docs/examples/.ipynb_checkpoints/pyiomica_examples-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ![Logo](https://raw.githubusercontent.com/gmiaslab/pyiomica/master/pyiomica/data/PyIOmica.png?token=AKZGC3KSHJC4BEYKHRJ3D3C5HBZZC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import PyIOmica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading PyIOmica (https://github.com/gmiaslab/pyiomica by G. Mias Lab)\n"
     ]
    }
   ],
   "source": [
    "from pyiomica import pyiomica\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examples of GO Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: results/EnrichmentOutputDirectory/GOAnalysis/goExample1.xlsx\n",
      "Saved: results/EnrichmentOutputDirectory/GOAnalysis/analysisGOAssociation.xlsx\n",
      "Saved: results/EnrichmentOutputDirectory/GOAnalysis/analysisGOLabel.xlsx\n",
      "Saved: results/EnrichmentOutputDirectory/GOAnalysis/analysisGOMixed.xlsx\n",
      "Saved: results/EnrichmentOutputDirectory/GOAnalysis/analysisGOMixedMulti.xlsx\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Domanskyi\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\indexes\\base.py:162: FutureWarning: the 'labels' keyword is deprecated, use 'codes' instead\n",
      "  return cls.__new__(cls, **d)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: results/EnrichmentOutputDirectory/GOAnalysis/ExampleClusteringObjectGO.xlsx\n"
     ]
    }
   ],
   "source": [
    "EnrichmentOutputDirectory = 'results/EnrichmentOutputDirectory/'\n",
    "\n",
    "#Let's do a GO analysis for a group of genes, annotated with their \"Gene Symbol\":\n",
    "goExample1 = pyiomica.GOAnalysis([\"TAB1\", \"TNFSF13B\", \"MALT1\", \"TIRAP\", \"CHUK\", \n",
    "                                \"TNFRSF13C\", \"PARP1\", \"CSNK2A1\", \"CSNK2A2\", \"CSNK2B\", \"LTBR\", \n",
    "                                \"LYN\", \"MYD88\", \"GADD45B\", \"ATM\", \"NFKB1\", \"NFKB2\", \"NFKBIA\", \n",
    "                                \"IRAK4\", \"PIAS4\", \"PLAU\"])\n",
    "\n",
    "pyiomica.ExportEnrichmentReport(goExample1, AppendString='goExample1', OutputDirectory=EnrichmentOutputDirectory + 'GOAnalysis/')\n",
    "\n",
    "#The information can be computed for multiple groups, if these are provided as an association:\n",
    "analysisGOAssociation = pyiomica.GOAnalysis({\"Group1\": [\"C6orf57\", \"CD46\", \"DHX58\", \"HMGB3\", \"MAP3K5\", \"NFKB2\", \"NOS2\", \"PYCARD\", \"PYDC1\", \"SSC5D\"], \n",
    "                                                \"Group2\": [\"TAB1\", \"TNFSF13B\", \"MALT1\", \"TIRAP\", \"CHUK\", \"TNFRSF13C\", \"PARP1\", \"CSNK2A1\", \"CSNK2A2\", \"CSNK2B\", \"LTBR\", \n",
    "                                                            \"LYN\", \"MYD88\", \"GADD45B\", \"ATM\", \"NFKB1\", \"NFKB2\", \"NFKBIA\", \"IRAK4\", \"PIAS4\", \"PLAU\"]})\n",
    "\n",
    "pyiomica.ExportEnrichmentReport(analysisGOAssociation, AppendString='analysisGOAssociation', OutputDirectory=EnrichmentOutputDirectory + 'GOAnalysis/')\n",
    "\n",
    "#The data can be computed with or without a label. If labeled, the gene ID must be the first element for each ID provided. The data is in the form {ID,label}:\n",
    "analysisGOLabel = pyiomica.GOAnalysis([[\"C6orf57\", \"Protein\"], [\"CD46\", \"Protein\"], [\"DHX58\", \"Protein\"], [\"HMGB3\", \"Protein\"], [\"MAP3K5\", \"Protein\"], \n",
    "                                        [\"NFKB2\", \"Protein\"], [\"NOS2\", \"Protein\"], [\"PYCARD\", \"Protein\"], [\"PYDC1\",\"Protein\"], [\"SSC5D\", \"Protein\"]])\n",
    "\n",
    "pyiomica.ExportEnrichmentReport(analysisGOLabel, AppendString='analysisGOLabel', OutputDirectory=EnrichmentOutputDirectory + 'GOAnalysis/')\n",
    "\n",
    "#The data can be mixed, e.g. proteins and RNA with different labels:\n",
    "analysisGOMixed = pyiomica.GOAnalysis([[\"C6orf57\", \"Protein\"], [\"CD46\", \"Protein\"], [\"DHX58\", \"Protein\"], [\"HMGB3\", \"RNA\"], [\"HMGB3\", \"Protein\"], [\"MAP3K5\", \"Protein\"], \n",
    "                                        [\"NFKB2\", \"RNA\"], [\"NFKB2\", \"Protein\"], [\"NOS2\", \"RNA\"], [\"PYCARD\", \"RNA\"], [\"PYDC1\", \"Protein\"], [\"SSC5D\", \"Protein\"]])\n",
    "\n",
    "pyiomica.ExportEnrichmentReport(analysisGOMixed, AppendString='analysisGOMixed', OutputDirectory=EnrichmentOutputDirectory + 'GOAnalysis/')\n",
    "\n",
    "#We can instead treat the data as different by setting the MultipleList and MultipleListCorrection options:\n",
    "analysisGOMixedMulti = pyiomica.GOAnalysis([[\"C6orf57\", \"Protein\"], [\"CD46\", \"Protein\"], [\"DHX58\", \"Protein\"], [\"HMGB3\", \"RNA\"], [\"HMGB3\", \"Protein\"], [\"MAP3K5\", \"Protein\"], \n",
    "                                            [\"NFKB2\", \"RNA\"], [\"NFKB2\", \"Protein\"], [\"NOS2\", \"RNA\"], [\"PYCARD\", \"RNA\"], [\"PYDC1\", \"Protein\"], [\"SSC5D\", \"Protein\"]],\n",
    "                                            MultipleList=True, MultipleListCorrection='Automatic')\n",
    "\n",
    "pyiomica.ExportEnrichmentReport(analysisGOMixedMulti, AppendString='analysisGOMixedMulti', OutputDirectory=EnrichmentOutputDirectory + 'GOAnalysis/')\n",
    "\n",
    "#Let's consider an example from real protein data. We will use already clustered data, from the examples. Let's import the data:\n",
    "ExampleClusteringObject = pyiomica.read(os.path.join(pyiomica.ConstantPyIOmicaExamplesDirectory, 'exampleClusteringObject_SLV_Delta_LAG1_Autocorr'))\n",
    "\n",
    "if not ExampleClusteringObject is None:\n",
    "    #We calculate the GOAnalysis for each group in each class:\n",
    "    ExampleClusteringObjectGO = pyiomica.GOAnalysis(ExampleClusteringObject, MultipleListCorrection='Automatic')\n",
    "\n",
    "    pyiomica.ExportEnrichmentReport(ExampleClusteringObjectGO, AppendString='ExampleClusteringObjectGO', OutputDirectory=EnrichmentOutputDirectory + 'GOAnalysis/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examples of KEGG Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: results/EnrichmentOutputDirectory/KEGGAnalysis/keggExample1.xlsx\n",
      "Saved: results/EnrichmentOutputDirectory/KEGGAnalysis/analysisKEGGAssociation.xlsx\n",
      "Saved: results/EnrichmentOutputDirectory/KEGGAnalysis/analysisKEGGLabel.xlsx\n",
      "Saved: results/EnrichmentOutputDirectory/KEGGAnalysis/analysisKEGGNoLabel.xlsx\n",
      "Saved: results/EnrichmentOutputDirectory/KEGGAnalysis/analysisKEGGstrings.xlsx\n",
      "Saved: results/EnrichmentOutputDirectory/KEGGAnalysis/analysisKEGGMixed.xlsx\n",
      "Saved: results/EnrichmentOutputDirectory/KEGGAnalysis/analysisKEGGMixedMulti.xlsx\n",
      "Saved: results/EnrichmentOutputDirectory/KEGGAnalysis/compoundsExampleKEGG.xlsx\n",
      "Saved: results/EnrichmentOutputDirectory/KEGGAnalysis/multiOmicsDataKEGG.xlsx\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Domanskyi\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\indexes\\base.py:162: FutureWarning: the 'labels' keyword is deprecated, use 'codes' instead\n",
      "  return cls.__new__(cls, **d)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: results/EnrichmentOutputDirectory/KEGGAnalysis/ExampleClusteringObject.xlsx\n"
     ]
    }
   ],
   "source": [
    "EnrichmentOutputDirectory = 'results/EnrichmentOutputDirectory/'\n",
    "\n",
    "#Let's do a KEGG pathway analysis for a group of genes (most in the NFKB pathway), annotated with their \"Gene Symbol\":\n",
    "keggExample1 = pyiomica.KEGGAnalysis([\"TAB1\", \"TNFSF13B\", \"MALT1\", \"TIRAP\", \"CHUK\", \"TNFRSF13C\", \"PARP1\", \"CSNK2A1\", \"CSNK2A2\", \"CSNK2B\", \"LTBR\", \"LYN\", \"MYD88\", \n",
    "                                        \"GADD45B\", \"ATM\", \"NFKB1\", \"NFKB2\", \"NFKBIA\", \"IRAK4\", \"PIAS4\", \"PLAU\", \"POLR3B\", \"NME1\", \"CTPS1\", \"POLR3A\"])\n",
