def _mean(a, axis=None, dtype=None, out=None):
arr = array_create.array(a)
is_float16_result = False
rcount = _count_reduce_items(arr, axis)
# Make this warning show up first
if rcount == 0:
warnings.warn("Mean of empty slice.", RuntimeWarning, stacklevel=2)
# Cast bool, unsigned int, and int to float64 by default
if dtype is None:
if issubclass(arr.dtype.type, (numpy.integer, numpy.bool_)):
dtype = numpy.dtype('f8')
elif issubclass(arr.dtype.type, numpy.float16):
dtype = numpy.dtype('f4')
is_float16_result = True
ret = sum(arr, axis, dtype, out)
if isinstance(ret, numpy.ndarray):
ret = ufuncs.true_divide(ret, rcount, out=ret)
if is_float16_result and out is None:
ret = a.dtype.type(ret)
elif hasattr(ret, 'dtype'):
if is_float16_result:
ret = a.dtype.type(ret / rcount)
else:
ret = ret.dtype.type(ret / rcount)
else:
ret = ret / rcount
return ret
def _mean(a, axis=None, dtype=None, out=None):
arr = array_create.array(a)
is_float16_result = False
rcount = _count_reduce_items(arr, axis)
# Make this warning show up first
if rcount == 0:
warnings.warn("Mean of empty slice.", RuntimeWarning, stacklevel=2)
# Cast bool, unsigned int, and int to float64 by default
if dtype is None:
if issubclass(arr.dtype.type, (numpy.integer, numpy.bool_)):
dtype = numpy.dtype('f8')
elif issubclass(arr.dtype.type, numpy.float16):
dtype = numpy.dtype('f4')
is_float16_result = True
ret = sum(arr, axis, dtype, out)
if isinstance(ret, numpy.ndarray):
ret = ufuncs.true_divide(ret, rcount, out=ret)
if is_float16_result and out is None:
ret = a.dtype.type(ret)
elif hasattr(ret, 'dtype'):
if is_float16_result:
ret = a.dtype.type(ret / rcount)
else:
ret = ret.dtype.type(ret / rcount)
else:
ret = ret / rcount
return ret
def random_array(self, shape, dtype=None):
"""Return a random array of the given shape and dtype. If dtype is None, the type is determent by
the --dtype command line arguments"""
dtype = self.dtype if dtype is None else dtype
size = functools.reduce(operator.mul, shape)
if issubclass(numpy.dtype(dtype).type, numpy.integer):
if bohrium is not None:
# If bohrium is installed, we always uses the random123 in Bohrium even when running pure NumPy
ret = bohrium.random.randint(1,
size=size,
bohrium=bh_is_loaded_as_np)
else:
ret = numpy.random.randint(1, size=size)
else:
if bohrium is not None:
# If bohrium is installed, we always uses the random123 in Bohrium even when running pure NumPy
ret = bohrium.random.rand(*shape, bohrium=bh_is_loaded_as_np)
else:
ret = numpy.random.rand(*shape)
return np.array(ret, dtype=dtype)
def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=float, bohrium=True):
"""
Return evenly spaced numbers over a specified interval.
Returns `num` evenly spaced samples, calculated over the
interval [`start`, `stop` ].
The endpoint of the interval can optionally be excluded.
Parameters
----------
start : scalar
The starting value of the sequence.
stop : scalar
The end value of the sequence, unless `endpoint` is set to False.
In that case, the sequence consists of all but the last of ``num + 1``
evenly spaced samples, so that `stop` is excluded. Note that the step
size changes when `endpoint` is False.
num : int, optional
Number of samples to generate. Default is 50.
endpoint : bool, optional
If True, `stop` is the last sample. Otherwise, it is not included.
Default is True.
retstep : bool, optional
If True, return (`samples`, `step`), where `step` is the spacing
between samples.
Returns
-------
samples : ndarray
There are `num` equally spaced samples in the closed interval
``[start, stop]`` or the half-open interval ``[start, stop)``
(depending on whether `endpoint` is True or False).
step : float (only if `retstep` is True)
Size of spacing between samples.
