ndio/remote/ndingest.py
def add_dataset(self, dataset_name, imagesize, voxelres, offset=None,
                    timerange=None, scalinglevels=None, scaling=None):
        """
        Add a new dataset to the ingest.

        Arguments:
            dataset_name (str): Dataset Name is the overarching name of the
                research effort. Standard naming convention is to do
                LabNamePublicationYear or LeadResearcherCurrentYear.
            imagesize (int, int, int): Image size is the pixel count
                dimensions of the data. For example is the data is stored
                as a series of 100 slices each 2100x2000 pixel TIFF images,
                the X,Y,Z dimensions are (2100, 2000, 100).
            voxelres (float, float, float): Voxel Resolution is the number
                of voxels per unit pixel. We store X,Y,Z voxel resolution
                separately.
            offset (int, int, int): If your data is not well aligned and
                there is "excess" image data you do not wish to examine, but
                are present in your images, offset is how you specify where
                your actual image starts. Offset is provided a pixel
                coordinate offset from origin which specifies the "actual"
                origin of the image. The offset is for X,Y,Z dimensions.
            timerange (int, int): Time Range is a parameter to support
                storage of Time Series data, so the value of the tuple is a
                0 to X range of how many images over time were taken. It
                takes 2 inputs timeStepStart and timeStepStop.
            scalinglevels (int): Scaling levels is the number of levels the
                data is scalable to (how many zoom levels are present in the
                data). The highest resolution of the data is at scaling level
                0, and for each level up the data is down sampled by 2x2
                (per slice). To learn more about the sampling service used,
                visit the the propagation service page.
            scaling (int): Scaling is the scaling method of the data being
                stored. 0 corresponds to a Z-slice orientation (as in a
                collection of tiff images in which each tiff is a slice on
                the z plane) where data will be scaled only on the xy plane,
                not the z plane. 1 corresponds to an isotropic orientation
                (in which each tiff is a slice on the y plane) where data
                is scaled along all axis.

        Returns:
            None
        """
        self.dataset = (dataset_name.strip().replace(" ", ""), imagesize,
                        voxelres, offset, timerange, scalinglevels, scaling)
def add_dataset(self, dataset_name, imagesize, voxelres, offset=None,
                    timerange=None, scalinglevels=None, scaling=None):
        self.dataset = (dataset_name.strip().replace(" ", ""), imagesize,
                        voxelres, offset, timerange, scalinglevels, scaling)
def add_dataset ( self , dataset_name , imagesize , voxelres , offset = None , timerange = None , scalinglevels = None , scaling = None ) : self . dataset = ( dataset_name . strip ( ) . replace ( " " , "" ) , imagesize , voxelres , offset , timerange , scalinglevels , scaling )
Add a new dataset to the ingest.

        Arguments:
            dataset_name (str): Dataset Name is the overarching name of the
                research effort. Standard naming convention is to do
                LabNamePublicationYear or LeadResearcherCurrentYear.
            imagesize (int, int, int): Image size is the pixel count
                dimensions of the data. For example is the data is stored
                as a series of 100 slices each 2100x2000 pixel TIFF images,
                the X,Y,Z dimensions are (2100, 2000, 100).
            voxelres (float, float, float): Voxel Resolution is the number
                of voxels per unit pixel. We store X,Y,Z voxel resolution
                separately.
            offset (int, int, int): If your data is not well aligned and
                there is "excess" image data you do not wish to examine, but
                are present in your images, offset is how you specify where
                your actual image starts. Offset is provided a pixel
                coordinate offset from origin which specifies the "actual"
                origin of the image. The offset is for X,Y,Z dimensions.
            timerange (int, int): Time Range is a parameter to support
                storage of Time Series data, so the value of the tuple is a
                0 to X range of how many images over time were taken. It
                takes 2 inputs timeStepStart and timeStepStop.
            scalinglevels (int): Scaling levels is the number of levels the
                data is scalable to (how many zoom levels are present in the
                data). The highest resolution of the data is at scaling level
                0, and for each level up the data is down sampled by 2x2
                (per slice). To learn more about the sampling service used,
                visit the the propagation service page.
            scaling (int): Scaling is the scaling method of the data being
                stored. 0 corresponds to a Z-slice orientation (as in a
                collection of tiff images in which each tiff is a slice on
                the z plane) where data will be scaled only on the xy plane,
                not the z plane. 1 corresponds to an isotropic orientation
                (in which each tiff is a slice on the y plane) where data
                is scaled along all axis.

        Returns:
            None
Add a new dataset to the ingest .