MusicAM_ExtractingStatistics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "from scipy.stats import wilcoxon, ttest_rel, ttest_ind, ttest_1samp, mannwhitneyu, spearmanr, pearsonr\n",
    "import scipy.io\n",
    "import scipy.io.wavfile\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import os\n",
    "import pickle\n",
    "import os, fnmatch\n",
    "from os import listdir\n",
    "import pandas as pd\n",
    "from pandas import ExcelWriter\n",
    "from pandas import ExcelFile\n",
    "import matplotlib.pyplot as plt\n",
    "from pylab import*\n",
    "from scipy.io import wavfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/deepanshishokeen/Desktop/Capstone/work/amplitude-modulation-analysis-module\n"
     ]
    }
   ],
   "source": [
    "get_ipython().run_line_magic('cd', '/Users/deepanshishokeen/desktop/capstone/work/amplitude-modulation-analysis-module')\n",
    "from am_analysis import am_analysis as ama\n",
    "mpl=matplotlib.pyplot\n",
    "mod_feat = 'power_modulation_spectrogram' # Controls which version of the amplitude modulation spectrogram\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "power_rms = lambda x: np.sqrt(x.mean())\n",
    "rms = lambda x: np.sqrt(np.square(x).mean())\n",
    "dB = lambda y: 10*np.log10(y)\n",
    "objfun = lambda x: dB((np.median(x,0).squeeze()/power_rms(x))+10**(-12)) \n",
    "\n",
    "def TTEST(x,y): \n",
    "    return mannwhitneyu(objfun(x.squeeze()),objfun(y.squeeze())) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stats(mod_spec, f_range=np.array([500,4000]), modf_range=np.array([30,60]), c_range=np.array([20,80]), ttl=None, avg=False, ref=None, hist=False):\n",
    "    mn=mod_spec[mod_feat] # MN is the M x N modulation envelope\n",
    "    amx=mod_spec['freq_mod_axis']\n",
    "    amy=mod_spec['freq_axis']\n",
    "    midx=np.where((amx>=modf_range[0]) & (amx<modf_range[1]))[0]\n",
    "    midy=np.where((amy>=f_range[0]) & (amy<f_range[1]))[0]\n",
    "    if ref is None: \n",
    "        rf=mn\n",
    "    else:\n",
    "        rf=ref\n",
    "    w=TTEST(mn[midy][:,midx,:],rf[midy][:,midx,:])\n",
    "    print(w)\n",
    "    p=w[1]\n",
    "    T=w[0]\n",
    "    #m= p*T\n",
    "    print(p,T)\n",
    "    return p,T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stats_avg_mod(fname='/Users/deepanshishokeen/Desktop/Capstone/work/modspectrum/MozartK448_SonataTwoPianosDMaj.wav', t_range=None, FL=2, FR=1, N=1024):\n",
    "    fs,x = scipy.io.wavfile.read(fname, mmap=True)\n",
    "    win_size = int(fs*FL)\n",
    "    win_shift = int(fs/FR)\n",
    "    if t_range is None:\n",
    "        t_range = [60,120]\n",
    "    sig = x[t_range[0]*fs:t_range[1]*fs,0].reshape(-1,1) # LEFT CHANNEL\n",
    "    sig = sig / rms(sig)# normalize RMS \n",
    "    x_segmented, _, _ = ama.epoching(sig, win_size, win_size-win_shift)\n",
    "    n_segments  = x_segmented.shape[2]\n",
    "    mod_spec = []\n",
    "    for i_segment in range(0,n_segments):\n",
    "        x_tmp = x_segmented[:,:,i_segment]\n",
    "        ms=ama.strfft_modulation_spectrogram(x_tmp, fs, N, int(fs*0.005))\n",
    "        mod_spec.append(ms)\n",
    "    data = []\n",
    "    for m in mod_spec:    \n",
    "        data.append(m[mod_feat][:,:,0])\n",
    "    data = np.array(data)    \n",
    "    nmm = data.mean(0) # average modulation spectrum\n",
    "    mod_spec[0][mod_feat][:,:,0]=nmm\n",
    "    return mod_spec[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rcParams['figure.figsize']=(12,6)\n",
    "def make_reference_mod_spec(x):\n",
    "    y=np.array([r[mod_feat] for r in x]).mean(0)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_values(fname='/Users/deepanshishokeen/Desktop/Capstone/work/modspectrum/MozartK448_SonataTwoPianosDMaj.wav', T=[0,1,2], ttl='Mozart K.448', plotting=True, ref=None):\n",
    "    res = []\n",
    "    gr  = []\n",
    "    for t in T:\n",
    "        res.append(stats_avg_mod(fname=fname, t_range=[t*60,(t+1)*60]))\n",
    "        if plotting:\n",
    "                z= stats(res[t], ref=ref)  \n",
    "                gr.append(z)\n",
    "                m=max(gr)\n",
    "                p = m[0]\n",
    "                T = m[1]\n",
    "    return res,p,T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MannwhitneyuResult(statistic=1740.5, pvalue=0.4989262789424854)\n",
      "0.4989262789424854 1740.5\n",
      "MannwhitneyuResult(statistic=1740.5, pvalue=0.4989262789424854)\n",
      "0.4989262789424854 1740.5\n",
      "MannwhitneyuResult(statistic=1740.5, pvalue=0.4989262789424854)\n",
      "0.4989262789424854 1740.5\n"
     ]
    }
   ],
   "source": [
    "res_448,p,t = get_values('/Users/deepanshishokeen/Desktop/Capstone/work/modspectrum/MozartK448_SonataTwoPianosDMaj.wav')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MannwhitneyuResult(statistic=420.0, pvalue=6.033983115530341e-13)\n",
      "6.033983115530341e-13 420.0\n",
      "MannwhitneyuResult(statistic=1488.0, pvalue=0.08749694638880912)\n",
      "0.08749694638880912 1488.0\n",
      "MannwhitneyuResult(statistic=1210.0, pvalue=0.0021681528383346474)\n",
      "0.0021681528383346474 1210.0\n"
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   ],
   "source": [
    "ref_448 = make_reference_mod_spec(res_448)\n",
    "res,p,t = get_values('/Users/deepanshishokeen/Desktop/Capstone/work/modspectrum/Mozart_PianoSonata16CMaj.wav', ttl='Mozart K545', ref=ref_448)\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "2.7520236810909273e-13 400.0\n",
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      "3.7973954466182726e-21 0.0\n",
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      "3.7973954466182726e-21 0.0\n",
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      "1.0567733607804095e-13 376.0\n",
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      "8.211021109424925e-16 260.0\n",
      "MannwhitneyuResult(statistic=410.0, pvalue=4.080807828406825e-13)\n",
      "4.080807828406825e-13 410.0\n",
      "MannwhitneyuResult(statistic=358.0, pvalue=5.0998033078105116e-14)\n",
      "5.0998033078105116e-14 358.0\n",
      "MannwhitneyuResult(statistic=22.0, pvalue=1.1573014905339642e-20)\n",
      "1.1573014905339642e-20 22.0\n"
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   ],
   "source": [
    "my_list=[]\n",
    "mypath= '/Volumes/LaCie/Preferred Music Corpus/Non-western'\n",
    "files = [f for f in os.listdir(mypath) if f.endswith(\".wav\") and not f.startswith(\"._\")]\n",
    "files\n",
    "\n",
    "z = []\n",
    "new_list=[]\n",
    "slash='/'\n",
    "for j in files:\n",
    "    ttl=j.replace('.wav','')\n",
    "    whole_path=mypath+slash+j\n",
    "    sampFreq, snd = wavfile.read(whole_path)\n",
    "    if snd.shape[0]/sampFreq > 140: \n",
    "        res,p,t=get_values(whole_path,ttl=ttl, ref=ref_448)\n",
    "        z=ttl,p,t\n",
    "        my_list.append(z)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df=pd.DataFrame(my_list, columns=['Title', 'Pvalue', 'Tstatistics' ])\n",
    "df=df.sort_values(by=['Tstatistics'], ascending=False)\n",
    "df.to_excel('/Users/deepanshishokeen/Desktop/Music/get_stats/Non-western.xlsx')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_excel('/Users/deepanshishokeen/Desktop/Music/get_stats/Non-western.xlsx')\n",
    "g1=df.Pvalue\n",
    "g1.shape\n",
    "g2=df.Tstatistics\n",
    "g2.shape\n",
    "\n",
    "k448=plt.scatter(1422,0.04,color='red')#k448\n",
    "rnr=plt.scatter(g2,g1,color='blue')\n",
    "plt.legend((k448, rnr),\n",
    "           ('K448', 'Non-western'),\n",
    "           scatterpoints=1,\n",
    "           fontsize=14)\n",
    "plt.title('Scatter plot for Non-western Music', fontsize=20)\n",
    "plt.xlabel('U statistics', fontsize=18)\n",
    "plt.ylabel('P values', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "MannwhitneyuResult(statistic=1183.0, pvalue=0.0013591362734606345)\n",
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      "MannwhitneyuResult(statistic=626.0, pvalue=1.0121566927114994e-09)\n",
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      "MannwhitneyuResult(statistic=193.0, pvalue=4.168125834456015e-17)\n",
      "4.168125834456015e-17 193.0\n",
      "MannwhitneyuResult(statistic=1670.0, pvalue=0.3531765060813923)\n",
      "0.3531765060813923 1670.0\n",
      "MannwhitneyuResult(statistic=1461.0, pvalue=0.06659348749666197)\n",
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      "0.005976682757569433 1273.0\n",
      "MannwhitneyuResult(statistic=1.0, pvalue=3.995900955029211e-21)\n",
      "3.995900955029211e-21 1.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=5.0, pvalue=4.8978537333060254e-21)\n",
      "4.8978537333060254e-21 5.0\n",
      "MannwhitneyuResult(statistic=597.0, pvalue=3.827291442085215e-10)\n",
      "3.827291442085215e-10 597.0\n",
      "MannwhitneyuResult(statistic=657.0, pvalue=2.7881301121400923e-09)\n",
      "2.7881301121400923e-09 657.0\n",
      "MannwhitneyuResult(statistic=1231.0, pvalue=0.0030759504629190963)\n",
      "0.0030759504629190963 1231.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=38.0, pvalue=2.5800913293807886e-20)\n",
      "2.5800913293807886e-20 38.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n"
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     "output_type": "stream",
     "text": [
      "/Users/deepanshishokeen/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in sqrt\n",
      "  \n",
      "/Users/deepanshishokeen/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py:3405: RuntimeWarning: Invalid value encountered in median for 59 results\n",
      "  r = func(a, **kwargs)\n"
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      "4.655075256185421e-21 4.0\n",
      "MannwhitneyuResult(statistic=132.0, pvalue=2.4714133677405823e-18)\n",
      "2.4714133677405823e-18 132.0\n",
      "MannwhitneyuResult(statistic=122.0, pvalue=1.5395322333209311e-18)\n",
      "1.5395322333209311e-18 122.0\n",
      "MannwhitneyuResult(statistic=1112.0, pvalue=0.00036234629379435396)\n",
      "0.00036234629379435396 1112.0\n",
      "MannwhitneyuResult(statistic=538.0, pvalue=4.916928804128722e-11)\n",
      "4.916928804128722e-11 538.0\n",
