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How do I apply FFT on a 3D Array

I have a 3D array that has the shape (features, timestep, samples). I would like to apply the numpy fft function on each feature for the length of timestep for each sample. I have this, but I am uncertain whether this is the best way or whether there needs to be a loop to iterate through each sample.
import numpy as np
x_train_fft = np.fft.fft(x_train, axis=0) #selected axis 0 as this is the axis of features

Looks like this was the way to do it
X_transform_FFT =[]
for i in range(x_train.shape[0]):
f = abs(np.fft.fft(x_train[i, :, :], axis = 1))
X_transform_FFT.append(f)
np.asarray(X_transform_FFT)
print(X_transform_FFT)

Python: Convert 2d point cloud to grayscale image

I have an array of variable length filled with 2d coordinate points (coming from a point cloud) which are distributed around (0,0) and i want to convert them into a 2d matrix (=grayscale image).
# have
array = [(1.0,1.1),(0.0,0.0),...]
# want
matrix = [[0,100,...],[255,255,...],...]
how would i achieve this using python and numpy

Looks like matplotlib.pyplot.hist2d is what you are looking for.
It basically bins your data into 2-dimensional bins (with a size of your choice).
here the documentation and a working example is given below.
import numpy as np
import matplotlib.pyplot as plt
data = [np.random.randn(1000), np.random.randn(1000)]
plt.scatter(data[0], data[1])
Then you can call hist2d on your data, for instance like this
plt.hist2d(data[0], data[1], bins=20)
note that the arguments of hist2d are two 1-dimensional arrays, so you will have to do a bit of reshaping of our data prior to feed it to hist2d.

Quick solution using only numpy without the need for matplotlib and therefor plots:
import numpy as np
# given a 2dArray "array" and a desired image shape "[x,y]"
matrix = np.histogram2d(array[:,0], array[:,1], bins=[x,y])

Error plotting scikit-learn dataset training and test data

I am trying to plot the training and test data from a scikit-learn dataset.
import sys, os
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
plt.switch_backend('agg')
%matplotllib inline
diabetes = datasets.load_diabetes()
diabetes_X = diabetes.data[:, np.newaxis, 2]
diabetes_X_train = diabetes_X[:-20]
diabetes_X_test = diabetes_X[-20:]
diabetes_y_train = np.matrix(diabetes.target[:-20]).T
diabetes_y_test = np.matrix(diabetes.target[-20:]).T
plt.scatter(diabetes_X_train, diabetes_y_train, color='black')
plt.scatter(diabetes_X_test, diabetes_y_test, color='red')
but I have the following error:
ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 422 and the array at index 1 has size 1
I checked the shape of the matrices and the training data has (422,1) and the test data (20,1). What is causing this error?

plt.scatter is expecting to plot two same-shaped datasets against each other. IF they aren't 1D, they will be flattened. It does not make sense to flatten X in a machine-learning problem.
Check the dimensions of X_train and y_train. You'll see that they aren't compatible. This is a 2D plot you're making, you can only plot one set of numbers against another. X is a matrix: every row is a bunch of numbers.
So you can do this:
import numpy as np
import matplotlib.pyplot as plt
x, y = np.random.random((422, 1)), np.random.random((422, 1))
plt.scatter(x, y)
But you can't do this:
X, y = np.random.random((422, 10)), np.random.random((422, 1))
plt.scatter(X, y)
Which is essentially what you're trying to do. (I don't think you want to transpose y by the way.)
So this should work for you:
plt.scatter(diabetes_X_train[:, 0], diabetes_y_train)
But that only shows the relationship with one feature of X.
Assuming you're just trying to explore the data, I recommend checking out seaborn.pairplot. It's perfect for this sort of thing.

Plot 3rd axis of a 3D numpy array

I have a 3D numpy array that is a stack of 2D (m,n) images at certain timestamps, t. So my array is of shape (t, m, n). I want to plot the value of one of the pixels as a function of time.
e.g.:
import numpy as np
import matplotlib.pyplot as plt
data_cube = []
for i in xrange(10):
a = np.random(100,100)
data_cube.append(a)
So my (t, m, n) now has shape (10,100,100). Say I wanted a 1D plot the value of index [12][12] at each of the 10 steps I would do:
plt.plot(data_cube[:][12][12])
plt.show()
But I'm getting index out of range errors. I thought I might have my indices mixed up, but every plot I generate seems to be in the 'wrong' axis, i.e. across one of the 2D arrays, but instead I want it 'through' the vertical stack. Thanks in advance!

Here is the solution: Since you are already using numpy, convert you final list to an array and just use slicing. The problem in your case was two-fold:
First: Your final data_cube was not an array. For a list, you will have to iterate over the values
Second: Slicing was incorrect.
import numpy as np
import matplotlib.pyplot as plt
data_cube = []
for i in range(10):
a = np.random.rand(100,100)
data_cube.append(a)
data_cube = np.array(data_cube) # Added this step
plt.plot(data_cube[:,12,12]) # Modified the slicing
Output
A less verbose version that avoids iteration:
data_cube = np.random.rand(10, 100,100)
plt.plot(data_cube[:,12,12])

Representing row vector as a line plot in matplotlib

Let's say I have a row vector with the shape (1, 100). Call it row.
I want to display its values in a line plot. The x-axis displays the vector indices. The y-axis displays the values at the corresponding indices. How would you do it in matplotlib?
EDIT:
Here is what I have tried:
indices = [n for n in range(100)]
values = list(row[:, :100])
pyplot.plot(indices, values)

The array indices is not necessary.
The values array syntax is unclear... and an error in python, unless you are using numpy.
The following will plot an array of random values, with indices as x and the random numbers stored in values as y
import matplotlib.pyplot as plt
import random
# indices = [n for n in range(100)]
values = [random.random() for _ in range(100)]
plt.plot(values)
plt.show()

import matplotlib.pyplot as plt
indices = range(100) # already returns a list, no need to iterate again
values = # insert 1d vector here, what you seem to use is multi-dim
plt.plot(indices, values)
plt.show()