Below, we will connect to EPICS IOC(s) controlling simulated hardware in lieu of actual motors and detectors. An EPICS IOC is control system software that allows communication with a wide variety of hardware using a common interface. The IOCs should already be running in the background. Run this command to verify that they are running: it should produce output with RUNNING on each line. In the event of a problem, edit this command to replace
restart all and run again.
from bluesky_tutorial_utils.beamline_configuration import *
Check that we can communicate with the hardware. If this doesn't raise an error, it worked.
In the example below, the Bluesky run engine is the interpreter of experiment plans and
count is an experiment plan used here to acquire one reading from a point detector.
from bluesky.plans import count RE(count([det]))
The return value is a list of the run IDs that uniquely identify this data set. The "scan num" is easier to remember but is not good for long-term reference because it may not be unique.
Let's looks at the documentation for
count to see what our other options are.
help(count) # or, equiavently, type count? or ?count
Executing the next cell will display an empty widget. In the sections below, the scans that we run will add figures to this widget.
If you are reading this in JupyterLab, right-click somewhere in the output area below and choose "Create New View for Output". This will display a up-to-date copy of the figures off to the side of this notebook, and avoid the need for frequent scrolling between this widget and the code that follows.
# five consecutive readings RE(count([det], num=5))
# five sequential readings separated by a 1-second delay RE(count([det], num=5, delay=1))
RE(scan([det], motor, -10, 10, 15))
Bluesky includes utilities to inspecting plans before they are run. You can imagine various reasons you might want to do this. Example:
from bluesky.simulators import summarize_plan summarize_plan(scan([det], motor, -1, 1, 3))
Define a custom "plan", using the Python syntax
yield from to dispatch out to built-in plans.
# The plan_stubs module contains smaller plans. # They can be used alone or as buildling blocks for larger plans. from bluesky.plan_stubs import mv def sweep_exposure_time(times): "Multiple scans: one per exposure time setting." for t in times: yield from mv(det.exp, t) yield from scan([det], motor, -10, 10, 5)
Before we run, let's make our simulated motor move faster, just to save time in this example.
motor.delay = 0
RE(sweep_exposure_time([0.01, 0.1, 1]))
Q1: Above we ran a
count with multiple readings separated by a fixed delay. The
delay parameter also accepts a list of values. Try a
count with a variable delay.
# Try your solution here. Fill in the blank: # RE(count(____)))
Execute the following cell to reveal a solution:
Q2: Write a custom plan that scans the same region twice, first with coarse steps and then with fine steps.
# Try your solution here. Fill in the blank: # def coarse_and_fine(detectors, motor, start, stop): # yield from scan(___) # yield from scan(___) # # RE(coarse_and_fine([det], motor, -10, 10))
Q3. All of the usages of scan we have seen so far scan from negative to positive. Scan from positive to negative.
# Try your solution here.
scan plan samples equally-spaced points. To sample arbitrary points, you can use
list_scan. Import it from the same module that we imported
scan from, then use
list_scan? to view its documentation and figure out how to use it. Scan the positions
[1, 1, 2, 3, 5, 8].
# Try your solution here.
Q5: What's wrong with this? (What does it do?)
# Broken example def sweep_exposure_time(times): "Multiple scans: one per exposure time setting." for t in times: mv(det.exp, t) scan([det], motor, -10, 10, 15)