Fitting Callback

[jackbdoughty]

Introduces our own data fitting callback as a wrapper to Bluesky's LiveFit.

• Adds extra functionality such as guessing heuristics, each extra piece of data will mean new guess generated before trying to fit. Plus new fitting models over what already was in scans library.
• Unit tests for each type of fit and the callback itself.
• Documentation.

Best way to test would be to go through all new docs. Then to spin up some fake noisy data and set/read values to blocks.

while True:
    val = g.get_pv("CS:SB:bob", is_local=True)
    g.set_pv("CS:SB:alice",  lorentzian(val, 1, 1, 0),is_local=True)

Then create a plan in a separate file and check that fits are working.

alice = block_r(float, "alice")
    bob = block_rw(float, "bob", write_config=BlockWriteConfig(settle_time_s=0.5))

    lf = LiveFit(Lorentzian.fit(), "alice", "bob", )

    fig, ax = plt.subplots()
    
    @subs_decorator(
        [
            LiveTable([bob.name, alice.name]),
            LiveFitPlot(lf, ax=ax, color="r", num_points=1000),
            LivePlot(y=alice.name, x=bob.name, ax=ax, marker="x", linestyle="none"),
        ]
    )
    def _inner() -> Generator[Msg, None, None]:
        yield from ensure_connected(bob, alice)
        yield from bp.scan([alice], bob, -5, 5, num=15)
    yield from _inner()

• Also worth checking that unit tests are appropriate and cover what they need to.
• And can check that fit results can be read from LiveFit.result

[LowriJenkins]

Just some minor documentation changes