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

# ## One-bin `A <-> 2C + D`, with 1st-order kinetics for each species, taken to equilibrium
# 
# Diffusion not applicable (just 1 bin)
# 
# 
# LAST REVISED: June 23, 2024 (using v. 1.0 beta34.1)

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


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from experiments.get_notebook_info import get_notebook_basename

from life123 import ChemData as chem
from life123 import BioSim1D

import plotly.express as px
from life123 import GraphicLog


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# 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_2"],
                  extra_js="https://cdnjs.cloudflare.com/ajax/libs/cytoscape/3.21.2/cytoscape.umd.js")


# In[4]:


# Initialize the system
chem_data = chem(names=["A", "C", "D"])     # NOTE: Diffusion not applicable (just 1 bin)



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

bio = BioSim1D(n_bins=1, chem_data=chem_data)

bio.set_all_uniform_concentrations( [4., 7., 2.] )

bio.describe_state()


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# Save the state of the concentrations of all species at bin 0
bio.add_snapshot(bio.bin_snapshot(bin_address = 0), caption="Initial state")
bio.get_history()


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chem_data.describe_reactions()


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# Send the plot to the HTML log file
chem_data.plot_reaction_network("vue_cytoscape_2")


# ### <a name="sec_2_first_step"></a>First step

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# First step
bio.react(time_step=0.2, n_steps=1)
bio.describe_state()


# ---   
#     Note:  the above values are quite inaccurate because of the large time step 0.2
# 
#     For example, the value for the concentration of D (0.4) is a wild overshot from the initial 2.0 to the equilibrium value of 1.68941267
#        
#     A more precise calculation with bio.react(time_step=0.1, n_steps=2) gives conc_D(0.2) = 2.304
#        
#     An even more precise calculation with bio.react(time_step=0.05, n_steps=4) gives conc_D(0.2) = 1.69037202
#        
#     I.e. the system is almost at equilibrium already at t=0.2 !
#     
#     TODO: explore the early dynamics of the system in a separate experiment
# ---

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# Save the state of the concentrations of all species at bin 0
bio.add_snapshot(bio.bin_snapshot(bin_address = 0))
bio.get_history()


# ### <a name="sec_2"></a>Numerous more steps

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# Numerous more steps
bio.react(time_step=0.05, n_steps=30, snapshots={"sample_bin": 0})

bio.describe_state()


# ### <a name="sec_2_equilibrium"></a>Equilibrium

# Consistent with the 5/2 ratio of forward/reverse rates (and the 1st order reactions),
# the systems settles in the following equilibrium:  
# [A] = 4.31058733 , [C] = 6.37882534 , [D] = 1.68941267

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# Verify that the reaction has reached equilibrium
bio.reaction_dynamics.is_in_equilibrium(rxn_index=0, conc=bio.bin_snapshot(bin_address = 0))


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bio.get_history()


# C and D get depleted, while A gets produced.
# A wild overshoot is present at t=0.2

# # Plots of changes of concentration with time

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fig = px.line(data_frame=bio.get_history(), x="SYSTEM TIME", y=["A", "C", "D"], 
              title="Reaction A <-> 2C + D .  Changes in concentrations",
              color_discrete_sequence = ['navy', 'violet', 'red'],
              labels={"value":"concentration", "variable":"Chemical"})
fig.show()


# ### Notice the **wild overshoot** present at t=0.2 !  (Too large a time step, early in the reaction!)
# Variable, adaptive time steps are explored at length in the  _"reactions_single_compartment"_ experiments

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