def test_bad_edge_costs_2(self):
with self.assertRaises(errors.BadEdgeCostError) as context:
edges = [
("A", "B", 5),
("B", "C", 4.3),
]
graph_utils.make_graph(edges)
self.assertTrue('must be an integer' in str(context.exception))
def test_good_edge_costs_1(self):
from collections import defaultdict
edges = [
("A", "B", 5),
("B", "C", 43),
]
graph = graph_utils.make_graph(edges)
self.assertTrue(type(graph) == defaultdict)
def setUp(self):
edges = [
("A", "B", 5),
("B", "C", 4),
("C", "D", 8),
("D", "C", 8),
("D", "E", 6),
("A", "D", 5),
("C", "E", 2),
("E", "B", 3),
("A", "E", 7),
]
self.graph = graph_utils.make_graph(edges)
def node_out_128(inp_dir, out_dir, out_coor):
'''exclude the 128 and -128 values from the node attribute txt files and
write new node attribute files'''
node_path = inp_dir
f = [f for f in listdir(node_path) if isfile(join(node_path, f))]
#f = f[0:10]
for i in f:
print(i)
gph = open(node_path + i, 'r')
cont = gph.readlines()
ls_node, ls_edge = gphtols_view(cont, False)
graph = make_graph(ls_node, ls_edge, range(len(ls_node)))
nodes = np.asarray(ls_node)
wh_128 = np.where(nodes == out_coor)
wh_128 = list(wh_128[0])
wh_128n = np.where(nodes == -out_coor)
wh_128n = list(wh_128n[0])
full_list = list(set(wh_128) | set(wh_128n)) #union of two lists
for j in range(len(full_list)):
graph.remove_n(full_list[j])
adj = nx.attr_matrix(graph.get_graph())[0]
edges = []
for j in range(adj.shape[0]):
for k in range(adj.shape[1]):
if adj[j, k] == 1.:
edges.append([j, k])
nodes = list(
nx.get_node_attributes(graph.get_graph(), name='coor').values())
#print(nodes,edges)
write_gph(out_dir + i, nodes, edges)
def createDataRecord(out_filename, addrs_y, img_path, gph_path):
array = np.load('./data/numpy_arrays/nodes_out.npy')
qt = QuantileTransformer(output_distribution='normal')
shob = qt.fit_transform(array)
#mean = np.load('./data/numpy_arrays/fixed_node/mean.npy')
#std = np.load('./data/numpy_arrays/fixed_node/std.npy')
a = np.load('./data/numpy_arrays/range/a.npy')
b = np.load('./data/numpy_arrays/range/b.npy')
#an = np.load('./data/numpy_arrays/nodes/a.npy')
#bn = np.load('./data/numpy_arrays/nodes/b.npy')
#num_n = []
writer = tf.io.TFRecordWriter(out_filename)
for i in range(len(addrs_y)):
print(i)
if i == 0:
print(addrs_y[i])
img_y = cv2.imread(img_path + str(addrs_y[i]))
img_y = img_y / 255
img_y = np.asarray(
img_y, dtype=np.float32
) #all data has to be converted to np.float32 before writing
gph = open(gph_path + addrs_y[i].split('.')[0] + '.txt', 'r')
cont = gph.readlines()
ls_node, ls_edge = gphtols_view(cont, flip=False)
if i == 0:
print(ls_node)
if len(ls_node) == 0:
continue
#node_attr = np.asarray(ls_node,dtype=np.float32)
#print(ls_node)
#node_attr = (a*((ls_node - mean)/std))+b
node_attr = np.asarray(ls_node, dtype=np.float32)
node_attr = qt.transform(node_attr)
node_attr = (a * node_attr) + b
node_attr = np.asarray(node_attr, dtype=np.float32)
#print(node_attr)
#ls_node, ls_edge = gphtols(cont)
#node = make_gph(ls_node, ls_edge, range(len(ls_node)))
graph = make_graph(ls_node, ls_edge, range(len(ls_node)))
#num_nodes = graph.get_num_nodes()
#num_nodes = np.log(num_nodes)
#num_nodes = (an*num_nodes)+bn
#num_nodes = np.asarray(num_nodes,dtype=np.float32)
adj_mtx = graph.get_adj()
adj_mtx = np.asarray(adj_mtx, dtype=np.float32)
#num_n.append(num_nodes)
if i == 0:
print(node_attr, node_attr.shape)
print(adj_mtx)
#print(num_nodes)
print(img_y)
feature = {
'image_y': _bytes_feature(img_y.tostring()),
'gph_nodes': _bytes_feature(node_attr.tostring()),
'gph_adj': _bytes_feature(adj_mtx.tostring())
#'gph_node_num' : _bytes_feature(num_nodes.tostring())
}
example = tf.train.Example(features=tf.train.Features(feature=feature))
writer.write(example.SerializeToString())
writer.close()
sys.stdout.flush()
