Extract payload builders into views.py; load places index at startup (#42)
app.py had grown three co-resident strata: HTTP/caching plumbing, payload
assembly, and search policy. This moves the payload layer — the four
build_* functions, cal_span clamping, hist_end, PAYLOAD_VER, NullRun and
their helpers — into a new views.py with no web dependencies (pure moves,
public names). app.py keeps routing, ETag/derived-store plumbing, and
page serving; migrate.py imports views instead of reaching into app's
privates.
That import previously constructed the whole FastAPI app and — because
places.start_loading() ran at import time — kicked off a background
GeoNames download from an offline batch script. The index load now hangs
off the app's lifespan hook, so it fires when a server starts, not when
the module is imported (tests, migrate).
New tests: cal_span clamping (months-back default, record bounds, 2-year
cap, inversion), builder shapes (grade window ordering, day obs from the
recent bundle, forecast future-only), a regression test that build_day
degrades to climatology when the recent fetch fails, and a subprocess
layering guard that importing views/migrate pulls in neither FastAPI nor
the app module and starts no index download.
2026-07-11 19:43:41 +00:00
|
|
|
"""The payload layer: pure builders, span clamping, and the layering guarantee
|
|
|
|
|
that offline callers (migrate) can use it without dragging in the web stack."""
|
Migrate backend dataframe layer from pandas to polars (#90)
* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
2026-07-15 19:07:38 +00:00
|
|
|
import datetime
|
Extract payload builders into views.py; load places index at startup (#42)
app.py had grown three co-resident strata: HTTP/caching plumbing, payload
assembly, and search policy. This moves the payload layer — the four
build_* functions, cal_span clamping, hist_end, PAYLOAD_VER, NullRun and
their helpers — into a new views.py with no web dependencies (pure moves,
public names). app.py keeps routing, ETag/derived-store plumbing, and
page serving; migrate.py imports views instead of reaching into app's
privates.
That import previously constructed the whole FastAPI app and — because
places.start_loading() ran at import time — kicked off a background
GeoNames download from an offline batch script. The index load now hangs
off the app's lifespan hook, so it fires when a server starts, not when
the module is imported (tests, migrate).
New tests: cal_span clamping (months-back default, record bounds, 2-year
cap, inversion), builder shapes (grade window ordering, day obs from the
recent bundle, forecast future-only), a regression test that build_day
degrades to climatology when the recent fetch fails, and a subprocess
layering guard that importing views/migrate pulls in neither FastAPI nor
the app module and starts no index download.
2026-07-11 19:43:41 +00:00
|
|
|
import os
|
|
|
|
|
import subprocess
|
|
|
|
|
import sys
|
|
|
|
|
|
Migrate backend dataframe layer from pandas to polars (#90)
* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
2026-07-15 19:07:38 +00:00
|
|
|
import polars as pl
|
Extract payload builders into views.py; load places index at startup (#42)
app.py had grown three co-resident strata: HTTP/caching plumbing, payload
assembly, and search policy. This moves the payload layer — the four
build_* functions, cal_span clamping, hist_end, PAYLOAD_VER, NullRun and
their helpers — into a new views.py with no web dependencies (pure moves,
public names). app.py keeps routing, ETag/derived-store plumbing, and
page serving; migrate.py imports views instead of reaching into app's
privates.
That import previously constructed the whole FastAPI app and — because
places.start_loading() ran at import time — kicked off a background
GeoNames download from an offline batch script. The index load now hangs
off the app's lifespan hook, so it fires when a server starts, not when
the module is imported (tests, migrate).
New tests: cal_span clamping (months-back default, record bounds, 2-year
cap, inversion), builder shapes (grade window ordering, day obs from the
recent bundle, forecast future-only), a regression test that build_day
degrades to climatology when the recent fetch fails, and a subprocess
layering guard that importing views/migrate pulls in neither FastAPI nor
the app module and starts no index download.
2026-07-11 19:43:41 +00:00
|
|
|
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)
|
|
|
|
|
|
|
|
|
|
|
Single-source cache identity; shared fetch preamble and cache flow (#43)
The derived store's key/token formats existed in three places — each
endpoint, api_cell's slice assembly, and migrate.py — where any drift
would silently split the cache (endpoints missing rows the bundle wrote,
migrate materializing rows nobody reads). They are now defined once in
views.py (grade_key/calendar_key/day_key/forecast_key, history_token/
recent_token/day_token) and consumed everywhere, with a pinning test so
a format change is always deliberate.
The four data endpoints shared two copy-pasted sequences, now helpers:
- _fetch_history: history (+ optional recent bundle) fetch with upstream
failures mapped to clean HTTP errors and an empty record to 404.
- _cached_response: If-None-Match 304 / token-valid store replay /
build + persist + serve, with calendar's dont-persist-placeless rule
as an explicit flag.
Each endpoint is now its audit run + identity + a build callback (~10
lines); api_day's hourly-token special case moved into day_token. New
tests: identity format pins, rate-limit 503 parametrized across all five
data routes, prefetch=1 never touching upstream (cold 204, warm
history-only slices), and calendar's placeless-payload retry behavior.
2026-07-11 19:49:15 +00:00
|
|
|
# ---- 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):
|
Migrate backend dataframe layer from pandas to polars (#90)
* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
2026-07-15 19:07:38 +00:00
|
|
|
t = datetime.date(2026, 6, 15)
|
Single-source cache identity; shared fetch preamble and cache flow (#43)
The derived store's key/token formats existed in three places — each
endpoint, api_cell's slice assembly, and migrate.py — where any drift
would silently split the cache (endpoints missing rows the bundle wrote,
migrate materializing rows nobody reads). They are now defined once in
views.py (grade_key/calendar_key/day_key/forecast_key, history_token/
recent_token/day_token) and consumed everywhere, with a pinning test so
a format change is always deliberate.
The four data endpoints shared two copy-pasted sequences, now helpers:
- _fetch_history: history (+ optional recent bundle) fetch with upstream
failures mapped to clean HTTP errors and an empty record to 404.
- _cached_response: If-None-Match 304 / token-valid store replay /
build + persist + serve, with calendar's dont-persist-placeless rule
as an explicit flag.
Each endpoint is now its audit run + identity + a build callback (~10
lines); api_day's hourly-token special case moved into day_token. New
tests: identity format pins, rate-limit 503 parametrized across all five
data routes, prefetch=1 never touching upstream (cold 204, warm
history-only slices), and calendar's placeless-payload retry behavior.
2026-07-11 19:49:15 +00:00
|
|
|
assert views.grade_key(t, 14, 7) == "2026-06-15:14:7"
|
Migrate backend dataframe layer from pandas to polars (#90)
* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
2026-07-15 19:07:38 +00:00
|
|
|
assert views.calendar_key(t, datetime.date(2026, 6, 20), 24) == "2026-06-15:2026-06-20:24"
|
Single-source cache identity; shared fetch preamble and cache flow (#43)
The derived store's key/token formats existed in three places — each
endpoint, api_cell's slice assembly, and migrate.py — where any drift
would silently split the cache (endpoints missing rows the bundle wrote,
migrate materializing rows nobody reads). They are now defined once in
views.py (grade_key/calendar_key/day_key/forecast_key, history_token/
recent_token/day_token) and consumed everywhere, with a pinning test so
a format change is always deliberate.
The four data endpoints shared two copy-pasted sequences, now helpers:
- _fetch_history: history (+ optional recent bundle) fetch with upstream
failures mapped to clean HTTP errors and an empty record to 404.
- _cached_response: If-None-Match 304 / token-valid store replay /
build + persist + serve, with calendar's dont-persist-placeless rule
as an explicit flag.
Each endpoint is now its audit run + identity + a build callback (~10
lines); api_day's hourly-token special case moved into day_token. New
tests: identity format pins, rate-limit 503 parametrized across all five
data routes, prefetch=1 never touching upstream (cold 204, warm
history-only slices), and calendar's placeless-payload retry behavior.
