"""
Class for creating plots on a geographic map using a Radar object using Cartopy
for drawing maps.
"""
import warnings
import numpy as np
import matplotlib.pyplot as plt
try:
import cartopy
_CARTOPY_AVAILABLE = True
except ImportError:
_CARTOPY_AVAILABLE = False
try:
import shapely.geometry as sgeom
from copy import copy
_LAMBERT_GRIDLINES = True
except ImportError:
_LAMBERT_GRIDLINES = False
from .radardisplay import RadarDisplay
from .common import parse_vmin_vmax, parse_cmap
from ..exceptions import MissingOptionalDependency
[docs]class RadarMapDisplay(RadarDisplay):
"""
A display object for creating plots on a geographic map from data in a
Radar object.
This class is still a work in progress. Some functionality may not work
correctly. Please report any problems to the Py-ART GitHub Issue Tracker.
Parameters
----------
radar : Radar
Radar object to use for creating plots.
shift : (float, float)
Shifts in km to offset the calculated x and y locations.
Attributes
----------
plots : list
List of plots created.
plot_vars : list
List of fields plotted, order matches plot list.
cbs : list
List of colorbars created.
origin : str
'Origin' or 'Radar'.
shift : (float, float)
Shift in meters.
loc : (float, float)
Latitude and Longitude of radar in degrees.
fields : dict
Radar fields.
scan_type : str
Scan type.
ranges : array
Gate ranges in meters.
azimuths : array
Azimuth angle in degrees.
elevations : array
Elevations in degrees.
fixed_angle : array
Scan angle in degrees.
grid_projection : cartopy.crs
AzimuthalEquidistant cartopy projection centered on radar.
Used to transform points into map projection.
"""
def __init__(self, radar, shift=(0.0, 0.0), grid_projection=None):
""" Initialize the object. """
# check that cartopy is available
if not _CARTOPY_AVAILABLE:
raise MissingOptionalDependency(
"Cartopy is required to use RadarMapDisplay but is " +
"not installed")
# initalize the base class
RadarDisplay.__init__(self, radar, shift=shift)
# additional attributes needed for plotting on a cartopy map.
if grid_projection is None:
lat_0 = self.loc[0]
lon_0 = self.loc[1]
grid_projection = cartopy.crs.AzimuthalEquidistant(
central_longitude=lon_0, central_latitude=lat_0)
elif not isinstance(grid_projection, cartopy.crs.Projection):
raise TypeError("grid_projection keyword must " +
"be a cartopy.crs object")
self.grid_projection = grid_projection
self.ax = None
self._x0 = None # x axis radar location in map coords (meters)
self._y0 = None # y axis radar location in map coords (meters)
return
def _check_ax(self):
""" Check that a GeoAxes object exists, raise ValueError if not """
if self.ax is None:
raise ValueError('no GeoAxes plotted')
[docs] def plot_ppi_map(
self, field, sweep=0, mask_tuple=None,
vmin=None, vmax=None, cmap=None, norm=None, mask_outside=False,
title=None, title_flag=True,
colorbar_flag=True, colorbar_label=None, ax=None, fig=None,
lat_lines=None, lon_lines=None, projection=None,
min_lon=None, max_lon=None, min_lat=None, max_lat=None,
width=None, height=None, lon_0=None, lat_0=None,
resolution='110m', shapefile=None, shapefile_kwargs=None,
edges=True, gatefilter=None,
filter_transitions=True, embelish=True, raster=False,
ticks=None, ticklabs=None, alpha=None):
"""
Plot a PPI volume sweep onto a geographic map.
Parameters
----------
field : str
Field to plot.
sweep : int, optional
Sweep number to plot.
Other Parameters
----------------
mask_tuple : (str, float)
Tuple containing the field name and value below which to mask
field prior to plotting, for example to mask all data where
NCP < 0.5 set mask_tuple to ['NCP', 0.5]. None performs no masking.
vmin : float
Luminance minimum value, None for default value.
Parameter is ignored is norm is not None.
vmax : float
Luminance maximum value, None for default value.
Parameter is ignored is norm is not None.
norm : Normalize or None, optional
matplotlib Normalize instance used to scale luminance data. If not
None the vmax and vmin parameters are ignored. If None, vmin and
vmax are used for luminance scaling.
cmap : str or None
Matplotlib colormap name. None will use the default colormap for
the field being plotted as specified by the Py-ART configuration.
mask_outside : bool
True to mask data outside of vmin, vmax. False performs no
masking.
title : str
Title to label plot with, None to use default title generated from
the field and tilt parameters. Parameter is ignored if title_flag
is False.
title_flag : bool
True to add a title to the plot, False does not add a title.
colorbar_flag : bool
True to add a colorbar with label to the axis. False leaves off
the colorbar.
ticks : array
Colorbar custom tick label locations.
ticklabs : array
Colorbar custom tick labels.
colorbar_label : str
Colorbar label, None will use a default label generated from the
field information.
ax : Cartopy GeoAxes instance
If None, create GeoAxes instance using other keyword info.
If provided, ax must have a Cartopy crs projection and projection
kwarg below is ignored.
fig : Figure
Figure to add the colorbar to. None will use the current figure.
lat_lines, lon_lines : array or None
Locations at which to draw latitude and longitude lines.
None will use default values which are resonable for maps of
North America.
projection : cartopy.crs class
Map projection supported by cartopy. Used for all subsequent calls
to the GeoAxes object generated. Defaults to LambertConformal
centered on radar.
min_lat, max_lat, min_lon, max_lon : float
Latitude and longitude ranges for the map projection region in
degrees.
width, height : float
Width and height of map domain in meters.
Only this set of parameters or the previous set of parameters
(min_lat, max_lat, min_lon, max_lon) should be specified.
If neither set is specified then the map domain will be determined
from the extend of the radar gate locations.
shapefile : str
Filename for a shapefile to add to map.
shapefile_kwargs : dict
Key word arguments used to format shapefile. Projection defaults
to lat lon (cartopy.crs.PlateCarree())
resolution : '10m', '50m', '110m'.
Resolution of NaturalEarthFeatures to use. See Cartopy
documentation for details.
gatefilter : GateFilter
GateFilter instance. None will result in no gatefilter mask being
applied to data.
filter_transitions : bool
True to remove rays where the antenna was in transition between
sweeps from the plot. False will include these rays in the plot.
No rays are filtered when the antenna_transition attribute of the
underlying radar is not present.
edges : bool
True will interpolate and extrapolate the gate edges from the
range, azimuth and elevations in the radar, treating these
as specifying the center of each gate. False treats these
coordinates themselved as the gate edges, resulting in a plot
in which the last gate in each ray and the entire last ray are not
not plotted.
embelish: bool
True by default. Set to False to supress drawing of coastlines
etc.. Use for speedup when specifying shapefiles.
Note that lat lon labels only work with certain projections.
raster : bool
False by default. Set to true to render the display as a raster
rather than a vector in call to pcolormesh. Saves time in plotting
high resolution data over large areas. Be sure to set the dpi
of the plot for your application if you save it as a vector format
(i.e., pdf, eps, svg).
alpha : float or None
Set the alpha tranparency of the radar plot. Useful for
overplotting radar over other datasets.
"""
# parse parameters
vmin, vmax = parse_vmin_vmax(self._radar, field, vmin, vmax)
cmap = parse_cmap(cmap, field)
if lat_lines is None:
lat_lines = np.arange(30, 46, 1)
if lon_lines is None:
lon_lines = np.arange(-110, -75, 1)
lat_0 = self.loc[0]
lon_0 = self.loc[1]
# get data for the plot
data = self._get_data(
field, sweep, mask_tuple, filter_transitions, gatefilter)
x, y = self._get_x_y(sweep, edges, filter_transitions)
# mask the data where outside the limits
if mask_outside:
data = np.ma.masked_outside(data, vmin, vmax)
# Define a figure if None is provided.
if fig is None:
fig = plt.gcf()
# initialize instance of GeoAxes if not provided
if ax is not None:
if hasattr(ax, 'projection'):
projection = ax.projection
else:
if projection is None:
# set map projection to LambertConformal if none is
# specified.
projection = cartopy.crs.LambertConformal(
central_longitude=lon_0, central_latitude=lat_0)
warnings.warn(
"No projection was defined for the axes."
+ " Overridding defined axes and using default "
+ "axes with projection Lambert Conformal.",
UserWarning)
ax = plt.axes(projection=projection)
# Define GeoAxes if None is provided.
else:
if projection is None:
# set map projection to LambertConformal if none is
# specified.
projection = cartopy.crs.LambertConformal(
central_longitude=lon_0, central_latitude=lat_0)
warnings.warn(
"No projection was defined for the axes."
