1. Towards a Boost Free PyTango
Daresbury Laboratory
Towards a Boost Free PyTango
G.R. Mant (STFC, Daresbury, Warrington, Cheshire, United Kingdom)
T.M. Coutinho, A. Götz, V. Michel (ESRF, Grenoble, France)

2. C++/Python bindings What are the choices SWIG - old style
Daresbury Laboratory
C++/Python bindings
What are the choices
SWIG - old style
Boost.Python - as we already know
Cython - very neat syntax close to Python
SIP - used in the LIMA project
pybind11 - Syntax similar to Boost.Python,
compact implementation thanks to C++11.
Why did we choose pybind11 to replace Boost?

3. Daresbury Laboratory
Boost: Why change
Boost is an enormously large and complex suite of utility libraries that works with almost every C++ compiler in existence.
It uses arcane template tricks and workarounds.
Now that C++11-compatible compilers are widely available it’s not necessary.
< 200 commits in the last 5 years

4. Daresbury Laboratory
The future is pybind11
pybind11’s syntax and initial API design was heavily influenced by Boost.Python
pybind11 is a lightweight header-only with no dependencies and doesn’t require specific build tools
Heavily optimized for binary size; fast compile time
Support for C++11, C++14 and C++17 language features
Support for NumPy without having to include NumPy headers
Support for embedding Python interpreter

5. More on pybind11 Useful features for PyTango that come as standard:
Daresbury Laboratory
More on pybind11
Useful features for PyTango that come as standard:
STL data types,
overloaded functions,
enumerations,
callbacks,
multiple inheritance,
smart pointers,
capturing lambdas.
Still actively developed at:

6. Daresbury Laboratory
PyTango with pybind11
In a conda environment with g++ version and python
PyTango codebase compiles in 8min.
_tango.so size with boost: 106M
pybind11: 97M
branch: pybind11 at

7. Progress so far Client Server - attributes - scaler, array, images
Daresbury Laboratory
Progress so far
Client
- attributes
- scaler, array, images
- pipes (reading)
- commands
- sync
- async no callback
- events
Server

8. We are trying to keep the same API However, should we deprecate?
Daresbury Laboratory
We are trying to keep the same API
However, should we deprecate?
ApiUtil methods is_notifd_event_consumer_created & is_zmq_event_consumer_created
ExtractAs: does anyone use anything other than numpy
Async command with callback

9. DeviceProxy method overloading
Daresbury Laboratory
DeviceProxy method overloading
.def("_get_property", [](Tango::DeviceProxy& self, string& prop_name) -> Tango::DbData {
Tango::DbData dbData;
self.get_property(prop_name, dbData);
return dbData; })
.def("_get_property", [](Tango::DeviceProxy& self, std::vector<std::string>& prop_names) -> Tango::DbData
self.get_property(prop_names, dbData);

10. DeviceProxy methods implemented, C++11 style, as lambda functions
Daresbury Laboratory
DeviceProxy methods implemented, C++11 style, as lambda functions
.def("_read_attribute", [](Tango::DeviceProxy& self, std::string& attr_name,
PyTango::ExtractAs extract_as=PyTango::ExtractAsNumpy) -> py::object {
// the method body here
}, py::arg("attr_name"), py::arg("extract_as")=PyTango::ExtractAsNumpy)
.def("_read_attributes", [](Tango::DeviceProxy& self, std::vector<std::string> &attr_names, PyTango::ExtractAs extract_as=PyTango::ExtractAsNumpy) -> py::list {
}, py::arg("attr_names"), py::arg("extract_as")=PyTango::ExtractAsNumpy)

11. dp = DeviceProxy('sys/tg_test/1') db_datum = DbDatum('test')
Daresbury Laboratory
dp = DeviceProxy('sys/tg_test/1')
db_datum = DbDatum('test')
db_datum.value_string = ['3.142']
dp.put_property(db_datum)
props = dp.get_property('test')
assert props.keys() == ["test"]
assert props.values() == [['3.142']]
db_datum = DbDatum('timeout')
db_datum.value_string = ['0.05']
props = dp.get_property(['test', 'timeout'])
assert props.keys() == ['test', 'timeout']
assert props.values() == [['3.142'], ['0.05']]

12. Reading/Writing attributes
Daresbury Laboratory
Reading/Writing attributes
dp['long_scalar_w','double_spectrum', 'string_scalar', 'long_spectrum'] = 1234, [3.142, 6.284, 9.426], 'ESRF', np.array([1,2,3,4,5])
attr = dp['long_scalar_w','double_spectrum', 'string_scalar', 'long_spectrum']
assert attr[0].value == 1234
spectre = np.array([3.142, 6.284, 9.426])
assert set(attr[1].value) == set(spectre)
assert attr[2].value == 'ESRF'
spectre = np.array((1,2,3,4,5))
assert set(attr[3].value) == set(spectre)

13. Vincent Michel for his invaluable support and encouragement
Daresbury Laboratory
Thanks to:
Vincent Michel for his invaluable support and encouragement
Andy Gotz for giving me the opportunity to do this work and the Tango Consortium for financial support