bool-pvalues.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import collections\n",
    "import itertools\n",
    "import copy\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "from scipy import stats\n",
    "from scipy.misc import derivative\n",
    "import coloredlogs\n",
    "import logging\n",
    "\n",
    "# Pval generation related functions, previously in the notebook\n",
    "from booltest.pvals import *\n",
    "\n",
    "coloredlogs.CHROOT_FILES = []\n",
    "coloredlogs.install(level=logging.INFO, use_chroot=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "def pvalue_comp(fnc, extremes, dx, bin_tup, by_bins=True):\n",
    "    \"\"\"Extremes = [(val, direction +1\\-1)] \"\"\"\n",
    "    nints = len(extremes)\n",
    "    areas = [0] * nints\n",
    "    nbounds = [x[0] for x in extremes]\n",
    "    nbins = [binize(x[0], bin_tup) for x in extremes]\n",
    "    bmin = min(nbounds)\n",
    "    bmax = max(nbounds)\n",
    "    cp = 0\n",
    "    iterc = 0\n",
    "    results = []\n",
    "    print('OK: ', nints, nbins, ' size: ', bin_tup[4])\n",
    "    \n",
    "    while cp <= 1.0:  # integration step\n",
    "        iterc += 1\n",
    "        if iterc > 10000:\n",
    "            raise ValueError('exc')  # Hard-termination to avoid infinite cycle.\n",
    "        \n",
    "        # Integration by increasing pvalue and tabulating.\n",
    "        # Each area grows at the same pace. pvalue is a sum of areas.\n",
    "        # Termination - bounds are crossing / touching.\n",
    "        \n",
    "        # Integrate each area with one step but in such a way the area is the same. \n",
    "        max_area = max(areas)\n",
    "        min_area = min(areas)\n",
    "        sum_area = sum(areas)\n",
    "        err = max([abs(x) for x in all_diffs(areas)])\n",
    "        areas_str = ['%.7f' % x for x in areas]\n",
    "        #print('Main iter: %s, cp: %.7f, mina: %.7f, maxa: %.7f, suma: %.7f, err: %.7f, a: [%s], n: %s' \n",
    "        #      % (iterc, cp, min_area, max_area, sum_area, err, ', '.join(areas_str), nbins))\n",
    "        \n",
    "        subit = 0\n",
    "        while any([x <= min_area for x in areas]):\n",
    "            subit += 1\n",
    "            #print('.. subit: %s' % subit)\n",
    "            \n",
    "            for ix in range(nints):\n",
    "                if areas[ix] > min_area :\n",
    "                    continue\n",
    "                if by_bins:\n",
    "                    areas[ix] += get_bin_val(nbins[ix], bin_tup)\n",
    "                    nbounds[ix] = get_bin_start(nbins[ix], bin_tup)\n",
    "                    nbins[ix] = move_bound(nbins[ix], 1, extremes[ix][1])\n",
    "                else:\n",
    "                    areas[ix] += fnc(nbounds[ix])\n",
    "                    nbounds[ix] = move_bound(nbounds[ix], dx, extremes[ix][1])        \n",
    "        cp = sum(areas)\n",
    "        \n",
    "        crit_int = [None]*nints\n",
    "        for i in range(nints):\n",
    "            crit_int[i] = (extremes[i][0], nbounds[i]) if extremes[i][1] > 0 else (nbounds[i], extremes[i][0])\n",
    "            \n",
    "        results.append((cp, crit_int, copy.deepcopy(areas), err))\n",
    "        \n",
    "    #print('Main iter: %s, cp: %s, mina: %s, maxa: %s, suma: %s, a: %s' \n",
    "    #          % (iterc, cp, min(areas), max(areas), sum(areas), areas))\n",
    "    #print('Total: %s' % (sum([get_bin_val(ix, bin_tup) for ix in range(len(bin_tup[0]))])))\n",
    "    #print(json.dumps(results, indent=2))\n",
    "    return results\n",
    "\n",
    "\n",
    "def tabulate_pvals(val, nbins=200, abs_val=False, target_pvals=[0.0, 0.00001, 0.0001, 0.0005, 0.001, 0.005, 0.01]):\n",
    "    inp_iter = val['zscores']\n",
    "    if abs_val:\n",
    "        inp_iter = [abs(x) for x in inp_iter]\n",
    "    \n",
    "    bin_tup = get_bins(inp_iter, nbins=nbins, full=True)\n",
    "    bb = get_distrib_fbins(inp_iter, bin_tup)\n",
    "    \n",
    "    bin_size = bin_tup[1]\n",
    "    minv, maxv = bin_tup[2], bin_tup[3] \n",
    "    bins = np.array([x[0] for x in bb])\n",
    "    \n",
    "    # Tabulate pvalues\n",
    "    # build_integrator(bin_tup)\n",
    "    extremes = [\n",
    "        [minv, 1],\n",
    "        [0, -1],\n",
    "        [0, +1],\n",
    "        [maxv, -1]\n",
    "    ] if not abs_val else [\n",
    "        [minv, 1],\n",
    "        [maxv, -1]\n",
    "    ]\n",
    "    \n",
    "    print('%s-%s-%s-%s-%s' % (val['method'], val['block'], val['deg'], val['comb_deg'], val['data_size']))\n",
    "    pvals = pvalue_comp(lambda x: binned_pmf(x, bin_tup), extremes, \n",
    "                        dx=1./(nbins/10.), bin_tup=bin_tup, by_bins=True)\n",
    "\n",
    "    res_pdata = []\n",
    "    for target in target_pvals:\n",
    "        chosen = 0\n",
    "        for i in range(len(pvals)):\n",
    "            chosen = i\n",
    "            if pvals[i][0] >= target:\n",
    "                chosen = i - 1 if i > 0 else 0\n",
    "                break\n",
    "                \n",
    "        cdata = pvals[chosen]\n",
    "        res_pdata.append(collections.OrderedDict([\n",
    "            ('pval_target', target),\n",
    "            ('pval', cdata[0]),\n",
    "            ('crit', cdata[1]),\n",
    "            ('areas', cdata[2]),\n",
    "            ('err', cdata[3]),\n",
    "        ]))\n",
    "\n",
    "    return collections.OrderedDict([\n",
    "        ('method', val['method']), \n",
    "        ('block', val['block']),\n",
    "        ('deg', val['deg']),\n",
    "        ('comb_deg', val['comb_deg']),\n",
    "        ('data_size', val['data_size']),\n",
    "        ('nsamples', len(inp_iter)),\n",
    "        ('nbins', nbins),\n",
    "        ('abs_val', abs_val),\n",
    "        ('binsize', bin_size),\n",
    "        ('minv', minv),\n",
    "        ('maxv', maxv),\n",
    "        ('extremes', extremes),\n",
    "        ('pvals', res_pdata)\n",
    "    ])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "js1 = json.load(open('/tmp/results_bat_1588463858-booltest1-ref3-40k.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "js2 = json.load(open('/tmp/results_bat_1588274711-booltest1-ref-60k.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "js = js1 + js2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-03 15:09:13 phx.local booltest.pvals[14921] DEBUG Input candidates: 3600000\n",
      "2020-05-03 15:09:29 phx.local booltest.pvals[14921] DEBUG Skipped same seed: 0\n",
      "2020-05-03 15:09:30 phx.local booltest.pvals[14921] DEBUG Skipped same hash: 0\n",
      "2020-05-03 15:09:30 phx.local booltest.pvals[14921] DEBUG Skipped same dfile: 0\n"
     ]
    }
   ],
   "source": [
    "if js:\n",
    "    rs = extract_zscores_from_bat(js)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rs[0]['zscores'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-128-1-1-10000000\n",
      "OK:  2 [0, 163]  size:  100000\n",
      "Dump 0\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-128-1-2-10000000\n",
      "OK:  2 [0, 149]  size:  100000\n",
      "Dump 1\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-128-1-3-10000000\n",
      "OK:  2 [0, 146]  size:  100000\n",
      "Dump 2\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-128-2-1-10000000\n",
      "OK:  2 [0, 155]  size:  100000\n",
      "Dump 3\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-128-2-2-10000000\n",
      "OK:  2 [0, 149]  size:  100000\n",
