Refactor CANveyor dashboard architecture
- Replace `stats.utils.extractor` with dedicated `loader` and `converter` modules to improve code organization. - Implement explicit pipeline stages for ingestion, decoding, and precomputation with caching. - Standardize data loading and J1939 parsing logic across sub-modules. - Enhance dashboard responsiveness by pre-calculating figures and downsampling ID-grouped data. - Enforce strict typing and add docstrings to public components.
This commit is contained in:
@@ -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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|
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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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|
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|
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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"),
|
||||
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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],
|
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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(
|
||||
label="Logs",
|
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tab_id="logs",
|
||||
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"),
|
||||
dbc.Col(
|
||||
_bus_dropdown("logs-bus-selector"),
|
||||
width=2,
|
||||
),
|
||||
],
|
||||
className="mb-3 mt-3", align="end",
|
||||
),
|
||||
get_logs_table_component(),
|
||||
],
|
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),
|
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dbc.Tab(
|
||||
label="Statistics",
|
||||
tab_id="statistics",
|
||||
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)
|
||||
|
||||
Reference in New Issue
Block a user