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-# tractor
-async, multicore, distributed Python built on trio
diff --git a/README.rst b/README.rst
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+tractor
+=======
+An async-native `actor model`_ built on trio_ and multiprocessing_.
+
+
+|travis|
+
+.. |travis| image:: https://img.shields.io/travis/tgoodlet/tractor/master.svg
+    :target: https://travis-ci.org/tgoodlet/tractor
+
+.. _actor model: https://en.wikipedia.org/wiki/Actor_model
+.. _trio: https://github.com/python-trio/trio
+.. _multiprocessing: https://docs.python.org/3/library/multiprocessing.html
+.. _trionic: https://trio.readthedocs.io/en/latest/design.html#high-level-design-principles
+.. _async sandwich: https://trio.readthedocs.io/en/latest/tutorial.html#async-sandwich
+.. _always propagate: https://trio.readthedocs.io/en/latest/design.html#exceptions-always-propagate
+.. _causality: https://vorpus.org/blog/some-thoughts-on-asynchronous-api-design-in-a-post-asyncawait-world/#c-c-c-c-causality-breaker
+.. _shared nothing architecture: https://en.wikipedia.org/wiki/Shared-nothing_architecture
+.. _cancellation: https://trio.readthedocs.io/en/latest/reference-core.html#cancellation-and-timeouts
+.. _channels: https://en.wikipedia.org/wiki/Channel_(programming)
+.. _chaos engineering: http://principlesofchaos.org/
+
+
+What's this? Spawning event loops in subprocesses?
+--------------------------------------------------
+Close, but not quite.
+
+``tractor`` is an attempt to take trionic_ concurrency concepts and apply
+them to distributed multi-core Python.
+
+``tractor`` lets you run and spawn *actors*: separate processes which run a ``trio``
+scheduler and task tree (also known as an `async sandwich`_).
+*Actors* communicate by sending messages_ over channels_ and avoid sharing any state.
+This `actor model`_ allows for highly distributed software architecture which works just as
+well on multiple cores as it does over many hosts.
+``tractor`` takes much inspiration from pulsar_ and execnet_ but attempts to be much more
+focussed on sophistication of the lower level distributed architecture
+as well as have first class support for modern async Python.
+``tractor`` does **not** use ``asyncio`` hence **no** event loops.
+
+The first step to grok ``tractor`` is to get the basics of ``trio``
+down. A great place to start is the `trio docs`_ and this `blog post`_
+by njsmith_.
+
+.. _messages: https://en.wikipedia.org/wiki/Message_passing
+.. _trio docs: https://trio.readthedocs.io/en/latest/
+.. _blog post: https://vorpus.org/blog/notes-on-structured-concurrency-or-go-statement-considered-harmful/
+.. _njsmith: https://github.com/njsmith/
+
+
+Philosophy
+----------
+``tractor``'s tenets non-comprehensively include:
+
+- no spawning of processes *willy-nilly*; causality_ is paramount!
+- `shared nothing architecture`_
+- remote errors `always propagate`_ back to the caller
+- verbatim support for ``trio``'s cancellation_ system
+- no use of *proxy* objects to wrap RPC calls
+- an immersive debugging experience
+- anti-fragility through `chaos engineering`_
+
+.. warning:: ``tractor`` is in alpha-alpha and is expected to change rapidly!
+    Expect nothing to be set in stone. Your ideas about where it should go
+    are greatly appreciated!
+
+.. _pulsar: http://quantmind.github.io/pulsar/design.html
+.. _execnet: https://codespeak.net/execnet/
+
+
+Install
+-------
+No PyPi release yet!
+
+::
+
+    pip install git+git://github.com/tgoodlet/tractor.git
+
+
+A trynamic first scene
+----------------------
+Let's direct a couple *actors* and have them run their lines for
+the hip new film we're shooting:
+
+.. code:: python
+
+    import tractor
+    from functools import partial
+
+    _this_module = __name__
+    the_line = 'Hi my name is {}'
+
+
+    async def hi():
+        return the_line.format(tractor.current_actor().name)
+
+
+    async def say_hello(other_actor):
+        await trio.sleep(0.4)  # wait for other actor to spawn
+        async with tractor.find_actor(other_actor) as portal:
+            return await portal.run(_this_module, 'hi')
+
+
+    async def main():
+        """Main tractor entry point, the "master" process (for now
+        acts as the "director").
+        """
+        async with tractor.open_nursery() as n:
+            print("Alright... Action!")
+
+            donny = await n.run_in_actor(
+                'donny',
+                say_hello,
+                other_actor='gretchen',
+            )
+            gretchen = await n.run_in_actor(
+                'gretchen',
+                say_hello,
+                other_actor='donny',
+            )
+            print(await gretchen.result())
+            print(await donny.result())
+            await donny.cancel_actor()
+            print("CUTTTT CUUTT CUT!!! Donny!! You're supposed to say...")
+
+
+    tractor.run(main)
+
+
+We spawn two *actors*, *donny* and *gretchen*.
+Each actor starts up and executes their *main task* defined by an
+async function, ``say_hello()``.  The function instructs each actor
+to find their partner and say hello by calling their partner's
+``hi()`` function using something called a *portal*. Each actor
+receives a response and relays that back to the parent actor (in
+this case our "director" executing ``main()``).
+
+
+Actor spawning and causality
+----------------------------
+``tractor`` tries to take ``trio``'s concept of causal task lifetimes
+to multi-process land. Accordingly, ``tractor``'s *actor nursery* behaves
+similar to ``trio``'s nursery_. That is, ``tractor.open_nursery()``
+opens an ``ActorNursery`` which waits on spawned *actors* to complete
+(or error) in the same causal_ way ``trio`` waits on spawned subtasks.
+This includes errors from any one actor causing all other actors
+spawned by the same nursery to be cancelled_.
+
+To spawn an actor and run a function in it, open a *nursery block*
+and use the ``run_in_actor()`` method:
+
+.. code:: python
+
+    import tractor
+
+
+        def cellar_door():
+            return "Dang that's beautiful"
+
+
+        async def main():
+            """The main ``tractor`` routine.
+            """
+            async with tractor.open_nursery() as n:
+
+                portal = await n.run_in_actor('frank', movie_theatre_question)
+
+            # The ``async with`` will unblock here since the 'frank'
+            # actor has completed its main task ``movie_theatre_question()``.
+
+            print(await portal.result())
+
+
+    tractor.run(main)
+
+
+What's going on?
+
+- an initial *actor* is started with ``tractor.run()`` and told to execute
+  its main task_: ``main()``
+
+- inside ``main()`` an actor is *spawned* using an ``ActorNusery`` and is told
+  to run a single function: ``cellar_door()``
+
+- a ``portal`` instance (we'll get to what it is shortly)
+  returned from ``nursery.run_in_actor()`` is used to communicate with
+  the newly spawned *sub-actor*
+
+- the second actor, *frank*, in a new *process* running a new ``trio`` task_
+  then executes ``cellar_door()`` and returns its result over a *channel* back
+  to the parent actor
+
+- the parent actor retrieves the subactor's (*frank*) *final result* using ``portal.result()``
+  much like you'd expect from a future_.
+
+This ``run_in_actor()`` API should look very familiar to users of
+``asyncio``'s run_in_executor_ which uses a ``concurrent.futures`` Executor_.
+
+Since you might also want to spawn long running *worker* or *daemon*
+actors, each actor's *lifetime* can be determined based on the spawn
+method:
+
+- if the actor is spawned using ``run_in_actor()`` it terminates when
+  its *main* task completes (i.e. when the (async) function submitted
+  to it *returns*). The ``with tractor.open_nursery()`` exits only once
+  all actors' main function/task complete (just like the nursery_ in ``trio``)
+
+- actors can be spawned to *live forever* using the ``start_actor()``
+  method and act like an RPC daemon that runs indefinitely (the
+  ``with tractor.open_nursery()`` wont' exit) until cancelled_
+
+Had we wanted the latter form in our example it would have looked like:
+
+.. code:: python
+
+    def movie_theatre_question():
+        """A question asked in a dark theatre, in a tangent
+        (errr, I mean different) process.
+        """
