How does it work¶
Note
This section is a modified tutorial: Remote Time Tagger with Python.
Time Tagger is a great instrument for data acquisition whenever you are detecting, counting or analyzing single photons. You can quickly setup a time correlation measurement, coincidence analysis and much more. However at some point in your project you may want to control your experiment remotely. While you can simply use a remote desktop software, like VNC, TeamViewer, Windows Remote Desktop, etc. What if you want to programmatically control your remote experiment? Are you using multiple computers and want to collect data from many of them at the same time? Then you will have to delve into programming remote control interfaces. Luckily, this task is very common and there are many software libraries that release you from burden of dealing with network sockets and messaging protocols.
In this article, you will learn the fundamentals of the TimeTaggerRPC and how it allows you to achieve practically seamless access to the Time Tagger’s API remotely.
Remote procedure calling¶
Remote procedure calling (RPC) is a technology that allows interaction with remote programs by calling their procedures and receiving the responses. This involves a real code execution on one computer (server), while the client computer has only a substitute object (proxy) that mimics the real object running on the sever. The proxy object knows how to send requests and data to the server and the server knows how to interpret these requests and how to execute the real code.
In the case of Pyro5 the proxy object and server code are provided by the library and we only need to tell Pyro5 what we want to become available remotely.
Initial setup¶
You will need to have a Python 3.6 or newer installed on your computer. We recommend to use Anaconda distribution.
Install the Time Tagger software if have not done it yet. The description below assumes that you have the Time Tagger hardware and are familiar with the Time Tagger API.
The last missing part, the Pyro5 package, you can install from PyPi as
pip install Pyro5
Minimal example¶
Here we start from the simplest functional example and demonstrate working remote communication. The example consists of two parts: the server and the client code. You will need to run those in two separate command windows.
Server code
We need to create an adapter class with methods that we want to access remotely and decorate it with
Pyro5.api.expose(). The following code is very simple. Later, we will extend it to expose more of the Time Tagger’s functionality.
import Pyro5.api
import TimeTagger as TT
@Pyro5.api.expose
class TimeTaggerRPC:
"""Adapter for the Time Tagger Library"""
def scanTimeTagger(self):
"""This method will become available remotely."""
return TT.scanTimeTagger()
if __name__ == '__main__':
# Start server and expose the TimeTaggerRPC class
with Pyro5.api.Daemon(host='localhost', port=23000) as daemon:
# Register class with Pyro
uri = daemon.register(TimeTaggerRPC, 'TimeTaggerRPC')
# Print the URI of the published object
print(uri)
# Start the server event loop
daemon.requestLoop()
Client code
On the client side, we need to know the unique identifier of the exposed object which was printed when you started server.py In Pyro5, every object is identified by a special string (URI) that contains object identity string and the server address. As you can see in the code below, we do not use the Time Tagger software directly, but rather communicate to the server that has it.
import Pyro5.api
# Connect to the TimeTaggerRPC object on the server
# This line is all we need to establish remote communication
TT = Pyro5.api.Proxy("PYRO:TimeTaggerRPC@localhost:23000")
# Now we can call methods that will be executed on the server.
# Lets check what Time Taggers are available at the server
timetaggers = TT.scanTimeTagger()
print(timetaggers)
>> ['1740000ABC', '1750000ABC']
Congratulations! Now you have a very simple but functional communication to your remote Time Tagger software.
Creating the Time Tagger¶
By now, our code can communicate over a network and can only report the serial numbers of the connected Time Taggers.
In this section, we will expand the server code and make it more useful.
The next most important feature of the server is to expose the createTimeTagger() method
to tell the server to initialize the Time Tagger hardware.
You may be tempted to extend the TimeTaggerRPC class as follows:
@Pyro5.api.expose
class TimeTaggerRPC:
"""Adapter for the Time Tagger Library"""
def scanTimeTagger(self):
"""Return the serial numbers of the available Time Taggers."""
return TT.scanTimeTagger()
def createTimeTagger(self):
"""Create the Time Tagger."""
return TT.createTimeTagger() # This will fail! :(
To our great disappointment, the createTimeTagger method will fail when you try to access it from the client. The reason is in how the RPC communication works. The data and the program code has a certain format in which it is stored in the computer’s memory, and this memory cannot be easily or safely accessed from a remote computer. The RPC communication overcomes this problem by using data serialization, i.e. converting the data into a generalized format suitable for sending over network and understandable by a client system.
The Pyro5, more specifically the serpent serializer it employs by default,
knows how to serialize the standard Python data types like list of strings returned by scanTimeTagger().
However, it has no idea how to interpret the TimeTagger object returned by the createTimeTagger().
Moreover, instead of sending the TimeTagger object to the client,
we want to send a proxy object which allows the client talk to the TimeTagger object on the server.
For the TimeTagger, we define an adapter class. Then we modify the TimeTaggerRPC.createTimeTagger()
so that it creates an instance of the adapter class, register it with Pyro and return it.
Pyro will automatically take care of creating a proxy object for the client.
@Pyro5.api.expose
class TimeTagger:
"""Adapter for the Time Tagger object"""
def __init__(self, args, kwargs):
self._obj = TT.createTimeTagger(*args, **kwargs)
def setTestSignal(self, *args):
return self._obj.setTestSignal(*args)
def getSerial(self):
return self._obj.getSerial()
# ... Other methods of the TT.TimeTagger class are omitted here.
@Pyro5.api.expose
class TimeTaggerRPC:
"""Adapter for the Time Tagger Library"""
def scanTimeTagger(self):
"""Return the serial numbers of the available Time Taggers."""
return TT.scanTimeTagger()
def createTimeTagger(self, *args, **kwargs):
"""Create the Time Tagger."""
tagger = TimeTagger(args, kwargs)
self._pyroDaemon.register(tagger)
return tagger
# Pyro will automatically create and send a proxy object
# to the client.
def freeTimeTagger(self, tagger_proxy):
"""Free Time Tagger. """
# Client only has a proxy object.
objectId = tagger_proxy._pyroUri.object
# Get adapter object from the server.
tagger = self._pyroDaemon.objectsById.get(objectId)
self._pyroDaemon.unregister(tagger)
return TT.freeTimeTagger(tagger._obj)
Measurements and virtual channels¶
By now we can list available Time Tagger devices and create TimeTagger objects. The remaining part is to implement access to the measurements and virtual channels. We will use the same approach as with the TimeTagger class and create adapter classes for them.
@Pyro5.api.expose
class Correlation:
"""Adapter class for Correlation measurement."""
def __init__(self, tagger, args, kwargs):
self._obj = TT.Correlation(tagger._obj, *args, **kwargs)
def start(self):
return self._obj.start()
def startFor(self, capture_duration, clear):
return self._obj.startFor(capture_duration, clear=clear)
def stop(self):
return self._obj.stop()
def clear(self):
return self._obj.clear()
def isRunning(self):
return self._obj.isRunning()
def getIndex(self):
return self._obj.getIndex().tolist()
def getData(self):
return self._obj.getData().tolist()
@Pyro5.api.expose
class DelayedChannel():
"""Adapter class for DelayedChannel."""
def __init__(self, tagger, args, kwargs):
self._obj = TT.DelayedChannel(tagger._obj, *args, **kwargs)
def getChannel(self):
return self._obj.getChannel()
@Pyro5.api.expose
class TimeTaggerRPC:
"""Adapter class for the Time Tagger Library"""
# Earlier code omitted (...)
def Correlation(self, tagger_proxy, *args, **kwargs):
"""Create Correlation measurement."""
objectId = tagger_proxy._pyroUri.object
tagger = self._pyroDaemon.objectsById.get(objectId)
pyro_obj = Correlation(tagger, args, kwargs)
self._pyroDaemon.register(pyro_obj)
return pyro_obj
def DelayedChannel(self, tagger_proxy, *args, **kwargs):
"""Create DelayedChannel."""
objectId = tagger_proxy._pyroUri.object
tagger = self._pyroDaemon.objectsById.get(objectId)
pyro_obj = DelayedChannel(tagger, args, kwargs)
self._pyroDaemon.register(pyro_obj)
return pyro_obj
Note
The methods Correlation.getIndex() and Correlation.getData() return numpy.ndarray arrays.
Pyro5 does not know how to serialize numpy.ndarray, therefore for simplicity of the example,
we convert them to the Python lists.
More efficient approach would be to register custom serializer functions for
numpy.ndarray on both, server and client sides,
see Customizing serialization section of the Pyro5 documentation.
Actual implementation¶
Instead of manually writing adapter classes for each object and function of the TimeTagger software we harness the power of Python metaprogramming and generate them dynamically. This enables substantial independence of the TimeTaggerPRC version from the version of the Time Tagger software engine. The relevant adapter classes will be created automatically for every new feature of the Time Tagger without updating the TimeTaggerRPC version.