      "MannwhitneyuResult(statistic=759.0, pvalue=6.459101951936023e-08)\n",
      "6.459101951936023e-08 759.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=35.0, pvalue=2.221223901814917e-20)\n",
      "2.221223901814917e-20 35.0\n",
      "MannwhitneyuResult(statistic=98.0, pvalue=4.886325659426072e-19)\n",
      "4.886325659426072e-19 98.0\n",
      "MannwhitneyuResult(statistic=3.0, pvalue=4.424204115202177e-21)\n",
      "4.424204115202177e-21 3.0\n",
      "MannwhitneyuResult(statistic=1482.0, pvalue=0.08247347521003057)\n",
      "0.08247347521003057 1482.0\n",
      "MannwhitneyuResult(statistic=1420.0, pvalue=0.042505329463994004)\n",
      "0.042505329463994004 1420.0\n",
      "MannwhitneyuResult(statistic=1597.0, pvalue=0.22074961788016073)\n",
      "0.22074961788016073 1597.0\n",
      "MannwhitneyuResult(statistic=43.0, pvalue=3.309716791602097e-20)\n",
      "3.309716791602097e-20 43.0\n",
      "MannwhitneyuResult(statistic=60.0, pvalue=7.677047428794833e-20)\n",
      "7.677047428794833e-20 60.0\n",
      "MannwhitneyuResult(statistic=231.0, pvalue=2.295863007690679e-16)\n",
      "2.295863007690679e-16 231.0\n",
      "MannwhitneyuResult(statistic=179.0, pvalue=2.1999913818807576e-17)\n",
      "2.1999913818807576e-17 179.0\n",
      "MannwhitneyuResult(statistic=83.0, pvalue=2.365061696464257e-19)\n",
      "2.365061696464257e-19 83.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=179.0, pvalue=2.1999913818807576e-17)\n",
      "2.1999913818807576e-17 179.0\n",
      "MannwhitneyuResult(statistic=525.0, pvalue=3.0872164748816275e-11)\n",
      "3.0872164748816275e-11 525.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=742.0, pvalue=3.904193801308547e-08)\n",
      "3.904193801308547e-08 742.0\n",
      "MannwhitneyuResult(statistic=1112.0, pvalue=0.00036234629379435396)\n",
      "0.00036234629379435396 1112.0\n",
      "MannwhitneyuResult(statistic=15.0, pvalue=8.130408528750288e-21)\n",
      "8.130408528750288e-21 15.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=675.0, pvalue=4.959359535760316e-09)\n",
      "4.959359535760316e-09 675.0\n",
      "MannwhitneyuResult(statistic=1204.0, pvalue=0.0019577248816888674)\n",
      "0.0019577248816888674 1204.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=1.0, pvalue=3.995900955029211e-21)\n",
      "3.995900955029211e-21 1.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=18.0, pvalue=9.460229326676324e-21)\n",
      "9.460229326676324e-21 18.0\n",
      "MannwhitneyuResult(statistic=636.0, pvalue=1.4076441584696402e-09)\n",
      "1.4076441584696402e-09 636.0\n",
      "MannwhitneyuResult(statistic=746.0, pvalue=4.398390256240159e-08)\n",
      "4.398390256240159e-08 746.0\n",
      "MannwhitneyuResult(statistic=489.0, pvalue=8.297764779332898e-12)\n",
      "8.297764779332898e-12 489.0\n"
     ]
    }
   ],
   "source": [
    "my_list=[]\n",
    "mypath= '/Volumes/LaCie/Preferred Music Corpus/Ambient Music'\n",
    "files = [f for f in os.listdir(mypath) if f.endswith(\".wav\") and not f.startswith(\"._\")]\n",
    "files\n",
    "\n",
    "z = []\n",
    "new_list=[]\n",
    "slash='/'\n",
    "for j in files:\n",
    "    ttl=j.replace('.wav','')\n",
    "    whole_path=mypath+slash+j\n",
    "    sampFreq, snd = wavfile.read(whole_path)\n",
    "    if snd.shape[0]/sampFreq > 140: \n",
    "        res,p,t=get_values(whole_path,ttl=ttl, ref=ref_448)\n",
    "        z=ttl,p,t\n",
    "        my_list.append(z)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df=pd.DataFrame(my_list, columns=['Title', 'Pvalue', 'Tstatistics' ])\n",
    "df=df.sort_values(by=['Tstatistics'], ascending=False)\n",
    "df.to_excel('/Users/deepanshishokeen/Desktop/Music/get_stats/Ambience.xlsx')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zrvQT4GHyB8nNyO0zmRyk/4R8zVU6FPh8RNwKzCV/uH4z+f3iNeA/+qjf68WNz78gzwv+OXLv9prk898L4w+tDpo5f6AvX6v7i9xj/Hlyb+tD5DG3r5NvtvsZOcgZ08O+7eRZJ14k39j4GLkHbYeqfEeQp857mTyX8TXA28mBeAJ2r8r/BvKcwM+QA+jqOXTfQg7Q/0L+WrzbHLlFnveR5z5+qjifhUUdvkEeH16Zdx55hoz+tl1bcexLyP+4f0IOBhaTg+0P1djniFr1LbbtVLTNwqLOC8hzIr+ph+N/GZhDDjpSvedA7/OQjwGOI4+DXly87imukVWug97K6uX4txf77V9H3oeKvNsX65fQ83zLNa+n3tq9q/7k4HMWOQh+lTwsptb8yrtXX48V29YGTina6yXycJfHyA9hmkbFHOy9XQe9tSt5asPp5A8Gi8nDuf5ADjqr5wAfTw62f03+G32tuKZ+Sf6GZMM6f1+3FPV5Sx15a86TXWzbnDyV4nNF29xNvqFxlTYlf+j5HvmhU13555Jnntm2H39Tk8kPUnqM/Df1F3KAP72/f+++fI3EV6Q0mPuNJKk5inG8jwE/SCkd0dTKSJJUxTHZkiRJUskMsiVJkqSSGWRLkiRJJXNMtiRJklQye7IlSZKkko2KeSo32mij1NbW1uxqSJIkaZS75557FqWUNu4r36gIstva2pg9e3azqyFJkqRRLiLm15PP4SKSJElSyQyyJUmSpJIZZEuSJEklM8iWJEmSSmaQLUmSJJVsVMwu0pvly5ezaNEiXnjhBZYtW9bs6qhEa665JpttthlrrLFGs6siSZLUzagPsp944gkigra2NtZYYw0iotlVUglSSvzlL3/hiSeeYMqUKc2ujiRJUjejfrjIyy+/zKRJkxg/frwB9igSEWy44Ya8+uqrza6KJEnSKkZ9kA0wZsxqcZqrHT80SZKk4croU5IkSSqZQbYkSZJGhM5OaGuDMWPysrOz2TXq2ai/8VGSJEkjX2cnTJsGixfn9fnz8zpAe3vz6tUTe7KHqSOOOIJ99923W9pPf/pTWlpamD59erf0H/7wh0TEKvkr/eu//isRwfHHH98t/aWXXuILX/gCm222GWuttRZve9vbOPfcc8s7EUmSpBJMn74ywO6yeHFOH47syR4hZs6cyVFHHcU555zDCSecsCL90Ucf5Z/+6Z/Ydddde9z3jjvu4Pzzz2e77bZbZduXv/xlbrrpJmbOnMmUKVO47bbbOProo9loo4047LDDhuRcJEmS+mvBgv6lN5s92SPAN7/5TY466iguuOCCbgH2kiVLOOSQQ5gxYwZbbLFFzX1ffPFF2tvbufDCC1l//fVX2f7rX/+aww47jD322IO2tjY+/elP8+53v5s777xzyM5HkiSpvyZP7l96sxlk16tJI+3/+Z//mZNPPpn/+q//WqVnefr06bS1tXH44Yf3uP+0adP4+Mc/zp577llz+/ve9z6uu+46Hn/8cSAH3ffddx/77LNPeSchSZI0SDNmQEtL97SWlpw+HDlcpB5NGml/4403cv311/PTn/6Uj370o922/eIXv+BHP/oR9913X4/7n3/++cydO5eZM2f2mOdb3/oWxxxzDJMnT2bcuHw5fPvb3+51fLckSVKjdYVc06fnISKTJ+cAezje9AgG2fXpbaT9EP5mt912W1588UX+5V/+hV122YX11lsPgEWLFnHEEUdw2WWX1RwCAvDQQw9xyimn8Ktf/Yrx48f3eIxvf/vb3H777Vx77bW0trZy22238ZWvfIW2tjZ7syVJ0rDS3j58g+pqBtn1aNJI+0033ZRrr72WPffck7333psbb7yR9ddfnwceeICnn36avffee0Xe5cuXAzBu3DgefPBBfvOb37Bo0SK23XbbFXmWLVvGbbfdxnnnncfLL7/M8uXLOfnkk7nyyivZb7/9ANhuu+247777+Ld/+zeDbEmSpAEyyK7H5Ml5iEit9CE2adIkbrnlFvbcc0/22msvbrzxRnbaaSfuv//+bvm++tWv8vzzz/Od73yHKVOmsMkmmzB16tRueT7zmc+w5ZZbcsoppzB+/Hj+9re/sWTJEsaOHdst39ixY1cE7ZIkSeo/g+x6zJjRfUw2NHSk/aabbsott9zCXnvtxZ577skvf/nLbj3UAOuttx5Lly5dkT5+/PgVw0u6TJgwgQ022GBFnnXWWYfddtuNk046iYkTJ9La2sqtt97KpZdeyjnnnNOQc5MkSRqNnF2kHu3t0NEBra0QkZcdHQ0dFLTJJptw8803A7DHHnvw7LPPllLu5Zdfzk477UR7eztbb701Z599NmeeeeYqD62RJElS/SKl1Ow6DNrUqVPT7Nmza26bM2cOW221VYNrpEbx9ytJkhopIu5JKU3tK5892ZIkSVLJDLIlSZKkkjU8yI6IfSLioYiYGxEn9ZLv4xGRIqLP7nhJkiRpOGlokB0RY4HvAB8GtgYOiYita+RbG/gicGcj6ydJkqTho7MT2tpgzJi87Oxsdo3q1+ie7J2BuSmlR1NKrwOXAwfUyHcmcA7waiMrJ0mSpOGhszPPoDx/PqSUl9OmjZxAu9FB9iTg8Yr1J4q0FSJie2DzlNJPG1kxSZIkDR/Tp3d/RAnk9enTm1Of/mp0kB010lbMIRgRY4BzgX/ss6CIaRExOyJmL1y4sMQqSpIkqdkWLOhf+nDT6CD7CWDzivXNgKcq1tcGtgVuiYh5wLuBa2vd/JhS6kgpTU0pTd14442HsMqSJElqtMmT+5c+3DQ6yL4b2DIipkTEeOBg4NqujSmlF1NKG6WU2lJKbcAdwP4ppdpPmpEkSdKoNGMGtLR0T2tpyekjQUOD7JTSUuB44AZgDnBFSunBiDgjIvZvZF1WV6effjrbbrvtoPNIkiQNpfZ26OiA1laIyMuOjpw+EjR8nuyU0s9SSm9NKb05pTSjSDs1pXRtjby7r+692L/97W8ZO3Ysu+yyS8OO+ZWvfIVbb721YceTJEmqpb0d5s2D5cvzcqQE2OATH4e9888/n+OOO44HHniAOXPmNOSYEydOZMMNN2zIsSRJkkYjg+xh7JVXXuGyyy7j6KOP5uMf/zgXXnjhim3z5s0jIrj88svZbbfdWGuttdh+++35/e9/zwMPPMB73/teJkyYwPve9z4ee+yxVcq+4IILmDx5MmuttRYHHnggixYtWrGt1nCRiy++mK233po111yTt771rZx77rksX758xfaIoKOjg4MOOogJEyawxRZbMGvWrG5lPPXUU7S3t7PhhhvS0tLCO9/5Tm6++eYV26+77jp23HFH1lxzTaZMmcL06dN5/fXXB92OkiRJjWaQXadmPHHoqquuorW1le22247DDjuMSy+9lCVLlnTLc9ppp3HiiSfy29/+lvXWW49DDz2UL3zhC8yYMYO77rqLV199lS9+8Yvd9pk3bx6zZs3iJz/5CTfddBMPP/wwRx55ZI/1OP/88znllFM444wzmDNnDv/+7//O1772Nb773e92y3fGGWdwwAEH8Lvf/Y5PfvKTHHnkkcyfPx+Al19+md1224158+ZxzTXXcP/993Pqqaeu2PeGG26gvb2d448/ngcffJCLLrqIq666ilNOOWWwzShJktR4KaUR/9pxxx1TT/7whz/0uK1es2al1NKSUn7eUH61tOT0ofT+978/ff3rX08ppbR8+fLU2tqarrrqqpRSSo899lgC0nnnnbci/3XXXZeAdPXVV69Iu/jii9OECRNWrJ922mlpzJgxaf78+SvSfvWrXyUg/elPf1qRZ5tttlmxffPNN0+XXnppt7qde+65aauttlqxDqSTTjppxfqSJUvSWmutlWbOnJlSSqmjoyNNnDgxLVy4sOa57rrrrumMM87olnbNNdekCRMmpOXLl/fYRmX8fiVJkuoFzE51xKf2ZNehGU8cmjt3LrfffjuHHnookIdjtLe3c8EFF3TLt9122634eZNNNgHg7W9/e7e0l19+mcUVJzBp0iQmV0wy+a53vYsxY8bUHPO9cOFCHn/8cT73uc8xceLEFa+TTjqJRx55pMe6jBs3jo033phnn30WyDdwbrfddmy00UY1z/eee+5hxowZ3Y5x6KGH8vLLL/PnP/+598aSJEkaZsY1uwIjQTOeOHTBBRewbNmybsFw/vAEjz++8sn0a6yxxoqfI6LHtMrx0/3Rtd95553He9/73l7zVh6369hd+3fVvbfjnHbaaRx00EGrbPNhQ5IkaaQxyK7D5MlQDC1eJX0oLF26lB/84AecddZZ7Lvvvt22HXbYYVx88cV8+tOfHnD5Tz75JI8//jibb54fvnnXXXexfPlyttpqq1XybrLJJkyaNIlHHnlkUMfcYYcdmDVrFosWLarZm73DDjvwxz/+kbe85S0DPoYkSdJwYZBdhxkzYNq07kNGhvKJQ9dffz2LFi3i6KOPXmUqvYMPPpjvfe97fOpTnxpw+WuttRaHH3443/jGN3jllVc45phj+OhHP8qWW25ZM//pp5/OF77wBdZbbz0+8pGPsGTJEu69916efPJJTj755LqOeeihh3L22Wdz4IEHctZZZ7HZZptx//33s/baa7PHHntw6qmnsu+++9La2sonPvEJxo0bxwMPPMBdd93FOeecM+BzlSRJagbHZNeh0U8cuvDCC9ljjz1qzlV90EEHMX/+fG666aYBl9/W1sbBBx/Mfvvtx5577skWW2zBxRdf3GP+o446iosuuoiZM2fyjne8g1133ZWOjg6mTJlS9zEnTJjArbfeyqRJk9hvv/3YZpttOO2001YMZ/nQhz7E9ddfz80338zOO+/MzjvvzNlnn91tuIwkSdJIEX2NlR0Jpk6dmmbPrv1gyDlz5tQcBqHRwd+vJElqpIi4J6U0ta989mRLkiRJJTPIliRJkkpmkC1JkiSVzCBbkiRJKtlqEWSPhps7tSp/r5Ikabga9UH2GmuswSuvvNLsamgILFmyhHHjnOpdkiQNP6M+yH7DG97Ak08+yeLFi+35HEWWL1/OM888w7rrrtvsqkiSJK1i1HcDrrPOOgA89dRTLFmypMm1UZkmTJhQ8xHtkiRJzTbqg2zIgXZXsC1JkiQNtVE/XESSJElqNINsSZIkqWQG2ZIkSVLJDLIlSZKkkhlkS5IkqSk6O6GtDcaMycvOzmbXqDyrxewikiRJGl46O2HaNFi8OK/Pn5/XAdrbm1evstiTLUmSpIabPn1lgN1l8eKcPhoYZEuSJKnh5s/vX/pIY5AtSZKkhhs7tn/pI41BtiRJkhpu2bL+pY80BtmSJElquNbW/qWPNAbZkiRJargZM6ClpXtaS0tOHw0MsiVJktRw7e3Q0ZF7riPysqNjdEzfB86TLUmSpCZpbx89QXU1e7IlSZKkkhlkS5IkSSUzyJYkSZJKZpAtSZIklcwgW5IkSSqZQbYkSZJUMoNsSZIkqWQG2ZIkSVLJDLIlSZKkkhlkS5IkSSUzyJYkSZJKZpAtSZIklcwgW5IkSSqZQbYkSZJUMoNsSZIkqWQG2ZIkSVLJDLIlSZKkkhlkS5IkSSUzyJYkSZJKZpAtSZIklcwgW5IkSSqZQbYkSZJUMoNsSZIkqWQG2ZIkSVLJDLIlSZKkkhlkS5IkSSUzyJYkSZJKZpAtSZIklazhQXZE7BMRD0XE3Ig4qcb2YyLi/oi4LyL+NyK2bnQdJUmSpMFoaJAdEWOB7wAfBrYGDqkRRF+WUnp7SumdwDnANxpZR0mSJGmwGt2TvTMwN6X0aErpdeBy4IDKDCmlv1asTgBSA+snSZIkDdq4Bh9vEvB4xfoTwLuqM0XE54EvA+OBPRtTNUmSJKkcje7Jjhppq/RUp5S+k1J6M3Ai8NWaBUVMi4jZETF74cKFJVdTkiRJGrhGB9lPAJtXrG8GPNVL/suBA2ttSCl1pJSmppSmbrzxxiVWUZIkSRqcRgfZdwNbRsSUiBgPHAxcW5khIrasWP0o8HAD6ydJkiQNWkPHZKeUlkbE8cANwFjgopTSgxFxBjA7pXQtcHxE7A0sAZ4HDm9kHSVJkqTBavSNj6SUfgb8rCrt1IqfT2h0nSRJkqQy+cRHSZIkqWQG2ZIkSVLJDLKiNP19AAAdDUlEQVQlSZKkkhlkS5IkSSUzyJYkSZJKZpAtSZIklcwgW5IkSSqZQbYkSZJUMoNsSZIkqWQG2ZIkSVLJDLIlSZKkkhlkS5IkSSUzyJYkSZJKZpAtSZIklcwgW5IkSSqZQbYkSZJUMoNsSZIkqWQG2ZIkSVLJDLIlSZKkkhlkS5IkSSUzyJYkSZJKZpAtSZIklcwgW5IkSSqZQbYkSZJUMoNsSZIkqWQDDrIjYoOI2DEi/q7MCkmSJEkjXV1BdkR8NSLOqlh/PzAPuAt4OCK2HJrqSZIkSSNPvT3ZnwIerVg/B/gdcCDwDHBmyfWSJEmSRqxxdeabBDwMEBEbAzsBe6