Move Participant/Group to pydantic and add a saveable/editable Plan model
Participant and Group are now pydantic BaseModels. Group.hosts can form cycles between groups, so it's kept as a private, non-persisted live list (set via set_hosts()/add_host()) backed by a serializable host_uuids field, re-linked via resolve_hosts() after a reload. A new Plan model (src/tatami/plan.py) bundles groups, the after-party group, and the event config (course_times, organizer_contacts, info_text, spreadsheet_id) and supports save()/load() to/from JSON. tatami_masterplan's __main__ now saves to masterplan.json (PLAN_FILE env var to override) on first run and loads it on later runs instead of recomputing, so the plan can be hand-edited (move a member between groups, change a course, fill in spreadsheet_id) and picked up on rerun without hitting the Routes API again. spreadsheet_id moves out of .env (GOOGLE_SHEETS_SPREADSHEET_ID) onto the plan itself, since it's part of the plan rather than a secret.
This commit is contained in:
+5
-5
@@ -4,9 +4,9 @@ GOOGLE_MAPS_API_KEY=your-api-key-here
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# Optional: export the masterplan to a shared Google Sheet for participants.
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# Set up once: create a Google Cloud service account, enable the Google
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# Sheets API for its project, download the service account's JSON key, then
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# create a blank Google Sheet and share it with the service account's
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# client_email (found in the JSON key) as Editor. Leave both unset to skip
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# sheet export entirely.
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# Sheets API for its project, and download the service account's JSON key.
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# Then create a blank Google Sheet, share it with the service account's
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# client_email (found in the JSON key) as Editor, and set its id as
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# `spreadsheet_id` in the saved masterplan.json (see README) - leave it unset
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# there to skip sheet export entirely.
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GOOGLE_SHEETS_CREDENTIALS_FILE=service-account.json
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GOOGLE_SHEETS_SPREADSHEET_ID=your-spreadsheet-id-here
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@@ -13,14 +13,13 @@ The actively developed package is `src/tatami/`. `running_dinner/running_dinner.
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This project uses `uv` for dependency management (Python >=3.13).
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- Install deps: `uv sync`
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- Run the masterplan script: `uv run python -m tatami.tatami_masterplan` (requires `test-config.csv` in the working directory and `GOOGLE_MAPS_API_KEY` set; optionally set `GOOGLE_SHEETS_CREDENTIALS_FILE` + `GOOGLE_SHEETS_SPREADSHEET_ID` to also export to a shared Google Sheet — see `.env.example`)
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- Run the masterplan script: `uv run python -m tatami.tatami_masterplan` (requires `test-config.csv` in the working directory and `GOOGLE_MAPS_API_KEY` set; optionally set `GOOGLE_SHEETS_CREDENTIALS_FILE` in `.env` + `spreadsheet_id` in the saved plan to also export to a shared Google Sheet — see `.env.example`). The first run computes a plan and saves it to `masterplan.json` (override via `PLAN_FILE`); later runs load that file instead of recomputing, so it's the place to hand-edit groups/courses/contacts/`spreadsheet_id` between runs.
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- Lint: `uv run ruff check`
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- Format: `uv run ruff format`
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- Type check: `uv run mypy --allow-redefinition src/` (mypy is configured to treat untyped imports as errors except where ignored)
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- Test: `uv run pytest` (offline suite, HTTP mocked); `uv run pytest -m e2e` for the opt-in live-API tests
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- Pre-commit runs ruff check, ruff format, and mypy automatically (see `.pre-commit-config.yaml`); install hooks with `uv run pre-commit install` if working interactively.
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There is currently no test suite in the repo.
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## Architecture
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The pipeline (see `src/tatami/tatami_masterplan.py` `__main__` block) is:
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@@ -32,13 +31,18 @@ The pipeline (see `src/tatami/tatami_masterplan.py` `__main__` block) is:
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- Reduces the full distance matrix to just host-to-host distances (`reduce_distance_matrix`), adding each host's kitchen-size penalty into their row.
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- Runs `run_simulated_annealing` / `simulated_annealing` (Boltzmann-style annealing over `itertools.permutations` of group order — note this is brute-force over all permutations per iteration, so it only scales to a small number of groups) to find a low-travel-time ordering of groups.
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- `assign_courses` assigns each group a course (starter/main/dessert cycling) and, via `get_courses`, determines which other groups host it for each course (offsets of `+1` and `-4` mod total groups — this fixed relationship is what defines the dinner-rotation topology).
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4. `get_masterplan` returns two lists of plain dicts (`group.dict()`, `participant.dict()`) suitable for serialization; `compute_masterplan_groups` returns the live `Group`/`Participant` objects, which is what the sheet export step needs (`.hosts`, `.get_guests(...)`).
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5. **Export to Google Sheets (optional)** — if `GOOGLE_SHEETS_SPREADSHEET_ID` is set, `__main__` calls `sheets_export.export_masterplan_to_sheet` to populate a pre-existing, pre-shared spreadsheet with an Overview tab and one tab per group. This is opt-in and never sends anything directly to participants — the organizer still shares the sheet link manually.
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4. `get_masterplan` returns two lists of plain dicts (`group.dict()`, `participant.dict()`) suitable for serialization; `compute_masterplan_groups` returns the live `Group`/`Participant` objects, which is what the `Plan`/sheet export step needs (`.hosts`, `.get_guests(...)`).
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5. **Wrap in a `Plan` and save** (`src/tatami/plan.py`) — `__main__` bundles the computed `groups` + `after_party_group` with the event config (`course_times`, `organizer_contacts`, `info_text`, `spreadsheet_id`) into a `Plan` and calls `plan.save(PLAN_FILE)`. On the next invocation, if that file exists, `Plan.load()` reads it back instead of recomputing — this is the save/reload/edit path: hand-edit `masterplan.json` (move a member between groups, change a course, fill in `spreadsheet_id`, ...) and rerun to pick up the edit without hitting the Routes API again.
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6. **Export to Google Sheets (optional)** — if the loaded/built `Plan.spreadsheet_id` is set, `__main__` calls `sheets_export.export_masterplan_to_sheet` to populate a pre-existing, pre-shared spreadsheet with an Overview tab and one tab per group. This is opt-in and never sends anything directly to participants — the organizer still shares the sheet link manually.
