escape-fel

Event Synchronous Categorisation And Processing Environment

escape is a high-level, object-oriented Python framework for processing event-based measurement data from Free-Electron Laser (FEL) experiments and similar facilities. It abstracts over large volumes of per-pulse data using dask for lazy, scalable evaluation and provides a clean interface for aligning, grouping, analysing, and storing event streams.

Getting Started

• Installation
• Core Concepts

User Guide

• The Array Object
  • Creating an Array
  • Key Properties
  • Dask-Backed Arrays
  • Aggregation Methods
  • Slicing and Selection
• Array Sorting and Operations
  • Arithmetic Operators
  • Normalisation Example
  • Boolean Filtering
  • Matching and Aligning Arrays
  • Digitize — Sorting by Value
  • Re-Sorting with Another Array
  • Concatenating Arrays
  • HDF5 Storage and Loading
• Scan Steps
  • What Is a Scan?
  • Parameter Table
  • Accessing Individual Steps
  • Per-Step Statistics
  • Plotting a Scan
  • Step Histogram
  • Scan Arithmetic
  • Number of Events Per Step
  • Merging Scans from Multiple Runs
• Grid — Multi-Dimensional Scans
  • What Is a Grid?
  • Creating a Grid
  • Indexing a Grid
  • Grid Statistics
  • Fill Count
  • Combining Grids with unravel_arrays
• DataSet — Managed Storage
  • Creating a DataSet
  • Appending Data
  • Loading a Saved DataSet
  • Computing and Storing Multiple Arrays Efficiently
  • Storing Small Quantities in Bulk
  • Using DataSet as a Context Manager
  • Merging Multiple DataSets
  • Converting Between File Formats
• Custom Processing
  • map_index_blocks — Block-Wise Processing
  • escaped — Lifting Functions to Array Level
  • Comparison: When to Use Which
  • Writing a Per-Event Loop Inside map_index_blocks

Examples

• Quick Start
• Pump-Probe Analysis Example

API Reference

• API Reference