Merge pull request #253 from SainsburyWellcomeCentre/update_pose_reader

aeon/schema/social.py

@@ -1,13 +1,13 @@
 """Readers for data relevant to Social experiments."""
 
-from pathlib import Path
-from typing import List, Union
 import json
+from pathlib import Path
 
+import numpy as np
 import pandas as pd
 
-from aeon import util
 import aeon.io.reader as _reader
+from aeon import util
 
 
 class Pose(_reader.Harp):
@@ -25,63 +25,79 @@ def __init__(self, pattern: str, extension: str="bin"):
         # `pattern` for this reader should typically be '<hpcnode>_<jobid>*'
         super().__init__(pattern, columns=None, extension=extension)
 
-    def read(self, file: Path, ceph_proc_dir: Path=Path("/ceph/aeon/aeon/data/processed")) -> pd.DataFrame:
+    def read(
+        self, file: Path, ceph_proc_dir: str | Path = "/ceph/aeon/aeon/data/processed"
+    ) -> pd.DataFrame:
         """Reads data from the Harp-binarized tracking file."""
         # Get config file from `file`, then bodyparts from config file.
         model_dir = Path(file.stem.replace("_", "/")).parent
         config_file_dir = ceph_proc_dir / model_dir
-        assert config_file_dir.exists(), f"Cannot find model dir {config_file_dir}"
+        if not config_file_dir.exists():
+            raise FileNotFoundError(f"Cannot find model dir {config_file_dir}")
         config_file = get_config_file(config_file_dir)
         parts = self.get_bodyparts(config_file)
-        
+
         # Using bodyparts, assign column names to Harp register values, and read data in default format.
         columns = ["class", "class_likelihood"]
         for part in parts:
             columns.extend([f"{part}_x", f"{part}_y", f"{part}_likelihood"])
         self.columns = columns
         data = super().read(file)
-
+
+        # Drop any repeat parts.
+        unique_parts, unique_idxs = np.unique(parts, return_index=True)
+        repeat_idxs = np.setdiff1d(np.arange(len(parts)), unique_idxs)
+        if repeat_idxs:  # drop x, y, and likelihood cols for repeat parts (skip first 5 cols)
+            init_rep_part_col_idx = (repeat_idxs - 1) * 3 + 5
+            rep_part_col_idxs = np.concatenate([np.arange(i, i + 3) for i in init_rep_part_col_idx])
+            keep_part_col_idxs = np.setdiff1d(np.arange(len(data.columns)), rep_part_col_idxs)
+            data = data.iloc[:, keep_part_col_idxs]
+            parts = unique_parts
+
         # Set new columns, and reformat `data`.
         n_parts = len(parts)
-        part_data_list = [None] * n_parts
+        part_data_list = [pd.DataFrame()] * n_parts
         new_columns = ["class", "class_likelihood", "part", "x", "y", "part_likelihood"]
         new_data = pd.DataFrame(columns=new_columns)
         for i, part in enumerate(parts):
             part_columns = ["class", "class_likelihood", f"{part}_x", f"{part}_y", f"{part}_likelihood"]
-            part_data = data[part_columns]
+            part_data = pd.DataFrame(data[part_columns])
             part_data.insert(2, "part", part)
             part_data.columns = new_columns
             part_data_list[i] = part_data
         new_data = pd.concat(part_data_list)
         return new_data.sort_index()
 
-    def get_bodyparts(self, file: Path) -> Union[None, List[str]]:
+    def get_bodyparts(self, file: Path) -> list[str]:
         """Returns a list of bodyparts from a model's config file."""
-        parts = None
+        parts = []
         with open(file) as f:
             config = json.load(f)
         if file.stem == "confmap_config":  # SLEAP
             try:
                 heads = config["model"]["heads"]
-                parts = util.find_nested_key(heads, "part_names")
+                parts = [util.find_nested_key(heads, "anchor_part")]
+                parts += util.find_nested_key(heads, "part_names")
             except KeyError as err:
-                raise KeyError(f"Cannot find bodyparts in {file}.") from err
+                if not parts:
+                    raise KeyError(f"Cannot find bodyparts in {file}.") from err
         return parts
 
 
 def get_config_file(
     config_file_dir: Path,
-    config_file_names: List[str]=[
-        "confmap_config.json",  # SLEAP (add others for other trackers to this list)
-    ],
-):
+    config_file_names: None | list[str] = None,
+) -> Path:
     """Returns the config file from a model's config directory."""
+    if config_file_names is None:
+        config_file_names = ["confmap_config.json"]  # SLEAP (add for other trackers to this list)
     config_file = None
     for f in config_file_names:
         if (config_file_dir / f).exists():
             config_file = config_file_dir / f
             break
-    assert config_file is not None, f"Cannot find config file in {config_file_dir}"
+    if config_file is None:
+         raise FileNotFoundError(f"Cannot find config file in {config_file_dir}")
     return config_file
 
 

pyproject.toml

@@ -130,7 +130,6 @@ reportAssertAlwaysTrue = "error"
 reportSelfClsParameterName = "error"
 reportUnusedExpression = "error"
 reportMatchNotExhaustive = "error"
-reportImplicitOverride = "error"
 reportShadowedImports = "error"
 # *Note*: we may want to set all 'ReportOptional*' rules to "none", but leaving 'em default for now
 venvPath = "."