diff options
Diffstat (limited to 'numpy/numarray/functions.py')
| -rw-r--r-- | numpy/numarray/functions.py | 25 |
1 files changed, 12 insertions, 13 deletions
diff --git a/numpy/numarray/functions.py b/numpy/numarray/functions.py index 2a6e5317b..329e08024 100644 --- a/numpy/numarray/functions.py +++ b/numpy/numarray/functions.py @@ -1,4 +1,3 @@ - # missing Numarray defined names (in from numarray import *) ##__all__ = ['ClassicUnpickler', 'Complex32_fromtype', ## 'Complex64_fromtype', 'ComplexArray', 'Error', @@ -8,7 +7,7 @@ ## 'PyINT_TYPES', 'PyLevel2Type', 'PyNUMERIC_TYPES', 'PyREAL_TYPES', ## 'SUPPRESS_SMALL', ## 'SuitableBuffer', 'USING_BLAS', -## 'UsesOpPriority', +## 'UsesOpPriority', ## 'codegenerator', 'generic', 'libnumarray', 'libnumeric', ## 'make_ufuncs', 'memory', ## 'numarrayall', 'numarraycore', 'numinclude', 'safethread', @@ -72,7 +71,7 @@ def type2dtype(typecode, type, dtype, use_default=True): def fromfunction(shape, dimensions, type=None, typecode=None, dtype=None): dtype = type2dtype(typecode, type, dtype, 1) - return N.fromfunction(shape, dimensions, dtype=dtype) + return N.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) @@ -90,7 +89,7 @@ def where(condition, x=None, y=None, out=None): out[...] = arr return out return arr - + def indices(shape, type=None): return N.indices(shape, type) @@ -118,7 +117,7 @@ def around(array, digits=0, output=None): if output is None: return ret return - + def array2list(arr): return arr.tolist() @@ -174,7 +173,7 @@ def fromfile(infile, type=None, shape=None, sizing=STRICT, shape = (-1,) if not isinstance(shape, tuple): shape = (shape,) - + if (list(shape).count(-1)>1): raise ValueError("At most one unspecified dimension in shape") @@ -208,7 +207,7 @@ def fromfile(infile, type=None, shape=None, sizing=STRICT, ##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 * N.product([i for i in shape if i != -1]) blocksize = max(_BLOCKSIZE/recsize, 1)*recsize @@ -265,7 +264,7 @@ def fromfile(infile, type=None, shape=None, sizing=STRICT, if a.dtype.char == '?': N.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: @@ -275,7 +274,7 @@ def fromstring(datastring, type=None, shape=None, typecode=None, dtype=None): res = N.fromstring(datastring, count=count) if shape is not None: res.shape = shape - return res + return res # check_overflow is ignored @@ -333,12 +332,12 @@ def getShape(shape, *args): if not issubclass(dummy.dtype.type, N.integer): raise TypeError if len(dummy) > N.MAXDIMS: - raise TypeError + 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 N.identity(n, dtype) @@ -455,8 +454,8 @@ def take(array, indices, axis=0, outarr=None, clipmode=RAISE): if outarr is None: return res out[...] = res - return - + return + def tensormultiply(a1, a2): a1, a2 = N.asarray(a1), N.asarray(a2) if (a1.shape[-1] != a2.shape[0]): |