def _findIndices(ArrSize, FilterSize):
N = FilterSize.shape[0]
n = int(FilterSize.prod())
CumSizeArr = numpy.ones([N], dtype=numpy.int32)
CumSizeArr[1:N] = ArrSize[0:N - 1].cumprod()
CumSize = numpy.ones([N], dtype=numpy.int32)
CumSize[1:N] = FilterSize[0:N - 1].cumprod()
vals = numpy.empty((n, N), dtype=numpy.int32)
for i in range(N):
vals[:, i] = numpy.linspace(0, n - 1, n)
vals = vals // CumSize
vals = vals % FilterSize
CurrPos = summations.sum(vals * CumSizeArr, axis=1)
return CurrPos.astype(numpy.int32)
def _findIndices(ArrSize, FilterSize):
N = FilterSize.shape[0]
n = int(FilterSize.prod())
CumSizeArr = numpy.ones([N], dtype=numpy.int32)
CumSizeArr[1:N] = ArrSize[0:N - 1].cumprod()
CumSize = numpy.ones([N], dtype=numpy.int32)
CumSize[1:N] = FilterSize[0:N - 1].cumprod()
vals = numpy.empty((n, N), dtype=numpy.int32)
for i in range(N):
vals[:, i] = numpy.linspace(0, n - 1, n)
vals = vals // CumSize
vals = vals % FilterSize
CurrPos = summations.sum(vals * CumSizeArr, axis=1)
return CurrPos.astype(numpy.int32)
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