How can I , using Numpy, compute how many entries in my array that gives the cumulative sum of 0,9 as an example?

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After preforming PCA I have my (rescaled and proportion of variance explained) array with the sum 1. I have to many components to get a good screeplot - therefore I would like to be able to somehow set the desired cumulative sum and get how many of the components of the array I will need to get it. As an example [0, 1, 2, 3, 4, 5] and I wanted the cumulative sum of 6 I'd get the answer that I'd need four entries.

My solution would be to use numpy.cumsum together with numpy.where:

arr = np.arange(6) # dummy data
max_cum = 6 # your stop certerion

arr_cum = arr.cumsum() # calculate cumulative sums of your array
num = np.where(arr_cum >= max_cum) # get indices where arr_cum passes your max_cum

>> In: num[0][0]

>> Out:
3

num contains all indices which contain values that are equal to or higher than your stop criterion, so you will need to fetch the first one (numpy.where returns a 2D-array, so you will need [0][0]).

One-liner function:

def lowest_cum(arr, max_cum):
    return np.where(arr.cumsum() >= max_cum)[0][0]

>> In: lowest_cum(arr=np.arange(6), max_cum=6)

>> Out:
3

Edit: needless to say that you need +1 for the number of entries, as the function will return the first index at which the cumsum is completed, so index 3 means 4 entries.

numpy.cumsum — NumPy v1.19 Manual, I have to many components to get a good screeplot - therefore I would like to how many entries in my array that gives the cumulative sum of 0,9 a As an example [0, 1, 2, 3, 4, 5] and I wanted the cumulative sum of 6 I'd get� numpy.sum(arr, axis, dtype, out): This function returns the sum of array elements over the specified axis. Parameters : arr : input array. axis : axis along which we want to calculate the sum value.

You can calculate the cummulative sum, and then calculate the .argmax(..) where it is greater than or equal to the threshold:

>>> np.argmax(np.hstack((a.cumsum() >= 6, True))) + 1
4

Since a.cumsum() is the cummulative sum, and thus:

>>> a.cumsum()
array([ 0,  1,  3,  6, 10, 15])

and we thus calculate the first index where the threshold is satisfied:

>>> a.cumsum() >= 6
array([False, False, False,  True,  True,  True])

We append a True, such that in case there is no equence satisfying the threshold, it will return the number of elements plus one.

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Assuming a is already sorted, simply count the number of entries which do not yet exceed the threshold, and add the final entry which does.

a = np.arange(6)
num = (a.cumsum() < 6).sum() + 1 # 4

As a function:

def cum_thresh(a, thresh):
    """
    the sequence a has to be sorted
    """
    return (np.cumsum(a) < thresh).sum() + 1

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Python numpy cumsum() function returns the cumulative sum of the elements Let's look at some examples of calculating cumulative sum of numpy array elements. Cumulative Sum of elements at 0-axis is: [[ 1 2] [ 4 6] [ 9 12]] Cumulative Sum of I love Open Source technologies and writing about my experience about� NumPy package contains an iterator object numpy.nditer. It is an efficient multidimensional iterator object using which it is possible to iterate over an array. Each element of an array is visited using Python’s standard Iterator interface. Let us create a 3X4 array using arange() function and iterate over it using nditer. Example 1

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Comments
  • I didn't get the one-liner to work but the code above worked like a charm. Thanks! Or should I say: Tack så mycket!
  • You can call the function like lowest_cum(arr=np.arange(6), max_cum=6)