# platz

A genetic-algorithm-based solver that assigns people to tables ("Tische") for an event,
given group memberships, table sizes/neighbor relationships, and a configurable penalty
scheme. It reads a guest list (XML) and a table layout (INI), searches for a good seating
using a small custom GA framework, and writes the result to text/CSV output files.

**This is Python 3 code** (migrated from an original Python 2 implementation). There are no
third-party dependencies (standard library only), so `requirements.txt` is empty — it
exists only so `bin/install_py` has something to install.

## Project structure

```
platz/
├── bin/           # environment + launcher scripts (.bat + .sh pairs)
├── cfg/           # GA cycle definition and penalty scheme
├── libs/          # the two Python modules (ga.py, Strukturdaten.py, platz.py)
├── tests/         # unittest suite for Strukturdaten.py
└── work/          # per-run input (table layout, guest list) and output
    ├── test1/
    └── test2/
```

- `libs/platz.py` — entry point; wires together config, input data, and the GA run.
- `libs/ga.py` — generic genetic-algorithm scaffolding (`Loesung`, `Zyklus`, `Farm`,
  `Entwicklung`).
- `libs/Strukturdaten.py` — domain model for the seating problem (`Person`, `Gruppe`,
  `Tisch`, `Strafliste`, `Sitzplatzverteilung`, `JSONConfig`).

## Running the tests

```bash
python -m unittest discover -s tests -p "test_*.py" -v
```

If a stray global `PYTHONPATH` entry shadows the standard-library `tests` package name
(unrelated to this project), clear it for the command, e.g. `PYTHONPATH= python -m
unittest discover -s tests -p "test_*.py" -v`.

## Setup

1. Install a Python interpreter (`py` on Windows / `python3` on Linux/macOS must be on
   `PATH`).
2. Run the installer for your platform to create a `.venv` and install `requirements.txt`:

   ```bat
   :: Windows
   bin\install_py.bat
   ```

   ```bash
   # Linux/macOS
   bin/install_py.sh
   ```

All `bin/` scripts derive `PLATZ` automatically from their own location (no path editing
required) by calling `setenv.bat`/`setenv.sh` first.

## Running

```bat
:: Windows
bin\activate_venv.bat
bin\platz.bat --indir work\test1
```

```bash
# Linux/macOS
source bin/activate_venv.sh
bin/platz.sh --indir work/test1
```

`bin/platz.bat` / `bin/platz.sh` call `setenv` to set `PLATZ`, `PLATZ_CFG`, `PLATZ_LIBS`,
`PLATZ_WORK`, `PLATZ_IN`, `PLATZ_OUT`, add `PLATZ_LIBS` to `PYTHONPATH`, and then run
`libs/platz.py`, forwarding any extra arguments (like `--indir`) to it.

Other helper scripts (`bin/setenv.*`, `bin/get_cmd.*`) follow the same environment
convention:

- `setenv` — sets all `PLATZ_*` environment variables and creates missing folders; sourced
  by every other script, not normally called directly.
- `activate_venv` — activates the `.venv` created by `install_py`.
- `get_cmd` — opens a new shell with the `PLATZ_*` environment already set.

### Configuring a run

`--indir <dir>` is required and points at a directory containing the input files and
receiving the output files for one run — e.g. `work/test1/` or `work/test2/`:

- `tische.ini` — one section per table: `Nummer`, `Hof` (venue/court), `Plaetze` (seat
  count), `Nachbarliste` (neighboring table ids), `Koordinaten`.
- `bestellung.json` — `{ "gruppen": [ ... ] }`, each group with a `"personen"` list
  (`vorname`/`nachname`/optional `titel`), an optional `"anzahl"` (how many bookings to
  expand each person into), an optional `"name"` (display label) and an optional `"tisch"`
  (pins the group as a VIP group to that table instead of placing it via the GA).

To run against a different dataset, create a new folder with a `tische.ini` and
`bestellung.json` following the same layout and pass it via `--indir`.

The GA cycle and penalty scheme are read directly from `$PLATZ_CFG/zyklus.cfg` and
`$PLATZ_CFG/strafen.cfg` (independent of `--indir`); there is no longer a `platz.cfg`
indirection. The GA profile is selected with the `--zyklus-art <name>` switch (default:
`Adaptiv`).

`cfg/zyklus.cfg` defines the GA profiles. Two styles exist:

- **static** profiles (`Easy`, `Simple`) give an explicit `Abfolge` (sequence of actions)
  and matching `Anzahl` (counts): `e` create, `s` select best, `S` select randomly, `m`
  mutate, `j` keep parents, `z` advance generation.
- the **adaptive** profile (`Adaptiv`, the default) has no fixed action list. It is
  recognised by a `Start` field and runs a generation loop: a large starting population
  that shrinks each generation (`Schrumpfung`, down to `MinPopulation`) and stops once the
  best score fails to improve by at least `Schwelle` for `Geduld` generations (or after
  `MaxGenerationen`).

Select a different profile per run, e.g. `bin/platz.bat --indir work/test2 --zyklus-art Easy`.

`cfg/strafen.cfg` defines the penalty scheme: cost of splitting a group across tables (by
group size and number of splits), cost of a person sitting alone or without a group-mate at
a neighboring table, and per-table seat-utilization penalties.

### Output

A run writes two files into the `--indir` directory:

- `TischePersonen.txt` — table → seated people, with venue/table/seat numbers.
- `PersonenTische.csv` — person → table, sorted alphabetically by name.

Pass `--tosvg` to additionally write `Sitzplatzverteilung.svg` into the `--indir`
directory: tables are drawn as white circles, people as colored circles inside their
table's circle (color = group). See `doc/docu_tests.md` for example renderings.

Two more optional graphs go into the `--indir` directory:

- `--show-development` → `Entwicklung.svg`: the course of the search (best/average/worst
  value over time, plus a panel with the number of solutions in the pool per step).
- `--show-lineage` → `Abstammung.svg`: the mutation tree of every solution. The final best
  solution's ancestral branch is highlighted and labelled with its id. Every solution gets a
  plain running number (`1..N`); the ancestry is recorded via each node's stored predecessor
  (parent id), not encoded in the id itself, and is followed back along the highlighted branch.

## Further details

See `CLAUDE.md` for a deeper architectural walkthrough of the GA framework and domain
model, intended for AI coding assistants but equally useful for human contributors.