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CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

pytelpoint is a Python library for analyzing telescope pointing performance at the MMT Observatory using Bayesian/probabilistic methods via PyMC. It fits standard TPOINT-compatible pointing models to az/el encoder data.

Environment Setup

pip install -e ".[test]"

On macOS, fitting.py enforces /usr/bin/clang++ to avoid conflicts between conda-forge's clang and Xcode command-line tools.

Common Commands

# Run all tests
pytest --pyargs pytelpoint docs

# Run a single test file
pytest pytelpoint/tests/test_io.py

# Run a single test
pytest pytelpoint/tests/test_io.py::test_read_azel_datfile

# Check code style (max line length 132)
flake8 pytelpoint --count --max-line-length=132

# Format code
black pytelpoint

# Run via tox (uses conda for pymc)
tox -e py313-test

# Build docs
tox -e build_docs

Architecture

All core modules use SkyCoord (AstropY) as the primary data carrier. The typical workflow is:

  1. IO (io.py): Read pointing run data → returns (coo_ref, coo_meas) pair of SkyCoord objects in an AltAz frame tied to MMT_LOCATION.
  2. Stats (stats.py): Compute sky RMS / population SD from coordinate pairs (TPOINT-equivalent diagnostics).
  3. Transform (transform.py): Apply an 8-term az/el pointing model (azel_model) analytically to raw encoder coordinates.
  4. Fitting (fitting.py): Fit the pointing model probabilistically using PyMC (azel_fit), returning an arviz.InferenceData object. best_fit_pars summarizes the posterior using arviz_stats.summary.
  5. Visualization (visualization.py): plot_posterior and plot_corner for inspecting PyMC fit results using arviz_plots.

Pointing model terms (TPOINT-compatible)

All terms are in arcseconds. The 8 standard az/el terms are: ia (az index), ie (el index), an (N-S axis tilt), aw (E-W axis tilt), ca (collimation), npae (az/el non-perpendicularity), tf (tube flexure ∝ cos(el)), tx (tube flexure ∝ cot(el)).

Key design note

fitting.py uses pm.math (Theano/pytensor ops) for the model functions, while transform.py uses numpy — they implement the same math but for different contexts (PyMC model graph vs. direct application).

Test data

pytelpoint/test_data/ contains three real pointing run files used in tests. k_and_e.dat is the processed (4-column) format; point_20210821.dat and point_20250326.dat are raw mount code output files.

Testing Notes

  • Tests live in pytelpoint/tests/. Doctests in .rst files under docs/ are also run.
  • test_mc_fitting runs a real PyMC MCMC chain (with reduced nsamp=200, ntune=200) — it is slow.
  • pytest.ini options (in pyproject.toml) enable --doctest-rst and treat most warnings as errors.