def _run_interface(self, runtime):
from pynets.core.utils import collect_pandas_df
combination_complete = collect_pandas_df(self.inputs.network,
self.inputs.ID,
self.inputs.net_mets_csv_list,
self.inputs.plot_switch,
self.inputs.multi_nets,
self.inputs.multimodal)
setattr(self, '_combination_complete', combination_complete)
return runtime
def test_collect_pandas_df(multi_nets, multimodal):
base_dir = str(Path(__file__).parent / "examples")
network = 'Default'
ID = '002'
func_path = base_dir + '/002/fmri'
dmri_path = base_dir + '/002/dmri'
plot_switch = False
# Rename to proper format.
shutil.copy(
str(dmri_path +
'/DesikanKlein2012/0021001_net_mets_Default_csd_0.1_parc'),
str(dmri_path +
'/DesikanKlein2012/0021001_Default_net_mets_csd_0.1_parc'))
if multi_nets is not None and multimodal is False:
net_mets_csv_list = [
str(func_path +
'/whole_brain_cluster_labels_PCA200/002_Default_net_mets_sps_0.9'
),
str(func_path +
'/whole_brain_cluster_labels_PCA200/002_Default_net_mets_sps_0.9'
)
]
elif multi_nets is not None and multimodal is True:
net_mets_csv_list = [
str(func_path +
'/whole_brain_cluster_labels_PCA200/002_Default_net_mets_sps_0.9'
),
str(dmri_path +
'/DesikanKlein2012/0021001_Default_net_mets_csd_0.1_parc')
]
elif multi_nets is None and multimodal is False:
net_mets_csv_list = [
str(func_path +
'/coords_dosenbach_2010/netmetrics/002_net_mets_partcorr_0.2_4mm'
)
]
elif multi_nets is None and multimodal is True:
net_mets_csv_list = [
str(func_path +
'/coords_dosenbach_2010/netmetrics/002_net_mets_partcorr_0.2_4mm'
),
str(dmri_path + '/DesikanKlein2012/0021001_net_mets_csd_0.05_parc')
]
else:
return
combination_complete = utils.collect_pandas_df(network, ID,
net_mets_csv_list,
plot_switch, multi_nets,
multimodal)
assert combination_complete is not None
def test_collect_pandas_df(plot_switch, embed):
"""
Test collect_pandas_df_make functionality
"""
import glob
base_dir = str(Path(__file__).parent/"examples")
multi_nets = None
multimodal = False
network = None
ID = '002'
net_mets_csv_list = [i for i in glob.glob(f"{base_dir}/topology/*.csv")
if '_neat.csv' not in i]
out = utils.collect_pandas_df(network, ID, net_mets_csv_list,
plot_switch, multi_nets, multimodal, embed)
assert out is True
assert isinstance(net_mets_csv_list, list)
assert len(net_mets_csv_list) == 9
def test_collect_pandas_df(plot_switch, embed):
"""
Test collect_pandas_df_make functionality
"""
import glob
base_dir = os.path.abspath(
pkg_resources.resource_filename("pynets", "../data/examples"))
multi_nets = None
multimodal = False
subnet = None
ID = '002'
net_mets_csv_list = [
i for i in glob.glob(f"{base_dir}/topology/*.csv")
if '_neat.csv' not in i
]
out = utils.collect_pandas_df(subnet, ID, net_mets_csv_list, plot_switch,
multi_nets, multimodal, embed)
assert out is True
assert isinstance(net_mets_csv_list, list)
assert len(net_mets_csv_list) == 9
def test_collect_pandas_df():
base_dir = str(Path(__file__).parent / "examples")
network = None
ID = '002'
plot_switch = False
multi_nets = None
multimodal = False
if multi_nets is not None and multimodal is False:
net_mets_csv_list = [
f"{base_dir}/miscellaneous/0021001_modality-dwi_nodetype-parc_est-csa_thrtype-PROP_thr-0.3_net_mets.csv",
f"{base_dir}/miscellaneous/0021001_modality-dwi_nodetype-parc_est-csa_thrtype-PROP_thr-0.2_net_mets.csv"
]
elif multi_nets is not None and multimodal is True:
net_mets_csv_list = [
f"{base_dir}/miscellaneous/0021001_modality-dwi_nodetype-parc_est-csa_thrtype-PROP_thr-0.3_net_mets.csv",
f"{base_dir}/miscellaneous/0021001_modality-dwi_nodetype-parc_est-csa_thrtype-PROP_thr-0.2_net_mets.csv"
]
elif multi_nets is None and multimodal is False:
net_mets_csv_list = [
f"{base_dir}/miscellaneous/0021001_modality-dwi_nodetype-parc_est-csa_thrtype-PROP_thr-0.3_net_mets.csv",
f"{base_dir}/miscellaneous/0021001_modality-dwi_nodetype-parc_est-csa_thrtype-PROP_thr-0.2_net_mets.csv"
]
elif multi_nets is None and multimodal is True:
net_mets_csv_list = [
f"{base_dir}/miscellaneous/0021001_modality-dwi_nodetype-parc_est-csa_thrtype-PROP_thr-0.3_net_mets.csv",
f"{base_dir}/miscellaneous/0021001_modality-dwi_nodetype-parc_est-csa_thrtype-PROP_thr-0.2_net_mets.csv"
]
else:
return
combination_complete = utils.collect_pandas_df(network, ID,
net_mets_csv_list,
plot_switch, multi_nets,
multimodal)
assert combination_complete is not None