The Virtual Brain Project

Source code for tvb.core.entities.transient.graph_structures

# -*- coding: utf-8 -*-
# TheVirtualBrain-Framework Package. This package holds all Data Management, and 
# Web-UI helpful to run brain-simulations. To use it, you also need do download
# TheVirtualBrain-Scientific Package (for simulators). See content of the
# documentation-folder for more details. See also
# (c) 2012-2017, Baycrest Centre for Geriatric Care ("Baycrest") and others
# This program is free software: you can redistribute it and/or modify it under the
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# program.  If not, see <>.
# 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)

.. moduleauthor:: Lia Domide <>
.. moduleauthor:: Ionel Ortelecan <>

from import dao 



[docs]class NodeData(object): """ Contains the meta-data that will be set on each GRAPH node. """ shape_size = None shape_color = None shape_type = None node_type = None node_entity_id = None node_subtitle = None def __init__(self, shape_size, shape_color, shape_type, node_type, node_entity_id, node_subtitle): self.shape_size = shape_size self.shape_color = shape_color self.shape_type = shape_type self.node_type = node_type self.node_entity_id = node_entity_id self.node_subtitle = node_subtitle
[docs] def to_json(self): """ Returns the JSON-ready representation of this NodeData instance. """ instance_json = {"$dim": self.shape_size, "$color": self.shape_color, "$type": self.shape_type, "node_type": self.node_type, "node_entity_id": self.node_entity_id, "node_subtitle": self.node_subtitle} return instance_json
[docs] def build_node_for_datatype(datatype_id, node_subtitle, shape_size=MAX_SHAPE_SIZE, is_group=False): shape = "circlesGroup" if is_group else "circle" return NodeData(shape_size, "#83548B", shape, NODE_DATATYPE_TYPE, datatype_id, node_subtitle)
[docs] def build_node_for_operation(operation, group_id=None): if group_id: entity_id = group_id node_type = NODE_OPERATION_GROUP_TYPE shape = "squaresGroup" else: entity_id = node_type = NODE_OPERATION_TYPE shape = "square" return NodeData(MAX_SHAPE_SIZE, "#660033", shape, node_type, entity_id, str(operation.start_date))
[docs]class NodeStructure(object): """ Define the full structure of a graph NODE (including meta-data of type NodeData and node Adjiacences) """ node_gid = None name = None data = None adjacencies = [] selected = False def __init__(self, node_gid, node_name): self.node_gid = node_gid = node_name
[docs] def to_json(self): """ Returns the JSON-ready representation of this NodeStructure instance. """ instance_json = {"id": self.node_gid, "name":, "data": if is not None else {}, "adjacencies": [{"nodeFrom": self.node_gid, "nodeTo": adj, "data": {}} for adj in self.adjacencies]} return instance_json
[docs] def build_structure_for_datatype(datatype_gid): datatype = dao.get_datatype_by_gid(datatype_gid) is_group = dao.is_datatype_group(datatype_gid) structure = NodeStructure(datatype_gid, datatype.type) = NodeData.build_node_for_datatype(, datatype.display_name, is_group=is_group) return structure
[docs] def build_structure_for_operation(operation): algo = dao.get_algorithm_by_id(operation.fk_from_algo) structure = NodeStructure(operation.gid, algo.displayname) = NodeData.build_node_for_operation(operation) return structure
[docs] def build_structure_for_operation_group(operation_group_gid): group = dao.get_operationgroup_by_gid(operation_group_gid) operation = dao.get_operations_in_group(, only_first_operation=True) algo = dao.get_algorithm_by_id(operation.fk_from_algo) structure = NodeStructure(operation_group_gid, algo.displayname) = NodeData.build_node_for_operation(operation, return structure
[docs] def build_artificial_root_structure(adjacencies_nodes): root_structure = NodeStructure("fakeRootNode", "fakeRootNode") = NodeData.build_node_for_datatype("fakeRootNode", "Fake root", shape_size=MAX_SHAPE_SIZE / 2) root_structure.adjacencies = adjacencies_nodes return root_structure
