I have an issue working with DiagramBuilder and ManipulationStation classes.
It appears to me, that c++ API and the python bindings work differently in my case.
C++ API behaves as expected, while the python bindings result in the runtime error:
DiagramBuilder: Cannot operate on ports of System plant until it has been registered using AddSystem
How I use C++ API
In one of the ManipulationStation::Setup...() methods I inject a block of code, that adds an extra manipuland
const std::string sdf_path = FindResourceOrThrow("drake/examples/manipulation_station/models/bolt_n_nut.sdf");
RigidTransform<double> X_WC(RotationMatrix<double>::Identity(), Vector3d(0.0, -0.3, 0.1));
bolt_n_nut_ = internal::AddAndWeldModelFrom(sdf_path, "nut_and_bolt", lant_->world_frame(), "bolt", X_WC, plant_);
I inject another block of code into the method ManipulationStation::Finalize:
auto zero_torque = builder.template AddSystem<systems::ConstantVectorSource<double>>(Eigen::VectorXd::Zero(plant_->num_velocities(bolt_n_nut_)));
builder.Connect(zero_torque->get_output_port(), plant_->get_actuation_input_port(bolt_n_nut_));
With these changes, the simulation runs as expected.
How I use python bindings
plant = station.get_multibody_plant()
manipuland_path = get_manipuland_resource_path()
bolt_with_nut = Parser(plant=plant).AddModelFromFile(manipuland_path)
X_WC = RigidTransform(RotationMatrix.Identity(), [0.0, -0.3, 0.1])
plant.WeldFrames(plant.world_frame(), plant.GetFrameByName('bolt', bolt_with_nut), X_WC)
...
station.Finalize()
zero_torque = builder.AddSystem(ConstantValueSource(AbstractValue.Make([0.])))
builder.Connect(zero_torque.get_output_port(), plant.get_actuation_input_port(bolt_with_nut_model))
This triggers a RuntimeError with a message as above; The port, which causes this error is nut_and_bolt_actuation.
My vague understanding of the problem is the (in) visibility of nut_and_bolt System, due to having two distinct DiagramBuilders in a process: 1) a one is inside ManipulationStation 2) another is in the python code, that instantiates this ManipulationStation object.
Using ManipulationStation via python bindings is a preference for me, because that way I would've avoided depending on a custom build of drake library.
Thanks for your insight!
I agree with your assessment: you have two different DiagramBuilder objects here. This does not have anything to due with C++ or Python; the ManipulationStation is itself a Diagram (created using its own DiagramBuilder), and you have a second DiagramBuilder (in either c++ or python) that is connecting the ManipulationStation together with other elements. You are trying to connect a system that is in the external diagram to a port that is in the internal diagram, but is not exposed.
The solution would be to have the ManipulationStation diagram expose the extra nut and bolt actuation port so that you can connect to it from the second builder.
If you prefer Python, I've switched my course to using a completely python version of the manipulation station. I find this version is much easier to adapt to different student projects. (To be clear, the setup is in python, but at simulation time all of the elements are c++ and it doesn't call back to python; so the performance is almost identical.)
Related
I'm trying to write a simple machine learning application in Ada, and also trying to find a good framework to use. My knowledge of one thing is extremely minimal, and of the other is somewhat minimal.
There are several nifty machine learning frameworks out there, and I'd like to leverage one for use with an Ada program, but I guess I'm just...at a loss. Can I use an existing framework written in Python, for instance and wrap (or I guess, bind?) the API calls in Ada? Should I just pass off the scripting capabilities? I'm trying to figure it out.
Case in point: Scikit (sklearn)
https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html#
This does some neat stuff, and I'd like to be able to leverage this, but with an Ada program. Does anyone have advice from a similar experience?
I am just researching, so I have tried finding information.
http://www.inspirel.com/articles/Ada_Python_Binding.html
https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html#
The inspirel solution is based on python2.7. If you're using anything from python3.5 onwards a few mods need to be made. On Linux, changing to say python 3.7, you'd just change
--for Default_Switches ("Ada") use ("-lpython2.7");
for Default_Switches ("Ada") use ("-lpython3.7");
but on windows, the libraries aren't dumped in a community lib so gnat doesn't know where to find them. All the packages are kept separately. The -L has to be added to tell the linker where to find the library. Alternatively, you can use for lib_dir. In my case, I did a non-admin install of python, so it looks something like
for Default_Switches ("Ada") use ("-L\Users\StdUser\AppData\Local\Programs\Python\Python37-32\libs", "-lpython37");
Note that on windows, the library is called python37: not python3.7. Use gprbuild instead of gnatmake -p, which has been deprecated. If you do all your mods correctly
gprbuild ada_main.gpr
should give you an executable in obj\ada_main.exe if it builds. If a later version of python is used, some edits need to be made
python_module.py
#print 'Hello from Python module'
print('Hello from Python module')
#print 'Python adding:', a, '+', b
print('Python adding:', a, '+', b)
ada_main.adb
-- Python.Execute_String("print 'Hello from Python!'");
Python.Execute_String("print('Hello from Python!')");
Some routines have been deprecated so the linkage has to change
python.adb
--pragma Import(C, PyInt_AsLong, "PyInt_AsLong");
pragma Import(C, PyInt_AsLong, "PyLong_AsLong");
--pragma Import(C, PyString_FromString, PyString_FromString");
pragma Import(C, PyString_FromString, "PyUnicode_FromString");
Running the build and executable should give
C:\Users\StdUser\My Documents\ada-python>gprbuild ada_main.gpr
Compile
[Ada] ada_main.adb
Bind
[gprbind] ada_main.bexch
[Ada] ada_main.ali
Link
[link] ada_main.adb
C:\Users\StdUser\My Documents\ada-python>obj\ada_main.exe
executing Python directly from Ada:
Hello from Python!
loading external Python module and calling functions from that module:
Hello from Python module!
asking Python to add two integers:
Python adding: 10 + 2
Ada got result from Python: 12
we can try other operations, too:
subtract: 8
multiply: 20
divide : 5
Remember to put the pythonxx.dll somewhere on your path otherwise it won't be able to find the library when it starts executing.
I would like to visualize pointcloud in drake-visualizer using python binding.
I imitated how to publish images through lcm from here, and checked out these two issues (14985, 14991). The snippet is as follows :
point_cloud_to_lcm_point_cloud = builder.AddSystem(PointCloudToLcm())
point_cloud_to_lcm_point_cloud.set_name('pointcloud_converter')
builder.Connect(
station.GetOutputPort('camera0_point_cloud'),
point_cloud_to_lcm_point_cloud.get_input_port()
)
point_cloud_lcm_publisher = builder.AddSystem(
LcmPublisherSystem.Make(
channel="DRAKE_POINT_CLOUD_camera0",
lcm_type=lcmt_point_cloud,
lcm=None,
publish_period=0.2,
# use_cpp_serializer=True
)
)
point_cloud_lcm_publisher.set_name('point_cloud_publisher')
builder.Connect(
point_cloud_to_lcm_point_cloud.get_output_port(),
point_cloud_lcm_publisher.get_input_port()
)
However, I got the following runtime error:
RuntimeError: DiagramBuilder::Connect: Mismatched value types while connecting output port lcmt_point_cloud of System pointcloud_converter (type drake::lcmt_point_cloud) to input port lcm_message of System point_cloud_publisher (type drake::pydrake::Object)
When I set 'use_cpp_serializer=True', the error becomes
LcmPublisherSystem.Make(
File "/opt/drake/lib/python3.8/site-packages/pydrake/systems/_lcm_extra.py", line 71, in _make_lcm_publisher
serializer = _Serializer_[lcm_type]()
File "/opt/drake/lib/python3.8/site-packages/pydrake/common/cpp_template.py", line 90, in __getitem__
return self.get_instantiation(param)[0]
File "/opt/drake/lib/python3.8/site-packages/pydrake/common/cpp_template.py", line 159, in get_instantiation
raise RuntimeError("Invalid instantiation: {}".format(
RuntimeError: Invalid instantiation: _Serializer_[lcmt_point_cloud]
I saw the cpp example here, so maybe this issue is specific to python binding.
I also saw this python example, but thought using 'PointCloudToLcm' might be more convenient.
P.S.
I am aware of the development in recent commits on MeshcatVisualizerCpp and MeshcatPointCloudVisualizerCpp, but I am still on the drake-dev stable build 0.35.0-1 and want to stay on drake visualizer until the meshcat c++ is more mature.
The old version in pydrake.systems.meshcat_visualizer.MeshcatVisualizer is a bit too slow on my current use-case (multiple objects drop). I can visualize the pointcloud with this visualization setting, but it took too much machine resources.
Only the message types that are specifically bound in lcm_py_bind_cpp_serializers.cc can be used on an LCM message input/output port connection between C++ and Python. For all other LCM message types, the input/output port connection must be from a Python system to a Python system or a C++ System to a C++ System.
The lcmt_image_array is listed there, but not the lcmt_point_cloud.
If you're stuck using Drake's v0.35.0 capabilities, then I don't see any great solutions. Some options:
(1) Write your own PointCloudToLcm system in Python (by re-working the C++ code into Python, possibly with a narrower set of supported features / channels for simplicity).
(2) Write your own small C++ helper function MakePointCloudPublisherSystem(...) that calls LcmPublisherSystem::Make<lcmt_point_cloud> function in C++, and bind it into Python. Then your Python code can call MakePointCloudPublisherSystem() and successfully connect that to the existing C++ PointCloudToLcm.
I've been experimenting with PyCharm in conjunction with LO and UNO for some time (win10).
What I don't understand:
Why do I get the error Unresolved reference 'com' from PyCharm when I enter the code in editor, but everything seems to be OK, when I run the script with PyCharm or entered in console?
Example:
import uno
from com.sun.star.awt import Point
p = Point(100, 200)
print(p)
=> (com.sun.star.awt.Point){ X = (long)0x64, Y = (long)0xc8 }
What I would expect would be a relationship between the UNO interfaces com.sun.star.awt and Python so that I can use Intellisense/autocomplete (for example) when entering the code from com.sun.star.awt import Point in PyCharm'.
What I know is that all interfaces are described in the IDL files (SDK). I thought it must be possible to define a so-called stub/mock "package" that enables me to do this. Unfortunately, this request is beyond my capabilities at the moment. The attempt to integrate the idl structure from sdk is only inadequate (path structure only).
Are there any ready-made solutions? (which I doubt - intensively searched all sources)
Do I have to live with it now, or which approach would go in the right direction (I love intellisense)? - Maybe I think too complicated?
I am working on new costume board based on (i. MX 6Solo6DualLite).and I use (TJA1100 100BASE-T1 PHY) for Automotive Ethernet..
please correct me if my questions don't make sense, or I am in the wrong way.
I want to write driver for this device.. and make sure that it works correctly.
1- I can make sure that the driver works properly in the U-boot step, Right? I mean no need to load Linux kernel, so I have just to add source code C driver in U-boot source code and compile it. I want to do this in U-boot step, so I can limit the numbers of files that initialise all peripheriques, and make it simple as possible since that U-boot can behave like (mini-Os)
2-I don't know how to write this driver (exactly..), so I am looking for the driver (source code) that initialise the Ethernet Controller in any other processor , and initialise another typeof ethernet phy, in order to get an idea and write a similar driver source code for I.MX6 and TJA1100,?
after this i think that i could maybe add some very basic file c For simple Protocol like ARP, for test purpose..
3- is this good idea writing driver code by inspiring from another driver code source?
4 - maybe, if you already have a driver for (i. MX 6Solo6DualLite and TJA1100 100BASE-T1 PHY) can you provide to me please... ?
for my second question i tried to extract from U-boot source code the C file that initialise Ethernet Controller in AM335x, and initialise LAN8710A phy,(in beaglebone black) in order to get an idea and write a similar driver source code but i couldn't found it .. i found network C file for protocol .... but that's disturp me i couldn't seperate them from the real C file that initialise ethernet controller and ethernent Phy .
http://www.denx.de/wiki/U-Boot/SourceCode
There is a driver published on the NXP forum:
https://community.nxp.com/thread/460767
It includes both some bare metal code that should be usable with U-Boot and a Linux driver.
I'm trying to understand what is happening behind the Rcpp::sourceCpp() call on a parallelized environment. Recently, this was partially addressed in the question: Using Rcpp function in parLapply on Windows.
Within this post, Dirk said,
"You need to run the sourceCpp() call in each spawned process, or else get them your code."
This was in response to questioner's use of distributing the Rcpp function to the worker processes. The questioner was sending the Rcpp function via:
clusterExport(cl = cl, varlist = "payoff")
I'm confused as to why this doesn't work. My thoughts are that this was what the objective of the clusterExport() is for.
The issue here is that the compiled code is not "exportable" to the spawned processes without being embedded in a package due to how binaries are linked into R's processes.
Traditionally, the clusterExport() statement allows for R specific code to be distributed to workers.
By using clusterExport() on an Rcpp function, you are only receiving the R declaration and not the underlying shared library. That is to say, the R CMD SHLIB given in Attributes.R is not shared with / exported to the workers. As a result, when a call is then made to an Rcpp function on the worker, R cannot find the correct shared library.
Take the previous question's function:
Rcpp::cppFunction("NumericVector payoff( double strike, NumericVector data) {
return pmax(data - strike, 0);
}")
Note: I'm using cppFunction() instead of sourceCpp() but the results are equivalent since cppFunction() calls sourceCpp() to create the function.
Typing the function name:
payoff
Yields the R declaration with a shared library pointer.
function (strike, data)
.Primitive(".Call")(<pointer: 0x1015ec130>, strike, data)
This shared library is only available on process that compiled the function.
Hence, why it is always ideal to embed compiled code within a package and then distribute the package.