Source code for allel.model.ndarray

# -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function, division
import collections
import bisect


# third-party imports
import numpy as np


# internal imports
from allel.util import check_integer_dtype, check_shape, check_dtype, ignore_invalid, \
    check_dim0_aligned, check_ploidy, check_ndim, asarray_ndim
from allel.compat import PY2, copy_method_doc, integer_types, memoryview_safe
from allel.io import write_vcf, gff3_to_recarray, recarray_from_hdf5_group, recarray_to_hdf5_group
from allel.abc import ArrayWrapper, DisplayAs1D, DisplayAs2D, DisplayAsTable
from allel.opt.model import genotype_array_pack_diploid, genotype_array_unpack_diploid, \
    genotype_array_count_alleles, genotype_array_count_alleles_masked, \
    genotype_array_count_alleles_subpop, genotype_array_count_alleles_subpop_masked, \
    haplotype_array_count_alleles, haplotype_array_count_alleles_subpop, \
    haplotype_array_map_alleles
from .generic import index_genotype_vector, compress_genotypes, \
    take_genotypes, concatenate_genotypes, index_genotype_array, subset_genotype_array, \
    index_haplotype_array, compress_haplotype_array, take_haplotype_array, \
    subset_haplotype_array, concatenate_haplotype_array, index_allele_counts_array, \
    compress_allele_counts_array, take_allele_counts_array, concatenate_allele_counts_array,\
    index_genotype_ac_array, index_genotype_ac_vector, compress_genotype_ac, \
    take_genotype_ac, concatenate_genotype_ac, subset_genotype_ac_array


__all__ = ['Genotypes', 'GenotypeArray', 'GenotypeVector', 'HaplotypeArray', 'AlleleCountsArray',
           'GenotypeAlleleCounts', 'GenotypeAlleleCountsArray', 'GenotypeAlleleCountsVector',
           'SortedIndex', 'UniqueIndex', 'SortedMultiIndex', 'VariantTable', 'FeatureTable']


# noinspection PyTypeChecker
_total_slice = slice(None)


def subset(data, sel0, sel1):
    """Apply selections on first and second axes."""

    # check inputs
    data = np.asarray(data)
    if data.ndim < 2:
        raise ValueError('data must have 2 or more dimensions')
    sel0 = asarray_ndim(sel0, 1, allow_none=True)
    sel1 = asarray_ndim(sel1, 1, allow_none=True)

    # ensure indices
    if sel0 is not None and sel0.dtype.kind == 'b':
        sel0, = np.nonzero(sel0)
    if sel1 is not None and sel1.dtype.kind == 'b':
        sel1, = np.nonzero(sel1)

    # ensure leading dimension indices can be broadcast correctly
    if sel0 is not None and sel1 is not None:
        sel0 = sel0[:, np.newaxis]

    # deal with None arguments
    if sel0 is None:
        sel0 = _total_slice
    if sel1 is None:
        sel1 = _total_slice

    return data[sel0, sel1]


class NumpyArrayWrapper(ArrayWrapper):
    """Abstract base class that wraps a NumPy array."""

    def __init__(self, data, copy=False, **kwargs):
        values = np.array(data, copy=copy, **kwargs)
        super(NumpyArrayWrapper, self).__init__(values)


class NumpyRecArrayWrapper(DisplayAsTable):

    def __init__(self, data, copy=False, **kwargs):
        if isinstance(data, dict):
            names = kwargs.get('names', sorted(data.keys()))
            arrays = [data[n] for n in names]
            values = np.rec.fromarrays(arrays, names=names)
        else:
            values = np.rec.array(data, copy=copy, **kwargs)
            check_ndim(values, 1)
        if not values.dtype.names:
            raise ValueError('expected recarray')
        super(NumpyRecArrayWrapper, self).__init__(values)

    def __getitem__(self, item):
        s = self.values[item]
        if isinstance(item, (slice, list, np.ndarray, type(Ellipsis))):
            return type(self)(s)
        return s

    @property
    def names(self):
        return tuple(self.dtype.names)

    @classmethod
    def fromdict(cls, data, **kwargs):
        names = kwargs.get('names', sorted(data.keys()))
        arrays = [data[n] for n in names]
        a = np.rec.fromarrays(arrays, names=names)
        return cls(a, copy=False)

    @classmethod
    def fromarrays(cls, *args, **kwargs):
        a = np.rec.fromarrays(*args, **kwargs)
        return cls(a, copy=False)

    @classmethod
    def fromrecords(cls, *args, **kwargs):
        a = np.rec.fromrecords(*args, **kwargs)
        return cls(a, copy=False)

    @classmethod
    def from_hdf5_group(cls, *args, **kwargs):
        a = recarray_from_hdf5_group(*args, **kwargs)
        return cls(a, copy=False)

    def to_hdf5_group(self, parent, name, **kwargs):
        return recarray_to_hdf5_group(self, parent, name, **kwargs)

    def eval(self, expression, vm='python'):
        """Evaluate an expression against the table columns.

        Parameters
        ----------
        expression : string
            Expression to evaluate.
        vm : {'numexpr', 'python'}
            Virtual machine to use.

        Returns
        -------
        result : ndarray

        """

        if vm == 'numexpr':
            import numexpr as ne
            return ne.evaluate(expression, local_dict=self)
        else:
            if PY2:
                # locals must be a mapping
                m = {k: self[k] for k in self.dtype.names}
            else:
                m = self
            return eval(expression, dict(), m)

    def query(self, expression, vm='python'):
        """Evaluate expression and then use it to extract rows from the table.

        Parameters
        ----------
        expression : string
            Expression to evaluate.
        vm : {'numexpr', 'python'}
            Virtual machine to use.

        Returns
        -------
        result : structured array

        """

        condition = self.eval(expression, vm=vm)
        return self.compress(condition)

    def copy(self, *args, **kwargs):
        data = self.values.copy(*args, **kwargs)
        # can always wrap this as sub-class type
        return type(self)(data)

    def compress(self, condition, axis=0, out=None):
        out = self.values.compress(condition, axis=axis, out=out)
        if axis == 0:
            out = type(self)(out)
        return out

    def take(self, indices, axis=0, out=None, mode='raise'):
        out = self.values.take(indices, axis=axis, out=out, mode=mode)
        if axis == 0:
            out = type(self)(out)
        return out

    def concatenate(self, others):
        """Concatenate arrays."""
        if not isinstance(others, (list, tuple)):
            others = others,
        tup = (self.values,) + tuple(o.values for o in others)
        out = np.concatenate(tup, axis=0)
        out = type(self)(out)
        return out


