def test_compute_node_roles():
""" test ComputeNodeRoles"""
_make_tmp_dir()
compute_node_roles = ComputeNodeRoles()
compute_node_roles.inputs.rada_lol_file = lol_file
compute_node_roles.inputs.Pajek_net_file = Pajek_net_file
val = compute_node_roles.run().outputs
print(val)
assert os.path.exists(val.node_roles_file)
assert os.path.exists(val.all_Z_com_degree_file)
assert os.path.exists(val.all_participation_coeff_file)
os.remove(val.node_roles_file)
os.remove(val.all_Z_com_degree_file)
os.remove(val.all_participation_coeff_file)
def create_pipeline_net_list_to_graph(
main_path, pipeline_name="graph_net_pipe", multi=False, mod=True,
plot=False, optim_seq="WS trfr 100", compute_ndi=False):
"""
Pipeline from net_List (txt file) to graph analysis
Inputs (inputnode):
* net_List_file
* coords_file
* labels_file
Could be used in the previous functions
(create_pipeline_conmat_to_graph_density and
create_pipeline_conmat_to_graph_threshold)
"""
# TODO plot=True is kept for sake of clarity but is now unused
pipeline = pe.Workflow(name=pipeline_name)
pipeline.base_dir = main_path
inputnode = pe.Node(niu.IdentityInterface(
fields=['net_List_file', 'coords_file', 'labels_file']),
name='inputnode')
if not multi:
# density-based graphs
# prepare net_list for radatools processing
prep_rada = pe.Node(interface=PrepRada(), name='prep_rada')
prep_rada.inputs.network_type = "U"
pipeline.connect(inputnode, 'net_List_file',
prep_rada, 'net_List_file')
if mod:
# compute community with radatools
community_rada = pe.Node(
interface=CommRada(), name='community_rada')
community_rada.inputs.optim_seq = optim_seq
pipeline.connect(prep_rada, 'Pajek_net_file',
community_rada, 'Pajek_net_file')
# node roles
node_roles = pe.Node(interface=ComputeNodeRoles(
role_type="4roles"), name='node_roles')
pipeline.connect(prep_rada, 'Pajek_net_file',
node_roles, 'Pajek_net_file')
pipeline.connect(community_rada, 'rada_lol_file',
node_roles, 'rada_lol_file')
node_roles.inputs.compute_ndi = compute_ndi
# compute network properties with rada
net_prop = pe.Node(interface=NetPropRada(
optim_seq="A"), name='net_prop')
pipeline.connect(prep_rada, 'Pajek_net_file',
net_prop, 'Pajek_net_file')
else:
assert False, "Error, never tested"
# density-based graphs
# radatools
# prepare net_list for radatools processing
prep_rada = pe.MapNode(interface=PrepRada(),
name='prep_rada', iterfield=["net_List_file"])
prep_rada.inputs.network_type = "U"
pipeline.connect(inputnode, 'net_List_file',
prep_rada, 'net_List_file')
if mod:
# compute community with radatools
community_rada = pe.MapNode(interface=CommRada(
), name='community_rada', iterfield=["Pajek_net_file"])
community_rada.inputs.optim_seq = optim_seq
pipeline.connect(prep_rada, 'Pajek_net_file',
community_rada, 'Pajek_net_file')
# node roles
node_roles = pe.MapNode(interface=ComputeNodeRoles(
role_type="4roles"), name='node_roles',
iterfield=['Pajek_net_file', 'rada_lol_file'])
pipeline.connect(prep_rada, 'Pajek_net_file',
node_roles, 'Pajek_net_file')
pipeline.connect(community_rada, 'rada_lol_file',
node_roles, 'rada_lol_file')
node_roles.inputs.compute_ndi = compute_ndi
# compute network properties with rada
net_prop = pe.MapNode(interface=NetPropRada(
optim_seq="A"), name='net_prop', iterfield=["Pajek_net_file"])
