NBK: convert callers to 3-arg check_local_stability()

dynamicslab · Aug 30, 2024 · c1bcc28 · c1bcc28
1 parent c8cd72d
commit c1bcc28
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Showing 5 changed files with 19 additions and 19 deletions.
diff --git a/examples/8_trapping_sindy_examples/example.ipynb b/examples/8_trapping_sindy_examples/example.ipynb
@@ -240,7 +240,7 @@
     "print(\"Frobenius Error = \", E_pred)\n",
     "mean_val = np.mean(x_test_pred, axis=0)\n",
     "mean_val = np.sqrt(np.sum(mean_val**2))\n",
-    "check_local_stability(r, Xi, sindy_opt, mean_val)\n",
+    "check_local_stability(Xi, sindy_opt, mean_val)\n",
     "\n",
     "# compute relative Frobenius error in the model coefficients\n",
     "terms = sindy_library.get_feature_names()\n",
@@ -433,7 +433,7 @@
     "print(\"Frobenius error = \", E_pred)\n",
     "mean_val = np.mean(x_test_pred, axis=0)\n",
     "mean_val = np.sqrt(np.sum(mean_val**2))\n",
-    "check_local_stability(r, Xi, sindy_opt, mean_val)\n",
+    "check_local_stability(Xi, sindy_opt, mean_val)\n",
     "\n",
     "# compute relative Frobenius error in the model coefficients\n",
     "terms = sindy_library.get_feature_names()\n",
@@ -694,7 +694,7 @@
     "\n",
     "mean_val = np.mean(x_test_pred, axis=0)\n",
     "mean_val = np.sqrt(np.sum(mean_val**2))\n",
-    "check_local_stability(r, Xi, sindy_opt, mean_val)\n",
+    "check_local_stability(Xi, sindy_opt, mean_val)\n",
     "E_pred = np.linalg.norm(x_test - x_test_pred) / np.linalg.norm(x_test)\n",
     "print(\"Frobenius error = \", E_pred)\n",
     "\n",
@@ -924,7 +924,7 @@
     "make_lissajou(r, x_train, x_test, x_train_pred, x_test_pred, \"mhd\")\n",
     "mean_val = np.mean(x_test_pred, axis=0)\n",
     "mean_val = np.sqrt(np.sum(mean_val**2))\n",
-    "check_local_stability(r, Xi, sindy_opt, mean_val)\n",
+    "check_local_stability(Xi, sindy_opt, mean_val)\n",
     "E_pred = np.linalg.norm(x_test - x_test_pred) / np.linalg.norm(x_test)\n",
     "print(E_pred)\n",
     "\n",
@@ -1355,7 +1355,7 @@
     "Q = np.tensordot(PQ_tensor, Xi, axes=([4, 3], [0, 1]))\n",
     "Q_sum = np.max(np.abs((Q + np.transpose(Q, [1, 2, 0]) + np.transpose(Q, [2, 0, 1]))))\n",
     "print(\"Max deviation from the constraints = \", Q_sum)\n",
-    "if check_local_stability(r, Xi, sindy_opt, 1):\n",
+    "if check_local_stability(Xi, sindy_opt, 1):\n",
     "    x_train_pred = model.simulate(x_train[0, :], t, integrator_kws=integrator_keywords)\n",
     "    x_test_pred = model.simulate(a0, t, integrator_kws=integrator_keywords)\n",
     "    make_progress_plots(r, sindy_opt)\n",
@@ -1365,7 +1365,7 @@
     "    make_lissajou(r, x_train, x_test, x_train_pred, x_test_pred, \"VonKarman\")\n",
     "    mean_val = np.mean(x_test_pred, axis=0)\n",
     "    mean_val = np.sqrt(np.sum(mean_val**2))\n",
-    "    check_local_stability(r, Xi, sindy_opt, mean_val)\n",
+    "    check_local_stability(Xi, sindy_opt, mean_val)\n",
     "    A_guess = sindy_opt.A_history_[-1]\n",
     "    m_guess = sindy_opt.m_history_[-1]\n",
     "    E_pred = np.linalg.norm(x_test - x_test_pred) / np.linalg.norm(x_test)\n",
@@ -1462,7 +1462,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

diff --git a/examples/8_trapping_sindy_examples/example_dysts.py b/examples/8_trapping_sindy_examples/example_dysts.py
@@ -255,7 +255,7 @@
     plt.grid(True)
     plt.legend()
 
-    check_local_stability(r, Xi, sindy_opt, 1.0)
+    check_local_stability(Xi, sindy_opt, 1.0)
     Xi_true = (true_coefficients[i].T)[: Xi.shape[0], :]
 
     # run simulated annealing on the true system to make sure the system is amenable to trapping theorem
@@ -356,7 +356,7 @@
     x_test_pred = model.simulate(x_test[0, :], t, integrator_kws=integrator_keywords)
 
     # Check stability and try simulated annealing with the IDENTIFIED model
-    check_local_stability(r, Xi, sindy_opt, 1.0)
+    check_local_stability(Xi, sindy_opt, 1.0)
     PL_tensor = sindy_opt.PL_
     PM_tensor = sindy_opt.PM_
     L = np.tensordot(PL_tensor, Xi, axes=([3, 2], [0, 1]))
@@ -483,7 +483,7 @@ def rhs(t, x):
 
 model.fit(x_train, t=t_train)
 Xi = model.coefficients().T
-check_local_stability(r, Xi, sindy_opt, 1.0)
+check_local_stability(Xi, sindy_opt, 1.0)
 
