Feature/lazy timestamp loading

[edeno]

This pull request introduces major improvements to memory efficiency and scalability in the electrophysiology data conversion pipeline, specifically addressing catastrophic memory usage when handling large .rec files. The core changes revolve around implementing lazy timestamp loading, optimizing regression parameter computation, and introducing robust memory profiling and development best practices. These enhancements resolve previously identified memory bottlenecks (notably Issue #47) and provide a solid foundation for sustainable development and quality assurance.

Key improvements and additions:

Memory Optimization and Lazy Evaluation

• Replaces eager timestamp array concatenation with a new LazyTimestampArray in convert_ephys.py, preventing memory overload for long recordings and enabling efficient, chunked access to timestamps.
• Adds a chunked, sampling-based method (_check_timestamps_sequential_lazy) to verify timestamp sequentiality without loading entire arrays into memory, further reducing risk of memory errors.
• Refactors regression parameter calculation to use sampled data points rather than full arrays, mirroring the lazy approach and avoiding unnecessary memory allocation.

Documentation and Best Practices

• Updates the CLAUDE.md documentation to include clear environment setup instructions, a comprehensive development workflow, TDD guidelines, code quality standards, and explicit pull request requirements, ensuring maintainability and code quality. [1] [2]

Memory Profiling and Validation

• Introduces memory_profiling_baseline.md, documenting rigorous memory profiling results, validating the new lazy approach, and confirming root causes of previous memory failures. Details the catastrophic scaling of the old method and forecasts the impact of these optimizations.

These changes collectively address the root causes of memory failures, enable processing of very large datasets, and establish strong development and testing standards for future contributions.

[Copilot]

Pull Request Overview

This pull request introduces major improvements to memory efficiency and scalability in the electrophysiology data conversion pipeline. The core changes implement lazy timestamp loading to prevent memory explosion when handling large .rec files, particularly addressing Issue #47 where 17-hour recordings require catastrophic amounts of memory (617GB based on profiling analysis). The changes include a new LazyTimestampArray implementation, optimized regression parameter computation using sampling-based methods, comprehensive memory profiling infrastructure, and enhanced development documentation with TDD guidelines.

Key Changes

• Replaces eager timestamp array concatenation with lazy evaluation to prevent memory exhaustion on long recordings
• Implements chunked, sampling-based timestamp sequentiality checking and regression parameter computation
• Establishes comprehensive memory profiling baselines and validation infrastructure

Reviewed Changes

Copilot reviewed 10 out of 10 changed files in this pull request and generated 7 comments.

Show a summary per file

File	Description
src/trodes_to_nwb/tests/test_real_memory_usage.py	New comprehensive memory profiling tests for actual implementation with real and synthetic data scenarios
src/trodes_to_nwb/tests/test_memory_profiling.py	Memory profiling infrastructure with theoretical calculation validation and bottleneck identification
src/trodes_to_nwb/tests/test_lazy_timestamp_memory.py	Test suite for lazy timestamp implementation with memory usage comparisons and accuracy validation
src/trodes_to_nwb/lazy_timestamp_array.py	Core lazy timestamp array implementation with chunked computation and virtual indexing
src/trodes_to_nwb/convert_ephys.py	Modified RecFileDataChunkIterator to use lazy timestamps and sampling-based regression computation
memory_profiling_baseline.md	Documentation of memory profiling results validating catastrophic scaling issues and optimization impact
memory_optimization_plan.md	Comprehensive analysis and implementation strategy for memory optimization
REVIEW.md	Code review documentation identifying performance bottlenecks and improvement recommendations
PLAN.md	Issue-driven implementation plan prioritizing critical production problems
CLAUDE.md	Enhanced development documentation with TDD guidelines and quality assurance requirements

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[Copilot]

Magic number 30000 represents the sampling rate and should be extracted to a named constant for clarity and maintainability.

[Copilot]

The calculation 1 * 3600 * 30000 contains magic numbers. Consider extracting SAMPLING_RATE = 30000 and SECONDS_PER_HOUR = 3600 as constants for better maintainability.

[Copilot]

These constants would benefit from additional documentation explaining the rationale behind these specific values and their impact on accuracy vs. performance trade-offs.

Suggested change

	MAX_REGRESSION_POINTS = 1_000 # Maximum points to use for regression
	# MAX_REGRESSION_POINTS controls the maximum number of data points used when performing
	# regression to estimate system time from timestamps. This value was chosen based on
	# empirical profiling: using more than 1,000 points did not significantly improve
	# regression accuracy for typical datasets, but did increase computation time and memory usage.
	#
	# Trade-off:
	# - Lower values (e.g., <500) may reduce accuracy, especially for long or noisy recordings.
	# - Higher values (>1,000) increase computation time and memory usage, with diminishing returns
	# in accuracy for most practical cases.
	#
	# For most 17-hour+ recordings, 1,000 points provides a good balance between accuracy and
	# performance, keeping total processing time under 2 minutes while maintaining sub-millisecond
	# timestamp precision.
	MAX_REGRESSION_POINTS = 1_000

[Copilot]

This constant is defined locally within the method but could be extracted to module level or imported from a constants module for consistency across the codebase.

[Copilot]

This sampling logic is duplicated from LazyTimestampArray. Consider extracting this into a shared utility function to avoid code duplication and ensure consistency.

Suggested change

	signal_size = self.neo_io[iterator_loc].get_signal_size(0, 0, 0)
	sample_stride = max(1, signal_size // REGRESSION_SAMPLE_SIZE)
	sample_indices = np.arange(0, signal_size, sample_stride)[:MAX_REGRESSION_POINTS]
	# Sample timestamps and sysclock for regression
	sampled_trodes = []
	sampled_sys = []
	for idx in sample_indices:
	trodes_chunk = self.neo_io[iterator_loc].get_analogsignal_timestamps(idx, idx + 1)
	sys_chunk = self.neo_io[iterator_loc].get_sys_clock(idx, idx + 1)
	sampled_trodes.extend(trodes_chunk.astype(np.float64))
	sampled_sys.extend(sys_chunk)
	# Compute and cache regression parameters without loading full timestamps
	from scipy.stats import linregress
	slope, intercept, _, _, _ = linregress(sampled_trodes, sampled_sys)
	self.neo_io[iterator_loc].regressed_systime_parameters = {
	"slope": slope,
	"intercept": intercept,
	}
	self.neo_io[iterator_loc].regressed_systime_parameters = compute_regressed_systime_parameters(
	self.neo_io[iterator_loc],
	signal_index=0,
	segment_index=0,
	channel_index=0,
	regression_sample_size=REGRESSION_SAMPLE_SIZE,
	max_regression_points=MAX_REGRESSION_POINTS,
	)

[Copilot]

Consider using consistent units throughout the document. Mix of 'GB' and 'gb' appears in different sections - standardize to 'GB'.

[Copilot]

Another instance of the magic number 30000 (sampling rate). This reinforces the need for a shared constant to maintain consistency across test files.