Trajectory Forecasting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>id</th>\n",
       "      <th>epoch</th>\n",
       "      <th>sat_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>Vx</th>\n",
       "      <th>Vy</th>\n",
       "      <th>Vz</th>\n",
       "      <th>x_sim</th>\n",
       "      <th>y_sim</th>\n",
       "      <th>z_sim</th>\n",
       "      <th>Vx_sim</th>\n",
       "      <th>Vy_sim</th>\n",
       "      <th>Vz_sim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2014-01-01 00:00:00.000</td>\n",
       "      <td>0</td>\n",
       "      <td>-8855.823863</td>\n",
       "      <td>13117.780146</td>\n",
       "      <td>-20728.353233</td>\n",
       "      <td>-0.908303</td>\n",
       "      <td>-3.808436</td>\n",
       "      <td>-2.022083</td>\n",
       "      <td>-8843.131454</td>\n",
       "      <td>13138.221690</td>\n",
       "      <td>-20741.615306</td>\n",
       "      <td>-0.907527</td>\n",
       "      <td>-3.804930</td>\n",
       "      <td>-2.024133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:46:43.000</td>\n",
       "      <td>0</td>\n",
       "      <td>-10567.672384</td>\n",
       "      <td>1619.746066</td>\n",
       "      <td>-24451.813271</td>\n",
       "      <td>-0.302590</td>\n",
       "      <td>-4.272617</td>\n",
       "      <td>-0.612796</td>\n",
       "      <td>-10555.500066</td>\n",
       "      <td>1649.289367</td>\n",
       "      <td>-24473.089556</td>\n",
       "      <td>-0.303704</td>\n",
       "      <td>-4.269816</td>\n",
       "      <td>-0.616468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2014-01-01 01:33:26.001</td>\n",
       "      <td>0</td>\n",
       "      <td>-10578.684043</td>\n",
       "      <td>-10180.467460</td>\n",
       "      <td>-24238.280949</td>\n",
       "      <td>0.277435</td>\n",
       "      <td>-4.047522</td>\n",
       "      <td>0.723155</td>\n",
       "      <td>-10571.858472</td>\n",
       "      <td>-10145.939908</td>\n",
       "      <td>-24271.169776</td>\n",
       "      <td>0.274880</td>\n",
       "      <td>-4.046788</td>\n",
       "      <td>0.718768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2014-01-01 02:20:09.001</td>\n",
       "      <td>0</td>\n",
       "      <td>-9148.251857</td>\n",
       "      <td>-20651.437460</td>\n",
       "      <td>-20720.381279</td>\n",
       "      <td>0.715600</td>\n",
       "      <td>-3.373762</td>\n",
       "      <td>1.722115</td>\n",
       "      <td>-9149.620794</td>\n",
       "      <td>-20618.200201</td>\n",
       "      <td>-20765.019094</td>\n",
       "      <td>0.712437</td>\n",
       "      <td>-3.375202</td>\n",
       "      <td>1.718306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2014-01-01 03:06:52.002</td>\n",
       "      <td>0</td>\n",
       "      <td>-6719.092336</td>\n",
       "      <td>-28929.061629</td>\n",
       "      <td>-14938.907967</td>\n",
       "      <td>0.992507</td>\n",
       "      <td>-2.519732</td>\n",
       "      <td>2.344703</td>\n",
       "      <td>-6729.358857</td>\n",
       "      <td>-28902.271436</td>\n",
       "      <td>-14992.399986</td>\n",
       "      <td>0.989382</td>\n",
       "      <td>-2.522618</td>\n",
       "      <td>2.342237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                    epoch  sat_id             x             y  \\\n",
       "0   0  2014-01-01 00:00:00.000       0  -8855.823863  13117.780146   \n",
       "1   1  2014-01-01 00:46:43.000       0 -10567.672384   1619.746066   \n",
       "2   2  2014-01-01 01:33:26.001       0 -10578.684043 -10180.467460   \n",
       "3   3  2014-01-01 02:20:09.001       0  -9148.251857 -20651.437460   \n",
       "4   4  2014-01-01 03:06:52.002       0  -6719.092336 -28929.061629   \n",
       "\n",
       "              z        Vx        Vy        Vz         x_sim         y_sim  \\\n",
       "0 -20728.353233 -0.908303 -3.808436 -2.022083  -8843.131454  13138.221690   \n",
       "1 -24451.813271 -0.302590 -4.272617 -0.612796 -10555.500066   1649.289367   \n",
       "2 -24238.280949  0.277435 -4.047522  0.723155 -10571.858472 -10145.939908   \n",
       "3 -20720.381279  0.715600 -3.373762  1.722115  -9149.620794 -20618.200201   \n",
