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use built in clip #309

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use built in clip #309

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5bcd13c
use built in clip
May 8, 2021
54ff8f2
Merge branch 'main' into fix-clip
raybellwaves May 8, 2021
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3 changes: 2 additions & 1 deletion CHANGELOG.rst
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Expand Up @@ -21,7 +21,8 @@ Internal Changes
:py:func:`~xskillscore.rps` expilicty checks for ``bin_dim`` if
``category_edges==None``. (:pr:`287`) `Aaron Spring`_
- Add doctest on the docstring examples. (:pr:`302`) `Ray Bell`_
- Removed a call to compute weights in testing (:pr:`306`) `Ray Bell`_
- Removed a call to compute weights in testing. (:pr:`306`) `Ray Bell`_
- Use built in ``xarray`` clip method. (:pr:`309`) `Ray Bell`_


xskillscore v0.0.19 (2021-03-12)
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2 changes: 1 addition & 1 deletion ci/dev.yml
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Expand Up @@ -24,7 +24,7 @@ dependencies:
- xarray>=0.16.1
# xhistogram 0.1.3 introduced an error that broke xskillscore
# see https://github.com/xgcm/xhistogram/issues/48
- xhistogram!=0.1.3
- xhistogram==0.1.2
# Package Management
- asv
- black
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2 changes: 1 addition & 1 deletion ci/doc.yml
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Expand Up @@ -21,6 +21,6 @@ dependencies:
- xarray>=0.16.1
# xhistogram 0.1.3 introduced an error that broke xskillscore
# see https://github.com/xgcm/xhistogram/issues/48
- xhistogram!=0.1.3
- xhistogram==0.1.2
- pip:
- -e ..
2 changes: 1 addition & 1 deletion ci/docs_notebooks.yml
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Expand Up @@ -14,7 +14,7 @@ dependencies:
- xarray>=0.16.1
# xhistogram 0.1.3 introduced an error that broke xskillscore
# see https://github.com/xgcm/xhistogram/issues/48
- xhistogram!=0.1.3
- xhistogram==0.1.2
- importlib_metadata
- jupyterlab
- matplotlib-base
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2 changes: 1 addition & 1 deletion ci/minimum-tests.yml
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Expand Up @@ -15,7 +15,7 @@ dependencies:
- xarray>=0.16.1
# xhistogram 0.1.3 introduced an error that broke xskillscore
# see https://github.com/xgcm/xhistogram/issues/48
- xhistogram!=0.1.3
- xhistogram==0.1.2
- coveralls
- pytest
- pytest-cov
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4 changes: 2 additions & 2 deletions xskillscore/core/probabilistic.py
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Expand Up @@ -1153,9 +1153,9 @@ def _auc(fpr, tpr, dim="probability_bin"):
area = xr.apply_ufunc(
np.trapz, tpr, fpr, input_core_dims=[[dim], [dim]], dask="allowed"
)
area = np.abs(area)
area = abs(area)
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isnt np faster?

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I see no difference in speed

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It calls __abs__ (unlike using the builtin sum, which would be much slower)

if ((area > 1)).any():
area = np.clip(area, 0, 1) # allow only values between 0 and 1
area = area.clip(0, 1) # allow only values between 0 and 1
return area


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4 changes: 2 additions & 2 deletions xskillscore/tests/test_probabilistic.py
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Expand Up @@ -996,7 +996,7 @@ def test_roc_bin_edges_continuous_against_sklearn(
forecast_1d_long, observation_1d_long, drop_intermediate_bool
):
"""Test xs.roc against sklearn.metrics.roc_curve/auc_score."""
fp = np.clip(forecast_1d_long, 0, 1) # prob
fp = forecast_1d_long.clip(0, 1) # prob
ob = observation_1d_long > 0 # binary
# sklearn
sk_fpr, sk_tpr, _ = roc_curve(ob, fp, drop_intermediate=drop_intermediate_bool)
Expand All @@ -1017,7 +1017,7 @@ def test_roc_bin_edges_continuous_against_sklearn(

def test_roc_bin_edges_drop_intermediate(forecast_1d_long, observation_1d_long):
"""Test that drop_intermediate reduces probability_bins in xs.roc ."""
fp = np.clip(forecast_1d_long, 0, 1) # prob
fp = forecast_1d_long.clip(0, 1) # prob
ob = observation_1d_long > 0 # binary
# xs
txs_fpr, txs_tpr, txs_area = roc(
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