def plot_map_native(lon_corners, lat_corners, data, **kwargs):
from matplotlib import pyplot, patches, path
import numpy
# Fix longitude coordinates; we need longitudes to run from
# -180 to 180, not from 0 to 360
lon_corners.values = numpy.where(lon_corners > 180, lon_corners - 360,
lon_corners)
# Loop over GLL nodes and create paths that describe the boundaries
# of each node; collect these into a list to plot all at once
path_list = []
for icol in range(lon_corners.shape[0]):
# Get corners for this node
x_corners = lon_corners[icol, :].values
y_corners = lat_corners[icol, :].values
# Repeat first vertex at end of array to close the path
x_corners = numpy.append(x_corners, x_corners[0])
y_corners = numpy.append(y_corners, y_corners[0])
# Create paths connecting the corners and append to list
vertices = numpy.column_stack([x_corners, y_corners])
path_list.append(path.Path(vertices, closed=True))
# Plot collection of patches
from matplotlib.collections import PathCollection
collection = PathCollection(path_list, transform=crs.Geodetic(), **kwargs)
collection.set_array(data)
ax = pyplot.gca()
pl = ax.add_collection(collection)
return pl
#서울 구별 인구수 반영 그림 그리기
fig = plt.figure()
ax = plt.gca()
for x in range(len(seoul)):
coord = seoul[x]['geometry']
verts = coord['coordinates'][0]
patch = Polygon.Polygon(
verts,
facecolor=seoulcolor[seoul[x]['properties']['SIG_KOR_NM']],
ec='k')
cols.append(patch)
ax.add_patch(patch)
plt.autoscale()
plt.axis('off')
p = PathCollection(cols, cmap=cm.Blues)
p.set_array(np.array(colors))
ax.add_collection(p)
ax.autoscale_view()
plt.colorbar(p)
plt.show()
#Draw Heat Map
dx, dy = 0.15, 0.05
y, x = np.mgrid[slice(-3, 3 + dy, dy), slice(-3, 3 + dx, dx)]
z = (1 - x / 2. + x**5 + y**3) * np.exp(-x**2 - y**2)
z = z[:-1, :-1]
z_min, z_max = -np.abs(z).max(), np.abs(z).max()
plt.pcolor(x, y, z, cmap='RdBu', vmin=z_min, vmax=z_max)
