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TVB-2663 Remove tvb.adapter dependency from model_operation.py
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79 changes: 79 additions & 0 deletions
79
framework_tvb/tvb/core/entities/transient/range_parameter.py
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# -*- 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 http://www.thevirtualbrain.org | ||
# | ||
# (c) 2012-2020, Baycrest Centre for Geriatric Care ("Baycrest") and others | ||
# | ||
# This program is free software: you can redistribute it and/or modify it under the | ||
# terms of the GNU General Public License as published by the Free Software Foundation, | ||
# either version 3 of the License, or (at your option) any later version. | ||
# This program is distributed in the hope that it will be useful, but WITHOUT ANY | ||
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A | ||
# PARTICULAR PURPOSE. See the GNU General Public License for more details. | ||
# You should have received a copy of the GNU General Public License along with this | ||
# program. If not, see <http://www.gnu.org/licenses/>. | ||
# | ||
# | ||
# CITATION: | ||
# 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) | ||
# | ||
# | ||
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import json | ||
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import numpy | ||
from tvb.basic.neotraits.api import HasTraits, Range | ||
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class RangeParameter(object): | ||
KEY_FLOAT_LO = 'lo' | ||
KEY_FLOAT_STEP = 'step' | ||
KEY_FLOAT_HI = 'hi' | ||
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def __init__(self, name, type, range_definition, is_array=False, range_values=None): | ||
self.name = name | ||
self.type = type | ||
self.is_array = is_array | ||
# range_definition is a Range object when type=float and a FilterChain otherwise | ||
self.range_definition = range_definition | ||
self.range_values = range_values | ||
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def to_json(self): | ||
if self.type is float: | ||
return json.dumps([self.name, {self.KEY_FLOAT_LO: self.range_definition.lo, | ||
self.KEY_FLOAT_STEP: self.range_definition.step, | ||
self.KEY_FLOAT_HI: self.range_definition.hi}]) | ||
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return json.dumps([self.name, self.range_definition]) | ||
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@staticmethod | ||
def from_json(range_json): | ||
range_list = json.loads(range_json) | ||
if isinstance(range_list[1], dict): | ||
return RangeParameter(range_list[0], float, | ||
Range(range_list[1][RangeParameter.KEY_FLOAT_LO], | ||
range_list[1][RangeParameter.KEY_FLOAT_HI], | ||
range_list[1][RangeParameter.KEY_FLOAT_STEP])) | ||
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return RangeParameter(range_list[0], HasTraits, range_list[1]) | ||
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def get_range_values(self): | ||
if self.type is float: | ||
self.range_values = self.range_definition.to_array() | ||
if self.is_array: | ||
self.range_values = [numpy.array([value]) for value in self.range_values] | ||
return self.range_values | ||
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def fill_from_default(self, range_param): | ||
# type: (RangeParameter) -> None | ||
self.type = range_param.type | ||
self.is_array = range_param.is_array |
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