#37 Update Logger.convert_to_json() to support Numpy values and Panda…

…s DataFrames

datalogger/_logger.py

@@ -3,10 +3,12 @@
 from __future__ import annotations
 from typing import TypeVar, Generic, Any, overload, get_type_hints, get_origin
 from collections.abc import Callable, Sequence, Collection, Mapping
+from abc import ABC, abstractmethod
 import os
 import sys
-from abc import ABC, abstractmethod
 from datetime import datetime, timezone
+import numpy as np
+import pandas as pd  # type: ignore
 from datalogger._variables import Coord, DataVar
 from datalogger._logs import LogMetadata, DataLog, DictLog
 from datalogger._get_filename import get_filename
@@ -216,39 +218,68 @@ def make_log(log_metadata: LogMetadata) -> DataLog:
         return self._log(make_log, description, commit_id)
 
     @classmethod
-    def _convert_to_json(cls, obj: Any) -> Any:
+    def convert_to_json(
+        cls, obj: Any, convert: Callable[[Any], Any] | None = None
+    ) -> Any:
         """
-        Return a JSON-serializable version of the given object by converting ``Mapping``
-        and ``Collection`` objects to dictionaries and lists, converting other
-        non-JSON-serializable values to ``repr`` strings, and converting all dictionary
-        keys to strings.
+        Return a JSON-serializable version of the given object. This function is used to
+        convert objects to JSON for :py:meth:`Logger.log_dict` and
+        :py:meth:`Logger.log_props`.
+
+        1. If provided, ``convert()`` will be used to convert the object.
+
+        2. Numpy scalars will be unpacked and Pandas DataFrames will be converted to
+           dictionaries.
+
+        3. ``Mapping`` and ``Collection`` objects will be converted to dictionaries and
+           lists, with keys converted to strings and values converted according to these
+           rules.
+
+        4. Other non-JSON-serializable values will be converted to ``repr()`` strings.
         """
+        if convert is not None:
+            obj = convert(obj)
+        if isinstance(obj, (np.generic, np.ndarray)) and obj.ndim == 0:
+            obj = obj.item()  # Unpack NumPy scalars to simple Python values
+        if isinstance(obj, pd.DataFrame):
+            obj = obj.to_dict()  # Convert DataFrames to dictionaries
         if isinstance(obj, (str, int, float, bool)) or obj is None:
             return obj
         if isinstance(obj, Mapping):
-            return {str(k): cls._convert_to_json(v) for k, v in obj.items()}
+            return {str(k): cls.convert_to_json(v, convert) for k, v in obj.items()}
         if isinstance(obj, Collection):
-            return [cls._convert_to_json(v) for v in obj]
+            return [cls.convert_to_json(v, convert) for v in obj]
         return repr(obj)
 
     def log_dict(
-        self, description: str, dict_data: dict[str, Any], commit_id: int | None = None
+        self,
+        description: str,
+        dict_data: dict[str, Any],
+        commit_id: int | None = None,
+        convert: Callable[[Any], Any] | None = None,
     ) -> DictLog:
         """
         Save the given dictionary data and corresponding metadata in a JSON file, and
         return a :py:class:`DictLog` with this data and metadata.
 
+        Objects will be converted according to :py:meth:`Logger.convert_to_json`, with
+        ``convert()`` passed to that function.
+
         The log will be tagged with the given commit ID, or the latest commit ID if none
         is given (and if this Logger has a corresponding ParamDB).
         """
 
         def make_log(log_metadata: LogMetadata) -> DictLog:
-            return DictLog(log_metadata, self._convert_to_json(dict_data))
+            return DictLog(log_metadata, self.convert_to_json(dict_data, convert))
 
         return self._log(make_log, description, commit_id)
 
     def log_props(
-        self, description: str, obj: Any, commit_id: int | None = None
+        self,
+        description: str,
+        obj: Any,
+        commit_id: int | None = None,
+        convert: Callable[[Any], Any] | None = None,
     ) -> DictLog:
         """
         Save a dictionary of the given object's properties and corresponding metadata in
@@ -261,11 +292,14 @@ class Example:
                 value: LoggedProp
                 number: LoggedProp[float]
 
+        Objects will be converted according to :py:meth:`Logger.convert_to_json`, with
+        ``convert()`` passed to that function.
+
         The log will be tagged with the given commit ID, or the latest commit ID if none
         is given (and if this Logger has a corresponding ParamDB).
         """
-        logged_props: dict[str, Any] = {}
         obj_class = type(obj)
+        logged_props: dict[str, Any] = {}
         try:
             type_hints = get_type_hints(obj_class)
         except Exception as exc:
@@ -278,4 +312,4 @@ class Example:
             if type_hint is LoggedProp or get_origin(type_hint) is LoggedProp:
                 if hasattr(obj, name):
                     logged_props[name] = getattr(obj, name)
-        return self.log_dict(description, logged_props, commit_id)
+        return self.log_dict(description, logged_props, commit_id, convert)