fix tests

mantis/cli/estimate_stabilization.py

@@ -67,8 +67,6 @@ def estimate_position_focus(
 
 
 def get_mean_z_positions(dataframe_path: Path, verbose: bool = False) -> None:
-    import matplotlib.pyplot as plt
-
     df = pd.read_csv(dataframe_path)
 
     # Sort the DataFrame based on 'time_idx'
@@ -79,13 +77,17 @@ def get_mean_z_positions(dataframe_path: Path, verbose: bool = False) -> None:
 
     # Get the mean of positions for each time point
     average_focus_idx = df.groupby("time_idx")["focus_idx"].mean().reset_index()
+
     if verbose:
+        import matplotlib.pyplot as plt
+
         # Get the moving average of the focus_idx
         plt.plot(average_focus_idx["focus_idx"], linestyle="--", label="mean of all positions")
         plt.xlabel('Time index')
         plt.ylabel('Focus index')
         plt.legend()
         plt.savefig(dataframe_path.parent / "z_drift.png")
+
     return average_focus_idx["focus_idx"].values
 
 

mantis/tests/test_analysis/test_stabilization.py

@@ -1,40 +1,30 @@
+import io
+
 import numpy as np
 import pandas as pd
 
-from mantis.cli.estimate_stabilization import estimate_position_focus, get_mean_z_positions
-
-
-def test_estimate_position_focus():
-    # Create input data
-    z_positions = [1, 2, 3, 4, 5]
-    focus_scores = [0.1, 0.2, 0.3, 0.4, 0.5]
-
-    # Call the function
-    result = estimate_position_focus(z_positions, focus_scores)
-
-    # Check the result
-    assert isinstance(result, tuple)
-    assert len(result) == 2
-    assert isinstance(result[0], float)
-    assert isinstance(result[1], float)
+from mantis.cli.estimate_stabilization import get_mean_z_positions
 
 
 def test_mean_z_positions():
     # Create input data
     df = pd.DataFrame(
         {
-            "channel": ["GFP"],
-            "channel_idx": [0, 0, 0, 0, 0, 0, 0, 0],
-            "time_min": [1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0],
-            "focal_idx": [20, 25, 19, 16, 24, 29, 21, 18],
-            "position_idx": [1, 2, 3, 4, 1, 2, 3, 4],
+            "position": ['0/2/000000'] * 4 + ['0/2/001002'] * 4,
+            "time_idx": [0, 1, 2, 3] * 2,
+            "channel": ["GFP"] * 8,
+            "focus_idx": [20, 25, np.nan, 16, 24, 29, 21, 18],
         }
     )
 
+    # Create a pretend file
+    s_buf = io.StringIO()
+    df.to_csv(s_buf)
+    s_buf.seek(0)
+
     # Call the function
-    result = get_mean_z_positions(df, 0)
-    pos_1 = np.array([20, 25, 19, 16]).mean()
-    pos_2 = np.array([24, 29, 21, 18]).mean()
+    result = get_mean_z_positions(s_buf, verbose=False)
+    correct_result = np.array([22, 27, 25, 17])
 
     # Check the result
-    assert result == [pos_1, pos_2]
+    assert all(result == correct_result)