pipeline.connect(prep_rada, 'Pajek_net_file',
net_prop, 'Pajek_net_file')
return pipeline
def create_pipeline_intmat_to_graph_threshold(main_path,analysis_name = "int_graph_thr_pipe",threshold = 50, mod = False, plot = False,radatools_optim = ""):
"""
Description:
Pipeline from integer matrices (normally coclassification matrices) to graph analysis
Threshold is value based, normally a pourcentage (threshold, 50 -> 50%)
Inputs (inputnode):
* int_mat_file
* coords_file
* labels_file
Comments:
Used for coclassification, not so much used anymore
"""
pipeline = pe.Workflow(name=analysis_name)
pipeline.base_dir = main_path
inputnode = pe.Node(niu.IdentityInterface(fields=['int_mat_file','coords_file','labels_file']),
name='inputnode')
if can_plot_igraph==False:
plot = False
print("########################### plot = " + str(plot) + " #####################################################")
print("can_plot_igraph = " + str(can_plot_igraph))
### compute Z_list from coclass matrix
compute_net_list = pe.Node(interface = ComputeIntNetList(),name='compute_net_list')
compute_net_list.inputs.threshold = threshold
pipeline.connect(inputnode, 'int_mat_file', compute_net_list, 'int_mat_file')
############################################### radatools ################################################################
###--- prepare net_list for radatools processing
prep_rada = pe.Node(interface = PrepRada(),name='prep_rada')
pipeline.connect(compute_net_list, 'net_List_file', prep_rada, 'net_List_file')
if mod == True:
### compute community with radatools
community_rada = pe.Node(interface = CommRada(), name='community_rada',iterfield = ["Pajek_net_file"])
community_rada.inputs.optim_seq = radatools_optim
pipeline.connect( prep_rada, 'Pajek_net_file',community_rada,'Pajek_net_file')
### node roles
node_roles = pe.Node(interface = ComputeNodeRoles(role_type = "4roles"), name='node_roles')
pipeline.connect( prep_rada, 'Pajek_net_file',node_roles,'Pajek_net_file')
pipeline.connect( community_rada, 'rada_lol_file',node_roles,'rada_lol_file')
if plot == True:
#### plot_igraph_modules_rada
plot_igraph_modules_rada = pe.Node(interface = PlotIGraphModules(),name='plot_igraph_modules_rada')
pipeline.connect(inputnode, 'labels_file',plot_igraph_modules_rada,'labels_file')
pipeline.connect(inputnode, 'coords_file',plot_igraph_modules_rada,'coords_file')
pipeline.connect(prep_rada, 'Pajek_net_file',plot_igraph_modules_rada,'Pajek_net_file')
pipeline.connect(community_rada, 'rada_lol_file',plot_igraph_modules_rada,'rada_lol_file')
pipeline.connect(node_roles, 'node_roles_file',plot_igraph_modules_rada,'node_roles_file')
############################################ compute network properties with rada ############################################
net_prop = pe.Node(interface = NetPropRada(optim_seq = "A"), name = 'net_prop')
pipeline.connect(prep_rada, 'Pajek_net_file',net_prop,'Pajek_net_file')
return pipeline
def create_pipeline_conmat_to_graph_threshold(
main_path, pipeline_name="graph_thr_pipe", con_thr=1.0, multi=False,
mod=True, plot=True, optim_seq="WS trfr 100", compute_ndi=False):
"""
Pipeline from connectivity matrices to graph analysis
Threshold is value based (con_thr)
Inputs (inputnode):
* conmat_file
* coords_file
* labels_file
"""
# TODO Warning, need to be checked...
# TODO Warning, should be merged with previous function
# create_pipeline_conmat_to_graph_density
# TODO plot=True is kept for sake of clarity but is now unused
pipeline = pe.Workflow(name=pipeline_name)
pipeline.base_dir = main_path
inputnode = pe.Node(niu.IdentityInterface(
fields=['conmat_file', 'coords_file', 'labels_file']),
name='inputnode')
if not multi:
# density-based graphs
# net_list
compute_net_List = pe.Node(
interface=ComputeNetList(), name='compute_net_List')
compute_net_List.inputs.threshold = con_thr
pipeline.connect(inputnode, 'conmat_file',
compute_net_List, 'Z_cor_mat_file')
# radatools
# prepare net_list for radatools processing
prep_rada = pe.Node(interface=PrepRada(),
name='prep_rada', iterfield=["net_List_file"])
pipeline.connect(compute_net_List, 'net_List_file',
prep_rada, 'net_List_file')
if mod:
# compute community with radatools
community_rada = pe.Node(interface=CommRada(
), name='community_rada', iterfield=["Pajek_net_file"])
community_rada.inputs.optim_seq = optim_seq
pipeline.connect(prep_rada, 'Pajek_net_file',
community_rada, 'Pajek_net_file')
# node roles
node_roles = pe.Node(
interface=ComputeNodeRoles(role_type="4roles"),
name='node_roles')
pipeline.connect(prep_rada, 'Pajek_net_file',
node_roles, 'Pajek_net_file')
pipeline.connect(community_rada, 'rada_lol_file',
node_roles, 'rada_lol_file')
node_roles.inputs.compute_ndi = compute_ndi
# compute network properties with rada
net_prop = pe.Node(interface=NetPropRada(
optim_seq="A"), name='net_prop')
pipeline.connect(prep_rada, 'Pajek_net_file',
net_prop, 'Pajek_net_file')
else:
# density-based graphs
# net_list
compute_net_List = pe.MapNode(interface=ComputeNetList(
), name='compute_net_List', iterfield=["Z_cor_mat_file"])
compute_net_List.inputs.threshold = con_thr
pipeline.connect(inputnode, 'conmat_file',
compute_net_List, 'Z_cor_mat_file')
# radatools
# prepare net_list for radatools processing
prep_rada = pe.MapNode(interface=PrepRada(),
name='prep_rada', iterfield=["net_List_file"])
prep_rada.inputs.network_type = "U"
pipeline.connect(compute_net_List, 'net_List_file',
prep_rada, 'net_List_file')
if mod:
# compute community with radatools
community_rada = pe.MapNode(interface=CommRada(
), name='community_rada', iterfield=["Pajek_net_file"])
community_rada.inputs.optim_seq = optim_seq
pipeline.connect(prep_rada, 'Pajek_net_file',
community_rada, 'Pajek_net_file')
# node roles
node_roles = pe.MapNode(
interface=ComputeNodeRoles(role_type="4roles"),
name='node_roles',
iterfield=['Pajek_net_file', 'rada_lol_file'])
pipeline.connect(prep_rada, 'Pajek_net_file',
node_roles, 'Pajek_net_file')
pipeline.connect(community_rada, 'rada_lol_file',
node_roles, 'rada_lol_file')
node_roles.inputs.compute_ndi = compute_ndi
# compute network properties with rada
net_prop = pe.MapNode(interface=NetPropRada(
optim_seq="A"), name='net_prop', iterfield=["Pajek_net_file"])
pipeline.connect(prep_rada, 'Pajek_net_file',
net_prop, 'Pajek_net_file')
return pipeline
def create_pipeline_net_list_to_graph( main_path, pipeline_name = "graph_net_pipe", multi = False,mod = True, plot = False, optim_seq = "WS trfr 100"):
"""
Description:
Pipeline from net_List (txt file) to graph analysis
Inputs (inputnode):
* net_List_file
* coords_file
* labels_file
Could be used in the previous functions (create_pipeline_conmat_to_graph_density and create_pipeline_conmat_to_graph_threshold)
"""
pipeline = pe.Workflow(name= pipeline_name)
pipeline.base_dir = main_path
inputnode = pe.Node(niu.IdentityInterface(fields=['net_List_file','coords_file','labels_file']),
name='inputnode')
if plot==True and can_plot_igraph==False:
plot = False
if multi == False:
################################################ density-based graphs
##### radatools ################################################################
### prepare net_list for radatools processing
prep_rada = pe.Node(interface = PrepRada(),name='prep_rada')
prep_rada.inputs.network_type = "U"
pipeline.connect(inputnode, 'net_List_file', prep_rada, 'net_List_file')
if mod == True:
### compute community with radatools
community_rada = pe.Node(interface = CommRada(), name='community_rada')
community_rada.inputs.optim_seq = optim_seq
pipeline.connect( prep_rada, 'Pajek_net_file',community_rada,'Pajek_net_file')
### node roles
node_roles = pe.Node(interface = ComputeNodeRoles(role_type = "4roles"), name='node_roles')
pipeline.connect( prep_rada, 'Pajek_net_file',node_roles,'Pajek_net_file')
pipeline.connect( community_rada, 'rada_lol_file',node_roles,'rada_lol_file')
if plot == True:
#### plot_igraph_modules_rada
plot_igraph_modules_rada = pe.Node(interface = PlotIGraphModules(),name='plot_igraph_modules_rada')
pipeline.connect(prep_rada, 'Pajek_net_file',plot_igraph_modules_rada,'Pajek_net_file')
pipeline.connect(community_rada, 'rada_lol_file',plot_igraph_modules_rada,'rada_lol_file')
pipeline.connect(node_roles, 'node_roles_file',plot_igraph_modules_rada,'node_roles_file')
pipeline.connect(inputnode,'coords_file',plot_igraph_modules_rada,'coords_file')
pipeline.connect(inputnode,'labels_file',plot_igraph_modules_rada,'labels_file')
############ compute network properties with rada
net_prop = pe.Node(interface = NetPropRada(optim_seq = "A"), name = 'net_prop')
#net_prop.inputs.radatools_path = radatools_path
pipeline.connect(prep_rada, 'Pajek_net_file',net_prop,'Pajek_net_file')
else:
################################################ density-based graphs #################################################
##### radatools ################################################################
### prepare net_list for radatools processing
prep_rada = pe.MapNode(interface = PrepRada(),name='prep_rada',iterfield = ["net_List_file"])
prep_rada.inputs.network_type = "U"
pipeline.connect(compute_net_List, 'net_List_file', prep_rada, 'net_List_file')
if mod == True:
### compute community with radatools
community_rada = pe.MapNode(interface = CommRada(), name='community_rada',iterfield = ["Pajek_net_file"])
#community_rada.inputs.optim_seq = radatools_optim
pipeline.connect( prep_rada, 'Pajek_net_file',community_rada,'Pajek_net_file')
### node roles
node_roles = pe.MapNode(interface = ComputeNodeRoles(role_type = "4roles"), name='node_roles', iterfield = ['Pajek_net_file','rada_lol_file'])
pipeline.connect( prep_rada, 'Pajek_net_file',node_roles,'Pajek_net_file')
pipeline.connect( community_rada, 'rada_lol_file',node_roles,'rada_lol_file')
if plot == True :
#### plot_igraph_modules_rada
plot_igraph_modules_rada = pe.MapNode(interface = PlotIGraphModules(),name='plot_igraph_modules_rada',iterfield = ['Pajek_net_file','rada_lol_file','node_roles_file'])
pipeline.connect(prep_rada, 'Pajek_net_file',plot_igraph_modules_rada,'Pajek_net_file')
pipeline.connect(community_rada, 'rada_lol_file',plot_igraph_modules_rada,'rada_lol_file')
pipeline.connect(node_roles, 'node_roles_file',plot_igraph_modules_rada,'node_roles_file')
############ compute network properties with rada
net_prop = pe.MapNode(interface = NetPropRada(optim_seq = "A"), name = 'net_prop',iterfield = ["Pajek_net_file"])
#net_prop.inputs.radatools_path = radatools_path
pipeline.connect(prep_rada, 'Pajek_net_file',net_prop,'Pajek_net_file')
return pipeline
def create_pipeline_conmat_to_graph_density( main_path, pipeline_name = "graph_den_pipe", con_den = 1.0,multi = False,mod = True, plot = False, optim_seq = "WS trfr 100"):
"""
Description:
Pipeline from connectivity matrices to graph analysis
Threshold is density based
Inputs (inputnode):
* conmat_file
* coords_file
* labels_file
"""
pipeline = pe.Workflow(name= pipeline_name + "_den_" + str(con_den).replace(".","_"))
pipeline.base_dir = main_path
inputnode = pe.Node(niu.IdentityInterface(fields=['conmat_file','coords_file','labels_file']),
name='inputnode')
if plot==True and can_plot_igraph==False:
print("warning, no igraph is installed, plot is set back to zero.")
plot = False
if multi:
################################################ density-based graphs #################################################
#### net_list
compute_net_List = pe.MapNode(interface = ComputeNetList(),name='compute_net_List',iterfield = ["Z_cor_mat_file"])
compute_net_List.inputs.density = con_den
pipeline.connect(inputnode, 'conmat_file',compute_net_List, 'Z_cor_mat_file')
##### radatools ################################################################
### prepare net_list for radatools processing
prep_rada = pe.MapNode(interface = PrepRada(),name='prep_rada',iterfield = ["net_List_file"])
prep_rada.inputs.network_type = "U"
pipeline.connect(compute_net_List, 'net_List_file', prep_rada, 'net_List_file')
if mod == True:
### compute community with radatools
community_rada = pe.MapNode(interface = CommRada(), name='community_rada',iterfield = ["Pajek_net_file"])
community_rada.inputs.optim_seq = optim_seq
pipeline.connect( prep_rada, 'Pajek_net_file',community_rada,'Pajek_net_file')
### node roles
node_roles = pe.MapNode(interface = ComputeNodeRoles(role_type = "4roles"), name='node_roles', iterfield = ['Pajek_net_file','rada_lol_file'])
pipeline.connect( prep_rada, 'Pajek_net_file',node_roles,'Pajek_net_file')
pipeline.connect( community_rada, 'rada_lol_file',node_roles,'rada_lol_file')
if plot == True :
#### plot_igraph_modules_rada
plot_igraph_modules_rada = pe.MapNode(interface = PlotIGraphModules(),name='plot_igraph_modules_rada',iterfield = ['Pajek_net_file','rada_lol_file','node_roles_file'])
pipeline.connect(prep_rada, 'Pajek_net_file',plot_igraph_modules_rada,'Pajek_net_file')
pipeline.connect(community_rada, 'rada_lol_file',plot_igraph_modules_rada,'rada_lol_file')
pipeline.connect(node_roles, 'node_roles_file',plot_igraph_modules_rada,'node_roles_file')
pipeline.connect(inputnode,'labels_file',plot_igraph_modules_rada,'labels_file')
############ compute network properties with rada
net_prop = pe.MapNode(interface = NetPropRada(optim_seq = "A"), name = 'net_prop',iterfield = ["Pajek_net_file"])
#net_prop.inputs.radatools_path = radatools_path
pipeline.connect(prep_rada, 'Pajek_net_file',net_prop,'Pajek_net_file')
else:
################################################ density-based graphs
#### net_list
compute_net_List = pe.Node(interface = ComputeNetList(),name='compute_net_List')
compute_net_List.inputs.density = con_den
pipeline.connect(inputnode, 'conmat_file',compute_net_List, 'Z_cor_mat_file')
##### radatools ################################################################
### prepare net_list for radatools processing
prep_rada = pe.Node(interface = PrepRada(),name='prep_rada')
prep_rada.inputs.network_type = "U"
pipeline.connect(compute_net_List, 'net_List_file', prep_rada, 'net_List_file')
if mod == True:
### compute community with radatools
community_rada = pe.Node(interface = CommRada(), name='community_rada')
community_rada.inputs.optim_seq = optim_seq
pipeline.connect( prep_rada, 'Pajek_net_file',community_rada,'Pajek_net_file')
### node roles
node_roles = pe.Node(interface = ComputeNodeRoles(role_type = "4roles"), name='node_roles')
pipeline.connect( prep_rada, 'Pajek_net_file',node_roles,'Pajek_net_file')
pipeline.connect( community_rada, 'rada_lol_file',node_roles,'rada_lol_file')
if plot == True:
#### plot_igraph_modules_rada
plot_igraph_modules_rada = pe.Node(interface = PlotIGraphModules(),name='plot_igraph_modules_rada')
pipeline.connect(prep_rada, 'Pajek_net_file',plot_igraph_modules_rada,'Pajek_net_file')
pipeline.connect(community_rada, 'rada_lol_file',plot_igraph_modules_rada,'rada_lol_file')
pipeline.connect(node_roles, 'node_roles_file',plot_igraph_modules_rada,'node_roles_file')
pipeline.connect(inputnode,'coords_file',plot_igraph_modules_rada,'coords_file')
pipeline.connect(inputnode,'labels_file',plot_igraph_modules_rada,'labels_file')
############ compute network properties with rada
net_prop = pe.Node(interface = NetPropRada(optim_seq = "A"), name = 'net_prop')
#net_prop.inputs.radatools_path = radatools_path
pipeline.connect(prep_rada, 'Pajek_net_file',net_prop,'Pajek_net_file')
return pipeline
def create_pipeline_conmat_to_graph_threshold(main_path,pipeline_name="graph_thr_pipe",con_thr = 1.0,multi = False,mod = True, plot = True, optim_seq = "WS trfr 100"):
"""
Description:
Pipeline from connectivity matrices to graph analysis
Threshold is value based (con_thr)
Inputs (inputnode):
* conmat_file
* coords_file
* labels_file
!Warning, need to be checked...
!Warning, should be merged with previous function create_pipeline_conmat_to_graph_density
"""
pipeline = pe.Workflow(name=pipeline_name)
pipeline.base_dir = main_path
inputnode = pe.Node(niu.IdentityInterface(fields=['conmat_file','coords_file','labels_file']), name='inputnode')
if plot==True and can_plot_igraph==False:
plot = False
if multi == False:
################################################ density-based graphs
#### net_list
compute_net_List = pe.Node(interface = ComputeNetList(),name='compute_net_List')
compute_net_List.inputs.threshold = con_thr
#compute_net_List.inputs.density = None
pipeline.connect(inputnode, 'conmat_file',compute_net_List, 'Z_cor_mat_file')
##### radatools ################################################################
### prepare net_list for radatools processing
prep_rada = pe.Node(interface = PrepRada(),name='prep_rada',iterfield = ["net_List_file"])
pipeline.connect(compute_net_List, 'net_List_file', prep_rada, 'net_List_file')
if mod == True:
### compute community with radatools
community_rada = pe.Node(interface = CommRada(), name='community_rada',iterfield = ["Pajek_net_file"])
community_rada.inputs.optim_seq = optim_seq
pipeline.connect( prep_rada, 'Pajek_net_file',community_rada,'Pajek_net_file')
if plot == True:
#### plot_igraph_modules_rada
plot_igraph_modules_rada = pe.Node(interface = PlotIGraphModules(),name='plot_igraph_modules_rada',iterfield = ['Pajek_net_file','rada_lol_file'])
pipeline.connect(prep_rada, 'Pajek_net_file',plot_igraph_modules_rada,'Pajek_net_file')
pipeline.connect(community_rada, 'rada_lol_file',plot_igraph_modules_rada,'rada_lol_file')
############ compute network properties with rada
net_prop = pe.Node(interface = NetPropRada(optim_seq = "A"), name = 'net_prop')
pipeline.connect(prep_rada, 'Pajek_net_file',net_prop,'Pajek_net_file')
else:
################################################ density-based graphs
#### net_list
compute_net_List = pe.MapNode(interface = ComputeNetList(),name='compute_net_List',iterfield = ["Z_cor_mat_file"])
compute_net_List.inputs.threshold = con_thr
pipeline.connect(inputnode, 'conmat_file',compute_net_List, 'Z_cor_mat_file')
##### radatools ################################################################
### prepare net_list for radatools processing
prep_rada = pe.MapNode(interface = PrepRada(),name='prep_rada',iterfield = ["net_List_file"])
prep_rada.inputs.network_type = "U"