       "4 -14938.907967  0.992507 -2.519732  2.344703  -6729.358857 -28902.271436   \n",
       "\n",
       "          z_sim    Vx_sim    Vy_sim    Vz_sim  \n",
       "0 -20741.615306 -0.907527 -3.804930 -2.024133  \n",
       "1 -24473.089556 -0.303704 -4.269816 -0.616468  \n",
       "2 -24271.169776  0.274880 -4.046788  0.718768  \n",
       "3 -20765.019094  0.712437 -3.375202  1.718306  \n",
       "4 -14992.399986  0.989382 -2.522618  2.342237  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_df = pd.read_csv(\"jan_train.csv\")\n",
    "meta_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(503227, 15)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 503227 entries, 0 to 503226\n",
      "Data columns (total 15 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   id      503227 non-null  int64  \n",
      " 1   epoch   503227 non-null  object \n",
      " 2   sat_id  503227 non-null  int64  \n",
      " 3   x       503227 non-null  float64\n",
      " 4   y       503227 non-null  float64\n",
      " 5   z       503227 non-null  float64\n",
      " 6   Vx      503227 non-null  float64\n",
      " 7   Vy      503227 non-null  float64\n",
      " 8   Vz      503227 non-null  float64\n",
      " 9   x_sim   503227 non-null  float64\n",
      " 10  y_sim   503227 non-null  float64\n",
      " 11  z_sim   503227 non-null  float64\n",
      " 12  Vx_sim  503227 non-null  float64\n",
      " 13  Vy_sim  503227 non-null  float64\n",
      " 14  Vz_sim  503227 non-null  float64\n",
      "dtypes: float64(12), int64(2), object(1)\n",
      "memory usage: 57.6+ MB\n"
     ]
    }
   ],
   "source": [
    "meta_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_df[\"epoch\"] = pd.to_datetime(meta_df[\"epoch\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SAT-0 Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "filt = (meta_df[\"sat_id\"]==0)\n",
    "sat0_df = meta_df.loc[filt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>epoch</th>\n",
       "      <th>sat_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>Vx</th>\n",
       "      <th>Vy</th>\n",
       "      <th>Vz</th>\n",
       "      <th>x_sim</th>\n",
       "      <th>y_sim</th>\n",
       "      <th>z_sim</th>\n",
       "      <th>Vx_sim</th>\n",
       "      <th>Vy_sim</th>\n",
       "      <th>Vz_sim</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2014-01-01 00:00:00.000</td>\n",
       "      <td>0</td>\n",
       "      <td>-8855.823863</td>\n",
       "      <td>13117.780146</td>\n",
       "      <td>-20728.353233</td>\n",
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       "      <td>-3.808436</td>\n",
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       "      <td>-3.804930</td>\n",
       "      <td>-2.024133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:46:43.000</td>\n",
       "      <td>0</td>\n",
       "      <td>-10567.672384</td>\n",
       "      <td>1619.746066</td>\n",
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       "      <td>-4.272617</td>\n",
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       "      <td>-24473.089556</td>\n",
       "      <td>-0.303704</td>\n",
       "      <td>-4.269816</td>\n",
       "      <td>-0.616468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2014-01-01 01:33:26.001</td>\n",
       "      <td>0</td>\n",
       "      <td>-10578.684043</td>\n",
       "      <td>-10180.467460</td>\n",
       "      <td>-24238.280949</td>\n",
       "      <td>0.277435</td>\n",
       "      <td>-4.047522</td>\n",
       "      <td>0.723155</td>\n",
       "      <td>-10571.858472</td>\n",
       "      <td>-10145.939908</td>\n",
       "      <td>-24271.169776</td>\n",
       "      <td>0.274880</td>\n",
       "      <td>-4.046788</td>\n",
       "      <td>0.718768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2014-01-01 02:20:09.001</td>\n",
       "      <td>0</td>\n",
       "      <td>-9148.251857</td>\n",
       "      <td>-20651.437460</td>\n",
       "      <td>-20720.381279</td>\n",
       "      <td>0.715600</td>\n",
       "      <td>-3.373762</td>\n",
       "      <td>1.722115</td>\n",
       "      <td>-9149.620794</td>\n",
       "      <td>-20618.200201</td>\n",
       "      <td>-20765.019094</td>\n",
       "      <td>0.712437</td>\n",
