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Add EE charting functions (#1855)
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* Add image chart functions

* Add LineChart class

* Add DataTable class

* Add BaseChart class

* Add image_byClass function

* Add chart.image_histogram function

* Add the Chart class

* Add image_series function

* Add image_seriesByRegion function

* Add transpose_df function

* Change function name to snake case

* Add doy_series_by_year function

* Add image_doy_series function

* Add image_doy_series_by_region function

* Add image_by_class function

* Add typehints and docstrings

* Add notebook template

* Add chart_features.ipynb

* Add chart_image.ipynb

* Add chart_image_collection.ipynb

* Add chart.array_values function

* Add AreaChart type

* Add more chart array examples

* Add IntervalChart

* Save save_png method

* Add images to notebook

* Update mkdocs.yml
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giswqs authored Aug 17, 2024
1 parent 49d29e9 commit bfb63d2
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352 changes: 352 additions & 0 deletions docs/notebooks/144_chart_features.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://githubtocolab.com/gee-community/geemap/blob/master/examples/notebooks/144_chart_features.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/></a>\n",
"\n",
"**Feature and FeatureCollection Charts**\n",
"\n",
"Uncomment the following line to install [geemap](https://geemap.org) if needed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# %pip install -U geemap"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import calendar\n",
"import ee\n",
"import geemap\n",
"from geemap import chart"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"geemap.ee_initialize()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## feature_by_feature\n",
"\n",
"Features are plotted along the x-axis, labeled by values of a selected property. Series are represented by adjacent columns defined by a list of property names whose values are plotted along the y-axis."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ecoregions = ee.FeatureCollection(\"projects/google/charts_feature_example\")\n",
"features = ecoregions.select(\"[0-9][0-9]_tmean|label\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"geemap.ee_to_df(features)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x_property = \"label\"\n",
"y_properties = [str(x).zfill(2) + \"_tmean\" for x in range(1, 13)]\n",
"\n",
"labels = calendar.month_abbr[1:] # a list of month labels, e.g. ['Jan', 'Feb', ...]\n",
"\n",
"colors = [\n",
" \"#604791\",\n",
" \"#1d6b99\",\n",
" \"#39a8a7\",\n",
" \"#0f8755\",\n",
" \"#76b349\",\n",
" \"#f0af07\",\n",
" \"#e37d05\",\n",
" \"#cf513e\",\n",
" \"#96356f\",\n",
" \"#724173\",\n",
" \"#9c4f97\",\n",
" \"#696969\",\n",
"]\n",
"title = \"Average Monthly Temperature by Ecoregion\"\n",
"x_label = \"Ecoregion\"\n",
"y_label = \"Temperature\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = chart.feature_by_feature(\n",
" features,\n",
" x_property,\n",
" y_properties,\n",
" colors=colors,\n",
" labels=labels,\n",
" title=title,\n",
" x_label=x_label,\n",
" y_label=y_label,\n",
")\n",
"fig"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](https://i.imgur.com/MZa99Vf.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## feature.by_property"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ecoregions = ee.FeatureCollection(\"projects/google/charts_feature_example\")\n",
"features = ecoregions.select(\"[0-9][0-9]_ppt|label\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"geemap.ee_to_df(features)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"keys = [str(x).zfill(2) + \"_ppt\" for x in range(1, 13)]\n",
"values = calendar.month_abbr[1:] # a list of month labels, e.g. ['Jan', 'Feb', ...]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x_properties = dict(zip(keys, values))\n",
"series_property = \"label\"\n",
"title = \"Average Ecoregion Precipitation by Month\"\n",
"colors = [\"#f0af07\", \"#0f8755\", \"#76b349\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = chart.feature_by_property(\n",
" features,\n",
" x_properties,\n",
" series_property,\n",
" title=title,\n",
" colors=colors,\n",
" x_label=\"Month\",\n",
" y_label=\"Precipitation (mm)\",\n",
" legend_location=\"top-left\",\n",
")\n",
"fig"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](https://i.imgur.com/6RhuUc7.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## feature_groups"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ecoregions = ee.FeatureCollection(\"projects/google/charts_feature_example\")\n",
"features = ecoregions.select(\"[0-9][0-9]_ppt|label\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"features = ee.FeatureCollection(\"projects/google/charts_feature_example\")\n",
"x_property = \"label\"\n",
"y_property = \"01_tmean\"\n",
"series_property = \"warm\"\n",
"title = \"Average January Temperature by Ecoregion\"\n",
"colors = [\"#cf513e\", \"#1d6b99\"]\n",
"labels = [\"Warm\", \"Cold\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"chart.feature_groups(\n",
" features,\n",
" x_property,\n",
" y_property,\n",
" series_property,\n",
" title=title,\n",
" colors=colors,\n",
" x_label=\"Ecoregion\",\n",
" y_label=\"January Temperature (°C)\",\n",
" legend_location=\"top-right\",\n",
" labels=labels,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](https://i.imgur.com/YFZlJtc.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## feature_histogram"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"source = ee.ImageCollection(\"OREGONSTATE/PRISM/Norm91m\").toBands()\n",
"region = ee.Geometry.Rectangle(-123.41, 40.43, -116.38, 45.14)\n",
"features = source.sample(region, 5000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"geemap.ee_to_df(features.limit(5).select([\"07_ppt\"]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"property = \"07_ppt\"\n",
"title = \"July Precipitation Distribution for NW USA\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = chart.feature_histogram(\n",
" features,\n",
" property,\n",
" max_buckets=None,\n",
" title=title,\n",
" x_label=\"Precipitation (mm)\",\n",
" y_label=\"Pixel Count\",\n",
" colors=[\"#1d6b99\"],\n",
")\n",
"fig"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](https://i.imgur.com/ErIp7Oy.png)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "geo",
"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": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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