Concept Modules

Seven production-level Python modules covering topics that surface in practical problem solving, system design, and code review rounds. Each module is heavily commented for self-study.

just test-concepts    # run concept module tests (installs numpy/scipy/flask/pydantic)
just study-concept    # watch mode

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• T-Strings (PEP 750)

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  Lazy interpolation, safe SQL/HTML templating, structured logging with Python 3.14 template strings.

  t-strings

• Benchmarking (T-Strings)

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  Interview timing patterns: timed(), bench_compare(), empirical Big-O verification, all rendered via t-strings.

  benchmarking

• Advanced Typing

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  Protocol, TypeVar, ParamSpec, TypeGuard, @overload, and the new PEP 695 type syntax.

  advanced-typing

• Hypothesis Patterns

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  Property-based testing with @given, @composite strategies, stateful testing, and SortedList.

  hypothesis-patterns

• FFT / DCT

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  Signal processing fundamentals: FFT, inverse FFT, DCT, frequency analysis. Relevant to ADS-B and weather radar.

  fft-dct

• Modern Flask

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  Flask 3.x patterns: async views, class-based views, nested blueprints, app factory, testing.

  modern-flask

• Validation (Pydantic v2)

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  Model validators, discriminated unions, serialization, and comparison with TypeScript's Zod.

  validation

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Concept-to-Algorithm Connections

Module	Connects To	Why
t_strings	Template pattern	Parameterized queries in sql_safe(), analogous to DP building solutions from templates
advanced_typing	Stack[T]	Same LIFO structure used in valid_parentheses.py and daily_temperatures.py
hypothesis_patterns	bisect	Same binary search strategy as longest_increasing_subseq.py
fft_dct	Sensor data pipelines	Relevant when ASI asks about ADS-B signal processing or weather radar
modern_flask	API layer	Pairs with validation.py for full request lifecycle
validation	Runtime types	Runtime counterpart to advanced_typing's static type system
benchmarking	top_k_frequent, kth_largest	Empirical complexity verification of bucket sort O(n) vs heap O(n log k)