2026-07-11 19:49:15 +00:00
|
|
|
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):
|
Migrate backend dataframe layer from pandas to polars (#90)
* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
2026-07-15 19:07:38 +00:00
|
|
|
last = history["date"].max()
|
Single-source cache identity; shared fetch preamble and cache flow (#43)
The derived store's key/token formats existed in three places — each
endpoint, api_cell's slice assembly, and migrate.py — where any drift
would silently split the cache (endpoints missing rows the bundle wrote,
migrate materializing rows nobody reads). They are now defined once in
views.py (grade_key/calendar_key/day_key/forecast_key, history_token/
recent_token/day_token) and consumed everywhere, with a pinning test so
a format change is always deliberate.
The four data endpoints shared two copy-pasted sequences, now helpers:
- _fetch_history: history (+ optional recent bundle) fetch with upstream
failures mapped to clean HTTP errors and an empty record to 404.
- _cached_response: If-None-Match 304 / token-valid store replay /
build + persist + serve, with calendar's dont-persist-placeless rule
as an explicit flag.
Each endpoint is now its audit run + identity + a build callback (~10
lines); api_day's hourly-token special case moved into day_token. New
tests: identity format pins, rate-limit 503 parametrized across all five
data routes, prefetch=1 never touching upstream (cold 204, warm
history-only slices), and calendar's placeless-payload retry behavior.
2026-07-11 19:49:15 +00:00
|
|
|
assert views.day_token(history, last) == views.history_token(history)
|
Migrate backend dataframe layer from pandas to polars (#90)
* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
2026-07-15 19:07:38 +00:00
|
|
|
future = views.day_token(history, last + datetime.timedelta(days=1))
|
Single-source cache identity; shared fetch preamble and cache flow (#43)
The derived store's key/token formats existed in three places — each
endpoint, api_cell's slice assembly, and migrate.py — where any drift
would silently split the cache (endpoints missing rows the bundle wrote,
migrate materializing rows nobody reads). They are now defined once in
views.py (grade_key/calendar_key/day_key/forecast_key, history_token/
recent_token/day_token) and consumed everywhere, with a pinning test so
a format change is always deliberate.
The four data endpoints shared two copy-pasted sequences, now helpers:
- _fetch_history: history (+ optional recent bundle) fetch with upstream
failures mapped to clean HTTP errors and an empty record to 404.
- _cached_response: If-None-Match 304 / token-valid store replay /
build + persist + serve, with calendar's dont-persist-placeless rule
as an explicit flag.
Each endpoint is now its audit run + identity + a build callback (~10
lines); api_day's hourly-token special case moved into day_token. New
tests: identity format pins, rate-limit 503 parametrized across all five
data routes, prefetch=1 never touching upstream (cold 204, warm
history-only slices), and calendar's placeless-payload retry behavior.
2026-07-11 19:49:15 +00:00
|
|
|
assert future.startswith(views.history_token(history) + ":h")
|
|
|
|
|
|
|
|
|
|
|
Extract payload builders into views.py; load places index at startup (#42)
app.py had grown three co-resident strata: HTTP/caching plumbing, payload
assembly, and search policy. This moves the payload layer — the four
build_* functions, cal_span clamping, hist_end, PAYLOAD_VER, NullRun and
their helpers — into a new views.py with no web dependencies (pure moves,
public names). app.py keeps routing, ETag/derived-store plumbing, and
page serving; migrate.py imports views instead of reaching into app's
privates.
That import previously constructed the whole FastAPI app and — because
places.start_loading() ran at import time — kicked off a background
GeoNames download from an offline batch script. The index load now hangs
off the app's lifespan hook, so it fires when a server starts, not when
the module is imported (tests, migrate).
New tests: cal_span clamping (months-back default, record bounds, 2-year
cap, inversion), builder shapes (grade window ordering, day obs from the
recent bundle, forecast future-only), a regression test that build_day
degrades to climatology when the recent fetch fails, and a subprocess
layering guard that importing views/migrate pulls in neither FastAPI nor
the app module and starts no index download.
