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

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# %load tutorial-part2.py
import sys
sys.path.append('/Users/alex/Documents/OpenPIV/alexlib/openpiv-python')


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import openpiv.tools
import openpiv.process
import openpiv.scaling
import numpy as np
get_ipython().run_line_magic('matplotlib', 'inline')


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frame_a  = openpiv.tools.imread( 'test001.png' )
frame_b  = openpiv.tools.imread( 'test002.png' )
import pylab
pylab.imshow(np.c_[frame_a,np.ones((frame_a.shape[0],20)),frame_b],cmap=pylab.cm.gray)


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get_ipython().run_cell_magic('time', '', "u, v, sig2noise = openpiv.process.extended_search_area_piv( frame_a.astype(np.int32), frame_b.astype(np.int32), \n                                                           window_size=24, overlap=12, dt=0.02, \n                                                           search_area_size=64, sig2noise_method='peak2peak' )\nx, y = openpiv.process.get_coordinates( image_size=frame_a.shape, window_size=24, overlap=12 )\nu, v, mask = openpiv.validation.sig2noise_val( u, v, sig2noise, threshold = 2.5 )\nu, v = openpiv.filters.replace_outliers( u, v, method='localmean', max_iter=10, kernel_size=2)\nx, y, u, v = openpiv.scaling.uniform(x, y, u, v, scaling_factor = 96.52 )\nopenpiv.tools.save(x, y, u, v, mask, 'exp1_001_extended.txt' )\nopenpiv.tools.display_vector_field('exp1_001_extended.txt', scale=100, width=0.0025)\n")


# In[14]:


get_ipython().run_cell_magic('time', '', "u, v= openpiv.pyprocess.piv( frame_a, frame_b, corr_method='fft', window_size=24, overlap=12, dt=0.02, sig2noise_method='peak2peak' )\nx, y = openpiv.pyprocess.get_coordinates( image_size=frame_a.shape, window_size=24, overlap=12 )\nu, v, mask = openpiv.validation.sig2noise_val( u, v, sig2noise, threshold = 2.5 )\nu, v = openpiv.filters.replace_outliers( u, v, method='localmean', max_iter=10, kernel_size=2.5)\nx, y, u, v = openpiv.scaling.uniform(x, y, u, v, scaling_factor = 96.52 )\nopenpiv.tools.save(x, y, u, v, mask, 'exp1_001_fft.txt' )\nopenpiv.tools.display_vector_field('exp1_001_fft.txt', scale=10, width=0.0025)\n")


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get_ipython().run_cell_magic('time', '', "u, v= openpiv.pyprocess.piv( frame_a, frame_b, corr_method='direct', window_size=24, overlap=12, dt=0.02, sig2noise_method='peak2peak' )\nx, y = openpiv.pyprocess.get_coordinates( image_size=frame_a.shape, window_size=24, overlap=12 )\nu, v, mask = openpiv.validation.sig2noise_val( u, v, sig2noise, threshold = 2.5 )\nu, v = openpiv.filters.replace_outliers( u, v, method='localmean', max_iter=10, kernel_size=2.5)\nx, y, u, v = openpiv.scaling.uniform(x, y, u, v, scaling_factor = 96.52 )\nopenpiv.tools.save(x, y, u, v, mask, 'exp1_001_direct.txt' )\nopenpiv.tools.display_vector_field('exp1_001_direct.txt', scale=10, width=0.0025)\n")


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a = np.loadtxt('exp1_001_extended.txt')
b = np.loadtxt('exp1_001_fft.txt')
c = np.loadtxt('exp1_001_direct.txt')

fig,ax = pylab.subplots(1,2)
ax[0].scatter(a[:,2]-b[:,2],a[:,3]-b[:,3])
ax[1].scatter(b[:,2]-c[:,2],b[:,3]-c[:,3])
#ax[1].set_aspect(1)
#ax[1].set_xticks([-.2,.2])
#ax[1].set_yticks([-.2,.2])
ax[0].set_xlabel('pixel/dt')
ax[1].set_xlabel('pixel/dt')
ax[0].set_ylabel('differences');


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