osekit update

src/post_processing/utils/filtering_utils.py

@@ -10,7 +10,7 @@
 import numpy as np
 import pytz
 from osekit.config import TIMESTAMP_FORMAT_AUDIO_FILE
-from osekit.utils.timestamp_utils import strftime_osmose_format, strptime_from_text
+from osekit.utils.timestamp import strftime_osmose_format, strptime_from_text
 from pandas import (
     DataFrame,
     Timedelta,

src/post_processing/utils/fpod_utils.py

@@ -22,7 +22,7 @@
     sqrt,
     zeros,
 )
-from osekit.utils.timestamp_utils import strftime_osmose_format, strptime_from_text
+from osekit.utils.timestamp import strftime_osmose_format, strptime_from_text
 from pandas import (
     DataFrame,
     DateOffset,
@@ -206,8 +206,9 @@ def get_feeding_buzz_datetime(row: Series) -> Timestamp:
         exceptions.append(e)
 
     try:
-        return (strptime_from_text(row["Minute"], "%-d/%-m/%Y %H:%M") +
-                Timedelta(microseconds=row["microsec"]))
+        return strptime_from_text(row["Minute"], "%-d/%-m/%Y %H:%M") + Timedelta(
+            microseconds=row["microsec"]
+        )
     except (KeyError, TypeError, ValueError) as e:
         exceptions.append(e)
 
@@ -347,10 +348,9 @@ def gmm_ici(
     """
     df, ici = log_ici(df)
 
-    gmm = mixture.GaussianMixture(n_components=comp,
-                                  covariance_type="full",
-                                  random_state=42,
-                                  n_init=20)
+    gmm = mixture.GaussianMixture(
+        n_components=comp, covariance_type="full", random_state=42, n_init=20
+    )
     labels = gmm.fit_predict(ici)
 
     rank = argsort(argsort(gmm.means_.flatten()))
@@ -416,7 +416,11 @@ def cluster_ici(
     gmm = mixture.GaussianMixture(n_components=comp, covariance_type="full")
     gmm.fit(ar_ici)
 
-    component_names = ["Buzz ICIs", "Regular ICIs", "Long ICIs",]
+    component_names = [
+        "Buzz ICIs",
+        "Regular ICIs",
+        "Long ICIs",
+    ]
     cluster_info = []
     for i in range(comp):
         means = sort(gmm.means_, axis=0)[i][0]
@@ -512,10 +516,12 @@ def plot_gmm_ici(
             x_axis,
             gmm_icis.weights_[idx]
             * stats.norm.pdf(
-                x_axis, gmm_icis.means_[idx, 0], sqrt(gmm_icis.covariances_[idx, 0, 0]),
+                x_axis,
+                gmm_icis.means_[idx, 0],
+                sqrt(gmm_icis.covariances_[idx, 0, 0]),
             ).ravel(),
             label=f"(μ={gmm_icis.means_[idx, 0]:.2f},"
-                  f"σ={sqrt(gmm_icis.covariances_[idx, 0, 0]):.2f})",
+            f"σ={sqrt(gmm_icis.covariances_[idx, 0, 0]):.2f})",
         )
         lines += [line]
     (mixture_line,) = ax.plot(
@@ -640,8 +646,7 @@ def percent_calc(
 
     # Aggregate and compute metrics
     df = (
-        data
-        .groupby(time_unit)
+        data.groupby(time_unit)
         .agg(
             {
                 "DPh": "sum",
@@ -656,7 +661,9 @@ def percent_calc(
     df["%click"] = df["dpm_count"] * 100 / (df["Day"] * 60)
     df["%DPh"] = df["DPh"] * 100 / df["Day"]
     df["FBR"] = df.apply(
-        lambda row: (row["Foraging"] * 100 / row["dpm_count"]) if row["dpm_count"] > 0 else 0,
+        lambda row: (row["Foraging"] * 100 / row["dpm_count"])
+        if row["dpm_count"] > 0
+        else 0,
         axis=1,
     )
     df["%buzzes"] = df["Foraging"] * 100 / (df["Day"] * 60)