    "\n",
    "pyiomica.ExportEnrichmentReport(keggExample1, AppendString='keggExample1', OutputDirectory=EnrichmentOutputDirectory + 'KEGGAnalysis/')\n",
    "\n",
    "#The information can be computed for multiple groups, if these are provided as an association:\n",
    "analysisKEGGAssociation = pyiomica.KEGGAnalysis({\"Group1\": [\"C6orf57\", \"CD46\", \"DHX58\", \"HMGB3\", \"MAP3K5\", \"NFKB2\", \"NOS2\", \"PYCARD\", \"PYDC1\", \"SSC5D\"], \n",
    "                                                    \"Group2\": [\"TAB1\", \"TNFSF13B\", \"MALT1\", \"TIRAP\", \"CHUK\", \"TNFRSF13C\", \"PARP1\", \"CSNK2A1\", \"CSNK2A2\", \"CSNK2B\", \"LTBR\", \n",
    "                                                \"LYN\", \"MYD88\", \"GADD45B\", \"ATM\", \"NFKB1\", \"NFKB2\", \"NFKBIA\", \"IRAK4\", \"PIAS4\", \"PLAU\", \"POLR3B\", \"NME1\", \"CTPS1\", \"POLR3A\"]})\n",
    "        \n",
    "pyiomica.ExportEnrichmentReport(analysisKEGGAssociation, AppendString='analysisKEGGAssociation', OutputDirectory=EnrichmentOutputDirectory + 'KEGGAnalysis/')\n",
    "\n",
    "#The data can be computed with or without a label. If labeled, the gene ID must be the first element for each ID provided. The data is in the form {ID,label}:\n",
    "analysisKEGGLabel = pyiomica.KEGGAnalysis([[\"C6orf57\", \"Protein\"], [\"CD46\", \"Protein\"], [\"DHX58\", \"Protein\"], [\"HMGB3\", \"Protein\"], [\"MAP3K5\", \"Protein\"], \n",
    "                                            [\"NFKB2\", \"Protein\"], [\"NOS2\", \"Protein\"], [\"PYCARD\", \"Protein\"], [\"PYDC1\",\"Protein\"], [\"SSC5D\", \"Protein\"]])\n",
    "\n",
    "pyiomica.ExportEnrichmentReport(analysisKEGGLabel, AppendString='analysisKEGGLabel', OutputDirectory=EnrichmentOutputDirectory + 'KEGGAnalysis/')\n",
    "\n",
    "#The same result is obtained if IDs are enclosed in list brackets:\n",
    "analysisKEGGNoLabel = pyiomica.KEGGAnalysis([[\"C6orf57\"], [\"CD46\"], [\"DHX58\"], [\"HMGB3\"], [\"MAP3K5\"], [\"NFKB2\"], [\"NOS2\"], [\"PYCARD\"], [\"PYDC1\"], [\"SSC5D\"]])\n",
    "\n",
    "pyiomica.ExportEnrichmentReport(analysisKEGGNoLabel, AppendString='analysisKEGGNoLabel', OutputDirectory=EnrichmentOutputDirectory + 'KEGGAnalysis/')\n",
    "\n",
    "#The same result is obtained if IDs are input as strings:\n",
    "analysisKEGGstrings = pyiomica.KEGGAnalysis([\"C6orf57\", \"CD46\", \"DHX58\", \"HMGB3\", \"MAP3K5\", \"NFKB2\", \"NOS2\", \"PYCARD\", \"PYDC1\", \"SSC5D\"])\n",
    "\n",
    "pyiomica.ExportEnrichmentReport(analysisKEGGstrings, AppendString='analysisKEGGstrings', OutputDirectory=EnrichmentOutputDirectory + 'KEGGAnalysis/')\n",
    "\n",
    "#The data can be mixed, e.g. proteins and RNA with different labels:\n",
    "analysisKEGGMixed = pyiomica.KEGGAnalysis([[\"C6orf57\", \"Protein\"], [\"CD46\", \"Protein\"], [\"DHX58\", \"Protein\"], [\"HMGB3\", \"RNA\"], [\"HMGB3\", \"Protein\"], [\"MAP3K5\", \"Protein\"], \n",
    "                                            [\"NFKB2\", \"RNA\"], [\"NFKB2\", \"Protein\"], [\"NOS2\", \"RNA\"], [\"PYCARD\", \"RNA\"], [\"PYDC1\", \"Protein\"], [\"SSC5D\", \"Protein\"]])\n",
    "\n",
    "pyiomica.ExportEnrichmentReport(analysisKEGGMixed, AppendString='analysisKEGGMixed', OutputDirectory=EnrichmentOutputDirectory + 'KEGGAnalysis/')\n",
    "\n",
    "#The data in this case treated as originating from a single population. Protein and RNA labeled data for the same identifier are treated as equivalent.\n",
    "#We can instead treat the data as different by setting the MultipleList and MultipleListCorrection options:\n",
    "analysisKEGGMixedMulti = pyiomica.KEGGAnalysis([[\"C6orf57\", \"Protein\"], [\"CD46\", \"Protein\"], [\"DHX58\", \"Protein\"], [\"HMGB3\", \"RNA\"], [\"HMGB3\", \"Protein\"], [\"MAP3K5\", \"Protein\"], \n",
    "                                                [\"NFKB2\", \"RNA\"], [\"NFKB2\", \"Protein\"], [\"NOS2\", \"RNA\"], [\"PYCARD\", \"RNA\"], [\"PYDC1\", \"Protein\"], [\"SSC5D\", \"Protein\"]], \n",
    "                                                MultipleList=True, MultipleListCorrection='Automatic')\n",
    "\n",
    "pyiomica.ExportEnrichmentReport(analysisKEGGMixedMulti, AppendString='analysisKEGGMixedMulti', OutputDirectory=EnrichmentOutputDirectory + 'KEGGAnalysis/')\n",
    "\n",
    "#We can carry out a \"Molecular\" analysis for compound data. We consider the following metabolomics data, which has labels \"Meta\" \n",
    "#and additional mass and retention time information in the form {identifier,mass, retention time, label}:\n",
    "compoundsExample = [[\"cpd:C19691\", 325.2075, 10.677681, \"Meta\"], [\"cpd:C17905\", 594.2002, 8.727458, \"Meta\"],[\"cpd:C09921\", 204.0784, 12.3909445, \"Meta\"], \n",
    "                    [\"cpd:C18218\", 272.2356, 13.473582, \"Meta\"], [\"cpd:C14169\", 235.1573, 12.267084, \"Meta\"],[\"cpd:C14245\", 262.2296, 13.545572, \"Meta\"], \n",
    "                    [\"cpd:C09137\", 352.2615, 14.0554285, \"Meta\"], [\"cpd:C09674\", 296.1624, 12.147417, \"Meta\"], [\"cpd:C00449\", 276.1334, 11.004139, \"Meta\"], \n",
    "                    [\"cpd:C02999\", 364.1497, 12.147243, \"Meta\"], [\"cpd:C07915\", 309.194, 7.3625283, \"Meta\"],[\"cpd:C08760\", 496.2309, 8.7241125, \"Meta\"], \n",
    "                    [\"cpd:C14549\", 276.0972, 11.078914, \"Meta\"], [\"cpd:C20533\", 601.3378, 12.75722, \"Meta\"], [\"cpd:C20790\", 212.1051, 7.127666, \"Meta\"], \n",
    "                    [\"cpd:C09137\", 352.2613, 12.869867, \"Meta\"], [\"cpd:C17648\", 400.2085, 10.843841, \"Meta\"], [\"cpd:C07807\", 240.1471, 0.48564285, \"Meta\"], \n",
    "                    [\"cpd:C08564\", 324.0948, 10.281, \"Meta\"], [\"cpd:C19426\", 338.2818, 13.758765, \"Meta\"], [\"cpd:C02943\", 468.3218, 14.263261, \"Meta\"], \n",
    "                    [\"cpd:C04882\", 1193.342, 14.707576, \"Meta\"]]\n",
    "\n",
    "compoundsExampleKEGG = pyiomica.KEGGAnalysis(compoundsExample, FilterSignificant=True, AnalysisType='Molecular')\n",
    "\n",
    "pyiomica.ExportEnrichmentReport(compoundsExampleKEGG, AppendString='compoundsExampleKEGG', OutputDirectory=EnrichmentOutputDirectory + 'KEGGAnalysis/')\n",
    "\n",
    "#We can carry out multiomics data analysis. We consider the following simple example:\n",
    "multiOmicsData = [[\"C6orf57\", \"Protein\"], [\"CD46\", \"Protein\"], [\"DHX58\", \"Protein\"], [\"HMGB3\", \"RNA\"], [\"HMGB3\", \"Protein\"], \n",
    "                    [\"MAP3K5\", \"Protein\"], [\"NFKB2\", \"RNA\"], [\"NFKB2\", \"Protein\"], [\"NOS2\", \"RNA\"], [\"PYCARD\", \"RNA\"], [\"PYDC1\", \"Protein\"], \n",
    "                    [\"SSC5D\", \"Protein\"], [\"cpd:C19691\", 325.2075, 10.677681, \"Meta\"], [\"cpd:C17905\", 594.2002, 8.727458, \"Meta\"], \n",
    "                    [\"cpd:C09921\", 204.0784, 12.3909445, \"Meta\"], [\"cpd:C18218\", 272.2356, 13.473582, \"Meta\"], \n",
    "                    [\"cpd:C14169\", 235.1573, 12.267084, \"Meta\"], [\"cpd:C14245\", 262.2296, 13.545572, \"Meta\"], \n",
    "                    [\"cpd:C09137\", 352.2615, 14.0554285, \"Meta\"], [\"cpd:C09674\", 296.1624, 12.147417, \"Meta\"],\n",
    "                    [\"cpd:C00449\", 276.1334, 11.004139, \"Meta\"],[\"cpd:C02999\", 364.1497, 12.147243, \"Meta\"],\n",
    "                    [\"cpd:C07915\", 309.194, 7.3625283, \"Meta\"],[\"cpd:C08760\", 496.2309, 8.7241125, \"Meta\"],\n",
    "                    [\"cpd:C14549\", 276.0972, 11.078914, \"Meta\"],[\"cpd:C20533\", 601.3378, 12.75722, \"Meta\"], \n",
    "                    [\"cpd:C20790\", 212.1051, 7.127666, \"Meta\"], [\"cpd:C09137\", 352.2613, 12.869867, \"Meta\"],\n",
    "                    [\"cpd:C17648\", 400.2085, 10.843841, \"Meta\"], [\"cpd:C07807\", 240.1471, 0.48564285, \"Meta\"], \n",
