code_for_RAMA_plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f4a1b889",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "#np.set_printoptions(threshold=np.inf)\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d1c64764",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('D:/data_protein.csv')\n",
    "df_1 = df[df['amino_name'] != 'PRO']\n",
    "df_new = df_1[df_1['amino_name'] != 'GLY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "51505631",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_list = []\n",
    "for i in range(36):\n",
    "    for j in range(36):\n",
    "        phi_x1 = -180+10*(j)\n",
    "        phi_x2 = -180+10*(j+1)\n",
    "        psi_y1 = 180-10*(i+1)\n",
    "        psi_y2 = 180-10*(i)\n",
    "        df_ij = df_new[(df_new['phi']>phi_x1) & \n",
    "                       (df_new['phi']<=phi_x2)& \n",
    "                       (df_new['psi']<psi_y2) & \n",
    "                       (df_new['psi']>=psi_y1)\n",
    "                      ]\n",
    "        count = len(df_ij.index.tolist())\n",
    "        count_list.append(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6481877e",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_matric = np.array(count_list).reshape(36,36)\n",
    "count_matric_copy =  count_matric.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f425d242",
   "metadata": {},
   "outputs": [],
   "source": [
    "TOTAL = len(df_new.index.tolist())\n",
    "CORE = np.sum(count_matric_copy[count_matric_copy>=100])\n",
    "ALLOWED = np.sum(count_matric_copy[(count_matric_copy>=8) & (count_matric_copy<100)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "af0889bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_count = pd.DataFrame(count_matric)\n",
    "GENEROUS_list = []\n",
    "for i in range(2,34):\n",
    "    for j in range(2,34):\n",
    "        if df_count.iat[i,j] >= 8:\n",
    "            df_25X = df_count.iloc[(i-2):(i+3),(j-2):(j+3)]\n",
    "            for m in range(5):\n",
    "                for n in range(5):\n",
    "                    num2 = df_25X.iat[m,n]\n",
    "                    if num2 >= 8:\n",
    "                        pass\n",
    "                    else:\n",
    "                        df_count.iat[(i+m-2),(j+n-2)] = -2\n",
    "                        GENEROUS_list.append(num2)\n",
    "                        \n",
    "        else:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "41f4ff9e",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2</td>\n",
       "      <td>51</td>\n",
       "      <td>65</td>\n",
       "      <td>52</td>\n",
       "      <td>68</td>\n",
       "      <td>50</td>\n",
       "      <td>34</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2</td>\n",
       "      <td>23</td>\n",
       "      <td>52</td>\n",
       "      <td>83</td>\n",
       "      <td>121</td>\n",
       "      <td>96</td>\n",
       "      <td>70</td>\n",
       "      <td>38</td>\n",
       "      <td>53</td>\n",
       "      <td>63</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2</td>\n",
       "      <td>29</td>\n",
       "      <td>35</td>\n",
       "      <td>37</td>\n",
       "      <td>129</td>\n",
       "      <td>108</td>\n",
       "      <td>90</td>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>58</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>91</td>\n",
       "      <td>159</td>\n",
       "      <td>123</td>\n",
       "      <td>99</td>\n",
       "      <td>77</td>\n",
       "      <td>99</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>27</td>\n",
       "      <td>56</td>\n",
       "      <td>133</td>\n",
       "      <td>137</td>\n",
       "      <td>150</td>\n",
       "      <td>98</td>\n",
       "      <td>67</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>36</td>\n",
       "      <td>68</td>\n",
       "      <td>83</td>\n",
       "      <td>88</td>\n",
       "      <td>67</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>8</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>29</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>14</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>8</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>9</td>\n",
       "      <td>-2</td>\n",
       "      <td>15</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>33</td>\n",
       "      <td>16</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>57</td>\n",
       "      <td>67</td>\n",
       "      <td>59</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>14</td>\n",
       "      <td>29</td>\n",
       "      <td>44</td>\n",
       "      <td>77</td>\n",
       "      <td>145</td>\n",
       "      <td>99</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>45</td>\n",
       "      <td>106</td>\n",
       "      <td>170</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>14</td>\n",
       "      <td>-2</td>\n",
       "      <td>26</td>\n",
       "      <td>77</td>\n",
       "      <td>96</td>\n",
       "      <td>...</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>18</td>\n",
       "      <td>34</td>\n",
       "      <td>47</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>81</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>14</td>\n",
       "      <td>-2</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2   3    4    5    6    7    8    9   ...  26  27  28  29  30  31  \\\n",
