def test_walk_through_graph_shortest_path():
"""Test the routine to go through the graph using shortest path."""
# Load the data with only a single serie
currdir = os.path.dirname(os.path.abspath(__file__))
path_data = os.path.join(currdir, 'data', 'dce')
# Create an object to handle the data
dce_mod = DCEModality()
# Read the data
dce_mod.read_data_from_path(path_data)
# Load the GT data
path_gt = [os.path.join(currdir, 'data', 'gt_folders', 'prostate')]
label_gt = ['prostate']
gt_mod = GTModality()
gt_mod.read_data_from_path(label_gt, path_gt)
# Build a heatmap from the dce data
# Reduce the number of bins to enforce low memory consumption
nb_bins = [10] * dce_mod.n_serie_
heatmap, bins_heatmap = dce_mod.build_heatmap(gt_mod.extract_gt_data(
label_gt[0]), nb_bins=nb_bins)
# Build the graph by taking the inverse exponential of the heatmap
heatmap_inv_exp = np.exp(img_as_float(1. - (heatmap / np.max(heatmap))))
graph = StandardTimeNormalization._build_graph(heatmap_inv_exp, .99)
start_end_tuple = ((0, 6), (3, 6))
# Call the algorithm to walk through the graph
path = StandardTimeNormalization._walk_through_graph(graph,
heatmap_inv_exp,
start_end_tuple,
'shortest-path')
gt_path = np.array([[0, 6], [1, 6], [2, 6], [3, 6]])
assert_array_equal(path, gt_path)
