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

# ## Association/Dissociation reaction `A + B <-> C`
# #### with 1st-order kinetics for each species, taken to equilibrium.
# #### Exploration of debugging and diagnostics options
# (Adaptive variable time substeps are used)
# 
# _See also the experiment "1D/reactions/reaction_4"_ 
# 
#   # For the 0-th reaction (the only reaction in our case)

# In[1]:


# Extend the sys.path variable, to contain the project's root directory
import set_path
set_path.add_ancestor_dir_to_syspath(2)  # The number of levels to go up 
                                         # to reach the project's home, from the folder containing this notebook


# In[2]:


from experiments.get_notebook_info import get_notebook_basename

from src.modules.reactions.reaction_data import ReactionData as chem
from src.modules.reactions.reaction_dynamics import ReactionDynamics

import numpy as np
import plotly.express as px
from src.modules.visualization.graphic_log import GraphicLog


# In[3]:


# Initialize the HTML logging (for the graphics)
log_file = get_notebook_basename() + ".log.htm"    # Use the notebook base filename for the log file

# Set up the use of some specified graphic (Vue) components
GraphicLog.config(filename=log_file,
                  components=["vue_cytoscape_1"],
                  extra_js="https://cdnjs.cloudflare.com/ajax/libs/cytoscape/3.21.2/cytoscape.umd.js")


# # Initialize the System
# Specify the chemicals and the reactions

# In[4]:


# Specify the chemicals
chem_data = chem(names=["A", "B", "C"])

# Reaction A + B <-> C , with 1st-order kinetics for each species
chem_data.add_reaction(reactants=["A" , "B"], products=["C"],
                       forward_rate=5., reverse_rate=2.)

print("Number of reactions: ", chem_data.number_of_reactions())


# In[5]:


chem_data.describe_reactions()


# In[6]:


# Send a plot of the network of reactions to the HTML log file
graph_data = chem_data.prepare_graph_network()
GraphicLog.export_plot(graph_data, "vue_cytoscape_1")


# # Start the simulation

# In[7]:


dynamics = ReactionDynamics(reaction_data=chem_data)


# In[8]:


# Initial concentrations of all the chemicals, in index order
dynamics.set_conc([10., 50., 20.], snapshot=True)


# In[9]:


dynamics.describe_state()


# In[10]:


dynamics.get_history()


# ## Run the reaction

# In[11]:


dynamics.set_diagnostics()       # To save diagnostic information about the call to single_compartment_react()
#dynamics.verbose_list = [1]      # Uncomment for detailed run information (meant for debugging the adaptive variable time step)

dynamics.single_compartment_react(time_step=0.004, reaction_duration=0.06,
                                  snapshots={"initial_caption": "1st reaction step",
                                             "final_caption": "last reaction step"},
                                  dynamic_substeps=2, rel_fast_threshold=1)


# ### Note: the argument _dynamic_step=2_ splits the time steps in 2 whenever the reaction is "fast" (as determined using the given value of _fast_threshold_ )

# In[12]:


df = dynamics.get_history()
df


# In[13]:


dynamics.explain_time_advance()


# ### Notice how the reaction proceeds in smaller steps in the early times, when the concentrations are changing much more rapidly

# ## Note: "A" (now largely depleted) is the limiting reagent

# ### Check the final equilibrium

# In[14]:


dynamics.get_system_conc()


# NOTE: Consistent with the 3/2 ratio of forward/reverse rates (and the 1st order reactions),  
#  the systems settles in the following equilibrium:
# 
# [A] = 0.29487741 , [B] = 40.29487741 , [C] = 29.70512259
# 

# In[15]:


# Verify that the reaction has reached equilibrium
dynamics.is_in_equilibrium()


# ## Plots of changes of concentration with time

# In[16]:


dynamics.plot_curves(colors=['red', 'violet', 'green'])


# In[ ]:





# # Everthing below is just for diagnostic insight 
# ## into the adaptive variable time steps

# ## WARNING: The explanations below are based on an older system of using RELATIVE concentration changes, and no longer applicable to the newer approach; it'll be corrected in future versions (COMMENTED OUT FOR NOW)

# In[17]:


# This approach, from the run data, is only usable with single-reaction runs
#dynamics.examine_run(df=df, time_step=0.004, rel_fast_threshold=1)
# The arguments step MUST match the value used in call to single_compartment_react()


# # Take a peek at internal diagnostic data from the reactions

# In[18]:


# This approach, from internal diagnostic data, 
# is more generally applicable also to runs with multiple reactions
#dynamics.diagnose_variable_time_steps()


# ### The above diagnostics are based on the following diagnostic data:

# In[19]:


dynamics.get_diagnostic_data(rxn_index=0)


# In[ ]: