def test_random_layout(nodes_without_positions, edges):
expected_x = [0.417022004703, 0.000114374817, 0.146755890817, 0.186260211378, 0.396767474231]
expected_y = [0.720324493442, 0.302332572632, 0.092338594769, 0.345560727043, 0.538816734003]
df = random_layout(nodes_without_positions, edges, seed=1)
assert np.allclose(df['x'], expected_x)
assert np.allclose(df['y'], expected_y)
def ds_layout(nodes, edges=[], layout='circular'):
'''
Return nodes positions based on set layout.]]]]]
'''
if layout == 'circular':
return circular_layout(nodes)
elif layout == 'forceatlas2':
return forceatlas2_layout(nodes, edges)
return random_layout(nodes)
def test_random_layout(nodes_without_positions, edges):
expected_x = [
0.417022004703, 0.000114374817, 0.146755890817, 0.186260211378,
0.396767474231
]
expected_y = [
0.720324493442, 0.302332572632, 0.092338594769, 0.345560727043,
0.538816734003
]
df = random_layout(nodes_without_positions, edges, seed=1)
assert np.allclose(df['x'], expected_x)
assert np.allclose(df['y'], expected_y)
'overflowX': 'scroll'
}
}
#Create nodes and edges
np.random.seed(0)
n=100
m=200
nodes = pd.DataFrame(["node"+str(i) for i in range(n)], columns=['name'])
#nodes.tail()
edges = pd.DataFrame(np.random.randint(0,len(nodes), size=(m, 2)),
columns=['source', 'target'])
#edges.tail()
randomloc = random_layout(nodes)
#randomloc.tail()
cvsopts = dict(plot_height=600, plot_width=800)
#Creates nodes in datasahder image
def nodesplot(nodes, name=None, canvas=None, cat=None):
canvas = ds.Canvas(**cvsopts) if canvas is None else canvas
aggregator=None if cat is None else ds.count_cat(cat)
agg=canvas.points(nodes,'x','y',aggregator)
return tf.spread(tf.shade(agg, cmap=["#FF3333"]), px=3, name=name)
forcedirected = random_layout(nodes, edges)
#creates edges in datashader image
def edgesplot(edges, name=None, canvas=None):