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Return the sum along diagonals of the masked array in Numpy
To return the sum along diagonals of the masked array elements, use the ma.MaskedArray.trace() in Numpy. The offset parameter is the offset of the diagonal from the main diagonal. Can be both positive and negative. Defaults to 0.
The axis 1 and axis 2 are the axes to be used as the first and second axis of the 2-D sub-arrays from which the diagonals should be taken. Defaults are the first two axes of a. The dtype determines the data-type of the returned array and of the accumulator where the elements are summed. If dtype has the value None and a is of integer type of precision less than the default integer precision, then the default integer precision is used. Otherwise, the precision is the same as that of array.
Steps
At first, import the required library −
import numpy as np import numpy.ma as ma
Create an array with int elements using the numpy.array() method −
arr = np.array([[55, 85, 68, 84], [67, 33, 39, 53], [29, 88, 51, 37], [56, 45, 99, 85]]) print("Array...\n", arr) print("\nArray type...\n", arr.dtype)
Get the dimensions of the Array −
print("\nArray Dimensions...\n",arr.ndim)
Create a masked array and mask some of them as invalid −
maskArr = ma.masked_array(arr, mask =[[1, 1, 0, 0], [ 0, 0, 1, 0], [0, 0, 0, 1], [0, 1, 0, 0]]) print("\nOur Masked Array\n", maskArr) print("\nOur Masked Array type...\n", maskArr.dtype)
Get the dimensions of the Masked Array −
print("\nOur Masked Array Dimensions...\n",maskArr.ndim)
Get the shape of the Masked Array −
print("\nOur Masked Array Shape...\n",maskArr.shape)
Get the number of elements of the Masked Array −
print("\nElements in the Masked Array...\n",maskArr.size)
To return the sum along diagonals of the masked array elements, use the ma.MaskedArray.trace() in Numpy −
res = maskArr.trace() print("\nResult..\n.", res)
Example
import numpy as np import numpy.ma as ma # Create an array with int elements using the numpy.array() method arr = np.array([[55, 85, 68, 84], [67, 33, 39, 53], [29, 88, 51, 37], [56, 45, 99, 85]]) print("Array...\n", arr) print("\nArray type...\n", arr.dtype) # Get the dimensions of the Array print("\nArray Dimensions...\n",arr.ndim) # Create a masked array and mask some of them as invalid maskArr = ma.masked_array(arr, mask =[[1, 1, 0, 0], [ 0, 0, 1, 0], [0, 0, 0, 1], [0, 1, 0, 0]]) print("\nOur Masked Array\n", maskArr) print("\nOur Masked Array type...\n", maskArr.dtype) # Get the dimensions of the Masked Array print("\nOur Masked Array Dimensions...\n",maskArr.ndim) # Get the shape of the Masked Array print("\nOur Masked Array Shape...\n",maskArr.shape) # Get the number of elements of the Masked Array print("\nElements in the Masked Array...\n",maskArr.size) # To return the sum along diagonals of the masked array elements, use the ma.MaskedArray.trace() in Numpy res = maskArr.trace() print("\nResult..\n.", res)
Output
Array... [[55 85 68 84] [67 33 39 53] [29 88 51 37] [56 45 99 85]] Array type... int64 Array Dimensions... 2 Our Masked Array [[-- -- 68 84] [67 33 -- 53] [29 88 51 --] [56 -- 99 85]] Our Masked Array type... int64 Our Masked Array Dimensions... 2 Our Masked Array Shape... (4, 4) Elements in the Masked Array... 16 Result.. . 169.0
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