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
Links
1 maintainer
Metadata
1 maintainer
Release files for escape-fel 0.2.16
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