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### Core domain model (`src/tatami/classes.py`)
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### Core domain model (`src/tatami/classes.py`, `src/tatami/plan.py`)
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`Participant` and `Group` are pydantic `BaseModel`s (so they support `model_dump()`/`model_dump_json()`/`model_validate_json()` directly); `Plan` wraps the whole thing for persistence.
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- `Participant`: a person with an address, phone, kitchen size (0–10, used as a "willingness/suitability to host" proxy via `get_penalty`, which adds travel-time-equivalent minutes for smaller kitchens), and allergies.
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- `Group`: a hosting unit with a `main_member` (used as the group's representative location for all distance lookups — other members' addresses are not used for travel calculations), a `course`, and a `hosts` list (the groups that host *this* group across the evening, kept sorted starter→main→dessert via `sort_hosts`). `get_total_time` sums travel + penalty across this group's full route (its hosts, then the after-party).
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- `Group`: a hosting unit with a `main_member` (a property resolved from `main_member_uuid` against `members` — used as the group's representative location for all distance lookups; other members' addresses are not used for travel calculations), a `course`, and a `hosts` property (the groups that host *this* group across the evening, kept sorted starter→main→dessert via `sort_hosts`). `get_total_time` sums travel + penalty across this group's full route (its hosts, then the after-party).
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- `hosts` references *other* `Group`s and these references are genuinely cyclic (a group's hosts can host it back), so they can't be embedded directly in JSON. The live `hosts` list is a private, non-persisted attribute set via `set_hosts()`/`add_host()`; the persisted field is `host_uuids` (kept in sync automatically). After `Plan.load()`, `Group.resolve_hosts()` re-links `hosts` from `host_uuids` against the sibling groups in the same `Plan` — call it yourself if you ever construct `Group`s outside of a `Plan` and need `.hosts` populated from `host_uuids`.
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- `Plan` (`plan.py`): bundles `groups`, `after_party_group`, and the event-wide config (`course_times`, `organizer_contacts`, `info_text`, `spreadsheet_id`). `Plan.save(path)` / `Plan.load(path)` round-trip the whole thing to/from JSON; `plan.participants` is a derived property (flattened, deduplicated `group.members` across all groups + the after party), not a separately stored field, so editing a participant's data in a group's `members` is the single source of truth.
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- Distance/time lookups throughout the codebase are keyed by `Participant.uuid` (host groups are addressed via `main_member.uuid`), not by name — when adding new matrix operations, index by uuid for consistency with `traveltimes.py` and `classes.py`.
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### Travel times (`src/tatami/traveltimes.py`)
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@@ -79,9 +79,9 @@ gitignored, so your secret never gets committed. An already‑exported
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> The package raises at import time if no key is found, so `GOOGLE_MAPS_API_KEY`
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> must be set (even to a dummy value) just to import `tatami.traveltimes`.
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Optionally, also set `GOOGLE_SHEETS_CREDENTIALS_FILE` and
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`GOOGLE_SHEETS_SPREADSHEET_ID` in `.env` to export the plan to a shared
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Google Sheet — see [Sharing the plan with participants](#sharing-the-plan-with-participants).
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Optionally, also set `GOOGLE_SHEETS_CREDENTIALS_FILE` in `.env` and a
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`spreadsheet_id` in the saved `masterplan.json` to export the plan to a
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shared Google Sheet — see [Sharing the plan with participants](#sharing-the-plan-with-participants).
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## Input: the participant CSV
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@@ -111,18 +111,26 @@ Charlie Hermann-Hesse-Str 50, 76189 Karlsruhe 555-8765 9 none
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uv run python -m tatami.tatami_masterplan
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```
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This reads `test-config.csv` from the current directory, calls the Routes API,
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and prints the generated masterplan. The after‑party address and travel mode are
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currently set in the `__main__` block of `tatami_masterplan.py` (see
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[What you can tweak](#what-you-can-tweak)).
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The first run reads `test-config.csv` from the current directory, calls the
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Routes API, computes the masterplan, and **saves it** to `masterplan.json`
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(override the path with the `PLAN_FILE` env var). The after‑party address and
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travel mode are currently set in the `__main__` block of
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`tatami_masterplan.py` (see [What you can tweak](#what-you-can-tweak)).
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Every subsequent run **loads `masterplan.json` instead of recomputing**, so
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you can hand-edit that file — move a participant between groups, change a
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course, fix an address, set `spreadsheet_id` — and rerun to pick up the edit
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without calling the Routes API again. Delete (or move) the file to force a
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fresh computation.
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To use Tatami from your own code:
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```python
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from tatami.tatami_masterplan import (
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get_after_party_group, get_masterplan, load_csv_to_participants,
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get_after_party_group, compute_masterplan_groups, load_csv_to_participants,
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)
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from tatami.traveltimes import get_participant_distance_matrix
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from tatami.plan import Plan
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participants = load_csv_to_participants("my-participants.csv")
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after_party = get_after_party_group("Some Street 1, 12345 City")
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@@ -130,14 +138,22 @@ after_party = get_after_party_group("Some Street 1, 12345 City")
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distance_matrix = get_participant_distance_matrix(
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[*participants, after_party.main_member], mode="BICYCLE"
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)
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groups, participants_out = get_masterplan(participants, distance_matrix, after_party)
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groups, participants_out = compute_masterplan_groups(
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participants, distance_matrix, after_party
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)
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plan = Plan(groups=groups, after_party_group=after_party)
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plan.save("masterplan.json")
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```
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## Output format
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`get_masterplan` returns a tuple `(group_dicts, participant_dicts)`.
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The domain model (`Participant`, `Group`, `Plan` in `src/tatami/`) is built on
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[pydantic](https://docs.pydantic.dev/), so everything supports
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`model_dump()`/`model_dump_json()` plus `Plan.save(path)`/`Plan.load(path)`
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for the full plan.
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Each **group** dict:
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`get_masterplan` (a thin convenience wrapper around `compute_masterplan_groups`)
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returns a tuple `(group_dicts, participant_dicts)`. Each **group** dict:
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```python
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{
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@@ -157,13 +173,20 @@ keyed by UUID, so resolve names/addresses by looking participants up by `uuid`.
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> Note: only a group's `main_member` address is used for all travel calculations;
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> other members are assumed to join at the main member's home.
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A `Group`'s `hosts` are *other* `Group`s, and these references can form
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cycles (a group's hosts can also host that group back), so a `Plan`'s JSON
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persists them as plain `host_uuids` id lists rather than embedding the full
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objects — `Plan.load()` re-links the live `group.hosts` list from those ids
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after loading, so `group.hosts[i].course` etc. works exactly as it does right
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after computing the plan.
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## Sharing the plan with participants
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`get_masterplan`'s dicts are great for code, but participants need something
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readable. `compute_masterplan_groups` (the same computation, returning live
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`Group`/`Participant` objects instead of dicts) feeds an optional Google
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Sheets export — the same kind of shared spreadsheet organizers have used in
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previous years, just generated automatically instead of by hand.
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`Plan` (`src/tatami/plan.py`) bundles the computed `groups`, the
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`after_party_group`, and the event-wide configuration below into one
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object that feeds an optional Google Sheets export — the same kind of shared
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spreadsheet organizers have used in previous years, just generated
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automatically instead of by hand.
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Set up once:
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@@ -172,12 +195,15 @@ Set up once:
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2. Download the service account's JSON key and point
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`GOOGLE_SHEETS_CREDENTIALS_FILE` at it (in `.env`).
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3. Create a blank Google Sheet, share it with the service account's
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`client_email` (from the JSON key) as **Editor**, and set
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`GOOGLE_SHEETS_SPREADSHEET_ID` to that sheet's ID (in `.env`).
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`client_email` (from the JSON key) as **Editor**, copy its sheet ID, and
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set `spreadsheet_id` to that ID **in the saved `masterplan.json`** (not
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`.env` — the spreadsheet to export to is part of the plan itself, so it
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round-trips with everything else).
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With both set, running `uv run python -m tatami.tatami_masterplan` populates
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that spreadsheet with an **Overview** tab (every participant, their group,
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course, address, phone, allergies — followed by a Meal Times table, a Support
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With `GOOGLE_SHEETS_CREDENTIALS_FILE` set and `spreadsheet_id` filled in,
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rerunning `uv run python -m tatami.tatami_masterplan` populates that
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spreadsheet with an **Overview** tab (every participant, their group, course,
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address, phone, allergies — followed by a Meal Times table, a Support
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Contacts table, and a free-text Info block, see below) and one tab per group
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(their own course, route with addresses and fixed course times, and the
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guest list — with allergies — for the course they host). Reruns are
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@@ -187,9 +213,9 @@ run are deleted.
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Tatami never contacts participants directly — sharing the sheet's link is
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still up to the organizer, exactly as before.
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The three extra Overview sections are plain configuration, passed straight
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through to the sheet with no logic in between — edit these in
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`tatami_masterplan.py`:
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The three extra Overview sections are plain configuration, carried on the
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`Plan` and passed straight through to the sheet with no logic in between —
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set them when first building the plan, in `tatami_masterplan.py`:
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```python
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COURSE_TIMES = {
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@@ -199,11 +225,13 @@ ORGANIZER_CONTACTS = [("Lars (Organizer)", "0151-23456789")] # -> "Support Cont
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INFO_TEXT = "Welcome to the running dinner! ..." # -> "Info" block (one row per line)
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```
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`organizer_contacts` and `info_text` are optional (`None`/empty skips that
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section); `course_times` is also reused for each group's own route table.
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or by editing `course_times` / `organizer_contacts` / `info_text` directly in
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the saved `masterplan.json` afterwards. `organizer_contacts` and `info_text`
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are optional (`None`/empty skips that section); `course_times` is also reused
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for each group's own route table.
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If neither variable is set, this step is skipped entirely and Tatami just
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prints the plan, as before.
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If `spreadsheet_id` is unset, this step is skipped entirely and Tatami just
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prints a reminder to fill it in.
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## What you can tweak
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@@ -211,14 +239,15 @@ All knobs currently live in the source. The most useful ones:
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| What | Where | Default | Effect |
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|------|-------|---------|--------|
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| **After‑party address** | `tatami_masterplan.py:251` (`__main__`) | a Karlsruhe address | Where everyone ends the night; also influences host ranking. |
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| **Travel mode** | `tatami_masterplan.py:254` (`mode="BICYCLE"`) | `BICYCLE` | Any Routes API `travelMode`: `BICYCLE`, `DRIVE`, `WALK`, `TWO_WHEELER`, `TRANSIT`. |
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| **Course start times** | `tatami_masterplan.py` (`COURSE_TIMES`) | `18:30` / `20:00` / `22:00` / `23:30` | Fixed slot times written into the Google Sheet export; the dinner runs on a synchronized schedule, not travel-derived timing. |
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| **Group sizing** | `tatami_masterplan.py:24` (`len(participants) / 6`) | 1 group per ~6 people | The divisor sets how many participants form one "course‑triple". Larger → fewer, bigger groups. |
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| **Kitchen‑size penalty** | `classes.py:19` (`minutes=3 * (10 - kitchen_size)`) | 3 min per point | Travel‑time‑equivalent penalty for small kitchens. Raise the `3` to push hosting toward big kitchens. |
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| **Annealing schedule** | `tatami_masterplan.py:48–50` | `T=1000`, `cooling=0.99`, `iters=10000` | Optimization quality vs. runtime. More iterations / slower cooling → better routes, slower. |
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| **Course names** | `tatami_masterplan.py:31` | `["starter", "main", "dessert"]` | The three courses. The 3‑course rotation is baked into the topology — changing the *count* needs more work (see below). |
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| **Rotation topology** | `tatami_masterplan.py:172–173` (offsets `+1`, `-4`) | — | Defines who hosts whom. Changing these changes who meets whom; keep the invariant that each group's three hosts cover all three courses. |
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| **Saved plan path** | `PLAN_FILE` env var | `masterplan.json` | Where the computed/edited `Plan` is saved to and (on the next run) loaded from. |
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| **After‑party address** | `tatami_masterplan.py` (`__main__`) | a Karlsruhe address | Where everyone ends the night; also influences host ranking. Only used the first time a plan is computed. |
|
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| **Travel mode** | `tatami_masterplan.py` (`mode="BICYCLE"`) | `BICYCLE` | Any Routes API `travelMode`: `BICYCLE`, `DRIVE`, `WALK`, `TWO_WHEELER`, `TRANSIT`. |
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| **Course start times** | `tatami_masterplan.py` (`COURSE_TIMES`), or `course_times` in the saved plan | `18:30` / `20:00` / `22:00` / `23:30` | Fixed slot times written into the Google Sheet export; the dinner runs on a synchronized schedule, not travel-derived timing. |
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| **Group sizing** | `tatami_masterplan.py:54` (`len(participants) / 6`) | 1 group per ~6 people | The divisor sets how many participants form one "course‑triple". Larger → fewer, bigger groups. |
|
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| **Kitchen‑size penalty** | `classes.py` (`minutes=3 * (10 - kitchen_size)`) | 3 min per point | Travel‑time‑equivalent penalty for small kitchens. Raise the `3` to push hosting toward big kitchens. |
|
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| **Annealing schedule** | `tatami_masterplan.py` (`run_simulated_annealing` call) | `T=1000`, `cooling=0.99`, `iters=10000` | Optimization quality vs. runtime. More iterations / slower cooling → better routes, slower. |
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| **Course names** | `tatami_masterplan.py` (`courses = [...]`) | `["starter", "main", "dessert"]` | The three courses. The 3‑course rotation is baked into the topology — changing the *count* needs more work (see below). |
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| **Rotation topology** | `tatami_masterplan.py` `get_courses` (offsets `+1`, `-4`) | — | Defines who hosts whom. Changing these changes who meets whom; keep the invariant that each group's three hosts cover all three courses. |
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| **Distance vs. duration** | `traveltimes.py` `get_distance_matrix(value=…)` | `"duration"` | Optimize on travel **time** (`"duration"`) or **distance** (`"distanceMeters"`). |
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After changing routing‑relevant knobs, run the test suite (`uv run pytest`) — the
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@@ -291,7 +320,8 @@ uv run pytest -m e2e # opt-in live test that calls the real Routes API
|
||||
|
||||
```
|
||||
src/tatami/
|
||||
classes.py # Participant and Group domain model
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||||
classes.py # Participant and Group domain model (pydantic)
|
||||
plan.py # Plan: bundles groups + config, save()/load() to/from JSON
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traveltimes.py # Google Routes API wrapper + matrix helpers
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sheets_export.py # optional Google Sheets export for participants
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tatami_masterplan.py # pipeline: load → fetch → group → optimize → assign
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||||
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@@ -9,6 +9,7 @@ dependencies = [
|
||||
"numpy>=2.2.4",
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"pandas>=2.2.3",
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||||
"pandas-stubs>=2.2.3.250308",
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||||
"pydantic>=2.13.4",
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||||
"python-dotenv>=1.2.2",
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"requests>=2.32.3",
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"tqdm>=4.67.1",
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+76
-36
@@ -1,19 +1,21 @@
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import datetime as dt
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import pandas as pd
|
||||
from typing import cast
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||||
from typing import Any, Literal, cast
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from uuid import uuid4
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||||
|
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import pandas as pd
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||||
from pydantic import BaseModel, Field, PrivateAttr, model_validator
|
||||
|
||||
class Participant:
|
||||
def __init__(
|
||||
self, name: str, address: str, phone: str, kitchen_size: float, allergies: str
|
||||
):
|
||||
self.uuid = str(uuid4())
|
||||
self.name = name
|
||||
self.address = address
|
||||
self.phone = phone
|
||||
self.kitchen_size = kitchen_size # bigger is better; range 0-10
|
||||
self.allergies = allergies
|
||||
Course = Literal["starter", "main", "dessert"]
|
||||
_COURSE_ORDER: dict[str, int] = {"starter": 0, "main": 1, "dessert": 2}
|
||||
|
||||
|
||||
class Participant(BaseModel):
|
||||
uuid: str = Field(default_factory=lambda: str(uuid4()))
|
||||
name: str
|
||||
address: str
|
||||
phone: str
|
||||
kitchen_size: float
|
||||
allergies: str
|
||||
|
||||
def get_penalty(self) -> dt.timedelta:
|
||||
return dt.timedelta(minutes=3 * (10 - self.kitchen_size))
|
||||
@@ -37,7 +39,7 @@ class Participant:
|
||||
def __str__(self) -> str:
|
||||
return self.__repr__()
|
||||
|
||||
def dict(self) -> dict:
|
||||
def dict(self) -> dict: # type: ignore[override]
|
||||
return {
|
||||
"uuid": self.uuid,
|
||||
"name": self.name,
|
||||
@@ -48,39 +50,77 @@ class Participant:
|
||||
}
|
||||
|
||||
|
||||
class Group:
|
||||
def __init__(self, members: list[Participant], main_member: int = 0):
|
||||
self.uuid = "Group_" + str(uuid4())
|
||||
self.members = members
|
||||
self.main_member = members[main_member]
|
||||
self.course: str | None = (
|
||||
class Group(BaseModel):
|
||||
uuid: str = Field(default_factory=lambda: "Group_" + str(uuid4()))
|
||||
members: list[Participant]
|
||||
# The persisted reference to one of `members`. `hosts`/`host_uuids` reference
|
||||
# *other* Groups, which can form cycles (a group's hosts can host it back) -
|
||||
# those are kept as plain uuid references and resolved on demand via
|
||||
# `set_hosts`/`resolve_hosts`, rather than embedded directly, so a Plan
|
||||
# containing many Groups can still be serialized to JSON.
|
||||
main_member_uuid: str = ""
|
||||
course: Course | None = (
|
||||
None # For ordering the groups allowed values: "starter", "main", "dessert"
|
||||
)
|
||||
self.hosts: list[Group] | None = None
|
||||
host_uuids: list[str] | None = None
|
||||
|
||||
def set_course(self, course: str):
|
||||
_hosts: list["Group"] | None = PrivateAttr(default=None)
|
||||
|
||||
@model_validator(mode="before")
|
||||
@classmethod
|
||||
def _default_main_member_uuid(cls, data: Any) -> Any:
|
||||
"""Support the legacy ``Group(members=[...], main_member=<index>)`` call style."""
