Stuart Campbell stuartcampbell

IOC connection time analysis

We will use caproto-shark to analyze CA network traffic. Note that the actual servers and clients involve may or may not be using caproto themselves; it does not matter.

Install caproto and pandas if they are not already installed.
```
pip install caproto[standard] pandas
```

rename a section of a filename, i. e. example.1.{txt,conf,db} or 12345.1.{wav,ogg,mp3} and change the 1 to a 2 in the filename while preserving the rest of it.

  zmv -n '(*.)(<->)(.[^.]#)' '$1$(($2+1))$3' # would rename x.0001.y to x.2.y.
  zmv -n '(*.0#)(<->)(.[^.]#)' '$1$(($2+1))$3'

Rename files to lower case

  $ zmv '*' '${(L)f}'

Initial setup of Docker worker VM

Start the Docker daemon and a local Docker registry.

sudo systemctl start docker
sudo docker run -d -p 5000:5000 --restart=always --name registry registry:2

Install repo2docker.

Notes on Refactoring DataBroker to Use Intake

This document is still a working draft.

Summary

We recently refactored DataBroker, a project maintained by the DOE Light Sources as part of the Bluesky Collaboration, to leverage intake and to join the nascent community around it. We adopted several good ideas from intake's design, and we

Replay

Replay documents from a bluesky run with realistic time spacing.

Example

This example acquires data with bluesky, saves the documents to disk, accesses the data using databroker, and finally uses replay to push the documents into a LiveTable callback.

	version: "3.9"

	services:

	rapids:
	image: rapidsai/rapidsai:0.18-cuda11.0-runtime-ubuntu16.04-py3.8
	ports:
	- "8888:8888" # Jupyter
	- "8786:8786" # Dask communication
	- "8787:8787" # Dask dashboard

	#!/bin/bash
	# ------------------------------------------------------------------
	# Simple Backup Script for MongoDB
	# ------------------------------------------------------------------

	# Definitions
	MONGO_HOST='localhost'
	MONGO_PORT="27017"
	DUMP_PATH='/backup/mongo_backup'
	DAYS_OF_BACKUPS=2

	from ophyd.sim import img as detector
	from bluesky.plan_stubs import open_run, stage, unstage, close_run, trigger_and_read, subscribe, unsubscribe
	from bluesky_live.bluesky_run import BlueskyRun, DocumentCache


	def plan():
	"""
	Take 10 shots with a detector and do some data processing on the result.

	Key points

	#!/bin/bash

	# To be run as:
	# $ docker run -it --rm -v $PWD:/build quay.io/condaforge/linux-anvil-comp7:latest bash /build/gen-conda-packed-env-collection-2021-1.2.sh

	set -e

	umask 0002

	sudo yum install mesa-libGL -y

	#!/bin/bash

	set -e

	bib_src="$HOME/Brookhaven National Laboratory/NSLS-II SUF AI-ML - Beamline Optimization/LAB-20-2261_BL-opt.bib"
	xml_src="$HOME/Brookhaven National Laboratory/NSLS-II SUF AI-ML - Beamline Optimization/LAB-20-2261_BL-opt.xml"
	xml_dest="$HOME/Library/Containers/com.microsoft.Word/Data/Library/Application Support/Microsoft/Office/Sources.xml"
	binary="/Applications/JabRef.localized/JabRef.app/Contents/MacOS/JabRef"

	# Convert .bib file to .xml: