Search is not available for this dataset
repo_id stringlengths 12 110 | file_path stringlengths 24 164 | content stringlengths 3 89.3M | __index_level_0__ int64 0 0 |
|---|---|---|---|
public_repos/numpy-user-dtypes/mpfdtype | public_repos/numpy-user-dtypes/mpfdtype/mpfdtype/__init__.py | import numpy as np
from ._mpfdtype_main import MPFDType, MPFloat
# Lets add some uglier hacks:
# NumPy uses repr as a fallback (as of writing this code), we want to
# customize the printing of MPFloats though...
def mystr(obj):
if isinstance(obj, MPFloat):
return f"'{obj}'"
return repr(obj)
np.set... | 0 |
public_repos/numpy-user-dtypes/mpfdtype/mpfdtype | public_repos/numpy-user-dtypes/mpfdtype/mpfdtype/src/umath.cpp |
#include "scalar.h"
#define PY_ARRAY_UNIQUE_SYMBOL MPFDType_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
extern "C" {
#include <Python.h>
#include "numpy/arrayobject.h"
#include "numpy/ndarraytypes.h"
#include "numpy/ufuncobject.h"
#include "numpy/experimen... | 0 |
public_repos/numpy-user-dtypes/mpfdtype/mpfdtype | public_repos/numpy-user-dtypes/mpfdtype/mpfdtype/src/ops.hpp | #include "mpfr.h"
typedef int unary_op_def(mpfr_t, mpfr_t);
typedef int binop_def(mpfr_t, mpfr_t, mpfr_t);
typedef npy_bool cmp_def(mpfr_t, mpfr_t);
/*
* Unary operations
*/
static inline int
negative(mpfr_t op, mpfr_t out)
{
return mpfr_neg(out, op, MPFR_RNDN);
}
static inline int
positive(mpfr_t op, mpfr_t ... | 0 |
public_repos/numpy-user-dtypes/mpfdtype/mpfdtype | public_repos/numpy-user-dtypes/mpfdtype/mpfdtype/src/casts.h | #ifndef _NPY_CASTS_H
#define _NPY_CASTS_H
#ifdef __cplusplus
extern "C" {
#endif
extern PyArrayMethod_Spec MPFToMPFCastSpec;
PyArrayMethod_Spec **
init_casts(void);
void
free_casts(void);
#ifdef __cplusplus
}
#endif
#endif /* _NPY_CASTS_H */
| 0 |
public_repos/numpy-user-dtypes/mpfdtype/mpfdtype | public_repos/numpy-user-dtypes/mpfdtype/mpfdtype/src/numbers.cpp | /*
* This file defines scalar numeric operations.
*/
#define PY_ARRAY_UNIQUE_SYMBOL MPFDType_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
extern "C" {
#include <Python.h>
#include "numpy/arrayobject.h"
#include "numpy/ndarraytypes.h"
#include "numpy/ufuncobjec... | 0 |
public_repos/numpy-user-dtypes/mpfdtype/mpfdtype | public_repos/numpy-user-dtypes/mpfdtype/mpfdtype/src/scalar.c | #include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL MPFDType_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
#include "numpy/arrayobject.h"
#include "numpy/ndarraytypes.h"
#include "numpy/experimental_dtype_api.h"
#include "scalar.h"
#include "numbers.h"
mpfr_prec_t
get_prec_from_... | 0 |
public_repos/numpy-user-dtypes/mpfdtype/mpfdtype | public_repos/numpy-user-dtypes/mpfdtype/mpfdtype/src/casts.cpp |
#define PY_ARRAY_UNIQUE_SYMBOL MPFDType_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
extern "C" {
#include <Python.h>
#include "numpy/arrayobject.h"
#include "numpy/ndarraytypes.h"
#include "numpy/experimental_dtype_api.h"
}
#include <vector>
#include "mpfr.h"
... | 0 |
public_repos/numpy-user-dtypes/mpfdtype/mpfdtype | public_repos/numpy-user-dtypes/mpfdtype/mpfdtype/src/dtype.c | #include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL MPFDType_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
#include "numpy/arrayobject.h"
#include "numpy/ndarraytypes.h"
#include "numpy/experimental_dtype_api.h"
#include "mpfr.h"
#include "scalar.h"
#include "casts.h"
#include "d... | 0 |
public_repos/numpy-user-dtypes/mpfdtype/mpfdtype | public_repos/numpy-user-dtypes/mpfdtype/mpfdtype/src/dtype.h | #ifndef _MPRFDTYPE_DTYPE_H
#define _MPRFDTYPE_DTYPE_H
#include "mpfr.h"
#ifdef __cplusplus
extern "C" {
#endif
#include "scalar.h"
typedef struct {
PyArray_Descr base;
mpfr_prec_t precision;
} MPFDTypeObject;
/*
* It would be more compat to just store the kind, exponent and signfificand,
* however. Fo... | 0 |
public_repos/numpy-user-dtypes/mpfdtype/mpfdtype | public_repos/numpy-user-dtypes/mpfdtype/mpfdtype/src/terrible_hacks.h | #include <Python.h>
int
init_terrible_hacks(void);
| 0 |
public_repos/numpy-user-dtypes/mpfdtype/mpfdtype | public_repos/numpy-user-dtypes/mpfdtype/mpfdtype/src/mpfdtype_main.c | #include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL MPFDType_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#include "numpy/arrayobject.h"
#include "numpy/experimental_dtype_api.h"
#include "dtype.h"
#include "umath.h"
#include "terrible_hacks.h"
static struct PyModuleDef moduledef = {
PyModuleDe... | 0 |
public_repos/numpy-user-dtypes/mpfdtype/mpfdtype | public_repos/numpy-user-dtypes/mpfdtype/mpfdtype/src/numbers.h | #ifndef _MPF_NUMBERS_H
#define _MPF_NUMBERS_H
#ifdef __cplusplus
extern "C" {
#endif
PyObject *
mpf_richcompare(MPFloatObject *self, PyObject *other, int cmp_op);
extern PyNumberMethods mpf_as_number;
#ifdef __cplusplus
}
#endif
#endif /* _MPF_NUMBERS_H */
| 0 |
public_repos/numpy-user-dtypes/mpfdtype/mpfdtype | public_repos/numpy-user-dtypes/mpfdtype/mpfdtype/src/scalar.h | #ifndef _MPRFDTYPE_SCALAR_H
#define _MPRFDTYPE_SCALAR_H
#include "mpfr.h"
#ifdef __cplusplus
extern "C" {
#endif
#include <Python.h>
typedef struct {
mpfr_t x;
mp_limb_t significand[];
} mpf_field;
typedef struct {
PyObject_VAR_HEAD;
mpf_field mpf;
} MPFloatObject;
extern PyTypeObject MPFloat_T... | 0 |
public_repos/numpy-user-dtypes/mpfdtype/mpfdtype | public_repos/numpy-user-dtypes/mpfdtype/mpfdtype/src/umath.h | #ifndef _MPRFDTYPE_UMATH_H
#define _MPRFDTYPE_UMATH_H
#ifdef __cplusplus
extern "C" {
#endif
int
init_mpf_umath(void);
#ifdef __cplusplus
}
#endif
#endif /*_MPRFDTYPE_UMATH_H */
| 0 |
public_repos/numpy-user-dtypes/mpfdtype/mpfdtype | public_repos/numpy-user-dtypes/mpfdtype/mpfdtype/src/terrible_hacks.c | #include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL MPFDType_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
#include "numpy/arrayobject.h"
#include "numpy/ndarraytypes.h"
#include "numpy/experimental_dtype_api.h"
#include "mpfr.h"
#include "dtype.h"
#include "scalar.h"
/*
* Some ... | 0 |
public_repos/numpy-user-dtypes/mpfdtype/mpfdtype | public_repos/numpy-user-dtypes/mpfdtype/mpfdtype/tests/conftest.py | import os
os.environ["NUMPY_EXPERIMENTAL_DTYPE_API"] = "1"
| 0 |
public_repos/numpy-user-dtypes/mpfdtype/mpfdtype | public_repos/numpy-user-dtypes/mpfdtype/mpfdtype/tests/test_scalar.py | import pytest
import sys
import numpy as np
import operator
from mpfdtype import MPFDType, MPFloat
def test_create_scalar_simple():
# currently inferring 53bit precision from float:
assert MPFloat(12.).prec == 53
# currently infers 64bit or 32bit depending on system:
assert MPFloat(1).prec == sys.ma... | 0 |
public_repos/numpy-user-dtypes/mpfdtype/mpfdtype | public_repos/numpy-user-dtypes/mpfdtype/mpfdtype/tests/test_array.py | import numpy as np
from numpy.testing import assert_array_equal
from mpfdtype import MPFDType
def test_advanced_indexing():
# As of writing the test, this relies on copyswap
arr = np.arange(100).astype(MPFDType(100))
orig = np.arange(100).astype(MPFDType(100)) # second one, not a copy
b = arr[[1, 2... | 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/asv_benchmarks/asv.conf.json | {
// The version of the config file format. Do not change, unless
// you know what you are doing.
"version": 1,
// The name of the project being benchmarked
"project": "numpy-user-dtypes",
// The project's homepage
"project_url": "https://github.com/numpy/numpy-user-dtypes",
// The U... | 0 |
public_repos/numpy-user-dtypes/asv_benchmarks | public_repos/numpy-user-dtypes/asv_benchmarks/benchmarks/strings.py | # Write the benchmarking functions here.
# See "Writing benchmarks" in the asv docs for more information.
import uuid
import numpy as np
from asciidtype import ASCIIDType
from stringdtype import StringDType
def generate_data(n=100000):
"""Generate data for the benchmarks.
The vast majority of the time spen... | 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/unytdtype/meson.build | project(
'unytdtype',
'c',
)
py_mod = import('python')
py = py_mod.find_installation()
incdir_numpy = run_command(py,
[
'-c',
'import numpy; print(numpy.get_include())'
],
check: true
).stdout().strip()
includes = include_directories(
[
incdir_numpy,
'unytdtype/src'
]
)
srcs = [
'uny... | 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/unytdtype/.clang-format | # A clang-format style that approximates Python's PEP 7
# Useful for IDE integration
#
# Based on Paul Ganssle's version at
# https://gist.github.com/pganssle/0e3a5f828b4d07d79447f6ced8e7e4db
# and modified for NumPy
BasedOnStyle: Google
AlignAfterOpenBracket: Align
AllowShortEnumsOnASingleLine: false
AllowShortIfState... | 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/unytdtype/pyproject.toml | [build-system]
requires = [
"meson>=0.63.0",
"meson-python",
"patchelf",
"wheel",
"numpy",
]
build-backend = "mesonpy"
[project]
name = "unytdtype"
description = "A unit dtype backed by unyt"
version = "0.0.1"
readme = 'README.md'
author = "Nathan Goldbaum"
requires-python = ">=3.9.0"
dependencies ... | 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/unytdtype/README.md | # A dtype that stores unit metadata
This is a simple proof-of-concept dtype using the (as of late 2022) experimental
[new dtype
implementation](https://numpy.org/neps/nep-0041-improved-dtype-support.html) in
NumPy. It leverages the [unyt](https://unyt.readthedocs.org) library's `Unit`
type to store unit metadata, but ... | 0 |
public_repos/numpy-user-dtypes/unytdtype | public_repos/numpy-user-dtypes/unytdtype/unytdtype/scalar.py | """A scalar type needed by the dtype machinery."""
