Merge pull request #21291 from oscargus/spaceandcos

DOC: Add space after argument name

8 files changed, 60 insertions, 61 deletions

diff --git a/numpy/core/code_generators/generate_umath.py b/numpy/core/code_generators/generate_umath.py
index a33b1f790..687b80665 100644
--- a/numpy/core/code_generators/generate_umath.py
+++ b/numpy/core/code_generators/generate_umath.py
@@ -54,10 +54,10 @@ class TypeDescription:
     cfunc_alias : str or none, optional
         Appended to inner loop C function name, e.g., FLOAT_{cfunc_alias}. See make_arrays.
         NOTE: it doesn't support 'astype'
-    simd: list
+    simd : list
         Available SIMD ufunc loops, dispatched at runtime in specified order
         Currently only supported for simples types (see make_arrays)
-    dispatch: str or None, optional
+    dispatch : str or None, optional
         Dispatch-able source name without its extension '.dispatch.c' that
         contains the definition of ufunc, dispatched at runtime depending on the
         specified targets of the dispatch-able source.
diff --git a/numpy/distutils/ccompiler.py b/numpy/distutils/ccompiler.py
index 74e2c51f6..df268d1de 100644
--- a/numpy/distutils/ccompiler.py
+++ b/numpy/distutils/ccompiler.py
@@ -120,7 +120,7 @@ def CCompiler_spawn(self, cmd, display=None, env=None):
     display : str or sequence of str, optional
         The text to add to the log file kept by `numpy.distutils`.
         If not given, `display` is equal to `cmd`.
-    env: a dictionary for environment variables, optional
+    env : a dictionary for environment variables, optional
 
     Returns
     -------
diff --git a/numpy/distutils/ccompiler_opt.py b/numpy/distutils/ccompiler_opt.py
index 6d2114c95..f22c6da63 100644
--- a/numpy/distutils/ccompiler_opt.py
+++ b/numpy/distutils/ccompiler_opt.py
@@ -769,18 +769,18 @@ class _Cache:
 
     Parameters
     ----------
-    cache_path: str or None
+    cache_path : str or None
         The path of cache file, if None then cache in file will disabled.
 
-    *factors:
+    *factors :
         The caching factors that need to utilize next to `conf_cache_factors`.
 
     Attributes
     ----------
-    cache_private: set
+    cache_private : set
         Hold the attributes that need be skipped from "in-memory cache".
 
-    cache_infile: bool
+    cache_infile : bool
         Utilized during initializing this class, to determine if the cache was able
         to loaded from the specified cache path in 'cache_path'.
     """
@@ -1231,12 +1231,12 @@ class _Feature:
 
         Parameters
         ----------
-        names: sequence or None, optional
+        names : sequence or None, optional
             Specify certain CPU features to test it against the **C** compiler.
             if None(default), it will test all current supported features.
             **Note**: feature names must be in upper-case.
 
-        force_flags: list or None, optional
+        force_flags : list or None, optional
             If None(default), default compiler flags for every CPU feature will
             be used during the test.
 
@@ -1305,10 +1305,10 @@ class _Feature:
 
         Parameters
         ----------
-        names: str or sequence of str
+        names : str or sequence of str
             CPU feature name(s) in uppercase.
 
-        keep_origins: bool
+        keep_origins : bool
             if False(default) then the returned set will not contain any
             features from 'names'. This case happens only when two features
             imply each other.
@@ -1498,10 +1498,10 @@ class _Feature:
 
         Parameters
         ----------
-        name: str
+        name : str
             Supported CPU feature name.
 
-        force_flags: list or None, optional
+        force_flags : list or None, optional
             If None(default), the returned flags from `feature_flags()`
             will be used.
 
@@ -1537,10 +1537,10 @@ class _Feature:
 
         Parameters
         ----------
-        name: str
+        name : str
             CPU feature name in uppercase.
 
-        force_flags: list or None, optional
+        force_flags : list or None, optional
             If None(default), default compiler flags for every CPU feature will
             be used during test.
 
@@ -1582,7 +1582,7 @@ class _Feature:
 
         Parameters
         ----------
-        names: str
+        names : str
             CPU feature name in uppercase.
         """
         assert isinstance(name, str)
@@ -1668,10 +1668,10 @@ class _Parse:
 
     Parameters
     ----------
-    cpu_baseline: str or None
+    cpu_baseline : str or None
         minimal set of required CPU features or special options.
 
-    cpu_dispatch: str or None
+    cpu_dispatch : str or None
         dispatched set of additional CPU features or special options.
 
     Special options can be:
@@ -1804,7 +1804,7 @@ class _Parse:
 
         Parameters
         ----------
-        source: str
+        source : str
             the path of **C** source file.
 
         Returns
@@ -2243,7 +2243,7 @@ class CCompilerOpt(_Config, _Distutils, _Cache, _CCompiler, _Feature, _Parse):
             Path of parent directory for the generated headers and wrapped sources.
             If None(default) the files will generated in-place.
 
-        ccompiler: CCompiler
+        ccompiler : CCompiler
             Distutils `CCompiler` instance to be used for compilation.
             If None (default), the provided instance during the initialization
             will be used instead.
diff --git a/numpy/lib/format.py b/numpy/lib/format.py
index 3967b43ee..264fff8d6 100644
--- a/numpy/lib/format.py
+++ b/numpy/lib/format.py
@@ -421,10 +421,10 @@ def _write_array_header(fp, d, version=None):
     d : dict
         This has the appropriate entries for writing its string representation
         to the header of the file.
-    version: tuple or None
-        None means use oldest that works
-        explicit version will raise a ValueError if the format does not
-        allow saving this data.  Default: None
+    version : tuple or None
+        None means use oldest that works. Providing an explicit version will
+        raise a ValueError if the format does not allow saving this data.
+        Default: None
     """
     header = ["{"]
     for key, value in sorted(d.items()):
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index f69604d6e..d611dd225 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -168,7 +168,7 @@ def rot90(m, k=1, axes=(0, 1)):
         Array of two or more dimensions.
     k : integer
         Number of times the array is rotated by 90 degrees.
-    axes: (2,) array_like
+    axes : (2,) array_like
         The array is rotated in the plane defined by the axes.
         Axes must be different.
 
@@ -1656,7 +1656,7 @@ def unwrap(p, discont=None, axis=-1, *, period=2*pi):
         larger than ``period/2``.
     axis : int, optional
         Axis along which unwrap will operate, default is the last axis.
-    period: float, optional
+    period : float, optional
         Size of the range over which the input wraps. By default, it is
         ``2 pi``.
 
@@ -2974,15 +2974,14 @@ def bartlett(M):
               \\frac{M-1}{2} - \\left|n - \\frac{M-1}{2}\\right|
               \\right)
 
-    Most references to the Bartlett window come from the signal
-    processing literature, where it is used as one of many windowing
-    functions for smoothing values.  Note that convolution with this
-    window produces linear interpolation.  It is also known as an
-    apodization (which means"removing the foot", i.e. smoothing
-    discontinuities at the beginning and end of the sampled signal) or
-    tapering function. The fourier transform of the Bartlett is the product
-    of two sinc functions.
-    Note the excellent discussion in Kanasewich.
+    Most references to the Bartlett window come from the signal processing
+    literature, where it is used as one of many windowing functions for
+    smoothing values.  Note that convolution with this window produces linear
+    interpolation.  It is also known as an apodization (which means "removing
+    the foot", i.e. smoothing discontinuities at the beginning and end of the
+    sampled signal) or tapering function. The Fourier transform of the
+    Bartlett window is the product of two sinc functions. Note the excellent
+    discussion in Kanasewich [2]_.
 
