt6B4eyF82tLarLOJ9d4KxjeXkS6z4Z93Nd2+6Pc02MtF2s88j7OZY3vsvRFF/nOrvwsLO8/VKuy5DqbN/by9srfcqROjtYkwwUcr684qUcLzlUybGzfYX8+QeOYl3K9Zo1foG9aen17L8KxLO3K28px0IPL+aceN2VrMOlpIVzxqW08MFMW8Y1cpHC7038Oz/U6uD0bsjr4NjajHXsGUzdyZ99oJD1054SPnbxzrnU+j5+f8461sSGKvjYBGawnpr1mqPvdrNvbs9lHPedfxmPbfMDrMkFEniiSG5hja/oVfYpBlavx2hIDefzG8xlfTypgWOZB/LDenBggrOC2qRnGMZ0whIOGIYxbVCdApqeYRhG5Aj8gdi2rBzWSS8QBwzkhX8FUppHXz+hk3WWzf/KPrIZj7Am1T6HBztrDeeHU6fObP9sp0aEowk2sYSHimdZExvOcOoiLGJdyccyFkpucuI1T2bdJa2W8/vFLd9E7cxZHN85fPToZu/C3zi1ZC/ifH4pu3l8B2t4fAez+fMldLPO1FET1hgz61jP7D2GNauhDB771uN5LOde/y61Uc2xrsN5PLbxKzZTu/9s9vG5iT6S3uL1830sIvYVsV66/VTewOy7WLMbnM01LHpK+P1pjaxx+r7neEJ5eJBax8ei4TNcWDf9UtbwWq5lX2F/Pl9dlS3lcz95I/skd1zGHSh6I7z/ht4DxhpEhGl6hmFMGyyfnmEY0wsN6nqxjE16hmFEFXt6OwLvIJBRF9Y6mhezZlSxZQa122ZzLGnJP1kn2XQNe61m/ZZ1jI5F7HPLfHwNtYcXLaR25yzWBKv/xrGmYEkLid2su0CdGhaL+PN5Czn2taucdaPUv3O8Zv+HFlG7dR4frpQ9/JO65aes88RVsg5VcSv3V15dhdFIOJO9bV0VrFsVPhrWKP3NLNC2fob7ktLMx67mD6zpbf0mf1YJOHHP97Pn0b2YcMM9i27lWFvHEoqeUv4sQx9nH1zZ3ayX7rjA0U+dc8GtqeHZyf2tu5Y1xJx13KPEPXzult/Ovrra37MeG+fYIJNZstsn7rx1IWukpS9yf/uLRmimE5izFKbpGYYxrbCIDMMwphmBQGxPegdbI+M7IlIvIqtCr/MObTcNwzgSUA3e3kbymiwOtkYGANysqjeNZ2fe/mGkrw1rP2lbWHfomcfxjeLoJqmvb6F28mLOUSavLaN25jtcN0GH2AuVsY01u5ynOZ5y439wXd3Uev6NKHyL+5f8D/aalb/Cml3PGVxjw9WdVPhESH2FvWVJTp0GfzIfvsEs1oFKHN0ssJpjm+tv5NjclEZWytJ38XgF+HCh9/iwzzB1M2te+W+xRrblCq4XktDOfU/fwfv2DHN7/de5juxRN3AuwIznN1C7x6lxkfwYH6zc17gGsm8z1xfpK+NjUfGUo9nV8fu7P8CaXftZ3C5/hmNdWxexxzR1Dfc/oaKM2iWPcq7FxDbWZ/vzWT+uv4qPXdlveTxlHecrbD9t8Xt/+96a2IR0xN/equrLIlJ16LtiGMZUINYtKxOJF7lORNaEbn8PWAJSRK4VkeUisnzI33+g1QzDmCLE+u3twU56vwYwE8BiAI0AfnagFVX1TlVdoqpLErzJB1rNMIwpgCKyCS/WNb19UNX3XEEi8hsAT0T0voQ4DJWGtZnGk1iDSm7i6+LcuzlfHnL4grL6D1xnoP9DXEu1p5h1Djc+sfzejdTuOp3zx1X/jXWRuB6OLx3MYZ9gQibrQo0XcWxtQjd/vsYfs5fN45Qbrfobe7f8Tl2D/nwW2Ur/xLG9O69iXannSzw+xc87sbQz+Tcw+74V1C7czp9HesI1P/oWsd44mMV9nXk/a3xSxzUo+k5lfbbxJH7/rLt47DvPYr01vZY9iUNp/Fnav876ZRyvjpKn+FxKSmA9tm0B68PZTv2UuF723SU38f67ZrKHM3U3r7/7y3wulD7PmqUbly39Tk2MWtb49pzAxyP+Wf4u6ZIF1B7MCp+b6sQtj5cYv7s9uElvb1GgUPNiAGtHW98wjGmCAhrjlpWDrZFxuogsRnBSrwPw2UPYR8MwjiCO+IiMA9TIuPsQ9MUwjClArD+9PbwRGd198L608r1m8kzWMeL7eLSaPu/UIl3OXqfNV7JOUvEM6yRxA7y9AQ59hfZzjYveQsdX59RZyF3Hv2BuXVzNYa9a0UOcD8/fwjpNguMla7uKNbzAU6xJDuRyO6WRdZwN/8Wa21E/5roMvQu47kTS0repnVXFOlDrJ3n8s37HNTj8Hzzmvb/dcu/Zy7jmLoZYk9N4PvUSOnj5jB+w57HrAo6TzlzJwaaDlVzTInsta2A5y1gD2/NBPhn8u9h3hzJne+scEfAt9jwmOvVXklfx2Ls1NBquXUztojd4uf9d1pv7nDq+GW/tonbbB/jYzfx31vAGz+PkkCnb2XdYc0c4mLepmcdqPEQz9lZE7gFwAYAmVV2wn+WnI1h+dm/Q+iOq+r2xtmthaIZhRA8FEL3b23ux/8CIkfxTVS8Yz0Zt0jMMI6pE6/b2UAVGxHYye8Mwjjw0wlfw4ejyEa9rD2JvJ4rIahF5SkTmj736Yb7SGy5Mxe4rw36pgVz+Sah8kiM2OuawZifD7CvLXcWX0YlPcuytx/HtZa9jTWzo+LnUdmNLu2azca7wl+xbiz+ax9jVYTocTaxrBvc3vc6JdX2IvWDxa7kuQtMS3l+yU1Z3zjfX8X/ksS417HjXUipKqb3jIta5yl5g3cfVWIseDH/epovYZ7ftUo6jnn2XU48jmz2OqWtZU9v9Cc6vl9bAeq0m8ft9yY4eu5BjdZNbfU6bzyUd5v71lPH2sx7lY+GEhaN9LsfS5gxzfz2pXOOjt4y3ID5ue2Zxbsn0d1kPHq7g8d1zEp9LCd2sFzdf1Uftqv9wxtM3YnwmdKkm47GstKjqkrFXOyArAVSqak8o6clfAcwa4z12pWcYRhQ5jFlWVLVLVXtCfz8JIF5E8sZ4m016hmFEmchvbyeEiBSJBFMTichxCM5nraO/yx5kGIYRdaJmWdlfYEQ8AKjqHQAuAfB5EfEB6AdwmerY9+aHd9ITIDBCVqv6T/Z9NV3H8ZFVj3K85mAha3x5b7MXa8NvuaZDyVP88bI3sA/Ou429ZKU7ePvF2azTBE5mb5W3jbfXdiXXzU1uZh1pKI01xTZHds1zSg2aPfYAACAASURBVFYUP+1Udjifc675W9u4f7w2PLkcq7yPrzCRRcyKezl2V5I5NrroOScWuD08/ikt3Nf8L7O+OHAq16V1vxfpD7DmhE+wxqdevilpOJfrzuav5Pcn73E0tV6nnsmeFmq6x24wm/c3dBIfLI+jL2f9ns/l/vPZFzc0h+OyZ97APrpex4eX7tRsHhnnDADNZ7L+Ous6zs3odTTBpEs4f55Ucw3ljV8Prz/wc9Yzx030nt7uLzBi5PLbELS0jAu70jMMI7pYRIZhGNOGIyDhQCQ1MspF5EURWSci74rI9aH/zxGR50Rkc+jfAyYSNQxjGnGYHmQcLJFc6fkA/LuqrhSRdAArROQ5AJ8C8IKq/khEbgRwI4BvjLah+N29KPlJWHvYfT1reAUrWLeQXo6NHcjm2FbPMGtOc3/GOojPqRnRvJg1u4IVHbz9Y1jnSHyKfX/ehezr669kL1jOk+zTE8ebtf0j7IuruZ+9YQm7WKNb54QbJpzEOpJbB7f4NfY5bjuNk7aWf591n56LWUfa9UXWnY76OetePfMdb9hVYV2tfClrZh4n92Gvk9swayMf6/aT+bPPXMbL1/yYfXsFy/lYb/0Yf9asjXy1kfsbjpXd/RU+90ruWEntvrM51ncok8c69UkWYDsvZ00w409O7KuzPHAq17H1JXF//bVcA3n4LNari+7h/u506p2U38LLhz7MdjjPK+w7zNwYrsGxm7924yfGs6yMeaWnqo2qujL0dzeA9QBKAVwI4L7QavcBuOhQddIwjCMH0chek8W4NL1QHNzRAN4EUDgikehuAIUHeM+1AK4FgCSk7G8VwzCmCpN86xoJEZuTRSQNwF8AfEVVKT4p5I3Z70cdWSMjHhN8FG4YRowjwdvbSF6TRERXeiISj+CE90dVfST033v2po0XkWIATQfeQmg7iQnwVob9QMWvcX68oUwn+LWUNbP0h1hj0xM5h9nOCzjWNG0Xa2BFD7HmNnAavz9li+N7c3Kkub7HnhIevpQU1pUaLmSNcPYXWFPbchPrPDMe5f4ndHH/+/I5vjSp1all6sRv5qxnr1qzEzvrdbSbrHf5ROyd69Qhdmp45KwL79+fyH1rPZ3zu+UuZ8+lP5P11rgy1jtrv5JP7fg0x7PoUPqSE7vq+Ojg4f6lNvLy3dccQ+3cdTw4/gS+PvDm5VI7500nf2Ap16nNWs9xzD3VHGed9dc11N5wl1vPhM+1RC9/noLlrKlKMp+LCc8sp7anmH2O6TvDB9c7NMFLtSP9Si8U5nE3gPWq+vMRix4HcFXo76sQTOZnGMZ0JxDha5KI5ErvZACfAPCOiOx9ZPUtAD8C8KCIXANgO4BLD00XDcM4YohuEtFDQiQ1Ml7BgYPpzoxudwzDONKZzCezkXBYIzL8yfHoXhDWapovZy9Wyous6eWsd+Ill3D8Zu0V/GAkkcsmoIWtUMh+mPencXx3r26dhAyOX6z9N/aazbyS4y1rv8+aWc1dXMeg6WpensPWMcS1c//qT+On3X0VLKod9Qv2GfrTeDyS2rjuRJKTf2I4zfGevcK+vMA2rvPQfTF7xZLawzpb4gureZlTA0Pn8Vh6+rhvffO5fkdPKY91cqtTV/bNOmrHlbJG5U/lc8nVf13cfH2dVTyWmXV8Lg5XOJqjE8ftL+fYWF3OVVLT69gT2f8Bju2d+0unJoffqfdyylHUTmzj/rV+hD2l2fezHj40m8e7Y1Z4vP3LJ3ilFuOTnqWWMgxjWmGxt4ZhRBW7vR2BZyiA1F3hWzjP7/mxuj+RH+nEt3NYVWA1h87kLuDbxfx/OrcYuZwaavhEvj1OeJov+Zs/w9vzJ/Blftkf+JbMO59TpM/8Gad+ariCb0FSmvgWKnMd355u+xe2iOSv5tvZsh++RW1dxNvvnMVhdoF47n+Ge4uWOvqFvieTb8HEeeIW3x0eD98pTsjcmjpqd8zmY+GW58x4fTu1AwkV1E5sdaQO4b67qfpdCwz8PPZpyl767io+F9Maeey9L3JYl8cJSRyq4dvF9rm8Pc98tiflPcHlQRP3OKm1HHuU7GigdnwmywXSz+emW65zy4/43K54lkMgC38RtlPVKYf4jZsj/UGGYRhGxCgm1Y4SCTbpGYYRVez21jCM6YVNeiNR0iqSn2KdRBbMprar4emJnF7ITcfTvoRtApm1Tnp4R5cZPI9TertuxL4SPnr+JLZRtM/mUKRAPLeL3mRNsreEbRDDOWxJKX+edZ3Gk3h583c4fVD1L1lDTHBCm3aeQ020LWAbR1Iz62KpDZy6a9BJJRXXz/ctIy0vDafw2FQOchha9moOQ2s6gUPuvEfz+o0ncpjVrF+yH8lNH+8eS+8mtt9sv5Q1t5TdfGwLnttJ7UAHlyLY8gCnmsp8lvXTxE4em+Q2R5/uYo2w8VLWgzO3syaX2MxhcIGjZ/L26lkPhpP6P84Jg5v1M6cUgDN+gREapWx6FRPCJj3DMKYLk502KhJs0jMMI7rY01vDMKYVR/qVnoiUA/gdgklCFcCdqnqriHwHwL8CaA6t+q1QlfEDMpTlxbaLwrrTzE72YgXWsndp8FzWaVLqWMcouINDe1qvPo7a3t2sI/U5KbNTV3KYlWeojNoJXaxTZS/jMLWBatbwEnfw/oZLWCOL73PSGZ3AXq6KB7g/gxc46ZnW8NnUeSZroL5E/oU96ib2LbYdz940j99JP7+A0z0Vvs66VvP7+fMkdoTf76aL9yfxqdVfxGnC8pfxWAXWsD5Z2cXlNv1lHPblS+VjE9/Fmhi6Wc8dyOPPWvHLd6ndcxp7HtM4qg4zf+Tk1drC5173h50Sl2N88QvueJPannmzeHtzeKzbjmKNs+oXW3h3VazhNV5Uxfu7ndOa+c7gkEJvf/jzuaVCx4vr54w1JlIjAwBuVtWbDl33DMM4opgKml4oJXxj6O9uEdlbI8MwDGNfYnzSG1fCAadGBgBcJyJrROSeA5WAFJFrRWS5iCz3904wvMUwjNgnxktAipsC/YArBmtk/APAD1T1EREpBNCCYPf/B0Cxql492jYyPLl6QuK577U9NVW0PJDCXqMd3+C+VX6fxQJ/Mus6cZvYazW4uJqXd3O84WAea1gpdaxhST/rVDsv5gvclCYnPfvbrFNJB6fD99Vz/GTnlRyPmfs6a3C+rXXU9n+QU5p7BjieNH43a56b/5d1oYwX2FuW//u3qR0YYG9Y+1VOKqz7V1C7/8Nh3S116+hpruJ2cDUB1wc3dBKnVop3jpVbSiChg4+NL52Xt8539Mk3OF27x4lVlR72SPq287lU75RYzN7IY5++gUsNuOdO0wf53Ml/wvHNxbFmNziHNbrhdL4pkwB/N5IauP89MzjWOWM1j3/fLPZgDqeFr3/eee5W9LTtPChhL6m0XCs/99WI1t307a+uUNUlY68ZXSK60ttfjQxV3aOqflUNAPgNgONG24ZhGEYscNA1MkLFgPZyMYC17nsNw5iGxPjt7URqZFwuIosR7H4dgM8ekh4ahnHkoFPAsjJKjYxRPXn7QxLi4akIaxvNx3H8ZX8h76b8EvYWdV3KGlhSK+synh5+UNJXwJpfxymsM2Vt5qOz5xLWOap/wV6u1EYnfrPJ8YY5+qi/iJ/tdJzJJSF9LDthx8dYx6l4iHUjbwPrUi0nsHet/WKO1S35o5NifTenIK/7OmuEqY1OjrttrKv5Hd0tbV1YJ2o9kdO15yxrprabPt2Tzvpi21w+Nr3lrNHNuoNT7weyOc7YLdFY+CbrqZ4ujoPumccey7Sl7MH0LGbfXVYtj2XKo+yza/wCa34Fv+JzN72eP3/Pyaw3p21kPTixjmOHEx1P5Y7L2eNa1MXj5ZZxHHbKqaZs5u2P1PjEP8HLsBh/emsRGYZhRA3BFPDpGYZhjAub9AzDmDZMhYiMaDKcHo89HwzHfya1s06Ru4o1p5Zr2CdW8AZ7oVDPvrb2/8c1H7PeZS8YxImF7eX9Vz/M21//Q855NvsLb1Dbze83UMI6U9KKrby/avZO9eezN6v0Be5v31yOle0tYo0y549c46PnG+wa6ip3SjxuYl2r9GXeXsJ61s22fb6G2rKANcWcB8L9a1vAemx/Pve99DmuP1l/PmuAqY2O53ED66X+BiefnuOjS85jja71PI5Lzvod19tMKOVzQTL42HXN5LZ3kL/JgVM4NrjnFPbJJbWz/py5gTVGbwZrcNLOY7vrcs6fN5DH+y9/jj2V27/gnMuXcz2V7o9zf/yVjga7JnzueYYn+CQiSpOeiNwD4AIATaq6YD/LBcCtAM4D0AfgU6q60l3PxUpAGoYRVSQQ2SsC7gVwzijLzwUwK/S6FsCvI9moTXqGYUSXKPn0VPVlAG2jrHIhgN9pkDcAZDn+4f1ik55hGNEj0gkvOrfApQBG6hy7EEEylMOq6QXigb7isPaT1sCfvP5M1lnyV3H84q5zWLcpuZVziuW8Ws/7a+N40MGT2Ks0kMUfP30N72/Ob1kD886oonZXERvt3BoS277EOdqUJTzEO/kXvE3s1Up4m32CyS+yj69VOd9g5d/4R7FrLn/etvezDzHnDdbJJJ41vmSnjkRcHetcaXVhnSq9lj+7p9P5cMKaX+Ey1sC8b3MuRXd9zOE6rx7HE7mP57OA359XzrkS+zP52PecxrkLh9Oc/r7CY9s9m8d2xi1O3dq3WP/Fsexx7KxyTJpf5xrDpVfysQ9Uc/9dqi/n9WUJS2B7juf1y1/g/ID1Z4Y/z1CDc6KOk3E8yMgTkeUj2neq6p0T2nkE2NNbwzCiS+STXssEEw7UAxj5a1UW+r9RsdtbwzCiyt7iQGO9osDjAD4pQU4A0BnK/zkqdqVnGEZ0iZ5l5U8ATkfwNngXgP8GEA8AqnoHgqGw5wGoRdCy8ulIthtJjYwkAC8DSAyt/7Cq/reIVAN4AEAugBUAPqGqQwfeUnB294xYo20e796tEzuQw16mjO0c/+g/+X3U3vM+rjmRVctepPxfv05tbyHHQ8LRtPA2193tP4O9WeLoSkm7WcfKXccaWMY/2LeHQdYQkce61M7/5HjOygu4cENODWuWjafx+926vTP+i319vec4sbcbOR4zqZ3fn/0a+/j65ocflO1ZwmM3lMUa1cwbWOPquo09gFkXssakR7NHsuVo9ji6mlvxzzjWVeL53PEv5BoU/nh+v5vLcPdZ/BBQ6/izp21jn6AO8anvfu+3XsLjkbqL95//fY6b9s3n2NzW+XxuJ3TzHro/zOdKxaOcP2/uL53cjvncn5IR+fh2dPP3bDxEswSkql4+xnIF8MXxbjeS29tBAGeo6iIAiwGcE7qU/DGCNTJqALQDuGa8OzcMYwoS46mlxpz0Qh6YvaES8aGXAjgDwMOh/78PwEWHpIeGYRxRHEZN76CINHOyN5RLrwnAcwC2AOhQ1b33JAf0x4yskeHrsxoZhjHlifErvYgeZKiqH8BiEckC8CiAuZHuIOS7uRMA0rLLNWdEbYH0jaxJqePNSlvO3q3h41jniX+X68QW9LOPza2B0XQd6x4d81hHOuo27o/X59S1beDY4CGnNulIrxMAJLXwke0/hvPpNS9m3anir6zD5K/m/m3/Csf6VjzNsbpZWzleNYvLMKD9Cvb15f2DdaqO4/l3K2sNx8sOzOZ42q6K8OlTeTPrjZ4sHpve83nfza382RLOZ720eRGfmhXfZc3Om+3UocplPdP1+fWWcP6+jM2scQ1W8PtTWth3qPPYJ9g1g7fXsoj3l9LI7VSWAJG6h3Wzuo+wplfjeFBzlTXGuFa+gGhZzLkVm07hdrIT596Xx9c7mXXhc0drJ2jqmEoJB1S1Q0ReBHAigiEfcaGrvYj8MYZhTHGOgCwrkdTIyA9d4UFEkgGcDWA9gBcBXBJa7SoAjx2qThqGcQQxBW5viwHcJyJeBCfJB1X1CRFZB+ABEfk+gLcRLB5kGMY0ZyrUyFiDYIFv9/+3YpxlH72DfqTVhnUo1/skSVwnoenyhdRO7OTR7Dqfc6al7mZNSz2sq3TOZh2l5n72Vm2+ijW8qidZt9n+Od7/zE+zjlX/S/YNlt7KulG3owEmdDqaXzXrVAPZHANZ/gPWtbZ9n/MN1ty+jdp7zmevVydb1eBPKHPaPF7x1axRNpzCp4svNdx/32dYb0zs4M/mHpu4OtZbxc/HpvqPjrE+nzWqztNZY0t7iGtWFL7OPjQ9k4v1DZzKNTB8KTzWvQV8E5TSwGPj1qCo+elGam/4Lp+bs77M/XM1x+Ih1jxr/43z6c14mM+l9mM4jnrWb9hn2L2QPagZb/PyjH7Ox9dydng8A/EHVfL2PWL99tYiMgzDiB6TfOsaCTbpGYYRXWzSMwxjumDV0BzUI/CnhnU7zxDrPHs+yRqeG/vp6ja1P+e8/zlrOXZ3OIt1o8LXOT7U88oqas98g4ej73yOTc17jHWe2nvYrpi+grevydzOWMu+N1ad9iXF+Tz1X2afYcYWHp+206uonXsXxxrnp7AXzPWe6XLWvVzKB4+ldtwLK977u+Fr3Le293Hfct5lDanmt07d1dms2fnzOG65z9GoMl/iOGZx8uW1XsEaXd0N7EHMX8M+wfR1fGxSGnmsvC2sqfUtYr2382rOnVjwFuu/Ox/m/Hbll/BYp25lz2WmE4eNVRuomd1dxcs9jgb5CH9X+j/EGZxa57NHNK0+3N8JP4iwSc8wjOmEm4gj1rBJzzCM6KFTwLJiGIYxLmL7Qu8wT3oKeHzhn4G2K1gjKlraPOrbm69mX9rse7mmRGDtZmrHBdj7lT2Hc7i1X8GaYF8+6yKl93HdgYHj2Og282befnc1/8QN5rDv0FfOOlHGa3XU7jylittVrEuV3MQ+PZzAGmjLQtaZPKnclng+3K3zWTfLHWZdasd57BusfIRjg1ET9gGWPcU1JDwtfGx8u7kexx6npnFiF49d6zzOn9czk8d61iN8rsQ5mt5QGWtiFc9y3DTeWEPNtiv5XIgb4G9uz3Hcn5L72Zenpaw51l3EYzfjS46G+WHW2BJbWL/NudfJP3gpF7lI28nr6//w+Lc+xONbtJSPXcke1vTgD39e78DB59MD7EGGYRjTDZv0DMOYNkyRhANJIvKWiKwWkXdF5Luh/79XRLaJyKrQa/FY2zIMYxowBRIO7E0X3yMi8QBeEZGnQsu+pqoPj/JeQr2C4YywlpDcytpB72zWYVJrWRfKf5V1nL4ZrJv0LuFQ4KzN7A0byGTf3GAmxxjmbHBKfBSzTuPt5/56ajl2OGOYc54N5XBdg54i1uiGPsQ+uYxt3N+M59mL1ufkpHNjJAv+yDqVfxFrmPWnscaXvZk/TyCRTwevW/HEiY9t/kC4Bkn+P1mzq/s0x45WPsZxx3l/WMmbPo5jYdNreeeylL8lrVexZpWzpova/YWsWaVv5uXuA0Y37jj3NY799SXzsR1cVEXtpC18bpYtZY+ov4jjupOX87Gt/Rrnikw4iz9f+dP8XWg+juOifY/xsS266y1qu/VQ4vr4WO76bNgzO3jjwcfeTglzcqj4xv7SxRuGYeyDBGJ7ejiodPGqutfu/QMRWSMiN4tI4iibMAxjOhDprW2s18hQVb+qLkYwQ/JxIrIAwDcRTBv/fgA5AL6xv/eOrJExPGw1MgxjqiOByF6TxcGmiz9HVW8K/fegiPwWwA0HeM97NTIyPLka/0o45jCpgOMttceZFEtYU2s+gTW/XEfHSdncR+3dZ3HdW7fuQfGjTh1aJ5/fpi9wvOYsJ1504P2sW/UWsmaY3MbxncV/4HjLwMxyaveXsS7Tew775npK+Teqt4w/z9zlrPMMZLCulcDhnQjEsXYzlMM61DBb0/bJ0Zb3VlhnCqSxfln0Ftf0DWzisVYfj43nn29z2/FUdhzN+eP6LuRjn7Gdj13GOtbABovYk5h4FHsu89/k2NvuRVwPxDvIlya9RXysu8uc2N6XWO/1bXeKZNRwrsPEVj4W6Tv42LYt5GMbcL65nUc7430969sFK538ee/jY1318XAc9R5lD+C4ie2724NOF79BRIpD/ycIln8cPVrdMIxpQayXgJxIuvilIpKP4AObVQA+dwj7aRjGkYACONITDoySLv6MQ9IjwzCOaCzhwAgkIR6e8nBtWm3heMHATI6fHCjkWNWUZvYWyXquCdF7Jucsg2M3iu/hoxHo5nhM6WMtI20796fpFNaVsjazjpLcwjpV4lPLqL3hFxw/Wfoi928ondWGnDVOXdvljtbiZd9f+ymsEWau5Tq+OUOsGe4+nnWw/DU8voksiyG5kXWhkTU/0up4LBNXOEV3nVx+9dfwsSq6hXP/uZ7NhG7uW/HNrFfGreRYWH8v68MNl3C+v+QmPpYZO/nYDabz2A7k8slUspTPXd3I56L/fawZ7v4YH5uSO7m+Slo9a4juuZu9wYkd9vG5nNTOmmXmX9kH6dYhPvp/Wf/etjnsAdVX+FiMhynh0zMMw4gY1SP/9tYwDGM82JWeYRjTC5v0RiACJIT9TW3nsw8t84+cQ6zXyZ+Xupt1l91Xc46DnPWssRUsY93Cs62B2nVf5Vqtlbe8Q+30Xby/1KXrqQ3HZ9c2l3WTpMs4R9vc2zg+U4Z5+y79s9jHmNzK3rShSt5f5t85/1/jp7kOb9YWrkmSuY11odR3OX42eSfrcAPFbNwb+YuuK9fRsl3/xsdOWSLDQD5/M/REzg3YvJBPzep72Oc3NJM9mFLF+mvHMawJVj/An61rIWt6yS9x/zs/zedG7lo+t5qP57jvwMncLn6az7Xi2zbx/i/mWNjuctZz117/K2qft/BMajddzHV1e0tYBMxyPKe7Ps6eUlyynZqdF4a/l/5lVvfWMAwjMhRAjMfe2qRnGEZUMcuKYRjTixh/eit6GDuYITl6vIS1iU13cn64hGaeg2f+Ygu1/Xs4z//gufx+7yD/xHTOcGJPe/izpj/AGuKQU7dgMIv7k7WS97/zQtaV8lex7hP//Apqe5I43nHLd9jzPeuOemoPzGBNr7eE4z0TO/nzpq1iHcm3k+M/B8/j8Wp5H2+v9KdcK9Wbzzng9lzEulD6CG9bymusWQ0ey7Gz8R3s8Ws4jfXIkn+wJ1EcH9ruU1kzG3KKBlc+zJqdJvBn61zAsavxvbx9dQIy3auVpL9xfjp3LJMb2BfYOYc72FXl1F/5B/vuvBs5Nnf3x7mmctZW1mPVkd2St7Jv0OfUDd6nLKNTIwQS3uCbgefRpW0HJeylZ5bpkhO+FNG6Lz174wpVXXKg5SJyDoBbAXgB3KWqP3KWfwrATwHs/eLcpqp3jbVfu9IzDCN6RCltVCjs9XYEY/13AVgmIo+r6jpn1T+r6nXj2XZEqaUMwzAiIRiRoRG9xuA4ALWqulVVhwA8AODCaPQx4kkvlEj0bRF5ItSuFpE3RaRWRP4sIgljbcMwjGlAIMIXkLc312bode2IrZQCGHnPvyv0fy7/L5TI+GERKd/P8n0Yz+3t9QDWA9grVvwYwM2q+oCI3AHgGgC/Hm0DkpQIb024FoC3i81bWRw+Ce1kX5o32/FGJbLskPzyBmpnD7OXSb28vtfJqZb0iuPDO5XrNgxU8v4r/sCa41AN11Fw++vmkKv5Lfv22o8vofbuU/jXcO53ua7vluv581W0s/csfojrTKS8yf0t8HGNDhd1YpEL/8rvR3o4lndgCWt4Se+wRrXlOtYDC1c4HkVHQ2s4nceu6Fau+Rs4jfVQ/ybuW9MXOdbWzQ1Ydgvn7+u6mLc3kM3nSpKHz9WUWtbQ1n+d+zv7Ttb4kpv5mqDtP536LS+zZ7XoNfaYds3gfIXxfc6AzWT9te5C7v+8n7DmWfs/7KPMeyd8rgWePfjYW2A/+uGBaRlN04uAvwH4k6oOishnAdwHYMxEKJGmiy8DcD6Au0JtCW18b1Gg+xDMqWcYxnRGNejTi+Q1OvUARl65lSH8wCK0K21V1b1PD+8CcGwkXYz09vYWAF9H+Pc4F0CHqu79uT7QpadhGNOMKCURXQZgVkhGSwBwGYDHaT+hRMYhPorgneiYjHl7KyIXAGhS1RUicnokG3Xefy2AawEgKT5jjLUNwzjiiYINTlV9InIdgGcQtKzco6rvisj3ACxX1ccBfFlEPgrAB6ANwKci2XYkmt7JAD4qIucBSEJQ07sVQJaIxIWu9va59BzR+fdqZCQXlmv92WHtoebP3bSudxdrXJscH1sGpyxDxxwe3Nwsjt/Mvpe1CT2J4ynrP8Q+uOJXOV6xs5qHJ3sTa2Qdp1ZRO66f+9P0KdZp8t9mHSdhFetQ6mVNbs7XOOeaxrP3zDPMuk3LQo6VLW53NMV41qXUw+9v/yTXVci+j32M2/6Dc+BV3xge3/itO2gZ0jh3X/VjfKzdehzeFvbpZexkEc7V8OIc35/k87FMr2fNMO0lFox7z+Zzoe1jrMHN+C/WMzWZ+9t+LO/vqG+wT3HHv7LPruwm9vmle3j/Be9ybPGGn7I+PPtHTt3bE/jY7lni+ABfcDS/AfaQFr/G47Pzw+FzY5jl0/Gh0YvIUNUnATzp/N+3R/z9TQQLlI2LMW9vVfWbqlqmqlUIXmIuVdUrAbwI4JLQalcBeGy8OzcMYwqyN6feWK9JYiI+vW8A+KqI1CKo8d0dnS4ZhnFEE+N1b8dbAvIlAC+F/t6KoIHQMAzjPcZhWZkUDmsYWkKXD6XPhHU76WYvklvzofpxXt5dyV6lOT9k31rX6ewVc314HeX8/uLXWGcazmRNL30n12XoqmSvVc5ajp/c9jHWoSqf4P63zeP9950+n9oz/o91nR3Xcc610hdZ96r4HosvW27i/H3FLzg1RRo4dlhLeLxyVrMvsvsS/k1L3XngcEzvHPb8SQ9rYh1zeGxynmYNrONM9hx2VfBNSEEHt30ZrLF5VrMe3J/Dny09nWNRIZzI0wAABR1JREFUU7ewRlZyJz9kC2zhB4HeMvZQZm52albkssbWO4v1X3UsGi0L+VwreYlrKs+6mX13gbXsQfUcyz67mf/Fcd67P8/2t9TG3bz/23n7NT8Jf/da2yYgyikAv016hmFMEwQRhZhNKjbpGYYRXWzSMwxjWmGTXpjhtDiqHevmBCv4A/vS4tLZd5azg3WPofkV1O6qZE2weTH73mpuY80Mju/Nl1ZA7dQNrBOlvtxK7cBs3n8Wyy5I2Omsv5jjoav/zNvfdg3rYpWPc3ynDHBONb8TD1rmaHjd81m3SdnItWhbF/Dnz0pi3c2N7yy4nfPtjcwp17zI2VYt9yXrYY51VUe/zXiK63tkVHKAT8NZTh3cLv5iZXnYx5dWz2PVN599b/FdrLntPIv12pom1gT9CfxVafgAa4Bld7FPsfRJPvcCJ3G9ktJn+di2XcH5+TpY4oT3PI4lTuS3Q4918u/V8uf3znLOrX9j/bZ3Xjg3pBujPi4U+8RRxxp2pWcYRlQxTc8wjOmFTXqGYUwbVIFAbN/fHtZJL66lF7m/Ccdrbvo1+8Dy7mRf21AF11FIeIZ9eduuZ02tYAUPds8i1m023lBN7Tk3cTBv3GusK/kHOV6x/0Lub/JjHE+Zt5M1wbYzWEfJqON4x4azOX6z/Pvsuxs6kzPlNC9ib1fORta5XJf7UBp723b9hnWjed9jHap3AeterfNZpytu43jRPUvCyyuedmJrs7mvIqwT6XzOr7fx0+xhPOpm1m8zt/HYuWPvreFjW/evXL+keDH71Dr/ystn3875/+Dlses4jn166bv4XOs9jTW11L+w/tl1BXsoe05kvTrN2V6V4/HscTymaQ/x9j0LeP+JTg2ModPZ8zmQx8c2dVfYVykT9dnF9pxnV3qGYUQX0/QMw5he2KRnGMa0QRFJVuRJ5bDWvRWRZgDbAeQBaBlj9ckklvsXy30DrH8TIVb6Vqmq+WOvti+ZSUV6UsVVEa379OafjFr39lBxWK/09g6kiCyfjA8bKbHcv1juG2D9mwix3LdxYbe3hmFMGxSAP7Yf39qkZxhGFFFAbdLbH3dO0n4jJZb7F8t9A6x/EyGW+xY5MX57e1gfZBiGMbXJTCjUk4ouj2jdp3feOvUfZBiGMQ2I8Qspm/QMw4guNukZhjFtUAX8/rHXm0Rs0jMMI7rYlZ5hGNMKm/QMw5g+aMzH3tqkZxhG9FBAzZxsGMa0wq70DMOYVpimZxjGtMEsK4ZhTDfUCgMZhjF9ULu9NQxjGnEEpIv3jL2KYRjGONBAZK8xEJFzRGSjiNSKyI37WZ4oIn8OLX9TRKoi6Z5NeoZhRA0FoAGN6DUaIuIFcDuAcwHMA3C5iMxzVrsGQLuq1gC4GcCPI+mjTXqGYUQP1Whd6R0HoFZVt6rqEIAHAFzorHMhgPtCfz8M4Exxq8rvB9P0DMOIKhody0opgJ0j2rsAHH+gdVTVJyKdAHIxRkU5m/QMw4ga3Wh/5nl9OC/C1ZNEZPmI9p2qeshT5tukZxhG1FDVc6K0qXoA5SPaZaH/2986u0QkDkAmgNaxNmyanmEYscgyALNEpFpEEgBcBuBxZ53HAeytLH4JgKUaQdEfu9IzDCPmCGl01wF4BoAXwD2q+q6IfA/AclV9HMDdAH4vIrUA2hCcGMfEqqEZhjGtsNtbwzCmFTbpGYYxrbBJzzCMaYVNeoZhTCts0jMMY1phk55hGNMKm/QMw5hW2KRnGMa04v8DufiHsTaFL2QAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:39,816  SpectraRegion  INFO: Processing Mass 11152.35079999999 with best existing mass 11152.962932082024\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11152.35079999999 [('Cxcl11', 11152.35079999999)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:39,928  SpectraRegion  INFO: Processing Mass 11538.699599999984 with best existing mass 11539.301034693031\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11538.699599999984 [('Crem', 11537.28529999999), ('Ccl17', 11538.699599999984)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:40,049  SpectraRegion  INFO: Processing Mass 21899.871200000005 with best existing mass 21899.500544164013\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21899.871200000005 [('Psmb1', 21900.825699999972), ('Il18bp', 21899.871200000005)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:40,167  SpectraRegion  INFO: Processing Mass 26763.888900000027 with best existing mass 26763.43689070807\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26763.888900000027 [('Il34', 26763.888900000027)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:40,279  SpectraRegion  INFO: Processing Mass 18538.088100000008 with best existing mass 18538.66087809091\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18538.088100000008 [('Il6st', 18538.088100000008), ('Mfap5', 18537.995099999996)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:40,385  SpectraRegion  INFO: Processing Mass 7292.779600000001 with best existing mass 7292.600172398589\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7292.779600000001 [('Il6st', 7292.779600000001)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:40,496  SpectraRegion  INFO: Processing Mass 10560.59809999999 with best existing mass 10559.873579245592\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10560.59809999999 [('Mrps21', 10561.238699999993), ('Cxcl12', 10560.59809999999)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:40,788  SpectraRegion  INFO: Processing Mass 13634.183899999978 with best existing mass 13633.977934787092\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13634.183899999978 [('Ube2a', 13635.07789999999), ('Cxcl12', 13634.183899999978)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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M5U4vU0SK+tiP+vNvEiKSCOBlAHcbY1qsxWsBFBhj2kRkEYBXAUyx2+iLPt4qihJShuCnVzfY06GIRMM34f3dGPMPe3nfSdAY86aI/FFEMge6ERvuw7cBsERE1oyGmp2KogwPXxLR0KSLFxEB8DiAbcaY3/azTo5/PYjIPPjmtPrDrXuA4d7pnW2MKReRMQCWish2Y8wKq1N3wF/PMzor5XBtKIoyaghpYaD5AG4GsElE1vv/9iMA4wHAGPMIgOsAfE1EPAA6AdxojBmwDKEMsjxoROR+AG3GmF/3t07mCZnmsqeuPGjXdrHGtaua/fTGZ3L+t+an88mee+c6st/+lDWuhH2WxpdiVeTay/1ruZD9+jy1rOnNm8PxlDue5SRyUT3cfuOZlqNdO3/HpORzfrrcH/H2xT9mjc651SqpZRXMsks2ioe/TfM/4JxwPYncn4bprHPZtE2w8hk2BS5uuxLcDbcvI/v5Jy4k223F/abu4oNpKeR/nJ6TLZ9Ly5ks800eq7pZA99JmGgeK7s6mhTy/noauP0Jr3J/91zH28fv5bHsTuf1YydybsVvn8Dj9cA/r+H+WPGwdhmKmAbev9Mqj+LssK6Na/hmyO0J/K8Uf+cxdBZXHJGylzszzdzy7IWDrwjgl7NeXjMSLz+PeEoWkQS/7wxEJAHAxQA2h6pjiqIcfxyIyAjmM1IM5/E2G8Ar/sdpJ4BnjDFvh6RXiqIct4zawkD+It+zBl1RUZSIwZdPT2NvD9LtdWJ/W8Bfq+kFK5Z2uqVpdbAuMraDdZG3V1oa3n7W8LzW0ZlJLHQ0xXCt1JgNrDFOXMouQetappHtZFcruFos364u7o+rkb8BW7o531/vQis/H5fwOMRXLMry7Sp8hfu76xbuYNkC1ijzPmBfrq4Ma8AmdLBt6VruvIBmGTWN9cu37l9Ads/nuG9ddTz27moeKztfXsqr3PfKC/jgPXE89lHj+FwnfMTntiuT1+9JtGpsvMT7cyfw4O+5iffvjLFqFnv52o2rturWpvDx/3zZVWTHdHD/zr50A9lrqlnf7ljPfpI91rWZUMZ2yzb2iZU+h+PtGl4YmSYcUBQlYvBpeqP08VZRFOVwHI0wtFCik56iKCHDQODxamGgg7i7nCgpzjlopzot3yJLQnJVcE61ulms84xZyXbdbLbtfHROJ+s2BT9eT7b7dY5FnX4dx6oWL2eXouRibt+dbH3DWS6QtuYXX8l200msCzmb+eIxDm4wcR8/Ruz4ButQEwu5/3vK2A/yW59/juxvv/4F3l83Xx52fGrd6QE75kMWkVpZrsX4dPa5LF/JjnoNp/PJkk4+9tgKti+bY/lotp7COyxnzaxtvOWXV8CaX0EGxwqXt7Nm5pliXZwdPDbOnby/rHV8PHuv5rGLauPjyVrDzffcwFFUn7zG+nXSPr6W3VascNcYXp68lzXIu+96hez/eeL6QN8srXioaI0MRVEiBn17qyhKxKEvMhRFiRi0RoZFlFsQvy+wyy5Oh3dIDQl3OosL4zjdHBpOsGJZd1k1IKycbK3j2Xdq+4Mnkz3hAd7fW+exMJX3KS8vvYTbz+PwSfTGcf/Gv8h1dXvT2XeseQbbcdXc/tgVHK+54w7W8Mbmc42O0k8tYW0s60w/feAWsqet5O2Lb2bfr2YrS1lsZWA83VYuCeeprOGVvltA9vk3sIi1dMkcstO28blsY4kNS3azz2RUPmtunjoem6h09knETo5jLslgH0Qp5PWjKng5LL++7iy+Ntpz+FqLsWqIpLzO57p2rqXX9g78MsCuTRtbx9ubKL7bku9Ukv3gQ9eT3ZMT2H64N2qq6SmKEjH40sXrpKcoSqRgwt9lZdAbWRF5QkRqRGRzn7+li8hSEdnl/5k2UBuKokQGoUwierQI5k7vLwD+F776kwe4B8AyY8wDInKP3/7hYA15o4HOnIAW4mq24iXddk4w/sYoP591CxdLUBArv5xVBhafmcbBrO8Vsy605zquO5C6kftTcQ73RzzcH+fXWTcpjGYNbb+bi1b0zud8emDJ75CcaS2TWQdK3sLfWV1rssnObOb+1ffy8WWtZt2t9pe8ft7DPWQ3zODtZ10fyCS2cjnX2I1ayd+DsY3cdobLqpNrxYY+9LOHyb71ibvI9lp+eNkza8iutjUx65Ere41VL+VqK1fgfvYRHfsRj4U3mse+9Dqrbm+t5dNYzf1tOJG79/rVnBj4cxtuI9vW2epP5uM57fxtZG/52wyyy1dz3V0r7BxzLgz8b9T93dI/h0i4P94Oeqfnz4RsTS+4CsBT/t+fAnA1FEWJeA5oeqMxn162vyYlAFTBl1tPURQl7O/0hv0iwxhjRKTfnPN9a2Q40lT6U5TRzGj206sWkVxjTKWI5AKo6W9Ffx3LRwEgLmecSSgLPFF35LIIl7ne8qu7kv3SetpZU8peyrpNw5dZJ/Ku40m2upPjPV0xrNM4K1no8F7MmpdrFbfXMYm3r1jFuknmBj6+6C9wPGX6I+zcts/ShaLcfHyVC6z2Utk3zbGVNb+MzVwTw9XCvmN7Pst+eMkv8ndX9Tw+H16rrsQnnwR0I5eV/619WjfZeeOryX71L1yH1Vgljf9r/xX8h1mcjy/Oit3FSo4r7l3EY5m9jI+9aRIrO2Nft47VycdavoC3t8cizYrdbbNqODv5UkbSSVyj4tonvkd2Vz5fW1lWnd/aM1iDXF/FPpm9lg+s7fPZex7ryU1fCmzQu3cY90IG8IR5RMaR9m4xgAOerbcAeC003VEU5XhmVGh6IvIsgAXwVSMvA3AfgAcAvCAitwPYB+D6/ltQFCWSOO4fb40xN/WzKLg6b4qiRAyjWdM7IryxBm1TA1rF5IlVtLxnWQ7ZNVYdhb5xuwDQE8+6hmMpa25tU1kD21bFL5l7alhIirPy72Umcs61skSuaZFQzBpjVwbrLk1TWJPr3s46T+cXWegZ8xrnpGuaar0fiubjmfhrtitYJsOeK/j4bD/GKI8dr8nLu8bw+I5ZxRdzzWcC59IN1ryklc9V8SYOns0vZs2t4EfsQ/n17OVsP3sn9+0zrPF5L2T9Mno950ZsH8t9T97Lg9E+hs9V06l8Mbgq+PjyPuHtOzfxufXypXFIrGz6r9iJNHq8FcubzSejkUssI30d97cngfXh7rms9+b+hc9H7NVWTekHA7HFvXcOrxa20UlPUZRIQhMOKIoSMRgzCjQ9RVGU4BH0esPbZeWYTnrONkHmJ4FdllayzpPIbm6QePb1clk6UcUlrDllj2Xfp+h17LuV+xTHU+5fxPvL3MTCS1ki+z5lrWPdpXWcVcPC8u0acz7XqKh5j9vrSGLNsnsOb3/HZzhB39+euojskmtZZ+rJYR2q4CX+xq2dZfuakYmWSVaNkQwe/+pzeIP4nYHxPPtqrlnxwduzyXanWxpaNo/d2spxZN+65Jtk95zNml2ik899TR377aXM5MjJrtWsuT35P78h+wv/8V2yo2IsvdjNIl1rHvff1uw6rbq63Vm8Qksh5+c78+7VZG//EseFV57Hml0Pu2SiYyyPb3wRX1stVo2QilpLgywONOix6k0PlXDX9MJ7SlYU5bgilH56IjJORJaLyFYR2SIidx1mHRGRh0SkWEQ2isicw7XVF328VRQldBifrhciPAC+a4xZKyJJANaIyFJjzNY+61wKYIr/czqAP/l/9ove6SmKElJClU/PGFNpjFnr/70VwDYAVg0EXAXgaeNjJYBUf2hsvxzTO73ozG7k3Vpy0N60n0W8FifrHAmbLfty9uvzdrFG1/kO+2a5T2AdRXr5K0g8PPD7r7JiWzlUFr0uXj/xEu6PZwX7GXY8zcfnuZxjg2O3sDCTcw4n1Hv6GdbwunO4f9LL/UktYt3JWEJT10zWxTLe5fH1XMs6WO43rdjepzkedFtTID/guj+yhhdl1WGd+iQfe4WlUXXvYk3ulAt3kL32X1PJ9k5mvXHKQ3ysFedYPpHTue+3/oA1vM5cq6ZElXVtjefto9tY92rNsXwe83msC/7G/2pVZ5KJN949jWzXj9kPMf51q/4Ldw/Rzdz/xHK+Vuqu5v70lrGfYOy0wP4k9sgL3xoMSdPLFJGiPvaj/lj9QxCRQgCnAFhlLcoDUNrHLvP/rRL9oI+3iqKEkCFFZNQZY+YO2qJIIoCXAdxtjGkZbP3B0ElPUZSQ4vWG7u2tiETDN+H93Rjzj8OsUg6g76v/fBySg5w50hoZ94tIuYis938WDdSGoiiRgTG+x9tgPoMhIgLgcQDbjDG/7We1xQC+4H+LewaA5j4Jjg/LkdbIAIAHjTG/DmL7g3R2xGDT2oAOZBysU4ydy30treZY2s4trNmlbuOBqz+ddZfEnay71FgvsxP2s51k1WmouJT93po7WEhZkMF+eO/FWJpetpXDrIx9p8Qq49D8MmuABS+xrrXzIfZl89awJudO5f01XMQ6msvDO3Qn8/opf2adbftdVp2Lt/k7MqpPneKudG6rZwbrgWU93HanpU+mW/VIVidPInv8Cqvm8Bg+9t3X8bnxpPK1kLGKL/Wxd+4ie+3ayWQnlfCxeurZbp3NdSQkiq/l6f/BPqOVD3EcdHctx1mL9b/Q3cX67F0/4Oxtv/s7V2jI+5D7Y4THM/0N3n9bPh9PhyugL3t7hlfNLIQRGfMB3Axgk4is9//tRwDGA4Ax5hEAbwJYBKAYQAeALw7WaDBZVlb4RURFUZRBCZXLijHmI2Dg17zGGAPgG0NpdzguK3f6nQGfGKgEpIjcISJFIlLU29be32qKoowSQvV4e7Q40knvTwAmAZgN36vh3/S3ojHmUWPMXGPMXEdiQn+rKYoyCjAIbsIbyUnviN7eGmMOFjwQkT8DeD24DQHpI80423jO3VfMml3meK470L2edYk7v/cS2Q8/+NlDd9iHFi47i7haHvjGadyfvrGlvvW5vY+fYZHQPYt9xxw7ePuk3dz+uBtKyC5eOpHs3d9m37T8v7MvWmcG97/lCs7P5+5kXShtCY9f8xQ+nuxPWReKbmU/woIXLX3YFdBMt/+Q13Xs5321T2aNrW8MNgDUncv66X+d9SrZv8y9mGxTxZqY06rR4WpmPbfhZNYQm/azPjr9D7Vkb/8mF5mIyWGNMi7K8pn8lDXLnV9lH9roT7l/U5bxU0/TNNZ76+Zw+0/+7Eqye2bxudt9A4+n7YMaW8nXXnYRj7cUBdqrbxxmPr1hbX30OaJJ70BRIL95DYDNA62vKEqEYAATQpeVo8GR1shYICKz4ZvU9wL4ylHso6IoxxHhnmXlSGtkPH4U+qIoyigghAkHjgrHNCLD0Q2k7Ap8C7SNtzrTxv5Bl+RvI3vVokKyf/4qa3ieOVat0xXcnteKV8wuYg2r+BZe39HAw+PoZl0keR/7jkX18A7StrPGt28R60xVbaxLZW7m/lfP5f5Et/Hy/dfz/nNe5hdFzROsOgsXsy41+b+t47+ZX8LnrOT2z391I9lP7woks0h+j/3m7PobnunsM3jmN4rJXv4Cx57+GOyH5mjgsSs8mfXFjJmske18hfVQpLCmaKr5XJV8nvVkh+VokPQGa5Y9CXw303w6j6WzjNuXU7jObM9q1jybrO5G9Vh+j3FWXd4YHt+YdI6t7a7j9ntjrXx65/J4Ju7rs+66I79TG2Ls7YigYWiKooQOA0AnPUVRIgl9vFUUJbLQSS+ApHoQfVXAH8q9L52Xx7Nm9c5DZ5MdV88ak+cyK1+elQcsaR/rOJ3/xhpbaz77VsUmsZCT/D7rHs0cDoqWAtbcOnKturezLd+xJNbUOt/nGh6NZ/H20c28fcVZrNNEOVnHqbP2l7SH28vP5gSBvUmcc27iK3z8JdeyRvjP/+D67nPvDWiuc776MS17rvRUsl0OPjedvexD6JzPufxQyxpayk4+tpPOs+KeX2RN0LDEiOhyy2dxG49Ne47l12bVQG6azusXnFZKtqeF9dmoau5A0hw+VwCfy4xN3L6tidr593I+YTtxv5UbcqFVIzqR/f5sTfD0L284+HvZCr5Oh4Yc/y4riqIoQWP0RYaiKJGGPt4qihJZ6J3eQTweB9UnLVjMXwkJ6zjh6YR/cuzt+ge4Dm01y6wAACAASURBVIMjkYWXnHTOJL37BtbMTAXrOu2LWOMb/3+83NXEsazR7axxtd7M+0t7LZVs77W8vH0tx3PG1lt1D6waHDHs2oX2s9nXzVvPulB0J2/fOoHbb9jF+fpm7NlHtqcwm+wxq+06v3y5rCorOPj7+43TubPC+85dyttudHFf2i9jPdHRxOtn38h9fedtzjLunsLXgiuJbXe9lXswydJjZ7PmlpvJg9+8h89dxTKO3Y0/i/VSj1UHt+EjzrXYdYVVhyLJFhH5WkQqL6+0/OzyrXonmMnX7vg/s9/g3iv4+Hd974SDv3eXvodhoXd6iqJEFDrpKYoSMRwHCQeCqZFx2CrjIpIuIktFZJf/Z7+JRBVFiSBMkJ8RIpg7vcNWGQdwK4BlxpgHROQeAPcA+OFADaXHteNzJ68+aBc9zBpd8Z2cT26nJS3kdbBQkvwha1rJ77MuVP9vrFvEsESIxEWswzRPYk2rN4Z1kJaprMMkL+N5XixX9I41rAN5p3H/PLNZU/SuZ7/F2Z/fRPZNmSvJ/uFvv8ztsWsbxr3LOlVXJutEZTex42FKCY9v6zgev/gaKyddQ2D8Z07lAiPby1nDqjqLL7V0DuOFt5f39V9XPM/7juKxunv8jWS7SvlcTfw21xepe5brP/9m4Qtkf/Phr5NdWcDt5XzK/a06l8eqo4J9PsWKxUUra3DGyWMZt9PSHGfwuYtP4PZau3i8ulJ4fGOXW/kGW/naG/cO3+/svziwf/eu4SRUR9iHoQ16dANUGb8KwFP+1Z4CrAhxRVEiEjHBfUaKIWl6VpXx7D6JRKsAZPezzR0A7gCA5Ny4w62iKMpoYYQfXYMh6PvYgaqM+ysSHfZQ+9bIiE+LOdwqiqKMGsT3eBvMZ4QI6k6vnyrj1QfSxotILoCawdoxEPSYgBZRfH3iAGsD0e08MIk/ZN3I8VP29Sq5iTW0aHaTw9i3qsiuvJA1rou/+i+ylzx+FtlnnLKT7IbHef+lC1nXGbOGdZ/aE1kT7F7NGp7T8u1a//RJZJfsP4HsLnZVQ/ZqjjUu+bqVg62H9x+/jU9/41S2u7L4e0zOtRwHGwN1HTo9rFnlvGL5RObw92vTCaxp9XayRvXIvvPInpZaTXbsLtbAzMnsl9Zy6Uyy21bz/u//3e1kR1n57OzY1po51j+pVaf2prmryH7uQw6WlV67ZgWPV08KtzfuaR4PT3wy2dFjrPG03CQ98Ty+nov42nBZcdvuysC1a5zDvFU73u/0BqgyvhjALf7fbwHwmr2toigRiDfIzwgRzJ1ef1XGHwDwgojcDmAfgOuPThcVRTluGA1JRAepMn5hP39XFCVCGck3s8FwTCMyWuoSSCeLt95rODt4tNwskR2iG1WexQ2cfAH7Zm34gIWabfdaGtoObu+lllN4h6ez7rH+LdbUohfw6u6TOA9Z/Wmso8RFs904hjW2hDLWcZIqWORrG2vpPHE8XqU38vq5lq5mx/ba+QFjT6vn/r7M4xX7seX7lR3oT/X2fFqWYPWt1zrXBbM5H17JPq5RUf8u66VLT+S+XHM1+yzuauPt7/rFM2T/x33s09iVYeVKnMd+gB1xfK4mZfHYVC4uIPuTAvYxLXiDz21vLCtJVTfytZX9EmuUzRO5f92pfO7Eejy0NTx75unczZqgcw/356YvfXTw96cSrAIhQyXMJ71heiEqiqIcHUTkCRGpEZHD1tUWkQUi0iwi6/2f/wymXY29VRQlpITw8fYvAP4XwNMDrPOhMebyoTR6TCc9rwPo7hO5NeYcfsSJ+0E82bs+z7fkMQ5+fLPL2u17lB9n3WfxI0bcbn7c687gR4Kcxby8/t/4cdUTb7lwWG4IuS9YYV5X8+Oo2cHP61Hp3F7XbN5fTSI7cyfuJxNOK5WU2cOPSBO/u4XsDzdPI9vRxNunPsNhddWnc/9ia6wU5EmB5ZP/WkvLGn/LY9u1nh8/b87nx9OflF5B9v1f+hvZ//nk58l+J5Glho5SfvT+cQcHCDVNtcaKTw2i6vlxMqqZn8drqzmtmGETrS9zmFvzxTx2trZ/98nvk/3ousvIFivzVFyd1Z71jCZe/sOUK3aRXfkIaxkd1zeS/fb/zT/4e3OtFXM3VEL0IsMYs8IfEBFS9PFWUZTQYXCsXVbOFJENIvKWiMwcfHV9vFUUJcQM4fE2U0SK+tiPGmMeHcKu1gIoMMa0icgiAK8CmDLYRjrpKYoSWoKf9OqMMXMHX62f3fQJhzXGvCkifxSRTGNM3UDbHdtJL85L4UIVa1gH6fkWuwlMHM+a37ZdeWTnr+V75PIFvLsxn7BwU3OGFecVw9tHd1glFN/mMLm4BhZaSq/g7bM28vbjx7Kbw35htwvTw+pCxjLW8BpnWppfJq/fXchuFs4q1hQ3PHci2fEsmcJthT6153L7+e/xeFXOt8La+gxv9bkcAtjVwTGA55zPabIe3rWA7KTNlk/LfAxI7Bus93aez6mXarZzqQAkWXqsh48l/10+t+n37iZ75xt8A9E+wUpztp3/leIncR6ztiYe/AeXLSTbTON08DFJfG57trJmGVdtuXelsr3jbe5vVjP/b1XVsSg588a9B3/f856Vun6oHCOXFRHJAVBtjDEiMg8+ua5+kM30Tk9RlNARyrRRIvIsgAXwPQaXAbgPQDQAGGMeAXAdgK+JiAdAJ4Ab/clPBkQnPUVRQkvo3t7eNMjy/4XPpWVI6KSnKEpoCfOIjEEnPREZB59zYDZ8h/OoMeb3InI/gC8DOOCg9SNjzJsDtZXgcuPUvNKD9pblM7gzJ7EOtH8Na3iZ27m95DWcaqp6HodCdaVb3zh2yhw3a1i1J1u6TJUV5nU5a3hRzby+28qU1dBk6TDb2Y/umhs+JHtxJmtwOX9n3coOG4suZw3vpHPZN+u0VC6b+MhqTteUvtIKxVrAfoId2QMnfe1NCuhgjfNZ40py8Fi193JfGxt5sMxU1pweKeW+2qmXJi3cQ7azndurrWON0Ovi/kx5kTWz6h+wjtXyzGTe3pIIxT2w319bGZ+7+FIrxLCU+1OziDVFdy1rgAl8atBohTjmv87tV53B13bphbw8911ur3z9hIO/9zRa5SeHiB0iF24Mp0YGADxojPn10eueoijHFSOcCj4YgsmyUgmg0v97q4gcqJGhKIpyKGE+6Q0pIsOqkQEAd4rIRn9g8GFLQIrIHSJSJCJFXU1dh1tFUZTRxCgoAQng0BoZIvInAD+Dr/s/A/AbALfZ2/k9rB8FgPgx48zWpwMxk11cJRBdtayB5a1mcaBiAa+fyuGYGPNHXr91POsY2SvYrj+RdZmubN4+mSUxZH7CGljdPNZhKi/i7TOWss7UncrtPbuJ/TK9HXw6xEol1Wn1z9nG/V+3bxzZa7ZNIDt+D/ffa0k3rm2sIznmsK9ZYizrYG5PoH+Xj+c435V1vO+1H3Lc75UXrSZ70w9mkV11J18Ldpz1pmLWb7Nzua/22ES38djWzeKxHXsfa3q7vmClxorncz39/9rI3v5VPtcpW3h/bWdaeukMvt/Ie5lPRk8C9z9lJ6fDr+rm8Ylp4BuK5N2sabpTeH+tfKkgY2tAI3R0D29GCvfH26Du9A5XI8MYU22M6TXGeAH8GcC8o9dNRVGU0HDENTL8xYAOcA2Aw+a8UhQlwhgFj7f91ci4SURmw9f9vQC+clR6qCjK8YMZBS4rA9TIGNAn73B4XUB7Hy0h1ioa6ei0fKssXy9JZU2p9GPWdXI62FesZz6nvfasZd+pBA7theMiDturc2eQnbWGv55cdawLuVp4OLsXsc7UtYvz6bl2sR9carF1vF5Lo+SM5Ejay3ZrFPsB2nTmWGUB461YWiv89dLx7Bj5vawVZF/xk+8f/H3+vVwe84MfcfnMuMm8r9fWzSY7+UTWtPJSrHKdhTz2PWv4vZlrMcc1f+6nH/D+HmG/PwdfSth1s3VuClizwzq+dnb9wBJE+VSjdZLlF/gb9qsrv8CqhWAl0OtK44ew5ktZw+vK5/ai2/nkJVZwex09fK26k/l81J4c0Hs9RcOMqAhzTU8jMhRFCRmC8H+RoZOeoiihRSc9RVEihtEQkRFKpBdwtgf0AttPrNeSpNyJrC3YGtiUC0rI3jSWNT7Usd9ZulVnwG2V1evs4A6kbeXmWm5gX6moDazLTLqMc7CVNFolFKc0k93eyvurSecBMWnsOzbpMatOgoP7H9fIfngN0yw/xbmsk3X28Pp3T1lGdkIUC1+L1n2J7NYTAv0Z5+Rjq5rHbbsL2Y/MGc2aV9ouPpayJnZq7NzLmlZ0NI9F1el8rM8sZg3PcMVG5L/LY9s8mcfeY2lgTkvm8lqxva4W1uAc3bxBxQJbw7PMXj6ejC1WbPBpvL+MT/lft9U6vtYJ3H87f2DGFh7/ts/1OX/PWwU6hopOeoqiRBLH/dtbRVGUIaF3eoqiRAwj7HgcDMe27q0L6MwL6AXOdNZ58jLZ2WlvCddKlVjWGuz4S+liHePEkzh4dpMZT3baBl7fs5XjJ7vYTQ8Jr7CvVvtYXt783xzQGJ/Fw+u1hCGx8v3ZvmOtEyzR8/5yMks/4eO/fNEqsl/dyvGs7W7W2WL+zprj/adcz8uncH5Dl9PKmVcS0LGuePk7tCxlDtdmcXzCNTS6M/kZ6P6HuQjW7a/dQfbMU/eSXf4Mx/bafmddGfyfZ/s07r2CxyKriJd3V3ENieaZ7BfnsmoA2+K9XdO58S2+WDpy+fi7U61r0Tqe5B3cfvx1rM9Gebg/LWt4vJNLuIM1c60aIX0S9jkdw3s+1RcZiqJEFjrpKYoSSeidnqIokcXxPumJSCyAFQBi/Ou/ZIy5T0QmAHgOQAaANQBuNsYMWDDT4epFSn7AHyjpSdbIolpZh5j6o0qyd+7mOrm2rmLrNt0n8PKCxXw29l3NGpWzkddP287re1kGQv4S1rxq5/Hx2DU6bM2ucy7nWPO08w6yP2Cdpziej79gJff/tTjO7vXFi98n+6//PJ/s9ms5vtTTxr5g0WvZtyzKqhly/tf/dfD3Jfun07Luj/lcxjRbNXytmhMvNHDfo618eCX1LLC2n8GXWtIm7ruzi7fPfZvrqTRPZY2tdh7rWEnFVtELB/ffOcPWO1lv3l/B/R1bysu90VYs7Kl8LjLeYU3RWPsvq+LY47+c8wTZt1R8mey0+fy/1PMy69tt2wLj4a23LvQhEMoSkEeLYPLpdQO4wBgzC8BsAAtF5AwAv4SvRsZkAI0Abj963VQU5bghzFNLDTrpGR8Hvoai/R8D4AIAL/n//hSAq49KDxVFOa44cLc32GekCDZzssOfS68GwFIAuwE0GWMOPF+VoZ9iQX1rZHia2w+3iqIoo4kwv9ML6kWGMaYXwGwRSQXwCoDpg2zSd9uDNTJi88aZjk0BLWLSd7lO64469svrXsV+b7FTWPd47DNPkX3Hn+8ke+c+qwjHlazzpOVyvGhzPMfquos5NrZ+Hmto1efw+p+ZvZHslS+zn1zbySzqzR1fSvbqrZwwz/brG/8Wmdh3LV85U29fSfYLr55CtquR27tq6nqys6NZp+o4jf0EHyk6l+y39gbqFnft4djYrH1WbGc+f7+6WrgvHz4/h+y8hazBlRTzucxdbtV8sGJPY9lNENu+zRpe9tRasps/ySa7awyPbXwJj0XWOiu29hzrXymfz3VXKve3czbruYkfs49ow4k8fhfM30T2e5+cRPYt77BfY3Qza4aO/7Fihc/g46u7OOAz61k+zDiyMNf0hvT21hjTJCLLAZwJIFVEnP67vXwA5QNvrSjKqGc0vMgQkSz/HR5EJA7ARQC2AVgO4Dr/arcAeO1odVJRlOOIMH+8DUbTywWwXEQ2AlgNYKkx5nUAPwTwHREphs9t5fGj101FUY4XxBvcZ9B2fPW0a0TksEXHxMdDIlLsr78953Dr2QRTI2MjfAW+7b+XYIhlH1NT23H1ZQHfro9+cQYtF0v3GbeIdZ3qpRxr+lUnF76ddilrhNOTqsl+zqoz27WKfamsEhFos2qDwmslQYth36t318wkO6va8k3LYF1o9ydTyR7baPmKlbDGVnox+83FF3N3Sn/MdSmcH/LymddyzYuvpv+L7G/tvYbsdTsKuT9ZrKmmxgV0oIocPrbmiZz7MOpU1k+zEljTKq3gOGDvMj7XrJ4CySWc27BpqlUzeTkvh+HldZ2sHydYNS5apvC56LFqUlTF8tUSW2fVG2lhPdhjHUB8Ef/Bju1N3cS+cv+qZn04tcm6ttL5X9lYbobd4zg/YfIt/L/V8VbgYo/qCOr9Zr+E8PH2LwD+F8DT/Sy/FMAU/+d0AH/y/xyQ4R2doihKX4J9tA1iYjTGrADQMMAqVwF42u9WtxK+9wy5A6wPQCc9RVFCTfCTXuYBdzb/547DN9gveQD6ukD06zrXF429VRQlZAyxGlqdMWbu4KuFlmM66bW4Y7C0dNpB22Wli2s/pZPsziKetJPmc13a+KdYp9ifzprX5omTyU4ps/LZefnsNJ/F+f3smhzTHmMdquJcjrUd9yzX7Eh7mdv7eD1reI0ncn/OmstJ04qWziDbwc3Bk8D9z5zLGuazM1gKufx3PyD7snPZN21SOju3xe1lXSnjZfYlK/1MYLzz32N9sz3b8gMrtHwgd1o1iKeyfpl4DmuAnUtZg9vxFdbUUteCl9/O585Vz/1J4XImyFrNol7beL6WEotYo7P/sQ/JvVjBK8TVs0bYdVsjt+ex8ukVc2xt50n8v+Eo4uPL3Mw+pPuvtWKBZ7Cd18v761sT2Y4xHzLH7s1sOYC+yntQrnP6eKsoSkgRY4L6hIDFAL7gf4t7BoBmY0zlYBvp462iKKHDhK4wkIg8C2ABfNpfGYD74Iv9hzHmEQBvAlgEoBhAB4AvBtOuTnqKooSWED3eGmNuGmS5AfCNobZ7bCe9Jiei/hnwx3rrF7+hxeet4exUbfEs+jXtZw3PFcuaWOMMHm1Xs1WL1G2dDesbydbw3FNZR9kxgXWkBHZ7w/bvF5Kd+ALvP28h1zWo3sCa2pa/sYYXa/ladVo62ZWLONZ2ydNnkn3+9u+R7WJJDt7NPJ75C9nxz7WID3B35TSyM9cH+tP9TfYs6OxkDWx+Dkstm2rYs+CELNYjN7zD4d1dMzl/XnQi2y1T+FLOKOLB68i2ahxzuj9ULOCxGPsxa2TxpZwsY++VrPnlv8+Ca+UZfPwJVi7CtiKrA9alOXYNx+7uO4VXaJ3CGl1cLV9r4uD+e5r5f6lxNcciR2UF2h+un124h6HpnZ6iKKFFJz1FUSKGUZJwIFZEPhWRDSKyRUR+4v/7X0Rkj4is939mH/3uKooS9oR5woFg7vQOpItvE5FoAB+JyIHMbt83xrw0wLaEcQDupIC2Muftu2h5QQHnOGtPY11kwhOsy5SdzxqbieKR9MSy3XffAJB/xV6yt2/iYNukRNZpYv/Buk/TVKuGRqJdB4G/Uxo+5Jxw1mJEX8bH3/kBF5JwtnP/X3+NNbz4C9nPTraz85g7jUXMrGm8/pI3TuP9saSJ+V9dR3bR44HvuRvHcW6+vzxzCdk7zuLY0oTnWBPbD7adVtxzdA07j035fxw7uvv3PFb1cazP2rjqWfMreJH11pKbWXOMv401sqjV3F7phXwtujN5/fIT+fidu3n/3eMsjbKUNbi49XzuYxr42qs+h6+9WCsXpHsKn8zWydxeUl7AT9KuLz0UhuicPCIEk3DAADhcunhFUZRDsJ3+w40jShdvjFnlX/Rzf0qXB0XETlKiKEqkEcKEA0eLoCY9Y0yvMWY2fGEe80TkRAD3wpc2/jQA6fDl1zuEvjUyeju0RoaijHZClU/vaHGk6eIXGmN+7f9zt4g8CeB7/WxzsEZGXO4409v3ftB6+K9pYUey6T9mzWnbd9m3KLbWqm36Mbcnt7HvV8NHrKlt38i1P6c+zTnY9i/i+EfPVRwPmvQmx4/GrufvkGYueXHIV0zSXktzrGddqn0aXxmxeZzPzrudc8RdPn4L2X+t53yFMworyC5ZNoFssdIFus7iWOcla7kug+uCwJfYmmYuUtGdzn33fsp+aV0LWPPKfZ8HJ7rVqlHBkht2fZP7nv7awPpt89msz6Z9yPvbfStfG+lbrBofrayPjlvB18qe7/D+4jbxtdw5kdtzGl4/ZS0/KLVM4OMZewb7Oe7byppjYgn/K8dZuRx76lnj7GZ5Gu0dgT94uy0H0aES3k+3R5wufvuBvFUiIvCVfzxsdlNFUSKLcC8BGcydXi6Ap0TEAd8k+YIx5nUReU9EsuB7YbMewFePYj8VRTkeMABCk0zgqDGcdPEXHJUeKYpyXDOSel0wHNuIjIReOE4P5BGLW8OamWM7+yaVX8WaWRKnq0M3b47cb3LsaOmfp5AtVk7VWCtesXkaa2Q9ifyNlf0s6zR1J3N7PSdzTriYf7Jw0pPMOk6v9b7bjg2Or+T+tceyLnPqeVwT5JM6FhFLLuZaTVNXfIHsOJYI0XIC+5L1tA3s6+YpD+TIW7eT43JNoZX8r4A1vOhdPJbOLvYNa7RiTRP2sJ+e7RbhtsbWE2f5oX3Kx9JhlUS26+Q2TrcEToudt3F70YZjZe1/fEcT/6u5M/l4z1nI6tBpyXvI/sUHl5MdV8PXhteS4Zr50oeL5ehDmHdWIM56yWNdA6w5MKPCT09RFCVojDn+H28VRVGGgt7pKYoSWeikF8Db40BbeUCnc1g1HjoLWVNyWTnTPBVcZyF5F+saG0qtWqnXct2DjDjWKhqXsa9TVA/3J8rDuk75JZZQ4+H1k15jDa/+VF4/9wPe3J3E/W8t5OUOK/Z1xn37yd5zJQs3XZnc3zPdnyW74E8s/NRYKSLmn8ga4aoVJ5AtSZbvXXJAp/O6WXPLfY31WRPFAmbKB1yk4rQl7If27Bvnkt2dxmM9Zg3b0W1WTYhL+FijMllzi93EmpyVfg5j1vIfWgosTa6A95f8Ll+b9afx9mPf43PdMYb7t3oH17X9VNiOY7kZcy9nDfCjHXwtREXzuWpv5PMRX8b7L/owkL+wo5XjdoeK3ukpihI5GABhHnurk56iKCFFXVYURYks9O1tH8TAuAJfAxmbWYNyuLk7VWew7U1gHcWTwDqJKWedpjWLdYv2Xay5xZ3Jml97p1WDw/JtcqdbdXMTWLdpOIn7k/8un/yGaXw8Sfv5K9HRaUUFWq5iZTdN4v7m8/bRLbxBbQP7OfZMGVirsetW2J5qMbU8ngnlgf7aNSgqzuNtjYP72nQDxxlXPcLHNu6zrPE1vchOlg0zeKxcTfbYWbkNG1wDLUaslZ+udBHb0x7jWNsoN/sZdlzKy6OLWYSrncP7c3HZW/TMYafJxHjLV+49jl1euXwm2bFWrkUHy+OH1EzuOpv7GxMdGJDh5NMDVNNTFCWSGOG0UcGgk56iKCHDF5ER3rNeUPn0gIOJRNeJyOt+e4KIrBKRYhF5XkRcg7WhKEoE4A3yM0IM5U7vLgDbABwQin4J4EFjzHMi8giA2wH8acAWPAJnQ2CXieXsO1U9lzWn2AIW1ZwfcR2FHquO69cvfYfsP7+4kOzuSSxsdHaw71huGWsZDdNZw0rbwHZPEg+f7UvmieHvlM5sPtOdLKEhiV3X0GrlYEvcx+3lzKwhu3oj19GNtur41s9lDTIus4Pswh+yELT9W7x/I1bNj7MD58908ljkFHAuvupi1qQ8HqtGxGQeu9jHOHdiO5cERtY6PlcON/c1oYrbr7qUfT67MvlYek9njavgCR67yvms0bXO5mt3zKt8MdbOsY7HivNO3c39r8pMINtdy+11z+MEvJmvcf/ab2R9urWJ/QYzVvA9SacV++xxBzRB0zG8fHqj4k5PRPIBXAbgMb8tAC4AcKAo0FPw5dRTFCWSMcbnpxfMZ4QI9k7vdwB+AODA110GgCZjzIFbhzIAeYfbUFGUyCLc394Gkzn5cgA1xpg1R7KDvjUyvO1aI0NRRj0HMq0M9hkEEVkoIjv87w3uOczyW0Wktk/t7S8F071g7vTmA7hSRBYBiIVP0/s9gFQRcfrv9vIBlB9u4741MhIyxpnMDYGD3XsZ6wx2TrOYd9nPzGv1NoZlIzz8rwv5D2NZo0pex5phTCMPfJNVC7RjHGtgxsEdmH3pNrJL/sg55Toz+Dtl7Ees41Rex7pQdwPrMGlbuT92/sD4n/H4XPnQKrLTolmze/6vnPc19lPWqXbdxnbKNt5/y2TWzVI+DYxndBuPZZUjnezMtZZPpcPSsC5nTaq+w8pFaNXcaJ7IupOzg9tvOoPHNvsdvtba8vnYvM08lp2Zdhw2mUhdxe3Vn8jLjZP7G1dj1amdZ+XDG8P9xWTWnx0beDzarXyAPUV8cbg4FPqQSSahlBcnlQUOsKJjGLdqJjQRGf5M7X+ArzxFGYDVIrLYGLPVWvV5Y8ydQ2l70Ds9Y8y9xph8Y0whgBsBvGeM+XcAywFc51/tFgCvDWXHiqKMUkJzpzcPQLExpsQY4wbwHICrQtG9oF1WDsMPAXxHRIrh0/geH2R9RVEigeDr3mYekL78nzv6tJIHoO/9aH/vDT7rr739koiMC6Z7Qy0B+T6A9/2/l8A3GyuKohxkCC4rdcaYucPY1T8BPGuM6RaRr8DnRTJo7Z5jGpHhSQSq5wcGxDgs3aTCrlnBy72JrNGlrWHhImk7265m3r7+VBZmEnM43jHqA9ZNUrfw8KSU8P63tHO+uby3t5O9+9us8TWdxP3Je5n9BJ1f4bq05evYkS/BUk33XsG+WuUvcZ3bzE18vG1XcP8TL2ggu/AB1rVKLFk4ZrtVO/X8PjVB3udto1pZc+vKsOrC1vFYtO9lH0xvPvvVwW1pglGWz2SCFRdtxdradXDbZ7JmNvVh3l/xDaxvxlr6cd77/FLOncJ+b47pXC/F9g6AYAAABWJJREFUbLHiupu4Pz1WwZTOWu7/xCW8v+p5rIl25bJenLeM+9tj1Qxpz+fxzNwUOH7pHZ6mh+FsH6AcQN87t0PeGxhj+p6VxwD8KpiGh/N4qyiKQggMxAT3GYTVAKb4I79c8L1PWEz78tfe9nMlfMETg6Kxt4qihJYQRGQYYzwicieAdwA4ADxhjNkiIj8FUGSMWQzgWyJyJQAPgAYAtwbTtk56iqKElhCFoRlj3gTwpvW3/+zz+70A7h1qu8d00ovqBhL2BrQY2++ucy77lTlLWENyW7qEiWLb1vBirqsmO7mLdZPuDezblFpr1RW4jnWZ/ftZtzLxrJG13zuVbLF8u+LK+YCbJvPy5MfZ+cp7Jh9P4We58O/moglk93D38N0H/0b2XR9+juzmjzlWN2oM60JR5ax+dGfx+Dx76pMHf7+h8eu0LHU966sdY/lY0nbxvuKtsWm34pjjS7i99O1WHHEVFxTxuljzK76DjyWmhH02a+byteGwJMWcVawB1p/EmlqqdTwNTr62Oibw8aRvseK0rRLDHWMtP74YPh43S4RILOHlji6+Nhum8fjZ9VdKrgmMR/feYaheBiOaTCAY9E5PUZSQEu4JB3TSUxQltOikpyhKxGAM4A3v59tjOuk5ug3SdgW0mNaxvPuC+yzfJqtOrZRz/jhvC/vZdVzGhVwb3meNbPxLlWT3ZvL2ZRewr1XXXhbJHG5LQ8xnDXLmCVVk1/6MNbemKex71VrIF4c7hdvPs9orfpPrSPROtuoE72Pd5vvP3UK2k2Ur9J7Ex181zcqjZhXJSPmYdbDb/3hXYNVCS6Oz9NExa3isHL/gQOvon7FPYskUW8OyYkc/YJ/Iils5+DVtJ49Nyqc8Nu15lp+g5esf3coHX3MXi2DRb/Fg2jVB4vhSQ2+8fa75+JL3WXV2J/Hy+hN47E+4gGsUN/68gOy9V1k1ROr5eCf/gfXhrff32T5qmHdq4T3n6Z2eoiihRTU9RVEiC530FEWJGAxGNCtyMIg5hrOyiNQC2AcgE0DdIKuPJOHcv3DuG6D9Gw7h0rcCY0zW4KsdSkpsjjlr/C2Drwjg7V2/WjPMhANHxDG90zswkCJSNBIHGyzh3L9w7hug/RsO4dy3IaGPt4qiRAwGQG94v77VSU9RlBBiAKOT3uF4dIT2Gyzh3L9w7hug/RsO4dy34Anzx9tj+iJDUZTRTYor25yVc1NQ675d+vvR/yJDUZQIIMxvpHTSUxQltOikpyhKxGAM0Ns7+HojiE56iqKEFr3TUxQlotBJT1GUyMGEfeytTnqKooQOAxh1TlYUJaLQOz1FUSIK1fQURYkY1GVFUZRIw2hhIEVRIgejj7eKokQQx0G6+KjBV1EURRkCxhvcZxBEZKGI7BCRYhG55zDLY0Tkef/yVSJSGEz3dNJTFCVkGADGa4L6DISIOAD8AcClAGYAuElEZlir3Q6g0RgzGcCDAH4ZTB910lMUJXQYE6o7vXkAio0xJcYYN4DnAFxlrXMVgKf8v78E4EIRsUrUH4pqeoqihBQTGpeVPAClfewyAKf3t44xxiMizQAyMEhFOZ30FEUJGa1ofOdd81JmkKvHikhRH/tRY8xRT5mvk56iKCHDGLMwRE2VAxjXx873/+1w65SJiBNACoD6wRpWTU9RlHBkNYApIjJBRFwAbgSw2FpnMYADlcWvA/CeCaLoj97pKYoSdvg1ujsBvAPAAeAJY8wWEfkpgCJjzGIAjwP4q4gUA2iAb2IcFK2GpihKRKGPt4qiRBQ66SmKElHopKcoSkShk56iKBGFTnqKokQUOukpihJR6KSnKEpEoZOeoigRxf8HmxHvj++WJuUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:40,905  SpectraRegion  INFO: Processing Mass 10031.886499999991 with best existing mass 10031.676954582104\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10031.886499999991 [('Tnfrsf12a', 10032.82969999999), ('Cxcl12', 10031.886499999991)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:41,020  SpectraRegion  INFO: Processing Mass 14569.692699999976 with best existing mass 14569.640527048126\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14569.692699999976 [('Ccl28', 14569.692699999976), ('Trmt2b', 14568.390499999981)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:41,132  SpectraRegion  INFO: Processing Mass 20308.787899999992 with best existing mass 20308.874137320254\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20308.787899999992 [('Il17b', 20308.54879999999), ('Il17c', 20308.787899999992)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:41,242  SpectraRegion  INFO: Processing Mass 11199.70799999999 with best existing mass 11199.746061695076\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11199.70799999999 [('Ss18l2', 11201.606299999994), ('Il31', 11199.70799999999)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:41,349  SpectraRegion  INFO: Processing Mass 18119.635499999997 with best existing mass 18119.121844786765\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18119.635499999997 [('Tpo', 18119.2567), ('Il31', 18119.635499999997)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:41,457  SpectraRegion  INFO: Processing Mass 20872.213499999998 with best existing mass 20871.780825890197\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20872.213499999998 [('Il24', 20872.213499999998)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:41,570  SpectraRegion  INFO: Processing Mass 17542.508099999995 with best existing mass 17542.6329572969\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17542.508099999995 [('Arl14epl', 17540.928299999996), ('Il20', 17542.508099999995)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:41,682  SpectraRegion  INFO: Processing Mass 11672.063399999986 with best existing mass 11672.104757465566\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11672.063399999986 [('Il19', 11672.063399999986)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:41,790  SpectraRegion  INFO: Processing Mass 20287.34260000001 with best existing mass 20287.746272333712\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20287.34260000001 [('Il19', 20287.34260000001)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:06:41,897  SpectraRegion  INFO: Processing Mass 22536.800200000012 with best existing mass 22536.354760186845\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22536.800200000012 [('Sult2a2', 22538.584699999978), ('Ildr2', 22536.800200000012)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for y in pw_theo.protein2mass:\n",
    "    if not y.startswith((\"Ccl\", \"Ccr\", \"Cxc\", \"Il\")):\n",
    "        continue\n",
    "    for x in pw_theo.get_masses_for_protein(y):\n",
    "        massprots = pw_theo.get_protein_from_mass(x)\n",
    "        \n",
    "        if len(massprots) <= 2:\n",
    "            print(x, massprots)\n",
    "            slided_0.mass_heatmap(x)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 10:55:57,023  SpectraRegion  INFO: Processing Mass 13870 with best existing mass 13869.402716065675\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 377.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.33562942, 0.29980934, ..., 0.08112048, 0.57262519,\n",
       "        0.        ],\n",
       "       [0.02545898, 0.28970706, 0.69557764, ..., 1.32654748, 0.20488023,\n",
       "        0.10190447],\n",
       "       [0.3632469 , 0.17660935, 0.16031279, ..., 0.66523704, 0.66826813,\n",
       "        0.07720524],\n",
       "       ...,\n",
       "       [0.31846044, 0.3063842 , 0.13660394, ..., 0.6416831 , 0.39386746,\n",
       "        0.42146079],\n",
       "       [0.35692512, 0.74327409, 0.        , ..., 0.33920547, 0.06606368,\n",
       "        0.6278585 ],\n",
       "       [1.12895917, 0.37550086, 0.18109881, ..., 0.37619895, 0.84541711,\n",
       "        0.65886899]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slided_0.mass_heatmap(Ccl9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Marker masses/proteins/genes are genes which are differentially regulated when compared to the specific cluster and all other clusters (including or excluding the background cluster).\n",
    "\n",
    "Making the distinction regarding the background cluster might be required if the target tissue is embedded in another tissue. Excluding the background might then deliver more sensitive results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mgenes = spec.find_all_markers(pw, includeBackground=False,\n",
    "                               replaceExisting=False,\n",
    "                               outdirectory=\"./deresults/\",\n",
    "                               use_methods=[\"ttest\", \"rank\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mgenes[\"ttest\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mgenes_bg = spec.find_all_markers(pw, includeBackground=True, replaceExisting=False, use_methods = [\"ttest\", \"rank\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After both find_all_markers runs we can list all DE results, which are then printed.\n",
    "Each row shows one contained DE results beginning with the test performed and then a tuple of two region/cluster IDs. In the first row here:\n",
    "\n",
    "a t-test was performed on cluster 9 versus clusters 8,10,11,12,13,14,15 ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.list_de_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's also easy to save the result to disk:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "markerGenes = mgenes[\"ttest\"]\n",
    "markerGenes.to_csv(\"deresults/marker_genes.tsv\", sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "markerGenesBG = mgenes_bg[\"ttest\"]\n",
    "markerGenesBG.to_csv(\"deresults/marker_genes_bg.tsv\", sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Number of unique, differentially detected proteins (from masses): {}\".format(len(set(mgenes[\"ttest\"][\"gene\"]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Number of unique, differentially detected proteins (from masses): {}\".format(len(set(mgenes_bg[\"ttest\"][\"gene\"]))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to save the results, the SpectraRegion object can be pickled:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"slideD_region_0.spec.pickle\", \"wb\") as fout:\n",
    "    pickle.dump(spec, fout)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cell-type detection\n",
    "\n",
    "Cell-type detection requires analyseMarkers.py from https://github.com/mjoppich/scrnaseq_celltype_prediction .\n",
    "\n",
    "One (obvious) question is: what can I do with those marker proteins? Well, a lot!\n",
    "\n",
    "First, these are the proteins which you are interested in: these define your clusters, because they are present.\n",
    "\n",
    "Second, these proteins give a clue about which cell types might be present, if cell-type specific genes were found.\n",
    "\n",
    "Using the scrnaseq-celltype-prediction tool, this can be analysed! Let's download it first (requires wget, python3 and several other libraries - maybe).\n",
    "Did you know? This tool achieves better predictions than SingleR :)\n",
    "\n",
    "Because we know what kind of sample we got (aorta embedded in liver), we can specify organs to specifically check for cell types.\n",
    "Organs to be considered are \"Connective tissue\" \"Vasculature\" \"Heart\" \"Skeletal Muscle\" \"Smooth muscle\" \"Immune system\" \"Blood\" \"Epithelium\" \"Liver\".\n",
    "This allows a context-specific evaluation!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! ls analyseMarkers.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! wget https://raw.githubusercontent.com/mjoppich/scrnaseq_celltype_prediction/master/analyseMarkers.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! python3 analyseMarkers.py --organs \"Connective tissue\" \"Vasculature\" \"Heart\" \"Skeletal Muscle\" \"Smooth muscle\" \"Immune system\" \"Blood\" \"Epithelium\" \"Liver\" --pvaladj qvalue --markers deresults/marker_genes.tsv -n 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is quite interesting. For cluster 8 this tool predicts mainly monocytes or gamma delta t cells - cells which do not migrate into other tissues.\n",
    "\n",
    "This makes sense, because one would expect these cell-types in the inner of the vessel.\n",
    "\n",
    "H2AFX is known to be upregulated in lymphoblasts (check wikipedia ;) ).\n",
    "Ifitm3 is highly expressed in both, Liver and Monocytes: https://www.proteinatlas.org/ENSG00000142089-IFITM3/tissue (Consensus dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.plot_segments(highlight=(8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! python3 analyseMarkers.py --organs \"Connective tissue\" \"Vasculature\" \"Heart\" \"Skeletal Muscle\" \"Smooth muscle\" \"Immune system\" \"Blood\" \"Epithelium\" \"Liver\" --pvaladj qvalue --markers deresults/marker_genes_bg.tsv -n 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this analysis, the background was kept in. More differential genes are found, but also more liver-related cell types are found.\n",
    "\n",
    "This suggests that the analysis which includes the liver background has influenced the found differential proteins too much.\n",
    "\n",
    "Nonetheless, the results remain consistent. For cluster 8, the missing monocytes are replaced by hepatocytes, which are monocyte-like cells residing in the liver.\n",
    "\n",
    "Cluster 9 showing high amounds of cardiomyocytes is also not unlikely, given its location at the outer area of the aorta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spec.plot_segments(highlight=(9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally the SpectraRegion can also be exported to Aorta3D."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#spec.to_aorta3d(\"./slided_test/\", \"slided\", 0, protWeights=pw, nodf=False, pathPrefix=\"../data/test_msi/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!ls slided_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparations for CombinedSpectra - A comparative analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spectra1 = imze.get_region_array(1, makeNullLine=True)\n",
    "imze.normalize_region_array(spectra1, normalize=\"vector\")\n",
    "imze.list_highest_peaks(spectra1, counter=True)\n",
    "print(\"Got spectra\", spectra1.shape)\n",
    "\n",
    "spec1 = SpectraRegion(spectra1, imze.mzValues)\n",
    "spec1.calculate_similarity(mode=\"spectra_log_dist\")\n",
    "spec1.segment(method=\"WARD\", number_of_regions=15)\n",
    "spec1.plot_segments()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far the results were only for one IMS sample.\n",
    "\n",
    "Now the idea is to integrate multiple samples, in order to compare the different samples which were measured on the same slide.\n",
    "\n",
    "Hence the remaining samples are processed, all in the same fashion. With the inter and intro normalization, the sample are made comparable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pw = ProteinWeights(\"protein_weights.tsv\")\n",
    "\n",
    "def process_imzeregion(imze, regionID, basename):\n",
    "    \n",
    "    print(\"Processing regionID\", regionID, \"for basename\", basename)\n",
    "    \n",
    "    spectra_orig = imze.get_region_array(regionID, makeNullLine=True)\n",
    "    spectra_intra = imze.normalize_region_array(spectra_orig, normalize=\"intra_median\")\n",
    "    spectra = imze.normalize_region_array(spectra_intra, normalize=\"inter_median\")\n",
    "    \n",
    "    rspec = SpectraRegion(spectra, imze.mzValues, name=basename + \"_\" + str(regionID))\n",
    "    rspec.calculate_similarity(mode=\"spectra_log_dist\")\n",
    "    rspec.segment(method=\"WARD\", number_of_regions=15)\n",
    "    rspec.plot_segments()\n",
    "    \n",
    "    rspec.filter_clusters(method='remove_singleton')\n",
    "    rspec.filter_clusters(method='merge_background')\n",
    "    rspec.filter_clusters(method='remove_islands')\n",
    "    rspec.filter_clusters(method='remove_islands', minIslandSize=15)\n",
    "    rspec.plot_segments()\n",
    "    \n",
    "    #rspec.consensus_spectra()\n",
    "    #rspec.consensus_similarity()\n",
    "    #rspec.plot_consensus_similarity()\n",
    "    \n",
    "    mgenes = None\n",
    "    mgenes_bg = None\n",
    "    mgenes = rspec.find_all_markers(pw, includeBackground=False, replaceExisting=False, use_methods = [\"ttest\", \"rank\"])\n",
    "    mgenes_bg = rspec.find_all_markers(pw, includeBackground=True, replaceExisting=False, use_methods = [\"ttest\", \"rank\"])\n",
    "    \n",
    "    return rspec, mgenes, mgenes_bg\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First set the logging level to warn, to remove too many messages ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loggers = [logging.getLogger(name) for name in logging.root.manager.loggerDict]\n",
    "#for logger in loggers:\n",
    "#    logger.setLevel(logging.WARN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fetching region range\n",
      "Fetching region shape\n",
      "Found region 0 with shape (45, 59, 17900)\n",
      "Fetching region spectra\n",
      "  5% (135 of 2655) |#                    | Elapsed Time: 0:00:00 ETA:   0:00:03"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing regionID 0 for basename slideD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (2655 of 2655) |####################| Elapsed Time: 0:00:03 Time:  0:00:03\n",
      "100% (2655 of 2655) |####################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "Started Log Value: 0.14000733569264412\n",
      "100% (45 of 45) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "Got 2655 median-enabled pixels\n",
      "5-Number stats for medians: (2655, 2655, 0.2905536462232088, 0.5712127712945096, 0.6654297160132764, 0.7571640240536153, 1.119313296797138)\n",
      "Started Log Value: 0.21725614592432976\n",
      "Collecting fold changes\n",
      "100% (45 of 45) |########################| Elapsed Time: 0:00:17 Time:  0:00:17\n",
      "Got a total of 47524500 fold changes\n",
      "Median elements [23762250]\n",
      "Median elements\n",
      "Global Median 0.64579\n",
      "2020-09-30 22:41:10,939  SpectraRegion  INFO: dimensions inputarray: 17900\n",
      "2020-09-30 22:41:10,939  SpectraRegion  INFO: Creating C++ obj\n",
      "2020-09-30 22:41:10,940  SpectraRegion  INFO: 17900 (45, 59, 17900)\n",
      "2020-09-30 22:41:10,940  SpectraRegion  INFO: dimensions 17900\n",
      "2020-09-30 22:41:10,941  SpectraRegion  INFO: input dimensions (45, 59, 17900)\n",
      "2020-09-30 22:41:10,942  SpectraRegion  INFO: Switching to dot mode\n",
      "2020-09-30 22:41:11,029  SpectraRegion  INFO: Starting calc similarity c++\n",
      "2020-09-30 22:41:59,819  SpectraRegion  INFO: outclust dimensions (2655, 2655)\n",
      "2020-09-30 22:41:59,821  SpectraRegion  INFO: Calculating spectra similarity\n",
      "2020-09-30 22:41:59,959  SpectraRegion  INFO: Calculating spectra similarity done\n",
      "2020-09-30 22:41:59,959  SpectraRegion  INFO: Calculating dist pixel map\n",
      "2020-09-30 22:42:34,879  SpectraRegion  INFO: Calculating dist pixel map done\n",
      "2020-09-30 22:42:34,903  SpectraRegion  INFO: Calculating clusters\n",
      "2020-09-30 22:42:35,018  SpectraRegion  INFO: Calculating clusters done\n",
      "2020-09-30 22:42:35,108  SpectraRegion  INFO: Calculating clusters saved\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-30 22:42:35,211  SpectraRegion  INFO: Assigning clusters to background: {10, 11, 12, 13, 14, 15}\n"
     ]
    },
    {
     "data": {
      "image/png": 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aaNNu1YH8pIGyHHg+x5oyEaSsMkE7q2ClXAucB040U9Lw2R0rI2Jlrs1HI2KHpPcA90v6cUQ8XHaylPVcd1Grk0VEvCFpCzAbWEptEW2AW4CHcHA1s8G1t9kAFUBE7Mg+75F0F7AIKA2uI8q5ZiW2TwE2AMdlgRfg58BxIzmXmdkgkTQt++sdSdOAT9CkxFXy0wKS3gXcAVwREa9L//EnXESEpNIJ3VleYhnAFKamXs7MrN8cB9yVxb4jgH+JiHWNGicFV0kTqQXWWyPizmz3bkmzImJXVrCwdOn5LGexEuAozejOcvBmZj0WES8CC1LbpxQoFLWaWVsi4uvDvnQ3cBHw1ezz2pF11coGR8oGsPLyA1ipJbFb6Vap61QpA1jlkwbq5VflKhvgm5vcq+ZSBoZSVjsr6+NLO1e1PE9+4okHqvpXyp3rmcDngKclHX6H/BW1oPo9SRcDLwN/XE0XzcwGT8rTAj8CGt1inNPd7piZjQ2eoWVmVgGvLTCK8ou0QDGnlp9UAPB8bjH8soVKupWHLdPOw/5ludP9O+tzpfnJAKny32tKyfBU+YVa1q76XMtjyqoM5HOjJ1HShtYl05ef2PLy1id852pmVgEHVzOzCji4mplVwDlXMxuzdEBM3l7d+EMzDq6jaPmJ8wv75g/V/0rKHja/8ex/qtsuG/RamxscaXewKOUh/na106d89QSAsz7/WN12N1cJy/9syyoa5K9XNhBVNsiVVyy9ntJD61dOC5iZVcDB1cysAg6uZmYVcM51FJWtIp+fWPDkmuIK+pdS/yB72YSByQmVXAvVXkvyq2U5zpbn6aL89fP5VSjmPOeuSetPvspAu1LyqSnm3XtJ/Y5VxTbdupZVz3euZmYVcHA1M6uAg6uZWSJJEyQ9IemeVm07Ka39FUk7JG3KPj7Vjc6bmfWxy4EtLVvRWWltgOUR8bU2Oznula2KdcNlf1a3XTboUqgzUVI1+offOr1uO2XQKWXwCmDi+b+oP+7/Hlto084gV8oEgTKFB/tLfh7tltbOD5blJ3AAMFQ/0aCdEtngwap+J2kO8GngeuCLrdp3UlrbzGw8WQFcBRyZ0riT0toAl0l6StJqSUeP5FxmZn1mpqSNwz6WHf6CpPOAPRExlHqy5OCaL60N3Aj8LrCQ2p3t3zY4btnhzh6gWJDPzKxP7I2I04Z9rBz2tTOBz0jaBtwOLJb0z81O1nZp7YjYPezr3wZKR89cWruxsuqvfPM7LY8rW6ilG5LzpPnJBglJopR8alleNL+vLAed0iZF2fVvzP0+yn72K2bV50rzFQVs8EXEtcC1AJLOBv4yIv602TEpTwuUltaWNGtYsz8CNrfRZzOzMamT0toXSloIBLAN+HwlPTQz6yMR8RDwUKt2nZTWvnfEvTIzGyc8Q8vMrAJeFavP5CcRvH5C8VeULx1dtjp+O+WlUysD7J9dXxK7bFWuo37a+kH6lIGo/EP8hQkUDY5Lkb/+1SWDiflqEfNLHsQpmwySV1adIM+TCMYW37mamVXAwdXMrAJOC5jZmPWOAzBt5yhde3Qua2Y2tvnOtc+sX/3tuu1Tv3JpoU1+AOmHj55eaEPCgFZ+IKzsPGUDUwuurz+urBRNivxAVNkMqbm57ZRrdat8C8CVW1uvLnfpQ/Vld5YOFQcYOdWDVeON71zNzCrg4GpmVgEHVzOzCjjnOorOfW9x9aSyctt5+TzotguKK07ly0unrCZ11vWtV/2H4oP9tHn9qrRbdSA/gaPMpHXFn9FLO+trYJf9Xm388Z2rmVkFHFzNzCrg4GpmVoGUxbKnSHpU0pNZae2/yfbPk7RB0lZJ35WUtuqHmdk4kDKgtR9YHBG/zMq9/EjSv1IrLbs8Im6X9A/AxdTqalkH8oMh129dXWiTH3gpWxWrrLx0XruDTKXXa3H9wiBY4nnyg3Vlx7Q7gJX//pe8++lCm9JSPDkewBr7JE0BHgYmU4ubayLiumbHtLxzjZpfZpsTs48AFgNrsv23AOe32W8zs353+CZzAbWirEskndHsgKScq6QJWYmXPcD9wE+AfRFx+Jmg7SSVqTMzGzxNbjIbSgquEXEoIhYCc4BFwO+ldsqltc1sQMw8HKuyj2XDv5i/yYyIDc1ONqJJBBGxT9J64CPAdElHZHevc4AdDY5xae0O5FfCB1gwNPJcaVleMmXhlG5ZWmV+N0FKfrksv5rPp6ZM8rCBtTciGpaMiIhDwEJJ04G7JH0wIhpWvU55WuDY7GRIeifwcWALsB64IGt2EbA2/XswMxtMEbGPWvxb0qxdSlpgFrBe0lPAY9Ruh+8Brga+KGkrcAxwU2ddNjPrTw1uMn/c7JiU0tpPUfJgT0S8SC3/amY21s0CbpE0gdpN6feym8yGvHCLmVkLjW4ym3FwHUBbTq1fFausbHPKQFCVA1jdulY7x5UNXuV/ZmXOpfVkAE8YsFReW8DMrAK+czWzMWvC/uDIl1vXk6uC71zNzCrgO9cBlM+xluVX8wulpDzE38scbDelLMCyYmdxgsAVu+p/jil5WbNUvnM1M6uAg6uZWQUcXM3MKuDgamZWAQ9oDaCTLtlYt71k65Zioy6NTaWU5O6lsv7kB7DWvfahQpt1rxXPlR/AKlvxypMGrF2+czUzq4CDq5lZBTqp/nqzpJckbco+/PeTmVmmk+qvAP87ItY0OdYqkM8NlucFWz8QP3+o9a+/W/nVdivNpuRT8zwZwPpBynquAYyoMJeZ2XjXVvXXYYW5rpf0lKTlkiZX1kszs1Ek6XhJ6yU9m6VHL291TFvVXyV9ELiWWhXY04EZ1Mq+lHXK1V/NbNAdBL4UEScDZwBfkHRyswNG9LTA8MJcEbErq+W9H/hHGpR8iYiVEXFaRJw2Ed/cmtngyeLd49nrN6gVaZ3d7JiWOVdJxwIHsrLahwtz3SBpVkTskiTgfKBhiVnrrqoebG930CnFD791emHf9Vetbnlcsdx1ccWrfJvlJecpq9aQd+57WzYxQ9JcatN0NjRrl/K0QGlhLkn/lgVeAZuA/95Rj83MRtdMScOnP66MiJXDG0h6F3AHcEVEvN7sZJ1Uf12c1l8zs4GwNyIa/omTPYp6B3BrRNzZ6mSeoWVm1kKW/rwJ2BIRX085xsHVzKy1M4HPAYuHzUr9VLMDvCrWOJZSorvMjWf/U9122ayp/MyqJVe1HohqV8oAX34lsTJeFcsaiYgfURtfSuY7VzOzCvjO1czGrHe8fYipL706OtcelauamY1xvnMdx4o5xrSc45JcmeolU4v5zHVv1c/GW37i/EKb/MP+o53zdH7Vusl3rmZmFXBwNTOrgIOrmVkFHFzNzCrgAa1xrN0BnJTj8oNTZStVlQ1g5V2ZKxteNjBm1o9852pmVgEHVzOzCiQH16yO1hOS7sm250naIGmrpO9KmlRdN83MBstIcq6XUyttcFS2fQOwPCJul/QPwMXAjV3unw2obj2Qn8+xjvZEA7NUqdVf5wCfBlZl2wIWA2uyJrdQK/ViZmakpwVWAFcBv8m2jwH2RcThNeu206JYl5nZeNIyuEo6D9gTEUPtXMCltc1sPEq5cz0T+IykbcDt1NIB3wCmSzqcs50D7Cg72KW1zWwskLRa0h5JSZWuUwoUXgtcm538bOAvI+JPJH0fuIBawL0IWNt2r23MSxmIKmuTMtHArEduBr4JfCelcSfPuV4NfFHSVmo52Js6OJeZWV+LiIeBV1Lbj2j6a0Q8BDyUvX4RWDSS483MxguvLWBmVjNT0vCV31dGxMp2T+bgaj1R9qB/O/lUTxiwCu2NiLQSyAm8toCZWQV852pmY1b8ej8Hn9valXNJug04m1r6YDtwXUQ0HMh3cDUzSxARF46kvdMCZmYV8J2r9UTK4JUHq2ws8Z2rmVkFHFzNzCrg4GpmVgHnXK0nujWJwGxQ+M7VzKwCDq5mZhVwcDUzq4CDq5lZBXo6oPUGr+59INa8DMwE9vby2l0yiP3u2z5PmJXf89s54H3b5xYGsd/93OffGe0OdKKnwTUijgWQtLGbS3v1yiD2233unUHs9yD2eVA4LWBmVgEHVzOzCoxWcG27dMIoG8R+u8+9M4j9HsQ+DwRFxGj3wcysEkdpRnxY5yS1fSDWDLnMi5lZj0laIuk5SVslXdOqvYOrmVkLkiYAfw98EjgZuFDSyc2OcXA1M2ttEbA1Il6MiLeB24GlzQ5wcDUza2028LNh29uzfQ15yUEzG7Pe4NX7Hog1MxObT5G0cdj2yoho+2kKB1czG7MiYkmXTrUDOH7Y9pxsX0NOC5iZtfYY8H5J8yRNAj4L3N3sAN+5mpm1EBEHJV0G3AdMAFZHxDPNjvEkAjOzCjgtYGZWAQdXM7MKOLiamVXAwdXMrAIOrmZmFXBwNTOrgIOrmVkFHFzNzCrw/wEawSOFRKTROwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-30 22:42:35,308  SpectraRegion  INFO: DE data for case: [5]\n",
      "2020-09-30 22:42:35,309  SpectraRegion  INFO: DE data for control: [7, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 22:42:35,309  SpectraRegion  INFO: Running [5] against [7, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 22:42:35,310  SpectraRegion  INFO: DE result key: ((5,), (1, 2, 3, 4, 6, 7, 8, 9))\n",
      "2020-09-30 22:42:35,664  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 725)\n",
      "2020-09-30 22:42:35,667  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (725, 3)\n",
      "2020-09-30 22:42:35,670  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:119: ImplicitModificationWarning: Transforming to str index.\n",
      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
      "2020-09-30 22:42:35,933  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((5,), (1, 2, 3, 4, 6, 7, 8, 9))\n",
      "2020-09-30 22:42:35,934  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:42:40,400  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((5,), (1, 2, 3, 4, 6, 7, 8, 9))\n",
      "2020-09-30 22:42:40,402  SpectraRegion  INFO: DE result for case ((5,), (1, 2, 3, 4, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:42:40,402  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:42:40,406  SpectraRegion  INFO: DE result for case ((5,), (1, 2, 3, 4, 6, 7, 8, 9)) with (207, 7) results (filtered)\n",
      "2020-09-30 22:42:40,426  SpectraRegion  INFO: Created matrices with shape (100, 17900) and (625, 17900) (target, bg)\n",
      "2020-09-30 22:42:40,879  SpectraRegion  INFO: DE result for case ((5,), (1, 2, 3, 4, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:42:40,880  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:42:40,883  SpectraRegion  INFO: DE result for case ((5,), (1, 2, 3, 4, 6, 7, 8, 9)) with (126, 7) results (filtered)\n",
      "2020-09-30 22:42:40,903  SpectraRegion  INFO: Created matrices with shape (100, 17900) and (625, 17900) (target, bg)\n",
      "2020-09-30 22:42:41,228  SpectraRegion  INFO: DE data for case: [7]\n",
      "2020-09-30 22:42:41,229  SpectraRegion  INFO: DE data for control: [5, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 22:42:41,230  SpectraRegion  INFO: Running [7] against [5, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 22:42:41,230  SpectraRegion  INFO: DE result key: ((7,), (1, 2, 3, 4, 5, 6, 8, 9))\n",
      "2020-09-30 22:42:41,588  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 725)\n",
      "2020-09-30 22:42:41,590  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (725, 3)\n",
      "2020-09-30 22:42:41,594  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:42:41,832  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((7,), (1, 2, 3, 4, 5, 6, 8, 9))\n",
      "2020-09-30 22:42:41,833  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:42:46,299  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((7,), (1, 2, 3, 4, 5, 6, 8, 9))\n",
      "2020-09-30 22:42:46,301  SpectraRegion  INFO: DE result for case ((7,), (1, 2, 3, 4, 5, 6, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:42:46,302  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:42:46,305  SpectraRegion  INFO: DE result for case ((7,), (1, 2, 3, 4, 5, 6, 8, 9)) with (158, 7) results (filtered)\n",
      "2020-09-30 22:42:46,326  SpectraRegion  INFO: Created matrices with shape (161, 17900) and (564, 17900) (target, bg)\n",
      "2020-09-30 22:42:46,683  SpectraRegion  INFO: DE result for case ((7,), (1, 2, 3, 4, 5, 6, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:42:46,684  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:42:46,686  SpectraRegion  INFO: DE result for case ((7,), (1, 2, 3, 4, 5, 6, 8, 9)) with (125, 7) results (filtered)\n",
      "2020-09-30 22:42:46,706  SpectraRegion  INFO: Created matrices with shape (161, 17900) and (564, 17900) (target, bg)\n",
      "2020-09-30 22:42:46,956  SpectraRegion  INFO: DE data for case: [9]\n",
      "2020-09-30 22:42:46,957  SpectraRegion  INFO: DE data for control: [5, 7, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 22:42:46,958  SpectraRegion  INFO: Running [9] against [5, 7, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 22:42:46,958  SpectraRegion  INFO: DE result key: ((9,), (1, 2, 3, 4, 5, 6, 7, 8))\n",
      "2020-09-30 22:42:47,303  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 725)\n",
      "2020-09-30 22:42:47,305  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (725, 3)\n",
      "2020-09-30 22:42:47,309  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:42:47,544  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((9,), (1, 2, 3, 4, 5, 6, 7, 8))\n",
      "2020-09-30 22:42:47,545  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:42:52,026  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((9,), (1, 2, 3, 4, 5, 6, 7, 8))\n",
      "2020-09-30 22:42:52,028  SpectraRegion  INFO: DE result for case ((9,), (1, 2, 3, 4, 5, 6, 7, 8)) with (17900, 7) results\n",
      "2020-09-30 22:42:52,029  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:42:52,032  SpectraRegion  INFO: DE result for case ((9,), (1, 2, 3, 4, 5, 6, 7, 8)) with (1163, 7) results (filtered)\n",
      "2020-09-30 22:42:52,051  SpectraRegion  INFO: Created matrices with shape (89, 17900) and (636, 17900) (target, bg)\n",
      "2020-09-30 22:42:55,168  SpectraRegion  INFO: DE result for case ((9,), (1, 2, 3, 4, 5, 6, 7, 8)) with (17900, 7) results\n",
      "2020-09-30 22:42:55,169  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:42:55,172  SpectraRegion  INFO: DE result for case ((9,), (1, 2, 3, 4, 5, 6, 7, 8)) with (1153, 7) results (filtered)\n",
      "2020-09-30 22:42:55,191  SpectraRegion  INFO: Created matrices with shape (89, 17900) and (636, 17900) (target, bg)\n",
      "2020-09-30 22:42:58,062  SpectraRegion  INFO: DE data for case: [3]\n",
      "2020-09-30 22:42:58,063  SpectraRegion  INFO: DE data for control: [5, 7, 9, 8, 2, 4, 6, 1]\n",
      "2020-09-30 22:42:58,064  SpectraRegion  INFO: Running [3] against [5, 7, 9, 8, 2, 4, 6, 1]\n",
      "2020-09-30 22:42:58,064  SpectraRegion  INFO: DE result key: ((3,), (1, 2, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:42:58,414  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 725)\n",
      "2020-09-30 22:42:58,416  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (725, 3)\n",
      "2020-09-30 22:42:58,419  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:42:58,664  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((3,), (1, 2, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:42:58,664  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:43:03,157  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((3,), (1, 2, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:43:03,160  SpectraRegion  INFO: DE result for case ((3,), (1, 2, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:43:03,161  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:43:03,164  SpectraRegion  INFO: DE result for case ((3,), (1, 2, 4, 5, 6, 7, 8, 9)) with (226, 7) results (filtered)\n",
      "2020-09-30 22:43:03,184  SpectraRegion  INFO: Created matrices with shape (51, 17900) and (674, 17900) (target, bg)\n",
      "2020-09-30 22:43:03,755  SpectraRegion  INFO: DE result for case ((3,), (1, 2, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:43:03,756  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:43:03,759  SpectraRegion  INFO: DE result for case ((3,), (1, 2, 4, 5, 6, 7, 8, 9)) with (215, 7) results (filtered)\n",
      "2020-09-30 22:43:03,777  SpectraRegion  INFO: Created matrices with shape (51, 17900) and (674, 17900) (target, bg)\n",
      "2020-09-30 22:43:04,305  SpectraRegion  INFO: DE data for case: [8]\n",
      "2020-09-30 22:43:04,306  SpectraRegion  INFO: DE data for control: [5, 7, 9, 3, 2, 4, 6, 1]\n",
      "2020-09-30 22:43:04,307  SpectraRegion  INFO: Running [8] against [5, 7, 9, 3, 2, 4, 6, 1]\n",
      "2020-09-30 22:43:04,308  SpectraRegion  INFO: DE result key: ((8,), (1, 2, 3, 4, 5, 6, 7, 9))\n",
      "2020-09-30 22:43:04,653  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 725)\n",
      "2020-09-30 22:43:04,656  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (725, 3)\n",
      "2020-09-30 22:43:04,659  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:43:04,947  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((8,), (1, 2, 3, 4, 5, 6, 7, 9))\n",
      "2020-09-30 22:43:04,949  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:43:09,524  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((8,), (1, 2, 3, 4, 5, 6, 7, 9))\n",
      "2020-09-30 22:43:09,526  SpectraRegion  INFO: DE result for case ((8,), (1, 2, 3, 4, 5, 6, 7, 9)) with (17900, 7) results\n",
      "2020-09-30 22:43:09,527  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:43:09,529  SpectraRegion  INFO: DE result for case ((8,), (1, 2, 3, 4, 5, 6, 7, 9)) with (671, 7) results (filtered)\n",
      "2020-09-30 22:43:09,550  SpectraRegion  INFO: Created matrices with shape (55, 17900) and (670, 17900) (target, bg)\n",
      "2020-09-30 22:43:11,156  SpectraRegion  INFO: DE result for case ((8,), (1, 2, 3, 4, 5, 6, 7, 9)) with (17900, 7) results\n",
      "2020-09-30 22:43:11,156  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:43:11,159  SpectraRegion  INFO: DE result for case ((8,), (1, 2, 3, 4, 5, 6, 7, 9)) with (619, 7) results (filtered)\n",
      "2020-09-30 22:43:11,179  SpectraRegion  INFO: Created matrices with shape (55, 17900) and (670, 17900) (target, bg)\n",
      "2020-09-30 22:43:12,705  SpectraRegion  INFO: DE data for case: [2]\n",
      "2020-09-30 22:43:12,706  SpectraRegion  INFO: DE data for control: [5, 7, 9, 3, 8, 4, 6, 1]\n",
      "2020-09-30 22:43:12,707  SpectraRegion  INFO: Running [2] against [5, 7, 9, 3, 8, 4, 6, 1]\n",
      "2020-09-30 22:43:12,707  SpectraRegion  INFO: DE result key: ((2,), (1, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:43:13,022  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 725)\n",
      "2020-09-30 22:43:13,025  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (725, 3)\n",
      "2020-09-30 22:43:13,028  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:43:13,274  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((2,), (1, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:43:13,274  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:43:17,938  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((2,), (1, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:43:17,942  SpectraRegion  INFO: DE result for case ((2,), (1, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:43:17,943  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:43:17,946  SpectraRegion  INFO: DE result for case ((2,), (1, 3, 4, 5, 6, 7, 8, 9)) with (739, 7) results (filtered)\n",
      "2020-09-30 22:43:17,966  SpectraRegion  INFO: Created matrices with shape (150, 17900) and (575, 17900) (target, bg)\n",
      "2020-09-30 22:43:19,971  SpectraRegion  INFO: DE result for case ((2,), (1, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:43:19,972  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:43:19,974  SpectraRegion  INFO: DE result for case ((2,), (1, 3, 4, 5, 6, 7, 8, 9)) with (659, 7) results (filtered)\n",
      "2020-09-30 22:43:19,994  SpectraRegion  INFO: Created matrices with shape (150, 17900) and (575, 17900) (target, bg)\n",
      "2020-09-30 22:43:21,786  SpectraRegion  INFO: DE data for case: [4]\n",
      "2020-09-30 22:43:21,787  SpectraRegion  INFO: DE data for control: [5, 7, 9, 3, 8, 2, 6, 1]\n",
      "2020-09-30 22:43:21,787  SpectraRegion  INFO: Running [4] against [5, 7, 9, 3, 8, 2, 6, 1]\n",
      "2020-09-30 22:43:21,788  SpectraRegion  INFO: DE result key: ((4,), (1, 2, 3, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:43:22,115  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 725)\n",
      "2020-09-30 22:43:22,118  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (725, 3)\n",
      "2020-09-30 22:43:22,122  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:43:22,377  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((4,), (1, 2, 3, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:43:22,378  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:43:26,863  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((4,), (1, 2, 3, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:43:26,865  SpectraRegion  INFO: DE result for case ((4,), (1, 2, 3, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:43:26,866  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:43:26,869  SpectraRegion  INFO: DE result for case ((4,), (1, 2, 3, 5, 6, 7, 8, 9)) with (1194, 7) results (filtered)\n",
      "2020-09-30 22:43:26,889  SpectraRegion  INFO: Created matrices with shape (70, 17900) and (655, 17900) (target, bg)\n",
      "2020-09-30 22:43:29,143  SpectraRegion  INFO: DE result for case ((4,), (1, 2, 3, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:43:29,143  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:43:29,146  SpectraRegion  INFO: DE result for case ((4,), (1, 2, 3, 5, 6, 7, 8, 9)) with (1194, 7) results (filtered)\n",
      "2020-09-30 22:43:29,166  SpectraRegion  INFO: Created matrices with shape (70, 17900) and (655, 17900) (target, bg)\n",
      "2020-09-30 22:43:31,449  SpectraRegion  INFO: DE data for case: [6]\n",
      "2020-09-30 22:43:31,450  SpectraRegion  INFO: DE data for control: [5, 7, 9, 3, 8, 2, 4, 1]\n",
      "2020-09-30 22:43:31,450  SpectraRegion  INFO: Running [6] against [5, 7, 9, 3, 8, 2, 4, 1]\n",
      "2020-09-30 22:43:31,451  SpectraRegion  INFO: DE result key: ((6,), (1, 2, 3, 4, 5, 7, 8, 9))\n",
      "2020-09-30 22:43:31,808  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 725)\n",
      "2020-09-30 22:43:31,811  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (725, 3)\n",
      "2020-09-30 22:43:31,814  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:43:32,095  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((6,), (1, 2, 3, 4, 5, 7, 8, 9))\n",
      "2020-09-30 22:43:32,096  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:43:36,630  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((6,), (1, 2, 3, 4, 5, 7, 8, 9))\n",
      "2020-09-30 22:43:36,633  SpectraRegion  INFO: DE result for case ((6,), (1, 2, 3, 4, 5, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:43:36,634  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:43:36,637  SpectraRegion  INFO: DE result for case ((6,), (1, 2, 3, 4, 5, 7, 8, 9)) with (876, 7) results (filtered)\n",
      "2020-09-30 22:43:36,657  SpectraRegion  INFO: Created matrices with shape (27, 17900) and (698, 17900) (target, bg)\n",
      "2020-09-30 22:43:39,190  SpectraRegion  INFO: DE result for case ((6,), (1, 2, 3, 4, 5, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:43:39,191  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:43:39,194  SpectraRegion  INFO: DE result for case ((6,), (1, 2, 3, 4, 5, 7, 8, 9)) with (851, 7) results (filtered)\n",
      "2020-09-30 22:43:39,212  SpectraRegion  INFO: Created matrices with shape (27, 17900) and (698, 17900) (target, bg)\n",
      "2020-09-30 22:43:41,633  SpectraRegion  INFO: DE data for case: [1]\n",
      "2020-09-30 22:43:41,633  SpectraRegion  INFO: DE data for control: [5, 7, 9, 3, 8, 2, 4, 6]\n",
      "2020-09-30 22:43:41,634  SpectraRegion  INFO: Running [1] against [5, 7, 9, 3, 8, 2, 4, 6]\n",
      "2020-09-30 22:43:41,635  SpectraRegion  INFO: DE result key: ((1,), (2, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:43:41,951  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 725)\n",
      "2020-09-30 22:43:41,953  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (725, 3)\n",
      "2020-09-30 22:43:41,958  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:43:42,208  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((1,), (2, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:43:42,209  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:43:46,705  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((1,), (2, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:43:46,707  SpectraRegion  INFO: DE result for case ((1,), (2, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:43:46,708  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:43:46,711  SpectraRegion  INFO: DE result for case ((1,), (2, 3, 4, 5, 6, 7, 8, 9)) with (2116, 7) results (filtered)\n",
      "2020-09-30 22:43:46,730  SpectraRegion  INFO: Created matrices with shape (22, 17900) and (703, 17900) (target, bg)\n",
      "2020-09-30 22:43:52,934  SpectraRegion  INFO: DE result for case ((1,), (2, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:43:52,934  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:43:52,937  SpectraRegion  INFO: DE result for case ((1,), (2, 3, 4, 5, 6, 7, 8, 9)) with (2057, 7) results (filtered)\n",
      "2020-09-30 22:43:52,957  SpectraRegion  INFO: Created matrices with shape (22, 17900) and (703, 17900) (target, bg)\n",
      "2020-09-30 22:43:58,938  SpectraRegion  INFO: DE data for case: [0]\n",
      "2020-09-30 22:43:58,939  SpectraRegion  INFO: DE data for control: [5, 7, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 22:43:58,939  SpectraRegion  INFO: Running [0] against [5, 7, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 22:43:58,940  SpectraRegion  INFO: DE result key: ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:44:01,962  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2655)\n",
      "2020-09-30 22:44:01,966  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2655, 3)\n",
      "2020-09-30 22:44:01,970  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:44:03,054  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:44:03,054  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:44:12,751  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:44:12,754  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:44:12,755  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:44:12,757  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9)) with (1307, 7) results (filtered)\n",
      "2020-09-30 22:44:12,820  SpectraRegion  INFO: Created matrices with shape (1930, 17900) and (725, 17900) (target, bg)\n",
      "2020-09-30 22:44:16,470  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:44:16,471  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:44:16,474  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9)) with (1307, 7) results (filtered)\n",
      "2020-09-30 22:44:16,533  SpectraRegion  INFO: Created matrices with shape (1930, 17900) and (725, 17900) (target, bg)\n",
      "2020-09-30 22:44:20,095  SpectraRegion  INFO: DE data for case: [5]\n",
      "2020-09-30 22:44:20,095  SpectraRegion  INFO: DE data for control: [0, 7, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 22:44:20,096  SpectraRegion  INFO: Running [5] against [0, 7, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 22:44:20,096  SpectraRegion  INFO: DE result key: ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9))\n",
      "2020-09-30 22:44:23,299  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2655)\n",
      "2020-09-30 22:44:23,302  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2655, 3)\n",
      "2020-09-30 22:44:23,306  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:44:24,389  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9))\n",
      "2020-09-30 22:44:24,390  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:44:34,434  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9))\n",
      "2020-09-30 22:44:34,438  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:44:34,439  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:44:34,442  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9)) with (450, 7) results (filtered)\n",
      "2020-09-30 22:44:34,512  SpectraRegion  INFO: Created matrices with shape (100, 17900) and (2555, 17900) (target, bg)\n",
      "2020-09-30 22:44:36,091  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:44:36,092  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:44:36,095  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9)) with (450, 7) results (filtered)\n",
      "2020-09-30 22:44:36,171  SpectraRegion  INFO: Created matrices with shape (100, 17900) and (2555, 17900) (target, bg)\n",
      "2020-09-30 22:44:37,735  SpectraRegion  INFO: DE data for case: [7]\n",
      "2020-09-30 22:44:37,736  SpectraRegion  INFO: DE data for control: [0, 5, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 22:44:37,736  SpectraRegion  INFO: Running [7] against [0, 5, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 22:44:37,737  SpectraRegion  INFO: DE result key: ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9))\n",
      "2020-09-30 22:44:40,747  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2655)\n",
      "2020-09-30 22:44:40,750  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2655, 3)\n",
      "2020-09-30 22:44:40,754  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:44:41,814  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9))\n",
      "2020-09-30 22:44:41,815  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:44:51,507  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9))\n",
      "2020-09-30 22:44:51,510  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:44:51,511  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:44:51,513  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9)) with (680, 7) results (filtered)\n",
      "2020-09-30 22:44:51,575  SpectraRegion  INFO: Created matrices with shape (161, 17900) and (2494, 17900) (target, bg)\n",
      "2020-09-30 22:44:53,629  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:44:53,629  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:44:53,632  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9)) with (680, 7) results (filtered)\n",
      "2020-09-30 22:44:53,700  SpectraRegion  INFO: Created matrices with shape (161, 17900) and (2494, 17900) (target, bg)\n",
      "2020-09-30 22:44:55,663  SpectraRegion  INFO: DE data for case: [9]\n",
      "2020-09-30 22:44:55,664  SpectraRegion  INFO: DE data for control: [0, 5, 7, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 22:44:55,665  SpectraRegion  INFO: Running [9] against [0, 5, 7, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 22:44:55,665  SpectraRegion  INFO: DE result key: ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8))\n",
      "2020-09-30 22:44:59,028  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2655)\n",
      "2020-09-30 22:44:59,031  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2655, 3)\n",
      "2020-09-30 22:44:59,036  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:45:00,122  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8))\n",
      "2020-09-30 22:45:00,123  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:45:10,082  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8))\n",
      "2020-09-30 22:45:10,085  SpectraRegion  INFO: DE result for case ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8)) with (17900, 7) results\n",
      "2020-09-30 22:45:10,086  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:45:10,088  SpectraRegion  INFO: DE result for case ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8)) with (88, 7) results (filtered)\n",
      "2020-09-30 22:45:10,149  SpectraRegion  INFO: Created matrices with shape (89, 17900) and (2566, 17900) (target, bg)\n",
      "2020-09-30 22:45:10,246  SpectraRegion  INFO: DE result for case ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8)) with (17900, 7) results\n",
      "2020-09-30 22:45:10,247  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:45:10,251  SpectraRegion  INFO: DE result for case ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8)) with (65, 7) results (filtered)\n",
      "2020-09-30 22:45:10,322  SpectraRegion  INFO: Created matrices with shape (89, 17900) and (2566, 17900) (target, bg)\n",
      "2020-09-30 22:45:10,389  SpectraRegion  INFO: DE data for case: [3]\n",
      "2020-09-30 22:45:10,390  SpectraRegion  INFO: DE data for control: [0, 5, 7, 9, 8, 2, 4, 6, 1]\n",
      "2020-09-30 22:45:10,390  SpectraRegion  INFO: Running [3] against [0, 5, 7, 9, 8, 2, 4, 6, 1]\n",
      "2020-09-30 22:45:10,391  SpectraRegion  INFO: DE result key: ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:45:13,479  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2655)\n",
      "2020-09-30 22:45:13,483  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2655, 3)\n",
      "2020-09-30 22:45:13,487  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:45:14,728  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:45:14,729  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:45:24,500  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:45:24,504  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:45:24,504  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:45:24,507  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9)) with (1263, 7) results (filtered)\n",
      "2020-09-30 22:45:24,567  SpectraRegion  INFO: Created matrices with shape (51, 17900) and (2604, 17900) (target, bg)\n",
      "2020-09-30 22:45:28,347  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:45:28,348  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:45:28,351  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9)) with (1261, 7) results (filtered)\n",
      "2020-09-30 22:45:28,419  SpectraRegion  INFO: Created matrices with shape (51, 17900) and (2604, 17900) (target, bg)\n",
      "2020-09-30 22:45:32,101  SpectraRegion  INFO: DE data for case: [8]\n",
      "2020-09-30 22:45:32,102  SpectraRegion  INFO: DE data for control: [0, 5, 7, 9, 3, 2, 4, 6, 1]\n",
      "2020-09-30 22:45:32,103  SpectraRegion  INFO: Running [8] against [0, 5, 7, 9, 3, 2, 4, 6, 1]\n",
      "2020-09-30 22:45:32,103  SpectraRegion  INFO: DE result key: ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9))\n",
      "2020-09-30 22:45:35,400  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2655)\n",
      "2020-09-30 22:45:35,403  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2655, 3)\n",
      "2020-09-30 22:45:35,407  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:45:36,533  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9))\n",
      "2020-09-30 22:45:36,534  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:45:46,385  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9))\n",
      "2020-09-30 22:45:46,389  SpectraRegion  INFO: DE result for case ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9)) with (17900, 7) results\n",
      "2020-09-30 22:45:46,389  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:45:46,392  SpectraRegion  INFO: DE result for case ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9)) with (253, 7) results (filtered)\n",
      "2020-09-30 22:45:46,452  SpectraRegion  INFO: Created matrices with shape (55, 17900) and (2600, 17900) (target, bg)\n",
      "2020-09-30 22:45:46,931  SpectraRegion  INFO: DE result for case ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9)) with (17900, 7) results\n",
      "2020-09-30 22:45:46,932  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:45:46,934  SpectraRegion  INFO: DE result for case ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9)) with (250, 7) results (filtered)\n",
      "2020-09-30 22:45:46,994  SpectraRegion  INFO: Created matrices with shape (55, 17900) and (2600, 17900) (target, bg)\n",
      "2020-09-30 22:45:47,441  SpectraRegion  INFO: DE data for case: [2]\n",
      "2020-09-30 22:45:47,442  SpectraRegion  INFO: DE data for control: [0, 5, 7, 9, 3, 8, 4, 6, 1]\n",
      "2020-09-30 22:45:47,443  SpectraRegion  INFO: Running [2] against [0, 5, 7, 9, 3, 8, 4, 6, 1]\n",
      "2020-09-30 22:45:47,443  SpectraRegion  INFO: DE result key: ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:45:50,457  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2655)\n",
      "2020-09-30 22:45:50,461  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2655, 3)\n",
      "2020-09-30 22:45:50,466  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:45:51,702  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:45:51,702  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:46:01,520  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:46:01,523  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:46:01,524  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:46:01,526  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9)) with (1796, 7) results (filtered)\n",
      "2020-09-30 22:46:01,589  SpectraRegion  INFO: Created matrices with shape (150, 17900) and (2505, 17900) (target, bg)\n",
      "2020-09-30 22:46:07,617  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:46:07,618  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:46:07,621  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9)) with (1796, 7) results (filtered)\n",
      "2020-09-30 22:46:07,683  SpectraRegion  INFO: Created matrices with shape (150, 17900) and (2505, 17900) (target, bg)\n",
      "2020-09-30 22:46:13,652  SpectraRegion  INFO: DE data for case: [4]\n",
      "2020-09-30 22:46:13,653  SpectraRegion  INFO: DE data for control: [0, 5, 7, 9, 3, 8, 2, 6, 1]\n",
      "2020-09-30 22:46:13,653  SpectraRegion  INFO: Running [4] against [0, 5, 7, 9, 3, 8, 2, 6, 1]\n",
      "2020-09-30 22:46:13,653  SpectraRegion  INFO: DE result key: ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:46:16,729  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2655)\n",
      "2020-09-30 22:46:16,732  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2655, 3)\n",
      "2020-09-30 22:46:16,737  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:46:17,761  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:46:17,762  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:46:27,516  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:46:27,520  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:46:27,520  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:46:27,523  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9)) with (1742, 7) results (filtered)\n",
      "2020-09-30 22:46:27,588  SpectraRegion  INFO: Created matrices with shape (70, 17900) and (2585, 17900) (target, bg)\n",
      "2020-09-30 22:46:31,784  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:46:31,785  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:46:31,787  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9)) with (1742, 7) results (filtered)\n",
      "2020-09-30 22:46:31,853  SpectraRegion  INFO: Created matrices with shape (70, 17900) and (2585, 17900) (target, bg)\n",
      "2020-09-30 22:46:35,893  SpectraRegion  INFO: DE data for case: [6]\n",
      "2020-09-30 22:46:35,893  SpectraRegion  INFO: DE data for control: [0, 5, 7, 9, 3, 8, 2, 4, 1]\n",
      "2020-09-30 22:46:35,894  SpectraRegion  INFO: Running [6] against [0, 5, 7, 9, 3, 8, 2, 4, 1]\n",
      "2020-09-30 22:46:35,894  SpectraRegion  INFO: DE result key: ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9))\n",
      "2020-09-30 22:46:39,320  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2655)\n",
      "2020-09-30 22:46:39,323  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2655, 3)\n",
      "2020-09-30 22:46:39,328  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:46:40,637  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9))\n",
      "2020-09-30 22:46:40,637  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:46:50,310  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9))\n",
      "2020-09-30 22:46:50,313  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:46:50,314  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:46:50,317  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9)) with (2053, 7) results (filtered)\n",
      "2020-09-30 22:46:50,380  SpectraRegion  INFO: Created matrices with shape (27, 17900) and (2628, 17900) (target, bg)\n",
      "2020-09-30 22:46:56,745  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:46:56,746  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:46:56,749  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9)) with (2058, 7) results (filtered)\n",
      "2020-09-30 22:46:56,809  SpectraRegion  INFO: Created matrices with shape (27, 17900) and (2628, 17900) (target, bg)\n",
      "2020-09-30 22:47:02,995  SpectraRegion  INFO: DE data for case: [1]\n",
      "2020-09-30 22:47:02,996  SpectraRegion  INFO: DE data for control: [0, 5, 7, 9, 3, 8, 2, 4, 6]\n",
      "2020-09-30 22:47:02,997  SpectraRegion  INFO: Running [1] against [0, 5, 7, 9, 3, 8, 2, 4, 6]\n",
      "2020-09-30 22:47:02,997  SpectraRegion  INFO: DE result key: ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:47:06,106  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2655)\n",
