Apply ruff/pycodestyle rule W291

W291 Trailing whitespace

Author: DimitriPapadopoulos

doc/source/user/plots/matplotlib1.py

@@ -3,5 +3,5 @@
 
 a = np.array([2, 1, 5, 7, 4, 6, 8, 14, 10, 9, 18, 20, 22])
 
-plt.plot(a) 
+plt.plot(a)
 plt.show()

numpy/__init__.py

@@ -169,7 +169,7 @@
         vstack, where, zeros, zeros_like
     )
 
-    # NOTE: It's still under discussion whether these aliases 
+    # NOTE: It's still under discussion whether these aliases
     # should be removed.
     for ta in ["float96", "float128", "complex192", "complex256"]:
         try:
@@ -184,21 +184,21 @@
         histogram, histogram_bin_edges, histogramdd
     )
     from .lib._nanfunctions_impl import (
-        nanargmax, nanargmin, nancumprod, nancumsum, nanmax, nanmean, 
+        nanargmax, nanargmin, nancumprod, nancumsum, nanmax, nanmean,
         nanmedian, nanmin, nanpercentile, nanprod, nanquantile, nanstd,
         nansum, nanvar
     )
     from .lib._function_base_impl import (
-        select, piecewise, trim_zeros, copy, iterable, percentile, diff, 
+        select, piecewise, trim_zeros, copy, iterable, percentile, diff,
         gradient, angle, unwrap, sort_complex, flip, rot90, extract, place,
         vectorize, asarray_chkfinite, average, bincount, digitize, cov,
         corrcoef, median, sinc, hamming, hanning, bartlett, blackman,
         kaiser, trapezoid, trapz, i0, meshgrid, delete, insert, append,
         interp, quantile
     )
     from .lib._twodim_base_impl import (
-        diag, diagflat, eye, fliplr, flipud, tri, triu, tril, vander, 
-        histogram2d, mask_indices, tril_indices, tril_indices_from, 
+        diag, diagflat, eye, fliplr, flipud, tri, triu, tril, vander,
+        histogram2d, mask_indices, tril_indices, tril_indices_from,
         triu_indices, triu_indices_from
     )
     from .lib._shape_base_impl import (
@@ -207,7 +207,7 @@
         take_along_axis, tile, vsplit
     )
     from .lib._type_check_impl import (
-        iscomplexobj, isrealobj, imag, iscomplex, isreal, nan_to_num, real, 
+        iscomplexobj, isrealobj, imag, iscomplex, isreal, nan_to_num, real,
         real_if_close, typename, mintypecode, common_type
     )
     from .lib._arraysetops_impl import (
@@ -232,7 +232,7 @@
     )
     from .lib._index_tricks_impl import (
         diag_indices_from, diag_indices, fill_diagonal, ndindex, ndenumerate,
-        ix_, c_, r_, s_, ogrid, mgrid, unravel_index, ravel_multi_index, 
+        ix_, c_, r_, s_, ogrid, mgrid, unravel_index, ravel_multi_index,
         index_exp
     )
 
@@ -246,7 +246,7 @@
     # (experimental label) are not added here, because `from numpy import *`
     # must not raise any warnings - that's too disruptive.
     __numpy_submodules__ = {
-        "linalg", "fft", "dtypes", "random", "polynomial", "ma", 
+        "linalg", "fft", "dtypes", "random", "polynomial", "ma",
         "exceptions", "lib", "ctypeslib", "testing", "typing",
         "f2py", "test", "rec", "char", "core", "strings",
     }
@@ -419,7 +419,7 @@ def __dir__():
             globals().keys() | __numpy_submodules__
         )
         public_symbols -= {
-            "matrixlib", "matlib", "tests", "conftest", "version", 
+            "matrixlib", "matlib", "tests", "conftest", "version",
             "compat", "distutils", "array_api"
         }
         return list(public_symbols)
@@ -495,7 +495,7 @@ def _mac_os_check():
     def hugepage_setup():
         """
         We usually use madvise hugepages support, but on some old kernels it
-        is slow and thus better avoided. Specifically kernel version 4.6 
+        is slow and thus better avoided. Specifically kernel version 4.6
         had a bug fix which probably fixed this:
         https://github.com/torvalds/linux/commit/7cf91a98e607c2f935dbcc177d70011e95b8faff
         """
@@ -504,7 +504,7 @@ def hugepage_setup():
             # If there is an issue with parsing the kernel version,
             # set use_hugepage to 0. Usage of LooseVersion will handle
             # the kernel version parsing better, but avoided since it
-            # will increase the import time. 
+            # will increase the import time.
             # See: #16679 for related discussion.
             try:
                 use_hugepage = 1

numpy/_core/_add_newdocs_scalars.py

@@ -294,10 +294,10 @@ def add_newdoc_for_scalar_type(obj, fixed_aliases, doc):
     or datetime format.
     
     When parsing a string to create a datetime object, if the string contains
-    a trailing timezone (A 'Z' or a timezone offset), the timezone will be 
+    a trailing timezone (A 'Z' or a timezone offset), the timezone will be
     dropped and a User Warning is given.
     
-    Datetime64 objects should be considered to be UTC and therefore have an 
+    Datetime64 objects should be considered to be UTC and therefore have an
     offset of +0000.
 
     >>> np.datetime64(10, 'Y')

numpy/_core/_type_aliases.py

@@ -63,7 +63,7 @@
 
 # extra aliases are added only to `sctypeDict`
 # to support dtype name access, such as`np.dtype("float")`
-_extra_aliases = {  
+_extra_aliases = {
     "float": "float64",
     "complex": "complex128",
     "object": "object_",
@@ -104,8 +104,8 @@
 
     # find proper group for each concrete type
     for type_group, abstract_type in [
-        ("int", ma.signedinteger), ("uint", ma.unsignedinteger), 
-        ("float", ma.floating), ("complex", ma.complexfloating), 
+        ("int", ma.signedinteger), ("uint", ma.unsignedinteger),
+        ("float", ma.floating), ("complex", ma.complexfloating),
         ("others", ma.generic)
     ]:
         if issubclass(concrete_type, abstract_type):

numpy/_core/code_generators/ufunc_docstrings.py

@@ -3707,7 +3707,7 @@ def add_newdoc(place, name, doc):
     There is more than one definition of sign in common use for complex
     numbers.  The definition used here, :math:`x/|x|`, is the more common
     and useful one, but is different from the one used in numpy prior to
-    version 2.0, :math:`x/\\sqrt{x*x}`, which is equivalent to 
+    version 2.0, :math:`x/\\sqrt{x*x}`, which is equivalent to
     ``sign(x.real) + 0j if x.real != 0 else sign(x.imag) + 0j``.
 
     Examples
@@ -4650,7 +4650,7 @@ def add_newdoc(place, name, doc):
     array(True)
     >>> a = np.array(["hello", "HELLO", "Hello"])
     >>> np.strings.isupper(a)
-    array([False,  True, False]) 
+    array([False,  True, False])
 
     """)
 

numpy/_core/defchararray.pyi

@@ -3,7 +3,7 @@ from typing import (
     overload,
     TypeVar,
     Any,
-    SupportsIndex, 
+    SupportsIndex,
     SupportsInt,
 )
 
@@ -16,8 +16,8 @@ from numpy import (
     int_,
     object_,
     _OrderKACF,
-    _ShapeType_co, 
-    _CharDType, 
+    _ShapeType_co,
+    _CharDType,
     _SupportsBuffer,
 )
 

numpy/_core/einsumfunc.py

@@ -219,7 +219,7 @@ def _optimal_path(input_sets, output_set, idx_dict, memory_limit):
     return path
 
 def _parse_possible_contraction(
-        positions, input_sets, output_set, idx_dict, 
+        positions, input_sets, output_set, idx_dict,
         memory_limit, path_cost, naive_cost
     ):
     """Compute the cost (removed size + flops) and resultant indices for
@@ -290,7 +290,7 @@ def _update_other_results(results, best):
     Parameters
     ----------
     results : list
-        List of contraction results produced by 
+        List of contraction results produced by
         ``_parse_possible_contraction``.
     best : list
         The best contraction of ``results`` i.e. the one that
@@ -833,7 +833,7 @@ def einsum_path(*operands, optimize='greedy', einsum_call=False):
 
     >>> print(path_info[0])
     ['einsum_path', (0, 2), (0, 3), (0, 2), (0, 1)]
-    >>> print(path_info[1]) 
+    >>> print(path_info[1])
       Complete contraction:  ea,fb,abcd,gc,hd->efgh # may vary
              Naive scaling:  8
          Optimized scaling:  5
@@ -1183,15 +1183,15 @@ def einsum(*operands, out=None, optimize=False, **kwargs):
     ``np.einsum('i->', a)`` is like :py:func:`np.sum(a) <numpy.sum>`
     if ``a`` is a 1-D array, and ``np.einsum('ii->i', a)``
     is like :py:func:`np.diag(a) <numpy.diag>` if ``a`` is a square 2-D array.
-    The difference is that `einsum` does not allow broadcasting by default. 
+    The difference is that `einsum` does not allow broadcasting by default.
     Additionally ``np.einsum('ij,jh->ih', a, b)`` directly specifies the
     order of the output subscript labels and therefore returns matrix
     multiplication, unlike the example above in implicit mode.
 