      "Dump 4\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-128-2-3-10000000\n",
      "OK:  2 [0, 149]  size:  100000\n",
      "Dump 5\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-128-3-1-10000000\n",
      "OK:  2 [0, 149]  size:  100000\n",
      "Dump 6\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-128-3-2-10000000\n",
      "OK:  2 [0, 145]  size:  100000\n",
      "Dump 7\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-128-3-3-10000000\n",
      "OK:  2 [0, 147]  size:  100000\n",
      "Dump 8\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-256-1-1-10000000\n",
      "OK:  2 [0, 153]  size:  100000\n",
      "Dump 9\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-256-1-2-10000000\n",
      "OK:  2 [0, 151]  size:  100000\n",
      "Dump 10\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-256-1-3-10000000\n",
      "OK:  2 [0, 149]  size:  100000\n",
      "Dump 11\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-256-2-1-10000000\n",
      "OK:  2 [0, 153]  size:  100000\n",
      "Dump 12\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-256-2-2-10000000\n",
      "OK:  2 [0, 147]  size:  100000\n",
      "Dump 13\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-256-2-3-10000000\n",
      "OK:  2 [0, 151]  size:  100000\n",
      "Dump 14\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-256-3-1-10000000\n",
      "OK:  2 [0, 144]  size:  100000\n",
      "Dump 15\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-256-3-2-10000000\n",
      "OK:  2 [0, 144]  size:  100000\n",
      "Dump 16\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-256-3-3-10000000\n",
      "OK:  2 [0, 141]  size:  100000\n",
      "Dump 17\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-384-1-1-10000000\n",
      "OK:  2 [0, 151]  size:  100000\n",
      "Dump 18\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-384-1-2-10000000\n",
      "OK:  2 [0, 148]  size:  100000\n",
      "Dump 19\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-384-1-3-10000000\n",
      "OK:  2 [0, 148]  size:  100000\n",
      "Dump 20\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-384-2-1-10000000\n",
      "OK:  2 [0, 147]  size:  100000\n",
      "Dump 21\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-384-2-2-10000000\n",
      "OK:  2 [0, 144]  size:  100000\n",
      "Dump 22\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-384-2-3-10000000\n",
      "OK:  2 [0, 146]  size:  100000\n",
      "Dump 23\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-384-3-1-10000000\n",
      "OK:  2 [0, 145]  size:  100000\n",
      "Dump 24\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-384-3-2-10000000\n",
      "OK:  2 [0, 138]  size:  100000\n",
      "Dump 25\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-384-3-3-10000000\n",
      "OK:  2 [0, 143]  size:  100000\n",
      "Dump 26\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-512-1-1-10000000\n",
      "OK:  2 [0, 153]  size:  100000\n",
      "Dump 27\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-512-1-2-10000000\n",
      "OK:  2 [0, 147]  size:  100000\n",
      "Dump 28\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-512-1-3-10000000\n",
      "OK:  2 [0, 145]  size:  100000\n",
      "Dump 29\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-512-2-1-10000000\n",
      "OK:  2 [0, 146]  size:  100000\n",
      "Dump 30\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-512-2-2-10000000\n",
      "OK:  2 [0, 145]  size:  100000\n",
      "Dump 31\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-512-2-3-10000000\n",
      "OK:  2 [0, 148]  size:  100000\n",
      "Dump 32\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-512-3-1-10000000\n",
      "OK:  2 [0, 140]  size:  100000\n",
      "Dump 33\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-512-3-2-10000000\n",
      "OK:  2 [0, 141]  size:  100000\n",
      "Dump 34\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7-512-3-3-10000000\n",
      "OK:  2 [0, 141]  size:  100000\n",
      "Dump 35\n"
     ]
    }
   ],
   "source": [
    "pval_db = []\n",
    "data = data_filt = rs\n",
    "for dix, val in enumerate(data_filt):\n",
    "    res = tabulate_pvals(val, abs_val=True)\n",
    "    pval_db.append(res)\n",
    "    print('Dump %s' % dix)\n",
    "json.dump(pval_db, open('pval_db.json', 'w+'), indent=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Old way - csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"method\": \"in0-kctr-ri1\", \"block\": 128, \"deg\": 1, \"comb_deg\": 2, \"data_size\": 10, \"zscores\": [-3.77991]}\n"
     ]
    }
   ],
   "source": [
    "#js = json.load(open('ref_1554219251.json'))\n",
    "#csv = open('ref_1554219251.csv').read()\n",
    "csv = open('ref_1558361146.csv').read()\n",
    "\n",
    "csv_data = []\n",
    "for rec in [x.strip() for x in csv.split(\"\\n\")]:\n",
    "    p = rec.split(';')\n",
    "    if len(p) < 6:\n",
    "        continue\n",
    "    cur = collections.OrderedDict([\n",
    "        ('method', p[0]), \n",
    "        ('block', int(p[1])),\n",
    "        ('deg', int(p[2])),\n",
    "        ('comb_deg', int(p[3])),\n",
    "        ('data_size', int(p[4])),\n",
    "        ('zscores', [float(x.replace(',','.')) for x in p[6:]])\n",
    "    ])\n",
    "    csv_data.append(cur)\n",
    "print(json.dumps(csv_data[0]))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#of inputs with data count over threshold:  144\n"
     ]
    }
   ],
   "source": [
    "data = csv_data\n",
    "data_filt = [x for x in data if x and len(x['zscores']) > 19000]\n",
    "data_filt.sort(key=lambda x: (x['method'], x['block'], x['deg'], x['comb_deg'], x['data_size']))\n",
    "np.random.seed(87655677)\n",
    "print('#of inputs with data count over threshold: ', len(data_filt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inctr-krnd-ri0-128-1-1-10\n",
      "OK:  2 [0, 153]  size:  20000\n",
      "Dump 0\n",
      "inctr-krnd-ri0-128-1-2-10\n",
      "OK:  2 [0, 146]  size:  19999\n",
      "Dump 1\n",
      "inctr-krnd-ri0-128-1-3-10\n",
      "OK:  2 [0, 138]  size:  20000\n",
      "Dump 2\n",
      "inctr-krnd-ri0-128-2-1-10\n",
      "OK:  2 [0, 149]  size:  20000\n",
      "Dump 3\n",
      "inctr-krnd-ri0-128-2-2-10\n",
      "OK:  2 [0, 147]  size:  20000\n",
      "Dump 4\n",
      "inctr-krnd-ri0-128-2-3-10\n",
      "OK:  2 [0, 143]  size:  20001\n",
      "Dump 5\n",
      "inctr-krnd-ri0-128-3-1-10\n",
      "OK:  2 [0, 144]  size:  20000\n",
      "Dump 6\n",
      "inctr-krnd-ri0-128-3-2-10\n",
      "OK:  2 [0, 139]  size:  20001\n",
      "Dump 7\n",
      "inctr-krnd-ri0-128-3-3-10\n",
      "OK:  2 [0, 145]  size:  20001\n",
      "Dump 8\n",
      "inctr-krnd-ri0-256-1-1-10\n",
      "OK:  2 [0, 148]  size:  20000\n",
      "Dump 9\n",
      "inctr-krnd-ri0-256-1-2-10\n",
      "OK:  2 [0, 149]  size:  19999\n",
      "Dump 10\n",
      "inctr-krnd-ri0-256-1-3-10\n",
      "OK:  2 [0, 149]  size:  20000\n",
      "Dump 11\n",
      "inctr-krnd-ri0-256-2-1-10\n",
      "OK:  2 [0, 144]  size:  20001\n",
      "Dump 12\n",
      "inctr-krnd-ri0-256-2-2-10\n",
      "OK:  2 [0, 142]  size:  20000\n",
      "Dump 13\n",
      "inctr-krnd-ri0-256-2-3-10\n",
      "OK:  2 [0, 144]  size:  20000\n",
      "Dump 14\n",
      "inctr-krnd-ri0-256-3-1-10\n",
      "OK:  2 [0, 141]  size:  20001\n",
      "Dump 15\n",
      "inctr-krnd-ri0-256-3-2-10\n",
      "OK:  2 [0, 132]  size:  20001\n",
      "Dump 16\n",
      "inctr-krnd-ri0-256-3-3-10\n",
      "OK:  2 [0, 138]  size:  20001\n",
      "Dump 17\n",
      "inctr-krnd-ri0-384-1-1-10\n",
      "OK:  2 [0, 149]  size:  20001\n",
      "Dump 18\n",
      "inctr-krnd-ri0-384-1-2-10\n",
      "OK:  2 [0, 139]  size:  20000\n",
      "Dump 19\n",
      "inctr-krnd-ri0-384-1-3-10\n",
      "OK:  2 [0, 135]  size:  20001\n",
      "Dump 20\n",
      "inctr-krnd-ri0-384-2-1-10\n",
      "OK:  2 [0, 144]  size:  20001\n",
      "Dump 21\n",
      "inctr-krnd-ri0-384-2-2-10\n",
      "OK:  2 [0, 143]  size:  20001\n",
      "Dump 22\n",
      "inctr-krnd-ri0-384-2-3-10\n",
      "OK:  2 [0, 144]  size:  20001\n",
      "Dump 23\n",
      "inctr-krnd-ri0-384-3-1-10\n",
      "OK:  2 [0, 140]  size:  20000\n",
      "Dump 24\n",
      "inctr-krnd-ri0-384-3-2-10\n",
      "OK:  2 [0, 135]  size:  20000\n",
      "Dump 25\n",
      "inctr-krnd-ri0-384-3-3-10\n",
      "OK:  2 [0, 136]  size:  20001\n",
      "Dump 26\n",
      "inctr-krnd-ri0-512-1-1-10\n",
      "OK:  2 [0, 150]  size:  20000\n",
      "Dump 27\n",
      "inctr-krnd-ri0-512-1-2-10\n",
      "OK:  2 [0, 145]  size:  20000\n",
      "Dump 28\n",
      "inctr-krnd-ri0-512-1-3-10\n",
      "OK:  2 [0, 142]  size:  20001\n",
      "Dump 29\n",
      "inctr-krnd-ri0-512-2-1-10\n",
      "OK:  2 [0, 141]  size:  20000\n",
      "Dump 30\n",
      "inctr-krnd-ri0-512-2-2-10\n",
      "OK:  2 [0, 139]  size:  20001\n",
      "Dump 31\n",
      "inctr-krnd-ri0-512-2-3-10\n",
      "OK:  2 [0, 150]  size:  20001\n",
      "Dump 32\n",
      "inctr-krnd-ri0-512-3-1-10\n",
      "OK:  2 [0, 134]  size:  20001\n",
      "Dump 33\n",
      "inctr-krnd-ri0-512-3-2-10\n",
      "OK:  2 [0, 130]  size:  20000\n",
      "Dump 34\n",
      "inctr-krnd-ri0-512-3-3-10\n",
      "OK:  2 [0, 137]  size:  20000\n",
      "Dump 35\n",
      "inhw-krnd-ri0-128-1-1-10\n",
      "OK:  2 [0, 155]  size:  40006\n",
      "Dump 36\n",
      "inhw-krnd-ri0-128-1-2-10\n",
      "OK:  2 [0, 145]  size:  40006\n",
      "Dump 37\n",
      "inhw-krnd-ri0-128-1-3-10\n",
      "OK:  2 [0, 146]  size:  40007\n",
      "Dump 38\n",
      "inhw-krnd-ri0-128-2-1-10\n",
      "OK:  2 [0, 148]  size:  40007\n",
      "Dump 39\n",
      "inhw-krnd-ri0-128-2-2-10\n",
      "OK:  2 [0, 148]  size:  40007\n",
      "Dump 40\n",
      "inhw-krnd-ri0-128-2-3-10\n",
      "OK:  2 [0, 146]  size:  40007\n",
      "Dump 41\n",
      "inhw-krnd-ri0-128-3-1-10\n",
      "OK:  2 [0, 155]  size:  40005\n",
      "Dump 42\n",
      "inhw-krnd-ri0-128-3-2-10\n",
      "OK:  2 [0, 146]  size:  40003\n",
      "Dump 43\n",
      "inhw-krnd-ri0-128-3-3-10\n",
      "OK:  2 [0, 142]  size:  40008\n",
      "Dump 44\n",
      "inhw-krnd-ri0-256-1-1-10\n",
      "OK:  2 [0, 154]  size:  40007\n",
      "Dump 45\n",
      "inhw-krnd-ri0-256-1-2-10\n",
      "OK:  2 [0, 151]  size:  40007\n",
      "Dump 46\n",
      "inhw-krnd-ri0-256-1-3-10\n",
      "OK:  2 [0, 138]  size:  40007\n",
      "Dump 47\n",
      "inhw-krnd-ri0-256-2-1-10\n",
      "OK:  2 [0, 153]  size:  40004\n",
      "Dump 48\n",
      "inhw-krnd-ri0-256-2-2-10\n",
      "OK:  2 [0, 149]  size:  40005\n",
      "Dump 49\n",
      "inhw-krnd-ri0-256-2-3-10\n",
      "OK:  2 [0, 142]  size:  40008\n",
      "Dump 50\n",
      "inhw-krnd-ri0-256-3-1-10\n",
      "OK:  2 [0, 138]  size:  40006\n",
      "Dump 51\n",
      "inhw-krnd-ri0-256-3-2-10\n",
      "OK:  2 [0, 140]  size:  40007\n",
      "Dump 52\n",
      "inhw-krnd-ri0-256-3-3-10\n",
      "OK:  2 [0, 138]  size:  40009\n",
      "Dump 53\n",
      "inhw-krnd-ri0-384-1-1-10\n",
      "OK:  2 [0, 153]  size:  40006\n",
      "Dump 54\n",
      "inhw-krnd-ri0-384-1-2-10\n",
      "OK:  2 [0, 144]  size:  40008\n",
      "Dump 55\n",
      "inhw-krnd-ri0-384-1-3-10\n",
      "OK:  2 [0, 138]  size:  40007\n",
      "Dump 56\n",
      "inhw-krnd-ri0-384-2-1-10\n",
      "OK:  2 [0, 147]  size:  40006\n",
      "Dump 57\n",
      "inhw-krnd-ri0-384-2-2-10\n",
      "OK:  2 [0, 146]  size:  40007\n",
      "Dump 58\n",
      "inhw-krnd-ri0-384-2-3-10\n",
      "OK:  2 [0, 148]  size:  40006\n",
      "Dump 59\n",
      "inhw-krnd-ri0-384-3-1-10\n",
      "OK:  2 [0, 138]  size:  40008\n",
      "Dump 60\n",
      "inhw-krnd-ri0-384-3-2-10\n",
      "OK:  2 [0, 136]  size:  40006\n",
      "Dump 61\n",
      "inhw-krnd-ri0-384-3-3-10\n",
      "OK:  2 [0, 140]  size:  40006\n",
      "Dump 62\n",
      "inhw-krnd-ri0-512-1-1-10\n",
      "OK:  2 [0, 151]  size:  40007\n",
      "Dump 63\n",
      "inhw-krnd-ri0-512-1-2-10\n",
      "OK:  2 [0, 154]  size:  40005\n",
      "Dump 64\n",
      "inhw-krnd-ri0-512-1-3-10\n",
      "OK:  2 [0, 141]  size:  40004\n",
      "Dump 65\n",
      "inhw-krnd-ri0-512-2-1-10\n",
      "OK:  2 [0, 142]  size:  40008\n",
      "Dump 66\n",
      "inhw-krnd-ri0-512-2-2-10\n",
      "OK:  2 [0, 152]  size:  40005\n",
      "Dump 67\n",
      "inhw-krnd-ri0-512-2-3-10\n",
      "OK:  2 [0, 150]  size:  40008\n",
      "Dump 68\n",
      "inhw-krnd-ri0-512-3-1-10\n",
      "OK:  2 [0, 136]  size:  40005\n",
      "Dump 69\n",
      "inhw-krnd-ri0-512-3-2-10\n",
      "OK:  2 [0, 138]  size:  40007\n",
      "Dump 70\n",
      "inhw-krnd-ri0-512-3-3-10\n",
      "OK:  2 [0, 145]  size:  40007\n",
      "Dump 71\n",
      "inhw4-krnd-ri0-128-1-1-10\n",
      "OK:  2 [0, 151]  size:  20000\n",
      "Dump 72\n",
      "inhw4-krnd-ri0-128-1-2-10\n",
      "OK:  2 [0, 139]  size:  20001\n",
      "Dump 73\n",
      "inhw4-krnd-ri0-128-1-3-10\n",
      "OK:  2 [0, 145]  size:  20000\n",
      "Dump 74\n",
      "inhw4-krnd-ri0-128-2-1-10\n",
      "OK:  2 [0, 147]  size:  20000\n",
      "Dump 75\n",
      "inhw4-krnd-ri0-128-2-2-10\n",
      "OK:  2 [0, 148]  size:  20000\n",
      "Dump 76\n",
      "inhw4-krnd-ri0-128-2-3-10\n",
      "OK:  2 [0, 144]  size:  20000\n",
      "Dump 77\n",
      "inhw4-krnd-ri0-128-3-1-10\n",
      "OK:  2 [0, 155]  size:  20000\n",
      "Dump 78\n",
      "inhw4-krnd-ri0-128-3-2-10\n",
      "OK:  2 [0, 146]  size:  20000\n",
      "Dump 79\n",
      "inhw4-krnd-ri0-128-3-3-10\n",
      "OK:  2 [0, 142]  size:  20001\n",
      "Dump 80\n",
      "inhw4-krnd-ri0-256-1-1-10\n",
      "OK:  2 [0, 154]  size:  20000\n",
      "Dump 81\n",
      "inhw4-krnd-ri0-256-1-2-10\n",
      "OK:  2 [0, 150]  size:  20000\n",
      "Dump 82\n",
      "inhw4-krnd-ri0-256-1-3-10\n",
      "OK:  2 [0, 136]  size:  20000\n",
      "Dump 83\n",
      "inhw4-krnd-ri0-256-2-1-10\n",
      "OK:  2 [0, 153]  size:  20000\n",
      "Dump 84\n",
      "inhw4-krnd-ri0-256-2-2-10\n",
      "OK:  2 [0, 141]  size:  20000\n",
      "Dump 85\n",
      "inhw4-krnd-ri0-256-2-3-10\n",
      "OK:  2 [0, 139]  size:  20002\n",
      "Dump 86\n",
      "inhw4-krnd-ri0-256-3-1-10\n",
      "OK:  2 [0, 136]  size:  20000\n",
      "Dump 87\n",
      "inhw4-krnd-ri0-256-3-2-10\n",
      "OK:  2 [0, 139]  size:  20000\n",
      "Dump 88\n",
      "inhw4-krnd-ri0-256-3-3-10\n",
      "OK:  2 [0, 133]  size:  20002\n",
      "Dump 89\n",
      "inhw4-krnd-ri0-384-1-1-10\n",
      "OK:  2 [0, 146]  size:  19999\n",
      "Dump 90\n",
      "inhw4-krnd-ri0-384-1-2-10\n",
      "OK:  2 [0, 143]  size:  20001\n",
      "Dump 91\n",
      "inhw4-krnd-ri0-384-1-3-10\n",
      "OK:  2 [0, 136]  size:  20000\n",
      "Dump 92\n",
      "inhw4-krnd-ri0-384-2-1-10\n",
      "OK:  2 [0, 145]  size:  20000\n",
      "Dump 93\n",
      "inhw4-krnd-ri0-384-2-2-10\n",
      "OK:  2 [0, 141]  size:  20001\n",
      "Dump 94\n",
      "inhw4-krnd-ri0-384-2-3-10\n",
      "OK:  2 [0, 147]  size:  20000\n",
      "Dump 95\n",