       "0   -2  14  13  25   18   16   12   25   20   23  ...   0   0   0   0   0   0   \n",
       "1   -2  51  65  52   68   50   34   21   27   43  ...   0   0   0   0   0   0   \n",
       "2   -2  23  52  83  121   96   70   38   53   63  ...   0   0   0   0   0   0   \n",
       "3   -2  29  35  37  129  108   90   60   55   58  ...   0   0   0   0   2   0   \n",
       "4   -2   9  27  35   91  159  123   99   77   99  ...   0   0   0   0   0   0   \n",
       "5   -2  -2  -2  27   56  133  137  150   98   67  ...   0   0   0   0   0   0   \n",
       "6   -2  -2   8  10   26   36   68   83   88   67  ...   0   0   0   0   0   0   \n",
       "7   -2  -2  -2  -2    9   13   21   25   31   26  ...   0   0   0   0   0   0   \n",
       "8   -2  -2  -2  -2   19   18   -2   -2   15   16  ...   0   0   0   0   0   0   \n",
       "9    0  -2  -2   8   -2   -2   29   -2   -2   33  ...   0   0   0   0   0   0   \n",
       "10   1  -2  -2  -2   12   32   -2   -2   15   25  ...   0   0   0   0   0   0   \n",
       "11   0  -2  -2  -2   -2   -2   -2   -2   14   22  ...  -2   0   0   0   0   0   \n",
       "12   0   0  -2  -2   -2   -2   -2   -2   -2   -2  ...  -2   0   0   0   0   0   \n",
       "13   0   0  -2  -2    8   -2   -2   -2   -2   -2  ...  -2   0   0   1   0   0   \n",
       "14   0   0  -2  -2   -2    9   -2   15   -2   -2  ...  -2  -2   0   0   0   0   \n",
       "15   0   0  -2  -2    8   24   23   33   16   -2  ...  -2  -2  -2   0   0   0   \n",
       "16   0   0  -2  -2   11   12   57   67   59   23  ...  -2  -2  -2   0   0   0   \n",
       "17   0   0  -2  -2   14   29   44   77  145   99  ...   9  -2  -2   0   0   0   \n",
       "18   0   0  -2  -2   15   24   32   45  106  170  ...  12  -2  -2   0   0   0   \n",
       "19   0   0  -2  -2   -2   14   -2   26   77   96  ...  -2  -2  -2   0   0   0   \n",
       "20   0   0  -2  -2   -2   -2   18   34   47  100  ...  -2  -2  -2   0   0   0   \n",
       "21   0   0   0  -2   -2   -2   -2   11   27   81  ...   0   0   0   0   0   0   \n",
       "22   0   0   1  -2   -2   -2    9   13   20   27  ...   1   0   0   0   0   0   \n",
       "23   0   0   0  -2   -2   14   -2   15   12   25  ...   0   0   0   0   0   0   \n",
       "24   0   0   0  -2   -2   -2   -2   -2   -2   -2  ...   0   0   0   0   0   0   \n",
       "25   0   0   0  -2   -2   -2   -2   -2   -2   -2  ...   0   0   0   0   0   0   \n",
       "26   0   0   0   0    1    1    0    1   -2   -2  ...   0   0   0   0   0   0   \n",
       "27   0   0   0   1    1    0    0    0    0    1  ...   0   0   0   0   0   0   \n",
       "28   0   0   0   0    0    0    0    0    0    0  ...   0   0   0   0   0   0   \n",
       "29   0   1   0   0    0    1    0    0    0    0  ...   0   0   0   0   0   0   \n",
       "30   0   0   0   0    0    2    0    0    0    1  ...   1   0   0   0   0   0   \n",
       "31   0   0   3   0    0    5    0    0    0    0  ...   0   0   0   0   0   0   \n",
       "32   0   1   0   1    0    2    1    3    0    0  ...   0   0   0   0   0   0   \n",
       "33   0   1   7   0    0    1    0    1    0    0  ...   0   0   0   0   0   0   \n",
       "34   0   3   5   2    1    0    1    3    1    2  ...   0   0   0   0   0   0   \n",
       "35   0   5  17   7    2    6    3   16   11   18  ...   0   0   0   0   0   0   \n",
       "\n",
       "    32  33  34  35  \n",
       "0    0   0   0   0  \n",
       "1    0   2   1   0  \n",
       "2    0   0   0   1  \n",
       "3    1   0   0   0  \n",
       "4    1   1   0   2  \n",
       "5    0   0   0   0  \n",
       "6    0   0   0   1  \n",
       "7    0   0   0   0  \n",
       "8    0   0   0   0  \n",
       "9    0   4   0   1  \n",
       "10   0   1   0   0  \n",
       "11   1   1   0   1  \n",
       "12   0   0   0   0  \n",
       "13   0   0   0   0  \n",
       "14   0   0   0   0  \n",
       "15   0   0   0   0  \n",
       "16   0   0   0   0  \n",
       "17   0   0   0   0  \n",
       "18   0   0   0   0  \n",
       "19   0   0   0   0  \n",
       "20   0   0   0   0  \n",
       "21   0   0   0   0  \n",
       "22   0   0   0   0  \n",
       "23   0   0   0   0  \n",
       "24   0   0   0   0  \n",
       "25   0   0   0   0  \n",
       "26   0   0   0   0  \n",
       "27   0   0   0   0  \n",
       "28   0   0   0   0  \n",
       "29   0   0   0   0  \n",
       "30   0   0   0   4  \n",
       "31   0   0   0   0  \n",
       "32   0   0   0   0  \n",
       "33   0   0   0   0  \n",
       "34   2   0   0   0  \n",
       "35   0   0   0   0  \n",
       "\n",
       "[36 rows x 36 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f2d85a1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "GENEROUS_matric = np.array(GENEROUS_list).reshape(len(GENEROUS_list),1)\n",
    "GENEROUS_matric[GENEROUS_matric==-2] = 0\n",
    "GENEROUS = np.sum(GENEROUS_matric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bc04520d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9635991354794676, 10848, 6094, 0, 17582]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def proportion(total,core,allowed,generous):\n",
    "    prop = (core+allowed+generous) / total\n",
    "    return [prop,core,allowed,generous,total]\n",
    "proportion(TOTAL,CORE,ALLOWED,GENEROUS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4af2f683",
   "metadata": {},
   "source": [
    "#### heatmap for count_matric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "85407f2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_matric[count_matric>=100] = 100\n",
