from datamind.core import DF
from datamind.stats import VBA

# Optional PATH to mask and common prefix path to images
import os.path
BASEPATH=os.path.join(os.environ['HOME'],"Data/07Deprim/")
IMAGESPATH=BASEPATH+"IRM/Data/"
MASKPATH=BASEPATH+"IRM/Mask/mask.img"


tab=DF.read("tuples.csv")[:,['IRM_G_file', 'Response', 'Sex','Age']]
print tab
#IRM_G_file      Response        Sex     Age
#smwc1S6182.img  N               2.0     45.0
#smwc1S7302.img  N               2.0     44.0
#....            .               ...     ....
#smwc1S10140.img Y               1.0     38.0
#smwc1S10291.img Y               2.0     49.5
#smwc1E10590.img Y               2.0     39.5

# If the table contain the full path, Y_common_prefix_url is optional
m=VBA(tab,mask_url=MASKPATH,Y_common_prefix_url=IMAGESPATH)
print m.yX
#url             Response_N      Response_Y      Sex     Age
#smwc1S6182.img  1.0             0.0             2.0     45.0
#smwc1S7302.img  1.0             0.0             2.0     44.0
#..............  ...             ...             ...     ....
#smwc1S10291.img 0.0             1.0             2.0     49.5
#smwc1E10590.img 0.0             1.0             2.0     39.5


m.fit()
m.contrast([1,-1,0,0])
t, p=m.test()
m.save("/tmp/test1")
#ls /tmp/test1.*
#/tmp/test1.minf  /tmp/test1.model.h5  /tmp/test1.yX.csv
#
# test1.minf     essentially contains urls (mask, output dir) 
# test1.yX.csv   contains the design matrix & url to images
# test1.model.h5 model parameters beta, s2 etc...

m.saveVol(t,"t")

m2=VBA("/tmp/test1")
m2.contrast([1,-1,0,0])
m2.save("/tmp/test2")
t2, p2=m2.test()
m2.saveVol(t2,"t")

import numpy as N
N.max(N.abs(t-t2))