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apply z-score for all 2D slice( dependently only by data in each single 2d image) in a 3D numpy array

I have a numpy 3D array input_array,with a dimension of 480x480x661,which contains 661 images each with a dimension of 480x480. I would like to create another numpy array out,where in all images I've applied z-score ( dependently only by data in each single 2d image). I'm using scipy.stats.zscore. This code works,but I've no idea if is doing exactly what i want,can you check?
for ii in range(np.size(input_array,2)):
out=scipy.stats.zscore(input_array[:,:,ii],axis=None)

How to balance data when they look like a 3-D array?

I've got a numpy_array of size (3275412, 50, 22) which represents my data reshaped for LSTM purposes and I have got a target vector of shape (3275412,).
I want to balance my data so that there is approximately the same number of data with target 0 and 1.
The way I prepared the data makes that I can not do this balancing operation before reshaping.
Firstly, I wanted to apply make_imbalance function (see this link for details) but I can't apply it on a 2-D array (got an error).
My question is : what's the most efficient way to do it for a 3D array ?
My thoughts: I thought about firstly "flatten" my 3-D array to a 2-D array by "concatenating" the second and third dimension (but don't know how so please tell me ??) then apply make_imbalance and then reshape the result to a 3-D array (again, don't know how to do). It seems a little bit tricky however...
So any help would be appreciated, either for an other imbalancing method or for help about reshaping 3D->2D or vice-versa

You can use np.reshape with -1 for unknown dimension size.
data2d = data3d.reshape(data3d.shape[0], -1)
will give you a 2d array of shape (n_samples, n_features)
with the second and the third dimensions merged.
data2d_new, y_new = make_imbalance(data2d, y)
After make_imbalance call, you will get a 2d array with a shape (n_samples_new, n_features), where the number of rows is "unknown" but you know your other two 'feature' dimensions of the original 3d array, so
data3d_new = data2d.reshape(-1, data3d.shape[1], data3d.shape[2])
will give you back the balanced 3d dataset.

How can I combine my three 2D tensors into a single 3D tensor in tensor flow?

Hello I am a newbie with the tensorflow and currently, I am working with colour Images and it's PCAS.
I have extracted PCAS in a form of "Red","Green" and "Blue" and also computed the weights which are associated with "Red","Green" and "Blue" components.
After doing the all the above stuff I want to combine all three 2D matrices into the single 3D matrix.
For a tensorflow it would be a 3D tensor.
def multi(h0,ppca,mu,i,scope=None):
with tf.variable_scope(scope or"multi"):
return tf.matmul(ppca[:,:,0],h0[i,:,:,0]) + tf.reshape(mu[:,0],[4096,1]) , tf.matmul(ppca[:,:,1],h0[i,:,:,1]) + tf.reshape(mu[:,1],[4096,1]) ,tf.matmul(ppca[:,:,2],h0[i,:,:,2]) + tf.reshape(mu[:,2],[4096,1])
So from the above function, I will get all three different 2D tensors and want to combine those 2D tensors to single 3D tensor which has dimensions [4096,1,3]
How can I do that?
any help is highly appreciated.

You need to concat them like this:
three_d_image = tf.concat(0, [[r], [g], [b]])
This tells tensorflow to concat them along the x dimension and treat each tensor as a matrix.
Doing the same without the additional brackets around the r,g,b tensors will try to concat them to one large 2D matrix

A clean, easy way to do it is using the tf.stack operation (tf.pack in older versions of tensorflow), it concatenats all tensors along a new dimension. If you want your new dimension to be after all previous, you need to set the axis argument to the number of dimensions of your tensors.
three_d_image = tf.stack([r,g,b], axis=2)

one of the solutions is that you can add one more empty dimension to your 2Ds so you will have 3 matrices of 3D dimension [4096,1,1] then you can concat these 3 matrices by axis 2 tf.concat(2,matrices) gives you [4096,1,3]
the second solution can be concat of axis 1, tf.concat(1,matrices) then reshape it to 3D

How to extract vector from 3d numpy array?

I have a set of numpy.arrays of NXM (two dimensions: Range and Azimuth).
I need to form a stack of three dimensions and extract a single dimension vector to compute a covariance matrix (the red vectors in the picture).
How i do this efficiently and easy in Python?

You can make a 3D numpy array pretty easily and then just use the indexing to pull out the bits that you're interested in:
stackOfImages = np.array((image1, image2)) #iterate over these if many more
redData = stackOfImages[:, N-1, M-1]

Interchange ordering of 4-D Numpy array

I have a 4-D Numpy array of dimensions 96x96x3x1000 - these correspond to an image dataset that I have imported : 1000 images each of 96X96 pixels and RGB values for each pixel.
However, I need to iterate over flattened arrays for each image, ie. only a 2-D array [1000][96*96*3]. I managed to transform the given array by first doing
a.reshape(-1,a.size[3])
and then assigning each column to an image using a loop. I wanted to ask if there is a simpler/slicing method for interchanging the ordering of ndarrays ?
Thanks

You can change the ordering of the axes using numpy.swapaxes
a.reshape(-1,1000).swapaxes(0,1)
or simply tranposing it
a.reshape(-1,1000).T
You can also change the ordering of the axis at the beginning with numpy.transpose and then apply reshape
a.transpose([3,0,1,2]).reshape(1000,-1)