|
||||
if isinstance(data, dict) and not data.get("main_member_uuid"):
|
||||
members = data.get("members") or []
|
||||
index = data.pop("main_member", 0)
|
||||
if members:
|
||||
member = members[index]
|
||||
data["main_member_uuid"] = (
|
||||
member.uuid if isinstance(member, Participant) else member["uuid"]
|
||||
)
|
||||
return data
|
||||
|
||||
@property
|
||||
def main_member(self) -> Participant:
|
||||
return next(m for m in self.members if m.uuid == self.main_member_uuid)
|
||||
|
||||
@property
|
||||
def hosts(self) -> list["Group"] | None:
|
||||
return self._hosts
|
||||
|
||||
def set_course(self, course: Course) -> None:
|
||||
self.course = course
|
||||
|
||||
def set_hosts(self, hosts: list["Group"]):
|
||||
self.hosts = hosts
|
||||
def set_hosts(self, hosts: list["Group"]) -> None:
|
||||
self._hosts = hosts
|
||||
self.sort_hosts()
|
||||
|
||||
def sort_hosts(self):
|
||||
if self.hosts is None:
|
||||
def sort_hosts(self) -> None:
|
||||
if self._hosts is None:
|
||||
return
|
||||
|
||||
courses = {"starter": 0, "main": 1, "dessert": 2}
|
||||
self.hosts.sort(key=lambda x: courses[x.course] if x.course in courses else 3)
|
||||
self._hosts.sort(
|
||||
key=lambda x: _COURSE_ORDER[x.course] if x.course in _COURSE_ORDER else 3
|
||||
)
|
||||
self.host_uuids = [h.uuid for h in self._hosts]
|
||||
|
||||
def add_host(self, host: "Group"):
|
||||
if self.hosts is None:
|
||||
self.hosts = []
|
||||
self.hosts.append(host)
|
||||
def add_host(self, host: "Group") -> None:
|
||||
if self._hosts is None:
|
||||
self._hosts = []
|
||||
self._hosts.append(host)
|
||||
self.sort_hosts()
|
||||
|
||||
def add_member(self, member: Participant, main_member: bool = False):
|
||||
def resolve_hosts(self, groups_by_uuid: dict[str, "Group"]) -> None:
|
||||
"""Re-link the live ``hosts`` list from ``host_uuids`` (e.g. after a JSON reload)."""
|
||||
self._hosts = (
|
||||
None
|
||||
if self.host_uuids is None
|
||||
else [groups_by_uuid[uuid] for uuid in self.host_uuids]
|
||||
)
|
||||
|
||||
def add_member(self, member: Participant, main_member: bool = False) -> None:
|
||||
if main_member:
|
||||
self.main_member = member
|
||||
self.main_member_uuid = member.uuid
|
||||
self.members.append(member)
|
||||
|
||||
def get_total_time(
|
||||
@@ -106,13 +146,13 @@ class Group:
|
||||
|
||||
return total_time
|
||||
|
||||
def dict(self) -> dict:
|
||||
def dict(self) -> dict: # type: ignore[override]
|
||||
return {
|
||||
"uuid": self.uuid,
|
||||
"members": [member.uuid for member in self.members],
|
||||
"main_member": self.main_member.uuid,
|
||||
"main_member": self.main_member_uuid,
|
||||
"course": self.course,
|
||||
"hosts": [host.uuid for host in self.hosts] if self.hosts else None,
|
||||
"hosts": list(self.host_uuids) if self.host_uuids else None,
|
||||
}
|
||||
|
||||
def get_guests(self, groups: list["Group"]) -> list[Participant]:
|
||||
|
||||
@@ -0,0 +1,44 @@
|
||||
from pathlib import Path
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from tatami.classes import Group, Participant
|
||||
|
||||
|
||||
class Plan(BaseModel):
|
||||
"""The full, persistable state of one running dinner.
|
||||
|
||||
Bundles the computed groups together with the event-wide configuration
|
||||
(course times, organizer contacts, info text, the Sheets spreadsheet id)
|
||||
so the whole thing can be saved to a JSON file, hand-edited (swap a
|
||||
member between groups, change a course, fill in `spreadsheet_id`, ...),
|
||||
and reloaded without recomputing routes.
|
||||
"""
|
||||
|
||||
groups: list[Group]
|
||||
after_party_group: Group
|
||||
course_times: dict[str, str] = Field(default_factory=dict)
|
||||
organizer_contacts: list[tuple[str, str]] = Field(default_factory=list)
|
||||
info_text: str | None = None
|
||||
spreadsheet_id: str | None = None
|
||||
|
||||
def model_post_init(self, __context: object) -> None:
|
||||
groups_by_uuid = {g.uuid: g for g in [*self.groups, self.after_party_group]}
|
||||
for group in self.groups:
|
||||
group.resolve_hosts(groups_by_uuid)
|
||||
|
||||
@property
|
||||
def participants(self) -> list[Participant]:
|
||||
"""Every participant across all groups (including the after party), deduplicated."""
|
||||
by_uuid: dict[str, Participant] = {}
|
||||
for group in [*self.groups, self.after_party_group]:
|
||||
for member in group.members:
|
||||
by_uuid[member.uuid] = member
|
||||
return list(by_uuid.values())
|
||||
|
||||
@classmethod
|
||||
def load(cls, path: str | Path) -> "Plan":
|
||||
return cls.model_validate_json(Path(path).read_text())
|
||||
|
||||
def save(self, path: str | Path) -> None:
|
||||
Path(path).write_text(self.model_dump_json(indent=2))
|
||||
@@ -1,4 +1,7 @@
|
||||
from tatami.classes import Participant, Group
|
||||
from pathlib import Path
|
||||
|
||||
from tatami.classes import Participant, Group, Course
|
||||
from tatami.plan import Plan
|
||||
from tatami.traveltimes import reduce_distance_matrix, get_participant_distance_matrix
|
||||
from tatami.sheets_export import load_sheets_client, export_masterplan_to_sheet
|
||||
import pandas as pd
|
||||
@@ -7,6 +10,10 @@ import random
|
||||
import os
|
||||
from tqdm import tqdm
|
||||
|
||||
# Where the computed/edited Plan (groups, course times, contacts, spreadsheet id, ...)
|
||||
# is saved to and loaded from. Override with the PLAN_FILE env var.
|
||||
PLAN_FILE = os.getenv("PLAN_FILE", "masterplan.json")
