Upgrade cvxopt (#348)

* Update `pre-commit` and make some other fixes

* Update version pins

* Remove references to `cvxopt`

* Add type

Co-authored-by: Alan R Lowe <[email protected]>

* Use `|=`

* Remove extra dependencies

---------

Co-authored-by: Alan R Lowe <[email protected]>

.napari-hub/DESCRIPTION.md

@@ -44,5 +44,5 @@ If working on Apple Silicon make sure to also install the following
 packages from `conda-forge`.
 
 ```sh
-conda install -c conda-forge cvxopt pyqt
+conda install -c conda-forge pyqt
 ```

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
     - repo: https://github.com/charliermarsh/ruff-pre-commit
-      rev: v0.0.272
+      rev: v0.0.278
       hooks:
         - id: ruff
     - repo: https://github.com/Lucas-C/pre-commit-hooks

README.md

@@ -38,17 +38,6 @@ it is now built against `C++=17`.
 pip install btrack
 ```
 
-#### Installing on Apple Silicon
-
-Install the following with
-[conda](https://github.com/conda-forge/miniforge):
-
-```sh
-conda install -c conda-forge cvxopt
-```
-
-then run the above `pip` install command.
-
 ## Usage examples
 
 Visit [btrack documentation](https://btrack.readthedocs.io) to learn how to use it and see other examples.

btrack/btypes.py

@@ -17,7 +17,7 @@
 
 import ctypes
 from collections import OrderedDict
-from typing import Any, NamedTuple, Optional
+from typing import Any, ClassVar, NamedTuple, Optional
 
 import numpy as np
 
@@ -86,7 +86,7 @@ class PyTrackObject(ctypes.Structure):
 
     """
 
-    _fields_ = [
+    _fields_: ClassVar[list] = [
         ("ID", ctypes.c_long),
         ("x", ctypes.c_double),
         ("y", ctypes.c_double),
@@ -109,9 +109,7 @@ def __init__(self):
 
     @property
     def properties(self) -> dict[str, Any]:
-        if self.dummy:
-            return {}
-        return self._properties
+        return {} if self.dummy else self._properties
 
     @properties.setter
     def properties(self, properties: dict[str, Any]):
@@ -129,7 +127,7 @@ def set_features(self, keys: list[str]) -> None:
             self.n_features = 0
             return
 
-        if not all(k in self.properties for k in keys):
+        if any(k not in self.properties for k in keys):
             missing_features = list(set(keys).difference(set(self.properties.keys())))
             raise KeyError(f"Feature(s) missing: {missing_features}.")
 
@@ -151,7 +149,7 @@ def to_dict(self) -> dict[str, Any]:
             for k, _ in PyTrackObject._fields_
             if k not in ("features", "n_features")
         }
-        node.update(self.properties)
+        node |= self.properties
         return node
 
     @staticmethod
@@ -217,7 +215,7 @@ class PyTrackingInfo(ctypes.Structure):
 
     """
 
-    _fields_ = [
+    _fields_: ClassVar[list] = [
         ("error", ctypes.c_uint),
         ("n_tracks", ctypes.c_uint),
         ("n_active", ctypes.c_uint),
@@ -235,8 +233,7 @@ def to_dict(self) -> dict[str, Any]:
         """Return a dictionary of the statistics"""
         # TODO(arl): make this more readable by converting seconds, ms
         # and interpreting error messages?
-        stats = {k: getattr(self, k) for k, typ in PyTrackingInfo._fields_}
-        return stats
+        return {k: getattr(self, k) for k, typ in PyTrackingInfo._fields_}
 
     @property
     def tracker_active(self) -> bool:
@@ -265,7 +262,7 @@ class PyGraphEdge(ctypes.Structure):
     source timestamp, we just assume that the tracker has done it's job.
     """
 
-    _fields_ = [
+    _fields_: ClassVar[list] = [
         ("source", ctypes.c_long),
         ("target", ctypes.c_long),
         ("score", ctypes.c_double),
@@ -274,8 +271,7 @@ class PyGraphEdge(ctypes.Structure):
 
     def to_dict(self) -> dict[str, Any]:
         """Return a dictionary describing the edge."""
-        edge = {k: getattr(self, k) for k, _ in PyGraphEdge._fields_}
-        return edge
+        return {k: getattr(self, k) for k, _ in PyGraphEdge._fields_}
 
 
 class Tracklet:
@@ -516,9 +512,9 @@ def to_dict(
         """Return a dictionary of the tracklet which can be used for JSON
         export. This is an ordered dictionary for nicer JSON output.
         """
-        trk_tuple = tuple([(p, getattr(self, p)) for p in properties])
+        trk_tuple = tuple((p, getattr(self, p)) for p in properties)
         data = OrderedDict(trk_tuple)
-        data.update(self.properties)
+        data |= self.properties
         return data
 
     def to_array(

btrack/config.py

@@ -2,7 +2,7 @@
 import logging
 import os
 from pathlib import Path
-from typing import Optional
+from typing import ClassVar, Optional
 
 import numpy as np
 from pydantic import BaseModel, conlist, validator
@@ -95,9 +95,7 @@ class TrackerConfig(BaseModel):
 
     @validator("volume", pre=True, always=True)
     def _parse_volume(cls, v):
-        if isinstance(v, tuple):
-            return ImagingVolume(*v)
-        return v
+        return ImagingVolume(*v) if isinstance(v, tuple) else v
 
     @validator("tracking_updates", pre=True, always=True)
     def _parse_tracking_updates(cls, v):
@@ -112,7 +110,7 @@ def _parse_tracking_updates(cls, v):
     class Config:
         arbitrary_types_allowed = True
         validate_assignment = True
-        json_encoders = {
+        json_encoders: ClassVar[dict] = {
             np.ndarray: lambda x: x.ravel().tolist(),
         }
 

btrack/io/_localization.py

@@ -140,9 +140,7 @@ def __call__(
             for dim in range(len(centroids))
         }
 
-        nodes = {"t": [frame] * num_nodes}
-        nodes.update(coords)
-
+        nodes = {"t": [frame] * num_nodes} | coords
         for img_prop in self.img_props:
             nodes[img_prop] = [
                 getattr(props[idx], img_prop) for idx in range(num_nodes)

btrack/io/exporters.py

@@ -114,7 +114,7 @@ def export_LBEP(filename: os.PathLike, tracks: list):
 def _export_HDF(
     filename: os.PathLike,
     tracker,
-    obj_type=None,
+    obj_type: Optional[str] = None,
     filter_by: Optional[str] = None,
 ):
     """Export to HDF."""

btrack/io/hdf.py

@@ -4,6 +4,7 @@
 import logging
 import os
 import re
+from ast import literal_eval
 from functools import wraps
 from typing import TYPE_CHECKING, Any, Optional, Union
 
@@ -276,7 +277,7 @@ def filtered_objects(
             else:
                 raise ValueError(f"Cannot filter objects by {f_expr}")
 
-            filtered_idx = [i for i, x in enumerate(data) if eval(f_eval)]
+            filtered_idx = [i for i, x in enumerate(data) if literal_eval(f_eval)]
 
         else:
             filtered_idx = range(txyz.shape[0])  # default filtering uses all
@@ -302,7 +303,7 @@ def filtered_objects(
 
         # add the filtered properties
         for key, props in properties.items():
-            objects_dict.update({key: props[filtered_idx]})
+            objects_dict[key] = props[filtered_idx]
 
         return objects_from_dict(objects_dict)
 
@@ -404,16 +405,16 @@ def write_properties(
 
             # Check if the property is already in the props_grp:
             if key in props_grp:
-                if allow_overwrite is False:
+                if allow_overwrite:
+                    del self._hdf[f"objects/{self.object_type}/properties"][key]
+                    logger.info(f"Property '{key}' erased to be overwritten...")
+
+                else:
                     logger.info(f"Property '{key}' already written in the file")
                     raise KeyError(
                         f"Property '{key}' already in file -> switch on "
                         "'overwrite' param to replace existing property "
                     )
-                else:
-                    del self._hdf[f"objects/{self.object_type}/properties"][key]
-                    logger.info(f"Property '{key}' erased to be overwritten...")
-
             # Now that you handled overwriting, write the values:
             logger.info(f"Writing properties/{self.object_type}/{key} {values.shape}")
             props_grp.create_dataset(key, data=data[key], dtype="float32")
@@ -451,9 +452,8 @@ def tracks(self) -> list[btypes.Tracklet]:
         obj = self.filtered_objects(f_expr=f_expr)
 
         def _get_txyz(_ref: int) -> int:
-            if _ref >= 0:
-                return obj[_ref]
-            return dummy_obj[abs(_ref) - 1]  # references are -ve for dummies
+            # references are -ve for dummies
+            return obj[_ref] if _ref >= 0 else dummy_obj[abs(_ref) - 1]
 
         tracks = []
         for i in range(track_map.shape[0]):

btrack/optimise/hypothesis.py

@@ -18,6 +18,7 @@
 
 
 import ctypes
+from typing import ClassVar
 
 from btrack import constants
 
@@ -49,7 +50,7 @@ class Hypothesis(ctypes.Structure):
       These are automatically generated by the optimiser
     """
 
-    _fields_ = [
+    _fields_: ClassVar[list] = [
         ("hypothesis", ctypes.c_uint),
         ("ID", ctypes.c_uint),
         ("probability", ctypes.c_double),
@@ -94,7 +95,7 @@ class PyHypothesisParams(ctypes.Structure):
 
     """
 
-    _fields_ = [
+    _fields_: ClassVar[list] = [
         ("lambda_time", ctypes.c_double),
         ("lambda_dist", ctypes.c_double),
         ("lambda_link", ctypes.c_double),

docs/dev_guide/index.rst

@@ -20,12 +20,6 @@ If you would rather install the latest development version, and/or compile direc
     ./build.sh
     pip install -e .
 
-If working on Apple Silicon then also run:
-
-.. code:: sh
-
-    conda install -c conda-forge cvxopt
-
 If developing the documentation then run the following
 
 .. code:: sh

docs/user_guide/installation.rst

@@ -52,7 +52,4 @@ After we've created and activated the virtual environment, on the same terminal,
 
     pip install btrack
 
-This will download and install ``btrack`` and all its dependencies. If working
-on Apple Silicon then also run::
-
-    conda install -c conda-forge cvxopt
+This will download and install ``btrack`` and all its dependencies.

docs/user_guide/napari.rst

@@ -11,7 +11,7 @@ dependencies needed to use these plugins, install the ``napari`` extra via.::
 
 If working on Apple Silicon then also run::
 
-    conda install -c conda-forge cvxopt pyqt
+    conda install -c conda-forge pyqt
 
 The Tracks layer
 ================

pyproject.toml

@@ -25,11 +25,12 @@ classifiers = [
     "Topic :: Scientific/Engineering :: Visualization",
 ]
 dependencies = [
-    "cvxopt>=1.2.0",
+    "cvxopt>=1.3.1",
     "h5py>=2.10.0",
     "numpy>=1.17.3",
+    "pandas>=2.0.3",
     "pooch>=1.0.0",
-    "pydantic~=1.9",
+    "pydantic<2",
     "scikit-image>=0.16.2",
     "scipy>=1.3.1",
     "tqdm>=4.65.0",