    "                    [\"cpd:C08564\", 324.0948, 10.281, \"Meta\"], [\"cpd:C19426\", 338.2818, 13.758765, \"Meta\"], \n",
    "                    [\"cpd:C02943\", 468.3218, 14.263261, \"Meta\"], [\"cpd:C04882\", 1193.342, 14.707576, \"Meta\"]]\n",
    "\n",
    "#We can carry out \"Genomic\" and \"Molecular\" analysis concurrently by setting AnalysisType = \"All\":\n",
    "multiOmicsDataKEGG = pyiomica.KEGGAnalysis(multiOmicsData, AnalysisType='All', MultipleList=True, MultipleListCorrection='Automatic') \n",
    "\n",
    "pyiomica.ExportEnrichmentReport(multiOmicsDataKEGG, AppendString='multiOmicsDataKEGG', OutputDirectory=EnrichmentOutputDirectory + 'KEGGAnalysis/')\n",
    "\n",
    "#Let's consider an example from real protein data. We will use already clustered data, from the examples. Let's import the data:\n",
    "ExampleClusteringObject = pyiomica.read(os.path.join(pyiomica.ConstantPyIOmicaExamplesDirectory, 'exampleClusteringObject_SLV_Delta_LAG1_Autocorr'))\n",
    "\n",
    "if not ExampleClusteringObject is None:\n",
    "    #We calculate the KEGGAnalysis for each group in each class:\n",
    "    ExampleClusteringObject = pyiomica.KEGGAnalysis(ExampleClusteringObject)\n",
    "\n",
    "    pyiomica.ExportEnrichmentReport(ExampleClusteringObject, AppendString='ExampleClusteringObject', OutputDirectory=EnrichmentOutputDirectory + 'KEGGAnalysis/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series Classification\n",
    "\n",
    "In the example below we use data in the 'results' directory to illustrate automatic time series classification. Two time series sets of gene expression intensities are available as '.csv' files:\n",
    "\n",
    "- Hourly1TimeSeries_SLV_KallistoNormedGeneGencodeGC.csv\n",
    "- Hourly2TimeSeries_SLV_KallistoNormedGeneGencodeGC.csv\n",
    "\n",
    "The corresponding times of the sampling are provided in the file \"TimesHourly.csv\" (hourly sampling).\n",
    "\n",
    "The custom function processDataTempFunction is executed 3 times, once for each time series, and once for a computation of the 'delta' temporal trends (which considers the paired differences between the different time signals for matching identifiers across the two main sets being compared). The temporal trends, classification and results are output in the corresponding results directories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ---------------------------------------------------------------------- \n",
      "\tProcessing SLV_Hourly2TimeSeries (Autocorrelations) \n",
      " ----------------------------------------------------------------------\n",
      "Filtering out all-zero signals...\n",
      "Removed  27065 signals out of 84647.\n",
      "Remaining  57582 signals!\n",
      "Filtering out first time point zeros signals...\n",
      "Removed  38822 signals out of 57582.\n",
      "Remaining  18760 signals!\n",
      "Filtering out low-quality signals (with more than 25.0% missing points)...\n",
      "Removed  11266 signals out of 18760.\n",
      "Remaining  7494 signals!\n",
      "Tagging missing (i.e. zero) values with NaN...\n",
      "Tagging low values (<=1.0) with 1.0...\n",
      "\n",
      "Removing constant genes. Cutoff value is 0.0\n",
      "Removed  1 signals out of 7494.\n",
      "Remaining  7493 signals!\n",
      "SLV_Hourly2TimeSeries_dataAutocorrelations not found in results/results SLV H2/SLV_Hourly2TimeSeries.h5\n",
      "results/results SLV H2/SLV_Hourly2TimeSeries_dataAutocorrelations.pklz not found.\n",
      "SLV_Hourly2TimeSeries_randomAutocorrelations not found in results/results SLV H2/SLV_Hourly2TimeSeries.h5\n",
      "results/results SLV H2/SLV_Hourly2TimeSeries_randomAutocorrelations.pklz not found.\n",
      "Autocorrelation of data and the corresponding null distribution not found. Calculating...\n",
      "Calculating null distribution (autocorrelation) of 100000 samples...\n",
      "Filtering out low-quality signals (with more than 25.0% missing points)...\n",
      "Normalizing signals to unity...\n",
      "\n",
      "Removing constant genes. Cutoff value is 0.0\n",
      "Removed  0 signals out of 100000.\n",
      "Remaining  100000 signals!\n",
      "\n",
      "Calculating autocorrelations of 100000 random samples (sampled with replacement)...\n",
      "Normalizing signals to unity...\n",
      "Calculating each Time Series Autocorrelations...\n",
      "Quantiles: [1.0, -0.0042241343633796, 0.5721377871216922, 0.2156127526083077, 0.4503704409087177, 0.2810881691713686, 0.3797343841843995, 0.2800944767678657, 0.327614926896233, 0.2519814847757104, 0.2930585806286578, 0.2214007055463047] \n",
      "\n",
      "Calculating spikes cutoffs...\n",
      "Filtering out low-quality signals (with more than 25.0% missing points)...\n",
      "Normalizing signals to unity...\n",
      "\n",
      "Removing constant genes. Cutoff value is 0.0\n",
      "Removed  0 signals out of 100000.\n",
      "Remaining  100000 signals!\n",
      "{14: (0.7520825893367992, -0.0744169731906372), 15: (0.9698148382939349, -0.30060744996766425), 16: (0.9920340982047694, -0.3268084249920958), 17: (0.9965782241522557, -0.3137214708845752), 18: (0.9978246212152344, -0.2990983533010763), 19: (0.9970355082367465, -0.29338656052878553), 20: (0.997685030957083, -0.2811037417173823), 21: (0.9977268666835453, -0.2724060279350771), 22: (0.9979694751981909, -0.26358279633743387), 23: (0.998000322743736, -0.25670940515859886), 24: (0.9972063755604893, -0.2411736382794897)}\n",
      "Normalizing signals to unity...\n",
      "Recording SpikeMax data...\n",
      "30\n",
      "Recording SpikeMin data...\n",
      "2666\n",
      "Recording Lag1-Lag12 data...\n",
      "\n",
      "\n",
      "LAG1 of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 1\n",
      "[1246]\n",
      "n_subgroups: 3\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 3\n",
      "\n",
      "\n",
      "LAG2 of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 1\n",
      "[414]\n",
      "n_subgroups: 12\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 12\n",
      "\n",
      "\n",
      "LAG3 of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 1\n",
      "[312]\n",
      "n_subgroups: 3\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 3\n",
      "\n",
      "\n",
      "SpikeMax of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 2\n",
      "[2, 28]\n",
      "n_subgroups: 1\n",
      "n_subgroups: 5\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 2\n",
      "Number of subgroups: 5\n",
      "\n",
      "\n",
      "SpikeMin of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 3\n",
      "[147, 171, 691]\n",
      "n_subgroups: 38\n",
      "n_subgroups: 6\n",
      "n_subgroups: 3\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 38\n",
      "Number of subgroups: 6\n",
      "Number of subgroups: 3\n",
      "\n",
      " ---------------------------------------------------------------------- \n",
      "\tProcessing SLV_Hourly2TimeSeries (Periodograms) \n",
      " ----------------------------------------------------------------------\n",
      "Filtering out all-zero signals...\n",
      "Removed  27065 signals out of 84647.\n",
      "Remaining  57582 signals!\n",
      "Filtering out first time point zeros signals...\n",
      "Removed  38822 signals out of 57582.\n",
      "Remaining  18760 signals!\n",
      "Filtering out low-quality signals (with more than 25.0% missing points)...\n",
      "Removed  11266 signals out of 18760.\n",
      "Remaining  7494 signals!\n",
      "Tagging missing (i.e. zero) values with NaN...\n",
      "Tagging low values (<=1.0) with 1.0...\n",
      "\n",
      "Removing constant genes. Cutoff value is 0.0\n",
      "Removed  1 signals out of 7494.\n",
