1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
|
#A place for code to be called from C-code
# that implements more complicated stuff.
import re
from multiarray import dtype, ndarray
def _makenames_list(adict):
allfields = []
fnames = adict.keys()
for fname in fnames:
obj = adict[fname]
n = len(obj)
if not isinstance(obj, tuple) or n not in [2,3]:
raise ValueError, "entry not a 2- or 3- tuple"
if (n > 2) and (obj[2] == fname):
continue
num = int(obj[1])
if (num < 0):
raise ValueError, "invalid offset."
format = dtype(obj[0])
if (format.itemsize == 0):
raise ValueError, "all itemsizes must be fixed."
if (n > 2):
title = obj[2]
else:
title = None
allfields.append((fname, format, num, title))
# sort by offsets
allfields.sort(lambda x,y: cmp(x[2],y[2]))
names = [x[0] for x in allfields]
formats = [x[1] for x in allfields]
offsets = [x[2] for x in allfields]
titles = [x[3] for x in allfields]
return names, formats, offsets, titles
# Called in PyArray_DescrConverter function when
# a dictionary without "names" and "formats"
# fields is used as a data-type descriptor.
def _usefields(adict, align):
try:
names = adict[-1]
except KeyError:
names = None
if names is None:
names, formats, offsets, titles = _makenames_list(adict)
else:
formats = []
offsets = []
titles = []
for name in names:
res = adict[name]
formats.append(res[0])
offsets.append(res[1])
if (len(res) > 2):
titles.append(res[2])
else:
titles.append(None)
return dtype({"names" : names,
"formats" : formats,
"offsets" : offsets,
"titles" : titles}, align)
# construct an array_protocol descriptor list
# from the fields attribute of a descriptor
# This calls itself recursively but should eventually hit
# a descriptor that has no fields and then return
# a simple typestring
def _array_descr(descriptor):
fields = descriptor.fields
if fields is None:
return descriptor.str
names = descriptor.names
ordered_fields = [fields[x] + (x,) for x in names]
result = []
offset = 0
for field in ordered_fields:
if field[1] > offset:
result.append(('','|V%d' % (field[1]-offset)))
if len(field) > 3:
name = (field[2],field[3])
else:
name = field[2]
if field[0].subdtype:
tup = (name, _array_descr(field[0].subdtype[0]),
field[0].subdtype[1])
else:
tup = (name, _array_descr(field[0]))
offset += field[0].itemsize
result.append(tup)
return result
# Build a new array from the information in a pickle.
# Note that the name numpy.core._internal._reconstruct is embedded in
# pickles of ndarrays made with NumPy before release 1.0
# so don't remove the name here, or you'll
# break backward compatibilty.
def _reconstruct(subtype, shape, dtype):
return ndarray.__new__(subtype, shape, dtype)
# format_re and _split were taken from numarray by J. Todd Miller
def _split(input):
"""Split the input formats string into field formats without splitting
the tuple used to specify multi-dimensional arrays."""
newlist = []
hold = ''
for element in input.split(','):
if hold != '':
item = hold + ',' + element
else:
item = element
left = item.count('(')
right = item.count(')')
# if the parenthesis is not balanced, hold the string
if left > right :
hold = item
# when balanced, append to the output list and reset the hold
elif left == right:
newlist.append(item.strip())
hold = ''
# too many close parenthesis is unacceptable
else:
raise SyntaxError, item
# if there is string left over in hold
if hold != '':
raise SyntaxError, hold
return newlist
format_re = re.compile(r'(?P<repeat> *[(]?[ ,0-9]*[)]? *)(?P<dtype>[><|A-Za-z0-9.]*)')
# astr is a string (perhaps comma separated)
def _commastring(astr):
res = _split(astr)
if (len(res)) < 1:
raise ValueError, "unrecognized formant"
result = []
for k,item in enumerate(res):
# convert item
try:
(repeats, dtype) = format_re.match(item).groups()
except (TypeError, AttributeError):
raise ValueError('format %s is not recognized' % item)
if (repeats == ''):
newitem = dtype
else:
newitem = (dtype, eval(repeats))
result.append(newitem)
return result
def _getintp_ctype():
if _getintp_ctype.cache:
return _getintp_ctype.cache
import ctypes
char = dtype('p').char
if (char == 'i'):
val = ctypes.c_int
elif char == 'l':
val = ctypes.c_long
elif char == 'q':
val = ctypes.c_longlong
else:
raise ValueError, "confused about intp->ctypes."
_getintp_ctype.cache = val
return val
_getintp_ctype.cache = None
# Used for .ctypes attribute of ndarray
class _ctypes(object):
def __init__(self, array):
try:
import ctypes
self._ctypes = ctypes
except ImportError:
raise AttributeError, "ctypes not available"
self._arr = array
if self._arr.ndim == 0:
self._zerod = True
else:
self._zerod = False
def data_as(self, obj):
return self._ctypes.cast(self._arr._as_parameter_, obj)
def shape_as(self, obj):
if self._zerod:
return None
return (obj*self._arr.ndim)(*self._arr.shape)
def strides_as(self, obj):
if self._zerod:
return None
return (obj*self._arr.ndim)(*self._arr.strides)
def get_data(self):
return self._ctypes.c_void_p(self._arr._as_parameter_)
def get_shape(self):
if self._zerod:
return None
return (_getintp_ctype()*self._arr.ndim)(*self._arr.shape)
def get_strides(self):
if self._zerod:
return None
return (_getintp_ctype()*self._arr.ndim)(*self._arr.strides)
data = property(get_data, None, doc="c-types data")
shape = property(get_shape, None, doc="c-types shape")
strides = property(get_strides, None, doc="c-types strides")
|
