Script GUI Interactions¶

Goal

Try a small BEC history analysis in the IPython client, smooth the data locally, fit it with a Waveform DAP curve, and continue adjusting the plot interactively.

Start directly in the BEC IPython client. This page shows how normal Python analysis can be combined with Waveform plotting without turning the workflow into an automated control script.

Prerequisites¶

BEC is running with Terminal + Dock.
At least one completed scan is available through bec.history.
The bpm4i signal was recorded in the recent scans.
You know the motor used for the scan, for example samx.
The Python environment provides numpy and scipy.

1. Try the commands in IPython¶

Fetch the most recent scan, read the motor and signal data, and smooth the signal:

import numpy as np
from scipy.ndimage import gaussian_filter1d

scan = bec.history[-1]
motor_name = "samx"
device_name = "bpm4i"
signal_name = "bpm4i"

x = np.asarray(scan.devices[motor_name][motor_name].read()["value"])
y = np.asarray(scan.devices[device_name][signal_name].read()["value"])

smoothed = gaussian_filter1d(y, sigma=2.0)
gradient = np.gradient(smoothed)

2. Plot the measurement and fit it with DAP¶

Create a Waveform and fill it with the measured signal, smoothed signal, and gradient. Attach a GaussianModel DAP curve to the smoothed data:

gui.bec.delete_all()
wf = gui.bec.new(gui.available_widgets.Waveform)

wf.title = f"{device_name} smoothed history fit"
wf.x_label = motor_name
wf.y_label = f"{device_name}-{signal_name}"

wf.plot(x=x, y=y, label="measured")
wf.plot(x=x, y=smoothed, label="smoothed", dap="GaussianModel")
wf.plot(x=x, y=gradient, label="gradient")

After the DAP curve has updated, inspect the fit output:

wf.get_dap_summary()
wf.get_dap_params()

You can also read plotted data back from the Waveform and add another processed curve:

data = wf.get_all_data()
smoothed_data = data["smoothed"]

x_from_waveform = np.asarray(smoothed_data["x"])
y_from_waveform = np.asarray(smoothed_data["y"])
y_shifted = y_from_waveform - np.min(y_from_waveform)

wf.plot(x=x_from_waveform, y=y_shifted, label="smoothed shifted")

3. Continue interactively¶

Use the Waveform reference when you want to inspect fit results or adjust the plot:

data = wf.get_all_data()

wf.get_dap_summary()
wf.title = "Updated smoothed history comparison"

You can keep adding derived curves while you inspect the data:

gradient_abs = np.abs(gradient)
wf.plot(x=x, y=gradient_abs, label="absolute gradient")

Use a profile when the layout already exists

You do not have to create the Waveform from scratch. You can load a Dock Area profile first, then fill the widgets that already exist in that profile:

gui.bec.load_profile("alignment_scan")
wf = gui.bec.Waveform

To start from the saved baseline version of a profile, use:

gui.bec.load_profile("alignment_scan", restore_baseline=True)

This is the short form for loading the profile after restoring its runtime copy from the baseline. You can also call the restore step explicitly:

gui.bec.restore_baseline_profile("alignment_scan", show_dialog=False)

Result

You combined BEC history access, Python analysis, Waveform plotting, and DAP fitting from the BEC IPython client.