from unyt import Unit
class UnytScalar:
def __init__(self, value, unit):
from . import UnytDType
self.value = value
if isinstance(unit, (str, Unit)):
self.dtype = UnytDType(unit)
elif isinstance(unit, UnytDType):
... | 0 |
public_repos/numpy-user-dtypes/unytdtype | public_repos/numpy-user-dtypes/unytdtype/unytdtype/__init__.py | """A dtype that carries around unit metadata.
This is an example usage of the experimental new dtype API
in Numpy and is not yet intended for any real purpose.
"""
from .scalar import UnytScalar # isort: skip
from ._unytdtype_main import UnytDType
__all__ = ["UnytDType", "UnytScalar"]
| 0 |
public_repos/numpy-user-dtypes/unytdtype/unytdtype | public_repos/numpy-user-dtypes/unytdtype/unytdtype/src/casts.h | #ifndef _NPY_CASTS_H
#define _NPY_CASTS_H
#include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL unytdtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
#include "numpy/arrayobject.h"
#include "numpy/experimental_dtype_api.h"
#include "numpy/ndarraytypes.h"
/* Gets the conversion be... | 0 |
public_repos/numpy-user-dtypes/unytdtype/unytdtype | public_repos/numpy-user-dtypes/unytdtype/unytdtype/src/dtype.c | #include "dtype.h"
#include "casts.h"
PyTypeObject *UnytScalar_Type = NULL;
/*
* `get_value` and `get_unit` are small helpers to deal with the scalar.
*/
// NJG hack: get_value assumes scalar is a float64 - possible to generalize?
static double
get_value(PyObject *scalar)
{
PyTypeObject *scalar_type = Py_TYPE... | 0 |
public_repos/numpy-user-dtypes/unytdtype/unytdtype | public_repos/numpy-user-dtypes/unytdtype/unytdtype/src/unytdtype_main.c | #include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL unytdtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#include "numpy/arrayobject.h"
#include "numpy/experimental_dtype_api.h"
#include "dtype.h"
#include "umath.h"
static struct PyModuleDef moduledef = {
PyModuleDef_HEAD_INIT,
.m_nam... | 0 |
public_repos/numpy-user-dtypes/unytdtype/unytdtype | public_repos/numpy-user-dtypes/unytdtype/unytdtype/src/dtype.h | #ifndef _NPY_DTYPE_H
#define _NPY_DTYPE_H
// clang-format off
#include <Python.h>
#include "structmember.h"
// clang-format on
#define PY_ARRAY_UNIQUE_SYMBOL unytdtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
#include "numpy/arrayobject.h"
#include "numpy/experimental_dtype_... | 0 |
public_repos/numpy-user-dtypes/unytdtype/unytdtype | public_repos/numpy-user-dtypes/unytdtype/unytdtype/src/casts.c | #include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL unytdtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
#include "numpy/arrayobject.h"
#include "numpy/experimental_dtype_api.h"
#include "numpy/ndarraytypes.h"
#include "casts.h"
#include "dtype.h"
/*
* Helper function also us... | 0 |
public_repos/numpy-user-dtypes/unytdtype/unytdtype | public_repos/numpy-user-dtypes/unytdtype/unytdtype/src/umath.h | #ifndef _NPY_UFUNC_H
#define _NPY_UFUNC_H
int
init_multiply_ufunc(void);
#endif /*_NPY_UFUNC_H */
| 0 |
public_repos/numpy-user-dtypes/unytdtype/unytdtype | public_repos/numpy-user-dtypes/unytdtype/unytdtype/src/umath.c | #include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL unytdtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
#include "numpy/arrayobject.h"
#include "numpy/experimental_dtype_api.h"
#include "numpy/ndarraytypes.h"
#include "numpy/ufuncobject.h"
#include "dtype.h"
#include "umath.h"... | 0 |
public_repos/numpy-user-dtypes/unytdtype | public_repos/numpy-user-dtypes/unytdtype/tests/test_unytdtype.py | import numpy as np
import unyt
from unytdtype import UnytDType, UnytScalar
def test_dtype_creation():
dtype = UnytDType("m")
assert str(dtype) == "UnytDType('m')"
dtype2 = UnytDType(unyt.Unit("m"))
assert str(dtype2) == "UnytDType('m')"
assert dtype == dtype2
def test_scalar_creation():
dt... | 0 |
public_repos/numpy-user-dtypes/unytdtype | public_repos/numpy-user-dtypes/unytdtype/tests/conftest.py | import os
os.environ["NUMPY_EXPERIMENTAL_DTYPE_API"] = "1"
| 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/metadatadtype/.flake8 | [flake8]
per-file-ignores = __init__.py:F401
| 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/metadatadtype/meson.build | project(
'metadatadtype',
'c',
)
py_mod = import('python')
py = py_mod.find_installation()
incdir_numpy = run_command(py,
[
'-c',
'import numpy; print(numpy.get_include())'
],
check: true
).stdout().strip()
includes = include_directories(
[
incdir_numpy,
'metadatadtype/src'
]
)
srcs = ... | 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/metadatadtype/.clang-format | # A clang-format style that approximates Python's PEP 7
# Useful for IDE integration
#
# Based on Paul Ganssle's version at
# https://gist.github.com/pganssle/0e3a5f828b4d07d79447f6ced8e7e4db
# and modified for NumPy
BasedOnStyle: Google
AlignAfterOpenBracket: Align
AllowShortEnumsOnASingleLine: false
AllowShortIfState... | 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/metadatadtype/pyproject.toml | [build-system]
requires = [
"meson>=0.63.0",
"meson-python",
"patchelf",
"wheel",
"numpy",
]
build-backend = "mesonpy"
[project]
name = "metadatadtype"
description = "A dtype that holds a piece of metadata"
version = "0.0.1"
readme = 'README.md'
author = "Nathan Goldbaum"
requires-python = ">=3.9.0... | 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/metadatadtype/README.md | # A dtype that stores metadata
This is a simple proof-of-concept dtype using the (as of late 2022) experimental
[new dtype
implementation](https://numpy.org/neps/nep-0041-improved-dtype-support.html) in
NumPy. For now all it does it storea piece of static metadata in the dtype
itself.
