diff --git a/stat/correlation.py b/stat/correlation.py index 06aa856..1c232d7 100644 --- a/stat/correlation.py +++ b/stat/correlation.py @@ -12,75 +12,96 @@ import plotly.graph_objects as go from utils.extractor import load_data from utils.extractor import to_int -def _format_can_id(x): - """Safely cleans CAN ID strings without altering their length or value.""" - if pd.isna(x): - return "UNKNOWN" +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') - s = str(x).strip() - if not s: - return "UNKNOWN" - - if s.lower().startswith('0x'): - s = s[2:] - - return s.upper() +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: - """Calculates inter-byte correlation grouped by identifier.""" - byte_cols = [f"b{i}" for i in range(8)] + byte_cols = [f"b{i}" for i in range(8) if f"b{i}" in df.columns] available_cols = [col for col in byte_cols if col 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 = df[can_id_col].apply(_format_can_id) + identifiers = _format_can_id_vec(df[can_id_col]).to_numpy() - df_bytes = df[available_cols].copy() - for col in available_cols: - df_bytes[col] = df_bytes[col].apply(to_int) + 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(target_id) - group = df_bytes[identifiers == target_id] - if group.empty: + 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") - return group[available_cols].corr(method=method).fillna(0.0) + 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) - def max_abs_corr(group: pd.DataFrame) -> pd.Series: - corr_arr = np.abs(group.corr(method=method).to_numpy().copy()) - np.fill_diagonal(corr_arr, 0.0) - return pd.Series(corr_arr.max(axis=0), index=group.columns).fillna(0.0) + unique_ids, inverse = np.unique(identifiers, return_inverse=True) + n_cols = len(available_cols) - result = df_bytes.groupby(identifiers)[available_cols].apply(max_abs_corr) + 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 = [] + + out = np.zeros((len(unique_ids), n_cols), dtype=np.float64) + for gi, sub in enumerate(groups): + 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) + out[gi] = c.max(axis=0) + + 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: - """Generates an interactive heatmap of inter-byte correlation.""" is_8x8 = target_id is not None + x = corr_df.columns.tolist() + y = corr_df.index.tolist() + z = corr_df.values + if is_8x8: - x = corr_df.columns.tolist() - y = corr_df.index.tolist() - z = corr_df.values z_min, z_max = -1.0, 1.0 - colorscale = [ - [0.0, "#2c7bb6"], [0.25, "#abd9e9"], [0.5, "#ffffff"], - [0.75, "#fdae61"], [1.0, "#d7191c"] - ] + colorscale = [[0.0, "#2c7bb6"], [0.25, "#abd9e9"], [0.5, "#ffffff"], [0.75, "#fdae61"], [1.0, "#d7191c"]] hover_template = "%{y} vs %{x}
Correlation: %{z:.2f}" else: - x = corr_df.columns.tolist() - y = corr_df.index.tolist() - z = corr_df.values 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"] - ] + colorscale = [[0.0, "#ffffff"], [0.2, "#fff5f0"], [0.4, "#fecc5c"], [0.6, "#fd8d3c"], [0.8, "#e31a1c"], [1.0, "#800026"]] hover_template = "%{y}
Byte %{x} max correlation: %{z:.2f}" fig = go.Figure( @@ -106,17 +127,17 @@ def plot_correlation_heatmap(corr_df: pd.DataFrame, target_id: str | None, title 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) if not is_8x8 else 600, + 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="top" if not is_8x8 else "bottom", + 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="PGN or CAN ID" if not is_8x8 else "Byte Position", font=dict(size=13, color="#1a1a1a")), + 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, ), @@ -131,17 +152,15 @@ 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"), help="Path to the output HTML report") - parser.add_argument("title", nargs="?", default="CAN Bus Inter-Byte Correlation", help="Title for the HTML report") - parser.add_argument("--identifier", type=str, default=None, help="Specific PGN/CAN ID to analyze (e.g., 'PGN: 65331'). If omitted, shows max correlation per byte for all IDs.") + 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, diff --git a/stat/entropy.py b/stat/entropy.py index fc010bc..51240e2 100644 --- a/stat/entropy.py +++ b/stat/entropy.py @@ -12,57 +12,76 @@ import plotly.graph_objects as go from utils.extractor import load_data from utils.extractor import to_int -def _format_can_id(x): - """Safely cleans CAN ID strings without altering their length or value.""" - if pd.isna(x): - return "UNKNOWN" +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') - s = str(x).strip() - if not s: - return "UNKNOWN" - - if s.lower().startswith('0x'): - s = s[2:] - - return s.upper() +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: - """Calculates Shannon entropy per byte position for each identifier.""" - byte_cols = [f"b{i}" for i in range(8)] - available_cols = [col for col in byte_cols if col in df.columns] + byte_cols = [f"b{i}" for i in range(8) if f"b{i}" in df.columns] + available_cols = byte_cols 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 = df[can_id_col].apply(_format_can_id) + identifiers = _format_can_id_vec(df[can_id_col]).to_numpy() - df_bytes = df[available_cols].copy() - for col in available_cols: - df_bytes[col] = df_bytes[col].apply(to_int) + 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) - def entropy(s: pd.Series) -> float: - s = s.dropna() - if s.empty: - return 0.0 - p = s.value_counts(normalize=True) - return -np.sum(p * np.log2(p)) + 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) - result = df_bytes.groupby(identifiers)[available_cols].agg(entropy) + 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 = [] + + out = np.zeros((len(unique_ids), n_cols), dtype=np.float64) + for gi, sub in enumerate(groups): + for ci in range(n_cols): + out[gi, ci] = _entropy_col(sub[:, ci]) + + 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: - """Generates an interactive heatmap of byte-level Shannon entropy.""" 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, + z=z, x=x, y=y, colorscale=[ [0.0, "#ffffff"], [0.15, "#fff7ec"], @@ -71,112 +90,54 @@ def plot_entropy_heatmap(entropy_df: pd.DataFrame, title: str) -> go.Figure: [0.75, "#fdbb84"], [1.0, "#ef6548"], ], - xgap=3, - ygap=3, + xgap=3, ygap=3, text=np.round(z, 2), texttemplate="%{text}", - textfont={ - "size": 11, - "color": "#2a2a2a", - "family": "Segoe UI, Arial, sans-serif", - }, + textfont={"size": 11, "color": "#2a2a2a", "family": "Segoe UI, Arial, sans-serif"}, hoverongaps=False, - hovertemplate=( - "%{y}
" - "Byte %{x}: %{z:.2f} bits" - ), + hovertemplate="%{y}
Byte %{x}: %{z:.2f} bits", 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", + 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", + 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), - ), + 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", + 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, + 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", - ), + 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"), - help="Path to the output HTML report", - ) - parser.add_argument( - "title", - nargs="?", - default="CAN Bus Byte-Level Entropy", - help="Title for the HTML report", - ) + 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, diff --git a/stat/frequency.py b/stat/frequency.py index 8c7598f..1e8981b 100644 --- a/stat/frequency.py +++ b/stat/frequency.py @@ -4,52 +4,57 @@ import argparse from pathlib import Path +import numpy as np import pandas as pd -import plotly.express as px import plotly.graph_objects as go from utils.extractor import load_data -def _format_can_id(x): - """Safely cleans CAN ID strings without altering their length or value.""" - if pd.isna(x): - return "UNKNOWN" - - s = str(x).strip() - if not s: - return "UNKNOWN" - - if s.lower().startswith('0x'): - s = s[2:] - - return s.upper() +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: - """Calculates frequency counts and percentages for identifiers.""" can_id_col = 'ID' if 'ID' in df.columns else 'Identifier' + formatted = _format_can_id_vec(df[can_id_col]) + df['Formatted_ID'] = formatted - df['Formatted_ID'] = df[can_id_col].apply(_format_can_id) - - freq_df = df['Formatted_ID'].value_counts().reset_index() - freq_df.columns = ['Identifier', 'Count'] - - total = freq_df['Count'].sum() - freq_df['Percentage'] = (freq_df['Count'] / total * 100).round(2) - return freq_df.sort_values('Count', ascending=True) + 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: - """Generates interactive horizontal bar chart with log x-axis.""" - fig = px.bar( - stats_df, y='Identifier', x='Count', orientation='h', title=title, log_x=True, - labels={'Identifier': 'PGN / CAN ID', 'Count': 'Message Count'}, - color='Count', color_continuous_scale='Turbo', - range_color=(stats_df['Count'].min(), stats_df['Count'].max()), - hover_data={'Percentage': ':.2f', 'Count': ':,', 'Identifier': True} - ) + 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="%{y}
Count: %{x:,}
Share: %{customdata[0]}%", + texttemplate='%{x:,}', + textposition='outside', + cliponaxis=False, + )) + fig.update_layout( - height=max(600, len(stats_df) * 18), + 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, @@ -69,11 +74,10 @@ def plot_frequency(stats_df: pd.DataFrame, title: str) -> go.Figure: ticklen=4, tickcolor="#cccccc", automargin=True, - type='category' + 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'), + 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( @@ -87,39 +91,27 @@ def plot_frequency(stats_df: pd.DataFrame, title: str) -> go.Figure: yanchor='bottom', tickformat=',', outlinecolor='#cccccc', - outlinewidth=0.5 + outlinewidth=0.5, ), - title=dict(font=dict(size=20, color='#1a1a1a'), x=0.5, xanchor='center', - pad=dict(b=20)) + 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) + 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 - ) - fig.update_traces( - hovertemplate="%{y}
Count: %{x:,}
