I am wondering what is the most convenient way for finding the numpy array value with customized coordinates.
For example, I create a numpy array like
[[1,2,3,4]
[5,6,7,8]]
The x coordinate is[1, 3, 5, 7], and the y coordinate is [1,1.5].
I hope to get the value '8' by using the coordinate (7,1.5) rather than using numpy index (3,1).
What is the most elegant way to do that?
Related
I have a 3D array, and I were to find the greatest Z-coordinate in that array. After that I need to find the corresponding X and Y coordinate values based on the Z-coordinate. How can I achieve it quickly via numpy?
What I did:
I used argsort to first sort the given 3D array, then used np. max(array) to find the greatest Z-coordinate. I do not know how else to continue. Can numpy.where be useful here?
Thanks!
What you are looking for is numpy argmax
quick example :
import numpy as np
data = np.random.rand(5,3)
print data
ind = np.argmax(data[:,2])
print data[ind, :]
outputs
[[0.92037795 0.59469121 0.02956843]
[0.82881039 0.23272832 0.97275488]
[0.98418468 0.45699429 0.44662552]
[0.62519115 0.16637013 0.40433299]
[0.98272718 0.01467489 0.57442259]]
[0.82881039 0.23272832 0.97275488]
I have a simple, one dimensional Python array with random numbers. What I want to do is convert it into a numpy Matrix of a specific shape. My current attempt looks like this:
randomWeights = []
for i in range(80):
randomWeights.append(random.uniform(-1, 1))
W = np.mat(randomWeights)
W.reshape(8,10)
Unfortunately it always creates a matrix of the form:
[[random1, random2, random3, ...]]
So only the first element of one dimension gets used and the reshape command has no effect. Is there a way to convert the 1D array to a matrix so that the first x items will be row 1 of the matrix, the next x items will be row 2 and so on?
Basically this would be the intended shape:
[[1, 2, 3, 4, 5, 6, 7, 8],
[9, 10, 11, ... , 16],
[..., 800]]
I suppose I can always build a new matrix in the desired form manually by parsing through the input array. But I'd like to know if there is a simpler, more eleganz solution with built-in functions I'm not seeing. If I have to build those matrices manually I'll have a ton of extra work in other areas of the code since all my source data comes in simple 1D arrays but will be computed as matrices.
reshape() doesn't reshape in place, you need to assign the result:
>>> W = W.reshape(8,10)
>>> W.shape
(8,10)
You can use W.resize(), ndarray.resize()
I've got a big numpy array full of coordinates (about 400):
[[102, 234],
[304, 104],
....
]
And a numpy 2d array my_map of size 800x800.
What's the fastest way to look up the coordinates given in that array? I tried things like paletting as described in this post: http://opencvpython.blogspot.com/2012/06/fast-array-manipulation-in-numpy.html but couldn't get it to work.
I was also thinking about turning each coordinate into a linear index of the map and then piping it straight into my_map like so:
my_map[linearized_coords]
but I couldn't get vectorize to properly translate the coordinates into a linear fashion. Any ideas?
Try using a tuple for indexing:
my_map[tuple(coords.T)]
This selects elements of the array via advanced indexing.
I have some numpy/scipy issue. I have a 3D array that represent an ellipsoid in a binary way [ 0 out of the ellipsoid].
The thing is I would like to rotate my shape of a certain degree. Do you think it's possible ?
Or is there an efficient way to write directly the ellipsoid equation with the rotation ?
Just a short answer. If you need more informations or you don't know how to do it, then I will edit this post and add a small example.
The right way to rotate your matrix of data points is to do a matrix multiplication. Your rotation matrix would be probably an n*n-matrix and you have to multiply it with every point. If you have your 3d-matrix you have some thing like i*j*k-points for plotting. This means for your case you have to do it i*j*k-times to find the new points. Maybe you should consider an other matrix for plotting which is just a 2D matrix and just store the plotting points and no zero values.
There are some algorithm to calculate faster the results for low valued matrix, but just google for this.
Did you understood me or do you still have some questions? Sorry for this rough overview.
Best regards
Take a look at the command numpy.shape
I used it once to transpose an array, but I don't know if it might fit your needs.
Cheers!
rotating by a non rectangular degree is tricky, because the rotated square does no longer fit in the matrix.
The simplest way would be to transpose a 2D array/matrix by:
import numpy as np
... x = np.array([[1,2,3],[4,5,6],[7,8,9]])
x
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
x.T # transpose the array
array([[1, 4, 7],
[2, 5, 8],
[3, 6, 9]])
if you require an explicit array rotation check the ndimage package from scipy library:
from scipy import ndimage, datasets
img = datasets.ascent()
img_45 = ndimage.rotate(img, 45, reshape=False)
I have a 3D array created using the numpy mgrid command so that each element has a certain value and the indexes retain the spatial information. For example, if one summed over the z-axis (3rd dimension) then the the resultant 2D array could be used in matplotlib with the function imshow() to obtain an image with different binned pixel values.
My question is: How can I obtain the index values for each element in this grid (a,b,c)?
I need to use the index values to calculate the relative angle of each point to the origin of the grid. (eg. theta=sin-1(sqrt(x^2+y^2)/sqrt(x^2+y^2+z^2))
Maybe this can be translated to another 3D grid where each element is the array [a,b,c]?
I'm not exactly clear on your meaning, but if you are looking for 3d arrays that contain the indices x, y, and z, then the following may suit your needs; assume your data is held in a 3D array called "abc":
import numpy as nm
x,y,z = nm.mgrid[[slice(dm) for dm in abc.shape]]