def test_age_sex():
contingency_table = {}
for tup in all_data:
age = split_age(int(tup[9]))
sex = int(tup[10])
contingency_table.setdefault(age, {2: 0, 3: 0})
contingency_table[age][sex] += 1
show_contingency_table(contingency_table)
def test_age():
contingency_table = {}
for tup in all_data:
sline = tup[14]
age = split_age(int(tup[9]))
if sline is None:
continue
contingency_table.setdefault(sline, {2: 0, 3: 0, 4: 0, 5: 0})
contingency_table[sline][age] += 1
show_contingency_table(contingency_table)
def test_age_month():
contingency_table = {}
for tup in all_data:
admit_date = tup[2]
age = split_age(int(tup[9]))
if len(admit_date) == 0:
continue
month = get_month(admit_date)
contingency_table.setdefault(month, {2: 0, 3: 0, 4: 0, 5: 0})
contingency_table[month][age] += 1
show_contingency_table(contingency_table)
def test_rlos_age():
contingency_table = {}
for tup in all_data:
admit_date = tup[2]
rlos = tup[5]
age = split_age(int(tup[9]))
if len(admit_date) == 0:
continue
if len(rlos) == 0:
continue
contingency_table.setdefault(age, dict([(x, 0) for x in xrange(6)]))
contingency_table[age][get_los_category(int(rlos))] += 1
show_contingency_table(contingency_table)
plt.show()
def add_zeros(data):
zero_count = 365 - len(data)
if zero_count<0: zero_count = 0
result=[]
result.extend([0 for x in xrange(zero_count)])
result.extend(data)
return result
def filter_data(data, (sexf, agef, slinef)):
result = []
for tup in data:
age = split_age(int(tup[9]))
sex = int(tup[10])
admit_date = tup[2]
sline = tup[14]
if sline is None:
continue
if len(admit_date) == 0:
continue
if age == agef and sex == sexf and sline == slinef:
result.append(tup)
return result
def analyze_hist(sline_code, data, bins=7):
def get_age_category(self):
return split_age(self.age)
if (sex, age, sline) not in self.patients_count_distr:
raise NotImplementedError(
"Patient flow for selection: sex %d, age %d, sline %s is not implemented" % (sex, age, sline)
)
if sline not in self.age_distr:
raise NotImplementedError("Age distribution for sline %s is not implemented" % sline)
if (sex, age, sline) not in self.rlos_distr:
raise NotImplementedError(
"RLos distribution for selection: sex %d, age %d, sline %s is not implemented" % (sex, age, sline)
)
rlos_flow_func = lambda: self.rlos_distr[(sex, age, sline)](100)
rlos_flow = rlos_flow_func()
age_flow_func = lambda: [a for a in self.age_distr[sline](500) if split_age(a) == age]
age_flow = self.recall_if_empty(age_flow_func)
sline_distr_info = self.patients_count_distr[(sex, age, sline)]
patients_flow = sline_distr_info.rvs(days).tolist()
result = []
for iday in range(days):
if sline_distr_info.prob == 1.0 or random.random() <= sline_distr_info.prob:
pat_count = patients_flow.pop()
admits = []
# todo: move convertion from 0 to 1 to rvs function
for p in xrange(1 if pat_count == 0 else pat_count):
admits.append(AdmitInfo(str(iday + 1), sex, age_flow.pop(), sline, rlos_flow.pop()))
if len(rlos_flow) == 0:
rlos_flow = rlos_flow_func()
if len(age_flow) == 0:
