Python 3 code. scipy.mgrid is a useful! This short implementation comes without installing numpy.
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Python 3 code.
scipy.mgrid is a useful!
This short implementation comes without installing numpy.
Supports complex step. "3j" means "three equi-spaced
numbers from the span including the ends".
>>> print(mgrid[1:2:3j])
[1.0, 1.5, 2.0]
Supports floating point step.
>>> print(mgrid[1:2:0.5])
[1.0, 1.5]
does not support multi-dimensions,
indicated by a tuple to scipy.mgrid
>>> print(mgrid[1:2,3:5])
Traceback (most recent call last):
File "p.py", line 16, in __getitem__
start,stop,step = s.start,s.stop,s.step
AttributeError: 'tuple' object has no attribute 'start'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "p.py", line 18, in __getitem__
raise TypeError('expected a slice')
TypeError: expected a slice
Deviates from scipy.mgrid.
>>> mgrid[2:-1] # scipy.mgrid returns an empty list.
[2, 1, 0]
'''
def convert_slice_to_list(Slice,list_length):
'''
A fun idiom.
This gets python to deal with the "None"
in slices like slice(None,None,3)
'''
return list(range(list_length))[Slice]
class poor_man_1D_mgrid:
'''
grid()[slice] emulates the scipy mgrid function for vectors.
'''
def __getitem__(self,s):
try:
start,stop,step = s.start,s.stop,s.step
except:
raise TypeError('expected a slice')
start = start or 0
step = step or 1
L = stop-start
if isinstance(step,complex):
intervals = max(int(0.5+abs(step)),2)
step = L/(intervals-1)
halfway = start+L/2
l,r = [],[]
for i in range(int(intervals/2)):
delta = step*i
r.append(stop-delta)
l.append(start+delta)
if intervals & 1:
l.append(l[-1]+(r[-1]-l[-1])/2)
l.extend(reversed(r))
return l
if (L < 0) and (step == 1):
step = -1
if step*L < 0:
raise ValueError('avoid infinite list')
return [start+step*i for i in range(max(int(0.5+L/step),1))]
mgrid = poor_man_1D_mgrid()
|
The known deviations from the scipy.mgrid are these:
1) poor_man.mgrid[:-3] == [0, -1, -2] scipy.mgrid[:-3] == [] (etceteras)
2) scipy.mgrid[slice0, slice1, slice2, ..., slicenm2, slicenm1] scipy.mgrid is multidimensional, whereas poor_man.mgrid is one dimensional.