      "Remaining  7493 signals!\n",
      "SLV_Hourly2TimeSeries_dataPeriodograms not found in results/results SLV H2/SLV_Hourly2TimeSeries.h5\n",
      "results/results SLV H2/SLV_Hourly2TimeSeries_dataPeriodograms.pklz not found.\n",
      "SLV_Hourly2TimeSeries_randomPeriodograms not found in results/results SLV H2/SLV_Hourly2TimeSeries.h5\n",
      "results/results SLV H2/SLV_Hourly2TimeSeries_randomPeriodograms.pklz not found.\n",
      "Periodograms of data and the corresponding null distribution not found. Calculating...\n",
      "Calculating null distribution (periodogram) of 100000 samples...\n",
      "Filtering out low-quality signals (with more than 25.0% missing points)...\n",
      "Normalizing signals to unity...\n",
      "\n",
      "Removing constant genes. Cutoff value is 0.0\n",
      "Removed  0 signals out of 100000.\n",
      "Remaining  100000 signals!\n",
      "\n",
      "Calculating periodograms of 100000 random samples (sampled with replacement)...\n",
      "Normalizing signals to unity...\n",
      "Calculating each Time Series Periodogram...\n",
      "Quantiles: [1.0, 2.264223662683751, 2.43172555668027, 2.2381452893928584, 2.174512155169937, 2.2126282847272156, 2.21487035984499, 2.2433360923688177, 2.2563834652336947, 2.208285122505163, 2.3041599333152796, 2.2087829978551508] \n",
      "\n",
      "Calculating spikes cutoffs...\n",
      "Filtering out low-quality signals (with more than 25.0% missing points)...\n",
      "Normalizing signals to unity...\n",
      "\n",
      "Removing constant genes. Cutoff value is 0.0\n",
      "Removed  0 signals out of 100000.\n",
      "Remaining  100000 signals!\n",
      "{14: (0.0, -0.3122016894009218), 15: (0.8471195987930332, -0.3142990450165016), 16: (0.9931693453169247, -0.33920237220149324), 17: (0.9987869400669401, -0.31841309376232374), 18: (0.9972995150694166, -0.3002976132503057), 19: (0.9974452205981766, -0.2919275124632928), 20: (0.9974888575477882, -0.2817144585421322), 21: (0.9977849195899745, -0.27298561413918615), 22: (0.9977690306593947, -0.2648375094806345), 23: (0.998439445663548, -0.255008059456785), 24: (0.9982830187244937, -0.23770615328347136)}\n",
      "Normalizing signals to unity...\n",
      "Recording SpikeMax data...\n",
      "34\n",
      "Recording SpikeMin data...\n",
      "2673\n",
      "Recording Lag1-Lag12 data...\n",
      "\n",
      "\n",
      "LAG1 of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 5\n",
      "[157, 115, 264, 65, 243]\n",
      "n_subgroups: 2\n",
      "n_subgroups: 2\n",
      "n_subgroups: 17\n",
      "n_subgroups: 2\n",
      "n_subgroups: 1\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Domanskyi\\AppData\\Roaming\\Python\\Python37\\site-packages\\pyiomica\\pyiomica.py:2967: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  fig = plt.figure(figsize=(12,8))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of subgroups: 2\n",
      "Number of subgroups: 2\n",
      "Number of subgroups: 17\n",
      "Number of subgroups: 2\n",
      "Number of subgroups: 1\n",
      "\n",
      "\n",
      "LAG2 of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 10\n",
      "[74, 61, 13, 58, 32, 163, 75, 44, 42, 134]\n",
      "n_subgroups: 5\n",
      "n_subgroups: 1\n",
      "n_subgroups: 3\n",
      "n_subgroups: 3\n",
      "n_subgroups: 2\n",
      "n_subgroups: 11\n",
      "n_subgroups: 5\n",
      "n_subgroups: 5\n",
      "n_subgroups: 3\n",
      "n_subgroups: 2\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 5\n",
      "Number of subgroups: 1\n",
      "Number of subgroups: 3\n",
      "Number of subgroups: 3\n",
      "Number of subgroups: 2\n",
      "Number of subgroups: 11\n",
      "Number of subgroups: 5\n",
      "Number of subgroups: 5\n",
      "Number of subgroups: 3\n",
      "Number of subgroups: 2\n",
      "\n",
      "\n",
      "LAG3 of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 10\n",
      "[26, 56, 33, 56, 47, 38, 63, 42, 57, 29]\n",
      "n_subgroups: 4\n",
      "n_subgroups: 2\n",
      "n_subgroups: 3\n",
      "n_subgroups: 7\n",
      "n_subgroups: 9\n",
      "n_subgroups: 24\n",
      "n_subgroups: 5\n",
      "n_subgroups: 7\n",
      "n_subgroups: 11\n",
      "n_subgroups: 2\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 4\n",
      "Number of subgroups: 2\n",
      "Number of subgroups: 3\n",
      "Number of subgroups: 7\n",
      "Number of subgroups: 9\n",
      "Number of subgroups: 24\n",
      "Number of subgroups: 5\n",
      "Number of subgroups: 7\n",
      "Number of subgroups: 11\n",
      "Number of subgroups: 2\n",
      "\n",
      "\n",
      "SpikeMax of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 1\n",
      "[34]\n",
      "n_subgroups: 6\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 6\n",
      "\n",
      "\n",
      "SpikeMin of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 4\n",
      "[189, 426, 254, 145]\n",
      "n_subgroups: 3\n",
      "n_subgroups: 16\n",
      "n_subgroups: 1\n",
      "n_subgroups: 3\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 3\n",
      "Number of subgroups: 16\n",
      "Number of subgroups: 1\n",
      "Number of subgroups: 3\n",
      "\n",
      " ---------------------------------------------------------------------- \n",
      "\tProcessing SLV_Hourly1TimeSeries (Autocorrelations) \n",
      " ----------------------------------------------------------------------\n",
      "Filtering out all-zero signals...\n",
      "Removed  8063 signals out of 84647.\n",
      "Remaining  76584 signals!\n",
      "Filtering out first time point zeros signals...\n",
      "Removed  53369 signals out of 76584.\n",
      "Remaining  23215 signals!\n",
      "Filtering out low-quality signals (with more than 25.0% missing points)...\n",
      "Removed  7592 signals out of 23215.\n",
      "Remaining  15623 signals!\n",
      "Tagging missing (i.e. zero) values with NaN...\n",
      "Tagging low values (<=1.0) with 1.0...\n",
      "\n",
      "Removing constant genes. Cutoff value is 0.0\n",
      "Removed  2 signals out of 15623.\n",
      "Remaining  15621 signals!\n",
      "SLV_Hourly1TimeSeries_dataAutocorrelations not found in results/results SLV H1/SLV_Hourly1TimeSeries.h5\n",
      "results/results SLV H1/SLV_Hourly1TimeSeries_dataAutocorrelations.pklz not found.\n",
      "SLV_Hourly1TimeSeries_randomAutocorrelations not found in results/results SLV H1/SLV_Hourly1TimeSeries.h5\n",
      "results/results SLV H1/SLV_Hourly1TimeSeries_randomAutocorrelations.pklz not found.\n",
      "Autocorrelation of data and the corresponding null distribution not found. Calculating...\n",
      "Calculating null distribution (autocorrelation) of 100000 samples...\n",
      "Filtering out low-quality signals (with more than 25.0% missing points)...\n",
      "Normalizing signals to unity...\n",
      "\n",
      "Removing constant genes. Cutoff value is 0.0\n",
      "Removed  0 signals out of 100000.\n",
      "Remaining  100000 signals!\n",
      "\n",
      "Calculating autocorrelations of 100000 random samples (sampled with replacement)...\n",
      "Normalizing signals to unity...\n",
      "Calculating each Time Series Autocorrelations...\n",
      "Quantiles: [1.0, -0.008819039644394, 0.5874528757682363, 0.2215051832155648, 0.4583362991293238, 0.2948606127852824, 0.4035515669258362, 0.2992151491578608, 0.3519904580794906, 0.2678199081871924, 0.3037180802472463, 0.2343780311561772] \n",
      "\n",
      "Calculating spikes cutoffs...\n",
      "Filtering out low-quality signals (with more than 25.0% missing points)...\n",
      "Normalizing signals to unity...\n",
      "\n",
      "Removing constant genes. Cutoff value is 0.0\n",
      "Removed  0 signals out of 100000.\n",
