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# (C) British Crown Copyright 2016 - 2020, Met Office
#
# This file is part of cartopy.
#
# cartopy is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the
# Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# cartopy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with cartopy. If not, see <https://www.gnu.org/licenses/>.
from __future__ import (absolute_import, division, print_function)
import matplotlib.pyplot as plt
from matplotlib.testing.decorators import cleanup
import numpy as np
from numpy.testing import assert_array_almost_equal
from scipy.interpolate import NearestNDInterpolator
from scipy.signal import convolve2d
import cartopy.crs as ccrs
@cleanup
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))
@cleanup
def test_contour_linear_ring():
"""Test contourf with a section that only has 3 points."""
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()
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