def nonzero(array):
"""Return the indices of the elements that are non-zero.
Returns a tuple of arrays, one for each dimension of a, containing the indices of the non-zero elements
in that dimension. The corresponding non-zero values can be obtained with:
Parameters:
a : array_like
Input array.
Returns:
tuple_of_arrays : tuple
Indices of elements that are non-zero.
>>> x = np.eye(3)
>>> x
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
>>> np.nonzero(x)
(array([0, 1, 2]), array([0, 1, 2]))
>>> x[np.nonzero(x)]
array([ 1., 1., 1.])
>>> np.transpose(np.nonzero(x))
array([[0, 0],
[1, 1],
[2, 2]])"""
rowList = IntArrayList()
columnList = IntArrayList()
coordinateList = DoubleArrayList()
array._M.getNonZeros(rowList, columnList, coordinateList)
#TODO update array function to deal with Int....
return (array(rowList), array(columnList))
def nonzero(array):
"""Return the indices of the elements that are non-zero.
Returns a tuple of arrays, one for each dimension of a, containing the indices of the non-zero elements
in that dimension. The corresponding non-zero values can be obtained with:
Parameters:
a : array_like
Input array.
Returns:
tuple_of_arrays : tuple
Indices of elements that are non-zero.
>>> x = np.eye(3)
>>> x
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
>>> np.nonzero(x)
(array([0, 1, 2]), array([0, 1, 2]))
>>> x[np.nonzero(x)]
array([ 1., 1., 1.])
>>> np.transpose(np.nonzero(x))
array([[0, 0],
[1, 1],
[2, 2]])"""
rowList = IntArrayList()
columnList = IntArrayList()
coordinateList = DoubleArrayList()
array._M.getNonZeros(rowList, columnList, coordinateList)
#TODO update array function to deal with Int....
return(array(rowList), array(columnList))
def vdot(a, b):
"""Returns the dot product of 2 vectors (or anything that can be made into
a vector). NB: this is not the same as `dot`, as it takes the conjugate
of its first argument if complex and always returns a scalar."""
try:
return _dotblas.vdot(Numeric.ravel(a), Numeric.ravel(b))
# in case we get an integer Value
except TypeError:
return multiarray.matrixproduct(multiarray.array(a).flat,
multiarray.array(b).flat)
def innerproduct(a, b):
"""returns inner product between a and b.
The product-sum is over the last dimension of a and b.
NB: No conjugation of complex arguments is performed.
This version uses the BLAS optimized routines where possible.
"""
try:
return _dotblas.innerproduct(a, b)
except TypeError:
try:
return multiarray.innerproduct(a, b)
except TypeError,detail:
if multiarray.array(a).shape == () or multiarray.array(b).shape == ():
return a*b
else:
raise TypeError, detail or "invalid types for innerproduct"
def _index_fields(ary, fields):
from multiarray import empty, dtype, array
dt = ary.dtype
names = [name for name in fields if name in dt.names]
formats = [dt.fields[name][0] for name in fields if name in dt.names]
offsets = [dt.fields[name][1] for name in fields if name in dt.names]
view_dtype = {'names':names, 'formats':formats, 'offsets':offsets, 'itemsize':dt.itemsize}
view = ary.view(dtype=view_dtype)
# Return a copy for now until behavior is fully deprecated
# in favor of returning view
copy_dtype = {'names':view_dtype['names'], 'formats':view_dtype['formats']}
return array(view, dtype=copy_dtype, copy=True)
def sign(x):
""" sign(x[, out])
Returns an element-wise indication of the sign of a number.
The `sign` function returns ``-1 if x < 0, 0 if x==0, 1 if x > 0``.
Parameters
----------
x : array_like
Input values.
Returns
-------
y : ndarray
The sign of `x`.
Examples
--------
>>> numjy.sign([-5., 4.5])
array([-1., 1.])
>>> numjy.sign(0)
0
"""
def sign_int(a_number):
if a_number < 0:
return(-1)
elif a_number == 0:
return(0)
else:
return(1)
if hasattr(x, '__iter__' ) or isinstance(x, ndarray):
return_value = array([map(sign_int, list(array(x)._M.toArray()))])
else:
#In the case the input is just a number return the sign of the number.
return_value = sign_int(x)
return(return_value)
def sign(x):
""" sign(x[, out])
Returns an element-wise indication of the sign of a number.
