def test_frequency_bias_from_sr_and_pod(self):
"""Ensures correct output from frequency_bias_from_sr_and_pod."""
this_frequency_bias_matrix = model_eval.frequency_bias_from_sr_and_pod(
SUCCESS_RATIO_MATRIX, POD_MATRIX)
self.assertTrue(
numpy.allclose(this_frequency_bias_matrix,
FREQUENCY_BIAS_MATRIX,
atol=TOLERANCE))
def plot_performance_diagram(axes_object,
pod_by_threshold,
success_ratio_by_threshold,
line_colour=DEFAULT_PERFORMANCE_COLOUR,
line_width=DEFAULT_PERFORMANCE_WIDTH,
bias_line_colour=DEFAULT_FREQ_BIAS_COLOUR,
bias_line_width=DEFAULT_FREQ_BIAS_WIDTH):
"""Plots performance diagram.
T = number of binarization thresholds
For the definition of a "binarization threshold" and the role they play in
performance diagrams, see
`model_evaluation.get_points_in_performance_diagram`.
:param axes_object: Instance of `matplotlib.axes._subplots.AxesSubplot`.
:param pod_by_threshold: length-T numpy array of POD (probability of
detection) values.
:param success_ratio_by_threshold: length-T numpy array of success ratios.
:param line_colour: Colour (in any format accepted by `matplotlib.colors`).
:param line_width: Line width (real positive number).
:param bias_line_colour: Colour of contour lines for frequency bias.
:param bias_line_width: Width of contour lines for frequency bias.
"""
error_checking.assert_is_numpy_array(pod_by_threshold, num_dimensions=1)
error_checking.assert_is_geq_numpy_array(pod_by_threshold,
0.,
allow_nan=True)
error_checking.assert_is_leq_numpy_array(pod_by_threshold,
1.,
allow_nan=True)
num_thresholds = len(pod_by_threshold)
error_checking.assert_is_numpy_array(success_ratio_by_threshold,
exact_dimensions=numpy.array(
[num_thresholds]))
error_checking.assert_is_geq_numpy_array(success_ratio_by_threshold,
0.,
allow_nan=True)
error_checking.assert_is_leq_numpy_array(success_ratio_by_threshold,
1.,
allow_nan=True)
success_ratio_matrix, pod_matrix = model_eval.get_sr_pod_grid()
csi_matrix = model_eval.csi_from_sr_and_pod(success_ratio_matrix,
pod_matrix)
frequency_bias_matrix = model_eval.frequency_bias_from_sr_and_pod(
success_ratio_matrix, pod_matrix)
this_colour_map_object, this_colour_norm_object = _get_csi_colour_scheme()
pyplot.contourf(success_ratio_matrix,
pod_matrix,
csi_matrix,
LEVELS_FOR_CSI_CONTOURS,
cmap=this_colour_map_object,
norm=this_colour_norm_object,
vmin=0.,
vmax=1.,
axes=axes_object)
colour_bar_object = plotting_utils.plot_colour_bar(
axes_object_or_matrix=axes_object,
data_matrix=csi_matrix,
colour_map_object=this_colour_map_object,
colour_norm_object=this_colour_norm_object,
orientation_string='vertical',
extend_min=False,
extend_max=False)
colour_bar_object.set_label('CSI (critical success index)')
bias_colour_tuple = plotting_utils.colour_from_numpy_to_tuple(
bias_line_colour)
bias_colours_2d_tuple = ()
for _ in range(len(LEVELS_FOR_FREQ_BIAS_CONTOURS)):
bias_colours_2d_tuple += (bias_colour_tuple, )
bias_contour_object = pyplot.contour(success_ratio_matrix,
pod_matrix,
frequency_bias_matrix,
LEVELS_FOR_FREQ_BIAS_CONTOURS,
colors=bias_colours_2d_tuple,
linewidths=bias_line_width,
linestyles='dashed',
axes=axes_object)
pyplot.clabel(bias_contour_object,
inline=True,
inline_spacing=PIXEL_PADDING_FOR_FREQ_BIAS_LABELS,
fmt=STRING_FORMAT_FOR_FREQ_BIAS_LABELS,
fontsize=FONT_SIZE)
nan_flags = numpy.logical_or(numpy.isnan(success_ratio_by_threshold),
numpy.isnan(pod_by_threshold))
if not numpy.all(nan_flags):
real_indices = numpy.where(numpy.invert(nan_flags))[0]
axes_object.plot(
success_ratio_by_threshold[real_indices],
pod_by_threshold[real_indices],
color=plotting_utils.colour_from_numpy_to_tuple(line_colour),
linestyle='solid',
linewidth=line_width)
axes_object.set_xlabel('Success ratio (1 - FAR)')
axes_object.set_ylabel('POD (probability of detection)')
axes_object.set_xlim(0., 1.)
axes_object.set_ylim(0., 1.)
def plot_performance_diagram(axes_object,
pod_by_threshold,
success_ratio_by_threshold,
line_colour=PERF_DIAGRAM_COLOUR,
plot_background=True):
"""Plots performance diagram.
