def _raw_ticks(self, vmin, vmax):
self._guess_steps(vmin, vmax)
return MaxNLocator._raw_ticks(self, vmin, vmax)
def _raw_ticks(self, vmin, vmax):
self._guess_steps(vmin, vmax)
return MaxNLocator._raw_ticks(self, vmin, vmax)
def get_plot(params):
time_min = dt.datetime.strptime(params['time_min'],
'%Y-%m-%dT%H:%M:00.000Z')
time_max = dt.datetime.strptime(params['time_max'],
'%Y-%m-%dT%H:%M:00.000Z')
barbs = 'barbs' in params.keys() and params['barbs'] == 'True'
pbl = 'pbl' in params.keys() and params['pbl'] == 'True'
var = params['var']
lgd = None
has_barbs = var == 'barbs' or barbs
if var == 'barbs':
lidars = get_barb_data(params)
else:
lidars = get_lidar_data(params)
if barbs:
ds_barbs = get_barb_data(params)
nds = len(lidars)
figsize = (10, 3.5 * nds)
sharey = False
f, axarr = plt.subplots(nds, #sharex=True,
sharey=sharey,
squeeze=False,
figsize=figsize)
cbar_kwargs = {'label': cb_dict[var]}
is_centered = centered_dict[var]
graph_center = 0 if is_centered else False
cmap = 'coolwarm' if is_centered else 'jet'
for i, ds in enumerate(lidars):
ax = axarr[i][0]
title = ds.attrs['lidar'] + ' (' + ds.attrs['scan'] + ')'
if has_barbs: # get nice range intervals
# find the scan range_res
ranges = ds.coords['Range'].values
ydelta = ranges[1] - ranges[0]
# get the desired average barb height locations
ysteps = np.arange(1, 10)
yloc = MaxNLocator(nbins=barb_heights, steps=ysteps)
heights = yloc._raw_ticks(0, len(ranges))
dheight = (heights[1] - heights[0]) * ydelta
if var == 'barbs':
ds['windspeed'].rasp.plot_barbs(x='Time', y='Range',
components=['x', 'y'],
# resample=barb_resample,
# resampley=barb_bins,
resampley=dheight,
ax=ax,
length=barb_length)
ax.set_title(title)
ax.set_xlim([time_min, time_max])
ax.set_xlabel('Time (UTC)')
ax.set_ylabel('Range (km)')
else:
vmin = vmin_dict[var] if var in vmin_dict.keys() else None
vmax = vmax_dict[var] if var in vmax_dict.keys() else None
try:
da = ds[var]
da.plot.pcolormesh(x='Time', y='Range', center=graph_center,
robust=True, cmap=cmap, ax=ax,
vmin=vmin, vmax=vmax,
cbar_kwargs=cbar_kwargs)
except:
# this happens occasionally when all data is NA, or
# there's only one profile of data
Z = np.zeros((ds.dims['Time'], ds.dims['Range'])).transpose()
# Z = da.values.transpose()
Zm = np.ma.masked_where(np.isnan(Z), Z)
ax.pcolormesh(ds.coords['Time'].values,
ds.coords['Range'].values, Zm)
if pbl:
# add pbl
ds['pbl'] = estimate_residual_layer(ds['cnr_whole'])
ax.scatter(ds.coords['Time'].values, ds['pbl'], 50, marker='.', color='red',
edgecolor='gray', lw=.5, label='PBL (Residual Layer)')
ax.legend(loc=1)
ax.set_title(title)
ax.set_xlim([time_min, time_max])
ax.set_xlabel('Time (UTC)')
ax.set_ylabel('Range (km)')
if barbs and ds_barbs[i] is not None:
ds = ds_barbs[i]
ds['windspeed'].rasp.plot_barbs(x='Time', y='Range',
components=['x', 'y'],
# resample=barb_resample,
# resampley=barb_bins,
resampley=dheight,
ax=ax,
length=barb_length,
lw=barb_lw)
png_output = BytesIO()
if lgd is None:
plt.tight_layout()
canvas = FigureCanvas(plt.gcf())
canvas.print_png(png_output)
else:
plt.savefig(png_output, format='png',
bbox_extra_artists=(lgd,),
bbox_inches='tight')
response = png_output.getvalue()
# have to make sure to close things duh
plt.close()
png_output.close()
return response
