Added edge_align function for beam edge alignment

CHANGES.rst

@@ -30,6 +30,11 @@ describe future plans.
 
 release expected by 2024-03-31
 
+New Features
+------------
+
+* Add new plan for edge alignment called edge_align
+
 Fixes
 -----
 

apstools/plans/__init__.py

@@ -2,6 +2,7 @@
 from .alignment import TuneResults
 from .alignment import lineup
 from .alignment import lineup2
+from .alignment import edge_align
 from .alignment import tune_axes
 from .command_list import CommandFileReadError
 from .command_list import command_list_as_table

apstools/plans/alignment.py

@@ -16,6 +16,9 @@
 
 import numpy as np
 import pyRestTable
+from scipy.optimize import curve_fit
+from scipy.special import erf
+
 from bluesky import plan_stubs as bps
 from bluesky import plans as bp
 from bluesky import preprocessors as bpp
@@ -25,6 +28,7 @@
 from ophyd import Signal
 from ophyd.scaler import ScalerCH
 from ophyd.scaler import ScalerChannel
+import warnings
 
 from .. import utils
 from .doc_run import write_stream
@@ -34,8 +38,14 @@
 
 def lineup(
     # fmt: off
-    detectors, axis, minus, plus, npts,
-    time_s=0.1, peak_factor=4, width_factor=0.8,
+    detectors,
+    axis,
+    minus,
+    plus,
+    npts,
+    time_s=0.1,
+    peak_factor=4,
+    width_factor=0.8,
     feature="cen",
     rescan=True,
     bec=None,
@@ -135,7 +145,8 @@ def peak_analysis():
 
         if det0.name not in bec.peaks[feature]:
             logger.error(
-                "No statistical analysis of scan peak for feature '%s'!" "  (bec.peaks=%s, bec=%s)",
+                "No statistical analysis of scan peak for feature '%s'!"
+                "  (bec.peaks=%s, bec=%s)",
                 feature,
                 bec.peaks,
                 bec,
@@ -213,10 +224,128 @@ def peak_analysis():
         scaler.stage_sigs = old_sigs
 
