change intervals data type

tests/test_remove_artifacts.py

@@ -1,15 +1,16 @@
-import numpy as np
 import os
 import tempfile
 
+import numpy as np
 from neuro_py.raw.preprocessing import remove_artifacts
 
+
 def test_remove_artifacts():
     # Parameters
     n_channels = 4
     n_samples = 100
     precision = "int16"
-    zero_intervals = [(20, 30), (50, 60)]
+    zero_intervals = np.array([[20, 30], [50, 60]])
 
     # Create a temporary binary file
     with tempfile.NamedTemporaryFile(delete=False) as tmpfile:
@@ -25,23 +26,33 @@ def test_remove_artifacts():
 
         # Test mode "zeros"
         remove_artifacts(filepath, n_channels, zero_intervals, precision, mode="zeros")
-        data = np.memmap(filepath, dtype=precision, mode="r", shape=(n_samples, n_channels))
+        data = np.memmap(
+            filepath, dtype=precision, mode="r", shape=(n_samples, n_channels)
+        )
         for start, end in zero_intervals:
             assert np.all(data[start:end, :] == 0)
         del data  # Close memmap
 
         # Test mode "linear"
         remove_artifacts(filepath, n_channels, zero_intervals, precision, mode="linear")
-        data = np.memmap(filepath, dtype=precision, mode="r", shape=(n_samples, n_channels))
+        data = np.memmap(
+            filepath, dtype=precision, mode="r", shape=(n_samples, n_channels)
+        )
         for start, end in zero_intervals:
             for ch in range(n_channels):
-                expected = np.linspace(original_data[start, ch], original_data[end, ch], end - start)
+                expected = np.linspace(
+                    original_data[start, ch], original_data[end, ch], end - start
+                )
                 np.testing.assert_allclose(data[start:end, ch], expected, rtol=1e-5)
         del data  # Close memmap
 
         # Test mode "gaussian"
-        remove_artifacts(filepath, n_channels, zero_intervals, precision, mode="gaussian")
-        data = np.memmap(filepath, dtype=precision, mode="r", shape=(n_samples, n_channels))
+        remove_artifacts(
+            filepath, n_channels, zero_intervals, precision, mode="gaussian"
+        )
+        data = np.memmap(
+            filepath, dtype=precision, mode="r", shape=(n_samples, n_channels)
+        )
         for start, end in zero_intervals:
             for ch in range(n_channels):
                 segment = data[start:end, ch]
@@ -53,4 +64,5 @@ def test_remove_artifacts():
         del data  # Close memmap
     finally:
         # Clean up temporary file
-        os.remove(filepath)
+
+        os.remove(filepath)