2018-01-01 (Last Updated: 2024-03-25)

Glaciers#

Glaciers explorer using Datashader#

This notebook provides an annotated hvPlot+Panel implementation of a dashboard originally developed by Fabien Maussion in Plotly+Dash for viewing data about the Earth’s glaciers from the Open Global Glacier Model.

import numpy as np
import pandas as pd
import holoviews as hv
import panel as pn
import hvplot.pandas # noqa

from colorcet import bmy
from holoviews.util.transform import lon_lat_to_easting_northing as ll_en

Load the data#

Here we will load the glaciers data and project the latitudes and longitudes to Google Mercator coordinates, which will allow us to plot it on top of a tile source. We do this by using the lon_lat_to_easting_northing function from holoviews.

We also use the pn.state.as_cached function to cache the data to ensure that only the first visitor to our app has to load the data.

def load_data():
    df = pd.read_csv('data/oggm_glacier_explorer.csv')
    df['x'], df['y'] = ll_en(df.cenlon, df.cenlat)
    return df

df = pn.state.as_cached('glaciers', load_data)

df.tail()

	rgi_id	cenlon	cenlat	area_km2	glacier_type	terminus_type	mean_elev	max_elev	min_elev	avg_temp_at_mean_elev	avg_prcp	x	y
213745	RGI60-18.03533	170.354	-43.4215	0.189	Glacier	Land-terminating	1704.858276	2102.0	1231.0	2.992555	6277.991881	1.896372e+07	-5.376350e+06
213746	RGI60-18.03534	170.349	-43.4550	0.040	Glacier	Land-terminating	2105.564209	2261.0	1906.0	0.502311	6274.274146	1.896316e+07	-5.381486e+06
213747	RGI60-18.03535	170.351	-43.4400	0.184	Glacier	Land-terminating	1999.645874	2270.0	1693.0	1.187901	6274.274146	1.896339e+07	-5.379186e+06
213748	RGI60-18.03536	170.364	-43.4106	0.111	Glacier	Land-terminating	1812.489014	1943.0	1597.0	2.392771	6154.064456	1.896483e+07	-5.374680e+06
213749	RGI60-18.03537	170.323	-43.3829	0.085	Glacier	Land-terminating	1887.771484	1991.0	1785.0	1.351039	6890.991816	1.896027e+07	-5.370436e+06

Add linked selections#

Linked selections are a way to interlink different plots which use the same data. With linked selections, you can explore how a particular subset of your data is rendered across the different types of plot you create.

All we have to do to add linked selections to static plots is make a hv.link_selections instance and apply it to our plots:

Let’s create a pane that renders the count of total selections:

ls = hv.link_selections.instance()

def clear_selections(event):
    ls.selection_expr = None

clear_button = pn.widgets.Button(name='Clear selection', align='center')

clear_button.param.watch(clear_selections, 'clicks');

total_area = df.area_km2.sum()

def count(data):
    selected_area  = np.sum(data['area_km2'])
    selected_percentage = selected_area / total_area * 100
    return f'## Glaciers selected: {len(data)} | Area: {selected_area:.0f} km² ({selected_percentage:.1f}%)</font>'

dynamic_count = pn.bind(count, ls.selection_param(df))

pn.Row(
    pn.pane.Markdown(pn.bind(count, ls.selection_param(df)), align='center', width=600),
    clear_button
)

Plot the data#

As you can see in the dataframe, there are a lot of things that could be plotted about this dataset, but following the previous version let’s focus on the lat/lon location, elevation, temperature, and precipitation. We’ll use tools from HoloViz, starting with HvPlot as an easy way to build interactive Bokeh plots.

We now create different types of plot to display different aspects of the data. With the created link_selections instance, we can inspect how selecting an area of one plot will also render the same data point in the other plots.

geo = df.hvplot.points(
    'x', 'y', rasterize=True, tools=['hover'], tiles='ESRI', cmap=bmy, logz=True, colorbar=True,
    xaxis=None, yaxis=False, ylim=(-7452837.583633271, 6349198.00989753), min_height=400, responsive=True
).opts('Tiles', alpha=0.8, bgcolor='black')

scatter = df.hvplot.scatter(
    'mean_elev', 'cenlat', rasterize=True, fontscale=1.2, grid=True,
    xlabel='Elevation', ylabel='Latitude (degree)', responsive=True, min_height=400,
)

temp = df.hvplot.hist(
    'avg_temp_at_mean_elev', fontscale=1.2, bins=50, responsive=True, min_height=350, fill_color='#f1948a'
)

precipitation = df.hvplot.hist(
    'avg_prcp', fontscale=1.2, bins=50, responsive=True, min_height=350, fill_color='#85c1e9'
)

plots = pn.pane.HoloViews(ls(geo + scatter + temp + precipitation).cols(2).opts(sizing_mode='stretch_both'))
plots