def plot_sensitivity_map(self, salib_si='S1', **kwargs):
"""
Plot a map of the largest sensitivity index in each exposure point
Requires the uncertainty distribution for eai_exp.
Parameters
----------
salib_si : str, optional
The name of the sensitivity index to plot.
The default is 'S1'.
kwargs :
Keyword arguments passed on to
climada.util.plot.geo_scatter_categorical
Raises
------
ValueError :
If no sensitivity data is found, raise error.
Returns
-------
ax: matplotlib.pyplot.axes
The axis handle of the plot.
See Also
--------
climada.util.plot.geo_scatter_categorical :
geographical plot for categorical variable
"""
eai_max_si_df = self.get_largest_si(salib_si, metric_list=['eai_exp'])
plot_val = eai_max_si_df['param']
coord = np.array([self.coord_df.latitude,
self.coord_df.longitude]).transpose()
if 'var_name' not in kwargs:
kwargs['var_name'] = 'Input parameter with largest ' + salib_si
if 'title' not in kwargs:
kwargs['title'] = ''
if 'figsize' not in kwargs:
kwargs['figsize'] = (8, 6)
if 'cmap' not in kwargs:
labels = np.unique(plot_val)
n = np.where(labels == 'None')[0]
if len(n) > 0:
n = n[0]
cmap = mpl.colors.ListedColormap(
plt.get_cmap(MAP_CMAP).colors[:len(labels)])
colors = list(cmap.colors)
colors[n] = tuple(np.repeat(0.93, 3))
cmap.colors = tuple(colors)
kwargs['cmap'] = cmap
ax = u_plot.geo_scatter_categorical(plot_val, coord, **kwargs)
return ax
def test_geo_scatter_categorical(self):
"""Plots ones with geo_scatteR_categorical"""
# test default with one plot
values = np.array([1, 2.0, 1, 'a'])
coord = np.array([[26, 0], [26, 1], [28, 0], [29, 1]])
u_plot.geo_scatter_categorical(values,
coord,
'value',
'test plot',
pop_name=True)
plt.close()
#test multiple plots with non default kwargs
values = np.array([[1, 2.0, 1, 'a'], [0, 0, 0, 0]])
coord = np.array([[26, 0], [26, 1], [28, 0], [29, 1]])
u_plot.geo_scatter_categorical(values,
coord,
'value',
'test plot',
cat_name={
0: 'zero',
1: 'int',
2.0: 'float',
'a': 'string'
},
pop_name=False,
cmap=plt.get_cmap('Set1'))
plt.close()
#test colormap warning
values = np.array([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11],
[12, 13, 14, 15]])
coord = np.array([[26, 0], [26, 4], [28, 0], [29, 1]])
u_plot.geo_scatter_categorical(values,
coord,
'value',
'test plot',
pop_name=False,
cmap='viridis')
plt.close()
#test colormap warning with 256 colors
values = np.array([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11],
[12, 13, 14, 15]])
coord = np.array([[26, 0], [26, 1], [28, 0], [29, 1]])
u_plot.geo_scatter_categorical(values,
coord,
'value',
'test plot',
pop_name=False,
cmap='tab20c')
plt.close()
def plot_sensitivity_map(self, exp=None, salib_si='S1', figsize=(8, 6)):
"""
Plot a map of the largest sensitivity index in each exposure point
Parameters
----------
exp : climada.exposure
The exposure from which to take the coordinates
salib_si : str, optional
The name of the sensitivity index to plot.
The default is 'S1'.
figsize: tuple(int or float, int or float), optional
The figsize argument of matplotlib.pyplot.subplots()
The default is (8, 6)
Raises
------
ValueError
If no sensitivity data is found, raise error.
Returns
-------
ax: matplotlib.pyplot.axes
The axis handle of the plot.
"""
try:
si_eai = self.sensitivity['eai_exp']
eai_max_si_idx = [
np.argmax(si_dict[salib_si]) for si_dict in si_eai.values()
]
except KeyError as verr:
raise ValueError(
"No sensitivity indices found for"
" impact.eai_exp. Please compute sensitivity first using"
" UncImpact.calc_sensitivity(calc_eai_exp=True)") from verr
if exp is None:
exp_input_vals = self.samples_df.loc[0][
self.unc_vars['exp'].labels].to_dict()
exp = self.unc_vars['exp'].uncvar_func(**exp_input_vals)
plot_val = np.array([eai_max_si_idx]).astype(float)
coord = np.array([exp.gdf.latitude, exp.gdf.longitude]).transpose()
ax = u_plot.geo_scatter_categorical(
plot_val,
coord,
var_name='Largest sensitivity index ' + salib_si,
title='Sensitivity map',
cat_name=self.param_labels,
figsize=figsize)
return ax