NetworkX provides a ready-to-use library for the A* Algorithm.

Basically the steps you want to take are:

1. Read the slope (the slope numbers are the weight, the more weight the less optimal)
2. Create a graph from the slope matrix. This is the hardest part.
3. Feed the NetworkX lib the graph and according to docs it should do the rest.

This is a canned solution imo. If you want to learn how the A* algorithm works you can create a T/F matrix, where T is the 'walkable' cells (with a slope less than 4?) and F the not walkables.

There are many worth reading tutorials on the subject for the A* around the intertubes that operate with that kind of arrays. Good learning material! (That will be my weekend project ;) )

NetworkX provides a ready-to-use library for the A* Algorithm.

Basically the steps you want to take are:

1. Read the slope (the slope numbers are the weight, the more weight the less optimal)
2. Create the graph from the slope matrix. This is the hardest part.
3. Feed the NetworkX lib the graph and according to docs it should do the rest.

This is a canned solution imo. If you want to learn how the A* algorithm works you can create a T/F matrix, where T is the 'walkable' cells (with a slope less than 4?) and F the not walkables.

There are many worth reading tutorials on the subject for the A* around the intertubes that operate with that kind of arrays. Good learning material! (That will be my weekend project ;) )

NetworkX provides a ready-to-use library for the A* Algorithm.

Basically the steps you want to take are:

1. Read the slope (the slope numbers are the weight, the more weight the less optimal)
2. Create a graph from the slope matrix. This is the hardest part.
3. Fed the NetworkX lib the graph and according to docs it should do the rest.

This is a canned solution imo. If you want to learn how the A* algorithm works you can create a T/F matrix, where T is the 'walkable' cells (with a slope less than 4?) and F the not walkables.

There are many worth reading tutorials on the subject for the A* around the intertubes that operate with that kind of arrays. Good learning material! (That will be my weekend project ;) )

NetworkX provides a ready-to-use library for the A* Algorithm.

Basically the steps you want to take are:

1. Read the slope (the slope numbers are the weight, the more weight the less optimal)
2. Create a graph from the slope matrix. This is the hardest part.
3. Feed the NetworkX lib the graph and according to docs it should do the rest.

This is a canned solution imo. If you want to learn how the A* algorithm works you can create a T/F matrix, where T is the 'walkable' cells (with a slope less than 4?) and F the not walkables.

There are many worth reading tutorials on the subject for the A* around the intertubes that operate with that kind of arrays. Good learning material! (That will be my weekend project ;) )