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// Copyright 2023 The TensorStore Authors
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <pybind11/pybind11.h>
// Other headers must be included after pybind11 to ensure header-order
// inclusion constraints are satisfied.
// Other headers
#include <stddef.h>
#include <string>
#include <utility>
#include <variant>
#include <vector>
#include "python/tensorstore/dim_expression.h"
#include "python/tensorstore/index.h" // IWYU pragma: keep
#include "python/tensorstore/keyword_arguments.h"
#include "python/tensorstore/result_type_caster.h"
#include "python/tensorstore/sequence_parameter.h"
#include "python/tensorstore/spec.h"
#include "python/tensorstore/tensorstore_class.h"
#include "python/tensorstore/tensorstore_module_components.h"
#include "tensorstore/index.h"
#include "tensorstore/internal/global_initializer.h"
#include "tensorstore/spec.h"
#include "tensorstore/stack.h"
#include "tensorstore/tensorstore.h"
#include "tensorstore/util/executor.h"
namespace tensorstore {
namespace internal_python {
namespace {
namespace py = ::pybind11;
constexpr auto ForwardStackSetters = [](auto callback, auto... other_param) {
callback(other_param..., open_setters::SetRead{}, open_setters::SetWrite{},
open_setters::SetContext{}, open_setters::SetTransaction{},
schema_setters::SetRank{}, schema_setters::SetDtype{},
schema_setters::SetDomain{}, schema_setters::SetShape{},
schema_setters::SetDimensionUnits{}, schema_setters::SetSchema{});
};
void RegisterStackBindings(pybind11::module m, Executor defer) {
defer([m]() mutable {
ForwardStackSetters([&](auto... param_def) {
{
std::string doc = R"(
Virtually overlays a sequence of :py:obj:`TensorStore` layers within a common domain.
>>> store = ts.overlay([
... ts.array([1, 2, 3, 4], dtype=ts.uint32),
... ts.array([5, 6, 7, 8], dtype=ts.uint32).translate_to[3]
... ])
>>> store
TensorStore({
'context': {'data_copy_concurrency': {}},
'driver': 'stack',
'dtype': 'uint32',
'layers': [
{
'array': [1, 2, 3, 4],
'driver': 'array',
'dtype': 'uint32',
'transform': {'input_exclusive_max': [4], 'input_inclusive_min': [0]},
},
{
'array': [5, 6, 7, 8],
'driver': 'array',
'dtype': 'uint32',
'transform': {
'input_exclusive_max': [7],
'input_inclusive_min': [3],
'output': [{'input_dimension': 0, 'offset': -3}],
},
},
],
'schema': {'domain': {'exclusive_max': [7], 'inclusive_min': [0]}},
'transform': {'input_exclusive_max': [7], 'input_inclusive_min': [0]},
})
>>> await store.read()
array([1, 2, 3, 5, 6, 7, 8], dtype=uint32)
Args:
layers: Sequence of layers to overlay. Later layers take precedence. If a
layer is specified as a :py:obj:`Spec` rather than a :py:obj:`TensorStore`,
it must have a known :py:obj:`~Spec.domain` and will be opened on-demand as
neneded for individual read and write operations.
)";
AppendKeywordArgumentDocs(doc, param_def...);
doc += R"(
See also:
- :ref:`driver/stack`
- :py:obj:`tensorstore.stack`
- :py:obj:`tensorstore.concat`
Group:
Views
)";
m.def(
"overlay",
[](SequenceParameter<
std::variant<PythonTensorStoreObject*, PythonSpecObject*>>
python_layers,
KeywordArgument<decltype(param_def)>... kwarg) -> TensorStore<> {
StackOpenOptions options;
ApplyKeywordArguments<decltype(param_def)...>(options, kwarg...);
std::vector<std::variant<TensorStore<>, Spec>> layers(
python_layers.size());
for (size_t i = 0; i < layers.size(); ++i) {
std::visit([&](auto* obj) { layers[i] = obj->value; },
python_layers[i]);
}
return ValueOrThrow(
tensorstore::Overlay(layers, std::move(options)));
},
doc.c_str(), py::arg("layers"), py::kw_only(),
MakeKeywordArgumentPyArg(param_def)...);
}
{
std::string doc = R"(
Virtually stacks a sequence of :py:obj:`TensorStore` layers along a new dimension.