     References
     ----------
@@ -3075,7 +3074,7 @@ def hanning(M):
     -----
     The Hanning window is defined as
 
-    .. math::  w(n) = 0.5 - 0.5cos\\left(\\frac{2\\pi{n}}{M-1}\\right)
+    .. math::  w(n) = 0.5 - 0.5\\cos\\left(\\frac{2\\pi{n}}{M-1}\\right)
                \\qquad 0 \\leq n \\leq M-1
 
     The Hanning was named for Julius von Hann, an Austrian meteorologist.
@@ -3179,7 +3178,7 @@ def hamming(M):
     -----
     The Hamming window is defined as
 
-    .. math::  w(n) = 0.54 - 0.46cos\\left(\\frac{2\\pi{n}}{M-1}\\right)
+    .. math::  w(n) = 0.54 - 0.46\\cos\\left(\\frac{2\\pi{n}}{M-1}\\right)
                \\qquad 0 \\leq n \\leq M-1
 
     The Hamming was named for R. W. Hamming, an associate of J. W. Tukey
@@ -4731,10 +4730,10 @@ def trapz(y, x=None, dx=1.0, axis=-1):
     Returns
     -------
     trapz : float or ndarray
-        Definite integral of 'y' = n-dimensional array as approximated along
-        a single axis by the trapezoidal rule. If 'y' is a 1-dimensional array,
-        then the result is a float. If 'n' is greater than 1, then the result
-        is an 'n-1' dimensional array.
+        Definite integral of `y` = n-dimensional array as approximated along
+        a single axis by the trapezoidal rule. If `y` is a 1-dimensional array,
+        then the result is a float. If `n` is greater than 1, then the result
+        is an `n`-1 dimensional array.
 
     See Also
     --------
@@ -4865,9 +4864,9 @@ def meshgrid(*xi, copy=True, sparse=False, indexing='xy'):
     Returns
     -------
     X1, X2,..., XN : ndarray
-        For vectors `x1`, `x2`,..., 'xn' with lengths ``Ni=len(xi)`` ,
-        return ``(N1, N2, N3,...Nn)`` shaped arrays if indexing='ij'
-        or ``(N2, N1, N3,...Nn)`` shaped arrays if indexing='xy'
+        For vectors `x1`, `x2`,..., `xn` with lengths ``Ni=len(xi)``,
+        returns ``(N1, N2, N3,..., Nn)`` shaped arrays if indexing='ij'
+        or ``(N2, N1, N3,..., Nn)`` shaped arrays if indexing='xy'
         with the elements of `xi` repeated to fill the matrix along
         the first dimension for `x1`, the second for `x2` and so on.
 
@@ -5016,7 +5015,7 @@ def delete(arr, obj, axis=None):
     >>> mask[[0,2,4]] = False
     >>> result = arr[mask,...]
 
-    Is equivalent to `np.delete(arr, [0,2,4], axis=0)`, but allows further
+    Is equivalent to ``np.delete(arr, [0,2,4], axis=0)``, but allows further
     use of `mask`.
 
     Examples
@@ -5207,9 +5206,9 @@ def insert(arr, obj, values, axis=None):
 
     Notes
     -----
-    Note that for higher dimensional inserts `obj=0` behaves very different
-    from `obj=[0]` just like `arr[:,0,:] = values` is different from
-    `arr[:,[0],:] = values`.
+    Note that for higher dimensional inserts ``obj=0`` behaves very different
+    from ``obj=[0]`` just like ``arr[:,0,:] = values`` is different from
+    ``arr[:,[0],:] = values``.
 
