def test_geo_im_from_array(self):
values = np.array([1, 2.0, 5, 1])
coord = np.array([[-17, 178], [-10, 180], [-27, 175], [-16, 186]])
var_name = 'test'
title = 'test'
projection = ccrs.PlateCarree()
cmap = 'viridis'
ax = u_plot.geo_im_from_array(values, coord, var_name, title,
proj=projection, smooth=True, axes=None, figsize=(9, 13), cmap=cmap)
self.assertEqual(var_name, ax.get_title())
self.assertAlmostEqual(np.max(values), ax.collections[0].colorbar.vmax)
self.assertAlmostEqual(np.min(values), ax.collections[0].colorbar.vmin)
self.assertEqual(cmap, ax.collections[0].cmap.name)
plt.close()
def _event_plot(self, event_id, mat_var, col_name, **kwargs):
""""Plot an event of the input matrix.
Parameters:
event_id (int or np.array(int)): If event_id > 0, plot mat_var of
event with id = event_id. If event_id = 0, plot maximum
mat_var in each centroid. If event_id < 0, plot
abs(event_id)-largest event.
mat_var (sparse matrix): Sparse matrix where each row is an event
col_name (sparse matrix): Colorbar label
kwargs (optional): arguments for pcolormesh matplotlib function
Returns:
matplotlib.figure.Figure, matplotlib.axes._subplots.AxesSubplot
"""
if not isinstance(event_id, np.ndarray):
event_id = np.array([event_id])
array_val = list()
l_title = list()
for ev_id in event_id:
if ev_id > 0:
try:
event_pos = np.where(self.event_id == ev_id)[0][0]
except IndexError:
LOGGER.error('Wrong event id: %s.', ev_id)
raise ValueError from IndexError
im_val = mat_var[event_pos, :].todense().transpose()
title = 'Event ID %s: %s' % (str(self.event_id[event_pos]), \
self.event_name[event_pos])
elif ev_id < 0:
max_inten = np.asarray(np.sum(mat_var, axis=1)).reshape(-1)
event_pos = np.argpartition(max_inten, ev_id)[ev_id:]
event_pos = event_pos[np.argsort(max_inten[event_pos])][0]
im_val = mat_var[event_pos, :].todense().transpose()
title = '%s-largest Event. ID %s: %s' % (np.abs(ev_id), \
str(self.event_id[event_pos]), self.event_name[event_pos])
else:
im_val = np.max(mat_var, axis=0).todense().transpose()
title = '%s max intensity at each point' % self.tag.haz_type
array_val.append(im_val)
l_title.append(title)
return u_plot.geo_im_from_array(array_val, self.centroids.coord,
col_name, l_title, **kwargs)
def plot_rp_imp(self,
return_periods=(25, 50, 100, 250),
log10_scale=True,
smooth=True,
axis=None,
**kwargs):
"""Compute and plot exceedance impact maps for different return periods.
Calls local_exceedance_imp.
Parameters:
return_periods (tuple(int), optional): return periods to consider
log10_scale (boolean, optional): plot impact as log10(impact)
smooth (bool, optional): smooth plot to plot.RESOLUTIONxplot.RESOLUTION
kwargs (optional): arguments for pcolormesh matplotlib function
used in event plots
Returns:
matplotlib.axes._subplots.AxesSubplot,
np.ndarray (return_periods.size x num_centroids)
"""
imp_stats = self.local_exceedance_imp(np.array(return_periods))
if imp_stats == []:
LOGGER.error('Error: Attribute imp_mat is empty. Recalculate Impact'\
'instance with parameter save_mat=True')
raise ValueError
if log10_scale:
if np.min(imp_stats) < 0:
imp_stats_log = np.log10(abs(imp_stats) + 1)
colbar_name = 'Log10(abs(Impact)+1) (' + self.unit + ')'
elif np.min(imp_stats) < 1:
imp_stats_log = np.log10(imp_stats + 1)
colbar_name = 'Log10(Impact+1) (' + self.unit + ')'
else:
imp_stats_log = np.log10(imp_stats)
colbar_name = 'Log10(Impact) (' + self.unit + ')'
else:
imp_stats_log = imp_stats
colbar_name = 'Impact (' + self.unit + ')'
title = list()
for ret in return_periods:
title.append('Return period: ' + str(ret) + ' years')
axis = u_plot.geo_im_from_array(imp_stats_log, self.coord_exp, \
colbar_name, title, smooth=smooth, axes=axis, **kwargs)
return axis, imp_stats
def plot_rp_intensity(self, return_periods=(25, 50, 100, 250), **kwargs):
"""Compute and plot hazard exceedance intensity maps for different
return periods. Calls local_exceedance_inten.
Parameters:
return_periods (tuple(int), optional): return periods to consider
kwargs (optional): arguments for pcolormesh matplotlib function
used in event plots
Returns:
matplotlib.figure.Figure, matplotlib.axes._subplots.AxesSubplot,
np.ndarray (return_periods.size x num_centroids)
"""
inten_stats = self.local_exceedance_inten(np.array(return_periods))
colbar_name = 'Intensity (' + self.units + ')'
title = list()
for ret in return_periods:
title.append('Return period: ' + str(ret) + ' years')
fig, axis = u_plot.geo_im_from_array(inten_stats, self.centroids.coord,
colbar_name, title, **kwargs)
return fig, axis, inten_stats
