Source code for tvb.adapters.visualizers.pse_isocline

# -*- coding: utf-8 -*-
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.. moduleauthor:: Dan Pop <>
.. moduleauthor:: Bogdan Neacsa <>

import json

import numpy
from tvb.adapters.visualizers.pse import PSEGroupModel, PSEModel, KEY_GID
from tvb.core.adapters.abcadapter import ABCAdapterForm
from tvb.core.adapters.abcdisplayer import ABCDisplayer
from tvb.core.adapters.exceptions import LaunchException
from tvb.core.entities.filters.chain import FilterChain
from tvb.core.entities.model.model_datatype import DataTypeGroup
from tvb.core.neotraits.forms import TraitDataTypeSelectField
from tvb.core.neotraits.view_model import ViewModel, DataTypeGidAttr

[docs]class PSEIsoGroupModel(PSEGroupModel): def __init__(self, datatype_group_gid): super(PSEIsoGroupModel, self).__init__(datatype_group_gid) self.apriori_x = self.range1_values self.apriori_y = self.range2_values self.apriori_data = dict() self._fill_apriori_data()
[docs] def parse_pse_data_for_display(self): pse_model_list = [] op_has_results = True for operation in self.operations: if not operation.has_finished: raise LaunchException("Not all operations from this range are complete. Cannot view until then.") pse_model = PSEModel(operation) pse_model_list.append(pse_model) if not pse_model.datatype_measure: op_has_results = False if not op_has_results: raise LaunchException("No datatypes were generated due to simulation errors. Nothing to display.") return pse_model_list
def _prepare_sorted_metrics(self, metric_key): coords_to_node_info = super(PSEIsoGroupModel, self).get_all_node_info() metric_values = numpy.zeros((len(self.apriori_x), len(self.apriori_y))) self.datatypes_gids = numpy.zeros((len(self.apriori_x), len(self.apriori_y)), object) for idx1, val1 in enumerate(self.apriori_x): for idx2, val2 in enumerate(self.apriori_y): try: dt_gid = coords_to_node_info[val1][val2][KEY_GID] metric_values[idx1][idx2] = self.get_all_metrics()[dt_gid][metric_key] except KeyError: dt_gid = None metric_values[idx1][idx2] = numpy.NaN self.datatypes_gids[idx1][idx2] = dt_gid return metric_values def _fill_apriori_data(self): """ Gather apriori data from the operations. Also gather the datatype gid's""" for metric_key in self.get_available_metric_keys(): metric_values = self._prepare_sorted_metrics(metric_key) self.apriori_data.update({metric_key: metric_values})
[docs] def get_all_node_info(self): all_node_info = dict() for pse_model in self.pse_model_list: all_node_info.update({pse_model.datatype_measure.gid: pse_model.prepare_node_info()}) return all_node_info
[docs]class IsoclinePSEAdapterModel(ViewModel): datatype_group = DataTypeGidAttr( linked_datatype=DataTypeGroup, label='Datatype Group' )
[docs]class IsoclinePSEAdapterForm(ABCAdapterForm): def __init__(self, prefix='', project_id=None): super(IsoclinePSEAdapterForm, self).__init__(prefix, project_id) self.datatype_group = TraitDataTypeSelectField(IsoclinePSEAdapterModel.datatype_group, self, name='datatype_group', conditions=self.get_filters()) @staticmethod
[docs] def get_view_model(): return IsoclinePSEAdapterModel
[docs] def get_required_datatype(): return DataTypeGroup
[docs] def get_input_name(): return 'datatype_group'
[docs] def get_filters(): return FilterChain(fields=[FilterChain.datatype + ".no_of_ranges"], operations=["=="], values=[2])
[docs]class IsoclinePSEAdapter(ABCDisplayer): """ Visualization adapter for Parameter Space Exploration. Will be used as a generic visualizer, accessible when input entity is DataTypeGroup. Will also be used in Burst as a supplementary navigation layer. """ _ui_name = "Isocline Parameter Space Exploration" _ui_subsection = "pse_iso" def __init__(self): ABCDisplayer.__init__(self) self.interp_models = {} self.nan_indices = {}
[docs] def get_form_class(self): return IsoclinePSEAdapterForm
[docs] def get_required_memory_size(self, view_model): # type: (IsoclinePSEAdapterModel) -> int """ Return the required memory to run this algorithm. """ # Don't know how much memory is needed. return -1
[docs] def burst_preview(self, view_model): # type: (IsoclinePSEAdapterModel) -> dict """ Generate the preview for the burst page. """ return self.launch(view_model)
[docs] def get_metric_matrix(self, datatype_group_gid, selected_metric=None): pse_iso = PSEIsoGroupModel(datatype_group_gid) if selected_metric is None: selected_metric = list(pse_iso.get_available_metric_keys())[0] data_matrix = pse_iso.apriori_data[selected_metric] data_matrix = numpy.rot90(data_matrix) data_matrix = numpy.flipud(data_matrix) vmin = numpy.nanmin(data_matrix) vmax = numpy.nanmax(data_matrix) # TODO: We replace NaN values here. To be addressed by task TVB-2660. data_matrix[numpy.isnan(data_matrix)] = vmin - 1 matrix_data = ABCDisplayer.dump_with_precision(data_matrix.flat) matrix_guids = pse_iso.datatypes_gids matrix_guids = numpy.rot90(matrix_guids) matrix_shape = json.dumps(data_matrix.squeeze().shape) x_min = pse_iso.apriori_x[0] x_max = pse_iso.apriori_x[-1] y_min = pse_iso.apriori_y[0] y_max = pse_iso.apriori_y[-1] return dict(matrix_data=matrix_data, matrix_guids=json.dumps(matrix_guids.flatten().tolist()), matrix_shape=matrix_shape, color_metric=selected_metric, xAxisName=pse_iso.get_range1_key(), yAxisName=pse_iso.get_range2_key(), available_metrics=pse_iso.get_available_metric_keys(), x_min=x_min, x_max=x_max, y_min=y_min, y_max=y_max, vmin=vmin, vmax=vmax)
[docs] def prepare_node_data(datatype_group_gid): pse_iso = PSEIsoGroupModel(datatype_group_gid) return pse_iso.get_all_node_info()
[docs] def launch(self, view_model): params = self.get_metric_matrix(view_model.datatype_group.hex) params["title"] = self._ui_name params["canvasName"] = "Interpolated values for PSE metric: " params["url_base"] = "/burst/explore/get_metric_matrix/" + view_model.datatype_group.hex params["node_info_url"] = "/burst/explore/get_node_matrix/" + view_model.datatype_group.hex return self.build_display_result('pse_isocline/view', params, pages=dict(controlPage="pse_isocline/controls"))