Glaciers

import numpy as np
import pandas as pd
import holoviews as hv
import panel as pn

from colorcet import bmy

pn.extension('tabulator', template='fast')
import hvplot.pandas

Create intro

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><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=600)

panel_logo = pn.pane.PNG(
    'https://panel.holoviz.org/_static/logo_stacked.png',
    link_url='https://panel.holoviz.org', height=95, align='center'
)

oggm_logo = pn.pane.PNG(
    'https://raw.githubusercontent.com/OGGM/oggm/master/docs/_static/logos/oggm_s_alpha.png',
    link_url='https://oggm.org/', height=100, align='center'
)

intro = pn.Row(
    oggm_logo,
    instruction,
    pn.layout.HSpacer(),
    panel_logo,
    sizing_mode='stretch_width'
)

intro

Load and cache data

from holoviews.element.tiles import lon_lat_to_easting_northing

@pn.cache
def load_data():
    df = pd.read_parquet('https://datasets.holoviz.org/oggm_glaciers/v1/oggm_glaciers.parq')
    df['latdeg'] = df.cenlat
    df['x'], df['y'] = lon_lat_to_easting_northing(df.cenlon, df.cenlat)
    return df

df = 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	latdeg	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	-43.4215	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	-43.4550	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	-43.4400	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	-43.4106	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	-43.3829	1.896027e+07	-5.370436e+06

Set up linked 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>'

pn.Row(
    pn.pane.Markdown(pn.bind(count, ls.selection_param(df)), align='center', width=600),
    clear_button
).servable(area='header', title='OGGM Glaciers')

Create 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)

scatter = df.hvplot.scatter(
    'avg_prcp', 'mean_elev', rasterize=True, fontscale=1.2, grid=True,
    xlabel='Avg. Precipitation', ylabel='Elevation', responsive=True, min_height=400
)

temp = df.hvplot.hist(
    'avg_temp_at_mean_elev', fontscale=1.2, responsive=True, min_height=350, fill_color='#85c1e9'
)

precipitation = df.hvplot.hist(
    'avg_prcp', fontscale=1.2, responsive=True, min_height=350, fill_color='#f1948a'
)

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

Dashboard content

pn.Column(intro, plots, sizing_mode='stretch_both').servable();