AstropiAnalisys.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b257a457",
   "metadata": {},
   "source": [
    "# ASTROPI CSV ANALYSIS\n",
    "\n",
    "This notebook analyses csv data received from the ISS through the pandas library"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5371d51",
   "metadata": {},
   "source": [
    "We import csv file data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6ec17c59",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date/time</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>Total g</th>\n",
       "      <th>Elevation (Km)</th>\n",
       "      <th>g ValorMedio</th>\n",
       "      <th>MotionDetected</th>\n",
       "      <th>Fastmode</th>\n",
       "      <th>FastmodeCounter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19:57:14:422517</td>\n",
       "      <td>0.020252</td>\n",
       "      <td>0.030744</td>\n",
       "      <td>0.014152</td>\n",
       "      <td>0.039441</td>\n",
       "      <td>421.756310</td>\n",
       "      <td>0.03944865100063209</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19:57:14:660847</td>\n",
       "      <td>0.019520</td>\n",
       "      <td>0.031232</td>\n",
       "      <td>0.012688</td>\n",
       "      <td>0.038955</td>\n",
       "      <td>421.759487</td>\n",
       "      <td>0.039360807919806576</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19:57:14:899534</td>\n",
       "      <td>0.019520</td>\n",
       "      <td>0.030988</td>\n",
       "      <td>0.015372</td>\n",
       "      <td>0.039719</td>\n",
       "      <td>421.762669</td>\n",
       "      <td>0.03933148327459286</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19:57:15:137869</td>\n",
       "      <td>0.020984</td>\n",
       "      <td>0.030988</td>\n",
       "      <td>0.014152</td>\n",
       "      <td>0.040011</td>\n",
       "      <td>421.765848</td>\n",
       "      <td>0.039440552869117816</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19:57:15:376665</td>\n",
       "      <td>0.019276</td>\n",
       "      <td>0.029768</td>\n",
       "      <td>0.011956</td>\n",
       "      <td>0.037425</td>\n",
       "      <td>421.769035</td>\n",
       "      <td>0.039110119704977034</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50803</th>\n",
       "      <td>22:54:11:680660</td>\n",
       "      <td>0.021228</td>\n",
       "      <td>0.031232</td>\n",
       "      <td>0.014884</td>\n",
       "      <td>0.040591</td>\n",
       "      <td>418.041024</td>\n",
       "      <td>0.039541066724478324</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50804</th>\n",
       "      <td>22:54:11:921103</td>\n",
       "      <td>0.020984</td>\n",
       "      <td>0.030500</td>\n",
       "      <td>0.013908</td>\n",
       "      <td>0.039548</td>\n",
       "      <td>418.041199</td>\n",
       "      <td>0.03986316232590582</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50805</th>\n",
       "      <td>22:54:12:170192</td>\n",
       "      <td>0.019520</td>\n",
       "      <td>0.030012</td>\n",
       "      <td>0.014884</td>\n",
       "      <td>0.038772</td>\n",
       "      <td>418.041381</td>\n",
       "      <td>0.03973221236937731</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50806</th>\n",
       "      <td>22:54:12:410667</td>\n",
       "      <td>0.018056</td>\n",
       "      <td>0.029524</td>\n",
       "      <td>0.013664</td>\n",
       "      <td>0.037207</td>\n",
       "      <td>418.041559</td>\n",
       "      <td>0.03927215977934761</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50807</th>\n",
       "      <td>22:54:12:650693</td>\n",
       "      <td>0.019764</td>\n",
       "      <td>0.029280</td>\n",
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       "      <td>0.037965</td>\n",
