Merge pull request #70 from BrainAnnex/dev

Dev beta39

Author: BrainAnnex

experiments/1D/diffusion/diffusion_1.ipynb

@@ -17,13 +17,22 @@
 #
 # The system starts out with a "concentration pulse" in bin 2 (the 3rd bin from the left) - 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.
-#
-# LAST REVISED: June 23, 2024 (using v. 1.0 beta34.1)
+
+# %% [markdown]
+# ### TAGS :  "diffusion 1D", "basic"
 
 # %%
-import set_path      # Importing this module will add the project's home directory to sys.path
+LAST_REVISED = "Oct. 6, 2024"
+LIFE123_VERSION = "1.0.0.beta.39"   # Library version this experiment is based on
 
 # %%
+#import set_path                    # Using MyBinder?  Uncomment this before running the next cell!
+
+# %%
+#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 experiments.get_notebook_info import get_notebook_basename
 
 from life123 import BioSim1D

experiments/1D/diffusion/diffusion_1.py

@@ -115,12 +115,12 @@
 
 # Output to the log file a heatmap for each chemical species
 for i in range(bio.n_species):
-    log.write(f"{bio.chem_data.get_name(i)}:", also_print=False)
+    log.write(f"{bio.chem_data.get_label(i)}:", also_print=False)
     bio.single_species_heatmap(species_index=i, heatmap_pars=heatmap_pars, graphic_component="vue_heatmap_11")
 
 # Output to the log file a one-curve line plot for each chemical species
 for i in range(bio.n_species):
-    log.write(f"{bio.chem_data.get_name(i)}:", also_print=False)
+    log.write(f"{bio.chem_data.get_label(i)}:", also_print=False)
     bio.single_species_line_plot(species_index=i, plot_pars=lineplot_pars, graphic_component="vue_curves_3")
 
 # Output to the log file a line plot for ALL the chemicals together (same color as used for plotly elsewhere)
@@ -167,12 +167,12 @@
 
 # Output to the log file a heatmap for each chemical species
 for i in range(bio.n_species):
-    log.write(f"{bio.chem_data.get_name(i)}:", also_print=False)
+    log.write(f"{bio.chem_data.get_label(i)}:", also_print=False)
     bio.single_species_heatmap(species_index=i, heatmap_pars=heatmap_pars, graphic_component="vue_heatmap_11")
 
 # Output to the log file a one-curve line plot for each chemical species
 for i in range(bio.n_species):
-    log.write(f"{bio.chem_data.get_name(i)}:", also_print=False)
+    log.write(f"{bio.chem_data.get_label(i)}:", also_print=False)
     bio.single_species_line_plot(species_index=i, plot_pars=lineplot_pars, graphic_component="vue_curves_3")
 
 # Output to the log file a line plot for ALL the chemicals together (same color as used for plotly elsewhere)
@@ -213,12 +213,12 @@
 
 # Output to the log file a heatmap for each chemical species
 for i in range(bio.n_species):
-    log.write(f"{bio.chem_data.get_name(i)}:", also_print=False)
+    log.write(f"{bio.chem_data.get_label(i)}:", also_print=False)
     bio.single_species_heatmap(species_index=i, heatmap_pars=heatmap_pars, graphic_component="vue_heatmap_11")
 
 # Output to the log file a one-curve line plot for each chemical species
 for i in range(bio.n_species):
-    log.write(f"{bio.chem_data.get_name(i)}:", also_print=False)
+    log.write(f"{bio.chem_data.get_label(i)}:", also_print=False)
     bio.single_species_line_plot(species_index=i, plot_pars=lineplot_pars, graphic_component="vue_curves_3")
 
 # Output to the log file a line plot for ALL the chemicals together (same color as used for plotly elsewhere)
@@ -260,12 +260,12 @@
 
 # Output to the log file a heatmap for each chemical species
 for i in range(bio.n_species):
-    log.write(f"{bio.chem_data.get_name(i)}:", also_print=False)
+    log.write(f"{bio.chem_data.get_label(i)}:", also_print=False)
     bio.single_species_heatmap(species_index=i, heatmap_pars=heatmap_pars, graphic_component="vue_heatmap_11")
 
 # Output to the log file a one-curve line plot for each chemical species
 for i in range(bio.n_species):
-    log.write(f"{bio.chem_data.get_name(i)}:", also_print=False)
+    log.write(f"{bio.chem_data.get_label(i)}:", also_print=False)
     bio.single_species_line_plot(species_index=i, plot_pars=lineplot_pars, graphic_component="vue_curves_3")
 
 # Output to the log file a line plot for ALL the chemicals together (same color as used for plotly elsewhere)

experiments/1D/reaction_diffusion/rd_1.py

@@ -40,12 +40,11 @@
 # ![2 Coupled Reactions](../../docs/2_coupled_reactions.png)
 
 # %%
-LAST_REVISED = "July 26, 2024"
-LIFE123_VERSION = "1.0.0.beta.38"      # Version this experiment is based on
+LAST_REVISED = "Oct. 11, 2024"
+LIFE123_VERSION = "1.0.0.beta.39"   # Library version this experiment is based on
 
 # %%
-#import set_path            # Using MyBinder?  Uncomment this before running the next cell!
-                            # Importing this local file will add the project's home directory to sys.path
+#import set_path                    # Using MyBinder?  Uncomment this before running the next cell!
 
 # %% tags=[]
 #import sys
@@ -184,13 +183,13 @@
 # %%
 # Concentration increments due to reaction 0 (A <-> B)
 # Note that [C] is not affected
-dynamics.diagnostics.get_diagnostic_rxn_data(rxn_index=0)
+dynamics.diagnostics.get_rxn_data(rxn_index=0)
 
 # %%
 # Concentration increments due to reaction 1 (B <-> C) 
 # Also notice that the 0-th row from the A <-> B reaction isn't seen here (start time 0 and step 0.02), 
 # because that step was aborted early on, BEFORE even getting to THIS reaction
-dynamics.diagnostics.get_diagnostic_rxn_data(rxn_index=1)
+dynamics.diagnostics.get_rxn_data(rxn_index=1)
 
 # %%
 

experiments/reactions_single_compartment/cascade_1.ipynb

@@ -51,6 +51,7 @@
 
 # %% tags=[]
 # Instantiate the simulator and specify the chemicals
+# Here we use the "mid" preset for the variable steps, a compromise between speed and accuracy
 dynamics = UniformCompartment(preset="mid")
 
 # Reaction A <-> B (slower, and with a smaller K)
@@ -67,7 +68,7 @@
 # ### Run the simulation
 
 # %%
-dynamics.set_conc({"A": 50.}, snapshot=True)  # Set the initial concentrations of all the chemicals, in their index order
+dynamics.set_conc({"A": 50.}, snapshot=True)  # Set the initial concentrations of all the chemicals
 dynamics.describe_state()
 
 # %%
@@ -132,8 +133,8 @@
 # We'll pick time **t=0.1** as the divider between the 2 domains of the `A <-> C` time evolution that we want to model. 
 
 # %%
-dynamics.plot_history(colors=['darkturquoise', 'orange', 'green'], xrange=[0, 0.4], 
-                      vertical_lines=[0.1])
+dynamics.plot_history(colors=['darkturquoise', 'orange', 'green'], range_x=[0, 0.4],
+                      vertical_lines_to_add=[0.1])
 
 # %% [markdown]
 # #### Let's locate where the t = 0.1 point occurs in the data
@@ -177,8 +178,8 @@
 # It's no surprise that an elementary reaction is a good fit, if one observes what happens to the time evolution of the concentrations.  Repeating the earlier plot, but only showing `A` and `C` (i.e. hiding the intermediary `B`):
 
 # %%
-dynamics.plot_history(colors=['darkturquoise', 'green'], xrange=[0, 0.4], vertical_lines=[0.1], 
-                     chemicals=['A', 'C'], title="Changes in concentration for `A <-> C`")
+dynamics.plot_history(colors=['darkturquoise', 'green'], range_x=[0, 0.4], vertical_lines_to_add=[0.1],
+                      chemicals=['A', 'C'], title="Changes in concentration for `A <-> C`")
 
 # %% [markdown]
 # In the zone to the left of the vertical dashed line:  
@@ -202,8 +203,8 @@
 # Let's see the graph again:
 
 # %%
-dynamics.plot_history(colors=['darkturquoise', 'orange', 'green'], xrange=[0, 0.4], 
-                      vertical_lines=[0.1])
+dynamics.plot_history(colors=['darkturquoise', 'orange', 'green'], range_x=[0, 0.4],
+                      vertical_lines_to_add=[0.1])
 
 # %% [markdown]
 # A possible conclusion to draw is that, in this case, the earlier part of the complex (compound) reaction `A <-> C` cannot be modeled by an elementary reaction, while the later part can indeed be modeled by a 1st order elementary reaction, with kinetics similar to the slower `A <-> B` reaction

experiments/reactions_single_compartment/cascade_1.py

@@ -125,8 +125,8 @@
 # Note that this is a much smaller time than we saw in experiment `cascade_2_a`
 
 # %%
-dynamics.plot_history(colors=['darkturquoise', 'orange', 'green'], xrange=[0, 0.4], 
-                      vertical_lines=[0.028])
+dynamics.plot_history(colors=['darkturquoise', 'orange', 'green'], range_x=[0, 0.4],
+                      vertical_lines_to_add=[0.028])
 
 # %% [markdown]
 # #### Let's locate where the t = 0.028 point occurs in the data
@@ -175,8 +175,8 @@
 # Let's see the graph again:
 
 # %%
-dynamics.plot_history(colors=['darkturquoise', 'orange', 'green'], xrange=[0, 0.4], 
-                      vertical_lines=[0.028])
+dynamics.plot_history(colors=['darkturquoise', 'orange', 'green'], range_x=[0, 0.4],
+                      vertical_lines_to_add=[0.028])
 
 # %% [markdown]
 # Just as we concluded in experiment `cascade_2_a`, the earlier part of the complex (compound) reaction `A <-> C` cannot be modeled by an elementary reaction, while the later part can indeed be modeled by a 1st order elementary reaction, with kinetics similar to the slower `A <-> B` reaction.  This time, with a greater disparity between the two elementary reaction, the transition happens much sooner.

experiments/reactions_single_compartment/cascade_2_a.ipynb

@@ -121,8 +121,8 @@
 # Let's visually locate at what times those [A] values occur:
 
 # %%
-dynamics.plot_history(chemicals='A', colors='darkturquoise', xrange=[0, 0.15], 
-                      vertical_lines=[0.028, 0.1], title="[A] as a function of time")
+dynamics.plot_history(chemicals='A', colors='darkturquoise', range_x=[0, 0.15],
+                      vertical_lines_to_add=[0.028, 0.1], title="[A] as a function of time")
 
 # %%
 dynamics.get_history(columns=["SYSTEM TIME", "A"], t_start=0.02, t_end=0.03) 
@@ -190,8 +190,8 @@
 # Let's see the time evolution again, but just for `A` and `C`:
 
 # %%
-dynamics.plot_history(chemicals=['A', 'C'], colors=['darkturquoise',  'green'], xrange=[0, 0.25], 
-                      vertical_lines=[0.028, 0.1], title='A <-> C compound reaction')
+dynamics.plot_history(chemicals=['A', 'C'], colors=['darkturquoise',  'green'], range_x=[0, 0.25],
+                      vertical_lines_to_add=[0.028, 0.1], title='A <-> C compound reaction')
 
 # %% [markdown]
 # For t > 0.1, the product [C] appears to have a lot of response to nearly non-existing changes in [A] !  

experiments/reactions_single_compartment/cascade_2_a.py

@@ -202,7 +202,7 @@
 
 # %%
 # Show the timestepe taken (vertical dashed lines) in a small section of the plot
-dynamics.plot_history(chemicals=["A", "B", "C"], show_intervals=True, xrange=[2.5, 3.5])
+dynamics.plot_history(chemicals=["A", "B", "C"], show_intervals=True, range_x=[2.5, 3.5])
 
 # %%
 dynamics.explain_time_advance()  # Notice a lot of timestep adjustments, as needed