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

# # OpenPIV tutorials
# 
# 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( 'exp1_001_a.bmp' )
frame_b  = tools.imread( '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), window_size=24, overlap=12, dt=0.02, search_area_size=64, 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' )\ntools.display_vector_field('exp1_001_extended.txt', scale=100, width=0.0025)\n")


# In[8]:


get_ipython().run_cell_magic('time', '', "u, v, sig2noise = pyprocess.extended_search_area_piv( frame_a, frame_b, corr_method='fft', 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_fft.txt' )\ntools.display_vector_field('exp1_001_fft.txt', scale=100, width=0.0025)\n")


# In[6]:


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' )\ntools.display_vector_field('exp1_001_direct.txt', scale=100, width=0.0025)\n")