     To enable and control broadcasting, use an ellipsis.  Default
     NumPy-style broadcasting is done by adding an ellipsis
     to the left of each term, like ``np.einsum('...ii->...i', a)``.
-    ``np.einsum('...i->...', a)`` is like 
+    ``np.einsum('...i->...', a)`` is like
     :py:func:`np.sum(a, axis=-1) <numpy.sum>` for array ``a`` of any shape.
     To take the trace along the first and last axes,
     you can do ``np.einsum('i...i', a)``, or to do a matrix-matrix
@@ -1413,7 +1413,7 @@ def einsum(*operands, out=None, optimize=False, **kwargs):
 
     Optimal `einsum` (best usage pattern in some use cases): ~110ms
 
-    >>> path = np.einsum_path('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, 
+    >>> path = np.einsum_path('ijk,ilm,njm,nlk,abc->',a,a,a,a,a,
     ...     optimize='optimal')[0]
     >>> for iteration in range(500):
     ...     _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize=path)

numpy/_core/fromnumeric.py

@@ -653,10 +653,10 @@ def transpose(a, axes=None):
         Input array.
     axes : tuple or list of ints, optional
         If specified, it must be a tuple or list which contains a permutation
-        of [0, 1, ..., N-1] where N is the number of axes of `a`. Negative 
-        indices can also be used to specify axes. The i-th axis of the returned 
-        array will correspond to the axis numbered ``axes[i]`` of the input. 
-        If not specified, defaults to ``range(a.ndim)[::-1]``, which reverses 
+        of [0, 1, ..., N-1] where N is the number of axes of `a`. Negative
+        indices can also be used to specify axes. The i-th axis of the returned
+        array will correspond to the axis numbered ``axes[i]`` of the input.
+        If not specified, defaults to ``range(a.ndim)[::-1]``, which reverses
         the order of the axes.
 
     Returns

numpy/_core/multiarray.py

@@ -1,7 +1,7 @@
 """
-Create the numpy._core.multiarray namespace for backward compatibility. 
-In v1.16 the multiarray and umath c-extension modules were merged into 
-a single _multiarray_umath extension module. So we replicate the old 
+Create the numpy._core.multiarray namespace for backward compatibility.
+In v1.16 the multiarray and umath c-extension modules were merged into
+a single _multiarray_umath extension module. So we replicate the old
 namespace by importing from the extension module.
 
 """
@@ -43,7 +43,7 @@
     'unpackbits', 'unravel_index', 'vdot', 'where', 'zeros',
     '_get_promotion_state', '_set_promotion_state']
 
-# For backward compatibility, make sure pickle imports 
+# For backward compatibility, make sure pickle imports
 # these functions from here
 _reconstruct.__module__ = 'numpy._core.multiarray'
 scalar.__module__ = 'numpy._core.multiarray'
@@ -162,10 +162,10 @@ def empty_like(
 def concatenate(arrays, axis=None, out=None, *, dtype=None, casting=None):
     """
     concatenate(
-        (a1, a2, ...), 
-        axis=0, 
-        out=None, 
-        dtype=None, 
+        (a1, a2, ...),
+        axis=0,
+        out=None,
+        dtype=None,
         casting="same_kind"
     )
 
@@ -1465,10 +1465,10 @@ def may_share_memory(a, b, max_work=None):
 def is_busday(dates, weekmask=None, holidays=None, busdaycal=None, out=None):
     """
     is_busday(
-        dates, 
-        weekmask='1111100', 
-        holidays=None, 
-        busdaycal=None, 
+        dates,
+        weekmask='1111100',
+        holidays=None,
+        busdaycal=None,
         out=None
     )
 
@@ -1527,12 +1527,12 @@ def busday_offset(dates, offsets, roll=None, weekmask=None, holidays=None,
                   busdaycal=None, out=None):
     """
     busday_offset(
-        dates, 
-        offsets, 
-        roll='raise', 
-        weekmask='1111100', 
-        holidays=None, 
-        busdaycal=None, 
+        dates,
+        offsets,
+        roll='raise',
+        weekmask='1111100',
+        holidays=None,
+        busdaycal=None,
         out=None
     )
 
@@ -1631,11 +1631,11 @@ def busday_count(begindates, enddates, weekmask=None, holidays=None,
                  busdaycal=None, out=None):
     """
     busday_count(
-        begindates, 
-        enddates, 
-        weekmask='1111100', 
-        holidays=[], 
-        busdaycal=None, 
+        begindates,
+        enddates,
+        weekmask='1111100',
+        holidays=[],
+        busdaycal=None,
         out=None
     )
 
@@ -1715,7 +1715,7 @@ def datetime_as_string(arr, unit=None, timezone=None, casting=None):
     arr : array_like of datetime64
         The array of UTC timestamps to format.
     unit : str
-        One of None, 'auto', or 
+        One of None, 'auto', or
         a :ref:`datetime unit <arrays.dtypes.dateunits>`.
     timezone : {'naive', 'UTC', 'local'} or tzinfo
         Timezone information to use when displaying the datetime. If 'UTC',

numpy/_core/numerictypes.py

@@ -88,8 +88,8 @@
 
 # we add more at the bottom
 __all__ = [
-    'ScalarType', 'typecodes', 'issubdtype', 'datetime_data', 
-    'datetime_as_string', 'busday_offset', 'busday_count', 
+    'ScalarType', 'typecodes', 'issubdtype', 'datetime_data',
+    'datetime_as_string', 'busday_offset', 'busday_count',
     'is_busday', 'busdaycalendar', 'isdtype'
 ]