def extract_clusters(self):
# List all the time window keys
deltas = list(self.cluster_data.keys())
# Reverse them so we can start with the last clustering data
deltas.reverse()
# Ignore delta 0 as it is an artifact of the clustering that isn't used
deltas = deltas[:-1]
print("Extracting clusters from each time delta: ".format(deltas))
# This is list of cluster labels which we will update at each time delta
# where a value of 0 should indicate an unclustered node
final_cluster_info = ak.zeros_like(
self.cluster_data[deltas[0]]['index'])
# A list of cluster labels that are selected
selected_clusters = self.selection_data['index'][
self.selection_data['selected']]
selected_clusters = selected_clusters[selected_clusters > 0]
for delta in tqdm(deltas):
cluster = self.cluster_data[delta]['labels']
cluster_positive = ak.where(cluster < 0, -cluster, 0)
# The cluster labels found in this delta
labels_this_delta = cluster_positive[cluster_positive > 0]
# A boolean array to indicate which "selected" clusters are labels this delta
m = ak.in1d(selected_clusters, labels_this_delta)
# A list of clusters selected for this delta
extract_this_delta = selected_clusters[m]
# A boolean array indicating which nodes are in clusters that are extracted this delta
m2 = ak.in1d(cluster_positive, extract_this_delta)
# Indicate the clusters for all the nodes in clusters that we extracted this delta
final_cluster_info[m2] = cluster_positive[m2]
v, c = ak.value_counts(cluster_positive[m2])
selected_clusters = selected_clusters[(~m)]
self.extracted_clusters = final_cluster_info
if selected_clusters.size > 0:
print("Failed. {} of the selected clusters remain.".format(
selected_clusters.size))
print("Failing cluster labels: {}".format(selected_clusters))
# We can refer to this list here:
self.unextracted = selected_clusters
else:
print("Extraction completed succesfully.")
def value_counts(self,sort=True):
"""Return a Series containing counts of unique values.
The resulting object will be in descending order so that the
first element is the most frequently-occurring element.
Parameters
----------
sort : Boolean. Whether or not to sort the results. Default is true.
"""
s = Series(ak.value_counts(self.values))
if sort:
s = s.sort_values(ascending=False)
return s
iv = ak.arange(0,5,1) b = ak.zeros(iv.size,dtype=ak.bool) a[iv] = b print(a) a = ak.randint(10,20,10) print(a) iv = ak.randint(0,10,5) print(iv) b = ak.zeros(iv.size,dtype=ak.int64) a[iv] = b print(a) ak.v = False a = ak.randint(10,30,40) vc = ak.value_counts(a) print(vc[0].size,vc[0]) print(vc[1].size,vc[1]) ak.v = False a = ak.arange(0,10,1) b = a[a<5] a = ak.linspace(0,9,10) b = a[a<5] print(b) ak.v = True ak.pdarrayIterThresh = 1000 a = ak.arange(0,10,1) print(list(a))