## Building
Ensure Meson and Nu... | 0 |
public_repos/numpy-user-dtypes/metadatadtype | public_repos/numpy-user-dtypes/metadatadtype/metadatadtype/scalar.py | """A scalar type needed by the dtype machinery."""
class MetadataScalar:
def __init__(self, value, dtype):
self.value = value
self.dtype = dtype
def __repr__(self):
return f"{self.value} {self.dtype._metadata}"
| 0 |
public_repos/numpy-user-dtypes/metadatadtype | public_repos/numpy-user-dtypes/metadatadtype/metadatadtype/__init__.py | """A dtype that carries around metadata.
This is an example usage of the experimental new dtype API
in Numpy and is not intended for any real purpose.
"""
from .scalar import MetadataScalar # isort: skip
from ._metadatadtype_main import MetadataDType
__all__ = ["MetadataDType", "MetadataScalar"]
| 0 |
public_repos/numpy-user-dtypes/metadatadtype/metadatadtype | public_repos/numpy-user-dtypes/metadatadtype/metadatadtype/src/casts.h | #ifndef _NPY_CASTS_H
#define _NPY_CASTS_H
#include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL metadatadtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
#include "numpy/arrayobject.h"
#include "numpy/experimental_dtype_api.h"
#include "numpy/ndarraytypes.h"
PyArrayMethod_Spec **... | 0 |
public_repos/numpy-user-dtypes/metadatadtype/metadatadtype | public_repos/numpy-user-dtypes/metadatadtype/metadatadtype/src/dtype.c | #include "dtype.h"
#include "casts.h"
PyTypeObject *MetadataScalar_Type = NULL;
/*
* `get_value` and `get_unit` are small helpers to deal with the scalar.
*/
static double
get_value(PyObject *scalar)
{
PyTypeObject *scalar_type = Py_TYPE(scalar);
if (scalar_type != MetadataScalar_Type) {
double re... | 0 |
public_repos/numpy-user-dtypes/metadatadtype/metadatadtype | public_repos/numpy-user-dtypes/metadatadtype/metadatadtype/src/dtype.h | #ifndef _NPY_DTYPE_H
#define _NPY_DTYPE_H
// clang-format off
#include <Python.h>
#include "structmember.h"
// clang-format on
#define PY_ARRAY_UNIQUE_SYMBOL metadatadtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
#include "numpy/arrayobject.h"
#include "numpy/experimental_dt... | 0 |
public_repos/numpy-user-dtypes/metadatadtype/metadatadtype | public_repos/numpy-user-dtypes/metadatadtype/metadatadtype/src/casts.c | #include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL metadatadtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
#include "numpy/arrayobject.h"
#include "numpy/experimental_dtype_api.h"
#include "numpy/ndarraytypes.h"
#include "casts.h"
#include "dtype.h"
/*
* And now the actual ... | 0 |
public_repos/numpy-user-dtypes/metadatadtype/metadatadtype | public_repos/numpy-user-dtypes/metadatadtype/metadatadtype/src/metadatadtype_main.c | #include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL metadatadtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#include "numpy/arrayobject.h"
#include "numpy/experimental_dtype_api.h"
#include "dtype.h"
#include "umath.h"
static struct PyModuleDef moduledef = {
PyModuleDef_HEAD_INIT,
.m... | 0 |
public_repos/numpy-user-dtypes/metadatadtype/metadatadtype | public_repos/numpy-user-dtypes/metadatadtype/metadatadtype/src/umath.h | #ifndef _NPY_UFUNC_H
#define _NPY_UFUNC_H
int
init_ufuncs(void);
#endif /*_NPY_UFUNC_H */
| 0 |
public_repos/numpy-user-dtypes/metadatadtype/metadatadtype | public_repos/numpy-user-dtypes/metadatadtype/metadatadtype/src/umath.c | #include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL metadatadtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
#include "numpy/arrayobject.h"
#include "numpy/experimental_dtype_api.h"
#include "numpy/ndarraytypes.h"
#include "numpy/ufuncobject.h"
#include "dtype.h"
#include "umat... | 0 |
public_repos/numpy-user-dtypes/metadatadtype | public_repos/numpy-user-dtypes/metadatadtype/tests/conftest.py | import os
os.environ["NUMPY_EXPERIMENTAL_DTYPE_API"] = "1"
| 0 |
public_repos/numpy-user-dtypes/metadatadtype | public_repos/numpy-user-dtypes/metadatadtype/tests/test_metadatadtype.py | import numpy as np
from metadatadtype import MetadataDType, MetadataScalar
def test_dtype_creation():
dtype = MetadataDType("some metadata")
assert str(dtype) == "MetadataDType('some metadata')"
def test_creation_from_zeros():
dtype = MetadataDType("test")
arr = np.zeros(3, dtype=dtype)
assert ... | 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/stringdtype/meson.build | project(
'stringdtype',
'c',
)
py_mod = import('python')
py = py_mod.find_installation()
incdir_numpy = run_command(py,
[
'-c',
'import numpy; print(numpy.get_include())'
],
check: true
).stdout().strip()
cc = meson.get_compiler('c')
npymath_path = incdir_numpy / '..' / 'lib'
npymath_lib = cc.find... | 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/stringdtype/.clang-format | # A clang-format style that approximates Python's PEP 7
# Useful for IDE integration
#
# Based on Paul Ganssle's version at
# https://gist.github.com/pganssle/0e3a5f828b4d07d79447f6ced8e7e4db
# and modified for NumPy
BasedOnStyle: Google
AlignAfterOpenBracket: Align
AllowShortEnumsOnASingleLine: false
AllowShortIfState... | 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/stringdtype/pyproject.toml | [build-system]
requires = [
"meson>=0.63.0",
"meson-python",
"patchelf",
"wheel",
"numpy",
]
build-backend = "mesonpy"
[tool.black]
line-length = 79
[tool.isort]
profile = "black"
line_length = 79
[project]
name = "stringdtype"
description = "A dtype for storing UTF-8 strings"
version = "0.0.1"
r... | 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/stringdtype/README.md | # A dtype that stores pointers to strings
This is a simple proof-of-concept dtype using the (as of early 2023) experimental
[new dtype
implementation](https://numpy.org/neps/nep-0041-improved-dtype-support.html) in
NumPy.
## Building
Ensure Meson and NumPy are installed in the python environment you would like to us... | 0 |
public_repos/numpy-user-dtypes/stringdtype | public_repos/numpy-user-dtypes/stringdtype/tests/conftest.py | import os
os.environ["NUMPY_EXPERIMENTAL_DTYPE_API"] = "1"
| 0 |
public_repos/numpy-user-dtypes/stringdtype | public_repos/numpy-user-dtypes/stringdtype/tests/test_stringdtype.py | import concurrent.futures
import os
import pickle
import string
import tempfile
import numpy as np
try:
from pandas import NA as pd_NA
except ImportError:
pd_NA = None
import pytest
from stringdtype import StringDType, StringScalar, _memory_usage
@pytest.fixture
def string_list():
return ["abc", "def",... | 0 |
public_repos/numpy-user-dtypes/stringdtype | public_repos/numpy-user-dtypes/stringdtype/tests/test_char.py | import numpy as np
import pytest
from numpy.testing import assert_array_equal
from stringdtype import StringDType
TEST_DATA = [
"hello" * 10,
"Ae¢☃€ 😊" * 100,
"entry\nwith\nnewlines",
"entry\twith\ttabs",
]
@pytest.fixture
def string_array():
return np.array(TEST_DATA, dtype=StringDType())
@p... | 0 |
public_repos/numpy-user-dtypes/stringdtype | public_repos/numpy-user-dtypes/stringdtype/stringdtype/scalar.py | """Scalar types needed by the dtype machinery."""
