def visit_tuple(self, node, visited_children):
ttuple, _, paren, [maybe_types], paren = visited_children
if not maybe_types:
return hl.ttuple()
else:
[first, rest] = maybe_types
return hl.ttuple(first, *(t for comma, t in rest))
def visit_tuple(self, node, visited_children):
ttuple, _, paren, [maybe_types], paren = visited_children
if not maybe_types:
return hl.ttuple()
else:
[first, rest] = maybe_types
return hl.ttuple(first, *(t for comma, t in rest))
def blockmatrix_irs(self):
scalar_ir = ir.F64(2)
vector_ir = ir.MakeArray([ir.F64(3), ir.F64(2)],
hl.tarray(hl.tfloat64))
read = ir.BlockMatrixRead(
ir.BlockMatrixNativeReader(resource('blockmatrix_example/0')))
add_two_bms = ir.BlockMatrixMap2(
read, read, 'l', 'r',
ir.ApplyBinaryPrimOp('+', ir.Ref('l'), ir.Ref('r')), "Union")
negate_bm = ir.BlockMatrixMap(
read, 'element', ir.ApplyUnaryPrimOp('-', ir.Ref('element')),
False)
sqrt_bm = ir.BlockMatrixMap(
read, 'element',
hl.sqrt(construct_expr(ir.Ref('element'), hl.tfloat64))._ir, False)
persisted = ir.BlockMatrixRead(ir.BlockMatrixPersistReader('x', read))
scalar_to_bm = ir.ValueToBlockMatrix(scalar_ir, [1, 1], 1)
col_vector_to_bm = ir.ValueToBlockMatrix(vector_ir, [2, 1], 1)
row_vector_to_bm = ir.ValueToBlockMatrix(vector_ir, [1, 2], 1)
broadcast_scalar = ir.BlockMatrixBroadcast(scalar_to_bm, [], [2, 2],
256)
broadcast_col = ir.BlockMatrixBroadcast(col_vector_to_bm, [0], [2, 2],
256)
broadcast_row = ir.BlockMatrixBroadcast(row_vector_to_bm, [1], [2, 2],
256)
transpose = ir.BlockMatrixBroadcast(broadcast_scalar, [1, 0], [2, 2],
256)
matmul = ir.BlockMatrixDot(broadcast_scalar, transpose)
rectangle = ir.Literal(hl.tarray(hl.tint64), [0, 1, 5, 6])
band = ir.Literal(hl.ttuple(hl.tint64, hl.tint64), (-1, 1))
intervals = ir.Literal(
hl.ttuple(hl.tarray(hl.tint64), hl.tarray(hl.tint64)),
([0, 1, 5, 6], [5, 6, 8, 9]))
sparsify1 = ir.BlockMatrixSparsify(read, rectangle,
ir.RectangleSparsifier)
sparsify2 = ir.BlockMatrixSparsify(read, band, ir.BandSparsifier(True))
sparsify3 = ir.BlockMatrixSparsify(read, intervals,
ir.RowIntervalSparsifier(True))
densify = ir.BlockMatrixDensify(read)
pow_ir = (construct_expr(ir.Ref('l'), hl.tfloat64)**construct_expr(
ir.Ref('r'), hl.tfloat64))._ir
squared_bm = ir.BlockMatrixMap2(scalar_to_bm, scalar_to_bm, 'l', 'r',
pow_ir, "NeedsDense")
slice_bm = ir.BlockMatrixSlice(
matmul, [slice(0, 2, 1), slice(0, 1, 1)])
return [
read, persisted, add_two_bms, negate_bm, sqrt_bm, scalar_to_bm,
col_vector_to_bm, row_vector_to_bm, broadcast_scalar,
broadcast_col, broadcast_row, squared_bm, transpose, sparsify1,
sparsify2, sparsify3, densify, matmul, slice_bm
]
def parse_as_ranksum(string, has_non_ref):
typ = hl.ttuple(hl.tfloat64, hl.tint32)
items = string.split(r'\|')
items = hl.cond(has_non_ref, items[:-1], items)
return items.map(lambda s: hl.cond(
(hl.len(s) == 0) | (s == '.'),
hl.null(typ),
hl.rbind(s.split(','), lambda ss: hl.cond(
hl.len(ss) != 2, # bad field, possibly 'NaN', just set it null
hl.null(hl.ttuple(hl.tfloat64, hl.tint32)),
hl.tuple([hl.float64(ss[0]), hl.int32(ss[1])])))))
def test_parses(self):
env = {'c': hl.tbool,
'a': hl.tarray(hl.tint32),
'aa': hl.tarray(hl.tarray(hl.tint32)),
'da': hl.tarray(hl.ttuple(hl.tint32, hl.tstr)),
'v': hl.tint32,
's': hl.tstruct(x=hl.tint32, y=hl.tint64, z=hl.tfloat64),
't': hl.ttuple(hl.tint32, hl.tint64, hl.tfloat64),
'call': hl.tcall,
'x': hl.tint32}
env = {name: t._parsable_string() for name, t in env.items()}
for x in self.value_irs():
Env.hail().expr.ir.IRParser.parse_value_ir(str(x), env, {})
def test_parses(self):
env = {'c': hl.tbool,
'a': hl.tarray(hl.tint32),
'aa': hl.tarray(hl.tarray(hl.tint32)),
'da': hl.tarray(hl.ttuple(hl.tint32, hl.tstr)),
'v': hl.tint32,
's': hl.tstruct(x=hl.tint32, y=hl.tint64, z=hl.tfloat64),
't': hl.ttuple(hl.tint32, hl.tint64, hl.tfloat64),
'call': hl.tcall,
'x': hl.tint32}
env = {name: t._jtype for name, t in env.items()}
for x in self.value_irs():
Env.hail().expr.ir.IRParser.parse_value_ir(str(x), env, {})
def test_parses(self):
env = {'c': hl.tbool,
'a': hl.tarray(hl.tint32),
'st': hl.tstream(hl.tint32),
'aa': hl.tarray(hl.tarray(hl.tint32)),
'sta': hl.tstream(hl.tarray(hl.tint32)),
'da': hl.tarray(hl.ttuple(hl.tint32, hl.tstr)),
'nd': hl.tndarray(hl.tfloat64, 1),
'v': hl.tint32,
's': hl.tstruct(x=hl.tint32, y=hl.tint64, z=hl.tfloat64),
't': hl.ttuple(hl.tint32, hl.tint64, hl.tfloat64),
'call': hl.tcall,
'x': hl.tint32}
for x in self.value_irs():
Env.spark_backend('ValueIRTests.test_parses')._parse_value_ir(str(x), env)
def qr(nd, mode="reduced"):
"""Performs a QR decomposition.
:param nd: A 2 dimensional ndarray, shape(M, N)
:param mode: One of "reduced", "complete", "r", or "raw".
If K = min(M, N), then:
- `reduced`: returns q and r with dimensions (M, K), (K, N)
- `complete`: returns q and r with dimensions (M, M), (M, N)
- `r`: returns only r with dimensions (K, N)
- `raw`: returns h, tau with dimensions (N, M), (K,)
Returns
-------
- q: ndarray of float64
A matrix with orthonormal columns.
- r: ndarray of float64
The upper-triangular matrix R.
- (h, tau): ndarrays of float64
The array h contains the Householder reflectors that generate q along with r. The tau array contains scaling factors for the reflectors
"""
assert nd.ndim == 2, "QR decomposition requires 2 dimensional ndarray"
if mode not in ["reduced", "r", "raw", "complete"]:
raise ValueError(f"Unrecognized mode '{mode}' for QR decomposition")
float_nd = nd.map(lambda x: hl.float64(x))
ir = NDArrayQR(float_nd._ir, mode)
if mode == "raw":
return construct_expr(
ir,
hl.ttuple(hl.tndarray(hl.tfloat64, 2), hl.tndarray(hl.tfloat64,
1)))
elif mode == "r":
return construct_expr(ir, hl.tndarray(hl.tfloat64, 2))
elif mode in ["complete", "reduced"]:
return construct_expr(
ir,
hl.ttuple(hl.tndarray(hl.tfloat64, 2), hl.tndarray(hl.tfloat64,
2)))
def test_ndarray_mixed():
assert hl.eval(
hl.null(hl.tndarray(hl.tint64, 2)).map(lambda x: x * x).reshape(
(4, 5)).T) is None
assert hl.eval((hl.nd.zeros((5, 10)).map(lambda x: x - 2) + hl.nd.ones(
(5, 10)).map(lambda x: x + 5)).reshape(
hl.null(hl.ttuple(hl.tint64, hl.tint64))).T.reshape(
(10, 5))) is None
assert hl.eval(
hl.or_missing(
False,
hl.nd.array(np.arange(10)).reshape(
(5, 2)).map(lambda x: x * 2)).map(lambda y: y * 2)) is None
def create_all_values():
return hl.struct(
f32=hl.float32(3.14),
i64=hl.int64(-9),
m=hl.null(hl.tfloat64),
astruct=hl.struct(a=hl.null(hl.tint32), b=5.5),
mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)),
aset=hl.set(['foo', 'bar', 'baz']),
mset=hl.null(hl.tset(hl.tfloat64)),
d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}),
md=hl.null(hl.tdict(hl.tint32, hl.tstr)),
h38=hl.locus('chr22', 33878978, 'GRCh38'),
ml=hl.null(hl.tlocus('GRCh37')),
i=hl.interval(
hl.locus('1', 999),
hl.locus('1', 1001)),
c=hl.call(0, 1),
mc=hl.null(hl.tcall),
t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]),
mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool))
)
def create_all_values():
return hl.struct(
f32=hl.float32(3.14),
i64=hl.int64(-9),
m=hl.null(hl.tfloat64),
astruct=hl.struct(a=hl.null(hl.tint32), b=5.5),
mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)),
aset=hl.set(['foo', 'bar', 'baz']),
mset=hl.null(hl.tset(hl.tfloat64)),
d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}),
md=hl.null(hl.tdict(hl.tint32, hl.tstr)),
h38=hl.locus('chr22', 33878978, 'GRCh38'),
ml=hl.null(hl.tlocus('GRCh37')),
i=hl.interval(
hl.locus('1', 999),
hl.locus('1', 1001)),
c=hl.call(0, 1),
mc=hl.null(hl.tcall),
t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]),
mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool))
)
def create_all_values_datasets():
all_values = hl.struct(
f32=hl.float32(3.14),
i64=hl.int64(-9),
m=hl.null(hl.tfloat64),
astruct=hl.struct(a=hl.null(hl.tint32), b=5.5),
mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)),
aset=hl.set(['foo', 'bar', 'baz']),
mset=hl.null(hl.tset(hl.tfloat64)),
d=hl.dict({hl.array(['a', 'b']): 0.5, hl.array(['x', hl.null(hl.tstr), 'z']): 0.3}),
md=hl.null(hl.tdict(hl.tint32, hl.tstr)),
h38=hl.locus('chr22', 33878978, 'GRCh38'),
ml=hl.null(hl.tlocus('GRCh37')),
i=hl.interval(
hl.locus('1', 999),
hl.locus('1', 1001)),
c=hl.call(0, 1),
mc=hl.null(hl.tcall),
t=hl.tuple([hl.call(1, 2, phased=True), 'foo', hl.null(hl.tstr)]),
mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool))
)
def prefix(s, p):
return hl.struct(**{p + k: s[k] for k in s})
all_values_table = (hl.utils.range_table(5, n_partitions=3)
.annotate_globals(**prefix(all_values, 'global_'))
.annotate(**all_values)
.cache())
all_values_matrix_table = (hl.utils.range_matrix_table(3, 2, n_partitions=2)
.annotate_globals(**prefix(all_values, 'global_'))
.annotate_rows(**prefix(all_values, 'row_'))
.annotate_cols(**prefix(all_values, 'col_'))
.annotate_entries(**prefix(all_values, 'entry_'))
.cache())
return all_values_table, all_values_matrix_table
def create_all_values_datasets():
all_values = hl.struct(
f32=hl.float32(3.14),
i64=hl.int64(-9),
m=hl.null(hl.tfloat64),
astruct=hl.struct(a=hl.null(hl.tint32), b=5.5),
mstruct=hl.null(hl.tstruct(x=hl.tint32, y=hl.tstr)),
aset=hl.set(['foo', 'bar', 'baz']),
mset=hl.null(hl.tset(hl.tfloat64)),
d=hl.dict({
hl.array(['a', 'b']): 0.5,
hl.array(['x', hl.null(hl.tstr), 'z']): 0.3
}),
md=hl.null(hl.tdict(hl.tint32, hl.tstr)),
h38=hl.locus('chr22', 33878978, 'GRCh38'),
ml=hl.null(hl.tlocus('GRCh37')),
i=hl.interval(hl.locus('1', 999), hl.locus('1', 1001)),
c=hl.call(0, 1),
mc=hl.null(hl.tcall),
t=hl.tuple([hl.call(1, 2, phased=True), 'foo',
hl.null(hl.tstr)]),
mt=hl.null(hl.ttuple(hl.tlocus('GRCh37'), hl.tbool)))
def prefix(s, p):
return hl.struct(**{p + k: s[k] for k in s})
all_values_table = (hl.utils.range_table(
5, n_partitions=3).annotate_globals(
**prefix(all_values, 'global_')).annotate(**all_values).cache())
all_values_matrix_table = (hl.utils.range_matrix_table(
3, 2, n_partitions=2).annotate_globals(
**prefix(all_values, 'global_')).annotate_rows(
**prefix(all_values, 'row_')).annotate_cols(
**prefix(all_values, 'col_')).annotate_entries(
**prefix(all_values, 'entry_')).cache())
return all_values_table, all_values_matrix_table
def _impute_type(x, partial_type):
from hail.genetics import Locus, Call
from hail.utils import Interval, Struct
def refine(t, refined):
if t is None:
return refined
if not isinstance(t, type(refined)):
raise ExpressionException(
"Incompatible partial_type, {}, for value {}".format(
partial_type, x))
return t
if isinstance(x, Expression):
return x.dtype
elif isinstance(x, bool):
return tbool
elif isinstance(x, int):
if hl.tint32.min_value <= x <= hl.tint32.max_value:
return tint32
elif hl.tint64.min_value <= x <= hl.tint64.max_value:
return tint64
else:
raise ValueError(
"Hail has no integer data type large enough to store {}".
format(x))
elif isinstance(x, float):
return tfloat64
elif isinstance(x, str):
return tstr
elif isinstance(x, Locus):
return tlocus(x.reference_genome)
elif isinstance(x, Interval):
return tinterval(x.point_type)
elif isinstance(x, Call):
return tcall
elif isinstance(x, Struct) or isinstance(x, dict) and isinstance(
partial_type, tstruct):
partial_type = refine(partial_type, hl.tstruct())
t = tstruct(**{k: _impute_type(x[k], partial_type.get(k)) for k in x})
return t
elif isinstance(x, tuple):
partial_type = refine(partial_type, hl.ttuple())
return ttuple(*[
_impute_type(
element,
partial_type[index] if index < len(partial_type) else None)
for index, element in enumerate(x)
])
elif isinstance(x, list):
partial_type = refine(partial_type, hl.tarray(None))
if len(x) == 0:
return partial_type
ts = {
_impute_type(element, partial_type.element_type)
for element in x
}
unified_type = super_unify_types(*ts)
if unified_type is None:
raise ExpressionException(
"Hail does not support heterogeneous arrays: "
"found list with elements of types {} ".format(list(ts)))
return tarray(unified_type)
elif is_setlike(x):
partial_type = refine(partial_type, hl.tset(None))
if len(x) == 0:
return partial_type
ts = {
_impute_type(element, partial_type.element_type)
for element in x
}
unified_type = super_unify_types(*ts)
if not unified_type:
raise ExpressionException(
"Hail does not support heterogeneous sets: "
"found set with elements of types {} ".format(list(ts)))
return tset(unified_type)
elif isinstance(x, Mapping):
user_partial_type = partial_type
partial_type = refine(partial_type, hl.tdict(None, None))
if len(x) == 0:
return partial_type
kts = {
_impute_type(element, partial_type.key_type)
for element in x.keys()
}
vts = {
_impute_type(element, partial_type.value_type)
for element in x.values()
}
unified_key_type = super_unify_types(*kts)
unified_value_type = super_unify_types(*vts)
if not unified_key_type:
raise ExpressionException(
"Hail does not support heterogeneous dicts: "
"found dict with keys {} of types {} ".format(
list(x.keys()), list(kts)))
if not unified_value_type:
if unified_key_type == hl.tstr and user_partial_type is None:
return tstruct(**{k: _impute_type(x[k], None) for k in x})
raise ExpressionException(
"Hail does not support heterogeneous dicts: "
"found dict with values of types {} ".format(list(vts)))
return tdict(unified_key_type, unified_value_type)
elif isinstance(x, np.generic):
return from_numpy(x.dtype)
elif isinstance(x, np.ndarray):
element_type = from_numpy(x.dtype)
return tndarray(element_type, x.ndim)
elif x is None or pd.isna(x):
return partial_type
elif isinstance(
x, (hl.expr.builders.CaseBuilder, hl.expr.builders.SwitchBuilder)):
raise ExpressionException(
"'switch' and 'case' expressions must end with a call to either"
"'default' or 'or_missing'")
else:
raise ExpressionException(
"Hail cannot automatically impute type of {}: {}".format(
type(x), x))
def test_ndarray_reshape():
np_single = np.array([8])
single = hl.nd.array([8])
np_zero_dim = np.array(4)
zero_dim = hl.nd.array(4)
np_a = np.array([1, 2, 3, 4, 5, 6])
a = hl.nd.array(np_a)
np_cube = np.array([0, 1, 2, 3, 4, 5, 6, 7]).reshape((2, 2, 2))
cube = hl.nd.array([0, 1, 2, 3, 4, 5, 6, 7]).reshape((2, 2, 2))
cube_to_rect = cube.reshape((2, 4))
np_cube_to_rect = np_cube.reshape((2, 4))
cube_t_to_rect = cube.transpose((1, 0, 2)).reshape((2, 4))
np_cube_t_to_rect = np_cube.transpose((1, 0, 2)).reshape((2, 4))
np_hypercube = np.arange(3 * 5 * 7 * 9).reshape((3, 5, 7, 9))
hypercube = hl.nd.array(np_hypercube)
np_shape_zero = np.array([])
shape_zero = hl.nd.array(np_shape_zero)
assert_ndarrays_eq((single.reshape(()), np_single.reshape(
())), (zero_dim.reshape(()), np_zero_dim.reshape(
())), (zero_dim.reshape((1, )), np_zero_dim.reshape(
(1, ))), (a.reshape((6, )), np_a.reshape((6, ))), (a.reshape(
(2, 3)), np_a.reshape((2, 3))), (a.reshape(
(3, 2)), np_a.reshape((3, 2))), (a.reshape(
(3, -1)), np_a.reshape((3, -1))), (a.reshape(
(-1, 2)), np_a.reshape(
(-1, 2))), (cube_to_rect, np_cube_to_rect),
(cube_t_to_rect, np_cube_t_to_rect), (hypercube.reshape(
(5, 7, 9, 3)).reshape(
(7, 9, 3, 5)), np_hypercube.reshape(
(7, 9, 3, 5))),
(hypercube.reshape(hl.tuple(
[5, 7, 9, 3])), np_hypercube.reshape(
(5, 7, 9, 3))), (shape_zero.reshape(
(0, 5)), np_shape_zero.reshape((0, 5))),
(shape_zero.reshape(
(-1, 5)), np_shape_zero.reshape((-1, 5))))
assert hl.eval(hl.null(hl.tndarray(hl.tfloat, 2)).reshape((4, 5))) is None
assert hl.eval(
hl.nd.array(hl.range(20)).reshape(
hl.null(hl.ttuple(hl.tint64, hl.tint64)))) is None
with pytest.raises(FatalError) as exc:
hl.eval(hl.literal(np_cube).reshape((-1, -1)))
assert "more than one -1" in str(exc)
with pytest.raises(FatalError) as exc:
hl.eval(hl.literal(np_cube).reshape((20, )))
assert "requested shape is incompatible with number of elements" in str(
exc)
with pytest.raises(FatalError) as exc:
hl.eval(a.reshape((3, )))
assert "requested shape is incompatible with number of elements" in str(
exc)
with pytest.raises(FatalError) as exc:
hl.eval(a.reshape(()))
assert "requested shape is incompatible with number of elements" in str(
exc)
with pytest.raises(FatalError) as exc:
hl.eval(hl.literal(np_cube).reshape((0, 2, 2)))
assert "requested shape is incompatible with number of elements" in str(
exc)
with pytest.raises(FatalError) as exc:
hl.eval(hl.literal(np_cube).reshape((2, 2, -2)))
assert "must contain only nonnegative numbers or -1" in str(exc)
with pytest.raises(FatalError) as exc:
hl.eval(shape_zero.reshape((0, -1)))
assert "Can't reshape" in str(exc)
def value_irs(self):
b = ir.TrueIR()
c = ir.Ref('c', hl.tbool)
i = ir.I32(5)
j = ir.I32(7)
st = ir.Str('Hail')
a = ir.Ref('a', hl.tarray(hl.tint32))
aa = ir.Ref('aa', hl.tarray(hl.tarray(hl.tint32)))
da = ir.Ref('da', hl.tarray(hl.ttuple(hl.tint32, hl.tstr)))
v = ir.Ref('v', hl.tint32)
s = ir.Ref('s', hl.tstruct(x=hl.tint32, y=hl.tint64, z=hl.tfloat64))
t = ir.Ref('t', hl.ttuple(hl.tint32, hl.tint64, hl.tfloat64))
call = ir.Ref('call', hl.tcall)
collect_sig = ir.AggSignature('Collect', [], None, [hl.tint32])
call_stats_sig = ir.AggSignature('CallStats', [], [hl.tint32],
[hl.tcall])
hist_sig = ir.AggSignature('Histogram',
[hl.tfloat64, hl.tfloat64, hl.tint32], None,
[hl.tfloat64])
take_by_sig = ir.AggSignature('TakeBy', [hl.tint32], None,
[hl.tfloat64, hl.tfloat64])
value_irs = [
i,
ir.I64(5),
ir.F32(3.14),
ir.F64(3.14),
s,
ir.TrueIR(),
ir.FalseIR(),
ir.Void(),
ir.Cast(i, hl.tfloat64),
ir.NA(hl.tint32),
ir.IsNA(i),
ir.If(b, i, j),
ir.Let('v', i, v),
ir.Ref('x', hl.tint32),
ir.ApplyBinaryOp('+', i, j),
ir.ApplyUnaryOp('-', i),
ir.ApplyComparisonOp('EQ', i, j),
ir.MakeArray([i, ir.NA(hl.tint32), ir.I32(-3)],
hl.tarray(hl.tint32)),
ir.ArrayRef(a, i),
ir.ArrayLen(a),
ir.ArrayRange(ir.I32(0), ir.I32(5), ir.I32(1)),
ir.ArraySort(a, b, False),
ir.ToSet(a),
ir.ToDict(da),
ir.ToArray(a),
ir.LowerBoundOnOrderedCollection(a, i, True),
ir.GroupByKey(da),
ir.ArrayMap(a, 'v', v),
ir.ArrayFilter(a, 'v', v),
ir.ArrayFlatMap(aa, 'v', v),
ir.ArrayFold(a, ir.I32(0), 'x', 'v', v),
ir.ArrayScan(a, ir.I32(0), 'x', 'v', v),
ir.ArrayFor(a, 'v', ir.Void()),
ir.ApplyAggOp(ir.I32(0), [], None, collect_sig),
ir.ApplyScanOp(ir.I32(0), [], None, collect_sig),
ir.ApplyAggOp(ir.F64(-2.11),
[ir.F64(-5.0),
ir.F64(5.0), ir.I32(100)], None, hist_sig),
ir.ApplyAggOp(call, [], [ir.I32(2)], call_stats_sig),
ir.ApplyAggOp(ir.F64(-2.11), [ir.I32(10)], None, take_by_sig),
ir.InitOp(ir.I32(0), [ir.I32(2)], call_stats_sig),
ir.SeqOp(ir.I32(0), [i], collect_sig),
ir.SeqOp(ir.I32(0), [ir.F64(-2.11), ir.I32(17)], take_by_sig),
ir.Begin([ir.Void()]),
ir.MakeStruct([('x', i)]),
ir.SelectFields(s, ['x', 'z']),
ir.InsertFields(s, [('x', i)]),
ir.GetField(s, 'x'),
ir.MakeTuple([i, b]),
ir.GetTupleElement(t, 1),
ir.StringSlice(st, ir.I32(1), ir.I32(2)),
ir.StringLength(st),
ir.In(2, hl.tfloat64),
ir.Die('mumblefoo', hl.tfloat64),
ir.Apply('&&', b, c),
ir.Apply('toFloat64', i),
ir.Apply('isDefined', s),
ir.Uniroot('x', ir.F64(3.14), ir.F64(-5.0), ir.F64(5.0)),
ir.Literal(hl.tarray(hl.tint32), [1, 2, None]),
]
return value_irs