Io

In bmmltools.operations.io all the input-output methods are collected.

Input

Input is used to declare the input data on which the trace have to work taken from a data object.

Transfer function

None

Initialization and parameters

In the layer initialization one have to specify:

  • the trace on which the this operation act.

The layer parameters of the apply() method are:

  • data: (bmmltools.operations.core.Data> object) Data object where the input dataset is stored. The dataset name have to be specified in the operation input.

Inputs and outputs

The operation has the following inputs:

input 0

description: Input dataset.
data type: numpy array or dataframe.

The operation has the following outputs:

output 0

description: Dataset used as input.
data type: numpy array or dataframe.

InputFromTrace

InputFromTrace is used to declare the input data on which the trace have to work taken from the same trace on which this operation act.

Transfer function

None

Initialization and parameters

In the layer initialization one have to specify:

  • the trace on which the this operation act.

The layer parameters of the apply() method are:

  • dataset_name: (str) Name of the dataset used as input.

  • dataset_group: (str) Name of hdf5 group in which the input dataset is located.

Inputs and outputs

The operation has the following outputs:

output 0

description: Dataset used as input.
data type: numpy array or dataframe.

OutputRawLabels

OutputRawLabels is used to produce the output labelling obtained from the Clustering or Clustering_HDBSCAN operations, applied on the inout dataset.

Transfer function

None

Initialization and parameters

In the layer initialization one have to specify:

  • the trace on which the this operation act.

The layer parameters of the apply() method are:

  • patch_shape: (tuple[int]) shape of the patch used to perform the clustering, i.e. the patch used to create the patch space.

  • save_separate_masks: (bool) optional, if True the mask for each label is saved separately, otherwise a colored mask is produced where all the labels are present.

Inputs and outputs

The operation has the following inputs:

input 0

description: Input dataset on which the labelling have to be applied.
data type: 3d numpy array.
data shape: \((N_z,N_y,N_x)\), where \(N_i\) is the number of voxels along the i-th dimension for the operation input.

input 1

description: Dataframe containing the labelling in the patch space, see output of the Clustering or Clustering_HDBSCAN operation.
data type: pandas dataframe.

The outputs of this operations are saved in the output folders of the trace.

OutputValidLabels

OutputValidLabels is used to produce the output labelling obtained from the ClusterValidator operation, applied on the inout dataset.

Transfer function

None

Initialization and parameters

In the layer initialization one have to specify:

  • the trace on which the this operation act.

The layer parameters of the apply() method are:

  • patch_shape: (tuple[int]) shape of the patch used to perform the clustering, i.e. the patch used to create the patch space.

  • `` label_kind``: (str) optional, it can be 'label' or 'RS_label' . Kind of label to plot (i.e. the usual one or the one identified via rotational similarity)

  • point_kind: (str) optional, it can be 'all', 'core', 'bilayer' or 'boundary'. Kind of point in the patch space used to produce the labelling, according to the classification done by the ClusterValidator operation.

  • save_separate_masks: (bool) optional, if True the mask for each label is saved separately, otherwise a colored mask is produced where all the labels are present.

Inputs and outputs

The operation has the following inputs:

input 0

description: Input dataset on which the labelling have to be applied.
data type: 3d numpy array.
data shape: \((N_z,N_y,N_x)\), where \(N_i\) is the number of voxels along the i-th dimension for the operation input.

input 1

description: Dataframe containing the labelling in the patch space, see output of the ClusterValidator operation.
data type: pandas dataframe.

The outputs of this operations are saved in the output folders of the trace.

OutputSegmentation

OutputSegmentation is used to produce the output labelling obtained from the RandomForestSegmenter operation, applied on the inout dataset.

Transfer function

None

Initialization and parameters

In the layer initialization one have to specify:

  • the trace on which the this operation act.

The layer parameters of the apply() method are:

  • use_RS_labels: (bool) optional, if True the rotationally similar labels are assumed in the rendering. Note that if this is True, the operation assumes that also RandomForestSegmenter was trained with these labels (i.e. setting label = 'RS_label' in this operation).

  • save_separate_masks: (bool) optional, if True the mask for each label is saved separately, otherwise a colored mask is produced where all the labels are present.

Inputs and outputs

The operation has the following inputs:

input 0

description: Input dataset on which the labelling have to be applied.
data type: 3d numpy array.
data shape: \((N_z,N_y,N_x)\), where \(N_i\) is the number of voxels along the i-th dimension for the operation input.

input 1

description: Labelled 3d dataset., see output of the RandomForestSegmenter operation.
data type: 3d numpy array.
data shape: \((N_z,N_y,N_x)\), where \(N_i\) is the number of voxels along the i-th dimension for the operation input.

input 2

description: Dataframe with the valid clusters, see output of the ClusterValidator operation.
data type: pandas dataframe.

The outputs of this operations are saved in the output folders of the trace.