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consecutive_gap_annotate.py
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240 lines (208 loc) · 9.57 KB
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from rdflib import Literal, URIRef, BNode
from rdflib.namespace import RDF
#from calc_gaps_slopes import gap_calc,trend_calc,monotonic_pred,mod_collector
s=URIRef("http://example.com/app#display-lab")
p=URIRef('http://example.com/slowmo#IsAboutMeasure')
def goal_consecutive_annotate(input_graph,s13,latest_measure_df,comparator_bnode):
s14=s13
p14=URIRef('http://purl.obolibrary.org/obo/RO_0000091')
latest_measure_df=latest_measure_df.reset_index(drop=True)
goal_gap_size=[]
goal_gap_size=latest_measure_df['goal_comparison_value']-latest_measure_df['Performance_Rate']
latest_measure_df["goal_gap_size"]=goal_gap_size
back_up_df=latest_measure_df
idx= latest_measure_df.groupby(['measure'])['month'].nlargest(3) .reset_index()
l=idx['level_1'].tolist()
latest_measure_df = latest_measure_df[latest_measure_df.index.isin(l)]
latest_measure_df = latest_measure_df.reset_index(drop=True)
if((latest_measure_df["goal_gap_size"][2]>0 and latest_measure_df["goal_gap_size"][1]>0 and latest_measure_df["goal_gap_size"][0]>=0)==True):
ac=BNode(latest_measure_df["measure"][0])
av=comparator_bnode
o14=BNode()
event="positive"
number=find_number(back_up_df,event)
input_graph.add((s14,p14,o14))
input_graph=annotate_consecutive_goal_positive_gap(input_graph,o14,ac,av,number)
if((latest_measure_df["goal_gap_size"][2]<0 and latest_measure_df["goal_gap_size"][1]<0 and latest_measure_df["goal_gap_size"][0]<0)==True):
ac=BNode(latest_measure_df["measure"][0])
av=comparator_bnode
event="negative"
number=find_number(back_up_df,event)
o14=BNode()
input_graph.add((s14,p14,o14))
input_graph=annotate_consecutive_goal_negative_gap(input_graph,o14,ac,av,number)
#print(latest_measure_df)
return input_graph
def peer_consecutive_annotate(input_graph,s13,latest_measure_df,comparator_bnode):
s14=s13
p14=URIRef('http://purl.obolibrary.org/obo/RO_0000091')
latest_measure_df=latest_measure_df.reset_index(drop=True)
goal_gap_size=[]
goal_gap_size=latest_measure_df['peer_average_comparator']-latest_measure_df['Performance_Rate']
latest_measure_df["goal_gap_size"]=goal_gap_size
back_up_df=latest_measure_df
idx= latest_measure_df.groupby(['measure'])['month'].nlargest(3) .reset_index()
l=idx['level_1'].tolist()
latest_measure_df = latest_measure_df[latest_measure_df.index.isin(l)]
latest_measure_df = latest_measure_df.reset_index(drop=True)
# print(latest_measure_df)
# print(latest_measure_df["goal_gap_size"][1])
if((latest_measure_df["goal_gap_size"][2]>0 and latest_measure_df["goal_gap_size"][1]>0 and latest_measure_df["goal_gap_size"][0]>=0)==True):
ac=BNode(latest_measure_df["measure"][0])
av=comparator_bnode
o14=BNode()
event="positive"
number=find_number(back_up_df,event)
input_graph.add((s14,p14,o14))
input_graph=annotate_consecutive_peer_positive_gap(input_graph,o14,ac,av,number)
if((latest_measure_df["goal_gap_size"][2]<0 and latest_measure_df["goal_gap_size"][1]<0 and latest_measure_df["goal_gap_size"][0]<0)==True):
ac=BNode(latest_measure_df["measure"][0])
av=comparator_bnode
event="negative"
number=find_number(back_up_df,event)
o14=BNode()
input_graph.add((s14,p14,o14))
input_graph=annotate_consecutive_peer_negative_gap(input_graph,o14,ac,av,number)
#print(latest_measure_df)
return input_graph
def top_10_consecutive_annotate(input_graph,s13,latest_measure_df,comparator_bnode):
s14=s13
p14=URIRef('http://purl.obolibrary.org/obo/RO_0000091')
latest_measure_df=latest_measure_df.reset_index(drop=True)
goal_gap_size=[]
goal_gap_size=latest_measure_df['peer_90th_percentile_benchmark']-latest_measure_df['Performance_Rate']
latest_measure_df["goal_gap_size"]=goal_gap_size
back_up_df=latest_measure_df
idx= latest_measure_df.groupby(['measure'])['month'].nlargest(3) .reset_index()
l=idx['level_1'].tolist()
latest_measure_df = latest_measure_df[latest_measure_df.index.isin(l)]
latest_measure_df = latest_measure_df.reset_index(drop=True)
# print(latest_measure_df)
# print(latest_measure_df["goal_gap_size"][1])
if((latest_measure_df["goal_gap_size"][2]>0 and latest_measure_df["goal_gap_size"][1]>0 and latest_measure_df["goal_gap_size"][0]>=0)==True):
ac=BNode(latest_measure_df["measure"][0])
av=comparator_bnode
o14=BNode()
event="positive"
number=find_number(back_up_df,event)
input_graph.add((s14,p14,o14))
input_graph=annotate_consecutive_peer_positive_gap(input_graph,o14,ac,av,number)
if((latest_measure_df["goal_gap_size"][2]<0 and latest_measure_df["goal_gap_size"][1]<0 and latest_measure_df["goal_gap_size"][0]<0)==True):
ac=BNode(latest_measure_df["measure"][0])
av=comparator_bnode
event="negative"
number=find_number(back_up_df,event)
o14=BNode()
input_graph.add((s14,p14,o14))
input_graph=annotate_consecutive_peer_negative_gap(input_graph,o14,ac,av,number)
#print(latest_measure_df)
return input_graph
def top_25_consecutive_annotate(input_graph,s13,latest_measure_df,comparator_bnode):
s14=s13
p14=URIRef('http://purl.obolibrary.org/obo/RO_0000091')
latest_measure_df=latest_measure_df.reset_index(drop=True)
goal_gap_size=[]
goal_gap_size=latest_measure_df['peer_75th_percentile_benchmark']-latest_measure_df['Performance_Rate']
latest_measure_df["goal_gap_size"]=goal_gap_size
back_up_df=latest_measure_df
idx= latest_measure_df.groupby(['measure'])['month'].nlargest(3) .reset_index()
l=idx['level_1'].tolist()
latest_measure_df = latest_measure_df[latest_measure_df.index.isin(l)]
latest_measure_df = latest_measure_df.reset_index(drop=True)
# print(latest_measure_df)
# print(latest_measure_df["goal_gap_size"][1])
if((latest_measure_df["goal_gap_size"][2]>0 and latest_measure_df["goal_gap_size"][1]>0 and latest_measure_df["goal_gap_size"][0]>=0)==True):
ac=BNode(latest_measure_df["measure"][0])
av=comparator_bnode
o14=BNode()
event="positive"
number=find_number(back_up_df,event)
input_graph.add((s14,p14,o14))
input_graph=annotate_consecutive_peer_positive_gap(input_graph,o14,ac,av,number)
if((latest_measure_df["goal_gap_size"][2]<0 and latest_measure_df["goal_gap_size"][1]<0 and latest_measure_df["goal_gap_size"][0]<0)==True):
ac=BNode(latest_measure_df["measure"][0])
av=comparator_bnode
event="negative"
number=find_number(back_up_df,event)
o14=BNode()
input_graph.add((s14,p14,o14))
input_graph=annotate_consecutive_peer_negative_gap(input_graph,o14,ac,av,number)
#print(latest_measure_df)
return input_graph
def annotate_consecutive_goal_positive_gap(a,s16,measure_Name,o16,number):
p15=RDF.type
o15=URIRef('http://example.com/slowmo#ConsecutiveGoalPositiveGap')
a.add((s16,p15,o15))
p16=URIRef('http://example.com/slowmo#RegardingComparator')
a.add((s16,p16,o16))
p17=URIRef('http://example.com/slowmo#RegardingMeasure')
o17=measure_Name
a.add((s16,p17,o17))
p18=URIRef('http://example.com/slowmo#Numberofmonths')
o18=Literal(number)
a.add((s16,p18,o18))
return a
def annotate_consecutive_goal_negative_gap(a,s16,measure_Name,o16,number):
p15=RDF.type
o15=URIRef('http://example.com/slowmo#ConsecutiveGoalNegativeGap')
a.add((s16,p15,o15))
p16=URIRef('http://example.com/slowmo#RegardingComparator')
a.add((s16,p16,o16))
p17=URIRef('http://example.com/slowmo#RegardingMeasure')
o17=measure_Name
a.add((s16,p17,o17))
p18=URIRef('http://example.com/slowmo#Numberofmonths')
o18=Literal(number)
a.add((s16,p18,o18))
return a
def annotate_consecutive_peer_positive_gap(a,s16,measure_Name,o16,number):
p15=RDF.type
o15=URIRef('http://example.com/slowmo#ConsecutivePeerPositiveGap')
a.add((s16,p15,o15))
p16=URIRef('http://example.com/slowmo#RegardingComparator')
a.add((s16,p16,o16))
p17=URIRef('http://example.com/slowmo#RegardingMeasure')
o17=measure_Name
a.add((s16,p17,o17))
p18=URIRef('http://example.com/slowmo#Numberofmonths')
o18=Literal(number)
a.add((s16,p18,o18))
return a
def annotate_consecutive_peer_negative_gap(a,s16,measure_Name,o16,number):
p15=RDF.type
o15=URIRef('http://example.com/slowmo#ConsecutivePeerNegativeGap')
a.add((s16,p15,o15))
p16=URIRef('http://example.com/slowmo#RegardingComparator')
a.add((s16,p16,o16))
p17=URIRef('http://example.com/slowmo#RegardingMeasure')
o17=measure_Name
a.add((s16,p17,o17))
p18=URIRef('http://example.com/slowmo#Numberofmonths')
o18=Literal(number)
a.add((s16,p18,o18))
return a
def find_number(backup_df,trend_sign1):
if(trend_sign1=="negative"):
lista=[]
lista=backup_df["goal_gap_size"].tolist()
count=0
y=-1
for x in range(len(lista)):
if lista[y]>0:
return count
if(lista[y]<0):
count=count+1
y=y-1
return count
if(trend_sign1=="positive"):
lista=[]
lista=backup_df["goal_gap_size"].tolist()
count=0
y=-1
for x in range(len(lista)):
if lista[y]<0:
return count
if(lista[y]>0):
count=count+1
y=y-1
return count