Merge branch '0.1.X' of https://github.com/BirdVox/birdvoxdetect into…

… 0.1.X

birdvoxdetect/core.py

@@ -540,7 +540,7 @@ def predict(pcen, detector, logger_level, padding=0):
 def predict_with_context(pcen, context, detector, logger_level, padding=0):
     # Truncate frequency spectrum (from 128 to 120 bins)
     pcen = pcen[:120, :]
-    context = context[:120, :]
+    context = context[:, :120]
 
     # Compute number of hops.
     clip_length = 104
@@ -636,10 +636,20 @@ def map_confidence(y, model_name):
         # Calibrated on BirdVox-300h.
         # See repository: github.com/BirdVox/birdvox-full-season
         # Notebook: detector/notebooks/BirdVoxDetect-v01_calibration.ipynb
+        # This model encodes "0" resp. "1" as "event" resp. "no event"
         log1my = np.log1p(np.clip(-y, np.finfo(np.float32).eps - 1, None))
         logy = np.log(np.clip(y, np.finfo(np.float32).tiny, None))
         y_inverse_sigmoid = log1my - logy
         y_out = 2.7 * y_inverse_sigmoid - 21
+    elif model_name == "birdvoxdetect_pcen_cnn":
+        # Calibrated on BirdVox-full-night_unit03_00-19-45_01min.
+        # See repository: github.com/BirdVox/birdvox-full-season
+        # Notebook: detector/notebooks/BirdVoxDetect-v01_calibration-nocontext.ipynb
+        # This model encodes "1" resp. "0" as "event" resp. "no event"
+        log1my = np.log1p(np.clip(-y, np.finfo(np.float32).eps - 1, None))
+        logy = np.log(np.clip(y, np.finfo(np.float32).tiny, None))
+        y_inverse_sigmoid = logy - log1my
+        y_out = 6 * y_inverse_sigmoid
     else:
         y_out = y
     return y_out

birdvoxdetect/version.py

@@ -1,2 +1,2 @@
 short_version = '0.1'
-version = '0.1b0-dev'
+version = '0.1b5'

tests/test_core.py

@@ -49,7 +49,10 @@ def test_process_file():
 
     # standard call
     tempdir = tempfile.mkdtemp()
-    process_file(FG_10SEC_PATH, output_dir=os.path.join(tempdir, "subfolder"))
+    process_file(
+        FG_10SEC_PATH,
+        output_dir=os.path.join(tempdir, "subfolder"),
+        detector_name="pcen_snr")
     csv_path = os.path.join(
         tempdir, "subfolder",
         'BirdVox-scaper_example_foreground_timestamps.csv')
@@ -60,20 +63,24 @@ def test_process_file():
     assert df.columns[1] == "Time (s)"
     assert df.columns[2] == "Confidence (%)"
     assert np.allclose(
-        np.array(df["Time (s)"]), np.array([2.45, 5.2, 6.8]), atol=0.05)
+        np.array(df["Time (s)"]), np.array([2.4, 5.1, 6.7]), atol=0.1)
     shutil.rmtree(tempdir)
 
     # export clips
     tempdir = tempfile.mkdtemp()
-    process_file(FG_10SEC_PATH, output_dir=tempdir, export_clips=True)
+    process_file(
+        FG_10SEC_PATH,
+        output_dir=tempdir,
+        export_clips=True,
+        detector_name="pcen_snr")
     clips_dir = os.path.join(
         tempdir, 'BirdVox-scaper_example_foreground_clips')
     assert os.path.exists(clips_dir)
     clips_list = sorted(os.listdir(clips_dir))
     assert len(clips_list) == 3
-    assert clips_list[0].startswith('BirdVox-scaper_example_foreground_02')
-    assert clips_list[1].startswith('BirdVox-scaper_example_foreground_05')
-    assert clips_list[2].startswith('BirdVox-scaper_example_foreground_06')
+    assert clips_list[0].startswith('BirdVox-scaper_example_foreground_00002')
+    assert clips_list[1].startswith('BirdVox-scaper_example_foreground_00005')
+    assert clips_list[2].startswith('BirdVox-scaper_example_foreground_00006')
     assert np.all([c.endswith(".wav") for c in clips_list])
     shutil.rmtree(tempdir)
 
@@ -84,7 +91,7 @@ def test_process_file():
         tempdir, 'BirdVox-scaper_example_foreground_confidence.hdf5')
     with h5py.File(confidence_path, "r") as f:
         confidence = f["confidence"].value
-    assert confidence.shape == (200,)
+    assert confidence.shape == (199,)
     shutil.rmtree(tempdir)
 
     # suffix