more tests on log cdf

tests/test_sam_resource.py

@@ -586,13 +586,13 @@ def test_bias_correct_wind_qdm():
     assert np.allclose(bc_ws, base_ws)
 
     res = WindResource.preload_SAM(h5, sites, hub_heights)
-    params = list(0.1*np.ones(10))
+    params = sorted(np.random.uniform(10, 20, 10))
     bc['params_oh'] = json.dumps(params)
     bc.loc[0, 'params_oh'] = json.dumps(base_params)
     res.bias_correct(bc)
     bc_ws = res._res_arrays['windspeed']
     assert np.allclose(bc_ws[:, 0], base_ws[:, 0])
-    assert ((bc_ws[:, 1:] > base_ws[:, 1:]).sum() / bc_ws[:, 1:].size) < 1e-4
+    assert ((bc_ws[:, 1:] != base_ws[:, 1:]).sum() / bc_ws[:, 1:].size) > 0.99
 
 
 def test_bias_correct_irrad_qdm():

tests/test_temporal_stats.py

@@ -281,13 +281,38 @@ def test_weighted_circular_means(weights):
     assert np.allclose(truth, test_stats[name].values, rtol=0, atol=0.01), msg
 
 
-def test_cdf(n=21):
+@pytest.mark.parametrize("n_samples", [21, 50])
+def test_cdf_linear(n_samples):
     """Test the CDF function which gets evenly spaced values (in quantile
     space) defining the empirical CDF of a dataset"""
     data = np.random.normal(0, 1, (1000,))
-    x = cdf(data, n=n, decimals=None)
+    x = cdf(data, n_samples=n_samples, decimals=None)
 
-    quantiles = np.linspace(0, 1, n)
+    assert x[0] == data.min()
+    assert x[-1] == data.max()
+
+    quantiles = np.linspace(0, 1, n_samples)
+    assert quantiles[0] == 0
+    assert quantiles[-1] == 1
+
+    for i, q in enumerate(quantiles):
+        assert np.allclose(np.percentile(data, 100 * q), x[i])
+
+
+@pytest.mark.parametrize(("n_samples", "log_base"), [(21, 10), (50, 2)])
+def test_cdf_invlog(n_samples, log_base):
+    """Test the CDF function which gets inverse-log-spaced spaced values
+    (in quantile space) defining the empirical CDF of a dataset"""
+    data = np.random.normal(0, 1, (10000,))
+    x = cdf(data, n_samples=n_samples, decimals=None, sampling='invlog',
+            log_base=log_base)
+
+    assert x[0] == data.min()
+    assert x[-1] == data.max()
+
+    quantiles = np.logspace(0, 1, n_samples, base=log_base)
+    quantiles = (quantiles - 1) / (log_base - 1)
+    quantiles = np.array(sorted(1 - quantiles))
     assert quantiles[0] == 0
     assert quantiles[-1] == 1