scientimate.replacemissing2d

xReplaced, NaN_Indx = scientimate.replacemissing2d(x, what2replace='both', gridsize_x=1, gridsize_y=1, interpMethod='nearest', dispout='no')

Description

Replace missing data points in 2d array

Inputs

x

Input data

what2replace=’both’

What needs to be replaced

‘NaN’: replacing NaN data points

‘Inf’: replacing Inf data points

‘both’: replacing NaN and Inf data points

Number: replacing data points equal to Number

gridsize_x=1

Grid size (distance between grid points) in x direction

Leave gridsize_x=1 if you do not have it

gridsize_y=1

Grid size (distance between grid points) in y direction

Leave gridsize_y=1 if you do not have it

interpMethod=’nearest’

Interpolation method

‘linear’: Use default or ‘linear’ method to interpolate

‘nearest’: Use nearest neighbor method to interpolate

‘knn’: Use nearest neighbor method to interpolate (Use ‘knn’ for large array)

dispout=’no’

Define to display outputs or not (‘yes’: display, ‘no’: not display)

Outputs

xReplaced

Replaced data

NaN_Indx

Logical index of replaced points

Examples

import scientimate as sm
import numpy as np
from numpy import random

x=[[1,0,3],[2,5,np.nan],[3,np.nan,1],[5,7,2]]
xReplaced, NaN_Indx = sm.replacemissing2d(x, 'NaN', 1, 1, 'nearest', 'yes')

rng = np.random.default_rng()
xgrid=rng.normal(size=(100,50))
xgrid(rng.integers(0,99,(20,1)),rng.integers(0,49,(10,1)))=np.nan
xReplaced, NaN_Indx = sm.replacemissing2d(xgrid, 'NaN', 1, 1, 'knn', 'yes')

References