Pathfinding algorithms for python 2 and 3.

Currently there are 6 path-finders bundled in this library, namely:

Dijkstra and A* take the weight of the fields on the map into account.



This library is provided by pypi, so you can just install the current stable version using pip:

pip install pathfinding

A simple usage example to find a path using A*.

1. import the required libraries:

```python

from pathfinding.core.diagonal_movement import DiagonalMovement

from pathfinding.core.grid import Grid

from pathfinding.finder.a_star import AStarFinder

```

1. Create a map using a 2D-list. Any value smaller or equal to 0 describes an obstacle. Any number bigger than 0 describes the weight of a field that can be walked on. The bigger the number the higher the cost to walk that field. In this example we like the algorithm to create a path from the upper left to the bottom right. To make it not to easy for the algorithm we added an obstacle in the middle, so it can not use the direct way. We ignore the weight for now, all fields have the same cost of 1. Feel free to create a more complex map or use some sensor data as input for it.

```python

matrix = [

[1, 1, 1],

]

```

1. we create a new grid from this map representation. This will create Node instances for every element of our map. It will also set the size of the map. We assume that your map is a square, so the size height is defined by the length of the outer list and the width by the length of the first list inside it.

```python

grid = Grid(matrix=matrix)

```

1. we get the start (top-left) and endpoint (bottom-right) from the map:

```python

start = grid.node(0, 0)

end = grid.node(2, 2)

```

1. create a new instance of our finder and let it do its work. We allow diagonal movement. The `find_path` function does not only return you the path from the start to the end point it also returns the number of times the algorithm needed to be called until a way was found.

```python

finder = AStarFinder(diagonal_movement=DiagonalMovement.always)

path, runs = finder.find_path(start, end, grid)

```

1. thats it. We found a way. Now we can print the result (or do something else with it). Note that the start and end points are part of the path.

```python

print('operations:', runs, 'path length:', len(path))

print(grid.grid_str(path=path, start=start, end=end))

```

The result should look like this:

('operations:', 5, 'path length:', 4)

+---+

| e|

+---+

```

You can ignore the +, - and | characters, they just show the border around your map, the blank space is a free field, 's' marks the start, 'e' the end and '#' our obstacle in the middle. You see the path from start to end marked by 'x' characters. We allow horizontal movement, so it is not using the upper-right corner. You can access `print(path)` to get the specific list of coordinates.

Here The whole example if you just want to copy-and-paste the code and play with it:

Take a look at the _`test/`_ folder for more examples.

While running the pathfinding algorithm it might set values on the nodes. Depending on your path finding algorithm things like calculated distances or visited flags might be stored on them. So if you want to run the algorithm in a loop you need to clean the grid first (see `Grid.cleanup`). Please note that because cleanup looks at all nodes of the grid it might be an operation that can take a bit of time!

All pathfinding algorithms in this library are inheriting the Finder class. It has some common functionality that can be overwritten by the implementation of a path finding algorithm.

The normal process works like this:

1. You call `find_path` on one of your finder implementations

1. `init_find` instantiates `open_list` and resets all values and counters.

1. The main loop starts on the `open_list`. This list gets filled with all nodes that will be processed next (e.g. all neighbors that are walkable). For this you need to implement `check_neighbors` in your own finder implementation.

1. finally `process_node` updates the open list so `find_path` can run `check_neighbors` on it in the next node in the next iteration of the main loop.

flow:

find_path

init_find # (re)set global values and open list

check_neighbors # for every node in open list