src/post_processing/utils/pamguard_utils.py

@@ -1,7 +1,7 @@
 from pathlib import Path
 
-from osekit.core_api.audio_data import AudioData
-from osekit.utils.timestamp_utils import strftime_osmose_format
+from osekit.core.audio_data import AudioData
+from osekit.utils.timestamp import strftime_osmose_format
 from pandas import DataFrame, Timedelta, Timestamp
 from pypamguard import load_pamguard_binary_folder
 from pypamguard.core.filters import DateFilter, Filters
@@ -11,11 +11,12 @@
 logger.set_verbosity(Verbosity.ERROR)
 
 
-def process_binary(audio: AudioData,
-                   binary: Path,
-                   dataset: str,
-                   annotation: str,
-                   ) -> DataFrame:
+def process_binary(
+    audio: AudioData,
+    binary: Path,
+    dataset: str,
+    annotation: str,
+) -> DataFrame:
     r"""Process PAMGuard binary files into APLOSE DataFrame.
 
     Parameters
@@ -64,7 +65,9 @@ def process_binary(audio: AudioData,
     """
     filter_obj = Filters(
         {
-        "daterange": DateFilter(start_date=audio.begin, end_date=audio.end, ordered=True),
+            "daterange": DateFilter(
+                start_date=audio.begin, end_date=audio.end, ordered=True
+            ),
         },
     )
 

tests/conftest.py

@@ -6,7 +6,7 @@
 import pytest
 import soundfile as sf
 import yaml
-from osekit.utils.timestamp_utils import strftime_osmose_format
+from osekit.utils.timestamp import strftime_osmose_format
 from pandas import DataFrame, read_csv
 from pandas.tseries import frequencies
 

tests/test_fpod_utils.py

@@ -1,4 +1,5 @@
 """FPOD/ CPOD processing functions tests."""
+
 import pytest
 import pytz
 from pandas import DataFrame
@@ -96,7 +97,7 @@
 #
 #     return data.reset_index(drop=True)
 
-#@pytest.fixture(scope="module")
+# @pytest.fixture(scope="module")
 # @dt.working_directory(__file__)
 # def df_raw() -> DataFrame:
 #     return read_csv("pod_raw.csv")
@@ -106,7 +107,7 @@
 # def df_ap() -> DataFrame:
 #     return read_csv("pod_aplose.csv")
 
-#@pytest.mark.mandatory
+# @pytest.mark.mandatory
 # def test_columns(df_raw: DataFrame) -> None:
 #     dt.validate(
 #         df_raw.columns,
@@ -142,7 +143,7 @@ def test_csv_folder_single_file(tmp_path) -> None:
     csv_file = tmp_path / "data.csv"
     csv_file.write_text("col1;col2\nval1;val2\nval3;val4", encoding="latin-1")
 
-    result = load_pod_folder(tmp_path)
+    result = load_pod_folder(tmp_path, "csv")
 
     assert isinstance(result, DataFrame)
     assert len(result) == 2
@@ -453,20 +454,17 @@ def test_pod2aplose_index_reset(timezone):
     """Test that index is properly reset after sorting."""
     df = DataFrame({
         "ChunkEnd": ["15/01/2024 12:00", "15/01/2024 10:00"],
-        "deploy.name": ["d1", "d2"]
+        "deploy.name": ["d1", "d2"],
     })
 
     result = pod2aplose(
-        df=df,
-        tz=timezone,
-        dataset_name="dataset",
-        annotation="click",
-        annotator="john"
+        df=df, tz=timezone, dataset_name="dataset", annotation="click", annotator="john"
     )
 
     # Index should be 0, 1 after reset
     assert result.index.tolist() == [0, 1]
 
+
 # meta_cut_aplose
 
 
@@ -596,4 +594,4 @@ def test_pod2aplose_index_reset(timezone):
 #     result = deploy_period(df)
 #     assert_frame_equal(result, expected)
 
-# actual_data
+# actual_data