IC to DIC. Labels on figures.

analysis.py

@@ -242,7 +242,7 @@ def run_model(scenario: Scenario):
     result = ModelResult()
     result.pOC = P
     result.dOC = D
-    result.IC = I
+    result.DIC = I
     result.cells = N
     result.t = t
 

model.jl

@@ -17,7 +17,7 @@ function run_model(p, u0)
     prob = ODEProblem(model, u0[1:end-1], (0.0, last(u0)), p)
     sol = solve(prob, Rosenbrock23(), callback=cbs, maxiters=1e6)
 
-    # Return the solution array - [pOC, dOC, IC, Cells, t]
+    # Return the solution array - [pOC, dOC, DIC, Cells, t]
     return [[x[1] for x in sol.u], [x[2] for x in sol.u], [x[3] for x in sol.u], [x[4] for x in sol.u], sol.t]
 end
 
@@ -88,7 +88,7 @@ function make_callbacks(;sensitivity_analysis=false, p=nothing)
     eea_min_condition(u, t, integrator) = integrator.p[10] * u[4] - u[1]
     eea_min_cb = ContinuousCallback(eea_min_condition, do_nothing)
 
-    # Min condition for IC fixation rate
+    # Min condition for DIC fixation rate
     ic_min_condition(u, t, integrator) = integrator.p[8] - u[3]
     ic_min_cb = ContinuousCallback(ic_min_condition, do_nothing)
 

plots.py

@@ -22,7 +22,7 @@ def plot_one_analysis(analysis: Analysis):
     """
     # Make seaborn data frame
     data = analysis.model_result.get_dataframe(analysis.scenario.title, analysis._variable_title)
-    melted = pd.melt(data, id_vars="Years from start", value_vars=("POC", "DOC", "IC", "Cells"), var_name="Variable",
+    melted = pd.melt(data, id_vars="Years from start", value_vars=("POC", "DOC", "DIC", "Cells"), var_name="Variable",
                      value_name="femtograms C/mL or cells/mL")
 
     # Make figure
@@ -61,7 +61,7 @@ def plot_all_scenarios_all_analyses(analyses: [Analysis], color_cycle: int = Non
         all_data = pd.concat([all_data, data]).reset_index(drop=True)
 
     melted_data = all_data.melt(id_vars=["Years from start", "Scenario", "Analysis type"],
-                                value_vars=["POC", "DOC", "IC", "Cells"], var_name="Data type")
+                                value_vars=["POC", "DOC", "DIC", "Cells"], var_name="Data type")
 
     # Plot
     grid = sns.relplot(data=melted_data, x="Years from start", y="value", palette=cp, aspect=1,
@@ -91,8 +91,12 @@ def plot_all_scenarios_all_analyses(analyses: [Analysis], color_cycle: int = Non
         ax.set_ylim(lim)
         ax.set_xlim([0.01, 10 ** 5])
 
+        # Add panel title to row
+        if i % 4 == 0:
+            ax.text(-0.1, 1.1, string.ascii_uppercase[i//4], tr ansform=ax.transAxes, size=20, weight='bold')
+
     grid.tight_layout()
-    grid.fig.subplots_adjust(hspace=0.2, wspace=0.4)
+    grid.fig.subplots_adjust(hspace=0.3, wspace=0.4)
 
     return grid
 
@@ -118,7 +122,7 @@ def plot_multiple_scenarios_one_row(analyses: [Analysis], color_cycle: int = Non
         all_data = pd.concat([all_data, data]).reset_index(drop=True)
 
     melted_data = all_data.melt(id_vars=["Years from start", "Scenario", "Analysis type"],
-                                value_vars=["POC", "DOC", "IC", "Cells"], var_name="Data type")
+                                value_vars=["POC", "DOC", "DIC", "Cells"], var_name="Data type")
 
     # Plot
     grid = sns.relplot(data=melted_data, x="Years from start", y="value", palette=cp, aspect=1,
@@ -190,7 +194,7 @@ def plot_one_result_type_all_analyses(analyses: [Analysis], data_type: str, main
         ax.text(-0.1, 1.1, string.ascii_uppercase[i], transform=ax.transAxes, size=20, weight='bold')
 
     grid.tight_layout()
-    grid.fig.subplots_adjust(wspace=0.4)
+    grid.fig.subplots_adjust(wspace=0.2)
 
     return grid
 
@@ -243,7 +247,7 @@ def hypothetical_growth_scenarios():
     y_rapid = 10**5 + 10**8 / (1 + np.exp(-(x-10)))
     y_cyclic = 10**5 + (10**8 - 10**5)/2 + np.sin(x/4244.1333333333 + 1.5*np.pi) * (10**8 - 10**5)/2
 
-    axis.loglog(x, y_ng, label='No growth', color="brown", linestyle='dashed')
+    axis.loglog(x, y_ng, label='No growth', color="purple", linestyle='dashed')
     axis.loglog(x, y_slow, label='Slow growth', color="green")
     axis.loglog(x, y_rapid, label='Rapid growth, no decline', color="blue")
     axis.loglog(x, y_cyclic, label='Growth, decline, OC added', color="red")

utils.py

@@ -36,7 +36,7 @@ class SensitivityResult:
     first_order_sobol_indices: [float] = None
     total_conf_int: [[float]] = None
     first_order_conf_int: [[float]] = None
-    variables: [str] = ["POC", "DOC", "IC", "Cells", "Mean"]
+    variables: [str] = ["POC", "DOC", "DIC", "Cells", "Mean"]
 
     def get_dataframe(self, scenario):
         """
@@ -74,21 +74,21 @@ class ModelResult:
     """
     pOC: [float] = None
     dOC: [float] = None
-    IC: [float] = None
+    DIC: [float] = None
     cells: [float] = None
     t: [float] = None
 
     def get_dataframe(self, scenario, variable_title):
         """
         Returns model results as a dataframe. Removes duplicate values (e.g. discontinuities).
         """
-        data = [self.t/365.25, self.pOC, self.dOC, self.IC, self.cells, [scenario]*len(self.t), [variable_title]*len(self.t)]
+        data = [self.t/365.25, self.pOC, self.dOC, self.DIC, self.cells, [scenario]*len(self.t), [variable_title]*len(self.t)]
         df = pandas.DataFrame(data=np.column_stack(data),
-                                columns=["Years from start", "POC", "DOC", "IC", "Cells", "Scenario", "Analysis type"])
+                                columns=["Years from start", "POC", "DOC", "DIC", "Cells", "Scenario", "Analysis type"])
 
         df.drop_duplicates(subset=["Years from start", "Scenario", "Analysis type"], keep="last", inplace=True)
 
-        return df.astype({"Years from start": float, "POC": float, "DOC": float, "IC": float, "Cells": float,
+        return df.astype({"Years from start": float, "POC": float, "DOC": float, "DIC": float, "Cells": float,
                            "Scenario": str, "Analysis type": str})