def test_completeOMIReproduction_coarsegrid(tmp_path):
errors = []
# Calculate EOFs
raw_olr = olr.load_noaa_interpolated_olr(olr_data_filename)
shorter_olr = olr.restrict_time_coverage(raw_olr,
np.datetime64('1979-01-01'),
np.datetime64('2012-12-31'))
coarse_lat = np.arange(-20., 20.1, 8.0)
coarse_long = np.arange(0., 359.9, 20.0)
interpolated_olr = olr.interpolate_spatial_grid(shorter_olr, coarse_lat,
coarse_long)
eofs = omi.calc_eofs_from_olr(interpolated_olr,
sign_doy1reference=True,
interpolate_eofs=True,
strict_leap_year_treatment=True)
eofs.save_all_eofs_to_npzfile(
tmp_path / "test_completeOMIReproduction_coarsegrid_EOFs.npz")
# Validate EOFs against mjoindices own reference (results should be equal)
mjoindices_reference_eofs = eof.restore_all_eofs_from_npzfile(
mjoindices_reference_eofs_filename_coarsegrid)
for idx, target_eof in enumerate(eofs.eof_list):
if not mjoindices_reference_eofs.eof_list[idx].close(target_eof):
errors.append(
"mjoindices-reference-validation: EOF data at index %i is incorrect"
% idx)
# Calculate PCs
raw_olr = olr.load_noaa_interpolated_olr(olr_data_filename)
pcs = omi.calculate_pcs_from_olr(raw_olr,
eofs,
np.datetime64("1979-01-01"),
np.datetime64("2018-08-28"),
use_quick_temporal_filter=False)
pc_temp_file = tmp_path / "test_completeOMIReproduction_coarsegrid_PCs.txt"
pcs.save_pcs_to_txt_file(pc_temp_file)
# Validate PCs against mjoindices own reference (results should be equal)
# Reload pcs instead of using the calculated ones, because the saving routine has truncated some decimals of the
# reference values. So do the same with the testing target pcs.
pcs = pc.load_pcs_from_txt_file(pc_temp_file)
mjoindices_reference_pcs = pc.load_pcs_from_txt_file(
mjoindices_reference_pcs_filename_coarsegrid)
if not np.all(mjoindices_reference_pcs.time == pcs.time):
errors.append(
"mjoindices-reference-validation: Dates of PCs do not match.")
if not np.allclose(mjoindices_reference_pcs.pc1, pcs.pc1):
errors.append(
"mjoindices-reference-validation: PC1 values do not match.")
if not np.allclose(mjoindices_reference_pcs.pc2, pcs.pc2):
errors.append(
"mjoindices-reference-validation: PC2 values do not match.")
assert not errors, "errors occurred:\n{}".format("\n".join(errors))
def generate_reference_data_for_pcquick_filter_tests():
orig_long = np.arange(0., 359.9, 2.5)
test_olr = olr.load_noaa_interpolated_olr(olr_data_filename)
lat = np.array([0])
test_olr_part = olr.interpolate_spatial_grid(test_olr, lat, orig_long)
olrdata_filtered = qfilter.filter_olr_for_mjo_pc_calculation_1d_spectral_smoothing(
test_olr_part)
filename = Path(
str(reference_file_filterOLRForMJO_PCQuick_Calculation_lat0) +
".newcalc")
olrdata_filtered.save_to_npzfile(filename)
lat = np.array([5])
test_olr_part = olr.interpolate_spatial_grid(test_olr, lat, orig_long)
olrdata_filtered = qfilter.filter_olr_for_mjo_pc_calculation_1d_spectral_smoothing(
test_olr_part)
filename = Path(
str(reference_file_filterOLRForMJO_PCQuick_Calculation_lat5) +
".newcalc")
olrdata_filtered.save_to_npzfile(filename)
lat = np.array([-10])
test_olr_part = olr.interpolate_spatial_grid(test_olr, lat, orig_long)
olrdata_filtered = qfilter.filter_olr_for_mjo_pc_calculation_1d_spectral_smoothing(
test_olr_part)
filename = Path(
str(reference_file_filterOLRForMJO_PCQuick_Calculation_latmin10) +
".newcalc")
olrdata_filtered.save_to_npzfile(filename)
def test_calculatePCsFromOLRWithOriginalEOFs(use_quick_temporal_filter,
expectedCorr1, expectedCorr2):
# This test is quicker than the complete integration tests below. The quality check is very simple and just checks
# the correlations of the PC time series. Hence, this test in thought the get a first idea and more intensive testing
# should be done using the tests below.
orig_omi = pc.load_original_pcs_from_txt_file(origOMIPCsFilename)
olrData = olr.load_noaa_interpolated_olr(olr_data_filename)
target = omi.calculate_pcs_from_olr_original_conditions(
olrData,
originalOMIDataDirname,
use_quick_temporal_filter=use_quick_temporal_filter)
errors = []
if not np.all(target.time == orig_omi.time):
errors.append(
"Test is not reasonable, because temporal coverages of original OMI and recalculation do not "
"fit. Maybe wrong original file downloaded? Supported is the one with coverage until August 28, "
"2018.")
