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

# ## OpenPIV tutorial 2
# 
# In this notebook we compare the time to run the same analysis using Cython (precompiled) version
# with the Python process using FFT and/or direct cross-correlation method

# In[1]:


from openpiv import tools, process, scaling, pyprocess, validation, filters
import numpy as np
import pylab
get_ipython().run_line_magic('matplotlib', 'inline')


# In[2]:


frame_a  = tools.imread( '../test1/exp1_001_a.bmp' )
frame_b  = tools.imread( '../test1/exp1_001_b.bmp' )
pylab.imshow(np.c_[frame_a,np.ones((frame_a.shape[0],20)),frame_b],cmap=pylab.cm.gray)


# In[3]:


get_ipython().run_cell_magic('time', '', "u, v, sig2noise = 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, \n                                                   sig2noise_method='peak2peak' )\nx, y = process.get_coordinates( image_size=frame_a.shape, window_size=24, overlap=12 )\nu, v, mask = validation.sig2noise_val( u, v, sig2noise, threshold = 2.5 )\nu, v = filters.replace_outliers( u, v, method='localmean', max_iter=10, kernel_size=2)\nx, y, u, v = scaling.uniform(x, y, u, v, scaling_factor = 96.52 )\ntools.save(x, y, u, v, mask, 'exp1_001_extended.txt' )\n")


# In[4]:


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


# In[5]:


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


# In[6]:


get_ipython().run_cell_magic('time', '', "# the pyprocess uses extended_search_area a bit different from the Cython version\n# the main difference is that we prepare as many windows as of the \n# search_area_size and inside those start with the small window of the \n# window_size. This creates a much coarser grid, but we do not need to \n# extrapolate with zeros for all the vectors outside the image. \n# Which approach is the right one, needs to be decided. \n\n\nwindow_size = 24\noverlap = 12\nsearch_area_size = 32\n\nu, v, sig2noise = pyprocess.extended_search_area_piv(frame_a, frame_b, \n                                                    window_size=window_size, \n                                                    overlap=overlap, \n                                                    dt=0.02, \n                                                    search_area_size=search_area_size, \n                                                    sig2noise_method='peak2peak' )\nx, y = pyprocess.get_coordinates( image_size=frame_a.shape, \n                                 window_size=search_area_size, # note this point\n                                 overlap=12 )\n")


# In[7]:


u, v, mask = validation.sig2noise_val( u, v, sig2noise, threshold = 2.5 )
u, v = filters.replace_outliers( u, v, method='localmean', max_iter=10, kernel_size=2.5)
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor = 96.52 )
tools.save(x, y, u, v, mask, 'exp1_001_pyprocess_extended.txt' )


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# In[8]:


# in order to get the same denser field 

window_size = 16
overlap = 12
search_area_size = 24

u, v, sig2noise = pyprocess.extended_search_area_piv(frame_a, frame_b, 
                                                    window_size=window_size, 
                                                    overlap=overlap, 
                                                    dt=0.02, 
                                                    search_area_size=search_area_size, 
                                                    sig2noise_method='peak2peak' )
x, y = pyprocess.get_coordinates( image_size=frame_a.shape, 
                                 window_size=search_area_size, # note this point
                                 overlap=overlap )
u, v, mask = validation.sig2noise_val( u, v, sig2noise, threshold = 2.5 )
u, v = filters.replace_outliers( u, v, method='localmean', max_iter=10, kernel_size=2.5)
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor = 96.52 )
tools.save(x, y, u, v, mask, 'exp1_001_pyprocess_extended_24.txt' )


# In[9]:


tools.display_vector_field('exp1_001_extended.txt', scale=30, width=0.0025)
tools.display_vector_field('exp1_001_direct.txt', scale=30, width=0.0025)
tools.display_vector_field('exp1_001_fft.txt', scale=30, width=0.0025);
tools.display_vector_field('exp1_001_pyprocess_extended.txt', scale=30, width=0.0025);
tools.display_vector_field('exp1_001_pyprocess_extended_24.txt', scale=30, width=0.0025);


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