Phase 1: scaffold, data models, selection logic, tests

Flask application skeleton with SQLAlchemy models for courses (including
per-course settings), students, enrollments, opt-outs, and calls; the
weighted and cycle selection logic ported from the manual coldcall
scripts; alembic migrations; and a pytest suite covering selection
behavior and the model query helpers.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

tests/test_selection.py

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+import random
+from collections import Counter
+
+from coldcall_lti import selection
+
+
+STUDENTS = ["a", "b", "c", "d"]
+
+
+def test_weights_halve_per_answered_call():
+    weights = selection.compute_weights(STUDENTS, {"a": 0, "b": 1, "c": 3}, 2.0)
+    assert weights == {"a": 1.0, "b": 0.5, "c": 0.125, "d": 1.0}
+
+
+def test_weight_factor_one_is_uniform():
+    weights = selection.compute_weights(STUDENTS, {"a": 5, "b": 2}, 1.0)
+    assert set(weights.values()) == {1.0}
+
+
+def test_heavily_called_student_selected_less():
+    rng = random.Random(42)
+    counts = Counter(
+        selection.select_student(STUDENTS, {"a": 4}, 2.0, rng)
+        for _ in range(4000)
+    )
+    # "a" has weight 1/16 against 1 for the others, so it should get
+    # roughly 1/49th of the picks; the others roughly a third each.
+    assert counts["a"] < 250
+    for s in ("b", "c", "d"):
+        assert 1000 < counts[s] < 1700
+
+
+def test_generate_call_list_downweights_within_list():
+    rng = random.Random(7)
+    picks = selection.generate_call_list(STUDENTS, {}, 200, 2.0, rng)
+    assert len(picks) == 200
+    counts = Counter(picks)
+    # Sequential downweighting keeps the distribution close to even.
+    assert all(35 < counts[s] < 65 for s in STUDENTS)
+
+
+def test_generate_cycle_list_covers_everyone_once():
+    rng = random.Random(1)
+    picks = selection.generate_cycle_list(STUDENTS, rng)
+    assert sorted(picks) == sorted(STUDENTS)