Remove pydantic v2 dep

Necessary to have the napari plugin install via GUI installer.

setup.cfg

@@ -38,8 +38,6 @@ install_requires =
     wandb
     edt
     joblib
-    pydantic >= 2.0
-    pydantic_numpy
 python_requires = >=3.10
 include_package_data = True
 

trackastra/data/utils.py

@@ -7,13 +7,11 @@
 
 # from .data import CTCData
 import tifffile
-from pydantic import validate_call
 from tqdm import tqdm
 
 logger = logging.getLogger(__name__)
 
 
-@validate_call
 def load_tiff_timeseries(
     dir: Path,
     dtype: str | type | None = None,

trackastra/data/wrfeat.py

@@ -13,8 +13,6 @@
 import numpy as np
 import pandas as pd
 from edt import edt
-from pydantic import validate_call
-from pydantic_numpy import NpNDArray
 from skimage.measure import regionprops, regionprops_table
 from tqdm import tqdm
 
@@ -89,7 +87,10 @@ def __init__(
         self.timepoints = timepoints
 
     def __repr__(self):
-        s = f"WindowRegionFeatures(ndim={self.ndim}, nregions={len(self.labels)}, ntimepoints={len(np.unique(self.timepoints))})\n\n"
+        s = (
+            f"WindowRegionFeatures(ndim={self.ndim}, nregions={len(self.labels)},"
+            f" ntimepoints={len(np.unique(self.timepoints))})\n\n"
+        )
         for k, v in self.features.items():
             s += f"{k:>20} -> {v.shape}\n"
         return s
@@ -445,10 +446,9 @@ def __call__(self, feats: WRFeatures):
         return feats
 
 
-@validate_call
 def get_features(
-    detections: NpNDArray,
-    imgs: NpNDArray | None = None,
+    detections: np.ndarray,
+    imgs: np.ndarray | None = None,
     features: Literal["none", "wrfeat"] = "wrfeat",
     ndim: int = 2,
     n_workers=0,
@@ -458,25 +458,34 @@ def get_features(
     imgs = _check_dimensions(imgs, ndim)
     logger.info(f"Extracting features from {len(detections)} detections")
     if n_workers > 0:
-        features = joblib.Parallel(n_jobs=n_workers, backend='multiprocessing')(
+        features = joblib.Parallel(n_jobs=n_workers, backend="multiprocessing")(
             joblib.delayed(WRFeatures.from_mask_img)(
                 # New axis for time component
                 mask=mask[np.newaxis, ...],
                 img=img[np.newaxis, ...],
                 t_start=t,
             )
-            for t, (mask, img) in progbar_class(enumerate(zip(detections, imgs)), total=len(imgs), desc="Extracting features")
+            for t, (mask, img) in progbar_class(
+                enumerate(zip(detections, imgs)),
+                total=len(imgs),
+                desc="Extracting features",
+            )
         )
     else:
         logger.info("Using single process for feature extraction")
-        features = tuple(WRFeatures.from_mask_img(
+        features = tuple(
+            WRFeatures.from_mask_img(
                 mask=mask[np.newaxis, ...],
                 img=img[np.newaxis, ...],
                 t_start=t,
             )
-            for t, (mask, img) in progbar_class(enumerate(zip(detections, imgs)), total=len(imgs), desc="Extracting features")
-        ) 
-
+            for t, (mask, img) in progbar_class(
+                enumerate(zip(detections, imgs)),
+                total=len(imgs),
+                desc="Extracting features",
+            )
+        )
+
     return features
 
 
@@ -495,9 +504,7 @@ def _check_dimensions(x: np.ndarray, ndim: int):
 
 
 def build_windows(
-    features: list[WRFeatures],
-    window_size: int,
-    progbar_class=tqdm
+    features: list[WRFeatures], window_size: int, progbar_class=tqdm
 ) -> list[dict]:
     windows = []
     for t1, t2 in progbar_class(

trackastra/model/model_api.py

@@ -4,7 +4,6 @@
 
 import numpy as np
 import yaml
-from pydantic import validate_call
 from tqdm import tqdm
 
 from ..data import build_windows, get_features
@@ -27,15 +26,13 @@ def __init__(self, transformer, train_args, device="cpu"):
         self.device = device
 
     @classmethod
-    @validate_call
     def from_folder(cls, dir: Path, device: str = "cpu"):
         transformer = TrackingTransformer.from_folder(dir, map_location=device)
         train_args = yaml.load(open(dir / "train_config.yaml"), Loader=yaml.FullLoader)
         return cls(transformer=transformer, train_args=train_args, device=device)
 
     # TODO make safer
     @classmethod
-    @validate_call
     def from_pretrained(
         cls, name: str, device: str = "cpu", download_dir: Path | None = None
     ):

trackastra/model/pretrained.py

@@ -5,13 +5,14 @@
 from pathlib import Path
 
 import requests
-from pydantic import validate_call
 from tqdm import tqdm
 
 logger = logging.getLogger(__name__)
 
 _MODELS = {
-    "ctc": "https://github.com/weigertlab/trackastra-models/releases/download/v0.1/ctc.zip",
+    "ctc": (
+        "https://github.com/weigertlab/trackastra-models/releases/download/v0.1/ctc.zip"
+    ),
     "general_2d": "https://github.com/weigertlab/trackastra-models/releases/download/v0.1.1/general_2d.zip",
 }
 
@@ -53,7 +54,6 @@ def download(url: str, fname: Path):
             bar.update(size)
 
 
-@validate_call
 def download_pretrained(name: str, download_dir: Path | None = None):
     # TODO make safe, introduce versioning
     if download_dir is None:
@@ -66,7 +66,8 @@ def download_pretrained(name: str, download_dir: Path | None = None):
         url = _MODELS[name]
     except KeyError:
         raise ValueError(
-            f"Pretrained model `name` is not available. Choose from {list(_MODELS.keys())}"
+            "Pretrained model `name` is not available. Choose from"
+            f" {list(_MODELS.keys())}"
         )
     folder = download_dir / name
     download_and_unzip(url=url, dst=folder)