      "inhw4-krnd-ri0-384-3-1-10\n",
      "OK:  2 [0, 138]  size:  20001\n",
      "Dump 96\n",
      "inhw4-krnd-ri0-384-3-2-10\n",
      "OK:  2 [0, 131]  size:  20000\n",
      "Dump 97\n",
      "inhw4-krnd-ri0-384-3-3-10\n",
      "OK:  2 [0, 133]  size:  20000\n",
      "Dump 98\n",
      "inhw4-krnd-ri0-512-1-1-10\n",
      "OK:  2 [0, 147]  size:  20000\n",
      "Dump 99\n",
      "inhw4-krnd-ri0-512-1-2-10\n",
      "OK:  2 [0, 143]  size:  19999\n",
      "Dump 100\n",
      "inhw4-krnd-ri0-512-1-3-10\n",
      "OK:  2 [0, 135]  size:  19997\n",
      "Dump 101\n",
      "inhw4-krnd-ri0-512-2-1-10\n",
      "OK:  2 [0, 142]  size:  20001\n",
      "Dump 102\n",
      "inhw4-krnd-ri0-512-2-2-10\n",
      "OK:  2 [0, 147]  size:  19999\n",
      "Dump 103\n",
      "inhw4-krnd-ri0-512-2-3-10\n",
      "OK:  2 [0, 150]  size:  20001\n",
      "Dump 104\n",
      "inhw4-krnd-ri0-512-3-1-10\n",
      "OK:  2 [0, 135]  size:  20000\n",
      "Dump 105\n",
      "inhw4-krnd-ri0-512-3-2-10\n",
      "OK:  2 [0, 138]  size:  20000\n",
      "Dump 106\n",
      "inhw4-krnd-ri0-512-3-3-10\n",
      "OK:  2 [0, 145]  size:  20001\n",
      "Dump 107\n",
      "inhw6-krnd-ri0-128-1-1-10\n",
      "OK:  2 [0, 155]  size:  20000\n",
      "Dump 108\n",
      "inhw6-krnd-ri0-128-1-2-10\n",
      "OK:  2 [0, 145]  size:  20000\n",
      "Dump 109\n",
      "inhw6-krnd-ri0-128-1-3-10\n",
      "OK:  2 [0, 140]  size:  20000\n",
      "Dump 110\n",
      "inhw6-krnd-ri0-128-2-1-10\n",
      "OK:  2 [0, 147]  size:  20000\n",
      "Dump 111\n",
      "inhw6-krnd-ri0-128-2-2-10\n",
      "OK:  2 [0, 141]  size:  20000\n",
      "Dump 112\n",
      "inhw6-krnd-ri0-128-2-3-10\n",
      "OK:  2 [0, 146]  size:  20000\n",
      "Dump 113\n",
      "inhw6-krnd-ri0-128-3-1-10\n",
      "OK:  2 [0, 138]  size:  20000\n",
      "Dump 114\n",
      "inhw6-krnd-ri0-128-3-2-10\n",
      "OK:  2 [0, 135]  size:  20000\n",
      "Dump 115\n",
      "inhw6-krnd-ri0-128-3-3-10\n",
      "OK:  2 [0, 140]  size:  20000\n",
      "Dump 116\n",
      "inhw6-krnd-ri0-256-1-1-10\n",
      "OK:  2 [0, 150]  size:  20000\n",
      "Dump 117\n",
      "inhw6-krnd-ri0-256-1-2-10\n",
      "OK:  2 [0, 142]  size:  20000\n",
      "Dump 118\n",
      "inhw6-krnd-ri0-256-1-3-10\n",
      "OK:  2 [0, 138]  size:  20000\n",
      "Dump 119\n",
      "inhw6-krnd-ri0-256-2-1-10\n",
      "OK:  2 [0, 146]  size:  20000\n",
      "Dump 120\n",
      "inhw6-krnd-ri0-256-2-2-10\n",
      "OK:  2 [0, 149]  size:  20000\n",
      "Dump 121\n",
      "inhw6-krnd-ri0-256-2-3-10\n",
      "OK:  2 [0, 141]  size:  20000\n",
      "Dump 122\n",
      "inhw6-krnd-ri0-256-3-1-10\n",
      "OK:  2 [0, 138]  size:  20000\n",
      "Dump 123\n",
      "inhw6-krnd-ri0-256-3-2-10\n",
      "OK:  2 [0, 135]  size:  20000\n",
      "Dump 124\n",
      "inhw6-krnd-ri0-256-3-3-10\n",
      "OK:  2 [0, 138]  size:  20000\n",
      "Dump 125\n",
      "inhw6-krnd-ri0-384-1-1-10\n",
      "OK:  2 [0, 152]  size:  20000\n",
      "Dump 126\n",
      "inhw6-krnd-ri0-384-1-2-10\n",
      "OK:  2 [0, 143]  size:  20000\n",
      "Dump 127\n",
      "inhw6-krnd-ri0-384-1-3-10\n",
      "OK:  2 [0, 137]  size:  20000\n",
      "Dump 128\n",
      "inhw6-krnd-ri0-384-2-1-10\n",
      "OK:  2 [0, 147]  size:  20000\n",
      "Dump 129\n",
      "inhw6-krnd-ri0-384-2-2-10\n",
      "OK:  2 [0, 146]  size:  20000\n",
      "Dump 130\n",
      "inhw6-krnd-ri0-384-2-3-10\n",
      "OK:  2 [0, 145]  size:  20000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dump 131\n",
      "inhw6-krnd-ri0-384-3-1-10\n",
      "OK:  2 [0, 134]  size:  20000\n",
      "Dump 132\n",
      "inhw6-krnd-ri0-384-3-2-10\n",
      "OK:  2 [0, 136]  size:  20000\n",
      "Dump 133\n",
      "inhw6-krnd-ri0-384-3-3-10\n",
      "OK:  2 [0, 139]  size:  19999\n",
      "Dump 134\n",
      "inhw6-krnd-ri0-512-1-1-10\n",
      "OK:  2 [0, 150]  size:  20000\n",
      "Dump 135\n",
      "inhw6-krnd-ri0-512-1-2-10\n",
      "OK:  2 [0, 154]  size:  20000\n",
      "Dump 136\n",
      "inhw6-krnd-ri0-512-1-3-10\n",
      "OK:  2 [0, 141]  size:  20000\n",
      "Dump 137\n",
      "inhw6-krnd-ri0-512-2-1-10\n",
      "OK:  2 [0, 140]  size:  20000\n",
      "Dump 138\n",
      "inhw6-krnd-ri0-512-2-2-10\n",
      "OK:  2 [0, 152]  size:  20000\n",
      "Dump 139\n",
      "inhw6-krnd-ri0-512-2-3-10\n",
      "OK:  2 [0, 138]  size:  20000\n",
      "Dump 140\n",
      "inhw6-krnd-ri0-512-3-1-10\n",
      "OK:  2 [0, 135]  size:  20000\n",
      "Dump 141\n",
      "inhw6-krnd-ri0-512-3-2-10\n",
      "OK:  2 [0, 131]  size:  20000\n",
      "Dump 142\n",
      "inhw6-krnd-ri0-512-3-3-10\n",
      "OK:  2 [0, 144]  size:  20000\n",
      "Dump 143\n"
     ]
    }
   ],
   "source": [
    "pval_db = []\n",
    "for dix, val in enumerate(data_filt):\n",
    "    res = tabulate_pvals(val, abs_val=True)\n",
    "    pval_db.append(res)\n",
    "    print('Dump %s' % dix)\n",
    "json.dump(pval_db, open('pval_db.json', 'w+'), indent=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7[128:1:1:10000000]: 100000 \n",
      "<class 'scipy.stats._distn_infrastructure.rv_discrete'>\n",
      "[0. 0. 0.]\n",
      "[1.0e-05 3.0e-05 1.6e-04]\n",
      "OK:  2 [0, 163]  size:  100000\n",
      "AES-r10-e-cfg80a9374f52f32147903b0b2240c7[128:1:1:10000000]: 100000 [5.00498027 4.48483105 3.93730555 3.52666142 3.3350275  2.76012572\n",
      " 0.692451   6.15478382 1.77457982 1.93883747 2.3494816  3.3350275 ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting area under:  (0.00444, [(1.692451, 1.9662137499999999), (4.293197125, 6.167706)], [0.00212, 0.0023200000000000004], 3.999999999999967e-05)\n"
     ]
    },
    {
     "data": {
      "image/png": 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GuyzZWM8TS2s8rkYk86k5mQBtnRGGluZx1+xpXpeSUq4+fQynjavgvoXrWbpJwxdFkk0t9ATI9rVbDibXl8OUEQMozvPTpk0vRJJOgZ4AraEwBbn6snMwBQGfhi+K9AMFegK0ZelyuX2VH/DT3hklqjXSRZJKgZ4AbZ2RvQtSyf66uqNWbWskHNEKjCLJokA/Ajua2vnveWuobWhXH/qH6Do3F971Mj965h2PqxHJXAr0IzB32TZ+OX8t7eEoU0Zkz2bQh+r40WUcPzq2t2ptQ7vH1YhkLl3JOwJdC08t/+F5BP1qoR/MpGEDeOKrp3Dx3a/QqtEuIkmjFvoRaA2F8eUYAZ9OY18U5Pq016hIEimJjkBrKEJBrpbL7asCLdYlklQK9CPQFoqQp4uhfZYf8NGmQBdJGgX6EdDqioemIOBj/c4WfvPSOuqaOrwuRyTjKNAPg3OOpZvq2bqnTePPD0FJXi4AP3l2NY9rsS6RhFOgH4a3axq45J5FLN1UT2Vx0Oty0saXP3oU//WpyUBs/1URSaw+BbqZzTKzNWa21sxu+JDjLjEzZ2YzEldi6tnVHOsuuO2SKdx5mVZY7KvBJXn880mjKc7z06KNL0QSrtdANzMfcDdwPjARmG1mEw9wXDFwHfB6ootMNV0jNaaPKqOsMOBxNemnQBdHRZKiLy30E4G1zrn1zrkQ8Chw8QGOuxW4Dcj4qYBdYaQFuQ5PYcBPi8ajiyRcXwJ9OLCl2/2a+GN7mdl0YKRz7pkPeyMzu8bMqs2suq6u7pCLTRVda3tru7nDkx/w0dQepqFN/egiiXTEF0XNLAe4A/hWb8c65+51zs1wzs2orKw80o/2TFeXi4YsHp7CgJ+X3q1j6s1/4+0te7wuRyRj9CXQtwIju90fEX+sSzEwGVhgZhuBk4E5mXphdEdjOzX1rZhB0K9BQofjwqlDGZAfG8JYU9/mcTUimaMvfQZLgPFmNoZYkF8GfL7rSedcA1DRdd/MFgDXO+eqE1uq91o6wpx2+3xC4SjlhQFN+T9MX5hZxVlHD+L02+erL10kgXoNdOdc2MyuBeYBPuAB59xKM7sFqHbOzUl2kaliT1snoXCUfzl5NLNPHOV1OWmtKBj7q9fSoUAXSZQ+XdVzzs0F5vZ47KaDHHvmkZeVmrpWCjxhTDkTh5V4XE16KwjGrj9osS6RxFEn8CHYezFU0/2PWMCXgz/HaGzvpFPb0okkhAL9EGh0S+KYGUF/Dr95aT3H3/o8je0awihypBToh0ATihLr09NHANDYHma7tqYTOWIK9D4KR6LsbgkBmlCUKLd+cjIPXnECAE26OCpyxJRMfXTJr1/dOwmmOE+nLVGK8jTaRSRRlEx9tH5HMyeNKWf2iaMYVprvdTkZozD+bae5XYEucqTU5dIHzjlaOyOcUFXOJ6cN7/0F0mdd33a+8ce3uPcf6zyuRiS9KdD7IBSJEok6XQxNgrLCAAF/Dh3hKD+eu9rrckTSmgK9D9o0XDFpioJ+Fn7nLL44czQAobDGpIscLgV6H7Qo0JNqcEkeVRWFgC6OihwJBXovnHN7QyZfwxWTpjC+tkuzAl3ksCmhenHT0yt5+LVNABQHdbqSpevcbtrVyuCSPAJamljkkOlfTS9WbWtk9MACbrzgWGaOHeh1ORmrq4V++f++ztd+v9TjakTSkwK9Fy0dYSYMLubLHz2KPC3KlTTHjSrlM8fHlgLYvLvV42pE0pP6EHrRGopQqIuhSVeSl8t/f3YqzsGr63Z6XY5IWlILvRetoQgF6jvvN8V5fq3rInKYFOi9aA2F1ULvR8V5fpo7wkSjzutSRNKOAv0gdjV38MTSmlgLXcMV+01R0I9zcMxNz/G7+OgiEekbBfpB/OYf6/nWn94GYLgW4+o3Jx01kIlDSwiFoyzZuNvrckTSigL9IPa0hqgsDrLohrP57IwRXpeTNY4bWcrc605n4tASrcAocojUl3AQraEIxXl+LZXrkSJdHBU5ZGqhH0RbKKK1WzxUkudXC13kECnQD6IlFKYgV19gvFIU9LNqWyM3PrmcBWt2eF2OSFpQoB9EWyii9c89NLK8AIDfv76ZX83XxhcifaFA76GpvZOHX9vEtoZ2CoMKdK/8x7kTWHTD2Zx77CAa2zu9LkckLahPoYdnl9fyg6dWADAmvka39L+cHGNYaT4lebk0tTd5XY5IWlCg99DVGnzlhrMZNiDP42qkOM9Pk1roIn2iLpceWjpiuxMNLg5iZh5XI8V5uTS2h7n5LytZXdvodTkiKU2B3kNrZ5igPwe/T6cmFYwoi80DePCVjVoKQKQXSq0eWjsiezdbEO9desJIXvr2mQwvzaexTePSRT6MAr2HllCYfG1kkTLMjNEDCykvDGi0i0gv1BSNW7xhN3e+8B6raxspLwx4XY70UJLvp7FNgS7yYdRCj3t+VS2L1u1kTEUhl0zXYlyppiQvlzc272HWz//B8poGr8sRSUlqoce1hCKUFwb401dO8boUOYAzJlTy+obdrK5tYvHG3UwZMcDrkkRSjlroca0dYW1kkcIuO3EU1TeeS45BQ2vI63JEUpICPa4lpNEtqS4nxxiQn0t9q/rSRQ5EgR6nvUPTQ1lBgIdf28Rn7lnEruYOr8sRSSlZH+jRqGPF1gbqmjooUAs95Z0/ZQgA1ZvqWV2rNV5Eusv6QH9h9Q4uvOtl3t3ezEANV0x53/74Mcz7xkcBqFdfusg++hToZjbLzNaY2Vozu+EAz3/TzFaZ2TIze8HMRie+1OTY0dQOwJ2zp/Gfn5jocTXSF2UFuQDqSxfpoddANzMfcDdwPjARmG1mPZPvTWCGc+4jwOPA7YkuNFla4vtWnn3MIEoL1EJPByX5sUD/w+ub+dFfV+Gc87gikdTQlxb6icBa59x651wIeBS4uPsBzrn5zrnW+N3XgLSZmdMcX12xQNP900Zero9Txg6kpr6V+1/ewO4Wdb2IQN8CfTiwpdv9mvhjB3MV8OyBnjCza8ys2syq6+rq+l5lErV0xEa35ORoqdx08siXT+bWT04G1PUi0iWhF0XN7HJgBvCzAz3vnLvXOTfDOTejsrIykR99WLY1tLGtoU3jz9NUWbyLTBdHRWL6EuhbgZHd7o+IP7YPMzsXuBG4yDmX8gOEa+pbmfmTF5m7vFaLcaWprkD/ydx3uGeBNpIW6UugLwHGm9kYMwsAlwFzuh9gZtOA3xAL8x2JLzPxahtio1u+fvY4fvn56R5XI4djTGUhk4aVsHZHM/ctXO91OSKe6zXQnXNh4FpgHvAO8JhzbqWZ3WJmF8UP+xlQBPzJzN4yszkHebuU0Rwf3XLWMYMYN6jI42rkcBQF/Tzz76dzxSlV1LeGiEQ12kWyW586j51zc4G5PR67qdvtcxNcV9J1BXqx+s/TXnlhAOdi10SGl+ZrL1jJWlk7U7S5PRbouiCa/iqKgwCcdtt8vvfkCo+rEfFOVgb6029t5bmVtQAU5SnQ092ZRw/i+vMmMKaikHe2NXpdjohnsi7NQuEo3/jjWzgHowcWUKQ10NNeUdDPtWePZ+2OZqo31Xtdjohnsq6F3tjeiXPw2eNHsOD6MzWhKINUFAXZ3tjO1b+t5n6NepEslH2BHt9oOC/g08WzDHPq+ApGlBWwZONu7l+4wetyRPpd9gV6/GKo1m7JPGcdPYj515/J5SePoq65g6iGMUqWyapAf25FLT9//l0A8rU7UcYaXJJHJOqYfd9rPP3WfpOaRTJWVgX6bc+t5h/vxRYFq4wPdZPMc/JRA5k6spQVWxt44g0FumSPrAr0hrbOvet/FOflelyNJMuEwcU8/W+nMnNsBTsa270uR6TfZE2gO+dobOtkl9bOzhqDSoJs2NnCVQ8t4Y3NGs4omS9rAr0jHCWsi2RZ5YwJlYwsL2DBu3XMXbbN63JEki4rAn11bSO/eOE9r8uQfvbxSUP4+zfPYFR5AdvU9SJZICsC/Rd/f0/rZWexISV5PLt8G1P+cx7LavZ4XY5I0mRFoNe3hhhcolEt2err54zj0hNG0dQR5u2aBq/LEUmarAj0hrZObSScxU4ZW8GPPjkZf47x8nt1vLh6u9cliSRFVgT6jsYOOiO6IJrNfDnG2Moi5q3czpUPVbN2R7PXJYkkXEYH+tJN9Xz+vtfUOhcAHvvXmdw1exoAm3a1eFyNSOJldKD/bVUtr63fhdrmAjCgIJeTjioH4Ku/e4ML71qIc/rbIZkjowN9T0sn+VqES7oZVJzHjRccywljylixtVETzSSjZHSg17eG6AhHvS5DUsyXP3oUV546BoArHlzMrxas9bgikcTIyECvbWjnXx+uZtG6nZodKgd0/OgyTh9fQV1TBw++stHrckQSIiMDfdG6ncxbuR11j8rBlBYEePiqk7jy1DHUNXUw5T/n8euXNPlM0ltGBvqu5li/aEso4nElkuo+OW04V546hpL8XP62spbtje10RtRNJ+kpowK9MxJl6abdrHi/AW0uJ30xuCSPmz4xkTOOruSNzXs46ccv8JWHl3pdlshhyagt7/+weDM3Pb3S6zIkDV171jiOGVLMs8trWbJxN0+/tZVTxlZoIxRJKxnVQt+ws4X8XB9DSvK8LkXSzLDSfL4ws4qLjxtGY3uY6x59i9ufW+11WSKHJCMCPRJ1vLh6O8trGigM+qjVUqlymD43YyR//+YZnHxUOc+tqOVzv3mV5VrQS9JERgT6onU7ufKhaqo31dPYHva6HEljOTnGuEFFXHnqGCYNL+HNzfX8z/NreHxpDWFdLJUUlxF96Jt2tQKQn5tDW6f+0cmRO2/SEM6bNIQrHlzMgjV1LFhTR3tnhHOOHcTQAflelydyQGndQnfOcecL73H3/NhMP4W5JNp9X5jBohvOJj/Xx/efWsEZty9g6abd1GvJAElBaR3o63e2cMfz77KtQX3mkhy5vhyGlebz6DUnc/NFkwhFolxyz6v8050Lae+MaHEvSSlp2+WypzXEAy9v8LoMyRJTR5YydWQpYyuLeHH1Dh54ZQPH/OA5Lpo6jB9eNImyglzMNPtBvJWWge6c42N3/IO65g6vS5Esc9r4CmZUlTGsNI+X3q1jztvvM+ft97nilCouPWEkowcWUBBIy39WkgHS7m/e7pYQq95vUJiLZ/JyfVx9+lF8atpwnlm+jT+/sZWHFm3koUUbGVwS5Otnj+e8iYMZpPkQ0s/SLtD/VL2FnzyrCR/ivYFFQb4ws4rzJw9lycbdPLG0hhdW7+D7T63g1r+u4tyJg7lh1jGMLC/wulTJEmkX6OdOHMyct99n5fuNXpciAkBlcZALpgzl/MlD2NUS4pHXN/PXZe/zzLJtPLNsG8V5fr525jhmVJVxQlW51+VKBku7QB9bWcSg4iBasUVSjZlRURTk388Zz7+fM57nVmxj0bpd/N+rm7gtvozACVVlHDeylHOOHczUEaXkB7SjliRO2gW6SLqYNXkosyYP5bpzxrNhZws/m7eGdXUtLNlYz30LNxDwwdThJZw8bhDDSvM5bVwFQwbkketL69HE4iEFukiSDSwKMrAoyB//dSbOOd7csodl1S8zv3oZqzZXsWTzB92HOQbTRpUxqryAE6rKGVaax7SRZRQEfQp66VWfAt3MZgG/AHzA/c65n/Z4Pgj8H3A8sAu41Dm3MbGliqQ/M2P6qDKmt8AVy24n4owdlLHNlfNqdCJroiNZvWUMczYN4ck3t+59XWEgh5HlhYwoK6CyOMi4QUUUBX2MLCugrDDAmIpC8rQhetbrNdDNzAfcDXwMqAGWmNkc59yqboddBdQ758aZ2WXAbcClyShYJJP4zDGU3Qy13UzP+WCz6maXR60r5y03lp1uACvDVTTUFbOybhSN0XxaXGC/98rNgTy/kR/wMag4SDA3l2Cuj9KCXAbk5xLw5RDM9TGwMEDQn0OuP4dcXw7FQT8Bfw6+HCPXl4M/xwj4cwjEn/flGLk5Ofh8hj8n/suXQzD+mhwzcgxNrEoBfWmhnwisdc6tBzCzR4GLge6BfjHww/jtx4Ffmpm5JM2LLsrLpbxw/7/QhyKgr6/iJV8ACgYe9OkiYBwdjNv7z+zVvc9FnbEtWko7uayLDKbFBdkcrdFXVEcAAASXSURBVKDD5dLh/DSG8tm5s4SQ8xOyAKtdCU0ujxB+OqI+2l3yelqNWLdRV8DHfmef0I8d0+2+xV7X8wfCB3et238P9HzXUbb/Qfsc3+P9e32/Hp9/0OcP/P49D+7+7HXnTuCiqcMOfvxh6suf7HBgS7f7NcBJBzvGORc2swZgILCz+0Fmdg1wDcCoUaMOs2S4a/a0w36tSEqY8HH4zvrDemkOsX9wAGMP8bXOOVpCEULhKOFIlI5wlKb2MJ2RKOFolM6IIxxxdMafi0Qd4WiUcCT+e/SD50ORKJGIwwFR54i62Ps7t+/96N7HiN+OP0fXMey3Jk7PpmDsUz7s+Z7/nx/++l7u7l9Pr+9/aK8vK8glGfr1oqhz7l7gXoAZM2ZoVSORfmZmFAX9oJ31MlJf+h22AiO73R8Rf+yAx5iZHxhA7OKoiIj0k74E+hJgvJmNMbMAcBkwp8cxc4Avxm9/BngxWf3nIiJyYL12ucT7xK8F5hEbtviAc26lmd0CVDvn5gD/CzxsZmuB3cRCX0RE+lGf+tCdc3OBuT0eu6nb7Xbgs4ktTUREDoXG7omIZAgFuohIhlCgi4hkCAW6iEiGMK9GF5pZHbDJkw8/sAp6zGzNcjof+9M52ZfOx77663yMds5VHugJzwI91ZhZtXNuhtd1pAqdj/3pnOxL52NfqXA+1OUiIpIhFOgiIhlCgf6Be70uIMXofOxP52RfOh/78vx8qA9dRCRDqIUuIpIhFOgiIhki6wPdzEaa2XwzW2VmK83sOq9r8pKZ5ZnZYjN7O34+bva6plRgZj4ze9PM/up1LanAzDaa2XIze8vMqr2ux2tmVmpmj5vZajN7x8xmelFHv+5YlKLCwLecc2+YWTGw1Mye77EJdjbpAM52zjWbWS7wspk965x7zevCPHYd8A5Q4nUhKeQs55wmFsX8AnjOOfeZ+L4RBV4UkfUtdOfcNufcG/HbTcT+0Q7/8FdlLhfTHL+bG/+V1VfOzWwE8E/A/V7XIqnHzAYAHyW2LwTOuZBzbo8XtWR9oHdnZlXANOB1byvxVrx74S1gB/C8cy6rzwfwc+A7QNTrQlKIA/5mZkvjm79nszFAHfBgvFvufjMr9KIQBXqcmRUBTwDfcM41el2Pl5xzEefcccT2jz3RzCZ7XZNXzOxCYIdzbqnXtaSY05xz04HzgX8zs496XZCH/MB04B7n3DSgBbjBi0IU6EC8r/gJ4PfOuT97XU+qiH9tnA/M8roWD50KXGRmG4FHgbPN7HfeluQ959zW+O87gCeBE72tyFM1QE23b7KPEwv4fpf1gW5mRqzv6x3n3B1e1+M1M6s0s9L47XzgY8Bqb6vyjnPuu865Ec65KmJ75b7onLvc47I8ZWaF8QEExLsWzgNWeFuVd5xztcAWMzs6/tA5gCeDKjTKJdYC+xdgebzfGOB78X1Us9FQ4Ldm5iP2A/8x55yG6kl3g4EnY20h/MAjzrnnvC3Jc18Hfh8f4bIe+JIXRWjqv4hIhsj6LhcRkUyhQBcRyRAKdBGRDKFAFxHJEAp0EZEMoUAXEckQCnQRkQzx/wEb573/6ENpnwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = 200\n",
    "abs_val = True\n",
    "    \n",
    "for dix, val in enumerate(data_filt):\n",
    "    inp_iter = (val['zscores'])\n",
    "    if abs_val:\n",
    "        inp_iter = [abs(x) for x in inp_iter]\n",
    "    \n",
    "    print('%s[%s:%s:%s:%s]: %s %s' \n",
    "          % (val['method'], val['block'], val['deg'], val['comb_deg'], \n",
    "             val['data_size'], len(val['zscores']),\n",
    "             '',#dst.ppf([1-0.0001, 1-0.001, 1-0.01, 1-0.05, 1-0.10, 1-0.5, 0, 1, 0.0001, 0.001, 0.1, 0.9])\n",
    "             #dst.stats(moments='mvsk')\n",
    "            ))\n",
    "    \n",
    "    bin_tup = get_bins(inp_iter, nbins=nbins, full=True)\n",
    "    bb = get_distrib_fbins(inp_iter, bin_tup)\n",
    "    \n",
    "    bin_size = bin_tup[1]\n",
    "    minv, maxv = bin_tup[2], bin_tup[3] \n",
    "    bins = np.array([x[0] for x in bb])\n",
    "    dst = stats.rv_discrete(values=([x[0] for x in bb], [x[1] for x in bb]))\n",
    "    print(stats.rv_discrete)\n",
    "    \n",
    "    x=np.array([bins[0], bins[1], bins[6]])\n",
    "    print(dst.pmf(x))\n",
    "    print(dst._pmf(x))\n",
    "    \n",
    "    # Tabulate pvalues\n",
    "    build_integrator(bin_tup)\n",
    "    extremes = [\n",
    "        [minv, 1],\n",
    "        [0, -1],\n",
    "        [0, +1],\n",
    "        [maxv, -1]\n",
    "    ] if not abs_val else [\n",
    "        [minv, 1],\n",
    "        [maxv, -1]\n",
    "    ]\n",
    "    \n",
    "    pvals = pvalue_comp(lambda x: binned_pmf(x, bin_tup), extremes, \n",
