def template_clustering(file, map_size, trust_order, sync_order = 0.999, show_dyn = False, show_layer1 = False, show_layer2 = False, show_clusters = True):
# Read sample
sample = read_sample(file);
# Create network
network = syncsom(sample, map_size[0], map_size[1]);
# Run processing
(ticks, (dyn_time, dyn_phase)) = timedcall(network.process, trust_order, show_dyn, sync_order);
print("Sample: ", file, "\t\tExecution time: ", ticks, "\n");
# Show dynamic of the last layer.
if (show_dyn == True):
draw_dynamics(dyn_time, dyn_phase, x_title = "Time", y_title = "Phase", y_lim = [0, 2 * 3.14]);
if (show_clusters == True):
clusters = network.get_som_clusters();
draw_clusters(network.som_layer.weights, clusters);
# Show network stuff.
if (show_layer1 == True):
network.show_som_layer();
if (show_layer2 == True):
network.show_sync_layer();
if (show_clusters == True):
clusters = network.get_clusters();
draw_clusters(sample, clusters);
def cluster(number_clusters, iterations, maxneighbours):
data = read_sample('data.data')
m_clarans = clarans(data, number_clusters, iterations, maxneighbours)
(ticks, result) = timedcall(m_clarans.process)
print("Execution time: ", ticks, "\n")
clusters = m_clarans.get_clusters()
draw_clusters(data, clusters)
def template_clustering(file, map_size, trust_order, sync_order = 0.999, show_dyn = False, show_layer1 = False, show_layer2 = False, show_clusters = True):
# Read sample
sample = read_sample(file);
# Create network
network = syncsom(sample, map_size[0], map_size[1]);
# Run processing
(ticks, (dyn_time, dyn_phase)) = timedcall(network.process, trust_order, show_dyn, sync_order);
print("Sample: ", file, "\t\tExecution time: ", ticks, "\n");
# Show dynamic of the last layer.
if (show_dyn == True):
draw_dynamics(dyn_time, dyn_phase, x_title = "Time", y_title = "Phase", y_lim = [0, 2 * 3.14]);
if (show_clusters == True):
clusters = network.get_som_clusters();
draw_clusters(network.som_layer.weights, clusters);
# Show network stuff.
if (show_layer1 == True):
network.show_som_layer();
if (show_layer2 == True):
network.show_sync_layer();
if (show_clusters == True):
clusters = network.get_clusters();
draw_clusters(sample, clusters);
def template_clustering(
start_centers,
path,
tolerance=0.025,
criterion=splitting_type.BAYESIAN_INFORMATION_CRITERION,
ccore=False):
sample = read_sample(path)
xmeans_instance = xmeans(sample, start_centers, 20, tolerance, criterion,
ccore)
(ticks, result) = timedcall(xmeans_instance.process)
clusters = xmeans_instance.get_clusters()
criterion_string = "UNKNOWN"
if (criterion == splitting_type.BAYESIAN_INFORMATION_CRITERION):
criterion_string = "BAYESIAN_INFORMATION_CRITERION"
elif (criterion == splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH):
criterion_string = "MINIMUM_NOISELESS_DESCRIPTION_LENGTH"
print("Sample: ", path, "\nInitial centers: '",
(start_centers is not None), "', Execution time: '", ticks,
"', Number of clusters:", len(clusters), ",", criterion_string, "\n")
draw_clusters(sample, clusters)
def template_clustering(file,
radius,
order,
show_dyn=False,
show_conn=False,
show_clusters=True,
ena_conn_weight=False,
ccore_flag=True,
tolerance=0.1):
sample = read_sample(file)
network = syncnet(sample,
radius,
enable_conn_weight=ena_conn_weight,
ccore=ccore_flag)
(ticks, analyser) = timedcall(network.process, order, solve_type.FAST,
show_dyn)
print("Sample: ", file, "\t\tExecution time: ", ticks, "\n")
if (show_dyn == True):
sync_visualizer.show_output_dynamic(analyser)
sync_visualizer.animate(analyser)
#sync_visualizer.animate_output_dynamic(analyser);
#sync_visualizer.animate_correlation_matrix(analyser, colormap = 'hsv');
if ((show_conn == True) and (ccore_flag == False)):
