def test_get_pofd_pod_grid(self):
"""Ensures correct output from get_pofd_pod_grid."""
this_pofd_matrix, this_pod_matrix = model_eval.get_pofd_pod_grid(
pofd_spacing=NON_POD_GRID_SPACING, pod_spacing=POD_GRID_SPACING)
self.assertTrue(
numpy.allclose(this_pofd_matrix, POFD_MATRIX, atol=TOLERANCE))
self.assertTrue(
numpy.allclose(this_pod_matrix, POD_MATRIX, atol=TOLERANCE))
def plot_roc_curve(axes_object,
pod_by_threshold,
pofd_by_threshold,
line_colour=DEFAULT_ROC_COLOUR,
line_width=DEFAULT_ROC_WIDTH,
random_line_colour=DEFAULT_RANDOM_ROC_COLOUR,
random_line_width=DEFAULT_RANDOM_ROC_WIDTH):
"""Plots ROC (receiver operating characteristic) curve.
T = number of binarization thresholds
For the definition of a "binarization threshold" and the role they play in
ROC curves, see `model_evaluation.get_points_in_roc_curve`.
:param axes_object: Instance of `matplotlib.axes._subplots.AxesSubplot`.
:param pod_by_threshold: length-T numpy array of POD (probability of
detection) values.
:param pofd_by_threshold: length-T numpy array of POFD (probability of false
detection) values.
:param line_colour: Colour (in any format accepted by `matplotlib.colors`).
:param line_width: Line width (real positive number).
:param random_line_colour: Colour of reference line (ROC curve for a random
predictor).
:param random_line_width: Width of reference line.
"""
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(pofd_by_threshold,
exact_dimensions=numpy.array(
[num_thresholds]))
error_checking.assert_is_geq_numpy_array(pofd_by_threshold,
0.,
allow_nan=True)
error_checking.assert_is_leq_numpy_array(pofd_by_threshold,
1.,
allow_nan=True)
pofd_matrix, pod_matrix = model_eval.get_pofd_pod_grid()
peirce_score_matrix = pod_matrix - pofd_matrix
this_colour_map_object, this_colour_norm_object = _get_peirce_colour_scheme(
)
pyplot.contourf(pofd_matrix,
pod_matrix,
peirce_score_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=peirce_score_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('Peirce score (POD minus POFD)')
random_x_coords, random_y_coords = model_eval.get_random_roc_curve()
axes_object.plot(
random_x_coords,
random_y_coords,
color=plotting_utils.colour_from_numpy_to_tuple(random_line_colour),
linestyle='dashed',
linewidth=random_line_width)
nan_flags = numpy.logical_or(numpy.isnan(pofd_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(
pofd_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('POFD (probability of false detection)')
axes_object.set_ylabel('POD (probability of detection)')
axes_object.set_xlim(0., 1.)
axes_object.set_ylim(0., 1.)
def plot_roc_curve(axes_object,
pod_by_threshold,
pofd_by_threshold,
line_colour=ROC_CURVE_COLOUR,
plot_background=True):
"""Plots ROC (receiver operating characteristic) curve.
T = number of binarization thresholds
For the definition of a "binarization threshold" and the role they play in
ROC curves, see `model_evaluation.get_points_in_roc_curve`.
:param axes_object: Instance of `matplotlib.axes._subplots.AxesSubplot`.
:param pod_by_threshold: length-T numpy array of POD (probability of
detection) values.
:param pofd_by_threshold: length-T numpy array of POFD (probability of false
detection) values.
:param line_colour: Line colour.
:param plot_background: Boolean flag. If True, will plot background
(reference line and Peirce-score 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(pofd_by_threshold,
exact_dimensions=expected_dim)
error_checking.assert_is_geq_numpy_array(pofd_by_threshold,
0.,
allow_nan=True)
error_checking.assert_is_leq_numpy_array(pofd_by_threshold,
1.,
allow_nan=True)
error_checking.assert_is_boolean(plot_background)
if plot_background:
pofd_matrix, pod_matrix = model_eval.get_pofd_pod_grid()
peirce_score_matrix = pod_matrix - pofd_matrix
this_colour_map_object, this_colour_norm_object = (
_get_peirce_colour_scheme())
pyplot.contourf(pofd_matrix,
pod_matrix,
peirce_score_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=peirce_score_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('Peirce score (POD minus POFD)')
random_x_coords, random_y_coords = model_eval.get_random_roc_curve()
axes_object.plot(
random_x_coords,
random_y_coords,
color=plotting_utils.colour_from_numpy_to_tuple(RANDOM_ROC_COLOUR),
linestyle='dashed',
linewidth=RANDOM_ROC_WIDTH)
nan_flags = numpy.logical_or(numpy.isnan(pofd_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(
pofd_by_threshold[real_indices],
pod_by_threshold[real_indices],
color=plotting_utils.colour_from_numpy_to_tuple(line_colour),
linestyle='solid',
linewidth=ROC_CURVE_WIDTH)[0]
axes_object.set_xlabel('POFD (probability of false detection)')
axes_object.set_ylabel('POD (probability of detection)')
axes_object.set_xlim(0., 1.)
axes_object.set_ylim(0., 1.)
return line_handle