Converting raster (array) to NetworkX graph and do * search? [closed]

Question

I want to use a slope-raster for a a_star path analysis in NetworkX. I am using Python for this project.

My test raster looks like this

First I read in the raster with GDAL

input_raster = "slope5.tif"
raster = gdal.Open(input_raster)

Next I read the raster as an array

bandraster = raster.GetRasterBand(1)
arr = bandraster.ReadAsArray()

The array looks like this

>>> arr
array([[ 15.26072407,  16.01073837,  27.81685448,  24.49266815,
         32.94010162],
       [ 26.0834446 ,  21.01086426,  30.02531815,  25.44641304,
         19.48436928],
       [ 21.98579979,  19.06574059,  24.44838905,  26.67520905,
         26.61030769],
       [ 20.42549133,  28.58900261,  17.00629425,  31.25236893,
         32.34066772],
       [  8.33120537,  24.303339  ,   7.85025167,  35.50911713,
         29.56856346]], dtype=float32)

Then I create from the array a graph

G = nx.DiGraph(arr)

As far as I understand it my graph looks like this

(0,0) (0,1) (0,2) (0,3) (0,4)
(1,0) (1,1) (1,2) (1,3) (1,4)
(2,0) (2,1) (2,2) (2,3) (2,4)
(3,0) (3,1) (3,2) (3,3) (3,4)
(4,0) (4,1) (4,2) (4,3) (4,4)

So for example the weight of G[3][2] is {'weight': 17.00629425048828}

How do I now find the A star path?

I tried the following to find the way from (1,0) to (3,3)?

nx.astar_path(G,(1,0),(3,3))

But that only gave

Traceback (most recent call last):
  File "<pyshell#44>", line 1, in <module>
    nx.astar_path(G,(1,4),(4, 2))
  File "C:\Python27\lib\site-packages\networkx\algorithms\shortest_paths\astar.py", line 108, in astar_path
    for neighbor, w in G[curnode].items():
  File "C:\Python27\lib\site-packages\networkx\classes\graph.py", line 319, in __getitem__
    return self.adj[n]
KeyError: (1, 4)

My nodes and edges look likes this:

>>> G.nodes()
[0, 1, 2, 3, 4]
>>> G.edges()
[(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (2, 0), (2, 1), (2, 2), (2, 3), (2, 4), (3, 0), (3, 1), (3, 2), (3, 3), (3, 4), (4, 0), (4, 1), (4, 2), (4, 3), (4, 4)]

1. Is this the right approach?
2. How do I find the path?
3. Did I mix up something with nodes and edges?