       "      <td>418.041737</td>\n",
       "      <td>0.038816628255913574</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50808 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date/time         x         y         z   Total g  \\\n",
       "0      19:57:14:422517  0.020252  0.030744  0.014152  0.039441   \n",
       "1      19:57:14:660847  0.019520  0.031232  0.012688  0.038955   \n",
       "2      19:57:14:899534  0.019520  0.030988  0.015372  0.039719   \n",
       "3      19:57:15:137869  0.020984  0.030988  0.014152  0.040011   \n",
       "4      19:57:15:376665  0.019276  0.029768  0.011956  0.037425   \n",
       "...                ...       ...       ...       ...       ...   \n",
       "50803  22:54:11:680660  0.021228  0.031232  0.014884  0.040591   \n",
       "50804  22:54:11:921103  0.020984  0.030500  0.013908  0.039548   \n",
       "50805  22:54:12:170192  0.019520  0.030012  0.014884  0.038772   \n",
       "50806  22:54:12:410667  0.018056  0.029524  0.013664  0.037207   \n",
       "50807  22:54:12:650693  0.019764  0.029280  0.013908  0.037965   \n",
       "\n",
       "       Elevation (Km)          g ValorMedio  MotionDetected  Fastmode  \\\n",
       "0          421.756310   0.03944865100063209           False     False   \n",
       "1          421.759487  0.039360807919806576           False     False   \n",
       "2          421.762669   0.03933148327459286           False     False   \n",
       "3          421.765848  0.039440552869117816           False     False   \n",
       "4          421.769035  0.039110119704977034           False     False   \n",
       "...               ...                   ...             ...       ...   \n",
       "50803      418.041024  0.039541066724478324           False     False   \n",
       "50804      418.041199   0.03986316232590582           False     False   \n",
       "50805      418.041381   0.03973221236937731           False     False   \n",
       "50806      418.041559   0.03927215977934761           False     False   \n",
       "50807      418.041737  0.038816628255913574           False     False   \n",
       "\n",
       "       FastmodeCounter  \n",
       "0                    0  \n",
       "1                    0  \n",
       "2                    0  \n",
       "3                    0  \n",
       "4                    0  \n",
       "...                ...  \n",
       "50803                0  \n",
       "50804                0  \n",
       "50805                0  \n",
       "50806                0  \n",
       "50807                0  \n",
       "\n",
       "[50808 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "\n",
    "prova = pd.read_csv('HeaderLog.csv') #dataset creation\n",
    "\n",
    "display(prova)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e803c78c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>50808.0</td>\n",
       "      <td>0.019922</td>\n",
       "      <td>0.001699</td>\n",
       "      <td>-0.047580</td>\n",
       "      <td>0.019276</td>\n",
       "      <td>0.020008</td>\n",
       "      <td>0.020496</td>\n",
       "      <td>0.111020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>50808.0</td>\n",
       "      <td>0.031141</td>\n",
       "      <td>0.000816</td>\n",
       "      <td>0.021960</td>\n",
       "      <td>0.030744</td>\n",
       "      <td>0.031232</td>\n",
       "      <td>0.031720</td>\n",
       "      <td>0.065148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>50808.0</td>\n",
       "      <td>0.013901</td>\n",
       "      <td>0.001642</td>\n",
       "      <td>-0.081252</td>\n",
       "      <td>0.013176</td>\n",
       "      <td>0.013908</td>\n",
       "      <td>0.014640</td>\n",
       "      <td>0.094672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total g</th>\n",
       "      <td>50808.0</td>\n",
       "      <td>0.039552</td>\n",
       "      <td>0.001317</td>\n",
       "      <td>0.030580</td>\n",
       "      <td>0.038958</td>\n",
       "      <td>0.039523</td>\n",
       "      <td>0.040095</td>\n",
       "      <td>0.129499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elevation (Km)</th>\n",
       "      <td>50808.0</td>\n",
       "      <td>422.820676</td>\n",
       "      <td>7.235707</td>\n",
       "      <td>413.275397</td>\n",
       "      <td>418.040997</td>\n",
       "      <td>420.210519</td>\n",
       "      <td>429.034662</td>\n",
       "      <td>436.330827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FastmodeCounter</th>\n",
       "      <td>50808.0</td>\n",
       "      <td>0.025094</td>\n",
       "      <td>0.918822</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count        mean       std         min         25%  \\\n",
       "x                50808.0    0.019922  0.001699   -0.047580    0.019276   \n",
       "y                50808.0    0.031141  0.000816    0.021960    0.030744   \n",
       "z                50808.0    0.013901  0.001642   -0.081252    0.013176   \n",
       "Total g          50808.0    0.039552  0.001317    0.030580    0.038958   \n",
       "Elevation (Km)   50808.0  422.820676  7.235707  413.275397  418.040997   \n",
       "FastmodeCounter  50808.0    0.025094  0.918822    0.000000    0.000000   \n",
       "\n",
       "                        50%         75%         max  \n",
       "x                  0.020008    0.020496    0.111020  \n",
       "y                  0.031232    0.031720    0.065148  \n",
       "z                  0.013908    0.014640    0.094672  \n",
       "Total g            0.039523    0.040095    0.129499  \n",
       "Elevation (Km)   420.210519  429.034662  436.330827  \n",
       "FastmodeCounter    0.000000    0.000000   50.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "prova.describe().T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c0509548",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index                  128\n",
       "Date/time          3658176\n",
       "x                   406464\n",
       "y                   406464\n",
       "z                   406464\n",
       "Total g             406464\n",
       "Elevation (Km)      406464\n",
       "g ValorMedio       3873064\n",
       "MotionDetected       50808\n",
       "Fastmode             50808\n",
       "FastmodeCounter     406464\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prova.memory_usage(index=True, deep=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a67452ed",
   "metadata": {},
   "source": [
    "Now we estract only the peaks from the entire sample file through the pandas library, in order to analyse thei duration and their intensity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cacd59c1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1294989598548432\n",
      "0.039552140044214014\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5f8e459ee0>"
      ]
     },
     "execution_count": 5,
     "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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i9L2eMEJfC4wOfB8F7EiyzExglXNudypGGkZO4CIJQjc5mkdvHr3hEUbolwPjRGSs55nPBhbGlFkI3OBl30wD6p1zOwPr59BB2MYw8gLfo7fQjZFndJp145xrFpGbgMVAIXC/c26diMzz1s8HFgFXAzXAUeCz/vYi0hfN2Pli+s03jAzSGuPR53roxk+vNKHv9YR6YMo5twgV8+Cy+YHPDrgxwbZHgcou2GgYuYHfGevH6P2uqVzNo7cYveFhQyAYRlhamwKhGwHxhN5CN0aOY0JvGGEJdsZK4NJJdx79rufh3fu7vh8TesPDhN4wwuBcNEZPjNCn26OvWQBrv931/ZjQGx4m9IYRBj8GfyJ0041C7yLQfLjr+2mxGL2hmNAbRhh8r/hE6CbwjKAUpjd009qsE4a42OcSk92PefSGYkJvGGHwveITWTcxMfp0evR+dk/LsS7ux9IrDcWE3jDC4Itla5zO2HTn0fuNSlfDN+bRGx4m9IYRhnahm1iPPo159H6jEUmT0FuMvtdjQm8YYTghmhnqjIWuT+xtHr3hYUJvGGEIhm7ipVemtTM2TaGbFhN6QzGhN4wwnBBLp6Le7oGppjQeK92hmxx8atfIKCb0hhGGYJzbRdoKfXH/rodZ4h0rXUJvHn2vx4TeMMIQFMuWJtpcOiWDIFIfGKc+Tcdq7mqM3tIrDcWE3jDCEBTL1qYYj34g4NLn1beaR2+kFxN6wwhDR0JfMkjfmw6k51jO0iuN9GJCbxhhCHa2tkZoF7qB9Al9urJuzKM3PEIJvYjMEJGNIlIjIrfHWS8icq+3fo2ITAmsGygij4rIBhFZLyLT0/kDDCMjZNKjb01THr2lVxoenQq9iBQC96ETfE8A5ojIhJhiM4Fx3msu8LPAuh8DTzvnxgPnAuvTYLdhZBaXx6EbE/peTxiPfipQ45zb5JxrAh4GZsWUmQU86JSlwEARGS4i/YHLgP8CcM41OecOptF+w8gMsR59PoVuLEbf6wkj9COBbYHvtd6yMGVOBeqA/xaR10XklyLSrwv2GkZ2yMfQjaVXGh5hhF7iLIsdKDtRmSJgCvAz59x5wBGgXYwfQETmisgKEVlRV1cXwizDyCBthD7mgamifjoMgoVujBwljNDXAqMD30cBO0KWqQVqnXOvecsfRYW/Hc65Bc65audcdVVVVRjbDSNzdBSjF1GvPh1C71x0JEzLujHSRBihXw6ME5GxIlICzAYWxpRZCNzgZd9MA+qdczudc7uAbSJyplfuSuCtdBlvGBmjTXplTIwe0if0QVHuikff2hJ9Utdi9L2eos4KOOeaReQmYDFQCNzvnFsnIvO89fOBRcDVQA1wFPhsYBdfAX7jNRKbYtYZRn4QG6MviulqSptH7x9Huhaj9xsmKTSP3uhc6AGcc4tQMQ8umx/47IAbE2y7Gqjugo2GkX066owFFfrje9NwHC8+XzJQG47WCBQUp7AfT+iL+kXnn5V4XWlGb8CejDWMMMR2xnZ36KZksL6nGr4JCn1wv0avxITeMMLQUWcspD904wt9qh2yvtAX9mu7X6NXYkJvGGEIE7qJHOz6UMV+6KbU9+hTjNP7OfTm0RuY0BtGODp6MhZU6F1r13PfYz36VPfnj3NTXK7vJvS9GhN6wwhD7FSB8Tx66Hr4Jt0x+kLz6A0TesMIR6xQJhL6SBeHcooN3XQ1Rl9kMXrDhN4wwhFW6Lvq0bcL3aQao7esGyOKCb1hhKGdR9zNoZvi/vre0pjifkzojSgm9IYRhrAefVcfmvJDN0VeJ2rL8RT3Y0JvRDGhN4wwdCb0fUZC2Umwc3HXjuPfORRXeMc9ltp+WmLSKy1G36sxoTeMMLRGoKA0sCDm0ikohJM/DtufhKb6rh0HoLCvvrekKPTm0RsBTOgNIwytTVDYJ/o91qMHGDNHH1SqfawLx/EEuaAYCsvSELqxPHrDhN4wwtEagaJOhL7yQug3FrY8lPpx/ElHCoqhoMw8eiMtmNAbRhhcpK1HH+/SEYHRH4E9S9oOhdC4O7xgt/HoS1OP0ZvQGwFM6A0jDK0RDaX4xPPoASrOUJFt9CZha6qHJ8+Ctd8JfxwA8UM3XRR6G9TMwITeMMLRGol2kEJioe83Rt8btuh7zc81t37/ynDHORG6KUpTjN48esOE3ugqkcPw4ofhyNZsW9K9tEY674wFKB+r70c268BiG3+s3w+tD38ciMbo05VeaULfqwkl9CIyQ0Q2ikiNiNweZ72IyL3e+jUiMiWwbouIrBWR1SKyIp3GGzlA/TrY/ieoeyXblnQvYWL0AP1O1veGzbD1dxrCGfo+OLoNIg2dH6dN6KbUOmONtNCp0ItIIXAfMBOYAMwRkQkxxWYC47zXXOBnMeuvcM5Nds7ZlII9jeaGtu89lTBZN6Dhlj4j4MgW2P0clFbBGV/RdYc2dH6cdqGbLgi9FET7FSxG36sJ49FPBWqcc5ucc03Aw8CsmDKzgAedshQYKCLD02yrkYv4XmqqoyzmC61NUFACePOuJhJ60Dh9w2bYtxwqL4ABnl8UJnwT9OgLuhijLyiJzjdrHn2vJozQjwS2Bb7XesvClnHAMyKyUkTmJjqIiMwVkRUisqKuri6EWUZO4HvyYcIS+UxrxBPfIm9BB5dO+Vg49JYK++ALoOJ0kCKoT0Lo/QemupJeWVCqNgf3a/RKwgh9vKnjXRJlLnbOTUHDOzeKyGXxDuKcW+Ccq3bOVVdVVYUwy8gJmnuLRx9R8RVP6Dvz6I/t0Vz6ygt0u4rTkwzdpCFGbx694RFG6GuB0YHvo4AdYcs45/z3PcBjaCjI6Cn0lhi9S0Lo/cwbgMFet1T/8UmGboq6/mRsUOgtRt+rCSP0y4FxIjJWREqA2cDCmDILgRu87JtpQL1zbqeI9BORCgAR6QdcBbyZRvuNbOOHbLo6V2qu0xpR4ZRC/d6hR+8Jfd/R0GeYfu5/Fhyuic7l2tFxIBC6STFG33LcPHrjBEWdFXDONYvITcBioBC43zm3TkTmeevnA4uAq4Ea4CjwWW/zYcBjIuIf67fOuafT/iuM7NHbQjehYvRj9L3yguiywedrWGbvKzDs8sTbuog2JiJts26O1emY9wWdXrKevU1QWGIxegMIIfQAzrlFqJgHl80PfHbAjXG22wSc20UbjVymN3XGhg3d9B0NZcPgpKuiy0bM0Cdrt/6uY6FvbY4ew4/RtzTBn06Hyd+HcV8Maa8fuikExIS+l2NPxhpdo9d49E1ts246EvqCYrhuG5weSDIr6gcjr4Wtj0ZnkYp7nEg03OLH6CMHde7Yhk3J2VtQErXHj9G3NKU+D62Rt5jQG12jN3TGOqdhl6BH39mlU1Cs4Zcgp3wCjtfBnhc7OFZA6AvLAAdNB/V75GB4m/0YvW+L79Gv+y4svjD8fowegQm90TVS7Yxt3A1vfrftcL65SjDlMUzoJhHDZ6pnv62DiUnahG68p1r9eWiTmXi8aR+UDPZsDQj94Xf0zsAFMqRX3Qq7O2h8jLzHhN7oGs0pCv2GH8Cab0H9W+m3Kd20GWgsRNZNIor6QP8J0FCTuEzQo/enLjwh9HE8+u1PwsE4iWzHdms/gW+3/xuaDmhYp6VRvzc36n+x9ZHkf4+RN5jQG10jGLpxsc/RJaC1Bbb8Wj8n46VmixNCXxI+dJOIviOiY9UnOlab0A0de/SvfR7W/VvbZa5VH9jqc5JnaiBGf3y/ty+v0Ti2U9+P1ib3O4y8woTe6Bq+0Lvm8Dnfu5+DRk9g8knopYuhG4A+I+Ho9g6O1RxNiWwn9DEefWuLxvwbY/Z3fB+4lsQePUTj/b4t/nvTgdQf0jJyFhN6o2s0N3BiBIywKZabH4wKZtP+bjErrbhg6KarQj9Cf3MiMXWR6DFihT4S0yg27Vfv/WjMHcKx3fruC30wRu/vw280/LsLv7F4ZjqsvTOpn2TkPib0RteINECZNzZR2BTLnc/AyGv0c1549N7TrF3tjAXo6431lyh809pJjD4YHjvuDf7XuL3t8mO79L0sELppjWij0JRA6I/t1rDOoY068qbRozChN1KnNaLhmjJvROowHbItx1WgBp0HSJ4IfcCj94dASPXS6TNC32O98OCxEoVuXEvbNNZje/S9pREi9dHljTEevR+jjxyOZjlFYoQeYM//eesC+zJ6BCb0Ruo0H9H3Pp7Qh8ml973NvqOheEB+CX3YB6Y6oo/v0SeI07vmxKEbaHu+jgeG8z66HV6/Ddb/MBq66RPj0Qe39T36YIOzZ4m3LkeEfs9LsOaObFvRIzChT4Vdz8P+Vdm2IvPEToLhC7svKGE8er/Tr88IHbslkdDvXwU7n03NznTTxqPvaujG8+jDhG7iCn2gQ9b36EEbjnd/qf0fx3ZphlDxAM/WOEIf9OhLBuln/0GuXPHoa34Ob37HOofTgAl9Kiz9LKz6h2xbkVl2PQe/H9DWA/Q7X8sSePQ7noqm8/n4AtdnBJQObis++1+PNqCvfx1e+7v02d8VXByhT/XSKR6oc88myrwJhm5iY/TQ9nwFhf7A69o5e2i97rtsWPTJ3BMefeC/OBGj3x4dSvnAG/qeTaGv3xBt4P3nAxq2ZM2cnoIJfbJEGuDoVjiwKj+e6kwXO5/RePzhjdFlJzx6X+gDHn1TPSy5Rr3MIL7A9R2pnmSwIVh5M7z6ae1Y3L9SJ9TOBW8umEff1dCNiDZyHYZuYjz6eJ44aOimqFw/73zGs7UJ6l6KdsRCNEYfG7pxThveAWfrgGv+XEHZFPq1d8BLH9W7R3/8/iPWOdxVeofQr7gZ1twZruzeZdHYczz8WYIih+Dwu102LW/Yv1zfg55oR6GbY7sApzndQRp3qKdaMliFPpgyeLQW6t/URjRSr9vnQgZIvM7YVIUetJHrKHQTOwQCRIcziPXo+47WdXUvR5cf3RbtiPXtDoZuisr1c/Nhret9R0azgUCX+QOvtTZD7cLMOTWH39Z6te2P0WynZAZzM+LS84W+NaJe5Vv/Bke2dVy2YTM8Mw3W3524THA6uP0r02NjruNaYd8K/RwUqOYOQjd+h2Csd9i4Qz1akbYxet+7BHj7p9HyhzsYLiBTxEuv7Mql02dEx6GbWI8eVNChfWdsWZXur7VJY/J+Q9QnIPQSE7opP1XvDI4Gwmh+J7HfQPgjXO58Gv5vFmz5bWq/NRmc07F4QOPzPib0XabnC/2BNzT9rDUC6/+j47JbHwUc7OhgbpRD6/ViKiiFAxkS+r1L9SnIbHFoYzQs0xgnRl9aqeekjUefSOi3RzskfaF3nufvC+qWh6Ies3/hZ5rmRm9I4Zb0Zt2AimrjjvhDRgRDN36MHry0TGnfGVs6NOqNDzwH+p+pn2NDN75HX1Ciobamg237S/qO0s9+vN7/345s1fcNPwo/xEWqHNsVvZve86Ke435jc0/odz4LL10fbrL3HKHnC33dX/R9+Ax49xdtO7Bi2faovu9fFn8AKdA/t+J0GDgpvkefbkE+uE6fVnzvofTuNxn2eWGbgtIYj94T9uIKKKpo69H7udyxqXq+Rw8q9K0RaDnadr+tx2HgZO247GgAsGRo2Kzn0mfvMnjmYg0RxKPm5/DyX8PST0eFJh1ZN6C/v6Ux/rDDiUI3xf3bp6Me2wNlQ6Pnc8DZMOAc/RwM3fihmqYDes5LBsUReq+xiBV6vy/hwKponn1XcK7t/xDEb9RLh+h7+en6m8KE7zb9Sl+pEjkMz14Cb32/43Ir/x5euErrzeYUj7fhHnjnZ6ltmyKhaquIzBCRjSJSIyK3x1kvInKvt36NiEyJWV8oIq+LyBPpMjwhR7bpn+XHFPe+An1Phsl3acfejkUJtnsP9i2DEVfrtrtfiF/u0Hqd/3Pw+Zoh4ns5Lcfg+Q/Ckpntt9nyECz5UGqNwH4vZLLvteS3jYdzmh7a0eQXsexbpmIx5ML4oZuichX75hAe/dEd0TCBH3c+vj+6X19oBp+vDWqi0M2+5eFz8I9u15Dcc5erp77lYXj2Ytj7KrzyyWi2SZD9K1Vwt/wGVtyov7HPiPQIve89xxtIrM2TsSXR5cXlUDIw6oC0NmsoprQqej4HnK1ePbQV+kGTVLAP10SFPnIwKsbT+4oAABmGSURBVOJ9RkDlhRoeqvSF3gvdNO7QY5RWwpp/bp9imyw7FsGiiZojH4sv9GP+Vt8HTtSJ1mOHVY7laC0smwtLP9NeqFua2qf9xvY3uFZ49VPqFG73JOrAas0ACrL7Rdh4j04oM7i6bb9IWHY+C6v+XoeZcE7/x87mEU4DndZWESkE7gNmAhOAOSIyIabYTGCc95oLxDZXXwUyc5/z7i9g9W2w/U/6fe8rUHWReuClVSpy8dj6B30/7269qP0shiCtEb1YfKGP1GvM3rVqtsiuP8OuZ3V+T5/mRlh1C+x4Qtd3Ru2fYPU34e3/1ApwYLUu70p/QKQB3pmv77WPwfNXqoCFwTk9h4PPVyEIxpb90E1RPz1nkU5i9JHD2hgEPXpQwfaF/uSP6fvgKVAxLr7QH9sDz1ykKZixHK6Bxl3R760R9cyP79c0xXd/ASu/ohfqNW9pY/Pite2fiziwCkbMhOm/hgvvh+tqNeQUZs7Yzqg4LWrrobfhf0dHvdZg6MafNxb0jinYp+GnXJYNjYbCBpyt5w2g38nR4w06T9/r/qK/t9hrMA5t0GuiuBxGfwSu2xoN+fh3Ykd3QL8xMOUeFba/zG7rJNS9CjW/CP/b/bvDzf/Tft3hd/S3nzLH+z0TtT+h+bCG9o7v10lT1t+t9fLlj8PyL8Ob/6rX4Ihr9doP9ics/yI8dV40/NbcqOc72A9XswBqH9cGuP5N3fdfZsOfL9P6vvc1WPc9TasuPw2m/Eing9y3PH5W2P5Verf42EhY/wNdtvEn8Jc58OoN2oAf2wP163T00Wcu7PbO7jC1dSpQ45zb5JxrAh4GZsWUmQU86JSlwEARGQ4gIqOAa4CYPLtuwu80XH+3evdHa2HIRXrRDLsCdj+vf2TtQnjuA/DyJ/T75gf04h9wFgy9HHYubu/1Hn5XL8QBZ+kcoAWl8NZd8PZ9OhfoKX+j5XYHGpOan2vssbAPvLsgurzu1baCBOpFvfpJ3eeKGzWUdNDzNg+sjn9HsP1JWPaltrY27oK134Hnr9Lb5BU3wfIvwcqvwtpva5ldz7Xdj3OwY3F7L3nLbzRHe/RHvbTAQGy5uUHPQUFx5x69a42KuR8miCf0p34Oxn4aRl2nHv3R99p7PFsf1f/hvUe0cVn7HW2YmxvVU3/p+mjZnc+q537hL1U4Vt2iIll9LwwYD5d7HtwzF8Fmb+jk5qMqgoOmwNi/hdM+CyX+w0dpyLqpGKfvh9/WcMjRWji4VpcFQzcQjdMXlUc9cYg+FVtWpXPTjv6o3nENnwFXvqAeuo8v9K3HPY9+oJ6/PS9HGwYf/yGrE6GbHfp/jf0knH8v1P6vNpYAx/ZqR+2yubDZcxycg+U3JZ7IpN77nVt/r07XYyPhtbmaK3/4HRX2wdUw4XYY+yn9DhrCe+Vv9e5yzbe8MfR/ryGQmvlw6qfhssf0Wl/+Ze1biDRoHWl4V8uCOi2NO3QfDVu0Xm74EVROhbO+oXXx0Hrtlzpep2HTZ6bBG/+o9X3a/VDUF6ou0T6lfSvai/S796ujUNgHNv5YG9XXv66aUjIALvmdltv2R7XvwOrEkYY0Eaa2jgSC6Sq13rKwZe4BvgF02GSJyFwRWSEiK+rq6joqmhjnNNRRVKHex8t/rcurLtH3Ye/X29V1/6oVdP8KFei37tIL7TTvAZ2xn9Lc3de/0Xb/u7wHOfqP19b/zK+qZ7L6Np09aPqv9ELZ9Zx6aK9/XZ/sG/Z+GPdlbVwad8HGn8KzF8GLMeGcml+q2F+1VD2tHYu0EhQP1Jiun/Gz9ffaQK25E/7vOq3o/vjurS0aa1x7hwrcMxdqLLH/eNh0PxxcozHQPS/o+Tq6Xb22lV+FJTM0lOEL+ZH3YMVX9OIZd6OGCFqbotkbzQ3qDYKe83idsb5n+MR4vVAhsUdfWqnCNf0BTdksP10voiOb215M7z2sMevmBt3n2jvU7rd/op7S3leifTN7lqgHdfLH4cyv6Hgxoz8KlRfo+kGTYcYqGDJdb9/XfU/PkWuFwee1r2PpCN0U91dP/PA70YlXToxnEwjdQNSjPxG68WLt/p1V6VC9Q7j0D3pnJaLeZnAaw9LB0O8U/ewLPah4Dor5jSWxQr89+n+dcZPWhXV3aeO76mtqy6DJ6jnXb9CG65379D+Jx8G1WrcjB+HFa7Rh2/yg1tkDb0D5OJ3cZfL3tKEvH6vbvfIpzQA6+x/1v3n96zDwXJi6QO9kzv4nvdu66H/0P37109ootTTqNbn+P7Re735eG2spVGdq+5+0wT3zq9Gwlx/rP+0LOpz2GV+Bjx2E6/fA0Mt03ZCL9L1mgTZW78yP/sY9L0LVZXDOHZrq+vo3tJG9/Gm4dgOMmqUN2Fv/rsuLKjrO9EsDRZ0XQeIsiw2YxS0jItcCe5xzK0Xk8o4O4pxbACwAqK6uTq17/+g2bYUn36WVsX4dnP+T6AU77P36vuZbGoq4cgk8caa21kX9YIx3y3jKx1UoNv5IL/r+Z+gDJRt/BFWXRi+Os29X78a1wIULtKINu0Ir5O4X9MGqIRdB9U+0Ym34gd42umatVPtXaNzzyBZdv+dFGPo+zzP7K9j2B62o427Ui2f/Sjj0lt5WFpRoIzXkIu3MXPsdjW3u+rN6MBf9FoZMgxdm6IV6+ZPq7bY2w+lf1PDF9oXw0seiU+VVTtXG5b1H1GtZ9gVdN+0BvfiCj++XVqrQFlXosuLytmOv+ELffFgbgGD2TCKh95f7+J7v09UqrCd/XMMpdS/BOd9Rgdi+UBuEhhptcAedp+d9/X9A1cWwe4l6t0V9YMyn9A5n/N+3PU5ZFVyxWDte3/jH6J3ZoBhvF9ITuvF/2+F31OuDqNC3JhB6P3RzZAv87ymc8JvKhoY73qDztOEuGaSOg09HHn1zo/43/v8iAhO/pf1Qi6fq3ebEf9H6tGii1qlSz549L2poquL06L6bj+qys/9RBTJSr+e9tUk9ZxyM/HBbe/qN1f++sVbDquNv0X6CDT+AKT+Ak66E078QLV9+qt55vPY5vRPtO0ptXDZXHbVdz2l9OPnj2lDtfFqzkEZ/LNq4bX5Q38/7d6j+KRSW0I6yKnWetnghqDX/rNdfy3EN/4yZo7+loEQ1ovz0qHMBMOxKXV5xhp6/1/9BQ0HBMmkkjNDXAqMD30cBsU97JCrzMeDDInI1UAb0F5FfO+c+mbrJHeB3XA69Qv+4ovK2+cQVp+sff3Q7XDBfxWnCberNnjJbPS2fKT/QClb3kgpf034V30v/EL3YSwZpRUWiHWwnfUA9CSnQhmTopdF9Tv8f9eBKh6h3ueQavZsoqtDykXq1C1TQfC/9lE+oN/72T7XyDrkILn9KO6kqxqmX8uK1un7PS+oxjb5eK+g1bwFObb5qqV5Uvjf46g3awJ37r3oxj/wQPH0+vOI1eP3P0jBAf09wT4y8uF0bquaG6JOZ/cborWnkkP6eY7ujaX1+nL3qUp3L1I8fB4X+aByhHzRJw2h9hmtD+N5D0SdtT5mtIrjmn+HSR2HDD/UCPfufVIDe/K6Gxw6sggnf1G2K+kD1j+PXncISDe/sXgLv/VYbMv8/DZIOjx70At/xVFTUTwh9cwKhL1eBbj6i57qon4p+39ib6wQMOk/rZcngqEfvLw9SWKrhoqb66OxTwWMM/yt1CPavgknf1RBLQSGcc6deRwiM+ghsfxw2PQDnfje6bb1XFwedBxf/Vsv6TtiZN2uYw69rPsXlcMUz+pv9vo3Jd8HYG7R+xOPUz2if2LY/asfp2E/pXfzKr6n3PuGbMP6rmoq6+jYYN0///8IqbTiP7VJh9utnIoZdqY3nlB9qaHTjj/XuAtRhKxmg13Ht49oIBO+yTvKEfswntaF68zsaz7/k4Y6PmSJhhH45ME5ExgLbgdnA38SUWQjcJCIPAxcC9c65ncA3vReeR39rt4k8aLxMirQCBFPTfES0QkYaotkFp31B43GxXl5BEZz/o+j3SEP01jhIbAs8fIaKwMR/aSvyoHHOINPu146s07+oleLgm+rNg15Q/o3SoMn62vuqeiOXP6kXgF/RR1ytoaNVt+ixx98S9UL8OU5BL+LCUq3gZSdphT733+CMG6NlLvqNhqP8eG9R3+i6EyMv7tDb58M10Vv9U2ZrRd/6B+1QbWlUj+fQBr24QBtVfxx60IZVCrQRbdyhWRZBivrBBwLZTxHvicmWIyoI42/Ri7jPSXqxVV2snYrDLtfOr5ev17utYZcTiqJ+cM6/aIx30JT2/zWkJ48etIE+9t/R78HQTbwYfXG51rX+Z2m/Qt+T1TEIOicd4Qt60KMvqojGwIOUDNB9Bweg8xGB9z2hjbwfVgEY9yV45z/1WjrnDu2k3PQAnP1NPa8Q7YcYeI7eJQeZ9F2gAEbGdv+hohikoCixyPs2Tl2gjeO4L6sWnHd3NJTr72/EDH0FGTARjj0fzrOefBdM+IY6Ljuf1oyfqov0Lm2wt/2pn1MHaOyn2m478loNF42bp/1bp38RNtwNDXdB+ZjOj50kndZW51wzcBOwGM2c+Z1zbp2IzBOReV6xRcAmoAb4BfDltFsahv0rtRLFE3mf0z6vrblPUR+44L62t5jxKC6Pf+HHUnEazNqqQt8ZfUfBpG9rSKSoX1TkQT3KIdNUEIor9FZz2BVwxVPtL24RuOyPGvuTwmhfQyJEtIL3GameVJCBE/WWdfRH24o8RMe0adyhjcHBNdFjVV6oXtCWX0fDNv7DO37YJjbMIAUqOsf3aaMT69HHUlwOp96gogLaiPnDL5RWqvcmBfp54rc0vlpQrPH3sJz2d+qNjf5o/PXp6IyFaFjK5/heL90uUeimXO/srn1LxbmgSH9nWIZM03M9cGLUox98XvzfUewJfTDPPkhZVVuRB7V5+q9h8r/DoHNhwtd1+1dviPavHFyrIlh+WpxjlsP5P4yGB7tKaaX2mfmiOfp6vTss7KvnIhG+Rx5G6IvLo3en59+rfSE7F2t98x2tUR+G6/dH70Z8ivrB+fdEJ+0582atWxvvCfsLkyKMR49zbhEq5sFl8wOfHXBj7HYx5ZcAS5K2MCx+R2yiCzSThL2d7oxpD0SfFBz/NX0lorAMLv2jiqwvyB1RfZ963b63FYbCUr2Adr+gccgh0/UWGrTxGPtJzerxU0FPZJf4Qj+s/T5LBml515K+8wbacVgzXxuz2AarIwqK4QNLEq9PxxAI0Fbo+53iCb0niJIgRt8VyqrgY/v1f/IfGowN2/gUD9DQTSKhT0RldfROedgVGv5cdQs8eykMvwp2PgUDJrS9y8wUInDJ7zWLqyNH0L+rHJxkrLzfyRrmfOl6zRoLUtSn8+37jtS+oc0P6p1CRzamQCihzwtcs3pxAzu4pcs3Ym9vO0MKwok8qPglI4A+/cZon0DxQLjgP9t6hGM8oX/Ti8tWePYf8kI3pVXt9zfyw9rJDeEFJQyFJfDBvxA/T6ALpC1G791BFpSqqBx8IzAccjB0E8i66Sr+HWnpEA1pxAqST9CjLyzrPFadiDO/Boj2L629Uz+HudPtLsqG6Ksj/I74qouS33/5WJjZhXkqJv0/7S9Ls8hDTxL6guKOPV4jPVz0Ww2zVE5rn41QcZqGmLY+4n33hL7hHfVI43k2U36goai3vqfx53RSFqdh6SrpitEX9dO7jdJKDT/tfq7tKJk+hYE8+nQhBRquTETJADi0S2P0/gB0KR1HoneiLce0UUt1X5miuFxDgNmg3+jOy6RIzxF6IzP0P6PjO41z7tC0T1zUaz2+T+P38RCBSXdqyl28NLZcI12hG1BBCebG+09ZdkfoJhmCHn267rK6wUs1wtPzBzUzMsuAszRu329s287CzvK980HkIX2dsaAZPmfeHB3Ey+/EDnr06QzdhKV4gDbOB16P3pUZeY159Eb6ufAX+pBUYZnerrceD/9gT66TrtBNEF/oG3e2PQboOfSHxc4UxQO0o76lMXthDCOtmEdvpJ+CYk01g2iefU8R+nSGbnxihT4YuikZqOszGdv2/7Mh09um/Bp5iwm90b34j9THS63MR7rToz/mDXIXDN2c9Q