Difference between revisions of "UHD Python API"

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'''What's the streaming performance?'''
 
'''What's the streaming performance?'''
  
Worse than straight C++. Better than I would have thought, thanks to NumPy. We have no benchmarks yet. Overall, <code>recv()</code> calls are pretty efficient if you've preallocated a NumPy array, because we can cast that to a straight pointer (and also skip any type checking!!!!!!) and then it's not that different from a <code>recv()</code> call in a C++ app. However, consuming the data is limited by how
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Worse than straight C++. Better than I would have thought, thanks to NumPy. We have no benchmarks yet. Overall, <code>recv()</code> calls are pretty efficient if you've preallocated a NumPy array, because we can cast that to a straight pointer (and also skip any type checking!!!!!!) and then it's not that different from a <code>recv()</code> call in a C++ app. However, consuming the data is limited by how fast you can handle that in Python.

Revision as of 12:08, 15 August 2017

What's the UHD Python API?

As the name suggests, it exposes the UHD API into Python. We use Boost.Python to generate a Python module which exposes most of the C++ API, and some extra features.

The Python API is currently in a public beta test. We very much encourage users to try it out and voice their feedback. It is our intention to merge this API into the master branch soon, and ship it as a regular feature.

The pre-release announcement covers most of the information and can be found here: http://lists.ettus.com/pipermail/usrp-users_lists.ettus.com/2017-June/025379.html

We'll be doing some more development on this branch before we're merging it, but most importantly, we'd like to get some feedback from the greater community.

The biggest thing missing is more documentation, but it already includes some examples. It's definitely ready for testing!

Feedback

Any feedback is encouraged, please use the Github issue linked below for Python API discussion/bugs/improvements:

https://github.com/EttusResearch/uhd/issues/105

How can I use it?

In order to test the Python API, check out the python-api branch (see: https://github.com/EttusResearch/uhd/tree/python-api) and build it like any other UHD branch. When running CMake, make sure that the Python API was enabled.

The output from CMake should look something like this:

-- ######################################################
-- # UHD enabled components                              
-- ######################################################
--   * LibUHD
--   * LibUHD - C API
--   * LibUHD - Python API
--   * Examples
--   * Utils
--   * Tests
--   * USB
--   * B100
--   * B200
--   * USRP1
--   * USRP2
--   * X300
--   * N230
--   * OctoClock
--   * Manual
--   * API/Doxygen
--   * Man Pages
-- 
-- ######################################################
-- # UHD disabled components                             
-- ######################################################
--   * GPSD
--   * E100
--   * E300

Once it's built and installed, you'll be able to import the uhd Python module:

>>> import uhd
>>> my_usrp = uhd.usrp.MultiUSRP("type=b200")
>>> my_usrp.set_rx_gain(70)

We have some examples in host/examples/python. The examples are very simple, but concise.

Example: pyuhd_rx_to_file.py

This Python example is based on the C++ example uhd/host/examples/rx_samples_to_file.cpp.

import uhd
import numpy as np
import argparse

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("-a", "--args", default="", type=str)
    parser.add_argument("-o", "--output-file", type=str, required=True)
    parser.add_argument("-f", "--freq", type=float, required=True)
    parser.add_argument("-r", "--rate", default=1e6, type=float)
    parser.add_argument("-d", "--duration", default=5.0, type=float)
    parser.add_argument("-c", "--channels", default=0, nargs="+", type=int)
    parser.add_argument("-g", "--gain", type=int, default=10)
    return parser.parse_args()

def main():
    args = parse_args()
    usrp = uhd.usrp.MultiUSRP(args.args)
    num_samps = int(np.ceil(args.duration*args.rate))
    if not isinstance(args.channels, list):
        args.channels = [args.channels]
    samps = usrp.recv_num_samps(num_samps, args.freq, args.rate, args.channels, args.gain)
    with open(args.output_file, 'wb') as f:
        np.save(f, samps, allow_pickle=False, fix_imports=False)

if __name__ == "__main__":
    main()

What about documentation?

Documentation is currently pretty sparse. The best we can do right now is to ask users to infer the documentation from the C++ API. For example, the Python has an object called MultiUSRP which is an equivalent of the C++ multi_usrp API. The methods on both classes are the same, and take the same arguments.

FAQ

Does it support Python 2 and 3?

Yes.

Does it require GNU Radio?

No.


Does it use SWIG?

No, it uses Boost.Python. We didn't want to add another dependency to UHD (i.e., SWIG) and Boost was already a dependency of UHD. It also doesn't require the C API.

How does this relate to the Python API in gr-uhd?

It serves an entirely different purpose. This Python API is for people writing standalone applications for USRPs that *don't* use GNU Radio. gr-uhd is staying the way it is, and is going nowhere. If you're using GNU Radio, you probably don't care about this.

Are the UHD Python API and the gr-uhd Python API compatible?

Short answer: No. Long answer: There are very few cases where it makes sense to mix these APIs, so no. However, this means that a TimeSpec from the Boost.Python API is not convertible into a time_spec_t from the gr-uhd API.

When will it be released?

TBD, but if we hear a lot of encouragement that'll drive things along faster. It'll go into master branch whenever it's considered stable enough, and then in the first major release after that merge.

Does it support RFNoC API?

Not yet, but it's not hard to add. We wanted to get the basics (i.e. multi_usrp API) right first.

What's the streaming performance?

Worse than straight C++. Better than I would have thought, thanks to NumPy. We have no benchmarks yet. Overall, recv() calls are pretty efficient if you've preallocated a NumPy array, because we can cast that to a straight pointer (and also skip any type checking!!!!!!) and then it's not that different from a recv() call in a C++ app. However, consuming the data is limited by how fast you can handle that in Python.