def __init__(self, fcst, velocity_variable,
speed_units='knots', ax=None, fig=None,
**kwdargs):
self.variable = velocity_variable
self.speed_units = speed_units
# set the default colormap if needed
kwdargs['cmap'] = kwdargs.get('cmap', velocity_cmap)
# convert the bins to knots
bins = xray.Variable('bins',
velocity_variable.speed_bins.copy(),
{'units': velocity_variable.units})
_, self.bins, _ = units.convert_units(bins, speed_units)
default_norm = plt.cm.colors.BoundaryNorm(self.bins, self.bins.size)
kwdargs['norm'] = kwdargs.get('norm', default_norm)
self.variable = velocity_variable
self.ax, self.fig = utils.axis_figure(ax, fig)
# add the color bar
self.cax = self.fig.add_axes([0.92, 0.05, 0.03, 0.9])
cbar = mpl.colorbar.ColorbarBase(self.cax,
cmap=kwdargs['cmap'],
norm=kwdargs['norm'])
cbar.set_label("Knots")
fcst = self.normalize(fcst)
# use the longitude grid to define the radius of the circles
sorted_lats = np.sort(fcst['latitude'].values)
resol = np.median(angles.angle_diff(sorted_lats[1:],
sorted_lats[:-1]))
# create the map
self.m = utils.get_basemap(fcst, ax=self.ax,
lon_pad=0.75 * resol,
lat_pad = 0.75 * resol)
def create_circle(one_loc):
# determine the circle center
x, y = self.m(one_loc['longitude'].values,
one_loc['latitude'].values)
speeds = one_lonlat[self.variable.speed_name].values
dirs = one_lonlat[self.variable.direction_name].values
orientation = self.variable.direction_orientation
return DirectionCircle(x, y,
speeds=np.atleast_1d(speeds),
directions=np.atleast_1d(dirs),
orientation=orientation,
radius=0.4 * resol,
ax=self.ax, **kwdargs)
self.circles = [[create_circle(one_lonlat)
for lo, one_lonlat in one_lat.groupby('longitude')]
for la, one_lat in fcst.groupby('latitude')]
self.set_title(fcst)
def template_temporal_plot(fcst, ax=None, fig=None):
"""
Adds a human readable time axis to a figure.
"""
ax, fig = utils.axis_figure(ax, fig)
# set the x-axis to show time
str_times = [x.strftime('%m-%d %Hh')
for x in pd.to_datetime(fcst['time'].values)]
ax.xaxis.set_ticks(np.arange(len(str_times)) + 0.5)
ax.xaxis.set_ticklabels(str_times, rotation=90)
ax.set_xlabel("Forecast Time (UTC)")
return ax
def binned_line_plot(variable, bin_divs, ax=None, **kwdargs):
"""
Creates a plot showing the binned probability of the data
in variable.
"""
ax, _ = utils.axis_figure(axis=ax)
assert variable.dims == ('time', 'realization')
n_times, n_real = variable.shape
bins = bin_matrix(variable.values, bin_divs)
xs = np.arange(n_times).repeat(n_real).reshape(bins.shape)
lines = plt.plot(xs + 0.5, bins - 0.5, **kwdargs)
return lines
def __init__(self, x, y, speeds, directions, radius,
cmap=None, norm=None, ax=None, fig=None,
arrow_alpha=0.7, circle_alpha=0.6,
orientation='from'):
self.axis, self.fig = utils.axis_figure(ax, fig)
self.x = x
self.y = y
self.center = np.array([self.x, self.y]).flatten()
self.radius = radius
self.cm = cmap or plt.cm.get_cmap('Blues')
self.norm = norm or plt.Normalize()
self.arrow_alpha = arrow_alpha
self.circle_alpha = circle_alpha
assert orientation in ['from', 'to']
self.orientation = orientation
self.polys = self._build_polys(speeds, directions)
self.circle = self._build_circle(speeds)
self.axis.add_patch(self.circle)
[self.axis.add_patch(poly) for poly in self.polys]
def __init__(self, fcsts, velocity_variable, speed_units='knot',
max_speed=None, ax=None, fig=None):
self.variable = velocity_variable
self.speed_units = speed_units
self.ax, self.fig = utils.axis_figure(ax, fig)
self.ax.set_ylabel("Speed (%s)" % speed_units)
# convert the bins to knots
bins = xray.Variable('bins',
velocity_variable.speed_bins.copy(),
{'units': velocity_variable.units})
_, self.bins, _ = units.convert_units(bins, speed_units)
# convert the data to knots and radians
fcsts = self.normalize(fcsts)
# determine the upper bound on speed so we can place
# the direction circles.
all_speeds =fcsts[self.variable.speed_name].values
max_speed = max_speed or np.max(all_speeds)
max_bin = np.sum(self.bins <= max_speed) + 2
self.max_bin = np.minimum(max_bin, self.bins.size)
self.plot(fcsts)
def binned_probability_plot(variable, bin_divs, ax=None, **kwdargs):
"""
Creates a plot showing the binned probability of the data
in variable.
"""
ax, _ = utils.axis_figure(axis=ax)
if variable.dims == ('time', ):
variable = (xray.concat([variable], 'realization')
.transpose('time', 'realization'))
assert variable.dims == ('time', 'realization')
n_times, n_real = variable.shape
# compute the binned probabilities
probs = bin_probs(variable, bin_divs)
# default to a blue colormap placed in the background
kwdargs['cmap'] = kwdargs.get('cmap', plt.cm.get_cmap('Blues'))
kwdargs['zorder'] = kwdargs.get('zorder', -100)
# plot the probabilities
y, x = np.meshgrid(np.arange(bin_divs.size),
np.arange(variable['time'].size + 1))
pm = ax.pcolormesh(x, y, probs.values,
norm=plt.Normalize(vmin=0., vmax=1.),
**kwdargs)
return pm
