Source code for tvb.adapters.datatypes.db.connectivity

# -*- coding: utf-8 -*-
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from sqlalchemy import Column, Integer, ForeignKey, Boolean, Float
from tvb.core.entities.filters.chain import FilterChain
from tvb.core.entities.model.model_datatype import DataType
from tvb.core.neotraits.db import from_ndarray
from tvb.datatypes.connectivity import Connectivity

[docs]class ConnectivityIndex(DataType): id = Column(Integer, ForeignKey(, primary_key=True) number_of_regions = Column(Integer, nullable=False) number_of_connections = Column(Integer, nullable=False) undirected = Column(Boolean) weights_min = Column(Float) weights_max = Column(Float) weights_mean = Column(Float) tract_lengths_min = Column(Float) tract_lengths_max = Column(Float) tract_lengths_mean = Column(Float) has_cortical_mask = Column(Boolean) has_hemispheres_mask = Column(Boolean)
[docs] def fill_from_has_traits(self, datatype): # type: (Connectivity) -> None super(ConnectivityIndex, self).fill_from_has_traits(datatype) self.has_cortical_mask = datatype.cortical is not None self.has_hemispheres_mask = datatype.hemispheres is not None self.number_of_regions = datatype.number_of_regions self.number_of_connections = datatype.number_of_connections self.undirected = datatype.undirected self.weights_min, self.weights_max, self.weights_mean = from_ndarray(datatype.weights) self.tract_lengths_min, self.tract_lengths_max, self.tract_lengths_mean = from_ndarray(datatype.tract_lengths)
@property def display_name(self): """ Overwrite from superclass and add number of regions field """ previous = "Connectivity" return previous + " [" + str(self.number_of_regions) + "]"
[docs] @staticmethod def accepted_filters(): filters = DataType.accepted_filters() filters.update({FilterChain.datatype + '.number_of_regions': {'type': 'int', 'display': 'No of Regions', 'operations': ['==', '<', '>']}}) return filters