Update requirements.txt and format code

Author: actions-user

Diff for: 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:

Diff for: mimic/utilities/utilities.py

@@ -8,21 +8,21 @@
 cols = ["red", "green", "blue", "royalblue", "orange", "black"]
 
 
-
 def plot_gLV(yobs, timepoints):
-    # fig, axs = plt.subplots(1, 2, layout='constrained')  # Optional alternative
+    # fig, axs = plt.subplots(1, 2, layout='constrained')  # Optional
+    # alternative
     fig, axs = plt.subplots(1, 1)
     for species_idx in range(yobs.shape[1]):
         label = f'Species {species_idx + 1}'  # Add a label for each species
-        axs.plot(timepoints, yobs[:, species_idx], color=cols[species_idx], label=label)
-    
+        axs.plot(timepoints, yobs[:, species_idx],
+                 color=cols[species_idx], label=label)
+
     axs.set_xlabel('time')
     axs.set_ylabel('[species]')
     axs.legend()  # Ensure the legend is called on the correct axes
     plt.show()
 
 
-
 def plot_gMLV(yobs, sobs, timepoints):
     # fig, axs = plt.subplots(1, 2, layout='constrained')
     fig, axs = plt.subplots(1, 2)