WUbomI8cC3hqh+kiRJ0ohTb0/2MmB88fP7gVeB24v1hcAGJddLkiRJGrHqDbIfBD4VEROBI4FbU0pLim2bA88OReUkSZKkkaje4SJnAD8B2oElwIcqtn0EuLfkekmSJEkjVl1BdkrphojYCtgBuC+l9EjF5tvIN0FKkiRJov6ebFJKjwGP1Uj/fqk1kiRJkka4uh9GExGTIuIbETE7Ih6NiG2L9C9FxLuGroqSJEnSyFLvw2i2Ae4HDgOeAlpZOdtIK3DCkNROkiRJGoHq7cn+d2AOMAX4ByAqtv0aeHfJ9ZIkSZJGrHrHZL8POCSl9FJEjK3a9gzwxnKrJUmSJI1c9fZkL+9l20bAKyXURZIkSRoV6g2y7wI+08O2T7Dy6Y+SJEnSaq/e4SJnAjdFxC+Ay4AE7B0RJwAfIz9qXZIkSRJ19mSnlG4FDiTf+HgR+cbHs4FdgQNTSncOWQ0lSZKkEaY/D6O5Hrg+It4CvAH4S0rpoSGrmSRJkjRC1R1kd0kpzQXmDkFdJEmSpFGhriA7Ij7dV56U0qWDr44kSZI08tXbk31JD+mp4meDbEmSJIn6g+wpNdI2BPYFDgU+VVqNJEmSpBGu3tlF5td43ZtSOgP4IfDloa2mJEmSGqWzE9raYMyYvOzsbHaNRp5+3/hYw68wyJYkSRoVOjth2jRYvDivz5+f1wHa25tXr5Gm3ic+9ubdwEsllCNJkqQmmz59ZYDdZfHinK761Tu7yKk1kscD2wIfBf6zzEpJkiSpORYs6F+6aqt3uMjpNdJeA+YDM4CzyqqQJEmSmmfy5DxEpFa66ldXkJ1SKmNYiSRJkoa5GTO6j8kGaGnJ6aqfwbMkSZJWaG+Hjg5obYWIvOzo8KbH/uqxJzsi+vWlQErJkTqSJEmjQHu7QfVg9TZcZB7dn+jYl7GDq4okSZI0OvQWZB9J/4JsSZIkSfQSZKeULmlgPSRJkqRRwxsfJUmSpJLV/Vj1iHgDcAjwNmDNqs0ppfTZMismSZIkjVT1PvHxbcAd5JsbJwCLgA2K9eeBF4eqgpIkSdJIU+9wka8DdwGbAAF8GFgLOApYDHys3gNGxD4R8VBEzI2Ik2ps/3JE/CEifh8Rv4yI1nrLliRJkoaDeoPsnYDvkh+lDjAmpbQ0pXQR8G3gP+opJCLGAt8hB+lbA4dExNZV2X4LTE0pbQdcBZxTZx0lSZKkYaHeIHsi8FxKaTl5aMhGFdtmk4PweuwMzE0pPZpSeh24HDigMkNK6eaUUteDPO8ANquzbEmSJGlYqDfInge8sfj5IeCgim37Ai/UWc4k4PGK9SeKtJ58Fvh5nWVLkiRJw0K9s4vcCHwAuBL4BnB5RLwPWAr8PTCjznKiRlrNB95ExKeAqcBuPWyfBkwDmDy5X0+AlyRJkoZUvUH2ycDfAaSUroiIV4BPAi3AN4Hz6yznCWDzivXNgKeqM0XE3sB0YLeU0mvV24t6dAAdAFOnTvXJlJIkSRo26gqyi0D3tYr164DrBnC8u4EtI2IK8CRwMHBoZYaI2B74PrBPSunZARxDkiRJaqq6xmRHxDci4p2DPVhKaSlwPHADMAe4IqX0YEScERH7F9m+Tr7R8sqIuC8irh3scSVJkqRGipT6HmkREc8D65AD4x8Al6WUnhziutVt6tSpafbs2c2uhiRJkka5iLgnpTS1r3z1zi6yCfAJYC5wJjA/Im6KiMMiYsIg6ilJkiSNOnUF2Sml11NKV6eUDgQ2Bb5IfuLjD4BnImLmENZRkiRJGlHq7cleIaX0fErpuymlXYA9gOepunlRkiRJWp3VO4XfCsXwkI8DnwJ2J8+VfXW51ZIkSZJGrnpnFxkTEftERCfwDHARed7s44A3ppQ+MYR1lCRJkkaUenuynwI2Jt/4+DVgZkpp3lBVSpIkSRrJ6g2yrwYuTSndOZSVkSRJkkaDep/4+PmhrogkSZI0WvR7dhFJkiRJvTPIliRJkkpmkC1JkiSVzCBbkiRpNdDZCW1tMGZMXnZ2NrtGo1tdNz5GxEbASymlV4e4PpIkSSpZZydMmwaLF+f1+fPzOkB7e/PqNZr12JMdEWMj4vSIeIH8AJq/RsTVEbFe46onSZKkwZo+fWWA3WXx4pyuodFbT/YxwKnALcDdwBbAx4C/Ap8Z8ppJkiSpFAsW9C9dg9fbmOyjgfNTSnumlE5MKR0EfB74VESMb0z1JEmSNFiTJ/cvXYPXW5C9BXBlVdqPgLFA65DVSJIkSaWaMQNaWrqntbTkdA2N3oLsieShIZX+VizXHprqSJIkqWzt7dDRAa2tEJGXHR3e9DiU+ppdZFJEbFGxPrYi/YXKjCmlR0utmSRJkkrT3m5Q3Uh9BdlX9ZD+4xppY2ukSZIkSaud3oJsZxCRJEmSBqDHIDul9INGVkSSJEkaLXysuiRJklQyg2xJkiSpZAbZkiRJUskMsiVJkqSSGWRLkiRJJTPIliRJkkpmkC1JkiSVzCBbkiRpFOjshLY2GDMmLzs7m12j1Vtfj1WXJEnSMNfZCdOmweLFeX3+/LwO0N7evHqtzuzJliRJGuGmT18ZYHdZvDinqzkMsiVJkka4BQv6l66hZ5AtSZI0wk2e3L90DT2DbEmSpBFuxgxoaeme1tKS09UcBtmSJEkjXHs7dHRAaytE5GVHhzc9NpOzi0iSJI0C7e0G1cOJPdmSJElSyQyyJUmSpJIZZEuSJEklM8iWJEmSSmaQLUmSJJXMIFuSJEkqmUG2JEmSVDKDbEmSJKlkBtmSJElSyQyyJUmSpJIZZEuSJEklM8iWJEmSSmaQLUmSJJXMIFuSJEkqmUG2JEmSVDKDbEmSJKlkBtmSJElSyQyyJUmSpJIZZEuSJEklM8iWJEmSSmaQLUmSJJXMIFuSJEkqmUG2JEmSVDKDbEmSJKlkDQ+yI2KfiHgoIuZGxEk1tr8/Iu6NiKUR8fFG10+SJEkarIYG2RExFvgO8GFga+CQiNi6KtsC4AjgskbWTZIkSSrLuAYfb2dgbkrpUYCIuBw4APhDV4aU0rxi2/IG102SJEkqRaOHi0wCHq9Yf6JIkyRJkkaNRgfZUSMtDaigiGkRMTsiZi9cuHCQ1ZIkSZLK0+gg+wlg84r1zYCnBlJQSqkjpTQ1pTR14403LqVykiRJUhkaHWTfDWwZEVMiYjxwMHBtg+sgSZIkDamGBtkppaXA8cANwBzgipTSgxFxRkTsDxARO0XEE8BBwPcj4sFG1lGSJEkarEbPLkJK6WfAz6rSTq34+W7yMBJJkiRpRPKJj5IkSVLJDLIlSZKkkhlkS5IkSSUzyJYkSZJKZpAtSZLUQJ2d0NYGY8bkZWdns2ukodDw2UUkSZJWV52dMG0aLF6c1+fPz+sA7e3Nq5fKZ0+2JElSg0yfvjLA7rJ4cU7X6GKQLUmS1CALFvQvXSOXQbYkSVKDTJ7cv3SNXAbZkiRJDTJjBrS0dE9racnpGl0MsiVJkhqkvR06OqC1FSLysqPDmx5HI2cXkSRJaqD2doPq1YE92ZIkSVLJDLIlSZKkkhlkS5IkSSUzyJYkSZJKZpAtSZIklcwgW5IkSSqZQbYkSZJUMoNsSZIkqWQG2ZIkSVLJDLIlSZKkkhlkS5IkDVBnJ7S1wZgxednZ2ewaabgY1+wKSJIkjUSdnTBtGixenNfnz8/rAO3tzauXhgd7siVJkgZg+vSVAXaXxYtzumSQLUmSNAALFvQvXasXg2xJkqQBmDy5f+lavRhkS5IkDcCMGdDS0j2tpSWnSwbZkiRJA9DeDh0d0NoKEXnZ0eFNj8qcXUSSJGmA2tsNqlWbPdmSJElSyQyyJUmSpJIZZEuSJEklM8iWJEmSSmaQLUmSVEtnJ7S1wZgxednZ2ewaaQRxdhFJkqRqnZ0wbdrK56bPn5/XwelEVBd7siVJkqpNn74ywO6yeHFOl+pgkC1JklRtwYL+pUtVDLIlSZKqTZ7cv3SpikG2JElStRkzoKWle1pLS06X6mCQLUmSVK29HTo6oLUVIvKyo8ObHlU3ZxeRJEmqpb3doFoDZk+2JEmSVDKDbEmSJKlkBtmSJElSyQyyJUmSpJIZZEuSJEklM8iWJEmSSmaQLUmSJJXMIFuSJEkqmUG2JEkaMTo7oa0NxozJy87OZtdIqs0nPkqSpBGhsxOmTYPFi/P6/Pl5HXwwo4Yfe7IlSdKwVN1rfcIJKwPsLosXw/Tpzaid1Dt7siVJ0rBz3HFw3nmQUl6fP7/nvAsWNKZOUn/Yky1JkoaVzs7uAXZfJk8e2vpIA2GQLUmShpXp0+sPsFtaYMaMoa2PNBAG2ZIkaUj1d0aQ3oZ/bLghtLZCRF52dHjTo4Ynx2RLkqQhM5AZQSZPrj0GOwK++U2Dao0M9mRLkqQhM316/2cEmTEjDwOpFAHHHGOArZHDIFuSJA2ZnoZ+9DYkpL09DwOpHBYycyZ897tDU0dpKBhkS5K0GqhnXPRQPE2xp5k/+poRpL0d5s2D5cvz0h5sjTQND7IjYp+IeCgi5kbESTW2/11E/KjYfmdEtDW6jpIkNUpZgW1v5XSNi54/P8/a0TUuur95BqLW0A9nBNHqoKFBdkSMBb4DfBjYGjgkIrauyvZZ4PmU0luAc4GvNbKO9Yro+7XRRrD33jBuXF4fOxYmTqz/jbTeXoe11+5+3L6OMRQ9FQMts3q/444rv26DrWN/9x2K9m2mns5ntJ1nf9R77qOljUbLeTTTccet/F8wblxeh/IC277KqWdc9EDGTtej1tAPZwTRaiGl1LAX8B7ghor1k4GTq/LcALyn+HkcsAiI3srdcccdUyPlt7DBv1paUpo1q/YxZs3K23vLP2tWSuPG9e8Y9ZTbXwMts9Z+/WmjRtSxv/sORfs2U0/nc+yxo+s8+6Pe3/FouRZGy3k007HH1n5/O/bYlFpba29rbe3fMfoqJ6L29oiVZdSTR1JKwOxUR9wbOW9jRMTHgX1SSkcV64cB70opHV+R54EizxPF+iNFnkU9lTt16tQ0e/bsoa18hYjyymptzWPNqrW11Z6+qDJ/T3kGsk9P9ajHQMscSP0HajDn3Z99h6J9m6mn8xk7FpYtWzV9pJ5nf9T7Ox4t18JoOY9mGjeu9t/L2LF5vHGtf8MReVu9xozpvZzB/E/xdy11FxH3pJSm9pWv0WOya4Wn1W8L9eQhIqZFxOyImL1w4cJSKtcM/b3rujK9tzuz+7tPvWX1Z9++yhxI/QdqMOfdn32Hon2bqad61woYess/mtT7Ox4t18JoOY9m6unvZdmygd8UWG/+rvR6xkU7dloqV6OD7CeAzSvWNwOe6ilPRIwD1gWeqy4opdSRUpqaUpq68cYbD1F1h15/32Ar0+t9E65nn/6+odezb19lDqT+AzWY8+7PvkPRvs3UU73Hju1f/tGk3t/xaLkWRst5NFNPfy9jx5YX2PZVTj3joh07LZWsnjElZb3IY6wfBaYA44HfAdtU5fk8cF7x88HAFX2V65js+o/hmOz+l+2Y7FXPxzHZfZ/7aLkWRst5NFNvY7JTym3Z2prHPre2DrxtyypHUu+oc0x2Q4PsXC8+AvwJeASYXqSdAexf/LwmcCUwF7gL2KKvMhsdZKdUXxC94YYp7bVXSmPH5vUxY1KaMKH+N8B63jBnzUpp4sTux+3rGEPxRjzQMqv367oRaCj+SQzmvPuz72j7R9fT+Yy28+yPes99tLTRaDmPZjr22JX/C8aOXRlgSxp56g2yG3rj41Bp9I2PkiRJWj0N1xsfJUmSpFHPIFuSJEkqmUG2JEmSVDKDbEmSJKlkBtmSJElSyQyyJUmSpJIZZEuSJEklM8iWJEmSSmaQLUmSJJXMIFuSJEkq2ah4rHpELATmN+nwGwGLmnTs1YVtPPRs46FnGzeG7Tz0bOOhZxs3xkDbuTWltHFfmUZFkN1METG7nufXa+Bs46FnGw8927gxbOehZxsPPdu4MYa6nR0uIkmSJJXMIFuSJEkqmUH24HU0uwKrAdt46NnGQ882bgzbeejZxkPPNm6MIW1nx2RLkiRJJbMnW5IkSSqZQfYARcQ+EfFQRMyNiJOaXZ+RKiI2j4ibI2JORDwYEScU6adHxJMRcV/x+kjFPicX7f5QRHyoebUfWSJiXkTcX7Tn7CJtg4i4MSIeLpbrF+kREd8q2vn3EbFDc2s//EXE2yqu1/si4q8R8SWv5cGJiIsi4tmIeKAird/XbUQcXuR/OCIOb8a5DFc9tPHXI+KPRTteExHrFeltEfFKxfV8XsU+OxbvMXOL30M043yGqx7aud/vD8YfPeuhjX9U0b7zIuK+In3or+WUkq9+voCxwCPAFsB44HfA1s2u10h8AZsCOxQ/rw38CdgaOB34So38Wxft/XfAlOL3MLbZ5zESXsA8YKOqtHOAk4qfTwK+Vvz8EeDnQADvBu5sdv1H0qt4j/gz0Oq1POi2fD+wA/BARVq/rltgA+DRYrl+8fP6zT634fLqoY0/CIwrfv5aRRu3VearKucu4D1F+/8c+HCzz204vXpo5369Pxh/9L+Nq7b/O3Bq8fOQX8v2ZA/MzsDclNKjKaXXgcuBA5pcpxEppfR0Sune4ue/AXOASb3scgBweUrptZTSY8Bc8u9DA3MA8IPi5x8AB1akX5qyO4D1ImLTZlRwhNoLeCSl1NtDsryW65BSug14riq5v9fth4AbU0rPpZSeB24E9hn62o8Mtdo4pfSLlNLSYvUOYLPeyijaeZ2U0m9SjlIuZeXvRfR4Lfekp/cH449e9NbGRW/0J4Af9lZGmdeyQfbATAIer1h/gt4DQ9UhItqA7YE7i6Tji68qL+r6OhjbfjAS8IuIuCciphVpm6SUnob8gQd4Q5FuOw/OwXR/I/daLld/r1vbenCOJPfmdZkSEb+NiFsjYtcibRK5XbvYxvXrz/uD1/LA7Qo8k1J6uCJtSK9lg+yBqTU2x2laBiEiJgJXA19KKf0V+B7wZuCdwNPkr3jAth+MXVJKOwAfBj4fEe/vJa/tPEARMR7YH7iySPJabpye2tS2HqCImA4sBTqLpKeBySml7YEvA5dFxDrYxgPV3/cH23ngDqF758eQX8sG2QPzBLB5xfpmwFNNqsuIFxFrkAPszpTSfwGklJ5JKS1LKS0Hzmfl1+i2/QCllJ4qls8C15Db9JmuYSDF8tkiu+08cB8G7k0pPQNey0Okv9etbT0AxQ2i+wLtxdfmFMMX/lL8fA95fPBbyW1cOaTENq7DAN4fvJYHICLGAf8A/KgrrRHXskH2wNwNbBkRU4peq4OBa5tcpxGpGCN1ITAnpfSNivTK8b8fA7ruFL4WODgi/i4ipgBbkm9QUC8iYkJErN31M/mmpgfI7dk108LhwE+Kn68FPl3M1vBu4MWur+fVp269JV7LQ6K/1+0NwAcjYv3i6/gPFmnqQUTsA5wI7J9SWlyRvnFEjC1+3oJ83T5atPPfIuLdxfv6p1n5e1EPBvD+YPwxMHsDf0wprRgG0ohredzg6736SSktjYjjyW/SY4GLUkoPNrlaI9UuwGHA/V3T6gCnAIdExDvJX9HMAz4HkFJ6MCKuAP5A/grz8ymlZQ2v9cizCXBNMQvROOCylNJ/R8TdwBUR8VlgAXBQkf9n5Jka5gKLgc80vsojT0S0AB+guF4L53gtD1xE/BDYHdgoIp4ATgPOph/XbUrpuYg4kxygAJyRUqr3BrRRr4c2Ppk8s8WNxfvGHSmlY8izN5wREUuBZcAxFW15LHAJsBZ5DHflOO7VXg/tvHt/3x+MP3pWq41TShey6n0y0IBr2Sc+SpIkSSVzuIgkSZJUMoNsSZIkqWQG2ZIkSVLJDLIlSZKkkhlkS5IkSSUzyJYkrRARt0TEvJLL3D0iUkQcUWa5kjScGWRL0gBExBG9BY4R0VZsv6TEY54eEQeWUM6Xyg54I+KdRf3ayixXkkYqg2xJGjlOAwYdZANfAo7oYdsHgbcNoMx3kuvXVmPbbeSHOswcQLmSNCL5xEdJ0goppdeHoMzlwKtllytJw5k92ZLUZBGxZjHU4qGIWBwRL0TE/RHx9WJ7W0R0PZ738GIYSqpIIyI+GRHXRsSCiHgtIhZFxI8jYruqYyWgFditspyuYR61xmRHxDYRcWVEPFmU/eeIuDkiPlpsPx24uMh+c0WZlxTba47JjuzoiLgzIl4qXvdHxBn1to0kDVf2ZEtS830HOBK4FDgXGAtsCexZbF8IHEYebvEroKNGGccDzxXb/gy8GZgG3B4RO6SUHi7yHVYcYxEwo2L/hbUqFhEbAv9TrJ4HzAc2AqYC7wKuB/4L2LQ43r8Cc4r8j/Rx3jOBduDOoi4vAH8PfBw4tcjTV9tI0rBkkC1Jzfcx4OcppcNrbUwpvQzMioiZwKMppVk1su1T5FshIi4F7gP+L3BcUdasiPj/gGd6KKfaLsAbgE+mlK7ooX6/j4jfkIPsG1NKt/RVaER8ghxgzwIOL4aUdG2r/Ja117aRpOHK4SKS1HwvAttExLYDLaArwC6GYKwTERuRe6cfIvc4D6ZuAB+OiHUGUU619mL5lcoAG1aM4a48/qDaRpKawSBbkoZW6jsLXwLWB+6PiEci4oKIOKCqR7dXEbF9RPwU+Bs5MF1YvN5elD0gKaVbyUM1jgAWRcTtEfEvEbH1QMssbAk8nVJ6po98g24bSWoG36QkaWBeKZYtPWyfUJWvRymln5CnvjuMPP55L+DHwC0RMb6v/SNiMnmavO2BM8lDLD4IfAB4kEG+1xdDNd4OfBX4C/CPwO8j4vhBFBvU8QFksG0jSc1ikC1JA/NYsdyqh+1d6Y/WU1hK6bmU0qyU0tHAFsA5wK7AAXXs/jFgIvCplNLXUko/TindmFK6Cdiw1uHqqVNV/R5IKZ2TUtof2Ix8U+PZEREDLPMh4E0RsUkdxx5M20hSUxhkS9LA3As8DhwcEW+q3FD0sB5PDjyv662QiBgbEetVpqWUEvDbYnWDik0vVa13WdZVXFXZRwNvrJG/p3Jq1W+D6qEZKaUXyB8yWoA1K8qsrm9vOovlOdXldwXu/WwbSRpWnF1EkgYgpbQ0Io4FriGPF76A3Lu7CfBJYBvgX1NKD/VR1NrA0xFxLTl4fBaYAhwLPE/3IP0OYO+IOBFYkKuRLgd+DiwGZkbEfxb77QJ8pKhT9Xv9HcBnI+JM8nR7y4HrqmcnKXwa+L8RcQ0wF1gC7AZ8CLgipdQ1HObuopzpEbE+8DLwWErpzlonnVK6MiJ+VJS/ZXH+zwNvLcretp9tI0nDikG2JA1QSun6iNgF+H/A4eShGS+TA8Iep7yrshj4D/JY473Jwz6eBq4FzkopPVWR9zjyvNHTyQEowOUppUci4sPkOapPIfds304Ohv+TVR91Pp3cC/x5YD1yD/iUou7VbiGP9d6XPBf2MnIv9leKsrvaYkFEHAmcCHwPWAP4AXkO7J4cSp73+7PkebG7yr5yAG0jScNK5G/eJEmSJJXFMdmSJElSyQyyJUmSpJIZZEuSJEklM8iWJEmSSmaQLUmSJJXMIFuSJEkqmUG2JEmSVDKDbEmSJKlkBtmSJElSyQyyJUmSpJL9/1nFTGDkHJ83AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_excel('/Users/deepanshishokeen/Desktop/Music/get_stats/Ambience.xlsx')\n",
    "g1=df.Pvalue\n",
    "g1.shape\n",
    "g2=df.Tstatistics\n",
    "g2.shape\n",
    "\n",
    "k448=plt.scatter(1422,0.04,color='red')#k448\n",
    "rnr=plt.scatter(g2,g1,color='blue')\n",
    "plt.legend((k448, rnr),\n",
    "           ('K448', 'Ambience'),\n",
    "           scatterpoints=1,\n",
    "           fontsize=14)\n",
    "plt.title('Scatter plot for Ambience Music', fontsize=20)\n",
    "plt.xlabel('U statistics', fontsize=18)\n",
    "plt.ylabel('P values', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/deepanshishokeen/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in sqrt\n",
