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- Updated `build_gap_fill_goal_text` to include expected skills in the generated text, improving clarity for users. - Enhanced `_roadmap_gap_snapshot_for_spec` to incorporate skill expectations from the progression stage, enriching the roadmap context. - Modified `_annotate_roadmap_step` to append skill expectations to the step reasons, providing additional insights. - Updated tests to verify the inclusion of expected skills in the gap fill goal text. - Incremented application version to 0.8.215 to reflect these changes.
122 lines
4.0 KiB
Python
122 lines
4.0 KiB
Python
"""Tests wiederverwendbare Fähigkeiten-Erwartungen (Progressionsgraph + später Planung)."""
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from unittest.mock import MagicMock
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from planning_skill_expectations import (
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SCOPE_PROGRESSION_PATH,
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SCOPE_PROGRESSION_STAGE,
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PlanningSkillExpectationInput,
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apply_expectations_to_target,
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build_planning_skill_expectations,
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expectation_input_from_progression_path,
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expectation_input_from_progression_stage,
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)
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class _FakeCursor:
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def __init__(self, skills):
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self._skills = skills
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def execute(self, query, params=None):
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del query
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if params and len(params) >= 2:
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needle = str(params[0]).strip("%").lower()
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exact = str(params[1]).lower()
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matches = [
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s
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for s in self._skills
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if needle in s["name"].lower() or s["name"].lower() == exact
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]
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self._row = matches[0] if matches else None
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else:
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self._row = None
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def fetchone(self):
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return self._row
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def _fake_skill_rows():
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return [
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(1, "Koordination", 0),
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(2, "Timing", 0),
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(3, "Distanz", 0),
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(4, "Kime", 0),
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]
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def test_expectation_input_from_progression_stage_merges_sources(monkeypatch):
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monkeypatch.setattr(
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"planning_skill_expectations._load_skills_for_text_match",
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lambda cur: _fake_skill_rows(),
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)
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inp = expectation_input_from_progression_stage(
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goal_query="Kumite Beinarbeit",
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goal_analysis={
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"primary_topic": "Kumite",
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"start_assumption": "gleichförmige Steppbewegung",
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"target_state": "explosiver Angriff",
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},
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resolved_structured={"roadmap_notes": "Kindergruppe"},
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stage_spec={
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"learning_goal": "variable Rhythmen",
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"load_profile": ["timing", "distanz"],
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"phase": "vertiefung",
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},
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semantic_brief_summary={"must_phrases": ["Beinarbeit"], "primary_topic": "Kumite"},
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major_step={"phase": "vertiefung", "learning_goal": "Major-Ziel"},
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)
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assert inp.scope == SCOPE_PROGRESSION_STAGE
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assert inp.primary_topic == "Kumite"
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assert inp.start_situation == "gleichförmige Steppbewegung"
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assert inp.load_profile == ["timing", "distanz"]
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assert "Beinarbeit" in inp.skill_hints
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def test_build_planning_skill_expectations_load_profile_and_text(monkeypatch):
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monkeypatch.setattr(
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"planning_skill_expectations._load_skills_for_text_match",
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lambda cur: _fake_skill_rows(),
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)
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cur = _FakeCursor(
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[
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{"id": 2, "name": "Timing"},
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{"id": 3, "name": "Distanz"},
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]
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)
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inp = PlanningSkillExpectationInput(
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scope=SCOPE_PROGRESSION_STAGE,
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primary_topic="Kumite",
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goal_query="Kumite Beinarbeit mit Timing",
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load_profile=["timing", "distanz"],
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)
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exp = build_planning_skill_expectations(cur, inp)
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assert exp.scope == SCOPE_PROGRESSION_STAGE
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assert "load_profile" in exp.sources or "text_match" in exp.sources
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names = {it.skill_name for it in exp.items}
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assert "Timing" in names or "Distanz" in names
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assert exp.skill_weights
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def test_expectation_input_from_progression_path_scope():
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inp = expectation_input_from_progression_path(
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goal_query="Mae Geri Perfektion",
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goal_analysis={"primary_topic": "Mae Geri"},
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resolved_structured={"start_situation": "Grundstellung", "target_state": "freier Kick"},
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)
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assert inp.scope == SCOPE_PROGRESSION_PATH
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assert inp.start_situation == "Grundstellung"
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assert inp.target_state == "freier Kick"
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def test_apply_expectations_to_target_noop_without_weights():
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class _Target:
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skill_weights = {}
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def to_summary_dict(self, cur):
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return {}
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target = _Target()
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from planning_skill_expectations import PlanningSkillExpectations
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empty = PlanningSkillExpectations(scope=SCOPE_PROGRESSION_STAGE, skill_weights={}, items=[], sources=[])
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assert apply_expectations_to_target(target, empty) is target
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