>>> store = ts.stack([
... ts.array([1, 2, 3, 4], dtype=ts.uint32),
... ts.array([5, 6, 7, 8], dtype=ts.uint32)
... ])
>>> store
TensorStore({
'context': {'data_copy_concurrency': {}},
'driver': 'stack',
'dtype': 'uint32',
'layers': [
{
'array': [1, 2, 3, 4],
'driver': 'array',
'dtype': 'uint32',
'transform': {
'input_exclusive_max': [1, 4],
'input_inclusive_min': [0, 0],
'output': [{'input_dimension': 1}],
},
},
{
'array': [5, 6, 7, 8],
'driver': 'array',
'dtype': 'uint32',
'transform': {
'input_exclusive_max': [2, 4],
'input_inclusive_min': [1, 0],
'output': [{'input_dimension': 1}],
},
},
],
'schema': {'domain': {'exclusive_max': [2, 4], 'inclusive_min': [0, 0]}},
'transform': {'input_exclusive_max': [2, 4], 'input_inclusive_min': [0, 0]},
})
>>> await store.read()
array([[1, 2, 3, 4],
[5, 6, 7, 8]], dtype=uint32)
>>> store = ts.stack([
... ts.array([1, 2, 3, 4], dtype=ts.uint32),
... ts.array([5, 6, 7, 8], dtype=ts.uint32)
... ],
... axis=-1)
>>> store
TensorStore({
'context': {'data_copy_concurrency': {}},
'driver': 'stack',
'dtype': 'uint32',
'layers': [
{
'array': [1, 2, 3, 4],
'driver': 'array',
'dtype': 'uint32',
'transform': {
'input_exclusive_max': [4, 1],
'input_inclusive_min': [0, 0],
'output': [{'input_dimension': 0}],
},
},
{
'array': [5, 6, 7, 8],
'driver': 'array',
'dtype': 'uint32',
'transform': {
'input_exclusive_max': [4, 2],
'input_inclusive_min': [0, 1],
'output': [{'input_dimension': 0}],
},
},
],
'schema': {'domain': {'exclusive_max': [4, 2], 'inclusive_min': [0, 0]}},
'transform': {'input_exclusive_max': [4, 2], 'input_inclusive_min': [0, 0]},
})
>>> await store.read()
array([[1, 5],
[2, 6],
[3, 7],
[4, 8]], dtype=uint32)
Args:
layers: Sequence of layers to stack. If a layer is specified as a
:py:obj:`Spec` rather than a :py:obj:`TensorStore`, it must have a known
:py:obj:`~Spec.domain` and will be opened on-demand as needed for individual
read and write operations.
axis: New dimension along which to stack. A negative number counts from the end.
)";
AppendKeywordArgumentDocs(doc, param_def...);
doc += R"(
See also:
- :py:obj:`numpy.stack`
- :ref:`driver/stack`
- :py:obj:`tensorstore.overlay`
- :py:obj:`tensorstore.concat`
Group:
Views
)";
m.def(
"stack",
[](SequenceParameter<
std::variant<PythonTensorStoreObject*, PythonSpecObject*>>
python_layers,
DimensionIndex stack_dimension,
KeywordArgument<decltype(param_def)>... kwarg) -> TensorStore<> {
StackOpenOptions options;
ApplyKeywordArguments<decltype(param_def)...>(options, kwarg...);
std::vector<std::variant<TensorStore<>, Spec>> layers(
python_layers.size());
for (size_t i = 0; i < layers.size(); ++i) {
std::visit([&](auto* obj) { layers[i] = obj->value; },
python_layers[i]);
}
return ValueOrThrow(tensorstore::Stack(layers, stack_dimension,
std::move(options)));
},
doc.c_str(), py::arg("layers"), py::arg("axis") = 0, py::kw_only(),
MakeKeywordArgumentPyArg(param_def)...);
}
{
std::string doc = R"(
Virtually concatenates a sequence of :py:obj:`TensorStore` layers along an existing dimension.
