def createItems():
doc1 = doctor('1', 'Ольга', 'Яновская', 'Андреевна', 'Психолог',
'высшая категория')
doc2 = doctor('2', 'Елена', 'Дорофеева', 'Борисовна', 'Психолог',
'1 категория')
h.appendDoctor(doc1)
h.appendDoctor(doc2)
r1 = procedure('1', 'тыкнуть носом', 50, doc1)
r2 = procedure('2', 'консультация', 350, doc2)
h.appendProcedure(r1)
h.appendProcedure(r2)
p1 = patient('1', 'Кирилл', 'Вторцев', 'Дмитриевич', '21.11.2001', 50)
p2 = patient('2', 'Марк', 'Захаров', '', '08.05.2001', -90)
h.appendPatient(p1)
h.appendPatient(p2)
d1 = deal('1', '02.11.2020', 'что-то там 1', p1, [r1])
d2 = deal('2', '03.11.2020', 'что-то там 2', p2, [r1, r2])
h.appendDeal(d1)
d1.appendProcedure(r1)
h.appendDeal(d2)
d2.appendProcedure(r1)
d2.appendProcedure(r2)
h.save('res.json')
def patientData(patientlist, i, j):
try:
MasterAssemble = sio.loadmat('MasterAssemble.mat')
data = MasterAssemble['MasterAssemble']
DataName = (data['playerName'][0][0][0])
DataX = (data['translationX'][0][:].tolist()) #[0][:][:][0][:])
DataY = (data['translationY'][0][:].tolist())
DataT = (data['translationTime'][0][:])
DataD = (data['translationDistance'][0][:])
DataEvents = (data['translationMessages'][0][:])
#segmentedRoute = segmenter(DataX, DataY, DataT, DataEvents)
#DataX = segmentedRoute[0]
#DataY = segmentedRoute[1]
#print(segmentedRoute)
#print(DataX)
#(DataY)
Patient = patient(
DataName, DataX, DataY, DataT, DataD, DataEvents
) # def __init__(self, name, x, y, time, distance, events):
return Patient
except:
return patient('none', 0, 0, 0, 0, [0])
print("Problem with reading MasterAssemble.mat" + patientlist[i] +
str(j))
def patientData(patientlist, i, j):
try:
MasterAssemble = sio.loadmat('MasterAssemble.mat')
data = MasterAssemble['MasterAssemble']
DataName = (data['playerName'][0][0][0])
DataX = (data['translationX'][0][:].tolist())#[0][:][:][0][:])
DataY = (data['translationY'][0][:].tolist())
DataT = (data['translationTime'][0][:])
DataD = (data['translationDistance'][0][:])
DataEvents = (data['translationMessages'][0][:])
#segmentedRoute = segmenter(DataX, DataY, DataT, DataEvents)
#DataX = segmentedRoute[0]
#DataY = segmentedRoute[1]
#print(segmentedRoute)
#print(DataX)
#(DataY)
Patient = patient(DataName, DataX, DataY, DataT, DataD, DataEvents) # def __init__(self, name, x, y, time, distance, events):
return Patient
except:
return patient('none', 0, 0, 0, 0, [0])
print("Problem with reading MasterAssemble.mat" + patientlist[i] + str(j))
def scan_input(feed):
# enrollment dictionary to store patient objects
enrollment = dict()
#### example full traversal filtrage with preconditions #######################
# we'll iterate through each procedure scheduled
# english only, at least 2 days until procedure date, location one for every entry, etc
# the patient class handles tour classification via procedures
for row in feed:
p = patient(dict(row))
# general enrollment for location 1
p.setLocation(1)
# initial tour qualifications; english language & 2 day schedule buffer
if (p.isLanguage('English') and p.isScheduled(2)):
# add the patient to the enrollment list; else if they already exist, update their new procedure
if (p.Patient not in enrollment):
p.addProcedure(p.Date, p.Type)
enrollment[p.Patient] = p
else:
enrollment[p.Patient].addProcedure(p.Date, p.Type)
else:
enrollment[p.Patient] = p
enrollment[p.Patient].tour = -1
