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 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')))
negate_bm = ir.BlockMatrixMap(
read, 'element', ir.ApplyUnaryPrimOp('-', ir.Ref('element')))
sqrt_bm = ir.BlockMatrixMap(
read, 'element',
hl.sqrt(construct_expr(ir.Ref('element'), hl.tfloat64))._ir)
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)
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)
slice_bm = ir.BlockMatrixSlice(
matmul, [slice(0, 2, 1), slice(0, 1, 1)])
return [
read, 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, matmul,
slice_bm
]
def block_matrix_irs(self):
scalar_ir = ir.F64(2)
vector_ir = ir.MakeArray([ir.F64(3), ir.F64(2)], hl.tarray(hl.tfloat64))
read = ir.BlockMatrixRead(resource('blockmatrix_example/0'))
add_two_bms = BlockMatrixIRTests._make_element_wise_op_ir(read, read, '+')
scalar_to_bm = ir.ValueToBlockMatrix(scalar_ir, [], 1, [])
vector_to_bm = ir.ValueToBlockMatrix(vector_ir, [2], 1, [False])
broadcast_scalar = ir.BlockMatrixBroadcast(scalar_to_bm, "scalar", [2, 2], 256, [False, False])
broadcast_col = ir.BlockMatrixBroadcast(vector_to_bm, "col", [2, 2], 256, [False, False])
broadcast_row = ir.BlockMatrixBroadcast(vector_to_bm, "row", [2, 2], 256, [False, False])
return [
read,
add_two_bms,
scalar_to_bm,
vector_to_bm,
broadcast_scalar,
broadcast_col,
broadcast_row,
]
def value_irs(self):
b = ir.TrueIR()
c = ir.Ref('c')
i = ir.I32(5)
j = ir.I32(7)
st = ir.Str('Hail')
a = ir.Ref('a')
aa = ir.Ref('aa')
da = ir.Ref('da')
nd = ir.Ref('nd')
v = ir.Ref('v')
s = ir.Ref('s')
t = ir.Ref('t')
call = ir.Ref('call')
table = ir.TableRange(5, 3)
matrix_read = ir.MatrixRead(
ir.MatrixNativeReader(
resource('backward_compatability/1.0.0/matrix_table/0.hmt'),
None, False), False, False)
block_matrix_read = ir.BlockMatrixRead(
ir.BlockMatrixNativeReader('fake_file_path'))
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.Coalesce(i, j),
ir.Let('v', i, v),
ir.Ref('x'),
ir.ApplyBinaryPrimOp('+', i, j),
ir.ApplyUnaryPrimOp('-', 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.Str('foo')),
ir.ArrayLen(a),
ir.ArrayRange(ir.I32(0), ir.I32(5), ir.I32(1)),
ir.ArraySort(a, 'l', 'r',
ir.ApplyComparisonOp("LT", ir.Ref('l'), ir.Ref('r'))),
ir.ToSet(a),
ir.ToDict(da),
ir.ToArray(a),
ir.MakeNDArray(
ir.MakeArray([ir.F64(-1.0), ir.F64(1.0)],
hl.tarray(hl.tfloat64)),
ir.MakeTuple([ir.I64(1), ir.I64(2)]), ir.TrueIR()),
ir.NDArrayShape(nd),
ir.NDArrayReshape(nd, ir.MakeTuple([ir.I64(5)])),
ir.NDArrayRef(nd, [ir.I64(1), ir.I64(2)]),
ir.NDArrayMap(nd, 'v', v),
ir.NDArrayMatMul(nd, nd),
ir.LowerBoundOnOrderedCollection(a, i, True),
ir.GroupByKey(da),
ir.ArrayMap(a, 'v', v),
ir.ArrayZip([a, a], ['a', 'b'], ir.TrueIR(), 'ExtendNA'),
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.ArrayLeftJoinDistinct(a, a, 'l', 'r', ir.I32(0), ir.I32(1)),
ir.ArrayFor(a, 'v', ir.Void()),
ir.AggFilter(ir.TrueIR(), ir.I32(0), False),
ir.AggExplode(ir.ArrayRange(ir.I32(0), ir.I32(2), ir.I32(1)), 'x',
ir.I32(0), False),
ir.AggGroupBy(ir.TrueIR(), ir.I32(0), False),
ir.AggArrayPerElement(
ir.ArrayRange(ir.I32(0), ir.I32(2), ir.I32(1)), 'x', 'y',
ir.I32(0), False),
ir.ApplyAggOp('Collect', [], [ir.I32(0)]),
ir.ApplyScanOp('Collect', [], [ir.I32(0)]),
ir.ApplyAggOp('CallStats', [ir.I32(2)], [call]),
ir.ApplyAggOp('TakeBy', [ir.I32(10)],
[ir.F64(-2.11), ir.F64(-2.11)]),
ir.Begin([ir.Void()]),
ir.MakeStruct([('x', i)]),
ir.SelectFields(s, ['x', 'z']),
ir.InsertFields(s, [('x', i)], None),
ir.GetField(s, 'x'),
ir.MakeTuple([i, b]),
ir.GetTupleElement(t, 1),
ir.Die(ir.Str('mumblefoo'), hl.tfloat64),
ir.Apply('&&', hl.tbool, b, c),
ir.Apply('toFloat64', hl.tfloat64, i),
ir.Literal(hl.tarray(hl.tint32), [1, 2, None]),
ir.TableCount(table),
ir.TableGetGlobals(table),
ir.TableCollect(ir.TableKeyBy(table, [], False)),
ir.TableToValueApply(table, {'name': 'ForceCountTable'}),
ir.MatrixToValueApply(matrix_read,
{'name': 'ForceCountMatrixTable'}),
ir.TableAggregate(
table,
ir.MakeStruct([('foo', ir.ApplyAggOp('Collect', [],
[ir.I32(0)]))])),
ir.TableWrite(
table,
ir.TableNativeWriter(new_temp_file(), False, True,
"fake_codec_spec$$")),
ir.TableWrite(
table, ir.TableTextWriter(new_temp_file(), None, True, 0,
",")),
ir.MatrixAggregate(
matrix_read,
ir.MakeStruct([('foo', ir.ApplyAggOp('Collect', [],
[ir.I32(0)]))])),
ir.MatrixWrite(
matrix_read,
ir.MatrixNativeWriter(new_temp_file(), False, False, "", None,
None)),
ir.MatrixWrite(
matrix_read,
ir.MatrixNativeWriter(
new_temp_file(), False, False, "",
'[{"start":{"row_idx":0},"end":{"row_idx": 10},"includeStart":true,"includeEnd":false}]',
hl.dtype('array<interval<struct{row_idx:int32}>>'))),
ir.MatrixWrite(
matrix_read,
ir.MatrixVCFWriter(new_temp_file(), None, False, None)),
ir.MatrixWrite(matrix_read, ir.MatrixGENWriter(new_temp_file(),
4)),
ir.MatrixWrite(matrix_read, ir.MatrixPLINKWriter(new_temp_file())),
ir.MatrixMultiWrite([matrix_read, matrix_read],
ir.MatrixNativeMultiWriter(
new_temp_file(), False, False)),
ir.BlockMatrixWrite(
block_matrix_read,
ir.BlockMatrixNativeWriter('fake_file_path', False, False,
False)),
ir.LiftMeOut(ir.I32(1))
]
return value_irs
def _to_expr(e, dtype):
if e is None:
return None
elif isinstance(e, Expression):
if e.dtype != dtype:
assert is_numeric(dtype), 'expected {}, got {}'.format(
dtype, e.dtype)
if dtype == tfloat64:
return hl.float64(e)
elif dtype == tfloat32:
return hl.float32(e)
elif dtype == tint64:
return hl.int64(e)
else:
assert dtype == tint32
