Added some heuristics to tdp_finder.py (related to issue #2)

Due the sparse nature of CPU spec repositories https://github.com/felixsteinke/cpu-spec-dataset
and https://github.com/JosuaCarl/cpu-spec-dataset , where, depending
on the processor model, their consumptions can be described in very
disparate columns, the code now implements some heuristics and
pattern matching instead of hard-coding the names of the columns.

Author: jmfernandez

README.md

@@ -102,25 +102,39 @@ For instance, the sample directory was obtained just running next command line:
 
 ## Digestion
 
-The program `tdp-finder.py` helps to obtain the TDP of a processor, using the gathered metadata stored at `cpu_details.json` within the series directory.
+The program `tdp-finder.py` helps to obtain the TDP of an Intel processor, using the gathered metadata stored at `cpu_details.json` within the series directory.
 
 Repository https://github.com/felixsteinke/cpu-spec-dataset contains at
 [dataset](https://github.com/felixsteinke/cpu-spec-dataset/tree/main/dataset) subdirectory several tables in CSV format with
-this and other details for many Intel, AMD and Ampere processors.
+this and other details for many Intel, AMD and Ampere processors. The key column here is `ProcessorNumber`.
 
-For instance:
+Forked repository https://github.com/JosuaCarl/cpu-spec-dataset contains at
+[dataset](https://github.com/JosuaCarl/cpu-spec-dataset/tree/main/dataset) subdirectory tables in CSV similar to the ones from original repo, but with different column names. The key column here to match the processor is `Processor Number`.
+
+Usage would be something like next:
 
 ```bash
 git clone https://github.com/felixsteinke/cpu-spec-dataset
 python tdp-finder.py sample-series/Wetlab2Variations_metrics/2025_05_20-02_19-14001/ cpu-spec-dataset/dataset/intel-cpus.csv 
 ```
 
 ```
-TDP => 28.0 W
+TDP (ConfigTDPMax) => 28.0 W
+```
+
+or
+
+```bash
+git clone https://github.com/JosuaCarl/cpu-spec-dataset cpu-spec-dataset_Josua
+python tdp-finder.py sample-series/Wetlab2Variations_metrics/2025_05_20-02_19-14001/ cpu-spec-dataset_Josua/dataset/intel-cpus.csv 
+```
+
+```
+TDP (Configurable TDP-up) => 28.0 W
 ```
 
 The program `metrics-aggregator.py` is an initial proof of concept to digest the gathered process tree time series. As it tries
-computing the Wh of each part being executed, it needs the TDP (Thermal Design Power) from the CPU.
+computing the Wh of each part being executed, it needs the TDP (Thermal Design Power) or similar from the CPU.
 
 For instance, getting all the consumptions from main steps of a workflow execution (which was using docker for its steps)
 and it was collected, would be:

execution_process_metrics_collector/tdp_finder.py

@@ -23,6 +23,12 @@
 import pathlib
 import re
 import sys
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from typing import (
+        Tuple,
+    )
 
 import pandas as pd
 
@@ -33,7 +39,9 @@
 logger = logging.getLogger(__name__)
 
 
-def tdp_finder(series_dir: "pathlib.Path", processors_file: "pathlib.Path") -> "float":
+def tdp_finder(
+    series_dir: "pathlib.Path", processors_file: "pathlib.Path"
+) -> "Tuple[str, float]":
     if not series_dir.is_dir():
         logger.error(f"Path {series_dir.as_posix()} is not a directory")
         raise Exception()
@@ -48,24 +56,56 @@ def tdp_finder(series_dir: "pathlib.Path", processors_file: "pathlib.Path") -> "
 
     model_name = cpu_details[0]["model name"]
 
-    cpus = pd.read_csv(processors_file)
+    # low_memory is needed to avoid a warning in some CSV files with mixed data
+    cpus = pd.read_csv(processors_file, low_memory=False)
 
-    tdp_str = cpus[
-        cpus["ProcessorNumber"].apply(lambda pn: str(pn) in model_name)
-    ].ConfigTDPMax.values[0]
-    matched = re.search(r"^([0-9]+(?:\.[0-9]+])?) W", tdp_str)
-    if matched is not None:
-        tdp_matched = matched.group(1)
+    for key_column in ("ProcessorNumber", "Processor Number"):
+        if key_column in cpus.columns:
+            break
     else:
-        tdp_matched = tdp_str
+        logger.error(
+            f"Unable to find a valid processor identification column in file {processors_file.as_posix()}"
+        )
+        raise Exception()
 
-    return float(tdp_matched)
+    filtered_cpus = cpus[cpus[key_column].apply(lambda pn: str(pn) in model_name)]
+
+    if len(filtered_cpus) == 0:
+        logger.error(
+            f"Unable to match a valid processor row for {model_name} in file {processors_file.as_posix()}"
+        )
+        raise Exception()
+
+    matches = []
+    for column_name, column in filtered_cpus.items():
+        if not column.hasnans:
+            putative_tdp_str = column.values[0]
+            if isinstance(putative_tdp_str, str):
+                matched = re.search(
+                    r"^(?:[0-9]+(?:\.[0-9]+])?-)?([0-9]+(?:\.[0-9]+])?) W",
+                    putative_tdp_str,
+                )
+                if matched:
+                    matches.append((str(column_name), matched.group(1)))
+
+    if len(matches) == 0:
+        logger.error(
+            f"Unable to find processor package consumption values for {model_name} in file {processors_file.as_posix()}"
+        )
+        raise Exception()
+
+    # Now, sort by consumption
+    matches.sort(key=lambda t: t[1], reverse=True)
+
+    return (matches[0][0], float(matches[0][1]))
 
 
 def main() -> "None":
     if len(sys.argv) >= 3:
-        tdp_in_w = tdp_finder(pathlib.Path(sys.argv[1]), pathlib.Path(sys.argv[2]))
-        print(f"TDP => {tdp_in_w} W")
+        tdp_column, tdp_in_w = tdp_finder(
+            pathlib.Path(sys.argv[1]), pathlib.Path(sys.argv[2])
+        )
+        print(f"TDP ({tdp_column}) => {tdp_in_w} W")
     else:
         print(
             f"Usage: {sys.argv[0]} {{series_dir}} {{intel_datasheets_dir}}",