How to use the pytesmo.validation_framework.validation.Validation function in pytesmo

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github TUW-GEO / pytesmo / tests / test_validation_framwork / test_validation.py View on Github external
def test_validation_n2_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={
            (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))
github TUW-GEO / pytesmo / tests / test_validation_framwork / test_validation.py View on Github external
'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()
github TUW-GEO / pytesmo / tests / test_validation_framwork / test_validation.py View on Github external
'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()
    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))
github TUW-GEO / pytesmo / tests / test_validation_framwork / test_validation.py View on Github external
'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()
    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={
                             (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))
github TUW-GEO / pytesmo / tests / test_validation_framwork / test_validation.py View on Github external
'class': ismn_reader,
            'columns': ['soil moisture'],
        },
        'ASCAT': {
            'class': ascat_reader,
            'columns': ['sm'],
            'kwargs': {'mask_frozen_prob': 80,
                       'mask_snow_prob': 80,
                       'mask_ssf': True},
        }}

    read_ts_names = {'ASCAT': 'read', 'ISMN': 'read_ts'}
    period = [datetime(2007, 1, 1), datetime(2014, 12, 31)]

    datasets = DataManager(datasets, 'ISMN', period, read_ts_names=read_ts_names)
    process = Validation(
        datasets, 'ISMN',
        temporal_ref='ASCAT',
        scaling='lin_cdf_match',
        scaling_ref='ASCAT',
        metrics_calculators={
            (2, 2): metrics_calculators.BasicMetrics(other_name='k1', metadata_template=metadata_dict_template).calc_metrics},
        period=period)

    for job in jobs:
        results = process.calc(*job)
        netcdf_results_manager(results, save_path)

    results_fname = os.path.join(
        save_path, 'ASCAT.sm_with_ISMN.soil moisture.nc')

    vars_should = [u'n_obs', u'tau', u'gpi', u'RMSD', u'lon', u'p_tau',
github TUW-GEO / pytesmo / tests / test_validation_framwork / test_validation.py View on Github external
'columns': ['soil moisture']
        },
        'ASCAT': {
            'class': ascat_reader,
            'columns': ['sm'],
            'kwargs': {'mask_frozen_prob': 80,
                       'mask_snow_prob': 80,
                       'mask_ssf': True}
        }}

    read_ts_names = {'ASCAT': 'read', 'ISMN': 'read_ts'}
    period = [datetime(2007, 1, 1), datetime(2014, 12, 31)]

    datasets = DataManager(datasets, 'ISMN', period, read_ts_names=read_ts_names)

    process = Validation(
        datasets, 'ISMN',
        temporal_ref='ASCAT',
        scaling='lin_cdf_match',
        scaling_ref='ASCAT',
        metrics_calculators={
            (2, 2): metrics_calculators.RollingMetrics(other_name='k1',
                                                       metadata_template=metadata_dict_template).calc_metrics},
        period=period)

    for job in jobs:
        results = process.calc(*job)
        netcdf_results_manager(results, save_path, ts_vars=[
                               'R', 'p_R', 'RMSD'])

    results_fname = os.path.join(
        save_path, 'ASCAT.sm_with_ISMN.soil moisture.nc')
github TUW-GEO / pytesmo / tests / test_validation_framwork / test_validation.py View on Github external
'columns': ['soil moisture']
        },
        'ASCAT': {
            'class': ascat_reader,
            'columns': ['sm'],
            'kwargs': {'mask_frozen_prob': 80,
                       'mask_snow_prob': 80,
                       'mask_ssf': True}
        }}

    read_ts_names = {'ASCAT': 'read', 'ISMN': 'read_ts'}
    period = [datetime(2007, 1, 1), datetime(2014, 12, 31)]

    datasets = DataManager(datasets, 'ISMN', period, read_ts_names=read_ts_names)

    process = Validation(
        datasets, 'ISMN',
        temporal_ref='ASCAT',
        scaling='lin_cdf_match',
        scaling_ref='ASCAT',
        metrics_calculators={
            (2, 2): metrics_calculators.BasicMetrics(other_name='k1').calc_metrics},
        period=period)

    for job in jobs:
        results = process.calc(*job)
        netcdf_results_manager(results, save_path)

    results_fname = os.path.join(
        save_path, 'ASCAT.sm_with_ISMN.soil moisture.nc')

    vars_should = [u'n_obs', u'tau', u'gpi', u'RMSD', u'lon', u'p_tau',
github TUW-GEO / pytesmo / tests / test_validation_framwork / test_validation.py View on Github external
'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_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={
            (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))
github TUW-GEO / pytesmo / tests / test_validation_framwork / test_validation.py View on Github external
'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()

    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={
            (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))
github TUW-GEO / pytesmo / tests / test_validation_framwork / test_validation.py View on Github external
'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()