DenseAdditiveProgenyGeneticCovarianceMatrix

class pybrops.model.pcvmat.DenseAdditiveProgenyGeneticCovarianceMatrix.DenseAdditiveProgenyGeneticCovarianceMatrix(mat, taxa=None, taxa_grp=None, trait=None, **kwargs)

Bases: DenseProgenyGeneticCovarianceMatrix, AdditiveProgenyGeneticCovarianceMatrix

A semi-concrete class for dense additive genetic covariance matrices.

The purpose of this semi-concrete class is to provide functionality for:

1. Object construction of dense additive genetic covariance matrices.

Methods responsible for estimating genetic variances from genomic models remain abstract and must be implemented by inheriting classes.

Constructor for the concrete class DenseAdditiveProgenyGeneticCovarianceMatrix.

Parameters:

• mat (numpy.ndarray) – Array used to construct the object.

• taxa (numpy.ndarray) – Taxa names.

• taxa_grp (numpy.ndarray) – Taxa groupings.

• trait (numpy.ndarray) – Trait names.

• kwargs (dict) – Additional keyword arguments.

Methods

adjoin	Add additional elements to the end of the Matrix along an axis.
adjoin_taxa	Add additional elements to the end of the Matrix along an axis.
adjoin_trait	Add additional elements to the end of the Matrix along an axis.
append	Append values to the matrix.
append_taxa	Append values to the Matrix along the taxa axis.
append_trait	Append values to the Matrix along the trait axis.
concat	Concatenate matrices together along an axis.
concat_taxa	Concatenate list of Matrix together along the taxa axis.
concat_trait	Concatenate list of Matrix together along the trait axis.
copy	Make a shallow copy of the Matrix.
deepcopy	Make a deep copy of the Matrix.
delete	Delete sub-arrays along an axis.
delete_taxa	Delete sub-arrays along the taxa axis.
delete_trait	Delete sub-arrays along the trait axis.
from_algmod	Estimate genetic variances from a GenomicModel.
from_csv	Read an object from a CSV file.
from_gmod	Estimate genetic variances from a GenomicModel.
from_hdf5	Read DenseProgenyGeneticCovarianceMatrix from an HDF5 file.
from_pandas	Read an object from a pandas.DataFrame.
group	Sort matrix along axis, then populate grouping indices for the axis.
group_taxa	Sort the Matrix along the taxa axis, then populate grouping indices for the taxa axis.
incorp	Incorporate values along the given axis before the given indices.
incorp_taxa	Incorporate values along the taxa axis before the given indices.
incorp_trait	Incorporate values along the trait axis before the given indices.
insert	Insert values along the given axis before the given indices.
insert_taxa	Insert values along the taxa axis before the given indices.
insert_trait	Insert values along the trait axis before the given indices.
is_grouped	Determine whether the Matrix has been sorted and grouped.
is_grouped_taxa	Determine whether the Matrix has been sorted and grouped along the taxa axis.
is_square	Determine whether the axis lengths for the square axes are identical.
is_square_taxa	Determine whether the taxa axes lengths for the square axes are identical.
is_square_trait	Determine whether the trait axes lengths for the square axes are identical.
lexsort	Perform an indirect stable sort using a tuple of keys.
lexsort_taxa	Perform an indirect stable sort using a sequence of keys along the taxa axis.
lexsort_trait	Perform an indirect stable sort using a sequence of keys along the trait axis.
remove	Remove sub-arrays along an axis.
remove_taxa	Remove sub-arrays along the taxa axis.
remove_trait	Remove sub-arrays along the trait axis.
reorder	Reorder the VariantMatrix.
reorder_taxa	Reorder elements of the Matrix along the taxa axis using an array of indices.
reorder_trait	Reorder elements of the Matrix along the trait axis using an array of indices.
select	Select certain values from the matrix.
select_taxa	Select certain values from the Matrix along the taxa axis.
select_trait	Select certain values from the Matrix along the trait axis.
sort	Reset metadata for corresponding axis: name, stix, spix, len.
sort_taxa	Sort slements of the Matrix along the taxa axis using a sequence of keys.
sort_trait	Sort slements of the Matrix along the trait axis using a sequence of keys.
to_csv	Write an object to a CSV file.
to_hdf5	Write DenseSquareTaxaSquareTraitMatrix to an HDF5 file.
to_pandas	Export an object to a pandas.DataFrame.
ungroup	Ungroup the DenseSquareTaxaMatrix along an axis by removing grouping metadata.
ungroup_taxa	Ungroup the DenseTaxaMatrix along the taxa axis by removing taxa group metadata.

