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Merge pull request 'Implement plotly resamper and precompute data' (#5) from dev-plotly-resampler into dev-dash

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Reviewed-on: erickahmed/CANveyor#5
This commit was merged in pull request #5.
This commit is contained in:
Erick
2026-07-22 18:40:44 +02:00
parent 65591bbc6b c06d813c26
commit 0a4dc5801e
2 changed files with 59 additions and 19 deletions
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main.py
+51 -14
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@@ -9,13 +9,13 @@ 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.id_viewer import prepare_data, plot_bits
from stats.correlation import calculate_correlation, plot_correlation_heatmap
from stats.entropy import calculate_byte_entropy, plot_entropy_heatmap
@@ -55,7 +55,35 @@ DATA = {
"Bus 2": load_data(BUS2_DECODED)
}
PRECOMPUTED_FIGURES = {}
DATA_BY_ID = {}
CORR_CACHE = {}
for bus, df in DATA.items():
PRECOMPUTED_FIGURES[f"{bus}_freq"] = plot_frequency(calculate_frequency(df), title=f"{bus} Frequency")
PRECOMPUTED_FIGURES[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('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)
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("CAN Bus Analyzer", className="my-4"),
@@ -86,12 +114,10 @@ def render_content(tab, bus):
df = DATA[bus]
if tab == 'freq':
stats = calculate_frequency(df)
fig = plot_frequency(stats, title=f"{bus} Frequency")
return dcc.Graph(figure=fig, style={'height': '80vh'})
return dcc.Graph(figure=PRECOMPUTED_FIGURES[f"{bus}_freq"], style={'height': '80vh'})
elif tab == 'id_viewer':
ids = sorted(df['ID'].unique().tolist())
ids = sorted(DATA_BY_ID[bus].keys())
return html.Div([
html.Label("Select CAN ID:"),
dcc.Dropdown(
@@ -105,7 +131,7 @@ def render_content(tab, bus):
])
elif tab == 'corr':
ids = sorted(df['ID'].unique().tolist())
ids = sorted(DATA_BY_ID[bus].keys())
return html.Div([
dbc.Row([
dbc.Col(html.Label("Method:"), width=1, className="mt-2"),
@@ -127,9 +153,7 @@ def render_content(tab, bus):
])
elif tab == 'entropy':
entropy_df = calculate_byte_entropy(df)
fig = plot_entropy_heatmap(entropy_df, title=f"{bus} Byte-Level Entropy")
return dcc.Graph(figure=fig, style={'height': '80vh'})
return dcc.Graph(figure=PRECOMPUTED_FIGURES[f"{bus}_entropy"], style={'height': '80vh'})
return html.Div("Tab not found")
@@ -142,8 +166,12 @@ def render_content(tab, bus):
def update_id_viewer(selected_id, bus, tab):
if tab != 'id_viewer' or not selected_id:
return dash.no_update
df = DATA[bus]
filtered_df, byte_cols = prepare_data(df, selected_id)
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(
@@ -156,12 +184,21 @@ def update_id_viewer(selected_id, bus, tab):
def update_corr(method, target, bus, tab):
if tab != 'corr':
return dash.no_update
df = DATA[bus]
target_id = None if target == 'all' or not target else target
corr_df = calculate_correlation(df, method=method, target_id=target_id)
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__':
stats/id_viewer.py
+8 -5
View File
@@ -7,6 +7,7 @@ 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:
@@ -40,22 +41,24 @@ def prepare_data(df, target_id):
return filtered, byte_cols
def plot_bits(df, byte_cols, can_id, title):
fig = go.Figure()
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.Scattergl(
x=x,
y=y,
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}])