       "      <td>-3.375202</td>\n",
       "      <td>1.718306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2014-01-01 03:06:52.002</td>\n",
       "      <td>0</td>\n",
       "      <td>-6719.092336</td>\n",
       "      <td>-28929.061629</td>\n",
       "      <td>-14938.907967</td>\n",
       "      <td>0.992507</td>\n",
       "      <td>-2.519732</td>\n",
       "      <td>2.344703</td>\n",
       "      <td>-6729.358857</td>\n",
       "      <td>-28902.271436</td>\n",
       "      <td>-14992.399986</td>\n",
       "      <td>0.989382</td>\n",
       "      <td>-2.522618</td>\n",
       "      <td>2.342237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   id                   epoch  sat_id             x             y  \\\n",
       "0   0 2014-01-01 00:00:00.000       0  -8855.823863  13117.780146   \n",
       "1   1 2014-01-01 00:46:43.000       0 -10567.672384   1619.746066   \n",
       "2   2 2014-01-01 01:33:26.001       0 -10578.684043 -10180.467460   \n",
       "3   3 2014-01-01 02:20:09.001       0  -9148.251857 -20651.437460   \n",
       "4   4 2014-01-01 03:06:52.002       0  -6719.092336 -28929.061629   \n",
       "\n",
       "              z        Vx        Vy        Vz         x_sim         y_sim  \\\n",
       "0 -20728.353233 -0.908303 -3.808436 -2.022083  -8843.131454  13138.221690   \n",
       "1 -24451.813271 -0.302590 -4.272617 -0.612796 -10555.500066   1649.289367   \n",
       "2 -24238.280949  0.277435 -4.047522  0.723155 -10571.858472 -10145.939908   \n",
       "3 -20720.381279  0.715600 -3.373762  1.722115  -9149.620794 -20618.200201   \n",
       "4 -14938.907967  0.992507 -2.519732  2.344703  -6729.358857 -28902.271436   \n",
       "\n",
       "          z_sim    Vx_sim    Vy_sim    Vz_sim  \n",
       "0 -20741.615306 -0.907527 -3.804930 -2.024133  \n",
       "1 -24473.089556 -0.303704 -4.269816 -0.616468  \n",
       "2 -24271.169776  0.274880 -4.046788  0.718768  \n",
       "3 -20765.019094  0.712437 -3.375202  1.718306  \n",
       "4 -14992.399986  0.989382 -2.522618  2.342237  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat0_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sat0_df = sat0_df[[\"epoch\", \"x\", \"y\", \"z\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>epoch</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-01 00:00:00.000</td>\n",
       "      <td>-8855.823863</td>\n",
       "      <td>13117.780146</td>\n",
       "      <td>-20728.353233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-01 00:46:43.000</td>\n",
       "      <td>-10567.672384</td>\n",
       "      <td>1619.746066</td>\n",
       "      <td>-24451.813271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01 01:33:26.001</td>\n",
       "      <td>-10578.684043</td>\n",
       "      <td>-10180.467460</td>\n",
       "      <td>-24238.280949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-01 02:20:09.001</td>\n",
       "      <td>-9148.251857</td>\n",
       "      <td>-20651.437460</td>\n",
       "      <td>-20720.381279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-01 03:06:52.002</td>\n",
       "      <td>-6719.092336</td>\n",
       "      <td>-28929.061629</td>\n",
       "      <td>-14938.907967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-01-01 03:53:35.002</td>\n",
       "      <td>-3708.453525</td>\n",
       "      <td>-34767.115528</td>\n",
       "      <td>-7863.224747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-01-01 04:40:18.003</td>\n",
       "      <td>-437.699227</td>\n",
       "      <td>-38249.612548</td>\n",
       "      <td>-234.351187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-01-01 05:27:01.003</td>\n",
       "      <td>2863.147037</td>\n",
       "      <td>-39594.503233</td>\n",
       "      <td>7420.538280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-01-01 06:13:44.004</td>\n",
       "      <td>6031.593902</td>\n",
       "      <td>-39056.319613</td>\n",
       "      <td>14731.102545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-01-01 07:00:27.004</td>\n",
       "      <td>8950.655291</td>\n",
       "      <td>-36886.362968</td>\n",
       "      <td>21432.111677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    epoch             x             y             z\n",
