===========
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)
::
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 appropriate Python object if the array
is 0d and matches a Python type.
steals reference to mp
::
PyObject *
PyArray_GetField(PyArrayObject *self, PyArray_Descr *typed, int
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管理系统是一种通过计算机技术实现的用于组织、监控和控制各种活动的软件系统。这些系统通常被设计用来提高效率、减少错误、加强安全性,同时提供数据和信息支持。以下是一些常见类型的管理系统: 学校管理系统: 用于学校或教育机构的学生信息、教职员工信息、课程管理、成绩记录、考勤管理等。学校管理系统帮助提高学校的组织效率和信息管理水平。 人力资源管理系统(HRM): 用于处理组织内的人事信息,包括员工招聘、培训记录、薪资管理、绩效评估等。HRM系统有助于企业更有效地管理人力资源,提高员工的工作效率和满意度。 库存管理系统: 用于追踪和管理商品或原材料的库存。这种系统可以帮助企业避免库存过剩或不足的问题,提高供应链的效率。 客户关系管理系统(CRM): 用于管理与客户之间的关系,包括客户信息、沟通记录、销售机会跟踪等。CRM系统有助于企业更好地理解客户需求,提高客户满意度和保留率。 医院管理系统: 用于管理医院或医疗机构的患者信息、医生排班、药品库存等。这种系统可以提高医疗服务的质量和效率。 财务管理系统: 用于记录和管理组织的财务信息,包括会计凭证、财务报表、预算管理等。财务管理系统
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农业大棚管理系统.zip (2000个子文件)
fortranobject.c 35KB
wrapmodule.c 8KB
gfortran_vs2003_hack.c 77B
jquery-ui.css 36KB
jquery-ui.min.css 31KB
jquery-ui.structure.css 18KB
jquery-ui.theme.css 18KB
responsive.css 18KB
select2.css 17KB
base.css 16KB
jquery-ui.structure.min.css 15KB
select2.min.css 15KB
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widgets.css 10KB
forms.css 8KB
autocomplete.css 8KB
changelists.css 6KB
rtl.css 4KB
boilerplate.css 2KB
responsive_rtl.css 2KB
page.css 2KB
fbm.css 1KB
login.css 1KB
ol3.css 657B
fonts.css 423B
dashboard.css 412B
ndarraytypes.h 64KB
__multiarray_api.h 60KB
npy_common.h 37KB
npy_math.h 23KB
npy_3kcompat.h 14KB
ufuncobject.h 12KB
__ufunc_api.h 12KB
ndarrayobject.h 11KB
distributions.h 9KB
noprefix.h 7KB
old_defines.h 6KB
fortranobject.h 5KB
npy_1_7_deprecated_api.h 5KB
npy_cpu.h 4KB
arrayscalars.h 3KB
npy_interrupt.h 3KB
npy_endian.h 3KB
halffloat.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
index.html 32KB
technical_500.html 17KB
default_urlconf.html 16KB
tabular.html 4KB
base.html 4KB
index.html 3KB
change_form.html 3KB
change_list.html 3KB
technical_404.html 2KB
delete_confirmation.html 2KB
stacked.html 2KB
change_password.html 2KB
delete_selected_confirmation.html 2KB
password_change_form.html 2KB
openlayers.html 2KB
login.html 2KB
openlayers.html 2KB
model_detail.html 2KB
fieldset.html 2KB
template_filter_index.html 2KB
template_tag_index.html 2KB
view_index.html 2KB
change_list_results.html 2KB
all_figures.html 1KB
related_widget_wrapper.html 1KB
object_history.html 1KB
password_reset_confirm.html 1KB
bookmarklets.html 1KB
model_index.html 1KB
index.html 1KB
ipython_inline_figure.html 1KB
single_figure.html 1KB
actions.html 1KB
submit_line.html 1024B
search_form.html 1020B
template_detail.html 995B
password_reset_form.html 966B
view_detail.html 896B
missing_docutils.html 734B
password_change_done.html 671B
password_reset_done.html 669B
password_reset_email.html 582B
clearable_file_input.html 568B
pagination.html 553B
500.html 527B
date_hierarchy.html 518B
password_reset_complete.html 505B
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