I'm starting to learn numpy and can't understand the very basic thing. I have a list of indexes
in a multidimensional array (one for each axis). How can I set the value to the point in an array that corresponds to that index? Basically, how can I use idxs variable in the folllowing code and produce same result?
A = np.zeros((2, 2))
idxs = [1, 0]
A[1, 0] = 1
A
array([[0., 0.],
[1., 0.]])
Thanks to Ivan,
A[tuple(idxs)] = 1
works
Related
Let's consider a multi-dimensional array
arr = np.zeros((3,2,4))
and some indexing array
index_arr = np.array([2, 1])
To clarify what I want to get, it's this (but I want to provide indices dynamically):
arr[2, 1] # array([0., 0., 0., 0.])
NOT this:
arr[[2, 1]] # which returns result with shape (2, 2, 4)
I would have liked to do something like this
arr[*index_arr] # using * to unpack the items of `index_arr`
but that gives a syntax error. Is there a native way to do what I'm asking for?
I have a list of python arrays like the following:
[array([[0., 0., 0.]]),
array([[0., 0., 0.]]),
array([[0., 0., 0.]])]
My goal is to change them to an array of lists like the following:
array([[ 0, 0, 0],
[ 0, 0, 0],
[ 0, 0, 0]])
I tried the following however I'm getting I'm an error:
np.array([my_array[i].tolist() for i in my_array])
The following is the error:
TypeError: only integer scalar arrays can be converted to a scalar index
Could anyone help me understand what's going and what I'm doing wrong.
You can just concatenate row-wise with vstack since they are of shape (1, 3) in your example to get the result of shape (n, 3).
np.vstack(my_array)
Why your current code fails is because you are iterating over every sub-array in my_array and trying to index my_array with that. So you are indexing a list with array([[0., 0., 0.]]) which is not a scaler index because it is an array.
Alternative options:
np.array(my_array).reshape(-1, 3)
np.array([e.reshape(-1) for e in my_array])
...
Try:
np.array([i[0] for i in my_array])
Since in your case, i is not the index of a, it represents each element in my_array
And each of your element in my_array is a nested list, you may want to flat the nested list when doing the conversion
I have a 2D Numpy array, in which I want to normalise each column to zero mean and unit variance. Since I'm primarily used to C++, the method in which I'm doing is to use loops to iterate over elements in a column and do the necessary operations, followed by repeating this for all columns. I wanted to know about a pythonic way to do so.
Let class_input_data be my 2D array. I can get the column mean as:
column_mean = numpy.sum(class_input_data, axis = 0)/class_input_data.shape[0]
I then subtract the mean from all columns by:
class_input_data = class_input_data - column_mean
By now, the data should be zero mean. However, the value of:
numpy.sum(class_input_data, axis = 0)
isn't equal to 0, implying that I have done something wrong in my normalisation. By isn't equal to 0, I don't mean very small numbers which can be attributed to floating point inaccuracies.
Something like:
import numpy as np
eg_array = 5 + (np.random.randn(10, 10) * 2)
normed = (eg_array - eg_array.mean(axis=0)) / eg_array.std(axis=0)
normed.mean(axis=0)
Out[14]:
array([ 1.16573418e-16, -7.77156117e-17, -1.77635684e-16,
9.43689571e-17, -2.22044605e-17, -6.09234885e-16,
-2.22044605e-16, -4.44089210e-17, -7.10542736e-16,
4.21884749e-16])
normed.std(axis=0)
Out[15]: array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
Most probably somebody else already asked this but I couldn't find it. The question is how can I assign values to a 2D array from two 1D arrays. For example:
import numpy as np
#a is the 2D array. b is the 1D array and should be assigned
#to second coordinate. In this exaple the first coordinate is 1.
a=np.zeros((3,2))
b=np.asarray([1,2,3])
c=np.ones(3)
a=np.vstack((c,b)).T
output:
[[ 1. 1.]
[ 1. 2.]
[ 1. 3.]]
I know the way I am doing it so naive, but I am sure there should be a one line way of doing this.
P.S. In real case that I am dealing with, this is a subarray of an array, and therefore I cannot set the first coordinate from the beginning to one. The whole array's first coordinate are different, but after applying np.where they become constant.
How about 2 lines?
>>> c = np.ones((3, 2))
>>> c[:, 1] = [1, 2, 3]
And the proof it works:
>>> c
array([[ 1., 1.],
[ 1., 2.],
[ 1., 3.]])
Or, perhaps you want np.column_stack:
>>> np.column_stack(([1.,1,1],[1,2,3]))
array([[ 1., 1.],
[ 1., 2.],
[ 1., 3.]])
First, there's absolutely no reason to create the original zeros array that you stick in a, never reference, and replace with a completely different array with the same name.
Second, if you want to create an array the same shape and dtype as b but with all ones, use ones_like.
So:
b = np.array([1,2,3])
c = np.ones_like(b)
d = np.vstack((c, b).T
You could of course expand b to a 3x1-array instead of a 3-array, in which case you can use hstack instead of needing to vstack then transpose… but I don't think that's any simpler:
b = np.array([1,2,3])
b = np.expand_dims(b, 1)
c = np.ones_like(b)
d = np.hstack((c, b))
If you insist on 1 line, use fancy indexing:
>>> a[:,0],a[:,1]=[1,1,1],[1,2,3]
I'm defining a function which will return a 3-d grid. During it, I use a function defined already that returns a 2-d array. I want to join these 2-d arrarys to form the 3-d during an iteration but I've looked at functions like meshgrid(), dstack(), concatenate() but can't seem to get any of them to fit right into the code.
The program models the spread of waves from a point source on the 2-d array, and the 3-d array shows how the displacement of the medium changes over the course of a wavelength.
def make_wave_snapshot(size,wavelength,phase):
waves_array = np.zeros((size,size),np.float)
if size%2==0:
for y in range(size):
for x in range(size):
r = math.hypot((size/2 - x - 0.5),(size/2 - y - 0.5))
d = np.sin((2*math.pi*r/wavelength)-phase)/np.sqrt(r)
waves_array[y,x] = d
dp.display_2d_array(waves_array) #This is in another module altogether
return waves_array #Displays array showing values
else:
return 'Please use integer of size.'
def make_wave_sequence(size,wavelength,nsteps):
waves_sequence = np.zeros((nsteps,size,size),np.float)
if nsteps%1==0:
for z in range(nsteps):
make_wave_snapshot(size,wavelength,(2*math.pi*z/nsteps))
waves_sequence = ???
return waves_sequence #Displays array showing values
else:
return 'Please use positive integer for number of steps'
The issue is turning the 'wave_array's into a 'wave_sequence'. Generous commenting would be very appreciated if you write any code. Many thanks!
If I understand correctly you have a three dimensional array, something like:
wave = np.zeros((2, 2, 2), np.float)
([[[0., 0.],
[0., 0.]],
[[0., 0.],
[0., 0.]]])
And you want to insert a two dimensional array, returned from your function like:
([[ 1., 2.],
[ 3., 4.]])
Such that your 3D array is now:
([[[1., 2.],
[3., 4.]],
[[0., 0.],
[0., 0.]]])
After the first iteration of your for loop. If that is correct, then it's actually pretty simple and you're most of the way there. You can assign an "element" to your 3D array that is a 2D array as long as you select the correct entry:
for z in range(nsteps):
waves_sequence[z] = make_wave_snapshot(size,wavelength,(2*math.pi*z/nsteps))