def test_visualization_plot_precip_field(
source, type, bbox, colorscale, probthr, title, colorbar, axis,
):
if type == "intensity":
field, metadata = get_precipitation_fields(0, 0, True, True, None, source)
field = field.squeeze()
field, metadata = conversion.to_rainrate(field, metadata)
elif type == "depth":
field, metadata = get_precipitation_fields(0, 0, True, True, None, source)
field = field.squeeze()
field, metadata = conversion.to_raindepth(field, metadata)
elif type == "prob":
field, metadata = get_precipitation_fields(0, 10, True, True, None, source)
field, metadata = conversion.to_rainrate(field, metadata)
field = ensemblestats.excprob(field, probthr)
ax = plot_precip_field(
field,
type=type,
bbox=bbox,
geodata=metadata,
colorscale=colorscale,
probthr=probthr,
units=metadata["unit"],
title=title,
colorbar=colorbar,
axis=axis,
)
pl.close()
def worker(lt, R_thr):
if not np.any(np.isfinite(R_obs[lt, :, :])):
return
P_fct = excprob(R_fct[:, lt, :, :], R_thr)
probscores.reldiag_accum(results[ws]["reldiag"][R_thr][lt],
P_fct, R_obs[lt, :, :])
ensscores.rankhist_accum(results[ws]["rankhist"][R_thr][lt],
R_fct[:, lt, :, :], R_obs[lt, :, :])
probscores.ROC_curve_accum(results[ws]["ROC"][R_thr][lt],
P_fct, R_obs[lt, :, :])
def test_visualization_plot_precip_field(
source,
plot_type,
bbox,
colorscale,
probthr,
title,
colorbar,
axis,
):
if plot_type == "intensity":
field, metadata = get_precipitation_fields(0, 0, True, True, None,
source)
field = field.squeeze()
field, metadata = conversion.to_rainrate(field, metadata)
elif plot_type == "depth":
field, metadata = get_precipitation_fields(0, 0, True, True, None,
source)
field = field.squeeze()
field, metadata = conversion.to_raindepth(field, metadata)
elif plot_type == "prob":
field, metadata = get_precipitation_fields(0, 10, True, True, None,
source)
field, metadata = conversion.to_rainrate(field, metadata)
field = ensemblestats.excprob(field, probthr)
field_orig = field.copy()
ax = plot_precip_field(
field.copy(),
type=plot_type,
bbox=bbox,
geodata=None,
colorscale=colorscale,
probthr=probthr,
units=metadata["unit"],
title=title,
colorbar=colorbar,
axis=axis,
)
# Check that plot_precip_field does not modify the input data
field_orig = np.ma.masked_invalid(field_orig)
field_orig.data[field_orig.mask] = -100
field = np.ma.masked_invalid(field)
field.data[field.mask] = -100
assert np.array_equal(field_orig.data, field.data)
def worker(lt, scale_km):
lt_idx = int(lt / v_accu_min - 1)
if not np.any(np.isfinite(R_obs_c[lt_idx, :, :])):
return
R_fct_s, _ = aggregate_fields_space(R_fct_c,
metafct_c,
scale_km * 1000,
ignore_nan=False)
R_obs_s, _ = aggregate_fields_space(R_obs_c,
metaobs_c,
scale_km * 1000,
ignore_nan=False)
for R_thr in R_thrs:
P_fct = excprob(R_fct_s[:, lt_idx, :, :],
R_thr,
ignore_nan=True)
probscores.reldiag_accum(
results[c, m]["reldiag"][R_thr][scale_km][lt],
P_fct, R_obs_s[lt_idx, :, :])
ensscores.rankhist_accum(
results[c, m]["rankhist"][R_thr][scale_km][lt],
R_fct_s[:, lt_idx, :, :], R_obs_s[lt_idx, :, :])
probscores.ROC_curve_accum(