      "2020-09-30 22:47:06,109  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2655, 3)\n",
      "2020-09-30 22:47:06,113  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:47:07,177  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:47:07,178  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:47:17,169  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 22:47:17,173  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:47:17,174  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:47:17,177  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9)) with (3036, 7) results (filtered)\n",
      "2020-09-30 22:47:17,239  SpectraRegion  INFO: Created matrices with shape (22, 17900) and (2633, 17900) (target, bg)\n",
      "2020-09-30 22:47:27,123  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 22:47:27,124  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:47:27,127  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9)) with (3039, 7) results (filtered)\n",
      "2020-09-30 22:47:27,186  SpectraRegion  INFO: Created matrices with shape (22, 17900) and (2633, 17900) (target, bg)\n",
      "Fetching region range\n",
      "Fetching region shape\n",
      "Found region 1 with shape (43, 56, 17900)\n",
      "Fetching region spectra\n",
      "  3% (92 of 2408) |                      | Elapsed Time: 0:00:00 ETA:   0:00:03"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing regionID 1 for basename slideD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (2408 of 2408) |####################| Elapsed Time: 0:00:04 Time:  0:00:04\n",
      "100% (2408 of 2408) |####################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "Started Log Value: 0.17063884288072587\n",
      "100% (43 of 43) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "Got 2408 median-enabled pixels\n",
      "5-Number stats for medians: (2408, 2408, 0.3039763115205163, 0.5889737648019792, 0.6580512343895084, 0.7246390240951075, 1.0161616910876121)\n",
      "Started Log Value: 0.2648863598704338\n",
      "Collecting fold changes\n",
      "100% (43 of 43) |########################| Elapsed Time: 0:00:15 Time:  0:00:15\n",
      "Got a total of 43103200 fold changes\n",
      "Median elements [21551600]\n",
      "Median elements\n",
      "Global Median 0.64556\n",
      "2020-09-30 22:48:00,321  SpectraRegion  INFO: dimensions inputarray: 17900\n",
      "2020-09-30 22:48:00,322  SpectraRegion  INFO: Creating C++ obj\n",
      "2020-09-30 22:48:00,322  SpectraRegion  INFO: 17900 (43, 56, 17900)\n",
      "2020-09-30 22:48:00,323  SpectraRegion  INFO: dimensions 17900\n",
      "2020-09-30 22:48:00,324  SpectraRegion  INFO: input dimensions (43, 56, 17900)\n",
      "2020-09-30 22:48:00,325  SpectraRegion  INFO: Switching to dot mode\n",
      "2020-09-30 22:48:00,380  SpectraRegion  INFO: Starting calc similarity c++\n",
      "2020-09-30 22:48:39,976  SpectraRegion  INFO: outclust dimensions (2408, 2408)\n",
      "2020-09-30 22:48:39,991  SpectraRegion  INFO: Calculating spectra similarity\n",
      "2020-09-30 22:48:40,029  SpectraRegion  INFO: Calculating spectra similarity done\n",
      "2020-09-30 22:48:40,030  SpectraRegion  INFO: Calculating dist pixel map\n",
      "2020-09-30 22:49:11,429  SpectraRegion  INFO: Calculating dist pixel map done\n",
      "2020-09-30 22:49:11,448  SpectraRegion  INFO: Calculating clusters\n",
      "2020-09-30 22:49:11,559  SpectraRegion  INFO: Calculating clusters done\n",
      "2020-09-30 22:49:11,610  SpectraRegion  INFO: Calculating clusters saved\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-30 22:49:11,718  SpectraRegion  INFO: Assigning clusters to background: {8, 9, 10, 11, 12, 13, 14}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-30 22:49:11,815  SpectraRegion  INFO: DE data for case: [15]\n",
      "2020-09-30 22:49:11,816  SpectraRegion  INFO: DE data for control: [2, 6, 3, 1, 7, 5, 4]\n",
      "2020-09-30 22:49:11,817  SpectraRegion  INFO: Running [15] against [2, 6, 3, 1, 7, 5, 4]\n",
      "2020-09-30 22:49:11,817  SpectraRegion  INFO: DE result key: ((15,), (1, 2, 3, 4, 5, 6, 7))\n",
      "2020-09-30 22:49:11,998  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 485)\n",
      "2020-09-30 22:49:12,000  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (485, 3)\n",
      "2020-09-30 22:49:12,004  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:49:12,165  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((15,), (1, 2, 3, 4, 5, 6, 7))\n",
      "2020-09-30 22:49:12,166  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:49:16,161  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((15,), (1, 2, 3, 4, 5, 6, 7))\n",
      "2020-09-30 22:49:16,163  SpectraRegion  INFO: DE result for case ((15,), (1, 2, 3, 4, 5, 6, 7)) with (17900, 7) results\n",
      "2020-09-30 22:49:16,164  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:49:16,167  SpectraRegion  INFO: DE result for case ((15,), (1, 2, 3, 4, 5, 6, 7)) with (569, 7) results (filtered)\n",
      "2020-09-30 22:49:16,182  SpectraRegion  INFO: Created matrices with shape (91, 17900) and (394, 17900) (target, bg)\n",
      "2020-09-30 22:49:17,585  SpectraRegion  INFO: DE result for case ((15,), (1, 2, 3, 4, 5, 6, 7)) with (17900, 7) results\n",
      "2020-09-30 22:49:17,585  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:49:17,588  SpectraRegion  INFO: DE result for case ((15,), (1, 2, 3, 4, 5, 6, 7)) with (569, 7) results (filtered)\n",
      "2020-09-30 22:49:17,602  SpectraRegion  INFO: Created matrices with shape (91, 17900) and (394, 17900) (target, bg)\n",
      "2020-09-30 22:49:18,996  SpectraRegion  INFO: DE data for case: [2]\n",
      "2020-09-30 22:49:18,997  SpectraRegion  INFO: DE data for control: [15, 6, 3, 1, 7, 5, 4]\n",
      "2020-09-30 22:49:18,997  SpectraRegion  INFO: Running [2] against [15, 6, 3, 1, 7, 5, 4]\n",
      "2020-09-30 22:49:18,998  SpectraRegion  INFO: DE result key: ((2,), (1, 3, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:49:19,183  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 485)\n",
      "2020-09-30 22:49:19,186  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (485, 3)\n",
      "2020-09-30 22:49:19,190  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:49:19,350  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((2,), (1, 3, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:49:19,351  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:49:23,360  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((2,), (1, 3, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:49:23,362  SpectraRegion  INFO: DE result for case ((2,), (1, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:49:23,363  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:49:23,366  SpectraRegion  INFO: DE result for case ((2,), (1, 3, 4, 5, 6, 7, 15)) with (140, 7) results (filtered)\n",
      "2020-09-30 22:49:23,382  SpectraRegion  INFO: Created matrices with shape (79, 17900) and (406, 17900) (target, bg)\n",
      "2020-09-30 22:49:23,548  SpectraRegion  INFO: DE result for case ((2,), (1, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:49:23,549  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:49:23,552  SpectraRegion  INFO: DE result for case ((2,), (1, 3, 4, 5, 6, 7, 15)) with (137, 7) results (filtered)\n",
      "2020-09-30 22:49:23,568  SpectraRegion  INFO: Created matrices with shape (79, 17900) and (406, 17900) (target, bg)\n",
      "2020-09-30 22:49:23,726  SpectraRegion  INFO: DE data for case: [6]\n",
      "2020-09-30 22:49:23,727  SpectraRegion  INFO: DE data for control: [15, 2, 3, 1, 7, 5, 4]\n",
      "2020-09-30 22:49:23,728  SpectraRegion  INFO: Running [6] against [15, 2, 3, 1, 7, 5, 4]\n",
      "2020-09-30 22:49:23,728  SpectraRegion  INFO: DE result key: ((6,), (1, 2, 3, 4, 5, 7, 15))\n",
      "2020-09-30 22:49:23,907  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 485)\n",
      "2020-09-30 22:49:23,910  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (485, 3)\n",
      "2020-09-30 22:49:23,914  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:49:24,075  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((6,), (1, 2, 3, 4, 5, 7, 15))\n",
      "2020-09-30 22:49:24,076  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:49:28,211  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((6,), (1, 2, 3, 4, 5, 7, 15))\n",
      "2020-09-30 22:49:28,213  SpectraRegion  INFO: DE result for case ((6,), (1, 2, 3, 4, 5, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:49:28,214  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:49:28,217  SpectraRegion  INFO: DE result for case ((6,), (1, 2, 3, 4, 5, 7, 15)) with (291, 7) results (filtered)\n",
      "2020-09-30 22:49:28,235  SpectraRegion  INFO: Created matrices with shape (86, 17900) and (399, 17900) (target, bg)\n",
      "2020-09-30 22:49:29,050  SpectraRegion  INFO: DE result for case ((6,), (1, 2, 3, 4, 5, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:49:29,051  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:49:29,054  SpectraRegion  INFO: DE result for case ((6,), (1, 2, 3, 4, 5, 7, 15)) with (290, 7) results (filtered)\n",
      "2020-09-30 22:49:29,073  SpectraRegion  INFO: Created matrices with shape (86, 17900) and (399, 17900) (target, bg)\n",
      "2020-09-30 22:49:29,867  SpectraRegion  INFO: DE data for case: [3]\n",
      "2020-09-30 22:49:29,868  SpectraRegion  INFO: DE data for control: [15, 2, 6, 1, 7, 5, 4]\n",
      "2020-09-30 22:49:29,868  SpectraRegion  INFO: Running [3] against [15, 2, 6, 1, 7, 5, 4]\n",
      "2020-09-30 22:49:29,869  SpectraRegion  INFO: DE result key: ((3,), (1, 2, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:49:30,048  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 485)\n",
      "2020-09-30 22:49:30,051  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (485, 3)\n",
      "2020-09-30 22:49:30,054  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:49:30,218  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((3,), (1, 2, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:49:30,218  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:49:34,366  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((3,), (1, 2, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:49:34,371  SpectraRegion  INFO: DE result for case ((3,), (1, 2, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:49:34,372  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:49:34,377  SpectraRegion  INFO: DE result for case ((3,), (1, 2, 4, 5, 6, 7, 15)) with (42, 7) results (filtered)\n",
      "2020-09-30 22:49:34,400  SpectraRegion  INFO: Created matrices with shape (99, 17900) and (386, 17900) (target, bg)\n",
      "2020-09-30 22:49:34,447  SpectraRegion  INFO: DE result for case ((3,), (1, 2, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:49:34,448  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:49:34,450  SpectraRegion  INFO: DE result for case ((3,), (1, 2, 4, 5, 6, 7, 15)) with (21, 7) results (filtered)\n",
      "2020-09-30 22:49:34,469  SpectraRegion  INFO: Created matrices with shape (99, 17900) and (386, 17900) (target, bg)\n",
      "2020-09-30 22:49:34,509  SpectraRegion  INFO: DE data for case: [1]\n",
      "2020-09-30 22:49:34,510  SpectraRegion  INFO: DE data for control: [15, 2, 6, 3, 7, 5, 4]\n",
      "2020-09-30 22:49:34,511  SpectraRegion  INFO: Running [1] against [15, 2, 6, 3, 7, 5, 4]\n",
      "2020-09-30 22:49:34,512  SpectraRegion  INFO: DE result key: ((1,), (2, 3, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:49:34,703  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 485)\n",
      "2020-09-30 22:49:34,706  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (485, 3)\n",
      "2020-09-30 22:49:34,711  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:49:34,894  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((1,), (2, 3, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:49:34,895  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:49:38,951  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((1,), (2, 3, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:49:38,952  SpectraRegion  INFO: DE result for case ((1,), (2, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:49:38,953  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:49:38,956  SpectraRegion  INFO: DE result for case ((1,), (2, 3, 4, 5, 6, 7, 15)) with (778, 7) results (filtered)\n",
      "2020-09-30 22:49:38,971  SpectraRegion  INFO: Created matrices with shape (61, 17900) and (424, 17900) (target, bg)\n",
      "2020-09-30 22:49:40,992  SpectraRegion  INFO: DE result for case ((1,), (2, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:49:40,992  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:49:40,996  SpectraRegion  INFO: DE result for case ((1,), (2, 3, 4, 5, 6, 7, 15)) with (766, 7) results (filtered)\n",
      "2020-09-30 22:49:41,013  SpectraRegion  INFO: Created matrices with shape (61, 17900) and (424, 17900) (target, bg)\n",
      "2020-09-30 22:49:42,987  SpectraRegion  INFO: DE data for case: [7]\n",
      "2020-09-30 22:49:42,988  SpectraRegion  INFO: DE data for control: [15, 2, 6, 3, 1, 5, 4]\n",
      "2020-09-30 22:49:42,988  SpectraRegion  INFO: Running [7] against [15, 2, 6, 3, 1, 5, 4]\n",
      "2020-09-30 22:49:42,989  SpectraRegion  INFO: DE result key: ((7,), (1, 2, 3, 4, 5, 6, 15))\n",
      "2020-09-30 22:49:43,183  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 485)\n",
      "2020-09-30 22:49:43,189  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (485, 3)\n",
      "2020-09-30 22:49:43,198  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:49:43,371  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((7,), (1, 2, 3, 4, 5, 6, 15))\n",
      "2020-09-30 22:49:43,371  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:49:47,358  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((7,), (1, 2, 3, 4, 5, 6, 15))\n",
      "2020-09-30 22:49:47,360  SpectraRegion  INFO: DE result for case ((7,), (1, 2, 3, 4, 5, 6, 15)) with (17900, 7) results\n",
      "2020-09-30 22:49:47,360  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:49:47,363  SpectraRegion  INFO: DE result for case ((7,), (1, 2, 3, 4, 5, 6, 15)) with (1026, 7) results (filtered)\n",
      "2020-09-30 22:49:47,378  SpectraRegion  INFO: Created matrices with shape (39, 17900) and (446, 17900) (target, bg)\n",
      "2020-09-30 22:49:49,818  SpectraRegion  INFO: DE result for case ((7,), (1, 2, 3, 4, 5, 6, 15)) with (17900, 7) results\n",
      "2020-09-30 22:49:49,819  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:49:49,822  SpectraRegion  INFO: DE result for case ((7,), (1, 2, 3, 4, 5, 6, 15)) with (1027, 7) results (filtered)\n",
      "2020-09-30 22:49:49,837  SpectraRegion  INFO: Created matrices with shape (39, 17900) and (446, 17900) (target, bg)\n",
      "2020-09-30 22:49:52,279  SpectraRegion  INFO: DE data for case: [5]\n",
      "2020-09-30 22:49:52,279  SpectraRegion  INFO: DE data for control: [15, 2, 6, 3, 1, 7, 4]\n",
      "2020-09-30 22:49:52,280  SpectraRegion  INFO: Running [5] against [15, 2, 6, 3, 1, 7, 4]\n",
      "2020-09-30 22:49:52,280  SpectraRegion  INFO: DE result key: ((5,), (1, 2, 3, 4, 6, 7, 15))\n",
      "2020-09-30 22:49:52,460  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 485)\n",
      "2020-09-30 22:49:52,462  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (485, 3)\n",
      "2020-09-30 22:49:52,467  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:49:52,631  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((5,), (1, 2, 3, 4, 6, 7, 15))\n",
      "2020-09-30 22:49:52,631  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:49:56,658  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((5,), (1, 2, 3, 4, 6, 7, 15))\n",
      "2020-09-30 22:49:56,660  SpectraRegion  INFO: DE result for case ((5,), (1, 2, 3, 4, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:49:56,661  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:49:56,663  SpectraRegion  INFO: DE result for case ((5,), (1, 2, 3, 4, 6, 7, 15)) with (1536, 7) results (filtered)\n",
      "2020-09-30 22:49:56,677  SpectraRegion  INFO: Created matrices with shape (19, 17900) and (466, 17900) (target, bg)\n",
      "2020-09-30 22:50:00,203  SpectraRegion  INFO: DE result for case ((5,), (1, 2, 3, 4, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:50:00,204  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:50:00,207  SpectraRegion  INFO: DE result for case ((5,), (1, 2, 3, 4, 6, 7, 15)) with (1536, 7) results (filtered)\n",
      "2020-09-30 22:50:00,223  SpectraRegion  INFO: Created matrices with shape (19, 17900) and (466, 17900) (target, bg)\n",
      "2020-09-30 22:50:03,371  SpectraRegion  INFO: DE data for case: [4]\n",
      "2020-09-30 22:50:03,372  SpectraRegion  INFO: DE data for control: [15, 2, 6, 3, 1, 7, 5]\n",
      "2020-09-30 22:50:03,372  SpectraRegion  INFO: Running [4] against [15, 2, 6, 3, 1, 7, 5]\n",
      "2020-09-30 22:50:03,373  SpectraRegion  INFO: DE result key: ((4,), (1, 2, 3, 5, 6, 7, 15))\n",
      "2020-09-30 22:50:03,552  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 485)\n",
      "2020-09-30 22:50:03,554  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (485, 3)\n",
      "2020-09-30 22:50:03,557  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:50:03,729  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((4,), (1, 2, 3, 5, 6, 7, 15))\n",
      "2020-09-30 22:50:03,729  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:50:07,893  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((4,), (1, 2, 3, 5, 6, 7, 15))\n",
      "2020-09-30 22:50:07,895  SpectraRegion  INFO: DE result for case ((4,), (1, 2, 3, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:50:07,896  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:50:07,899  SpectraRegion  INFO: DE result for case ((4,), (1, 2, 3, 5, 6, 7, 15)) with (419, 7) results (filtered)\n",
      "2020-09-30 22:50:07,913  SpectraRegion  INFO: Created matrices with shape (11, 17900) and (474, 17900) (target, bg)\n",
      "2020-09-30 22:50:09,207  SpectraRegion  INFO: DE result for case ((4,), (1, 2, 3, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:50:09,208  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:50:09,211  SpectraRegion  INFO: DE result for case ((4,), (1, 2, 3, 5, 6, 7, 15)) with (354, 7) results (filtered)\n",
      "2020-09-30 22:50:09,227  SpectraRegion  INFO: Created matrices with shape (11, 17900) and (474, 17900) (target, bg)\n",
      "2020-09-30 22:50:10,123  SpectraRegion  INFO: DE data for case: [0]\n",
      "2020-09-30 22:50:10,124  SpectraRegion  INFO: DE data for control: [15, 2, 6, 3, 1, 7, 5, 4]\n",
      "2020-09-30 22:50:10,124  SpectraRegion  INFO: Running [0] against [15, 2, 6, 3, 1, 7, 5, 4]\n",
      "2020-09-30 22:50:10,125  SpectraRegion  INFO: DE result key: ((0,), (1, 2, 3, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:50:12,878  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2408)\n",
      "2020-09-30 22:50:12,881  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2408, 3)\n",
      "2020-09-30 22:50:12,887  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:50:13,890  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((0,), (1, 2, 3, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:50:13,890  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:50:22,877  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((0,), (1, 2, 3, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:50:22,880  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:50:22,881  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:50:22,885  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 15)) with (1086, 7) results (filtered)\n",
      "2020-09-30 22:50:22,945  SpectraRegion  INFO: Created matrices with shape (1923, 17900) and (485, 17900) (target, bg)\n",
      "2020-09-30 22:50:26,042  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:50:26,043  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:50:26,046  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 15)) with (1086, 7) results (filtered)\n",
      "2020-09-30 22:50:26,107  SpectraRegion  INFO: Created matrices with shape (1923, 17900) and (485, 17900) (target, bg)\n",
      "2020-09-30 22:50:29,195  SpectraRegion  INFO: DE data for case: [15]\n",
      "2020-09-30 22:50:29,195  SpectraRegion  INFO: DE data for control: [0, 2, 6, 3, 1, 7, 5, 4]\n",
      "2020-09-30 22:50:29,196  SpectraRegion  INFO: Running [15] against [0, 2, 6, 3, 1, 7, 5, 4]\n",
      "2020-09-30 22:50:29,196  SpectraRegion  INFO: DE result key: ((15,), (0, 1, 2, 3, 4, 5, 6, 7))\n",
      "2020-09-30 22:50:31,998  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2408)\n",
      "2020-09-30 22:50:32,002  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2408, 3)\n",
      "2020-09-30 22:50:32,005  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:50:32,936  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((15,), (0, 1, 2, 3, 4, 5, 6, 7))\n",
      "2020-09-30 22:50:32,937  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:50:42,240  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((15,), (0, 1, 2, 3, 4, 5, 6, 7))\n",
      "2020-09-30 22:50:42,244  SpectraRegion  INFO: DE result for case ((15,), (0, 1, 2, 3, 4, 5, 6, 7)) with (17900, 7) results\n",
      "2020-09-30 22:50:42,245  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:50:42,247  SpectraRegion  INFO: DE result for case ((15,), (0, 1, 2, 3, 4, 5, 6, 7)) with (12, 7) results (filtered)\n",
      "2020-09-30 22:50:42,308  SpectraRegion  INFO: Created matrices with shape (91, 17900) and (2317, 17900) (target, bg)\n",
      "2020-09-30 22:50:42,326  SpectraRegion  INFO: DE result for case ((15,), (0, 1, 2, 3, 4, 5, 6, 7)) with (17900, 7) results\n",
      "2020-09-30 22:50:42,327  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:50:42,329  SpectraRegion  INFO: DE result for case ((15,), (0, 1, 2, 3, 4, 5, 6, 7)) with (12, 7) results (filtered)\n",
      "2020-09-30 22:50:42,613  SpectraRegion  INFO: Created matrices with shape (91, 17900) and (2317, 17900) (target, bg)\n",
      "2020-09-30 22:50:42,633  SpectraRegion  INFO: DE data for case: [2]\n",
      "2020-09-30 22:50:42,633  SpectraRegion  INFO: DE data for control: [0, 15, 6, 3, 1, 7, 5, 4]\n",
      "2020-09-30 22:50:42,634  SpectraRegion  INFO: Running [2] against [0, 15, 6, 3, 1, 7, 5, 4]\n",
      "2020-09-30 22:50:42,634  SpectraRegion  INFO: DE result key: ((2,), (0, 1, 3, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:50:45,730  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2408)\n",
      "2020-09-30 22:50:45,733  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2408, 3)\n",
      "2020-09-30 22:50:45,738  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:50:46,902  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((2,), (0, 1, 3, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:50:46,903  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:50:56,000  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((2,), (0, 1, 3, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:50:56,003  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:50:56,004  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:50:56,007  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 15)) with (970, 7) results (filtered)\n",
      "2020-09-30 22:50:56,065  SpectraRegion  INFO: Created matrices with shape (79, 17900) and (2329, 17900) (target, bg)\n",
      "2020-09-30 22:50:59,148  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:50:59,149  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:50:59,152  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 15)) with (970, 7) results (filtered)\n",
      "2020-09-30 22:50:59,212  SpectraRegion  INFO: Created matrices with shape (79, 17900) and (2329, 17900) (target, bg)\n",
      "2020-09-30 22:51:02,221  SpectraRegion  INFO: DE data for case: [6]\n",
      "2020-09-30 22:51:02,221  SpectraRegion  INFO: DE data for control: [0, 15, 2, 3, 1, 7, 5, 4]\n",
      "2020-09-30 22:51:02,222  SpectraRegion  INFO: Running [6] against [0, 15, 2, 3, 1, 7, 5, 4]\n",
      "2020-09-30 22:51:02,222  SpectraRegion  INFO: DE result key: ((6,), (0, 1, 2, 3, 4, 5, 7, 15))\n",
      "2020-09-30 22:51:04,916  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2408)\n",
      "2020-09-30 22:51:04,920  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2408, 3)\n",
      "2020-09-30 22:51:04,924  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:51:05,850  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((6,), (0, 1, 2, 3, 4, 5, 7, 15))\n",
      "2020-09-30 22:51:05,851  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:51:15,062  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((6,), (0, 1, 2, 3, 4, 5, 7, 15))\n",
      "2020-09-30 22:51:15,066  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:51:15,067  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:51:15,070  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 15)) with (1032, 7) results (filtered)\n",
      "2020-09-30 22:51:15,128  SpectraRegion  INFO: Created matrices with shape (86, 17900) and (2322, 17900) (target, bg)\n",
      "2020-09-30 22:51:18,011  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:51:18,011  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:51:18,014  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 15)) with (1032, 7) results (filtered)\n",
      "2020-09-30 22:51:18,142  SpectraRegion  INFO: Created matrices with shape (86, 17900) and (2322, 17900) (target, bg)\n",
      "2020-09-30 22:51:20,886  SpectraRegion  INFO: DE data for case: [3]\n",
      "2020-09-30 22:51:20,887  SpectraRegion  INFO: DE data for control: [0, 15, 2, 6, 1, 7, 5, 4]\n",
      "2020-09-30 22:51:20,888  SpectraRegion  INFO: Running [3] against [0, 15, 2, 6, 1, 7, 5, 4]\n",
      "2020-09-30 22:51:20,888  SpectraRegion  INFO: DE result key: ((3,), (0, 1, 2, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:51:23,606  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2408)\n",
      "2020-09-30 22:51:23,609  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2408, 3)\n",
      "2020-09-30 22:51:23,614  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:51:24,558  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((3,), (0, 1, 2, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:51:24,559  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:51:34,006  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((3,), (0, 1, 2, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:51:34,010  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:51:34,010  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:51:34,013  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 15)) with (977, 7) results (filtered)\n",
      "2020-09-30 22:51:34,072  SpectraRegion  INFO: Created matrices with shape (99, 17900) and (2309, 17900) (target, bg)\n",
      "2020-09-30 22:51:37,148  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:51:37,149  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:51:37,151  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 15)) with (977, 7) results (filtered)\n",
      "2020-09-30 22:51:37,211  SpectraRegion  INFO: Created matrices with shape (99, 17900) and (2309, 17900) (target, bg)\n",
      "2020-09-30 22:51:40,206  SpectraRegion  INFO: DE data for case: [1]\n",
      "2020-09-30 22:51:40,207  SpectraRegion  INFO: DE data for control: [0, 15, 2, 6, 3, 7, 5, 4]\n",
      "2020-09-30 22:51:40,208  SpectraRegion  INFO: Running [1] against [0, 15, 2, 6, 3, 7, 5, 4]\n",
      "2020-09-30 22:51:40,208  SpectraRegion  INFO: DE result key: ((1,), (0, 2, 3, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:51:43,075  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2408)\n",
      "2020-09-30 22:51:43,079  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2408, 3)\n",
      "2020-09-30 22:51:43,084  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:51:44,053  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((1,), (0, 2, 3, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:51:44,054  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:51:53,448  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((1,), (0, 2, 3, 4, 5, 6, 7, 15))\n",
      "2020-09-30 22:51:53,452  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:51:53,453  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:51:53,456  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 15)) with (1944, 7) results (filtered)\n",
      "2020-09-30 22:51:53,514  SpectraRegion  INFO: Created matrices with shape (61, 17900) and (2347, 17900) (target, bg)\n",
      "2020-09-30 22:51:59,860  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:51:59,861  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:51:59,863  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 15)) with (1944, 7) results (filtered)\n",
      "2020-09-30 22:51:59,924  SpectraRegion  INFO: Created matrices with shape (61, 17900) and (2347, 17900) (target, bg)\n",
      "2020-09-30 22:52:06,294  SpectraRegion  INFO: DE data for case: [7]\n",
      "2020-09-30 22:52:06,294  SpectraRegion  INFO: DE data for control: [0, 15, 2, 6, 3, 1, 5, 4]\n",
      "2020-09-30 22:52:06,295  SpectraRegion  INFO: Running [7] against [0, 15, 2, 6, 3, 1, 5, 4]\n",
      "2020-09-30 22:52:06,295  SpectraRegion  INFO: DE result key: ((7,), (0, 1, 2, 3, 4, 5, 6, 15))\n",
      "2020-09-30 22:52:09,056  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2408)\n",
      "2020-09-30 22:52:09,059  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2408, 3)\n",
      "2020-09-30 22:52:09,063  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:52:10,019  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((7,), (0, 1, 2, 3, 4, 5, 6, 15))\n",
      "2020-09-30 22:52:10,020  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:52:19,420  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((7,), (0, 1, 2, 3, 4, 5, 6, 15))\n",
      "2020-09-30 22:52:19,424  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 15)) with (17900, 7) results\n",
      "2020-09-30 22:52:19,425  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:52:19,427  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 15)) with (1882, 7) results (filtered)\n",
      "2020-09-30 22:52:19,491  SpectraRegion  INFO: Created matrices with shape (39, 17900) and (2369, 17900) (target, bg)\n",
      "2020-09-30 22:52:24,519  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 15)) with (17900, 7) results\n",
      "2020-09-30 22:52:24,520  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:52:24,523  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 15)) with (1888, 7) results (filtered)\n",
      "2020-09-30 22:52:24,583  SpectraRegion  INFO: Created matrices with shape (39, 17900) and (2369, 17900) (target, bg)\n",
      "2020-09-30 22:52:29,611  SpectraRegion  INFO: DE data for case: [5]\n",
      "2020-09-30 22:52:29,612  SpectraRegion  INFO: DE data for control: [0, 15, 2, 6, 3, 1, 7, 4]\n",
      "2020-09-30 22:52:29,612  SpectraRegion  INFO: Running [5] against [0, 15, 2, 6, 3, 1, 7, 4]\n",
      "2020-09-30 22:52:29,613  SpectraRegion  INFO: DE result key: ((5,), (0, 1, 2, 3, 4, 6, 7, 15))\n",
      "2020-09-30 22:52:32,508  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2408)\n",
      "2020-09-30 22:52:32,511  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2408, 3)\n",
      "2020-09-30 22:52:32,516  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:52:33,513  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((5,), (0, 1, 2, 3, 4, 6, 7, 15))\n",
      "2020-09-30 22:52:33,514  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:52:43,050  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((5,), (0, 1, 2, 3, 4, 6, 7, 15))\n",
      "2020-09-30 22:52:43,054  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:52:43,055  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:52:43,058  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 15)) with (1840, 7) results (filtered)\n",
      "2020-09-30 22:52:43,122  SpectraRegion  INFO: Created matrices with shape (19, 17900) and (2389, 17900) (target, bg)\n",
      "2020-09-30 22:52:47,526  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:52:47,527  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:52:47,531  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 15)) with (1874, 7) results (filtered)\n",
      "2020-09-30 22:52:47,590  SpectraRegion  INFO: Created matrices with shape (19, 17900) and (2389, 17900) (target, bg)\n",
      "2020-09-30 22:52:51,668  SpectraRegion  INFO: DE data for case: [4]\n",
      "2020-09-30 22:52:51,669  SpectraRegion  INFO: DE data for control: [0, 15, 2, 6, 3, 1, 7, 5]\n",
      "2020-09-30 22:52:51,670  SpectraRegion  INFO: Running [4] against [0, 15, 2, 6, 3, 1, 7, 5]\n",
      "2020-09-30 22:52:51,670  SpectraRegion  INFO: DE result key: ((4,), (0, 1, 2, 3, 5, 6, 7, 15))\n",
      "2020-09-30 22:52:54,271  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2408)\n",
      "2020-09-30 22:52:54,274  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2408, 3)\n",
      "2020-09-30 22:52:54,279  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:52:55,242  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((4,), (0, 1, 2, 3, 5, 6, 7, 15))\n",
      "2020-09-30 22:52:55,243  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:53:04,497  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((4,), (0, 1, 2, 3, 5, 6, 7, 15))\n",
      "2020-09-30 22:53:04,501  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:53:04,502  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:53:04,505  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 15)) with (946, 7) results (filtered)\n",
      "2020-09-30 22:53:04,568  SpectraRegion  INFO: Created matrices with shape (11, 17900) and (2397, 17900) (target, bg)\n",
      "2020-09-30 22:53:07,265  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 22:53:07,266  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:53:07,269  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 15)) with (1065, 7) results (filtered)\n",
      "2020-09-30 22:53:07,327  SpectraRegion  INFO: Created matrices with shape (11, 17900) and (2397, 17900) (target, bg)\n"
     ]
    }
   ],
   "source": [
    "slided_0, slided0_mgenes, slided0_mgenes_bg = process_imzeregion(imze=imze, regionID= 0, basename=\"slideD\")\n",
    "slided_1, slided1_mgenes, slided1_mgenes_bg = process_imzeregion(imze=imze, regionID= 1, basename=\"slideD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slided_0.mass_dabest(pw.protein2mass.get(\"Tmsb4x\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slided_1.mass_dabest(pw.protein2mass.get(\"Tmsb4x\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fetching region range\n",
      "Fetching region shape\n",
      "Found region 4 with shape (49, 56, 17900)\n",
      "Fetching region spectra\n",
      "  2% (70 of 2744) |                      | Elapsed Time: 0:00:00 ETA:   0:00:04"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing regionID 4 for basename slideD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (2744 of 2744) |####################| Elapsed Time: 0:00:04 Time:  0:00:04\n",
      "100% (2744 of 2744) |####################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "Started Log Value: 0.22751572877168655\n",
      "100% (49 of 49) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "Got 2744 median-enabled pixels\n",
      "5-Number stats for medians: (2744, 2744, 0.29120835853070265, 0.5888907621230302, 0.660637862228705, 0.7270544871043271, 0.9875144181541924)\n",
      "Started Log Value: 0.3518995396792889\n",
      "Collecting fold changes\n",
      "100% (49 of 49) |########################| Elapsed Time: 0:00:22 Time:  0:00:22\n",
      "Got a total of 49117600 fold changes\n",
      "Median elements [24558800]\n",
      "Median elements\n",
      "Global Median 0.64784\n",
      "2020-09-30 22:53:40,667  SpectraRegion  INFO: dimensions inputarray: 17900\n",
      "2020-09-30 22:53:40,667  SpectraRegion  INFO: Creating C++ obj\n",
      "2020-09-30 22:53:40,668  SpectraRegion  INFO: 17900 (49, 56, 17900)\n",
      "2020-09-30 22:53:40,668  SpectraRegion  INFO: dimensions 17900\n",
      "2020-09-30 22:53:40,669  SpectraRegion  INFO: input dimensions (49, 56, 17900)\n",
      "2020-09-30 22:53:40,669  SpectraRegion  INFO: Switching to dot mode\n",
      "2020-09-30 22:53:40,713  SpectraRegion  INFO: Starting calc similarity c++\n",
      "2020-09-30 22:54:36,001  SpectraRegion  INFO: outclust dimensions (2744, 2744)\n",
      "2020-09-30 22:54:36,002  SpectraRegion  INFO: Calculating spectra similarity\n",
      "2020-09-30 22:54:36,069  SpectraRegion  INFO: Calculating spectra similarity done\n",
      "2020-09-30 22:54:36,075  SpectraRegion  INFO: Calculating dist pixel map\n",
      "2020-09-30 22:55:15,270  SpectraRegion  INFO: Calculating dist pixel map done\n",
      "2020-09-30 22:55:15,293  SpectraRegion  INFO: Calculating clusters\n",
      "2020-09-30 22:55:15,505  SpectraRegion  INFO: Calculating clusters done\n",
      "2020-09-30 22:55:15,521  SpectraRegion  INFO: Calculating clusters saved\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-30 22:55:15,622  SpectraRegion  INFO: Assigning clusters to background: {6, 7, 8, 9, 14, 15}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-30 22:55:15,720  SpectraRegion  INFO: DE data for case: [13]\n",
      "2020-09-30 22:55:15,721  SpectraRegion  INFO: DE data for control: [3, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 22:55:15,721  SpectraRegion  INFO: Running [13] against [3, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 22:55:15,722  SpectraRegion  INFO: DE result key: ((13,), (1, 2, 3, 4, 5, 10, 11, 12))\n",
      "2020-09-30 22:55:15,938  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 526)\n",
      "2020-09-30 22:55:15,941  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (526, 3)\n",
      "2020-09-30 22:55:15,945  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:55:16,106  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((13,), (1, 2, 3, 4, 5, 10, 11, 12))\n",
      "2020-09-30 22:55:16,106  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:55:20,246  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((13,), (1, 2, 3, 4, 5, 10, 11, 12))\n",
      "2020-09-30 22:55:20,249  SpectraRegion  INFO: DE result for case ((13,), (1, 2, 3, 4, 5, 10, 11, 12)) with (17900, 7) results\n",
      "2020-09-30 22:55:20,249  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:55:20,252  SpectraRegion  INFO: DE result for case ((13,), (1, 2, 3, 4, 5, 10, 11, 12)) with (741, 7) results (filtered)\n",
      "2020-09-30 22:55:20,273  SpectraRegion  INFO: Created matrices with shape (101, 17900) and (425, 17900) (target, bg)\n",
      "2020-09-30 22:55:21,894  SpectraRegion  INFO: DE result for case ((13,), (1, 2, 3, 4, 5, 10, 11, 12)) with (17900, 7) results\n",
      "2020-09-30 22:55:21,895  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:55:21,898  SpectraRegion  INFO: DE result for case ((13,), (1, 2, 3, 4, 5, 10, 11, 12)) with (740, 7) results (filtered)\n",
      "2020-09-30 22:55:21,918  SpectraRegion  INFO: Created matrices with shape (101, 17900) and (425, 17900) (target, bg)\n",
      "2020-09-30 22:55:23,531  SpectraRegion  INFO: DE data for case: [3]\n",
      "2020-09-30 22:55:23,532  SpectraRegion  INFO: DE data for control: [13, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 22:55:23,532  SpectraRegion  INFO: Running [3] against [13, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 22:55:23,533  SpectraRegion  INFO: DE result key: ((3,), (1, 2, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:55:23,738  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 526)\n",
      "2020-09-30 22:55:23,741  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (526, 3)\n",
      "2020-09-30 22:55:23,744  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:55:23,910  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((3,), (1, 2, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:55:23,911  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:55:28,027  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((3,), (1, 2, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:55:28,029  SpectraRegion  INFO: DE result for case ((3,), (1, 2, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:55:28,030  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:55:28,033  SpectraRegion  INFO: DE result for case ((3,), (1, 2, 4, 5, 10, 11, 12, 13)) with (125, 7) results (filtered)\n",
      "2020-09-30 22:55:28,054  SpectraRegion  INFO: Created matrices with shape (102, 17900) and (424, 17900) (target, bg)\n",
      "2020-09-30 22:55:28,178  SpectraRegion  INFO: DE result for case ((3,), (1, 2, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:55:28,179  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:55:28,182  SpectraRegion  INFO: DE result for case ((3,), (1, 2, 4, 5, 10, 11, 12, 13)) with (79, 7) results (filtered)\n",
      "2020-09-30 22:55:28,203  SpectraRegion  INFO: Created matrices with shape (102, 17900) and (424, 17900) (target, bg)\n",
      "2020-09-30 22:55:28,293  SpectraRegion  INFO: DE data for case: [10]\n",
      "2020-09-30 22:55:28,294  SpectraRegion  INFO: DE data for control: [13, 3, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 22:55:28,295  SpectraRegion  INFO: Running [10] against [13, 3, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 22:55:28,295  SpectraRegion  INFO: DE result key: ((10,), (1, 2, 3, 4, 5, 11, 12, 13))\n",
      "2020-09-30 22:55:28,506  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 526)\n",
      "2020-09-30 22:55:28,509  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (526, 3)\n",
      "2020-09-30 22:55:28,512  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:55:28,679  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((10,), (1, 2, 3, 4, 5, 11, 12, 13))\n",
      "2020-09-30 22:55:28,679  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:55:32,791  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((10,), (1, 2, 3, 4, 5, 11, 12, 13))\n",
      "2020-09-30 22:55:32,793  SpectraRegion  INFO: DE result for case ((10,), (1, 2, 3, 4, 5, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:55:32,794  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:55:32,797  SpectraRegion  INFO: DE result for case ((10,), (1, 2, 3, 4, 5, 11, 12, 13)) with (267, 7) results (filtered)\n",
      "2020-09-30 22:55:32,814  SpectraRegion  INFO: Created matrices with shape (49, 17900) and (477, 17900) (target, bg)\n",
      "2020-09-30 22:55:33,482  SpectraRegion  INFO: DE result for case ((10,), (1, 2, 3, 4, 5, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:55:33,482  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:55:33,485  SpectraRegion  INFO: DE result for case ((10,), (1, 2, 3, 4, 5, 11, 12, 13)) with (203, 7) results (filtered)\n",
      "2020-09-30 22:55:33,502  SpectraRegion  INFO: Created matrices with shape (49, 17900) and (477, 17900) (target, bg)\n",
      "2020-09-30 22:55:34,092  SpectraRegion  INFO: DE data for case: [12]\n",
      "2020-09-30 22:55:34,093  SpectraRegion  INFO: DE data for control: [13, 3, 10, 2, 11, 1, 5, 4]\n",
      "2020-09-30 22:55:34,093  SpectraRegion  INFO: Running [12] against [13, 3, 10, 2, 11, 1, 5, 4]\n",
      "2020-09-30 22:55:34,094  SpectraRegion  INFO: DE result key: ((12,), (1, 2, 3, 4, 5, 10, 11, 13))\n",
      "2020-09-30 22:55:34,304  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 526)\n",
      "2020-09-30 22:55:34,306  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (526, 3)\n",
      "2020-09-30 22:55:34,310  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:55:34,481  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((12,), (1, 2, 3, 4, 5, 10, 11, 13))\n",
      "2020-09-30 22:55:34,482  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:55:38,592  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((12,), (1, 2, 3, 4, 5, 10, 11, 13))\n",
      "2020-09-30 22:55:38,594  SpectraRegion  INFO: DE result for case ((12,), (1, 2, 3, 4, 5, 10, 11, 13)) with (17900, 7) results\n",
      "2020-09-30 22:55:38,594  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:55:38,598  SpectraRegion  INFO: DE result for case ((12,), (1, 2, 3, 4, 5, 10, 11, 13)) with (1249, 7) results (filtered)\n",
      "2020-09-30 22:55:38,616  SpectraRegion  INFO: Created matrices with shape (61, 17900) and (465, 17900) (target, bg)\n",
      "2020-09-30 22:55:41,678  SpectraRegion  INFO: DE result for case ((12,), (1, 2, 3, 4, 5, 10, 11, 13)) with (17900, 7) results\n",
      "2020-09-30 22:55:41,679  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:55:41,682  SpectraRegion  INFO: DE result for case ((12,), (1, 2, 3, 4, 5, 10, 11, 13)) with (1243, 7) results (filtered)\n",
