#!/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: June 23, 2024 (using v. 1.0 beta34.1)

# 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 life123 import BioSim1D
from life123 import ChemData as chem
from life123 import Numerical as num

import plotly.express as px
from life123 import HtmlLog as log
from life123 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

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