fix doc tests

xarray-contrib · Mar 10, 2024 · 2ac0527 · 2ac0527
1 parent c8920c7
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Showing 4 changed files with 93 additions and 94 deletions.
diff --git a/xskillscore/core/comparative.py b/xskillscore/core/comparative.py
@@ -92,20 +92,20 @@ def sign_test(
     >>> (-1 * confidence).plot(color="gray")  # doctest: +ELLIPSIS
     [<matplotlib.lines.Line2D object at 0x...>]
     >>> walk
-    <xarray.DataArray (time: 30)>
+    <xarray.DataArray (time: 30)> Size: 240B
     array([ 1,  0,  1,  2,  1,  2,  3,  4,  5,  6,  5,  6,  7,  6,  7,  8,  9,
            10,  9, 10, 11, 12, 13, 12, 11, 12, 13, 14, 15, 14])
     Coordinates:
-      * time     (time) int64 0 1 2 3 4 5 6 7 8 9 ... 20 21 22 23 24 25 26 27 28 29
+      * time     (time) int64 240B 0 1 2 3 4 5 6 7 8 ... 21 22 23 24 25 26 27 28 29
     >>> significantly_different
-    <xarray.DataArray (time: 30)>
+    <xarray.DataArray (time: 30)> Size: 30B
     array([False, False, False, False, False, False, False, False, False,
            False, False, False, False, False, False,  True,  True,  True,
             True,  True,  True,  True,  True,  True,  True,  True,  True,
             True,  True,  True])
     Coordinates:
-      * time     (time) int64 0 1 2 3 4 5 6 7 8 9 ... 20 21 22 23 24 25 26 27 28 29
-        alpha    float64 0.05
+      * time     (time) int64 240B 0 1 2 3 4 5 6 7 8 ... 21 22 23 24 25 26 27 28 29
+        alpha    float64 8B 0.05
 
     References
     ----------
@@ -274,13 +274,13 @@ def halfwidth_ci_test(
     ...     f1, f2, o, "mae", time_dim="time", dim=[], alpha=0.05
     ... )
     >>> significantly_different
-    <xarray.DataArray ()>
+    <xarray.DataArray ()> Size: 1B
     array(False)
     >>> diff
-    <xarray.DataArray ()>
+    <xarray.DataArray ()> Size: 8B
     array(-0.01919449)
     >>> hwci
-    <xarray.DataArray ()>
+    <xarray.DataArray ()> Size: 8B
     array(0.38729387)
     >>> # absolute magnitude of difference is smaller than half-width of
     >>> # confidence interval, therefore not significant at level alpha=0.05
@@ -289,7 +289,7 @@ def halfwidth_ci_test(
     ...     f1, f2 + 2.0, o, "mae", time_dim="time", dim=[], alpha=0.05
     ... )
     >>> significantly_different
-    <xarray.DataArray ()>
+    <xarray.DataArray ()> Size: 1B
     array(True)
 
     References

diff --git a/xskillscore/core/deterministic.py b/xskillscore/core/deterministic.py
@@ -126,7 +126,7 @@ def linslope(
     >>> a = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> b = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> xs.linslope(a, b, dim="time")
-    <xarray.DataArray (x: 3, y: 3)>
+    <xarray.DataArray (x: 3, y: 3)> Size: 72B
     array([[-0.30948771, -0.21562529, -0.63141304],
            [ 0.31446077,  2.23858011,  0.44743617],
            [-0.22243944,  0.47034784,  1.08512859]])
@@ -205,7 +205,7 @@ def pearson_r(
     >>> a = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> b = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> xs.pearson_r(a, b, dim="time")
-    <xarray.DataArray (x: 3, y: 3)>
+    <xarray.DataArray (x: 3, y: 3)> Size: 72B
     array([[-0.17455755, -0.26648379, -0.74265833],
            [ 0.32535918,  0.42496646,  0.1940647 ],
            [-0.3203094 ,  0.33207755,  0.89250429]])
@@ -275,7 +275,7 @@ def pearson_r_p_value(
     >>> a = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> b = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> xs.pearson_r_p_value(a, b, dim="time")
-    <xarray.DataArray (x: 3, y: 3)>
+    <xarray.DataArray (x: 3, y: 3)> Size: 72B
     array([[0.77888033, 0.66476199, 0.15051516],
            [0.59316935, 0.47567465, 0.75446898],
            [0.59925464, 0.58509064, 0.04161894]])
@@ -357,7 +357,7 @@ def effective_sample_size(a, b, dim="time", skipna=False, keep_attrs=False):
     >>> a = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> b = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> xs.effective_sample_size(a, b, dim="time")
-    <xarray.DataArray (x: 3, y: 3)>
+    <xarray.DataArray (x: 3, y: 3)> Size: 72B
     array([[4., 0., 4.],
            [3., 4., 4.],
            [3., 4., 2.]])
@@ -465,7 +465,7 @@ def pearson_r_eff_p_value(
     >>> a = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> b = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> xs.pearson_r_eff_p_value(a, b, dim="time")
-    <xarray.DataArray (x: 3, y: 3)>
+    <xarray.DataArray (x: 3, y: 3)> Size: 72B
     array([[0.82544245,        nan, 0.25734167],
            [0.78902959, 0.57503354, 0.8059353 ],
            [0.79242625, 0.66792245,        nan]])
@@ -547,7 +547,7 @@ def spearman_r(
     >>> a = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> b = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> xs.spearman_r(a, b, dim="time")
-    <xarray.DataArray (x: 3, y: 3)>
+    <xarray.DataArray (x: 3, y: 3)> Size: 72B
     array([[-0.6, -0.5, -0.7],
            [ 0.4,  0.3,  0.3],
            [-0.3, -0.1,  0.9]])
@@ -616,7 +616,7 @@ def spearman_r_p_value(
     >>> a = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> b = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> xs.spearman_r_p_value(a, b, dim="time")
-    <xarray.DataArray (x: 3, y: 3)>
+    <xarray.DataArray (x: 3, y: 3)> Size: 72B
     array([[0.28475698, 0.39100222, 0.1881204 ],
            [0.50463158, 0.62383766, 0.62383766],
            [0.62383766, 0.87288857, 0.03738607]])
@@ -716,7 +716,7 @@ def spearman_r_eff_p_value(
     >>> a = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> b = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> xs.spearman_r_eff_p_value(a, b, dim="time")
-    <xarray.DataArray (x: 3, y: 3)>
+    <xarray.DataArray (x: 3, y: 3)> Size: 72B
     array([[0.4       ,        nan, 0.3       ],
            [0.73802024, 0.7       , 0.7       ],
            [0.80602663, 0.9       ,        nan]])
@@ -819,8 +819,8 @@ def r2(
     --------
     >>> a = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> b = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
-    >>> r2(a, b, dim="time")
-    <xarray.DataArray (x: 3, y: 3)>
+    >>> xs.r2(a, b, dim="time")
+    <xarray.DataArray (x: 3, y: 3)> Size: 72B
     array([[ -3.77828319,  -1.25687543,  -2.52495914],
            [ -0.67280201, -39.45271514,  -5.78241791],
            [ -1.66615797,  -1.56749317,   0.09843265]])
@@ -888,8 +888,8 @@ def me(
     --------
     >>> a = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> b = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
-    >>> me(a, b, dim="time")
-    <xarray.DataArray (x: 3, y: 3)>
+    >>> xs.me(a, b, dim="time")
+    <xarray.DataArray (x: 3, y: 3)> Size: 72B
     array([[ 0.01748202, -0.14165293,  0.22455357],
            [ 0.13893709, -0.23513353, -0.18174132],
            [-0.29317762,  0.16887445, -0.17297527]])
@@ -963,7 +963,7 @@ def rmse(
     >>> a = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> b = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> xs.rmse(a, b, dim="time")
-    <xarray.DataArray (x: 3, y: 3)>
+    <xarray.DataArray (x: 3, y: 3)> Size: 72B
     array([[0.30366872, 0.5147618 , 0.57410211],
            [0.2963848 , 0.37177283, 0.40563885],
            [0.55686111, 0.38189299, 0.21317579]])
@@ -1038,7 +1038,7 @@ def mse(
     >>> a = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> b = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> xs.mse(a, b, dim="time")
-    <xarray.DataArray (x: 3, y: 3)>
+    <xarray.DataArray (x: 3, y: 3)> Size: 72B
     array([[0.09221469, 0.26497971, 0.32959323],
            [0.08784395, 0.13821504, 0.16454288],
            [0.31009429, 0.14584225, 0.04544392]])
@@ -1111,8 +1111,8 @@ def mae(
     --------
     >>> a = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> b = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
-    >>> mae(a, b, dim="time")
-    <xarray.DataArray (x: 3, y: 3)>
+    >>> xs.mae(a, b, dim="time")
+    <xarray.DataArray (x: 3, y: 3)> Size: 72B
     array([[0.26014863, 0.40137207, 0.48871634],
            [0.18809417, 0.30197826, 0.2984658 ],
            [0.52934554, 0.19820357, 0.17335851]])
@@ -1181,7 +1181,7 @@ def median_absolute_error(
     >>> a = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> b = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> xs.median_absolute_error(a, b, dim="time")
-    <xarray.DataArray (x: 3, y: 3)>
+    <xarray.DataArray (x: 3, y: 3)> Size: 72B
     array([[0.28798217, 0.23322591, 0.62067468],
            [0.12146232, 0.20314509, 0.23442927],
            [0.59041981, 0.03289321, 0.21343494]])
@@ -1261,7 +1261,7 @@ def mape(
     >>> a = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> b = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> xs.mape(a, b, dim="time")
-    <xarray.DataArray (x: 3, y: 3)>
+    <xarray.DataArray (x: 3, y: 3)> Size: 72B
     array([[0.6268041 , 9.45134297, 3.28717608],
            [0.27099746, 1.58105176, 1.48258713],
            [6.55806162, 0.22271096, 0.39302745]])
@@ -1338,7 +1338,7 @@ def smape(
     >>> a = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> b = xr.DataArray(np.random.rand(5, 3, 3), dims=["time", "x", "y"])
     >>> xs.smape(a, b, dim="time")
-    <xarray.DataArray (x: 3, y: 3)>
+    <xarray.DataArray (x: 3, y: 3)> Size: 72B
     array([[0.35591619, 0.43662087, 0.55372571],
            [0.1864336 , 0.45831965, 0.38473469],
            [0.58730494, 0.18081757, 0.14960832]])

