Say I have a numpy array [[1,2],[3,4],[5,6]], how do I do element-wise mathematics such that I could iterate over each XY pair to get X^2 + Y^2 for each pair?
Since you tagged numpy:
(np.array(a)**2).sum(-1)
Output:
array([ 5, 25, 61])
import numpy as np
arr1 = np.array([[1,2],[3,4],[5,6]])
rows = arr1.shape[0]
cols = arr1.shape[1]
ans = []
for x in range(rows):
answer = arr1[x,0]**2 + arr1[x,1]**2
ans.append(answer)
print(ans)
Related
I have two matrices, m0.shape = [10, 3, 3] and m1.shape = [10, 3]. What I want to do would done this way using loops:
m0 = np.zeros((10, 3, 3))
m1 = np.zeros((10, 3))
a = np.zeros((10, 3))
for i in range(10):
a += m1 # m0[i]
The question is: Can I somehow achieve the same result by using builtin numpy operations?
I think you have two options:
import numpy as np
np.sum(m1 # m0, axis=0)
or using numpy.einsum
np.einsum('ij,kjl->il', m1, m0)
The code below allows me to add a vector to each row of a given matrix using Numpy:
import numpy as np
m = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 1, 0])
print("Original vector:")
print(v)
print("Original matrix:")
print(m)
result = np.empty_like(m)
for i in range(4):
result[i, :] = m[i, :] + v
print("\nAfter adding the vector v to each row of the matrix m:")
print(result)
How do I perform a similar addition operation, but going column by column?
I have tried the following:
import numpy as np
array1 = np.array([[5,5,3],[2,2,3]])
print(array1)
addition = np.array([[1],[1]])
print(addition)
for i in range(3):
array1[:,i] = array1[:,i] + addition
print(array1)
However, I get the following broadcasting error:
ValueError: could not broadcast input array from shape (2,2) into shape (2)
Just match the number of dimensions, numpy will broadcast the arrays as needed. In the first example, it should be:
result = m + v.reshape((1, -1))
In the second example, the addition is already 2D so it will be just:
array1 + addition
You can alternatively, add a dimension via Numpy None syntax and then do the addition:
array1 += addition[:,None]
I have a 4d numpy array temperature of data with the measured temperature at points x,y,z and time t. Assuming I have an array indices with the indices where the first instance of a condition is met, say temperature < 0, how do I extract a 3d array with the first temperatures satisfying this condition? That is I'm looking for the equivalent of numpy's 1d version (import numpy as np tacitly assumed)
>>> temperatures = np.arange(10,-10,-1)
>>> ind = np.argmax(temperatures < 0)
>>> T = temperature[ind]
I have tried the analogous
In [1]: temperatures = np.random.random((11,8,5,200)) * 1000
In [2]: temperatures.shape
Out[2]: (11, 8, 5, 200)
In [3]: indices= np.argmax(temperatures > 900,axis=3)
In [4]: indices.shape
Out[4]: (11, 8, 5)
In [5]: T = temperatures[:,:,:,indices]
In [6]: T.shape
Out[6]: (11, 8, 5, 11, 8, 5)
However, the dimensions if Tis 6.
I could of course do it with a for loop:
indices = np.argmax(temperatures > 900,axis=3)
x,y,z = temperatures.shape[:-1]
T = np.zeros((x,y,z))
for indx in range(x):
for indy in range(y):
for indz in range(z):
T[indx,indy,indz] = temperatures[indx,indy,indz,indices[indx,indy,indz]]
but I'm looking for something fore elegant and more pythonic. Is there someone more skilled with numpy out there who can help me out on this?
P.S. For the sake of clarity, I'm not just looking for the temperature at these points given by indices, I'm also looking for other quantities in arrays of the same shape as temperature, e.g. the time derivative. Also, in reality the arrays are much larger then this minimal example.
Numpy advanced indexing does always work:
import numpy as np
temperatures = np.random.random((11,8,5, 200)) * 1000
indices = np.argmax(temperatures > 900, axis=3)
x, y, z = temperatures.shape[:-1]
T = temperatures[np.arange(x)[:, np.newaxis, np.newaxis],
np.arange(y)[np.newaxis, :, np.newaxis],
np.arange(z)[np.newaxis, np.newaxis, :],
indices]
As jdehesa pointed out this can be made more concise:
x, y, z = np.ogrid[:x, :y, :z]
T = temperatures[x, y, z, i]
I think you need:
axis = 3
indices = np.argmax(temperatures > 900, axis=axis)
result = np.take_along_axis(temperatures, np.expand_dims(indices, axis), axis)
result = result.squeeze(axis)
I need to write a list comprehension to create a vector twice the square of the middle column of a matrix. (My matrix x = [[1,2,3],[4,5,6],[7,8,9]].) Problem is, I know how to extract the middle column BUT I don't know how to square it or double the square. Any help would be greatly appreciated (...still learning but trying my best)!
x = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(x)
z = [b[1] for b in x]
print(z)
To create a vector twice the square of the column:
import numpy as np
x = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(x)
with a list comprehension: (not recommended)
z = [2*b[1]**2 for b in x]
print(z)
The output is a python list:
[8, 50, 128]
using numpy indexing: (recommended)
more info here
z = 2 * x[:,1] ** 2
print(z)
The output is a numpy array:
[ 8 50 128]
I have an array with m rows and arrays as values, which indicate the index of columns and are bounded to a large number n.
E.g:
Y = [[1,34,203,2032],...,[2984]]
Now I want an efficient way to initialize a sparse numpy matrix X with dimensions m,n and values corresponding to Y (X[i,j] = 1, if j is in Y[i], = 0 otherwise).
Your data are already close to csr format, so I suggest using that:
import numpy as np
from scipy import sparse
from itertools import chain
# create an example
m, n = 20, 10
X = np.random.random((m, n)) < 0.1
Y = [list(np.where(y)[0]) for y in X]
# construct the sparse matrix
indptr = np.fromiter(chain((0,), map(len, Y)), int, len(Y) + 1).cumsum()
indices = np.fromiter(chain.from_iterable(Y), int, indptr[-1])
data = np.ones_like(indices)
S = sparse.csr_matrix((data, indices, indptr), (m, n))
# or
S = sparse.csr_matrix((data, indices, indptr))
# check
assert np.all(S==X)