Attributes

epgc	Expected parental genome contribution to the offspring from each parent.
mat	Pointer to raw numpy.ndarray object.
mat_ndim	Number of dimensions of the raw numpy.ndarray.
mat_shape	Shape of the raw numpy.ndarray.
nsquare	Number of axes that are square.
nsquare_taxa	Number of taxa axes that are square.
nsquare_trait	Number of trait axes that are square.
ntaxa	Number of taxa
ntrait	Number of traits.
square_axes	Axis indices for axes that are square.
square_axes_len	Axis lengths for axes that are square.
square_taxa_axes	Axis indices for taxa axes that are square.
square_taxa_axes_len	Axis lengths for axes that are square.
square_trait_axes	Axis indices for trait axes that are square.
square_trait_axes_len	Axis lengths for axes that are square.
taxa	Taxa label array
taxa_axis	First square axis along which taxa are stored
taxa_grp	Taxa group label.
taxa_grp_len	Taxa group length.
taxa_grp_name	Taxa group name.
taxa_grp_spix	Taxa group stop index.
taxa_grp_stix	Taxa group start index.
trait	Trait label.
trait_axis	First square axis along which trait are stored

__add__(value)

Elementwise add matrices

Parameters:

value (object) – Object which to add.

Returns:

out – An object resulting from the addition.

Return type:

object

__mul__(value)

Elementwise multiply matrices

Parameters:

value (object) – Object which to multiply.

Returns:

out – An object resulting from the multiplication.

Return type:

object

adjoin(values, axis=-1, taxa=None, taxa_grp=None, trait=None, **kwargs)

Add additional elements to the end of the Matrix along an axis.

Parameters:

• values (Matrix, numpy.ndarray) – Values are appended to append to the Matrix.

• axis (int) – The axis along which values are adjoined.

• taxa (numpy.ndarray) – Taxa names to adjoin to the Matrix. If values is a DenseHaplotypeMatrix that has a non-None taxa field, providing this argument overwrites the field.

• taxa_grp (numpy.ndarray) – Taxa groups to adjoin to the Matrix. If values is a DenseHaplotypeMatrix that has a non-None taxa_grp field, providing this argument overwrites the field.

• trait (numpy.ndarray) – Trait names to adjoin to the Matrix. If values is a DenseHaplotypeMatrix that has a non-None trait field, providing this argument overwrites the field.

• kwargs (dict) – Additional keyword arguments.

Returns:

out – A copy of DenseSquareTraitMatrix with values adjoined to axis. Note that adjoin does not occur in-place: a new DenseSquareTraitMatrix is allocated and filled.

Return type:

DenseSquareTraitMatrix

adjoin_taxa(values, taxa=None, taxa_grp=None, **kwargs)

Add additional elements to the end of the Matrix along an axis.

Parameters:

• values (Matrix, numpy.ndarray) – Values are appended to append to the Matrix.

• taxa (numpy.ndarray) – Taxa names to adjoin to the Matrix. If values is a DenseSquareTaxaMatrix that has a non-None taxa field, providing this argument overwrites the field.

• taxa_grp (numpy.ndarray) – Taxa groups to adjoin to the Matrix. If values is a DenseSquareTaxaMatrix that has a non-None taxa_grp field, providing this argument overwrites the field.

