===========
NumPy C-API
===========
::
unsigned int
PyArray_GetNDArrayCVersion(void )
Included at the very first so not auto-grabbed and thus not labeled.
::
int
PyArray_SetNumericOps(PyObject *dict)
Set internal structure with number functions that all arrays will use
::
PyObject *
PyArray_GetNumericOps(void )
Get dictionary showing number functions that all arrays will use
::
int
PyArray_INCREF(PyArrayObject *mp)
For object arrays, increment all internal references.
::
int
PyArray_XDECREF(PyArrayObject *mp)
Decrement all internal references for object arrays.
(or arrays with object fields)
::
void
PyArray_SetStringFunction(PyObject *op, int repr)
Set the array print function to be a Python function.
::
PyArray_Descr *
PyArray_DescrFromType(int type)
Get the PyArray_Descr structure for a type.
::
PyObject *
PyArray_TypeObjectFromType(int type)
Get a typeobject from a type-number -- can return NULL.
New reference
::
char *
PyArray_Zero(PyArrayObject *arr)
Get pointer to zero of correct type for array.
::
char *
PyArray_One(PyArrayObject *arr)
Get pointer to one of correct type for array
::
PyObject *
PyArray_CastToType(PyArrayObject *arr, PyArray_Descr *dtype, int
is_f_order)
For backward compatibility
Cast an array using typecode structure.
steals reference to dtype --- cannot be NULL
This function always makes a copy of arr, even if the dtype
doesn't change.
::
int
PyArray_CastTo(PyArrayObject *out, PyArrayObject *mp)
Cast to an already created array.
::
int
PyArray_CastAnyTo(PyArrayObject *out, PyArrayObject *mp)
Cast to an already created array. Arrays don't have to be "broadcastable"
Only requirement is they have the same number of elements.
::
int
PyArray_CanCastSafely(int fromtype, int totype)
Check the type coercion rules.
::
npy_bool
PyArray_CanCastTo(PyArray_Descr *from, PyArray_Descr *to)
leaves reference count alone --- cannot be NULL
PyArray_CanCastTypeTo is equivalent to this, but adds a 'casting'
parameter.
::
int
PyArray_ObjectType(PyObject *op, int minimum_type)
Return the typecode of the array a Python object would be converted to
Returns the type number the result should have, or NPY_NOTYPE on error.
::
PyArray_Descr *
PyArray_DescrFromObject(PyObject *op, PyArray_Descr *mintype)
new reference -- accepts NULL for mintype
::
PyArrayObject **
PyArray_ConvertToCommonType(PyObject *op, int *retn)
This function is only used in one place within NumPy and should
generally be avoided. It is provided mainly for backward compatibility.
The user of the function has to free the returned array.
::
PyArray_Descr *
PyArray_DescrFromScalar(PyObject *sc)
Return descr object from array scalar.
New reference
::
PyArray_Descr *
PyArray_DescrFromTypeObject(PyObject *type)
::
npy_intp
PyArray_Size(PyObject *op)
Compute the size of an array (in number of items)
::
PyObject *
PyArray_Scalar(void *data, PyArray_Descr *descr, PyObject *base)
Get scalar-equivalent to a region of memory described by a descriptor.
::
PyObject *
PyArray_FromScalar(PyObject *scalar, PyArray_Descr *outcode)
Get 0-dim array from scalar
0-dim array from array-scalar object
always contains a copy of the data
unless outcode is NULL, it is of void type and the referrer does
not own it either.
steals reference to outcode
::
void
PyArray_ScalarAsCtype(PyObject *scalar, void *ctypeptr)
Convert to c-type
no error checking is performed -- ctypeptr must be same type as scalar
in case of flexible type, the data is not copied
into ctypeptr which is expected to be a pointer to pointer
::
int
PyArray_CastScalarToCtype(PyObject *scalar, void
*ctypeptr, PyArray_Descr *outcode)
Cast Scalar to c-type
The output buffer must be large-enough to receive the value
Even for flexible types which is different from ScalarAsCtype
where only a reference for flexible types is returned
This may not work right on narrow builds for NumPy unicode scalars.
::
int
PyArray_CastScalarDirect(PyObject *scalar, PyArray_Descr
*indescr, void *ctypeptr, int outtype)
Cast Scalar to c-type
::
PyObject *
PyArray_ScalarFromObject(PyObject *object)
Get an Array Scalar From a Python Object
Returns NULL if unsuccessful but error is only set if another error occurred.
Currently only Numeric-like object supported.
::
PyArray_VectorUnaryFunc *
PyArray_GetCastFunc(PyArray_Descr *descr, int type_num)
Get a cast function to cast from the input descriptor to the
output type_number (must be a registered data-type).
Returns NULL if un-successful.
::
PyObject *
PyArray_FromDims(int NPY_UNUSED(nd) , int *NPY_UNUSED(d) , int
NPY_UNUSED(type) )
Deprecated, use PyArray_SimpleNew instead.
::
PyObject *
PyArray_FromDimsAndDataAndDescr(int NPY_UNUSED(nd) , int
*NPY_UNUSED(d) , PyArray_Descr
*descr, char *NPY_UNUSED(data) )
Deprecated, use PyArray_NewFromDescr instead.
