R2, p2 = fit.loglikelihood_ratio('truncated_power_law', 'lognormal')
R3, p3 = fit.loglikelihood_ratio('power_law', 'lognormal')
if p1>.05:
dist.powerlaw = 0
else:
dist.powerlaw = 1
if p2<.05:
dist.powerlaw = 2
if p3<.05:
dist.powerlaw = 3
dist.mean = data.mean()
dist.median = median(data)
dist.skew = skew(data)
session.add(dist)
if f=='All':
continue
for fn2 in range(fn+1, n_factors):
f2 = factors[fn2]
data_other = session.query(db.LangleyParticipant).filter(getattr(db.LangleyParticipant, i)==f2).values(getattr(db.LangleyParticipant, d))
data_other = [q for q in data_other if q[0]!=None]
data_other = asarray([q for q in data_other if ~isnan(q)]).flatten()
if len(data_other)>2:
H, p_krusk = kruskal(data, data_other)
D, p_KS = ks_2samp(data/median(data), data_other/median(data_other))
fs.append(f)
f2s.append(f2)
Ds.append(D)
mains = 50
dirList=os.listdir(data_path)
for fname in dirList:
file = data_path+fname
f = h5py.File(file)
group_name = f.attrs['group_name']
number_in_group = f.attrs['number_in_group']
species = f.attrs['species']
location = f.attrs['location']
subject = session.query(db.Subject).\
filter_by(species=species, group_name=group_name, number_in_group=number_in_group).first()
if not subject:
subject = db.Subject(species=species, group_name=group_name, number_in_group=number_in_group)
session.add(subject)
session.commit()
print file
conditions = [(v,t,e,s,rem) for v in visits for t in tasks for e in eyes for s in sensors for rem in remicas]
for visit, task_type, eye, sensor_type, rem in conditions:
base = str(visit)+'/'+task_type+'/'+eye+'/'+sensor_type+'/'+rem
base_filtered = base+'/filter_'+filter_type+'_'+str(taps)+'_'+window
#If this particular set of conditions doesn't exist for this subject, just continue to the next set of conditions
try:
f[base_filtered]
except KeyError:
continue
print base
p.Has_Children = (G.out_degree()[n_id]>0)
p.Relationship_with_Parent = n['source_from']
p.Heard_Through_Medium = n['source_through']
p.Join_Time = n['join_time']
p.Latitude = n['lat']
p.Longitude = n['lng']
p.Has_Parent = False
if 'parent_id' in n.keys():
p.Has_Parent = True
p.parent_id = n['parent_id']
p.Parent_Child_Registration_Interval = n['wait_time']
p.Parent_Child_Registration_Interval_Corrected = max(n['wait_time'], 0)
p.Distance_from_Parent = n['distance']
p.Parent_Age = n['parent_age']
p.Parent_City = n['parent_city']
p.Parent_Country = n['parent_country']
p.Parent_Gender = n['parent_gender']
p.Parent_Number_of_Children = n['parent_sigma']
p.Parent_Relationship_with_Grandparent = n['parent_source_from']
p.Parent_Heard_Through_Medium = n['parent_source_through']
p.Grandparent_Parent_Registration_Interval = n['parent_wait_time']
p.Same_Age_as_Parent = n['same_age']
p.Same_City_as_Parent = n['same_city']
p.Same_Country_as_Parent = n['same_country']
p.Same_Gender_as_Parent = n['same_gender']
p.Same_Relationship_to_Parent_as_They_Had_to_Their_Parent = n['same_source_from']
p.Heard_Through_Same_Medium_as_Parent = n['same_source_through']
p.Parent_Join_Time = n['parent_join_time']
session.add(p)
session.commit()