• kwargs (dict) – Additional keyword arguments.

Returns:

out – A copy of DenseSquareTaxaMatrix with values appended to axis. Note that adjoin does not occur in-place: a new DenseSquareTaxaMatrix is allocated and filled.

Return type:

DenseSquareTaxaMatrix

adjoin_trait(values, trait=None, **kwargs)

Add additional elements to the end of the Matrix along an axis.

Parameters:

• values (Matrix, numpy.ndarray) – Values are appended to append to the Matrix.

• trait (numpy.ndarray) – Trait names to adjoin to the Matrix. If values is a DenseHaplotypeMatrix that has a non-None trait field, providing this argument overwrites the field.

• kwargs (dict) – Additional keyword arguments.

Returns:

out – A copy of DenseSquareTraitMatrix with values appended to axis. Note that adjoin does not occur in-place: a new DenseSquareTraitMatrix is allocated and filled.

Return type:

DenseSquareTraitMatrix

append(values, axis=-1, taxa=None, taxa_grp=None, trait=None, **kwargs)

Append values to the matrix.

Parameters:

• values (DenseHaplotypeMatrix, numpy.ndarray) – Values are appended to append to the matrix. Must be of type int8. Must be of shape (m, n, p)

• axis (int) – The axis along which values are appended.

• taxa (numpy.ndarray) – Taxa names to append to the Matrix. If values is a DenseSquareTaxaSquareTraitMatrix that has a non-None taxa field, providing this argument overwrites the field.

• taxa_grp (numpy.ndarray) – Taxa groups to append to the Matrix. If values is a DenseSquareTaxaSquareTraitMatrix that has a non-None taxa_grp field, providing this argument overwrites the field.

• trait (numpy.ndarray) – Trait names to append to the Matrix. If values is a DenseSquareTaxaSquareTraitMatrix that has a non-None trait field, providing this argument overwrites the field.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

append_taxa(values, taxa=None, taxa_grp=None, **kwargs)

Append values to the Matrix along the taxa axis.

Parameters:

• values (Matrix, numpy.ndarray) – Values are appended to append to the matrix.

• taxa (numpy.ndarray) – Taxa names to append to the Matrix.

• taxa_grp (numpy.ndarray) – Taxa groups to append to the Matrix.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

append_trait(values, trait=None, **kwargs)

Append values to the Matrix along the trait axis.

Parameters:

• values (Matrix, numpy.ndarray) – Values are appended to append to the matrix.

• trait (numpy.ndarray) – Trait names to append to the Matrix. If values is a DenseSquareTraitMatrix that has a non-None trait field, providing this argument overwrites the field.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

classmethod concat(mats, axis=-1, **kwargs)

Concatenate matrices together along an axis.

Parameters:

• mats (Sequence of matrices) – List of Matrix to concatenate. The matrices must have the same shape, except in the dimension corresponding to axis.

• axis (int) – The axis along which the arrays will be joined.

• kwargs (dict) – Additional keyword arguments

Returns:

out – The concatenated DenseSquareTraitMatrix. Note that concat does not occur in-place: a new DenseSquareTraitMatrix is allocated and filled.

Return type:

DenseSquareTraitMatrix

classmethod concat_taxa(mats, **kwargs)

Concatenate list of Matrix together along the taxa axis.

Parameters:

• mats (Sequence of Matrix) – List of Matrix to concatenate. The matrices must have the same shape, except in the dimension corresponding to axis.

• kwargs (dict) – Additional keyword arguments

Returns:

out – The concatenated DenseSquareTaxaMatrix. Note that concat does not occur in-place: a new DenseSquareTaxaMatrix is allocated and filled.

Return type:

DenseSquareTaxaMatrix

classmethod concat_trait(mats, **kwargs)

Concatenate list of Matrix together along the trait axis.

Parameters:

• mats (Sequence of Matrix) – List of Matrix to concatenate. The matrices must have the same shape, except in the dimension corresponding to axis.

