dacapo.experiments.datasplits.datasets.arrays.ones_array

Attributes

logger

Classes

OnesArray

This is a wrapper around another source_array that simply provides ones

Module Contents

dacapo.experiments.datasplits.datasets.arrays.ones_array.logger

class dacapo.experiments.datasplits.datasets.arrays.ones_array.OnesArray(array_config)

This is a wrapper around another source_array that simply provides ones with the same metadata as the source_array.

This is useful for creating a mask array that is the same size as the original array, but with all values set to 1.

source_array

The source array that this array is based on.

like()

Create a new OnesArray with the same metadata as another array.

attrs()

Get the attributes of the array.

axes()

Get the axes of the array.

dims()

Get the dimensions of the array.

voxel_size()

Get the voxel size of the array.

roi()

Get the region of interest of the array.

writable()

Check if the array is writable.

data()

Get the data of the array.

dtype()

Get the data type of the array.

num_channels()

Get the number of channels of the array.

__getitem__()

Get a subarray of the array.

Note

This class is not meant to be instantiated directly. Instead, use the like method to create a new OnesArray with the same metadata as another array.

classmethod like(array: dacapo.experiments.datasplits.datasets.arrays.array.Array)

Create a new OnesArray with the same metadata as another array.

Parameters:

array – The source array.

Returns:

The new OnesArray with the same metadata as the source array.

Raises:

RuntimeError – If the source array is not specified.

Examples

>>> from dacapo.experiments.datasplits.datasets.arrays import OnesArray
>>> from dacapo.experiments.datasplits.datasets.arrays import NumpyArray
>>> import numpy as np
>>> source_array = NumpyArray(np.zeros((10, 10, 10)))
>>> ones_array = OnesArray.like(source_array)
>>> ones_array.source_array
NumpyArray(data=array([[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
                        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
                        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
                        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
                        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
                        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
                        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
                        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
                        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
                        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]]), voxel_size=(1.0, 1.0, 1.0), roi=Roi((0, 0, 0), (10, 10, 10)), num_channels=1)

Notes

This class is not meant to be instantiated directly. Instead, use the like method to create a new OnesArray with the same metadata as another array.

property attrs

Get the attributes of the array.

Returns:

An empty dictionary.

Examples

>>> from dacapo.experiments.datasplits.datasets.arrays import OnesArray
>>> from dacapo.experiments.datasplits.datasets.arrays import NumpyArray
>>> import numpy as np
>>> source_array = NumpyArray(np.zeros((10, 10, 10)))
>>> ones_array = OnesArray(source_array)
>>> ones_array.attrs
{}

Notes

This method is used to get the attributes of the array. The attributes are stored as key-value pairs in a dictionary. This method returns an empty dictionary because the OnesArray does not have any attributes.

property source_array: dacapo.experiments.datasplits.datasets.arrays.array.Array

Get the source array that this array is based on.

Returns:

The source array.

Raises:

RuntimeError – If the source array is not specified.

Examples

>>> from dacapo.experiments.datasplits.datasets.arrays import OnesArray
>>> from dacapo.experiments.datasplits.datasets.arrays import NumpyArray
>>> import numpy as np
>>> source_array = NumpyArray(np.zeros((10, 10, 10)))
>>> ones_array = OnesArray(source_array)
>>> ones_array.source_array
NumpyArray(data=array([[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
                        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
                        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
                        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
                        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
                        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
                        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
                        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
                        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
                        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]]), voxel_size=(1.0, 1.0, 1.0), roi=Roi((0, 0, 0), (10, 10, 10)), num_channels=1)

Notes

This method is used to get the source array that this array is based on. The source array is the array that the OnesArray is created from. This method returns the source array that was specified when the OnesArray was created.

property axes

Get the axes of the array.

Returns:

The axes of the array.

Raises:

RuntimeError – If the axes are not specified.

Examples

>>> from dacapo.experiments.datasplits.datasets.arrays import OnesArray
>>> from dacapo.experiments.datasplits.datasets.arrays import NumpyArray
>>> import numpy as np
>>> source_array = NumpyArray(np.zeros((10, 10, 10)))
>>> ones_array = OnesArray(source_array)
>>> ones_array.axes
'zyx'

Notes

This method is used to get the axes of the array. The axes are the order of the dimensions of the array. This method returns the axes of the array that was specified when the OnesArray was created.

property dims

Get the dimensions of the array.

Returns:

The dimensions of the array.

Raises:

RuntimeError – If the dimensions are not specified.

Examples

>>> from dacapo.experiments.datasplits.datasets.arrays import OnesArray
>>> from dacapo.experiments.datasplits.datasets.arrays import NumpyArray
>>> import numpy as np
>>> source_array = NumpyArray(np.zeros((10, 10, 10)))
>>> ones_array = OnesArray(source_array)
>>> ones_array.dims
(10, 10, 10)

Notes

This method is used to get the dimensions of the array. The dimensions are the size of the array along each axis. This method returns the dimensions of the array that was specified when the OnesArray was created.

property voxel_size

Get the voxel size of the array.

Returns:

The voxel size of the array.

Raises:

RuntimeError – If the voxel size is not specified.

Examples

>>> from dacapo.experiments.datasplits.datasets.arrays import OnesArray
>>> from dacapo.experiments.datasplits.datasets.arrays import NumpyArray
>>> import numpy as np
>>> source_array = NumpyArray(np.zeros((10, 10, 10)))
>>> ones_array = OnesArray(source_array)
>>> ones_array.voxel_size
(1.0, 1.0, 1.0)

Notes

This method is used to get the voxel size of the array. The voxel size is the size of each voxel in the array. This method returns the voxel size of the array that was specified when the OnesArray was created.

property roi

Get the region of interest of the array.

Returns:

The region of interest of the array.

Raises:

RuntimeError – If the region of interest is not specified.

Examples

>>> from dacapo.experiments.datasplits.datasets.arrays import OnesArray
>>> from dacapo.experiments.datasplits.datasets.arrays import NumpyArray
>>> from funlib.geometry import Roi
>>> import numpy as np
>>> source_array = NumpyArray(np.zeros((10, 10, 10)))
>>> ones_array = OnesArray(source_array)
>>> ones_array.roi
Roi((0, 0, 0), (10, 10, 10))

Notes

This method is used to get the region of interest of the array. The region of interest is the region of the array that contains the data. This method returns the region of interest of the array that was specified when the OnesArray was created.

property writable: bool

Check if the array is writable.

Returns:

False.

Raises:

RuntimeError – If the writability of the array is not specified.

Examples

>>> from dacapo.experiments.datasplits.datasets.arrays import OnesArray
>>> from dacapo.experiments.datasplits.datasets.arrays import NumpyArray
>>> import numpy as np
>>> source_array = NumpyArray(np.zeros((10, 10, 10)))
>>> ones_array = OnesArray(source_array)
>>> ones_array.writable
False

Notes

This method is used to check if the array is writable. An array is writable if it can be modified in place. This method returns False because the OnesArray is read-only and cannot be modified.

property data

Get the data of the array.

Returns:

The data of the array.

Raises:

RuntimeError – If the data is not specified.

Examples

>>> from dacapo.experiments.datasplits.datasets.arrays import OnesArray
>>> from dacapo.experiments.datasplits.datasets.arrays import NumpyArray
>>> import numpy as np
>>> source_array = NumpyArray(np.zeros((10, 10, 10)))
>>> ones_array = OnesArray(source_array)
>>> ones_array.data
array([[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]])

Notes

This method is used to get the data of the array. The data is the values that are stored in the array. This method returns a subarray of the array with all values set to 1.

property dtype

Get the data type of the array.

Returns:

The data type of the array.

Raises:

RuntimeError – If the data type is not specified.

Examples

>>> from dacapo.experiments.datasplits.datasets.arrays import OnesArray
>>> from dacapo.experiments.datasplits.datasets.arrays import NumpyArray
>>> import numpy as np
>>> source_array = NumpyArray(np.zeros((10, 10, 10)))
>>> ones_array = OnesArray(source_array)
>>> ones_array.dtype
<class 'numpy.bool_'>

Notes

This method is used to get the data type of the array. The data type is the type of the values that are stored in the array. This method returns the data type of the array that was specified when the OnesArray was created.

property num_channels

Get the number of channels of the array.

Returns:

The number of channels of the array.

Raises:

RuntimeError – If the number of channels is not specified.

Examples

>>> from dacapo.experiments.datasplits.datasets.arrays import OnesArray
>>> from dacapo.experiments.datasplits.datasets.arrays import NumpyArray
>>> import numpy as np
>>> source_array = NumpyArray(np.zeros((10, 10, 10)))
>>> ones_array = OnesArray(source_array)
>>> ones_array.num_channels
1

Notes

This method is used to get the number of channels of the array. The number of channels is the number of values that are stored at each voxel in the array. This method returns the number of channels of the array that was specified when the OnesArray was created.