def __init__(tclass, _from, at={'tags':1, 'types':2}):
if some(_from) and some(at):
data = load_data(_from)
tags = data[at['tags']]
types = data[at['types']]
if len(tags) != len(types): raise ValueError('Tag Type mismatch:', len(types)-len(tags))
super().__init__(tags=[t.strip() for t in tags], types=[t.strip() for t in types], items={})
def __init__(pclass, _from=None, line=1, args=None):
if some(_from):
args = load_data(_from)[line]
patient.DEF = {arg.strip():0 for arg in args}
patient._lines_ = len(patient.DEF)
elif some(pclass.DEF):
super().__init__(**pclass.DEF)
pclass.states.append(pclass)
pclass.set(**args)
def calculate(stats, on=.5, by='day', targets=['bio'], outcome_from=4, model=.9, virtual=None, models=None, good=(0,'good'), bad=(1,'bad'), skip=0, threshold=.75, **opts):
stats.set(**opts)
stats._ref = skip
stats.set(stats = {tag:[] for tag in [good[1], bad[1], 'before']+targets})
if stats.from_end: stats.set(in_room=[], dismissed=[])
else: stats.set(assessed=[])
n=stats._calculate(on, by, targets, outcome_from, model, models, good, bad, threshold)
while some(virtual) and n<virtual:
n=stats._calculate(on, by, targets, outcome_from, model, models, good, bad, threshold, n)
if not stats.stack: stats.unstacked += stats.best; stats.best = []
def load(data, file, translator, id_from='hn', prev_at=0, _from=1):
last, lines = None, load_data(file)[_from:]
for line in lines:
raw, id = translator.translate(line), id_from
new = patient(args=raw)
if raw[id] == prev_at and some(last):
reset = translator.translate(line, data.zero)
for field in raw:
if reset[field] == data.zero: reset[field] = last.get(field)
new.set(**reset)
last.link(new)
else: new.ID = raw[id]
last = new
def _count_id(_, n=0):
if some(_.ID): return _.ID, n
return _.prev._count_id(n+1)