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HospitalLocationAnalysis.py
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HospitalLocationAnalysis.py
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import urllib.request
import urllib.parse
import json
import dml
import prov.model
import datetime
import uuid
class HospitalLocationAnalysis(dml.Algorithm):
contributor = 'ggelinas'
reads = ['ggelinas.hospitals',
'ggelinas.incidents']
writes = ['ggelinas.kmeanshospital']
def dist(p, q):
(x1, y1) = p
(x2, y2) = q
return (x1-x2)**2 + (y1-y2)**2
def product(R, S):
return [(t, u) for t in R for u in S]
def plus(args):
p = [0, 0]
for (x,y) in args:
p[0] += x
p[1] += y
return tuple(p)
def scale(p, c):
(x, y) = p
return (x/c, y/c)
def aggregate(R, f):
keys = {r[0] for r in R}
return [(key, f([v for (k,v) in R if k == key])) for key in keys]
@staticmethod
def execute(trial=False):
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('ggelinas', 'ggelinas')
Hospital = []
Hospitalname = []
Crime = []
X = []
Y = []
for h in repo['ggelinas.hospitals'].find():
try:
if not (h['location']['coordinates'] == [0,0]):
Hospitalname.append(h['name'])
Hospital.append((h['location']['coordinates'][1], h['location']['coordinates'][0]))
X.append(h['location']['coordinates'][1])
Y.append(h['location']['coordinates'][0])
except KeyError:
print('oh');
count = 0
for c in repo['ggelinas.incidents'].find():
if trial:
count += 1
if count <= 200:
try:
if not (c['location']['coordinates'] == [0,0]):
Crime.append((c['location']['coordinates'][1], c['location']['coordinates'][0]))
except KeyError:
print('oops, something went wrong')
else:
try:
if not (c['location']['coordinates'] == [0,0]):
Crime.append((c['location']['coordinates'][1], c['location']['coordinates'][0]))
except KeyError:
print('oops, something went wrong')
old = []
while old != Hospital:
old = Hospital
A = [(m, p, HospitalLocationAnalysis.dist(m,p)) for (m, p) in HospitalLocationAnalysis.product(Hospital, Crime)]
B = [(p, HospitalLocationAnalysis.dist(m,p)) for (m,p,d) in A]
C = HospitalLocationAnalysis.aggregate(B, min)
D = [(m,p) for ((m,p,d), (p2,d2)) in HospitalLocationAnalysis.product(A, C) if p == p2 and d == d2]
E = HospitalLocationAnalysis.aggregate(D, HospitalLocationAnalysis.plus)
F = [(m, 1) for ((m,p,d), (p2,d2)) in HospitalLocationAnalysis.product(A, C) if p == p2 and d == d2]
G = HospitalLocationAnalysis.aggregate(F, sum)
M = [HospitalLocationAnalysis.scale(t,c) for ((m,t),(m2,c)) in HospitalLocationAnalysis.product(E, G) if m == m2]
repo.dropPermanent("kmeanshospital")
repo.createPermanent("kmeanshospital")
for i in M:
repo['ggelinas.kmeanshospital'].insert({'latitude': i[0], 'longitude': i[1]})
XM = [float(i[0]) for i in M]
XY = [float(i[1]) for i in M]
Xdiff = [abs(x-y) for x, y in zip(XM, X)]
Ydiff = [abs(x-y) for x, y in zip(XY, Y)]
XY = [(x,y) for x,y in zip(Xdiff, Ydiff)]
print(Hospital)
print(M)
###################################################
# plotting map
# import folium
# output = 'hospital.html'
# hospitalmap = folium.Map(location=[42.355, -71.0609], zoom_start=13)
# for i in range(len(Hospital)):
# lat, long = Hospital[i]
# name = Hospitalname[i]
# folium.CircleMarker(location=[lat, long], popup=name, color='#ff0000', fill_color='#ff0000', radius=50, fill_opacity=0.7).add_to(hospitalmap)
# for j in range(len(M)):
# lat, long = M[j]
# name = Hospitalname[j]
# folium.CircleMarker(location=[lat, long], popup='Optimal ' + name, color='#0000ff', fill_color='#0000ff', radius=50, fill_opacity=0.7).add_to(hospitalmap)
# hospitalmap.save('hospitalmap.html')
###################################################
print("this is the locations of current Hospital station: " + str(Hospital))
print("K means Hospital locations: " + str(M))
print("this is difference between Hospital and Clusters: " + str(XY))
repo.logout()
endTime = datetime.datetime.now()
return {"start": startTime, "end": endTime}
@staticmethod
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
'''
Create the provenance document describing everything happening
in this script. Each run of the script will generate a new
document describing that invocation event.
'''
# Set up the database connection.
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('ggelinas', 'ggelinas')
doc.add_namespace('alg',
'http://datamechanics.io/algorithm/ggelinas') # The scripts are in <folder>#<filename> format.
doc.add_namespace('dat',
'http://datamechanics.io/data/') # The data sets are in <user>#<collection> format.
doc.add_namespace('ont',
'http://datamechanics.io/ontology#') # 'Extension', 'DataResource', 'DataSet', 'Retrieval', 'Query', or 'Computation'.
doc.add_namespace('log', 'http://datamechanics.io/log/') # The event log.
doc.add_namespace('bdp', 'https://data.cityofboston.gov/resource/')
this_script = doc.agent('alg:ggelinas#HospitalLocationAnalysis',
{prov.model.PROV_TYPE: prov.model.PROV['SoftwareAgent'], 'ont:Extension': 'py'})
stations_resource = doc.entity('dat:ggelinas#hospitals', {'prov:label': 'Boston Hospital Locations',
prov.model.PROV_TYPE: 'ont:DataSet'})
this_run = doc.activity('log:a' + str(uuid.uuid4()), startTime, endTime,
{'prov:label': 'Get Boston Hospital Locations'})
doc.wasAssociatedWith(this_run, this_script)
doc.usage(
this_run,
stations_resource,
startTime,
None,
{prov.model.PROV_TYPE: 'ont:Computation'}
)
incidents_resource = doc.entity('dat:ggelinas#incidents', {'prov:label': 'Crime Incidents Location',
prov.model.PROV_TYPE: 'ont:DataSet'})
this_run2 = doc.activity('log:uuid' + str(uuid.uuid4()), startTime, endTime,
{'prov:label': 'Get Crime Locations'})
doc.wasAssociatedWith(this_run2, this_script)
doc.usage(
this_run2,
incidents_resource,
startTime,
None,
{prov.model.PROV_TYPE: 'ont:Computation'}
)
#Unsure about documenting since the algorithm does not write new data and stores it
stations = doc.entity('dat:ggelinas#kmeanshospital',
{prov.model.PROV_LABEL: 'K means Hospital locations', prov.model.PROV_TYPE: 'ont:DataSet'})
doc.wasAttributedTo(stations, this_script)
doc.wasGeneratedBy(stations, this_run, endTime)
doc.wasDerivedFrom(stations, stations_resource, this_run, this_run, this_run)
#
# incidents = doc.entity('dat:ggelinas#incidents',
# {prov.model.PROV_LABEL: 'Crime locations', prov.model.PROV_TYPE: 'ont:DataSet'})
# doc.wasAttributedTo(incidents, this_script)
# doc.wasGeneratedBy(incidents, this_run2, endTime)
# doc.wasDerivedFrom(incidents, incidents_resource, this_run2, this_run2, this_run2)
repo.record(doc.serialize())
repo.logout()
return doc
HospitalLocationAnalysis.execute()
doc = HospitalLocationAnalysis.provenance()
print(doc.get_provn())
print(json.dumps(json.loads(doc.serialize()), indent=4))