Mean Activity Calculation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "from scipy.stats import pearsonr,spearmanr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_1 = \"C:\\sun_project\\sun_project\\gong_108.txt\"\n",
    "path_2 = \"C:/sun_project/sun_project/radio.txt1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>y</th>\n",
       "      <th>m</th>\n",
       "      <th>d</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>147.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>150.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>145.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>148.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>140.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25221</th>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>68.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25222</th>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>69.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25223</th>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>71.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25224</th>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25225</th>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>70.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25226 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          y  m   d  activity\n",
       "0      1950  1   1     147.1\n",
       "1      1950  1   3     150.1\n",
       "2      1950  1   4     145.1\n",
       "3      1950  1   5     148.4\n",
       "4      1950  1   6     140.8\n",
       "...     ... ..  ..       ...\n",
       "25221  2020  4  20      68.9\n",
       "25222  2020  4  21      69.8\n",
       "25223  2020  4  22      71.4\n",
       "25224  2020  4  23      69.5\n",
       "25225  2020  4  24      70.5\n",
       "\n",
       "[25226 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1 = pd.read_table(path_1,delim_whitespace=True, names=('index', 'y1', 'm1', 'd1','y2','m2','d2'))\n",
    "f2 = pd.read_table(path_2,delim_whitespace=True, names=('y','m','d','activity'))\n",
    "f2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "251\n"
     ]
    }
   ],
   "source": [
    "sum1 = 0\n",
    "l = []\n",
    "for r in f1.iloc[:,1:].values:\n",
    "    i = next(iter(f2[(f2['y']==r[0]) & (f2['m']==r[1]) & (f2['d'] ==r[2])].index),'no match')\n",
    "    j = next(iter(f2[(f2['y']==r[3]) & (f2['m']==r[4]) & (f2['d'] ==r[5])].index),'no match')\n",
    "    m = f2.loc[i:j,'activity'].mean()\n",
    "    l.append(m)\n",
    "l = l[:251]\n",
    "print(len(l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe2 = pd.DataFrame({'Mean of solar activity':l})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe2.to_csv('C:\\sun_project\\Activity.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "freqshift = pd.read_csv('C:\\sun_project\\correct_order_shift.csv',index_col=False)\n",
    "arr = freqshift.loc[0].values #frequency shift data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9874382564303334"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pear , p = pearsonr(l[:248],arr)\n",
    "pear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting Activity vs Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>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>10</th>\n",
       "      <th>...</th>\n",
       "      <th>242</th>\n",
       "      <th>243</th>\n",
       "      <th>244</th>\n",
       "      <th>245</th>\n",
       "      <th>246</th>\n",
       "      <th>247</th>\n",
       "      <th>248</th>\n",
       "      <th>249</th>\n",
       "      <th>250</th>\n",
       "      <th>251</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>76.84537</td>\n",
       "      <td>75.111111</td>\n",
       "      <td>74.84537</td>\n",
       "      <td>74.499074</td>\n",
       "      <td>73.621296</td>\n",
       "      <td>71.207407</td>\n",
       "      <td>70.486111</td>\n",
       "      <td>69.936111</td>\n",
       "      <td>70.603704</td>\n",
       "      <td>71.607407</td>\n",
       "      <td>...</td>\n",
       "      <td>71.565455</td>\n",
       "      <td>71.960185</td>\n",
       "      <td>70.586111</td>\n",
       "      <td>69.491667</td>\n",
       "      <td>68.758333</td>\n",
       "      <td>68.247222</td>\n",
       "      <td>68.176087</td>\n",
       "      <td>68.650725</td>\n",
       "      <td>69.016667</td>\n",
       "      <td>69.618519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 251 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          1          2         3          4          5          6          7  \\\n",
       "0  76.84537  75.111111  74.84537  74.499074  73.621296  71.207407  70.486111   \n",
       "\n",
       "           8          9         10  ...        242        243        244  \\\n",
       "0  69.936111  70.603704  71.607407  ...  71.565455  71.960185  70.586111   \n",
       "\n",
       "         245        246        247        248        249        250        251  \n",
       "0  69.491667  68.758333  68.247222  68.176087  68.650725  69.016667  69.618519  \n",
       "\n",
       "[1 rows x 251 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "at = pd.DataFrame({})\n",
    "for i in range(0,251):\n",
    "    at[str(i+1)] = pd.Series(dataframe2.values[i])\n",
    "at"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Activity Index')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "at.columns = at.columns.astype(int)\n",
    "at.loc[0].plot(marker = '*')\n",
    "plt.title(\"Activity Index vs Time\")\n",
    "plt.xlabel(\"Time(Dates labelled as integers)\")\n",
    "plt.ylabel(\"Activity Index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "at.to_csv('C:/sun_project/correct_order_activity.csv',index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding the mean activity from Mgii dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_3 = 'C:/sun_project/sun_project/Mgii_Bremen.txt1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>y</th>\n",
       "      <th>m</th>\n",
       "      <th>d</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1978</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>0.16402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1978</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>0.16382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1978</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>0.16373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1978</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>0.16334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1978</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0.16214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15155</th>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.15153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15156</th>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0.15126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15157</th>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0.15126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15158</th>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0.15133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15159</th>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0.15146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15160 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          y   m   d  activity\n",
       "0      1978  11   7   0.16402\n",
       "1      1978  11   8   0.16382\n",
       "2      1978  11   9   0.16373\n",
       "3      1978  11  10   0.16334\n",
       "4      1978  11  11   0.16214\n",
       "...     ...  ..  ..       ...\n",
       "15155  2020   5   5   0.15153\n",
       "15156  2020   5   6   0.15126\n",
       "15157  2020   5   7   0.15126\n",
       "15158  2020   5   8   0.15133\n",
       "15159  2020   5   9   0.15146\n",
       "\n",
       "[15160 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f3 = pd.read_table(path_3,delim_whitespace=True, names=('y','m','d','activity'))\n",
    "f3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "309\n"
     ]
    }
   ],
   "source": [
    "l1 = []\n",
    "for r in f1.iloc[:,1:].values:\n",
    "    i = next(iter(f3[(f3['y']==r[0]) & (f3['m']==r[1]) & (f3['d'] ==r[2])].index),'no match')\n",
    "    j = next(iter(f3[(f3['y']==r[3]) & (f3['m']==r[4]) & (f3['d'] ==r[5])].index),'no match')\n",
    "    m = f3.loc[i:j,'activity'].mean()\n",
    "    l1.append(m)\n",
    "print(len(l1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9946326847863334"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearmg , gh = pearsonr(l1[:248],arr)\n",
    "pearmg # pearson's correlation between frequency shift and mgii activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9906660729374335"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spearmg , fg = spearmanr(l1[:248],arr)\n",
    "spearmg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x218e8038a48>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PMedYhSaiCKY5AAxKTqBGm5U0mqinU+FgrvhvB94A9gAvKKV2ich9IrIAQERmi0gpxsO+UER2med6MExKG0RkB4aJ6s9+l19MW5MSwCJgp4hsA1YBi5WuLxxxhBIOmcl2TmjNQaOJesJKgjPNO+sCxu7xe70Zw9wU7Ny3gDND7JsXZOwh4KFw5qXpP+pCmJUyU+zUaOGg0UQ9unyGpsu0erw0NntCaA4JVDe26GYyGk2Uo4WDpss43a0AQTWH+DjB41UcqGzo62lpNJpeRAsHTZepdxnCIT2I5rD5YDUAf3h7b5/OSaPR9C668J6my9S5jGgkf80hsCDfmm1HWbNtrW74o9FEKVpz0HSZYJqDVZDPHm/cUnab6IY/Gk0Uo4WDpstYwsFfc7AK8rWY2kOLR+mGPxpNFKOFg6bL1PvMSm2tkpVON9flGxHNM8cM0g1/NJooRvscYpTyOhe3PPkJIlC4bFaXVvgnNYe2t0/hsnyUUrz46RHOPi2LOy6d2Ktz1mg0fYfWHGKUVRv2sfVwDVtKalj19r4unVsfxCFtISIMSkrQlVk1mihHaw4xRmBUEcBTG0t4amNJ2JFFx2pdCFDT1BxU48hMsevKrBpNlKM1hxijaHkBwzPaPtBF4LIpQ8KOLPr4YBUKQmocRpa0Fg4aTTSjNYcYIpjWAKAUZKcmdup3CDw/lMaRmWzny6rG3pu4RqPpc7TmEEMULS9g+KCTAiBOIMEGCTYJK7LIymWIM9s/ORLiguYyZCbrng4aTbSjNYcYIZjW4FXg9YDdBicamimvd3WoPVi5DF5lCBZ3qzdoLkOm2fBHKYXZmkOj0UQZWnOIEYqWF/C1aUN92/Z4IS8rmUnD0mj2KIoPVYcVtVTpdJNstzF/Yi5L544JqnFkJifQ4lE0NHt69TNoNJq+Q2sOMUJuuoOmFkNzSLAJLR7F4RONeMzK2orwopb+dOMsJqx4ndNzU7nr8klBj8lMtgNQ3dBMaqK+xTSaaERrDjHEFxVOAB65YSZL547hgnHZLJg+HH/DT2ZyAqu/f27IazjdrTR7vGSl2EMek2nu+19PfUJ5vSvkcRqNJnIJSziIyGUi8rmI7BeRO4Psv1BEPhWRVhFZFLBvtIi8KSJ7RGS3iOSZ40+IyEER2Wr+zTDHRURWme+1XURm9vxjagBcppnnX3srWLlwKk/cPLddlnN1YwvPfFwS8honzPyFwSmJIY/JTDaS43aX1bHq7X2U17m4vvAjLSg0miiiU51fRGzAw8DFQCmwWUTWKKV2+x1WAtyE0S86kL8DDyil3hKRVMDfK3qHUuqfAcdfDowz/+YCfzT/1XSTjkJQWzxeAnu2dWReqjKFQyjNwf+9/E1VYORFrLxmWu98KI1Gc0oJR3OYA+xXSh1QSjUDzwFX+x+glDqklNpO2wc/IjIZiDf7SKOUciqlOguAvxr4uzL4GBgkIsPC/DyaIBQtL+DC8dm+bf8Q1I/vuohLpgzBP6jI1kFS3AmnpTkEFw5Fywu4fOrQoPue2lhC3p1rmbBifQ8+jUaj6QvCEQ4jgMN+26XmWDiMB2pE5CUR2SIivzE1EYsHTNPR70TEslOE9X4icouIFItIcUVFRZjTiU1y0x24TWe03RbXJgQ1N91BTmoi/i2fPR0kxZ00KwUXDrnpjnb7LLljtwXPi9BoNJFHOMIhWKB6uN3j44ELMMxNs4HTMMxPAHcBE83xwcBPu/J+SqlHlVL5Sqn8nJycMKcTuxytaSI+Dlbfdm67ENRKp5tRmUmcfdpgALJT7SGT4nxmpdTQDulKp5vJw9OwCTji43xfXrMneF6ERqOJPMKJMywFRvltjwTKwrx+KbBFKXUAQEReBs4GHldKHTWPcYvIXznpr+jJ+2lCMCTD0BKmjMhg5YiMNvsKl+UDcLzOxdz/3MCPLhrHsnPygl7nRIObpAQbyfbQt07hsnw+3F/JDY9txOPn68hKsbN2x1F++NVxWkBoNBFOOJrDZmCciIwVETuwGFgT5vU3A5kiYi3t5wO7ASw/ghgptAuBneYxa4BvmlFLZwO1foJE0w2O1zbx6ZfV5GWldHhcbloijoS4DusiVTmbQ5qULCasWM8Nj21sf25DMzVNLV0uEa7RaPqeToWDUqoVuB14A9gDvKCU2iUi94nIAgARmS0ipcB1QKGI7DLP9WBoBBtEZAeGyejP5qWfNsd2ANnASnN8HXAA2G8ee1uvfNIY5pfrP8Oj4HB1x7EAIsLowckc6kA4lNU2UdPY3GFYatHyAl/9pUCU0o5pjSYaCCt9VSm1DuOh7T92j9/rzRjmn2DnvgWcGWR8fojjFfD9cOal6ZjAENaNB0+Qd+faDjOgh6Q5+GB/Rcg6S3uPO2lo9nQYlpqb7mDhjBG8tOVI0P2OhDgunTKUu78WPMNao9H0PzpDegBjVVGNN5fxoaqo+lPhdNPU4m1n+pmwYj15d671RSt1tvpvaG5lXG4qghEaa2HroGCfRqOJHLRwGMBYVVRbvQqh44ey9fD/7Fg9cPLhP/5uQ2G0BI1FZ4KmcFk+p+WksPTsMbz6gwsYlZmE3SZMGpYesmCfRqOJHHRVtAFOpdNNit3GrDGZjM5KoSKEr6BoeQEr1+3hjZ3HcLd6EYz44aumGwIhN91BUryxlrDFSVirfysKCqDop/P56T+389ae47y2cGqvfT6NRnNq0MJhgPPrRdOZfu+bzD0ti+8XnBHyOEvL8C99AfDip0d48dMjJMbHMXeskQfxg/lnUOlsDiloQjFuSCrPFx+myukmKzV0bSaNRtP/aOEwgCmvc3HDnz8GYERGUqfHVzrdXDtzBCVVjRR/WQ20dR4frXHx732VTBuRwUWThnR5PuOHpAGw9LGN/P07c7TPQaOJYLTPYQCzasM+9lc0APDv/Z2XGClcls9/Xz+DCUONh3ig87iqwfATdJbnEIpxQ1IB+PxYvc510GgiHK05DECCtQR96dMjvGSah0KFsVpUOt1kp9oZmu5gxuhMn/moymlVZO26SShUtdZw5qPRaPoerTkMQIqWF3DJ5LZmn44qrQZSuCyfr04awtFaFysXTvU5ln1F9zqoq9TRnBbMONlYKJywWo1G039o4TBA8G+ok5vuwJHQ9qvtqNJqMM7ITaWqoZkqv5DTEw3N2OPjSLHbOjgzOJbD23J061wHjSay0WalAcKqDfvYfOiEL3P5YKVRAuPCcVmkOexsL63pUm7BONN5/I3Cj3DYbSTY4hgxKImsFDsiIWpjdEKl0820ERnsOFLL4tmjdK6DRhPBaOEQ5YTq8mZlJv960QyGZnR9dT4u13AeWw5tMKq2dlSquzMKl+Wzeksp/+f5bdx83lifANJoNJGHNitFOaFs+WmOeDwKHnqn61FBE1as59wH32k3XlbjYueRuh4VzBubbQidg5UNnRyp0Wj6Ey0copxAW76rxcsrW8uoc7UC3auAWrS8gEumBM9jGJbh6JETeaxZNvz+13Z3WNlVo9H0L1o4DAD8H7JpjnjSHSethd2JCrJahwajprGl+xMFMpITsNuEw9VNOtdB02uU17lY+PAHXPPIB3rR0Uto4TAA+PlVU3yv612tON2G1pAYH9ftqCCrdeiQ9ETGDE7GLKtEU4un2w91q7hfs8fQc3RfB01vsWrDPrYermFLSY1edPQSolS47aAjl/z8fFVcXNzf0+g3xt29jhZP++8xwSZ8Y/ZoKupdbYrgdZVgSXVAlxPYyutcrFy3h9e2leFVbUtz6JBWTXcIdW9C1+/PWEREPlFKBX04hKU5iMhlIvK5iOwXkTuD7L9QRD4VkVYRWRSwb7SIvCkie0Rkt4jkmeNPm9fcKSJ/EZEEc3yeiNSKyFbz757A99O05YGFwZvutHgU/yg+3CPBAIYP4rIpQ7AiWLubwGb5R7ymHNO5DpqeEsw/FteFhE9NaDoVDiJiAx4GLgcmA0tEZHLAYSXATcAzQS7xd+A3SqlJwByg3Bx/GpgITAOSgO/6nVOklJph/t0X/seJTZo97VdOtjjptQzk3HSHr4pqT0xVYOU6pCPA0jmjda6DJiz8kzz9yU13MDi5bXi1t4sJn5rghKM5zAH2K6UOKKWageeAq/0PUEodUkptB9o8pUwhEm+2CkUp5VRKNZqv1ykTYBMh2oxqOqe83njA+nde83hVr67KK51uls4dw+rbzutRs57CZfksPGskCrjj0ok91mo0sYF/kmcge47WATBpmJE3k5GUoBcdvUA4SXAjgMN+26XA3DCvPx6oEZGXgLHA28CdSimPdYBpTloG/MjvvHNEZBtQBvxEKbUr8MIicgtwC8Do0aPDnM7ApKLeKJR3Wk4Kc0/L4oY5o3lmU0mX+y10hP9DfGUPm/UMSkoAoLqxmYzkhB5dSzOwGX/3+jaacbCCja2mv23aiAy+rGrk6zNHcs9VgcYNTVcJRzgEq5UQrhc7HrgAOAvD9PQ8hvnpcb9jHgH+rZQqMrc/BcYopZwicgXwMjCu3QSUehR4FAyHdJjzGZBU1LvISXP06gP8VJKZYgiEmqaehcVqBj5fmzaU1VvLfNv+QQyBzugXiksBeOLDg1o49ALhmJVKgVF+2yMxVvThUApsMU1SrRgP+pnWThH5OZAD/NgaU0rVKaWc5ut1QIKIZIf5fjHJkZomymqaoia+OyPJsBFXNzb380w0kYoV9uwvGMBI8rTMpcH6muemJfqaSml6RjjCYTMwTkTGiogdWAysCfP6m4FMEckxt+cDuwFE5LvApcASpZRP/IvIUDEru4nIHHOOVWG+X0zyZVUjtU0tURPfnWmakmp7mFCnGbgULS/g0oAopJGZDr4+a4TPn5Cb7iA5wagQbPU1T09KoKJe+xt6g07NSkqpVhG5HXgDsAF/UUrtEpH7gGKl1BoRmQ2sBjKBq0TkXqXUFKWUR0R+AmwwH/ifAH82L/0n4EvgI1MWvGRGJi0CvicirUATsFgNhGSMU0CoonuRHt+dmaw1B03H5KY7+OxoPWDYtRUgCP913Yw2xx2paQLgh/PPoMLZzMYDVVQ1NLPojx/yyI0zdcRSDwirKqtp3lkXMHaP3+vNhIg2MiOVzgwyHvS9lVIPAQ+FM69Yp2h5AT9fs4v1O48Bbe2xkUx6UgIiPS/FoRmYBC56rJXh4eqmdsd+v+AMivZVMmvMYM4fl+2r+vvJl9W+8vWa7qFLdkcxuekObHFGvEC8qVZHQ1KZLU5IdyRQozUHTRCKlhe0y6Qfmu7gRJD75WitITCGDXLoVrS9jK6tFOUcrzOc0PdfPaVH+Qd9zaDkBB2tpAmKfyZ9nBiZ9IOS7dQ1nawbZnG01rj/h2UYDuqL/drj6la0PUNrDlHOTeeOZfOhamaOGcySuWP6ezphMyjZTrU2K2lCYCV2fiN/FDZbHNtLawBY8ujHPH5Tvk87PlrjIiMpgWR7PMn2eHLTjEx+m+jyLD1Faw5RTlWD8SPqSYe2/mBQUgK12qykCcFdl08EYFbeYFYunMq9C4zKwzuP1LaJyjta62KYX6fDSqeREDppWHpUadKRiNYcopxKZzMiJyOAooWkhDg+PlZPeb1Lr+w07fjyhNEDfUxWcoe+hDFZyRyvc/vuo8Jl+dz10g7W7zzKqz84v9v9zjVac4h6qpxuBifbfY7paOFgZQPuVm/U5GZo+paSKlM4DE42kt2mt012s3wJwXJ8JgxJpaaxxWea0nQPrTlEOVXO5qgyKUVrboamb9lztM5XIj433UGa2d3QclC/tq2MV/yyp/3voye+PQeAbz6+iSe/O0drpt1Eaw5RTlWDm6yU4C09IxGr5EGCzfjlJ8briBJNe4r2VaCUUY0VDF9Cbloik4alsXTuGC4Yl82F405W1fHXJiYONcpn7D1erzXTHqA1hyinytnM5OHp/T2NsLHCFK1Kms06okTjR0ea5eVTh1L8ZbWvqOQ3Cj8CwG472WPkgl+9q3MdegmtOUQ5RnRG9GgOYMz5okm5AFw0KVdHlEQRoZru9BbBiulZGsHQjCSO17nwmq0Ej9Y0ER8nrL7tXF9kUqBmCpCXlaw1026gNYcoo7zOxe3PbuGhG85iUJKdOlcrWSnR43MAozfE3uP1vL2nnIVnjeDKM1xH2ewAACAASURBVId3fpImIvBvunMqSlPkpjtwxBtrVltA1v/wQQ5aPIqqhmZy0hIZkuEgN93BlBEZrByR4buGlVltcaiqkTkPbNDaQxfRwiHK+P3be30/zsVzjCZH9vjoUwAHmwKtyqlzHaKBvgwkOFpjaCX/+6JxHK93+5pWDU03TI/Hal14PF4++bKar89sX9LtwnHZHKpq5HB1Ex6vIk7gqunDI77mWKShhUOUEOrHCfDBF5Xc+pXT+2tq3SIz2Y4IVDVo4RANWPWOXt1aZlRIFbhk8hDuPwVNpX5w0TiK9lcyfdQgLhyf4xsflpEEQFltE4+9fwCvghIzH8KfJ26ey92rd/CM+ftQCu3X6gbRt+SMUYqWFzBz9KCg+/69t5K8O9cyYcX6Pp5V97HFCZnJdqq0vyEqsAIJLGuNUnCgouGUPHAtf0Zueltf2lAzE/rWJz/xhbFuPHgi6L1f6XSz8KwRAMzOG6z9Wt1Aaw5RQm66g33lzjZjVp37xPg4Lpsa+aW6A8lKsWuzUhTx7KaSNtv7yp3k3bm2101LVrOeQMGTlWLHJpAQH0eLR+HxqpBl6guX5aOU4u09xzlY1cBDS8/vtfnFClpziAKslon1rrYVKa1VXLMnOsNBs1LtnNBmpajh5e+f12b7VFU9La93Ex8nDEpKaDMeFyc4Emy4Wrx4vAqh4+J6IkJygo2KerfOd+gGWnOIAoqWF/DjF7bx/v7KoPsFolJtzkpJZM+xuv6ehiZMAvsxnqqqpxX1bnLSEonzKwkT6HMDY3EUR/B7X2fi95ywNAcRuUxEPheR/SJyZ5D9F4rIpyLSKiKLAvaNFpE3RWSPiOwWkTxzfKyIbBSRfSLyvNmfGhFJNLf3m/vzevoho53cdAe1TcYK27+OmAAjBiXx8f+7iMJl+f0zuR6QlarNStFEpfkQHj8kFYCrpw8/JYuS8nq3r/S2hZW/EO8nMMbnpoa893Umfs/pVDiIiA14GLgcmAwsEZHJAYeVADcBzwS5xN+B3yilJgFzgHJz/FfA75RS44Bq4Dvm+HeAaqXUGcDvzONimvI6F3uO1pORFM/lU4b6xhUwODkh6sxJFoNT7NQ2tdDi8XZ+sKbfsYTDsnPyAPjmuXmnZFFSXuciJ+CethziHr8EBmdza8h7X2fi95xwNIc5wH6l1AGlVDPwHHC1/wFKqUNKqe1Am1+5KUTizT7SKKWcSqlGMerozgf+aR76N2Ch+fpqcxtz/0US43V3//utvbR6FcMHJeFRiiumnRQQLV7VwZmRTZaZ2X39n05dxq2m96g0tTyrdtGp0vrK61xsK61pd088u6kE/7u9rMbVYZRepdPNJVOMznDzJ+pM/K4SjnAYARz22y41x8JhPFAjIi+JyBYR+Y2piWQBNUopy8Pqf03f+5n7a83j2yAit4hIsYgUV1RUhDmd6MJyRD+32fjv33O0njd2HWfDnnLspro8Jiu5P6fYI6zM7q2Ha7TDMAqoqHeTlhjPiEFGvsGpCENu9Xg50dgS1In88V0XsWDGcOw247HVmUO8cFk+d1xqNA1aMGN4VJpe+5NwHNLBVu3hLlfjgQuAszBMT89jmJ/WdHDNsN5PKfUo8ChAfn5+9C6fO2D1bedyw2MbqTHbaTri42j2eNs42t7YdfyUhBOeanQz+Oij0ukmOy3xZHZ7L0eadeZEtkxFLV4vifFxYTnEs3QmfrcJR3MoBUb5bY8EykIcG+zcLaZJqhV4GZgJVAKDRMQSTv7X9L2fuT8DOBHm+w0ontlY4hMMArg9XhbOGNGl1VOkUrS8gPkTc33b0fo5YomKeqMFpyPBRlpivM8H0VsULS/gghBluC0qnW6Wzh3D6tvOC6sNaEZSAnEC1bolbZcJR3PYDIwTkbHAEWAxcEOY198MZIpIjlKqAsPPUKyUUiLyLrAIw4fxLeAV85w15vZH5v53lAoMohvYhArbQ8HLW4+wZM7oLq2eIpHcdIdvVRdYYE0TmRyrbaLO1Up5vYusVLvPB9Fb5KY7fA5n/zLc/veEv2loZRilO+KsTHydT9NlOtUczBX/7cAbwB7gBaXULhG5T0QWAIjIbBEpBa4DCkVkl3muB/gJsEFEdmAsgP9sXvqnwI9FZD+GT+Fxc/xxIMsc/zHQLnR2oFO0vIBLJg9pM2YTuGzKED7+fxd1efUUqdS5WrAJXHXmsKj+HLHC0VoX1Y1GS86s1MRT4nOwWnu+cOvZvXZPDE6xU62FQ5cJKwlOKbUOWBcwdo/f680YpqFg574FnBlk/ABGJFTguAtDyMQsuekOku22NmMeBdmpib4m6hbhrJ4ilcJl+XzlN+/iUdH9OQY6wXwBENw52FNmjc6ktqmFGaMzmTE6s1eumZmiNYfuoMtnRChHapoAmJs3mK9NG8aozKQBubLOTUukvE6HsUYyRcsLuGzKSU3WkRBHXlYymckJHZzVPQ5XNzIqM6lXr5mVosu0dActHCIUq079b6+fzsNLZ1L00/kDMhQvN83hK7QWyKnuOqYJj9x0BwlmAES86R9yxMdxorGFY7U9+24Cv+PD1Y2MzOzd8OxMbVbqFlo4RChfnmgkPk4YljGwHbQ5aYk+O3Mg/l3HNP2LJcBXXDmJpXPHcKzO2P7tm5+FPKcj4W7t+9Xrn7Hp4AmuXPU+O0prOHyiicEpvauRZKXYqW5s9rUX1YSHLrwXoZRUNTIyM4l428CW37npiTjdrTQ2t5JsN25HXTQt8vhewRl8fHATD6zdQ4vn5EP2n58c4Z+fHAn63XTUUvScX27Ao2DTQWO7vN7NVQ99ABjJnr1JZrIdr4JFf/qQPy2bpSPiwkQLhwhlf0U91Y0tlNe7BvTNbH22PWV1/OqNz/nFVZOZNCydnLRE3tp9HCBkzX5N31HbZOTbPHnzHJ7ZfJjXdxyj2ePFbovj8mnGd2P1N99SUt1GgPgLd6BdmHYgVgOf3loMZKUaIdNbSmpOWe/rgYgWDhGIUooDFQ20eNSAv5mt6pvf+VsxNU0tXPvHD3G3ehniV5XT1eIlXmRAC8lIxxIOp+WkGlnKZrHEFr9eIitW72DzoROMzkziyxNNvnPbCHcFK9ftYd32o7SGMPPY4+O4vJeaV+lM/O4zsG0WUciEFesZe9c638rrqY0lUdcCtCt892+bAagxHz6uFi9K4bNpW2w6FJNJ8hFDnfn9pCclUOl0c90sI2Bi1phMntlk3KNPbSxBKdoIBjC+U5sItz+zBcTo5xxKMEBbgdNTipYX8BW/PtQ6Ez98tHCIMIqWFzBvQmzczBNWrKfZE56T8HB104AWkpFOXVML9vg4HAk2Cpfl8+DXz8QWJ5x9WpavIJ6tg8SHl7Yc8fkfKp1uUuw2LhiXzajMJLJNsw9AVkpCryZE5qY7fA7ueJ2J3yW0WSnCyE13EGdWKE+wDeybuWh5ASvX7mHNts5LdUVrn+yBQm1TCxl+bTvj4oTBKXaqGtwney10IueVOplABzBj1CCe/M5cnO5Wpv78DQAmDkvv9YRIq73u0rmj8Sio0KHRYaGFQwRihQ0+fMNM/r2vcsDezLnpDtIc4d2CullL/1LnaiscwAgRrag38gcqnW7SHfHY4gSvUtQ2tWIT2gkMR0IcGY4Ejte72XO0rl1k2gf7q3q9yvBj35rNlHtexxYXx71XBfYp04RCC4cI5OoZw9lxpJa5Y7O4xK/z20Ck0unmxrPHcKDCSaXTTVaqnVLTZj1qcDJlNU0cqmpkdt7gAZkhHi3UNrWQHiDIs1MTqWowvpNVS85i8j1vcNu809l7vJ63d5fjCVIv09XixdVinPP2HqMpZJwYxRdbPOqURaZlmrkOmvDRwiECqWpoJsEmpCcN/K+ns6xvpRQz7nuTg5UNPLT0/D6alSaQ2qYWclLb9nXOSrVTUtIIQPGX1Xi8ity0RP7vJRMor3Oxct0e3tx1DFeLoRlkpdhpavHQ2OwBTkYx2eKE1VuOnNIqw1m6vlKX0Q7pCKSy3k1WSiIx3h0VABEh2R5PhbN9ZzBN31HX1BrErHSyMusj7+4H4OMDRlSZ5Ydwt3p9+Q1VDc0+wQCGFvHqtjIa3K2nvMqwLqHRdQb+0jQKqWpo9iXuxDI6UzpyqG1qIT1AOGSn2Wlo9pB351rf2NodR1lr+gzmTchh6dwx3DBnNN9/+hMOVTX6Wjra44XhGUnkZaf0SZXhwSl29h13npJrD1S05hCBVDndZAWo8LFI0fICFswYToIZI5kYP3DDeiMZr1cFdUhnpxj36MWTcokzlVz/0OvCZfmsXDiVycPTWXr2GJ9giDf9C+efkc0T325Xtf+UMDhZV2btKlo4RCCVzuY2sd+ximWaaDVDXnTEUv/gbG5FKXhl65E2RfQs7XbzoWq8CmwSOvQ6LyvF9/rBa6f1eXOnwamGv6PJz6yl6ZiwhIOIXCYin4vIfhFp15lNRC4UkU9FpFVEFgXs84jIVvNvjd94kd94mYi8bI7PE5Fav333BL7fQEYpZTRy15oDYEQzLZg+HIBzT8/SEUv9QK3Zx/zwiaY2fh9Lu7Wy239y6fiQD/287JPCYevhGlYunNqnJeitlrQndMRS2HTqcxARG/AwcDFQCmwWkTVKqd1+h5UAN2G0BA2kSSk1I3BQKXWB33u8yMke0gBFSqkrw/oEA4yGZg/uVq/vZo51Cpfl43S38sq2Mr4yIYdbLjy9v6cUU4SqTRSMX73+eVB/UCT4jjKTTeHgbGbEoN5tJjRQCUdzmAPsV0odUEo1A88BV/sfoJQ6pJTaDnRcbjEIIpIGzAde7uq5A5G9x+oAo/iYxiDFbiMpwUZ5ndYa+pqi5QWce3qWb9vyKaz74fk+P4M/7lZvuxInlu/Iilrqj5IwlgnMysvQdE44T6ARwGG/7VJzLFwcIlIsIh+LyMIg+68BNiil6vzGzhGRbSKyXkSmdOG9op7Cfx8A4N97K/p5JpGDiJCTlqhNSv1AbvpJ34HddjIPYfLwDBbOaPsYsMVJ0Ie+5Ttq9nhPaS5DR1iaw/2v7dadBcMknFDWYMH2XWmpNFopVSYipwHviMgOpdQXfvuXAI/5bX8KjFFKOUXkCgyNYly7SYncAtwCMHr06C5MJzIJVL3f/byi18sIRDM5aYkh24lqTi1W8tiT35nDq9uP+sq5NDS3Mi43lf3lTuIEPF4V8qFf6XT7wlqf2VTS5yVhsszIqgMVDQO+DH5vEY5wKAVG+W2PBDqvlGailCoz/z0gIu8BZwFfAIhIFobZ6hq/4+v8Xq8TkUdEJFspVRlw3UeBRwHy8/Ojvv9f0fKCNnXuHfFxXKoLzfnITUtkf7mOU+8P5k/M5UCFkzljBzP3tJMmpsJl+dz6ZDFzT8vq9KHfF7kMoeiop0PR8gJuf3YLD91wlo6CCyAcs9JmYJyIjBURO7AYWNPJOQCISKaIJJqvs4HzAH9H9nXAa0opl985Q8VMDRaROeYcq8J5v2jGF7bpVQjg7sWa9gOBjnpNa04tHWXs++cy9HUEUrhYPg8Lo2zHECYPT+dXr3+m+5SHoFPNQSnVKiK3A28ANuAvSqldInIfUKyUWiMis4HVQCZwlYjcq5SaAkwCCkXEi/GQfzAgymkx8GDAWy4CvicirUATsFipIBW8BiCl1UadmgvGZzN6cMqArcbaHXJSE6ltasHd6iEx3tbf04kpKp1ustOiN3rOWniBYSN3t3p5c9dxFEbrUNDZ98EIq3yGUmodsC5g7B6/15sxzE2B530IhDTuKaXmBRl7CHgonHkNNNIdRgZqc4u3z1XvSCfHbBv6jcKPePSb+Vqj6kOMpMzozrupdLoZl5vKvnJnSI/p1TOGazOuHzpeMgKYsGI9eXeu5dXtRwH42GywrruenSQ33Xg4bT1cy5Wr3tcRJ33IQEjKLFyWz/X5hut0/oTcdvvFbF+qFx0n0cIhArBsojYzcHwgtwbtDhNWrOfmJ4p92+X1buY8sEELz1NIeZ2L6ws/orzORdUA0BwAxmQlA7C3vL7NeHwcLJ0zWodKB6CFQwTga7PoVYgwoFuDdoei5QVhJ1xpeodVG/ax+dAJfvPG5zR7vAOi1tftz2wBoLS6qc14qxd+9NXxEelM70+0cIgQKp1uEmzCFVOH9nlRskgnN90RdsKVpmdYJs6nNpagFPzjk1IAHlz/WT/PrOe8/X8vbLPtSIjzZX8ve3yjNlUGoIVDhHD/1VNp8SjmjM2K2JDA/sRKuLLoKOFK032Klhdw6ZQhvu0EU2W77+roL1QwenAKNvOJZ0UtDTF9WZ8fq9fhrAFo4RAh7DMTvPwfgJqTFC7L57ScFN9Kb3BKgi/0V9N75KY7OOJndmnxGqE9H+6P7lQjSyPymEUIFKAUrN5S5tt+amOJDgTxQwuHCOHTL432ioOSEzo5MnYpXJbPL681IqOrG1oYmZnczzMaeCil+OxYfbvx13YcjeoHZ7Dif5dOGcKlU4b46gPpQJC26DahEcKabUYY6zMbS3TdlxB0VAZBJy71nMD6Xv4YD9PoLecSrPhfTmqioUFw0sykTZUn0ZpDP2Opu5ZZSau2obFWf8FaUoJf+KV2LHaLouUFnHPa4KD7XC1eXt1WFtUPTqv43+rbzvMFfVQ63UwYkkpSgk0HggSgNYd+pmh5Afe/ttuXABftK7RTibX6s4qpuFq8xIuAgusLP2JUZpKvTo7WvrpObrrDlzwsnEwktscLwzOS2nRzi0ZCFf8r/NcX/HL9Z/z0sgmkObRZ10JrDv1MbrqDVtPpFx8XugevxqDS6WbJnNHYbYb6sOnQCc7+5QY2HTzBi58eQSmtffWESqdRnvsSM2IpTqDFozj/jGye+Pac/pzaKWNohvFbO16nNU5/tOYQAew9bjgA//v66Ww6VK0L7nXAe59XtLGLHw5IaLK4dMoQ7tf1qbrMOadlUWmaVm48u//6L/Qlw822oWU1Ls7ITevn2UQOWjhEAM1mfN2mgye0OaQTrL4Xb+w8FtJ5CkZTF619dZ3jdS6GpDn6tf9CXzPU7HZ3rHbgCsDuoIVDPxIJjdejDf+oE3+7eCD7yp3k3bkWu03Y+8AVfTnFqOZ4nYshGbElVIeYwuGoFg5t0D6HfqRoeQEXTzpZIVLHWYdHpdNNHBJWr9qrpg/v/CCNj+N1boakRX+Rva5gj48jOzWRY3XBTZSxihYO/UhuusNnUkqwaWd0uBQuy+eju+Yzf2L70suBvPjpEe2cDhOPV1HhdPtW0rFEdqqddTuO6TBoP7Rw6GeO1Bg347O3nK3jrLtAbrqDYRkORPDlPQxJS+S07OQ2FUS1NhY+VU43Hq+KObMSgNPdSm1Ti66v5EdYPgcRuQz4A0ab0MeUUg8G7L8Q+D1wJkZbz3/67fMAO8zNEqXUAnP8CeArQK257yal1Fazf/QfgCuARnP80+59vMhnwtA0mlu95I8ZTP6Y4AlImuBYSU3+ETWFy/K5e/UOnt5YorNeu8ieo0bUnCM+dtaM2u8Xmk6Fg4jYgIeBi4FSYLOIrAnoBV0C3AT8JMglmpRSM0Jc/g5/QWJyOTDO/JsL/NH8d8BRXudiw57jzB2rhUJ3CBVRY7WEPFrrYuGM4VobC5O/fnAQgA2flXOd2TVtoGNFv63fcZQWjyIxPo7LpuokVAhPc5gD7FdKHQAQkeeAqwGfcFBKHTL3hY4tDJ+rgb8rpRTwsYgMEpFhSqmjvXDtiKG8zsXXVhXhavFyoqG5v6czoChcls9f3j/Ifa/t5n9fPH5AdDE7lQSunl/feYy8O9fGxOrZin5r9RjhDc1a0/QRjv44Ajjst11qjoWLQ0SKReRjEVkYsO8BEdkuIr8TEesXHNb7icgt5nWLKyoqujCd/mfCivXM+c8NVJjZqDuO1GmnaS9zuln6XDdx6RyrZlV8jLaprXS6uT5/JADTR2VoTdMkHOEQpEFjWFGEFqOVUvnADcDvReR0c/wuYCIwGxgM/LQr76eUelQpla+Uys/JyenCdPqXUJUv44SY+TH2BafnGHWAPjuqm7h0hm/1bJZxiTU/TeGyfH61aDrDBzkoOdGkM+tNwhEOpYC/AXIkUBbuGyilysx/DwDvAWeZ20eVgRv4K4b5qsfvF+kULS9gVGZSu/FrzhoRMz/GU82EFes5/1fvArqJS7hUOt2MykxizODkmI2aixPhREOzXkyYhONz2AyME5GxwBFgMYYW0Ckikgk0KqXcIpINnAf82tw3TCl11IxOWgjsNE9bA9xu+jbmArUDxd/QUb18p7u1j2czcLGcjK9uLUOhK92GQ+GyfK595AOS7LYBXy4jEB2xFJxONQelVCtwO/AGsAd4QSm1S0TuExErLHW2iJQC1wGFIrLLPH0SUCwi24B3gQf9opyeFpEdGGGu2cBKc3wdcADYD/wZuK0XPmdEULS8gAV+Gbv2eCEvK5l5E3J0z+hexFfa29yONTNJd6lwusmJQee95XNJMCv92m2x5XMJRVh5DkqpdRgPbf+xe/xeb8Yw/wSe9yEQtJKcUmp+iHEFfD+ceUUbuekO6l0tgFGe2yqFrIvt9T6VTjczRw/i05Iarps50mcmKa9zcfuzW3johrO0sPBDKUVlfXNMRnYF+lyaPV4++iK6e2b3BrGT7RIhfPplNQDzJuTErG23Lyhcls//uXg8AAtnjvBpZqs27PM1BNKcpKHZQ1OLh5wYq6tkYSVUXnBGNnEC5fVurlz1fkxHuumqrH1EoF3z7T3lAL6G55reZ+LQdADufHEHx+pcNGu7ckgq641FSixqDmAsJgJ/o+X1buY8sCFm7xH9ZOojipYXcOWZw3zbsRZL3h/kpCWSGB9HyYlGrpw2jK9NG9pmf15Wsv7/N7E02FjVHMD4jcYFCaR3t3pjMtJNaw59RG66A6/S7UD7isBV4EtbjrQ75lBVY0yvDP2Jdc0BjN/owhkj2t0reVnJvPC/zumnWfUfWnPoQ0rNlparlszQ/oZTTGDWrz/+I5dOGaK1B+BApRMACZaCGkM0NLe2y8K1FhGxpj1o4dCHzJuQS5zARZOGsHLhVB2+egrJTXfw2rYyXwSKP/4jup2owfodxwB46uMv+3km/Uvhsny+Mj6bvKzkNiamWFxEaLNSH7K/vJ4xWSkkxtv6eyoxwYXjsjlU1ciR6iZagggJONlONFZNS4Hmt6c3lvB0jDvqn7h5Lnev3sGXG0t8Y7G4iNCaQx/y2dE6qhuaYzo8ri954ua5nHdGNq1K+VaB1g1vbSfGx3ZggGV+s/4/dKCEwbObStpomNYiIpZMS1o49BEtHi8HKxup0d2m+hQrfv3s07IYPySVuacPZlxuKpYiEYslmsvrXFxf+BHl9S5fAphXGQJTB0oYfHzXRSyYMRy7zXhEJtgk5oSmNiv1Abp2S/8RzK9z65PF5Odl8uymw0wbEXslmv0TAVdeM42yGiNQ4ubzxuJq9VKhNVuf0GzxGr/bFo+KuaxpUaor1bcjk/z8fFVcXNzf0whJeZ2L257+lGIzO9q/EFysr9D6k3N/uYG5p2Xxu2+EalQ4sAhV+FEwnPRPfHs28ybk9vm8IpVbnywmJ83BkepG/rW3Aq+CG+eOHlDlbkTkE7OlQju0WakPyE134GrxAIaNW6vukUFedgqHqhr6exp9huVf8A/VTE20+WzrWSn2/phWxFK4LJ9/FB/m3c8rfGbIWCr/roVDH3G8zkWGI57Vt52ncxwihDFZKRysaPDZ3wc6Vnivv63A6fb4Xl/10Acx8dDrCkXLC7h0yhDfdiw57LVw6CPs8Ta+MiGXycPTdY5DhDA2O5maphY2HTwRM0XWUhI7DqOO1VIRochNd/iyxm0x5rDXDuk+YP9xJ0dqmlh4Vldab2tOJbFWZK2jRlMWtjjhyjOH6aZIAVQ63QxKTmBcTioThqXHjMNeaw59wK9e3wPA7rLafp6JxiKcImvldS4WPvwB1zzyQVRqFf7zX33buQzxK6pnE8hOtZOWGO/b9nhVzKyKu0LhsnzOPT2LyobmmNL6teZwCglcrb37eUVMZ+NGEqGKrCXGx7H6++cCRsjn1sM1xmsz7DOa+P3be33zv2LV+232eRScaGjm4slDyElzcMOc0TyzqSRmVsVdJS8rhTd3HafV4yXe1vmaeiA0lQpLOIjIZcAfABvwmFLqwYD9FwK/B84EFiul/um3z4PRChSgRClltRZ9GsgHWoBNwK1KqRYRmQe8Ahw0z3lJKXVf9z5e/2L1Ml67rQyP0r2MIw2ryJq/g9bd6uWKP7zf7thoyk3pzIRkjxeGZySRl53SZhUca72ju0JedgqtXkVpdRN52SkdHlte5+LK/3mfCqc7KhcVFp2KQBGxAQ8DlwOTgSUiMjngsBLgJuCZIJdoUkrNMP8W+I0/DUzEaCOaBHzXb1+R3zlRKRjgZCKNR2efRiRWkbVwCpHGCVwWJcXXipYXkBuiL0OC7WR72ie+PaePZxa9jDUFwi1PFndoYpywYj1z/nMD5fVulIru0NdwfA5zgP1KqQNKqWbgOeBq/wOUUoeUUtuBjj1ebc9Zp0wwNId2PagHAgcrjTj66/NH6RDWCOSJm+dyTUCggE0gJ7VtzL9RXkK4/Zkt7C6rjdjwV/+HUyBxAq98/3x9H3aDvCxDOOw77gxZ/mb83cE1tjghKhYVgYQjHEYAh/22S82xcHGISLGIfCwiCwN3ikgCsAx43W/4HBHZJiLrRWRKsIuKyC3mdYsrKiq6MJ2T+NeYOVXYzTagVU53TDmzoomG5lbG5ab6NAiPggpnMwAjM5NIjDf2/GtvBZsOnuDaP34YsX2oAzsO+uNVcMWqIv5RfFjfh11gwor1zH7gbcAwQYbSBkL9v19z1oiotBaE43MIpnV3pebGaKVUmYicBrwjIjuUUl/47X8E+LdSqsjc/hQYo5RyisgVwMvAuHYTUOpR4FEwymd0YT4+Vm3Yx+aD3jU34AAADxhJREFUJ3hw3WccqGyg1eslwRZH4bJZPf4yA+2+b+0p187oCKVwWT63PlnM3NOyePrjL9vc3FaDJoCGZiNhzNVifK+R6IfITXf4eljEx0Gr14xEUoaz/bKp2ufVVSzf4WvbyvAG8R2Ov3s9zZ7gRpO8rGSc7ta+nG6vEY7mUAqM8tseCZSF+wZKqTLz3wPAe8BZ1j4R+TmQA/zY7/g6pZTTfL0OSBCR7HDfLxwmrFhP3p1reWqjUZb3pS1H2Hq4hp1H6thSUtOthKjAsMfVt52L3XZSrsZSZmU0YpVK6OoqQ4g8k0GJWRLkz9+czbjcVJ9gaPZon1d38K9cC+19h4Eag01g1phMACrq3dwfpY7+cITDZmCciIwVETuwGFgTzsVFJFNEEs3X2cB5wG5z+7vApcASpZTX75yhIkazQhGZY86xV8shWjVmQmElRHXFiWSFPW4pqWHOAxu4YtX7NHtOPmpcLV5e3Vamf5gRjGWSCaYqh3Ja52WnRNx3On3UIDKSEpg3IYfTclK48ewxumxLD6l0upk/IQeADEcCpdWNvkVmYDi0R8H4IanEYWibkWh+DIdOzUpKqVYRuR14AyOU9S9KqV0ich9QrJRaIyKzgdVAJnCViNyrlJoCTAIKRcSL8ZB/UCm127z0n4AvgY9MWWCFrC4CvicirUATRmhsr5aOtVYCnWElRHVkMggn89TiwvE5Yc9R0/fkpjvISEpAYTgRvQpS7TYyU+yMGpzMrrJaapvamggOVjaQd+da7DZh7wNX9M/EA9hSUoNSigqnW4eq9hKFy/KpbWxh+n1vUtPUwsjMZFbfNrFd/ojFs5tOumkj0fwYDmHlOZjmnXUBY/f4vd5MkGgjpdSHGKGqwa4Z9L2VUg8BD4Uzr55Q6XRz49ljOOF0s27nsXb74wSumj48qH3WSnD5xVWTmTQsnUOVTmqaOrYrfn3mCP7r+tgoDR3NWPeFf1KY9YC99cliUhLjOeFs5t/7jEqdVp7EVdNDa6J9SXOrl73H6vESnYl7kUqonizBsAr1vb2nHI9XRW1+U8z3c7j1yWJ2l9XR7PHS4GrF2XyySmVuWiKv/fD8NmYDK8ElWKhgKMbnpjI2J0VHiAwQ7l69g6dDPBj6c3UYSouNthVrJFJe5zISWrcfxROiH7nFjXNHo4BnTJ+mAEsjtA9ER/0cYr58hv8D22ruUe9q4ZWtZZTXt81w7IoJCeBrU4eSmZrYZvWpiX4qnW6unTmCgxUNbDHLU4jAJZOHcP/CqZTXubjlyU8QoVci38KlaHkBN/9tMzuP1AE6I7838TmlOxAMF03MZdigJF8Jkq+dOYzXth/l3NOzgvp6+us+CZeYFw7+FC7L75L6GIzxuan8fvFZPpOEtvMOPCxBf/fqHT7hoBTsPVbP7c9sYVRmUr/UZMpNd1Bnmjd1U6nep9LpZsmcUTy/+TBWrInlmxqfm0q8Tdr83j1exYY95YwbksYvFrRP1+pK7a7+qNUU82alQMrrXKxcu4dX/Zqi5GUl88jSmdz8t80cqw1uTvJfNWgtITY47a61dGJh8NEXpp3jtU2c/ct3GJGZxKPL8tv5TDQ9I5TlIE7g4slDgv4/X/3Q+3xR4eSdn8zzPdQ7yosIdZ+sWL2DpzaWBDV19wRtVuoCuekOXtvetlvWoarGkFEJEHzVoBn4fHzXRdz76i7W7mgf0GBhMx8c9/XBvfHL9Z+hgKEZDl9TKU3vYSXDvb7zGM2t3rB6wbtbvTjdHn7/1l7+89ozASMvIjD8FaBgfDa/um56m7FAQWKF2fdFdJwWDkG4cFw2h6oaOVTVGHS/CKTYbUwcms5Es/mHXp3FHrnpDtYHiXTzx6MgxR7fLqihN00EgSva4kPVOhv/FGD5HVo83k7NdoHfyTObDvPMpsPtjvOntMbV7lqhBEmzRzFhxXqKlhecMnOTNiuFoKOIFDAiEiIx+kDTt9z0l41BFxK5qXbKzfpMozKTKPrpfJ8D8nB1I1XOZnLTEnni27P5xau7e/Tj9kXSBCkNr/0NvYsVtBIs1Nkf6zt5Y+cx3K1eEmzC/Im5AGzYU+4rcRKMRLMeW7jBLyKwdE73nkfarNQNnt0UXDAIcO2sETrTVAMYVV3vXr2DQ1UlPuekgE8wAByubiLvzrXtzi2vd/vMlT1xXOemO7CBLg3fB4SbVGhpGZZJqMWjyElNRAGtXuXLj8nLSuZwdZMvPNZqNpWdksjPXtnJG7uOdzonqzR4byfaaeEQgo/vusgotrW1rE0d8mtnjuC/rtPJbJqTBCbOlZ5oID3ZzrrtRztcIfrT1R93oGnqgy+MCjNnjsxg6ohBuqNbBFDpdLN07hh2HqnlYKWTtTuOMi43FYCrpg8jPcnOUx9/2eYcq9mUACMykwDaNaQKRW+XBtfCIQS+uGbahqtFa4VFzakj2GqyK5FMFv4tSkPhb5o60dDMOf+5Ab8SXmw9XMvWw7U+04Sm/7Dui1+//hnbDteAwOEThvmxqdnDqiVTKT3RwL/2VrZ7+CtOVgT235eXlcyRmiZaPO1vrt4uDa7voA6wVoSv/eACbjx7jM5y1oTNhePC6zDnj7VqnLBifcheI+f8cgNbD9dQ5WxGKQh8Rujqv5HFhBX/v73zj63qLOP459tuQLYhGxQWYOuAQTIXs2y1LiRM/sCoGyYWo2ZEgz+yZP+4REOWiCEmxJg4TPwx45xMt+iME83cFP9YdBEW/3FjuACFEaRUJB2EdhmMdWAL5fGP9z3l0nPv7aX33p72nOeTNOfc95ye+3zztPe57/u87/u8xM9eOYoRhn9Ong3D0ckW/v/sfSdVbKocXXcvYvfmj7FqeRsXLxktY/64mvHF1RPSjtMkNv5+b2qmyaI5s7gwcolzwyNc06rURn5jSea1f3TrrpoTlC2C3u99asJ2O42j/+z/yuYOSmtrfPtPB+gdeJ8j/YMVn5P4NEmI9w4M8vbgEPNumMHt82dPeMakJ6QdJwOSCnM9/YO0xII7a+5YcEXiuVwAKSWZ1w5w8+yZnKphTy/f/XfqsOADs3j5zXRSeehi2ML/8fX3XFFs6sTpc+w8fLmyZYugfe51LIk1rCdz5MKHlRynSWzb0Mmy+dfzxZW38Zc4NDl2ltv7wxdrHn6qJTB8tmMxv/rqvROw1mkWq1e0sWTedaOvRcgdlAbxbRs6+e66D3HtNS2jJWtbY67zvuVtmfjUh5UcJ2OStRJvnT7PhRqz2IvmzOLM+QucGx65YsKE58WmJptf7Oa53ceZ0Roq8lVbl1DrWopG4MNKjjOFSdZKPLf78lqJSrQozF5Zc8cCBgaHJu1DxKmPZFprqa8qMVUKNNXUc5B0P/A4oRLcL83ssTHXVwM/Bu4iVG57vuTaCNAdXx43s0/H9qXAdmAu8AawwcyGY1nRZ4EPE8qDPmhmx6rZ5z0HZ7ozNtE48N4Ql8xSCWvfBt5pJHX1HCS1Ak8AHwf6gNcl7Sgp9wlwHPgK8GiZR5w3s3KrxrYCPzKz7ZJ+DjwEPBmPp81suaT18b4Hx7PTcaYz5T7ok0JUd91yIwD7+85w0cw31HMmhVqGle4FesysF0DSdqALGA0OyTf7WCt6XBSKRq8BvhCbfg1sIQSHrngO8DzwU0lqdB1px5nqeM/AyZJaZistBkq3E+yLbbUyS9IeSa9KWhfb5gFnzCzpM5c+c/T94vV34/2O4zjOJFFLz6HcTLur+RbfbmYnJC0DdkrqBs5WeWZN7yfpYeBhgPb29qswx3EcxxmPWnoOfcCtJa9vAU7U+gZmdiIee4FXgHuAt4EbJSXBqfSZo+8Xr88B3inz3KfMrNPMOufP90U/juM4jaSW4PA6sELSUkkzgPXAjloeLummOPsISW3AKuDNmD/YBXwu3vpl4M/xfEd8Tby+0/MNjuM4k8u4wSGO+z8C/BU4BPzBzA5K+o6kZFrqRyT1AZ8Htkk6GH/9g8AeSfsIweCxkllO3wQ2Suoh5BSeju1PA/Ni+0ZgUyOEOo7jOLXjK6Qdx3EKSrV1DrkIDpIGgP+Oe2N52gg5kKLhuotDETVDMXVfrebbzKxs0jYXwaEeJO2pFDnzjOsuDkXUDMXU3UjNviur4ziOk8KDg+M4jpPCgwM8lbUBGeG6i0MRNUMxdTdMc+FzDo7jOE4a7zk4juM4KTw4OI7jOCkKHRwk3S/psKQeSbleiS3pmKRuSXsl7YltcyW9LOlIPN6UtZ31IOkZSf2SDpS0ldWowE+i7/dL6sjO8vqooHuLpLeiv/dKWlty7VtR92FJn8zG6vqQdKukXZIOSToo6euxPdf+rqK78f42s0L+EKraHQWWATOAfcCdWdvVRL3HgLYxbd8HNsXzTcDWrO2sU+NqoAM4MJ5GYC3wEmEX4JXAa1nb32DdW4BHy9x7Z/xbnwksjf8DrVlrmIDmhUBHPJ8N/Dtqy7W/q+huuL+L3HMYLWJkZsOEkqVdGds02XQRCi0Rj+uq3DvlMbN/kN7Bt5LGLuBZC7xK2CV44eRY2lgq6K5EF7DdzIbM7D9AD+F/YVphZifN7I14/h5h37fF5NzfVXRXYsL+LnJwqLeI0XTDgL9J+leshQFws5mdhPBHByzIzLrmUUljEfz/SBxCeaZkyDB3uiUtIZQCeI0C+XuMbmiwv4scHOotYjTdWGVmHcADwNckrc7aoIzJu/+fBG4H7gZOAj+I7bnSLekG4I/AN8ysXBGx0VvLtOVJd8P9XeTgUFcRo+mGXS661A+8SOhankq61vHYn52FTaOSxlz738xOmdmImV0CfsHloYTc6JZ0LeED8rdm9kJszr2/y+luhr+LHBwmXMRouiHpekmzk3PgE8ABriysVFpwKU9U0rgD+FKcxbISeDcZjsgDY8bTP0PwNwTd6yXNlLQUWAHsnmz76kWSCLVfDpnZD0su5drflXQ3xd9ZZ98zzvyvJWT7jwKbs7aniTqXEWYs7AMOJloJRZb+DhyJx7lZ21qnzt8RutQXCN+YHqqkkdDdfiL6vhvozNr+Buv+TdS1P35ALCy5f3PUfRh4IGv7J6j5PsLwyH5gb/xZm3d/V9HdcH/79hmO4zhOiiIPKzmO4zgV8ODgOI7jpPDg4DiO46Tw4OA4juOk8ODgOI7jpPDg4DiO46Tw4OA4juOk+D/6W8kySUmQoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mgiidf = pd.DataFrame({'Mean activity Mgii':l1[:248]})\n",