return enrollment
def add_patient(self):
name = input("Paitent Name : ")
phone = input("Paitent Phone Number : ")
problem = input("Nature of Problem :")
print("1.OPD 2.EMERGENCY ")
choice = int(input("Enter 1 for OPD or 2 for EMERGENCY :"))
if choice == 1:
fee = 400
ward = 'OPD'
else:
fee = 1000
ward = 'EMERGENCY'
self.show_doc()
print("Please select Doctors from above table")
docname = input("Doctor Name :")
date = input("Enter Date of Admission (YYYY-MM-DD) :")
print("Estimated fees is {} for doctor {} ".format(fee, docname))
payment = input("Payment Method (CASH/CARD) : ")
Patient = patient(0, name, phone, payment, problem, ward, docname,
date)
print("Patient Data added sucessfully ")
self.ps.add_patient(Patient)
Patients = self.ps.search_number(phone)
if Patients:
PatientService.paint_data_list(Patients)
else:
print("No data found with Patient ID :{}".format(phone))
def addPatients(df):
person = list()
added_patient = list()
patientList = {}
patient_years = {}
for index, row in df.iterrows():
sid = row.SUBJECT_ID
admittime = date.parse_date(row.ADMITTIME)
#Create patient
if sid not in added_patient:
dob = date.parse_date(row.DOB)
if row.EXPIRE_FLAG == 1:
dod = date.parse_date(row.DOD)
else:
dod = None
pat = patient(sid, row.GENDER, dob, dod, row.EXPIRE_FLAG,
admittime)
patientList[sid] = pat
added_patient.append(sid)
else:
patientList[sid].updateAdm(admittime)
##########################################
for sid in patientList:
maxdate = patientList[sid].getmaxadm()
yeardif = date.generateYearDif(maxdate)
patient_years[sid] = yeardif
patientList[sid].changeDate(yeardif)
val = (patientList[sid].getGender(), patientList[sid].getDoB(),
patientList[sid].getFlag(), patientList[sid].getDoD(), 1,
datetime.datetime.now())
person.append(val)
returndata = [person, added_patient, patient_years]
return returndata
def checkName(db, name):
p = PatientMod.patient(db.getPatientDetails(patientsurname=name))
percentageChance = check(p)
p.patientData[
'percentage_chance'] = percentageChance #Add the new data to the patient record
db.write(p) #Write the new record!
#TODO We should probably write back to the DB here the chance that they got
return percentageChance #get the id and check it.
def changeItems():
doc3 = doctor('3', 'Наталия', 'Чехонина', 'Павловна', 'стоматолог',
'1 категория')
h.appendDoctor(doc3)
r3 = procedure('3', 'консультация', 500, doc3)
h.appendProcedure(r3)
p2 = patient('2', 'Марк', 'Захаров', '', '08.05.2001', -90)
h.appendPatient(p2)
d3 = deal('3', '13.12.2020', 'Зубной налет', p2, [r3])
h.appendDeal(d3)
def checkID(db, patientID):
""" Checks if the patient has anthrax, sets that status in the DB, and returns true or false """
p = PatientMod.patient(db.getPatientDetails(patientid=patientID))
print(type(p.patientData))
percentageChance = check(p)
# p.patientData['percentage_chance'] = percentageChance #Add the new data to the patient record
# db.write(p) #Write the new record!
#TODO We should probably write back to the DB here the chance that they got
return percentageChance #get the id and check it.
def patient_details():
patient.patient()
# Creates a clinic, saves patient files and then retrieves all .pkl
# files from a particular day.
import file_manager
import patient
import datetime
fm = file_manager.FileManager()
fm.create_clinic_dir("my_clinic3", ".")