14/7PpO1YY/CybMy25oadgoRuje/GFvrSHefTdGboJPhkbJiUw3Yyapamzo6/vvKyRF5jQG91LcU8L3aSxM9bH77Ru8CabL8xgPD4eJYOiTx8bPQIL3RjdS0kPC910R4y+sEzz5Hc8qaMd+sNqG0aaMKE3uhd/AK2e4tF3R4weNHzjWuHkv+6eB72MXo0JvdG99LjQTTcKPejQBIaRZixGb3QvAyfq+DipjpeSa6Rr4pFYKsYBktxIm4YREhN6o3s57XP66il0l0c/7X6ddCTXx4Ix8hITesNIhu7IugHtkM3C6L1G78Bi9IaRDN2RdWMY3Uyo2ioiM0Rko4jUiMjtcdaLiNzrrV8jIlO85WUiskxE3hCRdSLy7XT/AMPIKN2VdWMY3UintVVECoH7gJnABGCOiEyIKTYTGOe95gI/85YfB97vnDsXmAzMEJEO5vEyjBynu2L0htGNhKmtU4Ea59wm51wT8DAQO4PvLOBBpywFBorIcO97g1em2Hu5dBlvGBmnciqcdWvH844aRo4RRuhHAtsC32u9ZaHKiEihiKwG9gDPOudei3cQEZkrIitEZEVdXV1Y+w0jsxT1gfP+I7m5dg0jy4QR+nj5XrFeecIyzrkW59xkYBQwVUQmxjuIc26Bc67aOVddVWVPBhqGYaSLMEJfCwQnMxwF7Ei2jHPuILAEmJG0lYZhGEbKhBH65cA4ERkrIiXAbGBhTJmFwA1e9s00oN45t1NEqkRkIICI9AE+AGxIo/2GYRhGJ3T6wJRzrllEbgIWo4903O+cWyci87z184FFwNVADXAU+Ky3+XDgV17mTgHwO+fcE+n/GYZhGEYixLncS4Kprq52K1asyLYZhmEYeYOIrHTOVcdbZ8nAhmEYPRwTesMwjB6OCb1hGEYPJydj9CJSB7wXZ9UQYG+GzQmD2ZU8uWpbrtoFuWtbrtoFuWtbd9h1inMu7kNIOSn0iRCRFYk6G7KJ2ZU8uWpbrtoFuWtbrtoFuWtbpu2y0I1hGEYPx4TeMAyjh5NvQr8g2wYkwOxKnly1LVftgty1LVftgty1LaN25VWM3jAMw0iefPPoDcMwjCQxoTcMw+jpOOcy9gLKgGXAG8A64Nsx629Fx7EfErP8ZKABuNX73hd4Eh0Jcx1wV6DsZ4A6YLX3+rtM2uYtWwJsDNgw1FteCjyCDv72GjAmg+esImDPajSH955MnjNgDNAYOM78QNnzgbXeubmXaFix289ZIrtyoZ51cs6yVs86OGdprWfJ2uUtmwS86pVfC5Slu46l07buqGftbE1lo1Rf6AQl5d7nYu+kTvO+j0ZHyHyP9qL1B+D3tBX6K7zPJcBLwMzAiflptmwLXIDVcY7x5cAFMRt4JJN2xaxfCVyWyXOGisObCfa1DJju7fOpwP/Z7ecskV25UM86OWdZq2cd2ZXOepaCXUXAGuBc73slUJjuOpZO27qjnsW+Mhq6cUqiOWR/BHyDmNmrROQ6YBPa0vn7Oeqce8H73ASsQic7ybptnTAL+JX3+VHgShGJNztXt9olIuOAoWiFSplUbEtgz3Cgv3PuVae1+0HgOm91Rs5Zgv3kRD1LgaydsyDpqGcp2HUVsMY594a3/T7nXEu661g6beuOehZLxmP08eaQFZEPA9v9ExAo2w+4Dfh2B/sbCHwIeC6w+HoRWSMij4rI6ASbdrdt/y0iq0XkW4FKc2JuXedcM1CPtuqZtAtgDuq1BCtht58zj7Ei8rqIvCgil3rLRqKzlPkE5yXu9nPWgV3B/WWlnoWwLSv1LIRdkKZ6lqRdZwBORBaLyCoR+Ya3PO11LI22BfeXtnrWhq7eEqT6AgYCL6Axq9eAAd7yLURvde4GPu59vpOYMAR6K/QU8LXAskqg1Ps8D3g+07YBI733CuAZ4Abv+zpgVKDcu0BlJs+Zt/wt4PwsnLNS//ei8dJtQH/gAuDPgX1dCvwpg+csrl05Us8S2pbletbhOeuOehbSrluBzehYMn3RePiV3VnHumpbd9cz51z2hN4z/A7gW2hruMV7NQNbgZPQWz5/+UFgP3BTYPv7gXs72H8hOq1hxm0L7OczeDE2NGY3PfCn7sXrEMrgOTsXeDsb5yxO+SVANToT2YbA8jnAzzN1zhLZlQv1rDPbslXPQpyzbqlnIer/bOCBQPlvAV/v7jrWFdsyUs9S2SjVF1AFDPQ+90FF6dqYMluI6Vj0lt9JW6/5u2iHY0FMueGBzx8BlmbSNq+i+C14MRrzm+d9v5G2nT6/y+Q585bdRfvsgIycM6+83zF2KrAdGOx9Xw5MI9pRdnWmzlkndmW1niWyLdv1rKNzls56loJdg9AYd1/vHP0ZuCbddawbbEtrPYt9dTpnbJpJyxyyIjIK+Cc0HWmVF5r8qXPul8DNXoysGfVmP5NJ29Bb2sUiUoy2wH8GfuGt+y/gf0SkxrNtdgbt8vk4Or9vkEyds8uA74hIM9CCCtN+b92XgAfQC+Yp7wWZOWdx7cqRepbItn5kt5519F9C+upZUnY55w6IyA9RUXfAIufck97qdNaxtNnWTfWsDTYEgmEYRg/Hnow1DMPo4ZjQG4Zh9HBM6A3DMHo4JvSGYRg9HBN6wzCMHo4JvWEYRg/HhN4wDKOH8/8BsxEYv78k6f4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import pyplot\n",
    "\n",
    "#prova[['Date/time','Total g']]\n",
    "prova = pd.read_csv('HeaderLog.csv')\n",
    "#prova['Total g']=prova['Total g'].transform(lambda y: y*8.59)   #accelerometer gives values in g;we moltiplied\n",
    "#for g value at ISS altitude\n",
    "\n",
    "#print(prova['Total g'])\n",
    "meanValue = prova['Total g'].mean()\n",
    "maxValue = prova['Total g'].max()\n",
    "\n",
    "#meanValue = h.mean()\n",
    "#maxValue = h.max()\n",
    "#indexPeaks=prova.index[prova['Total g'] > (meanValue+0.021)].tolist()\n",
    "\n",
    "#searching for g peaks\n",
    "indexPeaks=prova.index[prova['Total g'] > (meanValue+0.021)].tolist()\n",
    "print(maxValue)\n",
    "print(meanValue)\n",
    "#print(indexPeaks)\n",
    "#print(type(indexPeaks))\n",
    "\n",
    "j=1\n",
    "i=0\n",
    "indexPeaksExtended=[]\n",
    "indexPeaksExtended1=[]\n",
    "indexPeaksExtended2=[]\n",
    "indexPeaksExtended3=[]\n",
    "indexPeaksExtended4=[]\n",
    "indexPeaksExtended5=[]\n",
    "\n",
    "\n",
    "#we list the peaks\n",
    "indexPeaksExtended.append(indexPeaks[0])#indexPeaks.copy()\n",
    "#print(indexPeaksExtended)\n",
