Strange interaction with matplotlib magic

I have an app that uses jupyter_client to interact with an ipykernel. In that app I have an execution loop that sends an execute request and then waits for a reply on the iopub channel that has `"execution_state": "idle"`. I assume that when this message is received it is done executing and ready to execute something else. I never send a second execute request until I get the idle-state reply.

This works until I use the `%matplotlib` magic to load a gui backend (e.g., `%matplotlib qt`). After that, things begin to behave strangely. If I send code to execute, and that code raises an exception, I get the error reply normally. But then whatever code I send *next* is aborted without ever being executed. I can only assume this is due to some interaction with the gui event loop that that matplotlib magic is creating, but I can't figure out exactly where the problem lies. It seems that the "stop aborting" event somehow is not processed until after the next execution request I send (even if the app just sits idle in between the exception-raising code and my next execution request). In other words things seem to happen out of order: I send a request, it raises an exception, the kernel "aborts the queue" (even though there are no other requests in the queue), I then send a new request, and the kernel then aborts that (even though it wasn't in the queue when the kernel decided to abort).

I'm not sure if this is a problem with ipykernel or with jupyter_client, but I'm posting here to see if there's something else I should be doing on the jupyter_client end. In particular, I want to know what message I need to wait for after submitting an execution request that tells me "the kernel is *done* processing, the next execution request you send will be processed and will not be aborted because of some lingering earlier error". I thought that was the idle-state message, but apparently not, because when I receive that message it seems the kernel might still be in a state where it is going to abort whatever I send it (because it thinks they were all part of one execution queue). Right now I fixed it by inserting a `wait_for_ready` call after each execution, but I'm not sure if that is overkill or could have any other unexpected effects.

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Originally opened as jupyter/jupyter_client#904 by @BrenBarn, migration requested by @blink1073

Reproduce the execution loop using jupyter_client and ipykernel with `%matplotlib qt`, comparing the iopub `execution_state: idle` message with `wait_for_ready` after an exception. Trace the kernel's abort behavior and determine which signal reliably permits the next execution request; done means the ordering is understood and the reported behavior has a verified resolution.

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