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values | stars int64 0 1k | created_date stringdate 2023-07-10 19:21:08 2025-07-09 19:11:45 | license stringclasses 4
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#ifndef NUMPY_CORE_INCLUDE_NUMPY_UTILS_H_\n#define NUMPY_CORE_INCLUDE_NUMPY_UTILS_H_\n\n#ifndef __COMP_NPY_UNUSED\n #if defined(__GNUC__)\n #define __COMP_NPY_UNUSED __attribute__ ((__unused__))\n #elif defined(__ICC)\n #define __COMP_NPY_UNUSED __attribute__ ((__unused__))\n #elif defined(__clang__)\n #define __COMP_NPY_UNUSED __attribute__ ((unused))\n #else\n #define __COMP_NPY_UNUSED\n #endif\n#endif\n\n#if defined(__GNUC__) || defined(__ICC) || defined(__clang__)\n #define NPY_DECL_ALIGNED(x) __attribute__ ((aligned (x)))\n#elif defined(_MSC_VER)\n #define NPY_DECL_ALIGNED(x) __declspec(align(x))\n#else\n #define NPY_DECL_ALIGNED(x)\n#endif\n\n/* Use this to tag a variable as not used. It will remove unused variable\n * warning on support platforms (see __COM_NPY_UNUSED) and mangle the variable\n * to avoid accidental use */\n#define NPY_UNUSED(x) __NPY_UNUSED_TAGGED ## x __COMP_NPY_UNUSED\n#define NPY_EXPAND(x) x\n\n#define NPY_STRINGIFY(x) #x\n#define NPY_TOSTRING(x) NPY_STRINGIFY(x)\n\n#define NPY_CAT__(a, b) a ## b\n#define NPY_CAT_(a, b) NPY_CAT__(a, b)\n#define NPY_CAT(a, b) NPY_CAT_(a, b)\n\n#endif /* NUMPY_CORE_INCLUDE_NUMPY_UTILS_H_ */\n | .venv\Lib\site-packages\numpy\_core\include\numpy\utils.h | utils.h | C | 1,222 | 0.95 | 0.054054 | 1 | node-utils | 187 | 2024-05-11T19:41:12.282886 | MIT | false | 29f08ef2059c21c3001b05dacd8ebc48 |
#ifndef NUMPY_CORE_INCLUDE_NUMPY__NEIGHBORHOOD_IMP_H_\n#error You should not include this header directly\n#endif\n/*\n * Private API (here for inline)\n */\nstatic inline int\n_PyArrayNeighborhoodIter_IncrCoord(PyArrayNeighborhoodIterObject* iter);\n\n/*\n * Update to next item of the iterator\n *\n * Note: this simply increment the coordinates vector, last dimension\n * incremented first , i.e, for dimension 3\n * ...\n * -1, -1, -1\n * -1, -1, 0\n * -1, -1, 1\n * ....\n * -1, 0, -1\n * -1, 0, 0\n * ....\n * 0, -1, -1\n * 0, -1, 0\n * ....\n */\n#define _UPDATE_COORD_ITER(c) \\n wb = iter->coordinates[c] < iter->bounds[c][1]; \\n if (wb) { \\n iter->coordinates[c] += 1; \\n return 0; \\n } \\n else { \\n iter->coordinates[c] = iter->bounds[c][0]; \\n }\n\nstatic inline int\n_PyArrayNeighborhoodIter_IncrCoord(PyArrayNeighborhoodIterObject* iter)\n{\n npy_intp i, wb;\n\n for (i = iter->nd - 1; i >= 0; --i) {\n _UPDATE_COORD_ITER(i)\n }\n\n return 0;\n}\n\n/*\n * Version optimized for 2d arrays, manual loop unrolling\n */\nstatic inline int\n_PyArrayNeighborhoodIter_IncrCoord2D(PyArrayNeighborhoodIterObject* iter)\n{\n npy_intp wb;\n\n _UPDATE_COORD_ITER(1)\n _UPDATE_COORD_ITER(0)\n\n return 0;\n}\n#undef _UPDATE_COORD_ITER\n\n/*\n * Advance to the next neighbour\n */\nstatic inline int\nPyArrayNeighborhoodIter_Next(PyArrayNeighborhoodIterObject* iter)\n{\n _PyArrayNeighborhoodIter_IncrCoord (iter);\n iter->dataptr = iter->translate((PyArrayIterObject*)iter, iter->coordinates);\n\n return 0;\n}\n\n/*\n * Reset functions\n */\nstatic inline int\nPyArrayNeighborhoodIter_Reset(PyArrayNeighborhoodIterObject* iter)\n{\n npy_intp i;\n\n for (i = 0; i < iter->nd; ++i) {\n iter->coordinates[i] = iter->bounds[i][0];\n }\n iter->dataptr = iter->translate((PyArrayIterObject*)iter, iter->coordinates);\n\n return 0;\n}\n | .venv\Lib\site-packages\numpy\_core\include\numpy\_neighborhood_iterator_imp.h | _neighborhood_iterator_imp.h | C | 1,947 | 0.95 | 0.066667 | 0.435897 | node-utils | 779 | 2023-12-03T04:26:19.571187 | MIT | false | 5ef85e23eefd97f448d4d08111126905 |
/* #undef NPY_HAVE_ENDIAN_H */\n\n#define NPY_SIZEOF_SHORT 2\n#define NPY_SIZEOF_INT 4\n#define NPY_SIZEOF_LONG 4\n#define NPY_SIZEOF_FLOAT 4\n#define NPY_SIZEOF_COMPLEX_FLOAT 8\n#define NPY_SIZEOF_DOUBLE 8\n#define NPY_SIZEOF_COMPLEX_DOUBLE 16\n#define NPY_SIZEOF_LONGDOUBLE 8\n#define NPY_SIZEOF_COMPLEX_LONGDOUBLE 16\n#define NPY_SIZEOF_PY_INTPTR_T 8\n#define NPY_SIZEOF_INTP 8\n#define NPY_SIZEOF_UINTP 8\n#define NPY_SIZEOF_WCHAR_T 2\n#define NPY_SIZEOF_OFF_T 4\n#define NPY_SIZEOF_PY_LONG_LONG 8\n#define NPY_SIZEOF_LONGLONG 8\n\n/*\n * Defined to 1 or 0. Note that Pyodide hardcodes NPY_NO_SMP (and other defines\n * in this header) for better cross-compilation, so don't rename them without a\n * good reason.\n */\n#define NPY_NO_SMP 0\n\n#define NPY_VISIBILITY_HIDDEN\n#define NPY_ABI_VERSION 0x02000000\n#define NPY_API_VERSION 0x00000014\n\n#ifndef __STDC_FORMAT_MACROS\n#define __STDC_FORMAT_MACROS 1\n#endif\n | .venv\Lib\site-packages\numpy\_core\include\numpy\_numpyconfig.h | _numpyconfig.h | C | 902 | 0.95 | 0.030303 | 1 | awesome-app | 941 | 2024-09-14T08:07:52.867573 | Apache-2.0 | false | f965cc7ef71c965b0b366ddbdd6d2bd6 |
/*\n * Public exposure of the DType Classes. These are tricky to expose\n * via the Python API, so they are exposed through this header for now.\n *\n * These definitions are only relevant for the public API and we reserve\n * the slots 320-360 in the API table generation for this (currently).\n *\n * TODO: This file should be consolidated with the API table generation\n * (although not sure the current generation is worth preserving).\n */\n#ifndef NUMPY_CORE_INCLUDE_NUMPY__PUBLIC_DTYPE_API_TABLE_H_\n#define NUMPY_CORE_INCLUDE_NUMPY__PUBLIC_DTYPE_API_TABLE_H_\n\n#if !(defined(NPY_INTERNAL_BUILD) && NPY_INTERNAL_BUILD)\n\n/* All of these require NumPy 2.0 support */\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n\n/*\n * The type of the DType metaclass\n */\n#define PyArrayDTypeMeta_Type (*(PyTypeObject *)(PyArray_API + 320)[0])\n/*\n * NumPy's builtin DTypes:\n */\n#define PyArray_BoolDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[1])\n/* Integers */\n#define PyArray_ByteDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[2])\n#define PyArray_UByteDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[3])\n#define PyArray_ShortDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[4])\n#define PyArray_UShortDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[5])\n#define PyArray_IntDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[6])\n#define PyArray_UIntDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[7])\n#define PyArray_LongDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[8])\n#define PyArray_ULongDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[9])\n#define PyArray_LongLongDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[10])\n#define PyArray_ULongLongDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[11])\n/* Integer aliases */\n#define PyArray_Int8DType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[12])\n#define PyArray_UInt8DType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[13])\n#define PyArray_Int16DType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[14])\n#define PyArray_UInt16DType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[15])\n#define PyArray_Int32DType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[16])\n#define PyArray_UInt32DType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[17])\n#define PyArray_Int64DType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[18])\n#define PyArray_UInt64DType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[19])\n#define PyArray_IntpDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[20])\n#define PyArray_UIntpDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[21])\n/* Floats */\n#define PyArray_HalfDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[22])\n#define PyArray_FloatDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[23])\n#define PyArray_DoubleDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[24])\n#define PyArray_LongDoubleDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[25])\n/* Complex */\n#define PyArray_CFloatDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[26])\n#define PyArray_CDoubleDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[27])\n#define PyArray_CLongDoubleDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[28])\n/* String/Bytes */\n#define PyArray_BytesDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[29])\n#define PyArray_UnicodeDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[30])\n/* Datetime/Timedelta */\n#define PyArray_DatetimeDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[31])\n#define PyArray_TimedeltaDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[32])\n/* Object/Void */\n#define PyArray_ObjectDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[33])\n#define PyArray_VoidDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[34])\n/* Python types (used as markers for scalars) */\n#define PyArray_PyLongDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[35])\n#define PyArray_PyFloatDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[36])\n#define PyArray_PyComplexDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[37])\n/* Default integer type */\n#define PyArray_DefaultIntDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[38])\n/* New non-legacy DTypes follow in the order they were added */\n#define PyArray_StringDType (*(PyArray_DTypeMeta *)(PyArray_API + 320)[39])\n\n/* NOTE: offset 40 is free */\n\n/* Need to start with a larger offset again for the abstract classes: */\n#define PyArray_IntAbstractDType (*(PyArray_DTypeMeta *)PyArray_API[366])\n#define PyArray_FloatAbstractDType (*(PyArray_DTypeMeta *)PyArray_API[367])\n#define PyArray_ComplexAbstractDType (*(PyArray_DTypeMeta *)PyArray_API[368])\n\n#endif /* NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION */\n\n#endif /* NPY_INTERNAL_BUILD */\n#endif /* NUMPY_CORE_INCLUDE_NUMPY__PUBLIC_DTYPE_API_TABLE_H_ */\n | .venv\Lib\site-packages\numpy\_core\include\numpy\_public_dtype_api_table.h | _public_dtype_api_table.h | C | 4,660 | 0.95 | 0.081395 | 1 | python-kit | 118 | 2023-09-22T15:46:11.320465 | GPL-3.0 | false | 27c8601ffdceba559496f0eb8519d7fe |
\n/* These pointers will be stored in the C-object for use in other\n extension modules\n*/\n\nvoid *PyArray_API[] = {\n (void *) PyArray_GetNDArrayCVersion,\n NULL,\n (void *) &PyArray_Type,\n (void *) &PyArrayDescr_Type,\n NULL,\n (void *) &PyArrayIter_Type,\n (void *) &PyArrayMultiIter_Type,\n (int *) &NPY_NUMUSERTYPES,\n (void *) &PyBoolArrType_Type,\n (void *) &_PyArrayScalar_BoolValues,\n (void *) &PyGenericArrType_Type,\n (void *) &PyNumberArrType_Type,\n (void *) &PyIntegerArrType_Type,\n (void *) &PySignedIntegerArrType_Type,\n (void *) &PyUnsignedIntegerArrType_Type,\n (void *) &PyInexactArrType_Type,\n (void *) &PyFloatingArrType_Type,\n (void *) &PyComplexFloatingArrType_Type,\n (void *) &PyFlexibleArrType_Type,\n (void *) &PyCharacterArrType_Type,\n (void *) &PyByteArrType_Type,\n (void *) &PyShortArrType_Type,\n (void *) &PyIntArrType_Type,\n (void *) &PyLongArrType_Type,\n (void *) &PyLongLongArrType_Type,\n (void *) &PyUByteArrType_Type,\n (void *) &PyUShortArrType_Type,\n (void *) &PyUIntArrType_Type,\n (void *) &PyULongArrType_Type,\n (void *) &PyULongLongArrType_Type,\n (void *) &PyFloatArrType_Type,\n (void *) &PyDoubleArrType_Type,\n (void *) &PyLongDoubleArrType_Type,\n (void *) &PyCFloatArrType_Type,\n (void *) &PyCDoubleArrType_Type,\n (void *) &PyCLongDoubleArrType_Type,\n (void *) &PyObjectArrType_Type,\n (void *) &PyStringArrType_Type,\n (void *) &PyUnicodeArrType_Type,\n (void *) &PyVoidArrType_Type,\n NULL,\n NULL,\n (void *) PyArray_INCREF,\n (void *) PyArray_XDECREF,\n (void *) PyArray_SetStringFunction,\n (void *) PyArray_DescrFromType,\n (void *) PyArray_TypeObjectFromType,\n (void *) PyArray_Zero,\n (void *) PyArray_One,\n (void *) PyArray_CastToType,\n (void *) PyArray_CopyInto,\n (void *) PyArray_CopyAnyInto,\n (void *) PyArray_CanCastSafely,\n (void *) PyArray_CanCastTo,\n (void *) PyArray_ObjectType,\n (void *) PyArray_DescrFromObject,\n (void *) PyArray_ConvertToCommonType,\n (void *) PyArray_DescrFromScalar,\n (void *) PyArray_DescrFromTypeObject,\n (void *) PyArray_Size,\n (void *) PyArray_Scalar,\n (void *) PyArray_FromScalar,\n (void *) PyArray_ScalarAsCtype,\n (void *) PyArray_CastScalarToCtype,\n (void *) PyArray_CastScalarDirect,\n (void *) PyArray_Pack,\n NULL,\n NULL,\n NULL,\n (void *) PyArray_FromAny,\n (void *) PyArray_EnsureArray,\n (void *) PyArray_EnsureAnyArray,\n (void *) PyArray_FromFile,\n (void *) PyArray_FromString,\n (void *) PyArray_FromBuffer,\n (void *) PyArray_FromIter,\n (void *) PyArray_Return,\n (void *) PyArray_GetField,\n (void *) PyArray_SetField,\n (void *) PyArray_Byteswap,\n (void *) PyArray_Resize,\n NULL,\n NULL,\n NULL,\n (void *) PyArray_CopyObject,\n (void *) PyArray_NewCopy,\n (void *) PyArray_ToList,\n (void *) PyArray_ToString,\n (void *) PyArray_ToFile,\n (void *) PyArray_Dump,\n (void *) PyArray_Dumps,\n (void *) PyArray_ValidType,\n (void *) PyArray_UpdateFlags,\n (void *) PyArray_New,\n (void *) PyArray_NewFromDescr,\n (void *) PyArray_DescrNew,\n (void *) PyArray_DescrNewFromType,\n (void *) PyArray_GetPriority,\n (void *) PyArray_IterNew,\n (void *) PyArray_MultiIterNew,\n (void *) PyArray_PyIntAsInt,\n (void *) PyArray_PyIntAsIntp,\n (void *) PyArray_Broadcast,\n NULL,\n (void *) PyArray_FillWithScalar,\n (void *) PyArray_CheckStrides,\n (void *) PyArray_DescrNewByteorder,\n (void *) PyArray_IterAllButAxis,\n (void *) PyArray_CheckFromAny,\n (void *) PyArray_FromArray,\n (void *) PyArray_FromInterface,\n (void *) PyArray_FromStructInterface,\n (void *) PyArray_FromArrayAttr,\n (void *) PyArray_ScalarKind,\n (void *) PyArray_CanCoerceScalar,\n NULL,\n (void *) PyArray_CanCastScalar,\n NULL,\n (void *) PyArray_RemoveSmallest,\n (void *) PyArray_ElementStrides,\n (void *) PyArray_Item_INCREF,\n (void *) PyArray_Item_XDECREF,\n NULL,\n (void *) PyArray_Transpose,\n (void *) PyArray_TakeFrom,\n (void *) PyArray_PutTo,\n (void *) PyArray_PutMask,\n (void *) PyArray_Repeat,\n (void *) PyArray_Choose,\n (void *) PyArray_Sort,\n (void *) PyArray_ArgSort,\n (void *) PyArray_SearchSorted,\n (void *) PyArray_ArgMax,\n (void *) PyArray_ArgMin,\n (void *) PyArray_Reshape,\n (void *) PyArray_Newshape,\n (void *) PyArray_Squeeze,\n (void *) PyArray_View,\n (void *) PyArray_SwapAxes,\n (void *) PyArray_Max,\n (void *) PyArray_Min,\n (void *) PyArray_Ptp,\n (void *) PyArray_Mean,\n (void *) PyArray_Trace,\n (void *) PyArray_Diagonal,\n (void *) PyArray_Clip,\n (void *) PyArray_Conjugate,\n (void *) PyArray_Nonzero,\n (void *) PyArray_Std,\n (void *) PyArray_Sum,\n (void *) PyArray_CumSum,\n (void *) PyArray_Prod,\n (void *) PyArray_CumProd,\n (void *) PyArray_All,\n (void *) PyArray_Any,\n (void *) PyArray_Compress,\n (void *) PyArray_Flatten,\n (void *) PyArray_Ravel,\n (void *) PyArray_MultiplyList,\n (void *) PyArray_MultiplyIntList,\n (void *) PyArray_GetPtr,\n (void *) PyArray_CompareLists,\n (void *) PyArray_AsCArray,\n NULL,\n NULL,\n (void *) PyArray_Free,\n (void *) PyArray_Converter,\n (void *) PyArray_IntpFromSequence,\n (void *) PyArray_Concatenate,\n (void *) PyArray_InnerProduct,\n (void *) PyArray_MatrixProduct,\n NULL,\n (void *) PyArray_Correlate,\n NULL,\n (void *) PyArray_DescrConverter,\n (void *) PyArray_DescrConverter2,\n (void *) PyArray_IntpConverter,\n (void *) PyArray_BufferConverter,\n (void *) PyArray_AxisConverter,\n (void *) PyArray_BoolConverter,\n (void *) PyArray_ByteorderConverter,\n (void *) PyArray_OrderConverter,\n (void *) PyArray_EquivTypes,\n (void *) PyArray_Zeros,\n (void *) PyArray_Empty,\n (void *) PyArray_Where,\n (void *) PyArray_Arange,\n (void *) PyArray_ArangeObj,\n (void *) PyArray_SortkindConverter,\n (void *) PyArray_LexSort,\n (void *) PyArray_Round,\n (void *) PyArray_EquivTypenums,\n (void *) PyArray_RegisterDataType,\n (void *) PyArray_RegisterCastFunc,\n (void *) PyArray_RegisterCanCast,\n (void *) PyArray_InitArrFuncs,\n (void *) PyArray_IntTupleFromIntp,\n NULL,\n (void *) PyArray_ClipmodeConverter,\n (void *) PyArray_OutputConverter,\n (void *) PyArray_BroadcastToShape,\n NULL,\n NULL,\n (void *) PyArray_DescrAlignConverter,\n (void *) PyArray_DescrAlignConverter2,\n (void *) PyArray_SearchsideConverter,\n (void *) PyArray_CheckAxis,\n (void *) PyArray_OverflowMultiplyList,\n NULL,\n (void *) PyArray_MultiIterFromObjects,\n (void *) PyArray_GetEndianness,\n (void *) PyArray_GetNDArrayCFeatureVersion,\n (void *) PyArray_Correlate2,\n (void *) PyArray_NeighborhoodIterNew,\n (void *) &PyTimeIntegerArrType_Type,\n (void *) &PyDatetimeArrType_Type,\n (void *) &PyTimedeltaArrType_Type,\n (void *) &PyHalfArrType_Type,\n (void *) &NpyIter_Type,\n NULL,\n NULL,\n NULL,\n NULL,\n (void *) NpyIter_GetTransferFlags,\n (void *) NpyIter_New,\n (void *) NpyIter_MultiNew,\n (void *) NpyIter_AdvancedNew,\n (void *) NpyIter_Copy,\n (void *) NpyIter_Deallocate,\n (void *) NpyIter_HasDelayedBufAlloc,\n (void *) NpyIter_HasExternalLoop,\n (void *) NpyIter_EnableExternalLoop,\n (void *) NpyIter_GetInnerStrideArray,\n (void *) NpyIter_GetInnerLoopSizePtr,\n (void *) NpyIter_Reset,\n (void *) NpyIter_ResetBasePointers,\n (void *) NpyIter_ResetToIterIndexRange,\n (void *) NpyIter_GetNDim,\n (void *) NpyIter_GetNOp,\n (void *) NpyIter_GetIterNext,\n (void *) NpyIter_GetIterSize,\n (void *) NpyIter_GetIterIndexRange,\n (void *) NpyIter_GetIterIndex,\n (void *) NpyIter_GotoIterIndex,\n (void *) NpyIter_HasMultiIndex,\n (void *) NpyIter_GetShape,\n (void *) NpyIter_GetGetMultiIndex,\n (void *) NpyIter_GotoMultiIndex,\n (void *) NpyIter_RemoveMultiIndex,\n (void *) NpyIter_HasIndex,\n (void *) NpyIter_IsBuffered,\n (void *) NpyIter_IsGrowInner,\n (void *) NpyIter_GetBufferSize,\n (void *) NpyIter_GetIndexPtr,\n (void *) NpyIter_GotoIndex,\n (void *) NpyIter_GetDataPtrArray,\n (void *) NpyIter_GetDescrArray,\n (void *) NpyIter_GetOperandArray,\n (void *) NpyIter_GetIterView,\n (void *) NpyIter_GetReadFlags,\n (void *) NpyIter_GetWriteFlags,\n (void *) NpyIter_DebugPrint,\n (void *) NpyIter_IterationNeedsAPI,\n (void *) NpyIter_GetInnerFixedStrideArray,\n (void *) NpyIter_RemoveAxis,\n (void *) NpyIter_GetAxisStrideArray,\n (void *) NpyIter_RequiresBuffering,\n (void *) NpyIter_GetInitialDataPtrArray,\n (void *) NpyIter_CreateCompatibleStrides,\n (void *) PyArray_CastingConverter,\n (void *) PyArray_CountNonzero,\n (void *) PyArray_PromoteTypes,\n (void *) PyArray_MinScalarType,\n (void *) PyArray_ResultType,\n (void *) PyArray_CanCastArrayTo,\n (void *) PyArray_CanCastTypeTo,\n (void *) PyArray_EinsteinSum,\n (void *) PyArray_NewLikeArray,\n NULL,\n (void *) PyArray_ConvertClipmodeSequence,\n (void *) PyArray_MatrixProduct2,\n (void *) NpyIter_IsFirstVisit,\n (void *) PyArray_SetBaseObject,\n (void *) PyArray_CreateSortedStridePerm,\n (void *) PyArray_RemoveAxesInPlace,\n (void *) PyArray_DebugPrint,\n (void *) PyArray_FailUnlessWriteable,\n (void *) PyArray_SetUpdateIfCopyBase,\n (void *) PyDataMem_NEW,\n (void *) PyDataMem_FREE,\n (void *) PyDataMem_RENEW,\n NULL,\n (NPY_CASTING *) &NPY_DEFAULT_ASSIGN_CASTING,\n NULL,\n NULL,\n NULL,\n (void *) PyArray_Partition,\n (void *) PyArray_ArgPartition,\n (void *) PyArray_SelectkindConverter,\n (void *) PyDataMem_NEW_ZEROED,\n (void *) PyArray_CheckAnyScalarExact,\n NULL,\n (void *) PyArray_ResolveWritebackIfCopy,\n (void *) PyArray_SetWritebackIfCopyBase,\n (void *) PyDataMem_SetHandler,\n (void *) PyDataMem_GetHandler,\n (PyObject* *) &PyDataMem_DefaultHandler,\n (void *) NpyDatetime_ConvertDatetime64ToDatetimeStruct,\n (void *) NpyDatetime_ConvertDatetimeStructToDatetime64,\n (void *) NpyDatetime_ConvertPyDateTimeToDatetimeStruct,\n (void *) NpyDatetime_GetDatetimeISO8601StrLen,\n (void *) NpyDatetime_MakeISO8601Datetime,\n (void *) NpyDatetime_ParseISO8601Datetime,\n (void *) NpyString_load,\n (void *) NpyString_pack,\n (void *) NpyString_pack_null,\n (void *) NpyString_acquire_allocator,\n (void *) NpyString_acquire_allocators,\n (void *) NpyString_release_allocator,\n (void *) NpyString_release_allocators,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n NULL,\n (void *) PyArray_GetDefaultDescr,\n (void *) PyArrayInitDTypeMeta_FromSpec,\n (void *) PyArray_CommonDType,\n (void *) PyArray_PromoteDTypeSequence,\n (void *) _PyDataType_GetArrFuncs,\n NULL,\n NULL,\n NULL\n};\n | .venv\Lib\site-packages\numpy\_core\include\numpy\__multiarray_api.c | __multiarray_api.c | C | 13,074 | 0.8 | 0.00266 | 0.005348 | node-utils | 541 | 2025-01-06T13:39:06.664085 | Apache-2.0 | false | 0124d456e3dda15ce851c57b77149a66 |
\n#if defined(_MULTIARRAYMODULE) || defined(WITH_CPYCHECKER_STEALS_REFERENCE_TO_ARG_ATTRIBUTE)\n\ntypedef struct {\n PyObject_HEAD\n npy_bool obval;\n} PyBoolScalarObject;\n\nextern NPY_NO_EXPORT PyTypeObject PyArrayNeighborhoodIter_Type;\nextern NPY_NO_EXPORT PyBoolScalarObject _PyArrayScalar_BoolValues[2];\n\nNPY_NO_EXPORT unsigned int PyArray_GetNDArrayCVersion \\n (void);\nextern NPY_NO_EXPORT PyTypeObject PyArray_Type;\n\nextern NPY_NO_EXPORT PyArray_DTypeMeta PyArrayDescr_TypeFull;\n#define PyArrayDescr_Type (*(PyTypeObject *)(&PyArrayDescr_TypeFull))\n\nextern NPY_NO_EXPORT PyTypeObject PyArrayIter_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyArrayMultiIter_Type;\n\nextern NPY_NO_EXPORT int NPY_NUMUSERTYPES;\n\nextern NPY_NO_EXPORT PyTypeObject PyBoolArrType_Type;\n\nextern NPY_NO_EXPORT PyBoolScalarObject _PyArrayScalar_BoolValues[2];\n\nextern NPY_NO_EXPORT PyTypeObject PyGenericArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyNumberArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyIntegerArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PySignedIntegerArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyUnsignedIntegerArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyInexactArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyFloatingArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyComplexFloatingArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyFlexibleArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyCharacterArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyByteArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyShortArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyIntArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyLongArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyLongLongArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyUByteArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyUShortArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyUIntArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyULongArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyULongLongArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyFloatArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyDoubleArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyLongDoubleArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyCFloatArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyCDoubleArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyCLongDoubleArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyObjectArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyStringArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyUnicodeArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyVoidArrType_Type;\n\nNPY_NO_EXPORT int PyArray_INCREF \\n (PyArrayObject *);\nNPY_NO_EXPORT int PyArray_XDECREF \\n (PyArrayObject *);\nNPY_NO_EXPORT void PyArray_SetStringFunction \\n (PyObject *, int);\nNPY_NO_EXPORT PyArray_Descr * PyArray_DescrFromType \\n (int);\nNPY_NO_EXPORT PyObject * PyArray_TypeObjectFromType \\n (int);\nNPY_NO_EXPORT char * PyArray_Zero \\n (PyArrayObject *);\nNPY_NO_EXPORT char * PyArray_One \\n (PyArrayObject *);\nNPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_CastToType \\n (PyArrayObject *, PyArray_Descr *, int);\nNPY_NO_EXPORT int PyArray_CopyInto \\n (PyArrayObject *, PyArrayObject *);\nNPY_NO_EXPORT int PyArray_CopyAnyInto \\n (PyArrayObject *, PyArrayObject *);\nNPY_NO_EXPORT int PyArray_CanCastSafely \\n (int, int);\nNPY_NO_EXPORT npy_bool PyArray_CanCastTo \\n (PyArray_Descr *, PyArray_Descr *);\nNPY_NO_EXPORT int PyArray_ObjectType \\n (PyObject *, int);\nNPY_NO_EXPORT PyArray_Descr * PyArray_DescrFromObject \\n (PyObject *, PyArray_Descr *);\nNPY_NO_EXPORT PyArrayObject ** PyArray_ConvertToCommonType \\n (PyObject *, int *);\nNPY_NO_EXPORT PyArray_Descr * PyArray_DescrFromScalar \\n (PyObject *);\nNPY_NO_EXPORT PyArray_Descr * PyArray_DescrFromTypeObject \\n (PyObject *);\nNPY_NO_EXPORT npy_intp PyArray_Size \\n (PyObject *);\nNPY_NO_EXPORT PyObject * PyArray_Scalar \\n (void *, PyArray_Descr *, PyObject *);\nNPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_FromScalar \\n (PyObject *, PyArray_Descr *);\nNPY_NO_EXPORT void PyArray_ScalarAsCtype \\n (PyObject *, void *);\nNPY_NO_EXPORT int PyArray_CastScalarToCtype \\n (PyObject *, void *, PyArray_Descr *);\nNPY_NO_EXPORT int PyArray_CastScalarDirect \\n (PyObject *, PyArray_Descr *, void *, int);\nNPY_NO_EXPORT int PyArray_Pack \\n (PyArray_Descr *, void *, PyObject *);\nNPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_FromAny \\n (PyObject *, PyArray_Descr *, int, int, int, PyObject *);\nNPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(1) PyObject * PyArray_EnsureArray \\n (PyObject *);\nNPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(1) PyObject * PyArray_EnsureAnyArray \\n (PyObject *);\nNPY_NO_EXPORT PyObject * PyArray_FromFile \\n (FILE *, PyArray_Descr *, npy_intp, char *);\nNPY_NO_EXPORT PyObject * PyArray_FromString \\n (char *, npy_intp, PyArray_Descr *, npy_intp, char *);\nNPY_NO_EXPORT PyObject * PyArray_FromBuffer \\n (PyObject *, PyArray_Descr *, npy_intp, npy_intp);\nNPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_FromIter \\n (PyObject *, PyArray_Descr *, npy_intp);\nNPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(1) PyObject * PyArray_Return \\n (PyArrayObject *);\nNPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_GetField \\n (PyArrayObject *, PyArray_Descr *, int);\nNPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) int PyArray_SetField \\n (PyArrayObject *, PyArray_Descr *, int, PyObject *);\nNPY_NO_EXPORT PyObject * PyArray_Byteswap \\n (PyArrayObject *, npy_bool);\nNPY_NO_EXPORT PyObject * PyArray_Resize \\n (PyArrayObject *, PyArray_Dims *, int, NPY_ORDER NPY_UNUSED(order));\nNPY_NO_EXPORT int PyArray_CopyObject \\n (PyArrayObject *, PyObject *);\nNPY_NO_EXPORT PyObject * PyArray_NewCopy \\n (PyArrayObject *, NPY_ORDER);\nNPY_NO_EXPORT PyObject * PyArray_ToList \\n (PyArrayObject *);\nNPY_NO_EXPORT PyObject * PyArray_ToString \\n (PyArrayObject *, NPY_ORDER);\nNPY_NO_EXPORT int PyArray_ToFile \\n (PyArrayObject *, FILE *, char *, char *);\nNPY_NO_EXPORT int PyArray_Dump \\n (PyObject *, PyObject *, int);\nNPY_NO_EXPORT PyObject * PyArray_Dumps \\n (PyObject *, int);\nNPY_NO_EXPORT int PyArray_ValidType \\n (int);\nNPY_NO_EXPORT void PyArray_UpdateFlags \\n (PyArrayObject *, int);\nNPY_NO_EXPORT PyObject * PyArray_New \\n (PyTypeObject *, int, npy_intp const *, int, npy_intp const *, void *, int, int, PyObject *);\nNPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_NewFromDescr \\n (PyTypeObject *, PyArray_Descr *, int, npy_intp const *, npy_intp const *, void *, int, PyObject *);\nNPY_NO_EXPORT PyArray_Descr * PyArray_DescrNew \\n (PyArray_Descr *);\nNPY_NO_EXPORT PyArray_Descr * PyArray_DescrNewFromType \\n (int);\nNPY_NO_EXPORT double PyArray_GetPriority \\n (PyObject *, double);\nNPY_NO_EXPORT PyObject * PyArray_IterNew \\n (PyObject *);\nNPY_NO_EXPORT PyObject* PyArray_MultiIterNew \\n (int, ...);\nNPY_NO_EXPORT int PyArray_PyIntAsInt \\n (PyObject *);\nNPY_NO_EXPORT npy_intp PyArray_PyIntAsIntp \\n (PyObject *);\nNPY_NO_EXPORT int PyArray_Broadcast \\n (PyArrayMultiIterObject *);\nNPY_NO_EXPORT int PyArray_FillWithScalar \\n (PyArrayObject *, PyObject *);\nNPY_NO_EXPORT npy_bool PyArray_CheckStrides \\n (int, int, npy_intp, npy_intp, npy_intp const *, npy_intp const *);\nNPY_NO_EXPORT PyArray_Descr * PyArray_DescrNewByteorder \\n (PyArray_Descr *, char);\nNPY_NO_EXPORT PyObject * PyArray_IterAllButAxis \\n (PyObject *, int *);\nNPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_CheckFromAny \\n (PyObject *, PyArray_Descr *, int, int, int, PyObject *);\nNPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_FromArray \\n (PyArrayObject *, PyArray_Descr *, int);\nNPY_NO_EXPORT PyObject * PyArray_FromInterface \\n (PyObject *);\nNPY_NO_EXPORT PyObject * PyArray_FromStructInterface \\n (PyObject *);\nNPY_NO_EXPORT PyObject * PyArray_FromArrayAttr \\n (PyObject *, PyArray_Descr *, PyObject *);\nNPY_NO_EXPORT NPY_SCALARKIND PyArray_ScalarKind \\n (int, PyArrayObject **);\nNPY_NO_EXPORT int PyArray_CanCoerceScalar \\n (int, int, NPY_SCALARKIND);\nNPY_NO_EXPORT npy_bool PyArray_CanCastScalar \\n (PyTypeObject *, PyTypeObject *);\nNPY_NO_EXPORT int PyArray_RemoveSmallest \\n (PyArrayMultiIterObject *);\nNPY_NO_EXPORT int PyArray_ElementStrides \\n (PyObject *);\nNPY_NO_EXPORT void PyArray_Item_INCREF \\n (char *, PyArray_Descr *);\nNPY_NO_EXPORT void PyArray_Item_XDECREF \\n (char *, PyArray_Descr *);\nNPY_NO_EXPORT PyObject * PyArray_Transpose \\n (PyArrayObject *, PyArray_Dims *);\nNPY_NO_EXPORT PyObject * PyArray_TakeFrom \\n (PyArrayObject *, PyObject *, int, PyArrayObject *, NPY_CLIPMODE);\nNPY_NO_EXPORT PyObject * PyArray_PutTo \\n (PyArrayObject *, PyObject*, PyObject *, NPY_CLIPMODE);\nNPY_NO_EXPORT PyObject * PyArray_PutMask \\n (PyArrayObject *, PyObject*, PyObject*);\nNPY_NO_EXPORT PyObject * PyArray_Repeat \\n (PyArrayObject *, PyObject *, int);\nNPY_NO_EXPORT PyObject * PyArray_Choose \\n (PyArrayObject *, PyObject *, PyArrayObject *, NPY_CLIPMODE);\nNPY_NO_EXPORT int PyArray_Sort \\n (PyArrayObject *, int, NPY_SORTKIND);\nNPY_NO_EXPORT PyObject * PyArray_ArgSort \\n (PyArrayObject *, int, NPY_SORTKIND);\nNPY_NO_EXPORT PyObject * PyArray_SearchSorted \\n (PyArrayObject *, PyObject *, NPY_SEARCHSIDE, PyObject *);\nNPY_NO_EXPORT PyObject * PyArray_ArgMax \\n (PyArrayObject *, int, PyArrayObject *);\nNPY_NO_EXPORT PyObject * PyArray_ArgMin \\n (PyArrayObject *, int, PyArrayObject *);\nNPY_NO_EXPORT PyObject * PyArray_Reshape \\n (PyArrayObject *, PyObject *);\nNPY_NO_EXPORT PyObject * PyArray_Newshape \\n (PyArrayObject *, PyArray_Dims *, NPY_ORDER);\nNPY_NO_EXPORT PyObject * PyArray_Squeeze \\n (PyArrayObject *);\nNPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) PyObject * PyArray_View \\n (PyArrayObject *, PyArray_Descr *, PyTypeObject *);\nNPY_NO_EXPORT PyObject * PyArray_SwapAxes \\n (PyArrayObject *, int, int);\nNPY_NO_EXPORT PyObject * PyArray_Max \\n (PyArrayObject *, int, PyArrayObject *);\nNPY_NO_EXPORT PyObject * PyArray_Min \\n (PyArrayObject *, int, PyArrayObject *);\nNPY_NO_EXPORT PyObject * PyArray_Ptp \\n (PyArrayObject *, int, PyArrayObject *);\nNPY_NO_EXPORT PyObject * PyArray_Mean \\n (PyArrayObject *, int, int, PyArrayObject *);\nNPY_NO_EXPORT PyObject * PyArray_Trace \\n (PyArrayObject *, int, int, int, int, PyArrayObject *);\nNPY_NO_EXPORT PyObject * PyArray_Diagonal \\n (PyArrayObject *, int, int, int);\nNPY_NO_EXPORT PyObject * PyArray_Clip \\n (PyArrayObject *, PyObject *, PyObject *, PyArrayObject *);\nNPY_NO_EXPORT PyObject * PyArray_Conjugate \\n (PyArrayObject *, PyArrayObject *);\nNPY_NO_EXPORT PyObject * PyArray_Nonzero \\n (PyArrayObject *);\nNPY_NO_EXPORT PyObject * PyArray_Std \\n (PyArrayObject *, int, int, PyArrayObject *, int);\nNPY_NO_EXPORT PyObject * PyArray_Sum \\n (PyArrayObject *, int, int, PyArrayObject *);\nNPY_NO_EXPORT PyObject * PyArray_CumSum \\n (PyArrayObject *, int, int, PyArrayObject *);\nNPY_NO_EXPORT PyObject * PyArray_Prod \\n (PyArrayObject *, int, int, PyArrayObject *);\nNPY_NO_EXPORT PyObject * PyArray_CumProd \\n (PyArrayObject *, int, int, PyArrayObject *);\nNPY_NO_EXPORT PyObject * PyArray_All \\n (PyArrayObject *, int, PyArrayObject *);\nNPY_NO_EXPORT PyObject * PyArray_Any \\n (PyArrayObject *, int, PyArrayObject *);\nNPY_NO_EXPORT PyObject * PyArray_Compress \\n (PyArrayObject *, PyObject *, int, PyArrayObject *);\nNPY_NO_EXPORT PyObject * PyArray_Flatten \\n (PyArrayObject *, NPY_ORDER);\nNPY_NO_EXPORT PyObject * PyArray_Ravel \\n (PyArrayObject *, NPY_ORDER);\nNPY_NO_EXPORT npy_intp PyArray_MultiplyList \\n (npy_intp const *, int);\nNPY_NO_EXPORT int PyArray_MultiplyIntList \\n (int const *, int);\nNPY_NO_EXPORT void * PyArray_GetPtr \\n (PyArrayObject *, npy_intp const*);\nNPY_NO_EXPORT int PyArray_CompareLists \\n (npy_intp const *, npy_intp const *, int);\nNPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(5) int PyArray_AsCArray \\n (PyObject **, void *, npy_intp *, int, PyArray_Descr*);\nNPY_NO_EXPORT int PyArray_Free \\n (PyObject *, void *);\nNPY_NO_EXPORT int PyArray_Converter \\n (PyObject *, PyObject **);\nNPY_NO_EXPORT int PyArray_IntpFromSequence \\n (PyObject *, npy_intp *, int);\nNPY_NO_EXPORT PyObject * PyArray_Concatenate \\n (PyObject *, int);\nNPY_NO_EXPORT PyObject * PyArray_InnerProduct \\n (PyObject *, PyObject *);\nNPY_NO_EXPORT PyObject * PyArray_MatrixProduct \\n (PyObject *, PyObject *);\nNPY_NO_EXPORT PyObject * PyArray_Correlate \\n (PyObject *, PyObject *, int);\nNPY_NO_EXPORT int PyArray_DescrConverter \\n (PyObject *, PyArray_Descr **);\nNPY_NO_EXPORT int PyArray_DescrConverter2 \\n (PyObject *, PyArray_Descr **);\nNPY_NO_EXPORT int PyArray_IntpConverter \\n (PyObject *, PyArray_Dims *);\nNPY_NO_EXPORT int PyArray_BufferConverter \\n (PyObject *, PyArray_Chunk *);\nNPY_NO_EXPORT int PyArray_AxisConverter \\n (PyObject *, int *);\nNPY_NO_EXPORT int PyArray_BoolConverter \\n (PyObject *, npy_bool *);\nNPY_NO_EXPORT int PyArray_ByteorderConverter \\n (PyObject *, char *);\nNPY_NO_EXPORT int PyArray_OrderConverter \\n (PyObject *, NPY_ORDER *);\nNPY_NO_EXPORT unsigned char PyArray_EquivTypes \\n (PyArray_Descr *, PyArray_Descr *);\nNPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(3) PyObject * PyArray_Zeros \\n (int, npy_intp const *, PyArray_Descr *, int);\nNPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(3) PyObject * PyArray_Empty \\n (int, npy_intp const *, PyArray_Descr *, int);\nNPY_NO_EXPORT PyObject * PyArray_Where \\n (PyObject *, PyObject *, PyObject *);\nNPY_NO_EXPORT PyObject * PyArray_Arange \\n (double, double, double, int);\nNPY_NO_EXPORT PyObject * PyArray_ArangeObj \\n (PyObject *, PyObject *, PyObject *, PyArray_Descr *);\nNPY_NO_EXPORT int PyArray_SortkindConverter \\n (PyObject *, NPY_SORTKIND *);\nNPY_NO_EXPORT PyObject * PyArray_LexSort \\n (PyObject *, int);\nNPY_NO_EXPORT PyObject * PyArray_Round \\n (PyArrayObject *, int, PyArrayObject *);\nNPY_NO_EXPORT unsigned char PyArray_EquivTypenums \\n (int, int);\nNPY_NO_EXPORT int PyArray_RegisterDataType \\n (PyArray_DescrProto *);\nNPY_NO_EXPORT int PyArray_RegisterCastFunc \\n (PyArray_Descr *, int, PyArray_VectorUnaryFunc *);\nNPY_NO_EXPORT int PyArray_RegisterCanCast \\n (PyArray_Descr *, int, NPY_SCALARKIND);\nNPY_NO_EXPORT void PyArray_InitArrFuncs \\n (PyArray_ArrFuncs *);\nNPY_NO_EXPORT PyObject * PyArray_IntTupleFromIntp \\n (int, npy_intp const *);\nNPY_NO_EXPORT int PyArray_ClipmodeConverter \\n (PyObject *, NPY_CLIPMODE *);\nNPY_NO_EXPORT int PyArray_OutputConverter \\n (PyObject *, PyArrayObject **);\nNPY_NO_EXPORT PyObject * PyArray_BroadcastToShape \\n (PyObject *, npy_intp *, int);\nNPY_NO_EXPORT int PyArray_DescrAlignConverter \\n (PyObject *, PyArray_Descr **);\nNPY_NO_EXPORT int PyArray_DescrAlignConverter2 \\n (PyObject *, PyArray_Descr **);\nNPY_NO_EXPORT int PyArray_SearchsideConverter \\n (PyObject *, void *);\nNPY_NO_EXPORT PyObject * PyArray_CheckAxis \\n (PyArrayObject *, int *, int);\nNPY_NO_EXPORT npy_intp PyArray_OverflowMultiplyList \\n (npy_intp const *, int);\nNPY_NO_EXPORT PyObject* PyArray_MultiIterFromObjects \\n (PyObject **, int, int, ...);\nNPY_NO_EXPORT int PyArray_GetEndianness \\n (void);\nNPY_NO_EXPORT unsigned int PyArray_GetNDArrayCFeatureVersion \\n (void);\nNPY_NO_EXPORT PyObject * PyArray_Correlate2 \\n (PyObject *, PyObject *, int);\nNPY_NO_EXPORT PyObject* PyArray_NeighborhoodIterNew \\n (PyArrayIterObject *, const npy_intp *, int, PyArrayObject*);\nextern NPY_NO_EXPORT PyTypeObject PyTimeIntegerArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyDatetimeArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyTimedeltaArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyHalfArrType_Type;\n\nextern NPY_NO_EXPORT PyTypeObject NpyIter_Type;\n\nNPY_NO_EXPORT NPY_ARRAYMETHOD_FLAGS NpyIter_GetTransferFlags \\n (NpyIter *);\nNPY_NO_EXPORT NpyIter * NpyIter_New \\n (PyArrayObject *, npy_uint32, NPY_ORDER, NPY_CASTING, PyArray_Descr*);\nNPY_NO_EXPORT NpyIter * NpyIter_MultiNew \\n (int, PyArrayObject **, npy_uint32, NPY_ORDER, NPY_CASTING, npy_uint32 *, PyArray_Descr **);\nNPY_NO_EXPORT NpyIter * NpyIter_AdvancedNew \\n (int, PyArrayObject **, npy_uint32, NPY_ORDER, NPY_CASTING, npy_uint32 *, PyArray_Descr **, int, int **, npy_intp *, npy_intp);\nNPY_NO_EXPORT NpyIter * NpyIter_Copy \\n (NpyIter *);\nNPY_NO_EXPORT int NpyIter_Deallocate \\n (NpyIter *);\nNPY_NO_EXPORT npy_bool NpyIter_HasDelayedBufAlloc \\n (NpyIter *);\nNPY_NO_EXPORT npy_bool NpyIter_HasExternalLoop \\n (NpyIter *);\nNPY_NO_EXPORT int NpyIter_EnableExternalLoop \\n (NpyIter *);\nNPY_NO_EXPORT npy_intp * NpyIter_GetInnerStrideArray \\n (NpyIter *);\nNPY_NO_EXPORT npy_intp * NpyIter_GetInnerLoopSizePtr \\n (NpyIter *);\nNPY_NO_EXPORT int NpyIter_Reset \\n (NpyIter *, char **);\nNPY_NO_EXPORT int NpyIter_ResetBasePointers \\n (NpyIter *, char **, char **);\nNPY_NO_EXPORT int NpyIter_ResetToIterIndexRange \\n (NpyIter *, npy_intp, npy_intp, char **);\nNPY_NO_EXPORT int NpyIter_GetNDim \\n (NpyIter *);\nNPY_NO_EXPORT int NpyIter_GetNOp \\n (NpyIter *);\nNPY_NO_EXPORT NpyIter_IterNextFunc * NpyIter_GetIterNext \\n (NpyIter *, char **);\nNPY_NO_EXPORT npy_intp NpyIter_GetIterSize \\n (NpyIter *);\nNPY_NO_EXPORT void NpyIter_GetIterIndexRange \\n (NpyIter *, npy_intp *, npy_intp *);\nNPY_NO_EXPORT npy_intp NpyIter_GetIterIndex \\n (NpyIter *);\nNPY_NO_EXPORT int NpyIter_GotoIterIndex \\n (NpyIter *, npy_intp);\nNPY_NO_EXPORT npy_bool NpyIter_HasMultiIndex \\n (NpyIter *);\nNPY_NO_EXPORT int NpyIter_GetShape \\n (NpyIter *, npy_intp *);\nNPY_NO_EXPORT NpyIter_GetMultiIndexFunc * NpyIter_GetGetMultiIndex \\n (NpyIter *, char **);\nNPY_NO_EXPORT int NpyIter_GotoMultiIndex \\n (NpyIter *, npy_intp const *);\nNPY_NO_EXPORT int NpyIter_RemoveMultiIndex \\n (NpyIter *);\nNPY_NO_EXPORT npy_bool NpyIter_HasIndex \\n (NpyIter *);\nNPY_NO_EXPORT npy_bool NpyIter_IsBuffered \\n (NpyIter *);\nNPY_NO_EXPORT npy_bool NpyIter_IsGrowInner \\n (NpyIter *);\nNPY_NO_EXPORT npy_intp NpyIter_GetBufferSize \\n (NpyIter *);\nNPY_NO_EXPORT npy_intp * NpyIter_GetIndexPtr \\n (NpyIter *);\nNPY_NO_EXPORT int NpyIter_GotoIndex \\n (NpyIter *, npy_intp);\nNPY_NO_EXPORT char ** NpyIter_GetDataPtrArray \\n (NpyIter *);\nNPY_NO_EXPORT PyArray_Descr ** NpyIter_GetDescrArray \\n (NpyIter *);\nNPY_NO_EXPORT PyArrayObject ** NpyIter_GetOperandArray \\n (NpyIter *);\nNPY_NO_EXPORT PyArrayObject * NpyIter_GetIterView \\n (NpyIter *, npy_intp);\nNPY_NO_EXPORT void NpyIter_GetReadFlags \\n (NpyIter *, char *);\nNPY_NO_EXPORT void NpyIter_GetWriteFlags \\n (NpyIter *, char *);\nNPY_NO_EXPORT void NpyIter_DebugPrint \\n (NpyIter *);\nNPY_NO_EXPORT npy_bool NpyIter_IterationNeedsAPI \\n (NpyIter *);\nNPY_NO_EXPORT void NpyIter_GetInnerFixedStrideArray \\n (NpyIter *, npy_intp *);\nNPY_NO_EXPORT int NpyIter_RemoveAxis \\n (NpyIter *, int);\nNPY_NO_EXPORT npy_intp * NpyIter_GetAxisStrideArray \\n (NpyIter *, int);\nNPY_NO_EXPORT npy_bool NpyIter_RequiresBuffering \\n (NpyIter *);\nNPY_NO_EXPORT char ** NpyIter_GetInitialDataPtrArray \\n (NpyIter *);\nNPY_NO_EXPORT int NpyIter_CreateCompatibleStrides \\n (NpyIter *, npy_intp, npy_intp *);\nNPY_NO_EXPORT int PyArray_CastingConverter \\n (PyObject *, NPY_CASTING *);\nNPY_NO_EXPORT npy_intp PyArray_CountNonzero \\n (PyArrayObject *);\nNPY_NO_EXPORT PyArray_Descr * PyArray_PromoteTypes \\n (PyArray_Descr *, PyArray_Descr *);\nNPY_NO_EXPORT PyArray_Descr * PyArray_MinScalarType \\n (PyArrayObject *);\nNPY_NO_EXPORT PyArray_Descr * PyArray_ResultType \\n (npy_intp, PyArrayObject *arrs[], npy_intp, PyArray_Descr *descrs[]);\nNPY_NO_EXPORT npy_bool PyArray_CanCastArrayTo \\n (PyArrayObject *, PyArray_Descr *, NPY_CASTING);\nNPY_NO_EXPORT npy_bool PyArray_CanCastTypeTo \\n (PyArray_Descr *, PyArray_Descr *, NPY_CASTING);\nNPY_NO_EXPORT PyArrayObject * PyArray_EinsteinSum \\n (char *, npy_intp, PyArrayObject **, PyArray_Descr *, NPY_ORDER, NPY_CASTING, PyArrayObject *);\nNPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(3) PyObject * PyArray_NewLikeArray \\n (PyArrayObject *, NPY_ORDER, PyArray_Descr *, int);\nNPY_NO_EXPORT int PyArray_ConvertClipmodeSequence \\n (PyObject *, NPY_CLIPMODE *, int);\nNPY_NO_EXPORT PyObject * PyArray_MatrixProduct2 \\n (PyObject *, PyObject *, PyArrayObject*);\nNPY_NO_EXPORT npy_bool NpyIter_IsFirstVisit \\n (NpyIter *, int);\nNPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) int PyArray_SetBaseObject \\n (PyArrayObject *, PyObject *);\nNPY_NO_EXPORT void PyArray_CreateSortedStridePerm \\n (int, npy_intp const *, npy_stride_sort_item *);\nNPY_NO_EXPORT void PyArray_RemoveAxesInPlace \\n (PyArrayObject *, const npy_bool *);\nNPY_NO_EXPORT void PyArray_DebugPrint \\n (PyArrayObject *);\nNPY_NO_EXPORT int PyArray_FailUnlessWriteable \\n (PyArrayObject *, const char *);\nNPY_NO_EXPORT NPY_STEALS_REF_TO_ARG(2) int PyArray_SetUpdateIfCopyBase \\n (PyArrayObject *, PyArrayObject *);\nNPY_NO_EXPORT void * PyDataMem_NEW \\n (size_t);\nNPY_NO_EXPORT void PyDataMem_FREE \\n (void *);\nNPY_NO_EXPORT void * PyDataMem_RENEW \\n (void *, size_t);\nextern NPY_NO_EXPORT NPY_CASTING NPY_DEFAULT_ASSIGN_CASTING;\n\nNPY_NO_EXPORT int PyArray_Partition \\n (PyArrayObject *, PyArrayObject *, int, NPY_SELECTKIND);\nNPY_NO_EXPORT PyObject * PyArray_ArgPartition \\n (PyArrayObject *, PyArrayObject *, int, NPY_SELECTKIND);\nNPY_NO_EXPORT int PyArray_SelectkindConverter \\n (PyObject *, NPY_SELECTKIND *);\nNPY_NO_EXPORT void * PyDataMem_NEW_ZEROED \\n (size_t, size_t);\nNPY_NO_EXPORT int PyArray_CheckAnyScalarExact \\n (PyObject *);\nNPY_NO_EXPORT int PyArray_ResolveWritebackIfCopy \\n (PyArrayObject *);\nNPY_NO_EXPORT int PyArray_SetWritebackIfCopyBase \\n (PyArrayObject *, PyArrayObject *);\nNPY_NO_EXPORT PyObject * PyDataMem_SetHandler \\n (PyObject *);\nNPY_NO_EXPORT PyObject * PyDataMem_GetHandler \\n (void);\nextern NPY_NO_EXPORT PyObject* PyDataMem_DefaultHandler;\n\nNPY_NO_EXPORT int NpyDatetime_ConvertDatetime64ToDatetimeStruct \\n (PyArray_DatetimeMetaData *, npy_datetime, npy_datetimestruct *);\nNPY_NO_EXPORT int NpyDatetime_ConvertDatetimeStructToDatetime64 \\n (PyArray_DatetimeMetaData *, const npy_datetimestruct *, npy_datetime *);\nNPY_NO_EXPORT int NpyDatetime_ConvertPyDateTimeToDatetimeStruct \\n (PyObject *, npy_datetimestruct *, NPY_DATETIMEUNIT *, int);\nNPY_NO_EXPORT int NpyDatetime_GetDatetimeISO8601StrLen \\n (int, NPY_DATETIMEUNIT);\nNPY_NO_EXPORT int NpyDatetime_MakeISO8601Datetime \\n (npy_datetimestruct *, char *, npy_intp, int, int, NPY_DATETIMEUNIT, int, NPY_CASTING);\nNPY_NO_EXPORT int NpyDatetime_ParseISO8601Datetime \\n (char const *, Py_ssize_t, NPY_DATETIMEUNIT, NPY_CASTING, npy_datetimestruct *, NPY_DATETIMEUNIT *, npy_bool *);\nNPY_NO_EXPORT int NpyString_load \\n (npy_string_allocator *, const npy_packed_static_string *, npy_static_string *);\nNPY_NO_EXPORT int NpyString_pack \\n (npy_string_allocator *, npy_packed_static_string *, const char *, size_t);\nNPY_NO_EXPORT int NpyString_pack_null \\n (npy_string_allocator *, npy_packed_static_string *);\nNPY_NO_EXPORT npy_string_allocator * NpyString_acquire_allocator \\n (const PyArray_StringDTypeObject *);\nNPY_NO_EXPORT void NpyString_acquire_allocators \\n (size_t, PyArray_Descr *const descrs[], npy_string_allocator *allocators[]);\nNPY_NO_EXPORT void NpyString_release_allocator \\n (npy_string_allocator *);\nNPY_NO_EXPORT void NpyString_release_allocators \\n (size_t, npy_string_allocator *allocators[]);\nNPY_NO_EXPORT PyArray_Descr * PyArray_GetDefaultDescr \\n (PyArray_DTypeMeta *);\nNPY_NO_EXPORT int PyArrayInitDTypeMeta_FromSpec \\n (PyArray_DTypeMeta *, PyArrayDTypeMeta_Spec *);\nNPY_NO_EXPORT PyArray_DTypeMeta * PyArray_CommonDType \\n (PyArray_DTypeMeta *, PyArray_DTypeMeta *);\nNPY_NO_EXPORT PyArray_DTypeMeta * PyArray_PromoteDTypeSequence \\n (npy_intp, PyArray_DTypeMeta **);\nNPY_NO_EXPORT PyArray_ArrFuncs * _PyDataType_GetArrFuncs \\n (const PyArray_Descr *);\n\n#else\n\n#if defined(PY_ARRAY_UNIQUE_SYMBOL)\n #define PyArray_API PY_ARRAY_UNIQUE_SYMBOL\n #define _NPY_VERSION_CONCAT_HELPER2(x, y) x ## y\n #define _NPY_VERSION_CONCAT_HELPER(arg) \\n _NPY_VERSION_CONCAT_HELPER2(arg, PyArray_RUNTIME_VERSION)\n #define PyArray_RUNTIME_VERSION \\n _NPY_VERSION_CONCAT_HELPER(PY_ARRAY_UNIQUE_SYMBOL)\n#endif\n\n/* By default do not export API in an .so (was never the case on windows) */\n#ifndef NPY_API_SYMBOL_ATTRIBUTE\n #define NPY_API_SYMBOL_ATTRIBUTE NPY_VISIBILITY_HIDDEN\n#endif\n\n#if defined(NO_IMPORT) || defined(NO_IMPORT_ARRAY)\nextern NPY_API_SYMBOL_ATTRIBUTE void **PyArray_API;\nextern NPY_API_SYMBOL_ATTRIBUTE int PyArray_RUNTIME_VERSION;\n#else\n#if defined(PY_ARRAY_UNIQUE_SYMBOL)\nNPY_API_SYMBOL_ATTRIBUTE void **PyArray_API;\nNPY_API_SYMBOL_ATTRIBUTE int PyArray_RUNTIME_VERSION;\n#else\nstatic void **PyArray_API = NULL;\nstatic int PyArray_RUNTIME_VERSION = 0;\n#endif\n#endif\n\n#define PyArray_GetNDArrayCVersion \\n (*(unsigned int (*)(void)) \\n PyArray_API[0])\n#define PyArray_Type (*(PyTypeObject *)PyArray_API[2])\n#define PyArrayDescr_Type (*(PyTypeObject *)PyArray_API[3])\n#define PyArrayIter_Type (*(PyTypeObject *)PyArray_API[5])\n#define PyArrayMultiIter_Type (*(PyTypeObject *)PyArray_API[6])\n#define NPY_NUMUSERTYPES (*(int *)PyArray_API[7])\n#define PyBoolArrType_Type (*(PyTypeObject *)PyArray_API[8])\n#define _PyArrayScalar_BoolValues ((PyBoolScalarObject *)PyArray_API[9])\n#define PyGenericArrType_Type (*(PyTypeObject *)PyArray_API[10])\n#define PyNumberArrType_Type (*(PyTypeObject *)PyArray_API[11])\n#define PyIntegerArrType_Type (*(PyTypeObject *)PyArray_API[12])\n#define PySignedIntegerArrType_Type (*(PyTypeObject *)PyArray_API[13])\n#define PyUnsignedIntegerArrType_Type (*(PyTypeObject *)PyArray_API[14])\n#define PyInexactArrType_Type (*(PyTypeObject *)PyArray_API[15])\n#define PyFloatingArrType_Type (*(PyTypeObject *)PyArray_API[16])\n#define PyComplexFloatingArrType_Type (*(PyTypeObject *)PyArray_API[17])\n#define PyFlexibleArrType_Type (*(PyTypeObject *)PyArray_API[18])\n#define PyCharacterArrType_Type (*(PyTypeObject *)PyArray_API[19])\n#define PyByteArrType_Type (*(PyTypeObject *)PyArray_API[20])\n#define PyShortArrType_Type (*(PyTypeObject *)PyArray_API[21])\n#define PyIntArrType_Type (*(PyTypeObject *)PyArray_API[22])\n#define PyLongArrType_Type (*(PyTypeObject *)PyArray_API[23])\n#define PyLongLongArrType_Type (*(PyTypeObject *)PyArray_API[24])\n#define PyUByteArrType_Type (*(PyTypeObject *)PyArray_API[25])\n#define PyUShortArrType_Type (*(PyTypeObject *)PyArray_API[26])\n#define PyUIntArrType_Type (*(PyTypeObject *)PyArray_API[27])\n#define PyULongArrType_Type (*(PyTypeObject *)PyArray_API[28])\n#define PyULongLongArrType_Type (*(PyTypeObject *)PyArray_API[29])\n#define PyFloatArrType_Type (*(PyTypeObject *)PyArray_API[30])\n#define PyDoubleArrType_Type (*(PyTypeObject *)PyArray_API[31])\n#define PyLongDoubleArrType_Type (*(PyTypeObject *)PyArray_API[32])\n#define PyCFloatArrType_Type (*(PyTypeObject *)PyArray_API[33])\n#define PyCDoubleArrType_Type (*(PyTypeObject *)PyArray_API[34])\n#define PyCLongDoubleArrType_Type (*(PyTypeObject *)PyArray_API[35])\n#define PyObjectArrType_Type (*(PyTypeObject *)PyArray_API[36])\n#define PyStringArrType_Type (*(PyTypeObject *)PyArray_API[37])\n#define PyUnicodeArrType_Type (*(PyTypeObject *)PyArray_API[38])\n#define PyVoidArrType_Type (*(PyTypeObject *)PyArray_API[39])\n#define PyArray_INCREF \\n (*(int (*)(PyArrayObject *)) \\n PyArray_API[42])\n#define PyArray_XDECREF \\n (*(int (*)(PyArrayObject *)) \\n PyArray_API[43])\n#define PyArray_SetStringFunction \\n (*(void (*)(PyObject *, int)) \\n PyArray_API[44])\n#define PyArray_DescrFromType \\n (*(PyArray_Descr * (*)(int)) \\n PyArray_API[45])\n#define PyArray_TypeObjectFromType \\n (*(PyObject * (*)(int)) \\n PyArray_API[46])\n#define PyArray_Zero \\n (*(char * (*)(PyArrayObject *)) \\n PyArray_API[47])\n#define PyArray_One \\n (*(char * (*)(PyArrayObject *)) \\n PyArray_API[48])\n#define PyArray_CastToType \\n (*(PyObject * (*)(PyArrayObject *, PyArray_Descr *, int)) \\n PyArray_API[49])\n#define PyArray_CopyInto \\n (*(int (*)(PyArrayObject *, PyArrayObject *)) \\n PyArray_API[50])\n#define PyArray_CopyAnyInto \\n (*(int (*)(PyArrayObject *, PyArrayObject *)) \\n PyArray_API[51])\n#define PyArray_CanCastSafely \\n (*(int (*)(int, int)) \\n PyArray_API[52])\n#define PyArray_CanCastTo \\n (*(npy_bool (*)(PyArray_Descr *, PyArray_Descr *)) \\n PyArray_API[53])\n#define PyArray_ObjectType \\n (*(int (*)(PyObject *, int)) \\n PyArray_API[54])\n#define PyArray_DescrFromObject \\n (*(PyArray_Descr * (*)(PyObject *, PyArray_Descr *)) \\n PyArray_API[55])\n#define PyArray_ConvertToCommonType \\n (*(PyArrayObject ** (*)(PyObject *, int *)) \\n PyArray_API[56])\n#define PyArray_DescrFromScalar \\n (*(PyArray_Descr * (*)(PyObject *)) \\n PyArray_API[57])\n#define PyArray_DescrFromTypeObject \\n (*(PyArray_Descr * (*)(PyObject *)) \\n PyArray_API[58])\n#define PyArray_Size \\n (*(npy_intp (*)(PyObject *)) \\n PyArray_API[59])\n#define PyArray_Scalar \\n (*(PyObject * (*)(void *, PyArray_Descr *, PyObject *)) \\n PyArray_API[60])\n#define PyArray_FromScalar \\n (*(PyObject * (*)(PyObject *, PyArray_Descr *)) \\n PyArray_API[61])\n#define PyArray_ScalarAsCtype \\n (*(void (*)(PyObject *, void *)) \\n PyArray_API[62])\n#define PyArray_CastScalarToCtype \\n (*(int (*)(PyObject *, void *, PyArray_Descr *)) \\n PyArray_API[63])\n#define PyArray_CastScalarDirect \\n (*(int (*)(PyObject *, PyArray_Descr *, void *, int)) \\n PyArray_API[64])\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define PyArray_Pack \\n (*(int (*)(PyArray_Descr *, void *, PyObject *)) \\n PyArray_API[65])\n#endif\n#define PyArray_FromAny \\n (*(PyObject * (*)(PyObject *, PyArray_Descr *, int, int, int, PyObject *)) \\n PyArray_API[69])\n#define PyArray_EnsureArray \\n (*(PyObject * (*)(PyObject *)) \\n PyArray_API[70])\n#define PyArray_EnsureAnyArray \\n (*(PyObject * (*)(PyObject *)) \\n PyArray_API[71])\n#define PyArray_FromFile \\n (*(PyObject * (*)(FILE *, PyArray_Descr *, npy_intp, char *)) \\n PyArray_API[72])\n#define PyArray_FromString \\n (*(PyObject * (*)(char *, npy_intp, PyArray_Descr *, npy_intp, char *)) \\n PyArray_API[73])\n#define PyArray_FromBuffer \\n (*(PyObject * (*)(PyObject *, PyArray_Descr *, npy_intp, npy_intp)) \\n PyArray_API[74])\n#define PyArray_FromIter \\n (*(PyObject * (*)(PyObject *, PyArray_Descr *, npy_intp)) \\n PyArray_API[75])\n#define PyArray_Return \\n (*(PyObject * (*)(PyArrayObject *)) \\n PyArray_API[76])\n#define PyArray_GetField \\n (*(PyObject * (*)(PyArrayObject *, PyArray_Descr *, int)) \\n PyArray_API[77])\n#define PyArray_SetField \\n (*(int (*)(PyArrayObject *, PyArray_Descr *, int, PyObject *)) \\n PyArray_API[78])\n#define PyArray_Byteswap \\n (*(PyObject * (*)(PyArrayObject *, npy_bool)) \\n PyArray_API[79])\n#define PyArray_Resize \\n (*(PyObject * (*)(PyArrayObject *, PyArray_Dims *, int, NPY_ORDER NPY_UNUSED(order))) \\n PyArray_API[80])\n#define PyArray_CopyObject \\n (*(int (*)(PyArrayObject *, PyObject *)) \\n PyArray_API[84])\n#define PyArray_NewCopy \\n (*(PyObject * (*)(PyArrayObject *, NPY_ORDER)) \\n PyArray_API[85])\n#define PyArray_ToList \\n (*(PyObject * (*)(PyArrayObject *)) \\n PyArray_API[86])\n#define PyArray_ToString \\n (*(PyObject * (*)(PyArrayObject *, NPY_ORDER)) \\n PyArray_API[87])\n#define PyArray_ToFile \\n (*(int (*)(PyArrayObject *, FILE *, char *, char *)) \\n PyArray_API[88])\n#define PyArray_Dump \\n (*(int (*)(PyObject *, PyObject *, int)) \\n PyArray_API[89])\n#define PyArray_Dumps \\n (*(PyObject * (*)(PyObject *, int)) \\n PyArray_API[90])\n#define PyArray_ValidType \\n (*(int (*)(int)) \\n PyArray_API[91])\n#define PyArray_UpdateFlags \\n (*(void (*)(PyArrayObject *, int)) \\n PyArray_API[92])\n#define PyArray_New \\n (*(PyObject * (*)(PyTypeObject *, int, npy_intp const *, int, npy_intp const *, void *, int, int, PyObject *)) \\n PyArray_API[93])\n#define PyArray_NewFromDescr \\n (*(PyObject * (*)(PyTypeObject *, PyArray_Descr *, int, npy_intp const *, npy_intp const *, void *, int, PyObject *)) \\n PyArray_API[94])\n#define PyArray_DescrNew \\n (*(PyArray_Descr * (*)(PyArray_Descr *)) \\n PyArray_API[95])\n#define PyArray_DescrNewFromType \\n (*(PyArray_Descr * (*)(int)) \\n PyArray_API[96])\n#define PyArray_GetPriority \\n (*(double (*)(PyObject *, double)) \\n PyArray_API[97])\n#define PyArray_IterNew \\n (*(PyObject * (*)(PyObject *)) \\n PyArray_API[98])\n#define PyArray_MultiIterNew \\n (*(PyObject* (*)(int, ...)) \\n PyArray_API[99])\n#define PyArray_PyIntAsInt \\n (*(int (*)(PyObject *)) \\n PyArray_API[100])\n#define PyArray_PyIntAsIntp \\n (*(npy_intp (*)(PyObject *)) \\n PyArray_API[101])\n#define PyArray_Broadcast \\n (*(int (*)(PyArrayMultiIterObject *)) \\n PyArray_API[102])\n#define PyArray_FillWithScalar \\n (*(int (*)(PyArrayObject *, PyObject *)) \\n PyArray_API[104])\n#define PyArray_CheckStrides \\n (*(npy_bool (*)(int, int, npy_intp, npy_intp, npy_intp const *, npy_intp const *)) \\n PyArray_API[105])\n#define PyArray_DescrNewByteorder \\n (*(PyArray_Descr * (*)(PyArray_Descr *, char)) \\n PyArray_API[106])\n#define PyArray_IterAllButAxis \\n (*(PyObject * (*)(PyObject *, int *)) \\n PyArray_API[107])\n#define PyArray_CheckFromAny \\n (*(PyObject * (*)(PyObject *, PyArray_Descr *, int, int, int, PyObject *)) \\n PyArray_API[108])\n#define PyArray_FromArray \\n (*(PyObject * (*)(PyArrayObject *, PyArray_Descr *, int)) \\n PyArray_API[109])\n#define PyArray_FromInterface \\n (*(PyObject * (*)(PyObject *)) \\n PyArray_API[110])\n#define PyArray_FromStructInterface \\n (*(PyObject * (*)(PyObject *)) \\n PyArray_API[111])\n#define PyArray_FromArrayAttr \\n (*(PyObject * (*)(PyObject *, PyArray_Descr *, PyObject *)) \\n PyArray_API[112])\n#define PyArray_ScalarKind \\n (*(NPY_SCALARKIND (*)(int, PyArrayObject **)) \\n PyArray_API[113])\n#define PyArray_CanCoerceScalar \\n (*(int (*)(int, int, NPY_SCALARKIND)) \\n PyArray_API[114])\n#define PyArray_CanCastScalar \\n (*(npy_bool (*)(PyTypeObject *, PyTypeObject *)) \\n PyArray_API[116])\n#define PyArray_RemoveSmallest \\n (*(int (*)(PyArrayMultiIterObject *)) \\n PyArray_API[118])\n#define PyArray_ElementStrides \\n (*(int (*)(PyObject *)) \\n PyArray_API[119])\n#define PyArray_Item_INCREF \\n (*(void (*)(char *, PyArray_Descr *)) \\n PyArray_API[120])\n#define PyArray_Item_XDECREF \\n (*(void (*)(char *, PyArray_Descr *)) \\n PyArray_API[121])\n#define PyArray_Transpose \\n (*(PyObject * (*)(PyArrayObject *, PyArray_Dims *)) \\n PyArray_API[123])\n#define PyArray_TakeFrom \\n (*(PyObject * (*)(PyArrayObject *, PyObject *, int, PyArrayObject *, NPY_CLIPMODE)) \\n PyArray_API[124])\n#define PyArray_PutTo \\n (*(PyObject * (*)(PyArrayObject *, PyObject*, PyObject *, NPY_CLIPMODE)) \\n PyArray_API[125])\n#define PyArray_PutMask \\n (*(PyObject * (*)(PyArrayObject *, PyObject*, PyObject*)) \\n PyArray_API[126])\n#define PyArray_Repeat \\n (*(PyObject * (*)(PyArrayObject *, PyObject *, int)) \\n PyArray_API[127])\n#define PyArray_Choose \\n (*(PyObject * (*)(PyArrayObject *, PyObject *, PyArrayObject *, NPY_CLIPMODE)) \\n PyArray_API[128])\n#define PyArray_Sort \\n (*(int (*)(PyArrayObject *, int, NPY_SORTKIND)) \\n PyArray_API[129])\n#define PyArray_ArgSort \\n (*(PyObject * (*)(PyArrayObject *, int, NPY_SORTKIND)) \\n PyArray_API[130])\n#define PyArray_SearchSorted \\n (*(PyObject * (*)(PyArrayObject *, PyObject *, NPY_SEARCHSIDE, PyObject *)) \\n PyArray_API[131])\n#define PyArray_ArgMax \\n (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \\n PyArray_API[132])\n#define PyArray_ArgMin \\n (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \\n PyArray_API[133])\n#define PyArray_Reshape \\n (*(PyObject * (*)(PyArrayObject *, PyObject *)) \\n PyArray_API[134])\n#define PyArray_Newshape \\n (*(PyObject * (*)(PyArrayObject *, PyArray_Dims *, NPY_ORDER)) \\n PyArray_API[135])\n#define PyArray_Squeeze \\n (*(PyObject * (*)(PyArrayObject *)) \\n PyArray_API[136])\n#define PyArray_View \\n (*(PyObject * (*)(PyArrayObject *, PyArray_Descr *, PyTypeObject *)) \\n PyArray_API[137])\n#define PyArray_SwapAxes \\n (*(PyObject * (*)(PyArrayObject *, int, int)) \\n PyArray_API[138])\n#define PyArray_Max \\n (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \\n PyArray_API[139])\n#define PyArray_Min \\n (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \\n PyArray_API[140])\n#define PyArray_Ptp \\n (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \\n PyArray_API[141])\n#define PyArray_Mean \\n (*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \\n PyArray_API[142])\n#define PyArray_Trace \\n (*(PyObject * (*)(PyArrayObject *, int, int, int, int, PyArrayObject *)) \\n PyArray_API[143])\n#define PyArray_Diagonal \\n (*(PyObject * (*)(PyArrayObject *, int, int, int)) \\n PyArray_API[144])\n#define PyArray_Clip \\n (*(PyObject * (*)(PyArrayObject *, PyObject *, PyObject *, PyArrayObject *)) \\n PyArray_API[145])\n#define PyArray_Conjugate \\n (*(PyObject * (*)(PyArrayObject *, PyArrayObject *)) \\n PyArray_API[146])\n#define PyArray_Nonzero \\n (*(PyObject * (*)(PyArrayObject *)) \\n PyArray_API[147])\n#define PyArray_Std \\n (*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *, int)) \\n PyArray_API[148])\n#define PyArray_Sum \\n (*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \\n PyArray_API[149])\n#define PyArray_CumSum \\n (*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \\n PyArray_API[150])\n#define PyArray_Prod \\n (*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \\n PyArray_API[151])\n#define PyArray_CumProd \\n (*(PyObject * (*)(PyArrayObject *, int, int, PyArrayObject *)) \\n PyArray_API[152])\n#define PyArray_All \\n (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \\n PyArray_API[153])\n#define PyArray_Any \\n (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \\n PyArray_API[154])\n#define PyArray_Compress \\n (*(PyObject * (*)(PyArrayObject *, PyObject *, int, PyArrayObject *)) \\n PyArray_API[155])\n#define PyArray_Flatten \\n (*(PyObject * (*)(PyArrayObject *, NPY_ORDER)) \\n PyArray_API[156])\n#define PyArray_Ravel \\n (*(PyObject * (*)(PyArrayObject *, NPY_ORDER)) \\n PyArray_API[157])\n#define PyArray_MultiplyList \\n (*(npy_intp (*)(npy_intp const *, int)) \\n PyArray_API[158])\n#define PyArray_MultiplyIntList \\n (*(int (*)(int const *, int)) \\n PyArray_API[159])\n#define PyArray_GetPtr \\n (*(void * (*)(PyArrayObject *, npy_intp const*)) \\n PyArray_API[160])\n#define PyArray_CompareLists \\n (*(int (*)(npy_intp const *, npy_intp const *, int)) \\n PyArray_API[161])\n#define PyArray_AsCArray \\n (*(int (*)(PyObject **, void *, npy_intp *, int, PyArray_Descr*)) \\n PyArray_API[162])\n#define PyArray_Free \\n (*(int (*)(PyObject *, void *)) \\n PyArray_API[165])\n#define PyArray_Converter \\n (*(int (*)(PyObject *, PyObject **)) \\n PyArray_API[166])\n#define PyArray_IntpFromSequence \\n (*(int (*)(PyObject *, npy_intp *, int)) \\n PyArray_API[167])\n#define PyArray_Concatenate \\n (*(PyObject * (*)(PyObject *, int)) \\n PyArray_API[168])\n#define PyArray_InnerProduct \\n (*(PyObject * (*)(PyObject *, PyObject *)) \\n PyArray_API[169])\n#define PyArray_MatrixProduct \\n (*(PyObject * (*)(PyObject *, PyObject *)) \\n PyArray_API[170])\n#define PyArray_Correlate \\n (*(PyObject * (*)(PyObject *, PyObject *, int)) \\n PyArray_API[172])\n#define PyArray_DescrConverter \\n (*(int (*)(PyObject *, PyArray_Descr **)) \\n PyArray_API[174])\n#define PyArray_DescrConverter2 \\n (*(int (*)(PyObject *, PyArray_Descr **)) \\n PyArray_API[175])\n#define PyArray_IntpConverter \\n (*(int (*)(PyObject *, PyArray_Dims *)) \\n PyArray_API[176])\n#define PyArray_BufferConverter \\n (*(int (*)(PyObject *, PyArray_Chunk *)) \\n PyArray_API[177])\n#define PyArray_AxisConverter \\n (*(int (*)(PyObject *, int *)) \\n PyArray_API[178])\n#define PyArray_BoolConverter \\n (*(int (*)(PyObject *, npy_bool *)) \\n PyArray_API[179])\n#define PyArray_ByteorderConverter \\n (*(int (*)(PyObject *, char *)) \\n PyArray_API[180])\n#define PyArray_OrderConverter \\n (*(int (*)(PyObject *, NPY_ORDER *)) \\n PyArray_API[181])\n#define PyArray_EquivTypes \\n (*(unsigned char (*)(PyArray_Descr *, PyArray_Descr *)) \\n PyArray_API[182])\n#define PyArray_Zeros \\n (*(PyObject * (*)(int, npy_intp const *, PyArray_Descr *, int)) \\n PyArray_API[183])\n#define PyArray_Empty \\n (*(PyObject * (*)(int, npy_intp const *, PyArray_Descr *, int)) \\n PyArray_API[184])\n#define PyArray_Where \\n (*(PyObject * (*)(PyObject *, PyObject *, PyObject *)) \\n PyArray_API[185])\n#define PyArray_Arange \\n (*(PyObject * (*)(double, double, double, int)) \\n PyArray_API[186])\n#define PyArray_ArangeObj \\n (*(PyObject * (*)(PyObject *, PyObject *, PyObject *, PyArray_Descr *)) \\n PyArray_API[187])\n#define PyArray_SortkindConverter \\n (*(int (*)(PyObject *, NPY_SORTKIND *)) \\n PyArray_API[188])\n#define PyArray_LexSort \\n (*(PyObject * (*)(PyObject *, int)) \\n PyArray_API[189])\n#define PyArray_Round \\n (*(PyObject * (*)(PyArrayObject *, int, PyArrayObject *)) \\n PyArray_API[190])\n#define PyArray_EquivTypenums \\n (*(unsigned char (*)(int, int)) \\n PyArray_API[191])\n#define PyArray_RegisterDataType \\n (*(int (*)(PyArray_DescrProto *)) \\n PyArray_API[192])\n#define PyArray_RegisterCastFunc \\n (*(int (*)(PyArray_Descr *, int, PyArray_VectorUnaryFunc *)) \\n PyArray_API[193])\n#define PyArray_RegisterCanCast \\n (*(int (*)(PyArray_Descr *, int, NPY_SCALARKIND)) \\n PyArray_API[194])\n#define PyArray_InitArrFuncs \\n (*(void (*)(PyArray_ArrFuncs *)) \\n PyArray_API[195])\n#define PyArray_IntTupleFromIntp \\n (*(PyObject * (*)(int, npy_intp const *)) \\n PyArray_API[196])\n#define PyArray_ClipmodeConverter \\n (*(int (*)(PyObject *, NPY_CLIPMODE *)) \\n PyArray_API[198])\n#define PyArray_OutputConverter \\n (*(int (*)(PyObject *, PyArrayObject **)) \\n PyArray_API[199])\n#define PyArray_BroadcastToShape \\n (*(PyObject * (*)(PyObject *, npy_intp *, int)) \\n PyArray_API[200])\n#define PyArray_DescrAlignConverter \\n (*(int (*)(PyObject *, PyArray_Descr **)) \\n PyArray_API[203])\n#define PyArray_DescrAlignConverter2 \\n (*(int (*)(PyObject *, PyArray_Descr **)) \\n PyArray_API[204])\n#define PyArray_SearchsideConverter \\n (*(int (*)(PyObject *, void *)) \\n PyArray_API[205])\n#define PyArray_CheckAxis \\n (*(PyObject * (*)(PyArrayObject *, int *, int)) \\n PyArray_API[206])\n#define PyArray_OverflowMultiplyList \\n (*(npy_intp (*)(npy_intp const *, int)) \\n PyArray_API[207])\n#define PyArray_MultiIterFromObjects \\n (*(PyObject* (*)(PyObject **, int, int, ...)) \\n PyArray_API[209])\n#define PyArray_GetEndianness \\n (*(int (*)(void)) \\n PyArray_API[210])\n#define PyArray_GetNDArrayCFeatureVersion \\n (*(unsigned int (*)(void)) \\n PyArray_API[211])\n#define PyArray_Correlate2 \\n (*(PyObject * (*)(PyObject *, PyObject *, int)) \\n PyArray_API[212])\n#define PyArray_NeighborhoodIterNew \\n (*(PyObject* (*)(PyArrayIterObject *, const npy_intp *, int, PyArrayObject*)) \\n PyArray_API[213])\n#define PyTimeIntegerArrType_Type (*(PyTypeObject *)PyArray_API[214])\n#define PyDatetimeArrType_Type (*(PyTypeObject *)PyArray_API[215])\n#define PyTimedeltaArrType_Type (*(PyTypeObject *)PyArray_API[216])\n#define PyHalfArrType_Type (*(PyTypeObject *)PyArray_API[217])\n#define NpyIter_Type (*(PyTypeObject *)PyArray_API[218])\n\n#if NPY_FEATURE_VERSION >= NPY_2_3_API_VERSION\n#define NpyIter_GetTransferFlags \\n (*(NPY_ARRAYMETHOD_FLAGS (*)(NpyIter *)) \\n PyArray_API[223])\n#endif\n#define NpyIter_New \\n (*(NpyIter * (*)(PyArrayObject *, npy_uint32, NPY_ORDER, NPY_CASTING, PyArray_Descr*)) \\n PyArray_API[224])\n#define NpyIter_MultiNew \\n (*(NpyIter * (*)(int, PyArrayObject **, npy_uint32, NPY_ORDER, NPY_CASTING, npy_uint32 *, PyArray_Descr **)) \\n PyArray_API[225])\n#define NpyIter_AdvancedNew \\n (*(NpyIter * (*)(int, PyArrayObject **, npy_uint32, NPY_ORDER, NPY_CASTING, npy_uint32 *, PyArray_Descr **, int, int **, npy_intp *, npy_intp)) \\n PyArray_API[226])\n#define NpyIter_Copy \\n (*(NpyIter * (*)(NpyIter *)) \\n PyArray_API[227])\n#define NpyIter_Deallocate \\n (*(int (*)(NpyIter *)) \\n PyArray_API[228])\n#define NpyIter_HasDelayedBufAlloc \\n (*(npy_bool (*)(NpyIter *)) \\n PyArray_API[229])\n#define NpyIter_HasExternalLoop \\n (*(npy_bool (*)(NpyIter *)) \\n PyArray_API[230])\n#define NpyIter_EnableExternalLoop \\n (*(int (*)(NpyIter *)) \\n PyArray_API[231])\n#define NpyIter_GetInnerStrideArray \\n (*(npy_intp * (*)(NpyIter *)) \\n PyArray_API[232])\n#define NpyIter_GetInnerLoopSizePtr \\n (*(npy_intp * (*)(NpyIter *)) \\n PyArray_API[233])\n#define NpyIter_Reset \\n (*(int (*)(NpyIter *, char **)) \\n PyArray_API[234])\n#define NpyIter_ResetBasePointers \\n (*(int (*)(NpyIter *, char **, char **)) \\n PyArray_API[235])\n#define NpyIter_ResetToIterIndexRange \\n (*(int (*)(NpyIter *, npy_intp, npy_intp, char **)) \\n PyArray_API[236])\n#define NpyIter_GetNDim \\n (*(int (*)(NpyIter *)) \\n PyArray_API[237])\n#define NpyIter_GetNOp \\n (*(int (*)(NpyIter *)) \\n PyArray_API[238])\n#define NpyIter_GetIterNext \\n (*(NpyIter_IterNextFunc * (*)(NpyIter *, char **)) \\n PyArray_API[239])\n#define NpyIter_GetIterSize \\n (*(npy_intp (*)(NpyIter *)) \\n PyArray_API[240])\n#define NpyIter_GetIterIndexRange \\n (*(void (*)(NpyIter *, npy_intp *, npy_intp *)) \\n PyArray_API[241])\n#define NpyIter_GetIterIndex \\n (*(npy_intp (*)(NpyIter *)) \\n PyArray_API[242])\n#define NpyIter_GotoIterIndex \\n (*(int (*)(NpyIter *, npy_intp)) \\n PyArray_API[243])\n#define NpyIter_HasMultiIndex \\n (*(npy_bool (*)(NpyIter *)) \\n PyArray_API[244])\n#define NpyIter_GetShape \\n (*(int (*)(NpyIter *, npy_intp *)) \\n PyArray_API[245])\n#define NpyIter_GetGetMultiIndex \\n (*(NpyIter_GetMultiIndexFunc * (*)(NpyIter *, char **)) \\n PyArray_API[246])\n#define NpyIter_GotoMultiIndex \\n (*(int (*)(NpyIter *, npy_intp const *)) \\n PyArray_API[247])\n#define NpyIter_RemoveMultiIndex \\n (*(int (*)(NpyIter *)) \\n PyArray_API[248])\n#define NpyIter_HasIndex \\n (*(npy_bool (*)(NpyIter *)) \\n PyArray_API[249])\n#define NpyIter_IsBuffered \\n (*(npy_bool (*)(NpyIter *)) \\n PyArray_API[250])\n#define NpyIter_IsGrowInner \\n (*(npy_bool (*)(NpyIter *)) \\n PyArray_API[251])\n#define NpyIter_GetBufferSize \\n (*(npy_intp (*)(NpyIter *)) \\n PyArray_API[252])\n#define NpyIter_GetIndexPtr \\n (*(npy_intp * (*)(NpyIter *)) \\n PyArray_API[253])\n#define NpyIter_GotoIndex \\n (*(int (*)(NpyIter *, npy_intp)) \\n PyArray_API[254])\n#define NpyIter_GetDataPtrArray \\n (*(char ** (*)(NpyIter *)) \\n PyArray_API[255])\n#define NpyIter_GetDescrArray \\n (*(PyArray_Descr ** (*)(NpyIter *)) \\n PyArray_API[256])\n#define NpyIter_GetOperandArray \\n (*(PyArrayObject ** (*)(NpyIter *)) \\n PyArray_API[257])\n#define NpyIter_GetIterView \\n (*(PyArrayObject * (*)(NpyIter *, npy_intp)) \\n PyArray_API[258])\n#define NpyIter_GetReadFlags \\n (*(void (*)(NpyIter *, char *)) \\n PyArray_API[259])\n#define NpyIter_GetWriteFlags \\n (*(void (*)(NpyIter *, char *)) \\n PyArray_API[260])\n#define NpyIter_DebugPrint \\n (*(void (*)(NpyIter *)) \\n PyArray_API[261])\n#define NpyIter_IterationNeedsAPI \\n (*(npy_bool (*)(NpyIter *)) \\n PyArray_API[262])\n#define NpyIter_GetInnerFixedStrideArray \\n (*(void (*)(NpyIter *, npy_intp *)) \\n PyArray_API[263])\n#define NpyIter_RemoveAxis \\n (*(int (*)(NpyIter *, int)) \\n PyArray_API[264])\n#define NpyIter_GetAxisStrideArray \\n (*(npy_intp * (*)(NpyIter *, int)) \\n PyArray_API[265])\n#define NpyIter_RequiresBuffering \\n (*(npy_bool (*)(NpyIter *)) \\n PyArray_API[266])\n#define NpyIter_GetInitialDataPtrArray \\n (*(char ** (*)(NpyIter *)) \\n PyArray_API[267])\n#define NpyIter_CreateCompatibleStrides \\n (*(int (*)(NpyIter *, npy_intp, npy_intp *)) \\n PyArray_API[268])\n#define PyArray_CastingConverter \\n (*(int (*)(PyObject *, NPY_CASTING *)) \\n PyArray_API[269])\n#define PyArray_CountNonzero \\n (*(npy_intp (*)(PyArrayObject *)) \\n PyArray_API[270])\n#define PyArray_PromoteTypes \\n (*(PyArray_Descr * (*)(PyArray_Descr *, PyArray_Descr *)) \\n PyArray_API[271])\n#define PyArray_MinScalarType \\n (*(PyArray_Descr * (*)(PyArrayObject *)) \\n PyArray_API[272])\n#define PyArray_ResultType \\n (*(PyArray_Descr * (*)(npy_intp, PyArrayObject *arrs[], npy_intp, PyArray_Descr *descrs[])) \\n PyArray_API[273])\n#define PyArray_CanCastArrayTo \\n (*(npy_bool (*)(PyArrayObject *, PyArray_Descr *, NPY_CASTING)) \\n PyArray_API[274])\n#define PyArray_CanCastTypeTo \\n (*(npy_bool (*)(PyArray_Descr *, PyArray_Descr *, NPY_CASTING)) \\n PyArray_API[275])\n#define PyArray_EinsteinSum \\n (*(PyArrayObject * (*)(char *, npy_intp, PyArrayObject **, PyArray_Descr *, NPY_ORDER, NPY_CASTING, PyArrayObject *)) \\n PyArray_API[276])\n#define PyArray_NewLikeArray \\n (*(PyObject * (*)(PyArrayObject *, NPY_ORDER, PyArray_Descr *, int)) \\n PyArray_API[277])\n#define PyArray_ConvertClipmodeSequence \\n (*(int (*)(PyObject *, NPY_CLIPMODE *, int)) \\n PyArray_API[279])\n#define PyArray_MatrixProduct2 \\n (*(PyObject * (*)(PyObject *, PyObject *, PyArrayObject*)) \\n PyArray_API[280])\n#define NpyIter_IsFirstVisit \\n (*(npy_bool (*)(NpyIter *, int)) \\n PyArray_API[281])\n#define PyArray_SetBaseObject \\n (*(int (*)(PyArrayObject *, PyObject *)) \\n PyArray_API[282])\n#define PyArray_CreateSortedStridePerm \\n (*(void (*)(int, npy_intp const *, npy_stride_sort_item *)) \\n PyArray_API[283])\n#define PyArray_RemoveAxesInPlace \\n (*(void (*)(PyArrayObject *, const npy_bool *)) \\n PyArray_API[284])\n#define PyArray_DebugPrint \\n (*(void (*)(PyArrayObject *)) \\n PyArray_API[285])\n#define PyArray_FailUnlessWriteable \\n (*(int (*)(PyArrayObject *, const char *)) \\n PyArray_API[286])\n#define PyArray_SetUpdateIfCopyBase \\n (*(int (*)(PyArrayObject *, PyArrayObject *)) \\n PyArray_API[287])\n#define PyDataMem_NEW \\n (*(void * (*)(size_t)) \\n PyArray_API[288])\n#define PyDataMem_FREE \\n (*(void (*)(void *)) \\n PyArray_API[289])\n#define PyDataMem_RENEW \\n (*(void * (*)(void *, size_t)) \\n PyArray_API[290])\n#define NPY_DEFAULT_ASSIGN_CASTING (*(NPY_CASTING *)PyArray_API[292])\n#define PyArray_Partition \\n (*(int (*)(PyArrayObject *, PyArrayObject *, int, NPY_SELECTKIND)) \\n PyArray_API[296])\n#define PyArray_ArgPartition \\n (*(PyObject * (*)(PyArrayObject *, PyArrayObject *, int, NPY_SELECTKIND)) \\n PyArray_API[297])\n#define PyArray_SelectkindConverter \\n (*(int (*)(PyObject *, NPY_SELECTKIND *)) \\n PyArray_API[298])\n#define PyDataMem_NEW_ZEROED \\n (*(void * (*)(size_t, size_t)) \\n PyArray_API[299])\n#define PyArray_CheckAnyScalarExact \\n (*(int (*)(PyObject *)) \\n PyArray_API[300])\n#define PyArray_ResolveWritebackIfCopy \\n (*(int (*)(PyArrayObject *)) \\n PyArray_API[302])\n#define PyArray_SetWritebackIfCopyBase \\n (*(int (*)(PyArrayObject *, PyArrayObject *)) \\n PyArray_API[303])\n\n#if NPY_FEATURE_VERSION >= NPY_1_22_API_VERSION\n#define PyDataMem_SetHandler \\n (*(PyObject * (*)(PyObject *)) \\n PyArray_API[304])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_1_22_API_VERSION\n#define PyDataMem_GetHandler \\n (*(PyObject * (*)(void)) \\n PyArray_API[305])\n#endif\n#define PyDataMem_DefaultHandler (*(PyObject* *)PyArray_API[306])\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define NpyDatetime_ConvertDatetime64ToDatetimeStruct \\n (*(int (*)(PyArray_DatetimeMetaData *, npy_datetime, npy_datetimestruct *)) \\n PyArray_API[307])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define NpyDatetime_ConvertDatetimeStructToDatetime64 \\n (*(int (*)(PyArray_DatetimeMetaData *, const npy_datetimestruct *, npy_datetime *)) \\n PyArray_API[308])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define NpyDatetime_ConvertPyDateTimeToDatetimeStruct \\n (*(int (*)(PyObject *, npy_datetimestruct *, NPY_DATETIMEUNIT *, int)) \\n PyArray_API[309])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define NpyDatetime_GetDatetimeISO8601StrLen \\n (*(int (*)(int, NPY_DATETIMEUNIT)) \\n PyArray_API[310])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define NpyDatetime_MakeISO8601Datetime \\n (*(int (*)(npy_datetimestruct *, char *, npy_intp, int, int, NPY_DATETIMEUNIT, int, NPY_CASTING)) \\n PyArray_API[311])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define NpyDatetime_ParseISO8601Datetime \\n (*(int (*)(char const *, Py_ssize_t, NPY_DATETIMEUNIT, NPY_CASTING, npy_datetimestruct *, NPY_DATETIMEUNIT *, npy_bool *)) \\n PyArray_API[312])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define NpyString_load \\n (*(int (*)(npy_string_allocator *, const npy_packed_static_string *, npy_static_string *)) \\n PyArray_API[313])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define NpyString_pack \\n (*(int (*)(npy_string_allocator *, npy_packed_static_string *, const char *, size_t)) \\n PyArray_API[314])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define NpyString_pack_null \\n (*(int (*)(npy_string_allocator *, npy_packed_static_string *)) \\n PyArray_API[315])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define NpyString_acquire_allocator \\n (*(npy_string_allocator * (*)(const PyArray_StringDTypeObject *)) \\n PyArray_API[316])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define NpyString_acquire_allocators \\n (*(void (*)(size_t, PyArray_Descr *const descrs[], npy_string_allocator *allocators[])) \\n PyArray_API[317])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define NpyString_release_allocator \\n (*(void (*)(npy_string_allocator *)) \\n PyArray_API[318])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define NpyString_release_allocators \\n (*(void (*)(size_t, npy_string_allocator *allocators[])) \\n PyArray_API[319])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define PyArray_GetDefaultDescr \\n (*(PyArray_Descr * (*)(PyArray_DTypeMeta *)) \\n PyArray_API[361])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define PyArrayInitDTypeMeta_FromSpec \\n (*(int (*)(PyArray_DTypeMeta *, PyArrayDTypeMeta_Spec *)) \\n PyArray_API[362])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define PyArray_CommonDType \\n (*(PyArray_DTypeMeta * (*)(PyArray_DTypeMeta *, PyArray_DTypeMeta *)) \\n PyArray_API[363])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define PyArray_PromoteDTypeSequence \\n (*(PyArray_DTypeMeta * (*)(npy_intp, PyArray_DTypeMeta **)) \\n PyArray_API[364])\n#endif\n#define _PyDataType_GetArrFuncs \\n (*(PyArray_ArrFuncs * (*)(const PyArray_Descr *)) \\n PyArray_API[365])\n\n/*\n * The DType classes are inconvenient for the Python generation so exposed\n * manually in the header below (may be moved).\n */\n#include "numpy/_public_dtype_api_table.h"\n\n#if !defined(NO_IMPORT_ARRAY) && !defined(NO_IMPORT)\nstatic int\n_import_array(void)\n{\n int st;\n PyObject *numpy = PyImport_ImportModule("numpy._core._multiarray_umath");\n PyObject *c_api;\n if (numpy == NULL && PyErr_ExceptionMatches(PyExc_ModuleNotFoundError)) {\n PyErr_Clear();\n numpy = PyImport_ImportModule("numpy.core._multiarray_umath");\n }\n\n if (numpy == NULL) {\n return -1;\n }\n\n c_api = PyObject_GetAttrString(numpy, "_ARRAY_API");\n Py_DECREF(numpy);\n if (c_api == NULL) {\n return -1;\n }\n\n if (!PyCapsule_CheckExact(c_api)) {\n PyErr_SetString(PyExc_RuntimeError, "_ARRAY_API is not PyCapsule object");\n Py_DECREF(c_api);\n return -1;\n }\n PyArray_API = (void **)PyCapsule_GetPointer(c_api, NULL);\n Py_DECREF(c_api);\n if (PyArray_API == NULL) {\n PyErr_SetString(PyExc_RuntimeError, "_ARRAY_API is NULL pointer");\n return -1;\n }\n\n /*\n * On exceedingly few platforms these sizes may not match, in which case\n * We do not support older NumPy versions at all.\n */\n if (sizeof(Py_ssize_t) != sizeof(Py_intptr_t) &&\n PyArray_RUNTIME_VERSION < NPY_2_0_API_VERSION) {\n PyErr_Format(PyExc_RuntimeError,\n "module compiled against NumPy 2.0 but running on NumPy 1.x. "\n "Unfortunately, this is not supported on niche platforms where "\n "`sizeof(size_t) != sizeof(inptr_t)`.");\n }\n /*\n * Perform runtime check of C API version. As of now NumPy 2.0 is ABI\n * backwards compatible (in the exposed feature subset!) for all practical\n * purposes.\n */\n if (NPY_VERSION < PyArray_GetNDArrayCVersion()) {\n PyErr_Format(PyExc_RuntimeError, "module compiled against "\\n "ABI version 0x%x but this version of numpy is 0x%x", \\n (int) NPY_VERSION, (int) PyArray_GetNDArrayCVersion());\n return -1;\n }\n PyArray_RUNTIME_VERSION = (int)PyArray_GetNDArrayCFeatureVersion();\n if (NPY_FEATURE_VERSION > PyArray_RUNTIME_VERSION) {\n PyErr_Format(PyExc_RuntimeError,\n "module was compiled against NumPy C-API version 0x%x "\n "(NumPy " NPY_FEATURE_VERSION_STRING ") "\n "but the running NumPy has C-API version 0x%x. "\n "Check the section C-API incompatibility at the "\n "Troubleshooting ImportError section at "\n "https://numpy.org/devdocs/user/troubleshooting-importerror.html"\n "#c-api-incompatibility "\n "for indications on how to solve this problem.",\n (int)NPY_FEATURE_VERSION, PyArray_RUNTIME_VERSION);\n return -1;\n }\n\n /*\n * Perform runtime check of endianness and check it matches the one set by\n * the headers (npy_endian.h) as a safeguard\n */\n st = PyArray_GetEndianness();\n if (st == NPY_CPU_UNKNOWN_ENDIAN) {\n PyErr_SetString(PyExc_RuntimeError,\n "FATAL: module compiled as unknown endian");\n return -1;\n }\n#if NPY_BYTE_ORDER == NPY_BIG_ENDIAN\n if (st != NPY_CPU_BIG) {\n PyErr_SetString(PyExc_RuntimeError,\n "FATAL: module compiled as big endian, but "\n "detected different endianness at runtime");\n return -1;\n }\n#elif NPY_BYTE_ORDER == NPY_LITTLE_ENDIAN\n if (st != NPY_CPU_LITTLE) {\n PyErr_SetString(PyExc_RuntimeError,\n "FATAL: module compiled as little endian, but "\n "detected different endianness at runtime");\n return -1;\n }\n#endif\n\n return 0;\n}\n\n#define import_array() { \\n if (_import_array() < 0) { \\n PyErr_Print(); \\n PyErr_SetString( \\n PyExc_ImportError, \\n "numpy._core.multiarray failed to import" \\n ); \\n return NULL; \\n } \\n}\n\n#define import_array1(ret) { \\n if (_import_array() < 0) { \\n PyErr_Print(); \\n PyErr_SetString( \\n PyExc_ImportError, \\n "numpy._core.multiarray failed to import" \\n ); \\n return ret; \\n } \\n}\n\n#define import_array2(msg, ret) { \\n if (_import_array() < 0) { \\n PyErr_Print(); \\n PyErr_SetString(PyExc_ImportError, msg); \\n return ret; \\n } \\n}\n\n#endif\n\n#endif\n | .venv\Lib\site-packages\numpy\_core\include\numpy\__multiarray_api.h | __multiarray_api.h | C | 63,261 | 0.75 | 0.027127 | 0.248208 | awesome-app | 47 | 2024-08-09T20:13:40.588022 | BSD-3-Clause | false | 79a8e598da5ff19d64d0653b3e044b15 |
\n/* These pointers will be stored in the C-object for use in other\n extension modules\n*/\n\nvoid *PyUFunc_API[] = {\n (void *) &PyUFunc_Type,\n (void *) PyUFunc_FromFuncAndData,\n (void *) PyUFunc_RegisterLoopForType,\n NULL,\n (void *) PyUFunc_f_f_As_d_d,\n (void *) PyUFunc_d_d,\n (void *) PyUFunc_f_f,\n (void *) PyUFunc_g_g,\n (void *) PyUFunc_F_F_As_D_D,\n (void *) PyUFunc_F_F,\n (void *) PyUFunc_D_D,\n (void *) PyUFunc_G_G,\n (void *) PyUFunc_O_O,\n (void *) PyUFunc_ff_f_As_dd_d,\n (void *) PyUFunc_ff_f,\n (void *) PyUFunc_dd_d,\n (void *) PyUFunc_gg_g,\n (void *) PyUFunc_FF_F_As_DD_D,\n (void *) PyUFunc_DD_D,\n (void *) PyUFunc_FF_F,\n (void *) PyUFunc_GG_G,\n (void *) PyUFunc_OO_O,\n (void *) PyUFunc_O_O_method,\n (void *) PyUFunc_OO_O_method,\n (void *) PyUFunc_On_Om,\n NULL,\n NULL,\n (void *) PyUFunc_clearfperr,\n (void *) PyUFunc_getfperr,\n NULL,\n (void *) PyUFunc_ReplaceLoopBySignature,\n (void *) PyUFunc_FromFuncAndDataAndSignature,\n NULL,\n (void *) PyUFunc_e_e,\n (void *) PyUFunc_e_e_As_f_f,\n (void *) PyUFunc_e_e_As_d_d,\n (void *) PyUFunc_ee_e,\n (void *) PyUFunc_ee_e_As_ff_f,\n (void *) PyUFunc_ee_e_As_dd_d,\n (void *) PyUFunc_DefaultTypeResolver,\n (void *) PyUFunc_ValidateCasting,\n (void *) PyUFunc_RegisterLoopForDescr,\n (void *) PyUFunc_FromFuncAndDataAndSignatureAndIdentity,\n (void *) PyUFunc_AddLoopFromSpec,\n (void *) PyUFunc_AddPromoter,\n (void *) PyUFunc_AddWrappingLoop,\n (void *) PyUFunc_GiveFloatingpointErrors\n};\n | .venv\Lib\site-packages\numpy\_core\include\numpy\__ufunc_api.c | __ufunc_api.c | C | 1,809 | 0.8 | 0.018519 | 0.038462 | react-lib | 212 | 2023-08-04T20:09:29.059812 | Apache-2.0 | false | bfa8d5dc70f31db70c1eb4c403336be4 |
\n#ifdef _UMATHMODULE\n\nextern NPY_NO_EXPORT PyTypeObject PyUFunc_Type;\n\nextern NPY_NO_EXPORT PyTypeObject PyUFunc_Type;\n\nNPY_NO_EXPORT PyObject * PyUFunc_FromFuncAndData \\n (PyUFuncGenericFunction *, void *const *, const char *, int, int, int, int, const char *, const char *, int);\nNPY_NO_EXPORT int PyUFunc_RegisterLoopForType \\n (PyUFuncObject *, int, PyUFuncGenericFunction, const int *, void *);\nNPY_NO_EXPORT void PyUFunc_f_f_As_d_d \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_d_d \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_f_f \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_g_g \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_F_F_As_D_D \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_F_F \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_D_D \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_G_G \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_O_O \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_ff_f_As_dd_d \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_ff_f \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_dd_d \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_gg_g \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_FF_F_As_DD_D \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_DD_D \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_FF_F \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_GG_G \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_OO_O \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_O_O_method \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_OO_O_method \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_On_Om \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_clearfperr \\n (void);\nNPY_NO_EXPORT int PyUFunc_getfperr \\n (void);\nNPY_NO_EXPORT int PyUFunc_ReplaceLoopBySignature \\n (PyUFuncObject *, PyUFuncGenericFunction, const int *, PyUFuncGenericFunction *);\nNPY_NO_EXPORT PyObject * PyUFunc_FromFuncAndDataAndSignature \\n (PyUFuncGenericFunction *, void *const *, const char *, int, int, int, int, const char *, const char *, int, const char *);\nNPY_NO_EXPORT void PyUFunc_e_e \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_e_e_As_f_f \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_e_e_As_d_d \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_ee_e \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_ee_e_As_ff_f \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT void PyUFunc_ee_e_As_dd_d \\n (char **, npy_intp const *, npy_intp const *, void *);\nNPY_NO_EXPORT int PyUFunc_DefaultTypeResolver \\n (PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyObject *, PyArray_Descr **);\nNPY_NO_EXPORT int PyUFunc_ValidateCasting \\n (PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyArray_Descr *const *);\nNPY_NO_EXPORT int PyUFunc_RegisterLoopForDescr \\n (PyUFuncObject *, PyArray_Descr *, PyUFuncGenericFunction, PyArray_Descr **, void *);\nNPY_NO_EXPORT PyObject * PyUFunc_FromFuncAndDataAndSignatureAndIdentity \\n (PyUFuncGenericFunction *, void *const *, const char *, int, int, int, int, const char *, const char *, const int, const char *, PyObject *);\nNPY_NO_EXPORT int PyUFunc_AddLoopFromSpec \\n (PyObject *, PyArrayMethod_Spec *);\nNPY_NO_EXPORT int PyUFunc_AddPromoter \\n (PyObject *, PyObject *, PyObject *);\nNPY_NO_EXPORT int PyUFunc_AddWrappingLoop \\n (PyObject *, PyArray_DTypeMeta *new_dtypes[], PyArray_DTypeMeta *wrapped_dtypes[], PyArrayMethod_TranslateGivenDescriptors *, PyArrayMethod_TranslateLoopDescriptors *);\nNPY_NO_EXPORT int PyUFunc_GiveFloatingpointErrors \\n (const char *, int);\n\n#else\n\n#if defined(PY_UFUNC_UNIQUE_SYMBOL)\n#define PyUFunc_API PY_UFUNC_UNIQUE_SYMBOL\n#endif\n\n/* By default do not export API in an .so (was never the case on windows) */\n#ifndef NPY_API_SYMBOL_ATTRIBUTE\n #define NPY_API_SYMBOL_ATTRIBUTE NPY_VISIBILITY_HIDDEN\n#endif\n\n#if defined(NO_IMPORT) || defined(NO_IMPORT_UFUNC)\nextern NPY_API_SYMBOL_ATTRIBUTE void **PyUFunc_API;\n#else\n#if defined(PY_UFUNC_UNIQUE_SYMBOL)\nNPY_API_SYMBOL_ATTRIBUTE void **PyUFunc_API;\n#else\nstatic void **PyUFunc_API=NULL;\n#endif\n#endif\n\n#define PyUFunc_Type (*(PyTypeObject *)PyUFunc_API[0])\n#define PyUFunc_FromFuncAndData \\n (*(PyObject * (*)(PyUFuncGenericFunction *, void *const *, const char *, int, int, int, int, const char *, const char *, int)) \\n PyUFunc_API[1])\n#define PyUFunc_RegisterLoopForType \\n (*(int (*)(PyUFuncObject *, int, PyUFuncGenericFunction, const int *, void *)) \\n PyUFunc_API[2])\n#define PyUFunc_f_f_As_d_d \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[4])\n#define PyUFunc_d_d \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[5])\n#define PyUFunc_f_f \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[6])\n#define PyUFunc_g_g \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[7])\n#define PyUFunc_F_F_As_D_D \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[8])\n#define PyUFunc_F_F \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[9])\n#define PyUFunc_D_D \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[10])\n#define PyUFunc_G_G \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[11])\n#define PyUFunc_O_O \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[12])\n#define PyUFunc_ff_f_As_dd_d \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[13])\n#define PyUFunc_ff_f \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[14])\n#define PyUFunc_dd_d \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[15])\n#define PyUFunc_gg_g \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[16])\n#define PyUFunc_FF_F_As_DD_D \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[17])\n#define PyUFunc_DD_D \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[18])\n#define PyUFunc_FF_F \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[19])\n#define PyUFunc_GG_G \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[20])\n#define PyUFunc_OO_O \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[21])\n#define PyUFunc_O_O_method \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[22])\n#define PyUFunc_OO_O_method \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[23])\n#define PyUFunc_On_Om \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[24])\n#define PyUFunc_clearfperr \\n (*(void (*)(void)) \\n PyUFunc_API[27])\n#define PyUFunc_getfperr \\n (*(int (*)(void)) \\n PyUFunc_API[28])\n#define PyUFunc_ReplaceLoopBySignature \\n (*(int (*)(PyUFuncObject *, PyUFuncGenericFunction, const int *, PyUFuncGenericFunction *)) \\n PyUFunc_API[30])\n#define PyUFunc_FromFuncAndDataAndSignature \\n (*(PyObject * (*)(PyUFuncGenericFunction *, void *const *, const char *, int, int, int, int, const char *, const char *, int, const char *)) \\n PyUFunc_API[31])\n#define PyUFunc_e_e \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[33])\n#define PyUFunc_e_e_As_f_f \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[34])\n#define PyUFunc_e_e_As_d_d \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[35])\n#define PyUFunc_ee_e \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[36])\n#define PyUFunc_ee_e_As_ff_f \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[37])\n#define PyUFunc_ee_e_As_dd_d \\n (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \\n PyUFunc_API[38])\n#define PyUFunc_DefaultTypeResolver \\n (*(int (*)(PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyObject *, PyArray_Descr **)) \\n PyUFunc_API[39])\n#define PyUFunc_ValidateCasting \\n (*(int (*)(PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyArray_Descr *const *)) \\n PyUFunc_API[40])\n#define PyUFunc_RegisterLoopForDescr \\n (*(int (*)(PyUFuncObject *, PyArray_Descr *, PyUFuncGenericFunction, PyArray_Descr **, void *)) \\n PyUFunc_API[41])\n\n#if NPY_FEATURE_VERSION >= NPY_1_16_API_VERSION\n#define PyUFunc_FromFuncAndDataAndSignatureAndIdentity \\n (*(PyObject * (*)(PyUFuncGenericFunction *, void *const *, const char *, int, int, int, int, const char *, const char *, const int, const char *, PyObject *)) \\n PyUFunc_API[42])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define PyUFunc_AddLoopFromSpec \\n (*(int (*)(PyObject *, PyArrayMethod_Spec *)) \\n PyUFunc_API[43])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define PyUFunc_AddPromoter \\n (*(int (*)(PyObject *, PyObject *, PyObject *)) \\n PyUFunc_API[44])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define PyUFunc_AddWrappingLoop \\n (*(int (*)(PyObject *, PyArray_DTypeMeta *new_dtypes[], PyArray_DTypeMeta *wrapped_dtypes[], PyArrayMethod_TranslateGivenDescriptors *, PyArrayMethod_TranslateLoopDescriptors *)) \\n PyUFunc_API[45])\n#endif\n\n#if NPY_FEATURE_VERSION >= NPY_2_0_API_VERSION\n#define PyUFunc_GiveFloatingpointErrors \\n (*(int (*)(const char *, int)) \\n PyUFunc_API[46])\n#endif\n\nstatic inline int\n_import_umath(void)\n{\n PyObject *c_api;\n PyObject *numpy = PyImport_ImportModule("numpy._core._multiarray_umath");\n if (numpy == NULL && PyErr_ExceptionMatches(PyExc_ModuleNotFoundError)) {\n PyErr_Clear();\n numpy = PyImport_ImportModule("numpy.core._multiarray_umath");\n }\n\n if (numpy == NULL) {\n PyErr_SetString(PyExc_ImportError,\n "_multiarray_umath failed to import");\n return -1;\n }\n\n c_api = PyObject_GetAttrString(numpy, "_UFUNC_API");\n Py_DECREF(numpy);\n if (c_api == NULL) {\n PyErr_SetString(PyExc_AttributeError, "_UFUNC_API not found");\n return -1;\n }\n\n if (!PyCapsule_CheckExact(c_api)) {\n PyErr_SetString(PyExc_RuntimeError, "_UFUNC_API is not PyCapsule object");\n Py_DECREF(c_api);\n return -1;\n }\n PyUFunc_API = (void **)PyCapsule_GetPointer(c_api, NULL);\n Py_DECREF(c_api);\n if (PyUFunc_API == NULL) {\n PyErr_SetString(PyExc_RuntimeError, "_UFUNC_API is NULL pointer");\n return -1;\n }\n return 0;\n}\n\n#define import_umath() \\n do {\\n UFUNC_NOFPE\\n if (_import_umath() < 0) {\\n PyErr_Print();\\n PyErr_SetString(PyExc_ImportError,\\n "numpy._core.umath failed to import");\\n return NULL;\\n }\\n } while(0)\n\n#define import_umath1(ret) \\n do {\\n UFUNC_NOFPE\\n if (_import_umath() < 0) {\\n PyErr_Print();\\n PyErr_SetString(PyExc_ImportError,\\n "numpy._core.umath failed to import");\\n return ret;\\n }\\n } while(0)\n\n#define import_umath2(ret, msg) \\n do {\\n UFUNC_NOFPE\\n if (_import_umath() < 0) {\\n PyErr_Print();\\n PyErr_SetString(PyExc_ImportError, msg);\\n return ret;\\n }\\n } while(0)\n\n#define import_ufunc() \\n do {\\n UFUNC_NOFPE\\n if (_import_umath() < 0) {\\n PyErr_Print();\\n PyErr_SetString(PyExc_ImportError,\\n "numpy._core.umath failed to import");\\n }\\n } while(0)\n\n\nstatic inline int\nPyUFunc_ImportUFuncAPI()\n{\n if (NPY_UNLIKELY(PyUFunc_API == NULL)) {\n import_umath1(-1);\n }\n return 0;\n}\n\n#endif\n | .venv\Lib\site-packages\numpy\_core\include\numpy\__ufunc_api.h | __ufunc_api.h | C | 13,516 | 0.95 | 0.064516 | 0.227848 | vue-tools | 717 | 2023-07-12T14:20:03.476224 | GPL-3.0 | false | 7924bb5be488622cb7b4b6afccdaf28d |
#ifndef NUMPY_CORE_INCLUDE_NUMPY_RANDOM_BITGEN_H_\n#define NUMPY_CORE_INCLUDE_NUMPY_RANDOM_BITGEN_H_\n\n#pragma once\n#include <stddef.h>\n#include <stdbool.h>\n#include <stdint.h>\n\n/* Must match the declaration in numpy/random/<any>.pxd */\n\ntypedef struct bitgen {\n void *state;\n uint64_t (*next_uint64)(void *st);\n uint32_t (*next_uint32)(void *st);\n double (*next_double)(void *st);\n uint64_t (*next_raw)(void *st);\n} bitgen_t;\n\n\n#endif /* NUMPY_CORE_INCLUDE_NUMPY_RANDOM_BITGEN_H_ */\n | .venv\Lib\site-packages\numpy\_core\include\numpy\random\bitgen.h | bitgen.h | C | 508 | 0.95 | 0 | 0.533333 | awesome-app | 630 | 2025-04-11T17:10:36.871008 | BSD-3-Clause | false | b80b1830332f082e431fdccfff2f8489 |
#ifndef NUMPY_CORE_INCLUDE_NUMPY_RANDOM_DISTRIBUTIONS_H_\n#define NUMPY_CORE_INCLUDE_NUMPY_RANDOM_DISTRIBUTIONS_H_\n\n#ifdef __cplusplus\nextern "C" {\n#endif\n\n#include <Python.h>\n#include "numpy/npy_common.h"\n#include <stddef.h>\n#include <stdbool.h>\n#include <stdint.h>\n\n#include "numpy/npy_math.h"\n#include "numpy/random/bitgen.h"\n\n/*\n * RAND_INT_TYPE is used to share integer generators with RandomState which\n * used long in place of int64_t. If changing a distribution that uses\n * RAND_INT_TYPE, then the original unmodified copy must be retained for\n * use in RandomState by copying to the legacy distributions source file.\n */\n#ifdef NP_RANDOM_LEGACY\n#define RAND_INT_TYPE long\n#define RAND_INT_MAX LONG_MAX\n#else\n#define RAND_INT_TYPE int64_t\n#define RAND_INT_MAX INT64_MAX\n#endif\n\n#ifdef _MSC_VER\n#define DECLDIR __declspec(dllexport)\n#else\n#define DECLDIR extern\n#endif\n\n#ifndef MIN\n#define MIN(x, y) (((x) < (y)) ? x : y)\n#define MAX(x, y) (((x) > (y)) ? x : y)\n#endif\n\n#ifndef M_PI\n#define M_PI 3.14159265358979323846264338328\n#endif\n\ntypedef struct s_binomial_t {\n int has_binomial; /* !=0: following parameters initialized for binomial */\n double psave;\n RAND_INT_TYPE nsave;\n double r;\n double q;\n double fm;\n RAND_INT_TYPE m;\n double p1;\n double xm;\n double xl;\n double xr;\n double c;\n double laml;\n double lamr;\n double p2;\n double p3;\n double p4;\n} binomial_t;\n\nDECLDIR float random_standard_uniform_f(bitgen_t *bitgen_state);\nDECLDIR double random_standard_uniform(bitgen_t *bitgen_state);\nDECLDIR void random_standard_uniform_fill(bitgen_t *, npy_intp, double *);\nDECLDIR void random_standard_uniform_fill_f(bitgen_t *, npy_intp, float *);\n\nDECLDIR int64_t random_positive_int64(bitgen_t *bitgen_state);\nDECLDIR int32_t random_positive_int32(bitgen_t *bitgen_state);\nDECLDIR int64_t random_positive_int(bitgen_t *bitgen_state);\nDECLDIR uint64_t random_uint(bitgen_t *bitgen_state);\n\nDECLDIR double random_standard_exponential(bitgen_t *bitgen_state);\nDECLDIR float random_standard_exponential_f(bitgen_t *bitgen_state);\nDECLDIR void random_standard_exponential_fill(bitgen_t *, npy_intp, double *);\nDECLDIR void random_standard_exponential_fill_f(bitgen_t *, npy_intp, float *);\nDECLDIR void random_standard_exponential_inv_fill(bitgen_t *, npy_intp, double *);\nDECLDIR void random_standard_exponential_inv_fill_f(bitgen_t *, npy_intp, float *);\n\nDECLDIR double random_standard_normal(bitgen_t *bitgen_state);\nDECLDIR float random_standard_normal_f(bitgen_t *bitgen_state);\nDECLDIR void random_standard_normal_fill(bitgen_t *, npy_intp, double *);\nDECLDIR void random_standard_normal_fill_f(bitgen_t *, npy_intp, float *);\nDECLDIR double random_standard_gamma(bitgen_t *bitgen_state, double shape);\nDECLDIR float random_standard_gamma_f(bitgen_t *bitgen_state, float shape);\n\nDECLDIR double random_normal(bitgen_t *bitgen_state, double loc, double scale);\n\nDECLDIR double random_gamma(bitgen_t *bitgen_state, double shape, double scale);\nDECLDIR float random_gamma_f(bitgen_t *bitgen_state, float shape, float scale);\n\nDECLDIR double random_exponential(bitgen_t *bitgen_state, double scale);\nDECLDIR double random_uniform(bitgen_t *bitgen_state, double lower, double range);\nDECLDIR double random_beta(bitgen_t *bitgen_state, double a, double b);\nDECLDIR double random_chisquare(bitgen_t *bitgen_state, double df);\nDECLDIR double random_f(bitgen_t *bitgen_state, double dfnum, double dfden);\nDECLDIR double random_standard_cauchy(bitgen_t *bitgen_state);\nDECLDIR double random_pareto(bitgen_t *bitgen_state, double a);\nDECLDIR double random_weibull(bitgen_t *bitgen_state, double a);\nDECLDIR double random_power(bitgen_t *bitgen_state, double a);\nDECLDIR double random_laplace(bitgen_t *bitgen_state, double loc, double scale);\nDECLDIR double random_gumbel(bitgen_t *bitgen_state, double loc, double scale);\nDECLDIR double random_logistic(bitgen_t *bitgen_state, double loc, double scale);\nDECLDIR double random_lognormal(bitgen_t *bitgen_state, double mean, double sigma);\nDECLDIR double random_rayleigh(bitgen_t *bitgen_state, double mode);\nDECLDIR double random_standard_t(bitgen_t *bitgen_state, double df);\nDECLDIR double random_noncentral_chisquare(bitgen_t *bitgen_state, double df,\n double nonc);\nDECLDIR double random_noncentral_f(bitgen_t *bitgen_state, double dfnum,\n double dfden, double nonc);\nDECLDIR double random_wald(bitgen_t *bitgen_state, double mean, double scale);\nDECLDIR double random_vonmises(bitgen_t *bitgen_state, double mu, double kappa);\nDECLDIR double random_triangular(bitgen_t *bitgen_state, double left, double mode,\n double right);\n\nDECLDIR RAND_INT_TYPE random_poisson(bitgen_t *bitgen_state, double lam);\nDECLDIR RAND_INT_TYPE random_negative_binomial(bitgen_t *bitgen_state, double n,\n double p);\n\nDECLDIR int64_t random_binomial(bitgen_t *bitgen_state, double p,\n int64_t n, binomial_t *binomial);\n\nDECLDIR int64_t random_logseries(bitgen_t *bitgen_state, double p);\nDECLDIR int64_t random_geometric(bitgen_t *bitgen_state, double p);\nDECLDIR RAND_INT_TYPE random_geometric_search(bitgen_t *bitgen_state, double p);\nDECLDIR RAND_INT_TYPE random_zipf(bitgen_t *bitgen_state, double a);\nDECLDIR int64_t random_hypergeometric(bitgen_t *bitgen_state,\n int64_t good, int64_t bad, int64_t sample);\nDECLDIR uint64_t random_interval(bitgen_t *bitgen_state, uint64_t max);\n\n/* Generate random uint64 numbers in closed interval [off, off + rng]. */\nDECLDIR uint64_t random_bounded_uint64(bitgen_t *bitgen_state, uint64_t off,\n uint64_t rng, uint64_t mask,\n bool use_masked);\n\n/* Generate random uint32 numbers in closed interval [off, off + rng]. */\nDECLDIR uint32_t random_buffered_bounded_uint32(bitgen_t *bitgen_state,\n uint32_t off, uint32_t rng,\n uint32_t mask, bool use_masked,\n int *bcnt, uint32_t *buf);\nDECLDIR uint16_t random_buffered_bounded_uint16(bitgen_t *bitgen_state,\n uint16_t off, uint16_t rng,\n uint16_t mask, bool use_masked,\n int *bcnt, uint32_t *buf);\nDECLDIR uint8_t random_buffered_bounded_uint8(bitgen_t *bitgen_state, uint8_t off,\n uint8_t rng, uint8_t mask,\n bool use_masked, int *bcnt,\n uint32_t *buf);\nDECLDIR npy_bool random_buffered_bounded_bool(bitgen_t *bitgen_state, npy_bool off,\n npy_bool rng, npy_bool mask,\n bool use_masked, int *bcnt,\n uint32_t *buf);\n\nDECLDIR void random_bounded_uint64_fill(bitgen_t *bitgen_state, uint64_t off,\n uint64_t rng, npy_intp cnt,\n bool use_masked, uint64_t *out);\nDECLDIR void random_bounded_uint32_fill(bitgen_t *bitgen_state, uint32_t off,\n uint32_t rng, npy_intp cnt,\n bool use_masked, uint32_t *out);\nDECLDIR void random_bounded_uint16_fill(bitgen_t *bitgen_state, uint16_t off,\n uint16_t rng, npy_intp cnt,\n bool use_masked, uint16_t *out);\nDECLDIR void random_bounded_uint8_fill(bitgen_t *bitgen_state, uint8_t off,\n uint8_t rng, npy_intp cnt,\n bool use_masked, uint8_t *out);\nDECLDIR void random_bounded_bool_fill(bitgen_t *bitgen_state, npy_bool off,\n npy_bool rng, npy_intp cnt,\n bool use_masked, npy_bool *out);\n\nDECLDIR void random_multinomial(bitgen_t *bitgen_state, RAND_INT_TYPE n, RAND_INT_TYPE *mnix,\n double *pix, npy_intp d, binomial_t *binomial);\n\n/* multivariate hypergeometric, "count" method */\nDECLDIR int random_multivariate_hypergeometric_count(bitgen_t *bitgen_state,\n int64_t total,\n size_t num_colors, int64_t *colors,\n int64_t nsample,\n size_t num_variates, int64_t *variates);\n\n/* multivariate hypergeometric, "marginals" method */\nDECLDIR void random_multivariate_hypergeometric_marginals(bitgen_t *bitgen_state,\n int64_t total,\n size_t num_colors, int64_t *colors,\n int64_t nsample,\n size_t num_variates, int64_t *variates);\n\n/* Common to legacy-distributions.c and distributions.c but not exported */\n\nRAND_INT_TYPE random_binomial_btpe(bitgen_t *bitgen_state,\n RAND_INT_TYPE n,\n double p,\n binomial_t *binomial);\nRAND_INT_TYPE random_binomial_inversion(bitgen_t *bitgen_state,\n RAND_INT_TYPE n,\n double p,\n binomial_t *binomial);\ndouble random_loggam(double x);\nstatic inline double next_double(bitgen_t *bitgen_state) {\n return bitgen_state->next_double(bitgen_state->state);\n}\n\n#ifdef __cplusplus\n}\n#endif\n\n#endif /* NUMPY_CORE_INCLUDE_NUMPY_RANDOM_DISTRIBUTIONS_H_ */\n | .venv\Lib\site-packages\numpy\_core\include\numpy\random\distributions.h | distributions.h | C | 10,070 | 0.95 | 0.009569 | 0.243094 | node-utils | 531 | 2024-04-06T09:42:10.636055 | BSD-3-Clause | false | a44f47404af85258e2959cd100340685 |
// libdivide.h - Optimized integer division\n// https://libdivide.com\n//\n// Copyright (C) 2010 - 2019 ridiculous_fish, <libdivide@ridiculousfish.com>\n// Copyright (C) 2016 - 2019 Kim Walisch, <kim.walisch@gmail.com>\n//\n// libdivide is dual-licensed under the Boost or zlib licenses.\n// You may use libdivide under the terms of either of these.\n// See LICENSE.txt for more details.\n\n#ifndef NUMPY_CORE_INCLUDE_NUMPY_LIBDIVIDE_LIBDIVIDE_H_\n#define NUMPY_CORE_INCLUDE_NUMPY_LIBDIVIDE_LIBDIVIDE_H_\n\n#define LIBDIVIDE_VERSION "3.0"\n#define LIBDIVIDE_VERSION_MAJOR 3\n#define LIBDIVIDE_VERSION_MINOR 0\n\n#include <stdint.h>\n\n#if defined(__cplusplus)\n #include <cstdlib>\n #include <cstdio>\n #include <type_traits>\n#else\n #include <stdlib.h>\n #include <stdio.h>\n#endif\n\n#if defined(LIBDIVIDE_AVX512)\n #include <immintrin.h>\n#elif defined(LIBDIVIDE_AVX2)\n #include <immintrin.h>\n#elif defined(LIBDIVIDE_SSE2)\n #include <emmintrin.h>\n#endif\n\n#if defined(_MSC_VER)\n #include <intrin.h>\n // disable warning C4146: unary minus operator applied\n // to unsigned type, result still unsigned\n #pragma warning(disable: 4146)\n #define LIBDIVIDE_VC\n#endif\n\n#if !defined(__has_builtin)\n #define __has_builtin(x) 0\n#endif\n\n#if defined(__SIZEOF_INT128__)\n #define HAS_INT128_T\n // clang-cl on Windows does not yet support 128-bit division\n #if !(defined(__clang__) && defined(LIBDIVIDE_VC))\n #define HAS_INT128_DIV\n #endif\n#endif\n\n#if defined(__x86_64__) || defined(_M_X64)\n #define LIBDIVIDE_X86_64\n#endif\n\n#if defined(__i386__)\n #define LIBDIVIDE_i386\n#endif\n\n#if defined(__GNUC__) || defined(__clang__)\n #define LIBDIVIDE_GCC_STYLE_ASM\n#endif\n\n#if defined(__cplusplus) || defined(LIBDIVIDE_VC)\n #define LIBDIVIDE_FUNCTION __FUNCTION__\n#else\n #define LIBDIVIDE_FUNCTION __func__\n#endif\n\n#define LIBDIVIDE_ERROR(msg) \\n do { \\n fprintf(stderr, "libdivide.h:%d: %s(): Error: %s\n", \\n __LINE__, LIBDIVIDE_FUNCTION, msg); \\n abort(); \\n } while (0)\n\n#if defined(LIBDIVIDE_ASSERTIONS_ON)\n #define LIBDIVIDE_ASSERT(x) \\n do { \\n if (!(x)) { \\n fprintf(stderr, "libdivide.h:%d: %s(): Assertion failed: %s\n", \\n __LINE__, LIBDIVIDE_FUNCTION, #x); \\n abort(); \\n } \\n } while (0)\n#else\n #define LIBDIVIDE_ASSERT(x)\n#endif\n\n#ifdef __cplusplus\nnamespace libdivide {\n#endif\n\n// pack divider structs to prevent compilers from padding.\n// This reduces memory usage by up to 43% when using a large\n// array of libdivide dividers and improves performance\n// by up to 10% because of reduced memory bandwidth.\n#pragma pack(push, 1)\n\nstruct libdivide_u32_t {\n uint32_t magic;\n uint8_t more;\n};\n\nstruct libdivide_s32_t {\n int32_t magic;\n uint8_t more;\n};\n\nstruct libdivide_u64_t {\n uint64_t magic;\n uint8_t more;\n};\n\nstruct libdivide_s64_t {\n int64_t magic;\n uint8_t more;\n};\n\nstruct libdivide_u32_branchfree_t {\n uint32_t magic;\n uint8_t more;\n};\n\nstruct libdivide_s32_branchfree_t {\n int32_t magic;\n uint8_t more;\n};\n\nstruct libdivide_u64_branchfree_t {\n uint64_t magic;\n uint8_t more;\n};\n\nstruct libdivide_s64_branchfree_t {\n int64_t magic;\n uint8_t more;\n};\n\n#pragma pack(pop)\n\n// Explanation of the "more" field:\n//\n// * Bits 0-5 is the shift value (for shift path or mult path).\n// * Bit 6 is the add indicator for mult path.\n// * Bit 7 is set if the divisor is negative. We use bit 7 as the negative\n// divisor indicator so that we can efficiently use sign extension to\n// create a bitmask with all bits set to 1 (if the divisor is negative)\n// or 0 (if the divisor is positive).\n//\n// u32: [0-4] shift value\n// [5] ignored\n// [6] add indicator\n// magic number of 0 indicates shift path\n//\n// s32: [0-4] shift value\n// [5] ignored\n// [6] add indicator\n// [7] indicates negative divisor\n// magic number of 0 indicates shift path\n//\n// u64: [0-5] shift value\n// [6] add indicator\n// magic number of 0 indicates shift path\n//\n// s64: [0-5] shift value\n// [6] add indicator\n// [7] indicates negative divisor\n// magic number of 0 indicates shift path\n//\n// In s32 and s64 branchfree modes, the magic number is negated according to\n// whether the divisor is negated. In branchfree strategy, it is not negated.\n\nenum {\n LIBDIVIDE_32_SHIFT_MASK = 0x1F,\n LIBDIVIDE_64_SHIFT_MASK = 0x3F,\n LIBDIVIDE_ADD_MARKER = 0x40,\n LIBDIVIDE_NEGATIVE_DIVISOR = 0x80\n};\n\nstatic inline struct libdivide_s32_t libdivide_s32_gen(int32_t d);\nstatic inline struct libdivide_u32_t libdivide_u32_gen(uint32_t d);\nstatic inline struct libdivide_s64_t libdivide_s64_gen(int64_t d);\nstatic inline struct libdivide_u64_t libdivide_u64_gen(uint64_t d);\n\nstatic inline struct libdivide_s32_branchfree_t libdivide_s32_branchfree_gen(int32_t d);\nstatic inline struct libdivide_u32_branchfree_t libdivide_u32_branchfree_gen(uint32_t d);\nstatic inline struct libdivide_s64_branchfree_t libdivide_s64_branchfree_gen(int64_t d);\nstatic inline struct libdivide_u64_branchfree_t libdivide_u64_branchfree_gen(uint64_t d);\n\nstatic inline int32_t libdivide_s32_do(int32_t numer, const struct libdivide_s32_t *denom);\nstatic inline uint32_t libdivide_u32_do(uint32_t numer, const struct libdivide_u32_t *denom);\nstatic inline int64_t libdivide_s64_do(int64_t numer, const struct libdivide_s64_t *denom);\nstatic inline uint64_t libdivide_u64_do(uint64_t numer, const struct libdivide_u64_t *denom);\n\nstatic inline int32_t libdivide_s32_branchfree_do(int32_t numer, const struct libdivide_s32_branchfree_t *denom);\nstatic inline uint32_t libdivide_u32_branchfree_do(uint32_t numer, const struct libdivide_u32_branchfree_t *denom);\nstatic inline int64_t libdivide_s64_branchfree_do(int64_t numer, const struct libdivide_s64_branchfree_t *denom);\nstatic inline uint64_t libdivide_u64_branchfree_do(uint64_t numer, const struct libdivide_u64_branchfree_t *denom);\n\nstatic inline int32_t libdivide_s32_recover(const struct libdivide_s32_t *denom);\nstatic inline uint32_t libdivide_u32_recover(const struct libdivide_u32_t *denom);\nstatic inline int64_t libdivide_s64_recover(const struct libdivide_s64_t *denom);\nstatic inline uint64_t libdivide_u64_recover(const struct libdivide_u64_t *denom);\n\nstatic inline int32_t libdivide_s32_branchfree_recover(const struct libdivide_s32_branchfree_t *denom);\nstatic inline uint32_t libdivide_u32_branchfree_recover(const struct libdivide_u32_branchfree_t *denom);\nstatic inline int64_t libdivide_s64_branchfree_recover(const struct libdivide_s64_branchfree_t *denom);\nstatic inline uint64_t libdivide_u64_branchfree_recover(const struct libdivide_u64_branchfree_t *denom);\n\n//////// Internal Utility Functions\n\nstatic inline uint32_t libdivide_mullhi_u32(uint32_t x, uint32_t y) {\n uint64_t xl = x, yl = y;\n uint64_t rl = xl * yl;\n return (uint32_t)(rl >> 32);\n}\n\nstatic inline int32_t libdivide_mullhi_s32(int32_t x, int32_t y) {\n int64_t xl = x, yl = y;\n int64_t rl = xl * yl;\n // needs to be arithmetic shift\n return (int32_t)(rl >> 32);\n}\n\nstatic inline uint64_t libdivide_mullhi_u64(uint64_t x, uint64_t y) {\n#if defined(LIBDIVIDE_VC) && \\n defined(LIBDIVIDE_X86_64)\n return __umulh(x, y);\n#elif defined(HAS_INT128_T)\n __uint128_t xl = x, yl = y;\n __uint128_t rl = xl * yl;\n return (uint64_t)(rl >> 64);\n#else\n // full 128 bits are x0 * y0 + (x0 * y1 << 32) + (x1 * y0 << 32) + (x1 * y1 << 64)\n uint32_t mask = 0xFFFFFFFF;\n uint32_t x0 = (uint32_t)(x & mask);\n uint32_t x1 = (uint32_t)(x >> 32);\n uint32_t y0 = (uint32_t)(y & mask);\n uint32_t y1 = (uint32_t)(y >> 32);\n uint32_t x0y0_hi = libdivide_mullhi_u32(x0, y0);\n uint64_t x0y1 = x0 * (uint64_t)y1;\n uint64_t x1y0 = x1 * (uint64_t)y0;\n uint64_t x1y1 = x1 * (uint64_t)y1;\n uint64_t temp = x1y0 + x0y0_hi;\n uint64_t temp_lo = temp & mask;\n uint64_t temp_hi = temp >> 32;\n\n return x1y1 + temp_hi + ((temp_lo + x0y1) >> 32);\n#endif\n}\n\nstatic inline int64_t libdivide_mullhi_s64(int64_t x, int64_t y) {\n#if defined(LIBDIVIDE_VC) && \\n defined(LIBDIVIDE_X86_64)\n return __mulh(x, y);\n#elif defined(HAS_INT128_T)\n __int128_t xl = x, yl = y;\n __int128_t rl = xl * yl;\n return (int64_t)(rl >> 64);\n#else\n // full 128 bits are x0 * y0 + (x0 * y1 << 32) + (x1 * y0 << 32) + (x1 * y1 << 64)\n uint32_t mask = 0xFFFFFFFF;\n uint32_t x0 = (uint32_t)(x & mask);\n uint32_t y0 = (uint32_t)(y & mask);\n int32_t x1 = (int32_t)(x >> 32);\n int32_t y1 = (int32_t)(y >> 32);\n uint32_t x0y0_hi = libdivide_mullhi_u32(x0, y0);\n int64_t t = x1 * (int64_t)y0 + x0y0_hi;\n int64_t w1 = x0 * (int64_t)y1 + (t & mask);\n\n return x1 * (int64_t)y1 + (t >> 32) + (w1 >> 32);\n#endif\n}\n\nstatic inline int32_t libdivide_count_leading_zeros32(uint32_t val) {\n#if defined(__GNUC__) || \\n __has_builtin(__builtin_clz)\n // Fast way to count leading zeros\n return __builtin_clz(val);\n#elif defined(LIBDIVIDE_VC)\n unsigned long result;\n if (_BitScanReverse(&result, val)) {\n return 31 - result;\n }\n return 0;\n#else\n if (val == 0)\n return 32;\n int32_t result = 8;\n uint32_t hi = 0xFFU << 24;\n while ((val & hi) == 0) {\n hi >>= 8;\n result += 8;\n }\n while (val & hi) {\n result -= 1;\n hi <<= 1;\n }\n return result;\n#endif\n}\n\nstatic inline int32_t libdivide_count_leading_zeros64(uint64_t val) {\n#if defined(__GNUC__) || \\n __has_builtin(__builtin_clzll)\n // Fast way to count leading zeros\n return __builtin_clzll(val);\n#elif defined(LIBDIVIDE_VC) && defined(_WIN64)\n unsigned long result;\n if (_BitScanReverse64(&result, val)) {\n return 63 - result;\n }\n return 0;\n#else\n uint32_t hi = val >> 32;\n uint32_t lo = val & 0xFFFFFFFF;\n if (hi != 0) return libdivide_count_leading_zeros32(hi);\n return 32 + libdivide_count_leading_zeros32(lo);\n#endif\n}\n\n// libdivide_64_div_32_to_32: divides a 64-bit uint {u1, u0} by a 32-bit\n// uint {v}. The result must fit in 32 bits.\n// Returns the quotient directly and the remainder in *r\nstatic inline uint32_t libdivide_64_div_32_to_32(uint32_t u1, uint32_t u0, uint32_t v, uint32_t *r) {\n#if (defined(LIBDIVIDE_i386) || defined(LIBDIVIDE_X86_64)) && \\n defined(LIBDIVIDE_GCC_STYLE_ASM)\n uint32_t result;\n __asm__("divl %[v]"\n : "=a"(result), "=d"(*r)\n : [v] "r"(v), "a"(u0), "d"(u1)\n );\n return result;\n#else\n uint64_t n = ((uint64_t)u1 << 32) | u0;\n uint32_t result = (uint32_t)(n / v);\n *r = (uint32_t)(n - result * (uint64_t)v);\n return result;\n#endif\n}\n\n// libdivide_128_div_64_to_64: divides a 128-bit uint {u1, u0} by a 64-bit\n// uint {v}. The result must fit in 64 bits.\n// Returns the quotient directly and the remainder in *r\nstatic uint64_t libdivide_128_div_64_to_64(uint64_t u1, uint64_t u0, uint64_t v, uint64_t *r) {\n#if defined(LIBDIVIDE_X86_64) && \\n defined(LIBDIVIDE_GCC_STYLE_ASM)\n uint64_t result;\n __asm__("divq %[v]"\n : "=a"(result), "=d"(*r)\n : [v] "r"(v), "a"(u0), "d"(u1)\n );\n return result;\n#elif defined(HAS_INT128_T) && \\n defined(HAS_INT128_DIV)\n __uint128_t n = ((__uint128_t)u1 << 64) | u0;\n uint64_t result = (uint64_t)(n / v);\n *r = (uint64_t)(n - result * (__uint128_t)v);\n return result;\n#else\n // Code taken from Hacker's Delight:\n // http://www.hackersdelight.org/HDcode/divlu.c.\n // License permits inclusion here per:\n // http://www.hackersdelight.org/permissions.htm\n\n const uint64_t b = (1ULL << 32); // Number base (32 bits)\n uint64_t un1, un0; // Norm. dividend LSD's\n uint64_t vn1, vn0; // Norm. divisor digits\n uint64_t q1, q0; // Quotient digits\n uint64_t un64, un21, un10; // Dividend digit pairs\n uint64_t rhat; // A remainder\n int32_t s; // Shift amount for norm\n\n // If overflow, set rem. to an impossible value,\n // and return the largest possible quotient\n if (u1 >= v) {\n *r = (uint64_t) -1;\n return (uint64_t) -1;\n }\n\n // count leading zeros\n s = libdivide_count_leading_zeros64(v);\n if (s > 0) {\n // Normalize divisor\n v = v << s;\n un64 = (u1 << s) | (u0 >> (64 - s));\n un10 = u0 << s; // Shift dividend left\n } else {\n // Avoid undefined behavior of (u0 >> 64).\n // The behavior is undefined if the right operand is\n // negative, or greater than or equal to the length\n // in bits of the promoted left operand.\n un64 = u1;\n un10 = u0;\n }\n\n // Break divisor up into two 32-bit digits\n vn1 = v >> 32;\n vn0 = v & 0xFFFFFFFF;\n\n // Break right half of dividend into two digits\n un1 = un10 >> 32;\n un0 = un10 & 0xFFFFFFFF;\n\n // Compute the first quotient digit, q1\n q1 = un64 / vn1;\n rhat = un64 - q1 * vn1;\n\n while (q1 >= b || q1 * vn0 > b * rhat + un1) {\n q1 = q1 - 1;\n rhat = rhat + vn1;\n if (rhat >= b)\n break;\n }\n\n // Multiply and subtract\n un21 = un64 * b + un1 - q1 * v;\n\n // Compute the second quotient digit\n q0 = un21 / vn1;\n rhat = un21 - q0 * vn1;\n\n while (q0 >= b || q0 * vn0 > b * rhat + un0) {\n q0 = q0 - 1;\n rhat = rhat + vn1;\n if (rhat >= b)\n break;\n }\n\n *r = (un21 * b + un0 - q0 * v) >> s;\n return q1 * b + q0;\n#endif\n}\n\n// Bitshift a u128 in place, left (signed_shift > 0) or right (signed_shift < 0)\nstatic inline void libdivide_u128_shift(uint64_t *u1, uint64_t *u0, int32_t signed_shift) {\n if (signed_shift > 0) {\n uint32_t shift = signed_shift;\n *u1 <<= shift;\n *u1 |= *u0 >> (64 - shift);\n *u0 <<= shift;\n }\n else if (signed_shift < 0) {\n uint32_t shift = -signed_shift;\n *u0 >>= shift;\n *u0 |= *u1 << (64 - shift);\n *u1 >>= shift;\n }\n}\n\n// Computes a 128 / 128 -> 64 bit division, with a 128 bit remainder.\nstatic uint64_t libdivide_128_div_128_to_64(uint64_t u_hi, uint64_t u_lo, uint64_t v_hi, uint64_t v_lo, uint64_t *r_hi, uint64_t *r_lo) {\n#if defined(HAS_INT128_T) && \\n defined(HAS_INT128_DIV)\n __uint128_t ufull = u_hi;\n __uint128_t vfull = v_hi;\n ufull = (ufull << 64) | u_lo;\n vfull = (vfull << 64) | v_lo;\n uint64_t res = (uint64_t)(ufull / vfull);\n __uint128_t remainder = ufull - (vfull * res);\n *r_lo = (uint64_t)remainder;\n *r_hi = (uint64_t)(remainder >> 64);\n return res;\n#else\n // Adapted from "Unsigned Doubleword Division" in Hacker's Delight\n // We want to compute u / v\n typedef struct { uint64_t hi; uint64_t lo; } u128_t;\n u128_t u = {u_hi, u_lo};\n u128_t v = {v_hi, v_lo};\n\n if (v.hi == 0) {\n // divisor v is a 64 bit value, so we just need one 128/64 division\n // Note that we are simpler than Hacker's Delight here, because we know\n // the quotient fits in 64 bits whereas Hacker's Delight demands a full\n // 128 bit quotient\n *r_hi = 0;\n return libdivide_128_div_64_to_64(u.hi, u.lo, v.lo, r_lo);\n }\n // Here v >= 2**64\n // We know that v.hi != 0, so count leading zeros is OK\n // We have 0 <= n <= 63\n uint32_t n = libdivide_count_leading_zeros64(v.hi);\n\n // Normalize the divisor so its MSB is 1\n u128_t v1t = v;\n libdivide_u128_shift(&v1t.hi, &v1t.lo, n);\n uint64_t v1 = v1t.hi; // i.e. v1 = v1t >> 64\n\n // To ensure no overflow\n u128_t u1 = u;\n libdivide_u128_shift(&u1.hi, &u1.lo, -1);\n\n // Get quotient from divide unsigned insn.\n uint64_t rem_ignored;\n uint64_t q1 = libdivide_128_div_64_to_64(u1.hi, u1.lo, v1, &rem_ignored);\n\n // Undo normalization and division of u by 2.\n u128_t q0 = {0, q1};\n libdivide_u128_shift(&q0.hi, &q0.lo, n);\n libdivide_u128_shift(&q0.hi, &q0.lo, -63);\n\n // Make q0 correct or too small by 1\n // Equivalent to `if (q0 != 0) q0 = q0 - 1;`\n if (q0.hi != 0 || q0.lo != 0) {\n q0.hi -= (q0.lo == 0); // borrow\n q0.lo -= 1;\n }\n\n // Now q0 is correct.\n // Compute q0 * v as q0v\n // = (q0.hi << 64 + q0.lo) * (v.hi << 64 + v.lo)\n // = (q0.hi * v.hi << 128) + (q0.hi * v.lo << 64) +\n // (q0.lo * v.hi << 64) + q0.lo * v.lo)\n // Each term is 128 bit\n // High half of full product (upper 128 bits!) are dropped\n u128_t q0v = {0, 0};\n q0v.hi = q0.hi*v.lo + q0.lo*v.hi + libdivide_mullhi_u64(q0.lo, v.lo);\n q0v.lo = q0.lo*v.lo;\n\n // Compute u - q0v as u_q0v\n // This is the remainder\n u128_t u_q0v = u;\n u_q0v.hi -= q0v.hi + (u.lo < q0v.lo); // second term is borrow\n u_q0v.lo -= q0v.lo;\n\n // Check if u_q0v >= v\n // This checks if our remainder is larger than the divisor\n if ((u_q0v.hi > v.hi) ||\n (u_q0v.hi == v.hi && u_q0v.lo >= v.lo)) {\n // Increment q0\n q0.lo += 1;\n q0.hi += (q0.lo == 0); // carry\n\n // Subtract v from remainder\n u_q0v.hi -= v.hi + (u_q0v.lo < v.lo);\n u_q0v.lo -= v.lo;\n }\n\n *r_hi = u_q0v.hi;\n *r_lo = u_q0v.lo;\n\n LIBDIVIDE_ASSERT(q0.hi == 0);\n return q0.lo;\n#endif\n}\n\n////////// UINT32\n\nstatic inline struct libdivide_u32_t libdivide_internal_u32_gen(uint32_t d, int branchfree) {\n if (d == 0) {\n LIBDIVIDE_ERROR("divider must be != 0");\n }\n\n struct libdivide_u32_t result;\n uint32_t floor_log_2_d = 31 - libdivide_count_leading_zeros32(d);\n\n // Power of 2\n if ((d & (d - 1)) == 0) {\n // We need to subtract 1 from the shift value in case of an unsigned\n // branchfree divider because there is a hardcoded right shift by 1\n // in its division algorithm. Because of this we also need to add back\n // 1 in its recovery algorithm.\n result.magic = 0;\n result.more = (uint8_t)(floor_log_2_d - (branchfree != 0));\n } else {\n uint8_t more;\n uint32_t rem, proposed_m;\n proposed_m = libdivide_64_div_32_to_32(1U << floor_log_2_d, 0, d, &rem);\n\n LIBDIVIDE_ASSERT(rem > 0 && rem < d);\n const uint32_t e = d - rem;\n\n // This power works if e < 2**floor_log_2_d.\n if (!branchfree && (e < (1U << floor_log_2_d))) {\n // This power works\n more = floor_log_2_d;\n } else {\n // We have to use the general 33-bit algorithm. We need to compute\n // (2**power) / d. However, we already have (2**(power-1))/d and\n // its remainder. By doubling both, and then correcting the\n // remainder, we can compute the larger division.\n // don't care about overflow here - in fact, we expect it\n proposed_m += proposed_m;\n const uint32_t twice_rem = rem + rem;\n if (twice_rem >= d || twice_rem < rem) proposed_m += 1;\n more = floor_log_2_d | LIBDIVIDE_ADD_MARKER;\n }\n result.magic = 1 + proposed_m;\n result.more = more;\n // result.more's shift should in general be ceil_log_2_d. But if we\n // used the smaller power, we subtract one from the shift because we're\n // using the smaller power. If we're using the larger power, we\n // subtract one from the shift because it's taken care of by the add\n // indicator. So floor_log_2_d happens to be correct in both cases.\n }\n return result;\n}\n\nstruct libdivide_u32_t libdivide_u32_gen(uint32_t d) {\n return libdivide_internal_u32_gen(d, 0);\n}\n\nstruct libdivide_u32_branchfree_t libdivide_u32_branchfree_gen(uint32_t d) {\n if (d == 1) {\n LIBDIVIDE_ERROR("branchfree divider must be != 1");\n }\n struct libdivide_u32_t tmp = libdivide_internal_u32_gen(d, 1);\n struct libdivide_u32_branchfree_t ret = {tmp.magic, (uint8_t)(tmp.more & LIBDIVIDE_32_SHIFT_MASK)};\n return ret;\n}\n\nuint32_t libdivide_u32_do(uint32_t numer, const struct libdivide_u32_t *denom) {\n uint8_t more = denom->more;\n if (!denom->magic) {\n return numer >> more;\n }\n else {\n uint32_t q = libdivide_mullhi_u32(denom->magic, numer);\n if (more & LIBDIVIDE_ADD_MARKER) {\n uint32_t t = ((numer - q) >> 1) + q;\n return t >> (more & LIBDIVIDE_32_SHIFT_MASK);\n }\n else {\n // All upper bits are 0,\n // don't need to mask them off.\n return q >> more;\n }\n }\n}\n\nuint32_t libdivide_u32_branchfree_do(uint32_t numer, const struct libdivide_u32_branchfree_t *denom) {\n uint32_t q = libdivide_mullhi_u32(denom->magic, numer);\n uint32_t t = ((numer - q) >> 1) + q;\n return t >> denom->more;\n}\n\nuint32_t libdivide_u32_recover(const struct libdivide_u32_t *denom) {\n uint8_t more = denom->more;\n uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;\n\n if (!denom->magic) {\n return 1U << shift;\n } else if (!(more & LIBDIVIDE_ADD_MARKER)) {\n // We compute q = n/d = n*m / 2^(32 + shift)\n // Therefore we have d = 2^(32 + shift) / m\n // We need to ceil it.\n // We know d is not a power of 2, so m is not a power of 2,\n // so we can just add 1 to the floor\n uint32_t hi_dividend = 1U << shift;\n uint32_t rem_ignored;\n return 1 + libdivide_64_div_32_to_32(hi_dividend, 0, denom->magic, &rem_ignored);\n } else {\n // Here we wish to compute d = 2^(32+shift+1)/(m+2^32).\n // Notice (m + 2^32) is a 33 bit number. Use 64 bit division for now\n // Also note that shift may be as high as 31, so shift + 1 will\n // overflow. So we have to compute it as 2^(32+shift)/(m+2^32), and\n // then double the quotient and remainder.\n uint64_t half_n = 1ULL << (32 + shift);\n uint64_t d = (1ULL << 32) | denom->magic;\n // Note that the quotient is guaranteed <= 32 bits, but the remainder\n // may need 33!\n uint32_t half_q = (uint32_t)(half_n / d);\n uint64_t rem = half_n % d;\n // We computed 2^(32+shift)/(m+2^32)\n // Need to double it, and then add 1 to the quotient if doubling th\n // remainder would increase the quotient.\n // Note that rem<<1 cannot overflow, since rem < d and d is 33 bits\n uint32_t full_q = half_q + half_q + ((rem<<1) >= d);\n\n // We rounded down in gen (hence +1)\n return full_q + 1;\n }\n}\n\nuint32_t libdivide_u32_branchfree_recover(const struct libdivide_u32_branchfree_t *denom) {\n uint8_t more = denom->more;\n uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;\n\n if (!denom->magic) {\n return 1U << (shift + 1);\n } else {\n // Here we wish to compute d = 2^(32+shift+1)/(m+2^32).\n // Notice (m + 2^32) is a 33 bit number. Use 64 bit division for now\n // Also note that shift may be as high as 31, so shift + 1 will\n // overflow. So we have to compute it as 2^(32+shift)/(m+2^32), and\n // then double the quotient and remainder.\n uint64_t half_n = 1ULL << (32 + shift);\n uint64_t d = (1ULL << 32) | denom->magic;\n // Note that the quotient is guaranteed <= 32 bits, but the remainder\n // may need 33!\n uint32_t half_q = (uint32_t)(half_n / d);\n uint64_t rem = half_n % d;\n // We computed 2^(32+shift)/(m+2^32)\n // Need to double it, and then add 1 to the quotient if doubling th\n // remainder would increase the quotient.\n // Note that rem<<1 cannot overflow, since rem < d and d is 33 bits\n uint32_t full_q = half_q + half_q + ((rem<<1) >= d);\n\n // We rounded down in gen (hence +1)\n return full_q + 1;\n }\n}\n\n/////////// UINT64\n\nstatic inline struct libdivide_u64_t libdivide_internal_u64_gen(uint64_t d, int branchfree) {\n if (d == 0) {\n LIBDIVIDE_ERROR("divider must be != 0");\n }\n\n struct libdivide_u64_t result;\n uint32_t floor_log_2_d = 63 - libdivide_count_leading_zeros64(d);\n\n // Power of 2\n if ((d & (d - 1)) == 0) {\n // We need to subtract 1 from the shift value in case of an unsigned\n // branchfree divider because there is a hardcoded right shift by 1\n // in its division algorithm. Because of this we also need to add back\n // 1 in its recovery algorithm.\n result.magic = 0;\n result.more = (uint8_t)(floor_log_2_d - (branchfree != 0));\n } else {\n uint64_t proposed_m, rem;\n uint8_t more;\n // (1 << (64 + floor_log_2_d)) / d\n proposed_m = libdivide_128_div_64_to_64(1ULL << floor_log_2_d, 0, d, &rem);\n\n LIBDIVIDE_ASSERT(rem > 0 && rem < d);\n const uint64_t e = d - rem;\n\n // This power works if e < 2**floor_log_2_d.\n if (!branchfree && e < (1ULL << floor_log_2_d)) {\n // This power works\n more = floor_log_2_d;\n } else {\n // We have to use the general 65-bit algorithm. We need to compute\n // (2**power) / d. However, we already have (2**(power-1))/d and\n // its remainder. By doubling both, and then correcting the\n // remainder, we can compute the larger division.\n // don't care about overflow here - in fact, we expect it\n proposed_m += proposed_m;\n const uint64_t twice_rem = rem + rem;\n if (twice_rem >= d || twice_rem < rem) proposed_m += 1;\n more = floor_log_2_d | LIBDIVIDE_ADD_MARKER;\n }\n result.magic = 1 + proposed_m;\n result.more = more;\n // result.more's shift should in general be ceil_log_2_d. But if we\n // used the smaller power, we subtract one from the shift because we're\n // using the smaller power. If we're using the larger power, we\n // subtract one from the shift because it's taken care of by the add\n // indicator. So floor_log_2_d happens to be correct in both cases,\n // which is why we do it outside of the if statement.\n }\n return result;\n}\n\nstruct libdivide_u64_t libdivide_u64_gen(uint64_t d) {\n return libdivide_internal_u64_gen(d, 0);\n}\n\nstruct libdivide_u64_branchfree_t libdivide_u64_branchfree_gen(uint64_t d) {\n if (d == 1) {\n LIBDIVIDE_ERROR("branchfree divider must be != 1");\n }\n struct libdivide_u64_t tmp = libdivide_internal_u64_gen(d, 1);\n struct libdivide_u64_branchfree_t ret = {tmp.magic, (uint8_t)(tmp.more & LIBDIVIDE_64_SHIFT_MASK)};\n return ret;\n}\n\nuint64_t libdivide_u64_do(uint64_t numer, const struct libdivide_u64_t *denom) {\n uint8_t more = denom->more;\n if (!denom->magic) {\n return numer >> more;\n }\n else {\n uint64_t q = libdivide_mullhi_u64(denom->magic, numer);\n if (more & LIBDIVIDE_ADD_MARKER) {\n uint64_t t = ((numer - q) >> 1) + q;\n return t >> (more & LIBDIVIDE_64_SHIFT_MASK);\n }\n else {\n // All upper bits are 0,\n // don't need to mask them off.\n return q >> more;\n }\n }\n}\n\nuint64_t libdivide_u64_branchfree_do(uint64_t numer, const struct libdivide_u64_branchfree_t *denom) {\n uint64_t q = libdivide_mullhi_u64(denom->magic, numer);\n uint64_t t = ((numer - q) >> 1) + q;\n return t >> denom->more;\n}\n\nuint64_t libdivide_u64_recover(const struct libdivide_u64_t *denom) {\n uint8_t more = denom->more;\n uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;\n\n if (!denom->magic) {\n return 1ULL << shift;\n } else if (!(more & LIBDIVIDE_ADD_MARKER)) {\n // We compute q = n/d = n*m / 2^(64 + shift)\n // Therefore we have d = 2^(64 + shift) / m\n // We need to ceil it.\n // We know d is not a power of 2, so m is not a power of 2,\n // so we can just add 1 to the floor\n uint64_t hi_dividend = 1ULL << shift;\n uint64_t rem_ignored;\n return 1 + libdivide_128_div_64_to_64(hi_dividend, 0, denom->magic, &rem_ignored);\n } else {\n // Here we wish to compute d = 2^(64+shift+1)/(m+2^64).\n // Notice (m + 2^64) is a 65 bit number. This gets hairy. See\n // libdivide_u32_recover for more on what we do here.\n // TODO: do something better than 128 bit math\n\n // Full n is a (potentially) 129 bit value\n // half_n is a 128 bit value\n // Compute the hi half of half_n. Low half is 0.\n uint64_t half_n_hi = 1ULL << shift, half_n_lo = 0;\n // d is a 65 bit value. The high bit is always set to 1.\n const uint64_t d_hi = 1, d_lo = denom->magic;\n // Note that the quotient is guaranteed <= 64 bits,\n // but the remainder may need 65!\n uint64_t r_hi, r_lo;\n uint64_t half_q = libdivide_128_div_128_to_64(half_n_hi, half_n_lo, d_hi, d_lo, &r_hi, &r_lo);\n // We computed 2^(64+shift)/(m+2^64)\n // Double the remainder ('dr') and check if that is larger than d\n // Note that d is a 65 bit value, so r1 is small and so r1 + r1\n // cannot overflow\n uint64_t dr_lo = r_lo + r_lo;\n uint64_t dr_hi = r_hi + r_hi + (dr_lo < r_lo); // last term is carry\n int dr_exceeds_d = (dr_hi > d_hi) || (dr_hi == d_hi && dr_lo >= d_lo);\n uint64_t full_q = half_q + half_q + (dr_exceeds_d ? 1 : 0);\n return full_q + 1;\n }\n}\n\nuint64_t libdivide_u64_branchfree_recover(const struct libdivide_u64_branchfree_t *denom) {\n uint8_t more = denom->more;\n uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;\n\n if (!denom->magic) {\n return 1ULL << (shift + 1);\n } else {\n // Here we wish to compute d = 2^(64+shift+1)/(m+2^64).\n // Notice (m + 2^64) is a 65 bit number. This gets hairy. See\n // libdivide_u32_recover for more on what we do here.\n // TODO: do something better than 128 bit math\n\n // Full n is a (potentially) 129 bit value\n // half_n is a 128 bit value\n // Compute the hi half of half_n. Low half is 0.\n uint64_t half_n_hi = 1ULL << shift, half_n_lo = 0;\n // d is a 65 bit value. The high bit is always set to 1.\n const uint64_t d_hi = 1, d_lo = denom->magic;\n // Note that the quotient is guaranteed <= 64 bits,\n // but the remainder may need 65!\n uint64_t r_hi, r_lo;\n uint64_t half_q = libdivide_128_div_128_to_64(half_n_hi, half_n_lo, d_hi, d_lo, &r_hi, &r_lo);\n // We computed 2^(64+shift)/(m+2^64)\n // Double the remainder ('dr') and check if that is larger than d\n // Note that d is a 65 bit value, so r1 is small and so r1 + r1\n // cannot overflow\n uint64_t dr_lo = r_lo + r_lo;\n uint64_t dr_hi = r_hi + r_hi + (dr_lo < r_lo); // last term is carry\n int dr_exceeds_d = (dr_hi > d_hi) || (dr_hi == d_hi && dr_lo >= d_lo);\n uint64_t full_q = half_q + half_q + (dr_exceeds_d ? 1 : 0);\n return full_q + 1;\n }\n}\n\n/////////// SINT32\n\nstatic inline struct libdivide_s32_t libdivide_internal_s32_gen(int32_t d, int branchfree) {\n if (d == 0) {\n LIBDIVIDE_ERROR("divider must be != 0");\n }\n\n struct libdivide_s32_t result;\n\n // If d is a power of 2, or negative a power of 2, we have to use a shift.\n // This is especially important because the magic algorithm fails for -1.\n // To check if d is a power of 2 or its inverse, it suffices to check\n // whether its absolute value has exactly one bit set. This works even for\n // INT_MIN, because abs(INT_MIN) == INT_MIN, and INT_MIN has one bit set\n // and is a power of 2.\n uint32_t ud = (uint32_t)d;\n uint32_t absD = (d < 0) ? -ud : ud;\n uint32_t floor_log_2_d = 31 - libdivide_count_leading_zeros32(absD);\n // check if exactly one bit is set,\n // don't care if absD is 0 since that's divide by zero\n if ((absD & (absD - 1)) == 0) {\n // Branchfree and normal paths are exactly the same\n result.magic = 0;\n result.more = floor_log_2_d | (d < 0 ? LIBDIVIDE_NEGATIVE_DIVISOR : 0);\n } else {\n LIBDIVIDE_ASSERT(floor_log_2_d >= 1);\n\n uint8_t more;\n // the dividend here is 2**(floor_log_2_d + 31), so the low 32 bit word\n // is 0 and the high word is floor_log_2_d - 1\n uint32_t rem, proposed_m;\n proposed_m = libdivide_64_div_32_to_32(1U << (floor_log_2_d - 1), 0, absD, &rem);\n const uint32_t e = absD - rem;\n\n // We are going to start with a power of floor_log_2_d - 1.\n // This works if works if e < 2**floor_log_2_d.\n if (!branchfree && e < (1U << floor_log_2_d)) {\n // This power works\n more = floor_log_2_d - 1;\n } else {\n // We need to go one higher. This should not make proposed_m\n // overflow, but it will make it negative when interpreted as an\n // int32_t.\n proposed_m += proposed_m;\n const uint32_t twice_rem = rem + rem;\n if (twice_rem >= absD || twice_rem < rem) proposed_m += 1;\n more = floor_log_2_d | LIBDIVIDE_ADD_MARKER;\n }\n\n proposed_m += 1;\n int32_t magic = (int32_t)proposed_m;\n\n // Mark if we are negative. Note we only negate the magic number in the\n // branchfull case.\n if (d < 0) {\n more |= LIBDIVIDE_NEGATIVE_DIVISOR;\n if (!branchfree) {\n magic = -magic;\n }\n }\n\n result.more = more;\n result.magic = magic;\n }\n return result;\n}\n\nstruct libdivide_s32_t libdivide_s32_gen(int32_t d) {\n return libdivide_internal_s32_gen(d, 0);\n}\n\nstruct libdivide_s32_branchfree_t libdivide_s32_branchfree_gen(int32_t d) {\n struct libdivide_s32_t tmp = libdivide_internal_s32_gen(d, 1);\n struct libdivide_s32_branchfree_t result = {tmp.magic, tmp.more};\n return result;\n}\n\nint32_t libdivide_s32_do(int32_t numer, const struct libdivide_s32_t *denom) {\n uint8_t more = denom->more;\n uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;\n\n if (!denom->magic) {\n uint32_t sign = (int8_t)more >> 7;\n uint32_t mask = (1U << shift) - 1;\n uint32_t uq = numer + ((numer >> 31) & mask);\n int32_t q = (int32_t)uq;\n q >>= shift;\n q = (q ^ sign) - sign;\n return q;\n } else {\n uint32_t uq = (uint32_t)libdivide_mullhi_s32(denom->magic, numer);\n if (more & LIBDIVIDE_ADD_MARKER) {\n // must be arithmetic shift and then sign extend\n int32_t sign = (int8_t)more >> 7;\n // q += (more < 0 ? -numer : numer)\n // cast required to avoid UB\n uq += ((uint32_t)numer ^ sign) - sign;\n }\n int32_t q = (int32_t)uq;\n q >>= shift;\n q += (q < 0);\n return q;\n }\n}\n\nint32_t libdivide_s32_branchfree_do(int32_t numer, const struct libdivide_s32_branchfree_t *denom) {\n uint8_t more = denom->more;\n uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;\n // must be arithmetic shift and then sign extend\n int32_t sign = (int8_t)more >> 7;\n int32_t magic = denom->magic;\n int32_t q = libdivide_mullhi_s32(magic, numer);\n q += numer;\n\n // If q is non-negative, we have nothing to do\n // If q is negative, we want to add either (2**shift)-1 if d is a power of\n // 2, or (2**shift) if it is not a power of 2\n uint32_t is_power_of_2 = (magic == 0);\n uint32_t q_sign = (uint32_t)(q >> 31);\n q += q_sign & ((1U << shift) - is_power_of_2);\n\n // Now arithmetic right shift\n q >>= shift;\n // Negate if needed\n q = (q ^ sign) - sign;\n\n return q;\n}\n\nint32_t libdivide_s32_recover(const struct libdivide_s32_t *denom) {\n uint8_t more = denom->more;\n uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;\n if (!denom->magic) {\n uint32_t absD = 1U << shift;\n if (more & LIBDIVIDE_NEGATIVE_DIVISOR) {\n absD = -absD;\n }\n return (int32_t)absD;\n } else {\n // Unsigned math is much easier\n // We negate the magic number only in the branchfull case, and we don't\n // know which case we're in. However we have enough information to\n // determine the correct sign of the magic number. The divisor was\n // negative if LIBDIVIDE_NEGATIVE_DIVISOR is set. If ADD_MARKER is set,\n // the magic number's sign is opposite that of the divisor.\n // We want to compute the positive magic number.\n int negative_divisor = (more & LIBDIVIDE_NEGATIVE_DIVISOR);\n int magic_was_negated = (more & LIBDIVIDE_ADD_MARKER)\n ? denom->magic > 0 : denom->magic < 0;\n\n // Handle the power of 2 case (including branchfree)\n if (denom->magic == 0) {\n int32_t result = 1U << shift;\n return negative_divisor ? -result : result;\n }\n\n uint32_t d = (uint32_t)(magic_was_negated ? -denom->magic : denom->magic);\n uint64_t n = 1ULL << (32 + shift); // this shift cannot exceed 30\n uint32_t q = (uint32_t)(n / d);\n int32_t result = (int32_t)q;\n result += 1;\n return negative_divisor ? -result : result;\n }\n}\n\nint32_t libdivide_s32_branchfree_recover(const struct libdivide_s32_branchfree_t *denom) {\n return libdivide_s32_recover((const struct libdivide_s32_t *)denom);\n}\n\n///////////// SINT64\n\nstatic inline struct libdivide_s64_t libdivide_internal_s64_gen(int64_t d, int branchfree) {\n if (d == 0) {\n LIBDIVIDE_ERROR("divider must be != 0");\n }\n\n struct libdivide_s64_t result;\n\n // If d is a power of 2, or negative a power of 2, we have to use a shift.\n // This is especially important because the magic algorithm fails for -1.\n // To check if d is a power of 2 or its inverse, it suffices to check\n // whether its absolute value has exactly one bit set. This works even for\n // INT_MIN, because abs(INT_MIN) == INT_MIN, and INT_MIN has one bit set\n // and is a power of 2.\n uint64_t ud = (uint64_t)d;\n uint64_t absD = (d < 0) ? -ud : ud;\n uint32_t floor_log_2_d = 63 - libdivide_count_leading_zeros64(absD);\n // check if exactly one bit is set,\n // don't care if absD is 0 since that's divide by zero\n if ((absD & (absD - 1)) == 0) {\n // Branchfree and non-branchfree cases are the same\n result.magic = 0;\n result.more = floor_log_2_d | (d < 0 ? LIBDIVIDE_NEGATIVE_DIVISOR : 0);\n } else {\n // the dividend here is 2**(floor_log_2_d + 63), so the low 64 bit word\n // is 0 and the high word is floor_log_2_d - 1\n uint8_t more;\n uint64_t rem, proposed_m;\n proposed_m = libdivide_128_div_64_to_64(1ULL << (floor_log_2_d - 1), 0, absD, &rem);\n const uint64_t e = absD - rem;\n\n // We are going to start with a power of floor_log_2_d - 1.\n // This works if works if e < 2**floor_log_2_d.\n if (!branchfree && e < (1ULL << floor_log_2_d)) {\n // This power works\n more = floor_log_2_d - 1;\n } else {\n // We need to go one higher. This should not make proposed_m\n // overflow, but it will make it negative when interpreted as an\n // int32_t.\n proposed_m += proposed_m;\n const uint64_t twice_rem = rem + rem;\n if (twice_rem >= absD || twice_rem < rem) proposed_m += 1;\n // note that we only set the LIBDIVIDE_NEGATIVE_DIVISOR bit if we\n // also set ADD_MARKER this is an annoying optimization that\n // enables algorithm #4 to avoid the mask. However we always set it\n // in the branchfree case\n more = floor_log_2_d | LIBDIVIDE_ADD_MARKER;\n }\n proposed_m += 1;\n int64_t magic = (int64_t)proposed_m;\n\n // Mark if we are negative\n if (d < 0) {\n more |= LIBDIVIDE_NEGATIVE_DIVISOR;\n if (!branchfree) {\n magic = -magic;\n }\n }\n\n result.more = more;\n result.magic = magic;\n }\n return result;\n}\n\nstruct libdivide_s64_t libdivide_s64_gen(int64_t d) {\n return libdivide_internal_s64_gen(d, 0);\n}\n\nstruct libdivide_s64_branchfree_t libdivide_s64_branchfree_gen(int64_t d) {\n struct libdivide_s64_t tmp = libdivide_internal_s64_gen(d, 1);\n struct libdivide_s64_branchfree_t ret = {tmp.magic, tmp.more};\n return ret;\n}\n\nint64_t libdivide_s64_do(int64_t numer, const struct libdivide_s64_t *denom) {\n uint8_t more = denom->more;\n uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;\n\n if (!denom->magic) { // shift path\n uint64_t mask = (1ULL << shift) - 1;\n uint64_t uq = numer + ((numer >> 63) & mask);\n int64_t q = (int64_t)uq;\n q >>= shift;\n // must be arithmetic shift and then sign-extend\n int64_t sign = (int8_t)more >> 7;\n q = (q ^ sign) - sign;\n return q;\n } else {\n uint64_t uq = (uint64_t)libdivide_mullhi_s64(denom->magic, numer);\n if (more & LIBDIVIDE_ADD_MARKER) {\n // must be arithmetic shift and then sign extend\n int64_t sign = (int8_t)more >> 7;\n // q += (more < 0 ? -numer : numer)\n // cast required to avoid UB\n uq += ((uint64_t)numer ^ sign) - sign;\n }\n int64_t q = (int64_t)uq;\n q >>= shift;\n q += (q < 0);\n return q;\n }\n}\n\nint64_t libdivide_s64_branchfree_do(int64_t numer, const struct libdivide_s64_branchfree_t *denom) {\n uint8_t more = denom->more;\n uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;\n // must be arithmetic shift and then sign extend\n int64_t sign = (int8_t)more >> 7;\n int64_t magic = denom->magic;\n int64_t q = libdivide_mullhi_s64(magic, numer);\n q += numer;\n\n // If q is non-negative, we have nothing to do.\n // If q is negative, we want to add either (2**shift)-1 if d is a power of\n // 2, or (2**shift) if it is not a power of 2.\n uint64_t is_power_of_2 = (magic == 0);\n uint64_t q_sign = (uint64_t)(q >> 63);\n q += q_sign & ((1ULL << shift) - is_power_of_2);\n\n // Arithmetic right shift\n q >>= shift;\n // Negate if needed\n q = (q ^ sign) - sign;\n\n return q;\n}\n\nint64_t libdivide_s64_recover(const struct libdivide_s64_t *denom) {\n uint8_t more = denom->more;\n uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;\n if (denom->magic == 0) { // shift path\n uint64_t absD = 1ULL << shift;\n if (more & LIBDIVIDE_NEGATIVE_DIVISOR) {\n absD = -absD;\n }\n return (int64_t)absD;\n } else {\n // Unsigned math is much easier\n int negative_divisor = (more & LIBDIVIDE_NEGATIVE_DIVISOR);\n int magic_was_negated = (more & LIBDIVIDE_ADD_MARKER)\n ? denom->magic > 0 : denom->magic < 0;\n\n uint64_t d = (uint64_t)(magic_was_negated ? -denom->magic : denom->magic);\n uint64_t n_hi = 1ULL << shift, n_lo = 0;\n uint64_t rem_ignored;\n uint64_t q = libdivide_128_div_64_to_64(n_hi, n_lo, d, &rem_ignored);\n int64_t result = (int64_t)(q + 1);\n if (negative_divisor) {\n result = -result;\n }\n return result;\n }\n}\n\nint64_t libdivide_s64_branchfree_recover(const struct libdivide_s64_branchfree_t *denom) {\n return libdivide_s64_recover((const struct libdivide_s64_t *)denom);\n}\n\n#if defined(LIBDIVIDE_AVX512)\n\nstatic inline __m512i libdivide_u32_do_vector(__m512i numers, const struct libdivide_u32_t *denom);\nstatic inline __m512i libdivide_s32_do_vector(__m512i numers, const struct libdivide_s32_t *denom);\nstatic inline __m512i libdivide_u64_do_vector(__m512i numers, const struct libdivide_u64_t *denom);\nstatic inline __m512i libdivide_s64_do_vector(__m512i numers, const struct libdivide_s64_t *denom);\n\nstatic inline __m512i libdivide_u32_branchfree_do_vector(__m512i numers, const struct libdivide_u32_branchfree_t *denom);\nstatic inline __m512i libdivide_s32_branchfree_do_vector(__m512i numers, const struct libdivide_s32_branchfree_t *denom);\nstatic inline __m512i libdivide_u64_branchfree_do_vector(__m512i numers, const struct libdivide_u64_branchfree_t *denom);\nstatic inline __m512i libdivide_s64_branchfree_do_vector(__m512i numers, const struct libdivide_s64_branchfree_t *denom);\n\n//////// Internal Utility Functions\n\nstatic inline __m512i libdivide_s64_signbits(__m512i v) {;\n return _mm512_srai_epi64(v, 63);\n}\n\nstatic inline __m512i libdivide_s64_shift_right_vector(__m512i v, int amt) {\n return _mm512_srai_epi64(v, amt);\n}\n\n// Here, b is assumed to contain one 32-bit value repeated.\nstatic inline __m512i libdivide_mullhi_u32_vector(__m512i a, __m512i b) {\n __m512i hi_product_0Z2Z = _mm512_srli_epi64(_mm512_mul_epu32(a, b), 32);\n __m512i a1X3X = _mm512_srli_epi64(a, 32);\n __m512i mask = _mm512_set_epi32(-1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0);\n __m512i hi_product_Z1Z3 = _mm512_and_si512(_mm512_mul_epu32(a1X3X, b), mask);\n return _mm512_or_si512(hi_product_0Z2Z, hi_product_Z1Z3);\n}\n\n// b is one 32-bit value repeated.\nstatic inline __m512i libdivide_mullhi_s32_vector(__m512i a, __m512i b) {\n __m512i hi_product_0Z2Z = _mm512_srli_epi64(_mm512_mul_epi32(a, b), 32);\n __m512i a1X3X = _mm512_srli_epi64(a, 32);\n __m512i mask = _mm512_set_epi32(-1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0, -1, 0);\n __m512i hi_product_Z1Z3 = _mm512_and_si512(_mm512_mul_epi32(a1X3X, b), mask);\n return _mm512_or_si512(hi_product_0Z2Z, hi_product_Z1Z3);\n}\n\n// Here, y is assumed to contain one 64-bit value repeated.\n// https://stackoverflow.com/a/28827013\nstatic inline __m512i libdivide_mullhi_u64_vector(__m512i x, __m512i y) {\n __m512i lomask = _mm512_set1_epi64(0xffffffff);\n __m512i xh = _mm512_shuffle_epi32(x, (_MM_PERM_ENUM) 0xB1);\n __m512i yh = _mm512_shuffle_epi32(y, (_MM_PERM_ENUM) 0xB1);\n __m512i w0 = _mm512_mul_epu32(x, y);\n __m512i w1 = _mm512_mul_epu32(x, yh);\n __m512i w2 = _mm512_mul_epu32(xh, y);\n __m512i w3 = _mm512_mul_epu32(xh, yh);\n __m512i w0h = _mm512_srli_epi64(w0, 32);\n __m512i s1 = _mm512_add_epi64(w1, w0h);\n __m512i s1l = _mm512_and_si512(s1, lomask);\n __m512i s1h = _mm512_srli_epi64(s1, 32);\n __m512i s2 = _mm512_add_epi64(w2, s1l);\n __m512i s2h = _mm512_srli_epi64(s2, 32);\n __m512i hi = _mm512_add_epi64(w3, s1h);\n hi = _mm512_add_epi64(hi, s2h);\n\n return hi;\n}\n\n// y is one 64-bit value repeated.\nstatic inline __m512i libdivide_mullhi_s64_vector(__m512i x, __m512i y) {\n __m512i p = libdivide_mullhi_u64_vector(x, y);\n __m512i t1 = _mm512_and_si512(libdivide_s64_signbits(x), y);\n __m512i t2 = _mm512_and_si512(libdivide_s64_signbits(y), x);\n p = _mm512_sub_epi64(p, t1);\n p = _mm512_sub_epi64(p, t2);\n return p;\n}\n\n////////// UINT32\n\n__m512i libdivide_u32_do_vector(__m512i numers, const struct libdivide_u32_t *denom) {\n uint8_t more = denom->more;\n if (!denom->magic) {\n return _mm512_srli_epi32(numers, more);\n }\n else {\n __m512i q = libdivide_mullhi_u32_vector(numers, _mm512_set1_epi32(denom->magic));\n if (more & LIBDIVIDE_ADD_MARKER) {\n // uint32_t t = ((numer - q) >> 1) + q;\n // return t >> denom->shift;\n uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK;\n __m512i t = _mm512_add_epi32(_mm512_srli_epi32(_mm512_sub_epi32(numers, q), 1), q);\n return _mm512_srli_epi32(t, shift);\n }\n else {\n return _mm512_srli_epi32(q, more);\n }\n }\n}\n\n__m512i libdivide_u32_branchfree_do_vector(__m512i numers, const struct libdivide_u32_branchfree_t *denom) {\n __m512i q = libdivide_mullhi_u32_vector(numers, _mm512_set1_epi32(denom->magic));\n __m512i t = _mm512_add_epi32(_mm512_srli_epi32(_mm512_sub_epi32(numers, q), 1), q);\n return _mm512_srli_epi32(t, denom->more);\n}\n\n////////// UINT64\n\n__m512i libdivide_u64_do_vector(__m512i numers, const struct libdivide_u64_t *denom) {\n uint8_t more = denom->more;\n if (!denom->magic) {\n return _mm512_srli_epi64(numers, more);\n }\n else {\n __m512i q = libdivide_mullhi_u64_vector(numers, _mm512_set1_epi64(denom->magic));\n if (more & LIBDIVIDE_ADD_MARKER) {\n // uint32_t t = ((numer - q) >> 1) + q;\n // return t >> denom->shift;\n uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK;\n __m512i t = _mm512_add_epi64(_mm512_srli_epi64(_mm512_sub_epi64(numers, q), 1), q);\n return _mm512_srli_epi64(t, shift);\n }\n else {\n return _mm512_srli_epi64(q, more);\n }\n }\n}\n\n__m512i libdivide_u64_branchfree_do_vector(__m512i numers, const struct libdivide_u64_branchfree_t *denom) {\n __m512i q = libdivide_mullhi_u64_vector(numers, _mm512_set1_epi64(denom->magic));\n __m512i t = _mm512_add_epi64(_mm512_srli_epi64(_mm512_sub_epi64(numers, q), 1), q);\n return _mm512_srli_epi64(t, denom->more);\n}\n\n////////// SINT32\n\n__m512i libdivide_s32_do_vector(__m512i numers, const struct libdivide_s32_t *denom) {\n uint8_t more = denom->more;\n if (!denom->magic) {\n uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK;\n uint32_t mask = (1U << shift) - 1;\n __m512i roundToZeroTweak = _mm512_set1_epi32(mask);\n // q = numer + ((numer >> 31) & roundToZeroTweak);\n __m512i q = _mm512_add_epi32(numers, _mm512_and_si512(_mm512_srai_epi32(numers, 31), roundToZeroTweak));\n q = _mm512_srai_epi32(q, shift);\n __m512i sign = _mm512_set1_epi32((int8_t)more >> 7);\n // q = (q ^ sign) - sign;\n q = _mm512_sub_epi32(_mm512_xor_si512(q, sign), sign);\n return q;\n }\n else {\n __m512i q = libdivide_mullhi_s32_vector(numers, _mm512_set1_epi32(denom->magic));\n if (more & LIBDIVIDE_ADD_MARKER) {\n // must be arithmetic shift\n __m512i sign = _mm512_set1_epi32((int8_t)more >> 7);\n // q += ((numer ^ sign) - sign);\n q = _mm512_add_epi32(q, _mm512_sub_epi32(_mm512_xor_si512(numers, sign), sign));\n }\n // q >>= shift\n q = _mm512_srai_epi32(q, more & LIBDIVIDE_32_SHIFT_MASK);\n q = _mm512_add_epi32(q, _mm512_srli_epi32(q, 31)); // q += (q < 0)\n return q;\n }\n}\n\n__m512i libdivide_s32_branchfree_do_vector(__m512i numers, const struct libdivide_s32_branchfree_t *denom) {\n int32_t magic = denom->magic;\n uint8_t more = denom->more;\n uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;\n // must be arithmetic shift\n __m512i sign = _mm512_set1_epi32((int8_t)more >> 7);\n __m512i q = libdivide_mullhi_s32_vector(numers, _mm512_set1_epi32(magic));\n q = _mm512_add_epi32(q, numers); // q += numers\n\n // If q is non-negative, we have nothing to do\n // If q is negative, we want to add either (2**shift)-1 if d is\n // a power of 2, or (2**shift) if it is not a power of 2\n uint32_t is_power_of_2 = (magic == 0);\n __m512i q_sign = _mm512_srai_epi32(q, 31); // q_sign = q >> 31\n __m512i mask = _mm512_set1_epi32((1U << shift) - is_power_of_2);\n q = _mm512_add_epi32(q, _mm512_and_si512(q_sign, mask)); // q = q + (q_sign & mask)\n q = _mm512_srai_epi32(q, shift); // q >>= shift\n q = _mm512_sub_epi32(_mm512_xor_si512(q, sign), sign); // q = (q ^ sign) - sign\n return q;\n}\n\n////////// SINT64\n\n__m512i libdivide_s64_do_vector(__m512i numers, const struct libdivide_s64_t *denom) {\n uint8_t more = denom->more;\n int64_t magic = denom->magic;\n if (magic == 0) { // shift path\n uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK;\n uint64_t mask = (1ULL << shift) - 1;\n __m512i roundToZeroTweak = _mm512_set1_epi64(mask);\n // q = numer + ((numer >> 63) & roundToZeroTweak);\n __m512i q = _mm512_add_epi64(numers, _mm512_and_si512(libdivide_s64_signbits(numers), roundToZeroTweak));\n q = libdivide_s64_shift_right_vector(q, shift);\n __m512i sign = _mm512_set1_epi32((int8_t)more >> 7);\n // q = (q ^ sign) - sign;\n q = _mm512_sub_epi64(_mm512_xor_si512(q, sign), sign);\n return q;\n }\n else {\n __m512i q = libdivide_mullhi_s64_vector(numers, _mm512_set1_epi64(magic));\n if (more & LIBDIVIDE_ADD_MARKER) {\n // must be arithmetic shift\n __m512i sign = _mm512_set1_epi32((int8_t)more >> 7);\n // q += ((numer ^ sign) - sign);\n q = _mm512_add_epi64(q, _mm512_sub_epi64(_mm512_xor_si512(numers, sign), sign));\n }\n // q >>= denom->mult_path.shift\n q = libdivide_s64_shift_right_vector(q, more & LIBDIVIDE_64_SHIFT_MASK);\n q = _mm512_add_epi64(q, _mm512_srli_epi64(q, 63)); // q += (q < 0)\n return q;\n }\n}\n\n__m512i libdivide_s64_branchfree_do_vector(__m512i numers, const struct libdivide_s64_branchfree_t *denom) {\n int64_t magic = denom->magic;\n uint8_t more = denom->more;\n uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;\n // must be arithmetic shift\n __m512i sign = _mm512_set1_epi32((int8_t)more >> 7);\n\n // libdivide_mullhi_s64(numers, magic);\n __m512i q = libdivide_mullhi_s64_vector(numers, _mm512_set1_epi64(magic));\n q = _mm512_add_epi64(q, numers); // q += numers\n\n // If q is non-negative, we have nothing to do.\n // If q is negative, we want to add either (2**shift)-1 if d is\n // a power of 2, or (2**shift) if it is not a power of 2.\n uint32_t is_power_of_2 = (magic == 0);\n __m512i q_sign = libdivide_s64_signbits(q); // q_sign = q >> 63\n __m512i mask = _mm512_set1_epi64((1ULL << shift) - is_power_of_2);\n q = _mm512_add_epi64(q, _mm512_and_si512(q_sign, mask)); // q = q + (q_sign & mask)\n q = libdivide_s64_shift_right_vector(q, shift); // q >>= shift\n q = _mm512_sub_epi64(_mm512_xor_si512(q, sign), sign); // q = (q ^ sign) - sign\n return q;\n}\n\n#elif defined(LIBDIVIDE_AVX2)\n\nstatic inline __m256i libdivide_u32_do_vector(__m256i numers, const struct libdivide_u32_t *denom);\nstatic inline __m256i libdivide_s32_do_vector(__m256i numers, const struct libdivide_s32_t *denom);\nstatic inline __m256i libdivide_u64_do_vector(__m256i numers, const struct libdivide_u64_t *denom);\nstatic inline __m256i libdivide_s64_do_vector(__m256i numers, const struct libdivide_s64_t *denom);\n\nstatic inline __m256i libdivide_u32_branchfree_do_vector(__m256i numers, const struct libdivide_u32_branchfree_t *denom);\nstatic inline __m256i libdivide_s32_branchfree_do_vector(__m256i numers, const struct libdivide_s32_branchfree_t *denom);\nstatic inline __m256i libdivide_u64_branchfree_do_vector(__m256i numers, const struct libdivide_u64_branchfree_t *denom);\nstatic inline __m256i libdivide_s64_branchfree_do_vector(__m256i numers, const struct libdivide_s64_branchfree_t *denom);\n\n//////// Internal Utility Functions\n\n// Implementation of _mm256_srai_epi64(v, 63) (from AVX512).