thermograph/tests/test_views.py

"""The payload layer: pure builders, span clamping, and the layering guarantee
that offline callers (migrate) can use it without dragging in the web stack."""
import os
import subprocess
import sys

import pandas as pd
import pytest

import climate
import views

CELL = {"id": "1642_-4223", "center_lat": 47.6087, "center_lon": -122.29377}


def test_views_and_migrate_import_without_the_web_stack():
    """migrate.py must stay runnable offline: importing the payload layer may
    not construct the FastAPI app or start the places-index download."""
    code = ("import sys; import views, migrate; "
            "assert 'fastapi' not in sys.modules, 'views/migrate pulled in FastAPI'; "
            "assert 'app' not in sys.modules, 'views/migrate imported the web app'; "
            "import places; assert places._load_started is False")
    backend = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
    subprocess.run([sys.executable, "-c", code], cwd=backend, check=True)


# ---- cache identity --------------------------------------------------------------
# These formats are shared by the endpoints, the /cell bundle and migrate; pin
# them so a change is always deliberate (an accidental drift silently strands
# every existing derived row — change them only alongside a PAYLOAD_VER bump).

def test_cache_identity_formats_are_pinned(history):
    t = pd.Timestamp("2026-06-15")
    assert views.grade_key(t, 14, 7) == "2026-06-15:14:7"
    assert views.calendar_key(t, pd.Timestamp("2026-06-20"), 24) == "2026-06-15:2026-06-20:24"
    assert views.day_key(t) == "2026-06-15"
    assert views.forecast_key(t, 7) == "2026-06-15:7"
    assert views.history_token(history) == f"{views.PAYLOAD_VER}:{views.hist_end(history)}"


def test_recent_token_composes_history_and_stamp(history, monkeypatch):
    monkeypatch.setattr(climate, "recent_stamp", lambda cid: "stamp")
    assert views.recent_token(history, "1_2") == f"{views.history_token(history)}:stamp"


def test_day_token_expires_hourly_only_beyond_the_archive(history):
    last = pd.Timestamp(history["date"].max()).normalize()
    assert views.day_token(history, last) == views.history_token(history)
    future = views.day_token(history, last + pd.Timedelta(days=1))
    assert future.startswith(views.history_token(history) + ":h")


# ---- cal_span clamping ---------------------------------------------------------

def _hist(start, end):
    df = pd.DataFrame({"date": pd.date_range(start, end, freq="D")})
    return df


def test_cal_span_defaults_to_months_back_same_day_of_month():
    start_ts, end_ts = views.cal_span(_hist("2020-01-01", "2026-06-29"), None, None, 24)
    assert end_ts == pd.Timestamp("2026-06-29")
    assert start_ts == pd.Timestamp("2024-06-29")


def test_cal_span_clamps_to_the_record():
    h = _hist("2025-03-01", "2026-06-29")           # record shorter than the 2-year cap
    start_ts, end_ts = views.cal_span(h, "2020-01-01", None, 24)
    assert start_ts == pd.Timestamp("2025-03-01")   # can't start before the record
    _, end_ts = views.cal_span(h, None, "2030-01-01", 24)
    assert end_ts == pd.Timestamp("2026-06-29")     # nor end past it


def test_cal_span_caps_at_two_years():
    start_ts, end_ts = views.cal_span(_hist("2018-01-01", "2026-06-29"),
                                      "2018-01-01", "2026-06-29", 24)
    assert (end_ts - start_ts).days == views.CAL_MAX_SPAN_DAYS - 1


def test_cal_span_never_inverts():
    start_ts, end_ts = views.cal_span(_hist("2020-01-01", "2026-06-29"),
                                      "2026-06-01", "2021-01-01", 24)
    assert start_ts == end_ts


# ---- builders -------------------------------------------------------------------

def test_build_grade_window_and_shape(history, recent):
    target = pd.Timestamp.today().normalize()
    payload = views.build_grade(CELL, target, 14, history, recent,
                                {"cached": True}, "Testville")
    assert payload["target_date"] == target.date().isoformat()
    days = [d["date"] for d in payload["recent"]]
    assert days == sorted(days, reverse=True)          # newest first
    assert payload["climatology"]["tmax"] is not None
    assert all(d["dsr"] is not None for d in payload["recent"])


def test_build_day_pulls_future_obs_from_recent(history, recent):
    today = pd.Timestamp.today().normalize()
    payload = views.build_day(CELL, history, today, "Testville", recent=recent)
    assert payload["detail"]["date"] == today.date().isoformat()
    assert payload["detail"]["metrics"]["tmax"]["obs"] is not None


def test_build_day_survives_recent_fetch_failure(history, monkeypatch):
    def boom(cell):
        raise RuntimeError("upstream down")
    monkeypatch.setattr(climate, "get_recent_forecast", boom)
    today = pd.Timestamp.today().normalize()
    payload = views.build_day(CELL, history, today, "Testville")
    assert payload["detail"]["metrics"]["tmax"]["obs"] is None      # climatology only
    assert payload["detail"]["metrics"]["tmax"]["ladder"] is not None


def test_build_forecast_only_future_days(history, recent):
    today = pd.Timestamp.today().normalize()
    payload = views.build_forecast(CELL, 7, history, recent, today, None)
    days = [d["date"] for d in payload["recent"]]
    assert days == sorted(days, reverse=True)          # furthest-out first
    assert min(days) > today.date().isoformat()
    assert payload["forecast"] is True