def process_wrapper(in_queue, out_queue, plock, disease_file, ndo=0):
dx = bd.BeeStringDict(marketscan+'DX', keysize = 9, readonly=True)
idx = bd.BeeStringDict(marketscan+'indices', keysize = 51, readonly=True)
code2dx = pickle.load(open(disease_file))
for (county, index, tot_cty) in iter( in_queue.get, None ):
if index % 50000 == 0:
plock.acquire()
print 'AT {:1.2f} %\n'.format(index/float(tot_cty)*100)
sys.stdout.flush()
plock.release()
ret = {}
## Do DX for M and F separately
for gender in ['M','F']:
county_ids = set()
try:
county_ids = idx[gender + ' ' + county]
except KeyError:
print 'indices does not have key: F ' + county
continue
if ndo > 0:
county_ids = set(list(county_ids)[:ndo])
ret[gender] = do_county(dx, county_ids, code2dx)
if len(ret.keys()) > 0:
out_queue.put((county, ret))
out_queue.put(None)
def main():
rx = bd.BeeStringDict('RX', keysize=9, readonly=1)
rx_set = bd.BeeStringDict('RXset', keysize=9, readonly=0)
count = -1
empty = 0
keys = sorted(rx.keys())
print len(keys)
for key in keys:
line = rx.get(key, '')
if len(line) > 9:
subs = line.split('|')
codes = []
for sub in subs:
code = sub.split(':')[0]
if len(code) > 4:
codes.append(code)
#print codes
rx_set[key] = set(codes)
rx_set.commit()
else:
empty += 1
count += 1
if count % 10000 == 0:
print rich_string_wrap(num2comma2(count),'y',0,'k',0), \
rich_string_wrap(num2comma2(empty),'r',0,'k',0), ('%4.3f ' % (float(empty+0.5)/float(count+0.5)))
def __init__(self,
filename=None,
type=None,
index_attrs=None,
key_sizes=None,
from_dump=False):
if from_dump == True:
#
path = os.getcwd() + '/storage/' + type.__name__ + '/' + 'dumb.p'
#storage = Storage()
#storage.open_file(path)
#filetext = storage.current_file.read()
#c = pickle.load(StringIO.StringIO(filetext))
c = pickle.load(open(path, 'rb'))
self.filename = c.filename
self.name = type.__name__
self.gd = c.gd
self.pp = c.pp
self.type = c.type
self.counter = c.counter
self.size = c.size
self.m = c.m
self.index_attrs = c.index_attrs
self.key_sizes = c.key_sizes
self.trees = {}
print("Extracting ")
#storage.extract('storage/' + type.__name__)
print("Extracting done ")
for index_attr, key_size in zip(self.index_attrs, self.key_sizes):
tree = BeeDict.BeeStringDict(os.getcwd() + '/storage/' +
type(type).__name__ + index_attr,
keysize=key_size)
# tree.close()
self.trees[index_attr] = tree
else:
self.filename = filename
self.gd = 0
self.pp = [0]
self.type = type
self.name = type.__name__
self.counter = 0
self.size = 0
self.m = {}
if index_attrs is not None and key_sizes is not None:
self.index_attrs = index_attrs
self.key_sizes = key_sizes
self.trees = {}
for attr, key_size in zip(index_attrs, key_sizes):
tree = BeeDict.BeeStringDict(os.getcwd() + '/storage/' +
type(type).__name__ + attr,
keysize=key_size)
# tree.close()
self.trees[attr] = tree
def process_pat_worker(in_queue, out_queue, plock, process_function, db_list,
**kwargs):
for db in db_list:
kwargs[db] = bd.BeeStringDict(db_list[db][0] + db,
keysize=db_list[db][1],
readonly=True)
#people_dx = bd.BeeStringDict('kanix_matches_F_Breast_Cancer.txt_demo_bd',
# keysize = 9, readonly=True)
#kwargs['person_dx_info'] = people_dx ### BADDD
#rx = bd.BeeStringDict(marketscan+'RX', keysize = 9, readonly=True)
#dx = bd.BeeStringDict(marketscan+'DX', keysize = 9, readonly=True)
#demo = bd.BeeStringDict(marketscan+'ID_demographics', keysize = 9, readonly=True)
#rxset = bd.BeeStringDict(marketscan+'RXSet', keysize = 9, readonly=True)
#dx = bd.BeeStringDict(marketscan+'DX', keysize = 9, readonly=True)
## iter = reads until get "None"
for (patline, index, tot_pat) in iter(in_queue.get, None):
if index % 50000 == 0:
plock.acquire()
print 'AT {:1.2f} %\n'.format(index / float(tot_pat) * 100)
sys.stdout.flush()
plock.release()
result = process_function(patline, **kwargs)
if not result is None:
out_queue.put(result)
out_queue.put(None)
plock.acquire()
print 'child: is in q empty? ' + str(in_queue.empty())
plock.release()
def main():
global drug2set
drug2set = read_drug_sets()
clean = False
names = []
for name in sorted(drug2set.keys()):
imya = name.replace(" ", "_")
names.append(imya)
system('clear')
from mx.BeeBase import BeeDict as bd
rx_set = bd.BeeStringDict('../RXset', keysize=9, readonly=1)
all_pats = sorted(rx_set.keys())
ndrugs = len(names)
num = 0
for i in range(num_procs):
procs.append(multiprocessing.Process(target=worker))
procs[-1].daemon = True
procs[-1].start()
num = 0
alle = float(len(all_pats))
for item in all_pats:
drug_set = rx_set.get(item, set([]))
if num < len(all_pats):
num += 1
if len(drug_set) > 0:
q.put((drug_set, num, item, alle))
else:
break
if num < ndrugs:
q.join()
for p in procs:
q.put(None)
q.join()
for p in procs:
p.join()
print "Finished everything...."
print "num active children:", multiprocessing.active_children()
def page_test():
studs = get_dataset()
p = Ipage()
open('page.txt', 'w').close()
from mx.BeeBase import BeeDict
tree = BeeDict.BeeStringDict(os.getcwd() + '/storage/' + student.__name__ +
'name',
keysize=256)
for stud in studs[0:10]:
p.insert(stud)
p.store('page.txt', 0)
p.store_to_tree(tree, student, 'name', 'page.txt')
print(zip(tree.keys(), tree.values()))
tree.close()
def main():
global drug2set
drug2set = read_drug_sets()
clean = False
names = []
for name in sorted(drug2set.keys()):
imya = name.replace(" ", "_")
names.append(imya)
system('clear')
from mx.BeeBase import BeeDict as bd
rx_set = bd.BeeStringDict('../RXset', keysize=9, readonly=1)
all_pats = sorted(rx_set.keys())
print num2comma2(len(all_pats))
def tree_test():
from mx.BeeBase import BeeDict
# Here is a very simple file based string dictionary:
# d = BeeDict.BeeStringDict('storage/BeeStringDict.example', keysize=26)
# studs = get_shuffled_million()
# i = 0
# max = ''
# for stud in studs[:100000]:
# with Profiler() as p:
# d[stud.attrs['name']] = stud.get_string()
# i += 1
# d.commit()
# if i % 5000 == 0:
# print('#', i)
# d.close()
d = BeeDict.BeeStringDict('storage/BeeStringDict.example', keysize=26)
martha = d.cursor(key='Martha Morrow')
print(martha.next())
# print(len(d))
# print(d['Martha Morrow'])
print(martha.key, d[martha.key])
from mx.BeeBase import BeeDict as bd
import pandas as pd
from itertools import chain
data = '/Volumes/Macintosh HD-1/Users/Data/'
data = '/Users/Data/'
px = bd.BeeStringDict(data + 'PX', keysize=9, readonly=True)
rx = bd.BeeStringDict(data + 'RXset', keysize=9, readonly=True)
dx = bd.BeeStringDict(data + 'DX', keysize=9, readonly=True)
demo = bd.BeeStringDict(data + 'ID_demographics', keysize=9, readonly=True)
from pat_weeks import *
import billing_codes
[icd9, cpt] = billing_codes.load_icd9_cpt()
import time
t0 = time.time()
bcpat = open('ab').read().strip().split('\n')
ndo = 0 #100000
import numpy as np
lens = np.zeros((len(bcpat), 5))
for (i, pat) in enumerate(bcpat):
wkinfo = get_pat_weeks(pat, dx, px)
wnums = [int(w) for w in wkinfo['d'].keys()]
brca_diagnos_wk = [
int(w)
for w in get_diagnosis_weeks(wkinfo, ('174', '239.3', '238.3', '233'))
]
lens[i, :] = [
len(wkinfo['d']),
len(wkinfo['p']),
max(wnums) - min(wnums),
