def test_RegularGrid():
res = Grid.RegularGrid(time_seq=np.arange(2),
space_grid=np.array([[0., 5.], [1., 2.]]),
silence_level=2).sequence(0)
exp = np.array([0., 0., 5., 5.], dtype=np.float32)
assert np.allclose(res, exp, atol=1e-04)
res = Grid.RegularGrid(time_seq=np.arange(2),
space_grid=np.array([[0., 5.], [1., 2.]]),
silence_level=2).sequence(1)
exp = np.array([1., 2., 1., 2.], dtype=np.float32)
assert np.allclose(res, exp, atol=1e-04)
from pyunicorn.core.grid import Grid
"""
This example code offers an overview of the spatial network code for GeoModel1
and GeoModel2 can be called. Furthermore, with the python versions GeoModel1_py
and GeoModel2_py further analysis can be done, to visualize the working of the
network operations.
"""
# Create Random Grids
rect_grid_num = 20 # For larger network sizes this might take a while to compute!
grid = Grid.RegularGrid(time_seq=np.arange(2),
lat_grid=np.random.randint(low=0,
high=40,
size=rect_grid_num),
lon_grid=np.random.randint(low=0,
high=40,
size=rect_grid_num))
erdos_renyi = sp.SpatialNetwork.ErdosRenyi(grid=grid,
n_nodes=int(rect_grid_num**2),
link_probability=0.1)
geo_model = sp.SpatialNetwork(grid=erdos_renyi.grid,
adjacency=erdos_renyi.adjacency)
# Apply geoModel code
new_link_list = geo_model.GeoModel1(n_steps=int(5e4),
tolerance=1,
grid_type='euclidean',
verbose=False)