def test_validation_n3_k2_temporal_matching_no_matches():
tst_results = {}
datasets = setup_two_without_overlap()
dm = DataManager(
datasets,
"DS1",
read_ts_names={d: "read" for d in ["DS1", "DS2", "DS3"]},
)
process = Validation(
dm,
"DS1",
temporal_matcher=temporal_matchers.BasicTemporalMatching(
window=1 / 24.0
).combinatory_matcher,
scaling="lin_cdf_match",
metrics_calculators={
(3, 2): metrics_calculators.BasicMetrics(
other_name="k1"
).calc_metrics
},
)
jobs = process.get_processing_jobs()
for job in jobs:
results = process.calc(*job)
assert sorted(list(results)) == sorted(list(tst_results))
def test_validation_n3_k2_temporal_matching_no_matches2():
tst_results = {
(("DS1", "x"), ("DS3", "y")): {
"n_obs": np.array([1000], dtype=np.int32),
"tau": np.array([np.nan], dtype=np.float32),
"gpi": np.array([4], dtype=np.int32),
"RMSD": np.array([0.0], dtype=np.float32),
"lon": np.array([4.0]),
"p_tau": np.array([np.nan], dtype=np.float32),
"BIAS": np.array([0.0], dtype=np.float32),
"p_rho": np.array([0.0], dtype=np.float32),
"rho": np.array([1.0], dtype=np.float32),
"lat": np.array([4.0]),
"R": np.array([1.0], dtype=np.float32),
"p_R": np.array([0.0], dtype=np.float32),
},
(("DS1", "x"), ("DS3", "x")): {
"n_obs": np.array([1000], dtype=np.int32),
"tau": np.array([np.nan], dtype=np.float32),
"gpi": np.array([4], dtype=np.int32),
"RMSD": np.array([0.0], dtype=np.float32),
"lon": np.array([4.0]),
"p_tau": np.array([np.nan], dtype=np.float32),
"BIAS": np.array([0.0], dtype=np.float32),
"p_rho": np.array([0.0], dtype=np.float32),
"rho": np.array([1.0], dtype=np.float32),
"lat": np.array([4.0]),
"R": np.array([1.0], dtype=np.float32),
"p_R": np.array([0.0], dtype=np.float32),
},
}
datasets = setup_three_with_two_overlapping()
dm = DataManager(
datasets,
"DS1",
read_ts_names={d: "read" for d in ["DS1", "DS2", "DS3"]},
)
process = Validation(
dm,
"DS1",
temporal_matcher=temporal_matchers.BasicTemporalMatching(
window=1 / 24.0
).combinatory_matcher,
scaling="lin_cdf_match",
metrics_calculators={
(3, 2): metrics_calculators.BasicMetrics(
other_name="k1"
).calc_metrics
},
)
jobs = process.get_processing_jobs()
for job in jobs:
results = process.calc(*job)
assert sorted(list(results)) == sorted(list(tst_results))
def test_validation_n3_k2():
tst_results = {
(('DS1', 'x'), ('DS3', 'y')): {
'n_obs': np.array([1000], dtype=np.int32),
'tau': np.array([np.nan], dtype=np.float32),
'gpi': np.array([4], dtype=np.int32),
'RMSD': np.array([0.], dtype=np.float32),
'lon': np.array([4.]),
'p_tau': np.array([np.nan], dtype=np.float32),
'BIAS': np.array([0.], dtype=np.float32),
'p_rho': np.array([0.], dtype=np.float32),
'rho': np.array([1.], dtype=np.float32),
'lat': np.array([4.]),
'R': np.array([1.], dtype=np.float32),
'p_R': np.array([0.], dtype=np.float32)},
(('DS1', 'x'), ('DS2', 'y')): {
'n_obs': np.array([1000], dtype=np.int32),
'tau': np.array([np.nan], dtype=np.float32),
'gpi': np.array([4], dtype=np.int32),
'RMSD': np.array([0.], dtype=np.float32),
'lon': np.array([4.]),
'p_tau': np.array([np.nan], dtype=np.float32),
'BIAS': np.array([0.], dtype=np.float32),
'p_rho': np.array([0.], dtype=np.float32),
'rho': np.array([1.], dtype=np.float32),
'lat': np.array([4.]),
'R': np.array([1.], dtype=np.float32),
'p_R': np.array([0.], dtype=np.float32)},
(('DS1', 'x'), ('DS3', 'x')): {
'n_obs': np.array([1000], dtype=np.int32),
'tau': np.array([np.nan], dtype=np.float32),
'gpi': np.array([4], dtype=np.int32),
'RMSD': np.array([0.], dtype=np.float32),
'lon': np.array([4.]),
'p_tau': np.array([np.nan], dtype=np.float32),
'BIAS': np.array([0.], dtype=np.float32),
'p_rho': np.array([0.], dtype=np.float32),
'rho': np.array([1.], dtype=np.float32),
'lat': np.array([4.]),
'R': np.array([1.], dtype=np.float32),
'p_R': np.array([0.], dtype=np.float32)}}
datasets = setup_TestDatasets()
process = Validation(
datasets, 'DS1',
temporal_matcher=temporal_matchers.BasicTemporalMatching(
window=1 / 24.0).combinatory_matcher,
scaling='lin_cdf_match',
metrics_calculators={
(3, 2): metrics_calculators.BasicMetrics(other_name='k1').calc_metrics})
jobs = process.get_processing_jobs()
for job in jobs:
results = process.calc(*job)
assert sorted(list(results)) == sorted(list(tst_results))
def test_validation_n3_k2():
tst_results = {
(('DS1', 'x'), ('DS3', 'y')): {
'n_obs': np.array([1000], dtype=np.int32),
'tau': np.array([np.nan], dtype=np.float32),
'gpi': np.array([4], dtype=np.int32),
'RMSD': np.array([0.], dtype=np.float32),
'lon': np.array([4.]),
'p_tau': np.array([np.nan], dtype=np.float32),
'BIAS': np.array([0.], dtype=np.float32),
'p_rho': np.array([0.], dtype=np.float32),
'rho': np.array([1.], dtype=np.float32),
'lat': np.array([4.]),
'R': np.array([1.], dtype=np.float32),
'p_R': np.array([0.], dtype=np.float32)},
(('DS1', 'x'), ('DS2', 'y')): {
'n_obs': np.array([1000], dtype=np.int32),
'tau': np.array([np.nan], dtype=np.float32),
'gpi': np.array([4], dtype=np.int32),
'RMSD': np.array([0.], dtype=np.float32),
'lon': np.array([4.]),
'p_tau': np.array([np.nan], dtype=np.float32),
'BIAS': np.array([0.], dtype=np.float32),
'p_rho': np.array([0.], dtype=np.float32),
'rho': np.array([1.], dtype=np.float32),
'lat': np.array([4.]),
'R': np.array([1.], dtype=np.float32),
'p_R': np.array([0.], dtype=np.float32)},
(('DS1', 'x'), ('DS3', 'x')): {
'n_obs': np.array([1000], dtype=np.int32),
'tau': np.array([np.nan], dtype=np.float32),
'gpi': np.array([4], dtype=np.int32),
'RMSD': np.array([0.], dtype=np.float32),
'lon': np.array([4.]),
'p_tau': np.array([np.nan], dtype=np.float32),
'BIAS': np.array([0.], dtype=np.float32),
'p_rho': np.array([0.], dtype=np.float32),
'rho': np.array([1.], dtype=np.float32),
'lat': np.array([4.]),
'R': np.array([1.], dtype=np.float32),
'p_R': np.array([0.], dtype=np.float32)}}
datasets = setup_TestDatasets()
process = Validation(
datasets, 'DS1',
temporal_matcher=temporal_matchers.BasicTemporalMatching(
window=1 / 24.0).combinatory_matcher,
scaling='lin_cdf_match',
metrics_calculators={
(3, 2): metrics_calculators.BasicMetrics(other_name='k1').calc_metrics})
jobs = process.get_processing_jobs()
for job in jobs:
results = process.calc(*job)
assert sorted(list(results)) == sorted(list(tst_results))
def test_validation_n3_k2_masking_no_data_remains():
datasets = setup_TestDatasets()
# setup masking datasets
grid = grids.CellGrid(np.array([1, 2, 3, 4]), np.array([1, 2, 3, 4]),
np.array([4, 4, 2, 1]), gpis=np.array([1, 2, 3, 4]))
mds1 = GriddedTsBase("", grid, MaskingTestDataset)
mds2 = GriddedTsBase("", grid, MaskingTestDataset)
mds = {
'masking1': {
'class': mds1,
'columns': ['x'],
'args': [],
'kwargs': {'limit': 500},
'use_lut': False,
'grids_compatible': True},
'masking2': {
'class': mds2,
'columns': ['x'],
'args': [],
'kwargs': {'limit': 1000},
'use_lut': False,
'grids_compatible': True}
}
process = Validation(
datasets, 'DS1',
temporal_matcher=temporal_matchers.BasicTemporalMatching(
window=1 / 24.0).combinatory_matcher,
scaling='lin_cdf_match',
metrics_calculators={
(3, 2): metrics_calculators.BasicMetrics(other_name='k1').calc_metrics},
masking_datasets=mds)
gpi_info = (1, 1, 1)
ref_df = datasets['DS1']['class'].read(1)
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
new_ref_df = process.mask_dataset(ref_df, gpi_info)
assert len(new_ref_df) == 0
nptest.assert_allclose(new_ref_df.x.values, np.arange(1000, 1000))
jobs = process.get_processing_jobs()
for job in jobs:
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
results = process.calc(*job)
tst = []
assert sorted(list(results)) == sorted(list(tst))
for key, tst_key in zip(sorted(results),
sorted(tst)):
nptest.assert_almost_equal(results[key]['n_obs'],
tst[tst_key]['n_obs'])
def test_validation_n3_k2_masking_no_data_remains():
datasets = setup_TestDatasets()
# setup masking datasets
grid = grids.CellGrid(np.array([1, 2, 3, 4]), np.array([1, 2, 3, 4]),
np.array([4, 4, 2, 1]), gpis=np.array([1, 2, 3, 4]))
mds1 = GriddedTsBase("", grid, MaskingTestDataset)
mds2 = GriddedTsBase("", grid, MaskingTestDataset)
mds = {
'masking1': {
'class': mds1,
'columns': ['x'],
'args': [],
'kwargs': {'limit': 500},
'use_lut': False,
'grids_compatible': True},
'masking2': {
'class': mds2,
'columns': ['x'],
'args': [],
'kwargs': {'limit': 1000},
'use_lut': False,
'grids_compatible': True}
}
process = Validation(
datasets, 'DS1',
temporal_matcher=temporal_matchers.BasicTemporalMatching(
window=1 / 24.0).combinatory_matcher,
scaling='lin_cdf_match',
metrics_calculators={
(3, 2): metrics_calculators.BasicMetrics(other_name='k1').calc_metrics},
masking_datasets=mds)
gpi_info = (1, 1, 1)
ref_df = datasets['DS1']['class'].read_ts(1)
new_ref_df = process.mask_dataset(ref_df, gpi_info)
assert len(new_ref_df) == 0
nptest.assert_allclose(new_ref_df.x.values, np.arange(1000, 1000))
jobs = process.get_processing_jobs()
for job in jobs:
results = process.calc(*job)
tst = []
assert sorted(list(results)) == sorted(list(tst))
for key, tst_key in zip(sorted(results),
sorted(tst)):
nptest.assert_almost_equal(results[key]['n_obs'],
tst[tst_key]['n_obs'])
def test_validation_n2_k2_temporal_matching_no_matches():
tst_results = {}
datasets = setup_two_without_overlap()
process = Validation(
datasets, 'DS1',
temporal_matcher=temporal_matchers.BasicTemporalMatching(
window=1 / 24.0).combinatory_matcher,
scaling='lin_cdf_match',
metrics_calculators={
(2, 2): metrics_calculators.BasicMetrics(other_name='k1').calc_metrics})
jobs = process.get_processing_jobs()
for job in jobs:
results = process.calc(*job)
assert sorted(list(results)) == sorted(list(tst_results))
def test_validation_n2_k2_temporal_matching_no_matches():
tst_results = {}
datasets = setup_two_without_overlap()
process = Validation(
datasets, 'DS1',
temporal_matcher=temporal_matchers.BasicTemporalMatching(
window=1 / 24.0).combinatory_matcher,
scaling='lin_cdf_match',
metrics_calculators={
(2, 2): metrics_calculators.BasicMetrics(other_name='k1').calc_metrics})
jobs = process.get_processing_jobs()
for job in jobs:
results = process.calc(*job)
assert sorted(list(results)) == sorted(list(tst_results))
def test_validation_n3_k2_masking():
# test result for one gpi in a cell
tst_results_one = {
(('DS1', 'x'), ('DS3', 'y')): {
'n_obs': np.array([250], dtype=np.int32)},
(('DS1', 'x'), ('DS2', 'y')): {
'n_obs': np.array([250], dtype=np.int32)},
(('DS1', 'x'), ('DS3', 'x')): {
'n_obs': np.array([250], dtype=np.int32)}}
# test result for two gpis in a cell
tst_results_two = {
(('DS1', 'x'), ('DS3', 'y')): {
'n_obs': np.array([250, 250], dtype=np.int32)},
(('DS1', 'x'), ('DS2', 'y')): {
'n_obs': np.array([250, 250], dtype=np.int32)},
(('DS1', 'x'), ('DS3', 'x')): {
'n_obs': np.array([250, 250], dtype=np.int32)}}
# cell 4 in this example has two gpis so it returns different results.
tst_results = {1: tst_results_one,
1: tst_results_one,
2: tst_results_two}
datasets = setup_TestDatasets()
# setup masking datasets
grid = grids.CellGrid(np.array([1, 2, 3, 4]), np.array([1, 2, 3, 4]),
np.array([4, 4, 2, 1]), gpis=np.array([1, 2, 3, 4]))
mds1 = GriddedTsBase("", grid, MaskingTestDataset)
mds2 = GriddedTsBase("", grid, MaskingTestDataset)
mds = {
'masking1': {
'class': mds1,
'columns': ['x'],
'args': [],
'kwargs': {'limit': 500},
'use_lut': False,
'grids_compatible': True},
'masking2': {
'class': mds2,
'columns': ['x'],
'args': [],
'kwargs': {'limit': 750},
'use_lut': False,
'grids_compatible': True}
}
process = Validation(
datasets, 'DS1',
temporal_matcher=temporal_matchers.BasicTemporalMatching(
window=1 / 24.0).combinatory_matcher,
scaling='lin_cdf_match',
metrics_calculators={
(3, 2): metrics_calculators.BasicMetrics(other_name='k1').calc_metrics},
masking_datasets=mds)
gpi_info = (1, 1, 1)
ref_df = datasets['DS1']['class'].read_ts(1)
new_ref_df = process.mask_dataset(ref_df, gpi_info)
assert len(new_ref_df) == 250
nptest.assert_allclose(new_ref_df.x.values, np.arange(750, 1000))
jobs = process.get_processing_jobs()
for job in jobs:
results = process.calc(*job)
tst = tst_results[len(job[0])]
assert sorted(list(results)) == sorted(list(tst))
for key, tst_key in zip(sorted(results),
sorted(tst)):
nptest.assert_almost_equal(results[key]['n_obs'],
tst[tst_key]['n_obs'])
def test_validation_n3_k2_masking():
# test result for one gpi in a cell
tst_results_one = {
(("DS1", "x"), ("DS3", "y")): {
"n_obs": np.array([250], dtype=np.int32)
},
(("DS1", "x"), ("DS2", "y")): {
"n_obs": np.array([250], dtype=np.int32)
},
(("DS1", "x"), ("DS3", "x")): {
"n_obs": np.array([250], dtype=np.int32)
},
(("DS2", "y"), ("DS3", "x")): {
"n_obs": np.array([250], dtype=np.int32)
},
(("DS2", "y"), ("DS3", "y")): {
"n_obs": np.array([250], dtype=np.int32)
},
}
# test result for two gpis in a cell
tst_results_two = {
(("DS1", "x"), ("DS3", "y")): {
"n_obs": np.array([250, 250], dtype=np.int32)
},
(("DS1", "x"), ("DS2", "y")): {
"n_obs": np.array([250, 250], dtype=np.int32)
},
(("DS1", "x"), ("DS3", "x")): {
"n_obs": np.array([250, 250], dtype=np.int32)
},
(("DS2", "y"), ("DS3", "x")): {
"n_obs": np.array([250, 250], dtype=np.int32)
},
(("DS2", "y"), ("DS3", "y")): {
"n_obs": np.array([250, 250], dtype=np.int32)
},
}
# cell 4 in this example has two gpis so it returns different results.
tst_results = {1: tst_results_one, 1: tst_results_one, 2: tst_results_two}
datasets = setup_TestDatasets()
# setup masking datasets
grid = grids.CellGrid(
np.array([1, 2, 3, 4]),
np.array([1, 2, 3, 4]),
np.array([4, 4, 2, 1]),
gpis=np.array([1, 2, 3, 4]),
)
mds1 = GriddedTsBase("", grid, MaskingTestDataset)
mds2 = GriddedTsBase("", grid, MaskingTestDataset)
mds = {
"masking1": {
"class": mds1,
"columns": ["x"],
"args": [],
"kwargs": {"limit": 500},
"use_lut": False,
"grids_compatible": True,
},
"masking2": {
"class": mds2,
"columns": ["x"],
"args": [],
"kwargs": {"limit": 750},
"use_lut": False,
"grids_compatible": True,
},
}
process = Validation(
datasets,
"DS1",
temporal_matcher=temporal_matchers.BasicTemporalMatching(
window=1 / 24.0
).combinatory_matcher,
scaling="lin_cdf_match",
metrics_calculators={
(3, 2): metrics_calculators.BasicMetrics(
other_name="k1"
).calc_metrics
},
masking_datasets=mds,
)
gpi_info = (1, 1, 1)
ref_df = datasets["DS1"]["class"].read(1)
with warnings.catch_warnings():
warnings.simplefilter(
"ignore", category=DeprecationWarning
) # read_ts is hard coded when using mask_data
new_ref_df = process.mask_dataset(ref_df, gpi_info)
assert len(new_ref_df) == 250
nptest.assert_allclose(new_ref_df.x.values, np.arange(750, 1000))
jobs = process.get_processing_jobs()
for job in jobs:
with warnings.catch_warnings():
# most warnings here are caused by the read_ts function that cannot
# be changed when using a masking data set
warnings.simplefilter("ignore", category=DeprecationWarning)
results = process.calc(*job)
tst = tst_results[len(job[0])]
assert sorted(list(results)) == sorted(list(tst))
for key, tst_key in zip(sorted(results), sorted(tst)):
nptest.assert_almost_equal(
results[key]["n_obs"], tst[tst_key]["n_obs"]
)
def test_validation_n3_k2_masking_no_data_remains():
datasets = setup_TestDatasets()
# setup masking datasets
grid = grids.CellGrid(
np.array([1, 2, 3, 4]),
np.array([1, 2, 3, 4]),
np.array([4, 4, 2, 1]),
gpis=np.array([1, 2, 3, 4]),
)
mds1 = GriddedTsBase("", grid, MaskingTestDataset)
mds2 = GriddedTsBase("", grid, MaskingTestDataset)
mds = {
"masking1": {
"class": mds1,
"columns": ["x"],
"args": [],
"kwargs": {"limit": 500},
"use_lut": False,
"grids_compatible": True,
},
"masking2": {
"class": mds2,
"columns": ["x"],
"args": [],
"kwargs": {"limit": 1000},
"use_lut": False,
"grids_compatible": True,
},
}
process = Validation(
datasets,
"DS1",
temporal_matcher=temporal_matchers.BasicTemporalMatching(
window=1 / 24.0
).combinatory_matcher,
scaling="lin_cdf_match",
metrics_calculators={
(3, 2): metrics_calculators.BasicMetrics(
other_name="k1"
).calc_metrics
},
masking_datasets=mds,
)
gpi_info = (1, 1, 1)
ref_df = datasets["DS1"]["class"].read(1)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
new_ref_df = process.mask_dataset(ref_df, gpi_info)
assert len(new_ref_df) == 0
nptest.assert_allclose(new_ref_df.x.values, np.arange(1000, 1000))
jobs = process.get_processing_jobs()
for job in jobs:
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
results = process.calc(*job)
tst = []
assert sorted(list(results)) == sorted(list(tst))
for key, tst_key in zip(sorted(results), sorted(tst)):
nptest.assert_almost_equal(
results[key]["n_obs"], tst[tst_key]["n_obs"]
)
def test_validation_n3_k2_masking():
# test result for one gpi in a cell
tst_results_one = {
(('DS1', 'x'), ('DS3', 'y')): {
'n_obs': np.array([250], dtype=np.int32)},
(('DS1', 'x'), ('DS2', 'y')): {
'n_obs': np.array([250], dtype=np.int32)},
(('DS1', 'x'), ('DS3', 'x')): {
'n_obs': np.array([250], dtype=np.int32)}}
# test result for two gpis in a cell
tst_results_two = {
(('DS1', 'x'), ('DS3', 'y')): {
'n_obs': np.array([250, 250], dtype=np.int32)},
(('DS1', 'x'), ('DS2', 'y')): {
'n_obs': np.array([250, 250], dtype=np.int32)},
(('DS1', 'x'), ('DS3', 'x')): {
'n_obs': np.array([250, 250], dtype=np.int32)}}
# cell 4 in this example has two gpis so it returns different results.
tst_results = {1: tst_results_one,
1: tst_results_one,
2: tst_results_two}
datasets = setup_TestDatasets()
# setup masking datasets
grid = grids.CellGrid(np.array([1, 2, 3, 4]), np.array([1, 2, 3, 4]),
np.array([4, 4, 2, 1]), gpis=np.array([1, 2, 3, 4]))
mds1 = GriddedTsBase("", grid, MaskingTestDataset)
mds2 = GriddedTsBase("", grid, MaskingTestDataset)
mds = {
'masking1': {
'class': mds1,
'columns': ['x'],
'args': [],
'kwargs': {'limit': 500},
'use_lut': False,
'grids_compatible': True},
'masking2': {
'class': mds2,
'columns': ['x'],
'args': [],
'kwargs': {'limit': 750},
'use_lut': False,
'grids_compatible': True}
}
process = Validation(
datasets, 'DS1',
temporal_matcher=temporal_matchers.BasicTemporalMatching(
window=1 / 24.0).combinatory_matcher,
scaling='lin_cdf_match',
metrics_calculators={
(3, 2): metrics_calculators.BasicMetrics(other_name='k1').calc_metrics},
masking_datasets=mds)
gpi_info = (1, 1, 1)
ref_df = datasets['DS1']['class'].read_ts(1)
new_ref_df = process.mask_dataset(ref_df, gpi_info)
assert len(new_ref_df) == 250
nptest.assert_allclose(new_ref_df.x.values, np.arange(750, 1000))
jobs = process.get_processing_jobs()
for job in jobs:
results = process.calc(*job)
tst = tst_results[len(job[0])]
assert sorted(list(results)) == sorted(list(tst))
for key, tst_key in zip(sorted(results),
sorted(tst)):
nptest.assert_almost_equal(results[key]['n_obs'],
tst[tst_key]['n_obs'])
