import IHCtools as ihc
from ihc_settings import s
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats.stats as sss
import scikits.bootstrap as bootstrap
# seed the rng with clock or similarly sufficiently random state
np.random.RandomState(seed=None)
# disable truncated printing of arrays
np.set_printoptions(threshold=np.inf)
########### Loading and organising data
### load numpy array of GS data (nImages*nMeasures*nExperts). Also returns imIDs to keep track of what image each row in expert_data_np refers to
datExp, AllimIDs = ihc.get_gs_data_2r20_numpy(s["expertIDs"])
if s["project_short_name"] == "tb2-r2.0":
imIDs = list(AllimIDs.Utask_ID_PA)
elif s["project_short_name"] == "tb2-r2.0a":
imIDs = list(AllimIDs.Utask_ID_a)
elif s["project_short_name"] == "tb2-r2.1":
imIDs = list(AllimIDs.Utask_ID_2dot1)
elif s["project_short_name"] == "tb2-r2.1b":
imIDs = list(AllimIDs.GStask_ID.astype(int))
else:
raise Exception("add project to list to get imIDs")
print "number of images included:", len(imIDs)
### load numpy array of user data. Also returns userIDs in a list
(datUser, userIDs) = ihc.get_user_data(imIDs, s["project_short_name"])
### simulate user data from expert data
# (datUser,userIDs) = ihc.simulate_user_data(datExp)