       "0 2014-01-01 00:00:00.000  -8855.823863  13117.780146 -20728.353233\n",
       "1 2014-01-01 00:46:43.000 -10567.672384   1619.746066 -24451.813271\n",
       "2 2014-01-01 01:33:26.001 -10578.684043 -10180.467460 -24238.280949\n",
       "3 2014-01-01 02:20:09.001  -9148.251857 -20651.437460 -20720.381279\n",
       "4 2014-01-01 03:06:52.002  -6719.092336 -28929.061629 -14938.907967\n",
       "5 2014-01-01 03:53:35.002  -3708.453525 -34767.115528  -7863.224747\n",
       "6 2014-01-01 04:40:18.003   -437.699227 -38249.612548   -234.351187\n",
       "7 2014-01-01 05:27:01.003   2863.147037 -39594.503233   7420.538280\n",
       "8 2014-01-01 06:13:44.004   6031.593902 -39056.319613  14731.102545\n",
       "9 2014-01-01 07:00:27.004   8950.655291 -36886.362968  21432.111677"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat0_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.92, 'Satellite Tragectory')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "#from matplotlib.pyplot import figure\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "%matplotlib qt\n",
    "plt.style.use(\"dark_background\")\n",
    "# Creating figure\n",
    "fig = plt.figure(figsize = (10, 7))\n",
    "ax = plt.axes(projection =\"3d\")\n",
    "\n",
    "# Creating plot\n",
    "ax.plot(sat0_df[\"x\"], sat0_df[\"y\"], sat0_df[\"z\"], color = \"red\", label=\"Trajectory\")\n",
    "ax.set_xlabel(\"X\")\n",
    "ax.set_ylabel(\"Y\")\n",
    "ax.set_zlabel(\"Z\")\n",
    "ax.legend()\n",
    "\n",
    "plt.title(\"Satellite Tragectory\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>epoch</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>2014-01-24 16:23:10.363</td>\n",
       "      <td>11133.121927</td>\n",
       "      <td>-33859.313721</td>\n",
       "      <td>26747.171975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>2014-01-24 17:09:53.364</td>\n",
       "      <td>13379.880052</td>\n",
       "      <td>-29243.225228</td>\n",
       "      <td>31775.931674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>2014-01-24 17:56:36.364</td>\n",
       "      <td>15173.743957</td>\n",
       "      <td>-23635.665058</td>\n",
       "      <td>35728.949880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>2014-01-24 18:43:19.365</td>\n",
       "      <td>16461.691880</td>\n",
       "      <td>-17239.921573</td>\n",
       "      <td>38490.581501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>2014-01-24 19:30:02.365</td>\n",
       "      <td>17194.282667</td>\n",
       "      <td>-10263.631730</td>\n",
       "      <td>39953.578881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>2014-01-24 20:16:45.366</td>\n",
       "      <td>17323.752275</td>\n",
       "      <td>-2929.599994</td>\n",
       "      <td>40015.054282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>2014-01-24 21:03:28.366</td>\n",
       "      <td>16803.492692</td>\n",
       "      <td>4510.550397</td>\n",
       "      <td>38575.720092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>2014-01-24 21:50:11.367</td>\n",
       "      <td>15589.464016</td>\n",
       "      <td>11758.599852</td>\n",
       "      <td>35543.718040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>2014-01-24 22:36:54.367</td>\n",
       "      <td>13644.957424</td>\n",
       "      <td>18446.405418</td>\n",
       "      <td>30846.351215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>2014-01-24 23:23:37.368</td>\n",
       "      <td>10951.688066</td>\n",
       "      <td>24106.659914</td>\n",
       "      <td>24456.597220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      epoch             x             y             z\n",
       "731 2014-01-24 16:23:10.363  11133.121927 -33859.313721  26747.171975\n",
       "732 2014-01-24 17:09:53.364  13379.880052 -29243.225228  31775.931674\n",
       "733 2014-01-24 17:56:36.364  15173.743957 -23635.665058  35728.949880\n",
       "734 2014-01-24 18:43:19.365  16461.691880 -17239.921573  38490.581501\n",
       "735 2014-01-24 19:30:02.365  17194.282667 -10263.631730  39953.578881\n",
       "736 2014-01-24 20:16:45.366  17323.752275  -2929.599994  40015.054282\n",
       "737 2014-01-24 21:03:28.366  16803.492692   4510.550397  38575.720092\n",
