Numpy Adding two vectors with different sizes
Question:
If I have two numpy arrays of different sizes, how can I superimpose them.
a = numpy([0, 10, 20, 30])
b = numpy([20, 30, 40, 50, 60, 70])
What is the cleanest way to add these two vectors to produce a new vector (20, 40, 60, 80, 60, 70)?
This is my generic question. For background, I am specifically applying a Green’s transform function and need to superimpose the results for each time step in the evaulation unto the responses previously accumulated.
Answers:
This could be what you are looking for
if len(a) < len(b):
c = b.copy()
c[:len(a)] += a
else:
c = a.copy()
c[:len(b)] += b
basically you copy the longer one and then add in-place the shorter one
If you know that b is higher dimension, then:
>>> a.resize(b.shape)
>>> c = a+b
is all you need.
Very similar to the one above, but a little more compact:
l = sorted((a, b), key=len)
c = l[1].copy()
c[:len(l[0])] += l[0]
If I have two numpy arrays of different sizes, how can I superimpose them.
a = numpy([0, 10, 20, 30])
b = numpy([20, 30, 40, 50, 60, 70])
What is the cleanest way to add these two vectors to produce a new vector (20, 40, 60, 80, 60, 70)?
This is my generic question. For background, I am specifically applying a Green’s transform function and need to superimpose the results for each time step in the evaulation unto the responses previously accumulated.
This could be what you are looking for
if len(a) < len(b):
c = b.copy()
c[:len(a)] += a
else:
c = a.copy()
c[:len(b)] += b
basically you copy the longer one and then add in-place the shorter one
If you know that b is higher dimension, then:
>>> a.resize(b.shape)
>>> c = a+b
is all you need.
Very similar to the one above, but a little more compact:
l = sorted((a, b), key=len)
c = l[1].copy()
c[:len(l[0])] += l[0]