+ " Overridding defined axes and using default "
+ "axes with projection Lambert Conformal.",
UserWarning)
ax = plt.axes(projection=projection)
if min_lon:
ax.set_extent([min_lon, max_lon, min_lat, max_lat],
crs=cartopy.crs.PlateCarree())
elif width:
ax.set_extent([-width/2., width/2., -height/2., height/2.],
crs=self.grid_projection)
# plot the data
if norm is not None: # if norm is set do not override with vmin/vmax
vmin = vmax = None
pm = ax.pcolormesh(x * 1000., y * 1000., data, alpha=alpha,
vmin=vmin, vmax=vmax, cmap=cmap,
norm=norm, transform=self.grid_projection)
# plot as raster in vector graphics files
if raster:
pm.set_rasterized(True)
# add embelishments
if embelish is True:
# Create a feature for States/Admin 1 regions at 1:resolution
# from Natural Earth
states_provinces = cartopy.feature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale=resolution,
facecolor='none')
ax.coastlines(resolution=resolution)
ax.add_feature(states_provinces, edgecolor='gray')
# labeling gridlines poses some difficulties depending on the
# projection, so we need some projection-spectific methods
if ax.projection in [cartopy.crs.PlateCarree(),
cartopy.crs.Mercator()]:
gl = ax.gridlines(xlocs=lon_lines, ylocs=lat_lines,
draw_labels=True)
gl.xlabels_top = False
gl.ylabels_right = False
elif isinstance(ax.projection, cartopy.crs.LambertConformal):
ax.figure.canvas.draw()
ax.gridlines(xlocs=lon_lines, ylocs=lat_lines)
# Label the end-points of the gridlines using the custom
# tick makers:
ax.xaxis.set_major_formatter(
cartopy.mpl.gridliner.LONGITUDE_FORMATTER)
ax.yaxis.set_major_formatter(
cartopy.mpl.gridliner.LATITUDE_FORMATTER)
if _LAMBERT_GRIDLINES:
lambert_xticks(ax, lon_lines)
lambert_yticks(ax, lat_lines)
else:
ax.gridlines(xlocs=lon_lines, ylocs=lat_lines)
# plot the data and optionally the shape file
# we need to convert the radar gate locations (x and y) which are in
# km to meters we also need to give the original projection of the
# data which is stored in self.grid_projection
if shapefile is not None:
from cartopy.io.shapereader import Reader
if shapefile_kwargs is None:
shapefile_kwargs = {}
if 'crs' not in shapefile_kwargs:
shapefile_kwargs['crs'] = cartopy.crs.PlateCarree()
ax.add_geometries(Reader(shapefile).geometries(),
**shapefile_kwargs)
if title_flag:
self._set_title(field, sweep, title, ax)
# add plot and field to lists
self.plots.append(pm)
self.plot_vars.append(field)
if colorbar_flag:
self.plot_colorbar(
mappable=pm, label=colorbar_label, field=field, fig=fig,
ax=ax, ticks=ticks, ticklabs=ticklabs)
# keep track of this GeoAxes object for later
self.ax = ax
return
[docs] def plot_point(self, lon, lat, symbol='ro', label_text=None,
label_offset=(None, None), **kwargs):
"""
Plot a point on the current map.
Additional arguments are passed to ax.plot.
Parameters
----------
lon : float
Longitude of point to plot.
lat : float
Latitude of point to plot.
symbol : str
Matplotlib compatible string which specified the symbol of the
point.
label_text : str, optional.
Text to label symbol with. If None no label will be added.
label_offset : [float, float]
Offset in lon, lat degrees for the bottom left corner of the label
text relative to the point. A value of None will use 0.01.
"""
self._check_ax()
lon_offset, lat_offset = label_offset
if lon_offset is None:
lon_offset = 0.01
if lat_offset is None:
lat_offset = 0.01
if 'transform' not in kwargs.keys():
kwargs['transform'] = cartopy.crs.PlateCarree()
self.ax.plot(lon, lat, symbol, **kwargs)
if label_text is not None:
# the "plt.annotate call" does not have a "transform=" keyword,
# so for this one we transform the coordinates with a Cartopy call.
x_text, y_text = self.ax.projection.transform_point(
lon + lon_offset, lat + lat_offset,
src_crs=kwargs['transform'])
self.ax.annotate(label_text, xy=(x_text, y_text))
[docs] def plot_line_geo(self, line_lons, line_lats, line_style='r-', **kwargs):
"""
Plot a line segments on the current map given values in lat and lon.
Additional arguments are passed to ax.plot.
Parameters
----------
line_lons : array
Longitude of line segment to plot.
line_lats : array
Latitude of line segment to plot.
line_style : str
Matplotlib compatible string which specifies the line style.
"""
self._check_ax()
if 'transform' not in kwargs.keys():
kwargs['transform'] = cartopy.crs.PlateCarree()
self.ax.plot(line_lons, line_lats, line_style, **kwargs)
[docs] def plot_line_xy(self, line_x, line_y, color='r', line_style='-', **kwargs):
"""
Plot a line segments on the current map given radar x, y values.
