Refactor CANveyor dashboard architecture #8
@@ -2,205 +2,306 @@
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# Copyright (C) 2026 Erick Ahmed
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# SPDX-License-Identifier: AGPL-3.0-or-later
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import os
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from pathlib import Path
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from concurrent.futures import ThreadPoolExecutor
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"""CANveyor dashboard entry point.
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Handles raw log ingestion, J1939 decoding, precomputation of
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statistical figures, and exposes a Dash application for browsing the
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processed data.
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"""
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import os
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from concurrent.futures import ThreadPoolExecutor
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from pathlib import Path
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from typing import Dict, List, Tuple
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import polars as pl
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import dash
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from dash import dcc, html, Input, Output, State
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import dash_bootstrap_components as dbc
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import numpy as np
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import pandas as pd
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import polars as pl
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from dash import dcc, html, Input, Output, State
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from parser import parse_log, parse_csv
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from decoder import decode_j1939_frames
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from stats.utils.extractor import load_data
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from stats.id_viewer import _format_can_id_vec, plot_bits
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from stats.frequency import calculate_frequency, plot_frequency
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from logs.view import get_logs_table_component, prepare_logs_data
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from parser import parse_csv, parse_log
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from stats.correlation import calculate_correlation, plot_correlation_heatmap
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from stats.entropy import calculate_byte_entropy, plot_entropy_heatmap
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from logs.view import get_logs_table_component, prepare_logs_data
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from stats.frequency import calculate_frequency, plot_frequency
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from stats.id_viewer import _format_can_id_vec, plot_bits
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from stats.utils.loader import load_data
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from vehicle import get_vehicle_module
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RAW_LOG_DIR = "data/logs"
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PAGE_SIZE = 25000
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CSV_DIR = "data/csv"
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PARQUET_DIR = "data/parquet"
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PAGE_SIZE = 25_000
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BYTE_COLS = [f"b{i}" for i in range(8)]
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BUS_OPTIONS = [
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{"label": "Bus 1", "value": "Bus 1"},
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{"label": "Bus 2", "value": "Bus 2"},
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]
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def parse_vehicle_from_filename(filename: str):
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DATA: Dict[str, Dict[str, pd.DataFrame]] = {}
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VEHICLE_META: Dict[str, Dict[str, str]] = {}
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PRECOMPUTED_FIGURES: Dict[str, object] = {}
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DATA_BY_ID: Dict[Tuple[str, str], Dict[str, Tuple[pd.DataFrame, List[str]]]] = {}
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CORR_CACHE: Dict[Tuple, object] = {}
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PREPARED_LOGS_CACHE: Dict[Tuple[str, str], pd.DataFrame] = {}
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def parse_vehicle_from_filename(filename: str) -> Tuple[str, str, str]:
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"""Derive (vehicle, brand, model) from a log file name."""
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stem = Path(filename).stem
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if '-' in stem:
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brand, model_part = stem.split('-', 1)
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if "-" in stem:
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brand, model_part = stem.split("-", 1)
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else:
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brand, model_part = stem, "Unknown"
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model = model_part.replace('_', ' ')
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model = model_part.replace("_", " ")
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vehicle = f"{brand} {model}".strip()
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return vehicle, brand, model
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def run_pipeline():
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os.makedirs(RAW_LOG_DIR, exist_ok=True)
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os.makedirs("data/csv", exist_ok=True)
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os.makedirs("data/parquet", exist_ok=True)
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def _vehicle_paths(vehicle: str) -> Dict[str, str]:
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"""Return all intermediate file paths for a given vehicle."""
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return {
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"bus1_csv": f"{CSV_DIR}/{vehicle}_bus1.csv",
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"bus2_csv": f"{CSV_DIR}/{vehicle}_bus2.csv",
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"bus1_parquet": f"{PARQUET_DIR}/{vehicle}_bus1.parquet",
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"bus2_parquet": f"{PARQUET_DIR}/{vehicle}_bus2.parquet",
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"bus1_decoded": f"{PARQUET_DIR}/{vehicle}_bus1_decoded.parquet",
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"bus2_decoded": f"{PARQUET_DIR}/{vehicle}_bus2_decoded.parquet",
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}
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def run_pipeline() -> None:
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"""Parse raw log files, convert to parquet, and decode J1939 frames."""
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for directory in (RAW_LOG_DIR, CSV_DIR, PARQUET_DIR):
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os.makedirs(directory, exist_ok=True)
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for log_file in Path(RAW_LOG_DIR).glob("*.txt"):
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vehicle, _, _ = parse_vehicle_from_filename(log_file.name)
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paths = _vehicle_paths(vehicle)
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if Path(paths["bus1_decoded"]).exists() and Path(paths["bus2_decoded"]).exists():
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continue
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print(f"Parsing raw log: {log_file.name}...")
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parse_log(str(log_file), paths["bus1_csv"], paths["bus2_csv"])
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print("Converting to parquet...")
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parse_csv(paths["bus1_csv"]).sink_parquet(paths["bus1_parquet"])
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parse_csv(paths["bus2_csv"]).sink_parquet(paths["bus2_parquet"])
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print("Decoding J1939...")
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df1 = pl.read_parquet(paths["bus1_parquet"])
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df2 = pl.read_parquet(paths["bus2_parquet"])
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decode_j1939_frames(df1).write_parquet(paths["bus1_decoded"])
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decode_j1939_frames(df2).write_parquet(paths["bus2_decoded"])
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def load_vehicle_data() -> None:
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"""Load all decoded parquet files into the in-memory DATA store."""
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for log_file in Path(RAW_LOG_DIR).glob("*.txt"):
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vehicle, brand, model = parse_vehicle_from_filename(log_file.name)
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VEHICLE_META[vehicle] = {"brand": brand, "model": model}
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bus1_csv = f"data/csv/{vehicle}_bus1.csv"
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bus2_csv = f"data/csv/{vehicle}_bus2.csv"
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bus1_parquet = f"data/parquet/{vehicle}_bus1.parquet"
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bus2_parquet = f"data/parquet/{vehicle}_bus2.parquet"
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bus1_decoded = f"data/parquet/{vehicle}_bus1_decoded.parquet"
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bus2_decoded = f"data/parquet/{vehicle}_bus2_decoded.parquet"
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paths = _vehicle_paths(vehicle)
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if Path(paths["bus1_decoded"]).exists() and Path(paths["bus2_decoded"]).exists():
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DATA[vehicle] = {
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"Bus 1": load_data(paths["bus1_decoded"]),
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"Bus 2": load_data(paths["bus2_decoded"]),
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}
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if not Path(bus1_decoded).exists() or not Path(bus2_decoded).exists():
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print(f"Parsing raw log: {log_file.name}...")
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parse_log(str(log_file), bus1_csv, bus2_csv)
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print("Converting to parquet...")
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parse_csv(bus1_csv).sink_parquet(bus1_parquet)
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parse_csv(bus2_csv).sink_parquet(bus2_parquet)
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def _can_id_column(df: pd.DataFrame) -> str:
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return "ID" if "ID" in df.columns else "Identifier"
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print("Decoding J1939...")
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df1 = pl.read_parquet(bus1_parquet)
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df2 = pl.read_parquet(bus2_parquet)
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dec1 = decode_j1939_frames(df1)
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dec2 = decode_j1939_frames(df2)
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dec1.write_parquet(bus1_decoded)
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dec2.write_parquet(bus2_decoded)
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run_pipeline()
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def _downsample_unchanged(group: pd.DataFrame, byte_cols: List[str]) -> pd.DataFrame:
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"""Keep only rows where at least one byte changed vs. the previous row."""
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if group.empty or not byte_cols:
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return group
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arr = group[byte_cols].to_numpy(dtype=np.float32, copy=False)
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if len(arr) <= 1:
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return group
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changed = np.any(arr[1:] != arr[:-1], axis=1)
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keep = np.concatenate(([True], changed))
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return group.iloc[keep]
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print("Loading data into memory...")
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DATA = {}
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VEHICLE_META = {}
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for log_file in Path(RAW_LOG_DIR).glob("*.txt"):
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vehicle, brand, model = parse_vehicle_from_filename(log_file.name)
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VEHICLE_META[vehicle] = {"brand": brand, "model": model}
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def process_bus_data(
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vehicle: str, bus: str, df: pd.DataFrame
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) -> Tuple[str, str, Dict[str, object], Dict[str, Tuple[pd.DataFrame, List[str]]]]:
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"""Compute per-bus figures and ID-grouped, downsampled frames."""
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precomp = {
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f"{vehicle}_{bus}_freq": plot_frequency(
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calculate_frequency(df), title=f"{vehicle} {bus} Frequency"
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),
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f"{vehicle}_{bus}_entropy": plot_entropy_heatmap(
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calculate_byte_entropy(df), title=f"{vehicle} {bus} Byte-Level Entropy"
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),
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}
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bus1_decoded = f"data/parquet/{vehicle}_bus1_decoded.parquet"
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bus2_decoded = f"data/parquet/{vehicle}_bus2_decoded.parquet"
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df = df.assign(Formatted_ID=_format_can_id_vec(df[_can_id_column(df)]))
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df = df.sort_values(["Formatted_ID", "Timestamp"], kind="stable")
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if Path(bus1_decoded).exists() and Path(bus2_decoded).exists():
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DATA[vehicle] = {
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"Bus 1": load_data(bus1_decoded),
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"Bus 2": load_data(bus2_decoded)
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}
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PRECOMPUTED_FIGURES = {}
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DATA_BY_ID = {}
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CORR_CACHE = {}
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PREPARED_LOGS_CACHE = {}
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def process_bus_data(vehicle, bus, df):
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precomp = {}
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precomp[f"{vehicle}_{bus}_freq"] = plot_frequency(calculate_frequency(df), title=f"{vehicle} {bus} Frequency")
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precomp[f"{vehicle}_{bus}_entropy"] = plot_entropy_heatmap(calculate_byte_entropy(df), title=f"{vehicle} {bus} Byte-Level Entropy")
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can_id_col = 'ID' if 'ID' in df.columns else 'Identifier'
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formatted = _format_can_id_vec(df[can_id_col])
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df = df.assign(Formatted_ID=formatted)
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df = df.sort_values(['Formatted_ID', 'Timestamp'], kind='stable')
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grouped = {}
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for can_id, group in df.groupby(by='Formatted_ID'):
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byte_cols = [f"b{i}" for i in range(8) if f"b{i}" in group.columns]
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if not group.empty and len(byte_cols) > 0:
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arr = group[byte_cols].to_numpy(dtype=np.float32, copy=False)
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if len(arr) > 1:
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changed = np.any(arr[1:] != arr[:-1], axis=1)
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keep = np.concatenate(([True], changed))
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group = group.iloc[keep]
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grouped: Dict[str, Tuple[pd.DataFrame, List[str]]] = {}
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for can_id, group in df.groupby(by="Formatted_ID"):
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byte_cols = [c for c in BYTE_COLS if c in group.columns]
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group = _downsample_unchanged(group, byte_cols)
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grouped[can_id] = (group, byte_cols)
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return vehicle, bus, precomp, grouped
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with ThreadPoolExecutor() as executor:
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futures = []
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for vehicle, buses in DATA.items():
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for bus, df in buses.items():
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futures.append(executor.submit(process_bus_data, vehicle, bus, df))
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for future in futures:
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v, b, precomp, grouped = future.result()
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PRECOMPUTED_FIGURES.update(precomp)
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DATA_BY_ID[(v, b)] = grouped
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def precompute_all() -> None:
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"""Run :func:`process_bus_data` across every vehicle/bus pair in parallel."""
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with ThreadPoolExecutor() as executor:
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futures = [
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executor.submit(process_bus_data, vehicle, bus, df)
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for vehicle, buses in DATA.items()
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for bus, df in buses.items()
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]
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for future in futures:
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v, b, precomp, grouped = future.result()
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PRECOMPUTED_FIGURES.update(precomp)
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DATA_BY_ID[(v, b)] = grouped
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run_pipeline()
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print("Loading data into memory...")
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load_vehicle_data()
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precompute_all()
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app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
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app.config.suppress_callback_exceptions = True
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app.layout = dbc.Container([
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html.H1("CANveyor", className="my-4"),
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dbc.Tabs([
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dbc.Tab(label="Overview", tab_id="overview", children=[
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html.Div(id="overview-content")
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]),
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dbc.Tab(label="Vehicles", tab_id="vehicles", children=[
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dbc.Row([
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dbc.Col(html.Label("Vehicle:", className="mt-2"), width="auto"),
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dbc.Col(dcc.Dropdown(
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id='vehicles-vehicle-selector',
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options=[{'label': v, 'value': v} for v in DATA.keys()],
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value=list(DATA.keys())[0] if DATA else None,
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clearable=False
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), width=3, className="me-4"),
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], className="mb-3 mt-3", align="end"),
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html.Div(id='vehicles-content', className="mt-3")
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]),
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dbc.Tab(label="Logs", tab_id="logs", children=[
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dbc.Row([
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dbc.Col(html.Label("Vehicle:", className="mt-2"), width="auto"),
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dbc.Col(dcc.Dropdown(
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id='logs-vehicle-selector',
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options=[{'label': v, 'value': v} for v in DATA.keys()],
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value=list(DATA.keys())[0] if DATA else None,
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clearable=False
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), width=3, className="me-4"),
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dbc.Col(html.Label("Bus:", className="mt-2"), width="auto"),
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dbc.Col(dcc.Dropdown(
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id='logs-bus-selector',
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options=[{'label': 'Bus 1', 'value': 'Bus 1'}, {'label': 'Bus 2', 'value': 'Bus 2'}],
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value='Bus 1',
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clearable=False
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), width=2),
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], className="mb-3 mt-3", align="end"),
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get_logs_table_component()
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]),
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dbc.Tab(label="Statistics", tab_id="statistics", children=[
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dbc.Row([
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dbc.Col(html.Label("Vehicle:", className="mt-2"), width="auto"),
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dbc.Col(dcc.Dropdown(
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id='vehicle-selector',
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options=[{'label': v, 'value': v} for v in DATA.keys()],
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value=list(DATA.keys())[0] if DATA else None,
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clearable=False
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), width=3, className="me-4"),
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dbc.Col(html.Label("Bus:", className="mt-2"), width="auto"),
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dbc.Col(dcc.Dropdown(
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id='bus-selector',
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options=[{'label': 'Bus 1', 'value': 'Bus 1'}, {'label': 'Bus 2', 'value': 'Bus 2'}],
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value='Bus 1',
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clearable=False
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), width=2),
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], className="mb-3 mt-3", align="end"),
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dbc.Tabs([
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dbc.Tab(label="Frequency", tab_id="freq"),
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dbc.Tab(label="ID Viewer", tab_id="id_viewer"),
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dbc.Tab(label="Correlation", tab_id="corr"),
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dbc.Tab(label="Entropy", tab_id="entropy"),
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], id="tabs", active_tab="freq"),
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html.Div(id="tab-content", className="mt-3")
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])
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], id="main-tabs", active_tab="statistics")
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], fluid=True)
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def get_prepared_logs(vehicle, bus):
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def _vehicle_dropdown(dropdown_id: str) -> dcc.Dropdown:
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return dcc.Dropdown(
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id=dropdown_id,
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options=[{"label": v, "value": v} for v in DATA.keys()],
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value=list(DATA.keys())[0] if DATA else None,
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clearable=False,
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)
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def _bus_dropdown(dropdown_id: str) -> dcc.Dropdown:
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return dcc.Dropdown(
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id=dropdown_id,
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options=BUS_OPTIONS,
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value="Bus 1",
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clearable=False,
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)
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def _label(text: str) -> html.Label:
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return html.Label(text, className="mt-2")
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app.layout = dbc.Container(
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[
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html.H1("CANveyor", className="my-4"),
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dbc.Tabs(
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[
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dbc.Tab(
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label="Overview",
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tab_id="overview",
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children=[html.Div(id="overview-content")],
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||||
),
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dbc.Tab(
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label="Vehicles",
|
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tab_id="vehicles",
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children=[
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dbc.Row(
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[
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dbc.Col(_label("Vehicle:"), width="auto"),
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||||
dbc.Col(
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||||
_vehicle_dropdown("vehicles-vehicle-selector"),
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||||
width=3, className="me-4",
|
||||
),
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],
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className="mb-3 mt-3", align="end",
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||||
),
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html.Div(id="vehicles-content", className="mt-3"),
|
||||
],
|
||||
),
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||||
dbc.Tab(
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||||
label="Logs",
|
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tab_id="logs",
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||||
children=[
|
||||
dbc.Row(
|
||||
[
|
||||
dbc.Col(_label("Vehicle:"), width="auto"),
|
||||
dbc.Col(
|
||||
_vehicle_dropdown("logs-vehicle-selector"),
|
||||
width=3, className="me-4",
|
||||
),
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dbc.Col(_label("Bus:"), width="auto"),
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||||
dbc.Col(
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||||
_bus_dropdown("logs-bus-selector"),
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||||
width=2,
|
||||
),
|
||||
],
|
||||
className="mb-3 mt-3", align="end",
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||||
),
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||||
get_logs_table_component(),
|
||||
],
|
||||
),
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dbc.Tab(
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||||
label="Statistics",
|
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tab_id="statistics",
|
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children=[
|
||||
dbc.Row(
|
||||
[
|
||||
dbc.Col(_label("Vehicle:"), width="auto"),
|
||||
dbc.Col(
|
||||
_vehicle_dropdown("vehicle-selector"),
|
||||
width=3, className="me-4",
|
||||
),
|
||||
dbc.Col(_label("Bus:"), width="auto"),
|
||||
dbc.Col(
|
||||
_bus_dropdown("bus-selector"),
|
||||
width=2,
|
||||
),
|
||||
],
|
||||
className="mb-3 mt-3", align="end",
|
||||
),
|
||||
dbc.Tabs(
|
||||
[
|
||||
dbc.Tab(label="Frequency", tab_id="freq"),
|
||||
dbc.Tab(label="ID Viewer", tab_id="id_viewer"),
|
||||
dbc.Tab(label="Correlation", tab_id="corr"),
|
||||
dbc.Tab(label="Entropy", tab_id="entropy"),
|
||||
],
|
||||
id="tabs",
|
||||
active_tab="freq",
|
||||
),
|
||||
html.Div(id="tab-content", className="mt-3"),
|
||||
],
|
||||
),
|
||||
],
|
||||
id="main-tabs",
|
||||
active_tab="statistics",
|
||||
),
|
||||
],
|
||||
fluid=True,
|
||||
)
|
||||
|
||||
|
||||
def get_prepared_logs(vehicle: str, bus: str) -> pd.DataFrame:
|
||||
"""Lazily prepare and cache log table data for a vehicle/bus pair."""
