def get_analyze_groups(self):
"""
:return: list of AlgorithmTransientGroup entities
"""
categories = dao.get_launchable_categories(elimin_viewers=True)
categories_ids = [categ.id for categ in categories]
stored_adapters = dao.get_adapters_from_categories(categories_ids)
groups_list = []
for adapter in stored_adapters:
# For empty groups, this time, we fill the actual adapter
group = AlgorithmTransientGroup(adapter.group_name or adapter.displayname,
adapter.group_description or adapter.description)
group = self._find_group(groups_list, group)
group.children.append(adapter)
return categories[0], groups_list
def _group_adapters_by_category(self, stored_adapters, categories):
"""
:param stored_adapters: list StoredAdapter
:return: dict(category_name: List AlgorithmTransientGroup), empty groups all in the same AlgorithmTransientGroup
"""
categories_dict = dict()
for adapter in stored_adapters:
category_name = categories.get(adapter.fk_category)
if category_name in categories_dict:
groups_list = categories_dict.get(category_name)
else:
groups_list = []
categories_dict[category_name] = groups_list
group = AlgorithmTransientGroup(adapter.group_name, adapter.group_description)
group = self._find_group(groups_list, group)
group.children.append(adapter)
return categories_dict
#
# CITATION:
# When using The Virtual Brain for scientific publications, please cite it as follows:
#
# Paula Sanz Leon, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide,
# Jochen Mersmann, Anthony R. McIntosh, Viktor Jirsa (2013)
# The Virtual Brain: a simulator of primate brain network dynamics.
# Frontiers in Neuroinformatics (7:10. doi: 10.3389/fninf.2013.00010)
#
#
from tvb.core.entities.model import AlgorithmTransientGroup
from tvb.adapters.analyzers.bct_adapters import BaseBCT, bct_description
BCT_GROUP_DEGREE = AlgorithmTransientGroup("Degree and Similarity Algorithms",
"Brain Connectivity Toolbox",
"bctdegree")
BCT_GROUP_DENSITY = AlgorithmTransientGroup("Density Algorithms",
"Brain Connectivity Toolbox",
"bctdensity")
class Degree(BaseBCT):
"""
"""
_ui_group = BCT_GROUP_DEGREE
_ui_connectivity_label = "Undirected (binary/weighted) connection matrix:"
_ui_name = "Degree"
_ui_description = bct_description("degrees_und.m")
_matlab_code = "deg = degrees_und(CIJ);"
#
# CITATION:
# When using The Virtual Brain for scientific publications, please cite it as follows:
#
# Paula Sanz Leon, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide,
# Jochen Mersmann, Anthony R. McIntosh, Viktor Jirsa (2013)
# The Virtual Brain: a simulator of primate brain network dynamics.
# Frontiers in Neuroinformatics (7:10. doi: 10.3389/fninf.2013.00010)
#
#
from tvb.adapters.analyzers.bct_adapters import BaseBCT, BaseUndirected, bct_description, LABEL_CONNECTIVITY_BINARY
from tvb.core.entities.model import AlgorithmTransientGroup
BCT_GROUP_CENTRALITY = AlgorithmTransientGroup("Centrality Algorithms",
"Brain Connectivity Toolbox",
"bctcentrality")
class CentralityNodeBinary(BaseBCT):
"""
"""
_ui_group = BCT_GROUP_CENTRALITY
_ui_connectivity_label = LABEL_CONNECTIVITY_BINARY
_ui_name = "Node Betweenness Centrality Binary"
_ui_description = bct_description("betweenness_bin.m")
_matlab_code = "C = betweenness_bin(A);"
def launch(self, connectivity, **kwargs):
kwargs['A'] = connectivity.binarized_weights
# CITATION:
# When using The Virtual Brain for scientific publications, please cite it as follows:
#
# Paula Sanz Leon, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide,
# Jochen Mersmann, Anthony R. McIntosh, Viktor Jirsa (2013)
# The Virtual Brain: a simulator of primate brain network dynamics.
# Frontiers in Neuroinformatics (7:10. doi: 10.3389/fninf.2013.00010)
#
#
from tvb.core.entities.model import AlgorithmTransientGroup
from tvb.adapters.analyzers.bct_adapters import BaseBCT, BaseUndirected, bct_description, \
LABEL_CONN_WEIGHTED_UNDIRECTED, LABEL_CONN_WEIGHTED_DIRECTED
BCT_GROUP_CLUSTERING = AlgorithmTransientGroup("Clustering Algorithms", "Brain Connectivity Toolbox", "bctclustering")
class ClusteringCoefficient(BaseBCT):
"""
"""
_ui_group = BCT_GROUP_CLUSTERING
_ui_connectivity_label = "Binary directed connection matrix:"
_ui_name = "Clustering Coefficient BD"
_ui_description = bct_description("clustering_coef_bd.m")
_matlab_code = "C = clustering_coef_bd(A);"
def launch(self, connectivity, **kwargs):
kwargs['A'] = connectivity.weights
#
import os
from abc import abstractmethod
from tvb.adapters.analyzers.matlab_worker import MatlabWorker
from tvb.basic.filters.chain import FilterChain
from tvb.basic.profile import TvbProfile
from tvb.core.adapters.abcadapter import ABCAsynchronous
from tvb.core.entities.model import AlgorithmTransientGroup
from tvb.core.utils import extract_matlab_doc_string
from tvb.datatypes.connectivity import Connectivity
from tvb.datatypes.graph import ConnectivityMeasure
from tvb.datatypes.mapped_values import ValueWrapper
BCT_GROUP_MODULARITY = AlgorithmTransientGroup("Modularity Algorithms",
"Brain Connectivity Toolbox",
"bct")
BCT_GROUP_DISTANCE = AlgorithmTransientGroup("Distance Algorithms",
"Brain Connectivity Toolbox",
"bctdistance")
BCT_PATH = os.path.join(TvbProfile.current.EXTERNALS_FOLDER_PARENT,
"externals/BCT")
BCT_PATH_ENV = 'BCT_PATH'
if BCT_PATH_ENV in os.environ and os.path.exists(
os.environ[BCT_PATH_ENV]) and os.path.isdir(os.environ[BCT_PATH_ENV]):
BCT_PATH = os.environ[BCT_PATH_ENV]
LABEL_CONNECTIVITY_BINARY = "Binary (directed/undirected) connection matrix"
LABEL_CONN_WEIGHTED_DIRECTED = "Weighted directed connection matrix"
LABEL_CONN_WEIGHTED_UNDIRECTED = "Weighted undirected connection matrix"
