v0.6.1

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "fasttrackpy"
-version = "0.6.0"
+version = "0.6.1"
 requires-python = ">=3.10,<3.14"
 description = "A python implementation of FastTrack"
 authors = [

src/fasttrackpy/tracks.py

@@ -586,15 +586,15 @@ def __get_pitch(self) -> npt.NDArray:
         )
         pitch_array = np.array([
             pitch_obj.get_value_at_time(t)
-            for t in self.candidates[0].time_domain
+            for t in self.winner.time_domain
         ])
         return pitch_array
 
     def __get_intensty(self) -> npt.NDArray:
         intensity_obj = self.sound.to_intensity(time_step = self.time_step)
         intensity = np.array([
             intensity_obj.get_value(time = t)
-            for t in self.candidates[0].time_domain
+            for t in self.winner.time_domain
         ])
         return intensity
 

tests/test_outputs.py

@@ -25,23 +25,27 @@
 def dest(tmp_path_factory):
     dest = tmp_path_factory.mktemp("dest") 
     return dest
-@TEST_DATA
+
 class TestDataFrames:
-    def test_formant_df(self, candidates):
-
-        formant_df = candidates.to_df(output="formants")
-        big_formant_df = candidates.to_df(which='all', output="formants")
+    @CORPUS
+    def test_formant_df(self, candidates2):
+        for cand in candidates2:
+
+            formant_df = cand.to_df(output="formants")
+            big_formant_df = cand.to_df(which='all', output="formants")
 
-        assert isinstance(formant_df, pl.DataFrame)
-        assert isinstance(big_formant_df, pl.DataFrame)
+            assert isinstance(formant_df, pl.DataFrame)
+            assert isinstance(big_formant_df, pl.DataFrame)
 
+    @TEST_DATA
     def test_param_df(self, candidates):
         param_df = candidates.to_df(output="param")
         big_param_df = candidates.to_df(which='all', output="formants")
 
         assert isinstance(param_df, pl.DataFrame)
         assert isinstance(big_param_df, pl.DataFrame)
 
+    @TEST_DATA
     def test_log_param_df(self, candidates):
         log_param_df = candidates.to_df(output="log_param")
         big_log_param_df = candidates.to_df(which='all', output='log_param')