      "Remaining  100000 signals!\n",
      "{15: (0.9379834187904734, -0.0007221053485108023), 16: (0.7695688975664394, -0.2564929393187547), 17: (0.9828946769258555, -0.3183218466257354), 18: (0.9976427555822038, -0.2797497684052378), 19: (0.9977216059495129, -0.2763918597406204), 20: (0.9969619507464658, -0.268352485352599), 21: (0.9968787961166524, -0.2623008726708006), 22: (0.997196174598402, -0.2538707929076258), 23: (0.9971782124840951, -0.24601962982874304), 24: (0.9974365054239245, -0.24125901282826767)}\n",
      "Normalizing signals to unity...\n",
      "Recording SpikeMax data...\n",
      "184\n",
      "Recording SpikeMin data...\n",
      "5120\n",
      "Recording Lag1-Lag12 data...\n",
      "\n",
      "\n",
      "LAG1 of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 2\n",
      "[68, 2665]\n",
      "n_subgroups: 40\n",
      "n_subgroups: 6\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 40\n",
      "Number of subgroups: 6\n",
      "\n",
      "\n",
      "LAG2 of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 4\n",
      "[28, 25, 368, 476]\n",
      "n_subgroups: 3\n",
      "n_subgroups: 8\n",
      "n_subgroups: 6\n",
      "n_subgroups: 7\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 3\n",
      "Number of subgroups: 8\n",
      "Number of subgroups: 6\n",
      "Number of subgroups: 7\n",
      "\n",
      "\n",
      "LAG3 of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 4\n",
      "[375, 95, 150, 379]\n",
      "n_subgroups: 4\n",
      "n_subgroups: 4\n",
      "n_subgroups: 7\n",
      "n_subgroups: 5\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 4\n",
      "Number of subgroups: 4\n",
      "Number of subgroups: 7\n",
      "Number of subgroups: 5\n",
      "\n",
      "\n",
      "SpikeMax of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 1\n",
      "[184]\n",
      "n_subgroups: 3\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 3\n",
      "\n",
      "\n",
      "SpikeMin of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 5\n",
      "[587, 178, 583, 314, 288]\n",
      "n_subgroups: 6\n",
      "n_subgroups: 6\n",
      "n_subgroups: 24\n",
      "n_subgroups: 44\n",
      "n_subgroups: 6\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 6\n",
      "Number of subgroups: 6\n",
      "Number of subgroups: 24\n",
      "Number of subgroups: 44\n",
      "Number of subgroups: 6\n",
      "\n",
      " ---------------------------------------------------------------------- \n",
      "\tProcessing SLV_Hourly1TimeSeries (Periodograms) \n",
      " ----------------------------------------------------------------------\n",
      "Filtering out all-zero signals...\n",
      "Removed  8063 signals out of 84647.\n",
      "Remaining  76584 signals!\n",
      "Filtering out first time point zeros signals...\n",
      "Removed  53369 signals out of 76584.\n",
      "Remaining  23215 signals!\n",
      "Filtering out low-quality signals (with more than 25.0% missing points)...\n",
      "Removed  7592 signals out of 23215.\n",
      "Remaining  15623 signals!\n",
      "Tagging missing (i.e. zero) values with NaN...\n",
      "Tagging low values (<=1.0) with 1.0...\n",
      "\n",
      "Removing constant genes. Cutoff value is 0.0\n",
      "Removed  2 signals out of 15623.\n",
      "Remaining  15621 signals!\n",
      "SLV_Hourly1TimeSeries_dataPeriodograms not found in results/results SLV H1/SLV_Hourly1TimeSeries.h5\n",
      "results/results SLV H1/SLV_Hourly1TimeSeries_dataPeriodograms.pklz not found.\n",
      "SLV_Hourly1TimeSeries_randomPeriodograms not found in results/results SLV H1/SLV_Hourly1TimeSeries.h5\n",
      "results/results SLV H1/SLV_Hourly1TimeSeries_randomPeriodograms.pklz not found.\n",
      "Periodograms of data and the corresponding null distribution not found. Calculating...\n",
      "Calculating null distribution (periodogram) of 100000 samples...\n",
      "Filtering out low-quality signals (with more than 25.0% missing points)...\n",
      "Normalizing signals to unity...\n",
      "\n",
      "Removing constant genes. Cutoff value is 0.0\n",
      "Removed  0 signals out of 100000.\n",
      "Remaining  100000 signals!\n",
      "\n",
      "Calculating periodograms of 100000 random samples (sampled with replacement)...\n",
      "Normalizing signals to unity...\n",
      "Calculating each Time Series Periodogram...\n",
      "Quantiles: [1.0, 2.2304444659432754, 2.2684401835742256, 2.2597257273905598, 2.213518236457791, 2.282315692002894, 2.2309096250010643, 2.310794179343262, 2.2580564940677044, 2.2498870752677558, 2.3489042500371626, 2.2453903746637263] \n",
      "\n",
      "Calculating spikes cutoffs...\n",
      "Filtering out low-quality signals (with more than 25.0% missing points)...\n",
      "Normalizing signals to unity...\n",
      "\n",
      "Removing constant genes. Cutoff value is 0.0\n",
      "Removed  0 signals out of 100000.\n",
      "Remaining  100000 signals!\n",
      "{16: (0.6067783613629586, -0.013762427932705512), 17: (0.9990188685292228, -0.3156161981263354), 18: (0.9963303022043342, -0.2819416088559518), 19: (0.996327266130615, -0.2792522697065894), 20: (0.9964725590867424, -0.2707953624101156), 21: (0.9977661346769355, -0.2622843051288062), 22: (0.997485800507431, -0.2537321204022652), 23: (0.997011710792566, -0.24665852738803615), 24: (0.9975080897443435, -0.24002445799933786)}\n",
      "Normalizing signals to unity...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recording SpikeMax data...\n",
      "188\n",
      "Recording SpikeMin data...\n",
      "5127\n",
      "Recording Lag1-Lag12 data...\n",
      "\n",
      "\n",
      "LAG1 of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 9\n",
      "[136, 33, 56, 55, 143, 507, 177, 52, 138]\n",
      "n_subgroups: 4\n",
      "n_subgroups: 3\n",
      "n_subgroups: 4\n",
      "n_subgroups: 4\n",
      "n_subgroups: 3\n",
      "n_subgroups: 2\n",
      "n_subgroups: 3\n",
      "n_subgroups: 3\n",
      "n_subgroups: 15\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 4\n",
      "Number of subgroups: 3\n",
      "Number of subgroups: 4\n",
      "Number of subgroups: 4\n",
      "Number of subgroups: 3\n",
      "Number of subgroups: 2\n",
      "Number of subgroups: 3\n",
      "Number of subgroups: 3\n",
      "Number of subgroups: 15\n",
      "\n",
      "\n",
      "LAG2 of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 1\n",
      "[1168]\n",
      "n_subgroups: 14\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 14\n",
      "\n",
      "\n",
      "LAG3 of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 1\n",
      "[896]\n",
      "n_subgroups: 2\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 2\n",
      "\n",
      "\n",
      "SpikeMax of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 1\n",
      "[188]\n",
      "n_subgroups: 3\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 3\n",
      "\n",
      "\n",
      "SpikeMin of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 21\n",
      "[12, 38, 39, 55, 71, 89, 12, 34, 47, 27, 38, 235, 54, 44, 24, 32, 145, 84, 40, 55, 644]\n",
      "n_subgroups: 2\n",
      "n_subgroups: 14\n",
      "n_subgroups: 2\n",
      "n_subgroups: 28\n",
      "n_subgroups: 46\n",
      "n_subgroups: 18\n",
      "n_subgroups: 3\n",
      "n_subgroups: 9\n",
      "n_subgroups: 34\n",
      "n_subgroups: 12\n",
      "n_subgroups: 2\n",
      "n_subgroups: 7\n",
      "n_subgroups: 3\n",
      "n_subgroups: 25\n",
      "n_subgroups: 11\n",
      "n_subgroups: 2\n",
      "n_subgroups: 3\n",
      "n_subgroups: 6\n",
      "n_subgroups: 1\n",
      "n_subgroups: 3\n",
      "n_subgroups: 2\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 2\n",
