AdditiveGeneticVarianceMatrix

class pybrops.model.vmat.AdditiveGeneticVarianceMatrix.AdditiveGeneticVarianceMatrix

Bases: GeneticVarianceMatrix

An abstract class for additive genetic variance matrices.

The purpose of this abstract interface is to provide functionality for:

1. Estimation of additive genetic variance from an additive linear genomic model.

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 the taxa axis.
adjoin_trait	Add additional elements to the end of the Matrix along the trait axis.
append	Append values to the Matrix.
append_taxa	Append values to the Matrix along the taxa axis.
append_trait	Append values to the TraitMatrix 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 an object from an HDF5 file.
from_pandas	Read an object from a pandas.DataFrame.
group	Sort the GroupableMatrix along an axis, then populate grouping indices.
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 axes lengths for the square axes are identical.
is_square_taxa	Determine whether the taxa axes lengths for the square axes are identical.
lexsort	Perform an indirect stable sort using a sequence 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 elements of the Matrix using an array of indices.
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	Sort slements of the Matrix using a sequence of keys.
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 an object to an HDF5 file.
to_pandas	Export an object to a pandas.DataFrame.
ungroup	Ungroup the GroupableMatrix along an axis by removing grouping metadata.
ungroup_taxa	Ungroup the TaxaMatrix along the taxa axis by removing taxa group metadata.

Attributes

epgc	Expected parental genome contribution to the offspring.
mat	Pointer to raw matrix object.
mat_ndim	Number of dimensions of the raw matrix.
mat_shape	Shape of the raw matrix.
nsquare	Number of axes that are square.
nsquare_taxa	Number of taxa 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.
taxa	Taxa label.
taxa_axis	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	Axis along which traits are stored.

abstract __add__(value)

Elementwise add matrices

Parameters:

value (object) – Object which to add.

Returns:

out – An object resulting from the addition.

Return type:

object

abstract __mul__(value)

Elementwise multiply matrices

Parameters:

value (object) – Object which to multiply.

Returns:

out – An object resulting from the multiplication.

Return type:

object

abstract adjoin(values, axis, **kwargs)

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

Parameters:

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

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

• kwargs (dict) – Additional keyword arguments.

Returns:

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

Return type:

Matrix

abstract adjoin_taxa(values, taxa, taxa_grp, **kwargs)

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

Parameters:

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

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

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

• kwargs (dict) – Additional keyword arguments.

Returns:

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

Return type:

TaxaMatrix

abstract adjoin_trait(values, trait, **kwargs)

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

Parameters:

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

• trait (numpy.ndarray) – Trait names to adjoin to the TraitMatrix.

• kwargs (dict) – Additional keyword arguments.

Returns:

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

Return type:

TraitMatrix

abstract append(values, axis, **kwargs)

Append values to the Matrix.

Parameters:

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

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

• kwargs (dict) – Additional keyword arguments.

Return type:

None

abstract append_taxa(values, taxa, taxa_grp, **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

abstract append_trait(values, trait, **kwargs)

Append values to the TraitMatrix along the trait axis.

Parameters:

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

• trait (numpy.ndarray) – Trait names to append to the TraitMatrix.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

abstract classmethod concat(mats, axis, **kwargs)

Concatenate matrices together along an 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.

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

• kwargs (dict) – Additional keyword arguments

Returns:

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

Return type:

Matrix

abstract 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 TaxaMatrix. Note that concat does not occur in-place: a new TaxaMatrix is allocated and filled.

Return type:

TaxaMatrix

abstract classmethod concat_trait(mats, **kwargs)

Concatenate list of Matrix together along the trait axis.

Parameters:

• mats (Sequence of TraitMatrix) – List of TraitMatrix 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 TraitMatrix. Note that concat does not occur in-place: a new TraitMatrix is allocated and filled.

Return type:

TraitMatrix

abstract copy()

Make a shallow copy of the Matrix.

Returns:

out – A shallow copy of the original Matrix.

Return type:

Matrix

abstract deepcopy(memo)

Make a deep copy of the Matrix.

Parameters:

memo (dict) – Dictionary of memo metadata.

Returns:

out – A deep copy of the original Matrix.

Return type:

Matrix

abstract delete(obj, axis, **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 Matrix with deleted elements. Note that concat does not occur in-place: a new Matrix is allocated and filled.

Return type:

Matrix

abstract 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 TaxaMatrix with deleted elements. Note that concat does not occur in-place: a new TaxaMatrix is allocated and filled.

Return type:

TaxaMatrix

abstract 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 TraitMatrix with deleted elements. Note that concat does not occur in-place: a new TraitMatrix is allocated and filled.

Return type:

TraitMatrix

abstract property epgc: tuple

Expected parental genome contribution to the offspring.

abstract classmethod from_algmod(algmod, pgmat, nmating, 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.

• nmating (int) – Number of cross patterns to simulate for genetic variance estimation.

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

• 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 variance estimations.

Return type:

AdditiveGeneticVarianceMatrix

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, nmating, 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.

• nmating (int) – Number of cross patterns to simulate for genetic variance estimation.

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

• 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 variance estimations.