|
||||
|
||||
# Courses run on a synchronized schedule shared by every group, so start times
|
||||
# are fixed slots rather than derived from travel-time optimization.
|
||||
COURSE_TIMES = {
|
||||
@@ -36,7 +43,7 @@ def get_after_party_group(address: str) -> Group:
|
||||
participant = Participant(
|
||||
name="After Party", address=address, phone="", kitchen_size=10, allergies=""
|
||||
)
|
||||
return Group(members=[participant], main_member=0)
|
||||
return Group(members=[participant], main_member_uuid=participant.uuid)
|
||||
|
||||
|
||||
def compute_masterplan_groups(
|
||||
@@ -51,7 +58,7 @@ def compute_masterplan_groups(
|
||||
)
|
||||
hosts = participants[: 3 * groups_per_course]
|
||||
semi_hosts = participants[3 * groups_per_course :]
|
||||
courses = ["starter", "main", "dessert"] * groups_per_course
|
||||
courses: list[Course] = ["starter", "main", "dessert"] * groups_per_course
|
||||
|
||||
groups = []
|
||||
for host in hosts:
|
||||
@@ -90,7 +97,7 @@ def get_masterplan(
|
||||
return group_dicts, participant_dicts
|
||||
|
||||
|
||||
def assign_courses(groups: list[Group], courses: list[str]) -> None:
|
||||
def assign_courses(groups: list[Group], courses: list[Course]) -> None:
|
||||
"""
|
||||
Assign courses to groups.
|
||||
|
||||
@@ -243,7 +250,7 @@ def load_csv_to_participants(file_path: str) -> list[Participant]:
|
||||
"""
|
||||
Load participants from a CSV file.
|
||||
"""
|
||||
df = pd.read_csv(file_path, sep="\t")
|
||||
df = pd.read_csv(file_path, sep="\t", dtype={"phone": str, "allergies": str})
|
||||
participants = []
|
||||
for _, row in df.iterrows():
|
||||
participant = Participant(
|
||||
@@ -258,7 +265,16 @@ def load_csv_to_participants(file_path: str) -> list[Participant]:
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Example usage
|
||||
plan_path = Path(PLAN_FILE)
|
||||
|
||||
if plan_path.exists():
|
||||
# A previously computed (and possibly hand-edited) plan exists - reuse it
|
||||
# as-is instead of recomputing routes. This is how you edit group
|
||||
# assignments, course times, or fill in `spreadsheet_id`: edit the file,
|
||||
# then rerun.
|
||||
plan = Plan.load(plan_path)
|
||||
print(f"Loaded existing masterplan from {plan_path}.")
|
||||
else:
|
||||
participants = load_csv_to_participants("test-config.csv")
|
||||
after_party_group = get_after_party_group(
|
||||
"Sebastian-Kneipp-Straße 6, 76131 Karlsruhe"
|
||||
@@ -269,19 +285,30 @@ if __name__ == "__main__":
|
||||
groups, participants = compute_masterplan_groups(
|
||||
participants, distance_matrix, after_party_group
|
||||
)
|
||||
print([group.dict() for group in groups], [p.dict() for p in participants])
|
||||
print("Masterplan generated successfully.")
|
||||
|
||||
spreadsheet_id = os.getenv("GOOGLE_SHEETS_SPREADSHEET_ID")
|
||||
if spreadsheet_id:
|
||||
client = load_sheets_client()
|
||||
spreadsheet = client.open_by_key(spreadsheet_id)
|
||||
export_masterplan_to_sheet(
|
||||
spreadsheet,
|
||||
groups,
|
||||
after_party_group,
|
||||
COURSE_TIMES,
|
||||
plan = Plan(
|
||||
groups=groups,
|
||||
after_party_group=after_party_group,
|
||||
course_times=COURSE_TIMES,
|
||||
organizer_contacts=ORGANIZER_CONTACTS,
|
||||
info_text=INFO_TEXT,
|
||||
)
|
||||
plan.save(plan_path)
|
||||
print(f"Masterplan generated and saved to {plan_path}.")
|
||||
|
||||
if plan.spreadsheet_id:
|
||||
client = load_sheets_client()
|
||||
spreadsheet = client.open_by_key(plan.spreadsheet_id)
|
||||
export_masterplan_to_sheet(
|
||||
spreadsheet,
|
||||
plan.groups,
|
||||
plan.after_party_group,
|
||||
plan.course_times,
|
||||
organizer_contacts=plan.organizer_contacts,
|
||||
info_text=plan.info_text,
|
||||
)
|
||||
print(f"Masterplan exported to Google Sheet: {spreadsheet.url}")
|
||||
else:
|
||||
print(
|
||||
f"No spreadsheet_id set in {plan_path}; skipping Sheets export. "
|
||||
"Edit the file to add one and rerun."
|
||||
)
|
||||
|
||||
@@ -96,12 +96,26 @@ def reduce_distance_matrix(
|
||||
if __name__ == "__main__":
|
||||
# Example usage
|
||||
participants = [
|
||||
Participant("Alice", "Römerstr. 12 76189 Karlsruhe", "555-1234", 8.0, "None"),
|
||||
Participant(
|
||||
"Bob", "Gottesauerstr. 30 76131 Karlsruhe", "555-5678", 7.5, "Peanuts"
|
||||
name="Alice",
|
||||
address="Römerstr. 12 76189 Karlsruhe",
|
||||
phone="555-1234",
|
||||
kitchen_size=8.0,
|
||||
allergies="None",
|
||||
),
|
||||
Participant(
|
||||
"Charlie", "Hermann-Hesse-Str 50 76189 Karlsruhe", "555-8765", 9.0, "None"
|
||||
name="Bob",
|
||||
address="Gottesauerstr. 30 76131 Karlsruhe",
|
||||
phone="555-5678",
|
||||
kitchen_size=7.5,
|
||||
allergies="Peanuts",
|
||||
),
|
||||
Participant(
|
||||
name="Charlie",
|
||||
address="Hermann-Hesse-Str 50 76189 Karlsruhe",
|
||||
phone="555-8765",
|
||||
kitchen_size=9.0,
|
||||
allergies="None",
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
+16
-4
@@ -11,8 +11,12 @@ from conftest import make_participants, make_timedelta_matrix
|
||||
|
||||
class TestParticipant:
|
||||
def test_uuid_is_unique(self):
|
||||
a = Participant("A", "addr", "", 5, "")
|
||||
b = Participant("A", "addr", "", 5, "")
|
||||
a = Participant(
|
||||
name="A", address="addr", phone="", kitchen_size=5, allergies=""
|
||||
)
|
||||
b = Participant(
|
||||
name="A", address="addr", phone="", kitchen_size=5, allergies=""
|
||||
)
|
||||
assert a.uuid != b.uuid
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
@@ -20,7 +24,9 @@ class TestParticipant:
|
||||
[(10, 0), (7, 9), (0, 30), (5, 15)],
|
||||
)
|
||||
def test_penalty_scales_with_kitchen_size(self, kitchen_size, minutes):
|
||||
p = Participant("A", "addr", "", kitchen_size, "")
|
||||
p = Participant(
|
||||
name="A", address="addr", phone="", kitchen_size=kitchen_size, allergies=""
|
||||
)
|
||||
assert p.get_penalty() == dt.timedelta(minutes=minutes)
|
||||
|
||||
def test_after_party_time_combines_penalty_and_travel(self):
|
||||
@@ -32,7 +38,13 @@ class TestParticipant:
|
||||
assert result == dt.timedelta(minutes=9) + dt.timedelta(seconds=600)
|
||||
|
||||
def test_dict_roundtrip_fields(self):
|
||||
p = Participant("Alice", "addr", "555", 8, "peanuts")
|
||||
p = Participant(
|
||||
name="Alice",
|
||||
address="addr",
|
||||
phone="555",
|
||||
kitchen_size=8,
|
||||
allergies="peanuts",
|
||||
)
|
||||
d = p.dict()
|
||||
assert d == {
|
||||
"uuid": p.uuid,
|
||||
|
||||
+45
-33
@@ -71,57 +71,69 @@ def _make_mock_groups() -> tuple[list[Group], Group]:
|
||||
"""3 groups of 3 fictional participants with addresses around Karlsruhe."""
|
||||
hosts = [
|
||||
Participant(
|
||||
"Anna Wagner", "Kaiserstraße 12, 76131 Karlsruhe", "0721-1000001", 9, "none"
|
||||
name="Anna Wagner",
|
||||
address="Kaiserstraße 12, 76131 Karlsruhe",
|
||||
phone="0721-1000001",
|
||||
kitchen_size=9,
|
||||
allergies="none",
|
||||
),
|
||||
Participant(
|
||||
"Jonas Becker",
|
||||
"Waldstraße 5, 76133 Karlsruhe",
|
||||
"0721-1000002",
|
||||
7,
|
||||
"lactose",
|
||||
name="Jonas Becker",
|
||||
address="Waldstraße 5, 76133 Karlsruhe",
|
||||
phone="0721-1000002",
|
||||
kitchen_size=7,
|
||||
allergies="lactose",
|
||||
),
|
||||
Participant(
|
||||
"Mira Hofmann", "Yorckstraße 22, 76185 Karlsruhe", "0721-1000003", 8, "none"
|
||||
name="Mira Hofmann",
|
||||
address="Yorckstraße 22, 76185 Karlsruhe",
|
||||
phone="0721-1000003",
|
||||
kitchen_size=8,
|
||||
allergies="none",
|
||||
),
|
||||
]
|
||||
semi_hosts = [
|
||||
Participant(
|
||||
"Lukas Schreiber",
|
||||
"Sophienstraße 40, 76135 Karlsruhe",
|
||||
"0721-1000004",
|
||||
5,
|
||||
"nuts",
|
||||
name="Lukas Schreiber",
|
||||
address="Sophienstraße 40, 76135 Karlsruhe",
|
||||
phone="0721-1000004",
|
||||
kitchen_size=5,
|
||||
allergies="nuts",
|
||||
),
|
||||
Participant(
|
||||
"Sophie Lindner",
|
||||
"Beiertheimer Allee 18, 76137 Karlsruhe",
|
||||
"0721-1000005",
|
||||
6,
|
||||
"none",
|
||||
name="Sophie Lindner",
|
||||
address="Beiertheimer Allee 18, 76137 Karlsruhe",
|
||||
phone="0721-1000005",
|
||||
kitchen_size=6,
|
||||
allergies="none",
|
||||
),
|
||||
Participant(
|
||||
"Tom Vogel",
|
||||
"Durlacher Allee 75, 76131 Karlsruhe",
|
||||
"0721-1000006",
|
||||
4,
|
||||
"none",
|
||||
name="Tom Vogel",
|
||||
address="Durlacher Allee 75, 76131 Karlsruhe",
|
||||
phone="0721-1000006",
|
||||
kitchen_size=4,
|
||||
allergies="none",
|
||||
),
|
||||
Participant(
|
||||
"Lea Brandt",
|
||||
"Moltkestraße 30, 76133 Karlsruhe",
|
||||
"0721-1000007",
|
||||
3,
|
||||
"gluten",
|
||||
name="Lea Brandt",
|
||||
address="Moltkestraße 30, 76133 Karlsruhe",
|
||||
phone="0721-1000007",
|
||||
kitchen_size=3,
|
||||
allergies="gluten",
|
||||
),
|
||||
Participant(
|
||||
"Felix Krause", "Adlerstraße 14, 76133 Karlsruhe", "0721-1000008", 8, "none"
|
||||
name="Felix Krause",
|
||||
address="Adlerstraße 14, 76133 Karlsruhe",
|
||||
phone="0721-1000008",
|
||||
kitchen_size=8,
|
||||
allergies="none",
|
||||
),
|
||||
Participant(
|
||||
"Nora Fink",
|
||||
"Rüppurrer Straße 60, 76137 Karlsruhe",
|
||||
"0721-1000009",
|
||||
5,
|
||||
"none",
|
||||
name="Nora Fink",
|
||||
address="Rüppurrer Straße 60, 76137 Karlsruhe",
|
||||
phone="0721-1000009",
|
||||
kitchen_size=5,
|
||||
allergies="none",
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
@@ -0,0 +1,103 @@
|
||||
"""Tests for the Plan model: saving, reloading, and editing a masterplan."""