      "Number of subgroups: 14\n",
      "Number of subgroups: 2\n",
      "Number of subgroups: 28\n",
      "Number of subgroups: 46\n",
      "Number of subgroups: 18\n",
      "Number of subgroups: 3\n",
      "Number of subgroups: 9\n",
      "Number of subgroups: 34\n",
      "Number of subgroups: 12\n",
      "Number of subgroups: 2\n",
      "Number of subgroups: 7\n",
      "Number of subgroups: 3\n",
      "Number of subgroups: 25\n",
      "Number of subgroups: 11\n",
      "Number of subgroups: 2\n",
      "Number of subgroups: 3\n",
      "Number of subgroups: 6\n",
      "Number of subgroups: 1\n",
      "Number of subgroups: 3\n",
      "Number of subgroups: 2\n",
      "\n",
      " ---------------------------------------------------------------------- \n",
      "\tProcessing SLV_Delta (Autocorrelations) \n",
      " ----------------------------------------------------------------------\n",
      "Filtering out all-zero signals...\n",
      "Removed  0 signals out of 6133.\n",
      "Remaining  6133 signals!\n",
      "Filtering out first time point zeros signals...\n",
      "Removed  12 signals out of 6133.\n",
      "Remaining  6121 signals!\n",
      "Filtering out low-quality signals (with more than 25.0% missing points)...\n",
      "Removed  297 signals out of 6121.\n",
      "Remaining  5824 signals!\n",
      "Tagging missing (i.e. zero) values with NaN...\n",
      "Tagging low values (<=1.0) with 1.0...\n",
      "\n",
      "Removing constant genes. Cutoff value is 0.0\n",
      "Removed  1 signals out of 5824.\n",
      "Remaining  5823 signals!\n",
      "SLV_Delta_dataAutocorrelations not found in results/results SLV Delta/SLV_Delta.h5\n",
      "results/results SLV Delta/SLV_Delta_dataAutocorrelations.pklz not found.\n",
      "SLV_Delta_randomAutocorrelations not found in results/results SLV Delta/SLV_Delta.h5\n",
      "results/results SLV Delta/SLV_Delta_randomAutocorrelations.pklz not found.\n",
      "Autocorrelation of data and the corresponding null distribution not found. Calculating...\n",
      "Calculating null distribution (autocorrelation) of 100000 samples...\n",
      "Filtering out low-quality signals (with more than 25.0% missing points)...\n",
      "Normalizing signals to unity...\n",
      "\n",
      "Removing constant genes. Cutoff value is 0.0\n",
      "Removed  0 signals out of 100000.\n",
      "Remaining  100000 signals!\n",
      "\n",
      "Calculating autocorrelations of 100000 random samples (sampled with replacement)...\n",
      "Normalizing signals to unity...\n",
      "Calculating each Time Series Autocorrelations...\n",
      "Quantiles: [1.0, 0.0271192038740695, 0.5770498593047159, 0.212620203911524, 0.4445926897373778, 0.289151299346025, 0.3655403354306394, 0.3164431031034342, 0.3565167422377936, 0.2630330153123729, 0.3010877476500822, 0.228462762495317] \n",
      "\n",
      "Calculating spikes cutoffs...\n",
      "Filtering out low-quality signals (with more than 25.0% missing points)...\n",
      "Normalizing signals to unity...\n",
      "\n",
      "Removing constant genes. Cutoff value is 0.0\n",
      "Removed  0 signals out of 100000.\n",
      "Remaining  100000 signals!\n",
      "{14: (0.0, -0.29446915423136577), 15: (0.8653901166665467, -0.15257098454661625), 16: (0.9894343508364406, -0.29267591319995395), 17: (0.9884607679411079, -0.2913702428557354), 18: (0.9897928427495479, -0.27656815963238607), 19: (0.9916353625650253, -0.26917658738936406), 20: (0.9909165105292588, -0.26008547331094123), 21: (0.9897994230935381, -0.2528202507734705), 22: (0.9900189278525026, -0.24480164023185016), 23: (0.9906202670989371, -0.23697394049794646), 24: (0.9930125754206899, -0.22869683267388238)}\n",
      "Normalizing signals to unity...\n",
      "Recording SpikeMax data...\n",
      "122\n",
      "Recording SpikeMin data...\n",
      "821\n",
      "Recording Lag1-Lag12 data...\n",
      "\n",
      "\n",
      "LAG1 of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 3\n",
      "[290, 35, 278]\n",
      "n_subgroups: 2\n",
      "n_subgroups: 2\n",
      "n_subgroups: 1\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 2\n",
      "Number of subgroups: 2\n",
      "Number of subgroups: 1\n",
      "\n",
      "\n",
      "LAG2 of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 2\n",
      "[214, 94]\n",
      "n_subgroups: 17\n",
      "n_subgroups: 2\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 17\n",
      "Number of subgroups: 2\n",
      "\n",
      "\n",
      "LAG3 of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 1\n",
      "[256]\n",
      "n_subgroups: 2\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 2\n",
      "\n",
      "\n",
      "SpikeMax of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 1\n",
      "[122]\n",
      "n_subgroups: 10\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 10\n",
      "\n",
      "\n",
      "SpikeMin of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 1\n",
      "[337]\n",
      "n_subgroups: 3\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 3\n",
      "\n",
      " ---------------------------------------------------------------------- \n",
      "\tProcessing SLV_Delta (Periodograms) \n",
      " ----------------------------------------------------------------------\n",
      "Filtering out all-zero signals...\n",
      "Removed  0 signals out of 6133.\n",
      "Remaining  6133 signals!\n",
      "Filtering out first time point zeros signals...\n",
      "Removed  12 signals out of 6133.\n",
      "Remaining  6121 signals!\n",
      "Filtering out low-quality signals (with more than 25.0% missing points)...\n",
      "Removed  297 signals out of 6121.\n",
      "Remaining  5824 signals!\n",
      "Tagging missing (i.e. zero) values with NaN...\n",
      "Tagging low values (<=1.0) with 1.0...\n",
      "\n",
      "Removing constant genes. Cutoff value is 0.0\n",
      "Removed  1 signals out of 5824.\n",
      "Remaining  5823 signals!\n",
      "SLV_Delta_dataPeriodograms not found in results/results SLV Delta/SLV_Delta.h5\n",
      "results/results SLV Delta/SLV_Delta_dataPeriodograms.pklz not found.\n",
      "SLV_Delta_randomPeriodograms not found in results/results SLV Delta/SLV_Delta.h5\n",
      "results/results SLV Delta/SLV_Delta_randomPeriodograms.pklz not found.\n",
      "Periodograms of data and the corresponding null distribution not found. Calculating...\n",
      "Calculating null distribution (periodogram) of 100000 samples...\n",
      "Filtering out low-quality signals (with more than 25.0% missing points)...\n",
      "Normalizing signals to unity...\n",
      "\n",
      "Removing constant genes. Cutoff value is 0.0\n",
      "Removed  0 signals out of 100000.\n",
      "Remaining  100000 signals!\n",
      "\n",
      "Calculating periodograms of 100000 random samples (sampled with replacement)...\n",
      "Normalizing signals to unity...\n",
      "Calculating each Time Series Periodogram...\n",
      "Quantiles: [1.0, 2.3031175796071266, 2.5900480940781403, 2.1905284814567305, 2.1318514778109354, 2.2184648099342774, 2.145105819412733, 2.2286770083633165, 2.2673622144366576, 2.149169136637268, 2.279681856342899, 2.1498247518088553] \n",
      "\n",
      "Calculating spikes cutoffs...\n",
      "Filtering out low-quality signals (with more than 25.0% missing points)...\n",
      "Normalizing signals to unity...\n",
      "\n",
      "Removing constant genes. Cutoff value is 0.0\n",
      "Removed  0 signals out of 100000.\n",
      "Remaining  100000 signals!\n",
      "{15: (0.6777837136146504, -0.22879151451051075), 16: (0.9801504186414851, -0.2785046455169609), 17: (0.9911982551564479, -0.28926981062237533), 18: (0.9925611731922932, -0.2793309032317038), 19: (0.9898031369462673, -0.26772453816127173), 20: (0.9903776546049126, -0.2599241290168372), 21: (0.990262634296789, -0.2520903232830933), 22: (0.9900402058405001, -0.2457424435350169), 23: (0.9903802705867211, -0.23790606812288898), 24: (0.9934319334665737, -0.23075207391020955)}\n",
      "Normalizing signals to unity...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recording SpikeMax data...\n",
      "123\n",
      "Recording SpikeMin data...\n",
      "801\n",
      "Recording Lag1-Lag12 data...\n",
      "\n",
      "\n",
      "LAG1 of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 5\n",
      "[2, 96, 44, 349, 38]\n",
      "n_subgroups: 1\n",
      "n_subgroups: 1\n",
      "n_subgroups: 2\n",
      "n_subgroups: 10\n",
      "n_subgroups: 2\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 2\n",
      "Number of subgroups: 1\n",
      "Number of subgroups: 2\n",
      "Number of subgroups: 10\n",
      "Number of subgroups: 2\n",
      "\n",
      "\n",
      "LAG2 of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 8\n",
      "[107, 64, 20, 91, 157, 4, 2, 22]\n",
      "n_subgroups: 5\n",
      "n_subgroups: 2\n",
      "n_subgroups: 1\n",
      "n_subgroups: 1\n",
      "n_subgroups: 1\n",
      "n_subgroups: 1\n",
      "n_subgroups: 1\n",
      "n_subgroups: 11\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 5\n",
      "Number of subgroups: 2\n",
      "Number of subgroups: 1\n",
      "Number of subgroups: 1\n",
      "Number of subgroups: 1\n",
      "Number of subgroups: 1\n",
      "Number of subgroups: 2\n",
      "Number of subgroups: 11\n",
      "\n",
      "\n",
      "LAG3 of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 15\n",
      "[33, 1, 1, 18, 24, 16, 36, 43, 25, 8, 2, 9, 44, 19, 17]\n",
      "n_subgroups: 3\n",
      "n_subgroups: 1\n",
      "n_subgroups: 1\n",
      "n_subgroups: 1\n",
      "n_subgroups: 1\n",
      "n_subgroups: 4\n",
      "n_subgroups: 11\n",
      "n_subgroups: 3\n",
      "n_subgroups: 1\n",
      "n_subgroups: 2\n",
      "n_subgroups: 2\n",
      "n_subgroups: 7\n",
      "n_subgroups: 1\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 3\n",
      "Number of subgroups: 1\n",
      "Number of subgroups: 1\n",
      "Number of subgroups: 1\n",
      "Number of subgroups: 1\n",
      "Number of subgroups: 4\n",
      "Number of subgroups: 11\n",
      "Number of subgroups: 3\n",
      "Number of subgroups: 2\n",
      "Number of subgroups: 2\n",
      "Number of subgroups: 2\n",
      "Number of subgroups: 7\n",
      "Number of subgroups: 1\n",
      "\n",
      "\n",
      "SpikeMax of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 1\n",
      "[123]\n",
      "n_subgroups: 15\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 15\n",
      "\n",
      "\n",
      "SpikeMin of Time Series:\n",
      "Creating clustering object.\n",
      "n_groups: 10\n",
      "[13, 33, 10, 23, 23, 5, 56, 24, 24, 75]\n",
      "n_subgroups: 1\n",
      "n_subgroups: 3\n",
      "n_subgroups: 1\n",
      "n_subgroups: 15\n",
      "n_subgroups: 1\n",
      "n_subgroups: 1\n",
      "n_subgroups: 1\n",
      "n_subgroups: 7\n",
      "n_subgroups: 5\n",
      "n_subgroups: 1\n",
      "Exporting clustering object.\n",
      "Plotting Dendrogram with Heatmaps.\n",
      "Number of subgroups: 1\n",
      "Number of subgroups: 3\n",
      "Number of subgroups: 1\n",
      "Number of subgroups: 15\n",
      "Number of subgroups: 1\n",
      "Number of subgroups: 1\n",
      "Number of subgroups: 1\n",
      "Number of subgroups: 7\n",
      "Number of subgroups: 5\n",
      "Number of subgroups: 1\n"
     ]
    }
   ],
   "source": [
    "def processDataTempFunction(data_dir, dataFileName, timesFileName, saveDir, Delta=False):\n",
    "\n",
    "    if Delta:\n",
    "        dataName = 'SLV_Delta'\n",
    "        df_dataH1 = pyiomica.read('results/results SLV H1/SLV_Hourly1TimeSeries_df_data_transformed', hdf5fileName='results/results SLV H1/SLV_Hourly1TimeSeries.h5')\n",
    "        df_dataH2 = pyiomica.read('results/results SLV H2/SLV_Hourly2TimeSeries_df_data_transformed', hdf5fileName='results/results SLV H2/SLV_Hourly2TimeSeries.h5')\n",
    "        df_data = pyiomica.compareTwoTimeSeriesDataframe(df_dataH2, df_dataH1, function=np.subtract, compareAllLevelsInIndex=False, mergeFunction=np.median)\n",
    "        df_data[np.isnan(df_data)] = 0.0\n",
    "    else:\n",
    "        dataName = 'SLV' + '_' + dataFileName.split('_')[0]\n",
    "        df_data = pyiomica.prepareDataframe(data_dir, dataFileName, timesFileName)\n",
    "        df_data.index = pd.MultiIndex.from_tuples([(item.split(':')[1], \n",
    "                                                        item.split(':')[0].split('_')[0],\n",
    "                                                        (' '.join(item.split(':')[0].split('_')[1:]),)) for item in df_data.index.values], \n",
    "                                                        names=['source', 'id', 'metadata'])\n",
    "\n",
    "    pyiomica.timeSeriesClassification(df_data, dataName, saveDir, NumberOfRandomSamples = 10**5, NumberOfCPUs = 4, p_cutoff = 0.05, frequencyBasedClassification=False)\n",
    "    pyiomica.visualizeTimeSeriesClassification(dataName, saveDir, AutocorrNotPeriodogr=True, writeClusteringObjectToBinaries=True, vectorImage=False)\n",
    "\n",
    "    pyiomica.timeSeriesClassification(df_data, dataName, saveDir, NumberOfRandomSamples = 10**5, NumberOfCPUs = 4, p_cutoff = 0.05, frequencyBasedClassification=True)\n",
    "    pyiomica.visualizeTimeSeriesClassification(dataName, saveDir, AutocorrNotPeriodogr=False, writeClusteringObjectToBinaries=True, vectorImage=False)\n",
    "\n",
    "    return\n",
    "\n",
    "import zipfile\n",
    "\n",
    "with zipfile.ZipFile(os.path.join(pyiomica.ConstantPyIOmicaExamplesDirectory, 'SLV.zip'), \"r\") as zipFile:\n",
    "    zipFile.extractall(path=pyiomica.ConstantPyIOmicaExamplesDirectory)\n",
    "\n",
    "processDataTempFunction(os.path.join(pyiomica.ConstantPyIOmicaExamplesDirectory, \"SLV\"), \n",
    "                        'Hourly2TimeSeries_SLV_KallistoNormedGeneGencodeGC.csv', 'TimesHourly.csv', \n",
    "                        'results/results SLV H2/')\n",
    "\n",
    "processDataTempFunction(os.path.join(pyiomica.ConstantPyIOmicaExamplesDirectory, 'SLV'), \n",
    "                        'Hourly1TimeSeries_SLV_KallistoNormedGeneGencodeGC.csv', 'TimesHourly.csv', \n",
    "                        'results/results SLV H1/')\n",
    "\n",
    "processDataTempFunction(os.path.join(pyiomica.ConstantPyIOmicaExamplesDirectory, 'SLV'), \n",
    "                        'Hourly2TimeSeries_SLV_KallistoNormedGeneGencodeGC.csv', \n",
    "                        'TimesHourly.csv', \n",
    "                        'results/results SLV Delta/', Delta=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visibility Graph\n",
    "\n",
    "In this example we calculate the visibility graph of the mean signal of a clustered group. Both a circular as well as a horizontal layout are illustrated. Missing data are imputed to the mean of the time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Domanskyi\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas\\core\\indexes\\base.py:162: FutureWarning: the 'labels' keyword is deprecated, use 'codes' instead\n",
      "  return cls.__new__(cls, **d)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 6, 10, 16, 20, 22, 24] \n",
      "\n",
      "[0, 1, 2, 3, 4, 5]\n",
      "[6, 7, 8, 9]\n",
      "[10, 11, 12, 13, 14, 15]\n",
      "[16, 17, 18, 19]\n",
      "[20, 21]\n",
      "[22, 23]\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Domanskyi\\AppData\\Roaming\\Python\\Python37\\site-packages\\networkx\\drawing\\nx_pylab.py:579: MatplotlibDeprecationWarning: \n",