corr1 = (np.corrcoef(orig_omi.pc1, target.pc1))[0, 1]
if not corr1 > expectedCorr1:
errors.append("Correlation of PC1 too low!")
corr2 = (np.corrcoef(orig_omi.pc2, target.pc2))[0, 1]
if not corr2 > expectedCorr2:
errors.append("Correlation of PC2 too low!")
assert not errors, "errors occurred:\n{}".format("\n".join(errors))
def test_mjoindices_reference_validation_filterOLRForMJO_PC_Calculation():
errors = []
orig_long = np.arange(0., 359.9, 2.5)
test_olr = olr.load_noaa_interpolated_olr(olr_data_filename)
lat = np.array([0])
test_olr_part = olr.interpolate_spatial_grid(test_olr, lat, orig_long)
target = wkfilter.filter_olr_for_mjo_pc_calculation(test_olr_part)
control = olr.restore_from_npzfile(reference_file_filterOLRForMJO_PC_Calculation_lat0)
if not target.close(control):
errors.append("Filtered OLR for latitude 0 not identical")
lat = np.array([5])
test_olr_part = olr.interpolate_spatial_grid(test_olr, lat, orig_long)
target = wkfilter.filter_olr_for_mjo_pc_calculation(test_olr_part)
control = olr.restore_from_npzfile(reference_file_filterOLRForMJO_PC_Calculation_lat5)
if not target.close(control):
errors.append("Filtered OLR for latitude 5 not identical")
lat = np.array([-10])
test_olr_part = olr.interpolate_spatial_grid(test_olr, lat, orig_long)
target = wkfilter.filter_olr_for_mjo_pc_calculation(test_olr_part)
control = olr.restore_from_npzfile(reference_file_filterOLRForMJO_PC_Calculation_latmin10)
if not target.close(control):
errors.append("Filtered OLR for latitude -10 not identical")
assert not errors, "errors occurred:\n{}".format("\n".join(errors))
eofnpzfile = Path(os.path.abspath('')) / "example_data" / "EOFs_coarsegrid.npz"
# The PCs
pctxtfile = Path(os.path.abspath('')) / "example_data" / "PCs_coarsegrid.txt"
# Directory in which the figures are saved.
fig_dir = Path(os.path.abspath(
'')) / "example_data" / "omi_recalc_example_plots_coarsegrid"
# ############## Calculation of the EOFs ###################
if not fig_dir.exists():
fig_dir.mkdir(parents=True, exist_ok=False)
# Load the OLR data.
# This is the first place to insert your own OLR data, if you want to compute OMI for a different dataset.
raw_olr = olr.load_noaa_interpolated_olr(olr_data_filename)
# Restrict dataset to the original length for the EOF calculation (Kiladis, 2014).
shorter_olr = olr.restrict_time_coverage(raw_olr, np.datetime64('1979-01-01'),
np.datetime64('2012-12-31'))
# This is the line, where the spatial grid is changed.
interpolated_olr = olr.interpolate_spatial_grid(shorter_olr, coarse_lat,
coarse_long)
# Diagnosis plot of the loaded OLR data.
fig = olr.plot_olr_map_for_date(interpolated_olr, np.datetime64("2010-01-01"))
fig.show()
fig.savefig(fig_dir / "OLR_map.png")