Share: %{customdata[0]}%", - marker_line_width=0, - texttemplate='%{x:,}', - textposition='outside', - textfont=dict(size=10, color='#666666'), - cliponaxis=False, - selected=dict(marker=dict(opacity=0.6)), - unselected=dict(marker=dict(opacity=0.2)) + 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"), help="Path to the output HTML report") - parser.add_argument("title", nargs="?", default="Frequency", help="Title for the HTML report") + 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) @@ -130,6 +122,6 @@ if __name__ == "__main__": 'displaylogo': False, 'scrollZoom': True, 'modeBarButtonsToAdd': ['toggleSpikelines'], - 'toImageButtonOptions': {'format': 'png', 'scale': 2} + 'toImageButtonOptions': {'format': 'png', 'scale': 2}, } fig.write_html(str(args.output), include_plotlyjs='cdn', config=config) diff --git a/stat/id_viewer.py b/stat/id_viewer.py index 8c89267..273a041 100644 --- a/stat/id_viewer.py +++ b/stat/id_viewer.py @@ -1,75 +1,64 @@ +# 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 utils.extractor import load_data -def _format_can_id(x): - if pd.isna(x): - return "UNKNOWN" - s = str(x).strip() - if not s: - return "UNKNOWN" - if s.lower().startswith('0x'): - s = s[2:] - return s.upper() +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' - df['Formatted_ID'] = df[can_id_col].apply(_format_can_id) - target_id_clean = _format_can_id(target_id) - filtered = df[df['Formatted_ID'] == target_id_clean].copy() + 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 - byte_cols = [f"b{i}" for i in range(8)] for col in byte_cols: - filtered[col] = pd.to_numeric( - filtered[col].apply(lambda x: int(x, 16) if pd.notna(x) else None), - errors='coerce' - ) + 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') - mask = (filtered[byte_cols] != filtered[byte_cols].shift()).any(axis=1) - filtered = filtered[mask] + 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 = go.Figure() - 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): - fig.add_trace(go.Scatter( - x=[None], y=[None], - mode='markers', - marker=dict(symbol='square', size=10, color=colors[i]), - name=col.upper(), - showlegend=True, - legendgroup=col.upper(), - hoverinfo='skip' - )) - fig.add_trace(go.Scatter( - x=df['Timestamp'], - y=df[col], + 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, mode='lines', - line=dict(shape='hv', width=2, color=colors[i]), + line=dict(shape='hv', width=2, color=colors[i % len(colors)]), name=col.upper(), - showlegend=False, legendgroup=col.upper(), - hovertemplate=f"{col.upper()}
Time: %{{x}}
Value: %{{y}}" + hovertemplate=f"{col.upper()}
Time: %{{x}}
Value: %{{y}}", )) - all_button = dict( - label='ALL', - method='restyle', - args=[{'visible': [True] * 16}] - ) - - none_button = dict( - label='NONE', - method='restyle', - args=[{'visible': ['legendonly'] * 16}] - ) + 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, @@ -79,19 +68,15 @@ def plot_bits(df, byte_cols, can_id, title): text=f"{title} - ID: {can_id}", font=dict(size=20, color='#1a1a1a'), x=0.5, xanchor='center', - pad=dict(b=20) + 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' - ), + 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', + x=0.5, xanchor='center', + y=-0.18, yanchor='top', title=None, bgcolor='white', bordercolor='#cccccc', @@ -99,7 +84,7 @@ def plot_bits(df, byte_cols, can_id, title): font=dict(size=12, color="#2a2a2a"), itemsizing='constant', itemclick='toggle', - itemdoubleclick='toggleothers' + itemdoubleclick='toggleothers', ), xaxis=dict( title=dict(text="Timestamp", font=dict(size=13, color="#1a1a1a")), @@ -107,41 +92,38 @@ def plot_bits(df, byte_cols, can_id, title): 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) + 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" + ticks="outside", ticklen=4, tickcolor="#cccccc", ), updatemenus=[ dict( type='buttons', direction='right', - x=0.5, - xanchor='center', - y=-0.06, - yanchor='top', + 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) + 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"), help="Path to the output HTML report") - parser.add_argument("title", nargs="?", default="Byte Visualization", help="Title for the HTML report") + 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) @@ -153,6 +135,6 @@ if __name__ == "__main__": 'displaylogo': False, 'scrollZoom': True, 'modeBarButtonsToAdd': ['toggleSpikelines'], - 'toImageButtonOptions': {'format': 'png', 'scale': 2} + 'toImageButtonOptions': {'format': 'png', 'scale': 2}, } fig.write_html(str(args.output), include_plotlyjs='cdn', config=config) diff --git a/stat/utils/extractor.py b/stat/utils/extractor.py index c2d63e7..3472f07 100644 --- a/stat/utils/extractor.py +++ b/stat/utils/extractor.py @@ -7,9 +7,9 @@ from pathlib import Path import numpy as np import pandas as pd +import polars as pl def to_int(x): - """Convert a hex string or integer to int, returning NaN on failure.""" if isinstance(x, (int, np.integer)): return int(x) if isinstance(x, str): @@ -20,7 +20,6 @@ def to_int(x): return np.nan def extract_id(row: pd.Series) -> str: - """Extracts PGN from metadata or falls back to CAN ID.""" meta = row.get('j1939_metadata') if pd.isna(meta): return f"ID: {row['ID']}" @@ -34,7 +33,31 @@ def extract_id(row: pd.Series) -> str: return f"ID: {row['ID']}" def load_data(file_path: Path) -> pd.DataFrame: - """Loads Parquet file and adds an Identifier column.""" - df = pd.read_parquet(file_path) - df['Identifier'] = df.apply(extract_id, axis=1) - return df + 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()