from FakePatients import working_path
import scipy.stats as stats
import matplotlib.pyplot as plt
import math
import numpy as np
# print get_patients_freq()
repo = Repository()
repo.add_age_distr('050', lambda count: [18 if x < 18 else 103 if x > 103 else int(round(x))
for x in stats.norm.rvs(loc=70.0, scale=16, size=count)])
# Cardiology <35
repo.add_patients_count_distr((2, 2, '050'), DistrInfo(lambda x: stats.poisson.rvs(mu=1.1, size=x), 0.7))
repo.add_rlos_distr((2, 2, '050'),
lambda size: [int(math.floor(x)) for x in stats.expon.rvs(scale=2.0, size=size)],
lambda x: x >= 1)
# Cardiology 35-50
repo.add_patients_count_distr((2, 3, '050'), DistrInfo(lambda x: stats.poisson.rvs(mu=1.17, size=x), 0.14))
repo.add_rlos_distr((2, 3, '050'),
lambda size: [int(math.floor(x)) for x in stats.expon.rvs(scale=2.0, size=size)],
lambda x: x >= 1)
# Cardiology 50-70
repo.add_patients_count_distr((2, 4, '050'), DistrInfo(lambda x: stats.poisson.rvs(mu=1.46, size=x), 0.45))
repo.add_rlos_distr((2, 4, '050'),
lambda size: [int(math.floor(x)) for x in stats.expon.rvs(scale=2.2, size=size)],
lambda x: x >= 1)
__author__ = 'Andrew' from Repository import Repository from Repository import DistrInfo from FakePatients import get_patients_freq import scipy.stats as stats import matplotlib.pyplot as plt import math repo = Repository() # Cardiology 35-50 repo.add_patients_count_distr((2, 3, '050'), DistrInfo(lambda x: stats.poisson.rvs(mu=1.17, size=x), 0.14)) repo.add_rlos_distr((2, 3, '050'), lambda count: [int(math.floor(x)) for x in stats.expon.rvs(scale=2.0, size=count)]) # Cardiology 50-70 repo.add_patients_count_distr((2, 4, '050'), DistrInfo(lambda x: stats.poisson.rvs(mu=1.46, size=x), 0.45)) # Cardiology 50-70 repo.add_patients_count_distr((3, 4, '050'), DistrInfo(lambda x: stats.poisson.rvs(mu=1.3, size=x), 0.336)) # Urological Surgery 50-70 repo.add_patients_count_distr((2, 4, '387'), DistrInfo(lambda x: stats.poisson.rvs(mu=1.44, size=x), 0.267)) # Urological Surgery 50-70 repo.add_patients_count_distr((3, 4, '387'), DistrInfo(lambda x: stats.poisson.rvs(mu=1.17, size=x), 0.08)) repo.add_rlos_distr((3, 4, '387'), lambda count: [int(math.floor(x)) for x in stats.expon.rvs(scale=2.4, size=count)]) # put selection here selection = (2, 3, '050') # build chart generated data versus historical data predicted = repo.predict(selection, 30) historical, sd, ed = repo.history(selection, 30) title_common = " [%4d-%02d-%02d %4d-%02d-%02d]" \
