diff --git a/pxii_bec/bec_ipython_client/plugins/alignment_fit.py b/pxii_bec/bec_ipython_client/plugins/alignment_fit.py
new file mode 100644
index 0000000..5cd0418
--- /dev/null
+++ b/pxii_bec/bec_ipython_client/plugins/alignment_fit.py
@@ -0,0 +1,49 @@
+import numpy as np
+from scipy.ndimage import gaussian_filter1d
+from lmfit.models import GaussianModel
+
+
+def alignment_fit_and_plot(
+    history_index: int,
+    device_name: str,
+    signal_name: str | None = None,
+    smoothing_sigma: float = 2.0,
+):
+    """
+    Get data for a completed scan from the BEC history, apply smoothing, gaussian fit,
+    gradient, and plot all the results.
+
+    Args:
+        history_index (int): scan to fetch, e.g. -1 for the most recent scan
+        device_name (str): the device for which to get the monitoring data
+
+    """
+    # Fetch scan data from the history
+    # by default, signal = device name, unless otherwise specified
+    signal = signal_name or device_name
+    scan = bec.history[history_index]
+    md = scan.metadata["bec"]
+    data = scan.devices[device_name][signal].read()["value"]
+    # motor name is a bytes object in the metadata, so make a string
+    motor_name = md["scan_motors"][0].decode()
+
+    # Create a plot and a text box to display results
+    dock_area = bec.gui.new()
+    wf = dock_area.new().new(bec.gui.available_widgets.Waveform)
+    wf.title = f"Scan {md['scan_number']}: {md['scan_name']} of {motor_name}"
+    text = dock_area.new(position="right").new(widget=bec.gui.available_widgets.TextBox)
+
+    # Calculate some processed data and add everything to the plot
+    wf.plot(data, label="Raw data")
+    smoothed_data = gaussian_filter1d(data, smoothing_sigma)
+    wf.plot(smoothed_data, label="Smoothed")
+    gradient = np.gradient(smoothed_data)
+    wf.plot(gradient, label="gradient")
+
+    # Fit a Gaussian model to the smoothed data and show the fitting parameters in the textbox
+    x_data = scan.devices[motor_name][motor_name].read()["value"]
+    model = GaussianModel()
+    result = model.fit(smoothed_data, x=x_data)
+    text.set_plain_text(f"Fit parameters: \n{result.params.pretty_repr()}")
+
+    return result