• kwargs (dict) – Additional keyword arguments

Returns:

out – The concatenated DenseSquareTraitMatrix. Note that concat does not occur in-place: a new DenseSquareTraitMatrix is allocated and filled.

Return type:

DenseSquareTraitMatrix

copy()

Make a shallow copy of the Matrix.

Returns:

out – A shallow copy of the original DenseProgenyGeneticCovarianceMatrix.

Return type:

DenseProgenyGeneticCovarianceMatrix

deepcopy(memo=None)

Make a deep copy of the Matrix.

Parameters:

memo (dict) – Dictionary of memo metadata.

Returns:

out – A deep copy of the original DenseProgenyGeneticCovarianceMatrix.

Return type:

DenseProgenyGeneticCovarianceMatrix

delete(obj, axis=-1, **kwargs)

Delete sub-arrays along an axis.

Parameters:

• obj (int, slice, or Sequence of ints) – Indicate indices of sub-arrays to remove along the specified axis.

• axis (int) – The axis along which to delete the subarray defined by obj.

• kwargs (dict) – Additional keyword arguments.

Returns:

out – A DenseSquareTraitMatrix with deleted elements. Note that concat does not occur in-place: a new DenseSquareTraitMatrix is allocated and filled.

Return type:

DenseSquareTraitMatrix

delete_taxa(obj, **kwargs)

Delete sub-arrays along the taxa axis.

Parameters:

• obj (int, slice, or Sequence of ints) – Indicate indices of sub-arrays to remove along the specified axis.

• kwargs (dict) – Additional keyword arguments.

Returns:

out – A DenseSquareTaxaMatrix with deleted elements. Note that concat does not occur in-place: a new DenseSquareTaxaMatrix is allocated and filled.

Return type:

DenseSquareTaxaMatrix

delete_trait(obj, **kwargs)

Delete sub-arrays along the trait axis.

Parameters:

• obj (int, slice, or Sequence of ints) – Indicate indices of sub-arrays to remove along the specified axis.

• kwargs (dict) – Additional keyword arguments.

Returns:

out – A DenseSquareTraitMatrix with deleted elements. Note that concat does not occur in-place: a new DenseSquareTraitMatrix is allocated and filled.

Return type:

DenseSquareTraitMatrix

property epgc: tuple

Expected parental genome contribution to the offspring from each parent.

abstract classmethod from_algmod(algmod, pgmat, ncross, nprogeny, nself, gmapfn, mem, **kwargs)

Estimate genetic variances from a GenomicModel.

Parameters:

• algmod (AdditiveLinearGenomicModel) – AdditiveLinearGenomicModel with which to estimate genetic variances.

• pgmat (PhasedGenotypeMatrix) – Input genomes to use to estimate genetic variances.

• ncross (int) – Number of cross patterns to simulate for genetic covariance estimation.

• nprogeny (int) – Number of progeny to simulate per cross to estimate genetic covariance.

• nself (int) – Number of selfing generations post-cross pattern before ‘nprogeny’ individuals are simulated.

• gmapfn (GeneticMapFunction) – GeneticMapFunction to use to estimate covariance induced by recombination.

• mem (int) – Memory chunk size to use during matrix operations.

• kwargs (dict) – Additional keyword arguments.

Returns:

out – A matrix of additive genetic covariance estimations.

Return type:

ProgenyGeneticCovarianceMatrix

abstract classmethod from_csv(filename, **kwargs)

Read an object from a CSV file.

Parameters:

• filename (str) – CSV file name from which to read.

• kwargs (dict) – Additional keyword arguments to use for dictating importing from a CSV.

Returns:

out – An object read from a CSV file.

Return type:

CSVInputOutput

abstract classmethod from_gmod(gmod, pgmat, ncross, nprogeny, nself, gmapfn, **kwargs)

Estimate genetic variances from a GenomicModel.

Parameters:

• gmod (GenomicModel) – GenomicModel with which to estimate genetic variances.

• pgmat (PhasedGenotypeMatrix) – Input genomes to use to estimate genetic variances.

• ncross (int) – Number of cross patterns to simulate for genetic covariance estimation.

• nprogeny (int) – Number of progeny to simulate per cross to estimate genetic covariance.

• nself (int) – Number of selfing generations post-cross pattern before ‘nprogeny’ individuals are simulated.

• gmapfn (GeneticMapFunction) – Genetic map function with which to calculate recombination probabilities.

• kwargs (dict) – Additional keyword arguments.

Returns:

out – A matrix of genetic covariance estimations.

Return type:

ProgenyGeneticCovarianceMatrix

classmethod from_hdf5(filename, groupname=None)

Read DenseProgenyGeneticCovarianceMatrix from an HDF5 file.

Parameters:

• filename (str, Path, or h5py.File) – HDF5 file name which to read.

• groupname (str, None) – HDF5 group name under which DenseProgenyGeneticCovarianceMatrix data is stored. If None, DenseProgenyGeneticCovarianceMatrix is read from base HDF5 group.

Returns:

out – A dense matrix read from file.

Return type:

DenseProgenyGeneticCovarianceMatrix

abstract classmethod from_pandas(df, **kwargs)

Read an object from a pandas.DataFrame.

Parameters:

• df (pandas.DataFrame) – Pandas dataframe from which to read.

• kwargs (dict) – Additional keyword arguments to use for dictating importing from a pandas.DataFrame.

Returns:

out – An object read from a pandas.DataFrame.

Return type:

PandasInputOutput

group(axis=-1, **kwargs)

Sort matrix along axis, then populate grouping indices for the axis.

Parameters:

• axis (int) – Axis along which values should be grouped.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

group_taxa(**kwargs)

Sort the Matrix along the taxa axis, then populate grouping indices for the taxa axis.

Parameters:

kwargs (dict) – Additional keyword arguments.

Return type:

None

incorp(obj, values, axis=-1, taxa=None, taxa_grp=None, trait=None, **kwargs)

Incorporate values along the given axis before the given indices.

Parameters:

• obj (int, slice, or Sequence of ints) – Object that defines the index or indices before which values is incorporated.

• values (array_like) – Values to incorporate into the matrix.

• axis (int) – The axis along which values are incorporated.

• taxa (numpy.ndarray) – Taxa names to incorporate into the Matrix. If values is a DenseSquareTaxaSquareTraitMatrix that has a non-None taxa field, providing this argument overwrites the field.

• taxa_grp (numpy.ndarray) – Taxa groups to incorporate into the Matrix. If values is a DenseSquareTaxaSquareTraitMatrix that has a non-None taxa_grp field, providing this argument overwrites the field.

• trait (numpy.ndarray) – Trait names to incorporate into the Matrix. If values is a DenseSquareTaxaSquareTraitMatrix that has a non-None trait field, providing this argument overwrites the field.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

incorp_taxa(obj, values, taxa=None, taxa_grp=None, **kwargs)

Incorporate values along the taxa axis before the given indices.

Parameters:

• obj (int, slice, or Sequence of ints) – Object that defines the index or indices before which values is incorporated.

• values (Matrix, numpy.ndarray) – Values to incorporate into the matrix.

• taxa (numpy.ndarray) – Taxa names to incorporate into the Matrix.

• taxa_grp (numpy.ndarray) – Taxa groups to incorporate into the Matrix.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

incorp_trait(obj, values, trait=None, **kwargs)

Incorporate values along the trait axis before the given indices.

Parameters:

• obj (int, slice, or Sequence of ints) – Object that defines the index or indices before which values is incorporated.

• values (Matrix, numpy.ndarray) – Values to incorporate into the matrix.

• trait (numpy.ndarray) – Trait names to incorporate into the Matrix.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

insert(obj, values, axis=-1, taxa=None, taxa_grp=None, trait=None, **kwargs)

Insert values along the given axis before the given indices.

Parameters:

• obj (int, slice, or Sequence of ints) – Object that defines the index or indices before which values is inserted.

• values (Matrix, numpy.ndarray) – Values to insert into the matrix.

• axis (int) – The axis along which values are inserted.

• taxa (numpy.ndarray) – Taxa names to insert into the Matrix. If values is a DenseSquareTaxaSquareTraitMatrix that has a non-None taxa field, providing this argument overwrites the field.

• taxa_grp (numpy.ndarray) – Taxa groups to insert into the Matrix. If values is a DenseSquareTaxaSquareTraitMatrix that has a non-None taxa_grp field, providing this argument overwrites the field.

• trait (numpy.ndarray) – Trait names to insert into the Matrix. If values is a DenseSquareTaxaSquareTraitMatrix that has a non-None trait field, providing this argument overwrites the field.

• kwargs (dict) – Additional keyword arguments.

Returns:

out – A DenseSquareTaxaSquareTraitMatrix with values inserted. Note that insert does not occur in-place: a new DenseSquareTaxaSquareTraitMatrix is allocated and filled.

Return type:

DenseSquareTaxaSquareTraitMatrix

insert_taxa(obj, values, taxa=None, taxa_grp=None, **kwargs)

Insert values along the taxa axis before the given indices.

Parameters:

• obj (int, slice, or Sequence of ints) – Object that defines the index or indices before which values is inserted.

• values (Matrix, numpy.ndarray) – Values to insert into the matrix.

• taxa (numpy.ndarray) – Taxa names to insert into the Matrix.

• taxa_grp (numpy.ndarray) – Taxa groups to insert into the Matrix.

• kwargs (dict) – Additional keyword arguments.

Returns:

out – A DenseSquareTaxaMatrix with values inserted. Note that insert does not occur in-place: a new DenseSquareTaxaMatrix is allocated and filled.

Return type:

DenseSquareTaxaMatrix

insert_trait(obj, values, trait=None, **kwargs)

Insert values along the trait axis before the given indices.

Parameters:

• obj (int, slice, or Sequence of ints) – Object that defines the index or indices before which values is inserted.

• values (Matrix, numpy.ndarray) – Values to insert into the matrix.

• trait (numpy.ndarray) – Trait names to insert into the Matrix.

• kwargs (dict) – Additional keyword arguments.

Returns:

out – A DenseSquareTraitMatrix with values inserted. Note that insert does not occur in-place: a new DenseSquareTraitMatrix is allocated and filled.

Return type:

DenseSquareTraitMatrix

is_grouped(axis=-1, **kwargs)

Determine whether the Matrix has been sorted and grouped.

Parameters:

• axis (int) – Axis along which to test if values are grouped.

• kwargs (dict) – Additional keyword arguments.

Returns:

grouped – True or False indicating whether the GeneticMap has been sorted and grouped.

Return type:

bool

is_grouped_taxa(**kwargs)

Determine whether the Matrix has been sorted and grouped along the taxa axis.

Parameters:

kwargs (dict) – Additional keyword arguments.

Returns:

grouped – True or False indicating whether the Matrix has been sorted and grouped.

Return type:

bool

is_square()

Determine whether the axis lengths for the square axes are identical.

Returns:

out – True if all square axes are the same length. False if not all square axes are the same length.

Return type:

bool

is_square_taxa()

Determine whether the taxa axes lengths for the square axes are identical.

Returns:

out – True if all square taxa axes are the same length. False if not all square taxa axes are the same length.

Return type:

bool

is_square_trait()

Determine whether the trait axes lengths for the square axes are identical.

Returns:

out – True if all square trait axes are the same length. False if not all square trait axes are the same length.

Return type:

bool

lexsort(keys, axis=-1, **kwargs)

Perform an indirect stable sort using a tuple of keys.

Parameters:

• keys (tuple, None) – A tuple of columns to be sorted. The last column is the primary sort key.

• axis (int) – The axis of the Matrix over which to sort values.

• kwargs (dict) – Additional keyword arguments.

Returns:

indices – Array of indices that sort the keys.

Return type:

numpy.ndarray

lexsort_taxa(keys=None, **kwargs)

Perform an indirect stable sort using a sequence of keys along the taxa axis.

Parameters:

• keys (A (k, N) array or tuple containing k (N,)-shaped sequences) – The k different columns to be sorted. The last column (or row if keys is a 2D array) is the primary sort key.

• kwargs (dict) – Additional keyword arguments.

Returns:

indices – Array of indices that sort the keys along the specified axis.

Return type:

A (N,) ndarray of ints

lexsort_trait(keys=None, **kwargs)

Perform an indirect stable sort using a sequence of keys along the trait axis.

Parameters:

• keys (A (k, N) array or tuple containing k (N,)-shaped sequences) – The k different columns to be sorted. The last column (or row if keys is a 2D array) is the primary sort key.

• kwargs (dict) – Additional keyword arguments.

Returns:

indices – Array of indices that sort the keys along the specified axis.

Return type:

A (N,) ndarray of ints

property mat: ndarray

Pointer to raw numpy.ndarray object.

property mat_ndim: int

Number of dimensions of the raw numpy.ndarray.

property mat_shape: tuple

Shape of the raw numpy.ndarray.

property nsquare: int

Number of axes that are square.

property nsquare_taxa: int

Number of taxa axes that are square.

property nsquare_trait: int

Number of trait axes that are square.

property ntaxa: int

Number of taxa

property ntrait: int

Number of traits.

remove(obj, axis=-1, **kwargs)

Remove sub-arrays along an axis.

Parameters:

• obj (int, slice, or Sequence of ints) – Indicate indices of sub-arrays to remove along the specified axis.

• axis (int) – The axis along which to remove the subarray defined by obj.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

remove_taxa(obj, **kwargs)

Remove sub-arrays along the taxa axis.

Parameters:

• obj (int, slice, or Sequence of ints) – Indicate indices of sub-arrays to remove along the specified axis.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

remove_trait(obj, **kwargs)

Remove sub-arrays along the trait axis.

Parameters:

• obj (int, slice, or Sequence of ints) – Indicate indices of sub-arrays to remove along the specified axis.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

reorder(indices, axis=-1, **kwargs)

Reorder the VariantMatrix.

Parameters:

• indices (numpy.ndarray) – Indices of where to place elements.

• axis (int) – The axis over which to reorder values.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

reorder_taxa(indices, **kwargs)

Reorder elements of the Matrix along the taxa axis using an array of indices. Note this modifies the Matrix in-place.

Parameters:

• indices (A (N,) ndarray of ints) – Array of indices that reorder the matrix along the specified axis.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

reorder_trait(indices, **kwargs)

Reorder elements of the Matrix along the trait axis using an array of indices. Note this modifies the Matrix in-place.

Parameters:

• indices (A (N,) ndarray of ints) – Array of indices that reorder the matrix along the specified axis.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

select(indices, axis=-1, **kwargs)

Select certain values from the matrix.

Parameters:

• indices (array_like (Nj, ...)) – The indices of the values to select.

• axis (int) – The axis along which values are selected.

• kwargs (dict) – Additional keyword arguments.

Returns:

out – The output DenseSquareTraitMatrix with values selected. Note that select does not occur in-place: a new DenseSquareTraitMatrix is allocated and filled.

Return type:

DenseSquareTraitMatrix

select_taxa(indices, **kwargs)

Select certain values from the Matrix along the taxa axis.

Parameters:

• indices (array_like (Nj, ...)) – The indices of the values to select.

• kwargs (dict) – Additional keyword arguments.

Returns:

out – The output DenseSquareTaxaMatrix with values selected. Note that select does not occur in-place: a new DenseSquareTaxaMatrix is allocated and filled.

