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(),
gcrs.Stereographic(),
pytest.param(gcrs.TransverseMercator(), marks=pytest.mark.xfail),
"""
Voronoi of Melbourne primary schools
====================================
This example shows a ``pointplot`` combined with a ``voronoi`` mapping primary schools in
Melbourne. Schools in outlying, less densely populated areas serve larger zones than those in
central Melbourne.
This example inspired by the `Melbourne Schools Zones Webmap <http://melbourneschoolzones.com/>`_.
"""
import geopandas as gpd
import geoplot as gplt
import geoplot.crs as gcrs
import pandas as pd
import matplotlib.pyplot as plt
melbourne = gpd.read_file(gplt.datasets.get_path('melbourne'))
melbourne_primary_schools = gpd.read_file(gplt.datasets.get_path('melbourne_schools'))\
.query('School_Type == "Primary"')
ax = gplt.voronoi(
melbourne_primary_schools, clip=melbourne, linewidth=0.5, edgecolor='white',
projection=gcrs.Mercator()
)
gplt.polyplot(melbourne, edgecolor='None', facecolor='lightgray', ax=ax)
gplt.pointplot(melbourne_primary_schools, color='black', ax=ax, s=1, extent=melbourne.total_bounds)
plt.title('Primary Schools in Greater Melbourne, 2018')
plt.savefig("melbourne-schools.png", bbox_inches='tight', pad_inches=0)
def plotme(week=weeks[-1][0],
startweek=weeks[-1][0],
webmap=False,
setnorm=False,
stddev=1,
savename='RecentMap.jpg',
closefig=False,
figsize=(25, 14)):
projectioncode = 'EPSG:3395'
fig = plt.figure(figsize=figsize)
spec = gridspec.GridSpec(ncols=2, nrows=1, width_ratios=[1, 10])
colname = week.strftime('%Y-%m-%d') + '_chg'
validmask = np.logical_not(
np.logical_or(np.isnan(df[colname]), np.isinf(df[colname])))
normmin, normmax = (df[validmask][colname].min(),
df[validmask][colname].max())
print("DataMin=%d\tDataMax=%d" % (normmin, normmax))
if webmap:
ax1 = fig.add_subplot(spec[1], projection=gcrs.WebMercator())
gplt.webmap(df, ax=ax1)
projectioncode = 'EPSG:3785'
else:
ax1 = fig.add_subplot(spec[1], projection=gcrs.Mercator())
if setnorm:
norm = Normalize(vmin=setnorm[0], vmax=setnorm[1], clip=True)
else:
#maxmin = np.max(np.abs([df[colname].min(),df[colname].max()]))
#norm = Normalize(vmin=-maxmin,vmax=maxmin)
norm = Normalize(vmin=normmin, vmax=normmax.max())
gplt.choropleth(df[validmask],
zorder=10,
hue=colname,
norm=norm,
legend=True,
ax=ax1,
extent=extent,
edgecolor='black',
cmap='RdYlGn_r')
#color NaNs and inf
gplt.polyplot(df[np.logical_not(validmask)],
facecolor='#0000FF',
zorder=20,
ax=ax1,
extent=extent)
fig.suptitle(
"Covid-19 Week to Week %% Change in New Cases by Parish for %s" %
week.strftime('%b %d, %Y').ljust(14))
ax2 = fig.add_subplot(spec[0])
ax2.set_title('Weekly New Cases for Louisiana')
ax2.barh(la_series.index,
la_series,
color='y',
height=6.5,
label='Weekly New Cases')
ax2.axhline(week, color='red', label='Current Week')
ax2.yaxis.set_major_formatter(DateFormatter('%b %d'))
for tick in ax2.get_yticklabels():
tick.set_rotation(90)
ax2.legend()
fig.text(0.78,
0.93,
week.strftime('%b %d, %Y').rjust(12),
fontsize=18,
fontfamily='monospace',
bbox=dict(boxstyle='round', facecolor='#ffffa8', alpha=0.7))
#get outliers
mean = df[validmask][colname].mean()
outdev = df[validmask][colname].std() * stddev
print("outdev=%d" % outdev)
label_outliers = df[np.abs(df[colname] - mean) > outdev]
labels = label_outliers.append(label_places).drop_duplicates()
centroids = labels['geometry'].to_crs(projectioncode).centroid
for x, y, name in zip(centroids.x, centroids.y, labels['NAME']):
ax1.annotate(name.replace(' ', "\n"),
xy=(x, y),
xytext=(0, -6),
textcoords="offset points",
zorder=50,
ha='center',
va='top')
centroids = df['geometry'].to_crs(projectioncode).centroid
for x, y, value in zip(centroids.x, centroids.y, df[colname]):
ax1.annotate(labelgen(value),
xy=(x, y),
xytext=(0, 6),
textcoords="offset points",
zorder=50,
ha='center',
va='center',
fontsize=15,
bbox=dict(boxstyle='round',
facecolor=getfacecolor(value),
alpha=0.4))
fig.tight_layout()
if savename != None:
fig.savefig(savename)
if closefig:
plt.close()
return norm
layerMissing = allZipNA.to_crs("EPSG:3395").plot(edgecolor='grey',
color='grey',
ax=layerBorder)
WApoints.to_crs("EPSG:3395").plot(color='black',
markersize=0.1,
alpha=0.1,
ax=layerBorder)
#%%
import geoplot.crs as gcrs #activating!
layerBorder = gplt.polyplot(
waBorder,
projection=gcrs.Mercator(), #HERE!
edgecolor='grey',
facecolor='white')
layerMissing = gplt.polyplot(allZipNA,
edgecolor='grey',
facecolor='grey',
ax=layerBorder)
layerPoint = gplt.pointplot(WApoints,
color='black',
s=0.1,
alpha=0.1,
ax=layerBorder) # on top of!
#%%