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

# -*- coding: utf-8 -*-
#
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# TheVirtualBrain-Framework Package. This package holds all Data Management, and
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# This program is free software: you can redistribute it and/or modify it under the
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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


class ConnectivityIndex(DataType):
    id = Column(Integer, ForeignKey(DataType.id), 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)

    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) + "]"

    @staticmethod
    def accepted_filters():
        filters = DataType.accepted_filters()
        filters.update({FilterChain.datatype + '.number_of_regions':
                            {'type': 'int', 'display': 'No of Regions', 'operations': ['==', '<', '>']}})
        return filters