8000 Merge pull request #28023 from mattip/doctest-1.6.0 · numpy/numpy@cb34472 · GitHub
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Merge pull request #28023 from mattip/doctest-1.6.0
DOC: update to scipy-doctest 1.6.0 and fix tests
2 parents 965fdf5 + 84fa489 commit cb34472

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‎.github/workflows/linux.yml

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@@ -194,12 +194,13 @@ jobs:
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shell: 'script -q -e -c "bash --noprofile --norc -eo pipefail {0}"'
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run: |
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spin bench --quick
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- name: Check docstests
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shell: 'script -q -e -c "bash --noprofile --norc -eo pipefail {0}"'
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run: |
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pip install scipy-doctest==1.5.1 hypothesis==6.104.1 matplotlib scipy pytz pandas
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spin check-docs -v
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spin check-tutorials -v
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# These are run on CircleCI
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# - name: Check docstests
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# shell: 'script -q -e -c "bash --noprofile --norc -eo pipefail {0}"'
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# run: |
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# pip install scipy-doctest==1.6.0 hypothesis==6.104.1 matplotlib scipy pytz pandas
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# spin check-docs -v
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# spin check-tutorials -v
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sdist:
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needs: [smoke_test]

‎numpy/_core/_add_newdocs.py

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@@ -2771,7 +2771,7 @@
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>>> y.shape = (3, 6)
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Traceback (most recent call last):
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File "<stdin>", line 1, in <module>
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ValueError: total size of new array must be unchanged
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ValueError: cannot reshape array of size 24 into shape (3,6)
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>>> np.zeros((4,2))[::2].shape = (-1,)
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Traceback (most recent call last):
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File "<stdin>", line 1, in <module>
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Examples
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--------
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>>> import numpy as np
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>>> y = np.reshape(np.arange(2*3*4), (2,3,4))
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>>> y = np.reshape(np.arange(2 * 3 * 4, dtype=np.int32), (2, 3, 4))
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>>> y
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array([[[ 0, 1, 2, 3],
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[ 4, 5, 6, 7],
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[ 8, 9, 10, 11]],
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[[12, 13, 14, 15],
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[16, 17, 18, 19],
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[20, 21, 22, 23]]])
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[20, 21, 22, 23]]], dtype=np.int32)
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>>> y.strides
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(48, 16, 4)
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>>> y[1,1,1]
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17
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>>> offset=sum(y.strides * np.array((1,1,1)))
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>>> offset/y.itemsize
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17
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>>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)
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>>> y[1, 1, 1]
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np.int32(17)
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>>> offset = sum(y.strides * np.array((1, 1, 1)))
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>>> offset // y.itemsize
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np.int64(17)
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>>> x = np.reshape(np.arange(5*6*7*8, dtype=np.int32), (5, 6, 7, 8))
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>>> x = x.transpose(2, 3, 1, 0)
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>>> x.strides
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(32, 4, 224, 1344)
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>>> i = np.array([3,5,2,2])
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>>> i = np.array([3, 5, 2, 2], dtype=np.int32)
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>>> offset = sum(i * x.strides)
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>>> x[3,5,2,2]
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813
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>>> offset / x.itemsize
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813
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>>> x[3, 5, 2, 2]
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np.int32(813)
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>>> offset // x.itemsize
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np.int64(813)
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"""))
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@@ -4964,8 +4965,8 @@
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0
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>>> np.multiply.identity
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1
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>>> np.power.identity
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1
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>>> print(np.power.identity)
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None
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>>> print(np.exp.identity)
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None
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"""))
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--------
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>>> import numpy as np
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>>> np.add.ntypes
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18
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22
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>>> np.multiply.ntypes
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18
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23
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>>> np.power.ntypes
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17
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21
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>>> np.exp.ntypes
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7
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10
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>>> np.remainder.ntypes
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14
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16
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"""))
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@@ -5080,26 +5081,16 @@
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--------
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>>> import numpy as np
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>>> np.add.types
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['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',
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'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',
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'GG->G', 'OO->O']
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>>> np.multiply.types
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['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',
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'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',
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'GG->G', 'OO->O']
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['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', ...
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>>> np.power.types
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['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',
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'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D', 'GG->G',
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'OO->O']
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['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', ...
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>>> np.exp.types
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['f->f', 'd->d', 'g->g', 'F->F', 'D->D', 'G->G', 'O->O']
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['e->e', 'f->f', 'd->d', 'f->f', 'd->d', 'g->g', 'F->F', 'D->D', 'G->G', 'O->O']
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>>> np.remainder.types
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['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',
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'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'OO->O']
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['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', ...
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"""))
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>>> import numpy as np
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>>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
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>>> print(dt.fields)
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{'grades': (dtype(('float64',(2,))), 16), 'name': (dtype('|S16'), 0)}
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{'name': (dtype('|S16'), 0), 'grades': (dtype(('float64',(2,))), 16)}
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"""))
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@@ -6131,13 +6122,12 @@
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>>> (arr + arr).dtype.metadata
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mappingproxy({'key': 'value'})
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But if the arrays have different dtype metadata, the metadata may be
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dropped:
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If the arrays have different dtype metadata, the first one wins:
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>>> dt2 = np.dtype(float, metadata={"key2": "value2"})
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>>> arr2 = np.array([3, 2, 1], dtype=dt2)
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>>> (arr + arr2).dtype.metadata is None
6140-
True # The metadata field is cleared so None is returned
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>>> print((arr + arr2).dtype.metadata)
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{'key': 'value'}
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"""))
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add_newdoc('numpy._core.multiarray', 'dtype', ('name',

‎numpy/polynomial/_polybase.py

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@@ -685,6 +685,7 @@ def degree(self):
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Create a polynomial object for ``1 + 7*x + 4*x**2``:
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>>> np.polynomial.set_default_printstyle("unicode")
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>>> poly = np.polynomial.Polynomial([1, 7, 4])
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>>> print(poly)
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1.0 + 7.0·x + 4.0 6EC3 ·x²

‎requirements/doc_requirements.txt

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toml
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# for doctests, also needs pytz which is in test_requirements
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scipy-doctest==1.5.1
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scipy-doctest==1.6.0

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