results[c, m]["ROC"][R_thr][scale_km][lt], P_fct,
R_obs_s[lt_idx, :, :])
# Only for lowest threshold
probscores.CRPS_accum(
results[c, m]["CRPS"][R_thrs[0]][scale_km][lt],
R_fct_s[:, lt_idx, :, :], R_obs_s[lt_idx, :, :])
results[c,m][skill_varname][R_thrs[0]][scale_km][lt]["sum"] +=\
ensscores.ensemble_skill(R_fct_s[:, lt_idx, :, :],R_obs_s[lt_idx, :, :],spread_skill_metric)
results[c,m][spread_varname][R_thrs[0]][scale_km][lt]["sum"] +=\
ensscores.ensemble_spread(R_fct_s[:, lt_idx, :, :],spread_skill_metric)
results[c, m][skill_varname][
R_thrs[0]][scale_km][lt]["n"] += 1
results[c, m][spread_varname][
R_thrs[0]][scale_km][lt]["n"] += 1
R_f_mean = np.mean(R_f[:, -1, :, :], axis=0)
pyplot.figure()
plot_precip_field(R_f_mean,
map="basemap",
geodata=metadata,
drawlonlatlines=True,
basemap_resolution='h')
pyplot.savefig("STEPS_ensemble_mean.png", bbox_inches="tight", dpi=300)
pyplot.figure()
plot_precip_field(R_f[0, -1, :, :],
map="basemap",
geodata=metadata,
drawlonlatlines=True,
basemap_resolution='h')
pyplot.savefig("STEPS_ensemble_member.png", bbox_inches="tight", dpi=300)
pyplot.figure()
P = excprob(R_f[:, -1, :, :], 0.5)
plot_precip_field(P,
map="basemap",
geodata=metadata,
drawlonlatlines=True,
basemap_resolution='h',
type="prob",
units="mm/h",
probthr=0.5)
pyplot.savefig("STEPS_excprob_0.5.png", bbox_inches="tight", dpi=300)
def test_exceptions_excprob(X):
with pytest.raises(Exception):
excprob(X, 2.0)
def test_ensemblestats_excprob(X, X_thr, ignore_nan, expected):
"""Test ensemblestats excprob."""
assert_array_almost_equal(excprob(X, X_thr, ignore_nan), expected)
def test_ROC_curve_area(X_f, X_o, X_min, n_prob_thrs, compute_area, expected):
"""Test the ROC_curve."""
P_f = excprob(X_f, X_min, ignore_nan=False)
assert_array_almost_equal(
probscores.ROC_curve(P_f, X_o, X_min, n_prob_thrs, compute_area)[2],
expected)
def test_reldiag_sum(X_f, X_o, X_min, n_bins, min_count, expected):
"""Test the reldiag."""
P_f = excprob(X_f, X_min, ignore_nan=False)
assert_array_almost_equal(
np.sum(probscores.reldiag(P_f, X_o, X_min, n_bins, min_count)[1]),
expected)
fn_ext,
5,
0,
num_next_files=12)
# read the observations
Z_obs, _, metadata = io.read_timeseries(fns,
import_fmi_pgm,
gzipped=True,
num_next_files=12)
R_obs = conversion.to_rainrate(Z_obs, metadata, 223.0, 1.53)[0]
# iterate over the nowcast lead times
for lt in range(R_f.shape[1]):
# compute the exceedance probability of 0.1 mm/h from the ensemble
P_f = ensemblestats.excprob(R_f[:, lt, :, :], 0.1, ignore_nan=True)
# compute and plot the ROC curve
roc = verification.ROC_curve_init(0.1, n_prob_thrs=10)
verification.ROC_curve_accum(roc, P_f, R_obs[lt + 1, :, :])
fig = figure()
verification.plot_ROC(roc, ax=fig.gca(), opt_prob_thr=True)
pyplot.savefig("ROC_%02d.eps" % (lt + 1), bbox_inches="tight")
pyplot.close()
# compute and plot the reliability diagram
reldiag = verification.reldiag_init(0.1)
verification.reldiag_accum(reldiag, P_f, R_obs[lt + 1, :, :])
fig = figure()
verification.plot_reldiag(reldiag, ax=fig.gca())
pyplot.savefig("reldiag_%02d.eps" % (lt + 1), bbox_inches="tight")
