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

# # OpenPIV tutorial 1
# 
# 
# In this tutorial we read the pair of images using `imread`, compare them visually 
# and process using OpenPIV. Here the import is using directly the basic functions and methods

# In[1]:


from openpiv import tools, pyprocess, validation, filters, scaling 

import numpy as np
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')

import imageio


# In[2]:


frame_a  = tools.imread( '../test1/exp1_001_a.bmp' )
frame_b  = tools.imread( '../test1/exp1_001_b.bmp' )


# In[3]:


fig,ax = plt.subplots(1,2,figsize=(12,10))
ax[0].imshow(frame_a,cmap=plt.cm.gray)
ax[1].imshow(frame_b,cmap=plt.cm.gray)


# In[4]:


winsize = 32 # pixels, interrogation window size in frame A
searchsize = 38  # pixels, search in image B
overlap = 12 # pixels, 50% overlap
dt = 0.02 # sec, time interval between pulses


u0, v0, sig2noise = pyprocess.extended_search_area_piv(frame_a.astype(np.int32), 
                                                       frame_b.astype(np.int32), 
                                                       window_size=winsize, 
                                                       overlap=overlap, 
                                                       dt=dt, 
                                                       search_area_size=searchsize, 
                                                       sig2noise_method='peak2peak')


# In[5]:


x, y = pyprocess.get_coordinates( image_size=frame_a.shape, 
                                 search_area_size=searchsize, 
                                 overlap=overlap )


# In[6]:


u1, v1, mask = validation.sig2noise_val( u0, v0, 
                                        sig2noise, 
                                        threshold = 1.05 )
# if you need more detailed look, first create a histogram of sig2noise
# plt.hist(sig2noise.flatten())
# to see where is a reasonable limit


# In[7]:


# filter out outliers that are very different from the
# neighbours

u2, v2 = filters.replace_outliers( u1, v1, 
                                  method='localmean', 
                                  max_iter=3, 
                                  kernel_size=3)


# In[8]:


# convert x,y to mm
# convert u,v to mm/sec

x, y, u3, v3 = scaling.uniform(x, y, u2, v2, 
                               scaling_factor = 96.52 ) # 96.52 microns/pixel


# In[9]:


#save in the simple ASCII table format
tools.save(x, y, u3, v3, sig2noise, mask, 'exp1_001.txt' )


# In[10]:


fig, ax = plt.subplots(figsize=(8,8))
tools.display_vector_field('exp1_001.txt', 
                           ax=ax, scaling_factor=96.52, 
                           scale=50, # scale defines here the arrow length
                           width=0.0035, # width is the thickness of the arrow
                           on_img=True, # overlay on the image
                           image_name='../test1/exp1_001_a.bmp');


# In[ ]: