vm updates

pyproject.toml

@@ -13,6 +13,7 @@ aligned-textgrid = "^0.6.3"
 fasttrackpy = "^0.4.0"
 numpy = "^1.26.4"
 polars = "<0.20"
+tqdm = "^4.66.2"
 
 
 [tool.poetry.group.docs.dependencies]
@@ -25,6 +26,7 @@ jupyter = "^1.0.0"
 [tool.poetry.group.dev.dependencies]
 ipykernel = "^6.29.2"
 matplotlib = "^3.8.3"
+dill = "^0.3.8"
 
 [build-system]
 requires = ["poetry-core"]

src/new_fave/measurements/vowel_measurement.py

@@ -7,11 +7,18 @@
 
 import warnings
 
+def blank():
+    return VowelClass()
+
+def blank_list():
+    return []
+
 class VowelClassCollection(defaultdict):
     def __init__(self, track_list:list, param_optim = 3):
-        super().__init__(lambda : VowelClass())
+
+        super().__init__(blank)
         self.param_optim = param_optim
-        self.tracks_dict = defaultdict(lambda: [])
+        self.tracks_dict = defaultdict(blank_list)
         self._make_tracks_dict(track_list)
         self._dictify()
         self._vowel_system()
@@ -119,14 +126,14 @@ def winners_maximum_formant(self):
     @property
     def params_means(self):
         N = len(self.tracks)
-        winner_mean =  self.winner_param_norm.reshape(-1, N).mean(axis = 1)
+        winner_mean =  self.winner_params.reshape(-1, N).mean(axis = 1)
         winner_mean = winner_mean[:, np.newaxis]
         return winner_mean
 
     @property
     def params_covs(self):
         N = len(self.tracks)
-        square_param = self.winner_param_norm.reshape(-1, N)
+        square_param = self.winner_params.reshape(-1, N)
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
             param_cov = np.cov(square_param)
@@ -242,7 +249,7 @@ def cand_errors(self):
     @property
     def cand_mahals(self):
         N = len(self.candidates)
-        square_params = self.cand_param_norm.reshape(-1, N)
+        square_params = self.cand_params.reshape(-1, N)
         inv_covmat = self.vowel_class.params_icov
         x_mu = square_params - self.vowel_class.params_means
         left = np.dot(x_mu.T, inv_covmat)