Merge pull request #49 from int-brain-lab/processing_fix

added testing for processing module

src/iblphotometry/processing.py

@@ -582,15 +582,29 @@ def fillnan_kde(y: np.ndarray, w: int = 25):
 
 
 def remove_outliers(
-    F: pd.Series, w_size: int = 1000, alpha: float = 0.005, w: int = 25
+    F: pd.Series,
+    w_len: float = 60,
+    alpha: float = 0.005,
+    w: int = 25,
+    fs=None,
+    max_it=100,
 ):
     y, t = F.values, F.index.values
+    fs = 1 / np.median(np.diff(t)) if fs is None else fs
+    w_size = int(w_len * fs)
+
     y = copy(y)
     outliers = detect_outliers(y, w_size=w_size, alpha=alpha)
+    j = 0
     while len(outliers) > 0:
         y[outliers] = np.nan
         y = fillnan_kde(y, w=w)
         outliers = detect_outliers(y, w_size=w_size, alpha=alpha)
+        if j > max_it:
+            break
+        else:
+            j += 1
+
     return pd.Series(y, index=t)
 
 

src/iblphotometry_tests/test_processing.py

@@ -0,0 +1,34 @@
+import iblphotometry.io as fio
+import iblphotometry.processing as processing
+import pandas as pd
+
+from iblphotometry_tests.base_tests import PhotometryDataTestCase
+
+
+class TestProcessing(PhotometryDataTestCase):
+    # think here about the possible use cases
+
+    def test_processing(self):
+        self.set_paths('alejandro')
+        # get data
+        raw_dfs = fio.from_ibl_pqt(
+            self.paths['photometry_signal_pqt'],
+            self.paths['photometryROI_locations_pqt'],
+        )
+        trials = pd.read_parquet(self.paths['trials_table_pqt'])
+        raw_df = raw_dfs['GCaMP']['DMS']
+
+        # bleach corrections
+        processing.lowpass_bleachcorrect(raw_df)
+        processing.exponential_bleachcorrect(raw_df)
+
+        # outlier removal
+        processing.remove_outliers(raw_df)
+        processing.remove_spikes(raw_df)
+
+        # other functions
+        processing.make_sliding_window(raw_df.values, 100, method='stride_tricks')
+        processing.make_sliding_window(raw_df.values, 100, method='window_generator')
+        processing.sliding_dFF(raw_df, w_len=60)
+        processing.sliding_z(raw_df, w_len=60)
+        processing.sliding_mad(raw_df, w_len=60)