Welcome to vison’s documentation!

Contents:

• README

Author

Ruyman Azzollini

Contact

<e-mail Ruyman Azzollini at UCL>

issue

v1.1+19.g64b0cf3

version

v1.1+19.g64b0cf3

date

Feb 19, 2021

This Python package “vison” is the software pipeline that has been used at MSSL for ground calibration of the VIS detection chains (12 + 2 spares), including one ROE, one RPSU and three CCDs each.

Installation

The package is distributed via github. The repository is hosted at:

https://github.com/ruymanengithub/vison

Detailed instructions:

• Installation
  • Cloning vison from the repository using git
  • Installation
    • Installing conda and creating vison environment
    • Hackity-Hack: installing astromatic_wrapper
    • Installing vison

Dependencies

Instructions to acquire a copy of the “conda” environment that provides almost all dependencies is included in the package. See Installation instructions for details. The only package that will not be installed by this means is SAO-DS9, which must be installed separetly, in order to be able to use some of the interactive inspection capabilities of Eyegore (data acquisition monitoring).

How to Use it

This is some kind of “cook-book”. Written a bit like when your grandmother tells you to put a pinch of sugar and heat it for a while… that kind of accuracy.

• vison cook-book
  • Accessing “vison”, the pipeline
    • Connecting to the remote machine via ssh
    • Changing shell to bash and updating system paths
    • Activating the “vison” conda environment
  • Data Checks / Analysis Work Environment Setup
    • 1. Starting point: assumptions
    • 2. Creating a working CALCAMP directory tree in our $HOME folder.
    • 3. Copying the script-writer session-builder scripts from the templates folder
    • 4. Copying the check/analysis script from the templates folder
  • Workflow Description
    • WRITING SCRIPTS / SESSIONS
    • Real Time Data Inspections using Eyegore
    • Checking Data Quality / Analysis
    • How to Write Test Scripts and Create Sessions
      • Writing Test Scripts
      • Making Sessions
  • Data Analysis Cookbook
    • Introduction
    • Editing the vison_config_*.py file
    • Executing the pipeline
      • Advanced: using “nohup”
    • Pipeline Feedback and Results
    • Quick Reference on running the pipeline
    • Listening/Wait Mode
    • Aim Mode
    • Estimated Processing Times
  • Real-Time Data Acquisition Monitoring: “Eyegore”
    • Vison’s data acquisition monitoring tool
      • Asking for help
    • Use Case 1
    • Use Case 2
    • Use Case 3
  • Quickds9: quick-loading many images into ds9
  • PRACTICAL CASE: “NOSTROMO”
    • ENVIRONMENT SETUP
    • Writing Acquisition Scripts for NOSTROMO
    • UPDATES to the Scripts
    • Using vison to analyse acquired data
    • Session D00: running the Pipeline in “wait” mode
    • Session D11: running the Pipeline in “aim” mode
    • Cleaning Up after Ourselves
  • PRACTICAL CASE: Running the pipeline in “FPA” mode

Guide through the code

In this section we provide an overall description of the capabilities and organisation of the code.

• Code Guide
  • Capabilities of the Code
  • Code Architecture
    • Delving into the Task class
  • Code Flow
    • Data-set Analysis
  • Data Models
  • Reusing / adapting the code
    • Repurposing the vison package
      • You are still not deterred and want to reuse the code

Pipeline Core

Pipeline master classes.

• Pipeline
  • genmaster.py
  • master.py
  • task.py

Data Model

Modules with classes of data models for inputs and outputs: exposure log, HK files, FITS files, etc.

• Data Model
  • ccd.py
  • ccd_aux.py
  • cdp.py
  • ccdsim.py
  • compliance.py
  • core.py
  • elvis.py
  • EXPLOGtools.py
  • fpa_dm.py
  • generator.py
  • HKtools.py
  • inputs.py
  • QLAtools.py
  • scriptic.py

Analysis (Shared)

Some shared analysis tools.

• Analysis (Shared)
  • ellipse.py
  • Guyonnet15.py

Test Campaign Definitions

The tests usually have free parameters (e.g. the wavelength, or the number of frames of each fluence in a flat-field test). When executing a test campaign, one has to choose what specific values to use for each test, to generate scripts and do the corresponding, and consistent analysis. This is done through the main functions in the following modules.

• Test Campaign Descriptions
  • campaign.py
  • CEAFPAcampaign.py

Charge Injection Tools

Tools for analysis of charge injection data.

• Charge Injection Tools
  • InjTask.py
  • lib.py
  • plot.py

“Flat” Acq. Analysis Tools

Tools for analysis of flat-illumination tests.

• “Flat” Acq. Analysis Tools
  • FlatTask.py
  • FlatFielding.py
  • nl.py
  • ptc.py

FPA Tests

Analysing FPA-level data.

• FPA Tests
  • fpamaster.py
  • fpatask

Image

General purpose image analysis tools.

• Image Analysis
  • bits.py
  • calibration.py
  • cosmetics.py
  • covariance.py
  • ds9reg.py
  • performance.py
  • pixbounce.py
  • sextractor.py

Metatests

Tests that ingest results from a test / type of test across a collection of block-calibrations. This is used to compare results across the “as-built” FM FPA, and produce CDPs at FPA level from the results of the calibrations of the individual blocks that make up the FPA.

• Metatests
  • metacal.py
  • bf.py
  • bias.py
  • chinj01.py
  • chinj02.py
  • cosmetics.py
  • dark.py
  • flat.py
  • mot_warm.py
  • nl.py
  • persistence.py
  • psf.py
  • ptc.py
  • tpx1.py
  • tpx2.py

Monitoring (“Eyegore”)

Tools to monitor data acquisition on real time: plots of HK, auto-updating of visual display of Exposure Log with some interactive capabilities, and display of latest images.

• Monitoring (“Eyegore”)
  • eyegore.py
  • eyeCCDs.py
  • eyeHK.py
  • eyeObs.py
  • eyeWarnings.py

OGSE

OGSE stands for Optical Ground Support Equipment.

• OGSE Tools
  • ogse.py

Plotting

General use plotting facilities.

• Plotting
  • baseplotclasses.py
  • figclasses.py
  • plots_fpa.py
  • trends.py

Point-Source Analysis

General purpose tools for the analysis of point-source images.

• Point-Source Analysis
  • basis.py
  • display.py
    • Display Library for Point-Source Analysis
  • gauss.py
    • Gaussian Model of Point-like Sources
  • models.py
    • Models (Point-Like Sources)
  • photom.py
    • Aperture Photometry of point-like objects
  • shape.py
    • Quadrupole Moments Shape Measurement
  • spot.py
  • lib.py

Scripts

These are pipeline scripts, not the Test Scripts (for those keep scrolling down).

• Scripts
  • HKmonitor.py
  • QLA.py
  • quickds9.py
  • vis_mkscripts.py
  • vis_genDataSet.py
  • vison_run
  • vis_explogs_merger.py
  • vis_run_xtalk.py
  • vis_ROE_LinCalib.py
  • vis_ROETAB_LinCalib.py
  • vis_star_finder.py
  • vis_load_DD.py
  • vis_cosmetics_masker.py
  • vis_clear_space.py
  • vis_reports_merger.py
  • vis_mksession.py
  • vis_dnl_extract.py

Support Code

Other support software.

• Support Code
  • context.py
  • ET.py
  • excel.py
  • files.py
  • flags.py
  • latex.py
  • logger.py
  • report.py
  • utils.py
  • vistime.py
  • vjson.py

Unit Testing

• Unit Testing
  • test_ccdpile.py
  • test_ccd.py

Test Scripts

These are the scripts that hold the description, execution, data validation and analysis of the tests that make the campaign. They are served by the infrasctructure and tools provided by the pipeline.

Charge Injection Scripts

Charge Injection

• Charge Injection Scripts
  • CHINJ01
  • CHINJ02

Dark Scripts

Darks and Bias tests.

• “Dark Acquisitions” Scripts
  • BIAS01
  • DARK01

Flat-Illumination Scripts

Flat-fields, Non-Linearity, PTC, BFE.

• Flat-Illumination Scripts
  • FLAT0X
  • NL02
  • PTC0X
  • BF01

Point-Source Scripts

Point-source imaging tests.

• Point-Source Scripts
  • FOCUS00
  • PSF0X

Trap-Pumping Scripts

Trap-Pumping tests.

• Trap-Pumping Scripts
  • TP11
  • TP21
  • PumpTask
  • tptools

Other Test Scripts

Cosmetics, Persistence, etc.

• Other Scripts
  • COSMETICS00
  • MOT_FF
  • MOT_WARM
  • PERSIST01
  • STRAY00

Indices and tables

• Index

• Module Index

• Search Page