class StringScalar(str):
pass
| 0 |
public_repos/numpy-user-dtypes/stringdtype | public_repos/numpy-user-dtypes/stringdtype/stringdtype/__init__.py | """A dtype for working with variable-length string data
"""
from .scalar import StringScalar # isort: skip
from ._main import StringDType, _memory_usage
__all__ = [
"NA",
"StringDType",
"StringScalar",
"_memory_usage",
]
| 0 |
public_repos/numpy-user-dtypes/stringdtype/stringdtype | public_repos/numpy-user-dtypes/stringdtype/stringdtype/src/casts.h | #ifndef _NPY_CASTS_H
#define _NPY_CASTS_H
// needed for Py_UCS4
#include <Python.h>
// need these defines and includes for PyArrayMethod_Spec
#define PY_ARRAY_UNIQUE_SYMBOL stringdtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_2_0_API_VERSION
#define NPY_TARGET_VERSION NPY_2_0_API_VERSION
#define NO_IMPORT_ARRAY
#i... | 0 |
public_repos/numpy-user-dtypes/stringdtype/stringdtype | public_repos/numpy-user-dtypes/stringdtype/stringdtype/src/main.c | #include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL stringdtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_2_0_API_VERSION
#define NPY_TARGET_VERSION NPY_2_0_API_VERSION
#include "numpy/arrayobject.h"
#include "numpy/experimental_dtype_api.h"
#include "dtype.h"
#include "static_string.h"
#include "umath.h"
static Py... | 0 |
public_repos/numpy-user-dtypes/stringdtype/stringdtype | public_repos/numpy-user-dtypes/stringdtype/stringdtype/src/static_string.c | #include "static_string.h"
#include <stdint.h>
#include <string.h>
#if NPY_BYTE_ORDER == NPY_LITTLE_ENDIAN
// the high byte in vstring.size is reserved for flags
// SSSS SSSF
typedef struct _npy_static_string_t {
size_t offset;
size_t size;
} _npy_static_string_t;
typedef struct _short_string_buffer {
... | 0 |
public_repos/numpy-user-dtypes/stringdtype/stringdtype | public_repos/numpy-user-dtypes/stringdtype/stringdtype/src/dtype.c | #include "dtype.h"
#include "casts.h"
#include "static_string.h"
PyTypeObject *StringScalar_Type = NULL;
/*
* Internal helper to create new instances
*/
PyObject *
new_stringdtype_instance(PyObject *na_object, int coerce)
{
npy_string_allocator *allocator = NULL;
PyThread_type_lock *allocator_lock = NULL;
... | 0 |
public_repos/numpy-user-dtypes/stringdtype/stringdtype | public_repos/numpy-user-dtypes/stringdtype/stringdtype/src/dtype.h | #ifndef _NPY_DTYPE_H
#define _NPY_DTYPE_H
// clang-format off
#include <Python.h>
#include "structmember.h"
// clang-format on
#include "static_string.h"
#define PY_ARRAY_UNIQUE_SYMBOL stringdtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_2_0_API_VERSION
#define NPY_TARGET_VERSION NPY_2_0_API_VERSION
#define NO_IM... | 0 |
public_repos/numpy-user-dtypes/stringdtype/stringdtype | public_repos/numpy-user-dtypes/stringdtype/stringdtype/src/casts.c | #include "casts.h"
#include "dtype.h"
#include "static_string.h"
#define ANY_TO_STRING_RESOLVE_DESCRIPTORS(safety) \
static NPY_CASTING any_to_string_##safety##_resolve_descriptors( \
PyObject *NPY_UNUSED(self), \
PyArray_DT... | 0 |
public_repos/numpy-user-dtypes/stringdtype/stringdtype | public_repos/numpy-user-dtypes/stringdtype/stringdtype/src/umath.h | #ifndef _NPY_UFUNC_H
#define _NPY_UFUNC_H
int
init_ufuncs(void);
#endif /*_NPY_UFUNC_H */
| 0 |
public_repos/numpy-user-dtypes/stringdtype/stringdtype | public_repos/numpy-user-dtypes/stringdtype/stringdtype/src/static_string.h | #ifndef _NPY_STATIC_STRING_H
#define _NPY_STATIC_STRING_H
#include "stdint.h"
#include "stdlib.h"
typedef struct npy_packed_static_string {
char packed_buffer[sizeof(char *) + sizeof(size_t)];
} npy_packed_static_string;
typedef struct npy_static_string {
size_t size;
const char *buf;
} npy_static_string... | 0 |
public_repos/numpy-user-dtypes/stringdtype/stringdtype | public_repos/numpy-user-dtypes/stringdtype/stringdtype/src/umath.c | #include <Python.h>
#include "umath.h"
#include "dtype.h"
#include "static_string.h"
static NPY_CASTING
multiply_resolve_descriptors(
struct PyArrayMethodObject_tag *NPY_UNUSED(method),
PyArray_DTypeMeta *dtypes[], PyArray_Descr *given_descrs[],
PyArray_Descr *loop_descrs[], npy_intp *NPY_UNU... | 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/quaddtype/.flake8 | [flake8]
max-line-length = 100
ignore = D107,D104,W503
per-file-ignores = __init__.py:F401,tests/*:D100
docstring-convention = numpy
docstring_style = numpy
| 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/quaddtype/meson.build | project(
'quaddtype',
'c',
)
py_mod = import('python')
py = py_mod.find_installation()
incdir_numpy = run_command(py,
[
'-c',
'import numpy; print(numpy.get_include())'
],
check: true
).stdout().strip()
includes = include_directories(
[
incdir_numpy,
'quaddtype/src'
]
)
srcs = [
'qua... | 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/quaddtype/pyproject.toml | [build-system]
requires = [
"meson>=0.63.0",
"meson-python",
"patchelf",
"wheel",
"numpy"
]
build-backend = "mesonpy"
[project]
name = "quaddtype"
description = "Quad (128-bit) float dtype for numpy"
version = "0.0.1"
readme = 'README.md'
author = "Peyton Murray"
requires-python = ">=3.9.0"
depende... | 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/quaddtype/README.md | # quaddtype
Quad (128-bit) float dtype for numpy
## Installation
To install, make sure you have `numpy` nightly installed. Then build without
isolation so that the `quaddtype` can link against the experimental dtype API
headers, which aren't in the latest releases of `numpy`:
```bash
pip install -i https://pypi.ana... | 0 |
public_repos/numpy-user-dtypes/quaddtype | public_repos/numpy-user-dtypes/quaddtype/tests/conftest.py | import os
os.environ["NUMPY_EXPERIMENTAL_DTYPE_API"] = "1"
| 0 |
public_repos/numpy-user-dtypes/quaddtype | public_repos/numpy-user-dtypes/quaddtype/tests/test_quaddtype.py | import numpy as np
from quaddtype import QuadDType, QuadScalar
def test_dtype_creation():
assert str(QuadDType()) == "This is a quad (128-bit float) dtype."
def test_scalar_creation():
assert str(QuadScalar(3.1)) == "3.1"
def test_create_with_explicit_dtype():
assert (
repr(np.array([3.0, 3.1... | 0 |
public_repos/numpy-user-dtypes/quaddtype | public_repos/numpy-user-dtypes/quaddtype/quaddtype/quadscalar.py | """Quad scalar floating point type for numpy."""
class QuadScalar:
"""Quad scalar floating point type."""
def __init__(self, value):
self.value = value
def __repr__(self):
return f"{self.value}"
| 0 |
public_repos/numpy-user-dtypes/quaddtype | public_repos/numpy-user-dtypes/quaddtype/quaddtype/__init__.py | # Scalar quantity must be defined _before_ the dtype, so don't isort it.