See Also
--------
arange : Similiar to `linspace`, but uses a step size (instead of the
number of samples).
logspace : Samples uniformly distributed in log space.
Examples
--------
>>> np.linspace(2.0, 3.0, num=5)
array([ 2. , 2.25, 2.5 , 2.75, 3. ])
>>> np.linspace(2.0, 3.0, num=5, endpoint=False)
array([ 2. , 2.2, 2.4, 2.6, 2.8])
>>> np.linspace(2.0, 3.0, num=5, retstep=True)
(array([ 2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)
Graphical illustration:
>>> import matplotlib.pyplot as plt
>>> N = 8
>>> y = np.zeros(N)
>>> x1 = np.linspace(0, 10, N, endpoint=True)
>>> x2 = np.linspace(0, 10, N, endpoint=False)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
"""
if not bohrium:
# TODO: add copy=False to .astype()
return numpy.linspace(start, stop, num=num, endpoint=endpoint, retstep=retstep).astype(dtype)
num = int(num)
if num <= 0:
return array([], dtype=dtype)
if endpoint:
if num == 1:
return array([numpy.dtype(dtype).type(start)])
step = (stop - start) / float((num - 1))
else:
step = (stop - start) / float(num)
y = arange(num, dtype=dtype)
if step != 1: y *= step
if start != 0: y += start
if retstep:
return y, step
else:
return y
def arange(start, stop=None, step=1, dtype=None, bohrium=True):
"""
arange([start,] stop[, step,], dtype=None)
Return evenly spaced values within a given interval.
Values are generated within the half-open interval ``[start, stop)``
(in other words, the interval including `start` but excluding `stop`).
For integer arguments the function is equivalent to the Python built-in
`range <http://docs.python.org/lib/built-in-funcs.html>`_ function,
but returns a ndarray rather than a list.
When using a non-integer step, such as 0.1, the results will often not
be consistent. It is better to use ``linspace`` for these cases.
Parameters
----------
start : number, optional
Start of interval. The interval includes this value. The default
start value is 0.
stop : number
End of interval. The interval does not include this value, except
in some cases where `step` is not an integer and floating point
round-off affects the length of `out`.
step : number, optional
Spacing between values. For any output `out`, this is the distance
between two adjacent values, ``out[i+1] - out[i]``. The default
step size is 1. If `step` is specified, `start` must also be given.
dtype : dtype
The type of the output array. If `dtype` is not given, infer the data
type from the other input arguments.
Returns
-------
out : ndarray
Array of evenly spaced values.
For floating point arguments, the length of the result is
``ceil((stop - start)/step)``. Because of floating point overflow,
this rule may result in the last element of `out` being greater
than `stop`.
See Also
--------
linspace : Evenly spaced numbers with careful handling of endpoints.
ogrid: Arrays of evenly spaced numbers in N-dimensions
mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions
Examples
--------
>>> np.arange(3)
array([0, 1, 2])
>>> np.arange(3.0)
array([ 0., 1., 2.])
>>> np.arange(3,7)
array([3, 4, 5, 6])
>>> np.arange(3,7,2)
array([3, 5])
"""
if not bohrium:
return numpy.arange(start, stop, step, dtype)
if stop is None:
stop = start
start = type(stop)(0)
try:
integers = (int, long)
except:
integers = (int,)
if not (isinstance(stop, integers) and isinstance(start, integers)):
raise ValueError("arange(): start and stop must be integers")
if step == 0:
raise ValueError("arange(): step cannot be zero")
# Let's make sure that 'step' is always positive
swap_back = False
if step < 0:
step *= -1
(start, stop) = (stop, start)
swap_back = True
if start >= stop:
return array([], dtype=dtype, bohrium=bohrium)
size = int(math.ceil((float(stop) - float(start)) / float(step)))
if dtype is None:
dtype = numpy.int64
else:
start = numpy.dtype(dtype).type(start)
stop = numpy.dtype(dtype).type(stop)
step = numpy.dtype(dtype).type(step)
result = simply_range(size, dtype=dtype)
if swap_back:
step *= -1
(start, stop) = (stop, start)
if step != 1:
result *= step
if start != 0:
result += start
return result