       "738 2014-01-24 21:50:11.367  15589.464016  11758.599852  35543.718040\n",
       "739 2014-01-24 22:36:54.367  13644.957424  18446.405418  30846.351215\n",
       "740 2014-01-24 23:23:37.368  10951.688066  24106.659914  24456.597220"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat0_df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('23 days 23:23:37.368000')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat0_df['epoch'].max() - sat0_df['epoch'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(741, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = sat0_df[[\"x\", \"y\", \"z\"]]\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# total data of 24 days\n",
    "# set train data of 20days\n",
    "# reaminng 4 days for prediction/forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split data into train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.array(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -8855.8238626 ,  13117.78014618, -20728.35323298])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "741"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = data[:371]\n",
    "test = data[371:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "741"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train) + len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((371, 3), (370, 3))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scale the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_scaled = scaler.fit_transform(train)\n",
    "test_scaled = scaler.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of samples in the original training data =  371\n",
      "Total number of samples in the generated data =  351\n"
     ]
    }
   ],
   "source": [
    "#Sequence size has an impact on prediction\n",
    "seq_size = 20  ## number of steps (lookback)\n",
    "n_features = 3 ## number of features. (x, y and z)\n",
    "train_generator = TimeseriesGenerator(train_scaled, train_scaled, length = seq_size, batch_size=1)\n",
    "print(\"Total number of samples in the original training data = \", len(train_scaled)) # 521\n",
    "print(\"Total number of samples in the generated data = \", len(train_generator)) # 501 with seq_size=20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y =train_generator[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[[6.41722221e-02, 7.58692356e-01, 5.77184386e-02],\n",
       "         [2.92735477e-03, 5.93261579e-01, 0.00000000e+00],\n",
       "         [2.53339022e-03, 4.23483121e-01, 3.31002672e-03],\n",
       "         [5.37100150e-02, 2.72829479e-01, 5.78420142e-02],\n",
       "         [1.40618144e-01, 1.53733137e-01, 1.47462316e-01],\n",
       "         [2.48329880e-01, 6.97367084e-02, 2.57144543e-01],\n",
       "         [3.65347776e-01, 1.96314308e-02, 3.75401934e-01],\n",
       "         [4.83442274e-01, 2.81487169e-04, 4.94062605e-01],\n",
       "         [5.96799914e-01, 8.02473563e-03, 6.07385791e-01],\n",
       "         [7.01235280e-01, 3.92455176e-02, 7.11260086e-01],\n",
       "         [7.93616106e-01, 9.05528982e-02, 8.02623812e-01],\n",
       "         [8.71473592e-01, 1.58786017e-01, 8.79074666e-01],\n",
       "         [9.32743566e-01, 2.40945128e-01, 9.38612029e-01],\n",
       "         [9.75595474e-01, 3.34087022e-01, 9.79467998e-01],\n",
       "         [9.98322297e-01, 4.35195288e-01, 1.00000000e+00],\n",
       "         [9.99279135e-01, 5.41021585e-01, 9.98632762e-01],\n",
       "         [9.76872866e-01, 6.47885288e-01, 9.73852343e-01],\n",
       "         [9.29624813e-01, 7.51412809e-01, 9.24274592e-01],\n",
       "         [8.56360698e-01, 8.46197086e-01, 8.48842807e-01],\n",
       "         [7.56640094e-01, 9.25376095e-01, 7.47266975e-01]]]),\n",
       " array([[0.63163299, 0.98021086, 0.62091073]]))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of samples in the original training data =  370\n",
      "Total number of samples in the generated data =  350\n"
     ]
    }
   ],
   "source": [
    "#Also generate test data\n",
    "test_generator = TimeseriesGenerator(test_scaled, test_scaled, length=seq_size, batch_size=1)\n",
    "print(\"Total number of samples in the original training data = \", len(test_scaled)) \n",
