MeterExploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXPLORING FURTHER RELATIONS OF METRICAL STRENGTH AND PITCH RELATIONS IN TURKISH MAKAM MUSIC 2019\n",
    "## Francessca Ronchini, Dougal Shakespeare\n",
    "\n",
    "This research  aims  to  apply  a  three-fold  approach  to  further explore relations of metrical strength in the music of Turkish  Makam:  (1)  assess  the  extent  to  which  findings and conclusions drawn in Holzapfel et al’s work [1] are reproducible on a different data-set, (2) further extend Holzapfelet al’s methodology by exploring correlation relationships across different Makams to observe if any styles of Makam may also act to reinforce or diminish metrical strength, (3) pitch correlation analysis to observe if the makam structure reinforce or dimish the usul metrical structure. \n",
    "The dataset used for this work is the **SymbTr** which is a collection machine readable symbolic scores aimed at performing computational studies of Turkish Makam music. \n",
    "\n",
    "[1] http://mtg.upf.edu/node/3886"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can download the full pdf score dataset here: https://github.com/MTG/SymbTr-pdf \\\n",
    "You can download the full xml files dataset here: https://github.com/MTG/SymbTr/releases/tag/v2.4.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from music21 import *\n",
    "import os \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: change paths to your own dirs\n",
    "xml_path = 'path to MusicXML dir'\n",
    "pdf_path = 'path to SymbTr-pdf dir'\n",
    "#musescore_path = 'path to musescore'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Config Muse Score\n",
    "#us = environment.UserSettings()\n",
    "#us.create()\n",
    "#us['musicxmlPath'] = musescore_path # Path to MuseScore3.exe\n",
    "#us['ipythonShowFormat'] = 'musicxml'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing and Balancing of Data\n",
    "\n",
    "We will not work on the full dataset in order to have balance data.  \n",
    "For this reason, we will select only a subset of the usul and makam available. In particular, we will select the most popular usul based on value count.  \n",
    "Once we have decided which usul when want to work on, we will select only makam which share all the usul which has been selected. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting all the list of the different makam and usul used for each pdf file\n",
    "usul = []\n",
    "makam = []\n",
    "for makam_file in os.listdir(pdf_path):\n",
    "    if makam_file[-3:] == 'pdf':\n",
    "        usul.append(makam_file.split('--')[2])\n",
    "        makam.append(makam_file.split('--')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "aksak            319\n",
       "sofyan           293\n",
       "duyek            278\n",
       "aksaksemai       128\n",
       "curcuna          111\n",
       "                ... \n",
       "14_4               1\n",
       "devrituran_ii      1\n",
       "nimsakil           1\n",
       "bulgardarbi        1\n",
       "devrisureyya       1\n",
       "Length: 88, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#value count of different usul\n",
    "pd.Series(usul).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the most popolar usul in the dataset are:\n",
    "* aksak\n",
    "* sofyan \n",
    "* duyek \n",
    "* aksaksemai\n",
    "* curcuna \n",
    "\n",
    "Anyway, we have been not able to retrieve the weigths of düm, tek and ke for the aksaksemai usul, we decided to discart it and focused the research on aksak, sofyan, duyek and curcuna. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
    "    a = pd.Series(usul).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "makam            usul       \n",
      "acemasiran       agiraksak       7\n",
      "                 aksak           9\n",
      "                 duyek           7\n",
      "acemkurdi        aksak           6\n",
      "beyati           agiraksak       7\n",
      "                 aksak           7\n",
      "                 aksaksemai      7\n",
      "                 duyek          11\n",
      "buselik          duyek          10\n",
      "                 sofyan          6\n",
      "evic             aksak           8\n",
      "gerdaniye        sofyan          8\n",
      "hicaz            agiraksak      12\n",
      "                 aksak          26\n",
      "                 curcuna         9\n",
      "                 duyek          19\n",
      "                 nimsofyan      12\n",
      "                 semai           8\n",
      "                 senginsemai     6\n",
      "                 sofyan         20\n",
      "                 turkaksagi      8\n",
      "hicaz_humayun    sofyan          6\n",
      "hicazkar         aksak          11\n",
      "                 devrihindi      8\n",
      "                 duyek          15\n",
      "                 sofyan          7\n",
      "huseyni          agiraksak       6\n",
      "                 aksak          15\n",
      "                 curcuna         7\n",
      "                 duyek           7\n",
      "                 sofyan         17\n",
      "huzzam           agiraksak       6\n",
      "                 aksak          21\n",
      "                 curcuna        11\n",
      "                 duyek          16\n",
      "karcigar         aksak           8\n",
      "                 duyek           8\n",
      "kurdilihicazkar  aksak          13\n",
      "                 duyek           6\n",
      "mahur            aksak          18\n",
      "                 duyek          16\n",
      "                 sofyan         10\n",
      "muhayyer         aksak          12\n",
      "                 duyek           6\n",
      "                 nimsofyan       6\n",
      "                 sofyan         12\n",
      "muhayyerkurdi    duyek           6\n",
      "nihavent         aksak          15\n",
      "                 curcuna         7\n",
      "                 duyek          24\n",
      "                 nimsofyan      15\n",
      "                 semai          15\n",
      "                 sofyan         17\n",
      "                 turkaksagi      6\n",
      "rast             aksak           7\n",
      "                 curcuna         9\n",
      "                 duyek          18\n",
      "                 semai           7\n",
      "                 senginsemai     7\n",
      "                 sofyan         15\n",
      "saba             agiraksak       6\n",
      "                 aksak          10\n",
      "                 duyek          11\n",
      "                 sofyan          8\n",
      "segah            aksak          18\n",
      "                 duyek           9\n",
      "                 sofyan         11\n",
      "sehnaz           sofyan          7\n",
      "tahir            sofyan          6\n",
      "ussak            aksak          19\n",
      "                 curcuna         7\n",
      "                 duyek          15\n",
      "                 nimsofyan       9\n",
      "                 sofyan         19\n",
      "                 yuruksemai      6\n",
      "Name: makam, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Output makam / ursul distribution for all ursuls which have > 5 occurances in a makam\n",
    "makams = pd.DataFrame(np.array([usul, makam]).T, columns=['usul', 'makam'])\n",
    "makamsdf = makams.groupby(['makam', 'usul']).filter(lambda x: len(x) > 5)\n",
    "makamsdf = makamsdf.groupby(['makam', 'usul'])['makam'].count() \n",
    "\n",
    "with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
    "    print(makamsdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next step, only the makam which share all the four usul will be selected from the dataset and the research will be focused on those makams. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       makam     usul  count\n",
      "15     hicaz    aksak     26\n",
      "16     hicaz  curcuna      9\n",
      "17     hicaz    duyek     19\n",
      "18     hicaz   sofyan     20\n",
      "20  hicazkar    aksak     11\n",
      "21  hicazkar  curcuna      4\n",
      "22  hicazkar    duyek     15\n",
      "23  hicazkar   sofyan      7\n",
      "25   huseyni    aksak     15\n",
      "26   huseyni  curcuna      7\n",
      "27   huseyni    duyek      7\n",
      "28   huseyni   sofyan     17\n",
      "29    huzzam    aksak     21\n",
      "30    huzzam  curcuna     11\n",
      "31    huzzam    duyek     16\n",
      "32    huzzam   sofyan      5\n",
      "40     mahur    aksak     18\n",
      "41     mahur  curcuna      4\n",
      "42     mahur    duyek     16\n",
      "43     mahur   sofyan     10\n",
      "44  muhayyer    aksak     12\n",
      "45  muhayyer  curcuna      5\n",
      "46  muhayyer    duyek      6\n",
      "47  muhayyer   sofyan     12\n",
      "50  nihavent    aksak     15\n",
      "51  nihavent  curcuna      7\n",
      "52  nihavent    duyek     24\n",
      "53  nihavent   sofyan     17\n",
      "56      rast    aksak      7\n",
      "57      rast  curcuna      9\n",
      "58      rast    duyek     18\n",
      "59      rast   sofyan     15\n",
      "63     segah    aksak     18\n",
      "64     segah  curcuna      5\n",
      "65     segah    duyek      9\n",
      "66     segah   sofyan     11\n",
      "75     ussak    aksak     19\n",
      "76     ussak  curcuna      7\n",
      "77     ussak    duyek     15\n",
      "78     ussak   sofyan     19\n"
     ]
    }
   ],
   "source": [
    "high_pop_usuls = ['aksak', 'sofyan', 'duyek', 'curcuna'] #select 4 most popular ursuls for meaningful analysis\n",
    "filtered_makams = makams.loc[makams['usul'].isin(high_pop_usuls)] #select makams which contain those usul\n",
    "filtered_makams = filtered_makams.groupby(['makam', 'usul']).filter(lambda x: len(x) > 3) #remove usul occurances with <= 3\n",
    "df = pd.DataFrame({'count' : filtered_makams.groupby( [ \"makam\", \"usul\"] ).size()}).reset_index()\n",
    "\n",
    "# remove all makams which do not appear in all 4 usuls\n",
    "vc = df['makam'].value_counts()\n",
    "vc = vc[vc == 4].index\n",
    "df = df.loc[df['makam'].isin(vc)]\n",
    "\n",
    "# output chosen makams and usul counts\n",
    "with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
    "    print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We consider only makams which shared the same four usul selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Makam considered for the research: ['hicaz' 'hicazkar' 'huseyni' 'huzzam' 'mahur' 'muhayyer' 'nihavent'\n",
      " 'rast' 'segah' 'ussak']\n"
     ]
    }
   ],
   "source": [
    "#makams that will be considered for this research\n",
    "makam_considered = df.makam.unique()\n",
    "print(\"Makam considered for the research: {}\".format(makam_considered))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hard Coded Usul Weights\n",
    "\n",
    "All weights for the usul we considered are already defined in the following paper which we consider as a baseline [1]. In this paper, the weighted metrical distribution are taken form the Mus2okr software [2].\n",
    "We consider the same weigths for our research.\n",
    "\n",
    "[1] http://mtg.upf.edu/node/3886 \n",
    "[2] http://www.musiki.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#weigths_dictionary\n",
    "weights = {\n",
    "    \"aksak\": [3, 0, 0, 0, 2, 0, 1, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0],\n",
    "    \"sofyan\": [3, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0], \n",
    "    \"duyek\": [2, 0, 3, 0, 0, 0, 2, 0, 3, 0, 0, 0, 1, 0, 0, 0],\n",
    "    \"curcuna\": [3, 0, 0, 0, 1, 0, 2, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0]\n",
    "}\n",
    "\n",
    "\n",
    "# meter for usul\n",
    "usul_tempo = {\n",
    "    \"aksak\": [9, 8], \n",
    "    \"sofyan\": [4, 4], \n",
    "    \"duyek\": [8, 8], \n",
    "    \"curcuna\": [10, 8]\n",
    "}\n",
    "\n",
    "#creating a dictionary to save bins needed to plot the histogram for every usul\n",
    "usul_bins = {}\n",
    "for usul in usul_tempo:\n",
    "    y_bins = (16/usul_tempo[usul][1]) * usul_tempo[usul][0]\n",
    "    usul_bins[usul] = int(y_bins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot and see how the weights distribution looks like for each usul."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 711,
       "width": 1071
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting weigths for each usul\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2)\n",
    "\n",
    "fig.set_figheight(10)\n",
    "fig.set_figwidth(15)\n",
    "\n",
    "\n",
    "#create plot\n",
    "# usul aksak\n",
    "ax1.bar(np.arange(1, usul_bins[\"aksak\"]+1, dtype=int), weights[\"aksak\"])\n",
    "ax1.set_xlabel(\"Weight location (1/16)\")\n",
    "ax1.set_xlim(0.5, usul_bins[\"aksak\"])\n",
    "ax1.set_ylabel(\"Weight\")\n",
    "ax1.set_title('Aksak meter weights')\n",
    "\n",
    "#usul sofyan\n",
    "ax2.bar(np.arange(1, usul_bins[\"sofyan\"]+1, dtype=int), weights[\"sofyan\"])\n",
    "ax2.set_xlabel(\"Weight location (1/16)\")\n",
    "ax2.set_xlim(0.5, usul_bins[\"sofyan\"])\n",
    "ax2.set_ylabel(\"Weight\")\n",
    "ax2.set_title('Sofyan meter weights')\n",
    "\n",
    "#usul duyek\n",
    "ax3.bar(np.arange(1, usul_bins[\"duyek\"]+1, dtype=int), weights[\"duyek\"])\n",
    "ax3.set_xlabel(\"Weight location (1/16)\")\n",
    "ax3.set_xlim(0.5, usul_bins[\"duyek\"])\n",
    "ax3.set_ylabel(\"Weight\")\n",
    "ax3.set_title('Duyek meter weights')\n",
    " \n",
    "#usul curcuna\n",
    "ax4.bar(np.arange(1, usul_bins[\"curcuna\"]+1, dtype=int), weights[\"curcuna\"])\n",
    "ax4.set_xlabel(\"Weight location (1/16)\")\n",
    "ax4.set_xlim(0.5, usul_bins[\"curcuna\"])\n",
    "ax4.set_ylabel(\"Weight\")\n",
    "ax4.set_title('Curcuna meter weights')\n",
    " \n",
    "#Create names on the x-axis\n",
    "#plt.xticks(y_pos, bars)\n",
    " \n",
    "#Show graphic\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with makam scores\n",
    "Most of makam scores have non standard key signatures, and this might cause problems. We will show you an example of this problema and a work around we will apply to all the xml file we will process, in order to be able to work with MusicXML files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Music21 was not developed with makam music in mind, and many of the accidentals used in makam music are not recognized by music21."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# path to test xml file\n",
    "makamScore = xml_path + 'acemasiran--nakis--yuruksemai--ne_hevayi--dede_efendi.xml'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "music21 doesn't accept double-slash-flat as accidental\n",
      "music21 doesn't accept slash-flat as accidental\n",
      "music21 doesn't accept slash-quarter-sharp as accidental\n",
      "music21 doesn't accept slash-sharp as accidental\n",
      "done\n"
     ]
    },
    {
     "data": {
      "image/png": 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S4M1Er/RpwM+AlxKzrOZ9Efg98KVs+2O57aXH56cAGzH6cfoDgW2z5WeBL7fYZoBHga8A+wGr1Cm7XI1tK1A5a+xKLbYnTeyYYJek3vDmpiRJkiRJGgivozzezFXAzBbreTar4+Dc+plED/jSMU5qsf5W7EvxGOwv7dAxdk3qPKNDdbZqDo7BLmk4rEg59l7cxeM4BrskdY5jsEsaBpOIToXv6MGxHINdkir1dAz21A+AU7LlnYHrgG1aqGdO9r5Bsm4SMezK+tnnX9Be7/Vmza6xbc0OHcMe7JLUe47BLkmSpEG0LnAi8MF+N0SS1Lh2E+wAnwROzZY3Aa4hJhKd0kQdc7L3DbP3WcBPgFdnny8DjiTuCvTKPjW2vbxDx3CYAknqPWOvJLVvCvB1etPLUpLGm2nAelXWT8q9S5LGgE4k2AE+BrwJeJ54fPO/gduBtwLLN7D/nOx9S+A44J/AYdm6C4H9gfkdamsjNgReW2P7G4GVO3AcJ9qTpN5bRPmGrbFXklqzEfF3+3v73RBJGoPOI/Ig2/e5HZKkDmh1ktNqzgauJYZ12Ym4G/stYmzxS4E/AfcDjxDJ8qnADGAtYPOsjr2zF8TYtScTk5L2suf6OkRSf9kaZVYELiBuAjzRxrHsRSlJ/bEIWAZjryS1yl6WktS61XLvkqQxrJMJdoCbibHYXwt8gLgbuwzwiuzViIeB7wFfBR7ocPtqeTOwF3AoML2B8nsAtxHJ+LOISV6b5RjsktQfC4nrkz3YJUmSJElSyyYTw7t0wy+ISU83JXqzr8roxPVC4CngBcozWH8LWEIML9NLhwIrAPcATwPziN7pTwELsjLLZWVWyt5nEjcOVgL+3sIxZyTLe1G713y3zSKSTd36/0GSBkmpx+WWdC/uTSGGEzOuShqPSr0uZ9GbOFfqGLROj44nSd20TvZ+FPDSZP1K2fsMuh/rZmbvm/fgWJI0FuyavRflCQqfOppMDNHSTbdlL4gx30uTny4ClmbLKwD/AUwgLjT96M19bcH6yVQmwp/JXvfkyrVyHqcmy9NbrKNTJhP/ffrZBknqldLQY1PpXtybSG+us5LUD6UkUK/iXOnG6DI9Op4kddMy2fvKVM43V0p6T6L7sa40X960HhxLksaC+cAtRIfranHxftobKrzr1iCSHSNUn0l7vJpJ+Xsf2+e2zAEe73MbJKlX7iVi71e7eIxngX93sX5J6qetiDg6J/s8AdiM7o3JPjU73uVdql+SeulyIqbtl1u/Ubb+/uzz9sDWXWpDaRSB87tUvyQNjYn9bkAmvWM7o7DU+OMY7JLUH6WY6xjsktS+3YBriB4/n+pzWyRpPFiJ6KhxPXADMRyXJGlAdXqS01a9kCxP61srem9RwbIkqbsW5t4lSa1Zh8pe5esUFZQkFVoROAJ4c/Z5GjGfHcSTQcsR88NJkgZQp3qwvxK4EHiUSJbfCpxM5RjjtSxNlqcUlhp/FlMeB9gkjyT1jj3YJal1ywFvzJYH5YlYSRqL9gB+DDwEnAm8pL/NkSS1ot0e7JOBb1P+A7tkE+BU4FXEmGLP1qlnmWR5cZttGmsWEd8/n2CfRNy1rnfuJEnFimKpCXZJat4E4PXAZ4HZfW6LJI1VO1OOoR/qZ0MkSZ3RboL9a4xOrqd2I+7CHl2nnnRYmAVttmmsWUgk2BcBawL/CRxKPA42AXiAOM9fAJa0eayNiAlS1gCWJe6SXwnc12a9kjRoNgK+RNzknQI8A/wF+CbwKzo3RIxxVdIw+RLw7jpl2o1/RXH1sTbrlaR+Oxr4ALBFk/s9Bcxr47hFcXWk1k6SpN7YhhjaZaTOaymwcZ26XpSU37BL7R1UTxDf+ywiAVR0Hn/QYv0rAB8Dbi+odynwS2KW8sdb/RKSNEA2IRIxRfH098BN2fJ7Wqi/0bj6HDE5lSSNF9dTHFufBU4hkjfNaiSu/jpbvrygDkkadC9QP3+Sxr3LgHcBq7ZwrEbi6h+y5fNb/UKSpPZ9lMYvDsfVqWuPpOyMLrV3UD1M+bsvAk4D/i9wOqPP4zFN1v0u4Mkq9VR7LSaS/ZI0lk0E/k5lfLub6DH09irbTmiy/mbi6lLgzra+jSQNlmoJ9qXA94C1Wqyzmbg6AlzXevMlqa8aiXGlDnj3t3GcZuPqRW0cS5LUpjNpPGCfUqeuN1G+mAyb+yifp9fmtp1D5XlstCfkCsSks43+9ym9Xmjje0jSIDiA0bFt22T7dGBOsq3eDeCSVuPq0+18GUkaMNUS7HNarKvVuOoTl5LGqqK49iTxRPs+xJOYrSbYW42r7STzJUlt+gyNB+xj69RVSiRf0a3GDrA7ie9+W5VtuzP6XNZ7PGxl4G9V9mv0tczoKiVpzPgslTHtmiplTkq2v6WBOtuJq0sxrkoaP64iYtsDwCdpPcHeblyd0sZ3kKR+qTbE7gepHFprI1pLercTV5fQ/vx8kjTUJrax74MNllsM/LbG9mWAV2XLv26jPWPVouz9n1W2VeuxvkKNupYD/gRs10Z7atUvSYNu5dznW6qUuTpZrjfJabtxdQIws8V9JWnQvBc4kehh+eMW6+hEXJ3e4r6S1E9nEpOLvh/4R7buRtp/krzduDoRWL7NNkjSUGvnLuWlDZb7AnBPje1HU+6VfWEb7RmrSsmd+VW2VZskagrwioK6TqRyKIRmjRAX5SW59VdSvX2SNGjyN3+rxdF5yfIiYiLu9Qrq60Rc3Z7osZT6C+UbrJI0VlxBY0+cbkHxmOydiKs7Mjqu/rGNOiWpF96VLP+fJvd9MbBKwbZOxNWXUhlXFxF/r0qSeuBuaj+++WVq95KfntQxjMl1KI9leW6VbQdSeU4vI3oOtTr8S6uvzTr6jSWpe9JJs0eo3oN9k2T7a4DP0duYOsLonvaSNNZsRfEQMWfR25iaT7ZL0qC7nIhf++XWFw0R8wt6G1cf6dQXlaRh0O44W58B/idbfh44m3i06F7gfOJxp1pOA9Ynekx/rM22jFW1hid4e+7zp4kL7pNVyi5H9Z6azZhP9cfT8j3aJWlQXQpcAuyVfd6MSLqnT10tTpYXAgvofVwdabNeSRpk8+ltXDWmShrvnqW3cfWpNuuUJDVhEuUe2KVe6I2O6/6BZL8PdaV1Y8OlVO/BvjuVk6CcXaeeW2jvDvXdwIx2v4wkDYANiB8Fpfh2JZXXpnWSbfleQynjqiQVq9WDvYhxVZJCsz3YixhXJWmcWJd4fKgUoC+heMxFiGFhzkjK/5yYrGhY/ZE4Dz9M1q0I3E75HP2W+nel59P6RXUR8LKOfBtJGgx7ED19SnHuw8m29ZP1e9eow7gqScXyCfYjgZ8Bu9bYx7gqSaFegv1R4FjgD0RsnVRQj3FVksaRbYiJTEuB+jmix/VrgJcQE24cCJwOPJyU+ymwTO+bO1B+Q/lcQNw9vozyOfoSjQ3l8xitX1T/b2e+iiQNlK0p36xcCrwtW78p5Ri4e439jauSVKyUYJ9LTIRXioFn19jHuCpJoVqCfSJwBNXj4BoF9RhXJWmcWR24gMaC+XPEsDBFd2GHyc+Jc3IxcQ7/mn1+Fji8iXp+QPMX1XnAIZ34EpI0oGYC/0s57r0f2Dn5vHONfY2rklRsR6rHwfywhynjqiSFNMG+I9EZ8UGKY+GaBfUYVyVpnNoZ+DbVLw63AKcSw8oonE+cm4eJ8c9GiMlhN22yno2Bp2n8onoFMUyCJA2DdxKTmY4A11COhdvX2Me4KkmjTSXmUppH8wl246okhVKC/Qkai4dFCXbjqiQNgVnAJsCLaH9m6/Eqf8f5a8QPl1bsAtxB7Qvq9cBhDPe495KG04uBO6mMiVvX2ce4Kklle1A5LGS119l16jCuShpWM4FjiGFiF9Fcr/NVatRrXJWkPjO49t9ZwJuz5S8C72uzvknAnsBOxDhtI8TYmHcRY7vf22b9kjSWbQBcC6ycfd6Y+EFSi3FVksLlwG41tj9AjOl7dZ16jKuShtGtRAfEZtxDdML7Yp1yxlVJ0lDbHbgS+BQxqYkkqbu2BH5PDGdm3JWkxl1P9Z6RzwOfAZbvX9MkaeA102P9KWBf7BQpSZIkSZI0blRLsM8lng6SJNVWK6G+mBg65r3Z5/v71EZJkiRJkiR1SZpgvz97n9PPBknSGFItsf5vYuLotbIyG2GCXZIkSZIkaVz6H+Bu4C3ANphgl6RmPErEzfuAx7Pl/XJlTLBLkiRJkiQNga0wwS5JzdgA2IOYA+hyTLBL0rgxud8NkCRJkiRJGufuyl6SpHFmYr8bIEmSJEmSJEnSWGSCXZIkSZIkSZKkFphglyRJkiRJkiSpBSbYJUmSJDXrhdy7JKl9TwKLgEf63RBJUuOc5FSSJElSs24HDgPu63dDJGkcmQusDzzX53ZIkppggl2SJElSKy7sdwMkaYxakntPPdjLhkiS2jep3w2QJEmSJEkaIg8BdwE/Bhb2uS2SJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSBtykfjdATZkKbAscAOwJTAEeBRYnZVYCXg2sAdzV4/ZJknqrkevCBGBH4Ejgxtw2SRKsABwGrAPc0ee2SJIxSZKkLtkFeAQYyb3mA28ETgWuIBInI8CZ/WmmJKlHal0X9gDeBJwDPJRsW60vLZWkwbM68C7gD8BCIkae3c8GSRpqxiRJkrpsB2ABcZE9GdgPeIJywuQZYAmVCRYT7JI0ftW7LlwCPMfo5LsJdkkKr2T0389n97NBkoaaMUmSpC47l7jAPkE86g8xDMzSbP0pwMrArXQvwb4qsFGH65SkQbML5Tg7yBq5LgAcT/cS7F4XpOE0VuJkI5YnngA1mSVpEBiTJEnqkhWA54kL7B9y2zYixtUt+T7dSbBPBC4GvtvBOiVp0OxDJKjX63dD6mjmurAB3Umwe12QhtNYiZPN+DImsyQNDmOSJI1BE/vdANW1MbBstvx0btsdwHXJ5/z2TvkEsBfjp7eSJOWtBfyAiHODHuu8Lkjqh7EUJ5vxQr8bIEkJY5IkjUEm2Affun0+/quBj/S5DZLUTcsB5zN2xif3uiCp18ZanJQkSZJ6ZnK/G6BCLwY2JyY7KVkHODL5/Ddi3PVmrAu8BngRMX7uc8D1wE+BR3NljwC+R7mX0gbJ8R8C/tzksSWNH6sAuwMvJXpUP0jEpB8DM4G3AKcD84GtgK1z+9+alQc4CJiR234bEZtSE4GDiQk+1yWuYQ8AvyOGK6lmdSJuzQbeB2wCHAWsnbX1euB/iTGFSw4BHsuWfw/MTbYtn33vHYgY/TjwL+CCXLlm2vD7gv3yunVdaOa8el2QGjescRIiVu2StWN69l2upDhGdCpOlkzN6tuaOG93ATdnbfkLcH+d/TchntLZmfjvcz0xFOPiKmVbOecTgT2B1xIx9YasjoOIJ5G+Tvmcrpt9l1nAh4knCfYB9gAWAlcDvyGuR5IG13bA/sTfTk8RcfER4t/xb+rs20xMguZ+84MxSZI0jj1B5bi51V5H5/b5GrXHYD8NWJRtnwvMS8o/AmyZlH1ZnWOf1+b3kzR27U9xjFqULB+WlX8j8Gyu3FeS+n5ZZft/5465FXBntm0JcA/lCT1HgDOSstOIRPBFSXtuydqxJNlnPpHsKYpzi4HNknq3A24vKPsQsG+LbZhJY1q5LqyS257vfdrMefW6IDVuWOPkZOC/csdNX+dmx262DY3GSbL2lM7D88BfqTx3/5Er/1/JtrOBDxW0/VJgSm7fZs45wBbAZ4F7kzLHZW1Mj/UF4NjsmKX6biBi/MIqbbsX2LbREySp5z5HOa7NIZLrpX+/N+fKthOToLnf/MYkSdK4tzOwK5VJ8yuzdbsCO1XZp1aC/YPJtrOIu9RTgE8l689Pys/IjnNusv2m5Pj5XlSShsNJlPgkLpEAAB/ZSURBVP+w/h2wRrZ+e6KHYfrH9WHJftOI3pvVEkcQvQofTbaniaPViaTFCDEu5SbZ+vWJHwqlfUq9Pw9m9B/6+aRW6XUg0SMoXXcUEee2SNqwL7Agq+Pj2ff9T+DfyX5PEL2Emm1DmpyqpZXrQq0Ee7Pn1euC1JhhjZMTiImXR4C/Ez0a9wW+QyTiS/t+soU2NBonAW7M9vkb0cMSosfo3dn6/I3INJlVei0ALk/2Kb2OSfZr9pzD6KRV0euyrM6i7QsYfRPjKRzCRxpEh1L+d/qGbN0E4u/JEeIJm1SrMQma/81vTJIkDY33Ub5I/bpO2VoJ9oeSbQcl69dN1i9k9Lj8+TvokobXJpR7qVzB6F4ze1OcOAL4YbItnziCGAagWuLo2GT9X3P7nJVs+2iyfiZwTq49fwc2Ak4let38iviBMy1Xbv3cMSYTvSpHiF4+qY2pTBx9ucU2NKOZ60KtBHsr5xW8Lki1DGucBHh9tu1JIvmc+l6y7/PJ9k7HyQ0KviuUk1xvy63PJ7MuAVbMtk0nhgErbbsw2a+Vc74M0es9n4g6jhhe4WJiGIcDiHHvf5Ar9wfiaaKJxI2by3LbTy86MZL65vuUf2unsWwicSPwgVz5VmMSNP+b35gkSR3iJKfDY0HB8r3Aw9nyFLzLLKnY6ZSTRZ8hehmmLiV6qxR5uk79TxasL4pfANcmy2vnjvWnXNl3E+NInkIM93IQ8cd/PW+j3HvyW8QPjNLrAaIXe8mrutSGbmjlvEqqbVjj5LLE9wX4GfAMlbHyl7mye3ahDVCZ2D+YyqFlfkk8ATC1xv63EfNclM7zs0QP/JL0/LVyzhcC/yT+/i65FPhG9r43MZb7RZSH50kdQPRiXUr8/X5Arq5j8fedNGhKcWkKcHiyfikRXzoVk6D53/zGJEnqECc5HR7HAO8g7pKXJlyaSCSDlk/KrUL54itJJesQfzRDJEQuqlJmMfFY/Kwq29rxE2BTYhKltCfMbGICwZJVcvs9kyzPIXoAtWK/ZPnOOmU3A1amPBFUp9rQDa2eV0nVDXOc3IL4/gBvyl617Eb0xu9kGyCGbCnZiZhs751EUmgJMTzMgzX2v4rRN0XmJMvp+Wsnhqbf+TtVtpc8lyzfWKVtzwDfBD6dfZ5G9FSdg6RBkcal84CXAJ8g/v3+kMp/53nNxCRo/Te/MUmS2mSCfXhcmr0g7li/PXutkyvX7FAFkobD5slyaczZXlkAfCRbnkD05DmeGBd4UlKuVvx6vo3jb5wsf75O2cUU90AtasN0oodPkZuB39Q5bis6cV4llRknw83EsC61/LHJNjQaJx8Efkv5xujmRML+x8B7iTHxmzW/YH2nYmg75x3gp5STWQAbYjJLGiTfoTw01UTgPcDrgPcT89p8t8n6imISdOY3vzFJklpggn24zCLGs3w78SjaZcRjuF/HoWEk1ZYmjpbrUxteRSS4tyGGWPgikdD+SK2d2jSRGNMXovfjycTjtJ00i/guRZYS1+tuJev6cV6l8WhY4yRUJth/Sgzv0knNxMk3EZPJppOLHk4MNXM0cEGH29bvGHpv7vOyPTqupMZcTfzmPp1yYntNYv6Jo4AjqD10WLP6/ZvfmCRpKDke1vDYGrgJ+E9iBvCjiIlLLiB+BEhSLWsky72+dkwAvkT0StwG+AWRyPoglY/ddsMkYgKo0vJGXTjGIiIuF5lId3qR9/O8SuPRsMZJiGEASjYrLNW6ZuLkw8QQNF+n8sbk8sD5xETRndDvc16ygLgBXFJvKDNJvfcV4imXfPJ5P+BKYhiVThiE3/zGJElDyQT7cJgO/JryhftIYgZwSWpUOk7j2kRc6ZUTgJOy5SuA1wAP9ejYi6gcr/eVdcofDhzX5DEeBVYixm4vei1tss5G9PO8SuPRsMZJqHz8/2XU7sE/E/gao4csqKXRODkd2IUYB/idRKI9nXB0AjEZ64pNHLtIv895ycqUh6RZDNzVhzZIKrYlMVfDH4n5Kk6lcgLSLYATO3CcQfnNb0ySNJRMsA+HIyj/iJnL6LF8V2qwnmn1i0gap+5OlqcAHygo12icqPa4aNFkmu9Pln9EZQ+cRuNXM/LfIf1hcHKNY24LnJ29N2s+8ESNVzd04rx6XZDKjJNhTWr3Ev8G5fGIm9FInNyU6A06O/t8FbAz8GHKvdknUzl5dat6fc6LbJMs/4PRkw5K6q/PEZOaQkwS+jFgK+CWpMxBHThOp37zt8uYJGkomWAffMsky/XGL5tUsD6dMXwl4odPyerE2Gwl+d5W6Y+FLeocX9L4dSOVj3u+B1g/V2ZzavdITHvrHEJlzFoJeEXyefXsfQKViZytcnVukizX6i1a63q3JPc5H+suS5ZXAr7P6CTXLsSYv6Xekc22oRmduC60c169LkjVDXOcvAF4Nvn8AaJHd2oa8D9Er8pvA/c12YZmHJosjwCfBc5K1q2cLE8pWK62rjR/VafOOTT+nYvmzkqfmvpEQRlJ/XUglf+G7wL2TT63G5Ogvd/8KWOSJLXABPvgm50sr12n7A7JcnqHOr0ITwDOJH60fRK4LlfH4cSPotIF/5Fk26bED7vtgTuA19dpj6Tx417gh8nnacRj9+8g4sVJwJ/r1HF7srwmcC6wK5FsuZaYlKlkL+DVRCxLE8qHZ8d8AzGe5JuTbVtndZ2WfU7j4JoUX/MWAk8mn4/Kyp8G3ExMWDcv2X5gtv4bxHe+jegxuRrwaSqTRo22oRmtXhfy7Wn1vHpdkKob5jj5ZFInxHc/nxgS4RfA74gelW8DHiD+Bi3pRpx8H5XJJqicy+LqZDm9YZqOo1+yVrK8IuX2tXrOofI7pzG9li0ZPUzZIcT/AwCXAz9vsC5JvbUmcGxuXTr8XydiUju/+Y1JkqRxa33iwvcE0fOm9DqZ6CUzPVf2rUTvolK5p4DDiAvwdrlt6etu4LEq69+R1b1jwX53UHlXXNL4txExDm61mDBCjFWefj4st/9qRBKm2r6LgeurrH8Pkayots9S4vH//PpHid6Lt+TWn0EkOqoNz/DrgmOUhjl4I/GIa9F3L9VfMquFNtSzPo1fF5YjetVfnCv7A+LR3ak0f15LiXWvC1KxYY6TMwral74ep9z7vRtxcoeknhuJ8eDXIxL7z2Trz8/KziR62c9N9llIJMdLvT1fDvwr177PEjc3W4mhWxPDRaTr7yWSUtVumB6VK7uYiOsfIiZNfCFb/9eC/SX13y8p//v9MPAiIjZdmK1/HngJ7cekVn7zfxJjkiRpnFtK7R8o6eNW+R8m6euarExpJvHS+qeJ3pcziAvl4mTb16l8JPm7uTqvI36sSBo+GwJ/pzImPAP8lPjj/9ZkfT5xBNHbOZ9gmpOtP4nyj4AvAC8leuDMJv5QT/e5ieh1M4HoPVla/xDRi7xW/DyySrt2IBI/pTLziB9Bqd1y36/0uh14LZW9I49toQ31NHNdOLxO2f1p/rymveG9LkjFhjlOLkv0mpyfq28x8C0qe152I06ulx37YUYnmpYQCaBSz/Zaxz+L6JlZtP0iWouhteJ4+qRUST6ZdUfu85PEcDvOhyENrjOJ4b/yHSRGiOvBy7Ny7cYkaP43vzFJkqQWrArsQySJ8uO5rQkcQPG4oFsQwyKsVbBd0nBZi4gZ21J5Q65e4ggiAbM9MaHTdpQfbV0XeHHBPpOyYx3I6EdXJwK7E8Mo1BuTvJZZwN5EjCwaVxJinMy9iXg61nvntHNevS5ItQ1znJxEPFlzUNbeXiZbSkMdTCPi1P7Ed1+hC8fq9jlPk1k3EH+/bw8cTAzPMKF4V0kDYgqR4Ib4zb0bETM2pXiunHa085u/HmOSJEmS1AONJI4kaZgZJ9WofDJLkvrJmCRJBZzkVJIkSZIkSZKkFphglyR10vLJstcYSRrNOKlGpf+vOKaxpH4zJklSAf+olyR1yubE2I4l2/erIZI0oIyTasZuyfK6lMeXl6R+MCZJkiRJXXIw8CPgWcrjMo4AC4Bv4ySYkmScVDP2AX4GLKby/5c5xP8v+/atZZKGkTFJkuqY3O8GSJLGvJnAC8BPq2ybAszobXMkaeAYJ9WMOcA84Lwq26ZQOUyDJHXbHIxJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJQ2NSvxugpkwFtgUOAPYkJhR5lJjNu2Ql4NXAGsBdPW6fJLVie+BI4J/Awh4cbwKwY3bMG6mMoalGYm4rZgN7AIcAGwBPA0/lyhjLJaUGNU5CYzGtG3p9TiSpV1YADgPWAe7oc1skSRpXdgEeAUZyr/nAG4FTgSuIH0AjwJn9aaYkNWQf4GvAvZTj2fpdPN4awJuAc4CHkmOuVlC+VszdosU2TAHOqlLnCHA2kcQ3lksqGfQ4WS+mdUOvz4kk9crqwLuAPxA3DbsZSyVJGko7AAuIi+zJwH7AE5R/WDwDLKHyh41JGUmD7ClGJ2TW7+LxDgaeq3LMaomjejH33Bbb8JNs/78DrwK+lWvLvRjLJZUNcpyE+jFt4y60sdfnRJJ65ZWM/jvw7H42SJKk8eZc4gL7BPHILsTQAUuz9acAKwO30r2kzKrARh2uU9JwO5TeJ0mOp37iqJGY26yNk/1PzNZNAa7L1j0JzMRYLqnSoMbJRmNaN/TjnEhSLyxPPMlogl2SpA5bAXieuMD+IbdtI2J8zJLv052kzETgYuC7HaxTklag90mSDaidOGom5jbjv5Jj7pasXwbYixhzvcRYLqlkEOMkNBfTOq0f50SSeuXLmGCXpDFnYr8boLo2BpbNlp/ObbuD6ClEwfZO+QTxY2lCvYKS1IQX+nDMenGymZjbjK0K2rAQuIToLV9teycZy6WxZxDjJDQX0zqtH+dEknrFGCdJY5AJ9sG3bp+P/2rgI31ugyT1SrdirrFc0njS75gmSZIkDYzJ/W6ACr0Y2JyY7KRkHeDI5PPfiLF6m7Eu8BrgRcRYvM8B1wM/BR7NlT0C+B7l3o4bJMd/CPhzk8eWpHqWA3YHXk7Eq9uAnwH/KCjfTEyrpRsxd/OsXoD1kvUHUO79uQg4v4k6S4zl0vDqV5zsZkwDmErEpq2BGcBdwM3AdOAvwP019m32nEwkJnXdISs/GXgA+B0xlFY1q2ftmw28D9gEOApYG/gx8Pus3GTiWnIY8HXgJmBnYB/iKambs+9zRY3vI2n82Q7Yn/g77Cnib8pHiCd/flNn302IpxB3BuYTMfz7wOKC8s3G/YnAnsBrib8ZbyBi5EHEE5xfB+YmdR8JzAI+DKxFxLc9su9ydfZ9Hq/znSRJ6oknqBxfstrr6Nw+X6P2uL2nET98RogL5Lyk/CPAlknZl9U59nltfj9JmkZlXHk5kRTJx5vniUnt8pqJaSWr5OoujS3cSsyt5/0N1Hlnlf2M5ZJKBilOthrTGrFZtm/pu/wVeDap9z+Ssu2ek62SYy0B7qE8YesIcEbuWEcAF1E+j7cAb8z2Le0zn0gwnQE8lqx/C3B5lbaNAKcDk5o5SZLGrM9RjhlzqJzQ/uZc2XSOi7OBD1E9hlxKTC6d10zc3wL4LHBvUuY4Iganx/oCcGx2zFK8vIH423hhlbbdC2zb6MmRJKmbdgZ2pTLRcmW2bldgpyr71ErKfDDZdhZxl3oK8KlkfdrjaEZ2nHOT7Tclx5/R5veTpHySJE3QXEJl8uIOKhMRzca0kqLEUSsxt57Zyf4Lknrfkqxfq8p+xnJJJYMUJ1uNaY24Mavrb0SvSIhenndn69MbnO2ck9WJhPoIMc7xJtn69YnEU2m/rbP1Bxcca1GVdTcUlB0hElLPV1n/ueZOk6Qx6FDK/+bfkK2bAHw8W3dXrnyaYC+9FhA36+7OrT8mt2+zcT+fSC96XUbEzKLtC6i8UTlC9NKvNkm2JEl98T7KF6lf1ylbKynzULLtoGT9usn6hYwelz9/B12SOiWfJHmByiFZ3pzb/uJkW6sxrShxVNJMzG3Gc0m9O9YpayyXVDKIcRKai2n1bJDU9dHctlJi6m3JunbOybHJ+r/mjnVWQTtmAufk6vw7sBFwKpFY/xWRMHtxrtx84ATiHE4CXk9lcn4+sGbRiZE0LnyfcrxNJ5qfSNxUfCBXPp9gvwRYMds2HfhXsu3C3L7Nxv1liKd68snx44ghXy4mhpY5gBiK6we5cn8gnpacCKxBJOLT7afXOTeSNG44yenwWFCwfC/wcLY8Be8yS+qfo4EfJp+/R/R+KVk7WR7WmDas31tSGI9xcvVk+WAioV3yS2K84Kk19u/EOQG4tmCfp4E/5cq+m+gdfwoxrvJBRDLpRiLBVfJl4KvZd1hCPE30nmT7NEb3QJU0vpRi3BTg8GT9UuA71I5vtxFzOjyZfX4226dk7Vz5ZuP+QuCf2faSS4FvZO97E3NVXETcEPxz7ngHED3rl2b1H5Cr61jMOUkaEga74XEM8CNi/MzS5E0TiYlWlk/KrdLbZknS/3NN7vMS4L7kcxqfjmE4Y9oxDOf3lhTGY5y8J1neiZggb8/s8xIigf7nGvs3c05+AnyGeJLn+GT9bGKS1Gr7ADyTLM8hepQWea5G2wC+TYyJXLJpjbokjX1pjDsP+CLlIfp+CLy3xr5XEU+9pOYky/lYdQytxf00xn2HYml8u7FK254Bvpl8nkb0npekcW9yvxugnrk0e0HcsX579lonV24CkjQ45hes73dMeyXlMXrzRogfJ/MKtrej399b0uAZ63HyQeC3wH7Zts2JBPaPicTT71o4dtE5WQB8JFuekLXxeOBAKsdqr3VOnm+hPfm2/ZaYPBVgwzbrkzTYvkN5mKuJxFMsryOS4OcC322yvqL4Bp2J++3GuJ8Cn04+b0jlTQFJGpdMsA+XWcRYkW8nHkW7jHjE9ev0//FgSWpWP2Paa4G31tg+A/hEl45tLJfUqLESJ98E/J7KhPzhxNArRwMXdLhtrwI+D2xDDCfzRWAx5eR7t6VDKCzbo2NK6o+ribh7OuXE9prE3A5HETfbnqq+a0v6/XfivbnPxjhJQ8EhYobH1sBNwH8Sk0EdRUxccgHxg0KSxpJ+x7Rn6myfVGd7q/r9vSWNHf2OF83EyYeB3YgE0EiyfnngfGIC6k6YAHyJ6EG+DfALosf8B6kcxqHb0nNzZw+PK6k/vkI8MZNPPu8HXEnnhlHpd9yHeFJoSfLZGCdpKJhgHw7TgV9TvnAfScwALklj0SDEtPcAK9d4fbYLxxyE7y1pbBiEeNFonJwO7EIknd9JJNrTCUcnEOOmr9iBNp0AnJQtXwG8hspJSXslndj1tj4cX1LvbAmsBfwR2ILoXZ5OQLoFcGIHjjMIcR8ivpduoC4G7upDGySp50ywD4cjKI+7Nhf4TW77Sg3WM61jLZKk1nUqprVjBHiixmtB8a4tM5ZLatRYipObEj04Z2efrwJ2Bj5MuTf7ZMpjtLfj/cnyj6js0dmLc1KyTbJ8fQ+PK6n3Pkd5OKzngI8BWwG3JGUO6sBxBiHuQ2V8+wejJ0KVpHHJBPvgWyZZrjd+WdGQBOmM4SsRY76VrE6MzVYyPbdv+sNjizrHl6RmTKnzOb+uNG9IOzGt3tAtzcTcZqTfw1guqVGDGCfzx+xUrDw0WR4heriflaxbucqxq33OryudkwlU3mDcKrfPJsly/pykmvn9VG2+qy2IIRsAbgB+2UR9ksamA6mMB3cB+yafV06WpxQsV1uX1tlO3E81GuOK5vM7Llnu1nxEkjRwTLAPvtnJ8tp1yu6QLKd3qNOL8ATgTOAQ4JPAdbk6Dgc+QPmC/0iybVPgFcD2wB3A6+u0R5JqWSX3eY0qZdIfB9WSK83GtB1y2/O9eZqJuY1ajco2G8slNWoQ42SzMa1R76MyQQTlCQEhJgqE1s8JVN5EPRx4B/AGYnziNyfbtiaGVzgt+5yegzVp/DfUcVQm9Zcj/luUvI/KMecljU9rAsfm1i1Nlq9OltMYVy2+rZUsr0g5HrUT99MYN5vGbEmMK586BHh1tnw58PMG65IkqWvWJy58TxB/eJdeJxM9bqbnyr6VmEykVO4p4DDiArxdblv6uht4rMr6d2R171iw3x1U/niRpGZsCZxDZVz5AxFzJhNx7ZTc9tuIxPBLaD6mnUiM8Xtxbv0PiEdZN6XxmNuo5YlE1Q9zdf6d+EGTn9BqfYzlksoGLU6uTHMxrVE7JHXdCLwMWA94GzEu+wgx0Wm752QykUivdk6WEkPT5Nc/SvSsvyW3/gwiCV9tyK3bc2UfJBJdHySSXCPEkAnvauVkSRpzfkn8u19MDH31IiLOXZitf56I2TOJeSHmUo4fC4mbf6Ue6C8H/kVljPkscaOzlb8TP0kMYZOuu5dIlFe7eXpUruxi4prxIWIi1xey9X8t2F+SpJ5bSvWLY+mVPm6V/6M/fV2TlSnNJF5a/zTwDWAGcaFcnGz7OpWPB383V+d1xA8fSWpVUYxbQsSXKwu2jwA70XxMO7JGfaXkSqMxt1HH1qlzAZXJGWO5pNSgxcmv1tmej2mNWg+YDzzM6OTQEiJpU+rZ3u45mU0kftL1NxE3CCYANyfrHwI+Xec7H1nl+6QJ9rtybV5KjEm8TwvnSdLYdCYRH/OdOEaAW4mkOdT+u/Esord40faLsjqajfu1/v6dV+W75BPsd+Q+Pwl8G+f7kSSNc6sSf9Dvxujx3NYEDqA8MUreFsTYcWsVbJekXmsnpo1lxnJJjRorcbI0PME0Ik7tD+wOrNCFY00CtiViYX4ohInZcXel9bHl0wT7/yH+G+xJ/HfoxveRNNimEAluiLi7GxF/NqWxOS+a1c24nybYb8jq3x44mHjCaELxrpIkSZIkSVJ9+QS7JI0X+QS7JCnjJKeSJEmSJEmSJLXABLskSZIkdcbyybLjEEsaT4xvklTABLskSZIkte9FxBjHJdv1qyGS1AW7JcvrUp4/Q5IkSZIkSWrLfwN3Uh6feARYDPwa+HIf2yVJ7doH+BkR09IYNwf4NrBv31omSQNicr8bIEmSJElj3Ezg8uyVt2KP2yJJnTQHmAecV2XbFCqHjpEkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSRo+k/rdALVsS2LG7mc6WOc0YCdgGeDJDtYrSWNBP2LgIcAewN96dDxJ6iXjqiR1lnkASZI6YAfgV8AIcGKH6nwT8Gfg+azeEeAu4GvA8h06hiQNqm7GwI2BfxS87gCWAl9o8xiSNGiMq5LUWeYBJEnqgO2An1O+8HXqwvqepL4Lss+nAwuydZcAy3XgOJI0iLodA79BZdzOv5YAG7RRvyQNGuOqJHWOeQBJkjrkHKI3Tv4HRLsX1hOSui7KbXtvsu2PwMQ2jyVJg6bbMXA1yj9Sil4XttJwSRpQxlVJ6hzzAJIkddBHgVcAKxFjrXXiwjoDeDqrZwGwVm77JODG5FgHtnEsSRo0vYiBn8z2/QWwbZXXNsDkFuqVpEFkXJWkzjIPIElSl1xPZy6s6V3rSwrKnJyU+U0bx5KkQdPtGLg8MJfodbRti22UpLHEuCpJ3WMeQJIGmI/7jD1LOlDHBOD45POlBeXS9a8CNurAsSWp33oRA99K9DaaQPyQeSOwQhP7S9JYYlyVpO4yDyBJUgddS/t3rjdK6hgB9i0oNxV4ISl3fEE5SRpLuh0DJwNzcscYyer6BbBXi+2WpEFlXJWk7jIPIEkDzB7sw2nd3OcHC8o9DzyefF6nO82RpJ7qdgx8JbBelfXLAAcDFwPnET0xJWk8MK5K0uAzDyBJXWKCfTjlL6xP1Sibblu7C22RpF7rdgz8B/Be4AvAH4keQHmvA84lHtWVpLHOuCpJg888gCR1iQn24dTMhXVesuyFVdJ40O0YeB9wGvB+4tHbVYH3AA/nyu1PJIwkaawzrkrS4DMPIEldYoJ9OK2Y+/xsjbLpttW60BZJ6rVex8BngC8BGxC9K1MntVinJA0S46okDT7zAJLUJSbYh9Pjuc/L1Ci7bLJc6w63JI0V/YqBC4A3A79N1q0JrNJmvZLUb8ZVSRp85gEkqUtMsA+n+3OfV6hRdlayfF8X2iJJvdbPGLgIOBJYnKzbugP1SlI/GVclafCZB5CkLjHBPpzyF8hZVUuN3uaFVdJ40O8YOA+4PvlsT0tJY51xVZIGX79jtSSNWybYh1P+zvVKNcqm2/L7SdJYNAgx8I5k+fYO1itJ/WBclaTBNwixWpLGJRPsw+lO4J7k804F5TYDZiafr+haiySpdwYhBj6cvS8GbulgvZLUD8ZVSRp8gxCrJWlcMsE+nJYA30w+71lQ7uXJ8nXAtd1qkCT10CDEwB2y91uBFzpYryT1g3FVkgbfIMRqSZIGwvXASPY6sU7ZZYETgIuAfXLbViN+fIwQs4lPrbL/z5JjHd16kyVp4LQbA2vF1xWJCfamFBx7j6TeI1pouyQNonbj6ouBnwIfY/SwBcZVScOumTxArXhqHkCSNPQmAo9Rvth9rU7545Oyi4DZue2nJdtPz23bP9n2byKZJEnjSTsxsCi+bkoMTzACzCWS8JOT/TYmxgYeAX7doe8hSYOi1bg6GXgw2X5mss24KmnYNZMHqBVPS8wDSJKG0orA9sA3KF/sSj8y9gXWBZapst85ufKH5LZPAL6TbVsKfJToHXQ08BDli+qaHf02kjQY2omBRfF1r9z6EeCfwAeIHzBPZOv+l4jtkjSetBpXN6Qybt6YbDOuShpWreQBasXTEvMAkqSh9HNG/7DIv15eZb+DiQvmCHGhXK5KmYnAV4iLdFrfQuCPeFGVNL61GgNrxdeimL0UuAw4vNNfQpIGSKtx9bqk7Htz24yrkoZRq3mAWvG0xDyAJHXQ/w/zguWA/kqQUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 107,
       "width": 748
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# these are the names of all the accidentals used in makam scores, as contained in the MusicXML files\n",
    "makamAccidentals = ['double-slash-flat', 'flat', 'slash-flat', 'quarter-flat', 'quarter-sharp', 'sharp', 'slash-quarter-sharp', 'slash-sharp']\n",
    "\n",
    "# create a stream to contained altered notes\n",
    "makamNotes = stream.Stream()\n",
    "\n",
    "for i in range(len(makamAccidentals)): # create a note per accidental\n",
    "    try:\n",
    "        n = note.Note()\n",
    "        n.pitch.accidental = makamAccidentals[i] # add one accidental from the list\n",
    "        n.addLyric(makamAccidentals[i], applyRaw=True) # add the name of the accidental as lyric\n",
    "        n.addLyric(n.pitch.accidental.name, applyRaw=True) # add the name used by music21 as lyric\n",
    "        n.addLyric(n.pitch.accidental.alter) # add the number of semitones of the accidental as lyric\n",
    "        makamNotes.append(n)\n",
    "    except:\n",
    "        print(\"music21 doesn't accept {} as accidental\".format(makamAccidentals[i]))\n",
    "\n",
    "print('done')\n",
    "makamNotes.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the problem when loading the score with music21 is the non standard key signature, one solution might be to manipulate the MusicXML file to get rid of the time signature.\n",
    "<br/>\n",
    "We use ElementTree to find it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The key signature of this score has:\n",
      "- B flat\n"
     ]
    }
   ],
   "source": [
    "import xml.etree.ElementTree as ET\n",
    "\n",
    "tree = ET.parse(makamScore)\n",
    "root = tree.getroot()   \n",
    "\n",
    "notes = []\n",
    "accidentals = []\n",
    "alter = []\n",
    "\n",
    "for k in root.iter('key'):\n",
    "    for ks in k.findall('key-step'):\n",
    "        notes.append(ks.text)\n",
    "    for ka in k.findall('key-accidental'):\n",
    "        accidentals.append(ka.text)\n",
    "    for kalt in k.findall('key-alter'):\n",
    "        alter.append(kalt.text)\n",
    "\n",
    "print('The key signature of this score has:')\n",
    "for i in range(len(notes)):\n",
    "    print('-', notes[i], accidentals[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can remove it from the MusicXML file and create a new file without key signature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Element 'key' at 0x1aa62ce410>\n",
      "/Users/francescaronchini/repo/makam_project/makam/MusicXML_research/acemasiran--nakis--yuruksemai--ne_hevayi--dede_efendi-withoutKeySignature.xml\n"
     ]
    }
   ],
   "source": [
    "for k in root.iter('key'):\n",
    "    print(k)\n",
    "    for ks in k.findall('key-step'):\n",
    "        k.remove(ks)\n",
    "    for ka in k.findall('key-accidental'):\n",
    "        k.remove(ka)\n",
    "    for kalt in k.findall('key-alter'):\n",
    "        k.remove(kalt)\n",
    "\n",
    "newMakamScore = makamScore[:-4] + '-withoutKeySignature.xml'\n",
    "print(newMakamScore)\n",
    "\n",
    "tree.write(newMakamScore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now, music21 will load the score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1025,
       "width": 1106
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = converter.parse(newMakamScore)\n",
    "s.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing all the file\n",
    "\n",
    "Now that we have seen how it works with one score, we will go through all the scores, applying the work around we have just seen to remove accidentals to all of them, to be able to work with MusicXML. \n",
    "We will also remove scores which contain more than a time siganture per score and score which time signature is different compared to the usul time signature considered on this research. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_time_signature(usul, new_path):\n",
    "    '''\n",
    "    The function checks if the time signature of the current score is inline with the time signature declared\n",
    "    for the usul considered.\n",
    "    Input:\n",
    "    --------\n",
    "    :usul: usul considered\n",
    "    :new_path = path of the score considered\n",
    "    Output:\n",
    "    --------\n",
    "    return 0, the file can be keep\n",
    "    return 1: the file need to be removed, either for different time signature or multiple time siganture\n",
    "    '''\n",
    "    s = converter.parse(new_path) \n",
    "    p = s.parts # taking the first part of the score which contain the time signature information\n",
    "    tS = p.flat.getElementsByClass(meter.TimeSignature).stream()\n",
    "    #only scores with only 1 signature\n",
    "    if len(tS) == 1:\n",
    "        score_time = [tS[0].numerator, tS[0].denominator]\n",
    "        if score_time != usul_tempo[usul]:\n",
    "            #different meter\n",
    "            return 1         \n",
    "    else:\n",
    "        #more than a key siganture in the score\n",
    "        return 1 \n",
    "    \n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "makam_out_folder_path = 'path to processed score dir (where you want to save the processed scores)'\n",
    "\n",
    "\n",
    "if not os.path.exists(makam_out_folder_path):\n",
    "    os.makedirs(makam_out_folder_path)\n",
    "\n",
    "def remove_accidentals(makam_score):\n",
    "    '''\n",
    "    The function removes all the accidentals from the score.\n",
    "    Input:\n",
    "    -------\n",
    "    :makam_score: path of the makam score\n",
    "    '''\n",
    "    \n",
    "    tree = ET.parse(makam_score)\n",
    "    root = tree.getroot() \n",
    "    \n",
    "    for k in root.iter('key'):\n",
    "        print(k)\n",
    "    for ks in k.findall('key-step'):\n",
    "        k.remove(ks)\n",
    "    for ka in k.findall('key-accidental'):\n",
    "        k.remove(ka)\n",
    "    for kalt in k.findall('key-alter'):\n",
    "        k.remove(kalt)\n",
    "        \n",
    "    makam_score = makam_score.split('/')[-1]\n",
    "    new_Makam_Score = makam_out_folder_path + makam_score[:-4] + '-withoutKeySignature.xml'\n",
    "    tree.write(new_Makam_Score)\n",
    "     \n",
    "    return new_Makam_Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Element 'key' at 0x1aa7d03950>\n",
      "<Element 'key' at 0x1aa54edfb0>\n",
      "<Element 'key' at 0x1aa39d5b30>\n",
      "<Element 'key' at 0x1aa5885050>\n",
      "<Element 'key' at 0x1aa5519770>\n",
      "<Element 'key' at 0x1aa739b170>\n",
      "<Element 'key' at 0x1027f1650>\n",
      "<Element 'key' at 0x1aa79fa4d0>\n",
      "<Element 'key' at 0x1aa7352dd0>\n",
      "<Element 'key' at 0xa193fc3b0>\n",
      "<Element 'key' at 0xa18736e90>\n",
      "<Element 'key' at 0x1aa7a60170>\n",
      "<Element 'key' at 0xa17b27dd0>\n",
      "<Element 'key' at 0xa177395f0>\n",
      "<Element 'key' at 0x1aa708c710>\n",
      "<Element 'key' at 0xa176d5590>\n",
      "<Element 'key' at 0x1aa71375f0>\n",
      "<Element 'key' at 0x1aa7567290>\n",
      "<Element 'key' at 0xa18746a70>\n",
      "<Element 'key' at 0xa19414c50>\n",
      "<Element 'key' at 0x1aa71e98f0>\n",
      "<Element 'key' at 0xa16f88290>\n",
      "<Element 'key' at 0xa176c6410>\n",
      "<Element 'key' at 0xa19453710>\n",
      "<Element 'key' at 0x10281b650>\n",
      "<Element 'key' at 0x1aa73a24d0>\n",
      "<Element 'key' at 0xa187615f0>\n",
      "<Element 'key' at 0x1027eb650>\n",
      "<Element 'key' at 0xa17ab0cb0>\n",
      "<Element 'key' at 0x1aa199d7d0>\n",
      "<Element 'key' at 0x1027cd050>\n",
      "<Element 'key' at 0x1aa7837ad0>\n",
      "<Element 'key' at 0x1aa72af4d0>\n",
      "<Element 'key' at 0x1aa7126c50>\n",
      "<Element 'key' at 0x1aa736a590>\n",
      "<Element 'key' at 0xa17aade90>\n",
      "<Element 'key' at 0x1aa7293710>\n",
      "<Element 'key' at 0xa177636b0>\n",
      "<Element 'key' at 0xa19838b90>\n",
      "<Element 'key' at 0x1aa776c170>\n",
      "<Element 'key' at 0x1aa7a04c50>\n",
      "<Element 'key' at 0xa16f22530>\n",
      "<Element 'key' at 0x102763c50>\n",
      "<Element 'key' at 0x1aa70c3410>\n",
      "<Element 'key' at 0xa194f2dd0>\n",
      "The file ../makam/MusicXML_noaccidentals/ussak--kupe--duyek--sanma_ussakin--ahmet_avni_konuk-withoutKeySignature.xml will be removed for different time signature\n",
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      "The file ../makam/MusicXML_noaccidentals/nihavent--sarki--duyek--bulbul_asikmis--zeki_muren-withoutKeySignature.xml will be removed for different time signature\n",
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      "The file ../makam/MusicXML_noaccidentals/hicaz--sarki--duyek--istanbulda_bogazicinde--sekip_ayhan_ozisik-withoutKeySignature.xml will be removed for different time signature\n",
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      "The file ../makam/MusicXML_noaccidentals/mahur--sarki--aksak--muptelayim_hayli--kemenceci_usta_yani-withoutKeySignature.xml will be removed for different time signature\n",
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      "The file ../makam/MusicXML_noaccidentals/mahur--salatuselam--sofyan--allahumme---withoutKeySignature.xml will be removed for different time signature\n",
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      "The file ../makam/MusicXML_noaccidentals/muhayyer--turku--sofyan--binip_de--denizli-withoutKeySignature.xml will be removed for different time signature\n",
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      "The file ../makam/MusicXML_noaccidentals/hicaz--kupe--duyek--gerci--ahmet_avni_konuk-withoutKeySignature.xml will be removed for different time signature\n",
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      "The file ../makam/MusicXML_noaccidentals/hicaz--turku--sofyan--su_yaltadan--kirim-withoutKeySignature.xml will be removed for different time signature\n",
      "<Element 'key' at 0xa1984b590>\n",
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      "<Element 'key' at 0xa17739fb0>\n",
      "<Element 'key' at 0x1a956f5290>\n",
      "The file ../makam/MusicXML_noaccidentals/huzzam--sarki--curcuna--mahzun_gonul--kemani_salih_efendi-withoutKeySignature.xml will be removed for different time signature\n",
      "<Element 'key' at 0x1aa75939b0>\n"
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    {
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     "output_type": "stream",
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      "The file ../makam/MusicXML_noaccidentals/nihavent--sarki--sofyan--semalardan_gunes--misirli_udi_ibrahim_efendi-withoutKeySignature.xml will be removed for different time signature\n",
      "<Element 'key' at 0x1aa7786890>\n",
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      "The file ../makam/MusicXML_noaccidentals/segah--sarki--duyek--donulmez_aksamin--munir_nurettin_selcuk-withoutKeySignature.xml will be removed for different time signature\n",
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      "The file ../makam/MusicXML_noaccidentals/nihavent--sarki--aksak--mumkun_mu--rakim_elkutlu-withoutKeySignature.xml will be removed for different time signature\n",
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      "The file ../makam/MusicXML_noaccidentals/huseyni--kupe--duyek--pesden--ahmet_avni_konuk-withoutKeySignature.xml will be removed for different time signature\n",
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      "The file ../makam/MusicXML_noaccidentals/nihavent--sarki--duyek--istanbul_biricik--sadettin_kaynak-withoutKeySignature.xml will be removed for different time signature\n",
