The Virtual Brain Project

Source code for tvb.datatypes.lookup_tables

# -*- coding: utf-8 -*-
#  TheVirtualBrain-Scientific Package. This package holds all simulators, and 
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#   Paula Sanz Leon, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide,
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The LookUpTable datatype. This brings together the scientific and framework 
methods that are associated with the precalculated look up tables.

.. moduleauthor:: Paula Sanz Leon <Paula@tvb.invalid>


import numpy
from tvb.basic.readers import try_get_absolute_path
from tvb.datatypes import arrays
from tvb.basic.traits import types_basic as basic, types_mapped
from tvb.basic.logger.builder import get_logger

LOG = get_logger(__name__)

[docs]class LookUpTable(types_mapped.MappedType): """ Lookup Tables for storing pre-computed functions. Specific table subclasses are implemented below. """ _base_classes = ['LookUpTables'] equation = basic.String( label="String representation of the precalculated function", doc="""A latex representation of the function whose values are stored in the table, with the extra escaping needed for interpretation via sphinx.""") xmin = arrays.FloatArray( label="x-min", doc="""Minimum value""") xmax = arrays.FloatArray( label="x-max", doc="""Maximum value""") data = arrays.FloatArray( label="data", doc="""Tabulated values""") number_of_values = basic.Integer( label="Number of values", default=0, doc="""The number of values in the table """) df = arrays.FloatArray( label="df", doc=""".""") dx = arrays.FloatArray( label="dx", default=numpy.array([]), doc="""Tabulation step""") invdx = arrays.FloatArray( label="invdx", default=numpy.array([]), doc=""".""") @staticmethod def populate_table(result, source_file): source_full_path = try_get_absolute_path("tvb_data.tables", source_file) zip_data = numpy.load(source_full_path) result.df = zip_data['df'] result.xmin, result.xmax = zip_data['min_max'] = zip_data['f'] return result def configure(self): """ Invoke the compute methods for computable attributes that haven't been set during initialization. """ super(LookUpTable, self).configure() # Check if dx and invdx have been computed if self.number_of_values == 0: self.number_of_values =[0] if self.dx.size == 0: self.compute_search_indices() def _find_summary_info(self): """ Gather scientifically interesting summary information from an instance of this dataType, if any ... """ summary = {"Number of values": self.number_of_values} return summary def compute_search_indices(self): """ ... """ self.dx = ((self.xmax - self.xmin) / (self.number_of_values) - 1) self.invdx = 1 / self.dx def search_value(self, val): """ Search a value in this look up table """ if self.xmin: y = val - self.xmin else: y = val ind = numpy.array(y * self.invdx, dtype=int) try: return[ind] + self.df[ind] * (y - ind * self.dx) except IndexError: # out of bounds return numpy.NaN # NOTE: not sure if we should return a NaN or make val = self.max # At the moment, we force the input values to be within a known range
[docs]class PsiTable(LookUpTable): """ Look up table containing the values of a function representing the time-averaged gating variable :math:`\\psi(\\nu)` as a function of the presynaptic rates :math:`\\nu` """ __tablename__ = None @staticmethod def from_file(source_file="psi.npz", instance=None): return LookUpTable.populate_table(instance or PsiTable(), source_file)
[docs]class NerfTable(LookUpTable): """ Look up table containing the values of Nerf integral within the :math:`\\phi` function that describes how the discharge rate vary as a function of parameters defining the statistical properties of the membrane potential in presence of synaptic inputs. """ __tablename__ = None @staticmethod def from_file(source_file="nerf_int.npz", instance=None): return LookUpTable.populate_table(instance or NerfTable(), source_file)