      "2020-09-30 22:55:41,698  SpectraRegion  INFO: Created matrices with shape (61, 17900) and (465, 17900) (target, bg)\n",
      "2020-09-30 22:55:44,749  SpectraRegion  INFO: DE data for case: [2]\n",
      "2020-09-30 22:55:44,749  SpectraRegion  INFO: DE data for control: [13, 3, 10, 12, 11, 1, 5, 4]\n",
      "2020-09-30 22:55:44,750  SpectraRegion  INFO: Running [2] against [13, 3, 10, 12, 11, 1, 5, 4]\n",
      "2020-09-30 22:55:44,750  SpectraRegion  INFO: DE result key: ((2,), (1, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:55:44,961  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 526)\n",
      "2020-09-30 22:55:44,964  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (526, 3)\n",
      "2020-09-30 22:55:44,967  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:55:45,135  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((2,), (1, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:55:45,136  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:55:49,287  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((2,), (1, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:55:49,289  SpectraRegion  INFO: DE result for case ((2,), (1, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:55:49,290  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:55:49,293  SpectraRegion  INFO: DE result for case ((2,), (1, 3, 4, 5, 10, 11, 12, 13)) with (659, 7) results (filtered)\n",
      "2020-09-30 22:55:49,310  SpectraRegion  INFO: Created matrices with shape (45, 17900) and (481, 17900) (target, bg)\n",
      "2020-09-30 22:55:51,218  SpectraRegion  INFO: DE result for case ((2,), (1, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:55:51,219  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:55:51,222  SpectraRegion  INFO: DE result for case ((2,), (1, 3, 4, 5, 10, 11, 12, 13)) with (601, 7) results (filtered)\n",
      "2020-09-30 22:55:51,239  SpectraRegion  INFO: Created matrices with shape (45, 17900) and (481, 17900) (target, bg)\n",
      "2020-09-30 22:55:53,000  SpectraRegion  INFO: DE data for case: [11]\n",
      "2020-09-30 22:55:53,001  SpectraRegion  INFO: DE data for control: [13, 3, 10, 12, 2, 1, 5, 4]\n",
      "2020-09-30 22:55:53,002  SpectraRegion  INFO: Running [11] against [13, 3, 10, 12, 2, 1, 5, 4]\n",
      "2020-09-30 22:55:53,002  SpectraRegion  INFO: DE result key: ((11,), (1, 2, 3, 4, 5, 10, 12, 13))\n",
      "2020-09-30 22:55:53,213  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 526)\n",
      "2020-09-30 22:55:53,216  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (526, 3)\n",
      "2020-09-30 22:55:53,220  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:55:53,390  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((11,), (1, 2, 3, 4, 5, 10, 12, 13))\n",
      "2020-09-30 22:55:53,391  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:55:57,529  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((11,), (1, 2, 3, 4, 5, 10, 12, 13))\n",
      "2020-09-30 22:55:57,531  SpectraRegion  INFO: DE result for case ((11,), (1, 2, 3, 4, 5, 10, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:55:57,532  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:55:57,534  SpectraRegion  INFO: DE result for case ((11,), (1, 2, 3, 4, 5, 10, 12, 13)) with (122, 7) results (filtered)\n",
      "2020-09-30 22:55:57,551  SpectraRegion  INFO: Created matrices with shape (45, 17900) and (481, 17900) (target, bg)\n",
      "2020-09-30 22:55:58,083  SpectraRegion  INFO: DE result for case ((11,), (1, 2, 3, 4, 5, 10, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:55:58,084  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:55:58,086  SpectraRegion  INFO: DE result for case ((11,), (1, 2, 3, 4, 5, 10, 12, 13)) with (100, 7) results (filtered)\n",
      "2020-09-30 22:55:58,104  SpectraRegion  INFO: Created matrices with shape (45, 17900) and (481, 17900) (target, bg)\n",
      "2020-09-30 22:55:58,499  SpectraRegion  INFO: DE data for case: [1]\n",
      "2020-09-30 22:55:58,500  SpectraRegion  INFO: DE data for control: [13, 3, 10, 12, 2, 11, 5, 4]\n",
      "2020-09-30 22:55:58,500  SpectraRegion  INFO: Running [1] against [13, 3, 10, 12, 2, 11, 5, 4]\n",
      "2020-09-30 22:55:58,501  SpectraRegion  INFO: DE result key: ((1,), (2, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:55:58,715  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 526)\n",
      "2020-09-30 22:55:58,718  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (526, 3)\n",
      "2020-09-30 22:55:58,721  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:55:58,893  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((1,), (2, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:55:58,894  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:56:03,025  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((1,), (2, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:56:03,028  SpectraRegion  INFO: DE result for case ((1,), (2, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:56:03,028  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:56:03,032  SpectraRegion  INFO: DE result for case ((1,), (2, 3, 4, 5, 10, 11, 12, 13)) with (525, 7) results (filtered)\n",
      "2020-09-30 22:56:03,049  SpectraRegion  INFO: Created matrices with shape (57, 17900) and (469, 17900) (target, bg)\n",
      "2020-09-30 22:56:04,155  SpectraRegion  INFO: DE result for case ((1,), (2, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:56:04,156  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:56:04,159  SpectraRegion  INFO: DE result for case ((1,), (2, 3, 4, 5, 10, 11, 12, 13)) with (471, 7) results (filtered)\n",
      "2020-09-30 22:56:04,177  SpectraRegion  INFO: Created matrices with shape (57, 17900) and (469, 17900) (target, bg)\n",
      "2020-09-30 22:56:05,192  SpectraRegion  INFO: DE data for case: [5]\n",
      "2020-09-30 22:56:05,192  SpectraRegion  INFO: DE data for control: [13, 3, 10, 12, 2, 11, 1, 4]\n",
      "2020-09-30 22:56:05,193  SpectraRegion  INFO: Running [5] against [13, 3, 10, 12, 2, 11, 1, 4]\n",
      "2020-09-30 22:56:05,193  SpectraRegion  INFO: DE result key: ((5,), (1, 2, 3, 4, 10, 11, 12, 13))\n",
      "2020-09-30 22:56:05,408  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 526)\n",
      "2020-09-30 22:56:05,411  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (526, 3)\n",
      "2020-09-30 22:56:05,414  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:56:05,588  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((5,), (1, 2, 3, 4, 10, 11, 12, 13))\n",
      "2020-09-30 22:56:05,588  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:56:09,716  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((5,), (1, 2, 3, 4, 10, 11, 12, 13))\n",
      "2020-09-30 22:56:09,718  SpectraRegion  INFO: DE result for case ((5,), (1, 2, 3, 4, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:56:09,719  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:56:09,722  SpectraRegion  INFO: DE result for case ((5,), (1, 2, 3, 4, 10, 11, 12, 13)) with (544, 7) results (filtered)\n",
      "2020-09-30 22:56:09,739  SpectraRegion  INFO: Created matrices with shape (48, 17900) and (478, 17900) (target, bg)\n",
      "2020-09-30 22:56:11,287  SpectraRegion  INFO: DE result for case ((5,), (1, 2, 3, 4, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:56:11,288  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:56:11,291  SpectraRegion  INFO: DE result for case ((5,), (1, 2, 3, 4, 10, 11, 12, 13)) with (537, 7) results (filtered)\n",
      "2020-09-30 22:56:11,306  SpectraRegion  INFO: Created matrices with shape (48, 17900) and (478, 17900) (target, bg)\n",
      "2020-09-30 22:56:12,824  SpectraRegion  INFO: DE data for case: [4]\n",
      "2020-09-30 22:56:12,824  SpectraRegion  INFO: DE data for control: [13, 3, 10, 12, 2, 11, 1, 5]\n",
      "2020-09-30 22:56:12,825  SpectraRegion  INFO: Running [4] against [13, 3, 10, 12, 2, 11, 1, 5]\n",
      "2020-09-30 22:56:12,825  SpectraRegion  INFO: DE result key: ((4,), (1, 2, 3, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:56:13,040  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 526)\n",
      "2020-09-30 22:56:13,043  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (526, 3)\n",
      "2020-09-30 22:56:13,046  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:56:13,219  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((4,), (1, 2, 3, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:56:13,220  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:56:17,378  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((4,), (1, 2, 3, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:56:17,380  SpectraRegion  INFO: DE result for case ((4,), (1, 2, 3, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:56:17,381  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:56:17,384  SpectraRegion  INFO: DE result for case ((4,), (1, 2, 3, 5, 10, 11, 12, 13)) with (1615, 7) results (filtered)\n",
      "2020-09-30 22:56:17,402  SpectraRegion  INFO: Created matrices with shape (18, 17900) and (508, 17900) (target, bg)\n",
      "2020-09-30 22:56:21,995  SpectraRegion  INFO: DE result for case ((4,), (1, 2, 3, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:56:21,995  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:56:21,998  SpectraRegion  INFO: DE result for case ((4,), (1, 2, 3, 5, 10, 11, 12, 13)) with (1443, 7) results (filtered)\n",
      "2020-09-30 22:56:22,014  SpectraRegion  INFO: Created matrices with shape (18, 17900) and (508, 17900) (target, bg)\n",
      "2020-09-30 22:56:26,368  SpectraRegion  INFO: DE data for case: [0]\n",
      "2020-09-30 22:56:26,369  SpectraRegion  INFO: DE data for control: [13, 3, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 22:56:26,370  SpectraRegion  INFO: Running [0] against [13, 3, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 22:56:26,370  SpectraRegion  INFO: DE result key: ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:56:29,814  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2744)\n",
      "2020-09-30 22:56:29,817  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2744, 3)\n",
      "2020-09-30 22:56:29,821  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:56:31,253  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:56:31,254  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:56:41,518  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:56:41,522  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:56:41,523  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:56:41,526  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13)) with (1491, 7) results (filtered)\n",
      "2020-09-30 22:56:41,595  SpectraRegion  INFO: Created matrices with shape (2218, 17900) and (526, 17900) (target, bg)\n",
      "2020-09-30 22:56:46,150  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:56:46,150  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:56:46,154  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13)) with (1491, 7) results (filtered)\n",
      "2020-09-30 22:56:46,222  SpectraRegion  INFO: Created matrices with shape (2218, 17900) and (526, 17900) (target, bg)\n",
      "2020-09-30 22:56:50,742  SpectraRegion  INFO: DE data for case: [13]\n",
      "2020-09-30 22:56:50,744  SpectraRegion  INFO: DE data for control: [0, 3, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 22:56:50,745  SpectraRegion  INFO: Running [13] against [0, 3, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 22:56:50,746  SpectraRegion  INFO: DE result key: ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12))\n",
      "2020-09-30 22:56:54,236  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2744)\n",
      "2020-09-30 22:56:54,239  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2744, 3)\n",
      "2020-09-30 22:56:54,244  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:56:55,454  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12))\n",
      "2020-09-30 22:56:55,455  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:57:05,806  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12))\n",
      "2020-09-30 22:57:05,809  SpectraRegion  INFO: DE result for case ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12)) with (17900, 7) results\n",
      "2020-09-30 22:57:05,810  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:57:05,813  SpectraRegion  INFO: DE result for case ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12)) with (89, 7) results (filtered)\n",
      "2020-09-30 22:57:05,888  SpectraRegion  INFO: Created matrices with shape (101, 17900) and (2643, 17900) (target, bg)\n",
      "2020-09-30 22:57:06,147  SpectraRegion  INFO: DE result for case ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12)) with (17900, 7) results\n",
      "2020-09-30 22:57:06,148  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:57:06,151  SpectraRegion  INFO: DE result for case ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12)) with (89, 7) results (filtered)\n",
      "2020-09-30 22:57:06,220  SpectraRegion  INFO: Created matrices with shape (101, 17900) and (2643, 17900) (target, bg)\n",
      "2020-09-30 22:57:06,451  SpectraRegion  INFO: DE data for case: [3]\n",
      "2020-09-30 22:57:06,452  SpectraRegion  INFO: DE data for control: [0, 13, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 22:57:06,453  SpectraRegion  INFO: Running [3] against [0, 13, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 22:57:06,453  SpectraRegion  INFO: DE result key: ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:57:10,415  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2744)\n",
      "2020-09-30 22:57:10,418  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2744, 3)\n",
      "2020-09-30 22:57:10,423  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:57:11,718  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:57:11,718  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:57:22,495  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:57:22,500  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:57:22,500  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:57:22,504  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13)) with (1138, 7) results (filtered)\n",
      "2020-09-30 22:57:22,576  SpectraRegion  INFO: Created matrices with shape (102, 17900) and (2642, 17900) (target, bg)\n",
      "2020-09-30 22:57:25,925  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:57:25,926  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:57:25,929  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13)) with (1138, 7) results (filtered)\n",
      "2020-09-30 22:57:25,999  SpectraRegion  INFO: Created matrices with shape (102, 17900) and (2642, 17900) (target, bg)\n",
      "2020-09-30 22:57:29,305  SpectraRegion  INFO: DE data for case: [10]\n",
      "2020-09-30 22:57:29,305  SpectraRegion  INFO: DE data for control: [0, 13, 3, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 22:57:29,306  SpectraRegion  INFO: Running [10] against [0, 13, 3, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 22:57:29,306  SpectraRegion  INFO: DE result key: ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13))\n",
      "2020-09-30 22:57:32,863  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2744)\n",
      "2020-09-30 22:57:32,866  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2744, 3)\n",
      "2020-09-30 22:57:32,870  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:57:33,969  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13))\n",
      "2020-09-30 22:57:33,970  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:57:44,332  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13))\n",
      "2020-09-30 22:57:44,338  SpectraRegion  INFO: DE result for case ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:57:44,338  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:57:44,342  SpectraRegion  INFO: DE result for case ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13)) with (868, 7) results (filtered)\n",
      "2020-09-30 22:57:44,414  SpectraRegion  INFO: Created matrices with shape (49, 17900) and (2695, 17900) (target, bg)\n",
      "2020-09-30 22:57:46,757  SpectraRegion  INFO: DE result for case ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:57:46,758  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:57:46,762  SpectraRegion  INFO: DE result for case ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13)) with (868, 7) results (filtered)\n",
      "2020-09-30 22:57:46,840  SpectraRegion  INFO: Created matrices with shape (49, 17900) and (2695, 17900) (target, bg)\n",
      "2020-09-30 22:57:49,150  SpectraRegion  INFO: DE data for case: [12]\n",
      "2020-09-30 22:57:49,151  SpectraRegion  INFO: DE data for control: [0, 13, 3, 10, 2, 11, 1, 5, 4]\n",
      "2020-09-30 22:57:49,151  SpectraRegion  INFO: Running [12] against [0, 13, 3, 10, 2, 11, 1, 5, 4]\n",
      "2020-09-30 22:57:49,152  SpectraRegion  INFO: DE result key: ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13))\n",
      "2020-09-30 22:57:53,146  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2744)\n",
      "2020-09-30 22:57:53,150  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2744, 3)\n",
      "2020-09-30 22:57:53,155  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:57:54,389  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13))\n",
      "2020-09-30 22:57:54,390  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:58:04,808  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13))\n",
      "2020-09-30 22:58:04,812  SpectraRegion  INFO: DE result for case ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13)) with (17900, 7) results\n",
      "2020-09-30 22:58:04,812  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:58:04,815  SpectraRegion  INFO: DE result for case ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13)) with (1575, 7) results (filtered)\n",
      "2020-09-30 22:58:04,886  SpectraRegion  INFO: Created matrices with shape (61, 17900) and (2683, 17900) (target, bg)\n",
      "2020-09-30 22:58:08,856  SpectraRegion  INFO: DE result for case ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13)) with (17900, 7) results\n",
      "2020-09-30 22:58:08,857  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:58:08,860  SpectraRegion  INFO: DE result for case ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13)) with (1574, 7) results (filtered)\n",
      "2020-09-30 22:58:08,929  SpectraRegion  INFO: Created matrices with shape (61, 17900) and (2683, 17900) (target, bg)\n",
      "2020-09-30 22:58:12,785  SpectraRegion  INFO: DE data for case: [2]\n",
      "2020-09-30 22:58:12,786  SpectraRegion  INFO: DE data for control: [0, 13, 3, 10, 12, 11, 1, 5, 4]\n",
      "2020-09-30 22:58:12,787  SpectraRegion  INFO: Running [2] against [0, 13, 3, 10, 12, 11, 1, 5, 4]\n",
      "2020-09-30 22:58:12,787  SpectraRegion  INFO: DE result key: ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:58:16,534  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2744)\n",
      "2020-09-30 22:58:16,537  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2744, 3)\n",
      "2020-09-30 22:58:16,542  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:58:17,705  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:58:17,706  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:58:28,168  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:58:28,171  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:58:28,172  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:58:28,176  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13)) with (2184, 7) results (filtered)\n",
      "2020-09-30 22:58:28,248  SpectraRegion  INFO: Created matrices with shape (45, 17900) and (2699, 17900) (target, bg)\n",
      "2020-09-30 22:58:35,970  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:58:35,971  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:58:35,973  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13)) with (2184, 7) results (filtered)\n",
      "2020-09-30 22:58:36,039  SpectraRegion  INFO: Created matrices with shape (45, 17900) and (2699, 17900) (target, bg)\n",
      "2020-09-30 22:58:43,729  SpectraRegion  INFO: DE data for case: [11]\n",
      "2020-09-30 22:58:43,729  SpectraRegion  INFO: DE data for control: [0, 13, 3, 10, 12, 2, 1, 5, 4]\n",
      "2020-09-30 22:58:43,730  SpectraRegion  INFO: Running [11] against [0, 13, 3, 10, 12, 2, 1, 5, 4]\n",
      "2020-09-30 22:58:43,730  SpectraRegion  INFO: DE result key: ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13))\n",
      "2020-09-30 22:58:47,384  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2744)\n",
      "2020-09-30 22:58:47,387  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2744, 3)\n",
      "2020-09-30 22:58:47,392  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:58:48,515  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13))\n",
      "2020-09-30 22:58:48,515  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:58:59,200  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13))\n",
      "2020-09-30 22:58:59,204  SpectraRegion  INFO: DE result for case ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:58:59,205  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:58:59,208  SpectraRegion  INFO: DE result for case ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13)) with (1552, 7) results (filtered)\n",
      "2020-09-30 22:58:59,278  SpectraRegion  INFO: Created matrices with shape (45, 17900) and (2699, 17900) (target, bg)\n",
      "2020-09-30 22:59:03,809  SpectraRegion  INFO: DE result for case ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:59:03,810  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:59:03,812  SpectraRegion  INFO: DE result for case ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13)) with (1554, 7) results (filtered)\n",
      "2020-09-30 22:59:03,880  SpectraRegion  INFO: Created matrices with shape (45, 17900) and (2699, 17900) (target, bg)\n",
      "2020-09-30 22:59:08,409  SpectraRegion  INFO: DE data for case: [1]\n",
      "2020-09-30 22:59:08,409  SpectraRegion  INFO: DE data for control: [0, 13, 3, 10, 12, 2, 11, 5, 4]\n",
      "2020-09-30 22:59:08,410  SpectraRegion  INFO: Running [1] against [0, 13, 3, 10, 12, 2, 11, 5, 4]\n",
      "2020-09-30 22:59:08,410  SpectraRegion  INFO: DE result key: ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:59:11,913  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2744)\n",
      "2020-09-30 22:59:11,917  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2744, 3)\n",
      "2020-09-30 22:59:11,923  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:59:13,055  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:59:13,056  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:59:23,488  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 22:59:23,492  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:59:23,492  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:59:23,496  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13)) with (2040, 7) results (filtered)\n",
      "2020-09-30 22:59:23,568  SpectraRegion  INFO: Created matrices with shape (57, 17900) and (2687, 17900) (target, bg)\n",
      "2020-09-30 22:59:30,593  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:59:30,594  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:59:30,597  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13)) with (2038, 7) results (filtered)\n",
      "2020-09-30 22:59:30,666  SpectraRegion  INFO: Created matrices with shape (57, 17900) and (2687, 17900) (target, bg)\n",
      "2020-09-30 22:59:37,496  SpectraRegion  INFO: DE data for case: [5]\n",
      "2020-09-30 22:59:37,497  SpectraRegion  INFO: DE data for control: [0, 13, 3, 10, 12, 2, 11, 1, 4]\n",
      "2020-09-30 22:59:37,497  SpectraRegion  INFO: Running [5] against [0, 13, 3, 10, 12, 2, 11, 1, 4]\n",
      "2020-09-30 22:59:37,498  SpectraRegion  INFO: DE result key: ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13))\n",
      "2020-09-30 22:59:40,899  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2744)\n",
      "2020-09-30 22:59:40,902  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2744, 3)\n",
      "2020-09-30 22:59:40,907  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 22:59:42,048  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13))\n",
      "2020-09-30 22:59:42,049  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 22:59:52,418  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13))\n",
      "2020-09-30 22:59:52,421  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:59:52,422  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:59:52,425  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13)) with (1661, 7) results (filtered)\n",
      "2020-09-30 22:59:52,496  SpectraRegion  INFO: Created matrices with shape (48, 17900) and (2696, 17900) (target, bg)\n",
      "2020-09-30 22:59:57,205  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 22:59:57,205  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 22:59:57,210  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13)) with (1659, 7) results (filtered)\n",
      "2020-09-30 22:59:57,303  SpectraRegion  INFO: Created matrices with shape (48, 17900) and (2696, 17900) (target, bg)\n",
      "2020-09-30 23:00:01,937  SpectraRegion  INFO: DE data for case: [4]\n",
      "2020-09-30 23:00:01,937  SpectraRegion  INFO: DE data for control: [0, 13, 3, 10, 12, 2, 11, 1, 5]\n",
      "2020-09-30 23:00:01,938  SpectraRegion  INFO: Running [4] against [0, 13, 3, 10, 12, 2, 11, 1, 5]\n",
      "2020-09-30 23:00:01,938  SpectraRegion  INFO: DE result key: ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13))\n",
      "2020-09-30 23:00:05,739  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 2744)\n",
      "2020-09-30 23:00:05,743  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (2744, 3)\n",
      "2020-09-30 23:00:05,749  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 23:00:06,947  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13))\n",
      "2020-09-30 23:00:06,948  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 23:00:17,546  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13))\n",
      "2020-09-30 23:00:17,550  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 23:00:17,551  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:00:17,554  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13)) with (2953, 7) results (filtered)\n",
      "2020-09-30 23:00:17,621  SpectraRegion  INFO: Created matrices with shape (18, 17900) and (2726, 17900) (target, bg)\n",
      "2020-09-30 23:00:27,137  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 23:00:27,138  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:00:27,141  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13)) with (2920, 7) results (filtered)\n",
      "2020-09-30 23:00:27,210  SpectraRegion  INFO: Created matrices with shape (18, 17900) and (2726, 17900) (target, bg)\n",
      "Fetching region range\n",
      "Fetching region shape\n",
      "Found region 5 with shape (52, 59, 17900)\n",
      "Fetching region spectra\n",
      "  2% (78 of 3068) |                      | Elapsed Time: 0:00:00 ETA:   0:00:05"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing regionID 5 for basename slideD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (3068 of 3068) |####################| Elapsed Time: 0:00:05 Time:  0:00:05\n",
      "100% (3068 of 3068) |####################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "Started Log Value: 0.22961045652627946\n",
      "100% (52 of 52) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "Got 3068 median-enabled pixels\n",
      "5-Number stats for medians: (3068, 3068, 0.3318104865334491, 0.5890882557317825, 0.6596343025206233, 0.728007829747179, 0.9846777519403345)\n",
      "Started Log Value: 0.35571027994155885\n",
      "Collecting fold changes\n",
      "100% (52 of 52) |########################| Elapsed Time: 0:00:24 Time:  0:00:24\n",
      "Got a total of 54917200 fold changes\n",
      "Median elements [27458600]\n",
      "Median elements\n",
      "Global Median 0.64714\n",
      "2020-09-30 23:01:10,064  SpectraRegion  INFO: dimensions inputarray: 17900\n",
      "2020-09-30 23:01:10,065  SpectraRegion  INFO: Creating C++ obj\n",
      "2020-09-30 23:01:10,066  SpectraRegion  INFO: 17900 (52, 59, 17900)\n",
      "2020-09-30 23:01:10,066  SpectraRegion  INFO: dimensions 17900\n",
      "2020-09-30 23:01:10,066  SpectraRegion  INFO: input dimensions (52, 59, 17900)\n",
      "2020-09-30 23:01:10,067  SpectraRegion  INFO: Switching to dot mode\n",
      "2020-09-30 23:01:10,148  SpectraRegion  INFO: Starting calc similarity c++\n",
      "2020-09-30 23:02:17,213  SpectraRegion  INFO: outclust dimensions (3068, 3068)\n",
      "2020-09-30 23:02:17,215  SpectraRegion  INFO: Calculating spectra similarity\n",
      "2020-09-30 23:02:17,337  SpectraRegion  INFO: Calculating spectra similarity done\n",
      "2020-09-30 23:02:17,338  SpectraRegion  INFO: Calculating dist pixel map\n",
      "2020-09-30 23:03:06,611  SpectraRegion  INFO: Calculating dist pixel map done\n",
      "2020-09-30 23:03:06,647  SpectraRegion  INFO: Calculating clusters\n",
      "2020-09-30 23:03:06,866  SpectraRegion  INFO: Calculating clusters done\n",
      "2020-09-30 23:03:06,885  SpectraRegion  INFO: Calculating clusters saved\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-30 23:03:06,991  SpectraRegion  INFO: Assigning clusters to background: {1, 2, 3, 4, 5, 6, 7, 8, 13}\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-30 23:03:07,090  SpectraRegion  INFO: DE data for case: [15]\n",
      "2020-09-30 23:03:07,090  SpectraRegion  INFO: DE data for control: [12, 11, 14, 10, 9]\n",
      "2020-09-30 23:03:07,091  SpectraRegion  INFO: Running [15] against [12, 11, 14, 10, 9]\n",
      "2020-09-30 23:03:07,092  SpectraRegion  INFO: DE result key: ((15,), (9, 10, 11, 12, 14))\n",
      "2020-09-30 23:03:07,187  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 253)\n",
      "2020-09-30 23:03:07,189  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (253, 3)\n",
      "2020-09-30 23:03:07,192  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 23:03:07,287  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((15,), (9, 10, 11, 12, 14))\n",
      "2020-09-30 23:03:07,288  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 23:03:10,759  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((15,), (9, 10, 11, 12, 14))\n",
      "2020-09-30 23:03:10,762  SpectraRegion  INFO: DE result for case ((15,), (9, 10, 11, 12, 14)) with (17900, 7) results\n",
      "2020-09-30 23:03:10,763  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:03:10,766  SpectraRegion  INFO: DE result for case ((15,), (9, 10, 11, 12, 14)) with (367, 7) results (filtered)\n",
      "2020-09-30 23:03:10,778  SpectraRegion  INFO: Created matrices with shape (98, 17900) and (155, 17900) (target, bg)\n",
      "2020-09-30 23:03:11,526  SpectraRegion  INFO: DE result for case ((15,), (9, 10, 11, 12, 14)) with (17900, 7) results\n",
      "2020-09-30 23:03:11,526  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:03:11,529  SpectraRegion  INFO: DE result for case ((15,), (9, 10, 11, 12, 14)) with (365, 7) results (filtered)\n",
      "2020-09-30 23:03:11,541  SpectraRegion  INFO: Created matrices with shape (98, 17900) and (155, 17900) (target, bg)\n",
      "2020-09-30 23:03:12,289  SpectraRegion  INFO: DE data for case: [12]\n",
      "2020-09-30 23:03:12,290  SpectraRegion  INFO: DE data for control: [15, 11, 14, 10, 9]\n",
      "2020-09-30 23:03:12,290  SpectraRegion  INFO: Running [12] against [15, 11, 14, 10, 9]\n",
      "2020-09-30 23:03:12,291  SpectraRegion  INFO: DE result key: ((12,), (9, 10, 11, 14, 15))\n",
      "2020-09-30 23:03:12,381  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 253)\n",
      "2020-09-30 23:03:12,383  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (253, 3)\n",
      "2020-09-30 23:03:12,387  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 23:03:12,481  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((12,), (9, 10, 11, 14, 15))\n",
      "2020-09-30 23:03:12,482  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 23:03:15,982  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((12,), (9, 10, 11, 14, 15))\n",
      "2020-09-30 23:03:15,984  SpectraRegion  INFO: DE result for case ((12,), (9, 10, 11, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:03:15,984  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:03:15,987  SpectraRegion  INFO: DE result for case ((12,), (9, 10, 11, 14, 15)) with (293, 7) results (filtered)\n",
      "2020-09-30 23:03:15,998  SpectraRegion  INFO: Created matrices with shape (33, 17900) and (220, 17900) (target, bg)\n",
      "2020-09-30 23:03:16,291  SpectraRegion  INFO: DE result for case ((12,), (9, 10, 11, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:03:16,292  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:03:16,295  SpectraRegion  INFO: DE result for case ((12,), (9, 10, 11, 14, 15)) with (223, 7) results (filtered)\n",
      "2020-09-30 23:03:16,306  SpectraRegion  INFO: Created matrices with shape (33, 17900) and (220, 17900) (target, bg)\n",
      "2020-09-30 23:03:16,505  SpectraRegion  INFO: DE data for case: [11]\n",
      "2020-09-30 23:03:16,506  SpectraRegion  INFO: DE data for control: [15, 12, 14, 10, 9]\n",
      "2020-09-30 23:03:16,506  SpectraRegion  INFO: Running [11] against [15, 12, 14, 10, 9]\n",
      "2020-09-30 23:03:16,507  SpectraRegion  INFO: DE result key: ((11,), (9, 10, 12, 14, 15))\n",
      "2020-09-30 23:03:16,595  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 253)\n",
      "2020-09-30 23:03:16,598  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (253, 3)\n",
      "2020-09-30 23:03:16,602  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 23:03:16,703  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((11,), (9, 10, 12, 14, 15))\n",
      "2020-09-30 23:03:16,704  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 23:03:20,167  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((11,), (9, 10, 12, 14, 15))\n",
      "2020-09-30 23:03:20,169  SpectraRegion  INFO: DE result for case ((11,), (9, 10, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:03:20,170  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:03:20,174  SpectraRegion  INFO: DE result for case ((11,), (9, 10, 12, 14, 15)) with (2113, 7) results (filtered)\n",
      "2020-09-30 23:03:20,185  SpectraRegion  INFO: Created matrices with shape (24, 17900) and (229, 17900) (target, bg)\n",
      "2020-09-30 23:03:25,540  SpectraRegion  INFO: DE result for case ((11,), (9, 10, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:03:25,541  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:03:25,544  SpectraRegion  INFO: DE result for case ((11,), (9, 10, 12, 14, 15)) with (1869, 7) results (filtered)\n",
      "2020-09-30 23:03:25,554  SpectraRegion  INFO: Created matrices with shape (24, 17900) and (229, 17900) (target, bg)\n",
      "2020-09-30 23:03:30,401  SpectraRegion  INFO: DE data for case: [14]\n",
      "2020-09-30 23:03:30,402  SpectraRegion  INFO: DE data for control: [15, 12, 11, 10, 9]\n",
      "2020-09-30 23:03:30,402  SpectraRegion  INFO: Running [14] against [15, 12, 11, 10, 9]\n",
      "2020-09-30 23:03:30,403  SpectraRegion  INFO: DE result key: ((14,), (9, 10, 11, 12, 15))\n",
      "2020-09-30 23:03:30,494  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 253)\n",
      "2020-09-30 23:03:30,496  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (253, 3)\n",
      "2020-09-30 23:03:30,501  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 23:03:30,603  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((14,), (9, 10, 11, 12, 15))\n",
      "2020-09-30 23:03:30,604  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 23:03:34,078  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((14,), (9, 10, 11, 12, 15))\n",
      "2020-09-30 23:03:34,080  SpectraRegion  INFO: DE result for case ((14,), (9, 10, 11, 12, 15)) with (17900, 7) results\n",
      "2020-09-30 23:03:34,080  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:03:34,084  SpectraRegion  INFO: DE result for case ((14,), (9, 10, 11, 12, 15)) with (952, 7) results (filtered)\n",
      "2020-09-30 23:03:34,096  SpectraRegion  INFO: Created matrices with shape (28, 17900) and (225, 17900) (target, bg)\n",
      "2020-09-30 23:03:36,481  SpectraRegion  INFO: DE result for case ((14,), (9, 10, 11, 12, 15)) with (17900, 7) results\n",
      "2020-09-30 23:03:36,482  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:03:36,485  SpectraRegion  INFO: DE result for case ((14,), (9, 10, 11, 12, 15)) with (854, 7) results (filtered)\n",
      "2020-09-30 23:03:36,498  SpectraRegion  INFO: Created matrices with shape (28, 17900) and (225, 17900) (target, bg)\n",
      "2020-09-30 23:03:38,597  SpectraRegion  INFO: DE data for case: [10]\n",
      "2020-09-30 23:03:38,598  SpectraRegion  INFO: DE data for control: [15, 12, 11, 14, 9]\n",
      "2020-09-30 23:03:38,599  SpectraRegion  INFO: Running [10] against [15, 12, 11, 14, 9]\n",
      "2020-09-30 23:03:38,599  SpectraRegion  INFO: DE result key: ((10,), (9, 11, 12, 14, 15))\n",
      "2020-09-30 23:03:38,688  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 253)\n",
      "2020-09-30 23:03:38,690  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (253, 3)\n",
      "2020-09-30 23:03:38,694  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 23:03:38,795  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((10,), (9, 11, 12, 14, 15))\n",
      "2020-09-30 23:03:38,796  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 23:03:42,277  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((10,), (9, 11, 12, 14, 15))\n",
      "2020-09-30 23:03:42,279  SpectraRegion  INFO: DE result for case ((10,), (9, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:03:42,280  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:03:42,283  SpectraRegion  INFO: DE result for case ((10,), (9, 11, 12, 14, 15)) with (536, 7) results (filtered)\n",
      "2020-09-30 23:03:42,294  SpectraRegion  INFO: Created matrices with shape (36, 17900) and (217, 17900) (target, bg)\n",
      "2020-09-30 23:03:43,513  SpectraRegion  INFO: DE result for case ((10,), (9, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:03:43,514  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:03:43,517  SpectraRegion  INFO: DE result for case ((10,), (9, 11, 12, 14, 15)) with (540, 7) results (filtered)\n",
      "2020-09-30 23:03:43,528  SpectraRegion  INFO: Created matrices with shape (36, 17900) and (217, 17900) (target, bg)\n",
      "2020-09-30 23:03:44,758  SpectraRegion  INFO: DE data for case: [9]\n",
      "2020-09-30 23:03:44,758  SpectraRegion  INFO: DE data for control: [15, 12, 11, 14, 10]\n",
      "2020-09-30 23:03:44,759  SpectraRegion  INFO: Running [9] against [15, 12, 11, 14, 10]\n",
      "2020-09-30 23:03:44,759  SpectraRegion  INFO: DE result key: ((9,), (10, 11, 12, 14, 15))\n",
      "2020-09-30 23:03:44,847  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 253)\n",
      "2020-09-30 23:03:44,849  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (253, 3)\n",
      "2020-09-30 23:03:44,853  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 23:03:44,956  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((9,), (10, 11, 12, 14, 15))\n",
      "2020-09-30 23:03:44,956  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 23:03:48,437  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((9,), (10, 11, 12, 14, 15))\n",
      "2020-09-30 23:03:48,440  SpectraRegion  INFO: DE result for case ((9,), (10, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:03:48,440  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:03:48,443  SpectraRegion  INFO: DE result for case ((9,), (10, 11, 12, 14, 15)) with (1249, 7) results (filtered)\n",
      "2020-09-30 23:03:48,456  SpectraRegion  INFO: Created matrices with shape (34, 17900) and (219, 17900) (target, bg)\n",
      "2020-09-30 23:03:51,827  SpectraRegion  INFO: DE result for case ((9,), (10, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:03:51,828  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:03:51,831  SpectraRegion  INFO: DE result for case ((9,), (10, 11, 12, 14, 15)) with (1222, 7) results (filtered)\n",
      "2020-09-30 23:03:51,842  SpectraRegion  INFO: Created matrices with shape (34, 17900) and (219, 17900) (target, bg)\n",
      "2020-09-30 23:03:55,113  SpectraRegion  INFO: DE data for case: [0]\n",
      "2020-09-30 23:03:55,114  SpectraRegion  INFO: DE data for control: [15, 12, 11, 14, 10, 9]\n",
      "2020-09-30 23:03:55,114  SpectraRegion  INFO: Running [0] against [15, 12, 11, 14, 10, 9]\n",
      "2020-09-30 23:03:55,115  SpectraRegion  INFO: DE result key: ((0,), (9, 10, 11, 12, 14, 15))\n",
      "2020-09-30 23:03:59,833  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 3068)\n",
      "2020-09-30 23:03:59,836  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (3068, 3)\n",
      "2020-09-30 23:03:59,840  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 23:04:01,337  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((0,), (9, 10, 11, 12, 14, 15))\n",
      "2020-09-30 23:04:01,338  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 23:04:12,758  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((0,), (9, 10, 11, 12, 14, 15))\n",
      "2020-09-30 23:04:12,761  SpectraRegion  INFO: DE result for case ((0,), (9, 10, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:04:12,762  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:04:12,765  SpectraRegion  INFO: DE result for case ((0,), (9, 10, 11, 12, 14, 15)) with (1125, 7) results (filtered)\n",
      "2020-09-30 23:04:12,842  SpectraRegion  INFO: Created matrices with shape (2815, 17900) and (253, 17900) (target, bg)\n",
      "2020-09-30 23:04:15,510  SpectraRegion  INFO: DE result for case ((0,), (9, 10, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:04:15,511  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:04:15,513  SpectraRegion  INFO: DE result for case ((0,), (9, 10, 11, 12, 14, 15)) with (1125, 7) results (filtered)\n",
      "2020-09-30 23:04:15,591  SpectraRegion  INFO: Created matrices with shape (2815, 17900) and (253, 17900) (target, bg)\n",
      "2020-09-30 23:04:18,172  SpectraRegion  INFO: DE data for case: [15]\n",
      "2020-09-30 23:04:18,172  SpectraRegion  INFO: DE data for control: [0, 12, 11, 14, 10, 9]\n",
      "2020-09-30 23:04:18,173  SpectraRegion  INFO: Running [15] against [0, 12, 11, 14, 10, 9]\n",
      "2020-09-30 23:04:18,173  SpectraRegion  INFO: DE result key: ((15,), (0, 9, 10, 11, 12, 14))\n",
      "2020-09-30 23:04:23,163  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 3068)\n",
      "2020-09-30 23:04:23,166  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (3068, 3)\n",
      "2020-09-30 23:04:23,171  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 23:04:24,603  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((15,), (0, 9, 10, 11, 12, 14))\n",
      "2020-09-30 23:04:24,604  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 23:04:36,233  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((15,), (0, 9, 10, 11, 12, 14))\n",
      "2020-09-30 23:04:36,237  SpectraRegion  INFO: DE result for case ((15,), (0, 9, 10, 11, 12, 14)) with (17900, 7) results\n",
      "2020-09-30 23:04:36,238  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:04:36,241  SpectraRegion  INFO: DE result for case ((15,), (0, 9, 10, 11, 12, 14)) with (863, 7) results (filtered)\n",
      "2020-09-30 23:04:36,320  SpectraRegion  INFO: Created matrices with shape (98, 17900) and (2970, 17900) (target, bg)\n",
      "2020-09-30 23:04:38,420  SpectraRegion  INFO: DE result for case ((15,), (0, 9, 10, 11, 12, 14)) with (17900, 7) results\n",
      "2020-09-30 23:04:38,420  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:04:38,423  SpectraRegion  INFO: DE result for case ((15,), (0, 9, 10, 11, 12, 14)) with (863, 7) results (filtered)\n",
      "2020-09-30 23:04:38,505  SpectraRegion  INFO: Created matrices with shape (98, 17900) and (2970, 17900) (target, bg)\n",
      "2020-09-30 23:04:40,574  SpectraRegion  INFO: DE data for case: [12]\n",
      "2020-09-30 23:04:40,575  SpectraRegion  INFO: DE data for control: [0, 15, 11, 14, 10, 9]\n",
      "2020-09-30 23:04:40,576  SpectraRegion  INFO: Running [12] against [0, 15, 11, 14, 10, 9]\n",
      "2020-09-30 23:04:40,577  SpectraRegion  INFO: DE result key: ((12,), (0, 9, 10, 11, 14, 15))\n",
      "2020-09-30 23:04:45,346  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 3068)\n",
      "2020-09-30 23:04:45,350  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (3068, 3)\n",
      "2020-09-30 23:04:45,355  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 23:04:46,839  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((12,), (0, 9, 10, 11, 14, 15))\n",