network.show_network()
if (show_clusters == True):
clusters = analyser.allocate_clusters(tolerance)
print("amout of clusters: ", len(clusters))
draw_clusters(sample, clusters)
def template_clustering(number_clusters, path, branching_factor = 5, max_node_entries = 5, initial_diameter = 0.0, type_measurement = measurement_type.CENTROID_EUCLIDIAN_DISTANCE, entry_size_limit = 200, ccore = True):
sample = read_sample(path);
birch_instance = birch(sample, number_clusters, branching_factor, max_node_entries, initial_diameter, type_measurement, entry_size_limit, ccore);
(ticks, result) = timedcall(birch_instance.process);
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n");
clusters = birch_instance.get_clusters();
draw_clusters(sample, clusters);
def template_clustering(number_clusters, path, branching_factor = 5, max_node_entries = 5, initial_diameter = 0.0, type_measurement = measurement_type.CENTROID_EUCLIDIAN_DISTANCE, entry_size_limit = 200, ccore = True):
sample = read_sample(path);
birch_instance = birch(sample, number_clusters, branching_factor, max_node_entries, initial_diameter, type_measurement, entry_size_limit, ccore)
(ticks, result) = timedcall(birch_instance.process);
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n");
clusters = birch_instance.get_clusters();
draw_clusters(sample, clusters);
def template_clustering(number_clusters, path, iterations, maxneighbors):
sample = read_sample(path);
clarans_instance = clarans(sample, number_clusters, iterations, maxneighbors);
(ticks, result) = timedcall(clarans_instance.process);
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n");
clusters = clarans_instance.get_clusters();
draw_clusters(sample, clusters);
def template_clustering(number_clusters, path, iterations, maxneighbors):
sample = read_sample(path);
clarans_instance = clarans(sample, number_clusters, iterations, maxneighbors);
(ticks, result) = timedcall(clarans_instance.process);
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n");
clusters = clarans_instance.get_clusters();
draw_clusters(sample, clusters);
def template_clustering(start_medians, path, tolerance=0.25):
sample = read_sample(path)
kmedians_instance = kmedians(sample, start_medians, tolerance)
(ticks, result) = timedcall(kmedians_instance.process)
clusters = kmedians_instance.get_clusters()
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n")
draw_clusters(sample, clusters)
def template_clustering(start_centers, path, tolerance = 0.25):
sample = read_sample(path);
kmedians_instance = kmedians(sample, start_centers, tolerance);
(ticks, result) = timedcall(kmedians_instance.process);
clusters = kmedians_instance.get_clusters();
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n");
draw_clusters(sample, clusters);
def template_clustering(file, number_clusters, arg_order = 0.999, arg_collect_dynamic = True, ccore_flag = False):
sample = read_sample(file);
network = hsyncnet(sample, number_clusters, ccore = ccore_flag);
analyser = network.process(arg_order, collect_dynamic = arg_collect_dynamic);
clusters = analyser.allocate_clusters();
if (arg_collect_dynamic == True):
sync_visualizer.show_output_dynamic(analyser);
draw_clusters(sample, clusters);
def template_clustering(path, radius, cluster_numbers, threshold, draw = True, ccore = True):
sample = read_sample(path);
rock_instance = rock(sample, radius, cluster_numbers, threshold, ccore);
(ticks, result) = timedcall(rock_instance.process);
clusters = rock_instance.get_clusters();
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n");
if (draw == True):
draw_clusters(sample, clusters);
def template_clustering(file, number_clusters, arg_order = 0.999, arg_collect_dynamic = True, ccore_flag = False):
