Fitting Callback

Similar to LivePlot, ibex_bluesky_core provides a thin wrapper around Bluesky’s LiveFit class, enhancing it with additional functionality to better support real-time data fitting. This wrapper not only offers a wide selection of models to fit your data on, but also introduces guess generation for fit parameters. As new data points are acquired, the wrapper refines these guesses dynamically, improving the accuracy of the fit with each additional piece of data, allowing for more efficient and adaptive real-time fitting workflows.

In order to use the wrapper, import LiveFit from ibex_bluesky_core rather than bluesky directly:

from ibex_bluesky_core.callbacks.fitting import LiveFit

Note: that you do not need LivePlot for LiveFit to work but it may be useful to know visaully how well the model fits to the raw data.

Configuration

Below is a full example showing how to use standard matplotlib & bluesky functionality to apply fitting to a scan, using LivePlot and LiveFit. The fitting callback is set to expect data to take the form of a gaussian.

import matplotlib.pyplot as plt
from ibex_bluesky_core.callbacks.plotting import LivePlot
from ibex_bluesky_core.callbacks.fitting import LiveFit
from bluesky.callbacks import LiveFitPlot

# Create a new figure to plot onto.
plt.figure()
# Make a new set of axes on that figure
ax = plt.gca() 
# ax is shared by fit_callback and plot_callback 

plot_callback = LivePlot(y="y_variable", x="x_variable", ax=ax)
fit_callback = LiveFit(Gaussian.fit(), y="y_variable", x="x_variable", update_every=0.5)
# update_every = in seconds, how often to recompute the fit. If `None`, do not compute until the end. Default is 1.
fit_plot_callback = LiveFitPlot(fit_callback, ax=ax, color="r")

Note: that the LiveFit callback doesn’t directly do the plotting, it will return function parameters of the model its trying to fit to; a LiveFit object must be passed to LiveFitPlot which can then be subscribed to the RunEngine. See the Bluesky Documentation for information on the various arguments that can be passed to the LiveFitPlot class.

The plot_callback and fit_plot_callback objects can then be subscribed to the RunEngine, using the same methods as described in LivePlot. See the following example using @subs_decorator:

@subs_decorator(
    [
        fit_plot_callback,
        plot_callback
    ]
)

def plan() -> ... 

Models

We support standard fits for the following trends in data. See Standard Fits for more infomation on the behaviour of these fits.

Trend	Class Name in fitting_utils	Arguments
Linear	Linear	None
Polynomial	Polynomial	Polynomial Degree (int)
Gaussian	Gaussian	None
Lorentzian	Lorentzian	None
Damped Oscillator	DampedOsc	None
Slit Scan Fit	SlitScan	Max Slit Size (int)
Error Function	ERF	None
Complementary Error Function	ERFC	None
Top Hat	TopHat	None
Trapezoid	Trapezoid	None
PeakStats (COM) *	-	-

* Native to Bluesky there is support for PeakStats which “computes peak statsitics after a run finishes.” See Bluesky docs for more information on this. Similar to LiveFit and LiveFitPLot, PeakStats is a callback and must be passed to PeakStatsPlot to be plotted on a set of axes, which is subscribed to by the RunEngine.

Each of the above fit classes has a .fit() which returns an object of type FitMethod. This tells LiveFit how to perform fitting on the data. FitMethod is defined in ibex_bluesky_core.callbacks.fitting.

There are two ways that you can choose how to fit a model to your data:

Option 1: Use the standard fits

When only using the standard fits provided by ibex_bluesky_core, the following syntax can be used, replacing [FIT] with your chosen one from ibex_bluesky_core.callbacks.fitting.fitting_utils:

from bluesky.callbacks import LiveFitPlot
from ibex_bluesky_core.callbacks.fitting.fitting_utils import [FIT]

# Pass [FIT].fit() to the first parameter of LiveFit
lf = LiveFit([FIT].fit(), y="y_variable", x="x_variable", update_every=0.5)

# Then subscribe to LiveFitPlot(lf, ...)

The [FIT].fit() function will pass the FitMethod object straight to the LiveFit class.

Note: that for the fits in the above table that require parameters, you will need to pass value(s) to their .fit method. For example Polynomial fitting:

lf = LiveFit(Polynomial.fit(3),  y="y_variable", x="x_variable", update_every=0.5)
# For a polynomial of degree 3

Option 2: Use custom fits

If you wish, you can define your own non-standard FitMethod object. The FitMethod class takes two function arguments as follows:

model
- A function representing the behaviour of the model.
- Returns the y value (float) at the given x value and model parameters.
guess
- A function that must take two np.array arrays, representing x data and respective y data, of type float as arguments and must return a dict in the form dict[str, lmfit.Parameter].
- This will be called to guess the properties of the model, given the data already collected in the Bluesky run.

See the following example on how to define these.

# Linear Fitting

import lmfit

def model(x: float, m: float, c: float) -> float:
    
    return m * x + c # y = mx + c

def guess(x: npt.NDArray[np.float64], y: npt.NDArray[np.float64]) -> dict[str, lmfit.Parameter]:

    # Linear regression calculation
    # x = set of x data
    # y = set of respective y data
    # x[n] makes a pair with y[n]
    
    numerator = sum(x * y) - sum(x) * sum(y)
    denominator = sum(x**2) - sum(x) ** 2

    m = numerator / denominator
    c = (sum(y) - m * sum(x)) / len(x)

    init_guess = {
        "m": lmfit.Parameter("m", m), # gradient
        "c": lmfit.Parameter("c", c), # y - intercept
    }

    return init_guess

fit_method = FitMethod(model, guess) 
#Pass the model and guess function to FitMethod

lf = LiveFit(fit_method, y="y_variable", x="x_variable", update_every=0.5)

# Then subscribe to LiveFitPlot(lf, ...)

Note: that the parameters returned from the guess function must allocate to the arguments to the model function, ignoring the independant variable e.g x in this case. Array-like structures are not allowed. See the lmfit documentation for more information.

Option 2: Continued

Each Fit in ibex_bluesky_core.callbacks.fitting has a .model() and .guess(), which make up their fitting method. These are publically accessible class methods.

This means that aslong as the parameters returned from the guess function match to the arguments of the model function, you may mix and match user-made and standard, models and guess functions in the following manner:

import lmfit
from ibex_bluesky_core.callbacks.fitting.fitting_utils import Linear

def different_model(x: float, m: float, c: float) -> float:
    
    return m * x + c ** 2 # y = mx + (c ** 2)

fit_method = FitMethod(different_model, Linear.guess())
# Uses the user defined model and the standard Guessing. function for linear models

lf = LiveFit(fit_method, y="y_variable", x="x_variable", update_every=0.5)

# Then subscribe to LiveFitPlot(lf, ...)

… or the other way round …

import lmfit
from ibex_bluesky_core.callbacks.fitting.fitting_utils import Linear

# This Guessing. function isn't very good because it's return values don't change on the data already collected in the Bluesky run
# It always guesses that the linear function is y = x

def different_guess(x: float, m: float, c: float) -> float:
    
    init_guess = {
        "m": lmfit.Parameter("m", 1), # gradient
        "c": lmfit.Parameter("c", 0), # y - intercept
    }

    return init_guess

fit_method = FitMethod(Linear.model(), different_guess)
# Uses the standard linear model and the user defined Guessing. function

lf = LiveFit(fit_method, y="y_variable", x="x_variable", update_every=0.5)

# Then subscribe to LiveFitPlot(lf, ...)

Or you can create a completely user-defined fitting method.

Note: that for fits that require arguments, you will need to pass values to their respecitive .model and .guess functions. E.g for Polynomial fitting:

fit_method = FitMethod(Polynomial.model(3), different_guess) # If using a custom guess function
lf = LiveFit(fit_method, ...)

See the standard fits list above for standard fits which require parameters. It gets more complicated if you want to define your own custom model or guess which you want to pass parameters to. You will have to define a function that takes these parameters and returns the model / guess function with the subsituted values.