+        return 'have you ever seen a portal?'
+
+
+    async def main():
+        """The main ``tractor`` routine.
+        """
+        async with tractor.open_nursery() as n:
+
+            portal = await n.start_actor(
+                'frank',
+                # enable the actor to run funcs from this current module
+                rpc_module_paths=[__name__],
+            )
+
+            print(await portal.run(__name__, 'movie_theatre_question'))
+            # call the subactor a 2nd time
+            print(await portal.run(__name__, 'movie_theatre_question'))
+
+            # the async with will block here indefinitely waiting
+            # for our actor "frank" to complete, but since it's an
+            # "outlive_main" actor it will never end until cancelled
+            await portal.cancel_actor()
+
+
+The ``rpc_module_paths`` `kwarg` above is a list of module path
+strings that will be loaded and made accessible for execution in the
+remote actor through a call to ``Portal.run()``. For now this is
+a simple mechanism to restrict the functionality of the remote
+(and possibly daemonized) actor and uses Python's module system to
+limit the allowed remote function namespace(s).
+
+``tractor`` is opinionated about the underlying threading model used for
+each *actor*. Since Python has a GIL and an actor model by definition
+shares no state between actors, it fits naturally to use a multiprocessing_
+``Process``. This allows ``tractor`` programs to leverage not only multi-core
+hardware but also distribute over many hardware hosts (each *actor* can talk
+to all others with ease over standard network protocols).
+
+.. _task: https://trio.readthedocs.io/en/latest/reference-core.html#tasks-let-you-do-multiple-things-at-once
+.. _nursery: https://trio.readthedocs.io/en/latest/reference-core.html#nurseries-and-spawning
+.. _causal: https://vorpus.org/blog/some-thoughts-on-asynchronous-api-design-in-a-post-asyncawait-world/#causality
+.. _cancelled: https://trio.readthedocs.io/en/latest/reference-core.html#child-tasks-and-cancellation
+.. _run_in_executor: https://docs.python.org/3/library/asyncio-eventloop.html#executor
+.. _Executor: https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.Executor
+
+
+Transparent remote function calling using *portals*
+---------------------------------------------------
+``tractor`` introduces the concept of a *portal* which is an API
+borrowed_ from ``trio``. A portal may seem similar to the idea of
+a RPC future_ except a *portal* allows invoking remote *async* functions and
+generators and intermittently blocking to receive responses. This allows
+for fully async-native IPC between actors.
+
+When you invoke another actor's routines using a *portal* it looks as though
+it was called locally in the current actor. So when you see a call to
+``await portal.run()`` what you get back is what you'd expect
+to if you'd called the function directly in-process. This approach avoids
+the need to add any special RPC *proxy* objects to the library by instead just
+relying on the built-in (async) function calling semantics and protocols of Python.
+
+Depending on the function type ``Portal.run()`` tries to
+correctly interface exactly like a local version of the remote
+built-in Python *function type*. Currently async functions, generators,
+and regular functions are supported. Inspiration for this API comes
+from the way execnet_ does `remote function execution`_ but without
+the client code (necessarily) having to worry about the underlying
+channels_ system or shipping code over the network.
+
+This *portal* approach turns out to be paricularly exciting with the
+introduction of `asynchronous generators`_ in Python 3.6! It means that
+actors can compose nicely in a data processing pipeline.
+
+As an example here's an actor that streams for 1 second from a remote async
+generator function running in a separate actor:
+
+.. code:: python
+
+    from itertools import repeat
+    import trio
+    import tractor
+
+
+    async def stream_forever():
+        for i in repeat("I can see these little future bubble things"):
+            # each yielded value is sent over the ``Channel`` to the
+            # parent actor
+            yield i
+            await trio.sleep(0.01)
+
+
+    async def main():
+        # stream for at most 1 seconds
+        with trio.move_on_after(1) as cancel_scope:
+            async with tractor.open_nursery() as n:
+                portal = await n.start_actor(
+                    f'donny',
+                    rpc_module_paths=[__name__],
+                )
+
+                # this async for loop streams values from the above
+                # async generator running in a separate process
+                async for letter in await portal.run(__name__, 'stream_forever'):
+                    print(letter)
+
+        # we support trio's cancellation system
+        assert cancel_scope.cancelled_caught
+        assert n.cancelled
+
+
+    tractor.run(main)
+
+
+Alright, let's get fancy.
+
+Say you wanted to spawn two actors which each pulling data feeds from
+two different sources (and wanted this work spread across 2 cpus).
+You also want to aggregate these feeds, do some processing on them and then
+deliver the final result stream to a client (or in this case parent) actor
+and print the results to your screen:
+
+.. code:: python
+
+    import time
+    import trio
+    import tractor
+
+
+    # this is the first 2 actors, streamer_1 and streamer_2
+    async def stream_data(seed):
+        for i in range(seed):
+            yield i
+            await trio.sleep(0)  # trigger scheduler
+
+
+    # this is the third actor; the aggregator
+    async def aggregate(seed):
+        """Ensure that the two streams we receive match but only stream
+        a single set of values to the parent.
+        """
+        async with tractor.open_nursery() as nursery:
+            portals = []
+            for i in range(1, 3):
+                # fork point
+                portal = await nursery.start_actor(
+                    name=f'streamer_{i}',
+                    rpc_module_paths=[__name__],
+                )
+
+                portals.append(portal)
+
+            q = trio.Queue(500)
+
+            async def push_to_q(portal):
+                async for value in await portal.run(
+                    __name__, 'stream_data', seed=seed
+                ):
+                    # leverage trio's built-in backpressure
+                    await q.put(value)
+
+                await q.put(None)
+                print(f"FINISHED ITERATING {portal.channel.uid}")
+
+            # spawn 2 trio tasks to collect streams and push to a local queue
+            async with trio.open_nursery() as n:
+                for portal in portals:
+                    n.start_soon(push_to_q, portal)
+
+                unique_vals = set()
+                async for value in q:
+                    if value not in unique_vals:
+                        unique_vals.add(value)
+                        # yield upwards to the spawning parent actor
+                        yield value
+
+                        if value is None:
+                            break
+
+                    assert value in unique_vals
+
+                print("FINISHED ITERATING in aggregator")
+
+            await nursery.cancel()
+            print("WAITING on `ActorNursery` to finish")
+        print("AGGREGATOR COMPLETE!")
+
+
+    # this is the main actor and *arbiter*
+    async def main():
+        # a nursery which spawns "actors"
+        async with tractor.open_nursery() as nursery:
+
+            seed = int(1e3)
+            import time
+            pre_start = time.time()
+
+            portal = await nursery.run_in_actor(
+                'aggregator',
+                aggregate,
+                seed=seed,
+            )
+
+            start = time.time()
+            # the portal call returns exactly what you'd expect
+            # as if the remote "aggregate" function was called locally
+            result_stream = []
+            async for value in await portal.result():
+                result_stream.append(value)
+
+            print(f"STREAM TIME = {time.time() - start}")
+            print(f"STREAM + SPAWN TIME = {time.time() - pre_start}")
+            assert result_stream == list(range(seed)) + [None]
+            return result_stream
+
+
+    final_stream = tractor.run(main, arbiter_addr=('127.0.0.1', 1616))
+
+
+Here there's four actors running in separate processes (using all the
+cores on you machine). Two are streaming by *yielding* values from the
+``stream_data()`` async generator, one is aggregating values from
+those two in ``aggregate()`` (also an async generator) and shipping the
+single stream of unique values up the parent actor (the ``'MainProcess'``
+as ``multiprocessing`` calls it) which is running ``main()``. 
+
+.. _future: https://en.wikipedia.org/wiki/Futures_and_promises
+.. _borrowed:
+    https://trio.readthedocs.io/en/latest/reference-core.html#getting-back-into-the-trio-thread-from-another-thread
+.. _asynchronous generators: https://www.python.org/dev/peps/pep-0525/
+.. _remote function execution: https://codespeak.net/execnet/example/test_info.html#remote-exec-a-function-avoiding-inlined-source-part-i
+.. _asyncitertools: https://github.com/vodik/asyncitertools
+
+
+Cancellation
+------------
+``tractor`` supports ``trio``'s cancellation_ system verbatim.
+Cancelling a nursery block cancels all actors spawned by it.
+Eventually ``tractor`` plans to support different `supervision strategies`_ like ``erlang``.
+
+.. _supervision strategies: http://erlang.org/doc/man/supervisor.html#sup_flags
+
+
+Remote error propagation
+------------------------
+Any task invoked in a remote actor should ship any error(s) back to the calling
+actor where it is raised and expected to be dealt with. This way remote actors
+are never cancelled unless explicitly asked or there's a bug in ``tractor`` itself.
+
+.. code:: python
+
+    async def assert_err():
+        assert 0
+
+
+    async def main():
+        async with tractor.open_nursery() as n:
+            real_actors = []
+            for i in range(3):
+                real_actors.append(await n.start_actor(
+                    f'actor_{i}',
+                    rpc_module_paths=[__name__],
+                ))
+
+            # start one actor that will fail immediately
+            await n.run_in_actor('extra', assert_err)
+
+        # should error here with a ``RemoteActorError`` containing
+        # an ``AssertionError`` and all the other actors have been cancelled
+
+    try:
+        # also raises
+        tractor.run(main)
+    except tractor.RemoteActorError:
+        print("Look Maa that actor failed hard, hehhh!")
+
+
+You'll notice the nursery cancellation conducts a *one-cancels-all*
+supervisory strategy `exactly like trio`_. The plan is to add more
+`erlang strategies`_ in the near future by allowing nurseries to accept
+a ``Supervisor`` type.
+
+.. _exactly like trio: https://trio.readthedocs.io/en/latest/reference-core.html#cancellation-semantics
+.. _erlang strategies: http://learnyousomeerlang.com/supervisors
+
+
+Shared task state
+-----------------
+Although ``tractor`` uses a *shared-nothing* architecture between processes
+you can of course share state within an actor.  ``trio`` tasks spawned via
+multiple RPC calls to an actor can access global data using the per actor
+``statespace`` dictionary:
+
+.. code:: python
+
+
+        statespace = {'doggy': 10}
+
+
+        def check_statespace():
+            # Remember this runs in a new process so no changes
+            # will propagate back to the parent actor
+            assert tractor.current_actor().statespace == statespace
+
+
+        async def main():
+            async with tractor.open_nursery() as n:
+                await n.run_in_actor(
+                    'checker',
+                    check_statespace,
+                    statespace=statespace
+                )
+
+
+Of course you don't have to use the ``statespace`` variable (it's mostly
+a convenience for passing simple data to newly spawned actors); building
+out a state sharing system per-actor is totally up to you.
+
+
+How do actors find each other (a poor man's *service discovery*)?
+-----------------------------------------------------------------
+Though it will be built out much more in the near future, ``tractor``
+currently keeps track of actors by ``(name: str, id: str)`` using a
+special actor called the *arbiter*. Currently the *arbiter* must exist
+on a host (or it will be created if one can't be found) and keeps a
+simple ``dict`` of actor names to sockets for discovery by other actors.
+Obviously this can be made more sophisticated (help me with it!) but for
+now it does the trick.
+
+To find the arbiter from the current actor use the ``get_arbiter()`` function and to
+find an actor's socket address by name use the ``find_actor()`` function:
+
+.. code:: python
+
+    import tractor
+
+
+    async def main(service_name):
+
+        async with tractor.get_arbiter() as portal:
+            print(f"Arbiter is listening on {portal.channel}")
+
+        async with tractor.find_actor(service_name) as sockaddr:
+            print(f"my_service is found at {my_service}")
+
+
+    tractor.run(main, service_name)
+
+
+The ``name`` value you should pass to ``find_actor()`` is the one you passed as the
+*first* argument to either ``tractor.run()`` or ``ActorNursery.start_actor()``.
+
+
+Using ``Channel`` directly (undocumented)
+-----------------------------------------
+You can use the ``Channel`` api if necessary by simply defining a
+``chan`` and ``cid`` *kwarg* in your async function definition.
+``tractor`` will treat such async functions like async generators on
+the calling side (for now anyway) such that you can push stream values
+a little more granularly if you find *yielding* values to be restrictive.
+I am purposely not documenting this feature with code because I'm not yet
+sure yet how it should be used correctly. If you'd like more details
+please feel free to ask me on the `trio gitter channel`_.
+
+
+Running actors standalone (without spawning)
+--------------------------------------------
+You don't have to spawn any actors using ``open_nursery()`` if you just
+want to run a single actor that connects to an existing cluster.
+All the comms and arbiter registration stuff still works. This can
+somtimes turn out being handy when debugging mult-process apps when you
+need to hop into a debugger. You just need to pass the existing
+*arbiter*'s socket address you'd like to connect to:
+
+.. code:: python
+
+    tractor.run(main, arbiter_addr=('192.168.0.10', 1616))
+
+
+Enabling logging
+----------------
+Considering how complicated distributed software can become it helps to know
+what exactly it's doing (even at the lowest levels). Luckily ``tractor`` has
+tons of logging throughout the core. ``tractor`` isn't opinionated on
+how you use this information and users are expected to consume log messages in
+whichever way is appropriate for the system at hand. That being said, when hacking
+on ``tractor`` there is a prettified console formatter which you can enable to
+see what the heck is going on. Just put the following somewhere in your code:
+
+.. code:: python
+
+    from tractor.log import get_console_log
+    log = get_console_log('trace')
+
+
+What the future holds
+---------------------
+Stuff I'd like to see ``tractor`` do one day:
+
+- erlang-like supervisors_
+- native support for zeromq_ as a channel transport
+- native `gossip protocol`_ support for service discovery and arbiter election
+- a distributed log ledger for tracking cluster behaviour
+- a slick multi-process aware debugger much like in celery_
+  but with better `pdb++`_ support
+- an extensive `chaos engineering`_ test suite
+- support for reactive programming primitives and native support for asyncitertools_ like libs
+
+If you're interested in tackling any of these please do shout about it on the
+`trio gitter channel`_!
+
+.. _supervisors: http://learnyousomeerlang.com/supervisors
+.. _zeromq: https://en.wikipedia.org/wiki/ZeroMQ
+.. _gossip protocol: https://en.wikipedia.org/wiki/Gossip_protocol
+.. _trio gitter channel: https://gitter.im/python-trio/general
+.. _celery: http://docs.celeryproject.org/en/latest/userguide/debugging.html
+.. _pdb++: https://github.com/antocuni/pdb
diff --git a/setup.py b/setup.py
index e9cc810..c3001cf 100755
--- a/setup.py
+++ b/setup.py
@@ -18,7 +18,7 @@
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 from setuptools import setup
 
-with open('README.md', encoding='utf-8') as f:
+with open('README.rst', encoding='utf-8') as f:
     readme = f.read()
 
 
diff --git a/tests/test_tractor.py b/tests/test_tractor.py
index 8fbd22c..3683123 100644
--- a/tests/test_tractor.py
+++ b/tests/test_tractor.py
@@ -168,6 +168,8 @@ def test_remote_error(arb_addr):
 
 async def stream_forever():
     for i in repeat("I can see these little future bubble things"):
+        # each yielded value is sent over the ``Channel`` to the
+        # parent actor
         yield i
         await trio.sleep(0.01)
 
@@ -175,16 +177,20 @@ async def stream_forever():
 @tractor_test
 async def test_cancel_infinite_streamer():
 
-    # stream for at most 5 seconds
+    # stream for at most 1 seconds
     with trio.move_on_after(1) as cancel_scope:
         async with tractor.open_nursery() as n:
             portal = await n.start_actor(
                 f'donny',
                 rpc_module_paths=[__name__],
             )
+
+            # this async for loop streams values from the above
+            # async generator running in a separate process
             async for letter in await portal.run(__name__, 'stream_forever'):
                 print(letter)
 
+    # we support trio's cancellation system
     assert cancel_scope.cancelled_caught
     assert n.cancelled
 
@@ -230,6 +236,7 @@ async def test_movie_theatre_convo():
     """The main ``tractor`` routine.
     """
     async with tractor.open_nursery() as n:
+
         portal = await n.start_actor(
             'frank',
             # enable the actor to run funcs from this current module
@@ -237,7 +244,7 @@ async def test_movie_theatre_convo():
         )
 
         print(await portal.run(__name__, 'movie_theatre_question'))
-        # calls the subactor a 2nd time
+        # call the subactor a 2nd time
         print(await portal.run(__name__, 'movie_theatre_question'))
 
         # the async with will block here indefinitely waiting
@@ -246,17 +253,6 @@ async def test_movie_theatre_convo():
         await portal.cancel_actor()
 
 
-@tractor_test
-async def test_movie_theatre_convo_main_task():
-    async with tractor.open_nursery() as n:
-        portal = await n.run_in_actor('frank', movie_theatre_question)
-
-    # The ``async with`` will unblock here since the 'frank'
-    # actor has completed its main task ``movie_theatre_question()``.
-
-    print(await portal.result())
-
-
 def cellar_door():
     return "Dang that's beautiful"
 
@@ -266,6 +262,7 @@ async def test_most_beautiful_word():
     """The main ``tractor`` routine.
     """
     async with tractor.open_nursery() as n:
+
         portal = await n.run_in_actor('some_linguist', cellar_door)
 
     # The ``async with`` will unblock here since the 'some_linguist'
@@ -295,12 +292,14 @@ def test_cancel_single_subactor(arb_addr):
     tractor.run(main, arbiter_addr=arb_addr)
 
 
+# this is the first 2 actors, streamer_1 and streamer_2
 async def stream_data(seed):
     for i in range(seed):
         yield i
         await trio.sleep(0)  # trigger scheduler
 
 
+# this is the third actor; the aggregator
 async def aggregate(seed):
     """Ensure that the two streams we receive match but only stream
     a single set of values to the parent.
@@ -352,6 +351,7 @@ async def aggregate(seed):
     print("AGGREGATOR COMPLETE!")
 
 
+# this is the main actor and *arbiter*
 async def a_quadruple_example():
     # a nursery which spawns "actors"
     async with tractor.open_nursery() as nursery:
@@ -367,7 +367,7 @@ async def a_quadruple_example():
 
         start = time.time()
         # the portal call returns exactly what you'd expect
-        # as if the remote "main" function was called locally
+        # as if the remote "aggregate" function was called locally
         result_stream = []
         async for value in await portal.result():
             result_stream.append(value)