Glaciers#

panelgeoviewsdatashader

Published: January 1, 2018 · Updated: March 6, 2024

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

In the top left we’ve overlaid the location centers on a web-based map of the Earth, separately making a scatterplot of those same datapoints in the top right with elevation versus latitude. The bottom rows show histograms of temperature and precipitation for the whole set of glaciers. Of course, these are just some of the many plots that could be constructed from this data; see holoviews.org for inspiration.

Dashboard#

The code and plots above should be fine for exploring this data in a notebook, but let’s go further and make a dashboard using Panel.

We can add static text, Markdown, or HTML items like a title, instructions, and logos.

If you want detailed control over the formatting, you could define these items in a separate Jinja2 template. But here, let’s put it all together using the panel.pane module, which can display many python objects and plots from many different libraries. You’ll then get an app with widgets and plots usable from within the notebook:

instruction = pn.pane.Markdown("""
This dashboard visualizes all global glaciers and allows exploring the relationships 
between their locations and variables such as their elevation, temperature and annual precipitation.
<br>Box- or lasso-select on each plot to subselect and hit the "Clear selection" button to reset. 
See the notebook source code for how to build apps like this!""", width=250)

panel_logo = pn.pane.PNG(
    'https://panel.holoviz.org/_static/logo_stacked.png',
    link_url='https://panel.holoviz.org', width=250, align='center'
)

oggm_logo = pn.pane.PNG(
    './assets/oggm_logo.png',
    link_url='https://oggm.org/', width=250, align='center'
)

header = pn.Row(
        pn.layout.HSpacer(),
        dynamic_count,
        clear_button,
    )

sidebar = pn.Column(oggm_logo, instruction, panel_logo,)

pn.Column(header, pn.Row(sidebar, plots), min_height=900)

Template#

Now we have a fully functional dashboard here in the notebook. However, we can build a Template to give this dashboard a more polished look and feel when deployed, reflecting the image shown at the top of the notebook:

template = pn.template.MaterialTemplate(title='World Glaciers Explorer')

template.header.append(
    pn.Row(
        pn.layout.HSpacer(),
        dynamic_count,
        clear_button,
    )
)

template.sidebar.extend([
    oggm_logo,
    instruction,
    panel_logo,
])

template.main.append(plots)

template.servable();

Running the cell above will open a standalone dashboard in a new browser tab where you can select and explore your data to your heart’s content, and share it with anyone else interested in this topic! Or you can use the above approach to make your own custom dashboard for just about anything you want to visualize, with plots from just about any plotting library and arbitrary custom interactivity for libraries that support it.

This web page was generated from a Jupyter notebook and not all interactivity will work on this website.