Source code for tvb.core.neotraits.db

# -*- coding: utf-8 -*-
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# TheVirtualBrain-Framework Package. This package holds all Data Management, and
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import uuid
import numpy
from datetime import datetime
from sqlalchemy import Column, Integer, Text, DateTime, String, Boolean
from sqlalchemy.orm import declarative_base, declared_attr

SCALAR_MAPPING = {
    bool: Boolean,
    int: Integer,
    str: String
}

Base = declarative_base(name='DeclarativeBase')


[docs] class HasTraitsIndex(Base): id = Column(Integer, primary_key=True) gid = Column(String(32), unique=True) type_ = Column(String(50)) title = Column(Text) create_date = Column(DateTime, default=datetime.now) # Quick remainder about @declared_attr. It makes a class method. # Sqlalchemy will treat this class method like a statically declared class attribute # Another quick remainder, class methods are polymorphic @declared_attr def __tablename__(cls): # subclasses no longer need to define the __tablename__ as we do it here polymorphically for them. return cls.__name__ @declared_attr def __mapper_args__(cls): """ A polymorphic __maper_args__. Because of it the subclasses no longer need to declare the polymorphic_identity """ # this gets called by sqlalchemy before the HasTraitsIndex class declaration is finished (in it's metatype) # so we have to refer to the type by name if cls.__name__ == 'HasTraitsIndex' and cls.__module__ == __name__: # for the base class we have to define the discriminator column return { 'polymorphic_on': cls.type_, 'polymorphic_identity': cls.__name__ } elif cls.__name__ == "DataType": return { 'polymorphic_identity': cls.__name__, 'inherit_condition': cls.id == HasTraitsIndex.id } else: return { 'polymorphic_identity': cls.__name__ } def __init__(self): super(HasTraitsIndex, self).__init__() self.type_ = type(self).__name__ self.gid = uuid.uuid4().hex def __repr__(self): cls = type(self) return '<{}.{} gid="{}..." id="{}">'.format( cls.__module__, cls.__name__, self.gid[:4], self.id )
[docs] def get_subtype_attr(self): return None
[docs] def ensure_float(data): return numpy.float64(data)
[docs] def ensure_int(data): return int(data)
[docs] def from_ndarray(array): if array is None: return None if array.dtype.kind in 'iufc' and array.size != 0: # we compute these simple statistics for integer unsigned float or complex # arrays that are not empty min_value, max_value, mean_value = ensure_float(array.min()), ensure_float(array.max()),\ ensure_float(array.mean()) else: min_value, max_value, mean_value = None, None, None return min_value, max_value, mean_value
[docs] def prepare_array_shape_meta(shape_array): length_1d = None length_2d = None length_3d = None length_4d = None try: length_1d = shape_array[0] length_2d = shape_array[1] length_3d = shape_array[2] length_4d = shape_array[3] except IndexError: pass shape = [] for length in [length_1d, length_2d, length_3d, length_4d]: if length: shape.append(length) return tuple(shape)