      "  \n",
      "/Users/deepanshishokeen/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py:3405: RuntimeWarning: Invalid value encountered in median for 59 results\n",
      "  r = func(a, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "3.7973954466182726e-21 0.0\n",
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      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
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      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=616.0, pvalue=7.257294343566827e-10)\n",
      "7.257294343566827e-10 616.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=762.0, pvalue=7.053254496078401e-08)\n",
      "7.053254496078401e-08 762.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n"
     ]
    }
   ],
   "source": [
    "my_list=[]\n",
    "mypath= '/Volumes/LaCie/Preferred Music Corpus/Heavy Metal'\n",
    "files = [f for f in os.listdir(mypath) if f.endswith(\".wav\") and not f.startswith(\"._\")]\n",
    "files\n",
    "\n",
    "z = []\n",
    "new_list=[]\n",
    "slash='/'\n",
    "for j in files:\n",
    "    ttl=j.replace('.wav','')\n",
    "    whole_path=mypath+slash+j\n",
    "    sampFreq, snd = wavfile.read(whole_path)\n",
    "    if snd.shape[0]/sampFreq > 140: \n",
    "        res,p,t=get_values(whole_path,ttl=ttl, ref=ref_448)\n",
    "        z=ttl,p,t\n",
    "        my_list.append(z)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df=pd.DataFrame(my_list, columns=['Title', 'Pvalue', 'Tstatistics' ])\n",
    "df=df.sort_values(by=['Tstatistics'], ascending=False)\n",
    "df.to_excel('/Users/deepanshishokeen/Desktop/Music/get_stats/heavy_metal.xlsx')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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huMb2S5n53VXdiMz8cUSMBj5GcQLadRRvdA6j2B+uysxJq7r8ZsnMP5fjdRFFP39NsR+sQxEe96DYH99WPuVeitflIxHxB4rXfyjFN0POoLjkZVsupbiSzRnl8ts7cZbMvDMiHqA4KXExb7yhWS3l0e8/d7Lt4oj4CMXc7OvLbZ5GESRHUFzDeiuK/bQ1XN5K8YnTf0fEjpRHkTPzvzLz2Yi4gmI6xrSIuIniDeeHKc7lmAYs90VAzZKZ97Nyn7Z8g+IN4p3l79HfKU4kX4fiuwHeVdN2XYpPEWaXf1Mep3ij9WGKk1En139i18DK/C2Uer6uuoahN2/e3rhR/DM+meLo7AyKOb2vUZxYdQPlF4q08dyxFFcHWEDxT+4xiqsH7FTXbjzFP+hXKK5Y8DPgHbRxbWGKk/p+THEi4+vUXS+YYhrBLyiO1i2l7rrZZZvdKeaKPl1uz9yyD9+imH9e23Y2ddfv7eTYtZTrvpjin/XPKULLIoqwt1+D54xv1N+ybpdybOaWfX6C4kjq5m2s/7MU13x+tVxmp7aB9q+j3ofiKO/UcjsWUbwBOrnRftDesjo5du32mQbX+a6pe0c59o+XYzCfN77oaO+6thtTfFHQ7HJffRQ4CxjY0etPEdCSIkwP7cS2fbpsf/VqjMvvO9l+het81/0efa0ck0UUb9D+QvFG+hjqrstelk2jOJFyuXEvx+lM3rh++JMUnxpsQuPrZ+/FKl7nu5PtV7jOd03dCRSXP3yV4o3rDxv1kyKQ/zvFfP4nyu2aSzFt5ySgf91y29xP6OTfQm/eevotMqs+h0eSOi8iWij+yV6SmeO7tTPqMaL4evNxwIcy8zfd3B1J6jTnfEuS1igRMYJiesYjNLhuuyT1ZM75liStESLi4xRX0zia4prhX0w/vpW0hjF8S5LWFBMoLsf4JPCvmXltN/dHklaac74lSZKkijjnW5IkSapIr552Mnjw4GxpaenubkiSJKmXu+++++Zl5pCO2vXq8N3S0sLUqVO7uxuSJEnq5SLi8c60c9qJJEmSVBHDtyRJklQRw7ckSZJUEcO3JEmSVJHKw3dE7B8RMyJiZkSc2qB+QERcWdZPiYiWsrwlIv4WEdPK2w+q7rskSZK0Oiq92klE9AXOAz4MzAHujYjJmflwTbMTgBczc5uIOBr4OnBUWfdoZr67yj5LkqTeb+nSpcybN4+XXnqJ119/vbu7ox6mb9++bLjhhgwePJg+fVbv2HXVlxrcFZiZmbMAIuIKYDRQG75HA18uf74G+G5ERJWdlCRJa5c5c+YQEbS0tLDOOutg9FCrzGTx4sU899xzzJkzhy222GK1llf1tJNhwJM1j+eUZQ3bZOYSYAGwSVm3ZUT8MSJuj4g9urqzkiRp7fDKK68wbNgw+vfvb/DWciKC/v37M2zYMF555ZXVXl7VR74b7c3ZyTbPAFtk5gsRsTNwXUTskJl/Xe7JEROACcBqvzORJElrj9WdTqDerVn7R9V72RxgRM3j4cDTbbWJiH7AIGB+Zr6amS8AZOZ9wKPAdvUryMyJmTkqM0cNGdLhN3xKkiRJlak6fN8LbBsRW0ZEf+BoYHJdm8nAuPLnI4DfZmZGxJDyhE0iYitgW2BWRf2WJEmSVlul004yc0lEnALcCPQFLsrM6RHxFWBqZk4GLgQui4iZwHyKgA6wJ/CViFgCvA6clJnzq+y/JEmStDoqn9yUmTdk5naZuXVmnlmWfakM3mTm3zPzyMzcJjN3bb0ySmZem5k7ZOa7MnOnzPxF1X2XJEnqScaPH8/BBx+8XNkvf/lLBg4cyOmnn75c+U9+8hMiYoX2tc466ywiglNOOWW58oULF/LP//zPDB8+nHXXXZe3vvWtnHPOOc3bkLVI1SdcSpIkqYtcdtllfPKTn+Tss8/m05/+9LLyWbNm8W//9m/ssUfbF4u7++67Of/883nnO9+5Qt1nP/tZbrnlFi677DK23HJL7rjjDk488UQGDx7Mscce2yXb0lt5Wq8kSVIvcO655/LJT36SCy64YLngvXjxYsaMGcOZZ57JVltt1fC5CxYsYOzYsVx44YVstNFGK9T/4Q9/4Nhjj+WDH/wgLS0tfOITn+C9730vU6ZM6bLt6a0M35IkSc0yaRK0tECfPsX9pEmVrPaLX/wip512Gj/96U9XOBJ9+umn09LSwrhx49p4NkyYMIEjjjiCvffeu2H97rvvzi9+8QuefLL4upY//OEPTJs2jf333795G7GWcNqJJElSM0yaBBMmwKJFxePHHy8eA4wd22Wrvfnmm7n++uv55S9/yUEHHbRc3U033cSVV17JtGnT2nz++eefz8yZM7nsssvabPPtb3+bk046iS222IJ+/Yr4+J3vfKfd+eNqzCPfkiRJzXD66W8E71aLFhXlXWjHHXdk66235owzzuCll15aVj5v3jzGjx/PJZdc0nAqCcCMGTP4whe+wKRJk+jfv3+b6/jOd77DnXfeyeTJk7nvvvs455xz+NznPsevf/3rpm9Pb+eRb0mSpGZ44omVK2+SzTbbjMmTJ7P33nvzoQ99iJtvvpmNNtqIhx56iGeeeYYPfehDy9ouXboUgH79+jF9+nTuuusu5s2bx4477riszeuvv84dd9zBD37wA1555RWWLl3KaaedxtVXX80hhxwCwDvf+U6mTZvGN7/5TaeerCTDtyRJUjNssUUx1aRReRcbNmwYt912G3vvvTf77LMPN998M7vssgsPPvjgcu3+4z/+gxdffJHzzjuPLbfckqFDhzJq1Kjl2hx33HFsu+22fOELX6B///68/PLLLF68mL59+y7Xrm/fvsvCvDrP8C1JktQMZ565/JxvgIEDi/IKbLbZZtx2223ss88+7L333vzmN79Z7og2wIYbbsiSJUuWlffv358NN9xwuTbrrbceG2+88bI2G2ywAR/4wAc49dRTWX/99Rk5ciS33347l156KWeffXYl29abOOdbkiSpGcaOhYkTYeRIiCjuJ07s0pMt6w0dOpRbb70VgA9+8IM8//zzTVnuFVdcwS677MLYsWPZfvvt+drXvsZXv/rVFb6MRx2LzOzuPnSZUaNG5dSpU7u7G5IkqYd75JFHePvb397d3VAP195+EhH3ZeaohpU1PPItSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkARHBNddc06XrMHxLkiStocaPH8/BBx+8QvnUqVOJCGbPnl19p1ZSS0sLEcHll1++Qt2uu+5KRPDNb36z08u77bbbiAjmzZvXzG42jeFbkiRJ3WrEiBFceOGFy5U99NBDTJ8+nU022aSbetU1DN+SJElrgYcffpiDDjqIN7/5zWy66aaMGTOGZ599dln9vffey7777svgwYPZYIMN2H333bnrrruW1Y8ZM4aPfvSjyy1z6dKljBgxgnPOOYdLL72UTTbZhFdffXW5NmPHjuXQQw9tt28f//jHueuuu5g1a9aysgsvvJAjjjiC9ddff7m2r732Gp///OcZPnw46623Hrvssgs33ngjALNnz+aDH/wgAEOGDCEiGD9+PAC//vWv2WOPPdhoo43YeOON2W+//XjkkUc6OXrNY/iWJElqkkmToKUF+vQp7idN6u4eFZ555hn23HNPdtxxR+655x5uueUWFi5cyKGHHsrSpUsBePnllzn22GP53e9+xz333MO73/1uDjzwwGXTN4455hiuv/56XnrppWXLvf3223nmmWcYM2YMRx55JEuXLuXnP//5svoFCxbws5/9jBNOOKHd/g0ePJhDDjmEH/3oR0ARsC+//PKGzzvuuOO4/fbb+fGPf8yDDz7IuHHjOOSQQ3jggQcYMWIE1157LQDTp0/nmWee4dxzzwXglVde4TOf+Qz33HMPt912G4MGDeKQQw7htddeW42RXQWZ2WtvO++8c0qSJHXk4YcfXu1lXH555sCBmfDGbeDAoryrjBs3Lvv27Zvrrbfecrd11103gXzssccyM/OLX/xi7r333ss9d/78+QnklClTGi576dKl+Za3vCUvu+yyzMxcvHhxDhkyJC+44IJlbU444YTcd999lz0++eSTc7/99lv2+Hvf+14OHTo0Fy9e3OY2jBw5Mr/xjW/kDTfckCNGjMjXX389r7766txmm22Wq8/MnDlzZkZEPv7448stY/To0fmP//iPmZl56623JpBz585td+wWLlyYffr0yd/97nfLyoC8+uqr23xOe/sJMDU7kU898i1JktQEp58OixYtX7ZoUVHelfbcc0+mTZu23O3HP/7xcm3uu+8+7rjjDtZff/1ltxEjRgDw6KOPAvD888/zqU99iu22245Bgwbx5je/meeff54nnngCgH79+nHUUUcxqTyc/+qrr3LttddyzDHHLFvPiSeeyM0338ycOXMAuOiiixg3bhz9+vXrcDv2228/MpObb76ZCy+8kOOPP36FNvfffz+Zyfbbb7/ctlx//fXLtqMtjz76KB//+MfZeuut2WCDDRg6dChLly5dtn1V6XgkJEmS1KG2MlxXZ7uBAweyzTbbLFdWOzUEirnZBx10UMOrhgwdOhSAcePG8dxzz3HOOefQ0tLCgAED2GeffZablnHMMcfwD//wDzz11FNMmTKF1157jcMPP3xZ/bve9S522mknLr74Yg477DCmTp3a8ComjfTp04dx48Zx1llncffdd69wAmbrdkQE9957L+uss85ydeuuu267yz/kkEMYNmwYP/zhDxk2bBj9+vVj++23r3zaieFbkiSpCbbYAh5/vHF5d9tpp5246qqrGDly5AqhtdXvf/97vv3tb3PQQQcB8Nxzz/HMM88s12a33XZj66235ic/+Ql33XUXhx122AonRJ544omcffbZzJs3j/e///289a1v7XQ/jz/+eM466ywOPPBANt988xXq3/Oe95CZPPvss8tOrKzXv39/AF5//fVlZS+88AKPPPII55133rLn3X///SxZsqTTfWsWp51IkiQ1wZlnwsCBy5cNHFiUd7eTTz6ZBQsWcNRRRzFlyhRmzZrFLbfcwoQJE3j55ZcB2G677bj88st5+OGHuffeezn66KOXBdlaY8eO5YILLuD6669fbspJq9arqHz/+9/v8ETLeltttRXz5s3j6quvbli/3XbbMXbsWMaPH88111zDrFmzmDp1Kt/85jf56U9/CsDIkSOJCK6//nrmzp3LwoUL2WijjRg8eDDnn38+M2fO5Pbbb+ekk07q1HSYZjN8S5IkNcHYsTBxIowcCRHF/cSJRXl323zzzbnzzjvp06cP+++/PzvssAMnn3wyAwYMYMCAAUAxP3vhwoXsvPPOHH300Rx//PG0tLSssKxjjjmGGTNmMGjQID784Q+vUP/mN7+Zj33sY/Tv35+PfexjK93XjTfeuN0pJD/60Y847rjj+Pd//3fe9ra3cfDBB3PHHXcwcuRIAIYNG8YZZ5zB6aefztChQznllFPo06cPV155JX/605/YcccdOfnkk/nqV7+6bNurFMXJmb3TqFGjcurUqd3dDUmS1MM98sgjvP3tb+/ubvQaBxxwAMOHD+f888/v7q40VXv7SUTcl5mjOlqGc74lSZLUFPPnz+eWW27hpptu4oEHHuju7vRIhm9JkiQ1xU477cT8+fM566yz2HHHHbu7Oz2S4VuSJElNMXv27O7uQo/nCZeSJElSRQzfkiRJQG++CIVWX7P2D8O3JEla662zzjr87W9/6+5uqAf729/+1uYXFK0Mw7ckSVrrbbrppjz11FMsWrTII+BaTmayaNEinnrqKTbddNPVXp4nXEqSpLXeBhtsAMDTTz/N4sWLu7k36mnWWWcdhg4dumw/WR2Gb0mSJIoA3oxwJbXHaSeSJElSRQzfkiRJUkUM35IkSVJFDN+SJElSRQzfkiRJUkUM35IkSVJFDN+SJElSRQzfkiRJUkUM35IkSVJFDN+SJElSRQzfkiRJUkUqD98RsX9EzIiImRFxaoP6ARFxZVk/JSJa6uq3iIiFEfG5qvosSZIkNUOl4Tsi+gLnAQcA2wNjImL7umYnAC9m5jbAOcBOEeI6AAAd+ElEQVTX6+rPAX7V1X2VJEnSGmLSJGhpgT59ivtJk7q7R22q+sj3rsDMzJyVma8BVwCj69qMBi4pf74G2CciAiAiDgNmAdMr6q8kSZJ6skmTYMIEePxxyCzuJ0zosQG86vA9DHiy5vGcsqxhm8xcAiwANomI9YDPA2dU0E9JkiStCU4/HRYtWr5s0aKivAeqOnxHg7LsZJszgHMyc2G7K4iYEBFTI2Lq3LlzV7GbkiRJWiM88cTKlXezqsP3HGBEzePhwNNttYmIfsAgYD6wG3B2RMwGPgN8ISJOqV9BZk7MzFGZOWrIkCHN3wJJkiT1HFtssXLl3azq8H0vsG1EbBkR/YGjgcl1bSYD48qfjwB+m4U9MrMlM1uA/wXOyszvVtVxSZIk9UBnngkDBy5fNnBgUd4DVRq+yzncpwA3Ao8AV2Xm9Ij4SkQcWja7kGKO90zgs8AKlyOUJEmSABg7FiZOhJEjIaK4nzixKO+BIrN+ynXvMWrUqJw6dWp3d0OSJEm9XETcl5mjOmrnN1xKkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVMXxLkiRJFTF8S5IkSRUxfEuSJEkVqTx8R8T+ETEjImZGxKkN6gdExJVl/ZSIaCnLd42IaeXtgYg4vOq+S5IkSauj0vAdEX2B84ADgO2BMRGxfV2zE4AXM3Mb4Bzg62X5Q8CozHw3sD/ww4joV03PJUmSpNVX9ZHvXYGZmTkrM18DrgBG17UZDVxS/nwNsE9ERGYuyswlZfmbgKykx5IkSVKTVB2+hwFP1jyeU5Y1bFOG7QXAJgARsVtETAceBE6qCeOSJElSj1d1+I4GZfVHsNtsk5lTMnMHYBfgtIh40woriJgQEVMjYurcuXNXu8OSJElSs6xy+I6IjSNi54gYsBJPmwOMqHk8HHi6rTblnO5BwPzaBpn5CPAKsGP9CjJzYmaOysxRQ4YMWYmuSZIkSV2rU+E7Iv4jIv675vGewGzgHuAvEbFtJ9d3L7BtRGwZEf2Bo4HJdW0mA+PKn48AfpuZWT6nX7n+kcBbyz5IkiRJa4TOHvk+BphV8/hs4AHgMOA54KudWUg5R/sU4EbgEeCqzJweEV+JiEPLZhcCm0TETOCzQOvlCHcHHoiIacDPgH/KzHmd7L8kSZLU7Tp7qb5hwF8AImIIxZzrfTLztvII9rc7u8LMvAG4oa7sSzU//x04ssHzLgMu6+x6JEmSpJ6ms0e+Xwf6lz/vCfwduLN8PBfYuMn9kiRJknqdzobv6cAxEbE+cDxwe2YuLutGAM93ReckSZKk3qSz006+AvwcGAssBvarqTsQuL/J/ZIkSZJ6nU6F78y8MSLeDuwETMvMR2uq76A4+VKSJElSOzp75JvMfAx4rEH5D5vaI0mSJKmX6vSX7ETEsIj4VvntkbMiYsey/DMRsVvXdVGSJEnqHTr7JTs7AA8Cx1J8I+VI3rj6yUjg013SO0mSJKkX6eyR7/+h+FKcLYGPAFFT9wfgvU3ulyRJktTrdHbO9+7AmMxcGBF96+qeA97S3G5JkiRJvU9nj3wvbaduMPC3JvRFkiRJ6tU6G77vAY5ro+5jvPFtl5IkSZLa0NlpJ18FbomIm4AfAwl8KCI+DRxO8ZXzkiRJktrRqSPfmXk7cBjFCZcXUZxw+TVgD+CwzJzSZT2UJEmSeomV+ZKd64HrI2IbYFPghcyc0WU9kyRJknqZTofvVpk5E5jZBX2RJEmSerVOhe+I+ERHbTLz0tXvjiRJktR7dfbI98VtlGfNz4ZvSZIkqR2dDd9bNijbBDgY+DhwTNN6JEmSJPVSnQrfmfl4g+LHgfsjIoDPUoRwSZIkSW3o7JfstOd3wEFNWI4kSZLUqzUjfL8XWNiE5UiSJEm9WmevdvKlBsX9gR0pjnp/t5mdkiRJknqjzp5w+eUGZa9SzPs+E/jvZnVIkiRJ6q06e8JlM6anSJIkSWs1Q7UkSZJUkTaPfEfEFiuzoMx8YvW7I0mSJPVe7U07mc3y32DZkb6r1xVJkiSpd2svfB/PyoVvSZIkSe1oM3xn5sUV9kOSJEnq9TzhUpIkSapIZ6/zTURsCowB3gq8qa46M/OEZnZMkiRJ6m06+w2XbwXupjipcj1gHrBx+fhFYEFXdVCSJEnqLTo77eQbwD3AUCCAA4B1gU8Ci4DDu6R3kiRJUi/S2WknuwAnUXylPECfzFwCXBQRg4H/BT7YBf2TJEmSeo3OHvleH5ifmUspppgMrqmbShHOJUmSJLWjs+F7NvCW8ucZwJE1dQcDLzWxT5IkSVKv1NnwfTPw4fLnbwHHRcSMiJgOfBq4qCs6J0mSJPUmnZ3zfRowACAzr4qIvwFHAQOBc4Hzu6Z7kiRJUu/RqfCdma/yxsmWZOYvgF90VackSZKk3qhT004i4lsR8e6u7owkSZLUm3V2zvdxwH0R8VBE/FtEDOvKTkmSJEm9UWfD91DgY8BM4KvA4xFxS0QcGxHrdVnvJEmSpF6kU+E7M1/LzGsz8zBgM+BfKL7h8hLguYi4rAv7KEmSJPUKnT3yvUxmvpiZ38vM91N8q+WLwMeb3jNJkiSpl+nspQaXKaeZHAEcA+wFLAGubW63JEmSpN6ns1c76RMR+0fEJOA5ii/VGQD8E/CWzPxYF/ZRkiRJ6hU6e+T7aWAIxQmXXwcuy8zZXdUpSZIkqTfqbPi+Frg0M6d0ZWckSZKk3qyz33B5cld3RJIkSertVvpqJ5IkSZJWjeFbkiRJqkjl4bu8asqMiJgZEac2qB8QEVeW9VMioqUs/3BE3BcRD5b3e1fdd0mSJGl1VBq+I6IvcB5wALA9MCYitq9rdgLwYmZuA5xDcXUVgHnAIZn5DmAc4LdqSpIkaY3S2et8D46INzVhfbsCMzNzVma+BlwBjK5rM5ria+sBrgH2iYjIzD9m5tNl+XTgTRExoAl9kiRJkirRZviOiL4R8eWIeInii3X+GhHXRsSGq7G+YcCTNY/nlGUN22TmEmABsEldm48Cf8zMV1ejL5IkSVKl2rvU4EnAl4DbgHuBrYDDgb8Cx63i+qJBWa5Mm4jYgWIqyr4NVxAxAZgAsMUWW6xaLyVJkqQu0N60kxOB8zNz78z8fGYeCZwMHBMR/VdxfXOAETWPh1N8e2bDNhHRDxgEzC8fDwd+BnwiMx9ttILMnJiZozJz1JAhQ1axm5IkSVLztRe+twKuriu7EugLjFzF9d0LbBsRW5YB/mhgcl2byRQnVAIcAfw2M7Oc7nI9cFpm3rmK65ckSZK6TXvhe32KKSa1Xi7v37wqKyvncJ8C3Ag8AlyVmdMj4isRcWjZ7EJgk4iYCXwWaL0c4SnANsAXI2Jaedt0VfohSZIkdYfIrJ9yXVZELKU4sfGBmuK+wAyKK5JMr22fmbO6qI+rbNSoUTl16tTu7oYkSZJ6uYi4LzNHddSuvRMuobjUXyPXNSjr22GvJEmSpLVYe+F7Va9oIkmSJKmBNsN3Zl7SVp0kSZKklVfp18tLkiRJazPDtyRJklQRw7ckSZJUEcO3JEmSVBHDtyRJklQRw7ckSZJUEcO3JEmSVBHDtyRJklQRw7ckSZJUEcO3JEmSVBHDtyRJklQRw7ckSZJUEcO3JEmSVBHDtyRJklQRw7ckSZJUEcO3JEmSVBHDtyRJklQRw7ckSZJUEcO3JEmSVBHDtyRJklQRw7ckSZJUEcO3JEmSVBHDtyRJklQRw7ckSZJUEcO3JEmSVBHDtyRJklQRw7ckSZJUEcO3JEmSVBHDtyRJklQRw7ckSZJUEcO3JEmSVBHDtyRJklQRw7ckSZJUEcO3JEmSVBHDtyRJklQRw7ckSZJUEcO3JEmSVBHDtyRJklQRw7ckSZJUEcO3JEmSVBHDtyRJklQRw7ckSZJUEcO3JEmSVBHDtyRJklQRw7ckSZJUEcO3JEmSVBHDtyRJklQRw7ckSZJUkcrDd0TsHxEzImJmRJzaoH5ARFxZ1k+JiJayfJOIuDUiFkbEd6vutyRJkrS6Kg3fEdEXOA84ANgeGBMR29c1OwF4MTO3Ac4Bvl6W/x34IvC5irorSZIkNVXVR753BWZm5qzMfA24Ahhd12Y0cEn58zXAPhERmflKZv6eIoRLkiRJa5yqw/cw4Mmax3PKsoZtMnMJsADYpJLeSZIkSV2o6vAdDcpyFdq0vYKICRExNSKmzp07d6U6J0mSJHWlqsP3HGBEzePhwNNttYmIfsAgYH5nV5CZEzNzVGaOGjJkyGp2V5IkSWqeqsP3vcC2EbFlRPQHjgYm17WZDIwrfz4C+G1mdvrItyRJktRT9atyZZm5JCJOAW4E+gIXZeb0iPgKMDUzJwMXApdFxEyKI95Htz4/ImYDGwD9I+IwYN/MfLjKbZAkSZJWVaXhGyAzbwBuqCv7Us3PfweObOO5LV3aOUmSJKkL+Q2XkiRJUkUM35IkSVJFDN+SJElSRQzfkiRJUkUM35IkSVJFDN+SJElSRQzfkiRJUkUM35IkSVJFDN+SJElSRQzfkiRJUkUM35IkSVJFDN+SJElSRQzfkiRJUkUM35IkSVJFDN+SJElSRQzfkiRJUkUM35IkSVJFDN+SJElSRQzfkiRJUkUM35IkSVJFDN+SJElSRQzfkiRJUkUM35IkSVJFDN+SJElSRQzfkiRJUkUM35IkSVJFDN+SJElSRQzfkiRJUkUM35IkSVJFDN+SJElSRQzfkiRJUkUM35IkSVJFDN+SJElSRQzfkiRJUkUM35IkSVJFDN+SJElSRQzfkiRJUkUM35IkSVJFDN+SJEldaNIkaGmBPn2K+0mTurtH6k79ursDkiRJvdWkSTBhAixaVDx+/PHiMcDYsd3XL3Ufj3xLkiR1kdNPfyN4t1q0qCjX2snwLUmS1EWeeGLlytX7Gb4lSZK6yBZbrFy5ej/DtyRJUhc580wYOHD5soEDi3KtnQzfkiRJXWTsWJg4EUaOhIjifuJET7Zcm3m1E0mSpC40dqxhW2/wyLckSZJUEcO3JEmSVBHDtyRJklQRw7ckSZJUEcO3JEmSVJHKw3dE7B8RMyJiZkSc2qB+QERcWdZPiYiWmrrTyvIZEbFflf2WJEmSVlel4Tsi+gLnAQcA2wNjImL7umYnAC9m5jbAOcDXy+duDxwN7ADsD3yvXF6PErHirU8faGmBSZO6u3eqN2lS8dqsza+RY1CNtXmc1+ZtX1P5mkldp+rrfO8KzMzMWQARcQUwGni4ps1o4Mvlz9cA342IKMuvyMxXgcciYma5vLsq6nuHIhqXZ8Ljj8OECcVjr/XZM0yaVLwmixYVj9fG18gxqMbaPM5r87avqXzNpK4VmVndyiKOAPbPzE+Wj48FdsvMU2raPFS2mVM+fhTYjSKQ352Zl5flFwK/ysxr2lrfqFGjcurUqV21OStoK3zXGjkSZs/u8q6oE1pain8q9dam18gxqMbaPM5r87avqXzNpFUTEfdl5qiO2lU957tRPK1P/2216cxziYgJETE1IqbOnTt3FbrYtZ54ort7oFZtvRZr02vkGFRjbR7ntXnb11S+ZlLXqjp8zwFG1DweDjzdVpuI6AcMAuZ38rlk5sTMHJWZo4YMGdLErjfHFlt0dw/Uqq3XYm16jRyDaqzN47w2b/uaytdM6lpVh+97gW0jYsuI6E9xAuXkujaTgXHlz0cAv81ibsxk4OjyaihbAtsC91TU76YYOBDOPLO7e6FWZ55ZvCa11rbXyDGoxto8zmvztq+pfM2krlVp+M7MJcApwI3AI8BVmTk9Ir4SEYeWzS4ENilPqPwscGr53OnAVRQnZ/4aODkzX6+y/x1pa/p8RDFXbuJET1bpScaOLV6TkSPX3tfIMajG2jzOa/O2r6l8zaSuVekJl1Wr+oRLSZIkrZ166gmXkiRJ0lrL8C1JkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVxPAtSZIkVcTwLUmSJFXE8C1JkiRVJDKzu/vQZSJiLvB4N61+MDCvm9bdWzmmXcNx7RqOa/M5pl3DcW0+x7Rr9PRxHZmZQzpq1KvDd3eKiKmZOaq7+9GbOKZdw3HtGo5r8zmmXcNxbT7HtGv0lnF12okkSZJUEcO3JEmSVBHDd9eZ2N0d6IUc067huHYNx7X5HNOu4bg2n2PaNXrFuDrnW5IkSaqIR74lSZKkihi+mywi9o+IGRExMyJO7e7+rEkiYkRE3BoRj0TE9Ij4dFm+cUTcHBF/Ke83KssjIr5djvWfImKn7t2Cnisi+kbEHyPil+XjLSNiSjmmV0ZE/7J8QPl4Zlnf0p397skiYsOIuCYi/lzus+9zX109EfGv5e/+QxHxk4h4k/vqyouIiyLi+Yh4qKZspffNiBhXtv9LRIzrjm3pSdoY12+UfwP+FBE/i4gNa+pOK8d1RkTsV1NuTqjRaFxr6j4XERkRg8vHvWJ/NXw3UUT0Bc4DDgC2B8ZExPbd26s1yhLg/2Xm24H3AieX43cq8JvM3Bb4TfkYinHetrxNAL5ffZfXGJ8GHql5/HXgnHJMXwROKMtPAF7MzG2Ac8p2auxc4NeZ+TbgXRTj6766iiJiGPAvwKjM3BHoCxyN++qquBjYv65spfbNiNgY+E9gN2BX4D9bA/ta7GJWHNebgR0z853A/wGnAZT/u44Gdiif873yIIg5YUUXs+K4EhEjgA8DT9QU94r91fDdXLsCMzNzVma+BlwBjO7mPq0xMvOZzLy//PllijAzjGIMLymbXQIcVv48Grg0C3cDG0bEZhV3u8eLiOHAQcAF5eMA9gauKZvUj2nrWF8D7FO2V42I2ADYE7gQIDNfy8yXcF9dXf2AdSOiHzAQeAb31ZWWmXcA8+uKV3bf3A+4OTPnZ+aLFCFzhYC0Nmk0rpl5U2YuKR/eDQwvfx4NXJGZr2bmY8BMioxgTqjTxv4KxZvqfwdqT07sFfur4bu5hgFP1jyeU5ZpJZUfIb8HmAIMzcxnoAjowKZlM8e7c/6X4g/Y0vLxJsBLNf8wasdt2ZiW9QvK9lreVsBc4EfldJ4LImI93FdXWWY+BXyT4ijXMxT73n24rzbLyu6b7rMr73jgV+XPjutqiIhDgacy84G6ql4xrobv5mp01MXLyaykiFgfuBb4TGb+tb2mDcoc7xoRcTDwfGbeV1vcoGl2ok5v6AfsBHw/M98DvMIbH+M34rh2oPyIeDSwJbA5sB7FR8z13Febq61xdHxXQkScTjF1clJrUYNmjmsnRMRA4HTgS42qG5StceNq+G6uOcCImsfDgae7qS9rpIhYhyJ4T8rMn5bFz7V+RF/eP1+WO94dez9waETMpvh4c2+KI+Eblh/tw/LjtmxMy/pBNP44cG03B5iTmVPKx9dQhHH31VX3IeCxzJybmYuBnwL/gPtqs6zsvuk+20nlyX0HA2Pzjes3O66rbmuKN+EPlP+7hgP3R8Rb6CXjavhurnuBbcuz8/tTnGwxuZv7tMYo52teCDySmd+qqZoMtJ65PA74eU35J8qzn98LLGj9WFWFzDwtM4dnZgvF/vjbzBwL3AocUTarH9PWsT6ibN9jjx50l8x8FngyIt5aFu0DPIz76up4AnhvRAws/xa0jqn7anOs7L55I7BvRGxUfiqxb1mmGhGxP/B54NDMXFRTNRk4Ooqr8mxJcYLgPZgTOpSZD2bmppnZUv7vmgPsVP7d7R37a2Z6a+INOJDijOdHgdO7uz9r0g3YneJjoj8B08rbgRTzOH8D/KW837hsHxRnjT8KPEhxlYRu346eegP2An5Z/rwVxT+CmcDVwICy/E3l45ll/Vbd3e+eegPeDUwt99frgI3cV1d7TM8A/gw8BFwGDHBfXaVx/AnFvPnFFMHlhFXZNynmMM8sb8d193Z1962NcZ1JMde49X/WD2ran16O6wzggJpyc0IH41pXPxsYXP7cK/ZXv+FSkiRJqojTTiRJkqSKGL4lSZKkihi+JUmSpIoYviVJkqSKGL4lSZKkihi+JUkdiojbyi+8aOYy94qIjIjxzVyuJPVkhm9JaqKIGN9eoIyIlrL+4iau88sRcVgTlvOZZgfhiHh32b+WZi5XktZUhm9JWvP9J7Da4Rv4DDC+jbp9gbe2Udeed1P0r6VB3R3AuhRfqCNJa4V+3d0BSVLPl5mvdcEylwJ/b/ZyJakn88i3JPVQEfGmcsrGjIhYFBEvRcSDEfGNsr4lIlq/pnhcOZ0la8qIiKMiYnJEPBERr0bEvIi4LiLeWbeuBEYCH6hdTut0kUZzviNih4i4OiKeKpf9bETcGhEHlfVfBn5UNr+1ZpkXl/UN53xH4cSImBIRC8vbgxHxlc6OjST1VB75lqSe6zzgeOBS4BygL7AtsHdZPxc4lmLaxu+AiQ2WcQowv6x7FtgamADcGRE7ZeZfynbHluuYB5xZ8/y5jToWEZsAvy0f/gB4HBgMjAJ2A64HfgpsVq7vLOCRsv2jHWz3ZcBYYErZl5eAtwFHAF8q23Q0NpLUIxm+JannOhz4VWaOa1SZma8Al0fEZcCszLy8QbP9y3bLRMSlwDTgX4F/Kpd1eUT8F/BcG8up935gU+CozLyqjf79KSLuogjfN2fmbR0tNCI+RhG8LwfGlVNTWutqP61td2wkqady2okk9VwLgB0iYsdVXUBr8C6ncmwQEYMpjmbPoDhCvTp9AzggIjZYjeXUG1vef642eMOyOeK161+tsZGk7mD4lqTukR034TPARsCDEfFoRFwQEaPrjgC3KyLeExG/BF6mCKxzy9s7ymWvksy8nWLKx3hgXkTcGRFnRMT2q7rM0rbAM5n5XAftVntsJKk7+EdKkprrb+X9wDbq16tr16bM/DnFJfqOpZhfvQ9wHXBbRPTv6PkRsQXF5fzeA3yVYqrGvsCHgems5v+AcsrHO4D/AF4A/h/wp4g4ZTUWG3Tijcnqjo0kdRfDtyQ112Pl/dvbqG8tn9WZhWXm/My8PDNPBLYCzgb2AEZ34umHA+sDx2Tm1zPzusy8OTNvATZptLrO9Kmufw9l5v9v745dm4qiOI5/DyKIINQuleLSQRf9Dzq4FEQRV8VBweJQdRHEghkFlU4OBRcXEaHaodBSXF2EiqBzQREqWHCpg+2i9TjcF4jPptoanx2+nyUk7+Xkvrvkl5ubk4nMPA0cpPyY8m5ExDZrLgKDETHwB6/9N3MjSf+F4VuSeus18AE4GxGDnQeqFdmrlEA6t1mRiNgVEX2dj2VmAm+qu/0dh77U7rett8vVal8CDmxwfrc6G42vv77FIzM/Uz587AX2dNSsj3czj6vbiXr9dqDf4txI0o5itxNJ6qHM/BYRY8AMZT/yA8pq8ABwBjgC3M7Mxd+U2gcsR8QsJVR+AoaAMWCFn8P7AjASEePAUhlGTgHPgDXgUURMVs8bBk5WY6q/BywAoxFxi9IW8DswV++WUjkPXIuIGeAt8BU4BhwHnmZme1vNq6pOKyL2A6vA+8x8udFFZ+Z0RDyp6h+qrn8FOFzVPrrFuZGkHcXwLUk9lpnzETEM3AAuULZ4rFKCYtfWfDVrwD3KXuYRyvaRZWAWuJOZHzvOvUzpe92iBFOAqcx8FxEnKD22b1JWwl9QQvIkv/7le4uyanwF6KOsmA9VY697TtlLforSy3udsup9vardnouliLgIjAP3gd3AQ0oP727OUfqWj1L6erdrT29jbiRpR4nyTZ0kSZKkf80935IkSVJDDN+SJElSQwzfkiRJUkMM35IkSVJDDN+SJElSQwzfkiRJUkMM35IkSVJDDN+SJElSQwzfkiRJUkMM35IkSVJDfgDkJElJq601FwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_excel('/Users/deepanshishokeen/Desktop/Music/get_stats/heavy_metal.xlsx')\n",
    "g1=df.Pvalue\n",
    "g1.shape\n",
    "g2=df.Tstatistics\n",
    "g2.shape\n",
    "\n",
    "k448=plt.scatter(1422,0.04,color='red')#k448\n",
    "rnr=plt.scatter(g2,g1,color='blue')\n",
    "plt.legend((k448, rnr),\n",
    "           ('K448', 'Heavy Metal'),\n",
    "           scatterpoints=1,\n",
    "           fontsize=14)\n",
    "plt.title('Scatter plot for Heavy Metal Music', fontsize=20)\n",
    "plt.xlabel('U statistics', fontsize=18)\n",
    "plt.ylabel('P values', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=1411.0, pvalue=0.038299860531344994)\n",
      "0.038299860531344994 1411.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=6.0, pvalue=5.1531462799208714e-21)\n",
      "5.1531462799208714e-21 6.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=21.0, pvalue=1.1004719206961523e-20)\n",
      "1.1004719206961523e-20 21.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=13.0, pvalue=7.348363241496412e-21)\n",
      "7.348363241496412e-21 13.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=246.0, pvalue=4.451599613997991e-16)\n",
      "4.451599613997991e-16 246.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=1.0, pvalue=3.995900955029211e-21)\n",
      "3.995900955029211e-21 1.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=9.0, pvalue=6.0006428006017826e-21)\n",
      "6.0006428006017826e-21 9.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
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     "text": [
      "/Users/deepanshishokeen/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in sqrt\n",
      "  \n",
      "/Users/deepanshishokeen/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py:3405: RuntimeWarning: Invalid value encountered in median for 59 results\n",
      "  r = func(a, **kwargs)\n"
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      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=283.0, pvalue=2.2178193302639283e-15)\n",
      "2.2178193302639283e-15 283.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n"
     ]
    }
   ],
   "source": [
    "my_list=[]\n",
    "mypath= \"/Volumes/LaCie/Preferred Music Corpus/Rock n' Roll\"\n",
    "files = [f for f in os.listdir(mypath) if f.endswith(\".wav\") and not f.startswith(\"._\")]\n",
    "files\n",
    "\n",
    "z = []\n",
    "new_list=[]\n",
    "slash='/'\n",
    "for j in files:\n",
    "    ttl=j.replace('.wav','')\n",
    "    whole_path=mypath+slash+j\n",
    "    sampFreq, snd = wavfile.read(whole_path)\n",
    "    if snd.shape[0]/sampFreq > 140: \n",
    "        res,p,t=get_values(whole_path,ttl=ttl, ref=ref_448)\n",
