{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Create DataFrame of NDVI Timeseries\n",
    "*Written by Men Vuthy, 2021*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import modules**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rasterio\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create DataFrame**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data\n",
    "NDVI_Images = rasterio.open('output/1/mosaic_img/NDVI_2011.tif').read()\n",
    "\n",
    "# Reshape from array to list\n",
    "NDVI_list = []\n",
    "\n",
    "for array in NDVI_Images:\n",
    "    ndvi_by_day = array.reshape(-1)\n",
    "    NDVI_list.append(ndvi_by_day)\n",
    "\n",
    "# Create dataframe of timeseries data\n",
    "NDVI_DataFrame = pd.DataFrame(NDVI_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpolation to eliminate NaN inside timeseries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "        0   1   2   3   4   5   6   7   8   9   ...  59  60  61  62  63  64  \\\n",
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       "4      NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  ... NaN NaN NaN NaN NaN NaN   \n",
       "...     ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ...  ..  ..  ..  ..  ..  ..   \n",
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       "\n",
       "        65  66  67  68  \n",
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       "...     ..  ..  ..  ..  \n",
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       "\n",
       "[392313 rows x 69 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Interpolate based on Polynomial\n",
    "NDVI_DataFrame = NDVI_DataFrame.interpolate(method='polynomial', order=2)\n",
    "NDVI_DataFrame = NDVI_DataFrame.T\n",
    "NDVI_DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Extract date of each image**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2010/01/01', '2010/01/17', '2010/02/02', '2010/02/18', '2010/03/06', '2010/03/22', '2010/04/07', '2010/04/23', '2010/05/09', '2010/05/25', '2010/06/10', '2010/06/26', '2010/07/12', '2010/07/28', '2010/08/13', '2010/08/29', '2010/09/14', '2010/09/30', '2010/10/16', '2010/11/01', '2010/11/17', '2010/12/03', '2010/12/19', '2011/01/01', '2011/01/17', '2011/02/02', '2011/02/18', '2011/03/06', '2011/03/22', '2011/04/07', '2011/04/23', '2011/05/09', '2011/05/25', '2011/06/10', '2011/06/26', '2011/07/12', '2011/07/28', '2011/08/13', '2011/08/29', '2011/09/14', '2011/09/30', '2011/10/16', '2011/11/01', '2011/11/17', '2011/12/03', '2011/12/19', '2012/01/01', '2012/01/17', '2012/02/02', '2012/02/18', '2012/03/05', '2012/03/21', '2012/04/06', '2012/04/22', '2012/05/08', '2012/05/24', '2012/06/09', '2012/06/25', '2012/07/11', '2012/07/27', '2012/08/12', '2012/08/28', '2012/09/13', '2012/09/29', '2012/10/15', '2012/10/31', '2012/11/16', '2012/12/02', '2012/12/18']\n"
     ]
    }
   ],
   "source": [
    "file_path = \"input/ndvi_2011\"\n",
    "\n",
    "# Make a search criteria to select the ndvi files\n",
    "q = os.path.join(file_path, \"20*.tif\")\n",
    "\n",
    "# sorted files by name\n",
    "ndvi_fps = sorted(glob.glob(q)) \n",
    "\n",
    "# List of date of ndvi image\n",
    "img_date = []\n",
    "for file in ndvi_fps:\n",
    "    name = os.path.basename(file)\n",
    "    name_ver2 = name.replace(\"_\",\"/\")\n",
    "    name_ver3 = name_ver2.replace(\".tif\",\"\")\n",
    "    img_date.append(name_ver3)\n",
    "    \n",
    "print(img_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "date_frame = pd.DataFrame(img_date)\n",
    "date_frame\n",
    "\n",
    "date_img = []\n",
    "for date in date_frame[0]:\n",
    "    datetime_object = datetime.strptime(date, '%Y/%m/%d').date()\n",
    "    date_img.append(datetime_object)\n",
    "\n",
    "date_img = (date_img)\n",
    "date_img = [datetime.strftime(i,'%Y/%b/%d') for i in date_img]\n",
    "NDVI_DataFrame.columns = date_img\n",
    "\n",
    "# drop rows with nan in all columns\n",
    "NDVI_DataFrame.dropna(subset = NDVI_DataFrame.columns, inplace=True)\n",
    "\n",
    "DF_NDVI = NDVI_DataFrame.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save Date\n",
    "date = pd.DataFrame(date_img)\n",
    "date.to_csv('output/2/date_img/date_img.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Save result as csv**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF_NDVI.to_csv('output/2/timeseries/timeseries_ndvi.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualize data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,5))\n",
    "ax = DF_NDVI.iloc[:,5000:5001].plot(ax=ax)\n",
    "ax.set_title('NDVI_2011')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}