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

# # Exploring the change of delta_x (spatial resolution) in diffusion accuracy.
# #### From the same initial setup, diffusion is carried out over a fixed time span,
# #### at different spatial resolutions - and then the respective results are compared
# 
# LAST REVISED: May 27, 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.life_1D.bio_sim_1d import BioSim1D
from src.modules.reactions.reaction_data import ReactionData as chem
from src.modules.numerical.numerical import Numerical as num

import plotly.express as px
import plotly.graph_objects as go
from src.modules.html_log.html_log import HtmlLog as log
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_heatmap_11", "vue_curves_3"])


# In[4]:


# Set the heatmap parameters (for the log file)
heatmap_pars = {"range": [0, 150],
                "outer_width": 850, "outer_height": 150,
                "margins": {"top": 30, "right": 30, "bottom": 30, "left": 55}
                }

# Set the parameters of the line plots
lineplot_pars = {"range": [0, 150],
                "outer_width": 850, "outer_height": 250,
                "margins": {"top": 30, "right": 30, "bottom": 30, "left": 55}
                }


# ## Prepare the initial system

# In[5]:


chem_data = chem(names=["A"], diffusion_rates=[0.1])

conc_list=[10,13,17,21,25,28,30,38,42,55,65,47,35,32,27,23,20,17,14,8,3,10,16,18,
           20,25,30,35,40,65,85,115,150,92,73,69,65,50,42,36,20,45,50,55,69,82,95,
           77,60,43,37,31,25,22,20,18,15,11,9, 8]

bio = BioSim1D(n_bins=len(conc_list), chem_data=chem_data)

bio.set_species_conc(species_name="A", conc_list=conc_list)

bio.describe_state()


# In[6]:


# Line plot
fig = px.line(data_frame=bio.system_snapshot(), y=["A"], 
              title= f"Diffusion. System snapshot at time t={bio.system_time}",
              color_discrete_sequence = ['red'],
              labels={"value":"concentration", "variable":"Chemical", "index":"Bin number"})
fig.show()


# In[7]:


# Show as a heatmap
fig = px.imshow(bio.system_snapshot().T, 
                title= f"Diffusion. System snapshot as a heatmap at time t={bio.system_time}", 
                labels=dict(x="Bin number", y="Chem. species", color="Concentration"),
                text_auto=False, color_continuous_scale="gray_r")
fig.data[0].xgap=2
fig.data[0].ygap=4

fig.show()


# In[8]:


log.write("1-D diffusion of a single species, with Diffusion rate 0.1",
          style=log.h2)
log.write("Initial system state at time t=0:", blanks_before=2, style=log.bold)

# Output a heatmap to the log file
bio.single_species_heatmap(species_index=0, heatmap_pars=heatmap_pars, graphic_component="vue_heatmap_11")

# Output a line plot the log file
bio.single_species_line_plot(species_index=0, plot_pars=lineplot_pars, graphic_component="vue_curves_3")


# In[9]:


bio.describe_state(concise=True)


# ### Populate the data set with more bins, using interpolated concentration values
# ### IMPORTANT: we're **NOT** changing spacial resolution here; we're just creating a less ragged dataset, as *our initial system state*

# In[10]:


bio.smooth_spatial_resolution()
bio.describe_state()


# In[11]:


bio.n_bins


# # The STARTING POINT
# ### This system setup will be our starting point in exploring diffusion using different spacial resolutions

# In[12]:


original_state = bio.save_system()    # SAVE a copy of the system state, to do multiple runs starting from it


# In[13]:


# Line plot
fig = px.line(data_frame=bio.system_snapshot(), y=["A"], 
              title= f"Diffusion. System snapshot at time t={bio.system_time}",
              color_discrete_sequence = ['red'],
              labels={"value":"concentration", "variable":"Chemical", "index":"Bin number"})
fig.show()


# In[14]:


# Show as a heatmap
fig = px.imshow(bio.system_snapshot().T, 
                title= f"Diffusion. System snapshot as a heatmap at time t={bio.system_time}", 
                labels=dict(x="Bin number", y="Chem. species", color="Concentration"),
                text_auto=False, color_continuous_scale="gray_r")
fig.data[0].xgap=2
fig.data[0].ygap=4

fig.show()


# # Initial Diffusions with delta_x = 1

# In[15]:


bio.describe_state(concise=True)   # Our initial state


# In[16]:


bio.diffuse(total_duration=7, time_step=0.0005)
bio.describe_state(concise=True)


# In[17]:


# SAVE the above system data (a matrix of dimension n_species x n_bins):  this is the result of diffusion with delta_x = 1
diffuse_dx_1 = bio.system


# In[18]:


# Line plot
fig = px.line(data_frame=bio.system_snapshot(), y=["A"], 
              title= f"Diffusion. System snapshot at time t={bio.system_time}",
              color_discrete_sequence = ['red'],
              labels={"value":"concentration", "variable":"Chemical", "index":"Bin number"})
fig.show()


# ### Enough time has proceeded to result in some smoothing, and non-puny changes in most values - but still nowhere near equilibrium

# # Now restore the system to its initial (pre-diffusion) state
# ### and then perform a diffusion over the same time span, but with DOUBLE the spacial resolution
# #### delta_x will be be 1/2 instead of the original default 1

# In[19]:


bio.restore_system(original_state)


# In[20]:


bio.describe_state()


# In[21]:


# Double the spacial resolution
bio.increase_spatial_resolution(2)
bio.describe_state()


# In[22]:


# Now repeat the idential diffusion process as before, but with half the delta_x
bio.diffuse(total_duration=7, time_step=0.0005, delta_x=0.5)


# In[23]:


# Finally, halve the resolution, to return to the original number of bins
bio.decrease_spatial_resolution(2)


# In[24]:


bio.describe_state(concise=True)


# In[25]:


# SAVE the above system data: this is the result of diffusion with delta_x of 1/2
diffuse_dx_1_2 = bio.system


# ### Compare the last 2 runs (with dx=1 and dx=1/2)

# In[26]:


num.compare_states(diffuse_dx_1 , diffuse_dx_1_2, verbose=True)


# # Again, restore the system to its initial (pre-diffusion) state
# ### and then perform a diffusion over the same time span, but with QUADRUPLE the spacial resolution
# ### delta_x will be be 1/4 instead of the original default 1

# In[27]:


bio.restore_system(original_state)


# In[28]:


bio.describe_state()


# In[29]:


# Quadruple the spacial resolution
bio.increase_spatial_resolution(4)
bio.describe_state()


# In[30]:


# Now repeat the idential diffusion process as before, but with 1/4 the delta_x
bio.diffuse(total_duration=7, time_step=0.0005, delta_x=0.25)


# In[31]:


# Finally, reduce the resolution by a factor 4, to return to the original number of bins
bio.decrease_spatial_resolution(4)
bio.describe_state(concise=True)


# In[32]:


# SAVE the above system data: this is the result of diffusion with delta_x of 1/4
diffuse_dx_1_4 = bio.system


# ### Compare the latest 2 runs (with dx=1/2 and dx=1/4)

# In[33]:


num.compare_states(diffuse_dx_1_2 , diffuse_dx_1_4)


# ### Notice how the discrepancies have gone down

# # One last time, restore the system to its initial (pre-diffusion) state
# ### and then perform a diffusion over the same time span, but with 10x the spacial resolution
# ### delta_x will be be 1/10 instead of the original default 1

# In[34]:


bio.restore_system(original_state)

# Increase by a factor 10 the spacial resolution
bio.increase_spatial_resolution(10)
bio.n_bins


# In[35]:


# Now repeat the idential diffusion process as before, but with 1/10 the delta_x
bio.diffuse(total_duration=7, time_step=0.0005, delta_x=0.1)


# In[36]:


# Finally, reduce the resolution by a factor 10, to return to the original number of bins
bio.decrease_spatial_resolution(10)
bio.describe_state(concise=True)


# In[37]:


# SAVE the above system data: this is the result of diffusion with delta_x of 1/10
diffuse_dx_1_10 = bio.system


# ### Again, compare the latest 2 runs (with dx=1/4 and dx=1/10)

# In[38]:


num.compare_states(diffuse_dx_1_4 , diffuse_dx_1_10)


# ### Notice how the discrepancies have gone down even more
# ### This matches expectations that we're getting closer and closer to a "true" (very high precision) value, as we keep increasing the spacial resolution

# In[ ]: