import itertools
import pickle
from DBSpace.readout import DSV
#%%
#This sets up our clinical frame for the regression
print('Loading Clinical and BrainRadio Frames...')
ClinFrame = ClinVect.CFrame(norm_scales=True)
BRFrame = pickle.load(open('/home/virati/Chronic_Frame.pickle',"rb"))
print('Starting the readout analysis...')
analysis = DSV.ORegress(BRFrame,ClinFrame)
#%%
analysis.split_validation_set(do_split = True)
analysis.O_feat_extract()
all_pts = ['901','903','905','906','907','908']
#%%
regr_type = 'RIDGE'
test_scale = 'HDRS17'
do_detrend='Block'
ranson = True
if regr_type == 'OLSnite':
# Plotting Libraries
import matplotlib.pyplot as plt
import seaborn as sns
#Do some cleanup of the plotting space
plt.close('all')
sns.set_context('paper')
sns.set_style('white')
sns.set(font_scale=4)
# Misc libraries
import copy
import itertools
import scipy.stats as stats
#%%
ClinFrame = ClinVect.CFrame(norm_scales=True)
BRFrame = BRDF.BR_Data_Tree()
BRFrame.full_sequence(data_path='/home/virati/Chronic_Frame_july.npy')
BRFrame.check_empty_phases()
local_ro = DSV.ORegress(BRFrame, ClinFrame, trials=1, rmethod='RIDGE')
#%%
#Need to have a method that inserts models in
#need to have a method that zeros out coefficients, like right delta
#%%
# Now we want to just VIEW the coefficients, ensemble stuff, etc. Everything else from Partial_Biometric needs to be folded into ORegress class