\nstatic inline __m256i libdivide_s64_signbits(__m256i v) {\n __m256i hiBitsDuped = _mm256_shuffle_epi32(v, _MM_SHUFFLE(3, 3, 1, 1));\n __m256i signBits = _mm256_srai_epi32(hiBitsDuped, 31);\n return signBits;\n}\n\n// Implementation of _mm256_srai_epi64 (from AVX512).\nstatic inline __m256i libdivide_s64_shift_right_vector(__m256i v, int amt) {\n const int b = 64 - amt;\n __m256i m = _mm256_set1_epi64x(1ULL << (b - 1));\n __m256i x = _mm256_srli_epi64(v, amt);\n __m256i result = _mm256_sub_epi64(_mm256_xor_si256(x, m), m);\n return result;\n}\n\n// Here, b is assumed to contain one 32-bit value repeated.\nstatic inline __m256i libdivide_mullhi_u32_vector(__m256i a, __m256i b) {\n __m256i hi_product_0Z2Z = _mm256_srli_epi64(_mm256_mul_epu32(a, b), 32);\n __m256i a1X3X = _mm256_srli_epi64(a, 32);\n __m256i mask = _mm256_set_epi32(-1, 0, -1, 0, -1, 0, -1, 0);\n __m256i hi_product_Z1Z3 = _mm256_and_si256(_mm256_mul_epu32(a1X3X, b), mask);\n return _mm256_or_si256(hi_product_0Z2Z, hi_product_Z1Z3);\n}\n\n// b is one 32-bit value repeated.\nstatic inline __m256i libdivide_mullhi_s32_vector(__m256i a, __m256i b) {\n __m256i hi_product_0Z2Z = _mm256_srli_epi64(_mm256_mul_epi32(a, b), 32);\n __m256i a1X3X = _mm256_srli_epi64(a, 32);\n __m256i mask = _mm256_set_epi32(-1, 0, -1, 0, -1, 0, -1, 0);\n __m256i hi_product_Z1Z3 = _mm256_and_si256(_mm256_mul_epi32(a1X3X, b), mask);\n return _mm256_or_si256(hi_product_0Z2Z, hi_product_Z1Z3);\n}\n\n// Here, y is assumed to contain one 64-bit value repeated.\n// https://stackoverflow.com/a/28827013\nstatic inline __m256i libdivide_mullhi_u64_vector(__m256i x, __m256i y) {\n __m256i lomask = _mm256_set1_epi64x(0xffffffff);\n __m256i xh = _mm256_shuffle_epi32(x, 0xB1); // x0l, x0h, x1l, x1h\n __m256i yh = _mm256_shuffle_epi32(y, 0xB1); // y0l, y0h, y1l, y1h\n __m256i w0 = _mm256_mul_epu32(x, y); // x0l*y0l, x1l*y1l\n __m256i w1 = _mm256_mul_epu32(x, yh); // x0l*y0h, x1l*y1h\n __m256i w2 = _mm256_mul_epu32(xh, y); // x0h*y0l, x1h*y0l\n __m256i w3 = _mm256_mul_epu32(xh, yh); // x0h*y0h, x1h*y1h\n __m256i w0h = _mm256_srli_epi64(w0, 32);\n __m256i s1 = _mm256_add_epi64(w1, w0h);\n __m256i s1l = _mm256_and_si256(s1, lomask);\n __m256i s1h = _mm256_srli_epi64(s1, 32);\n __m256i s2 = _mm256_add_epi64(w2, s1l);\n __m256i s2h = _mm256_srli_epi64(s2, 32);\n __m256i hi = _mm256_add_epi64(w3, s1h);\n hi = _mm256_add_epi64(hi, s2h);\n\n return hi;\n}\n\n// y is one 64-bit value repeated.\nstatic inline __m256i libdivide_mullhi_s64_vector(__m256i x, __m256i y) {\n __m256i p = libdivide_mullhi_u64_vector(x, y);\n __m256i t1 = _mm256_and_si256(libdivide_s64_signbits(x), y);\n __m256i t2 = _mm256_and_si256(libdivide_s64_signbits(y), x);\n p = _mm256_sub_epi64(p, t1);\n p = _mm256_sub_epi64(p, t2);\n return p;\n}\n\n////////// UINT32\n\n__m256i libdivide_u32_do_vector(__m256i numers, const struct libdivide_u32_t *denom) {\n uint8_t more = denom->more;\n if (!denom->magic) {\n return _mm256_srli_epi32(numers, more);\n }\n else {\n __m256i q = libdivide_mullhi_u32_vector(numers, _mm256_set1_epi32(denom->magic));\n if (more & LIBDIVIDE_ADD_MARKER) {\n // uint32_t t = ((numer - q) >> 1) + q;\n // return t >> denom->shift;\n uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK;\n __m256i t = _mm256_add_epi32(_mm256_srli_epi32(_mm256_sub_epi32(numers, q), 1), q);\n return _mm256_srli_epi32(t, shift);\n }\n else {\n return _mm256_srli_epi32(q, more);\n }\n }\n}\n\n__m256i libdivide_u32_branchfree_do_vector(__m256i numers, const struct libdivide_u32_branchfree_t *denom) {\n __m256i q = libdivide_mullhi_u32_vector(numers, _mm256_set1_epi32(denom->magic));\n __m256i t = _mm256_add_epi32(_mm256_srli_epi32(_mm256_sub_epi32(numers, q), 1), q);\n return _mm256_srli_epi32(t, denom->more);\n}\n\n////////// UINT64\n\n__m256i libdivide_u64_do_vector(__m256i numers, const struct libdivide_u64_t *denom) {\n uint8_t more = denom->more;\n if (!denom->magic) {\n return _mm256_srli_epi64(numers, more);\n }\n else {\n __m256i q = libdivide_mullhi_u64_vector(numers, _mm256_set1_epi64x(denom->magic));\n if (more & LIBDIVIDE_ADD_MARKER) {\n // uint32_t t = ((numer - q) >> 1) + q;\n // return t >> denom->shift;\n uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK;\n __m256i t = _mm256_add_epi64(_mm256_srli_epi64(_mm256_sub_epi64(numers, q), 1), q);\n return _mm256_srli_epi64(t, shift);\n }\n else {\n return _mm256_srli_epi64(q, more);\n }\n }\n}\n\n__m256i libdivide_u64_branchfree_do_vector(__m256i numers, const struct libdivide_u64_branchfree_t *denom) {\n __m256i q = libdivide_mullhi_u64_vector(numers, _mm256_set1_epi64x(denom->magic));\n __m256i t = _mm256_add_epi64(_mm256_srli_epi64(_mm256_sub_epi64(numers, q), 1), q);\n return _mm256_srli_epi64(t, denom->more);\n}\n\n////////// SINT32\n\n__m256i libdivide_s32_do_vector(__m256i numers, const struct libdivide_s32_t *denom) {\n uint8_t more = denom->more;\n if (!denom->magic) {\n uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK;\n uint32_t mask = (1U << shift) - 1;\n __m256i roundToZeroTweak = _mm256_set1_epi32(mask);\n // q = numer + ((numer >> 31) & roundToZeroTweak);\n __m256i q = _mm256_add_epi32(numers, _mm256_and_si256(_mm256_srai_epi32(numers, 31), roundToZeroTweak));\n q = _mm256_srai_epi32(q, shift);\n __m256i sign = _mm256_set1_epi32((int8_t)more >> 7);\n // q = (q ^ sign) - sign;\n q = _mm256_sub_epi32(_mm256_xor_si256(q, sign), sign);\n return q;\n }\n else {\n __m256i q = libdivide_mullhi_s32_vector(numers, _mm256_set1_epi32(denom->magic));\n if (more & LIBDIVIDE_ADD_MARKER) {\n // must be arithmetic shift\n __m256i sign = _mm256_set1_epi32((int8_t)more >> 7);\n // q += ((numer ^ sign) - sign);\n q = _mm256_add_epi32(q, _mm256_sub_epi32(_mm256_xor_si256(numers, sign), sign));\n }\n // q >>= shift\n q = _mm256_srai_epi32(q, more & LIBDIVIDE_32_SHIFT_MASK);\n q = _mm256_add_epi32(q, _mm256_srli_epi32(q, 31)); // q += (q < 0)\n return q;\n }\n}\n\n__m256i libdivide_s32_branchfree_do_vector(__m256i numers, const struct libdivide_s32_branchfree_t *denom) {\n int32_t magic = denom->magic;\n uint8_t more = denom->more;\n uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;\n // must be arithmetic shift\n __m256i sign = _mm256_set1_epi32((int8_t)more >> 7);\n __m256i q = libdivide_mullhi_s32_vector(numers, _mm256_set1_epi32(magic));\n q = _mm256_add_epi32(q, numers); // q += numers\n\n // If q is non-negative, we have nothing to do\n // If q is negative, we want to add either (2**shift)-1 if d is\n // a power of 2, or (2**shift) if it is not a power of 2\n uint32_t is_power_of_2 = (magic == 0);\n __m256i q_sign = _mm256_srai_epi32(q, 31); // q_sign = q >> 31\n __m256i mask = _mm256_set1_epi32((1U << shift) - is_power_of_2);\n q = _mm256_add_epi32(q, _mm256_and_si256(q_sign, mask)); // q = q + (q_sign & mask)\n q = _mm256_srai_epi32(q, shift); // q >>= shift\n q = _mm256_sub_epi32(_mm256_xor_si256(q, sign), sign); // q = (q ^ sign) - sign\n return q;\n}\n\n////////// SINT64\n\n__m256i libdivide_s64_do_vector(__m256i numers, const struct libdivide_s64_t *denom) {\n uint8_t more = denom->more;\n int64_t magic = denom->magic;\n if (magic == 0) { // shift path\n uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK;\n uint64_t mask = (1ULL << shift) - 1;\n __m256i roundToZeroTweak = _mm256_set1_epi64x(mask);\n // q = numer + ((numer >> 63) & roundToZeroTweak);\n __m256i q = _mm256_add_epi64(numers, _mm256_and_si256(libdivide_s64_signbits(numers), roundToZeroTweak));\n q = libdivide_s64_shift_right_vector(q, shift);\n __m256i sign = _mm256_set1_epi32((int8_t)more >> 7);\n // q = (q ^ sign) - sign;\n q = _mm256_sub_epi64(_mm256_xor_si256(q, sign), sign);\n return q;\n }\n else {\n __m256i q = libdivide_mullhi_s64_vector(numers, _mm256_set1_epi64x(magic));\n if (more & LIBDIVIDE_ADD_MARKER) {\n // must be arithmetic shift\n __m256i sign = _mm256_set1_epi32((int8_t)more >> 7);\n // q += ((numer ^ sign) - sign);\n q = _mm256_add_epi64(q, _mm256_sub_epi64(_mm256_xor_si256(numers, sign), sign));\n }\n // q >>= denom->mult_path.shift\n q = libdivide_s64_shift_right_vector(q, more & LIBDIVIDE_64_SHIFT_MASK);\n q = _mm256_add_epi64(q, _mm256_srli_epi64(q, 63)); // q += (q < 0)\n return q;\n }\n}\n\n__m256i libdivide_s64_branchfree_do_vector(__m256i numers, const struct libdivide_s64_branchfree_t *denom) {\n int64_t magic = denom->magic;\n uint8_t more = denom->more;\n uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;\n // must be arithmetic shift\n __m256i sign = _mm256_set1_epi32((int8_t)more >> 7);\n\n // libdivide_mullhi_s64(numers, magic);\n __m256i q = libdivide_mullhi_s64_vector(numers, _mm256_set1_epi64x(magic));\n q = _mm256_add_epi64(q, numers); // q += numers\n\n // If q is non-negative, we have nothing to do.\n // If q is negative, we want to add either (2**shift)-1 if d is\n // a power of 2, or (2**shift) if it is not a power of 2.\n uint32_t is_power_of_2 = (magic == 0);\n __m256i q_sign = libdivide_s64_signbits(q); // q_sign = q >> 63\n __m256i mask = _mm256_set1_epi64x((1ULL << shift) - is_power_of_2);\n q = _mm256_add_epi64(q, _mm256_and_si256(q_sign, mask)); // q = q + (q_sign & mask)\n q = libdivide_s64_shift_right_vector(q, shift); // q >>= shift\n q = _mm256_sub_epi64(_mm256_xor_si256(q, sign), sign); // q = (q ^ sign) - sign\n return q;\n}\n\n#elif defined(LIBDIVIDE_SSE2)\n\nstatic inline __m128i libdivide_u32_do_vector(__m128i numers, const struct libdivide_u32_t *denom);\nstatic inline __m128i libdivide_s32_do_vector(__m128i numers, const struct libdivide_s32_t *denom);\nstatic inline __m128i libdivide_u64_do_vector(__m128i numers, const struct libdivide_u64_t *denom);\nstatic inline __m128i libdivide_s64_do_vector(__m128i numers, const struct libdivide_s64_t *denom);\n\nstatic inline __m128i libdivide_u32_branchfree_do_vector(__m128i numers, const struct libdivide_u32_branchfree_t *denom);\nstatic inline __m128i libdivide_s32_branchfree_do_vector(__m128i numers, const struct libdivide_s32_branchfree_t *denom);\nstatic inline __m128i libdivide_u64_branchfree_do_vector(__m128i numers, const struct libdivide_u64_branchfree_t *denom);\nstatic inline __m128i libdivide_s64_branchfree_do_vector(__m128i numers, const struct libdivide_s64_branchfree_t *denom);\n\n//////// Internal Utility Functions\n\n// Implementation of _mm_srai_epi64(v, 63) (from AVX512).\nstatic inline __m128i libdivide_s64_signbits(__m128i v) {\n __m128i hiBitsDuped = _mm_shuffle_epi32(v, _MM_SHUFFLE(3, 3, 1, 1));\n __m128i signBits = _mm_srai_epi32(hiBitsDuped, 31);\n return signBits;\n}\n\n// Implementation of _mm_srai_epi64 (from AVX512).\nstatic inline __m128i libdivide_s64_shift_right_vector(__m128i v, int amt) {\n const int b = 64 - amt;\n __m128i m = _mm_set1_epi64x(1ULL << (b - 1));\n __m128i x = _mm_srli_epi64(v, amt);\n __m128i result = _mm_sub_epi64(_mm_xor_si128(x, m), m);\n return result;\n}\n\n// Here, b is assumed to contain one 32-bit value repeated.\nstatic inline __m128i libdivide_mullhi_u32_vector(__m128i a, __m128i b) {\n __m128i hi_product_0Z2Z = _mm_srli_epi64(_mm_mul_epu32(a, b), 32);\n __m128i a1X3X = _mm_srli_epi64(a, 32);\n __m128i mask = _mm_set_epi32(-1, 0, -1, 0);\n __m128i hi_product_Z1Z3 = _mm_and_si128(_mm_mul_epu32(a1X3X, b), mask);\n return _mm_or_si128(hi_product_0Z2Z, hi_product_Z1Z3);\n}\n\n// SSE2 does not have a signed multiplication instruction, but we can convert\n// unsigned to signed pretty efficiently. Again, b is just a 32 bit value\n// repeated four times.\nstatic inline __m128i libdivide_mullhi_s32_vector(__m128i a, __m128i b) {\n __m128i p = libdivide_mullhi_u32_vector(a, b);\n // t1 = (a >> 31) & y, arithmetic shift\n __m128i t1 = _mm_and_si128(_mm_srai_epi32(a, 31), b);\n __m128i t2 = _mm_and_si128(_mm_srai_epi32(b, 31), a);\n p = _mm_sub_epi32(p, t1);\n p = _mm_sub_epi32(p, t2);\n return p;\n}\n\n// Here, y is assumed to contain one 64-bit value repeated.\n// https://stackoverflow.com/a/28827013\nstatic inline __m128i libdivide_mullhi_u64_vector(__m128i x, __m128i y) {\n __m128i lomask = _mm_set1_epi64x(0xffffffff);\n __m128i xh = _mm_shuffle_epi32(x, 0xB1); // x0l, x0h, x1l, x1h\n __m128i yh = _mm_shuffle_epi32(y, 0xB1); // y0l, y0h, y1l, y1h\n __m128i w0 = _mm_mul_epu32(x, y); // x0l*y0l, x1l*y1l\n __m128i w1 = _mm_mul_epu32(x, yh); // x0l*y0h, x1l*y1h\n __m128i w2 = _mm_mul_epu32(xh, y); // x0h*y0l, x1h*y0l\n __m128i w3 = _mm_mul_epu32(xh, yh); // x0h*y0h, x1h*y1h\n __m128i w0h = _mm_srli_epi64(w0, 32);\n __m128i s1 = _mm_add_epi64(w1, w0h);\n __m128i s1l = _mm_and_si128(s1, lomask);\n __m128i s1h = _mm_srli_epi64(s1, 32);\n __m128i s2 = _mm_add_epi64(w2, s1l);\n __m128i s2h = _mm_srli_epi64(s2, 32);\n __m128i hi = _mm_add_epi64(w3, s1h);\n hi = _mm_add_epi64(hi, s2h);\n\n return hi;\n}\n\n// y is one 64-bit value repeated.\nstatic inline __m128i libdivide_mullhi_s64_vector(__m128i x, __m128i y) {\n __m128i p = libdivide_mullhi_u64_vector(x, y);\n __m128i t1 = _mm_and_si128(libdivide_s64_signbits(x), y);\n __m128i t2 = _mm_and_si128(libdivide_s64_signbits(y), x);\n p = _mm_sub_epi64(p, t1);\n p = _mm_sub_epi64(p, t2);\n return p;\n}\n\n////////// UINT32\n\n__m128i libdivide_u32_do_vector(__m128i numers, const struct libdivide_u32_t *denom) {\n uint8_t more = denom->more;\n if (!denom->magic) {\n return _mm_srli_epi32(numers, more);\n }\n else {\n __m128i q = libdivide_mullhi_u32_vector(numers, _mm_set1_epi32(denom->magic));\n if (more & LIBDIVIDE_ADD_MARKER) {\n // uint32_t t = ((numer - q) >> 1) + q;\n // return t >> denom->shift;\n uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK;\n __m128i t = _mm_add_epi32(_mm_srli_epi32(_mm_sub_epi32(numers, q), 1), q);\n return _mm_srli_epi32(t, shift);\n }\n else {\n return _mm_srli_epi32(q, more);\n }\n }\n}\n\n__m128i libdivide_u32_branchfree_do_vector(__m128i numers, const struct libdivide_u32_branchfree_t *denom) {\n __m128i q = libdivide_mullhi_u32_vector(numers, _mm_set1_epi32(denom->magic));\n __m128i t = _mm_add_epi32(_mm_srli_epi32(_mm_sub_epi32(numers, q), 1), q);\n return _mm_srli_epi32(t, denom->more);\n}\n\n////////// UINT64\n\n__m128i libdivide_u64_do_vector(__m128i numers, const struct libdivide_u64_t *denom) {\n uint8_t more = denom->more;\n if (!denom->magic) {\n return _mm_srli_epi64(numers, more);\n }\n else {\n __m128i q = libdivide_mullhi_u64_vector(numers, _mm_set1_epi64x(denom->magic));\n if (more & LIBDIVIDE_ADD_MARKER) {\n // uint32_t t = ((numer - q) >> 1) + q;\n // return t >> denom->shift;\n uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK;\n __m128i t = _mm_add_epi64(_mm_srli_epi64(_mm_sub_epi64(numers, q), 1), q);\n return _mm_srli_epi64(t, shift);\n }\n else {\n return _mm_srli_epi64(q, more);\n }\n }\n}\n\n__m128i libdivide_u64_branchfree_do_vector(__m128i numers, const struct libdivide_u64_branchfree_t *denom) {\n __m128i q = libdivide_mullhi_u64_vector(numers, _mm_set1_epi64x(denom->magic));\n __m128i t = _mm_add_epi64(_mm_srli_epi64(_mm_sub_epi64(numers, q), 1), q);\n return _mm_srli_epi64(t, denom->more);\n}\n\n////////// SINT32\n\n__m128i libdivide_s32_do_vector(__m128i numers, const struct libdivide_s32_t *denom) {\n uint8_t more = denom->more;\n if (!denom->magic) {\n uint32_t shift = more & LIBDIVIDE_32_SHIFT_MASK;\n uint32_t mask = (1U << shift) - 1;\n __m128i roundToZeroTweak = _mm_set1_epi32(mask);\n // q = numer + ((numer >> 31) & roundToZeroTweak);\n __m128i q = _mm_add_epi32(numers, _mm_and_si128(_mm_srai_epi32(numers, 31), roundToZeroTweak));\n q = _mm_srai_epi32(q, shift);\n __m128i sign = _mm_set1_epi32((int8_t)more >> 7);\n // q = (q ^ sign) - sign;\n q = _mm_sub_epi32(_mm_xor_si128(q, sign), sign);\n return q;\n }\n else {\n __m128i q = libdivide_mullhi_s32_vector(numers, _mm_set1_epi32(denom->magic));\n if (more & LIBDIVIDE_ADD_MARKER) {\n // must be arithmetic shift\n __m128i sign = _mm_set1_epi32((int8_t)more >> 7);\n // q += ((numer ^ sign) - sign);\n q = _mm_add_epi32(q, _mm_sub_epi32(_mm_xor_si128(numers, sign), sign));\n }\n // q >>= shift\n q = _mm_srai_epi32(q, more & LIBDIVIDE_32_SHIFT_MASK);\n q = _mm_add_epi32(q, _mm_srli_epi32(q, 31)); // q += (q < 0)\n return q;\n }\n}\n\n__m128i libdivide_s32_branchfree_do_vector(__m128i numers, const struct libdivide_s32_branchfree_t *denom) {\n int32_t magic = denom->magic;\n uint8_t more = denom->more;\n uint8_t shift = more & LIBDIVIDE_32_SHIFT_MASK;\n // must be arithmetic shift\n __m128i sign = _mm_set1_epi32((int8_t)more >> 7);\n __m128i q = libdivide_mullhi_s32_vector(numers, _mm_set1_epi32(magic));\n q = _mm_add_epi32(q, numers); // q += numers\n\n // If q is non-negative, we have nothing to do\n // If q is negative, we want to add either (2**shift)-1 if d is\n // a power of 2, or (2**shift) if it is not a power of 2\n uint32_t is_power_of_2 = (magic == 0);\n __m128i q_sign = _mm_srai_epi32(q, 31); // q_sign = q >> 31\n __m128i mask = _mm_set1_epi32((1U << shift) - is_power_of_2);\n q = _mm_add_epi32(q, _mm_and_si128(q_sign, mask)); // q = q + (q_sign & mask)\n q = _mm_srai_epi32(q, shift); // q >>= shift\n q = _mm_sub_epi32(_mm_xor_si128(q, sign), sign); // q = (q ^ sign) - sign\n return q;\n}\n\n////////// SINT64\n\n__m128i libdivide_s64_do_vector(__m128i numers, const struct libdivide_s64_t *denom) {\n uint8_t more = denom->more;\n int64_t magic = denom->magic;\n if (magic == 0) { // shift path\n uint32_t shift = more & LIBDIVIDE_64_SHIFT_MASK;\n uint64_t mask = (1ULL << shift) - 1;\n __m128i roundToZeroTweak = _mm_set1_epi64x(mask);\n // q = numer + ((numer >> 63) & roundToZeroTweak);\n __m128i q = _mm_add_epi64(numers, _mm_and_si128(libdivide_s64_signbits(numers), roundToZeroTweak));\n q = libdivide_s64_shift_right_vector(q, shift);\n __m128i sign = _mm_set1_epi32((int8_t)more >> 7);\n // q = (q ^ sign) - sign;\n q = _mm_sub_epi64(_mm_xor_si128(q, sign), sign);\n return q;\n }\n else {\n __m128i q = libdivide_mullhi_s64_vector(numers, _mm_set1_epi64x(magic));\n if (more & LIBDIVIDE_ADD_MARKER) {\n // must be arithmetic shift\n __m128i sign = _mm_set1_epi32((int8_t)more >> 7);\n // q += ((numer ^ sign) - sign);\n q = _mm_add_epi64(q, _mm_sub_epi64(_mm_xor_si128(numers, sign), sign));\n }\n // q >>= denom->mult_path.shift\n q = libdivide_s64_shift_right_vector(q, more & LIBDIVIDE_64_SHIFT_MASK);\n q = _mm_add_epi64(q, _mm_srli_epi64(q, 63)); // q += (q < 0)\n return q;\n }\n}\n\n__m128i libdivide_s64_branchfree_do_vector(__m128i numers, const struct libdivide_s64_branchfree_t *denom) {\n int64_t magic = denom->magic;\n uint8_t more = denom->more;\n uint8_t shift = more & LIBDIVIDE_64_SHIFT_MASK;\n // must be arithmetic shift\n __m128i sign = _mm_set1_epi32((int8_t)more >> 7);\n\n // libdivide_mullhi_s64(numers, magic);\n __m128i q = libdivide_mullhi_s64_vector(numers, _mm_set1_epi64x(magic));\n q = _mm_add_epi64(q, numers); // q += numers\n\n // If q is non-negative, we have nothing to do.\n // If q is negative, we want to add either (2**shift)-1 if d is\n // a power of 2, or (2**shift) if it is not a power of 2.\n uint32_t is_power_of_2 = (magic == 0);\n __m128i q_sign = libdivide_s64_signbits(q); // q_sign = q >> 63\n __m128i mask = _mm_set1_epi64x((1ULL << shift) - is_power_of_2);\n q = _mm_add_epi64(q, _mm_and_si128(q_sign, mask)); // q = q + (q_sign & mask)\n q = libdivide_s64_shift_right_vector(q, shift); // q >>= shift\n q = _mm_sub_epi64(_mm_xor_si128(q, sign), sign); // q = (q ^ sign) - sign\n return q;\n}\n\n#endif\n\n/////////// C++ stuff\n\n#ifdef __cplusplus\n\n// The C++ divider class is templated on both an integer type\n// (like uint64_t) and an algorithm type.\n// * BRANCHFULL is the default algorithm type.\n// * BRANCHFREE is the branchfree algorithm type.\nenum {\n BRANCHFULL,\n BRANCHFREE\n};\n\n#if defined(LIBDIVIDE_AVX512)\n #define LIBDIVIDE_VECTOR_TYPE __m512i\n#elif defined(LIBDIVIDE_AVX2)\n #define LIBDIVIDE_VECTOR_TYPE __m256i\n#elif defined(LIBDIVIDE_SSE2)\n #define LIBDIVIDE_VECTOR_TYPE __m128i\n#endif\n\n#if !defined(LIBDIVIDE_VECTOR_TYPE)\n #define LIBDIVIDE_DIVIDE_VECTOR(ALGO)\n#else\n #define LIBDIVIDE_DIVIDE_VECTOR(ALGO) \\n LIBDIVIDE_VECTOR_TYPE divide(LIBDIVIDE_VECTOR_TYPE n) const { \\n return libdivide_##ALGO##_do_vector(n, &denom); \\n }\n#endif\n\n// The DISPATCHER_GEN() macro generates C++ methods (for the given integer\n// and algorithm types) that redirect to libdivide's C API.\n#define DISPATCHER_GEN(T, ALGO) \\n libdivide_##ALGO##_t denom; \\n dispatcher() { } \\n dispatcher(T d) \\n : denom(libdivide_##ALGO##_gen(d)) \\n { } \\n T divide(T n) const { \\n return libdivide_##ALGO##_do(n, &denom); \\n } \\n LIBDIVIDE_DIVIDE_VECTOR(ALGO) \\n T recover() const { \\n return libdivide_##ALGO##_recover(&denom); \\n }\n\n// The dispatcher selects a specific division algorithm for a given\n// type and ALGO using partial template specialization.\ntemplate<bool IS_INTEGRAL, bool IS_SIGNED, int SIZEOF, int ALGO> struct dispatcher { };\n\ntemplate<> struct dispatcher<true, true, sizeof(int32_t), BRANCHFULL> { DISPATCHER_GEN(int32_t, s32) };\ntemplate<> struct dispatcher<true, true, sizeof(int32_t), BRANCHFREE> { DISPATCHER_GEN(int32_t, s32_branchfree) };\ntemplate<> struct dispatcher<true, false, sizeof(uint32_t), BRANCHFULL> { DISPATCHER_GEN(uint32_t, u32) };\ntemplate<> struct dispatcher<true, false, sizeof(uint32_t), BRANCHFREE> { DISPATCHER_GEN(uint32_t, u32_branchfree) };\ntemplate<> struct dispatcher<true, true, sizeof(int64_t), BRANCHFULL> { DISPATCHER_GEN(int64_t, s64) };\ntemplate<> struct dispatcher<true, true, sizeof(int64_t), BRANCHFREE> { DISPATCHER_GEN(int64_t, s64_branchfree) };\ntemplate<> struct dispatcher<true, false, sizeof(uint64_t), BRANCHFULL> { DISPATCHER_GEN(uint64_t, u64) };\ntemplate<> struct dispatcher<true, false, sizeof(uint64_t), BRANCHFREE> { DISPATCHER_GEN(uint64_t, u64_branchfree) };\n\n// This is the main divider class for use by the user (C++ API).\n// The actual division algorithm is selected using the dispatcher struct\n// based on the integer and algorithm template parameters.\ntemplate<typename T, int ALGO = BRANCHFULL>\nclass divider {\npublic:\n // We leave the default constructor empty so that creating\n // an array of dividers and then initializing them\n // later doesn't slow us down.\n divider() { }\n\n // Constructor that takes the divisor as a parameter\n divider(T d) : div(d) { }\n\n // Divides n by the divisor\n T divide(T n) const {\n return div.divide(n);\n }\n\n // Recovers the divisor, returns the value that was\n // used to initialize this divider object.\n T recover() const {\n return div.recover();\n }\n\n bool operator==(const divider<T, ALGO>& other) const {\n return div.denom.magic == other.denom.magic &&\n div.denom.more == other.denom.more;\n }\n\n bool operator!=(const divider<T, ALGO>& other) const {\n return !(*this == other);\n }\n\n#if defined(LIBDIVIDE_VECTOR_TYPE)\n // Treats the vector as packed integer values with the same type as\n // the divider (e.g. s32, u32, s64, u64) and divides each of\n // them by the divider, returning the packed quotients.\n LIBDIVIDE_VECTOR_TYPE divide(LIBDIVIDE_VECTOR_TYPE n) const {\n return div.divide(n);\n }\n#endif\n\nprivate:\n // Storage for the actual divisor\n dispatcher<std::is_integral<T>::value,\n std::is_signed<T>::value, sizeof(T), ALGO> div;\n};\n\n// Overload of operator / for scalar division\ntemplate<typename T, int ALGO>\nT operator/(T n, const divider<T, ALGO>& div) {\n return div.divide(n);\n}\n\n// Overload of operator /= for scalar division\ntemplate<typename T, int ALGO>\nT& operator/=(T& n, const divider<T, ALGO>& div) {\n n = div.divide(n);\n return n;\n}\n\n#if defined(LIBDIVIDE_VECTOR_TYPE)\n // Overload of operator / for vector division\n template<typename T, int ALGO>\n LIBDIVIDE_VECTOR_TYPE operator/(LIBDIVIDE_VECTOR_TYPE n, const divider<T, ALGO>& div) {\n return div.divide(n);\n }\n // Overload of operator /= for vector division\n template<typename T, int ALGO>\n LIBDIVIDE_VECTOR_TYPE& operator/=(LIBDIVIDE_VECTOR_TYPE& n, const divider<T, ALGO>& div) {\n n = div.divide(n);\n return n;\n }\n#endif\n\n// libdivdie::branchfree_divider<T>\ntemplate <typename T>\nusing branchfree_divider = divider<T, BRANCHFREE>;\n\n} // namespace libdivide\n\n#endif // __cplusplus\n\n#endif // NUMPY_CORE_INCLUDE_NUMPY_LIBDIVIDE_LIBDIVIDE_H_\n | .venv\Lib\site-packages\numpy\_core\include\numpy\random\libdivide.h | libdivide.h | C | 82,217 | 0.75 | 0.08658 | 0.293413 | awesome-app | 968 | 2024-07-27T11:58:18.994505 | BSD-3-Clause | false | 4a27c048f093a7d867140c05ebca7c19 |
zlib License\n ------------\n\n Copyright (C) 2010 - 2019 ridiculous_fish, <libdivide@ridiculousfish.com>\n Copyright (C) 2016 - 2019 Kim Walisch, <kim.walisch@gmail.com>\n\n This software is provided 'as-is', without any express or implied\n warranty. In no event will the authors be held liable for any damages\n arising from the use of this software.\n\n Permission is granted to anyone to use this software for any purpose,\n including commercial applications, and to alter it and redistribute it\n freely, subject to the following restrictions:\n\n 1. The origin of this software must not be misrepresented; you must not\n claim that you wrote the original software. If you use this software\n in a product, an acknowledgment in the product documentation would be\n appreciated but is not required.\n 2. Altered source versions must be plainly marked as such, and must not be\n misrepresented as being the original software.\n 3. This notice may not be removed or altered from any source distribution.\n | .venv\Lib\site-packages\numpy\_core\include\numpy\random\LICENSE.txt | LICENSE.txt | Other | 1,039 | 0.85 | 0.095238 | 0 | react-lib | 325 | 2024-10-08T03:04:14.300316 | Apache-2.0 | false | e1c97b70a98c8ec5aff0aa275fdb2c91 |
[meta]\nName = mlib\nDescription = Math library used with this version of numpy\nVersion = 1.0\n\n[default]\nLibs=\nCflags=\n\n[msvc]\nLibs=m.lib\nCflags=\n | .venv\Lib\site-packages\numpy\_core\lib\npy-pkg-config\mlib.ini | mlib.ini | Other | 156 | 0.7 | 0 | 0 | react-lib | 104 | 2024-02-18T01:36:51.538937 | Apache-2.0 | false | f9f1205b6d47bc46f71266f60ca86c17 |
[meta]\nName=npymath\nDescription=Portable, core math library implementing C99 standard\nVersion=0.1\n\n[variables]\npkgname=numpy._core\nprefix=${pkgdir}\nlibdir=${prefix}\lib\nincludedir=${prefix}\include\n\n[default]\nLibs=-L${libdir} -lnpymath\nCflags=-I${includedir}\nRequires=mlib\n\n[msvc]\nLibs=/LIBPATH:${libdir} npymath.lib\nCflags=/INCLUDE:${includedir}\nRequires=mlib\n | .venv\Lib\site-packages\numpy\_core\lib\npy-pkg-config\npymath.ini | npymath.ini | Other | 381 | 0.7 | 0 | 0 | node-utils | 681 | 2025-06-19T11:54:52.991765 | BSD-3-Clause | false | 2d7ad4bea95b37fa1a036e7620159993 |
prefix=${pcfiledir}/../..\nincludedir=${prefix}/include\n\nName: numpy\nDescription: NumPy is the fundamental package for scientific computing with Python.\nVersion: 2.3.1\nCflags: -I${includedir}\n | .venv\Lib\site-packages\numpy\_core\lib\pkgconfig\numpy.pc | numpy.pc | Other | 198 | 0.7 | 0.142857 | 0 | python-kit | 670 | 2023-11-13T00:02:19.610435 | MIT | false | 279f3777df404c0b74bd29c1cdf5090b |
import numbers\n\nimport numpy as np\nfrom numpy._core.numerictypes import sctypes\nfrom numpy.testing import assert_\n\n\nclass TestABC:\n def test_abstract(self):\n assert_(issubclass(np.number, numbers.Number))\n\n assert_(issubclass(np.inexact, numbers.Complex))\n assert_(issubclass(np.complexfloating, numbers.Complex))\n assert_(issubclass(np.floating, numbers.Real))\n\n assert_(issubclass(np.integer, numbers.Integral))\n assert_(issubclass(np.signedinteger, numbers.Integral))\n assert_(issubclass(np.unsignedinteger, numbers.Integral))\n\n def test_floats(self):\n for t in sctypes['float']:\n assert_(isinstance(t(), numbers.Real),\n f"{t.__name__} is not instance of Real")\n assert_(issubclass(t, numbers.Real),\n f"{t.__name__} is not subclass of Real")\n assert_(not isinstance(t(), numbers.Rational),\n f"{t.__name__} is instance of Rational")\n assert_(not issubclass(t, numbers.Rational),\n f"{t.__name__} is subclass of Rational")\n\n def test_complex(self):\n for t in sctypes['complex']:\n assert_(isinstance(t(), numbers.Complex),\n f"{t.__name__} is not instance of Complex")\n assert_(issubclass(t, numbers.Complex),\n f"{t.__name__} is not subclass of Complex")\n assert_(not isinstance(t(), numbers.Real),\n f"{t.__name__} is instance of Real")\n assert_(not issubclass(t, numbers.Real),\n f"{t.__name__} is subclass of Real")\n\n def test_int(self):\n for t in sctypes['int']:\n assert_(isinstance(t(), numbers.Integral),\n f"{t.__name__} is not instance of Integral")\n assert_(issubclass(t, numbers.Integral),\n f"{t.__name__} is not subclass of Integral")\n\n def test_uint(self):\n for t in sctypes['uint']:\n assert_(isinstance(t(), numbers.Integral),\n f"{t.__name__} is not instance of Integral")\n assert_(issubclass(t, numbers.Integral),\n f"{t.__name__} is not subclass of Integral")\n | .venv\Lib\site-packages\numpy\_core\tests\test_abc.py | test_abc.py | Python | 2,275 | 0.85 | 0.185185 | 0 | awesome-app | 963 | 2025-02-15T18:43:04.813550 | GPL-3.0 | true | c63ea08b7e5a5723dcebfc7b925f52f9 |
import sys\n\nimport pytest\nfrom numpy._core._rational_tests import rational\n\nimport numpy as np\nimport numpy._core.umath as ncu\nfrom numpy.testing import (\n HAS_REFCOUNT,\n assert_,\n assert_array_equal,\n assert_equal,\n assert_raises,\n assert_warns,\n)\n\n\ndef test_array_array():\n tobj = type(object)\n ones11 = np.ones((1, 1), np.float64)\n tndarray = type(ones11)\n # Test is_ndarray\n assert_equal(np.array(ones11, dtype=np.float64), ones11)\n if HAS_REFCOUNT:\n old_refcount = sys.getrefcount(tndarray)\n np.array(ones11)\n assert_equal(old_refcount, sys.getrefcount(tndarray))\n\n # test None\n assert_equal(np.array(None, dtype=np.float64),\n np.array(np.nan, dtype=np.float64))\n if HAS_REFCOUNT:\n old_refcount = sys.getrefcount(tobj)\n np.array(None, dtype=np.float64)\n assert_equal(old_refcount, sys.getrefcount(tobj))\n\n # test scalar\n assert_equal(np.array(1.0, dtype=np.float64),\n np.ones((), dtype=np.float64))\n if HAS_REFCOUNT:\n old_refcount = sys.getrefcount(np.float64)\n np.array(np.array(1.0, dtype=np.float64), dtype=np.float64)\n assert_equal(old_refcount, sys.getrefcount(np.float64))\n\n # test string\n S2 = np.dtype((bytes, 2))\n S3 = np.dtype((bytes, 3))\n S5 = np.dtype((bytes, 5))\n assert_equal(np.array(b"1.0", dtype=np.float64),\n np.ones((), dtype=np.float64))\n assert_equal(np.array(b"1.0").dtype, S3)\n assert_equal(np.array(b"1.0", dtype=bytes).dtype, S3)\n assert_equal(np.array(b"1.0", dtype=S2), np.array(b"1."))\n assert_equal(np.array(b"1", dtype=S5), np.ones((), dtype=S5))\n\n # test string\n U2 = np.dtype((str, 2))\n U3 = np.dtype((str, 3))\n U5 = np.dtype((str, 5))\n assert_equal(np.array("1.0", dtype=np.float64),\n np.ones((), dtype=np.float64))\n assert_equal(np.array("1.0").dtype, U3)\n assert_equal(np.array("1.0", dtype=str).dtype, U3)\n assert_equal(np.array("1.0", dtype=U2), np.array("1."))\n assert_equal(np.array("1", dtype=U5), np.ones((), dtype=U5))\n\n builtins = getattr(__builtins__, '__dict__', __builtins__)\n assert_(hasattr(builtins, 'get'))\n\n # test memoryview\n dat = np.array(memoryview(b'1.0'), dtype=np.float64)\n assert_equal(dat, [49.0, 46.0, 48.0])\n assert_(dat.dtype.type is np.float64)\n\n dat = np.array(memoryview(b'1.0'))\n assert_equal(dat, [49, 46, 48])\n assert_(dat.dtype.type is np.uint8)\n\n # test array interface\n a = np.array(100.0, dtype=np.float64)\n o = type("o", (object,),\n {"__array_interface__": a.__array_interface__})\n assert_equal(np.array(o, dtype=np.float64), a)\n\n # test array_struct interface\n a = np.array([(1, 4.0, 'Hello'), (2, 6.0, 'World')],\n dtype=[('f0', int), ('f1', float), ('f2', str)])\n o = type("o", (object,),\n {"__array_struct__": a.__array_struct__})\n # wasn't what I expected... is np.array(o) supposed to equal a ?\n # instead we get a array([...], dtype=">V18")\n assert_equal(bytes(np.array(o).data), bytes(a.data))\n\n # test array\n def custom__array__(self, dtype=None, copy=None):\n return np.array(100.0, dtype=dtype, copy=copy)\n\n o = type("o", (object,), {"__array__": custom__array__})()\n assert_equal(np.array(o, dtype=np.float64), np.array(100.0, np.float64))\n\n # test recursion\n nested = 1.5\n for i in range(ncu.MAXDIMS):\n nested = [nested]\n\n # no error\n np.array(nested)\n\n # Exceeds recursion limit\n assert_raises(ValueError, np.array, [nested], dtype=np.float64)\n\n # Try with lists...\n # float32\n assert_equal(np.array([None] * 10, dtype=np.float32),\n np.full((10,), np.nan, dtype=np.float32))\n assert_equal(np.array([[None]] * 10, dtype=np.float32),\n np.full((10, 1), np.nan, dtype=np.float32))\n assert_equal(np.array([[None] * 10], dtype=np.float32),\n np.full((1, 10), np.nan, dtype=np.float32))\n assert_equal(np.array([[None] * 10] * 10, dtype=np.float32),\n np.full((10, 10), np.nan, dtype=np.float32))\n # float64\n assert_equal(np.array([None] * 10, dtype=np.float64),\n np.full((10,), np.nan, dtype=np.float64))\n assert_equal(np.array([[None]] * 10, dtype=np.float64),\n np.full((10, 1), np.nan, dtype=np.float64))\n assert_equal(np.array([[None] * 10], dtype=np.float64),\n np.full((1, 10), np.nan, dtype=np.float64))\n assert_equal(np.array([[None] * 10] * 10, dtype=np.float64),\n np.full((10, 10), np.nan, dtype=np.float64))\n\n assert_equal(np.array([1.0] * 10, dtype=np.float64),\n np.ones((10,), dtype=np.float64))\n assert_equal(np.array([[1.0]] * 10, dtype=np.float64),\n np.ones((10, 1), dtype=np.float64))\n assert_equal(np.array([[1.0] * 10], dtype=np.float64),\n np.ones((1, 10), dtype=np.float64))\n assert_equal(np.array([[1.0] * 10] * 10, dtype=np.float64),\n np.ones((10, 10), dtype=np.float64))\n\n # Try with tuples\n assert_equal(np.array((None,) * 10, dtype=np.float64),\n np.full((10,), np.nan, dtype=np.float64))\n assert_equal(np.array([(None,)] * 10, dtype=np.float64),\n np.full((10, 1), np.nan, dtype=np.float64))\n assert_equal(np.array([(None,) * 10], dtype=np.float64),\n np.full((1, 10), np.nan, dtype=np.float64))\n assert_equal(np.array([(None,) * 10] * 10, dtype=np.float64),\n np.full((10, 10), np.nan, dtype=np.float64))\n\n assert_equal(np.array((1.0,) * 10, dtype=np.float64),\n np.ones((10,), dtype=np.float64))\n assert_equal(np.array([(1.0,)] * 10, dtype=np.float64),\n np.ones((10, 1), dtype=np.float64))\n assert_equal(np.array([(1.0,) * 10], dtype=np.float64),\n np.ones((1, 10), dtype=np.float64))\n assert_equal(np.array([(1.0,) * 10] * 10, dtype=np.float64),\n np.ones((10, 10), dtype=np.float64))\n\n@pytest.mark.parametrize("array", [True, False])\ndef test_array_impossible_casts(array):\n # All builtin types can be forcibly cast, at least theoretically,\n # but user dtypes cannot necessarily.\n rt = rational(1, 2)\n if array:\n rt = np.array(rt)\n with assert_raises(TypeError):\n np.array(rt, dtype="M8")\n\n\ndef test_array_astype():\n a = np.arange(6, dtype='f4').reshape(2, 3)\n # Default behavior: allows unsafe casts, keeps memory layout,\n # always copies.\n b = a.astype('i4')\n assert_equal(a, b)\n assert_equal(b.dtype, np.dtype('i4'))\n assert_equal(a.strides, b.strides)\n b = a.T.astype('i4')\n assert_equal(a.T, b)\n assert_equal(b.dtype, np.dtype('i4'))\n assert_equal(a.T.strides, b.strides)\n b = a.astype('f4')\n assert_equal(a, b)\n assert_(not (a is b))\n\n # copy=False parameter skips a copy\n b = a.astype('f4', copy=False)\n assert_(a is b)\n\n # order parameter allows overriding of the memory layout,\n # forcing a copy if the layout is wrong\n b = a.astype('f4', order='F', copy=False)\n assert_equal(a, b)\n assert_(not (a is b))\n assert_(b.flags.f_contiguous)\n\n b = a.astype('f4', order='C', copy=False)\n assert_equal(a, b)\n assert_(a is b)\n assert_(b.flags.c_contiguous)\n\n # casting parameter allows catching bad casts\n b = a.astype('c8', casting='safe')\n assert_equal(a, b)\n assert_equal(b.dtype, np.dtype('c8'))\n\n assert_raises(TypeError, a.astype, 'i4', casting='safe')\n\n # subok=False passes through a non-subclassed array\n b = a.astype('f4', subok=0, copy=False)\n assert_(a is b)\n\n class MyNDArray(np.ndarray):\n pass\n\n a = np.array([[0, 1, 2], [3, 4, 5]], dtype='f4').view(MyNDArray)\n\n # subok=True passes through a subclass\n b = a.astype('f4', subok=True, copy=False)\n assert_(a is b)\n\n # subok=True is default, and creates a subtype on a cast\n b = a.astype('i4', copy=False)\n assert_equal(a, b)\n assert_equal(type(b), MyNDArray)\n\n # subok=False never returns a subclass\n b = a.astype('f4', subok=False, copy=False)\n assert_equal(a, b)\n assert_(not (a is b))\n assert_(type(b) is not MyNDArray)\n\n # Make sure converting from string object to fixed length string\n # does not truncate.\n a = np.array([b'a' * 100], dtype='O')\n b = a.astype('S')\n assert_equal(a, b)\n assert_equal(b.dtype, np.dtype('S100'))\n a = np.array(['a' * 100], dtype='O')\n b = a.astype('U')\n assert_equal(a, b)\n assert_equal(b.dtype, np.dtype('U100'))\n\n # Same test as above but for strings shorter than 64 characters\n a = np.array([b'a' * 10], dtype='O')\n b = a.astype('S')\n assert_equal(a, b)\n assert_equal(b.dtype, np.dtype('S10'))\n a = np.array(['a' * 10], dtype='O')\n b = a.astype('U')\n assert_equal(a, b)\n assert_equal(b.dtype, np.dtype('U10'))\n\n a = np.array(123456789012345678901234567890, dtype='O').astype('S')\n assert_array_equal(a, np.array(b'1234567890' * 3, dtype='S30'))\n a = np.array(123456789012345678901234567890, dtype='O').astype('U')\n assert_array_equal(a, np.array('1234567890' * 3, dtype='U30'))\n\n a = np.array([123456789012345678901234567890], dtype='O').astype('S')\n assert_array_equal(a, np.array(b'1234567890' * 3, dtype='S30'))\n a = np.array([123456789012345678901234567890], dtype='O').astype('U')\n assert_array_equal(a, np.array('1234567890' * 3, dtype='U30'))\n\n a = np.array(123456789012345678901234567890, dtype='S')\n assert_array_equal(a, np.array(b'1234567890' * 3, dtype='S30'))\n a = np.array(123456789012345678901234567890, dtype='U')\n assert_array_equal(a, np.array('1234567890' * 3, dtype='U30'))\n\n a = np.array('a\u0140', dtype='U')\n b = np.ndarray(buffer=a, dtype='uint32', shape=2)\n assert_(b.size == 2)\n\n a = np.array([1000], dtype='i4')\n assert_raises(TypeError, a.astype, 'S1', casting='safe')\n\n a = np.array(1000, dtype='i4')\n assert_raises(TypeError, a.astype, 'U1', casting='safe')\n\n # gh-24023\n assert_raises(TypeError, a.astype)\n\n@pytest.mark.parametrize("dt", ["S", "U"])\ndef test_array_astype_to_string_discovery_empty(dt):\n # See also gh-19085\n arr = np.array([""], dtype=object)\n # Note, the itemsize is the `0 -> 1` logic, which should change.\n # The important part the test is rather that it does not error.\n assert arr.astype(dt).dtype.itemsize == np.dtype(f"{dt}1").itemsize\n\n # check the same thing for `np.can_cast` (since it accepts arrays)\n assert np.can_cast(arr, dt, casting="unsafe")\n assert not np.can_cast(arr, dt, casting="same_kind")\n # as well as for the object as a descriptor:\n assert np.can_cast("O", dt, casting="unsafe")\n\n@pytest.mark.parametrize("dt", ["d", "f", "S13", "U32"])\ndef test_array_astype_to_void(dt):\n dt = np.dtype(dt)\n arr = np.array([], dtype=dt)\n assert arr.astype("V").dtype.itemsize == dt.itemsize\n\ndef test_object_array_astype_to_void():\n # This is different to `test_array_astype_to_void` as object arrays\n # are inspected. The default void is "V8" (8 is the length of double)\n arr = np.array([], dtype="O").astype("V")\n assert arr.dtype == "V8"\n\n@pytest.mark.parametrize("t",\n np._core.sctypes['uint'] +\n np._core.sctypes['int'] +\n np._core.sctypes['float']\n)\ndef test_array_astype_warning(t):\n # test ComplexWarning when casting from complex to float or int\n a = np.array(10, dtype=np.complex128)\n assert_warns(np.exceptions.ComplexWarning, a.astype, t)\n\n@pytest.mark.parametrize(["dtype", "out_dtype"],\n [(np.bytes_, np.bool),\n (np.str_, np.bool),\n (np.dtype("S10,S9"), np.dtype("?,?")),\n # The following also checks unaligned unicode access:\n (np.dtype("S7,U9"), np.dtype("?,?"))])\ndef test_string_to_boolean_cast(dtype, out_dtype):\n # Only the last two (empty) strings are falsy (the `\0` is stripped):\n arr = np.array(\n ["10", "10\0\0\0", "0\0\0", "0", "False", " ", "", "\0"],\n dtype=dtype)\n expected = np.array(\n [True, True, True, True, True, True, False, False],\n dtype=out_dtype)\n assert_array_equal(arr.astype(out_dtype), expected)\n # As it's similar, check that nonzero behaves the same (structs are\n # nonzero if all entries are)\n assert_array_equal(np.nonzero(arr), np.nonzero(expected))\n\n@pytest.mark.parametrize("str_type", [str, bytes, np.str_])\n@pytest.mark.parametrize("scalar_type",\n [np.complex64, np.complex128, np.clongdouble])\ndef test_string_to_complex_cast(str_type, scalar_type):\n value = scalar_type(b"1+3j")\n assert scalar_type(value) == 1 + 3j\n assert np.array([value], dtype=object).astype(scalar_type)[()] == 1 + 3j\n assert np.array(value).astype(scalar_type)[()] == 1 + 3j\n arr = np.zeros(1, dtype=scalar_type)\n arr[0] = value\n assert arr[0] == 1 + 3j\n\n@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])\ndef test_none_to_nan_cast(dtype):\n # Note that at the time of writing this test, the scalar constructors\n # reject None\n arr = np.zeros(1, dtype=dtype)\n arr[0] = None\n assert np.isnan(arr)[0]\n assert np.isnan(np.array(None, dtype=dtype))[()]\n assert np.isnan(np.array([None], dtype=dtype))[0]\n assert np.isnan(np.array(None).astype(dtype))[()]\n\ndef test_copyto_fromscalar():\n a = np.arange(6, dtype='f4').reshape(2, 3)\n\n # Simple copy\n np.copyto(a, 1.5)\n assert_equal(a, 1.5)\n np.copyto(a.T, 2.5)\n assert_equal(a, 2.5)\n\n # Where-masked copy\n mask = np.array([[0, 1, 0], [0, 0, 1]], dtype='?')\n np.copyto(a, 3.5, where=mask)\n assert_equal(a, [[2.5, 3.5, 2.5], [2.5, 2.5, 3.5]])\n mask = np.array([[0, 1], [1, 1], [1, 0]], dtype='?')\n np.copyto(a.T, 4.5, where=mask)\n assert_equal(a, [[2.5, 4.5, 4.5], [4.5, 4.5, 3.5]])\n\ndef test_copyto():\n a = np.arange(6, dtype='i4').reshape(2, 3)\n\n # Simple copy\n np.copyto(a, [[3, 1, 5], [6, 2, 1]])\n assert_equal(a, [[3, 1, 5], [6, 2, 1]])\n\n # Overlapping copy should work\n np.copyto(a[:, :2], a[::-1, 1::-1])\n assert_equal(a, [[2, 6, 5], [1, 3, 1]])\n\n # Defaults to 'same_kind' casting\n assert_raises(TypeError, np.copyto, a, 1.5)\n\n # Force a copy with 'unsafe' casting, truncating 1.5 to 1\n np.copyto(a, 1.5, casting='unsafe')\n assert_equal(a, 1)\n\n # Copying with a mask\n np.copyto(a, 3, where=[True, False, True])\n assert_equal(a, [[3, 1, 3], [3, 1, 3]])\n\n # Casting rule still applies with a mask\n assert_raises(TypeError, np.copyto, a, 3.5, where=[True, False, True])\n\n # Lists of integer 0's and 1's is ok too\n np.copyto(a, 4.0, casting='unsafe', where=[[0, 1, 1], [1, 0, 0]])\n assert_equal(a, [[3, 4, 4], [4, 1, 3]])\n\n # Overlapping copy with mask should work\n np.copyto(a[:, :2], a[::-1, 1::-1], where=[[0, 1], [1, 1]])\n assert_equal(a, [[3, 4, 4], [4, 3, 3]])\n\n # 'dst' must be an array\n assert_raises(TypeError, np.copyto, [1, 2, 3], [2, 3, 4])\n\n\ndef test_copyto_cast_safety():\n with pytest.raises(TypeError):\n np.copyto(np.arange(3), 3., casting="safe")\n\n # Can put integer and float scalars safely (and equiv):\n np.copyto(np.arange(3), 3, casting="equiv")\n np.copyto(np.arange(3.), 3., casting="equiv")\n # And also with less precision safely:\n np.copyto(np.arange(3, dtype="uint8"), 3, casting="safe")\n np.copyto(np.arange(3., dtype="float32"), 3., casting="safe")\n\n # But not equiv:\n with pytest.raises(TypeError):\n np.copyto(np.arange(3, dtype="uint8"), 3, casting="equiv")\n\n with pytest.raises(TypeError):\n np.copyto(np.arange(3., dtype="float32"), 3., casting="equiv")\n\n # As a special thing, object is equiv currently:\n np.copyto(np.arange(3, dtype=object), 3, casting="equiv")\n\n # The following raises an overflow error/gives a warning but not\n # type error (due to casting), though:\n with pytest.raises(OverflowError):\n np.copyto(np.arange(3), 2**80, casting="safe")\n\n with pytest.warns(RuntimeWarning):\n np.copyto(np.arange(3, dtype=np.float32), 2e300, casting="safe")\n\n\ndef test_copyto_permut():\n # test explicit overflow case\n pad = 500\n l = [True] * pad + [True, True, True, True]\n r = np.zeros(len(l) - pad)\n d = np.ones(len(l) - pad)\n mask = np.array(l)[pad:]\n np.copyto(r, d, where=mask[::-1])\n\n # test all permutation of possible masks, 9 should be sufficient for\n # current 4 byte unrolled code\n power = 9\n d = np.ones(power)\n for i in range(2**power):\n r = np.zeros(power)\n l = [(i & x) != 0 for x in range(power)]\n mask = np.array(l)\n np.copyto(r, d, where=mask)\n assert_array_equal(r == 1, l)\n assert_equal(r.sum(), sum(l))\n\n r = np.zeros(power)\n np.copyto(r, d, where=mask[::-1])\n assert_array_equal(r == 1, l[::-1])\n assert_equal(r.sum(), sum(l))\n\n r = np.zeros(power)\n np.copyto(r[::2], d[::2], where=mask[::2])\n assert_array_equal(r[::2] == 1, l[::2])\n assert_equal(r[::2].sum(), sum(l[::2]))\n\n r = np.zeros(power)\n np.copyto(r[::2], d[::2], where=mask[::-2])\n assert_array_equal(r[::2] == 1, l[::-2])\n assert_equal(r[::2].sum(), sum(l[::-2]))\n\n for c in [0xFF, 0x7F, 0x02, 0x10]:\n r = np.zeros(power)\n mask = np.array(l)\n imask = np.array(l).view(np.uint8)\n imask[mask != 0] = c\n np.copyto(r, d, where=mask)\n assert_array_equal(r == 1, l)\n assert_equal(r.sum(), sum(l))\n\n r = np.zeros(power)\n np.copyto(r, d, where=True)\n assert_equal(r.sum(), r.size)\n r = np.ones(power)\n d = np.zeros(power)\n np.copyto(r, d, where=False)\n assert_equal(r.sum(), r.size)\n\ndef test_copy_order():\n a = np.arange(24).reshape(2, 1, 3, 4)\n b = a.copy(order='F')\n c = np.arange(24).reshape(2, 1, 4, 3).swapaxes(2, 3)\n\n def check_copy_result(x, y, ccontig, fcontig, strides=False):\n assert_(not (x is y))\n assert_equal(x, y)\n assert_equal(res.flags.c_contiguous, ccontig)\n assert_equal(res.flags.f_contiguous, fcontig)\n\n # Validate the initial state of a, b, and c\n assert_(a.flags.c_contiguous)\n assert_(not a.flags.f_contiguous)\n assert_(not b.flags.c_contiguous)\n assert_(b.flags.f_contiguous)\n assert_(not c.flags.c_contiguous)\n assert_(not c.flags.f_contiguous)\n\n # Copy with order='C'\n res = a.copy(order='C')\n check_copy_result(res, a, ccontig=True, fcontig=False, strides=True)\n res = b.copy(order='C')\n check_copy_result(res, b, ccontig=True, fcontig=False, strides=False)\n res = c.copy(order='C')\n check_copy_result(res, c, ccontig=True, fcontig=False, strides=False)\n res = np.copy(a, order='C')\n check_copy_result(res, a, ccontig=True, fcontig=False, strides=True)\n res = np.copy(b, order='C')\n check_copy_result(res, b, ccontig=True, fcontig=False, strides=False)\n res = np.copy(c, order='C')\n check_copy_result(res, c, ccontig=True, fcontig=False, strides=False)\n\n # Copy with order='F'\n res = a.copy(order='F')\n check_copy_result(res, a, ccontig=False, fcontig=True, strides=False)\n res = b.copy(order='F')\n check_copy_result(res, b, ccontig=False, fcontig=True, strides=True)\n res = c.copy(order='F')\n check_copy_result(res, c, ccontig=False, fcontig=True, strides=False)\n res = np.copy(a, order='F')\n check_copy_result(res, a, ccontig=False, fcontig=True, strides=False)\n res = np.copy(b, order='F')\n check_copy_result(res, b, ccontig=False, fcontig=True, strides=True)\n res = np.copy(c, order='F')\n check_copy_result(res, c, ccontig=False, fcontig=True, strides=False)\n\n # Copy with order='K'\n res = a.copy(order='K')\n check_copy_result(res, a, ccontig=True, fcontig=False, strides=True)\n res = b.copy(order='K')\n check_copy_result(res, b, ccontig=False, fcontig=True, strides=True)\n res = c.copy(order='K')\n check_copy_result(res, c, ccontig=False, fcontig=False, strides=True)\n res = np.copy(a, order='K')\n check_copy_result(res, a, ccontig=True, fcontig=False, strides=True)\n res = np.copy(b, order='K')\n check_copy_result(res, b, ccontig=False, fcontig=True, strides=True)\n res = np.copy(c, order='K')\n check_copy_result(res, c, ccontig=False, fcontig=False, strides=True)\n\ndef test_contiguous_flags():\n a = np.ones((4, 4, 1))[::2, :, :]\n a.strides = a.strides[:2] + (-123,)\n b = np.ones((2, 2, 1, 2, 2)).swapaxes(3, 4)\n\n def check_contig(a, ccontig, fcontig):\n assert_(a.flags.c_contiguous == ccontig)\n assert_(a.flags.f_contiguous == fcontig)\n\n # Check if new arrays are correct:\n check_contig(a, False, False)\n check_contig(b, False, False)\n check_contig(np.empty((2, 2, 0, 2, 2)), True, True)\n check_contig(np.array([[[1], [2]]], order='F'), True, True)\n check_contig(np.empty((2, 2)), True, False)\n check_contig(np.empty((2, 2), order='F'), False, True)\n\n # Check that np.array creates correct contiguous flags:\n check_contig(np.array(a, copy=None), False, False)\n check_contig(np.array(a, copy=None, order='C'), True, False)\n check_contig(np.array(a, ndmin=4, copy=None, order='F'), False, True)\n\n # Check slicing update of flags and :\n check_contig(a[0], True, True)\n check_contig(a[None, ::4, ..., None], True, True)\n check_contig(b[0, 0, ...], False, True)\n check_contig(b[:, :, 0:0, :, :], True, True)\n\n # Test ravel and squeeze.\n check_contig(a.ravel(), True, True)\n check_contig(np.ones((1, 3, 1)).squeeze(), True, True)\n\ndef test_broadcast_arrays():\n # Test user defined dtypes\n a = np.array([(1, 2, 3)], dtype='u4,u4,u4')\n b = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)], dtype='u4,u4,u4')\n result = np.broadcast_arrays(a, b)\n assert_equal(result[0], np.array([(1, 2, 3), (1, 2, 3), (1, 2, 3)], dtype='u4,u4,u4'))\n assert_equal(result[1], np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)], dtype='u4,u4,u4'))\n\n@pytest.mark.parametrize(["shape", "fill_value", "expected_output"],\n [((2, 2), [5.0, 6.0], np.array([[5.0, 6.0], [5.0, 6.0]])),\n ((3, 2), [1.0, 2.0], np.array([[1.0, 2.0], [1.0, 2.0], [1.0, 2.0]]))])\ndef test_full_from_list(shape, fill_value, expected_output):\n output = np.full(shape, fill_value)\n assert_equal(output, expected_output)\n\ndef test_astype_copyflag():\n # test the various copyflag options\n arr = np.arange(10, dtype=np.intp)\n\n res_true = arr.astype(np.intp, copy=True)\n assert not np.shares_memory(arr, res_true)\n\n res_false = arr.astype(np.intp, copy=False)\n assert np.shares_memory(arr, res_false)\n\n res_false_float = arr.astype(np.float64, copy=False)\n assert not np.shares_memory(arr, res_false_float)\n\n # _CopyMode enum isn't allowed\n assert_raises(ValueError, arr.astype, np.float64,\n copy=np._CopyMode.NEVER)\n | .venv\Lib\site-packages\numpy\_core\tests\test_api.py | test_api.py | Python | 23,575 | 0.95 | 0.061192 | 0.151923 | node-utils | 629 | 2024-03-04T11:05:23.370623 | MIT | true | 88d8cecbd2253d75b7a161b572fedb1a |
"""\nTests for the private NumPy argument parsing functionality.\nThey mainly exists to ensure good test coverage without having to try the\nweirder cases on actual numpy functions but test them in one place.\n\nThe test function is defined in C to be equivalent to (errors may not always\nmatch exactly, and could be adjusted):\n\n def func(arg1, /, arg2, *, arg3):\n i = integer(arg1) # reproducing the 'i' parsing in Python.\n return None\n"""\n\nimport threading\n\nimport pytest\nfrom numpy._core._multiarray_tests import (\n argparse_example_function as func,\n)\nfrom numpy._core._multiarray_tests import (\n threaded_argparse_example_function as thread_func,\n)\n\nimport numpy as np\nfrom numpy.testing import IS_WASM\n\n\n@pytest.mark.skipif(IS_WASM, reason="wasm doesn't have support for threads")\ndef test_thread_safe_argparse_cache():\n b = threading.Barrier(8)\n\n def call_thread_func():\n b.wait()\n thread_func(arg1=3, arg2=None)\n\n tasks = [threading.Thread(target=call_thread_func) for _ in range(8)]\n [t.start() for t in tasks]\n [t.join() for t in tasks]\n\n\ndef test_invalid_integers():\n with pytest.raises(TypeError,\n match="integer argument expected, got float"):\n func(1.)\n with pytest.raises(OverflowError):\n func(2**100)\n\n\ndef test_missing_arguments():\n with pytest.raises(TypeError,\n match="missing required positional argument 0"):\n func()\n with pytest.raises(TypeError,\n match="missing required positional argument 0"):\n func(arg2=1, arg3=4)\n with pytest.raises(TypeError,\n match=r"missing required argument \'arg2\' \(pos 1\)"):\n func(1, arg3=5)\n\n\ndef test_too_many_positional():\n # the second argument is positional but can be passed as keyword.\n with pytest.raises(TypeError,\n match="takes from 2 to 3 positional arguments but 4 were given"):\n func(1, 2, 3, 4)\n\n\ndef test_multiple_values():\n with pytest.raises(TypeError,\n match=r"given by name \('arg2'\) and position \(position 1\)"):\n func(1, 2, arg2=3)\n\n\ndef test_string_fallbacks():\n # We can (currently?) use numpy strings to test the "slow" fallbacks\n # that should normally not be taken due to string interning.\n arg2 = np.str_("arg2")\n missing_arg = np.str_("missing_arg")\n func(1, **{arg2: 3})\n with pytest.raises(TypeError,\n match="got an unexpected keyword argument 'missing_arg'"):\n func(2, **{missing_arg: 3})\n\n\ndef test_too_many_arguments_method_forwarding():\n # Not directly related to the standard argument parsing, but we sometimes\n # forward methods to Python: arr.mean() calls np._core._methods._mean()\n # This adds code coverage for this `npy_forward_method`.\n arr = np.arange(3)\n args = range(1000)\n with pytest.raises(TypeError):\n arr.mean(*args)\n | .venv\Lib\site-packages\numpy\_core\tests\test_argparse.py | test_argparse.py | Python | 2,962 | 0.95 | 0.184783 | 0.084507 | react-lib | 260 | 2025-04-12T23:55:44.971216 | MIT | true | f37e342b0609b0c14b9702e4997223e9 |
"""\nThis file tests the generic aspects of ArrayMethod. At the time of writing\nthis is private API, but when added, public API may be added here.\n"""\n\nimport types\nfrom typing import Any\n\nimport pytest\nfrom numpy._core._multiarray_umath import _get_castingimpl as get_castingimpl\n\nimport numpy as np\n\n\nclass TestResolveDescriptors:\n # Test mainly error paths of the resolve_descriptors function,\n # note that the `casting_unittests` tests exercise this non-error paths.\n\n # Casting implementations are the main/only current user:\n method = get_castingimpl(type(np.dtype("d")), type(np.dtype("f")))\n\n @pytest.mark.parametrize("args", [\n (True,), # Not a tuple.\n ((None,)), # Too few elements\n ((None, None, None),), # Too many\n ((None, None),), # Input dtype is None, which is invalid.\n ((np.dtype("d"), True),), # Output dtype is not a dtype\n ((np.dtype("f"), None),), # Input dtype does not match method\n ])\n def test_invalid_arguments(self, args):\n with pytest.raises(TypeError):\n self.method._resolve_descriptors(*args)\n\n\nclass TestSimpleStridedCall:\n # Test mainly error paths of the resolve_descriptors function,\n # note that the `casting_unittests` tests exercise this non-error paths.\n\n # Casting implementations are the main/only current user:\n method = get_castingimpl(type(np.dtype("d")), type(np.dtype("f")))\n\n @pytest.mark.parametrize(["args", "error"], [\n ((True,), TypeError), # Not a tuple\n (((None,),), TypeError), # Too few elements\n ((None, None), TypeError), # Inputs are not arrays.\n (((None, None, None),), TypeError), # Too many\n (((np.arange(3), np.arange(3)),), TypeError), # Incorrect dtypes\n (((np.ones(3, dtype=">d"), np.ones(3, dtype="<f")),),\n TypeError), # Does not support byte-swapping\n (((np.ones((2, 2), dtype="d"), np.ones((2, 2), dtype="f")),),\n ValueError), # not 1-D\n (((np.ones(3, dtype="d"), np.ones(4, dtype="f")),),\n ValueError), # different length\n (((np.frombuffer(b"\0x00" * 3 * 2, dtype="d"),\n np.frombuffer(b"\0x00" * 3, dtype="f")),),\n ValueError), # output not writeable\n ])\n def test_invalid_arguments(self, args, error):\n # This is private API, which may be modified freely\n with pytest.raises(error):\n self.method._simple_strided_call(*args)\n\n\n@pytest.mark.parametrize(\n "cls", [\n np.ndarray, np.recarray, np.char.chararray, np.matrix, np.memmap\n ]\n)\nclass TestClassGetItem:\n def test_class_getitem(self, cls: type[np.ndarray]) -> None:\n """Test `ndarray.__class_getitem__`."""\n alias = cls[Any, Any]\n assert isinstance(alias, types.GenericAlias)\n assert alias.__origin__ is cls\n\n @pytest.mark.parametrize("arg_len", range(4))\n def test_subscript_tup(self, cls: type[np.ndarray], arg_len: int) -> None:\n arg_tup = (Any,) * arg_len\n if arg_len in (1, 2):\n assert cls[arg_tup]\n else:\n match = f"Too {'few' if arg_len == 0 else 'many'} arguments"\n with pytest.raises(TypeError, match=match):\n cls[arg_tup]\n | .venv\Lib\site-packages\numpy\_core\tests\test_arraymethod.py | test_arraymethod.py | Python | 3,307 | 0.95 | 0.130952 | 0.1 | node-utils | 658 | 2024-06-25T15:05:09.545753 | GPL-3.0 | true | c8158eea7861f5928c1831cfcc109d08 |
import pytest\n\nimport numpy as np\nfrom numpy.testing import assert_array_equal\n\n\ndef test_matrix_transpose_raises_error_for_1d():\n msg = "matrix transpose with ndim < 2 is undefined"\n arr = np.arange(48)\n with pytest.raises(ValueError, match=msg):\n arr.mT\n\n\ndef test_matrix_transpose_equals_transpose_2d():\n arr = np.arange(48).reshape((6, 8))\n assert_array_equal(arr.T, arr.mT)\n\n\nARRAY_SHAPES_TO_TEST = (\n (5, 2),\n (5, 2, 3),\n (5, 2, 3, 4),\n)\n\n\n@pytest.mark.parametrize("shape", ARRAY_SHAPES_TO_TEST)\ndef test_matrix_transpose_equals_swapaxes(shape):\n num_of_axes = len(shape)\n vec = np.arange(shape[-1])\n arr = np.broadcast_to(vec, shape)\n tgt = np.swapaxes(arr, num_of_axes - 2, num_of_axes - 1)\n mT = arr.mT\n assert_array_equal(tgt, mT)\n\n\nclass MyArr(np.ndarray):\n def __array_wrap__(self, arr, context=None, return_scalar=None):\n return super().__array_wrap__(arr, context, return_scalar)\n\n\nclass MyArrNoWrap(np.ndarray):\n pass\n\n\n@pytest.mark.parametrize("subclass_self", [np.ndarray, MyArr, MyArrNoWrap])\n@pytest.mark.parametrize("subclass_arr", [np.ndarray, MyArr, MyArrNoWrap])\ndef test_array_wrap(subclass_self, subclass_arr):\n # NumPy should allow `__array_wrap__` to be called on arrays, it's logic\n # is designed in a way that:\n #\n # * Subclasses never return scalars by default (to preserve their\n # information). They can choose to if they wish.\n # * NumPy returns scalars, if `return_scalar` is passed as True to allow\n # manual calls to `arr.__array_wrap__` to do the right thing.\n # * The type of the input should be ignored (it should be a base-class\n # array, but I am not sure this is guaranteed).\n\n arr = np.arange(3).view(subclass_self)\n\n arr0d = np.array(3, dtype=np.int8).view(subclass_arr)\n # With third argument True, ndarray allows "decay" to scalar.\n # (I don't think NumPy would pass `None`, but it seems clear to support)\n if subclass_self is np.ndarray:\n assert type(arr.__array_wrap__(arr0d, None, True)) is np.int8\n else:\n assert type(arr.__array_wrap__(arr0d, None, True)) is type(arr)\n\n # Otherwise, result should be viewed as the subclass\n assert type(arr.__array_wrap__(arr0d)) is type(arr)\n assert type(arr.__array_wrap__(arr0d, None, None)) is type(arr)\n assert type(arr.__array_wrap__(arr0d, None, False)) is type(arr)\n\n # Non 0-D array can't be converted to scalar, so we ignore that\n arr1d = np.array([3], dtype=np.int8).view(subclass_arr)\n assert type(arr.__array_wrap__(arr1d, None, True)) is type(arr)\n | .venv\Lib\site-packages\numpy\_core\tests\test_arrayobject.py | test_arrayobject.py | Python | 2,671 | 0.95 | 0.146667 | 0.232143 | awesome-app | 980 | 2023-07-31T13:20:14.951506 | MIT | true | 297c52c54ccc6cab01f82bb2689cd844 |
import gc\nimport sys\nimport textwrap\n\nimport pytest\nfrom hypothesis import given\nfrom hypothesis.extra import numpy as hynp\n\nimport numpy as np\nfrom numpy._core.arrayprint import _typelessdata\nfrom numpy.testing import (\n HAS_REFCOUNT,\n IS_WASM,\n assert_,\n assert_equal,\n assert_raises,\n assert_raises_regex,\n assert_warns,\n)\nfrom numpy.testing._private.utils import run_threaded\n\n\nclass TestArrayRepr:\n def test_nan_inf(self):\n x = np.array([np.nan, np.inf])\n assert_equal(repr(x), 'array([nan, inf])')\n\n def test_subclass(self):\n class sub(np.ndarray):\n pass\n\n # one dimensional\n x1d = np.array([1, 2]).view(sub)\n assert_equal(repr(x1d), 'sub([1, 2])')\n\n # two dimensional\n x2d = np.array([[1, 2], [3, 4]]).view(sub)\n assert_equal(repr(x2d),\n 'sub([[1, 2],\n'\n ' [3, 4]])')\n\n # two dimensional with flexible dtype\n xstruct = np.ones((2, 2), dtype=[('a', '<i4')]).view(sub)\n assert_equal(repr(xstruct),\n "sub([[(1,), (1,)],\n"\n " [(1,), (1,)]], dtype=[('a', '<i4')])"\n )\n\n @pytest.mark.xfail(reason="See gh-10544")\n def test_object_subclass(self):\n class sub(np.ndarray):\n def __new__(cls, inp):\n obj = np.asarray(inp).view(cls)\n return obj\n\n def __getitem__(self, ind):\n ret = super().__getitem__(ind)\n return sub(ret)\n\n # test that object + subclass is OK:\n x = sub([None, None])\n assert_equal(repr(x), 'sub([None, None], dtype=object)')\n assert_equal(str(x), '[None None]')\n\n x = sub([None, sub([None, None])])\n assert_equal(repr(x),\n 'sub([None, sub([None, None], dtype=object)], dtype=object)')\n assert_equal(str(x), '[None sub([None, None], dtype=object)]')\n\n def test_0d_object_subclass(self):\n # make sure that subclasses which return 0ds instead\n # of scalars don't cause infinite recursion in str\n class sub(np.ndarray):\n def __new__(cls, inp):\n obj = np.asarray(inp).view(cls)\n return obj\n\n def __getitem__(self, ind):\n ret = super().__getitem__(ind)\n return sub(ret)\n\n x = sub(1)\n assert_equal(repr(x), 'sub(1)')\n assert_equal(str(x), '1')\n\n x = sub([1, 1])\n assert_equal(repr(x), 'sub([1, 1])')\n assert_equal(str(x), '[1 1]')\n\n # check it works properly with object arrays too\n x = sub(None)\n assert_equal(repr(x), 'sub(None, dtype=object)')\n assert_equal(str(x), 'None')\n\n # plus recursive object arrays (even depth > 1)\n y = sub(None)\n x[()] = y\n y[()] = x\n assert_equal(repr(x),\n 'sub(sub(sub(..., dtype=object), dtype=object), dtype=object)')\n assert_equal(str(x), '...')\n x[()] = 0 # resolve circular references for garbage collector\n\n # nested 0d-subclass-object\n x = sub(None)\n x[()] = sub(None)\n assert_equal(repr(x), 'sub(sub(None, dtype=object), dtype=object)')\n assert_equal(str(x), 'None')\n\n # gh-10663\n class DuckCounter(np.ndarray):\n def __getitem__(self, item):\n result = super().__getitem__(item)\n if not isinstance(result, DuckCounter):\n result = result[...].view(DuckCounter)\n return result\n\n def to_string(self):\n return {0: 'zero', 1: 'one', 2: 'two'}.get(self.item(), 'many')\n\n def __str__(self):\n if self.shape == ():\n return self.to_string()\n else:\n fmt = {'all': lambda x: x.to_string()}\n return np.array2string(self, formatter=fmt)\n\n dc = np.arange(5).view(DuckCounter)\n assert_equal(str(dc), "[zero one two many many]")\n assert_equal(str(dc[0]), "zero")\n\n def test_self_containing(self):\n arr0d = np.array(None)\n arr0d[()] = arr0d\n assert_equal(repr(arr0d),\n 'array(array(..., dtype=object), dtype=object)')\n arr0d[()] = 0 # resolve recursion for garbage collector\n\n arr1d = np.array([None, None])\n arr1d[1] = arr1d\n assert_equal(repr(arr1d),\n 'array([None, array(..., dtype=object)], dtype=object)')\n arr1d[1] = 0 # resolve recursion for garbage collector\n\n first = np.array(None)\n second = np.array(None)\n first[()] = second\n second[()] = first\n assert_equal(repr(first),\n 'array(array(array(..., dtype=object), dtype=object), dtype=object)')\n first[()] = 0 # resolve circular references for garbage collector\n\n def test_containing_list(self):\n # printing square brackets directly would be ambiguous\n arr1d = np.array([None, None])\n arr1d[0] = [1, 2]\n arr1d[1] = [3]\n assert_equal(repr(arr1d),\n 'array([list([1, 2]), list([3])], dtype=object)')\n\n def test_void_scalar_recursion(self):\n # gh-9345\n repr(np.void(b'test')) # RecursionError ?\n\n def test_fieldless_structured(self):\n # gh-10366\n no_fields = np.dtype([])\n arr_no_fields = np.empty(4, dtype=no_fields)\n assert_equal(repr(arr_no_fields), 'array([(), (), (), ()], dtype=[])')\n\n\nclass TestComplexArray:\n def test_str(self):\n rvals = [0, 1, -1, np.inf, -np.inf, np.nan]\n cvals = [complex(rp, ip) for rp in rvals for ip in rvals]\n dtypes = [np.complex64, np.cdouble, np.clongdouble]\n actual = [str(np.array([c], dt)) for c in cvals for dt in dtypes]\n wanted = [\n '[0.+0.j]', '[0.+0.j]', '[0.+0.j]',\n '[0.+1.j]', '[0.+1.j]', '[0.+1.j]',\n '[0.-1.j]', '[0.-1.j]', '[0.-1.j]',\n '[0.+infj]', '[0.+infj]', '[0.+infj]',\n '[0.-infj]', '[0.-infj]', '[0.-infj]',\n '[0.+nanj]', '[0.+nanj]', '[0.+nanj]',\n '[1.+0.j]', '[1.+0.j]', '[1.+0.j]',\n '[1.+1.j]', '[1.+1.j]', '[1.+1.j]',\n '[1.-1.j]', '[1.-1.j]', '[1.-1.j]',\n '[1.+infj]', '[1.+infj]', '[1.+infj]',\n '[1.-infj]', '[1.-infj]', '[1.-infj]',\n '[1.+nanj]', '[1.+nanj]', '[1.+nanj]',\n '[-1.+0.j]', '[-1.+0.j]', '[-1.+0.j]',\n '[-1.+1.j]', '[-1.+1.j]', '[-1.+1.j]',\n '[-1.-1.j]', '[-1.-1.j]', '[-1.-1.j]',\n '[-1.+infj]', '[-1.+infj]', '[-1.+infj]',\n '[-1.-infj]', '[-1.-infj]', '[-1.-infj]',\n '[-1.+nanj]', '[-1.+nanj]', '[-1.+nanj]',\n '[inf+0.j]', '[inf+0.j]', '[inf+0.j]',\n '[inf+1.j]', '[inf+1.j]', '[inf+1.j]',\n '[inf-1.j]', '[inf-1.j]', '[inf-1.j]',\n '[inf+infj]', '[inf+infj]', '[inf+infj]',\n '[inf-infj]', '[inf-infj]', '[inf-infj]',\n '[inf+nanj]', '[inf+nanj]', '[inf+nanj]',\n '[-inf+0.j]', '[-inf+0.j]', '[-inf+0.j]',\n '[-inf+1.j]', '[-inf+1.j]', '[-inf+1.j]',\n '[-inf-1.j]', '[-inf-1.j]', '[-inf-1.j]',\n '[-inf+infj]', '[-inf+infj]', '[-inf+infj]',\n '[-inf-infj]', '[-inf-infj]', '[-inf-infj]',\n '[-inf+nanj]', '[-inf+nanj]', '[-inf+nanj]',\n '[nan+0.j]', '[nan+0.j]', '[nan+0.j]',\n '[nan+1.j]', '[nan+1.j]', '[nan+1.j]',\n '[nan-1.j]', '[nan-1.j]', '[nan-1.j]',\n '[nan+infj]', '[nan+infj]', '[nan+infj]',\n '[nan-infj]', '[nan-infj]', '[nan-infj]',\n '[nan+nanj]', '[nan+nanj]', '[nan+nanj]']\n\n for res, val in zip(actual, wanted):\n assert_equal(res, val)\n\nclass TestArray2String:\n def test_basic(self):\n """Basic test of array2string."""\n a = np.arange(3)\n assert_(np.array2string(a) == '[0 1 2]')\n assert_(np.array2string(a, max_line_width=4, legacy='1.13') == '[0 1\n 2]')\n assert_(np.array2string(a, max_line_width=4) == '[0\n 1\n 2]')\n\n def test_unexpected_kwarg(self):\n # ensure than an appropriate TypeError\n # is raised when array2string receives\n # an unexpected kwarg\n\n with assert_raises_regex(TypeError, 'nonsense'):\n np.array2string(np.array([1, 2, 3]),\n nonsense=None)\n\n def test_format_function(self):\n """Test custom format function for each element in array."""\n def _format_function(x):\n if np.abs(x) < 1:\n return '.'\n elif np.abs(x) < 2:\n return 'o'\n else:\n return 'O'\n\n x = np.arange(3)\n x_hex = "[0x0 0x1 0x2]"\n x_oct = "[0o0 0o1 0o2]"\n assert_(np.array2string(x, formatter={'all': _format_function}) ==\n "[. o O]")\n assert_(np.array2string(x, formatter={'int_kind': _format_function}) ==\n "[. o O]")\n assert_(np.array2string(x, formatter={'all': lambda x: f"{x:.4f}"}) ==\n "[0.0000 1.0000 2.0000]")\n assert_equal(np.array2string(x, formatter={'int': hex}),\n x_hex)\n assert_equal(np.array2string(x, formatter={'int': oct}),\n x_oct)\n\n x = np.arange(3.)\n assert_(np.array2string(x, formatter={'float_kind': lambda x: f"{x:.2f}"}) ==\n "[0.00 1.00 2.00]")\n assert_(np.array2string(x, formatter={'float': lambda x: f"{x:.2f}"}) ==\n "[0.00 1.00 2.00]")\n\n s = np.array(['abc', 'def'])\n assert_(np.array2string(s, formatter={'numpystr': lambda s: s * 2}) ==\n '[abcabc defdef]')\n\n def test_structure_format_mixed(self):\n dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])\n x = np.array([('Sarah', (8.0, 7.0)), ('John', (6.0, 7.0))], dtype=dt)\n assert_equal(np.array2string(x),\n "[('Sarah', [8., 7.]) ('John', [6., 7.])]")\n\n np.set_printoptions(legacy='1.13')\n try:\n # for issue #5692\n A = np.zeros(shape=10, dtype=[("A", "M8[s]")])\n A[5:].fill(np.datetime64('NaT'))\n assert_equal(\n np.array2string(A),\n textwrap.dedent("""\\n [('1970-01-01T00:00:00',) ('1970-01-01T00:00:00',) ('1970-01-01T00:00:00',)\n ('1970-01-01T00:00:00',) ('1970-01-01T00:00:00',) ('NaT',) ('NaT',)\n ('NaT',) ('NaT',) ('NaT',)]""")\n )\n finally:\n np.set_printoptions(legacy=False)\n\n # same again, but with non-legacy behavior\n assert_equal(\n np.array2string(A),\n textwrap.dedent("""\\n [('1970-01-01T00:00:00',) ('1970-01-01T00:00:00',)\n ('1970-01-01T00:00:00',) ('1970-01-01T00:00:00',)\n ('1970-01-01T00:00:00',) ( 'NaT',)\n ( 'NaT',) ( 'NaT',)\n ( 'NaT',) ( 'NaT',)]""")\n )\n\n # and again, with timedeltas\n A = np.full(10, 123456, dtype=[("A", "m8[s]")])\n A[5:].fill(np.datetime64('NaT'))\n assert_equal(\n np.array2string(A),\n textwrap.dedent("""\\n [(123456,) (123456,) (123456,) (123456,) (123456,) ( 'NaT',) ( 'NaT',)\n ( 'NaT',) ( 'NaT',) ( 'NaT',)]""")\n )\n\n def test_structure_format_int(self):\n # See #8160\n struct_int = np.array([([1, -1],), ([123, 1],)],\n dtype=[('B', 'i4', 2)])\n assert_equal(np.array2string(struct_int),\n "[([ 1, -1],) ([123, 1],)]")\n struct_2dint = np.array([([[0, 1], [2, 3]],), ([[12, 0], [0, 0]],)],\n dtype=[('B', 'i4', (2, 2))])\n assert_equal(np.array2string(struct_2dint),\n "[([[ 0, 1], [ 2, 3]],) ([[12, 0], [ 0, 0]],)]")\n\n def test_structure_format_float(self):\n # See #8172\n array_scalar = np.array(\n (1., 2.1234567890123456789, 3.), dtype=('f8,f8,f8'))\n assert_equal(np.array2string(array_scalar), "(1., 2.12345679, 3.)")\n\n def test_unstructured_void_repr(self):\n a = np.array([27, 91, 50, 75, 7, 65, 10, 8, 27, 91, 51, 49, 109, 82, 101, 100],\n dtype='u1').view('V8')\n assert_equal(repr(a[0]),\n r"np.void(b'\x1B\x5B\x32\x4B\x07\x41\x0A\x08')")\n assert_equal(str(a[0]), r"b'\x1B\x5B\x32\x4B\x07\x41\x0A\x08'")\n assert_equal(repr(a),\n r"array([b'\x1B\x5B\x32\x4B\x07\x41\x0A\x08',"\n "\n"\n r" b'\x1B\x5B\x33\x31\x6D\x52\x65\x64'], dtype='|V8')")\n\n assert_equal(eval(repr(a), vars(np)), a)\n assert_equal(eval(repr(a[0]), {'np': np}), a[0])\n\n def test_edgeitems_kwarg(self):\n # previously the global print options would be taken over the kwarg\n arr = np.zeros(3, int)\n assert_equal(\n np.array2string(arr, edgeitems=1, threshold=0),\n "[0 ... 0]"\n )\n\n def test_summarize_1d(self):\n A = np.arange(1001)\n strA = '[ 0 1 2 ... 998 999 1000]'\n assert_equal(str(A), strA)\n\n reprA = 'array([ 0, 1, 2, ..., 998, 999, 1000])'\n try:\n np.set_printoptions(legacy='2.1')\n assert_equal(repr(A), reprA)\n finally:\n np.set_printoptions(legacy=False)\n\n assert_equal(repr(A), reprA.replace(')', ', shape=(1001,))'))\n\n def test_summarize_2d(self):\n A = np.arange(1002).reshape(2, 501)\n strA = '[[ 0 1 2 ... 498 499 500]\n' \\n ' [ 501 502 503 ... 999 1000 1001]]'\n assert_equal(str(A), strA)\n\n reprA = 'array([[ 0, 1, 2, ..., 498, 499, 500],\n' \\n ' [ 501, 502, 503, ..., 999, 1000, 1001]])'\n try:\n np.set_printoptions(legacy='2.1')\n assert_equal(repr(A), reprA)\n finally:\n np.set_printoptions(legacy=False)\n\n assert_equal(repr(A), reprA.replace(')', ', shape=(2, 501))'))\n\n def test_summarize_2d_dtype(self):\n A = np.arange(1002, dtype='i2').reshape(2, 501)\n strA = '[[ 0 1 2 ... 498 499 500]\n' \\n ' [ 501 502 503 ... 999 1000 1001]]'\n assert_equal(str(A), strA)\n\n reprA = ('array([[ 0, 1, 2, ..., 498, 499, 500],\n'\n ' [ 501, 502, 503, ..., 999, 1000, 1001]],\n'\n ' shape=(2, 501), dtype=int16)')\n assert_equal(repr(A), reprA)\n\n def test_summarize_structure(self):\n A = (np.arange(2002, dtype="<i8").reshape(2, 1001)\n .view([('i', "<i8", (1001,))]))\n strA = ("[[([ 0, 1, 2, ..., 998, 999, 1000],)]\n"\n " [([1001, 1002, 1003, ..., 1999, 2000, 2001],)]]")\n assert_equal(str(A), strA)\n\n reprA = ("array([[([ 0, 1, 2, ..., 998, 999, 1000],)],\n"\n " [([1001, 1002, 1003, ..., 1999, 2000, 2001],)]],\n"\n " dtype=[('i', '<i8', (1001,))])")\n assert_equal(repr(A), reprA)\n\n B = np.ones(2002, dtype=">i8").view([('i', ">i8", (2, 1001))])\n strB = "[([[1, 1, 1, ..., 1, 1, 1], [1, 1, 1, ..., 1, 1, 1]],)]"\n assert_equal(str(B), strB)\n\n reprB = (\n "array([([[1, 1, 1, ..., 1, 1, 1], [1, 1, 1, ..., 1, 1, 1]],)],\n"\n " dtype=[('i', '>i8', (2, 1001))])"\n )\n assert_equal(repr(B), reprB)\n\n C = (np.arange(22, dtype="<i8").reshape(2, 11)\n .view([('i1', "<i8"), ('i10', "<i8", (10,))]))\n strC = "[[( 0, [ 1, ..., 10])]\n [(11, [12, ..., 21])]]"\n assert_equal(np.array2string(C, threshold=1, edgeitems=1), strC)\n\n def test_linewidth(self):\n a = np.full(6, 1)\n\n def make_str(a, width, **kw):\n return np.array2string(a, separator="", max_line_width=width, **kw)\n\n assert_equal(make_str(a, 8, legacy='1.13'), '[111111]')\n assert_equal(make_str(a, 7, legacy='1.13'), '[111111]')\n assert_equal(make_str(a, 5, legacy='1.13'), '[1111\n'\n ' 11]')\n\n assert_equal(make_str(a, 8), '[111111]')\n assert_equal(make_str(a, 7), '[11111\n'\n ' 1]')\n assert_equal(make_str(a, 5), '[111\n'\n ' 111]')\n\n b = a[None, None, :]\n\n assert_equal(make_str(b, 12, legacy='1.13'), '[[[111111]]]')\n assert_equal(make_str(b, 9, legacy='1.13'), '[[[111111]]]')\n assert_equal(make_str(b, 8, legacy='1.13'), '[[[11111\n'\n ' 1]]]')\n\n assert_equal(make_str(b, 12), '[[[111111]]]')\n assert_equal(make_str(b, 9), '[[[111\n'\n ' 111]]]')\n assert_equal(make_str(b, 8), '[[[11\n'\n ' 11\n'\n ' 11]]]')\n\n def test_wide_element(self):\n a = np.array(['xxxxx'])\n assert_equal(\n np.array2string(a, max_line_width=5),\n "['xxxxx']"\n )\n assert_equal(\n np.array2string(a, max_line_width=5, legacy='1.13'),\n "[ 'xxxxx']"\n )\n\n def test_multiline_repr(self):\n class MultiLine:\n def __repr__(self):\n return "Line 1\nLine 2"\n\n a = np.array([[None, MultiLine()], [MultiLine(), None]])\n\n assert_equal(\n np.array2string(a),\n '[[None Line 1\n'\n ' Line 2]\n'\n ' [Line 1\n'\n ' Line 2 None]]'\n )\n assert_equal(\n np.array2string(a, max_line_width=5),\n '[[None\n'\n ' Line 1\n'\n ' Line 2]\n'\n ' [Line 1\n'\n ' Line 2\n'\n ' None]]'\n )\n assert_equal(\n repr(a),\n 'array([[None, Line 1\n'\n ' Line 2],\n'\n ' [Line 1\n'\n ' Line 2, None]], dtype=object)'\n )\n\n class MultiLineLong:\n def __repr__(self):\n return "Line 1\nLooooooooooongestLine2\nLongerLine 3"\n\n a = np.array([[None, MultiLineLong()], [MultiLineLong(), None]])\n assert_equal(\n repr(a),\n 'array([[None, Line 1\n'\n ' LooooooooooongestLine2\n'\n ' LongerLine 3 ],\n'\n ' [Line 1\n'\n ' LooooooooooongestLine2\n'\n ' LongerLine 3 , None]], dtype=object)'\n )\n assert_equal(\n np.array_repr(a, 20),\n 'array([[None,\n'\n ' Line 1\n'\n ' LooooooooooongestLine2\n'\n ' LongerLine 3 ],\n'\n ' [Line 1\n'\n ' LooooooooooongestLine2\n'\n ' LongerLine 3 ,\n'\n ' None]],\n'\n ' dtype=object)'\n )\n\n def test_nested_array_repr(self):\n a = np.empty((2, 2), dtype=object)\n a[0, 0] = np.eye(2)\n a[0, 1] = np.eye(3)\n a[1, 0] = None\n a[1, 1] = np.ones((3, 1))\n assert_equal(\n repr(a),\n 'array([[array([[1., 0.],\n'\n ' [0., 1.]]), array([[1., 0., 0.],\n'\n ' [0., 1., 0.],\n'\n ' [0., 0., 1.]])],\n'\n ' [None, array([[1.],\n'\n ' [1.],\n'\n ' [1.]])]], dtype=object)'\n )\n\n @given(hynp.from_dtype(np.dtype("U")))\n def test_any_text(self, text):\n # This test checks that, given any value that can be represented in an\n # array of dtype("U") (i.e. unicode string), ...\n a = np.array([text, text, text])\n # casting a list of them to an array does not e.g. truncate the value\n assert_equal(a[0], text)\n text = text.item() # use raw python strings for repr below\n # and that np.array2string puts a newline in the expected location\n expected_repr = f"[{text!r} {text!r}\n {text!r}]"\n result = np.array2string(a, max_line_width=len(repr(text)) * 2 + 3)\n assert_equal(result, expected_repr)\n\n @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")\n def test_refcount(self):\n # make sure we do not hold references to the array due to a recursive\n # closure (gh-10620)\n gc.disable()\n a = np.arange(2)\n r1 = sys.getrefcount(a)\n np.array2string(a)\n np.array2string(a)\n r2 = sys.getrefcount(a)\n gc.collect()\n gc.enable()\n assert_(r1 == r2)\n\n def test_with_sign(self):\n # mixed negative and positive value array\n a = np.array([-2, 0, 3])\n assert_equal(\n np.array2string(a, sign='+'),\n '[-2 +0 +3]'\n )\n assert_equal(\n np.array2string(a, sign='-'),\n '[-2 0 3]'\n )\n assert_equal(\n np.array2string(a, sign=' '),\n '[-2 0 3]'\n )\n # all non-negative array\n a = np.array([2, 0, 3])\n assert_equal(\n np.array2string(a, sign='+'),\n '[+2 +0 +3]'\n )\n assert_equal(\n np.array2string(a, sign='-'),\n '[2 0 3]'\n )\n assert_equal(\n np.array2string(a, sign=' '),\n '[ 2 0 3]'\n )\n # all negative array\n a = np.array([-2, -1, -3])\n assert_equal(\n np.array2string(a, sign='+'),\n '[-2 -1 -3]'\n )\n assert_equal(\n np.array2string(a, sign='-'),\n '[-2 -1 -3]'\n )\n assert_equal(\n np.array2string(a, sign=' '),\n '[-2 -1 -3]'\n )\n # 2d array mixed negative and positive\n a = np.array([[10, -1, 1, 1], [10, 10, 10, 10]])\n assert_equal(\n np.array2string(a, sign='+'),\n '[[+10 -1 +1 +1]\n [+10 +10 +10 +10]]'\n )\n assert_equal(\n np.array2string(a, sign='-'),\n '[[10 -1 1 1]\n [10 10 10 10]]'\n )\n assert_equal(\n np.array2string(a, sign=' '),\n '[[10 -1 1 1]\n [10 10 10 10]]'\n )\n # 2d array all positive\n a = np.array([[10, 0, 1, 1], [10, 10, 10, 10]])\n assert_equal(\n np.array2string(a, sign='+'),\n '[[+10 +0 +1 +1]\n [+10 +10 +10 +10]]'\n )\n assert_equal(\n np.array2string(a, sign='-'),\n '[[10 0 1 1]\n [10 10 10 10]]'\n )\n assert_equal(\n np.array2string(a, sign=' '),\n '[[ 10 0 1 1]\n [ 10 10 10 10]]'\n )\n # 2d array all negative\n a = np.array([[-10, -1, -1, -1], [-10, -10, -10, -10]])\n assert_equal(\n np.array2string(a, sign='+'),\n '[[-10 -1 -1 -1]\n [-10 -10 -10 -10]]'\n )\n assert_equal(\n np.array2string(a, sign='-'),\n '[[-10 -1 -1 -1]\n [-10 -10 -10 -10]]'\n )\n assert_equal(\n np.array2string(a, sign=' '),\n '[[-10 -1 -1 -1]\n [-10 -10 -10 -10]]'\n )\n\n\nclass TestPrintOptions:\n """Test getting and setting global print options."""\n\n def setup_method(self):\n self.oldopts = np.get_printoptions()\n\n def teardown_method(self):\n np.set_printoptions(**self.oldopts)\n\n def test_basic(self):\n x = np.array([1.5, 0, 1.234567890])\n assert_equal(repr(x), "array([1.5 , 0. , 1.23456789])")\n ret = np.set_printoptions(precision=4)\n assert_equal(repr(x), "array([1.5 , 0. , 1.2346])")\n assert ret is None\n\n def test_precision_zero(self):\n np.set_printoptions(precision=0)\n for values, string in (\n ([0.], "0."), ([.3], "0."), ([-.3], "-0."), ([.7], "1."),\n ([1.5], "2."), ([-1.5], "-2."), ([-15.34], "-15."),\n ([100.], "100."), ([.2, -1, 122.51], " 0., -1., 123."),\n ([0], "0"), ([-12], "-12"), ([complex(.3, -.7)], "0.-1.j")):\n x = np.array(values)\n assert_equal(repr(x), f"array([{string}])")\n\n def test_formatter(self):\n x = np.arange(3)\n np.set_printoptions(formatter={'all': lambda x: str(x - 1)})\n assert_equal(repr(x), "array([-1, 0, 1])")\n\n def test_formatter_reset(self):\n x = np.arange(3)\n np.set_printoptions(formatter={'all': lambda x: str(x - 1)})\n assert_equal(repr(x), "array([-1, 0, 1])")\n np.set_printoptions(formatter={'int': None})\n assert_equal(repr(x), "array([0, 1, 2])")\n\n np.set_printoptions(formatter={'all': lambda x: str(x - 1)})\n assert_equal(repr(x), "array([-1, 0, 1])")\n np.set_printoptions(formatter={'all': None})\n assert_equal(repr(x), "array([0, 1, 2])")\n\n np.set_printoptions(formatter={'int': lambda x: str(x - 1)})\n assert_equal(repr(x), "array([-1, 0, 1])")\n np.set_printoptions(formatter={'int_kind': None})\n assert_equal(repr(x), "array([0, 1, 2])")\n\n x = np.arange(3.)\n np.set_printoptions(formatter={'float': lambda x: str(x - 1)})\n assert_equal(repr(x), "array([-1.0, 0.0, 1.0])")\n np.set_printoptions(formatter={'float_kind': None})\n assert_equal(repr(x), "array([0., 1., 2.])")\n\n def test_override_repr(self):\n x = np.arange(3)\n np.set_printoptions(override_repr=lambda x: "FOO")\n assert_equal(repr(x), "FOO")\n np.set_printoptions(override_repr=None)\n assert_equal(repr(x), "array([0, 1, 2])")\n\n with np.printoptions(override_repr=lambda x: "BAR"):\n assert_equal(repr(x), "BAR")\n assert_equal(repr(x), "array([0, 1, 2])")\n\n def test_0d_arrays(self):\n assert_equal(str(np.array('café', '<U4')), 'café')\n\n assert_equal(repr(np.array('café', '<U4')),\n "array('café', dtype='<U4')")\n assert_equal(str(np.array('test', np.str_)), 'test')\n\n a = np.zeros(1, dtype=[('a', '<i4', (3,))])\n assert_equal(str(a[0]), '([0, 0, 0],)')\n\n assert_equal(repr(np.datetime64('2005-02-25')[...]),\n "array('2005-02-25', dtype='datetime64[D]')")\n\n assert_equal(repr(np.timedelta64('10', 'Y')[...]),\n "array(10, dtype='timedelta64[Y]')")\n\n # repr of 0d arrays is affected by printoptions\n x = np.array(1)\n np.set_printoptions(formatter={'all': lambda x: "test"})\n assert_equal(repr(x), "array(test)")\n # str is unaffected\n assert_equal(str(x), "1")\n\n # check `style` arg raises\n assert_warns(DeprecationWarning, np.array2string,\n np.array(1.), style=repr)\n # but not in legacy mode\n np.array2string(np.array(1.), style=repr, legacy='1.13')\n # gh-10934 style was broken in legacy mode, check it works\n np.array2string(np.array(1.), legacy='1.13')\n\n def test_float_spacing(self):\n x = np.array([1., 2., 3.])\n y = np.array([1., 2., -10.])\n z = np.array([100., 2., -1.])\n w = np.array([-100., 2., 1.])\n\n assert_equal(repr(x), 'array([1., 2., 3.])')\n assert_equal(repr(y), 'array([ 1., 2., -10.])')\n assert_equal(repr(np.array(y[0])), 'array(1.)')\n assert_equal(repr(np.array(y[-1])), 'array(-10.)')\n assert_equal(repr(z), 'array([100., 2., -1.])')\n assert_equal(repr(w), 'array([-100., 2., 1.])')\n\n assert_equal(repr(np.array([np.nan, np.inf])), 'array([nan, inf])')\n assert_equal(repr(np.array([np.nan, -np.inf])), 'array([ nan, -inf])')\n\n x = np.array([np.inf, 100000, 1.1234])\n y = np.array([np.inf, 100000, -1.1234])\n z = np.array([np.inf, 1.1234, -1e120])\n np.set_printoptions(precision=2)\n assert_equal(repr(x), 'array([ inf, 1.00e+05, 1.12e+00])')\n assert_equal(repr(y), 'array([ inf, 1.00e+05, -1.12e+00])')\n assert_equal(repr(z), 'array([ inf, 1.12e+000, -1.00e+120])')\n\n def test_bool_spacing(self):\n assert_equal(repr(np.array([True, True])),\n 'array([ True, True])')\n assert_equal(repr(np.array([True, False])),\n 'array([ True, False])')\n assert_equal(repr(np.array([True])),\n 'array([ True])')\n assert_equal(repr(np.array(True)),\n 'array(True)')\n assert_equal(repr(np.array(False)),\n 'array(False)')\n\n def test_sign_spacing(self):\n a = np.arange(4.)\n b = np.array([1.234e9])\n c = np.array([1.0 + 1.0j, 1.123456789 + 1.123456789j], dtype='c16')\n\n assert_equal(repr(a), 'array([0., 1., 2., 3.])')\n assert_equal(repr(np.array(1.)), 'array(1.)')\n assert_equal(repr(b), 'array([1.234e+09])')\n assert_equal(repr(np.array([0.])), 'array([0.])')\n assert_equal(repr(c),\n "array([1. +1.j , 1.12345679+1.12345679j])")\n assert_equal(repr(np.array([0., -0.])), 'array([ 0., -0.])')\n\n np.set_printoptions(sign=' ')\n assert_equal(repr(a), 'array([ 0., 1., 2., 3.])')\n assert_equal(repr(np.array(1.)), 'array( 1.)')\n assert_equal(repr(b), 'array([ 1.234e+09])')\n assert_equal(repr(c),\n "array([ 1. +1.j , 1.12345679+1.12345679j])")\n assert_equal(repr(np.array([0., -0.])), 'array([ 0., -0.])')\n\n np.set_printoptions(sign='+')\n assert_equal(repr(a), 'array([+0., +1., +2., +3.])')\n assert_equal(repr(np.array(1.)), 'array(+1.)')\n assert_equal(repr(b), 'array([+1.234e+09])')\n assert_equal(repr(c),\n "array([+1. +1.j , +1.12345679+1.12345679j])")\n\n np.set_printoptions(legacy='1.13')\n assert_equal(repr(a), 'array([ 0., 1., 2., 3.])')\n assert_equal(repr(b), 'array([ 1.23400000e+09])')\n assert_equal(repr(-b), 'array([ -1.23400000e+09])')\n assert_equal(repr(np.array(1.)), 'array(1.0)')\n assert_equal(repr(np.array([0.])), 'array([ 0.])')\n assert_equal(repr(c),\n "array([ 1.00000000+1.j , 1.12345679+1.12345679j])")\n # gh-10383\n assert_equal(str(np.array([-1., 10])), "[ -1. 10.]")\n\n assert_raises(TypeError, np.set_printoptions, wrongarg=True)\n\n def test_float_overflow_nowarn(self):\n # make sure internal computations in FloatingFormat don't\n # warn about overflow\n repr(np.array([1e4, 0.1], dtype='f2'))\n\n def test_sign_spacing_structured(self):\n a = np.ones(2, dtype='<f,<f')\n assert_equal(repr(a),\n "array([(1., 1.), (1., 1.)], dtype=[('f0', '<f4'), ('f1', '<f4')])")\n assert_equal(repr(a[0]),\n "np.void((1.0, 1.0), dtype=[('f0', '<f4'), ('f1', '<f4')])")\n\n def test_floatmode(self):\n x = np.array([0.6104, 0.922, 0.457, 0.0906, 0.3733, 0.007244,\n 0.5933, 0.947, 0.2383, 0.4226], dtype=np.float16)\n y = np.array([0.2918820979355541, 0.5064172631089138,\n 0.2848750619642916, 0.4342965294660567,\n 0.7326538397312751, 0.3459503329096204,\n 0.0862072768214508, 0.39112753029631175],\n dtype=np.float64)\n z = np.arange(6, dtype=np.float16) / 10\n c = np.array([1.0 + 1.0j, 1.123456789 + 1.123456789j], dtype='c16')\n\n # also make sure 1e23 is right (is between two fp numbers)\n w = np.array([f'1e{i}' for i in range(25)], dtype=np.float64)\n # note: we construct w from the strings `1eXX` instead of doing\n # `10.**arange(24)` because it turns out the two are not equivalent in\n # python. On some architectures `1e23 != 10.**23`.\n wp = np.array([1.234e1, 1e2, 1e123])\n\n # unique mode\n np.set_printoptions(floatmode='unique')\n assert_equal(repr(x),\n "array([0.6104 , 0.922 , 0.457 , 0.0906 , 0.3733 , 0.007244,\n"\n " 0.5933 , 0.947 , 0.2383 , 0.4226 ], dtype=float16)")\n assert_equal(repr(y),\n "array([0.2918820979355541 , 0.5064172631089138 , 0.2848750619642916 ,\n"\n " 0.4342965294660567 , 0.7326538397312751 , 0.3459503329096204 ,\n"\n " 0.0862072768214508 , 0.39112753029631175])")\n assert_equal(repr(z),\n "array([0. , 0.1, 0.2, 0.3, 0.4, 0.5], dtype=float16)")\n assert_equal(repr(w),\n "array([1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05, 1.e+06, 1.e+07,\n"\n " 1.e+08, 1.e+09, 1.e+10, 1.e+11, 1.e+12, 1.e+13, 1.e+14, 1.e+15,\n"\n " 1.e+16, 1.e+17, 1.e+18, 1.e+19, 1.e+20, 1.e+21, 1.e+22, 1.e+23,\n"\n " 1.e+24])")\n assert_equal(repr(wp), "array([1.234e+001, 1.000e+002, 1.000e+123])")\n assert_equal(repr(c),\n "array([1. +1.j , 1.123456789+1.123456789j])")\n\n # maxprec mode, precision=8\n np.set_printoptions(floatmode='maxprec', precision=8)\n assert_equal(repr(x),\n "array([0.6104 , 0.922 , 0.457 , 0.0906 , 0.3733 , 0.007244,\n"\n " 0.5933 , 0.947 , 0.2383 , 0.4226 ], dtype=float16)")\n assert_equal(repr(y),\n "array([0.2918821 , 0.50641726, 0.28487506, 0.43429653, 0.73265384,\n"\n " 0.34595033, 0.08620728, 0.39112753])")\n assert_equal(repr(z),\n "array([0. , 0.1, 0.2, 0.3, 0.4, 0.5], dtype=float16)")\n assert_equal(repr(w[::5]),\n "array([1.e+00, 1.e+05, 1.e+10, 1.e+15, 1.e+20])")\n assert_equal(repr(wp), "array([1.234e+001, 1.000e+002, 1.000e+123])")\n assert_equal(repr(c),\n "array([1. +1.j , 1.12345679+1.12345679j])")\n\n # fixed mode, precision=4\n np.set_printoptions(floatmode='fixed', precision=4)\n assert_equal(repr(x),\n "array([0.6104, 0.9219, 0.4570, 0.0906, 0.3733, 0.0072, 0.5933, 0.9468,\n"\n " 0.2383, 0.4226], dtype=float16)")\n assert_equal(repr(y),\n "array([0.2919, 0.5064, 0.2849, 0.4343, 0.7327, 0.3460, 0.0862, 0.3911])")\n assert_equal(repr(z),\n "array([0.0000, 0.1000, 0.2000, 0.3000, 0.3999, 0.5000], dtype=float16)")\n assert_equal(repr(w[::5]),\n "array([1.0000e+00, 1.0000e+05, 1.0000e+10, 1.0000e+15, 1.0000e+20])")\n assert_equal(repr(wp), "array([1.2340e+001, 1.0000e+002, 1.0000e+123])")\n assert_equal(repr(np.zeros(3)), "array([0.0000, 0.0000, 0.0000])")\n assert_equal(repr(c),\n "array([1.0000+1.0000j, 1.1235+1.1235j])")\n # for larger precision, representation error becomes more apparent:\n np.set_printoptions(floatmode='fixed', precision=8)\n assert_equal(repr(z),\n "array([0.00000000, 0.09997559, 0.19995117, 0.30004883, 0.39990234,\n"\n " 0.50000000], dtype=float16)")\n\n # maxprec_equal mode, precision=8\n np.set_printoptions(floatmode='maxprec_equal', precision=8)\n assert_equal(repr(x),\n "array([0.610352, 0.921875, 0.457031, 0.090576, 0.373291, 0.007244,\n"\n " 0.593262, 0.946777, 0.238281, 0.422607], dtype=float16)")\n assert_equal(repr(y),\n "array([0.29188210, 0.50641726, 0.28487506, 0.43429653, 0.73265384,\n"\n " 0.34595033, 0.08620728, 0.39112753])")\n assert_equal(repr(z),\n "array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5], dtype=float16)")\n assert_equal(repr(w[::5]),\n "array([1.e+00, 1.e+05, 1.e+10, 1.e+15, 1.e+20])")\n assert_equal(repr(wp), "array([1.234e+001, 1.000e+002, 1.000e+123])")\n assert_equal(repr(c),\n "array([1.00000000+1.00000000j, 1.12345679+1.12345679j])")\n\n # test unique special case (gh-18609)\n a = np.float64.fromhex('-1p-97')\n assert_equal(np.float64(np.array2string(a, floatmode='unique')), a)\n\n test_cases_gh_28679 = [\n (np.half([999, 999]), "[999. 999.]"),\n (np.half([999, 1000]), "[9.99e+02 1.00e+03]"),\n (np.single([999999, 999999]), "[999999. 999999.]"),\n (np.single([999999, -1000000]), "[ 9.99999e+05 -1.00000e+06]"),\n (\n np.complex64([999999 + 999999j, 999999 + 999999j]),\n "[999999.+999999.j 999999.+999999.j]"\n ),\n (\n np.complex64([999999 + 999999j, 999999 + -1000000j]),\n "[999999.+9.99999e+05j 999999.-1.00000e+06j]"\n ),\n ]\n\n @pytest.mark.parametrize("input_array, expected_str", test_cases_gh_28679)\n def test_gh_28679(self, input_array, expected_str):\n # test cutoff to exponent notation for half, single, and complex64\n assert_equal(str(input_array), expected_str)\n\n test_cases_legacy_2_2 = [\n (np.half([1.e3, 1.e4, 65504]), "[ 1000. 10000. 65504.]"),\n (np.single([1.e6, 1.e7]), "[ 1000000. 10000000.]"),\n (np.single([1.e7, 1.e8]), "[1.e+07 1.e+08]"),\n ]\n\n @pytest.mark.parametrize("input_array, expected_str", test_cases_legacy_2_2)\n def test_legacy_2_2_mode(self, input_array, expected_str):\n # test legacy cutoff to exponent notation for half and single\n with np.printoptions(legacy='2.2'):\n assert_equal(str(input_array), expected_str)\n\n @pytest.mark.parametrize("legacy", ['1.13', '1.21', '1.25', '2.1', '2.2'])\n def test_legacy_get_options(self, legacy):\n # test legacy get options works okay\n with np.printoptions(legacy=legacy):\n p_opt = np.get_printoptions()\n assert_equal(p_opt["legacy"], legacy)\n\n def test_legacy_mode_scalars(self):\n # in legacy mode, str of floats get truncated, and complex scalars\n # use * for non-finite imaginary part\n np.set_printoptions(legacy='1.13')\n assert_equal(str(np.float64(1.123456789123456789)), '1.12345678912')\n assert_equal(str(np.complex128(complex(1, np.nan))), '(1+nan*j)')\n\n np.set_printoptions(legacy=False)\n assert_equal(str(np.float64(1.123456789123456789)),\n '1.1234567891234568')\n assert_equal(str(np.complex128(complex(1, np.nan))), '(1+nanj)')\n\n def test_legacy_stray_comma(self):\n np.set_printoptions(legacy='1.13')\n assert_equal(str(np.arange(10000)), '[ 0 1 2 ..., 9997 9998 9999]')\n\n np.set_printoptions(legacy=False)\n assert_equal(str(np.arange(10000)), '[ 0 1 2 ... 9997 9998 9999]')\n\n def test_dtype_linewidth_wrapping(self):\n np.set_printoptions(linewidth=75)\n assert_equal(repr(np.arange(10, 20., dtype='f4')),\n "array([10., 11., 12., 13., 14., 15., 16., 17., 18., 19.], dtype=float32)")\n assert_equal(repr(np.arange(10, 23., dtype='f4')), textwrap.dedent("""\\n array([10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22.],\n dtype=float32)"""))\n\n styp = '<U4'\n assert_equal(repr(np.ones(3, dtype=styp)),\n f"array(['1', '1', '1'], dtype='{styp}')")\n assert_equal(repr(np.ones(12, dtype=styp)), textwrap.dedent(f"""\\n array(['1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1'],\n dtype='{styp}')"""))\n\n @pytest.mark.parametrize(\n ['native'],\n [\n ('bool',),\n ('uint8',),\n ('uint16',),\n ('uint32',),\n ('uint64',),\n ('int8',),\n ('int16',),\n ('int32',),\n ('int64',),\n ('float16',),\n ('float32',),\n ('float64',),\n ('U1',), # 4-byte width string\n ],\n )\n def test_dtype_endianness_repr(self, native):\n '''\n there was an issue where\n repr(array([0], dtype='<u2')) and repr(array([0], dtype='>u2'))\n both returned the same thing:\n array([0], dtype=uint16)\n even though their dtypes have different endianness.\n '''\n native_dtype = np.dtype(native)\n non_native_dtype = native_dtype.newbyteorder()\n non_native_repr = repr(np.array([1], non_native_dtype))\n native_repr = repr(np.array([1], native_dtype))\n # preserve the sensible default of only showing dtype if nonstandard\n assert ('dtype' in native_repr) ^ (native_dtype in _typelessdata),\\n ("an array's repr should show dtype if and only if the type "\n 'of the array is NOT one of the standard types '\n '(e.g., int32, bool, float64).')\n if non_native_dtype.itemsize > 1:\n # if the type is >1 byte, the non-native endian version\n # must show endianness.\n assert non_native_repr != native_repr\n assert f"dtype='{non_native_dtype.byteorder}" in non_native_repr\n\n def test_linewidth_repr(self):\n a = np.full(7, fill_value=2)\n np.set_printoptions(linewidth=17)\n assert_equal(\n repr(a),\n textwrap.dedent("""\\n array([2, 2, 2,\n 2, 2, 2,\n 2])""")\n )\n np.set_printoptions(linewidth=17, legacy='1.13')\n assert_equal(\n repr(a),\n textwrap.dedent("""\\n array([2, 2, 2,\n 2, 2, 2, 2])""")\n )\n\n a = np.full(8, fill_value=2)\n\n np.set_printoptions(linewidth=18, legacy=False)\n assert_equal(\n repr(a),\n textwrap.dedent("""\\n array([2, 2, 2,\n 2, 2, 2,\n 2, 2])""")\n )\n\n np.set_printoptions(linewidth=18, legacy='1.13')\n assert_equal(\n repr(a),\n textwrap.dedent("""\\n array([2, 2, 2, 2,\n 2, 2, 2, 2])""")\n )\n\n def test_linewidth_str(self):\n a = np.full(18, fill_value=2)\n np.set_printoptions(linewidth=18)\n assert_equal(\n str(a),\n textwrap.dedent("""\\n [2 2 2 2 2 2 2 2\n 2 2 2 2 2 2 2 2\n 2 2]""")\n )\n np.set_printoptions(linewidth=18, legacy='1.13')\n assert_equal(\n str(a),\n textwrap.dedent("""\\n [2 2 2 2 2 2 2 2 2\n 2 2 2 2 2 2 2 2 2]""")\n )\n\n def test_edgeitems(self):\n np.set_printoptions(edgeitems=1, threshold=1)\n a = np.arange(27).reshape((3, 3, 3))\n assert_equal(\n repr(a),\n textwrap.dedent("""\\n array([[[ 0, ..., 2],\n ...,\n [ 6, ..., 8]],\n\n ...,\n\n [[18, ..., 20],\n ...,\n [24, ..., 26]]], shape=(3, 3, 3))""")\n )\n\n b = np.zeros((3, 3, 1, 1))\n assert_equal(\n repr(b),\n textwrap.dedent("""\\n array([[[[0.]],\n\n ...,\n\n [[0.]]],\n\n\n ...,\n\n\n [[[0.]],\n\n ...,\n\n [[0.]]]], shape=(3, 3, 1, 1))""")\n )\n\n # 1.13 had extra trailing spaces, and was missing newlines\n try:\n np.set_printoptions(legacy='1.13')\n assert_equal(repr(a), (\n "array([[[ 0, ..., 2],\n"\n " ..., \n"\n " [ 6, ..., 8]],\n"\n "\n"\n " ..., \n"\n " [[18, ..., 20],\n"\n " ..., \n"\n " [24, ..., 26]]])")\n )\n assert_equal(repr(b), (\n "array([[[[ 0.]],\n"\n "\n"\n " ..., \n"\n " [[ 0.]]],\n"\n "\n"\n "\n"\n " ..., \n"\n " [[[ 0.]],\n"\n "\n"\n " ..., \n"\n " [[ 0.]]]])")\n )\n finally:\n np.set_printoptions(legacy=False)\n\n def test_edgeitems_structured(self):\n np.set_printoptions(edgeitems=1, threshold=1)\n A = np.arange(5 * 2 * 3, dtype="<i8").view([('i', "<i8", (5, 2, 3))])\n reprA = (\n "array([([[[ 0, ..., 2], [ 3, ..., 5]], ..., "\n "[[24, ..., 26], [27, ..., 29]]],)],\n"\n " dtype=[('i', '<i8', (5, 2, 3))])"\n )\n assert_equal(repr(A), reprA)\n\n def test_bad_args(self):\n assert_raises(ValueError, np.set_printoptions, threshold=float('nan'))\n assert_raises(TypeError, np.set_printoptions, threshold='1')\n assert_raises(TypeError, np.set_printoptions, threshold=b'1')\n\n assert_raises(TypeError, np.set_printoptions, precision='1')\n assert_raises(TypeError, np.set_printoptions, precision=1.5)\n\ndef test_unicode_object_array():\n expected = "array(['é'], dtype=object)"\n x = np.array(['\xe9'], dtype=object)\n assert_equal(repr(x), expected)\n\n\nclass TestContextManager:\n def test_ctx_mgr(self):\n # test that context manager actually works\n with np.printoptions(precision=2):\n s = str(np.array([2.0]) / 3)\n assert_equal(s, '[0.67]')\n\n def test_ctx_mgr_restores(self):\n # test that print options are actually restored\n opts = np.get_printoptions()\n with np.printoptions(precision=opts['precision'] - 1,\n linewidth=opts['linewidth'] - 4):\n pass\n assert_equal(np.get_printoptions(), opts)\n\n def test_ctx_mgr_exceptions(self):\n # test that print options are restored even if an exception is raised\n opts = np.get_printoptions()\n try:\n with np.printoptions(precision=2, linewidth=11):\n raise ValueError\n except ValueError:\n pass\n assert_equal(np.get_printoptions(), opts)\n\n def test_ctx_mgr_as_smth(self):\n opts = {"precision": 2}\n with np.printoptions(**opts) as ctx:\n saved_opts = ctx.copy()\n assert_equal({k: saved_opts[k] for k in opts}, opts)\n\n\n@pytest.mark.parametrize("dtype", "bhilqpBHILQPefdgFDG")\n@pytest.mark.parametrize("value", [0, 1])\ndef test_scalar_repr_numbers(dtype, value):\n # Test NEP 51 scalar repr (and legacy option) for numeric types\n dtype = np.dtype(dtype)\n scalar = np.array(value, dtype=dtype)[()]\n assert isinstance(scalar, np.generic)\n\n string = str(scalar)\n repr_string = string.strip("()") # complex may have extra brackets\n representation = repr(scalar)\n if dtype.char == "g":\n assert representation == f"np.longdouble('{repr_string}')"\n elif dtype.char == 'G':\n assert representation == f"np.clongdouble('{repr_string}')"\n else:\n normalized_name = np.dtype(f"{dtype.kind}{dtype.itemsize}").type.__name__\n assert representation == f"np.{normalized_name}({repr_string})"\n\n with np.printoptions(legacy="1.25"):\n assert repr(scalar) == string\n\n\n@pytest.mark.parametrize("scalar, legacy_repr, representation", [\n (np.True_, "True", "np.True_"),\n (np.bytes_(b'a'), "b'a'", "np.bytes_(b'a')"),\n (np.str_('a'), "'a'", "np.str_('a')"),\n (np.datetime64("2012"),\n "numpy.datetime64('2012')", "np.datetime64('2012')"),\n (np.timedelta64(1), "numpy.timedelta64(1)", "np.timedelta64(1)"),\n (np.void((True, 2), dtype="?,<i8"),\n "(True, 2)",\n "np.void((True, 2), dtype=[('f0', '?'), ('f1', '<i8')])"),\n (np.void((1, 2), dtype="<f8,>f4"),\n "(1., 2.)",\n "np.void((1.0, 2.0), dtype=[('f0', '<f8'), ('f1', '>f4')])"),\n (np.void(b'a'), r"void(b'\x61')", r"np.void(b'\x61')"),\n ])\ndef test_scalar_repr_special(scalar, legacy_repr, representation):\n # Test NEP 51 scalar repr (and legacy option) for numeric types\n assert repr(scalar) == representation\n\n with np.printoptions(legacy="1.25"):\n assert repr(scalar) == legacy_repr\n\ndef test_scalar_void_float_str():\n # Note that based on this currently we do not print the same as a tuple\n # would, since the tuple would include the repr() inside for floats, but\n # we do not do that.\n scalar = np.void((1.0, 2.0), dtype=[('f0', '<f8'), ('f1', '>f4')])\n assert str(scalar) == "(1.0, 2.0)"\n\n@pytest.mark.skipif(IS_WASM, reason="wasm doesn't support asyncio")\n@pytest.mark.skipif(sys.version_info < (3, 11),\n reason="asyncio.barrier was added in Python 3.11")\ndef test_printoptions_asyncio_safe():\n asyncio = pytest.importorskip("asyncio")\n\n b = asyncio.Barrier(2)\n\n async def legacy_113():\n np.set_printoptions(legacy='1.13', precision=12)\n await b.wait()\n po = np.get_printoptions()\n assert po['legacy'] == '1.13'\n assert po['precision'] == 12\n orig_linewidth = po['linewidth']\n with np.printoptions(linewidth=34, legacy='1.21'):\n po = np.get_printoptions()\n assert po['legacy'] == '1.21'\n assert po['precision'] == 12\n assert po['linewidth'] == 34\n po = np.get_printoptions()\n assert po['linewidth'] == orig_linewidth\n assert po['legacy'] == '1.13'\n assert po['precision'] == 12\n\n async def legacy_125():\n np.set_printoptions(legacy='1.25', precision=7)\n await b.wait()\n po = np.get_printoptions()\n assert po['legacy'] == '1.25'\n assert po['precision'] == 7\n orig_linewidth = po['linewidth']\n with np.printoptions(linewidth=6, legacy='1.13'):\n po = np.get_printoptions()\n assert po['legacy'] == '1.13'\n assert po['precision'] == 7\n assert po['linewidth'] == 6\n po = np.get_printoptions()\n assert po['linewidth'] == orig_linewidth\n assert po['legacy'] == '1.25'\n assert po['precision'] == 7\n\n async def main():\n await asyncio.gather(legacy_125(), legacy_125())\n\n loop = asyncio.new_event_loop()\n asyncio.run(main())\n loop.close()\n\n@pytest.mark.skipif(IS_WASM, reason="wasm doesn't support threads")\ndef test_multithreaded_array_printing():\n # the dragon4 implementation uses a static scratch space for performance\n # reasons this test makes sure it is set up in a thread-safe manner\n\n run_threaded(TestPrintOptions().test_floatmode, 500)\n | .venv\Lib\site-packages\numpy\_core\tests\test_arrayprint.py | test_arrayprint.py | Python | 52,066 | 0.75 | 0.097139 | 0.061472 | python-kit | 919 | 2023-10-31T05:52:11.587517 | Apache-2.0 | true | c775d2d3f45797c36809fb8a01adfbcc |
import pytest\n\nimport numpy as np\n\ninfo = np.__array_namespace_info__()\n\n\ndef test_capabilities():\n caps = info.capabilities()\n assert caps["boolean indexing"] is True\n assert caps["data-dependent shapes"] is True\n\n # This will be added in the 2024.12 release of the array API standard.\n\n # assert caps["max rank"] == 64\n # np.zeros((1,)*64)\n # with pytest.raises(ValueError):\n # np.zeros((1,)*65)\n\n\ndef test_default_device():\n assert info.default_device() == "cpu" == np.asarray(0).device\n\n\ndef test_default_dtypes():\n dtypes = info.default_dtypes()\n assert dtypes["real floating"] == np.float64 == np.asarray(0.0).dtype\n assert dtypes["complex floating"] == np.complex128 == \\n np.asarray(0.0j).dtype\n assert dtypes["integral"] == np.intp == np.asarray(0).dtype\n assert dtypes["indexing"] == np.intp == np.argmax(np.zeros(10)).dtype\n\n with pytest.raises(ValueError, match="Device not understood"):\n info.default_dtypes(device="gpu")\n\n\ndef test_dtypes_all():\n dtypes = info.dtypes()\n assert dtypes == {\n "bool": np.bool_,\n "int8": np.int8,\n "int16": np.int16,\n "int32": np.int32,\n "int64": np.int64,\n "uint8": np.uint8,\n "uint16": np.uint16,\n "uint32": np.uint32,\n "uint64": np.uint64,\n "float32": np.float32,\n "float64": np.float64,\n "complex64": np.complex64,\n "complex128": np.complex128,\n }\n\n\ndtype_categories = {\n "bool": {"bool": np.bool_},\n "signed integer": {\n "int8": np.int8,\n "int16": np.int16,\n "int32": np.int32,\n "int64": np.int64,\n },\n "unsigned integer": {\n "uint8": np.uint8,\n "uint16": np.uint16,\n "uint32": np.uint32,\n "uint64": np.uint64,\n },\n "integral": ("signed integer", "unsigned integer"),\n "real floating": {"float32": np.float32, "float64": np.float64},\n "complex floating": {"complex64": np.complex64, "complex128":\n np.complex128},\n "numeric": ("integral", "real floating", "complex floating"),\n}\n\n\n@pytest.mark.parametrize("kind", dtype_categories)\ndef test_dtypes_kind(kind):\n expected = dtype_categories[kind]\n if isinstance(expected, tuple):\n assert info.dtypes(kind=kind) == info.dtypes(kind=expected)\n else:\n assert info.dtypes(kind=kind) == expected\n\n\ndef test_dtypes_tuple():\n dtypes = info.dtypes(kind=("bool", "integral"))\n assert dtypes == {\n "bool": np.bool_,\n "int8": np.int8,\n "int16": np.int16,\n "int32": np.int32,\n "int64": np.int64,\n "uint8": np.uint8,\n "uint16": np.uint16,\n "uint32": np.uint32,\n "uint64": np.uint64,\n }\n\n\ndef test_dtypes_invalid_kind():\n with pytest.raises(ValueError, match="unsupported kind"):\n info.dtypes(kind="invalid")\n\n\ndef test_dtypes_invalid_device():\n with pytest.raises(ValueError, match="Device not understood"):\n info.dtypes(device="gpu")\n\n\ndef test_devices():\n assert info.devices() == ["cpu"]\n | .venv\Lib\site-packages\numpy\_core\tests\test_array_api_info.py | test_array_api_info.py | Python | 3,176 | 0.95 | 0.088496 | 0.056818 | vue-tools | 3 | 2025-02-09T03:15:46.571453 | MIT | true | 8d838761700102bd66b8d0d5c9b5ece9 |
"""\nTests for array coercion, mainly through testing `np.array` results directly.\nNote that other such tests exist, e.g., in `test_api.py` and many corner-cases\nare tested (sometimes indirectly) elsewhere.\n"""\n\nfrom itertools import permutations, product\n\nimport numpy._core._multiarray_umath as ncu\nimport pytest\nfrom numpy._core._rational_tests import rational\nfrom pytest import param\n\nimport numpy as np\nfrom numpy.testing import IS_64BIT, IS_PYPY, assert_array_equal\n\n\ndef arraylikes():\n """\n Generator for functions converting an array into various array-likes.\n If full is True (default) it includes array-likes not capable of handling\n all dtypes.\n """\n # base array:\n def ndarray(a):\n return a\n\n yield param(ndarray, id="ndarray")\n\n # subclass:\n class MyArr(np.ndarray):\n pass\n\n def subclass(a):\n return a.view(MyArr)\n\n yield subclass\n\n class _SequenceLike:\n # Older NumPy versions, sometimes cared whether a protocol array was\n # also _SequenceLike. This shouldn't matter, but keep it for now\n # for __array__ and not the others.\n def __len__(self):\n raise TypeError\n\n def __getitem__(self, _, /):\n raise TypeError\n\n # Array-interface\n class ArrayDunder(_SequenceLike):\n def __init__(self, a):\n self.a = a\n\n def __array__(self, dtype=None, copy=None):\n if dtype is None:\n return self.a\n return self.a.astype(dtype)\n\n yield param(ArrayDunder, id="__array__")\n\n # memory-view\n yield param(memoryview, id="memoryview")\n\n # Array-interface\n class ArrayInterface:\n def __init__(self, a):\n self.a = a # need to hold on to keep interface valid\n self.__array_interface__ = a.__array_interface__\n\n yield param(ArrayInterface, id="__array_interface__")\n\n # Array-Struct\n class ArrayStruct:\n def __init__(self, a):\n self.a = a # need to hold on to keep struct valid\n self.__array_struct__ = a.__array_struct__\n\n yield param(ArrayStruct, id="__array_struct__")\n\n\ndef scalar_instances(times=True, extended_precision=True, user_dtype=True):\n # Hard-coded list of scalar instances.\n # Floats:\n yield param(np.sqrt(np.float16(5)), id="float16")\n yield param(np.sqrt(np.float32(5)), id="float32")\n yield param(np.sqrt(np.float64(5)), id="float64")\n if extended_precision:\n yield param(np.sqrt(np.longdouble(5)), id="longdouble")\n\n # Complex:\n yield param(np.sqrt(np.complex64(2 + 3j)), id="complex64")\n yield param(np.sqrt(np.complex128(2 + 3j)), id="complex128")\n if extended_precision:\n yield param(np.sqrt(np.clongdouble(2 + 3j)), id="clongdouble")\n\n # Bool:\n # XFAIL: Bool should be added, but has some bad properties when it\n # comes to strings, see also gh-9875\n # yield param(np.bool(0), id="bool")\n\n # Integers:\n yield param(np.int8(2), id="int8")\n yield param(np.int16(2), id="int16")\n yield param(np.int32(2), id="int32")\n yield param(np.int64(2), id="int64")\n\n yield param(np.uint8(2), id="uint8")\n yield param(np.uint16(2), id="uint16")\n yield param(np.uint32(2), id="uint32")\n yield param(np.uint64(2), id="uint64")\n\n # Rational:\n if user_dtype:\n yield param(rational(1, 2), id="rational")\n\n # Cannot create a structured void scalar directly:\n structured = np.array([(1, 3)], "i,i")[0]\n assert isinstance(structured, np.void)\n assert structured.dtype == np.dtype("i,i")\n yield param(structured, id="structured")\n\n if times:\n # Datetimes and timedelta\n yield param(np.timedelta64(2), id="timedelta64[generic]")\n yield param(np.timedelta64(23, "s"), id="timedelta64[s]")\n yield param(np.timedelta64("NaT", "s"), id="timedelta64[s](NaT)")\n\n yield param(np.datetime64("NaT"), id="datetime64[generic](NaT)")\n yield param(np.datetime64("2020-06-07 12:43", "ms"), id="datetime64[ms]")\n\n # Strings and unstructured void:\n yield param(np.bytes_(b"1234"), id="bytes")\n yield param(np.str_("2345"), id="unicode")\n yield param(np.void(b"4321"), id="unstructured_void")\n\n\ndef is_parametric_dtype(dtype):\n """Returns True if the dtype is a parametric legacy dtype (itemsize\n is 0, or a datetime without units)\n """\n if dtype.itemsize == 0:\n return True\n if issubclass(dtype.type, (np.datetime64, np.timedelta64)):\n if dtype.name.endswith("64"):\n # Generic time units\n return True\n return False\n\n\nclass TestStringDiscovery:\n @pytest.mark.parametrize("obj",\n [object(), 1.2, 10**43, None, "string"],\n ids=["object", "1.2", "10**43", "None", "string"])\n def test_basic_stringlength(self, obj):\n length = len(str(obj))\n expected = np.dtype(f"S{length}")\n\n assert np.array(obj, dtype="S").dtype == expected\n assert np.array([obj], dtype="S").dtype == expected\n\n # A nested array is also discovered correctly\n arr = np.array(obj, dtype="O")\n assert np.array(arr, dtype="S").dtype == expected\n # Also if we use the dtype class\n assert np.array(arr, dtype=type(expected)).dtype == expected\n # Check that .astype() behaves identical\n assert arr.astype("S").dtype == expected\n # The DType class is accepted by `.astype()`\n assert arr.astype(type(np.dtype("S"))).dtype == expected\n\n @pytest.mark.parametrize("obj",\n [object(), 1.2, 10**43, None, "string"],\n ids=["object", "1.2", "10**43", "None", "string"])\n def test_nested_arrays_stringlength(self, obj):\n length = len(str(obj))\n expected = np.dtype(f"S{length}")\n arr = np.array(obj, dtype="O")\n assert np.array([arr, arr], dtype="S").dtype == expected\n\n @pytest.mark.parametrize("arraylike", arraylikes())\n def test_unpack_first_level(self, arraylike):\n # We unpack exactly one level of array likes\n obj = np.array([None])\n obj[0] = np.array(1.2)\n # the length of the included item, not of the float dtype\n length = len(str(obj[0]))\n expected = np.dtype(f"S{length}")\n\n obj = arraylike(obj)\n # casting to string usually calls str(obj)\n arr = np.array([obj], dtype="S")\n assert arr.shape == (1, 1)\n assert arr.dtype == expected\n\n\nclass TestScalarDiscovery:\n def test_void_special_case(self):\n # Void dtypes with structures discover tuples as elements\n arr = np.array((1, 2, 3), dtype="i,i,i")\n assert arr.shape == ()\n arr = np.array([(1, 2, 3)], dtype="i,i,i")\n assert arr.shape == (1,)\n\n def test_char_special_case(self):\n arr = np.array("string", dtype="c")\n assert arr.shape == (6,)\n assert arr.dtype.char == "c"\n arr = np.array(["string"], dtype="c")\n assert arr.shape == (1, 6)\n assert arr.dtype.char == "c"\n\n def test_char_special_case_deep(self):\n # Check that the character special case errors correctly if the\n # array is too deep:\n nested = ["string"] # 2 dimensions (due to string being sequence)\n for i in range(ncu.MAXDIMS - 2):\n nested = [nested]\n\n arr = np.array(nested, dtype='c')\n assert arr.shape == (1,) * (ncu.MAXDIMS - 1) + (6,)\n with pytest.raises(ValueError):\n np.array([nested], dtype="c")\n\n def test_unknown_object(self):\n arr = np.array(object())\n assert arr.shape == ()\n assert arr.dtype == np.dtype("O")\n\n @pytest.mark.parametrize("scalar", scalar_instances())\n def test_scalar(self, scalar):\n arr = np.array(scalar)\n assert arr.shape == ()\n assert arr.dtype == scalar.dtype\n\n arr = np.array([[scalar, scalar]])\n assert arr.shape == (1, 2)\n assert arr.dtype == scalar.dtype\n\n # Additionally to string this test also runs into a corner case\n # with datetime promotion (the difference is the promotion order).\n @pytest.mark.filterwarnings("ignore:Promotion of numbers:FutureWarning")\n def test_scalar_promotion(self):\n for sc1, sc2 in product(scalar_instances(), scalar_instances()):\n sc1, sc2 = sc1.values[0], sc2.values[0]\n # test all combinations:\n try:\n arr = np.array([sc1, sc2])\n except (TypeError, ValueError):\n # The promotion between two times can fail\n # XFAIL (ValueError): Some object casts are currently undefined\n continue\n assert arr.shape == (2,)\n try:\n dt1, dt2 = sc1.dtype, sc2.dtype\n expected_dtype = np.promote_types(dt1, dt2)\n assert arr.dtype == expected_dtype\n except TypeError as e:\n # Will currently always go to object dtype\n assert arr.dtype == np.dtype("O")\n\n @pytest.mark.parametrize("scalar", scalar_instances())\n def test_scalar_coercion(self, scalar):\n # This tests various scalar coercion paths, mainly for the numerical\n # types. It includes some paths not directly related to `np.array`.\n if isinstance(scalar, np.inexact):\n # Ensure we have a full-precision number if available\n scalar = type(scalar)((scalar * 2)**0.5)\n\n if type(scalar) is rational:\n # Rational generally fails due to a missing cast. In the future\n # object casts should automatically be defined based on `setitem`.\n pytest.xfail("Rational to object cast is undefined currently.")\n\n # Use casting from object:\n arr = np.array(scalar, dtype=object).astype(scalar.dtype)\n\n # Test various ways to create an array containing this scalar:\n arr1 = np.array(scalar).reshape(1)\n arr2 = np.array([scalar])\n arr3 = np.empty(1, dtype=scalar.dtype)\n arr3[0] = scalar\n arr4 = np.empty(1, dtype=scalar.dtype)\n arr4[:] = [scalar]\n # All of these methods should yield the same results\n assert_array_equal(arr, arr1)\n assert_array_equal(arr, arr2)\n assert_array_equal(arr, arr3)\n assert_array_equal(arr, arr4)\n\n @pytest.mark.xfail(IS_PYPY, reason="`int(np.complex128(3))` fails on PyPy")\n @pytest.mark.filterwarnings("ignore::numpy.exceptions.ComplexWarning")\n @pytest.mark.parametrize("cast_to", scalar_instances())\n def test_scalar_coercion_same_as_cast_and_assignment(self, cast_to):\n """\n Test that in most cases:\n * `np.array(scalar, dtype=dtype)`\n * `np.empty((), dtype=dtype)[()] = scalar`\n * `np.array(scalar).astype(dtype)`\n should behave the same. The only exceptions are parametric dtypes\n (mainly datetime/timedelta without unit) and void without fields.\n """\n dtype = cast_to.dtype # use to parametrize only the target dtype\n\n for scalar in scalar_instances(times=False):\n scalar = scalar.values[0]\n\n if dtype.type == np.void:\n if scalar.dtype.fields is not None and dtype.fields is None:\n # Here, coercion to "V6" works, but the cast fails.\n # Since the types are identical, SETITEM takes care of\n # this, but has different rules than the cast.\n with pytest.raises(TypeError):\n np.array(scalar).astype(dtype)\n np.array(scalar, dtype=dtype)\n np.array([scalar], dtype=dtype)\n continue\n\n # The main test, we first try to use casting and if it succeeds\n # continue below testing that things are the same, otherwise\n # test that the alternative paths at least also fail.\n try:\n cast = np.array(scalar).astype(dtype)\n except (TypeError, ValueError, RuntimeError):\n # coercion should also raise (error type may change)\n with pytest.raises(Exception): # noqa: B017\n np.array(scalar, dtype=dtype)\n\n if (isinstance(scalar, rational) and\n np.issubdtype(dtype, np.signedinteger)):\n return\n\n with pytest.raises(Exception): # noqa: B017\n np.array([scalar], dtype=dtype)\n # assignment should also raise\n res = np.zeros((), dtype=dtype)\n with pytest.raises(Exception): # noqa: B017\n res[()] = scalar\n\n return\n\n # Non error path:\n arr = np.array(scalar, dtype=dtype)\n assert_array_equal(arr, cast)\n # assignment behaves the same\n ass = np.zeros((), dtype=dtype)\n ass[()] = scalar\n assert_array_equal(ass, cast)\n\n @pytest.mark.parametrize("pyscalar", [10, 10.32, 10.14j, 10**100])\n def test_pyscalar_subclasses(self, pyscalar):\n """NumPy arrays are read/write which means that anything but invariant\n behaviour is on thin ice. However, we currently are happy to discover\n subclasses of Python float, int, complex the same as the base classes.\n This should potentially be deprecated.\n """\n class MyScalar(type(pyscalar)):\n pass\n\n res = np.array(MyScalar(pyscalar))\n expected = np.array(pyscalar)\n assert_array_equal(res, expected)\n\n @pytest.mark.parametrize("dtype_char", np.typecodes["All"])\n def test_default_dtype_instance(self, dtype_char):\n if dtype_char in "SU":\n dtype = np.dtype(dtype_char + "1")\n elif dtype_char == "V":\n # Legacy behaviour was to use V8. The reason was float64 being the\n # default dtype and that having 8 bytes.\n dtype = np.dtype("V8")\n else:\n dtype = np.dtype(dtype_char)\n\n discovered_dtype, _ = ncu._discover_array_parameters([], type(dtype))\n\n assert discovered_dtype == dtype\n assert discovered_dtype.itemsize == dtype.itemsize\n\n @pytest.mark.parametrize("dtype", np.typecodes["Integer"])\n @pytest.mark.parametrize(["scalar", "error"],\n [(np.float64(np.nan), ValueError),\n (np.array(-1).astype(np.ulonglong)[()], OverflowError)])\n def test_scalar_to_int_coerce_does_not_cast(self, dtype, scalar, error):\n """\n Signed integers are currently different in that they do not cast other\n NumPy scalar, but instead use scalar.__int__(). The hardcoded\n exception to this rule is `np.array(scalar, dtype=integer)`.\n """\n dtype = np.dtype(dtype)\n\n # This is a special case using casting logic. It warns for the NaN\n # but allows the cast (giving undefined behaviour).\n with np.errstate(invalid="ignore"):\n coerced = np.array(scalar, dtype=dtype)\n cast = np.array(scalar).astype(dtype)\n assert_array_equal(coerced, cast)\n\n # However these fail:\n with pytest.raises(error):\n np.array([scalar], dtype=dtype)\n with pytest.raises(error):\n cast[()] = scalar\n\n\nclass TestTimeScalars:\n @pytest.mark.parametrize("dtype", [np.int64, np.float32])\n @pytest.mark.parametrize("scalar",\n [param(np.timedelta64("NaT", "s"), id="timedelta64[s](NaT)"),\n param(np.timedelta64(123, "s"), id="timedelta64[s]"),\n param(np.datetime64("NaT", "generic"), id="datetime64[generic](NaT)"),\n param(np.datetime64(1, "D"), id="datetime64[D]")],)\n def test_coercion_basic(self, dtype, scalar):\n # Note the `[scalar]` is there because np.array(scalar) uses stricter\n # `scalar.__int__()` rules for backward compatibility right now.\n arr = np.array(scalar, dtype=dtype)\n cast = np.array(scalar).astype(dtype)\n assert_array_equal(arr, cast)\n\n ass = np.ones((), dtype=dtype)\n if issubclass(dtype, np.integer):\n with pytest.raises(TypeError):\n # raises, as would np.array([scalar], dtype=dtype), this is\n # conversion from times, but behaviour of integers.\n ass[()] = scalar\n else:\n ass[()] = scalar\n assert_array_equal(ass, cast)\n\n @pytest.mark.parametrize("dtype", [np.int64, np.float32])\n @pytest.mark.parametrize("scalar",\n [param(np.timedelta64(123, "ns"), id="timedelta64[ns]"),\n param(np.timedelta64(12, "generic"), id="timedelta64[generic]")])\n def test_coercion_timedelta_convert_to_number(self, dtype, scalar):\n # Only "ns" and "generic" timedeltas can be converted to numbers\n # so these are slightly special.\n arr = np.array(scalar, dtype=dtype)\n cast = np.array(scalar).astype(dtype)\n ass = np.ones((), dtype=dtype)\n ass[()] = scalar # raises, as would np.array([scalar], dtype=dtype)\n\n assert_array_equal(arr, cast)\n assert_array_equal(cast, cast)\n\n @pytest.mark.parametrize("dtype", ["S6", "U6"])\n @pytest.mark.parametrize(["val", "unit"],\n [param(123, "s", id="[s]"), param(123, "D", id="[D]")])\n def test_coercion_assignment_datetime(self, val, unit, dtype):\n # String from datetime64 assignment is currently special cased to\n # never use casting. This is because casting will error in this\n # case, and traditionally in most cases the behaviour is maintained\n # like this. (`np.array(scalar, dtype="U6")` would have failed before)\n # TODO: This discrepancy _should_ be resolved, either by relaxing the\n # cast, or by deprecating the first part.\n scalar = np.datetime64(val, unit)\n dtype = np.dtype(dtype)\n cut_string = dtype.type(str(scalar)[:6])\n\n arr = np.array(scalar, dtype=dtype)\n assert arr[()] == cut_string\n ass = np.ones((), dtype=dtype)\n ass[()] = scalar\n assert ass[()] == cut_string\n\n with pytest.raises(RuntimeError):\n # However, unlike the above assignment using `str(scalar)[:6]`\n # due to being handled by the string DType and not be casting\n # the explicit cast fails:\n np.array(scalar).astype(dtype)\n\n @pytest.mark.parametrize(["val", "unit"],\n [param(123, "s", id="[s]"), param(123, "D", id="[D]")])\n def test_coercion_assignment_timedelta(self, val, unit):\n scalar = np.timedelta64(val, unit)\n\n # Unlike datetime64, timedelta allows the unsafe cast:\n np.array(scalar, dtype="S6")\n cast = np.array(scalar).astype("S6")\n ass = np.ones((), dtype="S6")\n ass[()] = scalar\n expected = scalar.astype("S")[:6]\n assert cast[()] == expected\n assert ass[()] == expected\n\nclass TestNested:\n def test_nested_simple(self):\n initial = [1.2]\n nested = initial\n for i in range(ncu.MAXDIMS - 1):\n nested = [nested]\n\n arr = np.array(nested, dtype="float64")\n assert arr.shape == (1,) * ncu.MAXDIMS\n with pytest.raises(ValueError):\n np.array([nested], dtype="float64")\n\n with pytest.raises(ValueError, match=".*would exceed the maximum"):\n np.array([nested]) # user must ask for `object` explicitly\n\n arr = np.array([nested], dtype=object)\n assert arr.dtype == np.dtype("O")\n assert arr.shape == (1,) * ncu.MAXDIMS\n assert arr.item() is initial\n\n def test_pathological_self_containing(self):\n # Test that this also works for two nested sequences\n l = []\n l.append(l)\n arr = np.array([l, l, l], dtype=object)\n assert arr.shape == (3,) + (1,) * (ncu.MAXDIMS - 1)\n\n # Also check a ragged case:\n arr = np.array([l, [None], l], dtype=object)\n assert arr.shape == (3, 1)\n\n @pytest.mark.parametrize("arraylike", arraylikes())\n def test_nested_arraylikes(self, arraylike):\n # We try storing an array like into an array, but the array-like\n # will have too many dimensions. This means the shape discovery\n # decides that the array-like must be treated as an object (a special\n # case of ragged discovery). The result will be an array with one\n # dimension less than the maximum dimensions, and the array being\n # assigned to it (which does work for object or if `float(arraylike)`\n # works).\n initial = arraylike(np.ones((1, 1)))\n\n nested = initial\n for i in range(ncu.MAXDIMS - 1):\n nested = [nested]\n\n with pytest.raises(ValueError, match=".*would exceed the maximum"):\n # It will refuse to assign the array into\n np.array(nested, dtype="float64")\n\n # If this is object, we end up assigning a (1, 1) array into (1,)\n # (due to running out of dimensions), this is currently supported but\n # a special case which is not ideal.\n arr = np.array(nested, dtype=object)\n assert arr.shape == (1,) * ncu.MAXDIMS\n assert arr.item() == np.array(initial).item()\n\n @pytest.mark.parametrize("arraylike", arraylikes())\n def test_uneven_depth_ragged(self, arraylike):\n arr = np.arange(4).reshape((2, 2))\n arr = arraylike(arr)\n\n # Array is ragged in the second dimension already:\n out = np.array([arr, [arr]], dtype=object)\n assert out.shape == (2,)\n assert out[0] is arr\n assert type(out[1]) is list\n\n # Array is ragged in the third dimension:\n with pytest.raises(ValueError):\n # This is a broadcast error during assignment, because\n # the array shape would be (2, 2, 2) but `arr[0, 0] = arr` fails.\n np.array([arr, [arr, arr]], dtype=object)\n\n def test_empty_sequence(self):\n arr = np.array([[], [1], [[1]]], dtype=object)\n assert arr.shape == (3,)\n\n # The empty sequence stops further dimension discovery, so the\n # result shape will be (0,) which leads to an error during:\n with pytest.raises(ValueError):\n np.array([[], np.empty((0, 1))], dtype=object)\n\n def test_array_of_different_depths(self):\n # When multiple arrays (or array-likes) are included in a\n # sequences and have different depth, we currently discover\n # as many dimensions as they share. (see also gh-17224)\n arr = np.zeros((3, 2))\n mismatch_first_dim = np.zeros((1, 2))\n mismatch_second_dim = np.zeros((3, 3))\n\n dtype, shape = ncu._discover_array_parameters(\n [arr, mismatch_second_dim], dtype=np.dtype("O"))\n assert shape == (2, 3)\n\n dtype, shape = ncu._discover_array_parameters(\n [arr, mismatch_first_dim], dtype=np.dtype("O"))\n assert shape == (2,)\n # The second case is currently supported because the arrays\n # can be stored as objects:\n res = np.asarray([arr, mismatch_first_dim], dtype=np.dtype("O"))\n assert res[0] is arr\n assert res[1] is mismatch_first_dim\n\n\nclass TestBadSequences:\n # These are tests for bad objects passed into `np.array`, in general\n # these have undefined behaviour. In the old code they partially worked\n # when now they will fail. We could (and maybe should) create a copy\n # of all sequences to be safe against bad-actors.\n\n def test_growing_list(self):\n # List to coerce, `mylist` will append to it during coercion\n obj = []\n\n class mylist(list):\n def __len__(self):\n obj.append([1, 2])\n return super().__len__()\n\n obj.append(mylist([1, 2]))\n\n with pytest.raises(RuntimeError):\n np.array(obj)\n\n # Note: We do not test a shrinking list. These do very evil things\n # and the only way to fix them would be to copy all sequences.\n # (which may be a real option in the future).\n\n def test_mutated_list(self):\n # List to coerce, `mylist` will mutate the first element\n obj = []\n\n class mylist(list):\n def __len__(self):\n obj[0] = [2, 3] # replace with a different list.\n return super().__len__()\n\n obj.append([2, 3])\n obj.append(mylist([1, 2]))\n # Does not crash:\n np.array(obj)\n\n def test_replace_0d_array(self):\n # List to coerce, `mylist` will mutate the first element\n obj = []\n\n class baditem:\n def __len__(self):\n obj[0][0] = 2 # replace with a different list.\n raise ValueError("not actually a sequence!")\n\n def __getitem__(self, _, /):\n pass\n\n # Runs into a corner case in the new code, the `array(2)` is cached\n # so replacing it invalidates the cache.\n obj.append([np.array(2), baditem()])\n with pytest.raises(RuntimeError):\n np.array(obj)\n\n\nclass TestArrayLikes:\n @pytest.mark.parametrize("arraylike", arraylikes())\n def test_0d_object_special_case(self, arraylike):\n arr = np.array(0.)\n obj = arraylike(arr)\n # A single array-like is always converted:\n res = np.array(obj, dtype=object)\n assert_array_equal(arr, res)\n\n # But a single 0-D nested array-like never:\n res = np.array([obj], dtype=object)\n assert res[0] is obj\n\n @pytest.mark.parametrize("arraylike", arraylikes())\n @pytest.mark.parametrize("arr", [np.array(0.), np.arange(4)])\n def test_object_assignment_special_case(self, arraylike, arr):\n obj = arraylike(arr)\n empty = np.arange(1, dtype=object)\n empty[:] = [obj]\n assert empty[0] is obj\n\n def test_0d_generic_special_case(self):\n class ArraySubclass(np.ndarray):\n def __float__(self):\n raise TypeError("e.g. quantities raise on this")\n\n arr = np.array(0.)\n obj = arr.view(ArraySubclass)\n res = np.array(obj)\n # The subclass is simply cast:\n assert_array_equal(arr, res)\n\n # If the 0-D array-like is included, __float__ is currently\n # guaranteed to be used. We may want to change that, quantities\n # and masked arrays half make use of this.\n with pytest.raises(TypeError):\n np.array([obj])\n\n # The same holds for memoryview:\n obj = memoryview(arr)\n res = np.array(obj)\n assert_array_equal(arr, res)\n with pytest.raises(ValueError):\n # The error type does not matter much here.\n np.array([obj])\n\n def test_arraylike_classes(self):\n # The classes of array-likes should generally be acceptable to be\n # stored inside a numpy (object) array. This tests all of the\n # special attributes (since all are checked during coercion).\n arr = np.array(np.int64)\n assert arr[()] is np.int64\n arr = np.array([np.int64])\n assert arr[0] is np.int64\n\n # This also works for properties/unbound methods:\n class ArrayLike:\n @property\n def __array_interface__(self):\n pass\n\n @property\n def __array_struct__(self):\n pass\n\n def __array__(self, dtype=None, copy=None):\n pass\n\n arr = np.array(ArrayLike)\n assert arr[()] is ArrayLike\n arr = np.array([ArrayLike])\n assert arr[0] is ArrayLike\n\n @pytest.mark.skipif(not IS_64BIT, reason="Needs 64bit platform")\n def test_too_large_array_error_paths(self):\n """Test the error paths, including for memory leaks"""\n arr = np.array(0, dtype="uint8")\n # Guarantees that a contiguous copy won't work:\n arr = np.broadcast_to(arr, 2**62)\n\n for i in range(5):\n # repeat, to ensure caching cannot have an effect:\n with pytest.raises(MemoryError):\n np.array(arr)\n with pytest.raises(MemoryError):\n np.array([arr])\n\n @pytest.mark.parametrize("attribute",\n ["__array_interface__", "__array__", "__array_struct__"])\n @pytest.mark.parametrize("error", [RecursionError, MemoryError])\n def test_bad_array_like_attributes(self, attribute, error):\n # RecursionError and MemoryError are considered fatal. All errors\n # (except AttributeError) should probably be raised in the future,\n # but shapely made use of it, so it will require a deprecation.\n\n class BadInterface:\n def __getattr__(self, attr):\n if attr == attribute:\n raise error\n super().__getattr__(attr)\n\n with pytest.raises(error):\n np.array(BadInterface())\n\n @pytest.mark.parametrize("error", [RecursionError, MemoryError])\n def test_bad_array_like_bad_length(self, error):\n # RecursionError and MemoryError are considered "critical" in\n # sequences. We could expand this more generally though. (NumPy 1.20)\n class BadSequence:\n def __len__(self):\n raise error\n\n def __getitem__(self, _, /):\n # must have getitem to be a Sequence\n return 1\n\n with pytest.raises(error):\n np.array(BadSequence())\n\n def test_array_interface_descr_optional(self):\n # The descr should be optional regression test for gh-27249\n arr = np.ones(10, dtype="V10")\n iface = arr.__array_interface__\n iface.pop("descr")\n\n class MyClass:\n __array_interface__ = iface\n\n assert_array_equal(np.asarray(MyClass), arr)\n\n\nclass TestAsArray:\n """Test expected behaviors of ``asarray``."""\n\n def test_dtype_identity(self):\n """Confirm the intended behavior for *dtype* kwarg.\n\n The result of ``asarray()`` should have the dtype provided through the\n keyword argument, when used. This forces unique array handles to be\n produced for unique np.dtype objects, but (for equivalent dtypes), the\n underlying data (the base object) is shared with the original array\n object.\n\n Ref https://github.com/numpy/numpy/issues/1468\n """\n int_array = np.array([1, 2, 3], dtype='i')\n assert np.asarray(int_array) is int_array\n\n # The character code resolves to the singleton dtype object provided\n # by the numpy package.\n assert np.asarray(int_array, dtype='i') is int_array\n\n # Derive a dtype from n.dtype('i'), but add a metadata object to force\n # the dtype to be distinct.\n unequal_type = np.dtype('i', metadata={'spam': True})\n annotated_int_array = np.asarray(int_array, dtype=unequal_type)\n assert annotated_int_array is not int_array\n assert annotated_int_array.base is int_array\n # Create an equivalent descriptor with a new and distinct dtype\n # instance.\n equivalent_requirement = np.dtype('i', metadata={'spam': True})\n annotated_int_array_alt = np.asarray(annotated_int_array,\n dtype=equivalent_requirement)\n assert unequal_type == equivalent_requirement\n assert unequal_type is not equivalent_requirement\n assert annotated_int_array_alt is not annotated_int_array\n assert annotated_int_array_alt.dtype is equivalent_requirement\n\n # Check the same logic for a pair of C types whose equivalence may vary\n # between computing environments.\n # Find an equivalent pair.\n integer_type_codes = ('i', 'l', 'q')\n integer_dtypes = [np.dtype(code) for code in integer_type_codes]\n typeA = None\n typeB = None\n for typeA, typeB in permutations(integer_dtypes, r=2):\n if typeA == typeB:\n assert typeA is not typeB\n break\n assert isinstance(typeA, np.dtype) and isinstance(typeB, np.dtype)\n\n # These ``asarray()`` calls may produce a new view or a copy,\n # but never the same object.\n long_int_array = np.asarray(int_array, dtype='l')\n long_long_int_array = np.asarray(int_array, dtype='q')\n assert long_int_array is not int_array\n assert long_long_int_array is not int_array\n assert np.asarray(long_int_array, dtype='q') is not long_int_array\n array_a = np.asarray(int_array, dtype=typeA)\n assert typeA == typeB\n assert typeA is not typeB\n assert array_a.dtype is typeA\n assert array_a is not np.asarray(array_a, dtype=typeB)\n assert np.asarray(array_a, dtype=typeB).dtype is typeB\n assert array_a is np.asarray(array_a, dtype=typeB).base\n\n\nclass TestSpecialAttributeLookupFailure:\n # An exception was raised while fetching the attribute\n\n class WeirdArrayLike:\n @property\n def __array__(self, dtype=None, copy=None): # noqa: PLR0206\n raise RuntimeError("oops!")\n\n class WeirdArrayInterface:\n @property\n def __array_interface__(self):\n raise RuntimeError("oops!")\n\n def test_deprecated(self):\n with pytest.raises(RuntimeError):\n np.array(self.WeirdArrayLike())\n with pytest.raises(RuntimeError):\n np.array(self.WeirdArrayInterface())\n\n\ndef test_subarray_from_array_construction():\n # Arrays are more complex, since they "broadcast" on success:\n arr = np.array([1, 2])\n\n res = arr.astype("2i")\n assert_array_equal(res, [[1, 1], [2, 2]])\n\n res = np.array(arr, dtype="(2,)i")\n\n assert_array_equal(res, [[1, 1], [2, 2]])\n\n res = np.array([[(1,), (2,)], arr], dtype="2i")\n assert_array_equal(res, [[[1, 1], [2, 2]], [[1, 1], [2, 2]]])\n\n # Also try a multi-dimensional example:\n arr = np.arange(5 * 2).reshape(5, 2)\n expected = np.broadcast_to(arr[:, :, np.newaxis, np.newaxis], (5, 2, 2, 2))\n\n res = arr.astype("(2,2)f")\n assert_array_equal(res, expected)\n\n res = np.array(arr, dtype="(2,2)f")\n assert_array_equal(res, expected)\n\n\ndef test_empty_string():\n # Empty strings are unfortunately often converted to S1 and we need to\n # make sure we are filling the S1 and not the (possibly) detected S0\n # result. This should likely just return S0 and if not maybe the decision\n # to return S1 should be moved.\n res = np.array([""] * 10, dtype="S")\n assert_array_equal(res, np.array("\0", "S1"))\n assert res.dtype == "S1"\n\n arr = np.array([""] * 10, dtype=object)\n\n res = arr.astype("S")\n assert_array_equal(res, b"")\n assert res.dtype == "S1"\n\n res = np.array(arr, dtype="S")\n assert_array_equal(res, b"")\n # TODO: This is arguably weird/wrong, but seems old:\n assert res.dtype == f"S{np.dtype('O').itemsize}"\n\n res = np.array([[""] * 10, arr], dtype="S")\n assert_array_equal(res, b"")\n assert res.shape == (2, 10)\n assert res.dtype == "S1"\n | .venv\Lib\site-packages\numpy\_core\tests\test_array_coercion.py | test_array_coercion.py | Python | 35,808 | 0.95 | 0.161361 | 0.209893 | node-utils | 376 | 2024-01-20T09:15:54.810480 | BSD-3-Clause | true | a350ad06e34a290789667676d547c818 |
import sys\nimport sysconfig\n\nimport pytest\n\nimport numpy as np\nfrom numpy.testing import IS_EDITABLE, IS_WASM, extbuild\n\n\n@pytest.fixture\ndef get_module(tmp_path):\n """ Some codes to generate data and manage temporary buffers use when\n sharing with numpy via the array interface protocol.\n """\n if sys.platform.startswith('cygwin'):\n pytest.skip('link fails on cygwin')\n if IS_WASM:\n pytest.skip("Can't build module inside Wasm")\n if IS_EDITABLE:\n pytest.skip("Can't build module for editable install")\n\n prologue = '''\n #include <Python.h>\n #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\n #include <numpy/arrayobject.h>\n #include <stdio.h>\n #include <math.h>\n\n NPY_NO_EXPORT\n void delete_array_struct(PyObject *cap) {\n\n /* get the array interface structure */\n PyArrayInterface *inter = (PyArrayInterface*)\n PyCapsule_GetPointer(cap, NULL);\n\n /* get the buffer by which data was shared */\n double *ptr = (double*)PyCapsule_GetContext(cap);\n\n /* for the purposes of the regression test set the elements\n to nan */\n for (npy_intp i = 0; i < inter->shape[0]; ++i)\n ptr[i] = nan("");\n\n /* free the shared buffer */\n free(ptr);\n\n /* free the array interface structure */\n free(inter->shape);\n free(inter);\n\n fprintf(stderr, "delete_array_struct\\ncap = %ld inter = %ld"\n " ptr = %ld\\n", (long)cap, (long)inter, (long)ptr);\n }\n '''\n\n functions = [\n ("new_array_struct", "METH_VARARGS", """\n\n long long n_elem = 0;\n double value = 0.0;\n\n if (!PyArg_ParseTuple(args, "Ld", &n_elem, &value)) {\n Py_RETURN_NONE;\n }\n\n /* allocate and initialize the data to share with numpy */\n long long n_bytes = n_elem*sizeof(double);\n double *data = (double*)malloc(n_bytes);\n\n if (!data) {\n PyErr_Format(PyExc_MemoryError,\n "Failed to malloc %lld bytes", n_bytes);\n\n Py_RETURN_NONE;\n }\n\n for (long long i = 0; i < n_elem; ++i) {\n data[i] = value;\n }\n\n /* calculate the shape and stride */\n int nd = 1;\n\n npy_intp *ss = (npy_intp*)malloc(2*nd*sizeof(npy_intp));\n npy_intp *shape = ss;\n npy_intp *stride = ss + nd;\n\n shape[0] = n_elem;\n stride[0] = sizeof(double);\n\n /* construct the array interface */\n PyArrayInterface *inter = (PyArrayInterface*)\n malloc(sizeof(PyArrayInterface));\n\n memset(inter, 0, sizeof(PyArrayInterface));\n\n inter->two = 2;\n inter->nd = nd;\n inter->typekind = 'f';\n inter->itemsize = sizeof(double);\n inter->shape = shape;\n inter->strides = stride;\n inter->data = data;\n inter->flags = NPY_ARRAY_WRITEABLE | NPY_ARRAY_NOTSWAPPED |\n NPY_ARRAY_ALIGNED | NPY_ARRAY_C_CONTIGUOUS;\n\n /* package into a capsule */\n PyObject *cap = PyCapsule_New(inter, NULL, delete_array_struct);\n\n /* save the pointer to the data */\n PyCapsule_SetContext(cap, data);\n\n fprintf(stderr, "new_array_struct\\ncap = %ld inter = %ld"\n " ptr = %ld\\n", (long)cap, (long)inter, (long)data);\n\n return cap;\n """)\n ]\n\n more_init = "import_array();"\n\n try:\n import array_interface_testing\n return array_interface_testing\n except ImportError:\n pass\n\n # if it does not exist, build and load it\n if sysconfig.get_platform() == "win-arm64":\n pytest.skip("Meson unable to find MSVC linker on win-arm64")\n return extbuild.build_and_import_extension('array_interface_testing',\n functions,\n prologue=prologue,\n include_dirs=[np.get_include()],\n build_dir=tmp_path,\n more_init=more_init)\n\n\n@pytest.mark.slow\ndef test_cstruct(get_module):\n\n class data_source:\n """\n This class is for testing the timing of the PyCapsule destructor\n invoked when numpy release its reference to the shared data as part of\n the numpy array interface protocol. If the PyCapsule destructor is\n called early the shared data is freed and invalid memory accesses will\n occur.\n """\n\n def __init__(self, size, value):\n self.size = size\n self.value = value\n\n @property\n def __array_struct__(self):\n return get_module.new_array_struct(self.size, self.value)\n\n # write to the same stream as the C code\n stderr = sys.__stderr__\n\n # used to validate the shared data.\n expected_value = -3.1415\n multiplier = -10000.0\n\n # create some data to share with numpy via the array interface\n # assign the data an expected value.\n stderr.write(' ---- create an object to share data ---- \n')\n buf = data_source(256, expected_value)\n stderr.write(' ---- OK!\n\n')\n\n # share the data\n stderr.write(' ---- share data via the array interface protocol ---- \n')\n arr = np.array(buf, copy=False)\n stderr.write(f'arr.__array_interface___ = {str(arr.__array_interface__)}\n')\n stderr.write(f'arr.base = {str(arr.base)}\n')\n stderr.write(' ---- OK!\n\n')\n\n # release the source of the shared data. this will not release the data\n # that was shared with numpy, that is done in the PyCapsule destructor.\n stderr.write(' ---- destroy the object that shared data ---- \n')\n buf = None\n stderr.write(' ---- OK!\n\n')\n\n # check that we got the expected data. If the PyCapsule destructor we\n # defined was prematurely called then this test will fail because our\n # destructor sets the elements of the array to NaN before free'ing the\n # buffer. Reading the values here may also cause a SEGV\n assert np.allclose(arr, expected_value)\n\n # read the data. If the PyCapsule destructor we defined was prematurely\n # called then reading the values here may cause a SEGV and will be reported\n # as invalid reads by valgrind\n stderr.write(' ---- read shared data ---- \n')\n stderr.write(f'arr = {str(arr)}\n')\n stderr.write(' ---- OK!\n\n')\n\n # write to the shared buffer. If the shared data was prematurely deleted\n # this will may cause a SEGV and valgrind will report invalid writes\n stderr.write(' ---- modify shared data ---- \n')\n arr *= multiplier\n expected_value *= multiplier\n stderr.write(f'arr.__array_interface___ = {str(arr.__array_interface__)}\n')\n stderr.write(f'arr.base = {str(arr.base)}\n')\n stderr.write(' ---- OK!\n\n')\n\n # read the data. If the shared data was prematurely deleted this\n # will may cause a SEGV and valgrind will report invalid reads\n stderr.write(' ---- read modified shared data ---- \n')\n stderr.write(f'arr = {str(arr)}\n')\n stderr.write(' ---- OK!\n\n')\n\n # check that we got the expected data. If the PyCapsule destructor we\n # defined was prematurely called then this test will fail because our\n # destructor sets the elements of the array to NaN before free'ing the\n # buffer. Reading the values here may also cause a SEGV\n assert np.allclose(arr, expected_value)\n\n # free the shared data, the PyCapsule destructor should run here\n stderr.write(' ---- free shared data ---- \n')\n arr = None\n stderr.write(' ---- OK!\n\n')\n | .venv\Lib\site-packages\numpy\_core\tests\test_array_interface.py | test_array_interface.py | Python | 8,065 | 0.95 | 0.085586 | 0.224138 | vue-tools | 295 | 2025-06-28T21:50:41.174283 | BSD-3-Clause | true | b0f22390e95e2304cbbff4966da228ff |
import pytest\nfrom pytest import param\n\nimport numpy as np\nfrom numpy.testing import IS_WASM\n\n\ndef values_and_dtypes():\n """\n Generate value+dtype pairs that generate floating point errors during\n casts. The invalid casts to integers will generate "invalid" value\n warnings, the float casts all generate "overflow".\n\n (The Python int/float paths don't need to get tested in all the same\n situations, but it does not hurt.)\n """\n # Casting to float16:\n yield param(70000, "float16", id="int-to-f2")\n yield param("70000", "float16", id="str-to-f2")\n yield param(70000.0, "float16", id="float-to-f2")\n yield param(np.longdouble(70000.), "float16", id="longdouble-to-f2")\n yield param(np.float64(70000.), "float16", id="double-to-f2")\n yield param(np.float32(70000.), "float16", id="float-to-f2")\n # Casting to float32:\n yield param(10**100, "float32", id="int-to-f4")\n yield param(1e100, "float32", id="float-to-f2")\n yield param(np.longdouble(1e300), "float32", id="longdouble-to-f2")\n yield param(np.float64(1e300), "float32", id="double-to-f2")\n # Casting to float64:\n # If longdouble is double-double, its max can be rounded down to the double\n # max. So we correct the double spacing (a bit weird, admittedly):\n max_ld = np.finfo(np.longdouble).max\n spacing = np.spacing(np.nextafter(np.finfo("f8").max, 0))\n if max_ld - spacing > np.finfo("f8").max:\n yield param(np.finfo(np.longdouble).max, "float64",\n id="longdouble-to-f8")\n\n # Cast to complex32:\n yield param(2e300, "complex64", id="float-to-c8")\n yield param(2e300 + 0j, "complex64", id="complex-to-c8")\n yield param(2e300j, "complex64", id="complex-to-c8")\n yield param(np.longdouble(2e300), "complex64", id="longdouble-to-c8")\n\n # Invalid float to integer casts:\n with np.errstate(over="ignore"):\n for to_dt in np.typecodes["AllInteger"]:\n for value in [np.inf, np.nan]:\n for from_dt in np.typecodes["AllFloat"]:\n from_dt = np.dtype(from_dt)\n from_val = from_dt.type(value)\n\n yield param(from_val, to_dt, id=f"{from_val}-to-{to_dt}")\n\n\ndef check_operations(dtype, value):\n """\n There are many dedicated paths in NumPy which cast and should check for\n floating point errors which occurred during those casts.\n """\n if dtype.kind != 'i':\n # These assignments use the stricter setitem logic:\n def assignment():\n arr = np.empty(3, dtype=dtype)\n arr[0] = value\n\n yield assignment\n\n def fill():\n arr = np.empty(3, dtype=dtype)\n arr.fill(value)\n\n yield fill\n\n def copyto_scalar():\n arr = np.empty(3, dtype=dtype)\n np.copyto(arr, value, casting="unsafe")\n\n yield copyto_scalar\n\n def copyto():\n arr = np.empty(3, dtype=dtype)\n np.copyto(arr, np.array([value, value, value]), casting="unsafe")\n\n yield copyto\n\n def copyto_scalar_masked():\n arr = np.empty(3, dtype=dtype)\n np.copyto(arr, value, casting="unsafe",\n where=[True, False, True])\n\n yield copyto_scalar_masked\n\n def copyto_masked():\n arr = np.empty(3, dtype=dtype)\n np.copyto(arr, np.array([value, value, value]), casting="unsafe",\n where=[True, False, True])\n\n yield copyto_masked\n\n def direct_cast():\n np.array([value, value, value]).astype(dtype)\n\n yield direct_cast\n\n def direct_cast_nd_strided():\n arr = np.full((5, 5, 5), fill_value=value)[:, ::2, :]\n arr.astype(dtype)\n\n yield direct_cast_nd_strided\n\n def boolean_array_assignment():\n arr = np.empty(3, dtype=dtype)\n arr[[True, False, True]] = np.array([value, value])\n\n yield boolean_array_assignment\n\n def integer_array_assignment():\n arr = np.empty(3, dtype=dtype)\n values = np.array([value, value])\n\n arr[[0, 1]] = values\n\n yield integer_array_assignment\n\n def integer_array_assignment_with_subspace():\n arr = np.empty((5, 3), dtype=dtype)\n values = np.array([value, value, value])\n\n arr[[0, 2]] = values\n\n yield integer_array_assignment_with_subspace\n\n def flat_assignment():\n arr = np.empty((3,), dtype=dtype)\n values = np.array([value, value, value])\n arr.flat[:] = values\n\n yield flat_assignment\n\n@pytest.mark.skipif(IS_WASM, reason="no wasm fp exception support")\n@pytest.mark.parametrize(["value", "dtype"], values_and_dtypes())\n@pytest.mark.filterwarnings("ignore::numpy.exceptions.ComplexWarning")\ndef test_floatingpoint_errors_casting(dtype, value):\n dtype = np.dtype(dtype)\n for operation in check_operations(dtype, value):\n dtype = np.dtype(dtype)\n\n match = "invalid" if dtype.kind in 'iu' else "overflow"\n with pytest.warns(RuntimeWarning, match=match):\n operation()\n\n with np.errstate(all="raise"):\n with pytest.raises(FloatingPointError, match=match):\n operation()\n | .venv\Lib\site-packages\numpy\_core\tests\test_casting_floatingpoint_errors.py | test_casting_floatingpoint_errors.py | Python | 5,230 | 0.95 | 0.149351 | 0.068376 | react-lib | 496 | 2023-11-11T06:19:48.734304 | BSD-3-Clause | true | e4643b3db941ae641462d613f57f372c |
"""\nThe tests exercise the casting machinery in a more low-level manner.\nThe reason is mostly to test a new implementation of the casting machinery.\n\nUnlike most tests in NumPy, these are closer to unit-tests rather\nthan integration tests.\n"""\n\nimport ctypes\nimport enum\nimport random\nimport textwrap\n\nimport pytest\nfrom numpy._core._multiarray_umath import _get_castingimpl as get_castingimpl\n\nimport numpy as np\nfrom numpy.lib.stride_tricks import as_strided\nfrom numpy.testing import assert_array_equal\n\n# Simple skips object, parametric and long double (unsupported by struct)\nsimple_dtypes = "?bhilqBHILQefdFD"\nif np.dtype("l").itemsize != np.dtype("q").itemsize:\n # Remove l and L, the table was generated with 64bit linux in mind.\n simple_dtypes = simple_dtypes.replace("l", "").replace("L", "")\nsimple_dtypes = [type(np.dtype(c)) for c in simple_dtypes]\n\n\ndef simple_dtype_instances():\n for dtype_class in simple_dtypes:\n dt = dtype_class()\n yield pytest.param(dt, id=str(dt))\n if dt.byteorder != "|":\n dt = dt.newbyteorder()\n yield pytest.param(dt, id=str(dt))\n\n\ndef get_expected_stringlength(dtype):\n """Returns the string length when casting the basic dtypes to strings.\n """\n if dtype == np.bool:\n return 5\n if dtype.kind in "iu":\n if dtype.itemsize == 1:\n length = 3\n elif dtype.itemsize == 2:\n length = 5\n elif dtype.itemsize == 4:\n length = 10\n elif dtype.itemsize == 8:\n length = 20\n else:\n raise AssertionError(f"did not find expected length for {dtype}")\n\n if dtype.kind == "i":\n length += 1 # adds one character for the sign\n\n return length\n\n # Note: Can't do dtype comparison for longdouble on windows\n if dtype.char == "g":\n return 48\n elif dtype.char == "G":\n return 48 * 2\n elif dtype.kind == "f":\n return 32 # also for half apparently.\n elif dtype.kind == "c":\n return 32 * 2\n\n raise AssertionError(f"did not find expected length for {dtype}")\n\n\nclass Casting(enum.IntEnum):\n no = 0\n equiv = 1\n safe = 2\n same_kind = 3\n unsafe = 4\n\n\ndef _get_cancast_table():\n table = textwrap.dedent("""\n X ? b h i l q B H I L Q e f d g F D G S U V O M m\n ? # = = = = = = = = = = = = = = = = = = = = = . =\n b . # = = = = . . . . . = = = = = = = = = = = . =\n h . ~ # = = = . . . . . ~ = = = = = = = = = = . =\n i . ~ ~ # = = . . . . . ~ ~ = = ~ = = = = = = . =\n l . ~ ~ ~ # # . . . . . ~ ~ = = ~ = = = = = = . =\n q . ~ ~ ~ # # . . . . . ~ ~ = = ~ = = = = = = . =\n B . ~ = = = = # = = = = = = = = = = = = = = = . =\n H . ~ ~ = = = ~ # = = = ~ = = = = = = = = = = . =\n I . ~ ~ ~ = = ~ ~ # = = ~ ~ = = ~ = = = = = = . =\n L . ~ ~ ~ ~ ~ ~ ~ ~ # # ~ ~ = = ~ = = = = = = . ~\n Q . ~ ~ ~ ~ ~ ~ ~ ~ # # ~ ~ = = ~ = = = = = = . ~\n e . . . . . . . . . . . # = = = = = = = = = = . .\n f . . . . . . . . . . . ~ # = = = = = = = = = . .\n d . . . . . . . . . . . ~ ~ # = ~ = = = = = = . .\n g . . . . . . . . . . . ~ ~ ~ # ~ ~ = = = = = . .\n F . . . . . . . . . . . . . . . # = = = = = = . .\n D . . . . . . . . . . . . . . . ~ # = = = = = . .\n G . . . . . . . . . . . . . . . ~ ~ # = = = = . .\n S . . . . . . . . . . . . . . . . . . # = = = . .\n U . . . . . . . . . . . . . . . . . . . # = = . .\n V . . . . . . . . . . . . . . . . . . . . # = . .\n O . . . . . . . . . . . . . . . . . . . . = # . .\n M . . . . . . . . . . . . . . . . . . . . = = # .\n m . . . . . . . . . . . . . . . . . . . . = = . #\n """).strip().split("\n")\n dtypes = [type(np.dtype(c)) for c in table[0][2::2]]\n\n convert_cast = {".": Casting.unsafe, "~": Casting.same_kind,\n "=": Casting.safe, "#": Casting.equiv,\n " ": -1}\n\n cancast = {}\n for from_dt, row in zip(dtypes, table[1:]):\n cancast[from_dt] = {}\n for to_dt, c in zip(dtypes, row[2::2]):\n cancast[from_dt][to_dt] = convert_cast[c]\n\n return cancast\n\n\nCAST_TABLE = _get_cancast_table()\n\n\nclass TestChanges:\n """\n These test cases exercise some behaviour changes\n """\n @pytest.mark.parametrize("string", ["S", "U"])\n @pytest.mark.parametrize("floating", ["e", "f", "d", "g"])\n def test_float_to_string(self, floating, string):\n assert np.can_cast(floating, string)\n # 100 is long enough to hold any formatted floating\n assert np.can_cast(floating, f"{string}100")\n\n def test_to_void(self):\n # But in general, we do consider these safe:\n assert np.can_cast("d", "V")\n assert np.can_cast("S20", "V")\n\n # Do not consider it a safe cast if the void is too smaller:\n assert not np.can_cast("d", "V1")\n assert not np.can_cast("S20", "V1")\n assert not np.can_cast("U1", "V1")\n # Structured to unstructured is just like any other:\n assert np.can_cast("d,i", "V", casting="same_kind")\n # Unstructured void to unstructured is actually no cast at all:\n assert np.can_cast("V3", "V", casting="no")\n assert np.can_cast("V0", "V", casting="no")\n\n\nclass TestCasting:\n size = 1500 # Best larger than NPY_LOWLEVEL_BUFFER_BLOCKSIZE * itemsize\n\n def get_data(self, dtype1, dtype2):\n if dtype2 is None or dtype1.itemsize >= dtype2.itemsize:\n length = self.size // dtype1.itemsize\n else:\n length = self.size // dtype2.itemsize\n\n # Assume that the base array is well enough aligned for all inputs.\n arr1 = np.empty(length, dtype=dtype1)\n assert arr1.flags.c_contiguous\n assert arr1.flags.aligned\n\n values = [random.randrange(-128, 128) for _ in range(length)]\n\n for i, value in enumerate(values):\n # Use item assignment to ensure this is not using casting:\n if value < 0 and dtype1.kind == "u":\n # Manually rollover unsigned integers (-1 -> int.max)\n value = value + np.iinfo(dtype1).max + 1\n arr1[i] = value\n\n if dtype2 is None:\n if dtype1.char == "?":\n values = [bool(v) for v in values]\n return arr1, values\n\n if dtype2.char == "?":\n values = [bool(v) for v in values]\n\n arr2 = np.empty(length, dtype=dtype2)\n assert arr2.flags.c_contiguous\n assert arr2.flags.aligned\n\n for i, value in enumerate(values):\n # Use item assignment to ensure this is not using casting:\n if value < 0 and dtype2.kind == "u":\n # Manually rollover unsigned integers (-1 -> int.max)\n value = value + np.iinfo(dtype2).max + 1\n arr2[i] = value\n\n return arr1, arr2, values\n\n def get_data_variation(self, arr1, arr2, aligned=True, contig=True):\n """\n Returns a copy of arr1 that may be non-contiguous or unaligned, and a\n matching array for arr2 (although not a copy).\n """\n if contig:\n stride1 = arr1.dtype.itemsize\n stride2 = arr2.dtype.itemsize\n elif aligned:\n stride1 = 2 * arr1.dtype.itemsize\n stride2 = 2 * arr2.dtype.itemsize\n else:\n stride1 = arr1.dtype.itemsize + 1\n stride2 = arr2.dtype.itemsize + 1\n\n max_size1 = len(arr1) * 3 * arr1.dtype.itemsize + 1\n max_size2 = len(arr2) * 3 * arr2.dtype.itemsize + 1\n from_bytes = np.zeros(max_size1, dtype=np.uint8)\n to_bytes = np.zeros(max_size2, dtype=np.uint8)\n\n # Sanity check that the above is large enough:\n assert stride1 * len(arr1) <= from_bytes.nbytes\n assert stride2 * len(arr2) <= to_bytes.nbytes\n\n if aligned:\n new1 = as_strided(from_bytes[:-1].view(arr1.dtype),\n arr1.shape, (stride1,))\n new2 = as_strided(to_bytes[:-1].view(arr2.dtype),\n arr2.shape, (stride2,))\n else:\n new1 = as_strided(from_bytes[1:].view(arr1.dtype),\n arr1.shape, (stride1,))\n new2 = as_strided(to_bytes[1:].view(arr2.dtype),\n arr2.shape, (stride2,))\n\n new1[...] = arr1\n\n if not contig:\n # Ensure we did not overwrite bytes that should not be written:\n offset = arr1.dtype.itemsize if aligned else 0\n buf = from_bytes[offset::stride1].tobytes()\n assert buf.count(b"\0") == len(buf)\n\n if contig:\n assert new1.flags.c_contiguous\n assert new2.flags.c_contiguous\n else:\n assert not new1.flags.c_contiguous\n assert not new2.flags.c_contiguous\n\n if aligned:\n assert new1.flags.aligned\n assert new2.flags.aligned\n else:\n assert not new1.flags.aligned or new1.dtype.alignment == 1\n assert not new2.flags.aligned or new2.dtype.alignment == 1\n\n return new1, new2\n\n @pytest.mark.parametrize("from_Dt", simple_dtypes)\n def test_simple_cancast(self, from_Dt):\n for to_Dt in simple_dtypes:\n cast = get_castingimpl(from_Dt, to_Dt)\n\n for from_dt in [from_Dt(), from_Dt().newbyteorder()]:\n default = cast._resolve_descriptors((from_dt, None))[1][1]\n assert default == to_Dt()\n del default\n\n for to_dt in [to_Dt(), to_Dt().newbyteorder()]:\n casting, (from_res, to_res), view_off = (\n cast._resolve_descriptors((from_dt, to_dt)))\n assert type(from_res) == from_Dt\n assert type(to_res) == to_Dt\n if view_off is not None:\n # If a view is acceptable, this is "no" casting\n # and byte order must be matching.\n assert casting == Casting.no\n # The above table lists this as "equivalent"\n assert Casting.equiv == CAST_TABLE[from_Dt][to_Dt]\n # Note that to_res may not be the same as from_dt\n assert from_res.isnative == to_res.isnative\n else:\n if from_Dt == to_Dt:\n # Note that to_res may not be the same as from_dt\n assert from_res.isnative != to_res.isnative\n assert casting == CAST_TABLE[from_Dt][to_Dt]\n\n if from_Dt is to_Dt:\n assert from_dt is from_res\n assert to_dt is to_res\n\n @pytest.mark.filterwarnings("ignore::numpy.exceptions.ComplexWarning")\n @pytest.mark.parametrize("from_dt", simple_dtype_instances())\n def test_simple_direct_casts(self, from_dt):\n """\n This test checks numeric direct casts for dtypes supported also by the\n struct module (plus complex). It tries to be test a wide range of\n inputs, but skips over possibly undefined behaviour (e.g. int rollover).\n Longdouble and CLongdouble are tested, but only using double precision.\n\n If this test creates issues, it should possibly just be simplified\n or even removed (checking whether unaligned/non-contiguous casts give\n the same results is useful, though).\n """\n for to_dt in simple_dtype_instances():\n to_dt = to_dt.values[0]\n cast = get_castingimpl(type(from_dt), type(to_dt))\n\n casting, (from_res, to_res), view_off = cast._resolve_descriptors(\n (from_dt, to_dt))\n\n if from_res is not from_dt or to_res is not to_dt:\n # Do not test this case, it is handled in multiple steps,\n # each of which should is tested individually.\n return\n\n safe = casting <= Casting.safe\n del from_res, to_res, casting\n\n arr1, arr2, values = self.get_data(from_dt, to_dt)\n\n cast._simple_strided_call((arr1, arr2))\n\n # Check via python list\n assert arr2.tolist() == values\n\n # Check that the same results are achieved for strided loops\n arr1_o, arr2_o = self.get_data_variation(arr1, arr2, True, False)\n cast._simple_strided_call((arr1_o, arr2_o))\n\n assert_array_equal(arr2_o, arr2)\n assert arr2_o.tobytes() == arr2.tobytes()\n\n # Check if alignment makes a difference, but only if supported\n # and only if the alignment can be wrong\n if ((from_dt.alignment == 1 and to_dt.alignment == 1) or\n not cast._supports_unaligned):\n return\n\n arr1_o, arr2_o = self.get_data_variation(arr1, arr2, False, True)\n cast._simple_strided_call((arr1_o, arr2_o))\n\n assert_array_equal(arr2_o, arr2)\n assert arr2_o.tobytes() == arr2.tobytes()\n\n arr1_o, arr2_o = self.get_data_variation(arr1, arr2, False, False)\n cast._simple_strided_call((arr1_o, arr2_o))\n\n assert_array_equal(arr2_o, arr2)\n assert arr2_o.tobytes() == arr2.tobytes()\n\n del arr1_o, arr2_o, cast\n\n @pytest.mark.parametrize("from_Dt", simple_dtypes)\n def test_numeric_to_times(self, from_Dt):\n # We currently only implement contiguous loops, so only need to\n # test those.\n from_dt = from_Dt()\n\n time_dtypes = [np.dtype("M8"), np.dtype("M8[ms]"), np.dtype("M8[4D]"),\n np.dtype("m8"), np.dtype("m8[ms]"), np.dtype("m8[4D]")]\n for time_dt in time_dtypes:\n cast = get_castingimpl(type(from_dt), type(time_dt))\n\n casting, (from_res, to_res), view_off = cast._resolve_descriptors(\n (from_dt, time_dt))\n\n assert from_res is from_dt\n assert to_res is time_dt\n del from_res, to_res\n\n assert casting & CAST_TABLE[from_Dt][type(time_dt)]\n assert view_off is None\n\n int64_dt = np.dtype(np.int64)\n arr1, arr2, values = self.get_data(from_dt, int64_dt)\n arr2 = arr2.view(time_dt)\n arr2[...] = np.datetime64("NaT")\n\n if time_dt == np.dtype("M8"):\n # This is a bit of a strange path, and could probably be removed\n arr1[-1] = 0 # ensure at least one value is not NaT\n\n # The cast currently succeeds, but the values are invalid:\n cast._simple_strided_call((arr1, arr2))\n with pytest.raises(ValueError):\n str(arr2[-1]) # e.g. conversion to string fails\n return\n\n cast._simple_strided_call((arr1, arr2))\n\n assert [int(v) for v in arr2.tolist()] == values\n\n # Check that the same results are achieved for strided loops\n arr1_o, arr2_o = self.get_data_variation(arr1, arr2, True, False)\n cast._simple_strided_call((arr1_o, arr2_o))\n\n assert_array_equal(arr2_o, arr2)\n assert arr2_o.tobytes() == arr2.tobytes()\n\n @pytest.mark.parametrize(\n ["from_dt", "to_dt", "expected_casting", "expected_view_off",\n "nom", "denom"],\n [("M8[ns]", None, Casting.no, 0, 1, 1),\n (str(np.dtype("M8[ns]").newbyteorder()), None,\n Casting.equiv, None, 1, 1),\n ("M8", "M8[ms]", Casting.safe, 0, 1, 1),\n # should be invalid cast:\n ("M8[ms]", "M8", Casting.unsafe, None, 1, 1),\n ("M8[5ms]", "M8[5ms]", Casting.no, 0, 1, 1),\n ("M8[ns]", "M8[ms]", Casting.same_kind, None, 1, 10**6),\n ("M8[ms]", "M8[ns]", Casting.safe, None, 10**6, 1),\n ("M8[ms]", "M8[7ms]", Casting.same_kind, None, 1, 7),\n ("M8[4D]", "M8[1M]", Casting.same_kind, None, None,\n # give full values based on NumPy 1.19.x\n [-2**63, 0, -1, 1314, -1315, 564442610]),\n ("m8[ns]", None, Casting.no, 0, 1, 1),\n (str(np.dtype("m8[ns]").newbyteorder()), None,\n Casting.equiv, None, 1, 1),\n ("m8", "m8[ms]", Casting.safe, 0, 1, 1),\n # should be invalid cast:\n ("m8[ms]", "m8", Casting.unsafe, None, 1, 1),\n ("m8[5ms]", "m8[5ms]", Casting.no, 0, 1, 1),\n ("m8[ns]", "m8[ms]", Casting.same_kind, None, 1, 10**6),\n ("m8[ms]", "m8[ns]", Casting.safe, None, 10**6, 1),\n ("m8[ms]", "m8[7ms]", Casting.same_kind, None, 1, 7),\n ("m8[4D]", "m8[1M]", Casting.unsafe, None, None,\n # give full values based on NumPy 1.19.x\n [-2**63, 0, 0, 1314, -1315, 564442610])])\n def test_time_to_time(self, from_dt, to_dt,\n expected_casting, expected_view_off,\n nom, denom):\n from_dt = np.dtype(from_dt)\n if to_dt is not None:\n to_dt = np.dtype(to_dt)\n\n # Test a few values for casting (results generated with NumPy 1.19)\n values = np.array([-2**63, 1, 2**63 - 1, 10000, -10000, 2**32])\n values = values.astype(np.dtype("int64").newbyteorder(from_dt.byteorder))\n assert values.dtype.byteorder == from_dt.byteorder\n assert np.isnat(values.view(from_dt)[0])\n\n DType = type(from_dt)\n cast = get_castingimpl(DType, DType)\n casting, (from_res, to_res), view_off = cast._resolve_descriptors(\n (from_dt, to_dt))\n assert from_res is from_dt\n assert to_res is to_dt or to_dt is None\n assert casting == expected_casting\n assert view_off == expected_view_off\n\n if nom is not None:\n expected_out = (values * nom // denom).view(to_res)\n expected_out[0] = "NaT"\n else:\n expected_out = np.empty_like(values)\n expected_out[...] = denom\n expected_out = expected_out.view(to_dt)\n\n orig_arr = values.view(from_dt)\n orig_out = np.empty_like(expected_out)\n\n if casting == Casting.unsafe and (to_dt == "m8" or to_dt == "M8"): # noqa: PLR1714\n # Casting from non-generic to generic units is an error and should\n # probably be reported as an invalid cast earlier.\n with pytest.raises(ValueError):\n cast._simple_strided_call((orig_arr, orig_out))\n return\n\n for aligned in [True, True]:\n for contig in [True, True]:\n arr, out = self.get_data_variation(\n orig_arr, orig_out, aligned, contig)\n out[...] = 0\n cast._simple_strided_call((arr, out))\n assert_array_equal(out.view("int64"), expected_out.view("int64"))\n\n def string_with_modified_length(self, dtype, change_length):\n fact = 1 if dtype.char == "S" else 4\n length = dtype.itemsize // fact + change_length\n return np.dtype(f"{dtype.byteorder}{dtype.char}{length}")\n\n @pytest.mark.parametrize("other_DT", simple_dtypes)\n @pytest.mark.parametrize("string_char", ["S", "U"])\n def test_string_cancast(self, other_DT, string_char):\n fact = 1 if string_char == "S" else 4\n\n string_DT = type(np.dtype(string_char))\n cast = get_castingimpl(other_DT, string_DT)\n\n other_dt = other_DT()\n expected_length = get_expected_stringlength(other_dt)\n string_dt = np.dtype(f"{string_char}{expected_length}")\n\n safety, (res_other_dt, res_dt), view_off = cast._resolve_descriptors(\n (other_dt, None))\n assert res_dt.itemsize == expected_length * fact\n assert safety == Casting.safe # we consider to string casts "safe"\n assert view_off is None\n assert isinstance(res_dt, string_DT)\n\n # These casts currently implement changing the string length, so\n # check the cast-safety for too long/fixed string lengths:\n for change_length in [-1, 0, 1]:\n if change_length >= 0:\n expected_safety = Casting.safe\n else:\n expected_safety = Casting.same_kind\n\n to_dt = self.string_with_modified_length(string_dt, change_length)\n safety, (_, res_dt), view_off = cast._resolve_descriptors(\n (other_dt, to_dt))\n assert res_dt is to_dt\n assert safety == expected_safety\n assert view_off is None\n\n # The opposite direction is always considered unsafe:\n cast = get_castingimpl(string_DT, other_DT)\n\n safety, _, view_off = cast._resolve_descriptors((string_dt, other_dt))\n assert safety == Casting.unsafe\n assert view_off is None\n\n cast = get_castingimpl(string_DT, other_DT)\n safety, (_, res_dt), view_off = cast._resolve_descriptors(\n (string_dt, None))\n assert safety == Casting.unsafe\n assert view_off is None\n assert other_dt is res_dt # returns the singleton for simple dtypes\n\n @pytest.mark.parametrize("string_char", ["S", "U"])\n @pytest.mark.parametrize("other_dt", simple_dtype_instances())\n def test_simple_string_casts_roundtrip(self, other_dt, string_char):\n """\n Tests casts from and to string by checking the roundtripping property.\n\n The test also covers some string to string casts (but not all).\n\n If this test creates issues, it should possibly just be simplified\n or even removed (checking whether unaligned/non-contiguous casts give\n the same results is useful, though).\n """\n string_DT = type(np.dtype(string_char))\n\n cast = get_castingimpl(type(other_dt), string_DT)\n cast_back = get_castingimpl(string_DT, type(other_dt))\n _, (res_other_dt, string_dt), _ = cast._resolve_descriptors(\n (other_dt, None))\n\n if res_other_dt is not other_dt:\n # do not support non-native byteorder, skip test in that case\n assert other_dt.byteorder != res_other_dt.byteorder\n return\n\n orig_arr, values = self.get_data(other_dt, None)\n str_arr = np.zeros(len(orig_arr), dtype=string_dt)\n string_dt_short = self.string_with_modified_length(string_dt, -1)\n str_arr_short = np.zeros(len(orig_arr), dtype=string_dt_short)\n string_dt_long = self.string_with_modified_length(string_dt, 1)\n str_arr_long = np.zeros(len(orig_arr), dtype=string_dt_long)\n\n assert not cast._supports_unaligned # if support is added, should test\n assert not cast_back._supports_unaligned\n\n for contig in [True, False]:\n other_arr, str_arr = self.get_data_variation(\n orig_arr, str_arr, True, contig)\n _, str_arr_short = self.get_data_variation(\n orig_arr, str_arr_short.copy(), True, contig)\n _, str_arr_long = self.get_data_variation(\n orig_arr, str_arr_long, True, contig)\n\n cast._simple_strided_call((other_arr, str_arr))\n\n cast._simple_strided_call((other_arr, str_arr_short))\n assert_array_equal(str_arr.astype(string_dt_short), str_arr_short)\n\n cast._simple_strided_call((other_arr, str_arr_long))\n assert_array_equal(str_arr, str_arr_long)\n\n if other_dt.kind == "b":\n # Booleans do not roundtrip\n continue\n\n other_arr[...] = 0\n cast_back._simple_strided_call((str_arr, other_arr))\n assert_array_equal(orig_arr, other_arr)\n\n other_arr[...] = 0\n cast_back._simple_strided_call((str_arr_long, other_arr))\n assert_array_equal(orig_arr, other_arr)\n\n @pytest.mark.parametrize("other_dt", ["S8", "<U8", ">U8"])\n @pytest.mark.parametrize("string_char", ["S", "U"])\n def test_string_to_string_cancast(self, other_dt, string_char):\n other_dt = np.dtype(other_dt)\n\n fact = 1 if string_char == "S" else 4\n div = 1 if other_dt.char == "S" else 4\n\n string_DT = type(np.dtype(string_char))\n cast = get_castingimpl(type(other_dt), string_DT)\n\n expected_length = other_dt.itemsize // div\n string_dt = np.dtype(f"{string_char}{expected_length}")\n\n safety, (res_other_dt, res_dt), view_off = cast._resolve_descriptors(\n (other_dt, None))\n assert res_dt.itemsize == expected_length * fact\n assert isinstance(res_dt, string_DT)\n\n expected_view_off = None\n if other_dt.char == string_char:\n if other_dt.isnative:\n expected_safety = Casting.no\n expected_view_off = 0\n else:\n expected_safety = Casting.equiv\n elif string_char == "U":\n expected_safety = Casting.safe\n else:\n expected_safety = Casting.unsafe\n\n assert view_off == expected_view_off\n assert expected_safety == safety\n\n for change_length in [-1, 0, 1]:\n to_dt = self.string_with_modified_length(string_dt, change_length)\n safety, (_, res_dt), view_off = cast._resolve_descriptors(\n (other_dt, to_dt))\n\n assert res_dt is to_dt\n if change_length <= 0:\n assert view_off == expected_view_off\n else:\n assert view_off is None\n if expected_safety == Casting.unsafe:\n assert safety == expected_safety\n elif change_length < 0:\n assert safety == Casting.same_kind\n elif change_length == 0:\n assert safety == expected_safety\n elif change_length > 0:\n assert safety == Casting.safe\n\n @pytest.mark.parametrize("order1", [">", "<"])\n @pytest.mark.parametrize("order2", [">", "<"])\n def test_unicode_byteswapped_cast(self, order1, order2):\n # Very specific tests (not using the castingimpl directly)\n # that tests unicode bytedwaps including for unaligned array data.\n dtype1 = np.dtype(f"{order1}U30")\n dtype2 = np.dtype(f"{order2}U30")\n data1 = np.empty(30 * 4 + 1, dtype=np.uint8)[1:].view(dtype1)\n data2 = np.empty(30 * 4 + 1, dtype=np.uint8)[1:].view(dtype2)\n if dtype1.alignment != 1:\n # alignment should always be >1, but skip the check if not\n assert not data1.flags.aligned\n assert not data2.flags.aligned\n\n element = "this is a ünicode string‽"\n data1[()] = element\n # Test both `data1` and `data1.copy()` (which should be aligned)\n for data in [data1, data1.copy()]:\n data2[...] = data1\n assert data2[()] == element\n assert data2.copy()[()] == element\n\n def test_void_to_string_special_case(self):\n # Cover a small special case in void to string casting that could\n # probably just as well be turned into an error (compare\n # `test_object_to_parametric_internal_error` below).\n assert np.array([], dtype="V5").astype("S").dtype.itemsize == 5\n assert np.array([], dtype="V5").astype("U").dtype.itemsize == 4 * 5\n\n def test_object_to_parametric_internal_error(self):\n # We reject casting from object to a parametric type, without\n # figuring out the correct instance first.\n object_dtype = type(np.dtype(object))\n other_dtype = type(np.dtype(str))\n cast = get_castingimpl(object_dtype, other_dtype)\n with pytest.raises(TypeError,\n match="casting from object to the parametric DType"):\n cast._resolve_descriptors((np.dtype("O"), None))\n\n @pytest.mark.parametrize("dtype", simple_dtype_instances())\n def test_object_and_simple_resolution(self, dtype):\n # Simple test to exercise the cast when no instance is specified\n object_dtype = type(np.dtype(object))\n cast = get_castingimpl(object_dtype, type(dtype))\n\n safety, (_, res_dt), view_off = cast._resolve_descriptors(\n (np.dtype("O"), dtype))\n assert safety == Casting.unsafe\n assert view_off is None\n assert res_dt is dtype\n\n safety, (_, res_dt), view_off = cast._resolve_descriptors(\n (np.dtype("O"), None))\n assert safety == Casting.unsafe\n assert view_off is None\n assert res_dt == dtype.newbyteorder("=")\n\n @pytest.mark.parametrize("dtype", simple_dtype_instances())\n def test_simple_to_object_resolution(self, dtype):\n # Simple test to exercise the cast when no instance is specified\n object_dtype = type(np.dtype(object))\n cast = get_castingimpl(type(dtype), object_dtype)\n\n safety, (_, res_dt), view_off = cast._resolve_descriptors(\n (dtype, None))\n assert safety == Casting.safe\n assert view_off is None\n assert res_dt is np.dtype("O")\n\n @pytest.mark.parametrize("casting", ["no", "unsafe"])\n def test_void_and_structured_with_subarray(self, casting):\n # test case corresponding to gh-19325\n dtype = np.dtype([("foo", "<f4", (3, 2))])\n expected = casting == "unsafe"\n assert np.can_cast("V4", dtype, casting=casting) == expected\n assert np.can_cast(dtype, "V4", casting=casting) == expected\n\n @pytest.mark.parametrize(["to_dt", "expected_off"],\n [ # Same as `from_dt` but with both fields shifted:\n (np.dtype({"names": ["a", "b"], "formats": ["i4", "f4"],\n "offsets": [0, 4]}), 2),\n # Additional change of the names\n (np.dtype({"names": ["b", "a"], "formats": ["i4", "f4"],\n "offsets": [0, 4]}), 2),\n # Incompatible field offset change\n (np.dtype({"names": ["b", "a"], "formats": ["i4", "f4"],\n "offsets": [0, 6]}), None)])\n def test_structured_field_offsets(self, to_dt, expected_off):\n # This checks the cast-safety and view offset for swapped and "shifted"\n # fields which are viewable\n from_dt = np.dtype({"names": ["a", "b"],\n "formats": ["i4", "f4"],\n "offsets": [2, 6]})\n cast = get_castingimpl(type(from_dt), type(to_dt))\n safety, _, view_off = cast._resolve_descriptors((from_dt, to_dt))\n if from_dt.names == to_dt.names:\n assert safety == Casting.equiv\n else:\n assert safety == Casting.safe\n # Shifting the original data pointer by -2 will align both by\n # effectively adding 2 bytes of spacing before `from_dt`.\n assert view_off == expected_off\n\n @pytest.mark.parametrize(("from_dt", "to_dt", "expected_off"), [\n # Subarray cases:\n ("i", "(1,1)i", 0),\n ("(1,1)i", "i", 0),\n ("(2,1)i", "(2,1)i", 0),\n # field cases (field to field is tested explicitly also):\n # Not considered viewable, because a negative offset would allow\n # may structured dtype to indirectly access invalid memory.\n ("i", {"names": ["a"], "formats": ["i"], "offsets": [2]}, None),\n ({"names": ["a"], "formats": ["i"], "offsets": [2]}, "i", 2),\n # Currently considered not viewable, due to multiple fields\n # even though they overlap (maybe we should not allow that?)\n ("i", {"names": ["a", "b"], "formats": ["i", "i"], "offsets": [2, 2]},\n None),\n # different number of fields can't work, should probably just fail\n # so it never reports "viewable":\n ("i,i", "i,i,i", None),\n # Unstructured void cases:\n ("i4", "V3", 0), # void smaller or equal\n ("i4", "V4", 0), # void smaller or equal\n ("i4", "V10", None), # void is larger (no view)\n ("O", "V4", None), # currently reject objects for view here.\n ("O", "V8", None), # currently reject objects for view here.\n ("V4", "V3", 0),\n ("V4", "V4", 0),\n ("V3", "V4", None),\n # Note that currently void-to-other cast goes via byte-strings\n # and is not a "view" based cast like the opposite direction:\n ("V4", "i4", None),\n # completely invalid/impossible cast:\n ("i,i", "i,i,i", None),\n ])\n def test_structured_view_offsets_parametric(\n self, from_dt, to_dt, expected_off):\n # TODO: While this test is fairly thorough, right now, it does not\n # really test some paths that may have nonzero offsets (they don't\n # really exists).\n from_dt = np.dtype(from_dt)\n to_dt = np.dtype(to_dt)\n cast = get_castingimpl(type(from_dt), type(to_dt))\n _, _, view_off = cast._resolve_descriptors((from_dt, to_dt))\n assert view_off == expected_off\n\n @pytest.mark.parametrize("dtype", np.typecodes["All"])\n def test_object_casts_NULL_None_equivalence(self, dtype):\n # None to <other> casts may succeed or fail, but a NULL'ed array must\n # behave the same as one filled with None's.\n arr_normal = np.array([None] * 5)\n arr_NULLs = np.empty_like(arr_normal)\n ctypes.memset(arr_NULLs.ctypes.data, 0, arr_NULLs.nbytes)\n # If the check fails (maybe it should) the test would lose its purpose:\n assert arr_NULLs.tobytes() == b"\x00" * arr_NULLs.nbytes\n\n try:\n expected = arr_normal.astype(dtype)\n except TypeError:\n with pytest.raises(TypeError):\n arr_NULLs.astype(dtype)\n else:\n assert_array_equal(expected, arr_NULLs.astype(dtype))\n\n @pytest.mark.parametrize("dtype",\n np.typecodes["AllInteger"] + np.typecodes["AllFloat"])\n def test_nonstandard_bool_to_other(self, dtype):\n # simple test for casting bool_ to numeric types, which should not\n # expose the detail that NumPy bools can sometimes take values other\n # than 0 and 1. See also gh-19514.\n nonstandard_bools = np.array([0, 3, -7], dtype=np.int8).view(bool)\n res = nonstandard_bools.astype(dtype)\n expected = [0, 1, 1]\n assert_array_equal(res, expected)\n | .venv\Lib\site-packages\numpy\_core\tests\test_casting_unittests.py | test_casting_unittests.py | Python | 35,153 | 0.95 | 0.141983 | 0.119883 | vue-tools | 749 | 2024-11-22T09:45:33.133981 | MIT | true | add333d25633ea3658784fe292ad066e |
"""\nTests for numpy/_core/src/multiarray/conversion_utils.c\n"""\nimport re\n\nimport numpy._core._multiarray_tests as mt\nimport pytest\n\nfrom numpy._core.multiarray import CLIP, RAISE, WRAP\nfrom numpy.testing import assert_raises\n\n\nclass StringConverterTestCase:\n allow_bytes = True\n case_insensitive = True\n exact_match = False\n warn = True\n\n def _check_value_error(self, val):\n pattern = fr'\(got {re.escape(repr(val))}\)'\n with pytest.raises(ValueError, match=pattern) as exc:\n self.conv(val)\n\n def _check_conv_assert_warn(self, val, expected):\n if self.warn:\n with assert_raises(ValueError) as exc:\n assert self.conv(val) == expected\n else:\n assert self.conv(val) == expected\n\n def _check(self, val, expected):\n """Takes valid non-deprecated inputs for converters,\n runs converters on inputs, checks correctness of outputs,\n warnings and errors"""\n assert self.conv(val) == expected\n\n if self.allow_bytes:\n assert self.conv(val.encode('ascii')) == expected\n else:\n with pytest.raises(TypeError):\n self.conv(val.encode('ascii'))\n\n if len(val) != 1:\n if self.exact_match:\n self._check_value_error(val[:1])\n self._check_value_error(val + '\0')\n else:\n self._check_conv_assert_warn(val[:1], expected)\n\n if self.case_insensitive:\n if val != val.lower():\n self._check_conv_assert_warn(val.lower(), expected)\n if val != val.upper():\n self._check_conv_assert_warn(val.upper(), expected)\n else:\n if val != val.lower():\n self._check_value_error(val.lower())\n if val != val.upper():\n self._check_value_error(val.upper())\n\n def test_wrong_type(self):\n # common cases which apply to all the below\n with pytest.raises(TypeError):\n self.conv({})\n with pytest.raises(TypeError):\n self.conv([])\n\n def test_wrong_value(self):\n # nonsense strings\n self._check_value_error('')\n self._check_value_error('\N{greek small letter pi}')\n\n if self.allow_bytes:\n self._check_value_error(b'')\n # bytes which can't be converted to strings via utf8\n self._check_value_error(b"\xFF")\n if self.exact_match:\n self._check_value_error("there's no way this is supported")\n\n\nclass TestByteorderConverter(StringConverterTestCase):\n """ Tests of PyArray_ByteorderConverter """\n conv = mt.run_byteorder_converter\n warn = False\n\n def test_valid(self):\n for s in ['big', '>']:\n self._check(s, 'NPY_BIG')\n for s in ['little', '<']:\n self._check(s, 'NPY_LITTLE')\n for s in ['native', '=']:\n self._check(s, 'NPY_NATIVE')\n for s in ['ignore', '|']:\n self._check(s, 'NPY_IGNORE')\n for s in ['swap']:\n self._check(s, 'NPY_SWAP')\n\n\nclass TestSortkindConverter(StringConverterTestCase):\n """ Tests of PyArray_SortkindConverter """\n conv = mt.run_sortkind_converter\n warn = False\n\n def test_valid(self):\n self._check('quicksort', 'NPY_QUICKSORT')\n self._check('heapsort', 'NPY_HEAPSORT')\n self._check('mergesort', 'NPY_STABLESORT') # alias\n self._check('stable', 'NPY_STABLESORT')\n\n\nclass TestSelectkindConverter(StringConverterTestCase):\n """ Tests of PyArray_SelectkindConverter """\n conv = mt.run_selectkind_converter\n case_insensitive = False\n exact_match = True\n\n def test_valid(self):\n self._check('introselect', 'NPY_INTROSELECT')\n\n\nclass TestSearchsideConverter(StringConverterTestCase):\n """ Tests of PyArray_SearchsideConverter """\n conv = mt.run_searchside_converter\n\n def test_valid(self):\n self._check('left', 'NPY_SEARCHLEFT')\n self._check('right', 'NPY_SEARCHRIGHT')\n\n\nclass TestOrderConverter(StringConverterTestCase):\n """ Tests of PyArray_OrderConverter """\n conv = mt.run_order_converter\n warn = False\n\n def test_valid(self):\n self._check('c', 'NPY_CORDER')\n self._check('f', 'NPY_FORTRANORDER')\n self._check('a', 'NPY_ANYORDER')\n self._check('k', 'NPY_KEEPORDER')\n\n def test_flatten_invalid_order(self):\n # invalid after gh-14596\n with pytest.raises(ValueError):\n self.conv('Z')\n for order in [False, True, 0, 8]:\n with pytest.raises(TypeError):\n self.conv(order)\n\n\nclass TestClipmodeConverter(StringConverterTestCase):\n """ Tests of PyArray_ClipmodeConverter """\n conv = mt.run_clipmode_converter\n\n def test_valid(self):\n self._check('clip', 'NPY_CLIP')\n self._check('wrap', 'NPY_WRAP')\n self._check('raise', 'NPY_RAISE')\n\n # integer values allowed here\n assert self.conv(CLIP) == 'NPY_CLIP'\n assert self.conv(WRAP) == 'NPY_WRAP'\n assert self.conv(RAISE) == 'NPY_RAISE'\n\n\nclass TestCastingConverter(StringConverterTestCase):\n """ Tests of PyArray_CastingConverter """\n conv = mt.run_casting_converter\n case_insensitive = False\n exact_match = True\n\n def test_valid(self):\n self._check("no", "NPY_NO_CASTING")\n self._check("equiv", "NPY_EQUIV_CASTING")\n self._check("safe", "NPY_SAFE_CASTING")\n self._check("same_kind", "NPY_SAME_KIND_CASTING")\n self._check("unsafe", "NPY_UNSAFE_CASTING")\n\n\nclass TestIntpConverter:\n """ Tests of PyArray_IntpConverter """\n conv = mt.run_intp_converter\n\n def test_basic(self):\n assert self.conv(1) == (1,)\n assert self.conv((1, 2)) == (1, 2)\n assert self.conv([1, 2]) == (1, 2)\n assert self.conv(()) == ()\n\n def test_none(self):\n with pytest.raises(TypeError):\n assert self.conv(None) == ()\n\n def test_float(self):\n with pytest.raises(TypeError):\n self.conv(1.0)\n with pytest.raises(TypeError):\n self.conv([1, 1.0])\n\n def test_too_large(self):\n with pytest.raises(ValueError):\n self.conv(2**64)\n\n def test_too_many_dims(self):\n assert self.conv([1] * 64) == (1,) * 64\n with pytest.raises(ValueError):\n self.conv([1] * 65)\n | .venv\Lib\site-packages\numpy\_core\tests\test_conversion_utils.py | test_conversion_utils.py | Python | 6,568 | 0.95 | 0.223301 | 0.030675 | node-utils | 75 | 2024-09-15T23:45:40.783964 | MIT | true | c1a36001c21909cfb035516716aa6113 |
from numpy._core._multiarray_umath import (\n __cpu_baseline__,\n __cpu_dispatch__,\n __cpu_features__,\n)\n\nfrom numpy._core import _umath_tests\nfrom numpy.testing import assert_equal\n\n\ndef test_dispatcher():\n """\n Testing the utilities of the CPU dispatcher\n """\n targets = (\n "SSE2", "SSE41", "AVX2",\n "VSX", "VSX2", "VSX3",\n "NEON", "ASIMD", "ASIMDHP",\n "VX", "VXE", "LSX"\n )\n highest_sfx = "" # no suffix for the baseline\n all_sfx = []\n for feature in reversed(targets):\n # skip baseline features, by the default `CCompilerOpt` do not generate separated objects\n # for the baseline, just one object combined all of them via 'baseline' option\n # within the configuration statements.\n if feature in __cpu_baseline__:\n continue\n # check compiler and running machine support\n if feature not in __cpu_dispatch__ or not __cpu_features__[feature]:\n continue\n\n if not highest_sfx:\n highest_sfx = "_" + feature\n all_sfx.append("func" + "_" + feature)\n\n test = _umath_tests.test_dispatch()\n assert_equal(test["func"], "func" + highest_sfx)\n assert_equal(test["var"], "var" + highest_sfx)\n\n if highest_sfx:\n assert_equal(test["func_xb"], "func" + highest_sfx)\n assert_equal(test["var_xb"], "var" + highest_sfx)\n else:\n assert_equal(test["func_xb"], "nobase")\n assert_equal(test["var_xb"], "nobase")\n\n all_sfx.append("func") # add the baseline\n assert_equal(test["all"], all_sfx)\n | .venv\Lib\site-packages\numpy\_core\tests\test_cpu_dispatcher.py | test_cpu_dispatcher.py | Python | 1,619 | 0.95 | 0.163265 | 0.095238 | awesome-app | 392 | 2025-06-15T10:43:37.586016 | BSD-3-Clause | true | b8e4d2fc45dff62ccdf1a89bd87d9e55 |
import os\nimport pathlib\nimport platform\nimport re\nimport subprocess\nimport sys\n\nimport pytest\nfrom numpy._core._multiarray_umath import (\n __cpu_baseline__,\n __cpu_dispatch__,\n __cpu_features__,\n)\n\n\ndef assert_features_equal(actual, desired, fname):\n __tracebackhide__ = True # Hide traceback for py.test\n actual, desired = str(actual), str(desired)\n if actual == desired:\n return\n detected = str(__cpu_features__).replace("'", "")\n try:\n with open("/proc/cpuinfo") as fd:\n cpuinfo = fd.read(2048)\n except Exception as err:\n cpuinfo = str(err)\n\n try:\n import subprocess\n auxv = subprocess.check_output(['/bin/true'], env={"LD_SHOW_AUXV": "1"})\n auxv = auxv.decode()\n except Exception as err:\n auxv = str(err)\n\n import textwrap\n error_report = textwrap.indent(\nf"""\n###########################################\n### Extra debugging information\n###########################################\n-------------------------------------------\n--- NumPy Detections\n-------------------------------------------\n{detected}\n-------------------------------------------\n--- SYS / CPUINFO\n-------------------------------------------\n{cpuinfo}....\n-------------------------------------------\n--- SYS / AUXV\n-------------------------------------------\n{auxv}\n""", prefix='\r')\n\n raise AssertionError((\n "Failure Detection\n"\n " NAME: '%s'\n"\n " ACTUAL: %s\n"\n " DESIRED: %s\n"\n "%s"\n ) % (fname, actual, desired, error_report))\n\ndef _text_to_list(txt):\n out = txt.strip("][\n").replace("'", "").split(', ')\n return None if out[0] == "" else out\n\nclass AbstractTest:\n features = []\n features_groups = {}\n features_map = {}\n features_flags = set()\n\n def load_flags(self):\n # a hook\n pass\n\n def test_features(self):\n self.load_flags()\n for gname, features in self.features_groups.items():\n test_features = [self.cpu_have(f) for f in features]\n assert_features_equal(__cpu_features__.get(gname), all(test_features), gname)\n\n for feature_name in self.features:\n cpu_have = self.cpu_have(feature_name)\n npy_have = __cpu_features__.get(feature_name)\n assert_features_equal(npy_have, cpu_have, feature_name)\n\n def cpu_have(self, feature_name):\n map_names = self.features_map.get(feature_name, feature_name)\n if isinstance(map_names, str):\n return map_names in self.features_flags\n return any(f in self.features_flags for f in map_names)\n\n def load_flags_cpuinfo(self, magic_key):\n self.features_flags = self.get_cpuinfo_item(magic_key)\n\n def get_cpuinfo_item(self, magic_key):\n values = set()\n with open('/proc/cpuinfo') as fd:\n for line in fd:\n if not line.startswith(magic_key):\n continue\n flags_value = [s.strip() for s in line.split(':', 1)]\n if len(flags_value) == 2:\n values = values.union(flags_value[1].upper().split())\n return values\n\n def load_flags_auxv(self):\n auxv = subprocess.check_output(['/bin/true'], env={"LD_SHOW_AUXV": "1"})\n for at in auxv.split(b'\n'):\n if not at.startswith(b"AT_HWCAP"):\n continue\n hwcap_value = [s.strip() for s in at.split(b':', 1)]\n if len(hwcap_value) == 2:\n self.features_flags = self.features_flags.union(\n hwcap_value[1].upper().decode().split()\n )\n\n@pytest.mark.skipif(\n sys.platform == 'emscripten',\n reason=(\n "The subprocess module is not available on WASM platforms and"\n " therefore this test class cannot be properly executed."\n ),\n)\nclass TestEnvPrivation:\n cwd = pathlib.Path(__file__).parent.resolve()\n env = os.environ.copy()\n _enable = os.environ.pop('NPY_ENABLE_CPU_FEATURES', None)\n _disable = os.environ.pop('NPY_DISABLE_CPU_FEATURES', None)\n SUBPROCESS_ARGS = {"cwd": cwd, "capture_output": True, "text": True, "check": True}\n unavailable_feats = [\n feat for feat in __cpu_dispatch__ if not __cpu_features__[feat]\n ]\n UNAVAILABLE_FEAT = (\n None if len(unavailable_feats) == 0\n else unavailable_feats[0]\n )\n BASELINE_FEAT = None if len(__cpu_baseline__) == 0 else __cpu_baseline__[0]\n SCRIPT = """\ndef main():\n from numpy._core._multiarray_umath import (\n __cpu_features__,\n __cpu_dispatch__\n )\n\n detected = [feat for feat in __cpu_dispatch__ if __cpu_features__[feat]]\n print(detected)\n\nif __name__ == "__main__":\n main()\n """\n\n @pytest.fixture(autouse=True)\n def setup_class(self, tmp_path_factory):\n file = tmp_path_factory.mktemp("runtime_test_script")\n file /= "_runtime_detect.py"\n file.write_text(self.SCRIPT)\n self.file = file\n\n def _run(self):\n return subprocess.run(\n [sys.executable, self.file],\n env=self.env,\n **self.SUBPROCESS_ARGS,\n )\n\n # Helper function mimicking pytest.raises for subprocess call\n def _expect_error(\n self,\n msg,\n err_type,\n no_error_msg="Failed to generate error"\n ):\n try:\n self._run()\n except subprocess.CalledProcessError as e:\n assertion_message = f"Expected: {msg}\nGot: {e.stderr}"\n assert re.search(msg, e.stderr), assertion_message\n\n assertion_message = (\n f"Expected error of type: {err_type}; see full "\n f"error:\n{e.stderr}"\n )\n assert re.search(err_type, e.stderr), assertion_message\n else:\n assert False, no_error_msg\n\n def setup_method(self):\n """Ensure that the environment is reset"""\n self.env = os.environ.copy()\n\n def test_runtime_feature_selection(self):\n """\n Ensure that when selecting `NPY_ENABLE_CPU_FEATURES`, only the\n features exactly specified are dispatched.\n """\n\n # Capture runtime-enabled features\n out = self._run()\n non_baseline_features = _text_to_list(out.stdout)\n\n if non_baseline_features is None:\n pytest.skip(\n "No dispatchable features outside of baseline detected."\n )\n feature = non_baseline_features[0]\n\n # Capture runtime-enabled features when `NPY_ENABLE_CPU_FEATURES` is\n # specified\n self.env['NPY_ENABLE_CPU_FEATURES'] = feature\n out = self._run()\n enabled_features = _text_to_list(out.stdout)\n\n # Ensure that only one feature is enabled, and it is exactly the one\n # specified by `NPY_ENABLE_CPU_FEATURES`\n assert set(enabled_features) == {feature}\n\n if len(non_baseline_features) < 2:\n pytest.skip("Only one non-baseline feature detected.")\n # Capture runtime-enabled features when `NPY_ENABLE_CPU_FEATURES` is\n # specified\n self.env['NPY_ENABLE_CPU_FEATURES'] = ",".join(non_baseline_features)\n out = self._run()\n enabled_features = _text_to_list(out.stdout)\n\n # Ensure that both features are enabled, and they are exactly the ones\n # specified by `NPY_ENABLE_CPU_FEATURES`\n assert set(enabled_features) == set(non_baseline_features)\n\n @pytest.mark.parametrize("enabled, disabled",\n [\n ("feature", "feature"),\n ("feature", "same"),\n ])\n def test_both_enable_disable_set(self, enabled, disabled):\n """\n Ensure that when both environment variables are set then an\n ImportError is thrown\n """\n self.env['NPY_ENABLE_CPU_FEATURES'] = enabled\n self.env['NPY_DISABLE_CPU_FEATURES'] = disabled\n msg = "Both NPY_DISABLE_CPU_FEATURES and NPY_ENABLE_CPU_FEATURES"\n err_type = "ImportError"\n self._expect_error(msg, err_type)\n\n @pytest.mark.skipif(\n not __cpu_dispatch__,\n reason=(\n "NPY_*_CPU_FEATURES only parsed if "\n "`__cpu_dispatch__` is non-empty"\n )\n )\n @pytest.mark.parametrize("action", ["ENABLE", "DISABLE"])\n def test_variable_too_long(self, action):\n """\n Test that an error is thrown if the environment variables are too long\n to be processed. Current limit is 1024, but this may change later.\n """\n MAX_VAR_LENGTH = 1024\n # Actual length is MAX_VAR_LENGTH + 1 due to null-termination\n self.env[f'NPY_{action}_CPU_FEATURES'] = "t" * MAX_VAR_LENGTH\n msg = (\n f"Length of environment variable 'NPY_{action}_CPU_FEATURES' is "\n f"{MAX_VAR_LENGTH + 1}, only {MAX_VAR_LENGTH} accepted"\n )\n err_type = "RuntimeError"\n self._expect_error(msg, err_type)\n\n @pytest.mark.skipif(\n not __cpu_dispatch__,\n reason=(\n "NPY_*_CPU_FEATURES only parsed if "\n "`__cpu_dispatch__` is non-empty"\n )\n )\n def test_impossible_feature_disable(self):\n """\n Test that a RuntimeError is thrown if an impossible feature-disabling\n request is made. This includes disabling a baseline feature.\n """\n\n if self.BASELINE_FEAT is None:\n pytest.skip("There are no unavailable features to test with")\n bad_feature = self.BASELINE_FEAT\n self.env['NPY_DISABLE_CPU_FEATURES'] = bad_feature\n msg = (\n f"You cannot disable CPU feature '{bad_feature}', since it is "\n "part of the baseline optimizations"\n )\n err_type = "RuntimeError"\n self._expect_error(msg, err_type)\n\n def test_impossible_feature_enable(self):\n """\n Test that a RuntimeError is thrown if an impossible feature-enabling\n request is made. This includes enabling a feature not supported by the\n machine, or disabling a baseline optimization.\n """\n\n if self.UNAVAILABLE_FEAT is None:\n pytest.skip("There are no unavailable features to test with")\n bad_feature = self.UNAVAILABLE_FEAT\n self.env['NPY_ENABLE_CPU_FEATURES'] = bad_feature\n msg = (\n f"You cannot enable CPU features \\({bad_feature}\\), since "\n "they are not supported by your machine."\n )\n err_type = "RuntimeError"\n self._expect_error(msg, err_type)\n\n # Ensure that it fails even when providing garbage in addition\n feats = f"{bad_feature}, Foobar"\n self.env['NPY_ENABLE_CPU_FEATURES'] = feats\n msg = (\n f"You cannot enable CPU features \\({bad_feature}\\), since they "\n "are not supported by your machine."\n )\n self._expect_error(msg, err_type)\n\n if self.BASELINE_FEAT is not None:\n # Ensure that only the bad feature gets reported\n feats = f"{bad_feature}, {self.BASELINE_FEAT}"\n self.env['NPY_ENABLE_CPU_FEATURES'] = feats\n msg = (\n f"You cannot enable CPU features \\({bad_feature}\\), since "\n "they are not supported by your machine."\n )\n self._expect_error(msg, err_type)\n\n\nis_linux = sys.platform.startswith('linux')\nis_cygwin = sys.platform.startswith('cygwin')\nmachine = platform.machine()\nis_x86 = re.match(r"^(amd64|x86|i386|i686)", machine, re.IGNORECASE)\n@pytest.mark.skipif(\n not (is_linux or is_cygwin) or not is_x86, reason="Only for Linux and x86"\n)\nclass Test_X86_Features(AbstractTest):\n features = [\n "MMX", "SSE", "SSE2", "SSE3", "SSSE3", "SSE41", "POPCNT", "SSE42",\n "AVX", "F16C", "XOP", "FMA4", "FMA3", "AVX2", "AVX512F", "AVX512CD",\n "AVX512ER", "AVX512PF", "AVX5124FMAPS", "AVX5124VNNIW", "AVX512VPOPCNTDQ",\n "AVX512VL", "AVX512BW", "AVX512DQ", "AVX512VNNI", "AVX512IFMA",\n "AVX512VBMI", "AVX512VBMI2", "AVX512BITALG", "AVX512FP16",\n ]\n features_groups = {\n "AVX512_KNL": ["AVX512F", "AVX512CD", "AVX512ER", "AVX512PF"],\n "AVX512_KNM": ["AVX512F", "AVX512CD", "AVX512ER", "AVX512PF", "AVX5124FMAPS",\n "AVX5124VNNIW", "AVX512VPOPCNTDQ"],\n "AVX512_SKX": ["AVX512F", "AVX512CD", "AVX512BW", "AVX512DQ", "AVX512VL"],\n "AVX512_CLX": ["AVX512F", "AVX512CD", "AVX512BW", "AVX512DQ", "AVX512VL", "AVX512VNNI"],\n "AVX512_CNL": ["AVX512F", "AVX512CD", "AVX512BW", "AVX512DQ", "AVX512VL", "AVX512IFMA",\n "AVX512VBMI"],\n "AVX512_ICL": ["AVX512F", "AVX512CD", "AVX512BW", "AVX512DQ", "AVX512VL", "AVX512IFMA",\n "AVX512VBMI", "AVX512VNNI", "AVX512VBMI2", "AVX512BITALG", "AVX512VPOPCNTDQ"],\n "AVX512_SPR": ["AVX512F", "AVX512CD", "AVX512BW", "AVX512DQ",\n "AVX512VL", "AVX512IFMA", "AVX512VBMI", "AVX512VNNI",\n "AVX512VBMI2", "AVX512BITALG", "AVX512VPOPCNTDQ",\n "AVX512FP16"],\n }\n features_map = {\n "SSE3": "PNI", "SSE41": "SSE4_1", "SSE42": "SSE4_2", "FMA3": "FMA",\n "AVX512VNNI": "AVX512_VNNI", "AVX512BITALG": "AVX512_BITALG",\n "AVX512VBMI2": "AVX512_VBMI2", "AVX5124FMAPS": "AVX512_4FMAPS",\n "AVX5124VNNIW": "AVX512_4VNNIW", "AVX512VPOPCNTDQ": "AVX512_VPOPCNTDQ",\n "AVX512FP16": "AVX512_FP16",\n }\n\n def load_flags(self):\n self.load_flags_cpuinfo("flags")\n\n\nis_power = re.match(r"^(powerpc|ppc)64", machine, re.IGNORECASE)\n@pytest.mark.skipif(not is_linux or not is_power, reason="Only for Linux and Power")\nclass Test_POWER_Features(AbstractTest):\n features = ["VSX", "VSX2", "VSX3", "VSX4"]\n features_map = {"VSX2": "ARCH_2_07", "VSX3": "ARCH_3_00", "VSX4": "ARCH_3_1"}\n\n def load_flags(self):\n self.load_flags_auxv()\n\n\nis_zarch = re.match(r"^(s390x)", machine, re.IGNORECASE)\n@pytest.mark.skipif(not is_linux or not is_zarch,\n reason="Only for Linux and IBM Z")\nclass Test_ZARCH_Features(AbstractTest):\n features = ["VX", "VXE", "VXE2"]\n\n def load_flags(self):\n self.load_flags_auxv()\n\n\nis_arm = re.match(r"^(arm|aarch64)", machine, re.IGNORECASE)\n@pytest.mark.skipif(not is_linux or not is_arm, reason="Only for Linux and ARM")\nclass Test_ARM_Features(AbstractTest):\n features = [\n "SVE", "NEON", "ASIMD", "FPHP", "ASIMDHP", "ASIMDDP", "ASIMDFHM"\n ]\n features_groups = {\n "NEON_FP16": ["NEON", "HALF"],\n "NEON_VFPV4": ["NEON", "VFPV4"],\n }\n\n def load_flags(self):\n self.load_flags_cpuinfo("Features")\n arch = self.get_cpuinfo_item("CPU architecture")\n # in case of mounting virtual filesystem of aarch64 kernel without linux32\n is_rootfs_v8 = (\n not re.match(r"^armv[0-9]+l$", machine) and\n (int('0' + next(iter(arch))) > 7 if arch else 0)\n )\n if re.match(r"^(aarch64|AARCH64)", machine) or is_rootfs_v8:\n self.features_map = {\n "NEON": "ASIMD", "HALF": "ASIMD", "VFPV4": "ASIMD"\n }\n else:\n self.features_map = {\n # ELF auxiliary vector and /proc/cpuinfo on Linux kernel(armv8 aarch32)\n # doesn't provide information about ASIMD, so we assume that ASIMD is supported\n # if the kernel reports any one of the following ARM8 features.\n "ASIMD": ("AES", "SHA1", "SHA2", "PMULL", "CRC32")\n }\n\n\nis_loongarch = re.match(r"^(loongarch)", machine, re.IGNORECASE)\n@pytest.mark.skipif(not is_linux or not is_loongarch, reason="Only for Linux and LoongArch")\nclass Test_LOONGARCH_Features(AbstractTest):\n features = ["LSX"]\n\n def load_flags(self):\n self.load_flags_cpuinfo("Features")\n | .venv\Lib\site-packages\numpy\_core\tests\test_cpu_features.py | test_cpu_features.py | Python | 16,135 | 0.95 | 0.178241 | 0.058047 | vue-tools | 326 | 2024-11-03T07:09:58.028466 | BSD-3-Clause | true | 28f105b35f9c130f7aa7437a466dcf54 |
from tempfile import NamedTemporaryFile\n\nimport pytest\nfrom numpy._core._multiarray_umath import (\n _discover_array_parameters as discover_array_params,\n)\nfrom numpy._core._multiarray_umath import _get_sfloat_dtype\n\nimport numpy as np\nfrom numpy.testing import assert_array_equal\n\nSF = _get_sfloat_dtype()\n\n\nclass TestSFloat:\n def _get_array(self, scaling, aligned=True):\n if not aligned:\n a = np.empty(3 * 8 + 1, dtype=np.uint8)[1:]\n a = a.view(np.float64)\n a[:] = [1., 2., 3.]\n else:\n a = np.array([1., 2., 3.])\n\n a *= 1. / scaling # the casting code also uses the reciprocal.\n return a.view(SF(scaling))\n\n def test_sfloat_rescaled(self):\n sf = SF(1.)\n sf2 = sf.scaled_by(2.)\n assert sf2.get_scaling() == 2.\n sf6 = sf2.scaled_by(3.)\n assert sf6.get_scaling() == 6.\n\n def test_class_discovery(self):\n # This does not test much, since we always discover the scaling as 1.\n # But most of NumPy (when writing) does not understand DType classes\n dt, _ = discover_array_params([1., 2., 3.], dtype=SF)\n assert dt == SF(1.)\n\n @pytest.mark.parametrize("scaling", [1., -1., 2.])\n def test_scaled_float_from_floats(self, scaling):\n a = np.array([1., 2., 3.], dtype=SF(scaling))\n\n assert a.dtype.get_scaling() == scaling\n assert_array_equal(scaling * a.view(np.float64), [1., 2., 3.])\n\n def test_repr(self):\n # Check the repr, mainly to cover the code paths:\n assert repr(SF(scaling=1.)) == "_ScaledFloatTestDType(scaling=1.0)"\n\n def test_dtype_str(self):\n assert SF(1.).str == "_ScaledFloatTestDType(scaling=1.0)"\n\n def test_dtype_name(self):\n assert SF(1.).name == "_ScaledFloatTestDType64"\n\n def test_sfloat_structured_dtype_printing(self):\n dt = np.dtype([("id", int), ("value", SF(0.5))])\n # repr of structured dtypes need special handling because the\n # implementation bypasses the object repr\n assert "('value', '_ScaledFloatTestDType64')" in repr(dt)\n\n @pytest.mark.parametrize("scaling", [1., -1., 2.])\n def test_sfloat_from_float(self, scaling):\n a = np.array([1., 2., 3.]).astype(dtype=SF(scaling))\n\n assert a.dtype.get_scaling() == scaling\n assert_array_equal(scaling * a.view(np.float64), [1., 2., 3.])\n\n @pytest.mark.parametrize("aligned", [True, False])\n @pytest.mark.parametrize("scaling", [1., -1., 2.])\n def test_sfloat_getitem(self, aligned, scaling):\n a = self._get_array(1., aligned)\n assert a.tolist() == [1., 2., 3.]\n\n @pytest.mark.parametrize("aligned", [True, False])\n def test_sfloat_casts(self, aligned):\n a = self._get_array(1., aligned)\n\n assert np.can_cast(a, SF(-1.), casting="equiv")\n assert not np.can_cast(a, SF(-1.), casting="no")\n na = a.astype(SF(-1.))\n assert_array_equal(-1 * na.view(np.float64), a.view(np.float64))\n\n assert np.can_cast(a, SF(2.), casting="same_kind")\n assert not np.can_cast(a, SF(2.), casting="safe")\n a2 = a.astype(SF(2.))\n assert_array_equal(2 * a2.view(np.float64), a.view(np.float64))\n\n @pytest.mark.parametrize("aligned", [True, False])\n def test_sfloat_cast_internal_errors(self, aligned):\n a = self._get_array(2e300, aligned)\n\n with pytest.raises(TypeError,\n match="error raised inside the core-loop: non-finite factor!"):\n a.astype(SF(2e-300))\n\n def test_sfloat_promotion(self):\n assert np.result_type(SF(2.), SF(3.)) == SF(3.)\n assert np.result_type(SF(3.), SF(2.)) == SF(3.)\n # Float64 -> SF(1.) and then promotes normally, so both of this work:\n assert np.result_type(SF(3.), np.float64) == SF(3.)\n assert np.result_type(np.float64, SF(0.5)) == SF(1.)\n\n # Test an undefined promotion:\n with pytest.raises(TypeError):\n np.result_type(SF(1.), np.int64)\n\n def test_basic_multiply(self):\n a = self._get_array(2.)\n b = self._get_array(4.)\n\n res = a * b\n # multiplies dtype scaling and content separately:\n assert res.dtype.get_scaling() == 8.\n expected_view = a.view(np.float64) * b.view(np.float64)\n assert_array_equal(res.view(np.float64), expected_view)\n\n def test_possible_and_impossible_reduce(self):\n # For reductions to work, the first and last operand must have the\n # same dtype. For this parametric DType that is not necessarily true.\n a = self._get_array(2.)\n # Addition reduction works (as of writing requires to pass initial\n # because setting a scaled-float from the default `0` fails).\n res = np.add.reduce(a, initial=0.)\n assert res == a.astype(np.float64).sum()\n\n # But each multiplication changes the factor, so a reduction is not\n # possible (the relaxed version of the old refusal to handle any\n # flexible dtype).\n with pytest.raises(TypeError,\n match="the resolved dtypes are not compatible"):\n np.multiply.reduce(a)\n\n def test_basic_ufunc_at(self):\n float_a = np.array([1., 2., 3.])\n b = self._get_array(2.)\n\n float_b = b.view(np.float64).copy()\n np.multiply.at(float_b, [1, 1, 1], float_a)\n np.multiply.at(b, [1, 1, 1], float_a)\n\n assert_array_equal(b.view(np.float64), float_b)\n\n def test_basic_multiply_promotion(self):\n float_a = np.array([1., 2., 3.])\n b = self._get_array(2.)\n\n res1 = float_a * b\n res2 = b * float_a\n\n # one factor is one, so we get the factor of b:\n assert res1.dtype == res2.dtype == b.dtype\n expected_view = float_a * b.view(np.float64)\n assert_array_equal(res1.view(np.float64), expected_view)\n assert_array_equal(res2.view(np.float64), expected_view)\n\n # Check that promotion works when `out` is used:\n np.multiply(b, float_a, out=res2)\n with pytest.raises(TypeError):\n # The promoter accepts this (maybe it should not), but the SFloat\n # result cannot be cast to integer:\n np.multiply(b, float_a, out=np.arange(3))\n\n def test_basic_addition(self):\n a = self._get_array(2.)\n b = self._get_array(4.)\n\n res = a + b\n # addition uses the type promotion rules for the result:\n assert res.dtype == np.result_type(a.dtype, b.dtype)\n expected_view = (a.astype(res.dtype).view(np.float64) +\n b.astype(res.dtype).view(np.float64))\n assert_array_equal(res.view(np.float64), expected_view)\n\n def test_addition_cast_safety(self):\n """The addition method is special for the scaled float, because it\n includes the "cast" between different factors, thus cast-safety\n is influenced by the implementation.\n """\n a = self._get_array(2.)\n b = self._get_array(-2.)\n c = self._get_array(3.)\n\n # sign change is "equiv":\n np.add(a, b, casting="equiv")\n with pytest.raises(TypeError):\n np.add(a, b, casting="no")\n\n # Different factor is "same_kind" (default) so check that "safe" fails\n with pytest.raises(TypeError):\n np.add(a, c, casting="safe")\n\n # Check that casting the output fails also (done by the ufunc here)\n with pytest.raises(TypeError):\n np.add(a, a, out=c, casting="safe")\n\n @pytest.mark.parametrize("ufunc",\n [np.logical_and, np.logical_or, np.logical_xor])\n def test_logical_ufuncs_casts_to_bool(self, ufunc):\n a = self._get_array(2.)\n a[0] = 0. # make sure first element is considered False.\n\n float_equiv = a.astype(float)\n expected = ufunc(float_equiv, float_equiv)\n res = ufunc(a, a)\n assert_array_equal(res, expected)\n\n # also check that the same works for reductions:\n expected = ufunc.reduce(float_equiv)\n res = ufunc.reduce(a)\n assert_array_equal(res, expected)\n\n # The output casting does not match the bool, bool -> bool loop:\n with pytest.raises(TypeError):\n ufunc(a, a, out=np.empty(a.shape, dtype=int), casting="equiv")\n\n def test_wrapped_and_wrapped_reductions(self):\n a = self._get_array(2.)\n float_equiv = a.astype(float)\n\n expected = np.hypot(float_equiv, float_equiv)\n res = np.hypot(a, a)\n assert res.dtype == a.dtype\n res_float = res.view(np.float64) * 2\n assert_array_equal(res_float, expected)\n\n # Also check reduction (keepdims, due to incorrect getitem)\n res = np.hypot.reduce(a, keepdims=True)\n assert res.dtype == a.dtype\n expected = np.hypot.reduce(float_equiv, keepdims=True)\n assert res.view(np.float64) * 2 == expected\n\n def test_astype_class(self):\n # Very simple test that we accept `.astype()` also on the class.\n # ScaledFloat always returns the default descriptor, but it does\n # check the relevant code paths.\n arr = np.array([1., 2., 3.], dtype=object)\n\n res = arr.astype(SF) # passing the class class\n expected = arr.astype(SF(1.)) # above will have discovered 1. scaling\n assert_array_equal(res.view(np.float64), expected.view(np.float64))\n\n def test_creation_class(self):\n # passing in a dtype class should return\n # the default descriptor\n arr1 = np.array([1., 2., 3.], dtype=SF)\n assert arr1.dtype == SF(1.)\n arr2 = np.array([1., 2., 3.], dtype=SF(1.))\n assert_array_equal(arr1.view(np.float64), arr2.view(np.float64))\n assert arr1.dtype == arr2.dtype\n\n assert np.empty(3, dtype=SF).dtype == SF(1.)\n assert np.empty_like(arr1, dtype=SF).dtype == SF(1.)\n assert np.zeros(3, dtype=SF).dtype == SF(1.)\n assert np.zeros_like(arr1, dtype=SF).dtype == SF(1.)\n\n def test_np_save_load(self):\n # this monkeypatch is needed because pickle\n # uses the repr of a type to reconstruct it\n np._ScaledFloatTestDType = SF\n\n arr = np.array([1.0, 2.0, 3.0], dtype=SF(1.0))\n\n # adapted from RoundtripTest.roundtrip in np.save tests\n with NamedTemporaryFile("wb", delete=False, suffix=".npz") as f:\n with pytest.warns(UserWarning) as record:\n np.savez(f.name, arr)\n\n assert len(record) == 1\n\n with np.load(f.name, allow_pickle=True) as data:\n larr = data["arr_0"]\n assert_array_equal(arr.view(np.float64), larr.view(np.float64))\n assert larr.dtype == arr.dtype == SF(1.0)\n\n del np._ScaledFloatTestDType\n\n def test_flatiter(self):\n arr = np.array([1.0, 2.0, 3.0], dtype=SF(1.0))\n\n for i, val in enumerate(arr.flat):\n assert arr[i] == val\n\n @pytest.mark.parametrize(\n "index", [\n [1, 2], ..., slice(None, 2, None),\n np.array([True, True, False]), np.array([0, 1])\n ], ids=["int_list", "ellipsis", "slice", "bool_array", "int_array"])\n def test_flatiter_index(self, index):\n arr = np.array([1.0, 2.0, 3.0], dtype=SF(1.0))\n np.testing.assert_array_equal(\n arr[index].view(np.float64), arr.flat[index].view(np.float64))\n\n arr2 = arr.copy()\n arr[index] = 5.0\n arr2.flat[index] = 5.0\n np.testing.assert_array_equal(\n arr.view(np.float64), arr2.view(np.float64))\n\ndef test_type_pickle():\n # can't actually unpickle, but we can pickle (if in namespace)\n import pickle\n\n np._ScaledFloatTestDType = SF\n\n s = pickle.dumps(SF)\n res = pickle.loads(s)\n assert res is SF\n\n del np._ScaledFloatTestDType\n\n\ndef test_is_numeric():\n assert SF._is_numeric\n | .venv\Lib\site-packages\numpy\_core\tests\test_custom_dtypes.py | test_custom_dtypes.py | Python | 12,081 | 0.95 | 0.12381 | 0.1417 | python-kit | 653 | 2024-07-29T07:37:18.184886 | BSD-3-Clause | true | bdc31eb4e5974dc8756dd98528179fcb |
import os\nimport subprocess\nimport sys\nimport sysconfig\nfrom datetime import datetime\n\nimport pytest\n\nimport numpy as np\nfrom numpy.testing import IS_EDITABLE, IS_WASM, assert_array_equal\n\n# This import is copied from random.tests.test_extending\ntry:\n import cython\n from Cython.Compiler.Version import version as cython_version\nexcept ImportError:\n cython = None\nelse:\n from numpy._utils import _pep440\n\n # Note: keep in sync with the one in pyproject.toml\n required_version = "3.0.6"\n if _pep440.parse(cython_version) < _pep440.Version(required_version):\n # too old or wrong cython, skip the test\n cython = None\n\npytestmark = pytest.mark.skipif(cython is None, reason="requires cython")\n\n\nif IS_EDITABLE:\n pytest.skip(\n "Editable install doesn't support tests with a compile step",\n allow_module_level=True\n )\n\n\n@pytest.fixture(scope='module')\ndef install_temp(tmpdir_factory):\n # Based in part on test_cython from random.tests.test_extending\n if IS_WASM:\n pytest.skip("No subprocess")\n\n srcdir = os.path.join(os.path.dirname(__file__), 'examples', 'cython')\n build_dir = tmpdir_factory.mktemp("cython_test") / "build"\n os.makedirs(build_dir, exist_ok=True)\n # Ensure we use the correct Python interpreter even when `meson` is\n # installed in a different Python environment (see gh-24956)\n native_file = str(build_dir / 'interpreter-native-file.ini')\n with open(native_file, 'w') as f:\n f.write("[binaries]\n")\n f.write(f"python = '{sys.executable}'\n")\n f.write(f"python3 = '{sys.executable}'")\n\n try:\n subprocess.check_call(["meson", "--version"])\n except FileNotFoundError:\n pytest.skip("No usable 'meson' found")\n if sysconfig.get_platform() == "win-arm64":\n pytest.skip("Meson unable to find MSVC linker on win-arm64")\n if sys.platform == "win32":\n subprocess.check_call(["meson", "setup",\n "--buildtype=release",\n "--vsenv", "--native-file", native_file,\n str(srcdir)],\n cwd=build_dir,\n )\n else:\n subprocess.check_call(["meson", "setup",\n "--native-file", native_file, str(srcdir)],\n cwd=build_dir\n )\n try:\n subprocess.check_call(["meson", "compile", "-vv"], cwd=build_dir)\n except subprocess.CalledProcessError:\n print("----------------")\n print("meson build failed when doing")\n print(f"'meson setup --native-file {native_file} {srcdir}'")\n print("'meson compile -vv'")\n print(f"in {build_dir}")\n print("----------------")\n raise\n\n sys.path.append(str(build_dir))\n\n\ndef test_is_timedelta64_object(install_temp):\n import checks\n\n assert checks.is_td64(np.timedelta64(1234))\n assert checks.is_td64(np.timedelta64(1234, "ns"))\n assert checks.is_td64(np.timedelta64("NaT", "ns"))\n\n assert not checks.is_td64(1)\n assert not checks.is_td64(None)\n assert not checks.is_td64("foo")\n assert not checks.is_td64(np.datetime64("now", "s"))\n\n\ndef test_is_datetime64_object(install_temp):\n import checks\n\n assert checks.is_dt64(np.datetime64(1234, "ns"))\n assert checks.is_dt64(np.datetime64("NaT", "ns"))\n\n assert not checks.is_dt64(1)\n assert not checks.is_dt64(None)\n assert not checks.is_dt64("foo")\n assert not checks.is_dt64(np.timedelta64(1234))\n\n\ndef test_get_datetime64_value(install_temp):\n import checks\n\n dt64 = np.datetime64("2016-01-01", "ns")\n\n result = checks.get_dt64_value(dt64)\n expected = dt64.view("i8")\n\n assert result == expected\n\n\ndef test_get_timedelta64_value(install_temp):\n import checks\n\n td64 = np.timedelta64(12345, "h")\n\n result = checks.get_td64_value(td64)\n expected = td64.view("i8")\n\n assert result == expected\n\n\ndef test_get_datetime64_unit(install_temp):\n import checks\n\n dt64 = np.datetime64("2016-01-01", "ns")\n result = checks.get_dt64_unit(dt64)\n expected = 10\n assert result == expected\n\n td64 = np.timedelta64(12345, "h")\n result = checks.get_dt64_unit(td64)\n expected = 5\n assert result == expected\n\n\ndef test_abstract_scalars(install_temp):\n import checks\n\n assert checks.is_integer(1)\n assert checks.is_integer(np.int8(1))\n assert checks.is_integer(np.uint64(1))\n\ndef test_default_int(install_temp):\n import checks\n\n assert checks.get_default_integer() is np.dtype(int)\n\n\ndef test_ravel_axis(install_temp):\n import checks\n\n assert checks.get_ravel_axis() == np.iinfo("intc").min\n\n\ndef test_convert_datetime64_to_datetimestruct(install_temp):\n # GH#21199\n import checks\n\n res = checks.convert_datetime64_to_datetimestruct()\n\n exp = {\n "year": 2022,\n "month": 3,\n "day": 15,\n "hour": 20,\n "min": 1,\n "sec": 55,\n "us": 260292,\n "ps": 0,\n "as": 0,\n }\n\n assert res == exp\n\n\nclass TestDatetimeStrings:\n def test_make_iso_8601_datetime(self, install_temp):\n # GH#21199\n import checks\n dt = datetime(2016, 6, 2, 10, 45, 19)\n # uses NPY_FR_s\n result = checks.make_iso_8601_datetime(dt)\n assert result == b"2016-06-02T10:45:19"\n\n def test_get_datetime_iso_8601_strlen(self, install_temp):\n # GH#21199\n import checks\n # uses NPY_FR_ns\n res = checks.get_datetime_iso_8601_strlen()\n assert res == 48\n\n\n@pytest.mark.parametrize(\n "arrays",\n [\n [np.random.rand(2)],\n [np.random.rand(2), np.random.rand(3, 1)],\n [np.random.rand(2), np.random.rand(2, 3, 2), np.random.rand(1, 3, 2)],\n [np.random.rand(2, 1)] * 4 + [np.random.rand(1, 1, 1)],\n ]\n)\ndef test_multiiter_fields(install_temp, arrays):\n import checks\n bcast = np.broadcast(*arrays)\n\n assert bcast.ndim == checks.get_multiiter_number_of_dims(bcast)\n assert bcast.size == checks.get_multiiter_size(bcast)\n assert bcast.numiter == checks.get_multiiter_num_of_iterators(bcast)\n assert bcast.shape == checks.get_multiiter_shape(bcast)\n assert bcast.index == checks.get_multiiter_current_index(bcast)\n assert all(\n x.base is y.base\n for x, y in zip(bcast.iters, checks.get_multiiter_iters(bcast))\n )\n\n\ndef test_dtype_flags(install_temp):\n import checks\n dtype = np.dtype("i,O") # dtype with somewhat interesting flags\n assert dtype.flags == checks.get_dtype_flags(dtype)\n\n\ndef test_conv_intp(install_temp):\n import checks\n\n class myint:\n def __int__(self):\n return 3\n\n # These conversion passes via `__int__`, not `__index__`:\n assert checks.conv_intp(3.) == 3\n assert checks.conv_intp(myint()) == 3\n\n\ndef test_npyiter_api(install_temp):\n import checks\n arr = np.random.rand(3, 2)\n\n it = np.nditer(arr)\n assert checks.get_npyiter_size(it) == it.itersize == np.prod(arr.shape)\n assert checks.get_npyiter_ndim(it) == it.ndim == 1\n assert checks.npyiter_has_index(it) == it.has_index == False\n\n it = np.nditer(arr, flags=["c_index"])\n assert checks.npyiter_has_index(it) == it.has_index == True\n assert (\n checks.npyiter_has_delayed_bufalloc(it)\n == it.has_delayed_bufalloc\n == False\n )\n\n it = np.nditer(arr, flags=["buffered", "delay_bufalloc"])\n assert (\n checks.npyiter_has_delayed_bufalloc(it)\n == it.has_delayed_bufalloc\n == True\n )\n\n it = np.nditer(arr, flags=["multi_index"])\n assert checks.get_npyiter_size(it) == it.itersize == np.prod(arr.shape)\n assert checks.npyiter_has_multi_index(it) == it.has_multi_index == True\n assert checks.get_npyiter_ndim(it) == it.ndim == 2\n assert checks.test_get_multi_index_iter_next(it, arr)\n\n arr2 = np.random.rand(2, 1, 2)\n it = np.nditer([arr, arr2])\n assert checks.get_npyiter_nop(it) == it.nop == 2\n assert checks.get_npyiter_size(it) == it.itersize == 12\n assert checks.get_npyiter_ndim(it) == it.ndim == 3\n assert all(\n x is y for x, y in zip(checks.get_npyiter_operands(it), it.operands)\n )\n assert all(\n np.allclose(x, y)\n for x, y in zip(checks.get_npyiter_itviews(it), it.itviews)\n )\n\n\ndef test_fillwithbytes(install_temp):\n import checks\n\n arr = checks.compile_fillwithbyte()\n assert_array_equal(arr, np.ones((1, 2)))\n\n\ndef test_complex(install_temp):\n from checks import inc2_cfloat_struct\n\n arr = np.array([0, 10 + 10j], dtype="F")\n inc2_cfloat_struct(arr)\n assert arr[1] == (12 + 12j)\n\n\ndef test_npystring_pack(install_temp):\n """Check that the cython API can write to a vstring array."""\n import checks\n\n arr = np.array(['a', 'b', 'c'], dtype='T')\n assert checks.npystring_pack(arr) == 0\n\n # checks.npystring_pack writes to the beginning of the array\n assert arr[0] == "Hello world"\n\ndef test_npystring_load(install_temp):\n """Check that the cython API can load strings from a vstring array."""\n import checks\n\n arr = np.array(['abcd', 'b', 'c'], dtype='T')\n result = checks.npystring_load(arr)\n assert result == 'abcd'\n\n\ndef test_npystring_multiple_allocators(install_temp):\n """Check that the cython API can acquire/release multiple vstring allocators."""\n import checks\n\n dt = np.dtypes.StringDType(na_object=None)\n arr1 = np.array(['abcd', 'b', 'c'], dtype=dt)\n arr2 = np.array(['a', 'b', 'c'], dtype=dt)\n\n assert checks.npystring_pack_multiple(arr1, arr2) == 0\n assert arr1[0] == "Hello world"\n assert arr1[-1] is None\n assert arr2[0] == "test this"\n\n\ndef test_npystring_allocators_other_dtype(install_temp):\n """Check that allocators for non-StringDType arrays is NULL."""\n import checks\n\n arr1 = np.array([1, 2, 3], dtype='i')\n arr2 = np.array([4, 5, 6], dtype='i')\n\n assert checks.npystring_allocators_other_types(arr1, arr2) == 0\n\n\n@pytest.mark.skipif(sysconfig.get_platform() == 'win-arm64', reason='no checks module on win-arm64')\ndef test_npy_uintp_type_enum():\n import checks\n assert checks.check_npy_uintp_type_enum()\n | .venv\Lib\site-packages\numpy\_core\tests\test_cython.py | test_cython.py | Python | 10,537 | 0.95 | 0.108262 | 0.04943 | node-utils | 40 | 2024-08-28T17:14:12.804858 | GPL-3.0 | true | 182e2f7c1217eb2daee98f8d2150602e |
import pytest\n\nimport numpy as np\nfrom numpy._core.multiarray import _vec_string\nfrom numpy.testing import (\n assert_,\n assert_array_equal,\n assert_equal,\n assert_raises,\n assert_raises_regex,\n)\n\nkw_unicode_true = {'unicode': True} # make 2to3 work properly\nkw_unicode_false = {'unicode': False}\n\nclass TestBasic:\n def test_from_object_array(self):\n A = np.array([['abc', 2],\n ['long ', '0123456789']], dtype='O')\n B = np.char.array(A)\n assert_equal(B.dtype.itemsize, 10)\n assert_array_equal(B, [[b'abc', b'2'],\n [b'long', b'0123456789']])\n\n def test_from_object_array_unicode(self):\n A = np.array([['abc', 'Sigma \u03a3'],\n ['long ', '0123456789']], dtype='O')\n assert_raises(ValueError, np.char.array, (A,))\n B = np.char.array(A, **kw_unicode_true)\n assert_equal(B.dtype.itemsize, 10 * np.array('a', 'U').dtype.itemsize)\n assert_array_equal(B, [['abc', 'Sigma \u03a3'],\n ['long', '0123456789']])\n\n def test_from_string_array(self):\n A = np.array([[b'abc', b'foo'],\n [b'long ', b'0123456789']])\n assert_equal(A.dtype.type, np.bytes_)\n B = np.char.array(A)\n assert_array_equal(B, A)\n assert_equal(B.dtype, A.dtype)\n assert_equal(B.shape, A.shape)\n B[0, 0] = 'changed'\n assert_(B[0, 0] != A[0, 0])\n C = np.char.asarray(A)\n assert_array_equal(C, A)\n assert_equal(C.dtype, A.dtype)\n C[0, 0] = 'changed again'\n assert_(C[0, 0] != B[0, 0])\n assert_(C[0, 0] == A[0, 0])\n\n def test_from_unicode_array(self):\n A = np.array([['abc', 'Sigma \u03a3'],\n ['long ', '0123456789']])\n assert_equal(A.dtype.type, np.str_)\n B = np.char.array(A)\n assert_array_equal(B, A)\n assert_equal(B.dtype, A.dtype)\n assert_equal(B.shape, A.shape)\n B = np.char.array(A, **kw_unicode_true)\n assert_array_equal(B, A)\n assert_equal(B.dtype, A.dtype)\n assert_equal(B.shape, A.shape)\n\n def fail():\n np.char.array(A, **kw_unicode_false)\n\n assert_raises(UnicodeEncodeError, fail)\n\n def test_unicode_upconvert(self):\n A = np.char.array(['abc'])\n B = np.char.array(['\u03a3'])\n assert_(issubclass((A + B).dtype.type, np.str_))\n\n def test_from_string(self):\n A = np.char.array(b'abc')\n assert_equal(len(A), 1)\n assert_equal(len(A[0]), 3)\n assert_(issubclass(A.dtype.type, np.bytes_))\n\n def test_from_unicode(self):\n A = np.char.array('\u03a3')\n assert_equal(len(A), 1)\n assert_equal(len(A[0]), 1)\n assert_equal(A.itemsize, 4)\n assert_(issubclass(A.dtype.type, np.str_))\n\nclass TestVecString:\n def test_non_existent_method(self):\n\n def fail():\n _vec_string('a', np.bytes_, 'bogus')\n\n assert_raises(AttributeError, fail)\n\n def test_non_string_array(self):\n\n def fail():\n _vec_string(1, np.bytes_, 'strip')\n\n assert_raises(TypeError, fail)\n\n def test_invalid_args_tuple(self):\n\n def fail():\n _vec_string(['a'], np.bytes_, 'strip', 1)\n\n assert_raises(TypeError, fail)\n\n def test_invalid_type_descr(self):\n\n def fail():\n _vec_string(['a'], 'BOGUS', 'strip')\n\n assert_raises(TypeError, fail)\n\n def test_invalid_function_args(self):\n\n def fail():\n _vec_string(['a'], np.bytes_, 'strip', (1,))\n\n assert_raises(TypeError, fail)\n\n def test_invalid_result_type(self):\n\n def fail():\n _vec_string(['a'], np.int_, 'strip')\n\n assert_raises(TypeError, fail)\n\n def test_broadcast_error(self):\n\n def fail():\n _vec_string([['abc', 'def']], np.int_, 'find', (['a', 'd', 'j'],))\n\n assert_raises(ValueError, fail)\n\n\nclass TestWhitespace:\n def setup_method(self):\n self.A = np.array([['abc ', '123 '],\n ['789 ', 'xyz ']]).view(np.char.chararray)\n self.B = np.array([['abc', '123'],\n ['789', 'xyz']]).view(np.char.chararray)\n\n def test1(self):\n assert_(np.all(self.A == self.B))\n assert_(np.all(self.A >= self.B))\n assert_(np.all(self.A <= self.B))\n assert_(not np.any(self.A > self.B))\n assert_(not np.any(self.A < self.B))\n assert_(not np.any(self.A != self.B))\n\nclass TestChar:\n def setup_method(self):\n self.A = np.array('abc1', dtype='c').view(np.char.chararray)\n\n def test_it(self):\n assert_equal(self.A.shape, (4,))\n assert_equal(self.A.upper()[:2].tobytes(), b'AB')\n\nclass TestComparisons:\n def setup_method(self):\n self.A = np.array([['abc', 'abcc', '123'],\n ['789', 'abc', 'xyz']]).view(np.char.chararray)\n self.B = np.array([['efg', 'efg', '123 '],\n ['051', 'efgg', 'tuv']]).view(np.char.chararray)\n\n def test_not_equal(self):\n assert_array_equal((self.A != self.B),\n [[True, True, False], [True, True, True]])\n\n def test_equal(self):\n assert_array_equal((self.A == self.B),\n [[False, False, True], [False, False, False]])\n\n def test_greater_equal(self):\n assert_array_equal((self.A >= self.B),\n [[False, False, True], [True, False, True]])\n\n def test_less_equal(self):\n assert_array_equal((self.A <= self.B),\n [[True, True, True], [False, True, False]])\n\n def test_greater(self):\n assert_array_equal((self.A > self.B),\n [[False, False, False], [True, False, True]])\n\n def test_less(self):\n assert_array_equal((self.A < self.B),\n [[True, True, False], [False, True, False]])\n\n def test_type(self):\n out1 = np.char.equal(self.A, self.B)\n out2 = np.char.equal('a', 'a')\n assert_(isinstance(out1, np.ndarray))\n assert_(isinstance(out2, np.ndarray))\n\nclass TestComparisonsMixed1(TestComparisons):\n """Ticket #1276"""\n\n def setup_method(self):\n TestComparisons.setup_method(self)\n self.B = np.array(\n [['efg', 'efg', '123 '],\n ['051', 'efgg', 'tuv']], np.str_).view(np.char.chararray)\n\nclass TestComparisonsMixed2(TestComparisons):\n """Ticket #1276"""\n\n def setup_method(self):\n TestComparisons.setup_method(self)\n self.A = np.array(\n [['abc', 'abcc', '123'],\n ['789', 'abc', 'xyz']], np.str_).view(np.char.chararray)\n\nclass TestInformation:\n def setup_method(self):\n self.A = np.array([[' abc ', ''],\n ['12345', 'MixedCase'],\n ['123 \t 345 \0 ', 'UPPER']]) \\n .view(np.char.chararray)\n self.B = np.array([[' \u03a3 ', ''],\n ['12345', 'MixedCase'],\n ['123 \t 345 \0 ', 'UPPER']]) \\n .view(np.char.chararray)\n # Array with longer strings, > MEMCHR_CUT_OFF in code.\n self.C = (np.array(['ABCDEFGHIJKLMNOPQRSTUVWXYZ',\n '01234567890123456789012345'])\n .view(np.char.chararray))\n\n def test_len(self):\n assert_(issubclass(np.char.str_len(self.A).dtype.type, np.integer))\n assert_array_equal(np.char.str_len(self.A), [[5, 0], [5, 9], [12, 5]])\n assert_array_equal(np.char.str_len(self.B), [[3, 0], [5, 9], [12, 5]])\n\n def test_count(self):\n assert_(issubclass(self.A.count('').dtype.type, np.integer))\n assert_array_equal(self.A.count('a'), [[1, 0], [0, 1], [0, 0]])\n assert_array_equal(self.A.count('123'), [[0, 0], [1, 0], [1, 0]])\n # Python doesn't seem to like counting NULL characters\n # assert_array_equal(self.A.count('\0'), [[0, 0], [0, 0], [1, 0]])\n assert_array_equal(self.A.count('a', 0, 2), [[1, 0], [0, 0], [0, 0]])\n assert_array_equal(self.B.count('a'), [[0, 0], [0, 1], [0, 0]])\n assert_array_equal(self.B.count('123'), [[0, 0], [1, 0], [1, 0]])\n # assert_array_equal(self.B.count('\0'), [[0, 0], [0, 0], [1, 0]])\n\n def test_endswith(self):\n assert_(issubclass(self.A.endswith('').dtype.type, np.bool))\n assert_array_equal(self.A.endswith(' '), [[1, 0], [0, 0], [1, 0]])\n assert_array_equal(self.A.endswith('3', 0, 3), [[0, 0], [1, 0], [1, 0]])\n\n def fail():\n self.A.endswith('3', 'fdjk')\n\n assert_raises(TypeError, fail)\n\n @pytest.mark.parametrize(\n "dtype, encode",\n [("U", str),\n ("S", lambda x: x.encode('ascii')),\n ])\n def test_find(self, dtype, encode):\n A = self.A.astype(dtype)\n assert_(issubclass(A.find(encode('a')).dtype.type, np.integer))\n assert_array_equal(A.find(encode('a')),\n [[1, -1], [-1, 6], [-1, -1]])\n assert_array_equal(A.find(encode('3')),\n [[-1, -1], [2, -1], [2, -1]])\n assert_array_equal(A.find(encode('a'), 0, 2),\n [[1, -1], [-1, -1], [-1, -1]])\n assert_array_equal(A.find([encode('1'), encode('P')]),\n [[-1, -1], [0, -1], [0, 1]])\n C = self.C.astype(dtype)\n assert_array_equal(C.find(encode('M')), [12, -1])\n\n def test_index(self):\n\n def fail():\n self.A.index('a')\n\n assert_raises(ValueError, fail)\n assert_(np.char.index('abcba', 'b') == 1)\n assert_(issubclass(np.char.index('abcba', 'b').dtype.type, np.integer))\n\n def test_isalnum(self):\n assert_(issubclass(self.A.isalnum().dtype.type, np.bool))\n assert_array_equal(self.A.isalnum(), [[False, False], [True, True], [False, True]])\n\n def test_isalpha(self):\n assert_(issubclass(self.A.isalpha().dtype.type, np.bool))\n assert_array_equal(self.A.isalpha(), [[False, False], [False, True], [False, True]])\n\n def test_isdigit(self):\n assert_(issubclass(self.A.isdigit().dtype.type, np.bool))\n assert_array_equal(self.A.isdigit(), [[False, False], [True, False], [False, False]])\n\n def test_islower(self):\n assert_(issubclass(self.A.islower().dtype.type, np.bool))\n assert_array_equal(self.A.islower(), [[True, False], [False, False], [False, False]])\n\n def test_isspace(self):\n assert_(issubclass(self.A.isspace().dtype.type, np.bool))\n assert_array_equal(self.A.isspace(), [[False, False], [False, False], [False, False]])\n\n def test_istitle(self):\n assert_(issubclass(self.A.istitle().dtype.type, np.bool))\n assert_array_equal(self.A.istitle(), [[False, False], [False, False], [False, False]])\n\n def test_isupper(self):\n assert_(issubclass(self.A.isupper().dtype.type, np.bool))\n assert_array_equal(self.A.isupper(), [[False, False], [False, False], [False, True]])\n\n def test_rfind(self):\n assert_(issubclass(self.A.rfind('a').dtype.type, np.integer))\n assert_array_equal(self.A.rfind('a'), [[1, -1], [-1, 6], [-1, -1]])\n assert_array_equal(self.A.rfind('3'), [[-1, -1], [2, -1], [6, -1]])\n assert_array_equal(self.A.rfind('a', 0, 2), [[1, -1], [-1, -1], [-1, -1]])\n assert_array_equal(self.A.rfind(['1', 'P']), [[-1, -1], [0, -1], [0, 2]])\n\n def test_rindex(self):\n\n def fail():\n self.A.rindex('a')\n\n assert_raises(ValueError, fail)\n assert_(np.char.rindex('abcba', 'b') == 3)\n assert_(issubclass(np.char.rindex('abcba', 'b').dtype.type, np.integer))\n\n def test_startswith(self):\n assert_(issubclass(self.A.startswith('').dtype.type, np.bool))\n assert_array_equal(self.A.startswith(' '), [[1, 0], [0, 0], [0, 0]])\n assert_array_equal(self.A.startswith('1', 0, 3), [[0, 0], [1, 0], [1, 0]])\n\n def fail():\n self.A.startswith('3', 'fdjk')\n\n assert_raises(TypeError, fail)\n\n\nclass TestMethods:\n def setup_method(self):\n self.A = np.array([[' abc ', ''],\n ['12345', 'MixedCase'],\n ['123 \t 345 \0 ', 'UPPER']],\n dtype='S').view(np.char.chararray)\n self.B = np.array([[' \u03a3 ', ''],\n ['12345', 'MixedCase'],\n ['123 \t 345 \0 ', 'UPPER']]).view(\n np.char.chararray)\n\n def test_capitalize(self):\n tgt = [[b' abc ', b''],\n [b'12345', b'Mixedcase'],\n [b'123 \t 345 \0 ', b'Upper']]\n assert_(issubclass(self.A.capitalize().dtype.type, np.bytes_))\n assert_array_equal(self.A.capitalize(), tgt)\n\n tgt = [[' \u03c3 ', ''],\n ['12345', 'Mixedcase'],\n ['123 \t 345 \0 ', 'Upper']]\n assert_(issubclass(self.B.capitalize().dtype.type, np.str_))\n assert_array_equal(self.B.capitalize(), tgt)\n\n def test_center(self):\n assert_(issubclass(self.A.center(10).dtype.type, np.bytes_))\n C = self.A.center([10, 20])\n assert_array_equal(np.char.str_len(C), [[10, 20], [10, 20], [12, 20]])\n\n C = self.A.center(20, b'#')\n assert_(np.all(C.startswith(b'#')))\n assert_(np.all(C.endswith(b'#')))\n\n C = np.char.center(b'FOO', [[10, 20], [15, 8]])\n tgt = [[b' FOO ', b' FOO '],\n [b' FOO ', b' FOO ']]\n assert_(issubclass(C.dtype.type, np.bytes_))\n assert_array_equal(C, tgt)\n\n def test_decode(self):\n A = np.char.array([b'\\u03a3'])\n assert_(A.decode('unicode-escape')[0] == '\u03a3')\n\n def test_encode(self):\n B = self.B.encode('unicode_escape')\n assert_(B[0][0] == ' \\u03a3 '.encode('latin1'))\n\n def test_expandtabs(self):\n T = self.A.expandtabs()\n assert_(T[2, 0] == b'123 345 \0')\n\n def test_join(self):\n # NOTE: list(b'123') == [49, 50, 51]\n # so that b','.join(b'123') results to an error on Py3\n A0 = self.A.decode('ascii')\n\n A = np.char.join([',', '#'], A0)\n assert_(issubclass(A.dtype.type, np.str_))\n tgt = np.array([[' ,a,b,c, ', ''],\n ['1,2,3,4,5', 'M#i#x#e#d#C#a#s#e'],\n ['1,2,3, ,\t, ,3,4,5, ,\x00, ', 'U#P#P#E#R']])\n assert_array_equal(np.char.join([',', '#'], A0), tgt)\n\n def test_ljust(self):\n assert_(issubclass(self.A.ljust(10).dtype.type, np.bytes_))\n\n C = self.A.ljust([10, 20])\n assert_array_equal(np.char.str_len(C), [[10, 20], [10, 20], [12, 20]])\n\n C = self.A.ljust(20, b'#')\n assert_array_equal(C.startswith(b'#'), [\n [False, True], [False, False], [False, False]])\n assert_(np.all(C.endswith(b'#')))\n\n C = np.char.ljust(b'FOO', [[10, 20], [15, 8]])\n tgt = [[b'FOO ', b'FOO '],\n [b'FOO ', b'FOO ']]\n assert_(issubclass(C.dtype.type, np.bytes_))\n assert_array_equal(C, tgt)\n\n def test_lower(self):\n tgt = [[b' abc ', b''],\n [b'12345', b'mixedcase'],\n [b'123 \t 345 \0 ', b'upper']]\n assert_(issubclass(self.A.lower().dtype.type, np.bytes_))\n assert_array_equal(self.A.lower(), tgt)\n\n tgt = [[' \u03c3 ', ''],\n ['12345', 'mixedcase'],\n ['123 \t 345 \0 ', 'upper']]\n assert_(issubclass(self.B.lower().dtype.type, np.str_))\n assert_array_equal(self.B.lower(), tgt)\n\n def test_lstrip(self):\n tgt = [[b'abc ', b''],\n [b'12345', b'MixedCase'],\n [b'123 \t 345 \0 ', b'UPPER']]\n assert_(issubclass(self.A.lstrip().dtype.type, np.bytes_))\n assert_array_equal(self.A.lstrip(), tgt)\n\n tgt = [[b' abc', b''],\n [b'2345', b'ixedCase'],\n [b'23 \t 345 \x00', b'UPPER']]\n assert_array_equal(self.A.lstrip([b'1', b'M']), tgt)\n\n tgt = [['\u03a3 ', ''],\n ['12345', 'MixedCase'],\n ['123 \t 345 \0 ', 'UPPER']]\n assert_(issubclass(self.B.lstrip().dtype.type, np.str_))\n assert_array_equal(self.B.lstrip(), tgt)\n\n def test_partition(self):\n P = self.A.partition([b'3', b'M'])\n tgt = [[(b' abc ', b'', b''), (b'', b'', b'')],\n [(b'12', b'3', b'45'), (b'', b'M', b'ixedCase')],\n [(b'12', b'3', b' \t 345 \0 '), (b'UPPER', b'', b'')]]\n assert_(issubclass(P.dtype.type, np.bytes_))\n assert_array_equal(P, tgt)\n\n def test_replace(self):\n R = self.A.replace([b'3', b'a'],\n [b'##########', b'@'])\n tgt = [[b' abc ', b''],\n [b'12##########45', b'MixedC@se'],\n [b'12########## \t ##########45 \x00 ', b'UPPER']]\n assert_(issubclass(R.dtype.type, np.bytes_))\n assert_array_equal(R, tgt)\n # Test special cases that should just return the input array,\n # since replacements are not possible or do nothing.\n S1 = self.A.replace(b'A very long byte string, longer than A', b'')\n assert_array_equal(S1, self.A)\n S2 = self.A.replace(b'', b'')\n assert_array_equal(S2, self.A)\n S3 = self.A.replace(b'3', b'3')\n assert_array_equal(S3, self.A)\n S4 = self.A.replace(b'3', b'', count=0)\n assert_array_equal(S4, self.A)\n\n def test_replace_count_and_size(self):\n a = np.array(['0123456789' * i for i in range(4)]\n ).view(np.char.chararray)\n r1 = a.replace('5', 'ABCDE')\n assert r1.dtype.itemsize == (3 * 10 + 3 * 4) * 4\n assert_array_equal(r1, np.array(['01234ABCDE6789' * i\n for i in range(4)]))\n r2 = a.replace('5', 'ABCDE', count=1)\n assert r2.dtype.itemsize == (3 * 10 + 4) * 4\n r3 = a.replace('5', 'ABCDE', count=0)\n assert r3.dtype.itemsize == a.dtype.itemsize\n assert_array_equal(r3, a)\n # Negative values mean to replace all.\n r4 = a.replace('5', 'ABCDE', count=-1)\n assert r4.dtype.itemsize == (3 * 10 + 3 * 4) * 4\n assert_array_equal(r4, r1)\n # We can do count on an element-by-element basis.\n r5 = a.replace('5', 'ABCDE', count=[-1, -1, -1, 1])\n assert r5.dtype.itemsize == (3 * 10 + 4) * 4\n assert_array_equal(r5, np.array(\n ['01234ABCDE6789' * i for i in range(3)]\n + ['01234ABCDE6789' + '0123456789' * 2]))\n\n def test_replace_broadcasting(self):\n a = np.array('0,0,0').view(np.char.chararray)\n r1 = a.replace('0', '1', count=np.arange(3))\n assert r1.dtype == a.dtype\n assert_array_equal(r1, np.array(['0,0,0', '1,0,0', '1,1,0']))\n r2 = a.replace('0', [['1'], ['2']], count=np.arange(1, 4))\n assert_array_equal(r2, np.array([['1,0,0', '1,1,0', '1,1,1'],\n ['2,0,0', '2,2,0', '2,2,2']]))\n r3 = a.replace(['0', '0,0', '0,0,0'], 'X')\n assert_array_equal(r3, np.array(['X,X,X', 'X,0', 'X']))\n\n def test_rjust(self):\n assert_(issubclass(self.A.rjust(10).dtype.type, np.bytes_))\n\n C = self.A.rjust([10, 20])\n assert_array_equal(np.char.str_len(C), [[10, 20], [10, 20], [12, 20]])\n\n C = self.A.rjust(20, b'#')\n assert_(np.all(C.startswith(b'#')))\n assert_array_equal(C.endswith(b'#'),\n [[False, True], [False, False], [False, False]])\n\n C = np.char.rjust(b'FOO', [[10, 20], [15, 8]])\n tgt = [[b' FOO', b' FOO'],\n [b' FOO', b' FOO']]\n assert_(issubclass(C.dtype.type, np.bytes_))\n assert_array_equal(C, tgt)\n\n def test_rpartition(self):\n P = self.A.rpartition([b'3', b'M'])\n tgt = [[(b'', b'', b' abc '), (b'', b'', b'')],\n [(b'12', b'3', b'45'), (b'', b'M', b'ixedCase')],\n [(b'123 \t ', b'3', b'45 \0 '), (b'', b'', b'UPPER')]]\n assert_(issubclass(P.dtype.type, np.bytes_))\n assert_array_equal(P, tgt)\n\n def test_rsplit(self):\n A = self.A.rsplit(b'3')\n tgt = [[[b' abc '], [b'']],\n [[b'12', b'45'], [b'MixedCase']],\n [[b'12', b' \t ', b'45 \x00 '], [b'UPPER']]]\n assert_(issubclass(A.dtype.type, np.object_))\n assert_equal(A.tolist(), tgt)\n\n def test_rstrip(self):\n assert_(issubclass(self.A.rstrip().dtype.type, np.bytes_))\n\n tgt = [[b' abc', b''],\n [b'12345', b'MixedCase'],\n [b'123 \t 345', b'UPPER']]\n assert_array_equal(self.A.rstrip(), tgt)\n\n tgt = [[b' abc ', b''],\n [b'1234', b'MixedCase'],\n [b'123 \t 345 \x00', b'UPP']\n ]\n assert_array_equal(self.A.rstrip([b'5', b'ER']), tgt)\n\n tgt = [[' \u03a3', ''],\n ['12345', 'MixedCase'],\n ['123 \t 345', 'UPPER']]\n assert_(issubclass(self.B.rstrip().dtype.type, np.str_))\n assert_array_equal(self.B.rstrip(), tgt)\n\n def test_strip(self):\n tgt = [[b'abc', b''],\n [b'12345', b'MixedCase'],\n [b'123 \t 345', b'UPPER']]\n assert_(issubclass(self.A.strip().dtype.type, np.bytes_))\n assert_array_equal(self.A.strip(), tgt)\n\n tgt = [[b' abc ', b''],\n [b'234', b'ixedCas'],\n [b'23 \t 345 \x00', b'UPP']]\n assert_array_equal(self.A.strip([b'15', b'EReM']), tgt)\n\n tgt = [['\u03a3', ''],\n ['12345', 'MixedCase'],\n ['123 \t 345', 'UPPER']]\n assert_(issubclass(self.B.strip().dtype.type, np.str_))\n assert_array_equal(self.B.strip(), tgt)\n\n def test_split(self):\n A = self.A.split(b'3')\n tgt = [\n [[b' abc '], [b'']],\n [[b'12', b'45'], [b'MixedCase']],\n [[b'12', b' \t ', b'45 \x00 '], [b'UPPER']]]\n assert_(issubclass(A.dtype.type, np.object_))\n assert_equal(A.tolist(), tgt)\n\n def test_splitlines(self):\n A = np.char.array(['abc\nfds\nwer']).splitlines()\n assert_(issubclass(A.dtype.type, np.object_))\n assert_(A.shape == (1,))\n assert_(len(A[0]) == 3)\n\n def test_swapcase(self):\n tgt = [[b' ABC ', b''],\n [b'12345', b'mIXEDcASE'],\n [b'123 \t 345 \0 ', b'upper']]\n assert_(issubclass(self.A.swapcase().dtype.type, np.bytes_))\n assert_array_equal(self.A.swapcase(), tgt)\n\n tgt = [[' \u03c3 ', ''],\n ['12345', 'mIXEDcASE'],\n ['123 \t 345 \0 ', 'upper']]\n assert_(issubclass(self.B.swapcase().dtype.type, np.str_))\n assert_array_equal(self.B.swapcase(), tgt)\n\n def test_title(self):\n tgt = [[b' Abc ', b''],\n [b'12345', b'Mixedcase'],\n [b'123 \t 345 \0 ', b'Upper']]\n assert_(issubclass(self.A.title().dtype.type, np.bytes_))\n assert_array_equal(self.A.title(), tgt)\n\n tgt = [[' \u03a3 ', ''],\n ['12345', 'Mixedcase'],\n ['123 \t 345 \0 ', 'Upper']]\n assert_(issubclass(self.B.title().dtype.type, np.str_))\n assert_array_equal(self.B.title(), tgt)\n\n def test_upper(self):\n tgt = [[b' ABC ', b''],\n [b'12345', b'MIXEDCASE'],\n [b'123 \t 345 \0 ', b'UPPER']]\n assert_(issubclass(self.A.upper().dtype.type, np.bytes_))\n assert_array_equal(self.A.upper(), tgt)\n\n tgt = [[' \u03a3 ', ''],\n ['12345', 'MIXEDCASE'],\n ['123 \t 345 \0 ', 'UPPER']]\n assert_(issubclass(self.B.upper().dtype.type, np.str_))\n assert_array_equal(self.B.upper(), tgt)\n\n def test_isnumeric(self):\n\n def fail():\n self.A.isnumeric()\n\n assert_raises(TypeError, fail)\n assert_(issubclass(self.B.isnumeric().dtype.type, np.bool))\n assert_array_equal(self.B.isnumeric(), [\n [False, False], [True, False], [False, False]])\n\n def test_isdecimal(self):\n\n def fail():\n self.A.isdecimal()\n\n assert_raises(TypeError, fail)\n assert_(issubclass(self.B.isdecimal().dtype.type, np.bool))\n assert_array_equal(self.B.isdecimal(), [\n [False, False], [True, False], [False, False]])\n\n\nclass TestOperations:\n def setup_method(self):\n self.A = np.array([['abc', '123'],\n ['789', 'xyz']]).view(np.char.chararray)\n self.B = np.array([['efg', '456'],\n ['051', 'tuv']]).view(np.char.chararray)\n\n def test_add(self):\n AB = np.array([['abcefg', '123456'],\n ['789051', 'xyztuv']]).view(np.char.chararray)\n assert_array_equal(AB, (self.A + self.B))\n assert_(len((self.A + self.B)[0][0]) == 6)\n\n def test_radd(self):\n QA = np.array([['qabc', 'q123'],\n ['q789', 'qxyz']]).view(np.char.chararray)\n assert_array_equal(QA, ('q' + self.A))\n\n def test_mul(self):\n A = self.A\n for r in (2, 3, 5, 7, 197):\n Ar = np.array([[A[0, 0] * r, A[0, 1] * r],\n [A[1, 0] * r, A[1, 1] * r]]).view(np.char.chararray)\n\n assert_array_equal(Ar, (self.A * r))\n\n for ob in [object(), 'qrs']:\n with assert_raises_regex(ValueError,\n 'Can only multiply by integers'):\n A * ob\n\n def test_rmul(self):\n A = self.A\n for r in (2, 3, 5, 7, 197):\n Ar = np.array([[A[0, 0] * r, A[0, 1] * r],\n [A[1, 0] * r, A[1, 1] * r]]).view(np.char.chararray)\n assert_array_equal(Ar, (r * self.A))\n\n for ob in [object(), 'qrs']:\n with assert_raises_regex(ValueError,\n 'Can only multiply by integers'):\n ob * A\n\n def test_mod(self):\n """Ticket #856"""\n F = np.array([['%d', '%f'], ['%s', '%r']]).view(np.char.chararray)\n C = np.array([[3, 7], [19, 1]], dtype=np.int64)\n FC = np.array([['3', '7.000000'],\n ['19', 'np.int64(1)']]).view(np.char.chararray)\n assert_array_equal(FC, F % C)\n\n A = np.array([['%.3f', '%d'], ['%s', '%r']]).view(np.char.chararray)\n A1 = np.array([['1.000', '1'],\n ['1', repr(np.array(1)[()])]]).view(np.char.chararray)\n assert_array_equal(A1, (A % 1))\n\n A2 = np.array([['1.000', '2'],\n ['3', repr(np.array(4)[()])]]).view(np.char.chararray)\n assert_array_equal(A2, (A % [[1, 2], [3, 4]]))\n\n def test_rmod(self):\n assert_(f"{self.A}" == str(self.A))\n assert_(f"{self.A!r}" == repr(self.A))\n\n for ob in [42, object()]:\n with assert_raises_regex(\n TypeError, "unsupported operand type.* and 'chararray'"):\n ob % self.A\n\n def test_slice(self):\n """Regression test for https://github.com/numpy/numpy/issues/5982"""\n\n arr = np.array([['abc ', 'def '], ['geh ', 'ijk ']],\n dtype='S4').view(np.char.chararray)\n sl1 = arr[:]\n assert_array_equal(sl1, arr)\n assert_(sl1.base is arr)\n assert_(sl1.base.base is arr.base)\n\n sl2 = arr[:, :]\n assert_array_equal(sl2, arr)\n assert_(sl2.base is arr)\n assert_(sl2.base.base is arr.base)\n\n assert_(arr[0, 0] == b'abc')\n\n @pytest.mark.parametrize('data', [['plate', ' ', 'shrimp'],\n [b'retro', b' ', b'encabulator']])\n def test_getitem_length_zero_item(self, data):\n # Regression test for gh-26375.\n a = np.char.array(data)\n # a.dtype.type() will be an empty string or bytes instance.\n # The equality test will fail if a[1] has the wrong type\n # or does not have length 0.\n assert_equal(a[1], a.dtype.type())\n\n\nclass TestMethodsEmptyArray:\n def setup_method(self):\n self.U = np.array([], dtype='U')\n self.S = np.array([], dtype='S')\n\n def test_encode(self):\n res = np.char.encode(self.U)\n assert_array_equal(res, [])\n assert_(res.dtype.char == 'S')\n\n def test_decode(self):\n res = np.char.decode(self.S)\n assert_array_equal(res, [])\n assert_(res.dtype.char == 'U')\n\n def test_decode_with_reshape(self):\n res = np.char.decode(self.S.reshape((1, 0, 1)))\n assert_(res.shape == (1, 0, 1))\n\n\nclass TestMethodsScalarValues:\n def test_mod(self):\n A = np.array([[' abc ', ''],\n ['12345', 'MixedCase'],\n ['123 \t 345 \0 ', 'UPPER']], dtype='S')\n tgt = [[b'123 abc ', b'123'],\n [b'12312345', b'123MixedCase'],\n [b'123123 \t 345 \0 ', b'123UPPER']]\n assert_array_equal(np.char.mod(b"123%s", A), tgt)\n\n def test_decode(self):\n bytestring = b'\x81\xc1\x81\xc1\x81\xc1'\n assert_equal(np.char.decode(bytestring, encoding='cp037'),\n 'aAaAaA')\n\n def test_encode(self):\n unicode = 'aAaAaA'\n assert_equal(np.char.encode(unicode, encoding='cp037'),\n b'\x81\xc1\x81\xc1\x81\xc1')\n\n def test_expandtabs(self):\n s = "\tone level of indentation\n\t\ttwo levels of indentation"\n assert_equal(\n np.char.expandtabs(s, tabsize=2),\n " one level of indentation\n two levels of indentation"\n )\n\n def test_join(self):\n seps = np.array(['-', '_'])\n assert_array_equal(np.char.join(seps, 'hello'),\n ['h-e-l-l-o', 'h_e_l_l_o'])\n\n def test_partition(self):\n assert_equal(np.char.partition('This string', ' '),\n ['This', ' ', 'string'])\n\n def test_rpartition(self):\n assert_equal(np.char.rpartition('This string here', ' '),\n ['This string', ' ', 'here'])\n\n def test_replace(self):\n assert_equal(np.char.replace('Python is good', 'good', 'great'),\n 'Python is great')\n\n\ndef test_empty_indexing():\n """Regression test for ticket 1948."""\n # Check that indexing a chararray with an empty list/array returns an\n # empty chararray instead of a chararray with a single empty string in it.\n s = np.char.chararray((4,))\n assert_(s[[]].size == 0)\n | .venv\Lib\site-packages\numpy\_core\tests\test_defchararray.py | test_defchararray.py | Python | 31,454 | 0.95 | 0.16 | 0.02406 | python-kit | 511 | 2025-04-04T21:12:36.650453 | GPL-3.0 | true | 6b253846f091f68d9881ac12f9297b23 |
"""\nTests related to deprecation warnings. Also a convenient place\nto document how deprecations should eventually be turned into errors.\n\n"""\nimport contextlib\nimport warnings\n\nimport numpy._core._struct_ufunc_tests as struct_ufunc\nimport pytest\nfrom numpy._core._multiarray_tests import fromstring_null_term_c_api # noqa: F401\n\nimport numpy as np\nfrom numpy.testing import assert_raises, temppath\n\ntry:\n import pytz # noqa: F401\n _has_pytz = True\nexcept ImportError:\n _has_pytz = False\n\n\nclass _DeprecationTestCase:\n # Just as warning: warnings uses re.match, so the start of this message\n # must match.\n message = ''\n warning_cls = DeprecationWarning\n\n def setup_method(self):\n self.warn_ctx = warnings.catch_warnings(record=True)\n self.log = self.warn_ctx.__enter__()\n\n # Do *not* ignore other DeprecationWarnings. Ignoring warnings\n # can give very confusing results because of\n # https://bugs.python.org/issue4180 and it is probably simplest to\n # try to keep the tests cleanly giving only the right warning type.\n # (While checking them set to "error" those are ignored anyway)\n # We still have them show up, because otherwise they would be raised\n warnings.filterwarnings("always", category=self.warning_cls)\n warnings.filterwarnings("always", message=self.message,\n category=self.warning_cls)\n\n def teardown_method(self):\n self.warn_ctx.__exit__()\n\n def assert_deprecated(self, function, num=1, ignore_others=False,\n function_fails=False,\n exceptions=np._NoValue,\n args=(), kwargs={}):\n """Test if DeprecationWarnings are given and raised.\n\n This first checks if the function when called gives `num`\n DeprecationWarnings, after that it tries to raise these\n DeprecationWarnings and compares them with `exceptions`.\n The exceptions can be different for cases where this code path\n is simply not anticipated and the exception is replaced.\n\n Parameters\n ----------\n function : callable\n The function to test\n num : int\n Number of DeprecationWarnings to expect. This should normally be 1.\n ignore_others : bool\n Whether warnings of the wrong type should be ignored (note that\n the message is not checked)\n function_fails : bool\n If the function would normally fail, setting this will check for\n warnings inside a try/except block.\n exceptions : Exception or tuple of Exceptions\n Exception to expect when turning the warnings into an error.\n The default checks for DeprecationWarnings. If exceptions is\n empty the function is expected to run successfully.\n args : tuple\n Arguments for `function`\n kwargs : dict\n Keyword arguments for `function`\n """\n __tracebackhide__ = True # Hide traceback for py.test\n\n # reset the log\n self.log[:] = []\n\n if exceptions is np._NoValue:\n exceptions = (self.warning_cls,)\n\n if function_fails:\n context_manager = contextlib.suppress(Exception)\n else:\n context_manager = contextlib.nullcontext()\n with context_manager:\n function(*args, **kwargs)\n\n # just in case, clear the registry\n num_found = 0\n for warning in self.log:\n if warning.category is self.warning_cls:\n num_found += 1\n elif not ignore_others:\n raise AssertionError(\n "expected %s but got: %s" %\n (self.warning_cls.__name__, warning.category))\n if num is not None and num_found != num:\n msg = f"{len(self.log)} warnings found but {num} expected."\n lst = [str(w) for w in self.log]\n raise AssertionError("\n".join([msg] + lst))\n\n with warnings.catch_warnings():\n warnings.filterwarnings("error", message=self.message,\n category=self.warning_cls)\n try:\n function(*args, **kwargs)\n if exceptions != ():\n raise AssertionError(\n "No error raised during function call")\n except exceptions:\n if exceptions == ():\n raise AssertionError(\n "Error raised during function call")\n\n def assert_not_deprecated(self, function, args=(), kwargs={}):\n """Test that warnings are not raised.\n\n This is just a shorthand for:\n\n self.assert_deprecated(function, num=0, ignore_others=True,\n exceptions=tuple(), args=args, kwargs=kwargs)\n """\n self.assert_deprecated(function, num=0, ignore_others=True,\n exceptions=(), args=args, kwargs=kwargs)\n\n\nclass _VisibleDeprecationTestCase(_DeprecationTestCase):\n warning_cls = np.exceptions.VisibleDeprecationWarning\n\n\nclass TestTestDeprecated:\n def test_assert_deprecated(self):\n test_case_instance = _DeprecationTestCase()\n test_case_instance.setup_method()\n assert_raises(AssertionError,\n test_case_instance.assert_deprecated,\n lambda: None)\n\n def foo():\n warnings.warn("foo", category=DeprecationWarning, stacklevel=2)\n\n test_case_instance.assert_deprecated(foo)\n test_case_instance.teardown_method()\n\n\nclass TestBincount(_DeprecationTestCase):\n # 2024-07-29, 2.1.0\n @pytest.mark.parametrize('badlist', [[0.5, 1.2, 1.5],\n ['0', '1', '1']])\n def test_bincount_bad_list(self, badlist):\n self.assert_deprecated(lambda: np.bincount(badlist))\n\n\nclass TestGeneratorSum(_DeprecationTestCase):\n # 2018-02-25, 1.15.0\n def test_generator_sum(self):\n self.assert_deprecated(np.sum, args=((i for i in range(5)),))\n\n\nclass BuiltInRoundComplexDType(_DeprecationTestCase):\n # 2020-03-31 1.19.0\n deprecated_types = [np.csingle, np.cdouble, np.clongdouble]\n not_deprecated_types = [\n np.int8, np.int16, np.int32, np.int64,\n np.uint8, np.uint16, np.uint32, np.uint64,\n np.float16, np.float32, np.float64,\n ]\n\n def test_deprecated(self):\n for scalar_type in self.deprecated_types:\n scalar = scalar_type(0)\n self.assert_deprecated(round, args=(scalar,))\n self.assert_deprecated(round, args=(scalar, 0))\n self.assert_deprecated(round, args=(scalar,), kwargs={'ndigits': 0})\n\n def test_not_deprecated(self):\n for scalar_type in self.not_deprecated_types:\n scalar = scalar_type(0)\n self.assert_not_deprecated(round, args=(scalar,))\n self.assert_not_deprecated(round, args=(scalar, 0))\n self.assert_not_deprecated(round, args=(scalar,), kwargs={'ndigits': 0})\n\n\nclass FlatteningConcatenateUnsafeCast(_DeprecationTestCase):\n # NumPy 1.20, 2020-09-03\n message = "concatenate with `axis=None` will use same-kind casting"\n\n def test_deprecated(self):\n self.assert_deprecated(np.concatenate,\n args=(([0.], [1.]),),\n kwargs={'axis': None, 'out': np.empty(2, dtype=np.int64)})\n\n def test_not_deprecated(self):\n self.assert_not_deprecated(np.concatenate,\n args=(([0.], [1.]),),\n kwargs={'axis': None, 'out': np.empty(2, dtype=np.int64),\n 'casting': "unsafe"})\n\n with assert_raises(TypeError):\n # Tests should notice if the deprecation warning is given first...\n np.concatenate(([0.], [1.]), out=np.empty(2, dtype=np.int64),\n casting="same_kind")\n\n\nclass TestCtypesGetter(_DeprecationTestCase):\n # Deprecated 2021-05-18, Numpy 1.21.0\n warning_cls = DeprecationWarning\n ctypes = np.array([1]).ctypes\n\n @pytest.mark.parametrize(\n "name", ["get_data", "get_shape", "get_strides", "get_as_parameter"]\n )\n def test_deprecated(self, name: str) -> None:\n func = getattr(self.ctypes, name)\n self.assert_deprecated(func)\n\n @pytest.mark.parametrize(\n "name", ["data", "shape", "strides", "_as_parameter_"]\n )\n def test_not_deprecated(self, name: str) -> None:\n self.assert_not_deprecated(lambda: getattr(self.ctypes, name))\n\n\nclass TestMachAr(_DeprecationTestCase):\n # Deprecated 2022-11-22, NumPy 1.25\n warning_cls = DeprecationWarning\n\n def test_deprecated_module(self):\n self.assert_deprecated(lambda: np._core.MachAr)\n\n\nclass TestQuantileInterpolationDeprecation(_DeprecationTestCase):\n # Deprecated 2021-11-08, NumPy 1.22\n @pytest.mark.parametrize("func",\n [np.percentile, np.quantile, np.nanpercentile, np.nanquantile])\n def test_deprecated(self, func):\n self.assert_deprecated(\n lambda: func([0., 1.], 0., interpolation="linear"))\n self.assert_deprecated(\n lambda: func([0., 1.], 0., interpolation="nearest"))\n\n @pytest.mark.parametrize("func",\n [np.percentile, np.quantile, np.nanpercentile, np.nanquantile])\n def test_both_passed(self, func):\n with warnings.catch_warnings():\n # catch the DeprecationWarning so that it does not raise:\n warnings.simplefilter("always", DeprecationWarning)\n with pytest.raises(TypeError):\n func([0., 1.], 0., interpolation="nearest", method="nearest")\n\n\nclass TestScalarConversion(_DeprecationTestCase):\n # 2023-01-02, 1.25.0\n def test_float_conversion(self):\n self.assert_deprecated(float, args=(np.array([3.14]),))\n\n def test_behaviour(self):\n b = np.array([[3.14]])\n c = np.zeros(5)\n with pytest.warns(DeprecationWarning):\n c[0] = b\n\n\nclass TestPyIntConversion(_DeprecationTestCase):\n message = r".*stop allowing conversion of out-of-bound.*"\n\n @pytest.mark.parametrize("dtype", np.typecodes["AllInteger"])\n def test_deprecated_scalar(self, dtype):\n dtype = np.dtype(dtype)\n info = np.iinfo(dtype)\n\n # Cover the most common creation paths (all end up in the\n # same place):\n def scalar(value, dtype):\n dtype.type(value)\n\n def assign(value, dtype):\n arr = np.array([0, 0, 0], dtype=dtype)\n arr[2] = value\n\n def create(value, dtype):\n np.array([value], dtype=dtype)\n\n for creation_func in [scalar, assign, create]:\n try:\n self.assert_deprecated(\n lambda: creation_func(info.min - 1, dtype))\n except OverflowError:\n pass # OverflowErrors always happened also before and are OK.\n\n try:\n self.assert_deprecated(\n lambda: creation_func(info.max + 1, dtype))\n except OverflowError:\n pass # OverflowErrors always happened also before and are OK.\n\n\n@pytest.mark.parametrize("name", ["str", "bytes", "object"])\ndef test_future_scalar_attributes(name):\n # FutureWarning added 2022-11-17, NumPy 1.24,\n assert name not in dir(np) # we may want to not add them\n with pytest.warns(FutureWarning,\n match=f"In the future .*{name}"):\n assert not hasattr(np, name)\n\n # Unfortunately, they are currently still valid via `np.dtype()`\n np.dtype(name)\n name in np._core.sctypeDict\n\n\n# Ignore the above future attribute warning for this test.\n@pytest.mark.filterwarnings("ignore:In the future:FutureWarning")\nclass TestRemovedGlobals:\n # Removed 2023-01-12, NumPy 1.24.0\n # Not a deprecation, but the large error was added to aid those who missed\n # the previous deprecation, and should be removed similarly to one\n # (or faster).\n @pytest.mark.parametrize("name",\n ["object", "float", "complex", "str", "int"])\n def test_attributeerror_includes_info(self, name):\n msg = f".*\n`np.{name}` was a deprecated alias for the builtin"\n with pytest.raises(AttributeError, match=msg):\n getattr(np, name)\n\n\nclass TestDeprecatedFinfo(_DeprecationTestCase):\n # Deprecated in NumPy 1.25, 2023-01-16\n def test_deprecated_none(self):\n self.assert_deprecated(np.finfo, args=(None,))\n\n\nclass TestMathAlias(_DeprecationTestCase):\n def test_deprecated_np_lib_math(self):\n self.assert_deprecated(lambda: np.lib.math)\n\n\nclass TestLibImports(_DeprecationTestCase):\n # Deprecated in Numpy 1.26.0, 2023-09\n def test_lib_functions_deprecation_call(self):\n from numpy import in1d, row_stack, trapz\n from numpy._core.numerictypes import maximum_sctype\n from numpy.lib._function_base_impl import disp\n from numpy.lib._npyio_impl import recfromcsv, recfromtxt\n from numpy.lib._shape_base_impl import get_array_wrap\n from numpy.lib._utils_impl import safe_eval\n from numpy.lib.tests.test_io import TextIO\n\n self.assert_deprecated(lambda: safe_eval("None"))\n\n data_gen = lambda: TextIO('A,B\n0,1\n2,3')\n kwargs = {'delimiter': ",", 'missing_values': "N/A", 'names': True}\n self.assert_deprecated(lambda: recfromcsv(data_gen()))\n self.assert_deprecated(lambda: recfromtxt(data_gen(), **kwargs))\n\n self.assert_deprecated(lambda: disp("test"))\n self.assert_deprecated(get_array_wrap)\n self.assert_deprecated(lambda: maximum_sctype(int))\n\n self.assert_deprecated(lambda: in1d([1], [1]))\n self.assert_deprecated(lambda: row_stack([[]]))\n self.assert_deprecated(lambda: trapz([1], [1]))\n self.assert_deprecated(lambda: np.chararray)\n\n\nclass TestDeprecatedDTypeAliases(_DeprecationTestCase):\n\n def _check_for_warning(self, func):\n with warnings.catch_warnings(record=True) as caught_warnings:\n func()\n assert len(caught_warnings) == 1\n w = caught_warnings[0]\n assert w.category is DeprecationWarning\n assert "alias 'a' was deprecated in NumPy 2.0" in str(w.message)\n\n def test_a_dtype_alias(self):\n for dtype in ["a", "a10"]:\n f = lambda: np.dtype(dtype)\n self._check_for_warning(f)\n self.assert_deprecated(f)\n f = lambda: np.array(["hello", "world"]).astype("a10")\n self._check_for_warning(f)\n self.assert_deprecated(f)\n\n\nclass TestDeprecatedArrayWrap(_DeprecationTestCase):\n message = "__array_wrap__.*"\n\n def test_deprecated(self):\n class Test1:\n def __array__(self, dtype=None, copy=None):\n return np.arange(4)\n\n def __array_wrap__(self, arr, context=None):\n self.called = True\n return 'pass context'\n\n class Test2(Test1):\n def __array_wrap__(self, arr):\n self.called = True\n return 'pass'\n\n test1 = Test1()\n test2 = Test2()\n self.assert_deprecated(lambda: np.negative(test1))\n assert test1.called\n self.assert_deprecated(lambda: np.negative(test2))\n assert test2.called\n\n\nclass TestDeprecatedDTypeParenthesizedRepeatCount(_DeprecationTestCase):\n message = "Passing in a parenthesized single number"\n\n @pytest.mark.parametrize("string", ["(2)i,", "(3)3S,", "f,(2)f"])\n def test_parenthesized_repeat_count(self, string):\n self.assert_deprecated(np.dtype, args=(string,))\n\n\nclass TestDeprecatedSaveFixImports(_DeprecationTestCase):\n # Deprecated in Numpy 2.1, 2024-05\n message = "The 'fix_imports' flag is deprecated and has no effect."\n\n def test_deprecated(self):\n with temppath(suffix='.npy') as path:\n sample_args = (path, np.array(np.zeros((1024, 10))))\n self.assert_not_deprecated(np.save, args=sample_args)\n self.assert_deprecated(np.save, args=sample_args,\n kwargs={'fix_imports': True})\n self.assert_deprecated(np.save, args=sample_args,\n kwargs={'fix_imports': False})\n for allow_pickle in [True, False]:\n self.assert_not_deprecated(np.save, args=sample_args,\n kwargs={'allow_pickle': allow_pickle})\n self.assert_deprecated(np.save, args=sample_args,\n kwargs={'allow_pickle': allow_pickle,\n 'fix_imports': True})\n self.assert_deprecated(np.save, args=sample_args,\n kwargs={'allow_pickle': allow_pickle,\n 'fix_imports': False})\n\n\nclass TestAddNewdocUFunc(_DeprecationTestCase):\n # Deprecated in Numpy 2.2, 2024-11\n def test_deprecated(self):\n self.assert_deprecated(\n lambda: np._core.umath._add_newdoc_ufunc(\n struct_ufunc.add_triplet, "new docs"\n )\n )\n | .venv\Lib\site-packages\numpy\_core\tests\test_deprecations.py | test_deprecations.py | Python | 17,555 | 0.95 | 0.237885 | 0.091413 | awesome-app | 519 | 2024-08-18T22:49:42.949618 | BSD-3-Clause | true | 83800727b10a007341fe63358a51f09f |
import sys\n\nimport pytest\n\nimport numpy as np\nfrom numpy.testing import IS_PYPY, assert_array_equal\n\n\ndef new_and_old_dlpack():\n yield np.arange(5)\n\n class OldDLPack(np.ndarray):\n # Support only the "old" version\n def __dlpack__(self, stream=None):\n return super().__dlpack__(stream=None)\n\n yield np.arange(5).view(OldDLPack)\n\n\nclass TestDLPack:\n @pytest.mark.skipif(IS_PYPY, reason="PyPy can't get refcounts.")\n @pytest.mark.parametrize("max_version", [(0, 0), None, (1, 0), (100, 3)])\n def test_dunder_dlpack_refcount(self, max_version):\n x = np.arange(5)\n y = x.__dlpack__(max_version=max_version)\n startcount = sys.getrefcount(x)\n del y\n assert startcount - sys.getrefcount(x) == 1\n\n def test_dunder_dlpack_stream(self):\n x = np.arange(5)\n x.__dlpack__(stream=None)\n\n with pytest.raises(RuntimeError):\n x.__dlpack__(stream=1)\n\n def test_dunder_dlpack_copy(self):\n # Checks the argument parsing of __dlpack__ explicitly.\n # Honoring the flag is tested in the from_dlpack round-tripping test.\n x = np.arange(5)\n x.__dlpack__(copy=True)\n x.__dlpack__(copy=None)\n x.__dlpack__(copy=False)\n\n with pytest.raises(ValueError):\n # NOTE: The copy converter should be stricter, but not just here.\n x.__dlpack__(copy=np.array([1, 2, 3]))\n\n def test_strides_not_multiple_of_itemsize(self):\n dt = np.dtype([('int', np.int32), ('char', np.int8)])\n y = np.zeros((5,), dtype=dt)\n z = y['int']\n\n with pytest.raises(BufferError):\n np.from_dlpack(z)\n\n @pytest.mark.skipif(IS_PYPY, reason="PyPy can't get refcounts.")\n @pytest.mark.parametrize("arr", new_and_old_dlpack())\n def test_from_dlpack_refcount(self, arr):\n arr = arr.copy()\n y = np.from_dlpack(arr)\n startcount = sys.getrefcount(arr)\n del y\n assert startcount - sys.getrefcount(arr) == 1\n\n @pytest.mark.parametrize("dtype", [\n np.bool,\n np.int8, np.int16, np.int32, np.int64,\n np.uint8, np.uint16, np.uint32, np.uint64,\n np.float16, np.float32, np.float64,\n np.complex64, np.complex128\n ])\n @pytest.mark.parametrize("arr", new_and_old_dlpack())\n def test_dtype_passthrough(self, arr, dtype):\n x = arr.astype(dtype)\n y = np.from_dlpack(x)\n\n assert y.dtype == x.dtype\n assert_array_equal(x, y)\n\n def test_invalid_dtype(self):\n x = np.asarray(np.datetime64('2021-05-27'))\n\n with pytest.raises(BufferError):\n np.from_dlpack(x)\n\n def test_invalid_byte_swapping(self):\n dt = np.dtype('=i8').newbyteorder()\n x = np.arange(5, dtype=dt)\n\n with pytest.raises(BufferError):\n np.from_dlpack(x)\n\n def test_non_contiguous(self):\n x = np.arange(25).reshape((5, 5))\n\n y1 = x[0]\n assert_array_equal(y1, np.from_dlpack(y1))\n\n y2 = x[:, 0]\n assert_array_equal(y2, np.from_dlpack(y2))\n\n y3 = x[1, :]\n assert_array_equal(y3, np.from_dlpack(y3))\n\n y4 = x[1]\n assert_array_equal(y4, np.from_dlpack(y4))\n\n y5 = np.diagonal(x).copy()\n assert_array_equal(y5, np.from_dlpack(y5))\n\n @pytest.mark.parametrize("ndim", range(33))\n def test_higher_dims(self, ndim):\n shape = (1,) * ndim\n x = np.zeros(shape, dtype=np.float64)\n\n assert shape == np.from_dlpack(x).shape\n\n def test_dlpack_device(self):\n x = np.arange(5)\n assert x.__dlpack_device__() == (1, 0)\n y = np.from_dlpack(x)\n assert y.__dlpack_device__() == (1, 0)\n z = y[::2]\n assert z.__dlpack_device__() == (1, 0)\n\n def dlpack_deleter_exception(self, max_version):\n x = np.arange(5)\n _ = x.__dlpack__(max_version=max_version)\n raise RuntimeError\n\n @pytest.mark.parametrize("max_version", [None, (1, 0)])\n def test_dlpack_destructor_exception(self, max_version):\n with pytest.raises(RuntimeError):\n self.dlpack_deleter_exception(max_version=max_version)\n\n def test_readonly(self):\n x = np.arange(5)\n x.flags.writeable = False\n # Raises without max_version\n with pytest.raises(BufferError):\n x.__dlpack__()\n\n # But works fine if we try with version\n y = np.from_dlpack(x)\n assert not y.flags.writeable\n\n def test_writeable(self):\n x_new, x_old = new_and_old_dlpack()\n\n # new dlpacks respect writeability\n y = np.from_dlpack(x_new)\n assert y.flags.writeable\n\n # old dlpacks are not writeable for backwards compatibility\n y = np.from_dlpack(x_old)\n assert not y.flags.writeable\n\n def test_ndim0(self):\n x = np.array(1.0)\n y = np.from_dlpack(x)\n assert_array_equal(x, y)\n\n def test_size1dims_arrays(self):\n x = np.ndarray(dtype='f8', shape=(10, 5, 1), strides=(8, 80, 4),\n buffer=np.ones(1000, dtype=np.uint8), order='F')\n y = np.from_dlpack(x)\n assert_array_equal(x, y)\n\n def test_copy(self):\n x = np.arange(5)\n\n y = np.from_dlpack(x)\n assert np.may_share_memory(x, y)\n y = np.from_dlpack(x, copy=False)\n assert np.may_share_memory(x, y)\n y = np.from_dlpack(x, copy=True)\n assert not np.may_share_memory(x, y)\n\n def test_device(self):\n x = np.arange(5)\n # requesting (1, 0), i.e. CPU device works in both calls:\n x.__dlpack__(dl_device=(1, 0))\n np.from_dlpack(x, device="cpu")\n np.from_dlpack(x, device=None)\n\n with pytest.raises(ValueError):\n x.__dlpack__(dl_device=(10, 0))\n with pytest.raises(ValueError):\n np.from_dlpack(x, device="gpu")\n | .venv\Lib\site-packages\numpy\_core\tests\test_dlpack.py | test_dlpack.py | Python | 6,020 | 0.95 | 0.136842 | 0.061224 | vue-tools | 991 | 2024-12-22T00:48:08.943789 | Apache-2.0 | true | 8189fdbc0ba52643b4192fe404e2e0b0 |
import ctypes\nimport gc\nimport operator\nimport pickle\nimport random\nimport sys\nimport types\nfrom itertools import permutations\nfrom typing import Any\n\nimport hypothesis\nimport pytest\nfrom hypothesis.extra import numpy as hynp\nfrom numpy._core._multiarray_tests import create_custom_field_dtype\nfrom numpy._core._rational_tests import rational\n\nimport numpy as np\nimport numpy.dtypes\nfrom numpy.testing import (\n HAS_REFCOUNT,\n IS_PYSTON,\n IS_WASM,\n assert_,\n assert_array_equal,\n assert_equal,\n assert_raises,\n)\n\n\ndef assert_dtype_equal(a, b):\n assert_equal(a, b)\n assert_equal(hash(a), hash(b),\n "two equivalent types do not hash to the same value !")\n\ndef assert_dtype_not_equal(a, b):\n assert_(a != b)\n assert_(hash(a) != hash(b),\n "two different types hash to the same value !")\n\nclass TestBuiltin:\n @pytest.mark.parametrize('t', [int, float, complex, np.int32, str, object])\n def test_run(self, t):\n """Only test hash runs at all."""\n dt = np.dtype(t)\n hash(dt)\n\n @pytest.mark.parametrize('t', [int, float])\n def test_dtype(self, t):\n # Make sure equivalent byte order char hash the same (e.g. < and = on\n # little endian)\n dt = np.dtype(t)\n dt2 = dt.newbyteorder("<")\n dt3 = dt.newbyteorder(">")\n if dt == dt2:\n assert_(dt.byteorder != dt2.byteorder, "bogus test")\n assert_dtype_equal(dt, dt2)\n else:\n assert_(dt.byteorder != dt3.byteorder, "bogus test")\n assert_dtype_equal(dt, dt3)\n\n def test_equivalent_dtype_hashing(self):\n # Make sure equivalent dtypes with different type num hash equal\n uintp = np.dtype(np.uintp)\n if uintp.itemsize == 4:\n left = uintp\n right = np.dtype(np.uint32)\n else:\n left = uintp\n right = np.dtype(np.ulonglong)\n assert_(left == right)\n assert_(hash(left) == hash(right))\n\n def test_invalid_types(self):\n # Make sure invalid type strings raise an error\n\n assert_raises(TypeError, np.dtype, 'O3')\n assert_raises(TypeError, np.dtype, 'O5')\n assert_raises(TypeError, np.dtype, 'O7')\n assert_raises(TypeError, np.dtype, 'b3')\n assert_raises(TypeError, np.dtype, 'h4')\n assert_raises(TypeError, np.dtype, 'I5')\n assert_raises(TypeError, np.dtype, 'e3')\n assert_raises(TypeError, np.dtype, 'f5')\n\n if np.dtype('g').itemsize == 8 or np.dtype('g').itemsize == 16:\n assert_raises(TypeError, np.dtype, 'g12')\n elif np.dtype('g').itemsize == 12:\n assert_raises(TypeError, np.dtype, 'g16')\n\n if np.dtype('l').itemsize == 8:\n assert_raises(TypeError, np.dtype, 'l4')\n assert_raises(TypeError, np.dtype, 'L4')\n else:\n assert_raises(TypeError, np.dtype, 'l8')\n assert_raises(TypeError, np.dtype, 'L8')\n\n if np.dtype('q').itemsize == 8:\n assert_raises(TypeError, np.dtype, 'q4')\n assert_raises(TypeError, np.dtype, 'Q4')\n else:\n assert_raises(TypeError, np.dtype, 'q8')\n assert_raises(TypeError, np.dtype, 'Q8')\n\n # Make sure negative-sized dtype raises an error\n assert_raises(TypeError, np.dtype, 'S-1')\n assert_raises(TypeError, np.dtype, 'U-1')\n assert_raises(TypeError, np.dtype, 'V-1')\n\n def test_richcompare_invalid_dtype_equality(self):\n # Make sure objects that cannot be converted to valid\n # dtypes results in False/True when compared to valid dtypes.\n # Here 7 cannot be converted to dtype. No exceptions should be raised\n\n assert not np.dtype(np.int32) == 7, "dtype richcompare failed for =="\n assert np.dtype(np.int32) != 7, "dtype richcompare failed for !="\n\n @pytest.mark.parametrize(\n 'operation',\n [operator.le, operator.lt, operator.ge, operator.gt])\n def test_richcompare_invalid_dtype_comparison(self, operation):\n # Make sure TypeError is raised for comparison operators\n # for invalid dtypes. Here 7 is an invalid dtype.\n\n with pytest.raises(TypeError):\n operation(np.dtype(np.int32), 7)\n\n @pytest.mark.parametrize("dtype",\n ['Bool', 'Bytes0', 'Complex32', 'Complex64',\n 'Datetime64', 'Float16', 'Float32', 'Float64',\n 'Int8', 'Int16', 'Int32', 'Int64',\n 'Object0', 'Str0', 'Timedelta64',\n 'UInt8', 'UInt16', 'Uint32', 'UInt32',\n 'Uint64', 'UInt64', 'Void0',\n "Float128", "Complex128"])\n def test_numeric_style_types_are_invalid(self, dtype):\n with assert_raises(TypeError):\n np.dtype(dtype)\n\n def test_expired_dtypes_with_bad_bytesize(self):\n match: str = r".*removed in NumPy 2.0.*"\n with pytest.raises(TypeError, match=match):\n np.dtype("int0")\n with pytest.raises(TypeError, match=match):\n np.dtype("uint0")\n with pytest.raises(TypeError, match=match):\n np.dtype("bool8")\n with pytest.raises(TypeError, match=match):\n np.dtype("bytes0")\n with pytest.raises(TypeError, match=match):\n np.dtype("str0")\n with pytest.raises(TypeError, match=match):\n np.dtype("object0")\n with pytest.raises(TypeError, match=match):\n np.dtype("void0")\n\n @pytest.mark.parametrize(\n 'value',\n ['m8', 'M8', 'datetime64', 'timedelta64',\n 'i4, (2,3)f8, f4', 'S3, 3u8, (3,4)S10',\n '>f', '<f', '=f', '|f',\n ])\n def test_dtype_bytes_str_equivalence(self, value):\n bytes_value = value.encode('ascii')\n from_bytes = np.dtype(bytes_value)\n from_str = np.dtype(value)\n assert_dtype_equal(from_bytes, from_str)\n\n def test_dtype_from_bytes(self):\n # Empty bytes object\n assert_raises(TypeError, np.dtype, b'')\n # Byte order indicator, but no type\n assert_raises(TypeError, np.dtype, b'|')\n\n # Single character with ordinal < NPY_NTYPES_LEGACY returns\n # type by index into _builtin_descrs\n assert_dtype_equal(np.dtype(bytes([0])), np.dtype('bool'))\n assert_dtype_equal(np.dtype(bytes([17])), np.dtype(object))\n\n # Single character where value is a valid type code\n assert_dtype_equal(np.dtype(b'f'), np.dtype('float32'))\n\n # Bytes with non-ascii values raise errors\n assert_raises(TypeError, np.dtype, b'\xff')\n assert_raises(TypeError, np.dtype, b's\xff')\n\n def test_bad_param(self):\n # Can't give a size that's too small\n assert_raises(ValueError, np.dtype,\n {'names': ['f0', 'f1'],\n 'formats': ['i4', 'i1'],\n 'offsets': [0, 4],\n 'itemsize': 4})\n # If alignment is enabled, the alignment (4) must divide the itemsize\n assert_raises(ValueError, np.dtype,\n {'names': ['f0', 'f1'],\n 'formats': ['i4', 'i1'],\n 'offsets': [0, 4],\n 'itemsize': 9}, align=True)\n # If alignment is enabled, the individual fields must be aligned\n assert_raises(ValueError, np.dtype,\n {'names': ['f0', 'f1'],\n 'formats': ['i1', 'f4'],\n 'offsets': [0, 2]}, align=True)\n\n def test_field_order_equality(self):\n x = np.dtype({'names': ['A', 'B'],\n 'formats': ['i4', 'f4'],\n 'offsets': [0, 4]})\n y = np.dtype({'names': ['B', 'A'],\n 'formats': ['i4', 'f4'],\n 'offsets': [4, 0]})\n assert_equal(x == y, False)\n # This is an safe cast (not equiv) due to the different names:\n assert np.can_cast(x, y, casting="safe")\n\n @pytest.mark.parametrize(\n ["type_char", "char_size", "scalar_type"],\n [["U", 4, np.str_],\n ["S", 1, np.bytes_]])\n def test_create_string_dtypes_directly(\n self, type_char, char_size, scalar_type):\n dtype_class = type(np.dtype(type_char))\n\n dtype = dtype_class(8)\n assert dtype.type is scalar_type\n assert dtype.itemsize == 8 * char_size\n\n def test_create_invalid_string_errors(self):\n one_too_big = np.iinfo(np.intc).max + 1\n with pytest.raises(TypeError):\n type(np.dtype("U"))(one_too_big // 4)\n\n with pytest.raises(TypeError):\n # Code coverage for very large numbers:\n type(np.dtype("U"))(np.iinfo(np.intp).max // 4 + 1)\n\n if one_too_big < sys.maxsize:\n with pytest.raises(TypeError):\n type(np.dtype("S"))(one_too_big)\n\n with pytest.raises(ValueError):\n type(np.dtype("U"))(-1)\n\n # OverflowError on 32 bit\n with pytest.raises((TypeError, OverflowError)):\n # see gh-26556\n type(np.dtype("S"))(2**61)\n\n with pytest.raises(TypeError):\n np.dtype("S1234hello")\n\n def test_leading_zero_parsing(self):\n dt1 = np.dtype('S010')\n dt2 = np.dtype('S10')\n\n assert dt1 == dt2\n assert repr(dt1) == "dtype('S10')"\n assert dt1.itemsize == 10\n\n\nclass TestRecord:\n def test_equivalent_record(self):\n """Test whether equivalent record dtypes hash the same."""\n a = np.dtype([('yo', int)])\n b = np.dtype([('yo', int)])\n assert_dtype_equal(a, b)\n\n def test_different_names(self):\n # In theory, they may hash the same (collision) ?\n a = np.dtype([('yo', int)])\n b = np.dtype([('ye', int)])\n assert_dtype_not_equal(a, b)\n\n def test_different_titles(self):\n # In theory, they may hash the same (collision) ?\n a = np.dtype({'names': ['r', 'b'],\n 'formats': ['u1', 'u1'],\n 'titles': ['Red pixel', 'Blue pixel']})\n b = np.dtype({'names': ['r', 'b'],\n 'formats': ['u1', 'u1'],\n 'titles': ['RRed pixel', 'Blue pixel']})\n assert_dtype_not_equal(a, b)\n\n @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")\n def test_refcount_dictionary_setting(self):\n names = ["name1"]\n formats = ["f8"]\n titles = ["t1"]\n offsets = [0]\n d = {"names": names, "formats": formats, "titles": titles, "offsets": offsets}\n refcounts = {k: sys.getrefcount(i) for k, i in d.items()}\n np.dtype(d)\n refcounts_new = {k: sys.getrefcount(i) for k, i in d.items()}\n assert refcounts == refcounts_new\n\n def test_mutate(self):\n # Mutating a dtype should reset the cached hash value.\n # NOTE: Mutating should be deprecated, but new API added to replace it.\n a = np.dtype([('yo', int)])\n b = np.dtype([('yo', int)])\n c = np.dtype([('ye', int)])\n assert_dtype_equal(a, b)\n assert_dtype_not_equal(a, c)\n a.names = ['ye']\n assert_dtype_equal(a, c)\n assert_dtype_not_equal(a, b)\n state = b.__reduce__()[2]\n a.__setstate__(state)\n assert_dtype_equal(a, b)\n assert_dtype_not_equal(a, c)\n\n def test_init_simple_structured(self):\n dt1 = np.dtype("i, i")\n assert dt1.names == ("f0", "f1")\n\n dt2 = np.dtype("i,")\n assert dt2.names == ("f0",)\n\n def test_mutate_error(self):\n # NOTE: Mutating should be deprecated, but new API added to replace it.\n a = np.dtype("i,i")\n\n with pytest.raises(ValueError, match="must replace all names at once"):\n a.names = ["f0"]\n\n with pytest.raises(ValueError, match=".*and not string"):\n a.names = ["f0", b"not a unicode name"]\n\n def test_not_lists(self):\n """Test if an appropriate exception is raised when passing bad values to\n the dtype constructor.\n """\n assert_raises(TypeError, np.dtype,\n {"names": {'A', 'B'}, "formats": ['f8', 'i4']})\n assert_raises(TypeError, np.dtype,\n {"names": ['A', 'B'], "formats": {'f8', 'i4'}})\n\n def test_aligned_size(self):\n # Check that structured dtypes get padded to an aligned size\n dt = np.dtype('i4, i1', align=True)\n assert_equal(dt.itemsize, 8)\n dt = np.dtype([('f0', 'i4'), ('f1', 'i1')], align=True)\n assert_equal(dt.itemsize, 8)\n dt = np.dtype({'names': ['f0', 'f1'],\n 'formats': ['i4', 'u1'],\n 'offsets': [0, 4]}, align=True)\n assert_equal(dt.itemsize, 8)\n dt = np.dtype({'f0': ('i4', 0), 'f1': ('u1', 4)}, align=True)\n assert_equal(dt.itemsize, 8)\n # Nesting should preserve that alignment\n dt1 = np.dtype([('f0', 'i4'),\n ('f1', [('f1', 'i1'), ('f2', 'i4'), ('f3', 'i1')]),\n ('f2', 'i1')], align=True)\n assert_equal(dt1.itemsize, 20)\n dt2 = np.dtype({'names': ['f0', 'f1', 'f2'],\n 'formats': ['i4',\n [('f1', 'i1'), ('f2', 'i4'), ('f3', 'i1')],\n 'i1'],\n 'offsets': [0, 4, 16]}, align=True)\n assert_equal(dt2.itemsize, 20)\n dt3 = np.dtype({'f0': ('i4', 0),\n 'f1': ([('f1', 'i1'), ('f2', 'i4'), ('f3', 'i1')], 4),\n 'f2': ('i1', 16)}, align=True)\n assert_equal(dt3.itemsize, 20)\n assert_equal(dt1, dt2)\n assert_equal(dt2, dt3)\n # Nesting should preserve packing\n dt1 = np.dtype([('f0', 'i4'),\n ('f1', [('f1', 'i1'), ('f2', 'i4'), ('f3', 'i1')]),\n ('f2', 'i1')], align=False)\n assert_equal(dt1.itemsize, 11)\n dt2 = np.dtype({'names': ['f0', 'f1', 'f2'],\n 'formats': ['i4',\n [('f1', 'i1'), ('f2', 'i4'), ('f3', 'i1')],\n 'i1'],\n 'offsets': [0, 4, 10]}, align=False)\n assert_equal(dt2.itemsize, 11)\n dt3 = np.dtype({'f0': ('i4', 0),\n 'f1': ([('f1', 'i1'), ('f2', 'i4'), ('f3', 'i1')], 4),\n 'f2': ('i1', 10)}, align=False)\n assert_equal(dt3.itemsize, 11)\n assert_equal(dt1, dt2)\n assert_equal(dt2, dt3)\n # Array of subtype should preserve alignment\n dt1 = np.dtype([('a', '|i1'),\n ('b', [('f0', '<i2'),\n ('f1', '<f4')], 2)], align=True)\n assert_equal(dt1.descr, [('a', '|i1'), ('', '|V3'),\n ('b', [('f0', '<i2'), ('', '|V2'),\n ('f1', '<f4')], (2,))])\n\n def test_empty_struct_alignment(self):\n # Empty dtypes should have an alignment of 1\n dt = np.dtype([], align=True)\n assert_equal(dt.alignment, 1)\n dt = np.dtype([('f0', [])], align=True)\n assert_equal(dt.alignment, 1)\n dt = np.dtype({'names': [],\n 'formats': [],\n 'offsets': []}, align=True)\n assert_equal(dt.alignment, 1)\n dt = np.dtype({'names': ['f0'],\n 'formats': [[]],\n 'offsets': [0]}, align=True)\n assert_equal(dt.alignment, 1)\n\n def test_union_struct(self):\n # Should be able to create union dtypes\n dt = np.dtype({'names': ['f0', 'f1', 'f2'], 'formats': ['<u4', '<u2', '<u2'],\n 'offsets': [0, 0, 2]}, align=True)\n assert_equal(dt.itemsize, 4)\n a = np.array([3], dtype='<u4').view(dt)\n a['f1'] = 10\n a['f2'] = 36\n assert_equal(a['f0'], 10 + 36 * 256 * 256)\n # Should be able to specify fields out of order\n dt = np.dtype({'names': ['f0', 'f1', 'f2'], 'formats': ['<u4', '<u2', '<u2'],\n 'offsets': [4, 0, 2]}, align=True)\n assert_equal(dt.itemsize, 8)\n # field name should not matter: assignment is by position\n dt2 = np.dtype({'names': ['f2', 'f0', 'f1'],\n 'formats': ['<u4', '<u2', '<u2'],\n 'offsets': [4, 0, 2]}, align=True)\n vals = [(0, 1, 2), (3, 2**15 - 1, 4)]\n vals2 = [(0, 1, 2), (3, 2**15 - 1, 4)]\n a = np.array(vals, dt)\n b = np.array(vals2, dt2)\n assert_equal(a.astype(dt2), b)\n assert_equal(b.astype(dt), a)\n assert_equal(a.view(dt2), b)\n assert_equal(b.view(dt), a)\n # Should not be able to overlap objects with other types\n assert_raises(TypeError, np.dtype,\n {'names': ['f0', 'f1'],\n 'formats': ['O', 'i1'],\n 'offsets': [0, 2]})\n assert_raises(TypeError, np.dtype,\n {'names': ['f0', 'f1'],\n 'formats': ['i4', 'O'],\n 'offsets': [0, 3]})\n assert_raises(TypeError, np.dtype,\n {'names': ['f0', 'f1'],\n 'formats': [[('a', 'O')], 'i1'],\n 'offsets': [0, 2]})\n assert_raises(TypeError, np.dtype,\n {'names': ['f0', 'f1'],\n 'formats': ['i4', [('a', 'O')]],\n 'offsets': [0, 3]})\n # Out of order should still be ok, however\n dt = np.dtype({'names': ['f0', 'f1'],\n 'formats': ['i1', 'O'],\n 'offsets': [np.dtype('intp').itemsize, 0]})\n\n @pytest.mark.parametrize(["obj", "dtype", "expected"],\n [([], ("2f4"), np.empty((0, 2), dtype="f4")),\n (3, "(3,)f4", [3, 3, 3]),\n (np.float64(2), "(2,)f4", [2, 2]),\n ([((0, 1), (1, 2)), ((2,),)], '(2,2)f4', None),\n (["1", "2"], "2i", None)])\n def test_subarray_list(self, obj, dtype, expected):\n dtype = np.dtype(dtype)\n res = np.array(obj, dtype=dtype)\n\n if expected is None:\n # iterate the 1-d list to fill the array\n expected = np.empty(len(obj), dtype=dtype)\n for i in range(len(expected)):\n expected[i] = obj[i]\n\n assert_array_equal(res, expected)\n\n def test_parenthesized_single_number(self):\n with pytest.raises(TypeError, match="not understood"):\n np.dtype("(2)f4")\n\n # Deprecation also tested in\n # test_deprecations.py::TestDeprecatedDTypeParenthesizedRepeatCount\n # Left here to allow easy conversion to an exception check.\n with pytest.warns(DeprecationWarning,\n match="parenthesized single number"):\n np.dtype("(2)f4,")\n\n def test_comma_datetime(self):\n dt = np.dtype('M8[D],datetime64[Y],i8')\n assert_equal(dt, np.dtype([('f0', 'M8[D]'),\n ('f1', 'datetime64[Y]'),\n ('f2', 'i8')]))\n\n def test_from_dictproxy(self):\n # Tests for PR #5920\n dt = np.dtype({'names': ['a', 'b'], 'formats': ['i4', 'f4']})\n assert_dtype_equal(dt, np.dtype(dt.fields))\n dt2 = np.dtype((np.void, dt.fields))\n assert_equal(dt2.fields, dt.fields)\n\n def test_from_dict_with_zero_width_field(self):\n # Regression test for #6430 / #2196\n dt = np.dtype([('val1', np.float32, (0,)), ('val2', int)])\n dt2 = np.dtype({'names': ['val1', 'val2'],\n 'formats': [(np.float32, (0,)), int]})\n\n assert_dtype_equal(dt, dt2)\n assert_equal(dt.fields['val1'][0].itemsize, 0)\n assert_equal(dt.itemsize, dt.fields['val2'][0].itemsize)\n\n def test_bool_commastring(self):\n d = np.dtype('?,?,?') # raises?\n assert_equal(len(d.names), 3)\n for n in d.names:\n assert_equal(d.fields[n][0], np.dtype('?'))\n\n def test_nonint_offsets(self):\n # gh-8059\n def make_dtype(off):\n return np.dtype({'names': ['A'], 'formats': ['i4'],\n 'offsets': [off]})\n\n assert_raises(TypeError, make_dtype, 'ASD')\n assert_raises(OverflowError, make_dtype, 2**70)\n assert_raises(TypeError, make_dtype, 2.3)\n assert_raises(ValueError, make_dtype, -10)\n\n # no errors here:\n dt = make_dtype(np.uint32(0))\n np.zeros(1, dtype=dt)[0].item()\n\n def test_fields_by_index(self):\n dt = np.dtype([('a', np.int8), ('b', np.float32, 3)])\n assert_dtype_equal(dt[0], np.dtype(np.int8))\n assert_dtype_equal(dt[1], np.dtype((np.float32, 3)))\n assert_dtype_equal(dt[-1], dt[1])\n assert_dtype_equal(dt[-2], dt[0])\n assert_raises(IndexError, lambda: dt[-3])\n\n assert_raises(TypeError, operator.getitem, dt, 3.0)\n\n assert_equal(dt[1], dt[np.int8(1)])\n\n @pytest.mark.parametrize('align_flag', [False, True])\n def test_multifield_index(self, align_flag):\n # indexing with a list produces subfields\n # the align flag should be preserved\n dt = np.dtype([\n (('title', 'col1'), '<U20'), ('A', '<f8'), ('B', '<f8')\n ], align=align_flag)\n\n dt_sub = dt[['B', 'col1']]\n assert_equal(\n dt_sub,\n np.dtype({\n 'names': ['B', 'col1'],\n 'formats': ['<f8', '<U20'],\n 'offsets': [88, 0],\n 'titles': [None, 'title'],\n 'itemsize': 96\n })\n )\n assert_equal(dt_sub.isalignedstruct, align_flag)\n\n dt_sub = dt[['B']]\n assert_equal(\n dt_sub,\n np.dtype({\n 'names': ['B'],\n 'formats': ['<f8'],\n 'offsets': [88],\n 'itemsize': 96\n })\n )\n assert_equal(dt_sub.isalignedstruct, align_flag)\n\n dt_sub = dt[[]]\n assert_equal(\n dt_sub,\n np.dtype({\n 'names': [],\n 'formats': [],\n 'offsets': [],\n 'itemsize': 96\n })\n )\n assert_equal(dt_sub.isalignedstruct, align_flag)\n\n assert_raises(TypeError, operator.getitem, dt, ())\n assert_raises(TypeError, operator.getitem, dt, [1, 2, 3])\n assert_raises(TypeError, operator.getitem, dt, ['col1', 2])\n assert_raises(KeyError, operator.getitem, dt, ['fake'])\n assert_raises(KeyError, operator.getitem, dt, ['title'])\n assert_raises(ValueError, operator.getitem, dt, ['col1', 'col1'])\n\n def test_partial_dict(self):\n # 'names' is missing\n assert_raises(ValueError, np.dtype,\n {'formats': ['i4', 'i4'], 'f0': ('i4', 0), 'f1': ('i4', 4)})\n\n def test_fieldless_views(self):\n a = np.zeros(2, dtype={'names': [], 'formats': [], 'offsets': [],\n 'itemsize': 8})\n assert_raises(ValueError, a.view, np.dtype([]))\n\n d = np.dtype((np.dtype([]), 10))\n assert_equal(d.shape, (10,))\n assert_equal(d.itemsize, 0)\n assert_equal(d.base, np.dtype([]))\n\n arr = np.fromiter((() for i in range(10)), [])\n assert_equal(arr.dtype, np.dtype([]))\n assert_raises(ValueError, np.frombuffer, b'', dtype=[])\n assert_equal(np.frombuffer(b'', dtype=[], count=2),\n np.empty(2, dtype=[]))\n\n assert_raises(ValueError, np.dtype, ([], 'f8'))\n assert_raises(ValueError, np.zeros(1, dtype='i4').view, [])\n\n assert_equal(np.zeros(2, dtype=[]) == np.zeros(2, dtype=[]),\n np.ones(2, dtype=bool))\n\n assert_equal(np.zeros((1, 2), dtype=[]) == a,\n np.ones((1, 2), dtype=bool))\n\n def test_nonstructured_with_object(self):\n # See gh-23277, the dtype here thinks it contain objects, if the\n # assert about that fails, the test becomes meaningless (which is OK)\n arr = np.recarray((0,), dtype="O")\n assert arr.dtype.names is None # no fields\n assert arr.dtype.hasobject # but claims to contain objects\n del arr # the deletion failed previously.\n\n\nclass TestSubarray:\n def test_single_subarray(self):\n a = np.dtype((int, (2)))\n b = np.dtype((int, (2,)))\n assert_dtype_equal(a, b)\n\n assert_equal(type(a.subdtype[1]), tuple)\n assert_equal(type(b.subdtype[1]), tuple)\n\n def test_equivalent_record(self):\n """Test whether equivalent subarray dtypes hash the same."""\n a = np.dtype((int, (2, 3)))\n b = np.dtype((int, (2, 3)))\n assert_dtype_equal(a, b)\n\n def test_nonequivalent_record(self):\n """Test whether different subarray dtypes hash differently."""\n a = np.dtype((int, (2, 3)))\n b = np.dtype((int, (3, 2)))\n assert_dtype_not_equal(a, b)\n\n a = np.dtype((int, (2, 3)))\n b = np.dtype((int, (2, 2)))\n assert_dtype_not_equal(a, b)\n\n a = np.dtype((int, (1, 2, 3)))\n b = np.dtype((int, (1, 2)))\n assert_dtype_not_equal(a, b)\n\n def test_shape_equal(self):\n """Test some data types that are equal"""\n assert_dtype_equal(np.dtype('f8'), np.dtype(('f8', ())))\n assert_dtype_equal(np.dtype('(1,)f8'), np.dtype(('f8', 1)))\n assert np.dtype(('f8', 1)).shape == (1,)\n assert_dtype_equal(np.dtype((int, 2)), np.dtype((int, (2,))))\n assert_dtype_equal(np.dtype(('<f4', (3, 2))), np.dtype(('<f4', (3, 2))))\n d = ([('a', 'f4', (1, 2)), ('b', 'f8', (3, 1))], (3, 2))\n assert_dtype_equal(np.dtype(d), np.dtype(d))\n\n def test_shape_simple(self):\n """Test some simple cases that shouldn't be equal"""\n assert_dtype_not_equal(np.dtype('f8'), np.dtype(('f8', (1,))))\n assert_dtype_not_equal(np.dtype(('f8', (1,))), np.dtype(('f8', (1, 1))))\n assert_dtype_not_equal(np.dtype(('f4', (3, 2))), np.dtype(('f4', (2, 3))))\n\n def test_shape_monster(self):\n """Test some more complicated cases that shouldn't be equal"""\n assert_dtype_not_equal(\n np.dtype(([('a', 'f4', (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),\n np.dtype(([('a', 'f4', (1, 2)), ('b', 'f8', (1, 3))], (2, 2))))\n assert_dtype_not_equal(\n np.dtype(([('a', 'f4', (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),\n np.dtype(([('a', 'f4', (2, 1)), ('b', 'i8', (1, 3))], (2, 2))))\n assert_dtype_not_equal(\n np.dtype(([('a', 'f4', (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),\n np.dtype(([('e', 'f8', (1, 3)), ('d', 'f4', (2, 1))], (2, 2))))\n assert_dtype_not_equal(\n np.dtype(([('a', [('a', 'i4', 6)], (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),\n np.dtype(([('a', [('a', 'u4', 6)], (2, 1)), ('b', 'f8', (1, 3))], (2, 2))))\n\n def test_shape_sequence(self):\n # Any sequence of integers should work as shape, but the result\n # should be a tuple (immutable) of base type integers.\n a = np.array([1, 2, 3], dtype=np.int16)\n l = [1, 2, 3]\n # Array gets converted\n dt = np.dtype([('a', 'f4', a)])\n assert_(isinstance(dt['a'].shape, tuple))\n assert_(isinstance(dt['a'].shape[0], int))\n # List gets converted\n dt = np.dtype([('a', 'f4', l)])\n assert_(isinstance(dt['a'].shape, tuple))\n #\n\n class IntLike:\n def __index__(self):\n return 3\n\n def __int__(self):\n # (a PyNumber_Check fails without __int__)\n return 3\n\n dt = np.dtype([('a', 'f4', IntLike())])\n assert_(isinstance(dt['a'].shape, tuple))\n assert_(isinstance(dt['a'].shape[0], int))\n dt = np.dtype([('a', 'f4', (IntLike(),))])\n assert_(isinstance(dt['a'].shape, tuple))\n assert_(isinstance(dt['a'].shape[0], int))\n\n def test_shape_matches_ndim(self):\n dt = np.dtype([('a', 'f4', ())])\n assert_equal(dt['a'].shape, ())\n assert_equal(dt['a'].ndim, 0)\n\n dt = np.dtype([('a', 'f4')])\n assert_equal(dt['a'].shape, ())\n assert_equal(dt['a'].ndim, 0)\n\n dt = np.dtype([('a', 'f4', 4)])\n assert_equal(dt['a'].shape, (4,))\n assert_equal(dt['a'].ndim, 1)\n\n dt = np.dtype([('a', 'f4', (1, 2, 3))])\n assert_equal(dt['a'].shape, (1, 2, 3))\n assert_equal(dt['a'].ndim, 3)\n\n def test_shape_invalid(self):\n # Check that the shape is valid.\n max_int = np.iinfo(np.intc).max\n max_intp = np.iinfo(np.intp).max\n # Too large values (the datatype is part of this)\n assert_raises(ValueError, np.dtype, [('a', 'f4', max_int // 4 + 1)])\n assert_raises(ValueError, np.dtype, [('a', 'f4', max_int + 1)])\n assert_raises(ValueError, np.dtype, [('a', 'f4', (max_int, 2))])\n # Takes a different code path (fails earlier:\n assert_raises(ValueError, np.dtype, [('a', 'f4', max_intp + 1)])\n # Negative values\n assert_raises(ValueError, np.dtype, [('a', 'f4', -1)])\n assert_raises(ValueError, np.dtype, [('a', 'f4', (-1, -1))])\n\n def test_alignment(self):\n # Check that subarrays are aligned\n t1 = np.dtype('(1,)i4', align=True)\n t2 = np.dtype('2i4', align=True)\n assert_equal(t1.alignment, t2.alignment)\n\n def test_aligned_empty(self):\n # Mainly regression test for gh-19696: construction failed completely\n dt = np.dtype([], align=True)\n assert dt == np.dtype([])\n dt = np.dtype({"names": [], "formats": [], "itemsize": 0}, align=True)\n assert dt == np.dtype([])\n\n def test_subarray_base_item(self):\n arr = np.ones(3, dtype=[("f", "i", 3)])\n # Extracting the field "absorbs" the subarray into a view:\n assert arr["f"].base is arr\n # Extract the structured item, and then check the tuple component:\n item = arr.item(0)\n assert type(item) is tuple and len(item) == 1\n assert item[0].base is arr\n\n def test_subarray_cast_copies(self):\n # Older versions of NumPy did NOT copy, but they got the ownership\n # wrong (not actually knowing the correct base!). Versions since 1.21\n # (I think) crashed fairly reliable. This defines the correct behavior\n # as a copy. Keeping the ownership would be possible (but harder)\n arr = np.ones(3, dtype=[("f", "i", 3)])\n cast = arr.astype(object)\n for fields in cast:\n assert type(fields) == tuple and len(fields) == 1\n subarr = fields[0]\n assert subarr.base is None\n assert subarr.flags.owndata\n\n\ndef iter_struct_object_dtypes():\n """\n Iterates over a few complex dtypes and object pattern which\n fill the array with a given object (defaults to a singleton).\n\n Yields\n ------\n dtype : dtype\n pattern : tuple\n Structured tuple for use with `np.array`.\n count : int\n Number of objects stored in the dtype.\n singleton : object\n A singleton object. The returned pattern is constructed so that\n all objects inside the datatype are set to the singleton.\n """\n obj = object()\n\n dt = np.dtype([('b', 'O', (2, 3))])\n p = ([[obj] * 3] * 2,)\n yield pytest.param(dt, p, 6, obj, id="<subarray>")\n\n dt = np.dtype([('a', 'i4'), ('b', 'O', (2, 3))])\n p = (0, [[obj] * 3] * 2)\n yield pytest.param(dt, p, 6, obj, id="<subarray in field>")\n\n dt = np.dtype([('a', 'i4'),\n ('b', [('ba', 'O'), ('bb', 'i1')], (2, 3))])\n p = (0, [[(obj, 0)] * 3] * 2)\n yield pytest.param(dt, p, 6, obj, id="<structured subarray 1>")\n\n dt = np.dtype([('a', 'i4'),\n ('b', [('ba', 'O'), ('bb', 'O')], (2, 3))])\n p = (0, [[(obj, obj)] * 3] * 2)\n yield pytest.param(dt, p, 12, obj, id="<structured subarray 2>")\n\n\n@pytest.mark.skipif(\n sys.version_info >= (3, 12),\n reason="Python 3.12 has immortal refcounts, this test will no longer "\n "work. See gh-23986"\n)\n@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")\nclass TestStructuredObjectRefcounting:\n """These tests cover various uses of complicated structured types which\n include objects and thus require reference counting.\n """\n @pytest.mark.parametrize(['dt', 'pat', 'count', 'singleton'],\n iter_struct_object_dtypes())\n @pytest.mark.parametrize(["creation_func", "creation_obj"], [\n pytest.param(np.empty, None,\n # None is probably used for too many things\n marks=pytest.mark.skip("unreliable due to python's behaviour")),\n (np.ones, 1),\n (np.zeros, 0)])\n def test_structured_object_create_delete(self, dt, pat, count, singleton,\n creation_func, creation_obj):\n """Structured object reference counting in creation and deletion"""\n # The test assumes that 0, 1, and None are singletons.\n gc.collect()\n before = sys.getrefcount(creation_obj)\n arr = creation_func(3, dt)\n\n now = sys.getrefcount(creation_obj)\n assert now - before == count * 3\n del arr\n now = sys.getrefcount(creation_obj)\n assert now == before\n\n @pytest.mark.parametrize(['dt', 'pat', 'count', 'singleton'],\n iter_struct_object_dtypes())\n def test_structured_object_item_setting(self, dt, pat, count, singleton):\n """Structured object reference counting for simple item setting"""\n one = 1\n\n gc.collect()\n before = sys.getrefcount(singleton)\n arr = np.array([pat] * 3, dt)\n assert sys.getrefcount(singleton) - before == count * 3\n # Fill with `1` and check that it was replaced correctly:\n before2 = sys.getrefcount(one)\n arr[...] = one\n after2 = sys.getrefcount(one)\n assert after2 - before2 == count * 3\n del arr\n gc.collect()\n assert sys.getrefcount(one) == before2\n assert sys.getrefcount(singleton) == before\n\n @pytest.mark.parametrize(['dt', 'pat', 'count', 'singleton'],\n iter_struct_object_dtypes())\n @pytest.mark.parametrize(\n ['shape', 'index', 'items_changed'],\n [((3,), ([0, 2],), 2),\n ((3, 2), ([0, 2], slice(None)), 4),\n ((3, 2), ([0, 2], [1]), 2),\n ((3,), ([True, False, True]), 2)])\n def test_structured_object_indexing(self, shape, index, items_changed,\n dt, pat, count, singleton):\n """Structured object reference counting for advanced indexing."""\n # Use two small negative values (should be singletons, but less likely\n # to run into race-conditions). This failed in some threaded envs\n # When using 0 and 1. If it fails again, should remove all explicit\n # checks, and rely on `pytest-leaks` reference count checker only.\n val0 = -4\n val1 = -5\n\n arr = np.full(shape, val0, dt)\n\n gc.collect()\n before_val0 = sys.getrefcount(val0)\n before_val1 = sys.getrefcount(val1)\n # Test item getting:\n part = arr[index]\n after_val0 = sys.getrefcount(val0)\n assert after_val0 - before_val0 == count * items_changed\n del part\n # Test item setting:\n arr[index] = val1\n gc.collect()\n after_val0 = sys.getrefcount(val0)\n after_val1 = sys.getrefcount(val1)\n assert before_val0 - after_val0 == count * items_changed\n assert after_val1 - before_val1 == count * items_changed\n\n @pytest.mark.parametrize(['dt', 'pat', 'count', 'singleton'],\n iter_struct_object_dtypes())\n def test_structured_object_take_and_repeat(self, dt, pat, count, singleton):\n """Structured object reference counting for specialized functions.\n The older functions such as take and repeat use different code paths\n then item setting (when writing this).\n """\n indices = [0, 1]\n\n arr = np.array([pat] * 3, dt)\n gc.collect()\n before = sys.getrefcount(singleton)\n res = arr.take(indices)\n after = sys.getrefcount(singleton)\n assert after - before == count * 2\n new = res.repeat(10)\n gc.collect()\n after_repeat = sys.getrefcount(singleton)\n assert after_repeat - after == count * 2 * 10\n\n\nclass TestStructuredDtypeSparseFields:\n """Tests subarray fields which contain sparse dtypes so that\n not all memory is used by the dtype work. Such dtype's should\n leave the underlying memory unchanged.\n """\n dtype = np.dtype([('a', {'names': ['aa', 'ab'], 'formats': ['f', 'f'],\n 'offsets': [0, 4]}, (2, 3))])\n sparse_dtype = np.dtype([('a', {'names': ['ab'], 'formats': ['f'],\n 'offsets': [4]}, (2, 3))])\n\n def test_sparse_field_assignment(self):\n arr = np.zeros(3, self.dtype)\n sparse_arr = arr.view(self.sparse_dtype)\n\n sparse_arr[...] = np.finfo(np.float32).max\n # dtype is reduced when accessing the field, so shape is (3, 2, 3):\n assert_array_equal(arr["a"]["aa"], np.zeros((3, 2, 3)))\n\n def test_sparse_field_assignment_fancy(self):\n # Fancy assignment goes to the copyswap function for complex types:\n arr = np.zeros(3, self.dtype)\n sparse_arr = arr.view(self.sparse_dtype)\n\n sparse_arr[[0, 1, 2]] = np.finfo(np.float32).max\n # dtype is reduced when accessing the field, so shape is (3, 2, 3):\n assert_array_equal(arr["a"]["aa"], np.zeros((3, 2, 3)))\n\n\nclass TestMonsterType:\n """Test deeply nested subtypes."""\n\n def test1(self):\n simple1 = np.dtype({'names': ['r', 'b'], 'formats': ['u1', 'u1'],\n 'titles': ['Red pixel', 'Blue pixel']})\n a = np.dtype([('yo', int), ('ye', simple1),\n ('yi', np.dtype((int, (3, 2))))])\n b = np.dtype([('yo', int), ('ye', simple1),\n ('yi', np.dtype((int, (3, 2))))])\n assert_dtype_equal(a, b)\n\n c = np.dtype([('yo', int), ('ye', simple1),\n ('yi', np.dtype((a, (3, 2))))])\n d = np.dtype([('yo', int), ('ye', simple1),\n ('yi', np.dtype((a, (3, 2))))])\n assert_dtype_equal(c, d)\n\n @pytest.mark.skipif(IS_PYSTON, reason="Pyston disables recursion checking")\n @pytest.mark.skipif(IS_WASM, reason="Pyodide/WASM has limited stack size")\n def test_list_recursion(self):\n l = []\n l.append(('f', l))\n with pytest.raises(RecursionError):\n np.dtype(l)\n\n @pytest.mark.skipif(IS_PYSTON, reason="Pyston disables recursion checking")\n @pytest.mark.skipif(IS_WASM, reason="Pyodide/WASM has limited stack size")\n def test_tuple_recursion(self):\n d = np.int32\n for i in range(100000):\n d = (d, (1,))\n with pytest.raises(RecursionError):\n np.dtype(d)\n\n @pytest.mark.skipif(IS_PYSTON, reason="Pyston disables recursion checking")\n @pytest.mark.skipif(IS_WASM, reason="Pyodide/WASM has limited stack size")\n def test_dict_recursion(self):\n d = {"names": ['self'], "formats": [None], "offsets": [0]}\n d['formats'][0] = d\n with pytest.raises(RecursionError):\n np.dtype(d)\n\n\nclass TestMetadata:\n def test_no_metadata(self):\n d = np.dtype(int)\n assert_(d.metadata is None)\n\n def test_metadata_takes_dict(self):\n d = np.dtype(int, metadata={'datum': 1})\n assert_(d.metadata == {'datum': 1})\n\n def test_metadata_rejects_nondict(self):\n assert_raises(TypeError, np.dtype, int, metadata='datum')\n assert_raises(TypeError, np.dtype, int, metadata=1)\n assert_raises(TypeError, np.dtype, int, metadata=None)\n\n def test_nested_metadata(self):\n d = np.dtype([('a', np.dtype(int, metadata={'datum': 1}))])\n assert_(d['a'].metadata == {'datum': 1})\n\n def test_base_metadata_copied(self):\n d = np.dtype((np.void, np.dtype('i4,i4', metadata={'datum': 1})))\n assert_(d.metadata == {'datum': 1})\n\nclass TestString:\n def test_complex_dtype_str(self):\n dt = np.dtype([('top', [('tiles', ('>f4', (64, 64)), (1,)),\n ('rtile', '>f4', (64, 36))], (3,)),\n ('bottom', [('bleft', ('>f4', (8, 64)), (1,)),\n ('bright', '>f4', (8, 36))])])\n assert_equal(str(dt),\n "[('top', [('tiles', ('>f4', (64, 64)), (1,)), "\n "('rtile', '>f4', (64, 36))], (3,)), "\n "('bottom', [('bleft', ('>f4', (8, 64)), (1,)), "\n "('bright', '>f4', (8, 36))])]")\n\n # If the sticky aligned flag is set to True, it makes the\n # str() function use a dict representation with an 'aligned' flag\n dt = np.dtype([('top', [('tiles', ('>f4', (64, 64)), (1,)),\n ('rtile', '>f4', (64, 36))],\n (3,)),\n ('bottom', [('bleft', ('>f4', (8, 64)), (1,)),\n ('bright', '>f4', (8, 36))])],\n align=True)\n assert_equal(str(dt),\n "{'names': ['top', 'bottom'],"\n " 'formats': [([('tiles', ('>f4', (64, 64)), (1,)), "\n "('rtile', '>f4', (64, 36))], (3,)), "\n "[('bleft', ('>f4', (8, 64)), (1,)), "\n "('bright', '>f4', (8, 36))]],"\n " 'offsets': [0, 76800],"\n " 'itemsize': 80000,"\n " 'aligned': True}")\n with np.printoptions(legacy='1.21'):\n assert_equal(str(dt),\n "{'names':['top','bottom'], "\n "'formats':[([('tiles', ('>f4', (64, 64)), (1,)), "\n "('rtile', '>f4', (64, 36))], (3,)),"\n "[('bleft', ('>f4', (8, 64)), (1,)), "\n "('bright', '>f4', (8, 36))]], "\n "'offsets':[0,76800], "\n "'itemsize':80000, "\n "'aligned':True}")\n assert_equal(np.dtype(eval(str(dt))), dt)\n\n dt = np.dtype({'names': ['r', 'g', 'b'], 'formats': ['u1', 'u1', 'u1'],\n 'offsets': [0, 1, 2],\n 'titles': ['Red pixel', 'Green pixel', 'Blue pixel']})\n assert_equal(str(dt),\n "[(('Red pixel', 'r'), 'u1'), "\n "(('Green pixel', 'g'), 'u1'), "\n "(('Blue pixel', 'b'), 'u1')]")\n\n dt = np.dtype({'names': ['rgba', 'r', 'g', 'b'],\n 'formats': ['<u4', 'u1', 'u1', 'u1'],\n 'offsets': [0, 0, 1, 2],\n 'titles': ['Color', 'Red pixel',\n 'Green pixel', 'Blue pixel']})\n assert_equal(str(dt),\n "{'names': ['rgba', 'r', 'g', 'b'],"\n " 'formats': ['<u4', 'u1', 'u1', 'u1'],"\n " 'offsets': [0, 0, 1, 2],"\n " 'titles': ['Color', 'Red pixel', "\n "'Green pixel', 'Blue pixel'],"\n " 'itemsize': 4}")\n\n dt = np.dtype({'names': ['r', 'b'], 'formats': ['u1', 'u1'],\n 'offsets': [0, 2],\n 'titles': ['Red pixel', 'Blue pixel']})\n assert_equal(str(dt),\n "{'names': ['r', 'b'],"\n " 'formats': ['u1', 'u1'],"\n " 'offsets': [0, 2],"\n " 'titles': ['Red pixel', 'Blue pixel'],"\n " 'itemsize': 3}")\n\n dt = np.dtype([('a', '<m8[D]'), ('b', '<M8[us]')])\n assert_equal(str(dt),\n "[('a', '<m8[D]'), ('b', '<M8[us]')]")\n\n def test_repr_structured(self):\n dt = np.dtype([('top', [('tiles', ('>f4', (64, 64)), (1,)),\n ('rtile', '>f4', (64, 36))], (3,)),\n ('bottom', [('bleft', ('>f4', (8, 64)), (1,)),\n ('bright', '>f4', (8, 36))])])\n assert_equal(repr(dt),\n "dtype([('top', [('tiles', ('>f4', (64, 64)), (1,)), "\n "('rtile', '>f4', (64, 36))], (3,)), "\n "('bottom', [('bleft', ('>f4', (8, 64)), (1,)), "\n "('bright', '>f4', (8, 36))])])")\n\n dt = np.dtype({'names': ['r', 'g', 'b'], 'formats': ['u1', 'u1', 'u1'],\n 'offsets': [0, 1, 2],\n 'titles': ['Red pixel', 'Green pixel', 'Blue pixel']},\n align=True)\n assert_equal(repr(dt),\n "dtype([(('Red pixel', 'r'), 'u1'), "\n "(('Green pixel', 'g'), 'u1'), "\n "(('Blue pixel', 'b'), 'u1')], align=True)")\n\n def test_repr_structured_not_packed(self):\n dt = np.dtype({'names': ['rgba', 'r', 'g', 'b'],\n 'formats': ['<u4', 'u1', 'u1', 'u1'],\n 'offsets': [0, 0, 1, 2],\n 'titles': ['Color', 'Red pixel',\n 'Green pixel', 'Blue pixel']}, align=True)\n assert_equal(repr(dt),\n "dtype({'names': ['rgba', 'r', 'g', 'b'],"\n " 'formats': ['<u4', 'u1', 'u1', 'u1'],"\n " 'offsets': [0, 0, 1, 2],"\n " 'titles': ['Color', 'Red pixel', "\n "'Green pixel', 'Blue pixel'],"\n " 'itemsize': 4}, align=True)")\n\n dt = np.dtype({'names': ['r', 'b'], 'formats': ['u1', 'u1'],\n 'offsets': [0, 2],\n 'titles': ['Red pixel', 'Blue pixel'],\n 'itemsize': 4})\n assert_equal(repr(dt),\n "dtype({'names': ['r', 'b'], "\n "'formats': ['u1', 'u1'], "\n "'offsets': [0, 2], "\n "'titles': ['Red pixel', 'Blue pixel'], "\n "'itemsize': 4})")\n\n def test_repr_structured_datetime(self):\n dt = np.dtype([('a', '<M8[D]'), ('b', '<m8[us]')])\n assert_equal(repr(dt),\n "dtype([('a', '<M8[D]'), ('b', '<m8[us]')])")\n\n def test_repr_str_subarray(self):\n dt = np.dtype(('<i2', (1,)))\n assert_equal(repr(dt), "dtype(('<i2', (1,)))")\n assert_equal(str(dt), "('<i2', (1,))")\n\n def test_base_dtype_with_object_type(self):\n # Issue gh-2798, should not error.\n np.array(['a'], dtype="O").astype(("O", [("name", "O")]))\n\n def test_empty_string_to_object(self):\n # Pull request #4722\n np.array(["", ""]).astype(object)\n\n def test_void_subclass_unsized(self):\n dt = np.dtype(np.record)\n assert_equal(repr(dt), "dtype('V')")\n assert_equal(str(dt), '|V0')\n assert_equal(dt.name, 'record')\n\n def test_void_subclass_sized(self):\n dt = np.dtype((np.record, 2))\n assert_equal(repr(dt), "dtype('V2')")\n assert_equal(str(dt), '|V2')\n assert_equal(dt.name, 'record16')\n\n def test_void_subclass_fields(self):\n dt = np.dtype((np.record, [('a', '<u2')]))\n assert_equal(repr(dt), "dtype((numpy.record, [('a', '<u2')]))")\n assert_equal(str(dt), "(numpy.record, [('a', '<u2')])")\n assert_equal(dt.name, 'record16')\n\n def test_custom_dtype_str(self):\n dt = np.dtypes.StringDType()\n assert_equal(dt.str, "StringDType()")\n\n\nclass TestDtypeAttributeDeletion:\n\n def test_dtype_non_writable_attributes_deletion(self):\n dt = np.dtype(np.double)\n attr = ["subdtype", "descr", "str", "name", "base", "shape",\n "isbuiltin", "isnative", "isalignedstruct", "fields",\n "metadata", "hasobject"]\n\n for s in attr:\n assert_raises(AttributeError, delattr, dt, s)\n\n def test_dtype_writable_attributes_deletion(self):\n dt = np.dtype(np.double)\n attr = ["names"]\n for s in attr:\n assert_raises(AttributeError, delattr, dt, s)\n\n\nclass TestDtypeAttributes:\n def test_descr_has_trailing_void(self):\n # see gh-6359\n dtype = np.dtype({\n 'names': ['A', 'B'],\n 'formats': ['f4', 'f4'],\n 'offsets': [0, 8],\n 'itemsize': 16})\n new_dtype = np.dtype(dtype.descr)\n assert_equal(new_dtype.itemsize, 16)\n\n def test_name_dtype_subclass(self):\n # Ticket #4357\n class user_def_subcls(np.void):\n pass\n assert_equal(np.dtype(user_def_subcls).name, 'user_def_subcls')\n\n def test_zero_stride(self):\n arr = np.ones(1, dtype="i8")\n arr = np.broadcast_to(arr, 10)\n assert arr.strides == (0,)\n with pytest.raises(ValueError):\n arr.dtype = "i1"\n\nclass TestDTypeMakeCanonical:\n def check_canonical(self, dtype, canonical):\n """\n Check most properties relevant to "canonical" versions of a dtype,\n which is mainly native byte order for datatypes supporting this.\n\n The main work is checking structured dtypes with fields, where we\n reproduce most the actual logic used in the C-code.\n """\n assert type(dtype) is type(canonical)\n\n # a canonical DType should always have equivalent casting (both ways)\n assert np.can_cast(dtype, canonical, casting="equiv")\n assert np.can_cast(canonical, dtype, casting="equiv")\n # a canonical dtype (and its fields) is always native (checks fields):\n assert canonical.isnative\n\n # Check that canonical of canonical is the same (no casting):\n assert np.result_type(canonical) == canonical\n\n if not dtype.names:\n # The flags currently never change for unstructured dtypes\n assert dtype.flags == canonical.flags\n return\n\n # Must have all the needs API flag set:\n assert dtype.flags & 0b10000\n\n # Check that the fields are identical (including titles):\n assert dtype.fields.keys() == canonical.fields.keys()\n\n def aligned_offset(offset, alignment):\n # round up offset:\n return - (-offset // alignment) * alignment\n\n totalsize = 0\n max_alignment = 1\n for name in dtype.names:\n # each field is also canonical:\n new_field_descr = canonical.fields[name][0]\n self.check_canonical(dtype.fields[name][0], new_field_descr)\n\n # Must have the "inherited" object related flags:\n expected = 0b11011 & new_field_descr.flags\n assert (canonical.flags & expected) == expected\n\n if canonical.isalignedstruct:\n totalsize = aligned_offset(totalsize, new_field_descr.alignment)\n max_alignment = max(new_field_descr.alignment, max_alignment)\n\n assert canonical.fields[name][1] == totalsize\n # if a title exists, they must match (otherwise empty tuple):\n assert dtype.fields[name][2:] == canonical.fields[name][2:]\n\n totalsize += new_field_descr.itemsize\n\n if canonical.isalignedstruct:\n totalsize = aligned_offset(totalsize, max_alignment)\n assert canonical.itemsize == totalsize\n assert canonical.alignment == max_alignment\n\n def test_simple(self):\n dt = np.dtype(">i4")\n assert np.result_type(dt).isnative\n assert np.result_type(dt).num == dt.num\n\n # dtype with empty space:\n struct_dt = np.dtype(">i4,<i1,i8,V3")[["f0", "f2"]]\n canonical = np.result_type(struct_dt)\n assert canonical.itemsize == 4 + 8\n assert canonical.isnative\n\n # aligned struct dtype with empty space:\n struct_dt = np.dtype(">i1,<i4,i8,V3", align=True)[["f0", "f2"]]\n canonical = np.result_type(struct_dt)\n assert canonical.isalignedstruct\n assert canonical.itemsize == np.dtype("i8").alignment + 8\n assert canonical.isnative\n\n def test_object_flag_not_inherited(self):\n # The following dtype still indicates "object", because its included\n # in the unaccessible space (maybe this could change at some point):\n arr = np.ones(3, "i,O,i")[["f0", "f2"]]\n assert arr.dtype.hasobject\n canonical_dt = np.result_type(arr.dtype)\n assert not canonical_dt.hasobject\n\n @pytest.mark.slow\n @hypothesis.given(dtype=hynp.nested_dtypes())\n def test_make_canonical_hypothesis(self, dtype):\n canonical = np.result_type(dtype)\n self.check_canonical(dtype, canonical)\n # result_type with two arguments should always give identical results:\n two_arg_result = np.result_type(dtype, dtype)\n assert np.can_cast(two_arg_result, canonical, casting="no")\n\n @pytest.mark.slow\n @hypothesis.given(\n dtype=hypothesis.extra.numpy.array_dtypes(\n subtype_strategy=hypothesis.extra.numpy.array_dtypes(),\n min_size=5, max_size=10, allow_subarrays=True))\n def test_structured(self, dtype):\n # Pick 4 of the fields at random. This will leave empty space in the\n # dtype (since we do not canonicalize it here).\n field_subset = random.sample(dtype.names, k=4)\n dtype_with_empty_space = dtype[field_subset]\n assert dtype_with_empty_space.itemsize == dtype.itemsize\n canonicalized = np.result_type(dtype_with_empty_space)\n self.check_canonical(dtype_with_empty_space, canonicalized)\n # promotion with two arguments should always give identical results:\n two_arg_result = np.promote_types(\n dtype_with_empty_space, dtype_with_empty_space)\n assert np.can_cast(two_arg_result, canonicalized, casting="no")\n\n # Ensure that we also check aligned struct (check the opposite, in\n # case hypothesis grows support for `align`. Then repeat the test:\n dtype_aligned = np.dtype(dtype.descr, align=not dtype.isalignedstruct)\n dtype_with_empty_space = dtype_aligned[field_subset]\n assert dtype_with_empty_space.itemsize == dtype_aligned.itemsize\n canonicalized = np.result_type(dtype_with_empty_space)\n self.check_canonical(dtype_with_empty_space, canonicalized)\n # promotion with two arguments should always give identical results:\n two_arg_result = np.promote_types(\n dtype_with_empty_space, dtype_with_empty_space)\n assert np.can_cast(two_arg_result, canonicalized, casting="no")\n\n\nclass TestPickling:\n\n def check_pickling(self, dtype):\n for proto in range(pickle.HIGHEST_PROTOCOL + 1):\n buf = pickle.dumps(dtype, proto)\n # The dtype pickling itself pickles `np.dtype` if it is pickled\n # as a singleton `dtype` should be stored in the buffer:\n assert b"_DType_reconstruct" not in buf\n assert b"dtype" in buf\n pickled = pickle.loads(buf)\n assert_equal(pickled, dtype)\n assert_equal(pickled.descr, dtype.descr)\n if dtype.metadata is not None:\n assert_equal(pickled.metadata, dtype.metadata)\n # Check the reconstructed dtype is functional\n x = np.zeros(3, dtype=dtype)\n y = np.zeros(3, dtype=pickled)\n assert_equal(x, y)\n assert_equal(x[0], y[0])\n\n @pytest.mark.parametrize('t', [int, float, complex, np.int32, str, object,\n bool])\n def test_builtin(self, t):\n self.check_pickling(np.dtype(t))\n\n def test_structured(self):\n dt = np.dtype(([('a', '>f4', (2, 1)), ('b', '<f8', (1, 3))], (2, 2)))\n self.check_pickling(dt)\n\n def test_structured_aligned(self):\n dt = np.dtype('i4, i1', align=True)\n self.check_pickling(dt)\n\n def test_structured_unaligned(self):\n dt = np.dtype('i4, i1', align=False)\n self.check_pickling(dt)\n\n def test_structured_padded(self):\n dt = np.dtype({\n 'names': ['A', 'B'],\n 'formats': ['f4', 'f4'],\n 'offsets': [0, 8],\n 'itemsize': 16})\n self.check_pickling(dt)\n\n def test_structured_titles(self):\n dt = np.dtype({'names': ['r', 'b'],\n 'formats': ['u1', 'u1'],\n 'titles': ['Red pixel', 'Blue pixel']})\n self.check_pickling(dt)\n\n @pytest.mark.parametrize('base', ['m8', 'M8'])\n @pytest.mark.parametrize('unit', ['', 'Y', 'M', 'W', 'D', 'h', 'm', 's',\n 'ms', 'us', 'ns', 'ps', 'fs', 'as'])\n def test_datetime(self, base, unit):\n dt = np.dtype(f'{base}[{unit}]' if unit else base)\n self.check_pickling(dt)\n if unit:\n dt = np.dtype(f'{base}[7{unit}]')\n self.check_pickling(dt)\n\n def test_metadata(self):\n dt = np.dtype(int, metadata={'datum': 1})\n self.check_pickling(dt)\n\n @pytest.mark.parametrize("DType",\n [type(np.dtype(t)) for t in np.typecodes['All']] +\n [type(np.dtype(rational)), np.dtype])\n def test_pickle_dtype_class(self, DType):\n # Check that DTypes (the classes/types) roundtrip when pickling\n for proto in range(pickle.HIGHEST_PROTOCOL + 1):\n roundtrip_DType = pickle.loads(pickle.dumps(DType, proto))\n assert roundtrip_DType is DType\n\n @pytest.mark.parametrize("dt",\n [np.dtype(t) for t in np.typecodes['All']] +\n [np.dtype(rational)])\n def test_pickle_dtype(self, dt):\n # Check that dtype instances roundtrip when pickling and that pickling\n # doesn't change the hash value\n pre_pickle_hash = hash(dt)\n for proto in range(pickle.HIGHEST_PROTOCOL + 1):\n roundtrip_dt = pickle.loads(pickle.dumps(dt, proto))\n assert roundtrip_dt == dt\n assert hash(dt) == pre_pickle_hash\n\n\nclass TestPromotion:\n """Test cases related to more complex DType promotions. Further promotion\n tests are defined in `test_numeric.py`\n """\n @pytest.mark.parametrize(["other", "expected"],\n [(2**16 - 1, np.complex64),\n (2**32 - 1, np.complex64),\n (np.float16(2), np.complex64),\n (np.float32(2), np.complex64),\n (np.longdouble(2), np.clongdouble),\n # Base of the double value to sidestep any rounding issues:\n (np.longdouble(np.nextafter(1.7e308, 0.)), np.clongdouble),\n # Additionally use "nextafter" so the cast can't round down:\n (np.longdouble(np.nextafter(1.7e308, np.inf)), np.clongdouble),\n # repeat for complex scalars:\n (np.complex64(2), np.complex64),\n (np.clongdouble(2), np.clongdouble),\n # Base of the double value to sidestep any rounding issues:\n (np.clongdouble(np.nextafter(1.7e308, 0.) * 1j), np.clongdouble),\n # Additionally use "nextafter" so the cast can't round down:\n (np.clongdouble(np.nextafter(1.7e308, np.inf)), np.clongdouble),\n ])\n def test_complex_other_value_based(self, other, expected):\n # This would change if we modify the value based promotion\n min_complex = np.dtype(np.complex64)\n\n res = np.result_type(other, min_complex)\n assert res == expected\n # Check the same for a simple ufunc call that uses the same logic:\n res = np.minimum(other, np.ones(3, dtype=min_complex)).dtype\n assert res == expected\n\n @pytest.mark.parametrize(["other", "expected"],\n [(np.bool, np.complex128),\n (np.int64, np.complex128),\n (np.float16, np.complex64),\n (np.float32, np.complex64),\n (np.float64, np.complex128),\n (np.longdouble, np.clongdouble),\n (np.complex64, np.complex64),\n (np.complex128, np.complex128),\n (np.clongdouble, np.clongdouble),\n ])\n def test_complex_scalar_value_based(self, other, expected):\n # This would change if we modify the value based promotion\n complex_scalar = 1j\n\n res = np.result_type(other, complex_scalar)\n assert res == expected\n # Check the same for a simple ufunc call that uses the same logic:\n res = np.minimum(np.ones(3, dtype=other), complex_scalar).dtype\n assert res == expected\n\n def test_complex_pyscalar_promote_rational(self):\n with pytest.raises(TypeError,\n match=r".* no common DType exists for the given inputs"):\n np.result_type(1j, rational)\n\n with pytest.raises(TypeError,\n match=r".* no common DType exists for the given inputs"):\n np.result_type(1j, rational(1, 2))\n\n @pytest.mark.parametrize("val", [2, 2**32, 2**63, 2**64, 2 * 100])\n def test_python_integer_promotion(self, val):\n # If we only pass scalars (mainly python ones!), NEP 50 means\n # that we get the default integer\n expected_dtype = np.dtype(int) # the default integer\n assert np.result_type(val, 0) == expected_dtype\n # With NEP 50, the NumPy scalar wins though:\n assert np.result_type(val, np.int8(0)) == np.int8\n\n @pytest.mark.parametrize(["other", "expected"],\n [(1, rational), (1., np.float64)])\n def test_float_int_pyscalar_promote_rational(self, other, expected):\n # Note that rationals are a bit awkward as they promote with float64\n # or default ints, but not float16 or uint8/int8 (which looks\n # inconsistent here). The new promotion fixed this (partially?)\n assert np.result_type(other, rational) == expected\n assert np.result_type(other, rational(1, 2)) == expected\n\n @pytest.mark.parametrize(["dtypes", "expected"], [\n # These promotions are not associative/commutative:\n ([np.uint16, np.int16, np.float16], np.float32),\n ([np.uint16, np.int8, np.float16], np.float32),\n ([np.uint8, np.int16, np.float16], np.float32),\n # The following promotions are not ambiguous, but cover code\n # paths of abstract promotion (no particular logic being tested)\n ([1, 1, np.float64], np.float64),\n ([1, 1., np.complex128], np.complex128),\n ([1, 1j, np.float64], np.complex128),\n ([1., 1., np.int64], np.float64),\n ([1., 1j, np.float64], np.complex128),\n ([1j, 1j, np.float64], np.complex128),\n ([1, True, np.bool], np.int_),\n ])\n def test_permutations_do_not_influence_result(self, dtypes, expected):\n # Tests that most permutations do not influence the result. In the\n # above some uint and int combinations promote to a larger integer\n # type, which would then promote to a larger than necessary float.\n for perm in permutations(dtypes):\n assert np.result_type(*perm) == expected\n\n\ndef test_rational_dtype():\n # test for bug gh-5719\n a = np.array([1111], dtype=rational).astype\n assert_raises(OverflowError, a, 'int8')\n\n # test that dtype detection finds user-defined types\n x = rational(1)\n assert_equal(np.array([x, x]).dtype, np.dtype(rational))\n\n\ndef test_dtypes_are_true():\n # test for gh-6294\n assert bool(np.dtype('f8'))\n assert bool(np.dtype('i8'))\n assert bool(np.dtype([('a', 'i8'), ('b', 'f4')]))\n\n\ndef test_invalid_dtype_string():\n # test for gh-10440\n assert_raises(TypeError, np.dtype, 'f8,i8,[f8,i8]')\n assert_raises(TypeError, np.dtype, 'Fl\xfcgel')\n\n\ndef test_keyword_argument():\n # test for https://github.com/numpy/numpy/pull/16574#issuecomment-642660971\n assert np.dtype(dtype=np.float64) == np.dtype(np.float64)\n\n\nclass TestFromDTypeAttribute:\n def test_simple(self):\n class dt:\n dtype = np.dtype("f8")\n\n assert np.dtype(dt) == np.float64\n assert np.dtype(dt()) == np.float64\n\n @pytest.mark.skipif(IS_PYSTON, reason="Pyston disables recursion checking")\n @pytest.mark.skipif(IS_WASM, reason="Pyodide/WASM has limited stack size")\n def test_recursion(self):\n class dt:\n pass\n\n dt.dtype = dt\n with pytest.raises(RecursionError):\n np.dtype(dt)\n\n dt_instance = dt()\n dt_instance.dtype = dt\n with pytest.raises(RecursionError):\n np.dtype(dt_instance)\n\n def test_void_subtype(self):\n class dt(np.void):\n # This code path is fully untested before, so it is unclear\n # what this should be useful for. Note that if np.void is used\n # numpy will think we are deallocating a base type [1.17, 2019-02].\n dtype = np.dtype("f,f")\n\n np.dtype(dt)\n np.dtype(dt(1))\n\n @pytest.mark.skipif(IS_PYSTON, reason="Pyston disables recursion checking")\n @pytest.mark.skipif(IS_WASM, reason="Pyodide/WASM has limited stack size")\n def test_void_subtype_recursion(self):\n class vdt(np.void):\n pass\n\n vdt.dtype = vdt\n\n with pytest.raises(RecursionError):\n np.dtype(vdt)\n\n with pytest.raises(RecursionError):\n np.dtype(vdt(1))\n\n\nclass TestDTypeClasses:\n @pytest.mark.parametrize("dtype", list(np.typecodes['All']) + [rational])\n def test_basic_dtypes_subclass_properties(self, dtype):\n # Note: Except for the isinstance and type checks, these attributes\n # are considered currently private and may change.\n dtype = np.dtype(dtype)\n assert isinstance(dtype, np.dtype)\n assert type(dtype) is not np.dtype\n if dtype.type.__name__ != "rational":\n dt_name = type(dtype).__name__.lower().removesuffix("dtype")\n if dt_name in {"uint", "int"}:\n # The scalar names has a `c` attached because "int" is Python\n # int and that is long...\n dt_name += "c"\n sc_name = dtype.type.__name__\n assert dt_name == sc_name.strip("_")\n assert type(dtype).__module__ == "numpy.dtypes"\n\n assert getattr(numpy.dtypes, type(dtype).__name__) is type(dtype)\n else:\n assert type(dtype).__name__ == "dtype[rational]"\n assert type(dtype).__module__ == "numpy"\n\n assert not type(dtype)._abstract\n\n # the flexible dtypes and datetime/timedelta have additional parameters\n # which are more than just storage information, these would need to be\n # given when creating a dtype:\n parametric = (np.void, np.str_, np.bytes_, np.datetime64, np.timedelta64)\n if dtype.type not in parametric:\n assert not type(dtype)._parametric\n assert type(dtype)() is dtype\n else:\n assert type(dtype)._parametric\n with assert_raises(TypeError):\n type(dtype)()\n\n def test_dtype_superclass(self):\n assert type(np.dtype) is not type\n assert isinstance(np.dtype, type)\n\n assert type(np.dtype).__name__ == "_DTypeMeta"\n assert type(np.dtype).__module__ == "numpy"\n assert np.dtype._abstract\n\n def test_is_numeric(self):\n all_codes = set(np.typecodes['All'])\n numeric_codes = set(np.typecodes['AllInteger'] +\n np.typecodes['AllFloat'] + '?')\n non_numeric_codes = all_codes - numeric_codes\n\n for code in numeric_codes:\n assert type(np.dtype(code))._is_numeric\n\n for code in non_numeric_codes:\n assert not type(np.dtype(code))._is_numeric\n\n @pytest.mark.parametrize("int_", ["UInt", "Int"])\n @pytest.mark.parametrize("size", [8, 16, 32, 64])\n def test_integer_alias_names(self, int_, size):\n DType = getattr(numpy.dtypes, f"{int_}{size}DType")\n sctype = getattr(numpy, f"{int_.lower()}{size}")\n assert DType.type is sctype\n assert DType.__name__.lower().removesuffix("dtype") == sctype.__name__\n\n @pytest.mark.parametrize("name",\n ["Half", "Float", "Double", "CFloat", "CDouble"])\n def test_float_alias_names(self, name):\n with pytest.raises(AttributeError):\n getattr(numpy.dtypes, name + "DType") is numpy.dtypes.Float16DType\n\n def test_scalar_helper_all_dtypes(self):\n for dtype in np.dtypes.__all__:\n dt_class = getattr(np.dtypes, dtype)\n dt = np.dtype(dt_class)\n if dt.char not in 'OTVM':\n assert np._core.multiarray.scalar(dt) == dt.type()\n elif dt.char == 'V':\n assert np._core.multiarray.scalar(dt) == dt.type(b'\x00')\n elif dt.char == 'M':\n # can't do anything with this without generating ValueError\n # because 'M' has no units\n _ = np._core.multiarray.scalar(dt)\n else:\n with pytest.raises(TypeError):\n np._core.multiarray.scalar(dt)\n\n\nclass TestFromCTypes:\n\n @staticmethod\n def check(ctype, dtype):\n dtype = np.dtype(dtype)\n assert np.dtype(ctype) == dtype\n assert np.dtype(ctype()) == dtype\n assert ctypes.sizeof(ctype) == dtype.itemsize\n\n def test_array(self):\n c8 = ctypes.c_uint8\n self.check( 3 * c8, (np.uint8, (3,)))\n self.check( 1 * c8, (np.uint8, (1,)))\n self.check( 0 * c8, (np.uint8, (0,)))\n self.check(1 * (3 * c8), ((np.uint8, (3,)), (1,)))\n self.check(3 * (1 * c8), ((np.uint8, (1,)), (3,)))\n\n def test_padded_structure(self):\n class PaddedStruct(ctypes.Structure):\n _fields_ = [\n ('a', ctypes.c_uint8),\n ('b', ctypes.c_uint16)\n ]\n expected = np.dtype([\n ('a', np.uint8),\n ('b', np.uint16)\n ], align=True)\n self.check(PaddedStruct, expected)\n\n def test_bit_fields(self):\n class BitfieldStruct(ctypes.Structure):\n _fields_ = [\n ('a', ctypes.c_uint8, 7),\n ('b', ctypes.c_uint8, 1)\n ]\n assert_raises(TypeError, np.dtype, BitfieldStruct)\n assert_raises(TypeError, np.dtype, BitfieldStruct())\n\n def test_pointer(self):\n p_uint8 = ctypes.POINTER(ctypes.c_uint8)\n assert_raises(TypeError, np.dtype, p_uint8)\n\n def test_size_t(self):\n assert np.dtype(np.uintp) is np.dtype("N")\n self.check(ctypes.c_size_t, np.uintp)\n\n def test_void_pointer(self):\n self.check(ctypes.c_void_p, "P")\n\n def test_union(self):\n class Union(ctypes.Union):\n _fields_ = [\n ('a', ctypes.c_uint8),\n ('b', ctypes.c_uint16),\n ]\n expected = np.dtype({\n "names": ['a', 'b'],\n "formats": [np.uint8, np.uint16],\n "offsets": [0, 0],\n "itemsize": 2\n })\n self.check(Union, expected)\n\n def test_union_with_struct_packed(self):\n class Struct(ctypes.Structure):\n _pack_ = 1\n _fields_ = [\n ('one', ctypes.c_uint8),\n ('two', ctypes.c_uint32)\n ]\n\n class Union(ctypes.Union):\n _fields_ = [\n ('a', ctypes.c_uint8),\n ('b', ctypes.c_uint16),\n ('c', ctypes.c_uint32),\n ('d', Struct),\n ]\n expected = np.dtype({\n "names": ['a', 'b', 'c', 'd'],\n "formats": ['u1', np.uint16, np.uint32, [('one', 'u1'), ('two', np.uint32)]],\n "offsets": [0, 0, 0, 0],\n "itemsize": ctypes.sizeof(Union)\n })\n self.check(Union, expected)\n\n def test_union_packed(self):\n class Struct(ctypes.Structure):\n _fields_ = [\n ('one', ctypes.c_uint8),\n ('two', ctypes.c_uint32)\n ]\n _pack_ = 1\n\n class Union(ctypes.Union):\n _pack_ = 1\n _fields_ = [\n ('a', ctypes.c_uint8),\n ('b', ctypes.c_uint16),\n ('c', ctypes.c_uint32),\n ('d', Struct),\n ]\n expected = np.dtype({\n "names": ['a', 'b', 'c', 'd'],\n "formats": ['u1', np.uint16, np.uint32, [('one', 'u1'), ('two', np.uint32)]],\n "offsets": [0, 0, 0, 0],\n "itemsize": ctypes.sizeof(Union)\n })\n self.check(Union, expected)\n\n def test_packed_structure(self):\n class PackedStructure(ctypes.Structure):\n _pack_ = 1\n _fields_ = [\n ('a', ctypes.c_uint8),\n ('b', ctypes.c_uint16)\n ]\n expected = np.dtype([\n ('a', np.uint8),\n ('b', np.uint16)\n ])\n self.check(PackedStructure, expected)\n\n def test_large_packed_structure(self):\n class PackedStructure(ctypes.Structure):\n _pack_ = 2\n _fields_ = [\n ('a', ctypes.c_uint8),\n ('b', ctypes.c_uint16),\n ('c', ctypes.c_uint8),\n ('d', ctypes.c_uint16),\n ('e', ctypes.c_uint32),\n ('f', ctypes.c_uint32),\n ('g', ctypes.c_uint8)\n ]\n expected = np.dtype({\n "formats": [np.uint8, np.uint16, np.uint8, np.uint16, np.uint32, np.uint32, np.uint8],\n "offsets": [0, 2, 4, 6, 8, 12, 16],\n "names": ['a', 'b', 'c', 'd', 'e', 'f', 'g'],\n "itemsize": 18})\n self.check(PackedStructure, expected)\n\n def test_big_endian_structure_packed(self):\n class BigEndStruct(ctypes.BigEndianStructure):\n _fields_ = [\n ('one', ctypes.c_uint8),\n ('two', ctypes.c_uint32)\n ]\n _pack_ = 1\n expected = np.dtype([('one', 'u1'), ('two', '>u4')])\n self.check(BigEndStruct, expected)\n\n def test_little_endian_structure_packed(self):\n class LittleEndStruct(ctypes.LittleEndianStructure):\n _fields_ = [\n ('one', ctypes.c_uint8),\n ('two', ctypes.c_uint32)\n ]\n _pack_ = 1\n expected = np.dtype([('one', 'u1'), ('two', '<u4')])\n self.check(LittleEndStruct, expected)\n\n def test_little_endian_structure(self):\n class PaddedStruct(ctypes.LittleEndianStructure):\n _fields_ = [\n ('a', ctypes.c_uint8),\n ('b', ctypes.c_uint16)\n ]\n expected = np.dtype([\n ('a', '<B'),\n ('b', '<H')\n ], align=True)\n self.check(PaddedStruct, expected)\n\n def test_big_endian_structure(self):\n class PaddedStruct(ctypes.BigEndianStructure):\n _fields_ = [\n ('a', ctypes.c_uint8),\n ('b', ctypes.c_uint16)\n ]\n expected = np.dtype([\n ('a', '>B'),\n ('b', '>H')\n ], align=True)\n self.check(PaddedStruct, expected)\n\n def test_simple_endian_types(self):\n self.check(ctypes.c_uint16.__ctype_le__, np.dtype('<u2'))\n self.check(ctypes.c_uint16.__ctype_be__, np.dtype('>u2'))\n self.check(ctypes.c_uint8.__ctype_le__, np.dtype('u1'))\n self.check(ctypes.c_uint8.__ctype_be__, np.dtype('u1'))\n\n all_types = set(np.typecodes['All'])\n all_pairs = permutations(all_types, 2)\n\n @pytest.mark.parametrize("pair", all_pairs)\n def test_pairs(self, pair):\n """\n Check that np.dtype('x,y') matches [np.dtype('x'), np.dtype('y')]\n Example: np.dtype('d,I') -> dtype([('f0', '<f8'), ('f1', '<u4')])\n """\n # gh-5645: check that np.dtype('i,L') can be used\n pair_type = np.dtype('{},{}'.format(*pair))\n expected = np.dtype([('f0', pair[0]), ('f1', pair[1])])\n assert_equal(pair_type, expected)\n\n\nclass TestUserDType:\n @pytest.mark.leaks_references(reason="dynamically creates custom dtype.")\n def test_custom_structured_dtype(self):\n class mytype:\n pass\n\n blueprint = np.dtype([("field", object)])\n dt = create_custom_field_dtype(blueprint, mytype, 0)\n assert dt.type == mytype\n # We cannot (currently) *create* this dtype with `np.dtype` because\n # mytype does not inherit from `np.generic`. This seems like an\n # unnecessary restriction, but one that has been around forever:\n assert np.dtype(mytype) == np.dtype("O")\n\n if HAS_REFCOUNT:\n # Create an array and test that memory gets cleaned up (gh-25949)\n o = object()\n startcount = sys.getrefcount(o)\n a = np.array([o], dtype=dt)\n del a\n assert sys.getrefcount(o) == startcount\n\n def test_custom_structured_dtype_errors(self):\n class mytype:\n pass\n\n blueprint = np.dtype([("field", object)])\n\n with pytest.raises(ValueError):\n # Tests what happens if fields are unset during creation\n # which is currently rejected due to the containing object\n # (see PyArray_RegisterDataType).\n create_custom_field_dtype(blueprint, mytype, 1)\n\n with pytest.raises(RuntimeError):\n # Tests that a dtype must have its type field set up to np.dtype\n # or in this case a builtin instance.\n create_custom_field_dtype(blueprint, mytype, 2)\n\n\nclass TestClassGetItem:\n def test_dtype(self) -> None:\n alias = np.dtype[Any]\n assert isinstance(alias, types.GenericAlias)\n assert alias.__origin__ is np.dtype\n\n @pytest.mark.parametrize("code", np.typecodes["All"])\n def test_dtype_subclass(self, code: str) -> None:\n cls = type(np.dtype(code))\n alias = cls[Any]\n assert isinstance(alias, types.GenericAlias)\n assert alias.__origin__ is cls\n\n @pytest.mark.parametrize("arg_len", range(4))\n def test_subscript_tuple(self, arg_len: int) -> None:\n arg_tup = (Any,) * arg_len\n if arg_len == 1:\n assert np.dtype[arg_tup]\n else:\n with pytest.raises(TypeError):\n np.dtype[arg_tup]\n\n def test_subscript_scalar(self) -> None:\n assert np.dtype[Any]\n\n\ndef test_result_type_integers_and_unitless_timedelta64():\n # Regression test for gh-20077. The following call of `result_type`\n # would cause a seg. fault.\n td = np.timedelta64(4)\n result = np.result_type(0, td)\n assert_dtype_equal(result, td.dtype)\n\n\ndef test_creating_dtype_with_dtype_class_errors():\n # Regression test for #25031, calling `np.dtype` with itself segfaulted.\n with pytest.raises(TypeError, match="Cannot convert np.dtype into a"):\n np.array(np.ones(10), dtype=np.dtype)\n | .venv\Lib\site-packages\numpy\_core\tests\test_dtype.py | test_dtype.py | Python | 80,792 | 0.75 | 0.134336 | 0.096491 | vue-tools | 792 | 2024-02-02T14:19:17.073771 | BSD-3-Clause | true | 5d6b6d63fffad671e2efc5ad9d64f296 |
import itertools\n\nimport pytest\n\nimport numpy as np\nfrom numpy.testing import (\n assert_,\n assert_allclose,\n assert_almost_equal,\n assert_array_equal,\n assert_equal,\n assert_raises,\n assert_raises_regex,\n suppress_warnings,\n)\n\n# Setup for optimize einsum\nchars = 'abcdefghij'\nsizes = np.array([2, 3, 4, 5, 4, 3, 2, 6, 5, 4, 3])\nglobal_size_dict = dict(zip(chars, sizes))\n\n\nclass TestEinsum:\n @pytest.mark.parametrize("do_opt", [True, False])\n @pytest.mark.parametrize("einsum_fn", [np.einsum, np.einsum_path])\n def test_einsum_errors(self, do_opt, einsum_fn):\n # Need enough arguments\n assert_raises(ValueError, einsum_fn, optimize=do_opt)\n assert_raises(ValueError, einsum_fn, "", optimize=do_opt)\n\n # subscripts must be a string\n assert_raises(TypeError, einsum_fn, 0, 0, optimize=do_opt)\n\n # issue 4528 revealed a segfault with this call\n assert_raises(TypeError, einsum_fn, *(None,) * 63, optimize=do_opt)\n\n # number of operands must match count in subscripts string\n assert_raises(ValueError, einsum_fn, "", 0, 0, optimize=do_opt)\n assert_raises(ValueError, einsum_fn, ",", 0, [0], [0],\n optimize=do_opt)\n assert_raises(ValueError, einsum_fn, ",", [0], optimize=do_opt)\n\n # can't have more subscripts than dimensions in the operand\n assert_raises(ValueError, einsum_fn, "i", 0, optimize=do_opt)\n assert_raises(ValueError, einsum_fn, "ij", [0, 0], optimize=do_opt)\n assert_raises(ValueError, einsum_fn, "...i", 0, optimize=do_opt)\n assert_raises(ValueError, einsum_fn, "i...j", [0, 0], optimize=do_opt)\n assert_raises(ValueError, einsum_fn, "i...", 0, optimize=do_opt)\n assert_raises(ValueError, einsum_fn, "ij...", [0, 0], optimize=do_opt)\n\n # invalid ellipsis\n assert_raises(ValueError, einsum_fn, "i..", [0, 0], optimize=do_opt)\n assert_raises(ValueError, einsum_fn, ".i...", [0, 0], optimize=do_opt)\n assert_raises(ValueError, einsum_fn, "j->..j", [0, 0], optimize=do_opt)\n assert_raises(ValueError, einsum_fn, "j->.j...", [0, 0],\n optimize=do_opt)\n\n # invalid subscript character\n assert_raises(ValueError, einsum_fn, "i%...", [0, 0], optimize=do_opt)\n assert_raises(ValueError, einsum_fn, "...j$", [0, 0], optimize=do_opt)\n assert_raises(ValueError, einsum_fn, "i->&", [0, 0], optimize=do_opt)\n\n # output subscripts must appear in input\n assert_raises(ValueError, einsum_fn, "i->ij", [0, 0], optimize=do_opt)\n\n # output subscripts may only be specified once\n assert_raises(ValueError, einsum_fn, "ij->jij", [[0, 0], [0, 0]],\n optimize=do_opt)\n\n # dimensions must match when being collapsed\n assert_raises(ValueError, einsum_fn, "ii",\n np.arange(6).reshape(2, 3), optimize=do_opt)\n assert_raises(ValueError, einsum_fn, "ii->i",\n np.arange(6).reshape(2, 3), optimize=do_opt)\n\n with assert_raises_regex(ValueError, "'b'"):\n # gh-11221 - 'c' erroneously appeared in the error message\n a = np.ones((3, 3, 4, 5, 6))\n b = np.ones((3, 4, 5))\n einsum_fn('aabcb,abc', a, b)\n\n def test_einsum_sorting_behavior(self):\n # Case 1: 26 dimensions (all lowercase indices)\n n1 = 26\n x1 = np.random.random((1,) * n1)\n path1 = np.einsum_path(x1, range(n1))[1] # Get einsum path details\n output_indices1 = path1.split("->")[-1].strip() # Extract output indices\n # Assert indices are only uppercase letters and sorted correctly\n assert all(c.isupper() for c in output_indices1), (\n "Output indices for n=26 should use uppercase letters only: "\n f"{output_indices1}"\n )\n assert_equal(\n output_indices1,\n ''.join(sorted(output_indices1)),\n err_msg=(\n "Output indices for n=26 are not lexicographically sorted: "\n f"{output_indices1}"\n )\n )\n\n # Case 2: 27 dimensions (includes uppercase indices)\n n2 = 27\n x2 = np.random.random((1,) * n2)\n path2 = np.einsum_path(x2, range(n2))[1]\n output_indices2 = path2.split("->")[-1].strip()\n # Assert indices include both uppercase and lowercase letters\n assert any(c.islower() for c in output_indices2), (\n "Output indices for n=27 should include uppercase letters: "\n f"{output_indices2}"\n )\n # Assert output indices are sorted uppercase before lowercase\n assert_equal(\n output_indices2,\n ''.join(sorted(output_indices2)),\n err_msg=(\n "Output indices for n=27 are not lexicographically sorted: "\n f"{output_indices2}"\n )\n )\n\n # Additional Check: Ensure dimensions correspond correctly to indices\n # Generate expected mapping of dimensions to indices\n expected_indices = [\n chr(i + ord('A')) if i < 26 else chr(i - 26 + ord('a'))\n for i in range(n2)\n ]\n assert_equal(\n output_indices2,\n ''.join(expected_indices),\n err_msg=(\n "Output indices do not map to the correct dimensions. Expected: "\n f"{''.join(expected_indices)}, Got: {output_indices2}"\n )\n )\n\n @pytest.mark.parametrize("do_opt", [True, False])\n def test_einsum_specific_errors(self, do_opt):\n # out parameter must be an array\n assert_raises(TypeError, np.einsum, "", 0, out='test',\n optimize=do_opt)\n\n # order parameter must be a valid order\n assert_raises(ValueError, np.einsum, "", 0, order='W',\n optimize=do_opt)\n\n # casting parameter must be a valid casting\n assert_raises(ValueError, np.einsum, "", 0, casting='blah',\n optimize=do_opt)\n\n # dtype parameter must be a valid dtype\n assert_raises(TypeError, np.einsum, "", 0, dtype='bad_data_type',\n optimize=do_opt)\n\n # other keyword arguments are rejected\n assert_raises(TypeError, np.einsum, "", 0, bad_arg=0, optimize=do_opt)\n\n # broadcasting to new dimensions must be enabled explicitly\n assert_raises(ValueError, np.einsum, "i", np.arange(6).reshape(2, 3),\n optimize=do_opt)\n assert_raises(ValueError, np.einsum, "i->i", [[0, 1], [0, 1]],\n out=np.arange(4).reshape(2, 2), optimize=do_opt)\n\n # Check order kwarg, asanyarray allows 1d to pass through\n assert_raises(ValueError, np.einsum, "i->i",\n np.arange(6).reshape(-1, 1), optimize=do_opt, order='d')\n\n def test_einsum_object_errors(self):\n # Exceptions created by object arithmetic should\n # successfully propagate\n\n class CustomException(Exception):\n pass\n\n class DestructoBox:\n\n def __init__(self, value, destruct):\n self._val = value\n self._destruct = destruct\n\n def __add__(self, other):\n tmp = self._val + other._val\n if tmp >= self._destruct:\n raise CustomException\n else:\n self._val = tmp\n return self\n\n def __radd__(self, other):\n if other == 0:\n return self\n else:\n return self.__add__(other)\n\n def __mul__(self, other):\n tmp = self._val * other._val\n if tmp >= self._destruct:\n raise CustomException\n else:\n self._val = tmp\n return self\n\n def __rmul__(self, other):\n if other == 0:\n return self\n else:\n return self.__mul__(other)\n\n a = np.array([DestructoBox(i, 5) for i in range(1, 10)],\n dtype='object').reshape(3, 3)\n\n # raised from unbuffered_loop_nop1_ndim2\n assert_raises(CustomException, np.einsum, "ij->i", a)\n\n # raised from unbuffered_loop_nop1_ndim3\n b = np.array([DestructoBox(i, 100) for i in range(27)],\n dtype='object').reshape(3, 3, 3)\n assert_raises(CustomException, np.einsum, "i...k->...", b)\n\n # raised from unbuffered_loop_nop2_ndim2\n b = np.array([DestructoBox(i, 55) for i in range(1, 4)],\n dtype='object')\n assert_raises(CustomException, np.einsum, "ij, j", a, b)\n\n # raised from unbuffered_loop_nop2_ndim3\n assert_raises(CustomException, np.einsum, "ij, jh", a, a)\n\n # raised from PyArray_EinsteinSum\n assert_raises(CustomException, np.einsum, "ij->", a)\n\n def test_einsum_views(self):\n # pass-through\n for do_opt in [True, False]:\n a = np.arange(6)\n a.shape = (2, 3)\n\n b = np.einsum("...", a, optimize=do_opt)\n assert_(b.base is a)\n\n b = np.einsum(a, [Ellipsis], optimize=do_opt)\n assert_(b.base is a)\n\n b = np.einsum("ij", a, optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, a)\n\n b = np.einsum(a, [0, 1], optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, a)\n\n # output is writeable whenever input is writeable\n b = np.einsum("...", a, optimize=do_opt)\n assert_(b.flags['WRITEABLE'])\n a.flags['WRITEABLE'] = False\n b = np.einsum("...", a, optimize=do_opt)\n assert_(not b.flags['WRITEABLE'])\n\n # transpose\n a = np.arange(6)\n a.shape = (2, 3)\n\n b = np.einsum("ji", a, optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, a.T)\n\n b = np.einsum(a, [1, 0], optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, a.T)\n\n # diagonal\n a = np.arange(9)\n a.shape = (3, 3)\n\n b = np.einsum("ii->i", a, optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, [a[i, i] for i in range(3)])\n\n b = np.einsum(a, [0, 0], [0], optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, [a[i, i] for i in range(3)])\n\n # diagonal with various ways of broadcasting an additional dimension\n a = np.arange(27)\n a.shape = (3, 3, 3)\n\n b = np.einsum("...ii->...i", a, optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, [[x[i, i] for i in range(3)] for x in a])\n\n b = np.einsum(a, [Ellipsis, 0, 0], [Ellipsis, 0], optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, [[x[i, i] for i in range(3)] for x in a])\n\n b = np.einsum("ii...->...i", a, optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, [[x[i, i] for i in range(3)]\n for x in a.transpose(2, 0, 1)])\n\n b = np.einsum(a, [0, 0, Ellipsis], [Ellipsis, 0], optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, [[x[i, i] for i in range(3)]\n for x in a.transpose(2, 0, 1)])\n\n b = np.einsum("...ii->i...", a, optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, [a[:, i, i] for i in range(3)])\n\n b = np.einsum(a, [Ellipsis, 0, 0], [0, Ellipsis], optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, [a[:, i, i] for i in range(3)])\n\n b = np.einsum("jii->ij", a, optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, [a[:, i, i] for i in range(3)])\n\n b = np.einsum(a, [1, 0, 0], [0, 1], optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, [a[:, i, i] for i in range(3)])\n\n b = np.einsum("ii...->i...", a, optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, [a.transpose(2, 0, 1)[:, i, i] for i in range(3)])\n\n b = np.einsum(a, [0, 0, Ellipsis], [0, Ellipsis], optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, [a.transpose(2, 0, 1)[:, i, i] for i in range(3)])\n\n b = np.einsum("i...i->i...", a, optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, [a.transpose(1, 0, 2)[:, i, i] for i in range(3)])\n\n b = np.einsum(a, [0, Ellipsis, 0], [0, Ellipsis], optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, [a.transpose(1, 0, 2)[:, i, i] for i in range(3)])\n\n b = np.einsum("i...i->...i", a, optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, [[x[i, i] for i in range(3)]\n for x in a.transpose(1, 0, 2)])\n\n b = np.einsum(a, [0, Ellipsis, 0], [Ellipsis, 0], optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, [[x[i, i] for i in range(3)]\n for x in a.transpose(1, 0, 2)])\n\n # triple diagonal\n a = np.arange(27)\n a.shape = (3, 3, 3)\n\n b = np.einsum("iii->i", a, optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, [a[i, i, i] for i in range(3)])\n\n b = np.einsum(a, [0, 0, 0], [0], optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, [a[i, i, i] for i in range(3)])\n\n # swap axes\n a = np.arange(24)\n a.shape = (2, 3, 4)\n\n b = np.einsum("ijk->jik", a, optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, a.swapaxes(0, 1))\n\n b = np.einsum(a, [0, 1, 2], [1, 0, 2], optimize=do_opt)\n assert_(b.base is a)\n assert_equal(b, a.swapaxes(0, 1))\n\n def check_einsum_sums(self, dtype, do_opt=False):\n dtype = np.dtype(dtype)\n # Check various sums. Does many sizes to exercise unrolled loops.\n\n # sum(a, axis=-1)\n for n in range(1, 17):\n a = np.arange(n, dtype=dtype)\n b = np.sum(a, axis=-1)\n if hasattr(b, 'astype'):\n b = b.astype(dtype)\n assert_equal(np.einsum("i->", a, optimize=do_opt), b)\n assert_equal(np.einsum(a, [0], [], optimize=do_opt), b)\n\n for n in range(1, 17):\n a = np.arange(2 * 3 * n, dtype=dtype).reshape(2, 3, n)\n b = np.sum(a, axis=-1)\n if hasattr(b, 'astype'):\n b = b.astype(dtype)\n assert_equal(np.einsum("...i->...", a, optimize=do_opt), b)\n assert_equal(np.einsum(a, [Ellipsis, 0], [Ellipsis], optimize=do_opt), b)\n\n # sum(a, axis=0)\n for n in range(1, 17):\n a = np.arange(2 * n, dtype=dtype).reshape(2, n)\n b = np.sum(a, axis=0)\n if hasattr(b, 'astype'):\n b = b.astype(dtype)\n assert_equal(np.einsum("i...->...", a, optimize=do_opt), b)\n assert_equal(np.einsum(a, [0, Ellipsis], [Ellipsis], optimize=do_opt), b)\n\n for n in range(1, 17):\n a = np.arange(2 * 3 * n, dtype=dtype).reshape(2, 3, n)\n b = np.sum(a, axis=0)\n if hasattr(b, 'astype'):\n b = b.astype(dtype)\n assert_equal(np.einsum("i...->...", a, optimize=do_opt), b)\n assert_equal(np.einsum(a, [0, Ellipsis], [Ellipsis], optimize=do_opt), b)\n\n # trace(a)\n for n in range(1, 17):\n a = np.arange(n * n, dtype=dtype).reshape(n, n)\n b = np.trace(a)\n if hasattr(b, 'astype'):\n b = b.astype(dtype)\n assert_equal(np.einsum("ii", a, optimize=do_opt), b)\n assert_equal(np.einsum(a, [0, 0], optimize=do_opt), b)\n\n # gh-15961: should accept numpy int64 type in subscript list\n np_array = np.asarray([0, 0])\n assert_equal(np.einsum(a, np_array, optimize=do_opt), b)\n assert_equal(np.einsum(a, list(np_array), optimize=do_opt), b)\n\n # multiply(a, b)\n assert_equal(np.einsum("..., ...", 3, 4), 12) # scalar case\n for n in range(1, 17):\n a = np.arange(3 * n, dtype=dtype).reshape(3, n)\n b = np.arange(2 * 3 * n, dtype=dtype).reshape(2, 3, n)\n assert_equal(np.einsum("..., ...", a, b, optimize=do_opt),\n np.multiply(a, b))\n assert_equal(np.einsum(a, [Ellipsis], b, [Ellipsis], optimize=do_opt),\n np.multiply(a, b))\n\n # inner(a,b)\n for n in range(1, 17):\n a = np.arange(2 * 3 * n, dtype=dtype).reshape(2, 3, n)\n b = np.arange(n, dtype=dtype)\n assert_equal(np.einsum("...i, ...i", a, b, optimize=do_opt), np.inner(a, b))\n assert_equal(np.einsum(a, [Ellipsis, 0], b, [Ellipsis, 0], optimize=do_opt),\n np.inner(a, b))\n\n for n in range(1, 11):\n a = np.arange(n * 3 * 2, dtype=dtype).reshape(n, 3, 2)\n b = np.arange(n, dtype=dtype)\n assert_equal(np.einsum("i..., i...", a, b, optimize=do_opt),\n np.inner(a.T, b.T).T)\n assert_equal(np.einsum(a, [0, Ellipsis], b, [0, Ellipsis], optimize=do_opt),\n np.inner(a.T, b.T).T)\n\n # outer(a,b)\n for n in range(1, 17):\n a = np.arange(3, dtype=dtype) + 1\n b = np.arange(n, dtype=dtype) + 1\n assert_equal(np.einsum("i,j", a, b, optimize=do_opt),\n np.outer(a, b))\n assert_equal(np.einsum(a, [0], b, [1], optimize=do_opt),\n np.outer(a, b))\n\n # Suppress the complex warnings for the 'as f8' tests\n with suppress_warnings() as sup:\n sup.filter(np.exceptions.ComplexWarning)\n\n # matvec(a,b) / a.dot(b) where a is matrix, b is vector\n for n in range(1, 17):\n a = np.arange(4 * n, dtype=dtype).reshape(4, n)\n b = np.arange(n, dtype=dtype)\n assert_equal(np.einsum("ij, j", a, b, optimize=do_opt),\n np.dot(a, b))\n assert_equal(np.einsum(a, [0, 1], b, [1], optimize=do_opt),\n np.dot(a, b))\n\n c = np.arange(4, dtype=dtype)\n np.einsum("ij,j", a, b, out=c,\n dtype='f8', casting='unsafe', optimize=do_opt)\n assert_equal(c,\n np.dot(a.astype('f8'),\n b.astype('f8')).astype(dtype))\n c[...] = 0\n np.einsum(a, [0, 1], b, [1], out=c,\n dtype='f8', casting='unsafe', optimize=do_opt)\n assert_equal(c,\n np.dot(a.astype('f8'),\n b.astype('f8')).astype(dtype))\n\n for n in range(1, 17):\n a = np.arange(4 * n, dtype=dtype).reshape(4, n)\n b = np.arange(n, dtype=dtype)\n assert_equal(np.einsum("ji,j", a.T, b.T, optimize=do_opt),\n np.dot(b.T, a.T))\n assert_equal(np.einsum(a.T, [1, 0], b.T, [1], optimize=do_opt),\n np.dot(b.T, a.T))\n\n c = np.arange(4, dtype=dtype)\n np.einsum("ji,j", a.T, b.T, out=c,\n dtype='f8', casting='unsafe', optimize=do_opt)\n assert_equal(c,\n np.dot(b.T.astype('f8'),\n a.T.astype('f8')).astype(dtype))\n c[...] = 0\n np.einsum(a.T, [1, 0], b.T, [1], out=c,\n dtype='f8', casting='unsafe', optimize=do_opt)\n assert_equal(c,\n np.dot(b.T.astype('f8'),\n a.T.astype('f8')).astype(dtype))\n\n # matmat(a,b) / a.dot(b) where a is matrix, b is matrix\n for n in range(1, 17):\n if n < 8 or dtype != 'f2':\n a = np.arange(4 * n, dtype=dtype).reshape(4, n)\n b = np.arange(n * 6, dtype=dtype).reshape(n, 6)\n assert_equal(np.einsum("ij,jk", a, b, optimize=do_opt),\n np.dot(a, b))\n assert_equal(np.einsum(a, [0, 1], b, [1, 2], optimize=do_opt),\n np.dot(a, b))\n\n for n in range(1, 17):\n a = np.arange(4 * n, dtype=dtype).reshape(4, n)\n b = np.arange(n * 6, dtype=dtype).reshape(n, 6)\n c = np.arange(24, dtype=dtype).reshape(4, 6)\n np.einsum("ij,jk", a, b, out=c, dtype='f8', casting='unsafe',\n optimize=do_opt)\n assert_equal(c,\n np.dot(a.astype('f8'),\n b.astype('f8')).astype(dtype))\n c[...] = 0\n np.einsum(a, [0, 1], b, [1, 2], out=c,\n dtype='f8', casting='unsafe', optimize=do_opt)\n assert_equal(c,\n np.dot(a.astype('f8'),\n b.astype('f8')).astype(dtype))\n\n # matrix triple product (note this is not currently an efficient\n # way to multiply 3 matrices)\n a = np.arange(12, dtype=dtype).reshape(3, 4)\n b = np.arange(20, dtype=dtype).reshape(4, 5)\n c = np.arange(30, dtype=dtype).reshape(5, 6)\n if dtype != 'f2':\n assert_equal(np.einsum("ij,jk,kl", a, b, c, optimize=do_opt),\n a.dot(b).dot(c))\n assert_equal(np.einsum(a, [0, 1], b, [1, 2], c, [2, 3],\n optimize=do_opt), a.dot(b).dot(c))\n\n d = np.arange(18, dtype=dtype).reshape(3, 6)\n np.einsum("ij,jk,kl", a, b, c, out=d,\n dtype='f8', casting='unsafe', optimize=do_opt)\n tgt = a.astype('f8').dot(b.astype('f8'))\n tgt = tgt.dot(c.astype('f8')).astype(dtype)\n assert_equal(d, tgt)\n\n d[...] = 0\n np.einsum(a, [0, 1], b, [1, 2], c, [2, 3], out=d,\n dtype='f8', casting='unsafe', optimize=do_opt)\n tgt = a.astype('f8').dot(b.astype('f8'))\n tgt = tgt.dot(c.astype('f8')).astype(dtype)\n assert_equal(d, tgt)\n\n # tensordot(a, b)\n if np.dtype(dtype) != np.dtype('f2'):\n a = np.arange(60, dtype=dtype).reshape(3, 4, 5)\n b = np.arange(24, dtype=dtype).reshape(4, 3, 2)\n assert_equal(np.einsum("ijk, jil -> kl", a, b),\n np.tensordot(a, b, axes=([1, 0], [0, 1])))\n assert_equal(np.einsum(a, [0, 1, 2], b, [1, 0, 3], [2, 3]),\n np.tensordot(a, b, axes=([1, 0], [0, 1])))\n\n c = np.arange(10, dtype=dtype).reshape(5, 2)\n np.einsum("ijk,jil->kl", a, b, out=c,\n dtype='f8', casting='unsafe', optimize=do_opt)\n assert_equal(c, np.tensordot(a.astype('f8'), b.astype('f8'),\n axes=([1, 0], [0, 1])).astype(dtype))\n c[...] = 0\n np.einsum(a, [0, 1, 2], b, [1, 0, 3], [2, 3], out=c,\n dtype='f8', casting='unsafe', optimize=do_opt)\n assert_equal(c, np.tensordot(a.astype('f8'), b.astype('f8'),\n axes=([1, 0], [0, 1])).astype(dtype))\n\n # logical_and(logical_and(a!=0, b!=0), c!=0)\n neg_val = -2 if dtype.kind != "u" else np.iinfo(dtype).max - 1\n a = np.array([1, 3, neg_val, 0, 12, 13, 0, 1], dtype=dtype)\n b = np.array([0, 3.5, 0., neg_val, 0, 1, 3, 12], dtype=dtype)\n c = np.array([True, True, False, True, True, False, True, True])\n\n assert_equal(np.einsum("i,i,i->i", a, b, c,\n dtype='?', casting='unsafe', optimize=do_opt),\n np.logical_and(np.logical_and(a != 0, b != 0), c != 0))\n assert_equal(np.einsum(a, [0], b, [0], c, [0], [0],\n dtype='?', casting='unsafe'),\n np.logical_and(np.logical_and(a != 0, b != 0), c != 0))\n\n a = np.arange(9, dtype=dtype)\n assert_equal(np.einsum(",i->", 3, a), 3 * np.sum(a))\n assert_equal(np.einsum(3, [], a, [0], []), 3 * np.sum(a))\n assert_equal(np.einsum("i,->", a, 3), 3 * np.sum(a))\n assert_equal(np.einsum(a, [0], 3, [], []), 3 * np.sum(a))\n\n # Various stride0, contiguous, and SSE aligned variants\n for n in range(1, 25):\n a = np.arange(n, dtype=dtype)\n if np.dtype(dtype).itemsize > 1:\n assert_equal(np.einsum("...,...", a, a, optimize=do_opt),\n np.multiply(a, a))\n assert_equal(np.einsum("i,i", a, a, optimize=do_opt), np.dot(a, a))\n assert_equal(np.einsum("i,->i", a, 2, optimize=do_opt), 2 * a)\n assert_equal(np.einsum(",i->i", 2, a, optimize=do_opt), 2 * a)\n assert_equal(np.einsum("i,->", a, 2, optimize=do_opt), 2 * np.sum(a))\n assert_equal(np.einsum(",i->", 2, a, optimize=do_opt), 2 * np.sum(a))\n\n assert_equal(np.einsum("...,...", a[1:], a[:-1], optimize=do_opt),\n np.multiply(a[1:], a[:-1]))\n assert_equal(np.einsum("i,i", a[1:], a[:-1], optimize=do_opt),\n np.dot(a[1:], a[:-1]))\n assert_equal(np.einsum("i,->i", a[1:], 2, optimize=do_opt), 2 * a[1:])\n assert_equal(np.einsum(",i->i", 2, a[1:], optimize=do_opt), 2 * a[1:])\n assert_equal(np.einsum("i,->", a[1:], 2, optimize=do_opt),\n 2 * np.sum(a[1:]))\n assert_equal(np.einsum(",i->", 2, a[1:], optimize=do_opt),\n 2 * np.sum(a[1:]))\n\n # An object array, summed as the data type\n a = np.arange(9, dtype=object)\n\n b = np.einsum("i->", a, dtype=dtype, casting='unsafe')\n assert_equal(b, np.sum(a))\n if hasattr(b, "dtype"):\n # Can be a python object when dtype is object\n assert_equal(b.dtype, np.dtype(dtype))\n\n b = np.einsum(a, [0], [], dtype=dtype, casting='unsafe')\n assert_equal(b, np.sum(a))\n if hasattr(b, "dtype"):\n # Can be a python object when dtype is object\n assert_equal(b.dtype, np.dtype(dtype))\n\n # A case which was failing (ticket #1885)\n p = np.arange(2) + 1\n q = np.arange(4).reshape(2, 2) + 3\n r = np.arange(4).reshape(2, 2) + 7\n assert_equal(np.einsum('z,mz,zm->', p, q, r), 253)\n\n # singleton dimensions broadcast (gh-10343)\n p = np.ones((10, 2))\n q = np.ones((1, 2))\n assert_array_equal(np.einsum('ij,ij->j', p, q, optimize=True),\n np.einsum('ij,ij->j', p, q, optimize=False))\n assert_array_equal(np.einsum('ij,ij->j', p, q, optimize=True),\n [10.] * 2)\n\n # a blas-compatible contraction broadcasting case which was failing\n # for optimize=True (ticket #10930)\n x = np.array([2., 3.])\n y = np.array([4.])\n assert_array_equal(np.einsum("i, i", x, y, optimize=False), 20.)\n assert_array_equal(np.einsum("i, i", x, y, optimize=True), 20.)\n\n # all-ones array was bypassing bug (ticket #10930)\n p = np.ones((1, 5)) / 2\n q = np.ones((5, 5)) / 2\n for optimize in (True, False):\n assert_array_equal(np.einsum("...ij,...jk->...ik", p, p,\n optimize=optimize),\n np.einsum("...ij,...jk->...ik", p, q,\n optimize=optimize))\n assert_array_equal(np.einsum("...ij,...jk->...ik", p, q,\n optimize=optimize),\n np.full((1, 5), 1.25))\n\n # Cases which were failing (gh-10899)\n x = np.eye(2, dtype=dtype)\n y = np.ones(2, dtype=dtype)\n assert_array_equal(np.einsum("ji,i->", x, y, optimize=optimize),\n [2.]) # contig_contig_outstride0_two\n assert_array_equal(np.einsum("i,ij->", y, x, optimize=optimize),\n [2.]) # stride0_contig_outstride0_two\n assert_array_equal(np.einsum("ij,i->", x, y, optimize=optimize),\n [2.]) # contig_stride0_outstride0_two\n\n def test_einsum_sums_int8(self):\n self.check_einsum_sums('i1')\n\n def test_einsum_sums_uint8(self):\n self.check_einsum_sums('u1')\n\n def test_einsum_sums_int16(self):\n self.check_einsum_sums('i2')\n\n def test_einsum_sums_uint16(self):\n self.check_einsum_sums('u2')\n\n def test_einsum_sums_int32(self):\n self.check_einsum_sums('i4')\n self.check_einsum_sums('i4', True)\n\n def test_einsum_sums_uint32(self):\n self.check_einsum_sums('u4')\n self.check_einsum_sums('u4', True)\n\n def test_einsum_sums_int64(self):\n self.check_einsum_sums('i8')\n\n def test_einsum_sums_uint64(self):\n self.check_einsum_sums('u8')\n\n def test_einsum_sums_float16(self):\n self.check_einsum_sums('f2')\n\n def test_einsum_sums_float32(self):\n self.check_einsum_sums('f4')\n\n def test_einsum_sums_float64(self):\n self.check_einsum_sums('f8')\n self.check_einsum_sums('f8', True)\n\n def test_einsum_sums_longdouble(self):\n self.check_einsum_sums(np.longdouble)\n\n def test_einsum_sums_cfloat64(self):\n self.check_einsum_sums('c8')\n self.check_einsum_sums('c8', True)\n\n def test_einsum_sums_cfloat128(self):\n self.check_einsum_sums('c16')\n\n def test_einsum_sums_clongdouble(self):\n self.check_einsum_sums(np.clongdouble)\n\n def test_einsum_sums_object(self):\n self.check_einsum_sums('object')\n self.check_einsum_sums('object', True)\n\n def test_einsum_misc(self):\n # This call used to crash because of a bug in\n # PyArray_AssignZero\n a = np.ones((1, 2))\n b = np.ones((2, 2, 1))\n assert_equal(np.einsum('ij...,j...->i...', a, b), [[[2], [2]]])\n assert_equal(np.einsum('ij...,j...->i...', a, b, optimize=True), [[[2], [2]]])\n\n # Regression test for issue #10369 (test unicode inputs with Python 2)\n assert_equal(np.einsum('ij...,j...->i...', a, b), [[[2], [2]]])\n assert_equal(np.einsum('...i,...i', [1, 2, 3], [2, 3, 4]), 20)\n assert_equal(np.einsum('...i,...i', [1, 2, 3], [2, 3, 4],\n optimize='greedy'), 20)\n\n # The iterator had an issue with buffering this reduction\n a = np.ones((5, 12, 4, 2, 3), np.int64)\n b = np.ones((5, 12, 11), np.int64)\n assert_equal(np.einsum('ijklm,ijn,ijn->', a, b, b),\n np.einsum('ijklm,ijn->', a, b))\n assert_equal(np.einsum('ijklm,ijn,ijn->', a, b, b, optimize=True),\n np.einsum('ijklm,ijn->', a, b, optimize=True))\n\n # Issue #2027, was a problem in the contiguous 3-argument\n # inner loop implementation\n a = np.arange(1, 3)\n b = np.arange(1, 5).reshape(2, 2)\n c = np.arange(1, 9).reshape(4, 2)\n assert_equal(np.einsum('x,yx,zx->xzy', a, b, c),\n [[[1, 3], [3, 9], [5, 15], [7, 21]],\n [[8, 16], [16, 32], [24, 48], [32, 64]]])\n assert_equal(np.einsum('x,yx,zx->xzy', a, b, c, optimize=True),\n [[[1, 3], [3, 9], [5, 15], [7, 21]],\n [[8, 16], [16, 32], [24, 48], [32, 64]]])\n\n # Ensure explicitly setting out=None does not cause an error\n # see issue gh-15776 and issue gh-15256\n assert_equal(np.einsum('i,j', [1], [2], out=None), [[2]])\n\n def test_object_loop(self):\n\n class Mult:\n def __mul__(self, other):\n return 42\n\n objMult = np.array([Mult()])\n objNULL = np.ndarray(buffer=b'\0' * np.intp(0).itemsize, shape=1, dtype=object)\n\n with pytest.raises(TypeError):\n np.einsum("i,j", [1], objNULL)\n with pytest.raises(TypeError):\n np.einsum("i,j", objNULL, [1])\n assert np.einsum("i,j", objMult, objMult) == 42\n\n def test_subscript_range(self):\n # Issue #7741, make sure that all letters of Latin alphabet (both uppercase & lowercase) can be used\n # when creating a subscript from arrays\n a = np.ones((2, 3))\n b = np.ones((3, 4))\n np.einsum(a, [0, 20], b, [20, 2], [0, 2], optimize=False)\n np.einsum(a, [0, 27], b, [27, 2], [0, 2], optimize=False)\n np.einsum(a, [0, 51], b, [51, 2], [0, 2], optimize=False)\n assert_raises(ValueError, lambda: np.einsum(a, [0, 52], b, [52, 2], [0, 2], optimize=False))\n assert_raises(ValueError, lambda: np.einsum(a, [-1, 5], b, [5, 2], [-1, 2], optimize=False))\n\n def test_einsum_broadcast(self):\n # Issue #2455 change in handling ellipsis\n # remove the 'middle broadcast' error\n # only use the 'RIGHT' iteration in prepare_op_axes\n # adds auto broadcast on left where it belongs\n # broadcast on right has to be explicit\n # We need to test the optimized parsing as well\n\n A = np.arange(2 * 3 * 4).reshape(2, 3, 4)\n B = np.arange(3)\n ref = np.einsum('ijk,j->ijk', A, B, optimize=False)\n for opt in [True, False]:\n assert_equal(np.einsum('ij...,j...->ij...', A, B, optimize=opt), ref)\n assert_equal(np.einsum('ij...,...j->ij...', A, B, optimize=opt), ref)\n assert_equal(np.einsum('ij...,j->ij...', A, B, optimize=opt), ref) # used to raise error\n\n A = np.arange(12).reshape((4, 3))\n B = np.arange(6).reshape((3, 2))\n ref = np.einsum('ik,kj->ij', A, B, optimize=False)\n for opt in [True, False]:\n assert_equal(np.einsum('ik...,k...->i...', A, B, optimize=opt), ref)\n assert_equal(np.einsum('ik...,...kj->i...j', A, B, optimize=opt), ref)\n assert_equal(np.einsum('...k,kj', A, B, optimize=opt), ref) # used to raise error\n assert_equal(np.einsum('ik,k...->i...', A, B, optimize=opt), ref) # used to raise error\n\n dims = [2, 3, 4, 5]\n a = np.arange(np.prod(dims)).reshape(dims)\n v = np.arange(dims[2])\n ref = np.einsum('ijkl,k->ijl', a, v, optimize=False)\n for opt in [True, False]:\n assert_equal(np.einsum('ijkl,k', a, v, optimize=opt), ref)\n assert_equal(np.einsum('...kl,k', a, v, optimize=opt), ref) # used to raise error\n assert_equal(np.einsum('...kl,k...', a, v, optimize=opt), ref)\n\n J, K, M = 160, 160, 120\n A = np.arange(J * K * M).reshape(1, 1, 1, J, K, M)\n B = np.arange(J * K * M * 3).reshape(J, K, M, 3)\n ref = np.einsum('...lmn,...lmno->...o', A, B, optimize=False)\n for opt in [True, False]:\n assert_equal(np.einsum('...lmn,lmno->...o', A, B,\n optimize=opt), ref) # used to raise error\n\n def test_einsum_fixedstridebug(self):\n # Issue #4485 obscure einsum bug\n # This case revealed a bug in nditer where it reported a stride\n # as 'fixed' (0) when it was in fact not fixed during processing\n # (0 or 4). The reason for the bug was that the check for a fixed\n # stride was using the information from the 2D inner loop reuse\n # to restrict the iteration dimensions it had to validate to be\n # the same, but that 2D inner loop reuse logic is only triggered\n # during the buffer copying step, and hence it was invalid to\n # rely on those values. The fix is to check all the dimensions\n # of the stride in question, which in the test case reveals that\n # the stride is not fixed.\n #\n # NOTE: This test is triggered by the fact that the default buffersize,\n # used by einsum, is 8192, and 3*2731 = 8193, is larger than that\n # and results in a mismatch between the buffering and the\n # striding for operand A.\n A = np.arange(2 * 3).reshape(2, 3).astype(np.float32)\n B = np.arange(2 * 3 * 2731).reshape(2, 3, 2731).astype(np.int16)\n es = np.einsum('cl, cpx->lpx', A, B)\n tp = np.tensordot(A, B, axes=(0, 0))\n assert_equal(es, tp)\n # The following is the original test case from the bug report,\n # made repeatable by changing random arrays to aranges.\n A = np.arange(3 * 3).reshape(3, 3).astype(np.float64)\n B = np.arange(3 * 3 * 64 * 64).reshape(3, 3, 64, 64).astype(np.float32)\n es = np.einsum('cl, cpxy->lpxy', A, B)\n tp = np.tensordot(A, B, axes=(0, 0))\n assert_equal(es, tp)\n\n def test_einsum_fixed_collapsingbug(self):\n # Issue #5147.\n # The bug only occurred when output argument of einssum was used.\n x = np.random.normal(0, 1, (5, 5, 5, 5))\n y1 = np.zeros((5, 5))\n np.einsum('aabb->ab', x, out=y1)\n idx = np.arange(5)\n y2 = x[idx[:, None], idx[:, None], idx, idx]\n assert_equal(y1, y2)\n\n def test_einsum_failed_on_p9_and_s390x(self):\n # Issues gh-14692 and gh-12689\n # Bug with signed vs unsigned char errored on power9 and s390x Linux\n tensor = np.random.random_sample((10, 10, 10, 10))\n x = np.einsum('ijij->', tensor)\n y = tensor.trace(axis1=0, axis2=2).trace()\n assert_allclose(x, y)\n\n def test_einsum_all_contig_non_contig_output(self):\n # Issue gh-5907, tests that the all contiguous special case\n # actually checks the contiguity of the output\n x = np.ones((5, 5))\n out = np.ones(10)[::2]\n correct_base = np.ones(10)\n correct_base[::2] = 5\n # Always worked (inner iteration is done with 0-stride):\n np.einsum('mi,mi,mi->m', x, x, x, out=out)\n assert_array_equal(out.base, correct_base)\n # Example 1:\n out = np.ones(10)[::2]\n np.einsum('im,im,im->m', x, x, x, out=out)\n assert_array_equal(out.base, correct_base)\n # Example 2, buffering causes x to be contiguous but\n # special cases do not catch the operation before:\n out = np.ones((2, 2, 2))[..., 0]\n correct_base = np.ones((2, 2, 2))\n correct_base[..., 0] = 2\n x = np.ones((2, 2), np.float32)\n np.einsum('ij,jk->ik', x, x, out=out)\n assert_array_equal(out.base, correct_base)\n\n @pytest.mark.parametrize("dtype",\n np.typecodes["AllFloat"] + np.typecodes["AllInteger"])\n def test_different_paths(self, dtype):\n # Test originally added to cover broken float16 path: gh-20305\n # Likely most are covered elsewhere, at least partially.\n dtype = np.dtype(dtype)\n # Simple test, designed to exercise most specialized code paths,\n # note the +0.5 for floats. This makes sure we use a float value\n # where the results must be exact.\n arr = (np.arange(7) + 0.5).astype(dtype)\n scalar = np.array(2, dtype=dtype)\n\n # contig -> scalar:\n res = np.einsum('i->', arr)\n assert res == arr.sum()\n # contig, contig -> contig:\n res = np.einsum('i,i->i', arr, arr)\n assert_array_equal(res, arr * arr)\n # noncontig, noncontig -> contig:\n res = np.einsum('i,i->i', arr.repeat(2)[::2], arr.repeat(2)[::2])\n assert_array_equal(res, arr * arr)\n # contig + contig -> scalar\n assert np.einsum('i,i->', arr, arr) == (arr * arr).sum()\n # contig + scalar -> contig (with out)\n out = np.ones(7, dtype=dtype)\n res = np.einsum('i,->i', arr, dtype.type(2), out=out)\n assert_array_equal(res, arr * dtype.type(2))\n # scalar + contig -> contig (with out)\n res = np.einsum(',i->i', scalar, arr)\n assert_array_equal(res, arr * dtype.type(2))\n # scalar + contig -> scalar\n res = np.einsum(',i->', scalar, arr)\n # Use einsum to compare to not have difference due to sum round-offs:\n assert res == np.einsum('i->', scalar * arr)\n # contig + scalar -> scalar\n res = np.einsum('i,->', arr, scalar)\n # Use einsum to compare to not have difference due to sum round-offs:\n assert res == np.einsum('i->', scalar * arr)\n # contig + contig + contig -> scalar\n arr = np.array([0.5, 0.5, 0.25, 4.5, 3.], dtype=dtype)\n res = np.einsum('i,i,i->', arr, arr, arr)\n assert_array_equal(res, (arr * arr * arr).sum())\n # four arrays:\n res = np.einsum('i,i,i,i->', arr, arr, arr, arr)\n assert_array_equal(res, (arr * arr * arr * arr).sum())\n\n def test_small_boolean_arrays(self):\n # See gh-5946.\n # Use array of True embedded in False.\n a = np.zeros((16, 1, 1), dtype=np.bool)[:2]\n a[...] = True\n out = np.zeros((16, 1, 1), dtype=np.bool)[:2]\n tgt = np.ones((2, 1, 1), dtype=np.bool)\n res = np.einsum('...ij,...jk->...ik', a, a, out=out)\n assert_equal(res, tgt)\n\n def test_out_is_res(self):\n a = np.arange(9).reshape(3, 3)\n res = np.einsum('...ij,...jk->...ik', a, a, out=a)\n assert res is a\n\n def optimize_compare(self, subscripts, operands=None):\n # Tests all paths of the optimization function against\n # conventional einsum\n if operands is None:\n args = [subscripts]\n terms = subscripts.split('->')[0].split(',')\n for term in terms:\n dims = [global_size_dict[x] for x in term]\n args.append(np.random.rand(*dims))\n else:\n args = [subscripts] + operands\n\n noopt = np.einsum(*args, optimize=False)\n opt = np.einsum(*args, optimize='greedy')\n assert_almost_equal(opt, noopt)\n opt = np.einsum(*args, optimize='optimal')\n assert_almost_equal(opt, noopt)\n\n def test_hadamard_like_products(self):\n # Hadamard outer products\n self.optimize_compare('a,ab,abc->abc')\n self.optimize_compare('a,b,ab->ab')\n\n def test_index_transformations(self):\n # Simple index transformation cases\n self.optimize_compare('ea,fb,gc,hd,abcd->efgh')\n self.optimize_compare('ea,fb,abcd,gc,hd->efgh')\n self.optimize_compare('abcd,ea,fb,gc,hd->efgh')\n\n def test_complex(self):\n # Long test cases\n self.optimize_compare('acdf,jbje,gihb,hfac,gfac,gifabc,hfac')\n self.optimize_compare('acdf,jbje,gihb,hfac,gfac,gifabc,hfac')\n self.optimize_compare('cd,bdhe,aidb,hgca,gc,hgibcd,hgac')\n self.optimize_compare('abhe,hidj,jgba,hiab,gab')\n self.optimize_compare('bde,cdh,agdb,hica,ibd,hgicd,hiac')\n self.optimize_compare('chd,bde,agbc,hiad,hgc,hgi,hiad')\n self.optimize_compare('chd,bde,agbc,hiad,bdi,cgh,agdb')\n self.optimize_compare('bdhe,acad,hiab,agac,hibd')\n\n def test_collapse(self):\n # Inner products\n self.optimize_compare('ab,ab,c->')\n self.optimize_compare('ab,ab,c->c')\n self.optimize_compare('ab,ab,cd,cd->')\n self.optimize_compare('ab,ab,cd,cd->ac')\n self.optimize_compare('ab,ab,cd,cd->cd')\n self.optimize_compare('ab,ab,cd,cd,ef,ef->')\n\n def test_expand(self):\n # Outer products\n self.optimize_compare('ab,cd,ef->abcdef')\n self.optimize_compare('ab,cd,ef->acdf')\n self.optimize_compare('ab,cd,de->abcde')\n self.optimize_compare('ab,cd,de->be')\n self.optimize_compare('ab,bcd,cd->abcd')\n self.optimize_compare('ab,bcd,cd->abd')\n\n def test_edge_cases(self):\n # Difficult edge cases for optimization\n self.optimize_compare('eb,cb,fb->cef')\n self.optimize_compare('dd,fb,be,cdb->cef')\n self.optimize_compare('bca,cdb,dbf,afc->')\n self.optimize_compare('dcc,fce,ea,dbf->ab')\n self.optimize_compare('fdf,cdd,ccd,afe->ae')\n self.optimize_compare('abcd,ad')\n self.optimize_compare('ed,fcd,ff,bcf->be')\n self.optimize_compare('baa,dcf,af,cde->be')\n self.optimize_compare('bd,db,eac->ace')\n self.optimize_compare('fff,fae,bef,def->abd')\n self.optimize_compare('efc,dbc,acf,fd->abe')\n self.optimize_compare('ba,ac,da->bcd')\n\n def test_inner_product(self):\n # Inner products\n self.optimize_compare('ab,ab')\n self.optimize_compare('ab,ba')\n self.optimize_compare('abc,abc')\n self.optimize_compare('abc,bac')\n self.optimize_compare('abc,cba')\n\n def test_random_cases(self):\n # Randomly built test cases\n self.optimize_compare('aab,fa,df,ecc->bde')\n self.optimize_compare('ecb,fef,bad,ed->ac')\n self.optimize_compare('bcf,bbb,fbf,fc->')\n self.optimize_compare('bb,ff,be->e')\n self.optimize_compare('bcb,bb,fc,fff->')\n self.optimize_compare('fbb,dfd,fc,fc->')\n self.optimize_compare('afd,ba,cc,dc->bf')\n self.optimize_compare('adb,bc,fa,cfc->d')\n self.optimize_compare('bbd,bda,fc,db->acf')\n self.optimize_compare('dba,ead,cad->bce')\n self.optimize_compare('aef,fbc,dca->bde')\n\n def test_combined_views_mapping(self):\n # gh-10792\n a = np.arange(9).reshape(1, 1, 3, 1, 3)\n b = np.einsum('bbcdc->d', a)\n assert_equal(b, [12])\n\n def test_broadcasting_dot_cases(self):\n # Ensures broadcasting cases are not mistaken for GEMM\n\n a = np.random.rand(1, 5, 4)\n b = np.random.rand(4, 6)\n c = np.random.rand(5, 6)\n d = np.random.rand(10)\n\n self.optimize_compare('ijk,kl,jl', operands=[a, b, c])\n self.optimize_compare('ijk,kl,jl,i->i', operands=[a, b, c, d])\n\n e = np.random.rand(1, 1, 5, 4)\n f = np.random.rand(7, 7)\n self.optimize_compare('abjk,kl,jl', operands=[e, b, c])\n self.optimize_compare('abjk,kl,jl,ab->ab', operands=[e, b, c, f])\n\n # Edge case found in gh-11308\n g = np.arange(64).reshape(2, 4, 8)\n self.optimize_compare('obk,ijk->ioj', operands=[g, g])\n\n def test_output_order(self):\n # Ensure output order is respected for optimize cases, the below\n # contraction should yield a reshaped tensor view\n # gh-16415\n\n a = np.ones((2, 3, 5), order='F')\n b = np.ones((4, 3), order='F')\n\n for opt in [True, False]:\n tmp = np.einsum('...ft,mf->...mt', a, b, order='a', optimize=opt)\n assert_(tmp.flags.f_contiguous)\n\n tmp = np.einsum('...ft,mf->...mt', a, b, order='f', optimize=opt)\n assert_(tmp.flags.f_contiguous)\n\n tmp = np.einsum('...ft,mf->...mt', a, b, order='c', optimize=opt)\n assert_(tmp.flags.c_contiguous)\n\n tmp = np.einsum('...ft,mf->...mt', a, b, order='k', optimize=opt)\n assert_(tmp.flags.c_contiguous is False)\n assert_(tmp.flags.f_contiguous is False)\n\n tmp = np.einsum('...ft,mf->...mt', a, b, optimize=opt)\n assert_(tmp.flags.c_contiguous is False)\n assert_(tmp.flags.f_contiguous is False)\n\n c = np.ones((4, 3), order='C')\n for opt in [True, False]:\n tmp = np.einsum('...ft,mf->...mt', a, c, order='a', optimize=opt)\n assert_(tmp.flags.c_contiguous)\n\n d = np.ones((2, 3, 5), order='C')\n for opt in [True, False]:\n tmp = np.einsum('...ft,mf->...mt', d, c, order='a', optimize=opt)\n assert_(tmp.flags.c_contiguous)\n\nclass TestEinsumPath:\n def build_operands(self, string, size_dict=global_size_dict):\n\n # Builds views based off initial operands\n operands = [string]\n terms = string.split('->')[0].split(',')\n for term in terms:\n dims = [size_dict[x] for x in term]\n operands.append(np.random.rand(*dims))\n\n return operands\n\n def assert_path_equal(self, comp, benchmark):\n # Checks if list of tuples are equivalent\n ret = (len(comp) == len(benchmark))\n assert_(ret)\n for pos in range(len(comp) - 1):\n ret &= isinstance(comp[pos + 1], tuple)\n ret &= (comp[pos + 1] == benchmark[pos + 1])\n assert_(ret)\n\n def test_memory_contraints(self):\n # Ensure memory constraints are satisfied\n\n outer_test = self.build_operands('a,b,c->abc')\n\n path, path_str = np.einsum_path(*outer_test, optimize=('greedy', 0))\n self.assert_path_equal(path, ['einsum_path', (0, 1, 2)])\n\n path, path_str = np.einsum_path(*outer_test, optimize=('optimal', 0))\n self.assert_path_equal(path, ['einsum_path', (0, 1, 2)])\n\n long_test = self.build_operands('acdf,jbje,gihb,hfac')\n path, path_str = np.einsum_path(*long_test, optimize=('greedy', 0))\n self.assert_path_equal(path, ['einsum_path', (0, 1, 2, 3)])\n\n path, path_str = np.einsum_path(*long_test, optimize=('optimal', 0))\n self.assert_path_equal(path, ['einsum_path', (0, 1, 2, 3)])\n\n def test_long_paths(self):\n # Long complex cases\n\n # Long test 1\n long_test1 = self.build_operands('acdf,jbje,gihb,hfac,gfac,gifabc,hfac')\n path, path_str = np.einsum_path(*long_test1, optimize='greedy')\n self.assert_path_equal(path, ['einsum_path',\n (3, 6), (3, 4), (2, 4), (2, 3), (0, 2), (0, 1)])\n\n path, path_str = np.einsum_path(*long_test1, optimize='optimal')\n self.assert_path_equal(path, ['einsum_path',\n (3, 6), (3, 4), (2, 4), (2, 3), (0, 2), (0, 1)])\n\n # Long test 2\n long_test2 = self.build_operands('chd,bde,agbc,hiad,bdi,cgh,agdb')\n path, path_str = np.einsum_path(*long_test2, optimize='greedy')\n self.assert_path_equal(path, ['einsum_path',\n (3, 4), (0, 3), (3, 4), (1, 3), (1, 2), (0, 1)])\n\n path, path_str = np.einsum_path(*long_test2, optimize='optimal')\n self.assert_path_equal(path, ['einsum_path',\n (0, 5), (1, 4), (3, 4), (1, 3), (1, 2), (0, 1)])\n\n def test_edge_paths(self):\n # Difficult edge cases\n\n # Edge test1\n edge_test1 = self.build_operands('eb,cb,fb->cef')\n path, path_str = np.einsum_path(*edge_test1, optimize='greedy')\n self.assert_path_equal(path, ['einsum_path', (0, 2), (0, 1)])\n\n path, path_str = np.einsum_path(*edge_test1, optimize='optimal')\n self.assert_path_equal(path, ['einsum_path', (0, 2), (0, 1)])\n\n # Edge test2\n edge_test2 = self.build_operands('dd,fb,be,cdb->cef')\n path, path_str = np.einsum_path(*edge_test2, optimize='greedy')\n self.assert_path_equal(path, ['einsum_path', (0, 3), (0, 1), (0, 1)])\n\n path, path_str = np.einsum_path(*edge_test2, optimize='optimal')\n self.assert_path_equal(path, ['einsum_path', (0, 3), (0, 1), (0, 1)])\n\n # Edge test3\n edge_test3 = self.build_operands('bca,cdb,dbf,afc->')\n path, path_str = np.einsum_path(*edge_test3, optimize='greedy')\n self.assert_path_equal(path, ['einsum_path', (1, 2), (0, 2), (0, 1)])\n\n path, path_str = np.einsum_path(*edge_test3, optimize='optimal')\n self.assert_path_equal(path, ['einsum_path', (1, 2), (0, 2), (0, 1)])\n\n # Edge test4\n edge_test4 = self.build_operands('dcc,fce,ea,dbf->ab')\n path, path_str = np.einsum_path(*edge_test4, optimize='greedy')\n self.assert_path_equal(path, ['einsum_path', (1, 2), (0, 1), (0, 1)])\n\n path, path_str = np.einsum_path(*edge_test4, optimize='optimal')\n self.assert_path_equal(path, ['einsum_path', (1, 2), (0, 2), (0, 1)])\n\n # Edge test5\n edge_test4 = self.build_operands('a,ac,ab,ad,cd,bd,bc->',\n size_dict={"a": 20, "b": 20, "c": 20, "d": 20})\n path, path_str = np.einsum_path(*edge_test4, optimize='greedy')\n self.assert_path_equal(path, ['einsum_path', (0, 1), (0, 1, 2, 3, 4, 5)])\n\n path, path_str = np.einsum_path(*edge_test4, optimize='optimal')\n self.assert_path_equal(path, ['einsum_path', (0, 1), (0, 1, 2, 3, 4, 5)])\n\n def test_path_type_input(self):\n # Test explicit path handling\n path_test = self.build_operands('dcc,fce,ea,dbf->ab')\n\n path, path_str = np.einsum_path(*path_test, optimize=False)\n self.assert_path_equal(path, ['einsum_path', (0, 1, 2, 3)])\n\n path, path_str = np.einsum_path(*path_test, optimize=True)\n self.assert_path_equal(path, ['einsum_path', (1, 2), (0, 1), (0, 1)])\n\n exp_path = ['einsum_path', (0, 2), (0, 2), (0, 1)]\n path, path_str = np.einsum_path(*path_test, optimize=exp_path)\n self.assert_path_equal(path, exp_path)\n\n # Double check einsum works on the input path\n noopt = np.einsum(*path_test, optimize=False)\n opt = np.einsum(*path_test, optimize=exp_path)\n assert_almost_equal(noopt, opt)\n\n def test_path_type_input_internal_trace(self):\n # gh-20962\n path_test = self.build_operands('cab,cdd->ab')\n exp_path = ['einsum_path', (1,), (0, 1)]\n\n path, path_str = np.einsum_path(*path_test, optimize=exp_path)\n self.assert_path_equal(path, exp_path)\n\n # Double check einsum works on the input path\n noopt = np.einsum(*path_test, optimize=False)\n opt = np.einsum(*path_test, optimize=exp_path)\n assert_almost_equal(noopt, opt)\n\n def test_path_type_input_invalid(self):\n path_test = self.build_operands('ab,bc,cd,de->ae')\n exp_path = ['einsum_path', (2, 3), (0, 1)]\n assert_raises(RuntimeError, np.einsum, *path_test, optimize=exp_path)\n assert_raises(\n RuntimeError, np.einsum_path, *path_test, optimize=exp_path)\n\n path_test = self.build_operands('a,a,a->a')\n exp_path = ['einsum_path', (1,), (0, 1)]\n assert_raises(RuntimeError, np.einsum, *path_test, optimize=exp_path)\n assert_raises(\n RuntimeError, np.einsum_path, *path_test, optimize=exp_path)\n\n def test_spaces(self):\n # gh-10794\n arr = np.array([[1]])\n for sp in itertools.product(['', ' '], repeat=4):\n # no error for any spacing\n np.einsum('{}...a{}->{}...a{}'.format(*sp), arr)\n\ndef test_overlap():\n a = np.arange(9, dtype=int).reshape(3, 3)\n b = np.arange(9, dtype=int).reshape(3, 3)\n d = np.dot(a, b)\n # sanity check\n c = np.einsum('ij,jk->ik', a, b)\n assert_equal(c, d)\n # gh-10080, out overlaps one of the operands\n c = np.einsum('ij,jk->ik', a, b, out=b)\n assert_equal(c, d)\n\ndef test_einsum_chunking_precision():\n """Most einsum operations are reductions and until NumPy 2.3 reductions\n never (or almost never?) used the `GROWINNER` mechanism to increase the\n inner loop size when no buffers are needed.\n Because einsum reductions work roughly:\n\n def inner(*inputs, out):\n accumulate = 0\n for vals in zip(*inputs):\n accumulate += prod(vals)\n out[0] += accumulate\n\n Calling the inner-loop more often actually improves accuracy slightly\n (same effect as pairwise summation but much less).\n Without adding pairwise summation to the inner-loop it seems best to just\n not use GROWINNER, a quick tests suggest that is maybe 1% slowdown for\n the simplest `einsum("i,i->i", x, x)` case.\n\n (It is not clear that we should guarantee precision to this extend.)\n """\n num = 1_000_000\n value = 1. + np.finfo(np.float64).eps * 8196\n res = np.einsum("i->", np.broadcast_to(np.array(value), num)) / num\n\n # At with GROWINNER 11 decimals succeed (larger will be less)\n assert_almost_equal(res, value, decimal=15)\n | .venv\Lib\site-packages\numpy\_core\tests\test_einsum.py | test_einsum.py | Python | 57,594 | 0.75 | 0.126803 | 0.149728 | awesome-app | 510 | 2023-12-01T11:58:00.249025 | GPL-3.0 | true | 8057d6952284735d05821fdd46739998 |
import sysconfig\n\nimport pytest\n\nimport numpy as np\nfrom numpy.testing import IS_WASM, assert_raises\n\n# The floating point emulation on ARM EABI systems lacking a hardware FPU is\n# known to be buggy. This is an attempt to identify these hosts. It may not\n# catch all possible cases, but it catches the known cases of gh-413 and\n# gh-15562.\nhosttype = sysconfig.get_config_var('HOST_GNU_TYPE')\narm_softfloat = False if hosttype is None else hosttype.endswith('gnueabi')\n\nclass TestErrstate:\n @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")\n @pytest.mark.skipif(arm_softfloat,\n reason='platform/cpu issue with FPU (gh-413,-15562)')\n def test_invalid(self):\n with np.errstate(all='raise', under='ignore'):\n a = -np.arange(3)\n # This should work\n with np.errstate(invalid='ignore'):\n np.sqrt(a)\n # While this should fail!\n with assert_raises(FloatingPointError):\n np.sqrt(a)\n\n @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")\n @pytest.mark.skipif(arm_softfloat,\n reason='platform/cpu issue with FPU (gh-15562)')\n def test_divide(self):\n with np.errstate(all='raise', under='ignore'):\n a = -np.arange(3)\n # This should work\n with np.errstate(divide='ignore'):\n a // 0\n # While this should fail!\n with assert_raises(FloatingPointError):\n a // 0\n # As should this, see gh-15562\n with assert_raises(FloatingPointError):\n a // a\n\n @pytest.mark.skipif(IS_WASM, reason="fp errors don't work in wasm")\n @pytest.mark.skipif(arm_softfloat,\n reason='platform/cpu issue with FPU (gh-15562)')\n def test_errcall(self):\n count = 0\n\n def foo(*args):\n nonlocal count\n count += 1\n\n olderrcall = np.geterrcall()\n with np.errstate(call=foo):\n assert np.geterrcall() is foo\n with np.errstate(call=None):\n assert np.geterrcall() is None\n assert np.geterrcall() is olderrcall\n assert count == 0\n\n with np.errstate(call=foo, invalid="call"):\n np.array(np.inf) - np.array(np.inf)\n\n assert count == 1\n\n def test_errstate_decorator(self):\n @np.errstate(all='ignore')\n def foo():\n a = -np.arange(3)\n a // 0\n\n foo()\n\n def test_errstate_enter_once(self):\n errstate = np.errstate(invalid="warn")\n with errstate:\n pass\n\n # The errstate context cannot be entered twice as that would not be\n # thread-safe\n with pytest.raises(TypeError,\n match="Cannot enter `np.errstate` twice"):\n with errstate:\n pass\n\n @pytest.mark.skipif(IS_WASM, reason="wasm doesn't support asyncio")\n def test_asyncio_safe(self):\n # asyncio may not always work, lets assume its fine if missing\n # Pyodide/wasm doesn't support it. If this test makes problems,\n # it should just be skipped liberally (or run differently).\n asyncio = pytest.importorskip("asyncio")\n\n @np.errstate(invalid="ignore")\n def decorated():\n # Decorated non-async function (it is not safe to decorate an\n # async one)\n assert np.geterr()["invalid"] == "ignore"\n\n async def func1():\n decorated()\n await asyncio.sleep(0.1)\n decorated()\n\n async def func2():\n with np.errstate(invalid="raise"):\n assert np.geterr()["invalid"] == "raise"\n await asyncio.sleep(0.125)\n assert np.geterr()["invalid"] == "raise"\n\n # for good sport, a third one with yet another state:\n async def func3():\n with np.errstate(invalid="print"):\n assert np.geterr()["invalid"] == "print"\n await asyncio.sleep(0.11)\n assert np.geterr()["invalid"] == "print"\n\n async def main():\n # simply run all three function multiple times:\n await asyncio.gather(\n func1(), func2(), func3(), func1(), func2(), func3(),\n func1(), func2(), func3(), func1(), func2(), func3())\n\n loop = asyncio.new_event_loop()\n with np.errstate(invalid="warn"):\n asyncio.run(main())\n assert np.geterr()["invalid"] == "warn"\n\n assert np.geterr()["invalid"] == "warn" # the default\n loop.close()\n | .venv\Lib\site-packages\numpy\_core\tests\test_errstate.py | test_errstate.py | Python | 4,758 | 0.95 | 0.152672 | 0.165138 | react-lib | 115 | 2024-05-14T14:33:01.069128 | BSD-3-Clause | true | 554bfc2847d5d13bdf1d91c6154a5f83 |
import contextlib\nimport itertools\nimport operator\n\nimport numpy._core._multiarray_tests as mt\nimport pytest\n\nimport numpy as np\nfrom numpy.testing import assert_equal, assert_raises\n\nINT64_MAX = np.iinfo(np.int64).max\nINT64_MIN = np.iinfo(np.int64).min\nINT64_MID = 2**32\n\n# int128 is not two's complement, the sign bit is separate\nINT128_MAX = 2**128 - 1\nINT128_MIN = -INT128_MAX\nINT128_MID = 2**64\n\nINT64_VALUES = (\n [INT64_MIN + j for j in range(20)] +\n [INT64_MAX - j for j in range(20)] +\n [INT64_MID + j for j in range(-20, 20)] +\n [2 * INT64_MID + j for j in range(-20, 20)] +\n [INT64_MID // 2 + j for j in range(-20, 20)] +\n list(range(-70, 70))\n)\n\nINT128_VALUES = (\n [INT128_MIN + j for j in range(20)] +\n [INT128_MAX - j for j in range(20)] +\n [INT128_MID + j for j in range(-20, 20)] +\n [2 * INT128_MID + j for j in range(-20, 20)] +\n [INT128_MID // 2 + j for j in range(-20, 20)] +\n list(range(-70, 70)) +\n [False] # negative zero\n)\n\nINT64_POS_VALUES = [x for x in INT64_VALUES if x > 0]\n\n\n@contextlib.contextmanager\ndef exc_iter(*args):\n """\n Iterate over Cartesian product of *args, and if an exception is raised,\n add information of the current iterate.\n """\n\n value = [None]\n\n def iterate():\n for v in itertools.product(*args):\n value[0] = v\n yield v\n\n try:\n yield iterate()\n except Exception:\n import traceback\n msg = f"At: {repr(value[0])!r}\n{traceback.format_exc()}"\n raise AssertionError(msg)\n\n\ndef test_safe_binop():\n # Test checked arithmetic routines\n\n ops = [\n (operator.add, 1),\n (operator.sub, 2),\n (operator.mul, 3)\n ]\n\n with exc_iter(ops, INT64_VALUES, INT64_VALUES) as it:\n for xop, a, b in it:\n pyop, op = xop\n c = pyop(a, b)\n\n if not (INT64_MIN <= c <= INT64_MAX):\n assert_raises(OverflowError, mt.extint_safe_binop, a, b, op)\n else:\n d = mt.extint_safe_binop(a, b, op)\n if c != d:\n # assert_equal is slow\n assert_equal(d, c)\n\n\ndef test_to_128():\n with exc_iter(INT64_VALUES) as it:\n for a, in it:\n b = mt.extint_to_128(a)\n if a != b:\n assert_equal(b, a)\n\n\ndef test_to_64():\n with exc_iter(INT128_VALUES) as it:\n for a, in it:\n if not (INT64_MIN <= a <= INT64_MAX):\n assert_raises(OverflowError, mt.extint_to_64, a)\n else:\n b = mt.extint_to_64(a)\n if a != b:\n assert_equal(b, a)\n\n\ndef test_mul_64_64():\n with exc_iter(INT64_VALUES, INT64_VALUES) as it:\n for a, b in it:\n c = a * b\n d = mt.extint_mul_64_64(a, b)\n if c != d:\n assert_equal(d, c)\n\n\ndef test_add_128():\n with exc_iter(INT128_VALUES, INT128_VALUES) as it:\n for a, b in it:\n c = a + b\n if not (INT128_MIN <= c <= INT128_MAX):\n assert_raises(OverflowError, mt.extint_add_128, a, b)\n else:\n d = mt.extint_add_128(a, b)\n if c != d:\n assert_equal(d, c)\n\n\ndef test_sub_128():\n with exc_iter(INT128_VALUES, INT128_VALUES) as it:\n for a, b in it:\n c = a - b\n if not (INT128_MIN <= c <= INT128_MAX):\n assert_raises(OverflowError, mt.extint_sub_128, a, b)\n else:\n d = mt.extint_sub_128(a, b)\n if c != d:\n assert_equal(d, c)\n\n\ndef test_neg_128():\n with exc_iter(INT128_VALUES) as it:\n for a, in it:\n b = -a\n c = mt.extint_neg_128(a)\n if b != c:\n assert_equal(c, b)\n\n\ndef test_shl_128():\n with exc_iter(INT128_VALUES) as it:\n for a, in it:\n if a < 0:\n b = -(((-a) << 1) & (2**128 - 1))\n else:\n b = (a << 1) & (2**128 - 1)\n c = mt.extint_shl_128(a)\n if b != c:\n assert_equal(c, b)\n\n\ndef test_shr_128():\n with exc_iter(INT128_VALUES) as it:\n for a, in it:\n if a < 0:\n b = -((-a) >> 1)\n else:\n b = a >> 1\n c = mt.extint_shr_128(a)\n if b != c:\n assert_equal(c, b)\n\n\ndef test_gt_128():\n with exc_iter(INT128_VALUES, INT128_VALUES) as it:\n for a, b in it:\n c = a > b\n d = mt.extint_gt_128(a, b)\n if c != d:\n assert_equal(d, c)\n\n\n@pytest.mark.slow\ndef test_divmod_128_64():\n with exc_iter(INT128_VALUES, INT64_POS_VALUES) as it:\n for a, b in it:\n if a >= 0:\n c, cr = divmod(a, b)\n else:\n c, cr = divmod(-a, b)\n c = -c\n cr = -cr\n\n d, dr = mt.extint_divmod_128_64(a, b)\n\n if c != d or d != dr or b * d + dr != a:\n assert_equal(d, c)\n assert_equal(dr, cr)\n assert_equal(b * d + dr, a)\n\n\ndef test_floordiv_128_64():\n with exc_iter(INT128_VALUES, INT64_POS_VALUES) as it:\n for a, b in it:\n c = a // b\n d = mt.extint_floordiv_128_64(a, b)\n\n if c != d:\n assert_equal(d, c)\n\n\ndef test_ceildiv_128_64():\n with exc_iter(INT128_VALUES, INT64_POS_VALUES) as it:\n for a, b in it:\n c = (a + b - 1) // b\n d = mt.extint_ceildiv_128_64(a, b)\n\n if c != d:\n assert_equal(d, c)\n | .venv\Lib\site-packages\numpy\_core\tests\test_extint128.py | test_extint128.py | Python | 5,842 | 0.95 | 0.290323 | 0.017442 | react-lib | 666 | 2023-10-28T14:37:27.992669 | Apache-2.0 | true | 6b9d897c30211066fa4e8929665db92f |
import platform\nimport sys\n\nimport pytest\n\nimport numpy as np\nfrom numpy import (\n arange,\n array,\n dtype,\n errstate,\n geomspace,\n isnan,\n linspace,\n logspace,\n ndarray,\n nextafter,\n sqrt,\n stack,\n)\nfrom numpy._core import sctypes\nfrom numpy._core.function_base import add_newdoc\nfrom numpy.testing import (\n IS_PYPY,\n assert_,\n assert_allclose,\n assert_array_equal,\n assert_equal,\n assert_raises,\n)\n\n\ndef _is_armhf():\n # Check if the current platform is ARMHF (32-bit ARM architecture)\n return platform.machine().startswith('arm') and platform.architecture()[0] == '32bit'\n\nclass PhysicalQuantity(float):\n def __new__(cls, value):\n return float.__new__(cls, value)\n\n def __add__(self, x):\n assert_(isinstance(x, PhysicalQuantity))\n return PhysicalQuantity(float(x) + float(self))\n __radd__ = __add__\n\n def __sub__(self, x):\n assert_(isinstance(x, PhysicalQuantity))\n return PhysicalQuantity(float(self) - float(x))\n\n def __rsub__(self, x):\n assert_(isinstance(x, PhysicalQuantity))\n return PhysicalQuantity(float(x) - float(self))\n\n def __mul__(self, x):\n return PhysicalQuantity(float(x) * float(self))\n __rmul__ = __mul__\n\n def __truediv__(self, x):\n return PhysicalQuantity(float(self) / float(x))\n\n def __rtruediv__(self, x):\n return PhysicalQuantity(float(x) / float(self))\n\n\nclass PhysicalQuantity2(ndarray):\n __array_priority__ = 10\n\n\nclass TestLogspace:\n\n def test_basic(self):\n y = logspace(0, 6)\n assert_(len(y) == 50)\n y = logspace(0, 6, num=100)\n assert_(y[-1] == 10 ** 6)\n y = logspace(0, 6, endpoint=False)\n assert_(y[-1] < 10 ** 6)\n y = logspace(0, 6, num=7)\n assert_array_equal(y, [1, 10, 100, 1e3, 1e4, 1e5, 1e6])\n\n def test_start_stop_array(self):\n start = array([0., 1.])\n stop = array([6., 7.])\n t1 = logspace(start, stop, 6)\n t2 = stack([logspace(_start, _stop, 6)\n for _start, _stop in zip(start, stop)], axis=1)\n assert_equal(t1, t2)\n t3 = logspace(start, stop[0], 6)\n t4 = stack([logspace(_start, stop[0], 6)\n for _start in start], axis=1)\n assert_equal(t3, t4)\n t5 = logspace(start, stop, 6, axis=-1)\n assert_equal(t5, t2.T)\n\n @pytest.mark.parametrize("axis", [0, 1, -1])\n def test_base_array(self, axis: int):\n start = 1\n stop = 2\n num = 6\n base = array([1, 2])\n t1 = logspace(start, stop, num=num, base=base, axis=axis)\n t2 = stack(\n [logspace(start, stop, num=num, base=_base) for _base in base],\n axis=(axis + 1) % t1.ndim,\n )\n assert_equal(t1, t2)\n\n @pytest.mark.parametrize("axis", [0, 1, -1])\n def test_stop_base_array(self, axis: int):\n start = 1\n stop = array([2, 3])\n num = 6\n base = array([1, 2])\n t1 = logspace(start, stop, num=num, base=base, axis=axis)\n t2 = stack(\n [logspace(start, _stop, num=num, base=_base)\n for _stop, _base in zip(stop, base)],\n axis=(axis + 1) % t1.ndim,\n )\n assert_equal(t1, t2)\n\n def test_dtype(self):\n y = logspace(0, 6, dtype='float32')\n assert_equal(y.dtype, dtype('float32'))\n y = logspace(0, 6, dtype='float64')\n assert_equal(y.dtype, dtype('float64'))\n y = logspace(0, 6, dtype='int32')\n assert_equal(y.dtype, dtype('int32'))\n\n def test_physical_quantities(self):\n a = PhysicalQuantity(1.0)\n b = PhysicalQuantity(5.0)\n assert_equal(logspace(a, b), logspace(1.0, 5.0))\n\n def test_subclass(self):\n a = array(1).view(PhysicalQuantity2)\n b = array(7).view(PhysicalQuantity2)\n ls = logspace(a, b)\n assert type(ls) is PhysicalQuantity2\n assert_equal(ls, logspace(1.0, 7.0))\n ls = logspace(a, b, 1)\n assert type(ls) is PhysicalQuantity2\n assert_equal(ls, logspace(1.0, 7.0, 1))\n\n\nclass TestGeomspace:\n\n def test_basic(self):\n y = geomspace(1, 1e6)\n assert_(len(y) == 50)\n y = geomspace(1, 1e6, num=100)\n assert_(y[-1] == 10 ** 6)\n y = geomspace(1, 1e6, endpoint=False)\n assert_(y[-1] < 10 ** 6)\n y = geomspace(1, 1e6, num=7)\n assert_array_equal(y, [1, 10, 100, 1e3, 1e4, 1e5, 1e6])\n\n y = geomspace(8, 2, num=3)\n assert_allclose(y, [8, 4, 2])\n assert_array_equal(y.imag, 0)\n\n y = geomspace(-1, -100, num=3)\n assert_array_equal(y, [-1, -10, -100])\n assert_array_equal(y.imag, 0)\n\n y = geomspace(-100, -1, num=3)\n assert_array_equal(y, [-100, -10, -1])\n assert_array_equal(y.imag, 0)\n\n def test_boundaries_match_start_and_stop_exactly(self):\n # make sure that the boundaries of the returned array exactly\n # equal 'start' and 'stop' - this isn't obvious because\n # np.exp(np.log(x)) isn't necessarily exactly equal to x\n start = 0.3\n stop = 20.3\n\n y = geomspace(start, stop, num=1)\n assert_equal(y[0], start)\n\n y = geomspace(start, stop, num=1, endpoint=False)\n assert_equal(y[0], start)\n\n y = geomspace(start, stop, num=3)\n assert_equal(y[0], start)\n assert_equal(y[-1], stop)\n\n y = geomspace(start, stop, num=3, endpoint=False)\n assert_equal(y[0], start)\n\n def test_nan_interior(self):\n with errstate(invalid='ignore'):\n y = geomspace(-3, 3, num=4)\n\n assert_equal(y[0], -3.0)\n assert_(isnan(y[1:-1]).all())\n assert_equal(y[3], 3.0)\n\n with errstate(invalid='ignore'):\n y = geomspace(-3, 3, num=4, endpoint=False)\n\n assert_equal(y[0], -3.0)\n assert_(isnan(y[1:]).all())\n\n def test_complex(self):\n # Purely imaginary\n y = geomspace(1j, 16j, num=5)\n assert_allclose(y, [1j, 2j, 4j, 8j, 16j])\n assert_array_equal(y.real, 0)\n\n y = geomspace(-4j, -324j, num=5)\n assert_allclose(y, [-4j, -12j, -36j, -108j, -324j])\n assert_array_equal(y.real, 0)\n\n y = geomspace(1 + 1j, 1000 + 1000j, num=4)\n assert_allclose(y, [1 + 1j, 10 + 10j, 100 + 100j, 1000 + 1000j])\n\n y = geomspace(-1 + 1j, -1000 + 1000j, num=4)\n assert_allclose(y, [-1 + 1j, -10 + 10j, -100 + 100j, -1000 + 1000j])\n\n # Logarithmic spirals\n y = geomspace(-1, 1, num=3, dtype=complex)\n assert_allclose(y, [-1, 1j, +1])\n\n y = geomspace(0 + 3j, -3 + 0j, 3)\n assert_allclose(y, [0 + 3j, -3 / sqrt(2) + 3j / sqrt(2), -3 + 0j])\n y = geomspace(0 + 3j, 3 + 0j, 3)\n assert_allclose(y, [0 + 3j, 3 / sqrt(2) + 3j / sqrt(2), 3 + 0j])\n y = geomspace(-3 + 0j, 0 - 3j, 3)\n assert_allclose(y, [-3 + 0j, -3 / sqrt(2) - 3j / sqrt(2), 0 - 3j])\n y = geomspace(0 + 3j, -3 + 0j, 3)\n assert_allclose(y, [0 + 3j, -3 / sqrt(2) + 3j / sqrt(2), -3 + 0j])\n y = geomspace(-2 - 3j, 5 + 7j, 7)\n assert_allclose(y, [-2 - 3j, -0.29058977 - 4.15771027j,\n 2.08885354 - 4.34146838j, 4.58345529 - 3.16355218j,\n 6.41401745 - 0.55233457j, 6.75707386 + 3.11795092j,\n 5 + 7j])\n\n # Type promotion should prevent the -5 from becoming a NaN\n y = geomspace(3j, -5, 2)\n assert_allclose(y, [3j, -5])\n y = geomspace(-5, 3j, 2)\n assert_allclose(y, [-5, 3j])\n\n def test_complex_shortest_path(self):\n # test the shortest logarithmic spiral is used, see gh-25644\n x = 1.2 + 3.4j\n y = np.exp(1j * (np.pi - .1)) * x\n z = np.geomspace(x, y, 5)\n expected = np.array([1.2 + 3.4j, -1.47384 + 3.2905616j,\n -3.33577588 + 1.36842949j, -3.36011056 - 1.30753855j,\n -1.53343861 - 3.26321406j])\n np.testing.assert_array_almost_equal(z, expected)\n\n def test_dtype(self):\n y = geomspace(1, 1e6, dtype='float32')\n assert_equal(y.dtype, dtype('float32'))\n y = geomspace(1, 1e6, dtype='float64')\n assert_equal(y.dtype, dtype('float64'))\n y = geomspace(1, 1e6, dtype='int32')\n assert_equal(y.dtype, dtype('int32'))\n\n # Native types\n y = geomspace(1, 1e6, dtype=float)\n assert_equal(y.dtype, dtype('float64'))\n y = geomspace(1, 1e6, dtype=complex)\n assert_equal(y.dtype, dtype('complex128'))\n\n def test_start_stop_array_scalar(self):\n lim1 = array([120, 100], dtype="int8")\n lim2 = array([-120, -100], dtype="int8")\n lim3 = array([1200, 1000], dtype="uint16")\n t1 = geomspace(lim1[0], lim1[1], 5)\n t2 = geomspace(lim2[0], lim2[1], 5)\n t3 = geomspace(lim3[0], lim3[1], 5)\n t4 = geomspace(120.0, 100.0, 5)\n t5 = geomspace(-120.0, -100.0, 5)\n t6 = geomspace(1200.0, 1000.0, 5)\n\n # t3 uses float32, t6 uses float64\n assert_allclose(t1, t4, rtol=1e-2)\n assert_allclose(t2, t5, rtol=1e-2)\n assert_allclose(t3, t6, rtol=1e-5)\n\n def test_start_stop_array(self):\n # Try to use all special cases.\n start = array([1.e0, 32., 1j, -4j, 1 + 1j, -1])\n stop = array([1.e4, 2., 16j, -324j, 10000 + 10000j, 1])\n t1 = geomspace(start, stop, 5)\n t2 = stack([geomspace(_start, _stop, 5)\n for _start, _stop in zip(start, stop)], axis=1)\n assert_equal(t1, t2)\n t3 = geomspace(start, stop[0], 5)\n t4 = stack([geomspace(_start, stop[0], 5)\n for _start in start], axis=1)\n assert_equal(t3, t4)\n t5 = geomspace(start, stop, 5, axis=-1)\n assert_equal(t5, t2.T)\n\n def test_physical_quantities(self):\n a = PhysicalQuantity(1.0)\n b = PhysicalQuantity(5.0)\n assert_equal(geomspace(a, b), geomspace(1.0, 5.0))\n\n def test_subclass(self):\n a = array(1).view(PhysicalQuantity2)\n b = array(7).view(PhysicalQuantity2)\n gs = geomspace(a, b)\n assert type(gs) is PhysicalQuantity2\n assert_equal(gs, geomspace(1.0, 7.0))\n gs = geomspace(a, b, 1)\n assert type(gs) is PhysicalQuantity2\n assert_equal(gs, geomspace(1.0, 7.0, 1))\n\n def test_bounds(self):\n assert_raises(ValueError, geomspace, 0, 10)\n assert_raises(ValueError, geomspace, 10, 0)\n assert_raises(ValueError, geomspace, 0, 0)\n\n\nclass TestLinspace:\n\n def test_basic(self):\n y = linspace(0, 10)\n assert_(len(y) == 50)\n y = linspace(2, 10, num=100)\n assert_(y[-1] == 10)\n y = linspace(2, 10, endpoint=False)\n assert_(y[-1] < 10)\n assert_raises(ValueError, linspace, 0, 10, num=-1)\n\n def test_corner(self):\n y = list(linspace(0, 1, 1))\n assert_(y == [0.0], y)\n assert_raises(TypeError, linspace, 0, 1, num=2.5)\n\n def test_type(self):\n t1 = linspace(0, 1, 0).dtype\n t2 = linspace(0, 1, 1).dtype\n t3 = linspace(0, 1, 2).dtype\n assert_equal(t1, t2)\n assert_equal(t2, t3)\n\n def test_dtype(self):\n y = linspace(0, 6, dtype='float32')\n assert_equal(y.dtype, dtype('float32'))\n y = linspace(0, 6, dtype='float64')\n assert_equal(y.dtype, dtype('float64'))\n y = linspace(0, 6, dtype='int32')\n assert_equal(y.dtype, dtype('int32'))\n\n def test_start_stop_array_scalar(self):\n lim1 = array([-120, 100], dtype="int8")\n lim2 = array([120, -100], dtype="int8")\n lim3 = array([1200, 1000], dtype="uint16")\n t1 = linspace(lim1[0], lim1[1], 5)\n t2 = linspace(lim2[0], lim2[1], 5)\n t3 = linspace(lim3[0], lim3[1], 5)\n t4 = linspace(-120.0, 100.0, 5)\n t5 = linspace(120.0, -100.0, 5)\n t6 = linspace(1200.0, 1000.0, 5)\n assert_equal(t1, t4)\n assert_equal(t2, t5)\n assert_equal(t3, t6)\n\n def test_start_stop_array(self):\n start = array([-120, 120], dtype="int8")\n stop = array([100, -100], dtype="int8")\n t1 = linspace(start, stop, 5)\n t2 = stack([linspace(_start, _stop, 5)\n for _start, _stop in zip(start, stop)], axis=1)\n assert_equal(t1, t2)\n t3 = linspace(start, stop[0], 5)\n t4 = stack([linspace(_start, stop[0], 5)\n for _start in start], axis=1)\n assert_equal(t3, t4)\n t5 = linspace(start, stop, 5, axis=-1)\n assert_equal(t5, t2.T)\n\n def test_complex(self):\n lim1 = linspace(1 + 2j, 3 + 4j, 5)\n t1 = array([1.0 + 2.j, 1.5 + 2.5j, 2.0 + 3j, 2.5 + 3.5j, 3.0 + 4j])\n lim2 = linspace(1j, 10, 5)\n t2 = array([0.0 + 1.j, 2.5 + 0.75j, 5.0 + 0.5j, 7.5 + 0.25j, 10.0 + 0j])\n assert_equal(lim1, t1)\n assert_equal(lim2, t2)\n\n def test_physical_quantities(self):\n a = PhysicalQuantity(0.0)\n b = PhysicalQuantity(1.0)\n assert_equal(linspace(a, b), linspace(0.0, 1.0))\n\n def test_subclass(self):\n a = array(0).view(PhysicalQuantity2)\n b = array(1).view(PhysicalQuantity2)\n ls = linspace(a, b)\n assert type(ls) is PhysicalQuantity2\n assert_equal(ls, linspace(0.0, 1.0))\n ls = linspace(a, b, 1)\n assert type(ls) is PhysicalQuantity2\n assert_equal(ls, linspace(0.0, 1.0, 1))\n\n def test_array_interface(self):\n # Regression test for https://github.com/numpy/numpy/pull/6659\n # Ensure that start/stop can be objects that implement\n # __array_interface__ and are convertible to numeric scalars\n\n class Arrayish:\n """\n A generic object that supports the __array_interface__ and hence\n can in principle be converted to a numeric scalar, but is not\n otherwise recognized as numeric, but also happens to support\n multiplication by floats.\n\n Data should be an object that implements the buffer interface,\n and contains at least 4 bytes.\n """\n\n def __init__(self, data):\n self._data = data\n\n @property\n def __array_interface__(self):\n return {'shape': (), 'typestr': '<i4', 'data': self._data,\n 'version': 3}\n\n def __mul__(self, other):\n # For the purposes of this test any multiplication is an\n # identity operation :)\n return self\n\n one = Arrayish(array(1, dtype='<i4'))\n five = Arrayish(array(5, dtype='<i4'))\n\n assert_equal(linspace(one, five), linspace(1, 5))\n\n # even when not explicitly enabled via FPSCR register\n @pytest.mark.xfail(_is_armhf(),\n reason="ARMHF/AArch32 platforms seem to FTZ subnormals")\n def test_denormal_numbers(self):\n # Regression test for gh-5437. Will probably fail when compiled\n # with ICC, which flushes denormals to zero\n for ftype in sctypes['float']:\n stop = nextafter(ftype(0), ftype(1)) * 5 # A denormal number\n assert_(any(linspace(0, stop, 10, endpoint=False, dtype=ftype)))\n\n def test_equivalent_to_arange(self):\n for j in range(1000):\n assert_equal(linspace(0, j, j + 1, dtype=int),\n arange(j + 1, dtype=int))\n\n def test_retstep(self):\n for num in [0, 1, 2]:\n for ept in [False, True]:\n y = linspace(0, 1, num, endpoint=ept, retstep=True)\n assert isinstance(y, tuple) and len(y) == 2\n if num == 2:\n y0_expect = [0.0, 1.0] if ept else [0.0, 0.5]\n assert_array_equal(y[0], y0_expect)\n assert_equal(y[1], y0_expect[1])\n elif num == 1 and not ept:\n assert_array_equal(y[0], [0.0])\n assert_equal(y[1], 1.0)\n else:\n assert_array_equal(y[0], [0.0][:num])\n assert isnan(y[1])\n\n def test_object(self):\n start = array(1, dtype='O')\n stop = array(2, dtype='O')\n y = linspace(start, stop, 3)\n assert_array_equal(y, array([1., 1.5, 2.]))\n\n def test_round_negative(self):\n y = linspace(-1, 3, num=8, dtype=int)\n t = array([-1, -1, 0, 0, 1, 1, 2, 3], dtype=int)\n assert_array_equal(y, t)\n\n def test_any_step_zero_and_not_mult_inplace(self):\n # any_step_zero is True, _mult_inplace is False\n start = array([0.0, 1.0])\n stop = array([2.0, 1.0])\n y = linspace(start, stop, 3)\n assert_array_equal(y, array([[0.0, 1.0], [1.0, 1.0], [2.0, 1.0]]))\n\n\nclass TestAdd_newdoc:\n\n @pytest.mark.skipif(sys.flags.optimize == 2, reason="Python running -OO")\n @pytest.mark.xfail(IS_PYPY, reason="PyPy does not modify tp_doc")\n def test_add_doc(self):\n # test that np.add_newdoc did attach a docstring successfully:\n tgt = "Current flat index into the array."\n assert_equal(np._core.flatiter.index.__doc__[:len(tgt)], tgt)\n assert_(len(np._core.ufunc.identity.__doc__) > 300)\n assert_(len(np.lib._index_tricks_impl.mgrid.__doc__) > 300)\n\n @pytest.mark.skipif(sys.flags.optimize == 2, reason="Python running -OO")\n def test_errors_are_ignored(self):\n prev_doc = np._core.flatiter.index.__doc__\n # nothing changed, but error ignored, this should probably\n # give a warning (or even error) in the future.\n add_newdoc("numpy._core", "flatiter", ("index", "bad docstring"))\n assert prev_doc == np._core.flatiter.index.__doc__\n | .venv\Lib\site-packages\numpy\_core\tests\test_function_base.py | test_function_base.py | Python | 18,154 | 0.95 | 0.141153 | 0.054632 | vue-tools | 729 | 2025-06-27T14:57:12.913987 | BSD-3-Clause | true | 82c5589829015d8c4d4785c69dfe4d12 |
""" Test functions for limits module.\n\n"""\nimport types\nimport warnings\n\nimport pytest\n\nimport numpy as np\nfrom numpy import double, half, longdouble, single\nfrom numpy._core import finfo, iinfo\nfrom numpy._core.getlimits import _discovered_machar, _float_ma\nfrom numpy.testing import assert_, assert_equal, assert_raises\n\n##################################################\n\nclass TestPythonFloat:\n def test_singleton(self):\n ftype = finfo(float)\n ftype2 = finfo(float)\n assert_equal(id(ftype), id(ftype2))\n\nclass TestHalf:\n def test_singleton(self):\n ftype = finfo(half)\n ftype2 = finfo(half)\n assert_equal(id(ftype), id(ftype2))\n\nclass TestSingle:\n def test_singleton(self):\n ftype = finfo(single)\n ftype2 = finfo(single)\n assert_equal(id(ftype), id(ftype2))\n\nclass TestDouble:\n def test_singleton(self):\n ftype = finfo(double)\n ftype2 = finfo(double)\n assert_equal(id(ftype), id(ftype2))\n\nclass TestLongdouble:\n def test_singleton(self):\n ftype = finfo(longdouble)\n ftype2 = finfo(longdouble)\n assert_equal(id(ftype), id(ftype2))\n\ndef assert_finfo_equal(f1, f2):\n # assert two finfo instances have the same attributes\n for attr in ('bits', 'eps', 'epsneg', 'iexp', 'machep',\n 'max', 'maxexp', 'min', 'minexp', 'negep', 'nexp',\n 'nmant', 'precision', 'resolution', 'tiny',\n 'smallest_normal', 'smallest_subnormal'):\n assert_equal(getattr(f1, attr), getattr(f2, attr),\n f'finfo instances {f1} and {f2} differ on {attr}')\n\ndef assert_iinfo_equal(i1, i2):\n # assert two iinfo instances have the same attributes\n for attr in ('bits', 'min', 'max'):\n assert_equal(getattr(i1, attr), getattr(i2, attr),\n f'iinfo instances {i1} and {i2} differ on {attr}')\n\nclass TestFinfo:\n def test_basic(self):\n dts = list(zip(['f2', 'f4', 'f8', 'c8', 'c16'],\n [np.float16, np.float32, np.float64, np.complex64,\n np.complex128]))\n for dt1, dt2 in dts:\n assert_finfo_equal(finfo(dt1), finfo(dt2))\n\n assert_raises(ValueError, finfo, 'i4')\n\n def test_regression_gh23108(self):\n # np.float32(1.0) and np.float64(1.0) have the same hash and are\n # equal under the == operator\n f1 = np.finfo(np.float32(1.0))\n f2 = np.finfo(np.float64(1.0))\n assert f1 != f2\n\n def test_regression_gh23867(self):\n class NonHashableWithDtype:\n __hash__ = None\n dtype = np.dtype('float32')\n\n x = NonHashableWithDtype()\n assert np.finfo(x) == np.finfo(x.dtype)\n\n\nclass TestIinfo:\n def test_basic(self):\n dts = list(zip(['i1', 'i2', 'i4', 'i8',\n 'u1', 'u2', 'u4', 'u8'],\n [np.int8, np.int16, np.int32, np.int64,\n np.uint8, np.uint16, np.uint32, np.uint64]))\n for dt1, dt2 in dts:\n assert_iinfo_equal(iinfo(dt1), iinfo(dt2))\n\n assert_raises(ValueError, iinfo, 'f4')\n\n def test_unsigned_max(self):\n types = np._core.sctypes['uint']\n for T in types:\n with np.errstate(over="ignore"):\n max_calculated = T(0) - T(1)\n assert_equal(iinfo(T).max, max_calculated)\n\nclass TestRepr:\n def test_iinfo_repr(self):\n expected = "iinfo(min=-32768, max=32767, dtype=int16)"\n assert_equal(repr(np.iinfo(np.int16)), expected)\n\n def test_finfo_repr(self):\n expected = "finfo(resolution=1e-06, min=-3.4028235e+38,"\\n " max=3.4028235e+38, dtype=float32)"\n assert_equal(repr(np.finfo(np.float32)), expected)\n\n\ndef test_instances():\n # Test the finfo and iinfo results on numeric instances agree with\n # the results on the corresponding types\n\n for c in [int, np.int16, np.int32, np.int64]:\n class_iinfo = iinfo(c)\n instance_iinfo = iinfo(c(12))\n\n assert_iinfo_equal(class_iinfo, instance_iinfo)\n\n for c in [float, np.float16, np.float32, np.float64]:\n class_finfo = finfo(c)\n instance_finfo = finfo(c(1.2))\n assert_finfo_equal(class_finfo, instance_finfo)\n\n with pytest.raises(ValueError):\n iinfo(10.)\n\n with pytest.raises(ValueError):\n iinfo('hi')\n\n with pytest.raises(ValueError):\n finfo(np.int64(1))\n\n\ndef assert_ma_equal(discovered, ma_like):\n # Check MachAr-like objects same as calculated MachAr instances\n for key, value in discovered.__dict__.items():\n assert_equal(value, getattr(ma_like, key))\n if hasattr(value, 'shape'):\n assert_equal(value.shape, getattr(ma_like, key).shape)\n assert_equal(value.dtype, getattr(ma_like, key).dtype)\n\n\ndef test_known_types():\n # Test we are correctly compiling parameters for known types\n for ftype, ma_like in ((np.float16, _float_ma[16]),\n (np.float32, _float_ma[32]),\n (np.float64, _float_ma[64])):\n assert_ma_equal(_discovered_machar(ftype), ma_like)\n # Suppress warning for broken discovery of double double on PPC\n with np.errstate(all='ignore'):\n ld_ma = _discovered_machar(np.longdouble)\n bytes = np.dtype(np.longdouble).itemsize\n if (ld_ma.it, ld_ma.maxexp) == (63, 16384) and bytes in (12, 16):\n # 80-bit extended precision\n assert_ma_equal(ld_ma, _float_ma[80])\n elif (ld_ma.it, ld_ma.maxexp) == (112, 16384) and bytes == 16:\n # IEE 754 128-bit\n assert_ma_equal(ld_ma, _float_ma[128])\n\n\ndef test_subnormal_warning():\n """Test that the subnormal is zero warning is not being raised."""\n with np.errstate(all='ignore'):\n ld_ma = _discovered_machar(np.longdouble)\n bytes = np.dtype(np.longdouble).itemsize\n with warnings.catch_warnings(record=True) as w:\n warnings.simplefilter('always')\n if (ld_ma.it, ld_ma.maxexp) == (63, 16384) and bytes in (12, 16):\n # 80-bit extended precision\n ld_ma.smallest_subnormal\n assert len(w) == 0\n elif (ld_ma.it, ld_ma.maxexp) == (112, 16384) and bytes == 16:\n # IEE 754 128-bit\n ld_ma.smallest_subnormal\n assert len(w) == 0\n else:\n # Double double\n ld_ma.smallest_subnormal\n # This test may fail on some platforms\n assert len(w) == 0\n\n\ndef test_plausible_finfo():\n # Assert that finfo returns reasonable results for all types\n for ftype in np._core.sctypes['float'] + np._core.sctypes['complex']:\n info = np.finfo(ftype)\n assert_(info.nmant > 1)\n assert_(info.minexp < -1)\n assert_(info.maxexp > 1)\n\n\nclass TestRuntimeSubscriptable:\n def test_finfo_generic(self):\n assert isinstance(np.finfo[np.float64], types.GenericAlias)\n\n def test_iinfo_generic(self):\n assert isinstance(np.iinfo[np.int_], types.GenericAlias)\n | .venv\Lib\site-packages\numpy\_core\tests\test_getlimits.py | test_getlimits.py | Python | 7,181 | 0.95 | 0.234146 | 0.103659 | vue-tools | 744 | 2024-10-22T06:30:57.415176 | BSD-3-Clause | true | b698d5535609562fa2b9ed481c3e7344 |
import platform\n\nimport pytest\n\nimport numpy as np\nfrom numpy import float16, float32, float64, uint16\nfrom numpy.testing import IS_WASM, assert_, assert_equal\n\n\ndef assert_raises_fpe(strmatch, callable, *args, **kwargs):\n try:\n callable(*args, **kwargs)\n except FloatingPointError as exc:\n assert_(str(exc).find(strmatch) >= 0,\n f"Did not raise floating point {strmatch} error")\n else:\n assert_(False,\n f"Did not raise floating point {strmatch} error")\n\nclass TestHalf:\n def setup_method(self):\n # An array of all possible float16 values\n self.all_f16 = np.arange(0x10000, dtype=uint16)\n self.all_f16.dtype = float16\n\n # NaN value can cause an invalid FP exception if HW is being used\n with np.errstate(invalid='ignore'):\n self.all_f32 = np.array(self.all_f16, dtype=float32)\n self.all_f64 = np.array(self.all_f16, dtype=float64)\n\n # An array of all non-NaN float16 values, in sorted order\n self.nonan_f16 = np.concatenate(\n (np.arange(0xfc00, 0x7fff, -1, dtype=uint16),\n np.arange(0x0000, 0x7c01, 1, dtype=uint16)))\n self.nonan_f16.dtype = float16\n self.nonan_f32 = np.array(self.nonan_f16, dtype=float32)\n self.nonan_f64 = np.array(self.nonan_f16, dtype=float64)\n\n # An array of all finite float16 values, in sorted order\n self.finite_f16 = self.nonan_f16[1:-1]\n self.finite_f32 = self.nonan_f32[1:-1]\n self.finite_f64 = self.nonan_f64[1:-1]\n\n def test_half_conversions(self):\n """Checks that all 16-bit values survive conversion\n to/from 32-bit and 64-bit float"""\n # Because the underlying routines preserve the NaN bits, every\n # value is preserved when converting to/from other floats.\n\n # Convert from float32 back to float16\n with np.errstate(invalid='ignore'):\n b = np.array(self.all_f32, dtype=float16)\n # avoid testing NaNs due to differing bit patterns in Q/S NaNs\n b_nn = b == b\n assert_equal(self.all_f16[b_nn].view(dtype=uint16),\n b[b_nn].view(dtype=uint16))\n\n # Convert from float64 back to float16\n with np.errstate(invalid='ignore'):\n b = np.array(self.all_f64, dtype=float16)\n b_nn = b == b\n assert_equal(self.all_f16[b_nn].view(dtype=uint16),\n b[b_nn].view(dtype=uint16))\n\n # Convert float16 to longdouble and back\n # This doesn't necessarily preserve the extra NaN bits,\n # so exclude NaNs.\n a_ld = np.array(self.nonan_f16, dtype=np.longdouble)\n b = np.array(a_ld, dtype=float16)\n assert_equal(self.nonan_f16.view(dtype=uint16),\n b.view(dtype=uint16))\n\n # Check the range for which all integers can be represented\n i_int = np.arange(-2048, 2049)\n i_f16 = np.array(i_int, dtype=float16)\n j = np.array(i_f16, dtype=int)\n assert_equal(i_int, j)\n\n @pytest.mark.parametrize("string_dt", ["S", "U"])\n def test_half_conversion_to_string(self, string_dt):\n # Currently uses S/U32 (which is sufficient for float32)\n expected_dt = np.dtype(f"{string_dt}32")\n assert np.promote_types(np.float16, string_dt) == expected_dt\n assert np.promote_types(string_dt, np.float16) == expected_dt\n\n arr = np.ones(3, dtype=np.float16).astype(string_dt)\n assert arr.dtype == expected_dt\n\n @pytest.mark.parametrize("string_dt", ["S", "U"])\n def test_half_conversion_from_string(self, string_dt):\n string = np.array("3.1416", dtype=string_dt)\n assert string.astype(np.float16) == np.array(3.1416, dtype=np.float16)\n\n @pytest.mark.parametrize("offset", [None, "up", "down"])\n @pytest.mark.parametrize("shift", [None, "up", "down"])\n @pytest.mark.parametrize("float_t", [np.float32, np.float64])\n def test_half_conversion_rounding(self, float_t, shift, offset):\n # Assumes that round to even is used during casting.\n max_pattern = np.float16(np.finfo(np.float16).max).view(np.uint16)\n\n # Test all (positive) finite numbers, denormals are most interesting\n # however:\n f16s_patterns = np.arange(0, max_pattern + 1, dtype=np.uint16)\n f16s_float = f16s_patterns.view(np.float16).astype(float_t)\n\n # Shift the values by half a bit up or a down (or do not shift),\n if shift == "up":\n f16s_float = 0.5 * (f16s_float[:-1] + f16s_float[1:])[1:]\n elif shift == "down":\n f16s_float = 0.5 * (f16s_float[:-1] + f16s_float[1:])[:-1]\n else:\n f16s_float = f16s_float[1:-1]\n\n # Increase the float by a minimal value:\n if offset == "up":\n f16s_float = np.nextafter(f16s_float, float_t(np.inf))\n elif offset == "down":\n f16s_float = np.nextafter(f16s_float, float_t(-np.inf))\n\n # Convert back to float16 and its bit pattern:\n res_patterns = f16s_float.astype(np.float16).view(np.uint16)\n\n # The above calculation tries the original values, or the exact\n # midpoints between the float16 values. It then further offsets them\n # by as little as possible. If no offset occurs, "round to even"\n # logic will be necessary, an arbitrarily small offset should cause\n # normal up/down rounding always.\n\n # Calculate the expected pattern:\n cmp_patterns = f16s_patterns[1:-1].copy()\n\n if shift == "down" and offset != "up":\n shift_pattern = -1\n elif shift == "up" and offset != "down":\n shift_pattern = 1\n else:\n # There cannot be a shift, either shift is None, so all rounding\n # will go back to original, or shift is reduced by offset too much.\n shift_pattern = 0\n\n # If rounding occurs, is it normal rounding or round to even?\n if offset is None:\n # Round to even occurs, modify only non-even, cast to allow + (-1)\n cmp_patterns[0::2].view(np.int16)[...] += shift_pattern\n else:\n cmp_patterns.view(np.int16)[...] += shift_pattern\n\n assert_equal(res_patterns, cmp_patterns)\n\n @pytest.mark.parametrize(["float_t", "uint_t", "bits"],\n [(np.float32, np.uint32, 23),\n (np.float64, np.uint64, 52)])\n def test_half_conversion_denormal_round_even(self, float_t, uint_t, bits):\n # Test specifically that all bits are considered when deciding\n # whether round to even should occur (i.e. no bits are lost at the\n # end. Compare also gh-12721. The most bits can get lost for the\n # smallest denormal:\n smallest_value = np.uint16(1).view(np.float16).astype(float_t)\n assert smallest_value == 2**-24\n\n # Will be rounded to zero based on round to even rule:\n rounded_to_zero = smallest_value / float_t(2)\n assert rounded_to_zero.astype(np.float16) == 0\n\n # The significand will be all 0 for the float_t, test that we do not\n # lose the lower ones of these:\n for i in range(bits):\n # slightly increasing the value should make it round up:\n larger_pattern = rounded_to_zero.view(uint_t) | uint_t(1 << i)\n larger_value = larger_pattern.view(float_t)\n assert larger_value.astype(np.float16) == smallest_value\n\n def test_nans_infs(self):\n with np.errstate(all='ignore'):\n # Check some of the ufuncs\n assert_equal(np.isnan(self.all_f16), np.isnan(self.all_f32))\n assert_equal(np.isinf(self.all_f16), np.isinf(self.all_f32))\n assert_equal(np.isfinite(self.all_f16), np.isfinite(self.all_f32))\n assert_equal(np.signbit(self.all_f16), np.signbit(self.all_f32))\n assert_equal(np.spacing(float16(65504)), np.inf)\n\n # Check comparisons of all values with NaN\n nan = float16(np.nan)\n\n assert_(not (self.all_f16 == nan).any())\n assert_(not (nan == self.all_f16).any())\n\n assert_((self.all_f16 != nan).all())\n assert_((nan != self.all_f16).all())\n\n assert_(not (self.all_f16 < nan).any())\n assert_(not (nan < self.all_f16).any())\n\n assert_(not (self.all_f16 <= nan).any())\n assert_(not (nan <= self.all_f16).any())\n\n assert_(not (self.all_f16 > nan).any())\n assert_(not (nan > self.all_f16).any())\n\n assert_(not (self.all_f16 >= nan).any())\n assert_(not (nan >= self.all_f16).any())\n\n def test_half_values(self):\n """Confirms a small number of known half values"""\n a = np.array([1.0, -1.0,\n 2.0, -2.0,\n 0.0999755859375, 0.333251953125, # 1/10, 1/3\n 65504, -65504, # Maximum magnitude\n 2.0**(-14), -2.0**(-14), # Minimum normal\n 2.0**(-24), -2.0**(-24), # Minimum subnormal\n 0, -1 / 1e1000, # Signed zeros\n np.inf, -np.inf])\n b = np.array([0x3c00, 0xbc00,\n 0x4000, 0xc000,\n 0x2e66, 0x3555,\n 0x7bff, 0xfbff,\n 0x0400, 0x8400,\n 0x0001, 0x8001,\n 0x0000, 0x8000,\n 0x7c00, 0xfc00], dtype=uint16)\n b.dtype = float16\n assert_equal(a, b)\n\n def test_half_rounding(self):\n """Checks that rounding when converting to half is correct"""\n a = np.array([2.0**-25 + 2.0**-35, # Rounds to minimum subnormal\n 2.0**-25, # Underflows to zero (nearest even mode)\n 2.0**-26, # Underflows to zero\n 1.0 + 2.0**-11 + 2.0**-16, # rounds to 1.0+2**(-10)\n 1.0 + 2.0**-11, # rounds to 1.0 (nearest even mode)\n 1.0 + 2.0**-12, # rounds to 1.0\n 65519, # rounds to 65504\n 65520], # rounds to inf\n dtype=float64)\n rounded = [2.0**-24,\n 0.0,\n 0.0,\n 1.0 + 2.0**(-10),\n 1.0,\n 1.0,\n 65504,\n np.inf]\n\n # Check float64->float16 rounding\n with np.errstate(over="ignore"):\n b = np.array(a, dtype=float16)\n assert_equal(b, rounded)\n\n # Check float32->float16 rounding\n a = np.array(a, dtype=float32)\n with np.errstate(over="ignore"):\n b = np.array(a, dtype=float16)\n assert_equal(b, rounded)\n\n def test_half_correctness(self):\n """Take every finite float16, and check the casting functions with\n a manual conversion."""\n\n # Create an array of all finite float16s\n a_bits = self.finite_f16.view(dtype=uint16)\n\n # Convert to 64-bit float manually\n a_sgn = (-1.0)**((a_bits & 0x8000) >> 15)\n a_exp = np.array((a_bits & 0x7c00) >> 10, dtype=np.int32) - 15\n a_man = (a_bits & 0x03ff) * 2.0**(-10)\n # Implicit bit of normalized floats\n a_man[a_exp != -15] += 1\n # Denormalized exponent is -14\n a_exp[a_exp == -15] = -14\n\n a_manual = a_sgn * a_man * 2.0**a_exp\n\n a32_fail = np.nonzero(self.finite_f32 != a_manual)[0]\n if len(a32_fail) != 0:\n bad_index = a32_fail[0]\n assert_equal(self.finite_f32, a_manual,\n "First non-equal is half value 0x%x -> %g != %g" %\n (a_bits[bad_index],\n self.finite_f32[bad_index],\n a_manual[bad_index]))\n\n a64_fail = np.nonzero(self.finite_f64 != a_manual)[0]\n if len(a64_fail) != 0:\n bad_index = a64_fail[0]\n assert_equal(self.finite_f64, a_manual,\n "First non-equal is half value 0x%x -> %g != %g" %\n (a_bits[bad_index],\n self.finite_f64[bad_index],\n a_manual[bad_index]))\n\n def test_half_ordering(self):\n """Make sure comparisons are working right"""\n\n # All non-NaN float16 values in reverse order\n a = self.nonan_f16[::-1].copy()\n\n # 32-bit float copy\n b = np.array(a, dtype=float32)\n\n # Should sort the same\n a.sort()\n b.sort()\n assert_equal(a, b)\n\n # Comparisons should work\n assert_((a[:-1] <= a[1:]).all())\n assert_(not (a[:-1] > a[1:]).any())\n assert_((a[1:] >= a[:-1]).all())\n assert_(not (a[1:] < a[:-1]).any())\n # All != except for +/-0\n assert_equal(np.nonzero(a[:-1] < a[1:])[0].size, a.size - 2)\n assert_equal(np.nonzero(a[1:] > a[:-1])[0].size, a.size - 2)\n\n def test_half_funcs(self):\n """Test the various ArrFuncs"""\n\n # fill\n assert_equal(np.arange(10, dtype=float16),\n np.arange(10, dtype=float32))\n\n # fillwithscalar\n a = np.zeros((5,), dtype=float16)\n a.fill(1)\n assert_equal(a, np.ones((5,), dtype=float16))\n\n # nonzero and copyswap\n a = np.array([0, 0, -1, -1 / 1e20, 0, 2.0**-24, 7.629e-6], dtype=float16)\n assert_equal(a.nonzero()[0],\n [2, 5, 6])\n a = a.byteswap()\n a = a.view(a.dtype.newbyteorder())\n assert_equal(a.nonzero()[0],\n [2, 5, 6])\n\n # dot\n a = np.arange(0, 10, 0.5, dtype=float16)\n b = np.ones((20,), dtype=float16)\n assert_equal(np.dot(a, b),\n 95)\n\n # argmax\n a = np.array([0, -np.inf, -2, 0.5, 12.55, 7.3, 2.1, 12.4], dtype=float16)\n assert_equal(a.argmax(),\n 4)\n a = np.array([0, -np.inf, -2, np.inf, 12.55, np.nan, 2.1, 12.4], dtype=float16)\n assert_equal(a.argmax(),\n 5)\n\n # getitem\n a = np.arange(10, dtype=float16)\n for i in range(10):\n assert_equal(a.item(i), i)\n\n def test_spacing_nextafter(self):\n """Test np.spacing and np.nextafter"""\n # All non-negative finite #'s\n a = np.arange(0x7c00, dtype=uint16)\n hinf = np.array((np.inf,), dtype=float16)\n hnan = np.array((np.nan,), dtype=float16)\n a_f16 = a.view(dtype=float16)\n\n assert_equal(np.spacing(a_f16[:-1]), a_f16[1:] - a_f16[:-1])\n\n assert_equal(np.nextafter(a_f16[:-1], hinf), a_f16[1:])\n assert_equal(np.nextafter(a_f16[0], -hinf), -a_f16[1])\n assert_equal(np.nextafter(a_f16[1:], -hinf), a_f16[:-1])\n\n assert_equal(np.nextafter(hinf, a_f16), a_f16[-1])\n assert_equal(np.nextafter(-hinf, a_f16), -a_f16[-1])\n\n assert_equal(np.nextafter(hinf, hinf), hinf)\n assert_equal(np.nextafter(hinf, -hinf), a_f16[-1])\n assert_equal(np.nextafter(-hinf, hinf), -a_f16[-1])\n assert_equal(np.nextafter(-hinf, -hinf), -hinf)\n\n assert_equal(np.nextafter(a_f16, hnan), hnan[0])\n assert_equal(np.nextafter(hnan, a_f16), hnan[0])\n\n assert_equal(np.nextafter(hnan, hnan), hnan)\n assert_equal(np.nextafter(hinf, hnan), hnan)\n assert_equal(np.nextafter(hnan, hinf), hnan)\n\n # switch to negatives\n a |= 0x8000\n\n assert_equal(np.spacing(a_f16[0]), np.spacing(a_f16[1]))\n assert_equal(np.spacing(a_f16[1:]), a_f16[:-1] - a_f16[1:])\n\n assert_equal(np.nextafter(a_f16[0], hinf), -a_f16[1])\n assert_equal(np.nextafter(a_f16[1:], hinf), a_f16[:-1])\n assert_equal(np.nextafter(a_f16[:-1], -hinf), a_f16[1:])\n\n assert_equal(np.nextafter(hinf, a_f16), -a_f16[-1])\n assert_equal(np.nextafter(-hinf, a_f16), a_f16[-1])\n\n assert_equal(np.nextafter(a_f16, hnan), hnan[0])\n assert_equal(np.nextafter(hnan, a_f16), hnan[0])\n\n def test_half_ufuncs(self):\n """Test the various ufuncs"""\n\n a = np.array([0, 1, 2, 4, 2], dtype=float16)\n b = np.array([-2, 5, 1, 4, 3], dtype=float16)\n c = np.array([0, -1, -np.inf, np.nan, 6], dtype=float16)\n\n assert_equal(np.add(a, b), [-2, 6, 3, 8, 5])\n assert_equal(np.subtract(a, b), [2, -4, 1, 0, -1])\n assert_equal(np.multiply(a, b), [0, 5, 2, 16, 6])\n assert_equal(np.divide(a, b), [0, 0.199951171875, 2, 1, 0.66650390625])\n\n assert_equal(np.equal(a, b), [False, False, False, True, False])\n assert_equal(np.not_equal(a, b), [True, True, True, False, True])\n assert_equal(np.less(a, b), [False, True, False, False, True])\n assert_equal(np.less_equal(a, b), [False, True, False, True, True])\n assert_equal(np.greater(a, b), [True, False, True, False, False])\n assert_equal(np.greater_equal(a, b), [True, False, True, True, False])\n assert_equal(np.logical_and(a, b), [False, True, True, True, True])\n assert_equal(np.logical_or(a, b), [True, True, True, True, True])\n assert_equal(np.logical_xor(a, b), [True, False, False, False, False])\n assert_equal(np.logical_not(a), [True, False, False, False, False])\n\n assert_equal(np.isnan(c), [False, False, False, True, False])\n assert_equal(np.isinf(c), [False, False, True, False, False])\n assert_equal(np.isfinite(c), [True, True, False, False, True])\n assert_equal(np.signbit(b), [True, False, False, False, False])\n\n assert_equal(np.copysign(b, a), [2, 5, 1, 4, 3])\n\n assert_equal(np.maximum(a, b), [0, 5, 2, 4, 3])\n\n x = np.maximum(b, c)\n assert_(np.isnan(x[3]))\n x[3] = 0\n assert_equal(x, [0, 5, 1, 0, 6])\n\n assert_equal(np.minimum(a, b), [-2, 1, 1, 4, 2])\n\n x = np.minimum(b, c)\n assert_(np.isnan(x[3]))\n x[3] = 0\n assert_equal(x, [-2, -1, -np.inf, 0, 3])\n\n assert_equal(np.fmax(a, b), [0, 5, 2, 4, 3])\n assert_equal(np.fmax(b, c), [0, 5, 1, 4, 6])\n assert_equal(np.fmin(a, b), [-2, 1, 1, 4, 2])\n assert_equal(np.fmin(b, c), [-2, -1, -np.inf, 4, 3])\n\n assert_equal(np.floor_divide(a, b), [0, 0, 2, 1, 0])\n assert_equal(np.remainder(a, b), [0, 1, 0, 0, 2])\n assert_equal(np.divmod(a, b), ([0, 0, 2, 1, 0], [0, 1, 0, 0, 2]))\n assert_equal(np.square(b), [4, 25, 1, 16, 9])\n assert_equal(np.reciprocal(b), [-0.5, 0.199951171875, 1, 0.25, 0.333251953125])\n assert_equal(np.ones_like(b), [1, 1, 1, 1, 1])\n assert_equal(np.conjugate(b), b)\n assert_equal(np.absolute(b), [2, 5, 1, 4, 3])\n assert_equal(np.negative(b), [2, -5, -1, -4, -3])\n assert_equal(np.positive(b), b)\n assert_equal(np.sign(b), [-1, 1, 1, 1, 1])\n assert_equal(np.modf(b), ([0, 0, 0, 0, 0], b))\n assert_equal(np.frexp(b), ([-0.5, 0.625, 0.5, 0.5, 0.75], [2, 3, 1, 3, 2]))\n assert_equal(np.ldexp(b, [0, 1, 2, 4, 2]), [-2, 10, 4, 64, 12])\n\n def test_half_coercion(self):\n """Test that half gets coerced properly with the other types"""\n a16 = np.array((1,), dtype=float16)\n a32 = np.array((1,), dtype=float32)\n b16 = float16(1)\n b32 = float32(1)\n\n assert np.power(a16, 2).dtype == float16\n assert np.power(a16, 2.0).dtype == float16\n assert np.power(a16, b16).dtype == float16\n assert np.power(a16, b32).dtype == float32\n assert np.power(a16, a16).dtype == float16\n assert np.power(a16, a32).dtype == float32\n\n assert np.power(b16, 2).dtype == float16\n assert np.power(b16, 2.0).dtype == float16\n assert np.power(b16, b16).dtype, float16\n assert np.power(b16, b32).dtype, float32\n assert np.power(b16, a16).dtype, float16\n assert np.power(b16, a32).dtype, float32\n\n assert np.power(a32, a16).dtype == float32\n assert np.power(a32, b16).dtype == float32\n assert np.power(b32, a16).dtype == float32\n assert np.power(b32, b16).dtype == float32\n\n @pytest.mark.skipif(platform.machine() == "armv5tel",\n reason="See gh-413.")\n @pytest.mark.skipif(IS_WASM,\n reason="fp exceptions don't work in wasm.")\n def test_half_fpe(self):\n with np.errstate(all='raise'):\n sx16 = np.array((1e-4,), dtype=float16)\n bx16 = np.array((1e4,), dtype=float16)\n sy16 = float16(1e-4)\n by16 = float16(1e4)\n\n # Underflow errors\n assert_raises_fpe('underflow', lambda a, b: a * b, sx16, sx16)\n assert_raises_fpe('underflow', lambda a, b: a * b, sx16, sy16)\n assert_raises_fpe('underflow', lambda a, b: a * b, sy16, sx16)\n assert_raises_fpe('underflow', lambda a, b: a * b, sy16, sy16)\n assert_raises_fpe('underflow', lambda a, b: a / b, sx16, bx16)\n assert_raises_fpe('underflow', lambda a, b: a / b, sx16, by16)\n assert_raises_fpe('underflow', lambda a, b: a / b, sy16, bx16)\n assert_raises_fpe('underflow', lambda a, b: a / b, sy16, by16)\n assert_raises_fpe('underflow', lambda a, b: a / b,\n float16(2.**-14), float16(2**11))\n assert_raises_fpe('underflow', lambda a, b: a / b,\n float16(-2.**-14), float16(2**11))\n assert_raises_fpe('underflow', lambda a, b: a / b,\n float16(2.**-14 + 2**-24), float16(2))\n assert_raises_fpe('underflow', lambda a, b: a / b,\n float16(-2.**-14 - 2**-24), float16(2))\n assert_raises_fpe('underflow', lambda a, b: a / b,\n float16(2.**-14 + 2**-23), float16(4))\n\n # Overflow errors\n assert_raises_fpe('overflow', lambda a, b: a * b, bx16, bx16)\n assert_raises_fpe('overflow', lambda a, b: a * b, bx16, by16)\n assert_raises_fpe('overflow', lambda a, b: a * b, by16, bx16)\n assert_raises_fpe('overflow', lambda a, b: a * b, by16, by16)\n assert_raises_fpe('overflow', lambda a, b: a / b, bx16, sx16)\n assert_raises_fpe('overflow', lambda a, b: a / b, bx16, sy16)\n assert_raises_fpe('overflow', lambda a, b: a / b, by16, sx16)\n assert_raises_fpe('overflow', lambda a, b: a / b, by16, sy16)\n assert_raises_fpe('overflow', lambda a, b: a + b,\n float16(65504), float16(17))\n assert_raises_fpe('overflow', lambda a, b: a - b,\n float16(-65504), float16(17))\n assert_raises_fpe('overflow', np.nextafter, float16(65504), float16(np.inf))\n assert_raises_fpe('overflow', np.nextafter, float16(-65504), float16(-np.inf))\n assert_raises_fpe('overflow', np.spacing, float16(65504))\n\n # Invalid value errors\n assert_raises_fpe('invalid', np.divide, float16(np.inf), float16(np.inf))\n assert_raises_fpe('invalid', np.spacing, float16(np.inf))\n assert_raises_fpe('invalid', np.spacing, float16(np.nan))\n\n # These should not raise\n float16(65472) + float16(32)\n float16(2**-13) / float16(2)\n float16(2**-14) / float16(2**10)\n np.spacing(float16(-65504))\n np.nextafter(float16(65504), float16(-np.inf))\n np.nextafter(float16(-65504), float16(np.inf))\n np.nextafter(float16(np.inf), float16(0))\n np.nextafter(float16(-np.inf), float16(0))\n np.nextafter(float16(0), float16(np.nan))\n np.nextafter(float16(np.nan), float16(0))\n float16(2**-14) / float16(2**10)\n float16(-2**-14) / float16(2**10)\n float16(2**-14 + 2**-23) / float16(2)\n float16(-2**-14 - 2**-23) / float16(2)\n\n def test_half_array_interface(self):\n """Test that half is compatible with __array_interface__"""\n class Dummy:\n pass\n\n a = np.ones((1,), dtype=float16)\n b = Dummy()\n b.__array_interface__ = a.__array_interface__\n c = np.array(b)\n assert_(c.dtype == float16)\n assert_equal(a, c)\n | .venv\Lib\site-packages\numpy\_core\tests\test_half.py | test_half.py | Python | 24,993 | 0.95 | 0.06338 | 0.132911 | react-lib | 865 | 2024-03-29T05:57:42.902444 | GPL-3.0 | true | badc8afaf6fdd2192527c47de93fb644 |
import random\n\nimport pytest\nfrom numpy._core._multiarray_tests import identityhash_tester\n\n\n@pytest.mark.parametrize("key_length", [1, 3, 6])\n@pytest.mark.parametrize("length", [1, 16, 2000])\ndef test_identity_hashtable(key_length, length):\n # use a 30 object pool for everything (duplicates will happen)\n pool = [object() for i in range(20)]\n keys_vals = []\n for i in range(length):\n keys = tuple(random.choices(pool, k=key_length))\n keys_vals.append((keys, random.choice(pool)))\n\n dictionary = dict(keys_vals)\n\n # add a random item at the end:\n keys_vals.append(random.choice(keys_vals))\n # the expected one could be different with duplicates:\n expected = dictionary[keys_vals[-1][0]]\n\n res = identityhash_tester(key_length, keys_vals, replace=True)\n assert res is expected\n\n if length == 1:\n return\n\n # add a new item with a key that is already used and a new value, this\n # should error if replace is False, see gh-26690\n new_key = (keys_vals[1][0], object())\n keys_vals[0] = new_key\n with pytest.raises(RuntimeError):\n identityhash_tester(key_length, keys_vals)\n | .venv\Lib\site-packages\numpy\_core\tests\test_hashtable.py | test_hashtable.py | Python | 1,182 | 0.95 | 0.171429 | 0.185185 | awesome-app | 746 | 2024-01-03T00:29:25.506288 | Apache-2.0 | true | ac6f4e5fbdd9c36a3b543e89be9c2c54 |
import numpy as np\nfrom numpy.testing import (\n assert_raises,\n assert_raises_regex,\n)\n\n\nclass TestIndexErrors:\n '''Tests to exercise indexerrors not covered by other tests.'''\n\n def test_arraytypes_fasttake(self):\n 'take from a 0-length dimension'\n x = np.empty((2, 3, 0, 4))\n assert_raises(IndexError, x.take, [0], axis=2)\n assert_raises(IndexError, x.take, [1], axis=2)\n assert_raises(IndexError, x.take, [0], axis=2, mode='wrap')\n assert_raises(IndexError, x.take, [0], axis=2, mode='clip')\n\n def test_take_from_object(self):\n # Check exception taking from object array\n d = np.zeros(5, dtype=object)\n assert_raises(IndexError, d.take, [6])\n\n # Check exception taking from 0-d array\n d = np.zeros((5, 0), dtype=object)\n assert_raises(IndexError, d.take, [1], axis=1)\n assert_raises(IndexError, d.take, [0], axis=1)\n assert_raises(IndexError, d.take, [0])\n assert_raises(IndexError, d.take, [0], mode='wrap')\n assert_raises(IndexError, d.take, [0], mode='clip')\n\n def test_multiindex_exceptions(self):\n a = np.empty(5, dtype=object)\n assert_raises(IndexError, a.item, 20)\n a = np.empty((5, 0), dtype=object)\n assert_raises(IndexError, a.item, (0, 0))\n\n def test_put_exceptions(self):\n a = np.zeros((5, 5))\n assert_raises(IndexError, a.put, 100, 0)\n a = np.zeros((5, 5), dtype=object)\n assert_raises(IndexError, a.put, 100, 0)\n a = np.zeros((5, 5, 0))\n assert_raises(IndexError, a.put, 100, 0)\n a = np.zeros((5, 5, 0), dtype=object)\n assert_raises(IndexError, a.put, 100, 0)\n\n def test_iterators_exceptions(self):\n "cases in iterators.c"\n def assign(obj, ind, val):\n obj[ind] = val\n\n a = np.zeros([1, 2, 3])\n assert_raises(IndexError, lambda: a[0, 5, None, 2])\n assert_raises(IndexError, lambda: a[0, 5, 0, 2])\n assert_raises(IndexError, lambda: assign(a, (0, 5, None, 2), 1))\n assert_raises(IndexError, lambda: assign(a, (0, 5, 0, 2), 1))\n\n a = np.zeros([1, 0, 3])\n assert_raises(IndexError, lambda: a[0, 0, None, 2])\n assert_raises(IndexError, lambda: assign(a, (0, 0, None, 2), 1))\n\n a = np.zeros([1, 2, 3])\n assert_raises(IndexError, lambda: a.flat[10])\n assert_raises(IndexError, lambda: assign(a.flat, 10, 5))\n a = np.zeros([1, 0, 3])\n assert_raises(IndexError, lambda: a.flat[10])\n assert_raises(IndexError, lambda: assign(a.flat, 10, 5))\n\n a = np.zeros([1, 2, 3])\n assert_raises(IndexError, lambda: a.flat[np.array(10)])\n assert_raises(IndexError, lambda: assign(a.flat, np.array(10), 5))\n a = np.zeros([1, 0, 3])\n assert_raises(IndexError, lambda: a.flat[np.array(10)])\n assert_raises(IndexError, lambda: assign(a.flat, np.array(10), 5))\n\n a = np.zeros([1, 2, 3])\n assert_raises(IndexError, lambda: a.flat[np.array([10])])\n assert_raises(IndexError, lambda: assign(a.flat, np.array([10]), 5))\n a = np.zeros([1, 0, 3])\n assert_raises(IndexError, lambda: a.flat[np.array([10])])\n assert_raises(IndexError, lambda: assign(a.flat, np.array([10]), 5))\n\n def test_mapping(self):\n "cases from mapping.c"\n\n def assign(obj, ind, val):\n obj[ind] = val\n\n a = np.zeros((0, 10))\n assert_raises(IndexError, lambda: a[12])\n\n a = np.zeros((3, 5))\n assert_raises(IndexError, lambda: a[(10, 20)])\n assert_raises(IndexError, lambda: assign(a, (10, 20), 1))\n a = np.zeros((3, 0))\n assert_raises(IndexError, lambda: a[(1, 0)])\n assert_raises(IndexError, lambda: assign(a, (1, 0), 1))\n\n a = np.zeros((10,))\n assert_raises(IndexError, lambda: assign(a, 10, 1))\n a = np.zeros((0,))\n assert_raises(IndexError, lambda: assign(a, 10, 1))\n\n a = np.zeros((3, 5))\n assert_raises(IndexError, lambda: a[(1, [1, 20])])\n assert_raises(IndexError, lambda: assign(a, (1, [1, 20]), 1))\n a = np.zeros((3, 0))\n assert_raises(IndexError, lambda: a[(1, [0, 1])])\n assert_raises(IndexError, lambda: assign(a, (1, [0, 1]), 1))\n\n def test_mapping_error_message(self):\n a = np.zeros((3, 5))\n index = (1, 2, 3, 4, 5)\n assert_raises_regex(\n IndexError,\n "too many indices for array: "\n "array is 2-dimensional, but 5 were indexed",\n lambda: a[index])\n\n def test_methods(self):\n "cases from methods.c"\n\n a = np.zeros((3, 3))\n assert_raises(IndexError, lambda: a.item(100))\n | .venv\Lib\site-packages\numpy\_core\tests\test_indexerrors.py | test_indexerrors.py | Python | 4,851 | 0.95 | 0.096 | 0.019417 | vue-tools | 398 | 2024-07-15T19:09:13.701208 | GPL-3.0 | true | 408f3468cd708607c34d405bce861ac6 |
import functools\nimport operator\nimport sys\nimport warnings\nfrom itertools import product\n\nimport pytest\nfrom numpy._core._multiarray_tests import array_indexing\n\nimport numpy as np\nfrom numpy.exceptions import ComplexWarning, VisibleDeprecationWarning\nfrom numpy.testing import (\n HAS_REFCOUNT,\n assert_,\n assert_array_equal,\n assert_equal,\n assert_raises,\n assert_raises_regex,\n assert_warns,\n)\n\n\nclass TestIndexing:\n def test_index_no_floats(self):\n a = np.array([[[5]]])\n\n assert_raises(IndexError, lambda: a[0.0])\n assert_raises(IndexError, lambda: a[0, 0.0])\n assert_raises(IndexError, lambda: a[0.0, 0])\n assert_raises(IndexError, lambda: a[0.0, :])\n assert_raises(IndexError, lambda: a[:, 0.0])\n assert_raises(IndexError, lambda: a[:, 0.0, :])\n assert_raises(IndexError, lambda: a[0.0, :, :])\n assert_raises(IndexError, lambda: a[0, 0, 0.0])\n assert_raises(IndexError, lambda: a[0.0, 0, 0])\n assert_raises(IndexError, lambda: a[0, 0.0, 0])\n assert_raises(IndexError, lambda: a[-1.4])\n assert_raises(IndexError, lambda: a[0, -1.4])\n assert_raises(IndexError, lambda: a[-1.4, 0])\n assert_raises(IndexError, lambda: a[-1.4, :])\n assert_raises(IndexError, lambda: a[:, -1.4])\n assert_raises(IndexError, lambda: a[:, -1.4, :])\n assert_raises(IndexError, lambda: a[-1.4, :, :])\n assert_raises(IndexError, lambda: a[0, 0, -1.4])\n assert_raises(IndexError, lambda: a[-1.4, 0, 0])\n assert_raises(IndexError, lambda: a[0, -1.4, 0])\n assert_raises(IndexError, lambda: a[0.0:, 0.0])\n assert_raises(IndexError, lambda: a[0.0:, 0.0, :])\n\n def test_slicing_no_floats(self):\n a = np.array([[5]])\n\n # start as float.\n assert_raises(TypeError, lambda: a[0.0:])\n assert_raises(TypeError, lambda: a[0:, 0.0:2])\n assert_raises(TypeError, lambda: a[0.0::2, :0])\n assert_raises(TypeError, lambda: a[0.0:1:2, :])\n assert_raises(TypeError, lambda: a[:, 0.0:])\n # stop as float.\n assert_raises(TypeError, lambda: a[:0.0])\n assert_raises(TypeError, lambda: a[:0, 1:2.0])\n assert_raises(TypeError, lambda: a[:0.0:2, :0])\n assert_raises(TypeError, lambda: a[:0.0, :])\n assert_raises(TypeError, lambda: a[:, 0:4.0:2])\n # step as float.\n assert_raises(TypeError, lambda: a[::1.0])\n assert_raises(TypeError, lambda: a[0:, :2:2.0])\n assert_raises(TypeError, lambda: a[1::4.0, :0])\n assert_raises(TypeError, lambda: a[::5.0, :])\n assert_raises(TypeError, lambda: a[:, 0:4:2.0])\n # mixed.\n assert_raises(TypeError, lambda: a[1.0:2:2.0])\n assert_raises(TypeError, lambda: a[1.0::2.0])\n assert_raises(TypeError, lambda: a[0:, :2.0:2.0])\n assert_raises(TypeError, lambda: a[1.0:1:4.0, :0])\n assert_raises(TypeError, lambda: a[1.0:5.0:5.0, :])\n assert_raises(TypeError, lambda: a[:, 0.4:4.0:2.0])\n # should still get the DeprecationWarning if step = 0.\n assert_raises(TypeError, lambda: a[::0.0])\n\n def test_index_no_array_to_index(self):\n # No non-scalar arrays.\n a = np.array([[[1]]])\n\n assert_raises(TypeError, lambda: a[a:a:a])\n\n def test_none_index(self):\n # `None` index adds newaxis\n a = np.array([1, 2, 3])\n assert_equal(a[None], a[np.newaxis])\n assert_equal(a[None].ndim, a.ndim + 1)\n\n def test_empty_tuple_index(self):\n # Empty tuple index creates a view\n a = np.array([1, 2, 3])\n assert_equal(a[()], a)\n assert_(a[()].base is a)\n a = np.array(0)\n assert_(isinstance(a[()], np.int_))\n\n def test_void_scalar_empty_tuple(self):\n s = np.zeros((), dtype='V4')\n assert_equal(s[()].dtype, s.dtype)\n assert_equal(s[()], s)\n assert_equal(type(s[...]), np.ndarray)\n\n def test_same_kind_index_casting(self):\n # Indexes should be cast with same-kind and not safe, even if that\n # is somewhat unsafe. So test various different code paths.\n index = np.arange(5)\n u_index = index.astype(np.uintp)\n arr = np.arange(10)\n\n assert_array_equal(arr[index], arr[u_index])\n arr[u_index] = np.arange(5)\n assert_array_equal(arr, np.arange(10))\n\n arr = np.arange(10).reshape(5, 2)\n assert_array_equal(arr[index], arr[u_index])\n\n arr[u_index] = np.arange(5)[:, None]\n assert_array_equal(arr, np.arange(5)[:, None].repeat(2, axis=1))\n\n arr = np.arange(25).reshape(5, 5)\n assert_array_equal(arr[u_index, u_index], arr[index, index])\n\n def test_empty_fancy_index(self):\n # Empty list index creates an empty array\n # with the same dtype (but with weird shape)\n a = np.array([1, 2, 3])\n assert_equal(a[[]], [])\n assert_equal(a[[]].dtype, a.dtype)\n\n b = np.array([], dtype=np.intp)\n assert_equal(a[[]], [])\n assert_equal(a[[]].dtype, a.dtype)\n\n b = np.array([])\n assert_raises(IndexError, a.__getitem__, b)\n\n def test_gh_26542(self):\n a = np.array([0, 1, 2])\n idx = np.array([2, 1, 0])\n a[idx] = a\n expected = np.array([2, 1, 0])\n assert_equal(a, expected)\n\n def test_gh_26542_2d(self):\n a = np.array([[0, 1, 2]])\n idx_row = np.zeros(3, dtype=int)\n idx_col = np.array([2, 1, 0])\n a[idx_row, idx_col] = a\n expected = np.array([[2, 1, 0]])\n assert_equal(a, expected)\n\n def test_gh_26542_index_overlap(self):\n arr = np.arange(100)\n expected_vals = np.copy(arr[:-10])\n arr[10:] = arr[:-10]\n actual_vals = arr[10:]\n assert_equal(actual_vals, expected_vals)\n\n def test_gh_26844(self):\n expected = [0, 1, 3, 3, 3]\n a = np.arange(5)\n a[2:][a[:-2]] = 3\n assert_equal(a, expected)\n\n def test_gh_26844_segfault(self):\n # check for absence of segfault for:\n # https://github.com/numpy/numpy/pull/26958/files#r1854589178\n a = np.arange(5)\n expected = [0, 1, 3, 3, 3]\n a[2:][None, a[:-2]] = 3\n assert_equal(a, expected)\n\n def test_ellipsis_index(self):\n a = np.array([[1, 2, 3],\n [4, 5, 6],\n [7, 8, 9]])\n assert_(a[...] is not a)\n assert_equal(a[...], a)\n # `a[...]` was `a` in numpy <1.9.\n assert_(a[...].base is a)\n\n # Slicing with ellipsis can skip an\n # arbitrary number of dimensions\n assert_equal(a[0, ...], a[0])\n assert_equal(a[0, ...], a[0, :])\n assert_equal(a[..., 0], a[:, 0])\n\n # Slicing with ellipsis always results\n # in an array, not a scalar\n assert_equal(a[0, ..., 1], np.array(2))\n\n # Assignment with `(Ellipsis,)` on 0-d arrays\n b = np.array(1)\n b[(Ellipsis,)] = 2\n assert_equal(b, 2)\n\n def test_single_int_index(self):\n # Single integer index selects one row\n a = np.array([[1, 2, 3],\n [4, 5, 6],\n [7, 8, 9]])\n\n assert_equal(a[0], [1, 2, 3])\n assert_equal(a[-1], [7, 8, 9])\n\n # Index out of bounds produces IndexError\n assert_raises(IndexError, a.__getitem__, 1 << 30)\n # Index overflow produces IndexError\n assert_raises(IndexError, a.__getitem__, 1 << 64)\n\n def test_single_bool_index(self):\n # Single boolean index\n a = np.array([[1, 2, 3],\n [4, 5, 6],\n [7, 8, 9]])\n\n assert_equal(a[np.array(True)], a[None])\n assert_equal(a[np.array(False)], a[None][0:0])\n\n def test_boolean_shape_mismatch(self):\n arr = np.ones((5, 4, 3))\n\n index = np.array([True])\n assert_raises(IndexError, arr.__getitem__, index)\n\n index = np.array([False] * 6)\n assert_raises(IndexError, arr.__getitem__, index)\n\n index = np.zeros((4, 4), dtype=bool)\n assert_raises(IndexError, arr.__getitem__, index)\n\n assert_raises(IndexError, arr.__getitem__, (slice(None), index))\n\n def test_boolean_indexing_onedim(self):\n # Indexing a 2-dimensional array with\n # boolean array of length one\n a = np.array([[0., 0., 0.]])\n b = np.array([True], dtype=bool)\n assert_equal(a[b], a)\n # boolean assignment\n a[b] = 1.\n assert_equal(a, [[1., 1., 1.]])\n\n def test_boolean_assignment_value_mismatch(self):\n # A boolean assignment should fail when the shape of the values\n # cannot be broadcast to the subscription. (see also gh-3458)\n a = np.arange(4)\n\n def f(a, v):\n a[a > -1] = v\n\n assert_raises(ValueError, f, a, [])\n assert_raises(ValueError, f, a, [1, 2, 3])\n assert_raises(ValueError, f, a[:1], [1, 2, 3])\n\n def test_boolean_assignment_needs_api(self):\n # See also gh-7666\n # This caused a segfault on Python 2 due to the GIL not being\n # held when the iterator does not need it, but the transfer function\n # does\n arr = np.zeros(1000)\n indx = np.zeros(1000, dtype=bool)\n indx[:100] = True\n arr[indx] = np.ones(100, dtype=object)\n\n expected = np.zeros(1000)\n expected[:100] = 1\n assert_array_equal(arr, expected)\n\n def test_boolean_indexing_twodim(self):\n # Indexing a 2-dimensional array with\n # 2-dimensional boolean array\n a = np.array([[1, 2, 3],\n [4, 5, 6],\n [7, 8, 9]])\n b = np.array([[ True, False, True],\n [False, True, False],\n [ True, False, True]])\n assert_equal(a[b], [1, 3, 5, 7, 9])\n assert_equal(a[b[1]], [[4, 5, 6]])\n assert_equal(a[b[0]], a[b[2]])\n\n # boolean assignment\n a[b] = 0\n assert_equal(a, [[0, 2, 0],\n [4, 0, 6],\n [0, 8, 0]])\n\n def test_boolean_indexing_list(self):\n # Regression test for #13715. It's a use-after-free bug which the\n # test won't directly catch, but it will show up in valgrind.\n a = np.array([1, 2, 3])\n b = [True, False, True]\n # Two variants of the test because the first takes a fast path\n assert_equal(a[b], [1, 3])\n assert_equal(a[None, b], [[1, 3]])\n\n def test_reverse_strides_and_subspace_bufferinit(self):\n # This tests that the strides are not reversed for simple and\n # subspace fancy indexing.\n a = np.ones(5)\n b = np.zeros(5, dtype=np.intp)[::-1]\n c = np.arange(5)[::-1]\n\n a[b] = c\n # If the strides are not reversed, the 0 in the arange comes last.\n assert_equal(a[0], 0)\n\n # This also tests that the subspace buffer is initialized:\n a = np.ones((5, 2))\n c = np.arange(10).reshape(5, 2)[::-1]\n a[b, :] = c\n assert_equal(a[0], [0, 1])\n\n def test_reversed_strides_result_allocation(self):\n # Test a bug when calculating the output strides for a result array\n # when the subspace size was 1 (and test other cases as well)\n a = np.arange(10)[:, None]\n i = np.arange(10)[::-1]\n assert_array_equal(a[i], a[i.copy('C')])\n\n a = np.arange(20).reshape(-1, 2)\n\n def test_uncontiguous_subspace_assignment(self):\n # During development there was a bug activating a skip logic\n # based on ndim instead of size.\n a = np.full((3, 4, 2), -1)\n b = np.full((3, 4, 2), -1)\n\n a[[0, 1]] = np.arange(2 * 4 * 2).reshape(2, 4, 2).T\n b[[0, 1]] = np.arange(2 * 4 * 2).reshape(2, 4, 2).T.copy()\n\n assert_equal(a, b)\n\n def test_too_many_fancy_indices_special_case(self):\n # Just documents behaviour, this is a small limitation.\n a = np.ones((1,) * 64) # 64 is NPY_MAXDIMS\n assert_raises(IndexError, a.__getitem__, (np.array([0]),) * 64)\n\n def test_scalar_array_bool(self):\n # NumPy bools can be used as boolean index (python ones as of yet not)\n a = np.array(1)\n assert_equal(a[np.bool(True)], a[np.array(True)])\n assert_equal(a[np.bool(False)], a[np.array(False)])\n\n # After deprecating bools as integers:\n #a = np.array([0,1,2])\n #assert_equal(a[True, :], a[None, :])\n #assert_equal(a[:, True], a[:, None])\n #\n #assert_(not np.may_share_memory(a, a[True, :]))\n\n def test_everything_returns_views(self):\n # Before `...` would return a itself.\n a = np.arange(5)\n\n assert_(a is not a[()])\n assert_(a is not a[...])\n assert_(a is not a[:])\n\n def test_broaderrors_indexing(self):\n a = np.zeros((5, 5))\n assert_raises(IndexError, a.__getitem__, ([0, 1], [0, 1, 2]))\n assert_raises(IndexError, a.__setitem__, ([0, 1], [0, 1, 2]), 0)\n\n def test_trivial_fancy_out_of_bounds(self):\n a = np.zeros(5)\n ind = np.ones(20, dtype=np.intp)\n ind[-1] = 10\n assert_raises(IndexError, a.__getitem__, ind)\n assert_raises(IndexError, a.__setitem__, ind, 0)\n ind = np.ones(20, dtype=np.intp)\n ind[0] = 11\n assert_raises(IndexError, a.__getitem__, ind)\n assert_raises(IndexError, a.__setitem__, ind, 0)\n\n def test_trivial_fancy_not_possible(self):\n # Test that the fast path for trivial assignment is not incorrectly\n # used when the index is not contiguous or 1D, see also gh-11467.\n a = np.arange(6)\n idx = np.arange(6, dtype=np.intp).reshape(2, 1, 3)[:, :, 0]\n assert_array_equal(a[idx], idx)\n\n # this case must not go into the fast path, note that idx is\n # a non-contiguous none 1D array here.\n a[idx] = -1\n res = np.arange(6)\n res[0] = -1\n res[3] = -1\n assert_array_equal(a, res)\n\n def test_nonbaseclass_values(self):\n class SubClass(np.ndarray):\n def __array_finalize__(self, old):\n # Have array finalize do funny things\n self.fill(99)\n\n a = np.zeros((5, 5))\n s = a.copy().view(type=SubClass)\n s.fill(1)\n\n a[[0, 1, 2, 3, 4], :] = s\n assert_((a == 1).all())\n\n # Subspace is last, so transposing might want to finalize\n a[:, [0, 1, 2, 3, 4]] = s\n assert_((a == 1).all())\n\n a.fill(0)\n a[...] = s\n assert_((a == 1).all())\n\n def test_array_like_values(self):\n # Similar to the above test, but use a memoryview instead\n a = np.zeros((5, 5))\n s = np.arange(25, dtype=np.float64).reshape(5, 5)\n\n a[[0, 1, 2, 3, 4], :] = memoryview(s)\n assert_array_equal(a, s)\n\n a[:, [0, 1, 2, 3, 4]] = memoryview(s)\n assert_array_equal(a, s)\n\n a[...] = memoryview(s)\n assert_array_equal(a, s)\n\n @pytest.mark.parametrize("writeable", [True, False])\n def test_subclass_writeable(self, writeable):\n d = np.rec.array([('NGC1001', 11), ('NGC1002', 1.), ('NGC1003', 1.)],\n dtype=[('target', 'S20'), ('V_mag', '>f4')])\n d.flags.writeable = writeable\n # Advanced indexing results are always writeable:\n ind = np.array([False, True, True], dtype=bool)\n assert d[ind].flags.writeable\n ind = np.array([0, 1])\n assert d[ind].flags.writeable\n # Views should be writeable if the original array is:\n assert d[...].flags.writeable == writeable\n assert d[0].flags.writeable == writeable\n\n def test_memory_order(self):\n # This is not necessary to preserve. Memory layouts for\n # more complex indices are not as simple.\n a = np.arange(10)\n b = np.arange(10).reshape(5, 2).T\n assert_(a[b].flags.f_contiguous)\n\n # Takes a different implementation branch:\n a = a.reshape(-1, 1)\n assert_(a[b, 0].flags.f_contiguous)\n\n def test_scalar_return_type(self):\n # Full scalar indices should return scalars and object\n # arrays should not call PyArray_Return on their items\n class Zero:\n # The most basic valid indexing\n def __index__(self):\n return 0\n\n z = Zero()\n\n class ArrayLike:\n # Simple array, should behave like the array\n def __array__(self, dtype=None, copy=None):\n return np.array(0)\n\n a = np.zeros(())\n assert_(isinstance(a[()], np.float64))\n a = np.zeros(1)\n assert_(isinstance(a[z], np.float64))\n a = np.zeros((1, 1))\n assert_(isinstance(a[z, np.array(0)], np.float64))\n assert_(isinstance(a[z, ArrayLike()], np.float64))\n\n # And object arrays do not call it too often:\n b = np.array(0)\n a = np.array(0, dtype=object)\n a[()] = b\n assert_(isinstance(a[()], np.ndarray))\n a = np.array([b, None])\n assert_(isinstance(a[z], np.ndarray))\n a = np.array([[b, None]])\n assert_(isinstance(a[z, np.array(0)], np.ndarray))\n assert_(isinstance(a[z, ArrayLike()], np.ndarray))\n\n def test_small_regressions(self):\n # Reference count of intp for index checks\n a = np.array([0])\n if HAS_REFCOUNT:\n refcount = sys.getrefcount(np.dtype(np.intp))\n # item setting always checks indices in separate function:\n a[np.array([0], dtype=np.intp)] = 1\n a[np.array([0], dtype=np.uint8)] = 1\n assert_raises(IndexError, a.__setitem__,\n np.array([1], dtype=np.intp), 1)\n assert_raises(IndexError, a.__setitem__,\n np.array([1], dtype=np.uint8), 1)\n\n if HAS_REFCOUNT:\n assert_equal(sys.getrefcount(np.dtype(np.intp)), refcount)\n\n def test_unaligned(self):\n v = (np.zeros(64, dtype=np.int8) + ord('a'))[1:-7]\n d = v.view(np.dtype("S8"))\n # unaligned source\n x = (np.zeros(16, dtype=np.int8) + ord('a'))[1:-7]\n x = x.view(np.dtype("S8"))\n x[...] = np.array("b" * 8, dtype="S")\n b = np.arange(d.size)\n # trivial\n assert_equal(d[b], d)\n d[b] = x\n # nontrivial\n # unaligned index array\n b = np.zeros(d.size + 1).view(np.int8)[1:-(np.intp(0).itemsize - 1)]\n b = b.view(np.intp)[:d.size]\n b[...] = np.arange(d.size)\n assert_equal(d[b.astype(np.int16)], d)\n d[b.astype(np.int16)] = x\n # boolean\n d[b % 2 == 0]\n d[b % 2 == 0] = x[::2]\n\n def test_tuple_subclass(self):\n arr = np.ones((5, 5))\n\n # A tuple subclass should also be an nd-index\n class TupleSubclass(tuple):\n pass\n index = ([1], [1])\n index = TupleSubclass(index)\n assert_(arr[index].shape == (1,))\n # Unlike the non nd-index:\n assert_(arr[index,].shape != (1,))\n\n def test_broken_sequence_not_nd_index(self):\n # See gh-5063:\n # If we have an object which claims to be a sequence, but fails\n # on item getting, this should not be converted to an nd-index (tuple)\n # If this object happens to be a valid index otherwise, it should work\n # This object here is very dubious and probably bad though:\n class SequenceLike:\n def __index__(self):\n return 0\n\n def __len__(self):\n return 1\n\n def __getitem__(self, item):\n raise IndexError('Not possible')\n\n arr = np.arange(10)\n assert_array_equal(arr[SequenceLike()], arr[SequenceLike(),])\n\n # also test that field indexing does not segfault\n # for a similar reason, by indexing a structured array\n arr = np.zeros((1,), dtype=[('f1', 'i8'), ('f2', 'i8')])\n assert_array_equal(arr[SequenceLike()], arr[SequenceLike(),])\n\n def test_indexing_array_weird_strides(self):\n # See also gh-6221\n # the shapes used here come from the issue and create the correct\n # size for the iterator buffering size.\n x = np.ones(10)\n x2 = np.ones((10, 2))\n ind = np.arange(10)[:, None, None, None]\n ind = np.broadcast_to(ind, (10, 55, 4, 4))\n\n # single advanced index case\n assert_array_equal(x[ind], x[ind.copy()])\n # higher dimensional advanced index\n zind = np.zeros(4, dtype=np.intp)\n assert_array_equal(x2[ind, zind], x2[ind.copy(), zind])\n\n def test_indexing_array_negative_strides(self):\n # From gh-8264,\n # core dumps if negative strides are used in iteration\n arro = np.zeros((4, 4))\n arr = arro[::-1, ::-1]\n\n slices = (slice(None), [0, 1, 2, 3])\n arr[slices] = 10\n assert_array_equal(arr, 10.)\n\n def test_character_assignment(self):\n # This is an example a function going through CopyObject which\n # used to have an untested special path for scalars\n # (the character special dtype case, should be deprecated probably)\n arr = np.zeros((1, 5), dtype="c")\n arr[0] = np.str_("asdfg") # must assign as a sequence\n assert_array_equal(arr[0], np.array("asdfg", dtype="c"))\n assert arr[0, 1] == b"s" # make sure not all were set to "a" for both\n\n @pytest.mark.parametrize("index",\n [True, False, np.array([0])])\n @pytest.mark.parametrize("num", [64, 80])\n @pytest.mark.parametrize("original_ndim", [1, 64])\n def test_too_many_advanced_indices(self, index, num, original_ndim):\n # These are limitations based on the number of arguments we can process.\n # For `num=32` (and all boolean cases), the result is actually define;\n # but the use of NpyIter (NPY_MAXARGS) limits it for technical reasons.\n arr = np.ones((1,) * original_ndim)\n with pytest.raises(IndexError):\n arr[(index,) * num]\n with pytest.raises(IndexError):\n arr[(index,) * num] = 1.\n\n def test_nontuple_ndindex(self):\n a = np.arange(25).reshape((5, 5))\n assert_equal(a[[0, 1]], np.array([a[0], a[1]]))\n assert_equal(a[[0, 1], [0, 1]], np.array([0, 6]))\n assert_raises(IndexError, a.__getitem__, [slice(None)])\n\n def test_flat_index_on_flatiter(self):\n a = np.arange(9).reshape((3, 3))\n b = np.array([0, 5, 6])\n assert_equal(a.flat[b.flat], np.array([0, 5, 6]))\n\n def test_empty_string_flat_index_on_flatiter(self):\n a = np.arange(9).reshape((3, 3))\n b = np.array([], dtype="S")\n assert_equal(a.flat[b.flat], np.array([]))\n\n def test_nonempty_string_flat_index_on_flatiter(self):\n a = np.arange(9).reshape((3, 3))\n b = np.array(["a"], dtype="S")\n with pytest.raises(IndexError, match="unsupported iterator index"):\n a.flat[b.flat]\n\n\nclass TestFieldIndexing:\n def test_scalar_return_type(self):\n # Field access on an array should return an array, even if it\n # is 0-d.\n a = np.zeros((), [('a', 'f8')])\n assert_(isinstance(a['a'], np.ndarray))\n assert_(isinstance(a[['a']], np.ndarray))\n\n\nclass TestBroadcastedAssignments:\n def assign(self, a, ind, val):\n a[ind] = val\n return a\n\n def test_prepending_ones(self):\n a = np.zeros((3, 2))\n\n a[...] = np.ones((1, 3, 2))\n # Fancy with subspace with and without transpose\n a[[0, 1, 2], :] = np.ones((1, 3, 2))\n a[:, [0, 1]] = np.ones((1, 3, 2))\n # Fancy without subspace (with broadcasting)\n a[[[0], [1], [2]], [0, 1]] = np.ones((1, 3, 2))\n\n def test_prepend_not_one(self):\n assign = self.assign\n s_ = np.s_\n a = np.zeros(5)\n\n # Too large and not only ones.\n assert_raises(ValueError, assign, a, s_[...], np.ones((2, 1)))\n assert_raises(ValueError, assign, a, s_[[1, 2, 3],], np.ones((2, 1)))\n assert_raises(ValueError, assign, a, s_[[[1], [2]],], np.ones((2, 2, 1)))\n\n def test_simple_broadcasting_errors(self):\n assign = self.assign\n s_ = np.s_\n a = np.zeros((5, 1))\n\n assert_raises(ValueError, assign, a, s_[...], np.zeros((5, 2)))\n assert_raises(ValueError, assign, a, s_[...], np.zeros((5, 0)))\n assert_raises(ValueError, assign, a, s_[:, [0]], np.zeros((5, 2)))\n assert_raises(ValueError, assign, a, s_[:, [0]], np.zeros((5, 0)))\n assert_raises(ValueError, assign, a, s_[[0], :], np.zeros((2, 1)))\n\n @pytest.mark.parametrize("index", [\n (..., [1, 2], slice(None)),\n ([0, 1], ..., 0),\n (..., [1, 2], [1, 2])])\n def test_broadcast_error_reports_correct_shape(self, index):\n values = np.zeros((100, 100)) # will never broadcast below\n\n arr = np.zeros((3, 4, 5, 6, 7))\n # We currently report without any spaces (could be changed)\n shape_str = str(arr[index].shape).replace(" ", "")\n\n with pytest.raises(ValueError) as e:\n arr[index] = values\n\n assert str(e.value).endswith(shape_str)\n\n def test_index_is_larger(self):\n # Simple case of fancy index broadcasting of the index.\n a = np.zeros((5, 5))\n a[[[0], [1], [2]], [0, 1, 2]] = [2, 3, 4]\n\n assert_((a[:3, :3] == [2, 3, 4]).all())\n\n def test_broadcast_subspace(self):\n a = np.zeros((100, 100))\n v = np.arange(100)[:, None]\n b = np.arange(100)[::-1]\n a[b] = v\n assert_((a[::-1] == v).all())\n\n\nclass TestSubclasses:\n def test_basic(self):\n # Test that indexing in various ways produces SubClass instances,\n # and that the base is set up correctly: the original subclass\n # instance for views, and a new ndarray for advanced/boolean indexing\n # where a copy was made (latter a regression test for gh-11983).\n class SubClass(np.ndarray):\n pass\n\n a = np.arange(5)\n s = a.view(SubClass)\n s_slice = s[:3]\n assert_(type(s_slice) is SubClass)\n assert_(s_slice.base is s)\n assert_array_equal(s_slice, a[:3])\n\n s_fancy = s[[0, 1, 2]]\n assert_(type(s_fancy) is SubClass)\n assert_(s_fancy.base is not s)\n assert_(type(s_fancy.base) is np.ndarray)\n assert_array_equal(s_fancy, a[[0, 1, 2]])\n assert_array_equal(s_fancy.base, a[[0, 1, 2]])\n\n s_bool = s[s > 0]\n assert_(type(s_bool) is SubClass)\n assert_(s_bool.base is not s)\n assert_(type(s_bool.base) is np.ndarray)\n assert_array_equal(s_bool, a[a > 0])\n assert_array_equal(s_bool.base, a[a > 0])\n\n def test_fancy_on_read_only(self):\n # Test that fancy indexing on read-only SubClass does not make a\n # read-only copy (gh-14132)\n class SubClass(np.ndarray):\n pass\n\n a = np.arange(5)\n s = a.view(SubClass)\n s.flags.writeable = False\n s_fancy = s[[0, 1, 2]]\n assert_(s_fancy.flags.writeable)\n\n def test_finalize_gets_full_info(self):\n # Array finalize should be called on the filled array.\n class SubClass(np.ndarray):\n def __array_finalize__(self, old):\n self.finalize_status = np.array(self)\n self.old = old\n\n s = np.arange(10).view(SubClass)\n new_s = s[:3]\n assert_array_equal(new_s.finalize_status, new_s)\n assert_array_equal(new_s.old, s)\n\n new_s = s[[0, 1, 2, 3]]\n assert_array_equal(new_s.finalize_status, new_s)\n assert_array_equal(new_s.old, s)\n\n new_s = s[s > 0]\n assert_array_equal(new_s.finalize_status, new_s)\n assert_array_equal(new_s.old, s)\n\n\nclass TestFancyIndexingCast:\n def test_boolean_index_cast_assign(self):\n # Setup the boolean index and float arrays.\n shape = (8, 63)\n bool_index = np.zeros(shape).astype(bool)\n bool_index[0, 1] = True\n zero_array = np.zeros(shape)\n\n # Assigning float is fine.\n zero_array[bool_index] = np.array([1])\n assert_equal(zero_array[0, 1], 1)\n\n # Fancy indexing works, although we get a cast warning.\n assert_warns(ComplexWarning,\n zero_array.__setitem__, ([0], [1]), np.array([2 + 1j]))\n assert_equal(zero_array[0, 1], 2) # No complex part\n\n # Cast complex to float, throwing away the imaginary portion.\n assert_warns(ComplexWarning,\n zero_array.__setitem__, bool_index, np.array([1j]))\n assert_equal(zero_array[0, 1], 0)\n\nclass TestFancyIndexingEquivalence:\n def test_object_assign(self):\n # Check that the field and object special case using copyto is active.\n # The right hand side cannot be converted to an array here.\n a = np.arange(5, dtype=object)\n b = a.copy()\n a[:3] = [1, (1, 2), 3]\n b[[0, 1, 2]] = [1, (1, 2), 3]\n assert_array_equal(a, b)\n\n # test same for subspace fancy indexing\n b = np.arange(5, dtype=object)[None, :]\n b[[0], :3] = [[1, (1, 2), 3]]\n assert_array_equal(a, b[0])\n\n # Check that swapping of axes works.\n # There was a bug that made the later assignment throw a ValueError\n # do to an incorrectly transposed temporary right hand side (gh-5714)\n b = b.T\n b[:3, [0]] = [[1], [(1, 2)], [3]]\n assert_array_equal(a, b[:, 0])\n\n # Another test for the memory order of the subspace\n arr = np.ones((3, 4, 5), dtype=object)\n # Equivalent slicing assignment for comparison\n cmp_arr = arr.copy()\n cmp_arr[:1, ...] = [[[1], [2], [3], [4]]]\n arr[[0], ...] = [[[1], [2], [3], [4]]]\n assert_array_equal(arr, cmp_arr)\n arr = arr.copy('F')\n arr[[0], ...] = [[[1], [2], [3], [4]]]\n assert_array_equal(arr, cmp_arr)\n\n def test_cast_equivalence(self):\n # Yes, normal slicing uses unsafe casting.\n a = np.arange(5)\n b = a.copy()\n\n a[:3] = np.array(['2', '-3', '-1'])\n b[[0, 2, 1]] = np.array(['2', '-1', '-3'])\n assert_array_equal(a, b)\n\n # test the same for subspace fancy indexing\n b = np.arange(5)[None, :]\n b[[0], :3] = np.array([['2', '-3', '-1']])\n assert_array_equal(a, b[0])\n\n\nclass TestMultiIndexingAutomated:\n """\n These tests use code to mimic the C-Code indexing for selection.\n\n NOTE:\n\n * This still lacks tests for complex item setting.\n * If you change behavior of indexing, you might want to modify\n these tests to try more combinations.\n * Behavior was written to match numpy version 1.8. (though a\n first version matched 1.7.)\n * Only tuple indices are supported by the mimicking code.\n (and tested as of writing this)\n * Error types should match most of the time as long as there\n is only one error. For multiple errors, what gets raised\n will usually not be the same one. They are *not* tested.\n\n Update 2016-11-30: It is probably not worth maintaining this test\n indefinitely and it can be dropped if maintenance becomes a burden.\n\n """\n\n def setup_method(self):\n self.a = np.arange(np.prod([3, 1, 5, 6])).reshape(3, 1, 5, 6)\n self.b = np.empty((3, 0, 5, 6))\n self.complex_indices = ['skip', Ellipsis,\n 0,\n # Boolean indices, up to 3-d for some special cases of eating up\n # dimensions, also need to test all False\n np.array([True, False, False]),\n np.array([[True, False], [False, True]]),\n np.array([[[False, False], [False, False]]]),\n # Some slices:\n slice(-5, 5, 2),\n slice(1, 1, 100),\n slice(4, -1, -2),\n slice(None, None, -3),\n # Some Fancy indexes:\n np.empty((0, 1, 1), dtype=np.intp), # empty and can be broadcast\n np.array([0, 1, -2]),\n np.array([[2], [0], [1]]),\n np.array([[0, -1], [0, 1]], dtype=np.dtype('intp').newbyteorder()),\n np.array([2, -1], dtype=np.int8),\n np.zeros([1] * 31, dtype=int), # trigger too large array.\n np.array([0., 1.])] # invalid datatype\n # Some simpler indices that still cover a bit more\n self.simple_indices = [Ellipsis, None, -1, [1], np.array([True]),\n 'skip']\n # Very simple ones to fill the rest:\n self.fill_indices = [slice(None, None), 0]\n\n def _get_multi_index(self, arr, indices):\n """Mimic multi dimensional indexing.\n\n Parameters\n ----------\n arr : ndarray\n Array to be indexed.\n indices : tuple of index objects\n\n Returns\n -------\n out : ndarray\n An array equivalent to the indexing operation (but always a copy).\n `arr[indices]` should be identical.\n no_copy : bool\n Whether the indexing operation requires a copy. If this is `True`,\n `np.may_share_memory(arr, arr[indices])` should be `True` (with\n some exceptions for scalars and possibly 0-d arrays).\n\n Notes\n -----\n While the function may mostly match the errors of normal indexing this\n is generally not the case.\n """\n in_indices = list(indices)\n indices = []\n # if False, this is a fancy or boolean index\n no_copy = True\n # number of fancy/scalar indexes that are not consecutive\n num_fancy = 0\n # number of dimensions indexed by a "fancy" index\n fancy_dim = 0\n # NOTE: This is a funny twist (and probably OK to change).\n # The boolean array has illegal indexes, but this is\n # allowed if the broadcast fancy-indices are 0-sized.\n # This variable is to catch that case.\n error_unless_broadcast_to_empty = False\n\n # We need to handle Ellipsis and make arrays from indices, also\n # check if this is fancy indexing (set no_copy).\n ndim = 0\n ellipsis_pos = None # define here mostly to replace all but first.\n for i, indx in enumerate(in_indices):\n if indx is None:\n continue\n if isinstance(indx, np.ndarray) and indx.dtype == bool:\n no_copy = False\n if indx.ndim == 0:\n raise IndexError\n # boolean indices can have higher dimensions\n ndim += indx.ndim\n fancy_dim += indx.ndim\n continue\n if indx is Ellipsis:\n if ellipsis_pos is None:\n ellipsis_pos = i\n continue # do not increment ndim counter\n raise IndexError\n if isinstance(indx, slice):\n ndim += 1\n continue\n if not isinstance(indx, np.ndarray):\n # This could be open for changes in numpy.\n # numpy should maybe raise an error if casting to intp\n # is not safe. It rejects np.array([1., 2.]) but not\n # [1., 2.] as index (same for ie. np.take).\n # (Note the importance of empty lists if changing this here)\n try:\n indx = np.array(indx, dtype=np.intp)\n except ValueError:\n raise IndexError\n in_indices[i] = indx\n elif indx.dtype.kind not in 'bi':\n raise IndexError('arrays used as indices must be of '\n 'integer (or boolean) type')\n if indx.ndim != 0:\n no_copy = False\n ndim += 1\n fancy_dim += 1\n\n if arr.ndim - ndim < 0:\n # we can't take more dimensions then we have, not even for 0-d\n # arrays. since a[()] makes sense, but not a[(),]. We will\n # raise an error later on, unless a broadcasting error occurs\n # first.\n raise IndexError\n\n if ndim == 0 and None not in in_indices:\n # Well we have no indexes or one Ellipsis. This is legal.\n return arr.copy(), no_copy\n\n if ellipsis_pos is not None:\n in_indices[ellipsis_pos:ellipsis_pos + 1] = ([slice(None, None)] *\n (arr.ndim - ndim))\n\n for ax, indx in enumerate(in_indices):\n if isinstance(indx, slice):\n # convert to an index array\n indx = np.arange(*indx.indices(arr.shape[ax]))\n indices.append(['s', indx])\n continue\n elif indx is None:\n # this is like taking a slice with one element from a new axis:\n indices.append(['n', np.array([0], dtype=np.intp)])\n arr = arr.reshape(arr.shape[:ax] + (1,) + arr.shape[ax:])\n continue\n if isinstance(indx, np.ndarray) and indx.dtype == bool:\n if indx.shape != arr.shape[ax:ax + indx.ndim]:\n raise IndexError\n\n try:\n flat_indx = np.ravel_multi_index(np.nonzero(indx),\n arr.shape[ax:ax + indx.ndim], mode='raise')\n except Exception:\n error_unless_broadcast_to_empty = True\n # fill with 0s instead, and raise error later\n flat_indx = np.array([0] * indx.sum(), dtype=np.intp)\n # concatenate axis into a single one:\n if indx.ndim != 0:\n arr = arr.reshape(arr.shape[:ax]\n + (np.prod(arr.shape[ax:ax + indx.ndim]),)\n + arr.shape[ax + indx.ndim:])\n indx = flat_indx\n else:\n # This could be changed, a 0-d boolean index can\n # make sense (even outside the 0-d indexed array case)\n # Note that originally this is could be interpreted as\n # integer in the full integer special case.\n raise IndexError\n # If the index is a singleton, the bounds check is done\n # before the broadcasting. This used to be different in <1.9\n elif indx.ndim == 0 and not (\n -arr.shape[ax] <= indx < arr.shape[ax]\n ):\n raise IndexError\n if indx.ndim == 0:\n # The index is a scalar. This used to be two fold, but if\n # fancy indexing was active, the check was done later,\n # possibly after broadcasting it away (1.7. or earlier).\n # Now it is always done.\n if indx >= arr.shape[ax] or indx < - arr.shape[ax]:\n raise IndexError\n if (len(indices) > 0 and\n indices[-1][0] == 'f' and\n ax != ellipsis_pos):\n # NOTE: There could still have been a 0-sized Ellipsis\n # between them. Checked that with ellipsis_pos.\n indices[-1].append(indx)\n else:\n # We have a fancy index that is not after an existing one.\n # NOTE: A 0-d array triggers this as well, while one may\n # expect it to not trigger it, since a scalar would not be\n # considered fancy indexing.\n num_fancy += 1\n indices.append(['f', indx])\n\n if num_fancy > 1 and not no_copy:\n # We have to flush the fancy indexes left\n new_indices = indices[:]\n axes = list(range(arr.ndim))\n fancy_axes = []\n new_indices.insert(0, ['f'])\n ni = 0\n ai = 0\n for indx in indices:\n ni += 1\n if indx[0] == 'f':\n new_indices[0].extend(indx[1:])\n del new_indices[ni]\n ni -= 1\n for ax in range(ai, ai + len(indx[1:])):\n fancy_axes.append(ax)\n axes.remove(ax)\n ai += len(indx) - 1 # axis we are at\n indices = new_indices\n # and now we need to transpose arr:\n arr = arr.transpose(*(fancy_axes + axes))\n\n # We only have one 'f' index now and arr is transposed accordingly.\n # Now handle newaxis by reshaping...\n ax = 0\n for indx in indices:\n if indx[0] == 'f':\n if len(indx) == 1:\n continue\n # First of all, reshape arr to combine fancy axes into one:\n orig_shape = arr.shape\n orig_slice = orig_shape[ax:ax + len(indx[1:])]\n arr = arr.reshape(arr.shape[:ax]\n + (np.prod(orig_slice).astype(int),)\n + arr.shape[ax + len(indx[1:]):])\n\n # Check if broadcasting works\n res = np.broadcast(*indx[1:])\n # unfortunately the indices might be out of bounds. So check\n # that first, and use mode='wrap' then. However only if\n # there are any indices...\n if res.size != 0:\n if error_unless_broadcast_to_empty:\n raise IndexError\n for _indx, _size in zip(indx[1:], orig_slice):\n if _indx.size == 0:\n continue\n if np.any(_indx >= _size) or np.any(_indx < -_size):\n raise IndexError\n if len(indx[1:]) == len(orig_slice):\n if np.prod(orig_slice) == 0:\n # Work around for a crash or IndexError with 'wrap'\n # in some 0-sized cases.\n try:\n mi = np.ravel_multi_index(indx[1:], orig_slice,\n mode='raise')\n except Exception:\n # This happens with 0-sized orig_slice (sometimes?)\n # here it is a ValueError, but indexing gives a:\n raise IndexError('invalid index into 0-sized')\n else:\n mi = np.ravel_multi_index(indx[1:], orig_slice,\n mode='wrap')\n else:\n # Maybe never happens...\n raise ValueError\n arr = arr.take(mi.ravel(), axis=ax)\n try:\n arr = arr.reshape(arr.shape[:ax]\n + mi.shape\n + arr.shape[ax + 1:])\n except ValueError:\n # too many dimensions, probably\n raise IndexError\n ax += mi.ndim\n continue\n\n # If we are here, we have a 1D array for take:\n arr = arr.take(indx[1], axis=ax)\n ax += 1\n\n return arr, no_copy\n\n def _check_multi_index(self, arr, index):\n """Check a multi index item getting and simple setting.\n\n Parameters\n ----------\n arr : ndarray\n Array to be indexed, must be a reshaped arange.\n index : tuple of indexing objects\n Index being tested.\n """\n # Test item getting\n try:\n mimic_get, no_copy = self._get_multi_index(arr, index)\n except Exception as e:\n if HAS_REFCOUNT:\n prev_refcount = sys.getrefcount(arr)\n assert_raises(type(e), arr.__getitem__, index)\n assert_raises(type(e), arr.__setitem__, index, 0)\n if HAS_REFCOUNT:\n assert_equal(prev_refcount, sys.getrefcount(arr))\n return\n\n self._compare_index_result(arr, index, mimic_get, no_copy)\n\n def _check_single_index(self, arr, index):\n """Check a single index item getting and simple setting.\n\n Parameters\n ----------\n arr : ndarray\n Array to be indexed, must be an arange.\n index : indexing object\n Index being tested. Must be a single index and not a tuple\n of indexing objects (see also `_check_multi_index`).\n """\n try:\n mimic_get, no_copy = self._get_multi_index(arr, (index,))\n except Exception as e:\n if HAS_REFCOUNT:\n prev_refcount = sys.getrefcount(arr)\n assert_raises(type(e), arr.__getitem__, index)\n assert_raises(type(e), arr.__setitem__, index, 0)\n if HAS_REFCOUNT:\n assert_equal(prev_refcount, sys.getrefcount(arr))\n return\n\n self._compare_index_result(arr, index, mimic_get, no_copy)\n\n def _compare_index_result(self, arr, index, mimic_get, no_copy):\n """Compare mimicked result to indexing result.\n """\n arr = arr.copy()\n if HAS_REFCOUNT:\n startcount = sys.getrefcount(arr)\n indexed_arr = arr[index]\n assert_array_equal(indexed_arr, mimic_get)\n # Check if we got a view, unless its a 0-sized or 0-d array.\n # (then its not a view, and that does not matter)\n if indexed_arr.size != 0 and indexed_arr.ndim != 0:\n assert_(np.may_share_memory(indexed_arr, arr) == no_copy)\n # Check reference count of the original array\n if HAS_REFCOUNT:\n if no_copy:\n # refcount increases by one:\n assert_equal(sys.getrefcount(arr), startcount + 1)\n else:\n assert_equal(sys.getrefcount(arr), startcount)\n\n # Test non-broadcast setitem:\n b = arr.copy()\n b[index] = mimic_get + 1000\n if b.size == 0:\n return # nothing to compare here...\n if no_copy and indexed_arr.ndim != 0:\n # change indexed_arr in-place to manipulate original:\n indexed_arr += 1000\n assert_array_equal(arr, b)\n return\n # Use the fact that the array is originally an arange:\n arr.flat[indexed_arr.ravel()] += 1000\n assert_array_equal(arr, b)\n\n def test_boolean(self):\n a = np.array(5)\n assert_equal(a[np.array(True)], 5)\n a[np.array(True)] = 1\n assert_equal(a, 1)\n # NOTE: This is different from normal broadcasting, as\n # arr[boolean_array] works like in a multi index. Which means\n # it is aligned to the left. This is probably correct for\n # consistency with arr[boolean_array,] also no broadcasting\n # is done at all\n self._check_multi_index(\n self.a, (np.zeros_like(self.a, dtype=bool),))\n self._check_multi_index(\n self.a, (np.zeros_like(self.a, dtype=bool)[..., 0],))\n self._check_multi_index(\n self.a, (np.zeros_like(self.a, dtype=bool)[None, ...],))\n\n def test_multidim(self):\n # Automatically test combinations with complex indexes on 2nd (or 1st)\n # spot and the simple ones in one other spot.\n with warnings.catch_warnings():\n # This is so that np.array(True) is not accepted in a full integer\n # index, when running the file separately.\n warnings.filterwarnings('error', '', DeprecationWarning)\n warnings.filterwarnings('error', '', VisibleDeprecationWarning)\n\n def isskip(idx):\n return isinstance(idx, str) and idx == "skip"\n\n for simple_pos in [0, 2, 3]:\n tocheck = [self.fill_indices, self.complex_indices,\n self.fill_indices, self.fill_indices]\n tocheck[simple_pos] = self.simple_indices\n for index in product(*tocheck):\n index = tuple(i for i in index if not isskip(i))\n self._check_multi_index(self.a, index)\n self._check_multi_index(self.b, index)\n\n # Check very simple item getting:\n self._check_multi_index(self.a, (0, 0, 0, 0))\n self._check_multi_index(self.b, (0, 0, 0, 0))\n # Also check (simple cases of) too many indices:\n assert_raises(IndexError, self.a.__getitem__, (0, 0, 0, 0, 0))\n assert_raises(IndexError, self.a.__setitem__, (0, 0, 0, 0, 0), 0)\n assert_raises(IndexError, self.a.__getitem__, (0, 0, [1], 0, 0))\n assert_raises(IndexError, self.a.__setitem__, (0, 0, [1], 0, 0), 0)\n\n def test_1d(self):\n a = np.arange(10)\n for index in self.complex_indices:\n self._check_single_index(a, index)\n\nclass TestFloatNonIntegerArgument:\n """\n These test that ``TypeError`` is raised when you try to use\n non-integers as arguments to for indexing and slicing e.g. ``a[0.0:5]``\n and ``a[0.5]``, or other functions like ``array.reshape(1., -1)``.\n\n """\n def test_valid_indexing(self):\n # These should raise no errors.\n a = np.array([[[5]]])\n\n a[np.array([0])]\n a[[0, 0]]\n a[:, [0, 0]]\n a[:, 0, :]\n a[:, :, :]\n\n def test_valid_slicing(self):\n # These should raise no errors.\n a = np.array([[[5]]])\n\n a[::]\n a[0:]\n a[:2]\n a[0:2]\n a[::2]\n a[1::2]\n a[:2:2]\n a[1:2:2]\n\n def test_non_integer_argument_errors(self):\n a = np.array([[5]])\n\n assert_raises(TypeError, np.reshape, a, (1., 1., -1))\n assert_raises(TypeError, np.reshape, a, (np.array(1.), -1))\n assert_raises(TypeError, np.take, a, [0], 1.)\n assert_raises(TypeError, np.take, a, [0], np.float64(1.))\n\n def test_non_integer_sequence_multiplication(self):\n # NumPy scalar sequence multiply should not work with non-integers\n def mult(a, b):\n return a * b\n\n assert_raises(TypeError, mult, [1], np.float64(3))\n # following should be OK\n mult([1], np.int_(3))\n\n def test_reduce_axis_float_index(self):\n d = np.zeros((3, 3, 3))\n assert_raises(TypeError, np.min, d, 0.5)\n assert_raises(TypeError, np.min, d, (0.5, 1))\n assert_raises(TypeError, np.min, d, (1, 2.2))\n assert_raises(TypeError, np.min, d, (.2, 1.2))\n\n\nclass TestBooleanIndexing:\n # Using a boolean as integer argument/indexing is an error.\n def test_bool_as_int_argument_errors(self):\n a = np.array([[[1]]])\n\n assert_raises(TypeError, np.reshape, a, (True, -1))\n assert_raises(TypeError, np.reshape, a, (np.bool(True), -1))\n # Note that operator.index(np.array(True)) does not work, a boolean\n # array is thus also deprecated, but not with the same message:\n assert_raises(TypeError, operator.index, np.array(True))\n assert_raises(TypeError, operator.index, np.True_)\n assert_raises(TypeError, np.take, args=(a, [0], False))\n\n def test_boolean_indexing_weirdness(self):\n # Weird boolean indexing things\n a = np.ones((2, 3, 4))\n assert a[False, True, ...].shape == (0, 2, 3, 4)\n assert a[True, [0, 1], True, True, [1], [[2]]].shape == (1, 2)\n assert_raises(IndexError, lambda: a[False, [0, 1], ...])\n\n def test_boolean_indexing_fast_path(self):\n # These used to either give the wrong error, or incorrectly give no\n # error.\n a = np.ones((3, 3))\n\n # This used to incorrectly work (and give an array of shape (0,))\n idx1 = np.array([[False] * 9])\n assert_raises_regex(IndexError,\n "boolean index did not match indexed array along axis 0; "\n "size of axis is 3 but size of corresponding boolean axis is 1",\n lambda: a[idx1])\n\n # This used to incorrectly give a ValueError: operands could not be broadcast together\n idx2 = np.array([[False] * 8 + [True]])\n assert_raises_regex(IndexError,\n "boolean index did not match indexed array along axis 0; "\n "size of axis is 3 but size of corresponding boolean axis is 1",\n lambda: a[idx2])\n\n # This is the same as it used to be. The above two should work like this.\n idx3 = np.array([[False] * 10])\n assert_raises_regex(IndexError,\n "boolean index did not match indexed array along axis 0; "\n "size of axis is 3 but size of corresponding boolean axis is 1",\n lambda: a[idx3])\n\n # This used to give ValueError: non-broadcastable operand\n a = np.ones((1, 1, 2))\n idx = np.array([[[True], [False]]])\n assert_raises_regex(IndexError,\n "boolean index did not match indexed array along axis 1; "\n "size of axis is 1 but size of corresponding boolean axis is 2",\n lambda: a[idx])\n\n\nclass TestArrayToIndexDeprecation:\n """Creating an index from array not 0-D is an error.\n\n """\n def test_array_to_index_error(self):\n # so no exception is expected. The raising is effectively tested above.\n a = np.array([[[1]]])\n\n assert_raises(TypeError, operator.index, np.array([1]))\n assert_raises(TypeError, np.reshape, a, (a, -1))\n assert_raises(TypeError, np.take, a, [0], a)\n\n\nclass TestNonIntegerArrayLike:\n """Tests that array_likes only valid if can safely cast to integer.\n\n For instance, lists give IndexError when they cannot be safely cast to\n an integer.\n\n """\n def test_basic(self):\n a = np.arange(10)\n\n assert_raises(IndexError, a.__getitem__, [0.5, 1.5])\n assert_raises(IndexError, a.__getitem__, (['1', '2'],))\n\n # The following is valid\n a.__getitem__([])\n\n\nclass TestMultipleEllipsisError:\n """An index can only have a single ellipsis.\n\n """\n def test_basic(self):\n a = np.arange(10)\n assert_raises(IndexError, lambda: a[..., ...])\n assert_raises(IndexError, a.__getitem__, ((Ellipsis,) * 2,))\n assert_raises(IndexError, a.__getitem__, ((Ellipsis,) * 3,))\n\n\nclass TestCApiAccess:\n def test_getitem(self):\n subscript = functools.partial(array_indexing, 0)\n\n # 0-d arrays don't work:\n assert_raises(IndexError, subscript, np.ones(()), 0)\n # Out of bound values:\n assert_raises(IndexError, subscript, np.ones(10), 11)\n assert_raises(IndexError, subscript, np.ones(10), -11)\n assert_raises(IndexError, subscript, np.ones((10, 10)), 11)\n assert_raises(IndexError, subscript, np.ones((10, 10)), -11)\n\n a = np.arange(10)\n assert_array_equal(a[4], subscript(a, 4))\n a = a.reshape(5, 2)\n assert_array_equal(a[-4], subscript(a, -4))\n\n def test_setitem(self):\n assign = functools.partial(array_indexing, 1)\n\n # Deletion is impossible:\n assert_raises(ValueError, assign, np.ones(10), 0)\n # 0-d arrays don't work:\n assert_raises(IndexError, assign, np.ones(()), 0, 0)\n # Out of bound values:\n assert_raises(IndexError, assign, np.ones(10), 11, 0)\n assert_raises(IndexError, assign, np.ones(10), -11, 0)\n assert_raises(IndexError, assign, np.ones((10, 10)), 11, 0)\n assert_raises(IndexError, assign, np.ones((10, 10)), -11, 0)\n\n a = np.arange(10)\n assign(a, 4, 10)\n assert_(a[4] == 10)\n\n a = a.reshape(5, 2)\n assign(a, 4, 10)\n assert_array_equal(a[-1], [10, 10])\n | .venv\Lib\site-packages\numpy\_core\tests\test_indexing.py | test_indexing.py | Python | 56,876 | 0.75 | 0.156014 | 0.191593 | node-utils | 260 | 2023-09-30T15:58:30.362761 | MIT | true | 1f89c783f9f7f6e8b1fadce9469b76b5 |
import sys\n\nimport pytest\n\nimport numpy as np\nfrom numpy.testing import HAS_REFCOUNT, assert_, assert_array_equal, assert_raises\n\n\nclass TestTake:\n def test_simple(self):\n a = [[1, 2], [3, 4]]\n a_str = [[b'1', b'2'], [b'3', b'4']]\n modes = ['raise', 'wrap', 'clip']\n indices = [-1, 4]\n index_arrays = [np.empty(0, dtype=np.intp),\n np.empty((), dtype=np.intp),\n np.empty((1, 1), dtype=np.intp)]\n real_indices = {'raise': {-1: 1, 4: IndexError},\n 'wrap': {-1: 1, 4: 0},\n 'clip': {-1: 0, 4: 1}}\n # Currently all types but object, use the same function generation.\n # So it should not be necessary to test all. However test also a non\n # refcounted struct on top of object, which has a size that hits the\n # default (non-specialized) path.\n types = int, object, np.dtype([('', 'i2', 3)])\n for t in types:\n # ta works, even if the array may be odd if buffer interface is used\n ta = np.array(a if np.issubdtype(t, np.number) else a_str, dtype=t)\n tresult = list(ta.T.copy())\n for index_array in index_arrays:\n if index_array.size != 0:\n tresult[0].shape = (2,) + index_array.shape\n tresult[1].shape = (2,) + index_array.shape\n for mode in modes:\n for index in indices:\n real_index = real_indices[mode][index]\n if real_index is IndexError and index_array.size != 0:\n index_array.put(0, index)\n assert_raises(IndexError, ta.take, index_array,\n mode=mode, axis=1)\n elif index_array.size != 0:\n index_array.put(0, index)\n res = ta.take(index_array, mode=mode, axis=1)\n assert_array_equal(res, tresult[real_index])\n else:\n res = ta.take(index_array, mode=mode, axis=1)\n assert_(res.shape == (2,) + index_array.shape)\n\n def test_refcounting(self):\n objects = [object() for i in range(10)]\n if HAS_REFCOUNT:\n orig_rcs = [sys.getrefcount(o) for o in objects]\n for mode in ('raise', 'clip', 'wrap'):\n a = np.array(objects)\n b = np.array([2, 2, 4, 5, 3, 5])\n a.take(b, out=a[:6], mode=mode)\n del a\n if HAS_REFCOUNT:\n assert_(all(sys.getrefcount(o) == rc + 1\n for o, rc in zip(objects, orig_rcs)))\n # not contiguous, example:\n a = np.array(objects * 2)[::2]\n a.take(b, out=a[:6], mode=mode)\n del a\n if HAS_REFCOUNT:\n assert_(all(sys.getrefcount(o) == rc + 1\n for o, rc in zip(objects, orig_rcs)))\n\n def test_unicode_mode(self):\n d = np.arange(10)\n k = b'\xc3\xa4'.decode("UTF8")\n assert_raises(ValueError, d.take, 5, mode=k)\n\n def test_empty_partition(self):\n # In reference to github issue #6530\n a_original = np.array([0, 2, 4, 6, 8, 10])\n a = a_original.copy()\n\n # An empty partition should be a successful no-op\n a.partition(np.array([], dtype=np.int16))\n\n assert_array_equal(a, a_original)\n\n def test_empty_argpartition(self):\n # In reference to github issue #6530\n a = np.array([0, 2, 4, 6, 8, 10])\n a = a.argpartition(np.array([], dtype=np.int16))\n\n b = np.array([0, 1, 2, 3, 4, 5])\n assert_array_equal(a, b)\n\n\nclass TestPutMask:\n @pytest.mark.parametrize("dtype", list(np.typecodes["All"]) + ["i,O"])\n def test_simple(self, dtype):\n if dtype.lower() == "m":\n dtype += "8[ns]"\n\n # putmask is weird and doesn't care about value length (even shorter)\n vals = np.arange(1001).astype(dtype=dtype)\n\n mask = np.random.randint(2, size=1000).astype(bool)\n # Use vals.dtype in case of flexible dtype (i.e. string)\n arr = np.zeros(1000, dtype=vals.dtype)\n zeros = arr.copy()\n\n np.putmask(arr, mask, vals)\n assert_array_equal(arr[mask], vals[:len(mask)][mask])\n assert_array_equal(arr[~mask], zeros[~mask])\n\n @pytest.mark.parametrize("dtype", list(np.typecodes["All"])[1:] + ["i,O"])\n @pytest.mark.parametrize("mode", ["raise", "wrap", "clip"])\n def test_empty(self, dtype, mode):\n arr = np.zeros(1000, dtype=dtype)\n arr_copy = arr.copy()\n mask = np.random.randint(2, size=1000).astype(bool)\n\n # Allowing empty values like this is weird...\n np.put(arr, mask, [])\n assert_array_equal(arr, arr_copy)\n\n\nclass TestPut:\n @pytest.mark.parametrize("dtype", list(np.typecodes["All"])[1:] + ["i,O"])\n @pytest.mark.parametrize("mode", ["raise", "wrap", "clip"])\n def test_simple(self, dtype, mode):\n if dtype.lower() == "m":\n dtype += "8[ns]"\n\n # put is weird and doesn't care about value length (even shorter)\n vals = np.arange(1001).astype(dtype=dtype)\n\n # Use vals.dtype in case of flexible dtype (i.e. string)\n arr = np.zeros(1000, dtype=vals.dtype)\n zeros = arr.copy()\n\n if mode == "clip":\n # Special because 0 and -1 value are "reserved" for clip test\n indx = np.random.permutation(len(arr) - 2)[:-500] + 1\n\n indx[-1] = 0\n indx[-2] = len(arr) - 1\n indx_put = indx.copy()\n indx_put[-1] = -1389\n indx_put[-2] = 1321\n else:\n # Avoid duplicates (for simplicity) and fill half only\n indx = np.random.permutation(len(arr) - 3)[:-500]\n indx_put = indx\n if mode == "wrap":\n indx_put = indx_put + len(arr)\n\n np.put(arr, indx_put, vals, mode=mode)\n assert_array_equal(arr[indx], vals[:len(indx)])\n untouched = np.ones(len(arr), dtype=bool)\n untouched[indx] = False\n assert_array_equal(arr[untouched], zeros[:untouched.sum()])\n\n @pytest.mark.parametrize("dtype", list(np.typecodes["All"])[1:] + ["i,O"])\n @pytest.mark.parametrize("mode", ["raise", "wrap", "clip"])\n def test_empty(self, dtype, mode):\n arr = np.zeros(1000, dtype=dtype)\n arr_copy = arr.copy()\n\n # Allowing empty values like this is weird...\n np.put(arr, [1, 2, 3], [])\n assert_array_equal(arr, arr_copy)\n | .venv\Lib\site-packages\numpy\_core\tests\test_item_selection.py | test_item_selection.py | Python | 6,798 | 0.95 | 0.215569 | 0.121429 | react-lib | 444 | 2023-08-19T02:22:23.071053 | GPL-3.0 | true | da23f67beac3bba027a578951bb6ac36 |
import os\nimport subprocess\nimport sys\nimport sysconfig\n\nimport pytest\n\nfrom numpy.testing import IS_EDITABLE, IS_PYPY, IS_WASM, NOGIL_BUILD\n\n# This import is copied from random.tests.test_extending\ntry:\n import cython\n from Cython.Compiler.Version import version as cython_version\nexcept ImportError:\n cython = None\nelse:\n from numpy._utils import _pep440\n\n # Note: keep in sync with the one in pyproject.toml\n required_version = "3.0.6"\n if _pep440.parse(cython_version) < _pep440.Version(required_version):\n # too old or wrong cython, skip the test\n cython = None\n\npytestmark = pytest.mark.skipif(cython is None, reason="requires cython")\n\n\nif IS_EDITABLE:\n pytest.skip(\n "Editable install doesn't support tests with a compile step",\n allow_module_level=True\n )\n\n\n@pytest.fixture(scope='module')\ndef install_temp(tmpdir_factory):\n # Based in part on test_cython from random.tests.test_extending\n if IS_WASM:\n pytest.skip("No subprocess")\n\n srcdir = os.path.join(os.path.dirname(__file__), 'examples', 'limited_api')\n build_dir = tmpdir_factory.mktemp("limited_api") / "build"\n os.makedirs(build_dir, exist_ok=True)\n # Ensure we use the correct Python interpreter even when `meson` is\n # installed in a different Python environment (see gh-24956)\n native_file = str(build_dir / 'interpreter-native-file.ini')\n with open(native_file, 'w') as f:\n f.write("[binaries]\n")\n f.write(f"python = '{sys.executable}'\n")\n f.write(f"python3 = '{sys.executable}'")\n\n try:\n subprocess.check_call(["meson", "--version"])\n except FileNotFoundError:\n pytest.skip("No usable 'meson' found")\n if sysconfig.get_platform() == "win-arm64":\n pytest.skip("Meson unable to find MSVC linker on win-arm64")\n if sys.platform == "win32":\n subprocess.check_call(["meson", "setup",\n "--werror",\n "--buildtype=release",\n "--vsenv", "--native-file", native_file,\n str(srcdir)],\n cwd=build_dir,\n )\n else:\n subprocess.check_call(["meson", "setup", "--werror",\n "--native-file", native_file, str(srcdir)],\n cwd=build_dir\n )\n try:\n subprocess.check_call(\n ["meson", "compile", "-vv"], cwd=build_dir)\n except subprocess.CalledProcessError as p:\n print(f"{p.stdout=}")\n print(f"{p.stderr=}")\n raise\n\n sys.path.append(str(build_dir))\n\n\n@pytest.mark.skipif(IS_WASM, reason="Can't start subprocess")\n@pytest.mark.xfail(\n sysconfig.get_config_var("Py_DEBUG"),\n reason=(\n "Py_LIMITED_API is incompatible with Py_DEBUG, Py_TRACE_REFS, "\n "and Py_REF_DEBUG"\n ),\n)\n@pytest.mark.xfail(\n NOGIL_BUILD,\n reason="Py_GIL_DISABLED builds do not currently support the limited API",\n)\n@pytest.mark.skipif(IS_PYPY, reason="no support for limited API in PyPy")\ndef test_limited_api(install_temp):\n """Test building a third-party C extension with the limited API\n and building a cython extension with the limited API\n """\n\n import limited_api1 # Earliest (3.6) # noqa: F401\n import limited_api2 # cython # noqa: F401\n import limited_api_latest # Latest version (current Python) # noqa: F401\n | .venv\Lib\site-packages\numpy\_core\tests\test_limited_api.py | test_limited_api.py | Python | 3,565 | 0.95 | 0.107843 | 0.068966 | vue-tools | 113 | 2024-05-23T01:28:46.046601 | MIT | true | 20cbf1a9ec795b95f716326df78ad1d4 |
import platform\nimport warnings\n\nimport pytest\n\nimport numpy as np\nfrom numpy._core.tests._locales import CommaDecimalPointLocale\nfrom numpy.testing import (\n IS_MUSL,\n assert_,\n assert_array_equal,\n assert_equal,\n assert_raises,\n temppath,\n)\n\nLD_INFO = np.finfo(np.longdouble)\nlongdouble_longer_than_double = (LD_INFO.eps < np.finfo(np.double).eps)\n\n\n_o = 1 + LD_INFO.eps\nstring_to_longdouble_inaccurate = (_o != np.longdouble(str(_o)))\ndel _o\n\n\ndef test_scalar_extraction():\n """Confirm that extracting a value doesn't convert to python float"""\n o = 1 + LD_INFO.eps\n a = np.array([o, o, o])\n assert_equal(a[1], o)\n\n\n# Conversions string -> long double\n\n# 0.1 not exactly representable in base 2 floating point.\nrepr_precision = len(repr(np.longdouble(0.1)))\n# +2 from macro block starting around line 842 in scalartypes.c.src.\n\n\n@pytest.mark.skipif(IS_MUSL,\n reason="test flaky on musllinux")\n@pytest.mark.skipif(LD_INFO.precision + 2 >= repr_precision,\n reason="repr precision not enough to show eps")\ndef test_str_roundtrip():\n # We will only see eps in repr if within printing precision.\n o = 1 + LD_INFO.eps\n assert_equal(np.longdouble(str(o)), o, f"str was {str(o)}")\n\n\n@pytest.mark.skipif(string_to_longdouble_inaccurate, reason="Need strtold_l")\ndef test_str_roundtrip_bytes():\n o = 1 + LD_INFO.eps\n assert_equal(np.longdouble(str(o).encode("ascii")), o)\n\n\n@pytest.mark.skipif(string_to_longdouble_inaccurate, reason="Need strtold_l")\n@pytest.mark.parametrize("strtype", (np.str_, np.bytes_, str, bytes))\ndef test_array_and_stringlike_roundtrip(strtype):\n """\n Test that string representations of long-double roundtrip both\n for array casting and scalar coercion, see also gh-15608.\n """\n o = 1 + LD_INFO.eps\n\n if strtype in (np.bytes_, bytes):\n o_str = strtype(str(o).encode("ascii"))\n else:\n o_str = strtype(str(o))\n\n # Test that `o` is correctly coerced from the string-like\n assert o == np.longdouble(o_str)\n\n # Test that arrays also roundtrip correctly:\n o_strarr = np.asarray([o] * 3, dtype=strtype)\n assert (o == o_strarr.astype(np.longdouble)).all()\n\n # And array coercion and casting to string give the same as scalar repr:\n assert (o_strarr == o_str).all()\n assert (np.asarray([o] * 3).astype(strtype) == o_str).all()\n\n\ndef test_bogus_string():\n assert_raises(ValueError, np.longdouble, "spam")\n assert_raises(ValueError, np.longdouble, "1.0 flub")\n\n\n@pytest.mark.skipif(string_to_longdouble_inaccurate, reason="Need strtold_l")\ndef test_fromstring():\n o = 1 + LD_INFO.eps\n s = (" " + str(o)) * 5\n a = np.array([o] * 5)\n assert_equal(np.fromstring(s, sep=" ", dtype=np.longdouble), a,\n err_msg=f"reading '{s}'")\n\n\ndef test_fromstring_complex():\n for ctype in ["complex", "cdouble"]:\n # Check spacing between separator\n assert_equal(np.fromstring("1, 2 , 3 ,4", sep=",", dtype=ctype),\n np.array([1., 2., 3., 4.]))\n # Real component not specified\n assert_equal(np.fromstring("1j, -2j, 3j, 4e1j", sep=",", dtype=ctype),\n np.array([1.j, -2.j, 3.j, 40.j]))\n # Both components specified\n assert_equal(np.fromstring("1+1j,2-2j, -3+3j, -4e1+4j", sep=",", dtype=ctype),\n np.array([1. + 1.j, 2. - 2.j, - 3. + 3.j, - 40. + 4j]))\n # Spaces at wrong places\n with assert_raises(ValueError):\n np.fromstring("1+2 j,3", dtype=ctype, sep=",")\n with assert_raises(ValueError):\n np.fromstring("1+ 2j,3", dtype=ctype, sep=",")\n with assert_raises(ValueError):\n np.fromstring("1 +2j,3", dtype=ctype, sep=",")\n with assert_raises(ValueError):\n np.fromstring("1+j", dtype=ctype, sep=",")\n with assert_raises(ValueError):\n np.fromstring("1+", dtype=ctype, sep=",")\n with assert_raises(ValueError):\n np.fromstring("1j+1", dtype=ctype, sep=",")\n\n\ndef test_fromstring_bogus():\n with assert_raises(ValueError):\n np.fromstring("1. 2. 3. flop 4.", dtype=float, sep=" ")\n\n\ndef test_fromstring_empty():\n with assert_raises(ValueError):\n np.fromstring("xxxxx", sep="x")\n\n\ndef test_fromstring_missing():\n with assert_raises(ValueError):\n np.fromstring("1xx3x4x5x6", sep="x")\n\n\nclass TestFileBased:\n\n ldbl = 1 + LD_INFO.eps\n tgt = np.array([ldbl] * 5)\n out = ''.join([str(t) + '\n' for t in tgt])\n\n def test_fromfile_bogus(self):\n with temppath() as path:\n with open(path, 'w') as f:\n f.write("1. 2. 3. flop 4.\n")\n\n with assert_raises(ValueError):\n np.fromfile(path, dtype=float, sep=" ")\n\n def test_fromfile_complex(self):\n for ctype in ["complex", "cdouble"]:\n # Check spacing between separator and only real component specified\n with temppath() as path:\n with open(path, 'w') as f:\n f.write("1, 2 , 3 ,4\n")\n\n res = np.fromfile(path, dtype=ctype, sep=",")\n assert_equal(res, np.array([1., 2., 3., 4.]))\n\n # Real component not specified\n with temppath() as path:\n with open(path, 'w') as f:\n f.write("1j, -2j, 3j, 4e1j\n")\n\n res = np.fromfile(path, dtype=ctype, sep=",")\n assert_equal(res, np.array([1.j, -2.j, 3.j, 40.j]))\n\n # Both components specified\n with temppath() as path:\n with open(path, 'w') as f:\n f.write("1+1j,2-2j, -3+3j, -4e1+4j\n")\n\n res = np.fromfile(path, dtype=ctype, sep=",")\n assert_equal(res, np.array([1. + 1.j, 2. - 2.j, - 3. + 3.j, - 40. + 4j]))\n\n # Spaces at wrong places\n with temppath() as path:\n with open(path, 'w') as f:\n f.write("1+2 j,3\n")\n\n with assert_raises(ValueError):\n np.fromfile(path, dtype=ctype, sep=",")\n\n # Spaces at wrong places\n with temppath() as path:\n with open(path, 'w') as f:\n f.write("1+ 2j,3\n")\n\n with assert_raises(ValueError):\n np.fromfile(path, dtype=ctype, sep=",")\n\n # Spaces at wrong places\n with temppath() as path:\n with open(path, 'w') as f:\n f.write("1 +2j,3\n")\n\n with assert_raises(ValueError):\n np.fromfile(path, dtype=ctype, sep=",")\n\n # Wrong sep\n with temppath() as path:\n with open(path, 'w') as f:\n f.write("1+j\n")\n\n with assert_raises(ValueError):\n np.fromfile(path, dtype=ctype, sep=",")\n\n # Wrong sep\n with temppath() as path:\n with open(path, 'w') as f:\n f.write("1+\n")\n\n with assert_raises(ValueError):\n np.fromfile(path, dtype=ctype, sep=",")\n\n # Wrong sep\n with temppath() as path:\n with open(path, 'w') as f:\n f.write("1j+1\n")\n\n with assert_raises(ValueError):\n np.fromfile(path, dtype=ctype, sep=",")\n\n @pytest.mark.skipif(string_to_longdouble_inaccurate,\n reason="Need strtold_l")\n def test_fromfile(self):\n with temppath() as path:\n with open(path, 'w') as f:\n f.write(self.out)\n res = np.fromfile(path, dtype=np.longdouble, sep="\n")\n assert_equal(res, self.tgt)\n\n @pytest.mark.skipif(string_to_longdouble_inaccurate,\n reason="Need strtold_l")\n def test_genfromtxt(self):\n with temppath() as path:\n with open(path, 'w') as f:\n f.write(self.out)\n res = np.genfromtxt(path, dtype=np.longdouble)\n assert_equal(res, self.tgt)\n\n @pytest.mark.skipif(string_to_longdouble_inaccurate,\n reason="Need strtold_l")\n def test_loadtxt(self):\n with temppath() as path:\n with open(path, 'w') as f:\n f.write(self.out)\n res = np.loadtxt(path, dtype=np.longdouble)\n assert_equal(res, self.tgt)\n\n @pytest.mark.skipif(string_to_longdouble_inaccurate,\n reason="Need strtold_l")\n def test_tofile_roundtrip(self):\n with temppath() as path:\n self.tgt.tofile(path, sep=" ")\n res = np.fromfile(path, dtype=np.longdouble, sep=" ")\n assert_equal(res, self.tgt)\n\n\n# Conversions long double -> string\n\n\ndef test_str_exact():\n o = 1 + LD_INFO.eps\n assert_(str(o) != '1')\n\n\n@pytest.mark.skipif(longdouble_longer_than_double, reason="BUG #2376")\n@pytest.mark.skipif(string_to_longdouble_inaccurate,\n reason="Need strtold_l")\ndef test_format():\n assert_(f"{1 + LD_INFO.eps:.40g}" != '1')\n\n\n@pytest.mark.skipif(longdouble_longer_than_double, reason="BUG #2376")\n@pytest.mark.skipif(string_to_longdouble_inaccurate,\n reason="Need strtold_l")\ndef test_percent():\n o = 1 + LD_INFO.eps\n assert_(f"{o:.40g}" != '1')\n\n\n@pytest.mark.skipif(longdouble_longer_than_double,\n reason="array repr problem")\n@pytest.mark.skipif(string_to_longdouble_inaccurate,\n reason="Need strtold_l")\ndef test_array_repr():\n o = 1 + LD_INFO.eps\n a = np.array([o])\n b = np.array([1], dtype=np.longdouble)\n if not np.all(a != b):\n raise ValueError("precision loss creating arrays")\n assert_(repr(a) != repr(b))\n\n#\n# Locale tests: scalar types formatting should be independent of the locale\n#\n\nclass TestCommaDecimalPointLocale(CommaDecimalPointLocale):\n\n def test_str_roundtrip_foreign(self):\n o = 1.5\n assert_equal(o, np.longdouble(str(o)))\n\n def test_fromstring_foreign_repr(self):\n f = 1.234\n a = np.fromstring(repr(f), dtype=float, sep=" ")\n assert_equal(a[0], f)\n\n def test_fromstring_foreign(self):\n s = "1.234"\n a = np.fromstring(s, dtype=np.longdouble, sep=" ")\n assert_equal(a[0], np.longdouble(s))\n\n def test_fromstring_foreign_sep(self):\n a = np.array([1, 2, 3, 4])\n b = np.fromstring("1,2,3,4,", dtype=np.longdouble, sep=",")\n assert_array_equal(a, b)\n\n def test_fromstring_foreign_value(self):\n with assert_raises(ValueError):\n np.fromstring("1,234", dtype=np.longdouble, sep=" ")\n\n\n@pytest.mark.parametrize("int_val", [\n # cases discussed in gh-10723\n # and gh-9968\n 2 ** 1024, 0])\ndef test_longdouble_from_int(int_val):\n # for issue gh-9968\n str_val = str(int_val)\n # we'll expect a RuntimeWarning on platforms\n # with np.longdouble equivalent to np.double\n # for large integer input\n with warnings.catch_warnings(record=True) as w:\n warnings.filterwarnings('always', '', RuntimeWarning)\n # can be inf==inf on some platforms\n assert np.longdouble(int_val) == np.longdouble(str_val)\n # we can't directly compare the int and\n # max longdouble value on all platforms\n if np.allclose(np.finfo(np.longdouble).max,\n np.finfo(np.double).max) and w:\n assert w[0].category is RuntimeWarning\n\n@pytest.mark.parametrize("bool_val", [\n True, False])\ndef test_longdouble_from_bool(bool_val):\n assert np.longdouble(bool_val) == np.longdouble(int(bool_val))\n\n\n@pytest.mark.skipif(\n not (IS_MUSL and platform.machine() == "x86_64"),\n reason="only need to run on musllinux_x86_64"\n)\ndef test_musllinux_x86_64_signature():\n # this test may fail if you're emulating musllinux_x86_64 on a different\n # architecture, but should pass natively.\n known_sigs = [b'\xcd\xcc\xcc\xcc\xcc\xcc\xcc\xcc\xfb\xbf']\n sig = (np.longdouble(-1.0) / np.longdouble(10.0))\n sig = sig.view(sig.dtype.newbyteorder('<')).tobytes()[:10]\n assert sig in known_sigs\n\n\ndef test_eps_positive():\n # np.finfo('g').eps should be positive on all platforms. If this isn't true\n # then something may have gone wrong with the MachArLike, e.g. if\n # np._core.getlimits._discovered_machar didn't work properly\n assert np.finfo(np.longdouble).eps > 0.\n | .venv\Lib\site-packages\numpy\_core\tests\test_longdouble.py | test_longdouble.py | Python | 12,760 | 0.95 | 0.116531 | 0.132867 | python-kit | 534 | 2025-05-15T23:50:57.120931 | MIT | true | 9895c9d370ea981d13996c34ef9343c6 |
"""\nTest machar. Given recent changes to hardcode type data, we might want to get\nrid of both MachAr and this test at some point.\n\n"""\nimport numpy._core.numerictypes as ntypes\nfrom numpy import array, errstate\nfrom numpy._core._machar import MachAr\n\n\nclass TestMachAr:\n def _run_machar_highprec(self):\n # Instantiate MachAr instance with high enough precision to cause\n # underflow\n try:\n hiprec = ntypes.float96\n MachAr(lambda v: array(v, hiprec))\n except AttributeError:\n # Fixme, this needs to raise a 'skip' exception.\n "Skipping test: no ntypes.float96 available on this platform."\n\n def test_underlow(self):\n # Regression test for #759:\n # instantiating MachAr for dtype = np.float96 raises spurious warning.\n with errstate(all='raise'):\n try:\n self._run_machar_highprec()\n except FloatingPointError as e:\n msg = f"Caught {e} exception, should not have been raised."\n raise AssertionError(msg)\n | .venv\Lib\site-packages\numpy\_core\tests\test_machar.py | test_machar.py | Python | 1,097 | 0.95 | 0.233333 | 0.192308 | vue-tools | 732 | 2024-09-09T20:55:33.105752 | Apache-2.0 | true | 8ae5ae6df0f29675362fb1117eda050d |
import mmap\nimport os\nimport sys\nfrom pathlib import Path\nfrom tempfile import NamedTemporaryFile, TemporaryFile\n\nimport pytest\n\nfrom numpy import (\n add,\n allclose,\n arange,\n asarray,\n average,\n isscalar,\n memmap,\n multiply,\n ndarray,\n prod,\n subtract,\n sum,\n)\nfrom numpy.testing import (\n IS_PYPY,\n assert_,\n assert_array_equal,\n assert_equal,\n break_cycles,\n suppress_warnings,\n)\n\n\nclass TestMemmap:\n def setup_method(self):\n self.tmpfp = NamedTemporaryFile(prefix='mmap')\n self.shape = (3, 4)\n self.dtype = 'float32'\n self.data = arange(12, dtype=self.dtype)\n self.data.resize(self.shape)\n\n def teardown_method(self):\n self.tmpfp.close()\n self.data = None\n if IS_PYPY:\n break_cycles()\n break_cycles()\n\n def test_roundtrip(self):\n # Write data to file\n fp = memmap(self.tmpfp, dtype=self.dtype, mode='w+',\n shape=self.shape)\n fp[:] = self.data[:]\n del fp # Test __del__ machinery, which handles cleanup\n\n # Read data back from file\n newfp = memmap(self.tmpfp, dtype=self.dtype, mode='r',\n shape=self.shape)\n assert_(allclose(self.data, newfp))\n assert_array_equal(self.data, newfp)\n assert_equal(newfp.flags.writeable, False)\n\n def test_open_with_filename(self, tmp_path):\n tmpname = tmp_path / 'mmap'\n fp = memmap(tmpname, dtype=self.dtype, mode='w+',\n shape=self.shape)\n fp[:] = self.data[:]\n del fp\n\n def test_unnamed_file(self):\n with TemporaryFile() as f:\n fp = memmap(f, dtype=self.dtype, shape=self.shape)\n del fp\n\n def test_attributes(self):\n offset = 1\n mode = "w+"\n fp = memmap(self.tmpfp, dtype=self.dtype, mode=mode,\n shape=self.shape, offset=offset)\n assert_equal(offset, fp.offset)\n assert_equal(mode, fp.mode)\n del fp\n\n def test_filename(self, tmp_path):\n tmpname = tmp_path / "mmap"\n fp = memmap(tmpname, dtype=self.dtype, mode='w+',\n shape=self.shape)\n abspath = Path(os.path.abspath(tmpname))\n fp[:] = self.data[:]\n assert_equal(abspath, fp.filename)\n b = fp[:1]\n assert_equal(abspath, b.filename)\n del b\n del fp\n\n def test_path(self, tmp_path):\n tmpname = tmp_path / "mmap"\n fp = memmap(Path(tmpname), dtype=self.dtype, mode='w+',\n shape=self.shape)\n # os.path.realpath does not resolve symlinks on Windows\n # see: https://bugs.python.org/issue9949\n # use Path.resolve, just as memmap class does internally\n abspath = str(Path(tmpname).resolve())\n fp[:] = self.data[:]\n assert_equal(abspath, str(fp.filename.resolve()))\n b = fp[:1]\n assert_equal(abspath, str(b.filename.resolve()))\n del b\n del fp\n\n def test_filename_fileobj(self):\n fp = memmap(self.tmpfp, dtype=self.dtype, mode="w+",\n shape=self.shape)\n assert_equal(fp.filename, self.tmpfp.name)\n\n @pytest.mark.skipif(sys.platform == 'gnu0',\n reason="Known to fail on hurd")\n def test_flush(self):\n fp = memmap(self.tmpfp, dtype=self.dtype, mode='w+',\n shape=self.shape)\n fp[:] = self.data[:]\n assert_equal(fp[0], self.data[0])\n fp.flush()\n\n def test_del(self):\n # Make sure a view does not delete the underlying mmap\n fp_base = memmap(self.tmpfp, dtype=self.dtype, mode='w+',\n shape=self.shape)\n fp_base[0] = 5\n fp_view = fp_base[0:1]\n assert_equal(fp_view[0], 5)\n del fp_view\n # Should still be able to access and assign values after\n # deleting the view\n assert_equal(fp_base[0], 5)\n fp_base[0] = 6\n assert_equal(fp_base[0], 6)\n\n def test_arithmetic_drops_references(self):\n fp = memmap(self.tmpfp, dtype=self.dtype, mode='w+',\n shape=self.shape)\n tmp = (fp + 10)\n if isinstance(tmp, memmap):\n assert_(tmp._mmap is not fp._mmap)\n\n def test_indexing_drops_references(self):\n fp = memmap(self.tmpfp, dtype=self.dtype, mode='w+',\n shape=self.shape)\n tmp = fp[(1, 2), (2, 3)]\n if isinstance(tmp, memmap):\n assert_(tmp._mmap is not fp._mmap)\n\n def test_slicing_keeps_references(self):\n fp = memmap(self.tmpfp, dtype=self.dtype, mode='w+',\n shape=self.shape)\n assert_(fp[:2, :2]._mmap is fp._mmap)\n\n def test_view(self):\n fp = memmap(self.tmpfp, dtype=self.dtype, shape=self.shape)\n new1 = fp.view()\n new2 = new1.view()\n assert_(new1.base is fp)\n assert_(new2.base is fp)\n new_array = asarray(fp)\n assert_(new_array.base is fp)\n\n def test_ufunc_return_ndarray(self):\n fp = memmap(self.tmpfp, dtype=self.dtype, shape=self.shape)\n fp[:] = self.data\n\n with suppress_warnings() as sup:\n sup.filter(FutureWarning, "np.average currently does not preserve")\n for unary_op in [sum, average, prod]:\n result = unary_op(fp)\n assert_(isscalar(result))\n assert_(result.__class__ is self.data[0, 0].__class__)\n\n assert_(unary_op(fp, axis=0).__class__ is ndarray)\n assert_(unary_op(fp, axis=1).__class__ is ndarray)\n\n for binary_op in [add, subtract, multiply]:\n assert_(binary_op(fp, self.data).__class__ is ndarray)\n assert_(binary_op(self.data, fp).__class__ is ndarray)\n assert_(binary_op(fp, fp).__class__ is ndarray)\n\n fp += 1\n assert fp.__class__ is memmap\n add(fp, 1, out=fp)\n assert fp.__class__ is memmap\n\n def test_getitem(self):\n fp = memmap(self.tmpfp, dtype=self.dtype, shape=self.shape)\n fp[:] = self.data\n\n assert_(fp[1:, :-1].__class__ is memmap)\n # Fancy indexing returns a copy that is not memmapped\n assert_(fp[[0, 1]].__class__ is ndarray)\n\n def test_memmap_subclass(self):\n class MemmapSubClass(memmap):\n pass\n\n fp = MemmapSubClass(self.tmpfp, dtype=self.dtype, shape=self.shape)\n fp[:] = self.data\n\n # We keep previous behavior for subclasses of memmap, i.e. the\n # ufunc and __getitem__ output is never turned into a ndarray\n assert_(sum(fp, axis=0).__class__ is MemmapSubClass)\n assert_(sum(fp).__class__ is MemmapSubClass)\n assert_(fp[1:, :-1].__class__ is MemmapSubClass)\n assert fp[[0, 1]].__class__ is MemmapSubClass\n\n def test_mmap_offset_greater_than_allocation_granularity(self):\n size = 5 * mmap.ALLOCATIONGRANULARITY\n offset = mmap.ALLOCATIONGRANULARITY + 1\n fp = memmap(self.tmpfp, shape=size, mode='w+', offset=offset)\n assert_(fp.offset == offset)\n\n def test_empty_array_with_offset_multiple_of_allocation_granularity(self):\n self.tmpfp.write(b'a' * mmap.ALLOCATIONGRANULARITY)\n size = 0\n offset = mmap.ALLOCATIONGRANULARITY\n fp = memmap(self.tmpfp, shape=size, mode='w+', offset=offset)\n assert_equal(fp.offset, offset)\n\n def test_no_shape(self):\n self.tmpfp.write(b'a' * 16)\n mm = memmap(self.tmpfp, dtype='float64')\n assert_equal(mm.shape, (2,))\n\n def test_empty_array(self):\n # gh-12653\n with pytest.raises(ValueError, match='empty file'):\n memmap(self.tmpfp, shape=(0, 4), mode='r')\n\n # gh-27723\n # empty memmap works with mode in ('w+','r+')\n memmap(self.tmpfp, shape=(0, 4), mode='w+')\n\n # ok now the file is not empty\n memmap(self.tmpfp, shape=(0, 4), mode='w+')\n\n def test_shape_type(self):\n memmap(self.tmpfp, shape=3, mode='w+')\n memmap(self.tmpfp, shape=self.shape, mode='w+')\n memmap(self.tmpfp, shape=list(self.shape), mode='w+')\n memmap(self.tmpfp, shape=asarray(self.shape), mode='w+')\n | .venv\Lib\site-packages\numpy\_core\tests\test_memmap.py | test_memmap.py | Python | 8,432 | 0.95 | 0.130081 | 0.071429 | node-utils | 261 | 2024-03-06T16:52:36.915290 | MIT | true | af74c4725f1c700d4932308627aa2f57 |
import itertools\n\nimport pytest\nfrom numpy._core._multiarray_tests import internal_overlap, solve_diophantine\n\nimport numpy as np\nfrom numpy._core import _umath_tests\nfrom numpy.lib.stride_tricks import as_strided\nfrom numpy.testing import assert_, assert_array_equal, assert_equal, assert_raises\n\nndims = 2\nsize = 10\nshape = tuple([size] * ndims)\n\nMAY_SHARE_BOUNDS = 0\nMAY_SHARE_EXACT = -1\n\n\ndef _indices_for_nelems(nelems):\n """Returns slices of length nelems, from start onwards, in direction sign."""\n\n if nelems == 0:\n return [size // 2] # int index\n\n res = []\n for step in (1, 2):\n for sign in (-1, 1):\n start = size // 2 - nelems * step * sign // 2\n stop = start + nelems * step * sign\n res.append(slice(start, stop, step * sign))\n\n return res\n\n\ndef _indices_for_axis():\n """Returns (src, dst) pairs of indices."""\n\n res = []\n for nelems in (0, 2, 3):\n ind = _indices_for_nelems(nelems)\n res.extend(itertools.product(ind, ind)) # all assignments of size "nelems"\n\n return res\n\n\ndef _indices(ndims):\n """Returns ((axis0_src, axis0_dst), (axis1_src, axis1_dst), ... ) index pairs."""\n\n ind = _indices_for_axis()\n return itertools.product(ind, repeat=ndims)\n\n\ndef _check_assignment(srcidx, dstidx):\n """Check assignment arr[dstidx] = arr[srcidx] works."""\n\n arr = np.arange(np.prod(shape)).reshape(shape)\n\n cpy = arr.copy()\n\n cpy[dstidx] = arr[srcidx]\n arr[dstidx] = arr[srcidx]\n\n assert_(np.all(arr == cpy),\n f'assigning arr[{dstidx}] = arr[{srcidx}]')\n\n\ndef test_overlapping_assignments():\n # Test automatically generated assignments which overlap in memory.\n\n inds = _indices(ndims)\n\n for ind in inds:\n srcidx = tuple(a[0] for a in ind)\n dstidx = tuple(a[1] for a in ind)\n\n _check_assignment(srcidx, dstidx)\n\n\n@pytest.mark.slow\ndef test_diophantine_fuzz():\n # Fuzz test the diophantine solver\n rng = np.random.RandomState(1234)\n\n max_int = np.iinfo(np.intp).max\n\n for ndim in range(10):\n feasible_count = 0\n infeasible_count = 0\n\n min_count = 500 // (ndim + 1)\n\n while min(feasible_count, infeasible_count) < min_count:\n # Ensure big and small integer problems\n A_max = 1 + rng.randint(0, 11, dtype=np.intp)**6\n U_max = rng.randint(0, 11, dtype=np.intp)**6\n\n A_max = min(max_int, A_max)\n U_max = min(max_int - 1, U_max)\n\n A = tuple(int(rng.randint(1, A_max + 1, dtype=np.intp))\n for j in range(ndim))\n U = tuple(int(rng.randint(0, U_max + 2, dtype=np.intp))\n for j in range(ndim))\n\n b_ub = min(max_int - 2, sum(a * ub for a, ub in zip(A, U)))\n b = int(rng.randint(-1, b_ub + 2, dtype=np.intp))\n\n if ndim == 0 and feasible_count < min_count:\n b = 0\n\n X = solve_diophantine(A, U, b)\n\n if X is None:\n # Check the simplified decision problem agrees\n X_simplified = solve_diophantine(A, U, b, simplify=1)\n assert_(X_simplified is None, (A, U, b, X_simplified))\n\n # Check no solution exists (provided the problem is\n # small enough so that brute force checking doesn't\n # take too long)\n ranges = tuple(range(0, a * ub + 1, a) for a, ub in zip(A, U))\n\n size = 1\n for r in ranges:\n size *= len(r)\n if size < 100000:\n assert_(not any(sum(w) == b for w in itertools.product(*ranges)))\n infeasible_count += 1\n else:\n # Check the simplified decision problem agrees\n X_simplified = solve_diophantine(A, U, b, simplify=1)\n assert_(X_simplified is not None, (A, U, b, X_simplified))\n\n # Check validity\n assert_(sum(a * x for a, x in zip(A, X)) == b)\n assert_(all(0 <= x <= ub for x, ub in zip(X, U)))\n feasible_count += 1\n\n\ndef test_diophantine_overflow():\n # Smoke test integer overflow detection\n max_intp = np.iinfo(np.intp).max\n max_int64 = np.iinfo(np.int64).max\n\n if max_int64 <= max_intp:\n # Check that the algorithm works internally in 128-bit;\n # solving this problem requires large intermediate numbers\n A = (max_int64 // 2, max_int64 // 2 - 10)\n U = (max_int64 // 2, max_int64 // 2 - 10)\n b = 2 * (max_int64 // 2) - 10\n\n assert_equal(solve_diophantine(A, U, b), (1, 1))\n\n\ndef check_may_share_memory_exact(a, b):\n got = np.may_share_memory(a, b, max_work=MAY_SHARE_EXACT)\n\n assert_equal(np.may_share_memory(a, b),\n np.may_share_memory(a, b, max_work=MAY_SHARE_BOUNDS))\n\n a.fill(0)\n b.fill(0)\n a.fill(1)\n exact = b.any()\n\n err_msg = ""\n if got != exact:\n err_msg = " " + "\n ".join([\n f"base_a - base_b = {a.__array_interface__['data'][0] - b.__array_interface__['data'][0]!r}",\n f"shape_a = {a.shape!r}",\n f"shape_b = {b.shape!r}",\n f"strides_a = {a.strides!r}",\n f"strides_b = {b.strides!r}",\n f"size_a = {a.size!r}",\n f"size_b = {b.size!r}"\n ])\n\n assert_equal(got, exact, err_msg=err_msg)\n\n\ndef test_may_share_memory_manual():\n # Manual test cases for may_share_memory\n\n # Base arrays\n xs0 = [\n np.zeros([13, 21, 23, 22], dtype=np.int8),\n np.zeros([13, 21, 23 * 2, 22], dtype=np.int8)[:, :, ::2, :]\n ]\n\n # Generate all negative stride combinations\n xs = []\n for x in xs0:\n for ss in itertools.product(*(([slice(None), slice(None, None, -1)],) * 4)):\n xp = x[ss]\n xs.append(xp)\n\n for x in xs:\n # The default is a simple extent check\n assert_(np.may_share_memory(x[:, 0, :], x[:, 1, :]))\n assert_(np.may_share_memory(x[:, 0, :], x[:, 1, :], max_work=None))\n\n # Exact checks\n check_may_share_memory_exact(x[:, 0, :], x[:, 1, :])\n check_may_share_memory_exact(x[:, ::7], x[:, 3::3])\n\n try:\n xp = x.ravel()\n if xp.flags.owndata:\n continue\n xp = xp.view(np.int16)\n except ValueError:\n continue\n\n # 0-size arrays cannot overlap\n check_may_share_memory_exact(x.ravel()[6:6],\n xp.reshape(13, 21, 23, 11)[:, ::7])\n\n # Test itemsize is dealt with\n check_may_share_memory_exact(x[:, ::7],\n xp.reshape(13, 21, 23, 11))\n check_may_share_memory_exact(x[:, ::7],\n xp.reshape(13, 21, 23, 11)[:, 3::3])\n check_may_share_memory_exact(x.ravel()[6:7],\n xp.reshape(13, 21, 23, 11)[:, ::7])\n\n # Check unit size\n x = np.zeros([1], dtype=np.int8)\n check_may_share_memory_exact(x, x)\n check_may_share_memory_exact(x, x.copy())\n\n\ndef iter_random_view_pairs(x, same_steps=True, equal_size=False):\n rng = np.random.RandomState(1234)\n\n if equal_size and same_steps:\n raise ValueError\n\n def random_slice(n, step):\n start = rng.randint(0, n + 1, dtype=np.intp)\n stop = rng.randint(start, n + 1, dtype=np.intp)\n if rng.randint(0, 2, dtype=np.intp) == 0:\n stop, start = start, stop\n step *= -1\n return slice(start, stop, step)\n\n def random_slice_fixed_size(n, step, size):\n start = rng.randint(0, n + 1 - size * step)\n stop = start + (size - 1) * step + 1\n if rng.randint(0, 2) == 0:\n stop, start = start - 1, stop - 1\n if stop < 0:\n stop = None\n step *= -1\n return slice(start, stop, step)\n\n # First a few regular views\n yield x, x\n for j in range(1, 7, 3):\n yield x[j:], x[:-j]\n yield x[..., j:], x[..., :-j]\n\n # An array with zero stride internal overlap\n strides = list(x.strides)\n strides[0] = 0\n xp = as_strided(x, shape=x.shape, strides=strides)\n yield x, xp\n yield xp, xp\n\n # An array with non-zero stride internal overlap\n strides = list(x.strides)\n if strides[0] > 1:\n strides[0] = 1\n xp = as_strided(x, shape=x.shape, strides=strides)\n yield x, xp\n yield xp, xp\n\n # Then discontiguous views\n while True:\n steps = tuple(rng.randint(1, 11, dtype=np.intp)\n if rng.randint(0, 5, dtype=np.intp) == 0 else 1\n for j in range(x.ndim))\n s1 = tuple(random_slice(p, s) for p, s in zip(x.shape, steps))\n\n t1 = np.arange(x.ndim)\n rng.shuffle(t1)\n\n if equal_size:\n t2 = t1\n else:\n t2 = np.arange(x.ndim)\n rng.shuffle(t2)\n\n a = x[s1]\n\n if equal_size:\n if a.size == 0:\n continue\n\n steps2 = tuple(rng.randint(1, max(2, p // (1 + pa)))\n if rng.randint(0, 5) == 0 else 1\n for p, s, pa in zip(x.shape, s1, a.shape))\n s2 = tuple(random_slice_fixed_size(p, s, pa)\n for p, s, pa in zip(x.shape, steps2, a.shape))\n elif same_steps:\n steps2 = steps\n else:\n steps2 = tuple(rng.randint(1, 11, dtype=np.intp)\n if rng.randint(0, 5, dtype=np.intp) == 0 else 1\n for j in range(x.ndim))\n\n if not equal_size:\n s2 = tuple(random_slice(p, s) for p, s in zip(x.shape, steps2))\n\n a = a.transpose(t1)\n b = x[s2].transpose(t2)\n\n yield a, b\n\n\ndef check_may_share_memory_easy_fuzz(get_max_work, same_steps, min_count):\n # Check that overlap problems with common strides are solved with\n # little work.\n x = np.zeros([17, 34, 71, 97], dtype=np.int16)\n\n feasible = 0\n infeasible = 0\n\n pair_iter = iter_random_view_pairs(x, same_steps)\n\n while min(feasible, infeasible) < min_count:\n a, b = next(pair_iter)\n\n bounds_overlap = np.may_share_memory(a, b)\n may_share_answer = np.may_share_memory(a, b)\n easy_answer = np.may_share_memory(a, b, max_work=get_max_work(a, b))\n exact_answer = np.may_share_memory(a, b, max_work=MAY_SHARE_EXACT)\n\n if easy_answer != exact_answer:\n # assert_equal is slow...\n assert_equal(easy_answer, exact_answer)\n\n if may_share_answer != bounds_overlap:\n assert_equal(may_share_answer, bounds_overlap)\n\n if bounds_overlap:\n if exact_answer:\n feasible += 1\n else:\n infeasible += 1\n\n\n@pytest.mark.slow\ndef test_may_share_memory_easy_fuzz():\n # Check that overlap problems with common strides are always\n # solved with little work.\n\n check_may_share_memory_easy_fuzz(get_max_work=lambda a, b: 1,\n same_steps=True,\n min_count=2000)\n\n\n@pytest.mark.slow\ndef test_may_share_memory_harder_fuzz():\n # Overlap problems with not necessarily common strides take more\n # work.\n #\n # The work bound below can't be reduced much. Harder problems can\n # also exist but not be detected here, as the set of problems\n # comes from RNG.\n\n check_may_share_memory_easy_fuzz(get_max_work=lambda a, b: max(a.size, b.size) // 2,\n same_steps=False,\n min_count=2000)\n\n\ndef test_shares_memory_api():\n x = np.zeros([4, 5, 6], dtype=np.int8)\n\n assert_equal(np.shares_memory(x, x), True)\n assert_equal(np.shares_memory(x, x.copy()), False)\n\n a = x[:, ::2, ::3]\n b = x[:, ::3, ::2]\n assert_equal(np.shares_memory(a, b), True)\n assert_equal(np.shares_memory(a, b, max_work=None), True)\n assert_raises(\n np.exceptions.TooHardError, np.shares_memory, a, b, max_work=1\n )\n\n\ndef test_may_share_memory_bad_max_work():\n x = np.zeros([1])\n assert_raises(OverflowError, np.may_share_memory, x, x, max_work=10**100)\n assert_raises(OverflowError, np.shares_memory, x, x, max_work=10**100)\n\n\ndef test_internal_overlap_diophantine():\n def check(A, U, exists=None):\n X = solve_diophantine(A, U, 0, require_ub_nontrivial=1)\n\n if exists is None:\n exists = (X is not None)\n\n if X is not None:\n assert_(sum(a * x for a, x in zip(A, X)) == sum(a * u // 2 for a, u in zip(A, U)))\n assert_(all(0 <= x <= u for x, u in zip(X, U)))\n assert_(any(x != u // 2 for x, u in zip(X, U)))\n\n if exists:\n assert_(X is not None, repr(X))\n else:\n assert_(X is None, repr(X))\n\n # Smoke tests\n check((3, 2), (2 * 2, 3 * 2), exists=True)\n check((3 * 2, 2), (15 * 2, (3 - 1) * 2), exists=False)\n\n\ndef test_internal_overlap_slices():\n # Slicing an array never generates internal overlap\n\n x = np.zeros([17, 34, 71, 97], dtype=np.int16)\n\n rng = np.random.RandomState(1234)\n\n def random_slice(n, step):\n start = rng.randint(0, n + 1, dtype=np.intp)\n stop = rng.randint(start, n + 1, dtype=np.intp)\n if rng.randint(0, 2, dtype=np.intp) == 0:\n stop, start = start, stop\n step *= -1\n return slice(start, stop, step)\n\n cases = 0\n min_count = 5000\n\n while cases < min_count:\n steps = tuple(rng.randint(1, 11, dtype=np.intp)\n if rng.randint(0, 5, dtype=np.intp) == 0 else 1\n for j in range(x.ndim))\n t1 = np.arange(x.ndim)\n rng.shuffle(t1)\n s1 = tuple(random_slice(p, s) for p, s in zip(x.shape, steps))\n a = x[s1].transpose(t1)\n\n assert_(not internal_overlap(a))\n cases += 1\n\n\ndef check_internal_overlap(a, manual_expected=None):\n got = internal_overlap(a)\n\n # Brute-force check\n m = set()\n ranges = tuple(range(n) for n in a.shape)\n for v in itertools.product(*ranges):\n offset = sum(s * w for s, w in zip(a.strides, v))\n if offset in m:\n expected = True\n break\n else:\n m.add(offset)\n else:\n expected = False\n\n # Compare\n if got != expected:\n assert_equal(got, expected, err_msg=repr((a.strides, a.shape)))\n if manual_expected is not None and expected != manual_expected:\n assert_equal(expected, manual_expected)\n return got\n\n\ndef test_internal_overlap_manual():\n # Stride tricks can construct arrays with internal overlap\n\n # We don't care about memory bounds, the array is not\n # read/write accessed\n x = np.arange(1).astype(np.int8)\n\n # Check low-dimensional special cases\n\n check_internal_overlap(x, False) # 1-dim\n check_internal_overlap(x.reshape([]), False) # 0-dim\n\n a = as_strided(x, strides=(3, 4), shape=(4, 4))\n check_internal_overlap(a, False)\n\n a = as_strided(x, strides=(3, 4), shape=(5, 4))\n check_internal_overlap(a, True)\n\n a = as_strided(x, strides=(0,), shape=(0,))\n check_internal_overlap(a, False)\n\n a = as_strided(x, strides=(0,), shape=(1,))\n check_internal_overlap(a, False)\n\n a = as_strided(x, strides=(0,), shape=(2,))\n check_internal_overlap(a, True)\n\n a = as_strided(x, strides=(0, -9993), shape=(87, 22))\n check_internal_overlap(a, True)\n\n a = as_strided(x, strides=(0, -9993), shape=(1, 22))\n check_internal_overlap(a, False)\n\n a = as_strided(x, strides=(0, -9993), shape=(0, 22))\n check_internal_overlap(a, False)\n\n\ndef test_internal_overlap_fuzz():\n # Fuzz check; the brute-force check is fairly slow\n\n x = np.arange(1).astype(np.int8)\n\n overlap = 0\n no_overlap = 0\n min_count = 100\n\n rng = np.random.RandomState(1234)\n\n while min(overlap, no_overlap) < min_count:\n ndim = rng.randint(1, 4, dtype=np.intp)\n\n strides = tuple(rng.randint(-1000, 1000, dtype=np.intp)\n for j in range(ndim))\n shape = tuple(rng.randint(1, 30, dtype=np.intp)\n for j in range(ndim))\n\n a = as_strided(x, strides=strides, shape=shape)\n result = check_internal_overlap(a)\n\n if result:\n overlap += 1\n else:\n no_overlap += 1\n\n\ndef test_non_ndarray_inputs():\n # Regression check for gh-5604\n\n class MyArray:\n def __init__(self, data):\n self.data = data\n\n @property\n def __array_interface__(self):\n return self.data.__array_interface__\n\n class MyArray2:\n def __init__(self, data):\n self.data = data\n\n def __array__(self, dtype=None, copy=None):\n return self.data\n\n for cls in [MyArray, MyArray2]:\n x = np.arange(5)\n\n assert_(np.may_share_memory(cls(x[::2]), x[1::2]))\n assert_(not np.shares_memory(cls(x[::2]), x[1::2]))\n\n assert_(np.shares_memory(cls(x[1::3]), x[::2]))\n assert_(np.may_share_memory(cls(x[1::3]), x[::2]))\n\n\ndef view_element_first_byte(x):\n """Construct an array viewing the first byte of each element of `x`"""\n from numpy.lib._stride_tricks_impl import DummyArray\n interface = dict(x.__array_interface__)\n interface['typestr'] = '|b1'\n interface['descr'] = [('', '|b1')]\n return np.asarray(DummyArray(interface, x))\n\n\ndef assert_copy_equivalent(operation, args, out, **kwargs):\n """\n Check that operation(*args, out=out) produces results\n equivalent to out[...] = operation(*args, out=out.copy())\n """\n\n kwargs['out'] = out\n kwargs2 = dict(kwargs)\n kwargs2['out'] = out.copy()\n\n out_orig = out.copy()\n out[...] = operation(*args, **kwargs2)\n expected = out.copy()\n out[...] = out_orig\n\n got = operation(*args, **kwargs).copy()\n\n if (got != expected).any():\n assert_equal(got, expected)\n\n\nclass TestUFunc:\n """\n Test ufunc call memory overlap handling\n """\n\n def check_unary_fuzz(self, operation, get_out_axis_size, dtype=np.int16,\n count=5000):\n shapes = [7, 13, 8, 21, 29, 32]\n\n rng = np.random.RandomState(1234)\n\n for ndim in range(1, 6):\n x = rng.randint(0, 2**16, size=shapes[:ndim]).astype(dtype)\n\n it = iter_random_view_pairs(x, same_steps=False, equal_size=True)\n\n min_count = count // (ndim + 1)**2\n\n overlapping = 0\n while overlapping < min_count:\n a, b = next(it)\n\n a_orig = a.copy()\n b_orig = b.copy()\n\n if get_out_axis_size is None:\n assert_copy_equivalent(operation, [a], out=b)\n\n if np.shares_memory(a, b):\n overlapping += 1\n else:\n for axis in itertools.chain(range(ndim), [None]):\n a[...] = a_orig\n b[...] = b_orig\n\n # Determine size for reduction axis (None if scalar)\n outsize, scalarize = get_out_axis_size(a, b, axis)\n if outsize == 'skip':\n continue\n\n # Slice b to get an output array of the correct size\n sl = [slice(None)] * ndim\n if axis is None:\n if outsize is None:\n sl = [slice(0, 1)] + [0] * (ndim - 1)\n else:\n sl = [slice(0, outsize)] + [0] * (ndim - 1)\n elif outsize is None:\n k = b.shape[axis] // 2\n if ndim == 1:\n sl[axis] = slice(k, k + 1)\n else:\n sl[axis] = k\n else:\n assert b.shape[axis] >= outsize\n sl[axis] = slice(0, outsize)\n b_out = b[tuple(sl)]\n\n if scalarize:\n b_out = b_out.reshape([])\n\n if np.shares_memory(a, b_out):\n overlapping += 1\n\n # Check result\n assert_copy_equivalent(operation, [a], out=b_out, axis=axis)\n\n @pytest.mark.slow\n def test_unary_ufunc_call_fuzz(self):\n self.check_unary_fuzz(np.invert, None, np.int16)\n\n @pytest.mark.slow\n def test_unary_ufunc_call_complex_fuzz(self):\n # Complex typically has a smaller alignment than itemsize\n self.check_unary_fuzz(np.negative, None, np.complex128, count=500)\n\n def test_binary_ufunc_accumulate_fuzz(self):\n def get_out_axis_size(a, b, axis):\n if axis is None:\n if a.ndim == 1:\n return a.size, False\n else:\n return 'skip', False # accumulate doesn't support this\n else:\n return a.shape[axis], False\n\n self.check_unary_fuzz(np.add.accumulate, get_out_axis_size,\n dtype=np.int16, count=500)\n\n def test_binary_ufunc_reduce_fuzz(self):\n def get_out_axis_size(a, b, axis):\n return None, (axis is None or a.ndim == 1)\n\n self.check_unary_fuzz(np.add.reduce, get_out_axis_size,\n dtype=np.int16, count=500)\n\n def test_binary_ufunc_reduceat_fuzz(self):\n def get_out_axis_size(a, b, axis):\n if axis is None:\n if a.ndim == 1:\n return a.size, False\n else:\n return 'skip', False # reduceat doesn't support this\n else:\n return a.shape[axis], False\n\n def do_reduceat(a, out, axis):\n if axis is None:\n size = len(a)\n step = size // len(out)\n else:\n size = a.shape[axis]\n step = a.shape[axis] // out.shape[axis]\n idx = np.arange(0, size, step)\n return np.add.reduceat(a, idx, out=out, axis=axis)\n\n self.check_unary_fuzz(do_reduceat, get_out_axis_size,\n dtype=np.int16, count=500)\n\n def test_binary_ufunc_reduceat_manual(self):\n def check(ufunc, a, ind, out):\n c1 = ufunc.reduceat(a.copy(), ind.copy(), out=out.copy())\n c2 = ufunc.reduceat(a, ind, out=out)\n assert_array_equal(c1, c2)\n\n # Exactly same input/output arrays\n a = np.arange(10000, dtype=np.int16)\n check(np.add, a, a[::-1].copy(), a)\n\n # Overlap with index\n a = np.arange(10000, dtype=np.int16)\n check(np.add, a, a[::-1], a)\n\n @pytest.mark.slow\n def test_unary_gufunc_fuzz(self):\n shapes = [7, 13, 8, 21, 29, 32]\n gufunc = _umath_tests.euclidean_pdist\n\n rng = np.random.RandomState(1234)\n\n for ndim in range(2, 6):\n x = rng.rand(*shapes[:ndim])\n\n it = iter_random_view_pairs(x, same_steps=False, equal_size=True)\n\n min_count = 500 // (ndim + 1)**2\n\n overlapping = 0\n while overlapping < min_count:\n a, b = next(it)\n\n if min(a.shape[-2:]) < 2 or min(b.shape[-2:]) < 2 or a.shape[-1] < 2:\n continue\n\n # Ensure the shapes are so that euclidean_pdist is happy\n if b.shape[-1] > b.shape[-2]:\n b = b[..., 0, :]\n else:\n b = b[..., :, 0]\n\n n = a.shape[-2]\n p = n * (n - 1) // 2\n if p <= b.shape[-1] and p > 0:\n b = b[..., :p]\n else:\n n = max(2, int(np.sqrt(b.shape[-1])) // 2)\n p = n * (n - 1) // 2\n a = a[..., :n, :]\n b = b[..., :p]\n\n # Call\n if np.shares_memory(a, b):\n overlapping += 1\n\n with np.errstate(over='ignore', invalid='ignore'):\n assert_copy_equivalent(gufunc, [a], out=b)\n\n def test_ufunc_at_manual(self):\n def check(ufunc, a, ind, b=None):\n a0 = a.copy()\n if b is None:\n ufunc.at(a0, ind.copy())\n c1 = a0.copy()\n ufunc.at(a, ind)\n c2 = a.copy()\n else:\n ufunc.at(a0, ind.copy(), b.copy())\n c1 = a0.copy()\n ufunc.at(a, ind, b)\n c2 = a.copy()\n assert_array_equal(c1, c2)\n\n # Overlap with index\n a = np.arange(10000, dtype=np.int16)\n check(np.invert, a[::-1], a)\n\n # Overlap with second data array\n a = np.arange(100, dtype=np.int16)\n ind = np.arange(0, 100, 2, dtype=np.int16)\n check(np.add, a, ind, a[25:75])\n\n def test_unary_ufunc_1d_manual(self):\n # Exercise ufunc fast-paths (that avoid creation of an `np.nditer`)\n\n def check(a, b):\n a_orig = a.copy()\n b_orig = b.copy()\n\n b0 = b.copy()\n c1 = ufunc(a, out=b0)\n c2 = ufunc(a, out=b)\n assert_array_equal(c1, c2)\n\n # Trigger "fancy ufunc loop" code path\n mask = view_element_first_byte(b).view(np.bool)\n\n a[...] = a_orig\n b[...] = b_orig\n c1 = ufunc(a, out=b.copy(), where=mask.copy()).copy()\n\n a[...] = a_orig\n b[...] = b_orig\n c2 = ufunc(a, out=b, where=mask.copy()).copy()\n\n # Also, mask overlapping with output\n a[...] = a_orig\n b[...] = b_orig\n c3 = ufunc(a, out=b, where=mask).copy()\n\n assert_array_equal(c1, c2)\n assert_array_equal(c1, c3)\n\n dtypes = [np.int8, np.int16, np.int32, np.int64, np.float32,\n np.float64, np.complex64, np.complex128]\n dtypes = [np.dtype(x) for x in dtypes]\n\n for dtype in dtypes:\n if np.issubdtype(dtype, np.integer):\n ufunc = np.invert\n else:\n ufunc = np.reciprocal\n\n n = 1000\n k = 10\n indices = [\n np.index_exp[:n],\n np.index_exp[k:k + n],\n np.index_exp[n - 1::-1],\n np.index_exp[k + n - 1:k - 1:-1],\n np.index_exp[:2 * n:2],\n np.index_exp[k:k + 2 * n:2],\n np.index_exp[2 * n - 1::-2],\n np.index_exp[k + 2 * n - 1:k - 1:-2],\n ]\n\n for xi, yi in itertools.product(indices, indices):\n v = np.arange(1, 1 + n * 2 + k, dtype=dtype)\n x = v[xi]\n y = v[yi]\n\n with np.errstate(all='ignore'):\n check(x, y)\n\n # Scalar cases\n check(x[:1], y)\n check(x[-1:], y)\n check(x[:1].reshape([]), y)\n check(x[-1:].reshape([]), y)\n\n def test_unary_ufunc_where_same(self):\n # Check behavior at wheremask overlap\n ufunc = np.invert\n\n def check(a, out, mask):\n c1 = ufunc(a, out=out.copy(), where=mask.copy())\n c2 = ufunc(a, out=out, where=mask)\n assert_array_equal(c1, c2)\n\n # Check behavior with same input and output arrays\n x = np.arange(100).astype(np.bool)\n check(x, x, x)\n check(x, x.copy(), x)\n check(x, x, x.copy())\n\n @pytest.mark.slow\n def test_binary_ufunc_1d_manual(self):\n ufunc = np.add\n\n def check(a, b, c):\n c0 = c.copy()\n c1 = ufunc(a, b, out=c0)\n c2 = ufunc(a, b, out=c)\n assert_array_equal(c1, c2)\n\n for dtype in [np.int8, np.int16, np.int32, np.int64,\n np.float32, np.float64, np.complex64, np.complex128]:\n # Check different data dependency orders\n\n n = 1000\n k = 10\n\n indices = []\n for p in [1, 2]:\n indices.extend([\n np.index_exp[:p * n:p],\n np.index_exp[k:k + p * n:p],\n np.index_exp[p * n - 1::-p],\n np.index_exp[k + p * n - 1:k - 1:-p],\n ])\n\n for x, y, z in itertools.product(indices, indices, indices):\n v = np.arange(6 * n).astype(dtype)\n x = v[x]\n y = v[y]\n z = v[z]\n\n check(x, y, z)\n\n # Scalar cases\n check(x[:1], y, z)\n check(x[-1:], y, z)\n check(x[:1].reshape([]), y, z)\n check(x[-1:].reshape([]), y, z)\n check(x, y[:1], z)\n check(x, y[-1:], z)\n check(x, y[:1].reshape([]), z)\n check(x, y[-1:].reshape([]), z)\n\n def test_inplace_op_simple_manual(self):\n rng = np.random.RandomState(1234)\n x = rng.rand(200, 200) # bigger than bufsize\n\n x += x.T\n assert_array_equal(x - x.T, 0)\n | .venv\Lib\site-packages\numpy\_core\tests\test_mem_overlap.py | test_mem_overlap.py | Python | 30,149 | 0.95 | 0.178495 | 0.089489 | react-lib | 663 | 2024-12-10T02:19:58.312258 | GPL-3.0 | true | ce8b9dd9efb311e94aa9d97d51c0047a |
import asyncio\nimport gc\nimport os\nimport sys\nimport sysconfig\nimport threading\n\nimport pytest\n\nimport numpy as np\nfrom numpy._core.multiarray import get_handler_name\nfrom numpy.testing import IS_EDITABLE, IS_WASM, assert_warns, extbuild\n\n\n@pytest.fixture\ndef get_module(tmp_path):\n """ Add a memory policy that returns a false pointer 64 bytes into the\n actual allocation, and fill the prefix with some text. Then check at each\n memory manipulation that the prefix exists, to make sure all alloc/realloc/\n free/calloc go via the functions here.\n """\n if sys.platform.startswith('cygwin'):\n pytest.skip('link fails on cygwin')\n if IS_WASM:\n pytest.skip("Can't build module inside Wasm")\n if IS_EDITABLE:\n pytest.skip("Can't build module for editable install")\n\n functions = [\n ("get_default_policy", "METH_NOARGS", """\n Py_INCREF(PyDataMem_DefaultHandler);\n return PyDataMem_DefaultHandler;\n """),\n ("set_secret_data_policy", "METH_NOARGS", """\n PyObject *secret_data =\n PyCapsule_New(&secret_data_handler, "mem_handler", NULL);\n if (secret_data == NULL) {\n return NULL;\n }\n PyObject *old = PyDataMem_SetHandler(secret_data);\n Py_DECREF(secret_data);\n return old;\n """),\n ("set_wrong_capsule_name_data_policy", "METH_NOARGS", """\n PyObject *wrong_name_capsule =\n PyCapsule_New(&secret_data_handler, "not_mem_handler", NULL);\n if (wrong_name_capsule == NULL) {\n return NULL;\n }\n PyObject *old = PyDataMem_SetHandler(wrong_name_capsule);\n Py_DECREF(wrong_name_capsule);\n return old;\n """),\n ("set_old_policy", "METH_O", """\n PyObject *old;\n if (args != NULL && PyCapsule_CheckExact(args)) {\n old = PyDataMem_SetHandler(args);\n }\n else {\n old = PyDataMem_SetHandler(NULL);\n }\n return old;\n """),\n ("get_array", "METH_NOARGS", """\n char *buf = (char *)malloc(20);\n npy_intp dims[1];\n dims[0] = 20;\n PyArray_Descr *descr = PyArray_DescrNewFromType(NPY_UINT8);\n return PyArray_NewFromDescr(&PyArray_Type, descr, 1, dims, NULL,\n buf, NPY_ARRAY_WRITEABLE, NULL);\n """),\n ("set_own", "METH_O", """\n if (!PyArray_Check(args)) {\n PyErr_SetString(PyExc_ValueError,\n "need an ndarray");\n return NULL;\n }\n PyArray_ENABLEFLAGS((PyArrayObject*)args, NPY_ARRAY_OWNDATA);\n // Maybe try this too?\n // PyArray_BASE(PyArrayObject *)args) = NULL;\n Py_RETURN_NONE;\n """),\n ("get_array_with_base", "METH_NOARGS", """\n char *buf = (char *)malloc(20);\n npy_intp dims[1];\n dims[0] = 20;\n PyArray_Descr *descr = PyArray_DescrNewFromType(NPY_UINT8);\n PyObject *arr = PyArray_NewFromDescr(&PyArray_Type, descr, 1, dims,\n NULL, buf,\n NPY_ARRAY_WRITEABLE, NULL);\n if (arr == NULL) return NULL;\n PyObject *obj = PyCapsule_New(buf, "buf capsule",\n (PyCapsule_Destructor)&warn_on_free);\n if (obj == NULL) {\n Py_DECREF(arr);\n return NULL;\n }\n if (PyArray_SetBaseObject((PyArrayObject *)arr, obj) < 0) {\n Py_DECREF(arr);\n Py_DECREF(obj);\n return NULL;\n }\n return arr;\n\n """),\n ]\n prologue = '''\n #define NPY_TARGET_VERSION NPY_1_22_API_VERSION\n #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\n #include <numpy/arrayobject.h>\n /*\n * This struct allows the dynamic configuration of the allocator funcs\n * of the `secret_data_allocator`. It is provided here for\n * demonstration purposes, as a valid `ctx` use-case scenario.\n */\n typedef struct {\n void *(*malloc)(size_t);\n void *(*calloc)(size_t, size_t);\n void *(*realloc)(void *, size_t);\n void (*free)(void *);\n } SecretDataAllocatorFuncs;\n\n NPY_NO_EXPORT void *\n shift_alloc(void *ctx, size_t sz) {\n SecretDataAllocatorFuncs *funcs = (SecretDataAllocatorFuncs *)ctx;\n char *real = (char *)funcs->malloc(sz + 64);\n if (real == NULL) {\n return NULL;\n }\n snprintf(real, 64, "originally allocated %ld", (unsigned long)sz);\n return (void *)(real + 64);\n }\n NPY_NO_EXPORT void *\n shift_zero(void *ctx, size_t sz, size_t cnt) {\n SecretDataAllocatorFuncs *funcs = (SecretDataAllocatorFuncs *)ctx;\n char *real = (char *)funcs->calloc(sz + 64, cnt);\n if (real == NULL) {\n return NULL;\n }\n snprintf(real, 64, "originally allocated %ld via zero",\n (unsigned long)sz);\n return (void *)(real + 64);\n }\n NPY_NO_EXPORT void\n shift_free(void *ctx, void * p, npy_uintp sz) {\n SecretDataAllocatorFuncs *funcs = (SecretDataAllocatorFuncs *)ctx;\n if (p == NULL) {\n return ;\n }\n char *real = (char *)p - 64;\n if (strncmp(real, "originally allocated", 20) != 0) {\n fprintf(stdout, "uh-oh, unmatched shift_free, "\n "no appropriate prefix\\n");\n /* Make C runtime crash by calling free on the wrong address */\n funcs->free((char *)p + 10);\n /* funcs->free(real); */\n }\n else {\n npy_uintp i = (npy_uintp)atoi(real +20);\n if (i != sz) {\n fprintf(stderr, "uh-oh, unmatched shift_free"\n "(ptr, %ld) but allocated %ld\\n", sz, i);\n /* This happens in some places, only print */\n funcs->free(real);\n }\n else {\n funcs->free(real);\n }\n }\n }\n NPY_NO_EXPORT void *\n shift_realloc(void *ctx, void * p, npy_uintp sz) {\n SecretDataAllocatorFuncs *funcs = (SecretDataAllocatorFuncs *)ctx;\n if (p != NULL) {\n char *real = (char *)p - 64;\n if (strncmp(real, "originally allocated", 20) != 0) {\n fprintf(stdout, "uh-oh, unmatched shift_realloc\\n");\n return realloc(p, sz);\n }\n return (void *)((char *)funcs->realloc(real, sz + 64) + 64);\n }\n else {\n char *real = (char *)funcs->realloc(p, sz + 64);\n if (real == NULL) {\n return NULL;\n }\n snprintf(real, 64, "originally allocated "\n "%ld via realloc", (unsigned long)sz);\n return (void *)(real + 64);\n }\n }\n /* As an example, we use the standard {m|c|re}alloc/free funcs. */\n static SecretDataAllocatorFuncs secret_data_handler_ctx = {\n malloc,\n calloc,\n realloc,\n free\n };\n static PyDataMem_Handler secret_data_handler = {\n "secret_data_allocator",\n 1,\n {\n &secret_data_handler_ctx, /* ctx */\n shift_alloc, /* malloc */\n shift_zero, /* calloc */\n shift_realloc, /* realloc */\n shift_free /* free */\n }\n };\n void warn_on_free(void *capsule) {\n PyErr_WarnEx(PyExc_UserWarning, "in warn_on_free", 1);\n void * obj = PyCapsule_GetPointer(capsule,\n PyCapsule_GetName(capsule));\n free(obj);\n };\n '''\n more_init = "import_array();"\n try:\n import mem_policy\n return mem_policy\n except ImportError:\n pass\n # if it does not exist, build and load it\n if sysconfig.get_platform() == "win-arm64":\n pytest.skip("Meson unable to find MSVC linker on win-arm64")\n return extbuild.build_and_import_extension('mem_policy',\n functions,\n prologue=prologue,\n include_dirs=[np.get_include()],\n build_dir=tmp_path,\n more_init=more_init)\n\n\ndef test_set_policy(get_module):\n\n get_handler_name = np._core.multiarray.get_handler_name\n get_handler_version = np._core.multiarray.get_handler_version\n orig_policy_name = get_handler_name()\n\n a = np.arange(10).reshape((2, 5)) # a doesn't own its own data\n assert get_handler_name(a) is None\n assert get_handler_version(a) is None\n assert get_handler_name(a.base) == orig_policy_name\n assert get_handler_version(a.base) == 1\n\n orig_policy = get_module.set_secret_data_policy()\n\n b = np.arange(10).reshape((2, 5)) # b doesn't own its own data\n assert get_handler_name(b) is None\n assert get_handler_version(b) is None\n assert get_handler_name(b.base) == 'secret_data_allocator'\n assert get_handler_version(b.base) == 1\n\n if orig_policy_name == 'default_allocator':\n get_module.set_old_policy(None) # tests PyDataMem_SetHandler(NULL)\n assert get_handler_name() == 'default_allocator'\n else:\n get_module.set_old_policy(orig_policy)\n assert get_handler_name() == orig_policy_name\n\n with pytest.raises(ValueError,\n match="Capsule must be named 'mem_handler'"):\n get_module.set_wrong_capsule_name_data_policy()\n\n\ndef test_default_policy_singleton(get_module):\n get_handler_name = np._core.multiarray.get_handler_name\n\n # set the policy to default\n orig_policy = get_module.set_old_policy(None)\n\n assert get_handler_name() == 'default_allocator'\n\n # re-set the policy to default\n def_policy_1 = get_module.set_old_policy(None)\n\n assert get_handler_name() == 'default_allocator'\n\n # set the policy to original\n def_policy_2 = get_module.set_old_policy(orig_policy)\n\n # since default policy is a singleton,\n # these should be the same object\n assert def_policy_1 is def_policy_2 is get_module.get_default_policy()\n\n\ndef test_policy_propagation(get_module):\n # The memory policy goes hand-in-hand with flags.owndata\n\n class MyArr(np.ndarray):\n pass\n\n get_handler_name = np._core.multiarray.get_handler_name\n orig_policy_name = get_handler_name()\n a = np.arange(10).view(MyArr).reshape((2, 5))\n assert get_handler_name(a) is None\n assert a.flags.owndata is False\n\n assert get_handler_name(a.base) is None\n assert a.base.flags.owndata is False\n\n assert get_handler_name(a.base.base) == orig_policy_name\n assert a.base.base.flags.owndata is True\n\n\nasync def concurrent_context1(get_module, orig_policy_name, event):\n if orig_policy_name == 'default_allocator':\n get_module.set_secret_data_policy()\n assert get_handler_name() == 'secret_data_allocator'\n else:\n get_module.set_old_policy(None)\n assert get_handler_name() == 'default_allocator'\n event.set()\n\n\nasync def concurrent_context2(get_module, orig_policy_name, event):\n await event.wait()\n # the policy is not affected by changes in parallel contexts\n assert get_handler_name() == orig_policy_name\n # change policy in the child context\n if orig_policy_name == 'default_allocator':\n get_module.set_secret_data_policy()\n assert get_handler_name() == 'secret_data_allocator'\n else:\n get_module.set_old_policy(None)\n assert get_handler_name() == 'default_allocator'\n\n\nasync def async_test_context_locality(get_module):\n orig_policy_name = np._core.multiarray.get_handler_name()\n\n event = asyncio.Event()\n # the child contexts inherit the parent policy\n concurrent_task1 = asyncio.create_task(\n concurrent_context1(get_module, orig_policy_name, event))\n concurrent_task2 = asyncio.create_task(\n concurrent_context2(get_module, orig_policy_name, event))\n await concurrent_task1\n await concurrent_task2\n\n # the parent context is not affected by child policy changes\n assert np._core.multiarray.get_handler_name() == orig_policy_name\n\n\ndef test_context_locality(get_module):\n if (sys.implementation.name == 'pypy'\n and sys.pypy_version_info[:3] < (7, 3, 6)):\n pytest.skip('no context-locality support in PyPy < 7.3.6')\n asyncio.run(async_test_context_locality(get_module))\n\n\ndef concurrent_thread1(get_module, event):\n get_module.set_secret_data_policy()\n assert np._core.multiarray.get_handler_name() == 'secret_data_allocator'\n event.set()\n\n\ndef concurrent_thread2(get_module, event):\n event.wait()\n # the policy is not affected by changes in parallel threads\n assert np._core.multiarray.get_handler_name() == 'default_allocator'\n # change policy in the child thread\n get_module.set_secret_data_policy()\n\n\ndef test_thread_locality(get_module):\n orig_policy_name = np._core.multiarray.get_handler_name()\n\n event = threading.Event()\n # the child threads do not inherit the parent policy\n concurrent_task1 = threading.Thread(target=concurrent_thread1,\n args=(get_module, event))\n concurrent_task2 = threading.Thread(target=concurrent_thread2,\n args=(get_module, event))\n concurrent_task1.start()\n concurrent_task2.start()\n concurrent_task1.join()\n concurrent_task2.join()\n\n # the parent thread is not affected by child policy changes\n assert np._core.multiarray.get_handler_name() == orig_policy_name\n\n\n@pytest.mark.skip(reason="too slow, see gh-23975")\ndef test_new_policy(get_module):\n a = np.arange(10)\n orig_policy_name = np._core.multiarray.get_handler_name(a)\n\n orig_policy = get_module.set_secret_data_policy()\n\n b = np.arange(10)\n assert np._core.multiarray.get_handler_name(b) == 'secret_data_allocator'\n\n # test array manipulation. This is slow\n if orig_policy_name == 'default_allocator':\n # when the np._core.test tests recurse into this test, the\n # policy will be set so this "if" will be false, preventing\n # infinite recursion\n #\n # if needed, debug this by\n # - running tests with -- -s (to not capture stdout/stderr\n # - setting verbose=2\n # - setting extra_argv=['-vv'] here\n assert np._core.test('full', verbose=1, extra_argv=[])\n # also try the ma tests, the pickling test is quite tricky\n assert np.ma.test('full', verbose=1, extra_argv=[])\n\n get_module.set_old_policy(orig_policy)\n\n c = np.arange(10)\n assert np._core.multiarray.get_handler_name(c) == orig_policy_name\n\n\n@pytest.mark.xfail(sys.implementation.name == "pypy",\n reason=("bad interaction between getenv and "\n "os.environ inside pytest"))\n@pytest.mark.parametrize("policy", ["0", "1", None])\ndef test_switch_owner(get_module, policy):\n a = get_module.get_array()\n assert np._core.multiarray.get_handler_name(a) is None\n get_module.set_own(a)\n\n if policy is None:\n # See what we expect to be set based on the env variable\n policy = os.getenv("NUMPY_WARN_IF_NO_MEM_POLICY", "0") == "1"\n oldval = None\n else:\n policy = policy == "1"\n oldval = np._core._multiarray_umath._set_numpy_warn_if_no_mem_policy(\n policy)\n try:\n # The policy should be NULL, so we have to assume we can call\n # "free". A warning is given if the policy == "1"\n if policy:\n with assert_warns(RuntimeWarning) as w:\n del a\n gc.collect()\n else:\n del a\n gc.collect()\n\n finally:\n if oldval is not None:\n np._core._multiarray_umath._set_numpy_warn_if_no_mem_policy(oldval)\n\n\ndef test_owner_is_base(get_module):\n a = get_module.get_array_with_base()\n with pytest.warns(UserWarning, match='warn_on_free'):\n del a\n gc.collect()\n gc.collect()\n | .venv\Lib\site-packages\numpy\_core\tests\test_mem_policy.py | test_mem_policy.py | Python | 17,246 | 0.95 | 0.115044 | 0.107143 | vue-tools | 729 | 2024-10-26T04:54:14.922052 | Apache-2.0 | true | f2a25af75a3a5f7593f4237a396a6cba |
import concurrent.futures\nimport string\nimport threading\n\nimport pytest\n\nimport numpy as np\nfrom numpy._core import _rational_tests\nfrom numpy.testing import IS_64BIT, IS_WASM\nfrom numpy.testing._private.utils import run_threaded\n\nif IS_WASM:\n pytest.skip(allow_module_level=True, reason="no threading support in wasm")\n\n\ndef test_parallel_randomstate_creation():\n # if the coercion cache is enabled and not thread-safe, creating\n # RandomState instances simultaneously leads to a data race\n def func(seed):\n np.random.RandomState(seed)\n\n run_threaded(func, 500, pass_count=True)\n\n\ndef test_parallel_ufunc_execution():\n # if the loop data cache or dispatch cache are not thread-safe\n # computing ufuncs simultaneously in multiple threads leads\n # to a data race that causes crashes or spurious exceptions\n def func():\n arr = np.random.random((25,))\n np.isnan(arr)\n\n run_threaded(func, 500)\n\n # see gh-26690\n NUM_THREADS = 50\n\n a = np.ones(1000)\n\n def f(b):\n b.wait()\n return a.sum()\n\n run_threaded(f, NUM_THREADS, pass_barrier=True)\n\n\ndef test_temp_elision_thread_safety():\n amid = np.ones(50000)\n bmid = np.ones(50000)\n alarge = np.ones(1000000)\n blarge = np.ones(1000000)\n\n def func(count):\n if count % 4 == 0:\n (amid * 2) + bmid\n elif count % 4 == 1:\n (amid + bmid) - 2\n elif count % 4 == 2:\n (alarge * 2) + blarge\n else:\n (alarge + blarge) - 2\n\n run_threaded(func, 100, pass_count=True)\n\n\ndef test_eigvalsh_thread_safety():\n # if lapack isn't thread safe this will randomly segfault or error\n # see gh-24512\n rng = np.random.RandomState(873699172)\n matrices = (\n rng.random((5, 10, 10, 3, 3)),\n rng.random((5, 10, 10, 3, 3)),\n )\n\n run_threaded(lambda i: np.linalg.eigvalsh(matrices[i]), 2,\n pass_count=True)\n\n\ndef test_printoptions_thread_safety():\n # until NumPy 2.1 the printoptions state was stored in globals\n # this verifies that they are now stored in a context variable\n b = threading.Barrier(2)\n\n def legacy_113():\n np.set_printoptions(legacy='1.13', precision=12)\n b.wait()\n po = np.get_printoptions()\n assert po['legacy'] == '1.13'\n assert po['precision'] == 12\n orig_linewidth = po['linewidth']\n with np.printoptions(linewidth=34, legacy='1.21'):\n po = np.get_printoptions()\n assert po['legacy'] == '1.21'\n assert po['precision'] == 12\n assert po['linewidth'] == 34\n po = np.get_printoptions()\n assert po['linewidth'] == orig_linewidth\n assert po['legacy'] == '1.13'\n assert po['precision'] == 12\n\n def legacy_125():\n np.set_printoptions(legacy='1.25', precision=7)\n b.wait()\n po = np.get_printoptions()\n assert po['legacy'] == '1.25'\n assert po['precision'] == 7\n orig_linewidth = po['linewidth']\n with np.printoptions(linewidth=6, legacy='1.13'):\n po = np.get_printoptions()\n assert po['legacy'] == '1.13'\n assert po['precision'] == 7\n assert po['linewidth'] == 6\n po = np.get_printoptions()\n assert po['linewidth'] == orig_linewidth\n assert po['legacy'] == '1.25'\n assert po['precision'] == 7\n\n task1 = threading.Thread(target=legacy_113)\n task2 = threading.Thread(target=legacy_125)\n\n task1.start()\n task2.start()\n\n\ndef test_parallel_reduction():\n # gh-28041\n NUM_THREADS = 50\n\n x = np.arange(1000)\n\n def closure(b):\n b.wait()\n np.sum(x)\n\n run_threaded(closure, NUM_THREADS, pass_barrier=True)\n\n\ndef test_parallel_flat_iterator():\n # gh-28042\n x = np.arange(20).reshape(5, 4).T\n\n def closure(b):\n b.wait()\n for _ in range(100):\n list(x.flat)\n\n run_threaded(closure, outer_iterations=100, pass_barrier=True)\n\n # gh-28143\n def prepare_args():\n return [np.arange(10)]\n\n def closure(x, b):\n b.wait()\n for _ in range(100):\n y = np.arange(10)\n y.flat[x] = x\n\n run_threaded(closure, pass_barrier=True, prepare_args=prepare_args)\n\n\ndef test_multithreaded_repeat():\n x0 = np.arange(10)\n\n def closure(b):\n b.wait()\n for _ in range(100):\n x = np.repeat(x0, 2, axis=0)[::2]\n\n run_threaded(closure, max_workers=10, pass_barrier=True)\n\n\ndef test_structured_advanced_indexing():\n # Test that copyswap(n) used by integer array indexing is threadsafe\n # for structured datatypes, see gh-15387. This test can behave randomly.\n\n # Create a deeply nested dtype to make a failure more likely:\n dt = np.dtype([("", "f8")])\n dt = np.dtype([("", dt)] * 2)\n dt = np.dtype([("", dt)] * 2)\n # The array should be large enough to likely run into threading issues\n arr = np.random.uniform(size=(6000, 8)).view(dt)[:, 0]\n\n rng = np.random.default_rng()\n\n def func(arr):\n indx = rng.integers(0, len(arr), size=6000, dtype=np.intp)\n arr[indx]\n\n tpe = concurrent.futures.ThreadPoolExecutor(max_workers=8)\n futures = [tpe.submit(func, arr) for _ in range(10)]\n for f in futures:\n f.result()\n\n assert arr.dtype is dt\n\n\ndef test_structured_threadsafety2():\n # Nonzero (and some other functions) should be threadsafe for\n # structured datatypes, see gh-15387. This test can behave randomly.\n from concurrent.futures import ThreadPoolExecutor\n\n # Create a deeply nested dtype to make a failure more likely:\n dt = np.dtype([("", "f8")])\n dt = np.dtype([("", dt)])\n dt = np.dtype([("", dt)] * 2)\n # The array should be large enough to likely run into threading issues\n arr = np.random.uniform(size=(5000, 4)).view(dt)[:, 0]\n\n def func(arr):\n arr.nonzero()\n\n tpe = ThreadPoolExecutor(max_workers=8)\n futures = [tpe.submit(func, arr) for _ in range(10)]\n for f in futures:\n f.result()\n\n assert arr.dtype is dt\n\n\ndef test_stringdtype_multithreaded_access_and_mutation(\n dtype, random_string_list):\n # this test uses an RNG and may crash or cause deadlocks if there is a\n # threading bug\n rng = np.random.default_rng(0x4D3D3D3)\n\n chars = list(string.ascii_letters + string.digits)\n chars = np.array(chars, dtype="U1")\n ret = rng.choice(chars, size=100 * 10, replace=True)\n random_string_list = ret.view("U100")\n\n def func(arr):\n rnd = rng.random()\n # either write to random locations in the array, compute a ufunc, or\n # re-initialize the array\n if rnd < 0.25:\n num = np.random.randint(0, arr.size)\n arr[num] = arr[num] + "hello"\n elif rnd < 0.5:\n if rnd < 0.375:\n np.add(arr, arr)\n else:\n np.add(arr, arr, out=arr)\n elif rnd < 0.75:\n if rnd < 0.875:\n np.multiply(arr, np.int64(2))\n else:\n np.multiply(arr, np.int64(2), out=arr)\n else:\n arr[:] = random_string_list\n\n with concurrent.futures.ThreadPoolExecutor(max_workers=8) as tpe:\n arr = np.array(random_string_list, dtype=dtype)\n futures = [tpe.submit(func, arr) for _ in range(500)]\n\n for f in futures:\n f.result()\n\n\n@pytest.mark.skipif(\n not IS_64BIT,\n reason="Sometimes causes failures or crashes due to OOM on 32 bit runners"\n)\ndef test_legacy_usertype_cast_init_thread_safety():\n def closure(b):\n b.wait()\n np.full((10, 10), 1, _rational_tests.rational)\n\n run_threaded(closure, 250, pass_barrier=True)\n\n@pytest.mark.parametrize("dtype", [bool, int, float])\ndef test_nonzero(dtype):\n # See: gh-28361\n #\n # np.nonzero uses np.count_nonzero to determine the size of the output array\n # In a second pass the indices of the non-zero elements are determined, but they can have changed\n #\n # This test triggers a data race which is suppressed in the TSAN CI. The test is to ensure\n # np.nonzero does not generate a segmentation fault\n x = np.random.randint(4, size=100).astype(dtype)\n\n def func(index):\n for _ in range(10):\n if index == 0:\n x[::2] = np.random.randint(2)\n else:\n try:\n _ = np.nonzero(x)\n except RuntimeError as ex:\n assert 'number of non-zero array elements changed during function execution' in str(ex)\n\n run_threaded(func, max_workers=10, pass_count=True, outer_iterations=5)\n | .venv\Lib\site-packages\numpy\_core\tests\test_multithreading.py | test_multithreading.py | Python | 8,893 | 0.95 | 0.181507 | 0.142222 | vue-tools | 599 | 2023-10-11T14:57:42.525849 | Apache-2.0 | true | 3870b7f18283e58dc603710b8df6a22f |
"""\nThis file adds basic tests to test the NEP 50 style promotion compatibility\nmode. Most of these test are likely to be simply deleted again once NEP 50\nis adopted in the main test suite. A few may be moved elsewhere.\n"""\n\nimport operator\n\nimport hypothesis\nimport pytest\nfrom hypothesis import strategies\n\nimport numpy as np\nfrom numpy.testing import IS_WASM, assert_array_equal\n\n\n@pytest.mark.skipif(IS_WASM, reason="wasm doesn't have support for fp errors")\ndef test_nep50_examples():\n res = np.uint8(1) + 2\n assert res.dtype == np.uint8\n\n res = np.array([1], np.uint8) + np.int64(1)\n assert res.dtype == np.int64\n\n res = np.array([1], np.uint8) + np.array(1, dtype=np.int64)\n assert res.dtype == np.int64\n\n with pytest.warns(RuntimeWarning, match="overflow"):\n res = np.uint8(100) + 200\n assert res.dtype == np.uint8\n\n with pytest.warns(RuntimeWarning, match="overflow"):\n res = np.float32(1) + 3e100\n\n assert np.isinf(res)\n assert res.dtype == np.float32\n\n res = np.array([0.1], np.float32) == np.float64(0.1)\n assert res[0] == False\n\n res = np.array([0.1], np.float32) + np.float64(0.1)\n assert res.dtype == np.float64\n\n res = np.array([1.], np.float32) + np.int64(3)\n assert res.dtype == np.float64\n\n\n@pytest.mark.parametrize("dtype", np.typecodes["AllInteger"])\ndef test_nep50_weak_integers(dtype):\n # Avoids warning (different code path for scalars)\n scalar_type = np.dtype(dtype).type\n\n maxint = int(np.iinfo(dtype).max)\n\n with np.errstate(over="warn"):\n with pytest.warns(RuntimeWarning):\n res = scalar_type(100) + maxint\n assert res.dtype == dtype\n\n # Array operations are not expected to warn, but should give the same\n # result dtype.\n res = np.array(100, dtype=dtype) + maxint\n assert res.dtype == dtype\n\n\n@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])\ndef test_nep50_weak_integers_with_inexact(dtype):\n # Avoids warning (different code path for scalars)\n scalar_type = np.dtype(dtype).type\n\n too_big_int = int(np.finfo(dtype).max) * 2\n\n if dtype in "dDG":\n # These dtypes currently convert to Python float internally, which\n # raises an OverflowError, while the other dtypes overflow to inf.\n # NOTE: It may make sense to normalize the behavior!\n with pytest.raises(OverflowError):\n scalar_type(1) + too_big_int\n\n with pytest.raises(OverflowError):\n np.array(1, dtype=dtype) + too_big_int\n else:\n # NumPy uses (or used) `int -> string -> longdouble` for the\n # conversion. But Python may refuse `str(int)` for huge ints.\n # In that case, RuntimeWarning would be correct, but conversion\n # fails earlier (seems to happen on 32bit linux, possibly only debug).\n if dtype in "gG":\n try:\n str(too_big_int)\n except ValueError:\n pytest.skip("`huge_int -> string -> longdouble` failed")\n\n # Otherwise, we overflow to infinity:\n with pytest.warns(RuntimeWarning):\n res = scalar_type(1) + too_big_int\n assert res.dtype == dtype\n assert res == np.inf\n\n with pytest.warns(RuntimeWarning):\n # We force the dtype here, since windows may otherwise pick the\n # double instead of the longdouble loop. That leads to slightly\n # different results (conversion of the int fails as above).\n res = np.add(np.array(1, dtype=dtype), too_big_int, dtype=dtype)\n assert res.dtype == dtype\n assert res == np.inf\n\n\n@pytest.mark.parametrize("op", [operator.add, operator.pow])\ndef test_weak_promotion_scalar_path(op):\n # Some additional paths exercising the weak scalars.\n\n # Integer path:\n res = op(np.uint8(3), 5)\n assert res == op(3, 5)\n assert res.dtype == np.uint8 or res.dtype == bool # noqa: PLR1714\n\n with pytest.raises(OverflowError):\n op(np.uint8(3), 1000)\n\n # Float path:\n res = op(np.float32(3), 5.)\n assert res == op(3., 5.)\n assert res.dtype == np.float32 or res.dtype == bool # noqa: PLR1714\n\n\ndef test_nep50_complex_promotion():\n with pytest.warns(RuntimeWarning, match=".*overflow"):\n res = np.complex64(3) + complex(2**300)\n\n assert type(res) == np.complex64\n\n\ndef test_nep50_integer_conversion_errors():\n # Implementation for error paths is mostly missing (as of writing)\n with pytest.raises(OverflowError, match=".*uint8"):\n np.array([1], np.uint8) + 300\n\n with pytest.raises(OverflowError, match=".*uint8"):\n np.uint8(1) + 300\n\n # Error message depends on platform (maybe unsigned int or unsigned long)\n with pytest.raises(OverflowError,\n match="Python integer -1 out of bounds for uint8"):\n np.uint8(1) + -1\n\n\ndef test_nep50_with_axisconcatenator():\n # Concatenate/r_ does not promote, so this has to error:\n with pytest.raises(OverflowError):\n np.r_[np.arange(5, dtype=np.int8), 255]\n\n\n@pytest.mark.parametrize("ufunc", [np.add, np.power])\ndef test_nep50_huge_integers(ufunc):\n # Very large integers are complicated, because they go to uint64 or\n # object dtype. This tests covers a few possible paths.\n with pytest.raises(OverflowError):\n ufunc(np.int64(0), 2**63) # 2**63 too large for int64\n\n with pytest.raises(OverflowError):\n ufunc(np.uint64(0), 2**64) # 2**64 cannot be represented by uint64\n\n # However, 2**63 can be represented by the uint64 (and that is used):\n res = ufunc(np.uint64(1), 2**63)\n\n assert res.dtype == np.uint64\n assert res == ufunc(1, 2**63, dtype=object)\n\n # The following paths fail to warn correctly about the change:\n with pytest.raises(OverflowError):\n ufunc(np.int64(1), 2**63) # np.array(2**63) would go to uint\n\n with pytest.raises(OverflowError):\n ufunc(np.int64(1), 2**100) # np.array(2**100) would go to object\n\n # This would go to object and thus a Python float, not a NumPy one:\n res = ufunc(1.0, 2**100)\n assert isinstance(res, np.float64)\n\n\ndef test_nep50_in_concat_and_choose():\n res = np.concatenate([np.float32(1), 1.], axis=None)\n assert res.dtype == "float32"\n\n res = np.choose(1, [np.float32(1), 1.])\n assert res.dtype == "float32"\n\n\n@pytest.mark.parametrize("expected,dtypes,optional_dtypes", [\n (np.float32, [np.float32],\n [np.float16, 0.0, np.uint16, np.int16, np.int8, 0]),\n (np.complex64, [np.float32, 0j],\n [np.float16, 0.0, np.uint16, np.int16, np.int8, 0]),\n (np.float32, [np.int16, np.uint16, np.float16],\n [np.int8, np.uint8, np.float32, 0., 0]),\n (np.int32, [np.int16, np.uint16],\n [np.int8, np.uint8, 0, np.bool]),\n ])\n@hypothesis.given(data=strategies.data())\ndef test_expected_promotion(expected, dtypes, optional_dtypes, data):\n # Sample randomly while ensuring "dtypes" is always present:\n optional = data.draw(strategies.lists(\n strategies.sampled_from(dtypes + optional_dtypes)))\n all_dtypes = dtypes + optional\n dtypes_sample = data.draw(strategies.permutations(all_dtypes))\n\n res = np.result_type(*dtypes_sample)\n assert res == expected\n\n\n@pytest.mark.parametrize("sctype",\n [np.int8, np.int16, np.int32, np.int64,\n np.uint8, np.uint16, np.uint32, np.uint64])\n@pytest.mark.parametrize("other_val",\n [-2 * 100, -1, 0, 9, 10, 11, 2**63, 2 * 100])\n@pytest.mark.parametrize("comp",\n [operator.eq, operator.ne, operator.le, operator.lt,\n operator.ge, operator.gt])\ndef test_integer_comparison(sctype, other_val, comp):\n # Test that comparisons with integers (especially out-of-bound) ones\n # works correctly.\n val_obj = 10\n val = sctype(val_obj)\n # Check that the scalar behaves the same as the python int:\n assert comp(10, other_val) == comp(val, other_val)\n assert comp(val, other_val) == comp(10, other_val)\n # Except for the result type:\n assert type(comp(val, other_val)) is np.bool\n\n # Check that the integer array and object array behave the same:\n val_obj = np.array([10, 10], dtype=object)\n val = val_obj.astype(sctype)\n assert_array_equal(comp(val_obj, other_val), comp(val, other_val))\n assert_array_equal(comp(other_val, val_obj), comp(other_val, val))\n\n\n@pytest.mark.parametrize("arr", [\n np.ones((100, 100), dtype=np.uint8)[::2], # not trivially iterable\n np.ones(20000, dtype=">u4"), # cast and >buffersize\n np.ones(100, dtype=">u4"), # fast path compatible with cast\n])\ndef test_integer_comparison_with_cast(arr):\n # Similar to above, but mainly test a few cases that cover the slow path\n # the test is limited to unsigned ints and -1 for simplicity.\n res = arr >= -1\n assert_array_equal(res, np.ones_like(arr, dtype=bool))\n res = arr < -1\n assert_array_equal(res, np.zeros_like(arr, dtype=bool))\n\n\n@pytest.mark.parametrize("comp",\n [np.equal, np.not_equal, np.less_equal, np.less,\n np.greater_equal, np.greater])\ndef test_integer_integer_comparison(comp):\n # Test that the NumPy comparison ufuncs work with large Python integers\n assert comp(2**200, -2**200) == comp(2**200, -2**200, dtype=object)\n\n\ndef create_with_scalar(sctype, value):\n return sctype(value)\n\n\ndef create_with_array(sctype, value):\n return np.array([value], dtype=sctype)\n\n\n@pytest.mark.parametrize("sctype",\n [np.int8, np.int16, np.int32, np.int64,\n np.uint8, np.uint16, np.uint32, np.uint64])\n@pytest.mark.parametrize("create", [create_with_scalar, create_with_array])\ndef test_oob_creation(sctype, create):\n iinfo = np.iinfo(sctype)\n\n with pytest.raises(OverflowError):\n create(sctype, iinfo.min - 1)\n\n with pytest.raises(OverflowError):\n create(sctype, iinfo.max + 1)\n\n with pytest.raises(OverflowError):\n create(sctype, str(iinfo.min - 1))\n\n with pytest.raises(OverflowError):\n create(sctype, str(iinfo.max + 1))\n\n assert create(sctype, iinfo.min) == iinfo.min\n assert create(sctype, iinfo.max) == iinfo.max\n | .venv\Lib\site-packages\numpy\_core\tests\test_nep50_promotions.py | test_nep50_promotions.py | Python | 10,355 | 0.95 | 0.108014 | 0.162037 | vue-tools | 794 | 2024-03-05T20:01:04.274209 | BSD-3-Clause | true | 35cb3c4c5b28b3e4b8f38dd63d59de9f |
import itertools\nimport sys\n\nimport pytest\n\nimport numpy as np\nimport numpy._core.numerictypes as nt\nfrom numpy._core.numerictypes import issctype, maximum_sctype, sctype2char, sctypes\nfrom numpy.testing import (\n IS_PYPY,\n assert_,\n assert_equal,\n assert_raises,\n assert_raises_regex,\n)\n\n# This is the structure of the table used for plain objects:\n#\n# +-+-+-+\n# |x|y|z|\n# +-+-+-+\n\n# Structure of a plain array description:\nPdescr = [\n ('x', 'i4', (2,)),\n ('y', 'f8', (2, 2)),\n ('z', 'u1')]\n\n# A plain list of tuples with values for testing:\nPbufferT = [\n # x y z\n ([3, 2], [[6., 4.], [6., 4.]], 8),\n ([4, 3], [[7., 5.], [7., 5.]], 9),\n ]\n\n\n# This is the structure of the table used for nested objects (DON'T PANIC!):\n#\n# +-+---------------------------------+-----+----------+-+-+\n# |x|Info |color|info |y|z|\n# | +-----+--+----------------+----+--+ +----+-----+ | |\n# | |value|y2|Info2 |name|z2| |Name|Value| | |\n# | | | +----+-----+--+--+ | | | | | | |\n# | | | |name|value|y3|z3| | | | | | | |\n# +-+-----+--+----+-----+--+--+----+--+-----+----+-----+-+-+\n#\n\n# The corresponding nested array description:\nNdescr = [\n ('x', 'i4', (2,)),\n ('Info', [\n ('value', 'c16'),\n ('y2', 'f8'),\n ('Info2', [\n ('name', 'S2'),\n ('value', 'c16', (2,)),\n ('y3', 'f8', (2,)),\n ('z3', 'u4', (2,))]),\n ('name', 'S2'),\n ('z2', 'b1')]),\n ('color', 'S2'),\n ('info', [\n ('Name', 'U8'),\n ('Value', 'c16')]),\n ('y', 'f8', (2, 2)),\n ('z', 'u1')]\n\nNbufferT = [\n # x Info color info y z\n # value y2 Info2 name z2 Name Value\n # name value y3 z3\n ([3, 2], (6j, 6., (b'nn', [6j, 4j], [6., 4.], [1, 2]), b'NN', True),\n b'cc', ('NN', 6j), [[6., 4.], [6., 4.]], 8),\n ([4, 3], (7j, 7., (b'oo', [7j, 5j], [7., 5.], [2, 1]), b'OO', False),\n b'dd', ('OO', 7j), [[7., 5.], [7., 5.]], 9),\n ]\n\n\nbyteorder = {'little': '<', 'big': '>'}[sys.byteorder]\n\ndef normalize_descr(descr):\n "Normalize a description adding the platform byteorder."\n\n out = []\n for item in descr:\n dtype = item[1]\n if isinstance(dtype, str):\n if dtype[0] not in ['|', '<', '>']:\n onebyte = dtype[1:] == "1"\n if onebyte or dtype[0] in ['S', 'V', 'b']:\n dtype = "|" + dtype\n else:\n dtype = byteorder + dtype\n if len(item) > 2 and np.prod(item[2]) > 1:\n nitem = (item[0], dtype, item[2])\n else:\n nitem = (item[0], dtype)\n out.append(nitem)\n elif isinstance(dtype, list):\n l = normalize_descr(dtype)\n out.append((item[0], l))\n else:\n raise ValueError(f"Expected a str or list and got {type(item)}")\n return out\n\n\n############################################################\n# Creation tests\n############################################################\n\nclass CreateZeros:\n """Check the creation of heterogeneous arrays zero-valued"""\n\n def test_zeros0D(self):\n """Check creation of 0-dimensional objects"""\n h = np.zeros((), dtype=self._descr)\n assert_(normalize_descr(self._descr) == h.dtype.descr)\n assert_(h.dtype.fields['x'][0].name[:4] == 'void')\n assert_(h.dtype.fields['x'][0].char == 'V')\n assert_(h.dtype.fields['x'][0].type == np.void)\n # A small check that data is ok\n assert_equal(h['z'], np.zeros((), dtype='u1'))\n\n def test_zerosSD(self):\n """Check creation of single-dimensional objects"""\n h = np.zeros((2,), dtype=self._descr)\n assert_(normalize_descr(self._descr) == h.dtype.descr)\n assert_(h.dtype['y'].name[:4] == 'void')\n assert_(h.dtype['y'].char == 'V')\n assert_(h.dtype['y'].type == np.void)\n # A small check that data is ok\n assert_equal(h['z'], np.zeros((2,), dtype='u1'))\n\n def test_zerosMD(self):\n """Check creation of multi-dimensional objects"""\n h = np.zeros((2, 3), dtype=self._descr)\n assert_(normalize_descr(self._descr) == h.dtype.descr)\n assert_(h.dtype['z'].name == 'uint8')\n assert_(h.dtype['z'].char == 'B')\n assert_(h.dtype['z'].type == np.uint8)\n # A small check that data is ok\n assert_equal(h['z'], np.zeros((2, 3), dtype='u1'))\n\n\nclass TestCreateZerosPlain(CreateZeros):\n """Check the creation of heterogeneous arrays zero-valued (plain)"""\n _descr = Pdescr\n\nclass TestCreateZerosNested(CreateZeros):\n """Check the creation of heterogeneous arrays zero-valued (nested)"""\n _descr = Ndescr\n\n\nclass CreateValues:\n """Check the creation of heterogeneous arrays with values"""\n\n def test_tuple(self):\n """Check creation from tuples"""\n h = np.array(self._buffer, dtype=self._descr)\n assert_(normalize_descr(self._descr) == h.dtype.descr)\n if self.multiple_rows:\n assert_(h.shape == (2,))\n else:\n assert_(h.shape == ())\n\n def test_list_of_tuple(self):\n """Check creation from list of tuples"""\n h = np.array([self._buffer], dtype=self._descr)\n assert_(normalize_descr(self._descr) == h.dtype.descr)\n if self.multiple_rows:\n assert_(h.shape == (1, 2))\n else:\n assert_(h.shape == (1,))\n\n def test_list_of_list_of_tuple(self):\n """Check creation from list of list of tuples"""\n h = np.array([[self._buffer]], dtype=self._descr)\n assert_(normalize_descr(self._descr) == h.dtype.descr)\n if self.multiple_rows:\n assert_(h.shape == (1, 1, 2))\n else:\n assert_(h.shape == (1, 1))\n\n\nclass TestCreateValuesPlainSingle(CreateValues):\n """Check the creation of heterogeneous arrays (plain, single row)"""\n _descr = Pdescr\n multiple_rows = 0\n _buffer = PbufferT[0]\n\nclass TestCreateValuesPlainMultiple(CreateValues):\n """Check the creation of heterogeneous arrays (plain, multiple rows)"""\n _descr = Pdescr\n multiple_rows = 1\n _buffer = PbufferT\n\nclass TestCreateValuesNestedSingle(CreateValues):\n """Check the creation of heterogeneous arrays (nested, single row)"""\n _descr = Ndescr\n multiple_rows = 0\n _buffer = NbufferT[0]\n\nclass TestCreateValuesNestedMultiple(CreateValues):\n """Check the creation of heterogeneous arrays (nested, multiple rows)"""\n _descr = Ndescr\n multiple_rows = 1\n _buffer = NbufferT\n\n\n############################################################\n# Reading tests\n############################################################\n\nclass ReadValuesPlain:\n """Check the reading of values in heterogeneous arrays (plain)"""\n\n def test_access_fields(self):\n h = np.array(self._buffer, dtype=self._descr)\n if not self.multiple_rows:\n assert_(h.shape == ())\n assert_equal(h['x'], np.array(self._buffer[0], dtype='i4'))\n assert_equal(h['y'], np.array(self._buffer[1], dtype='f8'))\n assert_equal(h['z'], np.array(self._buffer[2], dtype='u1'))\n else:\n assert_(len(h) == 2)\n assert_equal(h['x'], np.array([self._buffer[0][0],\n self._buffer[1][0]], dtype='i4'))\n assert_equal(h['y'], np.array([self._buffer[0][1],\n self._buffer[1][1]], dtype='f8'))\n assert_equal(h['z'], np.array([self._buffer[0][2],\n self._buffer[1][2]], dtype='u1'))\n\n\nclass TestReadValuesPlainSingle(ReadValuesPlain):\n """Check the creation of heterogeneous arrays (plain, single row)"""\n _descr = Pdescr\n multiple_rows = 0\n _buffer = PbufferT[0]\n\nclass TestReadValuesPlainMultiple(ReadValuesPlain):\n """Check the values of heterogeneous arrays (plain, multiple rows)"""\n _descr = Pdescr\n multiple_rows = 1\n _buffer = PbufferT\n\nclass ReadValuesNested:\n """Check the reading of values in heterogeneous arrays (nested)"""\n\n def test_access_top_fields(self):\n """Check reading the top fields of a nested array"""\n h = np.array(self._buffer, dtype=self._descr)\n if not self.multiple_rows:\n assert_(h.shape == ())\n assert_equal(h['x'], np.array(self._buffer[0], dtype='i4'))\n assert_equal(h['y'], np.array(self._buffer[4], dtype='f8'))\n assert_equal(h['z'], np.array(self._buffer[5], dtype='u1'))\n else:\n assert_(len(h) == 2)\n assert_equal(h['x'], np.array([self._buffer[0][0],\n self._buffer[1][0]], dtype='i4'))\n assert_equal(h['y'], np.array([self._buffer[0][4],\n self._buffer[1][4]], dtype='f8'))\n assert_equal(h['z'], np.array([self._buffer[0][5],\n self._buffer[1][5]], dtype='u1'))\n\n def test_nested1_acessors(self):\n """Check reading the nested fields of a nested array (1st level)"""\n h = np.array(self._buffer, dtype=self._descr)\n if not self.multiple_rows:\n assert_equal(h['Info']['value'],\n np.array(self._buffer[1][0], dtype='c16'))\n assert_equal(h['Info']['y2'],\n np.array(self._buffer[1][1], dtype='f8'))\n assert_equal(h['info']['Name'],\n np.array(self._buffer[3][0], dtype='U2'))\n assert_equal(h['info']['Value'],\n np.array(self._buffer[3][1], dtype='c16'))\n else:\n assert_equal(h['Info']['value'],\n np.array([self._buffer[0][1][0],\n self._buffer[1][1][0]],\n dtype='c16'))\n assert_equal(h['Info']['y2'],\n np.array([self._buffer[0][1][1],\n self._buffer[1][1][1]],\n dtype='f8'))\n assert_equal(h['info']['Name'],\n np.array([self._buffer[0][3][0],\n self._buffer[1][3][0]],\n dtype='U2'))\n assert_equal(h['info']['Value'],\n np.array([self._buffer[0][3][1],\n self._buffer[1][3][1]],\n dtype='c16'))\n\n def test_nested2_acessors(self):\n """Check reading the nested fields of a nested array (2nd level)"""\n h = np.array(self._buffer, dtype=self._descr)\n if not self.multiple_rows:\n assert_equal(h['Info']['Info2']['value'],\n np.array(self._buffer[1][2][1], dtype='c16'))\n assert_equal(h['Info']['Info2']['z3'],\n np.array(self._buffer[1][2][3], dtype='u4'))\n else:\n assert_equal(h['Info']['Info2']['value'],\n np.array([self._buffer[0][1][2][1],\n self._buffer[1][1][2][1]],\n dtype='c16'))\n assert_equal(h['Info']['Info2']['z3'],\n np.array([self._buffer[0][1][2][3],\n self._buffer[1][1][2][3]],\n dtype='u4'))\n\n def test_nested1_descriptor(self):\n """Check access nested descriptors of a nested array (1st level)"""\n h = np.array(self._buffer, dtype=self._descr)\n assert_(h.dtype['Info']['value'].name == 'complex128')\n assert_(h.dtype['Info']['y2'].name == 'float64')\n assert_(h.dtype['info']['Name'].name == 'str256')\n assert_(h.dtype['info']['Value'].name == 'complex128')\n\n def test_nested2_descriptor(self):\n """Check access nested descriptors of a nested array (2nd level)"""\n h = np.array(self._buffer, dtype=self._descr)\n assert_(h.dtype['Info']['Info2']['value'].name == 'void256')\n assert_(h.dtype['Info']['Info2']['z3'].name == 'void64')\n\n\nclass TestReadValuesNestedSingle(ReadValuesNested):\n """Check the values of heterogeneous arrays (nested, single row)"""\n _descr = Ndescr\n multiple_rows = False\n _buffer = NbufferT[0]\n\nclass TestReadValuesNestedMultiple(ReadValuesNested):\n """Check the values of heterogeneous arrays (nested, multiple rows)"""\n _descr = Ndescr\n multiple_rows = True\n _buffer = NbufferT\n\nclass TestEmptyField:\n def test_assign(self):\n a = np.arange(10, dtype=np.float32)\n a.dtype = [("int", "<0i4"), ("float", "<2f4")]\n assert_(a['int'].shape == (5, 0))\n assert_(a['float'].shape == (5, 2))\n\n\nclass TestMultipleFields:\n def setup_method(self):\n self.ary = np.array([(1, 2, 3, 4), (5, 6, 7, 8)], dtype='i4,f4,i2,c8')\n\n def _bad_call(self):\n return self.ary['f0', 'f1']\n\n def test_no_tuple(self):\n assert_raises(IndexError, self._bad_call)\n\n def test_return(self):\n res = self.ary[['f0', 'f2']].tolist()\n assert_(res == [(1, 3), (5, 7)])\n\n\nclass TestIsSubDType:\n # scalar types can be promoted into dtypes\n wrappers = [np.dtype, lambda x: x]\n\n def test_both_abstract(self):\n assert_(np.issubdtype(np.floating, np.inexact))\n assert_(not np.issubdtype(np.inexact, np.floating))\n\n def test_same(self):\n for cls in (np.float32, np.int32):\n for w1, w2 in itertools.product(self.wrappers, repeat=2):\n assert_(np.issubdtype(w1(cls), w2(cls)))\n\n def test_subclass(self):\n # note we cannot promote floating to a dtype, as it would turn into a\n # concrete type\n for w in self.wrappers:\n assert_(np.issubdtype(w(np.float32), np.floating))\n assert_(np.issubdtype(w(np.float64), np.floating))\n\n def test_subclass_backwards(self):\n for w in self.wrappers:\n assert_(not np.issubdtype(np.floating, w(np.float32)))\n assert_(not np.issubdtype(np.floating, w(np.float64)))\n\n def test_sibling_class(self):\n for w1, w2 in itertools.product(self.wrappers, repeat=2):\n assert_(not np.issubdtype(w1(np.float32), w2(np.float64)))\n assert_(not np.issubdtype(w1(np.float64), w2(np.float32)))\n\n def test_nondtype_nonscalartype(self):\n # See gh-14619 and gh-9505 which introduced the deprecation to fix\n # this. These tests are directly taken from gh-9505\n assert not np.issubdtype(np.float32, 'float64')\n assert not np.issubdtype(np.float32, 'f8')\n assert not np.issubdtype(np.int32, str)\n assert not np.issubdtype(np.int32, 'int64')\n assert not np.issubdtype(np.str_, 'void')\n # for the following the correct spellings are\n # np.integer, np.floating, or np.complexfloating respectively:\n assert not np.issubdtype(np.int8, int) # np.int8 is never np.int_\n assert not np.issubdtype(np.float32, float)\n assert not np.issubdtype(np.complex64, complex)\n assert not np.issubdtype(np.float32, "float")\n assert not np.issubdtype(np.float64, "f")\n\n # Test the same for the correct first datatype and abstract one\n # in the case of int, float, complex:\n assert np.issubdtype(np.float64, 'float64')\n assert np.issubdtype(np.float64, 'f8')\n assert np.issubdtype(np.str_, str)\n assert np.issubdtype(np.int64, 'int64')\n assert np.issubdtype(np.void, 'void')\n assert np.issubdtype(np.int8, np.integer)\n assert np.issubdtype(np.float32, np.floating)\n assert np.issubdtype(np.complex64, np.complexfloating)\n assert np.issubdtype(np.float64, "float")\n assert np.issubdtype(np.float32, "f")\n\n\nclass TestIsDType:\n """\n Check correctness of `np.isdtype`. The test considers different argument\n configurations: `np.isdtype(dtype, k1)` and `np.isdtype(dtype, (k1, k2))`\n with concrete dtypes and dtype groups.\n """\n dtype_group_dict = {\n "signed integer": sctypes["int"],\n "unsigned integer": sctypes["uint"],\n "integral": sctypes["int"] + sctypes["uint"],\n "real floating": sctypes["float"],\n "complex floating": sctypes["complex"],\n "numeric": (\n sctypes["int"] + sctypes["uint"] + sctypes["float"] +\n sctypes["complex"]\n )\n }\n\n @pytest.mark.parametrize(\n "dtype,close_dtype",\n [\n (np.int64, np.int32), (np.uint64, np.uint32),\n (np.float64, np.float32), (np.complex128, np.complex64)\n ]\n )\n @pytest.mark.parametrize(\n "dtype_group",\n [\n None, "signed integer", "unsigned integer", "integral",\n "real floating", "complex floating", "numeric"\n ]\n )\n def test_isdtype(self, dtype, close_dtype, dtype_group):\n # First check if same dtypes return `true` and different ones\n # give `false` (even if they're close in the dtype hierarchy!)\n if dtype_group is None:\n assert np.isdtype(dtype, dtype)\n assert not np.isdtype(dtype, close_dtype)\n assert np.isdtype(dtype, (dtype, close_dtype))\n\n # Check that dtype and a dtype group that it belongs to\n # return `true`, and `false` otherwise.\n elif dtype in self.dtype_group_dict[dtype_group]:\n assert np.isdtype(dtype, dtype_group)\n assert np.isdtype(dtype, (close_dtype, dtype_group))\n else:\n assert not np.isdtype(dtype, dtype_group)\n\n def test_isdtype_invalid_args(self):\n with assert_raises_regex(TypeError, r".*must be a NumPy dtype.*"):\n np.isdtype("int64", np.int64)\n with assert_raises_regex(TypeError, r".*kind argument must.*"):\n np.isdtype(np.int64, 1)\n with assert_raises_regex(ValueError, r".*not a known kind name.*"):\n np.isdtype(np.int64, "int64")\n\n def test_sctypes_complete(self):\n # issue 26439: int32/intc were masking each other on 32-bit builds\n assert np.int32 in sctypes['int']\n assert np.intc in sctypes['int']\n assert np.int64 in sctypes['int']\n assert np.uint32 in sctypes['uint']\n assert np.uintc in sctypes['uint']\n assert np.uint64 in sctypes['uint']\n\nclass TestSctypeDict:\n def test_longdouble(self):\n assert_(np._core.sctypeDict['float64'] is not np.longdouble)\n assert_(np._core.sctypeDict['complex128'] is not np.clongdouble)\n\n def test_ulong(self):\n assert np._core.sctypeDict['ulong'] is np.ulong\n assert np.dtype(np.ulong) is np.dtype("ulong")\n assert np.dtype(np.ulong).itemsize == np.dtype(np.long).itemsize\n\n\n@pytest.mark.filterwarnings("ignore:.*maximum_sctype.*:DeprecationWarning")\nclass TestMaximumSctype:\n\n # note that parametrizing with sctype['int'] and similar would skip types\n # with the same size (gh-11923)\n\n @pytest.mark.parametrize(\n 't', [np.byte, np.short, np.intc, np.long, np.longlong]\n )\n def test_int(self, t):\n assert_equal(maximum_sctype(t), np._core.sctypes['int'][-1])\n\n @pytest.mark.parametrize(\n 't', [np.ubyte, np.ushort, np.uintc, np.ulong, np.ulonglong]\n )\n def test_uint(self, t):\n assert_equal(maximum_sctype(t), np._core.sctypes['uint'][-1])\n\n @pytest.mark.parametrize('t', [np.half, np.single, np.double, np.longdouble])\n def test_float(self, t):\n assert_equal(maximum_sctype(t), np._core.sctypes['float'][-1])\n\n @pytest.mark.parametrize('t', [np.csingle, np.cdouble, np.clongdouble])\n def test_complex(self, t):\n assert_equal(maximum_sctype(t), np._core.sctypes['complex'][-1])\n\n @pytest.mark.parametrize('t', [np.bool, np.object_, np.str_, np.bytes_,\n np.void])\n def test_other(self, t):\n assert_equal(maximum_sctype(t), t)\n\n\nclass Test_sctype2char:\n # This function is old enough that we're really just documenting the quirks\n # at this point.\n\n def test_scalar_type(self):\n assert_equal(sctype2char(np.double), 'd')\n assert_equal(sctype2char(np.long), 'l')\n assert_equal(sctype2char(np.int_), np.array(0).dtype.char)\n assert_equal(sctype2char(np.str_), 'U')\n assert_equal(sctype2char(np.bytes_), 'S')\n\n def test_other_type(self):\n assert_equal(sctype2char(float), 'd')\n assert_equal(sctype2char(list), 'O')\n assert_equal(sctype2char(np.ndarray), 'O')\n\n def test_third_party_scalar_type(self):\n from numpy._core._rational_tests import rational\n assert_raises(KeyError, sctype2char, rational)\n assert_raises(KeyError, sctype2char, rational(1))\n\n def test_array_instance(self):\n assert_equal(sctype2char(np.array([1.0, 2.0])), 'd')\n\n def test_abstract_type(self):\n assert_raises(KeyError, sctype2char, np.floating)\n\n def test_non_type(self):\n assert_raises(ValueError, sctype2char, 1)\n\n@pytest.mark.parametrize("rep, expected", [\n (np.int32, True),\n (list, False),\n (1.1, False),\n (str, True),\n (np.dtype(np.float64), True),\n (np.dtype((np.int16, (3, 4))), True),\n (np.dtype([('a', np.int8)]), True),\n ])\ndef test_issctype(rep, expected):\n # ensure proper identification of scalar\n # data-types by issctype()\n actual = issctype(rep)\n assert type(actual) is bool\n assert_equal(actual, expected)\n\n\n@pytest.mark.skipif(sys.flags.optimize > 1,\n reason="no docstrings present to inspect when PYTHONOPTIMIZE/Py_OptimizeFlag > 1")\n@pytest.mark.xfail(IS_PYPY,\n reason="PyPy cannot modify tp_doc after PyType_Ready")\nclass TestDocStrings:\n def test_platform_dependent_aliases(self):\n if np.int64 is np.int_:\n assert_('int64' in np.int_.__doc__)\n elif np.int64 is np.longlong:\n assert_('int64' in np.longlong.__doc__)\n\n\nclass TestScalarTypeNames:\n # gh-9799\n\n numeric_types = [\n np.byte, np.short, np.intc, np.long, np.longlong,\n np.ubyte, np.ushort, np.uintc, np.ulong, np.ulonglong,\n np.half, np.single, np.double, np.longdouble,\n np.csingle, np.cdouble, np.clongdouble,\n ]\n\n def test_names_are_unique(self):\n # none of the above may be aliases for each other\n assert len(set(self.numeric_types)) == len(self.numeric_types)\n\n # names must be unique\n names = [t.__name__ for t in self.numeric_types]\n assert len(set(names)) == len(names)\n\n @pytest.mark.parametrize('t', numeric_types)\n def test_names_reflect_attributes(self, t):\n """ Test that names correspond to where the type is under ``np.`` """\n assert getattr(np, t.__name__) is t\n\n @pytest.mark.parametrize('t', numeric_types)\n def test_names_are_undersood_by_dtype(self, t):\n """ Test the dtype constructor maps names back to the type """\n assert np.dtype(t.__name__).type is t\n\n\nclass TestBoolDefinition:\n def test_bool_definition(self):\n assert nt.bool is np.bool\n | .venv\Lib\site-packages\numpy\_core\tests\test_numerictypes.py | test_numerictypes.py | Python | 23,893 | 0.95 | 0.159164 | 0.10325 | node-utils | 138 | 2025-04-24T01:03:15.697381 | MIT | true | 9682f2b7ea62c5570c51e95e563b59dc |
import inspect\nimport os\nimport pickle\nimport sys\nimport tempfile\nfrom io import StringIO\nfrom unittest import mock\n\nimport pytest\n\nimport numpy as np\nfrom numpy._core.overrides import (\n _get_implementing_args,\n array_function_dispatch,\n verify_matching_signatures,\n)\nfrom numpy.testing import assert_, assert_equal, assert_raises, assert_raises_regex\nfrom numpy.testing.overrides import get_overridable_numpy_array_functions\n\n\ndef _return_not_implemented(self, *args, **kwargs):\n return NotImplemented\n\n\n# need to define this at the top level to test pickling\n@array_function_dispatch(lambda array: (array,))\ndef dispatched_one_arg(array):\n """Docstring."""\n return 'original'\n\n\n@array_function_dispatch(lambda array1, array2: (array1, array2))\ndef dispatched_two_arg(array1, array2):\n """Docstring."""\n return 'original'\n\n\nclass TestGetImplementingArgs:\n\n def test_ndarray(self):\n array = np.array(1)\n\n args = _get_implementing_args([array])\n assert_equal(list(args), [array])\n\n args = _get_implementing_args([array, array])\n assert_equal(list(args), [array])\n\n args = _get_implementing_args([array, 1])\n assert_equal(list(args), [array])\n\n args = _get_implementing_args([1, array])\n assert_equal(list(args), [array])\n\n def test_ndarray_subclasses(self):\n\n class OverrideSub(np.ndarray):\n __array_function__ = _return_not_implemented\n\n class NoOverrideSub(np.ndarray):\n pass\n\n array = np.array(1).view(np.ndarray)\n override_sub = np.array(1).view(OverrideSub)\n no_override_sub = np.array(1).view(NoOverrideSub)\n\n args = _get_implementing_args([array, override_sub])\n assert_equal(list(args), [override_sub, array])\n\n args = _get_implementing_args([array, no_override_sub])\n assert_equal(list(args), [no_override_sub, array])\n\n args = _get_implementing_args(\n [override_sub, no_override_sub])\n assert_equal(list(args), [override_sub, no_override_sub])\n\n def test_ndarray_and_duck_array(self):\n\n class Other:\n __array_function__ = _return_not_implemented\n\n array = np.array(1)\n other = Other()\n\n args = _get_implementing_args([other, array])\n assert_equal(list(args), [other, array])\n\n args = _get_implementing_args([array, other])\n assert_equal(list(args), [array, other])\n\n def test_ndarray_subclass_and_duck_array(self):\n\n class OverrideSub(np.ndarray):\n __array_function__ = _return_not_implemented\n\n class Other:\n __array_function__ = _return_not_implemented\n\n array = np.array(1)\n subarray = np.array(1).view(OverrideSub)\n other = Other()\n\n assert_equal(_get_implementing_args([array, subarray, other]),\n [subarray, array, other])\n assert_equal(_get_implementing_args([array, other, subarray]),\n [subarray, array, other])\n\n def test_many_duck_arrays(self):\n\n class A:\n __array_function__ = _return_not_implemented\n\n class B(A):\n __array_function__ = _return_not_implemented\n\n class C(A):\n __array_function__ = _return_not_implemented\n\n class D:\n __array_function__ = _return_not_implemented\n\n a = A()\n b = B()\n c = C()\n d = D()\n\n assert_equal(_get_implementing_args([1]), [])\n assert_equal(_get_implementing_args([a]), [a])\n assert_equal(_get_implementing_args([a, 1]), [a])\n assert_equal(_get_implementing_args([a, a, a]), [a])\n assert_equal(_get_implementing_args([a, d, a]), [a, d])\n assert_equal(_get_implementing_args([a, b]), [b, a])\n assert_equal(_get_implementing_args([b, a]), [b, a])\n assert_equal(_get_implementing_args([a, b, c]), [b, c, a])\n assert_equal(_get_implementing_args([a, c, b]), [c, b, a])\n\n def test_too_many_duck_arrays(self):\n namespace = {'__array_function__': _return_not_implemented}\n types = [type('A' + str(i), (object,), namespace) for i in range(65)]\n relevant_args = [t() for t in types]\n\n actual = _get_implementing_args(relevant_args[:64])\n assert_equal(actual, relevant_args[:64])\n\n with assert_raises_regex(TypeError, 'distinct argument types'):\n _get_implementing_args(relevant_args)\n\n\nclass TestNDArrayArrayFunction:\n\n def test_method(self):\n\n class Other:\n __array_function__ = _return_not_implemented\n\n class NoOverrideSub(np.ndarray):\n pass\n\n class OverrideSub(np.ndarray):\n __array_function__ = _return_not_implemented\n\n array = np.array([1])\n other = Other()\n no_override_sub = array.view(NoOverrideSub)\n override_sub = array.view(OverrideSub)\n\n result = array.__array_function__(func=dispatched_two_arg,\n types=(np.ndarray,),\n args=(array, 1.), kwargs={})\n assert_equal(result, 'original')\n\n result = array.__array_function__(func=dispatched_two_arg,\n types=(np.ndarray, Other),\n args=(array, other), kwargs={})\n assert_(result is NotImplemented)\n\n result = array.__array_function__(func=dispatched_two_arg,\n types=(np.ndarray, NoOverrideSub),\n args=(array, no_override_sub),\n kwargs={})\n assert_equal(result, 'original')\n\n result = array.__array_function__(func=dispatched_two_arg,\n types=(np.ndarray, OverrideSub),\n args=(array, override_sub),\n kwargs={})\n assert_equal(result, 'original')\n\n with assert_raises_regex(TypeError, 'no implementation found'):\n np.concatenate((array, other))\n\n expected = np.concatenate((array, array))\n result = np.concatenate((array, no_override_sub))\n assert_equal(result, expected.view(NoOverrideSub))\n result = np.concatenate((array, override_sub))\n assert_equal(result, expected.view(OverrideSub))\n\n def test_no_wrapper(self):\n # Regular numpy functions have wrappers, but do not presume\n # all functions do (array creation ones do not): check that\n # we just call the function in that case.\n array = np.array(1)\n func = lambda x: x * 2\n result = array.__array_function__(func=func, types=(np.ndarray,),\n args=(array,), kwargs={})\n assert_equal(result, array * 2)\n\n def test_wrong_arguments(self):\n # Check our implementation guards against wrong arguments.\n a = np.array([1, 2])\n with pytest.raises(TypeError, match="args must be a tuple"):\n a.__array_function__(np.reshape, (np.ndarray,), a, (2, 1))\n with pytest.raises(TypeError, match="kwargs must be a dict"):\n a.__array_function__(np.reshape, (np.ndarray,), (a,), (2, 1))\n\n\nclass TestArrayFunctionDispatch:\n\n def test_pickle(self):\n for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):\n roundtripped = pickle.loads(\n pickle.dumps(dispatched_one_arg, protocol=proto))\n assert_(roundtripped is dispatched_one_arg)\n\n def test_name_and_docstring(self):\n assert_equal(dispatched_one_arg.__name__, 'dispatched_one_arg')\n if sys.flags.optimize < 2:\n assert_equal(dispatched_one_arg.__doc__, 'Docstring.')\n\n def test_interface(self):\n\n class MyArray:\n def __array_function__(self, func, types, args, kwargs):\n return (self, func, types, args, kwargs)\n\n original = MyArray()\n (obj, func, types, args, kwargs) = dispatched_one_arg(original)\n assert_(obj is original)\n assert_(func is dispatched_one_arg)\n assert_equal(set(types), {MyArray})\n # assert_equal uses the overloaded np.iscomplexobj() internally\n assert_(args == (original,))\n assert_equal(kwargs, {})\n\n def test_not_implemented(self):\n\n class MyArray:\n def __array_function__(self, func, types, args, kwargs):\n return NotImplemented\n\n array = MyArray()\n with assert_raises_regex(TypeError, 'no implementation found'):\n dispatched_one_arg(array)\n\n def test_where_dispatch(self):\n\n class DuckArray:\n def __array_function__(self, ufunc, method, *inputs, **kwargs):\n return "overridden"\n\n array = np.array(1)\n duck_array = DuckArray()\n\n result = np.std(array, where=duck_array)\n\n assert_equal(result, "overridden")\n\n\nclass TestVerifyMatchingSignatures:\n\n def test_verify_matching_signatures(self):\n\n verify_matching_signatures(lambda x: 0, lambda x: 0)\n verify_matching_signatures(lambda x=None: 0, lambda x=None: 0)\n verify_matching_signatures(lambda x=1: 0, lambda x=None: 0)\n\n with assert_raises(RuntimeError):\n verify_matching_signatures(lambda a: 0, lambda b: 0)\n with assert_raises(RuntimeError):\n verify_matching_signatures(lambda x: 0, lambda x=None: 0)\n with assert_raises(RuntimeError):\n verify_matching_signatures(lambda x=None: 0, lambda y=None: 0)\n with assert_raises(RuntimeError):\n verify_matching_signatures(lambda x=1: 0, lambda y=1: 0)\n\n def test_array_function_dispatch(self):\n\n with assert_raises(RuntimeError):\n @array_function_dispatch(lambda x: (x,))\n def f(y):\n pass\n\n # should not raise\n @array_function_dispatch(lambda x: (x,), verify=False)\n def f(y):\n pass\n\n\ndef _new_duck_type_and_implements():\n """Create a duck array type and implements functions."""\n HANDLED_FUNCTIONS = {}\n\n class MyArray:\n def __array_function__(self, func, types, args, kwargs):\n if func not in HANDLED_FUNCTIONS:\n return NotImplemented\n if not all(issubclass(t, MyArray) for t in types):\n return NotImplemented\n return HANDLED_FUNCTIONS[func](*args, **kwargs)\n\n def implements(numpy_function):\n """Register an __array_function__ implementations."""\n def decorator(func):\n HANDLED_FUNCTIONS[numpy_function] = func\n return func\n return decorator\n\n return (MyArray, implements)\n\n\nclass TestArrayFunctionImplementation:\n\n def test_one_arg(self):\n MyArray, implements = _new_duck_type_and_implements()\n\n @implements(dispatched_one_arg)\n def _(array):\n return 'myarray'\n\n assert_equal(dispatched_one_arg(1), 'original')\n assert_equal(dispatched_one_arg(MyArray()), 'myarray')\n\n def test_optional_args(self):\n MyArray, implements = _new_duck_type_and_implements()\n\n @array_function_dispatch(lambda array, option=None: (array,))\n def func_with_option(array, option='default'):\n return option\n\n @implements(func_with_option)\n def my_array_func_with_option(array, new_option='myarray'):\n return new_option\n\n # we don't need to implement every option on __array_function__\n # implementations\n assert_equal(func_with_option(1), 'default')\n assert_equal(func_with_option(1, option='extra'), 'extra')\n assert_equal(func_with_option(MyArray()), 'myarray')\n with assert_raises(TypeError):\n func_with_option(MyArray(), option='extra')\n\n # but new options on implementations can't be used\n result = my_array_func_with_option(MyArray(), new_option='yes')\n assert_equal(result, 'yes')\n with assert_raises(TypeError):\n func_with_option(MyArray(), new_option='no')\n\n def test_not_implemented(self):\n MyArray, implements = _new_duck_type_and_implements()\n\n @array_function_dispatch(lambda array: (array,), module='my')\n def func(array):\n return array\n\n array = np.array(1)\n assert_(func(array) is array)\n assert_equal(func.__module__, 'my')\n\n with assert_raises_regex(\n TypeError, "no implementation found for 'my.func'"):\n func(MyArray())\n\n @pytest.mark.parametrize("name", ["concatenate", "mean", "asarray"])\n def test_signature_error_message_simple(self, name):\n func = getattr(np, name)\n try:\n # all of these functions need an argument:\n func()\n except TypeError as e:\n exc = e\n\n assert exc.args[0].startswith(f"{name}()")\n\n def test_signature_error_message(self):\n # The lambda function will be named "<lambda>", but the TypeError\n # should show the name as "func"\n def _dispatcher():\n return ()\n\n @array_function_dispatch(_dispatcher)\n def func():\n pass\n\n try:\n func._implementation(bad_arg=3)\n except TypeError as e:\n expected_exception = e\n\n try:\n func(bad_arg=3)\n raise AssertionError("must fail")\n except TypeError as exc:\n if exc.args[0].startswith("_dispatcher"):\n # We replace the qualname currently, but it used `__name__`\n # (relevant functions have the same name and qualname anyway)\n pytest.skip("Python version is not using __qualname__ for "\n "TypeError formatting.")\n\n assert exc.args == expected_exception.args\n\n @pytest.mark.parametrize("value", [234, "this func is not replaced"])\n def test_dispatcher_error(self, value):\n # If the dispatcher raises an error, we must not attempt to mutate it\n error = TypeError(value)\n\n def dispatcher():\n raise error\n\n @array_function_dispatch(dispatcher)\n def func():\n return 3\n\n try:\n func()\n raise AssertionError("must fail")\n except TypeError as exc:\n assert exc is error # unmodified exception\n\n def test_properties(self):\n # Check that str and repr are sensible\n func = dispatched_two_arg\n assert str(func) == str(func._implementation)\n repr_no_id = repr(func).split("at ")[0]\n repr_no_id_impl = repr(func._implementation).split("at ")[0]\n assert repr_no_id == repr_no_id_impl\n\n @pytest.mark.parametrize("func", [\n lambda x, y: 0, # no like argument\n lambda like=None: 0, # not keyword only\n lambda *, like=None, a=3: 0, # not last (not that it matters)\n ])\n def test_bad_like_sig(self, func):\n # We sanity check the signature, and these should fail.\n with pytest.raises(RuntimeError):\n array_function_dispatch()(func)\n\n def test_bad_like_passing(self):\n # Cover internal sanity check for passing like as first positional arg\n def func(*, like=None):\n pass\n\n func_with_like = array_function_dispatch()(func)\n with pytest.raises(TypeError):\n func_with_like()\n with pytest.raises(TypeError):\n func_with_like(like=234)\n\n def test_too_many_args(self):\n # Mainly a unit-test to increase coverage\n objs = []\n for i in range(80):\n class MyArr:\n def __array_function__(self, *args, **kwargs):\n return NotImplemented\n\n objs.append(MyArr())\n\n def _dispatch(*args):\n return args\n\n @array_function_dispatch(_dispatch)\n def func(*args):\n pass\n\n with pytest.raises(TypeError, match="maximum number"):\n func(*objs)\n\n\nclass TestNDArrayMethods:\n\n def test_repr(self):\n # gh-12162: should still be defined even if __array_function__ doesn't\n # implement np.array_repr()\n\n class MyArray(np.ndarray):\n def __array_function__(*args, **kwargs):\n return NotImplemented\n\n array = np.array(1).view(MyArray)\n assert_equal(repr(array), 'MyArray(1)')\n assert_equal(str(array), '1')\n\n\nclass TestNumPyFunctions:\n\n def test_set_module(self):\n assert_equal(np.sum.__module__, 'numpy')\n assert_equal(np.char.equal.__module__, 'numpy.char')\n assert_equal(np.fft.fft.__module__, 'numpy.fft')\n assert_equal(np.linalg.solve.__module__, 'numpy.linalg')\n\n def test_inspect_sum(self):\n signature = inspect.signature(np.sum)\n assert_('axis' in signature.parameters)\n\n def test_override_sum(self):\n MyArray, implements = _new_duck_type_and_implements()\n\n @implements(np.sum)\n def _(array):\n return 'yes'\n\n assert_equal(np.sum(MyArray()), 'yes')\n\n def test_sum_on_mock_array(self):\n\n # We need a proxy for mocks because __array_function__ is only looked\n # up in the class dict\n class ArrayProxy:\n def __init__(self, value):\n self.value = value\n\n def __array_function__(self, *args, **kwargs):\n return self.value.__array_function__(*args, **kwargs)\n\n def __array__(self, *args, **kwargs):\n return self.value.__array__(*args, **kwargs)\n\n proxy = ArrayProxy(mock.Mock(spec=ArrayProxy))\n proxy.value.__array_function__.return_value = 1\n result = np.sum(proxy)\n assert_equal(result, 1)\n proxy.value.__array_function__.assert_called_once_with(\n np.sum, (ArrayProxy,), (proxy,), {})\n proxy.value.__array__.assert_not_called()\n\n def test_sum_forwarding_implementation(self):\n\n class MyArray(np.ndarray):\n\n def sum(self, axis, out):\n return 'summed'\n\n def __array_function__(self, func, types, args, kwargs):\n return super().__array_function__(func, types, args, kwargs)\n\n # note: the internal implementation of np.sum() calls the .sum() method\n array = np.array(1).view(MyArray)\n assert_equal(np.sum(array), 'summed')\n\n\nclass TestArrayLike:\n def setup_method(self):\n class MyArray:\n def __init__(self, function=None):\n self.function = function\n\n def __array_function__(self, func, types, args, kwargs):\n assert func is getattr(np, func.__name__)\n try:\n my_func = getattr(self, func.__name__)\n except AttributeError:\n return NotImplemented\n return my_func(*args, **kwargs)\n\n self.MyArray = MyArray\n\n class MyNoArrayFunctionArray:\n def __init__(self, function=None):\n self.function = function\n\n self.MyNoArrayFunctionArray = MyNoArrayFunctionArray\n\n class MySubclass(np.ndarray):\n def __array_function__(self, func, types, args, kwargs):\n result = super().__array_function__(func, types, args, kwargs)\n return result.view(self.__class__)\n\n self.MySubclass = MySubclass\n\n def add_method(self, name, arr_class, enable_value_error=False):\n def _definition(*args, **kwargs):\n # Check that `like=` isn't propagated downstream\n assert 'like' not in kwargs\n\n if enable_value_error and 'value_error' in kwargs:\n raise ValueError\n\n return arr_class(getattr(arr_class, name))\n setattr(arr_class, name, _definition)\n\n def func_args(*args, **kwargs):\n return args, kwargs\n\n def test_array_like_not_implemented(self):\n self.add_method('array', self.MyArray)\n\n ref = self.MyArray.array()\n\n with assert_raises_regex(TypeError, 'no implementation found'):\n array_like = np.asarray(1, like=ref)\n\n _array_tests = [\n ('array', *func_args((1,))),\n ('asarray', *func_args((1,))),\n ('asanyarray', *func_args((1,))),\n ('ascontiguousarray', *func_args((2, 3))),\n ('asfortranarray', *func_args((2, 3))),\n ('require', *func_args((np.arange(6).reshape(2, 3),),\n requirements=['A', 'F'])),\n ('empty', *func_args((1,))),\n ('full', *func_args((1,), 2)),\n ('ones', *func_args((1,))),\n ('zeros', *func_args((1,))),\n ('arange', *func_args(3)),\n ('frombuffer', *func_args(b'\x00' * 8, dtype=int)),\n ('fromiter', *func_args(range(3), dtype=int)),\n ('fromstring', *func_args('1,2', dtype=int, sep=',')),\n ('loadtxt', *func_args(lambda: StringIO('0 1\n2 3'))),\n ('genfromtxt', *func_args(lambda: StringIO('1,2.1'),\n dtype=[('int', 'i8'), ('float', 'f8')],\n delimiter=',')),\n ]\n\n def test_nep35_functions_as_array_functions(self,):\n all_array_functions = get_overridable_numpy_array_functions()\n like_array_functions_subset = {\n getattr(np, func_name) for func_name, *_ in self.__class__._array_tests\n }\n assert like_array_functions_subset.issubset(all_array_functions)\n\n nep35_python_functions = {\n np.eye, np.fromfunction, np.full, np.genfromtxt,\n np.identity, np.loadtxt, np.ones, np.require, np.tri,\n }\n assert nep35_python_functions.issubset(all_array_functions)\n\n nep35_C_functions = {\n np.arange, np.array, np.asanyarray, np.asarray,\n np.ascontiguousarray, np.asfortranarray, np.empty,\n np.frombuffer, np.fromfile, np.fromiter, np.fromstring,\n np.zeros,\n }\n assert nep35_C_functions.issubset(all_array_functions)\n\n @pytest.mark.parametrize('function, args, kwargs', _array_tests)\n @pytest.mark.parametrize('numpy_ref', [True, False])\n def test_array_like(self, function, args, kwargs, numpy_ref):\n self.add_method('array', self.MyArray)\n self.add_method(function, self.MyArray)\n np_func = getattr(np, function)\n my_func = getattr(self.MyArray, function)\n\n if numpy_ref is True:\n ref = np.array(1)\n else:\n ref = self.MyArray.array()\n\n like_args = tuple(a() if callable(a) else a for a in args)\n array_like = np_func(*like_args, **kwargs, like=ref)\n\n if numpy_ref is True:\n assert type(array_like) is np.ndarray\n\n np_args = tuple(a() if callable(a) else a for a in args)\n np_arr = np_func(*np_args, **kwargs)\n\n # Special-case np.empty to ensure values match\n if function == "empty":\n np_arr.fill(1)\n array_like.fill(1)\n\n assert_equal(array_like, np_arr)\n else:\n assert type(array_like) is self.MyArray\n assert array_like.function is my_func\n\n @pytest.mark.parametrize('function, args, kwargs', _array_tests)\n @pytest.mark.parametrize('ref', [1, [1], "MyNoArrayFunctionArray"])\n def test_no_array_function_like(self, function, args, kwargs, ref):\n self.add_method('array', self.MyNoArrayFunctionArray)\n self.add_method(function, self.MyNoArrayFunctionArray)\n np_func = getattr(np, function)\n\n # Instantiate ref if it's the MyNoArrayFunctionArray class\n if ref == "MyNoArrayFunctionArray":\n ref = self.MyNoArrayFunctionArray.array()\n\n like_args = tuple(a() if callable(a) else a for a in args)\n\n with assert_raises_regex(TypeError,\n 'The `like` argument must be an array-like that implements'):\n np_func(*like_args, **kwargs, like=ref)\n\n @pytest.mark.parametrize('function, args, kwargs', _array_tests)\n def test_subclass(self, function, args, kwargs):\n ref = np.array(1).view(self.MySubclass)\n np_func = getattr(np, function)\n like_args = tuple(a() if callable(a) else a for a in args)\n array_like = np_func(*like_args, **kwargs, like=ref)\n assert type(array_like) is self.MySubclass\n if np_func is np.empty:\n return\n np_args = tuple(a() if callable(a) else a for a in args)\n np_arr = np_func(*np_args, **kwargs)\n assert_equal(array_like.view(np.ndarray), np_arr)\n\n @pytest.mark.parametrize('numpy_ref', [True, False])\n def test_array_like_fromfile(self, numpy_ref):\n self.add_method('array', self.MyArray)\n self.add_method("fromfile", self.MyArray)\n\n if numpy_ref is True:\n ref = np.array(1)\n else:\n ref = self.MyArray.array()\n\n data = np.random.random(5)\n\n with tempfile.TemporaryDirectory() as tmpdir:\n fname = os.path.join(tmpdir, "testfile")\n data.tofile(fname)\n\n array_like = np.fromfile(fname, like=ref)\n if numpy_ref is True:\n assert type(array_like) is np.ndarray\n np_res = np.fromfile(fname, like=ref)\n assert_equal(np_res, data)\n assert_equal(array_like, np_res)\n else:\n assert type(array_like) is self.MyArray\n assert array_like.function is self.MyArray.fromfile\n\n def test_exception_handling(self):\n self.add_method('array', self.MyArray, enable_value_error=True)\n\n ref = self.MyArray.array()\n\n with assert_raises(TypeError):\n # Raises the error about `value_error` being invalid first\n np.array(1, value_error=True, like=ref)\n\n @pytest.mark.parametrize('function, args, kwargs', _array_tests)\n def test_like_as_none(self, function, args, kwargs):\n self.add_method('array', self.MyArray)\n self.add_method(function, self.MyArray)\n np_func = getattr(np, function)\n\n like_args = tuple(a() if callable(a) else a for a in args)\n # required for loadtxt and genfromtxt to init w/o error.\n like_args_exp = tuple(a() if callable(a) else a for a in args)\n\n array_like = np_func(*like_args, **kwargs, like=None)\n expected = np_func(*like_args_exp, **kwargs)\n # Special-case np.empty to ensure values match\n if function == "empty":\n array_like.fill(1)\n expected.fill(1)\n assert_equal(array_like, expected)\n\n\ndef test_function_like():\n # We provide a `__get__` implementation, make sure it works\n assert type(np.mean) is np._core._multiarray_umath._ArrayFunctionDispatcher\n\n class MyClass:\n def __array__(self, dtype=None, copy=None):\n # valid argument to mean:\n return np.arange(3)\n\n func1 = staticmethod(np.mean)\n func2 = np.mean\n func3 = classmethod(np.mean)\n\n m = MyClass()\n assert m.func1([10]) == 10\n assert m.func2() == 1 # mean of the arange\n with pytest.raises(TypeError, match="unsupported operand type"):\n # Tries to operate on the class\n m.func3()\n\n # Manual binding also works (the above may shortcut):\n bound = np.mean.__get__(m, MyClass)\n assert bound() == 1\n\n bound = np.mean.__get__(None, MyClass) # unbound actually\n assert bound([10]) == 10\n\n bound = np.mean.__get__(MyClass) # classmethod\n with pytest.raises(TypeError, match="unsupported operand type"):\n bound()\n | .venv\Lib\site-packages\numpy\_core\tests\test_overrides.py | test_overrides.py | Python | 28,309 | 0.95 | 0.238938 | 0.058333 | node-utils | 752 | 2025-04-16T17:49:04.651906 | BSD-3-Clause | true | 96aea7826d0dec7b9bf8b189ce642a26 |
import sys\nfrom io import StringIO\n\nimport pytest\n\nimport numpy as np\nfrom numpy._core.tests._locales import CommaDecimalPointLocale\nfrom numpy.testing import IS_MUSL, assert_, assert_equal\n\n_REF = {np.inf: 'inf', -np.inf: '-inf', np.nan: 'nan'}\n\n\n@pytest.mark.parametrize('tp', [np.float32, np.double, np.longdouble])\ndef test_float_types(tp):\n """ Check formatting.\n\n This is only for the str function, and only for simple types.\n The precision of np.float32 and np.longdouble aren't the same as the\n python float precision.\n\n """\n for x in [0, 1, -1, 1e20]:\n assert_equal(str(tp(x)), str(float(x)),\n err_msg=f'Failed str formatting for type {tp}')\n\n if tp(1e16).itemsize > 4:\n assert_equal(str(tp(1e16)), str(float('1e16')),\n err_msg=f'Failed str formatting for type {tp}')\n else:\n ref = '1e+16'\n assert_equal(str(tp(1e16)), ref,\n err_msg=f'Failed str formatting for type {tp}')\n\n\n@pytest.mark.parametrize('tp', [np.float32, np.double, np.longdouble])\ndef test_nan_inf_float(tp):\n """ Check formatting of nan & inf.\n\n This is only for the str function, and only for simple types.\n The precision of np.float32 and np.longdouble aren't the same as the\n python float precision.\n\n """\n for x in [np.inf, -np.inf, np.nan]:\n assert_equal(str(tp(x)), _REF[x],\n err_msg=f'Failed str formatting for type {tp}')\n\n\n@pytest.mark.parametrize('tp', [np.complex64, np.cdouble, np.clongdouble])\ndef test_complex_types(tp):\n """Check formatting of complex types.\n\n This is only for the str function, and only for simple types.\n The precision of np.float32 and np.longdouble aren't the same as the\n python float precision.\n\n """\n for x in [0, 1, -1, 1e20]:\n assert_equal(str(tp(x)), str(complex(x)),\n err_msg=f'Failed str formatting for type {tp}')\n assert_equal(str(tp(x * 1j)), str(complex(x * 1j)),\n err_msg=f'Failed str formatting for type {tp}')\n assert_equal(str(tp(x + x * 1j)), str(complex(x + x * 1j)),\n err_msg=f'Failed str formatting for type {tp}')\n\n if tp(1e16).itemsize > 8:\n assert_equal(str(tp(1e16)), str(complex(1e16)),\n err_msg=f'Failed str formatting for type {tp}')\n else:\n ref = '(1e+16+0j)'\n assert_equal(str(tp(1e16)), ref,\n err_msg=f'Failed str formatting for type {tp}')\n\n\n@pytest.mark.parametrize('dtype', [np.complex64, np.cdouble, np.clongdouble])\ndef test_complex_inf_nan(dtype):\n """Check inf/nan formatting of complex types."""\n TESTS = {\n complex(np.inf, 0): "(inf+0j)",\n complex(0, np.inf): "infj",\n complex(-np.inf, 0): "(-inf+0j)",\n complex(0, -np.inf): "-infj",\n complex(np.inf, 1): "(inf+1j)",\n complex(1, np.inf): "(1+infj)",\n complex(-np.inf, 1): "(-inf+1j)",\n complex(1, -np.inf): "(1-infj)",\n complex(np.nan, 0): "(nan+0j)",\n complex(0, np.nan): "nanj",\n complex(-np.nan, 0): "(nan+0j)",\n complex(0, -np.nan): "nanj",\n complex(np.nan, 1): "(nan+1j)",\n complex(1, np.nan): "(1+nanj)",\n complex(-np.nan, 1): "(nan+1j)",\n complex(1, -np.nan): "(1+nanj)",\n }\n for c, s in TESTS.items():\n assert_equal(str(dtype(c)), s)\n\n\n# print tests\ndef _test_redirected_print(x, tp, ref=None):\n file = StringIO()\n file_tp = StringIO()\n stdout = sys.stdout\n try:\n sys.stdout = file_tp\n print(tp(x))\n sys.stdout = file\n if ref:\n print(ref)\n else:\n print(x)\n finally:\n sys.stdout = stdout\n\n assert_equal(file.getvalue(), file_tp.getvalue(),\n err_msg=f'print failed for type{tp}')\n\n\n@pytest.mark.parametrize('tp', [np.float32, np.double, np.longdouble])\ndef test_float_type_print(tp):\n """Check formatting when using print """\n for x in [0, 1, -1, 1e20]:\n _test_redirected_print(float(x), tp)\n\n for x in [np.inf, -np.inf, np.nan]:\n _test_redirected_print(float(x), tp, _REF[x])\n\n if tp(1e16).itemsize > 4:\n _test_redirected_print(1e16, tp)\n else:\n ref = '1e+16'\n _test_redirected_print(1e16, tp, ref)\n\n\n@pytest.mark.parametrize('tp', [np.complex64, np.cdouble, np.clongdouble])\ndef test_complex_type_print(tp):\n """Check formatting when using print """\n # We do not create complex with inf/nan directly because the feature is\n # missing in python < 2.6\n for x in [0, 1, -1, 1e20]:\n _test_redirected_print(complex(x), tp)\n\n if tp(1e16).itemsize > 8:\n _test_redirected_print(complex(1e16), tp)\n else:\n ref = '(1e+16+0j)'\n _test_redirected_print(complex(1e16), tp, ref)\n\n _test_redirected_print(complex(np.inf, 1), tp, '(inf+1j)')\n _test_redirected_print(complex(-np.inf, 1), tp, '(-inf+1j)')\n _test_redirected_print(complex(-np.nan, 1), tp, '(nan+1j)')\n\n\ndef test_scalar_format():\n """Test the str.format method with NumPy scalar types"""\n tests = [('{0}', True, np.bool),\n ('{0}', False, np.bool),\n ('{0:d}', 130, np.uint8),\n ('{0:d}', 50000, np.uint16),\n ('{0:d}', 3000000000, np.uint32),\n ('{0:d}', 15000000000000000000, np.uint64),\n ('{0:d}', -120, np.int8),\n ('{0:d}', -30000, np.int16),\n ('{0:d}', -2000000000, np.int32),\n ('{0:d}', -7000000000000000000, np.int64),\n ('{0:g}', 1.5, np.float16),\n ('{0:g}', 1.5, np.float32),\n ('{0:g}', 1.5, np.float64),\n ('{0:g}', 1.5, np.longdouble),\n ('{0:g}', 1.5 + 0.5j, np.complex64),\n ('{0:g}', 1.5 + 0.5j, np.complex128),\n ('{0:g}', 1.5 + 0.5j, np.clongdouble)]\n\n for (fmat, val, valtype) in tests:\n try:\n assert_equal(fmat.format(val), fmat.format(valtype(val)),\n f"failed with val {val}, type {valtype}")\n except ValueError as e:\n assert_(False,\n "format raised exception (fmt='%s', val=%s, type=%s, exc='%s')" %\n (fmat, repr(val), repr(valtype), str(e)))\n\n\n#\n# Locale tests: scalar types formatting should be independent of the locale\n#\n\nclass TestCommaDecimalPointLocale(CommaDecimalPointLocale):\n\n def test_locale_single(self):\n assert_equal(str(np.float32(1.2)), str(1.2))\n\n def test_locale_double(self):\n assert_equal(str(np.double(1.2)), str(1.2))\n\n @pytest.mark.skipif(IS_MUSL,\n reason="test flaky on musllinux")\n def test_locale_longdouble(self):\n assert_equal(str(np.longdouble('1.2')), str(1.2))\n | .venv\Lib\site-packages\numpy\_core\tests\test_print.py | test_print.py | Python | 6,987 | 0.95 | 0.23 | 0.037267 | python-kit | 402 | 2025-05-11T05:34:50.149714 | GPL-3.0 | true | f2b08bca693427029f130a8c79d83b03 |
import warnings\n\nimport pytest\n\nimport numpy as np\n\n\n@pytest.mark.filterwarnings("error")\ndef test_getattr_warning():\n # issue gh-14735: make sure we clear only getattr errors, and let warnings\n # through\n class Wrapper:\n def __init__(self, array):\n self.array = array\n\n def __len__(self):\n return len(self.array)\n\n def __getitem__(self, item):\n return type(self)(self.array[item])\n\n def __getattr__(self, name):\n if name.startswith("__array_"):\n warnings.warn("object got converted", UserWarning, stacklevel=1)\n\n return getattr(self.array, name)\n\n def __repr__(self):\n return f"<Wrapper({self.array})>"\n\n array = Wrapper(np.arange(10))\n with pytest.raises(UserWarning, match="object got converted"):\n np.asarray(array)\n\n\ndef test_array_called():\n class Wrapper:\n val = '0' * 100\n\n def __array__(self, dtype=None, copy=None):\n return np.array([self.val], dtype=dtype, copy=copy)\n\n wrapped = Wrapper()\n arr = np.array(wrapped, dtype=str)\n assert arr.dtype == 'U100'\n assert arr[0] == Wrapper.val\n | .venv\Lib\site-packages\numpy\_core\tests\test_protocols.py | test_protocols.py | Python | 1,219 | 0.95 | 0.23913 | 0.0625 | node-utils | 390 | 2024-04-01T14:09:19.447437 | GPL-3.0 | true | e39954a7e419a43db2d39cd2053ac575 |
import collections.abc\nimport pickle\nimport textwrap\nfrom io import BytesIO\nfrom os import path\nfrom pathlib import Path\n\nimport pytest\n\nimport numpy as np\nfrom numpy.testing import (\n assert_,\n assert_array_almost_equal,\n assert_array_equal,\n assert_equal,\n assert_raises,\n temppath,\n)\n\n\nclass TestFromrecords:\n def test_fromrecords(self):\n r = np.rec.fromrecords([[456, 'dbe', 1.2], [2, 'de', 1.3]],\n names='col1,col2,col3')\n assert_equal(r[0].item(), (456, 'dbe', 1.2))\n assert_equal(r['col1'].dtype.kind, 'i')\n assert_equal(r['col2'].dtype.kind, 'U')\n assert_equal(r['col2'].dtype.itemsize, 12)\n assert_equal(r['col3'].dtype.kind, 'f')\n\n def test_fromrecords_0len(self):\n """ Verify fromrecords works with a 0-length input """\n dtype = [('a', float), ('b', float)]\n r = np.rec.fromrecords([], dtype=dtype)\n assert_equal(r.shape, (0,))\n\n def test_fromrecords_2d(self):\n data = [\n [(1, 2), (3, 4), (5, 6)],\n [(6, 5), (4, 3), (2, 1)]\n ]\n expected_a = [[1, 3, 5], [6, 4, 2]]\n expected_b = [[2, 4, 6], [5, 3, 1]]\n\n # try with dtype\n r1 = np.rec.fromrecords(data, dtype=[('a', int), ('b', int)])\n assert_equal(r1['a'], expected_a)\n assert_equal(r1['b'], expected_b)\n\n # try with names\n r2 = np.rec.fromrecords(data, names=['a', 'b'])\n assert_equal(r2['a'], expected_a)\n assert_equal(r2['b'], expected_b)\n\n assert_equal(r1, r2)\n\n def test_method_array(self):\n r = np.rec.array(\n b'abcdefg' * 100, formats='i2,S3,i4', shape=3, byteorder='big'\n )\n assert_equal(r[1].item(), (25444, b'efg', 1633837924))\n\n def test_method_array2(self):\n r = np.rec.array(\n [\n (1, 11, 'a'), (2, 22, 'b'), (3, 33, 'c'), (4, 44, 'd'),\n (5, 55, 'ex'), (6, 66, 'f'), (7, 77, 'g')\n ],\n formats='u1,f4,S1'\n )\n assert_equal(r[1].item(), (2, 22.0, b'b'))\n\n def test_recarray_slices(self):\n r = np.rec.array(\n [\n (1, 11, 'a'), (2, 22, 'b'), (3, 33, 'c'), (4, 44, 'd'),\n (5, 55, 'ex'), (6, 66, 'f'), (7, 77, 'g')\n ],\n formats='u1,f4,S1'\n )\n assert_equal(r[1::2][1].item(), (4, 44.0, b'd'))\n\n def test_recarray_fromarrays(self):\n x1 = np.array([1, 2, 3, 4])\n x2 = np.array(['a', 'dd', 'xyz', '12'])\n x3 = np.array([1.1, 2, 3, 4])\n r = np.rec.fromarrays([x1, x2, x3], names='a,b,c')\n assert_equal(r[1].item(), (2, 'dd', 2.0))\n x1[1] = 34\n assert_equal(r.a, np.array([1, 2, 3, 4]))\n\n def test_recarray_fromfile(self):\n data_dir = path.join(path.dirname(__file__), 'data')\n filename = path.join(data_dir, 'recarray_from_file.fits')\n fd = open(filename, 'rb')\n fd.seek(2880 * 2)\n r1 = np.rec.fromfile(fd, formats='f8,i4,S5', shape=3, byteorder='big')\n fd.seek(2880 * 2)\n r2 = np.rec.array(fd, formats='f8,i4,S5', shape=3, byteorder='big')\n fd.seek(2880 * 2)\n bytes_array = BytesIO()\n bytes_array.write(fd.read())\n bytes_array.seek(0)\n r3 = np.rec.fromfile(\n bytes_array, formats='f8,i4,S5', shape=3, byteorder='big'\n )\n fd.close()\n assert_equal(r1, r2)\n assert_equal(r2, r3)\n\n def test_recarray_from_obj(self):\n count = 10\n a = np.zeros(count, dtype='O')\n b = np.zeros(count, dtype='f8')\n c = np.zeros(count, dtype='f8')\n for i in range(len(a)):\n a[i] = list(range(1, 10))\n\n mine = np.rec.fromarrays([a, b, c], names='date,data1,data2')\n for i in range(len(a)):\n assert_(mine.date[i] == list(range(1, 10)))\n assert_(mine.data1[i] == 0.0)\n assert_(mine.data2[i] == 0.0)\n\n def test_recarray_repr(self):\n a = np.array([(1, 0.1), (2, 0.2)],\n dtype=[('foo', '<i4'), ('bar', '<f8')])\n a = np.rec.array(a)\n assert_equal(\n repr(a),\n textwrap.dedent("""\\n rec.array([(1, 0.1), (2, 0.2)],\n dtype=[('foo', '<i4'), ('bar', '<f8')])""")\n )\n\n # make sure non-structured dtypes also show up as rec.array\n a = np.array(np.ones(4, dtype='f8'))\n assert_(repr(np.rec.array(a)).startswith('rec.array'))\n\n # check that the 'np.record' part of the dtype isn't shown\n a = np.rec.array(np.ones(3, dtype='i4,i4'))\n assert_equal(repr(a).find('numpy.record'), -1)\n a = np.rec.array(np.ones(3, dtype='i4'))\n assert_(repr(a).find('dtype=int32') != -1)\n\n def test_0d_recarray_repr(self):\n arr_0d = np.rec.array((1, 2.0, '2003'), dtype='<i4,<f8,<M8[Y]')\n assert_equal(repr(arr_0d), textwrap.dedent("""\\n rec.array((1, 2., '2003'),\n dtype=[('f0', '<i4'), ('f1', '<f8'), ('f2', '<M8[Y]')])"""))\n\n record = arr_0d[()]\n assert_equal(repr(record),\n "np.record((1, 2.0, '2003'), "\n "dtype=[('f0', '<i4'), ('f1', '<f8'), ('f2', '<M8[Y]')])")\n # 1.13 converted to python scalars before the repr\n try:\n np.set_printoptions(legacy='1.13')\n assert_equal(repr(record), '(1, 2.0, datetime.date(2003, 1, 1))')\n finally:\n np.set_printoptions(legacy=False)\n\n def test_recarray_from_repr(self):\n a = np.array([(1, 'ABC'), (2, "DEF")],\n dtype=[('foo', int), ('bar', 'S4')])\n recordarr = np.rec.array(a)\n recarr = a.view(np.recarray)\n recordview = a.view(np.dtype((np.record, a.dtype)))\n\n recordarr_r = eval("np." + repr(recordarr), {'np': np})\n recarr_r = eval("np." + repr(recarr), {'np': np})\n # Prints the type `numpy.record` as part of the dtype:\n recordview_r = eval("np." + repr(recordview), {'np': np, 'numpy': np})\n\n assert_equal(type(recordarr_r), np.recarray)\n assert_equal(recordarr_r.dtype.type, np.record)\n assert_equal(recordarr, recordarr_r)\n\n assert_equal(type(recarr_r), np.recarray)\n assert_equal(recarr_r.dtype.type, np.record)\n assert_equal(recarr, recarr_r)\n\n assert_equal(type(recordview_r), np.ndarray)\n assert_equal(recordview.dtype.type, np.record)\n assert_equal(recordview, recordview_r)\n\n def test_recarray_views(self):\n a = np.array([(1, 'ABC'), (2, "DEF")],\n dtype=[('foo', int), ('bar', 'S4')])\n b = np.array([1, 2, 3, 4, 5], dtype=np.int64)\n\n # check that np.rec.array gives right dtypes\n assert_equal(np.rec.array(a).dtype.type, np.record)\n assert_equal(type(np.rec.array(a)), np.recarray)\n assert_equal(np.rec.array(b).dtype.type, np.int64)\n assert_equal(type(np.rec.array(b)), np.recarray)\n\n # check that viewing as recarray does the same\n assert_equal(a.view(np.recarray).dtype.type, np.record)\n assert_equal(type(a.view(np.recarray)), np.recarray)\n assert_equal(b.view(np.recarray).dtype.type, np.int64)\n assert_equal(type(b.view(np.recarray)), np.recarray)\n\n # check that view to non-structured dtype preserves type=np.recarray\n r = np.rec.array(np.ones(4, dtype="f4,i4"))\n rv = r.view('f8').view('f4,i4')\n assert_equal(type(rv), np.recarray)\n assert_equal(rv.dtype.type, np.record)\n\n # check that getitem also preserves np.recarray and np.record\n r = np.rec.array(np.ones(4, dtype=[('a', 'i4'), ('b', 'i4'),\n ('c', 'i4,i4')]))\n assert_equal(r['c'].dtype.type, np.record)\n assert_equal(type(r['c']), np.recarray)\n\n # and that it preserves subclasses (gh-6949)\n class C(np.recarray):\n pass\n\n c = r.view(C)\n assert_equal(type(c['c']), C)\n\n # check that accessing nested structures keep record type, but\n # not for subarrays, non-void structures, non-structured voids\n test_dtype = [('a', 'f4,f4'), ('b', 'V8'), ('c', ('f4', 2)),\n ('d', ('i8', 'i4,i4'))]\n r = np.rec.array([((1, 1), b'11111111', [1, 1], 1),\n ((1, 1), b'11111111', [1, 1], 1)], dtype=test_dtype)\n assert_equal(r.a.dtype.type, np.record)\n assert_equal(r.b.dtype.type, np.void)\n assert_equal(r.c.dtype.type, np.float32)\n assert_equal(r.d.dtype.type, np.int64)\n # check the same, but for views\n r = np.rec.array(np.ones(4, dtype='i4,i4'))\n assert_equal(r.view('f4,f4').dtype.type, np.record)\n assert_equal(r.view(('i4', 2)).dtype.type, np.int32)\n assert_equal(r.view('V8').dtype.type, np.void)\n assert_equal(r.view(('i8', 'i4,i4')).dtype.type, np.int64)\n\n # check that we can undo the view\n arrs = [np.ones(4, dtype='f4,i4'), np.ones(4, dtype='f8')]\n for arr in arrs:\n rec = np.rec.array(arr)\n # recommended way to view as an ndarray:\n arr2 = rec.view(rec.dtype.fields or rec.dtype, np.ndarray)\n assert_equal(arr2.dtype.type, arr.dtype.type)\n assert_equal(type(arr2), type(arr))\n\n def test_recarray_from_names(self):\n ra = np.rec.array([\n (1, 'abc', 3.7000002861022949, 0),\n (2, 'xy', 6.6999998092651367, 1),\n (0, ' ', 0.40000000596046448, 0)],\n names='c1, c2, c3, c4')\n pa = np.rec.fromrecords([\n (1, 'abc', 3.7000002861022949, 0),\n (2, 'xy', 6.6999998092651367, 1),\n (0, ' ', 0.40000000596046448, 0)],\n names='c1, c2, c3, c4')\n assert_(ra.dtype == pa.dtype)\n assert_(ra.shape == pa.shape)\n for k in range(len(ra)):\n assert_(ra[k].item() == pa[k].item())\n\n def test_recarray_conflict_fields(self):\n ra = np.rec.array([(1, 'abc', 2.3), (2, 'xyz', 4.2),\n (3, 'wrs', 1.3)],\n names='field, shape, mean')\n ra.mean = [1.1, 2.2, 3.3]\n assert_array_almost_equal(ra['mean'], [1.1, 2.2, 3.3])\n assert_(type(ra.mean) is type(ra.var))\n ra.shape = (1, 3)\n assert_(ra.shape == (1, 3))\n ra.shape = ['A', 'B', 'C']\n assert_array_equal(ra['shape'], [['A', 'B', 'C']])\n ra.field = 5\n assert_array_equal(ra['field'], [[5, 5, 5]])\n assert_(isinstance(ra.field, collections.abc.Callable))\n\n def test_fromrecords_with_explicit_dtype(self):\n a = np.rec.fromrecords([(1, 'a'), (2, 'bbb')],\n dtype=[('a', int), ('b', object)])\n assert_equal(a.a, [1, 2])\n assert_equal(a[0].a, 1)\n assert_equal(a.b, ['a', 'bbb'])\n assert_equal(a[-1].b, 'bbb')\n #\n ndtype = np.dtype([('a', int), ('b', object)])\n a = np.rec.fromrecords([(1, 'a'), (2, 'bbb')], dtype=ndtype)\n assert_equal(a.a, [1, 2])\n assert_equal(a[0].a, 1)\n assert_equal(a.b, ['a', 'bbb'])\n assert_equal(a[-1].b, 'bbb')\n\n def test_recarray_stringtypes(self):\n # Issue #3993\n a = np.array([('abc ', 1), ('abc', 2)],\n dtype=[('foo', 'S4'), ('bar', int)])\n a = a.view(np.recarray)\n assert_equal(a.foo[0] == a.foo[1], False)\n\n def test_recarray_returntypes(self):\n qux_fields = {'C': (np.dtype('S5'), 0), 'D': (np.dtype('S5'), 6)}\n a = np.rec.array([('abc ', (1, 1), 1, ('abcde', 'fgehi')),\n ('abc', (2, 3), 1, ('abcde', 'jklmn'))],\n dtype=[('foo', 'S4'),\n ('bar', [('A', int), ('B', int)]),\n ('baz', int), ('qux', qux_fields)])\n assert_equal(type(a.foo), np.ndarray)\n assert_equal(type(a['foo']), np.ndarray)\n assert_equal(type(a.bar), np.recarray)\n assert_equal(type(a['bar']), np.recarray)\n assert_equal(a.bar.dtype.type, np.record)\n assert_equal(type(a['qux']), np.recarray)\n assert_equal(a.qux.dtype.type, np.record)\n assert_equal(dict(a.qux.dtype.fields), qux_fields)\n assert_equal(type(a.baz), np.ndarray)\n assert_equal(type(a['baz']), np.ndarray)\n assert_equal(type(a[0].bar), np.record)\n assert_equal(type(a[0]['bar']), np.record)\n assert_equal(a[0].bar.A, 1)\n assert_equal(a[0].bar['A'], 1)\n assert_equal(a[0]['bar'].A, 1)\n assert_equal(a[0]['bar']['A'], 1)\n assert_equal(a[0].qux.D, b'fgehi')\n assert_equal(a[0].qux['D'], b'fgehi')\n assert_equal(a[0]['qux'].D, b'fgehi')\n assert_equal(a[0]['qux']['D'], b'fgehi')\n\n def test_zero_width_strings(self):\n # Test for #6430, based on the test case from #1901\n\n cols = [['test'] * 3, [''] * 3]\n rec = np.rec.fromarrays(cols)\n assert_equal(rec['f0'], ['test', 'test', 'test'])\n assert_equal(rec['f1'], ['', '', ''])\n\n dt = np.dtype([('f0', '|S4'), ('f1', '|S')])\n rec = np.rec.fromarrays(cols, dtype=dt)\n assert_equal(rec.itemsize, 4)\n assert_equal(rec['f0'], [b'test', b'test', b'test'])\n assert_equal(rec['f1'], [b'', b'', b''])\n\n\nclass TestPathUsage:\n # Test that pathlib.Path can be used\n def test_tofile_fromfile(self):\n with temppath(suffix='.bin') as path:\n path = Path(path)\n np.random.seed(123)\n a = np.random.rand(10).astype('f8,i4,S5')\n a[5] = (0.5, 10, 'abcde')\n with path.open("wb") as fd:\n a.tofile(fd)\n x = np._core.records.fromfile(\n path, formats='f8,i4,S5', shape=10\n )\n assert_array_equal(x, a)\n\n\nclass TestRecord:\n def setup_method(self):\n self.data = np.rec.fromrecords([(1, 2, 3), (4, 5, 6)],\n dtype=[("col1", "<i4"),\n ("col2", "<i4"),\n ("col3", "<i4")])\n\n def test_assignment1(self):\n a = self.data\n assert_equal(a.col1[0], 1)\n a[0].col1 = 0\n assert_equal(a.col1[0], 0)\n\n def test_assignment2(self):\n a = self.data\n assert_equal(a.col1[0], 1)\n a.col1[0] = 0\n assert_equal(a.col1[0], 0)\n\n def test_invalid_assignment(self):\n a = self.data\n\n def assign_invalid_column(x):\n x[0].col5 = 1\n\n assert_raises(AttributeError, assign_invalid_column, a)\n\n def test_nonwriteable_setfield(self):\n # gh-8171\n r = np.rec.array([(0,), (1,)], dtype=[('f', 'i4')])\n r.flags.writeable = False\n with assert_raises(ValueError):\n r.f = [2, 3]\n with assert_raises(ValueError):\n r.setfield([2, 3], *r.dtype.fields['f'])\n\n def test_out_of_order_fields(self):\n # names in the same order, padding added to descr\n x = self.data[['col1', 'col2']]\n assert_equal(x.dtype.names, ('col1', 'col2'))\n assert_equal(x.dtype.descr,\n [('col1', '<i4'), ('col2', '<i4'), ('', '|V4')])\n\n # names change order to match indexing, as of 1.14 - descr can't\n # represent that\n y = self.data[['col2', 'col1']]\n assert_equal(y.dtype.names, ('col2', 'col1'))\n assert_raises(ValueError, lambda: y.dtype.descr)\n\n def test_pickle_1(self):\n # Issue #1529\n a = np.array([(1, [])], dtype=[('a', np.int32), ('b', np.int32, 0)])\n for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):\n assert_equal(a, pickle.loads(pickle.dumps(a, protocol=proto)))\n assert_equal(a[0], pickle.loads(pickle.dumps(a[0],\n protocol=proto)))\n\n def test_pickle_2(self):\n a = self.data\n for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):\n assert_equal(a, pickle.loads(pickle.dumps(a, protocol=proto)))\n assert_equal(a[0], pickle.loads(pickle.dumps(a[0],\n protocol=proto)))\n\n def test_pickle_3(self):\n # Issue #7140\n a = self.data\n for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):\n pa = pickle.loads(pickle.dumps(a[0], protocol=proto))\n assert_(pa.flags.c_contiguous)\n assert_(pa.flags.f_contiguous)\n assert_(pa.flags.writeable)\n assert_(pa.flags.aligned)\n\n def test_pickle_void(self):\n # issue gh-13593\n dt = np.dtype([('obj', 'O'), ('int', 'i')])\n a = np.empty(1, dtype=dt)\n data = (bytearray(b'eman'),)\n a['obj'] = data\n a['int'] = 42\n ctor, args = a[0].__reduce__()\n # check the constructor is what we expect before interpreting the arguments\n assert ctor is np._core.multiarray.scalar\n dtype, obj = args\n # make sure we did not pickle the address\n assert not isinstance(obj, bytes)\n\n assert_raises(RuntimeError, ctor, dtype, 13)\n\n # Test roundtrip:\n dump = pickle.dumps(a[0])\n unpickled = pickle.loads(dump)\n assert a[0] == unpickled\n\n # Also check the similar (impossible) "object scalar" path:\n with assert_raises(TypeError):\n ctor(np.dtype("O"), data)\n\n def test_objview_record(self):\n # https://github.com/numpy/numpy/issues/2599\n dt = np.dtype([('foo', 'i8'), ('bar', 'O')])\n r = np.zeros((1, 3), dtype=dt).view(np.recarray)\n r.foo = np.array([1, 2, 3]) # TypeError?\n\n # https://github.com/numpy/numpy/issues/3256\n ra = np.recarray(\n (2,), dtype=[('x', object), ('y', float), ('z', int)]\n )\n ra[['x', 'y']] # TypeError?\n\n def test_record_scalar_setitem(self):\n # https://github.com/numpy/numpy/issues/3561\n rec = np.recarray(1, dtype=[('x', float, 5)])\n rec[0].x = 1\n assert_equal(rec[0].x, np.ones(5))\n\n def test_missing_field(self):\n # https://github.com/numpy/numpy/issues/4806\n arr = np.zeros((3,), dtype=[('x', int), ('y', int)])\n assert_raises(KeyError, lambda: arr[['nofield']])\n\n def test_fromarrays_nested_structured_arrays(self):\n arrays = [\n np.arange(10),\n np.ones(10, dtype=[('a', '<u2'), ('b', '<f4')]),\n ]\n arr = np.rec.fromarrays(arrays) # ValueError?\n\n @pytest.mark.parametrize('nfields', [0, 1, 2])\n def test_assign_dtype_attribute(self, nfields):\n dt = np.dtype([('a', np.uint8), ('b', np.uint8), ('c', np.uint8)][:nfields])\n data = np.zeros(3, dt).view(np.recarray)\n\n # the original and resulting dtypes differ on whether they are records\n assert data.dtype.type == np.record\n assert dt.type != np.record\n\n # ensure that the dtype remains a record even when assigned\n data.dtype = dt\n assert data.dtype.type == np.record\n\n @pytest.mark.parametrize('nfields', [0, 1, 2])\n def test_nested_fields_are_records(self, nfields):\n """ Test that nested structured types are treated as records too """\n dt = np.dtype([('a', np.uint8), ('b', np.uint8), ('c', np.uint8)][:nfields])\n dt_outer = np.dtype([('inner', dt)])\n\n data = np.zeros(3, dt_outer).view(np.recarray)\n assert isinstance(data, np.recarray)\n assert isinstance(data['inner'], np.recarray)\n\n data0 = data[0]\n assert isinstance(data0, np.record)\n assert isinstance(data0['inner'], np.record)\n\n def test_nested_dtype_padding(self):\n """ test that trailing padding is preserved """\n # construct a dtype with padding at the end\n dt = np.dtype([('a', np.uint8), ('b', np.uint8), ('c', np.uint8)])\n dt_padded_end = dt[['a', 'b']]\n assert dt_padded_end.itemsize == dt.itemsize\n\n dt_outer = np.dtype([('inner', dt_padded_end)])\n\n data = np.zeros(3, dt_outer).view(np.recarray)\n assert_equal(data['inner'].dtype, dt_padded_end)\n\n data0 = data[0]\n assert_equal(data0['inner'].dtype, dt_padded_end)\n\n\ndef test_find_duplicate():\n l1 = [1, 2, 3, 4, 5, 6]\n assert_(np.rec.find_duplicate(l1) == [])\n\n l2 = [1, 2, 1, 4, 5, 6]\n assert_(np.rec.find_duplicate(l2) == [1])\n\n l3 = [1, 2, 1, 4, 1, 6, 2, 3]\n assert_(np.rec.find_duplicate(l3) == [1, 2])\n\n l3 = [2, 2, 1, 4, 1, 6, 2, 3]\n assert_(np.rec.find_duplicate(l3) == [2, 1])\n | .venv\Lib\site-packages\numpy\_core\tests\test_records.py | test_records.py | Python | 21,091 | 0.95 | 0.102941 | 0.082251 | awesome-app | 351 | 2024-04-10T09:47:34.545128 | Apache-2.0 | true | 22c00956eff20359bb993383c8d7e3ce |
import copy\nimport gc\nimport pickle\nimport sys\nimport tempfile\nfrom io import BytesIO\nfrom itertools import chain\nfrom os import path\n\nimport pytest\n\nimport numpy as np\nfrom numpy._utils import asbytes, asunicode\nfrom numpy.exceptions import AxisError, ComplexWarning\nfrom numpy.testing import (\n HAS_REFCOUNT,\n IS_64BIT,\n IS_PYPY,\n IS_PYSTON,\n IS_WASM,\n _assert_valid_refcount,\n assert_,\n assert_almost_equal,\n assert_array_almost_equal,\n assert_array_equal,\n assert_equal,\n assert_raises,\n assert_raises_regex,\n assert_warns,\n suppress_warnings,\n)\nfrom numpy.testing._private.utils import _no_tracing, requires_memory\n\n\nclass TestRegression:\n def test_invalid_round(self):\n # Ticket #3\n v = 4.7599999999999998\n assert_array_equal(np.array([v]), np.array(v))\n\n def test_mem_empty(self):\n # Ticket #7\n np.empty((1,), dtype=[('x', np.int64)])\n\n def test_pickle_transposed(self):\n # Ticket #16\n a = np.transpose(np.array([[2, 9], [7, 0], [3, 8]]))\n for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):\n with BytesIO() as f:\n pickle.dump(a, f, protocol=proto)\n f.seek(0)\n b = pickle.load(f)\n assert_array_equal(a, b)\n\n def test_dtype_names(self):\n # Ticket #35\n # Should succeed\n np.dtype([(('name', 'label'), np.int32, 3)])\n\n def test_reduce(self):\n # Ticket #40\n assert_almost_equal(np.add.reduce([1., .5], dtype=None), 1.5)\n\n def test_zeros_order(self):\n # Ticket #43\n np.zeros([3], int, 'C')\n np.zeros([3], order='C')\n np.zeros([3], int, order='C')\n\n def test_asarray_with_order(self):\n # Check that nothing is done when order='F' and array C/F-contiguous\n a = np.ones(2)\n assert_(a is np.asarray(a, order='F'))\n\n def test_ravel_with_order(self):\n # Check that ravel works when order='F' and array C/F-contiguous\n a = np.ones(2)\n assert_(not a.ravel('F').flags.owndata)\n\n def test_sort_bigendian(self):\n # Ticket #47\n a = np.linspace(0, 10, 11)\n c = a.astype(np.dtype('<f8'))\n c.sort()\n assert_array_almost_equal(c, a)\n\n def test_negative_nd_indexing(self):\n # Ticket #49\n c = np.arange(125).reshape((5, 5, 5))\n origidx = np.array([-1, 0, 1])\n idx = np.array(origidx)\n c[idx]\n assert_array_equal(idx, origidx)\n\n def test_char_dump(self):\n # Ticket #50\n ca = np.char.array(np.arange(1000, 1010), itemsize=4)\n for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):\n with BytesIO() as f:\n pickle.dump(ca, f, protocol=proto)\n f.seek(0)\n ca = np.load(f, allow_pickle=True)\n\n def test_noncontiguous_fill(self):\n # Ticket #58.\n a = np.zeros((5, 3))\n b = a[:, :2,]\n\n def rs():\n b.shape = (10,)\n\n assert_raises(AttributeError, rs)\n\n def test_bool(self):\n # Ticket #60\n np.bool(1) # Should succeed\n\n def test_indexing1(self):\n # Ticket #64\n descr = [('x', [('y', [('z', 'c16', (2,)),]),]),]\n buffer = ((([6j, 4j],),),)\n h = np.array(buffer, dtype=descr)\n h['x']['y']['z']\n\n def test_indexing2(self):\n # Ticket #65\n descr = [('x', 'i4', (2,))]\n buffer = ([3, 2],)\n h = np.array(buffer, dtype=descr)\n h['x']\n\n def test_round(self):\n # Ticket #67\n x = np.array([1 + 2j])\n assert_almost_equal(x**(-1), [1 / (1 + 2j)])\n\n def test_scalar_compare(self):\n # Trac Ticket #72\n # https://github.com/numpy/numpy/issues/565\n a = np.array(['test', 'auto'])\n assert_array_equal(a == 'auto', np.array([False, True]))\n assert_(a[1] == 'auto')\n assert_(a[0] != 'auto')\n b = np.linspace(0, 10, 11)\n assert_array_equal(b != 'auto', np.ones(11, dtype=bool))\n assert_(b[0] != 'auto')\n\n def test_unicode_swapping(self):\n # Ticket #79\n ulen = 1\n ucs_value = '\U0010FFFF'\n ua = np.array([[[ucs_value * ulen] * 2] * 3] * 4, dtype=f'U{ulen}')\n ua.view(ua.dtype.newbyteorder()) # Should succeed.\n\n def test_object_array_fill(self):\n # Ticket #86\n x = np.zeros(1, 'O')\n x.fill([])\n\n def test_mem_dtype_align(self):\n # Ticket #93\n assert_raises(TypeError, np.dtype,\n {'names': ['a'], 'formats': ['foo']}, align=1)\n\n def test_endian_bool_indexing(self):\n # Ticket #105\n a = np.arange(10., dtype='>f8')\n b = np.arange(10., dtype='<f8')\n xa = np.where((a > 2) & (a < 6))\n xb = np.where((b > 2) & (b < 6))\n ya = ((a > 2) & (a < 6))\n yb = ((b > 2) & (b < 6))\n assert_array_almost_equal(xa, ya.nonzero())\n assert_array_almost_equal(xb, yb.nonzero())\n assert_(np.all(a[ya] > 0.5))\n assert_(np.all(b[yb] > 0.5))\n\n def test_endian_where(self):\n # GitHub issue #369\n net = np.zeros(3, dtype='>f4')\n net[1] = 0.00458849\n net[2] = 0.605202\n max_net = net.max()\n test = np.where(net <= 0., max_net, net)\n correct = np.array([0.60520202, 0.00458849, 0.60520202])\n assert_array_almost_equal(test, correct)\n\n def test_endian_recarray(self):\n # Ticket #2185\n dt = np.dtype([\n ('head', '>u4'),\n ('data', '>u4', 2),\n ])\n buf = np.recarray(1, dtype=dt)\n buf[0]['head'] = 1\n buf[0]['data'][:] = [1, 1]\n\n h = buf[0]['head']\n d = buf[0]['data'][0]\n buf[0]['head'] = h\n buf[0]['data'][0] = d\n assert_(buf[0]['head'] == 1)\n\n def test_mem_dot(self):\n # Ticket #106\n x = np.random.randn(0, 1)\n y = np.random.randn(10, 1)\n # Dummy array to detect bad memory access:\n _z = np.ones(10)\n _dummy = np.empty((0, 10))\n z = np.lib.stride_tricks.as_strided(_z, _dummy.shape, _dummy.strides)\n np.dot(x, np.transpose(y), out=z)\n assert_equal(_z, np.ones(10))\n # Do the same for the built-in dot:\n np._core.multiarray.dot(x, np.transpose(y), out=z)\n assert_equal(_z, np.ones(10))\n\n def test_arange_endian(self):\n # Ticket #111\n ref = np.arange(10)\n x = np.arange(10, dtype='<f8')\n assert_array_equal(ref, x)\n x = np.arange(10, dtype='>f8')\n assert_array_equal(ref, x)\n\n def test_arange_inf_step(self):\n ref = np.arange(0, 1, 10)\n x = np.arange(0, 1, np.inf)\n assert_array_equal(ref, x)\n\n ref = np.arange(0, 1, -10)\n x = np.arange(0, 1, -np.inf)\n assert_array_equal(ref, x)\n\n ref = np.arange(0, -1, -10)\n x = np.arange(0, -1, -np.inf)\n assert_array_equal(ref, x)\n\n ref = np.arange(0, -1, 10)\n x = np.arange(0, -1, np.inf)\n assert_array_equal(ref, x)\n\n def test_arange_underflow_stop_and_step(self):\n finfo = np.finfo(np.float64)\n\n ref = np.arange(0, finfo.eps, 2 * finfo.eps)\n x = np.arange(0, finfo.eps, finfo.max)\n assert_array_equal(ref, x)\n\n ref = np.arange(0, finfo.eps, -2 * finfo.eps)\n x = np.arange(0, finfo.eps, -finfo.max)\n assert_array_equal(ref, x)\n\n ref = np.arange(0, -finfo.eps, -2 * finfo.eps)\n x = np.arange(0, -finfo.eps, -finfo.max)\n assert_array_equal(ref, x)\n\n ref = np.arange(0, -finfo.eps, 2 * finfo.eps)\n x = np.arange(0, -finfo.eps, finfo.max)\n assert_array_equal(ref, x)\n\n def test_argmax(self):\n # Ticket #119\n a = np.random.normal(0, 1, (4, 5, 6, 7, 8))\n for i in range(a.ndim):\n a.argmax(i) # Should succeed\n\n def test_mem_divmod(self):\n # Ticket #126\n for i in range(10):\n divmod(np.array([i])[0], 10)\n\n def test_hstack_invalid_dims(self):\n # Ticket #128\n x = np.arange(9).reshape((3, 3))\n y = np.array([0, 0, 0])\n assert_raises(ValueError, np.hstack, (x, y))\n\n def test_squeeze_type(self):\n # Ticket #133\n a = np.array([3])\n b = np.array(3)\n assert_(type(a.squeeze()) is np.ndarray)\n assert_(type(b.squeeze()) is np.ndarray)\n\n def test_add_identity(self):\n # Ticket #143\n assert_equal(0, np.add.identity)\n\n def test_numpy_float_python_long_addition(self):\n # Check that numpy float and python longs can be added correctly.\n a = np.float64(23.) + 2**135\n assert_equal(a, 23. + 2**135)\n\n def test_binary_repr_0(self):\n # Ticket #151\n assert_equal('0', np.binary_repr(0))\n\n def test_rec_iterate(self):\n # Ticket #160\n descr = np.dtype([('i', int), ('f', float), ('s', '|S3')])\n x = np.rec.array([(1, 1.1, '1.0'),\n (2, 2.2, '2.0')], dtype=descr)\n x[0].tolist()\n list(x[0])\n\n def test_unicode_string_comparison(self):\n # Ticket #190\n a = np.array('hello', np.str_)\n b = np.array('world')\n a == b\n\n def test_tobytes_FORTRANORDER_discontiguous(self):\n # Fix in r2836\n # Create non-contiguous Fortran ordered array\n x = np.array(np.random.rand(3, 3), order='F')[:, :2]\n assert_array_almost_equal(x.ravel(), np.frombuffer(x.tobytes()))\n\n def test_flat_assignment(self):\n # Correct behaviour of ticket #194\n x = np.empty((3, 1))\n x.flat = np.arange(3)\n assert_array_almost_equal(x, [[0], [1], [2]])\n x.flat = np.arange(3, dtype=float)\n assert_array_almost_equal(x, [[0], [1], [2]])\n\n def test_broadcast_flat_assignment(self):\n # Ticket #194\n x = np.empty((3, 1))\n\n def bfa():\n x[:] = np.arange(3)\n\n def bfb():\n x[:] = np.arange(3, dtype=float)\n\n assert_raises(ValueError, bfa)\n assert_raises(ValueError, bfb)\n\n @pytest.mark.xfail(IS_WASM, reason="not sure why")\n @pytest.mark.parametrize("index",\n [np.ones(10, dtype=bool), np.arange(10)],\n ids=["boolean-arr-index", "integer-arr-index"])\n def test_nonarray_assignment(self, index):\n # See also Issue gh-2870, test for non-array assignment\n # and equivalent unsafe casted array assignment\n a = np.arange(10)\n\n with pytest.raises(ValueError):\n a[index] = np.nan\n\n with np.errstate(invalid="warn"):\n with pytest.warns(RuntimeWarning, match="invalid value"):\n a[index] = np.array(np.nan) # Only warns\n\n def test_unpickle_dtype_with_object(self):\n # Implemented in r2840\n dt = np.dtype([('x', int), ('y', np.object_), ('z', 'O')])\n for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):\n with BytesIO() as f:\n pickle.dump(dt, f, protocol=proto)\n f.seek(0)\n dt_ = pickle.load(f)\n assert_equal(dt, dt_)\n\n def test_mem_array_creation_invalid_specification(self):\n # Ticket #196\n dt = np.dtype([('x', int), ('y', np.object_)])\n # Wrong way\n assert_raises(ValueError, np.array, [1, 'object'], dt)\n # Correct way\n np.array([(1, 'object')], dt)\n\n def test_recarray_single_element(self):\n # Ticket #202\n a = np.array([1, 2, 3], dtype=np.int32)\n b = a.copy()\n r = np.rec.array(a, shape=1, formats=['3i4'], names=['d'])\n assert_array_equal(a, b)\n assert_equal(a, r[0][0])\n\n def test_zero_sized_array_indexing(self):\n # Ticket #205\n tmp = np.array([])\n\n def index_tmp():\n tmp[np.array(10)]\n\n assert_raises(IndexError, index_tmp)\n\n def test_chararray_rstrip(self):\n # Ticket #222\n x = np.char.chararray((1,), 5)\n x[0] = b'a '\n x = x.rstrip()\n assert_equal(x[0], b'a')\n\n def test_object_array_shape(self):\n # Ticket #239\n assert_equal(np.array([[1, 2], 3, 4], dtype=object).shape, (3,))\n assert_equal(np.array([[1, 2], [3, 4]], dtype=object).shape, (2, 2))\n assert_equal(np.array([(1, 2), (3, 4)], dtype=object).shape, (2, 2))\n assert_equal(np.array([], dtype=object).shape, (0,))\n assert_equal(np.array([[], [], []], dtype=object).shape, (3, 0))\n assert_equal(np.array([[3, 4], [5, 6], None], dtype=object).shape, (3,))\n\n def test_mem_around(self):\n # Ticket #243\n x = np.zeros((1,))\n y = [0]\n decimal = 6\n np.around(abs(x - y), decimal) <= 10.0**(-decimal)\n\n def test_character_array_strip(self):\n # Ticket #246\n x = np.char.array(("x", "x ", "x "))\n for c in x:\n assert_equal(c, "x")\n\n def test_lexsort(self):\n # Lexsort memory error\n v = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n assert_equal(np.lexsort(v), 0)\n\n def test_lexsort_invalid_sequence(self):\n # Issue gh-4123\n class BuggySequence:\n def __len__(self):\n return 4\n\n def __getitem__(self, key):\n raise KeyError\n\n assert_raises(KeyError, np.lexsort, BuggySequence())\n\n def test_lexsort_zerolen_custom_strides(self):\n # Ticket #14228\n xs = np.array([], dtype='i8')\n assert np.lexsort((xs,)).shape[0] == 0 # Works\n\n xs.strides = (16,)\n assert np.lexsort((xs,)).shape[0] == 0 # Was: MemoryError\n\n def test_lexsort_zerolen_custom_strides_2d(self):\n xs = np.array([], dtype='i8')\n\n xs.shape = (0, 2)\n xs.strides = (16, 16)\n assert np.lexsort((xs,), axis=0).shape[0] == 0\n\n xs.shape = (2, 0)\n xs.strides = (16, 16)\n assert np.lexsort((xs,), axis=0).shape[0] == 2\n\n def test_lexsort_invalid_axis(self):\n assert_raises(AxisError, np.lexsort, (np.arange(1),), axis=2)\n assert_raises(AxisError, np.lexsort, (np.array([]),), axis=1)\n assert_raises(AxisError, np.lexsort, (np.array(1),), axis=10)\n\n def test_lexsort_zerolen_element(self):\n dt = np.dtype([]) # a void dtype with no fields\n xs = np.empty(4, dt)\n\n assert np.lexsort((xs,)).shape[0] == xs.shape[0]\n\n def test_pickle_py2_bytes_encoding(self):\n # Check that arrays and scalars pickled on Py2 are\n # unpickleable on Py3 using encoding='bytes'\n\n test_data = [\n # (original, py2_pickle)\n (\n np.str_('\u6f2c'),\n b"cnumpy.core.multiarray\nscalar\np0\n(cnumpy\ndtype\np1\n(S'U1'\np2\nI0\nI1\ntp3\nRp4\n(I3\nS'<'\np5\nNNNI4\nI4\nI0\ntp6\nbS',o\\x00\\x00'\np7\ntp8\nRp9\n."\n ),\n\n (\n np.array([9e123], dtype=np.float64),\n b"cnumpy.core.multiarray\n_reconstruct\np0\n(cnumpy\nndarray\np1\n(I0\ntp2\nS'b'\np3\ntp4\nRp5\n(I1\n(I1\ntp6\ncnumpy\ndtype\np7\n(S'f8'\np8\nI0\nI1\ntp9\nRp10\n(I3\nS'<'\np11\nNNNI-1\nI-1\nI0\ntp12\nbI00\nS'O\\x81\\xb7Z\\xaa:\\xabY'\np13\ntp14\nb."\n ),\n\n (\n np.array([(9e123,)], dtype=[('name', float)]),\n b"cnumpy.core.multiarray\n_reconstruct\np0\n(cnumpy\nndarray\np1\n(I0\ntp2\nS'b'\np3\ntp4\nRp5\n(I1\n(I1\ntp6\ncnumpy\ndtype\np7\n(S'V8'\np8\nI0\nI1\ntp9\nRp10\n(I3\nS'|'\np11\nN(S'name'\np12\ntp13\n(dp14\ng12\n(g7\n(S'f8'\np15\nI0\nI1\ntp16\nRp17\n(I3\nS'<'\np18\nNNNI-1\nI-1\nI0\ntp19\nbI0\ntp20\nsI8\nI1\nI0\ntp21\nbI00\nS'O\\x81\\xb7Z\\xaa:\\xabY'\np22\ntp23\nb."\n ),\n ]\n\n for original, data in test_data:\n result = pickle.loads(data, encoding='bytes')\n assert_equal(result, original)\n\n if isinstance(result, np.ndarray) and result.dtype.names is not None:\n for name in result.dtype.names:\n assert_(isinstance(name, str))\n\n def test_pickle_dtype(self):\n # Ticket #251\n for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):\n pickle.dumps(float, protocol=proto)\n\n def test_swap_real(self):\n # Ticket #265\n assert_equal(np.arange(4, dtype='>c8').imag.max(), 0.0)\n assert_equal(np.arange(4, dtype='<c8').imag.max(), 0.0)\n assert_equal(np.arange(4, dtype='>c8').real.max(), 3.0)\n assert_equal(np.arange(4, dtype='<c8').real.max(), 3.0)\n\n def test_object_array_from_list(self):\n # Ticket #270 (gh-868)\n assert_(np.array([1, None, 'A']).shape == (3,))\n\n def test_multiple_assign(self):\n # Ticket #273\n a = np.zeros((3, 1), int)\n a[[1, 2]] = 1\n\n def test_empty_array_type(self):\n assert_equal(np.array([]).dtype, np.zeros(0).dtype)\n\n def test_void_copyswap(self):\n dt = np.dtype([('one', '<i4'), ('two', '<i4')])\n x = np.array((1, 2), dtype=dt)\n x = x.byteswap()\n assert_(x['one'] > 1 and x['two'] > 2)\n\n def test_method_args(self):\n # Make sure methods and functions have same default axis\n # keyword and arguments\n funcs1 = ['argmax', 'argmin', 'sum', 'any', 'all', 'cumsum',\n 'cumprod', 'prod', 'std', 'var', 'mean',\n 'round', 'min', 'max', 'argsort', 'sort']\n funcs2 = ['compress', 'take', 'repeat']\n\n for func in funcs1:\n arr = np.random.rand(8, 7)\n arr2 = arr.copy()\n res1 = getattr(arr, func)()\n res2 = getattr(np, func)(arr2)\n if res1 is None:\n res1 = arr\n\n if res1.dtype.kind in 'uib':\n assert_((res1 == res2).all(), func)\n else:\n assert_(abs(res1 - res2).max() < 1e-8, func)\n\n for func in funcs2:\n arr1 = np.random.rand(8, 7)\n arr2 = np.random.rand(8, 7)\n res1 = None\n if func == 'compress':\n arr1 = arr1.ravel()\n res1 = getattr(arr2, func)(arr1)\n else:\n arr2 = (15 * arr2).astype(int).ravel()\n if res1 is None:\n res1 = getattr(arr1, func)(arr2)\n res2 = getattr(np, func)(arr1, arr2)\n assert_(abs(res1 - res2).max() < 1e-8, func)\n\n def test_mem_lexsort_strings(self):\n # Ticket #298\n lst = ['abc', 'cde', 'fgh']\n np.lexsort((lst,))\n\n def test_fancy_index(self):\n # Ticket #302\n x = np.array([1, 2])[np.array([0])]\n assert_equal(x.shape, (1,))\n\n def test_recarray_copy(self):\n # Ticket #312\n dt = [('x', np.int16), ('y', np.float64)]\n ra = np.array([(1, 2.3)], dtype=dt)\n rb = np.rec.array(ra, dtype=dt)\n rb['x'] = 2.\n assert_(ra['x'] != rb['x'])\n\n def test_rec_fromarray(self):\n # Ticket #322\n x1 = np.array([[1, 2], [3, 4], [5, 6]])\n x2 = np.array(['a', 'dd', 'xyz'])\n x3 = np.array([1.1, 2, 3])\n np.rec.fromarrays([x1, x2, x3], formats="(2,)i4,S3,f8")\n\n def test_object_array_assign(self):\n x = np.empty((2, 2), object)\n x.flat[2] = (1, 2, 3)\n assert_equal(x.flat[2], (1, 2, 3))\n\n def test_ndmin_float64(self):\n # Ticket #324\n x = np.array([1, 2, 3], dtype=np.float64)\n assert_equal(np.array(x, dtype=np.float32, ndmin=2).ndim, 2)\n assert_equal(np.array(x, dtype=np.float64, ndmin=2).ndim, 2)\n\n def test_ndmin_order(self):\n # Issue #465 and related checks\n assert_(np.array([1, 2], order='C', ndmin=3).flags.c_contiguous)\n assert_(np.array([1, 2], order='F', ndmin=3).flags.f_contiguous)\n assert_(np.array(np.ones((2, 2), order='F'), ndmin=3).flags.f_contiguous)\n assert_(np.array(np.ones((2, 2), order='C'), ndmin=3).flags.c_contiguous)\n\n def test_mem_axis_minimization(self):\n # Ticket #327\n data = np.arange(5)\n data = np.add.outer(data, data)\n\n def test_mem_float_imag(self):\n # Ticket #330\n np.float64(1.0).imag\n\n def test_dtype_tuple(self):\n # Ticket #334\n assert_(np.dtype('i4') == np.dtype(('i4', ())))\n\n def test_dtype_posttuple(self):\n # Ticket #335\n np.dtype([('col1', '()i4')])\n\n def test_numeric_carray_compare(self):\n # Ticket #341\n assert_equal(np.array(['X'], 'c'), b'X')\n\n def test_string_array_size(self):\n # Ticket #342\n assert_raises(ValueError,\n np.array, [['X'], ['X', 'X', 'X']], '|S1')\n\n def test_dtype_repr(self):\n # Ticket #344\n dt1 = np.dtype(('uint32', 2))\n dt2 = np.dtype(('uint32', (2,)))\n assert_equal(dt1.__repr__(), dt2.__repr__())\n\n def test_reshape_order(self):\n # Make sure reshape order works.\n a = np.arange(6).reshape(2, 3, order='F')\n assert_equal(a, [[0, 2, 4], [1, 3, 5]])\n a = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])\n b = a[:, 1]\n assert_equal(b.reshape(2, 2, order='F'), [[2, 6], [4, 8]])\n\n def test_reshape_zero_strides(self):\n # Issue #380, test reshaping of zero strided arrays\n a = np.ones(1)\n a = np.lib.stride_tricks.as_strided(a, shape=(5,), strides=(0,))\n assert_(a.reshape(5, 1).strides[0] == 0)\n\n def test_reshape_zero_size(self):\n # GitHub Issue #2700, setting shape failed for 0-sized arrays\n a = np.ones((0, 2))\n a.shape = (-1, 2)\n\n def test_reshape_trailing_ones_strides(self):\n # GitHub issue gh-2949, bad strides for trailing ones of new shape\n a = np.zeros(12, dtype=np.int32)[::2] # not contiguous\n strides_c = (16, 8, 8, 8)\n strides_f = (8, 24, 48, 48)\n assert_equal(a.reshape(3, 2, 1, 1).strides, strides_c)\n assert_equal(a.reshape(3, 2, 1, 1, order='F').strides, strides_f)\n assert_equal(np.array(0, dtype=np.int32).reshape(1, 1).strides, (4, 4))\n\n def test_repeat_discont(self):\n # Ticket #352\n a = np.arange(12).reshape(4, 3)[:, 2]\n assert_equal(a.repeat(3), [2, 2, 2, 5, 5, 5, 8, 8, 8, 11, 11, 11])\n\n def test_array_index(self):\n # Make sure optimization is not called in this case.\n a = np.array([1, 2, 3])\n a2 = np.array([[1, 2, 3]])\n assert_equal(a[np.where(a == 3)], a2[np.where(a2 == 3)])\n\n def test_object_argmax(self):\n a = np.array([1, 2, 3], dtype=object)\n assert_(a.argmax() == 2)\n\n def test_recarray_fields(self):\n # Ticket #372\n dt0 = np.dtype([('f0', 'i4'), ('f1', 'i4')])\n dt1 = np.dtype([('f0', 'i8'), ('f1', 'i8')])\n for a in [np.array([(1, 2), (3, 4)], "i4,i4"),\n np.rec.array([(1, 2), (3, 4)], "i4,i4"),\n np.rec.array([(1, 2), (3, 4)]),\n np.rec.fromarrays([(1, 2), (3, 4)], "i4,i4"),\n np.rec.fromarrays([(1, 2), (3, 4)])]:\n assert_(a.dtype in [dt0, dt1])\n\n def test_random_shuffle(self):\n # Ticket #374\n a = np.arange(5).reshape((5, 1))\n b = a.copy()\n np.random.shuffle(b)\n assert_equal(np.sort(b, axis=0), a)\n\n def test_refcount_vdot(self):\n # Changeset #3443\n _assert_valid_refcount(np.vdot)\n\n def test_startswith(self):\n ca = np.char.array(['Hi', 'There'])\n assert_equal(ca.startswith('H'), [True, False])\n\n def test_noncommutative_reduce_accumulate(self):\n # Ticket #413\n tosubtract = np.arange(5)\n todivide = np.array([2.0, 0.5, 0.25])\n assert_equal(np.subtract.reduce(tosubtract), -10)\n assert_equal(np.divide.reduce(todivide), 16.0)\n assert_array_equal(np.subtract.accumulate(tosubtract),\n np.array([0, -1, -3, -6, -10]))\n assert_array_equal(np.divide.accumulate(todivide),\n np.array([2., 4., 16.]))\n\n def test_convolve_empty(self):\n # Convolve should raise an error for empty input array.\n assert_raises(ValueError, np.convolve, [], [1])\n assert_raises(ValueError, np.convolve, [1], [])\n\n def test_multidim_byteswap(self):\n # Ticket #449\n r = np.array([(1, (0, 1, 2))], dtype="i2,3i2")\n assert_array_equal(r.byteswap(),\n np.array([(256, (0, 256, 512))], r.dtype))\n\n def test_string_NULL(self):\n # Changeset 3557\n assert_equal(np.array("a\x00\x0b\x0c\x00").item(),\n 'a\x00\x0b\x0c')\n\n def test_junk_in_string_fields_of_recarray(self):\n # Ticket #483\n r = np.array([[b'abc']], dtype=[('var1', '|S20')])\n assert_(asbytes(r['var1'][0][0]) == b'abc')\n\n def test_take_output(self):\n # Ensure that 'take' honours output parameter.\n x = np.arange(12).reshape((3, 4))\n a = np.take(x, [0, 2], axis=1)\n b = np.zeros_like(a)\n np.take(x, [0, 2], axis=1, out=b)\n assert_array_equal(a, b)\n\n def test_take_object_fail(self):\n # Issue gh-3001\n d = 123.\n a = np.array([d, 1], dtype=object)\n if HAS_REFCOUNT:\n ref_d = sys.getrefcount(d)\n try:\n a.take([0, 100])\n except IndexError:\n pass\n if HAS_REFCOUNT:\n assert_(ref_d == sys.getrefcount(d))\n\n def test_array_str_64bit(self):\n # Ticket #501\n s = np.array([1, np.nan], dtype=np.float64)\n with np.errstate(all='raise'):\n np.array_str(s) # Should succeed\n\n def test_frompyfunc_endian(self):\n # Ticket #503\n from math import radians\n uradians = np.frompyfunc(radians, 1, 1)\n big_endian = np.array([83.4, 83.5], dtype='>f8')\n little_endian = np.array([83.4, 83.5], dtype='<f8')\n assert_almost_equal(uradians(big_endian).astype(float),\n uradians(little_endian).astype(float))\n\n def test_mem_string_arr(self):\n # Ticket #514\n s = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"\n t = []\n np.hstack((t, s))\n\n def test_arr_transpose(self):\n # Ticket #516\n x = np.random.rand(*(2,) * 16)\n x.transpose(list(range(16))) # Should succeed\n\n def test_string_mergesort(self):\n # Ticket #540\n x = np.array(['a'] * 32)\n assert_array_equal(x.argsort(kind='m'), np.arange(32))\n\n def test_argmax_byteorder(self):\n # Ticket #546\n a = np.arange(3, dtype='>f')\n assert_(a[a.argmax()] == a.max())\n\n def test_rand_seed(self):\n # Ticket #555\n for l in np.arange(4):\n np.random.seed(l)\n\n def test_mem_deallocation_leak(self):\n # Ticket #562\n a = np.zeros(5, dtype=float)\n b = np.array(a, dtype=float)\n del a, b\n\n def test_mem_on_invalid_dtype(self):\n "Ticket #583"\n assert_raises(ValueError, np.fromiter, [['12', ''], ['13', '']], str)\n\n def test_dot_negative_stride(self):\n # Ticket #588\n x = np.array([[1, 5, 25, 125., 625]])\n y = np.array([[20.], [160.], [640.], [1280.], [1024.]])\n z = y[::-1].copy()\n y2 = y[::-1]\n assert_equal(np.dot(x, z), np.dot(x, y2))\n\n def test_object_casting(self):\n # This used to trigger the object-type version of\n # the bitwise_or operation, because float64 -> object\n # casting succeeds\n def rs():\n x = np.ones([484, 286])\n y = np.zeros([484, 286])\n x |= y\n\n assert_raises(TypeError, rs)\n\n def test_unicode_scalar(self):\n # Ticket #600\n x = np.array(["DROND", "DROND1"], dtype="U6")\n el = x[1]\n for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):\n new = pickle.loads(pickle.dumps(el, protocol=proto))\n assert_equal(new, el)\n\n def test_arange_non_native_dtype(self):\n # Ticket #616\n for T in ('>f4', '<f4'):\n dt = np.dtype(T)\n assert_equal(np.arange(0, dtype=dt).dtype, dt)\n assert_equal(np.arange(0.5, dtype=dt).dtype, dt)\n assert_equal(np.arange(5, dtype=dt).dtype, dt)\n\n def test_bool_flat_indexing_invalid_nr_elements(self):\n s = np.ones(10, dtype=float)\n x = np.array((15,), dtype=float)\n\n def ia(x, s, v):\n x[(s > 0)] = v\n\n assert_raises(IndexError, ia, x, s, np.zeros(9, dtype=float))\n assert_raises(IndexError, ia, x, s, np.zeros(11, dtype=float))\n\n # Old special case (different code path):\n assert_raises(ValueError, ia, x.flat, s, np.zeros(9, dtype=float))\n assert_raises(ValueError, ia, x.flat, s, np.zeros(11, dtype=float))\n\n def test_mem_scalar_indexing(self):\n # Ticket #603\n x = np.array([0], dtype=float)\n index = np.array(0, dtype=np.int32)\n x[index]\n\n def test_binary_repr_0_width(self):\n assert_equal(np.binary_repr(0, width=3), '000')\n\n def test_fromstring(self):\n assert_equal(np.fromstring("12:09:09", dtype=int, sep=":"),\n [12, 9, 9])\n\n def test_searchsorted_variable_length(self):\n x = np.array(['a', 'aa', 'b'])\n y = np.array(['d', 'e'])\n assert_equal(x.searchsorted(y), [3, 3])\n\n def test_string_argsort_with_zeros(self):\n # Check argsort for strings containing zeros.\n x = np.frombuffer(b"\x00\x02\x00\x01", dtype="|S2")\n assert_array_equal(x.argsort(kind='m'), np.array([1, 0]))\n assert_array_equal(x.argsort(kind='q'), np.array([1, 0]))\n\n def test_string_sort_with_zeros(self):\n # Check sort for strings containing zeros.\n x = np.frombuffer(b"\x00\x02\x00\x01", dtype="|S2")\n y = np.frombuffer(b"\x00\x01\x00\x02", dtype="|S2")\n assert_array_equal(np.sort(x, kind="q"), y)\n\n def test_copy_detection_zero_dim(self):\n # Ticket #658\n np.indices((0, 3, 4)).T.reshape(-1, 3)\n\n def test_flat_byteorder(self):\n # Ticket #657\n x = np.arange(10)\n assert_array_equal(x.astype('>i4'), x.astype('<i4').flat[:])\n assert_array_equal(x.astype('>i4').flat[:], x.astype('<i4'))\n\n def test_sign_bit(self):\n x = np.array([0, -0.0, 0])\n assert_equal(str(np.abs(x)), '[0. 0. 0.]')\n\n def test_flat_index_byteswap(self):\n for dt in (np.dtype('<i4'), np.dtype('>i4')):\n x = np.array([-1, 0, 1], dtype=dt)\n assert_equal(x.flat[0].dtype, x[0].dtype)\n\n def test_copy_detection_corner_case(self):\n # Ticket #658\n np.indices((0, 3, 4)).T.reshape(-1, 3)\n\n def test_object_array_refcounting(self):\n # Ticket #633\n if not hasattr(sys, 'getrefcount'):\n return\n\n # NB. this is probably CPython-specific\n\n cnt = sys.getrefcount\n\n a = object()\n b = object()\n c = object()\n\n cnt0_a = cnt(a)\n cnt0_b = cnt(b)\n cnt0_c = cnt(c)\n\n # -- 0d -> 1-d broadcast slice assignment\n\n arr = np.zeros(5, dtype=np.object_)\n\n arr[:] = a\n assert_equal(cnt(a), cnt0_a + 5)\n\n arr[:] = b\n assert_equal(cnt(a), cnt0_a)\n assert_equal(cnt(b), cnt0_b + 5)\n\n arr[:2] = c\n assert_equal(cnt(b), cnt0_b + 3)\n assert_equal(cnt(c), cnt0_c + 2)\n\n del arr\n\n # -- 1-d -> 2-d broadcast slice assignment\n\n arr = np.zeros((5, 2), dtype=np.object_)\n arr0 = np.zeros(2, dtype=np.object_)\n\n arr0[0] = a\n assert_(cnt(a) == cnt0_a + 1)\n arr0[1] = b\n assert_(cnt(b) == cnt0_b + 1)\n\n arr[:, :] = arr0\n assert_(cnt(a) == cnt0_a + 6)\n assert_(cnt(b) == cnt0_b + 6)\n\n arr[:, 0] = None\n assert_(cnt(a) == cnt0_a + 1)\n\n del arr, arr0\n\n # -- 2-d copying + flattening\n\n arr = np.zeros((5, 2), dtype=np.object_)\n\n arr[:, 0] = a\n arr[:, 1] = b\n assert_(cnt(a) == cnt0_a + 5)\n assert_(cnt(b) == cnt0_b + 5)\n\n arr2 = arr.copy()\n assert_(cnt(a) == cnt0_a + 10)\n assert_(cnt(b) == cnt0_b + 10)\n\n arr2 = arr[:, 0].copy()\n assert_(cnt(a) == cnt0_a + 10)\n assert_(cnt(b) == cnt0_b + 5)\n\n arr2 = arr.flatten()\n assert_(cnt(a) == cnt0_a + 10)\n assert_(cnt(b) == cnt0_b + 10)\n\n del arr, arr2\n\n # -- concatenate, repeat, take, choose\n\n arr1 = np.zeros((5, 1), dtype=np.object_)\n arr2 = np.zeros((5, 1), dtype=np.object_)\n\n arr1[...] = a\n arr2[...] = b\n assert_(cnt(a) == cnt0_a + 5)\n assert_(cnt(b) == cnt0_b + 5)\n\n tmp = np.concatenate((arr1, arr2))\n assert_(cnt(a) == cnt0_a + 5 + 5)\n assert_(cnt(b) == cnt0_b + 5 + 5)\n\n tmp = arr1.repeat(3, axis=0)\n assert_(cnt(a) == cnt0_a + 5 + 3 * 5)\n\n tmp = arr1.take([1, 2, 3], axis=0)\n assert_(cnt(a) == cnt0_a + 5 + 3)\n\n x = np.array([[0], [1], [0], [1], [1]], int)\n tmp = x.choose(arr1, arr2)\n assert_(cnt(a) == cnt0_a + 5 + 2)\n assert_(cnt(b) == cnt0_b + 5 + 3)\n\n def test_mem_custom_float_to_array(self):\n # Ticket 702\n class MyFloat:\n def __float__(self):\n return 1.0\n\n tmp = np.atleast_1d([MyFloat()])\n tmp.astype(float) # Should succeed\n\n def test_object_array_refcount_self_assign(self):\n # Ticket #711\n class VictimObject:\n deleted = False\n\n def __del__(self):\n self.deleted = True\n\n d = VictimObject()\n arr = np.zeros(5, dtype=np.object_)\n arr[:] = d\n del d\n arr[:] = arr # refcount of 'd' might hit zero here\n assert_(not arr[0].deleted)\n arr[:] = arr # trying to induce a segfault by doing it again...\n assert_(not arr[0].deleted)\n\n def test_mem_fromiter_invalid_dtype_string(self):\n x = [1, 2, 3]\n assert_raises(ValueError,\n np.fromiter, list(x), dtype='S')\n\n def test_reduce_big_object_array(self):\n # Ticket #713\n oldsize = np.setbufsize(10 * 16)\n a = np.array([None] * 161, object)\n assert_(not np.any(a))\n np.setbufsize(oldsize)\n\n def test_mem_0d_array_index(self):\n # Ticket #714\n np.zeros(10)[np.array(0)]\n\n def test_nonnative_endian_fill(self):\n # Non-native endian arrays were incorrectly filled with scalars\n # before r5034.\n if sys.byteorder == 'little':\n dtype = np.dtype('>i4')\n else:\n dtype = np.dtype('<i4')\n x = np.empty([1], dtype=dtype)\n x.fill(1)\n assert_equal(x, np.array([1], dtype=dtype))\n\n def test_dot_alignment_sse2(self):\n # Test for ticket #551, changeset r5140\n x = np.zeros((30, 40))\n for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):\n y = pickle.loads(pickle.dumps(x, protocol=proto))\n # y is now typically not aligned on a 8-byte boundary\n z = np.ones((1, y.shape[0]))\n # This shouldn't cause a segmentation fault:\n np.dot(z, y)\n\n def test_astype_copy(self):\n # Ticket #788, changeset r5155\n # The test data file was generated by scipy.io.savemat.\n # The dtype is float64, but the isbuiltin attribute is 0.\n data_dir = path.join(path.dirname(__file__), 'data')\n filename = path.join(data_dir, "astype_copy.pkl")\n with open(filename, 'rb') as f:\n xp = pickle.load(f, encoding='latin1')\n xpd = xp.astype(np.float64)\n assert_(xp.__array_interface__['data'][0] !=\n xpd.__array_interface__['data'][0])\n\n def test_compress_small_type(self):\n # Ticket #789, changeset 5217.\n # compress with out argument segfaulted if cannot cast safely\n import numpy as np\n a = np.array([[1, 2], [3, 4]])\n b = np.zeros((2, 1), dtype=np.single)\n try:\n a.compress([True, False], axis=1, out=b)\n raise AssertionError("compress with an out which cannot be "\n "safely casted should not return "\n "successfully")\n except TypeError:\n pass\n\n def test_attributes(self):\n # Ticket #791\n class TestArray(np.ndarray):\n def __new__(cls, data, info):\n result = np.array(data)\n result = result.view(cls)\n result.info = info\n return result\n\n def __array_finalize__(self, obj):\n self.info = getattr(obj, 'info', '')\n\n dat = TestArray([[1, 2, 3, 4], [5, 6, 7, 8]], 'jubba')\n assert_(dat.info == 'jubba')\n dat.resize((4, 2))\n assert_(dat.info == 'jubba')\n dat.sort()\n assert_(dat.info == 'jubba')\n dat.fill(2)\n assert_(dat.info == 'jubba')\n dat.put([2, 3, 4], [6, 3, 4])\n assert_(dat.info == 'jubba')\n dat.setfield(4, np.int32, 0)\n assert_(dat.info == 'jubba')\n dat.setflags()\n assert_(dat.info == 'jubba')\n assert_(dat.all(1).info == 'jubba')\n assert_(dat.any(1).info == 'jubba')\n assert_(dat.argmax(1).info == 'jubba')\n assert_(dat.argmin(1).info == 'jubba')\n assert_(dat.argsort(1).info == 'jubba')\n assert_(dat.astype(TestArray).info == 'jubba')\n assert_(dat.byteswap().info == 'jubba')\n assert_(dat.clip(2, 7).info == 'jubba')\n assert_(dat.compress([0, 1, 1]).info == 'jubba')\n assert_(dat.conj().info == 'jubba')\n assert_(dat.conjugate().info == 'jubba')\n assert_(dat.copy().info == 'jubba')\n dat2 = TestArray([2, 3, 1, 0], 'jubba')\n choices = [[0, 1, 2, 3], [10, 11, 12, 13],\n [20, 21, 22, 23], [30, 31, 32, 33]]\n assert_(dat2.choose(choices).info == 'jubba')\n assert_(dat.cumprod(1).info == 'jubba')\n assert_(dat.cumsum(1).info == 'jubba')\n assert_(dat.diagonal().info == 'jubba')\n assert_(dat.flatten().info == 'jubba')\n assert_(dat.getfield(np.int32, 0).info == 'jubba')\n assert_(dat.imag.info == 'jubba')\n assert_(dat.max(1).info == 'jubba')\n assert_(dat.mean(1).info == 'jubba')\n assert_(dat.min(1).info == 'jubba')\n assert_(dat.prod(1).info == 'jubba')\n assert_(dat.ravel().info == 'jubba')\n assert_(dat.real.info == 'jubba')\n assert_(dat.repeat(2).info == 'jubba')\n assert_(dat.reshape((2, 4)).info == 'jubba')\n assert_(dat.round().info == 'jubba')\n assert_(dat.squeeze().info == 'jubba')\n assert_(dat.std(1).info == 'jubba')\n assert_(dat.sum(1).info == 'jubba')\n assert_(dat.swapaxes(0, 1).info == 'jubba')\n assert_(dat.take([2, 3, 5]).info == 'jubba')\n assert_(dat.transpose().info == 'jubba')\n assert_(dat.T.info == 'jubba')\n assert_(dat.var(1).info == 'jubba')\n assert_(dat.view(TestArray).info == 'jubba')\n # These methods do not preserve subclasses\n assert_(type(dat.nonzero()[0]) is np.ndarray)\n assert_(type(dat.nonzero()[1]) is np.ndarray)\n\n def test_recarray_tolist(self):\n # Ticket #793, changeset r5215\n # Comparisons fail for NaN, so we can't use random memory\n # for the test.\n buf = np.zeros(40, dtype=np.int8)\n a = np.recarray(2, formats="i4,f8,f8", names="id,x,y", buf=buf)\n b = a.tolist()\n assert_(a[0].tolist() == b[0])\n assert_(a[1].tolist() == b[1])\n\n def test_nonscalar_item_method(self):\n # Make sure that .item() fails graciously when it should\n a = np.arange(5)\n assert_raises(ValueError, a.item)\n\n def test_char_array_creation(self):\n a = np.array('123', dtype='c')\n b = np.array([b'1', b'2', b'3'])\n assert_equal(a, b)\n\n def test_unaligned_unicode_access(self):\n # Ticket #825\n for i in range(1, 9):\n msg = 'unicode offset: %d chars' % i\n t = np.dtype([('a', 'S%d' % i), ('b', 'U2')])\n x = np.array([(b'a', 'b')], dtype=t)\n assert_equal(str(x), "[(b'a', 'b')]", err_msg=msg)\n\n def test_sign_for_complex_nan(self):\n # Ticket 794.\n with np.errstate(invalid='ignore'):\n C = np.array([-np.inf, -3 + 4j, 0, 4 - 3j, np.inf, np.nan])\n have = np.sign(C)\n want = np.array([-1 + 0j, -0.6 + 0.8j, 0 + 0j, 0.8 - 0.6j, 1 + 0j,\n complex(np.nan, np.nan)])\n assert_equal(have, want)\n\n def test_for_equal_names(self):\n # Ticket #674\n dt = np.dtype([('foo', float), ('bar', float)])\n a = np.zeros(10, dt)\n b = list(a.dtype.names)\n b[0] = "notfoo"\n a.dtype.names = b\n assert_(a.dtype.names[0] == "notfoo")\n assert_(a.dtype.names[1] == "bar")\n\n def test_for_object_scalar_creation(self):\n # Ticket #816\n a = np.object_()\n b = np.object_(3)\n b2 = np.object_(3.0)\n c = np.object_([4, 5])\n d = np.object_([None, {}, []])\n assert_(a is None)\n assert_(type(b) is int)\n assert_(type(b2) is float)\n assert_(type(c) is np.ndarray)\n assert_(c.dtype == object)\n assert_(d.dtype == object)\n\n def test_array_resize_method_system_error(self):\n # Ticket #840 - order should be an invalid keyword.\n x = np.array([[0, 1], [2, 3]])\n assert_raises(TypeError, x.resize, (2, 2), order='C')\n\n def test_for_zero_length_in_choose(self):\n "Ticket #882"\n a = np.array(1)\n assert_raises(ValueError, lambda x: x.choose([]), a)\n\n def test_array_ndmin_overflow(self):\n "Ticket #947."\n assert_raises(ValueError, lambda: np.array([1], ndmin=65))\n\n def test_void_scalar_with_titles(self):\n # No ticket\n data = [('john', 4), ('mary', 5)]\n dtype1 = [(('source:yy', 'name'), 'O'), (('source:xx', 'id'), int)]\n arr = np.array(data, dtype=dtype1)\n assert_(arr[0][0] == 'john')\n assert_(arr[0][1] == 4)\n\n def test_void_scalar_constructor(self):\n # Issue #1550\n\n # Create test string data, construct void scalar from data and assert\n # that void scalar contains original data.\n test_string = np.array("test")\n test_string_void_scalar = np._core.multiarray.scalar(\n np.dtype(("V", test_string.dtype.itemsize)), test_string.tobytes())\n\n assert_(test_string_void_scalar.view(test_string.dtype) == test_string)\n\n # Create record scalar, construct from data and assert that\n # reconstructed scalar is correct.\n test_record = np.ones((), "i,i")\n test_record_void_scalar = np._core.multiarray.scalar(\n test_record.dtype, test_record.tobytes())\n\n assert_(test_record_void_scalar == test_record)\n\n # Test pickle and unpickle of void and record scalars\n for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):\n assert_(pickle.loads(\n pickle.dumps(test_string, protocol=proto)) == test_string)\n assert_(pickle.loads(\n pickle.dumps(test_record, protocol=proto)) == test_record)\n\n @_no_tracing\n def test_blasdot_uninitialized_memory(self):\n # Ticket #950\n for m in [0, 1, 2]:\n for n in [0, 1, 2]:\n for k in range(3):\n # Try to ensure that x->data contains non-zero floats\n x = np.array([123456789e199], dtype=np.float64)\n if IS_PYPY:\n x.resize((m, 0), refcheck=False)\n else:\n x.resize((m, 0))\n y = np.array([123456789e199], dtype=np.float64)\n if IS_PYPY:\n y.resize((0, n), refcheck=False)\n else:\n y.resize((0, n))\n\n # `dot` should just return zero (m, n) matrix\n z = np.dot(x, y)\n assert_(np.all(z == 0))\n assert_(z.shape == (m, n))\n\n def test_zeros(self):\n # Regression test for #1061.\n # Set a size which cannot fit into a 64 bits signed integer\n sz = 2 ** 64\n with assert_raises_regex(ValueError,\n 'Maximum allowed dimension exceeded'):\n np.empty(sz)\n\n def test_huge_arange(self):\n # Regression test for #1062.\n # Set a size which cannot fit into a 64 bits signed integer\n sz = 2 ** 64\n with assert_raises_regex(ValueError,\n 'Maximum allowed size exceeded'):\n np.arange(sz)\n assert_(np.size == sz)\n\n def test_fromiter_bytes(self):\n # Ticket #1058\n a = np.fromiter(list(range(10)), dtype='b')\n b = np.fromiter(list(range(10)), dtype='B')\n assert_(np.all(a == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))\n assert_(np.all(b == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))\n\n def test_array_from_sequence_scalar_array(self):\n # Ticket #1078: segfaults when creating an array with a sequence of\n # 0d arrays.\n a = np.array((np.ones(2), np.array(2)), dtype=object)\n assert_equal(a.shape, (2,))\n assert_equal(a.dtype, np.dtype(object))\n assert_equal(a[0], np.ones(2))\n assert_equal(a[1], np.array(2))\n\n a = np.array(((1,), np.array(1)), dtype=object)\n assert_equal(a.shape, (2,))\n assert_equal(a.dtype, np.dtype(object))\n assert_equal(a[0], (1,))\n assert_equal(a[1], np.array(1))\n\n def test_array_from_sequence_scalar_array2(self):\n # Ticket #1081: weird array with strange input...\n t = np.array([np.array([]), np.array(0, object)], dtype=object)\n assert_equal(t.shape, (2,))\n assert_equal(t.dtype, np.dtype(object))\n\n def test_array_too_big(self):\n # Ticket #1080.\n assert_raises(ValueError, np.zeros, [975] * 7, np.int8)\n assert_raises(ValueError, np.zeros, [26244] * 5, np.int8)\n\n def test_dtype_keyerrors_(self):\n # Ticket #1106.\n dt = np.dtype([('f1', np.uint)])\n assert_raises(KeyError, dt.__getitem__, "f2")\n assert_raises(IndexError, dt.__getitem__, 1)\n assert_raises(TypeError, dt.__getitem__, 0.0)\n\n def test_lexsort_buffer_length(self):\n # Ticket #1217, don't segfault.\n a = np.ones(100, dtype=np.int8)\n b = np.ones(100, dtype=np.int32)\n i = np.lexsort((a[::-1], b))\n assert_equal(i, np.arange(100, dtype=int))\n\n def test_object_array_to_fixed_string(self):\n # Ticket #1235.\n a = np.array(['abcdefgh', 'ijklmnop'], dtype=np.object_)\n b = np.array(a, dtype=(np.str_, 8))\n assert_equal(a, b)\n c = np.array(a, dtype=(np.str_, 5))\n assert_equal(c, np.array(['abcde', 'ijklm']))\n d = np.array(a, dtype=(np.str_, 12))\n assert_equal(a, d)\n e = np.empty((2, ), dtype=(np.str_, 8))\n e[:] = a[:]\n assert_equal(a, e)\n\n def test_unicode_to_string_cast(self):\n # Ticket #1240.\n a = np.array([['abc', '\u03a3'],\n ['asdf', 'erw']],\n dtype='U')\n assert_raises(UnicodeEncodeError, np.array, a, 'S4')\n\n def test_unicode_to_string_cast_error(self):\n # gh-15790\n a = np.array(['\x80'] * 129, dtype='U3')\n assert_raises(UnicodeEncodeError, np.array, a, 'S')\n b = a.reshape(3, 43)[:-1, :-1]\n assert_raises(UnicodeEncodeError, np.array, b, 'S')\n\n def test_mixed_string_byte_array_creation(self):\n a = np.array(['1234', b'123'])\n assert_(a.itemsize == 16)\n a = np.array([b'123', '1234'])\n assert_(a.itemsize == 16)\n a = np.array(['1234', b'123', '12345'])\n assert_(a.itemsize == 20)\n a = np.array([b'123', '1234', b'12345'])\n assert_(a.itemsize == 20)\n a = np.array([b'123', '1234', b'1234'])\n assert_(a.itemsize == 16)\n\n def test_misaligned_objects_segfault(self):\n # Ticket #1198 and #1267\n a1 = np.zeros((10,), dtype='O,c')\n a2 = np.array(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'], 'S10')\n a1['f0'] = a2\n repr(a1)\n np.argmax(a1['f0'])\n a1['f0'][1] = "FOO"\n a1['f0'] = "FOO"\n np.array(a1['f0'], dtype='S')\n np.nonzero(a1['f0'])\n a1.sort()\n copy.deepcopy(a1)\n\n def test_misaligned_scalars_segfault(self):\n # Ticket #1267\n s1 = np.array(('a', 'Foo'), dtype='c,O')\n s2 = np.array(('b', 'Bar'), dtype='c,O')\n s1['f1'] = s2['f1']\n s1['f1'] = 'Baz'\n\n def test_misaligned_dot_product_objects(self):\n # Ticket #1267\n # This didn't require a fix, but it's worth testing anyway, because\n # it may fail if .dot stops enforcing the arrays to be BEHAVED\n a = np.array([[(1, 'a'), (0, 'a')], [(0, 'a'), (1, 'a')]], dtype='O,c')\n b = np.array([[(4, 'a'), (1, 'a')], [(2, 'a'), (2, 'a')]], dtype='O,c')\n np.dot(a['f0'], b['f0'])\n\n def test_byteswap_complex_scalar(self):\n # Ticket #1259 and gh-441\n for dtype in [np.dtype('<' + t) for t in np.typecodes['Complex']]:\n z = np.array([2.2 - 1.1j], dtype)\n x = z[0] # always native-endian\n y = x.byteswap()\n if x.dtype.byteorder == z.dtype.byteorder:\n # little-endian machine\n assert_equal(x, np.frombuffer(y.tobytes(), dtype=dtype.newbyteorder()))\n else:\n # big-endian machine\n assert_equal(x, np.frombuffer(y.tobytes(), dtype=dtype))\n # double check real and imaginary parts:\n assert_equal(x.real, y.real.byteswap())\n assert_equal(x.imag, y.imag.byteswap())\n\n def test_structured_arrays_with_objects1(self):\n # Ticket #1299\n stra = 'aaaa'\n strb = 'bbbb'\n x = np.array([[(0, stra), (1, strb)]], 'i8,O')\n x[x.nonzero()] = x.ravel()[:1]\n assert_(x[0, 1] == x[0, 0])\n\n @pytest.mark.skipif(\n sys.version_info >= (3, 12),\n reason="Python 3.12 has immortal refcounts, this test no longer works."\n )\n @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")\n def test_structured_arrays_with_objects2(self):\n # Ticket #1299 second test\n stra = 'aaaa'\n strb = 'bbbb'\n numb = sys.getrefcount(strb)\n numa = sys.getrefcount(stra)\n x = np.array([[(0, stra), (1, strb)]], 'i8,O')\n x[x.nonzero()] = x.ravel()[:1]\n assert_(sys.getrefcount(strb) == numb)\n assert_(sys.getrefcount(stra) == numa + 2)\n\n def test_duplicate_title_and_name(self):\n # Ticket #1254\n dtspec = [(('a', 'a'), 'i'), ('b', 'i')]\n assert_raises(ValueError, np.dtype, dtspec)\n\n def test_signed_integer_division_overflow(self):\n # Ticket #1317.\n def test_type(t):\n min = np.array([np.iinfo(t).min])\n min //= -1\n\n with np.errstate(over="ignore"):\n for t in (np.int8, np.int16, np.int32, np.int64, int):\n test_type(t)\n\n def test_buffer_hashlib(self):\n from hashlib import sha256\n\n x = np.array([1, 2, 3], dtype=np.dtype('<i4'))\n assert_equal(\n sha256(x).hexdigest(),\n '4636993d3e1da4e9d6b8f87b79e8f7c6d018580d52661950eabc3845c5897a4d'\n )\n\n def test_0d_string_scalar(self):\n # Bug #1436; the following should succeed\n np.asarray('x', '>c')\n\n def test_log1p_compiler_shenanigans(self):\n # Check if log1p is behaving on 32 bit intel systems.\n assert_(np.isfinite(np.log1p(np.exp2(-53))))\n\n def test_fromiter_comparison(self):\n a = np.fromiter(list(range(10)), dtype='b')\n b = np.fromiter(list(range(10)), dtype='B')\n assert_(np.all(a == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))\n assert_(np.all(b == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))\n\n def test_fromstring_crash(self):\n with assert_raises(ValueError):\n np.fromstring(b'aa, aa, 1.0', sep=',')\n\n def test_ticket_1539(self):\n dtypes = [x for x in np._core.sctypeDict.values()\n if (issubclass(x, np.number)\n and not issubclass(x, np.timedelta64))]\n a = np.array([], np.bool) # not x[0] because it is unordered\n failures = []\n\n for x in dtypes:\n b = a.astype(x)\n for y in dtypes:\n c = a.astype(y)\n try:\n d = np.dot(b, c)\n except TypeError:\n failures.append((x, y))\n else:\n if d != 0:\n failures.append((x, y))\n if failures:\n raise AssertionError(f"Failures: {failures!r}")\n\n def test_ticket_1538(self):\n x = np.finfo(np.float32)\n for name in ('eps', 'epsneg', 'max', 'min', 'resolution', 'tiny'):\n assert_equal(type(getattr(x, name)), np.float32,\n err_msg=name)\n\n def test_ticket_1434(self):\n # Check that the out= argument in var and std has an effect\n data = np.array(((1, 2, 3), (4, 5, 6), (7, 8, 9)))\n out = np.zeros((3,))\n\n ret = data.var(axis=1, out=out)\n assert_(ret is out)\n assert_array_equal(ret, data.var(axis=1))\n\n ret = data.std(axis=1, out=out)\n assert_(ret is out)\n assert_array_equal(ret, data.std(axis=1))\n\n def test_complex_nan_maximum(self):\n cnan = complex(0, np.nan)\n assert_equal(np.maximum(1, cnan), cnan)\n\n def test_subclass_int_tuple_assignment(self):\n # ticket #1563\n class Subclass(np.ndarray):\n def __new__(cls, i):\n return np.ones((i,)).view(cls)\n\n x = Subclass(5)\n x[(0,)] = 2 # shouldn't raise an exception\n assert_equal(x[0], 2)\n\n def test_ufunc_no_unnecessary_views(self):\n # ticket #1548\n class Subclass(np.ndarray):\n pass\n x = np.array([1, 2, 3]).view(Subclass)\n y = np.add(x, x, x)\n assert_equal(id(x), id(y))\n\n @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")\n def test_take_refcount(self):\n # ticket #939\n a = np.arange(16, dtype=float)\n a.shape = (4, 4)\n lut = np.ones((5 + 3, 4), float)\n rgba = np.empty(shape=a.shape + (4,), dtype=lut.dtype)\n c1 = sys.getrefcount(rgba)\n try:\n lut.take(a, axis=0, mode='clip', out=rgba)\n except TypeError:\n pass\n c2 = sys.getrefcount(rgba)\n assert_equal(c1, c2)\n\n def test_fromfile_tofile_seeks(self):\n # tofile/fromfile used to get (#1610) the Python file handle out of sync\n with tempfile.NamedTemporaryFile() as f:\n f.write(np.arange(255, dtype='u1').tobytes())\n\n f.seek(20)\n ret = np.fromfile(f, count=4, dtype='u1')\n assert_equal(ret, np.array([20, 21, 22, 23], dtype='u1'))\n assert_equal(f.tell(), 24)\n\n f.seek(40)\n np.array([1, 2, 3], dtype='u1').tofile(f)\n assert_equal(f.tell(), 43)\n\n f.seek(40)\n data = f.read(3)\n assert_equal(data, b"\x01\x02\x03")\n\n f.seek(80)\n f.read(4)\n data = np.fromfile(f, dtype='u1', count=4)\n assert_equal(data, np.array([84, 85, 86, 87], dtype='u1'))\n\n def test_complex_scalar_warning(self):\n for tp in [np.csingle, np.cdouble, np.clongdouble]:\n x = tp(1 + 2j)\n assert_warns(ComplexWarning, float, x)\n with suppress_warnings() as sup:\n sup.filter(ComplexWarning)\n assert_equal(float(x), float(x.real))\n\n def test_complex_scalar_complex_cast(self):\n for tp in [np.csingle, np.cdouble, np.clongdouble]:\n x = tp(1 + 2j)\n assert_equal(complex(x), 1 + 2j)\n\n def test_complex_boolean_cast(self):\n # Ticket #2218\n for tp in [np.csingle, np.cdouble, np.clongdouble]:\n x = np.array([0, 0 + 0.5j, 0.5 + 0j], dtype=tp)\n assert_equal(x.astype(bool), np.array([0, 1, 1], dtype=bool))\n assert_(np.any(x))\n assert_(np.all(x[1:]))\n\n def test_uint_int_conversion(self):\n x = 2**64 - 1\n assert_equal(int(np.uint64(x)), x)\n\n def test_duplicate_field_names_assign(self):\n ra = np.fromiter(((i * 3, i * 2) for i in range(10)), dtype='i8,f8')\n ra.dtype.names = ('f1', 'f2')\n repr(ra) # should not cause a segmentation fault\n assert_raises(ValueError, setattr, ra.dtype, 'names', ('f1', 'f1'))\n\n def test_eq_string_and_object_array(self):\n # From e-mail thread "__eq__ with str and object" (Keith Goodman)\n a1 = np.array(['a', 'b'], dtype=object)\n a2 = np.array(['a', 'c'])\n assert_array_equal(a1 == a2, [True, False])\n assert_array_equal(a2 == a1, [True, False])\n\n def test_nonzero_byteswap(self):\n a = np.array([0x80000000, 0x00000080, 0], dtype=np.uint32)\n a.dtype = np.float32\n assert_equal(a.nonzero()[0], [1])\n a = a.byteswap()\n a = a.view(a.dtype.newbyteorder())\n assert_equal(a.nonzero()[0], [1]) # [0] if nonzero() ignores swap\n\n def test_empty_mul(self):\n a = np.array([1.])\n a[1:1] *= 2\n assert_equal(a, [1.])\n\n def test_array_side_effect(self):\n # The second use of itemsize was throwing an exception because in\n # ctors.c, discover_itemsize was calling PyObject_Length without\n # checking the return code. This failed to get the length of the\n # number 2, and the exception hung around until something checked\n # PyErr_Occurred() and returned an error.\n assert_equal(np.dtype('S10').itemsize, 10)\n np.array([['abc', 2], ['long ', '0123456789']], dtype=np.bytes_)\n assert_equal(np.dtype('S10').itemsize, 10)\n\n def test_any_float(self):\n # all and any for floats\n a = np.array([0.1, 0.9])\n assert_(np.any(a))\n assert_(np.all(a))\n\n def test_large_float_sum(self):\n a = np.arange(10000, dtype='f')\n assert_equal(a.sum(dtype='d'), a.astype('d').sum())\n\n def test_ufunc_casting_out(self):\n a = np.array(1.0, dtype=np.float32)\n b = np.array(1.0, dtype=np.float64)\n c = np.array(1.0, dtype=np.float32)\n np.add(a, b, out=c)\n assert_equal(c, 2.0)\n\n def test_array_scalar_contiguous(self):\n # Array scalars are both C and Fortran contiguous\n assert_(np.array(1.0).flags.c_contiguous)\n assert_(np.array(1.0).flags.f_contiguous)\n assert_(np.array(np.float32(1.0)).flags.c_contiguous)\n assert_(np.array(np.float32(1.0)).flags.f_contiguous)\n\n def test_squeeze_contiguous(self):\n # Similar to GitHub issue #387\n a = np.zeros((1, 2)).squeeze()\n b = np.zeros((2, 2, 2), order='F')[:, :, ::2].squeeze()\n assert_(a.flags.c_contiguous)\n assert_(a.flags.f_contiguous)\n assert_(b.flags.f_contiguous)\n\n def test_squeeze_axis_handling(self):\n # Issue #10779\n # Ensure proper handling of objects\n # that don't support axis specification\n # when squeezing\n\n class OldSqueeze(np.ndarray):\n\n def __new__(cls,\n input_array):\n obj = np.asarray(input_array).view(cls)\n return obj\n\n # it is perfectly reasonable that prior\n # to numpy version 1.7.0 a subclass of ndarray\n # might have been created that did not expect\n # squeeze to have an axis argument\n # NOTE: this example is somewhat artificial;\n # it is designed to simulate an old API\n # expectation to guard against regression\n def squeeze(self):\n return super().squeeze()\n\n oldsqueeze = OldSqueeze(np.array([[1], [2], [3]]))\n\n # if no axis argument is specified the old API\n # expectation should give the correct result\n assert_equal(np.squeeze(oldsqueeze),\n np.array([1, 2, 3]))\n\n # likewise, axis=None should work perfectly well\n # with the old API expectation\n assert_equal(np.squeeze(oldsqueeze, axis=None),\n np.array([1, 2, 3]))\n\n # however, specification of any particular axis\n # should raise a TypeError in the context of the\n # old API specification, even when using a valid\n # axis specification like 1 for this array\n with assert_raises(TypeError):\n # this would silently succeed for array\n # subclasses / objects that did not support\n # squeeze axis argument handling before fixing\n # Issue #10779\n np.squeeze(oldsqueeze, axis=1)\n\n # check for the same behavior when using an invalid\n # axis specification -- in this case axis=0 does not\n # have size 1, but the priority should be to raise\n # a TypeError for the axis argument and NOT a\n # ValueError for squeezing a non-empty dimension\n with assert_raises(TypeError):\n np.squeeze(oldsqueeze, axis=0)\n\n # the new API knows how to handle the axis\n # argument and will return a ValueError if\n # attempting to squeeze an axis that is not\n # of length 1\n with assert_raises(ValueError):\n np.squeeze(np.array([[1], [2], [3]]), axis=0)\n\n def test_reduce_contiguous(self):\n # GitHub issue #387\n a = np.add.reduce(np.zeros((2, 1, 2)), (0, 1))\n b = np.add.reduce(np.zeros((2, 1, 2)), 1)\n assert_(a.flags.c_contiguous)\n assert_(a.flags.f_contiguous)\n assert_(b.flags.c_contiguous)\n\n @pytest.mark.skipif(IS_PYSTON, reason="Pyston disables recursion checking")\n @pytest.mark.skipif(IS_WASM, reason="Pyodide/WASM has limited stack size")\n def test_object_array_self_reference(self):\n # Object arrays with references to themselves can cause problems\n a = np.array(0, dtype=object)\n a[()] = a\n assert_raises(RecursionError, int, a)\n assert_raises(RecursionError, float, a)\n a[()] = None\n\n @pytest.mark.skipif(IS_PYSTON, reason="Pyston disables recursion checking")\n @pytest.mark.skipif(IS_WASM, reason="Pyodide/WASM has limited stack size")\n def test_object_array_circular_reference(self):\n # Test the same for a circular reference.\n a = np.array(0, dtype=object)\n b = np.array(0, dtype=object)\n a[()] = b\n b[()] = a\n assert_raises(RecursionError, int, a)\n # NumPy has no tp_traverse currently, so circular references\n # cannot be detected. So resolve it:\n a[()] = None\n\n # This was causing a to become like the above\n a = np.array(0, dtype=object)\n a[...] += 1\n assert_equal(a, 1)\n\n def test_object_array_nested(self):\n # but is fine with a reference to a different array\n a = np.array(0, dtype=object)\n b = np.array(0, dtype=object)\n a[()] = b\n assert_equal(int(a), int(0)) # noqa: UP018\n assert_equal(float(a), float(0))\n\n def test_object_array_self_copy(self):\n # An object array being copied into itself DECREF'ed before INCREF'ing\n # causing segmentation faults (gh-3787)\n a = np.array(object(), dtype=object)\n np.copyto(a, a)\n if HAS_REFCOUNT:\n assert_(sys.getrefcount(a[()]) == 2)\n a[()].__class__ # will segfault if object was deleted\n\n def test_zerosize_accumulate(self):\n "Ticket #1733"\n x = np.array([[42, 0]], dtype=np.uint32)\n assert_equal(np.add.accumulate(x[:-1, 0]), [])\n\n def test_objectarray_setfield(self):\n # Setfield should not overwrite Object fields with non-Object data\n x = np.array([1, 2, 3], dtype=object)\n assert_raises(TypeError, x.setfield, 4, np.int32, 0)\n\n def test_setting_rank0_string(self):\n "Ticket #1736"\n s1 = b"hello1"\n s2 = b"hello2"\n a = np.zeros((), dtype="S10")\n a[()] = s1\n assert_equal(a, np.array(s1))\n a[()] = np.array(s2)\n assert_equal(a, np.array(s2))\n\n a = np.zeros((), dtype='f4')\n a[()] = 3\n assert_equal(a, np.array(3))\n a[()] = np.array(4)\n assert_equal(a, np.array(4))\n\n def test_string_astype(self):\n "Ticket #1748"\n s1 = b'black'\n s2 = b'white'\n s3 = b'other'\n a = np.array([[s1], [s2], [s3]])\n assert_equal(a.dtype, np.dtype('S5'))\n b = a.astype(np.dtype('S0'))\n assert_equal(b.dtype, np.dtype('S5'))\n\n def test_ticket_1756(self):\n # Ticket #1756\n s = b'0123456789abcdef'\n a = np.array([s] * 5)\n for i in range(1, 17):\n a1 = np.array(a, "|S%d" % i)\n a2 = np.array([s[:i]] * 5)\n assert_equal(a1, a2)\n\n def test_fields_strides(self):\n "gh-2355"\n r = np.frombuffer(b'abcdefghijklmnop' * 4 * 3, dtype='i4,(2,3)u2')\n assert_equal(r[0:3:2]['f1'], r['f1'][0:3:2])\n assert_equal(r[0:3:2]['f1'][0], r[0:3:2][0]['f1'])\n assert_equal(r[0:3:2]['f1'][0][()], r[0:3:2][0]['f1'][()])\n assert_equal(r[0:3:2]['f1'][0].strides, r[0:3:2][0]['f1'].strides)\n\n def test_alignment_update(self):\n # Check that alignment flag is updated on stride setting\n a = np.arange(10)\n assert_(a.flags.aligned)\n a.strides = 3\n assert_(not a.flags.aligned)\n\n def test_ticket_1770(self):\n "Should not segfault on python 3k"\n import numpy as np\n try:\n a = np.zeros((1,), dtype=[('f1', 'f')])\n a['f1'] = 1\n a['f2'] = 1\n except ValueError:\n pass\n except Exception:\n raise AssertionError\n\n def test_ticket_1608(self):\n "x.flat shouldn't modify data"\n x = np.array([[1, 2], [3, 4]]).T\n np.array(x.flat)\n assert_equal(x, [[1, 3], [2, 4]])\n\n def test_pickle_string_overwrite(self):\n import re\n\n data = np.array([1], dtype='b')\n blob = pickle.dumps(data, protocol=1)\n data = pickle.loads(blob)\n\n # Check that loads does not clobber interned strings\n s = re.sub(r"a(.)", "\x01\\1", "a_")\n assert_equal(s[0], "\x01")\n data[0] = 0x6a\n s = re.sub(r"a(.)", "\x01\\1", "a_")\n assert_equal(s[0], "\x01")\n\n def test_pickle_bytes_overwrite(self):\n for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):\n data = np.array([1], dtype='b')\n data = pickle.loads(pickle.dumps(data, protocol=proto))\n data[0] = 0x7d\n bytestring = "\x01 ".encode('ascii')\n assert_equal(bytestring[0:1], '\x01'.encode('ascii'))\n\n def test_pickle_py2_array_latin1_hack(self):\n # Check that unpickling hacks in Py3 that support\n # encoding='latin1' work correctly.\n\n # Python2 output for pickle.dumps(numpy.array([129], dtype='b'))\n data = b"cnumpy.core.multiarray\n_reconstruct\np0\n(cnumpy\nndarray\np1\n(I0\ntp2\nS'b'\np3\ntp4\nRp5\n(I1\n(I1\ntp6\ncnumpy\ndtype\np7\n(S'i1'\np8\nI0\nI1\ntp9\nRp10\n(I3\nS'|'\np11\nNNNI-1\nI-1\nI0\ntp12\nbI00\nS'\\x81'\np13\ntp14\nb."\n # This should work:\n result = pickle.loads(data, encoding='latin1')\n assert_array_equal(result, np.array([129]).astype('b'))\n # Should not segfault:\n assert_raises(Exception, pickle.loads, data, encoding='koi8-r')\n\n def test_pickle_py2_scalar_latin1_hack(self):\n # Check that scalar unpickling hack in Py3 that supports\n # encoding='latin1' work correctly.\n\n # Python2 output for pickle.dumps(...)\n datas = [\n # (original, python2_pickle, koi8r_validity)\n (np.str_('\u6bd2'),\n b"cnumpy.core.multiarray\nscalar\np0\n(cnumpy\ndtype\np1\n(S'U1'\np2\nI0\nI1\ntp3\nRp4\n(I3\nS'<'\np5\nNNNI4\nI4\nI0\ntp6\nbS'\\xd2k\\x00\\x00'\np7\ntp8\nRp9\n.",\n 'invalid'),\n\n (np.float64(9e123),\n b"cnumpy.core.multiarray\nscalar\np0\n(cnumpy\ndtype\np1\n(S'f8'\np2\nI0\nI1\ntp3\nRp4\n(I3\nS'<'\np5\nNNNI-1\nI-1\nI0\ntp6\nbS'O\\x81\\xb7Z\\xaa:\\xabY'\np7\ntp8\nRp9\n.",\n 'invalid'),\n\n # different 8-bit code point in KOI8-R vs latin1\n (np.bytes_(b'\x9c'),\n b"cnumpy.core.multiarray\nscalar\np0\n(cnumpy\ndtype\np1\n(S'S1'\np2\nI0\nI1\ntp3\nRp4\n(I3\nS'|'\np5\nNNNI1\nI1\nI0\ntp6\nbS'\\x9c'\np7\ntp8\nRp9\n.",\n 'different'),\n ]\n for original, data, koi8r_validity in datas:\n result = pickle.loads(data, encoding='latin1')\n assert_equal(result, original)\n\n # Decoding under non-latin1 encoding (e.g.) KOI8-R can\n # produce bad results, but should not segfault.\n if koi8r_validity == 'different':\n # Unicode code points happen to lie within latin1,\n # but are different in koi8-r, resulting to silent\n # bogus results\n result = pickle.loads(data, encoding='koi8-r')\n assert_(result != original)\n elif koi8r_validity == 'invalid':\n # Unicode code points outside latin1, so results\n # to an encoding exception\n assert_raises(\n ValueError, pickle.loads, data, encoding='koi8-r'\n )\n else:\n raise ValueError(koi8r_validity)\n\n def test_structured_type_to_object(self):\n a_rec = np.array([(0, 1), (3, 2)], dtype='i4,i8')\n a_obj = np.empty((2,), dtype=object)\n a_obj[0] = (0, 1)\n a_obj[1] = (3, 2)\n # astype records -> object\n assert_equal(a_rec.astype(object), a_obj)\n # '=' records -> object\n b = np.empty_like(a_obj)\n b[...] = a_rec\n assert_equal(b, a_obj)\n # '=' object -> records\n b = np.empty_like(a_rec)\n b[...] = a_obj\n assert_equal(b, a_rec)\n\n def test_assign_obj_listoflists(self):\n # Ticket # 1870\n # The inner list should get assigned to the object elements\n a = np.zeros(4, dtype=object)\n b = a.copy()\n a[0] = [1]\n a[1] = [2]\n a[2] = [3]\n a[3] = [4]\n b[...] = [[1], [2], [3], [4]]\n assert_equal(a, b)\n # The first dimension should get broadcast\n a = np.zeros((2, 2), dtype=object)\n a[...] = [[1, 2]]\n assert_equal(a, [[1, 2], [1, 2]])\n\n @pytest.mark.slow_pypy\n def test_memoryleak(self):\n # Ticket #1917 - ensure that array data doesn't leak\n for i in range(1000):\n # 100MB times 1000 would give 100GB of memory usage if it leaks\n a = np.empty((100000000,), dtype='i1')\n del a\n\n @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")\n def test_ufunc_reduce_memoryleak(self):\n a = np.arange(6)\n acnt = sys.getrefcount(a)\n np.add.reduce(a)\n assert_equal(sys.getrefcount(a), acnt)\n\n def test_search_sorted_invalid_arguments(self):\n # Ticket #2021, should not segfault.\n x = np.arange(0, 4, dtype='datetime64[D]')\n assert_raises(TypeError, x.searchsorted, 1)\n\n def test_string_truncation(self):\n # Ticket #1990 - Data can be truncated in creation of an array from a\n # mixed sequence of numeric values and strings (gh-2583)\n for val in [True, 1234, 123.4, complex(1, 234)]:\n for tostr, dtype in [(asunicode, "U"), (asbytes, "S")]:\n b = np.array([val, tostr('xx')], dtype=dtype)\n assert_equal(tostr(b[0]), tostr(val))\n b = np.array([tostr('xx'), val], dtype=dtype)\n assert_equal(tostr(b[1]), tostr(val))\n\n # test also with longer strings\n b = np.array([val, tostr('xxxxxxxxxx')], dtype=dtype)\n assert_equal(tostr(b[0]), tostr(val))\n b = np.array([tostr('xxxxxxxxxx'), val], dtype=dtype)\n assert_equal(tostr(b[1]), tostr(val))\n\n def test_string_truncation_ucs2(self):\n # Ticket #2081. Python compiled with two byte unicode\n # can lead to truncation if itemsize is not properly\n # adjusted for NumPy's four byte unicode.\n a = np.array(['abcd'])\n assert_equal(a.dtype.itemsize, 16)\n\n def test_unique_stable(self):\n # Ticket #2063 must always choose stable sort for argsort to\n # get consistent results\n v = np.array(([0] * 5 + [1] * 6 + [2] * 6) * 4)\n res = np.unique(v, return_index=True)\n tgt = (np.array([0, 1, 2]), np.array([0, 5, 11]))\n assert_equal(res, tgt)\n\n def test_unicode_alloc_dealloc_match(self):\n # Ticket #1578, the mismatch only showed up when running\n # python-debug for python versions >= 2.7, and then as\n # a core dump and error message.\n a = np.array(['abc'], dtype=np.str_)[0]\n del a\n\n def test_refcount_error_in_clip(self):\n # Ticket #1588\n a = np.zeros((2,), dtype='>i2').clip(min=0)\n x = a + a\n # This used to segfault:\n y = str(x)\n # Check the final string:\n assert_(y == "[0 0]")\n\n def test_searchsorted_wrong_dtype(self):\n # Ticket #2189, it used to segfault, so we check that it raises the\n # proper exception.\n a = np.array([('a', 1)], dtype='S1, int')\n assert_raises(TypeError, np.searchsorted, a, 1.2)\n # Ticket #2066, similar problem:\n dtype = np.rec.format_parser(['i4', 'i4'], [], [])\n a = np.recarray((2,), dtype)\n a[...] = [(1, 2), (3, 4)]\n assert_raises(TypeError, np.searchsorted, a, 1)\n\n def test_complex64_alignment(self):\n # Issue gh-2668 (trac 2076), segfault on sparc due to misalignment\n dtt = np.complex64\n arr = np.arange(10, dtype=dtt)\n # 2D array\n arr2 = np.reshape(arr, (2, 5))\n # Fortran write followed by (C or F) read caused bus error\n data_str = arr2.tobytes('F')\n data_back = np.ndarray(arr2.shape,\n arr2.dtype,\n buffer=data_str,\n order='F')\n assert_array_equal(arr2, data_back)\n\n def test_structured_count_nonzero(self):\n arr = np.array([0, 1]).astype('i4, 2i4')[:1]\n count = np.count_nonzero(arr)\n assert_equal(count, 0)\n\n def test_copymodule_preserves_f_contiguity(self):\n a = np.empty((2, 2), order='F')\n b = copy.copy(a)\n c = copy.deepcopy(a)\n assert_(b.flags.fortran)\n assert_(b.flags.f_contiguous)\n assert_(c.flags.fortran)\n assert_(c.flags.f_contiguous)\n\n def test_fortran_order_buffer(self):\n import numpy as np\n a = np.array([['Hello', 'Foob']], dtype='U5', order='F')\n arr = np.ndarray(shape=[1, 2, 5], dtype='U1', buffer=a)\n arr2 = np.array([[['H', 'e', 'l', 'l', 'o'],\n ['F', 'o', 'o', 'b', '']]])\n assert_array_equal(arr, arr2)\n\n def test_assign_from_sequence_error(self):\n # Ticket #4024.\n arr = np.array([1, 2, 3])\n assert_raises(ValueError, arr.__setitem__, slice(None), [9, 9])\n arr.__setitem__(slice(None), [9])\n assert_equal(arr, [9, 9, 9])\n\n def test_format_on_flex_array_element(self):\n # Ticket #4369.\n dt = np.dtype([('date', '<M8[D]'), ('val', '<f8')])\n arr = np.array([('2000-01-01', 1)], dt)\n formatted = f'{arr[0]}'\n assert_equal(formatted, str(arr[0]))\n\n def test_deepcopy_on_0d_array(self):\n # Ticket #3311.\n arr = np.array(3)\n arr_cp = copy.deepcopy(arr)\n\n assert_equal(arr, arr_cp)\n assert_equal(arr.shape, arr_cp.shape)\n assert_equal(int(arr), int(arr_cp))\n assert_(arr is not arr_cp)\n assert_(isinstance(arr_cp, type(arr)))\n\n def test_deepcopy_F_order_object_array(self):\n # Ticket #6456.\n a = {'a': 1}\n b = {'b': 2}\n arr = np.array([[a, b], [a, b]], order='F')\n arr_cp = copy.deepcopy(arr)\n\n assert_equal(arr, arr_cp)\n assert_(arr is not arr_cp)\n # Ensure that we have actually copied the item.\n assert_(arr[0, 1] is not arr_cp[1, 1])\n # Ensure we are allowed to have references to the same object.\n assert_(arr[0, 1] is arr[1, 1])\n # Check the references hold for the copied objects.\n assert_(arr_cp[0, 1] is arr_cp[1, 1])\n\n def test_deepcopy_empty_object_array(self):\n # Ticket #8536.\n # Deepcopy should succeed\n a = np.array([], dtype=object)\n b = copy.deepcopy(a)\n assert_(a.shape == b.shape)\n\n def test_bool_subscript_crash(self):\n # gh-4494\n c = np.rec.array([(1, 2, 3), (4, 5, 6)])\n masked = c[np.array([True, False])]\n base = masked.base\n del masked, c\n base.dtype\n\n def test_richcompare_crash(self):\n # gh-4613\n import operator as op\n\n # dummy class where __array__ throws exception\n class Foo:\n __array_priority__ = 1002\n\n def __array__(self, *args, **kwargs):\n raise Exception\n\n rhs = Foo()\n lhs = np.array(1)\n for f in [op.lt, op.le, op.gt, op.ge]:\n assert_raises(TypeError, f, lhs, rhs)\n assert_(not op.eq(lhs, rhs))\n assert_(op.ne(lhs, rhs))\n\n def test_richcompare_scalar_and_subclass(self):\n # gh-4709\n class Foo(np.ndarray):\n def __eq__(self, other):\n return "OK"\n\n x = np.array([1, 2, 3]).view(Foo)\n assert_equal(10 == x, "OK")\n assert_equal(np.int32(10) == x, "OK")\n assert_equal(np.array([10]) == x, "OK")\n\n def test_pickle_empty_string(self):\n # gh-3926\n for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):\n test_string = np.bytes_('')\n assert_equal(pickle.loads(\n pickle.dumps(test_string, protocol=proto)), test_string)\n\n def test_frompyfunc_many_args(self):\n # gh-5672\n\n def passer(*args):\n pass\n\n assert_raises(ValueError, np.frompyfunc, passer, 64, 1)\n\n def test_repeat_broadcasting(self):\n # gh-5743\n a = np.arange(60).reshape(3, 4, 5)\n for axis in chain(range(-a.ndim, a.ndim), [None]):\n assert_equal(a.repeat(2, axis=axis), a.repeat([2], axis=axis))\n\n def test_frompyfunc_nout_0(self):\n # gh-2014\n\n def f(x):\n x[0], x[-1] = x[-1], x[0]\n\n uf = np.frompyfunc(f, 1, 0)\n a = np.array([[1, 2, 3], [4, 5], [6, 7, 8, 9]], dtype=object)\n assert_equal(uf(a), ())\n expected = np.array([[3, 2, 1], [5, 4], [9, 7, 8, 6]], dtype=object)\n assert_array_equal(a, expected)\n\n @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")\n def test_leak_in_structured_dtype_comparison(self):\n # gh-6250\n recordtype = np.dtype([('a', np.float64),\n ('b', np.int32),\n ('d', (str, 5))])\n\n # Simple case\n a = np.zeros(2, dtype=recordtype)\n for i in range(100):\n a == a\n assert_(sys.getrefcount(a) < 10)\n\n # The case in the bug report.\n before = sys.getrefcount(a)\n u, v = a[0], a[1]\n u == v\n del u, v\n gc.collect()\n after = sys.getrefcount(a)\n assert_equal(before, after)\n\n def test_empty_percentile(self):\n # gh-6530 / gh-6553\n assert_array_equal(np.percentile(np.arange(10), []), np.array([]))\n\n def test_void_compare_segfault(self):\n # gh-6922. The following should not segfault\n a = np.ones(3, dtype=[('object', 'O'), ('int', '<i2')])\n a.sort()\n\n def test_reshape_size_overflow(self):\n # gh-7455\n a = np.ones(20)[::2]\n if IS_64BIT:\n # 64 bit. The following are the prime factors of 2**63 + 5,\n # plus a leading 2, so when multiplied together as int64,\n # the result overflows to a total size of 10.\n new_shape = (2, 13, 419, 691, 823, 2977518503)\n else:\n # 32 bit. The following are the prime factors of 2**31 + 5,\n # plus a leading 2, so when multiplied together as int32,\n # the result overflows to a total size of 10.\n new_shape = (2, 7, 7, 43826197)\n assert_raises(ValueError, a.reshape, new_shape)\n\n @pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),\n reason="PyPy bug in error formatting")\n def test_invalid_structured_dtypes(self):\n # gh-2865\n # mapping python objects to other dtypes\n assert_raises(ValueError, np.dtype, ('O', [('name', 'i8')]))\n assert_raises(ValueError, np.dtype, ('i8', [('name', 'O')]))\n assert_raises(ValueError, np.dtype,\n ('i8', [('name', [('name', 'O')])]))\n assert_raises(ValueError, np.dtype, ([('a', 'i4'), ('b', 'i4')], 'O'))\n assert_raises(ValueError, np.dtype, ('i8', 'O'))\n # wrong number/type of tuple elements in dict\n assert_raises(ValueError, np.dtype,\n ('i', {'name': ('i', 0, 'title', 'oops')}))\n assert_raises(ValueError, np.dtype,\n ('i', {'name': ('i', 'wrongtype', 'title')}))\n # disallowed as of 1.13\n assert_raises(ValueError, np.dtype,\n ([('a', 'O'), ('b', 'O')], [('c', 'O'), ('d', 'O')]))\n # allowed as a special case due to existing use, see gh-2798\n a = np.ones(1, dtype=('O', [('name', 'O')]))\n assert_equal(a[0], 1)\n # In particular, the above union dtype (and union dtypes in general)\n # should mainly behave like the main (object) dtype:\n assert a[0] is a.item()\n assert type(a[0]) is int\n\n def test_correct_hash_dict(self):\n # gh-8887 - __hash__ would be None despite tp_hash being set\n all_types = set(np._core.sctypeDict.values()) - {np.void}\n for t in all_types:\n val = t()\n\n try:\n hash(val)\n except TypeError:\n assert_(t.__hash__ is None)\n except ValueError:\n assert_(t is np.timedelta64)\n assert_(t.__hash__ is not None)\n else:\n assert_(t.__hash__ is not None)\n\n def test_scalar_copy(self):\n scalar_types = set(np._core.sctypeDict.values())\n values = {\n np.void: b"a",\n np.bytes_: b"a",\n np.str_: "a",\n np.datetime64: "2017-08-25",\n }\n for sctype in scalar_types:\n item = sctype(values.get(sctype, 1))\n item2 = copy.copy(item)\n assert_equal(item, item2)\n\n def test_void_item_memview(self):\n va = np.zeros(10, 'V4')\n x = va[:1].item()\n va[0] = b'\xff\xff\xff\xff'\n del va\n assert_equal(x, b'\x00\x00\x00\x00')\n\n def test_void_getitem(self):\n # Test fix for gh-11668.\n assert_(np.array([b'a'], 'V1').astype('O') == b'a')\n assert_(np.array([b'ab'], 'V2').astype('O') == b'ab')\n assert_(np.array([b'abc'], 'V3').astype('O') == b'abc')\n assert_(np.array([b'abcd'], 'V4').astype('O') == b'abcd')\n\n def test_structarray_title(self):\n # The following used to segfault on pypy, due to NPY_TITLE_KEY\n # not working properly and resulting to double-decref of the\n # structured array field items:\n # See: https://bitbucket.org/pypy/pypy/issues/2789\n for j in range(5):\n structure = np.array([1], dtype=[(('x', 'X'), np.object_)])\n structure[0]['x'] = np.array([2])\n gc.collect()\n\n def test_dtype_scalar_squeeze(self):\n # gh-11384\n values = {\n 'S': b"a",\n 'M': "2018-06-20",\n }\n for ch in np.typecodes['All']:\n if ch in 'O':\n continue\n sctype = np.dtype(ch).type\n scvalue = sctype(values.get(ch, 3))\n for axis in [None, ()]:\n squeezed = scvalue.squeeze(axis=axis)\n assert_equal(squeezed, scvalue)\n assert_equal(type(squeezed), type(scvalue))\n\n def test_field_access_by_title(self):\n # gh-11507\n s = 'Some long field name'\n if HAS_REFCOUNT:\n base = sys.getrefcount(s)\n t = np.dtype([((s, 'f1'), np.float64)])\n data = np.zeros(10, t)\n for i in range(10):\n str(data[['f1']])\n if HAS_REFCOUNT:\n assert_(base <= sys.getrefcount(s))\n\n @pytest.mark.parametrize('val', [\n # arrays and scalars\n np.ones((10, 10), dtype='int32'),\n np.uint64(10),\n ])\n @pytest.mark.parametrize('protocol',\n range(2, pickle.HIGHEST_PROTOCOL + 1)\n )\n def test_pickle_module(self, protocol, val):\n # gh-12837\n s = pickle.dumps(val, protocol)\n assert b'_multiarray_umath' not in s\n if protocol == 5 and len(val.shape) > 0:\n # unpickling ndarray goes through _frombuffer for protocol 5\n assert b'numpy._core.numeric' in s\n else:\n assert b'numpy._core.multiarray' in s\n\n def test_object_casting_errors(self):\n # gh-11993 update to ValueError (see gh-16909), since strings can in\n # principle be converted to complex, but this string cannot.\n arr = np.array(['AAAAA', 18465886.0, 18465886.0], dtype=object)\n assert_raises(ValueError, arr.astype, 'c8')\n\n def test_eff1d_casting(self):\n # gh-12711\n x = np.array([1, 2, 4, 7, 0], dtype=np.int16)\n res = np.ediff1d(x, to_begin=-99, to_end=np.array([88, 99]))\n assert_equal(res, [-99, 1, 2, 3, -7, 88, 99])\n\n # The use of safe casting means, that 1<<20 is cast unsafely, an\n # error may be better, but currently there is no mechanism for it.\n res = np.ediff1d(x, to_begin=(1 << 20), to_end=(1 << 20))\n assert_equal(res, [0, 1, 2, 3, -7, 0])\n\n def test_pickle_datetime64_array(self):\n # gh-12745 (would fail with pickle5 installed)\n d = np.datetime64('2015-07-04 12:59:59.50', 'ns')\n arr = np.array([d])\n for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):\n dumped = pickle.dumps(arr, protocol=proto)\n assert_equal(pickle.loads(dumped), arr)\n\n def test_bad_array_interface(self):\n class T:\n __array_interface__ = {}\n\n with assert_raises(ValueError):\n np.array([T()])\n\n def test_2d__array__shape(self):\n class T:\n def __array__(self, dtype=None, copy=None):\n return np.ndarray(shape=(0, 0))\n\n # Make sure __array__ is used instead of Sequence methods.\n def __iter__(self):\n return iter([])\n\n def __getitem__(self, idx):\n raise AssertionError("__getitem__ was called")\n\n def __len__(self):\n return 0\n\n t = T()\n # gh-13659, would raise in broadcasting [x=t for x in result]\n arr = np.array([t])\n assert arr.shape == (1, 0, 0)\n\n @pytest.mark.skipif(sys.maxsize < 2 ** 31 + 1, reason='overflows 32-bit python')\n def test_to_ctypes(self):\n # gh-14214\n arr = np.zeros((2 ** 31 + 1,), 'b')\n assert arr.size * arr.itemsize > 2 ** 31\n c_arr = np.ctypeslib.as_ctypes(arr)\n assert_equal(c_arr._length_, arr.size)\n\n def test_complex_conversion_error(self):\n # gh-17068\n with pytest.raises(TypeError, match=r"Unable to convert dtype.*"):\n complex(np.array("now", np.datetime64))\n\n def test__array_interface__descr(self):\n # gh-17068\n dt = np.dtype({'names': ['a', 'b'],\n 'offsets': [0, 0],\n 'formats': [np.int64, np.int64]})\n descr = np.array((1, 1), dtype=dt).__array_interface__['descr']\n assert descr == [('', '|V8')] # instead of [(b'', '|V8')]\n\n @pytest.mark.skipif(sys.maxsize < 2 ** 31 + 1, reason='overflows 32-bit python')\n @requires_memory(free_bytes=9e9)\n def test_dot_big_stride(self):\n # gh-17111\n # blas stride = stride//itemsize > int32 max\n int32_max = np.iinfo(np.int32).max\n n = int32_max + 3\n a = np.empty([n], dtype=np.float32)\n b = a[::n - 1]\n b[...] = 1\n assert b.strides[0] > int32_max * b.dtype.itemsize\n assert np.dot(b, b) == 2.0\n\n def test_frompyfunc_name(self):\n # name conversion was failing for python 3 strings\n # resulting in the default '?' name. Also test utf-8\n # encoding using non-ascii name.\n def cassé(x):\n return x\n\n f = np.frompyfunc(cassé, 1, 1)\n assert str(f) == "<ufunc 'cassé (vectorized)'>"\n\n @pytest.mark.parametrize("operation", [\n 'add', 'subtract', 'multiply', 'floor_divide',\n 'conjugate', 'fmod', 'square', 'reciprocal',\n 'power', 'absolute', 'negative', 'positive',\n 'greater', 'greater_equal', 'less',\n 'less_equal', 'equal', 'not_equal', 'logical_and',\n 'logical_not', 'logical_or', 'bitwise_and', 'bitwise_or',\n 'bitwise_xor', 'invert', 'left_shift', 'right_shift',\n 'gcd', 'lcm'\n ]\n )\n @pytest.mark.parametrize("order", [\n ('b->', 'B->'),\n ('h->', 'H->'),\n ('i->', 'I->'),\n ('l->', 'L->'),\n ('q->', 'Q->'),\n ]\n )\n def test_ufunc_order(self, operation, order):\n # gh-18075\n # Ensure signed types before unsigned\n def get_idx(string, str_lst):\n for i, s in enumerate(str_lst):\n if string in s:\n return i\n raise ValueError(f"{string} not in list")\n types = getattr(np, operation).types\n assert get_idx(order[0], types) < get_idx(order[1], types), (\n f"Unexpected types order of ufunc in {operation}"\n f"for {order}. Possible fix: Use signed before unsigned"\n "in generate_umath.py")\n\n def test_nonbool_logical(self):\n # gh-22845\n # create two arrays with bit patterns that do not overlap.\n # needs to be large enough to test both SIMD and scalar paths\n size = 100\n a = np.frombuffer(b'\x01' * size, dtype=np.bool)\n b = np.frombuffer(b'\x80' * size, dtype=np.bool)\n expected = np.ones(size, dtype=np.bool)\n assert_array_equal(np.logical_and(a, b), expected)\n\n @pytest.mark.skipif(IS_PYPY, reason="PyPy issue 2742")\n def test_gh_23737(self):\n with pytest.raises(TypeError, match="not an acceptable base type"):\n class Y(np.flexible):\n pass\n\n with pytest.raises(TypeError, match="not an acceptable base type"):\n class X(np.flexible, np.ma.core.MaskedArray):\n pass\n\n def test_load_ufunc_pickle(self):\n # ufuncs are pickled with a semi-private path in\n # numpy.core._multiarray_umath and must be loadable without warning\n # despite np.core being deprecated.\n test_data = b'\x80\x04\x95(\x00\x00\x00\x00\x00\x00\x00\x8c\x1cnumpy.core._multiarray_umath\x94\x8c\x03add\x94\x93\x94.'\n result = pickle.loads(test_data, encoding='bytes')\n assert result is np.add\n\n def test__array_namespace__(self):\n arr = np.arange(2)\n\n xp = arr.__array_namespace__()\n assert xp is np\n xp = arr.__array_namespace__(api_version="2021.12")\n assert xp is np\n xp = arr.__array_namespace__(api_version="2022.12")\n assert xp is np\n xp = arr.__array_namespace__(api_version="2023.12")\n assert xp is np\n xp = arr.__array_namespace__(api_version="2024.12")\n assert xp is np\n xp = arr.__array_namespace__(api_version=None)\n assert xp is np\n\n with pytest.raises(\n ValueError,\n match="Version \"2025.12\" of the Array API Standard "\n "is not supported."\n ):\n arr.__array_namespace__(api_version="2025.12")\n\n with pytest.raises(\n ValueError,\n match="Only None and strings are allowed as the Array API version"\n ):\n arr.__array_namespace__(api_version=2024)\n\n def test_isin_refcnt_bug(self):\n # gh-25295\n for _ in range(1000):\n np.isclose(np.int64(2), np.int64(2), atol=1e-15, rtol=1e-300)\n\n def test_replace_regression(self):\n # gh-25513 segfault\n carr = np.char.chararray((2,), itemsize=25)\n test_strings = [b' 4.52173913043478315E+00',\n b' 4.95652173913043548E+00']\n carr[:] = test_strings\n out = carr.replace(b"E", b"D")\n expected = np.char.chararray((2,), itemsize=25)\n expected[:] = [s.replace(b"E", b"D") for s in test_strings]\n assert_array_equal(out, expected)\n\n def test_logspace_base_does_not_determine_dtype(self):\n # gh-24957 and cupy/cupy/issues/7946\n start = np.array([0, 2], dtype=np.float16)\n stop = np.array([2, 0], dtype=np.float16)\n out = np.logspace(start, stop, num=5, axis=1, dtype=np.float32)\n expected = np.array([[1., 3.1621094, 10., 31.625, 100.],\n [100., 31.625, 10., 3.1621094, 1.]],\n dtype=np.float32)\n assert_almost_equal(out, expected)\n # Check test fails if the calculation is done in float64, as happened\n # before when a python float base incorrectly influenced the dtype.\n out2 = np.logspace(start, stop, num=5, axis=1, dtype=np.float32,\n base=np.array([10.0]))\n with pytest.raises(AssertionError, match="not almost equal"):\n assert_almost_equal(out2, expected)\n\n def test_vectorize_fixed_width_string(self):\n arr = np.array(["SOme wOrd DŽ ß ᾛ ΣΣ ffi⁵Å Ç Ⅰ"]).astype(np.str_)\n f = str.casefold\n res = np.vectorize(f, otypes=[arr.dtype])(arr)\n assert res.dtype == "U30"\n\n def test_repeated_square_consistency(self):\n # gh-26940\n buf = np.array([-5.171866611150749e-07 + 2.5618634555957426e-07j,\n 0, 0, 0, 0, 0])\n # Test buffer with regular and reverse strides\n for in_vec in [buf[:3], buf[:3][::-1]]:\n expected_res = np.square(in_vec)\n # Output vector immediately follows input vector\n # to reproduce off-by-one in nomemoverlap check.\n for res in [buf[3:], buf[3:][::-1]]:\n res = buf[3:]\n np.square(in_vec, out=res)\n assert_equal(res, expected_res)\n\n def test_sort_unique_crash(self):\n # gh-27037\n for _ in range(4):\n vals = np.linspace(0, 1, num=128)\n data = np.broadcast_to(vals, (128, 128, 128))\n data = data.transpose(0, 2, 1).copy()\n np.unique(data)\n\n def test_sort_overlap(self):\n # gh-27273\n size = 100\n inp = np.linspace(0, size, num=size, dtype=np.intc)\n out = np.sort(inp)\n assert_equal(inp, out)\n\n def test_searchsorted_structured(self):\n # gh-28190\n x = np.array([(0, 1.)], dtype=[('time', '<i8'), ('value', '<f8')])\n y = np.array((0, 0.), dtype=[('time', '<i8'), ('value', '<f8')])\n x.searchsorted(y)\n | .venv\Lib\site-packages\numpy\_core\tests\test_regression.py | test_regression.py | Python | 98,235 | 0.75 | 0.164419 | 0.165563 | python-kit | 951 | 2024-12-10T16:50:47.600221 | Apache-2.0 | true | 975b90234b942c66de55adba9ac0713d |
"""\nTest scalar buffer interface adheres to PEP 3118\n"""\nimport pytest\nfrom numpy._core._multiarray_tests import get_buffer_info\nfrom numpy._core._rational_tests import rational\n\nimport numpy as np\nfrom numpy.testing import assert_, assert_equal, assert_raises\n\n# PEP3118 format strings for native (standard alignment and byteorder) types\nscalars_and_codes = [\n (np.bool, '?'),\n (np.byte, 'b'),\n (np.short, 'h'),\n (np.intc, 'i'),\n (np.long, 'l'),\n (np.longlong, 'q'),\n (np.ubyte, 'B'),\n (np.ushort, 'H'),\n (np.uintc, 'I'),\n (np.ulong, 'L'),\n (np.ulonglong, 'Q'),\n (np.half, 'e'),\n (np.single, 'f'),\n (np.double, 'd'),\n (np.longdouble, 'g'),\n (np.csingle, 'Zf'),\n (np.cdouble, 'Zd'),\n (np.clongdouble, 'Zg'),\n]\nscalars_only, codes_only = zip(*scalars_and_codes)\n\n\nclass TestScalarPEP3118:\n\n @pytest.mark.parametrize('scalar', scalars_only, ids=codes_only)\n def test_scalar_match_array(self, scalar):\n x = scalar()\n a = np.array([], dtype=np.dtype(scalar))\n mv_x = memoryview(x)\n mv_a = memoryview(a)\n assert_equal(mv_x.format, mv_a.format)\n\n @pytest.mark.parametrize('scalar', scalars_only, ids=codes_only)\n def test_scalar_dim(self, scalar):\n x = scalar()\n mv_x = memoryview(x)\n assert_equal(mv_x.itemsize, np.dtype(scalar).itemsize)\n assert_equal(mv_x.ndim, 0)\n assert_equal(mv_x.shape, ())\n assert_equal(mv_x.strides, ())\n assert_equal(mv_x.suboffsets, ())\n\n @pytest.mark.parametrize('scalar, code', scalars_and_codes, ids=codes_only)\n def test_scalar_code_and_properties(self, scalar, code):\n x = scalar()\n expected = {'strides': (), 'itemsize': x.dtype.itemsize, 'ndim': 0,\n 'shape': (), 'format': code, 'readonly': True}\n\n mv_x = memoryview(x)\n assert self._as_dict(mv_x) == expected\n\n @pytest.mark.parametrize('scalar', scalars_only, ids=codes_only)\n def test_scalar_buffers_readonly(self, scalar):\n x = scalar()\n with pytest.raises(BufferError, match="scalar buffer is readonly"):\n get_buffer_info(x, ["WRITABLE"])\n\n def test_void_scalar_structured_data(self):\n dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])\n x = np.array(('ndarray_scalar', (1.2, 3.0)), dtype=dt)[()]\n assert_(isinstance(x, np.void))\n mv_x = memoryview(x)\n expected_size = 16 * np.dtype((np.str_, 1)).itemsize\n expected_size += 2 * np.dtype(np.float64).itemsize\n assert_equal(mv_x.itemsize, expected_size)\n assert_equal(mv_x.ndim, 0)\n assert_equal(mv_x.shape, ())\n assert_equal(mv_x.strides, ())\n assert_equal(mv_x.suboffsets, ())\n\n # check scalar format string against ndarray format string\n a = np.array([('Sarah', (8.0, 7.0)), ('John', (6.0, 7.0))], dtype=dt)\n assert_(isinstance(a, np.ndarray))\n mv_a = memoryview(a)\n assert_equal(mv_x.itemsize, mv_a.itemsize)\n assert_equal(mv_x.format, mv_a.format)\n\n # Check that we do not allow writeable buffer export (technically\n # we could allow it sometimes here...)\n with pytest.raises(BufferError, match="scalar buffer is readonly"):\n get_buffer_info(x, ["WRITABLE"])\n\n def _as_dict(self, m):\n return {'strides': m.strides, 'shape': m.shape, 'itemsize': m.itemsize,\n 'ndim': m.ndim, 'format': m.format, 'readonly': m.readonly}\n\n def test_datetime_memoryview(self):\n # gh-11656\n # Values verified with v1.13.3, shape is not () as in test_scalar_dim\n\n dt1 = np.datetime64('2016-01-01')\n dt2 = np.datetime64('2017-01-01')\n expected = {'strides': (1,), 'itemsize': 1, 'ndim': 1, 'shape': (8,),\n 'format': 'B', 'readonly': True}\n v = memoryview(dt1)\n assert self._as_dict(v) == expected\n\n v = memoryview(dt2 - dt1)\n assert self._as_dict(v) == expected\n\n dt = np.dtype([('a', 'uint16'), ('b', 'M8[s]')])\n a = np.empty(1, dt)\n # Fails to create a PEP 3118 valid buffer\n assert_raises((ValueError, BufferError), memoryview, a[0])\n\n # Check that we do not allow writeable buffer export\n with pytest.raises(BufferError, match="scalar buffer is readonly"):\n get_buffer_info(dt1, ["WRITABLE"])\n\n @pytest.mark.parametrize('s', [\n pytest.param("\x32\x32", id="ascii"),\n pytest.param("\uFE0F\uFE0F", id="basic multilingual"),\n pytest.param("\U0001f4bb\U0001f4bb", id="non-BMP"),\n ])\n def test_str_ucs4(self, s):\n s = np.str_(s) # only our subclass implements the buffer protocol\n\n # all the same, characters always encode as ucs4\n expected = {'strides': (), 'itemsize': 8, 'ndim': 0, 'shape': (), 'format': '2w',\n 'readonly': True}\n\n v = memoryview(s)\n assert self._as_dict(v) == expected\n\n # integers of the paltform-appropriate endianness\n code_points = np.frombuffer(v, dtype='i4')\n\n assert_equal(code_points, [ord(c) for c in s])\n\n # Check that we do not allow writeable buffer export\n with pytest.raises(BufferError, match="scalar buffer is readonly"):\n get_buffer_info(s, ["WRITABLE"])\n\n def test_user_scalar_fails_buffer(self):\n r = rational(1)\n with assert_raises(TypeError):\n memoryview(r)\n\n # Check that we do not allow writeable buffer export\n with pytest.raises(BufferError, match="scalar buffer is readonly"):\n get_buffer_info(r, ["WRITABLE"])\n | .venv\Lib\site-packages\numpy\_core\tests\test_scalarbuffer.py | test_scalarbuffer.py | Python | 5,791 | 0.95 | 0.078431 | 0.094488 | react-lib | 630 | 2023-08-07T13:43:40.751840 | BSD-3-Clause | true | 1831408926c306b46c73ff4d7c983e8e |
""" Test printing of scalar types.\n\n"""\nimport pytest\n\nimport numpy as np\nfrom numpy.testing import assert_, assert_raises\n\n\nclass A:\n pass\nclass B(A, np.float64):\n pass\n\nclass C(B):\n pass\nclass D(C, B):\n pass\n\nclass B0(np.float64, A):\n pass\nclass C0(B0):\n pass\n\nclass HasNew:\n def __new__(cls, *args, **kwargs):\n return cls, args, kwargs\n\nclass B1(np.float64, HasNew):\n pass\n\n\nclass TestInherit:\n def test_init(self):\n x = B(1.0)\n assert_(str(x) == '1.0')\n y = C(2.0)\n assert_(str(y) == '2.0')\n z = D(3.0)\n assert_(str(z) == '3.0')\n\n def test_init2(self):\n x = B0(1.0)\n assert_(str(x) == '1.0')\n y = C0(2.0)\n assert_(str(y) == '2.0')\n\n def test_gh_15395(self):\n # HasNew is the second base, so `np.float64` should have priority\n x = B1(1.0)\n assert_(str(x) == '1.0')\n\n # previously caused RecursionError!?\n with pytest.raises(TypeError):\n B1(1.0, 2.0)\n\n def test_int_repr(self):\n # Test that integer repr works correctly for subclasses (gh-27106)\n class my_int16(np.int16):\n pass\n\n s = repr(my_int16(3))\n assert s == "my_int16(3)"\n\nclass TestCharacter:\n def test_char_radd(self):\n # GH issue 9620, reached gentype_add and raise TypeError\n np_s = np.bytes_('abc')\n np_u = np.str_('abc')\n s = b'def'\n u = 'def'\n assert_(np_s.__radd__(np_s) is NotImplemented)\n assert_(np_s.__radd__(np_u) is NotImplemented)\n assert_(np_s.__radd__(s) is NotImplemented)\n assert_(np_s.__radd__(u) is NotImplemented)\n assert_(np_u.__radd__(np_s) is NotImplemented)\n assert_(np_u.__radd__(np_u) is NotImplemented)\n assert_(np_u.__radd__(s) is NotImplemented)\n assert_(np_u.__radd__(u) is NotImplemented)\n assert_(s + np_s == b'defabc')\n assert_(u + np_u == 'defabc')\n\n class MyStr(str, np.generic):\n # would segfault\n pass\n\n with assert_raises(TypeError):\n # Previously worked, but gave completely wrong result\n ret = s + MyStr('abc')\n\n class MyBytes(bytes, np.generic):\n # would segfault\n pass\n\n ret = s + MyBytes(b'abc')\n assert type(ret) is type(s)\n assert ret == b"defabc"\n\n def test_char_repeat(self):\n np_s = np.bytes_('abc')\n np_u = np.str_('abc')\n res_s = b'abc' * 5\n res_u = 'abc' * 5\n assert_(np_s * 5 == res_s)\n assert_(np_u * 5 == res_u)\n | .venv\Lib\site-packages\numpy\_core\tests\test_scalarinherit.py | test_scalarinherit.py | Python | 2,692 | 0.95 | 0.219048 | 0.083333 | node-utils | 67 | 2023-08-16T22:09:29.986598 | GPL-3.0 | true | b1d37cf34ac07d0a44c03ee04d7223bb |
import contextlib\nimport itertools\nimport operator\nimport platform\nimport sys\nimport warnings\n\nimport pytest\nfrom hypothesis import given, settings\nfrom hypothesis.extra import numpy as hynp\nfrom hypothesis.strategies import sampled_from\nfrom numpy._core._rational_tests import rational\n\nimport numpy as np\nfrom numpy._utils import _pep440\nfrom numpy.exceptions import ComplexWarning\nfrom numpy.testing import (\n IS_PYPY,\n _gen_alignment_data,\n assert_,\n assert_almost_equal,\n assert_array_equal,\n assert_equal,\n assert_raises,\n check_support_sve,\n suppress_warnings,\n)\n\ntypes = [np.bool, np.byte, np.ubyte, np.short, np.ushort, np.intc, np.uintc,\n np.int_, np.uint, np.longlong, np.ulonglong,\n np.single, np.double, np.longdouble, np.csingle,\n np.cdouble, np.clongdouble]\n\nfloating_types = np.floating.__subclasses__()\ncomplex_floating_types = np.complexfloating.__subclasses__()\n\nobjecty_things = [object(), None, np.array(None, dtype=object)]\n\nbinary_operators_for_scalars = [\n operator.lt, operator.le, operator.eq, operator.ne, operator.ge,\n operator.gt, operator.add, operator.floordiv, operator.mod,\n operator.mul, operator.pow, operator.sub, operator.truediv\n]\nbinary_operators_for_scalar_ints = binary_operators_for_scalars + [\n operator.xor, operator.or_, operator.and_\n]\n\n\n# This compares scalarmath against ufuncs.\n\nclass TestTypes:\n def test_types(self):\n for atype in types:\n a = atype(1)\n assert_(a == 1, f"error with {atype!r}: got {a!r}")\n\n def test_type_add(self):\n # list of types\n for k, atype in enumerate(types):\n a_scalar = atype(3)\n a_array = np.array([3], dtype=atype)\n for l, btype in enumerate(types):\n b_scalar = btype(1)\n b_array = np.array([1], dtype=btype)\n c_scalar = a_scalar + b_scalar\n c_array = a_array + b_array\n # It was comparing the type numbers, but the new ufunc\n # function-finding mechanism finds the lowest function\n # to which both inputs can be cast - which produces 'l'\n # when you do 'q' + 'b'. The old function finding mechanism\n # skipped ahead based on the first argument, but that\n # does not produce properly symmetric results...\n assert_equal(c_scalar.dtype, c_array.dtype,\n "error with types (%d/'%c' + %d/'%c')" %\n (k, np.dtype(atype).char, l, np.dtype(btype).char))\n\n def test_type_create(self):\n for atype in types:\n a = np.array([1, 2, 3], atype)\n b = atype([1, 2, 3])\n assert_equal(a, b)\n\n def test_leak(self):\n # test leak of scalar objects\n # a leak would show up in valgrind as still-reachable of ~2.6MB\n for i in range(200000):\n np.add(1, 1)\n\n\ndef check_ufunc_scalar_equivalence(op, arr1, arr2):\n scalar1 = arr1[()]\n scalar2 = arr2[()]\n assert isinstance(scalar1, np.generic)\n assert isinstance(scalar2, np.generic)\n\n if arr1.dtype.kind == "c" or arr2.dtype.kind == "c":\n comp_ops = {operator.ge, operator.gt, operator.le, operator.lt}\n if op in comp_ops and (np.isnan(scalar1) or np.isnan(scalar2)):\n pytest.xfail("complex comp ufuncs use sort-order, scalars do not.")\n if op == operator.pow and arr2.item() in [-1, 0, 0.5, 1, 2]:\n # array**scalar special case can have different result dtype\n # (Other powers may have issues also, but are not hit here.)\n # TODO: It would be nice to resolve this issue.\n pytest.skip("array**2 can have incorrect/weird result dtype")\n\n # ignore fpe's since they may just mismatch for integers anyway.\n with warnings.catch_warnings(), np.errstate(all="ignore"):\n # Comparisons DeprecationWarnings replacing errors (2022-03):\n warnings.simplefilter("error", DeprecationWarning)\n try:\n res = op(arr1, arr2)\n except Exception as e:\n with pytest.raises(type(e)):\n op(scalar1, scalar2)\n else:\n scalar_res = op(scalar1, scalar2)\n assert_array_equal(scalar_res, res, strict=True)\n\n\n@pytest.mark.slow\n@settings(max_examples=10000, deadline=2000)\n@given(sampled_from(binary_operators_for_scalars),\n hynp.arrays(dtype=hynp.scalar_dtypes(), shape=()),\n hynp.arrays(dtype=hynp.scalar_dtypes(), shape=()))\ndef test_array_scalar_ufunc_equivalence(op, arr1, arr2):\n """\n This is a thorough test attempting to cover important promotion paths\n and ensuring that arrays and scalars stay as aligned as possible.\n However, if it creates troubles, it should maybe just be removed.\n """\n check_ufunc_scalar_equivalence(op, arr1, arr2)\n\n\n@pytest.mark.slow\n@given(sampled_from(binary_operators_for_scalars),\n hynp.scalar_dtypes(), hynp.scalar_dtypes())\ndef test_array_scalar_ufunc_dtypes(op, dt1, dt2):\n # Same as above, but don't worry about sampling weird values so that we\n # do not have to sample as much\n arr1 = np.array(2, dtype=dt1)\n arr2 = np.array(3, dtype=dt2) # some power do weird things.\n\n check_ufunc_scalar_equivalence(op, arr1, arr2)\n\n\n@pytest.mark.parametrize("fscalar", [np.float16, np.float32])\ndef test_int_float_promotion_truediv(fscalar):\n # Promotion for mixed int and float32/float16 must not go to float64\n i = np.int8(1)\n f = fscalar(1)\n expected = np.result_type(i, f)\n assert (i / f).dtype == expected\n assert (f / i).dtype == expected\n # But normal int / int true division goes to float64:\n assert (i / i).dtype == np.dtype("float64")\n # For int16, result has to be ast least float32 (takes ufunc path):\n assert (np.int16(1) / f).dtype == np.dtype("float32")\n\n\nclass TestBaseMath:\n @pytest.mark.xfail(check_support_sve(), reason="gh-22982")\n def test_blocked(self):\n # test alignments offsets for simd instructions\n # alignments for vz + 2 * (vs - 1) + 1\n for dt, sz in [(np.float32, 11), (np.float64, 7), (np.int32, 11)]:\n for out, inp1, inp2, msg in _gen_alignment_data(dtype=dt,\n type='binary',\n max_size=sz):\n exp1 = np.ones_like(inp1)\n inp1[...] = np.ones_like(inp1)\n inp2[...] = np.zeros_like(inp2)\n assert_almost_equal(np.add(inp1, inp2), exp1, err_msg=msg)\n assert_almost_equal(np.add(inp1, 2), exp1 + 2, err_msg=msg)\n assert_almost_equal(np.add(1, inp2), exp1, err_msg=msg)\n\n np.add(inp1, inp2, out=out)\n assert_almost_equal(out, exp1, err_msg=msg)\n\n inp2[...] += np.arange(inp2.size, dtype=dt) + 1\n assert_almost_equal(np.square(inp2),\n np.multiply(inp2, inp2), err_msg=msg)\n # skip true divide for ints\n if dt != np.int32:\n assert_almost_equal(np.reciprocal(inp2),\n np.divide(1, inp2), err_msg=msg)\n\n inp1[...] = np.ones_like(inp1)\n np.add(inp1, 2, out=out)\n assert_almost_equal(out, exp1 + 2, err_msg=msg)\n inp2[...] = np.ones_like(inp2)\n np.add(2, inp2, out=out)\n assert_almost_equal(out, exp1 + 2, err_msg=msg)\n\n def test_lower_align(self):\n # check data that is not aligned to element size\n # i.e doubles are aligned to 4 bytes on i386\n d = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64)\n o = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64)\n assert_almost_equal(d + d, d * 2)\n np.add(d, d, out=o)\n np.add(np.ones_like(d), d, out=o)\n np.add(d, np.ones_like(d), out=o)\n np.add(np.ones_like(d), d)\n np.add(d, np.ones_like(d))\n\n\nclass TestPower:\n def test_small_types(self):\n for t in [np.int8, np.int16, np.float16]:\n a = t(3)\n b = a ** 4\n assert_(b == 81, f"error with {t!r}: got {b!r}")\n\n def test_large_types(self):\n for t in [np.int32, np.int64, np.float32, np.float64, np.longdouble]:\n a = t(51)\n b = a ** 4\n msg = f"error with {t!r}: got {b!r}"\n if np.issubdtype(t, np.integer):\n assert_(b == 6765201, msg)\n else:\n assert_almost_equal(b, 6765201, err_msg=msg)\n\n def test_integers_to_negative_integer_power(self):\n # Note that the combination of uint64 with a signed integer\n # has common type np.float64. The other combinations should all\n # raise a ValueError for integer ** negative integer.\n exp = [np.array(-1, dt)[()] for dt in 'bhilq']\n\n # 1 ** -1 possible special case\n base = [np.array(1, dt)[()] for dt in 'bhilqBHILQ']\n for i1, i2 in itertools.product(base, exp):\n if i1.dtype != np.uint64:\n assert_raises(ValueError, operator.pow, i1, i2)\n else:\n res = operator.pow(i1, i2)\n assert_(res.dtype.type is np.float64)\n assert_almost_equal(res, 1.)\n\n # -1 ** -1 possible special case\n base = [np.array(-1, dt)[()] for dt in 'bhilq']\n for i1, i2 in itertools.product(base, exp):\n if i1.dtype != np.uint64:\n assert_raises(ValueError, operator.pow, i1, i2)\n else:\n res = operator.pow(i1, i2)\n assert_(res.dtype.type is np.float64)\n assert_almost_equal(res, -1.)\n\n # 2 ** -1 perhaps generic\n base = [np.array(2, dt)[()] for dt in 'bhilqBHILQ']\n for i1, i2 in itertools.product(base, exp):\n if i1.dtype != np.uint64:\n assert_raises(ValueError, operator.pow, i1, i2)\n else:\n res = operator.pow(i1, i2)\n assert_(res.dtype.type is np.float64)\n assert_almost_equal(res, .5)\n\n def test_mixed_types(self):\n typelist = [np.int8, np.int16, np.float16,\n np.float32, np.float64, np.int8,\n np.int16, np.int32, np.int64]\n for t1 in typelist:\n for t2 in typelist:\n a = t1(3)\n b = t2(2)\n result = a**b\n msg = f"error with {t1!r} and {t2!r}:got {result!r}, expected {9!r}"\n if np.issubdtype(np.dtype(result), np.integer):\n assert_(result == 9, msg)\n else:\n assert_almost_equal(result, 9, err_msg=msg)\n\n def test_modular_power(self):\n # modular power is not implemented, so ensure it errors\n a = 5\n b = 4\n c = 10\n expected = pow(a, b, c) # noqa: F841\n for t in (np.int32, np.float32, np.complex64):\n # note that 3-operand power only dispatches on the first argument\n assert_raises(TypeError, operator.pow, t(a), b, c)\n assert_raises(TypeError, operator.pow, np.array(t(a)), b, c)\n\n\ndef floordiv_and_mod(x, y):\n return (x // y, x % y)\n\n\ndef _signs(dt):\n if dt in np.typecodes['UnsignedInteger']:\n return (+1,)\n else:\n return (+1, -1)\n\n\nclass TestModulus:\n\n def test_modulus_basic(self):\n dt = np.typecodes['AllInteger'] + np.typecodes['Float']\n for op in [floordiv_and_mod, divmod]:\n for dt1, dt2 in itertools.product(dt, dt):\n for sg1, sg2 in itertools.product(_signs(dt1), _signs(dt2)):\n fmt = 'op: %s, dt1: %s, dt2: %s, sg1: %s, sg2: %s'\n msg = fmt % (op.__name__, dt1, dt2, sg1, sg2)\n a = np.array(sg1 * 71, dtype=dt1)[()]\n b = np.array(sg2 * 19, dtype=dt2)[()]\n div, rem = op(a, b)\n assert_equal(div * b + rem, a, err_msg=msg)\n if sg2 == -1:\n assert_(b < rem <= 0, msg)\n else:\n assert_(b > rem >= 0, msg)\n\n def test_float_modulus_exact(self):\n # test that float results are exact for small integers. This also\n # holds for the same integers scaled by powers of two.\n nlst = list(range(-127, 0))\n plst = list(range(1, 128))\n dividend = nlst + [0] + plst\n divisor = nlst + plst\n arg = list(itertools.product(dividend, divisor))\n tgt = [divmod(*t) for t in arg]\n\n a, b = np.array(arg, dtype=int).T\n # convert exact integer results from Python to float so that\n # signed zero can be used, it is checked.\n tgtdiv, tgtrem = np.array(tgt, dtype=float).T\n tgtdiv = np.where((tgtdiv == 0.0) & ((b < 0) ^ (a < 0)), -0.0, tgtdiv)\n tgtrem = np.where((tgtrem == 0.0) & (b < 0), -0.0, tgtrem)\n\n for op in [floordiv_and_mod, divmod]:\n for dt in np.typecodes['Float']:\n msg = f'op: {op.__name__}, dtype: {dt}'\n fa = a.astype(dt)\n fb = b.astype(dt)\n # use list comprehension so a_ and b_ are scalars\n div, rem = zip(*[op(a_, b_) for a_, b_ in zip(fa, fb)])\n assert_equal(div, tgtdiv, err_msg=msg)\n assert_equal(rem, tgtrem, err_msg=msg)\n\n def test_float_modulus_roundoff(self):\n # gh-6127\n dt = np.typecodes['Float']\n for op in [floordiv_and_mod, divmod]:\n for dt1, dt2 in itertools.product(dt, dt):\n for sg1, sg2 in itertools.product((+1, -1), (+1, -1)):\n fmt = 'op: %s, dt1: %s, dt2: %s, sg1: %s, sg2: %s'\n msg = fmt % (op.__name__, dt1, dt2, sg1, sg2)\n a = np.array(sg1 * 78 * 6e-8, dtype=dt1)[()]\n b = np.array(sg2 * 6e-8, dtype=dt2)[()]\n div, rem = op(a, b)\n # Equal assertion should hold when fmod is used\n assert_equal(div * b + rem, a, err_msg=msg)\n if sg2 == -1:\n assert_(b < rem <= 0, msg)\n else:\n assert_(b > rem >= 0, msg)\n\n def test_float_modulus_corner_cases(self):\n # Check remainder magnitude.\n for dt in np.typecodes['Float']:\n b = np.array(1.0, dtype=dt)\n a = np.nextafter(np.array(0.0, dtype=dt), -b)\n rem = operator.mod(a, b)\n assert_(rem <= b, f'dt: {dt}')\n rem = operator.mod(-a, -b)\n assert_(rem >= -b, f'dt: {dt}')\n\n # Check nans, inf\n with suppress_warnings() as sup:\n sup.filter(RuntimeWarning, "invalid value encountered in remainder")\n sup.filter(RuntimeWarning, "divide by zero encountered in remainder")\n sup.filter(RuntimeWarning, "divide by zero encountered in floor_divide")\n sup.filter(RuntimeWarning, "divide by zero encountered in divmod")\n sup.filter(RuntimeWarning, "invalid value encountered in divmod")\n for dt in np.typecodes['Float']:\n fone = np.array(1.0, dtype=dt)\n fzer = np.array(0.0, dtype=dt)\n finf = np.array(np.inf, dtype=dt)\n fnan = np.array(np.nan, dtype=dt)\n rem = operator.mod(fone, fzer)\n assert_(np.isnan(rem), f'dt: {dt}')\n # MSVC 2008 returns NaN here, so disable the check.\n #rem = operator.mod(fone, finf)\n #assert_(rem == fone, 'dt: %s' % dt)\n rem = operator.mod(fone, fnan)\n assert_(np.isnan(rem), f'dt: {dt}')\n rem = operator.mod(finf, fone)\n assert_(np.isnan(rem), f'dt: {dt}')\n for op in [floordiv_and_mod, divmod]:\n div, mod = op(fone, fzer)\n assert_(np.isinf(div)) and assert_(np.isnan(mod))\n\n def test_inplace_floordiv_handling(self):\n # issue gh-12927\n # this only applies to in-place floordiv //=, because the output type\n # promotes to float which does not fit\n a = np.array([1, 2], np.int64)\n b = np.array([1, 2], np.uint64)\n with pytest.raises(TypeError,\n match=r"Cannot cast ufunc 'floor_divide' output from"):\n a //= b\n\nclass TestComparison:\n def test_comparision_different_types(self):\n x = np.array(1)\n y = np.array('s')\n eq = x == y\n neq = x != y\n assert eq is np.bool_(False)\n assert neq is np.bool_(True)\n\n\nclass TestComplexDivision:\n def test_zero_division(self):\n with np.errstate(all="ignore"):\n for t in [np.complex64, np.complex128]:\n a = t(0.0)\n b = t(1.0)\n assert_(np.isinf(b / a))\n b = t(complex(np.inf, np.inf))\n assert_(np.isinf(b / a))\n b = t(complex(np.inf, np.nan))\n assert_(np.isinf(b / a))\n b = t(complex(np.nan, np.inf))\n assert_(np.isinf(b / a))\n b = t(complex(np.nan, np.nan))\n assert_(np.isnan(b / a))\n b = t(0.)\n assert_(np.isnan(b / a))\n\n def test_signed_zeros(self):\n with np.errstate(all="ignore"):\n for t in [np.complex64, np.complex128]:\n # tupled (numerator, denominator, expected)\n # for testing as expected == numerator/denominator\n data = (\n (( 0.0, -1.0), ( 0.0, 1.0), (-1.0, -0.0)),\n (( 0.0, -1.0), ( 0.0, -1.0), ( 1.0, -0.0)),\n (( 0.0, -1.0), (-0.0, -1.0), ( 1.0, 0.0)),\n (( 0.0, -1.0), (-0.0, 1.0), (-1.0, 0.0)),\n (( 0.0, 1.0), ( 0.0, -1.0), (-1.0, 0.0)),\n (( 0.0, -1.0), ( 0.0, -1.0), ( 1.0, -0.0)),\n ((-0.0, -1.0), ( 0.0, -1.0), ( 1.0, -0.0)),\n ((-0.0, 1.0), ( 0.0, -1.0), (-1.0, -0.0))\n )\n for cases in data:\n n = cases[0]\n d = cases[1]\n ex = cases[2]\n result = t(complex(n[0], n[1])) / t(complex(d[0], d[1]))\n # check real and imag parts separately to avoid comparison\n # in array context, which does not account for signed zeros\n assert_equal(result.real, ex[0])\n assert_equal(result.imag, ex[1])\n\n def test_branches(self):\n with np.errstate(all="ignore"):\n for t in [np.complex64, np.complex128]:\n # tupled (numerator, denominator, expected)\n # for testing as expected == numerator/denominator\n data = []\n\n # trigger branch: real(fabs(denom)) > imag(fabs(denom))\n # followed by else condition as neither are == 0\n data.append((( 2.0, 1.0), ( 2.0, 1.0), (1.0, 0.0)))\n\n # trigger branch: real(fabs(denom)) > imag(fabs(denom))\n # followed by if condition as both are == 0\n # is performed in test_zero_division(), so this is skipped\n\n # trigger else if branch: real(fabs(denom)) < imag(fabs(denom))\n data.append(((1.0, 2.0), (1.0, 2.0), (1.0, 0.0)))\n\n for cases in data:\n n = cases[0]\n d = cases[1]\n ex = cases[2]\n result = t(complex(n[0], n[1])) / t(complex(d[0], d[1]))\n # check real and imag parts separately to avoid comparison\n # in array context, which does not account for signed zeros\n assert_equal(result.real, ex[0])\n assert_equal(result.imag, ex[1])\n\n\nclass TestConversion:\n def test_int_from_long(self):\n l = [1e6, 1e12, 1e18, -1e6, -1e12, -1e18]\n li = [10**6, 10**12, 10**18, -10**6, -10**12, -10**18]\n for T in [None, np.float64, np.int64]:\n a = np.array(l, dtype=T)\n assert_equal([int(_m) for _m in a], li)\n\n a = np.array(l[:3], dtype=np.uint64)\n assert_equal([int(_m) for _m in a], li[:3])\n\n def test_iinfo_long_values(self):\n for code in 'bBhH':\n with pytest.raises(OverflowError):\n np.array(np.iinfo(code).max + 1, dtype=code)\n\n for code in np.typecodes['AllInteger']:\n res = np.array(np.iinfo(code).max, dtype=code)\n tgt = np.iinfo(code).max\n assert_(res == tgt)\n\n for code in np.typecodes['AllInteger']:\n res = np.dtype(code).type(np.iinfo(code).max)\n tgt = np.iinfo(code).max\n assert_(res == tgt)\n\n def test_int_raise_behaviour(self):\n def overflow_error_func(dtype):\n dtype(np.iinfo(dtype).max + 1)\n\n for code in [np.int_, np.uint, np.longlong, np.ulonglong]:\n assert_raises(OverflowError, overflow_error_func, code)\n\n def test_int_from_infinite_longdouble(self):\n # gh-627\n x = np.longdouble(np.inf)\n assert_raises(OverflowError, int, x)\n with suppress_warnings() as sup:\n sup.record(ComplexWarning)\n x = np.clongdouble(np.inf)\n assert_raises(OverflowError, int, x)\n assert_equal(len(sup.log), 1)\n\n @pytest.mark.skipif(not IS_PYPY, reason="Test is PyPy only (gh-9972)")\n def test_int_from_infinite_longdouble___int__(self):\n x = np.longdouble(np.inf)\n assert_raises(OverflowError, x.__int__)\n with suppress_warnings() as sup:\n sup.record(ComplexWarning)\n x = np.clongdouble(np.inf)\n assert_raises(OverflowError, x.__int__)\n assert_equal(len(sup.log), 1)\n\n @pytest.mark.skipif(np.finfo(np.double) == np.finfo(np.longdouble),\n reason="long double is same as double")\n @pytest.mark.skipif(platform.machine().startswith("ppc"),\n reason="IBM double double")\n def test_int_from_huge_longdouble(self):\n # Produce a longdouble that would overflow a double,\n # use exponent that avoids bug in Darwin pow function.\n exp = np.finfo(np.double).maxexp - 1\n huge_ld = 2 * 1234 * np.longdouble(2) ** exp\n huge_i = 2 * 1234 * 2 ** exp\n assert_(huge_ld != np.inf)\n assert_equal(int(huge_ld), huge_i)\n\n def test_int_from_longdouble(self):\n x = np.longdouble(1.5)\n assert_equal(int(x), 1)\n x = np.longdouble(-10.5)\n assert_equal(int(x), -10)\n\n def test_numpy_scalar_relational_operators(self):\n # All integer\n for dt1 in np.typecodes['AllInteger']:\n assert_(1 > np.array(0, dtype=dt1)[()], f"type {dt1} failed")\n assert_(not 1 < np.array(0, dtype=dt1)[()], f"type {dt1} failed")\n\n for dt2 in np.typecodes['AllInteger']:\n assert_(np.array(1, dtype=dt1)[()] > np.array(0, dtype=dt2)[()],\n f"type {dt1} and {dt2} failed")\n assert_(not np.array(1, dtype=dt1)[()] < np.array(0, dtype=dt2)[()],\n f"type {dt1} and {dt2} failed")\n\n # Unsigned integers\n for dt1 in 'BHILQP':\n assert_(-1 < np.array(1, dtype=dt1)[()], f"type {dt1} failed")\n assert_(not -1 > np.array(1, dtype=dt1)[()], f"type {dt1} failed")\n assert_(-1 != np.array(1, dtype=dt1)[()], f"type {dt1} failed")\n\n # unsigned vs signed\n for dt2 in 'bhilqp':\n assert_(np.array(1, dtype=dt1)[()] > np.array(-1, dtype=dt2)[()],\n f"type {dt1} and {dt2} failed")\n assert_(not np.array(1, dtype=dt1)[()] < np.array(-1, dtype=dt2)[()],\n f"type {dt1} and {dt2} failed")\n assert_(np.array(1, dtype=dt1)[()] != np.array(-1, dtype=dt2)[()],\n f"type {dt1} and {dt2} failed")\n\n # Signed integers and floats\n for dt1 in 'bhlqp' + np.typecodes['Float']:\n assert_(1 > np.array(-1, dtype=dt1)[()], f"type {dt1} failed")\n assert_(not 1 < np.array(-1, dtype=dt1)[()], f"type {dt1} failed")\n assert_(-1 == np.array(-1, dtype=dt1)[()], f"type {dt1} failed")\n\n for dt2 in 'bhlqp' + np.typecodes['Float']:\n assert_(np.array(1, dtype=dt1)[()] > np.array(-1, dtype=dt2)[()],\n f"type {dt1} and {dt2} failed")\n assert_(not np.array(1, dtype=dt1)[()] < np.array(-1, dtype=dt2)[()],\n f"type {dt1} and {dt2} failed")\n assert_(np.array(-1, dtype=dt1)[()] == np.array(-1, dtype=dt2)[()],\n f"type {dt1} and {dt2} failed")\n\n def test_scalar_comparison_to_none(self):\n # Scalars should just return False and not give a warnings.\n # The comparisons are flagged by pep8, ignore that.\n with warnings.catch_warnings(record=True) as w:\n warnings.filterwarnings('always', '', FutureWarning)\n assert_(not np.float32(1) == None) # noqa: E711\n assert_(not np.str_('test') == None) # noqa: E711\n # This is dubious (see below):\n assert_(not np.datetime64('NaT') == None) # noqa: E711\n\n assert_(np.float32(1) != None) # noqa: E711\n assert_(np.str_('test') != None) # noqa: E711\n # This is dubious (see below):\n assert_(np.datetime64('NaT') != None) # noqa: E711\n assert_(len(w) == 0)\n\n # For documentation purposes, this is why the datetime is dubious.\n # At the time of deprecation this was no behaviour change, but\n # it has to be considered when the deprecations are done.\n assert_(np.equal(np.datetime64('NaT'), None))\n\n\n#class TestRepr:\n# def test_repr(self):\n# for t in types:\n# val = t(1197346475.0137341)\n# val_repr = repr(val)\n# val2 = eval(val_repr)\n# assert_equal( val, val2 )\n\n\nclass TestRepr:\n def _test_type_repr(self, t):\n finfo = np.finfo(t)\n last_fraction_bit_idx = finfo.nexp + finfo.nmant\n last_exponent_bit_idx = finfo.nexp\n storage_bytes = np.dtype(t).itemsize * 8\n # could add some more types to the list below\n for which in ['small denorm', 'small norm']:\n # Values from https://en.wikipedia.org/wiki/IEEE_754\n constr = np.array([0x00] * storage_bytes, dtype=np.uint8)\n if which == 'small denorm':\n byte = last_fraction_bit_idx // 8\n bytebit = 7 - (last_fraction_bit_idx % 8)\n constr[byte] = 1 << bytebit\n elif which == 'small norm':\n byte = last_exponent_bit_idx // 8\n bytebit = 7 - (last_exponent_bit_idx % 8)\n constr[byte] = 1 << bytebit\n else:\n raise ValueError('hmm')\n val = constr.view(t)[0]\n val_repr = repr(val)\n val2 = t(eval(val_repr))\n if not (val2 == 0 and val < 1e-100):\n assert_equal(val, val2)\n\n def test_float_repr(self):\n # long double test cannot work, because eval goes through a python\n # float\n for t in [np.float32, np.float64]:\n self._test_type_repr(t)\n\n\nif not IS_PYPY:\n # sys.getsizeof() is not valid on PyPy\n class TestSizeOf:\n\n def test_equal_nbytes(self):\n for type in types:\n x = type(0)\n assert_(sys.getsizeof(x) > x.nbytes)\n\n def test_error(self):\n d = np.float32()\n assert_raises(TypeError, d.__sizeof__, "a")\n\n\nclass TestMultiply:\n def test_seq_repeat(self):\n # Test that basic sequences get repeated when multiplied with\n # numpy integers. And errors are raised when multiplied with others.\n # Some of this behaviour may be controversial and could be open for\n # change.\n accepted_types = set(np.typecodes["AllInteger"])\n deprecated_types = {'?'}\n forbidden_types = (\n set(np.typecodes["All"]) - accepted_types - deprecated_types)\n forbidden_types -= {'V'} # can't default-construct void scalars\n\n for seq_type in (list, tuple):\n seq = seq_type([1, 2, 3])\n for numpy_type in accepted_types:\n i = np.dtype(numpy_type).type(2)\n assert_equal(seq * i, seq * int(i))\n assert_equal(i * seq, int(i) * seq)\n\n for numpy_type in deprecated_types:\n i = np.dtype(numpy_type).type()\n with assert_raises(TypeError):\n operator.mul(seq, i)\n\n for numpy_type in forbidden_types:\n i = np.dtype(numpy_type).type()\n assert_raises(TypeError, operator.mul, seq, i)\n assert_raises(TypeError, operator.mul, i, seq)\n\n def test_no_seq_repeat_basic_array_like(self):\n # Test that an array-like which does not know how to be multiplied\n # does not attempt sequence repeat (raise TypeError).\n # See also gh-7428.\n class ArrayLike:\n def __init__(self, arr):\n self.arr = arr\n\n def __array__(self, dtype=None, copy=None):\n return self.arr\n\n # Test for simple ArrayLike above and memoryviews (original report)\n for arr_like in (ArrayLike(np.ones(3)), memoryview(np.ones(3))):\n assert_array_equal(arr_like * np.float32(3.), np.full(3, 3.))\n assert_array_equal(np.float32(3.) * arr_like, np.full(3, 3.))\n assert_array_equal(arr_like * np.int_(3), np.full(3, 3))\n assert_array_equal(np.int_(3) * arr_like, np.full(3, 3))\n\n\nclass TestNegative:\n def test_exceptions(self):\n a = np.ones((), dtype=np.bool)[()]\n assert_raises(TypeError, operator.neg, a)\n\n def test_result(self):\n types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']\n with suppress_warnings() as sup:\n sup.filter(RuntimeWarning)\n for dt in types:\n a = np.ones((), dtype=dt)[()]\n if dt in np.typecodes['UnsignedInteger']:\n st = np.dtype(dt).type\n max = st(np.iinfo(dt).max)\n assert_equal(operator.neg(a), max)\n else:\n assert_equal(operator.neg(a) + a, 0)\n\nclass TestSubtract:\n def test_exceptions(self):\n a = np.ones((), dtype=np.bool)[()]\n assert_raises(TypeError, operator.sub, a, a)\n\n def test_result(self):\n types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']\n with suppress_warnings() as sup:\n sup.filter(RuntimeWarning)\n for dt in types:\n a = np.ones((), dtype=dt)[()]\n assert_equal(operator.sub(a, a), 0)\n\n\nclass TestAbs:\n def _test_abs_func(self, absfunc, test_dtype):\n x = test_dtype(-1.5)\n assert_equal(absfunc(x), 1.5)\n x = test_dtype(0.0)\n res = absfunc(x)\n # assert_equal() checks zero signedness\n assert_equal(res, 0.0)\n x = test_dtype(-0.0)\n res = absfunc(x)\n assert_equal(res, 0.0)\n\n x = test_dtype(np.finfo(test_dtype).max)\n assert_equal(absfunc(x), x.real)\n\n with suppress_warnings() as sup:\n sup.filter(UserWarning)\n x = test_dtype(np.finfo(test_dtype).tiny)\n assert_equal(absfunc(x), x.real)\n\n x = test_dtype(np.finfo(test_dtype).min)\n assert_equal(absfunc(x), -x.real)\n\n @pytest.mark.parametrize("dtype", floating_types + complex_floating_types)\n def test_builtin_abs(self, dtype):\n if (\n sys.platform == "cygwin" and dtype == np.clongdouble and\n (\n _pep440.parse(platform.release().split("-")[0])\n < _pep440.Version("3.3.0")\n )\n ):\n pytest.xfail(\n reason="absl is computed in double precision on cygwin < 3.3"\n )\n self._test_abs_func(abs, dtype)\n\n @pytest.mark.parametrize("dtype", floating_types + complex_floating_types)\n def test_numpy_abs(self, dtype):\n if (\n sys.platform == "cygwin" and dtype == np.clongdouble and\n (\n _pep440.parse(platform.release().split("-")[0])\n < _pep440.Version("3.3.0")\n )\n ):\n pytest.xfail(\n reason="absl is computed in double precision on cygwin < 3.3"\n )\n self._test_abs_func(np.abs, dtype)\n\nclass TestBitShifts:\n\n @pytest.mark.parametrize('type_code', np.typecodes['AllInteger'])\n @pytest.mark.parametrize('op',\n [operator.rshift, operator.lshift], ids=['>>', '<<'])\n def test_shift_all_bits(self, type_code, op):\n """Shifts where the shift amount is the width of the type or wider """\n # gh-2449\n dt = np.dtype(type_code)\n nbits = dt.itemsize * 8\n for val in [5, -5]:\n for shift in [nbits, nbits + 4]:\n val_scl = np.array(val).astype(dt)[()]\n shift_scl = dt.type(shift)\n res_scl = op(val_scl, shift_scl)\n if val_scl < 0 and op is operator.rshift:\n # sign bit is preserved\n assert_equal(res_scl, -1)\n else:\n assert_equal(res_scl, 0)\n\n # Result on scalars should be the same as on arrays\n val_arr = np.array([val_scl] * 32, dtype=dt)\n shift_arr = np.array([shift] * 32, dtype=dt)\n res_arr = op(val_arr, shift_arr)\n assert_equal(res_arr, res_scl)\n\n\nclass TestHash:\n @pytest.mark.parametrize("type_code", np.typecodes['AllInteger'])\n def test_integer_hashes(self, type_code):\n scalar = np.dtype(type_code).type\n for i in range(128):\n assert hash(i) == hash(scalar(i))\n\n @pytest.mark.parametrize("type_code", np.typecodes['AllFloat'])\n def test_float_and_complex_hashes(self, type_code):\n scalar = np.dtype(type_code).type\n for val in [np.pi, np.inf, 3, 6.]:\n numpy_val = scalar(val)\n # Cast back to Python, in case the NumPy scalar has less precision\n if numpy_val.dtype.kind == 'c':\n val = complex(numpy_val)\n else:\n val = float(numpy_val)\n assert val == numpy_val\n assert hash(val) == hash(numpy_val)\n\n if hash(float(np.nan)) != hash(float(np.nan)):\n # If Python distinguishes different NaNs we do so too (gh-18833)\n assert hash(scalar(np.nan)) != hash(scalar(np.nan))\n\n @pytest.mark.parametrize("type_code", np.typecodes['Complex'])\n def test_complex_hashes(self, type_code):\n # Test some complex valued hashes specifically:\n scalar = np.dtype(type_code).type\n for val in [np.pi + 1j, np.inf - 3j, 3j, 6. + 1j]:\n numpy_val = scalar(val)\n assert hash(complex(numpy_val)) == hash(numpy_val)\n\n\n@contextlib.contextmanager\ndef recursionlimit(n):\n o = sys.getrecursionlimit()\n try:\n sys.setrecursionlimit(n)\n yield\n finally:\n sys.setrecursionlimit(o)\n\n\n@given(sampled_from(objecty_things),\n sampled_from(binary_operators_for_scalar_ints),\n sampled_from(types + [rational]))\ndef test_operator_object_left(o, op, type_):\n try:\n with recursionlimit(200):\n op(o, type_(1))\n except TypeError:\n pass\n\n\n@given(sampled_from(objecty_things),\n sampled_from(binary_operators_for_scalar_ints),\n sampled_from(types + [rational]))\ndef test_operator_object_right(o, op, type_):\n try:\n with recursionlimit(200):\n op(type_(1), o)\n except TypeError:\n pass\n\n\n@given(sampled_from(binary_operators_for_scalars),\n sampled_from(types),\n sampled_from(types))\ndef test_operator_scalars(op, type1, type2):\n try:\n op(type1(1), type2(1))\n except TypeError:\n pass\n\n\n@pytest.mark.parametrize("op", binary_operators_for_scalars)\n@pytest.mark.parametrize("sctype", [np.longdouble, np.clongdouble])\ndef test_longdouble_operators_with_obj(sctype, op):\n # This is/used to be tricky, because NumPy generally falls back to\n # using the ufunc via `np.asarray()`, this effectively might do:\n # longdouble + None\n # -> asarray(longdouble) + np.array(None, dtype=object)\n # -> asarray(longdouble).astype(object) + np.array(None, dtype=object)\n # And after getting the scalars in the inner loop:\n # -> longdouble + None\n #\n # That would recurse infinitely. Other scalars return the python object\n # on cast, so this type of things works OK.\n #\n # As of NumPy 2.1, this has been consolidated into the np.generic binops\n # and now checks `.item()`. That also allows the below path to work now.\n try:\n op(sctype(3), None)\n except TypeError:\n pass\n try:\n op(None, sctype(3))\n except TypeError:\n pass\n\n\n@pytest.mark.parametrize("op", [operator.add, operator.pow, operator.sub])\n@pytest.mark.parametrize("sctype", [np.longdouble, np.clongdouble])\ndef test_longdouble_with_arrlike(sctype, op):\n # As of NumPy 2.1, longdouble behaves like other types and can coerce\n # e.g. lists. (Not necessarily better, but consistent.)\n assert_array_equal(op(sctype(3), [1, 2]), op(3, np.array([1, 2])))\n assert_array_equal(op([1, 2], sctype(3)), op(np.array([1, 2]), 3))\n\n\n@pytest.mark.parametrize("op", binary_operators_for_scalars)\n@pytest.mark.parametrize("sctype", [np.longdouble, np.clongdouble])\n@np.errstate(all="ignore")\ndef test_longdouble_operators_with_large_int(sctype, op):\n # (See `test_longdouble_operators_with_obj` for why longdouble is special)\n # NEP 50 means that the result is clearly a (c)longdouble here:\n if sctype == np.clongdouble and op in [operator.mod, operator.floordiv]:\n # The above operators are not support for complex though...\n with pytest.raises(TypeError):\n op(sctype(3), 2**64)\n with pytest.raises(TypeError):\n op(sctype(3), 2**64)\n else:\n assert op(sctype(3), -2**64) == op(sctype(3), sctype(-2**64))\n assert op(2**64, sctype(3)) == op(sctype(2**64), sctype(3))\n\n\n@pytest.mark.parametrize("dtype", np.typecodes["AllInteger"])\n@pytest.mark.parametrize("operation", [\n lambda min, max: max + max,\n lambda min, max: min - max,\n lambda min, max: max * max], ids=["+", "-", "*"])\ndef test_scalar_integer_operation_overflow(dtype, operation):\n st = np.dtype(dtype).type\n min = st(np.iinfo(dtype).min)\n max = st(np.iinfo(dtype).max)\n\n with pytest.warns(RuntimeWarning, match="overflow encountered"):\n operation(min, max)\n\n\n@pytest.mark.parametrize("dtype", np.typecodes["Integer"])\n@pytest.mark.parametrize("operation", [\n lambda min, neg_1: -min,\n lambda min, neg_1: abs(min),\n lambda min, neg_1: min * neg_1,\n pytest.param(lambda min, neg_1: min // neg_1,\n marks=pytest.mark.skip(reason="broken on some platforms"))],\n ids=["neg", "abs", "*", "//"])\ndef test_scalar_signed_integer_overflow(dtype, operation):\n # The minimum signed integer can "overflow" for some additional operations\n st = np.dtype(dtype).type\n min = st(np.iinfo(dtype).min)\n neg_1 = st(-1)\n\n with pytest.warns(RuntimeWarning, match="overflow encountered"):\n operation(min, neg_1)\n\n\n@pytest.mark.parametrize("dtype", np.typecodes["UnsignedInteger"])\ndef test_scalar_unsigned_integer_overflow(dtype):\n val = np.dtype(dtype).type(8)\n with pytest.warns(RuntimeWarning, match="overflow encountered"):\n -val\n\n zero = np.dtype(dtype).type(0)\n -zero # does not warn\n\n@pytest.mark.parametrize("dtype", np.typecodes["AllInteger"])\n@pytest.mark.parametrize("operation", [\n lambda val, zero: val // zero,\n lambda val, zero: val % zero, ], ids=["//", "%"])\ndef test_scalar_integer_operation_divbyzero(dtype, operation):\n st = np.dtype(dtype).type\n val = st(100)\n zero = st(0)\n\n with pytest.warns(RuntimeWarning, match="divide by zero"):\n operation(val, zero)\n\n\nops_with_names = [\n ("__lt__", "__gt__", operator.lt, True),\n ("__le__", "__ge__", operator.le, True),\n ("__eq__", "__eq__", operator.eq, True),\n # Note __op__ and __rop__ may be identical here:\n ("__ne__", "__ne__", operator.ne, True),\n ("__gt__", "__lt__", operator.gt, True),\n ("__ge__", "__le__", operator.ge, True),\n ("__floordiv__", "__rfloordiv__", operator.floordiv, False),\n ("__truediv__", "__rtruediv__", operator.truediv, False),\n ("__add__", "__radd__", operator.add, False),\n ("__mod__", "__rmod__", operator.mod, False),\n ("__mul__", "__rmul__", operator.mul, False),\n ("__pow__", "__rpow__", operator.pow, False),\n ("__sub__", "__rsub__", operator.sub, False),\n]\n\n\n@pytest.mark.parametrize(["__op__", "__rop__", "op", "cmp"], ops_with_names)\n@pytest.mark.parametrize("sctype", [np.float32, np.float64, np.longdouble])\ndef test_subclass_deferral(sctype, __op__, __rop__, op, cmp):\n """\n This test covers scalar subclass deferral. Note that this is exceedingly\n complicated, especially since it tends to fall back to the array paths and\n these additionally add the "array priority" mechanism.\n\n The behaviour was modified subtly in 1.22 (to make it closer to how Python\n scalars work). Due to its complexity and the fact that subclassing NumPy\n scalars is probably a bad idea to begin with. There is probably room\n for adjustments here.\n """\n class myf_simple1(sctype):\n pass\n\n class myf_simple2(sctype):\n pass\n\n def op_func(self, other):\n return __op__\n\n def rop_func(self, other):\n return __rop__\n\n myf_op = type("myf_op", (sctype,), {__op__: op_func, __rop__: rop_func})\n\n # inheritance has to override, or this is correctly lost:\n res = op(myf_simple1(1), myf_simple2(2))\n assert type(res) == sctype or type(res) == np.bool\n assert op(myf_simple1(1), myf_simple2(2)) == op(1, 2) # inherited\n\n # Two independent subclasses do not really define an order. This could\n # be attempted, but we do not since Python's `int` does neither:\n assert op(myf_op(1), myf_simple1(2)) == __op__\n assert op(myf_simple1(1), myf_op(2)) == op(1, 2) # inherited\n\n\ndef test_longdouble_complex():\n # Simple test to check longdouble and complex combinations, since these\n # need to go through promotion, which longdouble needs to be careful about.\n x = np.longdouble(1)\n assert x + 1j == 1 + 1j\n assert 1j + x == 1 + 1j\n\n\n@pytest.mark.parametrize(["__op__", "__rop__", "op", "cmp"], ops_with_names)\n@pytest.mark.parametrize("subtype", [float, int, complex, np.float16])\ndef test_pyscalar_subclasses(subtype, __op__, __rop__, op, cmp):\n # This tests that python scalar subclasses behave like a float64 (if they\n # don't override it).\n # In an earlier version of NEP 50, they behaved like the Python buildins.\n def op_func(self, other):\n return __op__\n\n def rop_func(self, other):\n return __rop__\n\n # Check that deferring is indicated using `__array_ufunc__`:\n myt = type("myt", (subtype,),\n {__op__: op_func, __rop__: rop_func, "__array_ufunc__": None})\n\n # Just like normally, we should never presume we can modify the float.\n assert op(myt(1), np.float64(2)) == __op__\n assert op(np.float64(1), myt(2)) == __rop__\n\n if op in {operator.mod, operator.floordiv} and subtype == complex:\n return # module is not support for complex. Do not test.\n\n if __rop__ == __op__:\n return\n\n # When no deferring is indicated, subclasses are handled normally.\n myt = type("myt", (subtype,), {__rop__: rop_func})\n behaves_like = lambda x: np.array(subtype(x))[()]\n\n # Check for float32, as a float subclass float64 may behave differently\n res = op(myt(1), np.float16(2))\n expected = op(behaves_like(1), np.float16(2))\n assert res == expected\n assert type(res) == type(expected)\n res = op(np.float32(2), myt(1))\n expected = op(np.float32(2), behaves_like(1))\n assert res == expected\n assert type(res) == type(expected)\n\n # Same check for longdouble (compare via dtype to accept float64 when\n # longdouble has the identical size), which is currently not perfectly\n # consistent.\n res = op(myt(1), np.longdouble(2))\n expected = op(behaves_like(1), np.longdouble(2))\n assert res == expected\n assert np.dtype(type(res)) == np.dtype(type(expected))\n res = op(np.float32(2), myt(1))\n expected = op(np.float32(2), behaves_like(1))\n assert res == expected\n assert np.dtype(type(res)) == np.dtype(type(expected))\n\n\ndef test_truediv_int():\n # This should work, as the result is float:\n assert np.uint8(3) / 123454 == np.float64(3) / 123454\n\n\n@pytest.mark.slow\n@pytest.mark.parametrize("op",\n # TODO: Power is a bit special, but here mostly bools seem to behave oddly\n [op for op in binary_operators_for_scalars if op is not operator.pow])\n@pytest.mark.parametrize("sctype", types)\n@pytest.mark.parametrize("other_type", [float, int, complex])\n@pytest.mark.parametrize("rop", [True, False])\ndef test_scalar_matches_array_op_with_pyscalar(op, sctype, other_type, rop):\n # Check that the ufunc path matches by coercing to an array explicitly\n val1 = sctype(2)\n val2 = other_type(2)\n\n if rop:\n _op = op\n op = lambda x, y: _op(y, x)\n\n try:\n res = op(val1, val2)\n except TypeError:\n try:\n expected = op(np.asarray(val1), val2)\n raise AssertionError("ufunc didn't raise.")\n except TypeError:\n return\n else:\n expected = op(np.asarray(val1), val2)\n\n # Note that we only check dtype equivalency, as ufuncs may pick the lower\n # dtype if they are equivalent.\n assert res == expected\n if isinstance(val1, float) and other_type is complex and rop:\n # Python complex accepts float subclasses, so we don't get a chance\n # and the result may be a Python complex (thus, the `np.array()``)\n assert np.array(res).dtype == expected.dtype\n else:\n assert res.dtype == expected.dtype\n | .venv\Lib\site-packages\numpy\_core\tests\test_scalarmath.py | test_scalarmath.py | Python | 47,759 | 0.95 | 0.193878 | 0.144711 | react-lib | 798 | 2023-12-28T05:56:48.678514 | GPL-3.0 | true | c323737b692e4f29ccea3c9ac4cb4006 |
""" Test printing of scalar types.\n\n"""\nimport platform\n\nimport pytest\n\nimport numpy as np\nfrom numpy.testing import IS_MUSL, assert_, assert_equal, assert_raises\n\n\nclass TestRealScalars:\n def test_str(self):\n svals = [0.0, -0.0, 1, -1, np.inf, -np.inf, np.nan]\n styps = [np.float16, np.float32, np.float64, np.longdouble]\n wanted = [\n ['0.0', '0.0', '0.0', '0.0' ], # noqa: E202\n ['-0.0', '-0.0', '-0.0', '-0.0'],\n ['1.0', '1.0', '1.0', '1.0' ], # noqa: E202\n ['-1.0', '-1.0', '-1.0', '-1.0'],\n ['inf', 'inf', 'inf', 'inf' ], # noqa: E202\n ['-inf', '-inf', '-inf', '-inf'],\n ['nan', 'nan', 'nan', 'nan' ]] # noqa: E202\n\n for wants, val in zip(wanted, svals):\n for want, styp in zip(wants, styps):\n msg = f'for str({np.dtype(styp).name}({val!r}))'\n assert_equal(str(styp(val)), want, err_msg=msg)\n\n def test_scalar_cutoffs(self):\n # test that both the str and repr of np.float64 behaves\n # like python floats in python3.\n def check(v):\n assert_equal(str(np.float64(v)), str(v))\n assert_equal(str(np.float64(v)), repr(v))\n assert_equal(repr(np.float64(v)), f"np.float64({v!r})")\n assert_equal(repr(np.float64(v)), f"np.float64({v})")\n\n # check we use the same number of significant digits\n check(1.12345678901234567890)\n check(0.0112345678901234567890)\n\n # check switch from scientific output to positional and back\n check(1e-5)\n check(1e-4)\n check(1e15)\n check(1e16)\n\n test_cases_gh_28679 = [\n (np.half, -0.000099, "-9.9e-05"),\n (np.half, 0.0001, "0.0001"),\n (np.half, 999, "999.0"),\n (np.half, -1000, "-1e+03"),\n (np.single, 0.000099, "9.9e-05"),\n (np.single, -0.000100001, "-0.000100001"),\n (np.single, 999999, "999999.0"),\n (np.single, -1000000, "-1e+06")\n ]\n\n @pytest.mark.parametrize("dtype, input_val, expected_str", test_cases_gh_28679)\n def test_gh_28679(self, dtype, input_val, expected_str):\n # test cutoff to exponent notation for half and single\n assert_equal(str(dtype(input_val)), expected_str)\n\n test_cases_legacy_2_2 = [\n (np.half(65504), "65500.0"),\n (np.single(1.e15), "1000000000000000.0"),\n (np.single(1.e16), "1e+16"),\n ]\n\n @pytest.mark.parametrize("input_val, expected_str", test_cases_legacy_2_2)\n def test_legacy_2_2_mode(self, input_val, expected_str):\n # test legacy cutoff to exponent notation for half and single\n with np.printoptions(legacy='2.2'):\n assert_equal(str(input_val), expected_str)\n\n def test_dragon4(self):\n # these tests are adapted from Ryan Juckett's dragon4 implementation,\n # see dragon4.c for details.\n\n fpos32 = lambda x, **k: np.format_float_positional(np.float32(x), **k)\n fsci32 = lambda x, **k: np.format_float_scientific(np.float32(x), **k)\n fpos64 = lambda x, **k: np.format_float_positional(np.float64(x), **k)\n fsci64 = lambda x, **k: np.format_float_scientific(np.float64(x), **k)\n\n preckwd = lambda prec: {'unique': False, 'precision': prec}\n\n assert_equal(fpos32('1.0'), "1.")\n assert_equal(fsci32('1.0'), "1.e+00")\n assert_equal(fpos32('10.234'), "10.234")\n assert_equal(fpos32('-10.234'), "-10.234")\n assert_equal(fsci32('10.234'), "1.0234e+01")\n assert_equal(fsci32('-10.234'), "-1.0234e+01")\n assert_equal(fpos32('1000.0'), "1000.")\n assert_equal(fpos32('1.0', precision=0), "1.")\n assert_equal(fsci32('1.0', precision=0), "1.e+00")\n assert_equal(fpos32('10.234', precision=0), "10.")\n assert_equal(fpos32('-10.234', precision=0), "-10.")\n assert_equal(fsci32('10.234', precision=0), "1.e+01")\n assert_equal(fsci32('-10.234', precision=0), "-1.e+01")\n assert_equal(fpos32('10.234', precision=2), "10.23")\n assert_equal(fsci32('-10.234', precision=2), "-1.02e+01")\n assert_equal(fsci64('9.9999999999999995e-08', **preckwd(16)),\n '9.9999999999999995e-08')\n assert_equal(fsci64('9.8813129168249309e-324', **preckwd(16)),\n '9.8813129168249309e-324')\n assert_equal(fsci64('9.9999999999999694e-311', **preckwd(16)),\n '9.9999999999999694e-311')\n\n # test rounding\n # 3.1415927410 is closest float32 to np.pi\n assert_equal(fpos32('3.14159265358979323846', **preckwd(10)),\n "3.1415927410")\n assert_equal(fsci32('3.14159265358979323846', **preckwd(10)),\n "3.1415927410e+00")\n assert_equal(fpos64('3.14159265358979323846', **preckwd(10)),\n "3.1415926536")\n assert_equal(fsci64('3.14159265358979323846', **preckwd(10)),\n "3.1415926536e+00")\n # 299792448 is closest float32 to 299792458\n assert_equal(fpos32('299792458.0', **preckwd(5)), "299792448.00000")\n assert_equal(fsci32('299792458.0', **preckwd(5)), "2.99792e+08")\n assert_equal(fpos64('299792458.0', **preckwd(5)), "299792458.00000")\n assert_equal(fsci64('299792458.0', **preckwd(5)), "2.99792e+08")\n\n assert_equal(fpos32('3.14159265358979323846', **preckwd(25)),\n "3.1415927410125732421875000")\n assert_equal(fpos64('3.14159265358979323846', **preckwd(50)),\n "3.14159265358979311599796346854418516159057617187500")\n assert_equal(fpos64('3.14159265358979323846'), "3.141592653589793")\n\n # smallest numbers\n assert_equal(fpos32(0.5**(126 + 23), unique=False, precision=149),\n "0.00000000000000000000000000000000000000000000140129846432"\n "4817070923729583289916131280261941876515771757068283889791"\n "08268586060148663818836212158203125")\n\n assert_equal(fpos64(5e-324, unique=False, precision=1074),\n "0.00000000000000000000000000000000000000000000000000000000"\n "0000000000000000000000000000000000000000000000000000000000"\n "0000000000000000000000000000000000000000000000000000000000"\n "0000000000000000000000000000000000000000000000000000000000"\n "0000000000000000000000000000000000000000000000000000000000"\n "0000000000000000000000000000000000049406564584124654417656"\n "8792868221372365059802614324764425585682500675507270208751"\n "8652998363616359923797965646954457177309266567103559397963"\n "9877479601078187812630071319031140452784581716784898210368"\n "8718636056998730723050006387409153564984387312473397273169"\n "6151400317153853980741262385655911710266585566867681870395"\n "6031062493194527159149245532930545654440112748012970999954"\n "1931989409080416563324524757147869014726780159355238611550"\n "1348035264934720193790268107107491703332226844753335720832"\n "4319360923828934583680601060115061698097530783422773183292"\n "4790498252473077637592724787465608477820373446969953364701"\n "7972677717585125660551199131504891101451037862738167250955"\n "8373897335989936648099411642057026370902792427675445652290"\n "87538682506419718265533447265625")\n\n # largest numbers\n f32x = np.finfo(np.float32).max\n assert_equal(fpos32(f32x, **preckwd(0)),\n "340282346638528859811704183484516925440.")\n assert_equal(fpos64(np.finfo(np.float64).max, **preckwd(0)),\n "1797693134862315708145274237317043567980705675258449965989"\n "1747680315726078002853876058955863276687817154045895351438"\n "2464234321326889464182768467546703537516986049910576551282"\n "0762454900903893289440758685084551339423045832369032229481"\n "6580855933212334827479782620414472316873817718091929988125"\n "0404026184124858368.")\n # Warning: In unique mode only the integer digits necessary for\n # uniqueness are computed, the rest are 0.\n assert_equal(fpos32(f32x),\n "340282350000000000000000000000000000000.")\n\n # Further tests of zero-padding vs rounding in different combinations\n # of unique, fractional, precision, min_digits\n # precision can only reduce digits, not add them.\n # min_digits can only extend digits, not reduce them.\n assert_equal(fpos32(f32x, unique=True, fractional=True, precision=0),\n "340282350000000000000000000000000000000.")\n assert_equal(fpos32(f32x, unique=True, fractional=True, precision=4),\n "340282350000000000000000000000000000000.")\n assert_equal(fpos32(f32x, unique=True, fractional=True, min_digits=0),\n "340282346638528859811704183484516925440.")\n assert_equal(fpos32(f32x, unique=True, fractional=True, min_digits=4),\n "340282346638528859811704183484516925440.0000")\n assert_equal(fpos32(f32x, unique=True, fractional=True,\n min_digits=4, precision=4),\n "340282346638528859811704183484516925440.0000")\n assert_raises(ValueError, fpos32, f32x, unique=True, fractional=False,\n precision=0)\n assert_equal(fpos32(f32x, unique=True, fractional=False, precision=4),\n "340300000000000000000000000000000000000.")\n assert_equal(fpos32(f32x, unique=True, fractional=False, precision=20),\n "340282350000000000000000000000000000000.")\n assert_equal(fpos32(f32x, unique=True, fractional=False, min_digits=4),\n "340282350000000000000000000000000000000.")\n assert_equal(fpos32(f32x, unique=True, fractional=False,\n min_digits=20),\n "340282346638528859810000000000000000000.")\n assert_equal(fpos32(f32x, unique=True, fractional=False,\n min_digits=15),\n "340282346638529000000000000000000000000.")\n assert_equal(fpos32(f32x, unique=False, fractional=False, precision=4),\n "340300000000000000000000000000000000000.")\n # test that unique rounding is preserved when precision is supplied\n # but no extra digits need to be printed (gh-18609)\n a = np.float64.fromhex('-1p-97')\n assert_equal(fsci64(a, unique=True), '-6.310887241768095e-30')\n assert_equal(fsci64(a, unique=False, precision=15),\n '-6.310887241768094e-30')\n assert_equal(fsci64(a, unique=True, precision=15),\n '-6.310887241768095e-30')\n assert_equal(fsci64(a, unique=True, min_digits=15),\n '-6.310887241768095e-30')\n assert_equal(fsci64(a, unique=True, precision=15, min_digits=15),\n '-6.310887241768095e-30')\n # adds/remove digits in unique mode with unbiased rnding\n assert_equal(fsci64(a, unique=True, precision=14),\n '-6.31088724176809e-30')\n assert_equal(fsci64(a, unique=True, min_digits=16),\n '-6.3108872417680944e-30')\n assert_equal(fsci64(a, unique=True, precision=16),\n '-6.310887241768095e-30')\n assert_equal(fsci64(a, unique=True, min_digits=14),\n '-6.310887241768095e-30')\n # test min_digits in unique mode with different rounding cases\n assert_equal(fsci64('1e120', min_digits=3), '1.000e+120')\n assert_equal(fsci64('1e100', min_digits=3), '1.000e+100')\n\n # test trailing zeros\n assert_equal(fpos32('1.0', unique=False, precision=3), "1.000")\n assert_equal(fpos64('1.0', unique=False, precision=3), "1.000")\n assert_equal(fsci32('1.0', unique=False, precision=3), "1.000e+00")\n assert_equal(fsci64('1.0', unique=False, precision=3), "1.000e+00")\n assert_equal(fpos32('1.5', unique=False, precision=3), "1.500")\n assert_equal(fpos64('1.5', unique=False, precision=3), "1.500")\n assert_equal(fsci32('1.5', unique=False, precision=3), "1.500e+00")\n assert_equal(fsci64('1.5', unique=False, precision=3), "1.500e+00")\n # gh-10713\n assert_equal(fpos64('324', unique=False, precision=5,\n fractional=False), "324.00")\n\n available_float_dtypes = [np.float16, np.float32, np.float64, np.float128]\\n if hasattr(np, 'float128') else [np.float16, np.float32, np.float64]\n\n @pytest.mark.parametrize("tp", available_float_dtypes)\n def test_dragon4_positional_interface(self, tp):\n # test is flaky for musllinux on np.float128\n if IS_MUSL and tp == np.float128:\n pytest.skip("Skipping flaky test of float128 on musllinux")\n\n fpos = np.format_float_positional\n\n # test padding\n assert_equal(fpos(tp('1.0'), pad_left=4, pad_right=4), " 1. ")\n assert_equal(fpos(tp('-1.0'), pad_left=4, pad_right=4), " -1. ")\n assert_equal(fpos(tp('-10.2'),\n pad_left=4, pad_right=4), " -10.2 ")\n\n # test fixed (non-unique) mode\n assert_equal(fpos(tp('1.0'), unique=False, precision=4), "1.0000")\n\n @pytest.mark.parametrize("tp", available_float_dtypes)\n def test_dragon4_positional_interface_trim(self, tp):\n # test is flaky for musllinux on np.float128\n if IS_MUSL and tp == np.float128:\n pytest.skip("Skipping flaky test of float128 on musllinux")\n\n fpos = np.format_float_positional\n # test trimming\n # trim of 'k' or '.' only affects non-unique mode, since unique\n # mode will not output trailing 0s.\n assert_equal(fpos(tp('1.'), unique=False, precision=4, trim='k'),\n "1.0000")\n\n assert_equal(fpos(tp('1.'), unique=False, precision=4, trim='.'),\n "1.")\n assert_equal(fpos(tp('1.2'), unique=False, precision=4, trim='.'),\n "1.2" if tp != np.float16 else "1.2002")\n\n assert_equal(fpos(tp('1.'), unique=False, precision=4, trim='0'),\n "1.0")\n assert_equal(fpos(tp('1.2'), unique=False, precision=4, trim='0'),\n "1.2" if tp != np.float16 else "1.2002")\n assert_equal(fpos(tp('1.'), trim='0'), "1.0")\n\n assert_equal(fpos(tp('1.'), unique=False, precision=4, trim='-'),\n "1")\n assert_equal(fpos(tp('1.2'), unique=False, precision=4, trim='-'),\n "1.2" if tp != np.float16 else "1.2002")\n assert_equal(fpos(tp('1.'), trim='-'), "1")\n assert_equal(fpos(tp('1.001'), precision=1, trim='-'), "1")\n\n @pytest.mark.parametrize("tp", available_float_dtypes)\n @pytest.mark.parametrize("pad_val", [10**5, np.iinfo("int32").max])\n def test_dragon4_positional_interface_overflow(self, tp, pad_val):\n # test is flaky for musllinux on np.float128\n if IS_MUSL and tp == np.float128:\n pytest.skip("Skipping flaky test of float128 on musllinux")\n\n fpos = np.format_float_positional\n\n # gh-28068\n with pytest.raises(RuntimeError,\n match="Float formatting result too large"):\n fpos(tp('1.047'), unique=False, precision=pad_val)\n\n with pytest.raises(RuntimeError,\n match="Float formatting result too large"):\n fpos(tp('1.047'), precision=2, pad_left=pad_val)\n\n with pytest.raises(RuntimeError,\n match="Float formatting result too large"):\n fpos(tp('1.047'), precision=2, pad_right=pad_val)\n\n @pytest.mark.parametrize("tp", available_float_dtypes)\n def test_dragon4_scientific_interface(self, tp):\n # test is flaky for musllinux on np.float128\n if IS_MUSL and tp == np.float128:\n pytest.skip("Skipping flaky test of float128 on musllinux")\n\n fsci = np.format_float_scientific\n\n # test exp_digits\n assert_equal(fsci(tp('1.23e1'), exp_digits=5), "1.23e+00001")\n\n # test fixed (non-unique) mode\n assert_equal(fsci(tp('1.0'), unique=False, precision=4),\n "1.0000e+00")\n\n @pytest.mark.skipif(not platform.machine().startswith("ppc64"),\n reason="only applies to ppc float128 values")\n def test_ppc64_ibm_double_double128(self):\n # check that the precision decreases once we get into the subnormal\n # range. Unlike float64, this starts around 1e-292 instead of 1e-308,\n # which happens when the first double is normal and the second is\n # subnormal.\n x = np.float128('2.123123123123123123123123123123123e-286')\n got = [str(x / np.float128('2e' + str(i))) for i in range(40)]\n expected = [\n "1.06156156156156156156156156156157e-286",\n "1.06156156156156156156156156156158e-287",\n "1.06156156156156156156156156156159e-288",\n "1.0615615615615615615615615615616e-289",\n "1.06156156156156156156156156156157e-290",\n "1.06156156156156156156156156156156e-291",\n "1.0615615615615615615615615615616e-292",\n "1.0615615615615615615615615615615e-293",\n "1.061561561561561561561561561562e-294",\n "1.06156156156156156156156156155e-295",\n "1.0615615615615615615615615616e-296",\n "1.06156156156156156156156156e-297",\n "1.06156156156156156156156157e-298",\n "1.0615615615615615615615616e-299",\n "1.06156156156156156156156e-300",\n "1.06156156156156156156155e-301",\n "1.0615615615615615615616e-302",\n "1.061561561561561561562e-303",\n "1.06156156156156156156e-304",\n "1.0615615615615615618e-305",\n "1.06156156156156156e-306",\n "1.06156156156156157e-307",\n "1.0615615615615616e-308",\n "1.06156156156156e-309",\n "1.06156156156157e-310",\n "1.0615615615616e-311",\n "1.06156156156e-312",\n "1.06156156154e-313",\n "1.0615615616e-314",\n "1.06156156e-315",\n "1.06156155e-316",\n "1.061562e-317",\n "1.06156e-318",\n "1.06155e-319",\n "1.0617e-320",\n "1.06e-321",\n "1.04e-322",\n "1e-323",\n "0.0",\n "0.0"]\n assert_equal(got, expected)\n\n # Note: we follow glibc behavior, but it (or gcc) might not be right.\n # In particular we can get two values that print the same but are not\n # equal:\n a = np.float128('2') / np.float128('3')\n b = np.float128(str(a))\n assert_equal(str(a), str(b))\n assert_(a != b)\n\n def float32_roundtrip(self):\n # gh-9360\n x = np.float32(1024 - 2**-14)\n y = np.float32(1024 - 2**-13)\n assert_(repr(x) != repr(y))\n assert_equal(np.float32(repr(x)), x)\n assert_equal(np.float32(repr(y)), y)\n\n def float64_vs_python(self):\n # gh-2643, gh-6136, gh-6908\n assert_equal(repr(np.float64(0.1)), repr(0.1))\n assert_(repr(np.float64(0.20000000000000004)) != repr(0.2))\n | .venv\Lib\site-packages\numpy\_core\tests\test_scalarprint.py | test_scalarprint.py | Python | 20,108 | 0.95 | 0.086849 | 0.129213 | vue-tools | 526 | 2024-08-24T15:30:34.904502 | MIT | true | 92dcb66d8c923743f4e300c8a09b6ba1 |
"""\nTest the scalar constructors, which also do type-coercion\n"""\nimport pytest\n\nimport numpy as np\nfrom numpy.testing import (\n assert_almost_equal,\n assert_equal,\n assert_warns,\n)\n\n\nclass TestFromString:\n def test_floating(self):\n # Ticket #640, floats from string\n fsingle = np.single('1.234')\n fdouble = np.double('1.234')\n flongdouble = np.longdouble('1.234')\n assert_almost_equal(fsingle, 1.234)\n assert_almost_equal(fdouble, 1.234)\n assert_almost_equal(flongdouble, 1.234)\n\n def test_floating_overflow(self):\n """ Strings containing an unrepresentable float overflow """\n fhalf = np.half('1e10000')\n assert_equal(fhalf, np.inf)\n fsingle = np.single('1e10000')\n assert_equal(fsingle, np.inf)\n fdouble = np.double('1e10000')\n assert_equal(fdouble, np.inf)\n flongdouble = assert_warns(RuntimeWarning, np.longdouble, '1e10000')\n assert_equal(flongdouble, np.inf)\n\n fhalf = np.half('-1e10000')\n assert_equal(fhalf, -np.inf)\n fsingle = np.single('-1e10000')\n assert_equal(fsingle, -np.inf)\n fdouble = np.double('-1e10000')\n assert_equal(fdouble, -np.inf)\n flongdouble = assert_warns(RuntimeWarning, np.longdouble, '-1e10000')\n assert_equal(flongdouble, -np.inf)\n\n\nclass TestExtraArgs:\n def test_superclass(self):\n # try both positional and keyword arguments\n s = np.str_(b'\\x61', encoding='unicode-escape')\n assert s == 'a'\n s = np.str_(b'\\x61', 'unicode-escape')\n assert s == 'a'\n\n # previously this would return '\\xx'\n with pytest.raises(UnicodeDecodeError):\n np.str_(b'\\xx', encoding='unicode-escape')\n with pytest.raises(UnicodeDecodeError):\n np.str_(b'\\xx', 'unicode-escape')\n\n # superclass fails, but numpy succeeds\n assert np.bytes_(-2) == b'-2'\n\n def test_datetime(self):\n dt = np.datetime64('2000-01', ('M', 2))\n assert np.datetime_data(dt) == ('M', 2)\n\n with pytest.raises(TypeError):\n np.datetime64('2000', garbage=True)\n\n def test_bool(self):\n with pytest.raises(TypeError):\n np.bool(False, garbage=True)\n\n def test_void(self):\n with pytest.raises(TypeError):\n np.void(b'test', garbage=True)\n\n\nclass TestFromInt:\n def test_intp(self):\n # Ticket #99\n assert_equal(1024, np.intp(1024))\n\n def test_uint64_from_negative(self):\n with pytest.raises(OverflowError):\n np.uint64(-2)\n\n\nint_types = [np.byte, np.short, np.intc, np.long, np.longlong]\nuint_types = [np.ubyte, np.ushort, np.uintc, np.ulong, np.ulonglong]\nfloat_types = [np.half, np.single, np.double, np.longdouble]\ncfloat_types = [np.csingle, np.cdouble, np.clongdouble]\n\n\nclass TestArrayFromScalar:\n """ gh-15467 and gh-19125 """\n\n def _do_test(self, t1, t2, arg=2):\n if arg is None:\n x = t1()\n elif isinstance(arg, tuple):\n if t1 is np.clongdouble:\n pytest.xfail("creating a clongdouble from real and "\n "imaginary parts isn't supported")\n x = t1(*arg)\n else:\n x = t1(arg)\n arr = np.array(x, dtype=t2)\n # type should be preserved exactly\n if t2 is None:\n assert arr.dtype.type is t1\n else:\n assert arr.dtype.type is t2\n\n @pytest.mark.parametrize('t1', int_types + uint_types)\n @pytest.mark.parametrize('t2', int_types + uint_types + [None])\n def test_integers(self, t1, t2):\n return self._do_test(t1, t2)\n\n @pytest.mark.parametrize('t1', float_types)\n @pytest.mark.parametrize('t2', float_types + [None])\n def test_reals(self, t1, t2):\n return self._do_test(t1, t2)\n\n @pytest.mark.parametrize('t1', cfloat_types)\n @pytest.mark.parametrize('t2', cfloat_types + [None])\n @pytest.mark.parametrize('arg', [2, 1 + 3j, (1, 2), None])\n def test_complex(self, t1, t2, arg):\n self._do_test(t1, t2, arg)\n\n @pytest.mark.parametrize('t', cfloat_types)\n def test_complex_errors(self, t):\n with pytest.raises(TypeError):\n t(1j, 1j)\n with pytest.raises(TypeError):\n t(1, None)\n with pytest.raises(TypeError):\n t(None, 1)\n\n\n@pytest.mark.parametrize("length",\n [5, np.int8(5), np.array(5, dtype=np.uint16)])\ndef test_void_via_length(length):\n res = np.void(length)\n assert type(res) is np.void\n assert res.item() == b"\0" * 5\n assert res.dtype == "V5"\n\n@pytest.mark.parametrize("bytes_",\n [b"spam", np.array(567.)])\ndef test_void_from_byteslike(bytes_):\n res = np.void(bytes_)\n expected = bytes(bytes_)\n assert type(res) is np.void\n assert res.item() == expected\n\n # Passing dtype can extend it (this is how filling works)\n res = np.void(bytes_, dtype="V100")\n assert type(res) is np.void\n assert res.item()[:len(expected)] == expected\n assert res.item()[len(expected):] == b"\0" * (res.nbytes - len(expected))\n # As well as shorten:\n res = np.void(bytes_, dtype="V4")\n assert type(res) is np.void\n assert res.item() == expected[:4]\n\ndef test_void_arraylike_trumps_byteslike():\n # The memoryview is converted as an array-like of shape (18,)\n # rather than a single bytes-like of that length.\n m = memoryview(b"just one mintleaf?")\n res = np.void(m)\n assert type(res) is np.ndarray\n assert res.dtype == "V1"\n assert res.shape == (18,)\n\ndef test_void_dtype_arg():\n # Basic test for the dtype argument (positional and keyword)\n res = np.void((1, 2), dtype="i,i")\n assert res.item() == (1, 2)\n res = np.void((2, 3), "i,i")\n assert res.item() == (2, 3)\n\n@pytest.mark.parametrize("data",\n [5, np.int8(5), np.array(5, dtype=np.uint16)])\ndef test_void_from_integer_with_dtype(data):\n # The "length" meaning is ignored, rather data is used:\n res = np.void(data, dtype="i,i")\n assert type(res) is np.void\n assert res.dtype == "i,i"\n assert res["f0"] == 5 and res["f1"] == 5\n\ndef test_void_from_structure():\n dtype = np.dtype([('s', [('f', 'f8'), ('u', 'U1')]), ('i', 'i2')])\n data = np.array(((1., 'a'), 2), dtype=dtype)\n res = np.void(data[()], dtype=dtype)\n assert type(res) is np.void\n assert res.dtype == dtype\n assert res == data[()]\n\ndef test_void_bad_dtype():\n with pytest.raises(TypeError,\n match="void: descr must be a `void.*int64"):\n np.void(4, dtype="i8")\n\n # Subarray dtype (with shape `(4,)` is rejected):\n with pytest.raises(TypeError,\n match=r"void: descr must be a `void.*\(4,\)"):\n np.void(4, dtype="4i")\n | .venv\Lib\site-packages\numpy\_core\tests\test_scalar_ctors.py | test_scalar_ctors.py | Python | 6,931 | 0.95 | 0.140097 | 0.075581 | vue-tools | 596 | 2024-03-18T14:19:30.084741 | BSD-3-Clause | true | 2f0ddb94436a906bb0bdc52e51e50f7d |
"""\nTest the scalar constructors, which also do type-coercion\n"""\nimport fractions\nimport platform\nimport types\nfrom typing import Any\n\nimport pytest\n\nimport numpy as np\nfrom numpy._core import sctypes\nfrom numpy.testing import assert_equal, assert_raises\n\n\nclass TestAsIntegerRatio:\n # derived in part from the cpython test "test_floatasratio"\n\n @pytest.mark.parametrize("ftype", [\n np.half, np.single, np.double, np.longdouble])\n @pytest.mark.parametrize("f, ratio", [\n (0.875, (7, 8)),\n (-0.875, (-7, 8)),\n (0.0, (0, 1)),\n (11.5, (23, 2)),\n ])\n def test_small(self, ftype, f, ratio):\n assert_equal(ftype(f).as_integer_ratio(), ratio)\n\n @pytest.mark.parametrize("ftype", [\n np.half, np.single, np.double, np.longdouble])\n def test_simple_fractions(self, ftype):\n R = fractions.Fraction\n assert_equal(R(0, 1),\n R(*ftype(0.0).as_integer_ratio()))\n assert_equal(R(5, 2),\n R(*ftype(2.5).as_integer_ratio()))\n assert_equal(R(1, 2),\n R(*ftype(0.5).as_integer_ratio()))\n assert_equal(R(-2100, 1),\n R(*ftype(-2100.0).as_integer_ratio()))\n\n @pytest.mark.parametrize("ftype", [\n np.half, np.single, np.double, np.longdouble])\n def test_errors(self, ftype):\n assert_raises(OverflowError, ftype('inf').as_integer_ratio)\n assert_raises(OverflowError, ftype('-inf').as_integer_ratio)\n assert_raises(ValueError, ftype('nan').as_integer_ratio)\n\n def test_against_known_values(self):\n R = fractions.Fraction\n assert_equal(R(1075, 512),\n R(*np.half(2.1).as_integer_ratio()))\n assert_equal(R(-1075, 512),\n R(*np.half(-2.1).as_integer_ratio()))\n assert_equal(R(4404019, 2097152),\n R(*np.single(2.1).as_integer_ratio()))\n assert_equal(R(-4404019, 2097152),\n R(*np.single(-2.1).as_integer_ratio()))\n assert_equal(R(4728779608739021, 2251799813685248),\n R(*np.double(2.1).as_integer_ratio()))\n assert_equal(R(-4728779608739021, 2251799813685248),\n R(*np.double(-2.1).as_integer_ratio()))\n # longdouble is platform dependent\n\n @pytest.mark.parametrize("ftype, frac_vals, exp_vals", [\n # dtype test cases generated using hypothesis\n # first five generated cases per dtype\n (np.half, [0.0, 0.01154830649280303, 0.31082276347447274,\n 0.527350517124794, 0.8308562335072596],\n [0, 1, 0, -8, 12]),\n (np.single, [0.0, 0.09248576989263226, 0.8160498218131407,\n 0.17389442853722373, 0.7956044195067877],\n [0, 12, 10, 17, -26]),\n (np.double, [0.0, 0.031066908499895136, 0.5214135908877832,\n 0.45780736035689296, 0.5906586745934036],\n [0, -801, 51, 194, -653]),\n pytest.param(\n np.longdouble,\n [0.0, 0.20492557202724854, 0.4277180662199366, 0.9888085019891495,\n 0.9620175814461964],\n [0, -7400, 14266, -7822, -8721],\n marks=[\n pytest.mark.skipif(\n np.finfo(np.double) == np.finfo(np.longdouble),\n reason="long double is same as double"),\n pytest.mark.skipif(\n platform.machine().startswith("ppc"),\n reason="IBM double double"),\n ]\n )\n ])\n def test_roundtrip(self, ftype, frac_vals, exp_vals):\n for frac, exp in zip(frac_vals, exp_vals):\n f = np.ldexp(ftype(frac), exp)\n assert f.dtype == ftype\n n, d = f.as_integer_ratio()\n\n try:\n nf = np.longdouble(n)\n df = np.longdouble(d)\n if not np.isfinite(df):\n raise OverflowError\n except (OverflowError, RuntimeWarning):\n # the values may not fit in any float type\n pytest.skip("longdouble too small on this platform")\n\n assert_equal(nf / df, f, f"{n}/{d}")\n\n\nclass TestIsInteger:\n @pytest.mark.parametrize("str_value", ["inf", "nan"])\n @pytest.mark.parametrize("code", np.typecodes["Float"])\n def test_special(self, code: str, str_value: str) -> None:\n cls = np.dtype(code).type\n value = cls(str_value)\n assert not value.is_integer()\n\n @pytest.mark.parametrize(\n "code", np.typecodes["Float"] + np.typecodes["AllInteger"]\n )\n def test_true(self, code: str) -> None:\n float_array = np.arange(-5, 5).astype(code)\n for value in float_array:\n assert value.is_integer()\n\n @pytest.mark.parametrize("code", np.typecodes["Float"])\n def test_false(self, code: str) -> None:\n float_array = np.arange(-5, 5).astype(code)\n float_array *= 1.1\n for value in float_array:\n if value == 0:\n continue\n assert not value.is_integer()\n\n\nclass TestClassGetItem:\n @pytest.mark.parametrize("cls", [\n np.number,\n np.integer,\n np.inexact,\n np.unsignedinteger,\n np.signedinteger,\n np.floating,\n ])\n def test_abc(self, cls: type[np.number]) -> None:\n alias = cls[Any]\n assert isinstance(alias, types.GenericAlias)\n assert alias.__origin__ is cls\n\n def test_abc_complexfloating(self) -> None:\n alias = np.complexfloating[Any, Any]\n assert isinstance(alias, types.GenericAlias)\n assert alias.__origin__ is np.complexfloating\n\n @pytest.mark.parametrize("arg_len", range(4))\n def test_abc_complexfloating_subscript_tuple(self, arg_len: int) -> None:\n arg_tup = (Any,) * arg_len\n if arg_len in (1, 2):\n assert np.complexfloating[arg_tup]\n else:\n match = f"Too {'few' if arg_len == 0 else 'many'} arguments"\n with pytest.raises(TypeError, match=match):\n np.complexfloating[arg_tup]\n\n @pytest.mark.parametrize("cls", [np.generic, np.flexible, np.character])\n def test_abc_non_numeric(self, cls: type[np.generic]) -> None:\n with pytest.raises(TypeError):\n cls[Any]\n\n @pytest.mark.parametrize("code", np.typecodes["All"])\n def test_concrete(self, code: str) -> None:\n cls = np.dtype(code).type\n with pytest.raises(TypeError):\n cls[Any]\n\n @pytest.mark.parametrize("arg_len", range(4))\n def test_subscript_tuple(self, arg_len: int) -> None:\n arg_tup = (Any,) * arg_len\n if arg_len == 1:\n assert np.number[arg_tup]\n else:\n with pytest.raises(TypeError):\n np.number[arg_tup]\n\n def test_subscript_scalar(self) -> None:\n assert np.number[Any]\n\n\nclass TestBitCount:\n # derived in part from the cpython test "test_bit_count"\n\n @pytest.mark.parametrize("itype", sctypes['int'] + sctypes['uint'])\n def test_small(self, itype):\n for a in range(max(np.iinfo(itype).min, 0), 128):\n msg = f"Smoke test for {itype}({a}).bit_count()"\n assert itype(a).bit_count() == a.bit_count(), msg\n\n def test_bit_count(self):\n for exp in [10, 17, 63]:\n a = 2**exp\n assert np.uint64(a).bit_count() == 1\n assert np.uint64(a - 1).bit_count() == exp\n assert np.uint64(a ^ 63).bit_count() == 7\n assert np.uint64((a - 1) ^ 510).bit_count() == exp - 8\n\n\nclass TestDevice:\n """\n Test scalar.device attribute and scalar.to_device() method.\n """\n scalars = [np.bool(True), np.int64(1), np.uint64(1), np.float64(1.0),\n np.complex128(1 + 1j)]\n\n @pytest.mark.parametrize("scalar", scalars)\n def test_device(self, scalar):\n assert scalar.device == "cpu"\n\n @pytest.mark.parametrize("scalar", scalars)\n def test_to_device(self, scalar):\n assert scalar.to_device("cpu") is scalar\n\n @pytest.mark.parametrize("scalar", scalars)\n def test___array_namespace__(self, scalar):\n assert scalar.__array_namespace__() is np\n\n\n@pytest.mark.parametrize("scalar", [np.bool(True), np.int8(1), np.float64(1)])\ndef test_array_wrap(scalar):\n # Test scalars array wrap as long as it exists. NumPy itself should\n # probably not use it, so it may not be necessary to keep it around.\n\n arr0d = np.array(3, dtype=np.int8)\n # Third argument not passed, None, or True "decays" to scalar.\n # (I don't think NumPy would pass `None`, but it seems clear to support)\n assert type(scalar.__array_wrap__(arr0d)) is np.int8\n assert type(scalar.__array_wrap__(arr0d, None, None)) is np.int8\n assert type(scalar.__array_wrap__(arr0d, None, True)) is np.int8\n\n # Otherwise, result should be the input\n assert scalar.__array_wrap__(arr0d, None, False) is arr0d\n\n # An old bug. A non 0-d array cannot be converted to scalar:\n arr1d = np.array([3], dtype=np.int8)\n assert scalar.__array_wrap__(arr1d) is arr1d\n assert scalar.__array_wrap__(arr1d, None, True) is arr1d\n | .venv\Lib\site-packages\numpy\_core\tests\test_scalar_methods.py | test_scalar_methods.py | Python | 9,363 | 0.95 | 0.154472 | 0.057416 | awesome-app | 90 | 2024-03-15T10:07:44.056092 | MIT | true | df3ea9facbda0b71c010b2aa48917878 |
import sys\n\nimport pytest\n\nimport numpy as np\nfrom numpy._core import (\n arange,\n array,\n atleast_1d,\n atleast_2d,\n atleast_3d,\n block,\n concatenate,\n hstack,\n newaxis,\n stack,\n vstack,\n)\nfrom numpy._core.shape_base import (\n _block_concatenate,\n _block_dispatcher,\n _block_setup,\n _block_slicing,\n)\nfrom numpy.exceptions import AxisError\nfrom numpy.testing import (\n IS_PYPY,\n assert_,\n assert_array_equal,\n assert_equal,\n assert_raises,\n assert_raises_regex,\n)\nfrom numpy.testing._private.utils import requires_memory\n\n\nclass TestAtleast1d:\n def test_0D_array(self):\n a = array(1)\n b = array(2)\n res = [atleast_1d(a), atleast_1d(b)]\n desired = [array([1]), array([2])]\n assert_array_equal(res, desired)\n\n def test_1D_array(self):\n a = array([1, 2])\n b = array([2, 3])\n res = [atleast_1d(a), atleast_1d(b)]\n desired = [array([1, 2]), array([2, 3])]\n assert_array_equal(res, desired)\n\n def test_2D_array(self):\n a = array([[1, 2], [1, 2]])\n b = array([[2, 3], [2, 3]])\n res = [atleast_1d(a), atleast_1d(b)]\n desired = [a, b]\n assert_array_equal(res, desired)\n\n def test_3D_array(self):\n a = array([[1, 2], [1, 2]])\n b = array([[2, 3], [2, 3]])\n a = array([a, a])\n b = array([b, b])\n res = [atleast_1d(a), atleast_1d(b)]\n desired = [a, b]\n assert_array_equal(res, desired)\n\n def test_r1array(self):\n """ Test to make sure equivalent Travis O's r1array function\n """\n assert_(atleast_1d(3).shape == (1,))\n assert_(atleast_1d(3j).shape == (1,))\n assert_(atleast_1d(3.0).shape == (1,))\n assert_(atleast_1d([[2, 3], [4, 5]]).shape == (2, 2))\n\n\nclass TestAtleast2d:\n def test_0D_array(self):\n a = array(1)\n b = array(2)\n res = [atleast_2d(a), atleast_2d(b)]\n desired = [array([[1]]), array([[2]])]\n assert_array_equal(res, desired)\n\n def test_1D_array(self):\n a = array([1, 2])\n b = array([2, 3])\n res = [atleast_2d(a), atleast_2d(b)]\n desired = [array([[1, 2]]), array([[2, 3]])]\n assert_array_equal(res, desired)\n\n def test_2D_array(self):\n a = array([[1, 2], [1, 2]])\n b = array([[2, 3], [2, 3]])\n res = [atleast_2d(a), atleast_2d(b)]\n desired = [a, b]\n assert_array_equal(res, desired)\n\n def test_3D_array(self):\n a = array([[1, 2], [1, 2]])\n b = array([[2, 3], [2, 3]])\n a = array([a, a])\n b = array([b, b])\n res = [atleast_2d(a), atleast_2d(b)]\n desired = [a, b]\n assert_array_equal(res, desired)\n\n def test_r2array(self):\n """ Test to make sure equivalent Travis O's r2array function\n """\n assert_(atleast_2d(3).shape == (1, 1))\n assert_(atleast_2d([3j, 1]).shape == (1, 2))\n assert_(atleast_2d([[[3, 1], [4, 5]], [[3, 5], [1, 2]]]).shape == (2, 2, 2))\n\n\nclass TestAtleast3d:\n def test_0D_array(self):\n a = array(1)\n b = array(2)\n res = [atleast_3d(a), atleast_3d(b)]\n desired = [array([[[1]]]), array([[[2]]])]\n assert_array_equal(res, desired)\n\n def test_1D_array(self):\n a = array([1, 2])\n b = array([2, 3])\n res = [atleast_3d(a), atleast_3d(b)]\n desired = [array([[[1], [2]]]), array([[[2], [3]]])]\n assert_array_equal(res, desired)\n\n def test_2D_array(self):\n a = array([[1, 2], [1, 2]])\n b = array([[2, 3], [2, 3]])\n res = [atleast_3d(a), atleast_3d(b)]\n desired = [a[:, :, newaxis], b[:, :, newaxis]]\n assert_array_equal(res, desired)\n\n def test_3D_array(self):\n a = array([[1, 2], [1, 2]])\n b = array([[2, 3], [2, 3]])\n a = array([a, a])\n b = array([b, b])\n res = [atleast_3d(a), atleast_3d(b)]\n desired = [a, b]\n assert_array_equal(res, desired)\n\n\nclass TestHstack:\n def test_non_iterable(self):\n assert_raises(TypeError, hstack, 1)\n\n def test_empty_input(self):\n assert_raises(ValueError, hstack, ())\n\n def test_0D_array(self):\n a = array(1)\n b = array(2)\n res = hstack([a, b])\n desired = array([1, 2])\n assert_array_equal(res, desired)\n\n def test_1D_array(self):\n a = array([1])\n b = array([2])\n res = hstack([a, b])\n desired = array([1, 2])\n assert_array_equal(res, desired)\n\n def test_2D_array(self):\n a = array([[1], [2]])\n b = array([[1], [2]])\n res = hstack([a, b])\n desired = array([[1, 1], [2, 2]])\n assert_array_equal(res, desired)\n\n def test_generator(self):\n with pytest.raises(TypeError, match="arrays to stack must be"):\n hstack(np.arange(3) for _ in range(2))\n with pytest.raises(TypeError, match="arrays to stack must be"):\n hstack(x for x in np.ones((3, 2)))\n\n def test_casting_and_dtype(self):\n a = np.array([1, 2, 3])\n b = np.array([2.5, 3.5, 4.5])\n res = np.hstack((a, b), casting="unsafe", dtype=np.int64)\n expected_res = np.array([1, 2, 3, 2, 3, 4])\n assert_array_equal(res, expected_res)\n\n def test_casting_and_dtype_type_error(self):\n a = np.array([1, 2, 3])\n b = np.array([2.5, 3.5, 4.5])\n with pytest.raises(TypeError):\n hstack((a, b), casting="safe", dtype=np.int64)\n\n\nclass TestVstack:\n def test_non_iterable(self):\n assert_raises(TypeError, vstack, 1)\n\n def test_empty_input(self):\n assert_raises(ValueError, vstack, ())\n\n def test_0D_array(self):\n a = array(1)\n b = array(2)\n res = vstack([a, b])\n desired = array([[1], [2]])\n assert_array_equal(res, desired)\n\n def test_1D_array(self):\n a = array([1])\n b = array([2])\n res = vstack([a, b])\n desired = array([[1], [2]])\n assert_array_equal(res, desired)\n\n def test_2D_array(self):\n a = array([[1], [2]])\n b = array([[1], [2]])\n res = vstack([a, b])\n desired = array([[1], [2], [1], [2]])\n assert_array_equal(res, desired)\n\n def test_2D_array2(self):\n a = array([1, 2])\n b = array([1, 2])\n res = vstack([a, b])\n desired = array([[1, 2], [1, 2]])\n assert_array_equal(res, desired)\n\n def test_generator(self):\n with pytest.raises(TypeError, match="arrays to stack must be"):\n vstack(np.arange(3) for _ in range(2))\n\n def test_casting_and_dtype(self):\n a = np.array([1, 2, 3])\n b = np.array([2.5, 3.5, 4.5])\n res = np.vstack((a, b), casting="unsafe", dtype=np.int64)\n expected_res = np.array([[1, 2, 3], [2, 3, 4]])\n assert_array_equal(res, expected_res)\n\n def test_casting_and_dtype_type_error(self):\n a = np.array([1, 2, 3])\n b = np.array([2.5, 3.5, 4.5])\n with pytest.raises(TypeError):\n vstack((a, b), casting="safe", dtype=np.int64)\n\n\nclass TestConcatenate:\n def test_returns_copy(self):\n a = np.eye(3)\n b = np.concatenate([a])\n b[0, 0] = 2\n assert b[0, 0] != a[0, 0]\n\n def test_exceptions(self):\n # test axis must be in bounds\n for ndim in [1, 2, 3]:\n a = np.ones((1,) * ndim)\n np.concatenate((a, a), axis=0) # OK\n assert_raises(AxisError, np.concatenate, (a, a), axis=ndim)\n assert_raises(AxisError, np.concatenate, (a, a), axis=-(ndim + 1))\n\n # Scalars cannot be concatenated\n assert_raises(ValueError, concatenate, (0,))\n assert_raises(ValueError, concatenate, (np.array(0),))\n\n # dimensionality must match\n assert_raises_regex(\n ValueError,\n r"all the input arrays must have same number of dimensions, but "\n r"the array at index 0 has 1 dimension\(s\) and the array at "\n r"index 1 has 2 dimension\(s\)",\n np.concatenate, (np.zeros(1), np.zeros((1, 1))))\n\n # test shapes must match except for concatenation axis\n a = np.ones((1, 2, 3))\n b = np.ones((2, 2, 3))\n axis = list(range(3))\n for i in range(3):\n np.concatenate((a, b), axis=axis[0]) # OK\n assert_raises_regex(\n ValueError,\n "all the input array dimensions except for the concatenation axis "\n f"must match exactly, but along dimension {i}, the array at "\n "index 0 has size 1 and the array at index 1 has size 2",\n np.concatenate, (a, b), axis=axis[1])\n assert_raises(ValueError, np.concatenate, (a, b), axis=axis[2])\n a = np.moveaxis(a, -1, 0)\n b = np.moveaxis(b, -1, 0)\n axis.append(axis.pop(0))\n\n # No arrays to concatenate raises ValueError\n assert_raises(ValueError, concatenate, ())\n\n @pytest.mark.slow\n @pytest.mark.skipif(sys.maxsize < 2**32, reason="only problematic on 64bit platforms")\n @requires_memory(2 * np.iinfo(np.intc).max)\n def test_huge_list_error(self):\n a = np.array([1])\n max_int = np.iinfo(np.intc).max\n arrs = (a,) * (max_int + 1)\n msg = fr"concatenate\(\) only supports up to {max_int} arrays but got {max_int + 1}."\n with pytest.raises(ValueError, match=msg):\n np.concatenate(arrs)\n\n def test_concatenate_axis_None(self):\n a = np.arange(4, dtype=np.float64).reshape((2, 2))\n b = list(range(3))\n c = ['x']\n r = np.concatenate((a, a), axis=None)\n assert_equal(r.dtype, a.dtype)\n assert_equal(r.ndim, 1)\n r = np.concatenate((a, b), axis=None)\n assert_equal(r.size, a.size + len(b))\n assert_equal(r.dtype, a.dtype)\n r = np.concatenate((a, b, c), axis=None, dtype="U")\n d = array(['0.0', '1.0', '2.0', '3.0',\n '0', '1', '2', 'x'])\n assert_array_equal(r, d)\n\n out = np.zeros(a.size + len(b))\n r = np.concatenate((a, b), axis=None)\n rout = np.concatenate((a, b), axis=None, out=out)\n assert_(out is rout)\n assert_equal(r, rout)\n\n def test_large_concatenate_axis_None(self):\n # When no axis is given, concatenate uses flattened versions.\n # This also had a bug with many arrays (see gh-5979).\n x = np.arange(1, 100)\n r = np.concatenate(x, None)\n assert_array_equal(x, r)\n\n # Once upon a time, this was the same as `axis=None` now it fails\n # (with an unspecified error, as multiple things are wrong here)\n with pytest.raises(ValueError):\n np.concatenate(x, 100)\n\n def test_concatenate(self):\n # Test concatenate function\n # One sequence returns unmodified (but as array)\n r4 = list(range(4))\n assert_array_equal(concatenate((r4,)), r4)\n # Any sequence\n assert_array_equal(concatenate((tuple(r4),)), r4)\n assert_array_equal(concatenate((array(r4),)), r4)\n # 1D default concatenation\n r3 = list(range(3))\n assert_array_equal(concatenate((r4, r3)), r4 + r3)\n # Mixed sequence types\n assert_array_equal(concatenate((tuple(r4), r3)), r4 + r3)\n assert_array_equal(concatenate((array(r4), r3)), r4 + r3)\n # Explicit axis specification\n assert_array_equal(concatenate((r4, r3), 0), r4 + r3)\n # Including negative\n assert_array_equal(concatenate((r4, r3), -1), r4 + r3)\n # 2D\n a23 = array([[10, 11, 12], [13, 14, 15]])\n a13 = array([[0, 1, 2]])\n res = array([[10, 11, 12], [13, 14, 15], [0, 1, 2]])\n assert_array_equal(concatenate((a23, a13)), res)\n assert_array_equal(concatenate((a23, a13), 0), res)\n assert_array_equal(concatenate((a23.T, a13.T), 1), res.T)\n assert_array_equal(concatenate((a23.T, a13.T), -1), res.T)\n # Arrays much match shape\n assert_raises(ValueError, concatenate, (a23.T, a13.T), 0)\n # 3D\n res = arange(2 * 3 * 7).reshape((2, 3, 7))\n a0 = res[..., :4]\n a1 = res[..., 4:6]\n a2 = res[..., 6:]\n assert_array_equal(concatenate((a0, a1, a2), 2), res)\n assert_array_equal(concatenate((a0, a1, a2), -1), res)\n assert_array_equal(concatenate((a0.T, a1.T, a2.T), 0), res.T)\n\n out = res.copy()\n rout = concatenate((a0, a1, a2), 2, out=out)\n assert_(out is rout)\n assert_equal(res, rout)\n\n @pytest.mark.skipif(IS_PYPY, reason="PYPY handles sq_concat, nb_add differently than cpython")\n def test_operator_concat(self):\n import operator\n a = array([1, 2])\n b = array([3, 4])\n n = [1, 2]\n res = array([1, 2, 3, 4])\n assert_raises(TypeError, operator.concat, a, b)\n assert_raises(TypeError, operator.concat, a, n)\n assert_raises(TypeError, operator.concat, n, a)\n assert_raises(TypeError, operator.concat, a, 1)\n assert_raises(TypeError, operator.concat, 1, a)\n\n def test_bad_out_shape(self):\n a = array([1, 2])\n b = array([3, 4])\n\n assert_raises(ValueError, concatenate, (a, b), out=np.empty(5))\n assert_raises(ValueError, concatenate, (a, b), out=np.empty((4, 1)))\n assert_raises(ValueError, concatenate, (a, b), out=np.empty((1, 4)))\n concatenate((a, b), out=np.empty(4))\n\n @pytest.mark.parametrize("axis", [None, 0])\n @pytest.mark.parametrize("out_dtype", ["c8", "f4", "f8", ">f8", "i8", "S4"])\n @pytest.mark.parametrize("casting",\n ['no', 'equiv', 'safe', 'same_kind', 'unsafe'])\n def test_out_and_dtype(self, axis, out_dtype, casting):\n # Compare usage of `out=out` with `dtype=out.dtype`\n out = np.empty(4, dtype=out_dtype)\n to_concat = (array([1.1, 2.2]), array([3.3, 4.4]))\n\n if not np.can_cast(to_concat[0], out_dtype, casting=casting):\n with assert_raises(TypeError):\n concatenate(to_concat, out=out, axis=axis, casting=casting)\n with assert_raises(TypeError):\n concatenate(to_concat, dtype=out.dtype,\n axis=axis, casting=casting)\n else:\n res_out = concatenate(to_concat, out=out,\n axis=axis, casting=casting)\n res_dtype = concatenate(to_concat, dtype=out.dtype,\n axis=axis, casting=casting)\n assert res_out is out\n assert_array_equal(out, res_dtype)\n assert res_dtype.dtype == out_dtype\n\n with assert_raises(TypeError):\n concatenate(to_concat, out=out, dtype=out_dtype, axis=axis)\n\n @pytest.mark.parametrize("axis", [None, 0])\n @pytest.mark.parametrize("string_dt", ["S", "U", "S0", "U0"])\n @pytest.mark.parametrize("arrs",\n [([0.],), ([0.], [1]), ([0], ["string"], [1.])])\n def test_dtype_with_promotion(self, arrs, string_dt, axis):\n # Note that U0 and S0 should be deprecated eventually and changed to\n # actually give the empty string result (together with `np.array`)\n res = np.concatenate(arrs, axis=axis, dtype=string_dt, casting="unsafe")\n # The actual dtype should be identical to a cast (of a double array):\n assert res.dtype == np.array(1.).astype(string_dt).dtype\n\n @pytest.mark.parametrize("axis", [None, 0])\n def test_string_dtype_does_not_inspect(self, axis):\n with pytest.raises(TypeError):\n np.concatenate(([None], [1]), dtype="S", axis=axis)\n with pytest.raises(TypeError):\n np.concatenate(([None], [1]), dtype="U", axis=axis)\n\n @pytest.mark.parametrize("axis", [None, 0])\n def test_subarray_error(self, axis):\n with pytest.raises(TypeError, match=".*subarray dtype"):\n np.concatenate(([1], [1]), dtype="(2,)i", axis=axis)\n\n\ndef test_stack():\n # non-iterable input\n assert_raises(TypeError, stack, 1)\n\n # 0d input\n for input_ in [(1, 2, 3),\n [np.int32(1), np.int32(2), np.int32(3)],\n [np.array(1), np.array(2), np.array(3)]]:\n assert_array_equal(stack(input_), [1, 2, 3])\n # 1d input examples\n a = np.array([1, 2, 3])\n b = np.array([4, 5, 6])\n r1 = array([[1, 2, 3], [4, 5, 6]])\n assert_array_equal(np.stack((a, b)), r1)\n assert_array_equal(np.stack((a, b), axis=1), r1.T)\n # all input types\n assert_array_equal(np.stack([a, b]), r1)\n assert_array_equal(np.stack(array([a, b])), r1)\n # all shapes for 1d input\n arrays = [np.random.randn(3) for _ in range(10)]\n axes = [0, 1, -1, -2]\n expected_shapes = [(10, 3), (3, 10), (3, 10), (10, 3)]\n for axis, expected_shape in zip(axes, expected_shapes):\n assert_equal(np.stack(arrays, axis).shape, expected_shape)\n assert_raises_regex(AxisError, 'out of bounds', stack, arrays, axis=2)\n assert_raises_regex(AxisError, 'out of bounds', stack, arrays, axis=-3)\n # all shapes for 2d input\n arrays = [np.random.randn(3, 4) for _ in range(10)]\n axes = [0, 1, 2, -1, -2, -3]\n expected_shapes = [(10, 3, 4), (3, 10, 4), (3, 4, 10),\n (3, 4, 10), (3, 10, 4), (10, 3, 4)]\n for axis, expected_shape in zip(axes, expected_shapes):\n assert_equal(np.stack(arrays, axis).shape, expected_shape)\n # empty arrays\n assert_(stack([[], [], []]).shape == (3, 0))\n assert_(stack([[], [], []], axis=1).shape == (0, 3))\n # out\n out = np.zeros_like(r1)\n np.stack((a, b), out=out)\n assert_array_equal(out, r1)\n # edge cases\n assert_raises_regex(ValueError, 'need at least one array', stack, [])\n assert_raises_regex(ValueError, 'must have the same shape',\n stack, [1, np.arange(3)])\n assert_raises_regex(ValueError, 'must have the same shape',\n stack, [np.arange(3), 1])\n assert_raises_regex(ValueError, 'must have the same shape',\n stack, [np.arange(3), 1], axis=1)\n assert_raises_regex(ValueError, 'must have the same shape',\n stack, [np.zeros((3, 3)), np.zeros(3)], axis=1)\n assert_raises_regex(ValueError, 'must have the same shape',\n stack, [np.arange(2), np.arange(3)])\n\n # do not accept generators\n with pytest.raises(TypeError, match="arrays to stack must be"):\n stack(x for x in range(3))\n\n # casting and dtype test\n a = np.array([1, 2, 3])\n b = np.array([2.5, 3.5, 4.5])\n res = np.stack((a, b), axis=1, casting="unsafe", dtype=np.int64)\n expected_res = np.array([[1, 2], [2, 3], [3, 4]])\n assert_array_equal(res, expected_res)\n # casting and dtype with TypeError\n with assert_raises(TypeError):\n stack((a, b), dtype=np.int64, axis=1, casting="safe")\n\n\ndef test_unstack():\n a = np.arange(24).reshape((2, 3, 4))\n\n for stacks in [np.unstack(a),\n np.unstack(a, axis=0),\n np.unstack(a, axis=-3)]:\n assert isinstance(stacks, tuple)\n assert len(stacks) == 2\n assert_array_equal(stacks[0], a[0])\n assert_array_equal(stacks[1], a[1])\n\n for stacks in [np.unstack(a, axis=1),\n np.unstack(a, axis=-2)]:\n assert isinstance(stacks, tuple)\n assert len(stacks) == 3\n assert_array_equal(stacks[0], a[:, 0])\n assert_array_equal(stacks[1], a[:, 1])\n assert_array_equal(stacks[2], a[:, 2])\n\n for stacks in [np.unstack(a, axis=2),\n np.unstack(a, axis=-1)]:\n assert isinstance(stacks, tuple)\n assert len(stacks) == 4\n assert_array_equal(stacks[0], a[:, :, 0])\n assert_array_equal(stacks[1], a[:, :, 1])\n assert_array_equal(stacks[2], a[:, :, 2])\n assert_array_equal(stacks[3], a[:, :, 3])\n\n assert_raises(ValueError, np.unstack, a, axis=3)\n assert_raises(ValueError, np.unstack, a, axis=-4)\n assert_raises(ValueError, np.unstack, np.array(0), axis=0)\n\n\n@pytest.mark.parametrize("axis", [0])\n@pytest.mark.parametrize("out_dtype", ["c8", "f4", "f8", ">f8", "i8"])\n@pytest.mark.parametrize("casting",\n ['no', 'equiv', 'safe', 'same_kind', 'unsafe'])\ndef test_stack_out_and_dtype(axis, out_dtype, casting):\n to_concat = (array([1, 2]), array([3, 4]))\n res = array([[1, 2], [3, 4]])\n out = np.zeros_like(res)\n\n if not np.can_cast(to_concat[0], out_dtype, casting=casting):\n with assert_raises(TypeError):\n stack(to_concat, dtype=out_dtype,\n axis=axis, casting=casting)\n else:\n res_out = stack(to_concat, out=out,\n axis=axis, casting=casting)\n res_dtype = stack(to_concat, dtype=out_dtype,\n axis=axis, casting=casting)\n assert res_out is out\n assert_array_equal(out, res_dtype)\n assert res_dtype.dtype == out_dtype\n\n with assert_raises(TypeError):\n stack(to_concat, out=out, dtype=out_dtype, axis=axis)\n\n\nclass TestBlock:\n @pytest.fixture(params=['block', 'force_concatenate', 'force_slicing'])\n def block(self, request):\n # blocking small arrays and large arrays go through different paths.\n # the algorithm is triggered depending on the number of element\n # copies required.\n # We define a test fixture that forces most tests to go through\n # both code paths.\n # Ultimately, this should be removed if a single algorithm is found\n # to be faster for both small and large arrays.\n def _block_force_concatenate(arrays):\n arrays, list_ndim, result_ndim, _ = _block_setup(arrays)\n return _block_concatenate(arrays, list_ndim, result_ndim)\n\n def _block_force_slicing(arrays):\n arrays, list_ndim, result_ndim, _ = _block_setup(arrays)\n return _block_slicing(arrays, list_ndim, result_ndim)\n\n if request.param == 'force_concatenate':\n return _block_force_concatenate\n elif request.param == 'force_slicing':\n return _block_force_slicing\n elif request.param == 'block':\n return block\n else:\n raise ValueError('Unknown blocking request. There is a typo in the tests.')\n\n def test_returns_copy(self, block):\n a = np.eye(3)\n b = block(a)\n b[0, 0] = 2\n assert b[0, 0] != a[0, 0]\n\n def test_block_total_size_estimate(self, block):\n _, _, _, total_size = _block_setup([1])\n assert total_size == 1\n\n _, _, _, total_size = _block_setup([[1]])\n assert total_size == 1\n\n _, _, _, total_size = _block_setup([[1, 1]])\n assert total_size == 2\n\n _, _, _, total_size = _block_setup([[1], [1]])\n assert total_size == 2\n\n _, _, _, total_size = _block_setup([[1, 2], [3, 4]])\n assert total_size == 4\n\n def test_block_simple_row_wise(self, block):\n a_2d = np.ones((2, 2))\n b_2d = 2 * a_2d\n desired = np.array([[1, 1, 2, 2],\n [1, 1, 2, 2]])\n result = block([a_2d, b_2d])\n assert_equal(desired, result)\n\n def test_block_simple_column_wise(self, block):\n a_2d = np.ones((2, 2))\n b_2d = 2 * a_2d\n expected = np.array([[1, 1],\n [1, 1],\n [2, 2],\n [2, 2]])\n result = block([[a_2d], [b_2d]])\n assert_equal(expected, result)\n\n def test_block_with_1d_arrays_row_wise(self, block):\n # # # 1-D vectors are treated as row arrays\n a = np.array([1, 2, 3])\n b = np.array([2, 3, 4])\n expected = np.array([1, 2, 3, 2, 3, 4])\n result = block([a, b])\n assert_equal(expected, result)\n\n def test_block_with_1d_arrays_multiple_rows(self, block):\n a = np.array([1, 2, 3])\n b = np.array([2, 3, 4])\n expected = np.array([[1, 2, 3, 2, 3, 4],\n [1, 2, 3, 2, 3, 4]])\n result = block([[a, b], [a, b]])\n assert_equal(expected, result)\n\n def test_block_with_1d_arrays_column_wise(self, block):\n # # # 1-D vectors are treated as row arrays\n a_1d = np.array([1, 2, 3])\n b_1d = np.array([2, 3, 4])\n expected = np.array([[1, 2, 3],\n [2, 3, 4]])\n result = block([[a_1d], [b_1d]])\n assert_equal(expected, result)\n\n def test_block_mixed_1d_and_2d(self, block):\n a_2d = np.ones((2, 2))\n b_1d = np.array([2, 2])\n result = block([[a_2d], [b_1d]])\n expected = np.array([[1, 1],\n [1, 1],\n [2, 2]])\n assert_equal(expected, result)\n\n def test_block_complicated(self, block):\n # a bit more complicated\n one_2d = np.array([[1, 1, 1]])\n two_2d = np.array([[2, 2, 2]])\n three_2d = np.array([[3, 3, 3, 3, 3, 3]])\n four_1d = np.array([4, 4, 4, 4, 4, 4])\n five_0d = np.array(5)\n six_1d = np.array([6, 6, 6, 6, 6])\n zero_2d = np.zeros((2, 6))\n\n expected = np.array([[1, 1, 1, 2, 2, 2],\n [3, 3, 3, 3, 3, 3],\n [4, 4, 4, 4, 4, 4],\n [5, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]])\n\n result = block([[one_2d, two_2d],\n [three_2d],\n [four_1d],\n [five_0d, six_1d],\n [zero_2d]])\n assert_equal(result, expected)\n\n def test_nested(self, block):\n one = np.array([1, 1, 1])\n two = np.array([[2, 2, 2], [2, 2, 2], [2, 2, 2]])\n three = np.array([3, 3, 3])\n four = np.array([4, 4, 4])\n five = np.array(5)\n six = np.array([6, 6, 6, 6, 6])\n zero = np.zeros((2, 6))\n\n result = block([\n [\n block([\n [one],\n [three],\n [four]\n ]),\n two\n ],\n [five, six],\n [zero]\n ])\n expected = np.array([[1, 1, 1, 2, 2, 2],\n [3, 3, 3, 2, 2, 2],\n [4, 4, 4, 2, 2, 2],\n [5, 6, 6, 6, 6, 6],\n [0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]])\n\n assert_equal(result, expected)\n\n def test_3d(self, block):\n a000 = np.ones((2, 2, 2), int) * 1\n\n a100 = np.ones((3, 2, 2), int) * 2\n a010 = np.ones((2, 3, 2), int) * 3\n a001 = np.ones((2, 2, 3), int) * 4\n\n a011 = np.ones((2, 3, 3), int) * 5\n a101 = np.ones((3, 2, 3), int) * 6\n a110 = np.ones((3, 3, 2), int) * 7\n\n a111 = np.ones((3, 3, 3), int) * 8\n\n result = block([\n [\n [a000, a001],\n [a010, a011],\n ],\n [\n [a100, a101],\n [a110, a111],\n ]\n ])\n expected = array([[[1, 1, 4, 4, 4],\n [1, 1, 4, 4, 4],\n [3, 3, 5, 5, 5],\n [3, 3, 5, 5, 5],\n [3, 3, 5, 5, 5]],\n\n [[1, 1, 4, 4, 4],\n [1, 1, 4, 4, 4],\n [3, 3, 5, 5, 5],\n [3, 3, 5, 5, 5],\n [3, 3, 5, 5, 5]],\n\n [[2, 2, 6, 6, 6],\n [2, 2, 6, 6, 6],\n [7, 7, 8, 8, 8],\n [7, 7, 8, 8, 8],\n [7, 7, 8, 8, 8]],\n\n [[2, 2, 6, 6, 6],\n [2, 2, 6, 6, 6],\n [7, 7, 8, 8, 8],\n [7, 7, 8, 8, 8],\n [7, 7, 8, 8, 8]],\n\n [[2, 2, 6, 6, 6],\n [2, 2, 6, 6, 6],\n [7, 7, 8, 8, 8],\n [7, 7, 8, 8, 8],\n [7, 7, 8, 8, 8]]])\n\n assert_array_equal(result, expected)\n\n def test_block_with_mismatched_shape(self, block):\n a = np.array([0, 0])\n b = np.eye(2)\n assert_raises(ValueError, block, [a, b])\n assert_raises(ValueError, block, [b, a])\n\n to_block = [[np.ones((2, 3)), np.ones((2, 2))],\n [np.ones((2, 2)), np.ones((2, 2))]]\n assert_raises(ValueError, block, to_block)\n\n def test_no_lists(self, block):\n assert_equal(block(1), np.array(1))\n assert_equal(block(np.eye(3)), np.eye(3))\n\n def test_invalid_nesting(self, block):\n msg = 'depths are mismatched'\n assert_raises_regex(ValueError, msg, block, [1, [2]])\n assert_raises_regex(ValueError, msg, block, [1, []])\n assert_raises_regex(ValueError, msg, block, [[1], 2])\n assert_raises_regex(ValueError, msg, block, [[], 2])\n assert_raises_regex(ValueError, msg, block, [\n [[1], [2]],\n [[3, 4]],\n [5] # missing brackets\n ])\n\n def test_empty_lists(self, block):\n assert_raises_regex(ValueError, 'empty', block, [])\n assert_raises_regex(ValueError, 'empty', block, [[]])\n assert_raises_regex(ValueError, 'empty', block, [[1], []])\n\n def test_tuple(self, block):\n assert_raises_regex(TypeError, 'tuple', block, ([1, 2], [3, 4]))\n assert_raises_regex(TypeError, 'tuple', block, [(1, 2), (3, 4)])\n\n def test_different_ndims(self, block):\n a = 1.\n b = 2 * np.ones((1, 2))\n c = 3 * np.ones((1, 1, 3))\n\n result = block([a, b, c])\n expected = np.array([[[1., 2., 2., 3., 3., 3.]]])\n\n assert_equal(result, expected)\n\n def test_different_ndims_depths(self, block):\n a = 1.\n b = 2 * np.ones((1, 2))\n c = 3 * np.ones((1, 2, 3))\n\n result = block([[a, b], [c]])\n expected = np.array([[[1., 2., 2.],\n [3., 3., 3.],\n [3., 3., 3.]]])\n\n assert_equal(result, expected)\n\n def test_block_memory_order(self, block):\n # 3D\n arr_c = np.zeros((3,) * 3, order='C')\n arr_f = np.zeros((3,) * 3, order='F')\n\n b_c = [[[arr_c, arr_c],\n [arr_c, arr_c]],\n [[arr_c, arr_c],\n [arr_c, arr_c]]]\n\n b_f = [[[arr_f, arr_f],\n [arr_f, arr_f]],\n [[arr_f, arr_f],\n [arr_f, arr_f]]]\n\n assert block(b_c).flags['C_CONTIGUOUS']\n assert block(b_f).flags['F_CONTIGUOUS']\n\n arr_c = np.zeros((3, 3), order='C')\n arr_f = np.zeros((3, 3), order='F')\n # 2D\n b_c = [[arr_c, arr_c],\n [arr_c, arr_c]]\n\n b_f = [[arr_f, arr_f],\n [arr_f, arr_f]]\n\n assert block(b_c).flags['C_CONTIGUOUS']\n assert block(b_f).flags['F_CONTIGUOUS']\n\n\ndef test_block_dispatcher():\n class ArrayLike:\n pass\n a = ArrayLike()\n b = ArrayLike()\n c = ArrayLike()\n assert_equal(list(_block_dispatcher(a)), [a])\n assert_equal(list(_block_dispatcher([a])), [a])\n assert_equal(list(_block_dispatcher([a, b])), [a, b])\n assert_equal(list(_block_dispatcher([[a], [b, [c]]])), [a, b, c])\n # don't recurse into non-lists\n assert_equal(list(_block_dispatcher((a, b))), [(a, b)])\n | .venv\Lib\site-packages\numpy\_core\tests\test_shape_base.py | test_shape_base.py | Python | 32,427 | 0.95 | 0.1156 | 0.062992 | vue-tools | 348 | 2025-05-03T09:57:25.155731 | MIT | true | 1258ec31989a81d38cf5e5a4894603ec |
# NOTE: Please avoid the use of numpy.testing since NPYV intrinsics\n# may be involved in their functionality.\nimport itertools\nimport math\nimport operator\nimport re\n\nimport pytest\nfrom numpy._core._multiarray_umath import __cpu_baseline__\n\nfrom numpy._core._simd import clear_floatstatus, get_floatstatus, targets\n\n\ndef check_floatstatus(divbyzero=False, overflow=False,\n underflow=False, invalid=False,\n all=False):\n #define NPY_FPE_DIVIDEBYZERO 1\n #define NPY_FPE_OVERFLOW 2\n #define NPY_FPE_UNDERFLOW 4\n #define NPY_FPE_INVALID 8\n err = get_floatstatus()\n ret = (all or divbyzero) and (err & 1) != 0\n ret |= (all or overflow) and (err & 2) != 0\n ret |= (all or underflow) and (err & 4) != 0\n ret |= (all or invalid) and (err & 8) != 0\n return ret\n\nclass _Test_Utility:\n # submodule of the desired SIMD extension, e.g. targets["AVX512F"]\n npyv = None\n # the current data type suffix e.g. 's8'\n sfx = None\n # target name can be 'baseline' or one or more of CPU features\n target_name = None\n\n def __getattr__(self, attr):\n """\n To call NPV intrinsics without the attribute 'npyv' and\n auto suffixing intrinsics according to class attribute 'sfx'\n """\n return getattr(self.npyv, attr + "_" + self.sfx)\n\n def _x2(self, intrin_name):\n return getattr(self.npyv, f"{intrin_name}_{self.sfx}x2")\n\n def _data(self, start=None, count=None, reverse=False):\n """\n Create list of consecutive numbers according to number of vector's lanes.\n """\n if start is None:\n start = 1\n if count is None:\n count = self.nlanes\n rng = range(start, start + count)\n if reverse:\n rng = reversed(rng)\n if self._is_fp():\n return [x / 1.0 for x in rng]\n return list(rng)\n\n def _is_unsigned(self):\n return self.sfx[0] == 'u'\n\n def _is_signed(self):\n return self.sfx[0] == 's'\n\n def _is_fp(self):\n return self.sfx[0] == 'f'\n\n def _scalar_size(self):\n return int(self.sfx[1:])\n\n def _int_clip(self, seq):\n if self._is_fp():\n return seq\n max_int = self._int_max()\n min_int = self._int_min()\n return [min(max(v, min_int), max_int) for v in seq]\n\n def _int_max(self):\n if self._is_fp():\n return None\n max_u = self._to_unsigned(self.setall(-1))[0]\n if self._is_signed():\n return max_u // 2\n return max_u\n\n def _int_min(self):\n if self._is_fp():\n return None\n if self._is_unsigned():\n return 0\n return -(self._int_max() + 1)\n\n def _true_mask(self):\n max_unsig = getattr(self.npyv, "setall_u" + self.sfx[1:])(-1)\n return max_unsig[0]\n\n def _to_unsigned(self, vector):\n if isinstance(vector, (list, tuple)):\n return getattr(self.npyv, "load_u" + self.sfx[1:])(vector)\n else:\n sfx = vector.__name__.replace("npyv_", "")\n if sfx[0] == "b":\n cvt_intrin = "cvt_u{0}_b{0}"\n else:\n cvt_intrin = "reinterpret_u{0}_{1}"\n return getattr(self.npyv, cvt_intrin.format(sfx[1:], sfx))(vector)\n\n def _pinfinity(self):\n return float("inf")\n\n def _ninfinity(self):\n return -float("inf")\n\n def _nan(self):\n return float("nan")\n\n def _cpu_features(self):\n target = self.target_name\n if target == "baseline":\n target = __cpu_baseline__\n else:\n target = target.split('__') # multi-target separator\n return ' '.join(target)\n\nclass _SIMD_BOOL(_Test_Utility):\n """\n To test all boolean vector types at once\n """\n def _nlanes(self):\n return getattr(self.npyv, "nlanes_u" + self.sfx[1:])\n\n def _data(self, start=None, count=None, reverse=False):\n true_mask = self._true_mask()\n rng = range(self._nlanes())\n if reverse:\n rng = reversed(rng)\n return [true_mask if x % 2 else 0 for x in rng]\n\n def _load_b(self, data):\n len_str = self.sfx[1:]\n load = getattr(self.npyv, "load_u" + len_str)\n cvt = getattr(self.npyv, f"cvt_b{len_str}_u{len_str}")\n return cvt(load(data))\n\n def test_operators_logical(self):\n """\n Logical operations for boolean types.\n Test intrinsics:\n npyv_xor_##SFX, npyv_and_##SFX, npyv_or_##SFX, npyv_not_##SFX,\n npyv_andc_b8, npvy_orc_b8, nvpy_xnor_b8\n """\n data_a = self._data()\n data_b = self._data(reverse=True)\n vdata_a = self._load_b(data_a)\n vdata_b = self._load_b(data_b)\n\n data_and = [a & b for a, b in zip(data_a, data_b)]\n vand = getattr(self, "and")(vdata_a, vdata_b)\n assert vand == data_and\n\n data_or = [a | b for a, b in zip(data_a, data_b)]\n vor = getattr(self, "or")(vdata_a, vdata_b)\n assert vor == data_or\n\n data_xor = [a ^ b for a, b in zip(data_a, data_b)]\n vxor = self.xor(vdata_a, vdata_b)\n assert vxor == data_xor\n\n vnot = getattr(self, "not")(vdata_a)\n assert vnot == data_b\n\n # among the boolean types, andc, orc and xnor only support b8\n if self.sfx not in ("b8"):\n return\n\n data_andc = [(a & ~b) & 0xFF for a, b in zip(data_a, data_b)]\n vandc = self.andc(vdata_a, vdata_b)\n assert data_andc == vandc\n\n data_orc = [(a | ~b) & 0xFF for a, b in zip(data_a, data_b)]\n vorc = self.orc(vdata_a, vdata_b)\n assert data_orc == vorc\n\n data_xnor = [~(a ^ b) & 0xFF for a, b in zip(data_a, data_b)]\n vxnor = self.xnor(vdata_a, vdata_b)\n assert data_xnor == vxnor\n\n def test_tobits(self):\n data2bits = lambda data: sum(int(x != 0) << i for i, x in enumerate(data, 0))\n for data in (self._data(), self._data(reverse=True)):\n vdata = self._load_b(data)\n data_bits = data2bits(data)\n tobits = self.tobits(vdata)\n bin_tobits = bin(tobits)\n assert bin_tobits == bin(data_bits)\n\n def test_pack(self):\n """\n Pack multiple vectors into one\n Test intrinsics:\n npyv_pack_b8_b16\n npyv_pack_b8_b32\n npyv_pack_b8_b64\n """\n if self.sfx not in ("b16", "b32", "b64"):\n return\n # create the vectors\n data = self._data()\n rdata = self._data(reverse=True)\n vdata = self._load_b(data)\n vrdata = self._load_b(rdata)\n pack_simd = getattr(self.npyv, f"pack_b8_{self.sfx}")\n # for scalar execution, concatenate the elements of the multiple lists\n # into a single list (spack) and then iterate over the elements of\n # the created list applying a mask to capture the first byte of them.\n if self.sfx == "b16":\n spack = [(i & 0xFF) for i in (list(rdata) + list(data))]\n vpack = pack_simd(vrdata, vdata)\n elif self.sfx == "b32":\n spack = [(i & 0xFF) for i in (2 * list(rdata) + 2 * list(data))]\n vpack = pack_simd(vrdata, vrdata, vdata, vdata)\n elif self.sfx == "b64":\n spack = [(i & 0xFF) for i in (4 * list(rdata) + 4 * list(data))]\n vpack = pack_simd(vrdata, vrdata, vrdata, vrdata,\n vdata, vdata, vdata, vdata)\n assert vpack == spack\n\n @pytest.mark.parametrize("intrin", ["any", "all"])\n @pytest.mark.parametrize("data", (\n [-1, 0],\n [0, -1],\n [-1],\n [0]\n ))\n def test_operators_crosstest(self, intrin, data):\n """\n Test intrinsics:\n npyv_any_##SFX\n npyv_all_##SFX\n """\n data_a = self._load_b(data * self._nlanes())\n func = eval(intrin)\n intrin = getattr(self, intrin)\n desired = func(data_a)\n simd = intrin(data_a)\n assert not not simd == desired\n\nclass _SIMD_INT(_Test_Utility):\n """\n To test all integer vector types at once\n """\n def test_operators_shift(self):\n if self.sfx in ("u8", "s8"):\n return\n\n data_a = self._data(self._int_max() - self.nlanes)\n data_b = self._data(self._int_min(), reverse=True)\n vdata_a, vdata_b = self.load(data_a), self.load(data_b)\n\n for count in range(self._scalar_size()):\n # load to cast\n data_shl_a = self.load([a << count for a in data_a])\n # left shift\n shl = self.shl(vdata_a, count)\n assert shl == data_shl_a\n # load to cast\n data_shr_a = self.load([a >> count for a in data_a])\n # right shift\n shr = self.shr(vdata_a, count)\n assert shr == data_shr_a\n\n # shift by zero or max or out-range immediate constant is not applicable and illogical\n for count in range(1, self._scalar_size()):\n # load to cast\n data_shl_a = self.load([a << count for a in data_a])\n # left shift by an immediate constant\n shli = self.shli(vdata_a, count)\n assert shli == data_shl_a\n # load to cast\n data_shr_a = self.load([a >> count for a in data_a])\n # right shift by an immediate constant\n shri = self.shri(vdata_a, count)\n assert shri == data_shr_a\n\n def test_arithmetic_subadd_saturated(self):\n if self.sfx in ("u32", "s32", "u64", "s64"):\n return\n\n data_a = self._data(self._int_max() - self.nlanes)\n data_b = self._data(self._int_min(), reverse=True)\n vdata_a, vdata_b = self.load(data_a), self.load(data_b)\n\n data_adds = self._int_clip([a + b for a, b in zip(data_a, data_b)])\n adds = self.adds(vdata_a, vdata_b)\n assert adds == data_adds\n\n data_subs = self._int_clip([a - b for a, b in zip(data_a, data_b)])\n subs = self.subs(vdata_a, vdata_b)\n assert subs == data_subs\n\n def test_math_max_min(self):\n data_a = self._data()\n data_b = self._data(self.nlanes)\n vdata_a, vdata_b = self.load(data_a), self.load(data_b)\n\n data_max = [max(a, b) for a, b in zip(data_a, data_b)]\n simd_max = self.max(vdata_a, vdata_b)\n assert simd_max == data_max\n\n data_min = [min(a, b) for a, b in zip(data_a, data_b)]\n simd_min = self.min(vdata_a, vdata_b)\n assert simd_min == data_min\n\n @pytest.mark.parametrize("start", [-100, -10000, 0, 100, 10000])\n def test_reduce_max_min(self, start):\n """\n Test intrinsics:\n npyv_reduce_max_##sfx\n npyv_reduce_min_##sfx\n """\n vdata_a = self.load(self._data(start))\n assert self.reduce_max(vdata_a) == max(vdata_a)\n assert self.reduce_min(vdata_a) == min(vdata_a)\n\n\nclass _SIMD_FP32(_Test_Utility):\n """\n To only test single precision\n """\n def test_conversions(self):\n """\n Round to nearest even integer, assume CPU control register is set to rounding.\n Test intrinsics:\n npyv_round_s32_##SFX\n """\n features = self._cpu_features()\n if not self.npyv.simd_f64 and re.match(r".*(NEON|ASIMD)", features):\n # very costly to emulate nearest even on Armv7\n # instead we round halves to up. e.g. 0.5 -> 1, -0.5 -> -1\n _round = lambda v: int(v + (0.5 if v >= 0 else -0.5))\n else:\n _round = round\n vdata_a = self.load(self._data())\n vdata_a = self.sub(vdata_a, self.setall(0.5))\n data_round = [_round(x) for x in vdata_a]\n vround = self.round_s32(vdata_a)\n assert vround == data_round\n\nclass _SIMD_FP64(_Test_Utility):\n """\n To only test double precision\n """\n def test_conversions(self):\n """\n Round to nearest even integer, assume CPU control register is set to rounding.\n Test intrinsics:\n npyv_round_s32_##SFX\n """\n vdata_a = self.load(self._data())\n vdata_a = self.sub(vdata_a, self.setall(0.5))\n vdata_b = self.mul(vdata_a, self.setall(-1.5))\n data_round = [round(x) for x in list(vdata_a) + list(vdata_b)]\n vround = self.round_s32(vdata_a, vdata_b)\n assert vround == data_round\n\nclass _SIMD_FP(_Test_Utility):\n """\n To test all float vector types at once\n """\n def test_arithmetic_fused(self):\n vdata_a, vdata_b, vdata_c = [self.load(self._data())] * 3\n vdata_cx2 = self.add(vdata_c, vdata_c)\n # multiply and add, a*b + c\n data_fma = self.load([a * b + c for a, b, c in zip(vdata_a, vdata_b, vdata_c)])\n fma = self.muladd(vdata_a, vdata_b, vdata_c)\n assert fma == data_fma\n # multiply and subtract, a*b - c\n fms = self.mulsub(vdata_a, vdata_b, vdata_c)\n data_fms = self.sub(data_fma, vdata_cx2)\n assert fms == data_fms\n # negate multiply and add, -(a*b) + c\n nfma = self.nmuladd(vdata_a, vdata_b, vdata_c)\n data_nfma = self.sub(vdata_cx2, data_fma)\n assert nfma == data_nfma\n # negate multiply and subtract, -(a*b) - c\n nfms = self.nmulsub(vdata_a, vdata_b, vdata_c)\n data_nfms = self.mul(data_fma, self.setall(-1))\n assert nfms == data_nfms\n # multiply, add for odd elements and subtract even elements.\n # (a * b) -+ c\n fmas = list(self.muladdsub(vdata_a, vdata_b, vdata_c))\n assert fmas[0::2] == list(data_fms)[0::2]\n assert fmas[1::2] == list(data_fma)[1::2]\n\n def test_abs(self):\n pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()\n data = self._data()\n vdata = self.load(self._data())\n\n abs_cases = ((-0, 0), (ninf, pinf), (pinf, pinf), (nan, nan))\n for case, desired in abs_cases:\n data_abs = [desired] * self.nlanes\n vabs = self.abs(self.setall(case))\n assert vabs == pytest.approx(data_abs, nan_ok=True)\n\n vabs = self.abs(self.mul(vdata, self.setall(-1)))\n assert vabs == data\n\n def test_sqrt(self):\n pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()\n data = self._data()\n vdata = self.load(self._data())\n\n sqrt_cases = ((-0.0, -0.0), (0.0, 0.0), (-1.0, nan), (ninf, nan), (pinf, pinf))\n for case, desired in sqrt_cases:\n data_sqrt = [desired] * self.nlanes\n sqrt = self.sqrt(self.setall(case))\n assert sqrt == pytest.approx(data_sqrt, nan_ok=True)\n\n data_sqrt = self.load([math.sqrt(x) for x in data]) # load to truncate precision\n sqrt = self.sqrt(vdata)\n assert sqrt == data_sqrt\n\n def test_square(self):\n pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()\n data = self._data()\n vdata = self.load(self._data())\n # square\n square_cases = ((nan, nan), (pinf, pinf), (ninf, pinf))\n for case, desired in square_cases:\n data_square = [desired] * self.nlanes\n square = self.square(self.setall(case))\n assert square == pytest.approx(data_square, nan_ok=True)\n\n data_square = [x * x for x in data]\n square = self.square(vdata)\n assert square == data_square\n\n @pytest.mark.parametrize("intrin, func", [("ceil", math.ceil),\n ("trunc", math.trunc), ("floor", math.floor), ("rint", round)])\n def test_rounding(self, intrin, func):\n """\n Test intrinsics:\n npyv_rint_##SFX\n npyv_ceil_##SFX\n npyv_trunc_##SFX\n npyv_floor##SFX\n """\n intrin_name = intrin\n intrin = getattr(self, intrin)\n pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()\n # special cases\n round_cases = ((nan, nan), (pinf, pinf), (ninf, ninf))\n for case, desired in round_cases:\n data_round = [desired] * self.nlanes\n _round = intrin(self.setall(case))\n assert _round == pytest.approx(data_round, nan_ok=True)\n\n for x in range(0, 2**20, 256**2):\n for w in (-1.05, -1.10, -1.15, 1.05, 1.10, 1.15):\n data = self.load([(x + a) * w for a in range(self.nlanes)])\n data_round = [func(x) for x in data]\n _round = intrin(data)\n assert _round == data_round\n\n # test large numbers\n for i in (\n 1.1529215045988576e+18, 4.6116860183954304e+18,\n 5.902958103546122e+20, 2.3611832414184488e+21\n ):\n x = self.setall(i)\n y = intrin(x)\n data_round = [func(n) for n in x]\n assert y == data_round\n\n # signed zero\n if intrin_name == "floor":\n data_szero = (-0.0,)\n else:\n data_szero = (-0.0, -0.25, -0.30, -0.45, -0.5)\n\n for w in data_szero:\n _round = self._to_unsigned(intrin(self.setall(w)))\n data_round = self._to_unsigned(self.setall(-0.0))\n assert _round == data_round\n\n @pytest.mark.parametrize("intrin", [\n "max", "maxp", "maxn", "min", "minp", "minn"\n ])\n def test_max_min(self, intrin):\n """\n Test intrinsics:\n npyv_max_##sfx\n npyv_maxp_##sfx\n npyv_maxn_##sfx\n npyv_min_##sfx\n npyv_minp_##sfx\n npyv_minn_##sfx\n npyv_reduce_max_##sfx\n npyv_reduce_maxp_##sfx\n npyv_reduce_maxn_##sfx\n npyv_reduce_min_##sfx\n npyv_reduce_minp_##sfx\n npyv_reduce_minn_##sfx\n """\n pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()\n chk_nan = {"xp": 1, "np": 1, "nn": 2, "xn": 2}.get(intrin[-2:], 0)\n func = eval(intrin[:3])\n reduce_intrin = getattr(self, "reduce_" + intrin)\n intrin = getattr(self, intrin)\n hf_nlanes = self.nlanes // 2\n\n cases = (\n ([0.0, -0.0], [-0.0, 0.0]),\n ([10, -10], [10, -10]),\n ([pinf, 10], [10, ninf]),\n ([10, pinf], [ninf, 10]),\n ([10, -10], [10, -10]),\n ([-10, 10], [-10, 10])\n )\n for op1, op2 in cases:\n vdata_a = self.load(op1 * hf_nlanes)\n vdata_b = self.load(op2 * hf_nlanes)\n data = func(vdata_a, vdata_b)\n simd = intrin(vdata_a, vdata_b)\n assert simd == data\n data = func(vdata_a)\n simd = reduce_intrin(vdata_a)\n assert simd == data\n\n if not chk_nan:\n return\n if chk_nan == 1:\n test_nan = lambda a, b: (\n b if math.isnan(a) else a if math.isnan(b) else b\n )\n else:\n test_nan = lambda a, b: (\n nan if math.isnan(a) or math.isnan(b) else b\n )\n cases = (\n (nan, 10),\n (10, nan),\n (nan, pinf),\n (pinf, nan),\n (nan, nan)\n )\n for op1, op2 in cases:\n vdata_ab = self.load([op1, op2] * hf_nlanes)\n data = test_nan(op1, op2)\n simd = reduce_intrin(vdata_ab)\n assert simd == pytest.approx(data, nan_ok=True)\n vdata_a = self.setall(op1)\n vdata_b = self.setall(op2)\n data = [data] * self.nlanes\n simd = intrin(vdata_a, vdata_b)\n assert simd == pytest.approx(data, nan_ok=True)\n\n def test_reciprocal(self):\n pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()\n data = self._data()\n vdata = self.load(self._data())\n\n recip_cases = ((nan, nan), (pinf, 0.0), (ninf, -0.0), (0.0, pinf), (-0.0, ninf))\n for case, desired in recip_cases:\n data_recip = [desired] * self.nlanes\n recip = self.recip(self.setall(case))\n assert recip == pytest.approx(data_recip, nan_ok=True)\n\n data_recip = self.load([1 / x for x in data]) # load to truncate precision\n recip = self.recip(vdata)\n assert recip == data_recip\n\n def test_special_cases(self):\n """\n Compare Not NaN. Test intrinsics:\n npyv_notnan_##SFX\n """\n nnan = self.notnan(self.setall(self._nan()))\n assert nnan == [0] * self.nlanes\n\n @pytest.mark.parametrize("intrin_name", [\n "rint", "trunc", "ceil", "floor"\n ])\n def test_unary_invalid_fpexception(self, intrin_name):\n intrin = getattr(self, intrin_name)\n for d in [float("nan"), float("inf"), -float("inf")]:\n v = self.setall(d)\n clear_floatstatus()\n intrin(v)\n assert check_floatstatus(invalid=True) is False\n\n @pytest.mark.parametrize('py_comp,np_comp', [\n (operator.lt, "cmplt"),\n (operator.le, "cmple"),\n (operator.gt, "cmpgt"),\n (operator.ge, "cmpge"),\n (operator.eq, "cmpeq"),\n (operator.ne, "cmpneq")\n ])\n def test_comparison_with_nan(self, py_comp, np_comp):\n pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()\n mask_true = self._true_mask()\n\n def to_bool(vector):\n return [lane == mask_true for lane in vector]\n\n intrin = getattr(self, np_comp)\n cmp_cases = ((0, nan), (nan, 0), (nan, nan), (pinf, nan),\n (ninf, nan), (-0.0, +0.0))\n for case_operand1, case_operand2 in cmp_cases:\n data_a = [case_operand1] * self.nlanes\n data_b = [case_operand2] * self.nlanes\n vdata_a = self.setall(case_operand1)\n vdata_b = self.setall(case_operand2)\n vcmp = to_bool(intrin(vdata_a, vdata_b))\n data_cmp = [py_comp(a, b) for a, b in zip(data_a, data_b)]\n assert vcmp == data_cmp\n\n @pytest.mark.parametrize("intrin", ["any", "all"])\n @pytest.mark.parametrize("data", (\n [float("nan"), 0],\n [0, float("nan")],\n [float("nan"), 1],\n [1, float("nan")],\n [float("nan"), float("nan")],\n [0.0, -0.0],\n [-0.0, 0.0],\n [1.0, -0.0]\n ))\n def test_operators_crosstest(self, intrin, data):\n """\n Test intrinsics:\n npyv_any_##SFX\n npyv_all_##SFX\n """\n data_a = self.load(data * self.nlanes)\n func = eval(intrin)\n intrin = getattr(self, intrin)\n desired = func(data_a)\n simd = intrin(data_a)\n assert not not simd == desired\n\nclass _SIMD_ALL(_Test_Utility):\n """\n To test all vector types at once\n """\n def test_memory_load(self):\n data = self._data()\n # unaligned load\n load_data = self.load(data)\n assert load_data == data\n # aligned load\n loada_data = self.loada(data)\n assert loada_data == data\n # stream load\n loads_data = self.loads(data)\n assert loads_data == data\n # load lower part\n loadl = self.loadl(data)\n loadl_half = list(loadl)[:self.nlanes // 2]\n data_half = data[:self.nlanes // 2]\n assert loadl_half == data_half\n assert loadl != data # detect overflow\n\n def test_memory_store(self):\n data = self._data()\n vdata = self.load(data)\n # unaligned store\n store = [0] * self.nlanes\n self.store(store, vdata)\n assert store == data\n # aligned store\n store_a = [0] * self.nlanes\n self.storea(store_a, vdata)\n assert store_a == data\n # stream store\n store_s = [0] * self.nlanes\n self.stores(store_s, vdata)\n assert store_s == data\n # store lower part\n store_l = [0] * self.nlanes\n self.storel(store_l, vdata)\n assert store_l[:self.nlanes // 2] == data[:self.nlanes // 2]\n assert store_l != vdata # detect overflow\n # store higher part\n store_h = [0] * self.nlanes\n self.storeh(store_h, vdata)\n assert store_h[:self.nlanes // 2] == data[self.nlanes // 2:]\n assert store_h != vdata # detect overflow\n\n @pytest.mark.parametrize("intrin, elsizes, scale, fill", [\n ("self.load_tillz, self.load_till", (32, 64), 1, [0xffff]),\n ("self.load2_tillz, self.load2_till", (32, 64), 2, [0xffff, 0x7fff]),\n ])\n def test_memory_partial_load(self, intrin, elsizes, scale, fill):\n if self._scalar_size() not in elsizes:\n return\n npyv_load_tillz, npyv_load_till = eval(intrin)\n data = self._data()\n lanes = list(range(1, self.nlanes + 1))\n lanes += [self.nlanes**2, self.nlanes**4] # test out of range\n for n in lanes:\n load_till = npyv_load_till(data, n, *fill)\n load_tillz = npyv_load_tillz(data, n)\n n *= scale\n data_till = data[:n] + fill * ((self.nlanes - n) // scale)\n assert load_till == data_till\n data_tillz = data[:n] + [0] * (self.nlanes - n)\n assert load_tillz == data_tillz\n\n @pytest.mark.parametrize("intrin, elsizes, scale", [\n ("self.store_till", (32, 64), 1),\n ("self.store2_till", (32, 64), 2),\n ])\n def test_memory_partial_store(self, intrin, elsizes, scale):\n if self._scalar_size() not in elsizes:\n return\n npyv_store_till = eval(intrin)\n data = self._data()\n data_rev = self._data(reverse=True)\n vdata = self.load(data)\n lanes = list(range(1, self.nlanes + 1))\n lanes += [self.nlanes**2, self.nlanes**4]\n for n in lanes:\n data_till = data_rev.copy()\n data_till[:n * scale] = data[:n * scale]\n store_till = self._data(reverse=True)\n npyv_store_till(store_till, n, vdata)\n assert store_till == data_till\n\n @pytest.mark.parametrize("intrin, elsizes, scale", [\n ("self.loadn", (32, 64), 1),\n ("self.loadn2", (32, 64), 2),\n ])\n def test_memory_noncont_load(self, intrin, elsizes, scale):\n if self._scalar_size() not in elsizes:\n return\n npyv_loadn = eval(intrin)\n for stride in range(-64, 64):\n if stride < 0:\n data = self._data(stride, -stride * self.nlanes)\n data_stride = list(itertools.chain(\n *zip(*[data[-i::stride] for i in range(scale, 0, -1)])\n ))\n elif stride == 0:\n data = self._data()\n data_stride = data[0:scale] * (self.nlanes // scale)\n else:\n data = self._data(count=stride * self.nlanes)\n data_stride = list(itertools.chain(\n *zip(*[data[i::stride] for i in range(scale)]))\n )\n data_stride = self.load(data_stride) # cast unsigned\n loadn = npyv_loadn(data, stride)\n assert loadn == data_stride\n\n @pytest.mark.parametrize("intrin, elsizes, scale, fill", [\n ("self.loadn_tillz, self.loadn_till", (32, 64), 1, [0xffff]),\n ("self.loadn2_tillz, self.loadn2_till", (32, 64), 2, [0xffff, 0x7fff]),\n ])\n def test_memory_noncont_partial_load(self, intrin, elsizes, scale, fill):\n if self._scalar_size() not in elsizes:\n return\n npyv_loadn_tillz, npyv_loadn_till = eval(intrin)\n lanes = list(range(1, self.nlanes + 1))\n lanes += [self.nlanes**2, self.nlanes**4]\n for stride in range(-64, 64):\n if stride < 0:\n data = self._data(stride, -stride * self.nlanes)\n data_stride = list(itertools.chain(\n *zip(*[data[-i::stride] for i in range(scale, 0, -1)])\n ))\n elif stride == 0:\n data = self._data()\n data_stride = data[0:scale] * (self.nlanes // scale)\n else:\n data = self._data(count=stride * self.nlanes)\n data_stride = list(itertools.chain(\n *zip(*[data[i::stride] for i in range(scale)])\n ))\n data_stride = list(self.load(data_stride)) # cast unsigned\n for n in lanes:\n nscale = n * scale\n llanes = self.nlanes - nscale\n data_stride_till = (\n data_stride[:nscale] + fill * (llanes // scale)\n )\n loadn_till = npyv_loadn_till(data, stride, n, *fill)\n assert loadn_till == data_stride_till\n data_stride_tillz = data_stride[:nscale] + [0] * llanes\n loadn_tillz = npyv_loadn_tillz(data, stride, n)\n assert loadn_tillz == data_stride_tillz\n\n @pytest.mark.parametrize("intrin, elsizes, scale", [\n ("self.storen", (32, 64), 1),\n ("self.storen2", (32, 64), 2),\n ])\n def test_memory_noncont_store(self, intrin, elsizes, scale):\n if self._scalar_size() not in elsizes:\n return\n npyv_storen = eval(intrin)\n data = self._data()\n vdata = self.load(data)\n hlanes = self.nlanes // scale\n for stride in range(1, 64):\n data_storen = [0xff] * stride * self.nlanes\n for s in range(0, hlanes * stride, stride):\n i = (s // stride) * scale\n data_storen[s:s + scale] = data[i:i + scale]\n storen = [0xff] * stride * self.nlanes\n storen += [0x7f] * 64\n npyv_storen(storen, stride, vdata)\n assert storen[:-64] == data_storen\n assert storen[-64:] == [0x7f] * 64 # detect overflow\n\n for stride in range(-64, 0):\n data_storen = [0xff] * -stride * self.nlanes\n for s in range(0, hlanes * stride, stride):\n i = (s // stride) * scale\n data_storen[s - scale:s or None] = data[i:i + scale]\n storen = [0x7f] * 64\n storen += [0xff] * -stride * self.nlanes\n npyv_storen(storen, stride, vdata)\n assert storen[64:] == data_storen\n assert storen[:64] == [0x7f] * 64 # detect overflow\n # stride 0\n data_storen = [0x7f] * self.nlanes\n storen = data_storen.copy()\n data_storen[0:scale] = data[-scale:]\n npyv_storen(storen, 0, vdata)\n assert storen == data_storen\n\n @pytest.mark.parametrize("intrin, elsizes, scale", [\n ("self.storen_till", (32, 64), 1),\n ("self.storen2_till", (32, 64), 2),\n ])\n def test_memory_noncont_partial_store(self, intrin, elsizes, scale):\n if self._scalar_size() not in elsizes:\n return\n npyv_storen_till = eval(intrin)\n data = self._data()\n vdata = self.load(data)\n lanes = list(range(1, self.nlanes + 1))\n lanes += [self.nlanes**2, self.nlanes**4]\n hlanes = self.nlanes // scale\n for stride in range(1, 64):\n for n in lanes:\n data_till = [0xff] * stride * self.nlanes\n tdata = data[:n * scale] + [0xff] * (self.nlanes - n * scale)\n for s in range(0, hlanes * stride, stride)[:n]:\n i = (s // stride) * scale\n data_till[s:s + scale] = tdata[i:i + scale]\n storen_till = [0xff] * stride * self.nlanes\n storen_till += [0x7f] * 64\n npyv_storen_till(storen_till, stride, n, vdata)\n assert storen_till[:-64] == data_till\n assert storen_till[-64:] == [0x7f] * 64 # detect overflow\n\n for stride in range(-64, 0):\n for n in lanes:\n data_till = [0xff] * -stride * self.nlanes\n tdata = data[:n * scale] + [0xff] * (self.nlanes - n * scale)\n for s in range(0, hlanes * stride, stride)[:n]:\n i = (s // stride) * scale\n data_till[s - scale:s or None] = tdata[i:i + scale]\n storen_till = [0x7f] * 64\n storen_till += [0xff] * -stride * self.nlanes\n npyv_storen_till(storen_till, stride, n, vdata)\n assert storen_till[64:] == data_till\n assert storen_till[:64] == [0x7f] * 64 # detect overflow\n\n # stride 0\n for n in lanes:\n data_till = [0x7f] * self.nlanes\n storen_till = data_till.copy()\n data_till[0:scale] = data[:n * scale][-scale:]\n npyv_storen_till(storen_till, 0, n, vdata)\n assert storen_till == data_till\n\n @pytest.mark.parametrize("intrin, table_size, elsize", [\n ("self.lut32", 32, 32),\n ("self.lut16", 16, 64)\n ])\n def test_lut(self, intrin, table_size, elsize):\n """\n Test lookup table intrinsics:\n npyv_lut32_##sfx\n npyv_lut16_##sfx\n """\n if elsize != self._scalar_size():\n return\n intrin = eval(intrin)\n idx_itrin = getattr(self.npyv, f"setall_u{elsize}")\n table = range(table_size)\n for i in table:\n broadi = self.setall(i)\n idx = idx_itrin(i)\n lut = intrin(table, idx)\n assert lut == broadi\n\n def test_misc(self):\n broadcast_zero = self.zero()\n assert broadcast_zero == [0] * self.nlanes\n for i in range(1, 10):\n broadcasti = self.setall(i)\n assert broadcasti == [i] * self.nlanes\n\n data_a, data_b = self._data(), self._data(reverse=True)\n vdata_a, vdata_b = self.load(data_a), self.load(data_b)\n\n # py level of npyv_set_* don't support ignoring the extra specified lanes or\n # fill non-specified lanes with zero.\n vset = self.set(*data_a)\n assert vset == data_a\n # py level of npyv_setf_* don't support ignoring the extra specified lanes or\n # fill non-specified lanes with the specified scalar.\n vsetf = self.setf(10, *data_a)\n assert vsetf == data_a\n\n # We're testing the sanity of _simd's type-vector,\n # reinterpret* intrinsics itself are tested via compiler\n # during the build of _simd module\n sfxes = ["u8", "s8", "u16", "s16", "u32", "s32", "u64", "s64"]\n if self.npyv.simd_f64:\n sfxes.append("f64")\n if self.npyv.simd_f32:\n sfxes.append("f32")\n for sfx in sfxes:\n vec_name = getattr(self, "reinterpret_" + sfx)(vdata_a).__name__\n assert vec_name == "npyv_" + sfx\n\n # select & mask operations\n select_a = self.select(self.cmpeq(self.zero(), self.zero()), vdata_a, vdata_b)\n assert select_a == data_a\n select_b = self.select(self.cmpneq(self.zero(), self.zero()), vdata_a, vdata_b)\n assert select_b == data_b\n\n # test extract elements\n assert self.extract0(vdata_b) == vdata_b[0]\n\n # cleanup intrinsic is only used with AVX for\n # zeroing registers to avoid the AVX-SSE transition penalty,\n # so nothing to test here\n self.npyv.cleanup()\n\n def test_reorder(self):\n data_a, data_b = self._data(), self._data(reverse=True)\n vdata_a, vdata_b = self.load(data_a), self.load(data_b)\n # lower half part\n data_a_lo = data_a[:self.nlanes // 2]\n data_b_lo = data_b[:self.nlanes // 2]\n # higher half part\n data_a_hi = data_a[self.nlanes // 2:]\n data_b_hi = data_b[self.nlanes // 2:]\n # combine two lower parts\n combinel = self.combinel(vdata_a, vdata_b)\n assert combinel == data_a_lo + data_b_lo\n # combine two higher parts\n combineh = self.combineh(vdata_a, vdata_b)\n assert combineh == data_a_hi + data_b_hi\n # combine x2\n combine = self.combine(vdata_a, vdata_b)\n assert combine == (data_a_lo + data_b_lo, data_a_hi + data_b_hi)\n\n # zip(interleave)\n data_zipl = self.load([\n v for p in zip(data_a_lo, data_b_lo) for v in p\n ])\n data_ziph = self.load([\n v for p in zip(data_a_hi, data_b_hi) for v in p\n ])\n vzip = self.zip(vdata_a, vdata_b)\n assert vzip == (data_zipl, data_ziph)\n vzip = [0] * self.nlanes * 2\n self._x2("store")(vzip, (vdata_a, vdata_b))\n assert vzip == list(data_zipl) + list(data_ziph)\n\n # unzip(deinterleave)\n unzip = self.unzip(data_zipl, data_ziph)\n assert unzip == (data_a, data_b)\n unzip = self._x2("load")(list(data_zipl) + list(data_ziph))\n assert unzip == (data_a, data_b)\n\n def test_reorder_rev64(self):\n # Reverse elements of each 64-bit lane\n ssize = self._scalar_size()\n if ssize == 64:\n return\n data_rev64 = [\n y for x in range(0, self.nlanes, 64 // ssize)\n for y in reversed(range(x, x + 64 // ssize))\n ]\n rev64 = self.rev64(self.load(range(self.nlanes)))\n assert rev64 == data_rev64\n\n def test_reorder_permi128(self):\n """\n Test permuting elements for each 128-bit lane.\n npyv_permi128_##sfx\n """\n ssize = self._scalar_size()\n if ssize < 32:\n return\n data = self.load(self._data())\n permn = 128 // ssize\n permd = permn - 1\n nlane128 = self.nlanes // permn\n shfl = [0, 1] if ssize == 64 else [0, 2, 4, 6]\n for i in range(permn):\n indices = [(i >> shf) & permd for shf in shfl]\n vperm = self.permi128(data, *indices)\n data_vperm = [\n data[j + (e & -permn)]\n for e, j in enumerate(indices * nlane128)\n ]\n assert vperm == data_vperm\n\n @pytest.mark.parametrize('func, intrin', [\n (operator.lt, "cmplt"),\n (operator.le, "cmple"),\n (operator.gt, "cmpgt"),\n (operator.ge, "cmpge"),\n (operator.eq, "cmpeq")\n ])\n def test_operators_comparison(self, func, intrin):\n if self._is_fp():\n data_a = self._data()\n else:\n data_a = self._data(self._int_max() - self.nlanes)\n data_b = self._data(self._int_min(), reverse=True)\n vdata_a, vdata_b = self.load(data_a), self.load(data_b)\n intrin = getattr(self, intrin)\n\n mask_true = self._true_mask()\n\n def to_bool(vector):\n return [lane == mask_true for lane in vector]\n\n data_cmp = [func(a, b) for a, b in zip(data_a, data_b)]\n cmp = to_bool(intrin(vdata_a, vdata_b))\n assert cmp == data_cmp\n\n def test_operators_logical(self):\n if self._is_fp():\n data_a = self._data()\n else:\n data_a = self._data(self._int_max() - self.nlanes)\n data_b = self._data(self._int_min(), reverse=True)\n vdata_a, vdata_b = self.load(data_a), self.load(data_b)\n\n if self._is_fp():\n data_cast_a = self._to_unsigned(vdata_a)\n data_cast_b = self._to_unsigned(vdata_b)\n cast, cast_data = self._to_unsigned, self._to_unsigned\n else:\n data_cast_a, data_cast_b = data_a, data_b\n cast, cast_data = lambda a: a, self.load\n\n data_xor = cast_data([a ^ b for a, b in zip(data_cast_a, data_cast_b)])\n vxor = cast(self.xor(vdata_a, vdata_b))\n assert vxor == data_xor\n\n data_or = cast_data([a | b for a, b in zip(data_cast_a, data_cast_b)])\n vor = cast(getattr(self, "or")(vdata_a, vdata_b))\n assert vor == data_or\n\n data_and = cast_data([a & b for a, b in zip(data_cast_a, data_cast_b)])\n vand = cast(getattr(self, "and")(vdata_a, vdata_b))\n assert vand == data_and\n\n data_not = cast_data([~a for a in data_cast_a])\n vnot = cast(getattr(self, "not")(vdata_a))\n assert vnot == data_not\n\n if self.sfx not in ("u8"):\n return\n data_andc = [a & ~b for a, b in zip(data_cast_a, data_cast_b)]\n vandc = cast(self.andc(vdata_a, vdata_b))\n assert vandc == data_andc\n\n @pytest.mark.parametrize("intrin", ["any", "all"])\n @pytest.mark.parametrize("data", (\n [1, 2, 3, 4],\n [-1, -2, -3, -4],\n [0, 1, 2, 3, 4],\n [0x7f, 0x7fff, 0x7fffffff, 0x7fffffffffffffff],\n [0, -1, -2, -3, 4],\n [0],\n [1],\n [-1]\n ))\n def test_operators_crosstest(self, intrin, data):\n """\n Test intrinsics:\n npyv_any_##SFX\n npyv_all_##SFX\n """\n data_a = self.load(data * self.nlanes)\n func = eval(intrin)\n intrin = getattr(self, intrin)\n desired = func(data_a)\n simd = intrin(data_a)\n assert not not simd == desired\n\n def test_conversion_boolean(self):\n bsfx = "b" + self.sfx[1:]\n to_boolean = getattr(self.npyv, f"cvt_{bsfx}_{self.sfx}")\n from_boolean = getattr(self.npyv, f"cvt_{self.sfx}_{bsfx}")\n\n false_vb = to_boolean(self.setall(0))\n true_vb = self.cmpeq(self.setall(0), self.setall(0))\n assert false_vb != true_vb\n\n false_vsfx = from_boolean(false_vb)\n true_vsfx = from_boolean(true_vb)\n assert false_vsfx != true_vsfx\n\n def test_conversion_expand(self):\n """\n Test expand intrinsics:\n npyv_expand_u16_u8\n npyv_expand_u32_u16\n """\n if self.sfx not in ("u8", "u16"):\n return\n totype = self.sfx[0] + str(int(self.sfx[1:]) * 2)\n expand = getattr(self.npyv, f"expand_{totype}_{self.sfx}")\n # close enough from the edge to detect any deviation\n data = self._data(self._int_max() - self.nlanes)\n vdata = self.load(data)\n edata = expand(vdata)\n # lower half part\n data_lo = data[:self.nlanes // 2]\n # higher half part\n data_hi = data[self.nlanes // 2:]\n assert edata == (data_lo, data_hi)\n\n def test_arithmetic_subadd(self):\n if self._is_fp():\n data_a = self._data()\n else:\n data_a = self._data(self._int_max() - self.nlanes)\n data_b = self._data(self._int_min(), reverse=True)\n vdata_a, vdata_b = self.load(data_a), self.load(data_b)\n\n # non-saturated\n data_add = self.load([a + b for a, b in zip(data_a, data_b)]) # load to cast\n add = self.add(vdata_a, vdata_b)\n assert add == data_add\n data_sub = self.load([a - b for a, b in zip(data_a, data_b)])\n sub = self.sub(vdata_a, vdata_b)\n assert sub == data_sub\n\n def test_arithmetic_mul(self):\n if self.sfx in ("u64", "s64"):\n return\n\n if self._is_fp():\n data_a = self._data()\n else:\n data_a = self._data(self._int_max() - self.nlanes)\n data_b = self._data(self._int_min(), reverse=True)\n vdata_a, vdata_b = self.load(data_a), self.load(data_b)\n\n data_mul = self.load([a * b for a, b in zip(data_a, data_b)])\n mul = self.mul(vdata_a, vdata_b)\n assert mul == data_mul\n\n def test_arithmetic_div(self):\n if not self._is_fp():\n return\n\n data_a, data_b = self._data(), self._data(reverse=True)\n vdata_a, vdata_b = self.load(data_a), self.load(data_b)\n\n # load to truncate f64 to precision of f32\n data_div = self.load([a / b for a, b in zip(data_a, data_b)])\n div = self.div(vdata_a, vdata_b)\n assert div == data_div\n\n def test_arithmetic_intdiv(self):\n """\n Test integer division intrinsics:\n npyv_divisor_##sfx\n npyv_divc_##sfx\n """\n if self._is_fp():\n return\n\n int_min = self._int_min()\n\n def trunc_div(a, d):\n """\n Divide towards zero works with large integers > 2^53,\n and wrap around overflow similar to what C does.\n """\n if d == -1 and a == int_min:\n return a\n sign_a, sign_d = a < 0, d < 0\n if a == 0 or sign_a == sign_d:\n return a // d\n return (a + sign_d - sign_a) // d + 1\n\n data = [1, -int_min] # to test overflow\n data += range(0, 2**8, 2**5)\n data += range(0, 2**8, 2**5 - 1)\n bsize = self._scalar_size()\n if bsize > 8:\n data += range(2**8, 2**16, 2**13)\n data += range(2**8, 2**16, 2**13 - 1)\n if bsize > 16:\n data += range(2**16, 2**32, 2**29)\n data += range(2**16, 2**32, 2**29 - 1)\n if bsize > 32:\n data += range(2**32, 2**64, 2**61)\n data += range(2**32, 2**64, 2**61 - 1)\n # negate\n data += [-x for x in data]\n for dividend, divisor in itertools.product(data, data):\n divisor = self.setall(divisor)[0] # cast\n if divisor == 0:\n continue\n dividend = self.load(self._data(dividend))\n data_divc = [trunc_div(a, divisor) for a in dividend]\n divisor_parms = self.divisor(divisor)\n divc = self.divc(dividend, divisor_parms)\n assert divc == data_divc\n\n def test_arithmetic_reduce_sum(self):\n """\n Test reduce sum intrinsics:\n npyv_sum_##sfx\n """\n if self.sfx not in ("u32", "u64", "f32", "f64"):\n return\n # reduce sum\n data = self._data()\n vdata = self.load(data)\n\n data_sum = sum(data)\n vsum = self.sum(vdata)\n assert vsum == data_sum\n\n def test_arithmetic_reduce_sumup(self):\n """\n Test extend reduce sum intrinsics:\n npyv_sumup_##sfx\n """\n if self.sfx not in ("u8", "u16"):\n return\n rdata = (0, self.nlanes, self._int_min(), self._int_max() - self.nlanes)\n for r in rdata:\n data = self._data(r)\n vdata = self.load(data)\n data_sum = sum(data)\n vsum = self.sumup(vdata)\n assert vsum == data_sum\n\n def test_mask_conditional(self):\n """\n Conditional addition and subtraction for all supported data types.\n Test intrinsics:\n npyv_ifadd_##SFX, npyv_ifsub_##SFX\n """\n vdata_a = self.load(self._data())\n vdata_b = self.load(self._data(reverse=True))\n true_mask = self.cmpeq(self.zero(), self.zero())\n false_mask = self.cmpneq(self.zero(), self.zero())\n\n data_sub = self.sub(vdata_b, vdata_a)\n ifsub = self.ifsub(true_mask, vdata_b, vdata_a, vdata_b)\n assert ifsub == data_sub\n ifsub = self.ifsub(false_mask, vdata_a, vdata_b, vdata_b)\n assert ifsub == vdata_b\n\n data_add = self.add(vdata_b, vdata_a)\n ifadd = self.ifadd(true_mask, vdata_b, vdata_a, vdata_b)\n assert ifadd == data_add\n ifadd = self.ifadd(false_mask, vdata_a, vdata_b, vdata_b)\n assert ifadd == vdata_b\n\n if not self._is_fp():\n return\n data_div = self.div(vdata_b, vdata_a)\n ifdiv = self.ifdiv(true_mask, vdata_b, vdata_a, vdata_b)\n assert ifdiv == data_div\n ifdivz = self.ifdivz(true_mask, vdata_b, vdata_a)\n assert ifdivz == data_div\n ifdiv = self.ifdiv(false_mask, vdata_a, vdata_b, vdata_b)\n assert ifdiv == vdata_b\n ifdivz = self.ifdivz(false_mask, vdata_a, vdata_b)\n assert ifdivz == self.zero()\n\n\nbool_sfx = ("b8", "b16", "b32", "b64")\nint_sfx = ("u8", "s8", "u16", "s16", "u32", "s32", "u64", "s64")\nfp_sfx = ("f32", "f64")\nall_sfx = int_sfx + fp_sfx\ntests_registry = {\n bool_sfx: _SIMD_BOOL,\n int_sfx: _SIMD_INT,\n fp_sfx: _SIMD_FP,\n ("f32",): _SIMD_FP32,\n ("f64",): _SIMD_FP64,\n all_sfx: _SIMD_ALL\n}\nfor target_name, npyv in targets.items():\n simd_width = npyv.simd if npyv else ''\n pretty_name = target_name.split('__') # multi-target separator\n if len(pretty_name) > 1:\n # multi-target\n pretty_name = f"({' '.join(pretty_name)})"\n else:\n pretty_name = pretty_name[0]\n\n skip = ""\n skip_sfx = {}\n if not npyv:\n skip = f"target '{pretty_name}' isn't supported by current machine"\n elif not npyv.simd:\n skip = f"target '{pretty_name}' isn't supported by NPYV"\n else:\n if not npyv.simd_f32:\n skip_sfx["f32"] = f"target '{pretty_name}' "\\n "doesn't support single-precision"\n if not npyv.simd_f64:\n skip_sfx["f64"] = f"target '{pretty_name}' doesn't"\\n "support double-precision"\n\n for sfxes, cls in tests_registry.items():\n for sfx in sfxes:\n skip_m = skip_sfx.get(sfx, skip)\n inhr = (cls,)\n attr = {"npyv": targets[target_name], "sfx": sfx, "target_name": target_name}\n tcls = type(f"Test{cls.__name__}_{simd_width}_{target_name}_{sfx}", inhr, attr)\n if skip_m:\n pytest.mark.skip(reason=skip_m)(tcls)\n globals()[tcls.__name__] = tcls\n | .venv\Lib\site-packages\numpy\_core\tests\test_simd.py | test_simd.py | Python | 50,164 | 0.95 | 0.184191 | 0.064946 | awesome-app | 112 | 2023-09-03T13:22:24.945479 | GPL-3.0 | true | 86ba21a3cfb12b8706bb82428c13e685 |
import pytest\n\nfrom numpy._core._simd import targets\n\n"""\nThis testing unit only for checking the sanity of common functionality,\ntherefore all we need is just to take one submodule that represents any\nof enabled SIMD extensions to run the test on it and the second submodule\nrequired to run only one check related to the possibility of mixing\nthe data types among each submodule.\n"""\nnpyvs = [npyv_mod for npyv_mod in targets.values() if npyv_mod and npyv_mod.simd]\nnpyv, npyv2 = (npyvs + [None, None])[:2]\n\nunsigned_sfx = ["u8", "u16", "u32", "u64"]\nsigned_sfx = ["s8", "s16", "s32", "s64"]\nfp_sfx = []\nif npyv and npyv.simd_f32:\n fp_sfx.append("f32")\nif npyv and npyv.simd_f64:\n fp_sfx.append("f64")\n\nint_sfx = unsigned_sfx + signed_sfx\nall_sfx = unsigned_sfx + int_sfx\n\n@pytest.mark.skipif(not npyv, reason="could not find any SIMD extension with NPYV support")\nclass Test_SIMD_MODULE:\n\n @pytest.mark.parametrize('sfx', all_sfx)\n def test_num_lanes(self, sfx):\n nlanes = getattr(npyv, "nlanes_" + sfx)\n vector = getattr(npyv, "setall_" + sfx)(1)\n assert len(vector) == nlanes\n\n @pytest.mark.parametrize('sfx', all_sfx)\n def test_type_name(self, sfx):\n vector = getattr(npyv, "setall_" + sfx)(1)\n assert vector.__name__ == "npyv_" + sfx\n\n def test_raises(self):\n a, b = [npyv.setall_u32(1)] * 2\n for sfx in all_sfx:\n vcb = lambda intrin: getattr(npyv, f"{intrin}_{sfx}")\n pytest.raises(TypeError, vcb("add"), a)\n pytest.raises(TypeError, vcb("add"), a, b, a)\n pytest.raises(TypeError, vcb("setall"))\n pytest.raises(TypeError, vcb("setall"), [1])\n pytest.raises(TypeError, vcb("load"), 1)\n pytest.raises(ValueError, vcb("load"), [1])\n pytest.raises(ValueError, vcb("store"), [1], getattr(npyv, f"reinterpret_{sfx}_u32")(a))\n\n @pytest.mark.skipif(not npyv2, reason=(\n "could not find a second SIMD extension with NPYV support"\n ))\n def test_nomix(self):\n # mix among submodules isn't allowed\n a = npyv.setall_u32(1)\n a2 = npyv2.setall_u32(1)\n pytest.raises(TypeError, npyv.add_u32, a2, a2)\n pytest.raises(TypeError, npyv2.add_u32, a, a)\n\n @pytest.mark.parametrize('sfx', unsigned_sfx)\n def test_unsigned_overflow(self, sfx):\n nlanes = getattr(npyv, "nlanes_" + sfx)\n maxu = (1 << int(sfx[1:])) - 1\n maxu_72 = (1 << 72) - 1\n lane = getattr(npyv, "setall_" + sfx)(maxu_72)[0]\n assert lane == maxu\n lanes = getattr(npyv, "load_" + sfx)([maxu_72] * nlanes)\n assert lanes == [maxu] * nlanes\n lane = getattr(npyv, "setall_" + sfx)(-1)[0]\n assert lane == maxu\n lanes = getattr(npyv, "load_" + sfx)([-1] * nlanes)\n assert lanes == [maxu] * nlanes\n\n @pytest.mark.parametrize('sfx', signed_sfx)\n def test_signed_overflow(self, sfx):\n nlanes = getattr(npyv, "nlanes_" + sfx)\n maxs_72 = (1 << 71) - 1\n lane = getattr(npyv, "setall_" + sfx)(maxs_72)[0]\n assert lane == -1\n lanes = getattr(npyv, "load_" + sfx)([maxs_72] * nlanes)\n assert lanes == [-1] * nlanes\n mins_72 = -1 << 71\n lane = getattr(npyv, "setall_" + sfx)(mins_72)[0]\n assert lane == 0\n lanes = getattr(npyv, "load_" + sfx)([mins_72] * nlanes)\n assert lanes == [0] * nlanes\n\n def test_truncate_f32(self):\n if not npyv.simd_f32:\n pytest.skip("F32 isn't support by the SIMD extension")\n f32 = npyv.setall_f32(0.1)[0]\n assert f32 != 0.1\n assert round(f32, 1) == 0.1\n\n def test_compare(self):\n data_range = range(npyv.nlanes_u32)\n vdata = npyv.load_u32(data_range)\n assert vdata == list(data_range)\n assert vdata == tuple(data_range)\n for i in data_range:\n assert vdata[i] == data_range[i]\n | .venv\Lib\site-packages\numpy\_core\tests\test_simd_module.py | test_simd_module.py | Python | 4,007 | 0.95 | 0.165049 | 0.011111 | awesome-app | 690 | 2024-05-24T09:49:34.343724 | GPL-3.0 | true | 9bae58ef7f2a54bbc168a9952d73979e |
import copy\nimport itertools\nimport os\nimport pickle\nimport sys\nimport tempfile\n\nimport pytest\n\nimport numpy as np\nfrom numpy._core.tests._natype import get_stringdtype_dtype as get_dtype\nfrom numpy._core.tests._natype import pd_NA\nfrom numpy.dtypes import StringDType\nfrom numpy.testing import IS_PYPY, assert_array_equal\n\n\n@pytest.fixture\ndef string_list():\n return ["abc", "def", "ghi" * 10, "A¢☃€ 😊" * 100, "Abc" * 1000, "DEF"]\n\n\n# second copy for cast tests to do a cartesian product over dtypes\n@pytest.fixture(params=[True, False])\ndef coerce2(request):\n return request.param\n\n\n@pytest.fixture(\n params=["unset", None, pd_NA, np.nan, float("nan"), "__nan__"],\n ids=["unset", "None", "pandas.NA", "np.nan", "float('nan')", "string nan"],\n)\ndef na_object2(request):\n return request.param\n\n\n@pytest.fixture()\ndef dtype2(na_object2, coerce2):\n # explicit is check for pd_NA because != with pd_NA returns pd_NA\n if na_object2 is pd_NA or na_object2 != "unset":\n return StringDType(na_object=na_object2, coerce=coerce2)\n else:\n return StringDType(coerce=coerce2)\n\n\ndef test_dtype_creation():\n hashes = set()\n dt = StringDType()\n assert not hasattr(dt, "na_object") and dt.coerce is True\n hashes.add(hash(dt))\n\n dt = StringDType(na_object=None)\n assert dt.na_object is None and dt.coerce is True\n hashes.add(hash(dt))\n\n dt = StringDType(coerce=False)\n assert not hasattr(dt, "na_object") and dt.coerce is False\n hashes.add(hash(dt))\n\n dt = StringDType(na_object=None, coerce=False)\n assert dt.na_object is None and dt.coerce is False\n hashes.add(hash(dt))\n\n assert len(hashes) == 4\n\n dt = np.dtype("T")\n assert dt == StringDType()\n assert dt.kind == "T"\n assert dt.char == "T"\n\n hashes.add(hash(dt))\n assert len(hashes) == 4\n\n\ndef test_dtype_equality(dtype):\n assert dtype == dtype\n for ch in "SU":\n assert dtype != np.dtype(ch)\n assert dtype != np.dtype(f"{ch}8")\n\n\ndef test_dtype_repr(dtype):\n if not hasattr(dtype, "na_object") and dtype.coerce:\n assert repr(dtype) == "StringDType()"\n elif dtype.coerce:\n assert repr(dtype) == f"StringDType(na_object={dtype.na_object!r})"\n elif not hasattr(dtype, "na_object"):\n assert repr(dtype) == "StringDType(coerce=False)"\n else:\n assert (\n repr(dtype)\n == f"StringDType(na_object={dtype.na_object!r}, coerce=False)"\n )\n\n\ndef test_create_with_na(dtype):\n if not hasattr(dtype, "na_object"):\n pytest.skip("does not have an na object")\n na_val = dtype.na_object\n string_list = ["hello", na_val, "world"]\n arr = np.array(string_list, dtype=dtype)\n assert str(arr) == "[" + " ".join([repr(s) for s in string_list]) + "]"\n assert arr[1] is dtype.na_object\n\n\n@pytest.mark.parametrize("i", list(range(5)))\ndef test_set_replace_na(i):\n # Test strings of various lengths can be set to NaN and then replaced.\n s_empty = ""\n s_short = "0123456789"\n s_medium = "abcdefghijklmnopqrstuvwxyz"\n s_long = "-=+" * 100\n strings = [s_medium, s_empty, s_short, s_medium, s_long]\n a = np.array(strings, StringDType(na_object=np.nan))\n for s in [a[i], s_medium + s_short, s_short, s_empty, s_long]:\n a[i] = np.nan\n assert np.isnan(a[i])\n a[i] = s\n assert a[i] == s\n assert_array_equal(a, strings[:i] + [s] + strings[i + 1:])\n\n\ndef test_null_roundtripping():\n data = ["hello\0world", "ABC\0DEF\0\0"]\n arr = np.array(data, dtype="T")\n assert data[0] == arr[0]\n assert data[1] == arr[1]\n\n\ndef test_string_too_large_error():\n arr = np.array(["a", "b", "c"], dtype=StringDType())\n with pytest.raises(OverflowError):\n arr * (sys.maxsize + 1)\n\n\n@pytest.mark.parametrize(\n "data",\n [\n ["abc", "def", "ghi"],\n ["🤣", "📵", "😰"],\n ["🚜", "🙃", "😾"],\n ["😹", "🚠", "🚌"],\n ],\n)\ndef test_array_creation_utf8(dtype, data):\n arr = np.array(data, dtype=dtype)\n assert str(arr) == "[" + " ".join(["'" + str(d) + "'" for d in data]) + "]"\n assert arr.dtype == dtype\n\n\n@pytest.mark.parametrize(\n "data",\n [\n [1, 2, 3],\n [b"abc", b"def", b"ghi"],\n [object, object, object],\n ],\n)\ndef test_scalars_string_conversion(data, dtype):\n try:\n str_vals = [str(d.decode('utf-8')) for d in data]\n except AttributeError:\n str_vals = [str(d) for d in data]\n if dtype.coerce:\n assert_array_equal(\n np.array(data, dtype=dtype),\n np.array(str_vals, dtype=dtype),\n )\n else:\n with pytest.raises(ValueError):\n np.array(data, dtype=dtype)\n\n\n@pytest.mark.parametrize(\n ("strings"),\n [\n ["this", "is", "an", "array"],\n ["€", "", "😊"],\n ["A¢☃€ 😊", " A☃€¢😊", "☃€😊 A¢", "😊☃A¢ €"],\n ],\n)\ndef test_self_casts(dtype, dtype2, strings):\n if hasattr(dtype, "na_object"):\n strings = strings + [dtype.na_object]\n elif hasattr(dtype2, "na_object"):\n strings = strings + [""]\n arr = np.array(strings, dtype=dtype)\n newarr = arr.astype(dtype2)\n\n if hasattr(dtype, "na_object") and not hasattr(dtype2, "na_object"):\n assert newarr[-1] == str(dtype.na_object)\n with pytest.raises(TypeError):\n arr.astype(dtype2, casting="safe")\n elif hasattr(dtype, "na_object") and hasattr(dtype2, "na_object"):\n assert newarr[-1] is dtype2.na_object\n arr.astype(dtype2, casting="safe")\n elif hasattr(dtype2, "na_object"):\n assert newarr[-1] == ""\n arr.astype(dtype2, casting="safe")\n else:\n arr.astype(dtype2, casting="safe")\n\n if hasattr(dtype, "na_object") and hasattr(dtype2, "na_object"):\n na1 = dtype.na_object\n na2 = dtype2.na_object\n if (na1 is not na2 and\n # check for pd_NA first because bool(pd_NA) is an error\n ((na1 is pd_NA or na2 is pd_NA) or\n # the second check is a NaN check, spelled this way\n # to avoid errors from math.isnan and np.isnan\n (na1 != na2 and not (na1 != na1 and na2 != na2)))):\n with pytest.raises(TypeError):\n arr[:-1] == newarr[:-1]\n return\n assert_array_equal(arr[:-1], newarr[:-1])\n\n\n@pytest.mark.parametrize(\n ("strings"),\n [\n ["this", "is", "an", "array"],\n ["€", "", "😊"],\n ["A¢☃€ 😊", " A☃€¢😊", "☃€😊 A¢", "😊☃A¢ €"],\n ],\n)\nclass TestStringLikeCasts:\n def test_unicode_casts(self, dtype, strings):\n arr = np.array(strings, dtype=np.str_).astype(dtype)\n expected = np.array(strings, dtype=dtype)\n assert_array_equal(arr, expected)\n\n arr_as_U8 = expected.astype("U8")\n assert_array_equal(arr_as_U8, np.array(strings, dtype="U8"))\n assert_array_equal(arr_as_U8.astype(dtype), arr)\n arr_as_U3 = expected.astype("U3")\n assert_array_equal(arr_as_U3, np.array(strings, dtype="U3"))\n assert_array_equal(\n arr_as_U3.astype(dtype),\n np.array([s[:3] for s in strings], dtype=dtype),\n )\n\n def test_void_casts(self, dtype, strings):\n sarr = np.array(strings, dtype=dtype)\n utf8_bytes = [s.encode("utf-8") for s in strings]\n void_dtype = f"V{max(len(s) for s in utf8_bytes)}"\n varr = np.array(utf8_bytes, dtype=void_dtype)\n assert_array_equal(varr, sarr.astype(void_dtype))\n assert_array_equal(varr.astype(dtype), sarr)\n\n def test_bytes_casts(self, dtype, strings):\n sarr = np.array(strings, dtype=dtype)\n try:\n utf8_bytes = [s.encode("ascii") for s in strings]\n bytes_dtype = f"S{max(len(s) for s in utf8_bytes)}"\n barr = np.array(utf8_bytes, dtype=bytes_dtype)\n assert_array_equal(barr, sarr.astype(bytes_dtype))\n assert_array_equal(barr.astype(dtype), sarr)\n if dtype.coerce:\n barr = np.array(utf8_bytes, dtype=dtype)\n assert_array_equal(barr, sarr)\n barr = np.array(utf8_bytes, dtype="O")\n assert_array_equal(barr.astype(dtype), sarr)\n else:\n with pytest.raises(ValueError):\n np.array(utf8_bytes, dtype=dtype)\n except UnicodeEncodeError:\n with pytest.raises(UnicodeEncodeError):\n sarr.astype("S20")\n\n\ndef test_additional_unicode_cast(random_string_list, dtype):\n arr = np.array(random_string_list, dtype=dtype)\n # test that this short-circuits correctly\n assert_array_equal(arr, arr.astype(arr.dtype))\n # tests the casts via the comparison promoter\n assert_array_equal(arr, arr.astype(random_string_list.dtype))\n\n\ndef test_insert_scalar(dtype, string_list):\n """Test that inserting a scalar works."""\n arr = np.array(string_list, dtype=dtype)\n scalar_instance = "what"\n arr[1] = scalar_instance\n assert_array_equal(\n arr,\n np.array(string_list[:1] + ["what"] + string_list[2:], dtype=dtype),\n )\n\n\ncomparison_operators = [\n np.equal,\n np.not_equal,\n np.greater,\n np.greater_equal,\n np.less,\n np.less_equal,\n]\n\n\n@pytest.mark.parametrize("op", comparison_operators)\n@pytest.mark.parametrize("o_dtype", [np.str_, object, StringDType()])\ndef test_comparisons(string_list, dtype, op, o_dtype):\n sarr = np.array(string_list, dtype=dtype)\n oarr = np.array(string_list, dtype=o_dtype)\n\n # test that comparison operators work\n res = op(sarr, sarr)\n ores = op(oarr, oarr)\n # test that promotion works as well\n orres = op(sarr, oarr)\n olres = op(oarr, sarr)\n\n assert_array_equal(res, ores)\n assert_array_equal(res, orres)\n assert_array_equal(res, olres)\n\n # test we get the correct answer for unequal length strings\n sarr2 = np.array([s + "2" for s in string_list], dtype=dtype)\n oarr2 = np.array([s + "2" for s in string_list], dtype=o_dtype)\n\n res = op(sarr, sarr2)\n ores = op(oarr, oarr2)\n olres = op(oarr, sarr2)\n orres = op(sarr, oarr2)\n\n assert_array_equal(res, ores)\n assert_array_equal(res, olres)\n assert_array_equal(res, orres)\n\n res = op(sarr2, sarr)\n ores = op(oarr2, oarr)\n olres = op(oarr2, sarr)\n orres = op(sarr2, oarr)\n\n assert_array_equal(res, ores)\n assert_array_equal(res, olres)\n assert_array_equal(res, orres)\n\n\ndef test_isnan(dtype, string_list):\n if not hasattr(dtype, "na_object"):\n pytest.skip("no na support")\n sarr = np.array(string_list + [dtype.na_object], dtype=dtype)\n is_nan = isinstance(dtype.na_object, float) and np.isnan(dtype.na_object)\n bool_errors = 0\n try:\n bool(dtype.na_object)\n except TypeError:\n bool_errors = 1\n if is_nan or bool_errors:\n # isnan is only true when na_object is a NaN\n assert_array_equal(\n np.isnan(sarr),\n np.array([0] * len(string_list) + [1], dtype=np.bool),\n )\n else:\n assert not np.any(np.isnan(sarr))\n\n\ndef test_pickle(dtype, string_list):\n arr = np.array(string_list, dtype=dtype)\n\n with tempfile.NamedTemporaryFile("wb", delete=False) as f:\n pickle.dump([arr, dtype], f)\n\n with open(f.name, "rb") as f:\n res = pickle.load(f)\n\n assert_array_equal(res[0], arr)\n assert res[1] == dtype\n\n os.remove(f.name)\n\n\ndef test_stdlib_copy(dtype, string_list):\n arr = np.array(string_list, dtype=dtype)\n\n assert_array_equal(copy.copy(arr), arr)\n assert_array_equal(copy.deepcopy(arr), arr)\n\n\n@pytest.mark.parametrize(\n "strings",\n [\n ["left", "right", "leftovers", "righty", "up", "down"],\n [\n "left" * 10,\n "right" * 10,\n "leftovers" * 10,\n "righty" * 10,\n "up" * 10,\n ],\n ["🤣🤣", "🤣", "📵", "😰"],\n ["🚜", "🙃", "😾"],\n ["😹", "🚠", "🚌"],\n ["A¢☃€ 😊", " A☃€¢😊", "☃€😊 A¢", "😊☃A¢ €"],\n ],\n)\ndef test_sort(dtype, strings):\n """Test that sorting matches python's internal sorting."""\n\n def test_sort(strings, arr_sorted):\n arr = np.array(strings, dtype=dtype)\n na_object = getattr(arr.dtype, "na_object", "")\n if na_object is None and None in strings:\n with pytest.raises(\n ValueError,\n match="Cannot compare null that is not a nan-like value",\n ):\n np.argsort(arr)\n argsorted = None\n elif na_object is pd_NA or na_object != '':\n argsorted = None\n else:\n argsorted = np.argsort(arr)\n np.random.default_rng().shuffle(arr)\n if na_object is None and None in strings:\n with pytest.raises(\n ValueError,\n match="Cannot compare null that is not a nan-like value",\n ):\n arr.sort()\n else:\n arr.sort()\n assert np.array_equal(arr, arr_sorted, equal_nan=True)\n if argsorted is not None:\n assert np.array_equal(argsorted, np.argsort(strings))\n\n # make a copy so we don't mutate the lists in the fixture\n strings = strings.copy()\n arr_sorted = np.array(sorted(strings), dtype=dtype)\n test_sort(strings, arr_sorted)\n\n if not hasattr(dtype, "na_object"):\n return\n\n # make sure NAs get sorted to the end of the array and string NAs get\n # sorted like normal strings\n strings.insert(0, dtype.na_object)\n strings.insert(2, dtype.na_object)\n # can't use append because doing that with NA converts\n # the result to object dtype\n if not isinstance(dtype.na_object, str):\n arr_sorted = np.array(\n arr_sorted.tolist() + [dtype.na_object, dtype.na_object],\n dtype=dtype,\n )\n else:\n arr_sorted = np.array(sorted(strings), dtype=dtype)\n\n test_sort(strings, arr_sorted)\n\n\n@pytest.mark.parametrize(\n "strings",\n [\n ["A¢☃€ 😊", " A☃€¢😊", "☃€😊 A¢", "😊☃A¢ €"],\n ["A¢☃€ 😊", "", " ", " "],\n ["", "a", "😸", "ááðfáíóåéë"],\n ],\n)\ndef test_nonzero(strings, na_object):\n dtype = get_dtype(na_object)\n arr = np.array(strings, dtype=dtype)\n is_nonzero = np.array(\n [i for i, item in enumerate(strings) if len(item) != 0])\n assert_array_equal(arr.nonzero()[0], is_nonzero)\n\n if na_object is not pd_NA and na_object == 'unset':\n return\n\n strings_with_na = np.array(strings + [na_object], dtype=dtype)\n is_nan = np.isnan(np.array([dtype.na_object], dtype=dtype))[0]\n\n if is_nan:\n assert strings_with_na.nonzero()[0][-1] == 4\n else:\n assert strings_with_na.nonzero()[0][-1] == 3\n\n # check that the casting to bool and nonzero give consistent results\n assert_array_equal(strings_with_na[strings_with_na.nonzero()],\n strings_with_na[strings_with_na.astype(bool)])\n\n\ndef test_where(string_list, na_object):\n dtype = get_dtype(na_object)\n a = np.array(string_list, dtype=dtype)\n b = a[::-1]\n res = np.where([True, False, True, False, True, False], a, b)\n assert_array_equal(res, [a[0], b[1], a[2], b[3], a[4], b[5]])\n\n\ndef test_fancy_indexing(string_list):\n sarr = np.array(string_list, dtype="T")\n assert_array_equal(sarr, sarr[np.arange(sarr.shape[0])])\n\n inds = [\n [True, True],\n [0, 1],\n ...,\n np.array([0, 1], dtype='uint8'),\n ]\n\n lops = [\n ['a' * 25, 'b' * 25],\n ['', ''],\n ['hello', 'world'],\n ['hello', 'world' * 25],\n ]\n\n # see gh-27003 and gh-27053\n for ind in inds:\n for lop in lops:\n a = np.array(lop, dtype="T")\n assert_array_equal(a[ind], a)\n rop = ['d' * 25, 'e' * 25]\n for b in [rop, np.array(rop, dtype="T")]:\n a[ind] = b\n assert_array_equal(a, b)\n assert a[0] == 'd' * 25\n\n\ndef test_creation_functions():\n assert_array_equal(np.zeros(3, dtype="T"), ["", "", ""])\n assert_array_equal(np.empty(3, dtype="T"), ["", "", ""])\n\n assert np.zeros(3, dtype="T")[0] == ""\n assert np.empty(3, dtype="T")[0] == ""\n\n\ndef test_concatenate(string_list):\n sarr = np.array(string_list, dtype="T")\n sarr_cat = np.array(string_list + string_list, dtype="T")\n\n assert_array_equal(np.concatenate([sarr], axis=0), sarr)\n\n\ndef test_resize_method(string_list):\n sarr = np.array(string_list, dtype="T")\n if IS_PYPY:\n sarr.resize(len(string_list) + 3, refcheck=False)\n else:\n sarr.resize(len(string_list) + 3)\n assert_array_equal(sarr, np.array(string_list + [''] * 3, dtype="T"))\n\n\ndef test_create_with_copy_none(string_list):\n arr = np.array(string_list, dtype=StringDType())\n # create another stringdtype array with an arena that has a different\n # in-memory layout than the first array\n arr_rev = np.array(string_list[::-1], dtype=StringDType())\n\n # this should create a copy and the resulting array\n # shouldn't share an allocator or arena with arr_rev, despite\n # explicitly passing arr_rev.dtype\n arr_copy = np.array(arr, copy=None, dtype=arr_rev.dtype)\n np.testing.assert_array_equal(arr, arr_copy)\n assert arr_copy.base is None\n\n with pytest.raises(ValueError, match="Unable to avoid copy"):\n np.array(arr, copy=False, dtype=arr_rev.dtype)\n\n # because we're using arr's dtype instance, the view is safe\n arr_view = np.array(arr, copy=None, dtype=arr.dtype)\n np.testing.assert_array_equal(arr, arr)\n np.testing.assert_array_equal(arr_view[::-1], arr_rev)\n assert arr_view is arr\n\n\ndef test_astype_copy_false():\n orig_dt = StringDType()\n arr = np.array(["hello", "world"], dtype=StringDType())\n assert not arr.astype(StringDType(coerce=False), copy=False).dtype.coerce\n\n assert arr.astype(orig_dt, copy=False).dtype is orig_dt\n\n@pytest.mark.parametrize(\n "strings",\n [\n ["left", "right", "leftovers", "righty", "up", "down"],\n ["🤣🤣", "🤣", "📵", "😰"],\n ["🚜", "🙃", "😾"],\n ["😹", "🚠", "🚌"],\n ["A¢☃€ 😊", " A☃€¢😊", "☃€😊 A¢", "😊☃A¢ €"],\n ],\n)\ndef test_argmax(strings):\n """Test that argmax/argmin matches what python calculates."""\n arr = np.array(strings, dtype="T")\n assert np.argmax(arr) == strings.index(max(strings))\n assert np.argmin(arr) == strings.index(min(strings))\n\n\n@pytest.mark.parametrize(\n "arrfunc,expected",\n [\n [np.sort, None],\n [np.nonzero, (np.array([], dtype=np.int_),)],\n [np.argmax, 0],\n [np.argmin, 0],\n ],\n)\ndef test_arrfuncs_zeros(arrfunc, expected):\n arr = np.zeros(10, dtype="T")\n result = arrfunc(arr)\n if expected is None:\n expected = arr\n assert_array_equal(result, expected, strict=True)\n\n\n@pytest.mark.parametrize(\n ("strings", "cast_answer", "any_answer", "all_answer"),\n [\n [["hello", "world"], [True, True], True, True],\n [["", ""], [False, False], False, False],\n [["hello", ""], [True, False], True, False],\n [["", "world"], [False, True], True, False],\n ],\n)\ndef test_cast_to_bool(strings, cast_answer, any_answer, all_answer):\n sarr = np.array(strings, dtype="T")\n assert_array_equal(sarr.astype("bool"), cast_answer)\n\n assert np.any(sarr) == any_answer\n assert np.all(sarr) == all_answer\n\n\n@pytest.mark.parametrize(\n ("strings", "cast_answer"),\n [\n [[True, True], ["True", "True"]],\n [[False, False], ["False", "False"]],\n [[True, False], ["True", "False"]],\n [[False, True], ["False", "True"]],\n ],\n)\ndef test_cast_from_bool(strings, cast_answer):\n barr = np.array(strings, dtype=bool)\n assert_array_equal(barr.astype("T"), np.array(cast_answer, dtype="T"))\n\n\n@pytest.mark.parametrize("bitsize", [8, 16, 32, 64])\n@pytest.mark.parametrize("signed", [True, False])\ndef test_sized_integer_casts(bitsize, signed):\n idtype = f"int{bitsize}"\n if signed:\n inp = [-(2**p - 1) for p in reversed(range(bitsize - 1))]\n inp += [2**p - 1 for p in range(1, bitsize - 1)]\n else:\n idtype = "u" + idtype\n inp = [2**p - 1 for p in range(bitsize)]\n ainp = np.array(inp, dtype=idtype)\n assert_array_equal(ainp, ainp.astype("T").astype(idtype))\n\n # safe casting works\n ainp.astype("T", casting="safe")\n\n with pytest.raises(TypeError):\n ainp.astype("T").astype(idtype, casting="safe")\n\n oob = [str(2**bitsize), str(-(2**bitsize))]\n with pytest.raises(OverflowError):\n np.array(oob, dtype="T").astype(idtype)\n\n with pytest.raises(ValueError):\n np.array(["1", np.nan, "3"],\n dtype=StringDType(na_object=np.nan)).astype(idtype)\n\n\n@pytest.mark.parametrize("typename", ["byte", "short", "int", "longlong"])\n@pytest.mark.parametrize("signed", ["", "u"])\ndef test_unsized_integer_casts(typename, signed):\n idtype = f"{signed}{typename}"\n\n inp = [1, 2, 3, 4]\n ainp = np.array(inp, dtype=idtype)\n assert_array_equal(ainp, ainp.astype("T").astype(idtype))\n\n\n@pytest.mark.parametrize(\n "typename",\n [\n pytest.param(\n "longdouble",\n marks=pytest.mark.xfail(\n np.dtypes.LongDoubleDType() != np.dtypes.Float64DType(),\n reason="numpy lacks an ld2a implementation",\n strict=True,\n ),\n ),\n "float64",\n "float32",\n "float16",\n ],\n)\ndef test_float_casts(typename):\n inp = [1.1, 2.8, -3.2, 2.7e4]\n ainp = np.array(inp, dtype=typename)\n assert_array_equal(ainp, ainp.astype("T").astype(typename))\n\n inp = [0.1]\n sres = np.array(inp, dtype=typename).astype("T")\n res = sres.astype(typename)\n assert_array_equal(np.array(inp, dtype=typename), res)\n assert sres[0] == "0.1"\n\n if typename == "longdouble":\n # let's not worry about platform-dependent rounding of longdouble\n return\n\n fi = np.finfo(typename)\n\n inp = [1e-324, fi.smallest_subnormal, -1e-324, -fi.smallest_subnormal]\n eres = [0, fi.smallest_subnormal, -0, -fi.smallest_subnormal]\n res = np.array(inp, dtype=typename).astype("T").astype(typename)\n assert_array_equal(eres, res)\n\n inp = [2e308, fi.max, -2e308, fi.min]\n eres = [np.inf, fi.max, -np.inf, fi.min]\n res = np.array(inp, dtype=typename).astype("T").astype(typename)\n assert_array_equal(eres, res)\n\n\ndef test_float_nan_cast_na_object():\n # gh-28157\n dt = np.dtypes.StringDType(na_object=np.nan)\n arr1 = np.full((1,), fill_value=np.nan, dtype=dt)\n arr2 = np.full_like(arr1, fill_value=np.nan)\n\n assert arr1.item() is np.nan\n assert arr2.item() is np.nan\n\n inp = [1.2, 2.3, np.nan]\n arr = np.array(inp).astype(dt)\n assert arr[2] is np.nan\n assert arr[0] == '1.2'\n\n\n@pytest.mark.parametrize(\n "typename",\n [\n "csingle",\n "cdouble",\n pytest.param(\n "clongdouble",\n marks=pytest.mark.xfail(\n np.dtypes.CLongDoubleDType() != np.dtypes.Complex128DType(),\n reason="numpy lacks an ld2a implementation",\n strict=True,\n ),\n ),\n ],\n)\ndef test_cfloat_casts(typename):\n inp = [1.1 + 1.1j, 2.8 + 2.8j, -3.2 - 3.2j, 2.7e4 + 2.7e4j]\n ainp = np.array(inp, dtype=typename)\n assert_array_equal(ainp, ainp.astype("T").astype(typename))\n\n inp = [0.1 + 0.1j]\n sres = np.array(inp, dtype=typename).astype("T")\n res = sres.astype(typename)\n assert_array_equal(np.array(inp, dtype=typename), res)\n assert sres[0] == "(0.1+0.1j)"\n\n\ndef test_take(string_list):\n sarr = np.array(string_list, dtype="T")\n res = sarr.take(np.arange(len(string_list)))\n assert_array_equal(sarr, res)\n\n # make sure it also works for out\n out = np.empty(len(string_list), dtype="T")\n out[0] = "hello"\n res = sarr.take(np.arange(len(string_list)), out=out)\n assert res is out\n assert_array_equal(sarr, res)\n\n\n@pytest.mark.parametrize("use_out", [True, False])\n@pytest.mark.parametrize(\n "ufunc_name,func",\n [\n ("min", min),\n ("max", max),\n ],\n)\ndef test_ufuncs_minmax(string_list, ufunc_name, func, use_out):\n """Test that the min/max ufuncs match Python builtin min/max behavior."""\n arr = np.array(string_list, dtype="T")\n uarr = np.array(string_list, dtype=str)\n res = np.array(func(string_list), dtype="T")\n assert_array_equal(getattr(arr, ufunc_name)(), res)\n\n ufunc = getattr(np, ufunc_name + "imum")\n\n if use_out:\n res = ufunc(arr, arr, out=arr)\n else:\n res = ufunc(arr, arr)\n\n assert_array_equal(uarr, res)\n assert_array_equal(getattr(arr, ufunc_name)(), func(string_list))\n\n\ndef test_max_regression():\n arr = np.array(['y', 'y', 'z'], dtype="T")\n assert arr.max() == 'z'\n\n\n@pytest.mark.parametrize("use_out", [True, False])\n@pytest.mark.parametrize(\n "other_strings",\n [\n ["abc", "def" * 500, "ghi" * 16, "🤣" * 100, "📵", "😰"],\n ["🚜", "🙃", "😾", "😹", "🚠", "🚌"],\n ["🥦", "¨", "⨯", "∰ ", "⨌ ", "⎶ "],\n ],\n)\ndef test_ufunc_add(dtype, string_list, other_strings, use_out):\n arr1 = np.array(string_list, dtype=dtype)\n arr2 = np.array(other_strings, dtype=dtype)\n result = np.array([a + b for a, b in zip(arr1, arr2)], dtype=dtype)\n\n if use_out:\n res = np.add(arr1, arr2, out=arr1)\n else:\n res = np.add(arr1, arr2)\n\n assert_array_equal(res, result)\n\n if not hasattr(dtype, "na_object"):\n return\n\n is_nan = isinstance(dtype.na_object, float) and np.isnan(dtype.na_object)\n is_str = isinstance(dtype.na_object, str)\n bool_errors = 0\n try:\n bool(dtype.na_object)\n except TypeError:\n bool_errors = 1\n\n arr1 = np.array([dtype.na_object] + string_list, dtype=dtype)\n arr2 = np.array(other_strings + [dtype.na_object], dtype=dtype)\n\n if is_nan or bool_errors or is_str:\n res = np.add(arr1, arr2)\n assert_array_equal(res[1:-1], arr1[1:-1] + arr2[1:-1])\n if not is_str:\n assert res[0] is dtype.na_object and res[-1] is dtype.na_object\n else:\n assert res[0] == dtype.na_object + arr2[0]\n assert res[-1] == arr1[-1] + dtype.na_object\n else:\n with pytest.raises(ValueError):\n np.add(arr1, arr2)\n\n\ndef test_ufunc_add_reduce(dtype):\n values = ["a", "this is a long string", "c"]\n arr = np.array(values, dtype=dtype)\n out = np.empty((), dtype=dtype)\n\n expected = np.array("".join(values), dtype=dtype)\n assert_array_equal(np.add.reduce(arr), expected)\n\n np.add.reduce(arr, out=out)\n assert_array_equal(out, expected)\n\n\ndef test_add_promoter(string_list):\n arr = np.array(string_list, dtype=StringDType())\n lresult = np.array(["hello" + s for s in string_list], dtype=StringDType())\n rresult = np.array([s + "hello" for s in string_list], dtype=StringDType())\n\n for op in ["hello", np.str_("hello"), np.array(["hello"])]:\n assert_array_equal(op + arr, lresult)\n assert_array_equal(arr + op, rresult)\n\n # The promoter should be able to handle things if users pass `dtype=`\n res = np.add("hello", string_list, dtype=StringDType)\n assert res.dtype == StringDType()\n\n # The promoter should not kick in if users override the input,\n # which means arr is cast, this fails because of the unknown length.\n with pytest.raises(TypeError, match="cannot cast dtype"):\n np.add(arr, "add", signature=("U", "U", None), casting="unsafe")\n\n # But it must simply reject the following:\n with pytest.raises(TypeError, match=".*did not contain a loop"):\n np.add(arr, "add", signature=(None, "U", None))\n\n with pytest.raises(TypeError, match=".*did not contain a loop"):\n np.add("a", "b", signature=("U", "U", StringDType))\n\n\ndef test_add_no_legacy_promote_with_signature():\n # Possibly misplaced, but useful to test with string DType. We check that\n # if there is clearly no loop found, a stray `dtype=` doesn't break things\n # Regression test for the bad error in gh-26735\n # (If legacy promotion is gone, this can be deleted...)\n with pytest.raises(TypeError, match=".*did not contain a loop"):\n np.add("3", 6, dtype=StringDType)\n\n\ndef test_add_promoter_reduce():\n # Exact TypeError could change, but ensure StringDtype doesn't match\n with pytest.raises(TypeError, match="the resolved dtypes are not"):\n np.add.reduce(np.array(["a", "b"], dtype="U"))\n\n # On the other hand, using `dtype=T` in the *ufunc* should work.\n np.add.reduce(np.array(["a", "b"], dtype="U"), dtype=np.dtypes.StringDType)\n\n\ndef test_multiply_reduce():\n # At the time of writing (NumPy 2.0) this is very limited (and rather\n # ridiculous anyway). But it works and actually makes some sense...\n # (NumPy does not allow non-scalar initial values)\n repeats = np.array([2, 3, 4])\n val = "school-🚌"\n res = np.multiply.reduce(repeats, initial=val, dtype=np.dtypes.StringDType)\n assert res == val * np.prod(repeats)\n\n\ndef test_multiply_two_string_raises():\n arr = np.array(["hello", "world"], dtype="T")\n with pytest.raises(np._core._exceptions._UFuncNoLoopError):\n np.multiply(arr, arr)\n\n\n@pytest.mark.parametrize("use_out", [True, False])\n@pytest.mark.parametrize("other", [2, [2, 1, 3, 4, 1, 3]])\n@pytest.mark.parametrize(\n "other_dtype",\n [\n None,\n "int8",\n "int16",\n "int32",\n "int64",\n "uint8",\n "uint16",\n "uint32",\n "uint64",\n "short",\n "int",\n "intp",\n "long",\n "longlong",\n "ushort",\n "uint",\n "uintp",\n "ulong",\n "ulonglong",\n ],\n)\ndef test_ufunc_multiply(dtype, string_list, other, other_dtype, use_out):\n """Test the two-argument ufuncs match python builtin behavior."""\n arr = np.array(string_list, dtype=dtype)\n if other_dtype is not None:\n other_dtype = np.dtype(other_dtype)\n try:\n len(other)\n result = [s * o for s, o in zip(string_list, other)]\n other = np.array(other)\n if other_dtype is not None:\n other = other.astype(other_dtype)\n except TypeError:\n if other_dtype is not None:\n other = other_dtype.type(other)\n result = [s * other for s in string_list]\n\n if use_out:\n arr_cache = arr.copy()\n lres = np.multiply(arr, other, out=arr)\n assert_array_equal(lres, result)\n arr[:] = arr_cache\n assert lres is arr\n arr *= other\n assert_array_equal(arr, result)\n arr[:] = arr_cache\n rres = np.multiply(other, arr, out=arr)\n assert rres is arr\n assert_array_equal(rres, result)\n else:\n lres = arr * other\n assert_array_equal(lres, result)\n rres = other * arr\n assert_array_equal(rres, result)\n\n if not hasattr(dtype, "na_object"):\n return\n\n is_nan = np.isnan(np.array([dtype.na_object], dtype=dtype))[0]\n is_str = isinstance(dtype.na_object, str)\n bool_errors = 0\n try:\n bool(dtype.na_object)\n except TypeError:\n bool_errors = 1\n\n arr = np.array(string_list + [dtype.na_object], dtype=dtype)\n\n try:\n len(other)\n other = np.append(other, 3)\n if other_dtype is not None:\n other = other.astype(other_dtype)\n except TypeError:\n pass\n\n if is_nan or bool_errors or is_str:\n for res in [arr * other, other * arr]:\n assert_array_equal(res[:-1], result)\n if not is_str:\n assert res[-1] is dtype.na_object\n else:\n try:\n assert res[-1] == dtype.na_object * other[-1]\n except (IndexError, TypeError):\n assert res[-1] == dtype.na_object * other\n else:\n with pytest.raises(TypeError):\n arr * other\n with pytest.raises(TypeError):\n other * arr\n\n\ndef test_findlike_promoters():\n r = "Wally"\n l = "Where's Wally?"\n s = np.int32(3)\n e = np.int8(13)\n for dtypes in [("T", "U"), ("U", "T")]:\n for function, answer in [\n (np.strings.index, 8),\n (np.strings.endswith, True),\n ]:\n assert answer == function(\n np.array(l, dtype=dtypes[0]), np.array(r, dtype=dtypes[1]), s, e\n )\n\n\ndef test_strip_promoter():\n arg = ["Hello!!!!", "Hello??!!"]\n strip_char = "!"\n answer = ["Hello", "Hello??"]\n for dtypes in [("T", "U"), ("U", "T")]:\n result = np.strings.strip(\n np.array(arg, dtype=dtypes[0]),\n np.array(strip_char, dtype=dtypes[1])\n )\n assert_array_equal(result, answer)\n assert result.dtype.char == "T"\n\n\ndef test_replace_promoter():\n arg = ["Hello, planet!", "planet, Hello!"]\n old = "planet"\n new = "world"\n answer = ["Hello, world!", "world, Hello!"]\n for dtypes in itertools.product("TU", repeat=3):\n if dtypes == ("U", "U", "U"):\n continue\n answer_arr = np.strings.replace(\n np.array(arg, dtype=dtypes[0]),\n np.array(old, dtype=dtypes[1]),\n np.array(new, dtype=dtypes[2]),\n )\n assert_array_equal(answer_arr, answer)\n assert answer_arr.dtype.char == "T"\n\n\ndef test_center_promoter():\n arg = ["Hello", "planet!"]\n fillchar = "/"\n for dtypes in [("T", "U"), ("U", "T")]:\n answer = np.strings.center(\n np.array(arg, dtype=dtypes[0]), 9, np.array(fillchar, dtype=dtypes[1])\n )\n assert_array_equal(answer, ["//Hello//", "/planet!/"])\n assert answer.dtype.char == "T"\n\n\nDATETIME_INPUT = [\n np.datetime64("1923-04-14T12:43:12"),\n np.datetime64("1994-06-21T14:43:15"),\n np.datetime64("2001-10-15T04:10:32"),\n np.datetime64("NaT"),\n np.datetime64("1995-11-25T16:02:16"),\n np.datetime64("2005-01-04T03:14:12"),\n np.datetime64("2041-12-03T14:05:03"),\n]\n\n\nTIMEDELTA_INPUT = [\n np.timedelta64(12358, "s"),\n np.timedelta64(23, "s"),\n np.timedelta64(74, "s"),\n np.timedelta64("NaT"),\n np.timedelta64(23, "s"),\n np.timedelta64(73, "s"),\n np.timedelta64(7, "s"),\n]\n\n\n@pytest.mark.parametrize(\n "input_data, input_dtype",\n [\n (DATETIME_INPUT, "M8[s]"),\n (TIMEDELTA_INPUT, "m8[s]")\n ]\n)\ndef test_datetime_timedelta_cast(dtype, input_data, input_dtype):\n\n a = np.array(input_data, dtype=input_dtype)\n\n has_na = hasattr(dtype, "na_object")\n is_str = isinstance(getattr(dtype, "na_object", None), str)\n\n if not has_na or is_str:\n a = np.delete(a, 3)\n\n sa = a.astype(dtype)\n ra = sa.astype(a.dtype)\n\n if has_na and not is_str:\n assert sa[3] is dtype.na_object\n assert np.isnat(ra[3])\n\n assert_array_equal(a, ra)\n\n if has_na and not is_str:\n # don't worry about comparing how NaT is converted\n sa = np.delete(sa, 3)\n a = np.delete(a, 3)\n\n if input_dtype.startswith("M"):\n assert_array_equal(sa, a.astype("U"))\n else:\n # The timedelta to unicode cast produces strings\n # that aren't round-trippable and we don't want to\n # reproduce that behavior in stringdtype\n assert_array_equal(sa, a.astype("int64").astype("U"))\n\n\ndef test_nat_casts():\n s = 'nat'\n all_nats = itertools.product(*zip(s.upper(), s.lower()))\n all_nats = list(map(''.join, all_nats))\n NaT_dt = np.datetime64('NaT')\n NaT_td = np.timedelta64('NaT')\n for na_object in [np._NoValue, None, np.nan, 'nat', '']:\n # numpy treats empty string and all case combinations of 'nat' as NaT\n dtype = StringDType(na_object=na_object)\n arr = np.array([''] + all_nats, dtype=dtype)\n dt_array = arr.astype('M8[s]')\n td_array = arr.astype('m8[s]')\n assert_array_equal(dt_array, NaT_dt)\n assert_array_equal(td_array, NaT_td)\n\n if na_object is np._NoValue:\n output_object = 'NaT'\n else:\n output_object = na_object\n\n for arr in [dt_array, td_array]:\n assert_array_equal(\n arr.astype(dtype),\n np.array([output_object] * arr.size, dtype=dtype))\n\n\ndef test_nat_conversion():\n for nat in [np.datetime64("NaT", "s"), np.timedelta64("NaT", "s")]:\n with pytest.raises(ValueError, match="string coercion is disabled"):\n np.array(["a", nat], dtype=StringDType(coerce=False))\n\n\ndef test_growing_strings(dtype):\n # growing a string leads to a heap allocation, this tests to make sure\n # we do that bookkeeping correctly for all possible starting cases\n data = [\n "hello", # a short string\n "abcdefghijklmnopqestuvwxyz", # a medium heap-allocated string\n "hello" * 200, # a long heap-allocated string\n ]\n\n arr = np.array(data, dtype=dtype)\n uarr = np.array(data, dtype=str)\n\n for _ in range(5):\n arr = arr + arr\n uarr = uarr + uarr\n\n assert_array_equal(arr, uarr)\n\n\nUFUNC_TEST_DATA = [\n "hello" * 10,\n "Ae¢☃€ 😊" * 20,\n "entry\nwith\nnewlines",\n "entry\twith\ttabs",\n]\n\n\n@pytest.fixture\ndef string_array(dtype):\n return np.array(UFUNC_TEST_DATA, dtype=dtype)\n\n\n@pytest.fixture\ndef unicode_array():\n return np.array(UFUNC_TEST_DATA, dtype=np.str_)\n\n\nNAN_PRESERVING_FUNCTIONS = [\n "capitalize",\n "expandtabs",\n "lower",\n "lstrip",\n "rstrip",\n "splitlines",\n "strip",\n "swapcase",\n "title",\n "upper",\n]\n\nBOOL_OUTPUT_FUNCTIONS = [\n "isalnum",\n "isalpha",\n "isdigit",\n "islower",\n "isspace",\n "istitle",\n "isupper",\n "isnumeric",\n "isdecimal",\n]\n\nUNARY_FUNCTIONS = [\n "str_len",\n "capitalize",\n "expandtabs",\n "isalnum",\n "isalpha",\n "isdigit",\n "islower",\n "isspace",\n "istitle",\n "isupper",\n "lower",\n "lstrip",\n "rstrip",\n "splitlines",\n "strip",\n "swapcase",\n "title",\n "upper",\n "isnumeric",\n "isdecimal",\n "isalnum",\n "islower",\n "istitle",\n "isupper",\n]\n\nUNIMPLEMENTED_VEC_STRING_FUNCTIONS = [\n "capitalize",\n "expandtabs",\n "lower",\n "splitlines",\n "swapcase",\n "title",\n "upper",\n]\n\nONLY_IN_NP_CHAR = [\n "join",\n "split",\n "rsplit",\n "splitlines"\n]\n\n\n@pytest.mark.parametrize("function_name", UNARY_FUNCTIONS)\ndef test_unary(string_array, unicode_array, function_name):\n if function_name in ONLY_IN_NP_CHAR:\n func = getattr(np.char, function_name)\n else:\n func = getattr(np.strings, function_name)\n dtype = string_array.dtype\n sres = func(string_array)\n ures = func(unicode_array)\n if sres.dtype == StringDType():\n ures = ures.astype(StringDType())\n assert_array_equal(sres, ures)\n\n if not hasattr(dtype, "na_object"):\n return\n\n is_nan = np.isnan(np.array([dtype.na_object], dtype=dtype))[0]\n is_str = isinstance(dtype.na_object, str)\n na_arr = np.insert(string_array, 0, dtype.na_object)\n\n if function_name in UNIMPLEMENTED_VEC_STRING_FUNCTIONS:\n if not is_str:\n # to avoid these errors we'd need to add NA support to _vec_string\n with pytest.raises((ValueError, TypeError)):\n func(na_arr)\n elif function_name == "splitlines":\n assert func(na_arr)[0] == func(dtype.na_object)[()]\n else:\n assert func(na_arr)[0] == func(dtype.na_object)\n return\n if function_name == "str_len" and not is_str:\n # str_len always errors for any non-string null, even NA ones because\n # it has an integer result\n with pytest.raises(ValueError):\n func(na_arr)\n return\n if function_name in BOOL_OUTPUT_FUNCTIONS:\n if is_nan:\n assert func(na_arr)[0] is np.False_\n elif is_str:\n assert func(na_arr)[0] == func(dtype.na_object)\n else:\n with pytest.raises(ValueError):\n func(na_arr)\n return\n if not (is_nan or is_str):\n with pytest.raises(ValueError):\n func(na_arr)\n return\n res = func(na_arr)\n if is_nan and function_name in NAN_PRESERVING_FUNCTIONS:\n assert res[0] is dtype.na_object\n elif is_str:\n assert res[0] == func(dtype.na_object)\n\n\nunicode_bug_fail = pytest.mark.xfail(\n reason="unicode output width is buggy", strict=True\n)\n\n# None means that the argument is a string array\nBINARY_FUNCTIONS = [\n ("add", (None, None)),\n ("multiply", (None, 2)),\n ("mod", ("format: %s", None)),\n ("center", (None, 25)),\n ("count", (None, "A")),\n ("encode", (None, "UTF-8")),\n ("endswith", (None, "lo")),\n ("find", (None, "A")),\n ("index", (None, "e")),\n ("join", ("-", None)),\n ("ljust", (None, 12)),\n ("lstrip", (None, "A")),\n ("partition", (None, "A")),\n ("replace", (None, "A", "B")),\n ("rfind", (None, "A")),\n ("rindex", (None, "e")),\n ("rjust", (None, 12)),\n ("rsplit", (None, "A")),\n ("rstrip", (None, "A")),\n ("rpartition", (None, "A")),\n ("split", (None, "A")),\n ("strip", (None, "A")),\n ("startswith", (None, "A")),\n ("zfill", (None, 12)),\n]\n\nPASSES_THROUGH_NAN_NULLS = [\n "add",\n "center",\n "ljust",\n "multiply",\n "replace",\n "rjust",\n "strip",\n "lstrip",\n "rstrip",\n "replace"\n "zfill",\n]\n\nNULLS_ARE_FALSEY = [\n "startswith",\n "endswith",\n]\n\nNULLS_ALWAYS_ERROR = [\n "count",\n "find",\n "rfind",\n]\n\nSUPPORTS_NULLS = (\n PASSES_THROUGH_NAN_NULLS +\n NULLS_ARE_FALSEY +\n NULLS_ALWAYS_ERROR\n)\n\n\ndef call_func(func, args, array, sanitize=True):\n if args == (None, None):\n return func(array, array)\n if args[0] is None:\n if sanitize:\n san_args = tuple(\n np.array(arg, dtype=array.dtype) if isinstance(arg, str) else\n arg for arg in args[1:]\n )\n else:\n san_args = args[1:]\n return func(array, *san_args)\n if args[1] is None:\n return func(args[0], array)\n # shouldn't ever happen\n assert 0\n\n\n@pytest.mark.parametrize("function_name, args", BINARY_FUNCTIONS)\ndef test_binary(string_array, unicode_array, function_name, args):\n if function_name in ONLY_IN_NP_CHAR:\n func = getattr(np.char, function_name)\n else:\n func = getattr(np.strings, function_name)\n sres = call_func(func, args, string_array)\n ures = call_func(func, args, unicode_array, sanitize=False)\n if not isinstance(sres, tuple) and sres.dtype == StringDType():\n ures = ures.astype(StringDType())\n assert_array_equal(sres, ures)\n\n dtype = string_array.dtype\n if function_name not in SUPPORTS_NULLS or not hasattr(dtype, "na_object"):\n return\n\n na_arr = np.insert(string_array, 0, dtype.na_object)\n is_nan = np.isnan(np.array([dtype.na_object], dtype=dtype))[0]\n is_str = isinstance(dtype.na_object, str)\n should_error = not (is_nan or is_str)\n\n if (\n (function_name in NULLS_ALWAYS_ERROR and not is_str)\n or (function_name in PASSES_THROUGH_NAN_NULLS and should_error)\n or (function_name in NULLS_ARE_FALSEY and should_error)\n ):\n with pytest.raises((ValueError, TypeError)):\n call_func(func, args, na_arr)\n return\n\n res = call_func(func, args, na_arr)\n\n if is_str:\n assert res[0] == call_func(func, args, na_arr[:1])\n elif function_name in NULLS_ARE_FALSEY:\n assert res[0] is np.False_\n elif function_name in PASSES_THROUGH_NAN_NULLS:\n assert res[0] is dtype.na_object\n else:\n # shouldn't ever get here\n assert 0\n\n\n@pytest.mark.parametrize("function, expected", [\n (np.strings.find, [[2, -1], [1, -1]]),\n (np.strings.startswith, [[False, False], [True, False]])])\n@pytest.mark.parametrize("start, stop", [\n (1, 4),\n (np.int8(1), np.int8(4)),\n (np.array([1, 1], dtype='u2'), np.array([4, 4], dtype='u2'))])\ndef test_non_default_start_stop(function, start, stop, expected):\n a = np.array([["--🐍--", "--🦜--"],\n ["-🐍---", "-🦜---"]], "T")\n indx = function(a, "🐍", start, stop)\n assert_array_equal(indx, expected)\n\n\n@pytest.mark.parametrize("count", [2, np.int8(2), np.array([2, 2], 'u2')])\ndef test_replace_non_default_repeat(count):\n a = np.array(["🐍--", "🦜-🦜-"], "T")\n result = np.strings.replace(a, "🦜-", "🦜†", count)\n assert_array_equal(result, np.array(["🐍--", "🦜†🦜†"], "T"))\n\n\ndef test_strip_ljust_rjust_consistency(string_array, unicode_array):\n rjs = np.char.rjust(string_array, 1000)\n rju = np.char.rjust(unicode_array, 1000)\n\n ljs = np.char.ljust(string_array, 1000)\n lju = np.char.ljust(unicode_array, 1000)\n\n assert_array_equal(\n np.char.lstrip(rjs),\n np.char.lstrip(rju).astype(StringDType()),\n )\n\n assert_array_equal(\n np.char.rstrip(ljs),\n np.char.rstrip(lju).astype(StringDType()),\n )\n\n assert_array_equal(\n np.char.strip(ljs),\n np.char.strip(lju).astype(StringDType()),\n )\n\n assert_array_equal(\n np.char.strip(rjs),\n np.char.strip(rju).astype(StringDType()),\n )\n\n\ndef test_unset_na_coercion():\n # a dtype instance with an unset na object is compatible\n # with a dtype that has one set\n\n # this test uses the "add" and "equal" ufunc but all ufuncs that\n # accept more than one string argument and produce a string should\n # behave this way\n # TODO: generalize to more ufuncs\n inp = ["hello", "world"]\n arr = np.array(inp, dtype=StringDType(na_object=None))\n for op_dtype in [None, StringDType(), StringDType(coerce=False),\n StringDType(na_object=None)]:\n if op_dtype is None:\n op = "2"\n else:\n op = np.array("2", dtype=op_dtype)\n res = arr + op\n assert_array_equal(res, ["hello2", "world2"])\n\n # dtype instances with distinct explicitly set NA objects are incompatible\n for op_dtype in [StringDType(na_object=pd_NA), StringDType(na_object="")]:\n op = np.array("2", dtype=op_dtype)\n with pytest.raises(TypeError):\n arr + op\n\n # comparisons only consider the na_object\n for op_dtype in [None, StringDType(), StringDType(coerce=True),\n StringDType(na_object=None)]:\n if op_dtype is None:\n op = inp\n else:\n op = np.array(inp, dtype=op_dtype)\n assert_array_equal(arr, op)\n\n for op_dtype in [StringDType(na_object=pd_NA),\n StringDType(na_object=np.nan)]:\n op = np.array(inp, dtype=op_dtype)\n with pytest.raises(TypeError):\n arr == op\n\n\ndef test_repeat(string_array):\n res = string_array.repeat(1000)\n # Create an empty array with expanded dimension, and fill it. Then,\n # reshape it to the expected result.\n expected = np.empty_like(string_array, shape=string_array.shape + (1000,))\n expected[...] = string_array[:, np.newaxis]\n expected = expected.reshape(-1)\n\n assert_array_equal(res, expected, strict=True)\n\n\n@pytest.mark.parametrize("tile", [1, 6, (2, 5)])\ndef test_accumulation(string_array, tile):\n """Accumulation is odd for StringDType but tests dtypes with references.\n """\n # Fill with mostly empty strings to not create absurdly big strings\n arr = np.zeros_like(string_array, shape=(100,))\n arr[:len(string_array)] = string_array\n arr[-len(string_array):] = string_array\n\n # Bloat size a bit (get above thresholds and test >1 ndim).\n arr = np.tile(string_array, tile)\n\n res = np.add.accumulate(arr, axis=0)\n res_obj = np.add.accumulate(arr.astype(object), axis=0)\n assert_array_equal(res, res_obj.astype(arr.dtype), strict=True)\n\n if arr.ndim > 1:\n res = np.add.accumulate(arr, axis=-1)\n res_obj = np.add.accumulate(arr.astype(object), axis=-1)\n\n assert_array_equal(res, res_obj.astype(arr.dtype), strict=True)\n\n\nclass TestImplementation:\n """Check that strings are stored in the arena when possible.\n\n This tests implementation details, so should be adjusted if\n the implementation changes.\n """\n\n @classmethod\n def setup_class(self):\n self.MISSING = 0x80\n self.INITIALIZED = 0x40\n self.OUTSIDE_ARENA = 0x20\n self.LONG = 0x10\n self.dtype = StringDType(na_object=np.nan)\n self.sizeofstr = self.dtype.itemsize\n sp = self.dtype.itemsize // 2 # pointer size = sizeof(size_t)\n # Below, size is not strictly correct, since it really uses\n # 7 (or 3) bytes, but good enough for the tests here.\n self.view_dtype = np.dtype([\n ('offset', f'u{sp}'),\n ('size', f'u{sp // 2}'),\n ('xsiz', f'V{sp // 2 - 1}'),\n ('size_and_flags', 'u1'),\n ] if sys.byteorder == 'little' else [\n ('size_and_flags', 'u1'),\n ('xsiz', f'V{sp // 2 - 1}'),\n ('size', f'u{sp // 2}'),\n ('offset', f'u{sp}'),\n ])\n self.s_empty = ""\n self.s_short = "01234"\n self.s_medium = "abcdefghijklmnopqrstuvwxyz"\n self.s_long = "-=+" * 100\n self.a = np.array(\n [self.s_empty, self.s_short, self.s_medium, self.s_long],\n self.dtype)\n\n def get_view(self, a):\n # Cannot view a StringDType as anything else directly, since\n # it has references. So, we use a stride trick hack.\n from numpy.lib._stride_tricks_impl import DummyArray\n interface = dict(a.__array_interface__)\n interface['descr'] = self.view_dtype.descr\n interface['typestr'] = self.view_dtype.str\n return np.asarray(DummyArray(interface, base=a))\n\n def get_flags(self, a):\n return self.get_view(a)['size_and_flags'] & 0xf0\n\n def is_short(self, a):\n return self.get_flags(a) == self.INITIALIZED | self.OUTSIDE_ARENA\n\n def is_on_heap(self, a):\n return self.get_flags(a) == (self.INITIALIZED\n | self.OUTSIDE_ARENA\n | self.LONG)\n\n def is_missing(self, a):\n return self.get_flags(a) & self.MISSING == self.MISSING\n\n def in_arena(self, a):\n return (self.get_flags(a) & (self.INITIALIZED | self.OUTSIDE_ARENA)\n == self.INITIALIZED)\n\n def test_setup(self):\n is_short = self.is_short(self.a)\n length = np.strings.str_len(self.a)\n assert_array_equal(is_short, (length > 0) & (length <= 15))\n assert_array_equal(self.in_arena(self.a), [False, False, True, True])\n assert_array_equal(self.is_on_heap(self.a), False)\n assert_array_equal(self.is_missing(self.a), False)\n view = self.get_view(self.a)\n sizes = np.where(is_short, view['size_and_flags'] & 0xf,\n view['size'])\n assert_array_equal(sizes, np.strings.str_len(self.a))\n assert_array_equal(view['xsiz'][2:],\n np.void(b'\x00' * (self.sizeofstr // 4 - 1)))\n # Check that the medium string uses only 1 byte for its length\n # in the arena, while the long string takes 8 (or 4).\n offsets = view['offset']\n assert offsets[2] == 1\n assert offsets[3] == 1 + len(self.s_medium) + self.sizeofstr // 2\n\n def test_empty(self):\n e = np.empty((3,), self.dtype)\n assert_array_equal(self.get_flags(e), 0)\n assert_array_equal(e, "")\n\n def test_zeros(self):\n z = np.zeros((2,), self.dtype)\n assert_array_equal(self.get_flags(z), 0)\n assert_array_equal(z, "")\n\n def test_copy(self):\n for c in [self.a.copy(), copy.copy(self.a), copy.deepcopy(self.a)]:\n assert_array_equal(self.get_flags(c), self.get_flags(self.a))\n assert_array_equal(c, self.a)\n offsets = self.get_view(c)['offset']\n assert offsets[2] == 1\n assert offsets[3] == 1 + len(self.s_medium) + self.sizeofstr // 2\n\n def test_arena_use_with_setting(self):\n c = np.zeros_like(self.a)\n assert_array_equal(self.get_flags(c), 0)\n c[:] = self.a\n assert_array_equal(self.get_flags(c), self.get_flags(self.a))\n assert_array_equal(c, self.a)\n\n def test_arena_reuse_with_setting(self):\n c = self.a.copy()\n c[:] = self.a\n assert_array_equal(self.get_flags(c), self.get_flags(self.a))\n assert_array_equal(c, self.a)\n\n def test_arena_reuse_after_missing(self):\n c = self.a.copy()\n c[:] = np.nan\n assert np.all(self.is_missing(c))\n # Replacing with the original strings, the arena should be reused.\n c[:] = self.a\n assert_array_equal(self.get_flags(c), self.get_flags(self.a))\n assert_array_equal(c, self.a)\n\n def test_arena_reuse_after_empty(self):\n c = self.a.copy()\n c[:] = ""\n assert_array_equal(c, "")\n # Replacing with the original strings, the arena should be reused.\n c[:] = self.a\n assert_array_equal(self.get_flags(c), self.get_flags(self.a))\n assert_array_equal(c, self.a)\n\n def test_arena_reuse_for_shorter(self):\n c = self.a.copy()\n # A string slightly shorter than the shortest in the arena\n # should be used for all strings in the arena.\n c[:] = self.s_medium[:-1]\n assert_array_equal(c, self.s_medium[:-1])\n # first empty string in original was never initialized, so\n # filling it in now leaves it initialized inside the arena.\n # second string started as a short string so it can never live\n # in the arena.\n in_arena = np.array([True, False, True, True])\n assert_array_equal(self.in_arena(c), in_arena)\n # But when a short string is replaced, it will go on the heap.\n assert_array_equal(self.is_short(c), False)\n assert_array_equal(self.is_on_heap(c), ~in_arena)\n # We can put the originals back, and they'll still fit,\n # and short strings are back as short strings\n c[:] = self.a\n assert_array_equal(c, self.a)\n assert_array_equal(self.in_arena(c), in_arena)\n assert_array_equal(self.is_short(c), self.is_short(self.a))\n assert_array_equal(self.is_on_heap(c), False)\n\n def test_arena_reuse_if_possible(self):\n c = self.a.copy()\n # A slightly longer string will not fit in the arena for\n # the medium string, but will fit for the longer one.\n c[:] = self.s_medium + "±"\n assert_array_equal(c, self.s_medium + "±")\n in_arena_exp = np.strings.str_len(self.a) >= len(self.s_medium) + 1\n # first entry started uninitialized and empty, so filling it leaves\n # it in the arena\n in_arena_exp[0] = True\n assert not np.all(in_arena_exp == self.in_arena(self.a))\n assert_array_equal(self.in_arena(c), in_arena_exp)\n assert_array_equal(self.is_short(c), False)\n assert_array_equal(self.is_on_heap(c), ~in_arena_exp)\n # And once outside arena, it stays outside, since offset is lost.\n # But short strings are used again.\n c[:] = self.a\n is_short_exp = self.is_short(self.a)\n assert_array_equal(c, self.a)\n assert_array_equal(self.in_arena(c), in_arena_exp)\n assert_array_equal(self.is_short(c), is_short_exp)\n assert_array_equal(self.is_on_heap(c), ~in_arena_exp & ~is_short_exp)\n\n def test_arena_no_reuse_after_short(self):\n c = self.a.copy()\n # If we replace a string with a short string, it cannot\n # go into the arena after because the offset is lost.\n c[:] = self.s_short\n assert_array_equal(c, self.s_short)\n assert_array_equal(self.in_arena(c), False)\n c[:] = self.a\n assert_array_equal(c, self.a)\n assert_array_equal(self.in_arena(c), False)\n assert_array_equal(self.is_on_heap(c), self.in_arena(self.a))\n | .venv\Lib\site-packages\numpy\_core\tests\test_stringdtype.py | test_stringdtype.py | Python | 57,794 | 0.75 | 0.132808 | 0.062078 | react-lib | 345 | 2024-03-02T07:34:37.148559 | MIT | true | 6b0c72f08568f1410542fc67861f2483 |
import operator\nimport sys\n\nimport pytest\n\nimport numpy as np\nfrom numpy.testing import IS_PYPY, assert_array_equal, assert_raises\nfrom numpy.testing._private.utils import requires_memory\n\nCOMPARISONS = [\n (operator.eq, np.equal, "=="),\n (operator.ne, np.not_equal, "!="),\n (operator.lt, np.less, "<"),\n (operator.le, np.less_equal, "<="),\n (operator.gt, np.greater, ">"),\n (operator.ge, np.greater_equal, ">="),\n]\n\nMAX = np.iinfo(np.int64).max\n\nIS_PYPY_LT_7_3_16 = IS_PYPY and sys.implementation.version < (7, 3, 16)\n\n@pytest.mark.parametrize(["op", "ufunc", "sym"], COMPARISONS)\ndef test_mixed_string_comparison_ufuncs_fail(op, ufunc, sym):\n arr_string = np.array(["a", "b"], dtype="S")\n arr_unicode = np.array(["a", "c"], dtype="U")\n\n with pytest.raises(TypeError, match="did not contain a loop"):\n ufunc(arr_string, arr_unicode)\n\n with pytest.raises(TypeError, match="did not contain a loop"):\n ufunc(arr_unicode, arr_string)\n\n@pytest.mark.parametrize(["op", "ufunc", "sym"], COMPARISONS)\ndef test_mixed_string_comparisons_ufuncs_with_cast(op, ufunc, sym):\n arr_string = np.array(["a", "b"], dtype="S")\n arr_unicode = np.array(["a", "c"], dtype="U")\n\n # While there is no loop, manual casting is acceptable:\n res1 = ufunc(arr_string, arr_unicode, signature="UU->?", casting="unsafe")\n res2 = ufunc(arr_string, arr_unicode, signature="SS->?", casting="unsafe")\n\n expected = op(arr_string.astype("U"), arr_unicode)\n assert_array_equal(res1, expected)\n assert_array_equal(res2, expected)\n\n\n@pytest.mark.parametrize(["op", "ufunc", "sym"], COMPARISONS)\n@pytest.mark.parametrize("dtypes", [\n ("S2", "S2"), ("S2", "S10"),\n ("<U1", "<U1"), ("<U1", ">U1"), (">U1", ">U1"),\n ("<U1", "<U10"), ("<U1", ">U10")])\n@pytest.mark.parametrize("aligned", [True, False])\ndef test_string_comparisons(op, ufunc, sym, dtypes, aligned):\n # ensure native byte-order for the first view to stay within unicode range\n native_dt = np.dtype(dtypes[0]).newbyteorder("=")\n arr = np.arange(2**15).view(native_dt).astype(dtypes[0])\n if not aligned:\n # Make `arr` unaligned:\n new = np.zeros(arr.nbytes + 1, dtype=np.uint8)[1:].view(dtypes[0])\n new[...] = arr\n arr = new\n\n arr2 = arr.astype(dtypes[1], copy=True)\n np.random.shuffle(arr2)\n arr[0] = arr2[0] # make sure one matches\n\n expected = [op(d1, d2) for d1, d2 in zip(arr.tolist(), arr2.tolist())]\n assert_array_equal(op(arr, arr2), expected)\n assert_array_equal(ufunc(arr, arr2), expected)\n assert_array_equal(\n np.char.compare_chararrays(arr, arr2, sym, False), expected\n )\n\n expected = [op(d2, d1) for d1, d2 in zip(arr.tolist(), arr2.tolist())]\n assert_array_equal(op(arr2, arr), expected)\n assert_array_equal(ufunc(arr2, arr), expected)\n assert_array_equal(\n np.char.compare_chararrays(arr2, arr, sym, False), expected\n )\n\n\n@pytest.mark.parametrize(["op", "ufunc", "sym"], COMPARISONS)\n@pytest.mark.parametrize("dtypes", [\n ("S2", "S2"), ("S2", "S10"), ("<U1", "<U1"), ("<U1", ">U10")])\ndef test_string_comparisons_empty(op, ufunc, sym, dtypes):\n arr = np.empty((1, 0, 1, 5), dtype=dtypes[0])\n arr2 = np.empty((100, 1, 0, 1), dtype=dtypes[1])\n\n expected = np.empty(np.broadcast_shapes(arr.shape, arr2.shape), dtype=bool)\n assert_array_equal(op(arr, arr2), expected)\n assert_array_equal(ufunc(arr, arr2), expected)\n assert_array_equal(\n np.char.compare_chararrays(arr, arr2, sym, False), expected\n )\n\n\n@pytest.mark.parametrize("str_dt", ["S", "U"])\n@pytest.mark.parametrize("float_dt", np.typecodes["AllFloat"])\ndef test_float_to_string_cast(str_dt, float_dt):\n float_dt = np.dtype(float_dt)\n fi = np.finfo(float_dt)\n arr = np.array([np.nan, np.inf, -np.inf, fi.max, fi.min], dtype=float_dt)\n expected = ["nan", "inf", "-inf", str(fi.max), str(fi.min)]\n if float_dt.kind == "c":\n expected = [f"({r}+0j)" for r in expected]\n\n res = arr.astype(str_dt)\n assert_array_equal(res, np.array(expected, dtype=str_dt))\n\n\n@pytest.mark.parametrize("str_dt", "US")\n@pytest.mark.parametrize("size", [-1, np.iinfo(np.intc).max])\ndef test_string_size_dtype_errors(str_dt, size):\n if size > 0:\n size = size // np.dtype(f"{str_dt}1").itemsize + 1\n\n with pytest.raises(ValueError):\n np.dtype((str_dt, size))\n with pytest.raises(TypeError):\n np.dtype(f"{str_dt}{size}")\n\n\n@pytest.mark.parametrize("str_dt", "US")\ndef test_string_size_dtype_large_repr(str_dt):\n size = np.iinfo(np.intc).max // np.dtype(f"{str_dt}1").itemsize\n size_str = str(size)\n\n dtype = np.dtype((str_dt, size))\n assert size_str in dtype.str\n assert size_str in str(dtype)\n assert size_str in repr(dtype)\n\n\n@pytest.mark.slow\n@requires_memory(2 * np.iinfo(np.intc).max)\n@pytest.mark.parametrize("str_dt", "US")\ndef test_large_string_coercion_error(str_dt):\n very_large = np.iinfo(np.intc).max // np.dtype(f"{str_dt}1").itemsize\n try:\n large_string = "A" * (very_large + 1)\n except Exception:\n # We may not be able to create this Python string on 32bit.\n pytest.skip("python failed to create huge string")\n\n class MyStr:\n def __str__(self):\n return large_string\n\n try:\n # TypeError from NumPy, or OverflowError from 32bit Python.\n with pytest.raises((TypeError, OverflowError)):\n np.array([large_string], dtype=str_dt)\n\n # Same as above, but input has to be converted to a string.\n with pytest.raises((TypeError, OverflowError)):\n np.array([MyStr()], dtype=str_dt)\n except MemoryError:\n # Catch memory errors, because `requires_memory` would do so.\n raise AssertionError("Ops should raise before any large allocation.")\n\n@pytest.mark.slow\n@requires_memory(2 * np.iinfo(np.intc).max)\n@pytest.mark.parametrize("str_dt", "US")\ndef test_large_string_addition_error(str_dt):\n very_large = np.iinfo(np.intc).max // np.dtype(f"{str_dt}1").itemsize\n\n a = np.array(["A" * very_large], dtype=str_dt)\n b = np.array("B", dtype=str_dt)\n try:\n with pytest.raises(TypeError):\n np.add(a, b)\n with pytest.raises(TypeError):\n np.add(a, a)\n except MemoryError:\n # Catch memory errors, because `requires_memory` would do so.\n raise AssertionError("Ops should raise before any large allocation.")\n\n\ndef test_large_string_cast():\n very_large = np.iinfo(np.intc).max // 4\n # Could be nice to test very large path, but it makes too many huge\n # allocations right now (need non-legacy cast loops for this).\n # a = np.array([], dtype=np.dtype(("S", very_large)))\n # assert a.astype("U").dtype.itemsize == very_large * 4\n\n a = np.array([], dtype=np.dtype(("S", very_large + 1)))\n # It is not perfect but OK if this raises a MemoryError during setup\n # (this happens due clunky code and/or buffer setup.)\n with pytest.raises((TypeError, MemoryError)):\n a.astype("U")\n\n\n@pytest.mark.parametrize("dt", ["S", "U", "T"])\nclass TestMethods:\n\n @pytest.mark.parametrize("in1,in2,out", [\n ("", "", ""),\n ("abc", "abc", "abcabc"),\n ("12345", "12345", "1234512345"),\n ("MixedCase", "MixedCase", "MixedCaseMixedCase"),\n ("12345 \0 ", "12345 \0 ", "12345 \0 12345 \0 "),\n ("UPPER", "UPPER", "UPPERUPPER"),\n (["abc", "def"], ["hello", "world"], ["abchello", "defworld"]),\n ])\n def test_add(self, in1, in2, out, dt):\n in1 = np.array(in1, dtype=dt)\n in2 = np.array(in2, dtype=dt)\n out = np.array(out, dtype=dt)\n assert_array_equal(np.strings.add(in1, in2), out)\n\n @pytest.mark.parametrize("in1,in2,out", [\n ("abc", 3, "abcabcabc"),\n ("abc", 0, ""),\n ("abc", -1, ""),\n (["abc", "def"], [1, 4], ["abc", "defdefdefdef"]),\n ])\n def test_multiply(self, in1, in2, out, dt):\n in1 = np.array(in1, dtype=dt)\n out = np.array(out, dtype=dt)\n assert_array_equal(np.strings.multiply(in1, in2), out)\n\n def test_multiply_raises(self, dt):\n with pytest.raises(TypeError, match="unsupported type"):\n np.strings.multiply(np.array("abc", dtype=dt), 3.14)\n\n with pytest.raises(OverflowError):\n np.strings.multiply(np.array("abc", dtype=dt), sys.maxsize)\n\n def test_inplace_multiply(self, dt):\n arr = np.array(['foo ', 'bar'], dtype=dt)\n arr *= 2\n if dt != "T":\n assert_array_equal(arr, np.array(['foo ', 'barb'], dtype=dt))\n else:\n assert_array_equal(arr, ['foo foo ', 'barbar'])\n\n with pytest.raises(OverflowError):\n arr *= sys.maxsize\n\n @pytest.mark.parametrize("i_dt", [np.int8, np.int16, np.int32,\n np.int64, np.int_])\n def test_multiply_integer_dtypes(self, i_dt, dt):\n a = np.array("abc", dtype=dt)\n i = np.array(3, dtype=i_dt)\n res = np.array("abcabcabc", dtype=dt)\n assert_array_equal(np.strings.multiply(a, i), res)\n\n @pytest.mark.parametrize("in_,out", [\n ("", False),\n ("a", True),\n ("A", True),\n ("\n", False),\n ("abc", True),\n ("aBc123", False),\n ("abc\n", False),\n (["abc", "aBc123"], [True, False]),\n ])\n def test_isalpha(self, in_, out, dt):\n in_ = np.array(in_, dtype=dt)\n assert_array_equal(np.strings.isalpha(in_), out)\n\n @pytest.mark.parametrize("in_,out", [\n ('', False),\n ('a', True),\n ('A', True),\n ('\n', False),\n ('123abc456', True),\n ('a1b3c', True),\n ('aBc000 ', False),\n ('abc\n', False),\n ])\n def test_isalnum(self, in_, out, dt):\n in_ = np.array(in_, dtype=dt)\n assert_array_equal(np.strings.isalnum(in_), out)\n\n @pytest.mark.parametrize("in_,out", [\n ("", False),\n ("a", False),\n ("0", True),\n ("012345", True),\n ("012345a", False),\n (["a", "012345"], [False, True]),\n ])\n def test_isdigit(self, in_, out, dt):\n in_ = np.array(in_, dtype=dt)\n assert_array_equal(np.strings.isdigit(in_), out)\n\n @pytest.mark.parametrize("in_,out", [\n ("", False),\n ("a", False),\n ("1", False),\n (" ", True),\n ("\t", True),\n ("\r", True),\n ("\n", True),\n (" \t\r \n", True),\n (" \t\r\na", False),\n (["\t1", " \t\r \n"], [False, True])\n ])\n def test_isspace(self, in_, out, dt):\n in_ = np.array(in_, dtype=dt)\n assert_array_equal(np.strings.isspace(in_), out)\n\n @pytest.mark.parametrize("in_,out", [\n ('', False),\n ('a', True),\n ('A', False),\n ('\n', False),\n ('abc', True),\n ('aBc', False),\n ('abc\n', True),\n ])\n def test_islower(self, in_, out, dt):\n in_ = np.array(in_, dtype=dt)\n assert_array_equal(np.strings.islower(in_), out)\n\n @pytest.mark.parametrize("in_,out", [\n ('', False),\n ('a', False),\n ('A', True),\n ('\n', False),\n ('ABC', True),\n ('AbC', False),\n ('ABC\n', True),\n ])\n def test_isupper(self, in_, out, dt):\n in_ = np.array(in_, dtype=dt)\n assert_array_equal(np.strings.isupper(in_), out)\n\n @pytest.mark.parametrize("in_,out", [\n ('', False),\n ('a', False),\n ('A', True),\n ('\n', False),\n ('A Titlecased Line', True),\n ('A\nTitlecased Line', True),\n ('A Titlecased, Line', True),\n ('Not a capitalized String', False),\n ('Not\ta Titlecase String', False),\n ('Not--a Titlecase String', False),\n ('NOT', False),\n ])\n def test_istitle(self, in_, out, dt):\n in_ = np.array(in_, dtype=dt)\n assert_array_equal(np.strings.istitle(in_), out)\n\n @pytest.mark.parametrize("in_,out", [\n ("", 0),\n ("abc", 3),\n ("12345", 5),\n ("MixedCase", 9),\n ("12345 \x00 ", 8),\n ("UPPER", 5),\n (["abc", "12345 \x00 "], [3, 8]),\n ])\n def test_str_len(self, in_, out, dt):\n in_ = np.array(in_, dtype=dt)\n assert_array_equal(np.strings.str_len(in_), out)\n\n @pytest.mark.parametrize("a,sub,start,end,out", [\n ("abcdefghiabc", "abc", 0, None, 0),\n ("abcdefghiabc", "abc", 1, None, 9),\n ("abcdefghiabc", "def", 4, None, -1),\n ("abc", "", 0, None, 0),\n ("abc", "", 3, None, 3),\n ("abc", "", 4, None, -1),\n ("rrarrrrrrrrra", "a", 0, None, 2),\n ("rrarrrrrrrrra", "a", 4, None, 12),\n ("rrarrrrrrrrra", "a", 4, 6, -1),\n ("", "", 0, None, 0),\n ("", "", 1, 1, -1),\n ("", "", MAX, 0, -1),\n ("", "xx", 0, None, -1),\n ("", "xx", 1, 1, -1),\n ("", "xx", MAX, 0, -1),\n pytest.param(99 * "a" + "b", "b", 0, None, 99,\n id="99*a+b-b-0-None-99"),\n pytest.param(98 * "a" + "ba", "ba", 0, None, 98,\n id="98*a+ba-ba-0-None-98"),\n pytest.param(100 * "a", "b", 0, None, -1,\n id="100*a-b-0-None--1"),\n pytest.param(30000 * "a" + 100 * "b", 100 * "b", 0, None, 30000,\n id="30000*a+100*b-100*b-0-None-30000"),\n pytest.param(30000 * "a", 100 * "b", 0, None, -1,\n id="30000*a-100*b-0-None--1"),\n pytest.param(15000 * "a" + 15000 * "b", 15000 * "b", 0, None, 15000,\n id="15000*a+15000*b-15000*b-0-None-15000"),\n pytest.param(15000 * "a" + 15000 * "b", 15000 * "c", 0, None, -1,\n id="15000*a+15000*b-15000*c-0-None--1"),\n (["abcdefghiabc", "rrarrrrrrrrra"], ["def", "arr"], [0, 3],\n None, [3, -1]),\n ("Ae¢☃€ 😊" * 2, "😊", 0, None, 6),\n ("Ae¢☃€ 😊" * 2, "😊", 7, None, 13),\n pytest.param("A" * (2 ** 17), r"[\w]+\Z", 0, None, -1,\n id=r"A*2**17-[\w]+\Z-0-None--1"),\n ])\n def test_find(self, a, sub, start, end, out, dt):\n if "😊" in a and dt == "S":\n pytest.skip("Bytes dtype does not support non-ascii input")\n a = np.array(a, dtype=dt)\n sub = np.array(sub, dtype=dt)\n assert_array_equal(np.strings.find(a, sub, start, end), out)\n\n @pytest.mark.parametrize("a,sub,start,end,out", [\n ("abcdefghiabc", "abc", 0, None, 9),\n ("abcdefghiabc", "", 0, None, 12),\n ("abcdefghiabc", "abcd", 0, None, 0),\n ("abcdefghiabc", "abcz", 0, None, -1),\n ("abc", "", 0, None, 3),\n ("abc", "", 3, None, 3),\n ("abc", "", 4, None, -1),\n ("rrarrrrrrrrra", "a", 0, None, 12),\n ("rrarrrrrrrrra", "a", 4, None, 12),\n ("rrarrrrrrrrra", "a", 4, 6, -1),\n (["abcdefghiabc", "rrarrrrrrrrra"], ["abc", "a"], [0, 0],\n None, [9, 12]),\n ("Ae¢☃€ 😊" * 2, "😊", 0, None, 13),\n ("Ae¢☃€ 😊" * 2, "😊", 0, 7, 6),\n ])\n def test_rfind(self, a, sub, start, end, out, dt):\n if "😊" in a and dt == "S":\n pytest.skip("Bytes dtype does not support non-ascii input")\n a = np.array(a, dtype=dt)\n sub = np.array(sub, dtype=dt)\n assert_array_equal(np.strings.rfind(a, sub, start, end), out)\n\n @pytest.mark.parametrize("a,sub,start,end,out", [\n ("aaa", "a", 0, None, 3),\n ("aaa", "b", 0, None, 0),\n ("aaa", "a", 1, None, 2),\n ("aaa", "a", 10, None, 0),\n ("aaa", "a", -1, None, 1),\n ("aaa", "a", -10, None, 3),\n ("aaa", "a", 0, 1, 1),\n ("aaa", "a", 0, 10, 3),\n ("aaa", "a", 0, -1, 2),\n ("aaa", "a", 0, -10, 0),\n ("aaa", "", 1, None, 3),\n ("aaa", "", 3, None, 1),\n ("aaa", "", 10, None, 0),\n ("aaa", "", -1, None, 2),\n ("aaa", "", -10, None, 4),\n ("aaa", "aaaa", 0, None, 0),\n pytest.param(98 * "a" + "ba", "ba", 0, None, 1,\n id="98*a+ba-ba-0-None-1"),\n pytest.param(30000 * "a" + 100 * "b", 100 * "b", 0, None, 1,\n id="30000*a+100*b-100*b-0-None-1"),\n pytest.param(30000 * "a", 100 * "b", 0, None, 0,\n id="30000*a-100*b-0-None-0"),\n pytest.param(30000 * "a" + 100 * "ab", "ab", 0, None, 100,\n id="30000*a+100*ab-ab-0-None-100"),\n pytest.param(15000 * "a" + 15000 * "b", 15000 * "b", 0, None, 1,\n id="15000*a+15000*b-15000*b-0-None-1"),\n pytest.param(15000 * "a" + 15000 * "b", 15000 * "c", 0, None, 0,\n id="15000*a+15000*b-15000*c-0-None-0"),\n ("", "", 0, None, 1),\n ("", "", 1, 1, 0),\n ("", "", MAX, 0, 0),\n ("", "xx", 0, None, 0),\n ("", "xx", 1, 1, 0),\n ("", "xx", MAX, 0, 0),\n (["aaa", ""], ["a", ""], [0, 0], None, [3, 1]),\n ("Ae¢☃€ 😊" * 100, "😊", 0, None, 100),\n ])\n def test_count(self, a, sub, start, end, out, dt):\n if "😊" in a and dt == "S":\n pytest.skip("Bytes dtype does not support non-ascii input")\n a = np.array(a, dtype=dt)\n sub = np.array(sub, dtype=dt)\n assert_array_equal(np.strings.count(a, sub, start, end), out)\n\n @pytest.mark.parametrize("a,prefix,start,end,out", [\n ("hello", "he", 0, None, True),\n ("hello", "hello", 0, None, True),\n ("hello", "hello world", 0, None, False),\n ("hello", "", 0, None, True),\n ("hello", "ello", 0, None, False),\n ("hello", "ello", 1, None, True),\n ("hello", "o", 4, None, True),\n ("hello", "o", 5, None, False),\n ("hello", "", 5, None, True),\n ("hello", "lo", 6, None, False),\n ("helloworld", "lowo", 3, None, True),\n ("helloworld", "lowo", 3, 7, True),\n ("helloworld", "lowo", 3, 6, False),\n ("", "", 0, 1, True),\n ("", "", 0, 0, True),\n ("", "", 1, 0, False),\n ("hello", "he", 0, -1, True),\n ("hello", "he", -53, -1, True),\n ("hello", "hello", 0, -1, False),\n ("hello", "hello world", -1, -10, False),\n ("hello", "ello", -5, None, False),\n ("hello", "ello", -4, None, True),\n ("hello", "o", -2, None, False),\n ("hello", "o", -1, None, True),\n ("hello", "", -3, -3, True),\n ("hello", "lo", -9, None, False),\n (["hello", ""], ["he", ""], [0, 0], None, [True, True]),\n ])\n def test_startswith(self, a, prefix, start, end, out, dt):\n a = np.array(a, dtype=dt)\n prefix = np.array(prefix, dtype=dt)\n assert_array_equal(np.strings.startswith(a, prefix, start, end), out)\n\n @pytest.mark.parametrize("a,suffix,start,end,out", [\n ("hello", "lo", 0, None, True),\n ("hello", "he", 0, None, False),\n ("hello", "", 0, None, True),\n ("hello", "hello world", 0, None, False),\n ("helloworld", "worl", 0, None, False),\n ("helloworld", "worl", 3, 9, True),\n ("helloworld", "world", 3, 12, True),\n ("helloworld", "lowo", 1, 7, True),\n ("helloworld", "lowo", 2, 7, True),\n ("helloworld", "lowo", 3, 7, True),\n ("helloworld", "lowo", 4, 7, False),\n ("helloworld", "lowo", 3, 8, False),\n ("ab", "ab", 0, 1, False),\n ("ab", "ab", 0, 0, False),\n ("", "", 0, 1, True),\n ("", "", 0, 0, True),\n ("", "", 1, 0, False),\n ("hello", "lo", -2, None, True),\n ("hello", "he", -2, None, False),\n ("hello", "", -3, -3, True),\n ("hello", "hello world", -10, -2, False),\n ("helloworld", "worl", -6, None, False),\n ("helloworld", "worl", -5, -1, True),\n ("helloworld", "worl", -5, 9, True),\n ("helloworld", "world", -7, 12, True),\n ("helloworld", "lowo", -99, -3, True),\n ("helloworld", "lowo", -8, -3, True),\n ("helloworld", "lowo", -7, -3, True),\n ("helloworld", "lowo", 3, -4, False),\n ("helloworld", "lowo", -8, -2, False),\n (["hello", "helloworld"], ["lo", "worl"], [0, -6], None,\n [True, False]),\n ])\n def test_endswith(self, a, suffix, start, end, out, dt):\n a = np.array(a, dtype=dt)\n suffix = np.array(suffix, dtype=dt)\n assert_array_equal(np.strings.endswith(a, suffix, start, end), out)\n\n @pytest.mark.parametrize("a,chars,out", [\n ("", None, ""),\n (" hello ", None, "hello "),\n ("hello", None, "hello"),\n (" \t\n\r\f\vabc \t\n\r\f\v", None, "abc \t\n\r\f\v"),\n ([" hello ", "hello"], None, ["hello ", "hello"]),\n ("", "", ""),\n ("", "xyz", ""),\n ("hello", "", "hello"),\n ("xyzzyhelloxyzzy", "xyz", "helloxyzzy"),\n ("hello", "xyz", "hello"),\n ("xyxz", "xyxz", ""),\n ("xyxzx", "x", "yxzx"),\n (["xyzzyhelloxyzzy", "hello"], ["xyz", "xyz"],\n ["helloxyzzy", "hello"]),\n (["ba", "ac", "baa", "bba"], "b", ["a", "ac", "aa", "a"]),\n ])\n def test_lstrip(self, a, chars, out, dt):\n a = np.array(a, dtype=dt)\n out = np.array(out, dtype=dt)\n if chars is not None:\n chars = np.array(chars, dtype=dt)\n assert_array_equal(np.strings.lstrip(a, chars), out)\n else:\n assert_array_equal(np.strings.lstrip(a), out)\n\n @pytest.mark.parametrize("a,chars,out", [\n ("", None, ""),\n (" hello ", None, " hello"),\n ("hello", None, "hello"),\n (" \t\n\r\f\vabc \t\n\r\f\v", None, " \t\n\r\f\vabc"),\n ([" hello ", "hello"], None, [" hello", "hello"]),\n ("", "", ""),\n ("", "xyz", ""),\n ("hello", "", "hello"),\n (["hello ", "abcdefghijklmnop"], None,\n ["hello", "abcdefghijklmnop"]),\n ("xyzzyhelloxyzzy", "xyz", "xyzzyhello"),\n ("hello", "xyz", "hello"),\n ("xyxz", "xyxz", ""),\n (" ", None, ""),\n ("xyxzx", "x", "xyxz"),\n (["xyzzyhelloxyzzy", "hello"], ["xyz", "xyz"],\n ["xyzzyhello", "hello"]),\n (["ab", "ac", "aab", "abb"], "b", ["a", "ac", "aa", "a"]),\n ])\n def test_rstrip(self, a, chars, out, dt):\n a = np.array(a, dtype=dt)\n out = np.array(out, dtype=dt)\n if chars is not None:\n chars = np.array(chars, dtype=dt)\n assert_array_equal(np.strings.rstrip(a, chars), out)\n else:\n assert_array_equal(np.strings.rstrip(a), out)\n\n @pytest.mark.parametrize("a,chars,out", [\n ("", None, ""),\n (" hello ", None, "hello"),\n ("hello", None, "hello"),\n (" \t\n\r\f\vabc \t\n\r\f\v", None, "abc"),\n ([" hello ", "hello"], None, ["hello", "hello"]),\n ("", "", ""),\n ("", "xyz", ""),\n ("hello", "", "hello"),\n ("xyzzyhelloxyzzy", "xyz", "hello"),\n ("hello", "xyz", "hello"),\n ("xyxz", "xyxz", ""),\n ("xyxzx", "x", "yxz"),\n (["xyzzyhelloxyzzy", "hello"], ["xyz", "xyz"],\n ["hello", "hello"]),\n (["bab", "ac", "baab", "bbabb"], "b", ["a", "ac", "aa", "a"]),\n ])\n def test_strip(self, a, chars, out, dt):\n a = np.array(a, dtype=dt)\n if chars is not None:\n chars = np.array(chars, dtype=dt)\n out = np.array(out, dtype=dt)\n assert_array_equal(np.strings.strip(a, chars), out)\n\n @pytest.mark.parametrize("buf,old,new,count,res", [\n ("", "", "", -1, ""),\n ("", "", "A", -1, "A"),\n ("", "A", "", -1, ""),\n ("", "A", "A", -1, ""),\n ("", "", "", 100, ""),\n ("", "", "A", 100, "A"),\n ("A", "", "", -1, "A"),\n ("A", "", "*", -1, "*A*"),\n ("A", "", "*1", -1, "*1A*1"),\n ("A", "", "*-#", -1, "*-#A*-#"),\n ("AA", "", "*-", -1, "*-A*-A*-"),\n ("AA", "", "*-", -1, "*-A*-A*-"),\n ("AA", "", "*-", 4, "*-A*-A*-"),\n ("AA", "", "*-", 3, "*-A*-A*-"),\n ("AA", "", "*-", 2, "*-A*-A"),\n ("AA", "", "*-", 1, "*-AA"),\n ("AA", "", "*-", 0, "AA"),\n ("A", "A", "", -1, ""),\n ("AAA", "A", "", -1, ""),\n ("AAA", "A", "", -1, ""),\n ("AAA", "A", "", 4, ""),\n ("AAA", "A", "", 3, ""),\n ("AAA", "A", "", 2, "A"),\n ("AAA", "A", "", 1, "AA"),\n ("AAA", "A", "", 0, "AAA"),\n ("AAAAAAAAAA", "A", "", -1, ""),\n ("ABACADA", "A", "", -1, "BCD"),\n ("ABACADA", "A", "", -1, "BCD"),\n ("ABACADA", "A", "", 5, "BCD"),\n ("ABACADA", "A", "", 4, "BCD"),\n ("ABACADA", "A", "", 3, "BCDA"),\n ("ABACADA", "A", "", 2, "BCADA"),\n ("ABACADA", "A", "", 1, "BACADA"),\n ("ABACADA", "A", "", 0, "ABACADA"),\n ("ABCAD", "A", "", -1, "BCD"),\n ("ABCADAA", "A", "", -1, "BCD"),\n ("BCD", "A", "", -1, "BCD"),\n ("*************", "A", "", -1, "*************"),\n ("^" + "A" * 1000 + "^", "A", "", 999, "^A^"),\n ("the", "the", "", -1, ""),\n ("theater", "the", "", -1, "ater"),\n ("thethe", "the", "", -1, ""),\n ("thethethethe", "the", "", -1, ""),\n ("theatheatheathea", "the", "", -1, "aaaa"),\n ("that", "the", "", -1, "that"),\n ("thaet", "the", "", -1, "thaet"),\n ("here and there", "the", "", -1, "here and re"),\n ("here and there and there", "the", "", -1, "here and re and re"),\n ("here and there and there", "the", "", 3, "here and re and re"),\n ("here and there and there", "the", "", 2, "here and re and re"),\n ("here and there and there", "the", "", 1, "here and re and there"),\n ("here and there and there", "the", "", 0, "here and there and there"),\n ("here and there and there", "the", "", -1, "here and re and re"),\n ("abc", "the", "", -1, "abc"),\n ("abcdefg", "the", "", -1, "abcdefg"),\n ("bbobob", "bob", "", -1, "bob"),\n ("bbobobXbbobob", "bob", "", -1, "bobXbob"),\n ("aaaaaaabob", "bob", "", -1, "aaaaaaa"),\n ("aaaaaaa", "bob", "", -1, "aaaaaaa"),\n ("Who goes there?", "o", "o", -1, "Who goes there?"),\n ("Who goes there?", "o", "O", -1, "WhO gOes there?"),\n ("Who goes there?", "o", "O", -1, "WhO gOes there?"),\n ("Who goes there?", "o", "O", 3, "WhO gOes there?"),\n ("Who goes there?", "o", "O", 2, "WhO gOes there?"),\n ("Who goes there?", "o", "O", 1, "WhO goes there?"),\n ("Who goes there?", "o", "O", 0, "Who goes there?"),\n ("Who goes there?", "a", "q", -1, "Who goes there?"),\n ("Who goes there?", "W", "w", -1, "who goes there?"),\n ("WWho goes there?WW", "W", "w", -1, "wwho goes there?ww"),\n ("Who goes there?", "?", "!", -1, "Who goes there!"),\n ("Who goes there??", "?", "!", -1, "Who goes there!!"),\n ("Who goes there?", ".", "!", -1, "Who goes there?"),\n ("This is a tissue", "is", "**", -1, "Th** ** a t**sue"),\n ("This is a tissue", "is", "**", -1, "Th** ** a t**sue"),\n ("This is a tissue", "is", "**", 4, "Th** ** a t**sue"),\n ("This is a tissue", "is", "**", 3, "Th** ** a t**sue"),\n ("This is a tissue", "is", "**", 2, "Th** ** a tissue"),\n ("This is a tissue", "is", "**", 1, "Th** is a tissue"),\n ("This is a tissue", "is", "**", 0, "This is a tissue"),\n ("bobob", "bob", "cob", -1, "cobob"),\n ("bobobXbobobob", "bob", "cob", -1, "cobobXcobocob"),\n ("bobob", "bot", "bot", -1, "bobob"),\n ("Reykjavik", "k", "KK", -1, "ReyKKjaviKK"),\n ("Reykjavik", "k", "KK", -1, "ReyKKjaviKK"),\n ("Reykjavik", "k", "KK", 2, "ReyKKjaviKK"),\n ("Reykjavik", "k", "KK", 1, "ReyKKjavik"),\n ("Reykjavik", "k", "KK", 0, "Reykjavik"),\n ("A.B.C.", ".", "----", -1, "A----B----C----"),\n ("Reykjavik", "q", "KK", -1, "Reykjavik"),\n ("spam, spam, eggs and spam", "spam", "ham", -1,\n "ham, ham, eggs and ham"),\n ("spam, spam, eggs and spam", "spam", "ham", -1,\n "ham, ham, eggs and ham"),\n ("spam, spam, eggs and spam", "spam", "ham", 4,\n "ham, ham, eggs and ham"),\n ("spam, spam, eggs and spam", "spam", "ham", 3,\n "ham, ham, eggs and ham"),\n ("spam, spam, eggs and spam", "spam", "ham", 2,\n "ham, ham, eggs and spam"),\n ("spam, spam, eggs and spam", "spam", "ham", 1,\n "ham, spam, eggs and spam"),\n ("spam, spam, eggs and spam", "spam", "ham", 0,\n "spam, spam, eggs and spam"),\n ("bobobob", "bobob", "bob", -1, "bobob"),\n ("bobobobXbobobob", "bobob", "bob", -1, "bobobXbobob"),\n ("BOBOBOB", "bob", "bobby", -1, "BOBOBOB"),\n ("one!two!three!", "!", "@", 1, "one@two!three!"),\n ("one!two!three!", "!", "", -1, "onetwothree"),\n ("one!two!three!", "!", "@", 2, "one@two@three!"),\n ("one!two!three!", "!", "@", 3, "one@two@three@"),\n ("one!two!three!", "!", "@", 4, "one@two@three@"),\n ("one!two!three!", "!", "@", 0, "one!two!three!"),\n ("one!two!three!", "!", "@", -1, "one@two@three@"),\n ("one!two!three!", "x", "@", -1, "one!two!three!"),\n ("one!two!three!", "x", "@", 2, "one!two!three!"),\n ("abc", "", "-", -1, "-a-b-c-"),\n ("abc", "", "-", 3, "-a-b-c"),\n ("abc", "", "-", 0, "abc"),\n ("abc", "ab", "--", 0, "abc"),\n ("abc", "xy", "--", -1, "abc"),\n (["abbc", "abbd"], "b", "z", [1, 2], ["azbc", "azzd"]),\n ])\n def test_replace(self, buf, old, new, count, res, dt):\n if "😊" in buf and dt == "S":\n pytest.skip("Bytes dtype does not support non-ascii input")\n buf = np.array(buf, dtype=dt)\n old = np.array(old, dtype=dt)\n new = np.array(new, dtype=dt)\n res = np.array(res, dtype=dt)\n assert_array_equal(np.strings.replace(buf, old, new, count), res)\n\n @pytest.mark.parametrize("buf,sub,start,end,res", [\n ("abcdefghiabc", "", 0, None, 0),\n ("abcdefghiabc", "def", 0, None, 3),\n ("abcdefghiabc", "abc", 0, None, 0),\n ("abcdefghiabc", "abc", 1, None, 9),\n ])\n def test_index(self, buf, sub, start, end, res, dt):\n buf = np.array(buf, dtype=dt)\n sub = np.array(sub, dtype=dt)\n assert_array_equal(np.strings.index(buf, sub, start, end), res)\n\n @pytest.mark.parametrize("buf,sub,start,end", [\n ("abcdefghiabc", "hib", 0, None),\n ("abcdefghiab", "abc", 1, None),\n ("abcdefghi", "ghi", 8, None),\n ("abcdefghi", "ghi", -1, None),\n ("rrarrrrrrrrra", "a", 4, 6),\n ])\n def test_index_raises(self, buf, sub, start, end, dt):\n buf = np.array(buf, dtype=dt)\n sub = np.array(sub, dtype=dt)\n with pytest.raises(ValueError, match="substring not found"):\n np.strings.index(buf, sub, start, end)\n\n @pytest.mark.parametrize("buf,sub,start,end,res", [\n ("abcdefghiabc", "", 0, None, 12),\n ("abcdefghiabc", "def", 0, None, 3),\n ("abcdefghiabc", "abc", 0, None, 9),\n ("abcdefghiabc", "abc", 0, -1, 0),\n ])\n def test_rindex(self, buf, sub, start, end, res, dt):\n buf = np.array(buf, dtype=dt)\n sub = np.array(sub, dtype=dt)\n assert_array_equal(np.strings.rindex(buf, sub, start, end), res)\n\n @pytest.mark.parametrize("buf,sub,start,end", [\n ("abcdefghiabc", "hib", 0, None),\n ("defghiabc", "def", 1, None),\n ("defghiabc", "abc", 0, -1),\n ("abcdefghi", "ghi", 0, 8),\n ("abcdefghi", "ghi", 0, -1),\n ("rrarrrrrrrrra", "a", 4, 6),\n ])\n def test_rindex_raises(self, buf, sub, start, end, dt):\n buf = np.array(buf, dtype=dt)\n sub = np.array(sub, dtype=dt)\n with pytest.raises(ValueError, match="substring not found"):\n np.strings.rindex(buf, sub, start, end)\n\n @pytest.mark.parametrize("buf,tabsize,res", [\n ("abc\rab\tdef\ng\thi", 8, "abc\rab def\ng hi"),\n ("abc\rab\tdef\ng\thi", 4, "abc\rab def\ng hi"),\n ("abc\r\nab\tdef\ng\thi", 8, "abc\r\nab def\ng hi"),\n ("abc\r\nab\tdef\ng\thi", 4, "abc\r\nab def\ng hi"),\n ("abc\r\nab\r\ndef\ng\r\nhi", 4, "abc\r\nab\r\ndef\ng\r\nhi"),\n (" \ta\n\tb", 1, " a\n b"),\n ])\n def test_expandtabs(self, buf, tabsize, res, dt):\n buf = np.array(buf, dtype=dt)\n res = np.array(res, dtype=dt)\n assert_array_equal(np.strings.expandtabs(buf, tabsize), res)\n\n def test_expandtabs_raises_overflow(self, dt):\n with pytest.raises(OverflowError, match="new string is too long"):\n np.strings.expandtabs(np.array("\ta\n\tb", dtype=dt), sys.maxsize)\n np.strings.expandtabs(np.array("\ta\n\tb", dtype=dt), 2**61)\n\n FILL_ERROR = "The fill character must be exactly one character long"\n\n def test_center_raises_multiple_character_fill(self, dt):\n buf = np.array("abc", dtype=dt)\n fill = np.array("**", dtype=dt)\n with pytest.raises(TypeError, match=self.FILL_ERROR):\n np.strings.center(buf, 10, fill)\n\n def test_ljust_raises_multiple_character_fill(self, dt):\n buf = np.array("abc", dtype=dt)\n fill = np.array("**", dtype=dt)\n with pytest.raises(TypeError, match=self.FILL_ERROR):\n np.strings.ljust(buf, 10, fill)\n\n def test_rjust_raises_multiple_character_fill(self, dt):\n buf = np.array("abc", dtype=dt)\n fill = np.array("**", dtype=dt)\n with pytest.raises(TypeError, match=self.FILL_ERROR):\n np.strings.rjust(buf, 10, fill)\n\n @pytest.mark.parametrize("buf,width,fillchar,res", [\n ('abc', 10, ' ', ' abc '),\n ('abc', 6, ' ', ' abc '),\n ('abc', 3, ' ', 'abc'),\n ('abc', 2, ' ', 'abc'),\n ('abc', 10, '*', '***abc****'),\n ])\n def test_center(self, buf, width, fillchar, res, dt):\n buf = np.array(buf, dtype=dt)\n fillchar = np.array(fillchar, dtype=dt)\n res = np.array(res, dtype=dt)\n assert_array_equal(np.strings.center(buf, width, fillchar), res)\n\n @pytest.mark.parametrize("buf,width,fillchar,res", [\n ('abc', 10, ' ', 'abc '),\n ('abc', 6, ' ', 'abc '),\n ('abc', 3, ' ', 'abc'),\n ('abc', 2, ' ', 'abc'),\n ('abc', 10, '*', 'abc*******'),\n ])\n def test_ljust(self, buf, width, fillchar, res, dt):\n buf = np.array(buf, dtype=dt)\n fillchar = np.array(fillchar, dtype=dt)\n res = np.array(res, dtype=dt)\n assert_array_equal(np.strings.ljust(buf, width, fillchar), res)\n\n @pytest.mark.parametrize("buf,width,fillchar,res", [\n ('abc', 10, ' ', ' abc'),\n ('abc', 6, ' ', ' abc'),\n ('abc', 3, ' ', 'abc'),\n ('abc', 2, ' ', 'abc'),\n ('abc', 10, '*', '*******abc'),\n ])\n def test_rjust(self, buf, width, fillchar, res, dt):\n buf = np.array(buf, dtype=dt)\n fillchar = np.array(fillchar, dtype=dt)\n res = np.array(res, dtype=dt)\n assert_array_equal(np.strings.rjust(buf, width, fillchar), res)\n\n @pytest.mark.parametrize("buf,width,res", [\n ('123', 2, '123'),\n ('123', 3, '123'),\n ('0123', 4, '0123'),\n ('+123', 3, '+123'),\n ('+123', 4, '+123'),\n ('+123', 5, '+0123'),\n ('+0123', 5, '+0123'),\n ('-123', 3, '-123'),\n ('-123', 4, '-123'),\n ('-0123', 5, '-0123'),\n ('000', 3, '000'),\n ('34', 1, '34'),\n ('0034', 4, '0034'),\n ])\n def test_zfill(self, buf, width, res, dt):\n buf = np.array(buf, dtype=dt)\n res = np.array(res, dtype=dt)\n assert_array_equal(np.strings.zfill(buf, width), res)\n\n @pytest.mark.parametrize("buf,sep,res1,res2,res3", [\n ("this is the partition method", "ti", "this is the par",\n "ti", "tion method"),\n ("http://www.python.org", "://", "http", "://", "www.python.org"),\n ("http://www.python.org", "?", "http://www.python.org", "", ""),\n ("http://www.python.org", "http://", "", "http://", "www.python.org"),\n ("http://www.python.org", "org", "http://www.python.", "org", ""),\n ("http://www.python.org", ["://", "?", "http://", "org"],\n ["http", "http://www.python.org", "", "http://www.python."],\n ["://", "", "http://", "org"],\n ["www.python.org", "", "www.python.org", ""]),\n ("mississippi", "ss", "mi", "ss", "issippi"),\n ("mississippi", "i", "m", "i", "ssissippi"),\n ("mississippi", "w", "mississippi", "", ""),\n ])\n def test_partition(self, buf, sep, res1, res2, res3, dt):\n buf = np.array(buf, dtype=dt)\n sep = np.array(sep, dtype=dt)\n res1 = np.array(res1, dtype=dt)\n res2 = np.array(res2, dtype=dt)\n res3 = np.array(res3, dtype=dt)\n act1, act2, act3 = np.strings.partition(buf, sep)\n assert_array_equal(act1, res1)\n assert_array_equal(act2, res2)\n assert_array_equal(act3, res3)\n assert_array_equal(act1 + act2 + act3, buf)\n\n @pytest.mark.parametrize("buf,sep,res1,res2,res3", [\n ("this is the partition method", "ti", "this is the parti",\n "ti", "on method"),\n ("http://www.python.org", "://", "http", "://", "www.python.org"),\n ("http://www.python.org", "?", "", "", "http://www.python.org"),\n ("http://www.python.org", "http://", "", "http://", "www.python.org"),\n ("http://www.python.org", "org", "http://www.python.", "org", ""),\n ("http://www.python.org", ["://", "?", "http://", "org"],\n ["http", "", "", "http://www.python."],\n ["://", "", "http://", "org"],\n ["www.python.org", "http://www.python.org", "www.python.org", ""]),\n ("mississippi", "ss", "missi", "ss", "ippi"),\n ("mississippi", "i", "mississipp", "i", ""),\n ("mississippi", "w", "", "", "mississippi"),\n ])\n def test_rpartition(self, buf, sep, res1, res2, res3, dt):\n buf = np.array(buf, dtype=dt)\n sep = np.array(sep, dtype=dt)\n res1 = np.array(res1, dtype=dt)\n res2 = np.array(res2, dtype=dt)\n res3 = np.array(res3, dtype=dt)\n act1, act2, act3 = np.strings.rpartition(buf, sep)\n assert_array_equal(act1, res1)\n assert_array_equal(act2, res2)\n assert_array_equal(act3, res3)\n assert_array_equal(act1 + act2 + act3, buf)\n\n @pytest.mark.parametrize("args", [\n (None,),\n (0,),\n (1,),\n (3,),\n (5,),\n (6,), # test index past the end\n (-1,),\n (-3,),\n ([3, 4],),\n ([2, 4],),\n ([-3, 5],),\n ([0, -5],),\n (1, 4),\n (-3, 5),\n (None, -1),\n (0, [4, 2]),\n ([1, 2], [-1, -2]),\n (1, 5, 2),\n (None, None, -1),\n ([0, 6], [-1, 0], [2, -1]),\n ])\n def test_slice(self, args, dt):\n buf = np.array(["hello", "world"], dtype=dt)\n act = np.strings.slice(buf, *args)\n bcast_args = tuple(np.broadcast_to(arg, buf.shape) for arg in args)\n res = np.array([s[slice(*arg)]\n for s, arg in zip(buf, zip(*bcast_args))],\n dtype=dt)\n assert_array_equal(act, res)\n\n def test_slice_unsupported(self, dt):\n with pytest.raises(TypeError, match="did not contain a loop"):\n np.strings.slice(np.array([1, 2, 3]), 4)\n\n with pytest.raises(TypeError, match=r"Cannot cast ufunc '_slice' input .* from .* to dtype\('int(64|32)'\)"):\n np.strings.slice(np.array(['foo', 'bar'], dtype=dt), np.array(['foo', 'bar'], dtype=dt))\n\n @pytest.mark.parametrize("int_dt", [np.int8, np.int16, np.int32, np.int64,\n np.uint8, np.uint16, np.uint32, np.uint64])\n def test_slice_int_type_promotion(self, int_dt, dt):\n buf = np.array(["hello", "world"], dtype=dt)\n\n assert_array_equal(np.strings.slice(buf, int_dt(4)), np.array(["hell", "worl"], dtype=dt))\n assert_array_equal(np.strings.slice(buf, np.array([4, 4], dtype=int_dt)), np.array(["hell", "worl"], dtype=dt))\n\n assert_array_equal(np.strings.slice(buf, int_dt(2), int_dt(4)), np.array(["ll", "rl"], dtype=dt))\n assert_array_equal(np.strings.slice(buf, np.array([2, 2], dtype=int_dt), np.array([4, 4], dtype=int_dt)), np.array(["ll", "rl"], dtype=dt))\n\n assert_array_equal(np.strings.slice(buf, int_dt(0), int_dt(4), int_dt(2)), np.array(["hl", "wr"], dtype=dt))\n assert_array_equal(np.strings.slice(buf, np.array([0, 0], dtype=int_dt), np.array([4, 4], dtype=int_dt), np.array([2, 2], dtype=int_dt)), np.array(["hl", "wr"], dtype=dt))\n\n@pytest.mark.parametrize("dt", ["U", "T"])\nclass TestMethodsWithUnicode:\n @pytest.mark.parametrize("in_,out", [\n ("", False),\n ("a", False),\n ("0", True),\n ("\u2460", False), # CIRCLED DIGIT 1\n ("\xbc", False), # VULGAR FRACTION ONE QUARTER\n ("\u0660", True), # ARABIC_INDIC DIGIT ZERO\n ("012345", True),\n ("012345a", False),\n (["0", "a"], [True, False]),\n ])\n def test_isdecimal_unicode(self, in_, out, dt):\n buf = np.array(in_, dtype=dt)\n assert_array_equal(np.strings.isdecimal(buf), out)\n\n @pytest.mark.parametrize("in_,out", [\n ("", False),\n ("a", False),\n ("0", True),\n ("\u2460", True), # CIRCLED DIGIT 1\n ("\xbc", True), # VULGAR FRACTION ONE QUARTER\n ("\u0660", True), # ARABIC_INDIC DIGIT ZERO\n ("012345", True),\n ("012345a", False),\n (["0", "a"], [True, False]),\n ])\n def test_isnumeric_unicode(self, in_, out, dt):\n buf = np.array(in_, dtype=dt)\n assert_array_equal(np.strings.isnumeric(buf), out)\n\n @pytest.mark.parametrize("buf,old,new,count,res", [\n ("...\u043c......<", "<", "<", -1, "...\u043c......<"),\n ("Ae¢☃€ 😊" * 2, "A", "B", -1, "Be¢☃€ 😊Be¢☃€ 😊"),\n ("Ae¢☃€ 😊" * 2, "😊", "B", -1, "Ae¢☃€ BAe¢☃€ B"),\n ])\n def test_replace_unicode(self, buf, old, new, count, res, dt):\n buf = np.array(buf, dtype=dt)\n old = np.array(old, dtype=dt)\n new = np.array(new, dtype=dt)\n res = np.array(res, dtype=dt)\n assert_array_equal(np.strings.replace(buf, old, new, count), res)\n\n @pytest.mark.parametrize("in_", [\n '\U00010401',\n '\U00010427',\n '\U00010429',\n '\U0001044E',\n '\U0001D7F6',\n '\U00011066',\n '\U000104A0',\n pytest.param('\U0001F107', marks=pytest.mark.xfail(\n sys.platform == 'win32' and IS_PYPY_LT_7_3_16,\n reason="PYPY bug in Py_UNICODE_ISALNUM",\n strict=True)),\n ])\n def test_isalnum_unicode(self, in_, dt):\n in_ = np.array(in_, dtype=dt)\n assert_array_equal(np.strings.isalnum(in_), True)\n\n @pytest.mark.parametrize("in_,out", [\n ('\u1FFc', False),\n ('\u2167', False),\n ('\U00010401', False),\n ('\U00010427', False),\n ('\U0001F40D', False),\n ('\U0001F46F', False),\n ('\u2177', True),\n pytest.param('\U00010429', True, marks=pytest.mark.xfail(\n sys.platform == 'win32' and IS_PYPY_LT_7_3_16,\n reason="PYPY bug in Py_UNICODE_ISLOWER",\n strict=True)),\n ('\U0001044E', True),\n ])\n def test_islower_unicode(self, in_, out, dt):\n in_ = np.array(in_, dtype=dt)\n assert_array_equal(np.strings.islower(in_), out)\n\n @pytest.mark.parametrize("in_,out", [\n ('\u1FFc', False),\n ('\u2167', True),\n ('\U00010401', True),\n ('\U00010427', True),\n ('\U0001F40D', False),\n ('\U0001F46F', False),\n ('\u2177', False),\n pytest.param('\U00010429', False, marks=pytest.mark.xfail(\n sys.platform == 'win32' and IS_PYPY_LT_7_3_16,\n reason="PYPY bug in Py_UNICODE_ISUPPER",\n strict=True)),\n ('\U0001044E', False),\n ])\n def test_isupper_unicode(self, in_, out, dt):\n in_ = np.array(in_, dtype=dt)\n assert_array_equal(np.strings.isupper(in_), out)\n\n @pytest.mark.parametrize("in_,out", [\n ('\u1FFc', True),\n ('Greek \u1FFcitlecases ...', True),\n pytest.param('\U00010401\U00010429', True, marks=pytest.mark.xfail(\n sys.platform == 'win32' and IS_PYPY_LT_7_3_16,\n reason="PYPY bug in Py_UNICODE_ISISTITLE",\n strict=True)),\n ('\U00010427\U0001044E', True),\n pytest.param('\U00010429', False, marks=pytest.mark.xfail(\n sys.platform == 'win32' and IS_PYPY_LT_7_3_16,\n reason="PYPY bug in Py_UNICODE_ISISTITLE",\n strict=True)),\n ('\U0001044E', False),\n ('\U0001F40D', False),\n ('\U0001F46F', False),\n ])\n def test_istitle_unicode(self, in_, out, dt):\n in_ = np.array(in_, dtype=dt)\n assert_array_equal(np.strings.istitle(in_), out)\n\n @pytest.mark.parametrize("buf,sub,start,end,res", [\n ("Ae¢☃€ 😊" * 2, "😊", 0, None, 6),\n ("Ae¢☃€ 😊" * 2, "😊", 7, None, 13),\n ])\n def test_index_unicode(self, buf, sub, start, end, res, dt):\n buf = np.array(buf, dtype=dt)\n sub = np.array(sub, dtype=dt)\n assert_array_equal(np.strings.index(buf, sub, start, end), res)\n\n def test_index_raises_unicode(self, dt):\n with pytest.raises(ValueError, match="substring not found"):\n np.strings.index("Ae¢☃€ 😊", "😀")\n\n @pytest.mark.parametrize("buf,res", [\n ("Ae¢☃€ \t 😊", "Ae¢☃€ 😊"),\n ("\t\U0001044E", " \U0001044E"),\n ])\n def test_expandtabs(self, buf, res, dt):\n buf = np.array(buf, dtype=dt)\n res = np.array(res, dtype=dt)\n assert_array_equal(np.strings.expandtabs(buf), res)\n\n @pytest.mark.parametrize("buf,width,fillchar,res", [\n ('x', 2, '\U0001044E', 'x\U0001044E'),\n ('x', 3, '\U0001044E', '\U0001044Ex\U0001044E'),\n ('x', 4, '\U0001044E', '\U0001044Ex\U0001044E\U0001044E'),\n ])\n def test_center(self, buf, width, fillchar, res, dt):\n buf = np.array(buf, dtype=dt)\n fillchar = np.array(fillchar, dtype=dt)\n res = np.array(res, dtype=dt)\n assert_array_equal(np.strings.center(buf, width, fillchar), res)\n\n @pytest.mark.parametrize("buf,width,fillchar,res", [\n ('x', 2, '\U0001044E', 'x\U0001044E'),\n ('x', 3, '\U0001044E', 'x\U0001044E\U0001044E'),\n ('x', 4, '\U0001044E', 'x\U0001044E\U0001044E\U0001044E'),\n ])\n def test_ljust(self, buf, width, fillchar, res, dt):\n buf = np.array(buf, dtype=dt)\n fillchar = np.array(fillchar, dtype=dt)\n res = np.array(res, dtype=dt)\n assert_array_equal(np.strings.ljust(buf, width, fillchar), res)\n\n @pytest.mark.parametrize("buf,width,fillchar,res", [\n ('x', 2, '\U0001044E', '\U0001044Ex'),\n ('x', 3, '\U0001044E', '\U0001044E\U0001044Ex'),\n ('x', 4, '\U0001044E', '\U0001044E\U0001044E\U0001044Ex'),\n ])\n def test_rjust(self, buf, width, fillchar, res, dt):\n buf = np.array(buf, dtype=dt)\n fillchar = np.array(fillchar, dtype=dt)\n res = np.array(res, dtype=dt)\n assert_array_equal(np.strings.rjust(buf, width, fillchar), res)\n\n @pytest.mark.parametrize("buf,sep,res1,res2,res3", [\n ("āāāāĀĀĀĀ", "Ă", "āāāāĀĀĀĀ", "", ""),\n ("āāāāĂĀĀĀĀ", "Ă", "āāāā", "Ă", "ĀĀĀĀ"),\n ("āāāāĂĂĀĀĀĀ", "ĂĂ", "āāāā", "ĂĂ", "ĀĀĀĀ"),\n ("𐌁𐌁𐌁𐌁𐌀𐌀𐌀𐌀", "𐌂", "𐌁𐌁𐌁𐌁𐌀𐌀𐌀𐌀", "", ""),\n ("𐌁𐌁𐌁𐌁𐌂𐌀𐌀𐌀𐌀", "𐌂", "𐌁𐌁𐌁𐌁", "𐌂", "𐌀𐌀𐌀𐌀"),\n ("𐌁𐌁𐌁𐌁𐌂𐌂𐌀𐌀𐌀𐌀", "𐌂𐌂", "𐌁𐌁𐌁𐌁", "𐌂𐌂", "𐌀𐌀𐌀𐌀"),\n ("𐌁𐌁𐌁𐌁𐌂𐌂𐌂𐌂𐌀𐌀𐌀𐌀", "𐌂𐌂𐌂𐌂", "𐌁𐌁𐌁𐌁", "𐌂𐌂𐌂𐌂", "𐌀𐌀𐌀𐌀"),\n ])\n def test_partition(self, buf, sep, res1, res2, res3, dt):\n buf = np.array(buf, dtype=dt)\n sep = np.array(sep, dtype=dt)\n res1 = np.array(res1, dtype=dt)\n res2 = np.array(res2, dtype=dt)\n res3 = np.array(res3, dtype=dt)\n act1, act2, act3 = np.strings.partition(buf, sep)\n assert_array_equal(act1, res1)\n assert_array_equal(act2, res2)\n assert_array_equal(act3, res3)\n assert_array_equal(act1 + act2 + act3, buf)\n\n @pytest.mark.parametrize("buf,sep,res1,res2,res3", [\n ("āāāāĀĀĀĀ", "Ă", "", "", "āāāāĀĀĀĀ"),\n ("āāāāĂĀĀĀĀ", "Ă", "āāāā", "Ă", "ĀĀĀĀ"),\n ("āāāāĂĂĀĀĀĀ", "ĂĂ", "āāāā", "ĂĂ", "ĀĀĀĀ"),\n ("𐌁𐌁𐌁𐌁𐌀𐌀𐌀𐌀", "𐌂", "", "", "𐌁𐌁𐌁𐌁𐌀𐌀𐌀𐌀"),\n ("𐌁𐌁𐌁𐌁𐌂𐌀𐌀𐌀𐌀", "𐌂", "𐌁𐌁𐌁𐌁", "𐌂", "𐌀𐌀𐌀𐌀"),\n ("𐌁𐌁𐌁𐌁𐌂𐌂𐌀𐌀𐌀𐌀", "𐌂𐌂", "𐌁𐌁𐌁𐌁", "𐌂𐌂", "𐌀𐌀𐌀𐌀"),\n ])\n def test_rpartition(self, buf, sep, res1, res2, res3, dt):\n buf = np.array(buf, dtype=dt)\n sep = np.array(sep, dtype=dt)\n res1 = np.array(res1, dtype=dt)\n res2 = np.array(res2, dtype=dt)\n res3 = np.array(res3, dtype=dt)\n act1, act2, act3 = np.strings.rpartition(buf, sep)\n assert_array_equal(act1, res1)\n assert_array_equal(act2, res2)\n assert_array_equal(act3, res3)\n assert_array_equal(act1 + act2 + act3, buf)\n\n @pytest.mark.parametrize("method", ["strip", "lstrip", "rstrip"])\n @pytest.mark.parametrize(\n "source,strip",\n [\n ("λμ", "μ"),\n ("λμ", "λ"),\n ("λ" * 5 + "μ" * 2, "μ"),\n ("λ" * 5 + "μ" * 2, "λ"),\n ("λ" * 5 + "A" + "μ" * 2, "μλ"),\n ("λμ" * 5, "μ"),\n ("λμ" * 5, "λ"),\n ])\n def test_strip_functions_unicode(self, source, strip, method, dt):\n src_array = np.array([source], dtype=dt)\n\n npy_func = getattr(np.strings, method)\n py_func = getattr(str, method)\n\n expected = np.array([py_func(source, strip)], dtype=dt)\n actual = npy_func(src_array, strip)\n\n assert_array_equal(actual, expected)\n\n @pytest.mark.parametrize("args", [\n (None,),\n (0,),\n (1,),\n (5,),\n (15,),\n (22,),\n (-1,),\n (-3,),\n ([3, 4],),\n ([-5, 5],),\n ([0, -8],),\n (1, 12),\n (-12, 15),\n (None, -1),\n (0, [17, 6]),\n ([1, 2], [-1, -2]),\n (1, 11, 2),\n (None, None, -1),\n ([0, 10], [-1, 0], [2, -1]),\n ])\n def test_slice(self, args, dt):\n buf = np.array(["Приве́т नमस्ते שָׁלוֹם", "😀😃😄😁😆😅🤣😂🙂🙃"],\n dtype=dt)\n act = np.strings.slice(buf, *args)\n bcast_args = tuple(np.broadcast_to(arg, buf.shape) for arg in args)\n res = np.array([s[slice(*arg)]\n for s, arg in zip(buf, zip(*bcast_args))],\n dtype=dt)\n assert_array_equal(act, res)\n\n\nclass TestMixedTypeMethods:\n def test_center(self):\n buf = np.array("😊", dtype="U")\n fill = np.array("*", dtype="S")\n res = np.array("*😊*", dtype="U")\n assert_array_equal(np.strings.center(buf, 3, fill), res)\n\n buf = np.array("s", dtype="S")\n fill = np.array("*", dtype="U")\n res = np.array("*s*", dtype="S")\n assert_array_equal(np.strings.center(buf, 3, fill), res)\n\n with pytest.raises(ValueError, match="'ascii' codec can't encode"):\n buf = np.array("s", dtype="S")\n fill = np.array("😊", dtype="U")\n np.strings.center(buf, 3, fill)\n\n def test_ljust(self):\n buf = np.array("😊", dtype="U")\n fill = np.array("*", dtype="S")\n res = np.array("😊**", dtype="U")\n assert_array_equal(np.strings.ljust(buf, 3, fill), res)\n\n buf = np.array("s", dtype="S")\n fill = np.array("*", dtype="U")\n res = np.array("s**", dtype="S")\n assert_array_equal(np.strings.ljust(buf, 3, fill), res)\n\n with pytest.raises(ValueError, match="'ascii' codec can't encode"):\n buf = np.array("s", dtype="S")\n fill = np.array("😊", dtype="U")\n np.strings.ljust(buf, 3, fill)\n\n def test_rjust(self):\n buf = np.array("😊", dtype="U")\n fill = np.array("*", dtype="S")\n res = np.array("**😊", dtype="U")\n assert_array_equal(np.strings.rjust(buf, 3, fill), res)\n\n buf = np.array("s", dtype="S")\n fill = np.array("*", dtype="U")\n res = np.array("**s", dtype="S")\n assert_array_equal(np.strings.rjust(buf, 3, fill), res)\n\n with pytest.raises(ValueError, match="'ascii' codec can't encode"):\n buf = np.array("s", dtype="S")\n fill = np.array("😊", dtype="U")\n np.strings.rjust(buf, 3, fill)\n\n\nclass TestUnicodeOnlyMethodsRaiseWithBytes:\n def test_isdecimal_raises(self):\n in_ = np.array(b"1")\n with assert_raises(TypeError):\n np.strings.isdecimal(in_)\n\n def test_isnumeric_bytes(self):\n in_ = np.array(b"1")\n with assert_raises(TypeError):\n np.strings.isnumeric(in_)\n\n\ndef check_itemsize(n_elem, dt):\n if dt == "T":\n return np.dtype(dt).itemsize\n if dt == "S":\n return n_elem\n if dt == "U":\n return n_elem * 4\n\n@pytest.mark.parametrize("dt", ["S", "U", "T"])\nclass TestReplaceOnArrays:\n\n def test_replace_count_and_size(self, dt):\n a = np.array(["0123456789" * i for i in range(4)], dtype=dt)\n r1 = np.strings.replace(a, "5", "ABCDE")\n assert r1.dtype.itemsize == check_itemsize(3 * 10 + 3 * 4, dt)\n r1_res = np.array(["01234ABCDE6789" * i for i in range(4)], dtype=dt)\n assert_array_equal(r1, r1_res)\n r2 = np.strings.replace(a, "5", "ABCDE", 1)\n assert r2.dtype.itemsize == check_itemsize(3 * 10 + 4, dt)\n r3 = np.strings.replace(a, "5", "ABCDE", 0)\n assert r3.dtype.itemsize == a.dtype.itemsize\n assert_array_equal(r3, a)\n # Negative values mean to replace all.\n r4 = np.strings.replace(a, "5", "ABCDE", -1)\n assert r4.dtype.itemsize == check_itemsize(3 * 10 + 3 * 4, dt)\n assert_array_equal(r4, r1)\n # We can do count on an element-by-element basis.\n r5 = np.strings.replace(a, "5", "ABCDE", [-1, -1, -1, 1])\n assert r5.dtype.itemsize == check_itemsize(3 * 10 + 4, dt)\n assert_array_equal(r5, np.array(\n ["01234ABCDE6789" * i for i in range(3)]\n + ["01234ABCDE6789" + "0123456789" * 2], dtype=dt))\n\n def test_replace_broadcasting(self, dt):\n a = np.array("0,0,0", dtype=dt)\n r1 = np.strings.replace(a, "0", "1", np.arange(3))\n assert r1.dtype == a.dtype\n assert_array_equal(r1, np.array(["0,0,0", "1,0,0", "1,1,0"], dtype=dt))\n r2 = np.strings.replace(a, "0", [["1"], ["2"]], np.arange(1, 4))\n assert_array_equal(r2, np.array([["1,0,0", "1,1,0", "1,1,1"],\n ["2,0,0", "2,2,0", "2,2,2"]],\n dtype=dt))\n r3 = np.strings.replace(a, ["0", "0,0", "0,0,0"], "X")\n assert_array_equal(r3, np.array(["X,X,X", "X,0", "X"], dtype=dt))\n\n\nclass TestOverride:\n @classmethod\n def setup_class(cls):\n class Override:\n\n def __array_function__(self, *args, **kwargs):\n return "function"\n\n def __array_ufunc__(self, *args, **kwargs):\n return "ufunc"\n\n cls.override = Override()\n\n @pytest.mark.parametrize("func, kwargs", [\n (np.strings.center, dict(width=10)),\n (np.strings.capitalize, {}),\n (np.strings.decode, {}),\n (np.strings.encode, {}),\n (np.strings.expandtabs, {}),\n (np.strings.ljust, dict(width=10)),\n (np.strings.lower, {}),\n (np.strings.mod, dict(values=2)),\n (np.strings.multiply, dict(i=2)),\n (np.strings.partition, dict(sep="foo")),\n (np.strings.rjust, dict(width=10)),\n (np.strings.rpartition, dict(sep="foo")),\n (np.strings.swapcase, {}),\n (np.strings.title, {}),\n (np.strings.translate, dict(table=None)),\n (np.strings.upper, {}),\n (np.strings.zfill, dict(width=10)),\n ])\n def test_override_function(self, func, kwargs):\n assert func(self.override, **kwargs) == "function"\n\n @pytest.mark.parametrize("func, args, kwargs", [\n (np.strings.add, (None, ), {}),\n (np.strings.lstrip, (), {}),\n (np.strings.rstrip, (), {}),\n (np.strings.strip, (), {}),\n (np.strings.equal, (None, ), {}),\n (np.strings.not_equal, (None, ), {}),\n (np.strings.greater_equal, (None, ), {}),\n (np.strings.less_equal, (None, ), {}),\n (np.strings.greater, (None, ), {}),\n (np.strings.less, (None, ), {}),\n (np.strings.count, ("foo", ), {}),\n (np.strings.endswith, ("foo", ), {}),\n (np.strings.find, ("foo", ), {}),\n (np.strings.index, ("foo", ), {}),\n (np.strings.isalnum, (), {}),\n (np.strings.isalpha, (), {}),\n (np.strings.isdecimal, (), {}),\n (np.strings.isdigit, (), {}),\n (np.strings.islower, (), {}),\n (np.strings.isnumeric, (), {}),\n (np.strings.isspace, (), {}),\n (np.strings.istitle, (), {}),\n (np.strings.isupper, (), {}),\n (np.strings.rfind, ("foo", ), {}),\n (np.strings.rindex, ("foo", ), {}),\n (np.strings.startswith, ("foo", ), {}),\n (np.strings.str_len, (), {}),\n ])\n def test_override_ufunc(self, func, args, kwargs):\n assert func(self.override, *args, **kwargs) == "ufunc"\n | .venv\Lib\site-packages\numpy\_core\tests\test_strings.py | test_strings.py | Python | 59,284 | 0.75 | 0.091034 | 0.01214 | node-utils | 384 | 2024-12-07T14:28:56.305340 | MIT | true | c6259eb438ca71b1be1b241a9358aae5 |
import os\nimport sys\nfrom ctypes import POINTER, c_double, c_float, c_int, c_longlong, cast, pointer\nfrom os import path\n\nimport pytest\nfrom numpy._core._multiarray_umath import __cpu_features__\n\nimport numpy as np\nfrom numpy.testing import assert_array_max_ulp\nfrom numpy.testing._private.utils import _glibc_older_than\n\nUNARY_UFUNCS = [obj for obj in np._core.umath.__dict__.values() if\n isinstance(obj, np.ufunc)]\nUNARY_OBJECT_UFUNCS = [uf for uf in UNARY_UFUNCS if "O->O" in uf.types]\n\n# Remove functions that do not support `floats`\nUNARY_OBJECT_UFUNCS.remove(np.invert)\nUNARY_OBJECT_UFUNCS.remove(np.bitwise_count)\n\nIS_AVX = __cpu_features__.get('AVX512F', False) or \\n (__cpu_features__.get('FMA3', False) and __cpu_features__.get('AVX2', False))\n\nIS_AVX512FP16 = __cpu_features__.get('AVX512FP16', False)\n\n# only run on linux with AVX, also avoid old glibc (numpy/numpy#20448).\nruntest = (sys.platform.startswith('linux')\n and IS_AVX and not _glibc_older_than("2.17"))\nplatform_skip = pytest.mark.skipif(not runtest,\n reason="avoid testing inconsistent platform "\n "library implementations")\n\n# convert string to hex function taken from:\n# https://stackoverflow.com/questions/1592158/convert-hex-to-float #\ndef convert(s, datatype="np.float32"):\n i = int(s, 16) # convert from hex to a Python int\n if (datatype == "np.float64"):\n cp = pointer(c_longlong(i)) # make this into a c long long integer\n fp = cast(cp, POINTER(c_double)) # cast the int pointer to a double pointer\n else:\n cp = pointer(c_int(i)) # make this into a c integer\n fp = cast(cp, POINTER(c_float)) # cast the int pointer to a float pointer\n\n return fp.contents.value # dereference the pointer, get the float\n\n\nstr_to_float = np.vectorize(convert)\n\nclass TestAccuracy:\n @platform_skip\n def test_validate_transcendentals(self):\n with np.errstate(all='ignore'):\n data_dir = path.join(path.dirname(__file__), 'data')\n files = os.listdir(data_dir)\n files = list(filter(lambda f: f.endswith('.csv'), files))\n for filename in files:\n filepath = path.join(data_dir, filename)\n with open(filepath) as fid:\n file_without_comments = (\n r for r in fid if r[0] not in ('$', '#')\n )\n data = np.genfromtxt(file_without_comments,\n dtype=('|S39', '|S39', '|S39', int),\n names=('type', 'input', 'output', 'ulperr'),\n delimiter=',',\n skip_header=1)\n npname = path.splitext(filename)[0].split('-')[3]\n npfunc = getattr(np, npname)\n for datatype in np.unique(data['type']):\n data_subset = data[data['type'] == datatype]\n inval = np.array(str_to_float(data_subset['input'].astype(str), data_subset['type'].astype(str)), dtype=eval(datatype))\n outval = np.array(str_to_float(data_subset['output'].astype(str), data_subset['type'].astype(str)), dtype=eval(datatype))\n perm = np.random.permutation(len(inval))\n inval = inval[perm]\n outval = outval[perm]\n maxulperr = data_subset['ulperr'].max()\n assert_array_max_ulp(npfunc(inval), outval, maxulperr)\n\n @pytest.mark.skipif(IS_AVX512FP16,\n reason="SVML FP16 have slightly higher ULP errors")\n @pytest.mark.parametrize("ufunc", UNARY_OBJECT_UFUNCS)\n def test_validate_fp16_transcendentals(self, ufunc):\n with np.errstate(all='ignore'):\n arr = np.arange(65536, dtype=np.int16)\n datafp16 = np.frombuffer(arr.tobytes(), dtype=np.float16)\n datafp32 = datafp16.astype(np.float32)\n assert_array_max_ulp(ufunc(datafp16), ufunc(datafp32),\n maxulp=1, dtype=np.float16)\n\n @pytest.mark.skipif(not IS_AVX512FP16,\n reason="lower ULP only apply for SVML FP16")\n def test_validate_svml_fp16(self):\n max_ulp_err = {\n "arccos": 2.54,\n "arccosh": 2.09,\n "arcsin": 3.06,\n "arcsinh": 1.51,\n "arctan": 2.61,\n "arctanh": 1.88,\n "cbrt": 1.57,\n "cos": 1.43,\n "cosh": 1.33,\n "exp2": 1.33,\n "exp": 1.27,\n "expm1": 0.53,\n "log": 1.80,\n "log10": 1.27,\n "log1p": 1.88,\n "log2": 1.80,\n "sin": 1.88,\n "sinh": 2.05,\n "tan": 2.26,\n "tanh": 3.00,\n }\n\n with np.errstate(all='ignore'):\n arr = np.arange(65536, dtype=np.int16)\n datafp16 = np.frombuffer(arr.tobytes(), dtype=np.float16)\n datafp32 = datafp16.astype(np.float32)\n for func in max_ulp_err:\n ufunc = getattr(np, func)\n ulp = np.ceil(max_ulp_err[func])\n assert_array_max_ulp(ufunc(datafp16), ufunc(datafp32),\n maxulp=ulp, dtype=np.float16)\n | .venv\Lib\site-packages\numpy\_core\tests\test_umath_accuracy.py | test_umath_accuracy.py | Python | 5,602 | 0.95 | 0.137097 | 0.036697 | node-utils | 166 | 2025-01-06T18:54:20.637352 | Apache-2.0 | true | fac9a7c9f825b0abbcb79029d1295c43 |
import platform\nimport sys\n\n# import the c-extension module directly since _arg is not exported via umath\nimport numpy._core._multiarray_umath as ncu\nimport pytest\n\nimport numpy as np\nfrom numpy.testing import (\n assert_almost_equal,\n assert_array_equal,\n assert_array_max_ulp,\n assert_equal,\n assert_raises,\n)\n\n# TODO: branch cuts (use Pauli code)\n# TODO: conj 'symmetry'\n# TODO: FPU exceptions\n\n# At least on Windows the results of many complex functions are not conforming\n# to the C99 standard. See ticket 1574.\n# Ditto for Solaris (ticket 1642) and OS X on PowerPC.\n# FIXME: this will probably change when we require full C99 compatibility\nwith np.errstate(all='ignore'):\n functions_seem_flaky = ((np.exp(complex(np.inf, 0)).imag != 0)\n or (np.log(complex(ncu.NZERO, 0)).imag != np.pi))\n# TODO: replace with a check on whether platform-provided C99 funcs are used\nxfail_complex_tests = (not sys.platform.startswith('linux') or functions_seem_flaky)\n\n# TODO This can be xfail when the generator functions are got rid of.\nplatform_skip = pytest.mark.skipif(xfail_complex_tests,\n reason="Inadequate C99 complex support")\n\n\nclass TestCexp:\n def test_simple(self):\n check = check_complex_value\n f = np.exp\n\n check(f, 1, 0, np.exp(1), 0, False)\n check(f, 0, 1, np.cos(1), np.sin(1), False)\n\n ref = np.exp(1) * complex(np.cos(1), np.sin(1))\n check(f, 1, 1, ref.real, ref.imag, False)\n\n @platform_skip\n def test_special_values(self):\n # C99: Section G 6.3.1\n\n check = check_complex_value\n f = np.exp\n\n # cexp(+-0 + 0i) is 1 + 0i\n check(f, ncu.PZERO, 0, 1, 0, False)\n check(f, ncu.NZERO, 0, 1, 0, False)\n\n # cexp(x + infi) is nan + nani for finite x and raises 'invalid' FPU\n # exception\n check(f, 1, np.inf, np.nan, np.nan)\n check(f, -1, np.inf, np.nan, np.nan)\n check(f, 0, np.inf, np.nan, np.nan)\n\n # cexp(inf + 0i) is inf + 0i\n check(f, np.inf, 0, np.inf, 0)\n\n # cexp(-inf + yi) is +0 * (cos(y) + i sin(y)) for finite y\n check(f, -np.inf, 1, ncu.PZERO, ncu.PZERO)\n check(f, -np.inf, 0.75 * np.pi, ncu.NZERO, ncu.PZERO)\n\n # cexp(inf + yi) is +inf * (cos(y) + i sin(y)) for finite y\n check(f, np.inf, 1, np.inf, np.inf)\n check(f, np.inf, 0.75 * np.pi, -np.inf, np.inf)\n\n # cexp(-inf + inf i) is +-0 +- 0i (signs unspecified)\n def _check_ninf_inf(dummy):\n msgform = "cexp(-inf, inf) is (%f, %f), expected (+-0, +-0)"\n with np.errstate(invalid='ignore'):\n z = f(np.array(complex(-np.inf, np.inf)))\n if z.real != 0 or z.imag != 0:\n raise AssertionError(msgform % (z.real, z.imag))\n\n _check_ninf_inf(None)\n\n # cexp(inf + inf i) is +-inf + NaNi and raised invalid FPU ex.\n def _check_inf_inf(dummy):\n msgform = "cexp(inf, inf) is (%f, %f), expected (+-inf, nan)"\n with np.errstate(invalid='ignore'):\n z = f(np.array(complex(np.inf, np.inf)))\n if not np.isinf(z.real) or not np.isnan(z.imag):\n raise AssertionError(msgform % (z.real, z.imag))\n\n _check_inf_inf(None)\n\n # cexp(-inf + nan i) is +-0 +- 0i\n def _check_ninf_nan(dummy):\n msgform = "cexp(-inf, nan) is (%f, %f), expected (+-0, +-0)"\n with np.errstate(invalid='ignore'):\n z = f(np.array(complex(-np.inf, np.nan)))\n if z.real != 0 or z.imag != 0:\n raise AssertionError(msgform % (z.real, z.imag))\n\n _check_ninf_nan(None)\n\n # cexp(inf + nan i) is +-inf + nan\n def _check_inf_nan(dummy):\n msgform = "cexp(-inf, nan) is (%f, %f), expected (+-inf, nan)"\n with np.errstate(invalid='ignore'):\n z = f(np.array(complex(np.inf, np.nan)))\n if not np.isinf(z.real) or not np.isnan(z.imag):\n raise AssertionError(msgform % (z.real, z.imag))\n\n _check_inf_nan(None)\n\n # cexp(nan + yi) is nan + nani for y != 0 (optional: raises invalid FPU\n # ex)\n check(f, np.nan, 1, np.nan, np.nan)\n check(f, np.nan, -1, np.nan, np.nan)\n\n check(f, np.nan, np.inf, np.nan, np.nan)\n check(f, np.nan, -np.inf, np.nan, np.nan)\n\n # cexp(nan + nani) is nan + nani\n check(f, np.nan, np.nan, np.nan, np.nan)\n\n # TODO This can be xfail when the generator functions are got rid of.\n @pytest.mark.skip(reason="cexp(nan + 0I) is wrong on most platforms")\n def test_special_values2(self):\n # XXX: most implementations get it wrong here (including glibc <= 2.10)\n # cexp(nan + 0i) is nan + 0i\n check = check_complex_value\n f = np.exp\n\n check(f, np.nan, 0, np.nan, 0)\n\nclass TestClog:\n def test_simple(self):\n x = np.array([1 + 0j, 1 + 2j])\n y_r = np.log(np.abs(x)) + 1j * np.angle(x)\n y = np.log(x)\n assert_almost_equal(y, y_r)\n\n @platform_skip\n @pytest.mark.skipif(platform.machine() == "armv5tel", reason="See gh-413.")\n def test_special_values(self):\n xl = []\n yl = []\n\n # From C99 std (Sec 6.3.2)\n # XXX: check exceptions raised\n # --- raise for invalid fails.\n\n # clog(-0 + i0) returns -inf + i pi and raises the 'divide-by-zero'\n # floating-point exception.\n with np.errstate(divide='raise'):\n x = np.array([ncu.NZERO], dtype=complex)\n y = complex(-np.inf, np.pi)\n assert_raises(FloatingPointError, np.log, x)\n with np.errstate(divide='ignore'):\n assert_almost_equal(np.log(x), y)\n\n xl.append(x)\n yl.append(y)\n\n # clog(+0 + i0) returns -inf + i0 and raises the 'divide-by-zero'\n # floating-point exception.\n with np.errstate(divide='raise'):\n x = np.array([0], dtype=complex)\n y = complex(-np.inf, 0)\n assert_raises(FloatingPointError, np.log, x)\n with np.errstate(divide='ignore'):\n assert_almost_equal(np.log(x), y)\n\n xl.append(x)\n yl.append(y)\n\n # clog(x + i inf returns +inf + i pi /2, for finite x.\n x = np.array([complex(1, np.inf)], dtype=complex)\n y = complex(np.inf, 0.5 * np.pi)\n assert_almost_equal(np.log(x), y)\n xl.append(x)\n yl.append(y)\n\n x = np.array([complex(-1, np.inf)], dtype=complex)\n assert_almost_equal(np.log(x), y)\n xl.append(x)\n yl.append(y)\n\n # clog(x + iNaN) returns NaN + iNaN and optionally raises the\n # 'invalid' floating- point exception, for finite x.\n with np.errstate(invalid='raise'):\n x = np.array([complex(1., np.nan)], dtype=complex)\n y = complex(np.nan, np.nan)\n #assert_raises(FloatingPointError, np.log, x)\n with np.errstate(invalid='ignore'):\n assert_almost_equal(np.log(x), y)\n\n xl.append(x)\n yl.append(y)\n\n with np.errstate(invalid='raise'):\n x = np.array([np.inf + 1j * np.nan], dtype=complex)\n #assert_raises(FloatingPointError, np.log, x)\n with np.errstate(invalid='ignore'):\n assert_almost_equal(np.log(x), y)\n\n xl.append(x)\n yl.append(y)\n\n # clog(- inf + iy) returns +inf + ipi , for finite positive-signed y.\n x = np.array([-np.inf + 1j], dtype=complex)\n y = complex(np.inf, np.pi)\n assert_almost_equal(np.log(x), y)\n xl.append(x)\n yl.append(y)\n\n # clog(+ inf + iy) returns +inf + i0, for finite positive-signed y.\n x = np.array([np.inf + 1j], dtype=complex)\n y = complex(np.inf, 0)\n assert_almost_equal(np.log(x), y)\n xl.append(x)\n yl.append(y)\n\n # clog(- inf + i inf) returns +inf + i3pi /4.\n x = np.array([complex(-np.inf, np.inf)], dtype=complex)\n y = complex(np.inf, 0.75 * np.pi)\n assert_almost_equal(np.log(x), y)\n xl.append(x)\n yl.append(y)\n\n # clog(+ inf + i inf) returns +inf + ipi /4.\n x = np.array([complex(np.inf, np.inf)], dtype=complex)\n y = complex(np.inf, 0.25 * np.pi)\n assert_almost_equal(np.log(x), y)\n xl.append(x)\n yl.append(y)\n\n # clog(+/- inf + iNaN) returns +inf + iNaN.\n x = np.array([complex(np.inf, np.nan)], dtype=complex)\n y = complex(np.inf, np.nan)\n assert_almost_equal(np.log(x), y)\n xl.append(x)\n yl.append(y)\n\n x = np.array([complex(-np.inf, np.nan)], dtype=complex)\n assert_almost_equal(np.log(x), y)\n xl.append(x)\n yl.append(y)\n\n # clog(NaN + iy) returns NaN + iNaN and optionally raises the\n # 'invalid' floating-point exception, for finite y.\n x = np.array([complex(np.nan, 1)], dtype=complex)\n y = complex(np.nan, np.nan)\n assert_almost_equal(np.log(x), y)\n xl.append(x)\n yl.append(y)\n\n # clog(NaN + i inf) returns +inf + iNaN.\n x = np.array([complex(np.nan, np.inf)], dtype=complex)\n y = complex(np.inf, np.nan)\n assert_almost_equal(np.log(x), y)\n xl.append(x)\n yl.append(y)\n\n # clog(NaN + iNaN) returns NaN + iNaN.\n x = np.array([complex(np.nan, np.nan)], dtype=complex)\n y = complex(np.nan, np.nan)\n assert_almost_equal(np.log(x), y)\n xl.append(x)\n yl.append(y)\n\n # clog(conj(z)) = conj(clog(z)).\n xa = np.array(xl, dtype=complex)\n ya = np.array(yl, dtype=complex)\n with np.errstate(divide='ignore'):\n for i in range(len(xa)):\n assert_almost_equal(np.log(xa[i].conj()), ya[i].conj())\n\n\nclass TestCsqrt:\n\n def test_simple(self):\n # sqrt(1)\n check_complex_value(np.sqrt, 1, 0, 1, 0)\n\n # sqrt(1i)\n rres = 0.5 * np.sqrt(2)\n ires = rres\n check_complex_value(np.sqrt, 0, 1, rres, ires, False)\n\n # sqrt(-1)\n check_complex_value(np.sqrt, -1, 0, 0, 1)\n\n def test_simple_conjugate(self):\n ref = np.conj(np.sqrt(complex(1, 1)))\n\n def f(z):\n return np.sqrt(np.conj(z))\n\n check_complex_value(f, 1, 1, ref.real, ref.imag, False)\n\n #def test_branch_cut(self):\n # _check_branch_cut(f, -1, 0, 1, -1)\n\n @platform_skip\n def test_special_values(self):\n # C99: Sec G 6.4.2\n\n check = check_complex_value\n f = np.sqrt\n\n # csqrt(+-0 + 0i) is 0 + 0i\n check(f, ncu.PZERO, 0, 0, 0)\n check(f, ncu.NZERO, 0, 0, 0)\n\n # csqrt(x + infi) is inf + infi for any x (including NaN)\n check(f, 1, np.inf, np.inf, np.inf)\n check(f, -1, np.inf, np.inf, np.inf)\n\n check(f, ncu.PZERO, np.inf, np.inf, np.inf)\n check(f, ncu.NZERO, np.inf, np.inf, np.inf)\n check(f, np.inf, np.inf, np.inf, np.inf)\n check(f, -np.inf, np.inf, np.inf, np.inf) # noqa: E221\n check(f, -np.nan, np.inf, np.inf, np.inf) # noqa: E221\n\n # csqrt(x + nani) is nan + nani for any finite x\n check(f, 1, np.nan, np.nan, np.nan)\n check(f, -1, np.nan, np.nan, np.nan)\n check(f, 0, np.nan, np.nan, np.nan)\n\n # csqrt(-inf + yi) is +0 + infi for any finite y > 0\n check(f, -np.inf, 1, ncu.PZERO, np.inf)\n\n # csqrt(inf + yi) is +inf + 0i for any finite y > 0\n check(f, np.inf, 1, np.inf, ncu.PZERO)\n\n # csqrt(-inf + nani) is nan +- infi (both +i infi are valid)\n def _check_ninf_nan(dummy):\n msgform = "csqrt(-inf, nan) is (%f, %f), expected (nan, +-inf)"\n z = np.sqrt(np.array(complex(-np.inf, np.nan)))\n # FIXME: ugly workaround for isinf bug.\n with np.errstate(invalid='ignore'):\n if not (np.isnan(z.real) and np.isinf(z.imag)):\n raise AssertionError(msgform % (z.real, z.imag))\n\n _check_ninf_nan(None)\n\n # csqrt(+inf + nani) is inf + nani\n check(f, np.inf, np.nan, np.inf, np.nan)\n\n # csqrt(nan + yi) is nan + nani for any finite y (infinite handled in x\n # + nani)\n check(f, np.nan, 0, np.nan, np.nan)\n check(f, np.nan, 1, np.nan, np.nan)\n check(f, np.nan, np.nan, np.nan, np.nan)\n\n # XXX: check for conj(csqrt(z)) == csqrt(conj(z)) (need to fix branch\n # cuts first)\n\nclass TestCpow:\n def setup_method(self):\n self.olderr = np.seterr(invalid='ignore')\n\n def teardown_method(self):\n np.seterr(**self.olderr)\n\n def test_simple(self):\n x = np.array([1 + 1j, 0 + 2j, 1 + 2j, np.inf, np.nan])\n y_r = x ** 2\n y = np.power(x, 2)\n assert_almost_equal(y, y_r)\n\n def test_scalar(self):\n x = np.array([1, 1j, 2, 2.5 + .37j, np.inf, np.nan])\n y = np.array([1, 1j, -0.5 + 1.5j, -0.5 + 1.5j, 2, 3])\n lx = list(range(len(x)))\n\n # Hardcode the expected `builtins.complex` values,\n # as complex exponentiation is broken as of bpo-44698\n p_r = [\n 1 + 0j,\n 0.20787957635076193 + 0j,\n 0.35812203996480685 + 0.6097119028618724j,\n 0.12659112128185032 + 0.48847676699581527j,\n complex(np.inf, np.nan),\n complex(np.nan, np.nan),\n ]\n\n n_r = [x[i] ** y[i] for i in lx]\n for i in lx:\n assert_almost_equal(n_r[i], p_r[i], err_msg='Loop %d\n' % i)\n\n def test_array(self):\n x = np.array([1, 1j, 2, 2.5 + .37j, np.inf, np.nan])\n y = np.array([1, 1j, -0.5 + 1.5j, -0.5 + 1.5j, 2, 3])\n lx = list(range(len(x)))\n\n # Hardcode the expected `builtins.complex` values,\n # as complex exponentiation is broken as of bpo-44698\n p_r = [\n 1 + 0j,\n 0.20787957635076193 + 0j,\n 0.35812203996480685 + 0.6097119028618724j,\n 0.12659112128185032 + 0.48847676699581527j,\n complex(np.inf, np.nan),\n complex(np.nan, np.nan),\n ]\n\n n_r = x ** y\n for i in lx:\n assert_almost_equal(n_r[i], p_r[i], err_msg='Loop %d\n' % i)\n\nclass TestCabs:\n def setup_method(self):\n self.olderr = np.seterr(invalid='ignore')\n\n def teardown_method(self):\n np.seterr(**self.olderr)\n\n def test_simple(self):\n x = np.array([1 + 1j, 0 + 2j, 1 + 2j, np.inf, np.nan])\n y_r = np.array([np.sqrt(2.), 2, np.sqrt(5), np.inf, np.nan])\n y = np.abs(x)\n assert_almost_equal(y, y_r)\n\n def test_fabs(self):\n # Test that np.abs(x +- 0j) == np.abs(x) (as mandated by C99 for cabs)\n x = np.array([1 + 0j], dtype=complex)\n assert_array_equal(np.abs(x), np.real(x))\n\n x = np.array([complex(1, ncu.NZERO)], dtype=complex)\n assert_array_equal(np.abs(x), np.real(x))\n\n x = np.array([complex(np.inf, ncu.NZERO)], dtype=complex)\n assert_array_equal(np.abs(x), np.real(x))\n\n x = np.array([complex(np.nan, ncu.NZERO)], dtype=complex)\n assert_array_equal(np.abs(x), np.real(x))\n\n def test_cabs_inf_nan(self):\n x, y = [], []\n\n # cabs(+-nan + nani) returns nan\n x.append(np.nan)\n y.append(np.nan)\n check_real_value(np.abs, np.nan, np.nan, np.nan)\n\n x.append(np.nan)\n y.append(-np.nan)\n check_real_value(np.abs, -np.nan, np.nan, np.nan)\n\n # According to C99 standard, if exactly one of the real/part is inf and\n # the other nan, then cabs should return inf\n x.append(np.inf)\n y.append(np.nan)\n check_real_value(np.abs, np.inf, np.nan, np.inf)\n\n x.append(-np.inf)\n y.append(np.nan)\n check_real_value(np.abs, -np.inf, np.nan, np.inf)\n\n # cabs(conj(z)) == conj(cabs(z)) (= cabs(z))\n def f(a):\n return np.abs(np.conj(a))\n\n def g(a, b):\n return np.abs(complex(a, b))\n\n xa = np.array(x, dtype=complex)\n assert len(xa) == len(x) == len(y)\n for xi, yi in zip(x, y):\n ref = g(xi, yi)\n check_real_value(f, xi, yi, ref)\n\nclass TestCarg:\n def test_simple(self):\n check_real_value(ncu._arg, 1, 0, 0, False)\n check_real_value(ncu._arg, 0, 1, 0.5 * np.pi, False)\n\n check_real_value(ncu._arg, 1, 1, 0.25 * np.pi, False)\n check_real_value(ncu._arg, ncu.PZERO, ncu.PZERO, ncu.PZERO)\n\n # TODO This can be xfail when the generator functions are got rid of.\n @pytest.mark.skip(\n reason="Complex arithmetic with signed zero fails on most platforms")\n def test_zero(self):\n # carg(-0 +- 0i) returns +- pi\n check_real_value(ncu._arg, ncu.NZERO, ncu.PZERO, np.pi, False)\n check_real_value(ncu._arg, ncu.NZERO, ncu.NZERO, -np.pi, False)\n\n # carg(+0 +- 0i) returns +- 0\n check_real_value(ncu._arg, ncu.PZERO, ncu.PZERO, ncu.PZERO)\n check_real_value(ncu._arg, ncu.PZERO, ncu.NZERO, ncu.NZERO)\n\n # carg(x +- 0i) returns +- 0 for x > 0\n check_real_value(ncu._arg, 1, ncu.PZERO, ncu.PZERO, False)\n check_real_value(ncu._arg, 1, ncu.NZERO, ncu.NZERO, False)\n\n # carg(x +- 0i) returns +- pi for x < 0\n check_real_value(ncu._arg, -1, ncu.PZERO, np.pi, False)\n check_real_value(ncu._arg, -1, ncu.NZERO, -np.pi, False)\n\n # carg(+- 0 + yi) returns pi/2 for y > 0\n check_real_value(ncu._arg, ncu.PZERO, 1, 0.5 * np.pi, False)\n check_real_value(ncu._arg, ncu.NZERO, 1, 0.5 * np.pi, False)\n\n # carg(+- 0 + yi) returns -pi/2 for y < 0\n check_real_value(ncu._arg, ncu.PZERO, -1, 0.5 * np.pi, False)\n check_real_value(ncu._arg, ncu.NZERO, -1, -0.5 * np.pi, False)\n\n #def test_branch_cuts(self):\n # _check_branch_cut(ncu._arg, -1, 1j, -1, 1)\n\n def test_special_values(self):\n # carg(-np.inf +- yi) returns +-pi for finite y > 0\n check_real_value(ncu._arg, -np.inf, 1, np.pi, False)\n check_real_value(ncu._arg, -np.inf, -1, -np.pi, False)\n\n # carg(np.inf +- yi) returns +-0 for finite y > 0\n check_real_value(ncu._arg, np.inf, 1, ncu.PZERO, False)\n check_real_value(ncu._arg, np.inf, -1, ncu.NZERO, False)\n\n # carg(x +- np.infi) returns +-pi/2 for finite x\n check_real_value(ncu._arg, 1, np.inf, 0.5 * np.pi, False)\n check_real_value(ncu._arg, 1, -np.inf, -0.5 * np.pi, False)\n\n # carg(-np.inf +- np.infi) returns +-3pi/4\n check_real_value(ncu._arg, -np.inf, np.inf, 0.75 * np.pi, False)\n check_real_value(ncu._arg, -np.inf, -np.inf, -0.75 * np.pi, False)\n\n # carg(np.inf +- np.infi) returns +-pi/4\n check_real_value(ncu._arg, np.inf, np.inf, 0.25 * np.pi, False)\n check_real_value(ncu._arg, np.inf, -np.inf, -0.25 * np.pi, False)\n\n # carg(x + yi) returns np.nan if x or y is nan\n check_real_value(ncu._arg, np.nan, 0, np.nan, False)\n check_real_value(ncu._arg, 0, np.nan, np.nan, False)\n\n check_real_value(ncu._arg, np.nan, np.inf, np.nan, False)\n check_real_value(ncu._arg, np.inf, np.nan, np.nan, False)\n\n\ndef check_real_value(f, x1, y1, x, exact=True):\n z1 = np.array([complex(x1, y1)])\n if exact:\n assert_equal(f(z1), x)\n else:\n assert_almost_equal(f(z1), x)\n\n\ndef check_complex_value(f, x1, y1, x2, y2, exact=True):\n z1 = np.array([complex(x1, y1)])\n z2 = complex(x2, y2)\n with np.errstate(invalid='ignore'):\n if exact:\n assert_equal(f(z1), z2)\n else:\n assert_almost_equal(f(z1), z2)\n\nclass TestSpecialComplexAVX:\n @pytest.mark.parametrize("stride", [-4, -2, -1, 1, 2, 4])\n @pytest.mark.parametrize("astype", [np.complex64, np.complex128])\n def test_array(self, stride, astype):\n arr = np.array([complex(np.nan, np.nan),\n complex(np.nan, np.inf),\n complex(np.inf, np.nan),\n complex(np.inf, np.inf),\n complex(0., np.inf),\n complex(np.inf, 0.),\n complex(0., 0.),\n complex(0., np.nan),\n complex(np.nan, 0.)], dtype=astype)\n abs_true = np.array([np.nan, np.inf, np.inf, np.inf, np.inf, np.inf, 0., np.nan, np.nan], dtype=arr.real.dtype)\n sq_true = np.array([complex(np.nan, np.nan),\n complex(np.nan, np.nan),\n complex(np.nan, np.nan),\n complex(np.nan, np.inf),\n complex(-np.inf, np.nan),\n complex(np.inf, np.nan),\n complex(0., 0.),\n complex(np.nan, np.nan),\n complex(np.nan, np.nan)], dtype=astype)\n with np.errstate(invalid='ignore'):\n assert_equal(np.abs(arr[::stride]), abs_true[::stride])\n assert_equal(np.square(arr[::stride]), sq_true[::stride])\n\nclass TestComplexAbsoluteAVX:\n @pytest.mark.parametrize("arraysize", [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 17, 18, 19])\n @pytest.mark.parametrize("stride", [-4, -3, -2, -1, 1, 2, 3, 4])\n @pytest.mark.parametrize("astype", [np.complex64, np.complex128])\n # test to ensure masking and strides work as intended in the AVX implementation\n def test_array(self, arraysize, stride, astype):\n arr = np.ones(arraysize, dtype=astype)\n abs_true = np.ones(arraysize, dtype=arr.real.dtype)\n assert_equal(np.abs(arr[::stride]), abs_true[::stride])\n\n# Testcase taken as is from https://github.com/numpy/numpy/issues/16660\nclass TestComplexAbsoluteMixedDTypes:\n @pytest.mark.parametrize("stride", [-4, -3, -2, -1, 1, 2, 3, 4])\n @pytest.mark.parametrize("astype", [np.complex64, np.complex128])\n @pytest.mark.parametrize("func", ['abs', 'square', 'conjugate'])\n def test_array(self, stride, astype, func):\n dtype = [('template_id', '<i8'), ('bank_chisq', '<f4'),\n ('bank_chisq_dof', '<i8'), ('chisq', '<f4'), ('chisq_dof', '<i8'),\n ('cont_chisq', '<f4'), ('psd_var_val', '<f4'), ('sg_chisq', '<f4'),\n ('mycomplex', astype), ('time_index', '<i8')]\n vec = np.array([\n (0, 0., 0, -31.666483, 200, 0., 0., 1. , 3.0 + 4.0j , 613090), # noqa: E203,E501\n (1, 0., 0, 260.91525 , 42, 0., 0., 1. , 5.0 + 12.0j , 787315), # noqa: E203,E501\n (1, 0., 0, 52.15155 , 42, 0., 0., 1. , 8.0 + 15.0j , 806641), # noqa: E203,E501\n (1, 0., 0, 52.430195, 42, 0., 0., 1. , 7.0 + 24.0j , 1363540), # noqa: E203,E501\n (2, 0., 0, 304.43646 , 58, 0., 0., 1. , 20.0 + 21.0j, 787323), # noqa: E203,E501\n (3, 0., 0, 299.42108 , 52, 0., 0., 1. , 12.0 + 35.0j, 787332), # noqa: E203,E501\n (4, 0., 0, 39.4836 , 28, 0., 0., 9.182192, 9.0 + 40.0j , 787304), # noqa: E203,E501\n (4, 0., 0, 76.83787 , 28, 0., 0., 1. , 28.0 + 45.0j, 1321869), # noqa: E203,E501\n (5, 0., 0, 143.26366 , 24, 0., 0., 10.996129, 11.0 + 60.0j, 787299)], # noqa: E203,E501\n dtype=dtype)\n myfunc = getattr(np, func)\n a = vec['mycomplex']\n g = myfunc(a[::stride])\n\n b = vec['mycomplex'].copy()\n h = myfunc(b[::stride])\n\n assert_array_max_ulp(h.real, g.real, 1)\n assert_array_max_ulp(h.imag, g.imag, 1)\n | .venv\Lib\site-packages\numpy\_core\tests\test_umath_complex.py | test_umath_complex.py | Python | 24,253 | 0.95 | 0.135783 | 0.184891 | node-utils | 490 | 2024-05-17T18:37:23.658363 | Apache-2.0 | true | 6b98b0fc7cdc3bcd70c77fb8c3d30cbe |
\nimport numpy as np\nfrom numpy.testing import assert_, assert_array_equal, assert_equal\n\n\ndef buffer_length(arr):\n if isinstance(arr, str):\n if not arr:\n charmax = 0\n else:\n charmax = max(ord(c) for c in arr)\n if charmax < 256:\n size = 1\n elif charmax < 65536:\n size = 2\n else:\n size = 4\n return size * len(arr)\n v = memoryview(arr)\n if v.shape is None:\n return len(v) * v.itemsize\n else:\n return np.prod(v.shape) * v.itemsize\n\n\n# In both cases below we need to make sure that the byte swapped value (as\n# UCS4) is still a valid unicode:\n# Value that can be represented in UCS2 interpreters\nucs2_value = '\u0900'\n# Value that cannot be represented in UCS2 interpreters (but can in UCS4)\nucs4_value = '\U00100900'\n\n\ndef test_string_cast():\n str_arr = np.array(["1234", "1234\0\0"], dtype='S')\n uni_arr1 = str_arr.astype('>U')\n uni_arr2 = str_arr.astype('<U')\n\n assert_array_equal(str_arr != uni_arr1, np.ones(2, dtype=bool))\n assert_array_equal(uni_arr1 != str_arr, np.ones(2, dtype=bool))\n assert_array_equal(str_arr == uni_arr1, np.zeros(2, dtype=bool))\n assert_array_equal(uni_arr1 == str_arr, np.zeros(2, dtype=bool))\n\n assert_array_equal(uni_arr1, uni_arr2)\n\n\n############################################################\n# Creation tests\n############################################################\n\nclass CreateZeros:\n """Check the creation of zero-valued arrays"""\n\n def content_check(self, ua, ua_scalar, nbytes):\n\n # Check the length of the unicode base type\n assert_(int(ua.dtype.str[2:]) == self.ulen)\n # Check the length of the data buffer\n assert_(buffer_length(ua) == nbytes)\n # Small check that data in array element is ok\n assert_(ua_scalar == '')\n # Encode to ascii and double check\n assert_(ua_scalar.encode('ascii') == b'')\n # Check buffer lengths for scalars\n assert_(buffer_length(ua_scalar) == 0)\n\n def test_zeros0D(self):\n # Check creation of 0-dimensional objects\n ua = np.zeros((), dtype=f'U{self.ulen}')\n self.content_check(ua, ua[()], 4 * self.ulen)\n\n def test_zerosSD(self):\n # Check creation of single-dimensional objects\n ua = np.zeros((2,), dtype=f'U{self.ulen}')\n self.content_check(ua, ua[0], 4 * self.ulen * 2)\n self.content_check(ua, ua[1], 4 * self.ulen * 2)\n\n def test_zerosMD(self):\n # Check creation of multi-dimensional objects\n ua = np.zeros((2, 3, 4), dtype=f'U{self.ulen}')\n self.content_check(ua, ua[0, 0, 0], 4 * self.ulen * 2 * 3 * 4)\n self.content_check(ua, ua[-1, -1, -1], 4 * self.ulen * 2 * 3 * 4)\n\n\nclass TestCreateZeros_1(CreateZeros):\n """Check the creation of zero-valued arrays (size 1)"""\n ulen = 1\n\n\nclass TestCreateZeros_2(CreateZeros):\n """Check the creation of zero-valued arrays (size 2)"""\n ulen = 2\n\n\nclass TestCreateZeros_1009(CreateZeros):\n """Check the creation of zero-valued arrays (size 1009)"""\n ulen = 1009\n\n\nclass CreateValues:\n """Check the creation of unicode arrays with values"""\n\n def content_check(self, ua, ua_scalar, nbytes):\n\n # Check the length of the unicode base type\n assert_(int(ua.dtype.str[2:]) == self.ulen)\n # Check the length of the data buffer\n assert_(buffer_length(ua) == nbytes)\n # Small check that data in array element is ok\n assert_(ua_scalar == self.ucs_value * self.ulen)\n # Encode to UTF-8 and double check\n assert_(ua_scalar.encode('utf-8') ==\n (self.ucs_value * self.ulen).encode('utf-8'))\n # Check buffer lengths for scalars\n if self.ucs_value == ucs4_value:\n # In UCS2, the \U0010FFFF will be represented using a\n # surrogate *pair*\n assert_(buffer_length(ua_scalar) == 2 * 2 * self.ulen)\n else:\n # In UCS2, the \uFFFF will be represented using a\n # regular 2-byte word\n assert_(buffer_length(ua_scalar) == 2 * self.ulen)\n\n def test_values0D(self):\n # Check creation of 0-dimensional objects with values\n ua = np.array(self.ucs_value * self.ulen, dtype=f'U{self.ulen}')\n self.content_check(ua, ua[()], 4 * self.ulen)\n\n def test_valuesSD(self):\n # Check creation of single-dimensional objects with values\n ua = np.array([self.ucs_value * self.ulen] * 2, dtype=f'U{self.ulen}')\n self.content_check(ua, ua[0], 4 * self.ulen * 2)\n self.content_check(ua, ua[1], 4 * self.ulen * 2)\n\n def test_valuesMD(self):\n # Check creation of multi-dimensional objects with values\n ua = np.array([[[self.ucs_value * self.ulen] * 2] * 3] * 4, dtype=f'U{self.ulen}')\n self.content_check(ua, ua[0, 0, 0], 4 * self.ulen * 2 * 3 * 4)\n self.content_check(ua, ua[-1, -1, -1], 4 * self.ulen * 2 * 3 * 4)\n\n\nclass TestCreateValues_1_UCS2(CreateValues):\n """Check the creation of valued arrays (size 1, UCS2 values)"""\n ulen = 1\n ucs_value = ucs2_value\n\n\nclass TestCreateValues_1_UCS4(CreateValues):\n """Check the creation of valued arrays (size 1, UCS4 values)"""\n ulen = 1\n ucs_value = ucs4_value\n\n\nclass TestCreateValues_2_UCS2(CreateValues):\n """Check the creation of valued arrays (size 2, UCS2 values)"""\n ulen = 2\n ucs_value = ucs2_value\n\n\nclass TestCreateValues_2_UCS4(CreateValues):\n """Check the creation of valued arrays (size 2, UCS4 values)"""\n ulen = 2\n ucs_value = ucs4_value\n\n\nclass TestCreateValues_1009_UCS2(CreateValues):\n """Check the creation of valued arrays (size 1009, UCS2 values)"""\n ulen = 1009\n ucs_value = ucs2_value\n\n\nclass TestCreateValues_1009_UCS4(CreateValues):\n """Check the creation of valued arrays (size 1009, UCS4 values)"""\n ulen = 1009\n ucs_value = ucs4_value\n\n\n############################################################\n# Assignment tests\n############################################################\n\nclass AssignValues:\n """Check the assignment of unicode arrays with values"""\n\n def content_check(self, ua, ua_scalar, nbytes):\n\n # Check the length of the unicode base type\n assert_(int(ua.dtype.str[2:]) == self.ulen)\n # Check the length of the data buffer\n assert_(buffer_length(ua) == nbytes)\n # Small check that data in array element is ok\n assert_(ua_scalar == self.ucs_value * self.ulen)\n # Encode to UTF-8 and double check\n assert_(ua_scalar.encode('utf-8') ==\n (self.ucs_value * self.ulen).encode('utf-8'))\n # Check buffer lengths for scalars\n if self.ucs_value == ucs4_value:\n # In UCS2, the \U0010FFFF will be represented using a\n # surrogate *pair*\n assert_(buffer_length(ua_scalar) == 2 * 2 * self.ulen)\n else:\n # In UCS2, the \uFFFF will be represented using a\n # regular 2-byte word\n assert_(buffer_length(ua_scalar) == 2 * self.ulen)\n\n def test_values0D(self):\n # Check assignment of 0-dimensional objects with values\n ua = np.zeros((), dtype=f'U{self.ulen}')\n ua[()] = self.ucs_value * self.ulen\n self.content_check(ua, ua[()], 4 * self.ulen)\n\n def test_valuesSD(self):\n # Check assignment of single-dimensional objects with values\n ua = np.zeros((2,), dtype=f'U{self.ulen}')\n ua[0] = self.ucs_value * self.ulen\n self.content_check(ua, ua[0], 4 * self.ulen * 2)\n ua[1] = self.ucs_value * self.ulen\n self.content_check(ua, ua[1], 4 * self.ulen * 2)\n\n def test_valuesMD(self):\n # Check assignment of multi-dimensional objects with values\n ua = np.zeros((2, 3, 4), dtype=f'U{self.ulen}')\n ua[0, 0, 0] = self.ucs_value * self.ulen\n self.content_check(ua, ua[0, 0, 0], 4 * self.ulen * 2 * 3 * 4)\n ua[-1, -1, -1] = self.ucs_value * self.ulen\n self.content_check(ua, ua[-1, -1, -1], 4 * self.ulen * 2 * 3 * 4)\n\n\nclass TestAssignValues_1_UCS2(AssignValues):\n """Check the assignment of valued arrays (size 1, UCS2 values)"""\n ulen = 1\n ucs_value = ucs2_value\n\n\nclass TestAssignValues_1_UCS4(AssignValues):\n """Check the assignment of valued arrays (size 1, UCS4 values)"""\n ulen = 1\n ucs_value = ucs4_value\n\n\nclass TestAssignValues_2_UCS2(AssignValues):\n """Check the assignment of valued arrays (size 2, UCS2 values)"""\n ulen = 2\n ucs_value = ucs2_value\n\n\nclass TestAssignValues_2_UCS4(AssignValues):\n """Check the assignment of valued arrays (size 2, UCS4 values)"""\n ulen = 2\n ucs_value = ucs4_value\n\n\nclass TestAssignValues_1009_UCS2(AssignValues):\n """Check the assignment of valued arrays (size 1009, UCS2 values)"""\n ulen = 1009\n ucs_value = ucs2_value\n\n\nclass TestAssignValues_1009_UCS4(AssignValues):\n """Check the assignment of valued arrays (size 1009, UCS4 values)"""\n ulen = 1009\n ucs_value = ucs4_value\n\n\n############################################################\n# Byteorder tests\n############################################################\n\nclass ByteorderValues:\n """Check the byteorder of unicode arrays in round-trip conversions"""\n\n def test_values0D(self):\n # Check byteorder of 0-dimensional objects\n ua = np.array(self.ucs_value * self.ulen, dtype=f'U{self.ulen}')\n ua2 = ua.view(ua.dtype.newbyteorder())\n # This changes the interpretation of the data region (but not the\n # actual data), therefore the returned scalars are not\n # the same (they are byte-swapped versions of each other).\n assert_(ua[()] != ua2[()])\n ua3 = ua2.view(ua2.dtype.newbyteorder())\n # Arrays must be equal after the round-trip\n assert_equal(ua, ua3)\n\n def test_valuesSD(self):\n # Check byteorder of single-dimensional objects\n ua = np.array([self.ucs_value * self.ulen] * 2, dtype=f'U{self.ulen}')\n ua2 = ua.view(ua.dtype.newbyteorder())\n assert_((ua != ua2).all())\n assert_(ua[-1] != ua2[-1])\n ua3 = ua2.view(ua2.dtype.newbyteorder())\n # Arrays must be equal after the round-trip\n assert_equal(ua, ua3)\n\n def test_valuesMD(self):\n # Check byteorder of multi-dimensional objects\n ua = np.array([[[self.ucs_value * self.ulen] * 2] * 3] * 4,\n dtype=f'U{self.ulen}')\n ua2 = ua.view(ua.dtype.newbyteorder())\n assert_((ua != ua2).all())\n assert_(ua[-1, -1, -1] != ua2[-1, -1, -1])\n ua3 = ua2.view(ua2.dtype.newbyteorder())\n # Arrays must be equal after the round-trip\n assert_equal(ua, ua3)\n\n def test_values_cast(self):\n # Check byteorder of when casting the array for a strided and\n # contiguous array:\n test1 = np.array([self.ucs_value * self.ulen] * 2, dtype=f'U{self.ulen}')\n test2 = np.repeat(test1, 2)[::2]\n for ua in (test1, test2):\n ua2 = ua.astype(dtype=ua.dtype.newbyteorder())\n assert_((ua == ua2).all())\n assert_(ua[-1] == ua2[-1])\n ua3 = ua2.astype(dtype=ua.dtype)\n # Arrays must be equal after the round-trip\n assert_equal(ua, ua3)\n\n def test_values_updowncast(self):\n # Check byteorder of when casting the array to a longer and shorter\n # string length for strided and contiguous arrays\n test1 = np.array([self.ucs_value * self.ulen] * 2, dtype=f'U{self.ulen}')\n test2 = np.repeat(test1, 2)[::2]\n for ua in (test1, test2):\n # Cast to a longer type with zero padding\n longer_type = np.dtype(f'U{self.ulen + 1}').newbyteorder()\n ua2 = ua.astype(dtype=longer_type)\n assert_((ua == ua2).all())\n assert_(ua[-1] == ua2[-1])\n # Cast back again with truncating:\n ua3 = ua2.astype(dtype=ua.dtype)\n # Arrays must be equal after the round-trip\n assert_equal(ua, ua3)\n\n\nclass TestByteorder_1_UCS2(ByteorderValues):\n """Check the byteorder in unicode (size 1, UCS2 values)"""\n ulen = 1\n ucs_value = ucs2_value\n\n\nclass TestByteorder_1_UCS4(ByteorderValues):\n """Check the byteorder in unicode (size 1, UCS4 values)"""\n ulen = 1\n ucs_value = ucs4_value\n\n\nclass TestByteorder_2_UCS2(ByteorderValues):\n """Check the byteorder in unicode (size 2, UCS2 values)"""\n ulen = 2\n ucs_value = ucs2_value\n\n\nclass TestByteorder_2_UCS4(ByteorderValues):\n """Check the byteorder in unicode (size 2, UCS4 values)"""\n ulen = 2\n ucs_value = ucs4_value\n\n\nclass TestByteorder_1009_UCS2(ByteorderValues):\n """Check the byteorder in unicode (size 1009, UCS2 values)"""\n ulen = 1009\n ucs_value = ucs2_value\n\n\nclass TestByteorder_1009_UCS4(ByteorderValues):\n """Check the byteorder in unicode (size 1009, UCS4 values)"""\n ulen = 1009\n ucs_value = ucs4_value\n | .venv\Lib\site-packages\numpy\_core\tests\test_unicode.py | test_unicode.py | Python | 13,335 | 0.95 | 0.157609 | 0.216783 | awesome-app | 161 | 2024-12-25T22:08:08.538328 | BSD-3-Clause | true | 40bc9c2efa1b3a663d093cbe2ffb2e1e |
"""\nTests of the ._exceptions module. Primarily for exercising the __str__ methods.\n"""\n\nimport pickle\n\nimport pytest\n\nimport numpy as np\nfrom numpy.exceptions import AxisError\n\n_ArrayMemoryError = np._core._exceptions._ArrayMemoryError\n_UFuncNoLoopError = np._core._exceptions._UFuncNoLoopError\n\nclass TestArrayMemoryError:\n def test_pickling(self):\n """ Test that _ArrayMemoryError can be pickled """\n error = _ArrayMemoryError((1023,), np.dtype(np.uint8))\n res = pickle.loads(pickle.dumps(error))\n assert res._total_size == error._total_size\n\n def test_str(self):\n e = _ArrayMemoryError((1023,), np.dtype(np.uint8))\n str(e) # not crashing is enough\n\n # testing these properties is easier than testing the full string repr\n def test__size_to_string(self):\n """ Test e._size_to_string """\n f = _ArrayMemoryError._size_to_string\n Ki = 1024\n assert f(0) == '0 bytes'\n assert f(1) == '1 bytes'\n assert f(1023) == '1023 bytes'\n assert f(Ki) == '1.00 KiB'\n assert f(Ki + 1) == '1.00 KiB'\n assert f(10 * Ki) == '10.0 KiB'\n assert f(int(999.4 * Ki)) == '999. KiB'\n assert f(int(1023.4 * Ki)) == '1023. KiB'\n assert f(int(1023.5 * Ki)) == '1.00 MiB'\n assert f(Ki * Ki) == '1.00 MiB'\n\n # 1023.9999 Mib should round to 1 GiB\n assert f(int(Ki * Ki * Ki * 0.9999)) == '1.00 GiB'\n assert f(Ki * Ki * Ki * Ki * Ki * Ki) == '1.00 EiB'\n # larger than sys.maxsize, adding larger prefixes isn't going to help\n # anyway.\n assert f(Ki * Ki * Ki * Ki * Ki * Ki * 123456) == '123456. EiB'\n\n def test__total_size(self):\n """ Test e._total_size """\n e = _ArrayMemoryError((1,), np.dtype(np.uint8))\n assert e._total_size == 1\n\n e = _ArrayMemoryError((2, 4), np.dtype((np.uint64, 16)))\n assert e._total_size == 1024\n\n\nclass TestUFuncNoLoopError:\n def test_pickling(self):\n """ Test that _UFuncNoLoopError can be pickled """\n assert isinstance(pickle.dumps(_UFuncNoLoopError), bytes)\n\n\n@pytest.mark.parametrize("args", [\n (2, 1, None),\n (2, 1, "test_prefix"),\n ("test message",),\n])\nclass TestAxisError:\n def test_attr(self, args):\n """Validate attribute types."""\n exc = AxisError(*args)\n if len(args) == 1:\n assert exc.axis is None\n assert exc.ndim is None\n else:\n axis, ndim, *_ = args\n assert exc.axis == axis\n assert exc.ndim == ndim\n\n def test_pickling(self, args):\n """Test that `AxisError` can be pickled."""\n exc = AxisError(*args)\n exc2 = pickle.loads(pickle.dumps(exc))\n\n assert type(exc) is type(exc2)\n for name in ("axis", "ndim", "args"):\n attr1 = getattr(exc, name)\n attr2 = getattr(exc2, name)\n assert attr1 == attr2, name\n | .venv\Lib\site-packages\numpy\_core\tests\test__exceptions.py | test__exceptions.py | Python | 3,012 | 0.95 | 0.144444 | 0.054054 | node-utils | 108 | 2024-06-03T20:56:52.352521 | MIT | true | 40606bd5b3ce35b34edc6c6b9a8f0b8a |
"""Provide class for testing in French locale\n\n"""\nimport locale\nimport sys\n\nimport pytest\n\n__ALL__ = ['CommaDecimalPointLocale']\n\n\ndef find_comma_decimal_point_locale():\n """See if platform has a decimal point as comma locale.\n\n Find a locale that uses a comma instead of a period as the\n decimal point.\n\n Returns\n -------\n old_locale: str\n Locale when the function was called.\n new_locale: {str, None)\n First French locale found, None if none found.\n\n """\n if sys.platform == 'win32':\n locales = ['FRENCH']\n else:\n locales = ['fr_FR', 'fr_FR.UTF-8', 'fi_FI', 'fi_FI.UTF-8']\n\n old_locale = locale.getlocale(locale.LC_NUMERIC)\n new_locale = None\n try:\n for loc in locales:\n try:\n locale.setlocale(locale.LC_NUMERIC, loc)\n new_locale = loc\n break\n except locale.Error:\n pass\n finally:\n locale.setlocale(locale.LC_NUMERIC, locale=old_locale)\n return old_locale, new_locale\n\n\nclass CommaDecimalPointLocale:\n """Sets LC_NUMERIC to a locale with comma as decimal point.\n\n Classes derived from this class have setup and teardown methods that run\n tests with locale.LC_NUMERIC set to a locale where commas (',') are used as\n the decimal point instead of periods ('.'). On exit the locale is restored\n to the initial locale. It also serves as context manager with the same\n effect. If no such locale is available, the test is skipped.\n\n """\n (cur_locale, tst_locale) = find_comma_decimal_point_locale()\n\n def setup_method(self):\n if self.tst_locale is None:\n pytest.skip("No French locale available")\n locale.setlocale(locale.LC_NUMERIC, locale=self.tst_locale)\n\n def teardown_method(self):\n locale.setlocale(locale.LC_NUMERIC, locale=self.cur_locale)\n\n def __enter__(self):\n if self.tst_locale is None:\n pytest.skip("No French locale available")\n locale.setlocale(locale.LC_NUMERIC, locale=self.tst_locale)\n\n def __exit__(self, type, value, traceback):\n locale.setlocale(locale.LC_NUMERIC, locale=self.cur_locale)\n | .venv\Lib\site-packages\numpy\_core\tests\_locales.py | _locales.py | Python | 2,248 | 0.85 | 0.25 | 0 | node-utils | 100 | 2024-09-23T09:44:04.405257 | Apache-2.0 | true | 336e8814325e21caa38b74dcb70a1e2f |
# Vendored implementation of pandas.NA, adapted from pandas/_libs/missing.pyx\n#\n# This is vendored to avoid adding pandas as a test dependency.\n\n__all__ = ["pd_NA"]\n\nimport numbers\n\nimport numpy as np\n\n\ndef _create_binary_propagating_op(name, is_divmod=False):\n is_cmp = name.strip("_") in ["eq", "ne", "le", "lt", "ge", "gt"]\n\n def method(self, other):\n if (\n other is pd_NA\n or isinstance(other, (str, bytes, numbers.Number, np.bool))\n or (isinstance(other, np.ndarray) and not other.shape)\n ):\n # Need the other.shape clause to handle NumPy scalars,\n # since we do a setitem on `out` below, which\n # won't work for NumPy scalars.\n if is_divmod:\n return pd_NA, pd_NA\n else:\n return pd_NA\n\n elif isinstance(other, np.ndarray):\n out = np.empty(other.shape, dtype=object)\n out[:] = pd_NA\n\n if is_divmod:\n return out, out.copy()\n else:\n return out\n\n elif is_cmp and isinstance(other, (np.datetime64, np.timedelta64)):\n return pd_NA\n\n elif isinstance(other, np.datetime64):\n if name in ["__sub__", "__rsub__"]:\n return pd_NA\n\n elif isinstance(other, np.timedelta64):\n if name in ["__sub__", "__rsub__", "__add__", "__radd__"]:\n return pd_NA\n\n return NotImplemented\n\n method.__name__ = name\n return method\n\n\ndef _create_unary_propagating_op(name: str):\n def method(self):\n return pd_NA\n\n method.__name__ = name\n return method\n\n\nclass NAType:\n def __repr__(self) -> str:\n return "<NA>"\n\n def __format__(self, format_spec) -> str:\n try:\n return self.__repr__().__format__(format_spec)\n except ValueError:\n return self.__repr__()\n\n def __bool__(self):\n raise TypeError("boolean value of NA is ambiguous")\n\n def __hash__(self):\n exponent = 31 if is_32bit else 61\n return 2**exponent - 1\n\n def __reduce__(self):\n return "pd_NA"\n\n # Binary arithmetic and comparison ops -> propagate\n\n __add__ = _create_binary_propagating_op("__add__")\n __radd__ = _create_binary_propagating_op("__radd__")\n __sub__ = _create_binary_propagating_op("__sub__")\n __rsub__ = _create_binary_propagating_op("__rsub__")\n __mul__ = _create_binary_propagating_op("__mul__")\n __rmul__ = _create_binary_propagating_op("__rmul__")\n __matmul__ = _create_binary_propagating_op("__matmul__")\n __rmatmul__ = _create_binary_propagating_op("__rmatmul__")\n __truediv__ = _create_binary_propagating_op("__truediv__")\n __rtruediv__ = _create_binary_propagating_op("__rtruediv__")\n __floordiv__ = _create_binary_propagating_op("__floordiv__")\n __rfloordiv__ = _create_binary_propagating_op("__rfloordiv__")\n __mod__ = _create_binary_propagating_op("__mod__")\n __rmod__ = _create_binary_propagating_op("__rmod__")\n __divmod__ = _create_binary_propagating_op("__divmod__", is_divmod=True)\n __rdivmod__ = _create_binary_propagating_op("__rdivmod__", is_divmod=True)\n # __lshift__ and __rshift__ are not implemented\n\n __eq__ = _create_binary_propagating_op("__eq__")\n __ne__ = _create_binary_propagating_op("__ne__")\n __le__ = _create_binary_propagating_op("__le__")\n __lt__ = _create_binary_propagating_op("__lt__")\n __gt__ = _create_binary_propagating_op("__gt__")\n __ge__ = _create_binary_propagating_op("__ge__")\n\n # Unary ops\n\n __neg__ = _create_unary_propagating_op("__neg__")\n __pos__ = _create_unary_propagating_op("__pos__")\n __abs__ = _create_unary_propagating_op("__abs__")\n __invert__ = _create_unary_propagating_op("__invert__")\n\n # pow has special\n def __pow__(self, other):\n if other is pd_NA:\n return pd_NA\n elif isinstance(other, (numbers.Number, np.bool)):\n if other == 0:\n # returning positive is correct for +/- 0.\n return type(other)(1)\n else:\n return pd_NA\n elif util.is_array(other):\n return np.where(other == 0, other.dtype.type(1), pd_NA)\n\n return NotImplemented\n\n def __rpow__(self, other):\n if other is pd_NA:\n return pd_NA\n elif isinstance(other, (numbers.Number, np.bool)):\n if other == 1:\n return other\n else:\n return pd_NA\n elif util.is_array(other):\n return np.where(other == 1, other, pd_NA)\n return NotImplemented\n\n # Logical ops using Kleene logic\n\n def __and__(self, other):\n if other is False:\n return False\n elif other is True or other is pd_NA:\n return pd_NA\n return NotImplemented\n\n __rand__ = __and__\n\n def __or__(self, other):\n if other is True:\n return True\n elif other is False or other is pd_NA:\n return pd_NA\n return NotImplemented\n\n __ror__ = __or__\n\n def __xor__(self, other):\n if other is False or other is True or other is pd_NA:\n return pd_NA\n return NotImplemented\n\n __rxor__ = __xor__\n\n __array_priority__ = 1000\n _HANDLED_TYPES = (np.ndarray, numbers.Number, str, np.bool)\n\n def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):\n types = self._HANDLED_TYPES + (NAType,)\n for x in inputs:\n if not isinstance(x, types):\n return NotImplemented\n\n if method != "__call__":\n raise ValueError(f"ufunc method '{method}' not supported for NA")\n result = maybe_dispatch_ufunc_to_dunder_op(\n self, ufunc, method, *inputs, **kwargs\n )\n if result is NotImplemented:\n # For a NumPy ufunc that's not a binop, like np.logaddexp\n index = next(i for i, x in enumerate(inputs) if x is pd_NA)\n result = np.broadcast_arrays(*inputs)[index]\n if result.ndim == 0:\n result = result.item()\n if ufunc.nout > 1:\n result = (pd_NA,) * ufunc.nout\n\n return result\n\n\npd_NA = NAType()\n\n\ndef get_stringdtype_dtype(na_object, coerce=True):\n # explicit is check for pd_NA because != with pd_NA returns pd_NA\n if na_object is pd_NA or na_object != "unset":\n return np.dtypes.StringDType(na_object=na_object, coerce=coerce)\n else:\n return np.dtypes.StringDType(coerce=coerce)\n | .venv\Lib\site-packages\numpy\_core\tests\_natype.py | _natype.py | Python | 6,730 | 0.95 | 0.214634 | 0.0875 | react-lib | 401 | 2025-05-07T03:24:48.984371 | GPL-3.0 | true | 41506ac5aa74d966a35abe13e6b52799 |
cnumpy.core.multiarray\n_reconstruct\nqcnumpy\nndarray\nqK | .venv\Lib\site-packages\numpy\_core\tests\data\astype_copy.pkl | astype_copy.pkl | Other | 716 | 0.7 | 0 | 0 | awesome-app | 996 | 2025-07-09T02:56:15.946014 | MIT | true | cd1b85e5d04b562819206983cdcc3e19 |
#include <algorithm>\n#include <fstream>\n#include <iostream>\n#include <math.h>\n#include <random>\n#include <cstdio>\n#include <ctime>\n#include <vector>\n\nstruct ufunc {\n std::string name;\n double (*f32func)(double);\n long double (*f64func)(long double);\n float f32ulp;\n float f64ulp;\n};\n\ntemplate <typename T>\nT\nRandomFloat(T a, T b)\n{\n T random = ((T)rand()) / (T)RAND_MAX;\n T diff = b - a;\n T r = random * diff;\n return a + r;\n}\n\ntemplate <typename T>\nvoid\nappend_random_array(std::vector<T> &arr, T min, T max, size_t N)\n{\n for (size_t ii = 0; ii < N; ++ii)\n arr.emplace_back(RandomFloat<T>(min, max));\n}\n\ntemplate <typename T1, typename T2>\nstd::vector<T1>\ncomputeTrueVal(const std::vector<T1> &in, T2 (*mathfunc)(T2))\n{\n std::vector<T1> out;\n for (T1 elem : in) {\n T2 elem_d = (T2)elem;\n T1 out_elem = (T1)mathfunc(elem_d);\n out.emplace_back(out_elem);\n }\n return out;\n}\n\n/*\n * FP range:\n * [-inf, -maxflt, -1., -minflt, -minden, 0., minden, minflt, 1., maxflt, inf]\n */\n\n#define MINDEN std::numeric_limits<T>::denorm_min()\n#define MINFLT std::numeric_limits<T>::min()\n#define MAXFLT std::numeric_limits<T>::max()\n#define INF std::numeric_limits<T>::infinity()\n#define qNAN std::numeric_limits<T>::quiet_NaN()\n#define sNAN std::numeric_limits<T>::signaling_NaN()\n\ntemplate <typename T>\nstd::vector<T>\ngenerate_input_vector(std::string func)\n{\n std::vector<T> input = {MINDEN, -MINDEN, MINFLT, -MINFLT, MAXFLT,\n -MAXFLT, INF, -INF, qNAN, sNAN,\n -1.0, 1.0, 0.0, -0.0};\n\n // [-1.0, 1.0]\n if ((func == "arcsin") || (func == "arccos") || (func == "arctanh")) {\n append_random_array<T>(input, -1.0, 1.0, 700);\n }\n // (0.0, INF]\n else if ((func == "log2") || (func == "log10")) {\n append_random_array<T>(input, 0.0, 1.0, 200);\n append_random_array<T>(input, MINDEN, MINFLT, 200);\n append_random_array<T>(input, MINFLT, 1.0, 200);\n append_random_array<T>(input, 1.0, MAXFLT, 200);\n }\n // (-1.0, INF]\n else if (func == "log1p") {\n append_random_array<T>(input, -1.0, 1.0, 200);\n append_random_array<T>(input, -MINFLT, -MINDEN, 100);\n append_random_array<T>(input, -1.0, -MINFLT, 100);\n append_random_array<T>(input, MINDEN, MINFLT, 100);\n append_random_array<T>(input, MINFLT, 1.0, 100);\n append_random_array<T>(input, 1.0, MAXFLT, 100);\n }\n // [1.0, INF]\n else if (func == "arccosh") {\n append_random_array<T>(input, 1.0, 2.0, 400);\n append_random_array<T>(input, 2.0, MAXFLT, 300);\n }\n // [-INF, INF]\n else {\n append_random_array<T>(input, -1.0, 1.0, 100);\n append_random_array<T>(input, MINDEN, MINFLT, 100);\n append_random_array<T>(input, -MINFLT, -MINDEN, 100);\n append_random_array<T>(input, MINFLT, 1.0, 100);\n append_random_array<T>(input, -1.0, -MINFLT, 100);\n append_random_array<T>(input, 1.0, MAXFLT, 100);\n append_random_array<T>(input, -MAXFLT, -100.0, 100);\n }\n\n std::random_shuffle(input.begin(), input.end());\n return input;\n}\n\nint\nmain()\n{\n srand(42);\n std::vector<struct ufunc> umathfunc = {\n {"sin", sin, sin, 1.49, 1.00},\n {"cos", cos, cos, 1.49, 1.00},\n {"tan", tan, tan, 3.91, 1.00},\n {"arcsin", asin, asin, 3.12, 1.00},\n {"arccos", acos, acos, 2.1, 1.00},\n {"arctan", atan, atan, 2.3, 1.00},\n {"sinh", sinh, sinh, 1.55, 1.00},\n {"cosh", cosh, cosh, 2.48, 1.00},\n {"tanh", tanh, tanh, 1.38, 2.00},\n {"arcsinh", asinh, asinh, 1.01, 1.00},\n {"arccosh", acosh, acosh, 1.16, 1.00},\n {"arctanh", atanh, atanh, 1.45, 1.00},\n {"cbrt", cbrt, cbrt, 1.94, 2.00},\n //{"exp",exp,exp,3.76,1.00},\n {"exp2", exp2, exp2, 1.01, 1.00},\n {"expm1", expm1, expm1, 2.62, 1.00},\n //{"log",log,log,1.84,1.00},\n {"log10", log10, log10, 3.5, 1.00},\n {"log1p", log1p, log1p, 1.96, 1.0},\n {"log2", log2, log2, 2.12, 1.00},\n };\n\n for (int ii = 0; ii < umathfunc.size(); ++ii) {\n // ignore sin/cos\n if ((umathfunc[ii].name != "sin") && (umathfunc[ii].name != "cos")) {\n std::string fileName =\n "umath-validation-set-" + umathfunc[ii].name + ".csv";\n std::ofstream txtOut;\n txtOut.open(fileName, std::ofstream::trunc);\n txtOut << "dtype,input,output,ulperrortol" << std::endl;\n\n // Single Precision\n auto f32in = generate_input_vector<float>(umathfunc[ii].name);\n auto f32out = computeTrueVal<float, double>(f32in,\n umathfunc[ii].f32func);\n for (int jj = 0; jj < f32in.size(); ++jj) {\n txtOut << "np.float32" << std::hex << ",0x"\n << *reinterpret_cast<uint32_t *>(&f32in[jj]) << ",0x"\n << *reinterpret_cast<uint32_t *>(&f32out[jj]) << ","\n << ceil(umathfunc[ii].f32ulp) << std::endl;\n }\n\n // Double Precision\n auto f64in = generate_input_vector<double>(umathfunc[ii].name);\n auto f64out = computeTrueVal<double, long double>(\n f64in, umathfunc[ii].f64func);\n for (int jj = 0; jj < f64in.size(); ++jj) {\n txtOut << "np.float64" << std::hex << ",0x"\n << *reinterpret_cast<uint64_t *>(&f64in[jj]) << ",0x"\n << *reinterpret_cast<uint64_t *>(&f64out[jj]) << ","\n << ceil(umathfunc[ii].f64ulp) << std::endl;\n }\n txtOut.close();\n }\n }\n return 0;\n}\n | .venv\Lib\site-packages\numpy\_core\tests\data\generate_umath_validation_data.cpp | generate_umath_validation_data.cpp | C++ | 6,012 | 0.8 | 0.058824 | 0.178344 | python-kit | 173 | 2024-07-23T21:23:12.334470 | GPL-3.0 | true | 80d8e19726afbea46b80e25c2c1e8ec2 |
SIMPLE = T / file does conform to FITS standard BITPIX = 16 / number of bits per data pixel NAXIS = 0 / number of data axes EXTEND = T / FITS dataset may contain extensions COMMENT FITS (Flexible Image Transport System) format defined in Astronomy andCOMMENT Astrophysics Supplement Series v44/p363, v44/p371, v73/p359, v73/p365.COMMENT Contact the NASA Science Office of Standards and Technology for the COMMENT FITS Definition document #100 and other FITS information. ORIGIN = 'STScI-STSDAS/TABLES' / Tables version 1999-09-07 FILENAME= 'tb.fits ' / name of file NEXTEND = 1 / number of extensions in file END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / 8-bit bytes NAXIS = 2 / 2-dimensional binary table NAXIS1 = 17 / width of table in bytes NAXIS2 = 3 PCOUNT = 0 / size of special data area GCOUNT = 1 / one data group (required keyword) TFIELDS = 3 TTYPE1 = 'a ' / label for field 1 TFORM1 = '1D ' / data format of field: 8-byte DOUBLE TTYPE2 = 'b ' / label for field 2 TFORM2 = '1J ' / data format of field: 4-byte INTEGER TTYPE3 = 'c ' / label for field 3 TFORM3 = '5A ' / data format of field: ASCII Character TDISP1 = 'G25.16 ' / display format TDISP2 = 'I11 ' / display format TNULL2 = -2147483647 / undefined value for column TDISP3 = 'A5 ' / display format HISTORY Created Fri 16:25:07 22-Jun-2001 END @fffffg | .venv\Lib\site-packages\numpy\_core\tests\data\recarray_from_file.fits | recarray_from_file.fits | Other | 8,640 | 0.95 | 0.1 | 0 | react-lib | 314 | 2025-01-06T00:47:24.182153 | GPL-3.0 | true | b8e122f880bc10fd52cabfaae9ba6ed5 |
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| .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-arccos.csv | umath-validation-set-arccos.csv | Other | 62,794 | 0.5 | 0 | 0 | vue-tools | 906 | 2023-08-15T00:03:12.688026 | BSD-3-Clause | true | e9f1c350a2b589b1e6e2cceb2f975b05 |
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| .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-arccosh.csv | umath-validation-set-arccosh.csv | Other | 62,794 | 0.5 | 0 | 0 | python-kit | 48 | 2024-04-26T15:41:13.570493 | Apache-2.0 | true | 5a5014dd42f94729f5cf69497fa16038 |
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| .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-arcsin.csv | umath-validation-set-arcsin.csv | Other | 62,768 | 0.5 | 0 | 0 | node-utils | 799 | 2023-09-17T13:10:24.945626 | GPL-3.0 | true | 24f78b261b6d3eff2f1fa5029fde6e64 |
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| .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-arcsinh.csv | umath-validation-set-arcsinh.csv | Other | 61,718 | 0.5 | 0 | 0 | react-lib | 805 | 2024-07-03T21:47:03.914412 | Apache-2.0 | true | 0f6718b99957ba62de9f2d6c1b73394f |
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| .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-arctan.csv | umath-validation-set-arctan.csv | Other | 61,734 | 0.5 | 0 | 0 | node-utils | 636 | 2023-11-27T09:30:15.504525 | BSD-3-Clause | true | 45988c00bb5ed694e1051a5b82508b99 |
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| .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-arctanh.csv | umath-validation-set-arctanh.csv | Other | 62,768 | 0.5 | 0 | 0 | react-lib | 693 | 2025-05-03T01:00:51.307015 | GPL-3.0 | true | eb69a7fada3fb706d61455324214de49 |
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| .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-cbrt.csv | umath-validation-set-cbrt.csv | Other | 62,275 | 0.5 | 0 | 0 | python-kit | 470 | 2025-03-31T09:17:21.627984 | MIT | true | 1526fa2a6a135f7f8fe1877b2d9ee57b |
dtype,input,output,ulperrortol\n## +ve denormals ##\nnp.float32,0x004b4716,0x3f800000,2\nnp.float32,0x007b2490,0x3f800000,2\nnp.float32,0x007c99fa,0x3f800000,2\nnp.float32,0x00734a0c,0x3f800000,2\nnp.float32,0x0070de24,0x3f800000,2\nnp.float32,0x007fffff,0x3f800000,2\nnp.float32,0x00000001,0x3f800000,2\n## -ve denormals ##\nnp.float32,0x80495d65,0x3f800000,2\nnp.float32,0x806894f6,0x3f800000,2\nnp.float32,0x80555a76,0x3f800000,2\nnp.float32,0x804e1fb8,0x3f800000,2\nnp.float32,0x80687de9,0x3f800000,2\nnp.float32,0x807fffff,0x3f800000,2\nnp.float32,0x80000001,0x3f800000,2\n## +/-0.0f, +/-FLT_MIN +/-FLT_MAX ##\nnp.float32,0x00000000,0x3f800000,2\nnp.float32,0x80000000,0x3f800000,2\nnp.float32,0x00800000,0x3f800000,2\nnp.float32,0x80800000,0x3f800000,2\n## 1.00f + 0x00000001 ##\nnp.float32,0x3f800000,0x3f0a5140,2\nnp.float32,0x3f800001,0x3f0a513f,2\nnp.float32,0x3f800002,0x3f0a513d,2\nnp.float32,0xc090a8b0,0xbe4332ce,2\nnp.float32,0x41ce3184,0x3f4d1de1,2\nnp.float32,0xc1d85848,0xbeaa8980,2\nnp.float32,0x402b8820,0xbf653aa3,2\nnp.float32,0x42b4e454,0xbf4a338b,2\nnp.float32,0x42a67a60,0x3c58202e,2\nnp.float32,0x41d92388,0xbed987c7,2\nnp.float32,0x422dd66c,0x3f5dcab3,2\nnp.float32,0xc28f5be6,0xbf5688d8,2\nnp.float32,0x41ab2674,0xbf53aa3b,2\nnp.float32,0x3f490fdb,0x3f3504f3,2\nnp.float32,0xbf490fdb,0x3f3504f3,2\nnp.float32,0x3fc90fdb,0xb33bbd2e,2\nnp.float32,0xbfc90fdb,0xb33bbd2e,2\nnp.float32,0x40490fdb,0xbf800000,2\nnp.float32,0xc0490fdb,0xbf800000,2\nnp.float32,0x3fc90fdb,0xb33bbd2e,2\nnp.float32,0xbfc90fdb,0xb33bbd2e,2\nnp.float32,0x40490fdb,0xbf800000,2\nnp.float32,0xc0490fdb,0xbf800000,2\nnp.float32,0x40c90fdb,0x3f800000,2\nnp.float32,0xc0c90fdb,0x3f800000,2\nnp.float32,0x4016cbe4,0xbf3504f3,2\nnp.float32,0xc016cbe4,0xbf3504f3,2\nnp.float32,0x4096cbe4,0x324cde2e,2\nnp.float32,0xc096cbe4,0x324cde2e,2\nnp.float32,0x4116cbe4,0xbf800000,2\nnp.float32,0xc116cbe4,0xbf800000,2\nnp.float32,0x40490fdb,0xbf800000,2\nnp.float32,0xc0490fdb,0xbf800000,2\nnp.float32,0x40c90fdb,0x3f800000,2\nnp.float32,0xc0c90fdb,0x3f800000,2\nnp.float32,0x41490fdb,0x3f800000,2\nnp.float32,0xc1490fdb,0x3f800000,2\nnp.float32,0x407b53d2,0xbf3504f1,2\nnp.float32,0xc07b53d2,0xbf3504f1,2\nnp.float32,0x40fb53d2,0xb4b5563d,2\nnp.float32,0xc0fb53d2,0xb4b5563d,2\nnp.float32,0x417b53d2,0xbf800000,2\nnp.float32,0xc17b53d2,0xbf800000,2\nnp.float32,0x4096cbe4,0x324cde2e,2\nnp.float32,0xc096cbe4,0x324cde2e,2\nnp.float32,0x4116cbe4,0xbf800000,2\nnp.float32,0xc116cbe4,0xbf800000,2\nnp.float32,0x4196cbe4,0x3f800000,2\nnp.float32,0xc196cbe4,0x3f800000,2\nnp.float32,0x40afede0,0x3f3504f7,2\nnp.float32,0xc0afede0,0x3f3504f7,2\nnp.float32,0x412fede0,0x353222c4,2\nnp.float32,0xc12fede0,0x353222c4,2\nnp.float32,0x41afede0,0xbf800000,2\nnp.float32,0xc1afede0,0xbf800000,2\nnp.float32,0x40c90fdb,0x3f800000,2\nnp.float32,0xc0c90fdb,0x3f800000,2\nnp.float32,0x41490fdb,0x3f800000,2\nnp.float32,0xc1490fdb,0x3f800000,2\nnp.float32,0x41c90fdb,0x3f800000,2\nnp.float32,0xc1c90fdb,0x3f800000,2\nnp.float32,0x40e231d6,0x3f3504f3,2\nnp.float32,0xc0e231d6,0x3f3504f3,2\nnp.float32,0x416231d6,0xb319a6a2,2\nnp.float32,0xc16231d6,0xb319a6a2,2\nnp.float32,0x41e231d6,0xbf800000,2\nnp.float32,0xc1e231d6,0xbf800000,2\nnp.float32,0x40fb53d2,0xb4b5563d,2\nnp.float32,0xc0fb53d2,0xb4b5563d,2\nnp.float32,0x417b53d2,0xbf800000,2\nnp.float32,0xc17b53d2,0xbf800000,2\nnp.float32,0x41fb53d2,0x3f800000,2\nnp.float32,0xc1fb53d2,0x3f800000,2\nnp.float32,0x410a3ae7,0xbf3504fb,2\nnp.float32,0xc10a3ae7,0xbf3504fb,2\nnp.float32,0x418a3ae7,0x35b08908,2\nnp.float32,0xc18a3ae7,0x35b08908,2\nnp.float32,0x420a3ae7,0xbf800000,2\nnp.float32,0xc20a3ae7,0xbf800000,2\nnp.float32,0x4116cbe4,0xbf800000,2\nnp.float32,0xc116cbe4,0xbf800000,2\nnp.float32,0x4196cbe4,0x3f800000,2\nnp.float32,0xc196cbe4,0x3f800000,2\nnp.float32,0x4216cbe4,0x3f800000,2\nnp.float32,0xc216cbe4,0x3f800000,2\nnp.float32,0x41235ce2,0xbf3504ef,2\nnp.float32,0xc1235ce2,0xbf3504ef,2\nnp.float32,0x41a35ce2,0xb53889b6,2\nnp.float32,0xc1a35ce2,0xb53889b6,2\nnp.float32,0x42235ce2,0xbf800000,2\nnp.float32,0xc2235ce2,0xbf800000,2\nnp.float32,0x412fede0,0x353222c4,2\nnp.float32,0xc12fede0,0x353222c4,2\nnp.float32,0x41afede0,0xbf800000,2\nnp.float32,0xc1afede0,0xbf800000,2\nnp.float32,0x422fede0,0x3f800000,2\nnp.float32,0xc22fede0,0x3f800000,2\nnp.float32,0x413c7edd,0x3f3504f4,2\nnp.float32,0xc13c7edd,0x3f3504f4,2\nnp.float32,0x41bc7edd,0x33800add,2\nnp.float32,0xc1bc7edd,0x33800add,2\nnp.float32,0x423c7edd,0xbf800000,2\nnp.float32,0xc23c7edd,0xbf800000,2\nnp.float32,0x41490fdb,0x3f800000,2\nnp.float32,0xc1490fdb,0x3f800000,2\nnp.float32,0x41c90fdb,0x3f800000,2\nnp.float32,0xc1c90fdb,0x3f800000,2\nnp.float32,0x42490fdb,0x3f800000,2\nnp.float32,0xc2490fdb,0x3f800000,2\nnp.float32,0x4155a0d9,0x3f3504eb,2\nnp.float32,0xc155a0d9,0x3f3504eb,2\nnp.float32,0x41d5a0d9,0xb5b3bc81,2\nnp.float32,0xc1d5a0d9,0xb5b3bc81,2\nnp.float32,0x4255a0d9,0xbf800000,2\nnp.float32,0xc255a0d9,0xbf800000,2\nnp.float32,0x416231d6,0xb319a6a2,2\nnp.float32,0xc16231d6,0xb319a6a2,2\nnp.float32,0x41e231d6,0xbf800000,2\nnp.float32,0xc1e231d6,0xbf800000,2\nnp.float32,0x426231d6,0x3f800000,2\nnp.float32,0xc26231d6,0x3f800000,2\nnp.float32,0x416ec2d4,0xbf3504f7,2\nnp.float32,0xc16ec2d4,0xbf3504f7,2\nnp.float32,0x41eec2d4,0x353ef0a7,2\nnp.float32,0xc1eec2d4,0x353ef0a7,2\nnp.float32,0x426ec2d4,0xbf800000,2\nnp.float32,0xc26ec2d4,0xbf800000,2\nnp.float32,0x417b53d2,0xbf800000,2\nnp.float32,0xc17b53d2,0xbf800000,2\nnp.float32,0x41fb53d2,0x3f800000,2\nnp.float32,0xc1fb53d2,0x3f800000,2\nnp.float32,0x427b53d2,0x3f800000,2\nnp.float32,0xc27b53d2,0x3f800000,2\nnp.float32,0x4183f268,0xbf3504e7,2\nnp.float32,0xc183f268,0xbf3504e7,2\nnp.float32,0x4203f268,0xb6059a13,2\nnp.float32,0xc203f268,0xb6059a13,2\nnp.float32,0x4283f268,0xbf800000,2\nnp.float32,0xc283f268,0xbf80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| .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-cos.csv | umath-validation-set-cos.csv | Other | 60,497 | 0.6 | 0 | 0.002909 | react-lib | 436 | 2025-01-01T06:32:29.349428 | Apache-2.0 | true | 6026a018dedc706fa673352e055543ca |
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| .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-cosh.csv | umath-validation-set-cosh.csv | Other | 62,298 | 0.5 | 0 | 0 | node-utils | 471 | 2024-10-22T18:22:57.559403 | GPL-3.0 | true | e8c7053b1623275eb369b75fe4094c11 |
dtype,input,output,ulperrortol\n## +ve denormals ##\nnp.float32,0x004b4716,0x3f800000,3\nnp.float32,0x007b2490,0x3f800000,3\nnp.float32,0x007c99fa,0x3f800000,3\nnp.float32,0x00734a0c,0x3f800000,3\nnp.float32,0x0070de24,0x3f800000,3\nnp.float32,0x00495d65,0x3f800000,3\nnp.float32,0x006894f6,0x3f800000,3\nnp.float32,0x00555a76,0x3f800000,3\nnp.float32,0x004e1fb8,0x3f800000,3\nnp.float32,0x00687de9,0x3f800000,3\n## -ve denormals ##\nnp.float32,0x805b59af,0x3f800000,3\nnp.float32,0x807ed8ed,0x3f800000,3\nnp.float32,0x807142ad,0x3f800000,3\nnp.float32,0x80772002,0x3f800000,3\nnp.float32,0x8062abcb,0x3f800000,3\nnp.float32,0x8045e31c,0x3f800000,3\nnp.float32,0x805f01c2,0x3f800000,3\nnp.float32,0x80506432,0x3f800000,3\nnp.float32,0x8060089d,0x3f800000,3\nnp.float32,0x8071292f,0x3f800000,3\n## floats that output a denormal ##\nnp.float32,0xc2cf3fc1,0x00000001,3\nnp.float32,0xc2c79726,0x00000021,3\nnp.float32,0xc2cb295d,0x00000005,3\nnp.float32,0xc2b49e6b,0x00068c4c,3\nnp.float32,0xc2ca8116,0x00000008,3\nnp.float32,0xc2c23f82,0x000001d7,3\nnp.float32,0xc2cb69c0,0x00000005,3\nnp.float32,0xc2cc1f4d,0x00000003,3\nnp.float32,0xc2ae094e,0x00affc4c,3\nnp.float32,0xc2c86c44,0x00000015,3\n## random floats between -87.0f and 88.0f ##\nnp.float32,0x4030d7e0,0x417d9a05,3\nnp.float32,0x426f60e8,0x6aa1be2c,3\nnp.float32,0x41a1b220,0x4e0efc11,3\nnp.float32,0xc20cc722,0x26159da7,3\nnp.float32,0x41c492bc,0x512ec79d,3\nnp.float32,0x40980210,0x42e73a0e,3\nnp.float32,0xbf1f7b80,0x3f094de3,3\nnp.float32,0x42a678a4,0x7b87a383,3\nnp.float32,0xc20f3cfd,0x25a1c304,3\nnp.float32,0x423ff34c,0x6216467f,3\nnp.float32,0x00000000,0x3f800000,3\n## floats that cause an overflow ##\nnp.float32,0x7f06d8c1,0x7f800000,3\nnp.float32,0x7f451912,0x7f800000,3\nnp.float32,0x7ecceac3,0x7f800000,3\nnp.float32,0x7f643b45,0x7f800000,3\nnp.float32,0x7e910ea0,0x7f800000,3\nnp.float32,0x7eb4756b,0x7f800000,3\nnp.float32,0x7f4ec708,0x7f800000,3\nnp.float32,0x7f6b4551,0x7f800000,3\nnp.float32,0x7d8edbda,0x7f800000,3\nnp.float32,0x7f730718,0x7f800000,3\nnp.float32,0x42b17217,0x7f7fff84,3\nnp.float32,0x42b17218,0x7f800000,3\nnp.float32,0x42b17219,0x7f800000,3\nnp.float32,0xfef2b0bc,0x00000000,3\nnp.float32,0xff69f83e,0x00000000,3\nnp.float32,0xff4ecb12,0x00000000,3\nnp.float32,0xfeac6d86,0x00000000,3\nnp.float32,0xfde0cdb8,0x00000000,3\nnp.float32,0xff26aef4,0x00000000,3\nnp.float32,0xff6f9277,0x00000000,3\nnp.float32,0xff7adfc4,0x00000000,3\nnp.float32,0xff0ad40e,0x00000000,3\nnp.float32,0xff6fd8f3,0x00000000,3\nnp.float32,0xc2cff1b4,0x00000001,3\nnp.float32,0xc2cff1b5,0x00000000,3\nnp.float32,0xc2cff1b6,0x00000000,3\nnp.float32,0x7f800000,0x7f800000,3\nnp.float32,0xff800000,0x00000000,3\nnp.float32,0x4292f27c,0x7480000a,3\nnp.float32,0x42a920be,0x7c7fff94,3\nnp.float32,0x41c214c9,0x50ffffd9,3\nnp.float32,0x41abe686,0x4effffd9,3\nnp.float32,0x4287db5a,0x707fffd3,3\nnp.float32,0x41902cbb,0x4c800078,3\nnp.float32,0x42609466,0x67ffffeb,3\nnp.float32,0x41a65af5,0x4e7fffd1,3\nnp.float32,0x417f13ff,0x4affffc9,3\nnp.float32,0x426d0e6c,0x6a3504f2,3\nnp.float32,0x41bc8934,0x507fff51,3\nnp.float32,0x42a7bdde,0x7c0000d6,3\nnp.float32,0x4120cf66,0x46b504f6,3\nnp.float32,0x4244da8f,0x62ffff1a,3\nnp.float32,0x41a0cf69,0x4e000034,3\nnp.float32,0x41cd2bec,0x52000005,3\nnp.float32,0x42893e41,0x7100009e,3\nnp.float32,0x41b437e1,0x4fb50502,3\nnp.float32,0x41d8430f,0x5300001d,3\nnp.float32,0x4244da92,0x62ffffda,3\nnp.float32,0x41a0cf63,0x4dffffa9,3\nnp.float32,0x3eb17218,0x3fb504f3,3\nnp.float32,0x428729e8,0x703504dc,3\nnp.float32,0x41a0cf67,0x4e000014,3\nnp.float32,0x4252b77d,0x65800011,3\nnp.float32,0x41902cb9,0x4c800058,3\nnp.float32,0x42a0cf67,0x79800052,3\nnp.float32,0x4152b77b,0x48ffffe9,3\nnp.float32,0x41265af3,0x46ffffc8,3\nnp.float32,0x42187e0b,0x5affff9a,3\nnp.float32,0xc0d2b77c,0x3ab504f6,3\nnp.float32,0xc283b2ac,0x10000072,3\nnp.float32,0xc1cff1b4,0x2cb504f5,3\nnp.float32,0xc05dce9e,0x3d000000,3\nnp.float32,0xc28ec9d2,0x0bfffea5,3\nnp.float32,0xc23c893a,0x1d7fffde,3\nnp.float32,0xc2a920c0,0x027fff6c,3\nnp.float32,0xc1f9886f,0x2900002b,3\nnp.float32,0xc2c42920,0x000000b5,3\nnp.float32,0xc2893e41,0x0dfffec5,3\nnp.float32,0xc2c4da93,0x00000080,3\nnp.float32,0xc17f1401,0x3400000c,3\nnp.float32,0xc1902cb6,0x327fffaf,3\nnp.float32,0xc27c4e3b,0x11ffffc5,3\nnp.float32,0xc268e5c5,0x157ffe9d,3\nnp.float32,0xc2b4e953,0x0005a826,3\nnp.float32,0xc287db5a,0x0e800016,3\nnp.float32,0xc207db5a,0x2700000b,3\nnp.float32,0xc2b2d4fe,0x000ffff1,3\nnp.float32,0xc268e5c0,0x157fffdd,3\nnp.float32,0xc22920bd,0x2100003b,3\nnp.float32,0xc2902caf,0x0b80011e,3\nnp.float32,0xc1902cba,0x327fff2f,3\nnp.float32,0xc2ca6625,0x00000008,3\nnp.float32,0xc280ece8,0x10fffeb5,3\nnp.float32,0xc2918f94,0x0b0000ea,3\nnp.float32,0xc29b43d5,0x077ffffc,3\nnp.float32,0xc1e61ff7,0x2ab504f5,3\nnp.float32,0xc2867878,0x0effff15,3\nnp.float32,0xc2a2324a,0x04fffff4,3\n#float64\n## near zero ##\nnp.float64,0x8000000000000000,0x3ff0000000000000,1\nnp.float64,0x8010000000000000,0x3ff0000000000000,1\nnp.float64,0x8000000000000001,0x3ff0000000000000,1\nnp.float64,0x8360000000000000,0x3ff0000000000000,1\nnp.float64,0x9a70000000000000,0x3ff0000000000000,1\nnp.float64,0xb9b0000000000000,0x3ff0000000000000,1\nnp.float64,0xb810000000000000,0x3ff0000000000000,1\nnp.float64,0xbc30000000000000,0x3ff0000000000000,1\nnp.float64,0xb6a0000000000000,0x3ff0000000000000,1\nnp.float64,0x0000000000000000,0x3ff0000000000000,1\nnp.float64,0x0010000000000000,0x3ff0000000000000,1\nnp.float64,0x0000000000000001,0x3ff0000000000000,1\nnp.float64,0x0360000000000000,0x3ff0000000000000,1\nnp.float64,0x1a70000000000000,0x3ff0000000000000,1\nnp.float64,0x3c30000000000000,0x3ff0000000000000,1\nnp.float64,0x36a0000000000000,0x3ff0000000000000,1\nnp.float64,0x39b0000000000000,0x3ff0000000000000,1\nnp.float64,0x3810000000000000,0x3ff0000000000000,1\n## underflow ##\nnp.float64,0xc0c6276800000000,0x0000000000000000,1\nnp.float64,0xc0c62d918ce2421d,0x0000000000000000,1\nnp.float64,0xc0c62d918ce2421e,0x0000000000000000,1\nnp.float64,0xc0c62d91a0000000,0x0000000000000000,1\nnp.float64,0xc0c62d9180000000,0x0000000000000000,1\nnp.float64,0xc0c62dea45ee3e06,0x0000000000000000,1\nnp.float64,0xc0c62dea45ee3e07,0x0000000000000000,1\nnp.float64,0xc0c62dea40000000,0x0000000000000000,1\nnp.float64,0xc0c62dea60000000,0x0000000000000000,1\nnp.float64,0xc0875f1120000000,0x0000000000000000,1\nnp.float64,0xc0875f113c30b1c8,0x0000000000000000,1\nnp.float64,0xc0875f1140000000,0x0000000000000000,1\nnp.float64,0xc093480000000000,0x0000000000000000,1\nnp.float64,0xffefffffffffffff,0x0000000000000000,1\nnp.float64,0xc7efffffe0000000,0x0000000000000000,1\n## overflow ##\nnp.float64,0x40862e52fefa39ef,0x7ff0000000000000,1\nnp.float64,0x40872e42fefa39ef,0x7ff0000000000000,1\n## +/- INF, +/- NAN ##\nnp.float64,0x7ff0000000000000,0x7ff0000000000000,1\nnp.float64,0xfff0000000000000,0x0000000000000000,1\nnp.float64,0x7ff8000000000000,0x7ff8000000000000,1\nnp.float64,0xfff8000000000000,0xfff8000000000000,1\n## output denormal ##\nnp.float64,0xc087438520000000,0x0000000000000001,1\nnp.float64,0xc08743853f2f4461,0x0000000000000001,1\nnp.float64,0xc08743853f2f4460,0x0000000000000001,1\nnp.float64,0xc087438540000000,0x0000000000000001,1\n## between -745.13321910 and 709.78271289 ##\nnp.float64,0xbff760cd14774bd9,0x3fcdb14ced00ceb6,1\nnp.float64,0xbff760cd20000000,0x3fcdb14cd7993879,1\nnp.float64,0xbff760cd00000000,0x3fcdb14d12fbd264,1\nnp.float64,0xc07f1cf360000000,0x130c1b369af14fda,1\nnp.float64,0xbeb0000000000000,0x3feffffe00001000,1\nnp.float64,0xbd70000000000000,0x3fefffffffffe000,1\nnp.float64,0xc084fd46e5c84952,0x0360000000000139,1\nnp.float64,0xc084fd46e5c84953,0x035ffffffffffe71,1\nnp.float64,0xc084fd46e0000000,0x0360000b9096d32c,1\nnp.float64,0xc084fd4700000000,0x035fff9721d12104,1\nnp.float64,0xc086232bc0000000,0x0010003af5e64635,1\nnp.float64,0xc086232bdd7abcd2,0x001000000000007c,1\nnp.float64,0xc086232bdd7abcd3,0x000ffffffffffe7c,1\nnp.float64,0xc086232be0000000,0x000ffffaf57a6fc9,1\nnp.float64,0xc086233920000000,0x000fe590e3b45eb0,1\nnp.float64,0xc086233938000000,0x000fe56133493c57,1\nnp.float64,0xc086233940000000,0x000fe5514deffbbc,1\nnp.float64,0xc086234c98000000,0x000fbf1024c32ccb,1\nnp.float64,0xc086234ca0000000,0x000fbf0065bae78d,1\nnp.float64,0xc086234c80000000,0x000fbf3f623a7724,1\nnp.float64,0xc086234ec0000000,0x000fbad237c846f9,1\nnp.float64,0xc086234ec8000000,0x000fbac27cfdec97,1\nnp.float64,0xc086234ee0000000,0x000fba934cfd3dc2,1\nnp.float64,0xc086234ef0000000,0x000fba73d7f618d9,1\nnp.float64,0xc086234f00000000,0x000fba54632dddc0,1\nnp.float64,0xc0862356e0000000,0x000faae0945b761a,1\nnp.float64,0xc0862356f0000000,0x000faac13eb9a310,1\nnp.float64,0xc086235700000000,0x000faaa1e9567b0a,1\nnp.float64,0xc086236020000000,0x000f98cd75c11ed7,1\nnp.float64,0xc086236ca0000000,0x000f8081b4d93f89,1\nnp.float64,0xc086236cb0000000,0x000f8062b3f4d6c5,1\nnp.float64,0xc086236cc0000000,0x000f8043b34e6f8c,1\nnp.float64,0xc086238d98000000,0x000f41220d9b0d2c,1\nnp.float64,0xc086238da0000000,0x000f4112cc80a01f,1\nnp.float64,0xc086238d80000000,0x000f414fd145db5b,1\nnp.float64,0xc08624fd00000000,0x000cbfce8ea1e6c4,1\nnp.float64,0xc086256080000000,0x000c250747fcd46e,1\nnp.float64,0xc08626c480000000,0x000a34f4bd975193,1\nnp.float64,0xbf50000000000000,0x3feff800ffeaac00,1\nnp.float64,0xbe10000000000000,0x3fefffffff800000,1\nnp.float64,0xbcd0000000000000,0x3feffffffffffff8,1\nnp.float64,0xc055d589e0000000,0x38100004bf94f63e,1\nnp.float64,0xc055d58a00000000,0x380ffff97f292ce8,1\nnp.float64,0xbfd962d900000000,0x3fe585a4b00110e1,1\nnp.float64,0x3ff4bed280000000,0x400d411e7a58a303,1\nnp.float64,0x3fff0b3620000000,0x401bd7737ffffcf3,1\nnp.float64,0x3ff0000000000000,0x4005bf0a8b145769,1\nnp.float64,0x3eb0000000000000,0x3ff0000100000800,1\nnp.float64,0x3d70000000000000,0x3ff0000000001000,1\nnp.float64,0x40862e42e0000000,0x7fefff841808287f,1\nnp.float64,0x40862e42fefa39ef,0x7fefffffffffff2a,1\nnp.float64,0x40862e0000000000,0x7feef85a11e73f2d,1\nnp.float64,0x4000000000000000,0x401d8e64b8d4ddae,1\nnp.float64,0x4009242920000000,0x40372a52c383a488,1\nnp.float64,0x4049000000000000,0x44719103e4080b45,1\nnp.float64,0x4008000000000000,0x403415e5bf6fb106,1\nnp.float64,0x3f50000000000000,0x3ff00400800aab55,1\nnp.float64,0x3e10000000000000,0x3ff0000000400000,1\nnp.float64,0x3cd0000000000000,0x3ff0000000000004,1\nnp.float64,0x40562e40a0000000,0x47effed088821c3f,1\nnp.float64,0x40562e42e0000000,0x47effff082e6c7ff,1\nnp.float64,0x40562e4300000000,0x47f00000417184b8,1\nnp.float64,0x3fe8000000000000,0x4000ef9db467dcf8,1\nnp.float64,0x402b12e8d4f33589,0x412718f68c71a6fe,1\nnp.float64,0x402b12e8d4f3358a,0x412718f68c71a70a,1\nnp.float64,0x402b12e8c0000000,0x412718f59a7f472e,1\nnp.float64,0x402b12e8e0000000,0x412718f70c0eac62,1\n##use 1th entry\nnp.float64,0x40631659AE147CB4,0x4db3a95025a4890f,1\nnp.float64,0xC061B87D2E85A4E2,0x332640c8e2de2c51,1\nnp.float64,0x405A4A50BE243AF4,0x496a45e4b7f0339a,1\nnp.float64,0xC0839898B98EC5C6,0x0764027828830df4,1\n#use 2th entry\nnp.float64,0xC072428C44B6537C,0x2596ade838b96f3e,1\nnp.float64,0xC053057C5E1AE9BF,0x3912c8fad18fdadf,1\nnp.float64,0x407E89C78328BAA3,0x6bfe35d5b9a1a194,1\nnp.float64,0x4083501B6DD87112,0x77a855503a38924e,1\n#use 3th entry\nnp.float64,0x40832C6195F24540,0x7741e73c80e5eb2f,1\nnp.float64,0xC083D4CD557C2EC9,0x06b61727c2d2508e,1\nnp.float64,0x400C48F5F67C99BD,0x404128820f02b92e,1\nnp.float64,0x4056E36D9B2DF26A,0x4830f52ff34a8242,1\n#use 4th entry\nnp.float64,0x4080FF700D8CBD06,0x70fa70df9bc30f20,1\nnp.float64,0x406C276D39E53328,0x543eb8e20a8f4741,1\nnp.float64,0xC070D6159BBD8716,0x27a4a0548c904a75,1\nnp.float64,0xC052EBCF8ED61F83,0x391c0e92368d15e4,1\n#use 5th entry\nnp.float64,0xC061F892A8AC5FBE,0x32f807a89efd3869,1\nnp.float64,0x4021D885D2DBA085,0x40bd4dc86d3e3270,1\nnp.float64,0x40767AEEEE7D4FCF,0x605e22851ee2afb7,1\nnp.float64,0xC0757C5D75D08C80,0x20f0751599b992a2,1\n#use 6th entry\nnp.float64,0x405ACF7A284C4CE3,0x499a4e0b7a27027c,1\nnp.float64,0xC085A6C9E80D7AF5,0x0175914009d62ec2,1\nnp.float64,0xC07E4C02F86F1DAE,0x1439269b29a9231e,1\nnp.float64,0x4080D80F9691CC87,0x7088a6cdafb041de,1\n#use 7th entry\nnp.float64,0x407FDFD84FBA0AC1,0x6deb1ae6f9bc4767,1\nnp.float64,0x40630C06A1A2213D,0x4dac7a9d51a838b7,1\nnp.float64,0x40685FDB30BB8B4F,0x5183f5cc2cac9e79,1\nnp.float64,0x408045A2208F77F4,0x6ee299e08e2aa2f0,1\n#use 8th entry\nnp.float64,0xC08104E391F5078B,0x0ed397b7cbfbd230,1\nnp.float64,0xC031501CAEFAE395,0x3e6040fd1ea35085,1\nnp.float64,0xC079229124F6247C,0x1babf4f923306b1e,1\nnp.float64,0x407FB65F44600435,0x6db03beaf2512b8a,1\n#use 9th entry\nnp.float64,0xC07EDEE8E8E8A5AC,0x136536cec9cbef48,1\nnp.float64,0x4072BB4086099A14,0x5af4d3c3008b56cc,1\nnp.float64,0x4050442A2EC42CB4,0x45cd393bd8fad357,1\nnp.float64,0xC06AC28FB3D419B4,0x2ca1b9d3437df85f,1\n#use 10th entry\nnp.float64,0x40567FC6F0A68076,0x480c977fd5f3122e,1\nnp.float64,0x40620A2F7EDA59BB,0x4cf278e96f4ce4d7,1\nnp.float64,0xC085044707CD557C,0x034aad6c968a045a,1\nnp.float64,0xC07374EA5AC516AA,0x23dd6afdc03e83d5,1\n#use 11th entry\nnp.float64,0x4073CC95332619C1,0x5c804b1498bbaa54,1\nnp.float64,0xC0799FEBBE257F31,0x1af6a954c43b87d2,1\nnp.float64,0x408159F19EA424F6,0x7200858efcbfc84d,1\nnp.float64,0x404A81F6F24C0792,0x44b664a07ce5bbfa,1\n#use 12th entry\nnp.float64,0x40295FF1EFB9A741,0x4113c0e74c52d7b0,1\nnp.float64,0x4073975F4CC411DA,0x5c32be40b4fec2c1,1\nnp.float64,0x406E9DE52E82A77E,0x56049c9a3f1ae089,1\nnp.float64,0x40748C2F52560ED9,0x5d93bc14fd4cd23b,1\n#use 13th entry\nnp.float64,0x4062A553CDC4D04C,0x4d6266bfde301318,1\nnp.float64,0xC079EC1D63598AB7,0x1a88cb184dab224c,1\nnp.float64,0xC0725C1CB3167427,0x25725b46f8a081f6,1\nnp.float64,0x407888771D9B45F9,0x6353b1ec6bd7ce80,1\n#use 14th entry\nnp.float64,0xC082CBA03AA89807,0x09b383723831ce56,1\nnp.float64,0xC083A8961BB67DD7,0x0735b118d5275552,1\nnp.float64,0xC076BC6ECA12E7E3,0x1f2222679eaef615,1\nnp.float64,0xC072752503AA1A5B,0x254eb832242c77e1,1\n#use 15th entry\nnp.float64,0xC058800792125DEC,0x371882372a0b48d4,1\nnp.float64,0x4082909FD863E81C,0x7580d5f386920142,1\nnp.float64,0xC071616F8FB534F9,0x26dbe20ef64a412b,1\nnp.float64,0x406D1AB571CAA747,0x54ee0d55cb38ac20,1\n#use 16th entry\nnp.float64,0x406956428B7DAD09,0x52358682c271237f,1\nnp.float64,0xC07EFC2D9D17B621,0x133b3e77c27a4d45,1\nnp.float64,0xC08469BAC5BA3CCA,0x050863e5f42cc52f,1\nnp.float64,0x407189D9626386A5,0x593cb1c0b3b5c1d3,1\n#use 17th entry\nnp.float64,0x4077E652E3DEB8C6,0x6269a10dcbd3c752,1\nnp.float64,0x407674C97DB06878,0x605485dcc2426ec2,1\nnp.float64,0xC07CE9969CF4268D,0x16386cf8996669f2,1\nnp.float64,0x40780EE32D5847C4,0x62a436bd1abe108d,1\n#use 18th entry\nnp.float64,0x4076C3AA5E1E8DA1,0x60c62f56a5e72e24,1\nnp.float64,0xC0730AFC7239B9BE,0x24758ead095cec1e,1\nnp.float64,0xC085CC2B9C420DDB,0x0109cdaa2e5694c1,1\nnp.float64,0x406D0765CB6D7AA4,0x54e06f8dd91bd945,1\n#use 19th entry\nnp.float64,0xC082D011F3B495E7,0x09a6647661d279c2,1\nnp.float64,0xC072826AF8F6AFBC,0x253acd3cd224507e,1\nnp.float64,0x404EB9C4810CEA09,0x457933dbf07e8133,1\nnp.float64,0x408284FBC97C58CE,0x755f6eb234aa4b98,1\n#use 20th entry\nnp.float64,0x40856008CF6EDC63,0x7d9c0b3c03f4f73c,1\nnp.float64,0xC077CB2E9F013B17,0x1d9b3d3a166a55db,1\nnp.float64,0xC0479CA3C20AD057,0x3bad40e081555b99,1\nnp.float64,0x40844CD31107332A,0x7a821d70aea478e2,1\n#use 21th entry\nnp.float64,0xC07C8FCC0BFCC844,0x16ba1cc8c539d19b,1\nnp.float64,0xC085C4E9A3ABA488,0x011ff675ba1a2217,1\nnp.float64,0x4074D538B32966E5,0x5dfd9d78043c6ad9,1\nnp.float64,0xC0630CA16902AD46,0x3231a446074cede6,1\n#use 22th entry\nnp.float64,0xC06C826733D7D0B7,0x2b5f1078314d41e1,1\nnp.float64,0xC0520DF55B2B907F,0x396c13a6ce8e833e,1\nnp.float64,0xC080712072B0F437,0x107eae02d11d98ea,1\nnp.float64,0x40528A6150E19EFB,0x469fdabda02228c5,1\n#use 23th entry\nnp.float64,0xC07B1D74B6586451,0x18d1253883ae3b48,1\nnp.float64,0x4045AFD7867DAEC0,0x43d7d634fc4c5d98,1\nnp.float64,0xC07A08B91F9ED3E2,0x1a60973e6397fc37,1\nnp.float64,0x407B3ECF0AE21C8C,0x673e03e9d98d7235,1\n#use 24th entry\nnp.float64,0xC078AEB6F30CEABF,0x1c530b93ab54a1b3,1\nnp.float64,0x4084495006A41672,0x7a775b6dc7e63064,1\nnp.float64,0x40830B1C0EBF95DD,0x76e1e6eed77cfb89,1\nnp.float64,0x407D93E8F33D8470,0x6a9adbc9e1e4f1e5,1\n#use 25th entry\nnp.float64,0x4066B11A09EFD9E8,0x504dd528065c28a7,1\nnp.float64,0x408545823723AEEB,0x7d504a9b1844f594,1\nnp.float64,0xC068C711F2CA3362,0x2e104f3496ea118e,1\nnp.float64,0x407F317FCC3CA873,0x6cf0732c9948ebf4,1\n#use 26th entry\nnp.float64,0x407AFB3EBA2ED50F,0x66dc28a129c868d5,1\nnp.float64,0xC075377037708ADE,0x21531a329f3d793e,1\nnp.float64,0xC07C30066A1F3246,0x174448baa16ded2b,1\nnp.float64,0xC06689A75DE2ABD3,0x2fad70662fae230b,1\n#use 27th entry\nnp.float64,0x4081514E9FCCF1E0,0x71e673b9efd15f44,1\nnp.float64,0xC0762C710AF68460,0x1ff1ed7d8947fe43,1\nnp.float64,0xC0468102FF70D9C4,0x3be0c3a8ff3419a3,1\nnp.float64,0xC07EA4CEEF02A83E,0x13b908f085102c61,1\n#use 28th entry\nnp.float64,0xC06290B04AE823C4,0x328a83da3c2e3351,1\nnp.float64,0xC0770EB1D1C395FB,0x1eab281c1f1db5fe,1\nnp.float64,0xC06F5D4D838A5BAE,0x29500ea32fb474ea,1\nnp.float64,0x40723B3133B54C5D,0x5a3c82c7c3a2b848,1\n#use 29th entry\nnp.float64,0x4085E6454CE3B4AA,0x7f20319b9638d06a,1\nnp.float64,0x408389F2A0585D4B,0x7850667c58aab3d0,1\nnp.float64,0xC0382798F9C8AE69,0x3dc1c79fe8739d6d,1\nnp.float64,0xC08299D827608418,0x0a4335f76cdbaeb5,1\n#use 30th entry\nnp.float64,0xC06F3DED43301BF1,0x2965670ae46750a8,1\nnp.float64,0xC070CAF6BDD577D9,0x27b4aa4ffdd29981,1\nnp.float64,0x4078529AD4B2D9F2,0x6305c12755d5e0a6,1\nnp.float64,0xC055B14E75A31B96,0x381c2eda6d111e5d,1\n#use 31th entry\nnp.float64,0x407B13EE414FA931,0x6700772c7544564d,1\nnp.float64,0x407EAFDE9DE3EC54,0x6c346a0e49724a3c,1\nnp.float64,0xC08362F398B9530D,0x07ffeddbadf980cb,1\nnp.float64,0x407E865CDD9EEB86,0x6bf866cac5e0d126,1\n#use 32th entry\nnp.float64,0x407FB62DBC794C86,0x6db009f708ac62cb,1\nnp.float64,0xC063D0BAA68CDDDE,0x31a3b2a51ce50430,1\nnp.float64,0xC05E7706A2231394,0x34f24bead6fab5c9,1\nnp.float64,0x4083E3A06FDE444E,0x79527b7a386d1937,1\n | .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-exp.csv | umath-validation-set-exp.csv | Other | 17,903 | 0.8 | 0 | 0.106796 | node-utils | 333 | 2025-05-28T08:33:51.381002 | Apache-2.0 | true | 839bf4ded6d4747b0dfe1ecc68282bf5 |
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| .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-exp2.csv | umath-validation-set-exp2.csv | Other | 60,053 | 0.5 | 0 | 0 | node-utils | 487 | 2024-09-23T09:41:02.594128 | MIT | true | 6d37c52e68d9f590fef8104ca1656d84 |
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| .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-expm1.csv | umath-validation-set-expm1.csv | Other | 61,728 | 0.5 | 0 | 0 | awesome-app | 54 | 2024-07-19T00:24:26.168347 | GPL-3.0 | true | cb326ba0da4387ce7e2c6d5b539f8b1b |
dtype,input,output,ulperrortol\n## +ve denormals ##\nnp.float32,0x004b4716,0xc2afbc1b,4\nnp.float32,0x007b2490,0xc2aec01e,4\nnp.float32,0x007c99fa,0xc2aeba17,4\nnp.float32,0x00734a0c,0xc2aee1dc,4\nnp.float32,0x0070de24,0xc2aeecba,4\nnp.float32,0x007fffff,0xc2aeac50,4\nnp.float32,0x00000001,0xc2ce8ed0,4\n## -ve denormals ##\nnp.float32,0x80495d65,0xffc00000,4\nnp.float32,0x806894f6,0xffc00000,4\nnp.float32,0x80555a76,0xffc00000,4\nnp.float32,0x804e1fb8,0xffc00000,4\nnp.float32,0x80687de9,0xffc00000,4\nnp.float32,0x807fffff,0xffc00000,4\nnp.float32,0x80000001,0xffc00000,4\n## +/-0.0f, +/-FLT_MIN +/-FLT_MAX ##\nnp.float32,0x00000000,0xff800000,4\nnp.float32,0x80000000,0xff800000,4\nnp.float32,0x7f7fffff,0x42b17218,4\nnp.float32,0x80800000,0xffc00000,4\nnp.float32,0xff7fffff,0xffc00000,4\n## 1.00f + 0x00000001 ##\nnp.float32,0x3f800000,0x00000000,4\nnp.float32,0x3f800001,0x33ffffff,4\nnp.float32,0x3f800002,0x347ffffe,4\nnp.float32,0x3f7fffff,0xb3800000,4\nnp.float32,0x3f7ffffe,0xb4000000,4\nnp.float32,0x3f7ffffd,0xb4400001,4\nnp.float32,0x402df853,0x3f7ffffe,4\nnp.float32,0x402df854,0x3f7fffff,4\nnp.float32,0x402df855,0x3f800000,4\nnp.float32,0x402df856,0x3f800001,4\nnp.float32,0x3ebc5ab0,0xbf800001,4\nnp.float32,0x3ebc5ab1,0xbf800000,4\nnp.float32,0x3ebc5ab2,0xbf800000,4\nnp.float32,0x3ebc5ab3,0xbf7ffffe,4\nnp.float32,0x423ef575,0x407768ab,4\nnp.float32,0x427b8c61,0x408485dd,4\nnp.float32,0x4211e9ee,0x406630b0,4\nnp.float32,0x424d5c41,0x407c0fed,4\nnp.float32,0x42be722a,0x4091cc91,4\nnp.float32,0x42b73d30,0x4090908b,4\nnp.float32,0x427e48e2,0x4084de7f,4\nnp.float32,0x428f759b,0x4088bba3,4\nnp.float32,0x41629069,0x4029a0cc,4\nnp.float32,0x4272c99d,0x40836379,4\nnp.float32,0x4d1b7458,0x4197463d,4\nnp.float32,0x4f10c594,0x41ace2b2,4\nnp.float32,0x4ea397c2,0x41a85171,4\nnp.float32,0x4fefa9d1,0x41b6769c,4\nnp.float32,0x4ebac6ab,0x41a960dc,4\nnp.float32,0x4f6efb42,0x41b0e535,4\nnp.float32,0x4e9ab8e7,0x41a7df44,4\nnp.float32,0x4e81b5d1,0x41a67625,4\nnp.float32,0x5014d9f2,0x41b832bd,4\nnp.float32,0x4f02175c,0x41ac07b8,4\nnp.float32,0x7f034f89,0x42b01c47,4\nnp.float32,0x7f56d00e,0x42b11849,4\nnp.float32,0x7f1cd5f6,0x42b0773a,4\nnp.float32,0x7e979174,0x42af02d7,4\nnp.float32,0x7f23369f,0x42b08ba2,4\nnp.float32,0x7f0637ae,0x42b0277d,4\nnp.float32,0x7efcb6e8,0x42b00897,4\nnp.float32,0x7f7907c8,0x42b163f6,4\nnp.float32,0x7e95c4c2,0x42aefcba,4\nnp.float32,0x7f4577b2,0x42b0ed2d,4\nnp.float32,0x3f49c92e,0xbe73ae84,4\nnp.float32,0x3f4a23d1,0xbe71e2f8,4\nnp.float32,0x3f4abb67,0xbe6ee430,4\nnp.float32,0x3f48169a,0xbe7c5532,4\nnp.float32,0x3f47f5fa,0xbe7cfc37,4\nnp.float32,0x3f488309,0xbe7a2ad8,4\nnp.float32,0x3f479df4,0xbe7ebf5f,4\nnp.float32,0x3f47cfff,0xbe7dbec9,4\nnp.float32,0x3f496704,0xbe75a125,4\nnp.float32,0x3f478ee8,0xbe7f0c92,4\nnp.float32,0x3f4a763b,0xbe7041ce,4\nnp.float32,0x3f47a108,0xbe7eaf94,4\nnp.float32,0x3f48136c,0xbe7c6578,4\nnp.float32,0x3f481c17,0xbe7c391c,4\nnp.float32,0x3f47cd28,0xbe7dcd56,4\nnp.float32,0x3f478be8,0xbe7f1bf7,4\nnp.float32,0x3f4c1f8e,0xbe67e367,4\nnp.float32,0x3f489b0c,0xbe79b03f,4\nnp.float32,0x3f4934cf,0xbe76a08a,4\nnp.float32,0x3f4954df,0xbe75fd6a,4\nnp.float32,0x3f47a3f5,0xbe7ea093,4\nnp.float32,0x3f4ba4fc,0xbe6a4b02,4\nnp.float32,0x3f47a0e1,0xbe7eb05c,4\nnp.float32,0x3f48c30a,0xbe78e42f,4\nnp.float32,0x3f48cab8,0xbe78bd05,4\nnp.float32,0x3f4b0569,0xbe6d6ea4,4\nnp.float32,0x3f47de32,0xbe7d7607,4\nnp.float32,0x3f477328,0xbe7f9b00,4\nnp.float32,0x3f496dab,0xbe757f52,4\nnp.float32,0x3f47662c,0xbe7fddac,4\nnp.float32,0x3f48ddd8,0xbe785b80,4\nnp.float32,0x3f481866,0xbe7c4bff,4\nnp.float32,0x3f48b119,0xbe793fb6,4\nnp.float32,0x3f48c7e8,0xbe78cb5c,4\nnp.float32,0x3f4985f6,0xbe7503da,4\nnp.float32,0x3f483fdf,0xbe7b8212,4\nnp.float32,0x3f4b1c76,0xbe6cfa67,4\nnp.float32,0x3f480b2e,0xbe7c8fa8,4\nnp.float32,0x3f48745f,0xbe7a75bf,4\nnp.float32,0x3f485bda,0xbe7af308,4\nnp.float32,0x3f47a660,0xbe7e942c,4\nnp.float32,0x3f47d4d5,0xbe7da600,4\nnp.float32,0x3f4b0a26,0xbe6d56be,4\nnp.float32,0x3f4a4883,0xbe712924,4\nnp.float32,0x3f4769e7,0xbe7fca84,4\nnp.float32,0x3f499702,0xbe74ad3f,4\nnp.float32,0x3f494ab1,0xbe763131,4\nnp.float32,0x3f476b69,0xbe7fc2c6,4\nnp.float32,0x3f4884e8,0xbe7a214a,4\nnp.float32,0x3f486945,0xbe7aae76,4\n#float64\n## +ve denormal ##\nnp.float64,0x0000000000000001,0xc0874385446d71c3,1\nnp.float64,0x0001000000000000,0xc086395a2079b70c,1\nnp.float64,0x000fffffffffffff,0xc086232bdd7abcd2,1\nnp.float64,0x0007ad63e2168cb6,0xc086290bc0b2980f,1\n## -ve denormal ##\nnp.float64,0x8000000000000001,0xfff8000000000001,1\nnp.float64,0x8001000000000000,0xfff8000000000001,1\nnp.float64,0x800fffffffffffff,0xfff8000000000001,1\nnp.float64,0x8007ad63e2168cb6,0xfff8000000000001,1\n## +/-0.0f, MAX, MIN##\nnp.float64,0x0000000000000000,0xfff0000000000000,1\nnp.float64,0x8000000000000000,0xfff0000000000000,1\nnp.float64,0x7fefffffffffffff,0x40862e42fefa39ef,1\nnp.float64,0xffefffffffffffff,0xfff8000000000001,1\n## near 1.0f ##\nnp.float64,0x3ff0000000000000,0x0000000000000000,1\nnp.float64,0x3fe8000000000000,0xbfd269621134db92,1\nnp.float64,0x3ff0000000000001,0x3cafffffffffffff,1\nnp.float64,0x3ff0000020000000,0x3e7fffffe000002b,1\nnp.float64,0x3ff0000000000001,0x3cafffffffffffff,1\nnp.float64,0x3fefffffe0000000,0xbe70000008000005,1\nnp.float64,0x3fefffffffffffff,0xbca0000000000000,1\n## random numbers ##\nnp.float64,0x02500186f3d9da56,0xc0855b8abf135773,1\nnp.float64,0x09200815a3951173,0xc082ff1ad7131bdc,1\nnp.float64,0x0da029623b0243d4,0xc0816fc994695bb5,1\nnp.float64,0x48703b8ac483a382,0x40579213a313490b,1\nnp.float64,0x09207b74c87c9860,0xc082fee20ff349ef,1\nnp.float64,0x62c077698e8df947,0x407821c996d110f0,1\nnp.float64,0x2350b45e87c3cfb0,0xc073d6b16b51d072,1\nnp.float64,0x3990a23f9ff2b623,0xc051aa60eadd8c61,1\nnp.float64,0x0d011386a116c348,0xc081a6cc7ea3b8fb,1\nnp.float64,0x1fe0f0303ebe273a,0xc0763870b78a81ca,1\nnp.float64,0x0cd1260121d387da,0xc081b7668d61a9d1,1\nnp.float64,0x1e6135a8f581d422,0xc077425ac10f08c2,1\nnp.float64,0x622168db5fe52d30,0x4077b3c669b9fadb,1\nnp.float64,0x69f188e1ec6d1718,0x407d1e2f18c63889,1\nnp.float64,0x3aa1bf1d9c4dd1a3,0xc04d682e24bde479,1\nnp.float64,0x6c81c4011ce4f683,0x407ee5190e8a8e6a,1\nnp.float64,0x2191fa55aa5a5095,0xc0750c0c318b5e2d,1\nnp.float64,0x32a1f602a32bf360,0xc06270caa493fc17,1\nnp.float64,0x16023c90ba93249b,0xc07d0f88e0801638,1\nnp.float64,0x1c525fe6d71fa9ff,0xc078af49c66a5d63,1\nnp.float64,0x1a927675815d65b7,0xc079e5bdd7fe376e,1\nnp.float64,0x41227b8fe70da028,0x402aa0c9f9a84c71,1\nnp.float64,0x4962bb6e853fe87d,0x405a34aa04c83747,1\nnp.float64,0x23d2cda00b26b5a4,0xc0737c13a06d00ea,1\nnp.float64,0x2d13083fd62987fa,0xc06a25055aeb474e,1\nnp.float64,0x10e31e4c9b4579a1,0xc0804e181929418e,1\nnp.float64,0x26d3247d556a86a9,0xc0716774171da7e8,1\nnp.float64,0x6603379398d0d4ac,0x407a64f51f8a887b,1\nnp.float64,0x02d38af17d9442ba,0xc0852d955ac9dd68,1\nnp.float64,0x6a2382b4818dd967,0x407d4129d688e5d4,1\nnp.float64,0x2ee3c403c79b3934,0xc067a091fefaf8b6,1\nnp.float64,0x6493a699acdbf1a4,0x4079663c8602bfc5,1\nnp.float64,0x1c8413c4f0de3100,0xc0788c99697059b6,1\nnp.float64,0x4573f1ed350d9622,0x404e9bd1e4c08920,1\nnp.float64,0x2f34265c9200b69c,0xc067310cfea4e986,1\nnp.float64,0x19b43e65fa22029b,0xc07a7f8877de22d6,1\nnp.float64,0x0af48ab7925ed6bc,0xc0825c4fbc0e5ade,1\nnp.float64,0x4fa49699cad82542,0x4065c76d2a318235,1\nnp.float64,0x7204a15e56ade492,0x40815bb87484dffb,1\nnp.float64,0x4734aa08a230982d,0x40542a4bf7a361a9,1\nnp.float64,0x1ae4ed296c2fd749,0xc079ac4921f20abb,1\nnp.float64,0x472514ea4370289c,0x4053ff372bd8f18f,1\nnp.float64,0x53a54b3f73820430,0x406b5411fc5f2e33,1\nnp.float64,0x64754de5a15684fa,0x407951592e99a5ab,1\nnp.float64,0x69358e279868a7c3,0x407c9c671a882c31,1\nnp.float64,0x284579ec61215945,0xc0706688e55f0927,1\nnp.float64,0x68b5c58806447adc,0x407c43d6f4eff760,1\nnp.float64,0x1945a83f98b0e65d,0xc07acc15eeb032cc,1\nnp.float64,0x0fc5eb98a16578bf,0xc080b0d02eddca0e,1\nnp.float64,0x6a75e208f5784250,0x407d7a7383bf8f05,1\nnp.float64,0x0fe63a029c47645d,0xc080a59ca1e98866,1\nnp.float64,0x37963ac53f065510,0xc057236281f7bdb6,1\nnp.float64,0x135661bb07067ff7,0xc07ee924930c21e4,1\nnp.float64,0x4b4699469d458422,0x405f73843756e887,1\nnp.float64,0x1a66d73e4bf4881b,0xc07a039ba1c63adf,1\nnp.float64,0x12a6b9b119a7da59,0xc07f62e49c6431f3,1\nnp.float64,0x24c719aa8fd1bdb5,0xc072d26da4bf84d3,1\nnp.float64,0x0fa6ff524ffef314,0xc080bb8514662e77,1\nnp.float64,0x1db751d66fdd4a9a,0xc077b77cb50d7c92,1\nnp.float64,0x4947374c516da82c,0x4059e9acfc7105bf,1\nnp.float64,0x1b1771ab98f3afc8,0xc07989326b8e1f66,1\nnp.float64,0x25e78805baac8070,0xc0720a818e6ef080,1\nnp.float64,0x4bd7a148225d3687,0x406082d004ea3ee7,1\nnp.float64,0x53d7d6b2bbbda00a,0x406b9a398967cbd5,1\nnp.float64,0x6997fb9f4e1c685f,0x407ce0a703413eba,1\nnp.float64,0x069802c2ff71b951,0xc083df39bf7acddc,1\nnp.float64,0x4d683ac9890f66d8,0x4062ae21d8c2acf0,1\nnp.float64,0x5a2825863ec14f4c,0x40722d718d549552,1\nnp.float64,0x0398799a88f4db80,0xc084e93dab8e2158,1\nnp.float64,0x5ed87a8b77e135a5,0x40756d7051777b33,1\nnp.float64,0x5828cd6d79b9bede,0x4070cafb22fc6ca1,1\nnp.float64,0x7b18ba2a5ec6f068,0x408481386b3ed6fe,1\nnp.float64,0x4938fd60922198fe,0x4059c206b762ea7e,1\nnp.float64,0x31b8f44fcdd1a46e,0xc063b2faa8b6434e,1\nnp.float64,0x5729341c0d918464,0x407019cac0c4a7d7,1\nnp.float64,0x13595e9228ee878e,0xc07ee7235a7d8088,1\nnp.float64,0x17698b0dc9dd4135,0xc07c1627e3a5ad5f,1\nnp.float64,0x63b977c283abb0cc,0x4078cf1ec6ed65be,1\nnp.float64,0x7349cc0d4dc16943,0x4081cc697ce4cb53,1\nnp.float64,0x4e49a80b732fb28d,0x4063e67e3c5cbe90,1\nnp.float64,0x07ba14b848a8ae02,0xc0837ac032a094e0,1\nnp.float64,0x3da9f17b691bfddc,0xc03929c25366acda,1\nnp.float64,0x02ea39aa6c3ac007,0xc08525af6f21e1c4,1\nnp.float64,0x3a6a42f04ed9563d,0xc04e98e825dca46b,1\nnp.float64,0x1afa877cd7900be7,0xc0799d6648cb34a9,1\nnp.float64,0x58ea986649e052c6,0x4071512e939ad790,1\nnp.float64,0x691abbc04647f536,0x407c89aaae0fcb83,1\nnp.float64,0x43aabc5063e6f284,0x4044b45d18106fd2,1\nnp.float64,0x488b003c893e0bea,0x4057df012a2dafbe,1\nnp.float64,0x77eb076ed67caee5,0x40836720de94769e,1\nnp.float64,0x5c1b46974aba46f4,0x40738731ba256007,1\nnp.float64,0x1a5b29ecb5d3c261,0xc07a0becc77040d6,1\nnp.float64,0x5d8b6ccf868c6032,0x4074865c1865e2db,1\nnp.float64,0x4cfb6690b4aaf5af,0x406216cd8c7e8ddb,1\nnp.float64,0x76cbd8eb5c5fc39e,0x4083038dc66d682b,1\nnp.float64,0x28bbd1fec5012814,0xc07014c2dd1b9711,1\nnp.f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| .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-log.csv | umath-validation-set-log.csv | Other | 11,963 | 0.8 | 0 | 0.0369 | vue-tools | 505 | 2023-12-27T14:50:51.095218 | Apache-2.0 | true | ff9012683118a838aa14d2145e62fcee |
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| .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-log10.csv | umath-validation-set-log10.csv | Other | 70,551 | 0.5 | 0 | 0 | awesome-app | 137 | 2024-09-01T16:19:31.438315 | MIT | true | 4cb030776907ced376d8cf0d1c5071ab |
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| .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-log1p.csv | umath-validation-set-log1p.csv | Other | 61,732 | 0.5 | 0 | 0 | vue-tools | 628 | 2024-07-30T20:21:53.218412 | BSD-3-Clause | true | 7bdefc7b6e128eeb4b0710889977ac47 |
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| .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-log2.csv | umath-validation-set-log2.csv | Other | 70,546 | 0.5 | 0 | 0 | awesome-app | 933 | 2024-04-07T15:49:26.056812 | MIT | true | 8404b9dfb20ebd0472af456699ca0016 |
Steps to validate transcendental functions:\n1) Add a file 'umath-validation-set-<ufuncname>.txt', where ufuncname is name of\n the function in NumPy you want to validate\n2) The file should contain 4 columns: dtype,input,expected output,ulperror\n a. dtype: one of np.float16, np.float32, np.float64\n b. input: floating point input to ufunc in hex. Example: 0x414570a4\n represents 12.340000152587890625\n c. expected output: floating point output for the corresponding input in hex.\n This should be computed using a high(er) precision library and then rounded to\n same format as the input.\n d. ulperror: expected maximum ulp error of the function. This\n should be same across all rows of the same dtype. Otherwise, the function is\n tested for the maximum ulp error among all entries of that dtype.\n3) Add file umath-validation-set-<ufuncname>.txt to the test file test_umath_accuracy.py\n which will then validate your ufunc.\n | .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-README.txt | umath-validation-set-README.txt | Other | 982 | 0.85 | 0.333333 | 0 | node-utils | 323 | 2023-11-22T11:40:28.905594 | Apache-2.0 | true | fd7efb77a74e2d8bb17c2866324fd1fd |
dtype,input,output,ulperrortol\n## +ve denormals ##\nnp.float32,0x004b4716,0x004b4716,2\nnp.float32,0x007b2490,0x007b2490,2\nnp.float32,0x007c99fa,0x007c99fa,2\nnp.float32,0x00734a0c,0x00734a0c,2\nnp.float32,0x0070de24,0x0070de24,2\nnp.float32,0x007fffff,0x007fffff,2\nnp.float32,0x00000001,0x00000001,2\n## -ve denormals ##\nnp.float32,0x80495d65,0x80495d65,2\nnp.float32,0x806894f6,0x806894f6,2\nnp.float32,0x80555a76,0x80555a76,2\nnp.float32,0x804e1fb8,0x804e1fb8,2\nnp.float32,0x80687de9,0x80687de9,2\nnp.float32,0x807fffff,0x807fffff,2\nnp.float32,0x80000001,0x80000001,2\n## +/-0.0f, +/-FLT_MIN +/-FLT_MAX ##\nnp.float32,0x00000000,0x00000000,2\nnp.float32,0x80000000,0x80000000,2\nnp.float32,0x00800000,0x00800000,2\nnp.float32,0x80800000,0x80800000,2\n## 1.00f ##\nnp.float32,0x3f800000,0x3f576aa4,2\nnp.float32,0x3f800001,0x3f576aa6,2\nnp.float32,0x3f800002,0x3f576aa7,2\nnp.float32,0xc090a8b0,0x3f7b4e48,2\nnp.float32,0x41ce3184,0x3f192d43,2\nnp.float32,0xc1d85848,0xbf7161cb,2\nnp.float32,0x402b8820,0x3ee3f29f,2\nnp.float32,0x42b4e454,0x3f1d0151,2\nnp.float32,0x42a67a60,0x3f7ffa4c,2\nnp.float32,0x41d92388,0x3f67beef,2\nnp.float32,0x422dd66c,0xbeffb0c1,2\nnp.float32,0xc28f5be6,0xbf0bae79,2\nnp.float32,0x41ab2674,0x3f0ffe2b,2\nnp.float32,0x3f490fdb,0x3f3504f3,2\nnp.float32,0xbf490fdb,0xbf3504f3,2\nnp.float32,0x3fc90fdb,0x3f800000,2\nnp.float32,0xbfc90fdb,0xbf800000,2\nnp.float32,0x40490fdb,0xb3bbbd2e,2\nnp.float32,0xc0490fdb,0x33bbbd2e,2\nnp.float32,0x3fc90fdb,0x3f800000,2\nnp.float32,0xbfc90fdb,0xbf800000,2\nnp.float32,0x40490fdb,0xb3bbbd2e,2\nnp.float32,0xc0490fdb,0x33bbbd2e,2\nnp.float32,0x40c90fdb,0x343bbd2e,2\nnp.float32,0xc0c90fdb,0xb43bbd2e,2\nnp.float32,0x4016cbe4,0x3f3504f3,2\nnp.float32,0xc016cbe4,0xbf3504f3,2\nnp.float32,0x4096cbe4,0xbf800000,2\nnp.float32,0xc096cbe4,0x3f800000,2\nnp.float32,0x4116cbe4,0xb2ccde2e,2\nnp.float32,0xc116cbe4,0x32ccde2e,2\nnp.float32,0x40490fdb,0xb3bbbd2e,2\nnp.float32,0xc0490fdb,0x33bbbd2e,2\nnp.float32,0x40c90fdb,0x343bbd2e,2\nnp.float32,0xc0c90fdb,0xb43bbd2e,2\nnp.float32,0x41490fdb,0x34bbbd2e,2\nnp.float32,0xc1490fdb,0xb4bbbd2e,2\nnp.float32,0x407b53d2,0xbf3504f5,2\nnp.float32,0xc07b53d2,0x3f3504f5,2\nnp.float32,0x40fb53d2,0x3f800000,2\nnp.float32,0xc0fb53d2,0xbf800000,2\nnp.float32,0x417b53d2,0xb535563d,2\nnp.float32,0xc17b53d2,0x3535563d,2\nnp.float32,0x4096cbe4,0xbf800000,2\nnp.float32,0xc096cbe4,0x3f800000,2\nnp.float32,0x4116cbe4,0xb2ccde2e,2\nnp.float32,0xc116cbe4,0x32ccde2e,2\nnp.float32,0x4196cbe4,0x334cde2e,2\nnp.float32,0xc196cbe4,0xb34cde2e,2\nnp.float32,0x40afede0,0xbf3504ef,2\nnp.float32,0xc0afede0,0x3f3504ef,2\nnp.float32,0x412fede0,0xbf800000,2\nnp.float32,0xc12fede0,0x3f800000,2\nnp.float32,0x41afede0,0xb5b222c4,2\nnp.float32,0xc1afede0,0x35b222c4,2\nnp.float32,0x40c90fdb,0x343bbd2e,2\nnp.float32,0xc0c90fdb,0xb43bbd2e,2\nnp.float32,0x41490fdb,0x34bbbd2e,2\nnp.float32,0xc1490fdb,0xb4bbbd2e,2\nnp.float32,0x41c90fdb,0x353bbd2e,2\nnp.float32,0xc1c90fdb,0xb53bbd2e,2\nnp.float32,0x40e231d6,0x3f3504f3,2\nnp.float32,0xc0e231d6,0xbf3504f3,2\nnp.float32,0x416231d6,0x3f800000,2\nnp.float32,0xc16231d6,0xbf800000,2\nnp.float32,0x41e231d6,0xb399a6a2,2\nnp.float32,0xc1e231d6,0x3399a6a2,2\nnp.float32,0x40fb53d2,0x3f800000,2\nnp.float32,0xc0fb53d2,0xbf800000,2\nnp.float32,0x417b53d2,0xb535563d,2\nnp.float32,0xc17b53d2,0x3535563d,2\nnp.float32,0x41fb53d2,0x35b5563d,2\nnp.float32,0xc1fb53d2,0xb5b5563d,2\nnp.float32,0x410a3ae7,0x3f3504eb,2\nnp.float32,0xc10a3ae7,0xbf3504eb,2\nnp.float32,0x418a3ae7,0xbf800000,2\nnp.float32,0xc18a3ae7,0x3f800000,2\nnp.float32,0x420a3ae7,0xb6308908,2\nnp.float32,0xc20a3ae7,0x36308908,2\nnp.float32,0x4116cbe4,0xb2ccde2e,2\nnp.float32,0xc116cbe4,0x32ccde2e,2\nnp.float32,0x4196cbe4,0x334cde2e,2\nnp.float32,0xc196cbe4,0xb34cde2e,2\nnp.float32,0x4216cbe4,0x33ccde2e,2\nnp.float32,0xc216cbe4,0xb3ccde2e,2\nnp.float32,0x41235ce2,0xbf3504f7,2\nnp.float32,0xc1235ce2,0x3f3504f7,2\nnp.float32,0x41a35ce2,0x3f800000,2\nnp.float32,0xc1a35ce2,0xbf800000,2\nnp.float32,0x42235ce2,0xb5b889b6,2\nnp.float32,0xc2235ce2,0x35b889b6,2\nnp.float32,0x412fede0,0xbf800000,2\nnp.float32,0xc12fede0,0x3f800000,2\nnp.float32,0x41afede0,0xb5b222c4,2\nnp.float32,0xc1afede0,0x35b222c4,2\nnp.float32,0x422fede0,0x363222c4,2\nnp.float32,0xc22fede0,0xb63222c4,2\nnp.float32,0x413c7edd,0xbf3504f3,2\nnp.float32,0xc13c7edd,0x3f3504f3,2\nnp.float32,0x41bc7edd,0xbf800000,2\nnp.float32,0xc1bc7edd,0x3f800000,2\nnp.float32,0x423c7edd,0xb4000add,2\nnp.float32,0xc23c7edd,0x34000add,2\nnp.float32,0x41490fdb,0x34bbbd2e,2\nnp.float32,0xc1490fdb,0xb4bbbd2e,2\nnp.float32,0x41c90fdb,0x353bbd2e,2\nnp.float32,0xc1c90fdb,0xb53bbd2e,2\nnp.float32,0x42490fdb,0x35bbbd2e,2\nnp.float32,0xc2490fdb,0xb5bbbd2e,2\nnp.float32,0x4155a0d9,0x3f3504fb,2\nnp.float32,0xc155a0d9,0xbf3504fb,2\nnp.float32,0x41d5a0d9,0x3f800000,2\nnp.float32,0xc1d5a0d9,0xbf800000,2\nnp.float32,0x4255a0d9,0xb633bc81,2\nnp.float32,0xc255a0d9,0x3633bc81,2\nnp.float32,0x416231d6,0x3f800000,2\nnp.float32,0xc16231d6,0xbf800000,2\nnp.float32,0x41e231d6,0xb399a6a2,2\nnp.float32,0xc1e231d6,0x3399a6a2,2\nnp.float32,0x426231d6,0x3419a6a2,2\nnp.float32,0xc26231d6,0xb419a6a2,2\nnp.float32,0x416ec2d4,0x3f3504ef,2\nnp.float32,0xc16ec2d4,0xbf3504ef,2\nnp.float32,0x41eec2d4,0xbf800000,2\nnp.float32,0xc1eec2d4,0x3f800000,2\nnp.float32,0x426ec2d4,0xb5bef0a7,2\nnp.float32,0xc26ec2d4,0x35bef0a7,2\nnp.float32,0x417b53d2,0xb535563d,2\nnp.float32,0xc17b53d2,0x3535563d,2\nnp.float32,0x41fb53d2,0x35b5563d,2\nnp.float32,0xc1fb53d2,0xb5b5563d,2\nnp.float32,0x427b53d2,0x3635563d,2\nnp.float32,0xc27b53d2,0xb635563d,2\nnp.float32,0x4183f268,0xbf3504ff,2\nnp.float32,0xc183f268,0x3f3504ff,2\nnp.float32,0x4203f268,0x3f800000,2\nnp.float32,0xc203f268,0xbf800000,2\nnp.float32,0x4283f268,0xb6859a13,2\nnp.float32,0xc283f268,0x3685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| .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-sin.csv | umath-validation-set-sin.csv | Other | 59,981 | 0.6 | 0 | 0.00292 | node-utils | 102 | 2024-03-03T23:39:26.030639 | Apache-2.0 | true | c70380b08736b821b046ea7bbd53f36a |
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| .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-sinh.csv | umath-validation-set-sinh.csv | Other | 61,722 | 0.5 | 0 | 0 | python-kit | 802 | 2024-12-06T02:10:37.425002 | Apache-2.0 | true | be13dc7172f984c9f2b1b7077bda69f6 |
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| .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-tan.csv | umath-validation-set-tan.csv | Other | 61,728 | 0.5 | 0 | 0 | node-utils | 7 | 2025-05-24T16:17:58.895219 | MIT | true | 62860199c86c5f81a1b3fd7c81e3cf27 |
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| .venv\Lib\site-packages\numpy\_core\tests\data\umath-validation-set-tanh.csv | umath-validation-set-tanh.csv | Other | 61,728 | 0.5 | 0 | 0 | python-kit | 849 | 2025-04-24T03:32:24.632153 | BSD-3-Clause | true | 525820cea48d1e4bd028614da460fbb3 |
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