MAINT: Misc. typo fixes (numpy#13664)

* DOC, MAINT: Misc. typo fixes

Found via `codespell`

benchmarks/benchmarks/bench_function_base.py

@@ -269,7 +269,7 @@ def setup(self):
     def time_sort_worst(self):
         np.sort(self.worst)
 
-    # Retain old benchmark name for backward compatability
+    # Retain old benchmark name for backward compatibility
     time_sort_worst.benchmark_name = "bench_function_base.Sort.time_sort_worst"
 
 

doc/DISTUTILS.rst.txt

@@ -302,7 +302,7 @@ Template files
 
 NumPy Distutils preprocesses C source files (extension: :file:`.c.src`) written
 in a custom templating language to generate C code. The :c:data:`@` symbol is
-used to wrap macro-style variables to empower a string substitution mechansim
+used to wrap macro-style variables to empower a string substitution mechanism
 that might describe (for instance) a set of data types.
 
 As a more detailed scenario, a loop in the NumPy C source code may

doc/Makefile

@@ -5,7 +5,7 @@
 # issues with the amendments to PYTHONPATH and install paths (see DIST_VARS).
 
 # Use explicit "version_info" indexing since make cannot handle colon characters, and
-# evaluate it now to allow easier debugging when printing the varaible
+# evaluate it now to allow easier debugging when printing the variable
 
 PYVER:=$(shell python3 -c 'from sys import version_info as v; print("{0}.{1}".format(v[0], v[1]))')
 PYTHON = python$(PYVER)

doc/source/reference/c-api.array.rst

@@ -219,7 +219,7 @@ From scratch
 
     If *data* is ``NULL``, then new unitinialized memory will be allocated and
     *flags* can be non-zero to indicate a Fortran-style contiguous array. Use
-    :c:func:`PyArray_FILLWBYTE` to initialze the memory.
+    :c:func:`PyArray_FILLWBYTE` to initialize the memory.
 
     If *data* is not ``NULL``, then it is assumed to point to the memory
     to be used for the array and the *flags* argument is used as the

doc/source/reference/c-api.coremath.rst

@@ -185,15 +185,15 @@ Those can be useful for precise floating point comparison.
     * NPY_FPE_INVALID
 
     Note that :c:func:`npy_get_floatstatus_barrier` is preferable as it prevents
-    agressive compiler optimizations reordering the call relative to
+    aggressive compiler optimizations reordering the call relative to
     the code setting the status, which could lead to incorrect results.
 
     .. versionadded:: 1.9.0
 
 .. c:function:: int npy_get_floatstatus_barrier(char*)
 
     Get floating point status. A pointer to a local variable is passed in to
-    prevent aggresive compiler optimizations from reodering this function call
+    prevent aggressive compiler optimizations from reodering this function call
     relative to the code setting the status, which could lead to incorrect
     results.
 
@@ -211,15 +211,15 @@ Those can be useful for precise floating point comparison.
     Clears the floating point status. Returns the previous status mask.
 
     Note that :c:func:`npy_clear_floatstatus_barrier` is preferable as it
-    prevents agressive compiler optimizations reordering the call relative to
+    prevents aggressive compiler optimizations reordering the call relative to
     the code setting the status, which could lead to incorrect results.
 
     .. versionadded:: 1.9.0
 
 .. c:function:: int npy_clear_floatstatus_barrier(char*)
 
     Clears the floating point status. A pointer to a local variable is passed in to
-    prevent aggresive compiler optimizations from reodering this function call.
+    prevent aggressive compiler optimizations from reodering this function call.
     Returns the previous status mask.
 
     .. versionadded:: 1.15.0

doc/source/reference/random/index.rst

@@ -5,7 +5,7 @@
 numpy.random
 ============
 
-Numpy's random number routines produce psuedo random numbers using
+Numpy's random number routines produce pseudo random numbers using
 combinations of a `BitGenerator` to create sequences and a `Generator`
 to use those sequences to sample from different statistical distributions:
 
@@ -41,7 +41,7 @@ which will be faster than the legacy methods in `RandomState`
 
 `Generator` can be used as a direct replacement for `~RandomState`, although
 the random values are generated by `~xoshiro256.Xoshiro256`. The
-`Generator` holds an instance of a BitGenerator. It is accessable as
+`Generator` holds an instance of a BitGenerator. It is accessible as
 ``gen.bit_generator``.
 
 .. code-block:: python
@@ -127,7 +127,7 @@ What's New or Different
   :ref:`Cython <randomgen_cython>`.
 * `~.Generator.integers` is now the canonical way to generate integer
   random numbers from a discrete uniform distribution. The ``rand`` and
-  ``randn`` methods are only availabe through the legacy `~.RandomState`.
+  ``randn`` methods are only available through the legacy `~.RandomState`.
   The ``endpoint`` keyword can be used to specify open or closed intervals.
   This replaces both ``randint`` and the deprecated ``random_integers``.
 * `~.Generator.random` is now the canonical way to generate floating-point

doc/source/reference/random/new-or-different.rst

@@ -54,7 +54,7 @@ And in more detail:
   `~.Generator.standard_gamma`.
 * `~.Generator.integers` is now the canonical way to generate integer
   random numbers from a discrete uniform distribution. The ``rand`` and
-  ``randn`` methods are only availabe through the legacy `~.RandomState`.
+  ``randn`` methods are only available through the legacy `~.RandomState`.
   This replaces both ``randint`` and the deprecated ``random_integers``.
 * The Box-Muller used to produce NumPy's normals is no longer available.
 * All bit generators can produce doubles, uint64s and

numpy/core/_dtype_ctypes.py

@@ -1,7 +1,7 @@
 """
 Conversion from ctypes to dtype.
 
-In an ideal world, we could acheive this through the PEP3118 buffer protocol,
+In an ideal world, we could achieve this through the PEP3118 buffer protocol,
 something like::
 
     def dtype_from_ctypes_type(t):

numpy/core/einsumfunc.py

@@ -287,7 +287,7 @@ def _update_other_results(results, best):
     Returns
     -------
     mod_results : list
-        The list of modifed results, updated with outcome of ``best`` contraction.
+        The list of modified results, updated with outcome of ``best`` contraction.
     """
 
     best_con = best[1]

numpy/core/numerictypes.py

@@ -275,7 +275,7 @@ def obj2sctype(rep, default=None):
     <class 'list'>
 
     """
-    # prevent abtract classes being upcast
+    # prevent abstract classes being upcast
     if isinstance(rep, type) and issubclass(rep, generic):
         return rep
     # extract dtype from arrays

numpy/core/shape_base.py

@@ -555,10 +555,10 @@ def _concatenate_shapes(shapes, axis):
         ret[(slice(None),) * axis + sl_c] == c
         ```
 
-        Thses are called slice prefixes since they are used in the recursive
+        These are called slice prefixes since they are used in the recursive
         blocking algorithm to compute the left-most slices during the
         recursion. Therefore, they must be prepended to rest of the slice
-        that was computed deeper in the recusion.
+        that was computed deeper in the recursion.
 
         These are returned as tuples to ensure that they can quickly be added
         to existing slice tuple without creating a new tuple everytime.
@@ -841,9 +841,9 @@ def block(arrays):
         return _block_concatenate(arrays, list_ndim, result_ndim)
 
 
-# Theses helper functions are mostly used for testing.
+# These helper functions are mostly used for testing.
 # They allow us to write tests that directly call `_block_slicing`
-# or `_block_concatenate` without blocking large arrays to forse the wisdom
+# or `_block_concatenate` without blocking large arrays to force the wisdom
 # to trigger the desired path.
 def _block_setup(arrays):
     """

numpy/core/src/multiarray/arraytypes.c.src

@@ -2260,7 +2260,7 @@ VOID_copyswapn (char *dst, npy_intp dstride, char *src, npy_intp sstride,
         char *dstptr, *srcptr;
         /*
          * In certain cases subarray copy can be optimized. This is when
-         * swapping is unecessary and the subarrays data type can certainly
+         * swapping is unnecessary and the subarrays data type can certainly
          * be simply copied (no object, fields, subarray, and not a user dtype).
          */
         npy_bool can_optimize_subarray = (!swap &&
@@ -2347,7 +2347,7 @@ VOID_copyswap (char *dst, char *src, int swap, PyArrayObject *arr)
         int subitemsize;
         /*
          * In certain cases subarray copy can be optimized. This is when
-         * swapping is unecessary and the subarrays data type can certainly
+         * swapping is unnecessary and the subarrays data type can certainly
          * be simply copied (no object, fields, subarray, and not a user dtype).
          */
         npy_bool can_optimize_subarray = (!swap &&

numpy/core/src/multiarray/ctors.c

@@ -3170,7 +3170,7 @@ PyArray_Zeros(int nd, npy_intp *dims, PyArray_Descr *type, int is_f_order)
  * Empty
  *
  * accepts NULL type
- * steals referenct to type
+ * steals a reference to type
  */
 NPY_NO_EXPORT PyObject *
 PyArray_Empty(int nd, npy_intp *dims, PyArray_Descr *type, int is_f_order)

numpy/core/src/multiarray/methods.c

@@ -1687,7 +1687,7 @@ array_reduce(PyArrayObject *self, PyObject *NPY_UNUSED(args))
 
