Chart_GPP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8b3b5276",
   "metadata": {},
   "source": [
    "**Reference**\n",
    "\n",
    "Github:https://github.com/bwilder95/Test-Area/blob/master/COLAB/GPP_Example.ipynb\n",
    "\n",
    "StackOverflow: https://stackoverflow.com/questions/20965046/cumulative-sum-and-percentage-on-column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc077362",
   "metadata": {},
   "source": [
    "## Instalacion de librerias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "6dda7b91",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import ee\n",
    "ee.Initialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b0a7e44",
   "metadata": {},
   "source": [
    "## Crear una coleccion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "341f42d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = ee.ImageCollection('MODIS/006/MOD17A2H')\n",
    "\n",
    "# Initial date of interest (inclusive).\n",
    "i_date = '2020-01-01'\n",
    "\n",
    "# Final date of interest (exclusive).\n",
    "f_date = '2020-12-31'\n",
    "\n",
    "# Selection of appropriate bands and dates.\n",
    "GPP = dataset.select('Gpp').filterDate(i_date, f_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8925cc8f",
   "metadata": {},
   "source": [
    "## Crear un punto de analisis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "f82f24cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Crear una geometria de tipo punto\n",
    "\n",
    "p_lon = -100.7917\n",
    "p_lat = 21.0721\n",
    "test_point = ee.Geometry.Point(p_lon, p_lat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "76113bc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPP Value: 101.56521739130434 kg*C/m^2\n"
     ]
    }
   ],
   "source": [
    "scale = 250  # scale in meters\n",
    "\n",
    "# Calculate and print the mean value of the collection at the point.\n",
    "GPP_test_point = GPP.mean().sample(test_point, scale).first().get('Gpp').getInfo()\n",
    "print('GPP Value:', GPP_test_point, 'kg*C/m^2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "c03d8e45",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create function that takes median of single band (gpp in this case) for ca\n",
    "def reduce(img):\n",
    "    mean = img.reduceRegion(reducer=ee.Reducer.mean(), geometry=test_point\n",
    "                               ,scale=250).get('Gpp')\n",
    "    return img.set('date', img.date().format()).set('mean',mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "feb01f9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reduce images\n",
    "reduce_dataset = GPP.map(reduce)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "adee4913",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Mean GPP (kg*C/m^2/8-day)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01T00:00:00</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-09T00:00:00</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-17T00:00:00</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25T00:00:00</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-02T00:00:00</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date  Mean GPP (kg*C/m^2/8-day)\n",
       "0  2020-01-01T00:00:00                         55\n",
       "1  2020-01-09T00:00:00                         54\n",
       "2  2020-01-17T00:00:00                         57\n",
       "3  2020-01-25T00:00:00                         65\n",
       "4  2020-02-02T00:00:00                         67"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save to pandas df\n",
    "reduce_list = reduce_dataset.reduceColumns(ee.Reducer.toList(2), ['date','mean',]).values().get(0)\n",
    "df = pd.DataFrame(reduce_list.getInfo(), columns=['Date','Mean GPP (kg*C/m^2/8-day)'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42a48289",
   "metadata": {},
   "source": [
    "## Generacion de los valores acumulativos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "d2f4c23f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Date</th>\n",
       "      <th>Mean GPP (kg*C/m^2/8-day)</th>\n",
       "      <th>cum_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01T00:00:00</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-09T00:00:00</td>\n",
       "      <td>54</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-17T00:00:00</td>\n",
       "      <td>57</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25T00:00:00</td>\n",
       "      <td>65</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-02T00:00:00</td>\n",
       "      <td>67</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date  Mean GPP (kg*C/m^2/8-day)  cum_sum\n",
       "0  2020-01-01T00:00:00                         55       55\n",
       "1  2020-01-09T00:00:00                         54      109\n",
       "2  2020-01-17T00:00:00                         57      166\n",
       "3  2020-01-25T00:00:00                         65      231\n",
       "4  2020-02-02T00:00:00                         67      298"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cum_sum'] = df['Mean GPP (kg*C/m^2/8-day)'].cumsum()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "8f932166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>Mean GPP (kg*C/m^2/8-day)</th>\n",
       "      <th>cum_sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-09</th>\n",
       "      <td>54</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-17</th>\n",
       "      <td>57</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>65</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-02</th>\n",
       "      <td>67</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Mean GPP (kg*C/m^2/8-day)  cum_sum\n",
       "Date                                          \n",
       "2020-01-01                         55       55\n",
       "2020-01-09                         54      109\n",
       "2020-01-17                         57      166\n",
       "2020-01-25                         65      231\n",
       "2020-02-02                         67      298"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df.set_index('Date', inplace=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0b1c09a",
   "metadata": {},
   "source": [
    "## Generar un grafico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "432faef8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb284f03278>"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb2850a1f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.set(rc={'figure.figsize':(14,14)})\n",
    "sns.lineplot(x='Date',y='cum_sum',data=df,legend='full', palette='winter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdd08f33",
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}