the old dict learning code was incompatible with multislice and OPRs, this should fix it (for Nopr = 1), for Nopr>1, not sure

[a4894z]

Features/fixes

The previous attempt at merging dict learn and multislice was hopeless, so I've rewritten the dict learn update rules to be compatible with multislice and OPRs

Related issues (optional)

See the previous pull request "#34"

Mentions

Ming Du

Checklist

Have you...

• Formatted your code properly adhering to PEP-8? Considering using RUFF to format your code automatically.
  doubt it

• Resolved all merge conflicts with the main branch?

not sure yet

• Checked the diffs between your branch and the main branch to ensure that your changes are not introducing any regressions or unnecessary/irrelevant changes?

I think so

[Copilot]

Pull Request Overview

This PR rewrites the dictionary learning update rules to support multislice objects and OPRs (for a single probe), and refactors sparse-code–based probe initialization and generation.

• Reimplemented compute_updates in the PIE reconstructor to handle multiple slices and integrate sparse code updates.
• Refactored probe data structure to derive and register sparse-code–based weights instead of initial weights.
• Updated step‐weight calculations and tensor assignments for sparse‐dictionary probes.

Reviewed Changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 2 comments.

File	Description
src/ptychi/reconstructors/pie.py	Overhauled compute_updates for multislice and sparse‐code probe updates; added slice loops
src/ptychi/data_structures/probe.py	Renamed get_initial_weights → get_sparse_code_weights; revised generate tensor handling

Comments suppressed due to low confidence (1)

src/ptychi/reconstructors/pie.py:95

• [nitpick] The variable unique_probes now holds data for all slices rather than a single slice; consider renaming it to shifted_probes_per_slice or similar for clarity.

unique_probes = self.forward_model.intermediate_variables.shifted_unique_probes

[Copilot]

Move heavy imports and plotting code out of the inner update loop; consider extracting debug visualization into a separate utility or removing before merge.

Suggested change

	#from PySide6.QtWidgets import QApplication
	import numpy as np
	import matplotlib.pyplot as plt
	import matplotlib as mpl
	from mpl_toolkits.axes_grid1 import make_axes_locatable
	#mpl.use('Agg')
	#mpl.use('TKAgg')
	#mpl.use('WebAgg')
	mpl.use('QTAgg')
	mpl.rcParams['agg.path.chunksize'] = 10000
	# Debug visualization (if needed)
	self._debug_visualize(sparse_code_probe)

[Copilot]

Directly manipulating .data is deprecated. Use APIs like with torch.no_grad(): self.tensor.copy_(...) or self.tensor.data = probe.view_as(self.tensor) to preserve autograd correctness.

Suggested change

	# is this the correct way to retain the originally initialized grad?
	self.tensor.data.data = torch.stack([probe.real, probe.imag], dim=-1)
	# Safely update self.tensor without breaking autograd
	with torch.no_grad():
	self.tensor.copy_(torch.stack([probe.real, probe.imag], dim=-1))