2. Setting up the Python environment for airbag result analysis

Airbag deployment result analysis sits at the intersection of crash-test data acquisition, signal processing, and regulatory reporting. Before any meaningful analysis can happen, the analyst needs a reproducible Python environment that can ingest raw sensor files, align them on a common time base, compute derived metrics, and emit structured reports. This section walks through the concrete setup: which libraries matter, how to organize the project, and how to build a minimal but functional data pipeline that turns a raw deployment log into a pass/fail summary.

Choosing the Runtime and Dependency Strategy

Airbag analysis workloads are numerically light compared to machine-learning pipelines, but they are sensitive to version drift because regulatory submissions may need to be reproduced months or years later. A practical approach is to pin the interpreter and all dependencies. Python 3.11 or 3.12 is a reasonable baseline; both are widely supported by the scientific stack and have stable wheels for Windows and Linux.

Use a virtual environment per project rather than a global install. With venv:

python -m venv .venv
# Windows
.venv\Scripts\activate
# Linux / macOS
source .venv/bin/activate
python -m pip install --upgrade pip

For dependency locking, pip-tools or uv both work well. A minimal requirements.in for this domain looks like:

numpy
pandas
scipy
matplotlib
pyarrow
pydantic
pytest

Compile it to a pinned requirements.txt with pip-compile requirements.in. Commit both files. This guarantees that a colleague re-running your analysis six months later gets identical numerical behavior.

Core Libraries and What Each One Does

The table below maps each library to the specific job it performs in an airbag result workflow. Avoid adding libraries that do not earn their place; every dependency is a future compatibility risk.

Two domain-specific notes are worth calling out. First, SAE J211 specifies a Channel Filter Class (CFC) for acceleration signals; CFC 60 corresponds to a cutoff near 100 Hz, CFC 180 to about 300 Hz, and CFC 1000 to about 1650 Hz. Your filtering code must expose the CFC as a parameter, not hard-code it. Second, time-zero alignment matters: different data loggers may define t=0 at different physical events (trigger, bumper contact, or fire signal). The pipeline should carry an explicit t_zero_event field so alignment is auditable.

Project Layout

A flat script directory becomes unmanageable once you have more than a handful of tests. A modest package layout keeps concerns separated:

airbag_analysis/
  pyproject.toml
  requirements.txt
  config/
    tests.yaml
  src/airbag_analysis/
    __init__.py
    io.py          # readers for CSV, MDF4, Parquet
    signals.py     # filtering, resampling, integration
    metrics.py     # peak, HIC-style, time-to-peak
    pipeline.py    # orchestration
    report.py      # HTML/CSV output
  tests/
    test_metrics.py
    fixtures/
      sample_run.csv
  data/
    raw/
    processed/

Keeping raw/ immutable is a discipline worth enforcing. All derived artifacts go to processed/, and every processed file should be traceable back to a raw file plus a config hash.

Building the Data Pipeline

The pipeline has four stages: load, condition, compute, and emit. The SVG below shows how data and metadata flow through these stages, including the validation gate that rejects malformed manifests before any numerical work begins.

The validation gate is not optional. In practice, the most common pipeline failures are not numerical but structural: a manifest missing the cfc field, a CSV with a different column order than expected, or a sample rate that silently changed between logger firmware versions. Catching these before filtering saves hours of debugging downstream.

A Minimal Working Example

Assume a raw CSV with columns time_s and accel_g, sampled at 20 kHz, and a manifest entry specifying CFC 1000. The following snippet loads, filters, and computes the peak:

import numpy as np
import pandas as pd
from scipy.signal import butter, sosfiltfilt

def cfc_cutoff_hz(cfc: int) -> float:
    # SAE J211: cutoff = CFC * 1.65 (approx, per standard table)
    return {60: 100.0, 180: 300.0, 600: 1000.0, 1000: 1650.0}[cfc]

def lowpass(sig, fs, fc, order=4):
    sos = butter(order, fc / (fs / 2), btype="low", output="sos")
    return sosfiltfilt(sos, sig)

df = pd.read_csv("data/raw/run_001.csv")
fs = 1.0 / np.median(np.diff(df["time_s"].values))
fc = cfc_cutoff_hz(1000)
df["accel_filt"] = lowpass(df["accel_g"].values, fs, fc)

peak_g = df["accel_filt"].max()
t_peak = df.loc[df["accel_filt"].idxmax(), "time_s"]
print(f"peak={peak_g:.2f} g at t={t_peak*1000:.1f} ms")

Expected output for a typical frontal test might read peak=42.17 g at t=63.4 ms. If the printed peak is implausibly high (say, above 200 g for a passenger-car frontal test) or the time-to-peak is near zero, the most likely causes are: the filter was skipped, the sample rate was miscomputed because time_s is not uniformly spaced, or the signal contains a pre-trigger spike that should have been excluded by the t-zero alignment step.

Verifying the Environment

Before trusting any result, run a smoke test that exercises the full chain on a known fixture. A pytest test that loads tests/fixtures/sample_run.csv, runs the pipeline, and asserts the peak is within 0.5 g of a recorded reference value will catch most environment-level regressions: a changed scipy version altering filter behavior, a numpy upgrade changing default dtypes, or a missing dependency silently falling back to a slower path.

With the environment pinned, the pipeline structured, and a smoke test in place, the analyst is ready to move from setup into the actual signal-processing and metric-definition work that the following sections cover.