The Virtual Brain Project

Source code for tvb.core.entities.transient.filtering

# -*- 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
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#   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.
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.. moduleauthor:: Bogdan Neacsa <>    

import json
import six
from tvb.core.entities import model
from tvb.basic.filters.chain import FilterChain

[docs]class StaticFiltersFactory(): """ Factory class to build lists with static used filters through the application. """ RELEVANT_VIEW = "Relevant view" FULL_VIEW = "Full view" @staticmethod
[docs] def build_datatype_filters(selected=RELEVANT_VIEW, single_filter=None): """ Return all visibility filters for data structure page, or only one filter. """ filters = {StaticFiltersFactory.FULL_VIEW: FilterChain(StaticFiltersFactory.FULL_VIEW), StaticFiltersFactory.RELEVANT_VIEW: FilterChain(StaticFiltersFactory.RELEVANT_VIEW, [FilterChain.datatype + '.visible'], [True], operations=["=="])} if selected is None or len(selected) == 0: selected = StaticFiltersFactory.RELEVANT_VIEW if selected in filters: filters[selected].selected = True if single_filter is not None: if single_filter in filters: return filters[single_filter] else: ### We have some custom filter to build return StaticFiltersFactory._build_custom_filter(single_filter) return filters.values()
@staticmethod def _build_custom_filter(filter_data): """ Param filter_data should be at this point a dictionary of the form: {'type' : 'fitler_type', 'value' : 'fitler_value'} If 'filter_type' is not handled just return None. """ filter_data = json.loads(filter_data) if filter_data['type'] == 'from_burst': return FilterChain('Burst', [FilterChain.datatype + '.fk_parent_burst'], [filter_data['value']], operations=["=="]) if filter_data['type'] == 'from_datatype': return FilterChain('Datatypes', [FilterChain.operation + '.parameters'], [filter_data['value']], operations=["like"]) return None @staticmethod
[docs] def build_operations_filters(simulation_algorithm, logged_user_id): """ :returns: list of filters that can be applied on Project View Operations page. """ new_filters = [] ### Filter by algorithm / categories new_filter = FilterChain("Omit Views", [FilterChain.algorithm_category + '.display'], [False], operations=["=="]) new_filters.append(new_filter) new_filter = FilterChain("Only Upload", [FilterChain.algorithm_category + '.rawinput'], [True], operations=["=="]) new_filters.append(new_filter) if simulation_algorithm is not None: new_filter = FilterChain("Only Simulations", [FilterChain.algorithm + '.id'], [], operations=["=="]) new_filters.append(new_filter) ### Filter by operation status filtered_statuses = {model.STATUS_STARTED: "Only Running", model.STATUS_ERROR: "Only with Errors", model.STATUS_CANCELED: "Only Canceled", model.STATUS_FINISHED: "Only Finished", model.STATUS_PENDING: "Only Pending"} for status, title in six.iteritems(filtered_statuses): new_filter = FilterChain(title, [FilterChain.operation + '.status'], [status], operations=["=="]) new_filters.append(new_filter) ### Filter by author new_filter = FilterChain("Only mine", [FilterChain.operation + '.fk_launched_by'], [logged_user_id], operations=["=="]) new_filters.append(new_filter) ### Filter by other flags new_filter = FilterChain("Only relevant", [FilterChain.operation + '.visible'], [True], operations=["=="]) new_filter.selected = True new_filters.append(new_filter) return new_filters