|
||||
cache_key = (vehicle, bus)
|
||||
if cache_key not in PREPARED_LOGS_CACHE:
|
||||
df = DATA[vehicle][bus]
|
||||
PREPARED_LOGS_CACHE[cache_key] = prepare_logs_data(df)
|
||||
PREPARED_LOGS_CACHE[cache_key] = prepare_logs_data(DATA[vehicle][bus])
|
||||
return PREPARED_LOGS_CACHE[cache_key]
|
||||
|
||||
def build_page_buttons(current_page: int, total_pages: int, max_buttons: int = 15) -> list:
|
||||
buttons: list = []
|
||||
|
||||
def build_page_buttons(
|
||||
current_page: int, total_pages: int, max_buttons: int = 15
|
||||
) -> List:
|
||||
"""Build the pagination button list with ellipses where appropriate."""
|
||||
buttons: List = []
|
||||
if total_pages <= 1:
|
||||
return buttons
|
||||
|
||||
@@ -212,17 +313,21 @@ def build_page_buttons(current_page: int, total_pages: int, max_buttons: int = 1
|
||||
|
||||
if start > 0:
|
||||
buttons.append(
|
||||
dbc.Button("1", id={'type': 'page-btn', 'index': 0}, color="secondary", outline=True, size="sm", className="me-1")
|
||||
dbc.Button(
|
||||
"1",
|
||||
id={"type": "page-btn", "index": 0},
|
||||
color="secondary", outline=True, size="sm", className="me-1",
|
||||
)
|
||||
)
|
||||
if start > 1:
|
||||
buttons.append(html.Span("…", className="mx-1 align-middle"))
|
||||
|
||||
for i in range(start, end):
|
||||
is_current = (i == current_page)
|
||||
is_current = i == current_page
|
||||
buttons.append(
|
||||
dbc.Button(
|
||||
str(i + 1),
|
||||
id={'type': 'page-btn', 'index': i},
|
||||
id={"type": "page-btn", "index": i},
|
||||
size="sm",
|
||||
color="primary" if is_current else "secondary",
|
||||
outline=not is_current,
|
||||
@@ -237,33 +342,33 @@ def build_page_buttons(current_page: int, total_pages: int, max_buttons: int = 1
|
||||
buttons.append(
|
||||
dbc.Button(
|
||||
str(total_pages),
|
||||
id={'type': 'page-btn', 'index': total_pages - 1},
|
||||
id={"type": "page-btn", "index": total_pages - 1},
|
||||
color="secondary", outline=True, size="sm", className="me-1",
|
||||
)
|
||||
)
|
||||
|
||||
return buttons
|
||||
|
||||
|
||||
@app.callback(
|
||||
Output('logs-table', 'data'),
|
||||
Output('logs-table', 'columns'),
|
||||
Output('logs-info-text', 'children'),
|
||||
Output('logs-page-nav', 'children'),
|
||||
Output('logs-current-page', 'data'),
|
||||
Input('logs-vehicle-selector', 'value'),
|
||||
Input('logs-bus-selector', 'value'),
|
||||
Input('logs-prev-btn', 'n_clicks'),
|
||||
Input('logs-next-btn', 'n_clicks'),
|
||||
Input({'type': 'page-btn', 'index': dash.ALL}, 'n_clicks'),
|
||||
State('logs-current-page', 'data'),
|
||||
Output("logs-table", "data"),
|
||||
Output("logs-table", "columns"),
|
||||
Output("logs-info-text", "children"),
|
||||
Output("logs-page-nav", "children"),
|
||||
Output("logs-current-page", "data"),
|
||||
Input("logs-vehicle-selector", "value"),
|
||||
Input("logs-bus-selector", "value"),
|
||||
Input("logs-prev-btn", "n_clicks"),
|
||||
Input("logs-next-btn", "n_clicks"),
|
||||
Input({"type": "page-btn", "index": dash.ALL}, "n_clicks"),
|
||||
State("logs-current-page", "data"),
|
||||
)
|
||||
def update_logs_table(vehicle, bus, prev_clicks, next_clicks, page_btn_clicks, current_page):
|
||||
if (not vehicle or not bus or vehicle not in DATA or bus not in DATA[vehicle]):
|
||||
if not vehicle or not bus or vehicle not in DATA or bus not in DATA[vehicle]:
|
||||
return [], [], "No data available", [], 0
|
||||
|
||||
prepared_df = get_prepared_logs(vehicle, bus)
|
||||
total_rows = len(prepared_df)
|
||||
|
||||
if total_rows == 0:
|
||||
return [], [], "No data available", [], 0
|
||||
|
||||
@@ -271,43 +376,37 @@ def update_logs_table(vehicle, bus, prev_clicks, next_clicks, page_btn_clicks, c
|
||||
|
||||
ctx = dash.callback_context
|
||||
triggered_id = ctx.triggered_id
|
||||
|
||||
current_page = current_page if current_page is not None else 0
|
||||
|
||||
if triggered_id in ('logs-vehicle-selector', 'logs-bus-selector'):
|
||||
if triggered_id in ("logs-vehicle-selector", "logs-bus-selector"):
|
||||
current_page = 0
|
||||
|
||||
elif triggered_id == 'logs-prev-btn':
|
||||
elif triggered_id == "logs-prev-btn":
|
||||
current_page = max(0, current_page - 1)
|
||||
|
||||
elif triggered_id == 'logs-next-btn':
|
||||
elif triggered_id == "logs-next-btn":
|
||||
current_page = current_page + 1
|
||||
|
||||
elif isinstance(triggered_id, dict) and triggered_id.get('type') == 'page-btn':
|
||||
if ctx.triggered and ctx.triggered[0]['value']:
|
||||
current_page = triggered_id['index']
|
||||
elif isinstance(triggered_id, dict) and triggered_id.get("type") == "page-btn":
|
||||
if ctx.triggered and ctx.triggered[0]["value"]:
|
||||
current_page = triggered_id["index"]
|
||||
|
||||
current_page = max(0, min(current_page, total_pages - 1))
|
||||
|
||||
start_idx = current_page * PAGE_SIZE
|
||||
end_idx = min(start_idx + PAGE_SIZE, total_rows)
|
||||
page_data = prepared_df.iloc[start_idx:end_idx].to_dict('records')
|
||||
|
||||
columns = [{"name": i, "id": i} for i in prepared_df.columns]
|
||||
|
||||
info_text = (f"Page {current_page + 1} of {total_pages} | "
|
||||
f"Showing rows {start_idx + 1:,}–{end_idx:,} "
|
||||
f"of {total_rows:,} total frames")
|
||||
|
||||
page_data = prepared_df.iloc[start_idx:end_idx].to_dict("records")
|
||||
columns = [{"name": c, "id": c} for c in prepared_df.columns]
|
||||
info_text = (
|
||||
f"Page {current_page + 1} of {total_pages} | "
|
||||
f"Showing rows {start_idx + 1:,}–{end_idx:,} "
|
||||
f"of {total_rows:,} total frames"
|
||||
)
|
||||
page_buttons = build_page_buttons(current_page, total_pages)
|
||||
|
||||
return page_data, columns, info_text, page_buttons, current_page
|
||||
|
||||
@app.callback(
|
||||
Output('tab-content', 'children'),
|
||||
Input('tabs', 'active_tab'),
|
||||
Input('vehicle-selector', 'value'),
|
||||
Input('bus-selector', 'value')
|
||||
Output("tab-content", "children"),
|
||||
Input("tabs", "active_tab"),
|
||||
Input("vehicle-selector", "value"),
|
||||
Input("bus-selector", "value"),
|
||||
)
|
||||
def render_content(tab, vehicle, bus):
|
||||
if not vehicle or not bus or vehicle not in DATA or bus not in DATA[vehicle]:
|
||||
@@ -315,59 +414,82 @@ def render_content(tab, vehicle, bus):
|
||||
|
||||
df = DATA[vehicle][bus]
|
||||
|
||||
if tab == 'freq':
|
||||
return dcc.Graph(figure=PRECOMPUTED_FIGURES[f"{vehicle}_{bus}_freq"], style={'height': '80vh'})
|
||||
if tab == "freq":
|
||||
return dcc.Graph(
|
||||
figure=PRECOMPUTED_FIGURES[f"{vehicle}_{bus}_freq"],
|
||||
style={"height": "80vh"},
|
||||
)
|
||||
|
||||
elif tab == 'id_viewer':
|
||||
if tab == "id_viewer":
|
||||
ids = sorted(DATA_BY_ID.get((vehicle, bus), {}).keys())
|
||||
return html.Div([
|
||||
html.Label("CAN ID:"),
|
||||
dcc.Dropdown(
|
||||
id='id-selector',
|
||||
options=[{'label': i, 'value': i} for i in ids],
|
||||
value=ids[0] if ids else None,
|
||||
clearable=False,
|
||||
style={'width': '50%', 'marginBottom': '10px'}
|
||||
),
|
||||
dcc.Graph(id='id-viewer-graph', style={'height': '70vh'})
|
||||
])
|
||||
return html.Div(
|
||||
[
|
||||
html.Label("CAN ID:"),
|
||||
dcc.Dropdown(
|
||||
id="id-selector",
|
||||
options=[{"label": i, "value": i} for i in ids],
|
||||
value=ids[0] if ids else None,
|
||||
clearable=False,
|
||||
style={"width": "50%", "marginBottom": "10px"},
|
||||
),
|
||||
dcc.Graph(id="id-viewer-graph", style={"height": "70vh"}),
|
||||
]
|
||||
)
|
||||
|
||||
elif tab == 'corr':
|
||||
if tab == "corr":
|
||||
ids = sorted(DATA_BY_ID.get((vehicle, bus), {}).keys())
|
||||
return html.Div([
|
||||
dbc.Row([
|
||||
dbc.Col(html.Label("Method:"), width=1, className="mt-2"),
|
||||
dbc.Col(dcc.Dropdown(
|
||||
id='corr-method',
|
||||
options=[{'label': 'Pearson', 'value': 'pearson'}, {'label': 'Spearman', 'value': 'spearman'}],
|
||||
value='pearson',
|
||||
clearable=False
|
||||
), width=2),
|
||||
dbc.Col(html.Label("Target ID:"), width=1, className="mt-2"),
|
||||
dbc.Col(dcc.Dropdown(
|
||||
id='corr-target',
|
||||
options=[{'label': 'All IDs (Max Corr)', 'value': 'all'}] + [{'label': i, 'value': i} for i in ids],
|
||||
value='all',
|
||||
clearable=True
|
||||
), width=4),
|
||||
], className="mb-3"),
|
||||
dcc.Graph(id='corr-graph', style={'height': '80vh'})
|
||||
])
|
||||
return html.Div(
|
||||
[
|
||||
dbc.Row(
|
||||
[
|
||||
dbc.Col(html.Label("Method:"), width=1, className="mt-2"),
|
||||
dbc.Col(
|
||||
dcc.Dropdown(
|
||||
id="corr-method",
|
||||
options=[
|
||||
{"label": "Pearson", "value": "pearson"},
|
||||
{"label": "Spearman", "value": "spearman"},
|
||||
],
|
||||
value="pearson",
|
||||
clearable=False,
|
||||
),
|
||||
width=2,
|
||||
),
|
||||
dbc.Col(html.Label("Target ID:"), width=1, className="mt-2"),
|
||||
dbc.Col(
|
||||
dcc.Dropdown(
|
||||
id="corr-target",
|
||||
options=[{"label": "All IDs (Max Corr)", "value": "all"}]
|
||||
+ [{"label": i, "value": i} for i in ids],
|
||||
value="all",
|
||||
clearable=True,
|
||||
),
|
||||
width=4,
|
||||
),
|
||||
],
|
||||
className="mb-3",
|
||||
),
|
||||
dcc.Graph(id="corr-graph", style={"height": "80vh"}),
|
||||
]
|
||||
)
|
||||
|
||||
elif tab == 'entropy':
|
||||
return dcc.Graph(figure=PRECOMPUTED_FIGURES[f"{vehicle}_{bus}_entropy"], style={'height': '80vh'})
|
||||
if tab == "entropy":
|
||||
return dcc.Graph(
|
||||
figure=PRECOMPUTED_FIGURES[f"{vehicle}_{bus}_entropy"],
|
||||
style={"height": "80vh"},
|
||||
)
|
||||
|
||||
return html.Div("Tab not found")
|
||||
|
||||
@app.callback(
|
||||
Output('id-viewer-graph', 'figure'),
|
||||
Input('id-selector', 'value'),
|
||||
Input('vehicle-selector', 'value'),
|
||||
Input('bus-selector', 'value'),
|
||||
Input('tabs', 'active_tab'),
|
||||
Output("id-viewer-graph", "figure"),
|
||||
Input("id-selector", "value"),
|
||||
Input("vehicle-selector", "value"),
|
||||
Input("bus-selector", "value"),
|
||||
Input("tabs", "active_tab"),
|
||||
)
|
||||
def update_id_viewer(selected_id, vehicle, bus, tab):
|
||||
if tab != 'id_viewer' or not selected_id or not vehicle or not bus:
|
||||
if tab != "id_viewer" or not selected_id or not vehicle or not bus:
|
||||
return dash.no_update
|
||||
|
||||
grouped_data = DATA_BY_ID.get((vehicle, bus), {})
|
||||
@@ -375,39 +497,41 @@ def update_id_viewer(selected_id, vehicle, bus, tab):
|
||||
return dash.no_update
|
||||
|
||||
filtered_df, byte_cols = grouped_data[selected_id]
|
||||
return plot_bits(filtered_df, byte_cols, selected_id, title=f"{vehicle} {bus} Byte Visualization")
|
||||
return plot_bits(
|
||||
filtered_df, byte_cols, selected_id,
|
||||
title=f"{vehicle} {bus} Byte Visualization",
|
||||
)
|
||||
|
||||
@app.callback(
|
||||
Output('corr-graph', 'figure'),
|
||||
Input('corr-method', 'value'),
|
||||
Input('corr-target', 'value'),
|
||||
Input('vehicle-selector', 'value'),
|
||||
Input('bus-selector', 'value'),
|
||||
Input('tabs', 'active_tab'),
|
||||
Output("corr-graph", "figure"),
|
||||
Input("corr-method", "value"),
|
||||
Input("corr-target", "value"),
|
||||
Input("vehicle-selector", "value"),
|
||||
Input("bus-selector", "value"),
|
||||
Input("tabs", "active_tab"),
|
||||
)
|
||||
def update_corr(method, target, vehicle, bus, tab):
|
||||
if tab != 'corr' or not vehicle or not bus:
|
||||
if tab != "corr" or not vehicle or not bus:
|
||||
return dash.no_update
|
||||
|
||||
target_id = None if target == 'all' or not target else target
|
||||
target_id = None if target == "all" or not target else target
|
||||
cache_key = (vehicle, bus, method, target_id)
|
||||
|
||||
if cache_key not in CORR_CACHE:
|
||||
df = DATA[vehicle][bus]
|
||||
corr_df = calculate_correlation(df, method=method, target_id=target_id)
|
||||
CORR_CACHE[cache_key] = corr_df
|
||||
else:
|
||||
corr_df = CORR_CACHE[cache_key]
|
||||
CORR_CACHE[cache_key] = calculate_correlation(
|
||||
df, method=method, target_id=target_id
|
||||
)
|
||||
corr_df = CORR_CACHE[cache_key]
|
||||
|
||||
title = f"{vehicle} {bus} Correlation"
|
||||
if target_id:
|
||||
title += f" ({target_id})"
|
||||
|
||||
return plot_correlation_heatmap(corr_df, target_id=target_id, title=title)
|
||||
|
||||
@app.callback(
|
||||
Output('vehicles-content', 'children'),
|
||||
Input('vehicles-vehicle-selector', 'value')
|
||||
Output("vehicles-content", "children"),
|
||||
Input("vehicles-vehicle-selector", "value"),
|
||||
)
|
||||
def render_vehicles(vehicle):
|
||||
if not vehicle or vehicle not in DATA:
|
||||
@@ -416,54 +540,58 @@ def render_vehicles(vehicle):
|
||||
brand = VEHICLE_META.get(vehicle, {}).get("brand", "")
|
||||
vehicle_module = get_vehicle_module(brand)
|
||||
|
||||
dfs = []
|
||||
for bus_df in DATA[vehicle].values():
|
||||
dfs.append(bus_df)
|
||||
|
||||
dfs = list(DATA[vehicle].values())
|
||||
if not dfs:
|
||||
return html.Div("No data available", className="text-muted")
|
||||
|
||||
df = pd.concat(dfs, ignore_index=True)
|
||||
|
||||
if 'Timestamp' in df.columns:
|
||||
df = df.sort_values('Timestamp', kind='stable').reset_index(drop=True)
|
||||
if "Timestamp" in df.columns:
|
||||
df = df.sort_values("Timestamp", kind="stable").reset_index(drop=True)
|
||||
|
||||
cards = []
|
||||
|
||||
for nid, frame_def in vehicle_module.DECODER_RULES.items():
|
||||
decoded = vehicle_module.decode_dataframe(df, frame_def.can_id)
|
||||
|
||||
for item in frame_def.signals:
|
||||
if hasattr(item, 'plot_func') and callable(item.plot_func):
|
||||
if hasattr(item, "plot_func") and callable(item.plot_func):
|
||||
title = f"{frame_def.can_id} - {item.name}"
|
||||
fig = item.plot_func(decoded, frame_def.color)
|
||||
else:
|
||||
sig = item
|
||||
if getattr(sig, 'skip_plot', False):
|
||||
if getattr(sig, "skip_plot", False):
|
||||
continue
|
||||
|
||||
unit_str = f" ({sig.unit})" if sig.unit else ""
|
||||
title = f"{frame_def.can_id} - {sig.name}{unit_str}"
|
||||
fig = vehicle_module.plot_signal(decoded, sig.name, title=title, color=frame_def.color)
|
||||
|
||||
card = dbc.Card([
|
||||
dbc.CardBody([
|
||||
dcc.Graph(figure=fig, config={'displayModeBar': False},
|
||||
style={'height': '280px'})
|
||||
], className="p-2"),
|
||||
], className="shadow-sm border-0 h-100")
|
||||
fig = vehicle_module.plot_signal(
|
||||
decoded, sig.name, title=title, color=frame_def.color
|
||||
)
|
||||
|
||||
cards.append(
|
||||
dbc.Col(card, xs=12, sm=6, md=4, lg=3, className="mb-3")
|
||||
dbc.Col(
|
||||
dbc.Card(
|
||||
[
|
||||
dbc.CardBody(
|
||||
[
|
||||
dcc.Graph(
|
||||
figure=fig,
|
||||
config={"displayModeBar": False},
|
||||
style={"height": "280px"},
|
||||
)
|
||||
],
|
||||
className="p-2",
|
||||
),
|
||||
],
|
||||
className="shadow-sm border-0 h-100",
|
||||
),
|
||||
xs=12, sm=6, md=4, lg=3, className="mb-3",
|
||||
)
|
||||
)
|
||||
|
||||
if not cards:
|
||||
return html.Div(
|
||||
"No decoded signals available. Add rules in the vehicle module.",
|
||||
className="text-muted"
|
||||
className="text-muted",
|
||||
)
|
||||
|
||||
return dbc.Row(cards)
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
app.run(debug=False)
|
||||
|
||||
+9
-10
@@ -1,25 +1,23 @@
|
||||
# File: correlation.py
|
||||
# File: stats/correlation.py
|
||||
# Copyright (C) 2026 Erick Ahmed
|
||||
# SPDX-License-Identifier: AGPL-3.0-or-later
|
||||
|
||||
"""CAN bus inter-byte correlation analyzer and plotter."""
|
||||
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from typing import List
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import plotly.graph_objects as go
|
||||
|
||||
from stats.utils.extractor import load_data
|
||||
from stats.utils.extractor import to_int
|
||||
from stats.utils.converter import format_can_id_vec as _format_can_id_vec, to_int
|
||||
from stats.utils.loader import load_data
|
||||
|
||||
def _format_can_id_vec(s: pd.Series) -> pd.Series:
|
||||
s = s.astype('string').str.strip()
|
||||
s = s.str.replace(r'^0x', '', case=False, regex=True)
|
||||
s = s.str.upper()
|
||||
return s.fillna('UNKNOWN').replace('', 'UNKNOWN')
|
||||
|
||||
def _ensure_int_bytes(df: pd.DataFrame, cols: list) -> pd.DataFrame:
|
||||
def _ensure_int_bytes(df: pd.DataFrame, cols: List[str]) -> pd.DataFrame:
|
||||
needs = [c for c in cols if not pd.api.types.is_numeric_dtype(df[c])]
|
||||
if needs:
|
||||
df = df.copy()
|
||||
@@ -27,6 +25,7 @@ def _ensure_int_bytes(df: pd.DataFrame, cols: list) -> pd.DataFrame:
|
||||
df[c] = df[c].apply(to_int)
|
||||
return df
|
||||
|
||||
|
||||
def calculate_correlation(df: pd.DataFrame, method: str, target_id: str | None = None) -> pd.DataFrame:
|
||||
available_cols = [f"b{i}" for i in range(8) if f"b{i}" in df.columns]
|
||||
|
||||
@@ -70,7 +69,7 @@ def calculate_correlation(df: pd.DataFrame, method: str, target_id: str | None =
|
||||
else:
|
||||
groups = []
|
||||
|
||||
def _process_group(sub):
|
||||
def _process_group(sub: np.ndarray) -> np.ndarray:
|
||||
mask = ~np.isnan(sub).any(axis=1)
|
||||
sub = sub[mask]
|
||||
if len(sub) > 1:
|
||||
|
||||
+8
-9
@@ -1,23 +1,21 @@
|
||||
# File: entropy.py
|
||||
# File: stats/entropy.py
|
||||
# Copyright (C) 2026 Erick Ahmed
|
||||
# SPDX-License-Identifier: AGPL-3.0-or-later
|
||||
|
||||
"""CAN bus byte-level entropy analyzer and plotter."""
|
||||
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from typing import List
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import plotly.graph_objects as go
|
||||
|
||||
from stats.utils.extractor import load_data
|
||||
from stats.utils.extractor import to_int
|
||||
from stats.utils.converter import format_can_id_vec as _format_can_id_vec, to_int
|
||||
from stats.utils.loader import load_data
|
||||
|
||||
def _format_can_id_vec(s: pd.Series) -> pd.Series:
|
||||
s = s.astype('string').str.strip()
|
||||
s = s.str.replace(r'^0x', '', case=False, regex=True)
|
||||
s = s.str.upper()
|
||||
return s.fillna('UNKNOWN').replace('', 'UNKNOWN')
|
||||
|
||||
def _entropy_col(a: np.ndarray) -> float:
|
||||
a = a[~np.isnan(a)]
|
||||
@@ -36,6 +34,7 @@ def _entropy_col(a: np.ndarray) -> float:
|
||||
p = counts / counts.sum()
|
||||
return float(-np.sum(p * np.log2(p)))
|
||||
|
||||
|
||||
def calculate_byte_entropy(df: pd.DataFrame) -> pd.DataFrame:
|
||||
available_cols = [f"b{i}" for i in range(8) if f"b{i}" in df.columns]
|
||||
if not available_cols:
|
||||
@@ -64,7 +63,7 @@ def calculate_byte_entropy(df: pd.DataFrame) -> pd.DataFrame:
|
||||
else:
|
||||
groups = []
|
||||
|
||||
def _process_group(sub):
|
||||
def _process_group(sub: np.ndarray) -> np.ndarray:
|
||||
res = np.zeros(n_cols, dtype=np.float64)
|
||||
for ci in range(n_cols):
|
||||
res[ci] = _entropy_col(sub[:, ci])
|
||||
|
||||
+9
-7
@@ -1,19 +1,19 @@
|
||||
# File: frequency.py
|
||||
# File: stats/frequency.py
|
||||
# Copyright (C) 2026 Erick Ahmed
|
||||
# SPDX-License-Identifier: AGPL-3.0-or-later
|
||||
|
||||
"""CAN bus message frequency analyzer and plotter."""
|
||||
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import plotly.graph_objects as go
|
||||
from stats.utils.extractor import load_data
|
||||
|
||||
def _format_can_id_vec(s: pd.Series) -> pd.Series:
|
||||
s = s.astype('string').str.strip()
|
||||
s = s.str.replace(r'^0x', '', case=False, regex=True)
|
||||
s = s.str.upper()
|
||||
return s.fillna('UNKNOWN').replace('', 'UNKNOWN')
|
||||
from stats.utils.converter import format_can_id_vec as _format_can_id_vec
|
||||
from stats.utils.loader import load_data
|
||||
|
||||
|
||||
def calculate_frequency(df: pd.DataFrame) -> pd.DataFrame:
|
||||
can_id_col = 'ID' if 'ID' in df.columns else 'Identifier'
|
||||
@@ -28,6 +28,7 @@ def calculate_frequency(df: pd.DataFrame) -> pd.DataFrame:
|
||||
freq_df['Percentage'] = np.round(freq_df['Count'] / total * 100, 2) if total else 0.0
|
||||
return freq_df.sort_values('Count', ascending=True).reset_index(drop=True)
|
||||
|
||||
|
||||
def plot_frequency(stats_df: pd.DataFrame, title: str) -> go.Figure:
|
||||
n = len(stats_df)
|
||||
fig = go.Figure(go.Bar(
|
||||
@@ -106,6 +107,7 @@ def plot_frequency(stats_df: pd.DataFrame, title: str) -> go.Figure:
|
||||
)
|
||||
return fig
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="Analyze CAN bus message frequency")
|
||||
parser.add_argument("input", type=Path, help="Path to the input CAN log file")
|
||||
|
||||
+22
-14
@@ -1,25 +1,30 @@
|
||||
# File: id_viewer.py
|
||||
# File: stats/id_viewer.py
|
||||
# Copyright (C) 2026 Erick Ahmed
|
||||
# SPDX-License-Identifier: AGPL-3.0-or-later
|
||||
|
||||
"""Interactive CAN bus byte-change visualizer."""
|
||||
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
from typing import List, Tuple
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import plotly.graph_objects as go
|
||||
from plotly_resampler import FigureResampler
|
||||
from stats.utils.extractor import load_data
|
||||
|
||||
def _format_can_id_vec(s: pd.Series) -> pd.Series:
|
||||
s = s.astype('string').str.strip()
|
||||
s = s.str.replace(r'^0x', '', case=False, regex=True)
|
||||
s = s.str.upper()
|
||||
return s.fillna('UNKNOWN').replace('', 'UNKNOWN')
|
||||
from stats.utils.converter import format_can_id_vec as _format_can_id_vec
|
||||
from stats.utils.loader import load_data
|
||||
|
||||
def prepare_data(df, target_id):
|
||||
_BYTE_COLORS = [
|
||||
'#e41a1c', '#377eb8', '#4daf4a', '#984ea3',
|
||||
'#ff7f00', '#ffff33', '#a65628', '#f781bf',
|
||||
]
|
||||
|
||||
|
||||
def prepare_data(df: pd.DataFrame, target_id: str) -> Tuple[pd.DataFrame, List[str]]:
|
||||
can_id_col = 'ID' if 'ID' in df.columns else 'Identifier'
|
||||
formatted = _format_can_id_vec(df[can_id_col])
|
||||
df = df.assign(Formatted_ID=formatted)
|
||||
df = df.assign(Formatted_ID=_format_can_id_vec(df[can_id_col]))
|
||||
target_id_clean = _format_can_id_vec(pd.Series([target_id])).iloc[0]
|
||||
filtered = df[df['Formatted_ID'] == target_id_clean]
|
||||
|
||||
@@ -29,7 +34,9 @@ def prepare_data(df, target_id):
|
||||
|
||||
for col in byte_cols:
|
||||
if not pd.api.types.is_numeric_dtype(filtered[col]):
|
||||
filtered = filtered.assign(**{col: pd.to_numeric(filtered[col], errors='coerce').astype('float32')})
|
||||
filtered = filtered.assign(
|
||||
**{col: pd.to_numeric(filtered[col], errors='coerce').astype('float32')}
|
||||
)
|
||||
|
||||
filtered = filtered.sort_values('Timestamp', kind='stable')
|
||||
arr = filtered[byte_cols].to_numpy(dtype=np.float32, copy=False)
|
||||
@@ -40,12 +47,12 @@ def prepare_data(df, target_id):
|
||||
|
||||
return filtered, byte_cols
|
||||
|
||||
def plot_bits(df, byte_cols, can_id, title):
|
||||
|
||||
def plot_bits(df: pd.DataFrame, byte_cols: List[str], can_id: str, title: str) -> FigureResampler:
|
||||
fig = FigureResampler(
|
||||
resampled_trace_prefix_suffix=("", ""),
|
||||
show_mean_aggregation_size=False
|
||||
)
|
||||
colors = ['#e41a1c', '#377eb8', '#4daf4a', '#984ea3', '#ff7f00', '#ffff33', '#a65628', '#f781bf']
|
||||
n = len(byte_cols)
|
||||
|
||||
x = df['Timestamp'].to_numpy() if not df.empty else np.array([])
|
||||
@@ -54,7 +61,7 @@ def plot_bits(df, byte_cols, can_id, title):
|
||||
|
||||
fig.add_trace(go.Scatter(
|
||||
mode='lines',
|
||||
line=dict(shape='hv', width=2, color=colors[i % len(colors)]),
|
||||
line=dict(shape='hv', width=2, color=_BYTE_COLORS[i % len(_BYTE_COLORS)]),
|
||||
name=col.upper(),
|
||||
legendgroup=col.upper(),
|
||||
hovertemplate=f"<b>{col.upper()}</b><br>Time: %{{x}}<br>Value: %{{y}}<extra></extra>",
|
||||
@@ -121,6 +128,7 @@ def plot_bits(df, byte_cols, can_id, title):
|
||||
)
|
||||
return fig
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="Visualize CAN bus byte changes over time")
|
||||
parser.add_argument("input", type=Path, help="Path to the input CAN log file")
|
||||
|
||||
@@ -1,63 +0,0 @@
|
||||
# File: extractor.py
|
||||
# Copyright (C) 2026 Erick Ahmed
|
||||
# SPDX-License-Identifier: AGPL-3.0-or-later
|
||||
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import polars as pl
|
||||
|
||||
def to_int(x):
|
||||
if isinstance(x, (int, np.integer)):
|
||||
return int(x)
|
||||
if isinstance(x, str):
|
||||
try:
|
||||
return int(x, 16)
|
||||
except ValueError:
|
||||
return np.nan
|
||||
return np.nan
|
||||
|
||||
def extract_id(row: pd.Series) -> str:
|
||||
meta = row.get('j1939_metadata')
|
||||
if pd.isna(meta):
|
||||
return f"ID: {row['ID']}"
|
||||
if isinstance(meta, str):
|
||||
try:
|
||||
meta = json.loads(meta)
|
||||
except json.JSONDecodeError:
|
||||
return f"ID: {row['ID']}"
|
||||
if isinstance(meta, dict) and 'PGN' in meta:
|
||||
return f"PGN: {meta['PGN']}"
|
||||
return f"ID: {row['ID']}"
|
||||
|
||||
def load_data(file_path: Path) -> pd.DataFrame:
|
||||
lf = pl.scan_parquet(file_path)
|
||||
schema = lf.collect_schema()
|
||||
names = schema.names()
|
||||
|
||||
byte_cols = [f"b{i}" for i in range(8) if f"b{i}" in names]
|
||||
if byte_cols:
|
||||
lf = lf.with_columns([
|
||||
pl.col(c).str.to_integer(base=16, strict=False).cast(pl.Int16).alias(c)
|
||||
for c in byte_cols
|
||||
])
|
||||
|
||||
id_col = 'ID' if 'ID' in names else 'Identifier'
|
||||
id_expr = pl.col(id_col).cast(pl.Utf8)
|
||||
|
||||
if 'j1939_metadata' in names:
|
||||
try:
|
||||
lf = lf.with_columns(
|
||||
pl.when(pl.col('j1939_metadata').is_not_null())
|
||||
.then(pl.lit('PGN: ') + pl.col('j1939_metadata').struct.field('PGN').cast(pl.Utf8))
|
||||
.otherwise(pl.lit('ID: ') + id_expr)
|
||||
.alias('Identifier')
|
||||
)
|
||||
except Exception:
|
||||
lf = lf.with_columns((pl.lit('ID: ') + id_expr).alias('Identifier'))
|
||||
else:
|
||||
lf = lf.with_columns((pl.lit('ID: ') + id_expr).alias('Identifier'))
|
||||
|
||||
return lf.collect().to_pandas()
|
||||
+77
-30
@@ -2,14 +2,20 @@
|
||||
# Copyright (C) 2026 Erick Ahmed
|
||||
# SPDX-License-Identifier: AGPL-3.0-or-later
|
||||
|
||||
"""Core data structures and helpers for J1939/CAN signal decoding."""
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
from typing import List
|
||||
from typing import Dict, List
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import plotly.graph_objects as go
|
||||
|
||||
|
||||
@dataclass
|
||||
class SignalDef:
|
||||
"""Definition of a single signal within a CAN frame."""
|
||||
|
||||
name: str
|
||||
bit_start: int
|
||||
bit_length: int
|
||||
@@ -19,27 +25,38 @@ class SignalDef:
|
||||
byte_order: str = "little"
|
||||
unit: str = ""
|
||||
|
||||
|
||||
@dataclass
|
||||
class FrameDef:
|
||||
"""Definition of a CAN frame and its contained signals."""
|
||||
|
||||
can_id: str
|
||||
description: str = ""
|
||||
color: str = "#377eb8"
|
||||
signals: List[SignalDef] = field(default_factory=list)
|
||||
|
||||
|
||||
def normalize_id(can_id: str) -> str:
|
||||
"""Normalize a CAN ID string to uppercase hex without leading zeros/0x."""
|
||||
s = str(can_id).strip().upper()
|
||||
if s.startswith("0X"):
|
||||
s = s[2:]
|
||||
return s.lstrip("0") or "0"
|
||||
|
||||
|
||||
def _byte_indices(sig: SignalDef) -> List[int]:
|
||||
"""Return the in-range byte positions spanned by *sig*."""
|
||||
byte_lo = sig.bit_start // 8
|
||||
byte_hi = (sig.bit_start + sig.bit_length - 1) // 8
|
||||
return [i for i in range(byte_lo, byte_hi + 1) if 0 <= i < 8]
|
||||
|
||||
|
||||
def _extract_signal(bytes_arr: np.ndarray, sig: SignalDef) -> np.ndarray:
|
||||
"""Extract raw signal values from an (N, 8) byte array and apply scaling."""
|
||||
if bytes_arr.size == 0:
|
||||
return np.zeros(0, dtype=np.float64)
|
||||
|
||||
byte_lo = sig.bit_start // 8
|
||||
byte_hi = (sig.bit_start + sig.bit_length - 1) // 8
|
||||
byte_indices = [i for i in range(byte_lo, byte_hi + 1) if 0 <= i < 8]
|
||||
|
||||
byte_indices = _byte_indices(sig)
|
||||
if not byte_indices:
|
||||
return np.full(bytes_arr.shape[0], np.nan, dtype=np.float64)
|
||||
|
||||
@@ -51,11 +68,8 @@ def _extract_signal(bytes_arr: np.ndarray, sig: SignalDef) -> np.ndarray:
|
||||
for shift, bi in enumerate(reversed(byte_indices)):
|
||||
raw += bytes_arr[:, bi].astype(np.int64) << (shift * 8)
|
||||
|
||||
intra_byte_shift = sig.bit_start % 8
|
||||
raw = raw >> intra_byte_shift
|
||||
|
||||
mask = (1 << sig.bit_length) - 1
|
||||
raw = raw & mask
|
||||
raw = raw >> (sig.bit_start % 8)
|
||||
raw = raw & ((1 << sig.bit_length) - 1)
|
||||
|
||||
if sig.is_signed and sig.bit_length < 64:
|
||||
sign_bit = 1 << (sig.bit_length - 1)
|
||||
@@ -63,7 +77,11 @@ def _extract_signal(bytes_arr: np.ndarray, sig: SignalDef) -> np.ndarray:
|
||||
|
||||
return raw.astype(np.float64) * sig.factor + sig.offset
|
||||
|
||||
def decode_dataframe(df: pd.DataFrame, can_id: str, decoder_rules: dict) -> pd.DataFrame:
|
||||
|
||||
def decode_dataframe(
|
||||
df: pd.DataFrame, can_id: str, decoder_rules: Dict[str, FrameDef]
|
||||
) -> pd.DataFrame:
|
||||
"""Decode all signals for *can_id* from *df* into a new DataFrame."""
|
||||
norm = normalize_id(can_id)
|
||||
if norm not in decoder_rules:
|
||||
return pd.DataFrame()
|
||||
@@ -72,8 +90,7 @@ def decode_dataframe(df: pd.DataFrame, can_id: str, decoder_rules: dict) -> pd.D
|
||||
|
||||
id_col = "ID" if "ID" in df.columns else "Identifier"
|
||||
df_ids = df[id_col].astype(str).map(normalize_id)
|
||||
mask = df_ids == norm
|
||||
sub = df.loc[mask].copy()
|
||||
sub = df.loc[df_ids == norm].copy()
|
||||
if sub.empty:
|
||||
return pd.DataFrame()
|
||||
|
||||
@@ -83,41 +100,69 @@ def decode_dataframe(df: pd.DataFrame, can_id: str, decoder_rules: dict) -> pd.D
|
||||
|
||||
arr = np.zeros((len(sub), 8), dtype=np.int64)
|
||||
for i, c in enumerate(byte_cols):
|
||||
arr[:, i] = pd.to_numeric(sub[c], errors="coerce").fillna(0).astype(np.int64).to_numpy()
|
||||
arr[:, i] = (
|
||||
pd.to_numeric(sub[c], errors="coerce")
|
||||
.fillna(0)
|
||||
.astype(np.int64)
|
||||
.to_numpy()
|
||||
)
|
||||
|
||||
out = pd.DataFrame()
|
||||
out["Timestamp"] = sub["Timestamp"].to_numpy() if "Timestamp" in sub.columns else np.arange(len(sub))
|
||||
out["Timestamp"] = (
|
||||
sub["Timestamp"].to_numpy()
|
||||
if "Timestamp" in sub.columns
|
||||
else np.arange(len(sub))
|
||||
)
|
||||
|
||||
for sig in frame_def.signals:
|
||||
out[sig.name] = _extract_signal(arr, sig)
|
||||
|
||||
return out
|
||||
|
||||
def plot_signal(df: pd.DataFrame, signal_name: str, title: str, color: str = "#377eb8", height: int = 280) -> go.Figure:
|
||||
|
||||
def plot_signal(
|
||||
df: pd.DataFrame,
|
||||
signal_name: str,
|
||||
title: str,
|
||||
color: str = "#377eb8",
|
||||
height: int = 280,
|
||||
) -> go.Figure:
|
||||
"""Plot a single signal over time as a line chart."""
|
||||
fig = go.Figure()
|
||||
|
||||
if df.empty or signal_name not in df.columns:
|
||||
fig.update_layout(
|
||||
title=dict(text=title, font=dict(size=14)),
|
||||
annotations=[dict(text="No data", showarrow=False, x=0.5, y=0.5,
|
||||
font=dict(size=13, color="#888"))],
|
||||
annotations=[
|
||||
dict(
|
||||
text="No data", showarrow=False, x=0.5, y=0.5,
|
||||
font=dict(size=13, color="#888"),
|
||||
)
|
||||
],
|
||||
height=height,
|
||||
template="plotly_white",
|
||||
)
|
||||
return fig
|
||||
|
||||
fig.add_trace(go.Scatter(
|
||||
x=df["Timestamp"],
|
||||
y=df[signal_name],
|
||||
mode="lines",
|
||||
line=dict(width=2, color=color),
|
||||
name=signal_name,
|
||||
hovertemplate=f"<b>{signal_name}</b><br>Time: %{{x}}<br>Value: %{{y:.2f}}<extra></extra>",
|
||||
))
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=df["Timestamp"],
|
||||
y=df[signal_name],
|
||||
mode="lines",
|
||||
line=dict(width=2, color=color),
|
||||
name=signal_name,
|
||||
hovertemplate=(
|
||||
f"<b>{signal_name}</b><br>Time: %{{x}}<br>"
|
||||
f"Value: %{{y:.2f}}<extra></extra>"
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
fig.update_layout(
|
||||
title=dict(text=title, font=dict(size=14, color="#1a1a1a"),
|
||||
x=0.5, xanchor="center", pad=dict(b=10)),
|
||||
title=dict(
|
||||
text=title, font=dict(size=14, color="#1a1a1a"),
|
||||
x=0.5, xanchor="center", pad=dict(b=10),
|
||||
),
|
||||
height=height,
|
||||
autosize=True,
|
||||
template="plotly_white",
|
||||
@@ -133,7 +178,9 @@ def plot_signal(df: pd.DataFrame, signal_name: str, title: str, color: str = "#3
|
||||
zeroline=False, linecolor="#bdbdbd",
|
||||
),
|
||||
font=dict(family="Segoe UI, Arial, sans-serif", size=11, color="#2a2a2a"),
|
||||
hoverlabel=dict(bgcolor="white", font_size=12,
|
||||
font_family="Segoe UI", bordercolor="#cccccc"),
|
||||
hoverlabel=dict(
|
||||
bgcolor="white", font_size=12,
|
||||
font_family="Segoe UI", bordercolor="#cccccc",
|
||||
),
|
||||
)
|
||||
return fig
|
||||
|
||||
+45
-30
@@ -2,17 +2,28 @@
|
||||
# Copyright (C) 2026 Erick Ahmed
|
||||
# SPDX-License-Identifier: AGPL-3.0-or-later
|
||||
|
||||
import plotly.express as px
|
||||
import pandas as pd
|
||||
from dataclasses import dataclass
|
||||
from typing import Callable
|
||||
"""Komatsu-specific CAN frame decoder rules and custom plot definitions."""
|
||||
|
||||
from copy import deepcopy
|
||||
from dataclasses import dataclass
|
||||
from typing import Callable, Dict
|
||||
|
||||
import pandas as pd
|
||||
import plotly.express as px
|
||||
|
||||
from vehicle.base import (
|
||||
SignalDef, FrameDef, normalize_id, decode_dataframe as _decode_dataframe, plot_signal
|
||||
FrameDef,
|
||||
SignalDef,
|
||||
decode_dataframe as _decode_dataframe,
|
||||
normalize_id,
|
||||
plot_signal,
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class CustomPlotDef:
|
||||
"""A non-signal entry in a FrameDef that carries its own plotting function."""
|
||||
|
||||
name: str
|
||||
plot_func: Callable
|
||||
|
||||
@@ -28,7 +39,7 @@ load_state_sig = SignalDef(
|
||||
)
|
||||
load_state_sig.skip_plot = True
|
||||
|
||||
DECODER_RULES = {
|
||||
DECODER_RULES: Dict[str, FrameDef] = {
|
||||
normalize_id("0x011F"): FrameDef(
|
||||
can_id="0x011F",
|
||||
description="Engine ECM Main Broadcast",
|
||||
@@ -84,7 +95,7 @@ DECODER_RULES = {
|
||||
load_state_sig,
|
||||
CustomPlotDef(
|
||||
name="Engine Load State",
|
||||
plot_func=lambda decoded, color: plot_load_state_pie(decoded, color)
|
||||
plot_func=lambda decoded, color: plot_load_state_pie(decoded, color),
|
||||
),
|
||||
],
|
||||
),
|
||||
@@ -93,54 +104,58 @@ DECODER_RULES = {
|
||||
LOAD_STATE_MAP = {
|
||||
0: "Boot up",
|
||||
16: "Normal load",
|
||||
32: "High load"
|
||||
32: "High load",
|
||||
}
|
||||
|
||||
_LOAD_STATE_COLORS = {
|
||||
"Boot up": "#ff9900",
|
||||
"Normal load": "#00cc00",
|
||||
"High load": "#cc0000",
|
||||
"Unknown": "#808080",
|
||||
}
|
||||
|
||||
|
||||
def plot_load_state_pie(decoded_df, color):
|
||||
"""Render a pie chart showing the distribution of engine load states."""
|
||||
if decoded_df is None or decoded_df.empty or "Engine Load State" not in decoded_df.columns:
|
||||
return px.pie(title="No data for Engine Load State")
|
||||
|
||||
states = pd.to_numeric(decoded_df["Engine Load State"], errors='coerce').dropna().astype(int)
|
||||
states = pd.to_numeric(decoded_df["Engine Load State"], errors="coerce").dropna().astype(int)
|
||||
|
||||
labels = states.map(LOAD_STATE_MAP).fillna("Unknown")
|
||||
counts = labels.value_counts().reset_index()
|
||||
counts.columns = ['State', 'Count']
|
||||
counts.columns = ["State", "Count"]
|
||||
|
||||
total = counts['Count'].sum()
|
||||
counts['Percentage'] = (counts['Count'] / total * 100).round(1)
|
||||
counts['Legend'] = counts['State'] + " (" + counts['Percentage'].astype(str) + "%)"
|
||||
total = counts["Count"].sum()
|
||||
counts["Percentage"] = (counts["Count"] / total * 100).round(1)
|
||||
counts["Legend"] = counts["State"] + " (" + counts["Percentage"].astype(str) + "%)"
|
||||
|
||||
fig = px.pie(
|
||||
counts,
|
||||
values='Count',
|
||||
names='Legend',
|
||||
color='State',
|
||||
title='Engine Load State Distribution',
|
||||
color_discrete_map={
|
||||
"Boot up": "#ff9900",
|
||||
"Normal load": "#00cc00",
|
||||
"High load": "#cc0000",
|
||||
"Unknown": "#808080"
|
||||
}
|
||||
values="Count",
|
||||
names="Legend",
|
||||
color="State",
|
||||
title="Engine Load State Distribution",
|
||||
color_discrete_map=_LOAD_STATE_COLORS,
|
||||
)
|
||||
|
||||
fig.update_traces(
|
||||
textinfo='none',
|
||||
hoverinfo='label+percent+value',
|
||||
domain={'x': [0.05, 0.55], 'y': [0.05, 0.95]}
|
||||
textinfo="none",
|
||||
hoverinfo="label+percent+value",
|
||||
domain={"x": [0.05, 0.55], "y": [0.05, 0.95]},
|
||||
)
|
||||
fig.update_layout(
|
||||
margin=dict(l=0, r=10, t=40, b=0),
|
||||
legend=dict(x=0.6, y=0.5)
|
||||
legend=dict(x=0.6, y=0.5),
|
||||
)
|
||||
return fig
|
||||
|
||||
def decode_dataframe(df, can_id):
|
||||
filtered_rules = {}
|
||||
"""Decode *can_id* from *df*, filtering out non-SignalDef entries first."""
|
||||
filtered_rules: Dict[str, FrameDef] = {}
|
||||
for nid, frame in DECODER_RULES.items():
|
||||
filtered_signals = [sig for sig in frame.signals if isinstance(sig, SignalDef)]
|
||||
new_frame = deepcopy(frame)
|
||||
new_frame.signals = filtered_signals
|
||||
new_frame.signals = [s for s in frame.signals if isinstance(s, SignalDef)]
|
||||
filtered_rules[nid] = new_frame
|
||||
|
||||
return _decode_dataframe(df, can_id, filtered_rules)
|
||||
|
||||
Reference in New Issue
Block a user