2026-07-11 19:43:41 +00:00
|
|
|
# ---- cal_span clamping ---------------------------------------------------------
|
|
|
|
|
|
|
|
|
|
def _hist(start, end):
|
Migrate backend dataframe layer from pandas to polars (#90)
* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
2026-07-15 19:07:38 +00:00
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# cal_span only reads the record's min/max date, so two rows pin the span.
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return pl.DataFrame({"date": [datetime.date.fromisoformat(start),
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datetime.date.fromisoformat(end)]})
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Extract payload builders into views.py; load places index at startup (#42)
app.py had grown three co-resident strata: HTTP/caching plumbing, payload
assembly, and search policy. This moves the payload layer — the four
build_* functions, cal_span clamping, hist_end, PAYLOAD_VER, NullRun and
their helpers — into a new views.py with no web dependencies (pure moves,
public names). app.py keeps routing, ETag/derived-store plumbing, and
page serving; migrate.py imports views instead of reaching into app's
privates.
That import previously constructed the whole FastAPI app and — because
places.start_loading() ran at import time — kicked off a background
GeoNames download from an offline batch script. The index load now hangs
off the app's lifespan hook, so it fires when a server starts, not when
the module is imported (tests, migrate).
New tests: cal_span clamping (months-back default, record bounds, 2-year
cap, inversion), builder shapes (grade window ordering, day obs from the
recent bundle, forecast future-only), a regression test that build_day
degrades to climatology when the recent fetch fails, and a subprocess
layering guard that importing views/migrate pulls in neither FastAPI nor
the app module and starts no index download.
2026-07-11 19:43:41 +00:00
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def test_cal_span_defaults_to_months_back_same_day_of_month():
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start_ts, end_ts = views.cal_span(_hist("2020-01-01", "2026-06-29"), None, None, 24)
|
Migrate backend dataframe layer from pandas to polars (#90)
* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
2026-07-15 19:07:38 +00:00
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assert end_ts == datetime.date(2026, 6, 29)
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assert start_ts == datetime.date(2024, 6, 29)
|
Extract payload builders into views.py; load places index at startup (#42)
app.py had grown three co-resident strata: HTTP/caching plumbing, payload
assembly, and search policy. This moves the payload layer — the four
build_* functions, cal_span clamping, hist_end, PAYLOAD_VER, NullRun and
their helpers — into a new views.py with no web dependencies (pure moves,
public names). app.py keeps routing, ETag/derived-store plumbing, and
page serving; migrate.py imports views instead of reaching into app's
privates.
That import previously constructed the whole FastAPI app and — because
places.start_loading() ran at import time — kicked off a background
GeoNames download from an offline batch script. The index load now hangs
off the app's lifespan hook, so it fires when a server starts, not when
the module is imported (tests, migrate).
New tests: cal_span clamping (months-back default, record bounds, 2-year
cap, inversion), builder shapes (grade window ordering, day obs from the
recent bundle, forecast future-only), a regression test that build_day
degrades to climatology when the recent fetch fails, and a subprocess
layering guard that importing views/migrate pulls in neither FastAPI nor
the app module and starts no index download.
2026-07-11 19:43:41 +00:00
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def test_cal_span_clamps_to_the_record():
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h = _hist("2025-03-01", "2026-06-29") # record shorter than the 2-year cap
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start_ts, end_ts = views.cal_span(h, "2020-01-01", None, 24)
|
Migrate backend dataframe layer from pandas to polars (#90)
* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
2026-07-15 19:07:38 +00:00
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assert start_ts == datetime.date(2025, 3, 1) # can't start before the record
|
Extract payload builders into views.py; load places index at startup (#42)
app.py had grown three co-resident strata: HTTP/caching plumbing, payload
assembly, and search policy. This moves the payload layer — the four
build_* functions, cal_span clamping, hist_end, PAYLOAD_VER, NullRun and
their helpers — into a new views.py with no web dependencies (pure moves,
public names). app.py keeps routing, ETag/derived-store plumbing, and
page serving; migrate.py imports views instead of reaching into app's
privates.
That import previously constructed the whole FastAPI app and — because
places.start_loading() ran at import time — kicked off a background
GeoNames download from an offline batch script. The index load now hangs
off the app's lifespan hook, so it fires when a server starts, not when
the module is imported (tests, migrate).
New tests: cal_span clamping (months-back default, record bounds, 2-year
cap, inversion), builder shapes (grade window ordering, day obs from the
recent bundle, forecast future-only), a regression test that build_day
degrades to climatology when the recent fetch fails, and a subprocess
layering guard that importing views/migrate pulls in neither FastAPI nor
the app module and starts no index download.
2026-07-11 19:43:41 +00:00
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_, end_ts = views.cal_span(h, None, "2030-01-01", 24)
|
Migrate backend dataframe layer from pandas to polars (#90)
* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
2026-07-15 19:07:38 +00:00
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assert end_ts == datetime.date(2026, 6, 29) # nor end past it
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Extract payload builders into views.py; load places index at startup (#42)
app.py had grown three co-resident strata: HTTP/caching plumbing, payload
assembly, and search policy. This moves the payload layer — the four
build_* functions, cal_span clamping, hist_end, PAYLOAD_VER, NullRun and
their helpers — into a new views.py with no web dependencies (pure moves,
public names). app.py keeps routing, ETag/derived-store plumbing, and
page serving; migrate.py imports views instead of reaching into app's
privates.
That import previously constructed the whole FastAPI app and — because
places.start_loading() ran at import time — kicked off a background
GeoNames download from an offline batch script. The index load now hangs
off the app's lifespan hook, so it fires when a server starts, not when
the module is imported (tests, migrate).
New tests: cal_span clamping (months-back default, record bounds, 2-year
cap, inversion), builder shapes (grade window ordering, day obs from the
recent bundle, forecast future-only), a regression test that build_day
degrades to climatology when the recent fetch fails, and a subprocess
layering guard that importing views/migrate pulls in neither FastAPI nor
the app module and starts no index download.
2026-07-11 19:43:41 +00:00
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def test_cal_span_caps_at_two_years():
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start_ts, end_ts = views.cal_span(_hist("2018-01-01", "2026-06-29"),
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"2018-01-01", "2026-06-29", 24)
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assert (end_ts - start_ts).days == views.CAL_MAX_SPAN_DAYS - 1
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def test_cal_span_never_inverts():
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start_ts, end_ts = views.cal_span(_hist("2020-01-01", "2026-06-29"),
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"2026-06-01", "2021-01-01", 24)
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assert start_ts == end_ts
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Migrate backend dataframe layer from pandas to polars (#90)
* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
2026-07-15 19:07:38 +00:00
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def test_months_before_clamps_month_end_and_leap():
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"""Calendar-aware month subtraction (replaces pandas DateOffset): land on the
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same day-of-month, clamped to the target month's last valid day."""
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assert views._months_before(datetime.date(2026, 3, 31), 1) == datetime.date(2026, 2, 28)
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assert views._months_before(datetime.date(2024, 3, 31), 1) == datetime.date(2024, 2, 29)
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assert views._months_before(datetime.date(2026, 1, 15), 14) == datetime.date(2024, 11, 15)
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def test_attach_dry_streaks_prefers_the_fresher_source_on_shared_dates():
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"""De-dup keeps the recent/forecast row over the archive one for a shared date
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(concat archive-first, keep='last') — the fresher precip drives the streak."""
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d = datetime.date(2026, 1, 1)
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hist = pl.DataFrame({"date": [d], "precip": [1.0]}) # archive: it rained (streak resets)
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rec = pl.DataFrame({"date": [d], "precip": [0.0]}) # fresher: dry (streak counts)
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graded = [{"date": d.isoformat()}]
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views._attach_dry_streaks(graded, hist, rec) # archive first, recent last
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assert graded[0]["dsr"] == 1 # recent (dry) won
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Extract payload builders into views.py; load places index at startup (#42)
app.py had grown three co-resident strata: HTTP/caching plumbing, payload
assembly, and search policy. This moves the payload layer — the four
build_* functions, cal_span clamping, hist_end, PAYLOAD_VER, NullRun and
their helpers — into a new views.py with no web dependencies (pure moves,
public names). app.py keeps routing, ETag/derived-store plumbing, and
page serving; migrate.py imports views instead of reaching into app's
privates.
That import previously constructed the whole FastAPI app and — because
places.start_loading() ran at import time — kicked off a background
GeoNames download from an offline batch script. The index load now hangs
off the app's lifespan hook, so it fires when a server starts, not when
the module is imported (tests, migrate).
New tests: cal_span clamping (months-back default, record bounds, 2-year
cap, inversion), builder shapes (grade window ordering, day obs from the
recent bundle, forecast future-only), a regression test that build_day
degrades to climatology when the recent fetch fails, and a subprocess
layering guard that importing views/migrate pulls in neither FastAPI nor
the app module and starts no index download.
2026-07-11 19:43:41 +00:00
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# ---- builders -------------------------------------------------------------------
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def test_build_grade_window_and_shape(history, recent):
|
Migrate backend dataframe layer from pandas to polars (#90)
* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
2026-07-15 19:07:38 +00:00
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target = datetime.date.today()
|
Extract payload builders into views.py; load places index at startup (#42)
app.py had grown three co-resident strata: HTTP/caching plumbing, payload
assembly, and search policy. This moves the payload layer — the four
build_* functions, cal_span clamping, hist_end, PAYLOAD_VER, NullRun and
their helpers — into a new views.py with no web dependencies (pure moves,
public names). app.py keeps routing, ETag/derived-store plumbing, and
page serving; migrate.py imports views instead of reaching into app's
privates.
That import previously constructed the whole FastAPI app and — because
places.start_loading() ran at import time — kicked off a background
GeoNames download from an offline batch script. The index load now hangs
off the app's lifespan hook, so it fires when a server starts, not when
the module is imported (tests, migrate).
New tests: cal_span clamping (months-back default, record bounds, 2-year
cap, inversion), builder shapes (grade window ordering, day obs from the
recent bundle, forecast future-only), a regression test that build_day
degrades to climatology when the recent fetch fails, and a subprocess
layering guard that importing views/migrate pulls in neither FastAPI nor
the app module and starts no index download.
2026-07-11 19:43:41 +00:00
|
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|
payload = views.build_grade(CELL, target, 14, history, recent,
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{"cached": True}, "Testville")
|
Migrate backend dataframe layer from pandas to polars (#90)
* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
2026-07-15 19:07:38 +00:00
|
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|
assert payload["target_date"] == target.isoformat()
|
Extract payload builders into views.py; load places index at startup (#42)
app.py had grown three co-resident strata: HTTP/caching plumbing, payload
assembly, and search policy. This moves the payload layer — the four
build_* functions, cal_span clamping, hist_end, PAYLOAD_VER, NullRun and
their helpers — into a new views.py with no web dependencies (pure moves,
public names). app.py keeps routing, ETag/derived-store plumbing, and
page serving; migrate.py imports views instead of reaching into app's
privates.
That import previously constructed the whole FastAPI app and — because
places.start_loading() ran at import time — kicked off a background
GeoNames download from an offline batch script. The index load now hangs
off the app's lifespan hook, so it fires when a server starts, not when
the module is imported (tests, migrate).
New tests: cal_span clamping (months-back default, record bounds, 2-year
cap, inversion), builder shapes (grade window ordering, day obs from the
recent bundle, forecast future-only), a regression test that build_day
degrades to climatology when the recent fetch fails, and a subprocess
layering guard that importing views/migrate pulls in neither FastAPI nor
the app module and starts no index download.
2026-07-11 19:43:41 +00:00
|
|
|
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):
|
Migrate backend dataframe layer from pandas to polars (#90)
* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
2026-07-15 19:07:38 +00:00
|
|
|
today = datetime.date.today()
|
Extract payload builders into views.py; load places index at startup (#42)
app.py had grown three co-resident strata: HTTP/caching plumbing, payload
assembly, and search policy. This moves the payload layer — the four
build_* functions, cal_span clamping, hist_end, PAYLOAD_VER, NullRun and
their helpers — into a new views.py with no web dependencies (pure moves,
public names). app.py keeps routing, ETag/derived-store plumbing, and
page serving; migrate.py imports views instead of reaching into app's
privates.
That import previously constructed the whole FastAPI app and — because
places.start_loading() ran at import time — kicked off a background
GeoNames download from an offline batch script. The index load now hangs
off the app's lifespan hook, so it fires when a server starts, not when
the module is imported (tests, migrate).
New tests: cal_span clamping (months-back default, record bounds, 2-year
cap, inversion), builder shapes (grade window ordering, day obs from the
recent bundle, forecast future-only), a regression test that build_day
degrades to climatology when the recent fetch fails, and a subprocess
layering guard that importing views/migrate pulls in neither FastAPI nor
the app module and starts no index download.
2026-07-11 19:43:41 +00:00
|
|
|
payload = views.build_day(CELL, history, today, "Testville", recent=recent)
|
Migrate backend dataframe layer from pandas to polars (#90)
* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
2026-07-15 19:07:38 +00:00
|
|
|
assert payload["detail"]["date"] == today.isoformat()
|
Extract payload builders into views.py; load places index at startup (#42)
app.py had grown three co-resident strata: HTTP/caching plumbing, payload
assembly, and search policy. This moves the payload layer — the four
build_* functions, cal_span clamping, hist_end, PAYLOAD_VER, NullRun and
their helpers — into a new views.py with no web dependencies (pure moves,
public names). app.py keeps routing, ETag/derived-store plumbing, and
page serving; migrate.py imports views instead of reaching into app's
privates.
That import previously constructed the whole FastAPI app and — because
places.start_loading() ran at import time — kicked off a background
GeoNames download from an offline batch script. The index load now hangs
off the app's lifespan hook, so it fires when a server starts, not when
the module is imported (tests, migrate).
New tests: cal_span clamping (months-back default, record bounds, 2-year
cap, inversion), builder shapes (grade window ordering, day obs from the
recent bundle, forecast future-only), a regression test that build_day
degrades to climatology when the recent fetch fails, and a subprocess
layering guard that importing views/migrate pulls in neither FastAPI nor
the app module and starts no index download.
2026-07-11 19:43:41 +00:00
|
|
|
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)
|
Migrate backend dataframe layer from pandas to polars (#90)
* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
2026-07-15 19:07:38 +00:00
|
|
|
today = datetime.date.today()
|
Extract payload builders into views.py; load places index at startup (#42)
app.py had grown three co-resident strata: HTTP/caching plumbing, payload
assembly, and search policy. This moves the payload layer — the four
build_* functions, cal_span clamping, hist_end, PAYLOAD_VER, NullRun and
their helpers — into a new views.py with no web dependencies (pure moves,
public names). app.py keeps routing, ETag/derived-store plumbing, and
page serving; migrate.py imports views instead of reaching into app's
privates.
That import previously constructed the whole FastAPI app and — because
places.start_loading() ran at import time — kicked off a background
GeoNames download from an offline batch script. The index load now hangs
off the app's lifespan hook, so it fires when a server starts, not when
the module is imported (tests, migrate).
New tests: cal_span clamping (months-back default, record bounds, 2-year
cap, inversion), builder shapes (grade window ordering, day obs from the
recent bundle, forecast future-only), a regression test that build_day
degrades to climatology when the recent fetch fails, and a subprocess
layering guard that importing views/migrate pulls in neither FastAPI nor
the app module and starts no index download.
2026-07-11 19:43:41 +00:00
|
|
|
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):
|
Migrate backend dataframe layer from pandas to polars (#90)
* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
2026-07-15 19:07:38 +00:00
|
|
|
today = datetime.date.today()
|
Extract payload builders into views.py; load places index at startup (#42)
app.py had grown three co-resident strata: HTTP/caching plumbing, payload
assembly, and search policy. This moves the payload layer — the four
build_* functions, cal_span clamping, hist_end, PAYLOAD_VER, NullRun and
their helpers — into a new views.py with no web dependencies (pure moves,
public names). app.py keeps routing, ETag/derived-store plumbing, and
page serving; migrate.py imports views instead of reaching into app's
privates.
That import previously constructed the whole FastAPI app and — because
places.start_loading() ran at import time — kicked off a background
GeoNames download from an offline batch script. The index load now hangs
off the app's lifespan hook, so it fires when a server starts, not when
the module is imported (tests, migrate).
New tests: cal_span clamping (months-back default, record bounds, 2-year
cap, inversion), builder shapes (grade window ordering, day obs from the
recent bundle, forecast future-only), a regression test that build_day
degrades to climatology when the recent fetch fails, and a subprocess
layering guard that importing views/migrate pulls in neither FastAPI nor
the app module and starts no index download.
2026-07-11 19:43:41 +00:00
|
|
|
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
|
Migrate backend dataframe layer from pandas to polars (#90)
* Migrate backend dataframe layer from pandas to polars
Replace pandas with polars across the backend, dropping both pandas and its
pyarrow parquet engine from the dependency set. numpy stays (the grading
percentile math is unchanged).
- climate.py: parquet IO, source→frame mappings, cache read/topup on polars.
New _normalize_read casts the cached `date` column to pl.Date (older files
were written by pandas as datetime64[ns]); frames now unify missing values as
null so the grading boundary drops them consistently across sources.
- grading.py: keep the numpy percentile core; swap the frame→numpy bridge to
.to_numpy()/.drop_nulls(), day-of-year/year to polars dt expressions, and the
per-row loop to iter_rows(named=True).
- views.py: filter/anti-join/concat replace boolean-mask, isin and pd.concat;
scalar dates are stdlib datetime.date; a local _months_before helper replaces
DateOffset(months=) for the calendar-range default.
- app.py, migrate.py: request-date parsing uses datetime.date, removing pandas
from the endpoint and migrate layers entirely.
- The date column is pl.Date end to end, eliminating the pandas normalize() calls
and comparing cleanly against stdlib dates.
Payloads are unchanged: calendar, day, grade and forecast responses are
byte-for-byte identical to the pandas implementation on the same cached record.
Tests ported to polars fixtures, with added coverage for the combined feels-like
fallback, calendar month-offset (month-end/leap), and the concat/dedup
"fresher source wins" rule.
* Port notify.py to polars after merging dev's account system
Merge origin/dev (accounts + notification subscriptions) and carry the pandas→
polars migration into the newly added notify.py, which the merge brought in still
using pandas — with pandas removed from requirements this broke its import.
- notify.py: _candidate_rows filters/sorts the recent bundle with polars
expressions and returns iter_rows dicts; date scalars are datetime.date;
history/recent emptiness via is_empty().
- test_notify.py: synthetic history/rows built with polars + datetime.
2026-07-15 19:07:38 +00:00
|
|
|
assert min(days) > today.isoformat()
|
Extract payload builders into views.py; load places index at startup (#42)
app.py had grown three co-resident strata: HTTP/caching plumbing, payload
assembly, and search policy. This moves the payload layer — the four
build_* functions, cal_span clamping, hist_end, PAYLOAD_VER, NullRun and
their helpers — into a new views.py with no web dependencies (pure moves,
public names). app.py keeps routing, ETag/derived-store plumbing, and
page serving; migrate.py imports views instead of reaching into app's
privates.
That import previously constructed the whole FastAPI app and — because
places.start_loading() ran at import time — kicked off a background
GeoNames download from an offline batch script. The index load now hangs
off the app's lifespan hook, so it fires when a server starts, not when
the module is imported (tests, migrate).
New tests: cal_span clamping (months-back default, record bounds, 2-year
cap, inversion), builder shapes (grade window ordering, day obs from the
recent bundle, forecast future-only), a regression test that build_day
degrades to climatology when the recent fetch fails, and a subprocess
layering guard that importing views/migrate pulls in neither FastAPI nor
the app module and starts no index download.
2026-07-11 19:43:41 +00:00
|
|
|
assert payload["forecast"] is True
|