Return type:

GeneticVarianceMatrix

abstract classmethod from_hdf5(filename, groupname)

Read an object from an HDF5 file.

Parameters:

• filename (str, Path, h5py.File) – If str, an HDF5 file name from which to read. If Path, an HDF5 file name from which to read. If h5py.File, an opened HDF5 file from which to read.

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

Returns:

out – An object read from an HDF5 file.

Return type:

HDF5InputOutput

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

abstract group(axis, **kwargs)

Sort the GroupableMatrix along an axis, then populate grouping indices.

Parameters:

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

• kwargs (dict) – Additional keyword arguments.

Return type:

None

abstract 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

abstract incorp(obj, values, axis, **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 (numpy.ndarray) – Values to incorporate into the matrix.

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

• kwargs (dict) – Additional keyword arguments.

Return type:

None

abstract incorp_taxa(obj, values, taxa, taxa_grp, **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

abstract incorp_trait(obj, values, trait, **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

abstract insert(obj, values, axis, **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 (ArrayLike) – Values to insert into the matrix.

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

• kwargs (dict) – Additional keyword arguments.

Returns:

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

Return type:

Matrix

abstract insert_taxa(obj, values, taxa, taxa_grp, **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 TaxaMatrix with values inserted. Note that insert does not occur in-place: a new TaxaMatrix is allocated and filled.

Return type:

TaxaMatrix

abstract insert_trait(obj, values, trait, **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 TraitMatrix.

• kwargs (dict) – Additional keyword arguments.

Returns:

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

Return type:

TraitMatrix

abstract is_grouped(axis, **kwargs)

Determine whether the Matrix has been sorted and grouped.

Parameters:

• axis (int) – Axis along which to determine whether elements have been sorted and grouped.

• kwargs (dict) – Additional keyword arguments.

Returns:

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

Return type:

bool

abstract 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

abstract is_square()

Determine whether the axes 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

abstract 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

abstract lexsort(keys, axis, **kwargs)

Perform an indirect stable sort using a sequence of keys.

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.

• axis (int) – Axis to be indirectly sorted.

• 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

abstract lexsort_taxa(keys, **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

abstract lexsort_trait(keys, **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

abstract property mat: object

Pointer to raw matrix object.

abstract property mat_ndim: int

Number of dimensions of the raw matrix.

abstract property mat_shape: tuple

Shape of the raw matrix.

abstract property nsquare: int

Number of axes that are square.

abstract property nsquare_taxa: int

Number of taxa axes that are square.

abstract property ntaxa: int

Number of taxa.

abstract property ntrait: int

Number of traits.

abstract remove(obj, axis, **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

abstract 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

abstract 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

abstract reorder(indices, axis, **kwargs)

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

Parameters:

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

• axis (int) – Axis to be reordered.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

abstract 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

abstract 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, Sequence of ints) – Array of indices that reorder the matrix along the specified axis.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

abstract select(indices, axis, **kwargs)

Select certain values from the matrix.

Parameters:

• indices (ArrayLike (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 matrix with values selected. Note that select does not occur in-place: a new Matrix is allocated and filled.

Return type:

Matrix

abstract 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 TaxaMatrix with values selected. Note that select does not occur in-place: a new TaxaMatrix is allocated and filled.

Return type:

TaxaMatrix

abstract select_trait(indices, **kwargs)

Select certain values from the Matrix along the trait axis.

Parameters:

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

• kwargs (dict) – Additional keyword arguments.

Returns:

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

Return type:

TraitMatrix

abstract sort(keys, axis, **kwargs)

Sort slements of the Matrix 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.

• axis (int) – Axis to be indirectly sorted.

• kwargs (dict) – Additional keyword arguments.

Return type:

None

abstract sort_taxa(keys, **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

abstract sort_trait(keys, **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

abstract property square_axes: tuple

Axis indices for axes that are square.

abstract property square_axes_len: tuple

Axis lengths for axes that are square.

abstract property square_taxa_axes: tuple

Axis indices for taxa axes that are square.

abstract property square_taxa_axes_len: tuple

Axis lengths for axes that are square.

abstract property taxa: object

Taxa label.

abstract property taxa_axis: int

Axis along which taxa are stored.

abstract property taxa_grp: object

Taxa group label.

abstract property taxa_grp_len: object

Taxa group length.

abstract property taxa_grp_name: object

Taxa group name.

abstract property taxa_grp_spix: object

Taxa group stop index.

abstract property taxa_grp_stix: object

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

abstract to_hdf5(filename, groupname, overwrite)

Write an object to an HDF5 file.

Parameters:

• filename (str, Path, h5py.File) – If str, an HDF5 file name to which to write. If Path, an HDF5 file path to which to write. If h5py.File, an opened HDF5 file to which to write.

• groupname (str, None) – If str, an HDF5 group name under which object data is stored. If None, object 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

abstract property trait: object

Trait label.

abstract property trait_axis: int

Axis along which traits are stored.

abstract ungroup(axis, **kwargs)

Ungroup the GroupableMatrix 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

abstract ungroup_taxa(**kwargs)

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

Parameters:

kwargs (dict) – Additional keyword arguments.

Return type:

None