     Examples
     --------
diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py
index 90424aab4..33b49127c 100644
--- a/numpy/lib/npyio.py
+++ b/numpy/lib/npyio.py
@@ -287,7 +287,7 @@ def load(file, mmap_mode=None, allow_pickle=False, fix_imports=True,
     ----------
     file : file-like object, string, or pathlib.Path
         The file to read. File-like objects must support the
-        ``seek()`` and ``read()`` methods and must always 
+        ``seek()`` and ``read()`` methods and must always
         be opened in binary mode.  Pickled files require that the
         file-like object support the ``readline()`` method as well.
     mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional
@@ -737,9 +737,9 @@ def _ensure_ndmin_ndarray(a, *, ndmin: int):
     """This is a helper function of loadtxt and genfromtxt to ensure
         proper minimum dimension as requested
 
-        ndim: int. Supported values 1, 2, 3
-                   ^^ whenever this changes, keep in sync with
-                      _ensure_ndmin_ndarray_check_param
+        ndim : int. Supported values 1, 2, 3
+                    ^^ whenever this changes, keep in sync with
+                       _ensure_ndmin_ndarray_check_param
     """
     # Verify that the array has at least dimensions `ndmin`.
     # Tweak the size and shape of the arrays - remove extraneous dimensions
diff --git a/numpy/ma/core.py b/numpy/ma/core.py
index 9c9dfac68..ed17b1b22 100644
--- a/numpy/ma/core.py
+++ b/numpy/ma/core.py
@@ -405,7 +405,7 @@ def _recursive_set_fill_value(fillvalue, dt):
 
     Returns
     -------
-    val: tuple
+    val : tuple
         A tuple of values corresponding to the structured fill value.
 
     """
@@ -2842,7 +2842,7 @@ class MaskedArray(ndarray):
         # still has the _mask attribute like MaskedArrays
         if hasattr(data, '_mask') and not isinstance(data, ndarray):
             _data._mask = data._mask
-            # FIXME: should we set `_data._sharedmask = True`? 
+            # FIXME: should we set `_data._sharedmask = True`?
         # Process mask.
         # Type of the mask
         mdtype = make_mask_descr(_data.dtype)
@@ -3584,7 +3584,7 @@ class MaskedArray(ndarray):
         By default, assigning definite values to masked array entries will
         unmask them.  When `hardmask` is ``True``, the mask will not change
         through assignments.
-        
+
         See Also
         --------
         ma.MaskedArray.harden_mask
@@ -8208,7 +8208,7 @@ class _convert2ma:
 
 
 arange = _convert2ma(
-    'arange', 
+    'arange',
     params=dict(fill_value=None, hardmask=False),
     np_ret='arange : ndarray',
     np_ma_ret='arange : MaskedArray',
@@ -8226,7 +8226,7 @@ diff = _convert2ma(
     np_ma_ret='diff : MaskedArray',
 )
 empty = _convert2ma(
-    'empty', 
+    'empty',
     params=dict(fill_value=None, hardmask=False),
     np_ret='out : ndarray',
     np_ma_ret='out : MaskedArray',
@@ -8247,7 +8247,7 @@ fromfunction = _convert2ma(
    np_ma_ret='fromfunction: MaskedArray',
 )
 identity = _convert2ma(
-    'identity', 
+    'identity',
     params=dict(fill_value=None, hardmask=False),
     np_ret='out : ndarray',
     np_ma_ret='out : MaskedArray',
diff --git a/numpy/random/_pickle.py b/numpy/random/_pickle.py
index a32f64f4a..5e89071e8 100644
--- a/numpy/random/_pickle.py
+++ b/numpy/random/_pickle.py
@@ -25,7 +25,7 @@ def __generator_ctor(bit_generator_name='MT19937'):
 
     Returns
     -------
-    rg: Generator
+    rg : Generator
         Generator using the named core BitGenerator
     """
     if bit_generator_name in BitGenerators:
@@ -48,7 +48,7 @@ def __bit_generator_ctor(bit_generator_name='MT19937'):
 
     Returns
     -------
-    bit_generator: BitGenerator
+    bit_generator : BitGenerator
         BitGenerator instance
     """
     if bit_generator_name in BitGenerators:
@@ -71,7 +71,7 @@ def __randomstate_ctor(bit_generator_name='MT19937'):
 
     Returns
     -------
-    rs: RandomState
+    rs : RandomState
         Legacy RandomState using the named core BitGenerator
     """
     if bit_generator_name in BitGenerators: