Add stub to decode J1939 and calculate useful values

COPYRIGHT.md

@@ -1,9 +0,0 @@
-# Copyright
-
-Copyright © 2026 Erick Ahmed
-
-The source code in this repository is licensed under the **GNU Affero General Public License v3.0 or later (AGPL-3.0-or-later)**.
-
-A copy of the license is provided in the `LICENSE` file. If any discrepancy exists between this notice and the `LICENSE` file, the `LICENSE` file shall prevail.
-
-Any third-party components included in this repository at any point during developement remain the property of their respective copyright holders and are subject to their own license terms.

LICENSE

@@ -1,21 +1,23 @@
-GNU AFFERO GENERAL PUBLIC LICENSE
-   Version 3, 19 November 2007
+GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
 
-Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
  Everyone is permitted to copy and distribute verbatim copies
  of this license document, but changing it is not allowed.
 
                             Preamble
 
-The GNU Affero General Public License is a free, copyleft license for
-software and other kinds of works, specifically designed to ensure
-cooperation with the community in the case of network server software.
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
 
   The licenses for most software and other practical works are designed
 to take away your freedom to share and change the works.  By contrast,
-our General Public Licenses are intended to guarantee your freedom to
+the GNU General Public License is intended to guarantee your freedom to
 share and change all versions of a program--to make sure it remains free
-software for all its users.
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
 
   When we speak of free software, we are referring to freedom, not
 price.  Our General Public Licenses are designed to make sure that you
@@ -24,34 +26,44 @@ them if you wish), that you receive source code or can get it if you
 want it, that you can change the software or use pieces of it in new
 free programs, and that you know you can do these things.
 
-Developers that use our General Public Licenses protect your rights
-with two steps: (1) assert copyright on the software, and (2) offer
-you this License which gives you legal permission to copy, distribute
-and/or modify the software.
+  To protect your rights, we need to prevent others from denying you
+these rights or asking you to surrender the rights.  Therefore, you have
+certain responsibilities if you distribute copies of the software, or if
+you modify it: responsibilities to respect the freedom of others.
 
-A secondary benefit of defending all users' freedom is that
-improvements made in alternate versions of the program, if they
-receive widespread use, become available for other developers to
-incorporate.  Many developers of free software are heartened and
-encouraged by the resulting cooperation.  However, in the case of
-software used on network servers, this result may fail to come about.
-The GNU General Public License permits making a modified version and
-letting the public access it on a server without ever releasing its
-source code to the public.
+  For example, if you distribute copies of such a program, whether
+gratis or for a fee, you must pass on to the recipients the same
+freedoms that you received.  You must make sure that they, too, receive
+or can get the source code.  And you must show them these terms so they
+know their rights.
 
-The GNU Affero General Public License is designed specifically to
-ensure that, in such cases, the modified source code becomes available
-to the community.  It requires the operator of a network server to
-provide the source code of the modified version running there to the
-users of that server.  Therefore, public use of a modified version, on
-a publicly accessible server, gives the public access to the source
-code of the modified version.
+  Developers that use the GNU GPL protect your rights with two steps:
+(1) assert copyright on the software, and (2) offer you this License
+giving you legal permission to copy, distribute and/or modify it.
 
-An older license, called the Affero General Public License and
-published by Affero, was designed to accomplish similar goals.  This is
-a different license, not a version of the Affero GPL, but Affero has
-released a new version of the Affero GPL which permits relicensing under
-this license.
+  For the developers' and authors' protection, the GPL clearly explains
+that there is no warranty for this free software.  For both users' and
+authors' sake, the GPL requires that modified versions be marked as
+changed, so that their problems will not be attributed erroneously to
+authors of previous versions.
+
+  Some devices are designed to deny users access to install or run
+modified versions of the software inside them, although the manufacturer
+can do so.  This is fundamentally incompatible with the aim of
+protecting users' freedom to change the software.  The systematic
+pattern of such abuse occurs in the area of products for individuals to
+use, which is precisely where it is most unacceptable.  Therefore, we
+have designed this version of the GPL to prohibit the practice for those
+products.  If such problems arise substantially in other domains, we
+stand ready to extend this provision to those domains in future versions
+of the GPL, as needed to protect the freedom of users.
+
+  Finally, every program is threatened constantly by software patents.
+States should not allow patents to restrict development and use of
+software on general-purpose computers, but in those that do, we wish to
+avoid the special danger that patents applied to a free program could
+make it effectively proprietary.  To prevent this, the GPL assures that
+patents cannot be used to render the program non-free.
 
   The precise terms and conditions for copying, distribution and
 modification follow.
@@ -60,7 +72,7 @@ modification follow.
 
   0. Definitions.
 
-"This License" refers to version 3 of the GNU Affero General Public License.
+  "This License" refers to version 3 of the GNU General Public License.
 
   "Copyright" also means copyright-like laws that apply to other kinds of
 works, such as semiconductor masks.
@@ -537,45 +549,35 @@ to collect a royalty for further conveying from those to whom you convey
 the Program, the only way you could satisfy both those terms and this
 License would be to refrain entirely from conveying the Program.
 
-13. Remote Network Interaction; Use with the GNU General Public License.
-
-Notwithstanding any other provision of this License, if you modify the
-Program, your modified version must prominently offer all users
-interacting with it remotely through a computer network (if your version
-supports such interaction) an opportunity to receive the Corresponding
-Source of your version by providing access to the Corresponding Source
-from a network server at no charge, through some standard or customary
-means of facilitating copying of software.  This Corresponding Source
-shall include the Corresponding Source for any work covered by version 3
-of the GNU General Public License that is incorporated pursuant to the
-following paragraph.
+  13. Use with the GNU Affero General Public License.
 
   Notwithstanding any other provision of this License, you have
 permission to link or combine any covered work with a work licensed
-under version 3 of the GNU General Public License into a single
+under version 3 of the GNU Affero General Public License into a single
 combined work, and to convey the resulting work.  The terms of this
 License will continue to apply to the part which is the covered work,
-but the work with which it is combined will remain governed by version
-3 of the GNU General Public License.
+but the special requirements of the GNU Affero General Public License,
+section 13, concerning interaction through a network will apply to the
+combination as such.
 
   14. Revised Versions of this License.
 
   The Free Software Foundation may publish revised and/or new versions of
-the GNU Affero General Public License from time to time.  Such new versions
-will be similar in spirit to the present version, but may differ in detail to
+the GNU General Public License from time to time.  Such new versions will
+be similar in spirit to the present version, but may differ in detail to
 address new problems or concerns.
 
   Each version is given a distinguishing version number.  If the
-Program specifies that a certain numbered version of the GNU Affero General
+Program specifies that a certain numbered version of the GNU General
 Public License "or any later version" applies to it, you have the
 option of following the terms and conditions either of that numbered
 version or of any later version published by the Free Software
 Foundation.  If the Program does not specify a version number of the
-GNU Affero General Public License, you may choose any version ever published
+GNU General Public License, you may choose any version ever published
 by the Free Software Foundation.
 
   If the Program specifies that a proxy can decide which future
-versions of the GNU Affero General Public License can be used, that proxy's
+versions of the GNU General Public License can be used, that proxy's
 public statement of acceptance of a version permanently authorizes you
 to choose that version for the Program.
 
@@ -633,29 +635,40 @@ the "copyright" line and a pointer to where the full notice is found.
     Copyright (C) <year>  <name of author>
 
     This program is free software: you can redistribute it and/or modify
-it under the terms of the GNU Affero General Public License as published
-by the Free Software Foundation, either version 3 of the License, or
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
     (at your option) any later version.
 
     This program is distributed in the hope that it will be useful,
     but WITHOUT ANY WARRANTY; without even the implied warranty of
     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-GNU Affero General Public License for more details.
+    GNU General Public License for more details.
 
-You should have received a copy of the GNU Affero General Public License
-along with this program.  If not, see <http://www.gnu.org/licenses/>.
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
 Also add information on how to contact you by electronic and paper mail.
 
-If your software can interact with users remotely through a computer
-network, you should also make sure that it provides a way for users to
-get its source.  For example, if your program is a web application, its
-interface could display a "Source" link that leads users to an archive
-of the code.  There are many ways you could offer source, and different
-solutions will be better for different programs; see section 13 for the
-specific requirements.
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
 
   You should also get your employer (if you work as a programmer) or school,
 if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU AGPL, see
-<http://www.gnu.org/licenses/>.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

decoder.py

@@ -1,86 +0,0 @@
-# File: decoder.py
-# Copyright (C) 2026 Erick Ahmed
-# SPDX-License-Identifier: AGPL-3.0-or-later
-
-import argparse
-import polars as pl
-
-def get_j1939_mask() -> pl.Expr:
-    """
-    Returns a Polars expression representing the strict J1939 filtering rules.
-    """
-    id_int = pl.col("ID").str.to_integer(base=16).cast(pl.UInt32)
-    return id_int > 0x7FF
-
-def decode_j1939_metadata(lf: pl.LazyFrame) -> pl.LazyFrame:
-    """
-    Decodes J1939 fields and bundles them into a Struct column.
-    """
-    id_int = pl.col("ID").str.to_integer(base=16).cast(pl.UInt32)
-
-    priority = ((id_int // 67108864) % 8).cast(pl.UInt8)
-    pf = ((id_int // 65536) % 256).cast(pl.UInt8)
-    ps = ((id_int // 256) % 256).cast(pl.UInt8)
-    sa = (id_int % 256).cast(pl.UInt8)
-
-    da = pl.when(pf < 240).then(ps).otherwise(pl.lit(255, dtype=pl.UInt8)).cast(pl.UInt8)
-
-    pgn = pl.when(pf < 240).then(
-        ((id_int // 256) & 0x3FF00)
-    ).otherwise(
-        ((id_int // 256) & 0x3FFFF)
-    ).cast(pl.UInt32)
-
-    return lf.with_columns(
-        pl.struct([
-            priority.alias("Priority"),
-            pf.alias("PF"),
-            ps.alias("PS"),
-            sa.alias("SA"),
-            da.alias("DA"),
-            pgn.alias("PGN")
-        ]).alias("j1939_metadata")
-    )
-
-def decode_j1939_frames(df: pl.DataFrame) -> pl.DataFrame:
-    id_int = pl.col("ID").str.to_integer(base=16).cast(pl.UInt32)
-
-    is_j1939 = id_int > 0x7FF
-
-    priority = ((id_int // 67108864) % 8).cast(pl.UInt8)
-    pf = ((id_int // 65536) % 256).cast(pl.UInt8)
-    ps = ((id_int // 256) % 256).cast(pl.UInt8)
-    sa = (id_int % 256).cast(pl.UInt8)
-
-    da = pl.when(pf < 240).then(ps).otherwise(pl.lit(255, dtype=pl.UInt8)).cast(pl.UInt8)
-
-    pgn = pl.when(pf < 240).then(
-        ((id_int // 256) & 0x3FF00)
-    ).otherwise(
-        ((id_int // 256) & 0x3FFFF)
-    ).cast(pl.UInt32)
-
-    j1939_meta = pl.when(is_j1939).then(
-        pl.struct([
-            priority.alias("Priority"),
-            pf.alias("PF"),
-            ps.alias("PS"),
-            sa.alias("SA"),
-            da.alias("DA"),
-            pgn.alias("PGN")
-        ])
-    ).otherwise(None)
-
-    return df.with_columns(j1939_meta.alias("j1939_metadata"))
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="J1939 decoder")
-    parser.add_argument("input_parquet", help="Path to the raw .parquet file")
-    parser.add_argument("output_parquet", help="Path to save the decoded .parquet file")
-    args = parser.parse_args()
-
-    df = pl.scan_parquet(args.input_parquet).collect()
-    decoded_df = decode_j1939_frames(df)
-
-    decoded_df.write_parquet(args.output_parquet)

logs/view.py

@@ -1,83 +0,0 @@
-# File: logs/view.py
-# Copyright (C) 2026 Erick Ahmed
-# SPDX-License-Identifier: AGPL-3.0-or-later
-
-import pandas as pd
-from dash import html, dash_table, dcc
-import dash_bootstrap_components as dbc
-
-PAGE_SIZE = 25000
-
-def prepare_logs_data(df: pd.DataFrame) -> pd.DataFrame:
-    if df is None or df.empty:
-        return pd.DataFrame()
-
-    df = df.copy()
-
-    if 'j1939_metadata' in df.columns:
-        df['Priority'] = df['j1939_metadata'].apply(lambda x: x.get('Priority') if isinstance(x, dict) else None)
-        df['PF'] = df['j1939_metadata'].apply(lambda x: x.get('PF') if isinstance(x, dict) else None)
-        df['PS'] = df['j1939_metadata'].apply(lambda x: x.get('PS') if isinstance(x, dict) else None)
-        df['SA'] = df['j1939_metadata'].apply(lambda x: x.get('SA') if isinstance(x, dict) else None)
-        df['DA'] = df['j1939_metadata'].apply(lambda x: x.get('DA') if isinstance(x, dict) else None)
-        df['PGN'] = df['j1939_metadata'].apply(lambda x: x.get('PGN') if isinstance(x, dict) else None)
-    else:
-        for col in ['Priority', 'PF', 'PS', 'SA', 'DA', 'PGN']:
-            df[col] = None
-
-    for i in range(8):
-        col = f'b{i}'
-        if col in df.columns:
-            df[col] = df[col].apply(lambda x: f"{int(x):02X}" if pd.notna(x) else "")
-        else:
-            df[col] = ""
-
-    if 'ID' in df.columns:
-        df['ID'] = df['ID'].astype(str)
-
-    display_cols = ['Timestamp', 'ID', 'DLC', 'b0', 'b1', 'b2', 'b3', 'b4', 'b5', 'b6', 'b7', 'Priority', 'PF', 'PS', 'SA', 'DA', 'PGN']
-    display_df = df[[c for c in display_cols if c in df.columns]]
-
-    return display_df.fillna("")
-
-def get_logs_table_component():
-    return html.Div([
-        html.Div(id='logs-info-text', className="text-muted mb-2"),
-        dash_table.DataTable(
-            id='logs-table',
-            virtualization=True,
-            page_action='none',
-            style_table={'overflowX': 'auto', 'height': '70vh', 'overflowY': 'auto'},
-            style_header={
-                'backgroundColor': '#1a1a1a',
-                'color': 'white',
-                'fontWeight': 'bold',
-                'textAlign': 'center',
-                'position': 'sticky',
-                'top': 0
-            },
-            style_data={
-                'backgroundColor': '#f8f9fa',
-                'color': '#2a2a2a',
-                'textAlign': 'center'
-            },
-            style_data_conditional=[
-                {
-                    'if': {'row_index': 'odd'},
-                    'backgroundColor': 'rgb(240, 240, 240)'
-                }
-            ],
-            style_cell={
-                'minWidth': '80px',
-                'padding': '5px',
-                'textAlign': 'center',
-                'fontFamily': 'Segoe UI, Arial, sans-serif'
-            }
-        ),
-        html.Div([
-            dbc.Button("Prev", id='logs-prev-btn', color="secondary", outline=True, size="sm", className="me-2"),
-            html.Div(id='logs-page-nav', className="d-inline-block", style={'verticalAlign': 'middle'}),
-            dbc.Button("Next", id='logs-next-btn', color="secondary", outline=True, size="sm", className="ms-2"),
-        ], className="d-flex justify-content-center align-items-center mt-3"),
-        dcc.Store(id='logs-current-page', data=0),
-    ])

main.py

@@ -1,597 +0,0 @@
-# File: main.py
-# Copyright (C) 2026 Erick Ahmed
-# SPDX-License-Identifier: AGPL-3.0-or-later
-
-"""CANveyor dashboard entry point.
-
-Handles raw log ingestion, J1939 decoding, precomputation of
-statistical figures, and exposes a Dash application for browsing the
-processed data.
-"""
-
-import os
-from concurrent.futures import ThreadPoolExecutor
-from pathlib import Path
-from typing import Dict, List, Tuple
-
-import dash
-import dash_bootstrap_components as dbc
-import numpy as np
-import pandas as pd
-import polars as pl
-from dash import dcc, html, Input, Output, State
-
-from decoder import decode_j1939_frames
-from logs.view import get_logs_table_component, prepare_logs_data
-from parser import parse_csv, parse_log
-from stats.correlation import calculate_correlation, plot_correlation_heatmap
-from stats.entropy import calculate_byte_entropy, plot_entropy_heatmap
-from stats.frequency import calculate_frequency, plot_frequency
-from stats.id_viewer import _format_can_id_vec, plot_bits
-from stats.utils.loader import load_data
-from vehicle import get_vehicle_module
-
-RAW_LOG_DIR = "data/logs"
-CSV_DIR = "data/csv"
-PARQUET_DIR = "data/parquet"
-PAGE_SIZE = 25_000
-BYTE_COLS = [f"b{i}" for i in range(8)]
-BUS_OPTIONS = [
-    {"label": "Bus 1", "value": "Bus 1"},
-    {"label": "Bus 2", "value": "Bus 2"},
-]
-
-DATA: Dict[str, Dict[str, pd.DataFrame]] = {}
-VEHICLE_META: Dict[str, Dict[str, str]] = {}
-PRECOMPUTED_FIGURES: Dict[str, object] = {}
-DATA_BY_ID: Dict[Tuple[str, str], Dict[str, Tuple[pd.DataFrame, List[str]]]] = {}
-CORR_CACHE: Dict[Tuple, object] = {}
-PREPARED_LOGS_CACHE: Dict[Tuple[str, str], pd.DataFrame] = {}
-
-
-def parse_vehicle_from_filename(filename: str) -> Tuple[str, str, str]:
-    """Derive (vehicle, brand, model) from a log file name."""
-    stem = Path(filename).stem
-    if "-" in stem:
-        brand, model_part = stem.split("-", 1)
-    else:
-        brand, model_part = stem, "Unknown"
-    model = model_part.replace("_", " ")
-    vehicle = f"{brand} {model}".strip()
-    return vehicle, brand, model
-
-
-def _vehicle_paths(vehicle: str) -> Dict[str, str]:
-    """Return all intermediate file paths for a given vehicle."""
-    return {
-        "bus1_csv": f"{CSV_DIR}/{vehicle}_bus1.csv",
-        "bus2_csv": f"{CSV_DIR}/{vehicle}_bus2.csv",
-        "bus1_parquet": f"{PARQUET_DIR}/{vehicle}_bus1.parquet",
-        "bus2_parquet": f"{PARQUET_DIR}/{vehicle}_bus2.parquet",
-        "bus1_decoded": f"{PARQUET_DIR}/{vehicle}_bus1_decoded.parquet",
-        "bus2_decoded": f"{PARQUET_DIR}/{vehicle}_bus2_decoded.parquet",
-    }
-
-
-def run_pipeline() -> None:
-    """Parse raw log files, convert to parquet, and decode J1939 frames."""
-    for directory in (RAW_LOG_DIR, CSV_DIR, PARQUET_DIR):
-        os.makedirs(directory, exist_ok=True)
-
-    for log_file in Path(RAW_LOG_DIR).glob("*.txt"):
-        vehicle, _, _ = parse_vehicle_from_filename(log_file.name)
-        paths = _vehicle_paths(vehicle)
-
-        if Path(paths["bus1_decoded"]).exists() and Path(paths["bus2_decoded"]).exists():
-            continue
-
-        print(f"Parsing raw log: {log_file.name}...")
-        parse_log(str(log_file), paths["bus1_csv"], paths["bus2_csv"])
-
-        print("Converting to parquet...")
-        parse_csv(paths["bus1_csv"]).sink_parquet(paths["bus1_parquet"])
-        parse_csv(paths["bus2_csv"]).sink_parquet(paths["bus2_parquet"])
-
-        print("Decoding J1939...")
-        df1 = pl.read_parquet(paths["bus1_parquet"])
-        df2 = pl.read_parquet(paths["bus2_parquet"])
-        decode_j1939_frames(df1).write_parquet(paths["bus1_decoded"])
-        decode_j1939_frames(df2).write_parquet(paths["bus2_decoded"])
-
-
-def load_vehicle_data() -> None:
-    """Load all decoded parquet files into the in-memory DATA store."""
-    for log_file in Path(RAW_LOG_DIR).glob("*.txt"):
-        vehicle, brand, model = parse_vehicle_from_filename(log_file.name)
-        VEHICLE_META[vehicle] = {"brand": brand, "model": model}
-
-        paths = _vehicle_paths(vehicle)
-        if Path(paths["bus1_decoded"]).exists() and Path(paths["bus2_decoded"]).exists():
-            DATA[vehicle] = {
-                "Bus 1": load_data(paths["bus1_decoded"]),
-                "Bus 2": load_data(paths["bus2_decoded"]),
-            }
-
-
-def _can_id_column(df: pd.DataFrame) -> str:
-    return "ID" if "ID" in df.columns else "Identifier"
-
-
-def _downsample_unchanged(group: pd.DataFrame, byte_cols: List[str]) -> pd.DataFrame:
-    """Keep only rows where at least one byte changed vs. the previous row."""
-    if group.empty or not byte_cols:
-        return group
-    arr = group[byte_cols].to_numpy(dtype=np.float32, copy=False)
-    if len(arr) <= 1:
-        return group
-    changed = np.any(arr[1:] != arr[:-1], axis=1)
-    keep = np.concatenate(([True], changed))
-    return group.iloc[keep]
-
-
-def process_bus_data(
-    vehicle: str, bus: str, df: pd.DataFrame
-) -> Tuple[str, str, Dict[str, object], Dict[str, Tuple[pd.DataFrame, List[str]]]]:
-    """Compute per-bus figures and ID-grouped, downsampled frames."""
-    precomp = {
-        f"{vehicle}_{bus}_freq": plot_frequency(
-            calculate_frequency(df), title=f"{vehicle} {bus} Frequency"
-        ),
-        f"{vehicle}_{bus}_entropy": plot_entropy_heatmap(
-            calculate_byte_entropy(df), title=f"{vehicle} {bus} Byte-Level Entropy"
-        ),
-    }
-
-    df = df.assign(Formatted_ID=_format_can_id_vec(df[_can_id_column(df)]))
-    df = df.sort_values(["Formatted_ID", "Timestamp"], kind="stable")
-
-    grouped: Dict[str, Tuple[pd.DataFrame, List[str]]] = {}
-    for can_id, group in df.groupby(by="Formatted_ID"):
-        byte_cols = [c for c in BYTE_COLS if c in group.columns]
-        group = _downsample_unchanged(group, byte_cols)
-        grouped[can_id] = (group, byte_cols)
-
-    return vehicle, bus, precomp, grouped
-
-
-def precompute_all() -> None:
-    """Run :func:`process_bus_data` across every vehicle/bus pair in parallel."""
-    with ThreadPoolExecutor() as executor:
-        futures = [
-            executor.submit(process_bus_data, vehicle, bus, df)
-            for vehicle, buses in DATA.items()
-            for bus, df in buses.items()
-        ]
-        for future in futures:
-            v, b, precomp, grouped = future.result()
-            PRECOMPUTED_FIGURES.update(precomp)
-            DATA_BY_ID[(v, b)] = grouped
-
-run_pipeline()
-
-print("Loading data into memory...")
-load_vehicle_data()
-precompute_all()
-
-app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
-app.config.suppress_callback_exceptions = True
-
-
-def _vehicle_dropdown(dropdown_id: str) -> dcc.Dropdown:
-    return dcc.Dropdown(
-        id=dropdown_id,
-        options=[{"label": v, "value": v} for v in DATA.keys()],
-        value=list(DATA.keys())[0] if DATA else None,
-        clearable=False,
-    )
-
-
-def _bus_dropdown(dropdown_id: str) -> dcc.Dropdown:
-    return dcc.Dropdown(
-        id=dropdown_id,
-        options=BUS_OPTIONS,
-        value="Bus 1",
-        clearable=False,
-    )
-
-
-def _label(text: str) -> html.Label:
-    return html.Label(text, className="mt-2")
-
-
-app.layout = dbc.Container(
-    [
-        html.H1("CANveyor", className="my-4"),
-        dbc.Tabs(
-            [
-                dbc.Tab(
-                    label="Overview",
-                    tab_id="overview",
-                    children=[html.Div(id="overview-content")],
-                ),
-                dbc.Tab(
-                    label="Vehicles",
-                    tab_id="vehicles",
-                    children=[
-                        dbc.Row(
-                            [
-                                dbc.Col(_label("Vehicle:"), width="auto"),
-                                dbc.Col(
-                                    _vehicle_dropdown("vehicles-vehicle-selector"),
-                                    width=3, className="me-4",
-                                ),
-                            ],
-                            className="mb-3 mt-3", align="end",
-                        ),
-                        html.Div(id="vehicles-content", className="mt-3"),
-                    ],
-                ),
-                dbc.Tab(
-                    label="Logs",
-                    tab_id="logs",
-                    children=[
-                        dbc.Row(
-                            [
-                                dbc.Col(_label("Vehicle:"), width="auto"),
-                                dbc.Col(
-                                    _vehicle_dropdown("logs-vehicle-selector"),
-                                    width=3, className="me-4",
-                                ),
-                                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(),
-                    ],
-                ),
-                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:
-        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:
-    """Build the pagination button list with ellipses where appropriate."""
-    buttons: List = []
-    if total_pages <= 1:
-        return buttons
-
-    half = max_buttons // 2
-    start = max(0, current_page - half)
-    end = min(total_pages, start + max_buttons)
-    if end - start < max_buttons:
-        start = max(0, end - max_buttons)
-
-    if start > 0:
-        buttons.append(
-            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
-        buttons.append(
-            dbc.Button(
-                str(i + 1),
-                id={"type": "page-btn", "index": i},
-                size="sm",
-                color="primary" if is_current else "secondary",
-                outline=not is_current,
-                className="me-1",
-                disabled=is_current,
-            )
-        )
-
-    if end < total_pages:
-        if end < total_pages - 1:
-            buttons.append(html.Span("…", className="mx-1 align-middle"))
-        buttons.append(
-            dbc.Button(
-                str(total_pages),
-                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"),
-)
-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]:
-        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
-
-    total_pages = max(1, (total_rows + PAGE_SIZE - 1) // PAGE_SIZE)
-
-    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"):
-        current_page = 0
-    elif triggered_id == "logs-prev-btn":
-        current_page = max(0, current_page - 1)
-    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"]
-
-    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": 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"),
-)
-def render_content(tab, vehicle, bus):
-    if not vehicle or not bus or vehicle not in DATA or bus not in DATA[vehicle]:
-        return html.Div("No data available")
-
-    df = DATA[vehicle][bus]
-
-    if tab == "freq":
-        return dcc.Graph(
-            figure=PRECOMPUTED_FIGURES[f"{vehicle}_{bus}_freq"],
-            style={"height": "80vh"},
-        )
-
-    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"}),
-            ]
-        )
-
-    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"}),
-            ]
-        )
-
-    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"),
-)
-def update_id_viewer(selected_id, vehicle, bus, tab):
-    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), {})
-    if selected_id not in grouped_data:
-        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",
-    )
-
-@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"),
-)
-def update_corr(method, target, vehicle, bus, tab):
-    if tab != "corr" or not vehicle or not bus:
-        return dash.no_update
-
-    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_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"),
-)
-def render_vehicles(vehicle):
-    if not vehicle or vehicle not in DATA:
-        return html.Div("No data available", className="text-muted")
-
-    brand = VEHICLE_META.get(vehicle, {}).get("brand", "")
-    vehicle_module = get_vehicle_module(brand)
-
-    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)
-
-    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):
-                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):
-                    continue
-                unit_str = f" ({sig.unit})" if sig.unit else ""
-                title = f"{sig.name}{unit_str}"
-                fig = vehicle_module.plot_signal(
-                    decoded, sig.name, title=title, color=frame_def.color
-                )
-
-            cards.append(
-                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",
-        )
-    return dbc.Row(cards)
-
-if __name__ == "__main__":
-    app.run(debug=False)

pyproject.toml

@@ -3,13 +3,5 @@ name = "CANveyor"
 version = "0.1.0"
 description = "J1939 CAN bus parser that works in pair with CANdigger"
 readme = "README.md"
-requires-python = ">=3.10"
-dependencies = [
-    "polars",
-    "dash",
-    "dash-bootstrap-components",
-    "numpy",
-    "pandas",
-    "plotly",
-    "plotly-resampler"
-]
+requires-python = ">=3.14"
+dependencies = ["polars", "pathlib", "typing"]

src/j1939_decoder.py

@@ -0,0 +1,99 @@
+import argparse
+import polars as pl
+
+def get_j1939_mask() -> pl.Expr:
+    """
+    Returns a Polars expression representing the strict J1939 filtering rules.
+    """
+    id_int = pl.col("ID").str.to_integer(base=16).cast(pl.UInt32)
+    return (
+        (id_int > 0x7FF) &
+        ((id_int % 33554432 // 16777216) == 0) &
+        (pl.col("DLC") <= 8)
+    )
+
+def decode_j1939_metadata(lf: pl.LazyFrame) -> pl.LazyFrame:
+    """
+    Decodes J1939 fields and bundles them into a Struct column.
+    """
+    id_int = pl.col("ID").str.to_integer(base=16).cast(pl.UInt32)
+
+    id_shifted_8 = id_int // 256
+    id_shifted_16 = id_int // 65536
+
+    priority = ((id_int // 67108864) % 8).cast(pl.UInt8)
+    pf = (id_shifted_16 % 256).cast(pl.UInt8)
+    ps = (id_shifted_8 % 256).cast(pl.UInt8)
+    sa = (id_int % 256).cast(pl.UInt8)
+
+    da = pl.when(pf < 240).then(ps).otherwise(pl.lit(255, dtype=pl.UInt8))
+
+    pgn = pl.when(pf < 240).then(id_shifted_8 % 65536).otherwise(id_shifted_8 % 262144).cast(pl.UInt32)
+
+    return lf.with_columns(
+        pl.struct([
+            priority.alias("Priority"),
+            pf.alias("PF"),
+            ps.alias("PS"),
+            sa.alias("SA"),
+            da.alias("DA"),
+            pgn.alias("PGN")
+        ]).alias("j1939_metadata")
+    )
+
+def decode_j1939_frames(df: pl.DataFrame) -> pl.DataFrame:
+    id_int = pl.col("ID").str.to_integer(base=16).cast(pl.UInt32)
+
+    is_j1939 = (id_int > 0x7FF) & ((id_int % 33554432 // 16777216) == 0) & (pl.col("DLC") <= 8)
+
+    priority = (id_int // 67108864) % 8
+    pf = (id_int // 65536) % 256
+    ps = (id_int // 256) % 256
+    sa = id_int % 256
+    da = pl.when(pf < 240).then(ps).otherwise(255)
+    pgn = pl.when(pf < 240).then((id_int // 256) % 65536).otherwise((id_int // 256) % 262144)
+
+    j1939_meta = pl.when(is_j1939).then(
+        pl.struct([
+            priority.cast(pl.UInt8).alias("Priority"),
+            pf.cast(pl.UInt8).alias("PF"),
+            ps.cast(pl.UInt8).alias("PS"),
+            sa.cast(pl.UInt8).alias("SA"),
+            da.cast(pl.UInt8).alias("DA"),
+            pgn.cast(pl.UInt32).alias("PGN")
+        ])
+    ).otherwise(None)
+
+    b = [pl.col(f"b{i}").fill_null("00").str.to_integer(base=16, strict=False) for i in range(8)]
+    pgn_j = pl.when(is_j1939).then(pgn).otherwise(None)
+
+    data_1 = pl.when(pgn_j.is_in([61444, 65331])).then(1 * 1)
+    data_2 = pl.when(pgn_j.is_in([61444, 65331])).then(1 * 1)
+    data_3 = pl.when(pgn_j == 65265).then(1 * 1)
+    data_4 = pl.when(pgn_j == 65226).then(1 * 1)
+    data_5 = pl.when(pgn_j == 65271).then(1 * 1)
+
+    data_col = pl.coalesce(
+        pl.when(data_1.is_not_null()).then(pl.lit("VALUE 1")),
+        pl.when(data_2.is_not_null()).then(pl.lit("VALUE 2")),
+        pl.when(data_3.is_not_null()).then(pl.lit("VALUE 3")),
+        pl.when(data_4.is_not_null()).then(pl.lit("VALUE 4")),
+        pl.when(data_5.is_not_null()).then(pl.lit("VALUE 5")),
+    )
+    value_col = pl.coalesce(data_1, data_2, data_3, data_4, data_5)
+
+    return df.with_columns([
+        j1939_meta.alias("j1939_metadata"),
+        data_col.alias("data"),
+        value_col.alias("value")
+    ])
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="CAN Bus Full Decoder")
+    parser.add_argument("input_parquet", help="Path to the raw .parquet file")
+    parser.add_argument("output_parquet", help="Path to save the decoded .parquet file")
+    args = parser.parse_args()
+
+    df = pl.scan_parquet(args.input_parquet).collect()
+    decoded_df = decode_j1939_frames(df)
+    decoded_df.write_parquet(args.output_parquet)

src/parser.py

@@ -1,7 +1,3 @@
-# File: parser.py
-# Copyright (C) 2026 Erick Ahmed
-# SPDX-License-Identifier: AGPL-3.0-or-later
-
 import re
 import csv
 import polars as pl
@@ -19,7 +15,7 @@ def parse_log(input_path: PathLike, out_bus1: PathLike, out_bus2: PathLike) -> N
     out1_file = Path(out_bus1)
     out2_file = Path(out_bus2)
 
-    start_pattern = re.compile(r'(C[12]):([0-9A-Fa-f]{7,8})\s+([0-9A-Fa-f]{1,2})\s+')
+    start_pattern = re.compile(r'(C[12]):([0-9A-Fa-f]{1,8})\s+([0-9A-Fa-f]{1,2})\s+')
     byte_pattern = re.compile(r'^[0-9A-Fa-f]{2}$')
 
     with input_file.open('r', encoding='utf-8') as f_in, \
@@ -35,12 +31,7 @@ def parse_log(input_path: PathLike, out_bus1: PathLike, out_bus2: PathLike) -> N
         for line in f_in:
             for match in start_pattern.finditer(line):
                 bus = match.group(1)
-
-                can_id = match.group(2).upper().zfill(8)
-
-                if int(can_id, 16) > 0x1FFFFFFF:
-                    continue
-
+                can_id = match.group(2).upper()
                 dlc_str = match.group(3)
 
                 try:
@@ -77,7 +68,7 @@ def parse_csv(csv_path: PathLike) -> pl.LazyFrame:
     """
     lf = pl.scan_csv(csv_path, schema_overrides={"ID": pl.String, "Data": pl.String})
 
-    if "Timestamp" not in lf.collect_schema().names():
+    if "Timestamp" not in lf.columns:
         lf = lf.with_row_index("Timestamp")
 
     byte_exprs = []
@@ -121,3 +112,4 @@ if __name__ == '__main__':
         print(f"[*] Processing {args.input_csv}...")
         lf = parse_csv(args.input_csv)
         lf.sink_parquet(args.output_parquet)
+        print(f"[+] Saved parquet file to {args.output_parquet}")

stats/correlation.py

@@ -1,177 +0,0 @@
-# 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.converter import format_can_id_vec as _format_can_id_vec, to_int
-from stats.utils.loader import load_data
-
-
-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()
-        for c in needs:
-            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]
-
-    if not available_cols:
-        raise ValueError("No byte columns (b0-b7) found in the DataFrame")
-
-    can_id_col = 'ID' if 'ID' in df.columns else 'Identifier'
-    identifiers = _format_can_id_vec(df[can_id_col]).to_numpy()
-
-    df_bytes = _ensure_int_bytes(df, available_cols)[available_cols]
-    data = df_bytes.to_numpy(dtype=np.float64, copy=False)
-
-    if target_id is not None:
-        target_id = _format_can_id_vec(pd.Series([target_id])).iloc[0]
-        mask = identifiers == target_id
-        if not mask.any():
-            raise ValueError(f"Identifier '{target_id}' not found in data")
-        sub = data[mask]
-        mask = ~np.isnan(sub).any(axis=1)
-        sub = sub[mask]
-        if method == 'spearman' and sub.shape[0] > 1:
-            sub = pd.DataFrame(sub).rank().to_numpy()
-        if sub.shape[0] > 1:
-            with np.errstate(divide='ignore', invalid='ignore'):
-                c = np.corrcoef(sub, rowvar=False)
-            np.nan_to_num(c, copy=False, nan=0.0)
-        else:
-            c = np.zeros((len(available_cols), len(available_cols)))
-        return pd.DataFrame(c, index=available_cols, columns=available_cols)
-
-    unique_ids, inverse = np.unique(identifiers, return_inverse=True)
-    n_cols = len(available_cols)
-
-    sort_idx = np.argsort(inverse, kind='stable')
-    data_sorted = data[sort_idx]
-    inverse_sorted = inverse[sort_idx]
-
-    if len(inverse_sorted) > 0:
-        split_points = np.flatnonzero(np.diff(inverse_sorted)) + 1
-        groups = np.split(data_sorted, split_points)
-    else:
-        groups = []
-
-    def _process_group(sub: np.ndarray) -> np.ndarray:
-        mask = ~np.isnan(sub).any(axis=1)
-        sub = sub[mask]
-        if len(sub) > 1:
-            if method == 'spearman':
-                sub = pd.DataFrame(sub).rank().to_numpy()
-            with np.errstate(divide='ignore', invalid='ignore'):
-                c = np.abs(np.corrcoef(sub, rowvar=False))
-            np.nan_to_num(c, copy=False, nan=0.0)
-            np.fill_diagonal(c, 0.0)
-            return c.max(axis=0)
-        return np.zeros(n_cols, dtype=np.float64)
-
-    out = np.zeros((len(unique_ids), n_cols), dtype=np.float64)
-    if len(groups) > 0:
-        with ThreadPoolExecutor() as executor:
-            results = list(executor.map(_process_group, groups))
-            for i, res in enumerate(results):
-                out[i] = res
-
-    result = pd.DataFrame(out, index=unique_ids, columns=available_cols)
-    result.index.name = 'Identifier'
-    return result
-
-
-def plot_correlation_heatmap(corr_df: pd.DataFrame, target_id: str | None, title: str) -> go.Figure:
-    is_8x8 = target_id is not None
-
-    x = corr_df.columns.tolist()
-    y = corr_df.index.tolist()
-    z = corr_df.values
-
-    if is_8x8:
-        z_min, z_max = -1.0, 1.0
-        colorscale = [[0.0, "#2c7bb6"], [0.25, "#abd9e9"], [0.5, "#ffffff"], [0.75, "#fdae61"], [1.0, "#d7191c"]]
-        hover_template = "<b>%{y}</b> vs <b>%{x}</b><br>Correlation: %{z:.2f}<extra></extra>"
-    else:
-        z_min, z_max = 0.0, 1.0
-        colorscale = [[0.0, "#ffffff"], [0.2, "#fff5f0"], [0.4, "#fecc5c"], [0.6, "#fd8d3c"], [0.8, "#e31a1c"], [1.0, "#800026"]]
-        hover_template = "<b>%{y}</b><br>Byte %{x} max correlation: %{z:.2f}<extra></extra>"
-
-    fig = go.Figure(
-        data=go.Heatmap(
-            z=z, x=x, y=y,
-            zmin=z_min, zmax=z_max,
-            colorscale=colorscale,
-            xgap=3, ygap=3,
-            text=np.round(z, 2),
-            texttemplate="%{text}",
-            textfont={"size": 11, "color": "#2a2a2a", "family": "Segoe UI, Arial, sans-serif"},
-            hoverongaps=False,
-            hovertemplate=hover_template,
-            colorbar=dict(
-                title=dict(text="Correlation", side="top", font=dict(size=13, color="#1a1a1a")),
-                orientation="h", thickness=15, len=0.35,
-                x=1.0, xanchor="right", y=1.02, yanchor="bottom",
-                tickfont=dict(size=11, color="#2a2a2a"),
-                tickformat=".1f", outlinewidth=0.5, outlinecolor="#cccccc",
-            ),
-        )
-    )
-
-    fig.update_layout(
-        title=dict(text=title, font=dict(size=20, color="#1a1a1a"), x=0.5, xanchor="center", pad=dict(b=20)),
-        height=600 if is_8x8 else max(600, len(y) * 28 + 150),
-        autosize=True,
-        template="plotly_white",
-        xaxis=dict(
-            title=dict(text="Byte Position", font=dict(size=13, color="#1a1a1a")),
-            side="bottom" if is_8x8 else "top",
-            dtick=1, showgrid=False, linecolor="#bdbdbd",
-            tickfont=dict(size=12, color="#2a2a2a"), ticks="outside", ticklen=4, tickcolor="#cccccc",
-        ),
-        yaxis=dict(
-            title=dict(text="Byte Position" if is_8x8 else "PGN or CAN ID", font=dict(size=13, color="#1a1a1a")),
-            autorange="reversed", showgrid=False, linecolor="#bdbdbd",
-            tickfont=dict(size=12, color="#2a2a2a"), ticks="outside", ticklen=4, tickcolor="#cccccc", automargin=True,
-        ),
-        font=dict(family="Segoe UI, Arial, sans-serif", size=12, color="#2a2a2a"),
-        hoverlabel=dict(bgcolor="white", font_size=13, font_family="Segoe UI", bordercolor="#cccccc"),
-        margin=dict(l=200, r=40, t=120, b=60),
-    )
-    return fig
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Analyze CAN bus inter-byte correlation")
-    parser.add_argument("method", choices=["pearson", "spearman"], help="Correlation method to use")
-    parser.add_argument("input", type=Path, help="Path to the input CAN log file")
-    parser.add_argument("output", type=Path, nargs="?", default=Path("correlation_report.html"))
-    parser.add_argument("title", nargs="?", default="CAN Bus Inter-Byte Correlation")
-    parser.add_argument("--identifier", type=str, default=None)
-    args = parser.parse_args()
-
-    df = load_data(args.input)
-    corr_df = calculate_correlation(df, method=args.method, target_id=args.identifier)
-    display_title = f"{args.title} ({args.identifier})" if args.identifier else args.title
-    fig = plot_correlation_heatmap(corr_df, target_id=args.identifier, title=display_title)
-    config = {
-        "responsive": True,
-        "displaylogo": False,
-        "scrollZoom": True,
-        "modeBarButtonsToAdd": ["toggleSpikelines"],
-        "toImageButtonOptions": {"format": "png", "scale": 2},
-    }
-    fig.write_html(str(args.output), include_plotlyjs="cdn", config=config)

stats/entropy.py

@@ -1,155 +0,0 @@
-# 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.converter import format_can_id_vec as _format_can_id_vec, to_int
-from stats.utils.loader import load_data
-
-
-def _entropy_col(a: np.ndarray) -> float:
-    a = a[~np.isnan(a)]
-    if a.size == 0:
-        return 0.0
-    a = a.astype(np.int64)
-    lo, hi = a.min(), a.max()
-    span = hi - lo + 1
-    if span <= 0:
-        return 0.0
-    if span > 1 << 20:
-        _, counts = np.unique(a, return_counts=True)
-    else:
-        counts = np.bincount(a - lo, minlength=span)
-        counts = counts[counts > 0]
-    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:
-        raise ValueError("No byte columns (b0-b7) found in the DataFrame")
-
-    can_id_col = 'ID' if 'ID' in df.columns else 'Identifier'
-    identifiers = _format_can_id_vec(df[can_id_col]).to_numpy()
-
-    needs = [c for c in available_cols if not pd.api.types.is_numeric_dtype(df[c])]
-    if needs:
-        df = df.copy()
-        for c in needs:
-            df[c] = df[c].apply(to_int)
-
-    data = df[available_cols].to_numpy(dtype=np.float64, copy=False)
-    unique_ids, inverse = np.unique(identifiers, return_inverse=True)
-    n_cols = len(available_cols)
-
-    sort_idx = np.argsort(inverse, kind='stable')
-    data_sorted = data[sort_idx]
-    inverse_sorted = inverse[sort_idx]
-
-    if len(inverse_sorted) > 0:
-        split_points = np.flatnonzero(np.diff(inverse_sorted)) + 1
-        groups = np.split(data_sorted, split_points)
-    else:
-        groups = []
-
-    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])
-        return res
-
-    out = np.zeros((len(unique_ids), n_cols), dtype=np.float64)
-    if len(groups) > 0:
-        with ThreadPoolExecutor() as executor:
-            results = list(executor.map(_process_group, groups))
-            for i, res in enumerate(results):
-                out[i] = res
-
-    result = pd.DataFrame(out, index=unique_ids, columns=available_cols)
-    result.index.name = 'Identifier'
-    return result
-
-
-def plot_entropy_heatmap(entropy_df: pd.DataFrame, title: str) -> go.Figure:
-    x = entropy_df.columns.tolist()
-    y = entropy_df.index.tolist()
-    z = entropy_df.values
-
-    fig = go.Figure(
-        data=go.Heatmap(
-            z=z, x=x, y=y,
-            colorscale=[
-                [0.0, "#ffffff"],
-                [0.15, "#fff7ec"],
-                [0.35, "#fee8c8"],
-                [0.55, "#fdd49e"],
-                [0.75, "#fdbb84"],
-                [1.0, "#ef6548"],
-            ],
-            xgap=3, ygap=3,
-            text=np.round(z, 2),
-            texttemplate="%{text}",
-            textfont={"size": 11, "color": "#2a2a2a", "family": "Segoe UI, Arial, sans-serif"},
-            hoverongaps=False,
-            hovertemplate="<b>%{y}</b><br>Byte %{x}: %{z:.2f} bits<extra></extra>",
-            colorbar=dict(
-                title=dict(text="Entropy (bits)", side="top", font=dict(size=13, color="#1a1a1a")),
-                orientation="h", thickness=15, len=0.35,
-                x=1.0, xanchor="right", y=1.02, yanchor="bottom",
-                tickfont=dict(size=11, color="#2a2a2a"),
-                tickformat=".1f", outlinewidth=0.5, outlinecolor="#cccccc",
-            ),
-        )
-    )
-
-    fig.update_layout(
-        title=dict(text=title, font=dict(size=20, color="#1a1a1a"), x=0.5, xanchor="center", pad=dict(b=20)),
-        height=max(600, len(y) * 28 + 150),
-        autosize=True,
-        template="plotly_white",
-        xaxis=dict(
-            title=dict(text="Byte Position", font=dict(size=13, color="#1a1a1a")),
-            side="top", dtick=1, showgrid=False, linecolor="#bdbdbd",
-            tickfont=dict(size=12, color="#2a2a2a"), ticks="outside", ticklen=4, tickcolor="#cccccc",
-        ),
-        yaxis=dict(
-            title=dict(text="PGN or CAN ID", font=dict(size=13, color="#1a1a1a")),
-            autorange="reversed", showgrid=False, linecolor="#bdbdbd",
-            tickfont=dict(size=12, color="#2a2a2a"), ticks="outside", ticklen=4, tickcolor="#cccccc", automargin=True,
-        ),
-        font=dict(family="Segoe UI, Arial, sans-serif", size=12, color="#2a2a2a"),
-        hoverlabel=dict(bgcolor="white", font_size=13, font_family="Segoe UI", bordercolor="#cccccc"),
-        margin=dict(l=200, r=40, t=120, b=60),
-    )
-    return fig
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Analyze CAN bus byte-level entropy")
-    parser.add_argument("input", type=Path, help="Path to the input CAN log file")
-    parser.add_argument("output", type=Path, nargs="?", default=Path("entropy_report.html"))
-    parser.add_argument("title", nargs="?", default="CAN Bus Byte-Level Entropy")
-    args = parser.parse_args()
-
-    df = load_data(args.input)
-    entropy_df = calculate_byte_entropy(df)
-    fig = plot_entropy_heatmap(entropy_df, title=args.title)
-    config = {
-        "responsive": True,
-        "displaylogo": False,
-        "scrollZoom": True,
-        "modeBarButtonsToAdd": ["toggleSpikelines"],
-        "toImageButtonOptions": {"format": "png", "scale": 2},
-    }
-    fig.write_html(str(args.output), include_plotlyjs="cdn", config=config)

stats/frequency.py

@@ -1,128 +0,0 @@
-# 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.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'
-    formatted = _format_can_id_vec(df[can_id_col])
-
-    counts = formatted.value_counts()
-    freq_df = pd.DataFrame({
-        'Identifier': counts.index,
-        'Count': counts.to_numpy(),
-    })
-    total = counts.sum()
-    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(
-        y=stats_df['Identifier'],
-        x=stats_df['Count'],
-        orientation='h',
-        marker=dict(
-            color=stats_df['Count'],
-            colorscale='Turbo',
-            cmin=int(stats_df['Count'].min()) if n else 0,
-            cmax=int(stats_df['Count'].max()) if n else 1,
-            line_width=0,
-        ),
-        customdata=stats_df[['Percentage']].to_numpy(),
-        hovertemplate="<b>%{y}</b><br>Count: %{x:,}<br>Share: %{customdata[0]}%<extra></extra>",
-        texttemplate='%{x:,}',
-        textposition='outside',
-        cliponaxis=False,
-    ))
-
-    fig.update_layout(
-        height=max(600, n * 18),
-        autosize=True,
-        template='plotly_white',
-        xaxis=dict(
-            type='log',
-            title=dict(text="Message count [log scale]", font=dict(size=13, color="#1a1a1a")),
-            side="top",
-            dtick=1,
-            showgrid=False,
-            linecolor="#bdbdbd",
-            tickfont=dict(size=12, color="#2a2a2a"),
-            ticks="outside",
-            ticklen=4,
-            tickcolor="#cccccc",
-        ),
-        yaxis=dict(
-            title=dict(text="PGN or CAN ID", font=dict(size=13, color="#1a1a1a")),
-            showgrid=False,
-            linecolor="#bdbdbd",
-            tickfont=dict(size=12, color="#2a2a2a"),
-            ticks="outside",
-            ticklen=4,
-            tickcolor="#cccccc",
-            automargin=True,
-            type='category',
-        ),
-        font=dict(family="Segoe UI, Arial, sans-serif", size=12, color='#2a2a2a'),
-        hoverlabel=dict(bgcolor="white", font_size=13, font_family="Segoe UI", bordercolor='#cccccc'),
-        margin=dict(l=200, r=40, t=120, b=60),
-        bargap=0.35,
-        coloraxis_colorbar=dict(
-            title=dict(text='Message Count', side='top'),
-            orientation='h',
-            thickness=15,
-            len=0.35,
-            x=1.0,
-            xanchor='right',
-            y=1.02,
-            yanchor='bottom',
-            tickformat=',',
-            outlinecolor='#cccccc',
-            outlinewidth=0.5,
-        ),
-        title=dict(text=title, font=dict(size=20, color='#1a1a1a'), x=0.5, xanchor='center', pad=dict(b=20)),
-    )
-    fig.update_xaxes(
-        showgrid=True, gridwidth=0.5, gridcolor='#e8e8e8',
-        zeroline=False, linecolor='#bdbdbd', mirror=False,
-        tickformat=',',
-        minor=dict(showgrid=True, gridcolor='#f4f4f4', gridwidth=0.5),
-    )
-    fig.update_yaxes(
-        showgrid=False, zeroline=False, linecolor='#bdbdbd',
-        ticks='outside', ticklen=4, tickcolor='#cccccc', automargin=True,
-    )
-    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")
-    parser.add_argument("output", type=Path, nargs="?", default=Path("freq_report.html"))
-    parser.add_argument("title", nargs="?", default="Frequency")
-    args = parser.parse_args()
-
-    df = load_data(args.input)
-    stats = calculate_frequency(df)
-    fig = plot_frequency(stats, title=args.title)
-    config = {
-        'responsive': True,
-        'displaylogo': False,
-        'scrollZoom': True,
-        'modeBarButtonsToAdd': ['toggleSpikelines'],
-        'toImageButtonOptions': {'format': 'png', 'scale': 2},
-    }
-    fig.write_html(str(args.output), include_plotlyjs='cdn', config=config)

stats/id_viewer.py

@@ -1,151 +0,0 @@
-# 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.converter import format_can_id_vec as _format_can_id_vec
-from stats.utils.loader import load_data
-
-_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'
-    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]
-
-    byte_cols = [f"b{i}" for i in range(8) if f"b{i}" in filtered.columns]
-    if filtered.empty:
-        return filtered, byte_cols
-
-    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.sort_values('Timestamp', kind='stable')
-    arr = filtered[byte_cols].to_numpy(dtype=np.float32, copy=False)
-    if len(arr) > 1:
-        changed = np.any(arr[1:] != arr[:-1], axis=1)
-        keep = np.concatenate(([True], changed))
-        filtered = filtered.iloc[keep]
-
-    return filtered, byte_cols
-
-
-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
-    )
-    n = len(byte_cols)
-
-    x = df['Timestamp'].to_numpy() if not df.empty else np.array([])
-    for i, col in enumerate(byte_cols):
-        y = df[col].to_numpy(dtype=np.float32, copy=False) if not df.empty else np.array([])
-
-        fig.add_trace(go.Scatter(
-            mode='lines',
-            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>",
-        ), hf_x=x, hf_y=y)
-
-    all_button = dict(label='ALL', method='restyle', args=[{'visible': [True] * n}])
-    none_button = dict(label='NONE', method='restyle', args=[{'visible': ['legendonly'] * n}])
-
-    fig.update_layout(
-        height=600,
-        autosize=True,
-        template='plotly_white',
-        title=dict(
-            text=f"{title} - ID: {can_id}",
-            font=dict(size=20, color='#1a1a1a'),
-            x=0.5, xanchor='center',
-            pad=dict(b=20),
-        ),
-        font=dict(family="Segoe UI, Arial, sans-serif", size=12, color='#2a2a2a'),
-        hoverlabel=dict(bgcolor="white", font_size=13, font_family="Segoe UI", bordercolor='#cccccc'),
-        margin=dict(l=60, r=40, t=120, b=140),
-        legend=dict(
-            orientation='h',
-            x=0.5, xanchor='center',
-            y=-0.18, yanchor='top',
-            title=None,
-            bgcolor='white',
-            bordercolor='#cccccc',
-            borderwidth=1,
-            font=dict(size=12, color="#2a2a2a"),
-            itemsizing='constant',
-            itemclick='toggle',
-            itemdoubleclick='toggleothers',
-        ),
-        xaxis=dict(
-            title=dict(text="Timestamp", font=dict(size=13, color="#1a1a1a")),
-            showgrid=True, gridwidth=0.5, gridcolor='#e8e8e8',
-            zeroline=False, linecolor="#bdbdbd",
-            tickfont=dict(size=12, color="#2a2a2a"),
-            ticks="outside", ticklen=4, tickcolor="#cccccc",
-            minor=dict(showgrid=True, gridcolor='#f4f4f4', gridwidth=0.5),
-        ),
-        yaxis=dict(
-            title=dict(text="Byte Value", font=dict(size=13, color="#1a1a1a")),
-            showgrid=True, gridwidth=0.5, gridcolor='#e8e8e8',
-            zeroline=False, linecolor="#bdbdbd",
-            tickfont=dict(size=12, color="#2a2a2a"),
-            ticks="outside", ticklen=4, tickcolor="#cccccc",
-        ),
-        updatemenus=[
-            dict(
-                type='buttons',
-                direction='right',
-                x=0.5, xanchor='center',
-                y=-0.06, yanchor='top',
-                buttons=[all_button, none_button],
-                bgcolor='white',
-                bordercolor='#cccccc',
-                borderwidth=1,
-                font=dict(size=11, color='#2a2a2a'),
-                pad=dict(l=5, r=5, t=5, b=5),
-            )
-        ],
-    )
-    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")
-    parser.add_argument("can_id", type=str, help="CAN ID to visualize")
-    parser.add_argument("output", type=Path, nargs="?", default=Path("bits_report.html"))
-    parser.add_argument("title", nargs="?", default="Byte Visualization")
-    args = parser.parse_args()
-
-    df = load_data(args.input)
-    filtered_df, byte_cols = prepare_data(df, args.can_id)
-    fig = plot_bits(filtered_df, byte_cols, args.can_id, title=args.title)
-
-    config = {
-        'responsive': True,
-        'displaylogo': False,
-        'scrollZoom': True,
-        'modeBarButtonsToAdd': ['toggleSpikelines'],
-        'toImageButtonOptions': {'format': 'png', 'scale': 2},
-    }
-    fig.write_html(str(args.output), include_plotlyjs='cdn', config=config)

vehicle/__init__.py

@@ -1,19 +0,0 @@
-# File: vehicle/__init__.py
-# Copyright (C) 2026 Erick Ahmed
-# SPDX-License-Identifier: AGPL-3.0-or-later
-
-import importlib
-
-def get_vehicle_module(brand: str):
-    """
-    Dynamically imports the correct decoder module based on the vehicle brand.
-    Falls back to 'vehicle.generic' if a specific brand module is not found.
-    """
-    if not brand:
-        return importlib.import_module("vehicle.generic")
-
-    module_name = f"vehicle.{brand.lower().replace(' ', '_')}"
-    try:
-        return importlib.import_module(module_name)
-    except ModuleNotFoundError:
-        return importlib.import_module("vehicle.generic")

vehicle/base.py

@@ -1,186 +0,0 @@
-# File: vehicle/base.py
-# 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 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
-    factor: float = 1.0
-    offset: float = 0.0
-    is_signed: bool = False
-    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_indices = _byte_indices(sig)
-    if not byte_indices:
-        return np.full(bytes_arr.shape[0], np.nan, dtype=np.float64)
-
-    raw = np.zeros(bytes_arr.shape[0], dtype=np.int64)
-    if sig.byte_order == "little":
-        for shift, bi in enumerate(byte_indices):
-            raw += bytes_arr[:, bi].astype(np.int64) << (shift * 8)
-    else:
-        for shift, bi in enumerate(reversed(byte_indices)):
-            raw += bytes_arr[:, bi].astype(np.int64) << (shift * 8)
-
-    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)
-        raw = (raw ^ sign_bit) - sign_bit
-
-    return raw.astype(np.float64) * sig.factor + sig.offset
-
-
-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()
-
-    frame_def = decoder_rules[norm]
-
-    id_col = "ID" if "ID" in df.columns else "Identifier"
-    df_ids = df[id_col].astype(str).map(normalize_id)
-    sub = df.loc[df_ids == norm].copy()
-    if sub.empty:
-        return pd.DataFrame()
-
-    byte_cols = [f"b{i}" for i in range(8) if f"b{i}" in sub.columns]
-    if not byte_cols:
-        return pd.DataFrame()
-
-    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()
-        )
-
-    out = pd.DataFrame()
-    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:
-    """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"),
-                )
-            ],
-            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>"
-                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),
-        ),
-        height=height,
-        autosize=True,
-        template="plotly_white",
-        margin=dict(l=55, r=20, t=55, b=45),
-        xaxis=dict(
-            title=dict(text="Time", font=dict(size=11)),
-            showgrid=True, gridwidth=0.5, gridcolor="#eee",
-            zeroline=False, linecolor="#bdbdbd",
-        ),
-        yaxis=dict(
-            title=dict(text=signal_name, font=dict(size=11)),
-            showgrid=True, gridwidth=0.5, gridcolor="#eee",
-            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",
-        ),
-    )
-    return fig

vehicle/komatsu.py

@@ -1,180 +0,0 @@
-# File: vehicle/komatsu.py
-# Copyright (C) 2026 Erick Ahmed
-# SPDX-License-Identifier: AGPL-3.0-or-later
-
-"""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 (
-    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
-
-load_state_sig = SignalDef(
-    name="Engine Load State",
-    bit_start=24,
-    bit_length=8,
-    factor=1,
-    offset=0.0,
-    is_signed=False,
-    byte_order="big",
-    unit="",
-)
-load_state_sig.skip_plot = True
-
-DECODER_RULES: Dict[str, FrameDef] = {
-    normalize_id("0x011F"): FrameDef(
-        can_id="0x011F",
-        description="ECM",
-        color="#e41a1c",
-        signals=[
-            SignalDef(
-                name="Engine",
-                bit_start=0,
-                bit_length=16,
-                factor=0.125,
-                offset=0.0,
-                is_signed=False,
-                byte_order="big",
-                unit="RPM",
-            ),
-            SignalDef(
-                name="Engine Load",
-                bit_start=16,
-                bit_length=16,
-                factor=0.05,
-                offset=0,
-                is_signed=False,
-                byte_order="little",
-                unit="%",
-            ),
-        ],
-    ),
-    normalize_id("0x0CFF3300"): FrameDef(
-        can_id="0x0CFF3300",
-        description="Engine temperatures",
-        color="#0080fe",
-        signals=[
-            SignalDef(
-                name="Engine coolant temp",
-                bit_start=8,
-                bit_length=8,
-                factor=1,
-                offset=0.0,
-                is_signed=False,
-                byte_order="big",
-                unit="℃",
-            ),
-            SignalDef(
-                name="Engine oil temp",
-                bit_start=40,
-                bit_length=8,
-                factor=1,
-                offset=0.0,
-                is_signed=False,
-                byte_order="big",
-                unit="℃",
-            ),
-            load_state_sig,
-            CustomPlotDef(
-                name="Engine Load",
-                plot_func=lambda decoded, color: plot_load_state_pie(decoded, color),
-            ),
-        ],
-    ),
-}
-
-LOAD_STATE_MAP = {
-    0: "Boot up",
-    16: "Normal 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:
-        fig = px.pie()
-        fig.update_layout(
-            title=dict(
-                text="Engine Load State",
-                font=dict(size=14, color="#1a1a1a"),
-                x=0.5, xanchor="center", pad=dict(b=10)
-            ),
-            height=280,
-            template="plotly_white",
-            annotations=[dict(text="No data", showarrow=False, x=0.5, y=0.5, font=dict(size=13, color="#888"))]
-        )
-        return fig
-
-    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"]
-
-    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",
-        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]},
-    )
-    fig.update_layout(
-        title=dict(
-            text="Engine Load State",
-            font=dict(size=14, color="#1a1a1a"),
-            x=0.5, xanchor="center", pad=dict(b=10)
-        ),
-        height=280,
-        autosize=True,
-        template="plotly_white",
-        margin=dict(l=20, r=20, t=55, b=45),
-        font=dict(family="Segoe UI, Arial, sans-serif", size=11, color="#2a2a2a"),
-        legend=dict(x=0.6, y=0.5),
-    )
-    return fig
-
-def decode_dataframe(df, can_id):
-    """Decode *can_id* from *df*, filtering out non-SignalDef entries first."""
-    filtered_rules: Dict[str, FrameDef] = {}
-    for nid, frame in DECODER_RULES.items():
-        new_frame = deepcopy(frame)
-        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)