T = number of binarization thresholds
For the definition of a "binarization threshold" and the role they play in
performance diagrams, see
`model_evaluation.get_points_in_performance_diagram`.
:param axes_object: Instance of `matplotlib.axes._subplots.AxesSubplot`.
:param pod_by_threshold: length-T numpy array of POD (probability of
detection) values.
:param success_ratio_by_threshold: length-T numpy array of success ratios.
:param line_colour: Line colour.
:param plot_background: Boolean flag. If True, will plot background
(frequency-bias and CSI contours).
:return: line_handle: Line handle for ROC curve.
"""
error_checking.assert_is_numpy_array(pod_by_threshold, num_dimensions=1)
error_checking.assert_is_geq_numpy_array(pod_by_threshold,
0.,
allow_nan=True)
error_checking.assert_is_leq_numpy_array(pod_by_threshold,
1.,
allow_nan=True)
num_thresholds = len(pod_by_threshold)
expected_dim = numpy.array([num_thresholds], dtype=int)
error_checking.assert_is_numpy_array(success_ratio_by_threshold,
exact_dimensions=expected_dim)
error_checking.assert_is_geq_numpy_array(success_ratio_by_threshold,
0.,
allow_nan=True)
error_checking.assert_is_leq_numpy_array(success_ratio_by_threshold,
1.,
allow_nan=True)
error_checking.assert_is_boolean(plot_background)
if plot_background:
success_ratio_matrix, pod_matrix = model_eval.get_sr_pod_grid()
csi_matrix = model_eval.csi_from_sr_and_pod(
success_ratio_array=success_ratio_matrix, pod_array=pod_matrix)
frequency_bias_matrix = model_eval.frequency_bias_from_sr_and_pod(
success_ratio_array=success_ratio_matrix, pod_array=pod_matrix)
this_colour_map_object, this_colour_norm_object = (
_get_csi_colour_scheme())
pyplot.contourf(success_ratio_matrix,
pod_matrix,
csi_matrix,
CSI_LEVELS,
cmap=this_colour_map_object,
norm=this_colour_norm_object,
vmin=0.,
vmax=1.,
axes=axes_object)
colour_bar_object = plotting_utils.plot_colour_bar(
axes_object_or_matrix=axes_object,
data_matrix=csi_matrix,
colour_map_object=this_colour_map_object,
colour_norm_object=this_colour_norm_object,
orientation_string='vertical',
extend_min=False,
extend_max=False,
fraction_of_axis_length=0.8)
colour_bar_object.set_label('CSI (critical success index)')
bias_colour_tuple = plotting_utils.colour_from_numpy_to_tuple(
FREQ_BIAS_COLOUR)
bias_colours_2d_tuple = ()
for _ in range(len(FREQ_BIAS_LEVELS)):
bias_colours_2d_tuple += (bias_colour_tuple, )
bias_contour_object = pyplot.contour(success_ratio_matrix,
pod_matrix,
frequency_bias_matrix,
FREQ_BIAS_LEVELS,
colors=bias_colours_2d_tuple,
linewidths=FREQ_BIAS_WIDTH,
linestyles='dashed',
axes=axes_object)
pyplot.clabel(bias_contour_object,
inline=True,
inline_spacing=FREQ_BIAS_PADDING,
fmt=FREQ_BIAS_STRING_FORMAT,
fontsize=FONT_SIZE)
nan_flags = numpy.logical_or(numpy.isnan(success_ratio_by_threshold),
numpy.isnan(pod_by_threshold))
if numpy.all(nan_flags):
line_handle = None
else:
real_indices = numpy.where(numpy.invert(nan_flags))[0]
line_handle = axes_object.plot(
success_ratio_by_threshold[real_indices],
pod_by_threshold[real_indices],
color=plotting_utils.colour_from_numpy_to_tuple(line_colour),
linestyle='solid',
linewidth=PERF_DIAGRAM_WIDTH)[0]
axes_object.set_xlabel('Success ratio (1 - FAR)')
axes_object.set_ylabel('POD (probability of detection)')
axes_object.set_xlim(0., 1.)
axes_object.set_ylim(0., 1.)
return line_handle
