I'm trying to do this:
h = [0.2, 0.2, 0.2, 0.2, 0.2]
Y = np.convolve(Y, h, "same")
Y looks like this:
While doing this I get this error:
ValueError: object too deep for desired array
Why is this?
My guess is because somehow the convolve function does not see Y as a 1D array.
The Y array in your screenshot is not a 1D array, it's a 2D array with 300 rows and 1 column, as indicated by its shape being (300, 1).
To remove the extra dimension, you can slice the array as Y[:, 0]. To generally convert an n-dimensional array to 1D, you can use np.reshape(a, a.size).
Another option for converting a 2D array into 1D is flatten() function from numpy.ndarray module, with the difference that it makes a copy of the array.
size is documented to return the number of elements in the array, which seems to me like the exact same thing as len() returns.size is the product of lengths across dimensions. Using a.size makes the recipe correctly reshape arrays with more than two dimensions, where using len would fail with "total size of new array must be unchanged". Thanks for the hint, I've now updated the answer.np.convolve() takes one dimension array. You need to check the input and convert it into 1D.
You can use the np.ravel(), to convert the array to one dimension.
np.convolve needs a flattened array as one of its inputs, you can use numpy.ndarray.flatten() which is quite fast, find it here.