    "        z=ttl,p,t\n",
    "        my_list.append(z)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df=pd.DataFrame(my_list, columns=['Title', 'Pvalue', 'Tstatistics' ])\n",
    "df=df.sort_values(by=['Tstatistics'], ascending=False)\n",
    "df.to_excel('/Users/deepanshishokeen/Desktop/Music/get_stats/rock_n_roll.xlsx')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e9sWI+DbFh6k/lieDj6I4H+CB8lFrC+C6iLiT4luP+yimLb2bYtrNqd2cEF3rBIq/he8B/jciLqTYN15N8Tv4ebred0BadfX12oQ+fPho7YPin8s/URydvYviiNMLFCdk/ZpizuSIbtbtoLgKxpMUJ1T+heLyejvUtZtGcfLaMxTXsz4feANFgp7A7nXtN6b4qvohisS6yzV/Kaay/Iri6O8yGlyHmOLDwU8p/lG/QPHhYAHwLYr557VtF9HgetNNjF17ue2zKG4m9EuKI/NLKZLwdzRYZ1qjeMu6ncqxeaSM+V6KI7ebd7P9zwJ3UlwdpOE1s7tZr6frqI+guOza/PJ1LKX4APRPjfaDnvpqcuy6jZniJkLLKK4FvVZN+VoUc7Jvo5hD/BRFMnlwD329heIymg+XY/sAxdSWAxq9nw3WP5jig+MiYKtm94tu6q+i7trWvfS3iB6u6V6Ox/3lWG1XV3dQOTZPUfyO3g7MpO766d39HvS0v3YTS2f7rzTZfoXrfJflARxDMc2o83fhJIoPWl3ipJiGchzFlV3uL38fHizH+WAgmtlnKRL8oyjuX/A0xd+rP1PMWZ/U133ch4+h+ojMlpynI0mVK69d/Bfg7MycNqjBSJLUBOd8S5IkSRUx+ZYkSZIqYvItSZIkVcQ535IkSVJFPPItSZIkVWRYX+d77Nix2d7ePthhSJIkaZi76aabHs3MXm8INayT7/b2dubPnz/YYUiSJGmYi4jFzbRz2okkSZJUEZNvSZIkqSIm35IkSVJFTL4lSZKkilSefEfEvhFxV0QsjIhjGtSvGRHnlvXXR0R7Wd4eEc9GxILy8b2qY5ckSZJWRqVXO4mIkcBpwD7AEuDGiJiXmXfUNPs48HhmToqIg4BvAAeWdXdn5puqjFmSJAlg2bJlPProozzxxBO8/PLLgx2OKjRy5Eg23HBDxo4dy4gRK3fsuupLDe4MLMzMewAi4ifAVKA2+Z4KHF/+/DPguxERVQYpSZJUb8mSJUQE7e3trLHGGpierB4ykxdffJGHHnqIJUuWMGHChJXqr+ppJ1sA99UsLynLGrbJzJeAJ4ExZd2WEfHHiPhdRLx1oIOVJEnq9Mwzz7DFFlswevRoE+/VSEQwevRotthiC5555pmV7q/qI9+N9tRsss2DwITM/GtE7AhcEBHbZObfuqwcMR2YDqz0JxNJkqRaKzvlQKuuVr33Ve9BS4BX1yyPBx7ork1EjAI2AB7LzOcz868AmXkTcDfwd/UbyMzZmTklM6eMG9frHT4lSZKkylSdfN8IbB0RW0bEaOAgYF5dm3nAoeXPBwBXZGZGxLjyhE0iYitga+CeiuKWJEmSVlql004y86WIOAq4BBgJnJmZt0fEl4H5mTkPOAOYExELgccoEnSAtwFfjoiXgJeBT2bmY1XGL0mSJK2MyicuZeavM/PvMvM1mTmrLDuuTLzJzOcy84OZOSkzd+68Mkpm/jwzt8nMN2bmDpn5q6pjlyRJWhVNmzaNd7/73V3KLrzwQtra2pg5c2aX8h//+MdExArta331q18lIjjqqKO6lD/99NMcffTRjB8/nrXXXpvXvva1nHLKKa17IcNA1SdcSpIkaZDNmTOHT3ziE5x00kl8+tOfXl5+zz338PnPf563vrX7i8pdd911nH766Wy33XYr1H32s5/lt7/9LXPmzGHLLbfk6quv5vDDD2fs2LEccsghA/JaVjWesitJkrQa+fa3v80nPvEJfvCDH3RJvF988UUOPvhgZs2axVZbbdVw3SeffJKOjg7OOOMMXvWqV61Qf+2113LIIYewxx570N7ezkc/+lHe/OY3c/311w/Y61nVmHxLkiRVae5caG+HESOK57lzK9v0F7/4RWbMmMEvfvGLFY5Ez5w5k/b2dg499NBu1obp06dzwAEHsOeeezas33XXXfnVr37FffcVt3W59tprWbBgAfvuu2/rXsQqzmknkiRJVZk7F6ZPh6VLi+XFi4tlgI6OAd30ZZddxkUXXcSFF17Iu971ri51l156Keeeey4LFizodv3TTz+dhQsXMmfOnG7bnHrqqXzyk59kwoQJjBpVpJnf+c53epw/vrrxyLckSVJVZs58JfHutHRpUT7Att12W17zmtdwwgkn8MQTTywvf/TRR5k2bRpnn312w6kkAHfddRfHHnssc+fOZfTo0d1u4zvf+Q7XXHMN8+bN46abbuKUU07hc5/7HBdffHHLX8+qyiPfkiRJVbn33r6Vt9Bmm23GvHnz2HPPPdl777257LLLeNWrXsVtt93Ggw8+yN5777287bJlywAYNWoUt99+O3/4wx949NFH2XbbbZe3efnll7n66qv53ve+xzPPPMOyZcuYMWMG5513Hu95z3sA2G677ViwYAEnn3yyU09KJt+SJElVmTChmGrSqLwCW2yxBVdddRV77rkne+21F5dddhk77bQTt956a5d2X/jCF3j88cc57bTT2HLLLdlkk02YMmVKlzaHHXYYW2+9NcceeyyjR4/mqaee4sUXX2TkyJFd2o0cOXJ5Mi+Tb0mSpOrMmtV1zjdAW1tRXpHNNtuMq666ir322os999yTyy+/vMsRbYANN9yQl156aXn56NGj2XDDDbu0WWedddhoo42Wt1l//fXZbbfdOOaYY1h33XWZOHEiv/vd7/jhD3/ISSedVM2LWwU451uSJKkqHR0wezZMnAgRxfPs2QN+smW9TTbZhCuvvBKAPfbYg4cffrgl/f7kJz9hp512oqOjg8mTJ/P1r3+dE088cYWb8azOIjMHO4YBM2XKlJw/f/5ghyFJkoaBO++8k9e//vWDHYYGUU/7QETclJlTGlbW8Mi3JEmSVBGTb0mSJKkiJt+SJElSRUy+JUmSpIqYfEuSJEkVMfmWJEmSKmLyLUmSJFXE5FuSJEmqiMm3JEmSVBGTb0mSJK209vZ2Tj755MEOo2nHH3882267bbfLA8XkW5IkaRibNm0aEUFEMGrUKCZMmMARRxzB448/Ptih9cvuu+++/PWMHj2a17zmNcyYMYPnn39+sENryqjBDkCSJEkDa++992bOnDm89NJL3HHHHXzsYx/jiSee4Mc//vFgh9Yvhx12GF/96ld54YUXuPHGGznssMMA+NrXvjbIkfXOI9+SJEnD3Jprrsmmm27K+PHjefvb386BBx7IpZde2qXNvffey/ve9z7WW2891ltvPd7//vezZMmSLm0uuugidtllF9Zee23GjBnDe97zHp577rmG2zznnHNYf/31mTdvXsP6q666iojg8ssvZ5dddqGtrY0pU6Zw88039/p62tra2HTTTZkwYQIf+MAH2GeffVZ4Pbfeeit77703a6+9NhtttBHTpk3jySef7LXvgWbyLUmSVKG5c6G9HUaMKJ7nzq12+/fccw8XX3wxa6yxxvKyzGT//ffnoYce4oorruDKK6/kgQceYP/99yczAbj44ouZOnUq++yzDzfddBNXXnklu+22G8uWLVthG6eeeipHH300F154Ie9973t7jGfGjBl8/etf5+abb2bMmDF0dHQs32YzbrnlFq655pour2fp0qXsu+++rLvuutxwww2cf/75XHvttXzsYx9rut+B4rQTSZKkisydC9Onw9KlxfLixcUyQEfHwG334osvZt111+Xll19efqT6W9/61vL63/72t9xyyy3cfffdtLe3A/CjH/2ISZMmcfnll7P33ntz4okncsABB/CVr3xl+XrbbbfdCts67rjj+P73v88VV1zB9ttv32tsJ554InvsscfydXfddVfuv/9+xo8f3+06s2fP5qyzzuLFF1/khRdeYMSIEZx22mnL6+fOncvTTz/NnDlzWG+99Zavs8cee7Bw4UImTZrUa1wDxSPfkiRJFZk585XEu9PSpUX5QHrb297GggULuOGGGzj66KN55zvfyac+9anl9XfeeSebb7758sQbYKuttmLzzTfnjjvuAOCPf/wje+21V4/b+fa3v82pp57K73//+6YSb+iawG+++eYAPPzwwz2uc+CBB7JgwQL+8Ic/8KEPfYjDDz+cD3zgA11ez3bbbbc88Qb4+7//e0aMGLH89QwWk29JkqSK3Htv38pbpa2tjUmTJvGGN7yBU089laVLl3LiiScur89MIqLhut2VN7LrrrsSEX06kbN2ukjnthpNZam1wQYbMGnSJHbYYQfOOeccrrjiCs4666zl9a16PQPB5FuSJKkiEyb0rXygfOlLX+Ib3/gGDzzwAACTJ0/m/vvvZ9GiRcvb3HPPPTzwwANMnjwZgO23357LL7+8x3533HFHLr30Ur71rW91Se4H0hprrMGxxx7LjBkzWFp+rTB58mRuueUWnnrqqeXtrr32WpYtW8brX//6SuLqjsm3JElSRWbNgra2rmVtbUV5lXbffXe22Wab5fO39957b974xjfS0dHBTTfdxPz58+no6GCHHXZgzz33BGDmzJmcd955fOELX+COO+7g9ttv55RTTlme8HbaaaeduPTSS/m3f/u3LvPDB9KHP/xhIoLvfve7AHR0dLDOOuvw0Y9+lFtvvZWrr76af/zHf+T973//oM73BpNvSZKkynR0wOzZMHEiRBTPs2cP7MmW3fnsZz/LGWecweLFi4kILrjgAsaNG8fuu+/OHnvswaabbsoFF1ywfJrGO9/5Ts4//3x+85vfsP3227Pbbrtx5ZVXMmLEiunkzjvvzKWXXsrJJ59cSQI+evRojjrqKE466SSeeuop2trauOSSS/jb3/7GzjvvzNSpU3nLW97CmWeeOeCx9Cb6cimXVc2UKVNy/vz5gx2GJEkaBu68885Bn7KgwdXTPhARN2XmlN768Mi3JEmSVBGTb0mSJKkiJt+SJElSRUy+JUmSpIqYfEuSJDVpOF+oQj1r1Xtv8i1JktSENdZYg2effXaww9AgefbZZ7vcjbO/TL4lSZKasPHGG3P//fezdOlSj4CvRjKTpUuXcv/997PxxhuvdH+jWhCTJEnSsLf++usD8MADD/Diiy8OcjSq0hprrMEmm2yyfB9YGSbfkiRJTVp//fVbkoBp9eW0E0mSJKkiJt+SJElSRUy+JUmSpIqYfEuSJEkVMfmWJEmSKmLyLUmSJFXE5FuSJEmqiMm3JEmSVBGTb0mSJKkiJt+SJElSRUy+JUmSpIpUnnxHxL4RcVdELIyIYxrUrxkR55b110dEe139hIh4OiI+V1XMkiRJUitUmnxHxEjgNGA/YDJwcERMrmv2ceDxzJwEnAJ8o67+FOA3Ax2rJEmSVhFz50J7O4wYUTzPnTvYEXWr6iPfOwMLM/OezHwB+Akwta7NVODs8uefAXtFRABExP7APcDtFcUrSZKkoWzuXJg+HRYvhsziefr0IZuAV518bwHcV7O8pCxr2CYzXwKeBMZExDrAvwInVBCnJEmSVgUzZ8LSpV3Lli4tyoegqpPvaFCWTbY5ATglM5/ucQMR0yNifkTMf+SRR/oZpiRJklYJ997bt/JBVnXyvQR4dc3yeOCB7tpExChgA+AxYBfgpIhYBHwGODYijqrfQGbOzswpmTll3LhxrX8FkiRJGjomTOhb+SCrOvm+Edg6IraMiNHAQcC8ujbzgEPLnw8ArsjCWzOzPTPbgX8HvpqZ360qcEmSJA1Bs2ZBW1vXsra2onwIqjT5LudwHwVcAtwJ/DQzb4+IL0fEe8tmZ1DM8V4IfBZY4XKEkiRJEgAdHTB7NkycCBHF8+zZRfkQFJn1U66HjylTpuT8+fMHOwxJkiQNcxFxU2ZO6a2dd7iUJEmSKmLyLUmSJFXE5FuSJEmrlFXohpYrGDXYAUiSJEnN6ryhZed9dTpvaAlD9hzLLjzyLUmSpFXGKnZDyxWYfEuSJGmVsYrd0HIFJt+SJElaZaxiN7Rcgcm3JEmSVhmr2A0tV2DyLUmSpFXGKnZDyxV4tRNJkiStUjo6Vp1ku55HviVJkqSKmHxLkiRJFTH5liRJkipi8i1JkiRVxORbkiRJqojJtyRJklQRk29JkiSpIibfkiRJUkVMviVJkqSKmHxLkiRJFTH5liRJkipi8i1JkiRVxORbkiRJqojJtyRJklQRk29JkiSpIibfkiRJUkUki3ZyAAAe80lEQVRMviVJkqSKmHxLkiRJFTH5liRJkipi8i1JkiRVxORbkiRJqojJtyRJklQRk29JkiSpIibfkiRJUkVMviVJkqSKmHxLkiRJFTH5liRJkipi8i1JkiRVxORbkiRJqojJtyRJklQRk29JkiSpIibfkiRJUkVMviVJkqSKmHxLkiRJFTH5liRJkipi8i1JkiRVxORbkiRJqojJtyRJklQRk29JkiSpIibfkiRJUkVMviVJkqSKVJ58R8S+EXFXRCyMiGMa1K8ZEeeW9ddHRHtZvnNELCgft0TE+6qOXZIkSVoZlSbfETESOA3YD5gMHBwRk+uafRx4PDMnAacA3yjLbwOmZOabgH2B70fEqGoilyRJklZe1Ue+dwYWZuY9mfkC8BNgal2bqcDZ5c8/A/aKiMjMpZn5Ulm+FpCVRCxJkiS1SNXJ9xbAfTXLS8qyhm3KZPtJYAxAROwSEbcDtwKfrEnGJUmSpCGv6uQ7GpTVH8Hutk1mXp+Z2wA7ATMiYq0VNhAxPSLmR8T8Rx55ZKUDliRJklql38l3RGwUETtGxJp9WG0J8Oqa5fHAA921Ked0bwA8VtsgM+8EngG2rd9AZs7OzCmZOWXcuHF9CE2SJEkaWE0l3xHxhYj4Ws3y24BFwA3AnyNi6ya3dyOwdURsGRGjgYOAeXVt5gGHlj8fAFyRmVmuM6rc/kTgtWUMkiRJ0iqh2SPfHwHuqVk+CbgF2B94CDixmU7KOdpHAZcAdwI/zczbI+LLEfHestkZwJiIWAh8Fui8HOGuwC0RsQA4HzgyMx9tMn5JkiRp0DV7qb4tgD8DRMQ4ijnXe2XmVeUR7FOb3WBm/hr4dV3ZcTU/Pwd8sMF6c4A5zW5HkiRJGmqaPfL9MjC6/PltwHPANeXyI8BGLY5LkiRJGnaaTb5vBz4SEesCHwN+l5kvlnWvBh4eiOAkSZKk4aTZaSdfBn4JdAAvAu+oqXsncHOL45IkSZKGnaaS78y8JCJeD+wALMjMu2uqr6Y4+VKSJElSD5o98k1m/gX4S4Py77c0IkmSJGmYavomOxGxRUR8q7x75D0RsW1Z/pmI2GXgQpQkSZKGh2ZvsrMNcCtwCMUdKSfyytVPJgKfHpDoJEmSpGGk2SPf/0ZxU5wtgfcDUVN3LfDmFsclSZIkDTvNzvneFTg4M5+OiJF1dQ8Bm7Y2LEmSJGn4afbI97Ie6sYCz7YgFkmSJGlYazb5vgE4rJu6D/HK3S4lSZIkdaPZaScnAr+NiEuBHwEJ7B0RnwbeR3HLeUmSJEk9aOrId2b+Dtif4oTLMylOuPw68FZg/8y8fsAilCRJkoaJvtxk5yLgooiYBGwM/DUz7xqwyCRJkqRhpunku1NmLgQWDkAskiRJ0rDWVPIdER/trU1m/nDlw5EkSZKGr2aPfJ/VTXnW/GzyLUmSJPWg2eR7ywZlY4B3Ax8GPtKyiCRJkqRhqqnkOzMXNyheDNwcEQF8liIJlyRJktSNZm+y05P/Bt7Vgn4kSZKkYa0Vyfebgadb0I8kSZI0rDV7tZPjGhSPBralOOr93VYGJUmSJA1HzZ5weXyDsucp5n3PAr7WqoAkSZKk4arZEy5bMT1FkiRJWq2ZVEuSJEkV6fbId0RM6EtHmXnvyocjSZIkDV89TTtZRNc7WPZm5MqFIkmSJA1vPSXfH6NvybckSZKkHnSbfGfmWRXGIUmSJA17nnApSZIkVaTZ63wTERsDBwOvBdaqq87M/HgrA5MkSZKGm2bvcPla4DqKkyrXAR4FNiqXHweeHKgAJUmSpOGi2Wkn3wRuADYBAtgPWBv4BLAUeN+ARCdJkiQNI81OO9kJ+CTFLeUBRmTmS8CZETEW+HdgjwGIT5IkSRo2mj3yvS7wWGYuo5hiMrambj5Fci5JkiSpB80m34uATcuf7wI+WFP3buCJFsYkSZIkDUvNJt+XAfuUP38LOCwi7oqI24FPA2cORHCSJEnScNLsnO8ZwJoAmfnTiHgWOBBoA74NnD4w4UmSJEnDR1PJd2Y+zysnW5KZvwJ+NVBBSZIkScNRU9NOIuJbEfGmgQ5GkiRJGs6anfN9GHBTRNwWEZ+PiC0GMihJkiRpOGo2+d4E+BCwEDgRWBwRv42IQyJinQGLTpIkSRpGmkq+M/OFzPx5Zu4PbAZ8iuIOl2cDD0XEnAGMUZIkSRoWmj3yvVxmPp6Z/5GZ/0BxV8vHgQ+3PDJJkiRpmGn2UoPLldNMDgA+AuwOvAT8vLVhSZIkScNPs1c7GRER+0bEXOAhipvqrAkcCWyamR8awBglSZKkYaHZI98PAOMoTrj8BjAnMxcNVFCSJEnScNRs8v1z4IeZef1ABiNJkiQNZ83e4fKfBjoQSZIkabjr89VOJEmSJPWPybckSZJUkcqT7/KqKXdFxMKIOKZB/ZoRcW5Zf31EtJfl+0TETRFxa/m8Z9WxS5IkSSuj0uQ7IkYCpwH7AZOBgyNicl2zjwOPZ+Yk4BSKq6sAPAq8JzPfABwKeFdNSZIkrVKavc732IhYqwXb2xlYmJn3ZOYLwE+AqXVtplLcth7gZ8BeERGZ+cfMfKAsvx1YKyLWbEFMkiRJUiW6Tb4jYmREHB8RT1DcWOdvEfHziNhwJba3BXBfzfKSsqxhm8x8CXgSGFPX5gPAHzPz+ZWIRZIkSapUT5ca/CRwHHAVcCOwFfA+4G/AYf3cXjQoy760iYhtKKaivL3hBiKmA9MBJkyY0L8oJUmSpAHQ07STw4HTM3PPzPzXzPwg8E/ARyJidD+3twR4dc3yeIq7ZzZsExGjgA2Ax8rl8cD5wEcz8+5GG8jM2Zk5JTOnjBs3rp9hSpIkSa3XU/K9FXBeXdm5wEhgYj+3dyOwdURsWSbwBwHz6trMozihEuAA4IrMzHK6y0XAjMy8pp/blyRJkgZNT8n3uhRTTGo9VT6v15+NlXO4jwIuAe4EfpqZt0fElyPivWWzM4AxEbEQ+CzQeTnCo4BJwBcjYkH52Lg/cUiSJEmDITLrp1yXFRHLKE5svKWmeCRwF8UVSW6vbZ+Z9wxQjP02ZcqUnD9//mCHIUmSpGEuIm7KzCm9tevphEsoLvXXyAUNykb2GpUkSZK0Gusp+e7vFU0kSZK0mpo7F2bOhHvvhQkTYNYs6OgY7KiGjm6T78w8u7s6SZIkqd7cuTB9OixdWiwvXlwsgwl4p0pvLy9JkqTha+bMVxLvTkuXFuUqmHxLkiSpJe69t2/lqyOTb0mSJLVEdzcX96bjrzD5liRJUkvMmgVtbV3L2tqKchVMviVJktQSHR0wezZMnAgRxfPs2Z5sWau363xLkiRJTevoMNnuiUe+JUmSpIqYfEuSJEkVMfmWJEmSKmLyLUmSJFXE5FuSJEmqiMm3JEmSVBGTb0mSJKkiJt+SJElSRUy+JUmSpIqYfEuSJEkVMfmWJEmSKmLyLUmSJFXE5FuSJEmqiMm3JEmSVBGTb0mSJKkiJt+SJElSRUy+JUmSpIqYfEuSJEkVMfmWJEmSKmLyLUmSJFXE5FuSJEmqiMm3JEmSVBGTb0mSJKkiJt+SJElSRUy+JUmSpIqYfEuSJEkVMfmWJEmSKmLyLUmSJFXE5FuSJEmqiMm3JEmSVBGTb0mSJKkiJt+SJElSRUy+JUmSpIqYfEuSJKmLuXOhvR1GjCie584d7IiGj1GDHYAkSZKGjrlzYfp0WLq0WF68uFgG6OgYvLiGC498S5IkabmZM19JvDstXVqUa+WZfEuSJGm5e+/tW7n6xuRbkiRJy02