def compute_contrasts(contrast_struct, misc, CompletePaths, glms=None,
model="default", **kargs):
"""
Contrast computation utility
Parameters
----------
contrast_struct, ConfigObj instance or string
it yields the set of contrasts of the multi-session model
or the path to a configobj that specifies the contarsts
misc: misc object instance,
misc information on the datasets used here
or path to a configobj file that yields the misc info
Complete_Paths: dictionary or string,
yields all paths, indexed by contrasts,
where outputs will be written
if it is a string, all the paths are re-generated
based on it as a output directory
glms, dictionary of nipy.neurospin.glm.glm.glm instances
indexed by sessions, optional
if it is not provided, a 'glm_config' instance should be provided
in kargs
"""
# read the msic info
if isinstance(misc, basestring):
misc = ConfigObj(misc)
if not misc.has_key(model):
misc[model] = {}
if not misc[model].has_key("con_dofs"):
misc[model]["con_dofs"] = {}
sessions = misc['sessions']
# get the contrasts
if isinstance(misc, basestring):
contrast_struct = ConfigObj(contrast_struct)
contrasts_names = contrast_struct["contrast"]
# get the glms
designs = {}
if glms is not None:
designs = glms
else:
if not kargs.has_key('glms_config'):
raise ValueError, "No glms provided"
else:
import nipy.neurospin.glm as GLM
for s in sessions:
designs[s] = GLM.load(kargs['glm_config'][s]["GlmDumpFile"])
# set the output paths
if isinstance(CompletePaths, basestring) :
CompletePaths = generate_brainvisa_ouput_paths(CompletePaths,
contrast_struct)
# compute the contrasts
for i, contrast in enumerate(contrasts_names):
contrast_type = contrast_struct[contrast]["Type"]
contrast_dimension = contrast_struct[contrast]["Dimension"]
final_contrast = []
k = i+1
multicon = dict()
for key, value in contrast_struct[contrast].items():
if key != "Type" and key != "Dimension":
session = "_".join(key.split("_")[:-1])
bv = [int(j) != 0 for j in value]
if contrast_type == "t" and sum(bv)>0:
_con = designs[session].contrast([int(i) for i in value])
final_contrast.append(_con)
if contrast_type == "F":
if not multicon.has_key(session):
multicon[session] = np.array(bv)
else:
multicon[session] = np.vstack((multicon[session], bv))
if contrast_type == "F":
for key, value in multicon.items():
if sum([j != 0 for j in value.reshape(-1)]) != 0:
_con = designs[key].contrast(value)
final_contrast.append(_con)
design = designs[session]
res_contrast = final_contrast[0]
for c in final_contrast[1:]:
res_contrast = res_contrast + c
res_contrast.type = contrast_type
# write misc information
cpp = CompletePaths[contrast]
save_all_textures(res_contrast, contrast_dimension, cpp)
misc[model]["con_dofs"][contrast] = res_contrast.dof
misc.write()
def ComputeContrasts(contrastFile, miscFile, glms, save_mode="Contrast Name",
model="default", verbose=0, **kargs):
"""
Contrast computation utility
Parameters
----------
contrastFile, string, path of the configobj contrast file
miscFile, string, path of the configobj miscinfo file
glms, dictionary indexed by sessions that yields paths to
nipy.neurospin.glm.glm instances
representing the glms of the individual sessions
in particular , it should have the key "GlmDumpFile".
save_mode="Contrast Name", string to be chosen
among "Contrast Name" or "Contrast Number"
model="default", string,
name of the contrast model used in miscfile
"""
import nipy.neurospin.glm as GLM
misc = ConfigObj(miscFile)
if not misc.has_key(model):
misc[model] = {}
if not misc[model].has_key("con_dofs"):
misc[model]["con_dofs"] = {}
contrasts = ConfigObj(contrastFile)
contrasts_names = contrasts["contrast"]
designs = {}
for i, contrast in enumerate(contrasts_names):
contrast_type = contrasts[contrast]["Type"]
contrast_dimension = contrasts[contrast]["Dimension"]
final_contrast = []
k = i+1
multicon = dict()
if save_mode == "Contrast Name":
ContrastId = contrast
elif save_mode == "Contrast Number":
ContrastId = "%04i" % k
else:
raise ValueError, "unknown save mode"
for key, value in contrasts[contrast].items():
if verbose: print key,value
if key != "Type" and key != "Dimension":
session = "_".join(key.split("_")[:-1])
if not designs.has_key(session):
print "Loading session : %s" % session
designs[session] = GLM.load(glms[session]["GlmDumpFile"])
bv=[int(j) != 0 for j in value]
if contrast_type == "t" and sum(bv)>0:
_con = designs[session].contrast([int(i) for i in value])
final_contrast.append(_con)
if contrast_type == "F":
if not multicon.has_key(session):
multicon[session] = np.array(bv)
else:
multicon[session] = np.vstack((multicon[session], bv))
if contrast_type == "F":
for key, value in multicon.items():
if sum([j != 0 for j in value.reshape(-1)]) != 0:
_con = designs[key].contrast(value)
final_contrast.append(_con)
design = designs[session]
res_contrast = final_contrast[0]
for c in final_contrast[1:]:
res_contrast = res_contrast + c
res_contrast.type = contrast_type
if contrasts.has_key('mask_url'):
mask_url = contrasts['mask_url']
elif misc.has_key('mask_url'):
mask_url = misc['mask_url']
else:
mask_url = None
if kargs.has_key('CompletePaths'):
cpp = kargs['CompletePaths']
print cpp, contrast
save_all_images(res_contrast, contrast_dimension, mask_url, cpp[contrast])
else:
save_all(res_contrast, ContrastId, contrast_dimension, mask_url, kargs)
misc[model]["con_dofs"][contrast] = res_contrast.dof
misc["Contrast Save Mode"] = save_mode
misc.write()