Source code for tvb.adapters.visualizers.time_series_volume

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Backend-side for TS Visualizer of TS Volume DataTypes.

.. moduleauthor:: Paula Popa <>
.. moduleauthor:: Robert Parcus <>
.. moduleauthor:: Lia Domide <>
.. moduleauthor:: Ciprian Tomoiaga <>


import json

import numpy
from tvb.adapters.datatypes.db.structural import StructuralMRIIndex
from tvb.adapters.datatypes.db.time_series import TimeSeriesIndex
from tvb.adapters.datatypes.h5.time_series_h5 import TimeSeriesVolumeH5, TimeSeriesRegionH5
from tvb.adapters.visualizers.region_volume_mapping import _MappedArrayVolumeBase, MappedArrayVolumeVisualizer
from tvb.core.adapters.abcadapter import ABCAdapterForm
from tvb.core.adapters.abcdisplayer import URLGenerator
from tvb.core.adapters.arguments_serialisation import postprocess_voxel_ts
from tvb.core.entities.filters.chain import FilterChain
from import dao
from tvb.core.neocom import h5
from tvb.core.neotraits.forms import TraitDataTypeSelectField
from tvb.core.neotraits.view_model import ViewModel, DataTypeGidAttr
from tvb.core.utils import prepare_time_slice
from tvb.datatypes.structural import StructuralMRI
from tvb.datatypes.time_series import TimeSeries

[docs] class TimeSeriesVolumeVisualiserModel(ViewModel): time_series = DataTypeGidAttr( linked_datatype=TimeSeries, label='Time Series' ) background = DataTypeGidAttr( linked_datatype=StructuralMRI, required=False, label='Background T1' )
[docs] class TimeSeriesVolumeVisualiserForm(ABCAdapterForm): def __init__(self): super(TimeSeriesVolumeVisualiserForm, self).__init__() self.time_series = TraitDataTypeSelectField(TimeSeriesVolumeVisualiserModel.time_series, name='time_series', conditions=self.get_filters()) self.background = TraitDataTypeSelectField(TimeSeriesVolumeVisualiserModel.background, name='background')
[docs] @staticmethod def get_view_model(): return TimeSeriesVolumeVisualiserModel
[docs] @staticmethod def get_input_name(): return 'time_series'
[docs] @staticmethod def get_filters(): return FilterChain(fields=[FilterChain.datatype + '.has_volume_mapping'], operations=["=="], values=[True])
[docs] @staticmethod def get_required_datatype(): return TimeSeriesIndex
[docs] class TimeSeriesVolumeVisualiser(_MappedArrayVolumeBase): _ui_name = "Time Series Volume Visualizer" _ui_subsection = "volume"
[docs] def get_form_class(self): return TimeSeriesVolumeVisualiserForm
[docs] def get_required_memory_size(self, view_model): # type: (TimeSeriesVolumeVisualiserModel) -> int """Return required memory.""" return -1
[docs] def launch(self, view_model): # type: (TimeSeriesVolumeVisualiserModel) -> dict url_volume_data = URLGenerator.build_url(, 'get_volume_view', view_model.time_series, '') url_timeseries_data = URLGenerator.build_url(, 'get_voxel_time_series', view_model.time_series, '') ts_index = self.load_entity_by_gid(view_model.time_series) ts_h5 = h5.h5_file_for_index(ts_index) min_value, max_value = ts_h5.get_min_max_values() if isinstance(ts_h5, TimeSeriesVolumeH5): volume_shape = volume = self.load_traited_by_gid(ts_h5.volume.load()) else: rm_index = self.load_entity_by_gid(ts_h5.region_mapping_volume.load()) volume = self.load_traited_by_gid(rm_index.fk_volume_gid) rmv = self.load_traited_by_gid(ts_h5.region_mapping_volume.load()) volume_shape = [[0]] volume_shape.extend(rmv.array_data.shape) background_index = None if view_model.background: background_index = self.load_entity_by_gid(view_model.background) params = dict(title="Volumetric Time Series", ts_title=ts_h5.title.load(), labelsStateVar=ts_index.get_labels_for_dimension(1), labelsModes=list(range(ts_index.data_length_4d)), minValue=min_value, maxValue=max_value, urlVolumeData=url_volume_data, urlTimeSeriesData=url_timeseries_data, samplePeriod=ts_h5.sample_period.load(), samplePeriodUnit=ts_h5.sample_period_unit.load(), volumeShape=json.dumps(volume_shape), volumeOrigin=json.dumps(volume.origin.tolist()), voxelUnit=volume.voxel_unit, voxelSize=json.dumps(volume.voxel_size.tolist())) params.update(self.ensure_background(background_index)) ts_h5.close() return self.build_display_result("time_series_volume/view", params, pages=dict(controlPage="time_series_volume/controls"))
[docs] def ensure_background(self, background_index): if background_index is None: background_index = dao.try_load_last_entity_of_type(self.current_project_id, StructuralMRIIndex) if background_index is None: return _MappedArrayVolumeBase.compute_background_params() with h5.h5_file_for_index(background_index) as background_h5: min_value, max_value = background_h5.get_min_max_values() url_volume_data = URLGenerator.build_url(, 'get_volume_view', background_index.gid, '') return _MappedArrayVolumeBase.compute_background_params(min_value, max_value, url_volume_data)
[docs] def get_voxel_time_series(self, entity_gid, **kwargs): """ Retrieve for a given voxel (x,y,z) the entire timeline. :param x: int coordinate :param y: int coordinate :param z: int coordinate :return: A complex dictionary with information about current voxel. The main part will be a vector with all the values over time from the x,y,z coordinates. """ with h5.h5_file_for_gid(entity_gid) as ts_h5: if isinstance(ts_h5, TimeSeriesRegionH5): return self.prepare_view_region(ts_h5, **kwargs) data = ts_h5.get_voxel_time_series(**kwargs) return data
[docs] def get_view_region(self, ts_h5, volume_rm_h5, from_idx, to_idx, x, y, z, var=0, mode=0): idx_slices = slice(x, x + 1), slice(y, y + 1), slice(z, z + 1) idx = int(volume_rm_h5.array_data[idx_slices]) time_length =[0] var, mode = int(var), int(mode) voxel_slices = prepare_time_slice(time_length), slice(var, var + 1), slice(idx, idx + 1), slice(mode, mode + 1) connectivity_gid = volume_rm_h5.connectivity.load() with h5.h5_file_for_gid(connectivity_gid) as connectivity_h5: label = connectivity_h5.region_labels.load()[idx] background, back_min, back_max = None, None, None if idx < 0: back_min, back_max = ts_h5.get_min_max_values() background = numpy.ones((time_length, 1)) * ts_h5.out_of_range(back_min) label = 'background' result = postprocess_voxel_ts(ts_h5, voxel_slices, background, back_min, back_max, label) return result