diff --git a/xskillscore/core/probabilistic.py b/xskillscore/core/probabilistic.py
@@ -101,11 +101,11 @@ def crps_gaussian(
     ... )
     >>> mu = forecasts.mean("member")
     >>> sig = forecasts.std("member")
-    >>> crps_gaussian(observations, mu, sig, dim="x")
-    <xarray.DataArray (y: 3)>
+    >>> xs.crps_gaussian(observations, mu, sig, dim="x")
+    <xarray.DataArray (y: 3)> Size: 24B
     array([1.0349773 , 0.36521376, 0.39017126])
     Coordinates:
-      * y        (y) int64 0 1 2
+      * y        (y) int64 24B 0 1 2
 
     See Also
     --------
@@ -178,10 +178,10 @@ def crps_quadrature(
     ... )
     >>> from scipy.stats import norm
     >>> xs.crps_quadrature(observations, norm, dim="x")
-    <xarray.DataArray (y: 3)>
+    <xarray.DataArray (y: 3)> Size: 24B
     array([0.80280921, 0.31818197, 0.32364912])
     Coordinates:
-      * y        (y) int64 0 1 2
+      * y        (y) int64 24B 0 1 2
 
     See Also
     --------
@@ -259,11 +259,11 @@ def crps_ensemble(
     ...     np.random.normal(size=(3, 3, 3)),
     ...     coords=[("x", np.arange(3)), ("y", np.arange(3)), ("member", np.arange(3))],
     ... )
-    >>> crps_ensemble(observations, forecasts, dim="x")
-    <xarray.DataArray (y: 3)>
+    >>> xs.crps_ensemble(observations, forecasts, dim="x")
+    <xarray.DataArray (y: 3)> Size: 24B
     array([1.04497153, 0.48997746, 0.47994095])
     Coordinates:
-      * y        (y) int64 0 1 2
+      * y        (y) int64 24B 0 1 2
 
     See Also
     --------
@@ -347,10 +347,10 @@ def brier_score(
     ...     coords=[("x", np.arange(3)), ("y", np.arange(3)), ("member", np.arange(3))],
     ... )
     >>> xs.brier_score(observations > 0.5, (forecasts > 0.5).mean("member"), dim="y")
-    <xarray.DataArray (x: 3)>
+    <xarray.DataArray (x: 3)> Size: 24B
     array([0.37037037, 0.14814815, 0.51851852])
     Coordinates:
-      * x        (x) int64 0 1 2
+      * x        (x) int64 24B 0 1 2
 
     See Also
     --------
@@ -451,10 +451,10 @@ def threshold_brier_score(
     ... )
     >>> threshold = [0.2, 0.5, 0.8]
     >>> xs.threshold_brier_score(observations, forecasts, threshold)
-    <xarray.DataArray (threshold: 3)>
+    <xarray.DataArray (threshold: 3)> Size: 24B
     array([0.27160494, 0.34567901, 0.18518519])
     Coordinates:
-      * threshold  (threshold) float64 0.2 0.5 0.8
+      * threshold  (threshold) float64 24B 0.2 0.5 0.8
 
     See Also
     --------
@@ -643,12 +643,12 @@ def rps(
     ... )
     >>> category_edges = np.array([0.33, 0.66])
     >>> xs.rps(o, f, category_edges, dim="x")
-    <xarray.DataArray (y: 3)>
+    <xarray.DataArray (y: 3)> Size: 24B
     array([0.37037037, 0.81481481, 1.        ])
     Coordinates:
-      * y                           (y) int64 0 1 2
-        observations_category_edge  <U45 '[-np.inf, 0.33), [0.33, 0.66), [0.66, n...
-        forecasts_category_edge     <U45 '[-np.inf, 0.33), [0.33, 0.66), [0.66, n...
+      * y                           (y) int64 24B 0 1 2
+        observations_category_edge  <U45 180B '[-np.inf, 0.33), [0.33, 0.66), [0....
+        forecasts_category_edge     <U45 180B '[-np.inf, 0.33), [0.33, 0.66), [0....
 
     You can also define multi-dimensional ``category_edges``, e.g. with xr.quantile.
     However, you still need to ensure that ``category_edges`` covers the forecasts
@@ -658,12 +658,12 @@ def rps(
     ...     {"quantile": "category_edge"}
     ... )
     >>> xs.rps(o, f, category_edges, dim="x")
-    <xarray.DataArray (y: 3)>
+    <xarray.DataArray (y: 3)> Size: 24B
     array([0.37037037, 0.81481481, 0.88888889])
     Coordinates:
-      * y                           (y) int64 0 1 2
-        observations_category_edge  <U45 '[-np.inf, 0.33), [0.33, 0.66), [0.66, n...
-        forecasts_category_edge     <U45 '[-np.inf, 0.33), [0.33, 0.66), [0.66, n...
+      * y                           (y) int64 24B 0 1 2
+        observations_category_edge  <U45 180B '[-np.inf, 0.33), [0.33, 0.66), [0....
+        forecasts_category_edge     <U45 180B '[-np.inf, 0.33), [0.33, 0.66), [0....
 