[docs]class GraphComponent(): """ Class used for representing a single component of the graph. One GraphComponent holds multiple lists of NodeStructure instances (for multiple layers). A GraphComponent will have the following structure:: input_datatypes operation_parent | | | | V V [operation_parent] [output_datatypes] * Currently Selected node | | | | V V output_datatypes in_operations """ input_datatypes = [] operation_parent = [] output_datatypes = [] in_operations = [] def __init__(self, dt_inputs, parent_op, dt_outputs, op_inputs): self.input_datatypes = dt_inputs self.operation_parent = parent_op self.output_datatypes = dt_outputs self.in_operations = op_inputs
[docs]class FullGraphStructure(): """ This class contains information for the entire graph to be displayed in UI. It holds a list of GraphComponent instances (e.g. multiple UPLOAD ops). """ graph_components = [] def __init__(self, components): self.graph_components = components self.fill_shape_size() self.fill_all_graph_adjiacences()
[docs] def prepare_for_json(self): """ Returns a list of NodeStructure instances to be serialized for browser-client rendering. """ artificial_root_adj = [] for component in self.graph_components: if len(component.input_datatypes): artificial_root_adj.extend(self._get_nodes_gids(component.input_datatypes)) else: artificial_root_adj.extend(self._get_nodes_gids(component.operation_parent)) result_to_serialize = [NodeStructure.build_artificial_root_structure(artificial_root_adj)] for component in self.graph_components: for level in [component.input_datatypes, component.operation_parent, component.output_datatypes, component.in_operations]: for node_structure in level: result_to_serialize.append(node_structure) return result_to_serialize
[docs] def fill_all_graph_adjiacences(self): for branch in self.graph_components: parent_ops_gids = self._get_nodes_gids(branch.operation_parent) dt_outputs_gids = self._get_nodes_gids(branch.output_datatypes) op_inputs_gids = self._get_nodes_gids(branch.in_operations) self._set_nodes_adjacencies(branch.input_datatypes, parent_ops_gids) self._set_nodes_adjacencies(branch.operation_parent, dt_outputs_gids) self._set_nodes_adjacencies(branch.output_datatypes, op_inputs_gids, True)
@staticmethod def _get_nodes_gids(list_of_nodes): return [node.node_gid for node in list_of_nodes] @staticmethod def _set_nodes_adjacencies(list_of_nodes, adjacencies, only_for_selected_node=False): """ Sets adjacencies for a list of nodes. """ if only_for_selected_node: for node in list_of_nodes: if node.selected: node.adjacencies = adjacencies else: node.adjacencies = [] else: for node in list_of_nodes: node.adjacencies = adjacencies
[docs] def fill_shape_size(self): """ Sets the correct size for each node from this graph. """ no_of_dt_inputs = 0 no_of_parent_op = 0 no_of_dt_outputs = 0 no_of_op_inputs = 0 for branch in self.graph_components: no_of_dt_inputs += len(branch.input_datatypes) no_of_parent_op += len(branch.operation_parent) no_of_dt_outputs += len(branch.output_datatypes) no_of_op_inputs += len(branch.in_operations) dt_input_size = self._compute_shape_size(no_of_dt_inputs) parent_op_size = self._compute_shape_size(no_of_parent_op) dt_output_size = self._compute_shape_size(no_of_dt_outputs) op_input = self._compute_shape_size(no_of_op_inputs) for branch in self.graph_components: self._set_nodes_size(branch.input_datatypes, dt_input_size) self._set_nodes_size(branch.operation_parent, parent_op_size) self._set_nodes_size(branch.output_datatypes, dt_output_size) self._set_nodes_size(branch.in_operations, op_input)
@staticmethod def _set_nodes_size(list_of_nodes, shape_size): """ Sets the size for each node from the given list of nodes. """ if not len(list_of_nodes): return for node in list_of_nodes: if is None: continue = shape_size @staticmethod def _compute_shape_size(no_of_elements): """ no_of_elements - represents the number of nodes that are displayed on a certain level We consider that the canvas width is 1000px. With a size of 50 the max number of shapes that may be displayed into the canvas are 9 (=> 111px per shape). max_shape_size = 50 points <=> 111px min_shape_size = 10 points <=> 20px 1 point <=> 2px => shape_size = (canvas_width) / [(no_of_nodes + 1) * 2] """ if not no_of_elements: return MAX_SHAPE_SIZE shape_size = 1000 / ((no_of_elements + 1) * 2) if shape_size > MAX_SHAPE_SIZE: return MAX_SHAPE_SIZE elif shape_size < MIN_SHAPE_SIZE: return MIN_SHAPE_SIZE else: return shape_size