[joyvelasquez]

Summary

This PR introduces XRTDEMIterative, a Python implementation of the IDL
routine xrt_dem_iterative2.pro for estimating Differential Emission
Measures (DEMs) from Hinode/XRT multi-filter observations.

What's Included

Core Solver (dem_solver.py)

• XRTDEMIterative class with full DEM solving pipeline
• Spline-parameterized DEM representation with iterative least-squares
  fitting via lmfit
• Temperature response interpolation onto a regular log10(T) grid
• User-supplied or default intensity uncertainty model:
  max(0.03 * intensity, 2 DN/s/pix), matching IDL default behavior
• Monte Carlo uncertainty estimation via monte_carlo_runs parameter
• Input validation with informative warnings (saturation, negative
  intensities, missing errors)
• summary() method for diagnostic output

Plotting (dem_plotting.py)

• plot_dem() — base DEM solution visualization
• plot_dem_mc() — base DEM + Monte Carlo ensemble overlay

Tests

• 70+ unit tests covering input validation, grid construction,
  response interpolation, spline system, residuals, solver, and
  Monte Carlo behavior
• Currently - heavy work in progress (UPDATED JULY 2026)

Documentation

• dem_overview.rst — introduction to DEM analysis with XRTpy
• API reference for xrtpy.xrt_dem_iterative
• Changelog entry in docs/changelog/0.6.0.rst

Validation -

• Validated against IDL xrt_dem_iterative2.pro across multiple
  solar observation cases using Monte Carlo comparison
• Currently - heavy work in progress (UPDATED JULY 2026)

[joyvelasquez]

Ensure the function checks for minimum required inputs (filters, observed intensities, etc.) and validates that all observation values are non-zero before proceeding.

[codecov]

Codecov Report

❌ Patch coverage is 0% with 916 lines in your changes missing coverage. Please review.
✅ Project coverage is 49.69%. Comparing base (357f41d) to head (fa18406).

Files with missing lines	Patch %	Lines
xrtpy/xrt_dem_iterative/dem_solver.py	0.00%	385 Missing ⚠️
...hysical_data_testing/test_dem_idl_comparison_mc.py	0.00%	168 Missing ⚠️
...rative/tests/physical_data_testing/utils_sav_io.py	0.00%	146 Missing ⚠️
...s/physical_data_testing/test_dem_idl_comparison.py	0.00%	111 Missing ⚠️
xrtpy/xrt_dem_iterative/dem_plotting.py	0.00%	99 Missing ⚠️
..._iterative/tests/physical_data_testing/conftest.py	0.00%	3 Missing ⚠️
xrtpy/xrt_dem_iterative/__init__.py	0.00%	2 Missing ⚠️
xrtpy/xrt_dem_iterative/tests/conftest.py	0.00%	2 Missing ⚠️

Additional details and impacted files

@@             Coverage Diff             @@
##             main     #367       +/-   ##
===========================================
- Coverage   87.97%   49.69%   -38.29%     
===========================================
  Files          26       35        +9     
  Lines        1855     3284     +1429     
  Branches      110      235      +125     
===========================================
  Hits         1632     1632               
- Misses        180     1609     +1429     
  Partials       43       43               

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[namurphy]

I'd suggest using this repository only for tests that get run in CI, and that we create a separate repository for cross-comparisons.

Moving stuff to a different repository could be done after this PR, but we'd want to update MANIFEST.in to exclude all the extra files so they don't get included in source distributions and wheels.

[namurphy]

Some ideas:

• Move the validation scripts not used in CI to a top-level folder, such as dem_validation or whatever you prefer
• Add the data files to a separate PR so that it's easier to review this one?
• Exclude the validation scripts/data from sdists and wheels (#420)
• Add regression tests to show that the results stay the same (similar) as they are now? Possibly with pytest-regression?

[joyvelasquez]

pre-commit.ci autofix

[namurphy]

need to change back ONGs → PNGs

[namurphy]

Two possibilities for this file:

• Instead of repeating the same commands, a possibility would be to use pytest fixtures.
• In tests with multiple assert statements, a possibility would be to use pytest-check. That way, if one of the early assertions fails, then the rest of the assertions will still get checked.

This might be a good opportunity to request AI to make the changes since they're pretty straightforward, even if confusing to humans. 😅