Metadata-Version: 2.1
Name: acert-nlsl
Version: 0.1.0.9
Summary: the ACERT NLSL package, now wrapped up in python!
Author: NLSL development team
License: Copyright (c) 2015, ACERT
         All rights reserved.
         
         This software is provided exclusively for the ACERT 2015 workshop, only for use by the attendees at the workshop.
         Distribution in any form is prohibited; the software is intended for demonstration purposes only. Please see the [ACERT webpage](http://www.acert.cornell.edu/index_files/acert_resources.php)
         to download freely available versions, or wait until this version has been
         properly licensed and released.
         
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Requires-Dist: sympy
Requires-Dist: numpy>=1.26.5
Requires-Dist: scipy
Requires-Dist: matplotlib
Requires-Dist: tables
Description-Content-Type: text/markdown

# NLSL

The NLSL project provides a Python interface to the ACERT nonlinear least
squares (NLSL) spectroscopic fitting toolkit, bundling the original Fortran
core with a modern Python API for building, fitting, and analysing spin
resonance models.【F:pyproject.toml†L11-L24】【F:nlsl/__init__.py†L1-L104】

## Key features

- **Pythonic parameter access** – the :class:`nlsl.nlsl` class exposes the
  complete parameter set from the legacy NLSL engine through mapping-style
  accessors, making it easy to read or update magnetic, diffusion, and fitting
  options from Python code.【F:nlsl/__init__.py†L60-L170】
- **Programmable fitting control** – the :class:`nlsl.fit_params` helper keeps
  the low-level Levenberg–Marquardt settings in sync with the Fortran
  structures, so tolerances, iteration limits, and other solver options can be
  modified directly without crafting text run files.【F:nlsl/__init__.py†L15-L58】
- **Runfile compatibility** – existing NLSL ``.run`` scripts can be replayed via
  :meth:`nlsl.nlsl.procline`, preserving the familiar workflow while allowing
  incremental migration to the Python API.【F:nlsl/__init__.py†L82-L109】
- **Spectrum capture** – helper methods such as
  :meth:`nlsl.nlsl.current_spectrum` and :meth:`nlsl.nlsl.write_spc` let you
  evaluate the current model or persist generated spectra for downstream
  analysis and visualisation.【F:nlsl/__init__.py†L105-L158】

## Installation

Clone the repository and install the package in editable mode so the compiled
Fortran components stay in sync with your working tree:

```bash
pip install -e . --no-build-isolation
```

## Usage overview

Instantiate :class:`nlsl.nlsl` to work with parameters programmatically, or
stream traditional runfiles back into the engine.

```python
import nlsl

n = nlsl.nlsl()
n["nsite"] = 1
n.fit_params["maxitr"] = 40
n.procline("data sampl1 ascii nspline 200 bc 20 shift")
site_spectra = n.fit()
total_spectrum = n["weights"] @ site_spectra
```

The mapping interface mirrors the parameter names defined by the original
Fortran code (see ``nlshlp.txt`` for a full command reference), while
:meth:`nlsl.nlsl.fit` runs the nonlinear least-squares optimiser and returns the
latest site spectra, while the companion weight matrix stays accessible through
the mapping interface.【F:nlsl/__init__.py†L60-L204】

## Examples

A curated set of runnable examples is included under ``examples/`` to help you
get started with real datasets and typical NLSL workflows:

### Download the examples directory

You can download the full repository ZIP directly from GitHub and extract only the `examples/` folder:

[⬇️ Download ZIP (GitHub)](https://github.com/acert-esr/nlsl/archive/refs/heads/main.zip)

```bash
unzip nlsl-main.zip "nlsl-main/examples/*"
```

### Description of examples

- **Runfile suite (`sampl1`–`sampl5`)** – canonical `.run` scripts paired with
  `.dat` inputs that demonstrate anisotropic rotations, multi-spectrum fits,
  and other standard analyses. Run them with the classic command-line
  interface and compare the generated `.spc` and `.log` outputs to the provided
  reference files to verify your setup.【F:examples/README.rst†L1-L22】
- **`runexample.py`** – command-line helper that executes one of the numbered
  runfile scenarios, cascades any nested `call` directives, reports relative
  RMS errors, and plots the resulting spectra with experimental overlays for
  quick diagnostics.【F:examples/runexample.py†L1-L93】
- **`runexample_first.py`** – simplified variant that focuses on the first
  sample run, computing a global relative RMS metric while rendering the fitted
  spectrum.【F:examples/runexample_first.py†L1-L74】
- **`break_down_example.py`** – step-by-step walkthrough of the first sample
  showcasing programmatic parameter updates, basis set configuration, and fit
  execution directly from Python without relying on runfiles.【F:examples/break_down_example.py†L1-L83】
- **`BL05CHSA.py`** – reproduces the `BL05CHSA.run` workflow, highlighting how
  to plot individual spectral components alongside the overall fit and
  cumulative integrals.【F:examples/BL05CHSA.py†L1-L40】
- **`c16pc371e.py`** – demonstrates interacting with a custom runfile while
  inspecting the solver configuration before and after execution and plotting
  the normalised spectra.【F:examples/c16pc371e.py†L1-L46】

Each script expects to be run from within the `examples/` directory so that the
associated data and reference outputs are discovered automatically.【F:examples/runexample.py†L22-L41】

## Additional resources

- The ``examples/README.rst`` file documents the legacy testing workflow for
  the runfile suite and points to the reference outputs for verification.
- ``nlshlp.txt`` lists every command available to the original interpreter along
  with detailed descriptions of their arguments and expected effects.