Y0Ldy9Y3JtyRJkpabNQva2rqWtbUV5Vp5Jt+SJElarqMDZs+GiRMhoniePduTLVvFq51IkiSpi44Ok+2B4pFvSZIkqSKVJ98RsW9E3BURCyPimAb1a0bEuWX99RHRXpaPiYgrI+LpiPhu1XFLkiRJK6vS5DsiRgKnAfsBk4GDI2JyXbOPA49n5iTgFOAbZflzwBeBz1UUriRJktRSVR/53hlYmJn3ZOYLwE+AqXVtpgJnlz//DNgrIiIzn8nM31Mk4ZIkSdIqp+rkewvgvprlJWVZwzaZ+RLwJDCmkugkSZKkAVR18h0NyrIfbbrfQMT0iJgfEfMfeeSRPgUnSZIkDaSqk+8lwKtrlscDD3TXJiJGARsAjzW7gcycnZlTMnPKuHHjVjJcSZIkqXWqTr5vBLaOiC0jYjRwEDCvrs084NDy5wOAKzKz6SPfkiRJ0lBV6U12MvOliDgKuAQYCZyZmbdHxJeB+Zk5DzgDmBMRCymOeB/UuX5ELALWB0ZHxP7A2zPzjipfgyRJktRfld/hMjN/Dfy6ruy4mp+fAz7YzbrtAxqcJEmSNIC8w6UkSZJUEZNvSZIkqSIm35IkSVJFTL4lSZKkiph8S5IkSRUx+ZYkSZIqYvItSZIkVcTkW5IkSaqIybckSZJUEZNvSZIkqSIm35IkSVJFTL4lSZKkiph8S5IkSRUx+ZYkSZIqYvItSZIkVcTkW5IkSaqIybckSZJUEZNvSZIkqSIm35IkSVJFTL4lSZKkiph8S5IkSRUx+ZYkSZIqYvItSZIkVcTkW5IkSaqIybckSZJUEZNvSZIkqSIm35IkSVJFTL4lSZKkiph8S5IkSRUx+ZYkSZIqYvItSZIkVcTkW5IkSaqIybckSZJUEZNvSZIkqSIm35IkSVJFTL4lSZKkiph8S5IkSRUx+ZYkSZIqYvItSZIkVcTkW5IkaQDMnQvt7TBiRPE8d+5gR6ShYNRgByBJkjTczJ0L06fD0qXF8uLFxTJAR8fgxaXB55FvSZKkFps585XEu9PSpUW5Vm8m35IkSS127719K9fqw+RbkiSpxSZM6Fu5Vh8m35IkSS02axa0tXUta2sryrV6M/mWJElqsY4OmD0bJk6EiOJ59mxPtpRXO5EkSRoQHR0m21qRR74lSZKkiph8S5IkSRUx+ZYkSas87yapVYVzviVJ0irNu0lqVVL5ke+I2Dci7oqIhRFxTIP6NSPi3LL++ohor6mbUZbfFRHvqDJuSZI0NHk3Sa1KKk2+I2IkcBqwHzAZODgiJtc1+zjweGZOAk4BvlGuOxk4CNgG2Bf4j7K/ISWif4811yy+Khs7tniMGAHrrVc8R8CoUXDkkV2/Vhs7FtZdt3F/667bta6zn/qv4uq/ptt7bxg5sms/zX51V9/XkUe25ivA3r5KnDu3GIvOmMeOXXHbtcu1Y9yfuPr61War2veln/ox6RyXnt77Vr+Oqvrqre9W7YcDqTPmzt/12udW/u50jkWr+q9aq/abKve/vvTd07pHHtl13zjyyNbFPBx4N0mtUjKzsgfwFuCSmuUZwIy6NpcAbyl/HgU8CkR929p23T123HHHrBIM/GPEiJXvo60t85xzikdbW3PbPOecnl97M311brcvGvVb288552SOHt2a8WhFPAPV/ogjmu+npzFZY43u3/tWvo5WjuHK9t2K/XAgVfm7s6qMSSOt2m+q3v+a7bundY84ovH7dsQRKx/zcDFxYuMxmjhxsCPT6gSYn9lEPtxMo1Y9gAOAH9QsHwJ8t67NbcD4muW7gbHAd4GP1JSfARzQ0/aGY/LdqsfEid3/serPH7Bm++rrH8Le/qD25TW0Iq6+/oFvVfuRI5vvp7cx6em9b9Xr6MlA/pMcqP1wIFX9u7MqjEkjrdpvBmP/a6bvntbt7vd/5MiVj3m4GMgPVVKzmk2+o2hbjYj4IPCOzPxEuXwIsHNmHl3T5vayzZJy+W5gZ+DLwB8y85yy/Azg15n587ptTAemA0yYMGHHxYsXD/wLW77tyja10jpjbfbtj4Bly7qvHzGiub5666fZfjv7aXa7rYqrt3gGqn13GvXTWx89vfeteh09aWVfzfY9ENtqlap/d1rVf9Vatd8Mxv7XTN89rdvT+1jhv/Ahb+7cYo73vffChAnFbdw92VJVioibMnNKb+2qPuFyCfDqmuXxwAPdtYmIUcAGwGNNrktmzs7MKZk5Zdy4cS0MfXiZMKF49KX9ytT3tV1v7TvL+9pfX7fT13gGqnxkN2c3NGrfzHs10PH2tv1W9dXfPlq137RC1b87rWpftVbtN4Ox/zXTd0/rdvf731356qqjAxYtKj7oLFpk4q2hq+rk+0Zg64jYMiJGU5xAOa+uzTzg0PLnA4ArykP584CDyquhbAlsDdxQUdxDxogWvGNtbcURgVmzip+b2easWT23aaavzu32RaN+a/uZNQtGj+5bnysTV2/xDFT76dOb76enMVljje7f+1a+jp60sq9m+q7Xqm21SpW/O63sv2qt2m+q3v+a7bundTsvmVevu3JJQ1wzc1Na+QDeCfwvxVzumWXZl4H3lj+vBZwHLKRIrreqWXdmud5dwH69bavqOd+ZfZtjWfsYPTozInPMmOIRkbnuusVz59y+I44o5q9NnPhK23XWadzfOut0revsZ+LErnPgavubODFzr726ntS5zjp9Oxmxtq8jjui63N+5d/X91vdzzjnFWHTGPGbMituuXa4d4/7E1Vs8A9W+L/3Uj0nnuPT03rf6dVTVV299t2o/HEidMXf+rtc+t/J3p3MsWtV/1Vq131S5//Wl757WPeKIrvuGJ1tKQw9Dcc531aZMmZLz588f7DAkSZI0zA3VOd+SJEnSasvkW5IkSaqIybckSZJUEZNvSZIkqSIm35IkSVJFTL4lSZKkiph8S5IkSRUx+ZYkSZIqYvItSZIkVcTkW5IkSaqIybckSZJUkcjMwY5hwETEI8DiQdr8WODRQdr2cOWYDgzHdWA4rq3nmA4Mx7X1HNOBMdTHdWJmjuut0bBOvgdTRMzPzCmDHcdw4pgODMd1YDiureeYDgzHtfUc04ExXMbVaSeSJElSRUy+JUmSpIqYfA+c2YMdwDDkmA4Mx3VgOK6t55gODMe19RzTgTEsxtU535IkSVJFPPItSZIkVcTku8UiYt+IuCsiFkbEMYMdz6okIl4dEVdGxJ0RcXtEfLos3ygiLouIP5fPryrLIyJOLcf6TxGxw+C+gqErIkZGxB8j4sJyecuIuL4c03MjYnRZvma5vLCsbx/MuIeyiNgwIn4WEf9T7rNvcV9dORHxz+Xv/m0R8eOIWMt9te8i4syIeDgibqsp6/O+GRGHlu3/HBGHDsZrGUq6Gddvln8D/hQR50fEhjV1M8pxvSsi3lFTbp5Qo9G41tR9LiIyIsaWy8NifzX5bqGIGAmcBuwHTAYOjojJgxvVKuUl4F8y8/XAm4F/KsfvGODyzNwauLxchmKcty4f04H/rD7kVcangTtrlr8BnFKO6ePAx8vyjwOPZ+Yk4JSynRr7NnBxZr4OeCPF+Lqv9lNEbAF8CpiSmdsCI4GDcF/tj7OAfevK+rRvRsRGwJeAXYCdgS91JuyrsbNYcVwvA7bNzO2A/wVmAJT/uw4CtinX+Y/yIIh5worOYsVxJSJeDewD3FtTPCz2V5Pv1toZWJiZ92TmC8BPgKmDHNMqIzMfzMyby5+fokhmtqAYw7PLZmcD+5c/TwV+mIXrgA0jYrOKwx7yImI88C7gB+VyAHsCPyub1I9p51j/DNirbK8aEbE+8DbgDIDMfCEzn8B9dWWNAtaOiFFAG/Ag7qt9lplXA4/VFfd133wHcFlmPpaZj1MkmSskSKuTRuOamZdm5kvl4nXA+PLnqcBPMvP5zPwLsJAiRzBPqNPN/grFh+r/B9SenDgs9leT79baArivZnlJWaY+Kr9C3h64HtgkMx+EIkEHNi6bOd7N+XeKP2DLyuUxwBM1/zBqx235mJb1T5bt1dVWwCPAf5XTeX4QEevgvtpvmXk/cDLFUa4HKfa9m3BfbZW+7pvus333MeA35c+O60qIiPcC92fmLXVVw2JcTb5bq9FRFy8n00cRsS7wc+Azmfm3npo2KHO8a0TEu4GHM/Om2uIGTbOJOr1iFLAD8J+ZuT3wDK98jd+I49qL8iviqcCWwObAOhRfMddzX22t7sbR8e2DiJhJMXVybmdRg2aOaxMiog2YCRzXqLpB2So3ribfrbUEeHXN8njggUGKZZUUEWtQJN5zM/MXZfFDnV/Rl88Pl+WOd+/+AXhvRCyi+HpzT4oj4RuWX+1D13FbPqZl/QY0/jpwdbcEWJKZ15fLP6NIxt1X+29v4C+Z+Uhmvgj8Avh73Fdbpa/7pvtsk8qT+94NdOQr1292XPvvNRQfwm8p/3eNB26OiE0ZJuNq8t1aNwJbl2fnj6Y42WLeIMe0yijna54B3JmZ36qpmgd0nrl8KPDLmvKPlmc/vxl4svNrVRUyc0Zmjs/Mdor98YrM7ACuBA4om9WPaedYH1C2H7JHDwZLZv4fcF9EvLYs2gu4A/fVlXEv8OaIaCv/FnSOqftqa/R137wEeHtEvKr8VuLtZZlqRMS+wL8C783MpTVV84CDorgqz5YUJwjegHlCrzLz1szcODPby/9dS4Adyr+7w2N/zUwfLXwA76Q44/luYOZgx7MqPYBdKb4m+hOwoHy8k2Ie5+XAn8vnjcr2QXHW+N3ArRRXSRj01zFUH8DuwIXlz1tR/CNYCJwHrFmWr1UuLyzrtxrsuIfqA3gTML/cXy8AXuW+utJjegLwP8BtwBxgTffVfo3jjynmzb9Ikbh8vD/7JsUc5oXl47DBfl2D/ehmXBdSzDXu/J/1vZr2M8txvQvYr6bcPKGXca2rXwSMLX8eFvurd7iUJEmSKuK0E0mSJKkiJt+SJElSRUy+JUmSpIqYfEuSJEkVMfmWJEmSKmLyLUnqVURcVd7wopV97h4RGRHTWtmvJA1lJt+S1EIRMa2nhDIi2sv6s1q4zeMjYv8W9POZVifCEfGmMr72VvYrSasqk29JWvV9CVjp5Bv4DDCtm7q3A6/tpq4nb6KIr71B3dXA2hQ31JGk1cKowQ5AkjT0ZeYLA9DnMuC5VvcrSUOZR74laYiKiLXKKRt3RcTSiHgiIm6NiG+W9e0R0Xmb4kPL6SxZU0ZEHBgR8yLi3oh4PiIejYgLImK7um0lMBHYrbafzukijeZ8R8Q2EXFeRNxf9v1/EXFlRLyrrD8e+K+y+ZU1fZ5V1jec8x2FwyPi+oh4unzcGhFfbnZsJGmo8si3JA1dpwEfA34InAKMBLYG9izrHwEOoZi28d/A7AZ9HAU8Vtb9H/AaYDpwTUTskJl/LtsdUm7jUWBWzfqPNAosIsYAV5SL3wMWA2OBKcAuwEXAL4DNyu19FbizbH93L697DtABXF/G8gTwOuAA4LiyTW9jI0lDksm3JA1d7wN+k5mHNqrMzGeAcyJiDnBPZp7ToNm+ZbvlIuKHwALgn4Ejy77OiYivAA9100+9fwA2Bg7MzJ92E9+fIuIPFMn3ZZl5VW+dRsSHKBLvc4BDy6kpnXW139b2ODaSNFQ57USShq4ngW0iYtv+dtCZeJdTOdaPiLEUR7PvojhCvTKxAewXEeuvRD/1Osrnz9Um3rB8jnjt9ldqbCRpMJh8S9LgyN6b8BngVcCtEXF3RPwgIqbWHQHuUURsHxEXAk9RJKyPlI83lH33S2b+jmLKxzTg0Yi4JiJOiIjJ/e2ztDXwYGY+1Eu7lR4bSRoM/pGSpNZ6tnxu66Z+nbp23crMX1Jcou8QivnVewEXAFdFxOje1o+ICRSX89seOJFiqsbbgX2A21nJ/wHllI83AF8A/gr8C/CniDhqJboNmvhgsrJjI0mDxeRbklrrL+Xz67up7yy/p5nOMvOxzDwnMw8HtgJOAt4KTG1i9fcB6wIfycxvZOYFmf+/vTt2bSqK4jj+PYggglDrUCkuHXTR/6CDS0EUcVUcFCwOVRdBLNhRUOnkUHBxERGqHQot4qCDi1ARdHEpKEIFCy51sF20Hof7AvGZ1rbWZ4fvZwnJS8677y755ebmJJ9m5jNgT6fTrWVMtfG9zczRzDwB7KP8mPJWRMQGa84CvRHRs4Zz/83cSNJ/YfiWpM31GvgInIqI3vYD1YrsJUognV6tSERsi4iu9scyM4E31d3utkNfa/dbllvlarXPA3s7PH+lOp3G113f4pGZXygfPnYCO9pq1se7mgfV7Wi9fivQr3NuJGlLsduJJG2izPweEUPAJGU/8l3KanAPcBI4CNzIzNk/lNoFzEfEFCVUfgb6gCFggV/D+wwwEBHDwFwZRo4DT4Al4H5EjFWv6weOVWOqvwfMAIMRcZ3SFvAHMF3vllI5A1yOiEngHfANOAwcAR5lZmtbzauqzkhE7AYWgQ+Z+bLTRWfmREQ8rOrvr65/AThQ1T60zrmRpC3F8C1JmywzH0dEP3AVOEvZ4rFICYortuarWQJuU/YyD1C2j8wDU8DNzPzU9twLlL7XI5RgCjCeme8j4iilx/Y1ykr4C0pIHuP3v3wfoawaXwS6KCvmfdXY655T9pIfp/TyXqasel+parfmYi4izgHDwB1gO3CP0sN7JacpfcsHKX29W7UnNjA3krSlRPmmTpIkSdK/5p5vSZIkqSGGb0mSJKkhhm9JkiSpIYZvSZIkqSGGb0mSJKkhhm9JkiSpIYZvSZIkqSGGb0mSJKkhhm9JkiSpIYZvSZIkqSE/AVavmNsz/RLbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_excel('/Users/deepanshishokeen/Desktop/Music/get_stats/rock_n_roll.xlsx')\n",
    "g1=df.Pvalue\n",
    "g1.shape\n",
    "g2=df.Tstatistics\n",
    "g2.shape\n",
    "\n",
    "k448=plt.scatter(1422,0.04,color='red')#k448\n",
    "rnr=plt.scatter(g2,g1,color='blue')\n",
    "plt.legend((k448, rnr),\n",
    "           ('K448', 'Rock n Roll'),\n",
    "           scatterpoints=1,\n",
    "           fontsize=14)\n",
    "plt.title('Scatter plot for Rock n Roll Music', fontsize=20)\n",
    "plt.xlabel('U statistics', fontsize=18)\n",
    "plt.ylabel('P values', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FOR CLASSICAL MUSIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=464.0, pvalue=3.260639736304906e-12)\n",
      "3.260639736304906e-12 464.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=627.0, pvalue=1.0462308243554688e-09)\n",
      "1.0462308243554688e-09 627.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=1301.0, pvalue=0.00906836950162186)\n",
      "0.00906836950162186 1301.0\n",
      "MannwhitneyuResult(statistic=23.0, pvalue=1.217030930839646e-20)\n",
      "1.217030930839646e-20 23.0\n",
      "MannwhitneyuResult(statistic=380.0, pvalue=1.240951882699375e-13)\n",
      "1.240951882699375e-13 380.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=590.0, pvalue=3.0157483307699843e-10)\n",
      "3.0157483307699843e-10 590.0\n",
      "MannwhitneyuResult(statistic=689.0, pvalue=7.712806741322271e-09)\n",
      "7.712806741322271e-09 689.0\n",
      "MannwhitneyuResult(statistic=695.0, pvalue=9.3040474565469e-09)\n",
      "9.3040474565469e-09 695.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=441.0, pvalue=1.359201527963937e-12)\n",
      "1.359201527963937e-12 441.0\n",
      "MannwhitneyuResult(statistic=15.0, pvalue=8.130408528750288e-21)\n",
      "8.130408528750288e-21 15.0\n",
      "MannwhitneyuResult(statistic=819.0, pvalue=3.578271035461306e-07)\n",
      "3.578271035461306e-07 819.0\n",
      "MannwhitneyuResult(statistic=1530.0, pvalue=0.12918001626428904)\n",
      "0.12918001626428904 1530.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
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      "1.9028805767740214e-14 334.0\n",
      "MannwhitneyuResult(statistic=425.0, pvalue=7.329420643677692e-13)\n",
      "7.329420643677692e-13 425.0\n",
      "MannwhitneyuResult(statistic=129.0, pvalue=2.1449090525518434e-18)\n",
      "2.1449090525518434e-18 129.0\n",
      "MannwhitneyuResult(statistic=20.0, pvalue=1.0464029986466468e-20)\n",
      "1.0464029986466468e-20 20.0\n",
      "MannwhitneyuResult(statistic=207.0, pvalue=7.852855480426387e-17)\n",
      "7.852855480426387e-17 207.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=987.0, pvalue=2.5299150458568554e-05)\n",
      "2.5299150458568554e-05 987.0\n",
      "MannwhitneyuResult(statistic=136.0, pvalue=2.9841463561075695e-18)\n",
      "2.9841463561075695e-18 136.0\n",
      "MannwhitneyuResult(statistic=637.0, pvalue=1.4546210910916075e-09)\n",
      "1.4546210910916075e-09 637.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=1004.0, pvalue=3.72639826936219e-05)\n",
      "3.72639826936219e-05 1004.0\n",
      "MannwhitneyuResult(statistic=1480.0, pvalue=0.08084817080489837)\n",
      "0.08084817080489837 1480.0\n",
      "MannwhitneyuResult(statistic=1517.0, pvalue=0.11502205426898143)\n",
      "0.11502205426898143 1517.0\n",
