# grilops
a GRId LOgic Puzzle Solver library, using Python 3 and
[z3](https://github.com/Z3Prover/z3).
This package contains a collection of libraries and helper functions that are
useful for solving and checking
[Nikoli](https://en.wikipedia.org/wiki/Nikoli_(publisher))-style logic puzzles
using z3.
To get a feel for how to use this package to model and solve puzzles, try
working through the [tutorial IPython
notebook](https://github.com/obijywk/grilops/blob/master/examples/tutorial.ipynb),
and refer to the
[examples](https://github.com/obijywk/grilops/tree/master/examples) and the
[API Documentation](https://obijywk.github.io/grilops/).
## Installation
grilops requires Python 3.6 or later.
To install grilops for use in your own programs:
```
$ pip3 install grilops
```
To install the source code (to run the examples and/or work with the code):
```
$ git clone https://github.com/obijywk/grilops.git
$ cd grilops
$ pip3 install -e .
```
## Basic Concepts and Usage
The `symbols`, `geometry`, and `grids` modules contain the core functionality
needed for modeling most puzzles. For convenience, their attributes can be
accessed directly from the top-level `grilops` module.
Symbols represent the marks that are determined and written into a grid by a
solver while solving a puzzle. For example, the symbol set of a
[Sudoku](https://en.wikipedia.org/wiki/Sudoku) puzzle would be the digits 1
through 9. The symbol set of a binary determination puzzle such as
[Nurikabe](https://en.wikipedia.org/wiki/Nurikabe_(puzzle)) could contain two
symbols, one representing a black cell and the other representing a white cell.
The geometry module defines Lattice classes that are used to manage the shapes
of grids and relationships between cells. Rectangular and hexagonal grids are
supported, as well as grids with empty spaces in them.
A symbol grid is used to keep track of the assignment of symbols to grid
cells. Generally, setting up a program to solve a puzzle using grilops involves:
* Constructing a symbol set
* Constructing a lattice for the grid
* Constructing a symbol grid in the shape of the lattice, limited to contain
symbols from the symbol set
* Adding puzzle-specific constraints to cells in the symbol grid
* Checking for satisfying assignments of symbols to symbol grid cells
Grid cells are exposed as z3 constants, so built-in z3 operators can and should
be used when adding puzzle-specific constraints. In addition, grilops provides
several modules to help automate and abstract away the introduction of common
kinds of constraints.
### Loops
The `grilops.loops` module is helpful for adding constraints that ensure symbols
connect to form closed loops. Some examples of puzzle types for which this is
useful are [Masyu](https://en.wikipedia.org/wiki/Masyu) and
[Slitherlink](https://en.wikipedia.org/wiki/Slitherlink).
~~~~
$ python3 examples/masyu.py $ python3 examples/slitherlink.py
┌───┐┌──┐ ┌──┐
┌┘ ┌─┘└─┐│ │┌┐│ ┌┐
└─┐│┌──┐││ └┘│└┐││
│││┌─┘││ │ └┘│
┌─┘└┘│ ┌┘│ └┐ │
│┌──┐│ │┌┘ ┌──┘┌┐│
││┌─┘└─┘└┐ └───┘└┘
│││ ┌───┐│
└┘│ │┌──┘│ Unique solution
└─┘└───┘
Unique solution
~~~~
### Regions
The `grilops.regions` module is helpful for adding constraints that ensure
cells are grouped into orthogonally contiguous regions (polyominos) of variable
shapes and sizes. Some examples of puzzle types for which this is useful are
[Nurikabe](https://en.wikipedia.org/wiki/Nurikabe_(puzzle)) and
[Fillomino](https://en.wikipedia.org/wiki/Fillomino).
~~~~
$ python3 examples/nurikabe.py $ python3 examples/fillomino.py
2 █ ██ 2 8 8 3 3 101010105
███ █2███ 8 8 8 3 1010105 5
█2█ 7█ █ █ 3 3 8 10104 4 4 5
█ ██████ █ 1 3 8 3 102 2 4 5
██ █ 3█3█ 2 2 8 3 3 1 3 2 2
█2████3██ 6 6 2 2 1 3 3 1 3
2██4 █ █ 6 4 4 4 2 2 1 3 3
██ █████ 6 4 2 2 4 3 3 4 4
█1███ 2█4 6 6 4 4 4 1 3 4 4
Unique solution Unique solution
~~~~
### Shapes
The `grilops.shapes` module is helpful for adding constraints that ensure
cells are grouped into orthogonally contiguous regions (polyominos) of fixed
shapes and sizes. Some examples of puzzle types for which this is useful are
[Battleship](https://en.wikipedia.org/wiki/Battleship_(puzzle)) and
[LITS](https://en.wikipedia.org/wiki/LITS).
~~~~
$ python3 examples/battleship.py $ python3 examples/lits.py
▴ IIII
◂▪▸ ▪ • SS L
▾ LSS L I
◂▪▪▸ • L IIIILLI
LL L I
▴ ◂▸ TTT L I
▾ ▴ SS T LL T
▾ • SSLL TT
L T T
Unique solution IIIILTTT
Unique solution
~~~~
### Sightlines
The `grilops.sightlines` module is helpful for adding constraints that ensure
properties hold along straight lines through the grid. These "sightlines" may
terminate before reaching the edge of the grid if certain conditions are met
(e.g. if a certain symbol, such as one representing a wall, is
encountered). Some examples of puzzle types for which this is useful are
[Akari](https://en.wikipedia.org/wiki/Light_Up_(puzzle)) and
[Skyscraper](https://www.puzzlemix.com/Skyscraper).
~~~~
$ python3 examples/akari.py $ python3 examples/skyscraper.py
█* █* █ 23541
* █ 15432
*█* █ * 34215
*█ █ █ 42153
███* 51324
*███*
█ * █* █* Unique solution
* █* █*
█ *
█ * █* █
Unique solution
~~~~
Raw data
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"description": "# grilops\n\na GRId LOgic Puzzle Solver library, using Python 3 and\n[z3](https://github.com/Z3Prover/z3).\n\nThis package contains a collection of libraries and helper functions that are\nuseful for solving and checking\n[Nikoli](https://en.wikipedia.org/wiki/Nikoli_(publisher))-style logic puzzles\nusing z3.\n\nTo get a feel for how to use this package to model and solve puzzles, try\nworking through the [tutorial IPython\nnotebook](https://github.com/obijywk/grilops/blob/master/examples/tutorial.ipynb),\nand refer to the\n[examples](https://github.com/obijywk/grilops/tree/master/examples) and the\n[API Documentation](https://obijywk.github.io/grilops/).\n\n## Installation\n\ngrilops requires Python 3.6 or later.\n\nTo install grilops for use in your own programs:\n\n```\n$ pip3 install grilops\n```\n\nTo install the source code (to run the examples and/or work with the code):\n\n```\n$ git clone https://github.com/obijywk/grilops.git\n$ cd grilops\n$ pip3 install -e .\n```\n\n## Basic Concepts and Usage\n\nThe `symbols`, `geometry`, and `grids` modules contain the core functionality\nneeded for modeling most puzzles. For convenience, their attributes can be\naccessed directly from the top-level `grilops` module.\n\nSymbols represent the marks that are determined and written into a grid by a\nsolver while solving a puzzle. For example, the symbol set of a\n[Sudoku](https://en.wikipedia.org/wiki/Sudoku) puzzle would be the digits 1\nthrough 9. The symbol set of a binary determination puzzle such as\n[Nurikabe](https://en.wikipedia.org/wiki/Nurikabe_(puzzle)) could contain two\nsymbols, one representing a black cell and the other representing a white cell.\n\nThe geometry module defines Lattice classes that are used to manage the shapes\nof grids and relationships between cells. Rectangular and hexagonal grids are\nsupported, as well as grids with empty spaces in them.\n\nA symbol grid is used to keep track of the assignment of symbols to grid\ncells. Generally, setting up a program to solve a puzzle using grilops involves:\n\n* Constructing a symbol set\n* Constructing a lattice for the grid\n* Constructing a symbol grid in the shape of the lattice, limited to contain\n symbols from the symbol set\n* Adding puzzle-specific constraints to cells in the symbol grid\n* Checking for satisfying assignments of symbols to symbol grid cells\n\nGrid cells are exposed as z3 constants, so built-in z3 operators can and should\nbe used when adding puzzle-specific constraints. In addition, grilops provides\nseveral modules to help automate and abstract away the introduction of common\nkinds of constraints.\n\n### Loops\n\nThe `grilops.loops` module is helpful for adding constraints that ensure symbols\nconnect to form closed loops. Some examples of puzzle types for which this is\nuseful are [Masyu](https://en.wikipedia.org/wiki/Masyu) and\n[Slitherlink](https://en.wikipedia.org/wiki/Slitherlink).\n\n~~~~\n$ python3 examples/masyu.py $ python3 examples/slitherlink.py \n \u250c\u2500\u2500\u2500\u2510\u250c\u2500\u2500\u2510 \u250c\u2500\u2500\u2510 \n\u250c\u2518 \u250c\u2500\u2518\u2514\u2500\u2510\u2502 \u2502\u250c\u2510\u2502 \u250c\u2510 \n\u2514\u2500\u2510\u2502\u250c\u2500\u2500\u2510\u2502\u2502 \u2514\u2518\u2502\u2514\u2510\u2502\u2502 \n \u2502\u2502\u2502\u250c\u2500\u2518\u2502\u2502 \u2502 \u2514\u2518\u2502 \n\u250c\u2500\u2518\u2514\u2518\u2502 \u250c\u2518\u2502 \u2514\u2510 \u2502 \n\u2502\u250c\u2500\u2500\u2510\u2502 \u2502\u250c\u2518 \u250c\u2500\u2500\u2518\u250c\u2510\u2502 \n\u2502\u2502\u250c\u2500\u2518\u2514\u2500\u2518\u2514\u2510 \u2514\u2500\u2500\u2500\u2518\u2514\u2518 \n\u2502\u2502\u2502 \u250c\u2500\u2500\u2500\u2510\u2502 \n\u2514\u2518\u2502 \u2502\u250c\u2500\u2500\u2518\u2502 Unique solution\n \u2514\u2500\u2518\u2514\u2500\u2500\u2500\u2518\n\nUnique solution\n~~~~\n\n### Regions\n\nThe `grilops.regions` module is helpful for adding constraints that ensure\ncells are grouped into orthogonally contiguous regions (polyominos) of variable\nshapes and sizes. Some examples of puzzle types for which this is useful are\n[Nurikabe](https://en.wikipedia.org/wiki/Nurikabe_(puzzle)) and\n[Fillomino](https://en.wikipedia.org/wiki/Fillomino).\n\n~~~~\n$ python3 examples/nurikabe.py $ python3 examples/fillomino.py \n2 \u2588 \u2588\u2588 2 8 8 3 3 101010105 \n\u2588\u2588\u2588 \u25882\u2588\u2588\u2588 8 8 8 3 1010105 5 \n\u25882\u2588 7\u2588 \u2588 \u2588 3 3 8 10104 4 4 5 \n\u2588 \u2588\u2588\u2588\u2588\u2588\u2588 \u2588 1 3 8 3 102 2 4 5 \n\u2588\u2588 \u2588 3\u25883\u2588 2 2 8 3 3 1 3 2 2 \n \u25882\u2588\u2588\u2588\u25883\u2588\u2588 6 6 2 2 1 3 3 1 3 \n2\u2588\u25884 \u2588 \u2588 6 4 4 4 2 2 1 3 3 \n\u2588\u2588 \u2588\u2588\u2588\u2588\u2588 6 4 2 2 4 3 3 4 4 \n\u25881\u2588\u2588\u2588 2\u25884 6 6 4 4 4 1 3 4 4 \n\nUnique solution Unique solution\n~~~~\n\n### Shapes\n\nThe `grilops.shapes` module is helpful for adding constraints that ensure\ncells are grouped into orthogonally contiguous regions (polyominos) of fixed\nshapes and sizes. Some examples of puzzle types for which this is useful are\n[Battleship](https://en.wikipedia.org/wiki/Battleship_(puzzle)) and\n[LITS](https://en.wikipedia.org/wiki/LITS).\n\n~~~~\n$ python3 examples/battleship.py $ python3 examples/lits.py\n \u25b4 IIII\n\u25c2\u25aa\u25b8 \u25aa \u2022 SS L \n \u25be LSS L I\n\u25c2\u25aa\u25aa\u25b8 \u2022 L IIIILLI\n LL L I\n \u25b4 \u25c2\u25b8 TTT L I\n \u25be \u25b4 SS T LL T\n \u25be \u2022 SSLL TT\n L T T\nUnique solution IIIILTTT\n\n Unique solution\n~~~~\n\n### Sightlines\n\nThe `grilops.sightlines` module is helpful for adding constraints that ensure\nproperties hold along straight lines through the grid. These \"sightlines\" may\nterminate before reaching the edge of the grid if certain conditions are met\n(e.g. if a certain symbol, such as one representing a wall, is\nencountered). Some examples of puzzle types for which this is useful are\n[Akari](https://en.wikipedia.org/wiki/Light_Up_(puzzle)) and\n[Skyscraper](https://www.puzzlemix.com/Skyscraper).\n\n~~~~\n$ python3 examples/akari.py $ python3 examples/skyscraper.py \n\u2588* \u2588* \u2588 23541 \n * \u2588 15432 \n*\u2588* \u2588 * 34215 \n *\u2588 \u2588 \u2588 42153 \n \u2588\u2588\u2588* 51324 \n *\u2588\u2588\u2588* \n\u2588 * \u2588* \u2588* Unique solution\n* \u2588* \u2588*\n \u2588 * \n\u2588 * \u2588* \u2588\n\nUnique solution\n~~~~\n\n\n",
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