1
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#define PY_SSIZE_T_CLEAN
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2
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#include <Python.h>
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3
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#include "structmember.h"
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4
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5
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#define NPY_NO_DEPRECATED_API NPY_API_VERSION
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6
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#define _MULTIARRAYMODULE
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7
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#include "numpy/arrayobject.h"
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8
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#include "numpy/arrayscalars.h"
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9
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10
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#include "numpy/npy_math.h"
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11
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12
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#include "npy_config.h"
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13
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14
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#include "npy_ctypes.h"
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#include "npy_pycompat.h"
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16
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#include "multiarraymodule.h"
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17
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18
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#include "common.h"
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19
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#include "ctors.h"
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20
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#include "convert_datatype.h"
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21
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#include "shape.h"
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22
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#include "npy_buffer.h"
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23
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#include "lowlevel_strided_loops.h"
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24
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#include "_datetime.h"
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25
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#include "datetime_strings.h"
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#include "array_assign.h"
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27
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#include "mapping.h" /* for array_item_asarray */
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28
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#include "templ_common.h" /* for npy_mul_with_overflow_intp */
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29
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#include "alloc.h"
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30
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#include <assert.h>
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32
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#include "get_attr_string.h"
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33
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#include "array_coercion.h"
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34
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35
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/*
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36
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* Reading from a file or a string.
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37
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*
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38
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* As much as possible, we try to use the same code for both files and strings,
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39
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* so the semantics for fromstring and fromfile are the same, especially with
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40
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* regards to the handling of text representations.
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41
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*/
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42
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43
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/*
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44
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* Scanning function for next element parsing and separator skipping.
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45
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* These functions return:
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46
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* - 0 to indicate more data to read
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47
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* - -1 when reading stopped at the end of the string/file
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48
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* - -2 when reading stopped before the end was reached.
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49
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*
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50
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* The dtype specific parsing functions may set the python error state
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* (they have to get the GIL first) additionally.
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52
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*/
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typedef int (*next_element)(void **, void *, PyArray_Descr *, void *);
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typedef int (*skip_separator)(void **, const char *, void *);
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55
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56
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57
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static npy_bool
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58
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string_is_fully_read(char const* start, char const* end) {
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59
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1
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if (end == NULL) {
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60
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1
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return *start == '\0'; /* null terminated */
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61
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}
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62
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else {
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63
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1
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return start >= end; /* fixed length */
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64
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}
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65
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}
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66
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67
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68
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static int
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1
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fromstr_next_element(char **s, void *dptr, PyArray_Descr *dtype,
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70
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const char *end)
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71
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{
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72
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1
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char *e = *s;
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73
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1
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int r = dtype->f->fromstr(*s, dptr, &e, dtype);
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74
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/*
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75
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* fromstr always returns 0 for basic dtypes; s points to the end of the
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76
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* parsed string. If s is not changed an error occurred or the end was
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77
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* reached.
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78
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*/
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1
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if (*s == e || r < 0) {
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80
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/* Nothing read, could be end of string or an error (or both) */
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81
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1
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if (string_is_fully_read(*s, end)) {
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82
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return -1;
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83
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}
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84
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1
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return -2;
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85
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}
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86
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1
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*s = e;
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87
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1
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if (end != NULL && *s > end) {
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88
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/* Stop the iteration if we read far enough */
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89
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return -1;
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90
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}
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91
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1
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return 0;
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92
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}
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93
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94
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static int
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95
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1
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fromfile_next_element(FILE **fp, void *dptr, PyArray_Descr *dtype,
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96
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void *NPY_UNUSED(stream_data))
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97
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{
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98
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/* the NULL argument is for backwards-compatibility */
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99
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1
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int r = dtype->f->scanfunc(*fp, dptr, NULL, dtype);
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100
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/* r can be EOF or the number of items read (0 or 1) */
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101
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1
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if (r == 1) {
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102
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return 0;
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103
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}
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1
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else if (r == EOF) {
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return -1;
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106
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}
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107
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else {
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108
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/* unable to read more, but EOF not reached indicating an error. */
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109
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1
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return -2;
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110
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}
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111
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}
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112
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113
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/*
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114
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* Remove multiple whitespace from the separator, and add a space to the
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115
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* beginning and end. This simplifies the separator-skipping code below.
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116
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*/
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117
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static char *
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118
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1
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swab_separator(const char *sep)
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119
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{
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120
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1
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int skip_space = 0;
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121
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char *s, *start;
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122
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123
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1
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s = start = malloc(strlen(sep)+3);
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124
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1
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if (s == NULL) {
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125
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0
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PyErr_NoMemory();
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126
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0
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return NULL;
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127
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}
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128
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/* add space to front if there isn't one */
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129
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1
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if (*sep != '\0' && !isspace(*sep)) {
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130
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1
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*s = ' '; s++;
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131
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}
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132
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1
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while (*sep != '\0') {
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133
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1
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if (isspace(*sep)) {
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134
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1
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if (skip_space) {
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135
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0
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sep++;
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136
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}
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137
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else {
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138
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1
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*s = ' ';
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139
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1
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s++;
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140
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1
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sep++;
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141
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1
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skip_space = 1;
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142
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}
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143
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}
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144
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else {
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145
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1
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*s = *sep;
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146
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1
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s++;
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147
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1
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sep++;
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148
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1
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skip_space = 0;
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149
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}
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150
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}
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151
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/* add space to end if there isn't one */
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152
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1
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if (s != start && s[-1] == ' ') {
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153
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1
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*s = ' ';
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154
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1
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s++;
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155
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}
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156
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1
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*s = '\0';
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157
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1
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return start;
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158
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}
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159
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160
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/*
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161
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* Assuming that the separator is the next bit in the string (file), skip it.
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162
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*
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163
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* Single spaces in the separator are matched to arbitrary-long sequences
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164
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* of whitespace in the input. If the separator consists only of spaces,
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165
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* it matches one or more whitespace characters.
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166
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*
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167
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* If we can't match the separator, return -2.
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168
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* If we hit the end of the string (file), return -1.
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169
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* Otherwise, return 0.
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170
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*/
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171
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static int
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172
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1
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fromstr_skip_separator(char **s, const char *sep, const char *end)
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173
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{
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174
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1
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char *string = *s;
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175
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1
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int result = 0;
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176
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177
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while (1) {
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178
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1
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char c = *string;
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179
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1
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if (string_is_fully_read(string, end)) {
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180
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result = -1;
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181
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break;
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182
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}
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183
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1
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else if (*sep == '\0') {
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184
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1
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if (string != *s) {
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185
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/* matched separator */
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186
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result = 0;
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187
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break;
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188
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}
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189
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else {
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190
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/* separator was whitespace wildcard that didn't match */
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191
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1
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result = -2;
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192
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1
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break;
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193
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}
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194
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}
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195
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1
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else if (*sep == ' ') {
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196
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/* whitespace wildcard */
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197
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1
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if (!isspace(c)) {
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198
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1
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sep++;
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199
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1
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continue;
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200
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}
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201
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}
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202
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1
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else if (*sep != c) {
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203
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result = -2;
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204
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break;
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205
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}
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206
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else {
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207
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1
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sep++;
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208
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}
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209
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1
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string++;
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210
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}
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211
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1
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*s = string;
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212
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1
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return result;
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213
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}
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214
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215
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static int
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216
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1
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fromfile_skip_separator(FILE **fp, const char *sep, void *NPY_UNUSED(stream_data))
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217
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{
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218
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1
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int result = 0;
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219
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1
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const char *sep_start = sep;
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220
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|
|
221
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while (1) {
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222
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1
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int c = fgetc(*fp);
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223
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|
|
224
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1
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if (c == EOF) {
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225
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result = -1;
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226
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break;
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227
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}
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228
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1
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else if (*sep == '\0') {
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229
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1
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ungetc(c, *fp);
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230
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1
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if (sep != sep_start) {
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231
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/* matched separator */
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232
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result = 0;
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233
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break;
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234
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}
|
235
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else {
|
236
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/* separator was whitespace wildcard that didn't match */
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237
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1
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result = -2;
|
238
|
1
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break;
|
239
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}
|
240
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}
|
241
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1
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else if (*sep == ' ') {
|
242
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/* whitespace wildcard */
|
243
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1
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if (!isspace(c)) {
|
244
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1
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sep++;
|
245
|
1
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sep_start++;
|
246
|
1
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ungetc(c, *fp);
|
247
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|
}
|
248
|
1
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else if (sep == sep_start) {
|
249
|
1
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sep_start--;
|
250
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|
}
|
251
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}
|
252
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1
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else if (*sep != c) {
|
253
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1
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ungetc(c, *fp);
|
254
|
1
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result = -2;
|
255
|
1
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break;
|
256
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}
|
257
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else {
|
258
|
1
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sep++;
|
259
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}
|
260
|
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}
|
261
|
1
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return result;
|
262
|
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}
|
263
|
|
|
264
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|
/*
|
265
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* Change a sub-array field to the base descriptor
|
266
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* and update the dimensions and strides
|
267
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* appropriately. Dimensions and strides are added
|
268
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|
* to the end.
|
269
|
|
*
|
270
|
|
* Strides are only added if given (because data is given).
|
271
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|
*/
|
272
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static int
|
273
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1
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_update_descr_and_dimensions(PyArray_Descr **des, npy_intp *newdims,
|
274
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npy_intp *newstrides, int oldnd)
|
275
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{
|
276
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PyArray_Descr *old;
|
277
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int newnd;
|
278
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int numnew;
|
279
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npy_intp *mydim;
|
280
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int i;
|
281
|
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int tuple;
|
282
|
|
|
283
|
1
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old = *des;
|
284
|
1
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*des = old->subarray->base;
|
285
|
|
|
286
|
|
|
287
|
1
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mydim = newdims + oldnd;
|
288
|
1
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tuple = PyTuple_Check(old->subarray->shape);
|
289
|
1
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if (tuple) {
|
290
|
1
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numnew = PyTuple_GET_SIZE(old->subarray->shape);
|
291
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}
|
292
|
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else {
|
293
|
|
numnew = 1;
|
294
|
|
}
|
295
|
|
|
296
|
|
|
297
|
1
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newnd = oldnd + numnew;
|
298
|
1
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if (newnd > NPY_MAXDIMS) {
|
299
|
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goto finish;
|
300
|
|
}
|
301
|
1
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if (tuple) {
|
302
|
1
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for (i = 0; i < numnew; i++) {
|
303
|
1
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mydim[i] = (npy_intp) PyLong_AsLong(
|
304
|
1
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PyTuple_GET_ITEM(old->subarray->shape, i));
|
305
|
|
}
|
306
|
|
}
|
307
|
|
else {
|
308
|
0
|
mydim[0] = (npy_intp) PyLong_AsLong(old->subarray->shape);
|
309
|
|
}
|
310
|
|
|
311
|
1
|
if (newstrides) {
|
312
|
|
npy_intp tempsize;
|
313
|
|
npy_intp *mystrides;
|
314
|
|
|
315
|
1
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mystrides = newstrides + oldnd;
|
316
|
|
/* Make new strides -- always C-contiguous */
|
317
|
1
|
tempsize = (*des)->elsize;
|
318
|
1
|
for (i = numnew - 1; i >= 0; i--) {
|
319
|
1
|
mystrides[i] = tempsize;
|
320
|
1
|
tempsize *= mydim[i] ? mydim[i] : 1;
|
321
|
|
}
|
322
|
|
}
|
323
|
|
|
324
|
1
|
finish:
|
325
|
1
|
Py_INCREF(*des);
|
326
|
1
|
Py_DECREF(old);
|
327
|
1
|
return newnd;
|
328
|
|
}
|
329
|
|
|
330
|
|
NPY_NO_EXPORT void
|
331
|
1
|
_unaligned_strided_byte_copy(char *dst, npy_intp outstrides, char *src,
|
332
|
|
npy_intp instrides, npy_intp N, int elsize)
|
333
|
|
{
|
334
|
|
npy_intp i;
|
335
|
1
|
char *tout = dst;
|
336
|
1
|
char *tin = src;
|
337
|
|
|
338
|
|
#define _COPY_N_SIZE(size) \
|
339
|
|
for(i=0; i<N; i++) { \
|
340
|
|
memcpy(tout, tin, size); \
|
341
|
|
tin += instrides; \
|
342
|
|
tout += outstrides; \
|
343
|
|
} \
|
344
|
|
return
|
345
|
|
|
346
|
1
|
switch(elsize) {
|
347
|
|
case 8:
|
348
|
1
|
_COPY_N_SIZE(8);
|
349
|
|
case 4:
|
350
|
1
|
_COPY_N_SIZE(4);
|
351
|
|
case 1:
|
352
|
1
|
_COPY_N_SIZE(1);
|
353
|
|
case 2:
|
354
|
1
|
_COPY_N_SIZE(2);
|
355
|
|
case 16:
|
356
|
1
|
_COPY_N_SIZE(16);
|
357
|
|
default:
|
358
|
1
|
_COPY_N_SIZE(elsize);
|
359
|
|
}
|
360
|
|
#undef _COPY_N_SIZE
|
361
|
|
|
362
|
|
}
|
363
|
|
|
364
|
|
NPY_NO_EXPORT void
|
365
|
1
|
_strided_byte_swap(void *p, npy_intp stride, npy_intp n, int size)
|
366
|
|
{
|
367
|
1
|
char *a, *b, c = 0;
|
368
|
|
int j, m;
|
369
|
|
|
370
|
1
|
switch(size) {
|
371
|
|
case 1: /* no byteswap necessary */
|
372
|
|
break;
|
373
|
1
|
case 4:
|
374
|
1
|
if (npy_is_aligned((void*)((npy_intp)p | stride), sizeof(npy_uint32))) {
|
375
|
1
|
for (a = (char*)p; n > 0; n--, a += stride) {
|
376
|
1
|
npy_uint32 * a_ = (npy_uint32 *)a;
|
377
|
1
|
*a_ = npy_bswap4(*a_);
|
378
|
|
}
|
379
|
|
}
|
380
|
|
else {
|
381
|
1
|
for (a = (char*)p; n > 0; n--, a += stride) {
|
382
|
1
|
npy_bswap4_unaligned(a);
|
383
|
|
}
|
384
|
|
}
|
385
|
|
break;
|
386
|
1
|
case 8:
|
387
|
1
|
if (npy_is_aligned((void*)((npy_intp)p | stride), sizeof(npy_uint64))) {
|
388
|
1
|
for (a = (char*)p; n > 0; n--, a += stride) {
|
389
|
1
|
npy_uint64 * a_ = (npy_uint64 *)a;
|
390
|
1
|
*a_ = npy_bswap8(*a_);
|
391
|
|
}
|
392
|
|
}
|
393
|
|
else {
|
394
|
1
|
for (a = (char*)p; n > 0; n--, a += stride) {
|
395
|
1
|
npy_bswap8_unaligned(a);
|
396
|
|
}
|
397
|
|
}
|
398
|
|
break;
|
399
|
1
|
case 2:
|
400
|
1
|
if (npy_is_aligned((void*)((npy_intp)p | stride), sizeof(npy_uint16))) {
|
401
|
1
|
for (a = (char*)p; n > 0; n--, a += stride) {
|
402
|
1
|
npy_uint16 * a_ = (npy_uint16 *)a;
|
403
|
1
|
*a_ = npy_bswap2(*a_);
|
404
|
|
}
|
405
|
|
}
|
406
|
|
else {
|
407
|
0
|
for (a = (char*)p; n > 0; n--, a += stride) {
|
408
|
0
|
npy_bswap2_unaligned(a);
|
409
|
|
}
|
410
|
|
}
|
411
|
|
break;
|
412
|
1
|
default:
|
413
|
1
|
m = size/2;
|
414
|
1
|
for (a = (char *)p; n > 0; n--, a += stride - m) {
|
415
|
1
|
b = a + (size - 1);
|
416
|
1
|
for (j = 0; j < m; j++) {
|
417
|
1
|
c=*a; *a++ = *b; *b-- = c;
|
418
|
|
}
|
419
|
|
}
|
420
|
|
break;
|
421
|
|
}
|
422
|
|
}
|
423
|
|
|
424
|
|
NPY_NO_EXPORT void
|
425
|
1
|
byte_swap_vector(void *p, npy_intp n, int size)
|
426
|
|
{
|
427
|
1
|
_strided_byte_swap(p, (npy_intp) size, n, size);
|
428
|
1
|
return;
|
429
|
|
}
|
430
|
|
|
431
|
|
/* If numitems > 1, then dst must be contiguous */
|
432
|
|
NPY_NO_EXPORT void
|
433
|
1
|
copy_and_swap(void *dst, void *src, int itemsize, npy_intp numitems,
|
434
|
|
npy_intp srcstrides, int swap)
|
435
|
|
{
|
436
|
1
|
if ((numitems == 1) || (itemsize == srcstrides)) {
|
437
|
1
|
memcpy(dst, src, itemsize*numitems);
|
438
|
|
}
|
439
|
|
else {
|
440
|
|
npy_intp i;
|
441
|
|
char *s1 = (char *)src;
|
442
|
|
char *d1 = (char *)dst;
|
443
|
|
|
444
|
0
|
for (i = 0; i < numitems; i++) {
|
445
|
0
|
memcpy(d1, s1, itemsize);
|
446
|
0
|
d1 += itemsize;
|
447
|
0
|
s1 += srcstrides;
|
448
|
|
}
|
449
|
|
}
|
450
|
|
|
451
|
1
|
if (swap) {
|
452
|
|
byte_swap_vector(dst, numitems, itemsize);
|
453
|
|
}
|
454
|
|
}
|
455
|
|
|
456
|
|
|
457
|
|
/*
|
458
|
|
* Recursive helper to assign using a coercion cache. This function
|
459
|
|
* must consume the cache depth first, just as the cache was originally
|
460
|
|
* produced.
|
461
|
|
*/
|
462
|
|
NPY_NO_EXPORT int
|
463
|
1
|
PyArray_AssignFromCache_Recursive(
|
464
|
|
PyArrayObject *self, const int ndim, coercion_cache_obj **cache)
|
465
|
|
{
|
466
|
|
/* Consume first cache element by extracting information and freeing it */
|
467
|
1
|
PyObject *original_obj = (*cache)->converted_obj;
|
468
|
1
|
PyObject *obj = (*cache)->arr_or_sequence;
|
469
|
1
|
Py_INCREF(obj);
|
470
|
1
|
npy_bool sequence = (*cache)->sequence;
|
471
|
1
|
int depth = (*cache)->depth;
|
472
|
1
|
*cache = npy_unlink_coercion_cache(*cache);
|
473
|
|
|
474
|
|
/*
|
475
|
|
* The maximum depth is special (specifically for objects), but usually
|
476
|
|
* unrolled in the sequence branch below.
|
477
|
|
*/
|
478
|
1
|
if (NPY_UNLIKELY(depth == ndim)) {
|
479
|
|
/*
|
480
|
|
* We have reached the maximum depth. We should simply assign to the
|
481
|
|
* element in principle. There is one exception. If this is a 0-D
|
482
|
|
* array being stored into a 0-D array (but we do not reach here then).
|
483
|
|
*/
|
484
|
1
|
if (PyArray_ISOBJECT(self)) {
|
485
|
|
assert(ndim != 0); /* guaranteed by PyArray_AssignFromCache */
|
486
|
|
assert(PyArray_NDIM(self) == 0);
|
487
|
1
|
Py_DECREF(obj);
|
488
|
1
|
return PyArray_Pack(PyArray_DESCR(self), PyArray_BYTES(self),
|
489
|
|
original_obj);
|
490
|
|
}
|
491
|
1
|
if (sequence) {
|
492
|
|
/*
|
493
|
|
* Sanity check which may be removed, the error is raised already
|
494
|
|
* in `PyArray_DiscoverDTypeAndShape`.
|
495
|
|
*/
|
496
|
|
assert(0);
|
497
|
0
|
PyErr_SetString(PyExc_RuntimeError,
|
498
|
|
"setting an array element with a sequence");
|
499
|
0
|
goto fail;
|
500
|
|
}
|
501
|
1
|
else if (original_obj != obj || !PyArray_CheckExact(obj)) {
|
502
|
|
/*
|
503
|
|
* If the leave node is an array-like, but not a numpy array,
|
504
|
|
* we pretend it is an arbitrary scalar. This means that in
|
505
|
|
* most cases (where the dtype is int or float), we will end
|
506
|
|
* up using float(array-like), or int(array-like). That does
|
507
|
|
* not support general casting, but helps Quantity and masked
|
508
|
|
* arrays, because it allows them to raise an error when
|
509
|
|
* `__float__()` or `__int__()` is called.
|
510
|
|
*/
|
511
|
1
|
Py_DECREF(obj);
|
512
|
1
|
return PyArray_SETITEM(self, PyArray_BYTES(self), original_obj);
|
513
|
|
}
|
514
|
|
}
|
515
|
|
|
516
|
|
/* The element is either a sequence, or an array */
|
517
|
1
|
if (!sequence) {
|
518
|
|
/* Straight forward array assignment */
|
519
|
|
assert(PyArray_Check(obj));
|
520
|
1
|
if (PyArray_CopyInto(self, (PyArrayObject *)obj) < 0) {
|
521
|
|
goto fail;
|
522
|
|
}
|
523
|
|
}
|
524
|
|
else {
|
525
|
|
assert(depth != ndim);
|
526
|
1
|
npy_intp length = PySequence_Length(obj);
|
527
|
1
|
if (length != PyArray_DIMS(self)[0]) {
|
528
|
1
|
PyErr_SetString(PyExc_RuntimeError,
|
529
|
|
"Inconsistent object during array creation? "
|
530
|
|
"Content of sequences changed (length inconsistent).");
|
531
|
1
|
goto fail;
|
532
|
|
}
|
533
|
|
|
534
|
1
|
for (npy_intp i = 0; i < length; i++) {
|
535
|
1
|
PyObject *value = PySequence_Fast_GET_ITEM(obj, i);
|
536
|
|
|
537
|
1
|
if (*cache == NULL || (*cache)->converted_obj != value ||
|
538
|
1
|
(*cache)->depth != depth + 1) {
|
539
|
1
|
if (ndim != depth + 1) {
|
540
|
1
|
PyErr_SetString(PyExc_RuntimeError,
|
541
|
|
"Inconsistent object during array creation? "
|
542
|
|
"Content of sequences changed (now too shallow).");
|
543
|
1
|
goto fail;
|
544
|
|
}
|
545
|
|
/* Straight forward assignment of elements */
|
546
|
|
char *item;
|
547
|
1
|
item = (PyArray_BYTES(self) + i * PyArray_STRIDES(self)[0]);
|
548
|
1
|
if (PyArray_Pack(PyArray_DESCR(self), item, value) < 0) {
|
549
|
|
goto fail;
|
550
|
|
}
|
551
|
|
}
|
552
|
|
else {
|
553
|
|
PyArrayObject *view;
|
554
|
1
|
view = (PyArrayObject *)array_item_asarray(self, i);
|
555
|
|
if (view < 0) {
|
556
|
|
goto fail;
|
557
|
|
}
|
558
|
1
|
if (PyArray_AssignFromCache_Recursive(view, ndim, cache) < 0) {
|
559
|
1
|
Py_DECREF(view);
|
560
|
|
goto fail;
|
561
|
|
}
|
562
|
1
|
Py_DECREF(view);
|
563
|
|
}
|
564
|
|
}
|
565
|
|
}
|
566
|
1
|
Py_DECREF(obj);
|
567
|
|
return 0;
|
568
|
|
|
569
|
1
|
fail:
|
570
|
1
|
Py_DECREF(obj);
|
571
|
|
return -1;
|
572
|
|
}
|
573
|
|
|
574
|
|
|
575
|
|
/**
|
576
|
|
* Fills an item based on a coercion cache object. It consumes the cache
|
577
|
|
* object while doing so.
|
578
|
|
*
|
579
|
|
* @param self Array to fill.
|
580
|
|
* @param cache coercion_cache_object, will be consumed. The cache must not
|
581
|
|
* contain a single array (must start with a sequence). The array case
|
582
|
|
* should be handled by `PyArray_FromArray()` before.
|
583
|
|
* @return 0 on success -1 on failure.
|
584
|
|
*/
|
585
|
|
NPY_NO_EXPORT int
|
586
|
1
|
PyArray_AssignFromCache(PyArrayObject *self, coercion_cache_obj *cache) {
|
587
|
1
|
int ndim = PyArray_NDIM(self);
|
588
|
|
/*
|
589
|
|
* Do not support ndim == 0 now with an array in the cache.
|
590
|
|
* The ndim == 0 is special because np.array(np.array(0), dtype=object)
|
591
|
|
* should unpack the inner array.
|
592
|
|
* Since the single-array case is special, it is handled previously
|
593
|
|
* in either case.
|
594
|
|
*/
|
595
|
|
assert(cache->sequence);
|
596
|
|
assert(ndim != 0); /* guaranteed if cache contains a sequence */
|
597
|
|
|
598
|
1
|
if (PyArray_AssignFromCache_Recursive(self, ndim, &cache) < 0) {
|
599
|
|
/* free the remaining cache. */
|
600
|
1
|
npy_free_coercion_cache(cache);
|
601
|
1
|
return -1;
|
602
|
|
}
|
603
|
|
|
604
|
|
/*
|
605
|
|
* Sanity check, this is the initial call, and when it returns, the
|
606
|
|
* cache has to be fully consumed, otherwise something is wrong.
|
607
|
|
* NOTE: May be nicer to put into a recursion helper.
|
608
|
|
*/
|
609
|
1
|
if (cache != NULL) {
|
610
|
1
|
PyErr_SetString(PyExc_RuntimeError,
|
611
|
|
"Inconsistent object during array creation? "
|
612
|
|
"Content of sequences changed (cache not consumed).");
|
613
|
1
|
return -1;
|
614
|
|
}
|
615
|
|
return 0;
|
616
|
|
}
|
617
|
|
|
618
|
|
|
619
|
|
static void
|
620
|
1
|
raise_memory_error(int nd, npy_intp const *dims, PyArray_Descr *descr)
|
621
|
|
{
|
622
|
|
static PyObject *exc_type = NULL;
|
623
|
|
|
624
|
1
|
npy_cache_import(
|
625
|
|
"numpy.core._exceptions", "_ArrayMemoryError",
|
626
|
|
&exc_type);
|
627
|
1
|
if (exc_type == NULL) {
|
628
|
|
goto fail;
|
629
|
|
}
|
630
|
|
|
631
|
1
|
PyObject *shape = PyArray_IntTupleFromIntp(nd, dims);
|
632
|
1
|
if (shape == NULL) {
|
633
|
|
goto fail;
|
634
|
|
}
|
635
|
|
|
636
|
|
/* produce an error object */
|
637
|
1
|
PyObject *exc_value = PyTuple_Pack(2, shape, (PyObject *)descr);
|
638
|
1
|
Py_DECREF(shape);
|
639
|
1
|
if (exc_value == NULL){
|
640
|
|
goto fail;
|
641
|
|
}
|
642
|
1
|
PyErr_SetObject(exc_type, exc_value);
|
643
|
1
|
Py_DECREF(exc_value);
|
644
|
|
return;
|
645
|
|
|
646
|
0
|
fail:
|
647
|
|
/* we couldn't raise the formatted exception for some reason */
|
648
|
0
|
PyErr_WriteUnraisable(NULL);
|
649
|
0
|
PyErr_NoMemory();
|
650
|
|
}
|
651
|
|
|
652
|
|
/*
|
653
|
|
* Generic new array creation routine.
|
654
|
|
* Internal variant with calloc argument for PyArray_Zeros.
|
655
|
|
*
|
656
|
|
* steals a reference to descr. On failure or descr->subarray, descr will
|
657
|
|
* be decrefed.
|
658
|
|
*/
|
659
|
|
NPY_NO_EXPORT PyObject *
|
660
|
1
|
PyArray_NewFromDescr_int(
|
661
|
|
PyTypeObject *subtype, PyArray_Descr *descr, int nd,
|
662
|
|
npy_intp const *dims, npy_intp const *strides, void *data,
|
663
|
|
int flags, PyObject *obj, PyObject *base, int zeroed,
|
664
|
|
int allow_emptystring)
|
665
|
|
{
|
666
|
|
PyArrayObject_fields *fa;
|
667
|
|
int i;
|
668
|
|
npy_intp nbytes;
|
669
|
|
|
670
|
1
|
if (descr->subarray) {
|
671
|
|
PyObject *ret;
|
672
|
|
npy_intp newdims[2*NPY_MAXDIMS];
|
673
|
1
|
npy_intp *newstrides = NULL;
|
674
|
1
|
memcpy(newdims, dims, nd*sizeof(npy_intp));
|
675
|
1
|
if (strides) {
|
676
|
1
|
newstrides = newdims + NPY_MAXDIMS;
|
677
|
1
|
memcpy(newstrides, strides, nd*sizeof(npy_intp));
|
678
|
|
}
|
679
|
1
|
nd =_update_descr_and_dimensions(&descr, newdims,
|
680
|
|
newstrides, nd);
|
681
|
1
|
ret = PyArray_NewFromDescr_int(
|
682
|
|
subtype, descr,
|
683
|
|
nd, newdims, newstrides, data,
|
684
|
|
flags, obj, base,
|
685
|
|
zeroed, allow_emptystring);
|
686
|
|
return ret;
|
687
|
|
}
|
688
|
|
|
689
|
1
|
if ((unsigned int)nd > (unsigned int)NPY_MAXDIMS) {
|
690
|
0
|
PyErr_Format(PyExc_ValueError,
|
691
|
|
"number of dimensions must be within [0, %d]",
|
692
|
|
NPY_MAXDIMS);
|
693
|
0
|
Py_DECREF(descr);
|
694
|
|
return NULL;
|
695
|
|
}
|
696
|
|
|
697
|
|
/* Check datatype element size */
|
698
|
1
|
nbytes = descr->elsize;
|
699
|
1
|
if (PyDataType_ISUNSIZED(descr)) {
|
700
|
1
|
if (!PyDataType_ISFLEXIBLE(descr)) {
|
701
|
0
|
PyErr_SetString(PyExc_TypeError, "Empty data-type");
|
702
|
0
|
Py_DECREF(descr);
|
703
|
|
return NULL;
|
704
|
|
}
|
705
|
1
|
else if (PyDataType_ISSTRING(descr) && !allow_emptystring &&
|
706
|
1
|
data == NULL) {
|
707
|
1
|
PyArray_DESCR_REPLACE(descr);
|
708
|
1
|
if (descr == NULL) {
|
709
|
|
return NULL;
|
710
|
|
}
|
711
|
1
|
if (descr->type_num == NPY_STRING) {
|
712
|
1
|
nbytes = descr->elsize = 1;
|
713
|
|
}
|
714
|
|
else {
|
715
|
1
|
nbytes = descr->elsize = sizeof(npy_ucs4);
|
716
|
|
}
|
717
|
|
}
|
718
|
|
}
|
719
|
|
|
720
|
|
/* Check dimensions and multiply them to nbytes */
|
721
|
1
|
for (i = 0; i < nd; i++) {
|
722
|
1
|
npy_intp dim = dims[i];
|
723
|
|
|
724
|
1
|
if (dim == 0) {
|
725
|
|
/*
|
726
|
|
* Compare to PyArray_OverflowMultiplyList that
|
727
|
|
* returns 0 in this case.
|
728
|
|
*/
|
729
|
1
|
continue;
|
730
|
|
}
|
731
|
|
|
732
|
1
|
if (dim < 0) {
|
733
|
0
|
PyErr_SetString(PyExc_ValueError,
|
734
|
|
"negative dimensions are not allowed");
|
735
|
0
|
Py_DECREF(descr);
|
736
|
|
return NULL;
|
737
|
|
}
|
738
|
|
|
739
|
|
/*
|
740
|
|
* Care needs to be taken to avoid integer overflow when
|
741
|
|
* multiplying the dimensions together to get the total size of the
|
742
|
|
* array.
|
743
|
|
*/
|
744
|
1
|
if (npy_mul_with_overflow_intp(&nbytes, nbytes, dim)) {
|
745
|
1
|
PyErr_SetString(PyExc_ValueError,
|
746
|
|
"array is too big; `arr.size * arr.dtype.itemsize` "
|
747
|
|
"is larger than the maximum possible size.");
|
748
|
1
|
Py_DECREF(descr);
|
749
|
|
return NULL;
|
750
|
|
}
|
751
|
|
}
|
752
|
|
|
753
|
1
|
fa = (PyArrayObject_fields *) subtype->tp_alloc(subtype, 0);
|
754
|
1
|
if (fa == NULL) {
|
755
|
0
|
Py_DECREF(descr);
|
756
|
|
return NULL;
|
757
|
|
}
|
758
|
1
|
fa->nd = nd;
|
759
|
1
|
fa->dimensions = NULL;
|
760
|
1
|
fa->data = NULL;
|
761
|
1
|
if (data == NULL) {
|
762
|
1
|
fa->flags = NPY_ARRAY_DEFAULT;
|
763
|
1
|
if (flags) {
|
764
|
1
|
fa->flags |= NPY_ARRAY_F_CONTIGUOUS;
|
765
|
1
|
if (nd > 1) {
|
766
|
1
|
fa->flags &= ~NPY_ARRAY_C_CONTIGUOUS;
|
767
|
|
}
|
768
|
|
flags = NPY_ARRAY_F_CONTIGUOUS;
|
769
|
|
}
|
770
|
|
}
|
771
|
|
else {
|
772
|
1
|
fa->flags = (flags & ~NPY_ARRAY_WRITEBACKIFCOPY);
|
773
|
1
|
fa->flags &= ~NPY_ARRAY_UPDATEIFCOPY;
|
774
|
|
}
|
775
|
1
|
fa->descr = descr;
|
776
|
1
|
fa->base = (PyObject *)NULL;
|
777
|
1
|
fa->weakreflist = (PyObject *)NULL;
|
778
|
|
|
779
|
1
|
if (nd > 0) {
|
780
|
1
|
fa->dimensions = npy_alloc_cache_dim(2 * nd);
|
781
|
1
|
if (fa->dimensions == NULL) {
|
782
|
0
|
PyErr_NoMemory();
|
783
|
0
|
goto fail;
|
784
|
|
}
|
785
|
1
|
fa->strides = fa->dimensions + nd;
|
786
|
|
if (nd) {
|
787
|
1
|
memcpy(fa->dimensions, dims, sizeof(npy_intp)*nd);
|
788
|
|
}
|
789
|
1
|
if (strides == NULL) { /* fill it in */
|
790
|
1
|
_array_fill_strides(fa->strides, dims, nd, descr->elsize,
|
791
|
|
flags, &(fa->flags));
|
792
|
|
}
|
793
|
|
else {
|
794
|
|
/*
|
795
|
|
* we allow strides even when we create
|
796
|
|
* the memory, but be careful with this...
|
797
|
|
*/
|
798
|
|
if (nd) {
|
799
|
1
|
memcpy(fa->strides, strides, sizeof(npy_intp)*nd);
|
800
|
|
}
|
801
|
|
}
|
802
|
|
}
|
803
|
|
else {
|
804
|
1
|
fa->dimensions = fa->strides = NULL;
|
805
|
1
|
fa->flags |= NPY_ARRAY_F_CONTIGUOUS;
|
806
|
|
}
|
807
|
|
|
808
|
1
|
if (data == NULL) {
|
809
|
|
/*
|
810
|
|
* Allocate something even for zero-space arrays
|
811
|
|
* e.g. shape=(0,) -- otherwise buffer exposure
|
812
|
|
* (a.data) doesn't work as it should.
|
813
|
|
* Could probably just allocate a few bytes here. -- Chuck
|
814
|
|
*/
|
815
|
1
|
if (nbytes == 0) {
|
816
|
1
|
nbytes = descr->elsize ? descr->elsize : 1;
|
817
|
|
}
|
818
|
|
/*
|
819
|
|
* It is bad to have uninitialized OBJECT pointers
|
820
|
|
* which could also be sub-fields of a VOID array
|
821
|
|
*/
|
822
|
1
|
if (zeroed || PyDataType_FLAGCHK(descr, NPY_NEEDS_INIT)) {
|
823
|
1
|
data = npy_alloc_cache_zero(nbytes);
|
824
|
|
}
|
825
|
|
else {
|
826
|
1
|
data = npy_alloc_cache(nbytes);
|
827
|
|
}
|
828
|
1
|
if (data == NULL) {
|
829
|
1
|
raise_memory_error(fa->nd, fa->dimensions, descr);
|
830
|
1
|
goto fail;
|
831
|
|
}
|
832
|
1
|
fa->flags |= NPY_ARRAY_OWNDATA;
|
833
|
|
}
|
834
|
|
else {
|
835
|
|
/*
|
836
|
|
* If data is passed in, this object won't own it by default.
|
837
|
|
* Caller must arrange for this to be reset if truly desired
|
838
|
|
*/
|
839
|
1
|
fa->flags &= ~NPY_ARRAY_OWNDATA;
|
840
|
|
}
|
841
|
1
|
fa->data = data;
|
842
|
|
|
843
|
|
/*
|
844
|
|
* always update the flags to get the right CONTIGUOUS, ALIGN properties
|
845
|
|
* not owned data and input strides may not be aligned and on some
|
846
|
|
* platforms (debian sparc) malloc does not provide enough alignment for
|
847
|
|
* long double types
|
848
|
|
*/
|
849
|
1
|
PyArray_UpdateFlags((PyArrayObject *)fa, NPY_ARRAY_UPDATE_ALL);
|
850
|
|
|
851
|
|
/* Set the base object. It's important to do it here so that
|
852
|
|
* __array_finalize__ below receives it
|
853
|
|
*/
|
854
|
1
|
if (base != NULL) {
|
855
|
1
|
Py_INCREF(base);
|
856
|
1
|
if (PyArray_SetBaseObject((PyArrayObject *)fa, base) < 0) {
|
857
|
|
goto fail;
|
858
|
|
}
|
859
|
|
}
|
860
|
|
|
861
|
|
/*
|
862
|
|
* call the __array_finalize__
|
863
|
|
* method if a subtype.
|
864
|
|
* If obj is NULL, then call method with Py_None
|
865
|
|
*/
|
866
|
1
|
if ((subtype != &PyArray_Type)) {
|
867
|
|
PyObject *res, *func, *args;
|
868
|
|
|
869
|
1
|
func = PyObject_GetAttr((PyObject *)fa, npy_ma_str_array_finalize);
|
870
|
1
|
if (func && func != Py_None) {
|
871
|
1
|
if (PyCapsule_CheckExact(func)) {
|
872
|
|
/* A C-function is stored here */
|
873
|
|
PyArray_FinalizeFunc *cfunc;
|
874
|
0
|
cfunc = PyCapsule_GetPointer(func, NULL);
|
875
|
0
|
Py_DECREF(func);
|
876
|
0
|
if (cfunc == NULL) {
|
877
|
|
goto fail;
|
878
|
|
}
|
879
|
0
|
if (cfunc((PyArrayObject *)fa, obj) < 0) {
|
880
|
|
goto fail;
|
881
|
|
}
|
882
|
|
}
|
883
|
|
else {
|
884
|
1
|
args = PyTuple_New(1);
|
885
|
1
|
if (obj == NULL) {
|
886
|
1
|
obj=Py_None;
|
887
|
|
}
|
888
|
1
|
Py_INCREF(obj);
|
889
|
1
|
PyTuple_SET_ITEM(args, 0, obj);
|
890
|
1
|
res = PyObject_Call(func, args, NULL);
|
891
|
1
|
Py_DECREF(args);
|
892
|
1
|
Py_DECREF(func);
|
893
|
1
|
if (res == NULL) {
|
894
|
|
goto fail;
|
895
|
|
}
|
896
|
|
else {
|
897
|
1
|
Py_DECREF(res);
|
898
|
|
}
|
899
|
|
}
|
900
|
|
}
|
901
|
1
|
else Py_XDECREF(func);
|
902
|
|
}
|
903
|
|
return (PyObject *)fa;
|
904
|
|
|
905
|
1
|
fail:
|
906
|
1
|
Py_DECREF(fa);
|
907
|
|
return NULL;
|
908
|
|
}
|
909
|
|
|
910
|
|
|
911
|
|
/*NUMPY_API
|
912
|
|
* Generic new array creation routine.
|
913
|
|
*
|
914
|
|
* steals a reference to descr. On failure or when dtype->subarray is
|
915
|
|
* true, dtype will be decrefed.
|
916
|
|
*/
|
917
|
|
NPY_NO_EXPORT PyObject *
|
918
|
1
|
PyArray_NewFromDescr(
|
919
|
|
PyTypeObject *subtype, PyArray_Descr *descr,
|
920
|
|
int nd, npy_intp const *dims, npy_intp const *strides, void *data,
|
921
|
|
int flags, PyObject *obj)
|
922
|
|
{
|
923
|
1
|
return PyArray_NewFromDescrAndBase(
|
924
|
|
subtype, descr,
|
925
|
|
nd, dims, strides, data,
|
926
|
|
flags, obj, NULL);
|
927
|
|
}
|
928
|
|
|
929
|
|
/*
|
930
|
|
* Sets the base object using PyArray_SetBaseObject
|
931
|
|
*/
|
932
|
|
NPY_NO_EXPORT PyObject *
|
933
|
1
|
PyArray_NewFromDescrAndBase(
|
934
|
|
PyTypeObject *subtype, PyArray_Descr *descr,
|
935
|
|
int nd, npy_intp const *dims, npy_intp const *strides, void *data,
|
936
|
|
int flags, PyObject *obj, PyObject *base)
|
937
|
|
{
|
938
|
1
|
return PyArray_NewFromDescr_int(subtype, descr, nd,
|
939
|
|
dims, strides, data,
|
940
|
|
flags, obj, base, 0, 0);
|
941
|
|
}
|
942
|
|
|
943
|
|
/*
|
944
|
|
* Creates a new array with the same shape as the provided one,
|
945
|
|
* with possible memory layout order, data type and shape changes.
|
946
|
|
*
|
947
|
|
* prototype - The array the new one should be like.
|
948
|
|
* order - NPY_CORDER - C-contiguous result.
|
949
|
|
* NPY_FORTRANORDER - Fortran-contiguous result.
|
950
|
|
* NPY_ANYORDER - Fortran if prototype is Fortran, C otherwise.
|
951
|
|
* NPY_KEEPORDER - Keeps the axis ordering of prototype.
|
952
|
|
* dtype - If not NULL, overrides the data type of the result.
|
953
|
|
* ndim - If not -1, overrides the shape of the result.
|
954
|
|
* dims - If ndim is not -1, overrides the shape of the result.
|
955
|
|
* subok - If 1, use the prototype's array subtype, otherwise
|
956
|
|
* always create a base-class array.
|
957
|
|
*
|
958
|
|
* NOTE: If dtype is not NULL, steals the dtype reference. On failure or when
|
959
|
|
* dtype->subarray is true, dtype will be decrefed.
|
960
|
|
*/
|
961
|
|
NPY_NO_EXPORT PyObject *
|
962
|
1
|
PyArray_NewLikeArrayWithShape(PyArrayObject *prototype, NPY_ORDER order,
|
963
|
|
PyArray_Descr *dtype, int ndim, npy_intp const *dims, int subok)
|
964
|
|
{
|
965
|
1
|
PyObject *ret = NULL;
|
966
|
|
|
967
|
1
|
if (ndim == -1) {
|
968
|
1
|
ndim = PyArray_NDIM(prototype);
|
969
|
1
|
dims = PyArray_DIMS(prototype);
|
970
|
|
}
|
971
|
1
|
else if (order == NPY_KEEPORDER && (ndim != PyArray_NDIM(prototype))) {
|
972
|
1
|
order = NPY_CORDER;
|
973
|
|
}
|
974
|
|
|
975
|
|
/* If no override data type, use the one from the prototype */
|
976
|
1
|
if (dtype == NULL) {
|
977
|
1
|
dtype = PyArray_DESCR(prototype);
|
978
|
1
|
Py_INCREF(dtype);
|
979
|
|
}
|
980
|
|
|
981
|
|
/* Handle ANYORDER and simple KEEPORDER cases */
|
982
|
1
|
switch (order) {
|
983
|
1
|
case NPY_ANYORDER:
|
984
|
1
|
order = PyArray_ISFORTRAN(prototype) ?
|
985
|
1
|
NPY_FORTRANORDER : NPY_CORDER;
|
986
|
|
break;
|
987
|
1
|
case NPY_KEEPORDER:
|
988
|
1
|
if (PyArray_IS_C_CONTIGUOUS(prototype) || ndim <= 1) {
|
989
|
|
order = NPY_CORDER;
|
990
|
|
break;
|
991
|
|
}
|
992
|
1
|
else if (PyArray_IS_F_CONTIGUOUS(prototype)) {
|
993
|
|
order = NPY_FORTRANORDER;
|
994
|
|
break;
|
995
|
|
}
|
996
|
|
break;
|
997
|
|
default:
|
998
|
|
break;
|
999
|
|
}
|
1000
|
|
|
1001
|
|
/* If it's not KEEPORDER, this is simple */
|
1002
|
1
|
if (order != NPY_KEEPORDER) {
|
1003
|
1
|
ret = PyArray_NewFromDescr(subok ? Py_TYPE(prototype) : &PyArray_Type,
|
1004
|
|
dtype,
|
1005
|
|
ndim,
|
1006
|
|
dims,
|
1007
|
|
NULL,
|
1008
|
|
NULL,
|
1009
|
|
order,
|
1010
|
|
subok ? (PyObject *)prototype : NULL);
|
1011
|
|
}
|
1012
|
|
/* KEEPORDER needs some analysis of the strides */
|
1013
|
|
else {
|
1014
|
|
npy_intp strides[NPY_MAXDIMS], stride;
|
1015
|
|
npy_stride_sort_item strideperm[NPY_MAXDIMS];
|
1016
|
|
int idim;
|
1017
|
|
|
1018
|
1
|
PyArray_CreateSortedStridePerm(ndim,
|
1019
|
1
|
PyArray_STRIDES(prototype),
|
1020
|
|
strideperm);
|
1021
|
|
|
1022
|
|
/* Build the new strides */
|
1023
|
1
|
stride = dtype->elsize;
|
1024
|
1
|
for (idim = ndim-1; idim >= 0; --idim) {
|
1025
|
1
|
npy_intp i_perm = strideperm[idim].perm;
|
1026
|
1
|
strides[i_perm] = stride;
|
1027
|
1
|
stride *= dims[i_perm];
|
1028
|
|
}
|
1029
|
|
|
1030
|
|
/* Finally, allocate the array */
|
1031
|
1
|
ret = PyArray_NewFromDescr(subok ? Py_TYPE(prototype) : &PyArray_Type,
|
1032
|
|
dtype,
|
1033
|
|
ndim,
|
1034
|
|
dims,
|
1035
|
|
strides,
|
1036
|
|
NULL,
|
1037
|
|
0,
|
1038
|
|
subok ? (PyObject *)prototype : NULL);
|
1039
|
|
}
|
1040
|
|
|
1041
|
1
|
return ret;
|
1042
|
|
}
|
1043
|
|
|
1044
|
|
/*NUMPY_API
|
1045
|
|
* Creates a new array with the same shape as the provided one,
|
1046
|
|
* with possible memory layout order and data type changes.
|
1047
|
|
*
|
1048
|
|
* prototype - The array the new one should be like.
|
1049
|
|
* order - NPY_CORDER - C-contiguous result.
|
1050
|
|
* NPY_FORTRANORDER - Fortran-contiguous result.
|
1051
|
|
* NPY_ANYORDER - Fortran if prototype is Fortran, C otherwise.
|
1052
|
|
* NPY_KEEPORDER - Keeps the axis ordering of prototype.
|
1053
|
|
* dtype - If not NULL, overrides the data type of the result.
|
1054
|
|
* subok - If 1, use the prototype's array subtype, otherwise
|
1055
|
|
* always create a base-class array.
|
1056
|
|
*
|
1057
|
|
* NOTE: If dtype is not NULL, steals the dtype reference. On failure or when
|
1058
|
|
* dtype->subarray is true, dtype will be decrefed.
|
1059
|
|
*/
|
1060
|
|
NPY_NO_EXPORT PyObject *
|
1061
|
1
|
PyArray_NewLikeArray(PyArrayObject *prototype, NPY_ORDER order,
|
1062
|
|
PyArray_Descr *dtype, int subok)
|
1063
|
|
{
|
1064
|
1
|
return PyArray_NewLikeArrayWithShape(prototype, order, dtype, -1, NULL, subok);
|
1065
|
|
}
|
1066
|
|
|
1067
|
|
/*NUMPY_API
|
1068
|
|
* Generic new array creation routine.
|
1069
|
|
*/
|
1070
|
|
NPY_NO_EXPORT PyObject *
|
1071
|
1
|
PyArray_New(
|
1072
|
|
PyTypeObject *subtype, int nd, npy_intp const *dims, int type_num,
|
1073
|
|
npy_intp const *strides, void *data, int itemsize, int flags,
|
1074
|
|
PyObject *obj)
|
1075
|
|
{
|
1076
|
|
PyArray_Descr *descr;
|
1077
|
|
PyObject *new;
|
1078
|
|
|
1079
|
1
|
descr = PyArray_DescrFromType(type_num);
|
1080
|
1
|
if (descr == NULL) {
|
1081
|
|
return NULL;
|
1082
|
|
}
|
1083
|
1
|
if (PyDataType_ISUNSIZED(descr)) {
|
1084
|
1
|
if (itemsize < 1) {
|
1085
|
0
|
PyErr_SetString(PyExc_ValueError,
|
1086
|
|
"data type must provide an itemsize");
|
1087
|
0
|
Py_DECREF(descr);
|
1088
|
|
return NULL;
|
1089
|
|
}
|
1090
|
1
|
PyArray_DESCR_REPLACE(descr);
|
1091
|
1
|
descr->elsize = itemsize;
|
1092
|
|
}
|
1093
|
1
|
new = PyArray_NewFromDescr(subtype, descr, nd, dims, strides,
|
1094
|
|
data, flags, obj);
|
1095
|
1
|
return new;
|
1096
|
|
}
|
1097
|
|
|
1098
|
|
|
1099
|
|
NPY_NO_EXPORT PyArray_Descr *
|
1100
|
1
|
_dtype_from_buffer_3118(PyObject *memoryview)
|
1101
|
|
{
|
1102
|
|
PyArray_Descr *descr;
|
1103
|
1
|
Py_buffer *view = PyMemoryView_GET_BUFFER(memoryview);
|
1104
|
1
|
if (view->format != NULL) {
|
1105
|
1
|
descr = _descriptor_from_pep3118_format(view->format);
|
1106
|
1
|
if (descr == NULL) {
|
1107
|
|
return NULL;
|
1108
|
|
}
|
1109
|
|
}
|
1110
|
|
else {
|
1111
|
|
/* If no format is specified, just assume a byte array
|
1112
|
|
* TODO: void would make more sense here, as it wouldn't null
|
1113
|
|
* terminate.
|
1114
|
|
*/
|
1115
|
0
|
descr = PyArray_DescrNewFromType(NPY_STRING);
|
1116
|
0
|
descr->elsize = view->itemsize;
|
1117
|
|
}
|
1118
|
|
return descr;
|
1119
|
|
}
|
1120
|
|
|
1121
|
|
|
1122
|
|
NPY_NO_EXPORT PyObject *
|
1123
|
1
|
_array_from_buffer_3118(PyObject *memoryview)
|
1124
|
|
{
|
1125
|
|
/* PEP 3118 */
|
1126
|
|
Py_buffer *view;
|
1127
|
1
|
PyArray_Descr *descr = NULL;
|
1128
|
1
|
PyObject *r = NULL;
|
1129
|
|
int nd, flags;
|
1130
|
|
Py_ssize_t d;
|
1131
|
|
npy_intp shape[NPY_MAXDIMS], strides[NPY_MAXDIMS];
|
1132
|
|
|
1133
|
1
|
view = PyMemoryView_GET_BUFFER(memoryview);
|
1134
|
1
|
nd = view->ndim;
|
1135
|
1
|
descr = _dtype_from_buffer_3118(memoryview);
|
1136
|
|
|
1137
|
1
|
if (descr == NULL) {
|
1138
|
|
return NULL;
|
1139
|
|
}
|
1140
|
|
|
1141
|
|
/* Sanity check */
|
1142
|
1
|
if (descr->elsize != view->itemsize) {
|
1143
|
|
/* Ctypes has bugs in its PEP3118 implementation, which we need to
|
1144
|
|
* work around.
|
1145
|
|
*
|
1146
|
|
* bpo-10746
|
1147
|
|
* bpo-32780
|
1148
|
|
* bpo-32782
|
1149
|
|
*
|
1150
|
|
* Note that even if the above are fixed in master, we have to drop the
|
1151
|
|
* early patch versions of python to actually make use of the fixes.
|
1152
|
|
*/
|
1153
|
1
|
if (!npy_ctypes_check(Py_TYPE(view->obj))) {
|
1154
|
|
/* This object has no excuse for a broken PEP3118 buffer */
|
1155
|
0
|
PyErr_Format(
|
1156
|
|
PyExc_RuntimeError,
|
1157
|
|
"Item size %zd for PEP 3118 buffer format "
|
1158
|
|
"string %s does not match the dtype %c item size %d.",
|
1159
|
0
|
view->itemsize, view->format, descr->type,
|
1160
|
|
descr->elsize);
|
1161
|
0
|
Py_DECREF(descr);
|
1162
|
|
return NULL;
|
1163
|
|
}
|
1164
|
|
|
1165
|
1
|
if (PyErr_Warn(
|
1166
|
|
PyExc_RuntimeWarning,
|
1167
|
|
"A builtin ctypes object gave a PEP3118 format "
|
1168
|
|
"string that does not match its itemsize, so a "
|
1169
|
|
"best-guess will be made of the data type. "
|
1170
|
|
"Newer versions of python may behave correctly.") < 0) {
|
1171
|
0
|
Py_DECREF(descr);
|
1172
|
|
return NULL;
|
1173
|
|
}
|
1174
|
|
|
1175
|
|
/* Thankfully, np.dtype(ctypes_type) works in most cases.
|
1176
|
|
* For an array input, this produces a dtype containing all the
|
1177
|
|
* dimensions, so the array is now 0d.
|
1178
|
|
*/
|
1179
|
1
|
nd = 0;
|
1180
|
1
|
Py_DECREF(descr);
|
1181
|
1
|
descr = (PyArray_Descr *)PyObject_CallFunctionObjArgs(
|
1182
|
1
|
(PyObject *)&PyArrayDescr_Type, Py_TYPE(view->obj), NULL);
|
1183
|
1
|
if (descr == NULL) {
|
1184
|
|
return NULL;
|
1185
|
|
}
|
1186
|
1
|
if (descr->elsize != view->len) {
|
1187
|
0
|
PyErr_SetString(
|
1188
|
|
PyExc_RuntimeError,
|
1189
|
|
"For the given ctypes object, neither the item size "
|
1190
|
|
"computed from the PEP 3118 buffer format nor from "
|
1191
|
|
"converting the type to a np.dtype matched the actual "
|
1192
|
|
"size. This is a bug both in python and numpy");
|
1193
|
0
|
Py_DECREF(descr);
|
1194
|
|
return NULL;
|
1195
|
|
}
|
1196
|
|
}
|
1197
|
|
|
1198
|
1
|
if (view->shape != NULL) {
|
1199
|
|
int k;
|
1200
|
1
|
if (nd > NPY_MAXDIMS || nd < 0) {
|
1201
|
1
|
PyErr_Format(PyExc_RuntimeError,
|
1202
|
|
"PEP3118 dimensions do not satisfy 0 <= ndim <= NPY_MAXDIMS");
|
1203
|
1
|
goto fail;
|
1204
|
|
}
|
1205
|
1
|
for (k = 0; k < nd; ++k) {
|
1206
|
1
|
shape[k] = view->shape[k];
|
1207
|
|
}
|
1208
|
1
|
if (view->strides != NULL) {
|
1209
|
1
|
for (k = 0; k < nd; ++k) {
|
1210
|
1
|
strides[k] = view->strides[k];
|
1211
|
|
}
|
1212
|
|
}
|
1213
|
|
else {
|
1214
|
0
|
d = view->len;
|
1215
|
0
|
for (k = 0; k < nd; ++k) {
|
1216
|
0
|
if (view->shape[k] != 0) {
|
1217
|
0
|
d /= view->shape[k];
|
1218
|
|
}
|
1219
|
0
|
strides[k] = d;
|
1220
|
|
}
|
1221
|
|
}
|
1222
|
|
}
|
1223
|
|
else {
|
1224
|
1
|
if (nd == 1) {
|
1225
|
0
|
shape[0] = view->len / view->itemsize;
|
1226
|
0
|
strides[0] = view->itemsize;
|
1227
|
|
}
|
1228
|
1
|
else if (nd > 1) {
|
1229
|
0
|
PyErr_SetString(PyExc_RuntimeError,
|
1230
|
|
"ndim computed from the PEP 3118 buffer format "
|
1231
|
|
"is greater than 1, but shape is NULL.");
|
1232
|
0
|
goto fail;
|
1233
|
|
}
|
1234
|
|
}
|
1235
|
|
|
1236
|
1
|
flags = NPY_ARRAY_BEHAVED & (view->readonly ? ~NPY_ARRAY_WRITEABLE : ~0);
|
1237
|
1
|
r = PyArray_NewFromDescrAndBase(
|
1238
|
|
&PyArray_Type, descr,
|
1239
|
|
nd, shape, strides, view->buf,
|
1240
|
|
flags, NULL, memoryview);
|
1241
|
1
|
return r;
|
1242
|
|
|
1243
|
|
|
1244
|
1
|
fail:
|
1245
|
1
|
Py_XDECREF(r);
|
1246
|
1
|
Py_XDECREF(descr);
|
1247
|
|
return NULL;
|
1248
|
|
|
1249
|
|
}
|
1250
|
|
|
1251
|
|
|
1252
|
|
/**
|
1253
|
|
* Attempts to extract an array from an array-like object.
|
1254
|
|
*
|
1255
|
|
* array-like is defined as either
|
1256
|
|
*
|
1257
|
|
* * an object implementing the PEP 3118 buffer interface;
|
1258
|
|
* * an object with __array_struct__ or __array_interface__ attributes;
|
1259
|
|
* * an object with an __array__ function.
|
1260
|
|
*
|
1261
|
|
* @param op The object to convert to an array
|
1262
|
|
* @param requested_type a requested dtype instance, may be NULL; The result
|
1263
|
|
* DType may be used, but is not enforced.
|
1264
|
|
* @param writeable whether the result must be writeable.
|
1265
|
|
* @param context Unused parameter, must be NULL (should be removed later).
|
1266
|
|
*
|
1267
|
|
* @returns The array object, Py_NotImplemented if op is not array-like,
|
1268
|
|
* or NULL with an error set. (A new reference to Py_NotImplemented
|
1269
|
|
* is returned.)
|
1270
|
|
*/
|
1271
|
|
NPY_NO_EXPORT PyObject *
|
1272
|
1
|
_array_from_array_like(PyObject *op,
|
1273
|
|
PyArray_Descr *requested_dtype, npy_bool writeable, PyObject *context) {
|
1274
|
|
PyObject* tmp;
|
1275
|
|
|
1276
|
|
/*
|
1277
|
|
* If op supports the PEP 3118 buffer interface.
|
1278
|
|
* We skip bytes and unicode since they are considered scalars. Unicode
|
1279
|
|
* would fail but bytes would be incorrectly converted to a uint8 array.
|
1280
|
|
*/
|
1281
|
1
|
if (!PyBytes_Check(op) && !PyUnicode_Check(op)) {
|
1282
|
1
|
PyObject *memoryview = PyMemoryView_FromObject(op);
|
1283
|
1
|
if (memoryview == NULL) {
|
1284
|
1
|
PyErr_Clear();
|
1285
|
|
}
|
1286
|
|
else {
|
1287
|
1
|
tmp = _array_from_buffer_3118(memoryview);
|
1288
|
1
|
Py_DECREF(memoryview);
|
1289
|
1
|
if (tmp == NULL) {
|
1290
|
|
return NULL;
|
1291
|
|
}
|
1292
|
|
|
1293
|
1
|
if (writeable
|
1294
|
0
|
&& PyArray_FailUnlessWriteable(
|
1295
|
|
(PyArrayObject *)tmp, "PEP 3118 buffer") < 0) {
|
1296
|
0
|
Py_DECREF(tmp);
|
1297
|
|
return NULL;
|
1298
|
|
}
|
1299
|
|
|
1300
|
|
return tmp;
|
1301
|
|
}
|
1302
|
|
}
|
1303
|
|
|
1304
|
|
/*
|
1305
|
|
* If op supports the __array_struct__ or __array_interface__ interface.