pipeline.connect(compute_net_List, 'net_List_file', prep_rada, 'net_List_file')
if mod == True:
### compute community with radatools
community_rada = pe.MapNode(interface = CommRada(), name='community_rada',iterfield = ["Pajek_net_file"])
community_rada.inputs.optim_seq = optim_seq
pipeline.connect( prep_rada, 'Pajek_net_file',community_rada,'Pajek_net_file')
### node roles
node_roles = pe.MapNode(interface = ComputeNodeRoles(role_type = "4roles"), name='node_roles', iterfield = ['Pajek_net_file','rada_lol_file'])
pipeline.connect( prep_rada, 'Pajek_net_file',node_roles,'Pajek_net_file')
pipeline.connect( community_rada, 'rada_lol_file',node_roles,'rada_lol_file')
if plot == True :
#### plot_igraph_modules_rada
plot_igraph_modules_rada = pe.MapNode(interface = PlotIGraphModules(),name='plot_igraph_modules_rada',iterfield = ['Pajek_net_file','rada_lol_file','node_roles_file'])
pipeline.connect(prep_rada, 'Pajek_net_file',plot_igraph_modules_rada,'Pajek_net_file')
pipeline.connect(community_rada, 'rada_lol_file',plot_igraph_modules_rada,'rada_lol_file')
pipeline.connect(node_roles, 'node_roles_file',plot_igraph_modules_rada,'node_roles_file')
pipeline.connect(inputnode,'labels_file',plot_igraph_modules_rada,'labels_file')
############ compute network properties with rada
net_prop = pe.MapNode(interface = NetPropRada(optim_seq = "A"), name = 'net_prop',iterfield = ["Pajek_net_file"])
#net_prop.inputs.radatools_path = radatools_path
pipeline.connect(prep_rada, 'Pajek_net_file',net_prop,'Pajek_net_file')
return pipeline
def create_pipeline_intmat_to_graph_threshold(
main_path,
analysis_name="int_graph_thr_pipe",
threshold=50,
mod=False,
plot=False,
radatools_optim=""):
"""
Pipeline from integer matrices (normally coclassification matrices)
to graph analysis
Threshold is value based, normally a pourcentage (threshold, 50 -> 50%)
Inputs (inputnode):
* int_mat_file
* coords_file
* labels_file
Comments:
Was used for coclassification, not so much used anymore
"""
# TODO plot=True is kept for sake of clarity but is now unused
# TODO should be merged with create_pipeline_net_list_to_graph in
# conmat_to_graph
pipeline = pe.Workflow(name=analysis_name)
pipeline.base_dir = main_path
inputnode = pe.Node(niu.IdentityInterface(
fields=['int_mat_file', 'coords_file', 'labels_file']),
name='inputnode')
# compute Z_list from coclass matrix
compute_net_list = pe.Node(interface=ComputeIntNetList(),
name='compute_net_list')
compute_net_list.inputs.threshold = threshold
pipeline.connect(inputnode, 'int_mat_file', compute_net_list,
'int_mat_file')
# radatools
# --- prepare net_list for radatools processing
prep_rada = pe.Node(interface=PrepRada(), name='prep_rada')
pipeline.connect(compute_net_list, 'net_List_file', prep_rada,
'net_List_file')
if mod:
# compute community with radatools
community_rada = pe.Node(interface=CommRada(),
name='community_rada',
iterfield=["Pajek_net_file"])
community_rada.inputs.optim_seq = radatools_optim
pipeline.connect(prep_rada, 'Pajek_net_file', community_rada,
'Pajek_net_file')
# node roles
node_roles = pe.Node(interface=ComputeNodeRoles(role_type="4roles"),
name='node_roles')
pipeline.connect(prep_rada, 'Pajek_net_file', node_roles,
'Pajek_net_file')
pipeline.connect(community_rada, 'rada_lol_file', node_roles,
'rada_lol_file')
# compute network properties with rada
net_prop = pe.Node(interface=NetPropRada(optim_seq="A"), name='net_prop')
pipeline.connect(prep_rada, 'Pajek_net_file', net_prop, 'Pajek_net_file')
return pipeline
