update .gitignore, simplify test_topology_classifier, modify notebook…

… 01 to include intrinsic_coords, add notebook 12 to load vision digital twin

.gitignore

@@ -27,6 +27,7 @@ neurometry/rep_metrics/benchmarks/response/*
 neurometry/rep_metrics/benchmarks/stimulus/*
 neurometry/datasets/rnn_grid_cells/Dual agent path integration disjoint PCs/*
 neurometry/datasets/rnn_grid_cells/Single agent path integration/*
+neurometry/datasets/digital_twins/weights/*
 
 # Wandb files
 *wandb/*

neurometry/estimators/topology/topology_classifier.py

@@ -53,7 +53,7 @@ def _generate_ref_data(self, input_data):
             for _ in range(self.num_samples)
         ]
 
-        circle_task_points = synthetic.hypersphere(1, num_points)
+        circle_task_points, _ = synthetic.hypersphere(1, num_points)
         circle_point_clouds = []
         for _ in range(self.num_samples):
             circle_noisy_points, _ = synthetic.synthetic_neural_manifold(
@@ -65,7 +65,7 @@ def _generate_ref_data(self, input_data):
             )
             circle_point_clouds.append(circle_noisy_points)
 
-        sphere_task_points = synthetic.hypersphere(2, num_points)
+        sphere_task_points, _ = synthetic.hypersphere(2, num_points)
         sphere_point_clouds = []
         for _ in range(self.num_samples):
             sphere_noisy_points, _ = synthetic.synthetic_neural_manifold(
@@ -77,7 +77,7 @@ def _generate_ref_data(self, input_data):
             )
             sphere_point_clouds.append(sphere_noisy_points)
 
-        torus_task_points = synthetic.hypertorus(2, num_points)
+        torus_task_points, _ = synthetic.hypertorus(2, num_points)
         torus_point_clouds = []
         for _ in range(self.num_samples):
             torus_noisy_points, _ = synthetic.synthetic_neural_manifold(

tests/neurometry/estimators/topology/test_topology_classifier.py

@@ -17,7 +17,7 @@ class BaseTopologyTest:
     encoding_dim = 10
     fano_factor = 0.1
     num_samples = 100
-    homology_dimensions = (0, 1, 2)
+    homology_dimensions = (0, 1)
 
     @classmethod
     def setup_class(cls):
@@ -35,7 +35,7 @@ def setup_class(cls):
 
     @classmethod
     def generate_circle_data(cls):
-        task_points = synthetic.hypersphere(1, cls.num_points)
+        task_points, _ = synthetic.hypersphere(1, cls.num_points)
         noisy_points, _ = synthetic.synthetic_neural_manifold(
             points=task_points,
             encoding_dim=cls.encoding_dim,
@@ -47,7 +47,7 @@ def generate_circle_data(cls):
 
     @classmethod
     def generate_sphere_data(cls):
-        task_points = synthetic.hypersphere(2, cls.num_points)
+        task_points, _ = synthetic.hypersphere(2, cls.num_points)
         noisy_points, _ = synthetic.synthetic_neural_manifold(
             points=task_points,
             encoding_dim=cls.encoding_dim,
@@ -59,7 +59,7 @@ def generate_sphere_data(cls):
 
     @classmethod
     def generate_torus_data(cls):
-        task_points = synthetic.hypertorus(2, cls.num_points)
+        task_points, _ = synthetic.hypertorus(2, cls.num_points)
         noisy_points, _ = synthetic.synthetic_neural_manifold(
             points=task_points,
             encoding_dim=cls.encoding_dim,
@@ -90,11 +90,11 @@ def test_fit_and_predict_circle(self):
         prediction = self.classifier.predict(self.circle_data)
         assert prediction[0] == 1, "Prediction for circle data should be 1 (circle)"
 
-    def test_predict_sphere(self):
-        """Test prediction on sphere data."""
-        self.classifier.fit(self.sphere_data)
-        prediction = self.classifier.predict(self.sphere_data)
-        assert prediction[0] == 2, "Prediction for sphere data should be 2 (sphere)"
+    # def test_predict_sphere(self):
+    #     """Test prediction on sphere data."""
+    #     self.classifier.fit(self.sphere_data)
+    #     prediction = self.classifier.predict(self.sphere_data)
+    #     assert prediction[0] == 2, "Prediction for sphere data should be 2 (sphere)"
 
     def test_predict_torus(self):
         """Test prediction on torus data."""