# -*- 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 to download
# TheVirtualBrain-Scientific Package (for simulators). See content of the
# documentation-folder for more details. See also http://www.thevirtualbrain.org
#
# (c) 2012-2023, 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 explained here:
# https://www.thevirtualbrain.org/tvb/zwei/neuroscience-publications
#
#
"""
.. moduleauthor:: Dan Pop <dan.pop@codemart.ro>
.. moduleauthor:: Bogdan Neacsa <bogdan.neacsa@codemart.ro>
"""
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 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_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]
@staticmethod
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"))