    "count_matric[(count_matric>=8) & (count_matric<100)] = 75\n",
    "count_matric[count_matric<8] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "aeaec946",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\system\\TEMP/ipykernel_22040/3444655610.py:25: MatplotlibDeprecationWarning: savefig() got unexpected keyword argument \"bbbox_inches\" which is no longer supported as of 3.3 and will become an error in 3.6\n",
      "  fig.savefig('D://CORE_plot.png',dpi=600,bbbox_inches='tight')\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 684x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set default font of the whole figure.\n",
    "font = {'family':'Arial', 'weight':'bold', 'size':10}\n",
    "matplotlib.rc('font',**font)\n",
    "# Generate class fig and ax.\n",
    "plt.style.use('seaborn-white')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9.5,8))\n",
    "\n",
    "sns.heatmap(count_matric,cmap=\"Blues\")\n",
    "\n",
    "ax.set_xlabel('phi_φ(degree)',fontname='Arial',size=15,fontweight='bold')\n",
    "ax.set_ylabel('psi_ψ(degree)',fontname='Arial',size=15,fontweight='bold')\n",
    "\n",
    "xlabel = [-180,-150,-120,-90,-60,-30,0,30,60,90,120,150,180]\n",
    "ylabel = [180,150,120,90,60,30,0,-30,-60,-90,-120,-150,-180]\n",
    "ax.set_xticks([0,3,6,9,12,15,18,21,23,25,28,32,35])\n",
    "ax.set_yticks([0,3,6,9,12,15,18,21,23,25,28,32,35])\n",
    "ax.set_xticklabels(xlabel)\n",
    "ax.set_yticklabels(ylabel)\n",
    "\n",
    "ax.set_xlabel('phi_φ(degree)',fontname='Arial',size=15,fontweight='bold')\n",
    "ax.set_ylabel('psi_ψ(degree)',fontname='Arial',size=15,fontweight='bold')\n",
    "fig.suptitle('Ramachandran plot',fontsize=20,y=0.93)\n",
    "\n",
    "fig.savefig('D://CORE_plot.png',dpi=600,bbbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c89bab0",
   "metadata": {},
   "source": [
    "#### heatmap for clean_matric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "caa0c430",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_matric = np.array(df_count)\n",
    "clean_matric[clean_matric>=100] = 100\n",
    "clean_matric[(clean_matric>=8) & (clean_matric<100)] = 75\n",
    "clean_matric[clean_matric == -2] = 30\n",
    "clean_matric[(clean_matric>=0) & (clean_matric<8)] = 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8d689e19",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\system\\TEMP/ipykernel_22040/2512538738.py:25: MatplotlibDeprecationWarning: savefig() got unexpected keyword argument \"bbbox_inches\" which is no longer supported as of 3.3 and will become an error in 3.6\n",
      "  fig.savefig('D://GENEROUS_plot.png',dpi=600,bbbox_inches='tight')\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhkAAAH8CAYAAAB8e70nAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABTq0lEQVR4nO3deVhU1f8H8PewDjLghqEigkuAiCAiuKDigkIuFfRLcUHNhMQNk/yGG2JaYkWmmeZWaWrydUHNNc0lC3MDNVGzZMk1ERUHZUDg/v7wYb6ioDPEvbPwfvXM88Dl3nM+d4T48Dnn3CMTBEEAERERUTUz0XUAREREZJyYZBAREZEomGQQERGRKJhkEBERkSiYZBAREZEomGQQERGRKJhkEP0LW7Zsgaura4WvNm3aoGvXrhg3bhxOnz6t61Cr3dWrV+Hq6oqxY8fqOpQKXbhwAa6uroiNjdV1KP9KdbzPt27dwubNm6sxKiLNmOk6ACJj4OfnBz8/v3LH7t+/j7Nnz2L//v04dOgQVq9ejfbt2+soQqqpcnNzERwcjI4dO+KNN97QdThUwzDJIKoGfn5+mDBhQoVfW7hwIZYsWYJPP/0UGzZskDgyqukKCgrw4MEDXYdBNRSHS4hEFhUVBXNzc6SlpaGgoEDX4RARSYZJBpHILCwsoFAoAABFRUXq448ePcLq1asxcOBA+Pj4wMPDAz169EBcXBzu3LlTrg1XV1dMnz4dx48fx5AhQ+Dl5YUuXbrgs88+Q0lJCf766y+8/fbb8Pb2RteuXTFnzpxnEpoHDx7gyy+/xGuvvQZvb2+0adMGffr0wccff4yHDx8+E/f+/fsRHh6O9u3bo0OHDhg5ciROnDhR4T0ePnwYAwcOhKenJzp16oSpU6c+cw8AcPDgQYwePRodO3ZE69at0bFjR4wdOxYXLlwod154eDh69uyJmzdvIiYmBh06dICXlxeGDh2KY8eOPdPuxYsXERUVBT8/P/j6+mLq1Km4d+/eM+fFxsbC1dUVZ8+eRd++fdGmTRuEhYWhbHcFseJ7Wtk8iwULFmD37t3o168fPD09ERQUhK+//hqlpaUvbEOpVOLjjz9GYGAgPDw80LlzZ8TExCAzM1N9zpYtW9CrVy8AwE8//QRXV1ds2bLlhW0TVRcmGUQiO3fuHO7evYvGjRujdu3a6uMxMTH46KOPYGZmhoEDB2LQoEGwsLBAUlISIiIinmnnzJkzGDVqFOrVq4fBgwfDwsICy5YtQ1xcHAYPHozS0lIMHjwYtWvXxtq1a7FgwQL1tcXFxXjrrbfwxRdfoEGDBhgyZAjeeOMNqFQqrFq16pnJkcuWLcO4ceNw+fJlBAUFoV+/fjh//jxGjhyJX375pdy5qampGDt2LOzs7DBs2DA0btwYW7ZswejRo/Hk1khr167FmDFjkJ2djf79+2P48OFo2bIlfvrpJwwdOhS3bt0q1+6DBw8wZMgQXLx4Ea+//joCAwORmpqKt99+G3///bf6vAsXLmDIkCE4cuQIunbtiv79++PXX3/FlClTKv03iYqKQtOmTREWFoYOHTpAJpOJFt/zHDlyBO+++y4cHR0RFhYGAJg/fz6mTZv23Ovu3r2LN998E6tWrUL9+vUxdOhQtG3bFrt27cL//d//4cyZMwCAVq1aYfjw4QCAZs2aYfz48WjVqpVGsRFVC4GIqmzz5s2Ci4uLsGjRonLHS0tLhby8POHQoUNCYGCg4OLiImzcuFH99bS0NMHFxUWIiYkpd92jR4+E/v37Cy4uLkJGRob6uIuLi+Di4iJ888036mOXL19WH09ISFAfVyqVQrt27YROnTqpj+3YsUNwcXERPvvss3L9KZVKoXPnzkKrVq2Ehw8fCoIgCBkZGYK7u7sQHBws3Lp1S31uVlaW0LZtW6F///6CIAjClStX1P1v375dfV5xcbEQEhIiuLi4COnp6YIgCEJhYaHQrl07oU+fPsKDBw/KxTBr1izBxcVF2LBhg/rYsGHDBBcXFyEqKkooKipSH1+6dKng4uIifP755+pjQ4cOFVq1aiWkpKSoj+Xm5gp9+/YVXFxchPfff199/P333xdcXFyE8ePHl4tBzPgq8uR7t3LlSvXxBw8eCAMHDhRcXFyE3377rdy5UVFR6vOmTp0quLi4CAsWLCjX7qFDhwRXV1ehT58+QnFxcaXXE0mFlQyiarB48eJyy1fd3Nzg6+uLyMhI3L17F7Gxsfi///s/9fkNGzZEQkICoqOjy7VjZmYGHx8fAI9XBTzJwsICQ4YMUX/evHlz1K1bFwAwatQo9XGFQoEWLVogNzcXKpUKAODu7o65c+di5MiR5dpUKBRwd3dHSUkJ8vLyAAB79uxBcXExxo4diwYNGqjPdXJywvvvv4833ngDjx49Uh93dHTEgAED1J+bmpqiW7duAIArV64AAEpKSjBnzhx8+OGHqFWrVrkYylblPH2/Zfdlbm6u/jwgIAAAkJWVBQD4559/cOLECXTt2hWdOnVSn1evXj2MGzfumfbKBAUFlftcrPhexMHBASNGjFB/XqtWLUyaNAkA8MMPP1R4TVFREXbu3AkHBwdMnDix3NcCAgLQp08fZGVl4eTJkxrFQCQmri4hqgZPLmHNz8/Hnj17cPPmTbz66quYM2cO5HJ5ufMbNmyIkJAQFBcXIz09HZmZmfj7779x4cIFpKSkAMAz4/KNGjWChYVFuWO1atXCw4cPyyUDAGBpaQng8S8kuVyOZs2aoVmzZigsLMSZM2fU/aWnp+P48eMAHv+iBR7PbwCAtm3bPnOfZSX9Jzk5OT1zrE6dOgCgnuthZWWFvn37AgAyMzNx+fJl/P333/jzzz9x9OjRCu8XAJydnct9/vTclrJYPTw8nrnW29v7mWNlHBwcyn0uVnwv4u3tDTOz8v8b9vT0BPC/e3taZmYmVCoV2rVrBxOTZ/9O9PHxwd69e3Hx4kV06NBBoziIxMIkg6gaPL2ENTo6GpGRkdi+fTtsbGwQFxf3zDUbNmzAl19+qR7rt7W1hZeXF1q0aIEzZ86Um88APP5FWJGnE4+KlJaWYtmyZfjmm2/UFYv69evD29sbDg4OuHz5srq/+/fvA/jfL8wXKUtoKvLkPZw4cQLz5s1Denq6+jo3Nze0bt0aN27ceOZ+K7o3mUxWrt2yWK2trZ+59sn5L097OukTK74Xsbe3f+aYtbU1rKysoFQqK7wmPz8fAGBjY1Ph11966SUAUFexiHSJSQaRCGrVqoXPP/8cr732GtatWwcXF5dyVYDdu3dj1qxZcHV1xaxZs9C6dWs0atQIADBr1iz1xL3q8vXXX+Pzzz+Hn58fIiIi0KpVK3X1Y/To0bh8+XK52IHHExvLhmPKqFQqWFhYVPgX9PNcu3YNERERsLS0xJw5c+Dj4wNnZ2eYmppi165d2L9/f5Xuy9bWFgAq/IVc0YoZqeN7kcLCwmeOFRUVQaVSPfPelylLqJ6eiFqmLPEqqyYR6RLnZBCJxM7ODvHx8QCAhIQEXL16Vf21HTt2AAASExMRGBioTjAAICMjA4Dmfw1rYseOHTA1NcXSpUvRrVs3dYIhCMIz/bm4uAAAzp49+0w7c+fOhZeXl3quhab279+PgoICTJw4EQMHDkSLFi1gamoKAOoEpyr36+7uDplMhtTU1Ge+du7cOZ3H9yK///77M8fKqlheXl4VXtO8eXNYWlri7NmzFQ7LlC0zbtmyJYD/VVeIdIFJBpGIevfujT59+qCgoECdcAD/G2K4fft2ufO3bt2qniNRXFxcbXFYWlqipKTkmWdXLFmyBNeuXSvXX//+/WFiYoKvvvoKd+/eVZ/7999/Y/fu3XB0dISjo6PW/QPP3u/FixexZs2acv1ro0GDBujatSt+++037N27V308Pz8fixcv1nl8L3LmzBns2rVL/Xl+fj4SExNhYmKCkJCQCq+xsLBAv379cOvWLSxatKjc137++Wfs3r0bTk5OaNeuHQCo53w8OVmXSCocLiES2YwZM5CSkoIjR45gx44d6N+/P1599VXs3LkT48ePR79+/aBQKPD777/j+PHjqF+/PnJzcyt8mFRVvfrqqzh9+jQGDx6MV155Bebm5jh27BjS09Of6a9FixYYP348Fi1ahNdeew09evSAIAjYtWsXCgsLMW/ePK3779GjBxITE7Fs2TJkZGSgadOmyM7OxsGDB9VzC6p6v3FxcQgLC8OkSZMQGBgIe3t7HDx4UKshHTHje57atWsjJiYGu3fvhr29PQ4dOoQrV65g7NixcHNzq/S6KVOmIDU1FStWrMCJEyfg7e2NK1eu4MCBA7C2tsYnn3yirmDUrVsXFhYWOHbsGObNm4fevXtzDx2SDCsZRCKzt7fHu+++CwD46KOPkJeXh+7du2PBggVo2rQpfvjhByQnJ6OwsBBxcXFYuXIlgMdP0awuQ4YMwcyZM1GnTh1s3LgRP/zwA6ytrfHZZ5/hgw8+eKa/cePGYcGCBWjUqBG2bduGH374AZ6enli7dm2lZfznsbe3xzfffIOOHTvit99+w/r165GZmYnw8HDs3r0bderUwZEjR6o0JOHo6IikpCT07dsXJ06cwObNm+Hu7o6lS5fqRXzP0759eyQmJuLSpUvYuHEjbG1t8fHHHz+ztPlp9erVw3//+1+MGjUKOTk5WLt2LX7//Xe8/vrr2LJlS7l/IwsLC8TFxaF27dpYv349fvvtt2q9B6LnkQliDDQSEVGlrl69il69eqFXr15YsmSJrsMhEg0rGURERCQKJhlEREQkCiYZREREJArOySAiIiJRsJJBREREomCSQURERKKQ7GFcJSUlyMjIgFKphI2NDZo3b65+bC8REREZH0mSjIMHDyI2Nhb3799XP8ymTp06mD9/PgICAqQIgYiIiCQmycTPnj17IiQkBD169ICVlRUKCgpw4MABbN26FQcOHBC7eyIiItIBSSoZKpUKDg4OcHR0hJWVFVQqFRwdHSvcQZCIiIiMgyQTP6OjoxEXF4eOHTvCy8sLHTp0wMyZM1/4fH4iIiKSxpo1a+Dq6orCwkJkZmYiNDQUPj4+iIuLQ2lpKQAgKSkJnTp1Qvfu3TUaiZDsORm5ubk4c+YMlEolFAoFPDw8YG9vL0XXREREVAmVSoWFCxfi22+/RWlpKc6ePYvo6Gg8evQIMTExGDhwIBITE+Hj44OAgADEx8fjxo0bWLduHX755ReYm5tX2rYkwyWFhYXYv38/zM3N0b9/f3zyySfYvXs3oqOj4ejoWOE1e9JzNG4/auXx6gqV9MjS0X66DqHKurs20HUIRCQSuUTrMq28x4vSbkHa4nKf5+TkIDMzE2PHjsXixY+/lpqaijFjxsDd3R3Ozs5IS0uDmZkZiouLERgYiKtXr+LLL79ERkYGXF1dK+1Lkrfq3XffxenTp2FmZoZvv/0Wjx49grm5OWJjY7Fu3TopQiAiIqIKODo64quvvsKWLVvUx5RKJeRyOQBALpdDqVRCqVQCAKysrGBlZaU+73kkSTKOHj2KHTt2oEGDBmjXrh02bNgAhUKB119/XYruiYiIDI9Md8/LVCgUKCwsBPB4OMXGxgYKhUL9eUFBAQDAxsbmue1IkmQ0adIEs2bNgo2NDczNzbFy5UpYWlpWOlRCREREuuPp6YmUlBT4+fkhOzsbEyZMQJs2bWBqaooDBw7g6tWrqFOnDpo3b/7cdiRJkz766CPI5XK4uLjg888/x/Hjx5GWlob4+HgpuiciIjI8Mpk4Lw3MmDEDd+7cQXh4OEJCQtC7d2/Y29tj1qxZSExMxJYtWzBv3rznTvoEJFpd8rznYVhYWFR4nBM/iRM/iUgfSTbx00ecxzwUnFooSrsVkeSt8vT0hKyS7OnChQtShEBERGRYdDgno7pIkmRMmzYNCQkJGDt2LBwcHKTokoiIyLBpOLShzyRJMoYPH44bN27g/PnzGD9enHW/REREpF8k2+p90qRJuHXrllTdERERGTYjGC6R7A64ZJWIiKhmkaySQURERFrgnAwiIiIShREMlzDJIL2l7fNPDPm5GkRExohJBhERkT4yguESw6/FEBERkV5iJYOIiEgfcU4GERERiYLDJUREREQVYyWDiIhIHxnBcInh3wERERHpJVYyiIiI9BHnZBARERFVjJUMIiIifWQEczKYZBAREekjJhlUkZuHdmt1fsPur4gUSc2iT3udHPojR7S29U131wa6DoGI9BSTDCIiIn1kwomfRERERBViJYOIiEgfcU4GERERiYLPySAiIiKqmGSVjJKSEmRkZECpVMLGxgbNmzeHqampVN0TEREZFg6XaObgwYOIjY3F/fv3IQgCAKBOnTqYP38+AgICpAiBiIiIJCZJkjFnzhwMGzYMPXr0gJWVFQoKCnDgwAHMnj0bBw4ckCIEIiIiw2IEczIkSTJUKhUcHBzg6OgIKysrqFQqODo6oqioSIruiYiIDA+HSzQTHR2NuLg4TJ8+XX3M1NQUs2bNkqJ7IiIi0gFJkoxBgwYhMDAQZ86cgVKphEKhgIeHB+zt7aXonoiIyPBwuEQz8fHxmDZtGlq1aoUPP/wQly5dQps2bTBjxgzUrVtXihD0mjZ7ndSkfU64BwwRkWGTZMAnKSkJRUVFmDZtGq5fv46goCBcuXIFU6dOlaJ7IiIiwyMzEeclIUmf+Hn16lUsXrwYrq6uuH79OgYMGCBl90RERIbDCIZLJElpBEFA7969kZubi3379kGlUuG9995D8+bNpeieiIiIdECSSsa+fftw7tw5pKeno3HjxrCwsICZmRk++OADKbonIiIyPFzCqhl7e3vY29ujV69eAIDi4mKsXLlSiq6JiIhIRyRJMjw9PSGrZGzpwoULUoRARERkWIxgToYkSca0adOQkJCAsWPHwsHBQYouiYiISMckSTKGDx+OGzdu4Pz58xg/frwUXRIRERk2zsnQ3KRJk3Dr1i2puiMiIjJsRpBkSHYHlpaWcHR0lKo7IiIi0jFJH8ZFREREGuLET6qItntoaLNHh6Hv56Ft/GK2HSVSHACwdLSfiK0DUSuPa3W+2PEQEVWESQYREZE+MoI5GUwyiIiI9JERDJcYfppEREREeomVDCIiIn1kBMMlhn8HREREpJdYySAiItJHRjAng0kGERGRHqpsY1FDwuESIiIiEgUrGURERHqIlQwiIiKiSrCSQUREpI8Mv5ABmSAIgq6DqMie9Bxdh6Cm7T4R2hJzPw9t6dteJ/qE+39UrLtrA12HQCQpuUR/nlu/+Y0o7T7Y+JYo7VaElQwiIiI9ZAxzMphkEBER6SFjSDI48ZOIiIhEwUoGERGRHmIlg4iIiKgSklUySkpKkJGRAaVSCRsbGzRv3hympqZSdU9ERGRQjKGSIUmScfDgQcTGxuL+/fsoWzFbp04dzJ8/HwEBAVKEQEREZFgMP8eQJsmYM2cOhg0bhh49esDKygoFBQU4cOAAZs+ejQMHDkgRAhEREUlMkiRDpVLBwcEBjo6OsLKygkqlgqOjI4qKiqTonoiIyOBwuERD0dHRiIuLw/Tp09XHTE1NERcXJ0X3REREpAOSJBmDBg1CYGAgzpw5A6VSCYVCAQ8PD9jb20vRPRERkcFhJUNDd+/exapVq1BaWoqwsDA4OztDpVIhJiYGiYmJUoRQjth7kWiL+4VUD+4tQkTGxBiSDEmek/Hee+9h27Zt2Lt3L1577TUcPHgQjx49wq5du6TonoiIiHRAkiQjNTUV69evx759+9C9e3dER0fjyJEjUnRNRERkkGQymSgvKUmSZDRu3BirVq1CQUEBEhMT0aVLF8TExEjRNREREemIJElGXFwcfvvtNzx48ABmZmZYuHAhBg8eDBMTPtWciIioQjKRXhKS5Le8t7c3duzYgXr16qGoqAiCICA2NhYHDx6UonsiIiLSAUlWl3h6elY4DiSTyXD+/HkpQiAiIjIoxrC6RJIkY9q0aUhISMDYsWPh4OAgRZdEREQGjUmGhoYPH44bN27g/PnzGD9+vBRdEhERkY5JttX7pEmTcOvWLam6IyIiMmjGUMmQbHmHpaUlHB0dpeqOiIiIdIxrSImIiPSRREtYi4qKEB0dDW9vbwQFBeHUqVPIzMxEaGgofHx8EBcXh9LS0irdgmTDJfpE7D0u9G1vFENVk/Yi6e7aQNchEJGekWq45Oeff8b+/fuRnJyM1atXY/78+ahXrx7q1q2LuXPnYuDAgfD390dQUJDWbbOSQUREVIO1bNkStWrVwksvvQRbW1uYm5sjNTUV/v7+cHd3h7OzM9LS0qrUdo2sZBAREek7qSoZtra2aNq0Kbp06YLS0lKsXr0aw4cPh1wuBwDI5XIolcoqtc0kg4iIqAZbtWoVsrOzsX79emzbtg3R0dFQKBQoLCwEAKhUKtjY2FSpbQ6XEBER6SGpdmG1traGhYUFLC0toVAokJeXh9atWyMlJQXp6enIzs6Gt7d3le6BlQwiIiI9JNVwyciRI3Hx4kWEhYXB1tYWc+bMgbe3N9577z2Eh4cjJCQEvXv3rlLbTDKIiIhqsFq1amHRokXPHN+8efO/bptJBhERkT4y/Ad+ck4GERERiYOVDCIiIj3EvUuIiIiIKsFKBhERkR4yhkoGkwwDY+j7rhjyfiTcX4SIpGQMSQaHS4iIiEgUrGQQERHpI8MvZLCSQUREROJgJYOIiEgPGcOcDCYZREREesgYkgwOlxAREZEoWMkgIiLSQ6xkEBEREVVCskrGvn37kJqaivz8fCgUCvj4+CAwMFCq7omIiAyKMVQyJEky4uLikJycDGdnZ8jlcqhUKqxbtw6hoaGIj4+XIgQiIiLDYvg5hjRJxq5du7Bx40a4ubmpj6Wnp2PEiBFMMoiIiIyUTBAEQexOQkNDUb9+ffj7+6srGUePHkVubi42bdpU4TWqYs3bP/RHTjVFWjFt9/MQc38OfYoFEHevE7Fj514kuiH2z6s+4feYcZJLNNGg+eRdorSb8VlfUdqtiCRv1WeffYaEhAQsXbpUPSfD09MTn376qRTdExERkQ5IkmQIgoCcnBzk5eUBAO7fv4/8/HyYmppK0T0REZHBMYaJn5IsYY2NjUWDBg0wd+5cDB06FE5OTvDw8EBsbKwU3RMREZEOSFLJuHz5MpKSktCiRQvcv38fSUlJeP311yudj0FERFTTGUEhQ5oko3fv3hg0aBCcnJxw9epVuLm54ddff4WHh4cU3RMRERkcYxgukSTJmDt3Lry9vXHhwgX07dsXgwYNQk5ODsLCwqTonoiIiHRAkiTD1NQUAwcOLHdMoVBI0TUREZFBMoJCBvcuISIiInFwF1YiIiI9xDkZREREJAojyDE4XEJERETiYCVDA9ruoWHI+3mIGTsZp5q0FwmRlExMDL+UwUoGERERiYKVDCIiIj1kDHMymGQQERHpIWNYXcLhEiIiIhIFKxlERER6yAgKGaxkEBERkThYySAiItJDnJNBREREVAlWMoiIiPSQMVQymGQQERHpISPIMThcQkREROJgJUMEYu8vQkSV02b/HbF/VrXd16W7awORIiFDZAzDJaxkEBERkShYySAiItJDRlDIYJJBRESkjzhcQkRERFQJVjKIiIj0kBEUMljJICIiInGwkkFERKSHjGFOBpMMIiIiPWQEOQaHS4iIiEgcklUy9u3bh9TUVOTn50OhUMDHxweBgYFSdU9ERGRQOFyiobi4OCQnJ8PZ2RlyuRwqlQrr1q1DaGgo4uPjpQiBiIiIJCZJkrFr1y5s3LgRbm5u6mPp6ekYMWJEtSQZ2j7vX9v9BGqSmrTvCveVMAza7EVCZEyMoJAhTZLRtGlTJCYmwt/fX13JOHr0KJydnaXonoiIiHRAkiTjs88+Q0JCApYuXYr8/HyUlpbC398fiYmJUnRPRERkcIxhToYkq0suXrwIX19fLF68GPXq1YMgCLh48SJu3rwpRfdEREQGRyYT5yUlSZKMjz76CDY2Nvjggw/QrVs3LFmyBAMGDEBcXJwU3RMREZEOSJJklJSUoG3btlAoFOjfvz969uyJnj174t69e1J0T0REZHBkMpkoLylJMidj+PDhGDRoEOrUqYOIiAg0aNAAt27dwujRo6XonoiIiHRAkiTjnXfegbu7O44ePYr79+9DoVDA19cXvXr1kqJ7IiIig2ME8z6le+Jn165d0bVrV6m6IyIiMmhcXUJERERUCe7CSkREpIdYySAiIiKqBCsZItB2r4WatF+IIdNmrxPuc2IY+LNK+swIChlMMoiIiPQRh0uIiIiIKsFKBhERkR4ygkIGKxlEREQkDlYyiIiI9JAxzMlgkkFERKSHjCDH4HAJERERiYOVDCIiIj1kYgSlDFYyiIiISBSsZBAREekhIyhksJJBRERE4mAlg4iISA/VyCWs169fR25uLkxNTWFnZ4eXXnpJjLgMmrabKGmzSZOYbVelfTEZcuxUfcT+ntcn2mzCB3AjPmNnYvg5hmZJxtGjR7Fx40akpKQgLy+v3Nfs7OzQpUsXvPHGG2jfvr0oQRIREZF4li1bhtWrV8PExATTpk1Dq1atEBMTg+zsbPTr1w/x8fEwMdF+hsVzk4yzZ89i9uzZOH/+PARBgLm5OZo1awYbGxsAwL1793Dt2jUkJydj69at8PLywrRp0+Dp6Vm1uyQiIiIA0g2XHDlyBF999RW+//57nDlzBteuXcP27dtRt25dzJ07FwMHDoS/vz+CgoK0bvu5ScagQYPQuHFjjBkzBj179oS7uztMTU3LnfPo0SP88ccfOHLkCHbt2oVBgwbhwoULWgdCRERE0ktJSUHt2rURHx8PpVKJWbNmYcWKFRgzZgzc3d3h7OyMtLS06k8yli5dioCAgOdmU+bm5vDw8ICHhweioqLw22+/aR0EERERlSfVvM/79+8jJycHH330EXbs2IHJkydDqVRCLpcDAORyOZRKZZXafm6S0b1792eO3b17F+np6ZDL5XBzc4NCoSj39Y4dO1YpECIiIvofGaTJMhQKBezs7NC5c2cUFBRg8+bNAIDCwkIAgEqlUk+T0JbGsziKi4sRHx+Prl27IiIiAt9++y02bdqEQYMG4d69e1XqnIiIiHTL19cXt27dwvHjx3Hy5En1go6UlBSkp6cjOzsb3t7eVWpb4yRj0aJF2LBhAxo1agRBEAAAV69exZkzZzBv3rwqdU5EREQVM5GJ83paYGAgIiIiMGHCBOzfvx+ffvopZsyYgTt37iA8PBwhISHo3bt3le5B4+dkbN26FU2bNsXOnTvRpk0bAEBsbCx+/vln/Pzzz1XqnIiIiHRv8uTJmDx5crljZcMm/4bGScbdu3fRvn17mJub/+9iMzPY2dnh1q1bL7x+3759SE1NRX5+PhQKBXx8fBAYGFi1qImIiIxcjXrip4uLC06cOIH169cDeJx0LF++HKmpqfDy8nrutXFxcUhOToazszPkcjlUKhXWrVuH0NBQxMfH/6sbICIiMkZGkGNonmRMmTIFERERmDNnDgAgNTUVqampMDU1xYQJE5577a5du7Bx40a4ubmpj6Wnp2PEiBFMMoiIiIyUxklGx44dsWPHDnz11Ve4dOkSiouL8fLLL2PkyJHw8PB47rVNmzZFYmIi/P391ZWMo0ePwtnZ+d/Gr5fE3DtB7H0ZDHm/EEOOnagqtNnrhPucGB4TIyhlaLVBmpOTU5VWknz22WdISEjA0qVLoVQqYWNjA09PTyQmJmrdFhERERkGrXY7SUtLw/jx49GlSxdER0dj//796jkaz+3ExAQWFhbo1q0btm7dCltbW1y4cAH//PNPlQMnIiIyZjKZOC8paVzJOHLkCKKiolBcXAyZTIaSkhKcPHkSq1evhiAIGDp0aKXXTpkyBUVFRTAzM8PgwYPRq1cvmJub44MPPsCOHTuq5UaIiIhIv2hcyViwYAHMzc3xzTffqB/GFRwcDCsrK3z33XfPvfby5ctYsGAB1q1bB0dHR4wdOxaRkZG4du3av4ueiIjISMlkMlFeUtI4yfjrr7/Qrl07dOrUSX2sbdu28PT0xPXr1597bbdu3TBq1CgUFBRg69atcHZ2Rnx8fJUfU0pERGTsatRwSd26dXHp0qVy+5Rcv34d58+fh52d3XOv/fDDD7Ft2zbUrl1bfSwgIAD9+/fXPmIiIiIyCBpXMt58803k5OSge/fukMlk+PXXXxEUFASlUolXX331uddaWVkhLCys3LGRI0e+MDkhIiKqqUxkMlFeUtK4kjFu3DgAwMqVKyEIAgoKCmBhYYHw8PAXPoyLiIiIah6Nk4yjR49i1KhRiIyMRHZ2NoqLi9G0aVNYW1uLGR8REVGNZPiP4tIiyZg0aRIaNmyI7du34+WXXxYzJiIiohrPGDZI03hOhp2dHUxMtHp2FxEREdVgGlcygoKCsGzZMgwaNAjt2rWDQqGAqamp+utjxowRJUDSb/q0Xwj3ItENbffE0Ga/jarQ5vtA2+9fffp+J+NnYviFDM2TjKVLlwIAzpw5g7Nnz6qPC4IAmUzGJIOIiIjK0TjJeP31141ifIiIiMgQGMPvXI2TjISEBDHjICIioicYQY6heZKxdevWSr9mYWEBOzs7tG3bFhYWFtURFxERERk4jZOM2NjYF5Zu7OzssGLFCri5uf3rwIiIiGoyYxgu0XhN6ltvvQVra2tYW1ujR48e6NmzJ6ytrSGXyxEcHAwvLy/k5OQgMTFRzHiJiIjIQGhcyQAAc3NzbN++HQ0aPF6ydvPmTbz22mto0aIFFixYgAEDBuD06dNixElERFSjGMMSVo0rGZs3b4abm5s6wQCAhg0bolWrVli3bh0AoHHjxigoKKj+KImIiMjgaJxkyGQynD59GqdOnVIfO3nyJM6cOYOSkhL8+eefSEtLg62trSiBEhER1SQymUyUl5Q0Hi559dVX8d1332HYsGGoV68eBEHA3bt3ATx+hsZvv/2G+/fvIygoSLRgiYiIagojGC3RvJLxn//8ByNHjoSZmRlyc3Nx584dWFhYYOjQoZg6dSru3r0LX19fTJs2Tcx4iYiIyEDIBEEQtLlApVLh77//RklJiahbvauKRWkWgPh7J2i7v4E+uXlot1bnN+z+ikiRaE+f9onQdj+PmkTsnz8xif2zLeb3ML8nq49cqyUTVTc66Zwo7a4c5CFKuxXR+q3KysrCmTNnULduXdSvXx9mZmawtLQUIzYiIiIyYBonGQ8ePMCkSZPwyy+/AAB69eqFrKwsbNiwAWvWrEGTJk1EC5KIiKimMYJncWk+J+Pjjz/GkSNH0K5dO5SNsDx48ADXr1/nviZERETVzBhWl2icZOzbtw8uLi7qZ2IAwLvvvouXX34Zx48b7hwEIiIiEofGScaDBw8qfAaGhYUFHj16VK1BERER1XQymTgvKWmcZHh5eeHUqVP45JNPAADXr19HXFwc0tPT4eXlJVqAREREZJg0TjKmTZsGW1tbrFq1CgBw4cIF/Pe//4WVlRViYmJEC5CIiKgmMpHJRHlJSePVJW5ubvjxxx/x3Xff4dKlSyguLsbLL7+MIUOGwN7eXswYiYiIahxjWF2i1XMyateujfHjx4sVCxERERmR5yYZw4cP16gRmUyG1atXV0tAREREBMmXm4rhuUnG00tTZTIZKnoK+YveiOzsbCQkJCAtLQ35+flQKBTw9vZGbGwsnJycqhA2ERER6bvnJhkff/yx+uObN29i4cKF6NChA3r16gWZTIa9e/fi3LlzmDdv3nM7iYmJQZ06dRAZGQm5XI6CggKkpKTgvffew8aNG6vnTkgntN3rRBva7oui7b4S+rTXSU2i7R4a+rTXibbfM4a8jxHpnsYrM/TYc5OMV199Vf3x22+/jWbNmuHrr79WHxs0aBBeffVVbNq0CX369Km0naysLKxZswbu7u7qY/7+/ggPD/83sRMREZEe03ji54kTJ+Di4lLumKmpKSwtLXHs2LHnXtu3b18MHjwYTk5OsLKygkqlQlZWFkJDQ6sWNRERkZEz+jkZT2rYsCHS09MRHx+PwMBACIKAvXv34vz582jWrNlzr509ezb8/f2RlpYGpVIJhUKBd955B8HBwf/6BoiIiIyRieHnGJonGePHj8d//vMfJCUlISkpCQAgCAJMTU0xZcqU514rk8kQFBSEoKCgfxctERERGQyNk4xXX30Vjo6OWL16NbKzsyGTydCyZUu8/fbbcHV1FTNGIiKiGqdGVTIAwNvbG97e3mLFQkREREbkuStkQkNDkZycjIKCghc2VFRUhD179mDYsGHVFhwREVFNJZPJRHlJ6bmVjAYNGmDq1KmYPXs2OnbsCC8vLzg7O8PW1hYlJSXIzc3FzZs3cfLkSaSmpkKlUqFbt25SxU5ERGS0jH64ZNmyZThw4ACWLVuGQ4cO4dChQ89kQWVPAPXz88Pbb7+NgIAA8aIlIiIig/HCORk9e/ZEz549kZmZicOHD+PSpUvIzc0FANjZ2cHDwwNdunSBo6Oj6MESERHVFEbwmAzNJ342a9bshc/DICIiIiqjcZIxc+ZM9ccymQwWFhZo3Lgx+vTpgyZNmogSnKESc38DsfdO0Ha/EG1ps9eJtvuiiB27NvRpvw3SHe6PQ/+GiRGUMjROMjZu3PjMLqwymQyLFi3CqlWr4OPjI0qARERENZHRb5D2pHHjxpWb9FlUVIRTp07h1KlTmDt3LpKTk0UJkIiIiAyTxknGhAkTKjw+dOhQpKamIi8vD7Vr1662wIiIiGoyIxgt0TzJqGzfkXv37gEAQkJCYG5uDgDYu3fvv4+MiIiIDJrGSUZ2dnalX5PJZLh+/br6YyIiIvp3atTEzzVr1lR4fMmSJTh27Bg+++wz2NnZVVtgREREZNg0TjKcnJzKff7o0SOcOnUKZ8+ehUKhwCuvvMIqBhERUTUxhl+pGicZ3bt3r/C4IAgYM2YMEwwiIqJqZPR7lzzpyedjAICFhQUaNWqE/v37Y/z48dUeGBERERk2jZOMixcvvvCcO3fuQKVSoXHjxv8qKCIioprOGCZ+VusDxWbOnInAwMDqbJKIiIgMlMaVDE09PaxCL6bN/gba7kUiNm33FyESW3fXBhqfyz1mSJ8ZQSGj+pMMIiIi+veMYeKnMey/QkRERHqIlQwiIiI9JIPhlzJYySAiIiJRsJJBRESkh4xhTka1JhlcWUJERFQ9mGQ8Zfjw4ejdu3d1NklEREQG6rlJxqhRo9CuXTuMHz8eo0aNqvQ8mUyGVatWoWPHjhV+PTs7GwkJCUhLS0N+fj4UCgW8vb0RGxv7zMZrREREBKPYE+y5SUZKSgpq1aql/rgyL3ojYmJiUKdOHURGRkIul6OgoAApKSl47733sHHjxiqETURERPruuUnGvHnz0KhRI/XHVZWVlYU1a9bA3d1dfczf3x/h4eFVbpOIiMiYGf2cjJCQkAo/1lbfvn0xePBgODk5wcrKCiqVCllZWQgNDa1ym0RERKTfZIIWS0L++usv2NjYwN7eHklJSTh8+DD8/f0xdOjQ514nCAJ+/PFHpKWlQalUQqFQwMvLC8HBwTAxqfhRHapi7W5ETIa8vwH3Oqlcw+6vaHW+NnvMiE3bf1dtYxfz+0bs91GbvUuIqkIu0cMfPvs5Q5R2J3drLkq7FdH4rTp48CAmTJiAuXPnwtHREbNmzVIfLykpwfDhwyu9ViaToXPnzvD394dCocDJkydRUFBQaYJBRERU09Word4XL14MQRBgZWWFHTt2wNTUFDNmzICVlRX++9//PvfaTZs2oVOnTujatSsWL16MYcOGITIyEgsWLPjXN0BERET6SeMkIzMzE+3bt0dQUBB+++03uLq6YtiwYfD29sa1a9eee+3nn3+OqKgoREdHY/HixVi4cCG++uorJCUl/esbICIiMkYmMnFekt6DpieamppCEAT8888/6oQDAO7evQu5XP7ca83MzNCsWTMMHz4cU6dOha+vL4qL9WjSBRERUQ139uxZeHh4YMuWLcjMzERoaCh8fHwQFxeH0tLSKrWpcZLRsmVLpKamYtSoUZDJZOjevTs+/vhjnD9/Hl5eXs+9dty4cZg6dSoePnyIESNGIDMzEx9//DGGDRtWpaCJiIiMnUwmzqsieXl5ePfdd/Ho0SMAwPz581G3bl1899132LJlC/bt21ele9A4yZg0aRLMzMxw+fJlBAQEoFOnTrh79y5sbGwwadKk51775ptv4tdff4VCoQAAREVFYeXKlRg/fnyVgiYiIjJ2JpCJ8qpIbGwsgoKC1J+npqbC398f7u7ucHZ2RlpaWpXuQePVJR06dMChQ4dw69YtuLi4AAAGDRqECRMmoHHjxi+8vizBAB4vaTU1Na1CuERERFSdvv76a+Tm5mLRokVYtWoVAECpVKqnQsjlciiVyiq1rdVq31q1auHcuXPYtm0bTE1N0aJFC7Ru3VrrTiMiImBra6v1dURERDWFVCtY165di9zcXHTq1AkAMHv2bJSWlqKwsBAAoFKpYGNjU6W2NU4y/vrrL0RERODmzZsQBEG9X8nixYuxfPlyNGvWTONOIyMjtY+UiIiIqt369evVizF69eqFiRMnIiUlBSkpKfDz80N2djYmTJhQpbY1npMRHx+PGzduwNnZGSNHjsTw4cPRokULXLlyBfHx8VXqnIiIiCom1RLWhg0bokmTJmjSpAkAoG7dupgxYwbu3LmD8PBwhISEoHfv3lW6B40rGWfPnoWDgwO2bt0KS0tLAEBRURH69etX5QkhREREVDFdPPHzjz/+UH+8efPmf92exkmGk5MTbG1t1QkGAFhYWKB+/fqwsrL614GQZvRtLxIyDGLudcLvSSKqjMZJxpw5czBq1CgsW7YMffr0waNHj7Bjxw6cO3cOn3zyCf755x/1ufb29qIES0REVFMYwdYlmicZI0aMQHFxMT7//HN8/vnn5b42efJk9ccymQznz5+vtgCJiIjIMGmcZJQtZXkRLXaOJyIiokoYwy6sGicZFy9eFDMOIiIiMjJaPYyLiIiIpGEEhQwmGURERPpI4wdZ6TFjuAciIiLSQ5JUMrKzs5GQkIC0tDTk5+dDoVDA29sbsbGxcHJykiIEIiIigyIzgvESSZKMmJgY1KlTB5GRkZDL5SgoKEBKSgree+89bNy4UYoQiIiISGKSJBlZWVlYs2YN3N3d1cf8/f0RHh4uRfdEREQGx/DrGBIlGX379sXgwYPh5OQEKysrqFQqZGVlITQ0VIruiYiIDI4xPCdDJkjw9CxBEPDjjz8iLS0NSqUSCoUCXl5eCA4OholJxXNPVcViR6W5Q3/kiNq+Nns/aLOnhLZt65ubh3aL2n7D7q9odb627z3pRnfXBroOgYycXKJ1mWtPXRWl3WE+TURptyKSLWFt27YtgoKC8Msvv+DSpUuwsbGpNMEgIiKq6Qy/jiFRkjFt2jRYW1vD2toay5YtQ+3atZGfn48RI0bgP//5jxQhEBERkcQkKSXs3bsXr7/+On744Qd89dVXOHbsGL7//nts2rRJiu6JiIgMjkwmzktKklQymjZtipSUFLi4uCAlJQVyuRz79u2DnZ2dFN0TEREZHD4nQ0MffPABJk6ciJs3bwIAvvvuO9SrVw8LFy6UonsiIiLSAUmSDE9PTxw4cAAZGRm4d+8ewsPD8f3336Np06ZSdE9ERGRwjGFphGSrS0xMTNCyZUsAgEKhgJkZ92YjIiIyZjr5TR8REQFbW1tddE1ERGQQOCejiiIjI3XRLREREUmIYxZERER6yPDrGEwyiIiI9BKHS6haiLknhr7tt6HNXirJa2eJ1jagf+8NEZGxYZJBRESkh4xhCasx3AMRERHpIVYyiIiI9BDnZBAREZEoDD/F4HAJERERiYSVDCIiIj1kBKMlrGQQERGROCSpZGRnZyMhIQFpaWnIz8+HQqGAt7c3YmNj4eTkJEUIREREBsXECGZlSJJkxMTEoE6dOoiMjIRcLkdBQQFSUlLw3nvvYePGjVKEQEREZFCMYbhEkiQjKysLa9asgbu7u/qYv78/wsPDpeieiIiIdECSJKNv374YPHgwnJycYGVlBZVKhaysLISGhkrRPRERkcGRGcFwiUwQBEHsTgRBwI8//oi0tDQolUooFAp4eXkhODgYJiYVzz1VFYsdleYO/ZGj6xDIyHV3baDrEIhIQ3KJ1mXuPHdLlHb7ebwkSrsVkeStkslkaN68OW7fvg2lUonatWujVatWlSYYRERENR3nZGhow4YNmD17NsqKJpaWligpKcHcuXPx+uuvSxECERGRQTGG1SWSlBKWLFmChIQEpKenY9OmTXjppZfwzTffYOHChVJ0T0RERDogSSXDxMQE9+7dQ0ZGBv766y9cu3YNubm5UCqVUnRPRERkcDhcoqHY2FhMnToV8+bNg5mZGcaNG4e///4bo0ePlqJ7IiIi0gFJkozg4GD4+/sjMzMTjRo1Qr9+/ZCcnAwHBwcpuiciIjI4rGRowcbGBp6engAeL2mVGcO7R0RERJXSyS6sERERsLW11UXXREREBsEYHsalkyQjMjJSF90SEREZDBPDzzG41TsRERGJQyeVDCIiIno+YxguYSWDiIiIRMFKhgZq0uZV3AyuetSk7xkiEocxLMJkkkFERKSHOFxCREREVAlWMoiIiPQQl7ASERERVYKVDCIiIj1kDHMymGQQERHpIWNYXcLhEiIiIhIFKxlERER6yAgKGaxkEBERkThYySAiItJDJkYwKYOVDCIiIhIFKxlUDvfcICLSD4Zfx2CSQUREpJ+MIMvgcAkRERGJgpUMIiIiPcQnfmph3759SE1NRX5+PhQKBXx8fBAYGChV90RERCQxSZKMuLg4JCcnw9nZGXK5HCqVCuvWrUNoaCji4+OlCIGIiMigGMEKVsgEQRDE7qR9+/ZYu3Yt3Nzc1MfS09MxYsQInDx5ssJrVMViR0VERKQ9uURjACcy8kRp17d5bVHarYgkb5WTkxMSExPh7++vrmQcPXoUzs7OUnRPREREOiBJJSMrKwsJCQlIS0uDUqmEQqGAp6cnZsyYUWmiwUoGERHpI8kqGZkiVTKaSVfJkCTJqExRUREsLCwq/BqTDCIi0kdMMjQnyXMyDh8+jM6dO6NDhw748ssvIQgClEolvLy8pOieiIjI4MhE+k9KkuRjc+fORUBAAOrXr4+lS5ciOzsb06dPhw6LKERERHrNGFaXSJJk3L59G2PHjoWjoyM8PDwQExODgoICyIzhHSQiIqIKSZJkdOzYEUOHDkVSUhKCg4Px6NEjTJ06VYquiYiIDJIx/BkuSZLx6aefYs2aNbCxsQEADBgwAI6OjlixYoUU3RMREZEO6HR1yfNwdQkREekjqVaXpGbfF6Xddk62orRbEUneqqSkpAqPy2QyDBw4UIoQiIiISGKSVDJ8fX2Rn5//zGoSmUyGCxcuVHgNKxlERKSPpKpkpGUrRWnX28lGlHYrIslzMrZv346XXnoJixYtwsWLF9WvyhIMIiKimk4mE+f1tPz8fERFRcHb2xuBgYE4dOgQMjMzERoaCh8fH8TFxaG0tLRK9yBJktGoUSPMnDkTBw8eBACkpqaiqKhIiq6JiIjoOdasWYPff/8dycnJ8PX1RWxsLObPn4+6deviu+++w5YtW7Bv374qtS1JkgEAgYGBmDdvHgBgzJgxuH37tlRdExERGRyZSK+nhYeHIykpCc7Ozqhfvz5KSkqQmpoKf39/uLu7w9nZGWlpaVW6B8mSjCfp6YIWIiKiGsfGxgYODg7Ys2cPvvnmGwwfPhxKpRJyuRwAIJfLoVRWbX6ITpKMiIgI2NpKt4SGiIjI4EhVygDwww8/YPLkyQgODsbYsWOhUChQWFgIAFCpVOrnXGlLJ0lGZGQkFAqFLromIiIyCFJtkHb69GnExsaiR48emDlzJh48eABPT0+kpKQgPT0d2dnZ8Pb2rtI96CTJICIiIv2wfPlyFBcXY//+/ejQoQN8fX0xZcoU3LlzB+Hh4QgJCUHv3r2r1Daf+ElERKQFqZ6T8fvVfFHabdNEupEEVjKIiIhIFBLlY0RERKQN7sJKRERE4jCCLIPDJURERCQKVjKIiIj0UEXLTQ0NKxlEREQkClYyiIiI9FBFO6YaGlYyiIiISBSsZBAREekhIyhkMMkgIiLSS0aQZXC4hIiIiETBSgYREZEe4hJWIiIiokqwkkFERKSHjGEJK5MMIiIiPWQEOQaHS4iIiEgcrGQQERHpIyMoZbCSQURERKJgJYOIiEgPGcMSViYZREREesgYVpdwuISIiIhEwUoGERGRHjKCQgYrGURERCQOVjKIiIj0kRGUMljJICIiIlGwkkFERKSHuISViIiIRGEMS1glSzL27duH1NRU5OfnQ6FQwMfHB4GBgVJ1T0RERBKTCYIgiN1JXFwckpOT4ezsDLlcDpVKhezsbISGhiI+Pr7Ca1TFYkdFRESkPblEf55n3VaJ0q6znVyUdisiSZLRvn17rF27Fm5ubupj6enpGDFiBE6ePFnhNUwyiIhIHzHJ0Jwkb5WTkxMSExPh7++vrmQcPXoUzs7OUnRPRERkeIxgToYklYysrCwkJCQgLS0NSqUSCoUCnp6emDFjRqWJBisZRESkj6SqZGTnForSrlN9S1HarYgkScaTcnNzkZWVhZYtW6J27dqVnsckg4iI9BGTDM1J8jAuLy8v5OfnY8+ePejZsyeGDRuG7t2748iRI1J0T0REZHBkMnFeUpIkySgqKgIALFiwAG+//TZ27tyJqKgofPjhh1J0T0RERDogSZIhCAJ++eUXyGQydOvWDc2bN0eHDh1w+/ZtKbonIiIyODKRXlKSZGTJx8cH06ZNw8OHD7Flyxa8/PLLGDJkCN544w0puiciIjI4xvDET8kmfgqCgIyMDABAixYtsHXrVgwYMACmpqYVns+Jn0REpI+kmvh59a44Ez+b1DXi1SWaYpJBRET6SLoko0iUdpvUtRCl3YpI8lYlJSVV+rVBgwZJEQIRERFJTJJKhq+vL/Lz8/F0VzKZDBcuXKjwGlYyiIhIH0lVybh2T5xKhkMd6SoZkiQZN27cQFhYGKZPn44+ffpodA2TDCIi0kdMMjQnyRLWRo0aYebMmTh48CAAIDU1Vf3sDCIiInqWMSxh1cnETz8/P2zduhWNGzeu9BxWMoiISB9JVcm4kSfOH+ONahtZJeNperqghYiIiKqRRPlYeREREbC1tdVF10RERAZBZgR7vfM5GURERFqQarjkZt4jUdptWNtclHYropNKBhEREb2A4RcymGQQERHpIyPIMXQz8ZOIiIiMHysZREREesgYdmFlJYOIiIhEwUoGERGRHjKGJaxMMoiIiPSR4ecYHC4hIiIicbCSQUREpIeMoJDBSgYRERGJg5UMIiIiPcQlrERERESVYCWDiIhID3EJKxEREYmCwyVERERElWCSQURERKKQbLhk3759SE1NRX5+PhQKBXx8fBAYGChV90RERCQxmSAIgtidxMXFITk5Gc7OzpDL5VCpVMjOzkZoaCji4+MrvEZVLHZURERE2pNL9Of5vYISUdqtY2UqSrsVkSTJaN++PdauXQs3Nzf1sfT0dIwYMQInT56s8BomGUREpI+kSjLyCkpFabe2lXQzJSR5q5ycnJCYmAh/f391JePo0aNwdnaWonsiIiLSAUkqGVlZWUhISEBaWhqUSiUUCgU8PT0xY8aMShMNVjKIiEgfSVXJuK8Sp5JhK5eukiFJkvG0oqIilJSUwMrKqtJzmGQQEZE+YpKhOUl6SktLQ1hYGMLDw3Hy5En06NEDvr6+2Lp1qxTdExERGRyZSC8pSVLJeOWVV+Dj4wMbGxt89913CAsLg7W1NbZv346DBw9WeI2YlYxWU3Zqdf6FT/qJFIn4tL1XfWLI7zsRGS+pKhnKQnEqGTaWRlbJuHnzJt566y2MGTMGABASEoJ+/fohLy9Piu6JiIhIByTJx7p164bXXnsN1tbWcHNzw1tvvQULCwt07dpViu6JiIgMDjdI09Cnn36Kn3/+GS1btoStrS1WrlwJhUKBkSNHStE9ERER6YAkSYa5uTl69eql/nzKlClSdEtERGSwjGEXVkmSjKSkpEq/NmjQIClCICIiIolJNlySn5+PpxeyyGQyJhlEREQVMIJChjRJxvbt2xEWFobp06ejT58+UnRJRERk2Iwgy5BkCWujRo0wc+ZM9TMxUlNTUVRUJEXXREREpCOSPZEjMDAQ8+bNAwCMGTMGt2/flqprIiIigyMT6b+nlZaWIi4uDj4+PggNDUVmZma13YN0j/16gg62SyEiIqIK7Nu3D8nJyVi9ejXq1q2L+fPnV1vbOkkyIiIiYGtrq4uuiYiIDIJMJs7raampqXB2doaHhwc6d+6MtLS0arsHiZ7AXl5kZOQLzxHz2fCZC2rOnhg16V6JiIyJZHukKJWQy+WP+5TLoVQqq61tnVQyiIiISD8oFAqoVCoAgEqlgo2NTbW1zSSDiIioBvPy8kJWVhYuXLiAlJQUeHt7V1vbOhkuISIiIv0QHByM48ePY9iwYXByckJiYmK1ta3zSsaNGzfQvn17fP/99wCAoqIiREdHw9vbG0FBQTh16hQAIDMzE6GhofDx8UFcXBxKS0tFjwUAhg4dCldXV7i6uqJNmzaSxVJRPPn5+YiKioK3tzcCAwNx6NAhSeMBgD179iAgIABdunRRxyXm8qfnOXv2LF577TV4e3vj/fffR0lJic5iAYCNGzciICAA/v7+2LhxIwDdvTe67rvMw4cPMXnyZPj4+ODNN99EVlZWtX6/Llu2DJ07d0aXLl2wa9euam27uLgYU6dOVcd++fJlSX/WnrRmzRq4urqisLAQx44dQ9++fdG2bVu8/fbbuHPnDoDH2zd06tQJ3bt3x4EDBySJSxNPxn7lyhX1/09dXV0RFxcHQLrYn4zl999/R//+/dG2bVuMGTMGBQUFksaiT0xNTTF79mycOnUKW7ZsQbNmzaqvcUGH9u3bJ3Tt2lVwcXER1q9frz7m7u4u/PHHH8K0adOEN998UxAEQXjnnXeEUaNGCenp6ULr1q2FPXv2iB5LSUmJ0LZtW2Hnzp1CXl6ecP/+fUliqSyeL7/8UvD39xcyMzOF2NhYoUOHDpLFIwiCkJeXJ7Rt21b4+uuvheTkZMHV1VW4ePGisGfPHsHDw0P4/fffhVGjRgnvvPOOKP0/qbi4WOjWrZuwdOlS4ezZs8Ls2bOFGzdu6CQWQRCE+/fvCx4eHsL27duFL774QnB3dxfu3buns3gEQdBp32XWr18vBAQECJcvXxZee+01Yfz48dX2/frzzz8Lbdu2FS5cuCBs2LBBWL58ebX+LCQlJQne3t7Cn3/+KURGRgohISGS/ayVKSgoEBISEgQ3NzfBxcVFUKlUQmBgoDB9+nQhIyND6NSpk5CQkCDk5OQI7u7uwn//+19h4cKFgp+fn1BUVCRqbFWJfdeuXYKfn5+Ql5cn5OXlCQUFBZLEXlEsY8eOFUaNGiX8/fffQseOHYX169fr5fto6HRaydi/fz+mTZtW7ljLli1Rq1YtvPTSS7C1tYW5uTmAx0ts/P394e7uDmdn52pdYlNZLJmZmXj48CEWLlyIN998E0eOHJEklsriCQ8PR1JSEpydnVG/fn2UlJRIFg8AZGVl4eHDh+jcuTP69+8PQRBw4sQJUZc/VSYjIwM3b97E6dOnMXHiRFhbW6Nhw4Y6iQUAbGxs8Ouvv6Jv375QKBQwMzODiYmJzuIBxF2WpqnBgwcjOTkZTZo0gZmZGSwsLKrt+zUlJQW1a9dGfHw81qxZAy8vr2r9WTh//jycnZ3RsmVLBAYGIj09HadOnZLkZ61MTk4OMjMzMXbsWPWx1atXY8qUKWjSpAksLCxQUlKCM2fOoLi4GIGBgejRowfu3buHjIwMUWN7kYpiP3fuHIqKihAaGoqJEyfizp07ksReUSytW7eGQqGAvb09zMzMYG5urpfvo6HTaZKRkJCA4ODgcsdsbW3RtGlTdOnSBatXr8akSZMAiLvEprJYBEHAwIEDMW/ePPTt2xfvv/8+bt++LXoslcVjY2MDBwcH7NmzB9988w2GDx8OQPz3pkyjRo1gamqK9PR0XLx4EQBw//59yfp/Ul5eHgCgSZMmmDlzJpYvX46ff/5ZJ7GUsbW1xXfffYd58+bh7bffho2NjU7j0WXfT6pbt656uCEyMrLa4rp//z5ycnIwceJEeHl5YfLkydV6z02aNMGVK1eQl5en/n7Pz8+X9D11dHTEV199BQcHB/Wxxo0bo1atWnjvvfeQm5uLsLAwdRxWVlawsrICAJ39e5epKPZmzZrhrbfewuLFi5GXl4e5c+dKEntFsbRu3RqHDx9Gu3btUKdOHQwYMEAv30dDJ3mSsXz5crRv3x7t27fHyZMnn/n6qlWrkJ2djfXr12Pw4MGIjo5GYWEhFAoFCgsLAVTfEpsXxdKyZUu8//77aNeuHfr374+ioiJkZWWJEosm8QDADz/8gMmTJyM4OFidlYsVz9NxvfLKKxg7diymT5+O6OhoWFhYoHbt2qIuf6oslmHDhgEAevXqhZ49e6J27dq4dOmSpLE8GU/Zv1n//v2xcOFCfPnllzhx4oTk8TxJl30/bcGCBQgMDMS4ceOq7ftVoVDAzs4OnTt3Rq9evZCTk4PS0tJq+1kICwtD06ZN0blzZ/zyyy+QyWSi/6xpomze2k8//YQFCxagefPmUCgU6pjK5hbo8t+7MiEhIYiIiICbmxv8/f3VP7OA9LFPnToV3bp1w/r16/Ho0SN88sknBvM+GhLJk4ywsDBs3boVW7duVU+kfJK1tTUsLCxgaWkJhUKBvLw8FBcXw9PTEykpKUhPT0d2dna1LLF5USzJycnw8/PD6dOn8dNPP8HKygotWrQQJRZN4jl9+jRiY2PRo0cPzJw5Ew8ePAAA0eJ5Oq7k5GRYWFggMTER8+bNQ2lpKTp27Cjq8qfKYtmzZw/q1auHH3/8EWfPnoVSqcTLL78saSxPxrN582ZMmzYNp0+fVv8F9PDhQ8njeZIu+y7z7bffYuTIkTAxMYG5uTkePnxYbd+vvr6+uHXrFo4fP46TJ0/Czs4OXbp0qbafhVu3bqFPnz5ISkpC165d0bZtW9F/1jSRkJCAAwcOICEhAX5+figoKECbNm1gamqKAwcO4ODBg6hTpw6aN28ueWwvMnLkSIwaNQrXrl3DsWPH0KZNG53Fbm1tDUtLS9jY2MDc3Bx37twxmPfRoOh6UoggCOUmNz548ECYMGGC0LZtW6Fbt27C5s2bBUEQhIyMDCE0NFTw9vYWZs6cKZSUlIgey6NHj4Tp06cL3t7eQp8+fYRDhw5JGsvT8URFRQkuLi7lXiqVStJ4lixZIrRr167cv01xcbEQFxcntGvXTggJCREyMjJE6/9Jx44dE4KDgwUfHx9h3rx5Oo1FEARh0aJFgp+fn+Dn5yd88sknOo9Hl32XuXXrljBixAjB09NTeOWVV4SUlJRq/X5NTEwU/Pz8hMDAwGpv+969e8KwYcMET09PISwsTMjKypL0Z+1JmzdvFlxcXISbN28KrVq1Kvf/gEmTJgmCIAgbNmwQOnfuLAQEBAg//fSTJHFpoix2lUolXLp0SQgNDRXatm0rjB49WsjNzRUEQbrYn4zlxIkTQr9+/YQ2bdoIgwYNErKysiSNpaaQCQJ3KyMiIqLqp/PnZBAREZFxYpJBREREomCSQURERKJgkkFERESiYJJBREREomCSQfQvhYeHw9XVFdu2bav0nC+++AKurq6YPn26aHGsWrUKbdq0UW+Y9bSbN2+qN6YyBHfv3oWnpydWrlyp61CIqIqYZBBJoHnz5ujVqxfc3d1FaT8nJweLFy/GK6+8gnr16onSh9Tq1q2L4OBgfPnll/jnn390HQ4RVQGTDCIJ9OvXD0uWLMHQoUNFaX/16tV4+PAhXn/9dVHa15WQkBA8fPgQ69ev13UoRFQFTDKInsPV1RVt2rRBWloaBgwYgDZt2mDo0KH4888/nzn37t27mDBhAry8vNCjRw9s2bJF/bWqDJc8ePAAH3zwAXr27InLly9j+PDh8PT0RGBgIL799tty527duhU2Njbw8/NTHzt37hzCwsLQpk0bDBgwAMePH68w5ilTpqB9+/bw9vZGZGQkMjMzy52TlJSEnj17wtPTE1FRUdi6dStcXV0RGxsLANiyZQtcXV0xZcoUjBo1Ct7e3li8eDEAYM+ePejXrx88PDzQq1cvfP311+Xavnr1KqKiouDt7Q1fX19MnjwZOTk56q/7+vrCxsYGmzZtAp8bSGR4mGQQvUBxcTFGjRoFhUKBBg0a4OTJkxg9erR687EyiYmJyMjIQOPGjXH9+nXMnDnzX5X53333Xaxbtw62trbYt28fjh07BisrK5SUlGDevHlYtmwZAODChQvIyclB69atYWZmBuDxbqERERFIS0tDgwYNYGNjo04KnhQdHY3t27ejQYMGaN26NX7++WeEh4erd7k9cOAA4uLicO3aNbRu3Rp//fUX5syZU2G8P/zwA/766y+0aNECPj4+OH78OKKjo3HlyhX4+fmhqKgI8+fPx/LlywEAjx49wujRo3HgwAE0a9YMTk5O2LlzJ0aNGoVHjx4BAMzMzODu7o7bt2/jjz/+qPJ7SUS6wSSD6AVKS0sRERGB77//Hrt27UKzZs1w8+ZN7Nixo9x5gYGB2LlzJ7Zt24ZGjRqhuLgYZ8+erVKfly9fxuHDh2Fubo5ly5bh+vXrAB5vyLZmzRrIZDIsX74cRUVF6l++zs7O6uu3bduGO3fuwMXFBXv27MH69evVu/aWOXHiBI4dOwZPT0/s2rULa9euxTvvvIOcnBwkJycDAFasWAEAmDhxovr+X3755QpjNjc3x7Zt27Bp0yZ06tRJXc1YtGgRvv76a+zcuRO1atXCqlWrUFJSgp07dyIzMxNBQUHYsmULNm3ahAEDBuDSpUs4fPiwut1mzZoBAM6fP1+l95KIdMdM1wEQGYKgoCAAgFwuR0BAADIzM3H58uVy53Tq1AkAYGFhgcaNG+PGjRvq7aK1VZY4vPzyy7C3t1f/ZW9paQlHR0fY2dkhJycHly9fxu3btwEAtra26uuzs7MBAAEBAbCwsAAA9OnTB1988cUzfZw9exZubm7l+j9z5gwAICMjo9z9m5ubIzg4GGlpac/E3LJlS9StW/eZ9t95551y5z18+BBZWVnqr+/du/eZFS+nT59GYGAggP9ttZ2bm1vxm0VEeotJBpEGyn7JA1DPDTA1NS13jqWlpfrjsq9VdR6BtbU1gMdDNU+3U1xcjPv37wMAFAqF+nhJSYn6Y5lM9sx1JiblC5dlbTdq1OiZVS9l21tX1H9lnozlyWv9/f0hl8ufOb/sPW3WrNkz22k3atTomfOffr+JSP9xuIRIAzt37gQAFBUV4ddffwXw+C93sbRq1Qrm5ua4dOnSM0Mue/bsQWFhIRo1agQHBwfY2dkBKP+Xfllshw8fVs8d2b17d7l2yoY9FAoFFi5ciCVLlqBXr15wcXFBQEBAuXP27t0L4PH9P91OmaeTgLJrQ0JCsGTJEsybNw8NGjRAp06d4OTkBBcXFwCAg4MDvvzySyxZsgQ+Pj5wd3dHx44d1e2UzQ8pu08iMhysZBBpYNWqVTh+/Dhyc3Nx5coVODg4oG/fvqL199JLL2HkyJFYsWIFRo8ejTp16gB4nDRcunQJMpkMsbGxMDExQZs2bQA8fthWmQEDBmDJkiX4888/0bdvX9jb2+P8+fMwNTVVVzw6d+4Md3d3nD9/HkFBQWjYsCFOnz4NExMT9OnTBwDw1ltvIS0tDV988QVSUlLwzz//qIdnXmT06NEYN24cYmNjsXHjRmRmZuLWrVsICgpCeHg4BgwYgC+++AK//PIL+vXrh1q1auH333+HjY0N3nzzTXU7ZatdPDw8/vX7SkTSYiWDSAOffvopHj58iJs3b8LPzw8rVqxQz3UQS0xMDN59910A/5tjcfr0adSvXx+LFy9GcHAwAKBFixZo1KgRzpw5g6KiIgCP546sWLECvr6+yMnJQX5+Pr766qtyQzoymQzLli3DgAED8ODBA6Snp6N169ZYvny5evgkKCgIM2fORMOGDZGeng5PT0+MGzcOAF54/4GBgZg/fz6aN2+O1NRUCIKAYcOG4eOPPwYAWFlZYc2aNejRowdu3ryJy5cvo2PHjli9ejXs7e0BPB4CunjxIhwcHNQTQInIcMgELj4nqlTZhMTDhw+jYcOGOomhuLgY0dHR2L9/P956660Kl6IuXrwYX3zxBdauXQtfX99q63vTpk3Iy8uDq6srunTpAuBxwrVixQqMGzcOEydOrLa+KnLq1CkMGTIE48ePx4QJE0Tti4iqH4dLiCS2c+dO9RyPyixZskT9sZmZmXqFxZMrSJ4UFhaGVatWYefOndWaZNy4cQOLFy+GmZkZ/Pz8UFhYiLS0NJiYmKBHjx7V1k9ldu/eDWtrawwePFj0voio+jHJIJJYRkYGfvrpp2pt087ODlFRUVi6dCliYmLUScm/NWbMGCiVSuzfvx/Hjx+HqakpWrVqhYiICPVcELEolUps2bIFY8aM4aRPIgPF4RIiIiISBSd+EhERkSiYZBAREZEomGQQERGRKJhkEBERkSiYZBAREZEomGQQERGRKP4feyRshoS3ADIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 684x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set default font of the whole figure.\n",
    "font = {'family':'Arial', 'weight':'bold', 'size':10}\n",
    "matplotlib.rc('font',**font)\n",
    "# Generate class fig and ax.\n",
    "plt.style.use('seaborn-white')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9.5,8))\n",
    "\n",
    "sns.heatmap(clean_matric,cmap=\"Blues\")\n",
    "\n",
    "ax.set_xlabel('phi_φ(degree)',fontname='Arial',size=15,fontweight='bold')\n",
    "ax.set_ylabel('psi_ψ(degree)',fontname='Arial',size=15,fontweight='bold')\n",
    "\n",
    "xlabel = [-180,-150,-120,-90,-60,-30,0,30,60,90,120,150,180]\n",
    "ylabel = [180,150,120,90,60,30,0,-30,-60,-90,-120,-150,-180]\n",
    "ax.set_xticks([0,3,6,9,12,15,18,21,23,25,28,32,35])\n",
    "ax.set_yticks([0,3,6,9,12,15,18,21,23,25,28,32,35])\n",
    "ax.set_xticklabels(xlabel)\n",
    "ax.set_yticklabels(ylabel)\n",
    "\n",
    "ax.set_xlabel('phi_φ(degree)',fontname='Arial',size=15,fontweight='bold')\n",
    "ax.set_ylabel('psi_ψ(degree)',fontname='Arial',size=15,fontweight='bold')\n",
    "fig.suptitle('Ramachandran plot',fontsize=20,y=0.93)\n",
    "\n",
    "fig.savefig('D://GENEROUS_plot.png',dpi=600,bbbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83b06644",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}