[docs]class Genotypes(NumpyArrayWrapper): """Base class for wrapping a NumPy array of genotype calls. See Also -------- GenotypeArray, GenotypeVector """ def __init__(self, data, copy=False, **kwargs): super(Genotypes, self).__init__(data, copy=copy, **kwargs) check_integer_dtype(self.values) self._mask = None self._is_phased = None @property def ploidy(self): """Sample ploidy.""" return self.shape[-1] @property def n_allele_calls(self): """Total number of allele calls.""" return np.prod(self.shape) @property def n_calls(self): """Total number of genotype calls.""" return self.n_allele_calls // self.ploidy @property def mask(self): """A boolean mask, indicating genotype calls that should be filtered (i.e., excluded) from genotype and allele counting operations. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 1], [1, 1]], ... [[0, 2], [-1, -1]]], dtype='i1') >>> g <GenotypeArray shape=(3, 2, 2) dtype=int8> 0/0 0/1 0/1 1/1 0/2 ./. >>> g.count_called() 5 >>> g.count_alleles() <AlleleCountsArray shape=(3, 3) dtype=int32> 3 1 0 1 3 0 1 0 1 >>> v = g[:, 1] >>> v <GenotypeVector shape=(3, 2) dtype=int8> 0/1 1/1 ./. >>> v.is_called() array([ True, True, False]) >>> mask = [[True, False], [False, True], [False, False]] >>> g.mask = mask >>> g <GenotypeArray shape=(3, 2, 2) dtype=int8> ./. 0/1 0/1 ./. 0/2 ./. >>> g.count_called() 3 >>> g.count_alleles() <AlleleCountsArray shape=(3, 3) dtype=int32> 1 1 0 1 1 0 1 0 1 >>> v = g[:, 1] >>> v <GenotypeVector shape=(3, 2) dtype=int8> 0/1 ./. ./. >>> v.is_called() array([ True, False, False]) Notes ----- This is a lightweight genotype call mask and **not** a mask in the sense of a numpy masked array. This means that the mask will only be taken into account by the genotype and allele counting methods of this class, and is ignored by any of the generic methods on the ndarray class or by any numpy ufuncs. """ return self._mask @mask.setter def mask(self, mask): if mask is not None: mask = np.asarray(mask, dtype=bool) check_shape(mask, self.shape[:-1]) self._mask = mask @property def is_phased(self): """A Boolean array indicating which genotype calls are phased and which are not. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 1], [1, 1]], ... [[0, 2], [-1, -1]]], dtype='i1') >>> g <GenotypeArray shape=(3, 2, 2) dtype=int8> 0/0 0/1 0/1 1/1 0/2 ./. >>> g.is_phased = [[True, True], [False, True], [False, False]] >>> g <GenotypeArray shape=(3, 2, 2) dtype=int8> 0|0 0|1 0/1 1|1 0/2 ./. """ return self._is_phased @is_phased.setter def is_phased(self, is_phased): if is_phased is not None: is_phased = np.asarray(is_phased, dtype=bool) check_shape(is_phased, self.shape[:-1]) self._is_phased = is_phased
[docs] def fill_masked(self, value=-1, copy=True): """Fill masked genotype calls with a given value. Parameters ---------- value : int, optional The fill value. copy : bool, optional If False, modify the array in place. Returns ------- g : GenotypeArray Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 1], [1, 1]], ... [[0, 2], [-1, -1]]], dtype='i1') >>> mask = [[True, False], [False, True], [False, False]] >>> g.mask = mask >>> g.fill_masked().values array([[[-1, -1], [ 0, 1]], [[ 0, 1], [-1, -1]], [[ 0, 2], [-1, -1]]], dtype=int8) """ if self.mask is None: raise ValueError('no mask is set') # apply the mask data = np.array(self.values, copy=copy) data[self.mask, ...] = value if copy: out = type(self)(data) # wrap out.is_phased = self.is_phased # don't set mask because it has been filled in else: out = self out.mask = None # reset mask return out
[docs] def is_called(self): """Find non-missing genotype calls. Returns ------- out : ndarray, bool, shape (n_variants, n_samples) Array where elements are True if the genotype call matches the condition. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 1], [1, 1]], ... [[0, 2], [-1, -1]]]) >>> g.is_called() array([[ True, True], [ True, True], [ True, False]]) >>> v = g[:, 1] >>> v <GenotypeVector shape=(3, 2) dtype=int64> 0/1 1/1 ./. >>> v.is_called() array([ True, True, False]) """ out = np.all(self.values >= 0, axis=-1) # handle mask if self.mask is not None: out &= ~self.mask return out
[docs] def is_missing(self): """Find missing genotype calls. Returns ------- out : ndarray, bool, shape (n_variants, n_samples) Array where elements are True if the genotype call matches the condition. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 1], [1, 1]], ... [[0, 2], [-1, -1]]]) >>> g.is_missing() array([[False, False], [False, False], [False, True]]) >>> v = g[:, 1] >>> v <GenotypeVector shape=(3, 2) dtype=int64> 0/1 1/1 ./. >>> v.is_missing() array([False, False, True]) """ out = np.any(self.values < 0, axis=-1) # handle mask if self.mask is not None: out |= self.mask return out
[docs] def is_hom(self, allele=None): """Find genotype calls that are homozygous. Parameters ---------- allele : int, optional Allele index. Returns ------- out : ndarray, bool, shape (n_variants, n_samples) Array where elements are True if the genotype call matches the condition. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 1], [1, 1]], ... [[2, 2], [-1, -1]]]) >>> g.is_hom() array([[ True, False], [False, True], [ True, False]]) >>> g.is_hom(allele=1) array([[False, False], [False, True], [False, False]]) >>> v = g[:, 0] >>> v <GenotypeVector shape=(3, 2) dtype=int64> 0/0 0/1 2/2 >>> v.is_hom() array([ True, False, True]) """ if allele is None: allele1 = self.values[..., 0, np.newaxis] other_alleles = self.values[..., 1:] tmp = (allele1 >= 0) & (allele1 == other_alleles) out = np.all(tmp, axis=-1) else: out = np.all(self.values == allele, axis=-1) # handle mask if self.mask is not None: out &= ~self.mask return out
[docs] def is_hom_ref(self): """Find genotype calls that are homozygous for the reference allele. Returns ------- out : ndarray, bool, shape (n_variants, n_samples) Array where elements are True if the genotype call matches the condition. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 1], [1, 1]], ... [[0, 2], [-1, -1]]]) >>> g.is_hom_ref() array([[ True, False], [False, False], [False, False]]) >>> v = g[:, 0] >>> v <GenotypeVector shape=(3, 2) dtype=int64> 0/0 0/1 0/2 >>> v.is_hom_ref() array([ True, False, False]) """ return self.is_hom(allele=0)
[docs] def is_hom_alt(self): """Find genotype calls that are homozygous for any alternate (i.e., non-reference) allele. Returns ------- out : ndarray, bool, shape (n_variants, n_samples) Array where elements are True if the genotype call matches the condition. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 1], [1, 1]], ... [[2, 2], [-1, -1]]]) >>> g.is_hom_alt() array([[False, False], [False, True], [ True, False]]) >>> v = g[:, 1] >>> v <GenotypeVector shape=(3, 2) dtype=int64> 0/1 1/1 ./. >>> v.is_hom_alt() array([False, True, False]) """ allele1 = self.values[..., 0, np.newaxis] other_alleles = self.values[..., 1:] tmp = (allele1 > 0) & (allele1 == other_alleles) out = np.all(tmp, axis=-1) # handle mask if self.mask is not None: out &= ~self.mask return out
[docs] def is_het(self, allele=None): """Find genotype calls that are heterozygous. Returns ------- out : ndarray, bool, shape (n_variants, n_samples) Array where elements are True if the genotype call matches the condition. allele : int, optional Heterozygous allele. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 1], [1, 1]], ... [[0, 2], [-1, -1]]]) >>> g.is_het() array([[False, True], [ True, False], [ True, False]]) >>> g.is_het(2) array([[False, False], [False, False], [ True, False]]) >>> v = g[:, 0] >>> v <GenotypeVector shape=(3, 2) dtype=int64> 0/0 0/1 0/2 >>> v.is_het() array([False, True, True]) """ allele1 = self.values[..., 0, np.newaxis] # type: np.ndarray other_alleles = self.values[..., 1:] # type: np.ndarray out = np.all(self.values >= 0, axis=-1) & np.any(allele1 != other_alleles, axis=-1) if allele is not None: out &= np.any(self.values == allele, axis=-1) # handle mask if self.mask is not None: out &= ~self.mask return out
[docs] def is_call(self, call): """Locate genotypes with a given call. Parameters ---------- call : array_like, int, shape (ploidy,) The genotype call to find. Returns ------- out : ndarray, bool, shape (n_variants, n_samples) Array where elements are True if the genotype is `call`. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 1], [1, 1]], ... [[0, 2], [-1, -1]]]) >>> g.is_call((0, 2)) array([[False, False], [False, False], [ True, False]]) >>> v = g[:, 0] >>> v <GenotypeVector shape=(3, 2) dtype=int64> 0/0 0/1 0/2 >>> v.is_call((0, 2)) array([False, False, True]) """ # guard conditions if not len(call) == self.shape[-1]: raise ValueError('invalid call ploidy: %s', repr(call)) if self.ndim == 2: call = np.asarray(call)[np.newaxis, :] else: call = np.asarray(call)[np.newaxis, np.newaxis, :] out = np.all(self.values == call, axis=-1) # handle mask if self.mask is not None: out &= ~self.mask return out
[docs] def count_called(self, axis=None): """Count called genotypes. Parameters ---------- axis : int, optional Axis over which to count, or None to perform overall count. """ b = self.is_called() return np.sum(b, axis=axis)
[docs] def count_missing(self, axis=None): """Count missing genotypes. Parameters ---------- axis : int, optional Axis over which to count, or None to perform overall count. """ b = self.is_missing() return np.sum(b, axis=axis)
[docs] def count_hom(self, allele=None, axis=None): """Count homozygous genotypes. Parameters ---------- allele : int, optional Allele index. axis : int, optional Axis over which to count, or None to perform overall count. """ b = self.is_hom(allele=allele) return np.sum(b, axis=axis)
[docs] def count_hom_ref(self, axis=None): """Count homozygous reference genotypes. Parameters ---------- axis : int, optional Axis over which to count, or None to perform overall count. """ b = self.is_hom_ref() return np.sum(b, axis=axis)
[docs] def count_hom_alt(self, axis=None): """Count homozygous alternate genotypes. Parameters ---------- axis : int, optional Axis over which to count, or None to perform overall count. """ b = self.is_hom_alt() return np.sum(b, axis=axis)
[docs] def count_het(self, allele=None, axis=None): """Count heterozygous genotypes. Parameters ---------- allele : int, optional Allele index. axis : int, optional Axis over which to count, or None to perform overall count. """ b = self.is_het(allele=allele) return np.sum(b, axis=axis)
[docs] def count_call(self, call, axis=None): """Count genotypes with a given call. Parameters ---------- call : array_like, int, shape (ploidy,) The genotype call to find. axis : int, optional Axis over which to count, or None to perform overall count. """ b = self.is_call(call=call) return np.sum(b, axis=axis)
[docs] def to_n_ref(self, fill=0, dtype='i1'): """Transform each genotype call into the number of reference alleles. Parameters ---------- fill : int, optional Use this value to represent missing calls. dtype : dtype, optional Output dtype. Returns ------- out : ndarray, int8, shape (n_variants, n_samples) Array of ref alleles per genotype call. Notes ----- By default this function returns 0 for missing genotype calls **and** for homozygous non-reference genotype calls. Use the `fill` argument to change how missing calls are represented. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 2], [1, 1]], ... [[2, 2], [-1, -1]]]) >>> g.to_n_ref() array([[2, 1], [1, 0], [0, 0]], dtype=int8) >>> g.to_n_ref(fill=-1) array([[ 2, 1], [ 1, 0], [ 0, -1]], dtype=int8) >>> v = g[:, 0] >>> v <GenotypeVector shape=(3, 2) dtype=int64> 0/0 0/2 2/2 >>> v.to_n_ref() array([2, 1, 0], dtype=int8) """ # count number of alternate alleles out = np.empty(self.shape[:-1], dtype=dtype) np.sum(self.values == 0, axis=-1, out=out) # fill missing calls if fill != 0: m = self.is_missing() out[m] = fill # handle mask if self.mask is not None: out[self.mask] = fill return out
[docs] def to_n_alt(self, fill=0, dtype='i1'): """Transform each genotype call into the number of non-reference alleles. Parameters ---------- fill : int, optional Use this value to represent missing calls. dtype : dtype, optional Output dtype. Returns ------- out : ndarray, int, shape (n_variants, n_samples) Array of non-ref alleles per genotype call. Notes ----- This function simply counts the number of non-reference alleles, it makes no distinction between different alternate alleles. By default this function returns 0 for missing genotype calls **and** for homozygous reference genotype calls. Use the `fill` argument to change how missing calls are represented. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 2], [1, 1]], ... [[2, 2], [-1, -1]]]) >>> g.to_n_alt() array([[0, 1], [1, 2], [2, 0]], dtype=int8) >>> g.to_n_alt(fill=-1) array([[ 0, 1], [ 1, 2], [ 2, -1]], dtype=int8) >>> v = g[:, 0] >>> v <GenotypeVector shape=(3, 2) dtype=int64> 0/0 0/2 2/2 >>> v.to_n_alt() array([0, 1, 2], dtype=int8) """ # count number of alternate alleles out = np.empty(self.shape[:-1], dtype=dtype) np.sum(self.values > 0, axis=-1, out=out) # fill missing calls if fill != 0: m = self.is_missing() out[m] = fill # handle mask if self.mask is not None: out[self.mask] = fill return out
[docs] def to_allele_counts(self, max_allele=None, dtype='u1'): """Transform genotype calls into allele counts per call. Parameters ---------- max_allele : int, optional Highest allele index. Provide this value to speed up computation. dtype : dtype, optional Output dtype. Returns ------- out : ndarray, uint8, shape (n_variants, n_samples, len(alleles)) Array of allele counts per call. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 2], [1, 1]], ... [[2, 2], [-1, -1]]]) >>> g.to_allele_counts() <GenotypeAlleleCountsArray shape=(3, 2, 3) dtype=uint8> 2:0:0 1:1:0 1:0:1 0:2:0 0:0:2 0:0:0 >>> v = g[:, 0] >>> v <GenotypeVector shape=(3, 2) dtype=int64> 0/0 0/2 2/2 >>> v.to_allele_counts() <GenotypeAlleleCountsVector shape=(3, 3) dtype=uint8> 2:0:0 1:0:1 0:0:2 """ # determine alleles to count if max_allele is None: max_allele = self.max() alleles = list(range(max_allele + 1)) # set up output array outshape = self.shape[:-1] + (len(alleles),) out = np.zeros(outshape, dtype=dtype) for allele in alleles: # count alleles along ploidy dimension allele_match = self.values == allele if self.mask is not None: allele_match &= ~self.mask[..., np.newaxis] np.sum(allele_match, axis=-1, out=out[..., allele]) if self.ndim == 2: out = GenotypeAlleleCountsVector(out) elif self.ndim == 3: out = GenotypeAlleleCountsArray(out) return out
[docs] def to_gt(self, max_allele=None): """Convert genotype calls to VCF-style string representation. Returns ------- gt : ndarray, string, shape (n_variants, n_samples) Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 2], [1, 1]], ... [[1, 2], [2, 1]], ... [[2, 2], [-1, -1]]]) >>> g.to_gt() chararray([[b'0/0', b'0/1'], [b'0/2', b'1/1'], [b'1/2', b'2/1'], [b'2/2', b'./.']], dtype='|S3') >>> v = g[:, 0] >>> v <GenotypeVector shape=(4, 2) dtype=int64> 0/0 0/2 1/2 2/2 >>> v.to_gt() chararray([b'0/0', b'0/2', b'1/2', b'2/2'], dtype='|S3') >>> g.is_phased = np.ones(g.shape[:-1]) >>> g.to_gt() chararray([[b'0|0', b'0|1'], [b'0|2', b'1|1'], [b'1|2', b'2|1'], [b'2|2', b'.|.']], dtype='|S3') >>> v = g[:, 0] >>> v <GenotypeVector shape=(4, 2) dtype=int64> 0|0 0|2 1|2 2|2 >>> v.to_gt() chararray([b'0|0', b'0|2', b'1|2', b'2|2'], dtype='|S3') """ # how many characters needed per allele call? if max_allele is None: max_allele = np.max(self) if max_allele <= 0: max_allele = 1 nchar = int(np.floor(np.log10(max_allele))) + 1 # convert to string a = self.astype((np.string_, nchar)).view(np.chararray) # recode missing alleles a[self < 0] = b'.' if self.mask is not None: a[self.mask] = b'.' # determine allele call separator if self.is_phased is None: sep = b'/' else: sep = np.empty(self.shape[:-1], dtype='S1').view(np.chararray) sep[self.is_phased] = b'|' sep[~self.is_phased] = b'/' # join via separator, coping with any ploidy gt = a[..., 0] for i in range(1, self.ploidy): gt = gt + sep + a[..., i] return gt
def copy(self, *args, **kwargs): data = self.values.copy(*args, **kwargs) out = type(self)(data) if self.mask is not None: out.mask = self.mask.copy() if self.is_phased is not None: out.is_phased = self.is_phased.copy() return out
[docs] def map_alleles(self, mapping, copy=True): """Transform alleles via a mapping. Parameters ---------- mapping : ndarray, int8, shape (n_variants, max_allele) An array defining the allele mapping for each variant. copy : bool, optional If True, return a new array; if False, apply mapping in place (only applies for arrays with dtype int8; all other dtypes require a copy). Returns ------- gm : GenotypeArray Examples -------- >>> import allel >>> import numpy as np >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 2], [1, 1]], ... [[1, 2], [2, 1]], ... [[2, 2], [-1, -1]]], dtype='i1') >>> mapping = np.array([[1, 2, 0], ... [2, 0, 1], ... [2, 1, 0], ... [0, 2, 1]], dtype='i1') >>> g.map_alleles(mapping) <GenotypeArray shape=(4, 2, 2) dtype=int8> 1/1 1/2 2/1 0/0 1/0 0/1 1/1 ./. >>> v = g[:, 0] >>> v <GenotypeVector shape=(4, 2) dtype=int8> 0/0 0/2 1/2 2/2 >>> v.map_alleles(mapping) <GenotypeVector shape=(4, 2) dtype=int8> 1/1 2/1 1/0 1/1 Notes ----- If a mask has been set, it is ignored by this function. For arrays with dtype int8 an optimised implementation is used which is faster and uses far less memory. It is recommended to convert arrays to dtype int8 where possible before calling this method. See Also -------- create_allele_mapping """ h = self.to_haplotypes() hm = h.map_alleles(mapping, copy=copy) if self.ndim == 2: gm = GenotypeVector(hm) else: gm = hm.to_genotypes(ploidy=self.ploidy) return gm
def to_haplotypes(self, copy=False): """Reshape a genotype array to view it as haplotypes. Parameters ---------- copy : bool, optional If True, copy data. Returns ------- h : HaplotypeArray, shape (n_variants, n_samples * ploidy) Haplotype array. Notes ----- If genotype calls are unphased, the haplotypes returned by this function will bear no resemblance to the true haplotypes. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 1], [1, 1]], ... [[0, 2], [-1, -1]]]) >>> g.to_haplotypes() <HaplotypeArray shape=(3, 4) dtype=int64> 0 0 0 1 0 1 1 1 0 2 . . >>> v = g[:, 0] >>> v <GenotypeVector shape=(3, 2) dtype=int64> 0/0 0/1 0/2 >>> v.to_haplotypes() <HaplotypeArray shape=(3, 2) dtype=int64> 0 0 0 1 0 2 """ # implement in sub-class raise NotImplementedError
[docs] def compress(self, condition, axis=0, out=None): """Return selected slices of an array along given axis. Parameters ---------- condition : array_like, bool Array that selects which entries to return. N.B., if len(condition) is less than the size of the given axis, then output is truncated to the length of the condition array. axis : int, optional Axis along which to take slices. If None, work on the flattened array. out : ndarray, optional Output array. Its type is preserved and it must be of the right shape to hold the output. Returns ------- out : Genotypes A copy of the array without the slices along axis for which `condition` is false. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 1], [1, 1]], ... [[0, 2], [-1, -1]]]) >>> g.compress([True, False, True], axis=0) <GenotypeArray shape=(2, 2, 2) dtype=int64> 0/0 0/1 0/2 ./. >>> v = g[:, 0] >>> v <GenotypeVector shape=(3, 2) dtype=int64> 0/0 0/1 0/2 >>> v.compress([True, False, True], axis=0) <GenotypeVector shape=(2, 2) dtype=int64> 0/0 0/2 """ # implement in sub-class raise NotImplementedError
[docs] def take(self, indices, axis=0, out=None, mode='raise'): """Take elements from an array along an axis. This function does the same thing as "fancy" indexing (indexing arrays using arrays); however, it can be easier to use if you need elements along a given axis. Parameters ---------- indices : array_like The indices of the values to extract. axis : int, optional The axis over which to select values. out : ndarray, optional If provided, the result will be placed in this array. It should be of the appropriate shape and dtype. mode : {'raise', 'wrap', 'clip'}, optional Specifies how out-of-bounds indices will behave. * 'raise' -- raise an error (default) * 'wrap' -- wrap around * 'clip' -- clip to the range 'clip' mode means that all indices that are too large are replaced by the index that addresses the last element along that axis. Note that this disables indexing with negative numbers. Returns ------- subarray : ndarray Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 1], [1, 1]], ... [[0, 2], [-1, -1]]]) >>> g.take([0, 2], axis=0) <GenotypeArray shape=(2, 2, 2) dtype=int64> 0/0 0/1 0/2 ./. >>> v = g[:, 0] >>> v <GenotypeVector shape=(3, 2) dtype=int64> 0/0 0/1 0/2 >>> v.take([0, 2], axis=0) <GenotypeVector shape=(2, 2) dtype=int64> 0/0 0/2 """ # implement in sub-class raise NotImplementedError
[docs] def concatenate(self, others, axis=0): """Join a sequence of arrays along an existing axis. Parameters ---------- others : sequence of array_like The arrays must have the same shape, except in the dimension corresponding to `axis` (the first, by default). axis : int, optional The axis along which the arrays will be joined. Default is 0. Returns ------- res : ndarray The concatenated array. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 1], [1, 1]], ... [[0, 2], [-1, -1]]]) >>> g.concatenate([g], axis=0) <GenotypeArray shape=(6, 2, 2) dtype=int64> 0/0 0/1 0/1 1/1 0/2 ./. 0/0 0/1 0/1 1/1 0/2 ./. >>> g.concatenate([g], axis=1) <GenotypeArray shape=(3, 4, 2) dtype=int64> 0/0 0/1 0/0 0/1 0/1 1/1 0/1 1/1 0/2 ./. 0/2 ./. >>> v1 = g[:, 0] >>> v2 = g[:, 1] >>> v1.concatenate([v2], axis=0) <GenotypeVector shape=(6, 2) dtype=int64> 0/0 0/1 0/2 0/1 1/1 ./. """ # implement in sub-class raise NotImplementedError
[docs]class GenotypeVector(Genotypes, DisplayAs1D): """Array of genotype calls for a sequence of variants or samples. Parameters ---------- data : array_like, int, shape (n_variants, n_samples, ploidy) Genotype data. copy : bool, optional If True, make a copy of `data`. **kwargs : keyword arguments All keyword arguments are passed through to :func:`numpy.array`. See Also -------- Genotypes, GenotypeArray, HaplotypeArray, GenotypeAlleleCountsArray """ def __init__(self, data, copy=False, **kwargs): super(GenotypeVector, self).__init__(data, copy=copy, **kwargs) check_ndim(self.values, 2) def __getitem__(self, item): return index_genotype_vector(self, item, type(self)) def compress(self, condition, axis=0, out=None): return compress_genotypes(self, condition=condition, axis=axis, wrap_axes={0}, cls=type(self), compress=np.compress, out=out) def take(self, indices, axis=0, out=None, mode='raise'): return take_genotypes(self, indices=indices, axis=axis, wrap_axes={0}, cls=type(self), take=np.take, out=out, mode=mode) def concatenate(self, others, axis=0): return concatenate_genotypes(self, others=others, axis=axis, wrap_axes={0}, cls=type(self), concatenate=np.concatenate) def to_haplotypes(self, copy=False): return HaplotypeArray(self.values, copy=copy) def str_items(self): gt = self.to_gt() if PY2: out = list(gt) else: out = [str(x, 'ascii') for x in gt] return out def to_str(self, threshold=10, edgeitems=5): _, items = self.get_display_items(threshold, edgeitems) s = ' '.join(items) return s
copy_method_doc(GenotypeVector.compress, Genotypes.compress) copy_method_doc(GenotypeVector.take, Genotypes.take) copy_method_doc(GenotypeVector.concatenate, Genotypes.concatenate) copy_method_doc(GenotypeVector.to_haplotypes, Genotypes.to_haplotypes)
[docs]class GenotypeArray(Genotypes, DisplayAs2D): """Array of discrete genotype calls for a matrix of variants and samples. Parameters ---------- data : array_like, int, shape (n_variants, n_samples, ploidy) Genotype data. copy : bool, optional If True, make a copy of `data`. **kwargs : keyword arguments All keyword arguments are passed through to :func:`numpy.array`. Notes ----- This class represents data on discrete genotype calls as a 3-dimensional numpy array of integers. By convention, the first dimension corresponds to the variants genotyped, the second dimension corresponds to the samples genotyped, and the third dimension corresponds to the ploidy of the samples. Each integer within the array corresponds to an **allele index**, where 0 is the reference allele, 1 is the first alternate allele, 2 is the second alternate allele, ... and -1 (or any other negative integer) is a missing allele call. A single byte integer dtype (int8) can represent up to 127 distinct alleles, which is usually sufficient. The actual alleles (i.e., the alternate nucleotide sequences) and the physical positions of the variants within the genome of an organism are stored in separate arrays, discussed elsewhere. Arrays of this class can store either **phased or unphased** genotype calls. If the genotypes are phased (i.e., haplotypes have been resolved) then individual haplotypes can be extracted by converting to a :class:`HaplotypeArray` then indexing the second dimension. If the genotype calls are unphased then the ordering of alleles along the third (ploidy) dimension is arbitrary. N.B., this means that an unphased diploid heterozygous call could be stored as (0, 1) or equivalently as (1, 0). A genotype array can store genotype calls with any ploidy > 1. For haploid calls, use a :class:`HaplotypeArray`. Note that genotype arrays are not capable of storing calls for samples with differing or variable ploidy, see :class:`GenotypeAlleleCountsArray` instead. With genotype data on large numbers of variants and/or samples, storing the genotype calls in memory as an uncompressed numpy array if integers may be impractical. For working with large arrays of genotype data, see the :mod:`allel.model.chunked` and :mod:`allel.model.dask` modules. Examples -------- Instantiate a genotype array:: >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 1], [1, 1]], ... [[0, 2], [-1, -1]]], dtype='i1') >>> g.dtype dtype('int8') >>> g.ndim 3 >>> g.shape (3, 2, 2) >>> g.n_variants 3 >>> g.n_samples 2 >>> g.ploidy 2 >>> g <GenotypeArray shape=(3, 2, 2) dtype=int8> 0/0 0/1 0/1 1/1 0/2 ./. Genotype calls for a single variant at all samples can be obtained by indexing the first dimension, e.g.:: >>> g[1] <GenotypeVector shape=(2, 2) dtype=int8> 0/1 1/1 Genotype calls for a single sample at all variants can be obtained by indexing the second dimension, e.g.:: >>> g[:, 1] <GenotypeVector shape=(3, 2) dtype=int8> 0/1 1/1 ./. A genotype call for a single sample at a single variant can be obtained by indexing the first and second dimensions, e.g.:: >>> g[1, 0] array([0, 1], dtype=int8) A genotype array can store polyploid calls, e.g.:: >>> g = allel.GenotypeArray([[[0, 0, 0], [0, 0, 1]], ... [[0, 1, 1], [1, 1, 1]], ... [[0, 1, 2], [-1, -1, -1]]], ... dtype='i1') >>> g.ploidy 3 >>> g <GenotypeArray shape=(3, 2, 3) dtype=int8> 0/0/0 0/0/1 0/1/1 1/1/1 0/1/2 ././. See Also -------- Genotypes, GenotypeVector, HaplotypeArray, GenotypeAlleleCountsArray """ def __init__(self, data, copy=False, **kwargs): super(GenotypeArray, self).__init__(data, copy=copy, **kwargs) check_ndim(self.values, 3) def __getitem__(self, item): return index_genotype_array(self, item, array_cls=type(self), vector_cls=GenotypeVector) @property def n_variants(self): """Number of variants.""" return self.shape[0] @property def n_samples(self): """Number of samples.""" return self.shape[1] def compress(self, condition, axis=0, out=None): return compress_genotypes(self, condition=condition, axis=axis, wrap_axes={0, 1}, cls=type(self), compress=np.compress, out=out) def take(self, indices, axis=0, out=None, mode='raise'): return take_genotypes(self, indices=indices, axis=axis, wrap_axes={0, 1}, cls=type(self), take=np.take, out=out, mode=mode)
[docs] def subset(self, sel0=None, sel1=None): """Make a sub-selection of variants and samples. Parameters ---------- sel0 : array_like Boolean array or array of indices selecting variants. sel1 : array_like Boolean array or array of indices selecting samples. Returns ------- out : GenotypeArray Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1], [1, 1]], ... [[0, 1], [1, 1], [1, 2]], ... [[0, 2], [-1, -1], [-1, -1]]]) >>> g.subset([0, 1], [0, 2]) <GenotypeArray shape=(2, 2, 2) dtype=int64> 0/0 1/1 0/1 1/2 See Also -------- Genotypes.take, Genotypes.compress """ return subset_genotype_array(self, sel0, sel1, cls=type(self), subset=subset)
def concatenate(self, others, axis=0): return concatenate_genotypes(self, others=others, axis=axis, wrap_axes={0, 1}, cls=type(self), concatenate=np.concatenate) def to_haplotypes(self, copy=False): # reshape, preserving size of variants dimension newshape = (self.shape[0], -1) data = np.reshape(self, newshape) h = HaplotypeArray(data, copy=copy) return h def str_items(self): gt = self.to_gt() n = gt.dtype.itemsize if PY2: out = [[x.rjust(n) for x in row] for row in gt] else: out = [[str(x, 'ascii').rjust(n) for x in row] for row in gt] return out
[docs] def to_packed(self, boundscheck=True): """Pack diploid genotypes into a single byte for each genotype, using the left-most 4 bits for the first allele and the right-most 4 bits for the second allele. Allows single byte encoding of diploid genotypes for variants with up to 15 alleles. Parameters ---------- boundscheck : bool, optional If False, do not check that minimum and maximum alleles are compatible with bit-packing. Returns ------- packed : ndarray, uint8, shape (n_variants, n_samples) Bit-packed genotype array. Notes ----- If a mask has been set, it is ignored by this function. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 2], [1, 1]], ... [[2, 2], [-1, -1]]], dtype='i1') >>> g.to_packed() array([[ 0, 1], [ 2, 17], [ 34, 239]], dtype=uint8) """ check_ploidy(self.ploidy, 2) if boundscheck: amx = self.max() if amx > 14: raise ValueError('max allele for packing is 14, found %s' % amx) amn = self.min() if amn < -1: raise ValueError('min allele for packing is -1, found %s' % amn) # pack data values = memoryview_safe(self.values) packed = genotype_array_pack_diploid(values) return packed
[docs] @classmethod def from_packed(cls, packed): """Unpack diploid genotypes that have been bit-packed into single bytes. Parameters ---------- packed : ndarray, uint8, shape (n_variants, n_samples) Bit-packed diploid genotype array. Returns ------- g : GenotypeArray, shape (n_variants, n_samples, 2) Genotype array. Examples -------- >>> import allel >>> import numpy as np >>> packed = np.array([[0, 1], ... [2, 17], ... [34, 239]], dtype='u1') >>> allel.GenotypeArray.from_packed(packed) <GenotypeArray shape=(3, 2, 2) dtype=int8> 0/0 0/1 0/2 1/1 2/2 ./. """ # check arguments packed = np.asarray(packed) check_ndim(packed, 2) check_dtype(packed, 'u1') packed = memoryview_safe(packed) data = genotype_array_unpack_diploid(packed) return cls(data)
# noinspection PyShadowingBuiltins
[docs] def to_sparse(self, format='csr', **kwargs): """Convert into a sparse matrix. Parameters ---------- format : {'coo', 'csc', 'csr', 'dia', 'dok', 'lil'} Sparse matrix format. kwargs : keyword arguments Passed through to sparse matrix constructor. Returns ------- m : scipy.sparse.spmatrix Sparse matrix Notes ----- If a mask has been set, it is ignored by this function. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 0]], ... [[0, 1], [0, 1]], ... [[1, 1], [0, 0]], ... [[0, 0], [-1, -1]]], dtype='i1') >>> m = g.to_sparse(format='csr') >>> m <4x4 sparse matrix of type '<class 'numpy.int8'>' with 6 stored elements in Compressed Sparse Row format> >>> m.data array([ 1, 1, 1, 1, -1, -1], dtype=int8) >>> m.indices array([1, 3, 0, 1, 2, 3], dtype=int32) >>> m.indptr array([0, 0, 2, 4, 6], dtype=int32) """ h = self.to_haplotypes() m = h.to_sparse(format=format, **kwargs) return m
[docs] @staticmethod def from_sparse(m, ploidy, order=None, out=None): """Construct a genotype array from a sparse matrix. Parameters ---------- m : scipy.sparse.spmatrix Sparse matrix ploidy : int The sample ploidy. order : {'C', 'F'}, optional Whether to store data in C (row-major) or Fortran (column-major) order in memory. out : ndarray, shape (n_variants, n_samples), optional Use this array as the output buffer. Returns ------- g : GenotypeArray, shape (n_variants, n_samples, ploidy) Genotype array. Examples -------- >>> import allel >>> import numpy as np >>> import scipy.sparse >>> data = np.array([ 1, 1, 1, 1, -1, -1], dtype=np.int8) >>> indices = np.array([1, 3, 0, 1, 2, 3], dtype=np.int32) >>> indptr = np.array([0, 0, 2, 4, 6], dtype=np.int32) >>> m = scipy.sparse.csr_matrix((data, indices, indptr)) >>> g = allel.GenotypeArray.from_sparse(m, ploidy=2) >>> g <GenotypeArray shape=(4, 2, 2) dtype=int8> 0/0 0/0 0/1 0/1 1/1 0/0 0/0 ./. """ h = HaplotypeArray.from_sparse(m, order=order, out=out) g = h.to_genotypes(ploidy=ploidy) return g
[docs] def haploidify_samples(self): """Construct a pseudo-haplotype for each sample by randomly selecting an allele from each genotype call. Returns ------- h : HaplotypeArray Notes ----- If a mask has been set, it is ignored by this function. Examples -------- >>> import allel >>> import numpy as np >>> np.random.seed(42) >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 2], [1, 1]], ... [[1, 2], [2, 1]], ... [[2, 2], [-1, -1]]]) >>> g.haploidify_samples() <HaplotypeArray shape=(4, 2) dtype=int64> 0 1 0 1 1 1 2 . >>> g = allel.GenotypeArray([[[0, 0, 0], [0, 0, 1]], ... [[0, 1, 1], [1, 1, 1]], ... [[0, 1, 2], [-1, -1, -1]]]) >>> g.haploidify_samples() <HaplotypeArray shape=(3, 2) dtype=int64> 0 0 1 1 2 . """ # N.B., this implementation is obscure and uses more memory than # necessary, TODO review # define the range of possible indices, e.g., diploid => (0, 1) index_range = np.arange(0, self.ploidy, dtype='u1') # create a random index for each genotype call indices = np.random.choice(index_range, size=self.n_calls, replace=True) # reshape genotype data so it's suitable for passing to np.choose # by merging the variants and samples dimensions choices = self.reshape(-1, self.ploidy).T # now use random indices to haploidify data = np.choose(indices, choices) # reshape the haploidified data to restore the variants and samples # dimensions data = data.reshape((self.n_variants, self.n_samples)) # view as haplotype array h = HaplotypeArray(data, copy=False) return h
[docs] def count_alleles(self, max_allele=None, subpop=None): """Count the number of calls of each allele per variant. Parameters ---------- max_allele : int, optional The highest allele index to count. Alleles above this will be ignored. subpop : sequence of ints, optional Indices of samples to include in count. Returns ------- ac : AlleleCountsArray Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 2], [1, 1]], ... [[2, 2], [-1, -1]]]) >>> g.count_alleles() <AlleleCountsArray shape=(3, 3) dtype=int32> 3 1 0 1 2 1 0 0 2 >>> g.count_alleles(max_allele=1) <AlleleCountsArray shape=(3, 2) dtype=int32> 3 1 1 2 0 0 """ # check inputs subpop = asarray_ndim(subpop, 1, allow_none=True, dtype=np.int64) if subpop is not None: if np.any(subpop >= self.shape[1]): raise ValueError('index out of bounds') if np.any(subpop < 0): raise ValueError('negative indices not supported') subpop = memoryview_safe(subpop) # determine alleles to count if max_allele is None: max_allele = self.max() # use optimisations values = memoryview_safe(self.values) mask = memoryview_safe(self.mask).view(dtype='u1') if self.mask is not None else None if subpop is None and mask is None: ac = genotype_array_count_alleles(values, max_allele) elif subpop is None: ac = genotype_array_count_alleles_masked(values, mask, max_allele) elif mask is None: ac = genotype_array_count_alleles_subpop(values, max_allele, subpop) else: ac = genotype_array_count_alleles_subpop_masked(values, mask, max_allele, subpop) return AlleleCountsArray(ac, copy=False)
[docs] def count_alleles_subpops(self, subpops, max_allele=None): """Count alleles for multiple subpopulations simultaneously. Parameters ---------- subpops : dict (string -> sequence of ints) Mapping of subpopulation names to sample indices. max_allele : int, optional The highest allele index to count. Alleles above this will be ignored. Returns ------- out : dict (string -> AlleleCountsArray) A mapping of subpopulation names to allele counts arrays. """ if max_allele is None: max_allele = self.max() out = {name: self.count_alleles(max_allele=max_allele, subpop=subpop) for name, subpop in subpops.items()} return out
copy_method_doc(GenotypeArray.compress, Genotypes.compress) copy_method_doc(GenotypeArray.take, Genotypes.take) copy_method_doc(GenotypeArray.concatenate, Genotypes.concatenate) copy_method_doc(GenotypeArray.to_haplotypes, Genotypes.to_haplotypes)
[docs]class HaplotypeArray(NumpyArrayWrapper, DisplayAs2D): """Array of haplotypes. Parameters ---------- data : array_like, int, shape (n_variants, n_haplotypes) Haplotype data. **kwargs : keyword arguments All keyword arguments are passed through to :func:`numpy.array`. Notes ----- This class represents haplotype data as a 2-dimensional numpy array of integers. By convention the first dimension corresponds to the variants genotyped, the second dimension corresponds to the haplotypes. Each integer within the array corresponds to an **allele index**, where 0 is the reference allele, 1 is the first alternate allele, 2 is the second alternate allele, ... and -1 (or any other negative integer) is a missing allele call. If adjacent haplotypes originate from the same sample, then a haplotype array can also be viewed as a genotype array. However, this is not a requirement. With data on large numbers of variants and/or haplotypes, storing the data in memory as an uncompressed numpy array if integers may be impractical. For working with large arrays of haplotype data, see the :mod:`allel.model.chunked` and :mod:`allel.model.dask` modules. Examples -------- Instantiate a haplotype array:: >>> import allel >>> h = allel.HaplotypeArray([[0, 0, 0, 1], ... [0, 1, 1, 1], ... [0, 2, -1, -1]], dtype='i1') >>> h.dtype dtype('int8') >>> h.ndim 2 >>> h.shape (3, 4) >>> h.n_variants 3 >>> h.n_haplotypes 4 >>> h <HaplotypeArray shape=(3, 4) dtype=int8> 0 0 0 1 0 1 1 1 0 2 . . Allele calls for a single variant at all haplotypes can be obtained by indexing the first dimension, e.g.:: >>> h[1] array([0, 1, 1, 1], dtype=int8) A single haplotype can be obtained by indexing the second dimension, e.g.:: >>> h[:, 1] array([0, 1, 2], dtype=int8) An allele call for a single haplotype at a single variant can be obtained by indexing the first and second dimensions, e.g.:: >>> h[1, 0] 0 View haplotypes as diploid genotypes:: >>> h.to_genotypes(ploidy=2) <GenotypeArray shape=(3, 2, 2) dtype=int8> 0/0 0/1 0/1 1/1 0/2 ./. See Also -------- Genotypes, GenotypeArray, GenotypeVector """ def __init__(self, data, copy=False, **kwargs): super(HaplotypeArray, self).__init__(data, copy=copy, **kwargs) check_integer_dtype(self.values) check_ndim(self.values, 2) @property def n_variants(self): """Number of variants.""" return self.shape[0] @property def n_haplotypes(self): """Number of haplotypes.""" return self.shape[1] def __getitem__(self, item): return index_haplotype_array(self, item, type(self))
[docs] def compress(self, condition, axis=0, out=None): """Return selected slices of an array along given axis. Parameters ---------- condition : array_like, bool Array that selects which entries to return. N.B., if len(condition) is less than the size of the given axis, then output is truncated to the length of the condition array. axis : int, optional Axis along which to take slices. If None, work on the flattened array. out : ndarray, optional Output array. Its type is preserved and it must be of the right shape to hold the output. Returns ------- out : HaplotypeArray A copy of the array without the slices along axis for which `condition` is false. Examples -------- >>> import allel >>> h = allel.HaplotypeArray([[0, 0, 0, 1], ... [0, 1, 1, 1], ... [0, 2, -1, -1]], dtype='i1') >>> h.compress([True, False, True], axis=0) <HaplotypeArray shape=(2, 4) dtype=int8> 0 0 0 1 0 2 . . >>> h.compress([True, False, True, False], axis=1) <HaplotypeArray shape=(3, 2) dtype=int8> 0 0 0 1 0 . """ return compress_haplotype_array(self, condition, axis=axis, cls=type(self), compress=np.compress, out=out)
[docs] def take(self, indices, axis=0, out=None, mode='raise'): """Take elements from an array along an axis. This function does the same thing as "fancy" indexing (indexing arrays using arrays); however, it can be easier to use if you need elements along a given axis. Parameters ---------- indices : array_like The indices of the values to extract. axis : int, optional The axis over which to select values. out : ndarray, optional If provided, the result will be placed in this array. It should be of the appropriate shape and dtype. mode : {'raise', 'wrap', 'clip'}, optional Specifies how out-of-bounds indices will behave. * 'raise' -- raise an error (default) * 'wrap' -- wrap around * 'clip' -- clip to the range 'clip' mode means that all indices that are too large are replaced by the index that addresses the last element along that axis. Note that this disables indexing with negative numbers. Returns ------- subarray : ndarray Examples -------- >>> import allel >>> h = allel.HaplotypeArray([[0, 0, 0, 1], ... [0, 1, 1, 1], ... [0, 2, -1, -1]], dtype='i1') >>> h.take([0, 2], axis=0) <HaplotypeArray shape=(2, 4) dtype=int8> 0 0 0 1 0 2 . . >>> h.take([0, 2], axis=1) <HaplotypeArray shape=(3, 2) dtype=int8> 0 0 0 1 0 . """ return take_haplotype_array(self, indices, axis=axis, cls=type(self), take=np.take, out=out, mode=mode)
[docs] def subset(self, sel0=None, sel1=None): """Make a sub-selection of variants and haplotypes. Parameters ---------- sel0 : array_like Boolean array or array of indices selecting variants. sel1 : array_like Boolean array or array of indices selecting haplotypes. Returns ------- out : HaplotypeArray See Also -------- HaplotypeArray.take, HaplotypeArray.compress """ return subset_haplotype_array(self, sel0, sel1, cls=type(self), subset=subset)
[docs] def concatenate(self, others, axis=0): """Join a sequence of arrays along an existing axis. Parameters ---------- others : sequence of array_like The arrays must have the same shape, except in the dimension corresponding to `axis` (the first, by default). axis : int, optional The axis along which the arrays will be joined. Default is 0. Returns ------- res : ndarray The concatenated array. Examples -------- >>> import allel >>> h = allel.HaplotypeArray([[0, 0, 0, 1], ... [0, 1, 1, 1], ... [0, 2, -1, -1]], dtype='i1') >>> h.concatenate([h], axis=0) <HaplotypeArray shape=(6, 4) dtype=int8> 0 0 0 1 0 1 1 1 0 2 . . 0 0 0 1 0 1 1 1 0 2 . . >>> h.concatenate([h], axis=1) <HaplotypeArray shape=(3, 8) dtype=int8> 0 0 0 1 0 0 0 1 0 1 1 1 0 1 1 1 0 2 . . 0 2 . . """ return concatenate_haplotype_array(self, others, axis=axis, cls=type(self), concatenate=np.concatenate)
def str_items(self): values = self.values max_allele = np.max(values) if max_allele <= 0: max_allele = 1 n = int(np.floor(np.log10(max_allele))) + 1 t = values.astype((np.string_, n)) # recode missing alleles t[values < 0] = b'.' if PY2: out = [[x.rjust(n) for x in row] for row in t] else: out = [[str(x, 'ascii').rjust(n) for x in row] for row in t] return out
[docs] def is_called(self): return self.values >= 0
[docs] def is_missing(self): return self.values < 0
[docs] def is_ref(self): return self.values == 0
[docs] def is_alt(self, allele=None): if allele is None: return self.values > 0 else: return self.values == allele
[docs] def is_call(self, allele): return self.values == allele
[docs] def count_called(self, axis=None): b = self.is_called() return np.sum(b, axis=axis)
[docs] def count_missing(self, axis=None): b = self.is_missing() return np.sum(b, axis=axis)
[docs] def count_ref(self, axis=None): b = self.is_ref() return np.sum(b, axis=axis)
[docs] def count_alt(self, axis=None): b = self.is_alt() return np.sum(b, axis=axis)
[docs] def count_call(self, allele, axis=None): b = self.is_call(allele=allele) return np.sum(b, axis=axis)
[docs] def to_genotypes(self, ploidy, copy=False): """Reshape a haplotype array to view it as genotypes by restoring the ploidy dimension. Parameters ---------- ploidy : int The sample ploidy. copy : bool, optional If True, make a copy of data. Returns ------- g : ndarray, int, shape (n_variants, n_samples, ploidy) Genotype array (sharing same underlying buffer). copy : bool, optional If True, copy the data. Examples -------- >>> import allel >>> h = allel.HaplotypeArray([[0, 0, 0, 1], ... [0, 1, 1, 1], ... [0, 2, -1, -1]], dtype='i1') >>> h.to_genotypes(ploidy=2) <GenotypeArray shape=(3, 2, 2) dtype=int8> 0/0 0/1 0/1 1/1 0/2 ./. """ # check ploidy is compatible if (self.shape[1] % ploidy) > 0: raise ValueError('incompatible ploidy') # reshape newshape = (self.shape[0], -1, ploidy) data = self.reshape(newshape) # wrap g = GenotypeArray(data, copy=copy) return g
# noinspection PyShadowingBuiltins
[docs] def to_sparse(self, format='csr', **kwargs): """Convert into a sparse matrix. Parameters ---------- format : {'coo', 'csc', 'csr', 'dia', 'dok', 'lil'} Sparse matrix format. kwargs : keyword arguments Passed through to sparse matrix constructor. Returns ------- m : scipy.sparse.spmatrix Sparse matrix Examples -------- >>> import allel >>> h = allel.HaplotypeArray([[0, 0, 0, 0], ... [0, 1, 0, 1], ... [1, 1, 0, 0], ... [0, 0, -1, -1]], dtype='i1') >>> m = h.to_sparse(format='csr') >>> m <4x4 sparse matrix of type '<class 'numpy.int8'>' with 6 stored elements in Compressed Sparse Row format> >>> m.data array([ 1, 1, 1, 1, -1, -1], dtype=int8) >>> m.indices array([1, 3, 0, 1, 2, 3], dtype=int32) >>> m.indptr array([0, 0, 2, 4, 6], dtype=int32) """ import scipy.sparse # check arguments f = { 'bsr': scipy.sparse.bsr_matrix, 'coo': scipy.sparse.coo_matrix, 'csc': scipy.sparse.csc_matrix, 'csr': scipy.sparse.csr_matrix, 'dia': scipy.sparse.dia_matrix, 'dok': scipy.sparse.dok_matrix, 'lil': scipy.sparse.lil_matrix } if format not in f: raise ValueError('invalid format: %r' % format) # create sparse matrix m = f[format](self, **kwargs) return m
[docs] @staticmethod def from_sparse(m, order=None, out=None): """Construct a haplotype array from a sparse matrix. Parameters ---------- m : scipy.sparse.spmatrix Sparse matrix order : {'C', 'F'}, optional Whether to store data in C (row-major) or Fortran (column-major) order in memory. out : ndarray, shape (n_variants, n_samples), optional Use this array as the output buffer. Returns ------- h : HaplotypeArray, shape (n_variants, n_haplotypes) Haplotype array. Examples -------- >>> import allel >>> import numpy as np >>> import scipy.sparse >>> data = np.array([ 1, 1, 1, 1, -1, -1], dtype=np.int8) >>> indices = np.array([1, 3, 0, 1, 2, 3], dtype=np.int32) >>> indptr = np.array([0, 0, 2, 4, 6], dtype=np.int32) >>> m = scipy.sparse.csr_matrix((data, indices, indptr)) >>> h = allel.HaplotypeArray.from_sparse(m) >>> h <HaplotypeArray shape=(4, 4) dtype=int8> 0 0 0 0 0 1 0 1 1 1 0 0 0 0 . . """ import scipy.sparse # check arguments if not scipy.sparse.isspmatrix(m): raise ValueError('not a sparse matrix: %r' % m) # convert to dense array data = m.toarray(order=order, out=out) # wrap h = HaplotypeArray(data) return h
[docs] def count_alleles(self, max_allele=None, subpop=None): """Count the number of calls of each allele per variant. Parameters ---------- max_allele : int, optional The highest allele index to count. Alleles greater than this index will be ignored. subpop : array_like, int, optional Indices of haplotypes to include. Returns ------- ac : AlleleCountsArray, int, shape (n_variants, n_alleles) Examples -------- >>> import allel >>> h = allel.HaplotypeArray([[0, 0, 0, 1], ... [0, 1, 1, 1], ... [0, 2, -1, -1]], dtype='i1') >>> ac = h.count_alleles() >>> ac <AlleleCountsArray shape=(3, 3) dtype=int32> 3 1 0 1 3 0 1 0 1 """ # check inputs subpop = asarray_ndim(subpop, 1, allow_none=True, dtype=np.int64) if subpop is not None: if np.any(subpop >= self.shape[1]): raise ValueError('index out of bounds') if np.any(subpop < 0): raise ValueError('negative indices not supported') subpop = memoryview_safe(subpop) # determine alleles to count if max_allele is None: max_allele = self.max() # use optimisations values = memoryview_safe(self.values) if subpop is None: ac = haplotype_array_count_alleles(values, max_allele) else: ac = haplotype_array_count_alleles_subpop(values, max_allele, subpop) return AlleleCountsArray(ac, copy=False)
[docs] def count_alleles_subpops(self, subpops, max_allele=None): """Count alleles for multiple subpopulations simultaneously. Parameters ---------- subpops : dict (string -> sequence of ints) Mapping of subpopulation names to sample indices. max_allele : int, optional The highest allele index to count. Alleles above this will be ignored. Returns ------- out : dict (string -> AlleleCountsArray) A mapping of subpopulation names to allele counts arrays. """ if max_allele is None: max_allele = self.max() out = {name: self.count_alleles(max_allele=max_allele, subpop=subpop) for name, subpop in subpops.items()} return out
[docs] def map_alleles(self, mapping, copy=True): """Transform alleles via a mapping. Parameters ---------- mapping : ndarray, int8, shape (n_variants, max_allele) An array defining the allele mapping for each variant. copy : bool, optional If True, return a new array; if False, apply mapping in place (only applies for arrays with dtype int8; all other dtypes require a copy). Returns ------- hm : HaplotypeArray Examples -------- >>> import allel >>> import numpy as np >>> h = allel.HaplotypeArray([[0, 0, 0, 1], ... [0, 1, 1, 1], ... [0, 2, -1, -1]], dtype='i1') >>> mapping = np.array([[1, 2, 0], ... [2, 0, 1], ... [2, 1, 0]], dtype='i1') >>> h.map_alleles(mapping) <HaplotypeArray shape=(3, 4) dtype=int8> 1 1 1 2 2 0 0 0 2 0 . . Notes ----- For arrays with dtype int8 an optimised implementation is used which is faster and uses far less memory. It is recommended to convert arrays to dtype int8 where possible before calling this method. See Also -------- allel.model.util.create_allele_mapping """ # check inputs mapping = asarray_ndim(mapping, 2) check_dim0_aligned(self, mapping) # use optimisation mapping = np.asarray(mapping, dtype=self.dtype) mapping = memoryview_safe(mapping) values = memoryview_safe(self.values) data = haplotype_array_map_alleles(values, mapping, copy=copy) return HaplotypeArray(data, copy=False)
[docs] def prefix_argsort(self): """Return indices that would sort the haplotypes by prefix.""" return np.lexsort(self.values[::-1])
[docs] def distinct(self): """Return sets of indices for each distinct haplotype.""" # setup collection d = collections.defaultdict(set) # iterate over haplotypes for i in range(self.shape[1]): # hash the haplotype k = hash(self.values[:, i].tobytes()) # collect d[k].add(i) # extract sets, sorted by most common return sorted(d.values(), key=len, reverse=True)
[docs] def distinct_counts(self): """Return counts for each distinct haplotype.""" # hash the haplotypes k = [hash(self.values[:, i].tobytes()) for i in range(self.shape[1])] # count and sort # noinspection PyArgumentList counts = sorted(collections.Counter(k).values(), reverse=True) return np.asarray(counts)
[docs] def distinct_frequencies(self): """Return frequencies for each distinct haplotype.""" c = self.distinct_counts() n = self.shape[1] return c / n
[docs]class AlleleCountsArray(NumpyArrayWrapper, DisplayAs2D): """Array of allele counts. Parameters ---------- data : array_like, int, shape (n_variants, n_alleles) Allele counts data. copy : bool, optional If True, make a copy of `data`. **kwargs : keyword arguments All keyword arguments are passed through to :func:`numpy.array`. Notes ----- This class represents allele counts as a 2-dimensional numpy array of integers. By convention the first dimension corresponds to the variants genotyped, the second dimension corresponds to the alleles counted. Examples -------- Obtain allele counts from a genotype array: >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 1], [1, 1]], ... [[0, 2], [-1, -1]]], dtype='i1') >>> ac = g.count_alleles() >>> ac <AlleleCountsArray shape=(3, 3) dtype=int32> 3 1 0 1 3 0 1 0 1 >>> ac.dtype dtype('int32') >>> ac.shape (3, 3) >>> ac.n_variants 3 >>> ac.n_alleles 3 Allele counts for a single variant can be obtained by indexing the first dimension, e.g.:: >>> ac[1] array([1, 3, 0], dtype=int32) Allele counts for a specific allele can be obtained by indexing the second dimension, e.g., reference allele counts: >>> ac[:, 0] array([3, 1, 1], dtype=int32) Calculate the total number of alleles called for each variant: >>> import numpy as np >>> an = np.sum(ac, axis=1) >>> an array([4, 4, 2]) Add allele counts from two populations: >>> ac + ac <AlleleCountsArray shape=(3, 3) dtype=int32> 6 2 0 2 6 0 2 0 2 """ def __init__(self, data, copy=False, **kwargs): super(AlleleCountsArray, self).__init__(data, copy=copy, **kwargs) check_integer_dtype(self.values) check_ndim(self.values, 2) def __add__(self, other): ret = super(AlleleCountsArray, self).__add__(other) if hasattr(ret, 'shape') and ret.shape == self.shape: ret = AlleleCountsArray(ret) return ret def __sub__(self, other): ret = super(AlleleCountsArray, self).__sub__(other) if hasattr(ret, 'shape') and ret.shape == self.shape: ret = AlleleCountsArray(ret) return ret @property def n_variants(self): """Number of variants.""" return self.shape[0] @property def n_alleles(self): """Number of alleles.""" return self.shape[1] def __getitem__(self, item): return index_allele_counts_array(self, item, type(self))
[docs] def compress(self, condition, axis=0, out=None): return compress_allele_counts_array(self, condition, axis=axis, cls=type(self), compress=np.compress, out=out)
[docs] def take(self, indices, axis=0, out=None, mode='raise'): return take_allele_counts_array(self, indices, axis=axis, cls=type(self), take=np.take, out=out, mode=mode)
[docs] def concatenate(self, others, axis=0): return concatenate_allele_counts_array(self, others, axis=axis, cls=type(self), concatenate=np.concatenate)
def str_items(self): values = self.values max_allele = np.max(values) if max_allele <= 0: max_allele = 1 n = int(np.floor(np.log10(max_allele))) + 1 t = values.astype((np.string_, n)) if PY2: out = [[x.rjust(n) for x in row] for row in t] else: out = [[str(x, 'ascii').rjust(n) for x in row] for row in t] return out
[docs] def to_frequencies(self, fill=np.nan): """Compute allele frequencies. Parameters ---------- fill : float, optional Value to use when number of allele calls is 0. Returns ------- af : ndarray, float, shape (n_variants, n_alleles) Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 2], [1, 1]], ... [[2, 2], [-1, -1]]]) >>> ac = g.count_alleles() >>> ac.to_frequencies() array([[0.75, 0.25, 0. ], [0.25, 0.5 , 0.25], [0. , 0. , 1. ]]) """ an = np.sum(self, axis=1)[:, None] with ignore_invalid(): af = np.where(an > 0, self / an, fill) return af
[docs] def allelism(self): """Determine the number of distinct alleles observed for each variant. Returns ------- n : ndarray, int, shape (n_variants,) Allelism array. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 2], [1, 1]], ... [[2, 2], [-1, -1]]]) >>> ac = g.count_alleles() >>> ac.allelism() array([2, 3, 1]) """ return np.sum(self.values > 0, axis=1)
[docs] def max_allele(self): """Return the highest allele index for each variant. Returns ------- n : ndarray, int, shape (n_variants,) Allele index array. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 1]], ... [[0, 2], [1, 1]], ... [[2, 2], [-1, -1]]]) >>> ac = g.count_alleles() >>> ac.max_allele() array([1, 2, 2], dtype=int8) """ out = np.empty(self.shape[0], dtype='i1') out.fill(-1) for i in range(self.shape[1]): d = self.values[:, i] > 0 out[d] = i return out
[docs] def is_variant(self): """Find variants with at least one non-reference allele call. Returns ------- out : ndarray, bool, shape (n_variants,) Boolean array where elements are True if variant matches the condition. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 0]], ... [[0, 0], [0, 1]], ... [[0, 2], [1, 1]], ... [[2, 2], [-1, -1]]]) >>> ac = g.count_alleles() >>> ac.is_variant() array([False, True, True, True]) """ t = self.values[:, 1:] > 0 # type: np.ndarray return np.any(t, axis=1)
[docs] def is_non_variant(self): """Find variants with no non-reference allele calls. Returns ------- out : ndarray, bool, shape (n_variants,) Boolean array where elements are True if variant matches the condition. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 0]], ... [[0, 0], [0, 1]], ... [[0, 2], [1, 1]], ... [[2, 2], [-1, -1]]]) >>> ac = g.count_alleles() >>> ac.is_non_variant() array([ True, False, False, False]) """ t = self.values[:, 1:] == 0 # type: np.ndarray return np.all(t, axis=1)
[docs] def is_segregating(self): """Find segregating variants (where more than one allele is observed). Returns ------- out : ndarray, bool, shape (n_variants,) Boolean array where elements are True if variant matches the condition. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 0]], ... [[0, 0], [0, 1]], ... [[0, 2], [1, 1]], ... [[2, 2], [-1, -1]]]) >>> ac = g.count_alleles() >>> ac.is_segregating() array([False, True, True, False]) """ return self.allelism() > 1
[docs] def is_non_segregating(self, allele=None): """Find non-segregating variants (where at most one allele is observed). Parameters ---------- allele : int, optional Allele index. Returns ------- out : ndarray, bool, shape (n_variants,) Boolean array where elements are True if variant matches the condition. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 0]], ... [[0, 0], [0, 1]], ... [[0, 2], [1, 1]], ... [[2, 2], [-1, -1]]]) >>> ac = g.count_alleles() >>> ac.is_non_segregating() array([ True, False, False, True]) >>> ac.is_non_segregating(allele=2) array([False, False, False, True]) """ if allele is None: return self.allelism() <= 1 else: return (self.allelism() == 1) & (self.values[:, allele] > 0)
[docs] def is_singleton(self, allele): """Find variants with a single call for the given allele. Parameters ---------- allele : int, optional Allele index. Returns ------- out : ndarray, bool, shape (n_variants,) Boolean array where elements are True if variant matches the condition. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 0]], ... [[0, 0], [0, 1]], ... [[1, 1], [1, 2]], ... [[2, 2], [-1, -1]]]) >>> ac = g.count_alleles() >>> ac.is_singleton(allele=1) array([False, True, False, False]) >>> ac.is_singleton(allele=2) array([False, False, True, False]) """ return self.values[:, allele] == 1
[docs] def is_doubleton(self, allele): """Find variants with exactly two calls for the given allele. Parameters ---------- allele : int, optional Allele index. Returns ------- out : ndarray, bool, shape (n_variants,) Boolean array where elements are True if variant matches the condition. Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 0]], ... [[0, 0], [1, 1]], ... [[1, 1], [1, 2]], ... [[2, 2], [-1, -1]]]) >>> ac = g.count_alleles() >>> ac.is_doubleton(allele=1) array([False, True, False, False]) >>> ac.is_doubleton(allele=2) array([False, False, False, True]) """ return self.values[:, allele] == 2
[docs] def is_biallelic(self): """Find biallelic variants. Returns ------- out : ndarray, bool, shape (n_variants,) Boolean array where elements are True if variant matches the condition. """ return self.allelism() == 2
[docs] def is_biallelic_01(self, min_mac=None): """Find variants biallelic for the reference (0) and first alternate (1) allele. Parameters ---------- min_mac : int, optional Minimum minor allele count. Returns ------- out : ndarray, bool, shape (n_variants,) Boolean array where elements are True if variant matches the condition. """ loc = self.is_biallelic() & (self.max_allele() == 1) if min_mac is not None: # noinspection PyAugmentAssignment loc = loc & (self.values[:, :2].min(axis=1) >= min_mac) return loc
[docs] def count_variant(self): return np.sum(self.is_variant())
[docs] def count_non_variant(self): return np.sum(self.is_non_variant())
[docs] def count_segregating(self): return np.sum(self.is_segregating())
[docs] def count_non_segregating(self, allele=None): return np.sum(self.is_non_segregating(allele=allele))
[docs] def count_singleton(self, allele=1): return np.sum(self.is_singleton(allele=allele))
[docs] def count_doubleton(self, allele=1): return np.sum(self.is_doubleton(allele=allele))
[docs] def map_alleles(self, mapping): """Transform alleles via a mapping. Parameters ---------- mapping : ndarray, int8, shape (n_variants, max_allele) An array defining the allele mapping for each variant. Returns ------- ac : AlleleCountsArray Examples -------- >>> import allel >>> g = allel.GenotypeArray([[[0, 0], [0, 0]], ... [[0, 0], [0, 1]], ... [[0, 2], [1, 1]], ... [[2, 2], [-1, -1]]]) >>> ac = g.count_alleles() >>> ac <AlleleCountsArray shape=(4, 3) dtype=int32> 4 0 0 3 1 0 1 2 1 0 0 2 >>> mapping = [[1, 0, 2], ... [1, 0, 2], ... [2, 1, 0], ... [1, 2, 0]] >>> ac.map_alleles(mapping) <AlleleCountsArray shape=(4, 3) dtype=int64> 0 4 0 1 3 0 1 2 1 2 0 0 See Also -------- create_allele_mapping """ mapping = asarray_ndim(mapping, 2) check_dim0_aligned(self, mapping) # setup output array out = np.empty_like(mapping) # apply transformation i = np.arange(self.shape[0]).reshape((-1, 1)) out[i, mapping] = self return type(self)(out)
[docs]class GenotypeAlleleCounts(NumpyArrayWrapper): """Base class for wrapping a NumPy array of genotype calls, stored as allele counts per call. See Also -------- GenotypeAlleleCountsArray, GenotypeAlleleCountsVector """ def __init__(self, data, copy=False, **kwargs): super(GenotypeAlleleCounts, self).__init__(data, copy=copy, **kwargs) check_integer_dtype(self.values)
[docs] def is_missing(self): return np.sum(self.values, axis=-1) == 0
[docs] def is_called(self): return np.sum(self.values, axis=-1) > 0
# noinspection PyAugmentAssignment
[docs] def is_hom(self, allele=None): out = np.sum(self.values > 0, axis=-1) == 1 if allele is not None: out = out & (self.values[..., allele] > 0) return out
[docs] def is_hom_ref(self): return self.is_hom(0)
# noinspection PyAugmentAssignment
[docs] def is_hom_alt(self): out = np.sum(self.values > 0, axis=-1) == 1 out = out & (self.values[..., 0] == 0) return out
# noinspection PyAugmentAssignment
[docs] def is_het(self, allele=None): out = np.sum(self.values > 0, axis=-1) > 1 if allele is not None: out = out & (self.values[..., allele] > 0) return out
def to_frequencies(self, fill=np.nan): an = np.sum(self, axis=-1)[..., np.newaxis] with ignore_invalid(): af = np.where(an > 0, self / an, fill) return af def allelism(self): return np.sum(self.values > 0, axis=-1) def max_allele(self): out = np.empty(self.shape[:-1], dtype='i1') out.fill(-1) for i in range(self.shape[-1]): d = self.values[..., i] > 0 out[d] = i return out def is_variant(self): t = self.values[..., 1:] > 0 # type: np.ndarray return np.any(t, axis=-1) def is_non_variant(self): t = self.values[..., 1:] == 0 # type: np.ndarray return np.all(t, axis=-1) def is_segregating(self): return self.allelism() > 1 def is_non_segregating(self, allele=None): if allele is None: return self.allelism() <= 1 else: return (self.allelism() == 1) & (self.values[:, allele] > 0) def is_biallelic(self): return self.allelism() == 2 def is_biallelic_01(self): loc = self.is_biallelic() & (self.max_allele() == 1) return loc def to_gt(self, max_count=None): # how many characters needed per allele? if max_count is None: max_count = np.max(self) nchar = int(np.floor(np.log10(max_count))) + 1 # convert to string a = self.astype((np.string_, nchar)).view(np.chararray) # determine allele count separator sep = b':' # join via separator gt = a[..., 0] for i in range(1, self.shape[-1]): gt = gt + sep + a[..., i] return gt
[docs] def compress(self, condition, axis=0, out=None): # implement in sub-class raise NotImplementedError
[docs] def take(self, indices, axis=0, out=None, mode='raise'): # implement in sub-class raise NotImplementedError
[docs] def concatenate(self, others, axis=0): # implement in sub-class raise NotImplementedError
[docs]class GenotypeAlleleCountsVector(GenotypeAlleleCounts, DisplayAs1D): """Array of genotype calls for a sequence of variants or samples, stored as allele counts per call. Parameters ---------- data : array_like, int, shape (n_variants, n_samples, ploidy) Genotype data. copy : bool, optional If True, make a copy of `data`. **kwargs : keyword arguments All keyword arguments are passed through to :func:`numpy.array`. See Also -------- GenotypeAlleleCounts, GenotypeAlleleCountsArray, GenotypeVector """ def __init__(self, data, copy=False, **kwargs): super(GenotypeAlleleCountsVector, self).__init__(data, copy=copy, **kwargs) check_ndim(self.values, 2) def __getitem__(self, item): return index_genotype_ac_vector(self, item, cls=type(self)) @property def n_calls(self): """Number of variants.""" return self.shape[0] @property def n_alleles(self): """Number of alleles.""" return self.shape[1] def compress(self, condition, axis=0, out=None): return compress_genotype_ac(self, condition=condition, axis=axis, wrap_axes={0}, cls=type(self), compress=np.compress, out=out) def take(self, indices, axis=0, out=None, mode='raise'): return take_genotype_ac(self, indices=indices, axis=axis, wrap_axes={0}, cls=type(self), take=np.take, out=out, mode=mode) def concatenate(self, others, axis=0): return concatenate_genotype_ac(self, others=others, axis=axis, wrap_axes={0}, cls=type(self), concatenate=np.concatenate) def str_items(self): gt = self.to_gt() if PY2: out = list(gt) else: out = [str(x, 'ascii') for x in gt] return out def to_str(self, threshold=10, edgeitems=5): _, items = self.get_display_items(threshold, edgeitems) s = ' '.join(items) return s
[docs]class GenotypeAlleleCountsArray(GenotypeAlleleCounts, DisplayAs2D): """Array of genotype calls for a matrix of variants and samples, stored as allele counts per call. Parameters ---------- data : array_like, int, shape (n_variants, n_samples, ploidy) Genotype data. copy : bool, optional If True, make a copy of `data`. **kwargs : keyword arguments All keyword arguments are passed through to :func:`numpy.array`. Notes ----- This class provides an alternative representation of genotype calls, allowing for variable copy number (effective ploidy) between chromosomes and/or genome regions. Genotype calls are represented as a 3-dimensional array of integers. By convention, the first dimension corresponds to the variants genotyped, the second dimension corresponds to the samples genotyped, and the third dimension corresponds to the alleles genotyped in index order. Each integer in the array records the **count** for the given in allele in the given variant and sample. Examples -------- Instantiate an array:: >>> import allel >>> g = allel.GenotypeAlleleCountsArray([[[2, 0, 0], [0, 2, 0]], ... [[1, 1, 0], [0, 1, 1]], ... [[0, 0, 4], [0, 0, 0]]], ... dtype='u1') >>> g.dtype dtype('uint8') >>> g.ndim 3 >>> g.shape (3, 2, 3) >>> g.n_variants 3 >>> g.n_samples 2 >>> g.n_alleles 3 >>> g <GenotypeAlleleCountsArray shape=(3, 2, 3) dtype=uint8> 2:0:0 0:2:0 1:1:0 0:1:1 0:0:4 0:0:0 Genotype calls for a single variant at all samples can be obtained by indexing the first dimension, e.g.:: >>> g[1] <GenotypeAlleleCountsVector shape=(2, 3) dtype=uint8> 1:1:0 0:1:1 Genotype calls for a single sample at all variants can be obtained by indexing the second dimension, e.g.:: >>> g[:, 1] <GenotypeAlleleCountsVector shape=(3, 3) dtype=uint8> 0:2:0 0:1:1 0:0:0 A genotype call for a single sample at a single variant can be obtained by indexing the first and second dimensions, e.g.:: >>> g[1, 0] array([1, 1, 0], dtype=uint8) Copy number (effective ploidy) may vary between calls:: >>> cn = g.sum(axis=2) >>> cn array([[2, 2], [2, 2], [4, 0]], dtype=uint64) See Also -------- GenotypeAlleleCounts, GenotypeAlleleCountsVector, GenotypeArray """ def __init__(self, data, copy=False, **kwargs): super(GenotypeAlleleCountsArray, self).__init__(data, copy=copy, **kwargs) check_ndim(self.values, 3) def __getitem__(self, item): return index_genotype_ac_array(self, item, array_cls=type(self), vector_cls=GenotypeAlleleCountsVector) @property def n_variants(self): """Number of variants.""" return self.shape[0] @property def n_samples(self): """Number of samples.""" return self.shape[1] @property def n_alleles(self): """Number of alleles.""" return self.shape[2]
[docs] def count_alleles(self, subpop=None): # deal with subpop if subpop: g = self.take(subpop, axis=1).values else: g = self.values out = g.sum(axis=1) out = AlleleCountsArray(out) return out
def compress(self, condition, axis=0, out=None): return compress_genotype_ac(self, condition=condition, axis=axis, wrap_axes={0, 1}, cls=type(self), compress=np.compress, out=out) def take(self, indices, axis=0, out=None, mode='raise'): return take_genotype_ac(self, indices=indices, axis=axis, wrap_axes={0, 1}, cls=type(self), take=np.take, out=out, mode=mode) def concatenate(self, others, axis=0): return concatenate_genotype_ac(self, others=others, axis=axis, wrap_axes={0, 1}, cls=type(self), concatenate=np.concatenate)
[docs] def subset(self, sel0=None, sel1=None): return subset_genotype_ac_array(self, sel0, sel1, cls=type(self), subset=subset)
def str_items(self): gt = self.to_gt() n = gt.dtype.itemsize if PY2: out = [[x.rjust(n) for x in row] for row in gt] else: out = [[str(x, 'ascii').rjust(n) for x in row] for row in gt] return out
[docs]class SortedIndex(NumpyArrayWrapper, DisplayAs1D): """Index of sorted values, e.g., positions from a single chromosome or contig. Parameters ---------- data : array_like Values in ascending order. **kwargs : keyword arguments All keyword arguments are passed through to :func:`numpy.array`. Notes ----- Values must be given in ascending order, although duplicate values may be present (i.e., values must be monotonically increasing). Examples -------- >>> import allel >>> idx = allel.SortedIndex([2, 5, 8, 14, 15, 23, 42, 42, 61, 77, 103], dtype='i4') >>> idx <SortedIndex shape=(11,) dtype=int32> [2, 5, 8, 14, 15, ..., 42, 42, 61, 77, 103] >>> idx.dtype dtype('int32') >>> idx.ndim 1 >>> idx.shape (11,) >>> idx.is_unique False See Also -------- SortedMultiIndex, UniqueIndex """ def __init__(self, data, copy=False, **kwargs): super(SortedIndex, self).__init__(data, copy=copy, **kwargs) check_ndim(self.values, 1) # check sorted ascending t = self.values[:-1] > self.values[1:] # type: np.ndarray if np.any(t): raise ValueError('values must be monotonically increasing') self._is_unique = None @property def is_unique(self): """True if no duplicate entries.""" if self._is_unique is None: t = self.values[:-1] == self.values[1:] # type: np.ndarray self._is_unique = ~np.any(t) return self._is_unique def __getitem__(self, item): s = self.values[item] if isinstance(item, (slice, list, np.ndarray, type(Ellipsis))): return type(self)(s) return s def compress(self, condition, axis=0, out=None): out = self.values.compress(condition, axis=axis, out=out) if axis == 0: out = type(self)(out) return out def take(self, indices, axis=0, out=None, mode='raise'): out = self.values.take(indices, axis=axis, out=out, mode=mode) if axis == 0: out = type(self)(out) return out
[docs] def locate_key(self, key): """Get index location for the requested key. Parameters ---------- key : object Value to locate. Returns ------- loc : int or slice Location of `key` (will be slice if there are duplicate entries). Examples -------- >>> import allel >>> idx = allel.SortedIndex([3, 6, 6, 11]) >>> idx.locate_key(3) 0 >>> idx.locate_key(11) 3 >>> idx.locate_key(6) slice(1, 3, None) >>> try: ... idx.locate_key(2) ... except KeyError as e: ... print(e) ... 2 """ left = bisect.bisect_left(self, key) right = bisect.bisect_right(self, key) diff = right - left if diff == 0: raise KeyError(key) elif diff == 1: return left else: return slice(left, right)
[docs] def locate_intersection(self, other): """Locate the intersection with another array. Parameters ---------- other : array_like, int Array of values to intersect. Returns ------- loc : ndarray, bool Boolean array with location of intersection. loc_other : ndarray, bool Boolean array with location in `other` of intersection. Examples -------- >>> import allel >>> idx1 = allel.SortedIndex([3, 6, 11, 20, 35]) >>> idx2 = allel.SortedIndex([4, 6, 20, 39]) >>> loc1, loc2 = idx1.locate_intersection(idx2) >>> loc1 array([False, True, False, True, False]) >>> loc2 array([False, True, True, False]) >>> idx1[loc1] <SortedIndex shape=(2,) dtype=int64> [6, 20] >>> idx2[loc2] <SortedIndex shape=(2,) dtype=int64> [6, 20] """ # check inputs other = SortedIndex(other, copy=False) # find intersection assume_unique = self.is_unique and other.is_unique loc = np.in1d(self, other, assume_unique=assume_unique) loc_other = np.in1d(other, self, assume_unique=assume_unique) return loc, loc_other
[docs] def locate_keys(self, keys, strict=True): """Get index locations for the requested keys. Parameters ---------- keys : array_like Array of keys to locate. strict : bool, optional If True, raise KeyError if any keys are not found in the index. Returns ------- loc : ndarray, bool Boolean array with location of values. Examples -------- >>> import allel >>> idx1 = allel.SortedIndex([3, 6, 11, 20, 35]) >>> idx2 = allel.SortedIndex([4, 6, 20, 39]) >>> loc = idx1.locate_keys(idx2, strict=False) >>> loc array([False, True, False, True, False]) >>> idx1[loc] <SortedIndex shape=(2,) dtype=int64> [6, 20] """ # check inputs keys = SortedIndex(keys, copy=False) # find intersection loc, found = self.locate_intersection(keys) if strict and np.any(~found): raise KeyError(keys[~found]) return loc
[docs] def intersect(self, other): """Intersect with `other` sorted index. Parameters ---------- other : array_like, int Array of values to intersect with. Returns ------- out : SortedIndex Values in common. Examples -------- >>> import allel >>> idx1 = allel.SortedIndex([3, 6, 11, 20, 35]) >>> idx2 = allel.SortedIndex([4, 6, 20, 39]) >>> idx1.intersect(idx2) <SortedIndex shape=(2,) dtype=int64> [6, 20] """ loc = self.locate_keys(other, strict=False) return self.compress(loc, axis=0)
[docs] def locate_range(self, start=None, stop=None): """Locate slice of index containing all entries within `start` and `stop` values **inclusive**. Parameters ---------- start : int, optional Start value. stop : int, optional Stop value. Returns ------- loc : slice Slice object. Examples -------- >>> import allel >>> idx = allel.SortedIndex([3, 6, 11, 20, 35]) >>> loc = idx.locate_range(4, 32) >>> loc slice(1, 4, None) >>> idx[loc] <SortedIndex shape=(3,) dtype=int64> [6, 11, 20] """ # locate start and stop indices if start is None: start_index = 0 else: start_index = bisect.bisect_left(self, start) if stop is None: stop_index = len(self) else: stop_index = bisect.bisect_right(self, stop) if stop_index - start_index == 0: raise KeyError(start, stop) loc = slice(start_index, stop_index) return loc
[docs] def intersect_range(self, start=None, stop=None): """Intersect with range defined by `start` and `stop` values **inclusive**. Parameters ---------- start : int, optional Start value. stop : int, optional Stop value. Returns ------- idx : SortedIndex Examples -------- >>> import allel >>> idx = allel.SortedIndex([3, 6, 11, 20, 35]) >>> idx.intersect_range(4, 32) <SortedIndex shape=(3,) dtype=int64> [6, 11, 20] """ try: loc = self.locate_range(start=start, stop=stop) except KeyError: return self.values[0:0] else: return self[loc]
[docs] def locate_intersection_ranges(self, starts, stops): """Locate the intersection with a set of ranges. Parameters ---------- starts : array_like, int Range start values. stops : array_like, int Range stop values. Returns ------- loc : ndarray, bool Boolean array with location of entries found. loc_ranges : ndarray, bool Boolean array with location of ranges containing one or more entries. Examples -------- >>> import allel >>> import numpy as np >>> idx = allel.SortedIndex([3, 6, 11, 20, 35]) >>> ranges = np.array([[0, 2], [6, 17], [12, 15], [31, 35], ... [100, 120]]) >>> starts = ranges[:, 0] >>> stops = ranges[:, 1] >>> loc, loc_ranges = idx.locate_intersection_ranges(starts, stops) >>> loc array([False, True, True, False, True]) >>> loc_ranges array([False, True, False, True, False]) >>> idx[loc] <SortedIndex shape=(3,) dtype=int64> [6, 11, 35] >>> ranges[loc_ranges] array([[ 6, 17], [31, 35]]) """ # check inputs starts = asarray_ndim(starts, 1) stops = asarray_ndim(stops, 1) check_dim0_aligned(starts, stops) # find indices of start and stop values in idx start_indices = np.searchsorted(self, starts) stop_indices = np.searchsorted(self, stops, side='right') # find intervals overlapping at least one value loc_ranges = start_indices < stop_indices # find values within at least one interval loc = np.zeros(self.shape, dtype=np.bool) for i, j in zip(start_indices[loc_ranges], stop_indices[loc_ranges]): loc[i:j] = True return loc, loc_ranges
[docs] def locate_ranges(self, starts, stops, strict=True): """Locate items within the given ranges. Parameters ---------- starts : array_like, int Range start values. stops : array_like, int Range stop values. strict : bool, optional If True, raise KeyError if any ranges contain no entries. Returns ------- loc : ndarray, bool Boolean array with location of entries found. Examples -------- >>> import allel >>> import numpy as np >>> idx = allel.SortedIndex([3, 6, 11, 20, 35]) >>> ranges = np.array([[0, 2], [6, 17], [12, 15], [31, 35], ... [100, 120]]) >>> starts = ranges[:, 0] >>> stops = ranges[:, 1] >>> loc = idx.locate_ranges(starts, stops, strict=False) >>> loc array([False, True, True, False, True]) >>> idx[loc] <SortedIndex shape=(3,) dtype=int64> [6, 11, 35] """ loc, found = self.locate_intersection_ranges(starts, stops) if strict and np.any(~found): raise KeyError(starts[~found], stops[~found]) return loc
[docs] def intersect_ranges(self, starts, stops): """Intersect with a set of ranges. Parameters ---------- starts : array_like, int Range start values. stops : array_like, int Range stop values. Returns ------- idx : SortedIndex Examples -------- >>> import allel >>> import numpy as np >>> idx = allel.SortedIndex([3, 6, 11, 20, 35]) >>> ranges = np.array([[0, 2], [6, 17], [12, 15], [31, 35], ... [100, 120]]) >>> starts = ranges[:, 0] >>> stops = ranges[:, 1] >>> idx.intersect_ranges(starts, stops) <SortedIndex shape=(3,) dtype=int64> [6, 11, 35] """ loc = self.locate_ranges(starts, stops, strict=False) return self.compress(loc, axis=0)
[docs]class UniqueIndex(NumpyArrayWrapper, DisplayAs1D): """Array of unique values (e.g., variant or sample identifiers). Parameters ---------- data : array_like Values. **kwargs : keyword arguments All keyword arguments are passed through to :func:`numpy.array`. Notes ----- This class represents an arbitrary set of unique values, e.g., sample or variant identifiers. There is no need for values to be sorted. However, all values must be unique within the array, and must be hashable objects. Examples -------- >>> import allel >>> idx = allel.UniqueIndex(['A', 'C', 'B', 'F']) >>> idx <UniqueIndex shape=(4,) dtype=object> ['A', 'C', 'B', 'F'] >>> idx.dtype dtype('O') >>> idx.ndim 1 >>> idx.shape (4,) See Also -------- SortedIndex, SortedMultiIndex """ def __init__(self, data, copy=False, dtype=object, **kwargs): super(UniqueIndex, self).__init__(data, copy=copy, dtype=dtype, **kwargs) check_ndim(self.values, 1) # check unique # noinspection PyTupleAssignmentBalance _, counts = np.unique(self.values, return_counts=True) t = counts > 1 # type: np.ndarray if np.any(t): raise ValueError('values are not unique') self.lookup = {v: i for i, v in enumerate(self.values)} def __getitem__(self, item): s = self.values[item] if isinstance(item, (slice, list, np.ndarray, type(Ellipsis))): return type(self)(s) return s def compress(self, condition, axis=0, out=None): out = self.values.compress(condition, axis=axis, out=out) if axis == 0: out = type(self)(out) return out def take(self, indices, axis=0, out=None, mode='raise'): out = self.values.take(indices, axis=axis, out=out, mode=mode) if axis == 0: out = type(self)(out) return out
[docs] def locate_key(self, key): """Get index location for the requested key. Parameters ---------- key : object Key to locate. Returns ------- loc : int Location of `key`. Examples -------- >>> import allel >>> idx = allel.UniqueIndex(['A', 'C', 'B', 'F']) >>> idx.locate_key('A') 0 >>> idx.locate_key('B') 2 >>> try: ... idx.locate_key('X') ... except KeyError as e: ... print(e) ... 'X' """ return self.lookup[key]
[docs] def locate_intersection(self, other): """Locate the intersection with another array. Parameters ---------- other : array_like Array to intersect. Returns ------- loc : ndarray, bool Boolean array with location of intersection. loc_other : ndarray, bool Boolean array with location in `other` of intersection. Examples -------- >>> import allel >>> idx1 = allel.UniqueIndex(['A', 'C', 'B', 'F'], dtype=object) >>> idx2 = allel.UniqueIndex(['X', 'F', 'G', 'C', 'Z'], dtype=object) >>> loc1, loc2 = idx1.locate_intersection(idx2) >>> loc1 array([False, True, False, True]) >>> loc2 array([False, True, False, True, False]) >>> idx1[loc1] <UniqueIndex shape=(2,) dtype=object> ['C', 'F'] >>> idx2[loc2] <UniqueIndex shape=(2,) dtype=object> ['F', 'C'] """ # check inputs other = UniqueIndex(other) # find intersection assume_unique = True loc = np.in1d(self, other, assume_unique=assume_unique) loc_other = np.in1d(other, self, assume_unique=assume_unique) return loc, loc_other
[docs] def locate_keys(self, keys, strict=True): """Get index locations for the requested keys. Parameters ---------- keys : array_like Array of keys to locate. strict : bool, optional If True, raise KeyError if any keys are not found in the index. Returns ------- loc : ndarray, bool Boolean array with location of keys. Examples -------- >>> import allel >>> idx = allel.UniqueIndex(['A', 'C', 'B', 'F']) >>> idx.locate_keys(['F', 'C']) array([False, True, False, True]) >>> idx.locate_keys(['X', 'F', 'G', 'C', 'Z'], strict=False) array([False, True, False, True]) """ # check inputs keys = UniqueIndex(keys) # find intersection loc, found = self.locate_intersection(keys) if strict and np.any(~found): raise KeyError(keys[~found]) return loc
[docs] def intersect(self, other): """Intersect with `other`. Parameters ---------- other : array_like Array to intersect. Returns ------- out : UniqueIndex Examples -------- >>> import allel >>> idx1 = allel.UniqueIndex(['A', 'C', 'B', 'F'], dtype=object) >>> idx2 = allel.UniqueIndex(['X', 'F', 'G', 'C', 'Z'], dtype=object) >>> idx1.intersect(idx2) <UniqueIndex shape=(2,) dtype=object> ['C', 'F'] >>> idx2.intersect(idx1) <UniqueIndex shape=(2,) dtype=object> ['F', 'C'] """ loc = self.locate_keys(other, strict=False) return self.compress(loc, axis=0)
# noinspection PyMissingConstructor
[docs]class SortedMultiIndex(DisplayAs1D): """Two-level index of sorted values, e.g., variant positions from two or more chromosomes/contigs. Parameters ---------- l1 : array_like First level values in ascending order. l2 : array_like Second level values, in ascending order within each sub-level. copy : bool, optional If True, inputs will be copied into new arrays. Examples -------- >>> import allel >>> chrom = ['chr1', 'chr1', 'chr2', 'chr2', 'chr2', 'chr3'] >>> pos = [1, 4, 2, 5, 5, 3] >>> idx = allel.SortedMultiIndex(chrom, pos) >>> idx <SortedMultiIndex shape=(6,), dtype=<U4/int64> chr1:1 chr1:4 chr2:2 chr2:5 chr2:5 chr3:3 >>> len(idx) 6 See Also -------- SortedIndex, UniqueIndex """ def __init__(self, l1, l2, copy=False): l1 = SortedIndex(l1, copy=copy) l2 = np.array(l2, copy=copy) check_ndim(l2, 1) check_dim0_aligned(l1, l2) self.l1 = l1 self.l2 = l2 def __repr__(self): s = '<SortedMultiIndex shape=(%s,), dtype=%s/%s>' % \ (len(self), self.l1.dtype, self.l2.dtype) s += '\n' + str(self) return s def str_items(self): return ['%s:%s' % (x, y) for x, y in zip(self.l1, self.l2)] def to_str(self, threshold=10, edgeitems=5): _, items = self.get_display_items(threshold, edgeitems) s = ' '.join(items) return s def __len__(self): return len(self.l1) def __getitem__(self, item): l1 = self.l1[item] l2 = self.l2[item] if isinstance(item, integer_types): return l1, l2 else: return SortedMultiIndex(l1, l2, copy=False) def compress(self, condition, axis=0, out=None): if out is not None: raise NotImplementedError('out argument not supported') l1 = self.l1.compress(condition, axis=axis) l2 = self.l2.compress(condition, axis=axis) return SortedMultiIndex(l1, l2, copy=False) def take(self, indices, axis=0, out=None, mode='raise'): if out is not None: raise NotImplementedError('out argument not supported') l1 = self.l1.take(indices, axis=axis, mode=mode) l2 = self.l2.take(indices, axis=axis, mode=mode) return SortedMultiIndex(l1, l2, copy=False) @property def shape(self): return len(self),
[docs] def locate_key(self, k1, k2=None): """ Get index location for the requested key. Parameters ---------- k1 : object Level 1 key. k2 : object, optional Level 2 key. Returns ------- loc : int or slice Location of requested key (will be slice if there are duplicate entries). Examples -------- >>> import allel >>> chrom = ['chr1', 'chr1', 'chr2', 'chr2', 'chr2', 'chr3'] >>> pos = [1, 4, 2, 5, 5, 3] >>> idx = allel.SortedMultiIndex(chrom, pos) >>> idx.locate_key('chr1') slice(0, 2, None) >>> idx.locate_key('chr1', 4) 1 >>> idx.locate_key('chr2', 5) slice(3, 5, None) >>> try: ... idx.locate_key('chr3', 4) ... except KeyError as e: ... print(e) ... ('chr3', 4) """ loc1 = self.l1.locate_key(k1) if k2 is None: return loc1 if isinstance(loc1, slice): offset = loc1.start try: loc2 = SortedIndex(self.l2[loc1], copy=False).locate_key(k2) except KeyError: # reraise with more information raise KeyError(k1, k2) else: if isinstance(loc2, slice): loc = slice(offset + loc2.start, offset + loc2.stop) else: # assume singleton loc = offset + loc2 else: # singleton match in l1 v = self.l2[loc1] if v == k2: loc = loc1 else: raise KeyError(k1, k2) return loc
[docs] def locate_range(self, key, start=None, stop=None): """Locate slice of index containing all entries within the range `key`:`start`-`stop` **inclusive**. Parameters ---------- key : object Level 1 key value. start : object, optional Level 2 start value. stop : object, optional Level 2 stop value. Returns ------- loc : slice Slice object. Examples -------- >>> import allel >>> chrom = ['chr1', 'chr1', 'chr2', 'chr2', 'chr2', 'chr3'] >>> pos = [1, 4, 2, 5, 5, 3] >>> idx = allel.SortedMultiIndex(chrom, pos) >>> idx.locate_range('chr1') slice(0, 2, None) >>> idx.locate_range('chr1', 1, 4) slice(0, 2, None) >>> idx.locate_range('chr2', 3, 7) slice(3, 5, None) >>> try: ... idx.locate_range('chr3', 4, 9) ... except KeyError as e: ... print(e) ('chr3', 4, 9) """ loc1 = self.l1.locate_key(key) if start is None and stop is None: loc = loc1 elif isinstance(loc1, slice): offset = loc1.start idx = SortedIndex(self.l2[loc1], copy=False) try: loc2 = idx.locate_range(start, stop) except KeyError: raise KeyError(key, start, stop) else: loc = slice(offset + loc2.start, offset + loc2.stop) else: # singleton match in l1 v = self.l2[loc1] if start <= v <= stop: loc = loc1 else: raise KeyError(key, start, stop) # ensure slice is always returned if not isinstance(loc, slice): loc = slice(loc, loc + 1) return loc
[docs]class VariantTable(NumpyRecArrayWrapper): """Table (catalogue) of variants. Parameters ---------- data : array_like, structured, shape (n_variants,) Variant records. index : string or pair of strings, optional Names of columns to use for positional index, e.g., 'POS' if table contains a 'POS' column and records from a single chromosome/contig, or ('CHROM', 'POS') if table contains records from multiple chromosomes/contigs. **kwargs : keyword arguments, optional Further keyword arguments are passed through to :func:`numpy.rec.array`. Examples -------- Instantiate a table from existing data:: >>> import allel >>> records = [[b'chr1', 2, 35, 4.5, (1, 2)], ... [b'chr1', 7, 12, 6.7, (3, 4)], ... [b'chr2', 3, 78, 1.2, (5, 6)], ... [b'chr2', 9, 22, 4.4, (7, 8)], ... [b'chr3', 6, 99, 2.8, (9, 10)]] >>> dtype = [('CHROM', 'S4'), ... ('POS', 'u4'), ... ('DP', int), ... ('QD', float), ... ('AC', (int, 2))] >>> vt = allel.VariantTable(records, dtype=dtype, ... index=('CHROM', 'POS')) >>> vt.names ('CHROM', 'POS', 'DP', 'QD', 'AC') >>> vt.n_variants 5 Access a column:: >>> vt['DP'] array([35, 12, 78, 22, 99]) Access multiple columns:: >>> vt[['DP', 'QD']] <VariantTable shape=(5,) dtype=(numpy.record, [('DP', '<i8'), ('QD', '<f8')])> [(35, 4.5) (12, 6.7) (78, 1.2) (22, 4.4) (99, 2.8)] Access a row:: >>> vt[2] (b'chr2', 3, 78, 1.2, [5, 6]) Access multiple rows:: >>> vt[2:4] <VariantTable shape=(2,) dtype=(numpy.record, [('CHROM', 'S4'), ('POS', '<u4'), ... [(b'chr2', 3, 78, 1.2, [5, 6]) (b'chr2', 9, 22, 4.4, ... Evaluate expressions against the table:: >>> vt.eval('DP > 30') array([ True, False, True, False, True]) >>> vt.eval('(DP > 30) & (QD > 4)') array([ True, False, False, False, False]) >>> vt.eval('DP * 2') array([ 70, 24, 156, 44, 198]) Query the table:: >>> vt.query('DP > 30') <VariantTable shape=(3,) dtype=(numpy.record, [('CHROM', 'S4'), ('POS', '<u4'), ... [(b'chr1', 2, 35, 4.5, [ 1, 2]) (b'chr2', 3, 78, 1.2, ... (b'chr3', 6, 99, 2.8, [ 9, 10])] >>> vt.query('(DP > 30) & (QD > 4)') <VariantTable shape=(1,) dtype=(numpy.record, [('CHROM', 'S4'), ('POS', '<u4'), ... [(b'chr1', 2, 35, 4.5, [1, 2])] Use the index to query variants:: >>> vt.query_region(b'chr2', 1, 10) <VariantTable shape=(2,) dtype=(numpy.record, [('CHROM', 'S4'), ('POS', '<u4'), ... [(b'chr2', 3, 78, 1.2, [5, 6]) (b'chr2', 9, 22, 4.4, ... """ def __init__(self, data, index=None, copy=False, **kwargs): super(VariantTable, self).__init__(data, copy=copy, **kwargs) self.set_index(index) @property def n_variants(self): """Number of variants (length of first dimension).""" return self.shape[0] # noinspection PyAttributeOutsideInit def set_index(self, index): """Set or reset the index. Parameters ---------- index : string or pair of strings, optional Names of columns to use for positional index, e.g., 'POS' if table contains a 'POS' column and records from a single chromosome/contig, or ('CHROM', 'POS') if table contains records from multiple chromosomes/contigs. """ if index is None: pass elif isinstance(index, str): index = SortedIndex(self[index], copy=False) elif isinstance(index, (tuple, list)) and len(index) == 2: index = SortedMultiIndex(self[index[0]], self[index[1]], copy=False) else: raise ValueError('invalid index argument, expected string or ' 'pair of strings, found %s' % repr(index)) self.index = index
[docs] def query_position(self, chrom=None, position=None): """Query the table, returning row or rows matching the given genomic position. Parameters ---------- chrom : string, optional Chromosome/contig. position : int, optional Position (1-based). Returns ------- result : row or VariantTable """ if self.index is None: raise ValueError('no index has been set') if isinstance(self.index, SortedIndex): # ignore chrom loc = self.index.locate_key(position) else: loc = self.index.locate_key(chrom, position) return self[loc]
[docs] def query_region(self, chrom=None, start=None, stop=None): """Query the table, returning row or rows within the given genomic region. Parameters ---------- chrom : string, optional Chromosome/contig. start : int, optional Region start position (1-based). stop : int, optional Region stop position (1-based). Returns ------- result : VariantTable """ if self.index is None: raise ValueError('no index has been set') if isinstance(self.index, SortedIndex): # ignore chrom loc = self.index.locate_range(start, stop) else: loc = self.index.locate_range(chrom, start, stop) return self[loc]
[docs] def to_vcf(self, path, rename=None, number=None, description=None, fill=None, write_header=True): r"""Write to a variant call format (VCF) file. Parameters ---------- path : string File path. rename : dict, optional Rename these columns in the VCF. number : dict, optional Override the number specified in INFO headers. description : dict, optional Descriptions for the INFO and FILTER headers. fill : dict, optional Fill values used for missing data in the table. write_header : bool, optional If True write VCF header. Examples -------- Setup a variant table to write out:: >>> import allel >>> chrom = [b'chr1', b'chr1', b'chr2', b'chr2', b'chr3'] >>> pos = [2, 6, 3, 8, 1] >>> ids = ['a', 'b', 'c', 'd', 'e'] >>> ref = [b'A', b'C', b'T', b'G', b'N'] >>> alt = [(b'T', b'.'), ... (b'G', b'.'), ... (b'A', b'C'), ... (b'C', b'A'), ... (b'X', b'.')] >>> qual = [1.2, 2.3, 3.4, 4.5, 5.6] >>> filter_qd = [True, True, True, False, False] >>> filter_dp = [True, False, True, False, False] >>> dp = [12, 23, 34, 45, 56] >>> qd = [12.3, 23.4, 34.5, 45.6, 56.7] >>> flg = [True, False, True, False, True] >>> ac = [(1, -1), (3, -1), (5, 6), (7, 8), (9, -1)] >>> xx = [(1.2, 2.3), (3.4, 4.5), (5.6, 6.7), (7.8, 8.9), ... (9.0, 9.9)] >>> columns = [chrom, pos, ids, ref, alt, qual, filter_dp, ... filter_qd, dp, qd, flg, ac, xx] >>> records = list(zip(*columns)) >>> dtype = [('CHROM', 'S4'), ... ('POS', 'u4'), ... ('ID', 'S1'), ... ('REF', 'S1'), ... ('ALT', ('S1', 2)), ... ('qual', 'f4'), ... ('filter_dp', bool), ... ('filter_qd', bool), ... ('dp', int), ... ('qd', float), ... ('flg', bool), ... ('ac', (int, 2)), ... ('xx', (float, 2))] >>> vt = allel.VariantTable(records, dtype=dtype) Now write out to VCF and inspect the result:: >>> rename = {'dp': 'DP', 'qd': 'QD', 'filter_qd': 'QD'} >>> fill = {'ALT': b'.', 'ac': -1} >>> number = {'ac': 'A'} >>> description = {'ac': 'Allele counts', 'filter_dp': 'Low depth'} >>> vt.to_vcf('example.vcf', rename=rename, fill=fill, ... number=number, description=description) >>> print(open('example.vcf').read()) ##fileformat=VCFv4.1 ##fileDate=... ##source=... ##INFO=<ID=DP,Number=1,Type=Integer,Description=""> ##INFO=<ID=QD,Number=1,Type=Float,Description=""> ##INFO=<ID=ac,Number=A,Type=Integer,Description="Allele counts"> ##INFO=<ID=flg,Number=0,Type=Flag,Description=""> ##INFO=<ID=xx,Number=2,Type=Float,Description=""> ##FILTER=<ID=QD,Description=""> ##FILTER=<ID=dp,Description="Low depth"> #CHROM POS ID REF ALT QUAL FILTER INFO chr1 2 a A T 1.2 QD;dp DP=12;QD=12.3;ac=1;flg;xx=... chr1 6 b C G 2.3 QD DP=23;QD=23.4;ac=3;xx=3.4,4.5 chr2 3 c T A,C 3.4 QD;dp DP=34;QD=34.5;ac=5,6;flg;x... chr2 8 d G C,A 4.5 PASS DP=45;QD=45.6;ac=7,8;xx=7... chr3 1 e N X 5.6 PASS DP=56;QD=56.7;ac=9;flg;xx=... """ write_vcf(path, callset=self, rename=rename, number=number, description=description, fill=fill, write_header=write_header)
[docs]class FeatureTable(NumpyRecArrayWrapper): """Table of genomic features (e.g., genes, exons, etc.). Parameters ---------- data : array_like, structured, shape (n_variants,) Variant records. copy : bool, optional If True, make a copy of `data`. **kwargs : keyword arguments, optional Further keyword arguments are passed through to :func:`numpy.rec.array`. """ def __init__(self, data, copy=False, **kwargs): super(FeatureTable, self).__init__(data, copy=copy, **kwargs) @property def n_features(self): """Number of features (length of first dimension).""" return self.shape[0]
[docs] def to_mask(self, size, start_name='start', stop_name='end'): """Construct a mask array where elements are True if the fall within features in the table. Parameters ---------- size : int Size of chromosome/contig. start_name : string, optional Name of column with start coordinates. stop_name : string, optional Name of column with stop coordinates. Returns ------- mask : ndarray, bool """ m = np.zeros(size, dtype=bool) for start, stop in self[[start_name, stop_name]]: m[start-1:stop] = True return m
[docs] @staticmethod def from_gff3(path, attributes=None, region=None, score_fill=-1, phase_fill=-1, attributes_fill=b'.', dtype=None): """Read a feature table from a GFF3 format file. Parameters ---------- path : string File path. attributes : list of strings, optional List of columns to extract from the "attributes" field. region : string, optional Genome region to extract. If given, file must be position sorted, bgzipped and tabix indexed. Tabix must also be installed and on the system path. score_fill : int, optional Value to use where score field has a missing value. phase_fill : int, optional Value to use where phase field has a missing value. attributes_fill : object or list of objects, optional Value(s) to use where attribute field(s) have a missing value. dtype : numpy dtype, optional Manually specify a dtype. Returns ------- ft : FeatureTable """ a = gff3_to_recarray(path, attributes=attributes, region=region, score_fill=score_fill, phase_fill=phase_fill, attributes_fill=attributes_fill, dtype=dtype) if a is None: return None else: return FeatureTable(a, copy=False)