Merge pull request 'Implement Plotly Dash app with efficient dynamic resampler' (#6) from dev-dash into main

Reviewed-on: erickahmed/CANveyor#6

COPYRIGHT.md

@@ -0,0 +1,9 @@
+# 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

@@ -1,23 +1,21 @@
-GNU GENERAL PUBLIC LICENSE
+GNU AFFERO GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
+   Version 3, 19 November 2007
 
- Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
 Everyone is permitted to copy and distribute verbatim copies
 of this license document, but changing it is not allowed.
 
         Preamble
 
-  The GNU General Public License is a free, copyleft license for
+The GNU Affero General Public License is a free, copyleft license for
-software and other kinds of works.
+software and other kinds of works, specifically designed to ensure
+cooperation with the community in the case of network server software.
 
 The licenses for most software and other practical works are designed
 to take away your freedom to share and change the works.  By contrast,
-the GNU General Public License is intended to guarantee your freedom to
+our General Public Licenses are 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.  We, the Free Software Foundation, use the
+software for all its users.
-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
@@ -26,44 +24,34 @@ 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.
 
-  To protect your rights, we need to prevent others from denying you
+Developers that use our General Public Licenses protect your rights
-these rights or asking you to surrender the rights.  Therefore, you have
+with two steps: (1) assert copyright on the software, and (2) offer
-certain responsibilities if you distribute copies of the software, or if
+you this License which gives you legal permission to copy, distribute
-you modify it: responsibilities to respect the freedom of others.
+and/or modify the software.
 
-  For example, if you distribute copies of such a program, whether
+A secondary benefit of defending all users' freedom is that
-gratis or for a fee, you must pass on to the recipients the same
+improvements made in alternate versions of the program, if they
-freedoms that you received.  You must make sure that they, too, receive
+receive widespread use, become available for other developers to
-or can get the source code.  And you must show them these terms so they
+incorporate.  Many developers of free software are heartened and
-know their rights.
+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.
 
-  Developers that use the GNU GPL protect your rights with two steps:
+The GNU Affero General Public License is designed specifically to
-(1) assert copyright on the software, and (2) offer you this License
+ensure that, in such cases, the modified source code becomes available
-giving you legal permission to copy, distribute and/or modify it.
+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.
 
-  For the developers' and authors' protection, the GPL clearly explains
+An older license, called the Affero General Public License and
-that there is no warranty for this free software.  For both users' and
+published by Affero, was designed to accomplish similar goals.  This is
-authors' sake, the GPL requires that modified versions be marked as
+a different license, not a version of the Affero GPL, but Affero has
-changed, so that their problems will not be attributed erroneously to
+released a new version of the Affero GPL which permits relicensing under
-authors of previous versions.
+this license.
-
-  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.
@@ -72,7 +60,7 @@ modification follow.
 
 0. Definitions.
 
-  "This License" refers to version 3 of the GNU General Public License.
+"This License" refers to version 3 of the GNU Affero General Public License.
 
 "Copyright" also means copyright-like laws that apply to other kinds of
 works, such as semiconductor masks.
@@ -549,35 +537,45 @@ 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. Use with the GNU Affero General Public License.
+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.
 
 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 Affero General Public License into a single
+under version 3 of the GNU 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 special requirements of the GNU Affero General Public License,
+but the work with which it is combined will remain governed by version
-section 13, concerning interaction through a network will apply to the
+3 of the GNU General Public License.
-combination as such.
 
 14. Revised Versions of this License.
 
 The Free Software Foundation may publish revised and/or new versions of
-the GNU General Public License from time to time.  Such new versions will
+the GNU Affero General Public License from time to time.  Such new versions
-be similar in spirit to the present version, but may differ in detail to
+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 General
+Program specifies that a certain numbered version of the GNU Affero 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 General Public License, you may choose any version ever published
+GNU Affero 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 General Public License can be used, that proxy's
+versions of the GNU Affero 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.
 
@@ -635,40 +633,29 @@ 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 General Public License as published by
+it under the terms of the GNU Affero General Public License as published
-    the Free Software Foundation, either version 3 of the License, or
+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 General Public License for more details.
+GNU Affero General Public License for more details.
 
-    You should have received a copy of the GNU General Public License
+You should have received a copy of the GNU Affero General Public License
-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+along with this program.  If not, see <http://www.gnu.org/licenses/>.
 
 Also add information on how to contact you by electronic and paper mail.
 
-  If the program does terminal interaction, make it output a short
+If your software can interact with users remotely through a computer
-notice like this when it starts in an interactive mode:
+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
-    <program>  Copyright (C) <year>  <name of author>
+interface could display a "Source" link that leads users to an archive
-    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+of the code.  There are many ways you could offer source, and different
-    This is free software, and you are welcome to redistribute it
+solutions will be better for different programs; see section 13 for the
-    under certain conditions; type `show c' for details.
+specific requirements.
-
-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 GPL, see
+For more information on this, and how to apply and follow the GNU AGPL, see
-<https://www.gnu.org/licenses/>.
+<http://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

@@ -0,0 +1,86 @@
+# 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)

main.py

@@ -0,0 +1,222 @@
+# File: main.py
+# Copyright (C) 2026 Erick Ahmed
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+import os
+from pathlib import Path
+from concurrent.futures import ThreadPoolExecutor
+
+import polars as pl
+import dash
+from dash import dcc, html, Input, Output
+import dash_bootstrap_components as dbc
+import numpy as np
+
+from parser import parse_log, parse_csv
+from decoder import decode_j1939_frames
+from stats.utils.extractor import load_data
+from stats.id_viewer import _format_can_id_vec, plot_bits
+from stats.frequency import calculate_frequency, plot_frequency
+from stats.correlation import calculate_correlation, plot_correlation_heatmap
+from stats.entropy import calculate_byte_entropy, plot_entropy_heatmap
+
+RAW_LOG = "data/logs/rawlog.txt"
+BUS1_CSV = "data/csv/bus1.csv"
+BUS2_CSV = "data/csv/bus2.csv"
+BUS1_PARQUET = "data/parquet/bus1.parquet"
+BUS2_PARQUET = "data/parquet/bus2.parquet"
+BUS1_DECODED = "data/parquet/bus1_decoded.parquet"
+BUS2_DECODED = "data/parquet/bus2_decoded.parquet"
+
+def run_pipeline():
+    os.makedirs("data/logs", exist_ok=True)
+    os.makedirs("data/csv", exist_ok=True)
+    os.makedirs("data/parquet", exist_ok=True)
+    if not Path(BUS1_DECODED).exists() or not Path(BUS2_DECODED).exists():
+        print("Parsing raw log...")
+        parse_log(RAW_LOG, BUS1_CSV, BUS2_CSV)
+
+        print("Converting to parquet...")
+        parse_csv(BUS1_CSV).sink_parquet(BUS1_PARQUET)
+        parse_csv(BUS2_CSV).sink_parquet(BUS2_PARQUET)
+
+        print("Decoding J1939...")
+        df1 = pl.read_parquet(BUS1_PARQUET)
+        df2 = pl.read_parquet(BUS2_PARQUET)
+        dec1 = decode_j1939_frames(df1)
+        dec2 = decode_j1939_frames(df2)
+        dec1.write_parquet(BUS1_DECODED)
+        dec2.write_parquet(BUS2_DECODED)
+
+run_pipeline()
+
+print("Loading data into memory...")
+DATA = {
+    "Bus 1": load_data(BUS1_DECODED),
+    "Bus 2": load_data(BUS2_DECODED)
+}
+
+PRECOMPUTED_FIGURES = {}
+DATA_BY_ID = {}
+CORR_CACHE = {}
+
+def process_bus_data(bus, df):
+    precomp = {}
+    precomp[f"{bus}_freq"] = plot_frequency(calculate_frequency(df), title=f"{bus} Frequency")
+    precomp[f"{bus}_entropy"] = plot_entropy_heatmap(calculate_byte_entropy(df), title=f"{bus} Byte-Level Entropy")
+
+    can_id_col = 'ID' if 'ID' in df.columns else 'Identifier'
+    formatted = _format_can_id_vec(df[can_id_col])
+    df = df.assign(Formatted_ID=formatted)
+
+    df = df.sort_values(['Formatted_ID', 'Timestamp'], kind='stable')
+
+    grouped = {}
+    for can_id, group in df.groupby(by='Formatted_ID'):
+        byte_cols = [f"b{i}" for i in range(8) if f"b{i}" in group.columns]
+        if not group.empty and len(byte_cols) > 0:
+            arr = group[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))
+                group = group.iloc[keep]
+        grouped[can_id] = (group, byte_cols)
+
+    return precomp, grouped
+
+with ThreadPoolExecutor() as executor:
+    futures = {executor.submit(process_bus_data, bus, df): bus for bus, df in DATA.items()}
+    for future in futures:
+        bus = futures[future]
+        precomp, grouped = future.result()
+        PRECOMPUTED_FIGURES.update(precomp)
+        DATA_BY_ID[bus] = grouped
+
+app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
+app.config.suppress_callback_exceptions = True
+
+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="Statistics", tab_id="statistics", children=[
+            dbc.Row([
+                dbc.Col(html.Label("Select Bus:"), width=1, className="mt-2"),
+                dbc.Col(dcc.Dropdown(
+                    id='bus-selector',
+                    options=[{'label': k, 'value': k} for k in DATA.keys()],
+                    value='Bus 1',
+                    clearable=False
+                ), width=2),
+            ], className="mb-3 mt-3"),
+            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)
+
+@app.callback(
+    Output('tab-content', 'children'),
+    Input('tabs', 'active_tab'),
+    Input('bus-selector', 'value')
+)
+def render_content(tab, bus):
+    df = DATA[bus]
+
+    if tab == 'freq':
+        return dcc.Graph(figure=PRECOMPUTED_FIGURES[f"{bus}_freq"], style={'height': '80vh'})
+
+    elif tab == 'id_viewer':
+        ids = sorted(DATA_BY_ID[bus].keys())
+        return html.Div([
+            html.Label("Select 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':
+        ids = sorted(DATA_BY_ID[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'})
+        ])
+
+    elif tab == 'entropy':
+        return dcc.Graph(figure=PRECOMPUTED_FIGURES[f"{bus}_entropy"], style={'height': '80vh'})
+
+    return html.Div("Tab not found")
+
+@app.callback(
+    Output('id-viewer-graph', 'figure'),
+    Input('id-selector', 'value'),
+    Input('bus-selector', 'value'),
+    Input('tabs', 'active_tab'),
+)
+def update_id_viewer(selected_id, bus, tab):
+    if tab != 'id_viewer' or not selected_id:
+        return dash.no_update
+
+    grouped_data = DATA_BY_ID.get(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"{bus} Byte Visualization")
+
+@app.callback(
+    Output('corr-graph', 'figure'),
+    Input('corr-method', 'value'),
+    Input('corr-target', 'value'),
+    Input('bus-selector', 'value'),
+    Input('tabs', 'active_tab'),
+)
+def update_corr(method, target, bus, tab):
+    if tab != 'corr':
+        return dash.no_update
+
+    target_id = None if target == 'all' or not target else target
+    cache_key = (bus, method, target_id)
+
+    if cache_key not in CORR_CACHE:
+        df = DATA[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]
+
+    title = f"{bus} Correlation"
+    if target_id:
+        title += f" ({target_id})"
+
+    return plot_correlation_heatmap(corr_df, target_id=target_id, title=title)
+
+if __name__ == '__main__':
+    app.run(debug=True)

parser.py

@@ -1,3 +1,7 @@
+# File: parser.py
+# Copyright (C) 2026 Erick Ahmed
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
 import re
 import csv
 import polars as pl
@@ -15,7 +19,7 @@ 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]{1,8})\s+([0-9A-Fa-f]{1,2})\s+')
+    start_pattern = re.compile(r'(C[12]):([0-9A-Fa-f]{7,8})\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, \
@@ -31,7 +35,12 @@ 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()
+
+                can_id = match.group(2).upper().zfill(8)
+
+                if int(can_id, 16) > 0x1FFFFFFF:
+                    continue
+
                 dlc_str = match.group(3)
 
                 try:
@@ -68,7 +77,7 @@ 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.columns:
+    if "Timestamp" not in lf.collect_schema().names():
         lf = lf.with_row_index("Timestamp")
 
     byte_exprs = []
@@ -112,4 +121,3 @@ 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}")

pyproject.toml

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

src/j1939_decoder.py

@@ -1,56 +0,0 @@
-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(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)
-    mask = get_j1939_mask()
-
-    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(pl.lit(255, dtype=pl.UInt8))
-    pgn = pl.when(pf < 240).then((id_int // 256) % 65536).otherwise((id_int // 256) % 262144).cast(pl.UInt32)
-
-    j1939_struct = 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.alias("PGN")
-    ])
-
-    return lf.with_columns(
-        pl.when(mask).then(j1939_struct).otherwise(None).alias("j1939_metadata")
-    )
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="CAN Log J1939 Decoder")
-    parser.add_argument("input_parquet", help="Path to the parsed .parquet file")
-    parser.add_argument("output_parquet", help="Path to save the decoded .parquet file")
-    args = parser.parse_args()
-
-    print(f"[*] Loading {args.input_parquet}")
-    lf = pl.scan_parquet(args.input_parquet)
-
-    print("[*] Decoding J1939 IDs into a nested Struct column")
-    lf_decoded = decode_j1939(lf)
-
-    print(f"[*] Saving decoded data to {args.output_parquet}")
-    lf_decoded.sink_parquet(args.output_parquet)

stats/correlation.py

@@ -0,0 +1,174 @@
+# File: correlation.py
+# Copyright (C) 2026 Erick Ahmed
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+import argparse
+from pathlib import Path
+from concurrent.futures import ThreadPoolExecutor
+
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+
+from stats.utils.extractor import load_data
+from stats.utils.extractor import to_int
+
+def _format_can_id_vec(s: pd.Series) -> pd.Series:
+    s = s.astype('string').str.strip()
+    s = s.str.replace(r'^0x', '', case=False, regex=True)
+    s = s.str.upper()
+    return s.fillna('UNKNOWN').replace('', 'UNKNOWN')
+
+def _ensure_int_bytes(df: pd.DataFrame, cols: list) -> pd.DataFrame:
+    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):
+        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)
+
+    with ThreadPoolExecutor() as executor:
+        out = np.array(list(executor.map(_process_group, groups)))
+
+    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

@@ -0,0 +1,152 @@
+# File: entropy.py
+# Copyright (C) 2026 Erick Ahmed
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+import argparse
+from pathlib import Path
+from concurrent.futures import ThreadPoolExecutor
+
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+
+from stats.utils.extractor import load_data
+from stats.utils.extractor import to_int
+
+def _format_can_id_vec(s: pd.Series) -> pd.Series:
+    s = s.astype('string').str.strip()
+    s = s.str.replace(r'^0x', '', case=False, regex=True)
+    s = s.str.upper()
+    return s.fillna('UNKNOWN').replace('', 'UNKNOWN')
+
+def _entropy_col(a: np.ndarray) -> float:
+    a = a[~np.isnan(a)]
+    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):
+        res = np.zeros(n_cols, dtype=np.float64)
+        for ci in range(n_cols):
+            res[ci] = _entropy_col(sub[:, ci])
+        return res
+
+    with ThreadPoolExecutor() as executor:
+        out = np.array(list(executor.map(_process_group, groups)))
+
+    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

@@ -0,0 +1,126 @@
+# File: frequency.py
+# Copyright (C) 2026 Erick Ahmed
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+import argparse
+from pathlib import Path
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+from stats.utils.extractor import load_data
+
+def _format_can_id_vec(s: pd.Series) -> pd.Series:
+    s = s.astype('string').str.strip()
+    s = s.str.replace(r'^0x', '', case=False, regex=True)
+    s = s.str.upper()
+    return s.fillna('UNKNOWN').replace('', 'UNKNOWN')
+
+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

@@ -0,0 +1,143 @@
+# File: id_viewer.py
+# Copyright (C) 2026 Erick Ahmed
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+import argparse
+from pathlib import Path
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+from plotly_resampler import FigureResampler
+from stats.utils.extractor import load_data
+
+def _format_can_id_vec(s: pd.Series) -> pd.Series:
+    s = s.astype('string').str.strip()
+    s = s.str.replace(r'^0x', '', case=False, regex=True)
+    s = s.str.upper()
+    return s.fillna('UNKNOWN').replace('', 'UNKNOWN')
+
+def prepare_data(df, target_id):
+    can_id_col = 'ID' if 'ID' in df.columns else 'Identifier'
+    formatted = _format_can_id_vec(df[can_id_col])
+    df = df.assign(Formatted_ID=formatted)
+    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, byte_cols, can_id, title):
+    fig = FigureResampler(
+        resampled_trace_prefix_suffix=("", ""),
+        show_mean_aggregation_size=False
+    )
+    colors = ['#e41a1c', '#377eb8', '#4daf4a', '#984ea3', '#ff7f00', '#ffff33', '#a65628', '#f781bf']
+    n = len(byte_cols)
+
+    x = df['Timestamp'].to_numpy() if not df.empty else np.array([])
+    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=colors[i % len(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)

stats/utils/extractor.py

@@ -0,0 +1,63 @@
+# File: extractor.py
+# Copyright (C) 2026 Erick Ahmed
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+import json
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import polars as pl
+
+def to_int(x):
+    if isinstance(x, (int, np.integer)):
+        return int(x)
+    if isinstance(x, str):
+        try:
+            return int(x, 16)
+        except ValueError:
+            return np.nan
+    return np.nan
+
+def extract_id(row: pd.Series) -> str:
+    meta = row.get('j1939_metadata')
+    if pd.isna(meta):
+        return f"ID: {row['ID']}"
+    if isinstance(meta, str):
+        try:
+            meta = json.loads(meta)
+        except json.JSONDecodeError:
+            return f"ID: {row['ID']}"
+    if isinstance(meta, dict) and 'PGN' in meta:
+        return f"PGN: {meta['PGN']}"
+    return f"ID: {row['ID']}"
+
+def load_data(file_path: Path) -> pd.DataFrame:
+    lf = pl.scan_parquet(file_path)
+    schema = lf.collect_schema()
+    names = schema.names()
+
+    byte_cols = [f"b{i}" for i in range(8) if f"b{i}" in names]
+    if byte_cols:
+        lf = lf.with_columns([
+            pl.col(c).str.to_integer(base=16, strict=False).cast(pl.Int16).alias(c)
+            for c in byte_cols
+        ])
+
+    id_col = 'ID' if 'ID' in names else 'Identifier'
+    id_expr = pl.col(id_col).cast(pl.Utf8)
+
+    if 'j1939_metadata' in names:
+        try:
+            lf = lf.with_columns(
+                pl.when(pl.col('j1939_metadata').is_not_null())
+                .then(pl.lit('PGN: ') + pl.col('j1939_metadata').struct.field('PGN').cast(pl.Utf8))
+                .otherwise(pl.lit('ID: ') + id_expr)
+                .alias('Identifier')
+            )
+        except Exception:
+            lf = lf.with_columns((pl.lit('ID: ') + id_expr).alias('Identifier'))
+    else:
+        lf = lf.with_columns((pl.lit('ID: ') + id_expr).alias('Identifier'))
+
+    return lf.collect().to_pandas()