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      "<Element 'key' at 0x1aa7a1f9b0>\n",
      "<Element 'key' at 0x1aa701ab90>\n",
      "<Element 'key' at 0x1aa71a79b0>\n",
      "<Element 'key' at 0x1a8f1641d0>\n",
      "<Element 'key' at 0x1aa7210290>\n",
      "<Element 'key' at 0x1aa738a4d0>\n",
      "<Element 'key' at 0x1aa75a7ef0>\n",
      "<Element 'key' at 0x1aa47a6a10>\n",
      "<Element 'key' at 0x1aa7596c50>\n",
      "<Element 'key' at 0x1aa75852f0>\n",
      "<Element 'key' at 0x10279f8f0>\n",
      "The file ../makam/MusicXML_noaccidentals/huseyni--turku--aksak--yanik_omer--sadettin_kaynak-withoutKeySignature.xml will be removed for different time signature\n",
      "<Element 'key' at 0xa16f8aa10>\n",
      "<Element 'key' at 0x1aa72ce710>\n",
      "<Element 'key' at 0x1aa4c7d5f0>\n",
      "<Element 'key' at 0xa17b2a230>\n",
      "The file ../makam/MusicXML_noaccidentals/nihavent--turku--aksak--evlerinin_onu--muzaffer_sarisozen-withoutKeySignature.xml will be removed for different time signature\n",
      "<Element 'key' at 0xa1985bad0>\n",
      "<Element 'key' at 0xa1874ab90>\n",
      "<Element 'key' at 0x1aa72c65f0>\n",
      "<Element 'key' at 0xa195174d0>\n",
      "<Element 'key' at 0x1aa5121590>\n",
      "<Element 'key' at 0x1aa71d91d0>\n",
      "The file ../makam/MusicXML_noaccidentals/nihavent--fantezi--duyek--bahar_meltemidir--emin_ongan-withoutKeySignature.xml will be removed for different time signature\n",
      "<Element 'key' at 0x1aa7761e30>\n",
      "<Element 'key' at 0x1aa711e650>\n",
      "<Element 'key' at 0x1aa756e890>\n",
      "<Element 'key' at 0x1aa72690b0>\n",
      "<Element 'key' at 0x1aa71c06b0>\n",
      "<Element 'key' at 0x1aa735c4d0>\n",
      "<Element 'key' at 0x1aa785ccb0>\n",
      "<Element 'key' at 0x1aa0bb97d0>\n",
      "<Element 'key' at 0x1aa7c1e410>\n",
      "<Element 'key' at 0x1aa4c76e30>\n",
      "<Element 'key' at 0x1aa71ecef0>\n",
      "<Element 'key' at 0x1aa7512110>\n",
      "<Element 'key' at 0x1aa7258c50>\n",
      "<Element 'key' at 0x1aa6ff6bf0>\n",
      "<Element 'key' at 0xa16ebd050>\n",
      "<Element 'key' at 0x1aa7a60770>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Element 'key' at 0x1aa737b050>\n",
      "<Element 'key' at 0x1aa75a7950>\n",
      "<Element 'key' at 0xa19b88c50>\n",
      "<Element 'key' at 0x1027f1a10>\n",
      "<Element 'key' at 0x1aa0713cb0>\n",
      "<Element 'key' at 0xa1775f8f0>\n",
      "<Element 'key' at 0x1aa785b5f0>\n",
      "<Element 'key' at 0xa176e2350>\n",
      "<Element 'key' at 0xa1983a230>\n",
      "<Element 'key' at 0x1aa7355290>\n",
      "<Element 'key' at 0x1aa75a4590>\n",
      "<Element 'key' at 0x1aa7a1eb30>\n",
      "The file ../makam/MusicXML_noaccidentals/muhayyer--kupe--duyek--nideyim--ahmet_avni_konuk-withoutKeySignature.xml will be removed for different time signature\n",
      "<Element 'key' at 0x102767e90>\n",
      "<Element 'key' at 0xa17b1e410>\n",
      "<Element 'key' at 0x1aa7245ef0>\n",
      "<Element 'key' at 0x1aa7108d10>\n",
      "<Element 'key' at 0xa176eb650>\n",
      "<Element 'key' at 0x1aa78fc530>\n",
      "<Element 'key' at 0x1aa775d0b0>\n",
      "<Element 'key' at 0x1aa72b3f50>\n",
      "<Element 'key' at 0xa1875e410>\n",
      "<Element 'key' at 0x1aa71c86b0>\n",
      "<Element 'key' at 0x1aa7bed2f0>\n",
      "<Element 'key' at 0x1aa2bff650>\n",
      "<Element 'key' at 0x1aa74b3710>\n",
      "<Element 'key' at 0xa16e999b0>\n",
      "<Element 'key' at 0x102794050>\n",
      "<Element 'key' at 0x1aa730afb0>\n",
      "<Element 'key' at 0xa17740b90>\n",
      "<Element 'key' at 0x1aa78679b0>\n",
      "<Element 'key' at 0x1aa0bb9290>\n",
      "<Element 'key' at 0x1aa7593830>\n",
      "<Element 'key' at 0x1aa72da230>\n",
      "<Element 'key' at 0xa19440290>\n",
      "<Element 'key' at 0x1aa309a5f0>\n",
      "<Element 'key' at 0xa1949c650>\n",
      "<Element 'key' at 0x1aa7271a10>\n",
      "<Element 'key' at 0x1a9aa6d530>\n",
      "<Element 'key' at 0x1aa7725290>\n",
      "<Element 'key' at 0x1aa708c3b0>\n",
      "The file ../makam/MusicXML_noaccidentals/huzzam--sarki--aksak--sevdim_yine--tanburi_mustafa_cavus-withoutKeySignature.xml will be removed for different time signature\n",
      "<Element 'key' at 0x1aa712b8f0>\n",
      "<Element 'key' at 0x1aa720fd70>\n",
      "The file ../makam/MusicXML_noaccidentals/segah--sarki--duyek--issiz_gecelerin--omer_sami_gupgup-withoutKeySignature.xml will be removed for different time signature\n",
      "<Element 'key' at 0x1aa7142110>\n",
      "<Element 'key' at 0x1aa71b06b0>\n",
      "<Element 'key' at 0xa1872eb90>\n",
      "<Element 'key' at 0x1a956f5b90>\n",
      "<Element 'key' at 0x1aa7761c50>\n",
      "<Element 'key' at 0xa19405cb0>\n",
      "<Element 'key' at 0xa18743410>\n",
      "The file ../makam/MusicXML_noaccidentals/segah--turku--aksak--yarim_istanbulu--kayseri-withoutKeySignature.xml will be removed for different time signature\n",
      "<Element 'key' at 0x1aa74d0cb0>\n",
      "<Element 'key' at 0x1aa4c31e30>\n",
      "<Element 'key' at 0x1027772f0>\n",
      "<Element 'key' at 0x1a8ff419b0>\n",
      "<Element 'key' at 0xa17aa3950>\n",
      "<Element 'key' at 0xa17b03fb0>\n",
      "<Element 'key' at 0x1aa39d5cb0>\n",
      "<Element 'key' at 0x1aa75656b0>\n",
      "<Element 'key' at 0xa194ec5f0>\n",
      "<Element 'key' at 0x1aa76fb290>\n",
      "<Element 'key' at 0x1aa199def0>\n",
      "<Element 'key' at 0x1aa73711d0>\n",
      "<Element 'key' at 0x10274e890>\n",
      "<Element 'key' at 0x1aa72576b0>\n",
      "<Element 'key' at 0xa17b06890>\n",
      "<Element 'key' at 0x1027f8ef0>\n",
      "<Element 'key' at 0x1aa753d230>\n",
      "<Element 'key' at 0xa17a96590>\n",
      "<Element 'key' at 0x1aa4c519b0>\n",
      "<Element 'key' at 0x1aa71c0770>\n",
      "The file ../makam/MusicXML_noaccidentals/hicaz--kupe--duyek--gerci-2--ahmet_avni_konuk-withoutKeySignature.xml will be removed for different time signature\n",
      "<Element 'key' at 0xa19410710>\n",
      "<Element 'key' at 0x10278f170>\n"
     ]
    }
   ],
   "source": [
    "usul_considered = high_pop_usuls\n",
    "makam_init_folder_path = xml_path\n",
    "\n",
    "makam_count_before = 0\n",
    "makam_count_noaccidentals = 0\n",
    "makam_count_different_time = 0 \n",
    "counter = 0\n",
    "\n",
    "#loop through the makam dataset\n",
    "for makam_file in os.listdir(makam_init_folder_path):\n",
    "    #for mac and .DS_store file\n",
    "    if not makam_file.startswith('.'):\n",
    "        usul = makam_file.split('--')[2]\n",
    "        makam = makam_file.split('--')[0]\n",
    "        counter = counter + 1\n",
    "        \n",
    "        #if the score refers to one of the usul considered and one the makam considered\n",
    "        if usul in usul_considered and makam in makam_considered:  \n",
    "            makam_count_before = makam_count_before + 1\n",
    "            #remove accidentals\n",
    "            path_score = makam_init_folder_path + makam_file\n",
    "            new_path = remove_accidentals(path_score)\n",
    "            \n",
    "            #check time signature for the current xml\n",
    "            different_time = check_time_signature(usul, new_path)\n",
    "            if different_time:\n",
    "                print(\"The file {} will be removed for different time signature\".format(new_path))\n",
    "                os.remove(new_path)\n",
    "                makam_count_different_time = makam_count_different_time + 1            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of makams in the dataset 2201\n"
     ]
    }
   ],
   "source": [
    "print(\"Total number of makams in the dataset {}\".format(counter))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We only analyzed makams between ['hicaz' 'hicazkar' 'huseyni' 'huzzam' 'mahur' 'muhayyer' 'nihavent'\n",
    " 'rast' 'segah' 'ussak'] and usul ['aksak', 'sofyan', 'duyek', 'curcuna']. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We analyzed 508 makams but we kept only 486 for time signature\n"
     ]
    }
   ],
   "source": [
    "print(\"We analyzed {} makams but we kept only {} for time signature\".format(makam_count_before, (makam_count_before-makam_count_different_time)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling to k \n",
    "Definition of function we will need to scale the bin according to the different usul metric. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scale note ditribution to k (bin of the usul)\n",
    "def scale_to_k(OldMin, OldMax, OldValue, k):\n",
    "    '''\n",
    "    The function scales note ditribution to k (bin of the usul considered)\n",
    "    Input:\n",
    "    -------\n",
    "    :oldMin: previous minimum for old value\n",
    "    :OldMax: previous maximum for old value\n",
    "    :OldValue: previous value to scale to new value\n",
    "    :k: usul bins\n",
    "    Return:\n",
    "    --------\n",
    "    :newValue: value scaled\n",
    "    '''\n",
    "    \n",
    "    NewMin = 0\n",
    "    NewMax = k \n",
    "    \n",
    "    NewValue = (((OldValue - OldMin) * (NewMax - NewMin)) / (OldMax - OldMin)) + NewMin\n",
    "    return NewValue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to calc measure seperation discarding measures which are glissandos\n",
    "def calc_measure_length(m1, m2, measures):\n",
    "    '''\n",
    "    The function calculates measure seperation discarding measures which are glissandos\n",
    "    Input:\n",
    "    -------\n",
    "    :m1: first measure\n",
    "    :m2: second measure\n",
    "    :measures: measures\n",
    "    Return:\n",
    "    --------\n",
    "    distance between measures\n",
    "    '''\n",
    "    if isinstance(measures[m1], m21.spanner.Glissando):\n",
    "        # shift m1 and m2\n",
    "        m1+=1\n",
    "        m2+=1\n",
    "        return calc_measure_length(m1,m2,measures)\n",
    "    if isinstance(measures[m2], m21.spanner.Glissando):\n",
    "        m2+=1\n",
    "        return calc_measure_length(m1,m2,measures)\n",
    "    else: return measures[m2].offset - measures[m1].offset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of dataset: note distributiona and pitch correlation\n",
    "\n",
    "We will first declare all the functions and variable we need to do the analyze note distribution and pitch correlation. Then we will perform the analysis of the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#counting how many beat there are per usul and note definition \n",
    "from collections import defaultdict\n",
    "\n",
    "#note considered for the pitch correlation task considerng only tone\n",
    "note_considered = {\n",
    "    'C': 0,\n",
    "    'D': 1,\n",
    "    'E': 2,\n",
    "    'F': 3, \n",
    "    'G': 4,\n",
    "    'A': 5, \n",
    "    'B': 6\n",
    "}\n",
    "\n",
    "octaves = np.arange(7)\n",
    "usul_weights_bins = defaultdict(dict)\n",
    "\n",
    "for usul in usul_bins.keys():\n",
    "    for o in octaves:\n",
    "        usul_weights_bins[usul][o] = np.zeros(shape = (len(weights[usul]), len(note_considered)), dtype=int)\n",
    "        \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check if a bin is a weigth bin in the usul considered\n",
    "def check_bin(usul, bin_value):\n",
    "    '''\n",
    "    The function checks if a bin is a weigthed bin in the usul considered\n",
    "    Input:\n",
    "    -------\n",
    "    :usul: usul considered\n",
    "    :bin_value: bin to check\n",
    "    Return:\n",
    "    --------\n",
    "    return 1: the bin is a weighted bin\n",
    "    return 0: the bin is not a weighted bin\n",
    "    '''\n",
    "    return weights[usul][bin_value] != 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import music21 as m21\n",
    "\n",
    "df_makam_bin_vals = pd.DataFrame(columns=['usul', 'makam', 'bin_vals', 'file_path'])\n",
    "\n",
    "\n",
    "def analyse_note_dist(k, makam, usul, scoreParts, makam_score):\n",
    "    '''\n",
    "    The function analyses the distance between notes\n",
    "    Input:\n",
    "    -------\n",
    "    :k: usul bin\n",
    "    :makam: makam considered\n",
    "    :scoreParts: parts of the score\n",
    "    :makam_score: makam score path\n",
    "    '''\n",
    "    \n",
    "    note_offsets = np.array([])\n",
    "    meter_bins = np.zeros(k)\n",
    "    measures0 = scoreParts[0].elements[1:]\n",
    "\n",
    "    beats_in_bar = calc_measure_length(1,2,measures0)\n",
    "    \n",
    "\n",
    "    for m in measures0:\n",
    "        \n",
    "        if isinstance(m, m21.spanner.Glissando) == False:\n",
    "            \n",
    "            for n1 in m.elements: \n",
    "                # only consider notes, not rests, time sigs ect.\n",
    "                if isinstance(n1, m21.note.Note):\n",
    "                    #offset of the note\n",
    "                    note_offset = n1.offset\n",
    "                    note_offsets = np.append(note_offsets, note_offset)\n",
    "                    #scaling to bins distribution of usul\n",
    "                    scaled_offset = scale_to_k(0, beats_in_bar, note_offset, k)\n",
    "                    bin_val = math.floor(scaled_offset)\n",
    "                    if check_bin(usul, bin_val):\n",
    "                        #it is a weigth bin, we it will considered for pitch correlation\n",
    "                        pitch = note_considered[n1.pitch.step]\n",
    "                        octave = n1.octave\n",
    "                        usul_weights_bins[usul][octave][bin_val][pitch] += 1\n",
    "                    meter_bins[bin_val] += 1\n",
    "        else: \n",
    "            print(makam_score)\n",
    "            print('glissando found:', m)\n",
    "    \n",
    "    # add row to df\n",
    "    df_makam_bin_vals.loc[len(df_makam_bin_vals)] = [usul, makam, meter_bins, makam_score]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "huzzam--sarki--aksak--zamani_var_ki--klarnet_ibrahim_efendi-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note C>>\n",
      "huzzam--sarki--aksak--zamani_var_ki--klarnet_ibrahim_efendi-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note C>>\n",
      "huzzam--sarki--aksak--zamani_var_ki--klarnet_ibrahim_efendi-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note C>>\n",
      "huzzam--sarki--aksak--zamani_var_ki--klarnet_ibrahim_efendi-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note C>>\n",
      "hicaz--sarki--duyek--artik_bu--alaeddin_yavasca-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note A>>\n",
      "hicaz--sarki--duyek--artik_bu--alaeddin_yavasca-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note A>>\n",
      "hicaz--sarki--duyek--artik_bu--alaeddin_yavasca-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note A>>\n",
      "huzzam--sarki--curcuna--aksam_donusu--udi_marko_colakoglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note C>>\n",
      "huzzam--sarki--curcuna--aksam_donusu--udi_marko_colakoglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B`><music21.note.Note D>>\n",
      "huseyni--turku--sofyan--kopruden_gecti--kirsehir-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note D><music21.note.Note C>>\n",
      "huseyni--turku--sofyan--kopruden_gecti--kirsehir-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note D><music21.note.Note C>>\n",
      "huseyni--turku--sofyan--kopruden_gecti--kirsehir-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note D><music21.note.Note C>>\n",
      "huseyni--turku--sofyan--kopruden_gecti--kirsehir-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note D><music21.note.Note C>>\n",
      "huseyni--turku--sofyan--kopruden_gecti--kirsehir-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note D><music21.note.Note C>>\n",
      "huseyni--turku--sofyan--kopruden_gecti--kirsehir-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note D><music21.note.Note C>>\n",
      "huseyni--turku--sofyan--kopruden_gecti--kirsehir-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note D><music21.note.Note C>>\n",
      "huseyni--turku--sofyan--kopruden_gecti--kirsehir-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F><music21.note.Note E>>\n",
      "huseyni--turku--sofyan--kopruden_gecti--kirsehir-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F><music21.note.Note G>>\n",
      "huseyni--turku--sofyan--kopruden_gecti--kirsehir-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F><music21.note.Note E>>\n",
      "huseyni--turku--sofyan--kopruden_gecti--kirsehir-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B`><music21.note.Note A>>\n",
      "huseyni--turku--sofyan--kopruden_gecti--kirsehir-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F><music21.note.Note E>>\n",
      "huseyni--turku--sofyan--kopruden_gecti--kirsehir-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F><music21.note.Note G>>\n",
      "huseyni--turku--sofyan--kopruden_gecti--kirsehir-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F><music21.note.Note E>>\n",
      "huseyni--turku--sofyan--kopruden_gecti--kirsehir-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B`><music21.note.Note A>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note E>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note E>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note E>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note E>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note E><music21.note.Note F#>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note A><music21.note.Note F#>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note E>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note A><music21.note.Note F#>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note E>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note E>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note E>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note A><music21.note.Note F#>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note E>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note A><music21.note.Note F#>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note E>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note A><music21.note.Note F#>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note E>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note D>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note A><music21.note.Note F#>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note E>>\n",
      "huzzam--turku--curcuna--al_yesil--urfa-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note D>>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mahur--sarki--sofyan--ne_dogan--munir_nurettin_selcuk-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note D><music21.note.Note G>>\n",
      "mahur--sarki--sofyan--ne_dogan--munir_nurettin_selcuk-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note E><music21.note.Note F>>\n",
      "mahur--sarki--sofyan--ne_dogan--munir_nurettin_selcuk-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note E>>\n",
      "mahur--sarki--sofyan--ne_dogan--munir_nurettin_selcuk-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note E>>\n",
      "segah--turku--sofyan--pancar_pezik--hacibektas-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note A><music21.note.Note G>>\n",
      "segah--turku--sofyan--pancar_pezik--hacibektas-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note F~>>\n",
      "segah--turku--sofyan--pancar_pezik--hacibektas-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note A><music21.note.Note G>>\n",
      "segah--turku--sofyan--pancar_pezik--hacibektas-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note F~>>\n",
      "segah--turku--sofyan--pancar_pezik--hacibektas-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note A><music21.note.Note G>>\n",
      "segah--turku--sofyan--pancar_pezik--hacibektas-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note F~>>\n",
      "segah--turku--sofyan--pancar_pezik--hacibektas-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note A><music21.note.Note G>>\n",
      "segah--turku--sofyan--pancar_pezik--hacibektas-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note F~>>\n",
      "mahur--sarki--duyek--ben_guzele--sadettin_kaynak-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note C><music21.note.Note E>>\n",
      "mahur--sarki--duyek--ben_guzele--sadettin_kaynak-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note C><music21.note.Note E>>\n",
      "mahur--sarki--duyek--ben_guzele--sadettin_kaynak-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B><music21.note.Note D>>\n",
      "mahur--sarki--duyek--ben_guzele--sadettin_kaynak-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B><music21.note.Note D>>\n",
      "mahur--sarki--duyek--ben_guzele--sadettin_kaynak-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note C><music21.note.Note E>>\n",
      "mahur--sarki--duyek--ben_guzele--sadettin_kaynak-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note C><music21.note.Note E>>\n",
      "mahur--sarki--duyek--ben_guzele--sadettin_kaynak-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B><music21.note.Note D>>\n",
      "mahur--sarki--duyek--ben_guzele--sadettin_kaynak-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B><music21.note.Note D>>\n",
      "mahur--sarki--duyek--ben_guzele--sadettin_kaynak-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note C><music21.note.Note E>>\n",
      "mahur--sarki--duyek--ben_guzele--sadettin_kaynak-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note C><music21.note.Note E>>\n",
