008_EE_meets_Geopandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Google Earth Engine meets Geopandas\n",
    "**Author:** René Kopeinig<br>\n",
    "**Description:** Extracting Landsat 8 TOA and CHIRPS precipitation data from Google Earth Engine and use Geopandas capabilities to create time series analysis. Furthermore, data will be visualized through a time series viewer and also a heat map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ee, datetime, folium\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from IPython.display import Image\n",
    "from pylab import *\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import geopandas as gpd\n",
    "from folium.plugins import HeatMap\n",
    "ee.Initialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create random point grid from shapefile\n",
    "Our area of interest is the Mexican state Chiapas. We only going to create two points per polygon. By increasing this number, the computation time on retrieving information through Google Earth Engine will increase. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rkopei/Documents/Nike/Development/aiml/lib/python3.10/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n"
     ]
    }
   ],
   "source": [
    "chiapas = gpd.read_file('data/chiapas/chiapas.shp')\n",
    "X = chiapas.to_crs({'init': 'epsg:4326'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_random_points(X):\n",
    "    n = 1\n",
    "    x_min, y_min, x_max, y_max = X.bounds\n",
    "\n",
    "    x = np.random.uniform(x_min, x_max, n)\n",
    "    y = np.random.uniform(y_min, y_max, n)\n",
    "\n",
    "    gdf_points = gpd.GeoSeries(gpd.points_from_xy(x, y))\n",
    "    gdf_points = gdf_points[gdf_points.within(X)]\n",
    "    \n",
    "    return gdf_points\n",
    "\n",
    "x_list = []\n",
    "for index, row in X.iterrows():\n",
    "    x = gpd.GeoDataFrame({'CVE_MUN':row['CVE_MUN'],\n",
    "                          'CVE_ENT':row['CVE_ENT'],\n",
    "                          'NOM_MUN':row['NOM_MUN'],\n",
    "                          'OID':row['OID'],\n",
    "                      'geometry':get_random_points(row['geometry'])})\n",
    "    x_list.append(x)\n",
    "Y = pd.concat(x_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Next Step takes between 5 and 30 minutes depending on our choice of random points per polygon. If you would like to avoid this step, continue with importing points file in data folder. <br><br> **Continue two code cells below!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve Landsat 8 TOA and CHIRPS Precipitation Data from Google Earth Engine\n",
    "Data will be also reshaped after retrieve. This step might take up to 8 minutes if your random point per polygon is set to 2. To be fair, the sample size is rather small for the analysis. I'd recommend to use the CSV File named **points.csv**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Total run time: 0:02:38.517681 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:36: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_landsat = l8_time_series.resample('M').mean()\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/1480478213.py:62: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_chirps = chirps_time_series.resample('M').mean()\n"
     ]
    }
   ],
   "source": [
    "# Set start and end date\n",
    "startTime = datetime.datetime(2013, 2, 3)\n",
    "endTime = datetime.datetime(2019, 5, 25)\n",
    "\n",
    "# Create image collection\n",
    "l8 = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT').filterDate(startTime, endTime)\n",
    "precipitation = ee.ImageCollection('UCSB-CHG/CHIRPS/PENTAD').filterDate(startTime, endTime)\n",
    "\n",
    "def _getInfo(image_collection, point):\n",
    "    return image_collection.getRegion(point,500).getInfo()\n",
    "\n",
    "def get_ndvi(x,point, row):\n",
    "    header = x[0]\n",
    "    data = array(x[1:])\n",
    "    \n",
    "    iTime = header.index('time')\n",
    "    time = [datetime.datetime.fromtimestamp(i/1000) for i in (data[0:,iTime].astype(int))]\n",
    "\n",
    "    # List of used image bands\n",
    "    band_list = ['B5',u'B4']\n",
    "\n",
    "    iBands = [header.index(b) for b in band_list]\n",
    "    yData = data[0:,iBands].astype(np.float64)\n",
    " \n",
    "    # Calculate NDVI\n",
    "    red = yData[:,0]\n",
    "    nir = yData[:,1]\n",
    "    ndvi = (nir - red) / (nir + red)\n",
    "\n",
    "    # Reshape NDVI array into Pandas Dataframe\n",
    "    df = pd.DataFrame(data = ndvi*-1, index = time, columns = ['ndvi'])\n",
    "    df_red = pd.DataFrame(data = red, index = time, columns = ['red'])\n",
    "    l8_time_series = df.dropna()\n",
    "\n",
    "    # Resampling\n",
    "    monthly_landsat = l8_time_series.resample('M').mean()\n",
    "    monthly_landsat['landsat_id'] = str(data[0][0])\n",
    "    monthly_landsat['x'] = point['coordinates'][0]\n",
    "    monthly_landsat['y'] = point['coordinates'][1]\n",
    "    monthly_landsat['CVE_MUN'] = row['CVE_MUN']\n",
    "    monthly_landsat['CVE_ENT'] = row['CVE_ENT']\n",
    "    monthly_landsat['NOM_MUN'] = row['NOM_MUN']\n",
    "    monthly_landsat['OID'] = row['OID']\n",
    "    return monthly_landsat\n",
    "\n",
    "\n",
    "def get_precipitation(x,point,row):\n",
    "    # Reshape Chirps precipitation data \n",
    "    header = x[0]\n",
    "    data = array(x[1:])\n",
    "    iTime = header.index('time')\n",
    "    time = [datetime.datetime.fromtimestamp(i/1000) for i in (data[0:,iTime].astype(int))]\n",
    "\n",
    "    band_list = ['precipitation']\n",
    "    iBands = [header.index(b) for b in band_list]\n",
    "    chirps = data[0:,iBands].astype(np.float64)\n",
    "\n",
    "    df = pd.DataFrame(data = chirps, index = time, columns = ['precipitation'])\n",
    "    chirps_time_series = df.dropna()\n",
    "\n",
    "    # Resampling\n",
    "    monthly_chirps = chirps_time_series.resample('M').mean()\n",
    "    monthly_chirps['chirps_id'] = str(data[0][0])\n",
    "    monthly_chirps['x'] = point['coordinates'][0]\n",
    "    monthly_chirps['y'] = point['coordinates'][1]\n",
    "    monthly_chirps['CVE_MUN'] = row['CVE_MUN']\n",
    "    monthly_chirps['CVE_ENT'] = row['CVE_ENT']\n",
    "    monthly_chirps['NOM_MUN'] = row['NOM_MUN']\n",
    "    monthly_chirps['OID'] = row['OID']\n",
    "    return monthly_chirps\n",
    "\n",
    "\n",
    "start_time = datetime.datetime.now()\n",
    "\n",
    "l8_list = []\n",
    "pp_list = []\n",
    "for index , row in Y.iterrows():\n",
    "    point = {'type':'Point', 'coordinates':[row['geometry'].x,row['geometry'].y]};\n",
    "    _x = _getInfo(l8, point)\n",
    "    _y = _getInfo(precipitation, point)\n",
    "    l8_list.append(get_ndvi(_x,point,row))\n",
    "    pp_list.append(get_precipitation(_y,point,row))\n",
    "\n",
    "df_l8 = pd.concat(l8_list)\n",
    "df_l8 = df_l8.reset_index()\n",
    "\n",
    "df_pp = pd.concat(pp_list)\n",
    "df_pp = df_pp.reset_index()\n",
    "\n",
    "pp = df_pp.copy()\n",
    "pp = df_pp[df_pp['index']>'2013-07-01']\n",
    "\n",
    "ll8 = df_l8.copy()\n",
    "ll8 = df_l8[df_l8['index']>'2013-07-01']\n",
    "\n",
    "df = pd.merge(pp,ll8, on = ['index','CVE_MUN','CVE_ENT','NOM_MUN','OID','x','y'])\n",
    "# df.to_csv('data/chiapas/points.csv',index=False)\n",
    "\n",
    "end_time = datetime.datetime.now()\n",
    "print('\\nTotal run time:', end_time - start_time, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:**\n",
    "Instead of waiting 5 to 30 minutes (depends on variable n), load points file with 554 points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/chiapas/points.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert Pandas to GeoPandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = gpd.GeoDataFrame(df, geometry = gpd.points_from_xy(df.y, df.x))\n",
    "gdf = gdf.drop(columns=['x','y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = gdf['index'].unique()\n",
    "time_series_list = []\n",
    "for i in dates:\n",
    "    time_series_list.append(gdf[gdf['index']==dates[0]].set_index(['NOM_MUN','index'])[['precipitation','chirps_id','ndvi']].groupby(level=[0,1]).mean())\n",
    "\n",
    "df_time_series = pd.concat(time_series_list).reset_index()\n",
    "df_time_series = df_time_series.merge(X[['geometry','NOM_MUN']])\n",
    "\n",
    "gdf_time_series = gpd.GeoDataFrame(df_time_series)\n",
    "gdf_time_series['index'] = pd.to_datetime(gdf_time_series['index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (12,10), subplot_kw = {'aspect':'equal'})\n",
    "gdf_time_series[gdf_time_series['index']=='2013-07-31'].plot(column = 'precipitation',cmap = 'Blues',ax = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (12,10), subplot_kw = {'aspect':'equal'})\n",
    "gdf_time_series[gdf_time_series['index']=='2013-07-31'].plot(column = 'ndvi',cmap = 'YlGn',ax = ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Time Series Viewer\n",
    "In this last part, we are going to use widgets to browse through the Time-Series in order to see all time steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a47624548bfd45d8bef33b68cb7c4530",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Choose Date:', layout=Layout(width='max-content'), options=(Timestamp('2013-07-31 00:00:…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, clear_output\n",
    "import ipywidgets as widgets\n",
    "\n",
    "\n",
    "drop_dfu = widgets.Dropdown(options=sorted(gdf_time_series['index'].unique()), description='Choose Date:', \n",
    "                            disabled=False,layout={'width': 'max-content'})\n",
    "display(drop_dfu)\n",
    "\n",
    "def c(i):\n",
    "    vmin, vmax = gdf_time_series['precipitation'].min(), gdf_time_series['precipitation'].max()\n",
    "\n",
    "    fig,ax = plt.subplots(1,2,figsize=(25,15),subplot_kw={'aspect':'equal'})\n",
    "    \n",
    "    gdf_time_series[gdf_time_series['index']==drop_dfu.value].plot(column='ndvi',cmap='YlGn',ax=ax[0])\n",
    "    gdf_time_series[gdf_time_series['index']==drop_dfu.value].plot(column='precipitation',cmap='Blues',ax=ax[1])\n",
    "    \n",
    "    for i in range(2):\n",
    "        ax[i].axis('off')\n",
    "        ax[i].set_title([\"ndvi\",\"precipitation\"][i], fontsize=20)\n",
    "\n",
    "def on_change(change):\n",
    "    if change['name'] == 'value' and (change['new'] != change['old']):\n",
    "        clear_output()\n",
    "        display(drop_dfu)\n",
    "        c(drop_dfu.value)\n",
    "\n",
    "drop_dfu.observe(on_change)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precipitation Heat Map\n",
    "Create a precipitation heat map on average precipitation values throughout the whole time series. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/3387998565.py:2: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  points_gdf['x'] = points_gdf.centroid.x\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/3387998565.py:3: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  points_gdf['y'] = points_gdf.centroid.y\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
       "&lt;html&gt;\n",
       "&lt;head&gt;\n",
       "    \n",
       "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
       "    \n",
       "        &lt;script&gt;\n",
       "            L_NO_TOUCH = false;\n",
       "            L_DISABLE_3D = false;\n",
       "        &lt;/script&gt;\n",
       "    \n",
       "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
       "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n",
       "    \n",
       "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
       "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
       "            &lt;style&gt;\n",
       "                #map_7cf7679c05f3e51cd4c8a781e6062adf {\n",
       "                    position: relative;\n",
       "                    width: 100.0%;\n",
       "                    height: 100.0%;\n",
       "                    left: 0.0%;\n",
       "                    top: 0.0%;\n",
       "                }\n",
       "                .leaflet-container { font-size: 1rem; }\n",
       "            &lt;/style&gt;\n",
       "        \n",
       "    &lt;script src=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium@main/folium/templates/leaflet_heat.min.js&quot;&gt;&lt;/script&gt;\n",
       "&lt;/head&gt;\n",
       "&lt;body&gt;\n",
       "    \n",
       "    \n",
       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_7cf7679c05f3e51cd4c8a781e6062adf&quot; &gt;&lt;/div&gt;\n",
       "        \n",
       "&lt;/body&gt;\n",
       "&lt;script&gt;\n",
       "    \n",
       "    \n",
       "            var map_7cf7679c05f3e51cd4c8a781e6062adf = L.map(\n",
       "                &quot;map_7cf7679c05f3e51cd4c8a781e6062adf&quot;,\n",
       "                {\n",
       "                    center: [16.25, -92.45],\n",
       "                    crs: L.CRS.EPSG3857,\n",
       "                    zoom: 7,\n",
       "                    zoomControl: true,\n",
       "                    preferCanvas: false,\n",
       "                }\n",
       "            );\n",
       "            L.control.scale().addTo(map_7cf7679c05f3e51cd4c8a781e6062adf);\n",
       "\n",
       "            \n",
       "\n",
       "        \n",
       "    \n",
       "            var tile_layer_16aa0a825ecd2fdbae33da5c05a2efdd = L.tileLayer(\n",
       "                &quot;https://stamen-tiles-{s}.a.ssl.fastly.net/toner/{z}/{x}/{y}.png&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Map tiles by \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://stamen.com\\&quot;\\u003eStamen Design\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://creativecommons.org/licenses/by/3.0\\&quot;\\u003eCC BY 3.0\\u003c/a\\u003e. Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_7cf7679c05f3e51cd4c8a781e6062adf);\n",
       "        \n",
       "    \n",
       "            var heat_map_b9563183c3936d821e41fed149208a22 = L.heatLayer(\n",
       "                [[15.414139836838071, -92.68168536043574], [16.53775291971715, -92.84090254141441], [15.231414472218532, -92.77402483018214], [16.921830923348455, -92.69017605914146], [16.6618845041544, -91.8625729811303], [15.556835061099859, -92.09716338981605], [17.385593893727943, -92.85733830116828], [15.758148321939423, -92.68044797203369], [16.2488838981223, -93.93736052542353], [15.491755248651295, -92.16129267347127], [15.619584599477866, -92.24241687597157], [16.28183600731508, -90.5625088017745], [16.866606687502433, -93.31907484517023], [17.014122761961193, -92.95879021724336], [15.086241069821021, -92.16413446298701], [17.779871278187183, -91.99760375213714], [17.02127054243214, -92.61180174771755], [16.81936582292508, -92.68016908624625], [17.32679743436176, -93.13153758849417], [16.950157171229637, -92.57043515376346], [16.598071403585262, -92.97063176405227], [16.55397282519367, -92.72594407247466], [16.995333049419674, -93.1115199702685], [15.799032255028974, -92.39128986117113], [17.10969121392168, -92.03514187582387], [16.712962518599888, -93.86747144422445], [17.1205748962268, -93.1260784247102], [16.296883585850107, -92.17897405227686], [17.09417440931395, -93.25286045487199], [17.031748222416226, -92.74129237365936], [15.432546422296285, -92.26392556453702], [15.364432466015387, -92.55816843428349], [17.294073619035196, -93.27618675790346], [15.762651422865305, -92.08776683790379], [14.750009274454976, -92.2131990197254], [15.009403991519493, -92.42377327551918], [17.243167273704604, -92.69492697218145], [17.29853247739465, -92.97862919638915], [17.42139780641583, -93.09902282863516], [16.81795071718749, -92.8969951475513], [17.46687274734702, -92.97559131158921], [16.52273562162797, -93.67207617857824], [17.089618439008998, -92.87441597855126], [17.69944720777233, -93.19333535570169], [15.960894948073946, -92.77124303791648], [15.512996713534115, -92.21840121800224], [16.204702237089737, -91.79579476597904], [17.606636195950866, -91.75107452024189], [16.02444312485997, -91.89940022964582], [16.92438368903565, -92.77667746275098], [16.38560262825539, -91.74028293332215], [16.365307564733673, -92.36472683440111], [15.461012123691035, -92.89163475486102], [16.19919393266687, -91.2485949074356], [16.246350141843152, -90.74342293225853], [15.37276629407694, -92.18576378592493], [14.88060482728667, -92.49755273854238], [14.821663642629339, -92.20159973525061], [16.87481012535272, -92.56310089298294], [15.671677651309736, -92.66439042542342], [15.313825902263021, -92.33146420002737], [16.438200421982195, -92.61005044613562], [16.702093865380412, -91.33749873317252], [17.22079739571642, -93.17387626530093], [16.827902459790337, -93.49225479964396], [17.43236300724401, -93.39966113024816], [16.89523329808198, -93.08380363438664], [17.444075590284175, -91.81265555612738], [17.060103478870605, -92.46406540499929], [17.189185985033482, -93.05690017797637], [17.530576526982895, -93.19914079263272], [15.66955613164759, -93.19240131574018], [17.217828546881766, -92.88120991556778], [17.192220473886593, -92.99657551775427], [17.864223308294918, -93.22938057998516], [17.31982569778416, -92.61076220998237], [17.437619002984068, -92.18714148741066], [16.678297105700118, -92.58994606168453], [16.91313318922676, -93.20967827166194], [16.930159947339508, -92.39166786030097], [16.62247027084254, -92.72378130556204], [16.949363970450754, -92.72154872161421], [15.547320028397579, -92.45622295792376], [17.138922908817808, -92.62501505354689], [17.037265979699225, -92.34828952992302], [16.10642758790304, -92.37594578079278], [17.38699233849557, -93.00479064102115], [16.925968942905527, -92.98867456741947], [16.585545567921304, -93.13633224794535], [14.639383532565013, -92.22712116937095], [17.488856410520384, -93.25158759450755], [14.933968484423131, -92.30479717320198], [17.22011355008479, -93.11231444610694], [17.250969064179433, -92.99727690349003], [17.19825633162223, -93.53320184694707], [16.833188473746397, -92.47200580366149], [16.561102933203525, -92.50881225179188], [17.379189289370395, -92.48582820616261], [15.99699062638118, -93.6905281119329], [16.515155333548964, -92.65996329025393], [17.300789783526582, -92.26962531407712], [14.894105146532105, -92.19868036359807], [16.73944664466165, -93.13728534700509], [15.140441356902206, -92.41156438690228], [16.12408314442959, -92.24643996023164], [15.059952718524057, -92.10254993690752], [16.317312604265304, -92.60789676203657], [15.16542248637973, -92.6195664670956], [16.112152136263777, -93.19760676570728], [16.346143650518904, -93.37518815969894], [17.185559242385093, -92.32584884923241], [16.721915888827056, -92.78198097670887]],\n",
       "                {&quot;blur&quot;: 15, &quot;maxZoom&quot;: 18, &quot;minOpacity&quot;: 0.5, &quot;radius&quot;: 25}\n",
       "            ).addTo(map_7cf7679c05f3e51cd4c8a781e6062adf);\n",
       "        \n",
       "&lt;/script&gt;\n",
       "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1779533a0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points_gdf = gdf_time_series.copy()\n",
    "points_gdf['x'] = points_gdf.centroid.x\n",
    "points_gdf['y'] = points_gdf.centroid.y\n",
    "precipitation = points_gdf.set_index('NOM_MUN')[['index','precipitation']].groupby(level=0).mean()\n",
    "points_gdf = points_gdf[['NOM_MUN','x','y']].drop_duplicates()\n",
    "points_gdf['precipitation']=precipitation.precipitation.values\n",
    "\n",
    "points_gdf = points_gdf[points_gdf['precipitation']>25]\n",
    "points_array = points_gdf[['y', 'x']].values\n",
    "\n",
    "m = folium.Map(location = [16.25, -92.45], tiles = 'stamentoner', zoom_start = 7, control_scale = True)\n",
    "HeatMap(points_array).add_to(m)\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NDVI Heat Map\n",
    "Create a NDVI heat map on average NDVI values throughout the whole time series. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/3768323428.py:2: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  points_gdf['x'] = points_gdf.centroid.x\n",
      "/var/folders/9h/5ccmk6p120n5hg6tprbr1mdr0000gp/T/ipykernel_15687/3768323428.py:3: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  points_gdf['y'] = points_gdf.centroid.y\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
       "&lt;html&gt;\n",
       "&lt;head&gt;\n",
       "    \n",
       "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
       "    \n",
       "        &lt;script&gt;\n",
       "            L_NO_TOUCH = false;\n",
       "            L_DISABLE_3D = false;\n",
       "        &lt;/script&gt;\n",
       "    \n",
       "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
       "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n",
       "    \n",
       "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
       "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
       "            &lt;style&gt;\n",
       "                #map_639ae4f6c713c5b675e702f29ef0acfb {\n",
       "                    position: relative;\n",
       "                    width: 100.0%;\n",
       "                    height: 100.0%;\n",
       "                    left: 0.0%;\n",
       "                    top: 0.0%;\n",
       "                }\n",
       "                .leaflet-container { font-size: 1rem; }\n",
       "            &lt;/style&gt;\n",
       "        \n",
       "    &lt;script src=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium@main/folium/templates/leaflet_heat.min.js&quot;&gt;&lt;/script&gt;\n",
       "&lt;/head&gt;\n",
       "&lt;body&gt;\n",
       "    \n",
       "    \n",
       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_639ae4f6c713c5b675e702f29ef0acfb&quot; &gt;&lt;/div&gt;\n",
       "        \n",
       "&lt;/body&gt;\n",
       "&lt;script&gt;\n",
       "    \n",
       "    \n",
       "            var map_639ae4f6c713c5b675e702f29ef0acfb = L.map(\n",
       "                &quot;map_639ae4f6c713c5b675e702f29ef0acfb&quot;,\n",
       "                {\n",
       "                    center: [16.25, -92.45],\n",
       "                    crs: L.CRS.EPSG3857,\n",
       "                    zoom: 7,\n",
       "                    zoomControl: true,\n",
       "                    preferCanvas: false,\n",
       "                }\n",
       "            );\n",
       "            L.control.scale().addTo(map_639ae4f6c713c5b675e702f29ef0acfb);\n",
       "\n",
       "            \n",
       "\n",
       "        \n",
       "    \n",
       "            var tile_layer_13e7df602d73acbe6f3217a9d5829385 = L.tileLayer(\n",
       "                &quot;https://stamen-tiles-{s}.a.ssl.fastly.net/toner/{z}/{x}/{y}.png&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Map tiles by \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://stamen.com\\&quot;\\u003eStamen Design\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://creativecommons.org/licenses/by/3.0\\&quot;\\u003eCC BY 3.0\\u003c/a\\u003e. Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_639ae4f6c713c5b675e702f29ef0acfb);\n",
       "        \n",
       "    \n",
       "            var heat_map_ea8b52f1368211624adc5adad2296308 = L.heatLayer(\n",
       "                [[15.414139836838071, -92.68168536043574], [16.53775291971715, -92.84090254141441], [15.231414472218532, -92.77402483018214], [16.921830923348455, -92.69017605914146], [16.6618845041544, -91.8625729811303], [15.556835061099859, -92.09716338981605], [16.515289554070144, -92.39542713428796], [17.385593893727943, -92.85733830116828], [15.758148321939423, -92.68044797203369], [16.2488838981223, -93.93736052542353], [15.491755248651295, -92.16129267347127], [16.28183600731508, -90.5625088017745], [16.866606687502433, -93.31907484517023], [17.014122761961193, -92.95879021724336], [15.086241069821021, -92.16413446298701], [17.779871278187183, -91.99760375213714], [17.02127054243214, -92.61180174771755], [16.81936582292508, -92.68016908624625], [16.611533215387308, -92.21697043797022], [17.32679743436176, -93.13153758849417], [16.950157171229637, -92.57043515376346], [16.598071403585262, -92.97063176405227], [16.55397282519367, -92.72594407247466], [16.995333049419674, -93.1115199702685], [15.799032255028974, -92.39128986117113], [17.10969121392168, -92.03514187582387], [16.712962518599888, -93.86747144422445], [17.1205748962268, -93.1260784247102], [16.296883585850107, -92.17897405227686], [17.09417440931395, -93.25286045487199], [17.031748222416226, -92.74129237365936], [15.432546422296285, -92.26392556453702], [15.364432466015387, -92.55816843428349], [17.294073619035196, -93.27618675790346], [15.762651422865305, -92.08776683790379], [14.750009274454976, -92.2131990197254], [15.009403991519493, -92.42377327551918], [17.243167273704604, -92.69492697218145], [16.69427742462277, -92.39963387178656], [17.29853247739465, -92.97862919638915], [17.42139780641583, -93.09902282863516], [16.81795071718749, -92.8969951475513], [17.46687274734702, -92.97559131158921], [16.52273562162797, -93.67207617857824], [17.089618439008998, -92.87441597855126], [17.69944720777233, -93.19333535570169], [15.960894948073946, -92.77124303791648], [15.512996713534115, -92.21840121800224], [17.606636195950866, -91.75107452024189], [16.92438368903565, -92.77667746275098], [15.461012123691035, -92.89163475486102], [16.19919393266687, -91.2485949074356], [16.246350141843152, -90.74342293225853], [15.37276629407694, -92.18576378592493], [14.88060482728667, -92.49755273854238], [14.821663642629339, -92.20159973525061], [16.87481012535272, -92.56310089298294], [15.671677651309736, -92.66439042542342], [15.313825902263021, -92.33146420002737], [16.438200421982195, -92.61005044613562], [16.702093865380412, -91.33749873317252], [17.22079739571642, -93.17387626530093], [16.827902459790337, -93.49225479964396], [17.43236300724401, -93.39966113024816], [16.89523329808198, -93.08380363438664], [16.780997981413844, -92.27478653421767], [17.444075590284175, -91.81265555612738], [17.060103478870605, -92.46406540499929], [17.189185985033482, -93.05690017797637], [17.530576526982895, -93.19914079263272], [15.66955613164759, -93.19240131574018], [17.217828546881766, -92.88120991556778], [17.192220473886593, -92.99657551775427], [17.864223308294918, -93.22938057998516], [17.31982569778416, -92.61076220998237], [17.437619002984068, -92.18714148741066], [16.678297105700118, -92.58994606168453], [16.91313318922676, -93.20967827166194], [16.930159947339508, -92.39166786030097], [16.62247027084254, -92.72378130556204], [16.949363970450754, -92.72154872161421], [15.547320028397579, -92.45622295792376], [17.138922908817808, -92.62501505354689], [17.037265979699225, -92.34828952992302], [17.38699233849557, -93.00479064102115], [16.925968942905527, -92.98867456741947], [16.585545567921304, -93.13633224794535], [14.639383532565013, -92.22712116937095], [17.488856410520384, -93.25158759450755], [14.933968484423131, -92.30479717320198], [17.22011355008479, -93.11231444610694], [17.250969064179433, -92.99727690349003], [17.19825633162223, -93.53320184694707], [16.833188473746397, -92.47200580366149], [17.379189289370395, -92.48582820616261], [15.99699062638118, -93.6905281119329], [16.515155333548964, -92.65996329025393], [17.300789783526582, -92.26962531407712], [14.894105146532105, -92.19868036359807], [16.73944664466165, -93.13728534700509], [15.140441356902206, -92.41156438690228], [15.059952718524057, -92.10254993690752], [15.16542248637973, -92.6195664670956], [16.112152136263777, -93.19760676570728], [16.346143650518904, -93.37518815969894], [17.185559242385093, -92.32584884923241], [16.721915888827056, -92.78198097670887]],\n",
       "                {&quot;blur&quot;: 15, &quot;maxZoom&quot;: 18, &quot;minOpacity&quot;: 0.5, &quot;radius&quot;: 25}\n",
       "            ).addTo(map_639ae4f6c713c5b675e702f29ef0acfb);\n",
       "        \n",
       "&lt;/script&gt;\n",
       "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x28b3d4370>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points_gdf = gdf_time_series.copy()\n",
    "points_gdf['x'] = points_gdf.centroid.x\n",
    "points_gdf['y'] = points_gdf.centroid.y\n",
    "ndvi = points_gdf.set_index('NOM_MUN')[['index','ndvi']].groupby(level=0).mean()\n",
    "points_gdf = points_gdf[['NOM_MUN','x','y']].drop_duplicates()\n",
    "points_gdf['ndvi'] = ndvi.ndvi.values\n",
    "\n",
    "points_gdf = points_gdf[points_gdf['ndvi']>0.25]\n",
    "points_array = points_gdf[['y', 'x']].values\n",
    "\n",
    "m = folium.Map(location = [16.25, -92.45], tiles = 'stamentoner', zoom_start = 7, control_scale = True)\n",
    "HeatMap(points_array).add_to(m)\n",
    "m"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}