def launch(self, view_model):
# type: (ConnectivityAnnotationsViewModel) -> dict
annotations_index = self.load_entity_by_gid(view_model.annotations_index)
if view_model.connectivity_index is None:
connectivity_index = self.load_entity_by_gid(annotations_index.connectivity_gid)
else:
connectivity_index = self.load_entity_by_gid(view_model.connectivity_index)
if view_model.region_mapping_index is None:
region_map = dao.get_generic_entity(RegionMappingIndex, connectivity_index.gid,
'connectivity_gid')
if len(region_map) < 1:
raise LaunchException(
"Can not launch this viewer unless we have at least a RegionMapping for the current Connectivity!")
region_mapping_index = region_map[0]
else:
region_mapping_index = self.load_entity_by_gid(view_model.region_mapping_index)
boundary_url = SurfaceURLGenerator.get_url_for_region_boundaries(region_mapping_index.surface_gid,
region_mapping_index.gid,
self.stored_adapter.id)
surface_index = self.load_entity_by_gid(region_mapping_index.surface_gid)
surface_h5 = h5.h5_file_for_index(surface_index)
assert isinstance(surface_h5, SurfaceH5)
url_vertices_pick, url_normals_pick, url_triangles_pick = SurfaceURLGenerator.get_urls_for_pick_rendering(
surface_h5)
url_vertices, url_normals, _, url_triangles, url_region_map = SurfaceURLGenerator.get_urls_for_rendering(
surface_h5, region_mapping_index.gid)
params = dict(title="Connectivity Annotations Visualizer",
baseUrl=TvbProfile.current.web.BASE_URL,
annotationsTreeUrl=URLGenerator.build_url(self.stored_adapter.id, 'tree_json',
view_model.annotations_index),
urlTriangleToRegion=URLGenerator.build_url(self.stored_adapter.id, "get_triangles_mapping",
region_mapping_index.gid),
urlActivationPatterns=URLGenerator.paths2url(view_model.annotations_index,
"get_activation_patterns"),
minValue=0,
maxValue=connectivity_index.number_of_regions - 1,
urlColors=json.dumps(url_region_map),
urlVerticesPick=json.dumps(url_vertices_pick),
urlTrianglesPick=json.dumps(url_triangles_pick),
urlNormalsPick=json.dumps(url_normals_pick),
brainCenter=json.dumps(surface_h5.center()),
urlVertices=json.dumps(url_vertices),
urlTriangles=json.dumps(url_triangles),
urlNormals=json.dumps(url_normals),
urlRegionBoundaries=boundary_url)
return self.build_display_result("annotations/annotations_view", params,
pages={"controlPage": "annotations/annotations_controls"})
def compute_parameters(self, time_series, shell_surface=None):
"""
Create the required parameter dictionary for the HTML/JS viewer.
:rtype: `dict`
:raises Exception: when
* number of measure points exceeds the maximum allowed
* a Face object cannot be found in database
"""
self.populate_surface_fields(time_series)
url_vertices, url_normals, url_lines, url_triangles, url_region_map = SurfaceURLGenerator.get_urls_for_rendering(
self.surface_h5, self.region_map_gid)
hemisphere_chunk_mask = self.surface_h5.get_slices_to_hemisphere_mask()
params = self.retrieve_measure_points_params(time_series)
if not self.one_to_one_map and self.measure_points_no > MAX_MEASURE_POINTS_LENGTH:
raise Exception("Max number of measure points " + str(MAX_MEASURE_POINTS_LENGTH) + " exceeded.")
time_series_h5 = h5.h5_file_for_index(time_series)
assert isinstance(time_series_h5, TimeSeriesH5)
base_adapter_url, time_urls = self._prepare_data_slices(time_series)
min_val, max_val = time_series_h5.get_min_max_values()
legend_labels = self._compute_legend_labels(min_val, max_val)
state_variables = time_series.get_labels_for_dimension(1)
if self.surface_gid and self.region_map_gid:
boundary_url = SurfaceURLGenerator.get_url_for_region_boundaries(self.surface_gid, self.region_map_gid,
self.stored_adapter.id)
else:
boundary_url = ''
shell_surface = ensure_shell_surface(self.current_project_id, shell_surface)
params.update(dict(title="Cerebral Activity: " + time_series.title, isOneToOneMapping=self.one_to_one_map,
urlVertices=json.dumps(url_vertices), urlTriangles=json.dumps(url_triangles),
urlLines=json.dumps(url_lines), urlNormals=json.dumps(url_normals),
urlRegionMap=json.dumps(url_region_map), base_adapter_url=base_adapter_url,
time=json.dumps(time_urls), minActivity=min_val, maxActivity=max_val,
legendLabels=legend_labels, labelsStateVar=state_variables,
labelsModes=list(range(time_series.data_length_4d)), extended_view=False,
shellObject=self.prepare_shell_surface_params(shell_surface, SurfaceURLGenerator),
biHemispheric=self.surface_h5.bi_hemispheric.load(),
hemisphereChunkMask=json.dumps(hemisphere_chunk_mask),
pageSize=self.PAGE_SIZE, urlRegionBoundaries=boundary_url,
measurePointsLabels=self.get_space_labels(time_series_h5),
measurePointsTitle=time_series.title))
params.update(self.build_params_for_subselectable_ts(time_series_h5))
time_series_h5.close()
if self.surface_h5:
self.surface_h5.close()
return params
def generate_preview(self,
time_series,
shell_surface=None,
figure_size=None):
"""
Generate the preview for the burst page
"""
self.populate_surface_fields(time_series)
url_vertices, url_normals, url_lines, url_triangles, url_region_map = \
SurfaceURLGenerator.get_urls_for_rendering(self.surface_h5, self.region_map_gid)
params = self.retrieve_measure_points_prams(time_series)
time_series_h5 = h5.h5_file_for_index(time_series)
assert isinstance(time_series_h5, TimeSeriesH5)
base_activity_url, time_urls = self._prepare_data_slices(
time_series_h5)
min_val, max_val = time_series_h5.get_min_max_values()
time_series_h5.close()
if self.surface_h5 and self.region_map_gid:
boundary_url = SurfaceURLGenerator.get_url_for_region_boundaries(
self.surface_h5, self.region_map_gid, self.stored_adapter.id)
else:
boundary_url = ''
params.update(urlVertices=json.dumps(url_vertices),
urlTriangles=json.dumps(url_triangles),
urlLines=json.dumps(url_lines),
urlNormals=json.dumps(url_normals),
urlRegionMap=json.dumps(url_region_map),
urlRegionBoundaries=boundary_url,
base_activity_url=base_activity_url,
isOneToOneMapping=self.one_to_one_map,
minActivity=min_val,
maxActivity=max_val)
normalization_factor = figure_size[0] / 800
if figure_size[1] / 600 < normalization_factor:
normalization_factor = figure_size[1] / 600
params['width'] = figure_size[0] * normalization_factor
params['height'] = figure_size[1] * normalization_factor
if self.surface_h5:
self.surface_h5.close()
return self.build_display_result("brain/portlet_preview", params)
