diff options
Diffstat (limited to 'numpy/numarray/functions.py')
| -rw-r--r-- | numpy/numarray/functions.py | 105 |
1 files changed, 55 insertions, 50 deletions
diff --git a/numpy/numarray/functions.py b/numpy/numarray/functions.py index 6e54456de..3f55e1a9c 100644 --- a/numpy/numarray/functions.py +++ b/numpy/numarray/functions.py @@ -36,8 +36,13 @@ __all__ += ['vdot', 'dot', 'matrixmultiply', 'ravel', 'indices', 'togglebyteorder' ] -import copy, copy_reg, types -import os, sys, math, operator +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, around, argsort, array_equal, array_equiv,\ @@ -45,7 +50,7 @@ from numpy import dot as matrixmultiply, dot, vdot, ravel, concatenate, all,\ diagonal, e, pi, indices, inner as innerproduct, nonzero, \ outer as outerproduct, kron as kroneckerproduct, lexsort, putmask, rank, \ resize, searchsorted, shape, size, sort, swapaxes, trace, transpose -import numpy as N +import numpy as np from numerictypes import typefrom @@ -62,46 +67,46 @@ 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 = N.dtype(typecode) + dtype = np.dtype(typecode) else: - dtype = N.dtype(type) + dtype = np.dtype(type) if use_default and dtype is None: - dtype = N.dtype('int') + dtype = np.dtype('int') return dtype def fromfunction(shape, dimensions, type=None, typecode=None, dtype=None): dtype = type2dtype(typecode, type, dtype, 1) - return N.fromfunction(shape, dimensions, dtype=dtype) + return np.fromfunction(shape, dimensions, dtype=dtype) def ones(shape, type=None, typecode=None, dtype=None): dtype = type2dtype(typecode, type, dtype, 1) - return N.ones(shape, dtype) + return np.ones(shape, dtype) def zeros(shape, type=None, typecode=None, dtype=None): dtype = type2dtype(typecode, type, dtype, 1) - return N.zeros(shape, dtype) + return np.zeros(shape, dtype) def where(condition, x=None, y=None, out=None): if x is None and y is None: - arr = N.where(condition) + arr = np.where(condition) else: - arr = N.where(condition, x, y) + arr = np.where(condition, x, y) if out is not None: out[...] = arr return out return arr def indices(shape, type=None): - return N.indices(shape, type) + 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 N.arange(a1, a2, stride, dtype) + return np.arange(a1, a2, stride, dtype) arrayrange = arange def alltrue(x, axis=0): - return N.alltrue(x, axis) + return np.alltrue(x, axis) def and_(a, b): """Same as a & b @@ -113,7 +118,7 @@ def divide_remainder(a, b): return (a/b,a%b) def around(array, digits=0, output=None): - ret = N.around(array, digits, output) + ret = np.around(array, digits, output) if output is None: return ret return @@ -123,14 +128,14 @@ def array2list(arr): def choose(selector, population, outarr=None, clipmode=RAISE): - a = N.asarray(selector) + 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 N.compress(condition, a, axis) + return np.compress(condition, a, axis) # only returns a view def explicit_type(a): @@ -180,7 +185,7 @@ def fromfile(infile, type=None, shape=None, sizing=STRICT, if -1 not in shape: if sizing != STRICT: raise ValueError("sizing must be STRICT if size complete") - arr = N.empty(shape, dtype) + arr = np.empty(shape, dtype) bytesleft=arr.nbytes bytesread=0 while(bytesleft > _BLOCKSIZE): @@ -208,7 +213,7 @@ def fromfile(infile, type=None, shape=None, sizing=STRICT, ##file whose size may be determined before allocation, should be ##quick -- only one allocation will be needed. - recsize = dtype.itemsize * N.product([i for i in shape if i != -1]) + recsize = dtype.itemsize * np.product([i for i in shape if i != -1]) blocksize = max(_BLOCKSIZE/recsize, 1)*recsize ##try to estimate file size @@ -222,7 +227,7 @@ def fromfile(infile, type=None, shape=None, sizing=STRICT, else: initsize=max(1,(endpos-curpos)/recsize)*recsize - buf = N.newbuffer(initsize) + buf = np.newbuffer(initsize) bytesread=0 while 1: @@ -260,9 +265,9 @@ def fromfile(infile, type=None, shape=None, sizing=STRICT, uidx = shape.index(-1) shape[uidx]=len(buf) / recsize - a = N.ndarray(shape=shape, dtype=type, buffer=buf) + a = np.ndarray(shape=shape, dtype=type, buffer=buf) if a.dtype.char == '?': - N.not_equal(a, 0, a) + np.not_equal(a, 0, a) return a def fromstring(datastring, type=None, shape=None, typecode=None, dtype=None): @@ -270,8 +275,8 @@ def fromstring(datastring, type=None, shape=None, typecode=None, dtype=None): if