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Source code for tvb.adapters.visualizers.time_series_volume

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#   Paula Sanz Leon, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide,
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"""
Backend-side for TS Visualizer of TS Volume DataTypes.

.. moduleauthor:: Paula Popa <paula.popa@codemart.ro>
.. moduleauthor:: Robert Parcus <betoparcus@gmail.com>
.. moduleauthor:: Lia Domide <lia.domide@codemart.ro>
.. moduleauthor:: Ciprian Tomoiaga <ciprian.tomoiaga@codemart.ro>

"""

import json
import numpy
from tvb.adapters.visualizers.region_volume_mapping import _MappedArrayVolumeBase
from tvb.adapters.datatypes.h5.time_series_h5 import TimeSeriesVolumeH5, TimeSeriesRegionH5
from tvb.adapters.datatypes.db.structural import StructuralMRIIndex
from tvb.adapters.datatypes.db.time_series import TimeSeriesIndex
from tvb.core.adapters.abcdisplayer import URLGenerator
from tvb.core.adapters.abcadapter import ABCAdapterForm
from tvb.core.adapters.arguments_serialisation import preprocess_space_parameters, postprocess_voxel_ts
from tvb.core.entities.filters.chain import FilterChain
from tvb.core.entities.storage import dao
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, prefix='', project_id=None): super(TimeSeriesVolumeVisualiserForm, self).__init__(prefix, project_id) self.time_series = TraitDataTypeSelectField(TimeSeriesVolumeVisualiserModel.time_series, self, name='time_series', conditions=self.get_filters()) self.background = TraitDataTypeSelectField(TimeSeriesVolumeVisualiserModel.background, self, name='background') @staticmethod
[docs] def get_view_model(): return TimeSeriesVolumeVisualiserModel
@staticmethod
[docs] def get_input_name(): return 'time_series'
@staticmethod
[docs] def get_filters(): return FilterChain(fields=[FilterChain.datatype + '.has_volume_mapping'], operations=["=="], values=[True])
@staticmethod
[docs] 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(self.stored_adapter.id, 'get_volume_view', view_model.time_series, '') url_timeseries_data = URLGenerator.build_url(self.stored_adapter.id, 'get_voxel_time_series', view_model.time_series, '') ts_h5_class, ts_h5_path = self._load_h5_of_gid(view_model.time_series.hex) ts_h5 = ts_h5_class(ts_h5_path) min_value, max_value = ts_h5.get_min_max_values() ts_index = self.load_entity_by_gid(view_model.time_series) if isinstance(ts_h5, TimeSeriesVolumeH5): volume_h5_class, volume_h5_path = self._load_h5_of_gid(ts_h5.volume.load()) volume_h5 = volume_h5_class(volume_h5_path) volume_shape = ts_h5.data.shape else: rmv_index = self.load_entity_by_gid(ts_h5.region_mapping_volume.load()) rmv_h5_class, rmv_h5_path = self._load_h5_of_gid(rmv_index.gid) rmv_h5 = rmv_h5_class(rmv_h5_path) volume_index = self.load_entity_by_gid(rmv_h5.volume.load()) volume_h5_class, volume_h5_path = self._load_h5_of_gid(volume_index.gid) volume_h5 = volume_h5_class(volume_h5_path) volume_shape = [ts_h5.data.shape[0]] volume_shape.extend(rmv_h5.array_data.shape) rmv_h5.close() 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_h5.origin.load().tolist()), voxelUnit=volume_h5.voxel_unit.load(), voxelSize=json.dumps(volume_h5.voxel_size.load().tolist())) params.update(self.ensure_background(background_index)) volume_h5.close() 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() background_class, background_path = self._load_h5_of_gid(background_index.gid) background_h5 = background_class(background_path) min_value, max_value = background_h5.get_min_max_values() background_h5.close() url_volume_data = URLGenerator.build_url(self.stored_adapter.id, '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. """ ts_h5_class, ts_h5_path = self._load_h5_of_gid(entity_gid) with ts_h5_class(ts_h5_path) as ts_h5: if ts_h5_class is TimeSeriesRegionH5: return self._get_voxel_time_series_region(ts_h5, **kwargs) return ts_h5.get_voxel_time_series(**kwargs)
def _get_voxel_time_series_region(self, ts_h5, x, y, z, var=0, mode=0): region_mapping_volume_gid = ts_h5.region_mapping_volume.load() if region_mapping_volume_gid is None: raise Exception("Invalid method called for TS without Volume Mapping!") volume_rm_h5_class, volume_rm_h5_path = self._load_h5_of_gid(region_mapping_volume_gid.hex) volume_rm_h5 = volume_rm_h5_class(volume_rm_h5_path) volume_rm_shape = volume_rm_h5.array_data.shape x, y, z = preprocess_space_parameters(x, y, z, volume_rm_shape[0], volume_rm_shape[1], volume_rm_shape[2]) 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 = ts_h5.data.shape[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() connectivity_h5_class, connectivity_h5_path = self._load_h5_of_gid(connectivity_gid.hex) connectivity_h5 = connectivity_h5_class(connectivity_h5_path) 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' volume_rm_h5.close() connectivity_h5.close() result = postprocess_voxel_ts(ts_h5, voxel_slices, background, back_min, back_max, label) return result