Add a time slider for visualizing time series data

This example shows how to add a time slider for visualizing time series data.

In [1]:

# %pip install -U leafmap

# %pip install -U leafmap

In [2]:

import os
import pandas as pd
import leafmap.maplibregl as leafmap

import os import pandas as pd import leafmap.maplibregl as leafmap

In [3]:

os.environ["TITILER_ENDPOINT"] = "https://titiler.xyz"

os.environ["TITILER_ENDPOINT"] = "https://titiler.xyz"

Visualizing single band images¶

In this example, we will use the NASA OPERA displacement data to visualize the displacement of the San Francisco area over time. The data is available on Hugging Face at NASA-OPERA.

In [4]:

url = "https://huggingface.co/datasets/giswqs/NASA-OPERA/resolve/main/SanFrancisco_OPERA-DISP-S1/filenames.csv"
df = pd.read_csv(url)
df.head()

url = "https://huggingface.co/datasets/giswqs/NASA-OPERA/resolve/main/SanFrancisco_OPERA-DISP-S1/filenames.csv" df = pd.read_csv(url) df.head()

Out[4]:

	filename	date	url
0	displacement_20160810_20160903.tif	2016-09-03	https://huggingface.co/datasets/giswqs/NASA-OP...
1	displacement_20160810_20160927.tif	2016-09-27	https://huggingface.co/datasets/giswqs/NASA-OP...
2	displacement_20160810_20161021.tif	2016-10-21	https://huggingface.co/datasets/giswqs/NASA-OP...
3	displacement_20160810_20161114.tif	2016-11-14	https://huggingface.co/datasets/giswqs/NASA-OP...
4	displacement_20160810_20161208.tif	2016-12-08	https://huggingface.co/datasets/giswqs/NASA-OP...

In [5]:

dates = df["date"].to_list()
images = df["url"].to_list()

dates = df["date"].to_list() images = df["url"].to_list()

In [6]:

m = leafmap.Map(projection="globe", sidebar_visible=True, layer_manager_expanded=False)
m.add_basemap("USGS.Imagery")
vmin = -0.02
vmax = 0.02
palette = "seismic"
m.add_time_slider(
    images,
    labels=dates,
    vmin=vmin,
    vmax=vmax,
    colormap_name=palette,
    opacity=0.7,
    expanded=True,
    time_interval=1,
)
m.add_colorbar(vmin=vmin, vmax=vmax, palette=palette, label="Displacement (m)")
m

m = leafmap.Map(projection="globe", sidebar_visible=True, layer_manager_expanded=False) m.add_basemap("USGS.Imagery") vmin = -0.02 vmax = 0.02 palette = "seismic" m.add_time_slider( images, labels=dates, vmin=vmin, vmax=vmax, colormap_name=palette, opacity=0.7, expanded=True, time_interval=1, ) m.add_colorbar(vmin=vmin, vmax=vmax, palette=palette, label="Displacement (m)") m

In [7]:

m.set_terrain()

m.set_terrain()

Visualize multi-band images¶

In [8]:

url = "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/landsat/filenames.csv"
df = pd.read_csv(url)
df.head()

url = "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/landsat/filenames.csv" df = pd.read_csv(url) df.head()

Out[8]:

	filename	year	url
0	1984.tif	1984	https://huggingface.co/datasets/giswqs/geospat...
1	1985.tif	1985	https://huggingface.co/datasets/giswqs/geospat...
2	1986.tif	1986	https://huggingface.co/datasets/giswqs/geospat...
3	1987.tif	1987	https://huggingface.co/datasets/giswqs/geospat...
4	1988.tif	1988	https://huggingface.co/datasets/giswqs/geospat...

In [9]:

years = df["year"].to_list()
years = [str(year) for year in years]
images = df["url"].to_list()

years = df["year"].to_list() years = [str(year) for year in years] images = df["url"].to_list()

In [10]:

m = leafmap.Map(projection="globe", sidebar_visible=True, layer_manager_expanded=False)
m.add_basemap("USGS.Imagery")
m.add_time_slider(
    images,
    labels=years,
    opacity=0.7,
    expanded=True,
    time_interval=1,
)
m

m = leafmap.Map(projection="globe", sidebar_visible=True, layer_manager_expanded=False) m.add_basemap("USGS.Imagery") m.add_time_slider( images, labels=years, opacity=0.7, expanded=True, time_interval=1, ) m