Say I have 2 numpy 2D arrays, mins, and maxs, that will always be the same dimension as one another. I'd like to create a third array, results, that is the result of applying linspace to max and min value. Is there some "numpy"/vectorized way to do this? Example non-vectorized code is below to show results I would like.
import numpy as np
mins = np.random.rand(2,2)
maxs = np.random.rand(2,2)
# Number of elements in the linspace
x = 3
m, n = mins.shape
results = np.zeros((m, n, x))
for i in range(m):
for j in range(n):
min = mins[i][j]
max = maxs[i][j]
results[i][j] = np.linspace(min, max, num=x)
The NumPy linspace function creates sequences of evenly spaced values within a defined interval. What is this? Essentally, you specify a starting point and an ending point of an interval, and then specify the total number of breakpoints you want within that interval (including the start and end points).
NumPy is a library in python adding support for large multidimensional arrays and matrices along with high level mathematical functions to operate these arrays.
linspace is an in-built function in Python's NumPy library. It is used to create an evenly spaced sequence in a specified interval.
numpy arrays can have 0, 1, 2 or more dimensions. C. shape returns a tuple of the dimensions; it may be of 0 length, () , 1 value, (81,) , or 2 (81,1) .
Here's one vectorized approach based on this post
to cover for generic n-dim cases -
def create_ranges_nd(start, stop, N, endpoint=True):
if endpoint==1:
divisor = N-1
else:
divisor = N
steps = (1.0/divisor) * (stop - start)
return start[...,None] + steps[...,None]*np.arange(N)
Sample run -
In [536]: mins = np.array([[3,5],[2,4]])
In [537]: maxs = np.array([[13,16],[11,12]])
In [538]: create_ranges_nd(mins, maxs, 6)
Out[538]:
array([[[ 3. , 5. , 7. , 9. , 11. , 13. ],
[ 5. , 7.2, 9.4, 11.6, 13.8, 16. ]],
[[ 2. , 3.8, 5.6, 7.4, 9.2, 11. ],
[ 4. , 5.6, 7.2, 8.8, 10.4, 12. ]]])
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With