Merge pull request #349 from digitraceslab/audio_bugfix

Audio bugfix (1.2.2)

niimpy/_version.py

@@ -1 +1 @@
-__version__ = '1.2.1'
+__version__ = '1.2.2'

niimpy/preprocessing/audio.py

@@ -144,7 +144,7 @@ def audio_count_loud(df_u, config=None):
     df_u[col_name] = pd.to_numeric(df_u[col_name])
 
     if len(df_u)>0:
-        df_s = df_u[df_u[col_name]>70] #check if environment was noisy
+        df_u = df_u[df_u[col_name]>70] #check if environment was noisy
         result = group_data(df_u)[col_name].resample(**config["resample_args"]).count()
         result = result.to_frame(name='audio_count_loud')
         result = reset_groups(result)

pyproject.toml

@@ -7,7 +7,7 @@ name = "niimpy"
 
 [project]
 name = "niimpy"
-version = "1.2.1"
+version = "1.2.2"
 readme = "README.md"
 description = "Python module for analysis of behavioral data"
 authors = [
@@ -56,7 +56,7 @@ Repository = "https://github.com/digitraceslab/niimpy"
 
 
 [bumpver]
-current_version = "1.2.1"
+current_version = "1.2.2"
 version_pattern = "MAJOR.MINOR.PATCH"
 commit_message = "bump version {old_version} -> {new_version}"
 commit = true

tests/preprocessing/test_audio.py

@@ -18,7 +18,7 @@ def test_audio_features():
     test_user1 = test[test["user"] == "jd9INuQ5BBlW"]
     assert test_user1["audio_count_silent"].sum() == 0
     assert test_user1.loc[pd.Timestamp("2020-01-09 02:00:00", tz='Europe/Helsinki')]["audio_count_silent"] == 0
-    assert test_user1["audio_count_loud"].sum() == 16
+    assert test_user1["audio_count_loud"].sum() == 12
     assert test_user1.loc[pd.Timestamp("2020-01-09 10:30:00", tz='Europe/Helsinki')]["audio_min_freq"] == 9601
     assert test_user1.loc[pd.Timestamp("2020-01-09 10:30:00", tz='Europe/Helsinki')]["audio_max_freq"] == 9601
     assert test_user1.loc[pd.Timestamp("2020-01-09 10:30:00", tz='Europe/Helsinki')]["audio_mean_freq"] == 9601
@@ -33,7 +33,7 @@ def test_audio_features():
     assert test_user2["audio_count_silent"].sum() == 3
     assert test_user2.loc[pd.Timestamp("2019-08-13 15:00:00", tz='Europe/Helsinki')]["audio_count_silent"] == 2
     assert test_user2.loc[pd.Timestamp("2019-08-13 15:30:00", tz='Europe/Helsinki')]["audio_count_speech"] == 1
-    assert test_user2["audio_count_loud"].sum() == 17
+    assert test_user2["audio_count_loud"].sum() == 10
     assert test_user2.loc[pd.Timestamp("2019-08-13 15:00:00", tz='Europe/Helsinki')]["audio_min_freq"] == 2914
     assert test_user2.loc[pd.Timestamp("2019-08-13 15:00:00", tz='Europe/Helsinki')]["audio_max_freq"] == 7195
     assert test_user2.loc[pd.Timestamp("2019-08-13 15:00:00", tz='Europe/Helsinki')]["audio_mean_freq"] == 5054.5
@@ -57,9 +57,9 @@ def test_audio_features():
     assert test_user1_dev1.loc[pd.Timestamp("2020-01-09", tz='Europe/Helsinki')]["audio_count_silent"] == 0
     assert test_user2.loc[pd.Timestamp("2019-08-13", tz='Europe/Helsinki')]["audio_count_silent"] == 3
     assert test_user2.loc[pd.Timestamp("2019-08-13", tz='Europe/Helsinki')]["audio_count_speech"] == 1
-    assert test_user1_dev1.loc[pd.Timestamp("2020-01-09", tz='Europe/Helsinki')]["audio_count_loud"] == 10
-    assert test_user1_dev2.loc[pd.Timestamp("2020-01-09", tz='Europe/Helsinki')]["audio_count_loud"] == 6
-    assert test_user2.loc[pd.Timestamp("2019-08-13", tz='Europe/Helsinki')]["audio_count_loud"] == 17
+    assert test_user1_dev1.loc[pd.Timestamp("2020-01-09", tz='Europe/Helsinki')]["audio_count_loud"] == 7
+    assert test_user1_dev2.loc[pd.Timestamp("2020-01-09", tz='Europe/Helsinki')]["audio_count_loud"] == 5
+    assert test_user2.loc[pd.Timestamp("2019-08-13", tz='Europe/Helsinki')]["audio_count_loud"] == 10
 
     features ={audio.audio_min_freq:{"audio_column_name":"double_frequency","resample_args":{"rule":"2h"}},
                audio.audio_max_freq:{"audio_column_name":"double_frequency","resample_args":{"rule":"2h"}},