      "2020-09-30 23:04:46,840  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 23:04:58,758  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((12,), (0, 9, 10, 11, 14, 15))\n",
      "2020-09-30 23:04:58,763  SpectraRegion  INFO: DE result for case ((12,), (0, 9, 10, 11, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:04:58,764  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:04:58,766  SpectraRegion  INFO: DE result for case ((12,), (0, 9, 10, 11, 14, 15)) with (856, 7) results (filtered)\n",
      "2020-09-30 23:04:58,854  SpectraRegion  INFO: Created matrices with shape (33, 17900) and (3035, 17900) (target, bg)\n",
      "2020-09-30 23:05:01,063  SpectraRegion  INFO: DE result for case ((12,), (0, 9, 10, 11, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:05:01,064  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:05:01,067  SpectraRegion  INFO: DE result for case ((12,), (0, 9, 10, 11, 14, 15)) with (855, 7) results (filtered)\n",
      "2020-09-30 23:05:01,152  SpectraRegion  INFO: Created matrices with shape (33, 17900) and (3035, 17900) (target, bg)\n",
      "2020-09-30 23:05:03,372  SpectraRegion  INFO: DE data for case: [11]\n",
      "2020-09-30 23:05:03,373  SpectraRegion  INFO: DE data for control: [0, 15, 12, 14, 10, 9]\n",
      "2020-09-30 23:05:03,374  SpectraRegion  INFO: Running [11] against [0, 15, 12, 14, 10, 9]\n",
      "2020-09-30 23:05:03,374  SpectraRegion  INFO: DE result key: ((11,), (0, 9, 10, 12, 14, 15))\n",
      "2020-09-30 23:05:08,311  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 3068)\n",
      "2020-09-30 23:05:08,315  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (3068, 3)\n",
      "2020-09-30 23:05:08,319  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 23:05:09,696  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((11,), (0, 9, 10, 12, 14, 15))\n",
      "2020-09-30 23:05:09,697  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 23:05:21,420  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((11,), (0, 9, 10, 12, 14, 15))\n",
      "2020-09-30 23:05:21,424  SpectraRegion  INFO: DE result for case ((11,), (0, 9, 10, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:05:21,425  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:05:21,429  SpectraRegion  INFO: DE result for case ((11,), (0, 9, 10, 12, 14, 15)) with (2583, 7) results (filtered)\n",
      "2020-09-30 23:05:21,507  SpectraRegion  INFO: Created matrices with shape (24, 17900) and (3044, 17900) (target, bg)\n",
      "2020-09-30 23:05:30,515  SpectraRegion  INFO: DE result for case ((11,), (0, 9, 10, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:05:30,516  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:05:30,520  SpectraRegion  INFO: DE result for case ((11,), (0, 9, 10, 12, 14, 15)) with (2542, 7) results (filtered)\n",
      "2020-09-30 23:05:30,598  SpectraRegion  INFO: Created matrices with shape (24, 17900) and (3044, 17900) (target, bg)\n",
      "2020-09-30 23:05:39,484  SpectraRegion  INFO: DE data for case: [14]\n",
      "2020-09-30 23:05:39,484  SpectraRegion  INFO: DE data for control: [0, 15, 12, 11, 10, 9]\n",
      "2020-09-30 23:05:39,485  SpectraRegion  INFO: Running [14] against [0, 15, 12, 11, 10, 9]\n",
      "2020-09-30 23:05:39,485  SpectraRegion  INFO: DE result key: ((14,), (0, 9, 10, 11, 12, 15))\n",
      "2020-09-30 23:05:44,160  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 3068)\n",
      "2020-09-30 23:05:44,164  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (3068, 3)\n",
      "2020-09-30 23:05:44,170  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 23:05:45,547  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((14,), (0, 9, 10, 11, 12, 15))\n",
      "2020-09-30 23:05:45,548  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 23:05:57,475  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((14,), (0, 9, 10, 11, 12, 15))\n",
      "2020-09-30 23:05:57,479  SpectraRegion  INFO: DE result for case ((14,), (0, 9, 10, 11, 12, 15)) with (17900, 7) results\n",
      "2020-09-30 23:05:57,480  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:05:57,483  SpectraRegion  INFO: DE result for case ((14,), (0, 9, 10, 11, 12, 15)) with (2209, 7) results (filtered)\n",
      "2020-09-30 23:05:57,560  SpectraRegion  INFO: Created matrices with shape (28, 17900) and (3040, 17900) (target, bg)\n",
      "2020-09-30 23:06:03,976  SpectraRegion  INFO: DE result for case ((14,), (0, 9, 10, 11, 12, 15)) with (17900, 7) results\n",
      "2020-09-30 23:06:03,977  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:06:03,981  SpectraRegion  INFO: DE result for case ((14,), (0, 9, 10, 11, 12, 15)) with (2219, 7) results (filtered)\n",
      "2020-09-30 23:06:04,074  SpectraRegion  INFO: Created matrices with shape (28, 17900) and (3040, 17900) (target, bg)\n",
      "2020-09-30 23:06:10,288  SpectraRegion  INFO: DE data for case: [10]\n",
      "2020-09-30 23:06:10,289  SpectraRegion  INFO: DE data for control: [0, 15, 12, 11, 14, 9]\n",
      "2020-09-30 23:06:10,289  SpectraRegion  INFO: Running [10] against [0, 15, 12, 11, 14, 9]\n",
      "2020-09-30 23:06:10,290  SpectraRegion  INFO: DE result key: ((10,), (0, 9, 11, 12, 14, 15))\n",
      "2020-09-30 23:06:15,066  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 3068)\n",
      "2020-09-30 23:06:15,070  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (3068, 3)\n",
      "2020-09-30 23:06:15,075  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 23:06:16,456  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((10,), (0, 9, 11, 12, 14, 15))\n",
      "2020-09-30 23:06:16,457  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 23:06:28,151  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((10,), (0, 9, 11, 12, 14, 15))\n",
      "2020-09-30 23:06:28,155  SpectraRegion  INFO: DE result for case ((10,), (0, 9, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:06:28,155  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:06:28,159  SpectraRegion  INFO: DE result for case ((10,), (0, 9, 11, 12, 14, 15)) with (1799, 7) results (filtered)\n",
      "2020-09-30 23:06:28,236  SpectraRegion  INFO: Created matrices with shape (36, 17900) and (3032, 17900) (target, bg)\n",
      "2020-09-30 23:06:32,655  SpectraRegion  INFO: DE result for case ((10,), (0, 9, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:06:32,655  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:06:32,659  SpectraRegion  INFO: DE result for case ((10,), (0, 9, 11, 12, 14, 15)) with (1807, 7) results (filtered)\n",
      "2020-09-30 23:06:32,735  SpectraRegion  INFO: Created matrices with shape (36, 17900) and (3032, 17900) (target, bg)\n",
      "2020-09-30 23:06:37,083  SpectraRegion  INFO: DE data for case: [9]\n",
      "2020-09-30 23:06:37,084  SpectraRegion  INFO: DE data for control: [0, 15, 12, 11, 14, 10]\n",
      "2020-09-30 23:06:37,085  SpectraRegion  INFO: Running [9] against [0, 15, 12, 11, 14, 10]\n",
      "2020-09-30 23:06:37,086  SpectraRegion  INFO: DE result key: ((9,), (0, 10, 11, 12, 14, 15))\n",
      "2020-09-30 23:06:41,784  SpectraRegion  INFO: DE DataFrame ready. Shape (17900, 3068)\n",
      "2020-09-30 23:06:41,789  SpectraRegion  INFO: DE Sample DataFrame ready. Shape (3068, 3)\n",
      "2020-09-30 23:06:41,793  SpectraRegion  INFO: Performing DE-test: ttest\n",
      "2020-09-30 23:06:43,204  SpectraRegion  INFO: DE-test (ttest) finished. Results available: ((9,), (0, 10, 11, 12, 14, 15))\n",
      "2020-09-30 23:06:43,205  SpectraRegion  INFO: Performing DE-test: rank\n",
      "2020-09-30 23:06:54,998  SpectraRegion  INFO: DE-test (rank) finished. Results available: ((9,), (0, 10, 11, 12, 14, 15))\n",
      "2020-09-30 23:06:55,002  SpectraRegion  INFO: DE result for case ((9,), (0, 10, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:06:55,003  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:06:55,007  SpectraRegion  INFO: DE result for case ((9,), (0, 10, 11, 12, 14, 15)) with (1932, 7) results (filtered)\n",
      "2020-09-30 23:06:55,087  SpectraRegion  INFO: Created matrices with shape (34, 17900) and (3034, 17900) (target, bg)\n",
      "2020-09-30 23:07:00,317  SpectraRegion  INFO: DE result for case ((9,), (0, 10, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:07:00,318  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:07:00,321  SpectraRegion  INFO: DE result for case ((9,), (0, 10, 11, 12, 14, 15)) with (1937, 7) results (filtered)\n",
      "2020-09-30 23:07:00,405  SpectraRegion  INFO: Created matrices with shape (34, 17900) and (3034, 17900) (target, bg)\n"
     ]
    }
   ],
   "source": [
    "slided_4, slided4_mgenes, slided4_mgenes_bg = process_imzeregion(imze=imze, regionID= 4, basename=\"slideD\")\n",
    "slided_5, slided5_mgenes, slided5_mgenes_bg = process_imzeregion(imze=imze, regionID= 5, basename=\"slideD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loggers = [logging.getLogger(name) for name in logging.root.manager.loggerDict]\n",
    "#for logger in loggers:\n",
    "#    logger.setLevel(logging.INFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "markerGenes0 = slided0_mgenes[\"ttest\"]\n",
    "markerGenes0.to_csv(\"deresults/marker_genes_region_0.tsv\", sep=\"\\t\", index=False)\n",
    "markerGenes1 = slided1_mgenes[\"ttest\"]\n",
    "markerGenes1.to_csv(\"deresults/marker_genes_region_1.tsv\", sep=\"\\t\", index=False)\n",
    "markerGenes4 = slided4_mgenes[\"ttest\"]\n",
    "markerGenes4.to_csv(\"deresults/marker_genes_region_4.tsv\", sep=\"\\t\", index=False)\n",
    "markerGenes5 = slided5_mgenes[\"ttest\"]\n",
    "markerGenes5.to_csv(\"deresults/marker_genes_region_5.tsv\", sep=\"\\t\", index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting number of predictions to 1\n",
      "Taking value gene from gene\n",
      "Taking value cluster from clusterID\n",
      "Taking value logfc from avg_logFC\n",
      "Taking value pvaladj from qvalue\n",
      "Taking value expr-mean from mean\n",
      "Taking value expressing-cell-count from num\n",
      "Taking value cluster-cell-count from anum\n",
      "Got 9 clusters.\n",
      "Starting analysis\n",
      "Loaded Databases\n",
      "known genes 38\n",
      "known (celltype, organ) 50\n",
      "Setting number of predictions to 1\n",
      "Taking value gene from gene\n",
      "Taking value cluster from clusterID\n",
      "Taking value logfc from avg_logFC\n",
      "Taking value pvaladj from qvalue\n",
      "Taking value expr-mean from mean\n",
      "Taking value expressing-cell-count from num\n",
      "Taking value cluster-cell-count from anum\n",
      "Got 7 clusters.\n",
      "Starting analysis\n",
      "Loaded Databases\n",
      "known genes 26\n",
      "known (celltype, organ) 41\n",
      "Setting number of predictions to 1\n",
      "Taking value gene from gene\n",
      "Taking value cluster from clusterID\n",
      "Taking value logfc from avg_logFC\n",
      "Taking value pvaladj from qvalue\n",
      "Taking value expr-mean from mean\n",
      "Taking value expressing-cell-count from num\n",
      "Taking value cluster-cell-count from anum\n",
      "Got 9 clusters.\n",
      "Starting analysis\n",
      "Loaded Databases\n",
      "known genes 33\n",
      "known (celltype, organ) 47\n",
      "Setting number of predictions to 1\n",
      "Taking value gene from gene\n",
      "Taking value cluster from clusterID\n",
      "Taking value logfc from avg_logFC\n",
      "Taking value pvaladj from qvalue\n",
      "Taking value expr-mean from mean\n",
      "Taking value expressing-cell-count from num\n",
      "Taking value cluster-cell-count from anum\n",
      "Got 6 clusters.\n",
      "Starting analysis\n",
      "Loaded Databases\n",
      "known genes 48\n",
      "known (celltype, organ) 54\n"
     ]
    }
   ],
   "source": [
    "! python3 analyseMarkers.py --organs \"Connective tissue\" \"Vasculature\" \"Heart\" \"Skeletal Muscle\" \"Smooth muscle\" \"Immune system\" \"Blood\" \"Epithelium\" --pvaladj qvalue --markers deresults/marker_genes_region_0.tsv --aorta3d --output deresults/marker_genes_region_0.pred.tsv \n",
    "! python3 analyseMarkers.py --organs \"Connective tissue\" \"Vasculature\" \"Heart\" \"Skeletal Muscle\" \"Smooth muscle\" \"Immune system\" \"Blood\" \"Epithelium\" --pvaladj qvalue --markers deresults/marker_genes_region_1.tsv --aorta3d --output deresults/marker_genes_region_1.pred.tsv \n",
    "! python3 analyseMarkers.py --organs \"Connective tissue\" \"Vasculature\" \"Heart\" \"Skeletal Muscle\" \"Smooth muscle\" \"Immune system\" \"Blood\" \"Epithelium\" --pvaladj qvalue --markers deresults/marker_genes_region_4.tsv --aorta3d --output deresults/marker_genes_region_4.pred.tsv \n",
    "! python3 analyseMarkers.py --organs \"Connective tissue\" \"Vasculature\" \"Heart\" \"Skeletal Muscle\" \"Smooth muscle\" \"Immune system\" \"Blood\" \"Epithelium\" --pvaladj qvalue --markers deresults/marker_genes_region_5.tsv --aorta3d --output deresults/marker_genes_region_5.pred.tsv "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-30 23:37:25,306  SpectraRegion  INFO: Cell-type assigned: 1 -> Monocytes;Immune system\n",
      "2020-09-30 23:37:25,307  SpectraRegion  INFO: Cell-type assigned: 2 -> Megakaryocytes;Immune system\n",
      "2020-09-30 23:37:25,308  SpectraRegion  INFO: Cell-type assigned: 4 -> Mast cells;Immune system\n",
      "2020-09-30 23:37:25,308  SpectraRegion  INFO: Cell-type assigned: 5 -> Monocytes;Immune system\n",
      "2020-09-30 23:37:25,309  SpectraRegion  INFO: Cell-type assigned: 6 -> Monocytes;Immune system\n",
      "2020-09-30 23:37:25,309  SpectraRegion  INFO: Cell-type assigned: 7 -> Mast cells;Immune system\n",
      "2020-09-30 23:37:25,309  SpectraRegion  INFO: Cell-type assigned: 8 -> Mast cells;Immune system\n",
      "2020-09-30 23:37:25,310  SpectraRegion  INFO: Cell-type assigned: 9 -> Smooth muscle cells;Smooth muscle\n",
      "2020-09-30 23:37:25,319  SpectraRegion  INFO: DE data for case: [0]\n",
      "2020-09-30 23:37:25,320  SpectraRegion  INFO: DE data for control: [5, 7, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 23:37:25,320  SpectraRegion  INFO: Running [0] against [5, 7, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 23:37:25,321  SpectraRegion  INFO: DE result key: ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 23:37:25,321  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:37:25,324  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 23:37:25,324  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:37:25,327  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9)) with (1307, 7) results (filtered)\n",
      "2020-09-30 23:37:25,411  SpectraRegion  INFO: Created matrices with shape (1930, 17900) and (725, 17900) (target, bg)\n",
      "2020-09-30 23:37:29,070  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 23:37:29,071  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:37:29,074  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9)) with (1307, 7) results (filtered)\n",
      "2020-09-30 23:37:29,131  SpectraRegion  INFO: Created matrices with shape (1930, 17900) and (725, 17900) (target, bg)\n",
      "2020-09-30 23:37:32,660  SpectraRegion  INFO: DE data for case: [5]\n",
      "2020-09-30 23:37:32,661  SpectraRegion  INFO: DE data for control: [0, 7, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 23:37:32,661  SpectraRegion  INFO: Running [5] against [0, 7, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 23:37:32,662  SpectraRegion  INFO: DE result key: ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9))\n",
      "2020-09-30 23:37:32,662  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:37:32,664  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 23:37:32,665  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:37:32,667  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9)) with (450, 7) results (filtered)\n",
      "2020-09-30 23:37:32,725  SpectraRegion  INFO: Created matrices with shape (100, 17900) and (2555, 17900) (target, bg)\n",
      "2020-09-30 23:37:34,183  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 23:37:34,184  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:37:34,187  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9)) with (450, 7) results (filtered)\n",
      "2020-09-30 23:37:34,243  SpectraRegion  INFO: Created matrices with shape (100, 17900) and (2555, 17900) (target, bg)\n",
      "2020-09-30 23:37:35,675  SpectraRegion  INFO: DE data for case: [7]\n",
      "2020-09-30 23:37:35,676  SpectraRegion  INFO: DE data for control: [0, 5, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 23:37:35,676  SpectraRegion  INFO: Running [7] against [0, 5, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 23:37:35,676  SpectraRegion  INFO: DE result key: ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9))\n",
      "2020-09-30 23:37:35,677  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:37:35,679  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 23:37:35,679  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:37:35,682  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9)) with (680, 7) results (filtered)\n",
      "2020-09-30 23:37:35,741  SpectraRegion  INFO: Created matrices with shape (161, 17900) and (2494, 17900) (target, bg)\n",
      "2020-09-30 23:37:37,578  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 23:37:37,579  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:37:37,581  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9)) with (680, 7) results (filtered)\n",
      "2020-09-30 23:37:37,639  SpectraRegion  INFO: Created matrices with shape (161, 17900) and (2494, 17900) (target, bg)\n",
      "2020-09-30 23:37:39,489  SpectraRegion  INFO: DE data for case: [9]\n",
      "2020-09-30 23:37:39,490  SpectraRegion  INFO: DE data for control: [0, 5, 7, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 23:37:39,490  SpectraRegion  INFO: Running [9] against [0, 5, 7, 3, 8, 2, 4, 6, 1]\n",
      "2020-09-30 23:37:39,491  SpectraRegion  INFO: DE result key: ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8))\n",
      "2020-09-30 23:37:39,491  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:37:39,493  SpectraRegion  INFO: DE result for case ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8)) with (17900, 7) results\n",
      "2020-09-30 23:37:39,493  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:37:39,496  SpectraRegion  INFO: DE result for case ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8)) with (88, 7) results (filtered)\n",
      "2020-09-30 23:37:39,553  SpectraRegion  INFO: Created matrices with shape (89, 17900) and (2566, 17900) (target, bg)\n",
      "2020-09-30 23:37:39,635  SpectraRegion  INFO: DE result for case ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8)) with (17900, 7) results\n",
      "2020-09-30 23:37:39,635  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:37:39,638  SpectraRegion  INFO: DE result for case ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8)) with (65, 7) results (filtered)\n",
      "2020-09-30 23:37:39,694  SpectraRegion  INFO: Created matrices with shape (89, 17900) and (2566, 17900) (target, bg)\n",
      "2020-09-30 23:37:39,752  SpectraRegion  INFO: DE data for case: [3]\n",
      "2020-09-30 23:37:39,753  SpectraRegion  INFO: DE data for control: [0, 5, 7, 9, 8, 2, 4, 6, 1]\n",
      "2020-09-30 23:37:39,754  SpectraRegion  INFO: Running [3] against [0, 5, 7, 9, 8, 2, 4, 6, 1]\n",
      "2020-09-30 23:37:39,754  SpectraRegion  INFO: DE result key: ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 23:37:39,754  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:37:39,757  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 23:37:39,757  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:37:39,760  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9)) with (1263, 7) results (filtered)\n",
      "2020-09-30 23:37:39,816  SpectraRegion  INFO: Created matrices with shape (51, 17900) and (2604, 17900) (target, bg)\n",
      "2020-09-30 23:37:43,327  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 23:37:43,328  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:37:43,331  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9)) with (1261, 7) results (filtered)\n",
      "2020-09-30 23:37:43,387  SpectraRegion  INFO: Created matrices with shape (51, 17900) and (2604, 17900) (target, bg)\n",
      "2020-09-30 23:37:46,934  SpectraRegion  INFO: DE data for case: [8]\n",
      "2020-09-30 23:37:46,935  SpectraRegion  INFO: DE data for control: [0, 5, 7, 9, 3, 2, 4, 6, 1]\n",
      "2020-09-30 23:37:46,935  SpectraRegion  INFO: Running [8] against [0, 5, 7, 9, 3, 2, 4, 6, 1]\n",
      "2020-09-30 23:37:46,936  SpectraRegion  INFO: DE result key: ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9))\n",
      "2020-09-30 23:37:46,937  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:37:46,938  SpectraRegion  INFO: DE result for case ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9)) with (17900, 7) results\n",
      "2020-09-30 23:37:46,939  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:37:46,941  SpectraRegion  INFO: DE result for case ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9)) with (253, 7) results (filtered)\n",
      "2020-09-30 23:37:47,002  SpectraRegion  INFO: Created matrices with shape (55, 17900) and (2600, 17900) (target, bg)\n",
      "2020-09-30 23:37:47,468  SpectraRegion  INFO: DE result for case ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9)) with (17900, 7) results\n",
      "2020-09-30 23:37:47,469  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:37:47,471  SpectraRegion  INFO: DE result for case ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9)) with (250, 7) results (filtered)\n",
      "2020-09-30 23:37:47,533  SpectraRegion  INFO: Created matrices with shape (55, 17900) and (2600, 17900) (target, bg)\n",
      "2020-09-30 23:37:47,974  SpectraRegion  INFO: DE data for case: [2]\n",
      "2020-09-30 23:37:47,975  SpectraRegion  INFO: DE data for control: [0, 5, 7, 9, 3, 8, 4, 6, 1]\n",
      "2020-09-30 23:37:47,975  SpectraRegion  INFO: Running [2] against [0, 5, 7, 9, 3, 8, 4, 6, 1]\n",
      "2020-09-30 23:37:47,976  SpectraRegion  INFO: DE result key: ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 23:37:47,976  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:37:47,978  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 23:37:47,979  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:37:47,982  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9)) with (1796, 7) results (filtered)\n",
      "2020-09-30 23:37:48,044  SpectraRegion  INFO: Created matrices with shape (150, 17900) and (2505, 17900) (target, bg)\n",
      "2020-09-30 23:37:53,614  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 23:37:53,614  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:37:53,617  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9)) with (1796, 7) results (filtered)\n",
      "2020-09-30 23:37:53,674  SpectraRegion  INFO: Created matrices with shape (150, 17900) and (2505, 17900) (target, bg)\n",
      "2020-09-30 23:37:59,200  SpectraRegion  INFO: DE data for case: [4]\n",
      "2020-09-30 23:37:59,201  SpectraRegion  INFO: DE data for control: [0, 5, 7, 9, 3, 8, 2, 6, 1]\n",
      "2020-09-30 23:37:59,201  SpectraRegion  INFO: Running [4] against [0, 5, 7, 9, 3, 8, 2, 6, 1]\n",
      "2020-09-30 23:37:59,202  SpectraRegion  INFO: DE result key: ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9))\n",
      "2020-09-30 23:37:59,202  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:37:59,204  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 23:37:59,205  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:37:59,207  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9)) with (1742, 7) results (filtered)\n",
      "2020-09-30 23:37:59,264  SpectraRegion  INFO: Created matrices with shape (70, 17900) and (2585, 17900) (target, bg)\n",
      "2020-09-30 23:38:03,136  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 23:38:03,137  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:38:03,140  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9)) with (1742, 7) results (filtered)\n",
      "2020-09-30 23:38:03,197  SpectraRegion  INFO: Created matrices with shape (70, 17900) and (2585, 17900) (target, bg)\n",
      "2020-09-30 23:38:07,049  SpectraRegion  INFO: DE data for case: [6]\n",
      "2020-09-30 23:38:07,050  SpectraRegion  INFO: DE data for control: [0, 5, 7, 9, 3, 8, 2, 4, 1]\n",
      "2020-09-30 23:38:07,050  SpectraRegion  INFO: Running [6] against [0, 5, 7, 9, 3, 8, 2, 4, 1]\n",
      "2020-09-30 23:38:07,051  SpectraRegion  INFO: DE result key: ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9))\n",
      "2020-09-30 23:38:07,051  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:38:07,053  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 23:38:07,054  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:38:07,056  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9)) with (2053, 7) results (filtered)\n",
      "2020-09-30 23:38:07,114  SpectraRegion  INFO: Created matrices with shape (27, 17900) and (2628, 17900) (target, bg)\n",
      "2020-09-30 23:38:12,974  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 23:38:12,974  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:38:12,977  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9)) with (2058, 7) results (filtered)\n",
      "2020-09-30 23:38:13,033  SpectraRegion  INFO: Created matrices with shape (27, 17900) and (2628, 17900) (target, bg)\n",
      "2020-09-30 23:38:18,905  SpectraRegion  INFO: DE data for case: [1]\n",
      "2020-09-30 23:38:18,906  SpectraRegion  INFO: DE data for control: [0, 5, 7, 9, 3, 8, 2, 4, 6]\n",
      "2020-09-30 23:38:18,907  SpectraRegion  INFO: Running [1] against [0, 5, 7, 9, 3, 8, 2, 4, 6]\n",
      "2020-09-30 23:38:18,908  SpectraRegion  INFO: DE result key: ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-09-30 23:38:18,908  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:38:18,910  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 23:38:18,910  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:38:18,913  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9)) with (3036, 7) results (filtered)\n",
      "2020-09-30 23:38:18,970  SpectraRegion  INFO: Created matrices with shape (22, 17900) and (2633, 17900) (target, bg)\n",
      "2020-09-30 23:38:28,282  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-09-30 23:38:28,283  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:38:28,285  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9)) with (3039, 7) results (filtered)\n",
      "2020-09-30 23:38:28,341  SpectraRegion  INFO: Created matrices with shape (22, 17900) and (2633, 17900) (target, bg)\n",
      "2020-09-30 23:38:37,734  SpectraRegion  INFO: No cell type info for cluster: 0\n",
      "2020-09-30 23:38:37,735  SpectraRegion  INFO: No cell type info for cluster: 3\n",
      "2020-09-30 23:38:37,745  SpectraRegion  INFO: Cell-type assigned: 1 -> Megakaryocytes;Immune system\n",
      "2020-09-30 23:38:37,746  SpectraRegion  INFO: Cell-type assigned: 4 -> B cells;Immune system\n",
      "2020-09-30 23:38:37,747  SpectraRegion  INFO: Cell-type assigned: 5 -> Basophils;Immune system\n",
      "2020-09-30 23:38:37,748  SpectraRegion  INFO: Cell-type assigned: 7 -> Adipocytes;Connective tissue\n",
      "2020-09-30 23:38:37,748  SpectraRegion  INFO: Cell-type assigned: 15 -> Cardiomyocytes;Heart\n",
      "2020-09-30 23:38:37,761  SpectraRegion  INFO: DE data for case: [0]\n",
      "2020-09-30 23:38:37,762  SpectraRegion  INFO: DE data for control: [15, 2, 6, 3, 1, 7, 5, 4]\n",
      "2020-09-30 23:38:37,763  SpectraRegion  INFO: Running [0] against [15, 2, 6, 3, 1, 7, 5, 4]\n",
      "2020-09-30 23:38:37,765  SpectraRegion  INFO: DE result key: ((0,), (1, 2, 3, 4, 5, 6, 7, 15))\n",
      "2020-09-30 23:38:37,766  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:38:37,768  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 23:38:37,769  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:38:37,774  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 15)) with (1086, 7) results (filtered)\n",
      "2020-09-30 23:38:37,869  SpectraRegion  INFO: Created matrices with shape (1923, 17900) and (485, 17900) (target, bg)\n",
      "2020-09-30 23:38:40,673  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 23:38:40,673  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:38:40,675  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 15)) with (1086, 7) results (filtered)\n",
      "2020-09-30 23:38:40,728  SpectraRegion  INFO: Created matrices with shape (1923, 17900) and (485, 17900) (target, bg)\n",
      "2020-09-30 23:38:43,559  SpectraRegion  INFO: DE data for case: [15]\n",
      "2020-09-30 23:38:43,560  SpectraRegion  INFO: DE data for control: [0, 2, 6, 3, 1, 7, 5, 4]\n",
      "2020-09-30 23:38:43,560  SpectraRegion  INFO: Running [15] against [0, 2, 6, 3, 1, 7, 5, 4]\n",
      "2020-09-30 23:38:43,560  SpectraRegion  INFO: DE result key: ((15,), (0, 1, 2, 3, 4, 5, 6, 7))\n",
      "2020-09-30 23:38:43,561  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:38:43,563  SpectraRegion  INFO: DE result for case ((15,), (0, 1, 2, 3, 4, 5, 6, 7)) with (17900, 7) results\n",
      "2020-09-30 23:38:43,563  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:38:43,566  SpectraRegion  INFO: DE result for case ((15,), (0, 1, 2, 3, 4, 5, 6, 7)) with (12, 7) results (filtered)\n",
      "2020-09-30 23:38:43,619  SpectraRegion  INFO: Created matrices with shape (91, 17900) and (2317, 17900) (target, bg)\n",
      "2020-09-30 23:38:43,637  SpectraRegion  INFO: DE result for case ((15,), (0, 1, 2, 3, 4, 5, 6, 7)) with (17900, 7) results\n",
      "2020-09-30 23:38:43,637  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:38:43,640  SpectraRegion  INFO: DE result for case ((15,), (0, 1, 2, 3, 4, 5, 6, 7)) with (12, 7) results (filtered)\n",
      "2020-09-30 23:38:43,748  SpectraRegion  INFO: Created matrices with shape (91, 17900) and (2317, 17900) (target, bg)\n",
      "2020-09-30 23:38:43,767  SpectraRegion  INFO: DE data for case: [2]\n",
      "2020-09-30 23:38:43,768  SpectraRegion  INFO: DE data for control: [0, 15, 6, 3, 1, 7, 5, 4]\n",
      "2020-09-30 23:38:43,768  SpectraRegion  INFO: Running [2] against [0, 15, 6, 3, 1, 7, 5, 4]\n",
      "2020-09-30 23:38:43,768  SpectraRegion  INFO: DE result key: ((2,), (0, 1, 3, 4, 5, 6, 7, 15))\n",
      "2020-09-30 23:38:43,769  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:38:43,771  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 23:38:43,771  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:38:43,774  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 15)) with (970, 7) results (filtered)\n",
      "2020-09-30 23:38:43,829  SpectraRegion  INFO: Created matrices with shape (79, 17900) and (2329, 17900) (target, bg)\n",
      "2020-09-30 23:38:46,598  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 23:38:46,598  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:38:46,601  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 15)) with (970, 7) results (filtered)\n",
      "2020-09-30 23:38:46,655  SpectraRegion  INFO: Created matrices with shape (79, 17900) and (2329, 17900) (target, bg)\n",
      "2020-09-30 23:38:49,444  SpectraRegion  INFO: DE data for case: [6]\n",
      "2020-09-30 23:38:49,445  SpectraRegion  INFO: DE data for control: [0, 15, 2, 3, 1, 7, 5, 4]\n",
      "2020-09-30 23:38:49,446  SpectraRegion  INFO: Running [6] against [0, 15, 2, 3, 1, 7, 5, 4]\n",
      "2020-09-30 23:38:49,446  SpectraRegion  INFO: DE result key: ((6,), (0, 1, 2, 3, 4, 5, 7, 15))\n",
      "2020-09-30 23:38:49,446  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:38:49,448  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 23:38:49,449  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:38:49,451  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 15)) with (1032, 7) results (filtered)\n",
      "2020-09-30 23:38:49,504  SpectraRegion  INFO: Created matrices with shape (86, 17900) and (2322, 17900) (target, bg)\n",
      "2020-09-30 23:38:52,025  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 23:38:52,026  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:38:52,028  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 15)) with (1032, 7) results (filtered)\n",
      "2020-09-30 23:38:52,082  SpectraRegion  INFO: Created matrices with shape (86, 17900) and (2322, 17900) (target, bg)\n",
      "2020-09-30 23:38:54,614  SpectraRegion  INFO: DE data for case: [3]\n",
      "2020-09-30 23:38:54,614  SpectraRegion  INFO: DE data for control: [0, 15, 2, 6, 1, 7, 5, 4]\n",
      "2020-09-30 23:38:54,615  SpectraRegion  INFO: Running [3] against [0, 15, 2, 6, 1, 7, 5, 4]\n",
      "2020-09-30 23:38:54,615  SpectraRegion  INFO: DE result key: ((3,), (0, 1, 2, 4, 5, 6, 7, 15))\n",
      "2020-09-30 23:38:54,616  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:38:54,616  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 23:38:54,617  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:38:54,620  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 15)) with (977, 7) results (filtered)\n",
      "2020-09-30 23:38:54,675  SpectraRegion  INFO: Created matrices with shape (99, 17900) and (2309, 17900) (target, bg)\n",
      "2020-09-30 23:38:57,443  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 23:38:57,444  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:38:57,447  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 15)) with (977, 7) results (filtered)\n",
      "2020-09-30 23:38:57,500  SpectraRegion  INFO: Created matrices with shape (99, 17900) and (2309, 17900) (target, bg)\n",
      "2020-09-30 23:39:00,256  SpectraRegion  INFO: DE data for case: [1]\n",
      "2020-09-30 23:39:00,257  SpectraRegion  INFO: DE data for control: [0, 15, 2, 6, 3, 7, 5, 4]\n",
      "2020-09-30 23:39:00,257  SpectraRegion  INFO: Running [1] against [0, 15, 2, 6, 3, 7, 5, 4]\n",
      "2020-09-30 23:39:00,257  SpectraRegion  INFO: DE result key: ((1,), (0, 2, 3, 4, 5, 6, 7, 15))\n",
      "2020-09-30 23:39:00,258  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:39:00,260  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 23:39:00,261  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:39:00,263  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 15)) with (1944, 7) results (filtered)\n",
      "2020-09-30 23:39:00,321  SpectraRegion  INFO: Created matrices with shape (61, 17900) and (2347, 17900) (target, bg)\n",
      "2020-09-30 23:39:06,161  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 23:39:06,162  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:39:06,165  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 15)) with (1944, 7) results (filtered)\n",
      "2020-09-30 23:39:06,217  SpectraRegion  INFO: Created matrices with shape (61, 17900) and (2347, 17900) (target, bg)\n",
      "2020-09-30 23:39:12,057  SpectraRegion  INFO: DE data for case: [7]\n",
      "2020-09-30 23:39:12,058  SpectraRegion  INFO: DE data for control: [0, 15, 2, 6, 3, 1, 5, 4]\n",
      "2020-09-30 23:39:12,059  SpectraRegion  INFO: Running [7] against [0, 15, 2, 6, 3, 1, 5, 4]\n",
      "2020-09-30 23:39:12,059  SpectraRegion  INFO: DE result key: ((7,), (0, 1, 2, 3, 4, 5, 6, 15))\n",
      "2020-09-30 23:39:12,059  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:39:12,061  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 15)) with (17900, 7) results\n",
      "2020-09-30 23:39:12,061  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:39:12,064  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 15)) with (1882, 7) results (filtered)\n",
      "2020-09-30 23:39:12,116  SpectraRegion  INFO: Created matrices with shape (39, 17900) and (2369, 17900) (target, bg)\n",
      "2020-09-30 23:39:16,707  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 15)) with (17900, 7) results\n",
      "2020-09-30 23:39:16,708  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:39:16,710  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 15)) with (1888, 7) results (filtered)\n",
      "2020-09-30 23:39:16,762  SpectraRegion  INFO: Created matrices with shape (39, 17900) and (2369, 17900) (target, bg)\n",
      "2020-09-30 23:39:21,385  SpectraRegion  INFO: DE data for case: [5]\n",
      "2020-09-30 23:39:21,386  SpectraRegion  INFO: DE data for control: [0, 15, 2, 6, 3, 1, 7, 4]\n",
      "2020-09-30 23:39:21,387  SpectraRegion  INFO: Running [5] against [0, 15, 2, 6, 3, 1, 7, 4]\n",
      "2020-09-30 23:39:21,388  SpectraRegion  INFO: DE result key: ((5,), (0, 1, 2, 3, 4, 6, 7, 15))\n",
      "2020-09-30 23:39:21,389  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:39:21,390  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 23:39:21,391  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:39:21,393  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 15)) with (1840, 7) results (filtered)\n",
      "2020-09-30 23:39:21,447  SpectraRegion  INFO: Created matrices with shape (19, 17900) and (2389, 17900) (target, bg)\n",
      "2020-09-30 23:39:25,444  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 23:39:25,444  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:39:25,447  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 15)) with (1874, 7) results (filtered)\n",
      "2020-09-30 23:39:25,500  SpectraRegion  INFO: Created matrices with shape (19, 17900) and (2389, 17900) (target, bg)\n",
      "2020-09-30 23:39:29,147  SpectraRegion  INFO: DE data for case: [4]\n",
      "2020-09-30 23:39:29,148  SpectraRegion  INFO: DE data for control: [0, 15, 2, 6, 3, 1, 7, 5]\n",
      "2020-09-30 23:39:29,148  SpectraRegion  INFO: Running [4] against [0, 15, 2, 6, 3, 1, 7, 5]\n",
      "2020-09-30 23:39:29,149  SpectraRegion  INFO: DE result key: ((4,), (0, 1, 2, 3, 5, 6, 7, 15))\n",
      "2020-09-30 23:39:29,149  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:39:29,151  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 23:39:29,151  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:39:29,154  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 15)) with (946, 7) results (filtered)\n",
      "2020-09-30 23:39:29,207  SpectraRegion  INFO: Created matrices with shape (11, 17900) and (2397, 17900) (target, bg)\n",
      "2020-09-30 23:39:31,549  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-09-30 23:39:31,550  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:39:31,552  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 15)) with (1065, 7) results (filtered)\n",
      "2020-09-30 23:39:31,605  SpectraRegion  INFO: Created matrices with shape (11, 17900) and (2397, 17900) (target, bg)\n",
      "2020-09-30 23:39:34,059  SpectraRegion  INFO: No cell type info for cluster: 0\n",
      "2020-09-30 23:39:34,059  SpectraRegion  INFO: No cell type info for cluster: 2\n",
      "2020-09-30 23:39:34,060  SpectraRegion  INFO: No cell type info for cluster: 6\n",
      "2020-09-30 23:39:34,060  SpectraRegion  INFO: No cell type info for cluster: 3\n",
      "2020-09-30 23:39:34,119  SpectraRegion  INFO: Cell-type assigned: 1 -> Cardiomyocytes;Heart\n",
      "2020-09-30 23:39:34,120  SpectraRegion  INFO: Cell-type assigned: 2 -> Adipocytes;Connective tissue\n",
      "2020-09-30 23:39:34,121  SpectraRegion  INFO: Cell-type assigned: 4 -> Platelets;Blood\n",
      "2020-09-30 23:39:34,121  SpectraRegion  INFO: Cell-type assigned: 5 -> Monocytes;Immune system\n",
      "2020-09-30 23:39:34,121  SpectraRegion  INFO: Cell-type assigned: 10 -> Basophils;Immune system\n",
      "2020-09-30 23:39:34,122  SpectraRegion  INFO: Cell-type assigned: 11 -> NK cells;Immune system\n",
      "2020-09-30 23:39:34,122  SpectraRegion  INFO: Cell-type assigned: 12 -> Cardiomyocytes;Heart\n",
      "2020-09-30 23:39:34,123  SpectraRegion  INFO: Cell-type assigned: 13 -> Cardiomyocytes;Heart\n",
      "2020-09-30 23:39:34,133  SpectraRegion  INFO: DE data for case: [0]\n",
      "2020-09-30 23:39:34,134  SpectraRegion  INFO: DE data for control: [13, 3, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 23:39:34,135  SpectraRegion  INFO: Running [0] against [13, 3, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 23:39:34,135  SpectraRegion  INFO: DE result key: ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 23:39:34,136  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:39:34,137  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 23:39:34,138  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:39:34,140  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13)) with (1491, 7) results (filtered)\n",
      "2020-09-30 23:39:34,244  SpectraRegion  INFO: Created matrices with shape (2218, 17900) and (526, 17900) (target, bg)\n",
      "2020-09-30 23:39:38,498  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 23:39:38,498  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:39:38,501  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13)) with (1491, 7) results (filtered)\n",
      "2020-09-30 23:39:38,569  SpectraRegion  INFO: Created matrices with shape (2218, 17900) and (526, 17900) (target, bg)\n",
      "2020-09-30 23:39:42,710  SpectraRegion  INFO: DE data for case: [13]\n",
      "2020-09-30 23:39:42,711  SpectraRegion  INFO: DE data for control: [0, 3, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 23:39:42,712  SpectraRegion  INFO: Running [13] against [0, 3, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 23:39:42,712  SpectraRegion  INFO: DE result key: ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12))\n",
      "2020-09-30 23:39:42,713  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:39:42,714  SpectraRegion  INFO: DE result for case ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12)) with (17900, 7) results\n",
      "2020-09-30 23:39:42,715  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:39:42,717  SpectraRegion  INFO: DE result for case ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12)) with (89, 7) results (filtered)\n",
      "2020-09-30 23:39:42,779  SpectraRegion  INFO: Created matrices with shape (101, 17900) and (2643, 17900) (target, bg)\n",
      "2020-09-30 23:39:42,987  SpectraRegion  INFO: DE result for case ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12)) with (17900, 7) results\n",
      "2020-09-30 23:39:42,988  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:39:42,990  SpectraRegion  INFO: DE result for case ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12)) with (89, 7) results (filtered)\n",
      "2020-09-30 23:39:43,052  SpectraRegion  INFO: Created matrices with shape (101, 17900) and (2643, 17900) (target, bg)\n",
      "2020-09-30 23:39:43,256  SpectraRegion  INFO: DE data for case: [3]\n",
      "2020-09-30 23:39:43,257  SpectraRegion  INFO: DE data for control: [0, 13, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 23:39:43,257  SpectraRegion  INFO: Running [3] against [0, 13, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 23:39:43,258  SpectraRegion  INFO: DE result key: ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 23:39:43,258  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:39:43,260  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 23:39:43,261  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:39:43,263  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13)) with (1138, 7) results (filtered)\n",
      "2020-09-30 23:39:43,324  SpectraRegion  INFO: Created matrices with shape (102, 17900) and (2642, 17900) (target, bg)\n",
      "2020-09-30 23:39:46,500  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 23:39:46,500  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:39:46,503  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13)) with (1138, 7) results (filtered)\n",
      "2020-09-30 23:39:46,564  SpectraRegion  INFO: Created matrices with shape (102, 17900) and (2642, 17900) (target, bg)\n",
      "2020-09-30 23:39:49,593  SpectraRegion  INFO: DE data for case: [10]\n",
      "2020-09-30 23:39:49,594  SpectraRegion  INFO: DE data for control: [0, 13, 3, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 23:39:49,594  SpectraRegion  INFO: Running [10] against [0, 13, 3, 12, 2, 11, 1, 5, 4]\n",
      "2020-09-30 23:39:49,595  SpectraRegion  INFO: DE result key: ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13))\n",
      "2020-09-30 23:39:49,595  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:39:49,597  SpectraRegion  INFO: DE result for case ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 23:39:49,598  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:39:49,600  SpectraRegion  INFO: DE result for case ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13)) with (868, 7) results (filtered)\n",
      "2020-09-30 23:39:49,661  SpectraRegion  INFO: Created matrices with shape (49, 17900) and (2695, 17900) (target, bg)\n",
