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