lightcurve_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "from fbprophet import Prophet\n",
    "import julian"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we have added the necessary modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('classic')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's read sample file. The first column is the date given in the Y-M-D format and the second column \n",
    "is some intensity (in units of millimcrab)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('xper_test.txt', delimiter= ' ') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is how our data look like. Just two columns with data and intensity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>date</th>\n",
       "      <th>GSC_ch1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-02-07</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-08</td>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-02-10</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  GSC_ch1\n",
       "0  2018-02-06    0.037\n",
       "1  2018-02-07    0.035\n",
       "2  2018-02-08    0.032\n",
       "3  2018-02-09    0.028\n",
       "4  2018-02-10    0.045"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And that's how the intensity varies for the specific dates in our data. This kind of plot is called a light curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x105bf0cc0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['GSC_ch1'].plot(kind='line') # line, barh, box, scatter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A necessary requeiremnt for prophet to work, is that we change the columns names into ds for date and y for whatever the other column represents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['ds','y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-02-07</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-08</td>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-02-10</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds      y\n",
       "0  2018-02-06  0.037\n",
       "1  2018-02-07  0.035\n",
       "2  2018-02-08  0.032\n",
       "3  2018-02-09  0.028\n",
       "4  2018-02-10  0.045"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x1a1bbdae48>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = Prophet(daily_seasonality=True)\n",
    "m.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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",
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       "    }\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>ds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>2018-09-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>2018-09-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>2018-09-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>2018-09-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>2018-09-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds\n",
       "218 2018-09-12\n",
       "219 2018-09-13\n",
       "220 2018-09-14\n",
       "221 2018-09-15\n",
       "222 2018-09-16"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future = m.make_future_dataframe(periods=10)\n",
    "future.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>ds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-02-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-02-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-02-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds\n",
       "0 2018-02-06\n",
       "1 2018-02-07\n",
       "2 2018-02-08\n",
       "3 2018-02-09\n",
       "4 2018-02-10"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast = m.predict(future)\n",
    "#forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>trend</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "      <th>trend_lower</th>\n",
       "      <th>trend_upper</th>\n",
       "      <th>additive_terms</th>\n",
       "      <th>additive_terms_lower</th>\n",
       "      <th>additive_terms_upper</th>\n",
       "      <th>daily</th>\n",
       "      <th>daily_lower</th>\n",
       "      <th>daily_upper</th>\n",
       "      <th>weekly</th>\n",
       "      <th>weekly_lower</th>\n",
       "      <th>weekly_upper</th>\n",
       "      <th>multiplicative_terms</th>\n",
       "      <th>multiplicative_terms_lower</th>\n",
       "      <th>multiplicative_terms_upper</th>\n",
       "      <th>yhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>0.001322</td>\n",
       "      <td>-0.003235</td>\n",
       "      <td>0.047895</td>\n",
       "      <td>0.001322</td>\n",
       "      <td>0.001322</td>\n",
       "      <td>0.020940</td>\n",
       "      <td>0.020940</td>\n",
       "      <td>0.020940</td>\n",
       "      <td>0.021754</td>\n",
       "      <td>0.021754</td>\n",
       "      <td>0.021754</td>\n",
       "      <td>-0.000814</td>\n",
       "      <td>-0.000814</td>\n",
       "      <td>-0.000814</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-02-07</td>\n",
       "      <td>0.001277</td>\n",
       "      <td>-0.006840</td>\n",
       "      <td>0.042773</td>\n",
       "      <td>0.001277</td>\n",
       "      <td>0.001277</td>\n",
       "      <td>0.017541</td>\n",
       "      <td>0.017541</td>\n",
       "      <td>0.017541</td>\n",
       "      <td>0.021754</td>\n",
       "      <td>0.021754</td>\n",
       "      <td>0.021754</td>\n",
       "      <td>-0.004213</td>\n",
       "      <td>-0.004213</td>\n",
       "      <td>-0.004213</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-08</td>\n",
       "      <td>0.001232</td>\n",
       "      <td>-0.008076</td>\n",
       "      <td>0.042997</td>\n",
       "      <td>0.001232</td>\n",
       "      <td>0.001232</td>\n",
       "      <td>0.017143</td>\n",
       "      <td>0.017143</td>\n",
       "      <td>0.017143</td>\n",
       "      <td>0.021754</td>\n",
       "      <td>0.021754</td>\n",
       "      <td>0.021754</td>\n",
       "      <td>-0.004612</td>\n",
       "      <td>-0.004612</td>\n",
       "      <td>-0.004612</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>0.001187</td>\n",
       "      <td>0.002967</td>\n",
       "      <td>0.053940</td>\n",
       "      <td>0.001187</td>\n",
       "      <td>0.001187</td>\n",
       "      <td>0.026634</td>\n",
       "      <td>0.026634</td>\n",
       "      <td>0.026634</td>\n",
       "      <td>0.021754</td>\n",
       "      <td>0.021754</td>\n",
       "      <td>0.021754</td>\n",
       "      <td>0.004880</td>\n",
       "      <td>0.004880</td>\n",
       "      <td>0.004880</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-02-10</td>\n",
       "      <td>0.001142</td>\n",
       "      <td>0.005423</td>\n",
       "      <td>0.054929</td>\n",
       "      <td>0.001142</td>\n",
       "      <td>0.001142</td>\n",
       "      <td>0.029087</td>\n",
       "      <td>0.029087</td>\n",
       "      <td>0.029087</td>\n",
       "      <td>0.021754</td>\n",
       "      <td>0.021754</td>\n",
       "      <td>0.021754</td>\n",
       "      <td>0.007333</td>\n",
       "      <td>0.007333</td>\n",
       "      <td>0.007333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds     trend  yhat_lower  yhat_upper  trend_lower  trend_upper  \\\n",
       "0 2018-02-06  0.001322   -0.003235    0.047895     0.001322     0.001322   \n",
       "1 2018-02-07  0.001277   -0.006840    0.042773     0.001277     0.001277   \n",
       "2 2018-02-08  0.001232   -0.008076    0.042997     0.001232     0.001232   \n",
       "3 2018-02-09  0.001187    0.002967    0.053940     0.001187     0.001187   \n",
       "4 2018-02-10  0.001142    0.005423    0.054929     0.001142     0.001142   \n",
       "\n",
       "   additive_terms  additive_terms_lower  additive_terms_upper     daily  \\\n",
       "0        0.020940              0.020940              0.020940  0.021754   \n",
       "1        0.017541              0.017541              0.017541  0.021754   \n",
       "2        0.017143              0.017143              0.017143  0.021754   \n",
       "3        0.026634              0.026634              0.026634  0.021754   \n",
       "4        0.029087              0.029087              0.029087  0.021754   \n",
       "\n",
       "   daily_lower  daily_upper    weekly  weekly_lower  weekly_upper  \\\n",
       "0     0.021754     0.021754 -0.000814     -0.000814     -0.000814   \n",
       "1     0.021754     0.021754 -0.004213     -0.004213     -0.004213   \n",
       "2     0.021754     0.021754 -0.004612     -0.004612     -0.004612   \n",
       "3     0.021754     0.021754  0.004880      0.004880      0.004880   \n",
       "4     0.021754     0.021754  0.007333      0.007333      0.007333   \n",
       "\n",
       "   multiplicative_terms  multiplicative_terms_lower  \\\n",
       "0                   0.0                         0.0   \n",
       "1                   0.0                         0.0   \n",
       "2                   0.0                         0.0   \n",
       "3                   0.0                         0.0   \n",
       "4                   0.0                         0.0   \n",
       "\n",
       "   multiplicative_terms_upper      yhat  \n",
       "0                         0.0  0.022262  \n",
       "1                         0.0  0.018818  \n",
       "2                         0.0  0.018375  \n",
       "3                         0.0  0.027821  \n",
       "4                         0.0  0.030229  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.register_matplotlib_converters()\n",
    "fig1 = m.plot(forecast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usually (almost always) astronomers use a different format for the date. We called it Modified Julian Date (MJD). For example the MDJ = 58202 correspond to 2018-03-25.\n",
    "In the first cell we imported *julian*, which converts for us the dates from MJD to UTC Y-M-D time format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-03-25\n"
     ]
    }
   ],
   "source": [
    "mjd = 58202\n",
    "utc = julian.from_jd(mjd, fmt='mjd')\n",
    "print(datetime.datetime.date(utc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's read a file where te first column is in MJD. Then we will use *julian* to change the date to UTS so we can let prophet can do its magic, like in the previous cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd = pd.read_csv('XPer_daily_small_sample.txt', delimiter= ' ') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>MJD</th>\n",
       "      <th>GSC_ch1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58155.5</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58156.5</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58157.5</td>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58158.5</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58159.5</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MJD  GSC_ch1\n",
       "0  58155.5    0.037\n",
       "1  58156.5    0.035\n",
       "2  58157.5    0.032\n",
       "3  58158.5    0.028\n",
       "4  58159.5    0.045"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd.head()\n",
    "#dd.columns = ['ds','y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd['MJD'] = [datetime.datetime.date(julian.from_jd(mjd, fmt='mjd')) for mjd in dd['MJD']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2018-02-06\n",
       "1      2018-02-07\n",
       "2      2018-02-08\n",
       "3      2018-02-09\n",
       "4      2018-02-10\n",
       "          ...    \n",
       "208    2018-09-02\n",
       "209    2018-09-03\n",
       "210    2018-09-04\n",
       "211    2018-09-05\n",
       "212    2018-09-06\n",
       "Name: MJD, Length: 213, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd['MJD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd.columns = ['ds','y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-02-07</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-08</td>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-02-10</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds      y\n",
       "0  2018-02-06  0.037\n",
       "1  2018-02-07  0.035\n",
       "2  2018-02-08  0.032\n",
       "3  2018-02-09  0.028\n",
       "4  2018-02-10  0.045"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n"
     ]
    },
    {
     "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>ds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>2018-09-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>2018-09-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>2018-09-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>2018-09-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>2018-09-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds\n",
       "218 2018-09-12\n",
       "219 2018-09-13\n",
       "220 2018-09-14\n",
       "221 2018-09-15\n",
       "222 2018-09-16"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dd.columns = ['ds','y']\n",
    "m = Prophet(daily_seasonality=True)\n",
    "m.fit(dd)\n",
    "future = m.make_future_dataframe(periods=10)\n",
    "future.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>2018-09-02</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>2018-09-03</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>2018-09-04</td>\n",
       "      <td>0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>2018-09-05</td>\n",
       "      <td>-0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>2018-09-06</td>\n",
       "      <td>0.024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ds      y\n",
       "208  2018-09-02  0.025\n",
       "209  2018-09-03  0.035\n",
       "210  2018-09-04  0.023\n",
       "211  2018-09-05 -0.006\n",
       "212  2018-09-06  0.024"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ds   2018-09-02\n",
       "Name: 208, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future.iloc[208]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast = m.predict(future)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.register_matplotlib_converters()\n",
    "fig1 = m.plot(forecast)"
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}