#!/usr/bin/env python
# coding: utf-8

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# let's make a test for subpixel localization


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import numpy as np
import matplotlib.pyplot as plt
from openpiv.pyprocess import find_first_peak


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N = 64

corr = np.zeros((N,N))

corr[2:5,2:5] = 1
corr[3,3] = 2
corr[3,4] = 3
corr[3,5] = 1
corr


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pos,height = find_first_peak(corr)


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pos,height


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from openpiv.pyprocess import find_subpixel_peak_position


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find_subpixel_peak_position(corr)


# ## let's find some corner cases

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# peak on the border 
corr = np.zeros((N,N))

corr[:3,:3] = 1
corr[0,0] = 2
corr[0,2] = 3
corr[0,3] = 1
corr


# ## Corner case 1: peak on the border
# 
# it is disregarded in our function because we cannot define well the subpixel
# position. Or do we? 

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find_subpixel_peak_position(corr)


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# peak on the border 
corr = np.flipud(corr)
corr


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find_subpixel_peak_position(corr)


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corr = np.fliplr(corr)
corr[-2,-1]=5
corr


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find_subpixel_peak_position(corr)


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## Corner case 2: zero next to the peak - the log(0) fails


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corr = np.zeros((N,N))

corr[2:5,2:5] = 1
corr[3,3] = 2
corr[3,4] = 3
# corr[3,5] = 1
corr


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find_subpixel_peak_position(corr)


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fig, ax = plt.subplots(figsize=(12,12))
ax.pcolor(corr[:8,:8])
for eps in np.logspace(-15,5):
    i,j = find_subpixel_peak_position(corr+eps)
    # print(i,j)
    ax.plot(i,j,'rx')


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import matplotlib.pyplot as plt


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plt.pcolor(corr[:8,:8])
plt.plot(i,j,'ro')


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for method in ['gaussian','parabolic','centroid']:
    i,j = find_subpixel_peak_position(corr,method)
    print(i,j)
    i,j = find_subpixel_peak_position(corr+eps,method)
    print(i,j)


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