    "\n",
    "#we take 200 samples before the peak and 200 after the peak\n",
    "for i in range(1):#(len(indexPeaks)):\n",
    "    #print(indexPeaks[i])\n",
    "    for j in range(200):  \n",
    "        indexPeaksExtended.append(indexPeaks[i]-j)\n",
    "        indexPeaksExtended.append(indexPeaks[i]+j)\n",
    "        \n",
    "indexPeaksExtended.sort()\n",
    "indexPeaksExtended = list(set(indexPeaksExtended))\n",
    "#print(indexPeaksExtended)  \n",
    "#prova.reset_index(inplace=True)\n",
    "#prova[['Date/time','Total g']]\n",
    "#prova.drop_duplicates(inplace=True)\n",
    "\n",
    "#we plot the peaks\n",
    "\n",
    "\n",
    "\"\"\"with pd.option_context('display.max_rows', None,\n",
    "                       'display.max_columns', None,\n",
    "                       'display.precision', 6,\n",
    "                       ):\n",
    "    print(prova.iloc[indexPeaksExtended,[0,4,7,8]])\"\"\"\n",
    "\n",
    "prova.iloc[indexPeaksExtended,[0,4,7,8]].plot(kind='line', color='red')\n",
    "\n",
    "indexPeaksExtended1.append(indexPeaks[1])\n",
    "j=1\n",
    "for j in range(100):\n",
    "    indexPeaksExtended1.append(indexPeaks[1]-j)\n",
    "    indexPeaksExtended1.append(indexPeaks[1]+j)\n",
    "indexPeaksExtended1.sort()\n",
    "indexPeaksExtended1 = list(set(indexPeaksExtended1))\n",
    "#print(prova.iloc[indexPeaksExtended1,[0,4,7,8]])\n",
    "\"\"\"with pd.option_context('display.max_rows', None,\n",
    "                       'display.max_columns', None,\n",
    "                       'display.precision', 6,\n",
    "                       ):\n",
    "    print(prova.iloc[indexPeaksExtended1,[0,4,7,8]])\"\"\"\n",
    "#print(prova.iloc[indexPeaksExtended1,[4]].mean())\n",
    "prova.iloc[indexPeaksExtended1,[0,4]].plot(kind='line', color='blue')\n",
    "\n",
    "indexPeaksExtended2.append(indexPeaks[2])\n",
    "j=1\n",
    "for j in range(100):\n",
    "    indexPeaksExtended2.append(indexPeaks[2]-j)\n",
    "    indexPeaksExtended2.append(indexPeaks[2]+j)\n",
    "indexPeaksExtended2.sort()\n",
    "indexPeaksExtended2 = list(set(indexPeaksExtended2))\n",
    "#print(prova.iloc[indexPeaksExtended2,[0,4,7,8]])\n",
    "\"\"\"with pd.option_context('display.max_rows', None,\n",
    "                       'display.max_columns', None,\n",
    "                       'display.precision', 6,\n",
    "                       ):\n",
    "   print(prova.iloc[indexPeaksExtended2,[0,4,7,8]])\"\"\"\n",
    "#print(prova.iloc[indexPeaksExtended1,[4]].mean())\n",
    "prova.iloc[indexPeaksExtended2,[0,4]].plot(kind='line', color='green')\n",
    "\n",
    "indexPeaksExtended3.append(indexPeaks[4])\n",
    "j=1\n",
    "for j in range(100):\n",
    "    indexPeaksExtended3.append(indexPeaks[4]-j)\n",
    "    indexPeaksExtended3.append(indexPeaks[4]+j)\n",
    "indexPeaksExtended3.sort()\n",
    "indexPeaksExtended3 = list(set(indexPeaksExtended3))\n",
    "#print(prova.iloc[indexPeaksExtended3,[0,4,7,8]])\n",
    "\"\"\"with pd.option_context('display.max_rows', None,\n",
    "                       'display.max_columns', None,\n",
    "                       'display.precision', 6,\n",
    "                       ):\n",
    "    print(prova.iloc[indexPeaksExtended3,[0,4,7,8]])\"\"\"\n",
    "#print(prova.iloc[indexPeaksExtended1,[4]].mean())\n",
    "prova.iloc[indexPeaksExtended3,[0,4]].plot(kind='line', color='orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de377fc8",
   "metadata": {},
   "source": [
    "we notice that the peaks are very short as the duration (one sample every 240ms in Normal mode sample; only the red trace case generated e fast mode sampling); the max value is 0.129g, so they seem to be  vibration shots, we exclude a reboost phase of the ISS. "
   ]
  }
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