      "2020-09-30 23:39:51,745  SpectraRegion  INFO: DE result for case ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 23:39:51,746  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:39:51,748  SpectraRegion  INFO: DE result for case ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13)) with (868, 7) results (filtered)\n",
      "2020-09-30 23:39:51,810  SpectraRegion  INFO: Created matrices with shape (49, 17900) and (2695, 17900) (target, bg)\n",
      "2020-09-30 23:39:53,900  SpectraRegion  INFO: DE data for case: [12]\n",
      "2020-09-30 23:39:53,900  SpectraRegion  INFO: DE data for control: [0, 13, 3, 10, 2, 11, 1, 5, 4]\n",
      "2020-09-30 23:39:53,901  SpectraRegion  INFO: Running [12] against [0, 13, 3, 10, 2, 11, 1, 5, 4]\n",
      "2020-09-30 23:39:53,901  SpectraRegion  INFO: DE result key: ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13))\n",
      "2020-09-30 23:39:53,902  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:39:53,904  SpectraRegion  INFO: DE result for case ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13)) with (17900, 7) results\n",
      "2020-09-30 23:39:53,905  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:39:53,908  SpectraRegion  INFO: DE result for case ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13)) with (1575, 7) results (filtered)\n",
      "2020-09-30 23:39:53,969  SpectraRegion  INFO: Created matrices with shape (61, 17900) and (2683, 17900) (target, bg)\n",
      "2020-09-30 23:39:57,577  SpectraRegion  INFO: DE result for case ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13)) with (17900, 7) results\n",
      "2020-09-30 23:39:57,578  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:39:57,580  SpectraRegion  INFO: DE result for case ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13)) with (1574, 7) results (filtered)\n",
      "2020-09-30 23:39:57,639  SpectraRegion  INFO: Created matrices with shape (61, 17900) and (2683, 17900) (target, bg)\n",
      "2020-09-30 23:40:01,187  SpectraRegion  INFO: DE data for case: [2]\n",
      "2020-09-30 23:40:01,187  SpectraRegion  INFO: DE data for control: [0, 13, 3, 10, 12, 11, 1, 5, 4]\n",
      "2020-09-30 23:40:01,188  SpectraRegion  INFO: Running [2] against [0, 13, 3, 10, 12, 11, 1, 5, 4]\n",
      "2020-09-30 23:40:01,189  SpectraRegion  INFO: DE result key: ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 23:40:01,189  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:40:01,191  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 23:40:01,192  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:40:01,195  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13)) with (2184, 7) results (filtered)\n",
      "2020-09-30 23:40:01,254  SpectraRegion  INFO: Created matrices with shape (45, 17900) and (2699, 17900) (target, bg)\n",
      "2020-09-30 23:40:08,343  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 23:40:08,344  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:40:08,346  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13)) with (2184, 7) results (filtered)\n",
      "2020-09-30 23:40:08,407  SpectraRegion  INFO: Created matrices with shape (45, 17900) and (2699, 17900) (target, bg)\n",
      "2020-09-30 23:40:15,385  SpectraRegion  INFO: DE data for case: [11]\n",
      "2020-09-30 23:40:15,385  SpectraRegion  INFO: DE data for control: [0, 13, 3, 10, 12, 2, 1, 5, 4]\n",
      "2020-09-30 23:40:15,386  SpectraRegion  INFO: Running [11] against [0, 13, 3, 10, 12, 2, 1, 5, 4]\n",
      "2020-09-30 23:40:15,386  SpectraRegion  INFO: DE result key: ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13))\n",
      "2020-09-30 23:40:15,387  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:40:15,389  SpectraRegion  INFO: DE result for case ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 23:40:15,390  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:40:15,392  SpectraRegion  INFO: DE result for case ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13)) with (1552, 7) results (filtered)\n",
      "2020-09-30 23:40:15,452  SpectraRegion  INFO: Created matrices with shape (45, 17900) and (2699, 17900) (target, bg)\n",
      "2020-09-30 23:40:19,566  SpectraRegion  INFO: DE result for case ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 23:40:19,566  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:40:19,569  SpectraRegion  INFO: DE result for case ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13)) with (1554, 7) results (filtered)\n",
      "2020-09-30 23:40:19,628  SpectraRegion  INFO: Created matrices with shape (45, 17900) and (2699, 17900) (target, bg)\n",
      "2020-09-30 23:40:23,792  SpectraRegion  INFO: DE data for case: [1]\n",
      "2020-09-30 23:40:23,793  SpectraRegion  INFO: DE data for control: [0, 13, 3, 10, 12, 2, 11, 5, 4]\n",
      "2020-09-30 23:40:23,793  SpectraRegion  INFO: Running [1] against [0, 13, 3, 10, 12, 2, 11, 5, 4]\n",
      "2020-09-30 23:40:23,794  SpectraRegion  INFO: DE result key: ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-09-30 23:40:23,794  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:40:23,795  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 23:40:23,796  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:40:23,800  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13)) with (2040, 7) results (filtered)\n",
      "2020-09-30 23:40:23,860  SpectraRegion  INFO: Created matrices with shape (57, 17900) and (2687, 17900) (target, bg)\n",
      "2020-09-30 23:40:30,028  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 23:40:30,029  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:40:30,032  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13)) with (2038, 7) results (filtered)\n",
      "2020-09-30 23:40:30,105  SpectraRegion  INFO: Created matrices with shape (57, 17900) and (2687, 17900) (target, bg)\n",
      "2020-09-30 23:40:36,316  SpectraRegion  INFO: DE data for case: [5]\n",
      "2020-09-30 23:40:36,317  SpectraRegion  INFO: DE data for control: [0, 13, 3, 10, 12, 2, 11, 1, 4]\n",
      "2020-09-30 23:40:36,317  SpectraRegion  INFO: Running [5] against [0, 13, 3, 10, 12, 2, 11, 1, 4]\n",
      "2020-09-30 23:40:36,318  SpectraRegion  INFO: DE result key: ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13))\n",
      "2020-09-30 23:40:36,318  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:40:36,319  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 23:40:36,320  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:40:36,324  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13)) with (1661, 7) results (filtered)\n",
      "2020-09-30 23:40:36,393  SpectraRegion  INFO: Created matrices with shape (48, 17900) and (2696, 17900) (target, bg)\n",
      "2020-09-30 23:40:40,526  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 23:40:40,527  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:40:40,530  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13)) with (1659, 7) results (filtered)\n",
      "2020-09-30 23:40:40,589  SpectraRegion  INFO: Created matrices with shape (48, 17900) and (2696, 17900) (target, bg)\n",
      "2020-09-30 23:40:44,728  SpectraRegion  INFO: DE data for case: [4]\n",
      "2020-09-30 23:40:44,729  SpectraRegion  INFO: DE data for control: [0, 13, 3, 10, 12, 2, 11, 1, 5]\n",
      "2020-09-30 23:40:44,729  SpectraRegion  INFO: Running [4] against [0, 13, 3, 10, 12, 2, 11, 1, 5]\n",
      "2020-09-30 23:40:44,730  SpectraRegion  INFO: DE result key: ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13))\n",
      "2020-09-30 23:40:44,730  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:40:44,733  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 23:40:44,733  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:40:44,736  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13)) with (2953, 7) results (filtered)\n",
      "2020-09-30 23:40:44,795  SpectraRegion  INFO: Created matrices with shape (18, 17900) and (2726, 17900) (target, bg)\n",
      "2020-09-30 23:40:53,414  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-09-30 23:40:53,415  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:40:53,417  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13)) with (2920, 7) results (filtered)\n",
      "2020-09-30 23:40:53,477  SpectraRegion  INFO: Created matrices with shape (18, 17900) and (2726, 17900) (target, bg)\n",
      "2020-09-30 23:41:02,695  SpectraRegion  INFO: No cell type info for cluster: 0\n",
      "2020-09-30 23:41:02,696  SpectraRegion  INFO: No cell type info for cluster: 3\n",
      "2020-09-30 23:41:02,705  SpectraRegion  INFO: Cell-type assigned: 9 -> Endothelial cells;Vasculature\n",
      "2020-09-30 23:41:02,706  SpectraRegion  INFO: Cell-type assigned: 10 -> Gamma delta T cells;Immune system\n",
      "2020-09-30 23:41:02,707  SpectraRegion  INFO: Cell-type assigned: 11 -> Platelets;Blood\n",
      "2020-09-30 23:41:02,707  SpectraRegion  INFO: Cell-type assigned: 14 -> Adipocytes;Connective tissue\n",
      "2020-09-30 23:41:02,708  SpectraRegion  INFO: Cell-type assigned: 15 -> Cardiomyocytes;Heart\n",
      "2020-09-30 23:41:02,720  SpectraRegion  INFO: DE data for case: [0]\n",
      "2020-09-30 23:41:02,720  SpectraRegion  INFO: DE data for control: [15, 12, 11, 14, 10, 9]\n",
      "2020-09-30 23:41:02,721  SpectraRegion  INFO: Running [0] against [15, 12, 11, 14, 10, 9]\n",
      "2020-09-30 23:41:02,721  SpectraRegion  INFO: DE result key: ((0,), (9, 10, 11, 12, 14, 15))\n",
      "2020-09-30 23:41:02,722  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:41:02,723  SpectraRegion  INFO: DE result for case ((0,), (9, 10, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:41:02,723  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:41:02,726  SpectraRegion  INFO: DE result for case ((0,), (9, 10, 11, 12, 14, 15)) with (1125, 7) results (filtered)\n",
      "2020-09-30 23:41:02,838  SpectraRegion  INFO: Created matrices with shape (2815, 17900) and (253, 17900) (target, bg)\n",
      "2020-09-30 23:41:05,162  SpectraRegion  INFO: DE result for case ((0,), (9, 10, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:41:05,163  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:41:05,165  SpectraRegion  INFO: DE result for case ((0,), (9, 10, 11, 12, 14, 15)) with (1125, 7) results (filtered)\n",
      "2020-09-30 23:41:05,234  SpectraRegion  INFO: Created matrices with shape (2815, 17900) and (253, 17900) (target, bg)\n",
      "2020-09-30 23:41:07,563  SpectraRegion  INFO: DE data for case: [15]\n",
      "2020-09-30 23:41:07,564  SpectraRegion  INFO: DE data for control: [0, 12, 11, 14, 10, 9]\n",
      "2020-09-30 23:41:07,564  SpectraRegion  INFO: Running [15] against [0, 12, 11, 14, 10, 9]\n",
      "2020-09-30 23:41:07,565  SpectraRegion  INFO: DE result key: ((15,), (0, 9, 10, 11, 12, 14))\n",
      "2020-09-30 23:41:07,565  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:41:07,567  SpectraRegion  INFO: DE result for case ((15,), (0, 9, 10, 11, 12, 14)) with (17900, 7) results\n",
      "2020-09-30 23:41:07,568  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:41:07,570  SpectraRegion  INFO: DE result for case ((15,), (0, 9, 10, 11, 12, 14)) with (863, 7) results (filtered)\n",
      "2020-09-30 23:41:07,639  SpectraRegion  INFO: Created matrices with shape (98, 17900) and (2970, 17900) (target, bg)\n",
      "2020-09-30 23:41:09,509  SpectraRegion  INFO: DE result for case ((15,), (0, 9, 10, 11, 12, 14)) with (17900, 7) results\n",
      "2020-09-30 23:41:09,509  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:41:09,512  SpectraRegion  INFO: DE result for case ((15,), (0, 9, 10, 11, 12, 14)) with (863, 7) results (filtered)\n",
      "2020-09-30 23:41:09,580  SpectraRegion  INFO: Created matrices with shape (98, 17900) and (2970, 17900) (target, bg)\n",
      "2020-09-30 23:41:11,447  SpectraRegion  INFO: DE data for case: [12]\n",
      "2020-09-30 23:41:11,448  SpectraRegion  INFO: DE data for control: [0, 15, 11, 14, 10, 9]\n",
      "2020-09-30 23:41:11,448  SpectraRegion  INFO: Running [12] against [0, 15, 11, 14, 10, 9]\n",
      "2020-09-30 23:41:11,449  SpectraRegion  INFO: DE result key: ((12,), (0, 9, 10, 11, 14, 15))\n",
      "2020-09-30 23:41:11,449  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:41:11,451  SpectraRegion  INFO: DE result for case ((12,), (0, 9, 10, 11, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:41:11,452  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:41:11,455  SpectraRegion  INFO: DE result for case ((12,), (0, 9, 10, 11, 14, 15)) with (856, 7) results (filtered)\n",
      "2020-09-30 23:41:11,522  SpectraRegion  INFO: Created matrices with shape (33, 17900) and (3035, 17900) (target, bg)\n",
      "2020-09-30 23:41:13,438  SpectraRegion  INFO: DE result for case ((12,), (0, 9, 10, 11, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:41:13,438  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:41:13,441  SpectraRegion  INFO: DE result for case ((12,), (0, 9, 10, 11, 14, 15)) with (855, 7) results (filtered)\n",
      "2020-09-30 23:41:13,506  SpectraRegion  INFO: Created matrices with shape (33, 17900) and (3035, 17900) (target, bg)\n",
      "2020-09-30 23:41:15,383  SpectraRegion  INFO: DE data for case: [11]\n",
      "2020-09-30 23:41:15,384  SpectraRegion  INFO: DE data for control: [0, 15, 12, 14, 10, 9]\n",
      "2020-09-30 23:41:15,384  SpectraRegion  INFO: Running [11] against [0, 15, 12, 14, 10, 9]\n",
      "2020-09-30 23:41:15,384  SpectraRegion  INFO: DE result key: ((11,), (0, 9, 10, 12, 14, 15))\n",
      "2020-09-30 23:41:15,385  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:41:15,388  SpectraRegion  INFO: DE result for case ((11,), (0, 9, 10, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:41:15,388  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:41:15,391  SpectraRegion  INFO: DE result for case ((11,), (0, 9, 10, 12, 14, 15)) with (2583, 7) results (filtered)\n",
      "2020-09-30 23:41:15,457  SpectraRegion  INFO: Created matrices with shape (24, 17900) and (3044, 17900) (target, bg)\n",
      "2020-09-30 23:41:23,438  SpectraRegion  INFO: DE result for case ((11,), (0, 9, 10, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:41:23,439  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:41:23,441  SpectraRegion  INFO: DE result for case ((11,), (0, 9, 10, 12, 14, 15)) with (2542, 7) results (filtered)\n",
      "2020-09-30 23:41:23,508  SpectraRegion  INFO: Created matrices with shape (24, 17900) and (3044, 17900) (target, bg)\n",
      "2020-09-30 23:41:31,414  SpectraRegion  INFO: DE data for case: [14]\n",
      "2020-09-30 23:41:31,415  SpectraRegion  INFO: DE data for control: [0, 15, 12, 11, 10, 9]\n",
      "2020-09-30 23:41:31,415  SpectraRegion  INFO: Running [14] against [0, 15, 12, 11, 10, 9]\n",
      "2020-09-30 23:41:31,416  SpectraRegion  INFO: DE result key: ((14,), (0, 9, 10, 11, 12, 15))\n",
      "2020-09-30 23:41:31,416  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:41:31,418  SpectraRegion  INFO: DE result for case ((14,), (0, 9, 10, 11, 12, 15)) with (17900, 7) results\n",
      "2020-09-30 23:41:31,419  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:41:31,421  SpectraRegion  INFO: DE result for case ((14,), (0, 9, 10, 11, 12, 15)) with (2209, 7) results (filtered)\n",
      "2020-09-30 23:41:31,488  SpectraRegion  INFO: Created matrices with shape (28, 17900) and (3040, 17900) (target, bg)\n",
      "2020-09-30 23:41:37,033  SpectraRegion  INFO: DE result for case ((14,), (0, 9, 10, 11, 12, 15)) with (17900, 7) results\n",
      "2020-09-30 23:41:37,034  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:41:37,036  SpectraRegion  INFO: DE result for case ((14,), (0, 9, 10, 11, 12, 15)) with (2219, 7) results (filtered)\n",
      "2020-09-30 23:41:37,103  SpectraRegion  INFO: Created matrices with shape (28, 17900) and (3040, 17900) (target, bg)\n",
      "2020-09-30 23:41:42,665  SpectraRegion  INFO: DE data for case: [10]\n",
      "2020-09-30 23:41:42,666  SpectraRegion  INFO: DE data for control: [0, 15, 12, 11, 14, 9]\n",
      "2020-09-30 23:41:42,667  SpectraRegion  INFO: Running [10] against [0, 15, 12, 11, 14, 9]\n",
      "2020-09-30 23:41:42,667  SpectraRegion  INFO: DE result key: ((10,), (0, 9, 11, 12, 14, 15))\n",
      "2020-09-30 23:41:42,668  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:41:42,669  SpectraRegion  INFO: DE result for case ((10,), (0, 9, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:41:42,669  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:41:42,672  SpectraRegion  INFO: DE result for case ((10,), (0, 9, 11, 12, 14, 15)) with (1799, 7) results (filtered)\n",
      "2020-09-30 23:41:42,742  SpectraRegion  INFO: Created matrices with shape (36, 17900) and (3032, 17900) (target, bg)\n",
      "2020-09-30 23:41:46,596  SpectraRegion  INFO: DE result for case ((10,), (0, 9, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:41:46,596  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:41:46,599  SpectraRegion  INFO: DE result for case ((10,), (0, 9, 11, 12, 14, 15)) with (1807, 7) results (filtered)\n",
      "2020-09-30 23:41:46,665  SpectraRegion  INFO: Created matrices with shape (36, 17900) and (3032, 17900) (target, bg)\n",
      "2020-09-30 23:41:50,500  SpectraRegion  INFO: DE data for case: [9]\n",
      "2020-09-30 23:41:50,500  SpectraRegion  INFO: DE data for control: [0, 15, 12, 11, 14, 10]\n",
      "2020-09-30 23:41:50,501  SpectraRegion  INFO: Running [9] against [0, 15, 12, 11, 14, 10]\n",
      "2020-09-30 23:41:50,501  SpectraRegion  INFO: DE result key: ((9,), (0, 10, 11, 12, 14, 15))\n",
      "2020-09-30 23:41:50,502  SpectraRegion  INFO: DE result key already exists\n",
      "2020-09-30 23:41:50,504  SpectraRegion  INFO: DE result for case ((9,), (0, 10, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:41:50,505  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:41:50,508  SpectraRegion  INFO: DE result for case ((9,), (0, 10, 11, 12, 14, 15)) with (1932, 7) results (filtered)\n",
      "2020-09-30 23:41:50,574  SpectraRegion  INFO: Created matrices with shape (34, 17900) and (3034, 17900) (target, bg)\n",
      "2020-09-30 23:41:55,153  SpectraRegion  INFO: DE result for case ((9,), (0, 10, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-09-30 23:41:55,154  SpectraRegion  INFO: DE result logFC inversed\n",
      "2020-09-30 23:41:55,156  SpectraRegion  INFO: DE result for case ((9,), (0, 10, 11, 12, 14, 15)) with (1937, 7) results (filtered)\n",
      "2020-09-30 23:41:55,222  SpectraRegion  INFO: Created matrices with shape (34, 17900) and (3034, 17900) (target, bg)\n",
      "2020-09-30 23:41:59,909  SpectraRegion  INFO: No cell type info for cluster: 0\n",
      "2020-09-30 23:41:59,909  SpectraRegion  INFO: No cell type info for cluster: 12\n"
     ]
    }
   ],
   "source": [
    "slided_0.to_aorta3d(\"./aorta3d/slided/\", \"slided\", 0, protWeights=pw, ctpred=\"deresults/marker_genes_region_0.pred.tsv\")\n",
    "slided_1.to_aorta3d(\"./aorta3d/slided/\", \"slided\", 1, protWeights=pw, ctpred=\"deresults/marker_genes_region_1.pred.tsv\")\n",
    "slided_4.to_aorta3d(\"./aorta3d/slided/\", \"slided\", 4, protWeights=pw, ctpred=\"deresults/marker_genes_region_4.pred.tsv\")\n",
    "slided_5.to_aorta3d(\"./aorta3d/slided/\", \"slided\", 5, protWeights=pw, ctpred=\"deresults/marker_genes_region_5.pred.tsv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! python3 analyseMarkers.py --organs \"Connective tissue\" \"Vasculature\" \"Heart\" \"Skeletal Muscle\" \"Smooth muscle\" \"Immune system\" \"Blood\" \"Epithelium\" --pvaladj qvalue --markers deresults/marker_genes_region_0.tsv -n 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! python3 analyseMarkers.py --organs \"Connective tissue\" \"Vasculature\" \"Heart\" \"Skeletal Muscle\" \"Smooth muscle\" \"Immune system\" \"Blood\" \"Epithelium\" --pvaladj qvalue --markers deresults/marker_genes_region_1.tsv -n 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! python3 analyseMarkers.py --organs \"Connective tissue\" \"Vasculature\" \"Heart\" \"Skeletal Muscle\" \"Smooth muscle\" \"Immune system\" \"Blood\" \"Epithelium\" --pvaladj qvalue --markers deresults/marker_genes_region_4.tsv -n 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! python3 analyseMarkers.py --organs \"Connective tissue\" \"Vasculature\" \"Heart\" \"Skeletal Muscle\" \"Smooth muscle\" \"Immune system\" \"Blood\" \"Epithelium\" --pvaladj qvalue --markers deresults/marker_genes_region_5.tsv -n 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From these cell type predictions it can be seen that mostly atherosclerosis relates cell types are showing up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a CombinedSpectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slided_0.name = \"slided_0\"\n",
    "slided_1.name = \"slided_1\"\n",
    "slided_4.name = \"slided_4\"\n",
    "slided_5.name = \"slided_5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#slided_0 = SpectraRegion.from_pickle(\"slideD_region_0.pickle\")\n",
    "#slided_1 = SpectraRegion.from_pickle(\"slideD_region_1.pickle\")\n",
    "#slided_4 = SpectraRegion.from_pickle(\"slideD_region_4.pickle\")\n",
    "#slided_5 = SpectraRegion.from_pickle(\"slideD_region_5pickle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course a SpectraRegion can also be re-processed to yield a more realistic clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slided_1.segment(method=\"WPGMA\", number_of_regions=15)\n",
    "slided_1.plot_segments()\n",
    "\n",
    "slided_1.filter_clusters(method='remove_singleton')\n",
    "slided_1.filter_clusters(method='merge_background')\n",
    "slided_1.filter_clusters(method='remove_islands')\n",
    "slided_1.filter_clusters(method='remove_islands', minIslandSize=15)\n",
    "\n",
    "\n",
    "\n",
    "slided_1.plot_segments()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = slided_1.segmented == 8\n",
    "mask[0:22,: ] = False\n",
    "mask[:,0:22 ] = False\n",
    "slided_1.segmented[mask] = 15\n",
    "slided_1.plot_segments()\n",
    "slided_1.plot_segments(highlight=[15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slided_5.segment(method=\"WPGMA\", number_of_regions=15)\n",
    "slided_5.plot_segments()\n",
    "\n",
    "slided_5.filter_clusters(method='remove_singleton')\n",
    "slided_5.filter_clusters(method='merge_background')\n",
    "slided_5.filter_clusters(method='remove_islands')\n",
    "slided_5.filter_clusters(method='remove_islands', minIslandSize=15)\n",
    "slided_5.plot_segments()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slided_0.to_pickle(\"deresults/slideD_region_0.pickle\")\n",
    "slided_1.to_pickle(\"deresults/slideD_region_1.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slided_4.to_pickle(\"deresults/slideD_region_4.pickle\")\n",
    "slided_5.to_pickle(\"deresults/slideD_region_5.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 18:53:10,097  SpectraRegion  INFO: Cell-type assigned: 1 -> Monocytes;Immune system\n",
      "INFO:SpectraRegion:Cell-type assigned: 1 -> Monocytes;Immune system\n",
      "2020-10-01 18:53:10,098  SpectraRegion  INFO: Cell-type assigned: 2 -> Megakaryocytes;Immune system\n",
      "INFO:SpectraRegion:Cell-type assigned: 2 -> Megakaryocytes;Immune system\n",
      "2020-10-01 18:53:10,099  SpectraRegion  INFO: Cell-type assigned: 4 -> Mast cells;Immune system\n",
      "INFO:SpectraRegion:Cell-type assigned: 4 -> Mast cells;Immune system\n",
      "2020-10-01 18:53:10,100  SpectraRegion  INFO: Cell-type assigned: 5 -> Monocytes;Immune system\n",
      "INFO:SpectraRegion:Cell-type assigned: 5 -> Monocytes;Immune system\n",
      "2020-10-01 18:53:10,101  SpectraRegion  INFO: Cell-type assigned: 6 -> Monocytes;Immune system\n",
      "INFO:SpectraRegion:Cell-type assigned: 6 -> Monocytes;Immune system\n",
      "2020-10-01 18:53:10,102  SpectraRegion  INFO: Cell-type assigned: 7 -> Mast cells;Immune system\n",
      "INFO:SpectraRegion:Cell-type assigned: 7 -> Mast cells;Immune system\n",
      "2020-10-01 18:53:10,103  SpectraRegion  INFO: Cell-type assigned: 8 -> Mast cells;Immune system\n",
      "INFO:SpectraRegion:Cell-type assigned: 8 -> Mast cells;Immune system\n",
      "2020-10-01 18:53:10,104  SpectraRegion  INFO: Cell-type assigned: 9 -> Smooth muscle cells;Smooth muscle\n",
      "INFO:SpectraRegion:Cell-type assigned: 9 -> Smooth muscle cells;Smooth muscle\n",
      "2020-10-01 18:53:10,109  SpectraRegion  INFO: Segment Image: ./aorta3d/slided/slided.0.clustering.png\n",
      "INFO:SpectraRegion:Segment Image: ./aorta3d/slided/slided.0.clustering.png\n",
      "2020-10-01 18:53:10,114  SpectraRegion  INFO: Segment Matrix: /mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.0.matrix.npy\n",
      "INFO:SpectraRegion:Segment Matrix: /mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.0.matrix.npy\n",
      "2020-10-01 18:53:10,119  SpectraRegion  INFO: Starting Marker Proteins Analysis\n",
      "INFO:SpectraRegion:Starting Marker Proteins Analysis\n",
      "2020-10-01 18:53:10,122  SpectraRegion  INFO: DE data for case: [0]\n",
      "INFO:SpectraRegion:DE data for case: [0]\n",
      "2020-10-01 18:53:10,123  SpectraRegion  INFO: DE data for control: [5, 7, 9, 3, 8, 2, 4, 6, 1]\n",
      "INFO:SpectraRegion:DE data for control: [5, 7, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-10-01 18:53:10,124  SpectraRegion  INFO: Running [0] against [5, 7, 9, 3, 8, 2, 4, 6, 1]\n",
      "INFO:SpectraRegion:Running [0] against [5, 7, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-10-01 18:53:10,125  SpectraRegion  INFO: DE result key: ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9))\n",
      "INFO:SpectraRegion:DE result key: ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-10-01 18:53:10,126  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:53:10,130  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-10-01 18:53:10,131  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:53:10,132  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:53:10,136  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9)) with (1307, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9)) with (1307, 7) results (filtered)\n",
      "2020-10-01 18:53:10,275  SpectraRegion  INFO: Created matrices with shape (1930, 17900) and (725, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (1930, 17900) and (725, 17900) (target, bg)\n",
      "2020-10-01 18:53:14,664  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-10-01 18:53:14,665  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:53:14,666  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:53:14,670  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9)) with (1307, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 8, 9)) with (1307, 7) results (filtered)\n",
      "2020-10-01 18:53:14,738  SpectraRegion  INFO: Created matrices with shape (1930, 17900) and (725, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (1930, 17900) and (725, 17900) (target, bg)\n",
      "2020-10-01 18:53:19,139  SpectraRegion  INFO: DE data for case: [5]\n",
      "INFO:SpectraRegion:DE data for case: [5]\n",
      "2020-10-01 18:53:19,141  SpectraRegion  INFO: DE data for control: [0, 7, 9, 3, 8, 2, 4, 6, 1]\n",
      "INFO:SpectraRegion:DE data for control: [0, 7, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-10-01 18:53:19,141  SpectraRegion  INFO: Running [5] against [0, 7, 9, 3, 8, 2, 4, 6, 1]\n",
      "INFO:SpectraRegion:Running [5] against [0, 7, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-10-01 18:53:19,143  SpectraRegion  INFO: DE result key: ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9))\n",
      "INFO:SpectraRegion:DE result key: ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9))\n",
      "2020-10-01 18:53:19,144  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:53:19,147  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-10-01 18:53:19,148  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:53:19,149  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:53:19,153  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9)) with (450, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9)) with (450, 7) results (filtered)\n",
      "2020-10-01 18:53:19,218  SpectraRegion  INFO: Created matrices with shape (100, 17900) and (2555, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (100, 17900) and (2555, 17900) (target, bg)\n",
      "2020-10-01 18:53:21,028  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-10-01 18:53:21,029  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:53:21,030  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:53:21,035  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9)) with (450, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 8, 9)) with (450, 7) results (filtered)\n",
      "2020-10-01 18:53:21,096  SpectraRegion  INFO: Created matrices with shape (100, 17900) and (2555, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (100, 17900) and (2555, 17900) (target, bg)\n",
      "2020-10-01 18:53:22,886  SpectraRegion  INFO: DE data for case: [7]\n",
      "INFO:SpectraRegion:DE data for case: [7]\n",
      "2020-10-01 18:53:22,887  SpectraRegion  INFO: DE data for control: [0, 5, 9, 3, 8, 2, 4, 6, 1]\n",
      "INFO:SpectraRegion:DE data for control: [0, 5, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-10-01 18:53:22,888  SpectraRegion  INFO: Running [7] against [0, 5, 9, 3, 8, 2, 4, 6, 1]\n",
      "INFO:SpectraRegion:Running [7] against [0, 5, 9, 3, 8, 2, 4, 6, 1]\n",
      "2020-10-01 18:53:22,888  SpectraRegion  INFO: DE result key: ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9))\n",
      "INFO:SpectraRegion:DE result key: ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9))\n",
      "2020-10-01 18:53:22,889  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:53:22,892  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9)) with (17900, 7) results\n",
      "2020-10-01 18:53:22,894  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:53:22,895  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:53:22,900  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9)) with (680, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9)) with (680, 7) results (filtered)\n",
      "2020-10-01 18:53:22,966  SpectraRegion  INFO: Created matrices with shape (161, 17900) and (2494, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (161, 17900) and (2494, 17900) (target, bg)\n",
      "2020-10-01 18:53:25,372  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9)) with (17900, 7) results\n",
      "2020-10-01 18:53:25,373  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:53:25,374  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:53:25,377  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9)) with (680, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 8, 9)) with (680, 7) results (filtered)\n",
      "2020-10-01 18:53:25,438  SpectraRegion  INFO: Created matrices with shape (161, 17900) and (2494, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (161, 17900) and (2494, 17900) (target, bg)\n",
      "2020-10-01 18:53:27,830  SpectraRegion  INFO: DE data for case: [9]\n",
      "INFO:SpectraRegion:DE data for case: [9]\n",
      "2020-10-01 18:53:27,831  SpectraRegion  INFO: DE data for control: [0, 5, 7, 3, 8, 2, 4, 6, 1]\n",
      "INFO:SpectraRegion:DE data for control: [0, 5, 7, 3, 8, 2, 4, 6, 1]\n",
      "2020-10-01 18:53:27,832  SpectraRegion  INFO: Running [9] against [0, 5, 7, 3, 8, 2, 4, 6, 1]\n",
      "INFO:SpectraRegion:Running [9] against [0, 5, 7, 3, 8, 2, 4, 6, 1]\n",
      "2020-10-01 18:53:27,833  SpectraRegion  INFO: DE result key: ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8))\n",
      "INFO:SpectraRegion:DE result key: ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8))\n",
      "2020-10-01 18:53:27,834  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:53:27,837  SpectraRegion  INFO: DE result for case ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8)) with (17900, 7) results\n",
      "2020-10-01 18:53:27,838  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:53:27,838  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:53:27,842  SpectraRegion  INFO: DE result for case ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8)) with (88, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8)) with (88, 7) results (filtered)\n",
      "2020-10-01 18:53:27,909  SpectraRegion  INFO: Created matrices with shape (89, 17900) and (2566, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (89, 17900) and (2566, 17900) (target, bg)\n",
      "2020-10-01 18:53:28,025  SpectraRegion  INFO: DE result for case ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8)) with (17900, 7) results\n",
      "2020-10-01 18:53:28,027  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:53:28,028  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:53:28,032  SpectraRegion  INFO: DE result for case ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8)) with (65, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((9,), (0, 1, 2, 3, 4, 5, 6, 7, 8)) with (65, 7) results (filtered)\n",
      "2020-10-01 18:53:28,098  SpectraRegion  INFO: Created matrices with shape (89, 17900) and (2566, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (89, 17900) and (2566, 17900) (target, bg)\n",
      "2020-10-01 18:53:28,180  SpectraRegion  INFO: DE data for case: [3]\n",
      "INFO:SpectraRegion:DE data for case: [3]\n",
      "2020-10-01 18:53:28,181  SpectraRegion  INFO: DE data for control: [0, 5, 7, 9, 8, 2, 4, 6, 1]\n",
      "INFO:SpectraRegion:DE data for control: [0, 5, 7, 9, 8, 2, 4, 6, 1]\n",
      "2020-10-01 18:53:28,182  SpectraRegion  INFO: Running [3] against [0, 5, 7, 9, 8, 2, 4, 6, 1]\n",
      "INFO:SpectraRegion:Running [3] against [0, 5, 7, 9, 8, 2, 4, 6, 1]\n",
      "2020-10-01 18:53:28,183  SpectraRegion  INFO: DE result key: ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9))\n",
      "INFO:SpectraRegion:DE result key: ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9))\n",
      "2020-10-01 18:53:28,184  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:53:28,188  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-10-01 18:53:28,189  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:53:28,190  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:53:28,195  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9)) with (1263, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9)) with (1263, 7) results (filtered)\n",
      "2020-10-01 18:53:28,261  SpectraRegion  INFO: Created matrices with shape (51, 17900) and (2604, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (51, 17900) and (2604, 17900) (target, bg)\n",
      "2020-10-01 18:53:32,627  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-10-01 18:53:32,628  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:53:32,630  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:53:32,633  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9)) with (1261, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 8, 9)) with (1261, 7) results (filtered)\n",
      "2020-10-01 18:53:32,695  SpectraRegion  INFO: Created matrices with shape (51, 17900) and (2604, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (51, 17900) and (2604, 17900) (target, bg)\n",
      "2020-10-01 18:53:37,065  SpectraRegion  INFO: DE data for case: [8]\n",
      "INFO:SpectraRegion:DE data for case: [8]\n",
      "2020-10-01 18:53:37,066  SpectraRegion  INFO: DE data for control: [0, 5, 7, 9, 3, 2, 4, 6, 1]\n",
      "INFO:SpectraRegion:DE data for control: [0, 5, 7, 9, 3, 2, 4, 6, 1]\n",
      "2020-10-01 18:53:37,067  SpectraRegion  INFO: Running [8] against [0, 5, 7, 9, 3, 2, 4, 6, 1]\n",
      "INFO:SpectraRegion:Running [8] against [0, 5, 7, 9, 3, 2, 4, 6, 1]\n",
      "2020-10-01 18:53:37,068  SpectraRegion  INFO: DE result key: ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9))\n",
      "INFO:SpectraRegion:DE result key: ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9))\n",
      "2020-10-01 18:53:37,069  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:53:37,072  SpectraRegion  INFO: DE result for case ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9)) with (17900, 7) results\n",
      "2020-10-01 18:53:37,073  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:53:37,074  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:53:37,078  SpectraRegion  INFO: DE result for case ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9)) with (253, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9)) with (253, 7) results (filtered)\n",
      "2020-10-01 18:53:37,141  SpectraRegion  INFO: Created matrices with shape (55, 17900) and (2600, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (55, 17900) and (2600, 17900) (target, bg)\n",
      "2020-10-01 18:53:37,717  SpectraRegion  INFO: DE result for case ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9)) with (17900, 7) results\n",
      "2020-10-01 18:53:37,718  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:53:37,719  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:53:37,723  SpectraRegion  INFO: DE result for case ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9)) with (250, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((8,), (0, 1, 2, 3, 4, 5, 6, 7, 9)) with (250, 7) results (filtered)\n",
      "2020-10-01 18:53:37,785  SpectraRegion  INFO: Created matrices with shape (55, 17900) and (2600, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (55, 17900) and (2600, 17900) (target, bg)\n",
      "2020-10-01 18:53:38,368  SpectraRegion  INFO: DE data for case: [2]\n",
      "INFO:SpectraRegion:DE data for case: [2]\n",
      "2020-10-01 18:53:38,370  SpectraRegion  INFO: DE data for control: [0, 5, 7, 9, 3, 8, 4, 6, 1]\n",
      "INFO:SpectraRegion:DE data for control: [0, 5, 7, 9, 3, 8, 4, 6, 1]\n",
      "2020-10-01 18:53:38,370  SpectraRegion  INFO: Running [2] against [0, 5, 7, 9, 3, 8, 4, 6, 1]\n",
      "INFO:SpectraRegion:Running [2] against [0, 5, 7, 9, 3, 8, 4, 6, 1]\n",
      "2020-10-01 18:53:38,371  SpectraRegion  INFO: DE result key: ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9))\n",
      "INFO:SpectraRegion:DE result key: ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-10-01 18:53:38,372  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:53:38,375  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-10-01 18:53:38,376  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:53:38,377  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:53:38,382  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9)) with (1796, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9)) with (1796, 7) results (filtered)\n",
      "2020-10-01 18:53:38,445  SpectraRegion  INFO: Created matrices with shape (150, 17900) and (2505, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (150, 17900) and (2505, 17900) (target, bg)\n",
      "2020-10-01 18:53:45,157  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-10-01 18:53:45,158  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:53:45,159  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:53:45,163  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9)) with (1796, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 8, 9)) with (1796, 7) results (filtered)\n",
      "2020-10-01 18:53:45,229  SpectraRegion  INFO: Created matrices with shape (150, 17900) and (2505, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (150, 17900) and (2505, 17900) (target, bg)\n",
      "2020-10-01 18:53:51,985  SpectraRegion  INFO: DE data for case: [4]\n",
      "INFO:SpectraRegion:DE data for case: [4]\n",
      "2020-10-01 18:53:51,986  SpectraRegion  INFO: DE data for control: [0, 5, 7, 9, 3, 8, 2, 6, 1]\n",
      "INFO:SpectraRegion:DE data for control: [0, 5, 7, 9, 3, 8, 2, 6, 1]\n",
      "2020-10-01 18:53:51,987  SpectraRegion  INFO: Running [4] against [0, 5, 7, 9, 3, 8, 2, 6, 1]\n",
      "INFO:SpectraRegion:Running [4] against [0, 5, 7, 9, 3, 8, 2, 6, 1]\n",
      "2020-10-01 18:53:51,988  SpectraRegion  INFO: DE result key: ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9))\n",
      "INFO:SpectraRegion:DE result key: ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9))\n",
      "2020-10-01 18:53:51,989  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:53:51,992  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-10-01 18:53:51,993  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:53:51,996  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:53:52,000  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9)) with (1742, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9)) with (1742, 7) results (filtered)\n",
      "2020-10-01 18:53:52,066  SpectraRegion  INFO: Created matrices with shape (70, 17900) and (2585, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (70, 17900) and (2585, 17900) (target, bg)\n",
      "2020-10-01 18:53:56,828  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-10-01 18:53:56,829  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:53:56,830  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:53:56,835  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9)) with (1742, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 8, 9)) with (1742, 7) results (filtered)\n",
      "2020-10-01 18:53:56,896  SpectraRegion  INFO: Created matrices with shape (70, 17900) and (2585, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (70, 17900) and (2585, 17900) (target, bg)\n",
      "2020-10-01 18:54:01,662  SpectraRegion  INFO: DE data for case: [6]\n",
      "INFO:SpectraRegion:DE data for case: [6]\n",
      "2020-10-01 18:54:01,664  SpectraRegion  INFO: DE data for control: [0, 5, 7, 9, 3, 8, 2, 4, 1]\n",
      "INFO:SpectraRegion:DE data for control: [0, 5, 7, 9, 3, 8, 2, 4, 1]\n",
      "2020-10-01 18:54:01,665  SpectraRegion  INFO: Running [6] against [0, 5, 7, 9, 3, 8, 2, 4, 1]\n",
      "INFO:SpectraRegion:Running [6] against [0, 5, 7, 9, 3, 8, 2, 4, 1]\n",
      "2020-10-01 18:54:01,666  SpectraRegion  INFO: DE result key: ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9))\n",
      "INFO:SpectraRegion:DE result key: ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9))\n",
      "2020-10-01 18:54:01,666  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:54:01,669  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9)) with (17900, 7) results\n",
      "2020-10-01 18:54:01,670  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:54:01,671  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:54:01,676  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9)) with (2053, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9)) with (2053, 7) results (filtered)\n",
      "2020-10-01 18:54:01,741  SpectraRegion  INFO: Created matrices with shape (27, 17900) and (2628, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (27, 17900) and (2628, 17900) (target, bg)\n",
      "2020-10-01 18:54:08,808  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9)) with (17900, 7) results\n",
      "2020-10-01 18:54:08,809  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:54:08,810  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:54:08,815  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9)) with (2058, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 8, 9)) with (2058, 7) results (filtered)\n",
      "2020-10-01 18:54:08,878  SpectraRegion  INFO: Created matrices with shape (27, 17900) and (2628, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (27, 17900) and (2628, 17900) (target, bg)\n",
      "2020-10-01 18:54:15,921  SpectraRegion  INFO: DE data for case: [1]\n",
      "INFO:SpectraRegion:DE data for case: [1]\n",
      "2020-10-01 18:54:15,922  SpectraRegion  INFO: DE data for control: [0, 5, 7, 9, 3, 8, 2, 4, 6]\n",
      "INFO:SpectraRegion:DE data for control: [0, 5, 7, 9, 3, 8, 2, 4, 6]\n",
      "2020-10-01 18:54:15,923  SpectraRegion  INFO: Running [1] against [0, 5, 7, 9, 3, 8, 2, 4, 6]\n",
      "INFO:SpectraRegion:Running [1] against [0, 5, 7, 9, 3, 8, 2, 4, 6]\n",
      "2020-10-01 18:54:15,924  SpectraRegion  INFO: DE result key: ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9))\n",
      "INFO:SpectraRegion:DE result key: ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9))\n",
      "2020-10-01 18:54:15,924  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:54:15,927  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-10-01 18:54:15,929  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:54:15,930  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:54:15,934  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9)) with (3036, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9)) with (3036, 7) results (filtered)\n",
      "2020-10-01 18:54:15,996  SpectraRegion  INFO: Created matrices with shape (22, 17900) and (2633, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (22, 17900) and (2633, 17900) (target, bg)\n",
      "2020-10-01 18:54:27,189  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9)) with (17900, 7) results\n",
      "2020-10-01 18:54:27,191  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:54:27,192  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:54:27,196  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9)) with (3039, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 8, 9)) with (3039, 7) results (filtered)\n",
      "2020-10-01 18:54:27,258  SpectraRegion  INFO: Created matrices with shape (22, 17900) and (2633, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (22, 17900) and (2633, 17900) (target, bg)\n",
      "2020-10-01 18:54:38,558  SpectraRegion  INFO: No cell type info for cluster: '0'\n",
      "INFO:SpectraRegion:No cell type info for cluster: '0'\n",
      "2020-10-01 18:54:38,560  SpectraRegion  INFO: No cell type info for cluster: '3'\n",
      "INFO:SpectraRegion:No cell type info for cluster: '3'\n",
      "2020-10-01 18:54:38,573  SpectraRegion  INFO: Cell-type assigned: 1 -> Megakaryocytes;Immune system\n",
      "INFO:SpectraRegion:Cell-type assigned: 1 -> Megakaryocytes;Immune system\n",
      "2020-10-01 18:54:38,575  SpectraRegion  INFO: Cell-type assigned: 4 -> B cells;Immune system\n",
      "INFO:SpectraRegion:Cell-type assigned: 4 -> B cells;Immune system\n",
      "2020-10-01 18:54:38,577  SpectraRegion  INFO: Cell-type assigned: 5 -> Basophils;Immune system\n",
      "INFO:SpectraRegion:Cell-type assigned: 5 -> Basophils;Immune system\n",
      "2020-10-01 18:54:38,578  SpectraRegion  INFO: Cell-type assigned: 7 -> Adipocytes;Connective tissue\n",
      "INFO:SpectraRegion:Cell-type assigned: 7 -> Adipocytes;Connective tissue\n",
      "2020-10-01 18:54:38,580  SpectraRegion  INFO: Cell-type assigned: 15 -> Cardiomyocytes;Heart\n",
      "INFO:SpectraRegion:Cell-type assigned: 15 -> Cardiomyocytes;Heart\n",
      "2020-10-01 18:54:38,588  SpectraRegion  INFO: Segment Image: ./aorta3d/slided/slided.1.clustering.png\n",
      "INFO:SpectraRegion:Segment Image: ./aorta3d/slided/slided.1.clustering.png\n",
      "2020-10-01 18:54:38,596  SpectraRegion  INFO: Segment Matrix: /mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.1.matrix.npy\n",
      "INFO:SpectraRegion:Segment Matrix: /mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.1.matrix.npy\n",
      "2020-10-01 18:54:38,602  SpectraRegion  INFO: Starting Marker Proteins Analysis\n",
      "INFO:SpectraRegion:Starting Marker Proteins Analysis\n",
      "2020-10-01 18:54:38,606  SpectraRegion  INFO: DE data for case: [0]\n",
      "INFO:SpectraRegion:DE data for case: [0]\n",
      "2020-10-01 18:54:38,608  SpectraRegion  INFO: DE data for control: [15, 2, 6, 3, 1, 7, 5, 4]\n",
      "INFO:SpectraRegion:DE data for control: [15, 2, 6, 3, 1, 7, 5, 4]\n",
      "2020-10-01 18:54:38,610  SpectraRegion  INFO: Running [0] against [15, 2, 6, 3, 1, 7, 5, 4]\n",
      "INFO:SpectraRegion:Running [0] against [15, 2, 6, 3, 1, 7, 5, 4]\n",
      "2020-10-01 18:54:38,611  SpectraRegion  INFO: DE result key: ((0,), (1, 2, 3, 4, 5, 6, 7, 15))\n",
      "INFO:SpectraRegion:DE result key: ((0,), (1, 2, 3, 4, 5, 6, 7, 15))\n",
      "2020-10-01 18:54:38,612  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:54:38,616  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-10-01 18:54:38,617  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:54:38,618  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:54:38,622  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 15)) with (1086, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 15)) with (1086, 7) results (filtered)\n",
      "2020-10-01 18:54:38,733  SpectraRegion  INFO: Created matrices with shape (1923, 17900) and (485, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (1923, 17900) and (485, 17900) (target, bg)\n",
      "2020-10-01 18:54:42,214  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-10-01 18:54:42,215  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:54:42,217  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:54:42,221  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 15)) with (1086, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((0,), (1, 2, 3, 4, 5, 6, 7, 15)) with (1086, 7) results (filtered)\n",
      "2020-10-01 18:54:42,280  SpectraRegion  INFO: Created matrices with shape (1923, 17900) and (485, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (1923, 17900) and (485, 17900) (target, bg)\n",
      "2020-10-01 18:54:45,762  SpectraRegion  INFO: DE data for case: [15]\n",
      "INFO:SpectraRegion:DE data for case: [15]\n",
      "2020-10-01 18:54:45,764  SpectraRegion  INFO: DE data for control: [0, 2, 6, 3, 1, 7, 5, 4]\n",
      "INFO:SpectraRegion:DE data for control: [0, 2, 6, 3, 1, 7, 5, 4]\n",
      "2020-10-01 18:54:45,764  SpectraRegion  INFO: Running [15] against [0, 2, 6, 3, 1, 7, 5, 4]\n",
      "INFO:SpectraRegion:Running [15] against [0, 2, 6, 3, 1, 7, 5, 4]\n",
      "2020-10-01 18:54:45,765  SpectraRegion  INFO: DE result key: ((15,), (0, 1, 2, 3, 4, 5, 6, 7))\n",
      "INFO:SpectraRegion:DE result key: ((15,), (0, 1, 2, 3, 4, 5, 6, 7))\n",
      "2020-10-01 18:54:45,766  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:54:45,770  SpectraRegion  INFO: DE result for case ((15,), (0, 1, 2, 3, 4, 5, 6, 7)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((15,), (0, 1, 2, 3, 4, 5, 6, 7)) with (17900, 7) results\n",
      "2020-10-01 18:54:45,771  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:54:45,772  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:54:45,776  SpectraRegion  INFO: DE result for case ((15,), (0, 1, 2, 3, 4, 5, 6, 7)) with (12, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((15,), (0, 1, 2, 3, 4, 5, 6, 7)) with (12, 7) results (filtered)\n",
      "2020-10-01 18:54:45,836  SpectraRegion  INFO: Created matrices with shape (91, 17900) and (2317, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (91, 17900) and (2317, 17900) (target, bg)\n",
      "2020-10-01 18:54:45,860  SpectraRegion  INFO: DE result for case ((15,), (0, 1, 2, 3, 4, 5, 6, 7)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((15,), (0, 1, 2, 3, 4, 5, 6, 7)) with (17900, 7) results\n",
      "2020-10-01 18:54:45,861  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:54:45,863  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:54:45,870  SpectraRegion  INFO: DE result for case ((15,), (0, 1, 2, 3, 4, 5, 6, 7)) with (12, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((15,), (0, 1, 2, 3, 4, 5, 6, 7)) with (12, 7) results (filtered)\n",
      "2020-10-01 18:54:46,024  SpectraRegion  INFO: Created matrices with shape (91, 17900) and (2317, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (91, 17900) and (2317, 17900) (target, bg)\n",
      "2020-10-01 18:54:46,049  SpectraRegion  INFO: DE data for case: [2]\n",
      "INFO:SpectraRegion:DE data for case: [2]\n",
      "2020-10-01 18:54:46,051  SpectraRegion  INFO: DE data for control: [0, 15, 6, 3, 1, 7, 5, 4]\n",
      "INFO:SpectraRegion:DE data for control: [0, 15, 6, 3, 1, 7, 5, 4]\n",
      "2020-10-01 18:54:46,052  SpectraRegion  INFO: Running [2] against [0, 15, 6, 3, 1, 7, 5, 4]\n",
      "INFO:SpectraRegion:Running [2] against [0, 15, 6, 3, 1, 7, 5, 4]\n",
      "2020-10-01 18:54:46,053  SpectraRegion  INFO: DE result key: ((2,), (0, 1, 3, 4, 5, 6, 7, 15))\n",
      "INFO:SpectraRegion:DE result key: ((2,), (0, 1, 3, 4, 5, 6, 7, 15))\n",
      "2020-10-01 18:54:46,054  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:54:46,057  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-10-01 18:54:46,058  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:54:46,059  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:54:46,064  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 15)) with (970, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 15)) with (970, 7) results (filtered)\n",
      "2020-10-01 18:54:46,126  SpectraRegion  INFO: Created matrices with shape (79, 17900) and (2329, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (79, 17900) and (2329, 17900) (target, bg)\n",
      "2020-10-01 18:54:49,555  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-10-01 18:54:49,556  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:54:49,557  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:54:49,561  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 15)) with (970, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((2,), (0, 1, 3, 4, 5, 6, 7, 15)) with (970, 7) results (filtered)\n",
      "2020-10-01 18:54:49,619  SpectraRegion  INFO: Created matrices with shape (79, 17900) and (2329, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (79, 17900) and (2329, 17900) (target, bg)\n",
      "2020-10-01 18:54:52,974  SpectraRegion  INFO: DE data for case: [6]\n",
      "INFO:SpectraRegion:DE data for case: [6]\n",
      "2020-10-01 18:54:52,975  SpectraRegion  INFO: DE data for control: [0, 15, 2, 3, 1, 7, 5, 4]\n",
      "INFO:SpectraRegion:DE data for control: [0, 15, 2, 3, 1, 7, 5, 4]\n",
      "2020-10-01 18:54:52,976  SpectraRegion  INFO: Running [6] against [0, 15, 2, 3, 1, 7, 5, 4]\n",
      "INFO:SpectraRegion:Running [6] against [0, 15, 2, 3, 1, 7, 5, 4]\n",
      "2020-10-01 18:54:52,977  SpectraRegion  INFO: DE result key: ((6,), (0, 1, 2, 3, 4, 5, 7, 15))\n",
      "INFO:SpectraRegion:DE result key: ((6,), (0, 1, 2, 3, 4, 5, 7, 15))\n",
      "2020-10-01 18:54:52,978  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:54:52,982  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 15)) with (17900, 7) results\n",
      "2020-10-01 18:54:52,983  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:54:52,984  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:54:52,987  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 15)) with (1032, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 15)) with (1032, 7) results (filtered)\n",
      "2020-10-01 18:54:53,047  SpectraRegion  INFO: Created matrices with shape (86, 17900) and (2322, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (86, 17900) and (2322, 17900) (target, bg)\n",
      "2020-10-01 18:54:56,182  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 15)) with (17900, 7) results\n",
      "2020-10-01 18:54:56,183  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:54:56,184  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:54:56,188  SpectraRegion  INFO: DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 15)) with (1032, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((6,), (0, 1, 2, 3, 4, 5, 7, 15)) with (1032, 7) results (filtered)\n",
      "2020-10-01 18:54:56,247  SpectraRegion  INFO: Created matrices with shape (86, 17900) and (2322, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (86, 17900) and (2322, 17900) (target, bg)\n",
      "2020-10-01 18:54:59,341  SpectraRegion  INFO: DE data for case: [3]\n",
      "INFO:SpectraRegion:DE data for case: [3]\n",
      "2020-10-01 18:54:59,342  SpectraRegion  INFO: DE data for control: [0, 15, 2, 6, 1, 7, 5, 4]\n",
      "INFO:SpectraRegion:DE data for control: [0, 15, 2, 6, 1, 7, 5, 4]\n",
      "2020-10-01 18:54:59,343  SpectraRegion  INFO: Running [3] against [0, 15, 2, 6, 1, 7, 5, 4]\n",
      "INFO:SpectraRegion:Running [3] against [0, 15, 2, 6, 1, 7, 5, 4]\n",
      "2020-10-01 18:54:59,344  SpectraRegion  INFO: DE result key: ((3,), (0, 1, 2, 4, 5, 6, 7, 15))\n",
      "INFO:SpectraRegion:DE result key: ((3,), (0, 1, 2, 4, 5, 6, 7, 15))\n",
      "2020-10-01 18:54:59,345  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:54:59,348  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-10-01 18:54:59,349  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:54:59,350  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:54:59,354  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 15)) with (977, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 15)) with (977, 7) results (filtered)\n",
      "2020-10-01 18:54:59,410  SpectraRegion  INFO: Created matrices with shape (99, 17900) and (2309, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (99, 17900) and (2309, 17900) (target, bg)\n",
      "2020-10-01 18:55:02,789  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-10-01 18:55:02,791  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:55:02,791  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:55:02,795  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 15)) with (977, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((3,), (0, 1, 2, 4, 5, 6, 7, 15)) with (977, 7) results (filtered)\n",
      "2020-10-01 18:55:02,853  SpectraRegion  INFO: Created matrices with shape (99, 17900) and (2309, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (99, 17900) and (2309, 17900) (target, bg)\n",
      "2020-10-01 18:55:06,220  SpectraRegion  INFO: DE data for case: [1]\n",
      "INFO:SpectraRegion:DE data for case: [1]\n",
      "2020-10-01 18:55:06,221  SpectraRegion  INFO: DE data for control: [0, 15, 2, 6, 3, 7, 5, 4]\n",
      "INFO:SpectraRegion:DE data for control: [0, 15, 2, 6, 3, 7, 5, 4]\n",
      "2020-10-01 18:55:06,222  SpectraRegion  INFO: Running [1] against [0, 15, 2, 6, 3, 7, 5, 4]\n",
      "INFO:SpectraRegion:Running [1] against [0, 15, 2, 6, 3, 7, 5, 4]\n",
      "2020-10-01 18:55:06,222  SpectraRegion  INFO: DE result key: ((1,), (0, 2, 3, 4, 5, 6, 7, 15))\n",
      "INFO:SpectraRegion:DE result key: ((1,), (0, 2, 3, 4, 5, 6, 7, 15))\n",
      "2020-10-01 18:55:06,224  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:55:06,227  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-10-01 18:55:06,228  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:55:06,229  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:55:06,233  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 15)) with (1944, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 15)) with (1944, 7) results (filtered)\n",
      "2020-10-01 18:55:06,291  SpectraRegion  INFO: Created matrices with shape (61, 17900) and (2347, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (61, 17900) and (2347, 17900) (target, bg)\n",
      "2020-10-01 18:55:13,341  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-10-01 18:55:13,342  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:55:13,343  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:55:13,348  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 15)) with (1944, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((1,), (0, 2, 3, 4, 5, 6, 7, 15)) with (1944, 7) results (filtered)\n",
      "2020-10-01 18:55:13,405  SpectraRegion  INFO: Created matrices with shape (61, 17900) and (2347, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (61, 17900) and (2347, 17900) (target, bg)\n",
      "2020-10-01 18:55:20,437  SpectraRegion  INFO: DE data for case: [7]\n",
      "INFO:SpectraRegion:DE data for case: [7]\n",
      "2020-10-01 18:55:20,438  SpectraRegion  INFO: DE data for control: [0, 15, 2, 6, 3, 1, 5, 4]\n",
      "INFO:SpectraRegion:DE data for control: [0, 15, 2, 6, 3, 1, 5, 4]\n",
      "2020-10-01 18:55:20,439  SpectraRegion  INFO: Running [7] against [0, 15, 2, 6, 3, 1, 5, 4]\n",
      "INFO:SpectraRegion:Running [7] against [0, 15, 2, 6, 3, 1, 5, 4]\n",
      "2020-10-01 18:55:20,440  SpectraRegion  INFO: DE result key: ((7,), (0, 1, 2, 3, 4, 5, 6, 15))\n",
      "INFO:SpectraRegion:DE result key: ((7,), (0, 1, 2, 3, 4, 5, 6, 15))\n",
      "2020-10-01 18:55:20,441  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:55:20,444  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 15)) with (17900, 7) results\n",
      "2020-10-01 18:55:20,445  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:55:20,446  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:55:20,450  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 15)) with (1882, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 15)) with (1882, 7) results (filtered)\n",
      "2020-10-01 18:55:20,507  SpectraRegion  INFO: Created matrices with shape (39, 17900) and (2369, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (39, 17900) and (2369, 17900) (target, bg)\n",
      "2020-10-01 18:55:26,145  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 15)) with (17900, 7) results\n",
      "2020-10-01 18:55:26,146  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:55:26,147  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:55:26,150  SpectraRegion  INFO: DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 15)) with (1888, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((7,), (0, 1, 2, 3, 4, 5, 6, 15)) with (1888, 7) results (filtered)\n",
      "2020-10-01 18:55:26,207  SpectraRegion  INFO: Created matrices with shape (39, 17900) and (2369, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (39, 17900) and (2369, 17900) (target, bg)\n",
      "2020-10-01 18:55:31,880  SpectraRegion  INFO: DE data for case: [5]\n",
      "INFO:SpectraRegion:DE data for case: [5]\n",
      "2020-10-01 18:55:31,881  SpectraRegion  INFO: DE data for control: [0, 15, 2, 6, 3, 1, 7, 4]\n",
      "INFO:SpectraRegion:DE data for control: [0, 15, 2, 6, 3, 1, 7, 4]\n",
      "2020-10-01 18:55:31,882  SpectraRegion  INFO: Running [5] against [0, 15, 2, 6, 3, 1, 7, 4]\n",
      "INFO:SpectraRegion:Running [5] against [0, 15, 2, 6, 3, 1, 7, 4]\n",
      "2020-10-01 18:55:31,883  SpectraRegion  INFO: DE result key: ((5,), (0, 1, 2, 3, 4, 6, 7, 15))\n",
      "INFO:SpectraRegion:DE result key: ((5,), (0, 1, 2, 3, 4, 6, 7, 15))\n",
      "2020-10-01 18:55:31,884  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:55:31,887  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 15)) with (17900, 7) results\n",
      "2020-10-01 18:55:31,888  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:55:31,889  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:55:31,892  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 15)) with (1840, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 15)) with (1840, 7) results (filtered)\n",
      "2020-10-01 18:55:31,949  SpectraRegion  INFO: Created matrices with shape (19, 17900) and (2389, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (19, 17900) and (2389, 17900) (target, bg)\n",
      "2020-10-01 18:55:36,968  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 15)) with (17900, 7) results\n",
      "2020-10-01 18:55:36,969  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:55:36,970  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:55:36,973  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 15)) with (1874, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((5,), (0, 1, 2, 3, 4, 6, 7, 15)) with (1874, 7) results (filtered)\n",
      "2020-10-01 18:55:37,031  SpectraRegion  INFO: Created matrices with shape (19, 17900) and (2389, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (19, 17900) and (2389, 17900) (target, bg)\n",
      "2020-10-01 18:55:41,699  SpectraRegion  INFO: DE data for case: [4]\n",
      "INFO:SpectraRegion:DE data for case: [4]\n",
      "2020-10-01 18:55:41,700  SpectraRegion  INFO: DE data for control: [0, 15, 2, 6, 3, 1, 7, 5]\n",
      "INFO:SpectraRegion:DE data for control: [0, 15, 2, 6, 3, 1, 7, 5]\n",
      "2020-10-01 18:55:41,702  SpectraRegion  INFO: Running [4] against [0, 15, 2, 6, 3, 1, 7, 5]\n",
      "INFO:SpectraRegion:Running [4] against [0, 15, 2, 6, 3, 1, 7, 5]\n",
      "2020-10-01 18:55:41,703  SpectraRegion  INFO: DE result key: ((4,), (0, 1, 2, 3, 5, 6, 7, 15))\n",
      "INFO:SpectraRegion:DE result key: ((4,), (0, 1, 2, 3, 5, 6, 7, 15))\n",
      "2020-10-01 18:55:41,704  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:55:41,707  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-10-01 18:55:41,708  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:55:41,709  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:55:41,713  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 15)) with (946, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 15)) with (946, 7) results (filtered)\n",
      "2020-10-01 18:55:41,770  SpectraRegion  INFO: Created matrices with shape (11, 17900) and (2397, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (11, 17900) and (2397, 17900) (target, bg)\n",
      "2020-10-01 18:55:44,710  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 15)) with (17900, 7) results\n",
      "2020-10-01 18:55:44,711  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:55:44,712  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:55:44,716  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 15)) with (1065, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((4,), (0, 1, 2, 3, 5, 6, 7, 15)) with (1065, 7) results (filtered)\n",
      "2020-10-01 18:55:44,774  SpectraRegion  INFO: Created matrices with shape (11, 17900) and (2397, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (11, 17900) and (2397, 17900) (target, bg)\n",
      "2020-10-01 18:55:47,823  SpectraRegion  INFO: No cell type info for cluster: '0'\n",
      "INFO:SpectraRegion:No cell type info for cluster: '0'\n",
      "2020-10-01 18:55:47,824  SpectraRegion  INFO: No cell type info for cluster: '2'\n",
      "INFO:SpectraRegion:No cell type info for cluster: '2'\n",
      "2020-10-01 18:55:47,825  SpectraRegion  INFO: No cell type info for cluster: '6'\n",
      "INFO:SpectraRegion:No cell type info for cluster: '6'\n",
      "2020-10-01 18:55:47,826  SpectraRegion  INFO: No cell type info for cluster: '3'\n",
      "INFO:SpectraRegion:No cell type info for cluster: '3'\n",
      "2020-10-01 18:55:47,857  SpectraRegion  INFO: Cell-type assigned: 1 -> Cardiomyocytes;Heart\n",
      "INFO:SpectraRegion:Cell-type assigned: 1 -> Cardiomyocytes;Heart\n",
      "2020-10-01 18:55:47,858  SpectraRegion  INFO: Cell-type assigned: 2 -> Adipocytes;Connective tissue\n",
      "INFO:SpectraRegion:Cell-type assigned: 2 -> Adipocytes;Connective tissue\n",
      "2020-10-01 18:55:47,862  SpectraRegion  INFO: Cell-type assigned: 4 -> Platelets;Blood\n",
      "INFO:SpectraRegion:Cell-type assigned: 4 -> Platelets;Blood\n",
      "2020-10-01 18:55:47,864  SpectraRegion  INFO: Cell-type assigned: 5 -> Monocytes;Immune system\n",
      "INFO:SpectraRegion:Cell-type assigned: 5 -> Monocytes;Immune system\n",
      "2020-10-01 18:55:47,865  SpectraRegion  INFO: Cell-type assigned: 10 -> Basophils;Immune system\n",
      "INFO:SpectraRegion:Cell-type assigned: 10 -> Basophils;Immune system\n",
      "2020-10-01 18:55:47,867  SpectraRegion  INFO: Cell-type assigned: 11 -> NK cells;Immune system\n",
      "INFO:SpectraRegion:Cell-type assigned: 11 -> NK cells;Immune system\n",
      "2020-10-01 18:55:47,868  SpectraRegion  INFO: Cell-type assigned: 12 -> Cardiomyocytes;Heart\n",
      "INFO:SpectraRegion:Cell-type assigned: 12 -> Cardiomyocytes;Heart\n",
      "2020-10-01 18:55:47,870  SpectraRegion  INFO: Cell-type assigned: 13 -> Cardiomyocytes;Heart\n",
      "INFO:SpectraRegion:Cell-type assigned: 13 -> Cardiomyocytes;Heart\n",
      "2020-10-01 18:55:47,878  SpectraRegion  INFO: Segment Image: ./aorta3d/slided/slided.4.clustering.png\n",
      "INFO:SpectraRegion:Segment Image: ./aorta3d/slided/slided.4.clustering.png\n",
      "2020-10-01 18:55:47,884  SpectraRegion  INFO: Segment Matrix: /mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.4.matrix.npy\n",
      "INFO:SpectraRegion:Segment Matrix: /mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.4.matrix.npy\n",
      "2020-10-01 18:55:47,890  SpectraRegion  INFO: Starting Marker Proteins Analysis\n",
      "INFO:SpectraRegion:Starting Marker Proteins Analysis\n",
      "2020-10-01 18:55:47,894  SpectraRegion  INFO: DE data for case: [0]\n",
      "INFO:SpectraRegion:DE data for case: [0]\n",
      "2020-10-01 18:55:47,895  SpectraRegion  INFO: DE data for control: [13, 3, 10, 12, 2, 11, 1, 5, 4]\n",
      "INFO:SpectraRegion:DE data for control: [13, 3, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-10-01 18:55:47,896  SpectraRegion  INFO: Running [0] against [13, 3, 10, 12, 2, 11, 1, 5, 4]\n",
      "INFO:SpectraRegion:Running [0] against [13, 3, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-10-01 18:55:47,897  SpectraRegion  INFO: DE result key: ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13))\n",
      "INFO:SpectraRegion:DE result key: ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-10-01 18:55:47,900  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:55:47,902  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-10-01 18:55:47,903  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:55:47,904  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:55:47,908  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13)) with (1491, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13)) with (1491, 7) results (filtered)\n",
      "2020-10-01 18:55:48,033  SpectraRegion  INFO: Created matrices with shape (2218, 17900) and (526, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (2218, 17900) and (526, 17900) (target, bg)\n",
      "2020-10-01 18:55:53,007  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-10-01 18:55:53,008  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:55:53,009  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:55:53,013  SpectraRegion  INFO: DE result for case ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13)) with (1491, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((0,), (1, 2, 3, 4, 5, 10, 11, 12, 13)) with (1491, 7) results (filtered)\n",
      "2020-10-01 18:55:53,079  SpectraRegion  INFO: Created matrices with shape (2218, 17900) and (526, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (2218, 17900) and (526, 17900) (target, bg)\n",
      "2020-10-01 18:55:58,096  SpectraRegion  INFO: DE data for case: [13]\n",
      "INFO:SpectraRegion:DE data for case: [13]\n",
      "2020-10-01 18:55:58,097  SpectraRegion  INFO: DE data for control: [0, 3, 10, 12, 2, 11, 1, 5, 4]\n",
      "INFO:SpectraRegion:DE data for control: [0, 3, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-10-01 18:55:58,098  SpectraRegion  INFO: Running [13] against [0, 3, 10, 12, 2, 11, 1, 5, 4]\n",
      "INFO:SpectraRegion:Running [13] against [0, 3, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-10-01 18:55:58,099  SpectraRegion  INFO: DE result key: ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12))\n",
      "INFO:SpectraRegion:DE result key: ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12))\n",
      "2020-10-01 18:55:58,099  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:55:58,102  SpectraRegion  INFO: DE result for case ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12)) with (17900, 7) results\n",
      "2020-10-01 18:55:58,103  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:55:58,105  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:55:58,108  SpectraRegion  INFO: DE result for case ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12)) with (89, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12)) with (89, 7) results (filtered)\n",
      "2020-10-01 18:55:58,173  SpectraRegion  INFO: Created matrices with shape (101, 17900) and (2643, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (101, 17900) and (2643, 17900) (target, bg)\n",
      "2020-10-01 18:55:58,478  SpectraRegion  INFO: DE result for case ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12)) with (17900, 7) results\n",
      "2020-10-01 18:55:58,479  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:55:58,480  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:55:58,484  SpectraRegion  INFO: DE result for case ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12)) with (89, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((13,), (0, 1, 2, 3, 4, 5, 10, 11, 12)) with (89, 7) results (filtered)\n",
      "2020-10-01 18:55:58,550  SpectraRegion  INFO: Created matrices with shape (101, 17900) and (2643, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (101, 17900) and (2643, 17900) (target, bg)\n",
      "2020-10-01 18:55:58,853  SpectraRegion  INFO: DE data for case: [3]\n",
      "INFO:SpectraRegion:DE data for case: [3]\n",
      "2020-10-01 18:55:58,854  SpectraRegion  INFO: DE data for control: [0, 13, 10, 12, 2, 11, 1, 5, 4]\n",
      "INFO:SpectraRegion:DE data for control: [0, 13, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-10-01 18:55:58,855  SpectraRegion  INFO: Running [3] against [0, 13, 10, 12, 2, 11, 1, 5, 4]\n",
      "INFO:SpectraRegion:Running [3] against [0, 13, 10, 12, 2, 11, 1, 5, 4]\n",
      "2020-10-01 18:55:58,856  SpectraRegion  INFO: DE result key: ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13))\n",
      "INFO:SpectraRegion:DE result key: ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13))\n",
      "2020-10-01 18:55:58,857  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:55:58,860  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-10-01 18:55:58,861  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:55:58,863  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:55:58,866  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13)) with (1138, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13)) with (1138, 7) results (filtered)\n",
      "2020-10-01 18:55:58,932  SpectraRegion  INFO: Created matrices with shape (102, 17900) and (2642, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (102, 17900) and (2642, 17900) (target, bg)\n",
      "2020-10-01 18:56:02,728  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-10-01 18:56:02,729  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:56:02,730  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:56:02,734  SpectraRegion  INFO: DE result for case ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13)) with (1138, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((3,), (0, 1, 2, 4, 5, 10, 11, 12, 13)) with (1138, 7) results (filtered)\n",
      "2020-10-01 18:56:02,798  SpectraRegion  INFO: Created matrices with shape (102, 17900) and (2642, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (102, 17900) and (2642, 17900) (target, bg)\n",
      "2020-10-01 18:56:06,585  SpectraRegion  INFO: DE data for case: [10]\n",
      "INFO:SpectraRegion:DE data for case: [10]\n",
      "2020-10-01 18:56:06,586  SpectraRegion  INFO: DE data for control: [0, 13, 3, 12, 2, 11, 1, 5, 4]\n",
      "INFO:SpectraRegion:DE data for control: [0, 13, 3, 12, 2, 11, 1, 5, 4]\n",
      "2020-10-01 18:56:06,587  SpectraRegion  INFO: Running [10] against [0, 13, 3, 12, 2, 11, 1, 5, 4]\n",
      "INFO:SpectraRegion:Running [10] against [0, 13, 3, 12, 2, 11, 1, 5, 4]\n",
      "2020-10-01 18:56:06,588  SpectraRegion  INFO: DE result key: ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13))\n",
      "INFO:SpectraRegion:DE result key: ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13))\n",
      "2020-10-01 18:56:06,589  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:56:06,593  SpectraRegion  INFO: DE result for case ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13)) with (17900, 7) results\n",
      "2020-10-01 18:56:06,594  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:56:06,595  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:56:06,599  SpectraRegion  INFO: DE result for case ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13)) with (868, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13)) with (868, 7) results (filtered)\n",
      "2020-10-01 18:56:06,665  SpectraRegion  INFO: Created matrices with shape (49, 17900) and (2695, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (49, 17900) and (2695, 17900) (target, bg)\n",
      "2020-10-01 18:56:09,314  SpectraRegion  INFO: DE result for case ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13)) with (17900, 7) results\n",
      "2020-10-01 18:56:09,315  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:56:09,316  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:56:09,320  SpectraRegion  INFO: DE result for case ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13)) with (868, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((10,), (0, 1, 2, 3, 4, 5, 11, 12, 13)) with (868, 7) results (filtered)\n",
      "2020-10-01 18:56:09,384  SpectraRegion  INFO: Created matrices with shape (49, 17900) and (2695, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (49, 17900) and (2695, 17900) (target, bg)\n",
      "2020-10-01 18:56:12,069  SpectraRegion  INFO: DE data for case: [12]\n",
      "INFO:SpectraRegion:DE data for case: [12]\n",
      "2020-10-01 18:56:12,070  SpectraRegion  INFO: DE data for control: [0, 13, 3, 10, 2, 11, 1, 5, 4]\n",
      "INFO:SpectraRegion:DE data for control: [0, 13, 3, 10, 2, 11, 1, 5, 4]\n",
      "2020-10-01 18:56:12,071  SpectraRegion  INFO: Running [12] against [0, 13, 3, 10, 2, 11, 1, 5, 4]\n",
      "INFO:SpectraRegion:Running [12] against [0, 13, 3, 10, 2, 11, 1, 5, 4]\n",
      "2020-10-01 18:56:12,072  SpectraRegion  INFO: DE result key: ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13))\n",
      "INFO:SpectraRegion:DE result key: ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13))\n",
      "2020-10-01 18:56:12,073  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:56:12,076  SpectraRegion  INFO: DE result for case ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13)) with (17900, 7) results\n",
      "2020-10-01 18:56:12,077  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:56:12,078  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:56:12,083  SpectraRegion  INFO: DE result for case ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13)) with (1575, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13)) with (1575, 7) results (filtered)\n",
      "2020-10-01 18:56:12,149  SpectraRegion  INFO: Created matrices with shape (61, 17900) and (2683, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (61, 17900) and (2683, 17900) (target, bg)\n",
      "2020-10-01 18:56:16,509  SpectraRegion  INFO: DE result for case ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13)) with (17900, 7) results\n",
      "2020-10-01 18:56:16,510  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:56:16,511  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:56:16,515  SpectraRegion  INFO: DE result for case ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13)) with (1574, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((12,), (0, 1, 2, 3, 4, 5, 10, 11, 13)) with (1574, 7) results (filtered)\n",
      "2020-10-01 18:56:16,581  SpectraRegion  INFO: Created matrices with shape (61, 17900) and (2683, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (61, 17900) and (2683, 17900) (target, bg)\n",
      "2020-10-01 18:56:20,951  SpectraRegion  INFO: DE data for case: [2]\n",
      "INFO:SpectraRegion:DE data for case: [2]\n",
      "2020-10-01 18:56:20,952  SpectraRegion  INFO: DE data for control: [0, 13, 3, 10, 12, 11, 1, 5, 4]\n",
      "INFO:SpectraRegion:DE data for control: [0, 13, 3, 10, 12, 11, 1, 5, 4]\n",
      "2020-10-01 18:56:20,953  SpectraRegion  INFO: Running [2] against [0, 13, 3, 10, 12, 11, 1, 5, 4]\n",
      "INFO:SpectraRegion:Running [2] against [0, 13, 3, 10, 12, 11, 1, 5, 4]\n",
      "2020-10-01 18:56:20,954  SpectraRegion  INFO: DE result key: ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13))\n",
      "INFO:SpectraRegion:DE result key: ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-10-01 18:56:20,955  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:56:20,958  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-10-01 18:56:20,959  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:56:20,961  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:56:20,964  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13)) with (2184, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13)) with (2184, 7) results (filtered)\n",
      "2020-10-01 18:56:21,029  SpectraRegion  INFO: Created matrices with shape (45, 17900) and (2699, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (45, 17900) and (2699, 17900) (target, bg)\n",
      "2020-10-01 18:56:29,447  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-10-01 18:56:29,448  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:56:29,449  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:56:29,453  SpectraRegion  INFO: DE result for case ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13)) with (2184, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((2,), (0, 1, 3, 4, 5, 10, 11, 12, 13)) with (2184, 7) results (filtered)\n",
      "2020-10-01 18:56:29,519  SpectraRegion  INFO: Created matrices with shape (45, 17900) and (2699, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (45, 17900) and (2699, 17900) (target, bg)\n",
      "2020-10-01 18:56:37,968  SpectraRegion  INFO: DE data for case: [11]\n",
      "INFO:SpectraRegion:DE data for case: [11]\n",
      "2020-10-01 18:56:37,969  SpectraRegion  INFO: DE data for control: [0, 13, 3, 10, 12, 2, 1, 5, 4]\n",
      "INFO:SpectraRegion:DE data for control: [0, 13, 3, 10, 12, 2, 1, 5, 4]\n",
      "2020-10-01 18:56:37,971  SpectraRegion  INFO: Running [11] against [0, 13, 3, 10, 12, 2, 1, 5, 4]\n",
      "INFO:SpectraRegion:Running [11] against [0, 13, 3, 10, 12, 2, 1, 5, 4]\n",
      "2020-10-01 18:56:37,972  SpectraRegion  INFO: DE result key: ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13))\n",
      "INFO:SpectraRegion:DE result key: ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13))\n",
      "2020-10-01 18:56:37,973  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:56:37,976  SpectraRegion  INFO: DE result for case ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13)) with (17900, 7) results\n",
      "2020-10-01 18:56:37,977  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:56:37,978  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:56:37,983  SpectraRegion  INFO: DE result for case ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13)) with (1552, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13)) with (1552, 7) results (filtered)\n",
      "2020-10-01 18:56:38,050  SpectraRegion  INFO: Created matrices with shape (45, 17900) and (2699, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (45, 17900) and (2699, 17900) (target, bg)\n",
      "2020-10-01 18:56:43,198  SpectraRegion  INFO: DE result for case ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13)) with (17900, 7) results\n",
      "2020-10-01 18:56:43,199  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:56:43,201  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:56:43,205  SpectraRegion  INFO: DE result for case ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13)) with (1554, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((11,), (0, 1, 2, 3, 4, 5, 10, 12, 13)) with (1554, 7) results (filtered)\n",
      "2020-10-01 18:56:43,270  SpectraRegion  INFO: Created matrices with shape (45, 17900) and (2699, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (45, 17900) and (2699, 17900) (target, bg)\n",
      "2020-10-01 18:56:48,420  SpectraRegion  INFO: DE data for case: [1]\n",
      "INFO:SpectraRegion:DE data for case: [1]\n",
      "2020-10-01 18:56:48,422  SpectraRegion  INFO: DE data for control: [0, 13, 3, 10, 12, 2, 11, 5, 4]\n",
      "INFO:SpectraRegion:DE data for control: [0, 13, 3, 10, 12, 2, 11, 5, 4]\n",
      "2020-10-01 18:56:48,423  SpectraRegion  INFO: Running [1] against [0, 13, 3, 10, 12, 2, 11, 5, 4]\n",
      "INFO:SpectraRegion:Running [1] against [0, 13, 3, 10, 12, 2, 11, 5, 4]\n",
      "2020-10-01 18:56:48,424  SpectraRegion  INFO: DE result key: ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13))\n",
      "INFO:SpectraRegion:DE result key: ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13))\n",
      "2020-10-01 18:56:48,425  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:56:48,428  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-10-01 18:56:48,429  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:56:48,429  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:56:48,434  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13)) with (2040, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13)) with (2040, 7) results (filtered)\n",
      "2020-10-01 18:56:48,500  SpectraRegion  INFO: Created matrices with shape (57, 17900) and (2687, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (57, 17900) and (2687, 17900) (target, bg)\n",
      "2020-10-01 18:56:56,073  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-10-01 18:56:56,074  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:56:56,075  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:56:56,079  SpectraRegion  INFO: DE result for case ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13)) with (2038, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((1,), (0, 2, 3, 4, 5, 10, 11, 12, 13)) with (2038, 7) results (filtered)\n",
      "2020-10-01 18:56:56,153  SpectraRegion  INFO: Created matrices with shape (57, 17900) and (2687, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (57, 17900) and (2687, 17900) (target, bg)\n",
      "2020-10-01 18:57:03,798  SpectraRegion  INFO: DE data for case: [5]\n",
      "INFO:SpectraRegion:DE data for case: [5]\n",
      "2020-10-01 18:57:03,799  SpectraRegion  INFO: DE data for control: [0, 13, 3, 10, 12, 2, 11, 1, 4]\n",
      "INFO:SpectraRegion:DE data for control: [0, 13, 3, 10, 12, 2, 11, 1, 4]\n",
      "2020-10-01 18:57:03,800  SpectraRegion  INFO: Running [5] against [0, 13, 3, 10, 12, 2, 11, 1, 4]\n",
      "INFO:SpectraRegion:Running [5] against [0, 13, 3, 10, 12, 2, 11, 1, 4]\n",
      "2020-10-01 18:57:03,802  SpectraRegion  INFO: DE result key: ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13))\n",
      "INFO:SpectraRegion:DE result key: ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13))\n",
      "2020-10-01 18:57:03,803  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:57:03,805  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-10-01 18:57:03,806  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:57:03,807  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:57:03,811  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13)) with (1661, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13)) with (1661, 7) results (filtered)\n",
      "2020-10-01 18:57:03,877  SpectraRegion  INFO: Created matrices with shape (48, 17900) and (2696, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (48, 17900) and (2696, 17900) (target, bg)\n",
      "2020-10-01 18:57:09,256  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-10-01 18:57:09,257  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:57:09,258  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:57:09,263  SpectraRegion  INFO: DE result for case ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13)) with (1659, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((5,), (0, 1, 2, 3, 4, 10, 11, 12, 13)) with (1659, 7) results (filtered)\n",
      "2020-10-01 18:57:09,329  SpectraRegion  INFO: Created matrices with shape (48, 17900) and (2696, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (48, 17900) and (2696, 17900) (target, bg)\n",
      "2020-10-01 18:57:14,534  SpectraRegion  INFO: DE data for case: [4]\n",
      "INFO:SpectraRegion:DE data for case: [4]\n",
      "2020-10-01 18:57:14,535  SpectraRegion  INFO: DE data for control: [0, 13, 3, 10, 12, 2, 11, 1, 5]\n",
      "INFO:SpectraRegion:DE data for control: [0, 13, 3, 10, 12, 2, 11, 1, 5]\n",
      "2020-10-01 18:57:14,537  SpectraRegion  INFO: Running [4] against [0, 13, 3, 10, 12, 2, 11, 1, 5]\n",
      "INFO:SpectraRegion:Running [4] against [0, 13, 3, 10, 12, 2, 11, 1, 5]\n",
      "2020-10-01 18:57:14,538  SpectraRegion  INFO: DE result key: ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13))\n",
      "INFO:SpectraRegion:DE result key: ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13))\n",
      "2020-10-01 18:57:14,539  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:57:14,542  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-10-01 18:57:14,543  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:57:14,544  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:57:14,548  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13)) with (2953, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13)) with (2953, 7) results (filtered)\n",
      "2020-10-01 18:57:14,611  SpectraRegion  INFO: Created matrices with shape (18, 17900) and (2726, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (18, 17900) and (2726, 17900) (target, bg)\n",
      "2020-10-01 18:57:25,081  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13)) with (17900, 7) results\n",
      "2020-10-01 18:57:25,082  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:57:25,083  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:57:25,087  SpectraRegion  INFO: DE result for case ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13)) with (2920, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((4,), (0, 1, 2, 3, 5, 10, 11, 12, 13)) with (2920, 7) results (filtered)\n",
      "2020-10-01 18:57:25,150  SpectraRegion  INFO: Created matrices with shape (18, 17900) and (2726, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (18, 17900) and (2726, 17900) (target, bg)\n",
      "2020-10-01 18:57:36,016  SpectraRegion  INFO: No cell type info for cluster: '0'\n",
      "INFO:SpectraRegion:No cell type info for cluster: '0'\n",
      "2020-10-01 18:57:36,018  SpectraRegion  INFO: No cell type info for cluster: '3'\n",
      "INFO:SpectraRegion:No cell type info for cluster: '3'\n",
      "2020-10-01 18:57:36,031  SpectraRegion  INFO: Cell-type assigned: 9 -> Endothelial cells;Vasculature\n",
      "INFO:SpectraRegion:Cell-type assigned: 9 -> Endothelial cells;Vasculature\n",
      "2020-10-01 18:57:36,033  SpectraRegion  INFO: Cell-type assigned: 10 -> Gamma delta T cells;Immune system\n",
      "INFO:SpectraRegion:Cell-type assigned: 10 -> Gamma delta T cells;Immune system\n",
      "2020-10-01 18:57:36,034  SpectraRegion  INFO: Cell-type assigned: 11 -> Platelets;Blood\n",
      "INFO:SpectraRegion:Cell-type assigned: 11 -> Platelets;Blood\n",
      "2020-10-01 18:57:36,036  SpectraRegion  INFO: Cell-type assigned: 14 -> Adipocytes;Connective tissue\n",
      "INFO:SpectraRegion:Cell-type assigned: 14 -> Adipocytes;Connective tissue\n",
      "2020-10-01 18:57:36,037  SpectraRegion  INFO: Cell-type assigned: 15 -> Cardiomyocytes;Heart\n",
      "INFO:SpectraRegion:Cell-type assigned: 15 -> Cardiomyocytes;Heart\n",
      "2020-10-01 18:57:36,044  SpectraRegion  INFO: Segment Image: ./aorta3d/slided/slided.5.clustering.png\n",
      "INFO:SpectraRegion:Segment Image: ./aorta3d/slided/slided.5.clustering.png\n",
      "2020-10-01 18:57:36,051  SpectraRegion  INFO: Segment Matrix: /mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.5.matrix.npy\n",
      "INFO:SpectraRegion:Segment Matrix: /mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.5.matrix.npy\n",
      "2020-10-01 18:57:36,056  SpectraRegion  INFO: Starting Marker Proteins Analysis\n",
      "INFO:SpectraRegion:Starting Marker Proteins Analysis\n",
      "2020-10-01 18:57:36,060  SpectraRegion  INFO: DE data for case: [0]\n",
      "INFO:SpectraRegion:DE data for case: [0]\n",
      "2020-10-01 18:57:36,061  SpectraRegion  INFO: DE data for control: [15, 12, 11, 14, 10, 9]\n",
      "INFO:SpectraRegion:DE data for control: [15, 12, 11, 14, 10, 9]\n",
      "2020-10-01 18:57:36,063  SpectraRegion  INFO: Running [0] against [15, 12, 11, 14, 10, 9]\n",
      "INFO:SpectraRegion:Running [0] against [15, 12, 11, 14, 10, 9]\n",
      "2020-10-01 18:57:36,065  SpectraRegion  INFO: DE result key: ((0,), (9, 10, 11, 12, 14, 15))\n",
      "INFO:SpectraRegion:DE result key: ((0,), (9, 10, 11, 12, 14, 15))\n",
      "2020-10-01 18:57:36,066  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:57:36,068  SpectraRegion  INFO: DE result for case ((0,), (9, 10, 11, 12, 14, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((0,), (9, 10, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-10-01 18:57:36,070  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:57:36,071  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:57:36,075  SpectraRegion  INFO: DE result for case ((0,), (9, 10, 11, 12, 14, 15)) with (1125, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((0,), (9, 10, 11, 12, 14, 15)) with (1125, 7) results (filtered)\n",
      "2020-10-01 18:57:36,211  SpectraRegion  INFO: Created matrices with shape (2815, 17900) and (253, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (2815, 17900) and (253, 17900) (target, bg)\n",
      "2020-10-01 18:57:39,206  SpectraRegion  INFO: DE result for case ((0,), (9, 10, 11, 12, 14, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((0,), (9, 10, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-10-01 18:57:39,207  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:57:39,208  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:57:39,212  SpectraRegion  INFO: DE result for case ((0,), (9, 10, 11, 12, 14, 15)) with (1125, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((0,), (9, 10, 11, 12, 14, 15)) with (1125, 7) results (filtered)\n",
      "2020-10-01 18:57:39,283  SpectraRegion  INFO: Created matrices with shape (2815, 17900) and (253, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (2815, 17900) and (253, 17900) (target, bg)\n",
      "2020-10-01 18:57:42,231  SpectraRegion  INFO: DE data for case: [15]\n",
      "INFO:SpectraRegion:DE data for case: [15]\n",
      "2020-10-01 18:57:42,233  SpectraRegion  INFO: DE data for control: [0, 12, 11, 14, 10, 9]\n",
      "INFO:SpectraRegion:DE data for control: [0, 12, 11, 14, 10, 9]\n",
      "2020-10-01 18:57:42,234  SpectraRegion  INFO: Running [15] against [0, 12, 11, 14, 10, 9]\n",
      "INFO:SpectraRegion:Running [15] against [0, 12, 11, 14, 10, 9]\n",
      "2020-10-01 18:57:42,235  SpectraRegion  INFO: DE result key: ((15,), (0, 9, 10, 11, 12, 14))\n",
      "INFO:SpectraRegion:DE result key: ((15,), (0, 9, 10, 11, 12, 14))\n",
      "2020-10-01 18:57:42,235  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:57:42,238  SpectraRegion  INFO: DE result for case ((15,), (0, 9, 10, 11, 12, 14)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((15,), (0, 9, 10, 11, 12, 14)) with (17900, 7) results\n",
      "2020-10-01 18:57:42,239  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:57:42,240  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:57:42,245  SpectraRegion  INFO: DE result for case ((15,), (0, 9, 10, 11, 12, 14)) with (863, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((15,), (0, 9, 10, 11, 12, 14)) with (863, 7) results (filtered)\n",
      "2020-10-01 18:57:42,315  SpectraRegion  INFO: Created matrices with shape (98, 17900) and (2970, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (98, 17900) and (2970, 17900) (target, bg)\n",
      "2020-10-01 18:57:44,680  SpectraRegion  INFO: DE result for case ((15,), (0, 9, 10, 11, 12, 14)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((15,), (0, 9, 10, 11, 12, 14)) with (17900, 7) results\n",
      "2020-10-01 18:57:44,681  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:57:44,682  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:57:44,686  SpectraRegion  INFO: DE result for case ((15,), (0, 9, 10, 11, 12, 14)) with (863, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((15,), (0, 9, 10, 11, 12, 14)) with (863, 7) results (filtered)\n",
      "2020-10-01 18:57:44,757  SpectraRegion  INFO: Created matrices with shape (98, 17900) and (2970, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (98, 17900) and (2970, 17900) (target, bg)\n",
      "2020-10-01 18:57:47,103  SpectraRegion  INFO: DE data for case: [12]\n",
      "INFO:SpectraRegion:DE data for case: [12]\n",
      "2020-10-01 18:57:47,104  SpectraRegion  INFO: DE data for control: [0, 15, 11, 14, 10, 9]\n",
      "INFO:SpectraRegion:DE data for control: [0, 15, 11, 14, 10, 9]\n",
      "2020-10-01 18:57:47,105  SpectraRegion  INFO: Running [12] against [0, 15, 11, 14, 10, 9]\n",
      "INFO:SpectraRegion:Running [12] against [0, 15, 11, 14, 10, 9]\n",
      "2020-10-01 18:57:47,106  SpectraRegion  INFO: DE result key: ((12,), (0, 9, 10, 11, 14, 15))\n",
      "INFO:SpectraRegion:DE result key: ((12,), (0, 9, 10, 11, 14, 15))\n",
      "2020-10-01 18:57:47,107  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:57:47,110  SpectraRegion  INFO: DE result for case ((12,), (0, 9, 10, 11, 14, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((12,), (0, 9, 10, 11, 14, 15)) with (17900, 7) results\n",
      "2020-10-01 18:57:47,111  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:57:47,112  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:57:47,116  SpectraRegion  INFO: DE result for case ((12,), (0, 9, 10, 11, 14, 15)) with (856, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((12,), (0, 9, 10, 11, 14, 15)) with (856, 7) results (filtered)\n",
      "2020-10-01 18:57:47,190  SpectraRegion  INFO: Created matrices with shape (33, 17900) and (3035, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (33, 17900) and (3035, 17900) (target, bg)\n",
      "2020-10-01 18:57:49,633  SpectraRegion  INFO: DE result for case ((12,), (0, 9, 10, 11, 14, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((12,), (0, 9, 10, 11, 14, 15)) with (17900, 7) results\n",
      "2020-10-01 18:57:49,634  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:57:49,635  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:57:49,639  SpectraRegion  INFO: DE result for case ((12,), (0, 9, 10, 11, 14, 15)) with (855, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((12,), (0, 9, 10, 11, 14, 15)) with (855, 7) results (filtered)\n",
      "2020-10-01 18:57:49,712  SpectraRegion  INFO: Created matrices with shape (33, 17900) and (3035, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (33, 17900) and (3035, 17900) (target, bg)\n",
      "2020-10-01 18:57:52,110  SpectraRegion  INFO: DE data for case: [11]\n",
      "INFO:SpectraRegion:DE data for case: [11]\n",
      "2020-10-01 18:57:52,111  SpectraRegion  INFO: DE data for control: [0, 15, 12, 14, 10, 9]\n",
      "INFO:SpectraRegion:DE data for control: [0, 15, 12, 14, 10, 9]\n",
      "2020-10-01 18:57:52,112  SpectraRegion  INFO: Running [11] against [0, 15, 12, 14, 10, 9]\n",
      "INFO:SpectraRegion:Running [11] against [0, 15, 12, 14, 10, 9]\n",
      "2020-10-01 18:57:52,113  SpectraRegion  INFO: DE result key: ((11,), (0, 9, 10, 12, 14, 15))\n",
      "INFO:SpectraRegion:DE result key: ((11,), (0, 9, 10, 12, 14, 15))\n",
      "2020-10-01 18:57:52,114  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:57:52,117  SpectraRegion  INFO: DE result for case ((11,), (0, 9, 10, 12, 14, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((11,), (0, 9, 10, 12, 14, 15)) with (17900, 7) results\n",
      "2020-10-01 18:57:52,118  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:57:52,119  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:57:52,125  SpectraRegion  INFO: DE result for case ((11,), (0, 9, 10, 12, 14, 15)) with (2583, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((11,), (0, 9, 10, 12, 14, 15)) with (2583, 7) results (filtered)\n",
      "2020-10-01 18:57:52,197  SpectraRegion  INFO: Created matrices with shape (24, 17900) and (3044, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (24, 17900) and (3044, 17900) (target, bg)\n",
      "2020-10-01 18:58:01,721  SpectraRegion  INFO: DE result for case ((11,), (0, 9, 10, 12, 14, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((11,), (0, 9, 10, 12, 14, 15)) with (17900, 7) results\n",
      "2020-10-01 18:58:01,722  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:58:01,723  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:58:01,727  SpectraRegion  INFO: DE result for case ((11,), (0, 9, 10, 12, 14, 15)) with (2542, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((11,), (0, 9, 10, 12, 14, 15)) with (2542, 7) results (filtered)\n",
      "2020-10-01 18:58:01,798  SpectraRegion  INFO: Created matrices with shape (24, 17900) and (3044, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (24, 17900) and (3044, 17900) (target, bg)\n",
      "2020-10-01 18:58:11,142  SpectraRegion  INFO: DE data for case: [14]\n",
      "INFO:SpectraRegion:DE data for case: [14]\n",
      "2020-10-01 18:58:11,144  SpectraRegion  INFO: DE data for control: [0, 15, 12, 11, 10, 9]\n",
      "INFO:SpectraRegion:DE data for control: [0, 15, 12, 11, 10, 9]\n",
      "2020-10-01 18:58:11,145  SpectraRegion  INFO: Running [14] against [0, 15, 12, 11, 10, 9]\n",
      "INFO:SpectraRegion:Running [14] against [0, 15, 12, 11, 10, 9]\n",
      "2020-10-01 18:58:11,146  SpectraRegion  INFO: DE result key: ((14,), (0, 9, 10, 11, 12, 15))\n",
      "INFO:SpectraRegion:DE result key: ((14,), (0, 9, 10, 11, 12, 15))\n",
      "2020-10-01 18:58:11,147  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:58:11,150  SpectraRegion  INFO: DE result for case ((14,), (0, 9, 10, 11, 12, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((14,), (0, 9, 10, 11, 12, 15)) with (17900, 7) results\n",
      "2020-10-01 18:58:11,150  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:58:11,151  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:58:11,155  SpectraRegion  INFO: DE result for case ((14,), (0, 9, 10, 11, 12, 15)) with (2209, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((14,), (0, 9, 10, 11, 12, 15)) with (2209, 7) results (filtered)\n",
      "2020-10-01 18:58:11,226  SpectraRegion  INFO: Created matrices with shape (28, 17900) and (3040, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (28, 17900) and (3040, 17900) (target, bg)\n",
      "2020-10-01 18:58:18,056  SpectraRegion  INFO: DE result for case ((14,), (0, 9, 10, 11, 12, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((14,), (0, 9, 10, 11, 12, 15)) with (17900, 7) results\n",
      "2020-10-01 18:58:18,057  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:58:18,058  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:58:18,062  SpectraRegion  INFO: DE result for case ((14,), (0, 9, 10, 11, 12, 15)) with (2219, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((14,), (0, 9, 10, 11, 12, 15)) with (2219, 7) results (filtered)\n",
      "2020-10-01 18:58:18,132  SpectraRegion  INFO: Created matrices with shape (28, 17900) and (3040, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (28, 17900) and (3040, 17900) (target, bg)\n",
      "2020-10-01 18:58:24,941  SpectraRegion  INFO: DE data for case: [10]\n",
      "INFO:SpectraRegion:DE data for case: [10]\n",
      "2020-10-01 18:58:24,942  SpectraRegion  INFO: DE data for control: [0, 15, 12, 11, 14, 9]\n",
      "INFO:SpectraRegion:DE data for control: [0, 15, 12, 11, 14, 9]\n",
      "2020-10-01 18:58:24,943  SpectraRegion  INFO: Running [10] against [0, 15, 12, 11, 14, 9]\n",
      "INFO:SpectraRegion:Running [10] against [0, 15, 12, 11, 14, 9]\n",
      "2020-10-01 18:58:24,944  SpectraRegion  INFO: DE result key: ((10,), (0, 9, 11, 12, 14, 15))\n",
      "INFO:SpectraRegion:DE result key: ((10,), (0, 9, 11, 12, 14, 15))\n",
      "2020-10-01 18:58:24,945  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:58:24,948  SpectraRegion  INFO: DE result for case ((10,), (0, 9, 11, 12, 14, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((10,), (0, 9, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-10-01 18:58:24,950  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:58:24,951  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:58:24,955  SpectraRegion  INFO: DE result for case ((10,), (0, 9, 11, 12, 14, 15)) with (1799, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((10,), (0, 9, 11, 12, 14, 15)) with (1799, 7) results (filtered)\n",
      "2020-10-01 18:58:25,027  SpectraRegion  INFO: Created matrices with shape (36, 17900) and (3032, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (36, 17900) and (3032, 17900) (target, bg)\n",
      "2020-10-01 18:58:29,770  SpectraRegion  INFO: DE result for case ((10,), (0, 9, 11, 12, 14, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((10,), (0, 9, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-10-01 18:58:29,771  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:58:29,772  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:58:29,776  SpectraRegion  INFO: DE result for case ((10,), (0, 9, 11, 12, 14, 15)) with (1807, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((10,), (0, 9, 11, 12, 14, 15)) with (1807, 7) results (filtered)\n",
      "2020-10-01 18:58:29,847  SpectraRegion  INFO: Created matrices with shape (36, 17900) and (3032, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (36, 17900) and (3032, 17900) (target, bg)\n",
      "2020-10-01 18:58:34,589  SpectraRegion  INFO: DE data for case: [9]\n",
      "INFO:SpectraRegion:DE data for case: [9]\n",
      "2020-10-01 18:58:34,590  SpectraRegion  INFO: DE data for control: [0, 15, 12, 11, 14, 10]\n",
      "INFO:SpectraRegion:DE data for control: [0, 15, 12, 11, 14, 10]\n",
      "2020-10-01 18:58:34,591  SpectraRegion  INFO: Running [9] against [0, 15, 12, 11, 14, 10]\n",
      "INFO:SpectraRegion:Running [9] against [0, 15, 12, 11, 14, 10]\n",
      "2020-10-01 18:58:34,592  SpectraRegion  INFO: DE result key: ((9,), (0, 10, 11, 12, 14, 15))\n",
      "INFO:SpectraRegion:DE result key: ((9,), (0, 10, 11, 12, 14, 15))\n",
      "2020-10-01 18:58:34,593  SpectraRegion  INFO: DE result key already exists\n",
      "INFO:SpectraRegion:DE result key already exists\n",
      "2020-10-01 18:58:34,596  SpectraRegion  INFO: DE result for case ((9,), (0, 10, 11, 12, 14, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((9,), (0, 10, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-10-01 18:58:34,598  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:58:34,599  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:58:34,603  SpectraRegion  INFO: DE result for case ((9,), (0, 10, 11, 12, 14, 15)) with (1932, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((9,), (0, 10, 11, 12, 14, 15)) with (1932, 7) results (filtered)\n",
      "2020-10-01 18:58:34,674  SpectraRegion  INFO: Created matrices with shape (34, 17900) and (3034, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (34, 17900) and (3034, 17900) (target, bg)\n",
      "2020-10-01 18:58:40,396  SpectraRegion  INFO: DE result for case ((9,), (0, 10, 11, 12, 14, 15)) with (17900, 7) results\n",
      "INFO:SpectraRegion:DE result for case ((9,), (0, 10, 11, 12, 14, 15)) with (17900, 7) results\n",
      "2020-10-01 18:58:40,397  SpectraRegion  INFO: DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "INFO:SpectraRegion:DF column names ['gene', 'pval', 'qval', 'log2fc', 'mean', 'zero_mean', 'zero_variance']\n",
      "2020-10-01 18:58:40,398  SpectraRegion  INFO: DE result logFC inversed\n",
      "INFO:SpectraRegion:DE result logFC inversed\n",
      "2020-10-01 18:58:40,403  SpectraRegion  INFO: DE result for case ((9,), (0, 10, 11, 12, 14, 15)) with (1937, 7) results (filtered)\n",
      "INFO:SpectraRegion:DE result for case ((9,), (0, 10, 11, 12, 14, 15)) with (1937, 7) results (filtered)\n",
      "2020-10-01 18:58:40,473  SpectraRegion  INFO: Created matrices with shape (34, 17900) and (3034, 17900) (target, bg)\n",
      "INFO:SpectraRegion:Created matrices with shape (34, 17900) and (3034, 17900) (target, bg)\n",
      "2020-10-01 18:58:46,290  SpectraRegion  INFO: No cell type info for cluster: '0'\n",
      "INFO:SpectraRegion:No cell type info for cluster: '0'\n",
      "2020-10-01 18:58:46,292  SpectraRegion  INFO: No cell type info for cluster: '12'\n",
      "INFO:SpectraRegion:No cell type info for cluster: '12'\n"
     ]
    }
   ],
   "source": [
    "slided_0.to_aorta3d(\"./aorta3d/slided/\", \"slided\", 0, protWeights=pw, ctpred=\"deresults/marker_genes_region_0.pred.tsv\")\n",
    "slided_1.to_aorta3d(\"./aorta3d/slided/\", \"slided\", 1, protWeights=pw, ctpred=\"deresults/marker_genes_region_1.pred.tsv\")\n",
    "slided_4.to_aorta3d(\"./aorta3d/slided/\", \"slided\", 4, protWeights=pw, ctpred=\"deresults/marker_genes_region_4.pred.tsv\")\n",
    "slided_5.to_aorta3d(\"./aorta3d/slided/\", \"slided\", 5, protWeights=pw, ctpred=\"deresults/marker_genes_region_5.pred.tsv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-10-02 13:02:52.506568: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'libcudart.so.10.1'; dlerror: libcudart.so.10.1: cannot open shared object file: No such file or directory\n",
      "2020-10-02 13:02:52.506609: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "<frozen importlib._bootstrap>:219: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "/mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.1.info\n",
      "['region', 'path_upgma', 'info', 'segment_file']\n",
      "/mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.0.info\n",
      "['region', 'path_upgma', 'info', 'segment_file']\n",
      "/mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.4.info\n",
      "['region', 'path_upgma', 'info', 'segment_file']\n",
      "/mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.5.info\n",
      "['region', 'path_upgma', 'info', 'segment_file']\n",
      "Determined most similar image: 3, /mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.5.clustering.png\n",
      "/mnt/f/dev/git/Aorta3D/files2model/register/register_pimz.py:270: FutureWarning: The behavior of rgb2gray will change in scikit-image 0.19. Currently, rgb2gray allows 2D grayscale image to be passed as inputs and leaves them unmodified as outputs. Starting from version 0.19, 2D arrays will be treated as 1D images with 3 channels.\n",
      "  trans = register_pair.start_ransac(img1=rgb2gray(masks[mostSimilar]), img2=rgb2gray(curImgMask), brief=True, common_factor=cf)\n",
      "(208, 236)\n",
      "(172, 224)\n",
      "/mnt/f/dev/git/Aorta3D/files2model/register/register_pair.py:106: FutureWarning: Until version 0.16, threshold_rel was set to 0.1 by default. Starting from version 0.16, the default value is set to None. Until version 0.18, a None value corresponds to a threshold value of 0.1. The default behavior will match skimage.feature.peak_local_max. To avoid this warning, set threshold_rel=0.\n",
      "  keypoints1 = corner_peaks(corner_harris(img1), min_distance=5)\n",
      "/mnt/f/dev/git/Aorta3D/files2model/register/register_pair.py:107: FutureWarning: Until version 0.16, threshold_rel was set to 0.1 by default. Starting from version 0.16, the default value is set to None. Until version 0.18, a None value corresponds to a threshold value of 0.1. The default behavior will match skimage.feature.peak_local_max. To avoid this warning, set threshold_rel=0.\n",
      "  keypoints2 = corner_peaks(corner_harris(img2), min_distance=5)\n",
      "/mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.1.clustering.png (172, 224) (172, 224) (172, 224) (172, 224)\n",
      "['/mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.1.clustering.png_reg_seg.png']\n",
      "0 /mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.1.clustering.png_reg_seg.png\n",
      "0 /mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.1.clustering.png_reg_seg.png\n",
      "broke at 0 /mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.1.clustering.png_reg_seg.png\n",
      "/mnt/f/dev/git/Aorta3D/files2model/register/register_pimz.py:82: RuntimeWarning: invalid value encountered in true_divide\n",
      "  V = V / np.max(V)\n",
      "/mnt/f/dev/git/Aorta3D/files2model/register/register_pimz.py:94: RuntimeWarning: invalid value encountered in greater\n",
      "  Vtaorta[Vtaorta > 10] = 100\n",
      "(172, 224, 30)\n",
      "Traceback (most recent call last):\n",
      "  File \"/mnt/f/dev/git/Aorta3D/files2model/register/register_pimz.py\", line 297, in <module>\n",
      "    createStlModel([outName + '_reg_seg.png'], outName + \"_model.stl\", None, full=True)\n",
      "  File \"/mnt/f/dev/git/Aorta3D/files2model/register/register_pimz.py\", line 103, in createStlModel\n",
      "    s_aortaVertices,s_aortaFaces = sn.surface_net(Vtaorta, 75)\n",
      "  File \"/mnt/f/dev/git/Aorta3D/files2model/register/stlstuff.py\", line 88, in surface_net\n",
      "    vdata[vidx]= data[i,j,k]\n",
      "ValueError: cannot convert float NaN to integer\n"
     ]
    }
   ],
   "source": [
    "! python3 /mnt/f/dev/git/Aorta3D/files2model/register/register_pimz.py --id slided \\\n",
    "--files \\\n",
    "/mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.1.info \\\n",
    "/mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.0.info \\\n",
    "/mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.4.info \\\n",
    "/mnt/f/dev/git/pyIMS/examples/aorta3d/slided/slided.5.info \\\n",
    "--output /mnt/f/dev/git/pyIMS/examples/aorta3d/slided/registered/slided_server.conf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here a CombinedSpectra is creates. This class allows to compare multiple SpectraRegions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec = CombinedSpectra({0: slided_0, 1: slided_1, 4: slided_4, 5: slided_5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The consensus_smilarity() function calculates the similarity between all the contained spectra's regions using the average cluster/region spectra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.consensus_similarity()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.consensus_similarity_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the inspection of this matrix becomes tidious, particularly if large, it an be plotted :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.plot_consensus_similarity()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can already be seen that some classes are more similar to each other, than others.\n",
    "\n",
    "Now the single slide/region clusters are reclustered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.cluster_concensus_spectra(number_of_clusters=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A detailed print out of the newly assigned cluster for each slide, region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.region_cluster2cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new assignment can also be plotted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.plot_common_segments()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And of course, interpreted.\n",
    "\n",
    "Slide 1 and Slide 5 appear to be similar, and slide 0 and slide 4.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.plot_common_segments(highlight=(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to a normal SpectraRegion, also specific masses can be plotted. Here we can see that mass 14954 is not present in slides 1 and 5!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = combSpec.mass_heatmap(14954)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A first DE Analysis: Tunica Media"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slide 0 cluster 2 and slide 4 cluster 1 seem to be very similar. We now want to see whether some differences can be observed.\n",
    "\n",
    "For this, we suspect that slides 0 and 4 are disease-samples, and slides 1 and 5 are controls.\n",
    "\n",
    "We now compare the outer regions of both samples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.regions[\"slided_0\"].plot_segments(highlight=[8,9])\n",
    "combSpec.regions[\"slided_1\"].plot_segments(highlight=[12,14])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.get_internormed_regions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.median(combSpec.region_array_scaled[\"slided_1\"]), np.median( combSpec.regions[\"slided_1\"].region_array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.mass_intensity(14954, regions=[\"slided_0\", \"slided_1\"], scaled=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.mass_intensity(14954, regions=[\"slided_0\", \"slided_1\"], scaled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "resdf, expr, pdata = combSpec.find_markers(\"slided_0\", [8,9], \"slided_1\", [12,14], pw, scaled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.list_de_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydf = resdf[\"ttest\"][('slided_0', (8,9,), 'slided_1', (12,14,))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to plot a volcano plot for the differential masses/proteins, the result DF is subset to only have masses displayed that have a mean intensity of at least 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from adjustText import adjust_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', None)\n",
    "mydf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(16,10))\n",
    "xydots = [(x,y) for x,y in zip(list(-mydf[\"avg_logFC\"]), list(-np.log10(mydf[\"qvalue\"])))]\n",
    "dotgene = list(mydf[\"gene\"])\n",
    "texts = []\n",
    "seenProts = set()\n",
    "\n",
    "for i in range(len(xydots)):\n",
    "    x = xydots[i][0]\n",
    "    y = xydots[i][1]\n",
    "    \n",
    "    if not dotgene[i] in seenProts and abs(y) >= 10 and abs(x) >= 0.5:\n",
    "        texts.append(plt.text(x * (1 + 0.01), y * (1 + 0.01) , dotgene[i], fontsize=12))\n",
    "        plt.plot(x, y, 'ro')\n",
    "        seenProts.add(dotgene[i])\n",
    "    else:\n",
    "        plt.plot(x, y, 'bo')\n",
    "\n",
    "adjust_text(texts, force_points=0.2, force_text=0.2, expand_points=(1, 1), expand_text=(1, 1), arrowprops=dict(arrowstyle=\"-\", color='black', lw=0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](https://medicalartlibrary.com/wp-content/uploads/atherosclerosis-renal.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this comparison the tunica media of atherosclerotic (slided_0) and control (slided_1) aorta was compared.\n",
    "It must be noted, that the selected regions are considerably thicker in slided_1 compared to slided_0, but it is a known phenomenon that \"The media underlying intimal athero- sclerotic plaque is considerably thinner\" (Milutinović, A., Šuput, D., & Zorc-Pleskovič, R. (2020). Pathogenesis of atherosclerosis in the tunica intima, media, and adventitia of coronary arteries: An updated review. In Bosnian Journal of Basic Medical Sciences (Vol. 20, Issue 1, pp. 21–30). Association of Basic Medical Sciences of FBIH. https://doi.org/10.17305/bjbms.2019.4320 ).\n",
    "\n",
    "However, due to the proximity to the background tissue (liver), for each mass it should be checked whether this mass is not also prevalent in the background tissue and therefore is present in the cluster due to diffusion, bad clustering, or other reasons. The mass_heatmap-function give a good and fast overview!\n",
    "\n",
    "#### Tmsb4x -\n",
    "First it is very interesting to see a difference in Tmsb4x between the (suspected) control and disease aortas.\n",
    "The diseased-aorta-media has significantly less Tmsb4x (-0.5).\n",
    "This is not surprising, but was already found 5 years ago ( Zaima, N., Sasaki, T., Tanaka, H., Cheng, X. W., Onoue, K., Hayasaka, T., Goto-Inoue, N., Enomoto, H., Unno, N., Kuzuya, M., & Setou, M. (2011). Imaging mass spectrometry-based histopathologic examination of atherosclerotic lesions. Atherosclerosis, 217(2), 427–432. https://doi.org/10.1016/j.atherosclerosis.2011.03.044 ). Indeed, this can be seen as proof-of-concept.\n",
    "\n",
    "#### Ptges3 +\n",
    "\n",
    "On the contrary, there are other proteins, which are up-regulated in the atherosclerotic aorta.\n",
    "Among these is Ptges3 (+1.25), which was shown to be involved in the inflammatory response of bovine endometrial epithelial cells.\n",
    "Almughlliq, F. B., Koh, Y. Q., Peiris, H. N., Vaswani, K., Arachchige, B. J., Reed, S., & Mitchell, M. D. (2018). Eicosanoid pathway expression in bovine endometrial epithelial and stromal cells in response to lipopolysaccharide, interleukin 1 beta, and tumor necrosis factor alpha. Reproductive Biology, 18(4), 390–396. https://doi.org/10.1016/j.repbio.2018.10.001\n",
    "\n",
    "Further proteins of interest include\n",
    "\n",
    "#### Chchd4 +\n",
    "Chchd4 was already identified as a biomarker for chronic obstructive pulmonary disease (COPD), a chronic inflammatory disease - in such, maybe? comparable to atherosclerosis.\n",
    "Chchd4 affects the mitochondrial metabolism, being named in the p53-axis controlling tumor proliferation.\n",
    "\n",
    "Maghsoudloo, M., Azimzadeh Jamalkandi, S., Najafi, A., & Masoudi-Nejad, A. (2020). An efficient hybrid feature selection method to identify potential biomarkers in common chronic lung inflammatory diseases. Genomics, 112(5), 3284–3293. https://doi.org/10.1016/j.ygeno.2020.06.010\n",
    "\n",
    "#### Coa6 --\n",
    "\n",
    "The respiratory metabolism appears to play an important role in atherosclerosis. Many regulated proteins fall into this category.\n",
    "A Coa6 deficiency is known to be causing many disease phenotypes, including cardiomyopathy, encephalomyopathy, skeletal muscle myopathy, Leigh syndrome, metabolic acidosis and occasional hepatic failure.\n",
    "In summary: Coa6 deficiency does not contribute to a healthy Aorta.\n",
    "\n",
    "Ghosh, A., Trivedi, P. P., Timbalia, S. A., Griffin, A. T., Rahn, J. J., Chan, S. S. L., & Gohil, V. M. (2014). Copper supplementation restores cytochrome c oxidase assembly defect in a mitochondrial disease model of COA6 deficiency. Human Molecular Genetics, 23(13), 3596–3606. https://doi.org/10.1093/hmg/ddu069\n",
    "\n",
    "#### Cst3 +\n",
    "\n",
    "Serum Cystein3 is associated with subclinical atherosclerosis, which can also be seen in the middle of the IMS aorta. It also does not seem unlikely, that Cst3 is elsewhere upregulated in atherosclerotic aorta.\n",
    "\n",
    "Chung, Y. K., Lee, Y. J., Kim, K. W., Cho, R. K., Chung, S. M., Moon, J. S., Yoon, J. S., Won, K. C., & Lee, H. W. (2018). Serum cystatin C is associated with subclinical atherosclerosis in patients with type 2 diabetes: A retrospective study. Diabetes and Vascular Disease Research, 15(1), 24–30. https://doi.org/10.1177/1479164117738156\n",
    "\n",
    "#### Ndufa11 +, Cox7a2 -\n",
    "\n",
    "This proteins is of interesting, and together with other identified proteins ( like Cox7a2 ), is part of the mitochondria.\n",
    "Atherosclerosis therefore might have an effect on the energy metabolism, similar to an ischemic stroke: James, R., Searcy, J. L., Le Bihan, T., Martin, S. F., Gliddon, C. M., Povey, J., Deighton, R. F., Kerr, L. E., McCulloch, J., & Horsburgh, K. (2012). Proteomic analysis of mitochondria in APOE transgenic mice and in response to an ischemic challenge. Journal of Cerebral Blood Flow and Metabolism, 32(1), 164–176. https://doi.org/10.1038/jcbfm.2011.120 \n",
    "\n",
    "#### Ndufb6 +\n",
    "\n",
    "Ndufb6 was identified to being critical for the development of PM2.5-induced fibrosis in mouse lungs. Therefore it is likely related to inflammatory processes.\n",
    "\n",
    "Han, X., Liu, H., Zhang, Z., Yang, W., Wu, C., Liu, X., Zhang, F., Sun, B., Zhao, Y., Jiang, G., Yang, Y. G., & Ding, W. (2020). Epitranscriptomic 5-Methylcytosine Profile in PM2.5-induced Mouse Pulmonary Fibrosis. Genomics, Proteomics and Bioinformatics, 18(1), 41–51. https://doi.org/10.1016/j.gpb.2019.11.005\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = combSpec.mass_heatmap(pw.protein2mass.get(\"Tmsb4x\"), scaled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.mass_intensity(pw.protein2mass.get(\"Tmsb4x\"), regions=[\"slided_0\", \"slided_1\"], scaled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for gene in np.unique(mydf[\"gene\"]):\n",
    "    print(gene)\n",
    "    _ = combSpec.mass_heatmap(pw.protein2mass.get(gene), scaled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.mass_intensity(pw.protein2mass.get(\"Ptges3\"), regions=[\"slided_0\", \"slided_1\"], scaled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Another DE Analysis: whole Aorta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having spotted several differences between the Tunica media of atherosclerotic and suspected healthy aorta, a full comparison might also be of interest.\n",
    "\n",
    "For this, all slided_0 and slided_1 regions are compared  - with the exception of the backgrounds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slided_0_regions = tuple(sorted([x for x in np.unique(slided_0.segmented) if x > 0]))\n",
    "slided_1_regions = tuple(sorted([x for x in np.unique(slided_1.segmented) if x > 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.regions[\"slided_0\"].plot_segments(highlight=slided_0_regions)\n",
    "combSpec.regions[\"slided_1\"].plot_segments(highlight=slided_1_regions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "resdf_all, expr, pdata = combSpec.find_markers(\"slided_0\", slided_0_regions, \"slided_1\", slided_1_regions, pw, scaled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.list_de_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydf_all = resdf_all[\"ttest\"][('slided_0', slided_0_regions, 'slided_1', slided_1_regions)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(16,10))\n",
    "xydots = [(x,y) for x,y in zip(list(-mydf_all[\"avg_logFC\"]), list(-np.log10(mydf_all[\"qvalue\"])))]\n",
    "dotgene = list(mydf_all[\"gene\"])\n",
    "texts = []\n",
    "seenProts = set()\n",
    "\n",
    "for i in range(len(xydots)):\n",
    "    x = xydots[i][0]\n",
    "    y = xydots[i][1]\n",
    "    \n",
    "    if not dotgene[i] in seenProts and abs(y) >= 10 and abs(x) >= 0.5:\n",
    "        texts.append(plt.text(x * (1 + 0.01), y * (1 + 0.01) , dotgene[i], fontsize=12))\n",
    "        plt.plot(x, y, 'ro')\n",
    "        seenProts.add(dotgene[i])\n",
    "    else:\n",
    "        plt.plot(x, y, 'bo')\n",
    "\n",
    "adjust_text(texts, force_points=0.2, force_text=0.2, expand_points=(1, 1), expand_text=(1, 1), arrowprops=dict(arrowstyle=\"-\", color='black', lw=0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The comparison of both aorta results again in several differential proteins.\n",
    "\n",
    "Again, several proteins involved in the respiratory metabolism are listed, like Ndufc2, Ndufa11.\n",
    "\n",
    "Of interest here are Ifitm3, Ccdc126 and Ubl5, which are all located in the inner part of the aorta.\n",
    "\n",
    "#### Ifitm3\n",
    "\n",
    "Known gene involved in the innate immune system (T cells): Yánez, D. C., Ross, S., & Crompton, T. (2020). The IFITM protein family in adaptive immunity. In Immunology (Vol. 159, Issue 4, pp. 365–372). Blackwell Publishing Ltd. https://doi.org/10.1111/imm.13163\n",
    "\n",
    "Ifitm3 is involved in the plaque uptake in Alzheimer's disease: Hur, J. Y., Frost, G. R., Wu, X., Crump, C., Pan, S. J., Wong, E., Barros, M., Li, T., Nie, P., Zhai, Y., Wang, J. C., Tcw, J., Guo, L., McKenzie, A., Ming, C., Zhou, X., Wang, M., Sagi, Y., Renton, A. E., … Li, Y. M. (2020). The innate immunity protein IFITM3 modulates γ-secretase in Alzheimer’s disease. Nature, 1–6. https://doi.org/10.1038/s41586-020-2681-2\n",
    "\n",
    "\n",
    "#### Mrps14\n",
    "\n",
    "Mrps14 effects the mitochondrial translation. \n",
    "Hence, again, a disturbation of mitochondrial work can be observed.\n",
    "\n",
    "Jackson, C. B., Huemer, M., Bolognini, R., Martin, F., Szinnai, G., Donner, B. C., Richter, U., Battersby, B. J., Nuoffer, J. M., Suomalainen, A., & Schaller, A. (2019). A variant in MRPS14 (uS14m) causes perinatal hypertrophic cardiomyopathy with neonatal lactic acidosis, growth retardation, dysmorphic features and neurological involvement. Human Molecular Genetics, 28(4), 639–649. https://doi.org/10.1093/hmg/ddy374\n",
    "\n",
    "#### Acot13\n",
    "\n",
    "In chicken it was observed that an Acot13 decreases works as inhibitor during Preadipocytes Differentiation.\n",
    "Hence, an upregulation of Acot13 leads to an increase in preadipocyte differentiation, which also plays a role in plaque formation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = combSpec.mass_heatmap(pw.protein2mass.get(\"Ifitm3\"), scaled=True)\n",
    "_ = combSpec.mass_heatmap(pw.protein2mass.get(\"Mrps14\"), scaled=True)\n",
    "_ = combSpec.mass_heatmap(pw.protein2mass.get(\"Acot13\"), scaled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in set(mydf_all[\"gene\"]):\n",
    "    print(x)\n",
    "    _ = combSpec.mass_heatmap(pw.protein2mass.get(x), scaled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.mass_intensity(pw.protein2mass.get(\"Ccdc126\"), regions=[\"slided_0\", \"slided_1\"], scaled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.regions[\"slided_0\"].plot_segments(highlight=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = combSpec.mass_heatmap(pw.protein2mass.get(\"Ccdc126\"), scaled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.mass_intensity(pw.protein2mass.get(\"Hsbp1\"), regions=[\"slided_0\", \"slided_1\"], scaled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = combSpec.mass_heatmap(pw.protein2mass.get(\"Hsbp1\"), scaled=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The core DE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slided_0_inner_regions = tuple([1,3,4,6])\n",
    "slided_1_inner_regions = tuple([2,3,7,8,9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combSpec.regions[\"slided_0\"].plot_segments(highlight=slided_0_inner_regions)\n",
    "combSpec.regions[\"slided_1\"].plot_segments(highlight=slided_1_inner_regions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "resdf_inner, expr, pdata = combSpec.find_markers(\"slided_0\", slided_0_inner_regions, \"slided_1\", slided_1_inner_regions, pw, scaled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydf_inner = resdf_inner[\"ttest\"][('slided_0', slided_0_inner_regions, 'slided_1', slided_1_inner_regions)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(16,10))\n",
    "xydots = [(x,y) for x,y in zip(list(-mydf_inner[\"avg_logFC\"]), list(-np.log10(mydf_inner[\"qvalue\"])))]\n",
    "dotgene = list(mydf_inner[\"gene\"])\n",
    "texts = []\n",
    "seenProts = set()\n",
    "\n",
    "for i in range(len(xydots)):\n",
    "    x = xydots[i][0]\n",
    "    y = xydots[i][1]\n",
    "    \n",
    "    if not dotgene[i] in seenProts and abs(y) >= 10 and abs(x) >= 0.5:\n",
    "        texts.append(plt.text(x * (1 + 0.01), y * (1 + 0.01) , dotgene[i], fontsize=12))\n",
    "        plt.plot(x, y, 'ro')\n",
    "        seenProts.add(dotgene[i])\n",
    "    else:\n",
    "        plt.plot(x, y, 'bo')\n",
    "\n",
    "adjust_text(texts, force_points=0.2, force_text=0.2, expand_points=(1, 1), expand_text=(1, 1), arrowprops=dict(arrowstyle=\"-\", color='black', lw=0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again the same candidates as previously shown can be seen.\n",
    "\n",
    "Mrps14, Ccdc126, Ndufa11 - which mostly relate to mitochondrial activity.\n",
    "\n",
    "\n",
    "#### Timm8b\n",
    "Likewise is Timm8b significantly upregulated.\n",
    "\n",
    "Timm8b is found to be upregulated in colon mucosa cardinogenesis of diabetes type 2 patients, and is associated with mitochondrial dysfunction.\n",
    "A similar pattern might be observable in atherosclerosis as well.\n",
    "\n",
    "Del Puerto-Nevado, L., Santiago-Hernandez, A., Solanes-Casado, S., Gonzalez, N., Ricote, M., Corton, M., Prieto, I., Mas, S., Sanz, A. B., Aguilera, O., Gomez-Guerrero, C., Ayuso, C., Ortiz, A., Rojo, F., Egido, J., Garcia-Foncillas, J., Minguez, P., & Alvarez-Llamas, G. (2019). Diabetes-mediated promotion of colon mucosa carcinogenesis is associated with mitochondrial dysfunction. Molecular Oncology, 13(9), 1887–1897. https://doi.org/10.1002/1878-0261.12531"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GO analysis for plaque-DE experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! wget http://ftp.ebi.ac.uk//pub/databases/GO/goa/HUMAN/goa_human.gaf.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! wget 'http://purl.obolibrary.org/obo/go/go-basic.obo'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gzip\n",
    "import Bio.UniProt.GOA as GOA\n",
    "from goatools.go_enrichment import GOEnrichmentStudy\n",
    "from goatools import obo_parser\n",
    "\n",
    "goa_human = \"goa_human.gaf.gz\"\n",
    "\n",
    "# File is a gunzip file, so we need to open it in this way\n",
    "with gzip.open(goa_human, 'rt') as arab_gaf_fp:\n",
    "    human_funcs = {}  # Initialise the dictionary of functions\n",
    "    \n",
    "    # Iterate on each function using Bio.UniProt.GOA library.\n",
    "    for entry in GOA.gafiterator(arab_gaf_fp):\n",
    "        uniprot_id = entry.pop('DB_Object_Symbol')\n",
    "        human_funcs[uniprot_id] = entry\n",
    "\n",
    "        \n",
    "go = obo_parser.GODag(\"go-basic.obo\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,x in enumerate(human_funcs):\n",
    "    if x.startswith(\"CCL\"):\n",
    "        print(i,x, human_funcs[x])\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "keyword = 'growth'\n",
    "growth_dict = {x: human_funcs[x]\n",
    "               for x in human_funcs \n",
    "               if keyword in human_funcs[x]['DB_Object_Name']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "print('UniProt IDs of annotations with \"growth\" in their name:')\n",
    "\n",
    "print(\"Total: {}\".format(len(growth_dict)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pop = [x.upper() for x in pw.protein2mass]\n",
    "assoc = {}\n",
    "\n",
    "for x in human_funcs:\n",
    "    if x not in assoc:\n",
    "        assoc[x] = set()\n",
    "    assoc[x].add(str(human_funcs[x]['GO_ID']))\n",
    "\n",
    "methods = [\"bonferroni\", \"fdr\"]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "study = list(set([x.upper() for x in mydf[\"gene\"]])) #mydf_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = GOEnrichmentStudy(pop, assoc, go,\n",
    "                         propagate_counts=True,\n",
    "                         alpha=0.05,\n",
    "                         methods=['bonferroni', 'fdr_bh'])\n",
    "g_res = g.run_study(study)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in sorted(g_res, key=lambda x: (x.study_count, -x.p_uncorrected), reverse=True):\n",
    "    if x.study_count > 1:\n",
    "        print(x.study_count, x)"
   ]
  }
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