# During initialization of _quaddtype_main, QuadScalar is imported from this
# (partially initialized)
# module, and therefore has to be defined first.
from .quadscalar import QuadScalar # isort: skip
from ._quaddtype_main import QuadDType
__all... | 0 |
public_repos/numpy-user-dtypes/quaddtype/quaddtype | public_repos/numpy-user-dtypes/quaddtype/quaddtype/src/casts.h | #ifndef _NPY_CASTS_H
#define _NPY_CASTS_H
#include "numpy/experimental_dtype_api.h"
extern PyArrayMethod_Spec QuadToQuadCastSpec;
extern PyArrayMethod_Spec QuadToFloat128CastSpec;
#endif /* _NPY_CASTS_H */
| 0 |
public_repos/numpy-user-dtypes/quaddtype/quaddtype | public_repos/numpy-user-dtypes/quaddtype/quaddtype/src/dtype.c | #include "dtype.h"
#include "abstract.h"
#include "casts.h"
PyTypeObject *QuadScalar_Type = NULL;
QuadDTypeObject *
new_quaddtype_instance(void)
{
QuadDTypeObject *new =
(QuadDTypeObject *)PyArrayDescr_Type.tp_new((PyTypeObject *)&QuadDType, NULL, NULL);
if (new == NULL) {
return NULL;
... | 0 |
public_repos/numpy-user-dtypes/quaddtype/quaddtype | public_repos/numpy-user-dtypes/quaddtype/quaddtype/src/dtype.h | #ifndef _NPY_DTYPE_H
#define _NPY_DTYPE_H
#include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL quaddtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
#include "numpy/ndarraytypes.h"
#include "numpy/arrayobject.h"
#include "numpy/experimental_dtype_api.h"
typedef struct {
PyAr... | 0 |
public_repos/numpy-user-dtypes/quaddtype/quaddtype | public_repos/numpy-user-dtypes/quaddtype/quaddtype/src/casts.c | #include "casts.h"
#include "dtype.h"
// And now the actual cast code! Starting with the "resolver" which tells
// us about cast safety.
// Note also the `view_offset`! It is a way for you to tell NumPy, that this
// cast does not require anything at all, but the cast can simply be done as
// a view.
// For `arr.ast... | 0 |
public_repos/numpy-user-dtypes/quaddtype/quaddtype | public_repos/numpy-user-dtypes/quaddtype/quaddtype/src/quaddtype_main.c | #include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL quaddtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#include "numpy/arrayobject.h"
#include "numpy/experimental_dtype_api.h"
#include "dtype.h"
#include "umath.h"
static struct PyModuleDef moduledef = {
PyModuleDef_HEAD_INIT,
.m_nam... | 0 |
public_repos/numpy-user-dtypes/quaddtype/quaddtype | public_repos/numpy-user-dtypes/quaddtype/quaddtype/src/umath.h | #ifndef _NPY_UFUNC_H
#define _NPY_UFUNC_H
int
init_multiply_ufunc(void);
#endif /*_NPY_UFUNC_H */
| 0 |
public_repos/numpy-user-dtypes/quaddtype/quaddtype | public_repos/numpy-user-dtypes/quaddtype/quaddtype/src/umath.c | #include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL quaddtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
#include "numpy/arrayobject.h"
#include "numpy/ndarraytypes.h"
#include "numpy/ufuncobject.h"
#include "numpy/experimental_dtype_api.h"
#include "dtype.h"
#include "umath.h... | 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/asciidtype/.flake8 | [flake8]
per-file-ignores = __init__.py:F401
max-line-length = 160
| 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/asciidtype/meson.build | project(
'asciidtype',
'c',
)
py_mod = import('python')
py = py_mod.find_installation()
incdir_numpy = run_command(py,
[
'-c',
'import numpy; print(numpy.get_include())'
],
check: true
).stdout().strip()
includes = include_directories(
[
incdir_numpy,
'asciidtype/src'
]
)
srcs = [
'a... | 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/asciidtype/.clang-format | # A clang-format style that approximates Python's PEP 7
# Useful for IDE integration
#
# Based on Paul Ganssle's version at
# https://gist.github.com/pganssle/0e3a5f828b4d07d79447f6ced8e7e4db
# and modified for NumPy
BasedOnStyle: Google
AlignAfterOpenBracket: Align
AllowShortEnumsOnASingleLine: false
AllowShortIfState... | 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/asciidtype/pyproject.toml | [build-system]
requires = [
"meson>=0.63.0",
"meson-python",
"patchelf",
"wheel",
"numpy",
]
build-backend = "mesonpy"
[tool.black]
line-length = 79
[project]
name = "asciidtype"
description = "A dtype for ASCII data"
version = "0.0.1"
readme = 'README.md'
author = "Nathan Goldbaum"
requires-pytho... | 0 |
public_repos/numpy-user-dtypes | public_repos/numpy-user-dtypes/asciidtype/README.md | # A dtype that stores ASCII data
This is a simple proof-of-concept dtype using the (as of late 2022) experimental
[new dtype
implementation](https://numpy.org/neps/nep-0041-improved-dtype-support.html) in
NumPy.
