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# Copyright Crown and Cartopy Contributors
#
# This file is part of Cartopy and is released under the BSD 3-clause license.
# See LICENSE in the root of the repository for full licensing details.
import matplotlib.pyplot as plt
import numpy as np
from numpy.testing import assert_array_almost_equal
import pytest
import cartopy.crs as ccrs
from cartopy.tests.conftest import requires_scipy
def test_contour_plot_bounds():
x = np.linspace(-2763217.0, 2681906.0, 200)
y = np.linspace(-263790.62, 3230840.5, 130)
data = np.hypot(*np.meshgrid(x, y)) / 2e5
proj_lcc = ccrs.LambertConformal(central_longitude=-95,
central_latitude=25,
standard_parallels=[25])
ax = plt.axes(projection=proj_lcc)
ax.contourf(x, y, data, levels=np.arange(0, 40, 1))
assert_array_almost_equal(ax.get_extent(),
np.array([x[0], x[-1], y[0], y[-1]]))
# Levels that don't include data should not fail.
plt.figure()
ax = plt.axes(projection=proj_lcc)
ax.contourf(x, y, data, levels=np.max(data) + np.arange(1, 3))
def test_contour_doesnt_shrink():
xglobal = np.linspace(-180, 180)
yglobal = np.linspace(-90, 90)
xsmall = np.linspace(-30, 30)
ysmall = np.linspace(-30, 30)
data = np.hypot(*np.meshgrid(xglobal, yglobal))
proj = ccrs.PlateCarree()
ax = plt.axes(projection=proj)
ax.contourf(xglobal, yglobal, data)
expected = np.array([xglobal[0], xglobal[-1], yglobal[0], yglobal[-1]])
assert_array_almost_equal(ax.get_extent(), expected)
# Make sure that a call to contour(f) doesn't shrink the already set bounds
ax.contour(xsmall, ysmall, data)
assert_array_almost_equal(ax.get_extent(), expected)
ax.contourf(xsmall, ysmall, data)
assert_array_almost_equal(ax.get_extent(), expected)
@pytest.mark.parametrize('func', ['contour', 'contourf'])
def test_plot_after_contour_doesnt_shrink(func):
xglobal = np.linspace(-180, 180)
yglobal = np.linspace(-90, 90.00001)
data = np.hypot(*np.meshgrid(xglobal, yglobal))
target_proj = ccrs.PlateCarree(central_longitude=200)
source_proj = ccrs.PlateCarree()
ax = plt.axes(projection=target_proj)
test_func = getattr(ax, func)
test_func(xglobal, yglobal, data, transform=source_proj)
ax.plot([10, 20], [20, 30], transform=source_proj)
expected = np.array([xglobal[0], xglobal[-1], yglobal[0], 90])
assert_array_almost_equal(ax.get_extent(), expected)
@requires_scipy
def test_contour_linear_ring():
"""Test contourf with a section that only has 3 points."""
from scipy.interpolate import NearestNDInterpolator
from scipy.signal import convolve2d
ax = plt.axes([0.01, 0.05, 0.898, 0.85], projection=ccrs.Mercator(),
aspect='equal')
ax.set_extent([-99.6, -89.0, 39.8, 45.5])
xbnds = ax.get_xlim()
ybnds = ax.get_ylim()
ll = ccrs.Geodetic().transform_point(xbnds[0], ybnds[0], ax.projection)
ul = ccrs.Geodetic().transform_point(xbnds[0], ybnds[1], ax.projection)
ur = ccrs.Geodetic().transform_point(xbnds[1], ybnds[1], ax.projection)
lr = ccrs.Geodetic().transform_point(xbnds[1], ybnds[0], ax.projection)
xi = np.linspace(min(ll[0], ul[0]), max(lr[0], ur[0]), 100)
yi = np.linspace(min(ll[1], ul[1]), max(ul[1], ur[1]), 100)
xi, yi = np.meshgrid(xi, yi)
nn = NearestNDInterpolator((np.arange(-94, -85), np.arange(36, 45)),
np.arange(9))
vals = nn(xi, yi)
lons = xi
lats = yi
window = np.ones((6, 6))
vals = convolve2d(vals, window / window.sum(), mode='same',
boundary='symm')
ax.contourf(lons, lats, vals, np.arange(9), transform=ccrs.PlateCarree())
plt.draw()
def test_contour_update_bounds():
"""Test that contour updates the extent"""
xs, ys = np.meshgrid(np.linspace(0, 360), np.linspace(-80, 80))
zs = ys**2
ax = plt.axes(projection=ccrs.Orthographic())
ax.contour(xs, ys, zs, transform=ccrs.PlateCarree())
# Force a draw, which is a smoke test to make sure contouring
# doesn't raise with an Orthographic projection
# GH issue 1673
plt.draw()
@pytest.mark.parametrize('func', ['contour', 'contourf'])
def test_contourf_transform_first(func):
"""Test the fast-path option for filled contours."""
# Gridded data that needs to be wrapped
x = np.arange(360)
y = np.arange(-25, 26)
xx, yy = np.meshgrid(x, y)
z = xx + yy**2
ax = plt.axes(projection=ccrs.PlateCarree())
test_func = getattr(ax, func)
# Can't handle just Z input with the transform_first
with pytest.raises(ValueError,
match="The X and Y arguments must be provided"):
test_func(z, transform=ccrs.PlateCarree(),
transform_first=True)
# X and Y must also be 2-dimensional
with pytest.raises(ValueError,
match="The X and Y arguments must be gridded"):
test_func(x, y, z, transform=ccrs.PlateCarree(),
transform_first=True)
# When calculating the contour in projection-space the extent
# will now be the extent of the transformed points (-179, 180, -25, 25)
test_func(xx, yy, z, transform=ccrs.PlateCarree(),
transform_first=True)
assert_array_almost_equal(ax.get_extent(), (-179, 180, -25, 25))
# The extent without the transform_first should be all the way out to -180
test_func(xx, yy, z, transform=ccrs.PlateCarree(),
transform_first=False)
assert_array_almost_equal(ax.get_extent(), (-180, 180, -25, 25))
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