The `sign` function returns ``-1 if x < 0, 0 if x==0, 1 if x > 0``.
Parameters
----------
x : array_like
Input values.
Returns
-------
y : ndarray
The sign of `x`.
Examples
--------
>>> numjy.sign([-5., 4.5])
array([-1., 1.])
>>> numjy.sign(0)
0
"""
def sign_int(a_number):
if a_number < 0:
return (-1)
elif a_number == 0:
return (0)
else:
return (1)
if hasattr(x, '__iter__') or isinstance(x, ndarray):
return_value = array([map(sign_int, list(array(x)._M.toArray()))])
else:
#In the case the input is just a number return the sign of the number.
return_value = sign_int(x)
return (return_value)
def _index_fields(ary, fields):
from multiarray import empty, dtype, array
dt = ary.dtype
names = [name for name in fields if name in dt.names]
formats = [dt.fields[name][0] for name in fields if name in dt.names]
offsets = [dt.fields[name][1] for name in fields if name in dt.names]
view_dtype = {'names':names, 'formats':formats, 'offsets':offsets, 'itemsize':dt.itemsize}
view = ary.view(dtype=view_dtype)
# Return a copy for now until behavior is fully deprecated
# in favor of returning view
copy_dtype = {'names':view_dtype['names'], 'formats':view_dtype['formats']}
return array(view, dtype=copy_dtype, copy=True)
def __init__(self, data):
if data.dtype.kind == 'm':
nat_value = array(['NaT'], dtype=data.dtype)[0]
int_dtype = dtype(data.dtype.byteorder + 'i8')
int_view = data.view(int_dtype)
v = int_view[not_equal(int_view, nat_value.view(int_dtype))]
if len(v) > 0:
# Max str length of non-NaT elements
max_str_len = max(len(str(maximum.reduce(v))),
len(str(minimum.reduce(v))))
else:
max_str_len = 0
if len(v) < len(data):
# data contains a NaT
max_str_len = max(max_str_len, 5)
self.format = '%' + str(max_str_len) + 'd'
self._nat = "'NaT'".rjust(max_str_len)
def _copy_fields(ary):
"""Return copy of structured array with padding between fields removed.
Parameters
----------
ary : ndarray
Structured array from which to remove padding bytes
Returns
-------
ary_copy : ndarray
Copy of ary with padding bytes removed
"""
dt = ary.dtype
copy_dtype = {
'names': dt.names,
'formats': [dt.fields[name][0] for name in dt.names]
}
return array(ary, dtype=copy_dtype, copy=True)
def array2string(a, max_line_width=None, precision=None,
suppress_small=None, separator=' ', prefix="",
style=repr, formatter=None):
"""
Return a string representation of an array.
Parameters
----------
a : ndarray
Input array.
max_line_width : int, optional
The maximum number of columns the string should span. Newline
characters splits the string appropriately after array elements.
precision : int, optional
Floating point precision. Default is the current printing
precision (usually 8), which can be altered using `set_printoptions`.
suppress_small : bool, optional
Represent very small numbers as zero. A number is "very small" if it
is smaller than the current printing precision.
separator : str, optional
Inserted between elements.
prefix : str, optional
An array is typically printed as::
'prefix(' + array2string(a) + ')'
The length of the prefix string is used to align the
output correctly.
style : function, optional
A function that accepts an ndarray and returns a string. Used only
when the shape of `a` is equal to ``()``, i.e. for 0-D arrays.
formatter : dict of callables, optional
If not None, the keys should indicate the type(s) that the respective
formatting function applies to. Callables should return a string.
Types that are not specified (by their corresponding keys) are handled
by the default formatters. Individual types for which a formatter
can be set are::
- 'bool'
- 'int'
- 'timedelta' : a `numpy.timedelta64`
- 'datetime' : a `numpy.datetime64`
- 'float'
- 'longfloat' : 128-bit floats
- 'complexfloat'
- 'longcomplexfloat' : composed of two 128-bit floats
- 'numpystr' : types `numpy.string_` and `numpy.unicode_`
- 'str' : all other strings
Other keys that can be used to set a group of types at once are::
- 'all' : sets all types
- 'int_kind' : sets 'int'
- 'float_kind' : sets 'float' and 'longfloat'
- 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat'
- 'str_kind' : sets 'str' and 'numpystr'
Returns
-------
array_str : str
String representation of the array.
