186 lines
6.7 KiB
Python
186 lines
6.7 KiB
Python
|
"""Built-in reports: counts, cost of nonconformance, aging, cycle times,
|
|||
|
|
top jobs. All queries respect the shared date-range/department/category
|
|||
|
|
filters."""
|
|||
|
|
from collections import defaultdict
|
|||
|
|
from decimal import Decimal
|
|||
|
|
|
|||
|
|
from fastapi import APIRouter, Depends, Query
|
|||
|
|
from sqlalchemy import func, select
|
|||
|
|
from sqlalchemy.ext.asyncio import AsyncSession
|
|||
|
|
|
|||
|
|
from app.auth.deps import CurrentUser, get_current_user
|
|||
|
|
from app.database import get_db
|
|||
|
|
from app.domain import STAGE_LABELS, Stage
|
|||
|
|
from app.models import Department, DeviationCategory, Ncr, StageTransition
|
|||
|
|
from app.models.base import utcnow
|
|||
|
|
from app.schemas.report import (
|
|||
|
|
AgingBucket,
|
|||
|
|
CostByMonth,
|
|||
|
|
CountByMonth,
|
|||
|
|
CountByName,
|
|||
|
|
ReportsSummaryOut,
|
|||
|
|
StageCycleTime,
|
|||
|
|
TopJob,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
router = APIRouter(tags=["reports"])
|
|||
|
|
|
|||
|
|
_AGING_BUCKETS = [(0, 7, "0–7 days"), (8, 14, "8–14 days"), (15, 30, "15–30 days"),
|
|||
|
|
(31, 60, "31–60 days"), (61, None, "60+ days")]
|
|||
|
|
|
|||
|
|
|
|||
|
|
def _base_filters(stmt, date_from, date_to, department_id, category_id):
|
|||
|
|
if date_from:
|
|||
|
|
stmt = stmt.where(Ncr.created_at >= date_from)
|
|||
|
|
if date_to:
|
|||
|
|
stmt = stmt.where(Ncr.created_at <= f"{date_to} 23:59:59")
|
|||
|
|
if department_id:
|
|||
|
|
stmt = stmt.where(Ncr.department_id == department_id)
|
|||
|
|
if category_id:
|
|||
|
|
stmt = stmt.where(Ncr.deviation_category_id == category_id)
|
|||
|
|
return stmt
|
|||
|
|
|
|||
|
|
|
|||
|
|
@router.get("/reports/summary", response_model=ReportsSummaryOut)
|
|||
|
|
async def reports_summary(
|
|||
|
|
date_from: str | None = Query(default=None, description="YYYY-MM-DD"),
|
|||
|
|
date_to: str | None = Query(default=None, description="YYYY-MM-DD"),
|
|||
|
|
department_id: int | None = None,
|
|||
|
|
category_id: int | None = None,
|
|||
|
|
_: CurrentUser = Depends(get_current_user),
|
|||
|
|
db: AsyncSession = Depends(get_db),
|
|||
|
|
) -> ReportsSummaryOut:
|
|||
|
|
filters = dict(
|
|||
|
|
date_from=date_from,
|
|||
|
|
date_to=date_to,
|
|||
|
|
department_id=department_id,
|
|||
|
|
category_id=category_id,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# Load the filtered NCR set once; aggregate in Python. NCR volume is a few
|
|||
|
|
# thousand rows a year, so this stays cheap and keeps the SQL portable.
|
|||
|
|
ncrs = (
|
|||
|
|
(await db.execute(_base_filters(select(Ncr), **filters))).scalars().unique().all()
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
dept_names = {
|
|||
|
|
d.id: d.name for d in (await db.execute(select(Department))).scalars().all()
|
|||
|
|
}
|
|||
|
|
cat_names = {
|
|||
|
|
c.id: c.name
|
|||
|
|
for c in (await db.execute(select(DeviationCategory))).scalars().all()
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
by_dept: dict[str, int] = defaultdict(int)
|
|||
|
|
by_cat: dict[str, int] = defaultdict(int)
|
|||
|
|
by_month: dict[str, int] = defaultdict(int)
|
|||
|
|
cost_by_month: dict[str, dict[str, Decimal]] = defaultdict(
|
|||
|
|
lambda: {"labor": Decimal(0), "material": Decimal(0), "service": Decimal(0), "other": Decimal(0)}
|
|||
|
|
)
|
|||
|
|
aging_counts: dict[str, int] = {label: 0 for _, _, label in _AGING_BUCKETS}
|
|||
|
|
job_counts: dict[str, int] = defaultdict(int)
|
|||
|
|
total_cost = Decimal(0)
|
|||
|
|
open_count = 0
|
|||
|
|
closed_count = 0
|
|||
|
|
now = utcnow()
|
|||
|
|
|
|||
|
|
for n in ncrs:
|
|||
|
|
by_dept[dept_names.get(n.department_id, "?")] += 1
|
|||
|
|
by_cat[cat_names.get(n.deviation_category_id, "?")] += 1
|
|||
|
|
by_month[n.created_at.strftime("%Y-%m")] += 1
|
|||
|
|
job_counts[n.job_number] += 1
|
|||
|
|
if n.stage == Stage.CLOSED.value:
|
|||
|
|
closed_count += 1
|
|||
|
|
month = (n.closed_at or n.created_at).strftime("%Y-%m")
|
|||
|
|
bucket = cost_by_month[month]
|
|||
|
|
bucket["labor"] += n.labor_cost or 0
|
|||
|
|
bucket["material"] += n.material_cost or 0
|
|||
|
|
bucket["service"] += n.service_cost or 0
|
|||
|
|
bucket["other"] += n.other_cost or 0
|
|||
|
|
total_cost += n.total_cost or 0
|
|||
|
|
else:
|
|||
|
|
open_count += 1
|
|||
|
|
days = max(0, (now - n.stage_entered_at).days)
|
|||
|
|
for lo, hi, label in _AGING_BUCKETS:
|
|||
|
|
if days >= lo and (hi is None or days <= hi):
|
|||
|
|
aging_counts[label] += 1
|
|||
|
|
break
|
|||
|
|
|
|||
|
|
# ── cycle times from the transition history ─────────────────────────────
|
|||
|
|
ncr_ids = [n.id for n in ncrs]
|
|||
|
|
stage_durations: dict[str, list[float]] = defaultdict(list)
|
|||
|
|
end_to_end: list[float] = []
|
|||
|
|
if ncr_ids:
|
|||
|
|
transitions = (
|
|||
|
|
(
|
|||
|
|
await db.execute(
|
|||
|
|
select(StageTransition)
|
|||
|
|
.where(StageTransition.ncr_id.in_(ncr_ids))
|
|||
|
|
.order_by(StageTransition.ncr_id, StageTransition.acted_at)
|
|||
|
|
)
|
|||
|
|
)
|
|||
|
|
.scalars()
|
|||
|
|
.all()
|
|||
|
|
)
|
|||
|
|
per_ncr: dict[int, list[StageTransition]] = defaultdict(list)
|
|||
|
|
for t in transitions:
|
|||
|
|
per_ncr[t.ncr_id].append(t)
|
|||
|
|
for items in per_ncr.values():
|
|||
|
|
for prev, nxt in zip(items, items[1:]):
|
|||
|
|
delta_days = (nxt.acted_at - prev.acted_at).total_seconds() / 86400
|
|||
|
|
stage_durations[prev.to_stage].append(delta_days)
|
|||
|
|
first, last = items[0], items[-1]
|
|||
|
|
if last.to_stage == Stage.CLOSED.value:
|
|||
|
|
end_to_end.append(
|
|||
|
|
(last.acted_at - first.acted_at).total_seconds() / 86400
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
cycle_times = [
|
|||
|
|
StageCycleTime(
|
|||
|
|
stage=s.value,
|
|||
|
|
stage_label=STAGE_LABELS[s],
|
|||
|
|
avg_days=round(sum(v) / len(v), 2),
|
|||
|
|
samples=len(v),
|
|||
|
|
)
|
|||
|
|
for s in Stage
|
|||
|
|
if s != Stage.CLOSED and (v := stage_durations.get(s.value))
|
|||
|
|
]
|
|||
|
|
|
|||
|
|
months = sorted(set(by_month) | set(cost_by_month))
|
|||
|
|
return ReportsSummaryOut(
|
|||
|
|
total_ncrs=len(ncrs),
|
|||
|
|
open_ncrs=open_count,
|
|||
|
|
closed_ncrs=closed_count,
|
|||
|
|
total_cost=total_cost,
|
|||
|
|
by_department=sorted(
|
|||
|
|
(CountByName(name=k, count=v) for k, v in by_dept.items()),
|
|||
|
|
key=lambda x: -x.count,
|
|||
|
|
),
|
|||
|
|
by_category=sorted(
|
|||
|
|
(CountByName(name=k, count=v) for k, v in by_cat.items()),
|
|||
|
|
key=lambda x: -x.count,
|
|||
|
|
),
|
|||
|
|
by_month=[CountByMonth(month=m, count=by_month.get(m, 0)) for m in months],
|
|||
|
|
cost_over_time=[
|
|||
|
|
CostByMonth(
|
|||
|
|
month=m,
|
|||
|
|
labor=c["labor"],
|
|||
|
|
material=c["material"],
|
|||
|
|
service=c["service"],
|
|||
|
|
other=c["other"],
|
|||
|
|
total=c["labor"] + c["material"] + c["service"] + c["other"],
|
|||
|
|
)
|
|||
|
|
for m in months
|
|||
|
|
if (c := cost_by_month.get(m))
|
|||
|
|
],
|
|||
|
|
aging=[AgingBucket(bucket=label, count=aging_counts[label]) for _, _, label in _AGING_BUCKETS],
|
|||
|
|
cycle_times=cycle_times,
|
|||
|
|
end_to_end_avg_days=(
|
|||
|
|
round(sum(end_to_end) / len(end_to_end), 2) if end_to_end else None
|
|||
|
|
),
|
|||
|
|
top_jobs=sorted(
|
|||
|
|
(TopJob(job_number=j, count=c) for j, c in job_counts.items()),
|
|||
|
|
key=lambda x: -x.count,
|
|||
|
|
)[:10],
|
|||
|
|
)
|