p = patient.patient()
p.date = datetime.datetime.now()
p.family_name = "smith"
p.id = "12345"
p.given_name = "john"
fm.save_patient_files(p, "tmp.img", "tmp.pkl")
fm.save_patient_files(p, "tmp.img", "tmp.pkl")
fm.save_patient_files(p, "tmp.img", "tmp.pkl")
today = datetime.datetime.now().strftime("%d")
files = fm.get_patient_files2(day=today, file_type=".pkl")
print "files = ", " ".join(files)
import MySQLdb
import patient
import doctor
from tabulate import tabulate
while True:
choice = input(
"\n1.Register\n2.Display Records\n3.Update\n4.Delete\n5.Exit\n\n>>> ")
if choice == 1:
choice = input("\n1.Patient\n2. Doctor\n3.Exit\n\n>>> ")
if choice == 1:
while True:
s = patient.patient()
s.register()
choice = raw_input("Add more? [Y/n] ")
if choice.lower() == "y":
pass
elif choice.lower() == "n":
break
else:
print "Please enter valid choice"
elif choice == 2:
while True:
s = doctor.doctor()
s.register()
choice = raw_input("Add more? [Y/n] ")
if choice.lower() == "y":
def analyzeID(db, Id):
"""Takes an ID, tracks the movement attached to it and finds other people that may have come in contact"""
p = patient(db.getPatientDetails(patientid=Id))
ret = analyze(p)
return json.dumps(ret, sort_keys=True)
def analyzeName(db, name):
""" Takes an ID, tracks the movement attached to it and finds other people that may have come in contact"""
p = patient(db.getPatientDetails(patientsurname=name))
return none
def build_patients(index=-1,
freq_idx=0,
if_weekly=0,
if_2weekly=0,
if_PSV=0,
if_yrandom=0,
if_sliding_window=0,
sw_size=31,
log=0,
if_shuffle_label=0,
reg=0):
col_rs = hp.col_rs
col_es = hp.col_es
col_le = hp.col_le
#build patient object
p222_1 = patient('222_1',
if_sliding_window=if_sliding_window,
sw_size=sw_size,
reg=reg)
p222_2 = patient('222_2',
if_sliding_window=if_sliding_window,
sw_size=sw_size,
reg=reg)
p222_3 = patient('222_3',
if_sliding_window=if_sliding_window,
sw_size=sw_size,
reg=reg)
p222 = patient('222',
if_sliding_window=if_sliding_window,
sw_size=sw_size,
reg=reg)
p231 = patient('231',
if_sliding_window=if_sliding_window,
sw_size=sw_size,
reg=reg)
# local means whether to use local(weekly) median as threshold
# p229 = patient('229', if_sliding_window = if_sliding_window, sw_size = sw_size, reg = reg)
p241 = patient('241',
if_sliding_window=if_sliding_window,
sw_size=sw_size,
reg=reg)
# 06/2019 239,225,251,217,229
# p239_1 = patient('239_1', if_sliding_window = if_sliding_window, sw_size = sw_size, reg = reg)
# p239_2 = patient('239_2', if_sliding_window = if_sliding_window, sw_size = sw_size, reg = reg)
# p225_1 = patient('225_1', if_sliding_window = if_sliding_window, sw_size = sw_size, reg = reg)
# p225_2 = patient('225_2', if_sliding_window = if_sliding_window, sw_size = sw_size, reg = reg)
# p251 = patient('251', if_sliding_window = if_sliding_window, sw_size = sw_size, reg = reg)
# p217 = patient('217', if_sliding_window = if_sliding_window, sw_size = sw_size, reg = reg)
# p229 = patient('229', if_sliding_window = if_sliding_window, sw_size = sw_size, reg = reg)
p239 = patient('239',
if_sliding_window=if_sliding_window,
sw_size=sw_size,
reg=reg)
p225 = patient('225',
if_sliding_window=if_sliding_window,
