You may find the following Numpy functions helpful as you complete the labs in CS355:

np.linspace(start,stop,num)	Returns a Numpy array of size num with evenly spaced values between start and stop.
arr.shape	Returns a tuple of the size of each dimension in a Numpy array.
np.zeros(arr.shape)	Returns a new Numpy array of zeros of the same shape as arr. This is great for making buffer images.
np.array(list)	Turns a standard Python list into a Numpy array.
arr[start:stop:step,...,...]	Returns the portion of arr described by standard Python slice notation. Commas separate slice notation for each dimension. A single colon returns every element of the specified dimension.
arr + number	Performs the element-wise addition of each element of arr with number. This also works with subtraction(-), multiplication(*), division(/) and others.
arr1 + arr2	Performs the element-wise addition between arr1 with arr2. This also works with subtraction(-), multiplication(), division(/) and others. Warning:* If arr1 and arr2 are both matrices, * performs a matrix multiplication.
np.multiply(arr1,arr2)	Returns the element-wise multiplication of arr1 and arr2.
np.matmul(arr1, arr2)	Returns the matrix multiplication arr1 and arr2.
np.dot(arr1, arr2)	Returns the dot product of arr1 and arr2. Warning: Make sure that arr1 and arr2 are both row vectors or both column vectors.
np.cross(arr1, arr2)	Returns the cross product of arr1 and arr2. Warning: Make sure that arr1 and arr2 are both row vectors or both column vectors.
np.sum(arr, axis=None)	Sums all elements in a Numpy array. If you specify an axis, it will only sum along that axis.
np.atleast_2D(arr)	Turns a 1D list into a 2D Numpy matrix array.
arr.T	If arr is 2D, it returns the matrix transpose.
np.amax(arr, axis=None)	Returns the maximum value of a Numpy array. If you specify an axis, it will return each maximum value along that axis.
np.argmax(arr, axis=None)	Returns the index of the maximum value of a Numpy array. If you specify an axis, it will return the index of each maximum value along that axis.
np.clip(arr, min, max)	Returns an array where each element is between min and max.