    "                        dx=1./(nbins/10.), bin_tup=bin_tup, by_bins=True)\n",
    "    \n",
    "    n_sample = 100\n",
    "    rvs = dst.rvs(size=n_sample)\n",
    "    f, l = np.histogram(rvs, bins=bins)\n",
    "    f = np.append(f, [0])\n",
    "    probs = np.array([x[1] for x in bb])\n",
    "    #print(bins, len(bins))\n",
    "    #print(probs, len(probs))\n",
    "    #print(f, len(f))\n",
    "    #sfreq = np.vstack([np.array([x[0] for x in bb]), f, probs*n_sample]).T\n",
    "    #print(sfreq)\n",
    "    \n",
    "    print('%s[%s:%s:%s:%s]: %s %s' \n",
    "          % (val['method'], val['block'], val['deg'], val['comb_deg'], \n",
    "             val['data_size'], len(val['zscores']),\n",
    "             dst.ppf([1-0.0001, 1-0.001, 1-0.01, 1-0.05, 1-0.10, 1-0.5, 0, 1, 0.0001, 0.001, 0.1, 0.9])\n",
    "             #dst.stats(moments='mvsk')\n",
    "            ))\n",
    "    \n",
    "    x = np.linspace(min(bins),max(bins),1000)\n",
    "    plt.plot(x, dst.cdf(x))\n",
    "    plt.show()\n",
    "    \n",
    "    cdf_dev = derivative(dst.cdf, x, dx=0.5)\n",
    "    plt.plot(x,cdf_dev)\n",
    "    \n",
    "    sec_x = pvals[40]  # 49\n",
    "    print('Plotting area under: ', sec_x)\n",
    "    for ix in range(len(sec_x[1])):\n",
    "        section = np.arange(sec_x[1][ix][0], sec_x[1][ix][1], 1/20.)\n",
    "        plt.fill_between(section, derivative(dst.cdf, section, dx=0.5))\n",
    "    plt.show()\n",
    "    \n",
    "    #for pv in pvals:\n",
    "    #    sec_x = pv\n",
    "    #    for ix in range(len(sec_x[1])):\n",
    "    #        section = np.arange(sec_x[1][ix][0], sec_x[1][ix][1], 1/20.)\n",
    "    #        plt.fill_between(section, derivative(dst.cdf, section, dx=0.5))\n",
    "    #    plt.show()\n",
    "    \n",
    "    x = np.linspace(0,100,10000)\n",
    "    plt.plot(x,dst.ppf(x))\n",
    "    plt.show()\n",
    "    \n",
    "    x = np.linspace(minv,maxv,10000)\n",
    "    plt.plot(bins, dst._pmf(bins))\n",
    "    plt.show()\n",
    "    \n",
    "    x = np.linspace(minv,maxv,10000)\n",
    "    plt.plot(x, [binned_pmf(y, bin_tup) for y in x])\n",
    "    for ix in range(len(sec_x[1])):\n",
    "        section = np.linspace(sec_x[1][ix][0], sec_x[1][ix][1], 10000) #np.arange(sec_x[1][ix][0], sec_x[1][ix][1], 1/20.)\n",
    "        plt.fill_between(section, [binned_pmf(y, bin_tup)+0.0005 for y in section])\n",
    "    plt.show()\n",
    "    \n",
    "    # Idea: pvalue function = pms of the distribution. \n",
    "    # If test returns z-score with p=0 then we reject the hypothesis as we didnt get such zscore\n",
    "    # If test returns with p=0.3 we dont reject as we have our alpha set somehow...\n",
    "    # Problem: number of bins. If too many, we have small probabilities -> some alphas not reachable.\n",
    "    #if dix > 3:\n",
    "    break\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(7)\n",
    "#np.zeros(np.shape(0.5),'d')\n",
    "#print(dst.ppf([1-0.01, 1-0.05, 1-0.10, 0.5, 0.6]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "def crit_regions(data, add_zscores=False):\n",
    "    iter_block = [128, 256, 384, 512]\n",
    "    iter_deg = [1, 2, 3]\n",
    "    iter_cdeg = [1, 2, 3]\n",
    "    iter = itertools.product(iter_block, iter_deg, iter_cdeg)\n",
    "    for ix, crec in enumerate(iter):\n",
    "        c = tuple(crec)\n",
    "        reldata = [x for x in data if (x['block'], x['deg'], x['comb_deg']) == c and len(x['zscores']) > 9999]\n",
    "        reldata.sort(key=lambda x: -len(x['zscores']))\n",
    "        relrecs = []\n",
    "        for crel in reldata:\n",
    "            zsz = [abs(x) for x in crel['zscores']]\n",
    "            relrecs.append((crel['method'], len(zsz), min(zsz), max(zsz), crel['zscores'] if add_zscores else None))\n",
    "        \n",
    "        yield (crec, relrecs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.5"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(list(range(10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|     block |       deg |  comb-deg |   samples |     alpha |       min |       max |      mean |    stddev |\n",
      "|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|\n",
      "|       128 |         1 |         1 |    100000 |   1.0e-05 |  1.692451 |  6.167706 |  2.827212 |  0.392073 |\n",
      "|       128 |         1 |         2 |    100000 |   1.0e-05 |  3.205285 |  6.203124 |  3.968438 |  0.296394 |\n",
      "|       128 |         1 |         3 |    100000 |   1.0e-05 |  4.075543 |  6.790043 |  4.786912 |  0.252671 |\n",
      "|       128 |         2 |         1 |    100000 |   1.0e-05 |  2.935793 |  6.447060 |  3.888612 |  0.336659 |\n",
      "|       128 |         2 |         2 |    100000 |   1.0e-05 |  4.358132 |  7.336712 |  5.284895 |  0.303586 |\n",
      "|       128 |         2 |         3 |    100000 |   1.0e-05 |  4.873983 |  7.796648 |  5.770370 |  0.295979 |\n",
      "|       128 |         3 |         1 |    100000 |   1.0e-05 |  3.866804 |  6.823321 |  4.708614 |  0.290063 |\n",
      "|       128 |         3 |         2 |    100000 |   1.0e-05 |  5.698085 |  8.376927 |  6.560998 |  0.265912 |\n",
      "|       128 |         3 |         3 |    100000 |   1.0e-05 |  6.749871 |  9.570848 |  7.616965 |  0.282312 |\n",
      "|       256 |         1 |         1 |    100000 |   1.0e-05 |  1.999939 |  5.302164 |  3.044472 |  0.368626 |\n",
      "|       256 |         1 |         2 |    100000 |   1.0e-05 |  3.191316 |  6.303923 |  3.969717 |  0.297037 |\n",
      "|       256 |         1 |         3 |    100000 |   1.0e-05 |  4.096477 |  7.026620 |  4.786630 |  0.252239 |\n",
      "|       256 |         2 |         1 |    100000 |   1.0e-05 |  3.304946 |  6.601629 |  4.221227 |  0.310189 |\n",
      "|       256 |         2 |         2 |    100000 |   1.0e-05 |  4.775841 |  7.609938 |  5.659143 |  0.293182 |\n",
      "|       256 |         2 |         3 |    100000 |   1.0e-05 |  5.188120 |  8.305500 |  6.051743 |  0.289642 |\n",
      "|       256 |         3 |         1 |    100000 |   1.0e-05 |  4.389393 |  7.001934 |  5.130026 |  0.263766 |\n",
      "|       256 |         3 |         2 |    100000 |   1.0e-05 |  6.402618 |  9.003259 |  7.139777 |  0.245306 |\n",
      "|       256 |         3 |         3 |    100000 |   1.0e-05 |  7.408662 |  9.833698 |  8.247723 |  0.263262 |\n",
      "|       384 |         1 |         1 |    100000 |   1.0e-05 |  2.175556 |  5.326058 |  3.165671 |  0.359287 |\n",
      "|       384 |         1 |         2 |    100000 |   1.0e-05 |  3.214039 |  6.110398 |  3.968937 |  0.296144 |\n",
      "|       384 |         1 |         3 |    100000 |   1.0e-05 |  4.138595 |  7.017427 |  4.787164 |  0.252258 |\n",
      "|       384 |         2 |         1 |    100000 |   1.0e-05 |  3.563257 |  6.406780 |  4.405090 |  0.297651 |\n",
      "|       384 |         2 |         2 |    100000 |   1.0e-05 |  4.972945 |  7.606778 |  5.859061 |  0.288056 |\n",
      "|       384 |         2 |         3 |    100000 |   1.0e-05 |  5.355741 |  8.065769 |  6.201906 |  0.284984 |\n",
      "|       384 |         3 |         1 |    100000 |   1.0e-05 |  4.621511 |  7.317737 |  5.363479 |  0.251736 |\n",
      "|       384 |         3 |         2 |    100000 |   1.0e-05 |  6.729801 |  8.957334 |  7.454564 |  0.235683 |\n",
      "|       384 |         3 |         3 |    100000 |   1.0e-05 |  7.666722 | 10.225207 |  8.585460 |  0.254527 |\n",
      "|       512 |         1 |         1 |    100000 |   1.0e-05 |  2.292019 |  5.580788 |  3.250047 |  0.351051 |\n",