sw_size=sw_size,
reg=reg)
p201 = patient('201',
if_sliding_window=if_sliding_window,
sw_size=sw_size,
reg=reg)
p226 = patient('226',
if_sliding_window=if_sliding_window,
sw_size=sw_size,
reg=reg)
#add epoch info
# start_222_1 = datetime.strptime('Feb 12 2016', '%b %d %Y')
# end_222_1 = datetime.strptime('Oct 24 2016', '%b %d %Y')
# num_per_epoch_222_1 = 31
start_222 = datetime.strptime('Feb 12 2016', '%b %d %Y')
end_222 = datetime.strptime('May 29 2017', '%b %d %Y')
num_per_epoch_222 = 31
start_222_2 = datetime.strptime('Oct 26 2016', '%b %d %Y')
end_222_2 = datetime.strptime('May 29 2017', '%b %d %Y')
num_per_epoch_222_2 = 30
start_222_3 = datetime.strptime('Sep 19 2017', '%b %d %Y')
end_222_3 = datetime.strptime('Jan 30 2018', '%b %d %Y')
num_per_epoch_222_3 = 31
# start_222 = [start_222_1, start_222_2, start_222_3]
# end_222 = [end_222_1, end_222_2, end_222_3]
# num_per_epoch_222 = [num_per_epoch_222_1, num_per_epoch_222_2, num_per_epoch_222_3]
start_231 = datetime.strptime('Dec 6 2016', '%b %d %Y')
end_231 = datetime.strptime('Feb 21 2018', '%b %d %Y')
num_per_epoch_231 = 31
# start_231 = datetime.strptime('Feb 7 2017', '%b %d %Y')
# end_231 = datetime.strptime('Feb 21 2018', '%b %d %Y')
# num_per_epoch_231 = 31
start_241 = datetime.strptime('Nov 14 2017', '%b %d %Y')
end_241 = datetime.strptime('Oct 4 2018', '%b %d %Y')
num_per_epoch_241 = 29
start_239 = datetime.strptime('Jan 4 2018', '%b %d %Y')
end_239 = datetime.strptime('May 24 2019', '%b %d %Y')
num_per_epoch_239 = 31
start_225 = datetime.strptime('Jul 19 2016', '%b %d %Y')
end_225 = datetime.strptime('Jul 12 2017', '%b %d %Y')
num_per_epoch_225 = 33
#5/22/2015 - 1/29/2016, 4/11/2017 - 10/22/2017
start_201 = datetime.strptime('Feb 7 2016', '%b %d %Y')
end_201 = datetime.strptime('Oct 22 2017', '%b %d %Y')
num_per_epoch_201 = 31
# Apr 26, 2017 to Nov 12, 2017
start_226 = datetime.strptime('Apr 26 2017', '%b %d %Y')
end_226 = datetime.strptime('Apr 16 2018', '%b %d %Y')
num_per_epoch_226 = 32
# start_239_1 = datetime.strptime('Jul 13 2018', '%b %d %Y')
# end_239_1 = datetime.strptime('Jan 31 2019', '%b %d %Y')
# num_per_epoch_239_1 = 28
# start_239_2 = datetime.strptime('Jan 4 2018', '%b %d %Y')
# end_239_2 = datetime.strptime('Jul 11 2018', '%b %d %Y')
# num_per_epoch_239_2 = 31
# start_225_1 = datetime.strptime('Aug 24 2016', '%b %d %Y')
# end_225_1 = datetime.strptime('Apr 12 2017', '%b %d %Y')
# num_per_epoch_225_1 = 33
# start_225_2 = datetime.strptime('Oct 13 2017', '%b %d %Y')
# end_225_2 = datetime.strptime('Jun 5 2018', '%b %d %Y')
# num_per_epoch_225_2 = 29
# start_251 = datetime.strptime('Sep 26 2018', '%b %d %Y')
# end_251 = datetime.strptime('Apr 28 2019', '%b %d %Y')
# num_per_epoch_251 = 30
# start_217 = datetime.strptime('Jul 19 2018', '%b %d %Y')
# end_217 = datetime.strptime('May 18 2019', '%b %d %Y')
# num_per_epoch_217 = 30
# start_229 = datetime.strptime('Oct 9 2017', '%b %d %Y')
# end_229 = datetime.strptime('Feb 13 2019', '%b %d %Y')
# num_per_epoch_229 = 27
p231.add_epochinfo(start_231, end_231, num_per_epoch_231)
# p222_1.add_epochinfo(start_222_1, end_222_1, num_per_epoch_222_1)
# p222_2.add_epochinfo(start_222_2, end_222_2, num_per_epoch_222_2)
# p222_3.add_epochinfo(start_222_3, end_222_3, num_per_epoch_222_3)
p222.add_epochinfo(start_222, end_222, num_per_epoch_222)
# p229.add_epochinfo(start_229, end_229, num_per_epoch_229)
p241.add_epochinfo(start_241, end_241, num_per_epoch_241)
# p239_1.add_epochinfo(start_239_1, end_239_1, num_per_epoch_239_1)
# p239_2.add_epochinfo(start_239_2, end_239_2, num_per_epoch_239_2)
# p225_1.add_epochinfo(start_225_1, end_225_1, num_per_epoch_225_1)
# p225_2.add_epochinfo(start_225_2, end_225_2, num_per_epoch_225_2)
# p251.add_epochinfo(start_251, end_251, num_per_epoch_251)
# p217.add_epochinfo(start_217, end_217, num_per_epoch_217)
# p229.add_epochinfo(start_229, end_229, num_per_epoch_229)
p239.add_epochinfo(start_239, end_239, num_per_epoch_239)
p225.add_epochinfo(start_225, end_225, num_per_epoch_225)
p201.add_epochinfo(start_201, end_201, num_per_epoch_201)
p226.add_epochinfo(start_226, end_226, num_per_epoch_226)
#add duration and daily
prepath = '../data/'
duration_222_1 = pd.read_csv(
prepath + 'NY222_3760120_2015-08-11_to_2016-08-11_20180809165417.csv',
skiprows=3)
duration_222_2 = pd.read_csv(
prepath + 'NY222_3760120_2016-08-11_to_2017-08-11_20180809003645.csv',
skiprows=3)
duration_222_3 = pd.read_csv(
prepath + 'NY222_3760120_2017-08-11_to_2018-08-08_20180809004150.csv',
skiprows=3)
duration_231_1 = pd.read_csv(
prepath + 'NY231_4086548_2016-07-05_to_2017-07-05_20180809005536.csv',
skiprows=3)
duration_231_2 = pd.read_csv(
prepath + 'NY231_4086548_2017-07-05_to_2018-08-08_20180809010451.csv',
skiprows=3)
duration_229_1 = pd.read_csv(prepath + '229_duration.csv', skiprows=3)
daily = {}
daily['222'] = prep.prep_daily(
pd.read_csv(prepath +
'NY222_2015-08-11_to_2018-06-12_daily_20180613153105.csv',
skiprows=3))
daily['231'] = prep.prep_daily(
pd.read_csv(prepath +
'NY231_2016-07-05_to_2018-06-12_daily_20180613153815.csv',
skiprows=3))
# daily['229'] = prep.prep_daily(pd.read_csv(prepath + 'NY229_2017-05-12_to_2018-09-07_daily_20180907183334.csv', skiprows=3))
daily['241'] = prep.prep_daily(
pd.read_csv(prepath +
'NY241_2017-06-13_to_2018-10-05_daily_20181005204526.csv',
skiprows=3))
daily['239'] = prep.prep_daily(
pd.read_csv(prepath +
'NY239_2017-03-28_to_2019-05-24_daily_20190524174640.csv',
skiprows=3))
daily['225'] = prep.prep_daily(
pd.read_csv(prepath +
'NY225_2016-01-14_to_2019-05-23_daily_20190524174650.csv',
skiprows=3))
daily['201'] = prep.prep_daily(
pd.read_csv(prepath + '201_daily.csv', skiprows=3))
daily['226'] = prep.prep_daily(
pd.read_csv(prepath +
'NY226_2016-02-09_to_2018-10-05_daily_20181005204146.csv',
skiprows=3))
# daily['251'] = prep.prep_daily(pd.read_csv(prepath + 'NY251_2018-02-06_to_2019-04-29_daily_20190524174653.csv', skiprows=3))
# daily['217'] = prep.prep_daily(pd.read_csv(prepath + 'NY217_2015-05-14_to_2019-05-18_daily_20190524174658.csv', skiprows=3))
# daily['229'] = prep.prep_daily(pd.read_csv(prepath + 'NY229_2016-05-12_to_2019-05-20_daily_20190524174704.csv', skiprows=3))
#all patients
if index == -1:
pat_list = [p231, p222_1, p222_2, p229]
elif index == 231:
pat_list = [p231]
elif index == 2221:
pat_list = [p222_1]
elif index == 241:
pat_list = [p241]
elif index == 2223:
pat_list = [p222_3]
elif index == -2:
pat_list = [p239, p225, p222]
elif index == -3:
pat_list = [p231, p222_1, p222_2, p241]
elif index == -4:
pat_list = [p231, p222, p201, p226]
for pat in pat_list:
if if_weekly:
pat.epoch_info['num_per_epoch'] = 7
if if_2weekly:
pat.epoch_info['num_per_epoch'] = 14
if if_PSV:
f = h5py.File('../data/features_sync_90' + pat.pat_id + '.mat',
'r')
elif freq_idx == 124:
f = h5py.File('../data/features_124' + pat.pat_id + '.mat', 'r')
elif freq_idx == 90:
f = h5py.File('../data/features_90' + pat.pat_id + '.mat', 'r')
else:
sys.exit(1)
# use daily file to identify the label for each epoch or each ECog
pat.add_daily(daily[pat.pat_id], log=log, reg=reg)
# if use random label
if if_yrandom:
prev_dict = pat.epoch_label_dict
pat.prev_dict = prev_dict
n = len(prev_dict)
print('prev_dict: ', prev_dict)
new_dict = {}
tf = np.array([True, False])
if pat.id == '241':
np.random.seed(40)
new_vals = np.random.choice(
tf,
size=n,
)
np.random.seed(42)
for i, key in enumerate(prev_dict):
new_dict[key] = new_vals[i]
print('new_dict', new_dict)
pat.epoch_label_dict = new_dict
pat.add_features(f, if_PSV=if_PSV)
if if_shuffle_label:
label_list = list(pat.features.label)
np.random.shuffle(label_list)
pat.features.label = label_list
pat.ngood = pat.features.loc[pat.features['label'] == True].shape[0]
pat.nbad = pat.features.loc[pat.features['label'] == False].shape[0]
pat.ndata = pat.features.shape[0]
pat.print_features_property()
if len(pat_list) == 1:
return pat_list[0]
return tuple(pat_list)
doctors = doctorList()
procedures = procedureList()
doc1 = doctor(1, 'Ольга', 'Яновская', 'Андреевна', 'Психолог',
'высшая категория')
doc2 = doctor(2, 'Елена', 'Дорофеева', 'Борисовна', 'Психолог', '1 категория')
doctors.appendList(doc1)
doctors.appendList(doc2)
r1 = procedure(0, 'тыкунть носом', 500, doc1)
r2 = procedure(0, 'консультация', 3500, doc2)
r3 = procedure(0, 'консультация2', 100, doc2)
procedures.appendList(r1)
procedures.appendList(r2)
procedures.appendList(r3)
p1 = patient(1, 'Кирилл', 'Вторцев', 'Дмитриевич', '21.11.2001', 50)
p2 = patient(2, 'Марк', 'Захаров', '', '08.05.2001', -90)
patients.appendList(p1)
patients.appendList(p2)
d1 = deal(1, '02.11.2020', 'что-то там 1', p1, [r1])
d2 = deal(2, '03.11.2020', 'что-то там 2', p2, [r1, r2])
d3 = deal(3, '11.11.2020', 'что-то там 3', p1, [r3])
#d1.setPatient(p1)
#d2.setPatient(p2)
deals.appendList(d1)
deals.appendList(d2)
deals.appendList(d3)
print(procedures)
import file_manager
import patient
import datetime
p1 = patient.patient ()
p1.given_name = 'John'
p1.family_name = 'Smith'
p1.date = datetime.datetime.now ()
p1.id = '12345678910'
fm = file_manager.FileManager ()
print "\n"
print "////////////////////// Starting Test 1 /////////////////////"
print "\n"
print "calling fm.create_clinic_dir ('my_clinic', '.')"
print "\n"
fm.create_clinic_dir ('my_clinic', '.')
print "calling fm.get_clinic_dir ()"
print "\n"
print "clinic_dir = ", fm.get_clinic_dir ()
print "\n"
print "saving patient John Smith's files"
print ("calling fm.save_patient_files ",
"(p1, 'tmp.pkl', 'tmp.docx', 'tmp.csv', 'tmp2.img')")
def get_list_data(rows):
return [patient(*row) for row in rows]
