attune

Tools for tuning optical parametric amplifiers and multidimensional spectrometers.

https://github.com/wright-group/attune

Science Score: 36.0%

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  • Committers with academic emails
    3 of 9 committers (33.3%) from academic institutions
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    Low similarity (8.7%) to scientific vocabulary

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Repository

Tools for tuning optical parametric amplifiers and multidimensional spectrometers.

Basic Info
  • Host: GitHub
  • Owner: wright-group
  • License: mit
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 4.81 MB
Statistics
  • Stars: 3
  • Watchers: 2
  • Forks: 3
  • Open Issues: 14
  • Releases: 15
Created almost 9 years ago · Last pushed about 1 year ago
Metadata Files
Readme Changelog Contributing License Zenodo

README.md

attune

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Tools for tuning optical parametric amplifiers and multidimensional spectrometers.

Documentation is available at https://attune.wright.tools/en/latest/.

Overview

attune has three primary jobs:

  1. attune parses calibration data to find optimal motor positions ```

    data has scans of a motor position ("OPA1SHScrystal") against a signal

    ("signal") for a set of second harmonic signal color setpoints

    ("opa_color")

    calibrationdata = wt.open(pathtodata.wt5) calibrationdata.transform("opacolor", "motor") args = { "data": calibrationdata, "channel": "signal", "arrangement": "SHS", "tune": "SHScrystal", "instrument": "OPA1", } tunedopa1 = attune.intensity(**args) ```

  2. attune organizes optimal motor positions. The motor positions are stored in a hierarchy of mappings. Beginning at the lowest level:

* Tune : a map of OPA color (the "independent") to positions of a single motor (the "dependent").  
    ```
    my_tune = attune.Tune(
        independent=[450, 600, 700],
        dependent=[3.225, 2.332, 1.987]
    )  # relate color to bbo angle
    ```

* Arrangement : a collection of Tunes that define a concerted process (e.g. to generate idler photons, one might move several motors (`bbo`, `g1`, etc.))
    ```
    idler = attune.Arrangement("idler", dict(bbo=my_tune, g1=my_other_tune))
    ```

* Instrument : a collection of Arrangements (e.g. an OPA may have signal and idler)
    ```
    my_opa = attune.Instrument({"idler": idler, "signal": signal}, name="opa1")
    ```

    Note: arrangements can be called as tunables if they exist in the same instrument.  This can allow nested naming
    ```
    shi = attune.Arrangement(Dict(
        idler = Tune(shi_colors, idler_colors), 
        sh_crystal = Tune(shi_colors, angles)
    )) 
    ```
  1. attune stores motor mappings and remembers them through version tracking.
    • save a new instrument (or update an existing one) attune.store(my_opa)
* lookup a saved instrument (by name)
    ```
    attune.catalog()  # lists all saved instruments
    my_opa = attune.load("opa1")  # fetches the most recent version of the instrument
    my_previous_opa = attune.undo(my_opa) # fetches the previous version of the instrument
    my_old_opa = attune.load("opa1", time="yesterday")  # optional kwarg specifies the version by time of usage    
    ```

Notes

  • attune uses default units of nanometers ("nm") for its independent variables. At this time, units cannot be changed, so alternate units must be handled externally (PRs are welcome!). WrightTools calibration data is automatically converted into "nm" units for parsing.

Owner

  • Name: Wright Group
  • Login: wright-group
  • Kind: organization
  • Email: wright@chem.wisc.edu
  • Location: University of Wisconsin–Madison

GitHub Events

Total
  • Create event: 5
  • Issues event: 1
  • Release event: 2
  • Delete event: 2
  • Issue comment event: 4
  • Push event: 9
  • Pull request event: 15
Last Year
  • Create event: 5
  • Issues event: 1
  • Release event: 2
  • Delete event: 2
  • Issue comment event: 4
  • Push event: 9
  • Pull request event: 15

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 256
  • Total Committers: 9
  • Avg Commits per committer: 28.444
  • Development Distribution Score (DDS): 0.645
Past Year
  • Commits: 11
  • Committers: 4
  • Avg Commits per committer: 2.75
  • Development Distribution Score (DDS): 0.545
Top Committers
Name Email Commits
Kyle Sunden s****n@w****u 91
Kyle Sunden g****t@k****e 59
pre-commit-ci[bot] 6****] 37
Blaise Thompson b****e@u****m 26
kameyer226 k****r@w****u 20
Daniel Kohler 1****r 13
wright_stuff w****m@w****u 4
p770193 d****r@g****m 3
Darien Morrow d****w@g****m 3
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 34
  • Total pull requests: 90
  • Average time to close issues: over 1 year
  • Average time to close pull requests: 23 days
  • Total issue authors: 7
  • Total pull request authors: 6
  • Average comments per issue: 1.03
  • Average comments per pull request: 0.81
  • Merged pull requests: 81
  • Bot issues: 0
  • Bot pull requests: 27
Past Year
  • Issues: 1
  • Pull requests: 11
  • Average time to close issues: N/A
  • Average time to close pull requests: 3 days
  • Issue authors: 1
  • Pull request authors: 3
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.27
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 2
Top Authors
Issue Authors
  • ksunden (13)
  • untzag (7)
  • darienmorrow (4)
  • DLafayetteII (4)
  • ddkohler (3)
  • jscheeler1 (2)
  • kameyer226 (1)
Pull Request Authors
  • ksunden (37)
  • pre-commit-ci[bot] (29)
  • ddkohler (21)
  • untzag (7)
  • darienmorrow (1)
  • kameyer226 (1)
Top Labels
Issue Labels
enhancement (10) discussion (5) bug (4) good first issue (1)
Pull Request Labels
enhancement (3) tests (1)

Dependencies

setup.py pypi
  • WrightTools >=3.2.5
  • appdirs *
  • matplotlib *
  • maya *
  • numpy *
  • python-dateutil *
  • scipy *