escape

Event Synchronous Categorisation And Processing Environment

escape is a Python framework for event-based data analysis at free-electron laser (FEL) facilities. It pairs raw measurement arrays with pulse IDs and scan metadata, providing index-aligned arithmetic, scan-step aggregation, and scalable storage — designed for the data volumes and workflows typical at instruments like SwissFEL Bernina.

Core concepts

Array — wraps a data array together with a pulse-ID index and optional scan grouping. Index-aligned arithmetic between arrays is automatic: operations find common pulse IDs and align data before computing.
Scan — partitions an Array into sequential steps with parameter metadata (e.g. delay values). Exposes per-step statistics (nanmean, median, weighted_stat, …) directly.
Grid — maps scan steps onto an N-dimensional parameter grid, enabling multi-dimensional scans and 2D result arrays.
DataSet — named collection of Arrays backed by HDF5 (.esc.h5) or zarr (.esc.zarr) files for persistent storage.
escaped — decorator that lifts any NumPy/dask function to operate on Arrays with automatic index alignment.

Quick example

import escape as esc
import numpy as np

# Load a dataset
ds = esc.DataSet.load("run042.esc.h5")
sig  = ds["JF_signal"]
i0   = ds["I0_monitor"]
pump = ds["pump_on"]

# Normalize signal to I0 — pulse IDs aligned automatically
sig_norm = sig / i0

# Per-step median across the pump-probe scan
median_on  = sig_norm[pump].scan.nanmedian()
median_off = sig_norm[~pump].scan.nanmedian()

Installation

conda (recommended — reuses existing environment packages):

conda install -c conda-forge escape-fel

pip:

pip install escape-fel

Latest development version:

pip install git+https://github.com/htlemke/escape-fel

Documentation

Full documentation including user guide and API reference: escape-fel.readthedocs.io

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