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# missing Numarray defined names (in from numarray import *)
##__all__ = ['ClassicUnpickler', 'Complex32_fromtype',
## 'Complex64_fromtype', 'ComplexArray', 'Error',
## 'MAX_ALIGN', 'MAX_INT_SIZE', 'MAX_LINE_WIDTH',
## 'NDArray', 'NewArray', 'NumArray',
## 'NumError', 'PRECISION', 'Py2NumType',
## 'PyINT_TYPES', 'PyLevel2Type', 'PyNUMERIC_TYPES', 'PyREAL_TYPES',
## 'SUPPRESS_SMALL',
## 'SuitableBuffer', 'USING_BLAS',
## 'UsesOpPriority',
## 'codegenerator', 'generic', 'libnumarray', 'libnumeric',
## 'make_ufuncs', 'memory',
## 'numarrayall', 'numarraycore', 'numinclude', 'safethread',
## 'typecode', 'typecodes', 'typeconv', 'ufunc', 'ufuncFactory',
## 'ieeemask']
__all__ = ['asarray', 'ones', 'zeros', 'array', 'where']
__all__ += ['vdot', 'dot', 'matrixmultiply', 'ravel', 'indices',
'arange', 'concatenate', 'all', 'allclose', 'alltrue', 'and_',
'any', 'argmax', 'argmin', 'argsort', 'around', 'array_equal',
'array_equiv', 'arrayrange', 'array_str', 'array_repr',
'array2list', 'average', 'choose', 'CLIP', 'RAISE', 'WRAP',
'clip', 'compress', 'copy', 'copy_reg',
'diagonal', 'divide_remainder', 'e', 'explicit_type', 'pi',
'flush_caches', 'fromfile', 'os', 'sys', 'STRICT',
'SLOPPY', 'WARN', 'EarlyEOFError', 'SizeMismatchError',
'SizeMismatchWarning', 'FileSeekWarning', 'fromstring',
'fromfunction', 'fromlist', 'getShape', 'getTypeObject',
'identity', 'info', 'innerproduct', 'inputarray',
'isBigEndian', 'kroneckerproduct', 'lexsort', 'math',
'operator', 'outerproduct', 'put', 'putmask', 'rank',
'repeat', 'reshape', 'resize', 'round', 'searchsorted',
'shape', 'size', 'sometrue', 'sort', 'swapaxes', 'take',
'tcode', 'tname', 'tensormultiply', 'trace', 'transpose',
'types', 'value', 'cumsum', 'cumproduct', 'nonzero', 'newobj',
'togglebyteorder'
]
import copy
import copy_reg
import types
import os
import sys
import math
import operator
from numpy import dot as matrixmultiply, dot, vdot, ravel, concatenate, all,\
allclose, any, argsort, array_equal, array_equiv,\
array_str, array_repr, CLIP, RAISE, WRAP, clip, concatenate, \
diagonal, e, pi, inner as innerproduct, nonzero, \
outer as outerproduct, kron as kroneckerproduct, lexsort, putmask, rank, \
resize, searchsorted, shape, size, sort, swapaxes, trace, transpose
import numpy as np
from numerictypes import typefrom
isBigEndian = sys.byteorder != 'little'
value = tcode = 'f'
tname = 'Float32'
# If dtype is not None, then it is used
# If type is not None, then it is used
# If typecode is not None then it is used
# If use_default is True, then the default
# data-type is returned if all are None
def type2dtype(typecode, type, dtype, use_default=True):
if dtype is None:
if type is None:
if use_default or typecode is not None:
dtype = np.dtype(typecode)
else:
dtype = np.dtype(type)
if use_default and dtype is None:
dtype = np.dtype('int')
return dtype
def fromfunction(shape, dimensions, type=None, typecode=None, dtype=None):
dtype = type2dtype(typecode, type, dtype, 1)
return np.fromfunction(shape, dimensions, dtype=dtype)
def ones(shape, type=None, typecode=None, dtype=None):
dtype = type2dtype(typecode, type, dtype, 1)
return np.ones(shape, dtype)
def zeros(shape, type=None, typecode=None, dtype=None):
dtype = type2dtype(typecode, type, dtype, 1)
return np.zeros(shape, dtype)
def where(condition, x=None, y=None, out=None):
if x is None and y is None:
arr = np.where(condition)
else:
arr = np.where(condition, x, y)
if out is not None:
out[...] = arr
return out
return arr
def indices(shape, type=None):
return np.indices(shape, type)
def arange(a1, a2=None, stride=1, type=None, shape=None,
typecode=None, dtype=None):
dtype = type2dtype(typecode, type, dtype, 0)
return np.arange(a1, a2, stride, dtype)
arrayrange = arange
def alltrue(x, axis=0):
return np.alltrue(x, axis)
def and_(a, b):
"""Same as a & b
"""
return a & b
def divide_remainder(a, b):
a, b = asarray(a), asarray(b)
return (a/b,a%b)
def around(array, digits=0, output=None):
ret = np.around(array, digits, output)
if output is None:
return ret
return
def array2list(arr):
return arr.tolist()
def choose(selector, population, outarr=None, clipmode=RAISE):
a = np.asarray(selector)
ret = a.choose(population, out=outarr, mode=clipmode)
if outarr is None:
return ret
return
def compress(condition, a, axis=0):
return np.compress(condition, a, axis)
# only returns a view
def explicit_type(a):
x = a.view()
return x
# stub
def flush_caches():
pass
class EarlyEOFError(Exception):
"Raised in fromfile() if EOF unexpectedly occurs."
pass
class SizeMismatchError(Exception):
"Raised in fromfile() if file size does not match shape."
pass
class SizeMismatchWarning(Warning):
"Issued in fromfile() if file size does not match shape."
pass
class FileSeekWarning(Warning):
"Issued in fromfile() if there is unused data and seek() fails"
pass
STRICT, SLOPPY, WARN = range(3)
_BLOCKSIZE=1024
# taken and adapted directly from numarray
def fromfile(infile, type=None, shape=None, sizing=STRICT,
typecode=None, dtype=None):
if isinstance(infile, (str, unicode)):
infile = open(infile, 'rb')
dtype = type2dtype(typecode, type, dtype, True)
if shape is None:
shape = (-1,)
if not isinstance(shape, tuple):
shape = (shape,)
if (list(shape).count(-1)>1):
raise ValueError("At most one unspecified dimension in shape")
if -1 not in shape:
if sizing != STRICT:
raise ValueError("sizing must be STRICT if size complete")
arr = np.empty(shape, dtype)
bytesleft=arr.nbytes
bytesread=0
while(bytesleft > _BLOCKSIZE):
data = infile.read(_BLOCKSIZE)
if len(data) != _BLOCKSIZE:
raise EarlyEOFError("Unexpected EOF reading data for size complete array")
arr.data[bytesread:bytesread+_BLOCKSIZE]=data
bytesread += _BLOCKSIZE
bytesleft -= _BLOCKSIZE
if bytesleft > 0:
data = infile.read(bytesleft)
if len(data) != bytesleft:
raise EarlyEOFError("Unexpected EOF reading data for size complete array")
arr.data[bytesread:bytesread+bytesleft]=data
return arr
##shape is incompletely specified
##read until EOF
##implementation 1: naively use memory blocks
##problematic because memory allocation can be double what is
##necessary (!)
##the most common case, namely reading in data from an unchanging
##file whose size may be determined before allocation, should be
##quick -- only one allocation will be needed.
recsize = dtype.itemsize * np.product([i for i in shape if i != -1])
blocksize = max(_BLOCKSIZE/recsize, 1)*recsize
##try to estimate file size
try:
curpos=infile.tell()
infile.seek(0,2)
endpos=infile.tell()
infile.seek(curpos)
except (AttributeError, IOError):
initsize=blocksize
else:
initsize=max(1,(endpos-curpos)/recsize)*recsize
buf = np.newbuffer(initsize)
bytesread=0
while 1:
data=infile.read(blocksize)
if len(data) != blocksize: ##eof
break
##do we have space?