sample = read_sample(file);
network = hsyncnet(sample, number_clusters, initial_neighbors = int(len(sample) * 0.15), osc_initial_phases = initial_type.EQUIPARTITION, ccore = ccore_flag);
(ticks, analyser) = timedcall(network.process, arg_order, solve_type.FAST, arg_collect_dynamic);
print("Sample: ", file, "\t\tExecution time: ", ticks, "\n");
clusters = analyser.allocate_clusters();
if (arg_collect_dynamic == True):
sync_visualizer.show_output_dynamic(analyser);
draw_clusters(sample, clusters);
def template_clustering(file, number_clusters, arg_order = 0.999, arg_collect_dynamic = True, ccore_flag = False):
sample = read_sample(file);
network = hsyncnet(sample, number_clusters, initial_neighbors = int(len(sample) * 0.15), osc_initial_phases = initial_type.EQUIPARTITION, ccore = ccore_flag);
(ticks, analyser) = timedcall(network.process, arg_order, solve_type.FAST, arg_collect_dynamic);
print("Sample: ", file, "\t\tExecution time: ", ticks, "\n");
clusters = analyser.allocate_clusters();
if (arg_collect_dynamic == True):
sync_visualizer.show_output_dynamic(analyser);
draw_clusters(sample, clusters);
def template_clustering(number_clusters, path, number_represent_points = 5, compression = 0.5, draw = True, ccore_flag = False):
sample = read_sample(path);
cure_instance = cure(sample, number_clusters, number_represent_points, compression, ccore_flag);
(ticks, result) = timedcall(cure_instance.process);
clusters = cure_instance.get_clusters();
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n");
if (draw is True):
if (ccore_flag is True):
draw_clusters(sample, clusters);
else:
draw_clusters(None, clusters);
def template_clustering(file,
number_clusters,
arg_order=0.999,
arg_collect_dynamic=True,
ccore_flag=False):
sample = read_sample(file)
network = hsyncnet(sample, number_clusters, ccore=ccore_flag)
analyser = network.process(arg_order, collect_dynamic=arg_collect_dynamic)
clusters = analyser.allocate_clusters()
if (arg_collect_dynamic == True):
sync_visualizer.show_output_dynamic(analyser)
draw_clusters(sample, clusters)
def template_clustering(start_centers, path, tolerance = 0.025, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION, ccore = False):
sample = read_sample(path);
xmeans_instance = xmeans(sample, start_centers, 20, tolerance, criterion, ccore);
(ticks, result) = timedcall(xmeans_instance.process);
clusters = xmeans_instance.get_clusters();
criterion_string = "UNKNOWN";
if (criterion == splitting_type.BAYESIAN_INFORMATION_CRITERION): criterion_string = "BAYESIAN_INFORMATION_CRITERION";
elif (criterion == splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH): criterion_string = "MINIMUM_NOISELESS_DESCRIPTION_LENGTH";
print("Sample: ", path, "\tExecution time: ", ticks, "Number of clusters: ", len(clusters), criterion_string, "\n");
draw_clusters(sample, clusters);
def template_clustering(path_sample, eps, minpts):
sample = read_sample(path_sample)
optics_instance = optics(sample, eps, minpts)
optics_instance.process()
clusters = optics_instance.get_clusters()
noise = optics_instance.get_noise()
draw_clusters(sample, clusters, [], ".")
ordering = optics_instance.get_cluster_ordering()
indexes = [i for i in range(0, len(ordering))]
# visualization of cluster ordering in line with reachability distance.
plt.bar(indexes, ordering)
plt.show()
def template_clustering(path_sample, eps, minpts):
sample = read_sample(path_sample)
optics_instance = optics(sample, eps, minpts)
optics_instance.process()
clusters = optics_instance.get_clusters()
noise = optics_instance.get_noise()
draw_clusters(sample, clusters, [], '.')
ordering = optics_instance.get_cluster_ordering()
indexes = [i for i in range(0, len(ordering))]
# visualization of cluster ordering in line with reachability distance.
plt.bar(indexes, ordering)
plt.show()
def template_clustering(file, radius, order, show_dyn = False, show_conn = False, show_clusters = True, ena_conn_weight = False, ccore_flag = True, tolerance = 0.1):
sample = read_sample(file);
network = syncnet(sample, radius, enable_conn_weight = ena_conn_weight, ccore = ccore_flag);
(ticks, analyser) = timedcall(network.process, order, solve_type.FAST, show_dyn);
print("Sample: ", file, "\t\tExecution time: ", ticks, "\n");
if (show_dyn == True):
sync_visualizer.show_output_dynamic(analyser);
sync_visualizer.animate_output_dynamic(analyser);
if ( (show_conn == True) and (ccore_flag == False) ):
network.show_network();
if (show_clusters == True):
clusters = analyser.allocate_clusters(tolerance);
draw_clusters(sample, clusters);
def template_clustering(number_clusters,
path,
number_represent_points=5,
compression=0.5,
draw=True,
ccore_flag=False):
sample = read_sample(path)
cure_instance = cure(sample, number_clusters, number_represent_points,
compression, ccore_flag)
(ticks, result) = timedcall(cure_instance.process)
clusters = cure_instance.get_clusters()
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n")
if (draw is True):
if (ccore_flag is True):
draw_clusters(sample, clusters)
else:
draw_clusters(None, clusters)
def templateDrawClustersNoFailure(self, data_path, amount_clusters):
sample = read_sample(data_path);
initial_centers = kmeans_plusplus_initializer(sample, amount_clusters).initialize();
kmeans_instance = kmeans(sample, initial_centers, amount_clusters);
kmeans_instance.process();
clusters = kmeans_instance.get_clusters();
ax = draw_clusters(sample, clusters);
assert None != ax;
def templateClusterAllocation(self, path, cluster_sizes, number_clusters, branching_factor = 5, max_node_entries = 5, initial_diameter = 0.1, type_measurement = measurement_type.CENTROID_EUCLIDIAN_DISTANCE, entry_size_limit = 200, ccore = True):
sample = read_sample(path);
cure_instance = birch(sample, number_clusters, branching_factor, max_node_entries, initial_diameter, type_measurement, entry_size_limit, ccore);
cure_instance.process();
clusters = cure_instance.get_clusters();
obtained_cluster_sizes = [len(cluster) for cluster in clusters];
total_length = sum(obtained_cluster_sizes);
if (total_length != len(sample)):
draw_clusters(sample, clusters);
assert total_length == len(sample);
cluster_sizes.sort();
obtained_cluster_sizes.sort();
if (cluster_sizes != obtained_cluster_sizes):
draw_clusters(sample, clusters);
assert cluster_sizes == obtained_cluster_sizes;
def clustering(genomes):
keys = set()
for gid, g in genomes:
for key in g.info.keys():
keys.add(key)
keys = sorted(list(keys))
keys_to_i = {keys[i]: i for i in range(len(keys))}
ng = len(genomes)
na = len(keys)
props = np.zeros((ng, na))
for i in range(len(genomes)):
gid, g = genomes[i]
for key, value in g.info.items():
props[i][keys_to_i[key]] = random.random()
props = scipy.stats.zscore(props)
init_center = kmeans_plusplus_initializer(props, 2).initialize()
xm = xmeans(props, init_center, ccore=False)
xm.process()
clusters = xm.get_clusters()
draw_clusters(props, clusters)
def template_clustering(file, radius, order, show_dyn = False, show_conn = False, show_clusters = True, ena_conn_weight = False, ccore_flag = True, tolerance = 0.1):
sample = read_sample(file)
syncnet_instance = syncnet(sample, radius, enable_conn_weight = ena_conn_weight, ccore = ccore_flag)
(ticks, analyser) = timedcall(syncnet_instance.process, order, solve_type.FAST, show_dyn)
print("Sample: ", file, "\t\tExecution time: ", ticks)
if show_dyn == True:
sync_visualizer.show_output_dynamic(analyser)
sync_visualizer.animate(analyser)
sync_visualizer.show_local_order_parameter(analyser, syncnet_instance)
#sync_visualizer.animate_output_dynamic(analyser);
#sync_visualizer.animate_correlation_matrix(analyser, colormap = 'hsv')
if show_conn == True:
syncnet_instance.show_network()
if show_clusters == True:
clusters = analyser.allocate_clusters(tolerance)
print("Amount of allocated clusters: ", len(clusters))
draw_clusters(sample, clusters)
print("----------------------------\n")
return (sample, clusters)
def template_clustering(file, radius, order, show_dyn = False, show_conn = False, show_clusters = True, ena_conn_weight = False, ccore_flag = True, tolerance = 0.1):
sample = read_sample(file)
syncnet_instance = syncnet(sample, radius, enable_conn_weight = ena_conn_weight, ccore = ccore_flag)
(ticks, analyser) = timedcall(syncnet_instance.process, order, solve_type.FAST, show_dyn)
print("Sample: ", file, "\t\tExecution time: ", ticks)
if show_dyn == True:
sync_visualizer.show_output_dynamic(analyser)
sync_visualizer.animate(analyser)
sync_visualizer.show_local_order_parameter(analyser, syncnet_instance)
#sync_visualizer.animate_output_dynamic(analyser);
#sync_visualizer.animate_correlation_matrix(analyser, colormap = 'hsv')
if show_conn == True:
syncnet_instance.show_network()
if show_clusters == True:
clusters = analyser.allocate_clusters(tolerance)
print("Amount of allocated clusters: ", len(clusters))
draw_clusters(sample, clusters)
print("----------------------------\n")
return (sample, clusters)
def calculate_clusters_and_save_plot(data,
plot_name,
tolerance=0.025,
kmax=20):
centers = data[np.random.choice(data.shape[0],
NUM_INIT_CLUSTERS,
replace=False)]
xmeans_instance = xmeans.xmeans(
data,
initial_centers=centers,
tolerance=tolerance,
criterion=xmeans.splitting_type.BAYESIAN_INFORMATION_CRITERION,
kmax=kmax,
ccore=False)
xmeans_instance.process()
clusters = xmeans_instance.get_clusters()
centers = xmeans_instance.get_centers()
plot = draw_clusters(unique_pixels, clusters)
plot.get_figure().save_fig(plot_name, dpi=200)
return clusters, centers
def display_two_dimensional_cluster_distances(path_sample, amount_clusters):
distances = ['euclidian', 'manhattan', 'avr-inter', 'avr-intra', 'variance'];
ajacency = [ [0] * amount_clusters for i in range(amount_clusters) ];
sample = utils.read_sample(path_sample);
agglomerative_instance = agglomerative(sample, amount_clusters);
agglomerative_instance.process();
obtained_clusters = agglomerative_instance.get_clusters();
stage = utils.draw_clusters(sample, obtained_clusters, display_result = False);
for index_cluster in range(len(ajacency)):
for index_neighbor_cluster in range(index_cluster + 1, len(ajacency)):
if ( (index_cluster == index_neighbor_cluster) or (ajacency[index_cluster][index_neighbor_cluster] is True) ):
continue;
ajacency[index_cluster][index_neighbor_cluster] = True;
ajacency[index_neighbor_cluster][index_cluster] = True;
cluster1 = obtained_clusters[index_cluster];
cluster2 = obtained_clusters[index_neighbor_cluster];
center_cluster1 = utils.centroid(sample, cluster1);
center_cluster2 = utils.centroid(sample, cluster2);
x_maximum, x_minimum, y_maximum, y_minimum = None, None, None, None;
x_index_maximum, y_index_maximum = 1, 1;
if (center_cluster2[0] > center_cluster1[0]):
x_maximum = center_cluster2[0];
x_minimum = center_cluster1[0];
x_index_maximum = 1;
else:
x_maximum = center_cluster1[0];
x_minimum = center_cluster2[0];
x_index_maximum = -1;
if (center_cluster2[1] > center_cluster1[1]):
y_maximum = center_cluster2[1];
y_minimum = center_cluster1[1];
y_index_maximum = 1;
else:
y_maximum = center_cluster1[1];
y_minimum = center_cluster2[1];
y_index_maximum = -1;
print("Cluster 1:", cluster1, ", center:", center_cluster1);
print("Cluster 2:", cluster2, ", center:", center_cluster2);
stage.annotate(s = '', xy = (center_cluster1[0], center_cluster1[1]), xytext = (center_cluster2[0], center_cluster2[1]), arrowprops = dict(arrowstyle = '<->'));
for index_distance_type in range(len(distances)):
distance = None;
distance_type = distances[index_distance_type];
if (distance_type == 'euclidian'):
distance = utils.euclidean_distance(center_cluster1, center_cluster2);
elif (distance_type == 'manhattan'):
distance = utils.manhattan_distance(center_cluster1, center_cluster2);
elif (distance_type == 'avr-inter'):
distance = utils.average_inter_cluster_distance(cluster1, cluster2, sample);
elif (distance_type == 'avr-intra'):
distance = utils.average_intra_cluster_distance(cluster1, cluster2, sample);
elif (distance_type == 'variance'):
distance = utils.variance_increase_distance(cluster1, cluster2, sample);
print("\tCluster distance -", distance_type, ":", distance);
x_multiplier = index_distance_type + 3;
if (x_index_maximum < 0):
x_multiplier = len(distances) - index_distance_type + 3;
y_multiplier = index_distance_type + 3;
if (y_index_maximum < 0):
y_multiplier = len(distances) - index_distance_type + 3;
x_text = x_multiplier * (x_maximum - x_minimum) / (len(distances) + 6) + x_minimum;
y_text = y_multiplier * (y_maximum - y_minimum) / (len(distances) + 6) + y_minimum;
#print(x_text, y_text, "\n");
stage.text(x_text, y_text, distance_type + " {:.3f}".format(distance), fontsize = 9, color='blue');
plt.show();
def display_two_dimensional_cluster_distances(path_sample, amount_clusters):
distances = [
'euclidian', 'manhattan', 'avr-inter', 'avr-intra', 'variance'
]
ajacency = [[0] * amount_clusters for i in range(amount_clusters)]
sample = utils.read_sample(path_sample)
agglomerative_instance = agglomerative(sample, amount_clusters)
agglomerative_instance.process()
obtained_clusters = agglomerative_instance.get_clusters()
stage = utils.draw_clusters(sample,
obtained_clusters,
display_result=False)
for index_cluster in range(len(ajacency)):
for index_neighbor_cluster in range(index_cluster + 1, len(ajacency)):
if ((index_cluster == index_neighbor_cluster) or
(ajacency[index_cluster][index_neighbor_cluster] is True)):
continue
ajacency[index_cluster][index_neighbor_cluster] = True
ajacency[index_neighbor_cluster][index_cluster] = True
cluster1 = obtained_clusters[index_cluster]
cluster2 = obtained_clusters[index_neighbor_cluster]
center_cluster1 = utils.centroid(sample, cluster1)
center_cluster2 = utils.centroid(sample, cluster2)
x_maximum, x_minimum, y_maximum, y_minimum = None, None, None, None
x_index_maximum, y_index_maximum = 1, 1
if (center_cluster2[0] > center_cluster1[0]):
x_maximum = center_cluster2[0]
x_minimum = center_cluster1[0]
x_index_maximum = 1
else:
x_maximum = center_cluster1[0]
x_minimum = center_cluster2[0]
x_index_maximum = -1
if (center_cluster2[1] > center_cluster1[1]):
y_maximum = center_cluster2[1]
y_minimum = center_cluster1[1]
y_index_maximum = 1
else:
y_maximum = center_cluster1[1]
y_minimum = center_cluster2[1]
y_index_maximum = -1
print("Cluster 1:", cluster1, ", center:", center_cluster1)
print("Cluster 2:", cluster2, ", center:", center_cluster2)
stage.annotate(s='',
xy=(center_cluster1[0], center_cluster1[1]),
xytext=(center_cluster2[0], center_cluster2[1]),
arrowprops=dict(arrowstyle='<->'))
for index_distance_type in range(len(distances)):
distance = None
distance_type = distances[index_distance_type]
if (distance_type == 'euclidian'):
distance = utils.euclidean_distance(
center_cluster1, center_cluster2)
elif (distance_type == 'manhattan'):
distance = utils.manhattan_distance(
center_cluster1, center_cluster2)
elif (distance_type == 'avr-inter'):
distance = utils.average_inter_cluster_distance(
cluster1, cluster2, sample)
elif (distance_type == 'avr-intra'):
distance = utils.average_intra_cluster_distance(
cluster1, cluster2, sample)
elif (distance_type == 'variance'):
distance = utils.variance_increase_distance(
cluster1, cluster2, sample)
print("\tCluster distance -", distance_type, ":", distance)
x_multiplier = index_distance_type + 3
if (x_index_maximum < 0):
x_multiplier = len(distances) - index_distance_type + 3
y_multiplier = index_distance_type + 3
if (y_index_maximum < 0):
y_multiplier = len(distances) - index_distance_type + 3
x_text = x_multiplier * (x_maximum - x_minimum) / (
len(distances) + 6) + x_minimum
y_text = y_multiplier * (y_maximum - y_minimum) / (
len(distances) + 6) + y_minimum
#print(x_text, y_text, "\n");
stage.text(x_text,
y_text,
distance_type + " {:.3f}".format(distance),
fontsize=9,
color='blue')
plt.show()