    "print(\"Total number of samples in the generated data = \", len(test_generator)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build a neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(150, activation='relu', return_sequences=True, input_shape=(seq_size, n_features)))\n",
    "model.add(LSTM(64, activation='relu'))\n",
    "#model.add(Dropout(0.2))\n",
    "model.add(Dense(64))\n",
    "model.add(Dense(3))\n",
    "\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 20, 150)           92400     \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 64)                55040     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 3)                 195       \n",
      "=================================================================\n",
      "Total params: 151,795\n",
      "Trainable params: 151,795\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:1940: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "10/10 [==============================] - 5s 193ms/step - loss: 0.1819 - val_loss: 0.2105\n",
      "Epoch 2/50\n",
      "10/10 [==============================] - 1s 150ms/step - loss: 0.2168 - val_loss: 0.1648\n",
      "Epoch 3/50\n",
      "10/10 [==============================] - 1s 128ms/step - loss: 0.1953 - val_loss: 0.1368\n",
      "Epoch 4/50\n",
      "10/10 [==============================] - 1s 128ms/step - loss: 0.1107 - val_loss: 0.1010\n",
      "Epoch 5/50\n",
      "10/10 [==============================] - 1s 129ms/step - loss: 0.0937 - val_loss: 0.0777\n",
      "Epoch 6/50\n",
      "10/10 [==============================] - 1s 124ms/step - loss: 0.0649 - val_loss: 0.0526\n",
      "Epoch 7/50\n",
      "10/10 [==============================] - 1s 126ms/step - loss: 0.0404 - val_loss: 0.0609\n",
      "Epoch 8/50\n",
      "10/10 [==============================] - 1s 131ms/step - loss: 0.0810 - val_loss: 0.0500\n",
      "Epoch 9/50\n",
      "10/10 [==============================] - 1s 135ms/step - loss: 0.0342 - val_loss: 0.0352\n",
      "Epoch 10/50\n",
      "10/10 [==============================] - 1s 143ms/step - loss: 0.0376 - val_loss: 0.0263\n",
      "Epoch 11/50\n",
      "10/10 [==============================] - 1s 141ms/step - loss: 0.0122 - val_loss: 0.0094\n",
      "Epoch 12/50\n",
      "10/10 [==============================] - 1s 133ms/step - loss: 0.0223 - val_loss: 0.0077\n",
      "Epoch 13/50\n",
      "10/10 [==============================] - 1s 143ms/step - loss: 0.0192 - val_loss: 0.0200\n",
      "Epoch 14/50\n",
      "10/10 [==============================] - 1s 127ms/step - loss: 0.0229 - val_loss: 0.0029\n",
      "Epoch 15/50\n",
      "10/10 [==============================] - 1s 128ms/step - loss: 0.0061 - val_loss: 0.0021\n",
      "Epoch 16/50\n",
      "10/10 [==============================] - 1s 135ms/step - loss: 0.0023 - val_loss: 0.0015\n",
      "Epoch 17/50\n",
      "10/10 [==============================] - 1s 134ms/step - loss: 0.0029 - val_loss: 0.0011\n",
      "Epoch 18/50\n",
      "10/10 [==============================] - 1s 127ms/step - loss: 0.0019 - val_loss: 5.4531e-04\n",
      "Epoch 19/50\n",
      "10/10 [==============================] - 1s 126ms/step - loss: 9.7737e-04 - val_loss: 6.5718e-04\n",
      "Epoch 20/50\n",
      "10/10 [==============================] - 1s 128ms/step - loss: 4.1363e-04 - val_loss: 4.1612e-04\n",
      "Epoch 21/50\n",
      "10/10 [==============================] - 1s 128ms/step - loss: 5.5186e-04 - val_loss: 0.0010\n",
      "Epoch 22/50\n",
      "10/10 [==============================] - 1s 127ms/step - loss: 0.0021 - val_loss: 0.0024\n",
      "Epoch 23/50\n",
      "10/10 [==============================] - 1s 125ms/step - loss: 0.0012 - val_loss: 0.0014\n",
      "Epoch 24/50\n",
      "10/10 [==============================] - 1s 127ms/step - loss: 0.0011 - val_loss: 0.0035\n",
      "Epoch 25/50\n",
      "10/10 [==============================] - 1s 128ms/step - loss: 0.0019 - val_loss: 0.0012\n",
      "Epoch 26/50\n",
      "10/10 [==============================] - 1s 133ms/step - loss: 0.0010 - val_loss: 0.0012\n",
      "Epoch 27/50\n",
      "10/10 [==============================] - 1s 126ms/step - loss: 8.2788e-04 - val_loss: 5.9755e-04\n",
      "Epoch 28/50\n",
      "10/10 [==============================] - 1s 130ms/step - loss: 4.7549e-04 - val_loss: 4.1954e-04\n",
