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

# ## Accurate results in the simulation of the coupled reactions `2 S <-> U` and `S <-> X`   
# Both mostly forward.  1st-order kinetics throughout.   
# 
# Same as `substeps_1`, but with fixed steps: a lot of tiny steps - as a proxy for the "exact value"
# 
# LAST REVISED: May 25, 2023

# In[1]:


import set_path      # Importing this module will add the project's home directory to sys.path


# 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
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

# In[4]:


# Initialize the system
chem_data = chem(names=["U", "X", "S"])

# Reaction 2 S <-> U , with 1st-order kinetics for all species (mostly forward)
chem_data.add_reaction(reactants=[(2, "S")], products="U",
                       forward_rate=8., reverse_rate=2.)

# Reaction S <-> X , with 1st-order kinetics for all species (mostly forward)
chem_data.add_reaction(reactants="S", products="X",
                       forward_rate=6., reverse_rate=3.)

chem_data.describe_reactions()

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


# ### Set the initial concentrations of all the chemicals

# In[5]:


dynamics = ReactionDynamics(reaction_data=chem_data)
dynamics.set_conc(conc={"U": 50., "X": 100., "S": 0.})
dynamics.describe_state()


# In[6]:


dynamics.set_diagnostics()       # To save diagnostic information about the call to single_compartment_react()

dynamics.single_compartment_react(initial_step=0.0001, target_end_time=0.3, 
                                  variable_steps=False)

df = dynamics.get_history()
df


# In[7]:


df.iloc[400]


# In[8]:


df.iloc[1850]      # This data point is used in experiment `substeps_1`


# In[9]:


(transition_times, step_sizes) = dynamics.explain_time_advance(return_times=True)


# In[10]:


np.array(step_sizes)


# In[11]:


np.array(transition_times)    # Note: there will be one more transition time (the end time) than step sizes


# ## Plots of changes of concentration with time

# In[12]:


dynamics.plot_curves(colors=['green', 'orange', 'blue'])


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