Cleanup

environment.yml

@@ -18,6 +18,9 @@ dependencies:
   - pyresample
   - matplotlib>=1.2.0
   - pip
+  # optional, for documentation and testing:
+  # - nb_conda
+  # ----------------------------------------
   - pip:
     - pynetcf>=0.4.0
     - pygeogrids

tests/test_validation_framework/__init__.py

@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-

tests/test_validation_framework/test_metric_calculators.py

@@ -798,68 +798,6 @@ def make_testdata_random():
     return make_datasets(df), expected
 
 
-@pytest.mark.parametrize(
-    "testdata_generator", [make_testdata_known_results, make_testdata_random]
-)
-@pytest.mark.parametrize("metrics_adapter", [MonthsMetricsAdapter])  # todo
-def test_PairwiseIntercomparisonMetrics_adapted(testdata_generator,
-                                                metrics_adapter):
-    datasets, expected = testdata_generator()
-    metrics_calculator = PairwiseIntercomparisonMetrics(
-        calc_spearman=True, analytical_cis=False
-    )
-    metrics_calculator = metrics_adapter(metrics_calculator)
-
-    val = Validation(
-        datasets,
-        "reference_name",
-        scaling="mean_std",  # doesn't work with the constant test data
-        temporal_matcher=make_combined_temporal_matcher(pd.Timedelta(6, "h")),
-        metrics_calculators={(4, 2): (metrics_calculator.calc_metrics)},
-    )
-    results_pw = val.calc(
-        [0], [1], [1], rename_cols=False, only_with_reference=True
-    )
-
-    expected_metrics = [
-        "R",
-        "p_R",
-        "BIAS",
-        "RMSD",
-        "mse",
-        "RSS",
-        "mse_corr",
-        "mse_bias",
-        "urmsd",
-        "mse_var",
-        "n_obs",
-        "gpi",
-        "lat",
-        "lon",
-        "rho",
-        "p_rho",
-        "tau",
-        "p_tau",
-    ]
-
-    seasons = ["ALL", "DJF", "MAM", "JJA", "SON"]
-
-    metrics = []
-    for seas in seasons:
-        metrics += list(map(lambda x: (seas, x), expected_metrics))
-
-    for key in results_pw:
-        assert isinstance(key, tuple)
-        assert len(key) == 2
-        assert all(map(lambda x: isinstance(x, tuple), key))
-        assert isinstance(results_pw[key], dict)
-        res_metrics = list(results_pw[key].keys())
-        assert all([v in res_metrics for v in ["lon", "lat", "gpi"]])
-        for m in metrics:
-            if m in expected[key]:
-                assert_equal(results_pw[key][m], expected[key][m])
-
-
 @pytest.mark.parametrize(
     "testdata_generator", [make_testdata_known_results, make_testdata_random]
 )
@@ -1341,14 +1279,3 @@ def test_TripleCollocationMetrics_failure():
 # results = val.calc(1, 1, 1)
 
 # assert 0
-
-if __name__ == '__main__':
-    test_PairwiseIntercomparisonMetrics(testdata_known_results)
-
-    #
-    # from pytesmo.df_metrics import mse_var
-    # data, results = testdata_known_results()
-    # ref = data['reference_name']['class'].read(0)
-    # can = data['plus2_name']['class'].read(0)
-    # mv = mse_var(pd.DataFrame(data={'one': ref.iloc[:, 0].values,
-    #                                 'two': can.iloc[:, 0].values}))