def test_init_extent_geoaxes(self):
"""Test the extent setter code in the GeoAxes case."""
# default, empty geometry case: set extent to default value of (0, 1)
plot = Plot(self.gdf, **{**self.kwargs, **{'projection': gcrs.PlateCarree()}})
assert plot.ax.get_xlim() == plot.ax.get_ylim() == (0, 1)
# default, non-empty geometry case: use a (relaxed) geometry envelope
plot = Plot(
gpd.GeoDataFrame(geometry=[Point(-1, -1), Point(1, 1)]),
**{**self.kwargs, **{'projection': gcrs.PlateCarree()}}
)
xmin, xmax = plot.ax.get_xlim()
ymin, ymax = plot.ax.get_ylim()
assert xmin < -1
assert xmax > 1
assert ymin < -1
assert ymax > 1
# empty geometry, valid extent case: reuse prior extent, which is (0, 1) by default
plot = Plot(self.gdf, **{
**self.kwargs, **{'extent': (-1, -1, 1, 1), 'projection': gcrs.PlateCarree()}
})
assert plot.ax.get_xlim() == plot.ax.get_ylim() == (0, 1)
# nonempty geometry, valid extent case: use extent
plot = Plot(self.nonempty_gdf, **{
**self.kwargs, **{'extent': (-1, -1, 1, 1), 'projection': gcrs.PlateCarree()}
})
xmin, xmax = plot.ax.get_xlim()
ymin, ymax = plot.ax.get_ylim()
assert xmin == -1
assert xmax == 1
assert ymin == -1
assert ymax == 1
# nonempty geometry, unsatisfiable extent case: warn and fall back to default
with pytest.warns(UserWarning):
# Orthographic can only show one half of the world at a time
Plot(self.nonempty_gdf, **{
**self.kwargs,
**{'extent': (-180, -90, 180, 90), 'projection': gcrs.Orthographic()}
})
gcrs.PlateCarree(),
gcrs.LambertCylindrical(),
gcrs.Mercator(),
gcrs.Miller(),
gcrs.Mollweide(),
gcrs.Robinson(),
gcrs.Sinusoidal(),
pytest.param(gcrs.InterruptedGoodeHomolosine(), marks=pytest.mark.xfail),
pytest.param(gcrs.Geostationary(), marks=pytest.mark.xfail),
gcrs.NorthPolarStereo(),
gcrs.SouthPolarStereo(),
gcrs.Gnomonic(),
gcrs.AlbersEqualArea(),
gcrs.AzimuthalEquidistant(),
gcrs.LambertConformal(),
gcrs.Orthographic(),
gcrs.Stereographic(),
pytest.param(gcrs.TransverseMercator(), marks=pytest.mark.xfail),
gcrs.LambertAzimuthalEqualArea(),
gcrs.WebMercator()
])
def test_basic_global_projections(proj, countries):
gplt.polyplot(countries, proj)
ax = plt.gca()
ax.set_global()
return plt.gcf()
@pytest.mark.mpl_image_compare
@pytest.mark.parametrize("proj", [
gcrs.EuroPP(),
############################################################################### # Plotting with Geoplot # ===================== # # We start out by replicating the basic GeoPandas world plot using Geoplot. import geoplot geoplot.polyplot(df, figsize=(8, 4)) ############################################################################### # Geoplot can re-project data into any of the map projections provided by # CartoPy (see the list # `here <http://scitools.org.uk/cartopy/docs/latest/crs/projections.html>`_). import geoplot.crs as gcrs ax = geoplot.polyplot(df, projection=gcrs.Orthographic(), figsize=(8, 4)) ax.set_global() ax.outline_patch.set_visible(True) ############################################################################### # ``polyplot`` is trivial and can only plot the geometries you pass to it. If # you want to use color as a visual variable, specify a ``choropleth``. Here # we sort GDP per person by country into five buckets by color. geoplot.choropleth(df, hue='gdp_pp', cmap='Greens', figsize=(8, 4)) ############################################################################### # If you want to use size as a visual variable, you want a ``cartogram``. Here # are population estimates for countries in Africa. geoplot.cartogram(df[df['continent'] == 'Africa'],
