143 Precipitation Timelapse animation¶

This notebook demonstrates how to generate the timelapse animation of IMERG precipitation dataset. In the animation, you can add map elements such as, title of map, north arrow, map scale, colorbar etc.

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# !pip install .
# !pip install cartopy scipy

# !pip install . # !pip install cartopy scipy

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import geemap
import ee

ee.Authenticate()
ee.Initialize(project="YOUR-PROJECT-ID")

print(geemap.__version__)

import geemap import ee ee.Authenticate() ee.Initialize(project="YOUR-PROJECT-ID") print(geemap.__version__)

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m = geemap.Map(center=(0.7893, 113.9213), zoom=5)
m

m = geemap.Map(center=(0.7893, 113.9213), zoom=5) m

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# Extract the required country boundary, in this case Indonesia
countries = ee.FeatureCollection("FAO/GAUL/2015/level0")
indonesia = countries.filter(ee.Filter.eq("ADM0_NAME", "Indonesia"))
indonesia_geom = indonesia.geometry()

# Load the IMERG precipitation data for the desired day
date = ee.Date("2023-07-15")
precipitation_collection = ee.ImageCollection("NASA/GPM_L3/IMERG_V06").filterDate(
    date, date.advance(1, "day")
)


# Define a function to mask out precipitation less than 0.5 mm/hr
def mask_less_ppt(collection):
    masked_collection = collection.map(lambda image: image.updateMask(image.gte(0.5)))
    return masked_collection


# Mask out precipitation less than 0.5 mm/hr
masked_imerg_collection = mask_less_ppt(precipitation_collection)

# add indonesia boundary to map
style = {"color": "000ffff0", "width": 2, "lineType": "solid", "fillColor": "00000000"}
m.addLayer(indonesia.style(**style), {}, "Indonesia_boundary")


# Define visualization parameters
palette = [
    "000096",
    "0064ff",
    "00b4ff",
    "33db80",
    "9beb4a",
    "ffeb00",
    "ffb300",
    "ff6400",
    "eb1e00",
    "af0000",
]
vis_params = {
    "min": 0,
    "max": 50,
    "bands": "precipitationCal",
    "palette": ["000096", "0064ff", "00b4ff", "eb1e00", "af0000"],
}

# # Add the precipitation data to the map
# m.addLayer(masked_imerg_collection, vis_params, 'Precipitation (IMERG)')
# m

# Extract the required country boundary, in this case Indonesia countries = ee.FeatureCollection("FAO/GAUL/2015/level0") indonesia = countries.filter(ee.Filter.eq("ADM0_NAME", "Indonesia")) indonesia_geom = indonesia.geometry() # Load the IMERG precipitation data for the desired day date = ee.Date("2023-07-15") precipitation_collection = ee.ImageCollection("NASA/GPM_L3/IMERG_V06").filterDate( date, date.advance(1, "day") ) # Define a function to mask out precipitation less than 0.5 mm/hr def mask_less_ppt(collection): masked_collection = collection.map(lambda image: image.updateMask(image.gte(0.5))) return masked_collection # Mask out precipitation less than 0.5 mm/hr masked_imerg_collection = mask_less_ppt(precipitation_collection) # add indonesia boundary to map style = {"color": "000ffff0", "width": 2, "lineType": "solid", "fillColor": "00000000"} m.addLayer(indonesia.style(**style), {}, "Indonesia_boundary") # Define visualization parameters palette = [ "000096", "0064ff", "00b4ff", "33db80", "9beb4a", "ffeb00", "ffb300", "ff6400", "eb1e00", "af0000", ] vis_params = { "min": 0, "max": 50, "bands": "precipitationCal", "palette": ["000096", "0064ff", "00b4ff", "eb1e00", "af0000"], } # # Add the precipitation data to the map # m.addLayer(masked_imerg_collection, vis_params, 'Precipitation (IMERG)') # m

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# clip the precipitation data to the Indonesia boundary
clipped_imerg_collection = masked_imerg_collection.map(
    lambda image: image.clip(indonesia_geom)
)

# Get the number of images in the clipped collection
clipped_imerg_collection.size().getInfo()

# clip the precipitation data to the Indonesia boundary clipped_imerg_collection = masked_imerg_collection.map( lambda image: image.clip(indonesia_geom) ) # Get the number of images in the clipped collection clipped_imerg_collection.size().getInfo()

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# get bounding box of indonesia
indonesia_bbox = indonesia.geometry().bounds()
indonesia_bbox

# get bounding box of indonesia indonesia_bbox = indonesia.geometry().bounds() indonesia_bbox

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# Display sample image
vis_params = {
    "min": 0,
    "max": 50,
    "bands": "precipitationCal",
    "palette": ["000096", "0064ff", "00b4ff", "eb1e00", "af0000"],
}

# Display the first image in the clipped collection
image = ee.Image(clipped_imerg_collection.first())
m.addLayer(image, vis_params, "First image")
m

# Display sample image vis_params = { "min": 0, "max": 50, "bands": "precipitationCal", "palette": ["000096", "0064ff", "00b4ff", "eb1e00", "af0000"], } # Display the first image in the clipped collection image = ee.Image(clipped_imerg_collection.first()) m.addLayer(image, vis_params, "First image") m

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import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import json

# import the cartoee functionality from geemap
from geemap import cartoee

# set the width and height of the map
w = 25
h = 10

# set the center of the map
lon = 118
lat = -3

# set the region of interest
region = [lon + w, lat - h, lon - w, lat + h]


# use cartoee to get a map
ax = cartoee.get_map(image, region=region, vis_params=vis_params)

# add the Indonesia boundary to the map
outline_style = {"color": "FF0000", "width": 2, "fillColor": "00000000"}
cartoee.add_layer(
    ax, indonesia.style(**outline_style), region=region, crs=ccrs.PlateCarree()
)

# add gridlines to the map at a specified interval
cartoee.add_gridlines(ax, interval=[5, 5], linestyle=":")

# add a colorbar to the map (right side)
cb = cartoee.add_colorbar(
    ax, vis_params, loc="right", label="Rainfall (mm)", orientation="vertical"
)

# add north arrow
north_arrow_dict = {
    "text": "N",
    "xy": (0.1, 0.21),
    "arrow_length": 0.10,
    "text_color": "black",
    "arrow_color": "white",
    "fontsize": 10,
    "width": 5,
    "headwidth": 10,
    "ha": "center",
    "va": "center",
}
cartoee.add_north_arrow(ax, **north_arrow_dict)

# add scale bar
scale_bar_dict = {
    "length": 100,
    "xy": (0.1, 0.05),
    "linewidth": 2,
    "fontsize": 8,
    "color": "black",
    "unit": "km",
    "ha": "center",
    "va": "bottom",
}
cartoee.add_scale_bar_lite(ax, **scale_bar_dict)

# add title
ax.set_title(label="Total Rainfall (2023-07-15)", fontsize=10)

plt.show()

import matplotlib.pyplot as plt import cartopy.crs as ccrs import json # import the cartoee functionality from geemap from geemap import cartoee # set the width and height of the map w = 25 h = 10 # set the center of the map lon = 118 lat = -3 # set the region of interest region = [lon + w, lat - h, lon - w, lat + h] # use cartoee to get a map ax = cartoee.get_map(image, region=region, vis_params=vis_params) # add the Indonesia boundary to the map outline_style = {"color": "FF0000", "width": 2, "fillColor": "00000000"} cartoee.add_layer( ax, indonesia.style(**outline_style), region=region, crs=ccrs.PlateCarree() ) # add gridlines to the map at a specified interval cartoee.add_gridlines(ax, interval=[5, 5], linestyle=":") # add a colorbar to the map (right side) cb = cartoee.add_colorbar( ax, vis_params, loc="right", label="Rainfall (mm)", orientation="vertical" ) # add north arrow north_arrow_dict = { "text": "N", "xy": (0.1, 0.21), "arrow_length": 0.10, "text_color": "black", "arrow_color": "white", "fontsize": 10, "width": 5, "headwidth": 10, "ha": "center", "va": "center", } cartoee.add_north_arrow(ax, **north_arrow_dict) # add scale bar scale_bar_dict = { "length": 100, "xy": (0.1, 0.05), "linewidth": 2, "fontsize": 8, "color": "black", "unit": "km", "ha": "center", "va": "bottom", } cartoee.add_scale_bar_lite(ax, **scale_bar_dict) # add title ax.set_title(label="Total Rainfall (2023-07-15)", fontsize=10) plt.show()

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# create a gif of the images in the clipped collection
cartoee.get_image_collection_gif(
    ee_ic=clipped_imerg_collection,
    out_dir="./img/gif_images",
    out_gif="./img/animation.gif",
    vis_params=vis_params,
    region=region,
    fps=2,
    mp4=True,
    grid_interval=(5, 5),
    plot_title="Indonesia Daily Rainfall",
    date_format="YYYY-MM-dd:HH-mm",
    fig_size=(7, 7),
    dpi_plot=100,
    file_format="png",
    overlay_layers=[indonesia],
    overlay_styles=[outline_style],
    colorbar_dict={"loc": "right", "label": "Rainfall (mm)", "orientation": "vertical"},
    north_arrow_dict=north_arrow_dict,
    scale_bar_dict=scale_bar_dict,
    verbose=True,
)

# create a gif of the images in the clipped collection cartoee.get_image_collection_gif( ee_ic=clipped_imerg_collection, out_dir="./img/gif_images", out_gif="./img/animation.gif", vis_params=vis_params, region=region, fps=2, mp4=True, grid_interval=(5, 5), plot_title="Indonesia Daily Rainfall", date_format="YYYY-MM-dd:HH-mm", fig_size=(7, 7), dpi_plot=100, file_format="png", overlay_layers=[indonesia], overlay_styles=[outline_style], colorbar_dict={"loc": "right", "label": "Rainfall (mm)", "orientation": "vertical"}, north_arrow_dict=north_arrow_dict, scale_bar_dict=scale_bar_dict, verbose=True, )