Return type:

DenseSquareTaxaMatrix

select_trait(indices, **kwargs)

Select certain values from the Matrix along the trait axis.

Parameters:

• indices (array_like (Nj, ...)) – The indices of the values to select.

• kwargs (dict) – Additional keyword arguments.

Returns:

out – The output DenseSquareTraitMatrix with values selected. Note that select does not occur in-place: a new DenseSquareTraitMatrix is allocated and filled.

Return type:

DenseSquareTraitMatrix

sort(keys=None, axis=-1, **kwargs)

Reset metadata for corresponding axis: name, stix, spix, len. Sort the VariantMatrix using a tuple of keys.

Parameters:

• keys (tuple, None) – A tuple of columns to be sorted. The last column is the primary sort key.

• axis (int) – The axis over which to sort values.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

sort_taxa(keys=None, **kwargs)

Sort slements of the Matrix along the taxa axis using a sequence of keys. Note this modifies the Matrix in-place.

Parameters:

• keys (A (k, N) array or tuple containing k (N,)-shaped sequences) – The k different columns to be sorted. The last column (or row if keys is a 2D array) is the primary sort key.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

sort_trait(keys=None, **kwargs)

Sort slements of the Matrix along the trait axis using a sequence of keys. Note this modifies the Matrix in-place.

Parameters:

• keys (A (k, N) array or tuple containing k (N,)-shaped sequences) – The k different columns to be sorted. The last column (or row if keys is a 2D array) is the primary sort key.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

property square_axes: tuple

Axis indices for axes that are square.

property square_axes_len: tuple

Axis lengths for axes that are square.

property square_taxa_axes: tuple

Axis indices for taxa axes that are square.

property square_taxa_axes_len: tuple

Axis lengths for axes that are square.

property square_trait_axes: tuple

Axis indices for trait axes that are square.

property square_trait_axes_len: tuple

Axis lengths for axes that are square.

property taxa: ndarray | None

Taxa label array

property taxa_axis: int

First square axis along which taxa are stored

property taxa_grp: ndarray | None

Taxa group label.

property taxa_grp_len: ndarray | None

Taxa group length.

property taxa_grp_name: ndarray | None

Taxa group name.

property taxa_grp_spix: ndarray | None

Taxa group stop index.

property taxa_grp_stix: ndarray | None

Taxa group start index.

abstract to_csv(filename, **kwargs)

Write an object to a CSV file.

Parameters:

• filename (str) – CSV file name to which to write.

• kwargs (dict) – Additional keyword arguments to use for dictating export to a CSV.

Return type:

None

to_hdf5(filename, groupname=None, overwrite=True)

Write DenseSquareTaxaSquareTraitMatrix to an HDF5 file.

Parameters:

• filename (str, Path, h5py.File) – If str, an HDF5 file name to which to write. File is closed after writing. If h5py.File, an opened HDF5 file to which to write. File is not closed after writing.

• groupname (str, None) – If str, an HDF5 group name under which the DenseSquareTaxaSquareTraitMatrix data is stored. If None, the DenseSquareTaxaSquareTraitMatrix is written to the base HDF5 group.

• overwrite (bool) – Whether to overwrite values in an HDF5 file if a field already exists.

Return type:

None

abstract to_pandas(**kwargs)

Export an object to a pandas.DataFrame.

Parameters:

kwargs (dict) – Additional keyword arguments to use for dictating export to a pandas.DataFrame.

Returns:

out – An output dataframe.

Return type:

pandas.DataFrame

property trait: ndarray | None

Trait label.

property trait_axis: int

First square axis along which trait are stored

ungroup(axis=-1, **kwargs)

Ungroup the DenseSquareTaxaMatrix along an axis by removing grouping metadata.

Parameters:

• axis (int) – The axis along which values should be ungrouped.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

ungroup_taxa(**kwargs)

Ungroup the DenseTaxaMatrix along the taxa axis by removing taxa group metadata.

Parameters:

kwargs (dict) – Additional keyword arguments.

Return type:

None