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

# ## Synthetic Image Generation examples ##
# In this Notebook a number of test cases using the PIV sythetic image generator will be presented.
# 
# The three examples shown are:
# 1. Using a fully synthetic flow field created by a random equation
# 2. Using pre attained experimental data
# 3. Using flow simulation results
# 
# In each of these cases the pattern to recieving the synthetic images is the same. The part that mostly varies is the data passed.
# 
# 1. Data is either syntheticly made or pass to the create_synimage_parameters function.
# 2. The parameters for both images (frame_a, frame_b) are created
# 3. The parameters are passed to the generate_particle_image function in order to create the image representation.
# 4. Finally the images are shown on the screen as grayschale images.

# In[1]:


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


# ### Example 1: synthetic flow field created by a random equation ###
# In this case no data is passed to the function, so a random equation is invoked from the cff module. (line 46,52 in synimagegen.py)
# 
# This equation defines the velocities U,V for each point in the X,Y plane.
# 
# This equation is ment to be changed to suit the testing needs of each users system.
# 

# In[2]:


ground_truth,cv,x_1,y_1,U_par,V_par,par_diam1,par_int1,x_2,y_2,par_diam2,par_int2 = synimagegen.create_synimage_parameters(None,[0,1],[0,1],[256,256],dt=0.0025)


# In[3]:


frame_a = synimagegen.generate_particle_image(256, 256, x_1, y_1, par_diam1, par_int1,16)
frame_b = synimagegen.generate_particle_image(256, 256, x_2, y_2, par_diam2, par_int2,16)


# In[4]:


fig = plt.figure(figsize=(20,10))
a = fig.add_subplot(1, 2, 1,)
imgplot = plt.imshow(frame_a, cmap='gray')
a.set_title('frame_a')
a = fig.add_subplot(1, 2, 2)
imgplot = plt.imshow(frame_b, cmap='gray')
a.set_title('frame_b')


# ### Example 2: pre attained experimental data ###
# In this case experiment data is passed to the function, and the interpolation flag is enabled. Thus using the data to create a continous flow field by interpolation and then using the field to create the paramters.

# In[5]:


data = np.load('PIV_experiment_data.npz')


# In[6]:


data =  np.stack([data['X'], data['Y'],data['U'] ,data['V']], axis=2)


# In[7]:


ground_truth,cv,x_1,y_1,U_par,V_par,par_diam1,par_int1,x_2,y_2,par_diam2,par_int2 = synimagegen.create_synimage_parameters(data,[0,1],[0,1],[256,256],inter=True,dt=0.0025)


# In[8]:


frame_a = synimagegen.generate_particle_image(256, 256, x_1, y_1, par_diam1, par_int1,16)
frame_b = synimagegen.generate_particle_image(256, 256, x_2, y_2, par_diam2, par_int2,16)


# In[9]:


fig = plt.figure(figsize=(20,10))
a = fig.add_subplot(1, 2, 1)
imgplot = plt.imshow(frame_a, cmap='gray')
a.set_title('frame_a')
a = fig.add_subplot(1, 2, 2)
imgplot = plt.imshow(frame_b, cmap='gray')
a.set_title('frame_b')


# ### Example 3: flow simulation results ###
# In this case flow simulation results are passed to the function, in the form of a tab-delimited text file.
# The file is parsed and the data is used in order to continous flow field by interpolation.
# 

# In[10]:


path_to_file = os.getcwd() + '/velocity_report.txt'
ground_truth,cv,x_1,y_1,U_par,V_par,par_diam1,par_int1,x_2,y_2,par_diam2,par_int2 = synimagegen.create_synimage_parameters(None,[0,1],[0,1],[256,256],path=path_to_file,inter=True,dt=0.0025)


# In[11]:


frame_a = synimagegen.generate_particle_image(256, 256, x_1, y_1, par_diam1, par_int1,16)
frame_b = synimagegen.generate_particle_image(256, 256, x_2, y_2, par_diam2, par_int2,16)


# In[12]:


fig = plt.figure(figsize=(20,10))
a = fig.add_subplot(1, 2, 1)
imgplot = plt.imshow(frame_a, cmap='gray')
a.set_title('frame_a')
a = fig.add_subplot(1, 2, 2)
imgplot = plt.imshow(frame_b, cmap='gray')
a.set_title('frame_b')