added test for iterator conversion

tests/test_exp_falsification_sampler.py

@@ -4,6 +4,7 @@
 from sklearn.linear_model import LinearRegression, LogisticRegression
 
 from autora.experimentalist.pipeline import Pipeline
+from autora.experimentalist.pooler.grid import grid_pool
 from autora.experimentalist.sampler.falsification import (
     falsification_sample,
     falsification_score_sample,
@@ -276,6 +277,52 @@ def test_falsification_reconstruction_without_model(
     assert np.round(X_selected[0, 0], 4) == 1.8 or np.round(X_selected[0, 0], 4) == 4.8
     assert np.round(X_selected[1, 0], 4) == 1.8 or np.round(X_selected[1, 0], 4) == 4.8
 
+def test_iterator_input(synthetic_linr_model):
+    # Import model and data_closed_loop
+    X_train, Y_train = get_sin_data()
+    model = synthetic_linr_model
+
+    # specify meta data_closed_loop
+
+    # Specify independent variables
+    iv = IV(
+        name="x",
+        value_range=(0, 2 * np.pi),
+        allowed_values=(np.linspace(0, 2 * np.pi, 100)),
+        units="intensity",
+        variable_label="stimulus",
+    )
+
+    # specify dependent variables
+    dv = DV(
+        name="y",
+        value_range=(-1, 1),
+        units="real",
+        variable_label="response",
+        type=ValueType.REAL,
+    )
+
+    # Variable collection with ivs and dvs
+    metadata = VariableCollection(
+        independent_variables=[iv],
+        dependent_variables=[dv],
+    )
+
+    X = grid_pool(metadata.independent_variables)
+
+    new_conditions, new_scores = falsification_score_sample(
+                condition_pool=X,
+                model=model,
+                reference_conditions=X_train,
+                reference_observations=Y_train,
+                metadata=metadata,
+                num_samples=5,
+                training_epochs=1000,
+                training_lr=1e-3,
+                plot=False,
+            )
+
+    assert new_conditions.shape[0] == 5
 
 def test_doc_example():
     # Specify X and Y