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API: Cleaning numpy/__init__.py and main namespace - Part 4 [NEP 52] (numpy#24445)
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‎doc/release/upcoming_changes/24445.deprecation.rst

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* ``np.trapz`` has been deprecated. Use ``scipy.interpolate.trapezoid`` instead.
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* ``np.in1d`` has been deprecated. Use ``np.isin`` instead.
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* Alias ``np.row_stack`` has been deprecated. Use ``np.vstack`` directly.

‎doc/source/reference/routines.array-manipulation.rst

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hstack
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dstack
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column_stack
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row_stack
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Splitting arrays
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================

‎doc/source/reference/routines.ma.rst

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ma.hstack
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ma.hsplit
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ma.mr_
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ma.row_stack
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ma.vstack
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‎doc/source/reference/routines.math.rst

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ediff1d
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gradient
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cross
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trapz
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Exponents and logarithms
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------------------------

‎doc/source/user/quickstart.rst

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array([[4., 3.],
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[2., 8.]])
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On the other hand, the function `row_stack` is equivalent to `vstack`
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for any input arrays. In fact, `row_stack` is an alias for `vstack`::
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>>> np.column_stack is np.hstack
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False
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>>> np.row_stack is np.vstack
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True
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In general, for arrays with more than two dimensions,
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`hstack` stacks along their second
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axes, `vstack` stacks along their

‎numpy/core/fromnumeric.py

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cumsum : Cumulative sum of array elements.
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trapz : Integration of array values using the composite trapezoidal rule.
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mean, average
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Notes
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See Also
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--------
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sum : Sum array elements.
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trapz : Integration of array values using the composite trapezoidal rule.
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diff : Calculate the n-th discrete difference along given axis.
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Notes

‎numpy/core/shape_base.py

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< 10000 /div>
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and r/g/b channels (third axis). The functions `concatenate`, `stack` and
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`block` provide more general stacking and concatenation operations.
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``np.row_stack`` is an alias for `vstack`. They are the same function.
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Parameters
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----------
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tup : sequence of ndarrays

‎numpy/core/tests/test_deprecations.py

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from numpy.lib._shape_base_impl import get_array_wrap
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from numpy.core.numerictypes import maximum_sctype
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from numpy.lib.tests.test_io import TextIO
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from numpy import in1d, row_stack, trapz
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self.assert_deprecated(lambda: safe_eval("None"))
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self.assert_deprecated(lambda: disp("test"))
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self.assert_deprecated(lambda: get_array_wrap())
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self.assert_deprecated(lambda: maximum_sctype(int))
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self.assert_deprecated(lambda: in1d([1], [1]))
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self.assert_deprecated(lambda: row_stack([[]]))
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self.assert_deprecated(lambda: trapz([1], [1]))

‎numpy/core/tests/test_overrides.py

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bound = np.mean.__get__(MyClass) # classmethod
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with pytest.raises(TypeError, match="unsupported operand type"):
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bound()
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def test_scipy_trapz_support_shim():
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# SciPy 1.10 and earlier "clone" trapz in this way, so we have a
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# support shim in place: https://github.com/scipy/scipy/issues/17811
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# That should be removed eventually. This test copies what SciPy does.
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# Hopefully removable 1 year after SciPy 1.11; shim added to NumPy 1.25.
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import types
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import functools
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def _copy_func(f):
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# Based on http://stackoverflow.com/a/6528148/190597 (Glenn Maynard)
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g = types.FunctionType(f.__code__, f.__globals__, name=f.__name__,
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argdefs=f.__defaults__, closure=f.__closure__)
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g = functools.update_wrapper(g, f)
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g.__kwdefaults__ = f.__kwdefaults__
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return g
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trapezoid = _copy_func(np.trapz)
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assert np.trapz([1, 2]) == trapezoid([1, 2])

‎numpy/lib/_arraysetops_impl.py

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"""
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import functools
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import warnings
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import numpy as np
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from numpy.core import overrides
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"""
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Test whether each element of a 1-D array is also present in a second array.
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.. deprecated:: 2.0
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Use :func:`isin` instead of `in1d` for new code.
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Returns a boolean array the same length as `ar1` that is True
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where an element of `ar1` is in `ar2` and False otherwise.
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We recommend using :func:`isin` instead of `in1d` for new code.
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Parameters
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----------
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ar1 : (M,) array_like
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>>> test[mask]
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array([1, 5])
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"""
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# Deprecated in NumPy 2.0, 2023-08-18
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warnings.warn(
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"`in1d` is deprecated. Use `np.isin` instead.",
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DeprecationWarning,
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stacklevel=2
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)
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return _in1d(ar1, ar2, assume_unique, invert, kind=kind)
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def _in1d(ar1, ar2, assume_unique=False, invert=False, *, kind=None):
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# Ravel both arrays, behavior for the first array could be different
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ar1 = np.asarray(ar1).ravel()
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ar2 = np.asarray(ar2).ravel()
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Has the same shape as `element`. The values `element[isin]`
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are in `test_elements`.
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See Also
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--------
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in1d : Flattened version of this function.
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Notes
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[ True, False]])
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"""
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element = np.asarray(element)
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return in1d(element, test_elements, assume_unique=assume_unique,
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invert=invert, kind=kind).reshape(element.shape)
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return _in1d(element, test_elements, assume_unique=assume_unique,
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invert=invert, kind=kind).reshape(element.shape)
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def _union1d_dispatcher(ar1, ar2):
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else:
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ar1 = unique(ar1)
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ar2 = unique(ar2)
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return ar1[in1d(ar1, ar2, assume_unique=True, invert=True)]
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return ar1[_in1d(ar1, ar2, assume_unique=True, invert=True)]

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