Some import statements just imported `scipy` when we needed
`scipy.sparse`. Import order differences made this an occasional
bug. Fixes #252.

This allows us to do a proper `bones-check` with `black`.
The hardware tests are still in 3.5.2 to support NxSDK.

This commit also fixes some slight changes by `nengo-bones` 0.6.0
that were missed in the upgrade commit because of the missing
`bones-check`.

Not backwards compatible with previous versions.

This is useful for testing SNIPs.

- Add a timer around the `Simulator._run_steps` call, to measure the
  time taken for all steps.
- Connect to the board outside the timing loop, so that this does not
  count towards the step time.
- Add a timer specific to SNIPs, to get the most accurate timing
  (after we call the board run function, so all setup has happened).

This reduces unnecessary communication with the chip

The host SNIP runs on the host and facilitates communication
with the superhost using sockets. This is faster than using
the default RPC interface.

We also take care to make sure both the host and chip SNIPs
end properly, by sending a message with a negative spike count.
This helps to eliminate board hangs.

To allow the host SNIP to work with multiple `run` calls, we
keep it idling in between `run` calls, waiting for a message.
If the board disconnects before a subsequent run call,
the negative spike count message will tell the host SNIP to stop.

This improves performance by reducing the number of channel reads.

This allows the Nengo model on the (super)host to be running
simultaneously with the chip, reducing time per step but adding
in a one step delay between the (super)host model and chip model.

We're typically calling it one step at a time, in which case we
can simplify it and make it faster.

TODO:
- Fix up original collect_receiver_info and remove profiling code
- The original can probably do the same thing were we get rid of
  the receive and collect functions on the nodes, since essentially
  these just populate and read lists in those nodes.
- Only use the single step function if we know we'll be running
  one step at a time (this might be always right now).

Previously, fixed checking of `neurons_per_dimension` and fixed
value for `add_to_container` made `get_ensemble` not particularly
useful for users trying to make their own `DecodeNeurons`. Now,
these are configurable, and default to the values that users would
likely want.

This benchmark has a number of ensembles in parallel on the chip.
It can be used to test chip input and output speeds, and learning.

TODO:
- Uses weird intercepts. Just use normal ones.
- Currently only set up for learning. Allow it to be used without
  learning, and without funnelling on inputs and outputs.

- Remove weird intercept distribution
- Allow input and output funnelling and learning to be configured