Additional arguments are passed to ax.plot.
Parameters
----------
line_x : array
X location of points to plot in meters from the radar.
line_y : array
Y location of points to plot in meters from the radar.
color : str, optional
Matplotlib compatible string which specifies the color for the
line style.
line_style : str, optional
Matplotlib compatible string which specifies the line style.
"""
self._check_ax()
if 'transform' not in kwargs.keys():
kwargs['transform'] = self.grid_projection
self.ax.plot(line_x, line_y, color, line_style, **kwargs)
[docs] def plot_range_ring(self, range_ring_location_km, npts=360,
color='k', line_style='-', **kwargs):
"""
Plot a single range ring on the map.
Additional arguments are passed to ax.plot.
Parameters
----------
range_ring_location_km : float
Location of range ring in km.
npts : int
Number of points in the ring, higher for better resolution.
color : str, optional
Matplotlib compatible string which specifies the color for the
line style.
line_style : str, optional
Matplotlib compatible string which specified the line
style of the ring.
"""
# The RadarDisplay.plot_range_rings uses a col parameter to specify
# the line color, deal with this here.
if 'col' in kwargs:
color = kwargs.pop('col')
kwargs['c'] = color
if 'ax' in kwargs:
kwargs.pop('ax')
self._check_ax()
angle = np.linspace(0., 2.0 * np.pi, npts)
xpts = range_ring_location_km * 1000. * np.sin(angle)
ypts = range_ring_location_km * 1000. * np.cos(angle)
self.plot_line_xy(xpts, ypts, color=color, line_style=line_style,
**kwargs)
# These methods are a hack to allow gridlines when the projection is lambert
# http://nbviewer.jupyter.org/gist/ajdawson/dd536f786741e987ae4e
def find_side(ls, side):
"""
Given a shapely LineString which is assumed to be rectangular, return the
line corresponding to a given side of the rectangle.
"""
minx, miny, maxx, maxy = ls.bounds
points = {'left': [(minx, miny), (minx, maxy)],
'right': [(maxx, miny), (maxx, maxy)],
'bottom': [(minx, miny), (maxx, miny)],
'top': [(minx, maxy), (maxx, maxy)]}
return sgeom.LineString(points[side])
def lambert_xticks(ax, ticks):
""" Draw ticks on the bottom x-axis of a Lambert Conformal projection. """
def te(xy):
return xy[0]
def lc(t, n, b):
return np.vstack((np.zeros(n) + t, np.linspace(b[2], b[3], n))).T
xticks, xticklabels = _lambert_ticks(ax, ticks, 'bottom', lc, te)
ax.xaxis.tick_bottom()
ax.set_xticks(xticks)
ax.set_xticklabels([ax.xaxis.get_major_formatter()(xtick) for
xtick in xticklabels])
def lambert_yticks(ax, ticks):
""" Draw ticks on the left y-axis of a Lambert Conformal projection. """
def te(xy):
return xy[1]
def lc(t, n, b):
return np.vstack((np.linspace(b[0], b[1], n), np.zeros(n) + t)).T
yticks, yticklabels = _lambert_ticks(ax, ticks, 'left', lc, te)
ax.yaxis.tick_left()
ax.set_yticks(yticks)
ax.set_yticklabels([ax.yaxis.get_major_formatter()(ytick) for
ytick in yticklabels])
def _lambert_ticks(ax, ticks, tick_location, line_constructor, tick_extractor):
""" Get the tick locations and labels for a Lambert Conformal projection. """
outline_patch = sgeom.LineString(
ax.spines['geo'].get_path().vertices.tolist())
axis = find_side(outline_patch, tick_location)
n_steps = 30
extent = ax.get_extent(cartopy.crs.PlateCarree())
_ticks = []
for t in ticks:
xy = line_constructor(t, n_steps, extent)
proj_xyz = ax.projection.transform_points(cartopy.crs.Geodetic(),
xy[:, 0], xy[:, 1])
xyt = proj_xyz[..., :2]
ls = sgeom.LineString(xyt.tolist())
locs = axis.intersection(ls)
if not locs:
tick = [None]
else:
try:
tick = tick_extractor(locs.xy)
except AttributeError:
tick = [None]
_ticks.append(tick[0])
# Remove ticks that aren't visible:
ticklabels = copy(ticks)
while True:
try:
index = _ticks.index(None)
except ValueError:
break
_ticks.pop(index)
ticklabels = np.delete(ticklabels, index)
return _ticks, ticklabels