Method _print_summary

Lib/profiling/sampling/pstats_collector.py:267–422

Print summary of interesting functions.

(self, stats_list, total_samples)

            return "μs", float(MICROSECONDS_PER_SECOND)

    def _print_summary(self, stats_list, total_samples):
        """Print summary of interesting functions."""
        print(
            f"\n{ANSIColors.BOLD_BLUE}Summary of Interesting Functions:{ANSIColors.RESET}"
        )

        # Aggregate stats by fully qualified function name (ignoring line numbers)
        func_aggregated = {}
        for (
            func,
            direct_calls,
            cumulative_calls,
            total_time,
            cumulative_time,
            callers,
        ) in stats_list:
            # Use filename:function_name as the key to get fully qualified name
            qualified_name = f"{func[0]}:{func[2]}"
            if qualified_name not in func_aggregated:
                func_aggregated[qualified_name] = [
                    0,
                    0,
                    0,
                    0,
                ]  # direct_calls, cumulative_calls, total_time, cumulative_time
            func_aggregated[qualified_name][0] += direct_calls
            func_aggregated[qualified_name][1] += cumulative_calls
            func_aggregated[qualified_name][2] += total_time
            func_aggregated[qualified_name][3] += cumulative_time

        # Convert aggregated data back to list format for processing
        aggregated_stats = []
        for qualified_name, (
            prim_calls,
            total_calls,
            total_time,
            cumulative_time,
        ) in func_aggregated.items():
            # Parse the qualified name back to filename and function name
            if ":" in qualified_name:
                filename, func_name = qualified_name.rsplit(":", 1)
            else:
                filename, func_name = "", qualified_name
            # Create a dummy func tuple with filename and function name for display
            dummy_func = (filename, "", func_name)
            aggregated_stats.append(
                (
                    dummy_func,
                    prim_calls,
                    total_calls,
                    total_time,
                    cumulative_time,
                    {},
                )
            )

        # Determine best units for summary metrics
        max_total_time = max(