|
||||
|
||||
import json
|
||||
|
||||
from tatami.classes import Group
|
||||
from tatami.plan import Plan
|
||||
from tatami.tatami_masterplan import assign_courses, get_after_party_group
|
||||
from conftest import make_participants
|
||||
|
||||
|
||||
def make_plan(n: int = 6) -> Plan:
|
||||
groups = [Group(members=[p]) for p in make_participants(n)]
|
||||
courses = ["starter", "main", "dessert"] * (n // 3)
|
||||
assign_courses(groups, courses)
|
||||
after_party = get_after_party_group("party street")
|
||||
return Plan(
|
||||
groups=groups,
|
||||
after_party_group=after_party,
|
||||
course_times={"starter": "18:30", "main": "20:00", "dessert": "22:00"},
|
||||
organizer_contacts=[("Lars (Organizer)", "0151-23456789")],
|
||||
info_text="Welcome!",
|
||||
)
|
||||
|
||||
|
||||
class TestPlanRoundTrip:
|
||||
def test_save_and_load_preserves_groups_and_hosts(self, tmp_path):
|
||||
plan = make_plan()
|
||||
path = tmp_path / "masterplan.json"
|
||||
plan.save(path)
|
||||
|
||||
loaded = Plan.load(path)
|
||||
|
||||
assert [g.uuid for g in loaded.groups] == [g.uuid for g in plan.groups]
|
||||
for original, reloaded in zip(plan.groups, loaded.groups):
|
||||
assert reloaded.course == original.course
|
||||
assert [h.uuid for h in reloaded.hosts] == [h.uuid for h in original.hosts]
|
||||
|
||||
def test_load_resolves_live_host_objects_not_just_uuids(self, tmp_path):
|
||||
plan = make_plan()
|
||||
path = tmp_path / "masterplan.json"
|
||||
plan.save(path)
|
||||
|
||||
loaded = Plan.load(path)
|
||||
|
||||
group = loaded.groups[0]
|
||||
assert group.hosts is not None
|
||||
# Hosts are live Group objects (so .course etc. is accessible), not just ids.
|
||||
assert all(isinstance(h, Group) for h in group.hosts)
|
||||
assert {h.course for h in group.hosts} == {"starter", "main", "dessert"}
|
||||
|
||||
def test_round_trip_is_idempotent(self, tmp_path):
|
||||
plan = make_plan()
|
||||
path = tmp_path / "masterplan.json"
|
||||
plan.save(path)
|
||||
first = json.loads(path.read_text())
|
||||
|
||||
Plan.load(path).save(path)
|
||||
second = json.loads(path.read_text())
|
||||
|
||||
assert first == second
|
||||
|
||||
def test_carries_course_times_contacts_info_and_spreadsheet_id(self, tmp_path):
|
||||
plan = make_plan()
|
||||
plan.spreadsheet_id = "abc123"
|
||||
path = tmp_path / "masterplan.json"
|
||||
plan.save(path)
|
||||
|
||||
loaded = Plan.load(path)
|
||||
|
||||
assert loaded.course_times == plan.course_times
|
||||
assert loaded.organizer_contacts == plan.organizer_contacts
|
||||
assert loaded.info_text == plan.info_text
|
||||
assert loaded.spreadsheet_id == "abc123"
|
||||
|
||||
def test_spreadsheet_id_defaults_to_none(self):
|
||||
assert make_plan().spreadsheet_id is None
|
||||
|
||||
|
||||
class TestPlanEditing:
|
||||
def test_editing_saved_json_changes_reloaded_plan(self, tmp_path):
|
||||
plan = make_plan()
|
||||
path = tmp_path / "masterplan.json"
|
||||
plan.save(path)
|
||||
|
||||
data = json.loads(path.read_text())
|
||||
data["spreadsheet_id"] = "edited-by-hand"
|
||||
data["groups"][0]["members"][0]["address"] = "New Address 1"
|
||||
path.write_text(json.dumps(data))
|
||||
|
||||
loaded = Plan.load(path)
|
||||
|
||||
assert loaded.spreadsheet_id == "edited-by-hand"
|
||||
assert loaded.groups[0].members[0].address == "New Address 1"
|
||||
|
||||
|
||||
class TestPlanParticipants:
|
||||
def test_participants_property_collects_everyone_once(self):
|
||||
plan = make_plan()
|
||||
uuids = [p.uuid for p in plan.participants]
|
||||
assert len(uuids) == len(set(uuids))
|
||||
member_uuids = {m.uuid for g in plan.groups for m in g.members}
|
||||
member_uuids.add(plan.after_party_group.main_member.uuid)
|
||||
assert set(uuids) == member_uuids
|
||||
@@ -2,6 +2,15 @@ version = 1
|
||||
revision = 3
|
||||
requires-python = ">=3.13"
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.7.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2025.1.31"
|
||||
@@ -347,6 +356,77 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/0c/c3/44f3fbbfa403ea2a7c779186dc20772604442dde72947e7d01069cbe98e3/pycparser-3.0-py3-none-any.whl", hash = "sha256:b727414169a36b7d524c1c3e31839a521725078d7b2ff038656844266160a992", size = 48172, upload-time = "2026-01-21T14:26:50.693Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pydantic"
|
||||
version = "2.13.4"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "annotated-types" },
|
||||
{ name = "pydantic-core" },
|
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{ name = "typing-extensions" },
|
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{ name = "typing-inspection" },
|
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]
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sdist = { url = "https://files.pythonhosted.org/packages/18/a5/b60d21ac674192f8ab0ba4e9fd860690f9b4a6e51ca5df118733b487d8d6/pydantic-2.13.4.tar.gz", hash = "sha256:c40756b57adaa8b1efeeced5c196f3f3b7c435f90e84ea7f443901bec8099ef6", size = 844775, upload-time = "2026-05-06T13:43:05.343Z" }
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wheels = [
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]
|
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|
||||
[[package]]
|
||||
name = "pydantic-core"
|
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version = "2.46.4"
|
||||
source = { registry = "https://pypi.org/simple" }
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|
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{ name = "typing-extensions" },
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sdist = { url = "https://files.pythonhosted.org/packages/9d/56/921726b776ace8d8f5db44c4ef961006580d91dc52b803c489fafd1aa249/pydantic_core-2.46.4.tar.gz", hash = "sha256:62f875393d7f270851f20523dd2e29f082bcc82292d66db2b64ea71f64b6e1c1", size = 471464, upload-time = "2026-05-06T13:37:06.98Z" }
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Reference in New Issue
Block a user