      "|       512 |         1 |         2 |    100000 |   1.0e-05 |  3.177457 |  5.980500 |  3.968829 |  0.295422 |\n",
      "|       512 |         1 |         3 |    100000 |   1.0e-05 |  4.143849 |  6.820400 |  4.786089 |  0.251366 |\n",
      "|       512 |         2 |         1 |    100000 |   1.0e-05 |  3.736198 |  6.505483 |  4.530410 |  0.289203 |\n",
      "|       512 |         2 |         2 |    100000 |   1.0e-05 |  5.156339 |  7.839674 |  5.997271 |  0.286677 |\n",
      "|       512 |         2 |         3 |    100000 |   1.0e-05 |  5.459804 |  8.324528 |  6.304574 |  0.283540 |\n",
      "|       512 |         3 |         1 |    100000 |   1.0e-05 |  4.840198 |  7.196232 |  5.520881 |  0.242361 |\n",
      "|       512 |         3 |         2 |    100000 |   1.0e-05 |  6.931561 |  9.324313 |  7.667548 |  0.228464 |\n",
      "|       512 |         3 |         3 |    100000 |   1.0e-05 |  7.886802 | 10.312977 |  8.815687 |  0.249666 |\n"
     ]
    }
   ],
   "source": [
    "# Prints markdown critical region table\n",
    "def crit_regions_table(data):\n",
    "    dt = list(crit_regions(data, True))\n",
    "    max_num = 9\n",
    "    hdr = ['block', 'deg', 'comb-deg', 'samples', 'alpha', 'min', 'max', 'mean', 'stddev']\n",
    "    max_hdr = max([len(x) for x in hdr])\n",
    "    max_field = max(max_hdr, max_num) + 1\n",
    "    \n",
    "    hdr_spaced = [x.rjust(max_field) for x in hdr]\n",
    "    print('|%s |' % ' |'.join(hdr_spaced))\n",
    "    print('|%s|' % '|'.join([''.rjust(max_field + 1, '-') for _ in hdr]))\n",
    "    for r in dt:\n",
    "        cr, cd = r[0], r[1][0]\n",
    "        zsz = [abs(x) for x in cd[4]]\n",
    "        elems = [*cr] + [cd[1], '%.1e' % (1./cd[1]), \n",
    "                         '%9.6f' % cd[2], '%9.6f' % cd[3], \n",
    "                         '%9.6f' % np.mean(zsz), '%9.6f' % np.std(zsz)]\n",
    "        elems = [str(x).rjust(max_field) for x in elems]\n",
    "        print('|%s |' % ' |'.join(elems))\n",
    "    \n",
    "\n",
    "crit_regions_table(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "cregions = list(crit_regions(data, True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB+oAAABICAYAAADRe3woAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAFXElEQVR4nO3dv6qcVRTG4XdUUAwGC4MEEQlYpjMgVjZegFdxeqtgI16AhbUXoTeinbegVQqLzJ8U52wbG4Mag/nWl7V8ni7DFPvHPjNzmBdODmutAAAAAAAAAAA1Xtn7AAAAAAAAAADwf2KoBwAAAAAAAIBChnoAAAAAAAAAKGSoBwAAAAAAAIBChnoAAAAAAAAAKGSoBwAAAAAAAIBCrz3rCYfD4SrJVZK8+fobH927+97mh9rLurnZ+wiburm53vsIm1nX0+9ueN8a3jf9/gb3TW5LklffvbX3ETZ1PfhzL/He2dn14LZk9t0lyc3N2vsIm5p/f3P73n7r/b2PsKn5r73pfXNfe8nsvuVnszV9vd29dXvvI2xm+t3N3xmm9/ns62wN/j5w+t1N7/v1t18erbXuPP34Ya1//6Zz/96H6/uvv3mhB3uZXI7HvY+wqSenuX3T7+40+O6S5HKe3Xca33fa+wibOQ5uS5LbX3y89xE29fj8eO8jbOp4mf3zOfn193j858Jl7yNs6ng+732ETZ2H39/pOLfv80+/3fsImzqenux9hE2dzsP7TnNfe0lyHvzeMv1z4Xyc+ztnklyG/95yGX5/Dz/5bO8jbOYy+H0zSZ6M/9yb/dq7HIe/dw7+viVJzoO3ovPwuzsN/y73qx++/Gmt9eDpx/3pewAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKHtdY/P+FwuEpy9cc/7yf5eetD7eidJI/2PsRGJrcl+rrT19fktkRfd/r6mtyW6OtOX1+T2xJ93enra3Jboq87fX1Nbkv0daevr8ltib7upvd9sNa68/SDzxzq//Tkw+HHtdaDF3qsl8jkvsltib7u9PU1uS3R152+via3Jfq609fX5LZEX3f6+prclujrTl9fk9sSfd3p62tyW6Kvu+l9f8efvgcAAAAAAACAQoZ6AAAAAAAAACj0vEP9d5uc4uUxuW9yW6KvO319TW5L9HWnr6/JbYm+7vT1Nbkt0dedvr4mtyX6utPX1+S2RF93+vqa3Jbo62563196rv+jHgAAAAAAAAD4b/zpewAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAoZKgHAAAAAAAAgEKGegAAAAAAAAAo9Ds2iK02JuwIYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2592x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = [\n",
    "    *sns.color_palette(\"ch:1.0,-.1,light=.7,dark=.4\", 9),\n",
    "    *sns.color_palette(\"ch:2.0,-.1,light=.7,dark=.4\", 9),\n",
    "    *sns.color_palette(\"ch:2.9,-.1,light=.7,dark=.4\", 9),\n",
    "    *sns.color_palette(\"ch:0.5,-.1,light=.7,dark=.4\", 9),\n",
    "]\n",
    "\n",
    "sns.palplot(colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z-score distributions, m=128 0 100000 1.0860145915038812\n",
      "z-score distributions, m=128 1 100000 1.4898752914235662\n",
      "z-score distributions, m=128 2 100000 1.775244407949746\n",
      "z-score distributions, m=128 3 100000 1.2619474040479957\n",
      "z-score distributions, m=128 4 100000 1.3988954645627572\n",
      "z-score distributions, m=128 5 100000 1.4293099840415195\n",
      "z-score distributions, m=128 6 100000 1.457388007034366\n",
      "z-score distributions, m=128 7 100000 1.5833428682501467\n",
      "z-score distributions, m=128 8 100000 1.470101465047176\n",
      "z-score distributions, m=256 0 100000 1.155550064087031\n",
      "z-score distributions, m=256 1 100000 1.4922073675233691\n",
      "z-score distributions, m=256 2 100000 1.7754761820092513\n",
      "z-score distributions, m=256 3 100000 1.390848892630593\n",
      "z-score distributions, m=256 4 100000 1.4526803121198308\n",
      "z-score distributions, m=256 5 100000 1.4782328530024624\n",
      "z-score distributions, m=256 6 100000 1.6457794888760504\n",
      "z-score distributions, m=256 7 100000 1.720981496703932\n",
      "z-score distributions, m=256 8 100000 1.5743692739576993\n",
      "z-score distributions, m=384 0 100000 1.1973841102488112\n",
      "z-score distributions, m=384 1 100000 1.49988259393262\n",
      "z-score distributions, m=384 2 100000 1.7893995977758541\n",
      "z-score distributions, m=384 3 100000 1.4387241480371875\n",
      "z-score distributions, m=384 4 100000 1.474419247403731\n",
      "z-score distributions, m=384 5 100000 1.484486413165005\n",
      "z-score distributions, m=384 6 100000 1.732010655541338\n",
      "z-score distributions, m=384 7 100000 1.8058103891297945\n",
      "z-score distributions, m=384 8 100000 1.6482672294324072\n",
      "z-score distributions, m=512 0 100000 1.2331322903255877\n",
      "z-score distributions, m=512 1 100000 1.4913518557787002\n",
      "z-score distributions, m=512 2 100000 1.7637543383491219\n",
      "z-score distributions, m=512 3 100000 1.5132793986675874\n",
      "z-score distributions, m=512 4 100000 1.4921456241920505\n",
      "z-score distributions, m=512 5 100000 1.5039691830383564\n",
      "z-score distributions, m=512 6 100000 1.7899256094378988\n",
      "z-score distributions, m=512 7 100000 1.8570929715831315\n",
      "z-score distributions, m=512 8 100000 1.6844789082574791\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 770.4x451.44 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1a2f2be80>)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 770.4x451.44 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 770.4x451.44 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 770.4x451.44 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 770.4x451.44 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# z-score dist plot\n",
    "a4_dims = (10.7, 6.27)  #None #(2*11.7, 8.27)\n",
    "ylim = (0.0, 2.2740367425079864)\n",
    "xlim = (1.1900278616886606, 11.30353802905374)\n",
    "\n",
    "\n",
    "def distplots(cregions, colors, desc, fname, xlim=None, ylim=None):\n",
    "    fig, ax = pyplot.subplots(figsize=a4_dims)\n",
    "    \n",
    "    for i in range(len(cregions)):\n",
    "        zs = [abs(x) for x in cregions[i][1][0][4]]\n",
    "        lbl = '-'.join([str(x) for x in cregions[i][0]])\n",
    "        \n",
    "        r = sns.distplot(a=zs, ax=ax, hist=True, norm_hist=False, bins='auto', label=lbl, kde=True, color=colors[i])\n",
    "        lx = r.lines[-1].get_xdata()\n",
    "        ly = r.lines[-1].get_ydata()\n",
