def test_edgelist_to_distance_matrix2():
edgelist = utils.read_edgelist_file(test_files + 'edgelist-pentax-pce.txt')
matrix, names = utils.edgelist_to_distance_matrix(edgelist)
expected = ['Pentax_OptioA40_0_30521.JPG', 'Pentax_OptioA40_0_30522.JPG',
'Pentax_OptioA40_0_30523.JPG', 'Pentax_OptioA40_0_30524.JPG',
'Pentax_OptioA40_0_30525.JPG']
assert all([n == e for n,e in zip(names,expected)])
assert matrix.shape == (5,5)
expected = [0.0, 704.17228119005165, 433.30115575430841, 154.4174623739334, 658.55780052635578]
assert all([n == e for n,e in zip(matrix[0,:],expected)])
expected = [704.17228119005165, 0.0, 476.46750462930402, 244.28991913458535, 585.63345061046823]
assert all([n == e for n,e in zip(matrix[1,:],expected)])
def test_edgelist_to_distance_matrix2():
edgelist = utils.read_edgelist_file(test_files + 'edgelist-pentax-pce.txt')
matrix, names = utils.edgelist_to_distance_matrix(edgelist)
expected = [
'Pentax_OptioA40_0_30521.JPG', 'Pentax_OptioA40_0_30522.JPG',
'Pentax_OptioA40_0_30523.JPG', 'Pentax_OptioA40_0_30524.JPG',
'Pentax_OptioA40_0_30525.JPG'
]
assert all([n == e for n, e in zip(names, expected)])
assert matrix.shape == (5, 5)
expected = [
0.0, 704.17228119005165, 433.30115575430841, 154.4174623739334,
658.55780052635578
]
assert all([n == e for n, e in zip(matrix[0, :], expected)])
expected = [
704.17228119005165, 0.0, 476.46750462930402, 244.28991913458535,
585.63345061046823
]
assert all([n == e for n, e in zip(matrix[1, :], expected)])
parser = argparse.ArgumentParser()
mode = parser.add_mutually_exclusive_group(required=True)
mode.add_argument("-e", "--edgelist", help="name of the edgelist file", metavar='edgelist')
mode.add_argument("-m", "--matrix", help="name of distance matrix file", metavar='matrix')
parser.add_argument("-n", "--names", help="filename storing a list of names for the items to be clustered, in case distance matrix is used", metavar='names')
parser.add_argument("-c", "--convert", help="convert similarity to distance with specified a cut-off value", metavar='convert')
parser.add_argument("clustering_algorithm", help="name of the clustering algorithm to use", choices=["hierarchical", "dbscan", "spectral", "agglomerative"], metavar='clustering_algorithm')
return parser.parse_args()
if __name__ == "__main__":
args = parse_arguments()
edgelist = None
if args.edgelist:
print("edgelist filename=" + args.edgelist)
edgelist = utils.read_edgelist_file(args.edgelist)
matrix = None
if args.matrix:
print("matrix filename=" + args.matrix)
matrix = utils.read_distance_matrix_file(args.matrix)
if args.convert:
print("convert=" + args.convert)
if args.edgelist:
edgelist['d'] = utils.similarity_to_distance(edgelist['d'], float(args.convert))
if args.matrix:
matrix = utils.similarity_to_distance(matrix, float(args.convert))
if args.names:
print("names filenname=" + args.names)
def test_read_edgelist_file():
output = utils.read_edgelist_file(test_files + 'edgelist.txt')
assert output.size == 3
assert output['n1'][0] == 'node002'
assert output['n2'][0] == 'node001'
assert output['d'][0] == 5.7
help="name of the clustering algorithm to use",
choices=[
"hierarchical", "dbscan", "hdbscan", "spectral",
"affinity", "largevis"
],
metavar='clustering_algorithm')
return parser.parse_args()
if __name__ == "__main__":
args = parse_arguments()
edgelist = None
if args.edgelist:
print("edgelist filename=" + args.edgelist)
edgelist = utils.read_edgelist_file(args.edgelist)
matrix = None
if args.matrix:
print("matrix filename=" + args.matrix)
matrix = utils.read_distance_matrix_file(args.matrix)
if args.convert:
print("convert=" + args.convert)
if args.edgelist:
edgelist['d'] = utils.similarity_to_distance(
edgelist['d'], float(args.convert))
if args.matrix:
matrix = utils.similarity_to_distance(matrix, float(args.convert))
if args.names:
def test_read_edgelist_file():
output = utils.read_edgelist_file(test_files + 'edgelist.txt')
assert output.size == 3
assert output['n1'][0] == 'node002'
assert output['n2'][0] == 'node001'
assert output['d'][0] == 5.7
