def scenario_generate(cls, dir_exp, cases, update_func, colname):
if not os.path.exists(dir_exp):
os.mkdir(dir_exp)
nchannels = 1000
para = Para()
chgrp = ChannelSet(para.r_olap)
vcases_info = []
case_id = 0
for setting in cases:
update_func(para, setting)
gen = SingleChannelGenerator(para)
for i in range(nchannels):
channel = gen.generate()
chgrp.append('C%d'%i, channel)
chgrp.adjust_by_overlap()
case_id += 1
case_name = 'Case%d' % case_id
vcases_info.append((case_name, setting))
chgrp.save(os.path.join(dir_exp, case_name))
dfinfo = pd.DataFrame(vcases_info).transpose()
vcases = list(zip(*vcases_info))
dfinfo = pd.Series(vcases[1], index=vcases[0])
dfinfo.name = colname
dfinfo.to_csv(os.path.join(dir_exp, 'cases_info.csv'), header=True)
para.save_to(sfd)
def save_overlapped_pairs(self, sfc):
if self.overlapped_pairs is None:
return
dflap = pd.DataFrame(self.overlapped_pairs, columns=['Name_a', 'Name_b'])
dflap.to_csv(sfc)
@classmethod
def make_dirs(cls, outdir):
dir_a = os.path.join(outdir, 'alerts')
dir_b = os.path.join(outdir, 'label')
dir_c = os.path.join(outdir, 'info')
for dr in [dir_a, dir_b, dir_c]:
if not os.path.exists(dr):
os.makedirs(dr)
return (dir_a, dir_b, dir_c)
if __name__=='__main__':
from alerts_single_gen import SingleChannelGenerator
para = Para()
gen = SingleChannelGenerator(para)
chgrp = ChannelSet(para.r_olap)
for i in range(5):
cha = gen.generate()
chgrp.append('C%d'%i, cha)
chgrp.adjust_by_overlap()
chgrp.save('/share/temp')
burst_b = channel.dfbursts
period = burst_a.loc[ibst_a]
burst_b = burst_b.drop(ibst_b);
burst_b = burst_b.append(burst_a.loc[ibst_a], ignore_index=True)
burst_b = burst_b.sort_index(by='Start')
burst_b.index = list(range(len(burst_b)))
channel.dfbursts = burst_b
@classmethod
def labels_disclosed(cls, vz, nlab):
isel = random.sample(range(0, len(vz)), nlab)
subindex = vz.index[isel]
return vz.loc[subindex]
@classmethod
def generate_score(cls, u0, u1, s0, s1, n0, n1):
ve0 = np.random.normal(loc=u0, scale=s0, size=n0)
ve1 = np.random.normal(loc=u1, scale=s1, size=n1)
ve = np.hstack([ve0, ve1])
return ve
if __name__=='__main__':
from alerts_single_gen import SingleChannelGenerator
para = Para()
gen = SingleChannelGenerator(para)
cha = gen.generate()
chb = gen.generate()
chc = gen.generate()