def test_workflow_mean_functional():
import os
import pkg_resources as p
from qap.functional_preproc import run_mean_functional
from qap.workflow_utils import build_test_case
func_motion = p.resource_filename(
"qap", os.path.join(test_sub_dir, "rest_1", "func_motion_correct", "rest_calc_tshift_resample_" "volreg.nii.gz")
)
ref_graph = p.resource_filename(
"qap", os.path.join("test_data", "workflow_reference", "mean_functional", "graph_mean_functional.dot")
)
ref_inputs = p.resource_filename(
"qap", os.path.join("test_data", "workflow_reference", "mean_functional", "wf_inputs.txt")
)
# build the workflow
wf, base_dir = run_mean_functional(func_motion, False)
# get the workflow inputs of the workflow being tested
wf_inputs_string = str(wf.inputs).replace("\n", "")
wf_inputs_string = wf_inputs_string.replace(base_dir, "BASE_DIRECTORY_HERE")
wf_inputs_string = wf_inputs_string.replace(func_motion, "IN_FILE_HERE")
flag, err = build_test_case(wf, ref_inputs, ref_graph, wf_inputs_string)
assert flag == 2, err
def test_run_mean_functional():
import os
import nibabel as nb
import pkg_resources as p
from qap.functional_preproc import run_mean_functional
if "mean_functional" in os.listdir(os.getcwd()):
err = "\n[!] The output folder for this workflow already exists.\n"
raise Exception(err)
func_motion = p.resource_filename("qap", os.path.join(test_sub_dir, \
"rest_1", \
"func_motion_correct", \
"rest_calc_tshift_resample_" \
"volreg.nii.gz"))
ref_out = p.resource_filename("qap", os.path.join(test_sub_dir, \
"rest_1", \
"mean_functional", \
"rest_calc_tshift_resample_volreg_" \
"tstat.nii.gz"))
# run the workflow
output = run_mean_functional(func_motion)
# make the correlation
ref_out_data = nb.load(ref_out).get_data()
output_data = nb.load(output).get_data()
os.system("rm -R mean_functional")
# create a vector of True and False values
bool_vector = ref_out_data == output_data
assert bool_vector.all()
def test_workflow_mean_functional():
import os
import pkg_resources as p
from qap.functional_preproc import run_mean_functional
from qap.workflow_utils import build_test_case
func_motion = p.resource_filename("qap", os.path.join(test_sub_dir, \
"rest_1", \
"func_motion_correct", \
"rest_calc_tshift_resample_" \
"volreg.nii.gz"))
ref_graph = p.resource_filename("qap", os.path.join("test_data", \
"workflow_reference", \
"mean_functional", \
"graph_mean_functional.dot"))
ref_inputs = p.resource_filename("qap", os.path.join("test_data", \
"workflow_reference", \
"mean_functional", \
"wf_inputs.txt"))
# build the workflow
wf, base_dir = run_mean_functional(func_motion, False)
# get the workflow inputs of the workflow being tested
wf_inputs_string = str(wf.inputs).replace("\n", "")
wf_inputs_string = wf_inputs_string.replace(base_dir, \
"BASE_DIRECTORY_HERE")
wf_inputs_string = wf_inputs_string.replace(func_motion, "IN_FILE_HERE")
flag, err = build_test_case(wf, ref_inputs, ref_graph, wf_inputs_string)
assert flag == 2, err
def test_run_mean_functional():
import os
import nibabel as nb
import pkg_resources as p
from qap.functional_preproc import run_mean_functional
if "mean_functional" in os.listdir(os.getcwd()):
err = "\n[!] The output folder for this workflow already exists.\n"
raise Exception(err)
func_motion = p.resource_filename(
"qap", os.path.join(test_sub_dir, "rest_1", "func_motion_correct", "rest_calc_tshift_resample_" "volreg.nii.gz")
)
ref_out = p.resource_filename(
"qap",
os.path.join(test_sub_dir, "rest_1", "mean_functional", "rest_calc_tshift_resample_volreg_" "tstat.nii.gz"),
)
# run the workflow
output = run_mean_functional(func_motion)
# make the correlation
ref_out_data = nb.load(ref_out).get_data()
output_data = nb.load(output).get_data()
os.system("rm -R mean_functional")
# create a vector of True and False values
bool_vector = ref_out_data == output_data
assert bool_vector.all()