    "mgiidf.plot(y='Mean activity Mgii',marker=\"*\",kind = \"line\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding the mean activity from ssn dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_4 = 'C:/sun_project/sun_project/ssn_SIDC.txt1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>y</th>\n",
       "      <th>m</th>\n",
       "      <th>d</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1901</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1901</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1901</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1901</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1901</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43459</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43460</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43461</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43462</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43463</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43464 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          y   m   d  activity\n",
       "0      1901   1   1         0\n",
       "1      1901   1   2         0\n",
       "2      1901   1   3         0\n",
       "3      1901   1   4         0\n",
       "4      1901   1   5         0\n",
       "...     ...  ..  ..       ...\n",
       "43459  2019  12  27         0\n",
       "43460  2019  12  28         0\n",
       "43461  2019  12  29         0\n",
       "43462  2019  12  30         0\n",
       "43463  2019  12  31         0\n",
       "\n",
       "[43464 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f4 = pd.read_table(path_4,delim_whitespace=True, names=('y','m','d','activity'))\n",
    "f4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "250\n"
     ]
    }
   ],
   "source": [
    "l2 = []\n",
    "for r in f1.iloc[:,1:].values:\n",
    "    i = next(iter(f4[(f4['y']==r[0]) & (f4['m']==r[1]) & (f4['d'] ==r[2])].index),'no match')\n",
    "    j = next(iter(f4[(f4['y']==r[3]) & (f4['m']==r[4]) & (f4['d'] ==r[5])].index),'no match')\n",
    "    m = f4.loc[i:j,'activity'].mean()\n",
    "    l2.append(m)\n",
    "l2 = l2[:250]\n",
    "print(len(l2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Mean activity ssn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.953704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.287037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.898148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.944444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.101852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>0.805556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>0.685185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>0.675926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>0.792208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>1.146341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mean activity ssn\n",
       "0            19.953704\n",
       "1            18.287037\n",
       "2            19.898148\n",
       "3            20.944444\n",
       "4            20.101852\n",
       "..                 ...\n",
       "245           0.805556\n",
       "246           0.685185\n",
       "247           0.675926\n",
       "248           0.792208\n",
       "249           1.146341\n",
       "\n",
       "[250 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssndf = pd.DataFrame({'Mean activity ssn':l2})\n",
    "ssndf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x283ba999b08>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ssndf.plot(y='Mean activity ssn',kind ='line',marker='*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9798000895960419"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pe , o = pearsonr(l2[:248],arr)\n",
    "pe #pearson's correlation coefficient between ssn activity and frequency shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9819139357144375"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spea, k = spearmanr(l2[:248],arr)\n",
    "spea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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