def _subdivide_shapes(fc, max_length):
shapes = []
for feature in fc.features:
# get the boundary of each shape
shape = shapely.geometry.shape(feature['geometry'])
if max_length is not None:
# subdivide the shape if it's too coarse
geom_type = shape.geom_type
shape = subdivide_geom(shape, geom_type, max_length)
shapes.append(shape)
return shapes
def plot(self,
projection,
maxLength=4.0,
figsize=None,
colors=None,
dpi=200):
'''
Plot the features on a map using cartopy.
Parameters
----------
projection : str or ``cartopy.crs.Projection``
A cartopy projection object or one of the internally defined
map names:
'cyl', 'merc', 'mill', 'mill2', 'moll', 'moll2', 'robin',
'robin2', 'ortho', 'northpole', 'southpole', 'atlantic',
'pacific', 'americas', 'asia'
maxLength : float, optional
Maximum allowed segment length after subdivision for smoother
plotting (0.0 indicates skip subdivision)
figsize : tuple of float
Size of the figure in inches
colors : list of str
Colors to cycle through for the shapes
dpi : int
Dots per inch for the figure
Returns
-------
fig : ``matplotlib.figure.Figure``
The figure
'''
# Authors
# -------
# Xylar Asay-Davis, `Phillip J. Wolfram
projectionName = 'custom'
if isinstance(projection, str):
projections = build_projections()
projectionName = projection
projection = projections[projectionName]
if figsize is None:
if projectionName in [
'cyl', 'merc', 'mill', 'mill2', 'moll', 'moll2', 'robin',
'robin2'
]:
figsize = (12, 6)
else:
figsize = (12, 9)
fig = plt.figure(figsize=figsize, dpi=dpi)
(ax, projection) = plot_base(projectionName, projection)
if colors is None:
# use colorbrewer qualitative 7 data class colors,
# "7-class Accent": http://colorbrewer2.org/
colors = [
'#7fc97f', '#beaed4', '#fdc086', '#ffff99', '#386cb0',
'#f0027f', '#bf5b17'
]
bounds = None
for featureIndex, feature in enumerate(self.features):
geomType = feature['geometry']['type']
shape = shapely.geometry.shape(feature['geometry'])
if (maxLength > 0.0):
shape = subdivide_geom(shape, geomType, maxLength)
refProjection = cartopy.crs.PlateCarree()
color = colors[featureIndex % len(colors)]
if geomType in ['Polygon', 'MultiPolygon']:
props = {
'linewidth': 2.0,
'edgecolor': color,
'alpha': 0.4,
'facecolor': color
}
elif geomType in ['LineString', 'MultiLineString']:
props = {
'linewidth': 4.0,
'edgecolor': color,
'alpha': 1.,
'facecolor': 'none'
}
if bounds is None:
bounds = list(shape.bounds)
else:
# expand the bounding box
bounds[:2] = np.minimum(bounds[:2], shape.bounds[:2])
bounds[2:] = np.maximum(bounds[2:], shape.bounds[2:])
if geomType == 'Point':
ax.scatter(shape.coords[0][0],
shape.coords[0][1],
s=9,
transform=cartopy.crs.PlateCarree(),
marker='o',
color='blue',
edgecolor='blue')
else:
ax.add_geometries((shape, ), crs=refProjection, **props)
box = shapely.geometry.box(*bounds)
if (maxLength > 0.0):
box = subdivide_geom(box, 'Polygon', maxLength)
boxProjected = projection.project_geometry(box, src_crs=refProjection)
try:
x1, y1, x2, y2 = boxProjected.bounds
ax.set_xlim([x1, x2])
ax.set_ylim([y1, y2])
except ValueError:
print("Warning: bounding box could not be projected into "
"projection {}".format(projectionName))
print("Defaulting to global bounds.")
ax.set_global()
fig.canvas.draw()
plt.tight_layout(pad=4.)
return fig
def distance_from_geojson(fc, lon_grd, lat_grd, nn_search, max_length=None):
# {{{
"""
Get the distance for each point on a lon/lat grid from the closest point
on the boundary of the geojson regions.
Parameters
----------
fc : geometrics_features.FeatureCollection
The regions to be rasterized
lon_grd : numpy.ndarray
A 1D array of evenly spaced longitude values
lat_grd : numpy.ndarray
A 1D array of evenly spaced latitude values
nn_search: {'kdtree', 'flann'}
The method used to find the nearest point on the shape boundary
max_length : float, optional
The maximum distance (in degrees) between points on the boundary of the
geojson region. If the boundary is too coarse, it will be subdivided.
Returns
-------
distance : numpy.ndarray
A 2D field of distances to the shape boundary
"""
print("Distance from geojson")
print("---------------------")
print(" Finding region boundaries")
boundary_lon = []
boundary_lat = []
for feature in fc.features:
# get the boundary of each shape
shape = shapely.geometry.shape(feature['geometry']).boundary
if max_length is not None:
# subdivide the shape if it's too coarse
geom_type = shape.geom_type
shape = subdivide_geom(shape, geom_type, max_length)
x, y = shape.coords.xy
boundary_lon.extend(x)
boundary_lat.extend(y)
boundary_lon = np.array(boundary_lon)
boundary_lat = np.array(boundary_lat)
# Remove point at +/- 180 lon and +/- 90 lat because these are "fake".
# Need a little buffer (0.01 degrees) to avoid misses due to rounding.
mask = np.logical_not(
np.logical_or(
np.logical_or(boundary_lon <= -179.99, boundary_lon >= 179.99),
np.logical_or(boundary_lat <= -89.99, boundary_lat >= 89.99)))
boundary_lon = boundary_lon[mask]
boundary_lat = boundary_lat[mask]
print(" Mean boundary latitude: {0:.2f}".format(np.mean(boundary_lat)))
# Convert coastline points to x,y,z and create kd-tree
npoints = len(boundary_lon)
boundary_xyz = np.zeros((npoints, 3))
boundary_xyz[:, 0], boundary_xyz[:, 1], boundary_xyz[:, 2] = \
lonlat2xyz(boundary_lon, boundary_lat)
flann = None
tree = None
if nn_search == "kdtree":
tree = spatial.KDTree(boundary_xyz)
elif nn_search == "flann":
flann = pyflann.FLANN()
flann.build_index(boundary_xyz,
algorithm='kdtree',
target_precision=1.0,
random_seed=0)
else:
raise ValueError('Bad nn_search: expected kdtree or flann, got '
'{}'.format(nn_search))
# Convert backgound grid coordinates to x,y,z and put in a nx_grd x 3 array
# for kd-tree query
Lon_grd, Lat_grd = np.meshgrid(lon_grd, lat_grd)
X_grd, Y_grd, Z_grd = lonlat2xyz(Lon_grd, Lat_grd)
pts = np.vstack([X_grd.ravel(), Y_grd.ravel(), Z_grd.ravel()]).T
# Find distances of background grid coordinates to the coast
print(" Finding distance")
start = timeit.default_timer()
distance = None
if nn_search == "kdtree":
distance, _ = tree.query(pts)
elif nn_search == "flann":
_, distance = flann.nn_index(pts, checks=2000, random_seed=0)
distance = np.sqrt(distance)
end = timeit.default_timer()
print(" Done")
print(" {0:.0f} seconds".format(end - start))
# Make distance array that corresponds with cell_width array
distance = np.reshape(distance, Lon_grd.shape)
return distance # }}}
def add_inset(fig,
fc,
latlonbuffer=45.,
polarbuffer=5.,
width=1.0,
height=1.0,
lowerleft=None,
xbuffer=None,
ybuffer=None,
maxlength=1.):
'''
Plots an inset map showing the location of a transect or polygon. Shapes
are plotted on a polar grid if they are entirely poleward of +/-50 deg.
latitude and with a lat/lon grid if not.
Parameters
----------
fig : ``matplotlib.figure.Figure``
A matplotlib figure to add the inset to
fc : ``geometric_features.FeatureCollection``
A collection of regions, transects and/or points to plot in the inset
latlonbuffer : float, optional
The number of degrees lat/lon to use as a buffer around the shape(s)
to plot if a lat/lon plot is used.
polarbuffer : float, optional
The number of degrees latitude to use as a buffer equatorward of the
shape(s) in polar plots
width, height : float, optional
width and height in inches of the inset
lowerleft : pair of floats, optional
the location of the lower left corner of the axis in inches, default
puts the inset in the upper right corner of ``fig``.
xbuffer, ybuffer : float, optional
right and top buffers from the top-right corner (in inches) if
lowerleft is ``None``.
maxlength : float or ``None``, optional
Any segments longer than maxlength will be subdivided in the plot to
ensure curvature. If ``None``, no subdivision is performed.
Returns
-------
inset : ``matplotlib.axes.Axes``
The new inset axis
'''
# Authors
# -------
# Xylar Asay-Davis
minLon, minLat, maxLon, maxLat = _get_bounds(fc)
figsize = fig.get_size_inches()
width /= figsize[0]
height /= figsize[1]
if lowerleft is None:
if xbuffer is None:
xbuffer = 0.1 * width
else:
xbuffer /= figsize[0]
if ybuffer is None:
ybuffer = xbuffer * figsize[0] / figsize[1]
else:
ybuffer /= figsize[1]
lowerleft = [1.0 - width - xbuffer, 1.0 - height - ybuffer]
else:
lowerleft = [lowerleft[0] / figsize[0], lowerleft[1] / figsize[1]]
bounds = [lowerleft[0], lowerleft[1], width, height]
if maxLat <= -50:
# an Antarctic-focused map makes the most sense
inset = fig.add_axes(bounds, projection=ccrs.SouthPolarStereo())
extent = [-180., 180., -90., max(-65., maxLat + polarbuffer)]
_set_circular_boundary(inset)
xlocator = mticker.FixedLocator(numpy.linspace(-180., 180., 9))
ylocator = mticker.FixedLocator(numpy.linspace(-90., -50., 9))
elif minLat >= 50:
# an Arctic-focused map makes the most sense
inset = fig.add_axes(bounds, projection=ccrs.NorthPolarStereo())
extent = [-180, 180, min(65., minLat - polarbuffer), 90]
_set_circular_boundary(inset)
xlocator = mticker.FixedLocator(numpy.linspace(-180., 180., 9))
ylocator = mticker.FixedLocator(numpy.linspace(50., 90., 9))
else:
inset = fig.add_axes(bounds, projection=ccrs.PlateCarree())
extent = [
max(-180., minLon - latlonbuffer),
min(180., maxLon + latlonbuffer),
max(-90., minLat - latlonbuffer),
min(90., maxLat + latlonbuffer)
]
xlocator = None
ylocator = None
# kind of like "top" justified -- graphics are toward the "north" end of
# the subplot
inset.set_anchor('N')
inset.set_extent(extent, ccrs.PlateCarree())
inset.add_feature(cartopy.feature.LAND, zorder=1)
inset.add_feature(cartopy.feature.OCEAN, zorder=0)
gl = inset.gridlines(crs=ccrs.PlateCarree(),
draw_labels=False,
linewidth=0.5,
color='gray',
alpha=0.5,
linestyle='--')
if xlocator is not None:
gl.xlocator = xlocator
if ylocator is not None:
gl.ylocator = ylocator
for feature in fc.features:
geomtype = feature['geometry']['type']
shape = shapely.geometry.shape(feature['geometry'])
if maxlength is not None:
shape = subdivide_geom(shape, shape.geom_type, maxlength)
if geomtype in ['Polygon', 'MultiPolygon']:
inset.add_geometries((shape, ),
crs=ccrs.PlateCarree(),
edgecolor='blue',
facecolor='blue',
alpha=0.4,
linewidth=1.)
elif geomtype in ['Point', 'MultiPoint']:
inset.add_geometries((shape, ),
crs=ccrs.PlateCarree(),
edgecolor='none',
facecolor='none',
alpha=1.,
markersize=3.,
markeredgecolor='k',
markerfacecolor='k')
else:
inset.add_geometries((shape, ),
crs=ccrs.PlateCarree(),
edgecolor='k',
facecolor='none',
alpha=1.,
linewidth=1.)
# put a red point at the beginning and a blue point at the end
# of the transect to help show the orientation
begin = shape.coords[0]
end = shape.coords[-1]
inset.plot(begin[0],
begin[1],
color='r',
marker='o',
markersize=3.,
transform=ccrs.PlateCarree())
inset.plot(end[0],
end[1],
color='g',
marker='o',
markersize=3.,
transform=ccrs.PlateCarree())
return inset