def test_contourf_transform_path_counting():
ax = plt.axes(projection=ccrs.Robinson())
plt.draw()
# Capture the size of the cache before our test.
gc.collect()
initial_cache_size = len(cgeoaxes._PATH_TRANSFORM_CACHE)
path_to_geos_counter = CallCounter(cartopy.mpl.patch, 'path_to_geos')
with path_to_geos_counter:
x, y, z = sample_data((30, 60))
cs = plt.contourf(x, y, z, 5, transform=ccrs.PlateCarree())
n_geom = sum([len(c.get_paths()) for c in cs.collections])
del cs
if not six.PY3:
del c
plt.draw()
# Before the performance enhancement, the count would have been 2 * n_geom,
# but should now be just n_geom.
msg = ('The given geometry was transformed too many times (expected: %s; '
'got %s) - the caching is not working.'
'' % (n_geom, path_to_geos_counter.count))
assert path_to_geos_counter.count == n_geom, msg
# Check the cache has an entry for each geometry.
assert len(cgeoaxes._PATH_TRANSFORM_CACHE) == initial_cache_size + n_geom
# Check that the cache is empty again once we've dropped all references
# to the source paths.
plt.clf()
gc.collect()
assert len(cgeoaxes._PATH_TRANSFORM_CACHE) == initial_cache_size
plt.close()
def test_contourf_transform_path_counting():
ax = plt.axes(projection=ccrs.Robinson())
plt.draw()
# Capture the size of the cache before our test.
gc.collect()
initial_cache_size = len(cgeoaxes._PATH_TRANSFORM_CACHE)
path_to_geos_counter = CallCounter(cartopy.mpl.patch, 'path_to_geos')
with path_to_geos_counter:
x, y, z = sample_data((30, 60))
cs = plt.contourf(x, y, z, 5, transform=ccrs.PlateCarree())
n_geom = sum([len(c.get_paths()) for c in cs.collections])
del cs
if not six.PY3:
del c
plt.draw()
# Before the performance enhancement, the count would have been 2 * n_geom,
# but should now be just n_geom.
msg = ('The given geometry was transformed too many times (expected: %s; '
'got %s) - the caching is not working.'
'' % (n_geom, path_to_geos_counter.count))
assert path_to_geos_counter.count == n_geom, msg
# Check the cache has an entry for each geometry.
assert len(cgeoaxes._PATH_TRANSFORM_CACHE) == initial_cache_size + n_geom
# Check that the cache is empty again once we've dropped all references
# to the source paths.
plt.clf()
gc.collect()
assert len(cgeoaxes._PATH_TRANSFORM_CACHE) == initial_cache_size
plt.close()
def main():
pc = ccrs.PlateCarree()
rob = ccrs.Robinson()
sph = ccrs.Geodetic()
ax = plt.axes(projection=rob)
# ax = plt.axes(projection=pc)
# ax = plt.axes(projection=ccrs.NorthPolarStereo())
ax.set_global()
from cartopy.examples.waves import sample_data
x, y, data = sample_data((20, 40))
# ax.contourf(x, y, data, transform=pc, alpha=0.3)
# ax.contour(x, y, data, transform=pc, alpha=0.3)
# ax.contourf(x, y, data, 3, transform=pc, alpha=0.3)
# ax.contourf(x, y, data, 5, transform=pc, alpha=0.3)
#print 'getting domain'
#print ax.native_extents()
#print ax.map_domain(ccrs.PlateCarree())
#print ax.ll_boundary_poly()
ax.stock_img()
ax.coastlines()
#ax.gshhs_line()
# ax.coastlines_land()
plt.plot(-0.08, 51.53, 'o', transform=pc)
plt.plot([-0.08, 132], [51.53, 43.17], transform=pc)
plt.plot([-0.08, 132], [51.53, 43.17], transform=sph)
# ax.gshhs_line(resolution='coarse', domain=ax.boundary_poly())
plt.show()
def main():
pc = cartopy.prj.PlateCarree()
geod = pc.as_geodetic()
rob = cartopy.prj.Robinson()
igh = cartopy.prj.InterruptedGoodeHomolosine()
nps = cartopy.prj.NorthPolarStereo()
x, y, z = sample_data()
slices = slice(20, 55), slice(85, 115)
x = x.__getitem__(slices)
y = y.__getitem__(slices)
z = z.__getitem__(slices)
ax = plt.subplot(2, 2, 1, projection=pc)
plt.scatter(x, y, c=z, transform=geod)
ax.coastlines()
ax = plt.subplot(2, 2, 2, projection=rob)
plt.scatter(x, y, c=z, transform=geod)
ax.coastlines()
# XXX Ask the mpl mailing list to find out how you might create a subplot and subsequently modify it's projection.
# plt.subplot(2, 2, 3, )#projection=igh)
# plt.scatter(x, y, c=z, transform=pc)
ax = plt.subplot(2, 2, 4, projection=nps)
plt.scatter(x, y, c=z, transform=geod)
ax.coastlines()
plt.show()
def main():
# Setup a global EckertIII map with faint coastlines.
ax = plt.axes(projection=ccrs.EckertIII())
ax.set_global()
ax.coastlines('110m', alpha=0.1)
# Use the waves example to provide some sample data, but make it
# more dependent on y for more interesting contours.
x, y, z = sample_data((20, 40))
z = z * -1.5 * y
# Add colourful filled contours.
filled_c = ax.contourf(x, y, z, transform=ccrs.PlateCarree())
# And black line contours.
line_c = ax.contour(x, y, z, levels=filled_c.levels,
colors=['black'],
transform=ccrs.PlateCarree())
# Uncomment to make the line contours invisible.
# plt.setp(line_c.collections, visible=False)
# Add a colorbar for the filled contour.
plt.colorbar(filled_c, orientation='horizontal')
# Use the line contours to place contour labels.
plt.clabel(
line_c, # Typically best results when labelling line contours.
colors=['black'],
manual=False, # Automatic placement vs manual placement.
inline=True, # Cut the line where the label will be placed.
fmt=' {:.0f} '.format, # Labes as integers, with some extra space.
)
plt.show()
def main():
pc = cartopy.prj.PlateCarree()
geod = pc.as_geodetic()
rob = cartopy.prj.Robinson()
igh = cartopy.prj.InterruptedGoodeHomolosine()
nps = cartopy.prj.NorthPolarStereo()
x, y, z = sample_data()
slices = slice(20, 55), slice(85, 115)
x = x.__getitem__(slices)
y = y.__getitem__(slices)
z = z.__getitem__(slices)
ax = plt.subplot(2, 2, 1, projection=pc)
plt.scatter(x, y, c=z, transform=geod)
ax.coastlines()
ax = plt.subplot(2, 2, 2, projection=rob)
plt.scatter(x, y, c=z, transform=geod)
ax.coastlines()
# XXX Ask the mpl mailing list to find out how you might create a subplot and subsequently modify it's projection.
# plt.subplot(2, 2, 3, )#projection=igh)
# plt.scatter(x, y, c=z, transform=pc)
ax = plt.subplot(2, 2, 4, projection=nps)
plt.scatter(x, y, c=z, transform=geod)
ax.coastlines()
plt.show()
def produce_axes2(ax):
coords = [(-0.08, 51.53), (132.00, 43.17)] # London to Vladivostock
r = ax.plot(*zip(*coords), transform=ll)
x, y, z = sample_data()
ax.contour(x, y, z, transform=ll)
ax.coastlines()
ax.gridlines()
def main():
fig = plt.figure()
# Setup a global EckertIII map with faint coastlines.
ax = fig.add_subplot(1, 1, 1, projection=ccrs.EckertIII())
ax.set_global()
ax.coastlines('110m', alpha=0.1)
# Use the waves example to provide some sample data, but make it
# more dependent on y for more interesting contours.
x, y, z = sample_data((20, 40))
z = z * -1.5 * y
# Add colourful filled contours.
filled_c = ax.contourf(x, y, z, transform=ccrs.PlateCarree())
# And black line contours.
line_c = ax.contour(x,
y,
z,
levels=filled_c.levels,
colors=['black'],
transform=ccrs.PlateCarree())
# Uncomment to make the line contours invisible.
# plt.setp(line_c.collections, visible=False)
# Add a colorbar for the filled contour.
fig.colorbar(filled_c, orientation='horizontal')
# Use the line contours to place contour labels.
ax.clabel(
line_c, # Typically best results when labelling line contours.
colors=['black'],
manual=False, # Automatic placement vs manual placement.
inline=True, # Cut the line where the label will be placed.
fmt=' {:.0f} '.format, # Labes as integers, with some extra space.
)
plt.show()
def main():
pc = ccrs.PlateCarree()
rob = ccrs.Robinson()
sph = ccrs.Geodetic()
ax = plt.axes(projection=rob)
# ax = plt.axes(projection=pc)
# ax = plt.axes(projection=ccrs.NorthPolarStereo())
ax.set_global()
from cartopy.examples.waves import sample_data
x, y, data = sample_data((20, 40))
# ax.contourf(x, y, data, transform=pc, alpha=0.3)
# ax.contour(x, y, data, transform=pc, alpha=0.3)
# ax.contourf(x, y, data, 3, transform=pc, alpha=0.3)
# ax.contourf(x, y, data, 5, transform=pc, alpha=0.3)
#print 'getting domain'
#print ax.native_extents()
#print ax.map_domain(ccrs.PlateCarree())
#print ax.ll_boundary_poly()
ax.stock_img()
ax.coastlines()
#ax.gshhs_line()
# ax.coastlines_land()
plt.plot(-0.08, 51.53, 'o', transform=pc)
plt.plot([-0.08, 132], [51.53, 43.17], transform=pc)
plt.plot([-0.08, 132], [51.53, 43.17], transform=sph)
# ax.gshhs_line(resolution='coarse', domain=ax.boundary_poly())
plt.show()
import cartopy.crs as ccrs
from cartopy.examples.waves import sample_data
import matplotlib.pyplot as plt
from cartopy.mpl.patch import path_to_geos
import shapely.ops
import json
import shapely.geometry as sgeom
import numpy as np
import skimage.io
lons, lats, data = sample_data()
plt.figure()
ax = plt.axes(projection=ccrs.PlateCarree())
cs = plt.contourf(
lons, lats, data, 5, # Choose approximately 5 sensible levels.
transform=ccrs.PlateCarree())
ax.coastlines()
plt.colorbar(orientation='horizontal')
plt.show()
paths=cs.collections[4].get_paths()
geoms=[]
for path in paths:
geoms.extend(path_to_geos(path))
polygon=shapely.ops.unray_union(geoms)
with open('contour.geojson', 'w') as fh:
json.dump(sgeom.mapping(polygon), fh)
import matplotlib.pyplot as plt import cartopy.crs as ccrs from cartopy.examples.waves import sample_data ax = plt.axes(projection=ccrs.Robinson()) ax.set_global() lons, lats, data = sample_data(shape=(20, 40)) plt.contourf(lons, lats, data, transform=ccrs.PlateCarree()) ax.coastlines() ax.gridlines() plt.show()