## Building
Ensure Meson and NumPy are installed in the python environment you would like to use:
```
$ ... | 0 |
public_repos/numpy-user-dtypes/asciidtype | public_repos/numpy-user-dtypes/asciidtype/tests/conftest.py | import os
os.environ["NUMPY_EXPERIMENTAL_DTYPE_API"] = "1"
| 0 |
public_repos/numpy-user-dtypes/asciidtype | public_repos/numpy-user-dtypes/asciidtype/tests/test_asciidtype.py | import os
import pickle
import re
import tempfile
import numpy as np
import pytest
from asciidtype import ASCIIDType, ASCIIScalar
def test_dtype_creation():
dtype = ASCIIDType(4)
assert str(dtype) == "ASCIIDType(4)"
def test_scalar_creation():
dtype = ASCIIDType(7)
ASCIIScalar("string", dtype)
d... | 0 |
public_repos/numpy-user-dtypes/asciidtype | public_repos/numpy-user-dtypes/asciidtype/asciidtype/scalar.py | """A scalar type needed by the dtype machinery."""
class ASCIIScalar(str):
def __new__(cls, value, dtype):
instance = super().__new__(cls, value)
instance.dtype = dtype
return instance
def partition(self, sep):
ret = super().partition(sep)
return (str(ret[0]), str(ret[... | 0 |
public_repos/numpy-user-dtypes/asciidtype | public_repos/numpy-user-dtypes/asciidtype/asciidtype/__init__.py | """A dtype for working with ASCII data
This is an example usage of the experimental new dtype API
in Numpy and is not intended for any real purpose.
"""
from .scalar import ASCIIScalar # isort: skip
from ._asciidtype_main import ASCIIDType
__all__ = [
"ASCIIDType",
"ASCIIScalar",
]
| 0 |
public_repos/numpy-user-dtypes/asciidtype/asciidtype | public_repos/numpy-user-dtypes/asciidtype/asciidtype/src/casts.h | #ifndef _NPY_CASTS_H
#define _NPY_CASTS_H
#include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL asciidtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
#include "numpy/arrayobject.h"
#include "numpy/experimental_dtype_api.h"
#include "numpy/ndarraytypes.h"
PyArrayMethod_Spec **
ge... | 0 |
public_repos/numpy-user-dtypes/asciidtype/asciidtype | public_repos/numpy-user-dtypes/asciidtype/asciidtype/src/dtype.c | #include "dtype.h"
#include "casts.h"
PyTypeObject *ASCIIScalar_Type = NULL;
static PyObject *
get_value(PyObject *scalar)
{
PyObject *ret_bytes = NULL;
PyTypeObject *scalar_type = Py_TYPE(scalar);
if (scalar_type == &PyUnicode_Type) {
// attempt to decode as ASCII
ret_bytes = PyUnicode_A... | 0 |
public_repos/numpy-user-dtypes/asciidtype/asciidtype | public_repos/numpy-user-dtypes/asciidtype/asciidtype/src/dtype.h | #ifndef _NPY_DTYPE_H
#define _NPY_DTYPE_H
// clang-format off
#include <Python.h>
#include "structmember.h"
// clang-format on
#define PY_ARRAY_UNIQUE_SYMBOL asciidtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
#include "numpy/arrayobject.h"
#include "numpy/experimental_dtype... | 0 |
public_repos/numpy-user-dtypes/asciidtype/asciidtype | public_repos/numpy-user-dtypes/asciidtype/asciidtype/src/casts.c | #include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL asciidtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
#include "numpy/arrayobject.h"
#include "numpy/experimental_dtype_api.h"
#include "numpy/ndarraytypes.h"
#include "casts.h"
#include "dtype.h"
static NPY_CASTING
ascii_to_... | 0 |
public_repos/numpy-user-dtypes/asciidtype/asciidtype | public_repos/numpy-user-dtypes/asciidtype/asciidtype/src/umath.h | #ifndef _NPY_UFUNC_H
#define _NPY_UFUNC_H
int
init_ufuncs(void);
#endif /*_NPY_UFUNC_H */
| 0 |
public_repos/numpy-user-dtypes/asciidtype/asciidtype | public_repos/numpy-user-dtypes/asciidtype/asciidtype/src/umath.c | #include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL asciidtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define NO_IMPORT_ARRAY
#include "numpy/arrayobject.h"
#include "numpy/experimental_dtype_api.h"
#include "numpy/ndarraytypes.h"
#include "numpy/ufuncobject.h"
#include "dtype.h"
#include "string.... | 0 |
public_repos/numpy-user-dtypes/asciidtype/asciidtype | public_repos/numpy-user-dtypes/asciidtype/asciidtype/src/asciidtype_main.c | #include <Python.h>
#define PY_ARRAY_UNIQUE_SYMBOL asciidtype_ARRAY_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#include "numpy/arrayobject.h"
#include "numpy/experimental_dtype_api.h"
#include "dtype.h"
#include "umath.h"
static struct PyModuleDef moduledef = {
PyModuleDef_HEAD_INIT,
.m_na... | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.