Binary Junction by Python calling ExaDiS¶
We can run the same Binary Junction example using ExaDiS (after compilation) using the following commands.
cd ${OPENDIS_DIR}
cd examples/03_binary_junction
export OMP_NUM_THREADS=8
python3 -i test_binary_junction_exadis.py
This is assuming ExaDis has been compiled using OpenMP, and OMP_NUM_THREADS=8 specifies the number of threads used for the OpenMP run. The behavior should be similar to the previous test case (using pydis), except that the simulation should run much faster.
Explore Dislocation Network¶
After the simulation has finished, we can examine the data stored in object G by typing the following command in the Python terminal.
G
The output is
<pyexadis_base.ExaDisNet object at 0xffffabedbca0>
We can see that G is an ExaDiSNet object (so that we cannot interact with it the same way as a DisNet object).
We can use the following command to see the content of G.
G.export_data()
Here is the output for this command.
{'cell': {'h': array([[8., 0., 0.],
[0., 8., 0.],
[0., 0., 8.]]), 'origin': array([0., 0., 0.]), 'is_periodic': [0, 0, 0]}, 'nodes': {'tags': array([[ 0, 0],
[ 0, 5],
[ 0, 2],
[ 0, 3],
[ 0, 4],
...,
[ 0, 26],
[ 0, 27],
[ 0, 28],
[ 0, 29],
[ 0, 30]]), 'positions': array([[4. , 3. , 3. ],
[5. , 4. , 5. ],
[4. , 5. , 5. ],
[3. , 4. , 3. ],
[4.23214185, 4.23214185, 4.23214185],
...,
[3.89698369, 3.35282098, 3.35282098],
[4.17372627, 4.39481591, 4.39481591],
[3.35282098, 3.89698369, 3.35282098],
[4.39481591, 4.17372627, 4.39481591],
[3.75646582, 3.75646582, 3.75646582]]), 'constraints': array([[7],
[7],
[7],
[7],
[0],
...,
[0],
[0],
[0],
[0],
[0]])}, 'segs': {'nodeids': array([[ 0, 18],
[ 5, 2],
[ 3, 20],
[21, 1],
[ 4, 11],
...,
[26, 6],
[27, 7],
[28, 8],
[29, 9],
[19, 13]]), 'burgers': array([[-1., 1., 1.],
[-1., 1., 1.],
[ 1., -1., 1.],
[ 1., -1., 1.],
[ 0., 0., -2.],
...,
[-1., 1., 1.],
[-1., 1., 1.],
[ 1., -1., 1.],
[ 1., -1., 1.],
[ 0., 0., -2.]]), 'planes': array([[ 0. , 0.70710678, -0.70710678],
[ 0. , 0.70710678, -0.70710678],
[-0.70710678, 0. , 0.70710678],
[-0.70710678, 0. , 0.70710678],
[ 0. , 0. , 0. ],
...,
[ 0. , 0.70710678, -0.70710678],
[ 0. , 0.70710678, -0.70710678],
[-0.70710678, 0. , 0.70710678],
[-0.70710678, 0. , 0.70710678],
[ 0. , 0. , 0. ]])}}
We can use the following command to convert an ExaDiSNet object to a DisNet object, so that we can interact with it using the same approach as in the previous test case.
from pydis import DisNet
G1=DisNet()
G1.import_data(G.export_data())
Hint
This can also be accomplished using the get_disnet function of DisNetManger net.
from pydis import DisNet
G1=net.get_disnet(DisNet)
Now the object G1 is a DisNet object. We can use the same command as in the previous test case to interact with G1, such as,
G1.all_nodes_tags()
>> dict_keys([(0, 0), (0, 5), (0, 2), (0, 3), (0, 4), (0, 31), (0, 6), (0, 7), (0, 8), (0, 9), (0, 10), (0, 1), (0, 12), (0, 32), (0, 14), (0, 15), (0, 16), (0, 17), (0, 18), (0, 19), (0, 20), (0, 33), (0, 22), (0, 23), (0, 24), (0, 25), (0, 26), (0, 27), (0, 28), (0, 29), (0, 30)])
G1.nodes((0,0)).view()
>> {'R': array([4., 3., 3.]), 'constraint': 7}
list(G1.all_segments_tags())
>> [((0, 0), (0, 18)), ((0, 31), (0, 2)), ((0, 3), (0, 20)), ((0, 33), (0, 5)), ((0, 4), (0, 1)), ((0, 6), (0, 22)), ((0, 7), (0, 23)), ((0, 8), (0, 24)), ((0, 9), (0, 25)), ((0, 10), (0, 26)), ((0, 4), (0, 27)), ((0, 12), (0, 28)), ((0, 4), (0, 29)), ((0, 14), (0, 30)), ((0, 15), (0, 31)), ((0, 16), (0, 30)), ((0, 17), (0, 33)), ((0, 18), (0, 10)), ((0, 1), (0, 19)), ((0, 20), (0, 12)), ((0, 32), (0, 30)), ((0, 22), (0, 14)), ((0, 23), (0, 15)), ((0, 24), (0, 16)), ((0, 25), (0, 17)), ((0, 26), (0, 6)), ((0, 27), (0, 7)), ((0, 28), (0, 8)), ((0, 29), (0, 9)), ((0, 19), (0, 32))]
G1.segments(((0, 0), (0, 18))).view()
>> {'source_tag': (0, 0), 'target_tag': (0, 18), 'burg_vec': array([-1., 1., 1.]), 'plane_normal': array([ 0. , 0.70710678, -0.70710678])}
G1.segments(((0, 0), (0, 18))).burg_vec_from((0, 0))
>> array([-1., 1., 1.])
G1.segments(((0, 0), (0, 18))).burg_vec_from((0, 18))
>> array([ 1., -1., -1.])