equilibrium fixed

marimo/equilibrium.py

@@ -1,6 +1,6 @@
 import marimo
 
-__generated_with = "0.11.7"
+__generated_with = "0.12.5"
 app = marimo.App(width="full")
 
 
@@ -10,12 +10,14 @@ def _():
     import numpy as np
     import matplotlib.pyplot as plt
     import copy
+    from typing import Dict
+    from numpy.typing import NDArray
 
     nspecies = mo.ui.number(2,10,value=2,label="Number of species")
     mo.vstack([
         mo.md("#**Numerical Solution of Equilibrium Problems**").center(),
         nspecies],gap=2)
-    return copy, mo, np, nspecies, plt
+    return Dict, NDArray, copy, mo, np, nspecies, plt
 
 
 @app.cell
@@ -297,7 +299,7 @@ def _(compute_Q, concentrations, execute, keq, mo, np, stoichiometry):
         mm = mo.md("##**Optimisation Achieved**")
     else:
         mm = mo.md("##**Optimisation Failed**")
-    
+
     mo.vstack([
             mm.center(),
             mo.hstack([plot_c,plot_f,plot_d],

marimo/equilibrium_basic.py

@@ -1,6 +1,6 @@
 import marimo
 
-__generated_with = "0.11.7"
+__generated_with = "0.12.5"
 app = marimo.App(width="medium")
 
 
@@ -9,6 +9,8 @@ def _():
     import marimo as mo
     import numpy as np
     import matplotlib.pyplot as plt
+    from typing import Dict
+    from numpy.typing import NDArray
 
     conc_a = mo.ui.text(value="0.2",label="$[\mathrm{A}]_0$")
     conc_b = mo.ui.text(value="0.1",label="$[\mathrm{B}]_0$")
@@ -28,7 +30,7 @@ def _():
         {step} {tol}
         """
     )
-    return conc_a, conc_b, keq, mo, np, plt, step, tol
+    return Dict, NDArray, conc_a, conc_b, keq, mo, np, plt, step, tol
 
 
 @app.cell
@@ -112,7 +114,7 @@ def _(
     ) -> NDArray:
         """
         Solves chemical equilibrium equations using an iterative approach.
-    
+
         Args:
             initial_conc: Dictionary of initial concentrations for each species
             stoichiometry: Dictionary of stoichiometric coefficients
@@ -120,7 +122,7 @@ def _(
             dc: Concentration step size for iterations
             rtol: Relative tolerance for convergence
             max_iterations: Maximum number of iterations before stopping
-    
+
         Returns:
             NDArray: Array with columns [iteration, conc_A, conc_B, force]
         """
@@ -129,14 +131,14 @@ def _(
         conc_A = np.zeros(max_iterations + 1)
         conc_B = np.zeros(max_iterations + 1)
         forces = np.zeros(max_iterations + 1)
-    
+
         # Set initial values
         conc = initial_conc.copy()
         force_0 = compute_force(conc, stoichiometry, pK_eq)
         conc_A[0] = conc['A']
         conc_B[0] = conc['B']
         forces[0] = force_0
-    
+
         # Iterate until convergence or max iterations
         for i in range(max_iterations):
             # Update values
@@ -145,13 +147,13 @@ def _(
             # if force*forces[i] < 0:
             #     dc /=2
             pQ = -np.log10(compute_Q(conc, stoichiometry))
-        
+
             # Store results
             iterations[i + 1] = i + 1
             conc_A[i + 1] = conc['A']
             conc_B[i + 1] = conc['B']
             forces[i + 1] = force
-        
+
             # Check convergence
             # if np.isclose(pQ, pK_eq, rtol=rtol):
             if np.abs(force) < rtol:
@@ -162,7 +164,7 @@ def _(
                     conc_B[:i + 2],
                     forces[:i + 2]
                 ])
-    
+
         # Return all iterations if no convergence
         return np.column_stack([iterations, conc_A, conc_B, forces])
 
@@ -172,7 +174,7 @@ def _(
         plt.figure(figsize=(4,4))
         for i in range(0,ncols-1):
             plt.plot(data[:,0],data[:,i+1],label=labels[i],color=colors[i])
-        
+
         if refs is not None:
             for i in range(len(refs)):
                 plt.axhline(refs[i],linestyle='dashed',label=labels[i]+"$_{exact}$",color=colors[i])
@@ -214,7 +216,6 @@ def _(
     mo.vstack([initial,final,
         mo.hstack([plot_c,plot_f])
         ])
-
     return (
         analytic_solution,
         conc,