Python abcdisplayer.ABCDisplayer類代碼示例

    def compute_parameters(input_data, colors=None, rays=None):
        """
        Having as inputs a Connectivity matrix(required) and two arrays that 
        represent the rays and colors of the nodes from the matrix(optional) 
        this method will build the required parameter dictionary that will be 
        sent to the HTML/JS 3D representation of the connectivity matrix.
        """
        if colors is not None:
            color_list = colors.array_data.tolist()
            color_list = ABCDisplayer.get_one_dimensional_list(color_list, input_data.number_of_regions,
                                                               "Invalid input size for Sphere Colors")
            color_list = numpy.nan_to_num(numpy.array(color_list, dtype=numpy.float64)).tolist()
        else:
            color_list = [1.0] * input_data.number_of_regions

        if rays is not None:
            rays_list = rays.array_data.tolist()
            rays_list = ABCDisplayer.get_one_dimensional_list(rays_list, input_data.number_of_regions,
                                                              "Invalid input size for Sphere Sizes")
            rays_list = numpy.nan_to_num(numpy.array(rays_list, dtype=numpy.float64)).tolist()
        else:
            rays_list = [1.0] * input_data.number_of_regions

        params = dict(raysArray=json.dumps(rays_list), rayMin=min(rays_list), rayMax=max(rays_list),
                      colorsArray=json.dumps(color_list), colorMin=min(color_list), colorMax=max(color_list))
        return params, {}

def prepare_mapped_sensors_as_measure_points_params(project_id, sensors, eeg_cap=None):
    """
    Compute sensors positions by mapping them to the ``eeg_cap`` surface
    If ``eeg_cap`` is not specified the mapping will use a default EEGCal DataType in current project.
    If no default EEGCap is found, return sensors as they are (not projected)

    :returns: dictionary to be used in Viewers for rendering measure_points
    :rtype: dict
    """

    if eeg_cap is None:
        eeg_cap = dao.try_load_last_entity_of_type(project_id, EEGCap)

    if eeg_cap:
        datatype_kwargs = json.dumps({'surface_to_map': eeg_cap.gid})
        sensor_locations = ABCDisplayer.paths2url(sensors, 'sensors_to_surface') + '/' + datatype_kwargs
        sensor_no = sensors.number_of_sensors
        sensor_labels = ABCDisplayer.paths2url(sensors, 'labels')

        return {'urlMeasurePoints': sensor_locations,
                'urlMeasurePointsLabels': sensor_labels,
                'noOfMeasurePoints': sensor_no,
                'minMeasure': 0,
                'maxMeasure': sensor_no,
                'urlMeasure': ''}

    return prepare_sensors_as_measure_points_params(sensors)

 def get_sensor_measure_points(sensors):
     """
         Returns urls from where to fetch the measure points and their labels
     """
     measure_points = ABCDisplayer.paths2url(sensors, 'locations')
     measure_points_no = sensors.number_of_sensors
     measure_points_labels = ABCDisplayer.paths2url(sensors, 'labels')
     return measure_points, measure_points_labels, measure_points_no

 def retrieve_measure_points(self, time_series):
     """
     To be overwritten method, for retrieving the measurement points (region centers, EEG sensors).
     """
     if isinstance(time_series, TimeSeriesSurface):
         return [], [], 0
     measure_points = ABCDisplayer.paths2url(time_series.connectivity, 'centres')
     measure_points_labels = ABCDisplayer.paths2url(time_series.connectivity, 'region_labels')
     measure_points_no = time_series.connectivity.number_of_regions
     return measure_points, measure_points_labels, measure_points_no

def prepare_sensors_as_measure_points_params(sensors):
    """
    Returns urls from where to fetch the measure points and their labels
    """
    sensor_locations = ABCDisplayer.paths2url(sensors, 'locations')
    sensor_no = sensors.number_of_sensors
    sensor_labels = ABCDisplayer.paths2url(sensors, 'labels')

    return {'urlMeasurePoints': sensor_locations,
            'urlMeasurePointsLabels': sensor_labels,
            'noOfMeasurePoints': sensor_no,
            'minMeasure': 0,
            'maxMeasure': sensor_no,
            'urlMeasure': ''}

    def compute_params(self, region_mapping_volume=None, measure=None, data_slice='', background=None):

        region_mapping_volume = self._ensure_region_mapping(region_mapping_volume)

        volume = region_mapping_volume.volume
        volume_shape = region_mapping_volume.read_data_shape()
        volume_shape = (1, ) + volume_shape

        if measure is None:
            params = self._compute_region_volume_map_params(region_mapping_volume)
        else:
            params = self._compute_measure_params(region_mapping_volume, measure, data_slice)

        url_voxel_region = ABCDisplayer.paths2url(region_mapping_volume, "get_voxel_region", parameter="")

        params.update(volumeShape=json.dumps(volume_shape),
                      volumeOrigin=json.dumps(volume.origin.tolist()),
                      voxelUnit=volume.voxel_unit,
                      voxelSize=json.dumps(volume.voxel_size.tolist()),
                      urlVoxelRegion=url_voxel_region)

        if background is None:
            background = dao.try_load_last_entity_of_type(self.current_project_id, StructuralMRI)

        params.update(self._compute_background(background))
        return params

    def get_metric_matrix(self, datatype_group, selected_metric=None):
        self.model = PseIsoModel.from_db(datatype_group.fk_operation_group)
        if selected_metric is None:
            selected_metric = self.model.metrics.keys()[0]

        data_matrix = self.model.apriori_data[selected_metric]
        data_matrix = numpy.rot90(data_matrix)
        data_matrix = numpy.flipud(data_matrix)
        matrix_data = ABCDisplayer.dump_with_precision(data_matrix.flat)
        matrix_guids = self.model.datatypes_gids
        matrix_guids = numpy.rot90(matrix_guids)
        matrix_shape = json.dumps(data_matrix.squeeze().shape)
        x_min = self.model.apriori_x[0]
        x_max = self.model.apriori_x[self.model.apriori_x.size - 1]
        y_min = self.model.apriori_y[0]
        y_max = self.model.apriori_y[self.model.apriori_y.size - 1]
        vmin = data_matrix.min()
        vmax = data_matrix.max()
        return dict(matrix_data=matrix_data,
                    matrix_guids=json.dumps(matrix_guids.flatten().tolist()),
                    matrix_shape=matrix_shape,
                    color_metric=selected_metric,
                    x_min=x_min,
                    x_max=x_max,
                    y_min=y_min,
                    y_max=y_max,
                    vmin=vmin,
                    vmax=vmax)

    def launch(self, **kwargs):
        self.log.debug("Plot started...")
        input_data = kwargs['input_data']
        shape = list(input_data.read_data_shape())
        state_list = input_data.source.labels_dimensions.get(input_data.source.labels_ordering[1], [])
        mode_list = range(shape[3])
        available_scales = ["Linear", "Logarithmic"]

        params = dict(matrix_shape=json.dumps([shape[0], shape[2]]),
                      plotName=input_data.source.type,
                      url_base=ABCDisplayer.paths2url(input_data, "get_fourier_data", parameter=""),
                      xAxisName="Frequency [kHz]",
                      yAxisName="Power",
                      available_scales=available_scales,
                      state_list=state_list,
                      mode_list=mode_list,
                      normalize_list=["no", "yes"],
                      normalize="no",
                      state_variable=state_list[0],
                      mode=mode_list[0],
                      xscale=available_scales[0],
                      yscale=available_scales[0],
                      x_values=json.dumps(input_data.frequency[slice(shape[0])].tolist()),
                      xmin=input_data.freq_step,
                      xmax=input_data.max_freq)
        return self.build_display_result("fourier_spectrum/view", params)

    def launch(self, connectivity):
        """Construct data for visualization and launch it."""

        pars = {"labels": json.dumps(connectivity.region_labels.tolist()),
                "url_base": ABCDisplayer.paths2url(connectivity, attribute_name="weights", flatten="True")
                }

        return self.build_display_result("connectivity_edge_bundle/view", pars)

    def retrieve_measure_points_prams(self, time_series):
        """
        To be overwritten method, for retrieving the measurement points (region centers, EEG sensors).
        """
        if self.connectivity is None:
            self.measure_points_no = 0
            return {'urlMeasurePoints': [],
                    'urlMeasurePointsLabels': [],
                    'noOfMeasurePoints': 0}

        measure_points = ABCDisplayer.paths2url(self.connectivity, 'centres')
        measure_points_labels = ABCDisplayer.paths2url(self.connectivity, 'region_labels')
        self.measure_points_no = self.connectivity.number_of_regions

        return {'urlMeasurePoints': measure_points,
                'urlMeasurePointsLabels': measure_points_labels,
                'noOfMeasurePoints': self.measure_points_no}

 def _compute_background(background):
     if background is not None:
         min_value, max_value = background.get_min_max_values()
         url_volume_data = ABCDisplayer.paths2url(background, 'get_volume_view', parameter='')
     else:
         min_value, max_value = 0, 0
         url_volume_data = None
     return dict( minBackgroundValue=min_value, maxBackgroundValue=max_value,
                  urlBackgroundVolumeData = url_volume_data )

    def compute_raw_matrix_params(matrix):
        """
        Serializes matrix data, shape and stride metadata to json
        """
        matrix_data = ABCDisplayer.dump_with_precision(matrix.flat)
        matrix_shape = json.dumps(matrix.shape)

        return dict(matrix_data=matrix_data,
                    matrix_shape=matrix_shape)

    def launch(self, datatype):
        """Construct data for visualization and launch it."""

        # get data from coher datatype, convert to json
        frequency = ABCDisplayer.dump_with_precision(datatype.get_data('frequency').flat)
        array_data = datatype.get_data('array_data')

        params = self.compute_raw_matrix_params(array_data)
        params.update(frequency=frequency)
        params.update(matrix_strides=json.dumps([x / array_data.itemsize for x in array_data.strides]))
        return self.build_display_result("cross_coherence/view", params)

    def compute_sensor_surfacemapped_measure_points(project_id, sensors, eeg_cap=None):
        """
        Compute sensors positions by mapping them to the ``eeg_cap`` surface
        If ``eeg_cap`` is not specified the mapping will use a default.
        It returns a url from where to fetch the positions
        If no default is available it returns None
        :returns: measure points, measure points labels, measure points number
        :rtype: tuple
        """

        if eeg_cap is None:
            eeg_cap = dao.get_values_of_datatype(project_id, EEGCap)[0]
            if eeg_cap:
                eeg_cap = ABCDisplayer.load_entity_by_gid(eeg_cap[-1][2])

        if eeg_cap:
            datatype_kwargs = json.dumps({'surface_to_map': eeg_cap.gid})
            measure_points = ABCDisplayer.paths2url(sensors, 'sensors_to_surface') + '/' + datatype_kwargs
            measure_points_no = sensors.number_of_sensors
            measure_points_labels = ABCDisplayer.paths2url(sensors, 'labels')
            return measure_points, measure_points_labels, measure_points_no

    def get_shell_surface_urls(shell_surface=None, project_id=0):

        if shell_surface is None:
            shell_surface = dao.get_values_of_datatype(project_id, FaceSurface)[0]

            if not shell_surface:
                raise Exception('No face object found in database.')

            shell_surface = ABCDisplayer.load_entity_by_gid(shell_surface[0][2])

        face_vertices, face_normals, _, face_triangles = shell_surface.get_urls_for_rendering()
        return json.dumps([face_vertices, face_normals, face_triangles])