 # Fit a baseline model -- this is almost always an unstable model!
 model_baseline = ps.SINDy(

diff --git a/examples/8_trapping_sindy_examples/trapping_extended.py b/examples/8_trapping_sindy_examples/trapping_extended.py
@@ -159,7 +159,7 @@
 mean_val = np.sqrt(np.sum(mean_val**2))
 E_pred = np.linalg.norm(x_test - x_test_pred) / np.linalg.norm(x_test)
 print("Frobenius error = ", E_pred)
-check_local_stability(r, Xi, sindy_opt, mean_val)
+check_local_stability(Xi, sindy_opt, mean_val)
 
 # compute relative Frobenius error in the model coefficients
 sigma = 10
@@ -327,7 +327,7 @@
 Qenergy = np.tensordot(PQ_tensor, Xi, axes=([4, 3], [0, 1]))
 mean_val = np.mean(x_test_pred, axis=0)
 mean_val = np.sqrt(np.sum(mean_val**2))
-check_local_stability(r, Xi, sindy_opt, mean_val)
+check_local_stability(Xi, sindy_opt, mean_val)
 Q = np.tensordot(sindy_opt.PQ_, Xi, axes=([4, 3], [0, 1]))
 
 # %% [markdown]
@@ -358,7 +358,7 @@
 mean_val = np.sqrt(np.sum(mean_val**2))
 E_pred = np.linalg.norm(x_test - x_test_pred) / np.linalg.norm(x_test)
 print("Frobenius error = ", E_pred)
-check_local_stability(r, Xi, sindy_opt, mean_val)
+check_local_stability(Xi, sindy_opt, mean_val)
 
 # compute relative Frobenius error in the model coefficients
 coef_pred = np.linalg.norm(Xi_lorenz - Xi) / np.linalg.norm(Xi_lorenz)
@@ -412,7 +412,7 @@
 Qenergy = np.tensordot(PQ_tensor, Xi, axes=([4, 3], [0, 1]))
 mean_val = np.mean(x_test_pred, axis=0)
 mean_val = np.sqrt(np.sum(mean_val**2))
-check_local_stability(r, Xi, sindy_opt, mean_val)
+check_local_stability(Xi, sindy_opt, mean_val)
 Q = np.tensordot(sindy_opt.PQ_, Xi, axes=([4, 3], [0, 1]))
 print(
     "Maximum deviation from having zero totally symmetric part: ",
@@ -473,7 +473,7 @@
 Qenergy = np.tensordot(PQ_tensor, Xi, axes=([4, 3], [0, 1]))
 mean_val = np.mean(x_test_pred, axis=0)
 mean_val = np.sqrt(np.sum(mean_val**2))
-check_local_stability(r, Xi, sindy_opt, mean_val)
+check_local_stability(Xi, sindy_opt, mean_val)
 Q = np.tensordot(sindy_opt.PQ_, Xi, axes=([4, 3], [0, 1]))
 print(
     "Maximum deviation from having zero totally symmetric part: ",
@@ -512,7 +512,7 @@
 mean_val = np.sqrt(np.sum(mean_val**2))
 E_pred = np.linalg.norm(x_test - x_test_pred) / np.linalg.norm(x_test)
 print("Frobenius error = ", E_pred)
-check_local_stability(r, Xi, sindy_opt, mean_val)
+check_local_stability(Xi, sindy_opt, mean_val)
 
 # compute relative Frobenius error in the model coefficients
 coef_pred = np.linalg.norm(Xi_lorenz - Xi) / np.linalg.norm(Xi_lorenz)

diff --git a/examples/8_trapping_sindy_examples/von_karman_trapping_extended.ipynb b/examples/8_trapping_sindy_examples/von_karman_trapping_extended.ipynb
@@ -610,7 +610,7 @@
     "Qenstrophy = np.tensordot(PQ_tensor, Xi, axes=([4, 3], [0, 1]))\n",
     "mean_val = np.mean(a, axis=0)\n",
     "mean_val = np.sqrt(np.sum(mean_val**2))\n",
-    "check_local_stability(r, Xi, sindy_opt, mean_val)\n",
+    "check_local_stability(Xi, sindy_opt, mean_val)\n",
     "enstrophy_model = model\n",
     "# Q = np.tensordot(sindy_opt.PQ_, Xi, axes=([4, 3], [0, 1]))\n",
     "# Q_sum = np.max(np.abs((Q + np.transpose(Q, [1, 2, 0]) + np.transpose(Q, [2, 0, 1]))))\n",
@@ -837,7 +837,7 @@
     "Qenstrophy = np.tensordot(PQ_tensor, Xi, axes=([4, 3], [0, 1]))\n",
     "mean_val = np.mean(a, axis=0)\n",
     "mean_val = np.sqrt(np.sum(mean_val**2))\n",
-    "check_local_stability(r, Xi, sindy_opt, mean_val)\n",
+    "check_local_stability(Xi, sindy_opt, mean_val)\n",
     "enstrophy_model = model\n",
     "Q = np.tensordot(sindy_opt.PQ_, Xi, axes=([4, 3], [0, 1]))\n",
     "Q = np.tensordot(mod_matrix, Q, axes=([1], [0]))\n",

diff --git a/examples/8_trapping_sindy_examples/von_karman_trapping_extended.py b/examples/8_trapping_sindy_examples/von_karman_trapping_extended.py
@@ -604,7 +604,7 @@
 mean_val = np.sqrt(np.sum(mean_val**2))
 check_stability(r, Xi, sindy_opt, mean_val, mod_matrix)
 
-check_local_stability(r, Xi, sindy_opt, mean_val, mod_matrix)
+check_local_stability(Xi, sindy_opt, mean_val, mod_matrix)
 enstrophy_model = model
 Q = np.tensordot(sindy_opt.PQ_, Xi, axes=([4, 3], [0, 1]))
 Q = np.tensordot(mod_matrix, Q, axes=([1], [0]))