Merge pull request #63 from BrainAnnex/dev

Dev Beta32

Author: BrainAnnex

VERSION_NUMBER.txt

@@ -1 +1 @@
-version 1.0.0-beta.31
+version 1.0.0-beta.32

experiments/reactions_single_compartment/cascade_1.ipynb

@@ -20,7 +20,8 @@ <h3>List of reactions:</h3><span style='padding-left:4ch'>&nbsp;</span>0: A <->
 
 <div id="vue-root-1">   <!-- DIV container for the VUE COMPONENTS below : Vue ROOT element -->
 
-    <vue_cytoscape_2 v-bind:graph_data="graph_data_json">        
+    <vue_cytoscape_2 v-bind:graph_data="graph_data_json"
+                     v-bind:component_id="1">        
     </vue_cytoscape_2>
 
 </div>	<!--  ~~~~~~~~~~~~~~~~~~~~~  END of Vue root element  ~~~~~~~~~~~~~~~~~  -->
@@ -37,7 +38,7 @@ <h3>List of reactions:</h3><span style='padding-left:4ch'>&nbsp;</span>0: A <->
     el: '#vue-root-1',
 
     data: {
-        graph_data_json: {"structure": [{"name": "RXN", "kF": "64", "kR": "8", "delta_G": "-5,154.85", "K": "8", "id": "R-0", "labels": ["Reaction"]}, {"name": "B", "diff_rate": null, "id": "C-1", "labels": ["Chemical"]}, {"name": "produces", "source": "R-0", "target": "C-1", "id": "edge-1", "stoich": 1, "rxn_order": 1}, {"name": "A", "diff_rate": null, "id": "C-0", "labels": ["Chemical"]}, {"name": "reacts", "source": "C-0", "target": "R-0", "id": "edge-2", "stoich": 1, "rxn_order": 1}, {"name": "RXN", "kF": "12", "kR": "2", "delta_G": "-4,441.69", "K": "6", "id": "R-1", "labels": ["Reaction"]}, {"name": "C", "diff_rate": null, "id": "C-2", "labels": ["Chemical"]}, {"name": "produces", "source": "R-1", "target": "C-2", "id": "edge-3", "stoich": 1, "rxn_order": 1}, {"name": "reacts", "source": "C-1", "target": "R-1", "id": "edge-4", "stoich": 1, "rxn_order": 1}], "color_mapping": {"Chemical": "#8DCC92", "Reaction": "#D9C8AD"}, "caption_mapping": {"Chemical": "name", "Reaction": "name"}, "component_id": 1}
+        graph_data_json: {"structure": [{"name": "RXN", "kF": "64", "kR": "8", "delta_G": "-5,154.85", "K": "8", "id": "R-0", "labels": ["Reaction"]}, {"name": "B", "diff_rate": null, "id": "C-1", "labels": ["Chemical"]}, {"name": "produces", "source": "R-0", "target": "C-1", "id": "edge-1", "stoich": 1, "rxn_order": 1}, {"name": "A", "diff_rate": null, "id": "C-0", "labels": ["Chemical"]}, {"name": "reacts", "source": "C-0", "target": "R-0", "id": "edge-2", "stoich": 1, "rxn_order": 1}, {"name": "RXN", "kF": "12", "kR": "2", "delta_G": "-4,441.69", "K": "6", "id": "R-1", "labels": ["Reaction"]}, {"name": "C", "diff_rate": null, "id": "C-2", "labels": ["Chemical"]}, {"name": "produces", "source": "R-1", "target": "C-2", "id": "edge-3", "stoich": 1, "rxn_order": 1}, {"name": "reacts", "source": "C-1", "target": "R-1", "id": "edge-4", "stoich": 1, "rxn_order": 1}], "color_mapping": {"Chemical": "#8DCC92", "Reaction": "#D9C8AD"}, "caption_mapping": {"Chemical": "name", "Reaction": "name"}}
     }
 
 });

experiments/reactions_single_compartment/cascade_1.log.htm

@@ -24,7 +24,7 @@
 # In part2, some diagnotic insight is explored.   
 # In part3, two identical runs ("adaptive variable steps" and "fixed small steps") are compared. 
 #
-# LAST REVISED: May 5, 2024
+# LAST REVISED: May 27, 2024  (using v. 1.0beta32)
 
 # %% [markdown]
 # ## Bathtub analogy:
@@ -46,6 +46,7 @@
 # %% tags=[]
 from experiments.get_notebook_info import get_notebook_basename
 
+from src.modules.chemicals.chem_data import ChemData
 from src.modules.reactions.reaction_dynamics import ReactionDynamics
 from src.modules.visualization.plotly_helper import PlotlyHelper
 
@@ -66,29 +67,31 @@
 
 # %% tags=[]
 # Instantiate the simulator and specify the chemicals
-dynamics = ReactionDynamics(names=["A", "B", "C"])
+chem = ChemData(names=["A", "B", "C"])
 
 # Reaction A <-> B (fast)
-dynamics.add_reaction(reactants=["A"], products=["B"],
+chem.add_reaction(reactants=["A"], products=["B"],
                        forward_rate=64., reverse_rate=8.) 
 
 # Reaction B <-> C (slow)
-dynamics.add_reaction(reactants=["B"], products=["C"],
+chem.add_reaction(reactants=["B"], products=["C"],
                        forward_rate=12., reverse_rate=2.) 
 
-print("Number of reactions: ", dynamics.number_of_reactions())
+print("Number of reactions: ", chem.number_of_reactions())
 
 # %%
-dynamics.describe_reactions()
+chem.describe_reactions()
 
 # %%
 # Send a plot of the network of reactions to the HTML log file
-dynamics.plot_reaction_network("vue_cytoscape_2")
+chem.plot_reaction_network("vue_cytoscape_2")
 
 # %% [markdown]
 # ## Run the simulation
 
 # %%
+dynamics = ReactionDynamics(chem_data=chem, preset="fast")
+
 dynamics.set_conc([50., 0, 0.], snapshot=True) # Set the initial concentrations of all the chemicals, in their index order
 dynamics.describe_state()
 
@@ -101,10 +104,6 @@
 # %%
 dynamics.set_diagnostics()         # To save diagnostic information about the call to single_compartment_react()
 
-# These settings can be tweaked to make the time resolution finer or coarser.  
-# Here we use a "fast" heuristic: less conservative about taking larger steps
-dynamics.use_adaptive_preset(preset="fast")
-
 dynamics.single_compartment_react(initial_step=0.02, reaction_duration=0.4,
                                   snapshots={"initial_caption": "1st reaction step",
                                              "final_caption": "last reaction step"},
@@ -119,13 +118,13 @@
                       colors=['blue', 'orange', 'green'], show_intervals=True)
 
 # %%
-dynamics.curve_intersection("A", "B", t_start=0, t_end=0.05)
+dynamics.curve_intersect("A", "B", t_start=0, t_end=0.05)
 
 # %%
-dynamics.curve_intersection("A", "C", t_start=0, t_end=0.05)
+dynamics.curve_intersect("A", "C", t_start=0, t_end=0.05)
 
 # %%
-dynamics.curve_intersection("B", "C", t_start=0.05, t_end=0.1)
+dynamics.curve_intersect("B", "C", t_start=0.05, t_end=0.1)
 
 # %%
 dynamics.get_history()
@@ -195,12 +194,13 @@
 # We'll use **constant steps of size 0.0005** , which is 1/4 of the smallest steps (the "substep" size) previously used in the variable-step run
 
 # %%
-dynamics2 = ReactionDynamics(shared=dynamics)  # Re-use the same chemicals and reactions of the previous simulation
+dynamics2 = ReactionDynamics(chem_data=chem)  # Re-use the same chemicals and reactions of the previous simulation
 
 # %% tags=[]
 dynamics2.set_conc([50., 0, 0.], snapshot=True)
 
 # %%
+# Notice that we're using FIXED steps this time
 dynamics2.single_compartment_react(initial_step=0.0005, reaction_duration=0.4,
                                    variable_steps=False,
                                    snapshots={"initial_caption": "1st reaction step",
@@ -215,13 +215,13 @@
 # _(Notice that the vertical steps are now equally spaced - and that there are so many of them that we're only showing some)_
 
 # %%
-dynamics2.curve_intersection(t_start=0, t_end=0.05, chem1="A", chem2="B")
+dynamics2.curve_intersect(t_start=0, t_end=0.05, chem1="A", chem2="B")
 
 # %%
-dynamics2.curve_intersection(t_start=0, t_end=0.05, chem1="A", chem2="C")
+dynamics2.curve_intersect(t_start=0, t_end=0.05, chem1="A", chem2="C")
 
 # %%
-dynamics2.curve_intersection(t_start=0.05, t_end=0.1, chem1="B", chem2="C")
+dynamics2.curve_intersect(t_start=0.05, t_end=0.1, chem1="B", chem2="C")
 
 # %%
 df2 = dynamics2.get_history()
@@ -251,6 +251,7 @@
                            curve_labels=["B (adaptive variable steps)", "B (fixed small steps)"])
 
 # %% [markdown]
-# #### They overlap fairly well!  The 800 fixed-timestep points vs. the 48 adaptable variable-timestep ones
+# #### They overlap fairly well!  The 800 fixed-timestep points vs. the 48 adaptable variable-timestep ones.
+# The adaptive algorithms avoided 752 extra steps of limited benefit...
 
 # %%

experiments/reactions_single_compartment/cascade_1.py

@@ -15,7 +15,7 @@
 
 # %% [markdown]
 # ## A cycle of reactions `A <-> B <-> C <-> A`
-# #### the "closing" of the above cycle (the "return" parth from `C` to `A`) is coupled with an "energy donor" reaction:
+# #### the "closing" of the above cycle (the "return" path from `C` to `A`) is coupled with an "energy donor" reaction:
 # #### `C + E_High <-> A + E_Low`
 # #### where `E_High` and `E_Low` are, respectively, the high- and low- energy molecules that drive the cycle (for example, think of ATP/ADP).   
 # Comparisons are made between results obtained with 3 different time resolutions.