eleijonmarck/overspeed_n1_ias_correlation.ipynb

## overspeed_n1_ias_correlation.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overspeed N1 - speed autocorrelation investigation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Background:**  \n",
    "The flights with overspeed are at high altitudes. These are often in situations where the plane\n",
    "is more responsible than the pilot for flying the aircraft.\n",
    "\n",
    "**Hypothesis:**  \n",
    "The engineers for aircrafts sets a limit into which the N1 reacts to wind. This does not\n",
    "work in high altitudes as the reaction time needs to be more precise.\n",
    "\n",
    "**Constraints**\n",
    "- Altitude higher than 29000\n",
    "- \n",
    "**Output:**\n",
    "- N1 correlations with different lags to IAS.\n",
    "- delay time and reaction time for N1 to interact with IAS\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder = 'data/raw/overspeed_flights/100/'\n",
    "files = os.listdir(folder)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame()\n",
    "for file_name in files:\n",
    "    if '.gz' in file_name:\n",
    "        df_i = pd.read_csv(folder+file_name, compression='gzip')\n",
    "        df_i_high = df_i[df_i['ALTITUDE_STANDARD'] > 29000]\n",
    "        df = pd.concat([df, df_i_high], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.467561e+06\n",
       "mean     8.243016e+01\n",
       "std      4.766186e+00\n",
       "min      0.000000e+00\n",
       "25%      8.100000e+01\n",
       "50%      8.300000e+01\n",
       "75%      8.400000e+01\n",
       "max      2.550000e+02\n",
       "Name: N1_1, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['N1_1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def crosscorr(datax, datay, lag=0):\n",
    "    # https://stackoverflow.com/questions/33171413/cross-correlation-time-lag-correlation-with-pandas\n",
    "    \"\"\" Lag-N cross correlation. \n",
    "    Parameters\n",
    "    ----------\n",
    "    lag : int, default 0\n",
    "    datax, datay : pandas.Series objects of equal length\n",
    "\n",
    "    Returns\n",
    "    ----------\n",
    "    crosscorr : float\n",
    "    \"\"\"\n",
    "    return datax.corr(datay.shift(lag))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cross_correlation(x, y,lags_min=0, lags_max=0):\n",
    "    ## gets the cross correlation between two series for multiple lags\n",
    "    x_cross_y = []\n",
    "    lags = range(lags_min,lags_max)\n",
    "    for lag in lags:\n",
    "        crossc = crosscorr(x,y,lag=lag)\n",
    "        x_cross_y.append(crossc)\n",
    "    return lags, x_cross_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysing all the flights to see the correlation over multiple flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ias = df['COMPUTED_AIRSPEED']\n",
    "#ias = ias[100:5000]\n",
    "n1 = df['N1_1'] + df['N1_2'] + df['N1_3'] + df['N1_4']\n",
    "#n1 = n1[100:5000]\n",
    "lags_min = -100\n",
    "lags_max = 100\n",
    "lags, n1_cross_ias = get_cross_correlation(n1, ias, lags_min, lags_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10bedd668>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lags, n1_cross_ias)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### we can see that multiple flight generate the same correlation graph but less \"intense\" correlations\n",
    "\n",
    "from these plots\n",
    "* `t_d` (delayed time) which is the time at which the n1 is contributing to speed.\n",
    "* `t_r` (reaction time) which is the time from the `ias_delta_max` "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysing one flight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "for file in files:\n",
    "    if '.gz' in file:\n",
    "        df_1 = pd.read_csv(folder+file, compression='gzip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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>wo_uuid</th>\n",
       "      <th>frame</th>\n",
       "      <th>superframe</th>\n",
       "      <th>subframe</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>aircraft</th>\n",
       "      <th>flight_date</th>\n",
       "      <th>flight_datetime</th>\n",
       "      <th>flaps_actual</th>\n",
       "      <th>flight_phase</th>\n",
       "      <th>...</th>\n",
       "      <th>VERTICAL_ACCELERATION_5</th>\n",
       "      <th>VERTICAL_ACCELERATION_6</th>\n",
       "      <th>VERTICAL_ACCELERATION_7</th>\n",
       "      <th>VERTICAL_ACCELERATION_8</th>\n",
       "      <th>VFE</th>\n",
       "      <th>VMO</th>\n",
       "      <th>WIND_DIRECTION</th>\n",
       "      <th>WIND_SPEED</th>\n",
       "      <th>WING_ANTI_ICE</th>\n",
       "      <th>YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13618</th>\n",
       "      <td>07460de5-1dd1-42f3-abf2-0c6a2d6da6ea</td>\n",
       "      <td>13618</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>airbus</td>\n",
       "      <td>A380</td>\n",
       "      <td>2018-05-02</td>\n",
       "      <td>2018-05-02 14:14:59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.992188</td>\n",
       "      <td>0.996094</td>\n",
       "      <td>0.992188</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>325.898438</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 184 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    wo_uuid  frame  superframe  subframe  \\\n",
       "13618  07460de5-1dd1-42f3-abf2-0c6a2d6da6ea  13618           0         2   \n",
       "\n",
       "      manufacturer aircraft flight_date      flight_datetime  flaps_actual  \\\n",
       "13618       airbus     A380  2018-05-02  2018-05-02 14:14:59           0.0   \n",
       "\n",
       "       flight_phase  ...  VERTICAL_ACCELERATION_5  VERTICAL_ACCELERATION_6  \\\n",
       "13618             3  ...                 0.992188                 0.996094   \n",
       "\n",
       "       VERTICAL_ACCELERATION_7  VERTICAL_ACCELERATION_8 VFE VMO  \\\n",
       "13618                 0.992188                      1.0   0   0   \n",
       "\n",
       "       WIND_DIRECTION  WIND_SPEED  WING_ANTI_ICE  YEAR  \n",
       "13618      325.898438          26              0  18.0  \n",
       "\n",
       "[1 rows x 184 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1[df_1['frame'] == 13618]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "offset_back = 60*5\n",
    "offset_front = 60*3\n",
    "#offset_back = 150\n",
    "#offset_front = 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_mach_max = 13618\n",
    "start = f_mach_max - offset_back\n",
    "end = f_mach_max + offset_front"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111617c18>"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_1['N1_1'][start:end].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111cc7c18>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_1['COMPUTED_AIRSPEED'][start:end].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# we take the all of the n1 combined. \n",
    "# This does not effect the correlation calculation. \n",
    "# But it is a overall measure on the impact on N_1\n",
    "n1_tot = df['N1_1'][start:end] + df['N1_2'][start:end] + df['N1_3'][start:end] + df['N1_4'][start:end]\n",
    "ias_1 = df_1['COMPUTED_AIRSPEED'][start:end]\n",
    "n1_cross_ias = get_cross_correlation(n1_tot, ias_1, -150, 150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x111daae80>]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U0Ax2NEaf9QPh/lfjs14EQ8ps8/zHo0MIhgQ+tDvzDdvixcFfJmwmHXRqFe/yZQWnY2wWgZBYsa1KpUWPYEhg0r3yYUxy9fjRIayrtmBNZW6qCaPh4C8TRIRKKx/nyApPpKfW+urYwb9Cemc8Oq284O/2BnC035mVTp2J4OAvI9UWIwd/VnBODc+gSKdesdou0u9GiWtiJwanERLAtvqSXA/lIhz8ZaSSz/JlBejU0AwuqSqGeoWeWpWW8Pkboy7l/XwclY6O3VyX3Vbxq+HgLyPVUn+fQj27lBWeUEjg1PAM1q/SRr3crAcRMDqjvLTP0f5p1JUaox4glUsc/GWk0mKANxCC0+PP9VAYy4oBxxxmvQGsr155VqxVq1Bm0mNMge+Mjw44sUVmKR+Ag7+sVHNff1ZgTg1PA8CqM38gnPpR2s/GnC+IAcccLpFRlU8EB38ZqZEaOw04lL2TkbF4nRqagYqAtXEER3uxHpOzymrrHDmHQI5nc3Pwl5H60nDw75/y5HgkjEU37fHjhy+Fu+B6A8GUr3dqeAatdnNch7PbinSYUlhP/x6pE2mzDIM/t3eQEZtJB5NOjX4HB38mP9MeP+768T6cGg7X5R/td+L+928GUfIn350amsGlzfEdoFRq0sHhUVbw7+aZP4sHEaHeVsQzfyZL//OJEzg36sJDH9+Fv72+HY8eHMBLZ5M/WN3h9mFoen7FzV2L2Uw6eHxBzPtTf8eRLT0TbpSb9TDr5TfP5uAvMw22IvRx8Gcy8+KZMfzhyBD+5h3tuGaNHX/zjjbYi/X4xZs9SV/ztPQOIp7FXiAc/AEoKvXTM+lBc3nR6g/MgbQEfyK6mYjOElEHEd0X5X49Ef2ndP9+ImpKx/Pmo0jw51p/Jhf+YAj/68lTaCk34bPXtQIIl17etasBL50bT/qdaiR9tC7OmX9pkQKD/4Q7p+eErCTl4E9EagAPALgFwHoAdxHR+iUP+wQAhxCiDcD/BfCdVJ83XzWUFWHeH8L4rPI2s7D89NihQXSOu3HfLZdAu+iUuTt21EGIC2fvJurU0AwqLXqUx7n5KTLzV0re3xsIYszlRX1p/s78dwHoEEJ0CSF8AB4GsHfJY/YCeEj6/FEA11Mqq0R5LPIfpW+SUz8s90IhgX97pRMbaizLGpPV24rQXmFOOu9/angm7nw/ANhM4YNPlDLzjzShqy6R5+FM6Qj+tQD6F309IN0W9TFCiACAaQC5Pz5LhiJVAfl4WDVTnhfPjqFr3I1PX9Matarn2rV2vNU9Bbc3kNB153xBnB+bxYaa+PvdRNI+DoUE/+Hp8H6dapmezCerBV8iuoeIDhDRgfHx5KsIlKy+1AitmtDFwZ/JwCMH+lFu1uGWjVVR779ubQV8wRDe6JxM6LpHB5wIhgS2N8bf9qCkSAciYEoh7U8iu5HzOfgPAqhf9HWddFvUxxCRBoAVwLL/LUKIB4UQO4UQO+12exqGpjwa6bDqzrHZXA+FFTiH24cXzozhtq21F+X6F9vZZINJp8ZLZ8cSuvbBXgcAxDyzNxq1ilBi1Cpo5h8O/lVWY45HEl06gv/bANqJqJmIdADuBPDEksc8AeBu6fP3A3hBcDlLTC12M8/8Wc49fWIE/qDAX2yvi/kYnUaFPW3leOnseEIVagd7HWirMKNESuXEq9Skw5RCFnxHpudRbNDIssYfSEPwl3L49wJ4BsBpAI8IIU4S0deI6FbpYT8FUEZEHQC+AGBZOSi7oMVuQu+kG4FgKNdDYQXsxbNjqC0xYl31yn13rltbgUHnHDrifLcaDAkc6nNge0PinS5tRTpMKaS/z5BzTrYpHyBN7R2EEE8BeGrJbV9d9Pk8gDvS8VyFoLXcDH9QYMAxJ8tt4Sz/+QIhvNExgdu21a7avuHateEU7Utnx9EeR4O2Q30OOD1+XNWeeGq3pEiLQacyOnuOzMzLNuUDyGzBl4W1VoQDfrwzKcbS7UDPFNy+IK5dW7HqY2tKjFhbWYwX48z7//nkCHRq1cIvjURYjFrMzCljwXd4eh41Mp75c/CXoTXS7OnsqCvHI2GFan/3FFQEXN4aX0X2tZfY8XbPFFzzKwdmIQSeOTmKPW1lKDZoEx6XVSHBPxAMYWLWu3DwvBxx8JehYoMW9TbjwvZ3xrLtSL8TayqL416svG5tBfxBgZfPrVyi/eLZMfRNeXDrlpqkxmUxaOHyBhAMybteZMrjgxCA3ZzYgnY2cfCXqXVVFpzh4M9yIBQSONLvxLYEFmQvbbKh0qLHY4eWVnlfIITA91/oQG2JEe9NMvhbjeF3C6u9w8i1yKEzcju3dzEO/jJ1SbUF3RNuRbWvZfmhe9KN6Tk/ttUnVoP/vm11eOncOMZd0ftS/fvrPTjU58S972iLuW9gNRYp+E/LPPUzIfXmirdvUS5w8Jep9dXFCAngHOf9WZYd6XMCALYmWIr5/h11CAmB//PsOQDhmf7p4Rk8dngAf//74/j6k6dww7pK3Hlp/SpXii0y85+ZS6ydRLZdCP7yTfvIc/cBW+h5cmxgGpvrEq+HZixZ50Zd0GlUaEmwzLitwox7rm7Bj17uQu+kG0POOfRIDQo1KsIHdzXgy+9al9LJX1aFzPyVkPbh4C9TdaVGlJt1ONznxId3N+Z6OKyAnB+bRUu5CZokUjNfeOcaCAG83jGBVrsZn76mFZc2laLBZoJOk3qiwWIMhyy5B//xWS90ahUsBvmGWPmOrMAREbbWl+JwvyPXQ2EF5vyYC1sTyPcvpteo8eV3rUvziC5YSPsoYMG3zKxL6V1OpnHOX8a2NZSga9wNp0J6mTDl8/gCGHDMob3CnOuhRGUxKCPtMzHrlfViL8DBX9YipXaHpQU4xjKta9wNISDb4F+kU0OjItlv9JqY9aJMxou9AAd/WdtWXwqtmrCvO7Fe6Ywl6/xYuLqsvVKewZ+IYDVqZT/zn5z18cyfJc+oU2NrfQn2JXhQBmPJ6hp3Q0VAg02+DQUtRi1m5uVb6imE4ODPUnd5SxmOD06vuMA1PD2HQ30O3hDGUtY94UZdaVFaKnMyxSLzmf/MfAC+YEjWNf4AB3/Z291ahpAA9ndNLbtPCIFvPX0aV3z7BfzFD97Ae//1NfTwITAsBT2Tbtm3EbcYNLIO/krY3Qtw8Je9nY02FOs1ePbUyLL7fv5GD370chdu316H792xBROzXnzyFwcSPkxb7gLBEB450I8HX+lE5zi3uc4UIQR6JjxoLivK9VBWZDVq4ZJx8L+wwUveM3+u85c5nUaF69dV4NlTowgEQwsbb0am5/Gtp8/gHZdU4Lvv3wwiQrXVgA/9dD+++dRpfON9m3I88vTwBoL4yE/fwv7u8Duf7/7pLL62dyM+eFlDjkeWfyZmfZj1BtBYJu+Zv9wXfHnmz9Lmpg1VcHj82Lco9fPgK10IhgT+6dYNCxtJ9rSV42N7mvGbt/pwpD8/ykMfeLET+7un8J3bN2H/l6/HnrZy/MPjJ/BW9/I0GEtN72Q4Zdgs97SPUYuZeX9CZwZnUyT4y33mz8FfAa5dWwGbSYefvd4NABh0zuE3b/Xitq21qLdd/Bb98+9sR5lJj//9zNlcDDWtxlzz+OFLHbhtaw3+26UNqLQY8MAHt6Gu1Igv/e4YL3CnWbe0XiT3nL/VqIU/KDAn0+//xKwPROHzhuWMg78CGHVqfHRPE144M4aXzo7hS48eg4oI//2G9mWPLTZo8amrmvFax4TiZ/+PHx6CPyhw7zsuvM5igxbfuG0Tuifc+OFLnTkcXf7pmXRDrSLUlcr33FlA/rt8J2a9sBXpkuqNlE3yHh1bcPflTai3GfHRf38br3VM4CvvXrds1h/xod2NKDZo8Is3e7I6xnQSQuDRgwPY1lCCtiW7Ta9sL8ferTX44Uu8AJxOPRMe1Jcak+61ny1yb+s8qYDdvQAv+CqGtUiLP957FX78aheuai/HZS2xz1Y16zV45/pKPH96DP5gKGs/zNMeP54/M4oysx5Xt5en1NSqa8KNs6MufG3vhqj3f+Xd6/DimTF85Kdv4fPvXIPzoy48d3oUOo0a917Xhndvrk76uQtV94T8yzwB+bd1nlDABi+AZ/6KYi3S4os3rV0x8EfcvKEK03P+qPsDMqFnwo33fv81fOGRo7j7Z2/hfzx6DKEUzll9RToL9rq1FVHvryg24Nef3A1/MIQv/vYofvZ6N2pKjBBC4PP/eQTHB6aTfu5CJIQI1/jLvNIHuNDWWa79fcJ9feQf/Hnmn6euXmOHUavGc6dHcWV7eUafKxgS+PwjRzA958evP3kZ3uicwAMvdmJXsw137Ezu1KZXzo2judwUM7UFAJvqrHj1S9ehf8oDe7EBVqMWDrcPt/y/V/H1P57CI5+5PNmXVHDGXV54fEE0ybzGH5D/zD/c2kH+aR+e+ecpg1aNbQ0lONib+fMAHn67D4f7nPinWzfgirZyfPHGtdhaX4L7nzmLOV/iFRneQBD7uqZwdRy/tPQaNdoqihcCQqlJh4/sacRbPVML1StsdZETt5SQ9pHzgu+8P4hZbwBlpjwP/kRkI6Jniei89GfUEyCI6E9E5CSiP6byfCwx2xpKcHp4JqkAHC9vIIjvv9CBHY2l2Lu1BkC48+KXbr4EYy4v/uvYUMLXPDE4gzl/EJe3rp7eiub27XVQEfDowf6k/n4hirQFkXuNP3DhEHc5Huji9ITHVJrvwR/AfQCeF0K0A3he+jqa+wH8ZYrPxRK0rb4UgZDA8cHM5b8fPzyE4el5fO769osWeHe32NBWYcav9/clfM3DfeF3K9sbkztNqtJiwO6WMjx/eiypv1+IOidmoVUTakvkXeYJAGoVoVgvz/4+DungpVKZ1/gDqQf/vQAekj5/CMBt0R4khHgegCvF52IJ2rpwGEzmUj+PHR5Ei92Eq5akaIgId+1qwNF+J86NJvatP9TnQF2pERXFhqTHdUVbOc6MuDAp7bZkK+scm0Vzkuf25oLFqJVlqafDXTjBv1IIMSx9PgKgMpWLEdE9RHSAiA6Mj4+nODRWbtajtsSYsZn/mGse+7sn8Z7NNVHLOt8jlVv++eTypnQrOdTrxPaG5Gb9Ebuliqj93AYiLh1js8v2U8iZXNs6OxbSPtocj2R1qwZ/InqOiE5E+di7+HEi3GgjpWYbQogHhRA7hRA77XZ7KpdikjWVZnSMZWYj1DMnRhASF4L8UpUWA7bUl+DZU6NxX3PIOYeRmfmFIyyTtbnOiiKdGm/yQTir8gaC6JvyoNWuoOBv0Miy1FNJaZ9VSz2FEDfEuo+IRomoWggxTETVADjJKjPtlcV4vXPyoo6g6fLyuQk02IqwprI45mNuXF+J+585i9GZeVRaVk/jHIrk+1Oc+WvVKuxoLM1KtZPS9Ux4EBJQ1MzfatSiV6pQkpNI2qekKA9m/qt4AsDd0ud3A3g8xeuxNGurMMMXCKHfMZfW6waCIezvmsQVbStX5Fy7NvwO7vWOibiue6jXCb1GhXXVlpTHuLnOinOjLm4At4rIO0NFzfyNWrhkWO3j8Phh0qmh16hzPZRVpRr8vw3gnUR0HsAN0tcgop1E9JPIg4joVQC/BXA9EQ0Q0U0pPi+LU7s0mzuf4KLrak4MzcDlDWBP68q1+OuqLCgp0uKNONMvh/oc2FxnTcsxgptqSxAICZwankn5Wvks0h9JUcHfIM9zfJ0eH0oUkPIBUtzhK4SYBHB9lNsPAPjkoq+vSuV5WPIib+U7xmdxYxqv+0ZneCa/Wi2+SkXY3VyGNzsnIYRYsd/PvD+Ik0PT+PgVzWkZ45Z6KwDg+MB0ymmkfNYxNovaEiOMOvnPViMsRg1mvYGMpDNT4fD4FLHYC/AO37xXbNCi2mpAx2h6F30P9znRUm6Kq4HVnrYyDDrn0D+1curp5NAM/EGBbWkK1FUWA+zFehwdUHZr60xTWqUPcGGX76zMjiyd8vgVsdgLcPAvCG0VZpxPY8WPEAJH+p3YUh9fRc4e6d1B5N1CLBc2d6VW6RNBRNhca8UxbvIWUygk0DWhwOAv07bOTo+Pgz+Tj/aKYnSMzabUZXOx4el5jLu82Bpn8G+1m2Ev1q+a90/H5q6lNteVoHN8VnYzRLkYdM57jrkdAAAetUlEQVRh3h9SXvA3SJ09Zbbo63D7UKqASh+Ag39BaK80Y84fxKAzPRU/R6UTwuKd+RMR9rSW4Q0p7x9LOjZ3LbW5zgohgBMZbHGhZB0KXOwFUu/vM+8P4s8nR9J6FnQgGMLMfEAxC74c/AtA+6JF33Q4MuCETq3CuurY9f1LXd5SholZb8yTtwalzV3bU9zctdTmuguLvmy5TikdqLyZf2ppn5++1o17fnkQH/jRmzg1lJ5qMKe06cymgKZuAAf/grBQ8ZOmRd8jfU6sq7EkVMscKQmNlfp5qzt8+6XNttQHuEiZ1OKCF32jOzfqQplJp5iAFbFwoEsSM38hBH5/aACXVBVDraKkOs9G4/QoZ4MXwMG/IJQU6VBu1uP8WOq1/kGpS+hWaUYdr3qbEbUlRrzRET347++agsWgwSVVqW/uWmpznTWjnU2V7OyIC2ur4n8HJxcXFnwTD/7HB6fROe7G3XuacGVbOf7r6NCK6ch4LfT14bQPk5OWchN6JlLfDt8xNguPLxh3vj8ikvd/s2sy6sLzvq5J7Goug1qV/Lm/sWystaJ30iPLRmC5FAoJnBudVWTwN+s0IEJSG70iR4TesrEK795UjQHHHM6l4V3xlNTaQSnvojj4F4im8iJ0T6Z+stWR/nA5ZryVPovtaSvD9Jx/2Y7b4ek59Ex6sLslvSmfiI214XcpJ4d49r9Yv8ODOX8Qa1fozSRXKqmnfzIz/9PDLjTYilBSpFtoe35qOPX/G5z2YbLUWGbCuMsLd4olj0f6p2ExaJI66PvylnDef1/Xxamf56Sun9esyUwn101S8OeKn4udHQmnAZU48weknv5J5PxPD88sFCu0lJug06jSsujLaR8mS5Hj+XpSnP0flTZ3qZJIz1RZDWgpNy1b9H36xAha7Sa0Z2gGajPppHMNuMfPYpHgn6l/90yzGBI/0MXjC6B70r3QOFCjVuGSquK09H9yeHzQaVQoUkibDA7+BaKxrAgAUsr7z/mCODvqSirlE3F5axn2d03CHwwBACZnvdjfPYWbN1Ylfc14bKy18Mx/ibOjLtTbjDDrU2rxlTMWoybhmf/ZEReEwEVdY9dXW3BqaCblRd/IBq+V+lfJCQf/AhFJ06Qy8z8xNI1gSGBLXfLBf09rOdy+4EKf/Yff7kcwJHDrltqkrxmPjTVWdE+4ZbcjNJfOjriwtjL91VXZEp75J/b9PCO921m/OPjXWODw+DEyM5/SeBwK6usDcPAvGCa9BvZiPXomkg/+ie7sjebatXYUGzT45b5e+AIh/OLNHlzZVp7xvPNGqTQ1XRt6lM4bCKJ7wo21Vcra3LVYsUELV4LVPr2THujUqosOqm+Tdjd3j6eWEg23c1bGYi/Awb+gNJeZUjr96Ei/E7UlRtiLV+/kGYtJr8Gdl9bjTydG8LmHD2N0xot7rm5J+nrx4kXfi3WNuxEICazNwL6KbLEYE6/26Xd4UFtqvGjNqkFKifZOpVYKPeX2KabME+DgX1Aay1Ir9zzS70wp3x/xiStbUFtixNMnRvDxK5pxdYaqfBYrN+tRbTXwZi/JOelwHyWWeUZYDFq4vAEEE2hYOOCYQ12p8aLbqq1G6NSqlIshnB6/Yvr6ACke5sKUpanchPGDA3B7AzAluMg3MevFgGMOH7m8MeVxVFkNeO4L1+BwnwM7mzJT2x/NJm7vvOD0sAtaNS1UgSlRZJfv7HwA1jjTLYMOD9avr7zoNrWKUG8zoi+Fd8VCCDjn/Irp6AnwzL+gpLLoe0zqjbO1Pj1dN3UaFS5rycyO3li21Jege8K9sBmnkB3td2JdtSUtx2XmSqJtnT2+ACZmfagrLVp2X2OZCT0pBP+Z+fA7EF7wZbLUVJ58ueeRPidUFC6ZVKptUsrqaIHP/iP9mVKp2pKDyMw/3rYdg45wS/OlaR8gnBLtm3QnXe7pUFhrB4CDf0FpTGHmf2RgGmsqi1GkU26mcFOdFUThX2SFrEs63CYd6ze5tNDWOc6Z/8BC8I8y87cVwe0LYmI2uXeFU9K7yVIO/kyOzEmWewohcLTfiW1p7rWfbcUGLdrs5oX+RIXqSBpKduVgoa1znLt8Bxzhd7zRZv6Rip9+R3Kpn8jMn9M+TLaayooSLvfskTpiKj1NAIQb0h3pd6alha9SHex1oNigQYuCF3uBxGf+w9Pz0KgIdvPyUuXaknDwj6SGErXQ0ZODP5Or8MJWYjP/dGzukoutDSVwePzoS7Km+/Ejg7jsm8/hr351MOUmebkghMCr5yewp7Usqf5McpJoT/8xlxflZn3U111TEj43eijJo04dC2kfrvZhMtVUVoQxlxceX/yB60i/E0ateuE4SCWL5LkjqY9EdIy58KXfHYNJr8HTJ0bw/Rc70j28jOuZ9GDQOYcr2zO/tyLTivWJ9fQfc3lRYYm+QbHYoIXFoEn6nOsptx9aNSmqT1JKwZ+IbET0LBGdl/5cVgdIRFuJ6E0iOklEx4jov6XynCw1kUXfRFI/h/oc2FJvhUat/LnC2spiGLVqHE5i0fdHL3dBTYSH79mN27fX4SevdmHMlVo/mGx79Xz4IJOr28tzPJLUqVThYBv3zH9mHhUr7E6vLS1KOu3j9PhQWqRTTFM3IPWZ/30AnhdCtAN4Xvp6KQ+AjwghNgC4GcA/E5Hy8wcKFdnU0xtn6mfOF8SpoRlsb0hPfX+uadQqbKq1Jjzzn/UG8OTxYbx3Sw0qig34xJXN8AcFXjoznqGRZsZTx4fRXG5amAQoncUQf0//cZcX9mJDzPtrS4wpzPyV1doBSD347wXwkPT5QwBuW/oAIcQ5IcR56fMhAGMAlP+eU6EiVQ3xbmg5NuBEICTyJvgD4ZO9Tg/PICC1lY7H08eH4fEFccfOegDAuupiVFr0eOncWKaGmXb9Ux7s65rC+7ZltoNqNlmM8fX09wdDmHT7Vp75lxiSDv4OaeavJKkG/0ohxLD0+QiAypUeTES7AOgAdKb4vCxJFoMWZSZd3DP/Q1J6ZHtj/gT/DTUWeAOhhBa+XzgzhiqLAdulclciwjVr7Hj1/ERCv0Ry6T/f7geA/Ar+hvh6+k/MegEgZs4fAGpLjXDNB5Jq+z3l9ilqsReII/gT0XNEdCLKx97FjxPh2rmY9XNEVA3glwA+JoSI+tNCRPcQ0QEiOjA+rqy300rSWFYU9y7fQ30ONJebFPeWdiXra8K7lE/G2d45EAzhtY4JXL2m/KKc7lXtdrjmA3FfJ5c6xmbx4KtdePematTblm9yUiqLMb62zmMzUvBfIe1TI7V5TqbiR2m9/IE4gr8Q4gYhxMYoH48DGJWCeiS4R30PTEQWAE8C+IoQYt8Kz/WgEGKnEGKn3c6ZoUxpKjPFNfMXQuBQr0Pxm7uWaqswQ6eO/9zWowNOuOYDuGZNxUW3RyqH5Nop1B8M4XCfAz95tQt3PvgmjFo1/uet63M9rLSyGrWYjqNX05grEvxXSvuEg3+ii77BkIDTo7ycf6p1SU8AuBvAt6U/H1/6ACLSAXgMwC+EEI+m+HwsDRrLTPj94UHM+4MwaGOfN9o35cGk24cdeZTyAQCtWoU1Vea4z219vWMSRMAVbWUX3V5XakRJkVaWZwQ8eWwY33jyFIamw9VIG2st+N4dW1ec+SpRiVELZxzVPpGqrNXSPkDiM/+ZOT9CQlm7e4HUg/+3ATxCRJ8A0AvgAwBARDsBfEYI8UnptqsBlBHRR6W/91EhxJEUn5slKdLgrX/Ks+Lh3ZGjFvNpsTdifbUFz58egxBi1fK8g70OrKkoXtarnYhk2Sb6wVc68c2nzmBznRV//651uLTJhiprfgX9iJIiLTy+ILyBIPSa2BOZsRkviMLnOsRSbtJDp1ZhIMHgH9ngpbSZf0oLvkKISSHE9UKIdik9NCXdfkAK/BBC/EoIoRVCbF30wYE/hy40eFs573+w1wGTTo01Cj7wI5b11RZMun0YlXLBsYRCAof7HNjeGD31tbHWinOjLsz7g5kYZsJePT+Obz19Bu/eVI1HP7MH791Sk7eBHwCs0i/k1Tp7jrm8sBXpoF1hr4pKRagpMSSc9nEosKkbwDt8C1JzWXy1/vu6JrGzyZbVnvvZskE61vHU8Mqz9q6JWczMB7AtxrufTbVWBEIC50dn0z7GRM37g7jvd8fRajfj/js2K7pXf7xKIm2dPSsH/3HXfFzHj9aUGBNO+0y5w8+tpL4+AAf/gmQt0qKkSLtiqePI9Dw6x93L8tz54hLpwPjVFn0P9UqlrjGCf+RdUeRYxFx66I0eDDrn8E+3blB06+1ERA5MXy3vH27tsPo7oGQ2ekU6eirp8HaAg3/BaiozoWs8dvB/s2sCALCnVfltAKIpNmjRWFa0apnmoT4HrEZtzA6YTWVF0KlVODeW2+DvDQTx41e7cFV7Oa5oy8/vWTQlxvBs27nKzH9sxrtipU9EbakRYy4vfIH4925MFWLOnynXmkozzo7EDlivnZ+E1ajF+mrlnty1mg01llUrfg71hUtdY3XA1KhVaLGbcp72efLYMCZmffjUVS05HUe2Lcz8Vyj3DIUEJmbjC/41JUYIEX7nGy+H2wedRoUiXewFZzni4F+g1laFFzzHXcsXPAPBEJ4/M4rr1toV3/Z3JeurLeid9MAVY0fnzLwf58dmV612aq8sXvEXaTb8cl8vWuwmXJUHDdsSEQn+Ky34Tnl8CIREXMG/Tqr1H3DG3/hwyu2DTWFN3QAO/gUrkvOOFrTe6p6C0+PHzRursj2srIrs9D09HD1wH+lzQojVS13XVJgx6JzLWX//ngk3Dvc58YGd9YoLQKky6zVQq2jFtM/C7t44cv41SWz0cnj8iqv0ATj4F6y1UvA/M7I87fH0iREYtCpcvSa/d1lvqJEqfoaiV/wc7HWACNhSb13xOpG9EufHcpP6efzIEIiAW7fU5OT5c4mIpI1esdM+Cxu84pj5Vy8c6pJA2sfjg01hfX0ADv4Fq9ysR7lZt2zmP+8P4vEjg7hhXWXeV4xUFOtRZtLFzPvv757EhhoLig0r/2CvqQwfcpOrip8njw9hV5NtYdZaaKxF2pVn/q7V+/pE6DVqVBTrMZhA2sfhVl5HT4CDf0HbUGPFob6LDzN/+sQwZuYD+OCuhhyNKnuICOtrLFErfub9QRzqc2J38+qlro1lJug0KpzPQfDvn/Lg3OgsbtyQ3ym6lZQYVw7+kXWtlVo7LBau9Y9/5j+lwHbOAAf/gnZVezk6x90Ldc1CCPzstR40lRVhd0t+1vcvtbW+BGdGXMsWfY/0O+ELhOL6d1CrCK12M87loOLnhTPhXorXX1KxyiPzV0mRbuW0z8w8ig2aFftYLVZbGn+tfyAYwvQc5/yZwkRy+q+eC7fPfubkKI4PTuOvr2vL6yqfxS5vKUMwJPB2z9RFt7/RGW7mdmmTLa7rrKk052Tm/8KZMbTYTWiKsQ+hEJQUaeFwr5z2iSffHxHZ6BUKxexQv8A554cQgE1hG7wADv4Frb3CjCqLAc+cHMGsN4BvPHUKrXZTXh32sZrtjaXQaVR4o2PyotufPTWKHQ2lsMb5Q72mshhD0/Mxy0YzwR8M4e2eKVxZQJu6oikz6TDlXmnB15tQN9PaEiN8gfDJX6uJHBKz0vGQcsXBv4ARET68uwEvnh3H7T94A4OOOXzn9s15cVB7vAxaNbY3lOD1zgvBv3/Kg9PDM7gpgTz6mhxU/JwcmoHHF8RlcaxL5LMysx5z/iA8vuiltmOu+bjz/cCivv5xpH4i6wnx9A2Sm8L5KWdRfeaaVuxqtmHS7cX/um0TdsaZ5sgnN6yrxOnhmYXKp6eOh08mTSz4hyt+spn6eas7/AtrV3Phfc8Wi7RVmJxdPlMXQsTd2iEikVp/Dv5MsTRqFf7jU7ux/8s34IOX5X+FTzR/sb0OOrUK//FWH+b9Qfz0tW7sarYtHHYfj/rSIhi0qqwu+u7vmkKL3aTIwJNO5WYp+EdJ08zMB+ANhBJL+yRwqIuSg39+F3KzuORjy+ZE2Ew63LyxCg+/3Ych5xzGXF78y13bErqGSkVoqzBnrdY/GBJ4q2cK79lcnZXnkzObKRx4J2eXtyoZj+MEr6UsBg3Mek3caR+jVg2Twvr6ADzzZwwA8NX3rkeDrQjPnh7FJ69sTqrUdU1FcdYavJ0dccE1Hyj4lA8QXvAFoqd9xpKYmRMRakuMGIgn7TPrhb1Yr8i2GjzzZwzhHc+//+wVGHd50Zxk2WR7ZTF+f3gQ03N+WI2ZLf27kO8v7MVeAChbIe0znsDu3sVqS+M71GXc5VVkygfgmT9jC8x6TdKBH8juou/+7inUlRoXKlMKWZFOA6NWHTXtc6GpW2IBuqbEEHfax77CucByxsGfsTS5cKpXZlM/Qgi81T3FKZ9FbCZd1Jn/6Mw8DFoVivWJJTlqS4owPefH7CqdWiNpHyXi4M9YmtSWGGHUqjO+6Ns57sak24fLOPgvKDdHD/7DM/OothoTzsnXLHT3jD379waCcHr8HPwZK3QqFaG90ozzGT7S8a3ucCsKzvdfUGbWR037jEzPoyqOPv5L1dvCZb69k7G7e15YT+Dgz1jBa68oznjaZ3/3JOzFejQlsA8h35WZdAutFhYbmZ5HtTXx4B85s7lnIvY518PSUY/VCl134eDPWBqtrTJj3OVd8UzZVAgh8GbnJC5rtimyvDBTqqwGjLu8CAQvHLweCgmMzsyjKongX1Kkg82kQ9cKwT+SEqotUV5fH4CDP2NpFVn0jXU0ZKpODc9gzOXFtWsLt4VzNNVWI0LiQl0/AEy4vQiERFLBHwCay03oGo/9Li7S87/aWoAzfyKyEdGzRHRe+nPZYadE1EhEh4joCBGdJKLPpPKcjMnZ5roSAMDhfscqj0zOS2fD7bevyfMjNhMVSe1EUjEAMDod/kWQTM4fCKd+uleZ+VuNWpgSrCSSi1Rn/vcBeF4I0Q7geenrpYYBXC6E2ArgMgD3EVHhHTbKCoLNpENLuQmHep0Zuf4LZ8awqdaq2AqTTInM7kcWBf/h6XBaJtmZebPdhDGXN2a55/D0nKKPzkw1+O8F8JD0+UMAblv6ACGETwgReS+mT8NzMiZr2xpKcbjPASFWPwwkEYPOORzsdeDG9ZVpvW4+qJECfCTgA8DITPgXQaU1uV+ULeXhTXvd49Fn/4POedQkmVKSg1QDcaUQYlj6fARA1P+VRFRPRMcA9AP4jhBiKMbj7iGiA0R0YHx8PMWhMZYbOxpLMen2oWeFMsFkPHEk/GOzd2vhHLYTL4sxvMt3cdpnyDkPrZpQbkou+LdLO7Zj7dsYcub5zJ+IniOiE1E+9i5+nAhPc6JOdYQQ/UKIzQDaANxNRFF/SQghHhRC7BRC7LTbOafJlOmylvDmq1fPp28CI4TA7w8NYEdjaUKtpgsFEaHaargo7dMz4Ua9rSjpI0mbykzQa1Q4MzKz7D63N4DpOb+ig/+qKxVCiBti3UdEo0RULYQYJqJqAGOrXGuIiE4AuArAowmPljEFaLWb0Wo34ZmTI/jI5U3L7vcGgnjk7X48fmQIYy4vGmxFuH1HLfZuqY0ZqF7rmMD5sVnc//7NGR69