roi_files = model_csv.func_outdir + \
"/func/bandpass_freqs_0.01.0.1/rois_random_k3200.nii.gz"
roi_files.to_csv(outfile, index=False, sep=" ")
outfile = config.glm['infuncs2']
func_files = model_csv.func_outdir + \
"/func/bandpass_freqs_0.01.0.1/functional_mni_4mm.nii.gz"
func_files.to_csv(outfile, index=False, sep=" ")
## Run glm
# output directory
base_glm = path.dirname(config.data['glm_dir'])
if not path.exists(base_glm):
os.mkdir(base_glm)
# ok now run
glm = GlmRun(**config.glm)
glm.run()
## Summarize
## Goal: To easily represent the 4D GLM results (2D matrix) as 3D (1D vector)
## for easy visualization and comparison
# output directory
summarize_out = config.summarize['out_dir']
if not path.exists(summarize_out):
os.mkdir(summarize_out)
# get args
arg_order = ["regressors", "mask", "rois", "tvals", "out_dir"]
args = [config.summarize[arg] for arg in arg_order]
arg3 = ""
config_file = "config_%s.yml" % scan
config = YamlReader(config_file)
config.compile()
## Create Model/Contrasts
model = GlmModel(**config.model)
model.run() # still not spitting out the stdout in real-time
model.save()
## Run glm
# output directory
base_glm = path.dirname(config.data['glm_dir'])
if not path.exists(base_glm):
os.mkdir(base_glm)
# ok now run
glm = GlmRun(**config.glm) # NOTE: this doesn't really work right now,
glm.run() # do this step manually
## Summarize
## Goal: To easily represent the 4D GLM results (2D matrix) as 3D (1D vector)
## for easy visualization and comparison
# output directory
summarize_out = config.summarize['out_dir']
if not path.exists(summarize_out):
os.mkdir(summarize_out)
# get args
arg_order = ["regressors", "mask", "tvals", "out_dir"]
args = [config.summarize[arg] for arg in arg_order]
arg3 = ""
config_file = "config_%s.yml" % scan
config = YamlReader(config_file)
config.compile()
## Create Model/Contrasts
model = GlmModel(**config.model)
model.run() # still not spitting out the stdout in real-time
model.save()
## Run glm
# output directory
base_glm = path.dirname(config.data['glm_dir'])
if not path.exists(base_glm):
os.mkdir(base_glm)
# ok now run
glm = GlmRun(**config.glm) # NOTE: this doesn't really work right now,
glm.run() # do this step manually
## Summarize
## Goal: To easily represent the 4D GLM results (2D matrix) as 3D (1D vector)
## for easy visualization and comparison
# output directory
summarize_out = config.summarize['out_dir']
if not path.exists(summarize_out):
os.mkdir(summarize_out)
# get args
arg_order = ["regressors", "mask", "tvals", "out_dir"]
args = [ config.summarize[arg] for arg in arg_order ]
arg3 = ""
"/func/bandpass_freqs_0.01.0.1/rois_random_k3200.nii.gz"
roi_files.to_csv(outfile, index=False, sep=" ")
outfile = config.glm['infuncs2']
func_files = model_csv.func_outdir + \
"/func/bandpass_freqs_0.01.0.1/functional_mni_4mm.nii.gz"
func_files.to_csv(outfile, index=False, sep=" ")
## Run glm
# output directory
base_glm = path.dirname(config.data['glm_dir'])
if not path.exists(base_glm):
os.mkdir(base_glm)
# ok now run
glm = GlmRun(**config.glm)
glm.run()
## Summarize
## Goal: To easily represent the 4D GLM results (2D matrix) as 3D (1D vector)
## for easy visualization and comparison
# output directory
summarize_out = config.summarize['out_dir']
if not path.exists(summarize_out):
os.mkdir(summarize_out)
# get args
arg_order = ["regressors", "mask", "rois", "tvals", "out_dir"]
args = [ config.summarize[arg] for arg in arg_order ]