>>> store = ts.concat([
... ts.array([1, 2, 3, 4], dtype=ts.uint32),
... ts.array([5, 6, 7, 8], dtype=ts.uint32)
... ],
... axis=0)
>>> store
TensorStore({
'context': {'data_copy_concurrency': {}},
'driver': 'stack',
'dtype': 'uint32',
'layers': [
{
'array': [1, 2, 3, 4],
'driver': 'array',
'dtype': 'uint32',
'transform': {'input_exclusive_max': [4], 'input_inclusive_min': [0]},
},
{
'array': [5, 6, 7, 8],
'driver': 'array',
'dtype': 'uint32',
'transform': {
'input_exclusive_max': [8],
'input_inclusive_min': [4],
'output': [{'input_dimension': 0, 'offset': -4}],
},
},
],
'schema': {'domain': {'exclusive_max': [8], 'inclusive_min': [0]}},
'transform': {'input_exclusive_max': [8], 'input_inclusive_min': [0]},
})
>>> await store.read()
array([1, 2, 3, 4, 5, 6, 7, 8], dtype=uint32)
>>> store = ts.concat([
... ts.array([[1, 2, 3], [4, 5, 6]], dtype=ts.uint32),
... ts.array([[7, 8, 9], [10, 11, 12]], dtype=ts.uint32)
... ],
... axis=0)
>>> store
TensorStore({
'context': {'data_copy_concurrency': {}},
'driver': 'stack',
'dtype': 'uint32',
'layers': [
{
'array': [[1, 2, 3], [4, 5, 6]],
'driver': 'array',
'dtype': 'uint32',
'transform': {
'input_exclusive_max': [2, 3],
'input_inclusive_min': [0, 0],
},
},
{
'array': [[7, 8, 9], [10, 11, 12]],
'driver': 'array',
'dtype': 'uint32',
'transform': {
'input_exclusive_max': [4, 3],
'input_inclusive_min': [2, 0],
'output': [
{'input_dimension': 0, 'offset': -2},
{'input_dimension': 1},
],
},
},
],
'schema': {'domain': {'exclusive_max': [4, 3], 'inclusive_min': [0, 0]}},
'transform': {'input_exclusive_max': [4, 3], 'input_inclusive_min': [0, 0]},
})
>>> await store.read()
array([[ 1, 2, 3],
[ 4, 5, 6],
[ 7, 8, 9],
[10, 11, 12]], dtype=uint32)
>>> store = ts.concat([
... ts.array([[1, 2, 3], [4, 5, 6]], dtype=ts.uint32),
... ts.array([[7, 8, 9], [10, 11, 12]], dtype=ts.uint32)
... ],
... axis=-1)
>>> store
TensorStore({
'context': {'data_copy_concurrency': {}},
'driver': 'stack',
'dtype': 'uint32',
'layers': [
{
'array': [[1, 2, 3], [4, 5, 6]],
'driver': 'array',
'dtype': 'uint32',
'transform': {
'input_exclusive_max': [2, 3],
'input_inclusive_min': [0, 0],
},
},
{
'array': [[7, 8, 9], [10, 11, 12]],
'driver': 'array',
'dtype': 'uint32',
'transform': {
'input_exclusive_max': [2, 6],
'input_inclusive_min': [0, 3],
'output': [
{'input_dimension': 0},
{'input_dimension': 1, 'offset': -3},
],
},
},
],
'schema': {'domain': {'exclusive_max': [2, 6], 'inclusive_min': [0, 0]}},
'transform': {'input_exclusive_max': [2, 6], 'input_inclusive_min': [0, 0]},
})
>>> await store.read()
array([[ 1, 2, 3, 7, 8, 9],
[ 4, 5, 6, 10, 11, 12]], dtype=uint32)
>>> await ts.concat([
... ts.array([[1, 2, 3], [4, 5, 6]], dtype=ts.uint32).label["x", "y"],
... ts.array([[7, 8, 9], [10, 11, 12]], dtype=ts.uint32)
... ],
... axis="y").read()
array([[ 1, 2, 3, 7, 8, 9],
[ 4, 5, 6, 10, 11, 12]], dtype=uint32)
Args:
layers: Sequence of layers to concatenate. If a layer is specified as a
:py:obj:`Spec` rather than a :py:obj:`TensorStore`, it must have a known
:py:obj:`~Spec.domain` and will be opened on-demand as needed for individual
read and write operations.
axis: Existing dimension along which to concatenate. A negative number counts
from the end. May also be specified by a
:ref:`dimension label<dimension-labels>`.
)";
AppendKeywordArgumentDocs(doc, param_def...);
doc += R"(
See also:
- :py:obj:`numpy.concatenate`
- :ref:`driver/stack`
- :py:obj:`tensorstore.overlay`
- :py:obj:`tensorstore.stack`
Group:
Views
)";
m.def(
"concat",
[](SequenceParameter<
std::variant<PythonTensorStoreObject*, PythonSpecObject*>>
python_layers,
PythonDimensionIdentifier concat_dimension,
KeywordArgument<decltype(param_def)>... kwarg) -> TensorStore<> {
StackOpenOptions options;
ApplyKeywordArguments<decltype(param_def)...>(options, kwarg...);
std::vector<std::variant<TensorStore<>, Spec>> layers(
python_layers.size());
for (size_t i = 0; i < layers.size(); ++i) {
std::visit([&](auto* obj) { layers[i] = obj->value; },
python_layers[i]);
}
return ValueOrThrow(tensorstore::Concat(
layers,
internal_python::ToDimensionIdentifier(concat_dimension),
std::move(options)));
},
doc.c_str(), py::arg("layers"), py::arg("axis"), py::kw_only(),
MakeKeywordArgumentPyArg(param_def)...);
}
});
});
}
TENSORSTORE_GLOBAL_INITIALIZER {
RegisterPythonComponent(RegisterStackBindings, /*priority=*/-360);
}
} // namespace
} // namespace internal_python
} // namespace tensorstore
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