===========
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
没有合适的资源?快使用搜索试试~ 我知道了~
复现双层贷款违约风险预测模型.zip
共2000个文件
py:1837个
c:63个
txt:42个
1.该资源内容由用户上传,如若侵权请联系客服进行举报
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
版权申诉
0 下载量 158 浏览量
2023-10-19
20:56:23
上传
评论 1
收藏 138.27MB ZIP 举报
温馨提示
复现双层贷款违约风险预测模型.zip
资源推荐
资源详情
资源评论
收起资源包目录
复现双层贷款违约风险预测模型.zip (2000个子文件)
ObjectHandling.c 87KB
Coroutine.c 85KB
ModuleSetupCode.c 50KB
CythonFunction.c 45KB
Optimize.c 44KB
StringTools.c 40KB
AsyncGen.c 39KB
fortranobject.c 36KB
TypeConversion.c 35KB
Buffer.c 29KB
MemoryView_C.c 29KB
Exceptions.c 26KB
ImportExport.c 22KB
Profile.c 16KB
Builtins.c 16KB
Overflow.c 12KB
FunctionArguments.c 12KB
ExtensionTypes.c 11KB
Complex.c 10KB
wrapmodule.c 7KB
Embed.c 7KB
Printing.c 5KB
CMath.c 3KB
CommonStructures.c 2KB
extra_avx512f_reduce.c 2KB
cpu_asimd.c 704B
extra_avx512bw_mask.c 636B
Capsule.c 505B
extra_avx512dq_mask.c 504B
cpu_neon_vfpv4.c 493B
cpu_vsx.c 478B
cpu_asimdfhm.c 431B
cpu_avx512_knm.c 415B
cpu_asimddp.c 380B
cpu_neon.c 372B
cpu_popcnt.c 370B
cpu_asimdhp.c 329B
cpu_avx512_cnl.c 326B
cpu_avx512_icl.c 324B
cpu_avx512_knl.c 281B
TestUtilityLoader.c 279B
cpu_fma4.c 278B
cpu_avx512_skx.c 269B
cpu_vsx2.c 263B
cpu_f16c.c 251B
cpu_neon_fp16.c 251B
cpu_vsx3.c 250B
cpu_xop.c 234B
cpu_avx512_clx.c 224B
cpu_fma3.c 219B
cpu_avx.c 173B
cpu_avx512cd.c 160B
cpu_avx512f.c 158B
cpu_avx2.c 158B
cpu_ssse3.c 155B
cpu_sse2.c 149B
cpu_sse3.c 141B
cpu_sse42.c 141B
cpu_sse.c 140B
cpu_sse41.c 124B
gfortran_vs2003_hack.c 77B
cfuncs.c 71B
test_flags.c 16B
CppSupport.cpp 2KB
boilerplate.css 2KB
page.css 2KB
mpl.css 2KB
fbm.css 1KB
ndarraytypes.h 68KB
__multiarray_api.h 61KB
npy_common.h 37KB
npy_math.h 20KB
npy_3kcompat.h 15KB
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
arrayarray.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
utils.h 1KB
_numpyconfig.h 862B
npy_os.h 817B
oldnumeric.h 708B
npy_no_deprecated_api.h 567B
bitgen.h 389B
arrayobject.h 164B
all_figures.html 2KB
ipython_inline_figure.html 1KB
single_figure.html 1KB
mpl.js 23KB
共 2000 条
- 1
- 2
- 3
- 4
- 5
- 6
- 20
资源评论
天天501
- 粉丝: 595
- 资源: 4666
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功