How to interpolate only zeros in 2D array without change nonzero values using scipy griddata?












0















I have 2D zeros array MxN (M is not equal to N). I placed some values in the array and now i would like to interpolate the missing data (the zeros).
i'm trying to use scipy griddata but the result of my code change dramatically the values (max value = 80 turn to 40). my question is, whats wrong with my code? what am i missing?
Please see my code below.



import numpy as np
import scipy
from scipy.interpolate import griddata

R = np.linspace(0.0, 10, num=100) #those are the columns
T = np.arange(37, 80, 1) #those are the rows
grid_x, grid_y = np.meshgrid(T,R) #those are the new grid for interpolation

#data_interpolated is the zeros matrix with my data

tmp_float = data_interpolated.astype(float, order='K')

points = np.array(np.nonzero(tmp_float)) #find nonzeros values indexs
values = tmp_float[np.nonzero(tmp_float)] #find the values in those indexs

#interpolate
interpolated_data = griddata(points.transpose(), values, (grid_x, grid_y),
...method='linear')









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    0















    I have 2D zeros array MxN (M is not equal to N). I placed some values in the array and now i would like to interpolate the missing data (the zeros).
    i'm trying to use scipy griddata but the result of my code change dramatically the values (max value = 80 turn to 40). my question is, whats wrong with my code? what am i missing?
    Please see my code below.



    import numpy as np
    import scipy
    from scipy.interpolate import griddata

    R = np.linspace(0.0, 10, num=100) #those are the columns
    T = np.arange(37, 80, 1) #those are the rows
    grid_x, grid_y = np.meshgrid(T,R) #those are the new grid for interpolation

    #data_interpolated is the zeros matrix with my data

    tmp_float = data_interpolated.astype(float, order='K')

    points = np.array(np.nonzero(tmp_float)) #find nonzeros values indexs
    values = tmp_float[np.nonzero(tmp_float)] #find the values in those indexs

    #interpolate
    interpolated_data = griddata(points.transpose(), values, (grid_x, grid_y),
    ...method='linear')









    share|improve this question

























      0












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      0








      I have 2D zeros array MxN (M is not equal to N). I placed some values in the array and now i would like to interpolate the missing data (the zeros).
      i'm trying to use scipy griddata but the result of my code change dramatically the values (max value = 80 turn to 40). my question is, whats wrong with my code? what am i missing?
      Please see my code below.



      import numpy as np
      import scipy
      from scipy.interpolate import griddata

      R = np.linspace(0.0, 10, num=100) #those are the columns
      T = np.arange(37, 80, 1) #those are the rows
      grid_x, grid_y = np.meshgrid(T,R) #those are the new grid for interpolation

      #data_interpolated is the zeros matrix with my data

      tmp_float = data_interpolated.astype(float, order='K')

      points = np.array(np.nonzero(tmp_float)) #find nonzeros values indexs
      values = tmp_float[np.nonzero(tmp_float)] #find the values in those indexs

      #interpolate
      interpolated_data = griddata(points.transpose(), values, (grid_x, grid_y),
      ...method='linear')









      share|improve this question














      I have 2D zeros array MxN (M is not equal to N). I placed some values in the array and now i would like to interpolate the missing data (the zeros).
      i'm trying to use scipy griddata but the result of my code change dramatically the values (max value = 80 turn to 40). my question is, whats wrong with my code? what am i missing?
      Please see my code below.



      import numpy as np
      import scipy
      from scipy.interpolate import griddata

      R = np.linspace(0.0, 10, num=100) #those are the columns
      T = np.arange(37, 80, 1) #those are the rows
      grid_x, grid_y = np.meshgrid(T,R) #those are the new grid for interpolation

      #data_interpolated is the zeros matrix with my data

      tmp_float = data_interpolated.astype(float, order='K')

      points = np.array(np.nonzero(tmp_float)) #find nonzeros values indexs
      values = tmp_float[np.nonzero(tmp_float)] #find the values in those indexs

      #interpolate
      interpolated_data = griddata(points.transpose(), values, (grid_x, grid_y),
      ...method='linear')






      python-2.7






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      asked Jan 18 at 18:51









      alonalon

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