feat(algorithms design, data-structures): continuous median

[BrianLusina]

Describe your change:

Continuous median using two heaps

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.
• If this pull request resolves one or more open issues then the commit message contains Fixes: #{$ISSUE_NO}.

Summary by CodeRabbit

• New Features

  • Added continuous median calculation capability to efficiently compute running medians as new values are inserted into the data stream.

• Documentation

  • Updated directory structure with references to the new continuous median feature documentation.

• Tests

  • Added comprehensive unit test coverage for continuous median calculations, validating insertion operations and median retrieval.

_{✏️ Tip: You can customize this high-level summary in your review settings.}

[coderabbitai]

📝 Walkthrough

Walkthrough

This PR introduces a new ContinuousMedianHandler class that maintains a running median using two heaps (max-heap for lower half, min-heap for upper half). The implementation includes insert and get_median methods, accompanied by test coverage and documentation updates in DIRECTORY.md.

Changes

Cohort / File(s)	Summary
Documentation DIRECTORY.md	Added "Continuous Median" subsection references under Browser History section (2 locations)
Implementation design_patterns/continuous_median/__init__.py	New ContinuousMedianHandler class with insert() and get_median() methods using dual-heap approach for median tracking
Test Suite design_patterns/continuous_median/test_continuous_median_handler.py	Parameterized unit tests validating insertion and median retrieval through the public API

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~12 minutes

Poem

🐰 Two heaps pirouette in balanced grace,
Max and min find their rightful place,
Numbers inserted, medians bloom,
Running averages fill the room! 📊✨

🚥 Pre-merge checks | ✅ 2 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly describes the main change: adding a continuous median algorithm using two heaps (a data structure/algorithm addition).
Description check	✅ Passed	The description follows the template with all required sections completed. The author checked all relevant checklist items and provided a clear summary of changes.

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✨ Finishing touches

• 📝 Generate docstrings

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[coderabbitai]

Actionable comments posted: 1

🤖 Fix all issues with AI agents

In `@DIRECTORY.md`:
- Around line 591-592: The new list entry under "Continuous Median" has
incorrect indentation causing MD007; adjust the bullet for "[Test Continuous
Median Handler]" to be a nested list item (two spaces before the asterisk) so it
aligns under the "Continuous Median" parent bullet, ensuring list depth is
consistent with surrounding entries.

🧹 Nitpick comments (2)

design_patterns/continuous_median/test_continuous_median_handler.py (2)

6-10: Normalize test case shape to simplify inputs.

The current nested tuple/list structure is hard to read. Consider representing each step as (list_of_ints, expected) inside a single requests list.

♻️ Suggested test case shape

-CONTINUOUS_MEDIA_HANDLER_TESTS = [
-    (
-        ([(1, 2), 1.50000], [(3), 2.00000]),
-    ),
-]
+CONTINUOUS_MEDIA_HANDLER_TESTS = [
+    (
+        [([1, 2], 1.5), ([3], 2.0)],
+    ),
+]

---

15-23: Streamline the loop once inputs are consistent.

With the normalized cases above, you can remove the type branch and iterate uniformly.

♻️ Simplified loop and type hint

-    def test_continuous_median_handler(self, requests: List[Tuple[List[Tuple[int]] | int, int | float]]):
+    def test_continuous_median_handler(self, requests: List[Tuple[List[int], int | float]]):
         median_handler = ContinuousMedianHandler()
-        for request in requests:
-            data, expected = request
-            if type(data) is int:
-                median_handler.insert(data)
-            else:
-                for d in data:
-                    median_handler.insert(d)
+        for data, expected in requests:
+            for value in data:
+                median_handler.insert(value)
 
             actual = median_handler.get_median()
             self.assertEqual(expected, actual)