tname.name = x[1] #The name attribute is also set as the Code
tname.openings = x[6] #Set the number of openings, comes from quota in the data file
tname.prog_type = x[4] #Set the Type, for example 'C' is categorical
tname.specialty = x[3] #Set the specialty, for example 140 is internal medicine
tname.institution = x[2] #Set the institution number, basicly the name without the prog_type and specialty info
tname.specialty_name = x[0] #This is the Speacialty name rather then code
tname.obs_match = x[7] # The is the number that matched in the actual data set
# Comment out the Following to use defualt
tname.quality = max(min(r.gauss(50, 30), 100), 1) # on a scale 0-100
tname.number_to_interview = 10 * tname.openings
tname.observe_1 = r.gauss(1, .2)
tname.observe_2 = tname.observe_2 = (r.gauss(1, .1)- tname.observe_1)
tname.observed_1 = r.gauss(1, .2)
tname.observed_2 = (r.gauss(1, .1)- tname.observed_1) # based on applicant interviewed, percentage
tname.num_to_rank = 7 * tname.openings
tname.accept_range = [.7, None] # as a % [.5, 1.5] if [.7, None] then there is no upper limit
all_institutions.append(tname) #add this instance to the list of Intitutions
#applicants choose institutions to apply to
for index, app in enumerate(all_applicants):
app.apply_list(all_institutions)
s1 = sum([x.quality for x in app.applied_to])
if (len(app.applied_to)):
pass
#print max([x.quality for x in app.applied_to]), float(s1) / len(app.applied_to), min([x.quality for x in app.applied_to]), len(app.applied_to), app.name
else:
print('to picky', index, app.quality)
#Institutions invite applicants for interview
print('Make a list of Applicants')
all_applicants = []
for x in range(1, 100):
tname = 'app' + str(x)
tname = Applicant()
tname.name = x
##### Comment out the Following to use defualt
tname.quality = max(min(r.gauss(50, 20), 100), 1) # on a scale 0-100
tname.observe_1 = 1 # r.gauss(1, .2) # as a percentage, Applicant error in observing the institutions quality
tname.observe_2 = 1 # (r.gauss(1, .1) - tname.observe_1)
tname.observed_1 = 1 # r.gauss(1, .2) # as a percentage, Institutions error "as seen" in observing the applicants quality
tname.observed_2 = 1 # (r.gauss(1, .1) - tname.observed_1) # CORRECT change to observed_1 after interview default = 1
tname.applied_to_range = [.5,
1.5] # as a percentage, for example [0.8, 1.2]
tname.num_applied_to = 5 # the maximum number that the applicant applies to
tname.num_to_rank = 2
all_applicants.append(tname) #Add Applicant to the list of Applicants
print(('number of applicants', len(all_applicants)))
### Make the list of Institutions
##print('importing institution data')
##data2008 = InstitutionData('/Users/vmd/Dropbox/Match/rms/matchdata2008.csv', 140, 'C', 1)
##data2008.read_data_file()
##data2008.prep_data()
##data2008.select_data()
print('Setting Institution Attributes')
all_institutions = []
for x in range(1, 10):
tname = Institution() # the Code is set as the object instance name
tname.name = x #The name attribute is also set as the Code