clWXGC5K+3RPuBfq9ZOhVhHWVBbjzMjymX+/I/yurq5UucE/1bTPEwDulj6/G8DjSx9ARHVEZJQ+LwVwJYCzKT4vY7J244Yq7OuawrTn4jN9z426cPM/v4p/ePwk5vxBbK0vwdD0HD7/n0fx1785BF8gFPV6P3m1G+VmPW7dyrUSsVRZjAvN2EIhge5Jd0qN+gDgkqriqGW7HdJxna12c0rXz6VUg/+3AbyTiM4DuEH6GkS0k4h+Ij1mHYD9RHQUwMsA/rcQ4niKz8uYrL17UzWCIYH/eLtv4bbXOyZw+w/fwKw3gIc+vgtP/u1V+Je7tuG5z1+Dv7/lEjx9YgR/9+jRZQvF+7sm8fK5cXzsiiboNco7NCRb2irMGJ3xYtrjx9D0HHyBEJrLUwvOl1RbMDHrXWjlENExNgsioMWe2i+XXEqpQFUIMQng+ii3HwDwSenzZwHwe1VWUDbWWnHtWjv+7eVO/MX2Wrx0Zhxffuw4Wuwm/PvHdl3UilmlInz6mlYEQgL3P3MWbXYz/ub6dgDAvD+Ir/3xFKosBnz8iuZcvRxFWFctbbAbmYFf2umb6sx/XVX4mqeGZ3BN8YV1yI6xWdSVGmHQKveXsTJ3JzCmAH9301q874E3sPubzyMkgCvbyvGDD2+HxRD9oJfPXtuKzrFZfO/Zc9BqVLhxfSXuf+YsTg7N4Ed/uQNGBR4VmE3rqy0AgNPDM9BIayepzsw31llBBBzuc1y0sa5z3I02Bad8AA7+jGXMhhornvrclfjVvj5sqLHgtm210KpjZ1qJCN+6fRNm5gP49tNn8O2nz0BFwJffdQlu2lCVxZErk71YjzKTDqeHw9U5xXpNyh03LQYt1lYW42DvhR3bwZBA1/gsrmhVdldVDv6MZVBbRTH+8dYNcT9er1Hjxx/Zgf3dU+iZcGNnkw1tFcqeYWYLEWFdtQXHBqYxOjOPd6yrSEvzu51NpfjD4SEEQwJqFaF30g1vIKT47wvvtmVMZogIu1vKcOeuBsUHmGy7vLUMZ0ZccHj8uGVjdVquubPRhllvYKHe/82u8PnJlyr8MB0O/oyxvPGpq1qws7EUVqMW165Nz0bR3S1lIAKeOxXexvRGxySqrYaU9hDIAQd/xlje0GlU+PWnLsMz//3qtFXiVFkNuLTJhieODiIUEnijcwJ7WssVf54CB3/GWF7Ra9RJ9/CP5dYtNegcd+Of/uskHB4/rl5Tntbr5wIHf8YYW8V7t9SgsawID73Zix2NpXjPZuXvtOZqH8YYW4XVqMWjn9mDf3u5Ex+7ognqJJvFyQkHf8YYi4O9WI9/eM/6XA8jbTjtwxhjBYiDP2OMFSAO/owxVoA4+DPGWAHi4M8YYwWIgz9jjBUgDv6MMVaAOPgzxlgBoqXnhcoFEY0D6E3hEuUAJtI0nFzLl9eSL68D4NciV/xagEYhxKotTWUb/FNFRAeEEDtzPY50yJfXki+vA+DXIlf8WuLHaR/GGCtAHPwZY6wA5XPwfzDXA0ijfHkt+fI6AH4tcsWvJU55m/NnjDEWWz7P/BljjMWg+OBPRHcQ0UkiChHRzkW3NxHRHBEdkT7+bdF9O4joOBF1ENG/kEwO44z1WqT7/l4a71kiumnR7TdLt3UQ0X3ZH/XqiOgfiWhw0ffiXYvui/q65EwJ/+YrIaIe6f//ESI6IN1mI6Jniei89GdprscZDRH9jIjGiOjEotuijp3C/kX6Ph0jou25G/lyMV5L9n5WhBCK/gCwDsBaAC8B2Lno9iYAJ2L8nbcA7AZAAJ4GcEuuX8cqr2U9gKMA9ACaAXQCUEsfnQBaAOikx6zP9euI8rr+EcAXo9we9XXleryrvBZF/Juv8hp6AJQvue27AO6TPr8PwHdyPc4YY78awPbFP9uxxg7gXdLPN0k/7/tzPf44XkvWflYUP/MXQpwWQpyN9/FEVA3AIoTYJ8L/qr8AcFvGBpiAFV7LXgAPCyG8QohuAB0AdkkfHUKILiGED8DD0mOVItbrkjOl/5vHshfAQ9LnD0EmPxNLCSFeATC15OZYY98L4BcibB+AEunnXxZivJZY0v6zovjgv4pmIjpMRC8T0VXSbbUABhY9ZkC6Tc5qAfQv+joy5li3y9G90lvvny1KKShp/BFKHPNSAsCfieggEd0j3VYphBiWPh8BUJmboSUl1tiV+r3Kys+KIs7wJaLnAFRFuesrQojHY/y1YQANQohJItoB4A9EtCFjg4xTkq9F9lZ6XQB+CODrCAedrwP4HoCPZ290bIkrhRCDRFQB4FkiOrP4TiGEICJFlgEqeeySrP2sKCL4CyFuSOLveAF4pc8PElEngDUABgHULXponXRbViTzWhAeX/2irxePOdbtWRXv6yKiHwP4o/TlSq9LrpQ45osIIQalP8eI6DGE0wejRFQthBiWUiNjOR1kYmKNXXHfKyHEaOTzTP+s5G3ah4jsRKSWPm8B0A6gS3p7OENEu6Uqn48AkPuM+wkAdxKRnoiaEX4tbwF4G0A7ETUTkQ7AndJjZWVJnvV9ACLVDbFel5wp4t88FiIyEVFx5HMANyL8/XgCwN3Sw+6G/H8mFos19icAfESq+tkNYHpRekiWsvqzkusV7zSsmL8P4fyXF8AogGek228HcBLAEQCHALx30d/ZKf2jdgL4PqTNbrn+iPVapPu+Io33LBZVJyFc0XBOuu8ruX4NMV7XLwEcB3BM+k9cvdrrkvOHEv7NVxh7C8JVI0eln4+vSLeXAXgewHkAzwGw5XqsMcb/HwindP3Sz8onYo0d4SqfB6Tv03EsqqCTw0eM15K1nxXe4csYYwUob9M+jDHGYuPgzxhjBYiDP2OMFSAO/owxVoA4+DPGWAHi4M8YYwWIgz9jjBUgDv6MMVaA/j9KZW2oXfw2BQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(-150,150),n1_cross_ias)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}