pyckmeans.distance package

Submodules

pyckmeans.distance.c_interop module

pyckmeans.distance.c_interop.jc_distance(alignment: numpy.ndarray, pairwise_deletion: bool = True) → numpy.ndarray

Calculate Jukes-Cantor distance for a nucleotide alignment.

Parameters

alignmentnumpy.ndarray: n*m numpy alignment, where n is the number of entries and m is the number of sites. Bases must be encoded in the format of pyckmeans.io.NucleotideAlignment.
pairwise_deletionbool, optional: Calculate distances with pairwise-deletion in case of missing data, by default True

Returns

numpy.ndarray: n*n distance matrix.

pyckmeans.distance.c_interop.k2p_distance(alignment: numpy.ndarray, pairwise_deletion: bool = True) → numpy.ndarray

jc_distance

Calculate Kimura 2-parameter distance for a nucleotide alignment.

Parameters

alignmentnumpy.ndarray: n*m numpy alignment, where n is the number of entries and m is the number of sites. Bases must be encoded in the format of pyckmeans.io.NucleotideAlignment.
pairwise_deletionbool, optional: Calculate distances with pairwise-deletion in case of missing data, by default True

Returns

numpy.ndarray: n*n distance matrix.

pyckmeans.distance.c_interop.p_distance(alignment: numpy.ndarray, pairwise_deletion: bool = True) → numpy.ndarray

Calculate p-distance for a nucleotide alignment.

Parameters

alignmentnumpy.ndarray: n*m numpy alignment, where n is the number of entries and m is the number of sites. Bases must be encoded in the format of pyckmeans.io.NucleotideAlignment.
pairwise_deletionbool, optional: Calculate distances with pairwise-deletion in case of missing data, by default True

Returns

numpy.ndarray: n*n distance matrix.

Module contents

distance

Module for distance calculations.

class pyckmeans.distance.DistanceMatrix(dist_mat: numpy.ndarray, names: Optional[Iterable[str]] = None)

Bases: object

Distance Matrix, optionally named.

Parameters

dist_matnumpy.ndarray: n*n distance matrix.
namesOptional[Iterable[str]]: Names, by default None.

Raises

IncompatibleNamesError: Raised if dimension of names and dist_mat are incompatible.

Attributes

shape: shape

Methods

`from_csv`(file_path[, header, index_col, sep])	read_csv_distmat
`from_phylip`(file_path)	Read PHYLIP distance matrix.
`to_csv`(file_path[, force])	Write DistanceMatrix object to CSV.
`to_phylip`(file_path[, force])	Write distance matrix to file in PHYLIP matrix format.

static from_csv(file_path: str, header: Optional[int] = 0, index_col: Optional[int] = 0, sep: str = ',', **kwargs) → pyckmeans.distance.DistanceMatrix

read_csv_distmat

Read distance matrix from CSV file.

Parameters

file_pathstr: Path to CSV file.
headerOptional[int]: Determines the row in the CSV file containing sample names. Is passed to pandas.read_csv(). By default 0, meaning the first row.
index_colOptional[int]: Determines the index column. By default, the first column is expected to contain sample names. Passed to pandas.read_csv().
sepstr: Column separator, be default ‘,’. Passed to Passed to pandas.read_csv().
**kwargs: Additional keyword arguments passed to pandas.read_csv().
Returns
——-
pyckmeans.distance.DistanceMatrix: DistanceMatrix object.

static from_phylip(file_path: str) → pyckmeans.distance.DistanceMatrix

Read PHYLIP distance matrix.

Returns

DistanceMatrix: DistanceMatrix object.

property shape: Tuple[int]

Get matrix shape.

Returns

Tuple[int]: Matrix shape.

to_csv(file_path: str, force: bool = False)

Write DistanceMatrix object to CSV.

Parameters

file_pathstr: CSV file path.
forcebool, optional: Force overwrite if file_path already exists, by default False

to_phylip(file_path: str, force: bool = False)

Write distance matrix to file in PHYLIP matrix format.

Parameters

file_pathstr: Output file path.
forcebool, optional: Force overwrite if file exists, by default False

exception pyckmeans.distance.IncompatibleNamesError: Bases: Exception

exception pyckmeans.distance.InvalidDistanceTypeError

Bases: Exception

UnknownDistanceTypeError

pyckmeans.distance.alignment_distance(alignment: pyckmeans.io.nucleotide_alignment.NucleotideAlignment, distance_type: str = 'p', pairwise_deletion: bool = True) → pyckmeans.distance.DistanceMatrix

genetic_distance

Calculate genetic distance based on a nucleotide alignment.

Parameters

alignmentpyckmeans.io.NucleotideAlignment: Nucleotide alignment.
distance_typestr, optional: Type of genetic distance to calculate, by default ‘p’. Available distance types are p-distances (‘p’), Jukes-Cantor distances (‘jc’), and Kimura 2-paramater distances (‘k2p’).
pairwise_deletionbool: Use pairwise deletion as action to deal with missing data. If False, complete deletion is applied. Gaps (“-”, “~”, ” “), “?”, and ambiguous bases are treated as missing data.
Returns
——-
DistanceMatrix: n*n distance matrix.

Raises

InvalidDistanceTypeError: Raised if invalid distance_type is passed.