# Calculate the eofs. In the postprocessing, the signs of the EOFs are adjusted and the EOF in a period
# around DOY 300 are replaced by an interpolation see Kiladis, 2014).
def test_completeOMIReproduction(tmp_path):
errors = []
# Calculate EOFs
raw_olr = olr.load_noaa_interpolated_olr(olr_data_filename)
shorter_olr = olr.restrict_time_coverage(raw_olr,
np.datetime64('1979-01-01'),
np.datetime64('2012-12-31'))
interpolated_olr = olr.interpolate_spatial_grid_to_original(shorter_olr)
eofs = omi.calc_eofs_from_olr(interpolated_olr,
sign_doy1reference=True,
interpolate_eofs=True,
strict_leap_year_treatment=False)
eofs.save_all_eofs_to_npzfile(tmp_path /
"test_completeOMIReproduction_EOFs.npz")
# Validate EOFs against original (results are inexact but close)
orig_eofs = eof.load_all_original_eofs_from_directory(
originalOMIDataDirname)
corr_1, diff_mean_1, diff_std_1, diff_abs_percent68_1, diff_abs_percent95_1, diff_abs_percent99_1 = mjoindices.evaluation_tools.calc_comparison_stats_for_eofs_all_doys(
orig_eofs,
eofs,
1,
exclude_doy366=False,
percentage=False,
do_print=False)
if not np.all(corr_1 > 0.994):
errors.append(
"original-validation: Correlation for EOF1 at least for one DOY too low!"
)
if not np.all(diff_abs_percent99_1 < 0.0084):
errors.append(
"original-validation: 99% percentile for EOF1 at least for one DOY too high!"
)
if not np.all(diff_abs_percent68_1 < 0.0018):
errors.append(
"original-validation: 68% percentile for EOF1 at least for one DOY too high!"
)
corr_2, diff_mean_2, diff_std_2, diff_abs_percent68_2, diff_abs_percent95_2, diff_abs_percent99_2 = mjoindices.evaluation_tools.calc_comparison_stats_for_eofs_all_doys(
orig_eofs,
eofs,
2,
exclude_doy366=False,
percentage=False,
do_print=False)
if not np.all(corr_2 > 0.993):
errors.append(
"original-validation: Correlation for EOF2 at least for one DOY too low!"
)
if not np.all(diff_abs_percent99_2 < 0.0065):
errors.append(
"original-validation: 99% percentile for EOF2 at least for one DOY too high!"
)
if not np.all(diff_abs_percent68_2 < 0.0018):
errors.append(
"original-validation: 68% percentile for EOF2 at least for one DOY too high!"
)
# Validate explained variance against original (results are inexact but close)
orig_explained_variance_1, orig_explained_variance_2 = mjoindices.evaluation_tools.load_omi_explained_variance(
original_omi_explained_variance_file)
corr_var1, diff_mean_var1, diff_std_var1, diff_vec_var1, diff_abs_percent68_var1, diff_abs_percent95_var1, diff_abs_percent99_var1 = mjoindices.evaluation_tools.calc_comparison_stats_for_explained_variance(
orig_explained_variance_1,
eofs.explained_variance1_for_all_doys(),
do_print=False,
exclude_doy366=False)
if not diff_std_var1 < 0.0008:
errors.append(
"original-validation: Std.Dev. of the difference of both explained variances for EOF1 is to too high!"
)
if not diff_abs_percent99_var1 < 0.0017:
errors.append(
"original-validation: 99% percentile of the difference of both explained variances for EOF1 is to too high!"
)
if not diff_abs_percent68_var1 < 0.0009:
errors.append(
"original-validation: 68% percentile of the difference of both explained variances for EOF1 is to too high!"
)
corr_var2, diff_mean_var2, diff_std_var2, diff_vec_var2, diff_abs_percent68_var2, diff_abs_percent95_var2, diff_abs_percent99_var2 = mjoindices.evaluation_tools.calc_comparison_stats_for_explained_variance(
orig_explained_variance_2,
eofs.explained_variance2_for_all_doys(),
do_print=False,
exclude_doy366=False)
if not diff_std_var2 < 0.0008:
errors.append(
"original-validation: Std.Dev. of the difference of both explained variances for EOF2 is to too high!"
)
if not diff_abs_percent99_var2 < 0.0018:
errors.append(
"original-validation: 99% percentile of the difference of both explained variances for EOF2 is to too high!"
)
if not diff_abs_percent68_var2 < 0.001:
errors.append(
"original-validation: 68% percentile of the difference of both explained variances for EOF2 is to too high!"
)
# Validate EOFs against mjoindices own reference (results should be equal)
mjoindices_reference_eofs = eof.restore_all_eofs_from_npzfile(
mjoindices_reference_eofs_filename)
for idx, target_eof in enumerate(eofs.eof_list):
if not mjoindices_reference_eofs.eof_list[idx].close(target_eof):
errors.append(
"mjoindices-reference-validation: EOF data at index %i is incorrect"
% idx)
# Calculate PCs
raw_olr = olr.load_noaa_interpolated_olr(olr_data_filename)
pcs = omi.calculate_pcs_from_olr(raw_olr,
eofs,
np.datetime64("1979-01-01"),
np.datetime64("2018-08-28"),
use_quick_temporal_filter=False)
pc_temp_file = tmp_path / "test_completeOMIReproduction_PCs.txt"
pcs.save_pcs_to_txt_file(pc_temp_file)
# Validate PCs against original (results are inexact but close)
orig_pcs = pc.load_original_pcs_from_txt_file(origOMIPCsFilename)
# Reload pcs instead of using the calculated ones, because the saving routine has truncated some decimals of the
# reference values. So do the same with the testing target pcs.
pcs = pc.load_pcs_from_txt_file(pc_temp_file)
tempa, tempb, corr_pc1, diff_mean_pc1, diff_std_pc1, diff_ts_abs_pc1, diff_abs_percent68_pc1, diff_abs_percent95_pc1, diff_abs_percent99_pc1 = mjoindices.evaluation_tools.calc_timeseries_agreement(
orig_pcs.pc1,
orig_pcs.time,
pcs.pc1,
pcs.time,
exclude_doy366=False,
do_print=False)
if not corr_pc1 > 0.998:
errors.append(
"original-validation: Correlation for PC1 timeseries is to too low!"
)
if not diff_std_pc1 < 0.0458:
errors.append(
"original-validation: Std.Dev. of the difference of both PC1 timeseries is to too high!"
)
if not diff_abs_percent99_pc1 < 0.157:
errors.append(
"original-validation: 99% percentile of the difference of both PC1 timeseries is to too high!"
)
if not diff_abs_percent68_pc1 < 0.0327:
errors.append(
"original-validation: 68% percentile of the difference of both PC1 timeseries is to too high!"
)
tempa, tempb, corr_pc2, diff_mean_pc2, diff_std_pc2, diff_ts_abs_pc2, diff_abs_percent68_pc2, diff_abs_percent95_pc2, diff_abs_percent99_pc2 = mjoindices.evaluation_tools.calc_timeseries_agreement(
orig_pcs.pc2,
orig_pcs.time,
pcs.pc2,
pcs.time,
exclude_doy366=False,
do_print=False)
if not corr_pc2 > 0.998:
errors.append(
"original-validation: Correlation for PC2 timeseries is to too low!"
)
if not diff_std_pc2 < 0.0488:
errors.append(
"original-validation: Std.Dev. of the difference of both PC2 timeseries is to too high!"
)
if not diff_abs_percent99_pc2 < 0.1704:
errors.append(
"original-validation: 99% percentile of the difference of both PC2 timeseries is to too high!"
)
if not diff_abs_percent68_pc2 < 0.0353:
errors.append(
"original-validation: 68% percentile of the difference of both PC2 timeseries is to too high!"
)
strength = np.sqrt(np.square(pcs.pc1) + np.square(pcs.pc2))
orig_strength = np.sqrt(np.square(orig_pcs.pc1) + np.square(orig_pcs.pc2))
tempa, tempb, corr_strength, diff_mean_strength, diff_std_strength, diff_ts_abs_strength, diff_abs_percent68_strength, diff_abs_percent95_strength, diff_abs_percent99_strength = mjoindices.evaluation_tools.calc_timeseries_agreement(
orig_strength,
orig_pcs.time,
strength,
pcs.time,
exclude_doy366=False,
do_print=False)
if not corr_strength > 0.9998:
errors.append(
"original-validation: Correlation for strength timeseries is to too low!"
)
if not diff_std_strength < 0.0103:
errors.append(
"original-validation: Std.Dev. of the difference of both strength timeseries is to too high!"
)
if not diff_abs_percent99_strength < 0.0341:
errors.append(
"original-validation: 99% percentile of the difference of both strength timeseries is to too high!"
)
if not diff_abs_percent68_strength < 0.0079:
errors.append(
"original-validation: 68% percentile of the difference of both strength timeseries is to too high!"
)
# Validate PCs against mjoindices own reference (results should be equal)
mjoindices_reference_pcs = pc.load_pcs_from_txt_file(
mjoindices_reference_pcs_filename)
if not np.all(mjoindices_reference_pcs.time == pcs.time):
errors.append(
"mjoindices-reference-validation: Dates of PCs do not match.")
if not np.allclose(mjoindices_reference_pcs.pc1, pcs.pc1):
errors.append(
"mjoindices-reference-validation: PC1 values do not match.")
if not np.allclose(mjoindices_reference_pcs.pc2, pcs.pc2):
errors.append(
"mjoindices-reference-validation: PC2 values do not match.")
assert not errors, "errors occurred:\n{}".format("\n".join(errors))