      "mahur--sarki--duyek--ben_guzele--sadettin_kaynak-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B><music21.note.Note D>>\n",
      "mahur--sarki--duyek--ben_guzele--sadettin_kaynak-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B><music21.note.Note D>>\n",
      "mahur--sarki--duyek--ben_guzele--sadettin_kaynak-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note C><music21.note.Note E>>\n",
      "mahur--sarki--duyek--ben_guzele--sadettin_kaynak-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note C><music21.note.Note E>>\n",
      "mahur--sarki--duyek--ben_guzele--sadettin_kaynak-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B><music21.note.Note D>>\n",
      "mahur--sarki--duyek--ben_guzele--sadettin_kaynak-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B><music21.note.Note D>>\n",
      "huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note G>>\n",
      "huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note G>>\n",
      "huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note A><music21.note.Note G>>\n",
      "huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note F#>>\n",
      "huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note G>>\n",
      "huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note G>>\n",
      "huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note A><music21.note.Note G>>\n",
      "huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note F#>>\n",
      "huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note A>>\n",
      "huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note A>>\n",
      "huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note G>>\n",
      "huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note G>>\n",
      "huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note A><music21.note.Note G>>\n",
      "huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note F#>>\n",
      "huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note G>>\n",
      "huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note F#><music21.note.Note G>>\n",
      "huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note A><music21.note.Note G>>\n",
      "huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note G><music21.note.Note F#>>\n",
      "ussak--kanto--sofyan--telgirafin_tellerine---withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B`><music21.note.Note C>>\n",
      "ussak--kanto--sofyan--telgirafin_tellerine---withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note C><music21.note.Note A>>\n",
      "ussak--kanto--sofyan--telgirafin_tellerine---withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B`><music21.note.Note C>>\n",
      "ussak--kanto--sofyan--telgirafin_tellerine---withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note C><music21.note.Note A>>\n",
      "ussak--kanto--sofyan--telgirafin_tellerine---withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B`><music21.note.Note C>>\n",
      "ussak--kanto--sofyan--telgirafin_tellerine---withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note C><music21.note.Note A>>\n",
      "ussak--kanto--sofyan--telgirafin_tellerine---withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B`><music21.note.Note C>>\n",
      "ussak--kanto--sofyan--telgirafin_tellerine---withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note C><music21.note.Note A>>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mahur--mars--sofyan--gafil_ne--muallim_ismail_hakki_bey-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note E><music21.note.Note F>>\n",
      "mahur--mars--sofyan--gafil_ne--muallim_ismail_hakki_bey-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note C><music21.note.Note E>>\n",
      "mahur--mars--sofyan--gafil_ne--muallim_ismail_hakki_bey-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B><music21.note.Note D>>\n",
      "mahur--mars--sofyan--gafil_ne--muallim_ismail_hakki_bey-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note E><music21.note.Note F>>\n",
      "mahur--mars--sofyan--gafil_ne--muallim_ismail_hakki_bey-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note C><music21.note.Note E>>\n",
      "mahur--mars--sofyan--gafil_ne--muallim_ismail_hakki_bey-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B><music21.note.Note D>>\n",
      "mahur--mars--sofyan--gafil_ne--muallim_ismail_hakki_bey-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note E><music21.note.Note F>>\n",
      "mahur--mars--sofyan--gafil_ne--muallim_ismail_hakki_bey-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note C><music21.note.Note E>>\n",
      "mahur--mars--sofyan--gafil_ne--muallim_ismail_hakki_bey-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B><music21.note.Note D>>\n",
      "mahur--mars--sofyan--gafil_ne--muallim_ismail_hakki_bey-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note E><music21.note.Note F>>\n",
      "mahur--mars--sofyan--gafil_ne--muallim_ismail_hakki_bey-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note C><music21.note.Note E>>\n",
      "mahur--mars--sofyan--gafil_ne--muallim_ismail_hakki_bey-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B><music21.note.Note D>>\n",
      "nihavent--sarki--duyek--askimin_ilk--arif_sami_toker-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note E-><music21.note.Note A>>\n",
      "nihavent--sarki--duyek--askimin_ilk--arif_sami_toker-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note D><music21.note.Note E->>\n",
      "nihavent--sarki--duyek--askimin_ilk--arif_sami_toker-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note D><music21.note.Note E->>\n",
      "huzzam--sarki--curcuna--omrum_seni--yesari_asim_arsoy-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note D><music21.note.Note G>>\n",
      "huzzam--sarki--curcuna--omrum_seni--yesari_asim_arsoy-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note D><music21.note.Note G>>\n",
      "segah--sarki--aksak--yine_hicran_ile--melahat_pars-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B`><music21.note.Note D>>\n",
      "segah--sarki--aksak--yine_hicran_ile--melahat_pars-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B`><music21.note.Note D>>\n",
      "segah--sarki--aksak--yine_hicran_ile--melahat_pars-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B`><music21.note.Note D>>\n",
      "huzzam--sarki--curcuna--bir_gun--omer_sami_gupgup-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note B`><music21.note.Note A#>>\n",
      "huzzam--sarki--curcuna--bir_gun--omer_sami_gupgup-withoutKeySignature.xml\n",
      "glissando found: <music21.spanner.Glissando <music21.note.Note A#><music21.note.Note G>>\n"
     ]
    }
   ],
   "source": [
    "counter = 0\n",
    "\n",
    "for makam_score in os.listdir(makam_out_folder_path):\n",
    "    usul = makam_score.split('--')[2]\n",
    "    makam = makam_score.split('--')[0]\n",
    "    \n",
    "    k = usul_bins[usul] #value to scale to\n",
    "    makam_path = makam_out_folder_path + makam_score\n",
    "    counter = counter + 1 \n",
    "        \n",
    "    s = converter.parse(makam_path)\n",
    "    scoreParts = s.parts.stream()\n",
    "                \n",
    "    analyse_note_dist(k, makam, usul, scoreParts, makam_score)\n",
    "    \n",
    "       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pitch correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next step, we will gather information related to which note and which octave as been played for each weighted bin. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------\n",
      "Usul: aksak\n",
      "\n",
      "BIN: 0\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [  0   8   2  21 457 591 599]\n",
      "octave 5: [ 709 1467  744  569  853  343  117]\n",
      "octave 6: [66  8  0  0  0  0  0]\n",
      "\n",
      "BIN: 1\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 2\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 3\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 4\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [  0   5  14  56 169 283 644]\n",
      "octave 5: [702 883 912 663 632 233 123]\n",
      "octave 6: [57  9  1  0  0  0  0]\n",
      "\n",
      "BIN: 5\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 6\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [  0  15   8  29 190 386 562]\n",
      "octave 5: [ 674 1045  887  742  601  378  146]\n",
      "octave 6: [50 13  1  0  0  0  0]\n",
      "\n",
      "BIN: 7\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 8\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [  0   8  13  20 234 583 712]\n",
      "octave 5: [ 819 1215  944  800  710  413  102]\n",
      "octave 6: [77  8  0  0  0  0  0]\n",
      "\n",
      "BIN: 9\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 10\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 11\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 12\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [  0  11   5   7 296 610 614]\n",
      "octave 5: [ 721 1245  984  542  780  431  162]\n",
      "octave 6: [85 30  3  0  0  0  0]\n",
      "\n",
      "BIN: 13\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 14\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 15\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 16\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [  0  14   0  40 228 426 646]\n",
      "octave 5: [675 803 686 512 341 308  76]\n",
      "octave 6: [20  4  0  0  0  0  0]\n",
      "\n",
      "BIN: 17\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "---------------------------\n",
      "Usul: sofyan\n",
      "\n",
      "BIN: 0\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [  0  25   2  17 349 601 521]\n",
      "octave 5: [ 635 1102  500  390  477  197   82]\n",
      "octave 6: [39 10  0  0  0  0  0]\n",
      "\n",
      "BIN: 1\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 2\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 3\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 4\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 5\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 6\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 7\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 8\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [  0  13  12  20 311 662 686]\n",
      "octave 5: [806 938 728 387 467 311  92]\n",
      "octave 6: [38 11  0  0  0  0  0]\n",
      "\n",
      "BIN: 9\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 10\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 11\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 12\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [  0  17   9  44 269 492 752]\n",
      "octave 5: [573 850 550 383 297 170  63]\n",
      "octave 6: [8 4 0 0 0 0 0]\n",
      "\n",
      "BIN: 13\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 14\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 15\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "---------------------------\n",
      "Usul: duyek\n",
      "\n",
      "BIN: 0\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [  0   7   2  35 532 450 498]\n",
      "octave 5: [ 641 1209  588  590  736  341  127]\n",
      "octave 6: [73 11  0  0  0  0  0]\n",
      "\n",
      "BIN: 1\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 2\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 4 0 0]\n",
      "octave 4: [  0  14  16  35 234 362 489]\n",
      "octave 5: [ 625  977  712  640 1039  475  258]\n",
      "octave 6: [152  22   0   0   0   0   0]\n",
      "\n",
      "BIN: 3\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 4\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 5\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 6\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 4]\n",
      "octave 4: [  0  12   2  72 127 509 598]\n",
      "octave 5: [783 930 915 757 741 539 228]\n",
      "octave 6: [85 15  0  0  0  0  0]\n",
      "\n",
      "BIN: 7\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 8\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [  4  20  20  24 475 402 738]\n",
      "octave 5: [ 837 1264  953  819 1024  561  207]\n",
      "octave 6: [103  17   0   0   0   0   0]\n",
      "\n",
      "BIN: 9\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 10\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
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      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
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      "\n",
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      "\n",
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      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [  0  10  38  28 353 420 783]\n",
      "octave 5: [724 983 879 668 660 350 142]\n",
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      "\n",
      "BIN: 13\n",
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      "\n",
      "BIN: 14\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
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      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 15\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
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      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "---------------------------\n",
      "Usul: curcuna\n",
      "\n",
      "BIN: 0\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [  0  17   0  14 156 257 289]\n",
      "octave 5: [293 794 316 261 371 225  96]\n",
      "octave 6: [54 12  0  0  0  0  0]\n",
      "\n",
      "BIN: 1\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
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      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 2\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
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      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
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      "\n",
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      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [  0   0   3   2  88 154 284]\n",
      "octave 5: [378 491 343 457 305 139  93]\n",
      "octave 6: [61  9  1  0  0  0  0]\n",
      "\n",
      "BIN: 5\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 6\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [  0   0   2   3  74 139 293]\n",
      "octave 5: [269 524 517 268 379 241 107]\n",
      "octave 6: [30  8  0  0  0  0  0]\n",
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      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 8\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 9\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
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      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [  0  12   0   5 127 188 341]\n",
      "octave 5: [442 575 428 471 418 217  69]\n",
      "octave 6: [30  6  0  0  0  0  0]\n",
      "\n",
      "BIN: 11\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 12\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 13\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 14\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [  0   2   0   6 166 185 423]\n",
      "octave 5: [370 606 496 290 337 244  46]\n",
      "octave 6: [15  1  2  0  0  0  0]\n",
      "\n",
      "BIN: 15\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 16\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 17\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n",
      "\n",
      "BIN: 18\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [  0   6   0  10 152 207 308]\n",
      "octave 5: [233 408 186 164  90  77  24]\n",
      "octave 6: [5 4 0 0 0 0 0]\n",
      "\n",
      "BIN: 19\n",
      "octave 0: [0 0 0 0 0 0 0]\n",
      "octave 1: [0 0 0 0 0 0 0]\n",
      "octave 2: [0 0 0 0 0 0 0]\n",
      "octave 3: [0 0 0 0 0 0 0]\n",
      "octave 4: [0 0 0 0 0 0 0]\n",
      "octave 5: [0 0 0 0 0 0 0]\n",
      "octave 6: [0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "from operator import itemgetter\n",
    "from heapq import nlargest\n",
    "\n",
    "# Analyse the top 3 pitchs for each bin (across all octaves)\n",
    "\n",
    "notes = ['C', 'D', 'E', 'F', 'G', 'A', 'B']\n",
    "usul_pitch_bins_dfs = {} # collection of all df for pitch/bin info for all usuls\n",
    "\n",
    "for usul in usul_considered:\n",
    "    print('---------------------------')\n",
    "    print(\"Usul:\", usul)\n",
    "    \n",
    "    df_data = {'bin' : [], 'note' : [], 'octave' : [], 'count' : []} # reset df\n",
    "\n",
    "    # for every bin...\n",
    "    for i in range(len(weights[usul])):\n",
    "        # init new variable to append max vals to\n",
    "        max_vals_list = []\n",
    "        \n",
    "        print('\\nBIN:', i)\n",
    "        non_zero = False\n",
    "        max_val = 0\n",
    "        max_octave = 0\n",
    "        for o in octaves:\n",
    "            bin_array = usul_weights_bins[usul][o][i]\n",
    "            print('octave {}: {}'.format(o, bin_array))\n",
    "        \n",
    "            if np.sum(bin_array) != 0:\n",
    "                largest3 = nlargest(3, enumerate(bin_array), key=lambda x: x[1])\n",
    "                largest3 = [x[::-1] for x in largest3] #reverse list to get value first\n",
    "                # append octave also to tuple\n",
    "                max_vals = [list(tup)+[o] for tup in largest3]\n",
    "                max_vals_list += max_vals\n",
    "                  \n",
    "                index_max = np.where(bin_array == np.max(bin_array))\n",
    "                max_val = np.max(bin_array)\n",
    "                non_zero = True # flag to consider if at least one octave has value counts in\n",
    "        \n",
    "        if non_zero:\n",
    "            for max_note in nlargest(3,max_vals_list):\n",
    "                # returns [note_counts, index, octave] for top 3 note counts\n",
    "                df_data['bin'].append(i)\n",
    "                df_data['note'].append(notes[max_note[1]])\n",
    "                df_data['octave'].append(max_note[2])\n",
    "                df_data['count'].append(max_note[0])\n",
    "    \n",
    "    usul_pitch_bins_dfs[usul] = pd.DataFrame.from_dict(df_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we know which note and which octave as been played for each weighted bin, we can have a look at which are the most played for each weigthed bin. We will considered only the 3 most popular ones. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>984</td>\n",
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       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>G</td>\n",
       "      <td>5</td>\n",
       "      <td>780</td>\n",
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       "      <td>15</td>\n",
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      ],
      "text/plain": [
       "    bin note  octave  count\n",
       "0     0    D       5   1467\n",
       "1     0    G       5    853\n",
       "2     0    E       5    744\n",
       "3     4    E       5    912\n",
       "4     4    D       5    883\n",
       "5     4    C       5    702\n",
       "6     6    D       5   1045\n",
       "7     6    E       5    887\n",
       "8     6    F       5    742\n",
       "9     8    D       5   1215\n",
       "10    8    E       5    944\n",
       "11    8    C       5    819\n",
       "12   12    D       5   1245\n",
       "13   12    E       5    984\n",
       "14   12    G       5    780\n",
       "15   16    D       5    803\n",
       "16   16    E       5    686\n",
       "17   16    C       5    675"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pitch correlation for usul aksak\n",
    "usul_pitch_bins_dfs['aksak']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
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       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>E</td>\n",
       "      <td>5</td>\n",
       "      <td>728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bin note  octave  count\n",
       "0    0    D       5   1102\n",
       "1    0    C       5    635\n",
       "2    0    A       4    601\n",
       "3    8    D       5    938\n",
       "4    8    C       5    806\n",
       "5    8    E       5    728\n",
       "6   12    D       5    850\n",
       "7   12    B       4    752\n",
       "8   12    C       5    573"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pitch correlation for usul sofyan\n",
    "usul_pitch_bins_dfs['sofyan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bin</th>\n",
       "      <th>note</th>\n",
       "      <th>octave</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>1209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>G</td>\n",
       "      <td>5</td>\n",
       "      <td>736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>G</td>\n",
       "      <td>5</td>\n",
       "      <td>1039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>E</td>\n",
       "      <td>5</td>\n",
       "      <td>712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>E</td>\n",
       "      <td>5</td>\n",
       "      <td>915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>1264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>G</td>\n",
       "      <td>5</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>E</td>\n",
       "      <td>5</td>\n",
       "      <td>953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>E</td>\n",
       "      <td>5</td>\n",
       "      <td>879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    bin note  octave  count\n",
       "0     0    D       5   1209\n",
       "1     0    G       5    736\n",
       "2     0    C       5    641\n",
       "3     2    G       5   1039\n",
       "4     2    D       5    977\n",
       "5     2    E       5    712\n",
       "6     6    D       5    930\n",
       "7     6    E       5    915\n",
       "8     6    C       5    783\n",
       "9     8    D       5   1264\n",
       "10    8    G       5   1024\n",
       "11    8    E       5    953\n",
       "12   12    D       5    983\n",
       "13   12    E       5    879\n",
       "14   12    B       4    783"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pitch correlation for usul duyek\n",
    "usul_pitch_bins_dfs['duyek']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bin</th>\n",
       "      <th>note</th>\n",
       "      <th>octave</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>G</td>\n",
       "      <td>5</td>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>E</td>\n",
       "      <td>5</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>E</td>\n",
       "      <td>5</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>G</td>\n",
       "      <td>5</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>E</td>\n",
       "      <td>5</td>\n",
       "      <td>496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    bin note  octave  count\n",
       "0     0    D       5    794\n",
       "1     0    G       5    371\n",
       "2     0    E       5    316\n",
       "3     4    D       5    491\n",
       "4     4    F       5    457\n",
       "5     4    C       5    378\n",
       "6     6    D       5    524\n",
       "7     6    E       5    517\n",
       "8     6    G       5    379\n",
       "9    10    D       5    575\n",
       "10   10    F       5    471\n",
       "11   10    C       5    442\n",
       "12   14    D       5    606\n",
       "13   14    E       5    496\n",
       "14   14    B       4    423\n",
       "15   18    D       5    408\n",
       "16   18    B       4    308\n",
       "17   18    C       5    233"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pitch correlation for usul curcuna\n",
    "usul_pitch_bins_dfs['curcuna']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File that have been considered: 486\n"
     ]
    }
   ],
   "source": [
    "print('File that have been considered: {}'.format(counter)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrical strenght correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will have a look at the bin values for each score. The bin_vals represent how many notes have been played on each bin. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>usul</th>\n",
       "      <th>makam</th>\n",
       "      <th>bin_vals</th>\n",
       "      <th>file_path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>duyek</td>\n",
       "      <td>mahur</td>\n",
       "      <td>[52.0, 0.0, 58.0, 0.0, 10.0, 0.0, 66.0, 0.0, 9...</td>\n",
       "      <td>mahur--sarki--duyek--ayrilik_ruzgari--kamuran_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>aksak</td>\n",
       "      <td>huzzam</td>\n",
       "      <td>[46.0, 10.0, 14.0, 20.0, 32.0, 13.0, 44.0, 28....</td>\n",
       "      <td>huzzam--sarki--aksak--yine_bir--serif_icli-wit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>curcuna</td>\n",
       "      <td>muhayyer</td>\n",
       "      <td>[80.0, 0.0, 4.0, 4.0, 76.0, 0.0, 76.0, 0.0, 24...</td>\n",
       "      <td>muhayyer--sarki--curcuna--batan_gun--sadettin_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>duyek</td>\n",
       "      <td>hicazkar</td>\n",
       "      <td>[98.0, 10.0, 72.0, 24.0, 65.0, 27.0, 100.0, 13...</td>\n",
       "      <td>hicazkar--sarki--duyek--aglatip_kustureceksen-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>duyek</td>\n",
       "      <td>segah</td>\n",
       "      <td>[73.0, 0.0, 59.0, 3.0, 46.0, 0.0, 48.0, 0.0, 7...</td>\n",
       "      <td>segah--sarki--duyek--gonul_bahcemizde--omer_sa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>481</td>\n",
       "      <td>duyek</td>\n",
       "      <td>huzzam</td>\n",
       "      <td>[32.0, 0.0, 30.0, 7.0, 34.0, 0.0, 42.0, 8.0, 5...</td>\n",
       "      <td>huzzam--sarki--duyek--seni_sesini--sadi_hosses...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>482</td>\n",
       "      <td>curcuna</td>\n",
       "      <td>rast</td>\n",
       "      <td>[48.0, 7.0, 9.0, 14.0, 43.0, 3.0, 37.0, 4.0, 3...</td>\n",
       "      <td>rast--sarki--curcuna--aylar_geciyor--selahaddi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>483</td>\n",
       "      <td>duyek</td>\n",
       "      <td>rast</td>\n",
       "      <td>[29.0, 11.0, 38.0, 34.0, 36.0, 20.0, 40.0, 26....</td>\n",
       "      <td>rast--sarki--duyek--bir_gonul--ferit_sidal-wit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>484</td>\n",
       "      <td>duyek</td>\n",
       "      <td>nihavent</td>\n",
       "      <td>[18.0, 0.0, 25.0, 0.0, 13.0, 0.0, 29.0, 2.0, 3...</td>\n",
       "      <td>nihavent--sarki--duyek--bugun_yine--emin_ongan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>485</td>\n",
       "      <td>duyek</td>\n",
       "      <td>ussak</td>\n",
       "      <td>[26.0, 12.0, 42.0, 32.0, 42.0, 14.0, 42.0, 36....</td>\n",
       "      <td>ussak--turku--duyek--bahceye_indim_ki---withou...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>486 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        usul     makam                                           bin_vals  \\\n",
       "0      duyek     mahur  [52.0, 0.0, 58.0, 0.0, 10.0, 0.0, 66.0, 0.0, 9...   \n",
       "1      aksak    huzzam  [46.0, 10.0, 14.0, 20.0, 32.0, 13.0, 44.0, 28....   \n",
       "2    curcuna  muhayyer  [80.0, 0.0, 4.0, 4.0, 76.0, 0.0, 76.0, 0.0, 24...   \n",
       "3      duyek  hicazkar  [98.0, 10.0, 72.0, 24.0, 65.0, 27.0, 100.0, 13...   \n",
       "4      duyek     segah  [73.0, 0.0, 59.0, 3.0, 46.0, 0.0, 48.0, 0.0, 7...   \n",
       "..       ...       ...                                                ...   \n",
       "481    duyek    huzzam  [32.0, 0.0, 30.0, 7.0, 34.0, 0.0, 42.0, 8.0, 5...   \n",
       "482  curcuna      rast  [48.0, 7.0, 9.0, 14.0, 43.0, 3.0, 37.0, 4.0, 3...   \n",
       "483    duyek      rast  [29.0, 11.0, 38.0, 34.0, 36.0, 20.0, 40.0, 26....   \n",
       "484    duyek  nihavent  [18.0, 0.0, 25.0, 0.0, 13.0, 0.0, 29.0, 2.0, 3...   \n",
       "485    duyek     ussak  [26.0, 12.0, 42.0, 32.0, 42.0, 14.0, 42.0, 36....   \n",
       "\n",
       "                                             file_path  \n",
       "0    mahur--sarki--duyek--ayrilik_ruzgari--kamuran_...  \n",
       "1    huzzam--sarki--aksak--yine_bir--serif_icli-wit...  \n",
       "2    muhayyer--sarki--curcuna--batan_gun--sadettin_...  \n",
       "3    hicazkar--sarki--duyek--aglatip_kustureceksen-...  \n",
       "4    segah--sarki--duyek--gonul_bahcemizde--omer_sa...  \n",
       "..                                                 ...  \n",
       "481  huzzam--sarki--duyek--seni_sesini--sadi_hosses...  \n",
       "482  rast--sarki--curcuna--aylar_geciyor--selahaddi...  \n",
       "483  rast--sarki--duyek--bir_gonul--ferit_sidal-wit...  \n",
       "484  nihavent--sarki--duyek--bugun_yine--emin_ongan...  \n",
       "485  ussak--turku--duyek--bahceye_indim_ki---withou...  \n",
       "\n",
       "[486 rows x 4 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_makam_bin_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#normalization to 3\n",
    "def scale_to_3(x):\n",
    "    '''\n",
    "    The function scales the input value to between 0 and 3\n",
    "    Input:\n",
    "    -------\n",
    "    :x: value to scale\n",
    "    Return:\n",
    "    --------\n",
    "    :x: value scaled\n",
    "    '''\n",
    "    NewMin = 0\n",
    "    NewMax = 3\n",
    "    OldMin = x.min()\n",
    "    OldMax = x.max()\n",
    "    \n",
    "    x = (((x - OldMin) * (NewMax - NewMin)) / (OldMax - OldMin)) + NewMin\n",
    "    #print(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalise bin_vals to be 0 - 3\n",
    "df_makam_bin_vals['bin_vals'] = df_makam_bin_vals['bin_vals'].apply(scale_to_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bins value have been normalized between 0 and 3, so we can calculate the correlation between usul and makam pattern. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>usul</th>\n",
       "      <th>makam</th>\n",
       "      <th>bin_vals</th>\n",
       "      <th>file_path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>duyek</td>\n",
       "      <td>mahur</td>\n",
       "      <td>[1.6595744680851063, 0.0, 1.851063829787234, 0...</td>\n",
       "      <td>mahur--sarki--duyek--ayrilik_ruzgari--kamuran_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>aksak</td>\n",
       "      <td>huzzam</td>\n",
       "      <td>[2.918918918918919, 0.0, 0.32432432432432434, ...</td>\n",
       "      <td>huzzam--sarki--aksak--yine_bir--serif_icli-wit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>curcuna</td>\n",
       "      <td>muhayyer</td>\n",
       "      <td>[2.6373626373626373, 0.0, 0.13186813186813187,...</td>\n",
       "      <td>muhayyer--sarki--curcuna--batan_gun--sadettin_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>duyek</td>\n",
       "      <td>hicazkar</td>\n",
       "      <td>[2.838709677419355, 0.0, 2.0, 0.45161290322580...</td>\n",
       "      <td>hicazkar--sarki--duyek--aglatip_kustureceksen-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>duyek</td>\n",
       "      <td>segah</td>\n",
       "      <td>[2.92, 0.0, 2.36, 0.12, 1.84, 0.0, 1.92, 0.0, ...</td>\n",
       "      <td>segah--sarki--duyek--gonul_bahcemizde--omer_sa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>481</td>\n",
       "      <td>duyek</td>\n",
       "      <td>huzzam</td>\n",
       "      <td>[1.92, 0.0, 1.8, 0.42, 2.04, 0.0, 2.52, 0.48, ...</td>\n",
       "      <td>huzzam--sarki--duyek--seni_sesini--sadi_hosses...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>482</td>\n",
       "      <td>curcuna</td>\n",
       "      <td>rast</td>\n",
       "      <td>[2.938775510204082, 0.42857142857142855, 0.551...</td>\n",
       "      <td>rast--sarki--curcuna--aylar_geciyor--selahaddi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>483</td>\n",
       "      <td>duyek</td>\n",
       "      <td>rast</td>\n",
       "      <td>[1.5, 0.07894736842105263, 2.210526315789474, ...</td>\n",
       "      <td>rast--sarki--duyek--bir_gonul--ferit_sidal-wit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>484</td>\n",
       "      <td>duyek</td>\n",
       "      <td>nihavent</td>\n",
       "      <td>[1.7419354838709677, 0.0, 2.4193548387096775, ...</td>\n",
       "      <td>nihavent--sarki--duyek--bugun_yine--emin_ongan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>485</td>\n",
       "      <td>duyek</td>\n",
       "      <td>ussak</td>\n",
       "      <td>[1.5, 0.1875, 3.0, 2.0625, 3.0, 0.375, 3.0, 2....</td>\n",
       "      <td>ussak--turku--duyek--bahceye_indim_ki---withou...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>486 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        usul     makam                                           bin_vals  \\\n",
       "0      duyek     mahur  [1.6595744680851063, 0.0, 1.851063829787234, 0...   \n",
       "1      aksak    huzzam  [2.918918918918919, 0.0, 0.32432432432432434, ...   \n",
       "2    curcuna  muhayyer  [2.6373626373626373, 0.0, 0.13186813186813187,...   \n",
       "3      duyek  hicazkar  [2.838709677419355, 0.0, 2.0, 0.45161290322580...   \n",
       "4      duyek     segah  [2.92, 0.0, 2.36, 0.12, 1.84, 0.0, 1.92, 0.0, ...   \n",
       "..       ...       ...                                                ...   \n",
       "481    duyek    huzzam  [1.92, 0.0, 1.8, 0.42, 2.04, 0.0, 2.52, 0.48, ...   \n",
       "482  curcuna      rast  [2.938775510204082, 0.42857142857142855, 0.551...   \n",
       "483    duyek      rast  [1.5, 0.07894736842105263, 2.210526315789474, ...   \n",
       "484    duyek  nihavent  [1.7419354838709677, 0.0, 2.4193548387096775, ...   \n",
       "485    duyek     ussak  [1.5, 0.1875, 3.0, 2.0625, 3.0, 0.375, 3.0, 2....   \n",
       "\n",
       "                                             file_path  \n",
       "0    mahur--sarki--duyek--ayrilik_ruzgari--kamuran_...  \n",
       "1    huzzam--sarki--aksak--yine_bir--serif_icli-wit...  \n",
       "2    muhayyer--sarki--curcuna--batan_gun--sadettin_...  \n",
       "3    hicazkar--sarki--duyek--aglatip_kustureceksen-...  \n",
       "4    segah--sarki--duyek--gonul_bahcemizde--omer_sa...  \n",
       "..                                                 ...  \n",
       "481  huzzam--sarki--duyek--seni_sesini--sadi_hosses...  \n",
       "482  rast--sarki--curcuna--aylar_geciyor--selahaddi...  \n",
       "483  rast--sarki--duyek--bir_gonul--ferit_sidal-wit...  \n",
       "484  nihavent--sarki--duyek--bugun_yine--emin_ongan...  \n",
       "485  ussak--turku--duyek--bahceye_indim_ki---withou...  \n",
       "\n",
       "[486 rows x 4 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_makam_bin_vals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation evaluation\n",
    "\n",
    "We will first plot the note distribution for each makam (only the first four will be plot, but all the plots will be saved in an external folder. The user can define the path in the variable: plot_folder_path).\n",
    "\n",
    "Then, the spearmanr correlation between the makam score note distribution and the relative usul will be calculated. \n",
    " \n",
    "The same will be done in relation with makam, to see if there is any makam which contribute the most to the correlation between note distribution and usul pattern. \n",
    "\n",
    "The correlation is firstly evaluated between each score and usul. \n",
    "The final correlation is the average of the sum of previous correlation for each usul. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot folder\n",
    "plot_folder_path = 'path to dir where to save plots (the dir will be created next)'\n",
    "\n",
    "if not os.path.exists(plot_folder_path):\n",
    "    os.makedirs(plot_folder_path)\n",
    "\n",
    "#plot function\n",
    "def plot_hist(x, makam, usul, i, file_path):\n",
    "    # PLOT        \n",
    "    plt.style.use('ggplot')\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    bars = np.arange(1, len(x) + 1)\n",
    "    y_pos = np.arange(len(bars))\n",
    " \n",
    "    # Create bars\n",
    "    plt.bar(y_pos, x)\n",
    "    plt.xlabel(\"Location (1/16)\")\n",
    "    plt.ylabel(\"Normalized count\")\n",
    "    plt.title('Metrical strength for {}'.format(file_path))\n",
    " \n",
    "    # Create names on the x-axis\n",
    "    plt.xticks(y_pos, bars)\n",
    "    \n",
    "    #save plot inside the folder\n",
    "    fig.savefig(plot_folder_path + '/' + file_path + '.png', dpi=200)\n",
    "    \n",
    "    #plotting 4 just to give an idea, all the plot will be saved into the folder defined\n",
    "    if i < 4: plt.show()\n",
    "    \n",
    "    plt.close()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mahur--sarki--duyek--ayrilik_ruzgari--kamuran_yarkin\n"
     ]
    },
    {
     "data": {
      "image/png": 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r3Q8zMjJQv359eHl5oXHjxnBwcIChoSGePHmCM2fOYM2aNUhNTcXw4cNRrVo1fPbZZ2rj1uV2LqhevXrhwYMHGD16NLy9vWFra4tnz57p9NXY8+bNw8mTJwEALVq0gK+vL2rUqIGyZcsiISEBd+/exZkzZ5QeY4Hs7fLJJ5+gW7duaNKkCapWrQpjY2M8e/YMAQEBWLZsGRISEjB16lRUrVoV3t7eauP57bffEBYWhhYtWmDChAmoXbs2UlJSVPY6zC0uLg7dunXDpUuXIJPJsGjRIsycOTN/K0VHbty4gZ49e+L9+/cwMDDAiBEj4OPjgwoVKiAiIgJ//fUX9u7dm6cXly40bdoUjRs3RlBQENavX4/58+ervPt95coV8a70iBEj8pQLCwuDm5sbkpOTYW5uDl9fX7i5ucHJyQlpaWk4f/48lixZgujoaPj4+ODkyZNwd3eXHOu7d+8wcOBAcbD9L774AkuXLoWBgU5eophDRkYGrK2t0a1bN7Ru3Ro1atSApaUlYmJicPv2bfzzzz+4f/8+duzYATs7OyxZsiTH/Npcz3IBAQFYv349KlWqhK+//hrNmzeHoaEhgoKCYGVlpd0VUEAnT56Et7c3EhMTUalSJRw+fDhHj2RtGjZsGK5fv47+/ftjwIABcHR0RExMDF69eqXV5bRs2RI3btxQW+bixYsYN24cACj9LZ46dap4Pu/g4IBvvvkGzZo1gyAIuHDhAn755RcMHz4ctWrVUrucwrbL3EpCm8pN6nEhIyMDZmZm4rWAq6srKlSogLdv3+Lhw4dYt24dzp07h+vXr6NXr14ICAhQ25soKSkJPXv2REpKCubPn4927drBxMQEZ8+exffff4+kpCRMnDgRHh4e6N+/PyIiIjBt2jR06dIFZcqUQVBQEObMmYOYmBj8/vvv6N69e76Oh4Uxbdo0hIWFoVOnThg5ciScnZ0RHx+Pe/fuiWW0vb6Sk5Ph5eWFFy9eYPr06ejYsSPKlSuHyMhI/PjjjwgNDUVISAhmzJiBlStXauV7VqpUCd27d8eePXuwZ88exMXFoXz58krLPn/+HEeOHAEA9O7dO8/QGRkZGXB0dET37t3RokULODs7w9zcHC9fvkRoaCj+/vtvREVF4c8//0SVKlUwZcoUtbHt2rULYWFhqFWrlngenpGRgUuXLkm6BtXF7+GsWbNw4cIFdOrUCSNGjICLiwvevn2LHTt2YOXKlXj37h2GDBmCiIgIlC1btsDLERU2E5O7R4ogCMK9e/fEz/bt25ej/P79+8Vp4eHhgiAIGnukbNu2TZz+888/q8yS/fnnn2qzXu7u7gIAwcnJSeP3yt2DQlXvGmXllfVIUczIVa9eXW2vkRcvXghpaWk5Pnvz5o0QHR2tNobly5eLy9i4caPSMoXtkfLvv/+K8+/du1dt2dyZUEHQfEdbkWLbAiC0adNGSElJUVn+wYMHYg8Bb29vlWUvXbokllN2J0IxRgDC6tWrldYzZMgQscyBAwfyTL9586bYs8HZ2VmIiorKU+bZs2dC5cqVcyxPmcK03VmzZiktN2fOHLHMX3/9pbFeKctT1xtL/h0ACEuXLlVa5scffxTLdOjQIc/0/LYJKXLX2a1btzz7nyAIgre3t1jGxsZGaNq0qfD27ds85aZMmaLy+Cd39+5dtTE9ePBAvPvZrl07pWW0tZ111SNFJpOp/P66Ir+r1qJFC6U9DeTevn2rdBtr2i4xMTFCrVq1BABCzZo1lf4W5b7TO3DgwDw9SBSputPy+PFjoXbt2gIAwdjYWOUxXRCKp0dKy5YtxWUq6zmVmZkp+Pj45FgXuuqRIgiCsGrVKrEef39/lXWMHDlSbJ8PHjzIMS0jI0OoWbOmAECoVauW8OzZM6V1REVFiXcka9eunWf7qroDFxcXJ7Ru3VqctnDhQrXfV9sePXqktudLRkaG0K9fPwGAYGRkJDx58iRPGW2sZ8XeAAAEV1dXpecLysoXdY+UzZs3iz0batasqfG8pSAU9wEAwvLly9WW10aPFE0iIyMFa2trAYBQtmxZ4datWzmmh4aGiuc31atXF2JiYvLU8eTJE3G7AKp7pGijXeqiTWmbNo4LcXFxwqtXr9Qu56+//hLr2rp1q9Iy8jYEZPeQVPbbt2fPnhznPKampsLFixfzlLt69arYI6Fv375Kl6eLHikAhBkzZqitTxfrq0yZMkp7TSclJYm9cE1NTbXaK+X48ePi8v/880+V5ebPny+WO3PmTJ7pkZGRQkZGhsr5k5OThTZt2ggAhHLlyglJSUl5yigeVwAIHh4eQmpqqso6tdHu89MjRd257Lfffiv5OCuVThIpgvB/ja5nz545yvfq1UsAIHz22WfiZ5oSKa6urgIAoX379hrjadKkiXiBlVtBL0ZdXFzUnpTnLq/sYlK+bJlMJgQFBWlcfkHJHxno0aOH0umFTaQsXLhQnD8hISHf8xc0kWJgYCBERkaqLT9hwgQBgFC+fHmlF7iKpk6dKh7scl+IK8bYvXt3lXXcunVLLKcs0TZx4kRxurJEi9zOnTtzHACUKWjbbdCggcrEY3x8vHiCqNhtOL+kJFIUH037/PPP1dan+IOV+/Gr/LYJKRTrNDc3V3pCKAiCcPr06RzbSd5FOLf79++LZdR1ddXkjz/+EI8ZcXFxeaZrazvrKpEydOhQSd9Tm+Qn9tOnT9fZMhT3V2VtQPGkwcrKSuOxSNkJgmJX61KlSmnshlrUiRTF7tK5f+MVvXnzRihXrpzaE2NtJVKSkpKEMmXKCACErl27Kp3/7du3QqlSpVSeT2zdulWMJSAgQG0se/fuFcuePXs2xzRlJ45PnjwRz2WMjIyEdevWqa2/uERHR4v7kbLEqzbWc+6L3nPnzqmNqbgSKb/88ot4gdi8eXO1F+aFobgPqEqca4pVmYImUuLi4sSEopGRkXDixIk8ZcaNGyfWfeTIEZV1Kd4IVZVIkUJTu9RFm9K2ojwu1KlTR+25neJ5lqoLyqysrByJsJkzZ6pcnjyxbmtrq3S6LhIpn3zyidqkQH7kZ32puhEoCIKwZMkSSftFfmVlZYnJ+7p16yotk5mZKT6S+sknnxR4WYq/78quXRSPK4aGhhqv5bTR7vOTSGncuLHK8+HXr18LhoaGApD9qKA26KwvqXwAykOHDoldEl+9eiU+8iF1kNnw8HBxYJgBAwZoLC/vUhYQEKC1LuT9+/dX29VLkzdv3uD8+fMAsgfjbNSoUaFjyszMxPPnzxEeHo6bN2+Kf46OjgCAkJCQQi9DGQcHB/Hfa9as0ckylGnZsiVcXFzUltmzZw8AoGvXrihdurTash4eHgCy3zhz7do1leWGDh2qcpp8UC4ASgfeO3HiBADAxsYGXbp0UVlPz549dfa4hXyQN2XKli2LTz75BIDy+LVJ8XGksWPHqi07fvx48d/qBpiT0ibyq3379rCxsVE6rX79+uK/P/30U9StW1dpOWdnZ7H9PXjwQNJyX79+jfv37+PWrVvivlyqVCkAgCAICA0NVTu/vmxnRUOGDCmyZcnJHxXct28fYmNjC11ffHw8Hjx4kGO7KD42puk426NHD43HotzOnDkDNzc3vHjxAra2tjhz5gw6duxYoPh1RXF/9vX1VVnOysoqxyCZulSqVCkMHjwYAHDkyJEcr2WX27p1qzgQ4JgxY/JM3717N4DsfbhZs2Zqlyf/DQGyH4FQ5+bNm2jZsiVu376NUqVKYf/+/Rg+fLjaeYpCSkoKnjx5gtu3b4vtOyYmRnz8QVn71sZ6VuTk5IQ2bdoU9qtolSAImDJlCqZPnw5BEODl5YXTp08XyRsmi+O4qSg9PR3e3t64e/cuAGDZsmVKBwiVn99UrFhR7fGpd+/e+e4+X5B2qUgf25Qy2jguZGRkFOpawMDAAP3791c6TSaT4dNPPxX/P3DgQJX1yB91i4mJ0fpgq6oMGDBA8vAQcoVdXzKZTDz+KaP4u6HN8y2ZTCYeS2/evIkrV67kKXPixAnxJQCjR4+WVG9iYiIePXqU4xxH8VxS0/po3bq12kfwldH17+GQIUNUng+XL19eHDBYW9tH62OkyPn4+GDSpElISUnBli1b8NVXX2Hz5s1IT0+HhYUFfHx8JNWj+Cz5yJEjxfEvNElLS8Pr169VjrmRH4V9FjYkJARZWVkAUKhnB9+/f4+VK1di69atCA0NVfuGHm1cRCjTo0cPVKhQAa9fvxafj+3RowfatGmD5s2bixd/2qZpGzx58kR8i8LGjRuxceNGyXW/fPlS5bTatWurnbdcuXJISkrC27dvc3z+/v178TnNRo0aqX3ez8jICA0bNsTp06clxyyVpvjlz1nmjl/brl+/Lv67ZcuWassqjgcSFhamspwunlGvWbOmymmKz1arKycvm5iYqHa9/vfff1i+fDlOnz6N169fq61P0/6sL9tZkabto+5NQuXLlxeTIs+ePVP5NhOZTIY6deqI//f19cX333+Pe/fuwdnZGT179oSnpydatWol+W0dQUFBWLp0KY4fP67xzSyatkt+2+jOnTuxcuVKvH//Hi4uLjh69Gih3jICZL9pRNWJrampKWrUqAEgO+Gv7KJYTv62AyDn/ty8eXO1y2/evDlWr16d37ALZNy4cVi2bBkyMzOxbt06cewmOXny39bWFj169Mgzv/x848GDBypPxJRR9xty7tw5TJw4EQkJCbCxscHBgwc1Jml0sc0UY/3jjz+wb98+REZGiucmyqhq34Vdz4p0NdZIQaWnp2PAgAHiG3l8fX2xcuXKfF+wFVRxr48xY8aIbwGcPn260guylJQU8SKkcePGavcVY2Nj1K9fH+fOnVO7XG20S7nCrsPk5GQ8fPhQ5fTKlSuLyaG7d+8iPT1daTlLS0uVF5kFOS7IvXv3DitWrMC2bdsQGhqq9o0zmtaVnZ2d2nFjpJ73KJZ7+/atzq4FFEndztpcXxUrVlR741Nx7BJtn2+NGDECc+bMwfv377FmzRq0aNEix3T5cdfExATDhg1TWU9ERAT++OMPHDp0CE+ePFG7TG3va4Vp91IV9fmwzhIppUuXRp8+fbBx40asW7cOX331lThYbJ8+fVCmTBlJ9cTExBQ4hpSUlALPq6iwvQUUBwnLPbiqVM+fP0f79u3F18lpoq3vnpuVlRWOHDmCgQMHIjIyEiEhIWLGUv7asd69e2PUqFFaHdRL0zbQVTvJfRKamzxBkrv305s3b8RXcUtJ5mkj4adMQePXNsVEgbrBfYHsgbWUzZebLnrxqFtfismwwq7XyZMnY+nSpZLj0rQ/68t2VqRu+2RkZKBevXoqp/v6+oonBTNnzlT5Kl1DQ8McrzGePXs2YmNjsWLFCiQmJmLTpk3iaxkrVqwoDkqn6m7lTz/9hO+++07cdzXRtF3y20blbcLAwAB79uwpdBIFyL47o6rHhIuLi/ia+t27d6u9i3X+/Hm0bt0awP/tlwYGBhpf5axpf9emevXqoWXLlrh8+TLWrl2LWbNmiRd5oaGhYu/D4cOHKx0Ir6C/I+ragWISad26dZJOGnWxzQDg1KlT6NWrFxITEzXGAKj+XoVdz4qKavBrqZ48eSJeXDRp0qRIe94Cxbs+Fi5cKJ6ne3t74+eff1ZaLi4uTvy3Ns5vtNUu5Qq7Di9fvoz27durnL5p0yaxV4Knp6fKZKanp6c4+HluBTkuANmvm2/fvr3YY0gTbZ07GBoaqh3EX/H8qKjOM6Rs5+JaX4D214O1tTW8vb2xbds2bN++HX/88YfY4/XVq1fYt28fgOxe7qp6Vm/evBm+vr4aX/csp+19raDtPj+K+nxYp8PEyx/fuX79OlatWiXexZL6WA+AHCfJ//77L27cuCH5T/ExlMLQ5p2I/NzlUjR06FAxieLl5YU9e/bg/v37SEpKQmZmJoTs8W6KpFto06ZNcefOHfj7+2PUqFGoXbs2ZDIZMjIycOXKFUyfPh0uLi4qf0AKQtM2UGwn48ePz1c76dmzp9biLCipF24fA8V9RN3+UlR3CLVt06ZN4gWzk5MTli5dipCQEMTFxSEtLU3cl0+dOiXOUxLbR3FsHyMjI/z999+4d+8eFi5ciPbt24snGlFRUaEbXKMAACAASURBVFi/fj3c3NwwYMCAPHcRT58+jW+//RaCIMDGxgY//fQTAgMD8erVK7x//17cLoonZJq2S37XQd++fQEAWVlZ6Nevn9qeDiVFUbdd+VtGHj16lOM3SH4CJ5PJVCYf5CdWzZs3z9dvyPz581XG06tXL/HR4FGjRkm+GaJtr1+/ho+PDxITE2FkZITJkyfj1KlTePbsGVJTU5GVlSW2cXkyW922K8x6VqRvx3F7e3u0atUKAHDt2jV89dVXRbr84lofO3bswJw5cwBkn+Nt2rSpwOerualrR9pul4D+tSllCnpcGDx4sPgb1L17d+zdu1fptYB8KISSeO4glZTt/KGtL/lxNzk5Gdu3bxc/X79+vXhOo+pxyrt372LkyJFIS0tDqVKlMHv2bFy4cAFRUVF49+6duC4Un3bQ9r6mL7+H2qSzHilA9mMs1apVw8OHDzF58mQA2a9pzc/jLYpZtczMTJXjEugzxe+grhuuKvfu3RMf++jfvz+2bdumsqzinQJdMjIyQo8ePcRuu69fv8Z///2H9evX49ChQ4iLi4O3tzcePHig8Y6lNiiu47dv3xZ7O1HM0kq5y1mYHjUlgeKz5VFRUWqTnIoXj6pe8VaSyV//bWVlhYCAANjZ2SktV1T7suIdFHXdqQFo7dlnIyMjyScsmzdvFl+xKZWzszO+++47fPfdd8jKykJoaCj279+P5cuXIyYmBtu3b0f16tWxYMECcZ4VK1aIsZ0/f15lN2ZdbpdJkyahTZs2mDRpEsLDw+Hu7o7Tp0+Lz28XxIULFySVGzVqFEaNGiWprHx/zsrKQmxsrMq7X4DmY5u225+Pjw+mTJmCuLg4rF69Gu3bt0dqaqrYq6lt27Yqe/pYW1sjKioKUVFRWvsN8fb2xuDBg9G/f39ERUXBw8MDJ0+eVNsjSxfbbMeOHWLbXbFihcqxbQRBUPkonaLCrGd9Zm5ujqNHj6Jr1644e/YslixZgvT0dPz9999aSywUlnyf0dbx+vLlyxg+fDgEQUCVKlWwf/9+tXd1FX+XC3t+o+12qQ2ff/655N+nZ8+eFWgZBTku3L59W3xEavDgwWJvS2WK6vxBKm23WSlK8vpSxc3NDa6urrh9+zZWr14tJqvlPedcXFzQrl07pfOuXbtWTLbs27cPnp6eSsvpcl0UpN3rO532SJHJZOIAMu/evQOQ3dUzPz9GjRs3Fv8tfzd2YeIpDopjZJw9ezbf89+4cUP8t7oBd7OyshAUFJT/ALWgQoUK6NOnDw4ePIgJEyYAyB7E6NChQznK6WobVKtWTfxxP3nyZI4eKsVB8Tn24OBgtT8eGRkZGgcT1ZcTuIJSHLBM2SBZii5duiT+W3GA1w+FfH9u27atyiQKAFy9erVI4lEcDPXNmzdqy4aHh+s6HK0zMDBAo0aNMG/ePFy+fBlmZmYAkONuDvB/26Vhw4ZqnwXX9Xb58ssvsWLFCshkMkRERMDd3R2PHz/W6TLzS3F/DggIUFtW03Sp7S86OlrShZSZmZn4fPi+ffvw6tUr+Pn5ISEhAYD6wU/l5xuPHz/W6p0yb29v7N69G6ampoiJiUHbtm01HvO1Tep5xM2bN5GamqqxvsKsZ31naWmJw4cPixcay5Ytw9ixY/XmbrV8n9HG8frhw4fo0aMH3r17h9KlS+PgwYMaH8ezsLBAtWrVAGSPKaVuvaSnp6sd60zb7bIkye9xIT/XAsHBwVqNtbC02WalKsnrSx35CxuuXr2K69ev4+zZs+KYjKNHj1Z5vSBfHzY2NiqTKPJ6dUkffg+1SaeJFAAYNmwYzM3NYWpqCnNzc7UD4ChTv359ODs7A8j+sVbcMfJLfgKtbpBWXbCysoKbmxuA7AEm87vDKiYF1GVr9+zZo3FwxKKgOIK74vgwwP9tA0C728HAwEDsHRMdHV1kgxuqI3/G9tWrVzh8+LDKcv7+/hp/XIqr7WqLYptYtWqV2rLyngEA0KFDB53FVFzk+7O6fTkpKQkbNmwoknjKlSsnJiHV/YBGR0dr9XG94uDs7CwmOHMfm6Rsl4yMDKxcuVJ3Af5/Y8eOxZo1a2BgYIAHDx7A3d1d7eCHRU1xf/73339VlktISMDOnTvV1qX45i117S8/vZLkJ5ppaWnYuHGjeLfO2toavXr1Ujmft7e3+O8ffvhB8vKk6NatG/z9/WFmZobXr1+jXbt2RXrjQ+p5xD///CO5zoKu55LAwsICBw8eRKdOnQBkP7I0cuRIjXfUi4J8n4mPjxfHy1FG0z4THx8PLy8vvHr1CoaGhvDz85N8Z1h+fhMVFZXjLV657d69W+2gjrpolyVJfo4LUteVn59fnt+34iZvs3fu3EFSUpLSMoIgqBwPrSBK8vpSZ+jQoTA3NweQfVySH3eNjY3Vvv1Gvj5SU1PVJj+LYl8r7t9DbdJ5IsXJyQkpKSl49+4dUlJS4OTklK/5ZTIZ5s2bByD70R5vb2+NrywKCgpSeuEqf5wgJiZGvGtSVOSj2guCgH79+omvqFImKioqx/P78leXAtknrcp2gDt37uCLL77QXsAqHD16FE+fPtVYRk6eBJNTfKQjIiJCq7F99913MDU1BQBMnTpVYw+m58+fq70IKKxx48aJPZEmT56M6OjoPGVevHiBr7/+WmNdxdl2taFBgwbiI33Hjx8XH2/J7ddffxVfFd6hQ4ccb2T5UMj35wsXLigdAC09PR3Dhg1T2l50Rf4q14CAAKW95t69e4ehQ4eKPQv1UWxsLPbt26f2Qufhw4fiOs99bJJvl/DwcKUDfQqCgMmTJ+PWrVtajFq1kSNHYv369TA0NMTjx4/h5uam9qKpKDVp0kR8W8/evXvz9O4BstfX+PHjNSaJW7duLT4zvXTpUqVtLDg4WDwPkKJmzZpim/7111/FR2WGDRumdsDEIUOGiI+jbN26FQsXLlS7nOTkZPz++++S4+rUqRMOHDgACwsLvHnzBp6enhp77GiL4nnE2rVrlZbZvn27xkS3ooKu55LCzMwM/v7+6Nq1K4DscQiGDh1apIN2K9O2bVvx34sXL1ZaZvXq1Th48KDKOtLT09GnTx+x59XSpUvFpJEU48aNE+98f/nll0ovRJ8+fYpp06aprUcX7bKkkXpckHItcOvWLXz55Zc6jbcg5G32/fv3Kgfanz17dqFuludWkteXOlZWVujXrx+A7GTprl27AGSPAaOul7N8fSQlJYlvJcvt559/VpsY1abi/D3UJp0nUrRhyJAh4nPAkZGR+PTTTzFx4kTs378fwcHBCAwMxP79+zF37lw0bNgQTZo0yfF4gJz8TQ1ZWVkYPXo0Ll68iIiICERGRiIyMlKn3QY9PT3FHxT5d5g2bRqOHTuG0NBQXLp0Cf/++y98fHxQtWrVHNnTBg0aiK+YOnnyJDw8PLBz505cu3YNp0+fxowZM9CsWTMkJibmeBRKF7Zv345q1arB09MTixcvxtGjRxEUFIQrV67Az88P3t7e4kWys7MzunTpkmP+Vq1aiT++s2bNwuHDh3H37l1xGxTm2bzq1atjzZo1kMlkSE1NhZeXF7y9vbFlyxYEBgYiODgYx48fx2+//YaOHTuiatWqOk2k1KtXD5MmTQKQ/TrNRo0aYcmSJQgICMCVK1fw+++/o1GjRoiOjkbDhg3V1lWcbVdbVq1aBUtLSwDAhAkTMHDgQBw6dAghISE4cOAA+vbti+nTpwPI/qFQ7JnyIZEPtp2SkgJ3d3f8/vvvOH/+PAICArBq1So0bNgQe/bsUflmGV1QPJHo0aMHFi9ejMDAQAQGBmLVqlVo3LgxTp48qfHV1cUpPj4ePXv2RJUqVTBx4kRs2rQJFy9eREhICE6ePIkffvgBrVq1Ekerz33yJN8uWVlZ8PLywoIFC3DmzBlcvXoVGzduxGeffYZly5YV6XYZMmQINm/eDCMjIzx79gzu7u6S3z6gaytWrBAvlgcNGoQxY8bgxIkTCA4Oxo4dO9CmTRts27ZN46j8tra26N+/P4DsrsceHh7YvXs3QkJCcOrUKUyfPh1t2rRB5cqVc4y1pIl8UD7FXpqaBj81NjbGnj17xG7os2fPRrNmzbBs2TJcvHgRoaGhOHv2LFauXIlBgwahYsWKmDp1quSYgOzxFw4fPoxSpUohISEB7du3V/mGHm0aMGCA2LNx9uzZGD16NI4cOYLg4GDs27cPAwYMwMCBA1GrVi2dr+eSxNTUFHv27BF72GzZsgUDBw4s1seHu3btKiaCV69ejcGDB+PkyZMICQnBvn370L9/f4wZMybHG5tymzdvnjigebdu3eDm5oabN2+q/MudQG7YsKF48y4yMhINGzbE0qVLxfObX3/9FY0bN0ZsbKx4/qrskQNdtcuSRspxoUmTJmKPoaNHj8LT0xO7du1CUFAQTp06hW+++QbNmzdHamoqGjVqVBxfQ6UhQ4aIb/KcPXs2Jk2ahLNnzyI4OBh+fn7o2LEjFi1apLbN5ldJXl+ayI+78fHx4s0HTY9TKvZWGTFiBKZNm4YTJ04gKCgIfn5+6Ny5M2bOnFmk5zjF9XuoVUIhPXz4UAAgABB8fX0LVMfYsWPFOh4+fKi0TFZWljB//nzB2NhYLKvub/HixXnqSElJEWrXrq1ynv/++08su27dOqWfqyK1/Pz58wUjIyON8b958ybHfLdu3RJsbW1Vli9durTg7+8vDBs2TPxMGU3TNVGcX91f9erVhdu3byutY/jw4Srnmzt3rlhOsW0pfq7Jvn37BGtra0lxdunSJc/8c+fO1dge5ZycnAQAgru7u9LpGRkZwuDBg1Uu38TERFi/fr0wZMgQAYBgZmamtB5dtV13d3cBgODk5KS2nDr5WV5gYKBQqVIltdukcuXKQkhIiNL5C9om1MlPnfJyw4YNU1tOXbvIyMgQevTooXYdDB8+XDh16pT4/3Xr1uWpR9vbefbs2SrjMTY2FlauXKlx3yjs8aUwIiIiJO3zBgYGwpw5c5TWMW7cOI3Hixs3boj/X7BgQZ46Tpw4IU7ftGmTxrhXr14tlj9//rzSMrt27RJ/++zs7ISbN2/mmD5r1iyxjqdPn0pYW9px8OBBoVSpUirXV8+ePYUjR45oXB+xsbFCnTp1VNZTo0YNITIyUnBwcBAACJ6enhpjS0tLE+zs7MQ63NzcJH+vW7duCXXr1pXUnsqUKZNnfilt4MKFC0Lp0qUFAIKlpaVw5swZyfEV1KZNm9Sef3zyySdCRESEztdzenq6WF7KeaOU8ppibtWqlQBAcHFxKdD09PR0wcfHR4zD29tbSEtL0xi7FAXZf69cuSKULVu2wMeqQYMGSWrf8j9DQ0Ol62TAgAEq5zExMRE2b94slrG0tFT6XbTRLnXRprRNG8eF69evCzY2NmqPRwcOHBC3r7LtJgia27ucpnrkpLTh/fv3CyYmJipjHzVqlHD8+HG160jK76Wiol5fiuchys4PtKlhw4bisqpWrSpkZWVpnOfHH39Uu583a9ZMePnypdp9I7/fURvtXtN2z0+7kLotpSoRPVKA7Ez2nDlz8ODBA8yZMwetWrWCjY0NjIyMYG5uDicnJ3Ts2BE//PADbty4Id7ZVmRubo6LFy/i22+/RYMGDVCmTJkiH8Rzzpw5CA8Px7Rp09CgQQNYWVnBxMQEjo6OaNGiBWbOnIng4GAxcyvn6uqKsLAwTJkyBZ988glMTU1RunRp1K5dG1OmTEFYWJg4RoguLVmyBP7+/pg0aRJatGgBJycnmJubw8TEBA4ODujcuTNWrlyJmzdvonbt2krrWLNmDZYtWwY3NzdUqFBB7NatLd27d8ejR4/w119/oUuXLnBwcICpqSlMTExgZ2eH1q1bY+rUqTh16hQOHDig1WXnZmhoiE2bNmHv3r3o1KkTrK2tYWpqiipVqmDo0KG4cuUKhg0bJg6iWLZsWaX16EPb1YamTZsiIiICv/76K9q0aQNra2sYGRnB2toa7u7u+OOPPxAeHi7ewfoQGRoaYu/evVi7di1at26NMmXKiMeAXr164cCBA1i3bl2Ot5kUhQULFuDAgQPo2LEjypcvDxMTE1SpUgVDhgxBQECA3g8e6eLigtDQUPz+++/o3r07XF1dYW1tDUNDQ5QpUwYNGjTApEmTEBoaqvJ1tcuXL8euXbvw+eefo1y5cjA2NkalSpXQqVMnbNmyBYcOHcoxzlNR6d27N3bt2gUTExNER0ejbdu2uH79epHHkZuXlxdu3bqF8ePHo1q1ajA1NYW1tTU8PDywfv167NmzR9LxvUKFCrh8+TK+//571KtXD+bm5rC0tMSnn36KBQsWIDg4OMdYKlIYGxvnGGAwP+1X/nu7c+dODBgwAM7OzrC0tISRkRHKly+PRo0aYdSoUdixY0eBxyVr1aoVTpw4gbJlyyIpKQldunTJ8cpzXRg8eDACAgLQv39/2Nvbw8jICBUqVEDz5s3x888/Izg4ON9v2inMei5JjIyMsHXrVgwaNAhA9ph0ffr0EXu4FbXmzZsjJCQEY8aMgZOTE0xMTFChQgW4u7tj/fr1OHjwoM6PVfJ1snv3bnTs2BEVKlSAqakpnJycMHz4cAQGBmLQoEEaz2900S5LKk3HhXr16iEsLAxfffUVatSoIV4LuLq6YurUqQgLCxMfRdM33bp1w9WrVzFw4EDY29vD2NgYtra26NSpE/z9/bF69Wqtn9eW5PWlieKYo6NGjZK07mbOnIlTp06he/fu4jW0ra0t3N3dxZ6XRfGm1dyK4/dQW2SCoCfDkBN9xJydnfHw4UO4ubkV6M1ORESUU7t27fDff/+hfPnyeP78ebEkwT4GXM+kTpUqVfD06VO0a9euxFwcEem7kSNHYt26dTAyMsKTJ09QqVKl4g7po1RieqQQfaguXrwovpHjs88+K+ZoiIhKvoiICJw5cwZA9p07XtzrBtczqXP27FnxBQU8vyHSjoSEBHHA2G7dujGJUoyYSCHSoYSEBMTExKic/vLlS3EgZZlMpvbVZUREJM2iRYsgCAIMDAwwfvz44g7ng8X1/PGKj49X+9rYFy9eiI968fyGSHv+/PNPpKSkAAAmTpxYzNF83LQ7OAUR5fDw4UN89tln6N69Ozp27IiaNWvCwsICMTExOHfuHFasWIHXr18DACZNmoSaNWsWc8RERCXP27dvERMTg8TEROzevRsbNmwAAPTv3x81atQo5ug+HFzPJBcZGQk3Nzf07NkTHTp0QM2aNWFubo6YmBicOXMGK1euFN/E+PXXX+d7nCMiypaamornz58jNTUVJ0+exKJFiwBk9/Jq165dMUf3ceMYKUQ6FBoaqvHVxkD2s44rVqyAsbFxEURFRPRhWbNmTZ7X7trZ2SEkJITdnrWI6zmnu3fvIj09vUDz1qpVS+uD7Rela9euoWnTphrLjR49GsuWLSvR35WooJKTk8XH9/PL0tISVatWxcmTJ9G+ffsc00qVKoXLly+Lr3im4sGjGpEO1apVC1u3bsXx48cRFBSEmJgYxMXFwdTUFPb29mjdujVGjhyJVq1aFXeoREQlnkwmg4ODAzw8PLBgwYKP8uK+KHA9Z/P09MTz588LNO/Tp0/h6Oio5YiKTp06dbBlyxbx/ObVq1eIi4uDmZmZeH7j6+uLli1bFneoRMXm8uXLeZIgUnl6euLkyZM5PqtYsSKaN2+OBQsWMImiB9gjhYiIiIgonxwdHT/aRAoRaaasN4lUyhIppF+YSCEiIiIiIiIikohv7SEiIiIiIiIikoiJFCIiIiIiIiIiiZhIISIiIiIiIiKSiIkUIiIiIiIiIiKJmEghIiIiIiIiIpLIqLgDIPpYvHjxorhDyBdra2sAQGxsbDFHkpe+xqavcQH6G5u+xgXob2z6Ghegv7Hpa1yA/samr3EB+hubvsYFMLaC0Ne4NLG3ty/uEIg+CuyRQkREREREREQkERMpREREREREREQSMZFCRERERERERCQREylERERERERERBIxkUJEREREREREJBETKUREREREREREEjGRQkREREREREQkERMpREREREREREQSMZFCRERERERERCQREylERERERERERBIxkUJEREREREREJBETKUREREREREREEjGRQkREREREREQkkVFxB0BUUJs3b8aDBw/w8uVLvH37FiYmJrCxsUHTpk3RqVMnlC5dWnJdr1+/xo4dOxAWFobExESUK1cOTZs2RZ8+fWBpaanDb0FEREREREQlCXukUIl16NAhvH//HvXq1UOXLl3Qpk0bGBoaYufOnZg2bRpiY2Ml1RMVFYWZM2fizJkzcHFxgZeXF2xtbXH48GHMnj0biYmJOv4mREREREREVFKwRwqVWBs2bICJiUmez7dt24a9e/fC398fo0aN0ljP2rVrkZCQgBEjRqBz58456j906BC2bduGMWPGaDV2IiIiIiIiKpnYI4VKLGVJFABo2bIlAODly5ca64iOjkZYWBhsbGzQsWPHHNN8fHxgamqK8+fP4927d4UPmIiIiIiIiEo89kihD05QUBAAwMnJSWPZmzdvAgDq168PA4OceUVzc3PUqlULYWFhiIiIQL169dTWNWPGDKWf//zzzwAAa2trjfHoEyOj7MODPsatr7Hpa1yA/samr3EB+hubvsYF6G9s+hoXoL+x6WtcgP7Gpq9xAYytIPQ1LiLSD0ykUIm3f/9+vHv3DikpKXjw4AHCw8Ph5OSEnj17apz3xYsXAIBKlSopnV6xYkWEhYXh5cuXGhMpRESkW9G9Piu2ZdvtvVRsyyYiIiL9wkQKlXgHDhxAQkKC+P8GDRpgwoQJKFOmjMZ5U1JSAAAWFhZKp8s/T05O1liXvOeJKlIHv9UX8jsw+hi3vsamr3EB+hubvsYF6G9s+hoXoN+xFZauvpO+rjN9jQvQ39j0NS6AsRWEvsalib29fXGHQPRRYCKFSrzVq1cDAOLj43Hv3j1s2bIFM2bMwIwZM+Ds7FyougVBAADIZLJCx0lEREREREQlHwebpQ+GlZUVmjVrJr6y+J9//tE4j7zHibxnSm6pqak5yhEREREREdHHjYkU+uDY2NjA0dERT58+xdu3b9WWlXd/VPWGn6ioKACqx1AhIiIiIiKijwsTKfRBevPmDQDkeRNPbnXq1AEAhIWFISsrK8e01NRUhIeHw8TEBDVq1NBNoERERERERFSiMJFCJdLz588RHx+f5/OsrCxs27YNCQkJqFmzJiwtLQEAGRkZeP78udjDRK5ixYqoX78+Xr16hWPHjuWY5ufnh/fv38PNzQ1mZma6+zJERERERERUYnCwWSqRQkNDsXnzZtSuXRt2dnYoXbo04uPjcefOHURHR8PKygpjx44Vy8fFxWHKlCmwsbHJM3aKr68v5syZg3Xr1uHGjRtwdHREREQEbt26hUqVKmHAgAFF/fWIiIiIiIhITzGRQiVSvXr14Onpibt37+Lx48dITk6Gqakp7O3t0aZNG3Tp0kXsjaJJxYoV8eOPP8LPzw+hoaEICQlBuXLl0LlzZ/Tt21dyPURERERERPThYyKFSqQqVapg1KhRksvb2trCz89P5XRra2tMmDBBG6ERERERERHRB4xjpBARERERERERScREChERERERERGRREykEBERERERERFJxEQKEREREREREZFETKQQEREREREREUnERAoRERERERERkURMpBARERERERERScREChERERERERGRREykEBERERERERFJxEQKEREREREREZFETKQQEREREREREUnERAoRERERERERkURMpBARERERERERScREChERERERERGRREykEBERERERERFJxEQKEREREREREZFETKQQEREREREREUnERAoRERERERERkURMpBARERERERERScREChERERERERGRREykEBERERERERFJxEQKEREREREREZFETKQQEREREREREUnERAoRERERERERkURMpBARERERERERScREChERERERERGRREykEBERERERERFJxEQKEREREREREZFETKQQEREREREREUnERAoRERERERERkURMpBARERERERERScREChERERERERGRREykEBERERERERFJxEQKEREREREREZFETKQQEREREREREUnERAoRERERERERkURMpBARERERERERScREChERERERERGRREykEBERERERERFJxEQKEREREREREZFETKQQEREREREREUnERAoRERERERERkURMpBARERERERERScREChERERERERGRREykEBERERERERFJxEQKEREREREREZFETKQQEREREREREUnERAoRERERERERkURMpBARERERERERScREChERERERERGRREykEBERERERERFJxEQKEREREREREZFETKQQEREREREREUnERAoRERERERERkURGxR0AUUEkJiYiMDAQwcHBePLkCeLi4mBkZIQqVaqgbdu28PDwgIGBtDzhF198gVevXimdVrZsWaxevVqboRMREREREVEJxkQKlUiXL1/GmjVrUK5cOdSpUwfW1taIj49HYGAgVqxYgZCQEHz99deQyWSS6rOwsECXLl3yfG5mZqbt0ImIiIiIiKgEYyKFSiR7e3t88803aNSoUY6eJwMHDsS3336LgIAABAQEoEWLFpLqK1WqFHx8fHQVLhEREREREX0gmEihEqlu3bpKP7eyskL79u2xfft23L59W3IihYiIiAgAMkd3L9B80VpYtuHq/VqohYiIdI2JFPrgGBllN2upY6QAQHp6Os6dO4fY2FiYmZmhSpUqcHV1zVcdM2bMUPr5zz//DACwtraWXJc+kK9HfYxbX2PT17gA/Y1NX+MC9Dc2fY0L0H1s2rhQLShdfSd93Z76GhfAdlYQH/P2LCh9jYuI9AMTKfRByczMxNmzZwEADRo0kDxffHw8qxM49gAAIABJREFU/v777xyf2draYsKECXB1ddVqjERERERERFRyMZFCH5QtW7bg6dOnaNiwoeREioeHB2rXrg1HR0eYm5sjOjoaR48exalTp7Bo0SL88MMPqFq1qsZ65D1PVImNjZUUj76Q34HRx7j1NTZ9jQvQ39j0NS5Af2PT17gA/Y6tsHT1nfR1nelrXIB+x1ZYH1s7A/Q3Nn2NSxN7e/viDoHooyD9uQUiPXf48GEcPHgQDg4O+PLLLyXP17dvX9StWxdWVlYwNTVFlSpVMGbMGHh5eSEtLQ07d+7UYdRERERERERUkjCRQh+Eo0ePYv369XB0dMTcuXNhaWlZ6Do7dOgAALhz506h6yIiIiIiIqIPAx/toRLv0KFD2LBhAypXroz//e9/KFu2rFbqLVOmDADg/fv3WqmPiIiIiIiISj4mUqhE8/f3x9atW1G1alXMnj1bTH5ow7179wBkDzpLREREREREBPDRHirBdu3aha1bt8LZ2Rn/+9//1CZRMjIy8Pz5c0RFReX4/OnTp0hKSspT/tWrV/j3338BAG3atNFu4ERERERERFRisUcKlUhnzpyBn58fDAwMUKtWLRw+fDhPGVtbW3h4eAAA4uLiMGXKFNjY2OCff/4Ry1y+fBn79u1DnTp1YGtrCzMzM0RHRyM4OBjp6elo2LAhunfvXlRfi4iIiIiIiPQcEylUIsXExAAAsrKylCZRAMDV1VVMpKhSt25dvHjxAo8ePcK9e/fw/v17WFhYoFatWnBzc4ObmxtkMpm2wyciIiIiIqISiokUKpF8fHzg4+MjubytrS38/PzyfO7q6gpXV1dthkZEREREREQfMI6RQkREREREREQkERMpREREREREREQSMZFCRERERERERCQREylERERERERERBIxkUJEREREREREJBETKUREREREREREEjGRQkREREREREQkERMpREREREREREQSMZFCRERERERERCQREylERERERERERBIxkUJEREREREREJBETKUREREREREREEjGRQkREREREREQkERMpREREREREREQSMZFChRIbG4u4uDjJ5ePi4hAbG6vDiIiIiIiIiIh0x6i4A6CS7YsvvoCVlRVWrlwpqfycOXPw+vVrbN++XceREREREREREWkfe6RQkRMEobhDICIiIiIiIioQJlKoSKWlpcHQ0LC4wyAiIiIiIiIqECZSqMhERUXh7du3sLKyKu5QiIiIiIiIiAqEY6RQvly9ehVXr17N8VlKSgqWLVumdr7k5GSEh4cDAOrUqaOz+IiIiIiIiIh0iYkUypdHjx7h7NmzOT5LS0vL85kqdnZ26Nevny5CIyIiIiIiItI5JlIoX3L3Jtm1axfMzMzQtWtXlfPIZDJYWFjA0dERderU4RgpREREREREVGIxkUL54urqCldXV/H/8kRK3759izEqIiIiIiIioqLBRAoVyt9//w0DA45ZTERERERERB8HJlKoUGxsbIo7BCIiIiIiIqIiw0QKaU1mZiaioqKQnJyMjIwMtWUVHw8iIiIiIiIiKimYSKFCi4mJwdatW3Ht2jWkp6drLC+TybB9+/YiiIyIiIiIiIhIu5hIoUKJiorCrFmzkJSUJH5WtmxZGBsbF2NURERERERERLrBRAoVyvbt25GUlITy5ctj+PDhaNKkCV9vTERERERERB8sJlKoUG7dugUAmDx5MmrVqlXM0RARERERERHpFt9bS4WSmpoKExMTJlGIiIiIiIjoo8BEChWKtbU1BEGAIAjFHQoRERERERGRzjGRQoXy2WefIT09HTdu3CjuUIiIiIiIiIh0jokUKpSePXuiatWqWL16NWJiYoo7HCIiIiIiIiKd4mCzVCiXL1+Gh4cH/Pz8MHXqVLRo0QIuLi4wNzdXO5+7u3sRRUhERERERESkPUykUKEsW7Ysx//PnTuHc+fOaZyPiRQiIiIiIiIqiZhIoUKpXbs2ZDJZcYdBREREREREVCSYSKFCmTdvXnGHQERERERERFRkONgsEREREREREZFETKQQEREREREREUnERAoRERERERERkUQcI4UKpV+/fvmeRyaTYfv27TqIhoiIiIiIiEi32COFipwgCMUdAhEREREREVGBsEcKFcrff/+tdnpKSgru37+PQ4cOIT4+HuPHj4eTk1MRRUdERERERESkXUykUKHY2NhoLOPk5AQ3NzcsWrQIK1aswE8//VQEkRERERERERFpHxMpVCSMjIwwYsQITJs2Dbt27cK4ceOKOyQiomKVObp7geaL1sKyDVfv10ItRERERB8njpFCRaZy5cowNzdHaGhocYdCRERERERE9P/Yu/O4rupE/+NvviCbrLIIigyaK2Koo6bmWjmFZctNMW1xus54Jy3n6q2s39XRxplpmvXeyqypXJocBXPcyTQnl0pFE0hFBVNcAUFkkR2//P7gATcC5QvfXV/Pv+qcz/ec9/f4BfTN53xOmzAjBTZTU1OjyspKVVdX2zsKAAAAAABtwowU2MyXX34po9GoDh062DsKAAAAAABtwowUmCU/P/+m+6uqqlRQUKCDBw9q586dkqShQ4faIhoAAAAAABZHkQKzzJo1q1Xju3fvrscff9xKaQAAAAAAsC6KFFiVwWCQt7e3IiMjNWzYMN17771ydXW1dywAAAAAANqEIgVmSUhIsHcEAAAAAABshsVmAQAAAAAATMSMFDilkpISJScn6/Dhwzp37pwKCgrk5uamyMhIjR07VmPGjJHBYHpPeOXKFSUkJCgtLU0lJSUKDAzU4MGDNXHiRPn4+FjxnQAAAAAAnAlFCiymoqJChw8f1pkzZ1RcXCxJ8vPzU9euXTVw4EB5enpa7Fz79u3TBx98oMDAQPXt21fBwcEqLCxUcnKy3n33XaWkpGju3LlycXFp8Vg5OTlasGCBioqKNGjQIHXu3FmnTp1SUlKSUlNTtXjxYvn6+losOwAAAADAeVGkwGy1tbVav369Nm7cqIqKimbHeHp66rHHHtMjjzxiUrnRkk6dOunll1/WwIEDG808mTp1ql599VUdOHBABw4cMOlRyx9++KGKior07LPPKi4urmH7ypUrtXXrVq1evVozZswwOzMAAAAAwPmxRgrMtmTJEiUkJKiiokLt2rVTr169NGzYMA0bNky9evVSu3btVFFRodWrV2vJkiUWOWdMTIwGDRrU5PadgIAAjRs3TpKUnp7e4nFyc3OVlpamkJAQ3X///Y32xcfHy8PDQ3v37r1hQQQAAAAAuL0wIwVmOXDggPbu3StJevTRR/XII4/I29u70ZiysjJt2LBBGzdu1N69ezVkyBANGTLEapnc3Oo+1qaskXL06FFJUmxsbJPxXl5e6t27t9LS0pSZmal+/frd9Fjz5s1rdvsbb7whSQoODm4xjyOpv46OmNtRszlqLslxszlqLsn62XKtclTTWOs9cc1az1G/Bhw1l8TnrC1u5z/PtnLUXAAcAzNSYJbPP/9ckjRlyhRNmTKlSYkiSd7e3po6daomT57c6DXWcP36de3evVuS1L9//xbHX7p0SZIUHh7e7P6wsDBJUnZ2toUSAgAAAACcGTNSYJbTp0/LYDA0WlvkRsaPH6+1a9fqu+++s1qeVatW6fz58xowYIBJRUpZWZkkNVsAfX97aWlpi8eqn3lyI/n5+S0ew5HU/wbGEXM7ajZHzSU5bjZHzSU5djZzWes9cc1az1GvmaPmkhw7m7lut8+Z5LjZHDVXSzp16mTvCMBtgRkpMEtFRYW8vLzk4eHR4lhPT095eXlZbb2RpKQkbdmyRZ07d9YLL7xgkWPW1tZKkkUWyAUAAAAAOD+KFJjFz89PpaWlKigoaHFsQUGBSktL5efnZ/Ec27Zt04oVKxQREaGFCxfKx8fHpNfVzzipn5nyQ+Xl5Y3GAQAAAABubxQpMEufPn0kSR999FHD7I0bWblypSQpOjraohm2bt2qZcuWqUuXLlq4cKECAgJMfm399McbrYGSk5Mj6cZrqAAAAAAAbi+skQKzPPzww/r666+1b98+Xb16VY899pj69OnTcKtPSUmJjh07po0bNzaspzJhwgSLnX/Dhg36xz/+oaioKM2fP7/Vs1369u0rSUpLS5PRaGz05J7y8nKdOHFC7u7u6tGjh8UyAwAAAACcF0UKzBIVFaWf/exn+uCDD3TixAm9/vrrcnFxkbe3t6qrq1VVVdUw1sXFRdOnT1dUVJRFzv3JJ58oMTFR3bp10/z58296O09NTY1yc3Pl6ura8CQeqe6pPLGxsUpLS9Nnn33WaNHcxMREVVZW6r777pOnp6dFMgMAAAAAnBtFCsx23333qUuXLkpISNCxY8dUW1vb5Ck3MTExmjx5snr27GmRc+7atUuJiYkyGAzq3bu3kpKSmowJDQ3VmDFjJNWtzzJnzhyFhIRoyZIljcZNnz5dCxYs0PLly3XkyBFFREQoMzNTx44dU3h4uKZMmWKRzAAAAAAA50eRAovo1auXfvWrX+natWvKyspScXGxpLrFaKOiokxe/NVUly9fliQZjcZmSxSpbi2W+iLlZsLCwvT6668rMTFRqampSklJUWBgoOLi4jRp0iSLZwcAAAAAOC+KFFiUj4+PYmJirH6e+Ph4xcfHmzw+NDRUiYmJN9wfHBysmTNnWiIaAAAAAOAWxlN7AAAAAAAATMSMFJjl9OnT+vvf/65u3brp6aefvunY5cuX69y5c5o2bZrFFpwFAAAAAMCWmJECs+zevVvp6enq2rVri2O7dOmi9PR07dmzxwbJAAAAAACwPIoUmOXYsWOSZNK6KIMGDZIkHT161KqZAAAAAACwFooUmOXKlStyd3dXQEBAi2MDAgLk7u6uK1eu2CAZAAAAAACWR5ECs9TU1MhgMP1jZDAYVFlZacVEAAAAAABYD0UKzNKhQwdVVFTo0qVLLY69dOmSKioqFBgYaINkAAAAAABYHkUKzNK3b19JUmJiYotjExISGr0GAAAAAABnQ5ECszz44IMyGAzat2+f3nrrLV29erXJmKtXr+rNN9/U/v37ZTAYNH78eDskBQAAAADAfG72DgDn1rlzZz3zzDNasWKFvvzyS3399deKiopSUFCQJCk/P19nz56V0WiUJD311FOKjIy0Z2QAAAAAANqMIgVmi4uLU0BAgD766CMVFBTo9OnTOn36dKMxHTp00NNPP63hw4fbKSUAAAAAAOajSIFFDBs2TEOGDNGRI0eUmZmpoqIi1dbWKiAgQD169FC/fv3k6upq75iASa7//OE2vS7XAud2fX+TBY4CAAAAwFooUmAxrq6u6t+/v/r372/vKAAAAAAAWAWLzQIAAAAAAJiIIgUAAAAAAMBEFCkAAAAAAAAmokgBAAAAAAAwEUUKAAAAAACAiShSAAAAAAAATESRAgAAAAAAYCKKFAAAAAAAABNRpAAAAAAAAJjIzd4B4DzS09Mtdqzo6GiLHQsAAAAAAFuhSIHJXnvtNYscx8XFRWvWrLHIsQAAAAAAsCWKFJgsODj4hvuKi4tVVVUlSTIYDPLz81Ntba1KSkpkNBolSR4eHvL19bVJVgAAAAAArIEiBSZbsmRJs9s//fRT/f3vf1e/fv302GOPqVevXnJzq/to1dTUKCMjQ+vXr1d6eromTJigBx54wJaxAQAAAACwGIoUmOXw4cNasWKFRo8erZkzZzbZ7+bmpujoaEVHR+udd97R8uXLFRYWpv79+9shLQAAAAAA5uGpPTDLli1b5OLioqeeeqrFsU8++aQkafPmzdaOBQAAAACAVVCkwCxZWVny9vaWn59fi2P9/f3Vvn17nTlzxgbJAAAAAACwPIoUmKWmpkbl5eUqKytrcWxZWZnKy8tVU1Njg2QAAAAAAFgeRQrM0qVLFxmNRq1fv77FsevXr5fRaFRkZKQNkgEAAAAAYHkUKTBL/RN4Nm3apKVLlyo7O7vJmJycHL377rvatGlTo9cAAAAAAOBseGoPzDJy5EhlZGRo+/bt2rVrl3bt2iV/f3916NBBknT16lUVFhY2jL///vs1YsQIe8UFAAAAAMAsFCkw2/Tp09WzZ0+tXbtWubm5KioqUlFRUaMxYWFhmjRpEiUKAAAAAMCpUaTAIkaOHKmRI0cqKytLp0+fVnFxsSTJz89P3bp1U1RUlH0DAgAAAABgARQpsKioqChKEwAAAADALYvFZgEAAAAAAEzEjBRYRFlZmf71r3/p22+/1ZUrV1RVVaW33nqr0f6DBw/KxcVFo0aNsmPS28/1nz/cptflWuDcru9vssBRAAAAAMBxUKTAbBkZGfrzn//c6Ok8P+Tt7a2kpCRlZWUpNDRUvXv3tmFCAAAAAAAsg1t7YJYrV67o97//vQoLCzVgwAA9//zzat++fbNjx40bJ0nav3+/LSMCAAAAAGAxFCkwy6ZNm1RaWqpRo0bplVde0ciRI+Xm1vxEpwEDBkiS0tPTbRkRAAAAAACLoUiBWVJTUyVJkydPbnFsUFCQ3N3ddfnyZWvHAgAAAADAKihSYJb8/Hx5enoqODjYpPHu7u6qqqqycioAAAAAAKyDIgVmadeunaqrq2U0GlscW1FRodLS0huuoQIAAAAAgKOjSIFZwsPDdf36dZ07d67FsQcOHFBtba0iIyNtkAwAAAAAAMujSIFZBg8eLElat27dTcddunRJH3/8sSRp6NChVs8FAAAAAIA1NP94FcBE48eP186dO5WcnKw//elPevDBB1VbWyup7laeCxcu6MCBA9q+fbsqKioUERGhsWPH2jk1AAAAAABtQ5ECs3h6eurVV1/V66+/roMHD+rgwYMN+6ZNm9ZobGhoqF5++eUbPh4ZAAAAAABHx79oYbaIiAj98Y9/1KZNm7R7924VFBQ02u/v768xY8bo0Ucflbe3t51SAgAAAABgPooUWIS3t7eeeOIJPfHEE7py5YquXr2q2tpa+fv7KzQ01N7xAAAAAACwCIoUWFxQUJCCgoLsHQMAAAAAAIujSIFZdu/eLUnq1auXwsLCWhy/b98+VVVVafTo0daOBgAAABu4/vOH2/zaXDPP7fr+JjOPAACtR5ECs7zzzjuS6m7tmT17tgYMGHDT8cuXL1dxcTFFCgAAAADAKRnsHQC3hrKyMv3hD3/Qpk0t/1ag/vHIAAAAAAA4G2akwGx+fn4aO3asNm7cqFWrVikrK0vPPfec2rVrZ9Xz7t+/X+np6crKytLZs2dVXl6uESNGaPbs2a06zqxZs5SXl9fsPn9/f73//vuWiAsAAAAAuAVQpMBsBoNBU6dO1Y9+9CO9++67+uqrr5STk6OXXnpJgYGBVjvvunXrdPbsWXl6eiooKEgXL15s87G8vb01fvz4Jts9PT3NiQgAAAAAuMVQpMBi7r77boWHh+uPf/yjvvvuO73yyit68cUX1aNHD6ucb9q0aQoKClJYWJjS09P12muvtflY7du3V3x8vAXTAQAAAABuRayRAovq1q2b3njjDfXq1UuFhYVatGiRdu3aZZVzxcTEKDw8XC4uLlY5PgAAAAAAP0SRAovz8/PTwoULde+996qmpkZLly7VypUrHXqR2erqau3Zs0f//Oc/lZSUpKNHj8poNNo7FgAAAADAwXBrD6zC1dVVM2bMUGRkpFauXKmkpCSdP39e1dXV9o7WrMLCQr399tuNtoWGhmrmzJmKjo426Rjz5s1rdvsbb7whSQoODjYvZBvl2uWsdaz1nt3c3Kx6fK6Z7ThqLonPWVtwzVrPUb8GHDWXxOesLbhmrefIXwMA7I8iBVb1wAMPKDIyUn/+85915MgRe8dp1pgxY9SnTx9FRETIy8tLubm52rZtm3bu3Knf/e53+s1vfqOoqCh7xwQAAAAAOACKFJglODhY/v7+Nx0THR2t3//+9/rDH/6gc+fO2SiZ6SZNmtTo/yMjIzVjxgx5enpqy5YtWrt2rV566aUWj1M/8+RG8vPzzcrpjKz1nut/O3QrXtPb7Zo5ai7JsbOZ63b7nFnC7XbNHDWX5NjZzHW7fc4sgWvWWKdOnewdAbgtUKTALEuWLDFpXEhIiF5//XVdvXrVyoks5yc/+Ym2bNmi48eP2zsKAAAAAMBBUKTAZtzc3BQSEmLvGCbz8/OTJFVWVto5CQAAAADAUfDUHuAGMjIyJNUtOgsAAAAAgMSMFLTCO++8I0kKDAzUlClTGm1rDRcXFz333HMWzdaSmpoa5ebmytXVVWFhYQ3bz58/r8DAQPn4+DQan5eXp2XLlkmSRo4cadOsAAAAAADHRZECk+3evVtS3SJW9UVK/bbWskSRkpycrIMHD0qqe3yxJGVmZjas2+Lr66tnnnlGklRQUKA5c+YoJCSk0bou+/bt08aNG9W3b1+FhobK09NTubm5Onz4sKqrqzVgwAA9/PDDZmcFAAAAANwaKFJgsokTJ0qqKyh+uM0esrKymhQ5ubm5ys3NlVS3wG19kXIjMTExunTpkrKyspSRkaHKykp5e3urd+/eGjVqlEaNGiUXFxervQcAAAAAgHOhSIHJfviY4Btts5X4+HjFx8ebNDY0NFSJiYlNtkdHRys6OtrS0QAAAAAAtygWmwUAAAAAADARRQoAAAAAAICJKFIAAAAAAABMxBopMNnkyZMtchwXFxetWbPGIscCAAAAAMCWmJECm6utrbV3BAAAAAAA2oQZKTDZ22+/be8IAAAAAADYFUUKTBYSEmLvCAAAAAAA2BW39gAAAAAAAJiIIgUAAAAAAMBE3NoDi8nLy1NGRoauXr2qioqKm46dOHGijVIBAAAAAGA5FCkwW0FBgf72t78pJSXF5NdQpAAAAAAAnBFFCsxSVlamRYsWKTc3V35+furZs6cOHTokd3d33XXXXSoqKlJGRoYqKirk6+urgQMH2jsyAAAAAABtRpECs2zZskW5ubnq3r27/t//+39q3769Jk+eLG9vbz3//POSpMrKSq1bt04bN26UwWDQL37xCzunBgAAAACgbShSYJZvvvlGkvTUU0+pffv2zY7x8PDQ1KlTdf36dW3ZskV9+/bVyJEjbRkTAAAAAACL4Kk9MEtOTo4MBoN69erVaHtNTU2TsY888ogkaefOnTbJBgAAAACApVGkwCxGo1FeXl4yGP7vo+Th4aGysjLV1tY2Guvn56f27dvr3Llzto4JAAAAAIBFUKTALB06dFBpaWmjGShBQUEyGo26dOlSo7FVVVUqLS1VZWWlrWMCAAAAAGARFCkwS3h4uCQpNze3YVuPHj0kSTt27Gg0duvWrZKksLAwG6UDAAAAAMCyWGwWZhk4cKBSUlKUnJysxx57TJL0k5/8RLt379ann36q7OxsRUVF6dy5czp8+LAkafTo0faMDAAAAABAm1GkwCxDhgzR0aNHVVFR0bCte/fuevLJJ/WPf/xDqampSk1NbTT+oYceskdUAAAAAADMRpECswQEBGju3LlNtj/88MMaOHCg9u/fr4KCAnl7e+vOO+/UnXfeaYeUAAAAAABYBkUKrCYiIkITJ060dwwAAAAAACyGxWYBAAAAAABMRJECAAAAAABgIm7tgUWkpqZq//79On/+vK5du6br16/fcKyLi4veeustG6YDAAAAAMAyKFJglqqqKv31r39teLQxAAAAAAC3MooUmGXt2rU6fPiwDAaDRo8erZiYGAUEBMhg4K4xAAAAAMCthyIFZvnyyy8lSTNmzNDYsWPtnAYAAAAAAOti2gDMUlJSIjc3N40aNcreUQAAAAAAsDqKFJglKChIbm5ucnV1tXcUAAAAAACsjiIFZhk6dKgqKiqUkZFh7ygAAAAAAFgdRQrM8sgjjygiIkJLly7V5cuX7R0HAAAAAACrYrFZmMXb21sLFy7U+++/rzlz5mjo0KHq0qWLAgMDb/q60aNH2yghAAAAAACWQ5ECs2VnZ+vKlSuqqalpeIpPSyhSAAAAAADOiCIFZsnIyNDixYtVXV0tSQoPD5e/v78MBu4aAwAAAADceihSYJbExERVV1erZ8+e+uUvf6ng4GB7RwIAAAAAwGqYNgCzfPfdd5JEiQIAAAAAuC1QpMAsLi4u8vb2pkQBAAAAANwWKFJglq5du6qiokJlZWX2jgIAAAAAgNVRpMAsEyZMkNFo1ObNm+0dBQAAAAAAq6NIgVn69++v6dOna9OmTXr33XeVk5Nj70gAAAAAAFgNT+2BWZ5//nlJksFg0BdffKEvvvhC7u7u8vf3v+FrXFxc9NZbb9kqIgAAAAAAFkORArPk5eU12VZVVdXsdgAAAAAAnB1FCsyycOFCe0cAAAAAAMBmKFJglujoaHtHAAAAAADAZihSYJaVK1dKkh588EEFBwfbOQ0AAAAAANbFU3tglk8//VTbt29Xhw4d7B0FAAAAAACro0iBWfz9/eXu7i6DgY8SAAAAAODWx79+YZaePXuqrKxM+fn59o4CAAAAAIDVUaTALBMmTJDBYNDf//53e0cBAAAAAMDqKFJglp49e2r27NlKTU3VwoULdfDgQRUVFam2ttbe0QAAAAAAsDie2gOzTJ48ueG/T5w4oRMnTrT4GhcXF61Zs8aasQAAAAAAsApmpMDmmK0CAAAAAHBWzEiBWd5++217RwAAAAAAwGYoUmCWkJAQu517//79Sk9PV1ZWls6ePavy8nKNGDFCs2fPbvWxrly5ooSEBKWlpamkpESBgYEaPHiwJk6cKB8fHyukBwAAAAA4I4oUOK1169bp7Nmz8vT0VFBQkC5evNim4+Tk5GjBggUqKirSoEGD1LlzZ506dUpJSUlKTU3V4sWL5evra+H0AAAAAABnRJECiyoqKtKZM2dUXFwsSfLz81PXrl3l7+9v8XNNmzZNQUFBCgsLU3p6ul577bU2HefDDz9UUVGRnn32WcXFxTVsX7lypbZu3arVq1drxowZlooNAAAAAHBiFCmwiBNH1FdeAAAgAElEQVQnTmjNmjU6fvx4s/ujo6M1efJk9e7d22LnjImJMfsYubm5SktLU0hIiO6///5G++Lj4/X5559r7969euaZZ+Tp6Wn2+QAAAAAAzo2n9sBs27dv12uvvdZQohgMBvn7+8vf318GQ91HrH7GyI4dO+wZtYmjR49KkmJjYxuy1vPy8lLv3r1VWVmpzMxMe8QDAAAAADgYZqTALGfOnNGyZctUW1ur3r176/HHH1efPn3Url07SVJ1dbXS09O1bt06nTx5UsuWLVP37t3VtWtXOyevc+nSJUlSeHh4s/vDwsKUlpam7Oxs9evX76bHmjdvXrPb33jjDUlScHCwGUnbLtcuZ61jrffs5uZm1eNzzWzHUXNJfM7agmvWeo76NeCouSQ+Z23BNWs9R/4aAGB/zEiBWTZv3qza2loNGzZMCxcu1J133tlQokhSu3btFBsbq0WLFumuu+6S0WjUli1b7Ji4sbKyMkmSt7d3s/vrt5eWltosEwAAAADAcTEjBWapv51n2rRpTW6N+T6DwaCf/vSnOnDggNLT020Vz2y1tbWSJBcXlxbH1s88uZH8/HyLZHIm1nrP9b8duhWv6e12zRw1l+TY2cx1u33OLOF2u2aOmkty7Gzmut0+Z5bANWusU6dO9o4A3BaYkQKzFBcXq3379goMDGxxbIcOHdS+ffuGJ/o4gvoZJ/UzU36ovLy80TgAAAAAwO2NIgVm8fLyUnl5uSoqKlocW1FRofLycnl5edkgmWnqW/vs7Oxm9+fk5Ei68RoqAAAAAIDbC0UKzNK1a1cZjUZ9+umnLY5NSkqS0WhUt27dbJDMNH379pUkpaWlyWg0NtpXXl6uEydOyN3dXT169LBHPAAAAACAg6FIgVnuu+8+SVJCQoLWrFnT7C0yV69e1cqVK5WYmNjoNbZUU1OjixcvNswwqRcWFqbY2Fjl5eXps88+a7QvMTFRlZWVGjVqlDw9PW0ZFwAAAADgoFhsFma56667NHLkSO3du1fr16/X5s2bFRUVpcDAQNXU1CgvL085OTmqqamRJI0ePVpDhgyxyLmTk5N18OBBSVJhYaEkKTMzU0uWLJEk+fr66plnnpEkFRQUaM6cOQoJCWnYX2/69OlasGCBli9friNHjigiIkKZmZk6duyYwsPDNWXKFIvkBQAAAAA4P4oUmG3WrFmKiIjQhg0bVF5erlOnTjUZ4+Xlpccee0wTJkyw2HmzsrK0e/fuRttyc3OVm5srSQoJCWkoUm4mLCxMr7/+uhITE5WamqqUlBQFBgYqLi5OkyZNko+Pj8UyAwAAAACcG0UKzObi4qJHH31UcXFxSktL05kzZxqezOPn56euXbsqNjZWHh4eFj1vfHy84uPjTRobGhracGtRc4KDgzVz5kxLRQMAAAAA3KIoUmAxHh4eGjJkiMVu3QEAAAAAwNFQpAAAANyirv/84Ta9LtcC53Z9f5MFjgIAgOPhqT0AAAAAAAAmYkYKWuWdd94x+xguLi567rnnLJAGAAAAAADbokhBq/zwKTltRZECAAAAAHBGFClolbvvvlsuLi6tft3Vq1d17NgxKyQCAAAAAMB2KFLQKrNnz27V+JKSEm3YsEHJyckN27p162bpWAAAAAAA2ARFCqyivLxcmzdvVlJSksrLyyVJERERio+P11133WXndAAAAAAAtA1FCiyqqqpKSUlJ2rx5s65duyZJ6tixoyZNmqQRI0a06bYgAAAAAAAcBUUKLKKmpkY7duzQ+vXrVVRUJEkKCgrS448/rrFjx8pg4EnbAAAAAADnR5ECsxiNRu3atUvr1q1Tfn6+JMnf31+PPfaYxo0bJzc3PmIAAAAAgFsH/8pFm3355Zdau3atcnJyJEk+Pj56+OGHFRcXJ3d3dzunAwAAAADA8ihS0GrJyclKTEzU+fPnJUleXl568MEH9dBDD8nLy8vO6QAAAAAAsB6KFLTKq6++qtOnT0uS3N3dFRcXp0ceeUTt27e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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 553