        Notice because Python does not describe a mechanism to write
        raw data to the pickle, this performs a copy to a string first
-       This issue is now adressed in protocol 5, where a buffer is serialized
+       This issue is now addressed in protocol 5, where a buffer is serialized
        instead of a string,
     */
 

numpy/core/src/npymath/npy_math_complex.c.src

@@ -1246,7 +1246,7 @@ _clog_for_large_values@c@(@type@ x, @type@ y,
      * Divide x and y by E, and then add 1 to the logarithm.  This depends
      * on E being larger than sqrt(2).
      * Dividing by E causes an insignificant loss of accuracy; however
-     * this method is still poor since it is uneccessarily slow.
+     * this method is still poor since it is unnecessarily slow.
      */
     if (ax > @TMAX@ / 2) {
         *rr = npy_log@c@(npy_hypot@c@(x / NPY_E@c@, y / NPY_E@c@)) + 1;

numpy/core/src/npysort/selection.c.src

@@ -40,7 +40,7 @@ static NPY_INLINE void store_pivot(npy_intp pivot, npy_intp kth,
     }
 
     /*
-     * If pivot is the requested kth store it, overwritting other pivots if
+     * If pivot is the requested kth store it, overwriting other pivots if
      * required. This must be done so iterative partition can work without
      * manually shifting lower data offset by kth each time
      */

numpy/core/src/umath/ufunc_type_resolution.c

@@ -1737,7 +1737,7 @@ set_ufunc_loop_data_types(PyUFuncObject *self, PyArrayObject **op,
         }
         /*
          * For outputs, copy the dtype from op[0] if the type_num
-         * matches, similarly to preserve metdata.
+         * matches, similarly to preserve metadata.
          */
         else if (i >= nin && op[0] != NULL &&
                             PyArray_DESCR(op[0])->type_num == type_nums[i]) {

numpy/core/tests/test_dtype.py

@@ -603,7 +603,7 @@ class TestStructuredDtypeSparseFields(object):
                                     'offsets':[4]}, (2, 3))])
 
     @pytest.mark.xfail(reason="inaccessible data is changed see gh-12686.")
-    @pytest.mark.valgrind_error(reason="reads from unitialized buffers.")
+    @pytest.mark.valgrind_error(reason="reads from uninitialized buffers.")
     def test_sparse_field_assignment(self):
         arr = np.zeros(3, self.dtype)
         sparse_arr = arr.view(self.sparse_dtype)

numpy/core/tests/test_half.py

@@ -104,7 +104,7 @@ def test_half_conversion_rounding(self, float_t, shift, offset):
         # logic will be necessary, an arbitrarily small offset should cause
         # normal up/down rounding always.
 
-        # Calculate the expecte pattern:
+        # Calculate the expected pattern:
         cmp_patterns = f16s_patterns[1:-1].copy()
 
         if shift == "down" and offset != "up":

numpy/core/tests/test_nditer.py

@@ -2292,7 +2292,7 @@ def test_dtype_copy(self):
         assert_equal(vals, [[0, 1, 2], [3, 4, 5]])
         vals = None
 
-        # writebackifcopy - using conext manager
+        # writebackifcopy - using context manager
         a = arange(6, dtype='f4').reshape(2, 3)
         i, j = np.nested_iters(a, [[0], [1]],
                             op_flags=['readwrite', 'updateifcopy'],

numpy/core/tests/test_scalar_methods.py

@@ -64,7 +64,7 @@ def test_against_known_values(self):
                      R(*np.double(2.1).as_integer_ratio()))
         assert_equal(R(-4728779608739021, 2251799813685248),
                      R(*np.double(-2.1).as_integer_ratio()))
-        # longdouble is platform depedent
+        # longdouble is platform dependent
 
     @pytest.mark.parametrize("ftype, frac_vals, exp_vals", [
         # dtype test cases generated using hypothesis

numpy/core/tests/test_scalarprint.py

@@ -51,7 +51,7 @@ def check(v):
 
     def test_py2_float_print(self):
         # gh-10753
-        # In python2, the python float type implements an obsolte method
+        # In python2, the python float type implements an obsolete method
         # tp_print, which overrides tp_repr and tp_str when using "print" to
         # output to a "real file" (ie, not a StringIO). Make sure we don't
         # inherit it.

numpy/distutils/fcompiler/__init__.py

@@ -484,11 +484,11 @@ def customize(self, dist = None):
         # XXX Assuming that free format is default for f90 compiler.
         fix = self.command_vars.compiler_fix
         # NOTE: this and similar examples are probably just
-        # exluding --coverage flag when F90 = gfortran --coverage
+        # excluding --coverage flag when F90 = gfortran --coverage
         # instead of putting that flag somewhere more appropriate
         # this and similar examples where a Fortran compiler
         # environment variable has been customized by CI or a user
-        # should perhaps eventually be more throughly tested and more
+        # should perhaps eventually be more thoroughly tested and more
         # robustly handled
         if fix:
             fix = _shell_utils.NativeParser.split(fix)

numpy/doc/basics.py

@@ -276,7 +276,7 @@
 The behaviour of NumPy and Python integer types differs significantly for
 integer overflows and may confuse users expecting NumPy integers to behave
 similar to Python's ``int``. Unlike NumPy, the size of Python's ``int`` is
-flexible. This means Python integers may expand to accomodate any integer and
+flexible. This means Python integers may expand to accommodate any integer and
 will not overflow.
 