if len(buf) < bytesread+blocksize:
buf=_resizebuf(buf,len(buf)+blocksize)
## or rather a=resizebuf(a,2*len(a)) ?
assert len(buf) >= bytesread+blocksize
buf[bytesread:bytesread+blocksize]=data
bytesread += blocksize
if len(data) % recsize != 0:
if sizing == STRICT:
raise SizeMismatchError("Filesize does not match specified shape")
if sizing == WARN:
_warnings.warn("Filesize does not match specified shape",
SizeMismatchWarning)
try:
infile.seek(-(len(data) % recsize),1)
except AttributeError:
_warnings.warn("Could not rewind (no seek support)",
FileSeekWarning)
except IOError:
_warnings.warn("Could not rewind (IOError in seek)",
FileSeekWarning)
datasize = (len(data)/recsize) * recsize
if len(buf) != bytesread+datasize:
buf=_resizebuf(buf,bytesread+datasize)
buf[bytesread:bytesread+datasize]=data[:datasize]
##deduce shape from len(buf)
shape = list(shape)
uidx = shape.index(-1)
shape[uidx]=len(buf) / recsize
a = np.ndarray(shape=shape, dtype=type, buffer=buf)
if a.dtype.char == '?':
np.not_equal(a, 0, a)
return a
def fromstring(datastring, type=None, shape=None, typecode=None, dtype=None):
dtype = type2dtype(typecode, type, dtype, True)
if shape is None:
count = -1
else:
count = np.product(shape)
res = np.fromstring(datastring, dtype=dtype, count=count)
if shape is not None:
res.shape = shape
return res
# check_overflow is ignored
def fromlist(seq, type=None, shape=None, check_overflow=0, typecode=None, dtype=None):
dtype = type2dtype(typecode, type, dtype, False)
return np.array(seq, dtype)
def array(sequence=None, typecode=None, copy=1, savespace=0,
type=None, shape=None, dtype=None):
dtype = type2dtype(typecode, type, dtype, 0)
if sequence is None:
if shape is None:
return None
if dtype is None:
dtype = 'l'
return np.empty(shape, dtype)
if isinstance(sequence, file):
return fromfile(sequence, dtype=dtype, shape=shape)
if isinstance(sequence, str):
return fromstring(sequence, dtype=dtype, shape=shape)
if isinstance(sequence, buffer):
arr = np.frombuffer(sequence, dtype=dtype)
else:
arr = np.array(sequence, dtype, copy=copy)
if shape is not None:
arr.shape = shape
return arr
def asarray(seq, type=None, typecode=None, dtype=None):
if isinstance(seq, np.ndarray) and type is None and \
typecode is None and dtype is None:
return seq
return array(seq, type=type, typecode=typecode, copy=0, dtype=dtype)
inputarray = asarray
def getTypeObject(sequence, type):
if type is not None:
return type
try:
return typefrom(np.array(sequence))
except:
raise TypeError("Can't determine a reasonable type from sequence")
def getShape(shape, *args):
try:
if shape is () and not args:
return ()
if len(args) > 0:
shape = (shape, ) + args
else:
shape = tuple(shape)
dummy = np.array(shape)
if not issubclass(dummy.dtype.type, np.integer):
raise TypeError
if len(dummy) > np.MAXDIMS:
raise TypeError
except:
raise TypeError("Shape must be a sequence of integers")
return shape
def identity(n, type=None, typecode=None, dtype=None):
dtype = type2dtype(typecode, type, dtype, True)
return np.identity(n, dtype)
def info(obj, output=sys.stdout, numpy=0):
if numpy:
bp = lambda x: x
else:
bp = lambda x: int(x)
cls = getattr(obj, '__class__', type(obj))
if numpy:
nm = getattr(cls, '__name__', cls)
else:
nm = cls
print >> output, "class: ", nm
print >> output, "shape: ", obj.shape
strides = obj.strides
print >> output, "strides: ", strides
if not numpy:
print >> output, "byteoffset: 0"
if len(strides) > 0:
bs = obj.strides[0]
else:
bs = obj.itemsize
print >> output, "bytestride: ", bs
print >> output, "itemsize: ", obj.itemsize
print >> output, "aligned: ", bp(obj.flags.aligned)
print >> output, "contiguous: ", bp(obj.flags.contiguous)
if numpy:
print >> output, "fortran: ", obj.flags.fortran
if not numpy:
print >> output, "buffer: ", repr(obj.data)
if not numpy:
extra = " (DEBUG ONLY)"
tic = "'"
else:
extra = ""
tic = ""
print >> output, "data pointer: %s%s" % (hex(obj.ctypes._as_parameter_.value), extra)
print >> output, "byteorder: ",
endian = obj.dtype.byteorder
if endian in ['|','=']:
print >> output, "%s%s%s" % (tic, sys.byteorder, tic)
byteswap = False
elif endian == '>':
print >> output, "%sbig%s" % (tic, tic)
byteswap = sys.byteorder != "big"
else:
print >> output, "%slittle%s" % (tic, tic)
byteswap = sys.byteorder != "little"
print >> output, "byteswap: ", bp(byteswap)
if not numpy:
print >> output, "type: ", typefrom(obj).name
else:
print >> output, "type: %s" % obj.dtype
#clipmode is ignored if axis is not 0 and array is not 1d
def put(array, indices, values, axis=0, clipmode=RAISE):
if not isinstance(array, np.ndarray):
raise TypeError("put only works on subclass of ndarray")
work = asarray(array)
if axis == 0:
if array.ndim == 1:
work.put(indices, values, clipmode)
else:
work[indices] = values
elif isinstance(axis, (int, long, np.integer)):
work = work.swapaxes(0, axis)
work[indices] = values
work = work.swapaxes(0, axis)
else:
def_axes = range(work.ndim)
for x in axis:
def_axes.remove(x)
axis = list(axis)+def_axes
work = work.transpose(axis)
work[indices] = values
work = work.transpose(axis)
def repeat(array, repeats, axis=0):
return np.repeat(array, repeats, axis)
def reshape(array, shape, *args):
if len(args) > 0:
shape = (shape,) + args
return np.reshape(array, shape)
import warnings as _warnings
def round(*args, **keys):
_warnings.warn("round() is deprecated. Switch to around()",
DeprecationWarning)
return around(*args, **keys)
def sometrue(array, axis=0):
return np.sometrue(array, axis)
#clipmode is ignored if axis is not an integer
def take(array, indices, axis=0, outarr=None, clipmode=RAISE):
array = np.asarray(array)
if isinstance(axis, (int, long, np.integer)):
res = array.take(indices, axis, outarr, clipmode)
if outarr is None:
return res
return
else:
def_axes = range(array.ndim)
for x in axis:
def_axes.remove(x)
axis = list(axis) + def_axes
work = array.transpose(axis)
res = work[indices]
if outarr is None:
return res
outarr[...] = res
return
def tensormultiply(a1, a2):
a1, a2 = np.asarray(a1), np.asarray(a2)
if (a1.shape[-1] != a2.shape[0]):
raise ValueError("Unmatched dimensions")
shape = a1.shape[:-1] + a2.shape[1:]
return np.reshape(dot(np.reshape(a1, (-1, a1.shape[-1])),
np.reshape(a2, (a2.shape[0],-1))),
shape)
def cumsum(a1, axis=0, out=None, type=None, dim=0):
return np.asarray(a1).cumsum(axis,dtype=type,out=out)
def cumproduct(a1, axis=0, out=None, type=None, dim=0):
return np.asarray(a1).cumprod(axis,dtype=type,out=out)
def argmax(x, axis=-1):
return np.argmax(x, axis)
def argmin(x, axis=-1):
return np.argmin(x, axis)
def newobj(self, type):
if type is None:
return np.empty_like(self)
else:
return np.empty(self.shape, type)
def togglebyteorder(self):
self.dtype=self.dtype.newbyteorder()
def average(a, axis=0, weights=None, returned=0):
return np.average(a, axis, weights, returned)
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