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

# ## An initial concentration pulse (near the left edge of the system, and halfway vertically) moving towards equilibrium
# 
# The system starts out with a "concentration pulse" in just one bin - i.e. that bin is initially the only one with a non-zero concentration of the only chemical species.
# Then the system is left undisturbed, and followed to equilibrium.
# 
# (Note: this is the 2D counterpart of the 1D experiment by the same name)

# ### TAGS :  "diffusion 2D"

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LAST_REVISED = "Dec. 16, 2024"
LIFE123_VERSION = "1.0-rc.1"        # Library version this experiment is based on


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#import set_path                    # Using MyBinder?  Uncomment this before running the next cell!


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#import sys
#sys.path.append("C:/some_path/my_env_or_install")   # CHANGE to the folder containing your venv or libraries installation!
# NOTE: If any of the imports below can't find a module, uncomment the lines above, or try:  import set_path   

from life123 import BioSim2D, ChemData

import plotly.express as px


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# Prepare the initial system, with a single non-zero bin, near the left edge of the system, positioned halfway vertically
chem_data = ChemData(names="A", diffusion_rates=0.02)
bio = BioSim2D(n_bins=(5, 8), chem_data=chem_data)

bio.inject_conc_to_bin(bin_address=(2, 1), species_index=0, delta_conc=10.)

bio.describe_state()


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bio.system_snapshot()


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fig = px.imshow(bio.system_snapshot(), 
                title= f"Diffusion. System snapshot as a heatmap at time t={bio.system_time}", 
                labels=dict(x="x (col. number)", y="y (row number)", color="Concentration"),
                text_auto=True, color_continuous_scale="gray_r")         # text_auto=’.2f’

fig.data[0].xgap=2
fig.data[0].ygap=2

fig.show()


# # Initial Diffusion Step

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delta_time = 10.

status = bio.diffuse(total_duration=delta_time, time_step=0.1)
print("\n", status)

bio.describe_state()


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fig = px.imshow(bio.system_snapshot(), 
                title= f"Diffusion. System snapshot as a heatmap at time t={bio.system_time}", 
                labels=dict(x="x (col. number)", y="y (row number)", color="Concentration"),
                text_auto='.2f', color_continuous_scale="gray_r")

fig.data[0].xgap=2
fig.data[0].ygap=2

fig.show()


# ## This is still an early stage in the diffusion process; let's advance it more... (Visualization from results shown at selected times)

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for i in range(200):
    status = bio.diffuse(total_duration=delta_time, time_step=0.1)

    if i<2 or i==6 or i>=199:
        bio.describe_state()
        fig = px.imshow(bio.system_snapshot(), 
                title= f"Diffusion. System snapshot as a heatmap at time t={bio.system_time}", 
                labels=dict(x="x (col. number)", y="y (row number)", color="Concentration"),
                text_auto='.2f', color_continuous_scale="gray_r")

        fig.data[0].xgap=2
        fig.data[0].ygap=2

        fig.show()


# ## All bins now have essentially uniform concentration
# 
# Notice the continued symmetry across the mid-row.
# 
# **Mass conservations**: the "10. units of concentration" are now uniformly spread across the 40 bins, leading to a near-constant concentration of 10./40

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10./40


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