Update requirements.txt and format code

Author: actions-user

mimic/model_infer/infer_CRM_bayes.py

@@ -441,7 +441,7 @@ def run_inference(self) -> None:
             # Priors for unknown model parameters
 
             sigma = pm.HalfNormal(
-                'sigma', sigma = 0.5, shape=(
+                'sigma', sigma=0.5, shape=(
                     1,))  # Same sigma for all responses
 
             # Conditionally define parameters based on whether priors are

mimic/model_infer/infer_VAR_bayes.py

@@ -279,7 +279,7 @@ def _run_inference_large(self, **kwargs) -> None:
             c2 = pm.InverseGamma("c2", 2, 8)
             tau = pm.HalfCauchy("tau", beta=tau0)
             lam = pm.HalfCauchy("lam", beta=1, shape=(ndim, ndim))
-            A = pm.Normal('A', mu=A_prior_mu, sigma=tau * lam *
+            A = pm.Normal('A', mu=A_prior_mu, sigma=tau * lam * \
                           at.sqrt(c2 / (c2 + tau**2 * lam**2)), shape=(ndim, ndim))
 
             # If noise covariance is provided, use it as a prior
@@ -438,14 +438,14 @@ def _run_inference_large_xs(self, **kwargs) -> None:
             c2_A = pm.InverseGamma("c2_A", 2, 1)
             tau_A = pm.HalfCauchy("tau_A", beta=tau0_A)
             lam_A = pm.HalfCauchy("lam_A", beta=1, shape=(nX, nX))
-            Ah = pm.Normal('Ah', mu=A_prior_mu, sigma=tau_A * lam_A *
+            Ah = pm.Normal('Ah', mu=A_prior_mu, sigma=tau_A * lam_A * \
                            at.sqrt(c2_A / (c2_A + tau_A**2 * lam_A**2)), shape=(nX, nX))
 
             tau0_B = (DB0 / (DB - DB0)) * 0.1 / np.sqrt(N)
             c2_B = pm.InverseGamma("c2_B", 2, 1)
             tau_B = pm.HalfCauchy("tau_B", beta=tau0_B)
             lam_B = pm.HalfCauchy("lam_B", beta=1, shape=(nS, nX))
-            Bh = pm.Normal('Bh', mu=0, sigma=tau_B * lam_B *
+            Bh = pm.Normal('Bh', mu=0, sigma=tau_B * lam_B * \
                            at.sqrt(c2_B / (c2_B + tau_B**2 * lam_B**2)), shape=(nS, nX))
 
             if noise_cov_prior is not None:

mimic/utilities/utilities.py

@@ -102,49 +102,80 @@ def plot_CRM(observed_species, observed_resources, timepoints, csv_file=None):
 
     return fig, ax
 
-def plot_CRM_with_intervals(observed_species, observed_resources, species_lower, species_upper, 
-                           resource_lower, resource_upper, times, filename=None):
+
+def plot_CRM_with_intervals(
+        observed_species,
+        observed_resources,
+        species_lower,
+        species_upper,
+        resource_lower,
+        resource_upper,
+        times,
+        filename=None):
     fig, ax = plt.subplots(figsize=(12, 8))
-    
+
     # Plot median trajectories
     for i in range(observed_species.shape[1]):
-        ax.plot(times, observed_species[:, i], label=f'Species {i+1}', linewidth=2)
-    
+        ax.plot(times, observed_species[:, i],
+                label=f'Species {i+1}', linewidth=2)
+
     for i in range(observed_resources.shape[1]):
-        ax.plot(times, observed_resources[:, i], label=f'Resource {i+1}', linewidth=2, linestyle='--')
-    
-    # Add confidence ribbons 
+        ax.plot(times,
+                observed_resources[:,
+                                   i],
+                label=f'Resource {i+1}',
+                linewidth=2,
+                linestyle='--')
+
+    # Add confidence ribbons
     for i in range(observed_species.shape[1]):
-        ax.fill_between(times, species_lower[:, i], species_upper[:, i], 
-                       alpha=0.2, color=plt.cm.tab10(i))
-    
+        ax.fill_between(times, species_lower[:, i], species_upper[:, i],
+                        alpha=0.2, color=plt.cm.tab10(i))
+
     for i in range(observed_resources.shape[1]):
-        ax.fill_between(times, resource_lower[:, i], resource_upper[:, i], 
-                       alpha=0.2, color=plt.cm.tab10(i + observed_species.shape[1]))
-    
+        ax.fill_between(times,
+                        resource_lower[:,
+                                       i],
+                        resource_upper[:,
+                                       i],
+                        alpha=0.2,
+                        color=plt.cm.tab10(i + observed_species.shape[1]))
+
     if filename:
         true_data = pd.read_csv(filename)
         true_times = true_data['time'].values
-        
+
         for i in range(observed_species.shape[1]):
             col_name = f'species_{i+1}'
             if col_name in true_data.columns:
-                ax.scatter(true_times, true_data[col_name], 
-                          marker='o', s=30, color=plt.cm.tab10(i), label=f'True {col_name}')
-        
+                ax.scatter(
+                    true_times,
+                    true_data[col_name],
+                    marker='o',
+                    s=30,
+                    color=plt.cm.tab10(i),
+                    label=f'True {col_name}')
+
         for i in range(observed_resources.shape[1]):
             col_name = f'resource_{i+1}'
             if col_name in true_data.columns:
-                ax.scatter(true_times, true_data[col_name], 
-                          marker='s', s=30, color=plt.cm.tab10(i + observed_species.shape[1]), 
-                          label=f'True {col_name}')
-    
+                ax.scatter(
+                    true_times,
+                    true_data[col_name],
+                    marker='s',
+                    s=30,
+                    color=plt.cm.tab10(
+                        i + observed_species.shape[1]),
+                    label=f'True {col_name}')
+
     ax.set_xlabel('Time', fontsize=14)
     ax.set_ylabel('Concentration', fontsize=14)
-    ax.set_title('Consumer-Resource Model Dynamics with 95% Credible Intervals', fontsize=16)
+    ax.set_title(
+        'Consumer-Resource Model Dynamics with 95% Credible Intervals',
+        fontsize=16)
     ax.legend(loc='best', fontsize=12)
     ax.grid(True, alpha=0.3)
-    
+
     plt.tight_layout()
     if filename:
         plt.savefig(f"{filename.split('.')[0]}_with_intervals.png", dpi=300)