     If you have probabilistic forecasts, i.e. without a ``member`` dimension but
     different ``category`` probabilities, you can also provide probability
@@ -705,21 +705,21 @@ def rps(
     ... ).mean("member")
     >>> assert (f_p.sum("category") == 1).all()
     >>> xs.rps(o_p, f_p, category_edges=None, dim="x", input_distributions="p")
-    <xarray.DataArray (y: 3)>
+    <xarray.DataArray (y: 3)> Size: 24B
     array([0.37037037, 0.81481481, 0.88888889])
     Coordinates:
-      * y        (y) int64 0 1 2
+      * y        (y) int64 24B 0 1 2
 
     Providing cumulative distribution inputs yields identical results, where highest
     category equals 1 by default and can be ignored:
 
     >>> o_c = o < category_edges
     >>> f_c = (f < category_edges).mean("member")
     >>> xs.rps(o_c, f_c, category_edges=None, dim="x", input_distributions="c")
-    <xarray.DataArray (y: 3)>
+    <xarray.DataArray (y: 3)> Size: 24B
     array([0.37037037, 0.81481481, 0.88888889])
     Coordinates:
-      * y        (y) int64 0 1 2
+      * y        (y) int64 24B 0 1 2
 
     References
     ----------
@@ -915,13 +915,13 @@ def rank_histogram(
     ...     coords=[("x", np.arange(3)), ("y", np.arange(3)), ("member", np.arange(3))],
     ... )
     >>> xs.rank_histogram(observations, forecasts, dim="x")
-    <xarray.DataArray 'histogram_rank' (y: 3, rank: 4)>
+    <xarray.DataArray 'histogram_rank' (y: 3, rank: 4)> Size: 96B
     array([[0, 0, 1, 2],
            [0, 1, 2, 0],
            [0, 0, 2, 1]])
     Coordinates:
-      * y        (y) int64 0 1 2
-      * rank     (rank) float64 1.0 2.0 3.0 4.0
+      * y        (y) int64 24B 0 1 2
+      * rank     (rank) float64 32B 1.0 2.0 3.0 4.0
 
     Notes
     -----
@@ -1025,12 +1025,12 @@ def discrimination(
     >>> forecast_event_likelihood = (forecasts > 0).mean("member")
     >>> observed_event = observations > 0
     >>> xs.discrimination(observed_event, forecast_event_likelihood, dim=["x", "y"])
-    <xarray.DataArray (event: 2, forecast_probability: 5)>
+    <xarray.DataArray (event: 2, forecast_probability: 5)> Size: 80B
     array([[0.00437637, 0.15536105, 0.66739606, 0.12472648, 0.04814004],
            [0.00451467, 0.16704289, 0.66365688, 0.1241535 , 0.04063205]])
     Coordinates:
-      * forecast_probability  (forecast_probability) float64 0.1 0.3 0.5 0.7 0.9
-      * event                 (event) bool True False
+      * forecast_probability  (forecast_probability) float64 40B 0.1 0.3 0.5 0.7 0.9
+      * event                 (event) bool 2B True False
 
     References
     ----------
@@ -1108,14 +1108,14 @@ def reliability(
     ...     coords=[("x", np.arange(3)), ("y", np.arange(3))],
     ... )
     >>> xs.reliability(observations > 0.1, (forecasts > 0.1).mean("member"), dim="x")
-    <xarray.DataArray (y: 3, forecast_probability: 5)>
+    <xarray.DataArray (y: 3, forecast_probability: 5)> Size: 120B
     array([[nan, 0. , nan, 1. , nan],
            [1. , 0.5, nan, nan, nan],
            [nan, 0. , nan, 0. , nan]])
     Coordinates:
-      * y                     (y) int64 0 1 2
-      * forecast_probability  (forecast_probability) float64 0.1 0.3 0.5 0.7 0.9
-        samples               (y, forecast_probability) float64 0.0 2.0 ... 1.0 0.0
+      * y                     (y) int64 24B 0 1 2
+      * forecast_probability  (forecast_probability) float64 40B 0.1 0.3 0.5 0.7 0.9
+        samples               (y, forecast_probability) float64 120B 0.0 2.0 ... 0.0
 
     Notes
     -----
@@ -1264,7 +1264,7 @@ def roc(
     >>> o = f.copy()
     >>> category_edges = np.linspace(-2, 2, 5)
     >>> xs.roc(o, f, category_edges, dim=["time"])
-    <xarray.DataArray 'histogram_observations_forecasts' ()>
+    <xarray.DataArray 'histogram_observations_forecasts' ()> Size: 8B
     array(1.)
 
     See also