Lars
ca8cee990b
- Updated the README to include new activity production architecture and phases, improving clarity on the development roadmap. - Enhanced the `ACTIVITY_SESSION_METRICS_EAV_AGENT_GUIDE` with details on the target architecture and phase plan for production readiness. - Introduced a new function `merge_column_backed_and_eav_metrics` to streamline the merging of metrics from column-backed and EAV sources, ensuring data integrity and reducing duplication. - Refactored session metrics handling to eliminate deprecated synchronization methods, improving the overall efficiency of data processing. - Added unit tests for the new merging logic, ensuring robust validation of metrics handling.
308 lines
8.7 KiB
Python
308 lines
8.7 KiB
Python
"""Unit tests for data_layer.activity_session_metrics (no DB for most cases)."""
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import uuid
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from unittest.mock import patch
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import pytest
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from data_layer.activity_session_metrics import (
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ActivitySessionMetricsError,
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enrich_sessions_with_metrics,
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merge_column_backed_and_eav_metrics,
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merge_parameter_schema_rows,
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resolve_activity_attribute_schema,
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_row_value_tuple,
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_validate_single_value,
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)
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def _tp_row(
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pid: int,
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key: str,
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*,
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data_type: str = "integer",
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cat_sort: int = 0,
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cat_required: bool = False,
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name_de: str = "X",
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name_en: str = "X",
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param_category: str = "physical",
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unit: str | None = None,
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validation_rules: dict | None = None,
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source_field: str | None = None,
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):
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return {
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"training_parameter_id": pid,
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"cat_sort": cat_sort,
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"cat_required": cat_required,
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"cat_ui_group": None,
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"key": key,
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"name_de": name_de,
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"name_en": name_en,
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"param_category": param_category,
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"data_type": data_type,
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"unit": unit,
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"validation_rules": validation_rules or {},
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"source_field": source_field,
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}
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def _ttp_row(
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pid: int,
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key: str,
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*,
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typ_sort: int | None = None,
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typ_required: bool | None = None,
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typ_ui_group: str | None = None,
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data_type: str = "integer",
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name_de: str = "X",
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name_en: str = "X",
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param_category: str = "physical",
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unit: str | None = None,
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validation_rules: dict | None = None,
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source_field: str | None = None,
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):
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return {
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"training_parameter_id": pid,
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"typ_sort": typ_sort,
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"typ_required": typ_required,
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"typ_ui_group": typ_ui_group,
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"key": key,
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"name_de": name_de,
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"name_en": name_en,
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"param_category": param_category,
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"data_type": data_type,
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"unit": unit,
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"validation_rules": validation_rules or {},
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"source_field": source_field,
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}
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def test_merge_category_only_sorted_by_sort_order_then_key():
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cat = [
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_tp_row(2, "zebra", cat_sort=10),
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_tp_row(1, "alpha", cat_sort=5),
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]
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merged = merge_parameter_schema_rows(cat, [])
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assert [m["key"] for m in merged] == ["alpha", "zebra"]
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assert merged[0]["required"] is False
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def test_merge_type_overrides_required_and_sort():
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cat = [_tp_row(1, "rpe", cat_sort=0, cat_required=False)]
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typ = [_ttp_row(1, "rpe", typ_sort=99, typ_required=True)]
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merged = merge_parameter_schema_rows(cat, typ)
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assert len(merged) == 1
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assert merged[0]["sort_order"] == 99
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assert merged[0]["required"] is True
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def test_merge_type_adds_parameter_not_in_category():
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typ = [_ttp_row(7, "cadence", typ_sort=1, typ_required=True, data_type="integer")]
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merged = merge_parameter_schema_rows([], typ)
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assert len(merged) == 1
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assert merged[0]["key"] == "cadence"
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assert merged[0]["required"] is True
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def test_validate_integer_range():
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_validate_single_value("integer", 5, {"min": 1, "max": 10})
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with pytest.raises(ActivitySessionMetricsError) as ei:
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_validate_single_value("integer", 0, {"min": 1})
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assert ei.value.status_code == 400
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def test_validate_float_accepts_int():
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_validate_single_value("float", 3, {"min": 0, "max": 10})
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def test_validate_boolean_rejects_int():
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with pytest.raises(ActivitySessionMetricsError):
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_validate_single_value("boolean", 1, {})
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def test_row_value_tuple_mapping():
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assert _row_value_tuple("integer", 42) == (None, 42, None, None)
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assert _row_value_tuple("float", 1.5) == (1.5, None, None, None)
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assert _row_value_tuple("string", "hi") == (None, None, "hi", None)
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assert _row_value_tuple("boolean", True) == (None, None, None, True)
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class _FakeCursor:
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"""Sequences fetchone/fetchall for resolve_activity_attribute_schema."""
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def __init__(self, fetchone_chain, fetchall_chain):
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self._fetchone = list(fetchone_chain)
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self._fetchall = list(fetchall_chain)
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self.executes: list[tuple] = []
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def execute(self, sql, params=None):
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self.executes.append((sql, params))
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def fetchone(self):
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return self._fetchone.pop(0)
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def fetchall(self):
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return self._fetchall.pop(0)
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def test_resolve_with_explicit_category_no_type():
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cur = _FakeCursor(
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fetchone_chain=[],
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fetchall_chain=[
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[
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_tp_row(1, "rpe", cat_sort=0),
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],
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],
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)
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out = resolve_activity_attribute_schema(cur, "cardio", None)
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assert len(out) == 1
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assert out[0]["key"] == "rpe"
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assert len(cur.executes) == 1
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def test_resolve_loads_category_from_training_type_id():
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cur = _FakeCursor(
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fetchone_chain=[{"category": "strength"}],
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fetchall_chain=[
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[_tp_row(1, "rpe", cat_sort=0)],
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[],
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],
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)
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out = resolve_activity_attribute_schema(cur, None, 42)
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assert len(out) == 1
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assert cur.executes[0][1] == (42,)
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@patch("data_layer.activity_session_metrics.resolve_activity_attribute_schema", return_value=[])
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def test_enrich_sessions_batch(mock_resolve):
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aid = str(uuid.uuid4())
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bid = str(uuid.uuid4())
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class _Cur:
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def __init__(self):
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self.params = None
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self._fetch_n = 0
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def execute(self, sql, params=None):
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self.sql = sql
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self.params = params
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def fetchall(self):
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self._fetch_n += 1
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if self._fetch_n == 1:
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return [
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{
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"id": uuid.UUID(aid),
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"training_category": None,
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"training_type_id": None,
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},
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{
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"id": uuid.UUID(bid),
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"training_category": None,
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"training_type_id": None,
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},
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]
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return [
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{
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"activity_log_id": uuid.UUID(aid),
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"training_parameter_id": 3,
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"key": "rpe",
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"data_type": "integer",
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"unit": None,
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"value_num": None,
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"value_int": 7,
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"value_text": None,
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"value_bool": None,
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},
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]
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sessions = [{"id": aid}, {"id": bid}]
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enrich_sessions_with_metrics(_Cur(), sessions)
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assert sessions[0]["session_metrics"][0]["value"] == 7
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assert sessions[0]["session_metrics"][0]["key"] == "rpe"
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assert sessions[1]["session_metrics"] == []
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def test_merge_column_backed_prefers_column_over_stale_eav():
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schema = [
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{
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"training_parameter_id": 1,
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"key": "hr_avg",
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"data_type": "float",
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"unit": "bpm",
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"validation_rules": {},
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"source_field": "hr_avg",
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}
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]
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eav = [
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{
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"training_parameter_id": 1,
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"key": "hr_avg",
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"data_type": "float",
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"unit": "bpm",
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"value": 99.0,
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}
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]
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out = merge_column_backed_and_eav_metrics({"hr_avg": 140.0}, schema, eav)
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assert len(out) == 1
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assert out[0]["value"] == 140.0
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def test_merge_falls_back_to_eav_when_column_empty():
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schema = [
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{
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"training_parameter_id": 1,
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"key": "hr_avg",
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"data_type": "float",
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"unit": "bpm",
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"validation_rules": {},
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"source_field": "hr_avg",
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}
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]
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eav = [
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{
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"training_parameter_id": 1,
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"key": "hr_avg",
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"data_type": "float",
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"unit": "bpm",
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"value": 99.0,
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}
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]
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out = merge_column_backed_and_eav_metrics({"hr_avg": None}, schema, eav)
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assert len(out) == 1
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assert out[0]["value"] == 99.0
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def test_merge_keeps_eav_only_keys():
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schema = []
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eav = [
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{
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"training_parameter_id": 2,
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"key": "custom_param",
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"data_type": "string",
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"unit": None,
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"value": "x",
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}
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]
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out = merge_column_backed_and_eav_metrics({}, schema, eav)
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assert len(out) == 1
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assert out[0]["key"] == "custom_param"
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def test_merge_eav_primary_falls_back_to_legacy_hr_min_column():
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"""Kanon: min_hr ohne source_field / ohne EAV — Lesefallback Spalte hr_min."""
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schema = [
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{
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"training_parameter_id": 9,
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"key": "min_hr",
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"data_type": "integer",
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"unit": "bpm",
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"validation_rules": {},
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"source_field": None,
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}
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]
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out = merge_column_backed_and_eav_metrics({"hr_min": 88}, schema, [])
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assert len(out) == 1
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assert out[0]["key"] == "min_hr"
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assert out[0]["value"] == 88
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