import numpy as np
import hail as hl
from hail.expr.expressions import expr_float64, expr_locus, raise_unless_row_indexed
from hail.typecheck import nullable, oneof, typecheck
from hail.utils.java import Env
[docs]@typecheck(a=np.ndarray, radius=oneof(int, float))
def array_windows(a, radius):
"""Returns start and stop indices for window around each array value.
Examples
--------
>>> hl.linalg.utils.array_windows(np.array([1, 2, 4, 4, 6, 8]), 2)
(array([0, 0, 1, 1, 2, 4]), array([2, 4, 5, 5, 6, 6]))
>>> hl.linalg.utils.array_windows(np.array([-10.0, -2.5, 0.0, 0.0, 1.2, 2.3, 3.0]), 2.5)
(array([0, 1, 1, 1, 2, 2, 4]), array([1, 4, 6, 6, 7, 7, 7]))
Notes
-----
For an array ``a`` in ascending order, the resulting ``starts`` and ``stops``
arrays have the same length as ``a`` and the property that, for all indices
``i``, ``[starts[i], stops[i])`` is the maximal range of indices ``j`` such
that ``a[i] - radius <= a[j] <= a[i] + radius``.
Index ranges are start-inclusive and stop-exclusive. This function is
especially useful in conjunction with
:meth:`.BlockMatrix.sparsify_row_intervals`.
Parameters
----------
a: :obj:`numpy.ndarray` of signed integer or float values
1-dimensional array of values, non-decreasing with respect to index.
radius: :obj:`float`
Non-negative radius of window for values.
Returns
-------
(:class:`numpy.ndarray` of :obj:`int`, :class:`numpy.ndarray` of :obj:`int`)
Tuple of start indices array and stop indices array.
"""
if radius < 0:
raise ValueError(f'array_windows: radius must be non-negative, found {radius}')
if a.ndim != 1:
raise ValueError("array_windows: 'a' must be 1-dimensional")
if not (np.issubdtype(a.dtype, np.signedinteger) or np.issubdtype(a.dtype, np.floating)):
raise ValueError(
f"array_windows: 'a' must be an ndarray of signed integer or float values, " f"found dtype {a.dtype!s}"
)
size = a.size
if size == 0:
return np.zeros(shape=0, dtype=np.int64), np.zeros(shape=0, dtype=np.int64)
if (not np.all(a[:-1] <= a[1:])) or np.isnan(a[0]):
raise ValueError("array_windows: 'a' must be in ascending order with no nan elements")
if a[0] - radius > a[0]:
raise ValueError('array_windows: underflow for a[0] - radius')
if a[-1] + radius < a[-1]:
raise ValueError('array_windows: overflow for a[-1] + radius')
starts, stops = np.zeros(size, dtype=np.int64), np.zeros(size, dtype=np.int64)
j, k = 0, 0
for i in range(size):
min_val = a[i] - radius
while j < size and a[j] < min_val:
j += 1
starts[i] = j
max_val = a[i] + radius
while k < size and a[k] <= max_val:
k += 1
stops[i] = k
return starts, stops
[docs]@typecheck(locus_expr=expr_locus(), radius=oneof(int, float), coord_expr=nullable(expr_float64), _localize=bool)
def locus_windows(locus_expr, radius, coord_expr=None, _localize=True):
"""Returns start and stop indices for window around each locus.
Examples
--------
Windows with 2bp radius for one contig with positions 1, 2, 3, 4, 5:
>>> starts, stops = hl.linalg.utils.locus_windows(
... hl.balding_nichols_model(1, 5, 5).locus,
... radius=2)
>>> starts, stops
(array([0, 0, 0, 1, 2]), array([3, 4, 5, 5, 5]))
The following examples involve three contigs.
>>> loci = [{'locus': hl.Locus('1', 1), 'cm': 1.0},
... {'locus': hl.Locus('1', 2), 'cm': 3.0},
... {'locus': hl.Locus('1', 4), 'cm': 4.0},
... {'locus': hl.Locus('2', 1), 'cm': 2.0},
... {'locus': hl.Locus('2', 1), 'cm': 2.0},
... {'locus': hl.Locus('3', 3), 'cm': 5.0}]
>>> ht = hl.Table.parallelize(
... loci,
... hl.tstruct(locus=hl.tlocus('GRCh37'), cm=hl.tfloat64),
... key=['locus'])
Windows with 1bp radius:
>>> hl.linalg.utils.locus_windows(ht.locus, 1)
(array([0, 0, 2, 3, 3, 5]), array([2, 2, 3, 5, 5, 6]))
Windows with 1cm radius:
>>> hl.linalg.utils.locus_windows(ht.locus, 1.0, coord_expr=ht.cm)
(array([0, 1, 1, 3, 3, 5]), array([1, 3, 3, 5, 5, 6]))
Notes
-----
This function returns two 1-dimensional ndarrays of integers,
``starts`` and ``stops``, each of size equal to the number of rows.
By default, for all indices ``i``, ``[starts[i], stops[i])`` is the maximal
range of row indices ``j`` such that ``contig[i] == contig[j]`` and
``position[i] - radius <= position[j] <= position[i] + radius``.
If the :meth:`.global_position` on `locus_expr` is not in ascending order,
this method will fail. Ascending order should hold for a matrix table keyed
by locus or variant (and the associated row table), or for a table that has
been ordered by `locus_expr`.
Set `coord_expr` to use a value other than position to define the windows.
This row-indexed numeric expression must be non-missing, non-``nan``, on the
same source as `locus_expr`, and ascending with respect to locus
position for each contig; otherwise the function will fail.
The last example above uses centimorgan coordinates, so
``[starts[i], stops[i])`` is the maximal range of row indices ``j`` such
that ``contig[i] == contig[j]`` and
``cm[i] - radius <= cm[j] <= cm[i] + radius``.
Index ranges are start-inclusive and stop-exclusive. This function is
especially useful in conjunction with
:meth:`.BlockMatrix.sparsify_row_intervals`.
Parameters
----------
locus_expr : :class:`.LocusExpression`
Row-indexed locus expression on a table or matrix table.
radius: :obj:`int`
Radius of window for row values.
coord_expr: :class:`.Float64Expression`, optional
Row-indexed numeric expression for the row value.
Must be on the same table or matrix table as `locus_expr`.
By default, the row value is given by the locus position.
Returns
-------
(:class:`numpy.ndarray` of :obj:`int`, :class:`numpy.ndarray` of :obj:`int`)
Tuple of start indices array and stop indices array.
"""
if radius < 0:
raise ValueError(f"locus_windows: 'radius' must be non-negative, found {radius}")
raise_unless_row_indexed('locus_windows', locus_expr)
if coord_expr is not None:
raise_unless_row_indexed('locus_windows', coord_expr)
src = locus_expr._indices.source
if locus_expr not in src._fields_inverse:
locus = Env.get_uid()
annotate_fields = {locus: locus_expr}
if coord_expr is not None:
if coord_expr not in src._fields_inverse:
coords = Env.get_uid()
annotate_fields[coords] = coord_expr
else:
coords = src._fields_inverse[coord_expr]
if isinstance(src, hl.MatrixTable):
new_src = src.annotate_rows(**annotate_fields)
else:
new_src = src.annotate(**annotate_fields)
locus_expr = new_src[locus]
if coord_expr is not None:
coord_expr = new_src[coords]
if coord_expr is None:
coord_expr = locus_expr.position
rg = locus_expr.dtype.reference_genome
contig_group_expr = hl.agg.group_by(hl.locus(locus_expr.contig, 1, reference_genome=rg), hl.agg.collect(coord_expr))
# check loci are in sorted order
last_pos = hl.fold(
lambda a, elt: (
hl.case()
.when(a <= elt, elt)
.or_error(
hl.str("locus_windows: 'locus_expr' global position must be in ascending order. ")
+ hl.str(a)
+ hl.str(" was not less then or equal to ")
+ hl.str(elt)
)
),
-1,
hl.agg.collect(
hl.case()
.when(hl.is_defined(locus_expr), locus_expr.global_position())
.or_error("locus_windows: missing value for 'locus_expr'.")
),
)
checked_contig_groups = (
hl.case().when(last_pos >= 0, contig_group_expr).or_error("locus_windows: 'locus_expr' has length 0")
)
contig_groups = locus_expr._aggregation_method()(checked_contig_groups, _localize=False)
coords = hl.sorted(hl.array(contig_groups)).map(lambda t: t[1])
starts_and_stops = hl._locus_windows_per_contig(coords, radius)
if not _localize:
return starts_and_stops
starts, stops = hl.eval(starts_and_stops)
return np.array(starts), np.array(stops)
def _check_dims(a, name, ndim, min_size=1):
if len(a.shape) != ndim:
raise ValueError(f'{name} must be {ndim}-dimensional, ' f'found {a.ndim}')
for i in range(ndim):
if a.shape[i] < min_size:
raise ValueError(f'{name}.shape[{i}] must be at least ' f'{min_size}, found {a.shape[i]}')
def _ndarray_matmul_ndim(left, right):
if left == 1 and right == 1:
return 0
elif left == 1:
return right - 1
elif right == 1:
return left - 1
else:
assert left == right
return left