def test_cartogram(self):
try:
gplt.cartogram(dataframe_gaussian_polys, scale='hue_var', projection=gcrs.PlateCarree(), facecolor='white')
gplt.cartogram(dataframe_gaussian_polys, scale='hue_var',
projection=gcrs.PlateCarree(), legend_kwargs={'fancybox': False})
finally:
plt.close()
def test_sankey(self):
gplt.sankey(path=list_paths, projection=gcrs.PlateCarree(), edgecolor='white')
gplt.sankey(path=list_paths, projection=gcrs.PlateCarree(), color='white')
gplt.sankey(path=list_paths, projection=gcrs.PlateCarree(), linewidth=1)
gplt.sankey(path=list_paths, projection=gcrs.PlateCarree(), linestyle='--')
def test_pointplot(self):
try:
gplt.pointplot(list_gaussian_points, projection=gcrs.PlateCarree(), color='white')
gplt.pointplot(list_gaussian_points, projection=gcrs.PlateCarree(), s=5)
gplt.pointplot(list_gaussian_points, projection=gcrs.PlateCarree(), legend_kwargs={'fancybox': False})
finally: plt.close()
def test_aggplot(self):
try:
gplt.aggplot(dataframe_gaussian_points, hue='mock_category', projection=gcrs.PlateCarree())
gplt.aggplot(dataframe_gaussian_points, hue='mock_category', by='mock_category',
projection=gcrs.PlateCarree())
finally:
plt.close()
def test_kdeplot(self):
# Just four code paths.
try:
gplt.kdeplot(gaussian_points, projection=None, clip=None)
gplt.kdeplot(gaussian_points, projection=None, clip=gaussian_polys)
gplt.kdeplot(gaussian_points,
projection=gcrs.PlateCarree(),
clip=gaussian_polys)
gplt.kdeplot(gaussian_points,
projection=gcrs.PlateCarree(),
clip=gaussian_polys)
finally:
plt.close()
def test_polyplot(self):
try:
gplt.polyplot(list_gaussian_polys,
projection=gcrs.PlateCarree(),
color='white')
finally:
plt.close()
def test_choropleth(self):
try:
gplt.choropleth(dataframe_gaussian_polys,
hue='hue_var',
projection=gcrs.PlateCarree(),
legend_kwargs={'fancybox': False})
finally:
plt.close()
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()}
})
def test_base_init(self):
"""Test the base init all plotters pass to Plot."""
plot = Plot(self.gdf, **self.kwargs)
assert plot.figsize == (8, 6)
assert isinstance(plot.ax, SubplotBase) # SO 11690597
assert plot.extent == None
assert plot.projection == None
plot = Plot(self.gdf, **{**self.kwargs, **{'projection': gcrs.PlateCarree()}})
assert plot.figsize == (8, 6)
assert isinstance(plot.ax, GeoAxesSubplot)
assert plot.extent == None
assert isinstance(plot.projection, ccrs.PlateCarree)
import pytest
import geoplot as gplt
import geoplot.crs as gcrs
import geopandas as gpd
import matplotlib.pyplot as plt
import warnings
@pytest.fixture(scope="module")
def countries():
return gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
@pytest.mark.mpl_image_compare
@pytest.mark.parametrize("proj", [
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(),
manhattan = manhattan.to_crs(epsg=4326)
manhattan = manhattan.reset_index(drop=True)
manhattan = manhattan.reset_index().rename(columns={'index': 'n'})
# Plot the data.
# This plot demonstrates an extremely useful trick. When used with a provided geometry, the aggplot plot type expects
# an iterable of geometries to be used for binning observations. The idea is that, in general, we have n observations
# and some smaller number k of locations containing them, and we will match observations within the same bin,
# average them in some way, and plot the result.
#
# Of course, what if n == k? In other words, what if every observation comes with its own location? In that case we
# can can pass those locations to the ``geometry`` parameter and pass the data's index to the ``by`` parameter,
# and ``aggplot`` will plot all of our records one at a time!
#
# This is a nice feature to have, and very useful for a wide variety of datasets. In this case we are plotting
# building ages in Manhattan using data taken from MapPLUTO
# (http://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page).
#
# Note that this plot is for the purposes of example only: it contains 40,000 geometries (far more than palatable)
# and so takes a long time to render. To explore the data for real take a look at this all-NYC webmap:
# http://pureinformation.net/building-age-nyc/.
ax = gplt.aggplot(manhattan,
projection=gcrs.PlateCarree(),
geometry=manhattan.geometry,
by=pd.Series(manhattan.index),
hue='YearBuilt',
linewidth=0)
ax.set_title("Buildings in Manhattan by Year Built")
plt.savefig("aggplot-singular.png", bbox_inches='tight', pad_inches=0.1)
def test_polyplot(self):
gplt.polyplot(gaussian_polys, projection=None)
gplt.polyplot(gaussian_polys, projection=gcrs.PlateCarree())
plt.close()
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import shapely
import numpy as np
# Define strategies.
# Static inputs.
gaussian_points = gplt.utils.gaussian_points(n=10)
gaussian_polys = gplt.utils.gaussian_polygons(
gplt.utils.gaussian_points(n=100), n=2).append(
gplt.utils.gaussian_multi_polygons(gplt.utils.gaussian_points(n=100),
n=2))
# Projections.
projections = st.sampled_from((None, gcrs.PlateCarree()))
# Hue data.
schemes = st.sampled_from(
(None, "equal_interval", "quantiles", "fisher_jenks"))
k = st.integers(min_value=4, max_value=9).map(lambda f: None if f == 4 else f)
datasets_numeric = st.lists(st.floats(allow_nan=False,
allow_infinity=False,
min_value=10**-10,
max_value=10**10),
min_size=10,
max_size=10,
unique=True)
datasets_categorical = st.lists(st.text(st.characters(
max_codepoint=1000, blacklist_categories=('Cc', 'Cs')),
max_size=20),
def test_init_projection(self):
"""Test that passing a projection works as expected."""
plot = Plot(self.gdf, **{
**self.kwargs, 'projection': gcrs.PlateCarree()
})
assert isinstance(plot.projection, ccrs.PlateCarree)
usecols="A:F")
df = df.loc[df['Year'] == 2017]
print(df.columns.values[-1], "column_names")
df = df.rename(columns={df.columns.values[-1]: "Unbanked"})
df = df.rename(columns={'Country': "name"})
df['Unbanked'] = df.apply(take_inverse, axis=1)
df = df.filter(items=['name', 'Unbanked'])
df = df.reset_index(drop=True)
df.to_csv("HeatMap.csv", sep='\t', encoding='utf-8')
dfWorld = get_world_data_frame()
print(df.columns.values, "columnNames")
dfWorld = pd.merge(dfWorld, df, on=['name', 'name'])
print(df.head())
#proj = gcrs.AlbersEqualArea()
proj = gcrs.PlateCarree()
#ax = gplt.polyplot(dfWorld, projection=proj)
#print(ax)
legend_kwargs = {'loc': 'upper left'}
#hue is the data series to visualize
#dfWorld is geometry specified
#proj is the projection type
gplt.choropleth(
dfWorld,
hue=dfWorld['Unbanked'], # Display data, passed as a Series
projection=proj,
cmap='Purples',
k=None, # Do not bin our counties.
legend=True,
edgecolor='white',
linewidth=0.5,