::
PyObject *
PyArray_FromAny(PyObject *op, PyArray_Descr *newtype, int
min_depth, int max_depth, int flags, PyObject
*context)
Does not check for NPY_ARRAY_ENSURECOPY and NPY_ARRAY_NOTSWAPPED in flags
Steals a reference to newtype --- which can be NULL
::
PyObject *
PyArray_EnsureArray(PyObject *op)
This is a quick wrapper around
PyArray_FromAny(op, NULL, 0, 0, NPY_ARRAY_ENSUREARRAY, NULL)
that special cases Arrays and PyArray_Scalars up front
It *steals a reference* to the object
It also guarantees that the result is PyArray_Type
Because it decrefs op if any conversion needs to take place
so it can be used like PyArray_EnsureArray(some_function(...))
::
PyObject *
PyArray_EnsureAnyArray(PyObject *op)
::
PyObject *
PyArray_FromFile(FILE *fp, PyArray_Descr *dtype, npy_intp num, char
*sep)
Given a ``FILE *`` pointer ``fp``, and a ``PyArray_Descr``, return an
array corresponding to the data encoded in that file.
The reference to `dtype` is stolen (it is possible that the passed in
dtype is not held on to).
The number of elements to read is given as ``num``; if it is < 0, then
then as many as possible are read.
If ``sep`` is NULL or empty, then binary data is assumed, else
text data, with ``sep`` as the separator between elements. Whitespace in
the separator matches any length of whitespace in the text, and a match
for whitespace around the separator is added.
For memory-mapped files, use the buffer interface. No more data than
necessary is read by this routine.
::
PyObject *
PyArray_FromString(char *data, npy_intp slen, PyArray_Descr
*dtype, npy_intp num, char *sep)
Given a pointer to a string ``data``, a string length ``slen``, and
a ``PyArray_Descr``, return an array corresponding to the data
encoded in that string.
If the dtype is NULL, the default array type is used (double).
If non-null, the reference is stolen.
If ``slen`` is < 0, then the end of string is used for text data.
It is an error for ``slen`` to be < 0 for binary data (since embedded NULLs
would be the norm).
The number of elements to read is given as ``num``; if it is < 0, then
then as many as possible are read.
If ``sep`` is NULL or empty, then binary data is assumed, else
text data, with ``sep`` as the separator between elements. Whitespace in
the separator matches any length of whitespace in the text, and a match
for whitespace around the separator is added.
::
PyObject *
PyArray_FromBuffer(PyObject *buf, PyArray_Descr *type, npy_intp
count, npy_intp offset)
::
PyObject *
PyArray_FromIter(PyObject *obj, PyArray_Descr *dtype, npy_intp count)
steals a reference to dtype (which cannot be NULL)
::
PyObject *
PyArray_Return(PyArrayObject *mp)
Return either an array or the app
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基于Python的金融网站数据爬虫分析与应用项目源码+数据库 (2000个子文件)
fortranobject.c 34KB
wrapmodule.c 7KB
gfortran_vs2003_hack.c 77B
bootstrap.css 143KB
bootstrap.min.css 118KB
bootstrap-theme.css 26KB
bootstrap-theme.min.css 23KB
ndarraytypes.h 63KB
__multiarray_api.h 60KB
npy_common.h 37KB
npy_math.h 21KB
npy_3kcompat.h 14KB
ufuncobject.h 12KB
__ufunc_api.h 12KB
ndarrayobject.h 10KB
distributions.h 9KB
noprefix.h 7KB
old_defines.h 6KB
fortranobject.h 4KB
npy_1_7_deprecated_api.h 4KB
npy_cpu.h 4KB
arrayscalars.h 4KB
npy_endian.h 3KB
halffloat.h 2KB
npy_interrupt.h 2KB
_neighborhood_iterator_imp.h 2KB
numpyconfig.h 1KB
_numpyconfig.h 862B
npy_os.h 817B
utils.h 729B
oldnumeric.h 708B
npy_no_deprecated_api.h 567B
bitgen.h 389B
arrayobject.h 164B
wtf.html 8KB
pagination.html 2KB
google.html 2KB
utils.html 1KB
simple_globe.html 1KB
nb_jupyter_globe.html 1KB
base.html 1KB
simple_page.html 896B
simple_tab.html 771B
nb_jupyter_notebook_tab.html 608B
nb_jupyter_notebook.html 489B
nb_jupyter_lab_tab.html 438B
nb_jupyter_lab.html 343B
fixes.html 287B
simple_chart.html 253B
nb_nteract.html 253B
components.html 204B
nb_components.html 116B
jquery.js 287KB
jquery.min.js 95KB
bootstrap.js 68KB
bootstrap.min.js 36KB
npm.js 484B
city_coordinates.json 161KB
map_filename.json 31KB
countries_regions_db.json 5KB
metadata.json 1KB
metadata.json 1KB
metadata.json 1KB
metadata.json 983B
metadata.json 923B
metadata.json 828B
version.json 202B
pbr.json 47B
pbr.json 47B
LICENSE.md 1KB
README.md 1KB
README.md 984B
test_multiarray.py 310KB
table_zero.py 303KB
pyparsing.py 267KB
core.py 258KB
pyparsing.py 221KB
pyparsing.py 221KB
_add_newdocs.py 198KB
uts46data.py 197KB
uts46data.py 197KB
test_core.py 194KB
test_offsets.py 159KB
function_base.py 152KB
test_strings.py 131KB
test_frame.py 130KB
crackfortran.py 126KB
test_function_base.py 123KB
test_numeric.py 121KB
test_umath.py 118KB
fromnumeric.py 116KB
html5parser.py 114KB
test_nditer.py 110KB
univ.py 106KB
__init__.py 106KB
test_datetime.py 106KB
__init__.py 104KB
system_info.py 103KB
test_concat.py 102KB
test_generator_mt19937.py 100KB
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