shape is None: count = -1 else: - count = N.product(shape) - res = N.fromstring(datastring, dtype=dtype, count=count) + count = np.product(shape) + res = np.fromstring(datastring, dtype=dtype, count=count) if shape is not None: res.shape = shape return res @@ -280,7 +285,7 @@ def fromstring(datastring, type=None, shape=None, typecode=None, dtype=None): # 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 N.array(seq, dtype) + return np.array(seq, dtype) def array(sequence=None, typecode=None, copy=1, savespace=0, type=None, shape=None, dtype=None): @@ -290,21 +295,21 @@ def array(sequence=None, typecode=None, copy=1, savespace=0, return None if dtype is None: dtype = 'l' - return N.empty(shape, dtype) + 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 = N.frombuffer(sequence, dtype=dtype) + arr = np.frombuffer(sequence, dtype=dtype) else: - arr = N.array(sequence, dtype, copy=copy) + 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, N.ndarray) and type is None and \ + 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) @@ -328,10 +333,10 @@ def getShape(shape, *args): shape = (shape, ) + args else: shape = tuple(shape) - dummy = N.array(shape) - if not issubclass(dummy.dtype.type, N.integer): + dummy = np.array(shape) + if not issubclass(dummy.dtype.type, np.integer): raise TypeError - if len(dummy) > N.MAXDIMS: + if len(dummy) > np.MAXDIMS: raise TypeError except: raise TypeError("Shape must be a sequence of integers") @@ -340,7 +345,7 @@ def getShape(shape, *args): def identity(n, type=None, typecode=None, dtype=None): dtype = type2dtype(typecode, type, dtype, True) - return N.identity(n, dtype) + return np.identity(n, dtype) def info(obj, output=sys.stdout, numpy=0): if numpy: @@ -396,7 +401,7 @@ def info(obj, output=sys.stdout, numpy=0): #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, N.ndarray): + if not isinstance(array, np.ndarray): raise TypeError("put only works on subclass of ndarray") work = asarray(array) if axis == 0: @@ -404,7 +409,7 @@ def put(array, indices, values, axis=0, clipmode=RAISE): work.put(indices, values, clipmode) else: work[indices] = values - elif isinstance(axis, (int, long, N.integer)): + elif isinstance(axis, (int, long, np.integer)): work = work.swapaxes(0, axis) work[indices] = values work = work.swapaxes(0, axis) @@ -418,13 +423,13 @@ def put(array, indices, values, axis=0, clipmode=RAISE): work = work.transpose(axis) def repeat(array, repeats, axis=0): - return N.repeat(array, repeats, axis) + return np.repeat(array, repeats, axis) def reshape(array, shape, *args): if len(args) > 0: shape = (shape,) + args - return N.reshape(array, shape) + return np.reshape(array, shape) import warnings as _warnings @@ -434,12 +439,12 @@ def round(*args, **keys): return around(*args, **keys) def sometrue(array, axis=0): - return N.sometrue(array, axis) + 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 = N.asarray(array) - if isinstance(axis, (int, long, N.integer)): + array = np.asarray(array) + if isinstance(axis, (int, long, np.integer)): res = array.take(indices, axis, outarr, clipmode) if outarr is None: return res @@ -457,34 +462,34 @@ def take(array, indices, axis=0, outarr=None, clipmode=RAISE): return def tensormultiply(a1, a2): - a1, a2 = N.asarray(a1), N.asarray(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 N.reshape(dot(N.reshape(a1, (-1, a1.shape[-1])), - N.reshape(a2, (a2.shape[0],-1))), + return np.reshape(dot(N.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 N.asarray(a1).cumsum(axis,dtype=type,out=out) + return np.asarray(a1).cumsum(axis,dtype=type,out=out) def cumproduct(a1, axis=0, out=None, type=None, dim=0): - return N.asarray(a1).cumprod(axis,dtype=type,out=out) + return np.asarray(a1).cumprod(axis,dtype=type,out=out) def argmax(x, axis=-1): - return N.argmax(x, axis) + return np.argmax(x, axis) def argmin(x, axis=-1): - return N.argmin(x, axis) + return np.argmin(x, axis) def newobj(self, type): if type is None: - return N.empty_like(self) + return np.empty_like(self) else: - return N.empty(self.shape, type) + return np.empty(self.shape, type) def togglebyteorder(self): self.dtype=self.dtype.newbyteorder() def average(a, axis=0, weights=None, returned=0): - return N.average(a, axis, weights, returned) + return = np.average(a, axis, weights, returned) |