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app.py
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app.py
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import os
import intake
import numpy as np
import pandas as pd
import xarray as xr
import hvplot.pandas
import hvplot.xarray
import holoviews as hv
from holoviews.streams import Selection1D, Params
import panel as pn
import colorcet as cc
import geoviews as gv
import geoviews.tile_sources as gts
import cartopy.crs as ccrs
import pyproj
hv.extension('bokeh')
df = intake.open_csv('./data/bird_migration/{species}.csv').read()
def fill_day(v):
next_year = v.assign(day=v.day + v.day.max())
last_year = v.assign(day=v.day - v.day.max())
surrounding_years = pd.concat([last_year, v, next_year])
filled = surrounding_years.assign(
lat=surrounding_years.lat.interpolate(),
lon=surrounding_years.lon.interpolate())
this_year = filled[filled.day.isin(v.day)]
return this_year
g = pyproj.Geod(ellps='WGS84')
def calculate_speed(v):
today_lat = v['lat'].values
today_lon = v['lon'].values
tomorrow_lat = np.append(v['lat'][1:].values, v['lat'][0])
tomorrow_lon = np.append(v['lon'][1:].values, v['lon'][0])
_, _, dist = g.inv(today_lon, today_lat, tomorrow_lon, tomorrow_lat)
return v.assign(speed=dist/1000.)
df = pd.concat([calculate_speed(fill_day(v)) for k, v in df.groupby('species')])
species_cmap = dict(zip(df.species.cat.categories, cc.glasbey))
birds = df.hvplot.points('lon', 'lat', color='species', groupby='day', geo=True,
cmap=species_cmap, legend=False, width=400, height=600,
size=100).options(tools=['tap', 'hover', 'box_select'])
data_url = 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep/air.day.ltm.nc'
# I downloaded the file locally because I was hitting rate limits.
local_file = './data/air.day.ltm.nc'
if os.path.isfile(local_file):
data_url = local_file
ds = xr.open_dataset(data_url)
ds = ds.rename(time='day').sel(level=1000)
ds['day'] = list(range(1,366))
## convert to F
ds = ds.assign(air_F = (ds['air'] - 273.15) * 9/5 + 32)
ROI = ds.sel(lon=slice(205, 310), lat=slice(75, -55))
grouped_air = (ROI.hvplot.quadmesh('lon', 'lat', 'air_F', groupby='day', geo=True)
.options(height=600, width=500, tools=[])
.redim.range(air_F=(-20, 100)))
tiles = gts.EsriImagery()
tiles.extents = df.lon.min(), df.lat.min(), df.lon.max(), df.lat.max()
def timeseries(species=None, y='lat'):
data = df[df.species.isin(species)] if species else df
plots = [
(data.groupby(['day', 'species'], observed=True)[y]
.mean()
.groupby('day').agg([np.min, np.max])
.hvplot.area('day', 'amin', 'amax', alpha=0.2, fields={'amin': y}))]
if not species or len(species) > 7:
plots.append(data.groupby('day')[y].mean().hvplot().relabel('mean'))
else:
gb = data.groupby('species', observed=True)
plots.extend([v.hvplot('day', y, color=species_cmap[k]).relabel(k) for k, v in gb])
return hv.Overlay(plots).options(width=900, height=250, toolbar='below', legend_position='right', legend_offset=(20, 0), label_width=150)
def daily_table(species=None, day=None):
def temp_calc(ds, row):
lat_lon_day = row[['lat', 'lon', 'day']]
return round(ds.sel(**lat_lon_day, method='nearest')['air_F'].item())
if not species or not day:
return hv.Table(pd.DataFrame(columns=['Species', 'Air [F]', 'Speed [km/day]'])).relabel('No species selected')
subset = df[df.species.isin(species)]
subset = subset[subset.day==day]
temps = [temp_calc(ds, row) for _, row in subset.iterrows()]
return hv.Table(pd.DataFrame({'Species': species, 'Air [F]': temps, 'Speed [km/day]': subset['speed']})).relabel('day: {}'.format(day))
species = pn.widgets.MultiSelect(options=df.species.cat.categories.tolist(), size=10)
day = pn.widgets.Player(value=1, start=1, end=365, step=5, loop_policy='loop', name='day', width=350)
toggle = pn.widgets.Toggle(name='Air Temperature Layer', active=True)
highlight = pn.widgets.Toggle(name='Highlight Birds', active=False)
species_stream = Params(species, ['value'], rename={'value': 'species'})
day_stream = Params(day, ['value'], rename={'value': 'day'})
toggle_stream = Params(toggle, ['active'])
highlight_stream = Params(highlight, ['active'])
def reset(arg=None):
day_stream.update(value=1)
species_stream.update(value=[])
toggle_stream.update(active=True)
highlight_stream.update(active=False)
reset_button = pn.widgets.Button(name='Reset')
reset_button.param.watch(reset, 'clicks')
def toggle_temp(layer, active=True):
return layer.options(fill_alpha=int(active))
def do_highlight(points, active=True):
return points.options(line_alpha=(0.5 if active else 0), selection_line_alpha=active)
bird_dmap = hv.util.Dynamic(birds.clone(streams=[day_stream]).options(line_color='white'),
operation=do_highlight, streams=[highlight_stream])
air_dmap = grouped_air.clone(streams=[day_stream])
temp_layer = hv.util.Dynamic(air_dmap, operation=toggle_temp, streams=[toggle_stream])
ts_lat = hv.DynamicMap(lambda species: timeseries(species, 'lat'), streams=[species_stream])
ts_speed = hv.DynamicMap(lambda species: timeseries(species, 'speed'), streams=[species_stream])
table = hv.DynamicMap(daily_table, streams=[species_stream, day_stream])
def on_map_select(index):
if index:
species = df.species.cat.categories[index].tolist()
if set(species_stream.contents['species']) != set(species):
species_stream.update(value=species)
map_selected_stream = Selection1D(source=bird_dmap)
map_selected_stream.param.watch_values(on_map_select, ['index'])
dashboard = pn.Column(
pn.Row('## Bird Migration Dashboard', pn.Spacer(width=200, height=80)),
pn.Row(
pn.Column(
pn.Row(
pn.Row(tiles * temp_layer * gv.feature.coastline * bird_dmap)[0][0],
pn.Spacer(width=20),
pn.Column(
'**Day of Year**', day,
'**Species**:',
'This selector does not affect the map. Use plot selectors.', species,
toggle,
highlight,
'This reset button only resets widgets - otherwise use the plot reset 🔄',
reset_button
),
pn.Spacer(width=100),
),
pn.Row(pn.layout.Tabs(('Latitude', ts_lat), ('Speed', ts_speed))),
),
pn.Column(table.options(width=300, height=850))
)
)
dashboard.servable()