      "Epoch 29/50\n",
      "10/10 [==============================] - 1s 128ms/step - loss: 3.5401e-04 - val_loss: 2.6106e-04\n",
      "Epoch 30/50\n",
      "10/10 [==============================] - 1s 138ms/step - loss: 3.9330e-04 - val_loss: 4.1003e-04\n",
      "Epoch 31/50\n",
      "10/10 [==============================] - 1s 125ms/step - loss: 2.8512e-04 - val_loss: 1.0910e-04\n",
      "Epoch 32/50\n",
      "10/10 [==============================] - 2s 181ms/step - loss: 2.4284e-04 - val_loss: 1.2632e-04\n",
      "Epoch 33/50\n",
      "10/10 [==============================] - 2s 181ms/step - loss: 1.4263e-04 - val_loss: 2.7355e-04\n",
      "Epoch 34/50\n",
      "10/10 [==============================] - 1s 148ms/step - loss: 2.6110e-04 - val_loss: 1.0203e-04\n",
      "Epoch 35/50\n",
      "10/10 [==============================] - 1s 143ms/step - loss: 1.2843e-04 - val_loss: 1.1744e-04\n",
      "Epoch 36/50\n",
      "10/10 [==============================] - 1s 134ms/step - loss: 9.6969e-05 - val_loss: 2.1812e-04\n",
      "Epoch 37/50\n",
      "10/10 [==============================] - 1s 138ms/step - loss: 0.0010 - val_loss: 8.2026e-04\n",
      "Epoch 38/50\n",
      "10/10 [==============================] - 1s 144ms/step - loss: 9.2349e-04 - val_loss: 6.3094e-04\n",
      "Epoch 39/50\n",
      "10/10 [==============================] - 1s 136ms/step - loss: 5.2094e-04 - val_loss: 4.0966e-04\n",
      "Epoch 40/50\n",
      "10/10 [==============================] - 1s 138ms/step - loss: 0.0013 - val_loss: 0.0013\n",
      "Epoch 41/50\n",
      "10/10 [==============================] - 1s 128ms/step - loss: 5.7215e-04 - val_loss: 0.0022\n",
      "Epoch 42/50\n",
      "10/10 [==============================] - 1s 124ms/step - loss: 0.0016 - val_loss: 8.7560e-04\n",
      "Epoch 43/50\n",
      "10/10 [==============================] - 1s 131ms/step - loss: 8.1068e-04 - val_loss: 3.7347e-04\n",
      "Epoch 44/50\n",
      "10/10 [==============================] - 1s 134ms/step - loss: 4.9253e-04 - val_loss: 1.9270e-04\n",
      "Epoch 45/50\n",
      "10/10 [==============================] - 1s 132ms/step - loss: 2.8166e-04 - val_loss: 5.6996e-04\n",
      "Epoch 46/50\n",
      "10/10 [==============================] - 1s 147ms/step - loss: 5.8934e-04 - val_loss: 3.6275e-04\n",
      "Epoch 47/50\n",
      "10/10 [==============================] - 1s 142ms/step - loss: 4.2411e-04 - val_loss: 5.0931e-04\n",
      "Epoch 48/50\n",
      "10/10 [==============================] - 1s 143ms/step - loss: 6.2811e-04 - val_loss: 5.8050e-04\n",
      "Epoch 49/50\n",
      "10/10 [==============================] - 1s 145ms/step - loss: 7.6251e-04 - val_loss: 9.1282e-04\n",
      "Epoch 50/50\n",
      "10/10 [==============================] - 1s 140ms/step - loss: 8.2346e-04 - val_loss: 6.7566e-04\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_generator, \n",
    "                              validation_data=test_generator, \n",
    "                              epochs=50, steps_per_epoch=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the training and validation accuracy and loss at each epoch\n",
    "\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs = range(1, len(loss) + 1)\n",
    "plt.style.use(\"seaborn\")\n",
    "plt.plot(epochs, loss, 'y', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = model.predict(test_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = scaler.inverse_transform(test_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(test_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  -527.33777, -36838.625  ,   -140.44067],\n",
       "       [  2794.722  , -37999.81   ,   7594.961  ],\n",
       "       [  5994.5503 , -37142.246  ,  15096.323  ],\n",
       "       ...,\n",
       "       [ 16067.626  ,  16146.238  ,  36359.363  ],\n",
       "       [ 13837.179  ,  22765.402  ,  31339.275  ],\n",
       "       [ 10764.316  ,  28120.32   ,  24300.506  ]], dtype=float32)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((371, 3), (370, 3))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = [\"X\", \"Y\", \"Z\"]\n",
    "# creating the dataframe\n",
    "df = pd.DataFrame(data = train, columns = column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-8855.823863</td>\n",
       "      <td>13117.780146</td>\n",
       "      <td>-20728.353233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-10567.672384</td>\n",
       "      <td>1619.746066</td>\n",
       "      <td>-24451.813271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-10578.684043</td>\n",
       "      <td>-10180.467460</td>\n",
       "      <td>-24238.280949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-9148.251857</td>\n",
       "      <td>-20651.437460</td>\n",
       "      <td>-20720.381279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-6719.092336</td>\n",
       "      <td>-28929.061629</td>\n",
       "      <td>-14938.907967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              X             Y             Z\n",
       "0  -8855.823863  13117.780146 -20728.353233\n",
       "1 -10567.672384   1619.746066 -24451.813271\n",
       "2 -10578.684043 -10180.467460 -24238.280949\n",
       "3  -9148.251857 -20651.437460 -20720.381279\n",
       "4  -6719.092336 -28929.061629 -14938.907967"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"PredX\", \"PredY\", \"PredZ\"]\n",
    "# creating the dataframe\n",
    "df_pred = pd.DataFrame(data = test_predictions, columns = columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>PredX</th>\n",
       "      <th>PredY</th>\n",
       "      <th>PredZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-527.337769</td>\n",
       "      <td>-36838.625000</td>\n",
       "      <td>-140.440674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2794.721924</td>\n",
       "      <td>-37999.808594</td>\n",
       "      <td>7594.960938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5994.550293</td>\n",
       "      <td>-37142.246094</td>\n",
       "      <td>15096.323242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8861.881836</td>\n",
       "      <td>-34829.457031</td>\n",
       "      <td>21942.708984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11423.527344</td>\n",
       "      <td>-31057.597656</td>\n",
       "      <td>27978.414062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PredX         PredY         PredZ\n",
       "0   -527.337769 -36838.625000   -140.440674\n",
       "1   2794.721924 -37999.808594   7594.960938\n",
       "2   5994.550293 -37142.246094  15096.323242\n",
       "3   8861.881836 -34829.457031  21942.708984\n",
       "4  11423.527344 -31057.597656  27978.414062"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.92, 'Satellite Tragectory')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "\n",
    "#plt.style.use(\"seaborn\")\n",
    "plt.style.use('dark_background')\n",
    "\n",
    "# Creating figure\n",
    "fig = plt.figure(figsize = (10, 7))\n",
    "ax = plt.axes(projection =\"3d\")\n",
    "\n",
    "# Creating plot\n",
    "#ax.plot(sat0_df[\"x\"], sat0_df[\"y\"], sat0_df[\"z\"], color = \"green\")\n",
    "ax.scatter3D(df[\"X\"], df[\"Y\"], df[\"Z\"], color = \"red\", label=\"True Trajectory\")\n",
    "ax.scatter3D(df_pred[\"PredX\"], df_pred[\"PredY\"], df_pred[\"PredZ\"], color=\"green\", label=\"Forecasted Trajectory\")\n",
    "#ax.plt(test_predictions)\n",
    "ax.set_xlabel(\"X\")\n",
    "ax.set_ylabel(\"Y\")\n",
    "ax.set_zlabel(\"Z\")\n",
    "ax.legend()\n",
    "plt.title(\"Satellite Tragectory\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.92, 'Satellite Tragectory')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "\n",
    "#plt.style.use(\"seaborn\")\n",
    "plt.style.use('dark_background')\n",
    "\n",
    "# Creating figure\n",
    "fig = plt.figure(figsize = (10, 7))\n",
    "ax = plt.axes(projection =\"3d\")\n",
    "\n",
    "# Creating plot\n",
    "#ax.plot(sat0_df[\"x\"], sat0_df[\"y\"], sat0_df[\"z\"], color = \"green\")\n",
    "ax.plot(df[\"X\"], df[\"Y\"], df[\"Z\"], color = \"red\", label=\"True Trajectory\")\n",
    "ax.plot(df_pred[\"PredX\"], df_pred[\"PredY\"], df_pred[\"PredZ\"], color=\"green\", label=\"Forecasted Trajectory\")\n",
    "#ax.plt(test_predictions)\n",
    "ax.set_xlabel(\"X\")\n",
    "ax.set_ylabel(\"Y\")\n",
    "ax.set_zlabel(\"Z\")\n",
    "ax.legend()\n",
    "plt.title(\"Satellite Tragectory\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}