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "huzzam--sarki--aksak--yine_bir--serif_icli\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 457
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "muhayyer--sarki--curcuna--batan_gun--sadettin_kaynak\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 569
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hicazkar--sarki--duyek--aglatip_kustureceksen--alaattin_sensoy\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 623
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "segah--sarki--duyek--gonul_bahcemizde--omer_sami_gupgup\n",
      "hicaz--sarki--aksak--bir_an--omer_sami_gupgup\n",
      "ussak--sarki--duyek--her_mevsim--semahat_ozdenses\n",
      "hicazkar--etud--sofyan----ozer_ozel\n",
      "nihavent--sarki--sofyan--gonlumle_oturdum--sekip_memduh_bey\n",
      "huseyni--turku--aksak--a_fadimem--\n",
      "huzzam--sarki--aksak--zamani_var_ki--klarnet_ibrahim_efendi\n",
      "nihavent--fantezi--sofyan--mevsimlere_aldanirim--omer_sami_gupgup\n",
      "hicazkar--kocekce--aksak--bekliyorum_salinarak--muallim_ismail_hakki_bey\n",
      "muhayyer--etud--sofyan----hasan_esen\n",
      "nihavent--etud--sofyan----ozer_ozel\n",
      "mahur--tavsanca--aksak--nazar_etti--tanburi_mustafa_cavus\n",
      "huzzam--seyir--duyek--1--erol_bingol\n",
      "mahur--sarki--duyek--bu_son_sarkimda--muzaffer_ilkar\n",
      "huzzam--sarki--aksak--ey_husn--ahmet_irsoy\n",
      "rast--sarki--duyek--ne_sarkilarda--omer_dilek\n",
      "ussak--sarki--aksak--gitti_de--dede_efendi\n",
      "huzzam--aranagme--duyek--1--\n",
      "muhayyer--turku--sofyan--el_vurup--tokat-zile\n",
      "hicazkar--sarki--duyek--visali_yar--rakim_elkutlu\n",
      "segah--turku--sofyan--keten_koynek--kirsehir-mucur\n",
      "hicazkar--sarki--curcuna--ben_sana--corlulu_asik\n",
      "hicaz--sarki--duyek--sensizligi_ben--zekai_tunca\n",
      "huseyni--rumeliturkusu--aksak--akzadeler_giyer--rumeli\n",
      "ussak--sarki--aksak--aman_felek--dede_efendi\n",
      "ussak--aranagme--duyek--1--\n",
      "hicaz--sarki--curcuna--severim_her--lemi_atli\n",
      "ussak--sarki--duyek--hem_bakarsin--ugur_uzunhekim\n",
      "hicaz--sarki--aksak--sazlar_calinir--yesari_asim_arsoy\n",
      "muhayyer--turku--curcuna--kerpic_kerpic--diyarbakir\n",
      "rast--sarki--sofyan--bir_acaib--civan_aga\n",
      "rast--sarki--duyek--ben_kuskunum--baki_duyarlar\n",
      "nihavent--sarki--duyek--ahimi_hicranimi--selahattin_inal\n",
      "mahur--sarki--duyek--petek_petek--erol_sayan\n",
      "ussak--sarki--aksak--daglar_dayanmaz--sevki_bey\n",
      "hicazkar--sarki--aksak--ey_gul--tahir_aga\n",
      "segah--sazeseri--sofyan--uc_turkoglu--aksehir\n",
      "nihavent--sarki--aksak--seni_tenhada--ismet_aga\n",
      "mahur--sarki--duyek--ipek_saclarin--sadettin_kaynak\n",
      "huzzam--rumeliturkusu--sofyan--aksam_oldu--rumeli\n",
      "huzzam--sarki--duyek--a_canim--tanburi_mustafa_cavus\n",
      "muhayyer--ilahi--sofyan--duseli_bu_askin--dede_efendi\n",
      "huzzam--seyir--duyek--1--sefik_gurmeric\n",
      "nihavent--sarki--sofyan--gelmedin--ismail_gokce\n",
      "nihavent--sarki--curcuna--yagmur_verdim--omer_dilek\n",
      "rast--sarki--curcuna--nihansin_dideden--haci_faik_bey\n",
      "segah--ilahi--duyek--ey_acep--seyh_huseyin_halis_efendi\n",
      "nihavent--sarki--duyek--sarkilar_seni--muzaffer_ilkar\n",
      "rast--sarki--duyek--gorsem_seni--dede_efendi\n",
      "hicaz--sarki--sofyan--enginde_yavas--sadettin_kaynak\n",
      "rast--sarki--aksak--sen_nazla_gezerken--kazim_nami_erdolen\n",
      "hicazkar--aranagme--sofyan--1--\n",
      "hicaz--sarki--duyek--kederden_mi--nasibin_mehmet_yuru\n",
      "huseyni--sarki--aksak--arzetmedigim--emin_ongan\n",
      "rast--turku--sofyan--adananin_yollari--\n",
      "rast--sarki--aksak--bir_dame--haci_faik_bey\n",
      "huseyni--turku--sofyan--ciktim_belen--mugla\n",
      "ussak--turku--aksak--sari_gulum--\n",
      "huseyni--sarki--aksak--bir_dilberdir--tanburi_mustafa_cavus\n",
      "nihavent--karsilama--sofyan----merzifon\n",
      "ussak--sarki--aksak--siyah_ebrulerin--lemi_atli\n",
      "hicaz--sarki--duyek--artik_bu--alaeddin_yavasca\n",
      "huzzam--sarki--curcuna--aksam_donusu--udi_marko_colakoglu\n",
      "mahur--sarki--aksak--acirim_asik--giriftzen_asim_bey\n",
      "rast--sarki--sofyan--sazin_gibi--lemi_atli\n",
      "ussak--sarki--aksak--muntazirdir_sana--dellalzade_haci_ismail_efendi\n",
      "hicazkar--sarki--duyek--serpil_yagmur--cevdet_cagla\n",
      "huzzam--sarki--aksak--gonul_verdim--iii_selim\n",
      "muhayyer--seyir--sofyan--1--erol_bingol\n",
      "huzzam--ornek_oz--sofyan--1--ruhi_ayangil\n",
      "muhayyer--turku--duyek--sari_kizin--kirsehir\n",
      "segah--turku--aksak--beyaz_giyme--bolu\n",
      "segah--sarki--aksak--suyi_kagithanede--lavtaci_hristo\n",
      "huzzam--sarki--aksak--sonbaharin_bizi--bekir_sitki_sezgin\n",
      "hicazkar--sarki--sofyan--bir_halet--haci_arif_bey\n",
      "ussak--sarki--curcuna--bu_yaz--yesari_asim_arsoy\n",
      "rast--sarki--duyek--hicran_olacaksa--ferit_sidal\n",
      "rast--sarki--duyek--saclarin_tarumar--sekip_ayhan_ozisik\n",
      "nihavent--sarki--aksak--gonlum_yine--seyh_ethem_efendi\n",
      "mahur--turku--sofyan--su_dere--\n",
      "ussak--ilahi--duyek--dostun_senden--ali_rifat_cagatay\n",
      "ussak--nefes--duyek--ben_melamet--\n",
      "huseyni--sarki--aksak--ezelden_asinanim--serif_icli\n",
      "nihavent--sarki--sofyan--korfezde_meltem--ismail_otenkaya\n",
      "hicaz--turku--sofyan--mektebin_bacalari--mus\n",
      "hicaz--turku--aksak--necibemin_ikidir--istanbul\n",
      "segah--sarki--duyek--ayrilik_yaman--sadettin_kaynak\n",
      "nihavent--sarki--curcuna--guzel_bir--osman_nihat_akin\n",
      "hicaz--sarki--aksak--sen_bu_yerden--sevki_bey\n",
      "mahur--sarki--curcuna--askinla_harab--emin_ongan\n",
      "huzzam--turku--sofyan--aliverin_baglamami--\n",
      "ussak--turku--sofyan--eledim_eledim--erzurum\n",
      "hicaz--sarki--duyek--bir_atesim--avni_anil\n",
      "hicazkar--sarki--sofyan--sarsam_seni--sadettin_kaynak\n",
      "ussak--sarki--aksak--bir_melek--haci_arif_bey\n",
      "rast--sarki--duyek--her_telden--sakir_aga\n",
      "segah--turku--aksak--ey_su--usak\n",
      "nihavent--sarki--sofyan--karsida_kandilli--amir_ates\n",
      "ussak--ilahi--sofyan--ey_sehinsah-i--tiznam_yusuf_celebi\n",
      "huseyni--turku--aksak--yuksek_yuksek--\n",
      "ussak--oyunhavasi--aksak--bandirma_dortlemesi--\n",
      "mahur--sarki--aksak--bir_gonulde--mazhar_bey\n",
      "huseyni--aranagme--aksak--1--\n",
      "muhayyer--sarki--aksak--iltimas_etmeye--haci_arif_bey\n",
      "huzzam--sarki--aksak--ettigin_cevri--cinucen_tanrikorur\n",
      "nihavent--sarki--aksak--gece_sahilden--serif_icli\n",
      "hicaz--rumeliturkusu--sofyan--gemi_kalkar--rumeli\n",
      "hicaz--ilahi--duyek--icimde_bir--amir_ates\n",
      "huseyni--turku--sofyan--kopruden_gecti--kirsehir\n",
      "mahur--turku--sofyan--niksarin_fidanlari--tokat\n",
      "hicaz--turku--sofyan--gokteki_yildizin--\n",
      "hicaz--sarki--aksak--ben_gamli_hazan--melahat_pars\n",
      "hicazkar--aranagme--curcuna--1--\n",
      "ussak--turku--aksak--kizilciklar_oldu--edirne-kesan\n",
      "mahur--sarki--curcuna--zahiri_hale--haci_arif_bey\n",
      "ussak--sarki--aksak--ducar-i_hicr-i--sevki_bey\n",
      "hicaz--sarki--aksak--su_karsiki--dede_efendi\n",
      "hicaz--sarki--duyek--yalan_degil--yusuf_nalkesen\n",
      "nihavent--sarki--duyek--gokyuzunde--teoman_alpay\n",
      "hicaz--sarki--curcuna--bahar_olsa--fahri_kopuz\n",
      "ussak--sarki--sofyan--gamzedeyim_deva--tatyos_efendi\n",
      "mahur--sarki--aksak--yeniden_eski--ismail_baha_surelsan\n",
      "hicazkar--fantezi--duyek--aldi_beni--corlulu\n",
      "hicaz--turku--sofyan--bahce_duvarini--neset_ertas\n",
      "huseyni--sarki--duyek--bahcede_gordum--medeni_aziz_efendi\n",
      "rast--sarki--sofyan--karli_dagi--rifat_bey\n",
      "ussak--ilahi--sofyan--seyyah_olup--\n",
      "ussak--sarki--duyek--menekse_gozler--yesari_asim_arsoy\n",
      "hicaz--turku--sofyan--suda_balik--kirsehir\n",
      "nihavent--sarki--duyek--bir_demet--zeki_muren\n",
      "huseyni--turku--sofyan--cikayim_gideyim--\n",
      "hicaz--turku--sofyan--cadir_alti--gaziantep\n",
      "ussak--seyir--sofyan--1--erol_bingol\n",
      "segah--seyir--sofyan--1--sefik_gurmeric\n",
      "rast--sarki--aksak--ay_gulsun--ismail_baha_surelsan\n",
      "huseyni--turku--curcuna--coban_kizi--\n",
      "segah--fantezi--aksak--gul_ne--omer_dilek\n",
      "mahur--sarki--duyek--gul_mevsimi--tanburi_mustafa_cavus\n",
      "ussak--sarki--aksak--aglatirlar_guldururler--dede_efendi\n",
      "segah--sarki--aksak--gel_ey--tanburi_cemil_bey\n",
      "hicaz--sarki--duyek--yalancinin_birine--necdet_tokatlioglu\n",
      "ussak--sarki--duyek--aksam_oldu_huzunlendim--semahat_ozdenses\n",
      "hicaz--sarki--aksak--bilmiyorum--sevki_bey\n",
      "huseyni--ornek_oz--sofyan--1--ruhi_ayangil\n",
      "hicaz--sarki--curcuna--sislendi_hava--rifat_bey\n",
      "nihavent--sarki--aksak--vuslat_yaya--omer_dilek\n",
      "huseyni--sarki--aksak--bir_damlacik--omer_dilek\n",
      "segah--ilahi--sofyan--sordum_sari--\n",
      "huseyni--aranagme--duyek--1--\n",
      "hicaz--sarki--aksak--dil_yaresini--sevki_bey\n",
      "huseyni--turku--aksak--leylak_takivermis--bahri_altintas\n",
      "huzzam--sarki--duyek--birak_duygulari--omer_sami_gupgup\n",
      "huseyni--turku--sofyan--keklik_daglarda--\n",
      "huseyni--ornek_oz--sofyan--2--ruhi_ayangil\n",
      "huseyni--sarki--aksak--varalim_kuy-i--munir_nurettin_selcuk\n",
      "hicazkar--sarki--duyek--sen_gozlerinle--alaeddin_yavasca\n",
      "huseyni--seyir--sofyan--1--sefik_gurmeric\n",
      "segah--turku--sofyan--oy_kemence--ordu\n",
      "muhayyer--sarki--aksak--yadimda_o--sukru_tunar\n",
      "nihavent--sarki--aksak--kiz_sen--faize_ergin\n",
      "ussak--sarki--aksak--bu_aksam--tatyos_efendi\n",
      "huzzam--kanto--aksak--bana_bir--lavtaci_hristo\n",
      "nihavent--seyir--sofyan--1--erol_bingol\n",
      "segah--ornek_oz--aksak--1--ruhi_ayangil\n",
      "huseyni--sarki--curcuna--bak_su--danbeni_riza_bey\n",
      "hicazkar--kosma--sofyan--melin_mahzun--muallim_ismail_hakki_bey\n",
      "mahur--sarki--duyek--senle_durmak--kanuni_haci_arif_bey\n",
      "huseyni--sarki--curcuna--edali_bir--ali_rifat_cagatay\n",
      "ussak--kurthavasi--sofyan--karsida_kurt--\n",
      "ussak--turku--sofyan--sigaramin_dumani--\n",
      "huseyni--ornek_oz--sofyan--3--ruhi_ayangil\n",
      "huzzam--turku--aksak--arabaya_tas--istanbul\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "huzzam--sarki--sofyan--solgun_durma--munir_nurettin_selcuk\n",
      "mahur--sarki--aksak--gonul_adli--dellalzade_haci_ismail_efendi\n",
      "mahur--pesrev--duyek----gazi_giray_han\n",
      "nihavent--sarki--duyek--su_guzeller--necip_mirkelamoglu\n",
      "hicaz--sarki--curcuna--aksam_erdi--rahmi_bey\n",
      "segah--sarki--aksak--gece_sessiz--gultekin_ceki\n",
      "rast--sarki--curcuna--icime_hep--yildirim_gurses\n",
      "hicazkar--sarki--duyek--yuzun_gullerden--sadettin_kaynak\n",
      "huzzam--turku--curcuna--al_yesil--urfa\n",
      "huseyni--turku--sofyan--yaylanin_cimenine--trabzon\n",
      "mahur--tavsanca--aksak--aldi_aklim--ii_mahmud\n",
      "segah--turku--aksak--boyda_bosun--istanbul\n",
      "muhayyer--turku--sofyan--yandim_allah--sivas\n",
      "hicazkar--sarki--duyek--harap_oldu--arif_sami_toker\n",
      "mahur--sarki--aksak--alamam_dogrusu--serif_icli\n",
      "rast--sarki--duyek--sensiz_kalan--irfan_ozbakir\n",
      "segah--turku--curcuna--cayda_cira--elazig\n",
      "hicazkar--sarki--duyek--birer_birer--omer_sami_gupgup\n",
      "huseyni--turku--sofyan--hey_onbesli--tokat\n",
      "nihavent--sarki--duyek--ak_duvaklar--omer_dilek\n",
      "huzzam--sarki--aksak--adanin_yesil_camlari--sukru_tunar\n",
      "segah--sarki--aksak--seni_her--misirli_udi_ibrahim_efendi\n",
      "muhayyer--turku--aksak--serenler--burdur\n",
      "hicaz--rumeliturkusu--aksak--yine_de--rumeli\n",
      "huzzam--sarki--aksak--hicran_yine_hicran--serif_icli\n",
      "ussak--sarki--curcuna--yad_ile--sevki_bey\n",
      "hicazkar--sarki--curcuna--usandirdi_felek--ahmet_mukerrem_akinci\n",
      "hicaz--sarki--aksak--hastayim_yasiyorum--udi_hrant\n",
      "huseyni--beste--duyek--muheyya_oldu--rakim_elkutlu\n",
      "rast--sarki--curcuna--anlatayim_halimi--tanburi_ali_efendi\n",
      "huseyni--turku--aksak--yuce_dag--sadettin_kaynak\n",
      "hicaz--seyir--duyek--1--sefik_gurmeric\n",
      "hicaz--ornek_oz--sofyan--1--ruhi_ayangil\n",
      "hicazkar--sarki--duyek--beyhude_askina--nikogos_aga\n",
      "rast--ornek_oz--sofyan--1--huseyin_sadettin_arel\n",
      "mahur--sarki--duyek--kadehin_dudagimda--rustu_eric\n",
      "segah--sarki--aksak--benim_sen--ahmet_rasim_bey\n",
      "huseyni--sarki--duyek--her_seherde--nihat_adlim\n",
      "segah--turku--sofyan--kalaliyam--urfa\n",
      "nihavent--sarki--aksak--suzis-i_askinla--musa_sureyya_bey\n",
      "segah--sarki--sofyan--dayanilmaz_hasretin--omer_sami_gupgup\n",
      "muhayyer--turku--sofyan--keklik_gibi--erzincan\n",
      "hicaz--sarki--aksak--pencerenin_perdesini--muhlis_sabahaddin_ezgi\n",
      "muhayyer--turku--duyek--zehram_bana--yesari_asim_arsoy\n",
      "ussak--popsarkisi--sofyan--ellerimde_buyuttugum--baris_manco\n",
      "muhayyer--sarki--aksak--humari_yok--haci_arif_bey\n",
      "hicazkar--sarki--duyek--yaziktir_etme--erol_sayan\n",
      "rast--cocuksarkisi--sofyan--yavru_kuslar--\n",
      "rast--sarki--duyek--sevdim_seni--adnan_aydemir\n",
      "ussak--sarki--duyek--sevgi_dolu--bilge_ozgen\n",
      "nihavent--sarki--aksak--hatirlar_misin_beni--vecdi_seyhun\n",
      "rast--sarki--duyek--senin_askinla_cakoldum--basmaci_abdi_efendi\n",
      "hicazkar--sarki--aksak--nolur_bir--mildan_niyazi_bey\n",
      "hicaz--sarki--aksak--gulsen-i_husnune--rifat_bey\n",
      "ussak--sarki--curcuna--derdimi_anlatirdim--tahsin_karakus\n",
      "mahur--turku--aksak--setiremin_dugmeleri--istanbul\n",
      "hicaz--turku--sofyan--sandalim_geliyor--\n",
      "mahur--sarki--sofyan--ne_dogan--munir_nurettin_selcuk\n",
      "hicaz--turku--sofyan--ela_gozlerine--sadettin_kaynak\n",
      "nihavent--sarki--duyek--gizli_ask--zeynettin_maras\n",
      "nihavent--sarki--duyek--ayrilik_bukuverdi--arif_sami_toker\n",
      "mahur--sarki--duyek--saba_tarf-i--enderunlu_hafiz_husnu_efendi\n",
      "ussak--turku--aksak--findikli_bizim--\n",
      "mahur--tavsanca--duyek--ne_ararsam--iii_selim\n",
      "rast--fantezi--duyek--haydi_biraz--omer_sami_gupgup\n",
      "huseyni--turku--curcuna--sari_gelin--erzurum\n",
      "nihavent--sarki--duyek--yillari_durduracak--necdet_tokatlioglu\n",
      "ussak--aranagme--aksak--1--\n",
      "mahur--turku--sofyan--yemenimde_hare_var--\n",
      "huzzam--sarki--aksak--guzellerde_ne--tanburi_mustafa_cavus\n",
      "ussak--sarki--aksak--gulzara_nazar--sevki_bey\n",
      "hicaz--sarki--sofyan--egilmez_basin--kaptanzade_ali_riza_bey\n",
      "rast--mars--sofyan--baska_bir--musa_sureyya_bey\n",
      "rast--sarki--duyek--belki_bir_sabah--sekip_ayhan_ozisik\n",
      "nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
      "segah--turku--sofyan--pancar_pezik--hacibektas\n",
      "mahur--sarki--duyek--dudagimda_acan--erol_sayan\n",
      "nihavent--sarki--curcuna--yine_bu--osman_nihat_akin\n",
      "ussak--fantezi--sofyan--soyle_sevgili--munir_nurettin_selcuk\n",
      "segah--turku--aksak--cemberim_de--biga\n",
      "mahur--sarki--duyek--seb-i_vuslat--nasibin_mehmet_yuru\n",
      "huzzam--sarki--aksak--manada_guzel--sadi_isilay\n",
      "rast--sarki--duyek--omrumce_hep--irfan_ozbakir\n",
      "huzzam--turku--aksak--yaniyor_mu--\n",
      "segah--sarki--duyek--leyla_bir--sadettin_kaynak\n",
      "segah--sarki--curcuna--bir_emele--neveser_kokdes\n",
      "hicaz--sarki--aksak--beni_sev--misirli_udi_ibrahim_efendi\n",
      "ussak--sarki--curcuna--esiri_zulfunum--sevki_bey\n",
      "ussak--turku--aksak--su_dalmanin--aydin\n",
      "segah--turku--aksak--binnaz--usak\n",
      "segah--turku--aksak--izmirin_kavaklari--\n",
      "hicaz--kanto--sofyan--ada_sahillerinde--\n",
      "nihavent--sarki--duyek--doymadim_sana--nevzat_akay\n",
      "muhayyer--sarki--sofyan--bir_gun--ziya_taskent\n",
      "hicazkar--aranagme--aksak--1--\n",
      "hicaz--ninni--sofyan--dagda_tavsanlar--tanburi_cemil_bey\n",
      "hicaz--seyir--duyek--1--erol_bingol\n",
      "nihavent--sarki--curcuna--goze_mi--osman_nihat_akin\n",
      "segah--turku--sofyan--ana_beni--\n",
      "hicaz--sarki--aksak--kis_geldi--sevki_bey\n",
      "segah--sarki--duyek--askin_o_sihirli--erol_sayan\n",
      "huzzam--sarki--aksak--gozunun_rengini--selahattin_pinar\n",
      "nihavent--sarki--duyek--seninle_bu--dursun_karaca\n",
      "segah--seyir--sofyan--1--erol_bingol\n",
      "hicaz--kocekce--aksak--baharin_zamani--dede_efendi\n",
      "ussak--ornek_oz--sofyan--1--huseyin_sadettin_arel\n",
      "hicaz--turku--sofyan--su_gelen--\n",
      "nihavent--mars--sofyan--istiklal_marsi--zeki_ungor\n",
      "huzzam--sarki--duyek--zannetme_ki--kenan_savkli\n",
      "huseyni--divan--sofyan----\n",
      "rast--sarki--curcuna--mey-i_lalinle--tatyos_efendi\n",
      "rast--sarki--curcuna--gamdan_azade--rifat_bey\n",
      "mahur--sarki--duyek--ben_guzele--sadettin_kaynak\n",
      "nihavent--sarki--duyek--sevil_neselen--sadettin_oktenay\n",
      "hicaz--turku--aksak--karanfil_oylum_oylum--\n",
      "mahur--seyir--sofyan--1--sefik_gurmeric\n",
      "huzzam--sarki--duyek--omrunde_sarmadin--omer_sami_gupgup\n",
      "hicaz--sarki--aksak--gormedim_omrumun--kadri_sencalar\n",
      "ussak--turku--sofyan--mevlam_bircok--malatya\n",
      "huzzam--sarki--curcuna--kusade_taliim--sevki_bey\n",
      "ussak--sarki--aksak--zeybeklerle_gezer--sevki_bey\n",
      "mahur--sarki--curcuna--gucendim_ben--muhlis_sabahaddin_ezgi\n",
      "hicaz--sarki--curcuna--acaba_sen_misin--bimen_sen\n",
      "huseyni--turku--curcuna--sinemde_bir--\n",
      "mahur--sarki--aksak--iki_gozum--kemal_emin_bara\n",
      "hicaz--sarki--curcuna--ruya_gibi--avni_anil\n",
      "hicazkar--sarki--duyek--gul_dalinda--neveser_kokdes\n",
      "muhayyer--sarki--sofyan--nicin_mahzun--haci_arif_bey\n",
      "nihavent--sarki--duyek--askim_bahardi--yildirim_gurses\n",
      "hicaz--sarki--sofyan--gul_acarken--nihal_erkutun\n",
      "ussak--ilahi--duyek--allah_emrin--zekai_dede\n",
      "ussak--turku--sofyan--uzun_ince--asik_veysel\n",
      "hicaz--turku--aksak--ihtiyatlar_silah--\n",
      "huzzam--sarki--duyek--yalniz_aksamlarimda--omer_sami_gupgup\n",
      "ussak--seyir--sofyan--1--sefik_gurmeric\n",
      "hicazkar--sarki--curcuna--senelerce--sabri_suha_ansen\n",
      "rast--sarki--duyek--uftadenim_ey--dede_efendi\n",
      "rast--seyir--duyek--1--sefik_gurmeric\n",
      "rast--nakis--duyek--amed_nesim-i--abdulkadir_meragi\n",
      "rast--sarki--sofyan--acilan_bir--dramali_hasan_hasguler\n",
      "ussak--sarki--sofyan--bir_perikizi--\n",
      "huseyni--sarki--curcuna--cektim_elimi--tatyos_efendi\n",
      "huseyni--mars--sofyan--ceddin_deden--kaptanzade_ali_riza_bey\n",
      "hicaz--turku--sofyan--esmerim_bicim--diyarbakir\n",
      "mahur--sarki--duyek--hosgeldin_elimize--sadettin_kaynak\n",
      "hicazkar--sarki--aksak--bir_yar--k_hafiz_yasar\n",
      "muhayyer--sarki--aksak--hala_gonlum--tanburi_mustafa_cavus\n",
      "muhayyer--zeybek--aksak--serenler--burdur\n",
      "hicaz--sarki--duyek--ayrilik_canlari--omer_sami_gupgup\n",
      "huzzam--sarki--duyek--sacin_yuzume--teoman_alpay\n",
      "huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu\n",
      "rast--sarki--aksak--bin_can_ile--tanburi_buyuk_osman_bey\n",
      "hicaz--aranagme--curcuna--1--\n",
      "ussak--ilahi--sofyan--ey_enbiyalar--\n",
      "hicaz--sarki--duyek--bogazici_sen--alaeddin_yavasca\n",
      "hicaz--turku--aksak--el_zanneder--urfa\n",