      "MannwhitneyuResult(statistic=875.0, pvalue=1.61472402779243e-06)\n",
      "1.61472402779243e-06 875.0\n",
      "MannwhitneyuResult(statistic=247.0, pvalue=4.651448426381074e-16)\n",
      "4.651448426381074e-16 247.0\n",
      "MannwhitneyuResult(statistic=1009.0, pvalue=4.169610727049428e-05)\n",
      "4.169610727049428e-05 1009.0\n",
      "MannwhitneyuResult(statistic=414.0, pvalue=4.773507719566193e-13)\n",
      "4.773507719566193e-13 414.0\n",
      "MannwhitneyuResult(statistic=969.0, pvalue=1.6643614199594907e-05)\n",
      "1.6643614199594907e-05 969.0\n",
      "MannwhitneyuResult(statistic=801.0, pvalue=2.1638535056125265e-07)\n",
      "2.1638535056125265e-07 801.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=4.0, pvalue=4.655075256185421e-21)\n",
      "4.655075256185421e-21 4.0\n",
      "MannwhitneyuResult(statistic=59.0, pvalue=7.308014716472632e-20)\n",
      "7.308014716472632e-20 59.0\n",
      "MannwhitneyuResult(statistic=1.0, pvalue=3.995900955029211e-21)\n",
      "3.995900955029211e-21 1.0\n",
      "MannwhitneyuResult(statistic=760.0, pvalue=6.651559531699215e-08)\n",
      "6.651559531699215e-08 760.0\n",
      "MannwhitneyuResult(statistic=1356.0, pvalue=0.019376972307399588)\n",
      "0.019376972307399588 1356.0\n",
      "MannwhitneyuResult(statistic=1308.0, pvalue=0.010032270606451663)\n",
      "0.010032270606451663 1308.0\n",
      "MannwhitneyuResult(statistic=954.0, pvalue=1.1660653387365248e-05)\n",
      "1.1660653387365248e-05 954.0\n",
      "MannwhitneyuResult(statistic=873.0, pvalue=1.5324321758575064e-06)\n",
      "1.5324321758575064e-06 873.0\n",
      "MannwhitneyuResult(statistic=696.0, pvalue=9.59855016622064e-09)\n",
      "9.59855016622064e-09 696.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=45.0, pvalue=3.655662160850387e-20)\n",
      "3.655662160850387e-20 45.0\n",
      "MannwhitneyuResult(statistic=118.0, pvalue=1.2729700905553703e-18)\n",
      "1.2729700905553703e-18 118.0\n",
      "MannwhitneyuResult(statistic=69.0, pvalue=1.1945046782616295e-19)\n",
      "1.1945046782616295e-19 69.0\n",
      "MannwhitneyuResult(statistic=35.0, pvalue=2.221223901814917e-20)\n",
      "2.221223901814917e-20 35.0\n",
      "MannwhitneyuResult(statistic=270.0, pvalue=1.2671390203086889e-15)\n",
      "1.2671390203086889e-15 270.0\n",
      "MannwhitneyuResult(statistic=659.0, pvalue=2.9737159717915487e-09)\n",
      "2.9737159717915487e-09 659.0\n",
      "MannwhitneyuResult(statistic=506.0, pvalue=1.5502615013280787e-11)\n",
      "1.5502615013280787e-11 506.0\n",
      "MannwhitneyuResult(statistic=157.0, pvalue=7.968953598914714e-18)\n",
      "7.968953598914714e-18 157.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=123.0, pvalue=1.6143599137006172e-18)\n",
      "1.6143599137006172e-18 123.0\n",
      "MannwhitneyuResult(statistic=175.0, pvalue=1.8309759146207256e-17)\n",
      "1.8309759146207256e-17 175.0\n",
      "MannwhitneyuResult(statistic=84.0, pvalue=2.482783397543183e-19)\n",
      "2.482783397543183e-19 84.0\n",
      "MannwhitneyuResult(statistic=77.0, pvalue=1.7660508901012605e-19)\n",
      "1.7660508901012605e-19 77.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=1481.0, pvalue=0.08165777439893185)\n",
      "0.08165777439893185 1481.0\n",
      "MannwhitneyuResult(statistic=1290.0, pvalue=0.007717260072608577)\n",
      "0.007717260072608577 1290.0\n",
      "MannwhitneyuResult(statistic=1590.0, pvalue=0.20973535545432986)\n",
      "0.20973535545432986 1590.0\n"
     ]
    }
   ],
   "source": [
    "roots=[]\n",
    "files=[]\n",
    "my_list=[]\n",
    "mypath= '/Volumes/LaCie/PSYC90 Corpus Study'\n",
    "for root, dirnames, filenames in os.walk(mypath):\n",
    "    for filename in filenames:\n",
    "        if filename.endswith('.wav') and not filename.startswith(\"._\"):\n",
    "                files.append(os.path.join(filename))\n",
    "                roots.append(os.path.join(root,filename))\n",
    "\n",
    "z = []\n",
    "slash='/'\n",
    "for i,j in zip(roots,files)\n",
    "    ttl=j.replace('.wav','')\n",
    "    sampFreq, snd = wavfile.read(i)\n",
    "    if snd.shape[0]/sampFreq > 140: \n",
    "        res,p,t=get_values(i,ttl=ttl, ref=ref_448)\n",
    "        z=ttl,p,t\n",
    "        my_list.append(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(my_list, columns=['Title', 'Pvalue', 'Tstatistics' ])\n",
    "df=df.sort_values(by=['Tstatistics'], ascending=False)\n",
    "df.to_excel('/Users/deepanshishokeen/Desktop/Music/get_stats/classical.xlsx')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_excel('/Users/deepanshishokeen/Desktop/Music/get_stats/classical.xlsx')\n",
    "g1=df.Pvalue\n",
    "g1.shape\n",
    "g2=df.Tstatistics\n",
    "g2.shape\n",
    "\n",
    "k448=plt.scatter(1422,0.04,color='red')#k448\n",
    "rnr=plt.scatter(g2,g1,color='blue')\n",
    "plt.legend((k448, rnr),\n",
    "           ('K448', 'Classical Music'),\n",
    "           scatterpoints=1,\n",
    "           fontsize=14)\n",
    "plt.title('Scatter plot for Classical Music', fontsize=20)\n",
    "plt.xlabel('U statistics', fontsize=18)\n",
    "plt.ylabel('P values', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MannwhitneyuResult(statistic=742.0, pvalue=3.904193801308547e-08)\n",
      "3.904193801308547e-08 742.0\n",
      "MannwhitneyuResult(statistic=745.0, pvalue=4.269445599288243e-08)\n",
      "4.269445599288243e-08 745.0\n",
      "MannwhitneyuResult(statistic=723.0, pvalue=2.2028763150262704e-08)\n",
      "2.2028763150262704e-08 723.0\n",
      "MannwhitneyuResult(statistic=155.0, pvalue=7.261235743248243e-18)\n",
      "7.261235743248243e-18 155.0\n",
      "MannwhitneyuResult(statistic=112.0, pvalue=9.562897194418603e-19)\n",
      "9.562897194418603e-19 112.0\n",
      "MannwhitneyuResult(statistic=4.0, pvalue=4.655075256185421e-21)\n",
      "4.655075256185421e-21 4.0\n",
      "MannwhitneyuResult(statistic=438.0, pvalue=1.2112521711899665e-12)\n",
      "1.2112521711899665e-12 438.0\n",
      "MannwhitneyuResult(statistic=642.0, pvalue=1.7133684900722006e-09)\n",
      "1.7133684900722006e-09 642.0\n",
      "MannwhitneyuResult(statistic=427.0, pvalue=7.920806014307796e-13)\n",
      "7.920806014307796e-13 427.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=69.0, pvalue=1.1945046782616295e-19)\n",
      "1.1945046782616295e-19 69.0\n",
      "MannwhitneyuResult(statistic=3.0, pvalue=4.424204115202177e-21)\n",
      "4.424204115202177e-21 3.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=604.0, pvalue=4.85049052952426e-10)\n",
      "4.85049052952426e-10 604.0\n",
      "MannwhitneyuResult(statistic=458.0, pvalue=2.5989450825774804e-12)\n",
      "2.5989450825774804e-12 458.0\n",
      "MannwhitneyuResult(statistic=517.0, pvalue=2.3128442148201062e-11)\n",
      "2.3128442148201062e-11 517.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=0.0, pvalue=3.7973954466182726e-21)\n",
      "3.7973954466182726e-21 0.0\n",
      "MannwhitneyuResult(statistic=19.0, pvalue=9.949621087608652e-21)\n",
      "9.949621087608652e-21 19.0\n",
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      "9.594752477677674e-18 161.0\n",
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      "0.3939212543571017 1690.0\n",
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      "1.5645537567022417e-20 28.0\n",
      "MannwhitneyuResult(statistic=150.0, pvalue=5.751965388406566e-18)\n",
      "5.751965388406566e-18 150.0\n",
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      "5.513426079664112e-12 478.0\n"
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    }
   ],
   "source": [
    "my_list=[]\n",
    "mypath= '/Volumes/LaCie/Preferred Music Corpus/Country/Classic Country'\n",
    "files = [f for f in os.listdir(mypath) if f.endswith(\".wav\") and not f.startswith(\"._\")]\n",
    "files\n",
    "\n",
    "z = []\n",
    "new_list=[]\n",
    "slash='/'\n",
    "for j in files:\n",
    "    ttl=j.replace('.wav','')\n",
    "    whole_path=mypath+slash+j\n",
    "    sampFreq, snd = wavfile.read(whole_path)\n",
    "    if snd.shape[0]/sampFreq > 140: \n",
    "        res,p,t=get_values(whole_path,ttl=ttl, ref=ref_448)\n",
    "        z=ttl,p,t\n",
    "        my_list.append(z)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(my_list, columns=['Title', 'Pvalue', 'Tstatistics' ])\n",
    "df=df.sort_values(by=['Tstatistics'], ascending=False)\n",
    "df.to_excel('/Users/deepanshishokeen/Desktop/Music/get_stats/classic_country.xlsx')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_excel('/Users/deepanshishokeen/Desktop/Music/get_stats/classic_country.xlsx')\n",
    "g1=df.Pvalue\n",
    "g1.shape\n",
    "g2=df.Tstatistics\n",
    "g2.shape\n",
    "\n",
    "k448=plt.scatter(1422,0.04,color='red')#k448\n",
    "rnr=plt.scatter(g2,g1,color='blue')\n",
    "plt.legend((k448, rnr),\n",
    "           ('K448', 'Classic country Music'),\n",
    "           scatterpoints=1,\n",
    "           fontsize=14)\n",
    "plt.title('Scatter plot for Classic country Music', fontsize=20)\n",
    "plt.xlabel('U statistics', fontsize=18)\n",
    "plt.ylabel('P values', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nw = pd.read_excel('/Users/deepanshishokeen/Desktop/Music/get_stats/Non-western.xlsx')\n",
    "df_amb = pd.read_excel('/Users/deepanshishokeen/Desktop/Music/get_stats/Ambience.xlsx')\n",
    "df_hm = pd.read_excel('/Users/deepanshishokeen/Desktop/Music/get_stats/heavy_metal.xlsx')\n",
    "df_class = pd.read_excel('/Users/deepanshishokeen/Desktop/Music/get_stats/classical.xlsx')\n",
    "df_rnr = pd.read_excel('/Users/deepanshishokeen/Desktop/Music/get_stats/rock_n_roll.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_rnr.append(df_amb, ignore_index=True)\n",
    "df = df.append(df_nw,ignore_index=True)\n",
    "df = df.append(df_hm,ignore_index=True)\n",
    "df = df.append(df_class,ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'count')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Tstatistics'], color = 'blue', edgecolor = 'black', bins=30)\n",
    "plt.title('Histogram of U stats')\n",
    "plt.xlabel('U stats')\n",
    "plt.ylabel('count')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}