Raises
------
TypeError
if a callable in `formatter` does not return a string.
See Also
--------
array_str, array_repr, set_printoptions, get_printoptions
Notes
-----
If a formatter is specified for a certain type, the `precision` keyword is
ignored for that type.
This is a very flexible function; `array_repr` and `array_str` are using
`array2string` internally so keywords with the same name should work
identically in all three functions.
Examples
--------
>>> x = np.array([1e-16,1,2,3])
>>> print(np.array2string(x, precision=2, separator=',',
... suppress_small=True))
[ 0., 1., 2., 3.]
>>> x = np.arange(3.)
>>> np.array2string(x, formatter={'float_kind':lambda x: "%.2f" % x})
'[0.00 1.00 2.00]'
>>> x = np.arange(3)
>>> np.array2string(x, formatter={'int':lambda x: hex(x)})
'[0x0L 0x1L 0x2L]'
"""
if max_line_width is None:
max_line_width = _line_width
if precision is None:
precision = _float_output_precision
if suppress_small is None:
suppress_small = _float_output_suppress_small
if formatter is None:
formatter = _formatter
if a.shape == ():
x = a.item()
if a.dtype.fields is not None:
arr = array([x], dtype=a.dtype)
format_function = _get_format_function(
arr, precision, suppress_small, formatter)
lst = format_function(arr[0])
else:
lst = style(x)
elif functools.reduce(product, a.shape) == 0:
# treat as a null array if any of shape elements == 0
lst = "[]"
else:
lst = _array2string(a, max_line_width, precision, suppress_small,
separator, prefix, formatter=formatter)
return lst
"""Numeric module defining a multi-dimensional array and useful procedures for
def sarray(a, typecode=None, copy=0):
"""sarray(a, typecode=None, copy=0) calls array with savespace=1."""
return multiarray.array(a, typecode, copy, savespace=1)
def asarray(a, typecode=None, savespace=0):
"""asarray(a,typecode=None) returns a as a NumPy array. Unlike array(),
no copy is performed if a is already an array.
"""
return multiarray.array(a, typecode, copy=0, savespace=savespace)
"""Numeric module defining a multi-dimensional array and useful procedures for
def asarray(a, typecode=None, savespace=0):
"""asarray(a,typecode=None) returns a as a NumPy array. Unlike array(),
no copy is performed if a is already an array.
"""
return multiarray.array(a, typecode, copy=0, savespace=savespace)
def asanyarray(a, dtype=None, order=None):
""" Convert the input to an ndarray, but pass ndarray subclasses through """
return ma.array(a, dtype, copy=False, order=order, subok=True)
cast = _typedict()
try:
ScalarType = [
_types.IntType, _types.FloatType, _types.ComplexType, _types.LongType,
_types.BooleanType, _types.StringType, _types.UnicodeType,
_types.BufferType
]
except AttributeError:
# Py3K
ScalarType = [int, float, complex, int, bool, bytes, str, memoryview]
ScalarType.extend(_sctype2char_dict.keys())
ScalarType = tuple(ScalarType)
for key in _sctype2char_dict.keys():
cast[key] = lambda x, k=key: array(x, copy=False).astype(k)
# Create the typestring lookup dictionary
_typestr = _typedict()
for key in _sctype2char_dict.keys():
if issubclass(key, allTypes['flexible']):
_typestr[key] = _sctype2char_dict[key]
else:
_typestr[key] = empty((1, ), key).dtype.str[1:]
# Make sure all typestrings are in sctypeDict
for key, val in _typestr.items():
if val not in sctypeDict:
sctypeDict[val] = key
# Add additional strings to the sctypeDict
def asanyarray(a, dtype=None, order=None):
""" Convert the input to an ndarray, but pass ndarray subclasses through """
return ma.array(a, dtype, copy=False, order=order, subok=True)
def sarray(a, typecode=None, copy=0):
"""sarray(a, typecode=None, copy=0) calls array with savespace=1."""
return multiarray.array(a, typecode, copy, savespace=1)
a -= 3 print a a *= a print a a *= 3 print a a /= a print a a /= 3 print a print a.__nonzero__() print where(a) b = array(a) print b c = random.rand(3) print c d = array([1.,2.,0.]) print c.dtype print d.dtype print a[a] print c[a] e = (c > 0.5) print e.dtype print c[(c > 0.5)] print c[d] #print d[a]