import pandas as pd
import matplotlib.pyplot as plt
import cartopy
# This script demonstrates using the cartopy feature interface alongside geoplot.
# For more information visit http://scitools.org.uk/cartopy/docs/latest/matplotlib/feature_interface.html.
# Fetch the data. Note: this script doesn't actually work! For the moment.
airline_city_pairs = pd.read_csv('../../data/world_flights/flights.csv', index_col=0)
# Plot the data.
f, axarr = plt.subplots(2, 2, figsize=(12, 12), subplot_kw={
'projection': gcrs.Orthographic(central_latitude=40.7128, central_longitude=-74.0059)
})
plt.suptitle('Popular Flights out of Los Angeles, 2016', fontsize=16)
plt.subplots_adjust(top=0.95)
ax = gplt.sankey(airline_city_pairs.query('Origin == "Los Angeles, CA"'), start='Starting Point', end='Ending Point',
projection=gcrs.Orthographic(), scale='PASSENGERS', hue='PASSENGERS', cmap='Purples', ax=axarr[0][0])
ax.set_global()
ax.outline_patch.set_visible(True)
ax.coastlines()
ax = gplt.sankey(airline_city_pairs.query('Origin == "Los Angeles, CA"'), start='Starting Point', end='Ending Point',
projection=gcrs.Orthographic(), scale='PASSENGERS', hue='PASSENGERS', cmap='Purples', ax=axarr[0][1])
ax.set_global()
ax.outline_patch.set_visible(True)
ax.stock_img()
gcrs.PlateCarree(),
# gcrs.LambertCylindrical(),
gcrs.Mercator(),
gcrs.Miller(),
gcrs.Mollweide(),
gcrs.Robinson(),
gcrs.Sinusoidal(),
gcrs.InterruptedGoodeHomolosine(),
gcrs.Geostationary(),
gcrs.NorthPolarStereo(),
gcrs.SouthPolarStereo(),
gcrs.Gnomonic(),
gcrs.AlbersEqualArea(),
gcrs.AzimuthalEquidistant(),
gcrs.LambertConformal(),
gcrs.Orthographic(),
gcrs.Stereographic(),
gcrs.TransverseMercator(),
gcrs.LambertAzimuthalEqualArea()
# # TODO: Include other new ones.
])
def test_basic_global_projections(proj, countries):
gplt.polyplot(countries, proj)
ax = plt.gca()
ax.set_global()
return plt.gcf()
@pytest.mark.mpl_image_compare
@pytest.mark.parametrize("proj", [
import geoplot as gplt
import geoplot.crs as gcrs
import matplotlib.pyplot as plt
import cartopy
# load the data
la_flights = gpd.read_file(gplt.datasets.get_path('la_flights'))
la_flights = la_flights.assign(start=la_flights.geometry.map(lambda mp: mp[0]),
end=la_flights.geometry.map(lambda mp: mp[1]))
f, axarr = plt.subplots(2,
2,
figsize=(12, 12),
subplot_kw={
'projection':
gcrs.Orthographic(central_latitude=40.7128,
central_longitude=-74.0059)
})
plt.suptitle('Popular Flights out of Los Angeles, 2016', fontsize=16)
plt.subplots_adjust(top=0.95)
ax = gplt.sankey(la_flights,
start='start',
end='end',
projection=gcrs.Orthographic(),
scale='Passengers',
hue='Passengers',
cmap='Purples',
ax=axarr[0][0])
ax.set_global()
ax.outline_patch.set_visible(True)
ax.coastlines()
"""
import geopandas as gpd
import geoplot as gplt
import geoplot.crs as gcrs
import matplotlib.pyplot as plt
import cartopy
la_flights = gpd.read_file(gplt.datasets.get_path('la_flights'))
f, axarr = plt.subplots(2,
2,
figsize=(12, 12),
subplot_kw={
'projection':
gcrs.Orthographic(central_latitude=40.7128,
central_longitude=-74.0059)
})
plt.suptitle('Popular Flights out of Los Angeles, 2016', fontsize=16)
plt.subplots_adjust(top=0.95)
ax = gplt.sankey(la_flights,
scale='Passengers',
hue='Passengers',
cmap='Purples',
k=5,
ax=axarr[0][0])
ax.set_global()
ax.outline_patch.set_visible(True)
ax.coastlines()
ax = gplt.sankey(la_flights,