    "        maxi = np.argmax(ly)\n",
    "        \n",
    "        ax.annotate(lbl, (lx[maxi], ly[maxi] + 0.1), xytext=(lx[maxi], 2.1),\n",
    "                    rotation=90, ha='center', va='center', \n",
    "                    arrowprops={'arrowstyle':'->', 'connectionstyle':'arc3',\n",
    "                                \"linestyle\":\"--\", \"linewidth\":0.8, \"color\":'0.6'})\n",
    "        print(desc, i, len(zs), ly[maxi])\n",
    "    \n",
    "    plt.legend(loc='upper right')\n",
    "    plt.title(desc if desc else 'z-score distributions')\n",
    "    ax.set_xlabel(\"z-score\")  \n",
    "    ax.set_ylabel(\"Hist\")\n",
    "    if xlim: ax.set_xlim(xlim)\n",
    "    if ylim: ax.set_ylim(ylim)\n",
    "    \n",
    "    plt.savefig(fname, bbox_inches='tight', dpi=400)\n",
    "    #plt.savefig(os.path.splitext(fname)[0] + '.pdf', bbox_inches='tight', dpi=400)\n",
    "    return fig, ax\n",
    "\n",
    "# fig, ax = distplots(cregions, colors, 'z-score distributions', 'zscore-dists.png', xlim, ylim)\n",
    "# print('ylim', ax.get_ylim())\n",
    "# print('xlim', ax.get_xlim())\n",
    "\n",
    "distplots(cregions[0*9:1*9], colors[0*9:1*9], 'z-score distributions, m=128', 'zscore-dists-128.png', xlim, ylim)\n",
    "distplots(cregions[1*9:2*9], colors[1*9:2*9], 'z-score distributions, m=256', 'zscore-dists-256.png', xlim, ylim)\n",
    "distplots(cregions[2*9:3*9], colors[2*9:3*9], 'z-score distributions, m=384', 'zscore-dists-384.png', xlim, ylim)\n",
    "distplots(cregions[3*9:4*9], colors[3*9:4*9], 'z-score distributions, m=512', 'zscore-dists-512.png', xlim, ylim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "a4_dims = (2*11.7, 8.27)\n",
    "fig, ax = pyplot.subplots(figsize=a4_dims)\n",
    "zs = data_filt[1]['zscores']\n",
    "\n",
    "for  i in range(5):\n",
    "    zs = [(x) for x in data_filt[i]['zscores']]\n",
    "    print(len(zs))\n",
    "    sns.distplot(a=zs, ax=ax, hist=True, norm_hist=False, bins='auto')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pvalues\n",
    " - pvalue = probability (in the null hypothesis distribution) to be observed as a value equal to or more extreme than the value observed\n",
    " \n",
    "## computation \n",
    " - Derive CDF -> find 0 regions = extremes\n",
    " - Integrate from 0 regions towards region of increasing integral value. \n",
    " - Once sum of all integrations is alpha, stop. Integrated area is a critical region\n",
    " - Computation for x: integrate until the first integral boundary hits x. pvalue = sum of integrals\n",
    " - Tabulation: for each desired pvalue compute boundaries (4 values) where critical region starts. \n",
    " - pvalue(x): need to do the integration OR function table (\\forall zscores: P(zscore) > 0).\n",
    " - In our case 4 extremes, integrate: \n",
    "   - -\\inf towards 0\n",
    "   - +\\inf towards 0\n",
    "   - 0 towards +\\inf\n",
    "   - 0 towards -\\inf\n",
    "   - 10000 samples, pvalue = 0 -> 1/10000. \n",
    " - absolutize -> we have a new distribution -> 2x more datapoints, 2 tails.  \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.0000000e+00, -9.5000000e-01, -9.0000000e-01, -8.5000000e-01,\n",
       "       -8.0000000e-01, -7.5000000e-01, -7.0000000e-01, -6.5000000e-01,\n",
       "       -6.0000000e-01, -5.5000000e-01, -5.0000000e-01, -4.5000000e-01,\n",
       "       -4.0000000e-01, -3.5000000e-01, -3.0000000e-01, -2.5000000e-01,\n",
       "       -2.0000000e-01, -1.5000000e-01, -1.0000000e-01, -5.0000000e-02,\n",
       "        8.8817842e-16,  5.0000000e-02,  1.0000000e-01,  1.5000000e-01,\n",
       "        2.0000000e-01,  2.5000000e-01,  3.0000000e-01,  3.5000000e-01,\n",
       "        4.0000000e-01,  4.5000000e-01,  5.0000000e-01,  5.5000000e-01,\n",
       "        6.0000000e-01,  6.5000000e-01,  7.0000000e-01,  7.5000000e-01,\n",
       "        8.0000000e-01,  8.5000000e-01,  9.0000000e-01,  9.5000000e-01])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(-1, 1, 1/20.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 0, 0, 1]\n",
      "[0, 0, 0, 0, 0, 0, 1, 0]\n",
      "[0, 0, 0, 0, 0, 0, 1, 1]\n",
      "[0, 0, 0, 0, 0, 1, 0, 0]\n",
      "[0, 0, 0, 0, 0, 1, 0, 1]\n",
      "[0, 0, 0, 0, 0, 1, 1, 0]\n",
      "[0, 0, 0, 0, 0, 1, 1, 1]\n",
      "[0, 0, 0, 0, 1, 0, 0, 0]\n",
      "[0, 0, 0, 0, 1, 0, 0, 1]\n",
      "[0, 0, 0, 0, 1, 0, 1, 0]\n",
      "[0, 0, 0, 0, 1, 0, 1, 1]\n",
      "[0, 0, 0, 0, 1, 1, 0, 0]\n",
      "[0, 0, 0, 0, 1, 1, 0, 1]\n",
      "[0, 0, 0, 0, 1, 1, 1, 0]\n",
      "[0, 0, 0, 0, 1, 1, 1, 1]\n",
      "[0, 0, 0, 1, 0, 0, 0, 0]\n",
      "[0, 0, 0, 1, 0, 0, 0, 1]\n",
      "[0, 0, 0, 1, 0, 0, 1, 0]\n",
      "[0, 0, 0, 1, 0, 0, 1, 1]\n",
      "[0, 0, 0, 1, 0, 1, 0, 0]\n",
      "--------------------------------------------------------------------------------\n",
      "[0, 0, 0, 1, 0, 0, 1, 1]\n",
      "[0, 0, 0, 1, 0, 0, 1, 0]\n",
      "[0, 0, 0, 1, 0, 0, 0, 1]\n",
      "[0, 0, 0, 1, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 1, 1, 1, 1]\n",
      "[0, 0, 0, 0, 1, 1, 1, 0]\n",
      "[0, 0, 0, 0, 1, 1, 0, 1]\n",
      "[0, 0, 0, 0, 1, 1, 0, 0]\n",
      "[0, 0, 0, 0, 1, 0, 1, 1]\n",
      "[0, 0, 0, 0, 1, 0, 1, 0]\n",
      "[0, 0, 0, 0, 1, 0, 0, 1]\n",
      "[0, 0, 0, 0, 1, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 1, 1, 1]\n",
      "[0, 0, 0, 0, 0, 1, 1, 0]\n",
      "[0, 0, 0, 0, 0, 1, 0, 1]\n",
      "[0, 0, 0, 0, 0, 1, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0, 1, 1]\n",
      "[0, 0, 0, 0, 0, 0, 1, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 1]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "counter = [0] * 8\n",
    "MAXV = 2\n",
    "\n",
    "def inc(counter):\n",
    "    global MAXV\n",
    "    ln = len(counter) - 1\n",
    "    while ln >= 0:\n",
    "        counter[ln] = (counter[ln] + 1) % MAXV\n",
    "        if (counter[ln] != 0):\n",
    "            return(counter)\n",
    "        ln-=1\n",
    "    raise ValueError('Overflow')\n",
    "\n",
    "    \n",
    "def dec(counter):\n",
    "    global MAXV\n",
    "    ln = len(counter) - 1\n",
    "    while ln >= 0:\n",
    "        counter[ln] = (counter[ln] - 1) % MAXV\n",
    "        if (counter[ln] != MAXV-1):\n",
    "            return counter\n",
    "        ln-=1\n",
    "    raise ValueError('Underflow')\n",
    "    \n",
    "    \n",
    "for i in range(20):\n",
    "    print(inc(counter))\n",
    "print('-'*80)\n",
    "for i in range(20):\n",
    "    print(dec(counter))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "from booltest import common\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'c2bf37890011dfed'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common.generate_seed(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<DirEntry 'com.apple.launchd.RxJEu0LeJ5'>\n",
      "<DirEntry 'dumps'>\n",
      "<DirEntry 'powerlog'>\n",
      "<DirEntry 'com.apple.launchd.nOl6PZPwzO'>\n",
      "<DirEntry 'com.adobe.reader.rna.0.1f5.DC'>\n",
      "<DirEntry 'com.apple.launchd.w3Z9dzVsCy'>\n",
      "<DirEntry 'com.adobe.reader.rna.225.1f5'>\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import time\n",
    "tmp_files = os.scandir('/tmp')\n",
    "for i in tmp_files:\n",
    "    print(i)\n",
    "    time.sleep(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "810000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3*3*3*10000*3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": []
  }
 ],
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