Numpy recarray.clip() function | Python
Last Updated :
23 Apr, 2019
In numpy, arrays may have a data-types containing fields, analogous to columns in a spreadsheet. An example is [(a, int), (b, float)]
, where each entry in the array is a pair of (int, float). Normally, these attributes are accessed using dictionary lookups such as arr['a'] and arr['b']
.
Record arrays allow the fields to be accessed as members of the array, using arr.a and arr.b
. numpy.recarray.clip()
function return an array whose values are limited to [min, max]
. One of max or min must be given.
Syntax : numpy.recarray.clip(min=None, max=None, out=None)
Parameters:
min : Minimum value.
–> If None, clipping is not performed on lower interval edge. Not more than one of a_min and a_max may be None.
max : Maximum value.
–> If None, clipping is not performed on upper interval edge. Not more than one of a_min and a_max may be None.
–> If a_min or a_max are array_like, then the three arrays will be broadcasted to match their shapes.
out : Results will be placed in this array. It may be the input array for in-place clipping. out must be of the right shape to hold the output. Its type is preserved.
Return : [clipped_array, ndarray] An array where values less than minimum are replaced with min, and values greater then maximum with max.
Code #1 :
import numpy as geek
in_arr = geek.array([[( 5.0 , 2 ), ( 3.0 , - 4 ), ( 6.0 , 9 )], [( 9.0 , 1 ), ( 5.0 , 4 ), ( - 12.0 , - 7 )]],
dtype = [( 'a' , float ), ( 'b' , int )])
print ( "Input array : " , in_arr)
rec_arr = in_arr.view(geek.recarray)
print ( "Record array of int: " , rec_arr.b)
float_rec_arr = rec_arr.a
print ( "Record array of float: " , float_rec_arr)
out_arr = (rec_arr.a).clip( min = - 1.0 , max = 5.0 )
print ( "Output clipped array : " , out_arr)
int_rec_arr = rec_arr.b
print ( "Record array of int: " , int_rec_arr)
out_arr = int_rec_arr.clip( min = 2 , max = 6 )
print ( "Output clipped array : " , out_arr)
|
Output:
Input array : [[(5.0, 2) (3.0, -4) (6.0, 9)]
[(9.0, 1) (5.0, 4) (-12.0, -7)]]
Record array of int: [[ 2 -4 9]
[ 1 4 -7]]
Record array of float: [[ 5. 3. 6.]
[ 9. 5. -12.]]
Output clipped array : [[ 5. 3. 5.]
[ 5. 5. -1.]]
Record array of int: [[ 2 -4 9]
[ 1 4 -7]]
Output clipped array : [[2 2 6]
[2 4 2]]
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