for yr in gapminder_ds.keys()
})
# Define options
# Combine Points and Text
hvgapminder = (gapminder_ds * text).opts(
opts.Points(alpha=0.6,
color='Group',
cmap='Set1',
line_color='black',
size=np.sqrt(dim('Population')) * 0.005,
tools=['hover'],
title='Gapminder Demo',
responsive=True,
show_grid=True),
opts.Text(text_font_size='52pt', text_color='lightgray'))
# Define custom widgets
def animate_update():
year = slider.value + 1
if year > end:
year = int(start)
slider.value = year
# Update the holoviews plot by calling update with the new year.
def slider_update(event):
hvplot.update((event.new, ))
# width=500, height=400)
ds = xr.DataArray(zi, dims=['Lat', 'Lon'],
coords={'Lat': lats, 'Lon': lons}).to_dataset(name='PM2_5')
aqi_ds = gv.Dataset(ds, ['Lon', 'Lat'], 'PM2_5')
background = gvts.EsriImagery * gv.Image(aqi_ds).opts(alpha=0.7, width=500, height=400,
colorbar=True, cmap='magma',clim=(0,70), color_levels = levels)
contour = gvts.EsriReference * aqi_ds.to(gv.FilledContours, #gvts.CartoEco is the original
['Lon', 'Lat']).opts(alpha=0.5, width=500, height=400,
colorbar=True, cmap='magma', levels=10, color_levels=10,
tools=['hover'])
text_opts = opts.Text(text_align='center', text_baseline='middle', text_font='Arial',width = 500, height = 400)
test3 = (test + background + contour +
hv.Text(0.5, 0.5, begin.strftime("%m/%d/%Y %H:%M")).opts(text_opts)).cols(2)
print(type(test))
print(type(background))
print(type(contour))
print(type(test3))
#renderer = hv.Store.renderers['matplotlib'].instance(fig='svg', holomap='gif')
#renderer.save(test3, 'test3')
#renderer = hv.plotting.mpl.MPLRenderer.instance(dpi=120)
#layout_test3 = renderer.get_plot(test3)
#print(type(layout_test3))
#print(type(test3))
import pandas as pd
import numpy as np
import holoviews as hv
from holoviews import opts, dim
hv.extension('bokeh')
macro_df = pd.read_csv('http://assets.holoviews.org/macro.csv', '\t')
key_dimensions = [('year', 'Year'), ('country', 'Country')]
value_dimensions = [('unem', 'Unemployment'), ('capmob', 'Capital Mobility'),
('gdp', 'GDP Growth'), ('trade', 'Trade')]
macro = hv.Table(macro_df, key_dimensions, value_dimensions)
gdp_curves = macro.to.curve('Year', 'GDP Growth')
gdp_unem_scatter = macro.to.scatter('Year', ['GDP Growth', 'Unemployment'])
annotations = hv.Arrow(1973, 8, 'Oil Crisis', 'v') * hv.Arrow(1975, 6, 'Stagflation', 'v') *\
hv.Arrow(1979, 8, 'Energy Crisis', 'v') * hv.Arrow(1981.9, 5, 'Early Eighties\n Recession', 'v')
composition=(gdp_curves * gdp_unem_scatter* annotations)
composition.opts(
opts.Curve(color='k'),
opts.Scatter(cmap='Blues', color='Unemployment',
line_color='k', size=dim('Unemployment')*1.5),
opts.Text(text_font_size='13px'),
opts.Overlay(height=400, show_frame=False, width=700))
hv.save(composition, 'holomap.html')
default_fontsizes = dict(title=8, labels=8, ticks=7, minor_ticks=7, legend=7)
fig_opts = [
opts.Layout(aspect_weight=1,
fig_inches=(3.42, None),
sublabel_size=10,
fontsize=8),
opts.Overlay(fontsize=default_fontsizes, ),
opts.Area(fontsize=default_fontsizes),
opts.Arrow(textsize=default_fontsizes),
opts.Curve(fontsize=default_fontsizes),
opts.HexTiles(fontsize=default_fontsizes),
opts.Histogram(fontsize=default_fontsizes),
opts.Raster(fontsize=default_fontsizes),
opts.Scatter(fontsize=default_fontsizes),
opts.Text(fontsize=default_fontsizes),
opts.QuadMesh(fontsize=default_fontsizes),
opts.Violin(fontsize=default_fontsizes),
opts.VLine(fontsize=default_fontsizes),
]
# hv hooks
class Suptitle:
def __init__(self, suptitle, color, y=1.175):
self.suptitle = suptitle
self.color = color
self.y = y
def __call__(self, plot, element):