|
1306
|
|
*/
|
1307
|
1
|
tmp = PyArray_FromStructInterface(op);
|
1308
|
1
|
if (tmp == NULL) {
|
1309
|
|
return NULL;
|
1310
|
|
}
|
1311
|
1
|
if (tmp == Py_NotImplemented) {
|
1312
|
|
/* Until the return, NotImplemented is always a borrowed reference*/
|
1313
|
1
|
tmp = PyArray_FromInterface(op);
|
1314
|
1
|
if (tmp == NULL) {
|
1315
|
|
return NULL;
|
1316
|
|
}
|
1317
|
|
}
|
1318
|
|
|
1319
|
|
/*
|
1320
|
|
* If op supplies the __array__ function.
|
1321
|
|
* The documentation says this should produce a copy, so
|
1322
|
|
* we skip this method if writeable is true, because the intent
|
1323
|
|
* of writeable is to modify the operand.
|
1324
|
|
* XXX: If the implementation is wrong, and/or if actual
|
1325
|
|
* usage requires this behave differently,
|
1326
|
|
* this should be changed!
|
1327
|
|
*/
|
1328
|
1
|
if (!writeable && tmp == Py_NotImplemented) {
|
1329
|
1
|
tmp = PyArray_FromArrayAttr(op, requested_dtype, context);
|
1330
|
1
|
if (tmp == NULL) {
|
1331
|
|
return NULL;
|
1332
|
|
}
|
1333
|
|
}
|
1334
|
|
|
1335
|
1
|
if (tmp != Py_NotImplemented) {
|
1336
|
1
|
if (writeable &&
|
1337
|
0
|
PyArray_FailUnlessWriteable((PyArrayObject *)tmp,
|
1338
|
|
"array interface object") < 0) {
|
1339
|
0
|
Py_DECREF(tmp);
|
1340
|
|
return NULL;
|
1341
|
|
}
|
1342
|
|
return tmp;
|
1343
|
|
}
|
1344
|
|
|
1345
|
|
/* Until here Py_NotImplemented was borrowed */
|
1346
|
1
|
Py_INCREF(Py_NotImplemented);
|
1347
|
1
|
return Py_NotImplemented;
|
1348
|
|
}
|
1349
|
|
|
1350
|
|
|
1351
|
|
/*NUMPY_API*/
|
1352
|
|
NPY_NO_EXPORT int
|
1353
|
0
|
PyArray_GetArrayParamsFromObject(PyObject *NPY_UNUSED(op),
|
1354
|
|
PyArray_Descr *NPY_UNUSED(requested_dtype),
|
1355
|
|
npy_bool NPY_UNUSED(writeable),
|
1356
|
|
PyArray_Descr **NPY_UNUSED(out_dtype),
|
1357
|
|
int *NPY_UNUSED(out_ndim), npy_intp *NPY_UNUSED(out_dims),
|
1358
|
|
PyArrayObject **NPY_UNUSED(out_arr), PyObject *NPY_UNUSED(context))
|
1359
|
|
{
|
1360
|
|
/* Deprecated in NumPy 1.19, removed in NumPy 1.20. */
|
1361
|
0
|
PyErr_SetString(PyExc_RuntimeError,
|
1362
|
|
"PyArray_GetArrayParamsFromObject() C-API function is removed "
|
1363
|
|
"`PyArray_FromAny()` should be used at this time. New C-API "
|
1364
|
|
"may be exposed in the future (please do request this if it "
|
1365
|
|
"would help you).");
|
1366
|
0
|
return -1;
|
1367
|
|
}
|
1368
|
|
|
1369
|
|
|
1370
|
|
/*NUMPY_API
|
1371
|
|
* Does not check for NPY_ARRAY_ENSURECOPY and NPY_ARRAY_NOTSWAPPED in flags
|
1372
|
|
* Steals a reference to newtype --- which can be NULL
|
1373
|
|
*/
|
1374
|
|
NPY_NO_EXPORT PyObject *
|
1375
|
1
|
PyArray_FromAny(PyObject *op, PyArray_Descr *newtype, int min_depth,
|
1376
|
|
int max_depth, int flags, PyObject *context)
|
1377
|
|
{
|
1378
|
|
/*
|
1379
|
|
* This is the main code to make a NumPy array from a Python
|
1380
|
|
* Object. It is called from many different places.
|
1381
|
|
*/
|
1382
|
1
|
PyArrayObject *arr = NULL, *ret;
|
1383
|
1
|
PyArray_Descr *dtype = NULL;
|
1384
|
1
|
coercion_cache_obj *cache = NULL;
|
1385
|
1
|
int ndim = 0;
|
1386
|
|
npy_intp dims[NPY_MAXDIMS];
|
1387
|
|
|
1388
|
1
|
if (context != NULL) {
|
1389
|
0
|
PyErr_SetString(PyExc_RuntimeError, "'context' must be NULL");
|
1390
|
0
|
return NULL;
|
1391
|
|
}
|
1392
|
|
|
1393
|
|
PyArray_Descr *fixed_descriptor;
|
1394
|
|
PyArray_DTypeMeta *fixed_DType;
|
1395
|
1
|
if (PyArray_ExtractDTypeAndDescriptor((PyObject *)newtype,
|
1396
|
|
&fixed_descriptor, &fixed_DType) < 0) {
|
1397
|
0
|
Py_XDECREF(newtype);
|
1398
|
|
return NULL;
|
1399
|
|
}
|
1400
|
1
|
Py_XDECREF(newtype);
|
1401
|
|
|
1402
|
1
|
ndim = PyArray_DiscoverDTypeAndShape(op,
|
1403
|
|
NPY_MAXDIMS, dims, &cache, fixed_DType, fixed_descriptor, &dtype);
|
1404
|
|
|
1405
|
1
|
Py_XDECREF(fixed_descriptor);
|
1406
|
1
|
Py_XDECREF(fixed_DType);
|
1407
|
1
|
if (ndim < 0) {
|
1408
|
|
return NULL;
|
1409
|
|
}
|
1410
|
1
|
if (dtype == NULL) {
|
1411
|
1
|
dtype = PyArray_DescrFromType(NPY_DEFAULT_TYPE);
|
1412
|
|
}
|
1413
|
|
|
1414
|
1
|
if (min_depth != 0 && ndim < min_depth) {
|
1415
|
1
|
PyErr_SetString(PyExc_ValueError,
|
1416
|
|
"object of too small depth for desired array");
|
1417
|
1
|
Py_DECREF(dtype);
|
1418
|
1
|
npy_free_coercion_cache(cache);
|
1419
|
1
|
return NULL;
|
1420
|
|
}
|
1421
|
1
|
if (max_depth != 0 && ndim > max_depth) {
|
1422
|
1
|
PyErr_SetString(PyExc_ValueError,
|
1423
|
|
"object too deep for desired array");
|
1424
|
1
|
Py_DECREF(dtype);
|
1425
|
1
|
npy_free_coercion_cache(cache);
|
1426
|
1
|
return NULL;
|
1427
|
|
}
|
1428
|
|
|
1429
|
|
/* Got the correct parameters, but the cache may already hold the result */
|
1430
|
1
|
if (cache != NULL && !(cache->sequence)) {
|
1431
|
|
/*
|
1432
|
|
* There is only a single array-like and it was converted, it
|
1433
|
|
* may still have the incorrect type, but that is handled below.
|
1434
|
|
*/
|
1435
|
|
assert(cache->converted_obj == op);
|
1436
|
1
|
arr = (PyArrayObject *)(cache->arr_or_sequence);
|
1437
|
|
/* we may need to cast or assert flags (e.g. copy) */
|
1438
|
1
|
PyObject *res = PyArray_FromArray(arr, dtype, flags);
|
1439
|
1
|
npy_unlink_coercion_cache(cache);
|
1440
|
1
|
return res;
|
1441
|
|
}
|
1442
|
1
|
else if (cache == NULL && PyArray_IsScalar(op, Void) &&
|
1443
|
1
|
!(((PyVoidScalarObject *)op)->flags & NPY_ARRAY_OWNDATA) &&
|
1444
|
|
newtype == NULL) {
|
1445
|
|
/*
|
1446
|
|
* Special case, we return a *view* into void scalars, mainly to
|
1447
|
|
* allow things similar to the "reversed" assignment:
|
1448
|
|
* arr[indx]["field"] = val # instead of arr["field"][indx] = val
|
1449
|
|
*
|
1450
|
|
* It is unclear that this is necessary in this particular code path.
|
1451
|
|
* Note that this path is only activated when the user did _not_
|
1452
|
|
* provide a dtype (newtype is NULL).
|
1453
|
|
*/
|
1454
|
|
assert(ndim == 0);
|
1455
|
|
|
1456
|
1
|
return PyArray_NewFromDescrAndBase(
|
1457
|
|
&PyArray_Type, dtype,
|
1458
|
|
0, NULL, NULL,
|
1459
|
1
|
((PyVoidScalarObject *)op)->obval,
|
1460
|
|
((PyVoidScalarObject *)op)->flags,
|
1461
|
|
NULL, op);
|
1462
|
|
}
|
1463
|
|
|
1464
|
|
/* There was no array (or array-like) passed in directly. */
|
1465
|
1
|
if ((flags & NPY_ARRAY_WRITEBACKIFCOPY) ||
|
1466
|
|
(flags & NPY_ARRAY_UPDATEIFCOPY)) {
|
1467
|
1
|
PyErr_SetString(PyExc_TypeError,
|
1468
|
|
"WRITEBACKIFCOPY used for non-array input.");
|
1469
|
1
|
Py_DECREF(dtype);
|
1470
|
|
return NULL;
|
1471
|
|
}
|
1472
|
|
|
1473
|
|
/* Create a new array and copy the data */
|
1474
|
1
|
ret = (PyArrayObject *)PyArray_NewFromDescr(
|
1475
|
|
&PyArray_Type, dtype, ndim, dims, NULL, NULL,
|
1476
|
|
flags&NPY_ARRAY_F_CONTIGUOUS, NULL);
|
1477
|
1
|
if (ret == NULL) {
|
1478
|
|
return NULL;
|
1479
|
|
}
|
1480
|
1
|
if (cache == NULL) {
|
1481
|
|
/* This is a single item. Set it directly. */
|
1482
|
|
assert(ndim == 0);
|
1483
|
1
|
if (PyArray_Pack(PyArray_DESCR(ret), PyArray_DATA(ret), op) < 0) {
|
1484
|
1
|
Py_DECREF(ret);
|
1485
|
|
return NULL;
|
1486
|
|
}
|
1487
|
|
return (PyObject *)ret;
|
1488
|
|
}
|
1489
|
|
assert(ndim != 0);
|
1490
|
|
assert(op == cache->converted_obj);
|
1491
|
1
|
if (PyArray_AssignFromCache(ret, cache) < 0) {
|
1492
|
1
|
Py_DECREF(ret);
|
1493
|
|
return NULL;
|
1494
|
|
}
|
1495
|
|
return (PyObject *)ret;
|
1496
|
|
}
|
1497
|
|
|
1498
|
|
/*
|
1499
|
|
* flags is any of
|
1500
|
|
* NPY_ARRAY_C_CONTIGUOUS (formerly CONTIGUOUS),
|
1501
|
|
* NPY_ARRAY_F_CONTIGUOUS (formerly FORTRAN),
|
1502
|
|
* NPY_ARRAY_ALIGNED,
|
1503
|
|
* NPY_ARRAY_WRITEABLE,
|
1504
|
|
* NPY_ARRAY_NOTSWAPPED,
|
1505
|
|
* NPY_ARRAY_ENSURECOPY,
|
1506
|
|
* NPY_ARRAY_UPDATEIFCOPY,
|
1507
|
|
* NPY_ARRAY_WRITEBACKIFCOPY,
|
1508
|
|
* NPY_ARRAY_FORCECAST,
|
1509
|
|
* NPY_ARRAY_ENSUREARRAY,
|
1510
|
|
* NPY_ARRAY_ELEMENTSTRIDES
|
1511
|
|
*
|
1512
|
|
* or'd (|) together
|
1513
|
|
*
|
1514
|
|
* Any of these flags present means that the returned array should
|
1515
|
|
* guarantee that aspect of the array. Otherwise the returned array
|
1516
|
|
* won't guarantee it -- it will depend on the object as to whether or
|
1517
|
|
* not it has such features.
|
1518
|
|
*
|
1519
|
|
* Note that NPY_ARRAY_ENSURECOPY is enough
|
1520
|
|
* to guarantee NPY_ARRAY_C_CONTIGUOUS, NPY_ARRAY_ALIGNED and
|
1521
|
|
* NPY_ARRAY_WRITEABLE and therefore it is redundant to include
|
1522
|
|
* those as well.
|
1523
|
|
*
|
1524
|
|
* NPY_ARRAY_BEHAVED == NPY_ARRAY_ALIGNED | NPY_ARRAY_WRITEABLE
|
1525
|
|
* NPY_ARRAY_CARRAY = NPY_ARRAY_C_CONTIGUOUS | NPY_ARRAY_BEHAVED
|
1526
|
|
* NPY_ARRAY_FARRAY = NPY_ARRAY_F_CONTIGUOUS | NPY_ARRAY_BEHAVED
|
1527
|
|
*
|
1528
|
|
* NPY_ARRAY_F_CONTIGUOUS can be set in the FLAGS to request a FORTRAN array.
|
1529
|
|
* Fortran arrays are always behaved (aligned,
|
1530
|
|
* notswapped, and writeable) and not (C) CONTIGUOUS (if > 1d).
|
1531
|
|
*
|
1532
|
|
* NPY_ARRAY_UPDATEIFCOPY is deprecated in favor of
|
1533
|
|
* NPY_ARRAY_WRITEBACKIFCOPY in 1.14
|
1534
|
|
|
1535
|
|
* NPY_ARRAY_WRITEBACKIFCOPY flag sets this flag in the returned
|
1536
|
|
* array if a copy is made and the base argument points to the (possibly)
|
1537
|
|
* misbehaved array. Before returning to python, PyArray_ResolveWritebackIfCopy
|
1538
|
|
* must be called to update the contents of the original array from the copy.
|
1539
|
|
*
|
1540
|
|
* NPY_ARRAY_FORCECAST will cause a cast to occur regardless of whether or not
|
1541
|
|
* it is safe.
|
1542
|
|
*
|
1543
|
|
* context is passed through to PyArray_GetArrayParamsFromObject
|
1544
|
|
*/
|
1545
|
|
|
1546
|
|
/*NUMPY_API
|
1547
|
|
* steals a reference to descr -- accepts NULL
|
1548
|
|
*/
|
1549
|
|
NPY_NO_EXPORT PyObject *
|
1550
|
1
|
PyArray_CheckFromAny(PyObject *op, PyArray_Descr *descr, int min_depth,
|
1551
|
|
int max_depth, int requires, PyObject *context)
|
1552
|
|
{
|
1553
|
|
PyObject *obj;
|
1554
|
1
|
if (requires & NPY_ARRAY_NOTSWAPPED) {
|
1555
|
1
|
if (!descr && PyArray_Check(op) &&
|
1556
|
1
|
PyArray_ISBYTESWAPPED((PyArrayObject* )op)) {
|
1557
|
0
|
descr = PyArray_DescrNew(PyArray_DESCR((PyArrayObject *)op));
|
1558
|
|
}
|
1559
|
1
|
else if (descr && !PyArray_ISNBO(descr->byteorder)) {
|
1560
|
0
|
PyArray_DESCR_REPLACE(descr);
|
1561
|
|
}
|
1562
|
1
|
if (descr && descr->byteorder != NPY_IGNORE) {
|
1563
|
1
|
descr->byteorder = NPY_NATIVE;
|
1564
|
|
}
|
1565
|
|
}
|
1566
|
|
|
1567
|
1
|
obj = PyArray_FromAny(op, descr, min_depth, max_depth, requires, context);
|
1568
|
1
|
if (obj == NULL) {
|
1569
|
|
return NULL;
|
1570
|
|
}
|
1571
|
1
|
if ((requires & NPY_ARRAY_ELEMENTSTRIDES) &&
|
1572
|
0
|
!PyArray_ElementStrides(obj)) {
|
1573
|
|
PyObject *ret;
|
1574
|
0
|
ret = PyArray_NewCopy((PyArrayObject *)obj, NPY_ANYORDER);
|
1575
|
0
|
Py_DECREF(obj);
|
1576
|
|
obj = ret;
|
1577
|
|
}
|
1578
|
|
return obj;
|
1579
|
|
}
|
1580
|
|
|
1581
|
|
|
1582
|
|
/*NUMPY_API
|
1583
|
|
* steals reference to newtype --- acc. NULL
|
1584
|
|
*/
|
1585
|
|
NPY_NO_EXPORT PyObject *
|
1586
|
1
|
PyArray_FromArray(PyArrayObject *arr, PyArray_Descr *newtype, int flags)
|
1587
|
|
{
|
1588
|
|
|
1589
|
1
|
PyArrayObject *ret = NULL;
|
1590
|
1
|
int copy = 0;
|
1591
|
|
int arrflags;
|
1592
|
|
PyArray_Descr *oldtype;
|
1593
|
1
|
NPY_CASTING casting = NPY_SAFE_CASTING;
|
1594
|
|
|
1595
|
1
|
oldtype = PyArray_DESCR(arr);
|
1596
|
1
|
if (newtype == NULL) {
|
1597
|
|
/*
|
1598
|
|
* Check if object is of array with Null newtype.
|
1599
|
|
* If so return it directly instead of checking for casting.
|
1600
|
|
*/
|
1601
|
1
|
if (flags == 0) {
|
1602
|
1
|
Py_INCREF(arr);
|
1603
|
1
|
return (PyObject *)arr;
|
1604
|
|
}
|
1605
|
1
|
newtype = oldtype;
|
1606
|
1
|
Py_INCREF(oldtype);
|
1607
|
|
}
|
1608
|
1
|
else if (PyDataType_ISUNSIZED(newtype)) {
|
1609
|
1
|
PyArray_DESCR_REPLACE(newtype);
|
1610
|
1
|
if (newtype == NULL) {
|
1611
|
|
return NULL;
|
1612
|
|
}
|
1613
|
1
|
newtype->elsize = oldtype->elsize;
|
1614
|
|
}
|
1615
|
|
|
1616
|
|
/* If the casting if forced, use the 'unsafe' casting rule */
|
1617
|
1
|
if (flags & NPY_ARRAY_FORCECAST) {
|
1618
|
1
|
casting = NPY_UNSAFE_CASTING;
|
1619
|
|
}
|
1620
|
|
|
1621
|
|
/* Raise an error if the casting rule isn't followed */
|
1622
|
1
|
if (!PyArray_CanCastArrayTo(arr, newtype, casting)) {
|
1623
|
1
|
PyErr_Clear();
|
1624
|
1
|
npy_set_invalid_cast_error(
|
1625
|
1
|
PyArray_DESCR(arr), newtype, casting, PyArray_NDIM(arr) == 0);
|
1626
|
1
|
Py_DECREF(newtype);
|
1627
|
|
return NULL;
|
1628
|
|
}
|
1629
|
|
|
1630
|
1
|
arrflags = PyArray_FLAGS(arr);
|
1631
|
|
/* If a guaranteed copy was requested */
|
1632
|
1
|
copy = (flags & NPY_ARRAY_ENSURECOPY) ||
|
1633
|
|
/* If C contiguous was requested, and arr is not */
|
1634
|
1
|
((flags & NPY_ARRAY_C_CONTIGUOUS) &&
|
1635
|
1
|
(!(arrflags & NPY_ARRAY_C_CONTIGUOUS))) ||
|
1636
|
|
/* If an aligned array was requested, and arr is not */
|
1637
|
1
|
((flags & NPY_ARRAY_ALIGNED) &&
|
1638
|
1
|
(!(arrflags & NPY_ARRAY_ALIGNED))) ||
|
1639
|
|
/* If a Fortran contiguous array was requested, and arr is not */
|
1640
|
1
|
((flags & NPY_ARRAY_F_CONTIGUOUS) &&
|
1641
|
1
|
(!(arrflags & NPY_ARRAY_F_CONTIGUOUS))) ||
|
1642
|
|
/* If a writeable array was requested, and arr is not */
|
1643
|
1
|
((flags & NPY_ARRAY_WRITEABLE) &&
|
1644
|
1
|
(!(arrflags & NPY_ARRAY_WRITEABLE))) ||
|
1645
|
1
|
!PyArray_EquivTypes(oldtype, newtype);
|
1646
|
|
|
1647
|
|
if (copy) {
|
1648
|
1
|
NPY_ORDER order = NPY_KEEPORDER;
|
1649
|
1
|
int subok = 1;
|
1650
|
|
|
1651
|
|
/* Set the order for the copy being made based on the flags */
|
1652
|
1
|
if (flags & NPY_ARRAY_F_CONTIGUOUS) {
|
1653
|
|
order = NPY_FORTRANORDER;
|
1654
|
|
}
|
1655
|
1
|
else if (flags & NPY_ARRAY_C_CONTIGUOUS) {
|
1656
|
1
|
order = NPY_CORDER;
|
1657
|
|
}
|
1658
|
|
|
1659
|
1
|
if ((flags & NPY_ARRAY_ENSUREARRAY)) {
|
1660
|
1
|
subok = 0;
|
1661
|
|
}
|
1662
|
1
|
ret = (PyArrayObject *)PyArray_NewLikeArray(arr, order,
|
1663
|
|
newtype, subok);
|
1664
|
1
|
if (ret == NULL) {
|
1665
|
|
return NULL;
|
1666
|
|
}
|
1667
|
|
|
1668
|
1
|
if (PyArray_CopyInto(ret, arr) < 0) {
|
1669
|
1
|
Py_DECREF(ret);
|
1670
|
|
return NULL;
|
1671
|
|
}
|
1672
|
|
|
1673
|
1
|
if (flags & NPY_ARRAY_UPDATEIFCOPY) {
|
1674
|
|
/* This is the ONLY place the NPY_ARRAY_UPDATEIFCOPY flag
|
1675
|
|
* is still used.
|
1676
|
|
* Can be deleted once the flag itself is removed
|
1677
|
|
*/
|
1678
|
|
|
1679
|
|
/* 2017-Nov-10 1.14 */
|
1680
|
1
|
if (DEPRECATE(
|
1681
|
|
"NPY_ARRAY_UPDATEIFCOPY, NPY_ARRAY_INOUT_ARRAY, and "
|
1682
|
|
"NPY_ARRAY_INOUT_FARRAY are deprecated, use NPY_WRITEBACKIFCOPY, "
|
1683
|
|
"NPY_ARRAY_INOUT_ARRAY2, or NPY_ARRAY_INOUT_FARRAY2 respectively "
|
1684
|
|
"instead, and call PyArray_ResolveWritebackIfCopy before the "
|
1685
|
|
"array is deallocated, i.e. before the last call to Py_DECREF.") < 0) {
|
1686
|
1
|
Py_DECREF(ret);
|
1687
|
|
return NULL;
|
1688
|
|
}
|
1689
|
1
|
Py_INCREF(arr);
|
1690
|
1
|
if (PyArray_SetWritebackIfCopyBase(ret, arr) < 0) {
|
1691
|
0
|
Py_DECREF(ret);
|
1692
|
|
return NULL;
|
1693
|
|
}
|
1694
|
1
|
PyArray_ENABLEFLAGS(ret, NPY_ARRAY_UPDATEIFCOPY);
|
1695
|
|
PyArray_CLEARFLAGS(ret, NPY_ARRAY_WRITEBACKIFCOPY);
|
1696
|
|
}
|
1697
|
1
|
else if (flags & NPY_ARRAY_WRITEBACKIFCOPY) {
|
1698
|
1
|
Py_INCREF(arr);
|
1699
|
1
|
if (PyArray_SetWritebackIfCopyBase(ret, arr) < 0) {
|
1700
|
0
|
Py_DECREF(ret);
|
1701
|
|
return NULL;
|
1702
|
|
}
|
1703
|
|
}
|
1704
|
|
}
|
1705
|
|
/*
|
1706
|
|
* If no copy then take an appropriate view if necessary, or
|
1707
|
|
* just return a reference to ret itself.
|
1708
|
|
*/
|
1709
|
|
else {
|
1710
|
1
|
int needview = ((flags & NPY_ARRAY_ENSUREARRAY) &&
|
1711
|
1
|
!PyArray_CheckExact(arr));
|
1712
|
|
|
1713
|
1
|
Py_DECREF(newtype);
|
1714
|
1
|
if (needview) {
|
1715
|
1
|
PyTypeObject *subtype = NULL;
|
1716
|
|
|
1717
|
1
|
if (flags & NPY_ARRAY_ENSUREARRAY) {
|
1718
|
1
|
subtype = &PyArray_Type;
|
1719
|
|
}
|
1720
|
|
|
1721
|
1
|
ret = (PyArrayObject *)PyArray_View(arr, NULL, subtype);
|
1722
|
1
|
if (ret == NULL) {
|
1723
|
|
return NULL;
|
1724
|
|
}
|
1725
|
|
}
|
1726
|
|
else {
|
1727
|
1
|
Py_INCREF(arr);
|
1728
|
1
|
ret = arr;
|
1729
|
|
}
|
1730
|
|
}
|
1731
|
|
|
1732
|
|
return (PyObject *)ret;
|
1733
|
|
}
|
1734
|
|
|
1735
|
|
/*NUMPY_API */
|
1736
|
|
NPY_NO_EXPORT PyObject *
|
1737
|
1
|
PyArray_FromStructInterface(PyObject *input)
|
1738
|
|
{
|
1739
|
1
|
PyArray_Descr *thetype = NULL;
|
1740
|
|
PyArrayInterface *inter;
|
1741
|
|
PyObject *attr;
|
1742
|
1
|
char endian = NPY_NATBYTE;
|
1743
|
|
|
1744
|
1
|
attr = PyArray_LookupSpecial_OnInstance(input, "__array_struct__");
|
1745
|
1
|
if (attr == NULL) {
|
1746
|
1
|
if (PyErr_Occurred()) {
|
1747
|
|
return NULL;
|
1748
|
|
} else {
|
1749
|
1
|
return Py_NotImplemented;
|
1750
|
|
}
|
1751
|
|
}
|
1752
|
1
|
if (!PyCapsule_CheckExact(attr)) {
|
1753
|
1
|
if (PyType_Check(input) && PyObject_HasAttrString(attr, "__get__")) {
|
1754
|
|
/*
|
1755
|
|
* If the input is a class `attr` should be a property-like object.
|
1756
|
|
* This cannot be interpreted as an array, but is a valid.
|
1757
|
|
* (Needed due to the lookup being on the instance rather than type)
|
1758
|
|
*/
|
1759
|
1
|
Py_DECREF(attr);
|
1760
|
|
return Py_NotImplemented;
|
1761
|
|
}
|
1762
|
|
goto fail;
|
1763
|
|
}
|
1764
|
1
|
inter = PyCapsule_GetPointer(attr, NULL);
|
1765
|
1
|
if (inter == NULL) {
|
1766
|
|
goto fail;
|
1767
|
|
}
|
1768
|
1
|
if (inter->two != 2) {
|
1769
|
|
goto fail;
|
1770
|
|
}
|
1771
|
1
|
if ((inter->flags & NPY_ARRAY_NOTSWAPPED) != NPY_ARRAY_NOTSWAPPED) {
|
1772
|
1
|
endian = NPY_OPPBYTE;
|
1773
|
1
|
inter->flags &= ~NPY_ARRAY_NOTSWAPPED;
|
1774
|
|
}
|
1775
|
|
|
1776
|
1
|
if (inter->flags & NPY_ARR_HAS_DESCR) {
|
1777
|
0
|
if (PyArray_DescrConverter(inter->descr, &thetype) == NPY_FAIL) {
|
1778
|
0
|
thetype = NULL;
|
1779
|
0
|
PyErr_Clear();
|
1780
|
|
}
|
1781
|
|
}
|
1782
|
|
|
1783
|
1
|
if (thetype == NULL) {
|
1784
|
1
|
PyObject *type_str = PyUnicode_FromFormat(
|
1785
|
1
|
"%c%c%d", endian, inter->typekind, inter->itemsize);
|
1786
|
1
|
if (type_str == NULL) {
|
1787
|
0
|
Py_DECREF(attr);
|
1788
|
|
return NULL;
|
1789
|
|
}
|
1790
|
1
|
int ok = PyArray_DescrConverter(type_str, &thetype);
|
1791
|
1
|
Py_DECREF(type_str);
|
1792
|
1
|
if (ok != NPY_SUCCEED) {
|
1793
|
0
|
Py_DECREF(attr);
|
1794
|
|
return NULL;
|
1795
|
|
}
|
1796
|
|
}
|
1797
|
|
|
1798
|
1
|
PyObject *ret = PyArray_NewFromDescrAndBase(
|
1799
|
|
&PyArray_Type, thetype,
|
1800
|
1
|
inter->nd, inter->shape, inter->strides, inter->data,
|
1801
|
|
inter->flags, NULL, input);
|
1802
|
1
|
Py_DECREF(attr);
|
1803
|
|
return ret;
|
1804
|
|
|
1805
|
0
|
fail:
|
1806
|
0
|
PyErr_SetString(PyExc_ValueError, "invalid __array_struct__");
|
1807
|
0
|
Py_DECREF(attr);
|
1808
|
|
return NULL;
|
1809
|
|
}
|
1810
|
|
|
1811
|
|
/*
|
1812
|
|
* Checks if the object in descr is the default 'descr' member for the
|
1813
|
|
* __array_interface__ dictionary with 'typestr' member typestr.
|
1814
|
|
*/
|
1815
|
|
NPY_NO_EXPORT int
|
1816
|
1
|
_is_default_descr(PyObject *descr, PyObject *typestr) {
|
1817
|
1
|
if (!PyList_Check(descr) || PyList_GET_SIZE(descr) != 1) {
|
1818
|
|
return 0;
|
1819
|
|
}
|
1820
|
1
|
PyObject *tuple = PyList_GET_ITEM(descr, 0);
|
1821
|
1
|
if (!(PyTuple_Check(tuple) && PyTuple_GET_SIZE(tuple) == 2)) {
|
1822
|
|
return 0;
|
1823
|
|
}
|
1824
|
1
|
PyObject *name = PyTuple_GET_ITEM(tuple, 0);
|
1825
|
1
|
if (!(PyUnicode_Check(name) && PyUnicode_GetLength(name) == 0)) {
|
1826
|
|
return 0;
|
1827
|
|
}
|
1828
|
1
|
PyObject *typestr2 = PyTuple_GET_ITEM(tuple, 1);
|
1829
|
1
|
return PyObject_RichCompareBool(typestr, typestr2, Py_EQ);
|
1830
|
|
}
|
1831
|
|
|
1832
|
|
/*NUMPY_API*/
|
1833
|
|
NPY_NO_EXPORT PyObject *
|
1834
|
1
|
PyArray_FromInterface(PyObject *origin)
|
1835
|
|
{
|
1836
|
1
|
PyObject *iface = NULL;
|
1837
|
1
|
PyObject *attr = NULL;
|
1838
|
1
|
PyObject *base = NULL;
|
1839
|
|
PyArrayObject *ret;
|
1840
|
1
|
PyArray_Descr *dtype = NULL;
|
1841
|
1
|
char *data = NULL;
|
1842
|
|
Py_buffer view;
|
1843
|
|
int i, n;
|
1844
|
|
npy_intp dims[NPY_MAXDIMS], strides[NPY_MAXDIMS];
|
1845
|
1
|
int dataflags = NPY_ARRAY_BEHAVED;
|
1846
|
|
|
1847
|
1
|
iface = PyArray_LookupSpecial_OnInstance(origin, "__array_interface__");
|
1848
|
|
|
1849
|
1
|
if (iface == NULL) {
|
1850
|
1
|
if (PyErr_Occurred()) {
|
1851
|
0
|
PyErr_Clear(); /* TODO[gh-14801]: propagate crashes during attribute access? */
|
1852
|
|
}
|
1853
|
|
return Py_NotImplemented;
|
1854
|
|
}
|
1855
|
1
|
if (!PyDict_Check(iface)) {
|
1856
|
1
|
if (PyType_Check(origin) && PyObject_HasAttrString(iface, "__get__")) {
|
1857
|
|
/*
|
1858
|
|
* If the input is a class `iface` should be a property-like object.
|
1859
|
|
* This cannot be interpreted as an array, but is a valid.
|
1860
|
|
* (Needed due to the lookup being on the instance rather than type)
|
1861
|
|
*/
|
1862
|
1
|
Py_DECREF(iface);
|
1863
|
|
return Py_NotImplemented;
|
1864
|
|
}
|
1865
|
|
|
1866
|
0
|
Py_DECREF(iface);
|
1867
|
0
|
PyErr_SetString(PyExc_ValueError,
|
1868
|
|
"Invalid __array_interface__ value, must be a dict");
|
1869
|
0
|
return NULL;
|
1870
|
|
}
|
1871
|
|
|
1872
|
|
/* Get type string from interface specification */
|
1873
|
1
|
attr = _PyDict_GetItemStringWithError(iface, "typestr");
|
1874
|
1
|
if (attr == NULL) {
|
1875
|
1
|
Py_DECREF(iface);
|
1876
|
1
|
if (!PyErr_Occurred()) {
|
1877
|
1
|
PyErr_SetString(PyExc_ValueError,
|
1878
|
|
"Missing __array_interface__ typestr");
|
1879
|
|
}
|
1880
|
|
return NULL;
|
1881
|
|
}
|
1882
|
|
|
1883
|
|
/* allow bytes for backwards compatibility */
|
1884
|
1
|
if (!PyBytes_Check(attr) && !PyUnicode_Check(attr)) {
|
1885
|
0
|
PyErr_SetString(PyExc_TypeError,
|
1886
|
|
"__array_interface__ typestr must be a string");
|
1887
|
0
|
goto fail;
|
1888
|
|
}
|
1889
|
|
|
1890
|
|
/* Get dtype from type string */
|
1891
|
1
|
if (PyArray_DescrConverter(attr, &dtype) != NPY_SUCCEED) {
|
1892
|
|
goto fail;
|
1893
|
|
}
|
1894
|
|
|
1895
|
|
/*
|
1896
|
|
* If the dtype is NPY_VOID, see if there is extra information in
|
1897
|
|
* the 'descr' attribute.
|
1898
|
|
*/
|
1899
|
1
|
if (dtype->type_num == NPY_VOID) {
|
1900
|
1
|
PyObject *descr = _PyDict_GetItemStringWithError(iface, "descr");
|
1901
|
1
|
if (descr == NULL && PyErr_Occurred()) {
|
1902
|
|
goto fail;
|
1903
|
|
}
|
1904
|
1
|
PyArray_Descr *new_dtype = NULL;
|
1905
|
1
|
if (descr != NULL) {
|
1906
|
1
|
int is_default = _is_default_descr(descr, attr);
|
1907
|
1
|
if (is_default < 0) {
|
1908
|
|
goto fail;
|
1909
|
|
}
|
1910
|
1
|
if (!is_default) {
|
1911
|
1
|
if (PyArray_DescrConverter2(descr, &new_dtype) != NPY_SUCCEED) {
|
1912
|
|
goto fail;
|
1913
|
|
}
|
1914
|
1
|
if (new_dtype != NULL) {
|
1915
|
1
|
Py_DECREF(dtype);
|
1916
|
1
|
dtype = new_dtype;
|
1917
|
|
}
|
1918
|
|
}
|
1919
|
|
|
1920
|
|
}
|
1921
|
|
|
1922
|
|
}
|
1923
|
|
|
1924
|
|
/* Get shape tuple from interface specification */
|
1925
|
1
|
attr = _PyDict_GetItemStringWithError(iface, "shape");
|
1926
|
1
|
if (attr == NULL) {
|
1927
|
1
|
if (PyErr_Occurred()) {
|
1928
|
|
return NULL;
|
1929
|
|
}
|
1930
|
|
/* Shape must be specified when 'data' is specified */
|
1931
|
1
|
PyObject *data = _PyDict_GetItemStringWithError(iface, "data");
|
1932
|
1
|
if (data == NULL && PyErr_Occurred()) {
|
1933
|
|
return NULL;
|
1934
|
|
}
|
1935
|
1
|
else if (data != NULL) {
|
1936
|
0
|
Py_DECREF(iface);
|
1937
|
0
|
PyErr_SetString(PyExc_ValueError,
|
1938
|
|
"Missing __array_interface__ shape");
|
1939
|
0
|
return NULL;
|
1940
|
|
}
|
1941
|
|
/* Assume shape as scalar otherwise */
|
1942
|
|
else {
|
1943
|
|
/* NOTE: pointers to data and base should be NULL */
|
1944
|
1
|
n = dims[0] = 0;
|
1945
|
|
}
|
1946
|
|
}
|
1947
|
|
/* Make sure 'shape' is a tuple */
|
1948
|
1
|
else if (!PyTuple_Check(attr)) {
|
1949
|
1
|
PyErr_SetString(PyExc_TypeError,
|
1950
|
|
"shape must be a tuple");
|
1951
|
1
|
goto fail;
|
1952
|
|
}
|
1953
|
|
/* Get dimensions from shape tuple */
|
1954
|
|
else {
|
1955
|
1
|
n = PyTuple_GET_SIZE(attr);
|
1956
|
1
|
for (i = 0; i < n; i++) {
|
1957
|
1
|
PyObject *tmp = PyTuple_GET_ITEM(attr, i);
|
1958
|
1
|
dims[i] = PyArray_PyIntAsIntp(tmp);
|
1959
|
1
|
if (error_converting(dims[i])) {
|
1960
|
|
goto fail;
|
1961
|
|
}
|
1962
|
|
}
|
1963
|
|
}
|
1964
|
|
|
1965
|
|
/* Get data buffer from interface specification */
|
1966
|
1
|
attr = _PyDict_GetItemStringWithError(iface, "data");
|
1967
|
1
|
if (attr == NULL && PyErr_Occurred()){
|
1968
|
|
return NULL;
|
1969
|
|
}
|
1970
|
|
|
1971
|
|
/* Case for data access through pointer */
|
1972
|
1
|
if (attr && PyTuple_Check(attr)) {
|
1973
|
|
PyObject *dataptr;
|
1974
|
1
|
if (PyTuple_GET_SIZE(attr) != 2) {
|
1975
|
0
|
PyErr_SetString(PyExc_TypeError,
|
1976
|
|
"__array_interface__ data must be a 2-tuple with "
|
1977
|
|
"(data pointer integer, read-only flag)");
|
1978
|
0
|
goto fail;
|
1979
|
|
}
|
1980
|
1
|
dataptr = PyTuple_GET_ITEM(attr, 0);
|
1981
|
1
|
if (PyLong_Check(dataptr)) {
|
1982
|
1
|
data = PyLong_AsVoidPtr(dataptr);
|
1983
|
1
|
if (data == NULL && PyErr_Occurred()) {
|
1984
|
|
goto fail;
|
1985
|
|
}
|
1986
|
|
}
|
1987
|
|
else {
|
1988
|
0
|
PyErr_SetString(PyExc_TypeError,
|
1989
|
|
"first element of __array_interface__ data tuple "
|
1990
|
|
"must be an integer.");
|
1991
|
0
|
goto fail;
|
1992
|
|
}
|
1993
|
1
|
if (PyObject_IsTrue(PyTuple_GET_ITEM(attr,1))) {
|
1994
|
0
|
dataflags &= ~NPY_ARRAY_WRITEABLE;
|
1995
|
|
}
|
1996
|
|
base = origin;
|
1997
|
|
}
|
1998
|
|
|
1999
|
|
/* Case for data access through buffer */
|
2000
|
1
|
else if (attr) {
|
2001
|
1
|
if (attr != Py_None) {
|
2002
|
|
base = attr;
|
2003
|
|
}
|
2004
|
|
else {
|
2005
|
0
|
base = origin;
|
2006
|
|
}
|
2007
|
1
|
if (PyObject_GetBuffer(base, &view,
|
2008
|
|
PyBUF_WRITABLE|PyBUF_SIMPLE) < 0) {
|
2009
|
1
|
PyErr_Clear();
|
2010
|
1
|
if (PyObject_GetBuffer(base, &view,
|
2011
|
|
PyBUF_SIMPLE) < 0) {
|
2012
|
|
goto fail;
|
2013
|
|
}
|
2014
|
|
dataflags &= ~NPY_ARRAY_WRITEABLE;
|
2015
|
|
}
|
2016
|
1
|
data = (char *)view.buf;
|
2017
|
|
/*
|
2018
|
|
* In Python 3 both of the deprecated functions PyObject_AsWriteBuffer and
|
2019
|
|
* PyObject_AsReadBuffer that this code replaces release the buffer. It is
|
2020
|
|
* up to the object that supplies the buffer to guarantee that the buffer
|
2021
|
|
* sticks around after the release.
|
2022
|
|
*/
|
2023
|
1
|
PyBuffer_Release(&view);
|
2024
|
|
|
2025
|
|
/* Get offset number from interface specification */
|
2026
|
1
|
attr = _PyDict_GetItemStringWithError(iface, "offset");
|
2027
|
1
|
if (attr == NULL && PyErr_Occurred()) {
|
2028
|
|
goto fail;
|
2029
|
|
}
|
2030
|
1
|
else if (attr) {
|
2031
|
1
|
npy_longlong num = PyLong_AsLongLong(attr);
|
2032
|
1
|
if (error_converting(num)) {
|
2033
|
0
|
PyErr_SetString(PyExc_TypeError,
|
2034
|
|
"__array_interface__ offset must be an integer");
|
2035
|
0
|
goto fail;
|
2036
|
|
}
|
2037
|
1
|
data += num;
|
2038
|
|
}
|
2039
|
|
}
|
2040
|
|
|
2041
|
1
|
ret = (PyArrayObject *)PyArray_NewFromDescrAndBase(
|
2042
|
|
&PyArray_Type, dtype,
|
2043
|
|
n, dims, NULL, data,
|
2044
|
|
dataflags, NULL, base);
|
2045
|
|
/*
|
2046
|
|
* Ref to dtype was stolen by PyArray_NewFromDescrAndBase
|
2047
|
|
* Prevent DECREFing dtype in fail codepath by setting to NULL
|
2048
|
|
*/
|
2049
|
1
|
dtype = NULL;
|
2050
|
1
|
if (ret == NULL) {
|
2051
|
|
goto fail;
|
2052
|
|
}
|
2053
|
1
|
if (data == NULL) {
|
2054
|
1
|
if (PyArray_SIZE(ret) > 1) {
|
2055
|
1
|
PyErr_SetString(PyExc_ValueError,
|
2056
|
|
"cannot coerce scalar to array with size > 1");
|
2057
|
1
|
Py_DECREF(ret);
|
2058
|
|
goto fail;
|
2059
|
|
}
|
2060
|
1
|
if (PyArray_SETITEM(ret, PyArray_DATA(ret), origin) < 0) {
|
2061
|
0
|
Py_DECREF(ret);
|
2062
|
|
goto fail;
|
2063
|
|
}
|
2064
|
|
}
|
2065
|
1
|
attr = _PyDict_GetItemStringWithError(iface, "strides");
|
2066
|
1
|
if (attr == NULL && PyErr_Occurred()){
|
2067
|
|
return NULL;
|
2068
|
|
}
|
2069
|
1
|
if (attr != NULL && attr != Py_None) {
|
2070
|
1
|
if (!PyTuple_Check(attr)) {
|
2071
|
1
|
PyErr_SetString(PyExc_TypeError,
|
2072
|
|
"strides must be a tuple");
|
2073
|
1
|
Py_DECREF(ret);
|
2074
|
|
goto fail;
|
2075
|
|
}
|
2076
|
1
|
if (n != PyTuple_GET_SIZE(attr)) {
|
2077
|
1
|
PyErr_SetString(PyExc_ValueError,
|
2078
|
|
"mismatch in length of strides and shape");
|
2079
|
1
|
Py_DECREF(ret);
|
2080
|
|
goto fail;
|
2081
|
|
}
|
2082
|
1
|
for (i = 0; i < n; i++) {
|
2083
|
1
|
PyObject *tmp = PyTuple_GET_ITEM(attr, i);
|
2084
|
1
|
strides[i] = PyArray_PyIntAsIntp(tmp);
|
2085
|
1
|
if (error_converting(strides[i])) {
|
2086
|
0
|
Py_DECREF(ret);
|
2087
|
|
goto fail;
|
2088
|
|
}
|
2089
|
|
}
|
2090
|
1
|
if (n) {
|
2091
|
1
|
memcpy(PyArray_STRIDES(ret), strides, n*sizeof(npy_intp));
|
2092
|
|
}
|
2093
|
|
}
|
2094
|
1
|
PyArray_UpdateFlags(ret, NPY_ARRAY_UPDATE_ALL);
|
2095
|
1
|
Py_DECREF(iface);
|
2096
|
|
return (PyObject *)ret;
|
2097
|
|
|
2098
|
1
|
fail:
|
2099
|
1
|
Py_XDECREF(dtype);
|
2100
|
1
|
Py_XDECREF(iface);
|
2101
|
|
return NULL;
|
2102
|
|
}
|
2103
|
|
|
2104
|
|
/*NUMPY_API
|
2105
|
|
*/
|
2106
|
|
NPY_NO_EXPORT PyObject *
|
2107
|
1
|
PyArray_FromArrayAttr(PyObject *op, PyArray_Descr *typecode, PyObject *context)
|
2108
|
|
{
|
2109
|
|
PyObject *new;
|
2110
|
|
PyObject *array_meth;
|
2111
|
|
|
2112
|
1
|
if (context != NULL) {
|
2113
|
0
|
PyErr_SetString(PyExc_RuntimeError, "'context' must be NULL");
|
2114
|
0
|
return NULL;
|
2115
|
|
}
|
2116
|
1
|
array_meth = PyArray_LookupSpecial_OnInstance(op, "__array__");
|
2117
|
1
|
if (array_meth == NULL) {
|
2118
|
1
|
if (PyErr_Occurred()) {
|
2119
|
0
|
PyErr_Clear(); /* TODO[gh-14801]: propagate crashes during attribute access? */
|
2120
|
|
}
|
2121
|
|
return Py_NotImplemented;
|
2122
|
|
}
|
2123
|
1
|
if (PyType_Check(op) && PyObject_HasAttrString(array_meth, "__get__")) {
|
2124
|
|
/*
|
2125
|
|
* If the input is a class `array_meth` may be a property-like object.
|
2126
|
|
* This cannot be interpreted as an array (called), but is a valid.
|
2127
|
|
* Trying `array_meth.__call__()` on this should not be useful.
|
2128
|
|
* (Needed due to the lookup being on the instance rather than type)
|
2129
|
|
*/
|
2130
|
1
|
Py_DECREF(array_meth);
|
2131
|
|
return Py_NotImplemented;
|
2132
|
|
}
|
2133
|
1
|
if (typecode == NULL) {
|
2134
|
1
|
new = PyObject_CallFunction(array_meth, NULL);
|
2135
|
|
}
|
2136
|
|
else {
|
2137
|
1
|
new = PyObject_CallFunction(array_meth, "O", typecode);
|
2138
|
|
}
|
2139
|
1
|
Py_DECREF(array_meth);
|
2140
|
1
|
if (new == NULL) {
|
2141
|
|
return NULL;
|
2142
|
|
}
|
2143
|
1
|
if (!PyArray_Check(new)) {
|
2144
|
1
|
PyErr_SetString(PyExc_ValueError,
|
2145
|
|
"object __array__ method not " \
|
2146
|
|
"producing an array");
|
2147
|
1
|
Py_DECREF(new);
|
2148
|
|
return NULL;
|
2149
|
|
}
|
2150
|
|
return new;
|
2151
|
|
}
|
2152
|
|
|
2153
|
|
/*NUMPY_API
|
2154
|
|
* new reference -- accepts NULL for mintype
|
2155
|
|
*/
|
2156
|
|
NPY_NO_EXPORT PyArray_Descr *
|
2157
|
1
|
PyArray_DescrFromObject(PyObject *op, PyArray_Descr *mintype)
|
2158
|
|
{
|
2159
|
|
PyArray_Descr *dtype;
|
2160
|
|
|
2161
|
1
|
dtype = mintype;
|
2162
|
1
|
Py_XINCREF(dtype);
|
2163
|
|
|
2164
|
1
|
if (PyArray_DTypeFromObject(op, NPY_MAXDIMS, &dtype) < 0) {
|
2165
|
|
return NULL;
|
2166
|
|
}
|
2167
|
|
|
2168
|
1
|
if (dtype == NULL) {
|
2169
|
0
|
return PyArray_DescrFromType(NPY_DEFAULT_TYPE);
|
2170
|
|
}
|
2171
|
|
else {
|
2172
|
|
return dtype;
|
2173
|
|
}
|
2174
|
|
}
|
2175
|
|
|
2176
|
|
/* These are also old calls (should use PyArray_NewFromDescr) */
|
2177
|
|
|
2178
|
|
/* They all zero-out the memory as previously done */
|
2179
|
|
|
2180
|
|
/* steals reference to descr -- and enforces native byteorder on it.*/
|
2181
|
|
|
2182
|
|
/*NUMPY_API
|
2183
|
|
Deprecated, use PyArray_NewFromDescr instead.
|
2184
|
|
*/
|
2185
|
|
NPY_NO_EXPORT PyObject *
|
2186
|
0
|
PyArray_FromDimsAndDataAndDescr(int NPY_UNUSED(nd), int *NPY_UNUSED(d),
|
2187
|
|
PyArray_Descr *descr,
|
2188
|
|
char *NPY_UNUSED(data))
|
2189
|
|
{
|
2190
|
0
|
PyErr_SetString(PyExc_NotImplementedError,
|
2191
|
|
"PyArray_FromDimsAndDataAndDescr: use PyArray_NewFromDescr.");
|
2192
|
0
|
Py_DECREF(descr);
|
2193
|
0
|
return NULL;
|
2194
|
|
}
|
2195
|
|
|
2196
|
|
/*NUMPY_API
|
2197
|
|
Deprecated, use PyArray_SimpleNew instead.
|
2198
|
|
*/
|
2199
|
|
NPY_NO_EXPORT PyObject *
|
2200
|
0
|
PyArray_FromDims(int NPY_UNUSED(nd), int *NPY_UNUSED(d), int NPY_UNUSED(type))
|
2201
|
|
{
|
2202
|
0
|
PyErr_SetString(PyExc_NotImplementedError,
|
2203
|
|
"PyArray_FromDims: use PyArray_SimpleNew.");
|
2204
|
0
|
return NULL;
|
2205
|
|
}
|
2206
|
|
|
2207
|
|
/* end old calls */
|
2208
|
|
|
2209
|
|
/*NUMPY_API
|
2210
|
|
* This is a quick wrapper around
|
2211
|
|
* PyArray_FromAny(op, NULL, 0, 0, NPY_ARRAY_ENSUREARRAY, NULL)
|
2212
|
|
* that special cases Arrays and PyArray_Scalars up front
|
2213
|
|
* It *steals a reference* to the object
|
2214
|
|
* It also guarantees that the result is PyArray_Type
|
2215
|
|
* Because it decrefs op if any conversion needs to take place
|
2216
|
|
* so it can be used like PyArray_EnsureArray(some_function(...))
|
2217
|
|
*/
|
2218
|
|
NPY_NO_EXPORT PyObject *
|
2219
|
1
|
PyArray_EnsureArray(PyObject *op)
|
2220
|
|
{
|
2221
|
|
PyObject *new;
|
2222
|
|
|
2223
|
1
|
if ((op == NULL) || (PyArray_CheckExact(op))) {
|
2224
|
1
|
new = op;
|
2225
|
1
|
Py_XINCREF(new);
|
2226
|
|
}
|
2227
|
1
|
else if (PyArray_Check(op)) {
|
2228
|
0
|
new = PyArray_View((PyArrayObject *)op, NULL, &PyArray_Type);
|
2229
|
|
}
|
2230
|
1
|
else if (PyArray_IsScalar(op, Generic)) {
|
2231
|
0
|
new = PyArray_FromScalar(op, NULL);
|
2232
|
|
}
|
2233
|
|
else {
|
2234
|
1
|
new = PyArray_FROM_OF(op, NPY_ARRAY_ENSUREARRAY);
|
2235
|
|
}
|
2236
|
1
|
Py_XDECREF(op);
|
2237
|
1
|
return new;
|
2238
|
|
}
|
2239
|
|
|
2240
|
|
/*NUMPY_API*/
|
2241
|
|
NPY_NO_EXPORT PyObject *
|
2242
|
1
|
PyArray_EnsureAnyArray(PyObject *op)
|
2243
|
|
{
|
2244
|
1
|
if (op && PyArray_Check(op)) {
|
2245
|
|
return op;
|
2246
|
|
}
|
2247
|
0
|
return PyArray_EnsureArray(op);
|
2248
|
|
}
|
2249
|
|
|
2250
|
|
/*
|
2251
|
|
* Private implementation of PyArray_CopyAnyInto with an additional order
|
2252
|
|
* parameter.
|
2253
|
|
*/
|
2254
|
|
NPY_NO_EXPORT int
|
2255
|
1
|
PyArray_CopyAsFlat(PyArrayObject *dst, PyArrayObject *src, NPY_ORDER order)
|
2256
|
|
{
|
2257
|
1
|
PyArray_StridedUnaryOp *stransfer = NULL;
|
2258
|
1
|
NpyAuxData *transferdata = NULL;
|
2259
|
|
NpyIter *dst_iter, *src_iter;
|
2260
|
|
|
2261
|
|
NpyIter_IterNextFunc *dst_iternext, *src_iternext;
|
2262
|
|
char **dst_dataptr, **src_dataptr;
|
2263
|
|
npy_intp dst_stride, src_stride;
|
2264
|
|
npy_intp *dst_countptr, *src_countptr;
|
2265
|
|
npy_uint32 baseflags;
|
2266
|
|
|
2267
|
|
char *dst_data, *src_data;
|
2268
|
|
npy_intp dst_count, src_count, count;
|
2269
|
|
npy_intp src_itemsize;
|
2270
|
|
npy_intp dst_size, src_size;
|
2271
|
|
int needs_api;
|
2272
|
|
|
2273
|
1
|
NPY_BEGIN_THREADS_DEF;
|
2274
|
|
|
2275
|
1
|
if (PyArray_FailUnlessWriteable(dst, "destination array") < 0) {
|
2276
|
|
return -1;
|
2277
|
|
}
|
2278
|
|
|
2279
|
|
/*
|
2280
|
|
* If the shapes match and a particular order is forced
|
2281
|
|
* for both, use the more efficient CopyInto
|
2282
|
|
*/
|
2283
|
1
|
if (order != NPY_ANYORDER && order != NPY_KEEPORDER &&
|
2284
|
1
|
PyArray_NDIM(dst) == PyArray_NDIM(src) &&
|
2285
|
1
|
PyArray_CompareLists(PyArray_DIMS(dst), PyArray_DIMS(src),
|
2286
|
|
PyArray_NDIM(dst))) {
|
2287
|
1
|
return PyArray_CopyInto(dst, src);
|
2288
|
|
}
|
2289
|
|
|
2290
|
1
|
dst_size = PyArray_SIZE(dst);
|
2291
|
1
|
src_size = PyArray_SIZE(src);
|
2292
|
1
|
if (dst_size != src_size) {
|
2293
|
0
|
PyErr_Format(PyExc_ValueError,
|
2294
|
|
"cannot copy from array of size %" NPY_INTP_FMT " into an array "
|
2295
|
|
"of size %" NPY_INTP_FMT, src_size, dst_size);
|
2296
|
0
|
return -1;
|
2297
|
|
}
|
2298
|
|
|
2299
|
|
/* Zero-sized arrays require nothing be done */
|
2300
|
1
|
if (dst_size == 0) {
|
2301
|
|
return 0;
|
2302
|
|
}
|
2303
|
|
|
2304
|
1
|
baseflags = NPY_ITER_EXTERNAL_LOOP |
|
2305
|
|
NPY_ITER_DONT_NEGATE_STRIDES |
|
2306
|
|
NPY_ITER_REFS_OK;
|
2307
|
|
|
2308
|
|
/*
|
2309
|
|
* This copy is based on matching C-order traversals of src and dst.
|
2310
|
|
* By using two iterators, we can find maximal sub-chunks that
|
2311
|
|
* can be processed at once.
|
2312
|
|
*/
|
2313
|
1
|
dst_iter = NpyIter_New(dst, NPY_ITER_WRITEONLY | baseflags,
|
2314
|
|
order,
|
2315
|
|
NPY_NO_CASTING,
|
2316
|
|
NULL);
|
2317
|
1
|
if (dst_iter == NULL) {
|
2318
|
|
return -1;
|
2319
|
|
}
|
2320
|
1
|
src_iter = NpyIter_New(src, NPY_ITER_READONLY | baseflags,
|
2321
|
|
order,
|
2322
|
|
NPY_NO_CASTING,
|
2323
|
|
NULL);
|
2324
|
1
|
if (src_iter == NULL) {
|
2325
|
0
|
NpyIter_Deallocate(dst_iter);
|
2326
|
0
|
return -1;
|
2327
|
|
}
|
2328
|
|
|
2329
|
|
/* Get all the values needed for the inner loop */
|
2330
|
1
|
dst_iternext = NpyIter_GetIterNext(dst_iter, NULL);
|
2331
|
1
|
dst_dataptr = NpyIter_GetDataPtrArray(dst_iter);
|
2332
|
|
/* Since buffering is disabled, we can cache the stride */
|
2333
|
1
|
dst_stride = NpyIter_GetInnerStrideArray(dst_iter)[0];
|
2334
|
1
|
dst_countptr = NpyIter_GetInnerLoopSizePtr(dst_iter);
|
2335
|
|
|
2336
|
1
|
src_iternext = NpyIter_GetIterNext(src_iter, NULL);
|
2337
|
1
|
src_dataptr = NpyIter_GetDataPtrArray(src_iter);
|
2338
|
|
/* Since buffering is disabled, we can cache the stride */
|
2339
|
1
|
src_stride = NpyIter_GetInnerStrideArray(src_iter)[0];
|
2340
|
1
|
src_countptr = NpyIter_GetInnerLoopSizePtr(src_iter);
|
2341
|
1
|
src_itemsize = PyArray_DESCR(src)->elsize;
|
2342
|
|
|
2343
|
1
|
if (dst_iternext == NULL || src_iternext == NULL) {
|
2344
|
0
|
NpyIter_Deallocate(dst_iter);
|
2345
|
0
|
NpyIter_Deallocate(src_iter);
|
2346
|
0
|
return -1;
|
2347
|
|
}
|
2348
|
|
|
2349
|
1
|
needs_api = NpyIter_IterationNeedsAPI(dst_iter) ||
|
2350
|
1
|
NpyIter_IterationNeedsAPI(src_iter);
|
2351
|
|
|
2352
|
|
/*
|
2353
|
|
* Because buffering is disabled in the iterator, the inner loop
|
2354
|
|
* strides will be the same throughout the iteration loop. Thus,
|
2355
|
|
* we can pass them to this function to take advantage of
|
2356
|
|
* contiguous strides, etc.
|
2357
|
|
*/
|
2358
|
1
|
if (PyArray_GetDTypeTransferFunction(
|
2359
|
1
|
IsUintAligned(src) && IsAligned(src) &&
|
2360
|
1
|
IsUintAligned(dst) && IsAligned(dst),
|
2361
|
|
src_stride, dst_stride,
|
2362
|
|
PyArray_DESCR(src), PyArray_DESCR(dst),
|
2363
|
|
0,
|
2364
|
|
&stransfer, &transferdata,
|
2365
|
|
&needs_api) != NPY_SUCCEED) {
|
2366
|
0
|
NpyIter_Deallocate(dst_iter);
|
2367
|
0
|
NpyIter_Deallocate(src_iter);
|
2368
|
0
|
return -1;
|
2369
|
|
}
|
2370
|
|
|
2371
|
1
|
if (!needs_api) {
|
2372
|
1
|
NPY_BEGIN_THREADS;
|
2373
|
|
}
|
2374
|
|
|
2375
|
1
|
dst_count = *dst_countptr;
|
2376
|
1
|
src_count = *src_countptr;
|
2377
|
1
|
dst_data = dst_dataptr[0];
|
2378
|
1
|
src_data = src_dataptr[0];
|
2379
|
1
|
int res = 0;
|
2380
|
|
for(;;) {
|
2381
|
|
/* Transfer the biggest amount that fits both */
|
2382
|
1
|
count = (src_count < dst_count) ? src_count : dst_count;
|
2383
|
1
|
if (stransfer(
|
2384
|
|
dst_data, dst_stride, src_data, src_stride,
|
2385
|
|
count, src_itemsize, transferdata) < 0) {
|
2386
|
|
res = -1;
|
2387
|
|
break;
|
2388
|
|
}
|
2389
|
|
|
2390
|
|
/* If we exhausted the dst block, refresh it */
|
2391
|
1
|
if (dst_count == count) {
|
2392
|
1
|
res = dst_iternext(dst_iter);
|
2393
|
1
|
if (!res) {
|
2394
|
|
break;
|
2395
|
|
}
|
2396
|
0
|
dst_count = *dst_countptr;
|
2397
|
0
|
dst_data = dst_dataptr[0];
|
2398
|
|
}
|
2399
|
|
else {
|
2400
|
1
|
dst_count -= count;
|
2401
|
1
|
dst_data += count*dst_stride;
|
2402
|
|
}
|
2403
|
|
|
2404
|
|
/* If we exhausted the src block, refresh it */
|
2405
|
1
|
if (src_count == count) {
|
2406
|
1
|
res = src_iternext(src_iter);
|
2407
|
1
|
if (!res) {
|
2408
|
|
break;
|
2409
|
|
}
|
2410
|
1
|
src_count = *src_countptr;
|
2411
|
1
|
src_data = src_dataptr[0];
|
2412
|
|
}
|
2413
|
|
else {
|
2414
|
0
|
src_count -= count;
|
2415
|
0
|
src_data += count*src_stride;
|
2416
|
|
}
|
2417
|
|
}
|
2418
|
|
|
2419
|
1
|
NPY_END_THREADS;
|
2420
|
|
|
2421
|
1
|
NPY_AUXDATA_FREE(transferdata);
|
2422
|
1
|
NpyIter_Deallocate(dst_iter);
|
2423
|
1
|
NpyIter_Deallocate(src_iter);
|
2424
|
1
|
if (res > 0) {
|
2425
|
|
/* The iteration stopped successfully, do not report an error */
|
2426
|
|
return 0;
|
2427
|
|
}
|
2428
|
1
|
return res;
|
2429
|
|
}
|
2430
|
|
|
2431
|
|
/*NUMPY_API
|
2432
|
|
* Copy an Array into another array -- memory must not overlap
|
2433
|
|
* Does not require src and dest to have "broadcastable" shapes
|
2434
|
|
* (only the same number of elements).
|
2435
|
|
*
|
2436
|
|
* TODO: For NumPy 2.0, this could accept an order parameter which
|
2437
|
|
* only allows NPY_CORDER and NPY_FORDER. Could also rename
|
2438
|
|
* this to CopyAsFlat to make the name more intuitive.
|
2439
|
|
*
|
2440
|
|
* Returns 0 on success, -1 on error.
|
2441
|
|
*/
|
2442
|
|
NPY_NO_EXPORT int
|
2443
|
1
|
PyArray_CopyAnyInto(PyArrayObject *dst, PyArrayObject *src)
|
2444
|
|
{
|
2445
|
1
|
return PyArray_CopyAsFlat(dst, src, NPY_CORDER);
|
2446
|
|
}
|
2447
|
|
|
2448
|
|
/*NUMPY_API
|
2449
|
|
* Copy an Array into another array.
|
2450
|
|
* Broadcast to the destination shape if necessary.
|
2451
|
|
*
|
2452
|
|
* Returns 0 on success, -1 on failure.
|
2453
|
|
*/
|
2454
|
|
NPY_NO_EXPORT int
|
2455
|
1
|
PyArray_CopyInto(PyArrayObject *dst, PyArrayObject *src)
|
2456
|
|
{
|
2457
|
1
|
return PyArray_AssignArray(dst, src, NULL, NPY_UNSAFE_CASTING);
|
2458
|
|
}
|
2459
|
|
|
2460
|
|
/*NUMPY_API
|
2461
|
|
* Move the memory of one array into another, allowing for overlapping data.
|
2462
|
|
*
|
2463
|
|
* Returns 0 on success, negative on failure.
|
2464
|
|
*/
|
2465
|
|
NPY_NO_EXPORT int
|
2466
|
1
|
PyArray_MoveInto(PyArrayObject *dst, PyArrayObject *src)
|
2467
|
|
{
|
2468
|
1
|
return PyArray_AssignArray(dst, src, NULL, NPY_UNSAFE_CASTING);
|
2469
|
|
}
|
2470
|
|
|
2471
|
|
/*NUMPY_API
|
2472
|
|
* PyArray_CheckAxis
|
2473
|
|
*
|
2474
|
|
* check that axis is valid
|
2475
|
|
* convert 0-d arrays to 1-d arrays
|
2476
|
|
*/
|
2477
|
|
NPY_NO_EXPORT PyObject *
|
2478
|
1
|
PyArray_CheckAxis(PyArrayObject *arr, int *axis, int flags)
|
2479
|
|
{
|
2480
|
|
PyObject *temp1, *temp2;
|
2481
|
1
|
int n = PyArray_NDIM(arr);
|
2482
|
|
|
2483
|
1
|
if (*axis == NPY_MAXDIMS || n == 0) {
|
2484
|
1
|
if (n != 1) {
|
2485
|
1
|
temp1 = PyArray_Ravel(arr,0);
|
2486
|
1
|
if (temp1 == NULL) {
|
2487
|
0
|
*axis = 0;
|
2488
|
0
|
return NULL;
|
2489
|
|
}
|
2490
|
1
|
if (*axis == NPY_MAXDIMS) {
|
2491
|
1
|
*axis = PyArray_NDIM((PyArrayObject *)temp1)-1;
|
2492
|
|
}
|
2493
|
|
}
|
2494
|
|
else {
|
2495
|
1
|
temp1 = (PyObject *)arr;
|
2496
|
1
|
Py_INCREF(temp1);
|
2497
|
1
|
*axis = 0;
|
2498
|
|
}
|
2499
|
1
|
if (!flags && *axis == 0) {
|
2500
|
|
return temp1;
|
2501
|
|
}
|
2502
|
|
}
|
2503
|
|
else {
|
2504
|
1
|
temp1 = (PyObject *)arr;
|
2505
|
1
|
Py_INCREF(temp1);
|
2506
|
|
}
|
2507
|
1
|
if (flags) {
|
2508
|
1
|
temp2 = PyArray_CheckFromAny((PyObject *)temp1, NULL,
|
2509
|
|
0, 0, flags, NULL);
|
2510
|
1
|
Py_DECREF(temp1);
|
2511
|
1
|
if (temp2 == NULL) {
|
2512
|
|
return NULL;
|
2513
|
|
}
|
2514
|
|
}
|
2515
|
|
else {
|
2516
|
|
temp2 = (PyObject *)temp1;
|
2517
|
|
}
|
2518
|
1
|
n = PyArray_NDIM((PyArrayObject *)temp2);
|
2519
|
1
|
if (check_and_adjust_axis(axis, n) < 0) {
|
2520
|
1
|
Py_DECREF(temp2);
|
2521
|
|
return NULL;
|
2522
|
|
}
|
2523
|
|
return temp2;
|
2524
|
|
}
|
2525
|
|
|
2526
|
|
/*NUMPY_API
|
2527
|
|
* Zeros
|
2528
|
|
*
|
2529
|
|
* steals a reference to type. On failure or when dtype->subarray is
|
2530
|
|
* true, dtype will be decrefed.
|
2531
|
|
* accepts NULL type
|
2532
|
|
*/
|
2533
|
|
NPY_NO_EXPORT PyObject *
|
2534
|
1
|
PyArray_Zeros(int nd, npy_intp const *dims, PyArray_Descr *type, int is_f_order)
|
2535
|
|
{
|
2536
|
|
PyArrayObject *ret;
|
2537
|
|
|
2538
|
1
|
if (!type) {
|
2539
|
1
|
type = PyArray_DescrFromType(NPY_DEFAULT_TYPE);
|
2540
|
|
}
|
2541
|
|
|
2542
|
1
|
ret = (PyArrayObject *)PyArray_NewFromDescr_int(
|
2543
|
|
&PyArray_Type, type,
|
2544
|
|
nd, dims, NULL, NULL,
|
2545
|
|
is_f_order, NULL, NULL,
|
2546
|
|
1, 0);
|
2547
|
|
|
2548
|
1
|
if (ret == NULL) {
|
2549
|
|
return NULL;
|
2550
|
|
}
|
2551
|
|
|
2552
|
|
/* handle objects */
|
2553
|
1
|
if (PyDataType_REFCHK(PyArray_DESCR(ret))) {
|
2554
|
1
|
if (_zerofill(ret) < 0) {
|
2555
|
0
|
Py_DECREF(ret);
|
2556
|
|
return NULL;
|
2557
|
|
}
|
2558
|
|
}
|
2559
|
|
|
2560
|
|
|
2561
|
|
return (PyObject *)ret;
|
2562
|
|
|
2563
|
|
}
|
2564
|
|
|
2565
|
|
/*NUMPY_API
|
2566
|
|
* Empty
|
2567
|
|
*
|
2568
|
|
* accepts NULL type
|
2569
|
|
* steals a reference to type
|
2570
|
|
*/
|
2571
|
|
NPY_NO_EXPORT PyObject *
|
2572
|
1
|
PyArray_Empty(int nd, npy_intp const *dims, PyArray_Descr *type, int is_f_order)
|
2573
|
|
{
|
2574
|
|
PyArrayObject *ret;
|
2575
|
|
|
2576
|
1
|
if (!type) type = PyArray_DescrFromType(NPY_DEFAULT_TYPE);
|
2577
|
|
|
2578
|
|
/*
|
2579
|
|
* PyArray_NewFromDescr steals a ref,
|
2580
|
|
* but we need to look at type later.
|
2581
|
|
* */
|
2582
|
1
|
Py_INCREF(type);
|
2583
|
|
|
2584
|
1
|
ret = (PyArrayObject *)PyArray_NewFromDescr(&PyArray_Type,
|
2585
|
|
type, nd, dims,
|
2586
|
|
NULL, NULL,
|
2587
|
|
is_f_order, NULL);
|
2588
|
1
|
if (ret != NULL && PyDataType_REFCHK(type)) {
|
2589
|
1
|
PyArray_FillObjectArray(ret, Py_None);
|
2590
|
1
|
if (PyErr_Occurred()) {
|
2591
|
0
|
Py_DECREF(ret);
|
2592
|
0
|
Py_DECREF(type);
|
2593
|
|
return NULL;
|
2594
|
|
}
|
2595
|
|
}
|
2596
|
|
|
2597
|
1
|
Py_DECREF(type);
|
2598
|
|
return (PyObject *)ret;
|
2599
|
|
}
|
2600
|
|
|
2601
|
|
/*
|
2602
|
|
* Like ceil(value), but check for overflow.
|
2603
|
|
*
|
2604
|
|
* Return 0 on success, -1 on failure. In case of failure, set a PyExc_Overflow
|
2605
|
|
* exception
|
2606
|
|
*/
|
2607
|
|
static npy_intp
|
2608
|
1
|
_arange_safe_ceil_to_intp(double value)
|
2609
|
|
{
|
2610
|
|
double ivalue;
|
2611
|
|
|
2612
|
1
|
ivalue = npy_ceil(value);
|
2613
|
|
/* condition inverted to handle NaN */
|
2614
|
1
|
if (npy_isnan(ivalue)) {
|
2615
|
1
|
PyErr_SetString(PyExc_ValueError,
|
2616
|
|
"arange: cannot compute length");
|
2617
|
1
|
return -1;
|
2618
|
|
}
|
2619
|
1
|
if (!(NPY_MIN_INTP <= ivalue && ivalue <= NPY_MAX_INTP)) {
|
2620
|
1
|
PyErr_SetString(PyExc_OverflowError,
|
2621
|
|
"arange: overflow while computing length");
|
2622
|
1
|
return -1;
|
2623
|
|
}
|
2624
|
|
|
2625
|
1
|
return (npy_intp)ivalue;
|
2626
|
|
}
|
2627
|
|
|
2628
|
|
|
2629
|
|
/*NUMPY_API
|
2630
|
|
Arange,
|
2631
|
|
*/
|
2632
|
|
NPY_NO_EXPORT PyObject *
|
2633
|
0
|
PyArray_Arange(double start, double stop, double step, int type_num)
|
2634
|
|
{
|
2635
|
|
npy_intp length;
|
2636
|
|
PyArrayObject *range;
|
2637
|
|
PyArray_ArrFuncs *funcs;
|
2638
|
|
PyObject *obj;
|
2639
|
|
int ret;
|
2640
|
|
double delta, tmp_len;
|
2641
|
0
|
NPY_BEGIN_THREADS_DEF;
|
2642
|
|
|
2643
|
0
|
delta = stop - start;
|
2644
|
0
|
tmp_len = delta/step;
|
2645
|
|
|
2646
|
|
/* Underflow and divide-by-inf check */
|
2647
|
0
|
if (tmp_len == 0.0 && delta != 0.0) {
|
2648
|
0
|
if (npy_signbit(tmp_len)) {
|
2649
|
0
|
length = 0;
|
2650
|
|
}
|
2651
|
|
else {
|
2652
|
0
|
length = 1;
|
2653
|
|
}
|
2654
|
|
}
|
2655
|
|
else {
|
2656
|
0
|
length = _arange_safe_ceil_to_intp(tmp_len);
|
2657
|
0
|
if (error_converting(length)) {
|
2658
|
|
return NULL;
|
2659
|
|
}
|
2660
|
|
}
|
2661
|
|
|
2662
|
0
|
if (length <= 0) {
|
2663
|
0
|
length = 0;
|
2664
|
0
|
return PyArray_New(&PyArray_Type, 1, &length, type_num,
|
2665
|
|
NULL, NULL, 0, 0, NULL);
|
2666
|
|
}
|
2667
|
0
|
range = (PyArrayObject *)PyArray_New(&PyArray_Type, 1, &length, type_num,
|
2668
|
|
NULL, NULL, 0, 0, NULL);
|
2669
|
0
|
if (range == NULL) {
|
2670
|
|
return NULL;
|
2671
|
|
}
|
2672
|
0
|
funcs = PyArray_DESCR(range)->f;
|
2673
|
|
|
2674
|
|
/*
|
2675
|
|
* place start in the buffer and the next value in the second position
|
2676
|
|
* if length > 2, then call the inner loop, otherwise stop
|
2677
|
|
*/
|
2678
|
0
|
obj = PyFloat_FromDouble(start);
|
2679
|
0
|
ret = funcs->setitem(obj, PyArray_DATA(range), range);
|
2680
|
0
|
Py_DECREF(obj);
|
2681
|
0
|
if (ret < 0) {
|
2682
|
|
goto fail;
|
2683
|
|
}
|
2684
|
0
|
if (length == 1) {
|
2685
|
|
return (PyObject *)range;
|
2686
|
|
}
|
2687
|
0
|
obj = PyFloat_FromDouble(start + step);
|
2688
|
0
|
ret = funcs->setitem(obj, PyArray_BYTES(range)+PyArray_ITEMSIZE(range),
|
2689
|
|
range);
|
2690
|
0
|
Py_DECREF(obj);
|
2691
|
0
|
if (ret < 0) {
|
2692
|
|
goto fail;
|
2693
|
|
}
|
2694
|
0
|
if (length == 2) {
|
2695
|
|
return (PyObject *)range;
|
2696
|
|
}
|
2697
|
0
|
if (!funcs->fill) {
|
2698
|
0
|
PyErr_SetString(PyExc_ValueError,
|
2699
|
|
"no fill-function for data-type.");
|
2700
|
0
|
Py_DECREF(range);
|
2701
|
|
return NULL;
|
2702
|
|
}
|
2703
|
0
|
NPY_BEGIN_THREADS_DESCR(PyArray_DESCR(range));
|
2704
|
0
|
funcs->fill(PyArray_DATA(range), length, range);
|
2705
|
0
|
NPY_END_THREADS;
|
2706
|
0
|
if (PyErr_Occurred()) {
|
2707
|
|
goto fail;
|
2708
|
|
}
|
2709
|
|
return (PyObject *)range;
|
2710
|
|
|
2711
|
0
|
fail:
|
2712
|
0
|
Py_DECREF(range);
|
2713
|
|
return NULL;
|
2714
|
|
}
|
2715
|
|
|
2716
|
|
/*
|
2717
|
|
* the formula is len = (intp) ceil((stop - start) / step);
|
2718
|
|
*/
|
2719
|
|
static npy_intp
|
2720
|
1
|
_calc_length(PyObject *start, PyObject *stop, PyObject *step, PyObject **next, int cmplx)
|
2721
|
|
{
|
2722
|
|
npy_intp len, tmp;
|
2723
|
|
PyObject *zero, *val;
|
2724
|
|
int next_is_nonzero, val_is_zero;
|
2725
|
|
double value;
|
2726
|
|
|
2727
|
1
|
*next = PyNumber_Subtract(stop, start);
|
2728
|
1
|
if (!(*next)) {
|
2729
|
0
|
if (PyTuple_Check(stop)) {
|
2730
|
0
|
PyErr_Clear();
|
2731
|
0
|
PyErr_SetString(PyExc_TypeError,
|
2732
|
|
"arange: scalar arguments expected "\
|
2733
|
|
"instead of a tuple.");
|
2734
|
|
}
|
2735
|
|
return -1;
|
2736
|
|
}
|
2737
|
|
|
2738
|
1
|
zero = PyLong_FromLong(0);
|
2739
|
1
|
if (!zero) {
|
2740
|
0
|
Py_DECREF(*next);
|
2741
|
0
|
*next = NULL;
|
2742
|
0
|
return -1;
|
2743
|
|
}
|
2744
|
|
|
2745
|
1
|
next_is_nonzero = PyObject_RichCompareBool(*next, zero, Py_NE);
|
2746
|
1
|
if (next_is_nonzero == -1) {
|
2747
|
0
|
Py_DECREF(zero);
|
2748
|
0
|
Py_DECREF(*next);
|
2749
|
0
|
*next = NULL;
|
2750
|
0
|
return -1;
|
2751
|
|
}
|
2752
|
1
|
val = PyNumber_TrueDivide(*next, step);
|
2753
|
1
|
Py_DECREF(*next);
|
2754
|
1
|
*next = NULL;
|
2755
|
|
|
2756
|
1
|
if (!val) {
|
2757
|
1
|
Py_DECREF(zero);
|
2758
|
|
return -1;
|
2759
|
|
}
|
2760
|
|
|
2761
|
1
|
val_is_zero = PyObject_RichCompareBool(val, zero, Py_EQ);
|
2762
|
1
|
Py_DECREF(zero);
|
2763
|
1
|
if (val_is_zero == -1) {
|
2764
|
0
|
Py_DECREF(val);
|
2765
|
|
return -1;
|
2766
|
|
}
|
2767
|
|
|
2768
|
1
|
if (cmplx && PyComplex_Check(val)) {
|
2769
|
0
|
value = PyComplex_RealAsDouble(val);
|
2770
|
0
|
if (error_converting(value)) {
|
2771
|
0
|
Py_DECREF(val);
|
2772
|
|
return -1;
|
2773
|
|
}
|
2774
|
0
|
len = _arange_safe_ceil_to_intp(value);
|
2775
|
0
|
if (error_converting(len)) {
|
2776
|
0
|
Py_DECREF(val);
|
2777
|
|
return -1;
|
2778
|
|
}
|
2779
|
0
|
value = PyComplex_ImagAsDouble(val);
|
2780
|
0
|
Py_DECREF(val);
|
2781
|
0
|
if (error_converting(value)) {
|
2782
|
|
return -1;
|
2783
|
|
}
|
2784
|
0
|
tmp = _arange_safe_ceil_to_intp(value);
|
2785
|
0
|
if (error_converting(tmp)) {
|
2786
|
|
return -1;
|
2787
|
|
}
|
2788
|
0
|
len = PyArray_MIN(len, tmp);
|
2789
|
|
}
|
2790
|
|
else {
|
2791
|
1
|
value = PyFloat_AsDouble(val);
|
2792
|
1
|
Py_DECREF(val);
|
2793
|
1
|
if (error_converting(value)) {
|
2794
|
|
return -1;
|
2795
|
|
}
|
2796
|
|
|
2797
|
|
/* Underflow and divide-by-inf check */
|
2798
|
1
|
if (val_is_zero && next_is_nonzero) {
|
2799
|
1
|
if (npy_signbit(value)) {
|
2800
|
|
len = 0;
|
2801
|
|
}
|
2802
|
|
else {
|
2803
|
|
len = 1;
|
2804
|
|
}
|
2805
|
|
}
|
2806
|
|
else {
|
2807
|
1
|
len = _arange_safe_ceil_to_intp(value);
|
2808
|
1
|
if (error_converting(len)) {
|
2809
|
|
return -1;
|
2810
|
|
}
|
2811
|
|
}
|
2812
|
|
}
|
2813
|
|
|
2814
|
1
|
if (len > 0) {
|
2815
|
1
|
*next = PyNumber_Add(start, step);
|
2816
|
1
|
if (!*next) {
|
2817
|
|
return -1;
|
2818
|
|
}
|
2819
|
|
}
|
2820
|
|
return len;
|
2821
|
|
}
|
2822
|
|
|
2823
|
|
/*NUMPY_API
|
2824
|
|
*
|
2825
|
|
* ArangeObj,
|
2826
|
|
*
|
2827
|
|
* this doesn't change the references
|
2828
|
|
*/
|
2829
|
|
NPY_NO_EXPORT PyObject *
|
2830
|
1
|
PyArray_ArangeObj(PyObject *start, PyObject *stop, PyObject *step, PyArray_Descr *dtype)
|
2831
|
|
{
|
2832
|
|
PyArrayObject *range;
|
2833
|
|
PyArray_ArrFuncs *funcs;
|
2834
|
|
PyObject *next, *err;
|
2835
|
|
npy_intp length;
|
2836
|
1
|
PyArray_Descr *native = NULL;
|
2837
|
|
int swap;
|
2838
|
1
|
NPY_BEGIN_THREADS_DEF;
|
2839
|
|
|
2840
|
|
/* Datetime arange is handled specially */
|
2841
|
1
|
if ((dtype != NULL && (dtype->type_num == NPY_DATETIME ||
|
2842
|
1
|
dtype->type_num == NPY_TIMEDELTA)) ||
|
2843
|
1
|
(dtype == NULL && (is_any_numpy_datetime_or_timedelta(start) ||
|
2844
|
1
|
is_any_numpy_datetime_or_timedelta(stop) ||
|
2845
|
1
|
is_any_numpy_datetime_or_timedelta(step)))) {
|
2846
|
1
|
return (PyObject *)datetime_arange(start, stop, step, dtype);
|
2847
|
|
}
|
2848
|
|
|
2849
|
1
|
if (!dtype) {
|
2850
|
|
PyArray_Descr *deftype;
|
2851
|
|
PyArray_Descr *newtype;
|
2852
|
|
|
2853
|
|
/* intentionally made to be at least NPY_LONG */
|
2854
|
1
|
deftype = PyArray_DescrFromType(NPY_LONG);
|
2855
|
1
|
newtype = PyArray_DescrFromObject(start, deftype);
|
2856
|
1
|
Py_DECREF(deftype);
|
2857
|
1
|
if (newtype == NULL) {
|
2858
|
|
return NULL;
|
2859
|
|
}
|
2860
|
1
|
deftype = newtype;
|
2861
|
1
|
if (stop && stop != Py_None) {
|
2862
|
1
|
newtype = PyArray_DescrFromObject(stop, deftype);
|
2863
|
1
|
Py_DECREF(deftype);
|
2864
|
1
|
if (newtype == NULL) {
|
2865
|
|
return NULL;
|
2866
|
|
}
|
2867
|
|
deftype = newtype;
|
2868
|
|
}
|
2869
|
1
|
if (step && step != Py_None) {
|
2870
|
1
|
newtype = PyArray_DescrFromObject(step, deftype);
|
2871
|
1
|
Py_DECREF(deftype);
|
2872
|
1
|
if (newtype == NULL) {
|
2873
|
|
return NULL;
|
2874
|
|
}
|
2875
|
|
deftype = newtype;
|
2876
|
|
}
|
2877
|
|
dtype = deftype;
|
2878
|
|
}
|
2879
|
|
else {
|
2880
|
1
|
Py_INCREF(dtype);
|
2881
|
|
}
|
2882
|
1
|
if (!step || step == Py_None) {
|
2883
|
1
|
step = PyLong_FromLong(1);
|
2884
|
|
}
|
2885
|
|
else {
|
2886
|
1
|
Py_XINCREF(step);
|
2887
|
|
}
|
2888
|
1
|
if (!stop || stop == Py_None) {
|
2889
|
1
|
stop = start;
|
2890
|
1
|
start = PyLong_FromLong(0);
|
2891
|
|
}
|
2892
|
|
else {
|
2893
|
1
|
Py_INCREF(start);
|
2894
|
|
}
|
2895
|
|
/* calculate the length and next = start + step*/
|
2896
|
1
|
length = _calc_length(start, stop, step, &next,
|
2897
|
1
|
PyTypeNum_ISCOMPLEX(dtype->type_num));
|
2898
|
1
|
err = PyErr_Occurred();
|
2899
|
1
|
if (err) {
|
2900
|
1
|
Py_DECREF(dtype);
|
2901
|
1
|
if (err && PyErr_GivenExceptionMatches(err, PyExc_OverflowError)) {
|
2902
|
1
|
PyErr_SetString(PyExc_ValueError, "Maximum allowed size exceeded");
|
2903
|
|
}
|
2904
|
|
goto fail;
|
2905
|
|
}
|
2906
|
1
|
if (length <= 0) {
|
2907
|
1
|
length = 0;
|
2908
|
1
|
range = (PyArrayObject *)PyArray_SimpleNewFromDescr(1, &length, dtype);
|
2909
|
1
|
Py_DECREF(step);
|
2910
|
1
|
Py_DECREF(start);
|
2911
|
|
return (PyObject *)range;
|
2912
|
|
}
|
2913
|
|
|
2914
|
|
/*
|
2915
|
|
* If dtype is not in native byte-order then get native-byte
|
2916
|
|
* order version. And then swap on the way out.
|
2917
|
|
*/
|
2918
|
1
|
if (!PyArray_ISNBO(dtype->byteorder)) {
|
2919
|
1
|
native = PyArray_DescrNewByteorder(dtype, NPY_NATBYTE);
|
2920
|
1
|
swap = 1;
|
2921
|
|
}
|
2922
|
|
else {
|
2923
|
|
native = dtype;
|
2924
|
|
swap = 0;
|
2925
|
|
}
|
2926
|
|
|
2927
|
1
|
range = (PyArrayObject *)PyArray_SimpleNewFromDescr(1, &length, native);
|
2928
|
1
|
if (range == NULL) {
|
2929
|
|
goto fail;
|
2930
|
|
}
|
2931
|
|
|
2932
|
|
/*
|
2933
|
|
* place start in the buffer and the next value in the second position
|
2934
|
|
* if length > 2, then call the inner loop, otherwise stop
|
2935
|
|
*/
|
2936
|
1
|
funcs = PyArray_DESCR(range)->f;
|
2937
|
1
|
if (funcs->setitem(start, PyArray_DATA(range), range) < 0) {
|
2938
|
|
goto fail;
|
2939
|
|
}
|
2940
|
1
|
if (length == 1) {
|
2941
|
|
goto finish;
|
2942
|
|
}
|
2943
|
1
|
if (funcs->setitem(next, PyArray_BYTES(range)+PyArray_ITEMSIZE(range),
|
2944
|
|
range) < 0) {
|
2945
|
|
goto fail;
|
2946
|
|
}
|
2947
|
1
|
if (length == 2) {
|
2948
|
|
goto finish;
|
2949
|
|
}
|
2950
|
1
|
if (!funcs->fill) {
|
2951
|
0
|
PyErr_SetString(PyExc_ValueError, "no fill-function for data-type.");
|
2952
|
0
|
Py_DECREF(range);
|
2953
|
|
goto fail;
|
2954
|
|
}
|
2955
|
1
|
NPY_BEGIN_THREADS_DESCR(PyArray_DESCR(range));
|
2956
|
1
|
funcs->fill(PyArray_DATA(range), length, range);
|
2957
|
1
|
NPY_END_THREADS;
|
2958
|
1
|
if (PyErr_Occurred()) {
|
2959
|
|
goto fail;
|
2960
|
|
}
|
2961
|
1
|
finish:
|
2962
|
|
/* TODO: This swapping could be handled on the fly by the nditer */
|
2963
|
1
|
if (swap) {
|
2964
|
|
PyObject *new;
|
2965
|
1
|
new = PyArray_Byteswap(range, 1);
|
2966
|
1
|
Py_DECREF(new);
|
2967
|
1
|
Py_DECREF(PyArray_DESCR(range));
|
2968
|
|
/* steals the reference */
|
2969
|
1
|
((PyArrayObject_fields *)range)->descr = dtype;
|
2970
|
|
}
|
2971
|
1
|
Py_DECREF(start);
|
2972
|
1
|
Py_DECREF(step);
|
2973
|
1
|
Py_DECREF(next);
|
2974
|
|
return (PyObject *)range;
|
2975
|
|
|
2976
|
0
|
fail:
|
2977
|
1
|
Py_DECREF(start);
|
2978
|
1
|
Py_DECREF(step);
|
2979
|
1
|
Py_XDECREF(next);
|
2980
|
|
return NULL;
|
2981
|
|
}
|
2982
|
|
|
2983
|
|
/* This array creation function steals the reference to dtype. */
|
2984
|
|
static PyArrayObject *
|
2985
|
1
|
array_fromfile_binary(FILE *fp, PyArray_Descr *dtype, npy_intp num, size_t *nread)
|
2986
|
|
{
|
2987
|
|
PyArrayObject *r;
|
2988
|
|
npy_off_t start, numbytes;
|
2989
|
|
int elsize;
|
2990
|
|
|
2991
|
1
|
if (num < 0) {
|
2992
|
1
|
int fail = 0;
|
2993
|
1
|
start = npy_ftell(fp);
|
2994
|
1
|
if (start < 0) {
|
2995
|
0
|
fail = 1;
|
2996
|
|
}
|
2997
|
1
|
if (npy_fseek(fp, 0, SEEK_END) < 0) {
|
2998
|
0
|
fail = 1;
|
2999
|
|
}
|
3000
|
1
|
numbytes = npy_ftell(fp);
|
3001
|
1
|
if (numbytes < 0) {
|
3002
|
0
|
fail = 1;
|
3003
|
|
}
|
3004
|
1
|
numbytes -= start;
|
3005
|
1
|
if (npy_fseek(fp, start, SEEK_SET) < 0) {
|
3006
|
|
fail = 1;
|
3007
|
|
}
|
3008
|
1
|
if (fail) {
|
3009
|
0
|
PyErr_SetString(PyExc_IOError,
|
3010
|
|
"could not seek in file");
|
3011
|
0
|
Py_DECREF(dtype);
|
3012
|
|
return NULL;
|
3013
|
|
}
|
3014
|
1
|
num = numbytes / dtype->elsize;
|
3015
|
|
}
|
3016
|
|
|
3017
|
|
/*
|
3018
|
|
* Array creation may move sub-array dimensions from the dtype to array
|
3019
|
|
* dimensions, so we need to use the original element size when reading.
|
3020
|
|
*/
|
3021
|
1
|
elsize = dtype->elsize;
|
3022
|
|
|
3023
|
1
|
r = (PyArrayObject *)PyArray_NewFromDescr(&PyArray_Type, dtype, 1, &num,
|
3024
|
|
NULL, NULL, 0, NULL);
|
3025
|
1
|
if (r == NULL) {
|
3026
|
|
return NULL;
|
3027
|
|
}
|
3028
|
|
|
3029
|
1
|
NPY_BEGIN_ALLOW_THREADS;
|
3030
|
1
|
*nread = fread(PyArray_DATA(r), elsize, num, fp);
|
3031
|
1
|
NPY_END_ALLOW_THREADS;
|
3032
|
1
|
return r;
|
3033
|
|
}
|
3034
|
|
|
3035
|
|
/*
|
3036
|
|
* Create an array by reading from the given stream, using the passed
|
3037
|
|
* next_element and skip_separator functions.
|
3038
|
|
* As typical for array creation functions, it steals the reference to dtype.
|
3039
|
|
*/
|
3040
|
|
#define FROM_BUFFER_SIZE 4096
|
3041
|
|
static PyArrayObject *
|
3042
|
1
|
array_from_text(PyArray_Descr *dtype, npy_intp num, char const *sep, size_t *nread,
|
3043
|
|
void *stream, next_element next, skip_separator skip_sep,
|
3044
|
|
void *stream_data)
|
3045
|
|
{
|
3046
|
|
PyArrayObject *r;
|
3047
|
|
npy_intp i;
|
3048
|
|
char *dptr, *clean_sep, *tmp;
|
3049
|
1
|
int err = 0;
|
3050
|
1
|
int stop_reading_flag = 0; /* -1 means end reached; -2 a parsing error */
|
3051
|
1
|
npy_intp thisbuf = 0;
|
3052
|
|
npy_intp size;
|
3053
|
|
npy_intp bytes, totalbytes;
|
3054
|
|
|
3055
|
1
|
size = (num >= 0) ? num : FROM_BUFFER_SIZE;
|
3056
|
|
|
3057
|
|
/*
|
3058
|
|
* Array creation may move sub-array dimensions from the dtype to array
|
3059
|
|
* dimensions, so we need to use the original dtype when reading.
|
3060
|
|
*/
|
3061
|
1
|
Py_INCREF(dtype);
|
3062
|
|
|
3063
|
1
|
r = (PyArrayObject *)
|
3064
|
|
PyArray_NewFromDescr(&PyArray_Type, dtype, 1, &size,
|
3065
|
|
NULL, NULL, 0, NULL);
|
3066
|
1
|
if (r == NULL) {
|
3067
|
0
|
Py_DECREF(dtype);
|
3068
|
|
return NULL;
|
3069
|
|
}
|
3070
|
|
|
3071
|
1
|
clean_sep = swab_separator(sep);
|
3072
|
1
|
if (clean_sep == NULL) {
|
3073
|
|
err = 1;
|
3074
|
|
goto fail;
|
3075
|
|
}
|
3076
|
|
|
3077
|
1
|
NPY_BEGIN_ALLOW_THREADS;
|
3078
|
1
|
totalbytes = bytes = size * dtype->elsize;
|
3079
|
1
|
dptr = PyArray_DATA(r);
|
3080
|
1
|
for (i = 0; num < 0 || i < num; i++) {
|
3081
|
1
|
stop_reading_flag = next(&stream, dptr, dtype, stream_data);
|
3082
|
1
|
if (stop_reading_flag < 0) {
|
3083
|
|
break;
|
3084
|
|
}
|
3085
|
1
|
*nread += 1;
|
3086
|
1
|
thisbuf += 1;
|
3087
|
1
|
dptr += dtype->elsize;
|
3088
|
1
|
if (num < 0 && thisbuf == size) {
|
3089
|
0
|
totalbytes += bytes;
|
3090
|
0
|
tmp = PyDataMem_RENEW(PyArray_DATA(r), totalbytes);
|
3091
|
0
|
if (tmp == NULL) {
|
3092
|
|
err = 1;
|
3093
|
|
break;
|
3094
|
|
}
|
3095
|
0
|
((PyArrayObject_fields *)r)->data = tmp;
|
3096
|
0
|
dptr = tmp + (totalbytes - bytes);
|
3097
|
0
|
thisbuf = 0;
|
3098
|
|
}
|
3099
|
1
|
stop_reading_flag = skip_sep(&stream, clean_sep, stream_data);
|
3100
|
1
|
if (stop_reading_flag < 0) {
|
3101
|
1
|
if (num == i + 1) {
|
3102
|
|
/* if we read as much as requested sep is optional */
|
3103
|
1
|
stop_reading_flag = -1;
|
3104
|
|
}
|
3105
|
|
break;
|
3106
|
|
}
|
3107
|
|
}
|
3108
|
1
|
if (num < 0) {
|
3109
|
1
|
const size_t nsize = PyArray_MAX(*nread,1)*dtype->elsize;
|
3110
|
|
|
3111
|
1
|
if (nsize != 0) {
|
3112
|
1
|
tmp = PyDataMem_RENEW(PyArray_DATA(r), nsize);
|
3113
|
1
|
if (tmp == NULL) {
|
3114
|
|
err = 1;
|
3115
|
|
}
|
3116
|
|
else {
|
3117
|
1
|
PyArray_DIMS(r)[0] = *nread;
|
3118
|
1
|
((PyArrayObject_fields *)r)->data = tmp;
|
3119
|
|
}
|
3120
|
|
}
|
3121
|
|
}
|
3122
|
1
|
NPY_END_ALLOW_THREADS;
|
3123
|
|
|
3124
|
1
|
free(clean_sep);
|
3125
|
|
|
3126
|
1
|
if (stop_reading_flag == -2) {
|
3127
|
1
|
if (PyErr_Occurred()) {
|
3128
|
|
/* If an error is already set (unlikely), do not create new one */
|
3129
|
0
|
Py_DECREF(r);
|
3130
|
0
|
Py_DECREF(dtype);
|
3131
|
|
return NULL;
|
3132
|
|
}
|
3133
|
|
/* 2019-09-12, NumPy 1.18 */
|
3134
|
1
|
if (DEPRECATE(
|
3135
|
|
"string or file could not be read to its end due to unmatched "
|
3136
|
|
"data; this will raise a ValueError in the future.") < 0) {
|
3137
|
|
goto fail;
|
3138
|
|
}
|
3139
|
|
}
|
3140
|
|
|
3141
|
1
|
fail:
|
3142
|
1
|
Py_DECREF(dtype);
|
3143
|
1
|
if (err == 1) {
|
3144
|
0
|
PyErr_NoMemory();
|
3145
|
|
}
|
3146
|
1
|
if (PyErr_Occurred()) {
|
3147
|
1
|
Py_DECREF(r);
|
3148
|
|
return NULL;
|
3149
|
|
}
|
3150
|
|
return r;
|
3151
|
|
}
|
3152
|
|
#undef FROM_BUFFER_SIZE
|
3153
|
|
|
3154
|
|
/*NUMPY_API
|
3155
|
|
*
|
3156
|
|
* Given a ``FILE *`` pointer ``fp``, and a ``PyArray_Descr``, return an
|
3157
|
|
* array corresponding to the data encoded in that file.
|
3158
|
|
*
|
3159
|
|
* The reference to `dtype` is stolen (it is possible that the passed in
|
3160
|
|
* dtype is not held on to).
|
3161
|
|
*
|
3162
|
|
* The number of elements to read is given as ``num``; if it is < 0, then
|
3163
|
|
* then as many as possible are read.
|
3164
|
|
*
|
3165
|
|
* If ``sep`` is NULL or empty, then binary data is assumed, else
|
3166
|
|
* text data, with ``sep`` as the separator between elements. Whitespace in
|
3167
|
|
* the separator matches any length of whitespace in the text, and a match
|
3168
|
|
* for whitespace around the separator is added.
|
3169
|
|
*
|
3170
|
|
* For memory-mapped files, use the buffer interface. No more data than
|
3171
|
|
* necessary is read by this routine.
|
3172
|
|
*/
|