      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
      "  if not cb.iterable(width):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ExampleClusteringObject = pyiomica.read(os.path.join(pyiomica.ConstantPyIOmicaExamplesDirectory, 'exampleClusteringObject_SLV_Delta_LAG1_Autocorr'))\n",
    "\n",
    "data = ExampleClusteringObject[1][2]['data'].values\n",
    "times = ExampleClusteringObject[1][2]['data'].columns.values\n",
    "\n",
    "def imputeWithMedian(data):\n",
    "    data[np.isnan(data)] = np.median(data[np.isnan(data) == False])\n",
    "    return data\n",
    "\n",
    "data = pd.DataFrame(data=data).apply(imputeWithMedian, axis=1).apply(lambda data: np.sum(data[data > 0.0]) / len(data), axis=0).values\n",
    "\n",
    "import matplotlib\n",
    "matplotlib.use('Agg')\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "%matplotlib inline\n",
    "\n",
    "def add_colorbar(fig, data):\n",
    "    \n",
    "    axisColor = fig.add_axes([0.90,0.7,0.02,0.2])\n",
    "    mapp=cm.ScalarMappable(norm=matplotlib.colors.Normalize(vmin=np.min(data), vmax=np.max(data)), \n",
    "                           cmap=matplotlib.colors.LinearSegmentedColormap.from_list('GR', [(0, 1, 0), (1, 0, 0)], N=100))\n",
    "    mapp.set_array(data)\n",
    "    fig.colorbar(mapp, cax=axisColor, ticks=[np.max(data),np.min(data)])\n",
    "    axisColor.tick_params(labelsize=12)\n",
    "    axisColor.set_yticklabels([np.round(np.max(data),2),np.round(np.min(data),2)])\n",
    "    \n",
    "    return\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "pyiomica.addVisibilityGraph(data, times, fig=fig, fontsize=16, nodesize=500, level=0.5, \n",
    "                            commLineWidth=3.0, lineWidth=2.0, withLabel=False, layout='circle', \n",
    "                            printCommunities=True, radius=0.03)\n",
    "add_colorbar(fig, data)\n",
    "plt.show()\n",
    "plt.close(fig)\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "pyiomica.addVisibilityGraph(data, times, fig=fig, fontsize=8, nodesize=180, level=0.5, \n",
    "                            commLineWidth=2.0, lineWidth=2.0, withLabel=False, layout='line', \n",
    "                            printCommunities=False, radius=0.022)\n",
    "add_colorbar(fig, data)\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantile transform\n",
    "\n",
    "In this example we calculate the quantile normalized values of a given dataframe (column-wise)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     0    1    2\n",
      "0  1.0  2.0  3.0\n",
      "1  4.0  2.0  6.0\n",
      "2  7.0  3.0  4.0\n",
      "3  2.0  2.0  8.0\n",
      "4  4.0  4.0  4.0\n",
      "Quantile normalizing signals...\n",
      "          0         1         2\n",
      "0 -5.199338 -5.199338 -5.199338\n",
      "1  0.318639 -5.199338  0.674490\n",
      "2  5.199338  0.674490 -0.318639\n",
      "3 -0.674490 -5.199338  5.199338\n",
      "4  0.318639  5.199338 -0.318639\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(np.array([[1,2,3],[4,2,6],[7,3,4],[2,2,8],[4,4,4]]).astype(float))\n",
    "print(df)\n",
    "\n",
    "df_Q_normed = pyiomica.quantileNormalizeDataframe(df)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import of MathIOmica Objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Domanskyi\\AppData\\Roaming\\Python\\Python37\\site-packages\\pyiomica\\data\\ExampleData \n",
      "\n",
      "rnaExample: {'7': {('FAM138A', 'RNA'): (0, 'OK'), ('OR4F5', 'RNA'): (0, 'OK'), ('LOC729737', 'RNA'): (2.73998, 'OK'), ('DDX11L1', 'RNA'): (6.75461, 'OK'), ('WASH7P', 'RNA'): (11.8883, 'OK'), ('MIR6859-1', 'RNA'): (0.256318, 'OK'), ('OR4F16', 'RNA'): (0.0053034, 'OK'), ('LOC100132287', 'RNA'): (1.97164, 'OK'), ('LOC100133331', 'RNA'): (5.80254e-05, 'OK'), ('MIR6723', 'RNA'): (20.3672, 'OK'), ('OR4F29', 'RNA'): \t. . .\n",
      "\n",
      "proteinClassificationExample: {'SpikeMax': {('O14933', 'Protein'): ((0.4849004228596584, 0.050070636189644156, 0.5664473466604335, 0.30949242460233894, 0.23311633547831045, 0.5386852734130892, 0.03451527514359191), (-0.04912828102266407, 0.0, 0.047486747232116745, 0.05781001938939018, 0.0018554794650575278, 0.07692798803247838, 0.889969492713528, -0.05132183205490717, 0.08748941292339298, 0.040475366762846916, -0.1336772226107 \t. . .\n",
      "\n",
      "proteinTimeSeriesExample: {('A0AVT1', 'Protein'): (-0.20659318820947548, 0.0, 0.00940325600745479, -0.11002681881280515, -0.05719318431295019, 0.1969020264180568, 0.5311951869517098, 0.07384981442020351, -0.5337739620790529, 0.26082873245760485, 0.21703366523988002, 'Missing[]', -0.15708162036637413, -0.43507907967714743, -0.03798709370006283), ('A0FGR8', 'Protein'): (0.1560814138062252, 0.0, -0.1865886054264821, 0.0155952 \t. . .\n",
      "\n",
      "proteinExample: {'7': {('A0AVT1', 'Protein'): (0.937, 17), ('A0FGR8', 'Protein'): (1.073, 24), ('A0MZ66', 'Protein'): (1.059, 9), ('A1A4S6', 'Protein'): (1.03, 11), ('A1L0T0', 'Protein'): (1.268, 4), ('A1X283', 'Protein'): (1.277, 1), ('A2RRP1', 'Protein'): (0.977, 12), ('A2RTX5', 'Protein'): (1.351, 4), ('A2RUS2', 'Protein'): (1.017, 12), ('A3KMH1', 'Protein'): 'Missing[]', ('A3KN83', 'Protein'): (0.88, 1), ('A4 \t. . .\n",
      "\n",
      "metabolomicsPositiveModeExample: {'8': {(202.0329, 0.33260712, 'Meta'): ((263741, 276622, 337241), ('', '')), (174.0375, 0.33451426, 'Meta'): ((78435, 88529, 121073), ('', '')), (142.012, 0.3335833, 'Meta'): ((318908, 371272, 297537), ('', '')), (100.0009, 0.33358333, 'Meta'): ((42319, 35074, 34575), ('', '')), (81.9906, 0.33332258, 'Meta'): ((36822, 30341, 30344), ('', '')), (942.9842, 0.34920588, 'Meta'): ((160507, 132428, 1322 \t. . .\n",
      "\n",
      "metabolomicsNegativeModeExample: {'8': {(457.0024, 0.34764, 'Meta'): ((23444, 16317, 1), (' [ C16 H11 N9 S4, db=0.00, overall=47.55, mfg=95.11 ]', '')), (530.0453, 0.35665718, 'Meta'): ((24079, 27071, 27347), (' [ C23 H18 N2 O9 S2, db=0.00, overall=44.30, mfg=88.61 ]', '')), (694.0508, 0.3540571, 'Meta'): ((15664, 15419, 16108), (' [ C24 H18 N14 O4 S4, db=0.00, overall=41.90, mfg=83.80 ]', '')), (776.0537, 0.3550645, 'Meta'): ((1 \t. . .\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(pyiomica.ConstantPyIOmicaExamplesDirectory, '\\n')\n",
    "tempPath = os.path.join(pyiomica.ConstantPyIOmicaExamplesDirectory, 'MathIOmicaExamples')\n",
    "\n",
    "rnaExample = pyiomica.readMathIOmicaData(os.path.join(tempPath, 'rnaExample'))\n",
    "print('rnaExample:', str(rnaExample)[:400], '\\t. . .\\n')\n",
    "\n",
    "proteinClassificationExample = pyiomica.readMathIOmicaData(os.path.join(tempPath, 'proteinClassificationExample'))\n",
    "print('proteinClassificationExample:', str(proteinClassificationExample)[:400], '\\t. . .\\n')\n",
    "\n",
    "proteinTimeSeriesExample = pyiomica.readMathIOmicaData(os.path.join(tempPath, 'proteinTimeSeriesExample'))\n",
    "print('proteinTimeSeriesExample:', str(proteinTimeSeriesExample)[:400], '\\t. . .\\n')\n",
    "\n",
    "proteinExample = pyiomica.readMathIOmicaData(os.path.join(tempPath, 'proteinExample'))\n",
    "print('proteinExample:', str(proteinExample)[:400], '\\t. . .\\n')\n",
    "\n",
    "metabolomicsPositiveModeExample = pyiomica.readMathIOmicaData(os.path.join(tempPath, 'metabolomicsPositiveModeExample'))\n",
    "print('metabolomicsPositiveModeExample:', str(metabolomicsPositiveModeExample)[:400], '\\t. . .\\n')\n",
    "\n",
    "metabolomicsNegativeModeExample = pyiomica.readMathIOmicaData(os.path.join(tempPath, 'metabolomicsNegativeModeExample'))\n",
    "print('metabolomicsNegativeModeExample:', str(metabolomicsNegativeModeExample)[:400], '\\t. . .\\n')"
   ]
  }
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