 
+def edge_align(detectors, mover, start, end, points, cat=None, md={}):
+
+    """
+    Align to the edge given mover & detector data, relative to absolute position.
+
+    This plan can be used in the queueserver, Jupyter notebooks, and IPython
+    consoles.
+
+    PARAMETERS
+    ----------
+    detectors *Readable* or [*Readable*]:
+        Detector object or list of detector objects (each is a Device or
+        Signal).
+
+    mover *Movable*:
+        Mover object, such as motor or other positioner.
+
+    start *float*:
+        Starting point for the scan. This is an absolute motor location.
+
+    end *float*:
+        Ending point for the scan. This is an absolute motor location.
+
+    points *int*:
+        Number of points in the scan.
+
+    cat *databroker.temp().v2*:
+        Catalog where bluesky data is saved and can be retrieved from.
+
+    md *dict*:
+        User-supplied metadata for this scan.
+    """
+
+    def guess_erf_params(x_data, y_data):
+        """
+        Provide an initial guess for the parameters of an error function.
+
+        Parameters
+        ----------
+        x_data : A numpy array of the values on the x_axis
+        y_data : A numpy array of the values on the y_axis
+
+        Returns
+        -------
+        guess : dict
+            A dictionary containing the guessed parameters 'low_y_data', 'high_y_data', 'width', and 'midpoint'.
+        """
+
+        # Sort data to make finding the mid-point easier and to assist in other estimations
+        y_data_sorted = np.sort(y_data)
+        x_data_sorted = np.sort(x_data)
+
+        # Estimate low and high as the first and last elements (assuming sorted data)
+        low_y_data = np.min(y_data_sorted)
+        high_y_data = np.max(y_data_sorted)
+
+        low_x_data = np.min(x_data_sorted)
+        high_x_data = np.max(x_data_sorted)
+
+        # Estimate wid as a fraction of the range. This is very arbitrary and might need tuning!
+        width = (
+            (high_x_data - low_x_data) / 10
+        )  # This is a guess and might need adjustment based on your data's characteristics
+
+        # Estimate the midpoint of the x values
+        midpoint = x_data[int(len(x_data) / 2)]
+
+        return [low_y_data, high_y_data, width, midpoint]
+
+    def erf_model(x, low, high, width, midpoint):
+        """
+        Create error function for fitting and simulation
+
+        Parameters
+        ----------
+        x		:	input upon which error function is evaluated
+        low		: 	min value of error function
+        high	: 	max value of error function
+        width  	:	"spread" of error function transition region
+        midpoint:	location of error function's "center"
+        """
+        return (high - low) * 0.5 * (1 - erf((x - midpoint) / width)) + low
+
+    if not isinstance(detectors, (tuple, list)):
+        detectors = [detectors]
+
+    _md = dict(purpose="edge_align")
+    _md.update(md or {})
+
+    uid = yield from bp.scan(detectors, mover, start, end, points, md=_md)
+    cat = cat or utils.getCatalog()
+    run = cat[uid]  # return uids
+    ds = run.primary.read()
+
+    x = ds["mover"]
+    y = ds["noisy"]
+
+    initial_guess = guess_erf_params(x, y)
+
+    try:
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "error", "Covariance of the parameters could not be estimated"
+            )
+            popt, pcov = curve_fit(erf_model, x, y, p0=initial_guess)
+
+            yield from bps.mv(mover, popt[3])
+
+    except UserWarning as e:
+        print(f"Warning caught as exception: {e}")
+        print("\n Check to see if signal is linear.\n")
+
+
 def lineup2(
     # fmt: off
-    detectors, mover, rel_start, rel_end, points,
-    peak_factor=2.5, width_factor=0.8,
+    detectors,
+    mover,
+    rel_start,
+    rel_end,
+    points,
+    peak_factor=2.5,
+    width_factor=0.8,
     feature="centroid",
     nscans=2,
     signal_stats=None,
@@ -621,7 +750,9 @@ def _scan(width=1, step_factor=10, num=10, snake=True):
                 }
                 _md.update(md or {})
 
-                yield from self.tune(width=width, num=num, peak_factor=peak_factor, md=_md)
+                yield from self.tune(
+                    width=width, num=num, peak_factor=peak_factor, md=_md
+                )
 
                 if not self.tune_ok:
                     return
@@ -633,7 +764,9 @@ def _scan(width=1, step_factor=10, num=10, snake=True):
                 if snake:
                     width *= -1
 
-        return (yield from _scan(width=width, step_factor=step_factor, num=num, snake=snake))
+        return (
+            yield from _scan(width=width, step_factor=step_factor, num=num, snake=snake)
+        )
 
     def multi_pass_tune_summary(self):
         t = pyRestTable.Table()
@@ -724,7 +857,10 @@ class TuneResults(Device):
     peakstats_attrs = "x y cen com fwhm min max crossings".split()
 
     def report(self, title=None):
-        keys = self.peakstats_attrs + "tune_ok center initial_position final_position".split()
+        keys = (
+            self.peakstats_attrs
+            + "tune_ok center initial_position final_position".split()
+        )
         t = pyRestTable.Table()
         t.addLabel("key")
         t.addLabel("result")

docs/source/api/_plans.rst

@@ -29,14 +29,15 @@ Custom Scans
 
 .. autosummary::
 
-   ~apstools.plans.doc_run.documentation_run
-   ~apstools.plans.labels_to_streams.label_stream_decorator
+   ~apstools.plans.alignment.edge_align
    ~apstools.plans.alignment.lineup
    ~apstools.plans.alignment.lineup2
+   ~apstools.plans.alignment.tune_axes
+   ~apstools.plans.alignment.TuneAxis
+   ~apstools.plans.doc_run.documentation_run
+   ~apstools.plans.labels_to_streams.label_stream_decorator
    ~apstools.plans.nscan_support.nscan
    ~apstools.plans.sscan_support.sscan_1D
-   ~apstools.plans.alignment.TuneAxis
-   ~apstools.plans.alignment.tune_axes
 
 .. _plans.overall: