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jdbcode/ee_pdsi_chart_altair_ggplot2.ipynb

Last active April 27, 2021 21:20
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Earth Engine PDSI data reduction plotted with Altair and ggplot2
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ee_pdsi_chart_altair_ggplot2.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "ee_pdsi_chart_altair_ggplot2.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/jdbcode/a33e21d2c8eaf5ee52bde090593bd15f/ee_pdsi_chart_altair_ggplot2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vTL0i0GkRBx0"
},
"source": [
"# Setup"
]
},
{
"cell_type": "code",
"metadata": {
"id": "SvSPxYYwpVyC"
},
"source": [
"import ee\n",
"ee.Authenticate()\n",
"ee.Initialize()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "phryoeEgp5rH"
},
"source": [
"import pandas as pd\n",
"import altair as alt"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "G-2BwN5PRFhD"
},
"source": [
"A function to return a pandas dataframe for a mapped region reduction on an image collection."
]
},
{
"cell_type": "code",
"metadata": {
"id": "0fxbsgpy8n4C"
},
"source": [
"def reduce_col_to_df(col, reduce_params):\n",
" def create_reduce_region_function(reduce_params):\n",
" def reduce_region_function(img):\n",
" stat = img.reduceRegion(**reduce_params)\n",
" return (ee.Feature(None, stat)\n",
" .set({'millis': img.date().millis()}))\n",
" return reduce_region_function\n",
" \n",
" reduce = create_reduce_region_function(reduce_params)\n",
" fc = ee.FeatureCollection(col.map(reduce))\n",
" prop_names = fc.first().propertyNames()\n",
" prop_lists = fc.reduceColumns(\n",
" reducer=ee.Reducer.toList().repeat(prop_names.size()),\n",
" selectors=prop_names).get('list')\n",
"\n",
" return pd.DataFrame(ee.Dictionary.fromLists(prop_names, prop_lists).getInfo())"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "cOiMl6-AqpEb"
},
"source": [
"# Get drought data (PDSI)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zIflx14uQhYZ"
},
"source": [
"Define collection ('GRIDMET/DROUGHT') and region of interest (Monterey country, CA)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "LS0KsCvjqRau"
},
"source": [
"date_range = ['2010-01-01', '2021-05-01']\n",
"pdsi = (ee.ImageCollection('GRIDMET/DROUGHT')\n",
" .filterDate(date_range[0], date_range[1])\n",
" .select('pdsi'))\n",
"\n",
"aoi = (ee.FeatureCollection('TIGER/2018/Counties')\n",
" .filter(ee.Filter.eq('NAME', 'Monterey'))\n",
" .geometry())"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "9TKL3Vi1QlGR"
},
"source": [
"Get a pandas dataframe that contains the median regional PDSI per observation."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "42WCwHKz-SJg",
"outputId": "9e23a766-a676-4387-ec88-0a9094dc6fe0"
},
"source": [
"reduce_params = {\n",
" 'geometry': aoi,\n",
" 'reducer': ee.Reducer.median(),\n",
" 'scale': 4000,\n",
" 'crs': 'EPSG:3310'\n",
"}\n",
"pdsi_df = reduce_col_to_df(pdsi, reduce_params)\n",
"display(pdsi_df)"
],
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"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>millis</th>\n",
" <th>pdsi</th>\n",
" <th>system:index</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1262671200000</td>\n",
" <td>1.018947</td>\n",
" <td>20100105</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1263103200000</td>\n",
" <td>0.922500</td>\n",
" <td>20100110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1263535200000</td>\n",
" <td>0.797780</td>\n",
" <td>20100115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1263967200000</td>\n",
" <td>0.800977</td>\n",
" <td>20100120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1264399200000</td>\n",
" <td>0.857332</td>\n",
" <td>20100125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>819</th>\n",
" <td>1616738400000</td>\n",
" <td>-2.828437</td>\n",
" <td>20210326</td>\n",
" </tr>\n",
" <tr>\n",
" <th>820</th>\n",
" <td>1617170400000</td>\n",
" <td>-2.855532</td>\n",
" <td>20210331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>821</th>\n",
" <td>1617602400000</td>\n",
" <td>-2.889107</td>\n",
" <td>20210405</td>\n",
" </tr>\n",
" <tr>\n",
" <th>822</th>\n",
" <td>1618034400000</td>\n",
" <td>-2.950056</td>\n",
" <td>20210410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>823</th>\n",
" <td>1618466400000</td>\n",
" <td>-3.048842</td>\n",
" <td>20210415</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>824 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" millis pdsi system:index\n",
"0 1262671200000 1.018947 20100105\n",
"1 1263103200000 0.922500 20100110\n",
"2 1263535200000 0.797780 20100115\n",
"3 1263967200000 0.800977 20100120\n",
"4 1264399200000 0.857332 20100125\n",
".. ... ... ...\n",
"819 1616738400000 -2.828437 20210326\n",
"820 1617170400000 -2.855532 20210331\n",
"821 1617602400000 -2.889107 20210405\n",
"822 1618034400000 -2.950056 20210410\n",
"823 1618466400000 -3.048842 20210415\n",
"\n",
"[824 rows x 3 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qE8ypAGIQ4cz"
},
"source": [
"Clean up the dataframe."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "4xBchcpTAXGG",
"outputId": "7a583df8-e5cf-4fe2-d0d1-94af72b1083d"
},
"source": [
"pdsi_df['Datetime'] = pd.to_datetime(pdsi_df['millis'], unit='ms')\n",
"pdsi_df = pdsi_df.rename(columns={\n",
" 'pdsi': 'PDSI'\n",
"}).drop(columns=['millis', 'system:index'])\n",
"display(pdsi_df)"
],
"execution_count": 6,
"outputs": [
{
"output_type": "display_data",
"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>PDSI</th>\n",
" <th>Datetime</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.018947</td>\n",
" <td>2010-01-05 06:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.922500</td>\n",
" <td>2010-01-10 06:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.797780</td>\n",
" <td>2010-01-15 06:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.800977</td>\n",
" <td>2010-01-20 06:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.857332</td>\n",
" <td>2010-01-25 06:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>819</th>\n",
" <td>-2.828437</td>\n",
" <td>2021-03-26 06:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>820</th>\n",
" <td>-2.855532</td>\n",
" <td>2021-03-31 06:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>821</th>\n",
" <td>-2.889107</td>\n",
" <td>2021-04-05 06:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>822</th>\n",
" <td>-2.950056</td>\n",
" <td>2021-04-10 06:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>823</th>\n",
" <td>-3.048842</td>\n",
" <td>2021-04-15 06:00:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>824 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" PDSI Datetime\n",
"0 1.018947 2010-01-05 06:00:00\n",
"1 0.922500 2010-01-10 06:00:00\n",
"2 0.797780 2010-01-15 06:00:00\n",
"3 0.800977 2010-01-20 06:00:00\n",
"4 0.857332 2010-01-25 06:00:00\n",
".. ... ...\n",
"819 -2.828437 2021-03-26 06:00:00\n",
"820 -2.855532 2021-03-31 06:00:00\n",
"821 -2.889107 2021-04-05 06:00:00\n",
"822 -2.950056 2021-04-10 06:00:00\n",
"823 -3.048842 2021-04-15 06:00:00\n",
"\n",
"[824 rows x 2 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fV1DUIklQWZ_"
},
"source": [
"# Plot with Altair"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 368
},
"id": "fs4i_O0mApBe",
"outputId": "76a284f8-bc33-4efa-e6f5-3f75b662f453"
},
"source": [
"alt.Chart(pdsi_df).mark_bar(size=3).encode(\n",
" x='Datetime:T',\n",
" y='PDSI:Q',\n",
" color=alt.Color(\n",
" 'PDSI:Q', scale=alt.Scale(scheme='redblue', domain=(-5, 5))),\n",
" tooltip=[\n",
" alt.Tooltip('Datetime:T', title='Date'),\n",
" alt.Tooltip('PDSI:Q', title='PDSI')\n",
" ]).properties(width=1000, height=300).interactive()"
],
"execution_count": 7,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "3Hq1d_JpQSUy"
},
"source": [
"# Plot with ggplot2"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jAobEE5iBaNN"
},
"source": [
"Load R."
]
},
{
"cell_type": "code",
"metadata": {
"id": "jF0HZLDgBZOu"
},
"source": [
"%load_ext rpy2.ipython"
],
"execution_count": 8,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "3mHhmhsIFVZh"
},
"source": [
"Transfer pandas DF to R kernel."
]
},
{
"cell_type": "code",
"metadata": {
"id": "mxearcMdEmzb"
},
"source": [
"%R -i pdsi_df"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "cEklpbXqP0K3"
},
"source": [
"Load R libraries."
]
},
{
"cell_type": "code",
"metadata": {
"id": "UwrCV8q4BhAN"
},
"source": [
"%%R\n",
"\n",
"library('ggplot2')\n",
"library(scales)"
],
"execution_count": 10,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "j0SQhkx4QMH_"
},
"source": [
"Plot PDSI using ggplot2."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 497
},
"id": "xXWBfD7ZCOwE",
"outputId": "180e7919-33a7-4b04-ade8-fab21a5fa39d"
},
"source": [
"%%R\n",
"\n",
"ggplot(pdsi_df, aes(Datetime, PDSI)) +\n",
" geom_col(aes(fill = PDSI)) +\n",
" scale_fill_distiller(\n",
" palette = \"RdBu\", limits = c(-5, 5), direction = 1, oob=squish) +\n",
" theme_bw()"
],
"execution_count": 11,
"outputs": []
}
]
}
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