diff --git a/parser.py b/parser.py index b219036..ad7fc32 100644 --- a/parser.py +++ b/parser.py @@ -19,7 +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, \ @@ -35,7 +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: @@ -116,4 +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}") diff --git a/stat/correlation.py b/stat/correlation.py index 4d0f70c..06aa856 100644 --- a/stat/correlation.py +++ b/stat/correlation.py @@ -12,6 +12,20 @@ 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" + + s = str(x).strip() + if not s: + return "UNKNOWN" + + if s.lower().startswith('0x'): + s = s[2:] + + return s.upper() + 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)] @@ -20,12 +34,16 @@ def calculate_correlation(df: pd.DataFrame, method: str, target_id: str | None = if not available_cols: raise ValueError("No byte columns (b0-b7) found in the DataFrame") - df_bytes = df[["Identifier"] + available_cols].copy() + can_id_col = 'ID' if 'ID' in df.columns else 'Identifier' + identifiers = df[can_id_col].apply(_format_can_id) + + df_bytes = df[available_cols].copy() for col in available_cols: df_bytes[col] = df_bytes[col].apply(to_int) - if target_id: - group = df_bytes[df_bytes["Identifier"] == target_id] + if target_id is not None: + target_id = _format_can_id(target_id) + group = df_bytes[identifiers == target_id] if group.empty: raise ValueError(f"Identifier '{target_id}' not found in data") return group[available_cols].corr(method=method).fillna(0.0) @@ -35,7 +53,9 @@ def calculate_correlation(df: pd.DataFrame, method: str, target_id: str | None = np.fill_diagonal(corr_arr, 0.0) return pd.Series(corr_arr.max(axis=0), index=group.columns).fillna(0.0) - return df_bytes.groupby("Identifier")[available_cols].apply(max_abs_corr) + result = df_bytes.groupby(identifiers)[available_cols].apply(max_abs_corr) + result.index.name = 'Identifier' + return result def plot_correlation_heatmap(corr_df: pd.DataFrame, target_id: str | None, title: str) -> go.Figure: diff --git a/stat/entropy.py b/stat/entropy.py index ca314ea..fc010bc 100644 --- a/stat/entropy.py +++ b/stat/entropy.py @@ -12,6 +12,20 @@ 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" + + s = str(x).strip() + if not s: + return "UNKNOWN" + + if s.lower().startswith('0x'): + s = s[2:] + + return s.upper() + 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)] @@ -19,6 +33,9 @@ def calculate_byte_entropy(df: pd.DataFrame) -> pd.DataFrame: 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) + df_bytes = df[available_cols].copy() for col in available_cols: df_bytes[col] = df_bytes[col].apply(to_int) @@ -30,7 +47,9 @@ def calculate_byte_entropy(df: pd.DataFrame) -> pd.DataFrame: p = s.value_counts(normalize=True) return -np.sum(p * np.log2(p)) - return df.groupby("Identifier")[available_cols].agg(entropy) + result = df_bytes.groupby(identifiers)[available_cols].agg(entropy) + result.index.name = 'Identifier' + return result def plot_entropy_heatmap(entropy_df: pd.DataFrame, title: str) -> go.Figure: diff --git a/stat/frequency.py b/stat/frequency.py index b3e185a..4c3d9bc 100644 --- a/stat/frequency.py +++ b/stat/frequency.py @@ -9,10 +9,29 @@ 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 calc_freq(df: pd.DataFrame) -> pd.DataFrame: """Calculates frequency counts and percentages for identifiers.""" - freq_df = df['Identifier'].value_counts().reset_index() + can_id_col = 'ID' if 'ID' in df.columns else 'Identifier' + + 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) @@ -43,7 +62,6 @@ def plot_freq(stats_df: pd.DataFrame, title: str) -> go.Figure: ), yaxis=dict( title=dict(text="PGN or CAN ID", font=dict(size=13, color="#1a1a1a")), - #autorange="", showgrid=False, linecolor="#bdbdbd", tickfont=dict(size=12, color="#2a2a2a"), @@ -51,6 +69,7 @@ def plot_freq(stats_df: pd.DataFrame, title: str) -> go.Figure: 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",