return hl.int32(e)
return e
elif not is_compound(dtype):
# these are not container types and cannot contain expressions if we got here
return e
elif isinstance(dtype, tstruct):
new_fields = []
found_expr = False
for f, t in dtype.items():
value = _to_expr(e[f], t)
found_expr = found_expr or isinstance(value, Expression)
new_fields.append(value)
if not found_expr:
return e
else:
exprs = [
new_fields[i] if isinstance(new_fields[i], Expression) else
hl.literal(new_fields[i], dtype[i])
for i in range(len(new_fields))
]
fields = {name: expr for name, expr in zip(dtype.keys(), exprs)}
from .typed_expressions import StructExpression
return StructExpression._from_fields(fields)
elif isinstance(dtype, tarray):
elements = []
found_expr = False
for element in e:
value = _to_expr(element, dtype.element_type)
found_expr = found_expr or isinstance(value, Expression)
elements.append(value)
if not found_expr:
return e
else:
assert len(elements) > 0
exprs = [
element if isinstance(element, Expression) else hl.literal(
element, dtype.element_type) for element in elements
]
indices, aggregations = unify_all(*exprs)
x = ir.MakeArray([e._ir for e in exprs], None)
return expressions.construct_expr(x, dtype, indices, aggregations)
elif isinstance(dtype, tset):
elements = []
found_expr = False
for element in e:
value = _to_expr(element, dtype.element_type)
found_expr = found_expr or isinstance(value, Expression)
elements.append(value)
if not found_expr:
return e
else:
assert len(elements) > 0
exprs = [
element if isinstance(element, Expression) else hl.literal(
element, dtype.element_type) for element in elements
]
indices, aggregations = unify_all(*exprs)
x = ir.ToSet(
ir.ToStream(ir.MakeArray([e._ir for e in exprs], None)))
return expressions.construct_expr(x, dtype, indices, aggregations)
elif isinstance(dtype, ttuple):
elements = []
found_expr = False
assert len(e) == len(dtype.types)
for i in range(len(e)):
value = _to_expr(e[i], dtype.types[i])
found_expr = found_expr or isinstance(value, Expression)
elements.append(value)
if not found_expr:
return e
else:
exprs = [
elements[i] if isinstance(elements[i], Expression) else
hl.literal(elements[i], dtype.types[i])
for i in range(len(elements))
]
indices, aggregations = unify_all(*exprs)
x = ir.MakeTuple([expr._ir for expr in exprs])
return expressions.construct_expr(x, dtype, indices, aggregations)
elif isinstance(dtype, tdict):
keys = []
values = []
found_expr = False
for k, v in e.items():
k_ = _to_expr(k, dtype.key_type)
v_ = _to_expr(v, dtype.value_type)
found_expr = found_expr or isinstance(k_, Expression)
found_expr = found_expr or isinstance(v_, Expression)
keys.append(k_)
values.append(v_)
if not found_expr:
return e
else:
assert len(keys) > 0
# Here I use `to_expr` to call `lit` the keys and values separately.
# I anticipate a common mode is statically-known keys and Expression
# values.
key_array = to_expr(keys, tarray(dtype.key_type))
value_array = to_expr(values, tarray(dtype.value_type))
return hl.dict(hl.zip(key_array, value_array))
elif isinstance(dtype, hl.tndarray):
return hl.nd.array(e)
else:
raise NotImplementedError(dtype)
def value_irs(self):
b = ir.TrueIR()
c = ir.Ref('c')
i = ir.I32(5)
j = ir.I32(7)
st = ir.Str('Hail')
a = ir.Ref('a')
aa = ir.Ref('aa')
da = ir.Ref('da')
v = ir.Ref('v')
s = ir.Ref('s')
t = ir.Ref('t')
call = ir.Ref('call')
table = ir.TableRange(5, 3)
matrix_read = ir.MatrixRead(ir.MatrixNativeReader(
resource('backward_compatability/1.0.0/matrix_table/0.hmt')), False, False)
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'),
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.AggFilter(ir.TrueIR(), ir.I32(0)),
ir.AggExplode(ir.ArrayRange(ir.I32(0), ir.I32(2), ir.I32(1)), 'x', ir.I32(0)),
ir.AggGroupBy(ir.TrueIR(), ir.I32(0)),
ir.ApplyAggOp('Collect', [], None, [ir.I32(0)]),
ir.ApplyScanOp('Collect', [], None, [ir.I32(0)]),
ir.ApplyAggOp('Histogram', [ir.F64(-5.0), ir.F64(5.0), ir.I32(100)], None, [ir.F64(-2.11)]),
ir.ApplyAggOp('CallStats', [], [ir.I32(2)], [call]),
ir.ApplyAggOp('TakeBy', [ir.I32(10)], None, [ir.F64(-2.11), ir.F64(-2.11)]),
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(ir.Str('mumblefoo'), hl.tfloat64),
ir.Apply('&&', b, c),
ir.Apply('toFloat64', i),
ir.Uniroot('x', ir.F64(3.14), ir.F64(-5.0), ir.F64(5.0)),
ir.Literal(hl.tarray(hl.tint32), [1, 2, None]),
ir.TableCount(table),
ir.TableGetGlobals(table),
ir.TableCollect(table),
ir.TableAggregate(table, ir.MakeStruct([('foo', ir.ApplyAggOp('Collect', [], None, [ir.I32(0)]))])),
ir.TableWrite(table, new_temp_file(), False, True, "fake_codec_spec$$"),
ir.MatrixAggregate(matrix_read, ir.MakeStruct([('foo', ir.ApplyAggOp('Collect', [], None, [ir.I32(0)]))])),
ir.MatrixWrite(matrix_read, ir.MatrixNativeWriter(new_temp_file(), False, False, "")),
ir.MatrixWrite(matrix_read, ir.MatrixVCFWriter(new_temp_file(), None, False, None)),
ir.MatrixWrite(matrix_read, ir.MatrixGENWriter(new_temp_file(), 4)),
ir.MatrixWrite(matrix_read, ir.MatrixPLINKWriter(new_temp_file())),
]
return value_irs
def value_irs(self):
b = ir.TrueIR()
c = ir.Ref('c')
i = ir.I32(5)
j = ir.I32(7)
st = ir.Str('Hail')
a = ir.Ref('a')
aa = ir.Ref('aa')
da = ir.Ref('da')
v = ir.Ref('v')
s = ir.Ref('s')
t = ir.Ref('t')
call = ir.Ref('call')
collect_sig = ir.AggSignature('Collect', [], None, [hl.tint32])
call_stats_sig = ir.AggSignature('CallStats', [], [hl.tint32], [hl.tcall])
call_stats_type = hl.tstruct(AC=hl.tarray(hl.tint32),
AF=hl.tarray(hl.tfloat64),
AN=hl.tint32,
homozygote_count=hl.tarray(hl.tint32))
hist_sig = ir.AggSignature(
'Histogram', [hl.tfloat64, hl.tfloat64, hl.tint32], None, [hl.tfloat64])
hist_type = hl.tstruct(bin_edges=hl.tarray(hl.tfloat64),
bin_freq=hl.tarray(hl.tint64),
n_smaller=hl.tint64,
n_larger=hl.tint64)
take_by_sig = ir.AggSignature('TakeBy', [hl.tint32], None, [hl.tfloat64, hl.tfloat64])
take_by_type = hl.tarray(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'),
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.AggFilter(ir.TrueIR(), ir.I32(0)),
ir.AggExplode(ir.ArrayRange(ir.I32(0), ir.I32(2), ir.I32(1)), 'x', ir.I32(0)),
ir.AggGroupBy(ir.TrueIR(), ir.I32(0)),
ir.ApplyAggOp([], None, [ir.I32(0)], collect_sig),
ir.ApplyScanOp([], None, [ir.I32(0)], collect_sig),
ir.ApplyAggOp([ir.F64(-5.0), ir.F64(5.0), ir.I32(100)], None, [ir.F64(-2.11)], hist_sig),
ir.ApplyAggOp([], [ir.I32(2)], [call], call_stats_sig),
ir.ApplyAggOp([ir.I32(10)], None, [ir.F64(-2.11), ir.F64(-2.11)], 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.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
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.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))
]
return value_irs