mirror of
https://github.com/gentoo-mirror/gentoo.git
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52 lines
2.2 KiB
Diff
52 lines
2.2 KiB
Diff
diff --git a/patsy/highlevel.py b/patsy/highlevel.py
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index 78d2942..298739d 100644
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--- a/patsy/highlevel.py
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+++ b/patsy/highlevel.py
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@@ -178,7 +178,7 @@ def _do_highlevel_design(formula_like, data, eval_env,
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else:
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# subok=True is necessary here to allow DesignMatrixes to pass
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# through
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- (lhs, rhs) = (None, asarray_or_pandas(formula_like, subok=True))
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+ (lhs, rhs) = (None, asarray_or_pandas(formula_like, subok=True, copy=None))
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# some sort of explicit matrix or matrices were given. Currently we
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# have them in one of these forms:
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# -- an ndarray or subclass
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diff --git a/patsy/state.py b/patsy/state.py
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index 933c588..c489a4b 100644
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--- a/patsy/state.py
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+++ b/patsy/state.py
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@@ -103,7 +103,7 @@ class Center(object):
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pass
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def transform(self, x):
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- x = asarray_or_pandas(x)
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+ x = asarray_or_pandas(x, copy=None)
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# This doesn't copy data unless our input is a DataFrame that has
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# heterogeneous types. And in that case we're going to be munging the
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# types anyway, so copying isn't a big deal.
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diff --git a/patsy/util.py b/patsy/util.py
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index 3116e11..7ac6f79 100644
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--- a/patsy/util.py
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+++ b/patsy/util.py
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@@ -69,7 +69,7 @@ def asarray_or_pandas(a, copy=False, dtype=None, subok=False):
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def test_asarray_or_pandas():
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import warnings
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- assert type(asarray_or_pandas([1, 2, 3])) is np.ndarray
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+ assert type(asarray_or_pandas([1, 2, 3], copy=True)) is np.ndarray
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with warnings.catch_warnings() as w:
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warnings.filterwarnings('ignore', 'the matrix subclass',
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PendingDeprecationWarning)
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@@ -83,9 +83,9 @@ def test_asarray_or_pandas():
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assert np.array_equal(a, a_copy)
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a_copy[0] = 100
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assert not np.array_equal(a, a_copy)
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- assert np.allclose(asarray_or_pandas([1, 2, 3], dtype=float),
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+ assert np.allclose(asarray_or_pandas([1, 2, 3], dtype=float, copy=None),
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[1.0, 2.0, 3.0])
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- assert asarray_or_pandas([1, 2, 3], dtype=float).dtype == np.dtype(float)
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+ assert asarray_or_pandas([1, 2, 3], dtype=float, copy=None).dtype == np.dtype(float)
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a_view = asarray_or_pandas(a, dtype=a.dtype)
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a_view[0] = 99
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assert a[0] == 99
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