      "muhayyer--turku--sofyan--cayir_ince--\n",
      "hicazkar--sarki--aksak--bekledim_fecre--rakim_elkutlu\n",
      "huzzam--turku--aksak--duriyemin_gugumleri--\n",
      "segah--turku--curcuna--havuz_basinin--erzincan\n",
      "ussak--ilahi--sofyan--hak_taala--ali_ufki\n",
      "huseyni--turku--aksak--bursanin_ufak--bursa\n",
      "mahur--sarki--duyek--ay_dogar--sadettin_kaynak\n",
      "mahur--sarki--aksak--olmusum_divane--komurcuzade_hafiz_mehmet_efendi\n",
      "huseyni--sarki--aksak--sensizlige_soylendi--ilgun_soysev\n",
      "ussak--sarki--duyek--bahari_beklerken--selcuk_tekay\n",
      "ussak--kanto--sofyan--telgirafin_tellerine--\n",
      "ussak--aranagme--curcuna--1--\n",
      "nihavent--sarki--curcuna--saclarina_baglanali--rahmi_bey\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "segah--turku--curcuna--bahcada_yesil--diyarbakir\n",
      "mahur--sarki--sofyan--gordum_bugun--dede_efendi\n",
      "segah--ornek_oz--duyek--1--ruhi_ayangil\n",
      "rast--sarki--sofyan--ask_denilen--erol_sayan\n",
      "muhayyer--sarki--curcuna--yetmez_mi_sana--rahmi_bey\n",
      "segah--sarki--duyek--o_gece--omer_sami_gupgup\n",
      "segah--turku--aksak--bir_gemim--ankara\n",
      "huseyni--ornek_oz--duyek--1--ruhi_ayangil\n",
      "hicaz--kalenderi--aksak--ebrulerinin_zahmi--ii_mahmud\n",
      "nihavent--sarki--sofyan--ne_dert--sadettin_kaynak\n",
      "hicaz--ilahi--sofyan--daglar_ile--kutbi_dede\n",
      "muhayyer--sarki--duyek--cile_bulbulum--sadettin_kaynak\n",
      "huseyni--turku--sofyan--sabahin_seher--\n",
      "mahur--sarki--aksak--servinazi_seyret--rahmi_bey\n",
      "huzzam--sarki--curcuna--guzel_gun--haci_arif_bey\n",
      "ussak--turku--sofyan--kalenin_burcu_muyam--\n",
      "nihavent--sarki--duyek--ellerim_boyle--sekip_ayhan_ozisik\n",
      "nihavent--sarki--curcuna--vucud_ikliminin--haci_arif_bey\n",
      "muhayyer--sarki--aksak--dilde_rast--serif_icli\n",
      "mahur--sarki--curcuna--sakiz_hanim--bahadir_akkuzu\n",
      "hicaz--zeybek--aksak----izmir\n",
      "rast--sugul--duyek--ya_men--tanburi_ali_efendi\n",
      "ussak--sarki--duyek--gun_gelir_de--sekip_ayhan_ozisik\n",
      "nihavent--sarki--curcuna--ayrilmak_ne--ekrem_guyer\n",
      "huzzam--sarki--duyek--icimde_bin--avni_anil\n",
      "nihavent--sarki--duyek--sen_benim--emin_ongan\n",
      "rast--sarki--aksak--reng-i_ruhsarina--erol_sayan\n",
      "hicaz--sarki--duyek--aski_seninle--fehmi_tokay\n",
      "huzzam--sarki--duyek--omrumuzun_son--selahattin_altinbas\n",
      "mahur--mars--sofyan--gafil_ne--muallim_ismail_hakki_bey\n",
      "nihavent--sarki--duyek--bir_ihtimal--osman_nihat_akin\n",
      "mahur--sarki--aksak--cektikce_sineye--selahattin_icli\n",
      "hicazkar--sarki--aksak--acil_ey_gonce--haci_arif_bey\n",
      "muhayyer--turku--duyek--iste_seni--sadettin_kaynak\n",
      "segah--sarki--aksak--kirdin_ummidimi--fehmi_tokay\n",
      "huzzam--turku--sofyan--indim_havuz--istanbul\n",
      "huseyni--turku--curcuna--dersim_dort--tunceli\n",
      "huseyni--turku--sofyan--koskum_var--\n",
      "nihavent--fantezi--duyek--ufacik_tefeciktin--sekip_ayhan_ozisik\n",
      "hicaz--sarki--curcuna--gonul_yarasindan--selahattin_pinar\n",
      "huzzam--sarki--aksak--sevdigin_dunyalar--munir_nurettin_selcuk\n",
      "nihavent--sarki--aksak--camlica_yolunda--nuri_halil_poyraz\n",
      "huzzam--sarki--aksak--gonul_dustu--kemenceci_usta_yani\n",
      "nihavent--fantezi--sofyan--durusun_andirir--unal_narcin\n",
      "muhayyer--sarki--aksak--serapa_husn--rahmi_bey\n",
      "rast--sarki--duyek--bana_nasil--alaeddin_yavasca\n",
      "hicaz--fantezi--duyek--sevemez_kimse--suat_sayin\n",
      "muhayyer--turku--curcuna--yar_yuregim--urfa\n",
      "hicaz--sarki--aksak--matemzedeyim--tanburi_cemil_bey\n",
      "nihavent--seyir--sofyan--1--sefik_gurmeric\n",
      "ussak--sarki--curcuna--omrumun_gulusun--haydar_tatliyay\n",
      "nihavent--sarki--duyek--askimin_ilk--arif_sami_toker\n",
      "ussak--sarki--aksak--bag-i_husnun--kemal_emin_bara\n",
      "mahur--seyir--sofyan--1--erol_bingol\n",
      "hicaz--sarki--duyek--hicbir_seyde--fethi_karamahmutoglu\n",
      "muhayyer--sarki--aksak--bir_gonulde--ahmet_rasim_bey\n",
      "hicaz--sarki--aksak--bir_bahar--selahattin_pinar\n",
      "hicazkar--sarki--aksak--nerdesin--leyla_saz\n",
      "hicaz--turku--aksak--pencere_acildi--\n",
      "rast--sarki--curcuna--asik_oldur--haci_arif_bey\n",
      "huzzam--turku--curcuna--hayatlari_degirmi--\n",
      "ussak--sarki--duyek--dalinda_solarken--arif_sami_toker\n",
      "nihavent--kasaphavasi--duyek----\n",
      "nihavent--sarki--aksak--gel_guzelim--faiz_kapanci\n",
      "muhayyer--sarki--sofyan--ne_zaman--sadettin_kaynak\n",
      "muhayyer--sarki--aksak--aglamaktan_dideden--rifat_bey\n",
      "muhayyer--seyir--sofyan--1--sefik_gurmeric\n",
      "mahur--sarki--aksak--sana_layik_mi--dede_efendi\n",
      "mahur--sarki--aksak--al_yanina--dellalzade_haci_ismail_efendi\n",
      "segah--turku--sofyan--acem_ulkesinde--erzurum\n",
      "huseyni--sarki--sofyan--zeytin_gozlum--selahattin_icli\n",
      "huzzam--sarki--duyek--icimde_kim--yusuf_nalkesen\n",
      "ussak--sarki--curcuna--hastasin_zannim--sevki_bey\n",
      "huzzam--sarki--aksak--vefa_yoktur--tanburi_mustafa_cavus\n",
      "hicazkar--sarki--duyek--kulagimdan_gitmiyor--ozcan_korkut\n",
      "huseyni--seyir--duyek--1--erol_bingol\n",
      "nihavent--fantezi--sofyan--sarahaten_acaba--ali_rifat_cagatay\n",
      "ussak--ilahi--sofyan--askinla_cak--haci_nafiz_bey\n",
      "hicazkar--sarki--duyek--ciceklerin_guluyor--sadettin_kaynak\n",
      "nihavent--sarki--aksak--bakmiyor_cesm-i--haci_arif_bey\n",
      "mahur--sarki--aksak--su_guzele--semseddin_ziya_bey\n",
      "hicaz--popsarkisi--sofyan--basin_one--kerem_guney\n",
      "hicazkar--aranagme--aksak--1--muallim_ismail_hakki_bey\n",
      "huzzam--sarki--curcuna--omrum_seni--yesari_asim_arsoy\n",
      "rast--sarki--sofyan--yarali_gonlumde--omer_sami_gupgup\n",
      "huzzam--sarki--aksak--gonlum_seher--sadettin_kaynak\n",
      "mahur--sarki--aksak--ver_saki--refik_fersan\n",
      "muhayyer--sarki--aksak--her_halinle--erol_sayan\n",
      "huzzam--sarki--curcuna--ayrilik_umitlerin--avni_anil\n",
      "nihavent--sarki--sofyan--anla_artik--yildirim_gurses\n",
      "huzzam--sarki--curcuna--askinla_surunsem--selahaddin_pinar\n",
      "segah--sarki--aksak--yine_hicran_ile--melahat_pars\n",
      "huzzam--sarki--curcuna--gokyuzune_ciksan--omer_sami_gupgup\n",
      "nihavent--sarki--aksak--yok_baska--munir_nurettin_selcuk\n",
      "rast--mersiye--sofyan--ey_padiseh-i--rifat_bey\n",
      "hicaz--sarki--aksak--ne_gunah--alaeddin_yavasca\n",
      "hicazkar--sarki--duyek--yollari_gurbete--sadi_isilay\n",
      "ussak--sarki--duyek--mehtapli_gecelerde--sevim_sengul\n",
      "nihavent--sarki--duyek--unutturamaz_seni--ekrem_guyer\n",
      "hicazkar--sarki--sofyan--ben_sozune--numan_aga\n",
      "rast--sarki--aksak--calima_bak--lavtaci_hristo\n",
      "huzzam--sarki--aksak--indirip_yerlere--cinucen_tanrikorur\n",
      "rast--turku--sofyan--kalenin_dibinde--kerkuk\n",
      "rast--sarki--curcuna--aglasam_her--gulbenkyan\n",
      "nihavent--sarki--aksak--ruya_mi--omer_dilek\n",
      "hicazkar--kocekce--aksak--gece_gunduz--muallim_ismail_hakki_bey\n",
      "segah--sarki--aksak--bulbul_yetisir--haci_arif_bey\n",
      "hicazkar--sarki--aksak--severim_her--nefise_ozses\n",
      "ussak--turku--aksak--yangin_olur--\n",
      "rast--turku--sofyan--su_dereler--orta_anadolu\n",
      "huzzam--sarki--duyek--pisman_olur_da--irfan_ozbakir\n",
      "huseyni--sarki--aksak--sevdigim_cemalin--mahmud_celaleddin_pasa\n",
      "hicaz--sarki--curcuna--unutulmus_degilsin--irfan_ozbakir\n",
      "rast--sarki--aksak--aski_huznumle--mildan_niyazi_bey\n",
      "hicaz--sarki--duyek--sarkilardan_fal--erdogan_berker\n",
      "huzzam--sarki--duyek--nicin_nalendesin--nikogos_aga\n",
      "huseyni--etud--sofyan----hasan_esen\n",
      "hicazkar--seyir--sofyan--1--sefik_gurmeric\n",
      "muhayyer--sarki--aksak--gunes_gibi--sadettin_kaynak\n",
      "hicaz--turku--aksak--gemilerde_talim--\n",
      "rast--aranagme--curcuna--1--\n",
      "hicaz--rumeliturkusu--sofyan--kirmizi_gulun--rumeli\n",
      "rast--sarki--sofyan--gelmez_oldu--dramali_hasan_hasguler\n",
      "hicaz--sarki--duyek--anilsin_yar--semseddin_ziya_bey\n",
      "huzzam--sarki--curcuna--bir_gun--omer_sami_gupgup\n",
      "muhayyer--aranagme--curcuna--1--\n",
      "nihavent--sarki--aksak--koklasam_saclarini--artaki_candan\n",
      "segah--sarki--curcuna--olmaz_ilac--haci_arif_bey\n",
      "huseyni--sarki--sofyan--tez_gecse_de--selahattin_inal\n",
      "muhayyer--ilahi--duyek--toprakta_yatacak--dede_efendi\n",
      "rast--etud--sofyan----ozer_ozel\n",
      "mahur--turku--sofyan--beyoglunda_gezersin--pirincci_riza_bey\n",
      "hicazkar--sarki--duyek--cozmek_elinde--sadettin_kaynak\n",
      "nihavent--sarki--sofyan--sahilde_o--yesari_asim_arsoy\n",
      "mahur--sarki--aksak--gor_nitti_cana--dede_efendi\n",
      "mahur--sarki--duyek--olsa_da_hos--sadettin_kaynak\n",
      "huzzam--sarki--duyek--seni_sesini--sadi_hosses\n",
      "rast--sarki--curcuna--aylar_geciyor--selahaddin_pinar\n",
      "rast--sarki--duyek--bir_gonul--ferit_sidal\n",
      "nihavent--sarki--duyek--bugun_yine--emin_ongan\n",
      "ussak--turku--duyek--bahceye_indim_ki--\n",
      "Total number of makam processed: 485\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as sc\n",
    "\n",
    "#correlations dictionary \n",
    "correlation = {\n",
    "    \"aksak\": [0, 0, 0], \n",
    "    \"sofyan\": [0, 0, 0], \n",
    "    \"duyek\": [0, 0, 0], \n",
    "    \"curcuna\": [0, 0, 0]\n",
    "}\n",
    "\n",
    "# first key = makam, second key = usul\n",
    "correlation_makam = {\n",
    "    \"hicaz\": {\"aksak\": [0, 0, 0], \"sofyan\": [0, 0, 0], \"duyek\": [0, 0, 0], \"curcuna\": [0, 0, 0]}, \n",
    "    \"hicazkar\": {\"aksak\": [0, 0, 0], \"sofyan\": [0, 0, 0], \"duyek\": [0, 0, 0], \"curcuna\": [0, 0, 0]},\n",
    "    \"huseyni\": {\"aksak\": [0, 0, 0], \"sofyan\": [0, 0, 0], \"duyek\": [0, 0, 0], \"curcuna\": [0, 0, 0]},\n",
    "    \"huzzam\": {\"aksak\": [0, 0, 0], \"sofyan\": [0, 0, 0], \"duyek\": [0, 0, 0], \"curcuna\": [0, 0, 0]},\n",
    "    \"mahur\": {\"aksak\": [0, 0, 0], \"sofyan\": [0, 0, 0], \"duyek\": [0, 0, 0], \"curcuna\": [0, 0, 0]}, \n",
    "    \"muhayyer\": {\"aksak\": [0, 0, 0], \"sofyan\": [0, 0, 0], \"duyek\": [0, 0, 0], \"curcuna\": [0, 0, 0]}, \n",
    "    \"nihavent\": {\"aksak\": [0, 0, 0], \"sofyan\": [0, 0, 0], \"duyek\": [0, 0, 0], \"curcuna\": [0, 0, 0]}, \n",
    "    \"rast\": {\"aksak\": [0, 0, 0], \"sofyan\": [0, 0, 0], \"duyek\": [0, 0, 0], \"curcuna\": [0, 0, 0]},\n",
    "    \"segah\": {\"aksak\": [0, 0, 0], \"sofyan\": [0, 0, 0], \"duyek\": [0, 0, 0], \"curcuna\": [0, 0, 0]},\n",
    "    \"ussak\": {\"aksak\": [0, 0, 0], \"sofyan\": [0, 0, 0], \"duyek\": [0, 0, 0], \"curcuna\": [0, 0, 0]}\n",
    "}\n",
    "\n",
    "\n",
    "for i in df_makam_bin_vals.index:\n",
    "    sample = df_makam_bin_vals.loc[i]\n",
    "    usul = sample['usul']\n",
    "    makam = sample['makam']\n",
    "    x = sample['bin_vals']\n",
    "    file_path = sample['file_path'].split('-withoutKeySignature.xml')[0]\n",
    "    print(file_path)\n",
    "    \n",
    "    plot_hist(x, makam, usul, i, file_path) \n",
    "    #correlation and p-value for each usul\n",
    "    correlation[usul][0] = int(correlation[usul][0]) + 1\n",
    "    correlation[usul][1] = correlation[usul][1] + sc.spearmanr(x, weights[usul])[0]\n",
    "    correlation[usul][2] = correlation[usul][2] + sc.spearmanr(x, weights[usul])[1]\n",
    "    \n",
    "    #correlation and p-value for each usul considering the makam as well\n",
    "    correlation_makam[makam][usul][0] += 1\n",
    "    correlation_makam[makam][usul][1] += sc.spearmanr(x, weights[usul])[0]\n",
    "    correlation_makam[makam][usul][2] += sc.spearmanr(x, weights[usul])[1]\n",
    "        \n",
    "print(\"Total number of makam processed: {}\".format(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aksak</th>\n",
       "      <th>sofyan</th>\n",
       "      <th>duyek</th>\n",
       "      <th>curcuna</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Total makam</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>correlation</td>\n",
       "      <td>116.350183</td>\n",
       "      <td>66.764226</td>\n",
       "      <td>97.305141</td>\n",
       "      <td>52.308055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>p-value</td>\n",
       "      <td>1.309801</td>\n",
       "      <td>12.756624</td>\n",
       "      <td>1.351413</td>\n",
       "      <td>0.007665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  aksak      sofyan       duyek    curcuna\n",
       "Total makam  156.000000  129.000000  134.000000  67.000000\n",
       "correlation  116.350183   66.764226   97.305141  52.308055\n",
       "p-value        1.309801   12.756624    1.351413   0.007665"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create DataFrame \n",
    "correlation_df = pd.DataFrame(correlation, index =['Total makam', 'correlation', 'p-value']) \n",
    "  \n",
    "correlation_df.iloc[0].apply(int)\n",
    "correlation_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation for aksak usul: 0.7458345090355403, p-value: 0.008396162270744313\n",
      "Correlation for sofyan usul: 0.5175521401380984, p-value: 0.0988885603112329\n",
      "Correlation for duyek usul: 0.72615776736694, p-value: 0.010085168848546065\n",
      "Correlation for curcuna usul: 0.7807172346217971, p-value: 0.00011440739158565087\n"
     ]
    }
   ],
   "source": [
    "for usul in usul_considered:\n",
    "    #average correlations per usul\n",
    "    print(\"Correlation for {} usul: {}, p-value: {}\".format(usul, correlation[usul][1]/correlation[usul][0], \n",
    "          correlation[usul][2]/correlation[usul][0]))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of makam processed: 486\n"
     ]
    }
   ],
   "source": [
    "#todo: make a data frame as well \n",
    "correlation_makam_avg = {\n",
    "    \"hicaz\": {\"aksak\": [0, 0], \"sofyan\": [0, 0], \"duyek\": [0, 0], \"curcuna\": [0, 0]}, \n",
    "    \"hicazkar\": {\"aksak\": [0, 0], \"sofyan\": [0, 0], \"duyek\": [0, 0], \"curcuna\": [0, 0]}, \n",
    "    \"huseyni\": {\"aksak\": [0, 0], \"sofyan\": [0, 0], \"duyek\": [0, 0], \"curcuna\": [0, 0]}, \n",
    "    \"huzzam\": {\"aksak\": [0, 0], \"sofyan\": [0, 0], \"duyek\": [0, 0], \"curcuna\": [0, 0]}, \n",
    "    \"mahur\": {\"aksak\": [0, 0], \"sofyan\": [0, 0], \"duyek\": [0, 0], \"curcuna\": [0, 0]},  \n",
    "    \"muhayyer\": {\"aksak\": [0, 0], \"sofyan\": [0, 0], \"duyek\": [0, 0], \"curcuna\": [0, 0]},  \n",
    "    \"nihavent\": {\"aksak\": [0, 0], \"sofyan\": [0, 0], \"duyek\": [0, 0], \"curcuna\": [0, 0]}, \n",
    "    \"rast\": {\"aksak\": [0, 0], \"sofyan\": [0, 0], \"duyek\": [0, 0], \"curcuna\": [0, 0]}, \n",
    "    \"segah\": {\"aksak\": [0, 0], \"sofyan\": [0, 0], \"duyek\": [0, 0], \"curcuna\": [0, 0]}, \n",
    "    \"ussak\": {\"aksak\": [0, 0], \"sofyan\": [0, 0], \"duyek\": [0, 0], \"curcuna\": [0, 0]}\n",
    "}\n",
    "\n",
    "#change name of this\n",
    "total = 0\n",
    "for makam, value in correlation_makam.items():\n",
    "    for usul, value1 in value.items():\n",
    "        avg_correlation = value1[1]/value1[0]\n",
    "        avg_pval = value1[2]/value1[0]\n",
    "        # update our dict\n",
    "        correlation_makam_avg[makam][usul][0] = avg_correlation\n",
    "        correlation_makam_avg[makam][usul][1] = avg_pval\n",
    "        total += value1[0]\n",
    "\n",
    "# assert we have considered all makams \n",
    "print(\"Total number of makam processed: {}\".format(total))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Makam: hicaz\n",
      "Correlation for aksak usul: 0.7320392192787798, p-value: 0.006164838354526331\n",
      "Correlation for sofyan usul: 0.520049566101943, p-value: 0.08614469914858622\n",
      "Correlation for duyek usul: 0.7170030359327098, p-value: 0.022556871402976287\n",
      "Correlation for curcuna usul: 0.7690186699385412, p-value: 0.00011282880494285053\n",
      "Average correlation for hicaz makam: 0.6845276228129934, p-value: 0.02874480942775792\n",
      "\n",
      "Makam: hicazkar\n",
      "Correlation for aksak usul: 0.6974127996040231, p-value: 0.028937647370021802\n",
      "Correlation for sofyan usul: 0.4917772588792948, p-value: 0.09057781584198354\n",
      "Correlation for duyek usul: 0.7385577733342558, p-value: 0.0031734748646174613\n",
      "Correlation for curcuna usul: 0.7736287914916942, p-value: 0.00015489280454099948\n",
      "Average correlation for hicazkar makam: 0.675344155827317, p-value: 0.03071095772029095\n",
      "\n",
      "Makam: huseyni\n",
      "Correlation for aksak usul: 0.781527137349743, p-value: 0.008631947578610358\n",
      "Correlation for sofyan usul: 0.5130985017395008, p-value: 0.1262873619307815\n",
      "Correlation for duyek usul: 0.7299750569845117, p-value: 0.0268619283348325\n",
      "Correlation for curcuna usul: 0.7826666114853366, p-value: 0.0001502203146131495\n",
      "Average correlation for huseyni makam: 0.701816826889773, p-value: 0.040482864539709375\n",
      "\n",
      "Makam: huzzam\n",
      "Correlation for aksak usul: 0.7868779437721497, p-value: 0.0002958661441669477\n",
      "Correlation for sofyan usul: 0.6131263883496649, p-value: 0.017338523135270324\n",
      "Correlation for duyek usul: 0.7090745563829175, p-value: 0.005790083325250499\n",
      "Correlation for curcuna usul: 0.7714797955275687, p-value: 9.893397888116797e-05\n",
      "Average correlation for huzzam makam: 0.7201396710080752, p-value: 0.005880851645892235\n",
      "\n",
      "Makam: mahur\n",
      "Correlation for aksak usul: 0.7469042231985328, p-value: 0.004825201218027721\n",
      "Correlation for sofyan usul: 0.6118342796006337, p-value: 0.02578048645282646\n",
      "Correlation for duyek usul: 0.7094954096724704, p-value: 0.007974638807418843\n",
      "Correlation for curcuna usul: 0.7837410894497946, p-value: 6.932041287092506e-05\n",
      "Average correlation for mahur makam: 0.712993750480358, p-value: 0.009662411722785987\n",
      "\n",
      "Makam: muhayyer\n",
      "Correlation for aksak usul: 0.70489111885096, p-value: 0.010163711045595002\n",
      "Correlation for sofyan usul: 0.49538460192047273, p-value: 0.1145157717913495\n",
      "Correlation for duyek usul: 0.6557420966391009, p-value: 0.01704739026873154\n",
      "Correlation for curcuna usul: 0.7942822677468474, p-value: 0.0001274613942718345\n",
      "Average correlation for muhayyer makam: 0.6625750212893452, p-value: 0.03546358362498697\n",
      "\n",
      "Makam: nihavent\n",
      "Correlation for aksak usul: 0.7882317033887767, p-value: 0.00023222850890345338\n",
      "Correlation for sofyan usul: 0.5323773121218847, p-value: 0.07864675224516726\n",
      "Correlation for duyek usul: 0.7557468726010758, p-value: 0.016477784993726225\n",
      "Correlation for curcuna usul: 0.7846955731339984, p-value: 0.00011513460070376666\n",
      "Average correlation for nihavent makam: 0.7152628653114339, p-value: 0.023867975087125175\n",
      "\n",
      "Makam: rast\n",
      "Correlation for aksak usul: 0.77939299185853, p-value: 0.00033662507099969897\n",
      "Correlation for sofyan usul: 0.5078697062033339, p-value: 0.10227959213745619\n",
      "Correlation for duyek usul: 0.724698729431808, p-value: 0.0030619430586701305\n",
      "Correlation for curcuna usul: 0.7802327848581214, p-value: 0.00019343393232972088\n",
      "Average correlation for rast makam: 0.6980485530879483, p-value: 0.026467898549863936\n",
      "\n",
      "Makam: segah\n",
      "Correlation for aksak usul: 0.7412293467768434, p-value: 0.0037006336555651075\n",
      "Correlation for sofyan usul: 0.44899776853943224, p-value: 0.14799695983959055\n",
      "Correlation for duyek usul: 0.7744220157166248, p-value: 0.0008091124774384449\n",
      "Correlation for curcuna usul: 0.8012352632666785, p-value: 2.6045513538598468e-05\n",
      "Average correlation for segah makam: 0.6914710985748947, p-value: 0.03813318787153318\n",
      "\n",
      "Makam: ussak\n",
      "Correlation for aksak usul: 0.7108924690239837, p-value: 0.022745143971228607\n",
      "Correlation for sofyan usul: 0.5064073610493692, p-value: 0.12316032489116252\n",
      "Correlation for duyek usul: 0.7187735575594624, p-value: 0.004960302681896156\n",
      "Correlation for curcuna usul: 0.7816273142922612, p-value: 5.6816999436501454e-05\n",
      "Average correlation for ussak makam: 0.679425175481269, p-value: 0.037730647135930946\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Create DataFrame \n",
    "correlation_df_makams = pd.DataFrame(correlation_makam_avg) \n",
    "for makam in makam_considered:\n",
    "    print('Makam:', makam)\n",
    "    total_correlation = 0\n",
    "    total_pval = 0\n",
    "    usul_count = 0\n",
    "    for usul in usul_considered:\n",
    "        correlation_val = correlation_df_makams[makam][usul][0]\n",
    "        p_val = correlation_df_makams[makam][usul][1]\n",
    "        total_correlation += correlation_val\n",
    "        total_pval += p_val\n",
    "        usul_count += 1   \n",
    "        # output stats\n",
    "        print(\"Correlation for {} usul: {}, p-value: {}\".format(usul, correlation_val, p_val))\n",
    "    print('Average correlation for {} makam: {}, p-value: {}\\n'.format(makam, total_correlation/usul_count, \n",
    "                                                                         total_pval/usul_count))"
   ]
  }
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