grid_data_api.py

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from allensdk.api.warehouse_cache.cache import cacheable
from allensdk.deprecated import deprecated
from .rma_api import RmaApi


class GridDataApi(RmaApi):
    '''HTTP Client for the Allen 3-D Expression Grid Data Service.

    See: `Downloading 3-D Expression Grid Data <http://help.brain-map.org/display/api/Downloading+3-D+Expression+Grid+Data>`_
    '''

    INJECTION_DENSITY = 'injection_density'
    PROJECTION_DENSITY = 'projection_density'
    INJECTION_FRACTION = 'injection_fraction'
    INJECTION_ENERGY = 'injection_energy'
    PROJECTION_ENERGY = 'projection_energy'
    DATA_MASK = 'data_mask'

    ENERGY = 'energy'
    DENSITY = 'density'
    INTENSITY = 'intensity'

    def __init__(self,
                 resolution=None,
                 base_uri=None):
        super(GridDataApi, self).__init__(base_uri)

        if resolution is None:
            resolution = 25
        self.resolution = resolution


    def download_gene_expression_grid_data(self, 
                                           section_data_set_id, 
                                           volume_type, 
                                           path):
        ''' Download a metaimage file containing registered gene expression grid data

        Parameters
        ----------
        section_data_set_id : int
            Download data from this experiment
        volume_type : str
            Download this type of data (options are GridDataApi.ENERGY, 
            GridDataApi.DENSITY, GridDataApi.INTENSITY)
        path : str
            Download to this path

        '''

        include = '?include={}'.format(volume_type)
        url = ''.join([self.grid_data_endpoint, '/download/', str(section_data_set_id), include])
        self.retrieve_file_over_http(url, path, zipped=True)


    @deprecated(message='Use download_gene_expression_grid_data instead')
    def download_expression_grid_data(self,
                                      section_data_set_id,
                                      include=None,
                                      path=None):
        '''Download in zipped metaimage format.

        Parameters
        ----------
        section_data_set_id : integer
            What to download.
        include : list of strings, optional
            Image volumes. 'energy' (default), 'density', 'intensity'.
        path : string, optional
            File name to save as.
        
        Returns
        -------
            file : 3-D expression grid data packaged into a compressed archive file (.zip).

        Notes
        -----
        '''
        if include is not None:
            include_clause = ''.join(['?include=',
                                      ','.join(include)])
        else:
            include_clause = ''

        url = ''.join([self.grid_data_endpoint,
                       '/download/',
                       str(section_data_set_id),
                       include_clause])

        if path is None:
            path = str(section_data_set_id) + '.zip'

        self.retrieve_file_over_http(url, path)

    def download_projection_grid_data(self,
                                      section_data_set_id,
                                      image=None,
                                      resolution=None,
                                      save_file_path=None):
        '''Download in NRRD format.

        Parameters
        ----------
        section_data_set_id : integer
            What to download.
        image : list of strings, optional
            Image volume. 'projection_density', 'projection_energy', 'injection_fraction', 'injection_density', 'injection_energy', 'data_mask'.
        resolution : integer, optional
            in microns. 10, 25, 50, or 100 (default).
        save_file_path : string, optional
            File name to save as.

        Notes
        -----
        See `Downloading 3-D Projection Grid Data <http://help.brain-map.org/display/api/Downloading+3-D+Expression+Grid+Data#name="Downloading3-DExpressionGridData-DOWNLOADING3DPROJECTIONGRIDDATA">`_
        for additional documentation.
        '''
        params_list = []

        if image is not None:
            params_list.append('image=' + ','.join(image))

        if resolution is not None:
            params_list.append('resolution=%d' % (resolution))

        if len(params_list) > 0:
            params_clause = '?' + '&'.join(params_list)
        else:
            params_clause = ''

        url = ''.join([self.grid_data_endpoint,
                       '/download_file/',
                       str(section_data_set_id),
                       params_clause])

        if save_file_path is None:
            save_file_path = str(section_data_set_id) + '.nrrd'

        self.retrieve_file_over_http(url, save_file_path)


    def download_deformation_field(self, 
        section_data_set_id, 
        header_path=None,
        voxel_path=None,
        voxel_type='DeformationFieldVoxels', 
        header_type='DeformationFieldHeader'
    ):
        ''' Download the local alignment parameters for this dataset. This a 3D vector image (3 components) describing 
        a deformable local mapping from CCF voxels to this section data set's affine-aligned image stack.

        Parameters
        ----------
            section_data_set_id : int
                Download the deformation field for this data set
            header_path : str, optional
                If supplied, the deformation field header will be downloaded to this path.
            voxel_path : str, optiona
                If supplied, the deformation field voxels will be downloaded to this path.
            voxel_type : str
                WellKnownFileType of this dataset's data file
            header_type : str
                WellKnownFileType of this dataset's header file
        '''

        header_path = '{}_dfmfld.mhd'.format(section_data_set_id) if header_path is None else header_path
        voxel_path = '{}_dfmfld.raw'.format(section_data_set_id) if voxel_path is None else voxel_path

        well_known_files = self.model_query(
            model='WellKnownFile',
            filters={'attachable_id': section_data_set_id},
            criteria='well_known_file_type[name$in\'DeformationFieldHeader\',\'DeformationFieldVoxels\']',
            include='well_known_file_type'
        )

        well_known_file_urls = {
            wkf['well_known_file_type']['name']: 
            self.construct_well_known_file_download_url(wkf['id']) for wkf in well_known_files
        }
        
        self.retrieve_file_over_http(well_known_file_urls[header_type], header_path)
        self.retrieve_file_over_http(well_known_file_urls[voxel_type], voxel_path)


    @cacheable()
    def download_alignment3d(self, section_data_set_id, num_rows='all', count=False, **kwargs):
        ''' Download the parameters of the 3D affine tranformation mapping this section data set's image-space stack to 
        CCF-space (or vice-versa).

        Parameters
        ----------
        section_data_set_id : int
            download the parameters for this data set.
 
        Returns
        -------
        dict :
            parameters of this section data set's alignment3d
        '''

        results = self.model_query(
            model='SectionDataSet',
            filters={'id': section_data_set_id},
            include='alignment3d',
            num_rows=num_rows,
            count=count,
            **kwargs
        )

        results = [result for result in results if 'alignment3d' in result]
        if len(results) == 0:
            raise ValueError('no SectionDataSet with attached alignment3d found for id {}'.format(section_data_set_id))
        elif len(results) > 1:
            raise ValueError('found multiple SectionDataSets with attached alignment3ds for id {}: {}'.format(section_data_set_id, results))

        return results[0]['alignment3d']