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Bug fixes and notebook updates #340

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merged 3 commits into from
Nov 1, 2024
Merged

Bug fixes and notebook updates #340

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7f95e1c
agg dimensions bug and check metrics formatting
allibco Oct 31, 2024
5f6650a
updates to notebooks
allibco Nov 1, 2024
a85d551
Notebook text changes
allibco Nov 1, 2024
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511 changes: 409 additions & 102 deletions docs/source/notebooks/CAMNotebook.ipynb
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635 changes: 326 additions & 309 deletions docs/source/notebooks/LargeDataGladeNotebook.ipynb
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220 changes: 125 additions & 95 deletions docs/source/notebooks/MetricsNotebook.ipynb
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117 changes: 54 additions & 63 deletions docs/source/notebooks/TutorialNotebook.ipynb
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541 changes: 269 additions & 272 deletions docs/source/notebooks/error_bias.ipynb
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1 change: 1 addition & 0 deletions ldcpy/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@
open_datasets,
save_metrics,
subset_data,
combine_datasets,
)

try:
Expand Down
10 changes: 9 additions & 1 deletion ldcpy/calcs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1943,11 +1943,19 @@ def ks_p_value(self):
if not self._is_memoized('_ks_p_value'):
# Apply the KS test across the specified dimensions
# This will create a DataArray of p-values

if self._aggregate_dims is None:
lat_n = self._ds1.cf['latitude'].name
lon_n = self._ds2.cf['longitude'].name
core_d = [[lat_n, lon_n], [lat_n, lon_n]]
else:
core_d = [self._aggregate_dims, self._aggregate_dims]
# print(core_d)
self._ks_p_value = xr.apply_ufunc(
lambda x, y: ss.ks_2samp(x.ravel(), y.ravel())[1],
self._ds1,
self._ds2,
input_core_dims=[self._aggregate_dims, self._aggregate_dims],
input_core_dims=core_d,
dask='parallelized',
dask_gufunc_kwargs={'allow_rechunk': True},
vectorize=True,
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32 changes: 21 additions & 11 deletions ldcpy/util.py
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Original file line number Diff line number Diff line change
Expand Up @@ -487,7 +487,6 @@ def compare_stats(
else:
temp_zeros.append(temp_return)

# Alex is fixing ..
temp_info.append(da_set_calcs[i].get_single_calc('real_information_cutoff'))

if data_type == 'cam-fv':
Expand Down Expand Up @@ -593,7 +592,10 @@ def compare_stats(
for i in range(num - 1):
temp_nrms.append(diff_calcs[i].get_diff_calc('n_rms'))
temp_max_pe.append(diff_calcs[i].get_diff_calc('n_emax'))
temp_pcc.append(diff_calcs[i].get_diff_calc('pearson_correlation_coefficient'))

temptemp = diff_calcs[i].get_diff_calc('pearson_correlation_coefficient')
temp_pcc.append(temptemp)

temp_ks.append(diff_calcs[i].get_diff_calc('ks_p_value'))
diff_calcs[i].spre_tol = rel_errors[0]
temp_sre.append(diff_calcs[i].get_diff_calc('spatial_rel_error'))
Expand All @@ -610,7 +612,6 @@ def compare_stats(
df_dict2['normalized max pointwise error'] = temp_max_pe
df_dict2['pearson correlation coefficient'] = temp_pcc
df_dict2['ks p-value'] = temp_ks

tmp_str = 'spatial relative error(% > ' + str(rel_errors[0]) + ')'
df_dict2[tmp_str] = temp_sre

Expand Down Expand Up @@ -717,28 +718,37 @@ def check_metrics(
num_fail = 0
# Pearson less than pcc_tol means fail
pcc = diff_calcs.get_diff_calc('pearson_correlation_coefficient')
# print(type(pcc))
if pcc < pcc_tol:

print(' *FAILED pearson correlation coefficient test...(pcc = {0:.5f}'.format(pcc), ')')
print(
' *FAILED pearson correlation coefficient test...(pcc = {0:.5f}'.format(pcc.values),
')',
)
num_fail = num_fail + 1
else:
print(' PASSED pearson correlation coefficient test...(pcc = {0: .5f}'.format(pcc), ')')
print(
' PASSED pearson correlation coefficient test...(pcc = {0:.5f}'.format(pcc.values),
')',
)
# K-S p-value less than ks_tol means fail (can reject null hypo)
ks = diff_calcs.get_diff_calc('ks_p_value')
if ks < ks_tol:
print(' *FAILED ks test...(ks p_val = {0:.4f}'.format(ks), ')')
print(' *FAILED ks test...(ks p_val = {0:.5f}'.format(ks.values), ')')
num_fail = num_fail + 1
else:
print(' PASSED ks test...(ks p_val = {0:.4f}'.format(ks), ')')
print(' PASSED ks test...(ks p_val = {0:.5f}'.format(ks.values), ')')
# Spatial rel error fails if more than spre_tol
spre = diff_calcs.get_diff_calc('spatial_rel_error')

if spre > spre_tol:
print(' *FAILED spatial relative error test ... (spre = {0:.2f}'.format(spre), ' %)')
print(
' *FAILED spatial relative error test ... (spre = {0:.2f}'.format(spre.values),
' %)',
)
num_fail = num_fail + 1
else:
print(' PASSED spatial relative error test ...(spre = {0:.2f}'.format(spre), ' %)')
print(
' PASSED spatial relative error test ...(spre = {0:.2f}'.format(spre.values), ' %)'
)
# SSIM less than of ssim_tol is failing
ssim_val = diff_calcs.get_diff_calc('ssim_fp')
if ssim_val < ssim_tol:
Expand Down
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