 NumPy provides `numpy.iinfo` and `numpy.finfo` to verify the

numpy/doc/indexing.py

@@ -1,4 +1,5 @@
-"""==============
+"""
+==============
 Array indexing
 ==============
 
@@ -107,7 +108,7 @@
 
 It is possible to use special features to effectively increase the
 number of dimensions in an array through indexing so the resulting
-array aquires the shape needed for use in an expression or with a
+array acquires the shape needed for use in an expression or with a
 specific function.
 
 Index arrays

numpy/lib/function_base.py

@@ -1,7 +1,7 @@
 from __future__ import division, absolute_import, print_function
 
 try:
-    # Accessing collections abstact classes from collections
+    # Accessing collections abstract classes from collections
     # has been deprecated since Python 3.3
     import collections.abc as collections_abc
 except ImportError:
@@ -4341,7 +4341,7 @@ def delete(arr, obj, axis=None):
         else:
             slobj[axis] = slice(None, start)
             new[tuple(slobj)] = arr[tuple(slobj)]
-        # copy end chunck
+        # copy end chunk
         if stop == N:
             pass
         else:

numpy/lib/recfunctions.py

@@ -146,7 +146,7 @@ def get_names(adtype):
 def get_names_flat(adtype):
     """
     Returns the field names of the input datatype as a tuple. Nested structure
-    are flattend beforehand.
+    are flattened beforehand.
 
     Parameters
     ----------

numpy/lib/tests/test_histograms.py

@@ -798,7 +798,7 @@ def test_density_non_uniform_2d(self):
         hist, edges = histogramdd((y, x), bins=(y_edges, x_edges))
         assert_equal(hist, relative_areas)
 
-        # resulting histogram should be uniform, since counts and areas are propotional
+        # resulting histogram should be uniform, since counts and areas are proportional
         hist, edges = histogramdd((y, x), bins=(y_edges, x_edges), density=True)
         assert_equal(hist, 1 / (8*8))
 

numpy/random/generator.pyx

@@ -440,7 +440,7 @@ cdef class Generator:
                              'when required.')
 
         # Implementation detail: the old API used a masked method to generate
-        # bounded uniform integers. Lemire's method is preferrable since it is
+        # bounded uniform integers. Lemire's method is preferable since it is
         # faster. randomgen allows a choice, we will always use the faster one.
         cdef bint _masked = True
 

numpy/random/mtrand.pyx

@@ -621,7 +621,7 @@ cdef class RandomState:
                           'ValueError', DeprecationWarning)
 
         # Implementation detail: the use a masked method to generate
-        # bounded uniform integers. Lemire's method is preferrable since it is
+        # bounded uniform integers. Lemire's method is preferable since it is
         # faster. randomgen allows a choice, we will always use the slower but
         # backward compatible one.
         cdef bint _masked = True

numpy/random/src/philox/philox-benchmark.c

@@ -5,7 +5,7 @@
  *
  *  gcc philox-benchmark.c -O3 -o philox-benchmark
  *
- * Requres the Random123 directory containing header files to be located in the
+ * Requires the Random123 directory containing header files to be located in the
  * same directory (not included).
  */
 #include "Random123/philox.h"

numpy/random/src/philox/philox-test-data-gen.c

@@ -7,7 +7,7 @@
  *  gcc philox-test-data-gen.c -o philox-test-data-gen
  *  ./philox-test-data-gen
  *
- * Requres the Random123 directory containing header files to be located in the
+ * Requires the Random123 directory containing header files to be located in the
  * same directory (not included).
  *
  */

numpy/random/src/threefry/threefry-benchmark.c

@@ -5,7 +5,7 @@
  *
  *  gcc threefry-benchmark.c -O3 -o threefry-benchmark
  *
- * Requres the Random123 directory containing header files to be located in the
+ * Requires the Random123 directory containing header files to be located in the
  * same directory (not included).
  */
 #include "Random123/threefry.h"

numpy/random/src/threefry/threefry-test-data-gen.c

@@ -8,7 +8,7 @@
  * threefry-test-data-gen
  *  ./threefry-test-data-gen
  *
- * Requres the Random123 directory containing header files to be located in the
+ * Requires the Random123 directory containing header files to be located in the
  * same directory (not included).
  *
  */