From 521e4c3691cc3b6a5bc0994069f3a5fb3ba3635b Mon Sep 17 00:00:00 2001 From: Matthew Feickert Date: Tue, 20 Aug 2024 21:38:43 -0500 Subject: [PATCH 1/4] feat: Add pre-commit config * Add pre-commit/pre-commit-hooks for general linting. - Ignore CSV files. * Add python-jsonschema/check-jsonschema for GitHub Actions. schema validation * Add pre-commit.ci config. - Set autoupdate schedule to quarterly (the longest possible). --- .pre-commit-config.yaml | 38 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) create mode 100644 .pre-commit-config.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000000..59dd6d3a7f --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,38 @@ +ci: + autoupdate_commit_msg: "chore: [pre-commit.ci] pre-commit autoupdate" + autoupdate_schedule: quarterly + +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.6.0 + hooks: + - id: check-case-conflict + - id: check-json + - id: check-merge-conflict + - id: check-symlinks + - id: check-xml + - id: check-yaml + - id: debug-statements + - id: end-of-file-fixer + exclude: > + (?x)^( + .*\.csv + )$ + - id: mixed-line-ending + exclude: > + (?x)^( + .*\.csv| + .*\.css + )$ + - id: requirements-txt-fixer + - id: trailing-whitespace + exclude: > + (?x)^( + .*\.csv + )$ + + - repo: https://github.com/python-jsonschema/check-jsonschema + rev: 0.29.1 + hooks: + - id: check-github-workflows + args: ["--verbose"] From a9bb62f48bec4cd015eb62877322ffc20a46c46b Mon Sep 17 00:00:00 2001 From: Matthew Feickert Date: Tue, 20 Aug 2024 22:10:42 -0500 Subject: [PATCH 2/4] style: Apply pre-commit hooks --- papers/00_bibderwalt/00_bibderwalt.rst | 1 - papers/00_texderwalt/00_texderwalt.tex | 1 - papers/00_texderwalt/metadata.yaml | 2 +- publisher/Makefile | 4 +- publisher/_static/common.css | 12 ++-- publisher/_static/scipy.sty | 2 +- publisher/_static/screen.css | 23 ++++--- publisher/_templates/students.html.tmpl | 2 +- publisher/build_template.py | 2 +- publisher/conf.py | 1 - publisher/getting_ready_for_new_year.md | 17 +++--- publisher/mail/requirements.txt | 2 +- .../mail/templates/author-invitation.txt.tmpl | 2 - .../mail/templates/reviewer-final.txt.tmpl | 2 - .../reviewer-invitation-2024.txt.tmpl | 2 +- .../mail/templates/reviewer-invite.txt.tmpl | 2 - .../mail/templates/reviewer-reminder.txt.tmpl | 2 - publisher/metadata/2010/doi_batch_papers.xml | 2 +- publisher/metadata/2010/doi_batch_slides.xml | 2 +- publisher/metadata/2010/scipy_proc.json | 2 +- publisher/metadata/2010/toc.json | 2 +- publisher/metadata/2011/doi_batch_papers.xml | 2 +- publisher/metadata/2011/doi_batch_slides.xml | 2 +- publisher/metadata/2011/scipy_proc.json | 2 +- publisher/metadata/2011/toc.json | 2 +- publisher/metadata/2012/doi_batch_papers.xml | 2 +- publisher/metadata/2012/doi_batch_slides.xml | 2 +- publisher/metadata/2012/scipy_proc.json | 2 +- publisher/metadata/2012/toc.json | 2 +- publisher/metadata/2013/doi_batch_papers.xml | 2 +- .../doi_batch_papers_second_submission.xml | 2 +- publisher/metadata/2013/doi_batch_slides.xml | 2 +- publisher/metadata/2013/scipy_proc.json | 2 +- publisher/metadata/2013/toc.json | 2 +- publisher/metadata/2014/doi_batch.xml | 2 +- publisher/metadata/2014/emails.txt | 34 +++++------ publisher/metadata/2014/scipy_proc.json | 2 +- publisher/metadata/2014/toc.json | 2 +- publisher/metadata/2015/doi_batch.xml | 2 +- publisher/metadata/2015/email.txt | 60 +++++++++---------- publisher/metadata/2015/scipy_proc.json | 2 +- publisher/metadata/2015/toc.json | 2 +- publisher/metadata/2016/doi_batch.xml | 2 +- publisher/metadata/2016/emails.txt | 40 ++++++------- publisher/metadata/2016/scipy_proc.json | 2 +- publisher/metadata/2016/toc.json | 2 +- publisher/metadata/2018/doi_batch.xml | 2 +- publisher/metadata/2018/toc.json | 2 +- publisher/metadata/2020/doi_batch_papers.xml | 2 +- publisher/metadata/2020/doi_batch_slides.xml | 2 +- publisher/metadata/2020/other.json | 2 +- publisher/metadata/2020/scipy_proc.json | 2 +- publisher/metadata/2020/toc.json | 2 +- publisher/metadata/2021/doi_batch_papers.xml | 2 +- publisher/metadata/2021/doi_batch_slides.xml | 2 +- publisher/metadata/2021/other.json | 2 +- publisher/metadata/2021/scipy_proc.json | 2 +- publisher/metadata/2021/toc.json | 2 +- publisher/metadata/2022/doi_batch_papers.xml | 2 +- publisher/metadata/2022/doi_batch_slides.xml | 2 +- publisher/metadata/2022/other.json | 2 +- publisher/metadata/2022/scipy_proc.json | 2 +- publisher/metadata/2022/toc.json | 2 +- publisher/tempita/__init__.py | 6 +- publisher/tests/test_options.py | 2 +- publisher/writer/rstmath.py | 1 - requirements.txt | 2 +- reviews/invite-paper-reviews.rst | 2 +- reviews/invite-reviewer.txt.in | 12 ++-- scipy_proc.json | 2 +- 70 files changed, 156 insertions(+), 170 deletions(-) diff --git a/papers/00_bibderwalt/00_bibderwalt.rst b/papers/00_bibderwalt/00_bibderwalt.rst index 2386d51426..03d81d11c4 100644 --- a/papers/00_bibderwalt/00_bibderwalt.rst +++ b/papers/00_bibderwalt/00_bibderwalt.rst @@ -341,4 +341,3 @@ proceedings editors. :usepackage: somepackage Some custom LaTeX source here. - diff --git a/papers/00_texderwalt/00_texderwalt.tex b/papers/00_texderwalt/00_texderwalt.tex index 8ef922df68..4ecd47c1ef 100644 --- a/papers/00_texderwalt/00_texderwalt.tex +++ b/papers/00_texderwalt/00_texderwalt.tex @@ -281,4 +281,3 @@ \subsection{Important Part \label{important-part}} \emph{Muddy water, let stand, becomes clear.} \end{quote} \end{quotation} - diff --git a/papers/00_texderwalt/metadata.yaml b/papers/00_texderwalt/metadata.yaml index fe334c4154..0593973197 100644 --- a/papers/00_texderwalt/metadata.yaml +++ b/papers/00_texderwalt/metadata.yaml @@ -26,4 +26,4 @@ authors: name: Cleopatra bibliography: mybib video: "https://www.youtube.com/watch?v=Hz1JWzyvv8A" -title: A Numerical Perspective to Terraforming a Desert -- latex edition \ No newline at end of file +title: A Numerical Perspective to Terraforming a Desert -- latex edition diff --git a/publisher/Makefile b/publisher/Makefile index 5325662b1c..a9f986be02 100644 --- a/publisher/Makefile +++ b/publisher/Makefile @@ -32,8 +32,8 @@ $(TEXDIR)/%.pdf: $(TEXDIR)/%.tex title-pdf: $(TEXDIR)/title.pdf copyright-pdf: $(TEXDIR)/copyright.pdf organization-pdf: $(TEXDIR)/organization.pdf -students-pdf: $(TEXDIR)/students.pdf -slides-pdf: $(TEXDIR)/slides.pdf +students-pdf: $(TEXDIR)/students.pdf +slides-pdf: $(TEXDIR)/slides.pdf front-pdf: title-pdf copyright-pdf organization-pdf students-pdf slides-pdf diff --git a/publisher/_static/common.css b/publisher/_static/common.css index 1a02cb5b4c..90c949685f 100644 --- a/publisher/_static/common.css +++ b/publisher/_static/common.css @@ -22,8 +22,8 @@ h1, h2, h3, h4, h5 { line-height: 1.2em; border-bottom: 2px solid rgb(100,135,220); border-left: 1px solid rgb(100,135,220); -} - +} + h1 {font-size: 1.4em;} h2 {font-size: 1.25em;} h3 {font-size: 1.15em;} @@ -123,17 +123,17 @@ hr { /* eye catchers */ -.warning +.warning { color: red; } -.error +.error { color: red; } -strong.highlight +strong.highlight { background-color: #ffcc99; padding: 1pt; @@ -143,7 +143,7 @@ strong.highlight /* Recent changes */ div.recentchanges table { - border: 1px solid #e5e5e5; + border: 1px solid #e5e5e5; } .recentchanges p { diff --git a/publisher/_static/scipy.sty b/publisher/_static/scipy.sty index d7733c659f..ac3f3ec016 100644 --- a/publisher/_static/scipy.sty +++ b/publisher/_static/scipy.sty @@ -90,4 +90,4 @@ % Support for algorithms listings \usepackage{algorithm} -\usepackage{algpseudocode} \ No newline at end of file +\usepackage{algpseudocode} diff --git a/publisher/_static/screen.css b/publisher/_static/screen.css index bf2a204436..7cfecb4673 100644 --- a/publisher/_static/screen.css +++ b/publisher/_static/screen.css @@ -68,7 +68,7 @@ a {text-decoration: none; } .midHeader a { - text-decoration: none; + text-decoration: none; color: rgb(39,78,144); margin: 5px 0px 5px 10px; } @@ -114,7 +114,7 @@ a {text-decoration: none; font-weight: bold; margin: 0; padding: 0 0.75ex 0 0.5ex; -} +} .subHeader a:hover { text-decoration: underline; @@ -132,7 +132,7 @@ a {text-decoration: none; float: left; /* background-color: green; for debugging IE surprises*/ width: 212px; - padding: 0; + padding: 0; overflow: hidden; display: inline; /* forces IE to calc correct margin */ } @@ -161,7 +161,7 @@ a {text-decoration: none; .sidepanel { margin: 5px; float: left; - width: 200px; + width: 200px; border: 1px solid #9c9c9c; background: #e5e5e5; overflow: hidden; /* new 18:11 */ @@ -316,7 +316,7 @@ pre { clear: right; } -/* for broken IE */ +/* for broken IE */ * html #pagetrail li { border-right: 1px solid #AAA; padding: 0 0.3em 0 0; @@ -432,7 +432,7 @@ pre { margin: 5px 5px; padding: 0px; text-align: center; - font-size: 1em; + font-size: 1em; color: #6C7680; } @@ -440,14 +440,14 @@ pre { margin: 5px 5px; padding: 0px; text-align: center; - font-size: 1em; + font-size: 1em; } #timings{ margin: 5px 0px; padding: 0; text-align: center; - font-size: 0.8em; + font-size: 0.8em; color: #6C7680; } #credits li { @@ -554,7 +554,7 @@ TD.tip { #content .sidebar p {text-align: left;} #content[dir="rtl"] .sidebar p {text-align: right;} /* All headings use darker blue and some white space above */ -#content .sidebar h3, #content .sidebar h4 {margin: 1.5em 0 0 0; +#content .sidebar h3, #content .sidebar h4 {margin: 1.5em 0 0 0; padding: 2px 8px; background: #B8C5D9;} #content .sidebar *:first-child {margin-top: 0} /* all block elements use light blue background */ @@ -567,14 +567,13 @@ TD.tip { #content .sidebar li { display: block;} #content .sidebar li p {margin: 0; padding: 0; border: none;} -/* Content Figures +/* Content Figures Default figure float to the end of the page. Left of right classes float to the left or right :-) */ -#content .figure {float: right; margin: 0 0 0 20px; padding: 0; +#content .figure {float: right; margin: 0 0 0 20px; padding: 0; font-size: 0.85em;} #content[dir="rtl"] .figure {float: left; margin: 0 20px 0 0;} #content .figure.left {float: left; margin: 0 20px 0 0;} #content .figure.right {float: right; margin: 0 0 0 20px;} #content .figure p {margin: 0; text-align: center; font-weight: bold;} - diff --git a/publisher/_templates/students.html.tmpl b/publisher/_templates/students.html.tmpl index 59cdbb07ec..2fb7464c8e 100644 --- a/publisher/_templates/students.html.tmpl +++ b/publisher/_templates/students.html.tmpl @@ -7,7 +7,7 @@ {{for student in scipy_scholarship}}
  • {{html_quote(student['name']) | html}}, {{html_quote(student['org']) | html}} - {{if student.get('location')}}, + {{if student.get('location')}}, {{html_quote(student['location']) | html}} {{endif}}
    • {{endfor}} diff --git a/publisher/build_template.py b/publisher/build_template.py index f888652953..0bfb88b269 100755 --- a/publisher/build_template.py +++ b/publisher/build_template.py @@ -96,7 +96,7 @@ def copy_static_files(dest_fn): if not os.path.exists(template_fn): print("Cannot find template.") sys.exit(-1) - + config = get_config() from_template(dest_fn, config, dest_fn) copy_static_files(dest_fn) diff --git a/publisher/conf.py b/publisher/conf.py index 407a1811e9..424528247b 100644 --- a/publisher/conf.py +++ b/publisher/conf.py @@ -47,4 +47,3 @@ for subdir in glob.glob('%s/*' % dir) if os.path.isdir(subdir) and not any(e in subdir for e in excludes)]) for dir in (slides_dir, posters_dir, lightning_dir, tools_dir)} - diff --git a/publisher/getting_ready_for_new_year.md b/publisher/getting_ready_for_new_year.md index 6513d091ee..eec78c46e8 100644 --- a/publisher/getting_ready_for_new_year.md +++ b/publisher/getting_ready_for_new_year.md @@ -1,10 +1,10 @@ # Getting ready for the new year's proceedings We're going to assume that you are working in year *x* to set up the proceedings -system for SciPy *x*. +system for SciPy *x*. This workflow requires a few steps. We will list those steps here, but if you -don't know what some of the words mean, we highly recommend that you read +don't know what some of the words mean, we highly recommend that you read [the problem section](#What-problem-are-we-solving). # Workflow @@ -22,10 +22,10 @@ All development work should have been occurring on the `dev` branch of the repo. - `scipy_proceedings/publisher/README.md` - `scipy_proceedings/publisher/getting_ready_for_new_year.md` -## What problem are we solving? +## What problem are we solving? For the purposes of this explanation, we're going to describe the workflow in -terms of what needs to be done in the repo in the year `x = 2018`. +terms of what needs to be done in the repo in the year `x = 2018`. We organise the repo around git branches, specifically we're going to be thinking about 4 branches: @@ -55,14 +55,14 @@ the proceedings system. Additionally, this keeps `master` as small as possible. So, we only want to put the changes to the build system in master. But people have to submit their proceedings to `2017` in 2017, and `2018` in 2018. How can -we get the improvements to the system without getting the papers? +we get the improvements to the system without getting the papers? ### `dev` is where build changes go -The solution to the previous section problems is the `dev` branch. +The solution to the previous section problems is the `dev` branch. `dev` specifically exists to house the improvements to the system, without any -of the papers. +of the papers. That means during the 2018 proceedings any work that is being done to improve the system should be in PRs made against `dev`, not `2018`. @@ -71,7 +71,7 @@ the system should be in PRs made against `dev`, not `2018`. After merging `dev` into `master`, can branch `2018` off of `master`. -### Merge `dev` into `2018` on an on-going basis. +### Merge `dev` into `2018` on an on-going basis. But as you are developing the system, you will be making changes to address specific problems that come up for authors. If we aren't putting any changes in @@ -92,4 +92,3 @@ TODO: Figure out workflow to move previous year's branch into its own repo. Once we write up the workflow, this should be added to the todo list under [](#Workflow). - diff --git a/publisher/mail/requirements.txt b/publisher/mail/requirements.txt index dbb739d659..ac5847e6de 100644 --- a/publisher/mail/requirements.txt +++ b/publisher/mail/requirements.txt @@ -2,4 +2,4 @@ attrs google google-api-python-client google_auth_oauthlib -pandas \ No newline at end of file +pandas diff --git a/publisher/mail/templates/author-invitation.txt.tmpl b/publisher/mail/templates/author-invitation.txt.tmpl index e17987d1ef..a27ce46dd9 100644 --- a/publisher/mail/templates/author-invitation.txt.tmpl +++ b/publisher/mail/templates/author-invitation.txt.tmpl @@ -20,5 +20,3 @@ The {{proceedings['title']['acronym']}} {{proceedings['year']}} Proceedings Comm {{for n in proceedings['editor']}} {{n}} {{endfor}} - - diff --git a/publisher/mail/templates/reviewer-final.txt.tmpl b/publisher/mail/templates/reviewer-final.txt.tmpl index cee4b80525..9398d82d91 100644 --- a/publisher/mail/templates/reviewer-final.txt.tmpl +++ b/publisher/mail/templates/reviewer-final.txt.tmpl @@ -11,5 +11,3 @@ The {{proceedings['title']['acronym']}} {{proceedings['year']}} Proceedings Comm {{for n in proceedings['editor']}} {{n}} {{endfor}} - - diff --git a/publisher/mail/templates/reviewer-invitation-2024.txt.tmpl b/publisher/mail/templates/reviewer-invitation-2024.txt.tmpl index be037abd56..e0fc841b9e 100644 --- a/publisher/mail/templates/reviewer-invitation-2024.txt.tmpl +++ b/publisher/mail/templates/reviewer-invitation-2024.txt.tmpl @@ -28,7 +28,7 @@ Initial reviews complete 2024-06-21 Final review deadline 2024-07-26 -(Authors continue to make revisions in response to final review comments during this period.) +(Authors continue to make revisions in response to final review comments during this period.) Final author revision deadline 2024-07-31 diff --git a/publisher/mail/templates/reviewer-invite.txt.tmpl b/publisher/mail/templates/reviewer-invite.txt.tmpl index ea1c8e1989..92ffc025b6 100644 --- a/publisher/mail/templates/reviewer-invite.txt.tmpl +++ b/publisher/mail/templates/reviewer-invite.txt.tmpl @@ -13,5 +13,3 @@ The {{proceedings['title']['acronym']}} {{proceedings['year']}} Proceedings Comm {{for n in proceedings['editor']}} {{n}} {{endfor}} - - diff --git a/publisher/mail/templates/reviewer-reminder.txt.tmpl b/publisher/mail/templates/reviewer-reminder.txt.tmpl index 210d513ab2..eb191b363d 100644 --- a/publisher/mail/templates/reviewer-reminder.txt.tmpl +++ b/publisher/mail/templates/reviewer-reminder.txt.tmpl @@ -11,5 +11,3 @@ The {{proceedings['title']['acronym']}} {{proceedings['year']}} Proceedings Comm {{for n in proceedings['editor']}} {{n}} {{endfor}} - - diff --git a/publisher/metadata/2010/doi_batch_papers.xml b/publisher/metadata/2010/doi_batch_papers.xml index f2cbcddab2..582a3f4ac9 100644 --- a/publisher/metadata/2010/doi_batch_papers.xml +++ b/publisher/metadata/2010/doi_batch_papers.xml @@ -1 +1 @@ -Majora.92bf1922-17114a00b251585210565Dillon Niederhutdillon.niederhut@gmail.comCrossrefPython in Science ConferenceSciPy9thAustin, TexasJune 28 - July 3 2010Proceedings of the Python in Science ConferenceProceedings of the Python in Science Conference2575-975210.25080/issn.2575-9752https://conference.scipy.org/proceedingsProceedings of the 9th Python in Science ConferenceScientific Computing with PythonSciPy201010.25080/Majora-92bf1922-012https://conference.scipy.org/proceedings/scipy2010TomAldcroftKeeping the Chandra Satellite Cool with Python20101510.25080/Majora-92bf1922-000https://conference.scipy.org/proceedings/scipy2010/aldcroft.htmlCraigAllenAstrodata201061110.25080/Majora-92bf1922-001https://conference.scipy.org/proceedings/scipy2010/allen.htmlRobSpeerKennethArnoldCatherineHavasiDivisi: Learning from Semantic Networks and Sparse SVD2010121710.25080/Majora-92bf1922-002https://conference.scipy.org/proceedings/scipy2010/arnold.htmlJamesBergstraOlivierBreuleuxFrédéricBastienPascalLamblinRazvanPascanuGuillaumeDesjardinsJosephTurianDavidWarde-FarleyYoshuaBengioTheano: A CPU and GPU Math Compiler in Python2010182410.25080/Majora-92bf1922-003https://conference.scipy.org/proceedings/scipy2010/bergstra.htmlStevenColbertGregorFranzKonradWoellhafRedwanAlqasemiRajivDubeyA High Performance Robot Vision Algorithm Implemented in Python2010253010.25080/Majora-92bf1922-004https://conference.scipy.org/proceedings/scipy2010/colbert.htmlKadambariDevarajanMariaEcheverry-GalvisRajmondaSuloJenniferPetersonUnusual Relationships: Python and Weaver Birds2010313510.25080/Majora-92bf1922-005https://conference.scipy.org/proceedings/scipy2010/devarajan.htmlEricFloehrWeather Forecast Accuracy Analysis2010363910.25080/Majora-92bf1922-006https://conference.scipy.org/proceedings/scipy2010/floehr.htmlPerryGreenfieldIvoBuskoRosaDiazVickiLaidlerToddMillerMarkSienkiewiczMeganSoseyRebuilding the Hubble Exposure Time Calculator2010404410.25080/Majora-92bf1922-007https://conference.scipy.org/proceedings/scipy2010/greenfield.htmlWarrenHackPerryGreenfieldBabakSaifBenteEegholmUsing Python with Smoke and JWST Mirrors2010454810.25080/Majora-92bf1922-008https://conference.scipy.org/proceedings/scipy2010/hack.htmlSeanDavisMatthewHendersonAndrewSmithModeling Sudoku Puzzles with Python2010495510.25080/Majora-92bf1922-009https://conference.scipy.org/proceedings/scipy2010/henderson.htmlWesMcKinneyData Structures for Statistical Computing in Python2010566110.25080/Majora-92bf1922-00ahttps://conference.scipy.org/proceedings/scipy2010/mckinney.htmlJanMeinkeProtein Folding with Python on Supercomputers2010626610.25080/Majora-92bf1922-00bhttps://conference.scipy.org/proceedings/scipy2010/meinke.htmlStevenMorleyDanielWellingJosefKollerBrianLarsenMichaelHendersonJonathanNiehofSpacePy - A Python-based Library of Tools for the Space Sciences2010677210.25080/Majora-92bf1922-00chttps://conference.scipy.org/proceedings/scipy2010/morley.htmlKristopherOverholtNumerical Pyromaniacs: The Use of Python in Fire Research2010737610.25080/Majora-92bf1922-00dhttps://conference.scipy.org/proceedings/scipy2010/overholt.htmlMinRagan-KelleyJohnVerboncoeurA Programmatic Interface for Particle Plasma Simulation in Python2010777910.25080/Majora-92bf1922-00ehttps://conference.scipy.org/proceedings/scipy2010/ragan-kelley.htmlPrabhuRamachandranChandrashekharKaushikPySPH: A Python Framework for Smoothed Particle Hydrodynamics2010808410.25080/Majora-92bf1922-00fhttps://conference.scipy.org/proceedings/scipy2010/ramachandran.htmlHelgeReikerasBenHerbstJohandu PreezHermanEngelbrechtAudio-Visual Speech Recognition using SciPy2010859110.25080/Majora-92bf1922-010https://conference.scipy.org/proceedings/scipy2010/reikeras.htmlSkipperSeaboldJosefPerktoldStatsmodels: Econometric and Statistical Modeling with Python2010929610.25080/Majora-92bf1922-011https://conference.scipy.org/proceedings/scipy2010/seabold.html \ No newline at end of file +Majora.92bf1922-17114a00b251585210565Dillon Niederhutdillon.niederhut@gmail.comCrossrefPython in Science ConferenceSciPy9thAustin, TexasJune 28 - July 3 2010Proceedings of the Python in Science ConferenceProceedings of the Python in Science Conference2575-975210.25080/issn.2575-9752https://conference.scipy.org/proceedingsProceedings of the 9th Python in Science ConferenceScientific Computing with PythonSciPy201010.25080/Majora-92bf1922-012https://conference.scipy.org/proceedings/scipy2010TomAldcroftKeeping the Chandra Satellite Cool with Python20101510.25080/Majora-92bf1922-000https://conference.scipy.org/proceedings/scipy2010/aldcroft.htmlCraigAllenAstrodata201061110.25080/Majora-92bf1922-001https://conference.scipy.org/proceedings/scipy2010/allen.htmlRobSpeerKennethArnoldCatherineHavasiDivisi: Learning from Semantic Networks and Sparse SVD2010121710.25080/Majora-92bf1922-002https://conference.scipy.org/proceedings/scipy2010/arnold.htmlJamesBergstraOlivierBreuleuxFrédéricBastienPascalLamblinRazvanPascanuGuillaumeDesjardinsJosephTurianDavidWarde-FarleyYoshuaBengioTheano: A CPU and GPU Math Compiler in Python2010182410.25080/Majora-92bf1922-003https://conference.scipy.org/proceedings/scipy2010/bergstra.htmlStevenColbertGregorFranzKonradWoellhafRedwanAlqasemiRajivDubeyA High Performance Robot Vision Algorithm Implemented in Python2010253010.25080/Majora-92bf1922-004https://conference.scipy.org/proceedings/scipy2010/colbert.htmlKadambariDevarajanMariaEcheverry-GalvisRajmondaSuloJenniferPetersonUnusual Relationships: Python and Weaver Birds2010313510.25080/Majora-92bf1922-005https://conference.scipy.org/proceedings/scipy2010/devarajan.htmlEricFloehrWeather Forecast Accuracy Analysis2010363910.25080/Majora-92bf1922-006https://conference.scipy.org/proceedings/scipy2010/floehr.htmlPerryGreenfieldIvoBuskoRosaDiazVickiLaidlerToddMillerMarkSienkiewiczMeganSoseyRebuilding the Hubble Exposure Time Calculator2010404410.25080/Majora-92bf1922-007https://conference.scipy.org/proceedings/scipy2010/greenfield.htmlWarrenHackPerryGreenfieldBabakSaifBenteEegholmUsing Python with Smoke and JWST Mirrors2010454810.25080/Majora-92bf1922-008https://conference.scipy.org/proceedings/scipy2010/hack.htmlSeanDavisMatthewHendersonAndrewSmithModeling Sudoku Puzzles with Python2010495510.25080/Majora-92bf1922-009https://conference.scipy.org/proceedings/scipy2010/henderson.htmlWesMcKinneyData Structures for Statistical Computing in 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Texans2011444710.25080/Majora-ebaa42b7-007https://conference.scipy.org/proceedings/scipy2011/dharhas_pothina.htmlJonathanJackyPyModel: Model-based testing in Python2011485210.25080/Majora-ebaa42b7-008https://conference.scipy.org/proceedings/scipy2011/jacky.htmlMatthewTerryJosephKoningAutomation of Inertial Fusion Target Design with Python2011535710.25080/Majora-ebaa42b7-009https://conference.scipy.org/proceedings/scipy2011/koningterry.htmlMinwooLeeCharlesAndersonMarkDeMariaHurricane Prediction with Python2011586210.25080/Majora-ebaa42b7-00ahttps://conference.scipy.org/proceedings/scipy2011/lee.htmlMartinLingAlexYoungIMUSim - Simulating inertial and magnetic sensor systems in Python2011636910.25080/Majora-ebaa42b7-00bhttps://conference.scipy.org/proceedings/scipy2011/ling.htmlKyleMandliAmalAlghamdiAronAhmadiaDavidKetchesonWilliamScullinUsing Python to Construct a Scalable Parallel Nonlinear Wave 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PythonSciPy201110.25080/Majora-ebaa42b7-014https://conference.scipy.org/proceedings/scipy2011MineshAminA Technical Anatomy of SPM.Python, a Scalable, Parallel Version of Python20111910.25080/Majora-ebaa42b7-000https://conference.scipy.org/proceedings/scipy2011/amin.htmlBrianRefsdalStephenDoeDanNguyenAnetaSiemiginowskaFitting and Estimating Parameter Confidence Limits with Sherpa2011101610.25080/Majora-ebaa42b7-001https://conference.scipy.org/proceedings/scipy2011/brefsdal.htmlMarcelCaracioloBrunoMeloRicardoCaspirroCrab: A Recommendation Engine Framework for Python2011172310.25080/Majora-ebaa42b7-002https://conference.scipy.org/proceedings/scipy2011/caraciolo.htmlAndrewCronWesMcKinneygpustats: GPU Library for Statistical Computing in Python2011242810.25080/Majora-ebaa42b7-003https://conference.scipy.org/proceedings/scipy2011/cron_mckinney.htmlJeffDailyRobertLewisUsing the Global Arrays Toolkit to Reimplement NumPy for Distributed Computation2011293510.25080/Majora-ebaa42b7-004https://conference.scipy.org/proceedings/scipy2011/daily.htmlScottDetermanVision Spreadsheet: An Environment for Computer Vision2011363910.25080/Majora-ebaa42b7-005https://conference.scipy.org/proceedings/scipy2011/determan.htmlMarkDewingConstructing scientific programs using SymPy2011404310.25080/Majora-ebaa42b7-006https://conference.scipy.org/proceedings/scipy2011/dewing.htmlDharhasPothinaAndrewWilsonUsing Python, Partnerships, Standards and Web Services to provide Water Data for Texans2011444710.25080/Majora-ebaa42b7-007https://conference.scipy.org/proceedings/scipy2011/dharhas_pothina.htmlJonathanJackyPyModel: Model-based testing in Python2011485210.25080/Majora-ebaa42b7-008https://conference.scipy.org/proceedings/scipy2011/jacky.htmlMatthewTerryJosephKoningAutomation of Inertial Fusion Target Design with Python2011535710.25080/Majora-ebaa42b7-009https://conference.scipy.org/proceedings/scipy2011/koningterry.htmlMinwooLeeCharlesAndersonMarkDeMariaHurricane Prediction with Python2011586210.25080/Majora-ebaa42b7-00ahttps://conference.scipy.org/proceedings/scipy2011/lee.htmlMartinLingAlexYoungIMUSim - Simulating inertial and magnetic sensor systems in Python2011636910.25080/Majora-ebaa42b7-00bhttps://conference.scipy.org/proceedings/scipy2011/ling.htmlKyleMandliAmalAlghamdiAronAhmadiaDavidKetchesonWilliamScullinUsing Python to Construct a Scalable Parallel Nonlinear Wave Solver2011707510.25080/Majora-ebaa42b7-00chttps://conference.scipy.org/proceedings/scipy2011/mandli_etal.htmlMichaelMcKernsLeifStrandTimSullivanAltaFangMichaelAivazisBuilding a Framework for Predictive Science2011768610.25080/Majora-ebaa42b7-00dhttps://conference.scipy.org/proceedings/scipy2011/mckerns.htmlNickBrayPyStream: Compiling Python onto the 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PythonSciPy201210.25080/Majora-54c7f2c8-00dhttps://conference.scipy.org/proceedings/scipy2012AakashPrasadDavidHowardShoaibKamilArmandoFoxParallel High Performance Bootstrapping in Python20121510.25080/Majora-54c7f2c8-000https://conference.scipy.org/proceedings/scipy2012/aakash_prasad.htmlAdamHughesA Computational Framework for Plasmonic Nanobiosensing201261010.25080/Majora-54c7f2c8-001https://conference.scipy.org/proceedings/scipy2012/adam_hughes.htmlAlejandroWeinsteinMichaelWakinA Tale of Four Libraries2012111510.25080/Majora-54c7f2c8-002https://conference.scipy.org/proceedings/scipy2012/alejandro_weinstein.htmlAnthonyScopatzTotal Recall: flmake and the Quest for Reproducibility2012162210.25080/Majora-54c7f2c8-003https://conference.scipy.org/proceedings/scipy2012/anthony_scopatz_flmake.htmlCyrusHarrisonHarinarayanKrishnanPython's Role in VisIt2012232910.25080/Majora-54c7f2c8-004https://conference.scipy.org/proceedings/scipy2012/cyrus_harrison.htmlGeoffreyPoorePythonTeX: Fast Access to Python from within LaTeX2012303610.25080/Majora-54c7f2c8-005https://conference.scipy.org/proceedings/scipy2012/geoffrey_poore.htmlIqbalMohomedSelf-driving Lego Mindstorms Robot2012374010.25080/Majora-54c7f2c8-006https://conference.scipy.org/proceedings/scipy2012/iqbal_mohomed.htmlJacobBarhakThe Reference Model for Disease Progression2012414510.25080/Majora-54c7f2c8-007https://conference.scipy.org/proceedings/scipy2012/jacob_barhak.htmlJacobFrelingerAdamRichardsCliburnChanFcm - A python library for flow cytometry2012465010.25080/Majora-54c7f2c8-008https://conference.scipy.org/proceedings/scipy2012/jacob_frelinger.htmlMatthewRocklinUncertainty Modeling with SymPy Stats2012515510.25080/Majora-54c7f2c8-009https://conference.scipy.org/proceedings/scipy2012/matthew_rocklin.htmlRobertJohanssonPaulNationQuTiP: A framework for the dynamics of open quantum systems using SciPy and 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PythonSciPy201210.25080/Majora-54c7f2c8-00dhttps://conference.scipy.org/proceedings/scipy2012AakashPrasadDavidHowardShoaibKamilArmandoFoxParallel High Performance Bootstrapping in Python20121510.25080/Majora-54c7f2c8-000https://conference.scipy.org/proceedings/scipy2012/aakash_prasad.htmlAdamHughesA Computational Framework for Plasmonic Nanobiosensing201261010.25080/Majora-54c7f2c8-001https://conference.scipy.org/proceedings/scipy2012/adam_hughes.htmlAlejandroWeinsteinMichaelWakinA Tale of Four Libraries2012111510.25080/Majora-54c7f2c8-002https://conference.scipy.org/proceedings/scipy2012/alejandro_weinstein.htmlAnthonyScopatzTotal Recall: flmake and the Quest for Reproducibility2012162210.25080/Majora-54c7f2c8-003https://conference.scipy.org/proceedings/scipy2012/anthony_scopatz_flmake.htmlCyrusHarrisonHarinarayanKrishnanPython's Role in VisIt2012232910.25080/Majora-54c7f2c8-004https://conference.scipy.org/proceedings/scipy2012/cyrus_harrison.htmlGeoffreyPoorePythonTeX: Fast Access to Python from within LaTeX2012303610.25080/Majora-54c7f2c8-005https://conference.scipy.org/proceedings/scipy2012/geoffrey_poore.htmlIqbalMohomedSelf-driving Lego Mindstorms Robot2012374010.25080/Majora-54c7f2c8-006https://conference.scipy.org/proceedings/scipy2012/iqbal_mohomed.htmlJacobBarhakThe Reference Model for Disease Progression2012414510.25080/Majora-54c7f2c8-007https://conference.scipy.org/proceedings/scipy2012/jacob_barhak.htmlJacobFrelingerAdamRichardsCliburnChanFcm - A python library for flow cytometry2012465010.25080/Majora-54c7f2c8-008https://conference.scipy.org/proceedings/scipy2012/jacob_frelinger.htmlMatthewRocklinUncertainty Modeling with SymPy Stats2012515510.25080/Majora-54c7f2c8-009https://conference.scipy.org/proceedings/scipy2012/matthew_rocklin.htmlRobertJohanssonPaulNationQuTiP: A framework for the dynamics of open quantum systems using SciPy and 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package2014657110.25080/Majora-14bd3278-00bhttps://conference.scipy.org/proceedings/scipy2014/sanders.htmlRamalingamSaravananCreating a browser-based virtual computer lab for classroom instruction2014727810.25080/Majora-14bd3278-00chttps://conference.scipy.org/proceedings/scipy2014/saravanan.htmlKristenThyngRobertHetlandTracPy: Wrapping the Fortran Lagrangian trajectory model TRACMASS2014798410.25080/Majora-14bd3278-00dhttps://conference.scipy.org/proceedings/scipy2014/thyng.htmlJakeVanderPlasFrequentism and Bayesianism: A Python-driven Primer2014859310.25080/Majora-14bd3278-00ehttps://conference.scipy.org/proceedings/scipy2014/vanderplas.htmlMarkWiebeMatthewRocklinTJAlumbaughAndyTerrelBlaze: Building A Foundation for Array-Oriented Computing in Python2014949710.25080/Majora-14bd3278-00fhttps://conference.scipy.org/proceedings/scipy2014/wiebe.htmlJohnZuHoneVeronicaBiffiEricHallmanScottRandallAdamFosterChristianSchmidSimulating X-ray Observations with Python20149810410.25080/Majora-14bd3278-010https://conference.scipy.org/proceedings/scipy2014/zuhone.html \ No newline at end of file +Majora.14bd3278-16805574b2d1546299433Dillon Niederhutdillon.niederhut@gmail.comCrossrefPython in Science ConferenceSciPy13thAustin, TexasJuly 6 - 12 2014Proceedings of the Python in Science ConferenceProceedings of the Python in Science Conference2575-975210.25080/issn.2575-9752https://conference.scipy.org/proceedingsProceedings of the 13th Python in Science ConferenceScientific Computing with PythonSciPy201410.25080/Majora-14bd3278-011https://conference.scipy.org/proceedings/scipy2014AndyTerrelJonathanRocherStéfanvan der WaltJamesBergstraPreface20141110.25080/Majora-14bd3278-000https://conference.scipy.org/proceedings/scipy2014/00_intro.htmlGBaxterScientific Computing with SciPy for Undergraduate Physics Majors20142410.25080/Majora-14bd3278-001https://conference.scipy.org/proceedings/scipy2014/baxter.htmlDavClarkAaronCulichBrianHamlinRyanLovettBCE: Berkeley's Common Scientific Compute Environment for Research and Education201451210.25080/Majora-14bd3278-002https://conference.scipy.org/proceedings/scipy2014/clark.htmlScottCollisScottGiangrandeJonathanHelmusDiWuAnnFridlindMarcusvan Lier-WalquiAdamTheisenMeasuring rainshafts: Bringing Python to bear on remote sensing data2014131810.25080/Majora-14bd3278-003https://conference.scipy.org/proceedings/scipy2014/collis.htmlDavidKetchesonTeaching numerical methods with IPython notebooks and inquiry-based learning2014192410.25080/Majora-14bd3278-004https://conference.scipy.org/proceedings/scipy2014/ketcheson.htmlJenniferKlayProject-based introduction to scientific computing for physics majors2014253110.25080/Majora-14bd3278-005https://conference.scipy.org/proceedings/scipy2014/klay.htmlBrentKomerJamesBergstraChrisEliasmithHyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn2014323710.25080/Majora-14bd3278-006https://conference.scipy.org/proceedings/scipy2014/komer.htmlJamesKuiperAndrewAyersMichaelHolmMichaelNowakPython Coding of Geospatial Processing in Web-based Mapping Applications2014384410.25080/Majora-14bd3278-007https://conference.scipy.org/proceedings/scipy2014/kuiper.htmlJasonLauraSergioReyScaling Polygon Adjacency Algorithms to Big Data Geospatial Analysis2014455010.25080/Majora-14bd3278-008https://conference.scipy.org/proceedings/scipy2014/laura.htmlKannanMoudgalyaCampaign for IT literacy through FOSS and Spoken Tutorials2014515810.25080/Majora-14bd3278-009https://conference.scipy.org/proceedings/scipy2014/moudgalya.htmlDavidPughPython for research and teaching economics2014596410.25080/Majora-14bd3278-00ahttps://conference.scipy.org/proceedings/scipy2014/pugh.htmlDavidSandersLuisBenetValidated numerics with Python: the ValidiPy package2014657110.25080/Majora-14bd3278-00bhttps://conference.scipy.org/proceedings/scipy2014/sanders.htmlRamalingamSaravananCreating a browser-based virtual computer lab for classroom instruction2014727810.25080/Majora-14bd3278-00chttps://conference.scipy.org/proceedings/scipy2014/saravanan.htmlKristenThyngRobertHetlandTracPy: Wrapping the Fortran Lagrangian trajectory model TRACMASS2014798410.25080/Majora-14bd3278-00dhttps://conference.scipy.org/proceedings/scipy2014/thyng.htmlJakeVanderPlasFrequentism and Bayesianism: A Python-driven Primer2014859310.25080/Majora-14bd3278-00ehttps://conference.scipy.org/proceedings/scipy2014/vanderplas.htmlMarkWiebeMatthewRocklinTJAlumbaughAndyTerrelBlaze: Building A Foundation for Array-Oriented Computing in Python2014949710.25080/Majora-14bd3278-00fhttps://conference.scipy.org/proceedings/scipy2014/wiebe.htmlJohnZuHoneVeronicaBiffiEricHallmanScottRandallAdamFosterChristianSchmidSimulating X-ray Observations with Python20149810410.25080/Majora-14bd3278-010https://conference.scipy.org/proceedings/scipy2014/zuhone.html diff --git a/publisher/metadata/2014/emails.txt b/publisher/metadata/2014/emails.txt index 4eec148ea7..4c943c8c9d 100644 --- a/publisher/metadata/2014/emails.txt +++ b/publisher/metadata/2014/emails.txt @@ -4,7 +4,7 @@ Dry run -> not sending mail to aterrell@tacc.utexas.edu,jrocher@enthought.com,st From: dillon.niederhut@gmail.com Subject: Your SciPy 2014 Paper DOI To: aterrell@tacc.utexas.edu, jrocher@enthought.com, stefanv@berkeley.edu, james.bergstra@gmail.com -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -23,7 +23,7 @@ Dry run -> not sending mail to gwb6@psu.edu From: dillon.niederhut@gmail.com Subject: Your SciPy 2014 Paper DOI To: gwb6@psu.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -42,7 +42,7 @@ Dry run -> not sending mail to davclark@berkeley.edu,aculich@berkeley.edu,maplab From: dillon.niederhut@gmail.com Subject: Your SciPy 2014 Paper DOI To: davclark@berkeley.edu, aculich@berkeley.edu, maplabs@light42.com, rylo@berkeley.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -61,7 +61,7 @@ Dry run -> not sending mail to scollis@anl.gov,sgrande@bnl.gov,jhelmus@anl.gov,d From: dillon.niederhut@gmail.com Subject: Your SciPy 2014 Paper DOI To: scollis@anl.gov, sgrande@bnl.gov, jhelmus@anl.gov, di.wu@nasa.gov, ann.fridlind@nasa.gov, marcus.vanlier-walqui@nasa.gov, atheisen@ou.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -80,7 +80,7 @@ Dry run -> not sending mail to david.ketcheson@kaust.edu.sa From: dillon.niederhut@gmail.com Subject: Your SciPy 2014 Paper DOI To: david.ketcheson@kaust.edu.sa -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -99,7 +99,7 @@ Dry run -> not sending mail to jklay@calpoly.edu From: dillon.niederhut@gmail.com Subject: Your SciPy 2014 Paper DOI To: jklay@calpoly.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -118,7 +118,7 @@ Dry run -> not sending mail to bjkomer@uwaterloo.ca,james.bergstra@uwaterloo.ca, From: dillon.niederhut@gmail.com Subject: Your SciPy 2014 Paper DOI To: bjkomer@uwaterloo.ca, james.bergstra@uwaterloo.ca, celiasmith@uwaterloo.ca -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -137,7 +137,7 @@ Dry run -> not sending mail to jkuiper@anl.gov,aayers@anl.gov,mholm@anl.gov,mnow From: dillon.niederhut@gmail.com Subject: Your SciPy 2014 Paper DOI To: jkuiper@anl.gov, aayers@anl.gov, mholm@anl.gov, mnowak@anl.gov -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -156,7 +156,7 @@ Dry run -> not sending mail to jlaura@asu.edu,srey@asu.edu From: dillon.niederhut@gmail.com Subject: Your SciPy 2014 Paper DOI To: jlaura@asu.edu, srey@asu.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -175,7 +175,7 @@ Dry run -> not sending mail to kannan@iitb.ac.in From: dillon.niederhut@gmail.com Subject: Your SciPy 2014 Paper DOI To: kannan@iitb.ac.in -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -194,7 +194,7 @@ Dry run -> not sending mail to pugh@maths.ox.ac.uk From: dillon.niederhut@gmail.com Subject: Your SciPy 2014 Paper DOI To: pugh@maths.ox.ac.uk -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -213,7 +213,7 @@ Dry run -> not sending mail to dpsanders@ciencias.unam.mx,benet@fis.unam.mx From: dillon.niederhut@gmail.com Subject: Your SciPy 2014 Paper DOI To: dpsanders@ciencias.unam.mx, benet@fis.unam.mx -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -232,7 +232,7 @@ Dry run -> not sending mail to sarava@tamu.edu From: dillon.niederhut@gmail.com Subject: Your SciPy 2014 Paper DOI To: sarava@tamu.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -251,7 +251,7 @@ Dry run -> not sending mail to kthyng@tamu.edu,hetland@tamu.edu From: dillon.niederhut@gmail.com Subject: Your SciPy 2014 Paper DOI To: kthyng@tamu.edu, hetland@tamu.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -270,7 +270,7 @@ Dry run -> not sending mail to jakevdp@cs.washington.edu From: dillon.niederhut@gmail.com Subject: Your SciPy 2014 Paper DOI To: jakevdp@cs.washington.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -289,7 +289,7 @@ Dry run -> not sending mail to mwiebe@continuum.io,mrocklin@continuum.io,tj.alum From: dillon.niederhut@gmail.com Subject: Your SciPy 2014 Paper DOI To: mwiebe@continuum.io, mrocklin@continuum.io, tj.alumbaugh@continuum.io, aterrel@continuum.io -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -308,7 +308,7 @@ Dry run -> not sending mail to jzuhone@milkyway.gsfc.nasa.gov,biffi@sissa.it,hal From: dillon.niederhut@gmail.com Subject: Your SciPy 2014 Paper DOI To: jzuhone@milkyway.gsfc.nasa.gov, biffi@sissa.it, hallman13@gmail.com, srandall@cfa.harvard.edu, afoster@cfa.harvard.edu, christian.schmid@sternwarte.uni-erlangen.de -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain diff --git a/publisher/metadata/2014/scipy_proc.json b/publisher/metadata/2014/scipy_proc.json index 4680dd97c2..802ae3514b 100644 --- a/publisher/metadata/2014/scipy_proc.json +++ b/publisher/metadata/2014/scipy_proc.json @@ -492,4 +492,4 @@ }, "doi": "10.25080/issn.2575-9752" } -} \ No newline at end of file +} diff --git a/publisher/metadata/2014/toc.json b/publisher/metadata/2014/toc.json index e5eb47f8a5..09bbac817d 100644 --- a/publisher/metadata/2014/toc.json +++ b/publisher/metadata/2014/toc.json @@ -713,4 +713,4 @@ "keywords": "astronomical observations, astrophysics simulations, visualization" } ] -} \ No newline at end of file +} diff --git a/publisher/metadata/2015/doi_batch.xml b/publisher/metadata/2015/doi_batch.xml index 45ed9c482e..a2d3395858 100644 --- a/publisher/metadata/2015/doi_batch.xml +++ b/publisher/metadata/2015/doi_batch.xml @@ -1 +1 @@ -Majora.7b98e3ed-166e55fe44a1541446558Dillon Niederhutdniederhut@enthought.comCrossrefPython in Science ConferenceSciPy14thAustin, TexasJuly 6 - 12 2015Proceedings of the Python in Science ConferenceProceedings of the Python in Science Conference2575-975210.25080/issn.2575-9752https://conference.scipy.org/proceedingsProceedings of the 14th Python in Science ConferenceScientific Computing with PythonSciPy201510.25080/Majora-7b98e3ed-01ehttps://conference.scipy.org/proceedings/scipy2015AllenDowneyWill Millennials Ever Get Married?20151510.25080/Majora-7b98e3ed-000https://conference.scipy.org/proceedings/scipy2015/allen_downey.htmlAnkurAnkanAbinashPandapgmpy: Probabilistic Graphical Models using Python201561110.25080/Majora-7b98e3ed-001https://conference.scipy.org/proceedings/scipy2015/ankur_ankan.htmlBrianChapmanJeannieIrwinPython as a First Programming Language for Biomedical Scientists2015121710.25080/Majora-7b98e3ed-002https://conference.scipy.org/proceedings/scipy2015/brian_chapman.htmlBrianMcFeeColinRaffelDawenLiangDanielEllisMattMcVicarEricBattenbergOriolNietolibrosa: Audio and Music Signal Analysis in Python2015182410.25080/Majora-7b98e3ed-003https://conference.scipy.org/proceedings/scipy2015/brian_mcfee.htmlChrisDrakePyEDA: Data Structures and Algorithms for Electronic Design Automation2015253010.25080/Majora-7b98e3ed-004https://conference.scipy.org/proceedings/scipy2015/chris_drake.htmlCoryQuammenScientific Data Analysis and Visualization with Python, VTK, and ParaView2015313810.25080/Majora-7b98e3ed-005https://conference.scipy.org/proceedings/scipy2015/cory_quammen.htmlDavidLippaJasonVertreesCreating a Real-Time Recommendation Engine using Modified K-Means Clustering and Remote Sensing Signature Matching Algorithms2015394210.25080/Majora-7b98e3ed-006https://conference.scipy.org/proceedings/scipy2015/david_lippa.htmlHowardBushouseMichaelDroettboomPerryGreenfieldThe James Webb Space Telescope Data Calibration Pipeline2015434710.25080/Majora-7b98e3ed-007https://conference.scipy.org/proceedings/scipy2015/howard_bushouse.htmlIanHenriksenCircumventing The Linker: Using SciPy's BLAS and LAPACK Within Cython2015485010.25080/Majora-7b98e3ed-008https://conference.scipy.org/proceedings/scipy2015/ian_henriksen.htmlDavidMasadJacquelineKazilMesa: An Agent-Based Modeling Framework2015515810.25080/Majora-7b98e3ed-009https://conference.scipy.org/proceedings/scipy2015/jacqueline_kazil.htmlJean-LucStevensPhilippRudigerJamesBednarHoloViews: Building Complex Visualizations Easily for Reproducible Science2015596610.25080/Majora-7b98e3ed-00ahttps://conference.scipy.org/proceedings/scipy2015/jean-luc_stevens.htmlJordiTorrentsFabrizioFerraroStructural Cohesion: Visualization and Heuristics for Fast Computation with NetworkX and matplotlib2015677610.25080/Majora-7b98e3ed-00bhttps://conference.scipy.org/proceedings/scipy2015/jordi_torrents.htmlJoshWalawenderAutomated Image Quality Monitoring with IQMon2015778310.25080/Majora-7b98e3ed-00chttps://conference.scipy.org/proceedings/scipy2015/josh_walawender.htmlKathrynHuffPyRK: A Python Package For Nuclear Reactor Kinetics2015849010.25080/Majora-7b98e3ed-00dhttps://conference.scipy.org/proceedings/scipy2015/kathryn_huff.htmlLukeCampagnolaAlmarKleinEricLarsonCyrilleRossantNicolasRougierVisPy: Harnessing The GPU For Fast, High-Level Visualization2015919610.25080/Majora-7b98e3ed-00ehttps://conference.scipy.org/proceedings/scipy2015/luke_campagnola-vispy.htmlMargaretMahanChelleyChornApostolosGeorgopoulosWhite Noise Test: detecting autocorrelation and nonstationarities in long time series after ARIMA modeling20159710410.25080/Majora-7b98e3ed-00fhttps://conference.scipy.org/proceedings/scipy2015/margaret_mahan.htmlMarkWickertSignal Processing and Communications: Teaching and Research Using IPython Notebook201510511210.25080/Majora-7b98e3ed-010https://conference.scipy.org/proceedings/scipy2015/mark_wickert.htmlMattheusUeckermannRobertChambersChristopherBrooksWilliamAudetteJerryBieszczadpyDEM: Global Digital Elevation Model 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Bayesian NetworkX201514415110.25080/Majora-7b98e3ed-016https://conference.scipy.org/proceedings/scipy2015/mike_pacer.htmlNicolaCreatiRobertoVidmarPaoloSterzaiGeodynamic simulations in HPC with Python201515215710.25080/Majora-7b98e3ed-017https://conference.scipy.org/proceedings/scipy2015/nicola_creati.htmlThe QiitaTeamQiita: report of progress towards an open access microbiome data analysis and visualization platform201515816310.25080/Majora-7b98e3ed-018https://conference.scipy.org/proceedings/scipy2015/qiita_development_team.htmlRandyPaffenrothXiangnanKongPython in Data Science Research and Education201516417010.25080/Majora-7b98e3ed-019https://conference.scipy.org/proceedings/scipy2015/randy_paffenroth.htmlScottJamesJamesLarkinRelation: The Missing Container201517117410.25080/Majora-7b98e3ed-01ahttps://conference.scipy.org/proceedings/scipy2015/scott_james.htmlSebastianBenthallTesting Generative Models of Online Collaboration with BigBang201517518110.25080/Majora-7b98e3ed-01bhttps://conference.scipy.org/proceedings/scipy2015/sebastian_benthall.htmlSebastiánSepúlvedaPabloReyesAlejandroWeinsteinVisualizing physiological signals in real-time201518218610.25080/Majora-7b98e3ed-01chttps://conference.scipy.org/proceedings/scipy2015/sebastian_sepulveda.htmlFaicalCongoBuilding a Cloud Service for Reproducible Simulation Management201518719310.25080/Majora-7b98e3ed-01dhttps://conference.scipy.org/proceedings/scipy2015/yannick_congo.html \ No newline at end of file +Majora.7b98e3ed-166e55fe44a1541446558Dillon Niederhutdniederhut@enthought.comCrossrefPython in Science ConferenceSciPy14thAustin, TexasJuly 6 - 12 2015Proceedings of the Python in Science ConferenceProceedings of the Python in Science Conference2575-975210.25080/issn.2575-9752https://conference.scipy.org/proceedingsProceedings of the 14th Python in Science ConferenceScientific Computing with PythonSciPy201510.25080/Majora-7b98e3ed-01ehttps://conference.scipy.org/proceedings/scipy2015AllenDowneyWill Millennials Ever Get Married?20151510.25080/Majora-7b98e3ed-000https://conference.scipy.org/proceedings/scipy2015/allen_downey.htmlAnkurAnkanAbinashPandapgmpy: Probabilistic Graphical Models using Python201561110.25080/Majora-7b98e3ed-001https://conference.scipy.org/proceedings/scipy2015/ankur_ankan.htmlBrianChapmanJeannieIrwinPython as a First Programming Language for Biomedical Scientists2015121710.25080/Majora-7b98e3ed-002https://conference.scipy.org/proceedings/scipy2015/brian_chapman.htmlBrianMcFeeColinRaffelDawenLiangDanielEllisMattMcVicarEricBattenbergOriolNietolibrosa: Audio and Music Signal Analysis in Python2015182410.25080/Majora-7b98e3ed-003https://conference.scipy.org/proceedings/scipy2015/brian_mcfee.htmlChrisDrakePyEDA: Data Structures and Algorithms for Electronic Design Automation2015253010.25080/Majora-7b98e3ed-004https://conference.scipy.org/proceedings/scipy2015/chris_drake.htmlCoryQuammenScientific Data Analysis and Visualization with Python, VTK, and ParaView2015313810.25080/Majora-7b98e3ed-005https://conference.scipy.org/proceedings/scipy2015/cory_quammen.htmlDavidLippaJasonVertreesCreating a Real-Time Recommendation Engine using Modified K-Means Clustering and Remote Sensing Signature Matching Algorithms2015394210.25080/Majora-7b98e3ed-006https://conference.scipy.org/proceedings/scipy2015/david_lippa.htmlHowardBushouseMichaelDroettboomPerryGreenfieldThe James Webb Space Telescope Data Calibration Pipeline2015434710.25080/Majora-7b98e3ed-007https://conference.scipy.org/proceedings/scipy2015/howard_bushouse.htmlIanHenriksenCircumventing The Linker: Using SciPy's BLAS and LAPACK Within Cython2015485010.25080/Majora-7b98e3ed-008https://conference.scipy.org/proceedings/scipy2015/ian_henriksen.htmlDavidMasadJacquelineKazilMesa: An Agent-Based 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Undergraduates201512112510.25080/Majora-7b98e3ed-012https://conference.scipy.org/proceedings/scipy2015/matthew_craig.htmlMatthewRocklinDask: Parallel Computation with Blocked algorithms and Task Scheduling201512613210.25080/Majora-7b98e3ed-013https://conference.scipy.org/proceedings/scipy2015/matthew_rocklin.htmlMellissaCrossPySPLIT: a Package for the Generation, Analysis, and Visualization of HYSPLIT Air Parcel Trajectories201513313710.25080/Majora-7b98e3ed-014https://conference.scipy.org/proceedings/scipy2015/mellissa_cross_p.htmlMellissaCrossTrendVis: an Elegant Interface for dense, sparkline-like, quantitative visualizations of multiple series using matplotlib201513814310.25080/Majora-7b98e3ed-015https://conference.scipy.org/proceedings/scipy2015/mellissa_cross_t.htmlMichaelPacerCausal Bayesian NetworkX201514415110.25080/Majora-7b98e3ed-016https://conference.scipy.org/proceedings/scipy2015/mike_pacer.htmlNicolaCreatiRobertoVidmarPaoloSterzaiGeodynamic simulations in HPC with Python201515215710.25080/Majora-7b98e3ed-017https://conference.scipy.org/proceedings/scipy2015/nicola_creati.htmlThe QiitaTeamQiita: report of progress towards an open access microbiome data analysis and visualization platform201515816310.25080/Majora-7b98e3ed-018https://conference.scipy.org/proceedings/scipy2015/qiita_development_team.htmlRandyPaffenrothXiangnanKongPython in Data Science Research and Education201516417010.25080/Majora-7b98e3ed-019https://conference.scipy.org/proceedings/scipy2015/randy_paffenroth.htmlScottJamesJamesLarkinRelation: The Missing Container201517117410.25080/Majora-7b98e3ed-01ahttps://conference.scipy.org/proceedings/scipy2015/scott_james.htmlSebastianBenthallTesting Generative Models of Online Collaboration with BigBang201517518110.25080/Majora-7b98e3ed-01bhttps://conference.scipy.org/proceedings/scipy2015/sebastian_benthall.htmlSebastiánSepúlvedaPabloReyesAlejandroWeinsteinVisualizing physiological signals in real-time201518218610.25080/Majora-7b98e3ed-01chttps://conference.scipy.org/proceedings/scipy2015/sebastian_sepulveda.htmlFaicalCongoBuilding a Cloud Service for Reproducible Simulation Management201518719310.25080/Majora-7b98e3ed-01dhttps://conference.scipy.org/proceedings/scipy2015/yannick_congo.html diff --git a/publisher/metadata/2015/email.txt b/publisher/metadata/2015/email.txt index af82ddb88c..e3d0e4ae43 100644 --- a/publisher/metadata/2015/email.txt +++ b/publisher/metadata/2015/email.txt @@ -4,7 +4,7 @@ Dry run -> not sending mail to allen.downey@olin.edu From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: allen.downey@olin.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -23,7 +23,7 @@ Dry run -> not sending mail to ankurankan@gmail.com,mailme.abinashpanda@gmail.co From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: ankurankan@gmail.com, mailme.abinashpanda@gmail.com -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -42,7 +42,7 @@ Dry run -> not sending mail to brian.chapman@utah.edu,jeannieirwin@gmail.com From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: brian.chapman@utah.edu, jeannieirwin@gmail.com -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -61,7 +61,7 @@ Dry run -> not sending mail to brian.mcfee@nyu.edu,craffel@gmail.com,dliang@ee.c From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: brian.mcfee@nyu.edu, craffel@gmail.com, dliang@ee.columbia.edu, dpwe@ee.columbia.edu, mattjamesmcvicar@gmail.com, ericbattenberg@baidu.com, oriol@nyu.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -80,7 +80,7 @@ Dry run -> not sending mail to cjdrake@gmail.com From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: cjdrake@gmail.com -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -99,7 +99,7 @@ Dry run -> not sending mail to cory.quammen@kitware.com From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: cory.quammen@kitware.com -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -118,7 +118,7 @@ Dry run -> not sending mail to david.lippa@realmassive.com,jason.vertrees@realma From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: david.lippa@realmassive.com, jason.vertrees@realmassive.com -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -137,7 +137,7 @@ Dry run -> not sending mail to bushouse@stsci.edu,mdroe@stsci.edu,perry@stsci.ed From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: bushouse@stsci.edu, mdroe@stsci.edu, perry@stsci.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -156,7 +156,7 @@ Dry run -> not sending mail to iandh@byu.edu From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: iandh@byu.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -175,7 +175,7 @@ Dry run -> not sending mail to david.masad@gmail.com, jackiekazil@gmail.com,jack From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: david.masad@gmail.com, jackiekazil@gmail.com, jackiekazil@gmail.com -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -194,7 +194,7 @@ Dry run -> not sending mail to jlstevens@ed.ac.uk,p.rudiger@ed.ac.uk,jbednar@inf From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: jlstevens@ed.ac.uk, p.rudiger@ed.ac.uk, jbednar@inf.ed.ac.uk -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -213,7 +213,7 @@ Dry run -> not sending mail to jordi.t21@gmail.com,fferraro@iese.edu From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: jordi.t21@gmail.com, fferraro@iese.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -232,7 +232,7 @@ Dry run -> not sending mail to joshwalawender@me.com From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: joshwalawender@me.com -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -251,7 +251,7 @@ Dry run -> not sending mail to katyhuff@gmail.com From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: katyhuff@gmail.com -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -270,7 +270,7 @@ Dry run -> not sending mail to luke.campagnola@gmail.com,almar.klein@gmail.com,e From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: luke.campagnola@gmail.com, almar.klein@gmail.com, eric.larson.d@gmail.com, cyrille.rossant@gmail.com, Nicolas.Rougier@inria.fr -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -289,7 +289,7 @@ Dry run -> not sending mail to mahan027@umn.edu From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: mahan027@umn.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -308,7 +308,7 @@ Dry run -> not sending mail to mwickert@uccs.edu From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: mwickert@uccs.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -327,7 +327,7 @@ Dry run -> not sending mail to mpu@creare.com,rxc@creare.com,cab@creare.com,wea@ From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: mpu@creare.com, rxc@creare.com, cab@creare.com, wea@creare.com, jyb@creare.com -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -346,7 +346,7 @@ Dry run -> not sending mail to mcraig@mnstate.edu From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: mcraig@mnstate.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -365,7 +365,7 @@ Dry run -> not sending mail to mrocklin@gmail.com From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: mrocklin@gmail.com -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -384,7 +384,7 @@ Dry run -> not sending mail to cros0324@umn.edu, mellissa.cross@gmail.com From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: cros0324@umn.edu, mellissa.cross@gmail.com -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -403,7 +403,7 @@ Dry run -> not sending mail to cros0324@umn.edu, mellissa.cross@gmail.com From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: cros0324@umn.edu, mellissa.cross@gmail.com -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -422,7 +422,7 @@ Dry run -> not sending mail to mpacer@berkeley.edu From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: mpacer@berkeley.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -441,7 +441,7 @@ Dry run -> not sending mail to ncreati@inogs.it,rvidmar@inogs.it,psterzai@inogs. From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: ncreati@inogs.it, rvidmar@inogs.it, psterzai@inogs.it -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -460,7 +460,7 @@ Dry run -> not sending mail to robknight@ucsd.edu From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: robknight@ucsd.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -479,7 +479,7 @@ Dry run -> not sending mail to rcpaffenroth@wpi.edu,xkong@wpi.edu From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: rcpaffenroth@wpi.edu, xkong@wpi.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -498,7 +498,7 @@ Dry run -> not sending mail to scott.james@noblis.org,james.larkin@noblis.org From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: scott.james@noblis.org, james.larkin@noblis.org -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -517,7 +517,7 @@ Dry run -> not sending mail to sb@ischool.berkeley.edu From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: sb@ischool.berkeley.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -536,7 +536,7 @@ Dry run -> not sending mail to ssepulveda.sm@gmail.com,pablo.reyes@uv.cl,alejand From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: ssepulveda.sm@gmail.com, pablo.reyes@uv.cl, alejandro.weinstein@uv.cl -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -555,7 +555,7 @@ Dry run -> not sending mail to yannick.congo@gmail.com From: dniederhut@enthought.com Subject: Your SciPy 2015 Paper DOI To: yannick.congo@gmail.com -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain diff --git a/publisher/metadata/2015/scipy_proc.json b/publisher/metadata/2015/scipy_proc.json index a891f5c367..46f2535b1e 100644 --- a/publisher/metadata/2015/scipy_proc.json +++ b/publisher/metadata/2015/scipy_proc.json @@ -723,4 +723,4 @@ "full": "Proceedings of the Python in Science Conference" } } -} \ No newline at end of file +} diff --git a/publisher/metadata/2015/toc.json b/publisher/metadata/2015/toc.json index 0c88c7f134..4f23662da9 100644 --- a/publisher/metadata/2015/toc.json +++ b/publisher/metadata/2015/toc.json @@ -1150,4 +1150,4 @@ "pages": 7 } ] -} \ No newline at end of file +} diff --git a/publisher/metadata/2016/doi_batch.xml b/publisher/metadata/2016/doi_batch.xml index 1a9c6eacfc..bbe22ff240 100644 --- a/publisher/metadata/2016/doi_batch.xml +++ b/publisher/metadata/2016/doi_batch.xml @@ -1 +1 @@ -Majora.629e541a-166e53e5a0a1541444360Dillon Niederhutdniederhut@enthought.comCrossrefPython in Science ConferenceSciPy15thAustin, TexasJuly 11 - July 17 2016Proceedings of the Python in Science ConferenceProceedings of the Python in Science Conference2575-975210.25080/issn.2575-9752https://conference.scipy.org/proceedingsProceedings of the 15th Python in Science ConferenceScientific Computing with PythonSciPy201610.25080/Majora-629e541a-014https://conference.scipy.org/proceedings/scipy2016AlejandroWeinsteinWaelEl-DeredyStérenChabertMyriamFuentesFitting Human Decision Making Models using Python20161610.25080/Majora-629e541a-000https://conference.scipy.org/proceedings/scipy2016/alejandro_weinstein.htmlAndrewFraserStephenAndrewsFunctional Uncertainty Constrained by Law and Experiment201671410.25080/Majora-629e541a-001https://conference.scipy.org/proceedings/scipy2016/andrew_fraser.htmlAntonMalakhovComposable Multi-Threading for Python Libraries2016151910.25080/Majora-629e541a-002https://conference.scipy.org/proceedings/scipy2016/anton_malakhov.htmlBenLasscockGeneralized earthquake classification2016202610.25080/Majora-629e541a-003https://conference.scipy.org/proceedings/scipy2016/ben_lasscock.htmlBrettNaulStéfanvan der WaltArienCrellin-QuickJoshuaBloomFernandoPérezcesium: Open-Source Platform for Time-Series Inference2016273510.25080/Majora-629e541a-004https://conference.scipy.org/proceedings/scipy2016/brett_naul.htmlBryanWeberChih-JenSungUConnRCMPy: Python-based data analysis for Rapid Compression Machines2016364410.25080/Majora-629e541a-005https://conference.scipy.org/proceedings/scipy2016/bryan_weber.htmlChristianOxvigThomasArildsenTorbenLarsenStoring Reproducible Results from Computational Experiments using Scientific Python Packages2016455010.25080/Majora-629e541a-006https://conference.scipy.org/proceedings/scipy2016/christian_oxvig.htmlDavidDotsonSeanSeylerMaxLinkeRichardGowersOliverBecksteindatreant: persistent, Pythonic trees for heterogeneous data2016515610.25080/Majora-629e541a-007https://conference.scipy.org/proceedings/scipy2016/david_dotson.htmlDavidNicholsonComparison of machine learning methods applied to birdsong element classification2016576110.25080/Majora-629e541a-008https://conference.scipy.org/proceedings/scipy2016/david_nicholson.htmlWilliamJonathon SmithMONTE Python for Deep Space Navigation2016626810.25080/Majora-629e541a-009https://conference.scipy.org/proceedings/scipy2016/jonathon_smith.htmlJoyMonteiroRodrigoCaballeroThe Climate Modelling Toolkit2016697410.25080/Majora-629e541a-00ahttps://conference.scipy.org/proceedings/scipy2016/joy_monteiro.htmlJuanShishidoJayaNarasimhanMatarHallerTell Me Something I Don't Know: Analyzing OkCupid Profiles2016758110.25080/Majora-629e541a-00bhttps://conference.scipy.org/proceedings/scipy2016/juan_shishido.htmlKyleNiemeyerPyTeCK: a Python-based automatic testing package for chemical kinetic models2016828910.25080/Majora-629e541a-00chttps://conference.scipy.org/proceedings/scipy2016/kyle_niemeyer.htmlMichaelPacerJordanSuchowLinting science prose and the science of prose linting2016909710.25080/Majora-629e541a-00dhttps://conference.scipy.org/proceedings/scipy2016/mikejordan_pacersuchow.htmlRichardGowersMaxLinkeJonathanBarnoudTylerReddyManuelMeloSeanSeylerJanDomańskiDavidDotsonSébastienBuchouxIanKenneyOliverBecksteinMDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations20169810510.25080/Majora-629e541a-00ehttps://conference.scipy.org/proceedings/scipy2016/oliver_beckstein.htmlPatrickPedersenChristianOxvigJanØstergaardTorbenLarsenValidating Function Arguments in Python Signal Processing Applications201610611310.25080/Majora-629e541a-00fhttps://conference.scipy.org/proceedings/scipy2016/patrick_pedersen.htmlPrabhuRamachandranSpreading the Adoption of Python in India: the FOSSEE Python Project201611412110.25080/Majora-629e541a-010https://conference.scipy.org/proceedings/scipy2016/prabhu_ramachandran_fossee.htmlPrabhuRamachandranPySPH: a reproducible and high-performance framework for smoothed particle hydrodynamics201612212910.25080/Majora-629e541a-011https://conference.scipy.org/proceedings/scipy2016/prabhu_ramachandran_pysph.htmlSebastianBenthallTravisPinneyJCHerzKitPlummerAn Ecological Approach to Software Supply Chain Risk Management201613013610.25080/Majora-629e541a-012https://conference.scipy.org/proceedings/scipy2016/sebastian_benthall.htmlYuFengNickHandLaunching Python Applications on Peta-scale Massively Parallel Systems201613714310.25080/Majora-629e541a-013https://conference.scipy.org/proceedings/scipy2016/yu_feng.html \ No newline at end of file +Majora.629e541a-166e53e5a0a1541444360Dillon Niederhutdniederhut@enthought.comCrossrefPython in Science ConferenceSciPy15thAustin, TexasJuly 11 - July 17 2016Proceedings of the Python in Science ConferenceProceedings of the Python in Science Conference2575-975210.25080/issn.2575-9752https://conference.scipy.org/proceedingsProceedings of the 15th Python in Science ConferenceScientific Computing with PythonSciPy201610.25080/Majora-629e541a-014https://conference.scipy.org/proceedings/scipy2016AlejandroWeinsteinWaelEl-DeredyStérenChabertMyriamFuentesFitting Human Decision Making Models using Python20161610.25080/Majora-629e541a-000https://conference.scipy.org/proceedings/scipy2016/alejandro_weinstein.htmlAndrewFraserStephenAndrewsFunctional Uncertainty Constrained by Law and Experiment201671410.25080/Majora-629e541a-001https://conference.scipy.org/proceedings/scipy2016/andrew_fraser.htmlAntonMalakhovComposable Multi-Threading for Python Libraries2016151910.25080/Majora-629e541a-002https://conference.scipy.org/proceedings/scipy2016/anton_malakhov.htmlBenLasscockGeneralized earthquake classification2016202610.25080/Majora-629e541a-003https://conference.scipy.org/proceedings/scipy2016/ben_lasscock.htmlBrettNaulStéfanvan der WaltArienCrellin-QuickJoshuaBloomFernandoPérezcesium: Open-Source Platform for Time-Series Inference2016273510.25080/Majora-629e541a-004https://conference.scipy.org/proceedings/scipy2016/brett_naul.htmlBryanWeberChih-JenSungUConnRCMPy: Python-based data analysis for Rapid Compression Machines2016364410.25080/Majora-629e541a-005https://conference.scipy.org/proceedings/scipy2016/bryan_weber.htmlChristianOxvigThomasArildsenTorbenLarsenStoring Reproducible Results from Computational Experiments using Scientific Python Packages2016455010.25080/Majora-629e541a-006https://conference.scipy.org/proceedings/scipy2016/christian_oxvig.htmlDavidDotsonSeanSeylerMaxLinkeRichardGowersOliverBecksteindatreant: persistent, Pythonic trees for heterogeneous data2016515610.25080/Majora-629e541a-007https://conference.scipy.org/proceedings/scipy2016/david_dotson.htmlDavidNicholsonComparison of machine learning methods applied to birdsong element classification2016576110.25080/Majora-629e541a-008https://conference.scipy.org/proceedings/scipy2016/david_nicholson.htmlWilliamJonathon SmithMONTE Python for Deep Space Navigation2016626810.25080/Majora-629e541a-009https://conference.scipy.org/proceedings/scipy2016/jonathon_smith.htmlJoyMonteiroRodrigoCaballeroThe Climate Modelling Toolkit2016697410.25080/Majora-629e541a-00ahttps://conference.scipy.org/proceedings/scipy2016/joy_monteiro.htmlJuanShishidoJayaNarasimhanMatarHallerTell Me Something I Don't Know: Analyzing OkCupid Profiles2016758110.25080/Majora-629e541a-00bhttps://conference.scipy.org/proceedings/scipy2016/juan_shishido.htmlKyleNiemeyerPyTeCK: a Python-based automatic testing package for chemical kinetic models2016828910.25080/Majora-629e541a-00chttps://conference.scipy.org/proceedings/scipy2016/kyle_niemeyer.htmlMichaelPacerJordanSuchowLinting science prose and the science of prose linting2016909710.25080/Majora-629e541a-00dhttps://conference.scipy.org/proceedings/scipy2016/mikejordan_pacersuchow.htmlRichardGowersMaxLinkeJonathanBarnoudTylerReddyManuelMeloSeanSeylerJanDomańskiDavidDotsonSébastienBuchouxIanKenneyOliverBecksteinMDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations20169810510.25080/Majora-629e541a-00ehttps://conference.scipy.org/proceedings/scipy2016/oliver_beckstein.htmlPatrickPedersenChristianOxvigJanØstergaardTorbenLarsenValidating Function Arguments in Python Signal Processing Applications201610611310.25080/Majora-629e541a-00fhttps://conference.scipy.org/proceedings/scipy2016/patrick_pedersen.htmlPrabhuRamachandranSpreading the Adoption of Python in India: the FOSSEE Python Project201611412110.25080/Majora-629e541a-010https://conference.scipy.org/proceedings/scipy2016/prabhu_ramachandran_fossee.htmlPrabhuRamachandranPySPH: a reproducible and high-performance framework for smoothed particle hydrodynamics201612212910.25080/Majora-629e541a-011https://conference.scipy.org/proceedings/scipy2016/prabhu_ramachandran_pysph.htmlSebastianBenthallTravisPinneyJCHerzKitPlummerAn Ecological Approach to Software Supply Chain Risk Management201613013610.25080/Majora-629e541a-012https://conference.scipy.org/proceedings/scipy2016/sebastian_benthall.htmlYuFengNickHandLaunching Python Applications on Peta-scale Massively Parallel Systems201613714310.25080/Majora-629e541a-013https://conference.scipy.org/proceedings/scipy2016/yu_feng.html diff --git a/publisher/metadata/2016/emails.txt b/publisher/metadata/2016/emails.txt index be7475f138..0187cdb934 100644 --- a/publisher/metadata/2016/emails.txt +++ b/publisher/metadata/2016/emails.txt @@ -4,7 +4,7 @@ Dry run -> not sending mail to alejandro.weinstein@uv.cl,wael.el-deredy@uv.cl,st From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: alejandro.weinstein@uv.cl, wael.el-deredy@uv.cl, steren.chabert@uv.cl, mfuentes018@gmail.com -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -23,7 +23,7 @@ Dry run -> not sending mail to afraser@lanl.gov,saandrews@lanl.gov From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: afraser@lanl.gov, saandrews@lanl.gov -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -42,7 +42,7 @@ Dry run -> not sending mail to Anton.Malakhov@intel.com From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: Anton.Malakhov@intel.com -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -61,7 +61,7 @@ Dry run -> not sending mail to blasscoc@gmail.com From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: blasscoc@gmail.com -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -80,7 +80,7 @@ Dry run -> not sending mail to bnaul@berkeley.edu,stefanv@berkeley.edu,arien@ber From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: bnaul@berkeley.edu, stefanv@berkeley.edu, arien@berkeley.edu, joshbloom@berkeley.edu, fperez@lbl.gov -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -99,7 +99,7 @@ Dry run -> not sending mail to bryan.w.weber@gmail.com,chih-jen.sung@uconn.edu From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: bryan.w.weber@gmail.com, chih-jen.sung@uconn.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -118,7 +118,7 @@ Dry run -> not sending mail to cso@es.aau.dk,tha@es.aau.dk,tl@es.aau.dk From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: cso@es.aau.dk, tha@es.aau.dk, tl@es.aau.dk -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -137,7 +137,7 @@ Dry run -> not sending mail to dldotson@asu.edu,slseyler@asu.edu,max.linke@bioph From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: dldotson@asu.edu, slseyler@asu.edu, max.linke@biophys.mpg.de, richardjgowers@gmail.com, oliver.beckstein@asu.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -156,7 +156,7 @@ Dry run -> not sending mail to dnicho4@emory.edu From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: dnicho4@emory.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -175,7 +175,7 @@ Dry run -> not sending mail to jonathon.j.smith@jpl.nasa.gov From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: jonathon.j.smith@jpl.nasa.gov -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -196,7 +196,7 @@ Dry run -> not sending mail to joy.merwin@gmail.com,rodrigo@misu.su.se From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: joy.merwin@gmail.com, rodrigo@misu.su.se -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -215,7 +215,7 @@ Dry run -> not sending mail to juanshishido@berkeley.edu,jnaras@berkeley.edu,mat From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: juanshishido@berkeley.edu, jnaras@berkeley.edu, matar@berkeley.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -234,7 +234,7 @@ Dry run -> not sending mail to Kyle.Niemeyer@oregonstate.edu From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: Kyle.Niemeyer@oregonstate.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -253,7 +253,7 @@ Dry run -> not sending mail to mpacer@berkeley.edu From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: mpacer@berkeley.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -272,7 +272,7 @@ Dry run -> not sending mail to richardjgowers@gmail.com,max.linke@biophys.mpg.de From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: richardjgowers@gmail.com, max.linke@biophys.mpg.de, j.barnoud@rug.nl, tyler.reddy@bioch.ox.ac.uk, m.n.melo@rug.nl, slseyler@asu.edu, jan.domanski@bioch.ox.ac.uk, dldotson@asu.edu, sebastien.buchoux@u-picardie.fr, Ian.Kenney@asu.edu, oliver.beckstein@asu.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -291,7 +291,7 @@ Dry run -> not sending mail to psp@es.aau.dk,cso@es.aau.dk,jo@es.aau.dk,tola@adm From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: psp@es.aau.dk, cso@es.aau.dk, jo@es.aau.dk, tola@adm.aau.dk -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -310,7 +310,7 @@ Dry run -> not sending mail to prabhu@aero.iitb.ac.in From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: prabhu@aero.iitb.ac.in -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -329,7 +329,7 @@ Dry run -> not sending mail to prabhu@aero.iitb.ac.in From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: prabhu@aero.iitb.ac.in -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -348,7 +348,7 @@ Dry run -> not sending mail to sb@ischool.berkeley.edu,travis.pinney@ionchannel. From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: sb@ischool.berkeley.edu, travis.pinney@ionchannel.io, jc.herz@ionchannel.io, kit.plummer@ionchannel.io -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain @@ -367,7 +367,7 @@ Dry run -> not sending mail to yfeng1@berkeley.edu,nhand@berkeley.edu From: dniederhut@enthought.com Subject: Your SciPy 2016 Paper DOI To: yfeng1@berkeley.edu, nhand@berkeley.edu -Cc: +Cc: MIME-Version: 1.0 Content-Type: text/plain diff --git a/publisher/metadata/2016/scipy_proc.json b/publisher/metadata/2016/scipy_proc.json index b8de34fa28..030f33e36c 100644 --- a/publisher/metadata/2016/scipy_proc.json +++ b/publisher/metadata/2016/scipy_proc.json @@ -389,4 +389,4 @@ "org": "Golkhou ASU" } ] -} \ No newline at end of file +} diff --git a/publisher/metadata/2016/toc.json b/publisher/metadata/2016/toc.json index 4b50cc4b8d..5e0e7947f3 100644 --- a/publisher/metadata/2016/toc.json +++ b/publisher/metadata/2016/toc.json @@ -886,4 +886,4 @@ ] } ] -} \ No newline at end of file +} diff --git a/publisher/metadata/2018/doi_batch.xml b/publisher/metadata/2018/doi_batch.xml index ea245ff99f..10a6576090 100644 --- a/publisher/metadata/2018/doi_batch.xml +++ b/publisher/metadata/2018/doi_batch.xml @@ -1 +1 @@ -Majora.4af1f417-16514c7e1281533673568Dillon Niederhutdniederhut@enthought.comCrossrefPython in Science ConferenceSciPy17thAustin, TexasJuly 9 - July 15 2018Proceedings of the Python in Science ConferenceProceedings of the Python in Science Conference2575-975210.25080/issn.2575-9752https://conference.scipy.org/proceedingsProceedings of the 17th Python in Science ConferenceScientific Computing with PythonSciPy201810.25080/Majora-4af1f417-018https://conference.scipy.org/proceedings/scipy2018AndrewDurdenAllysonLoyBarbaraReavesMojtabaFazliAbigailCourtneyFrederickQuinnSChennubhotlaShannonQuinnDynamic Social Network Modeling of Diffuse Subcellular Morphologies20181710.25080/Majora-4af1f417-000https://conference.scipy.org/proceedings/scipy2018/Andrew_Durden.htmlAdamRichie-HalfordArielRokemCloudknot: A Python Library to Run your Existing Code on AWS Batch201881410.25080/Majora-4af1f417-001https://conference.scipy.org/proceedings/scipy2018/adam_richie-halford.htmlAnthonySuenLauraNorénAlanLiangAndreaTuEquity, Scalability, and Sustainability of Data Science Infrastructure2018151710.25080/Majora-4af1f417-002https://conference.scipy.org/proceedings/scipy2018/anthony_suen_laura_noren_alan_liang_andrea_tu.htmlAntonMalakhovDavidLiuAntonGorshkovTerryWilmarthComposable Multi-Threading and Multi-Processing for Numeric Libraries2018182410.25080/Majora-4af1f417-003https://conference.scipy.org/proceedings/scipy2018/anton_malakhov.htmlChristopherCarrollAlexanderKaufmanJacquelineKazilNathanPalmerMatthewWhiteThe Econ-ARK and HARK: Open Source Tools for Computational Economics2018253010.25080/Majora-4af1f417-004https://conference.scipy.org/proceedings/scipy2018/carroll_et_al.htmlChristianMcDanielShannonQuinnDeveloping a Start-to-Finish Pipeline for Accelerometer-Based Activity Recognition Using Long Short-Term Memory Recurrent Neural Networks2018314010.25080/Majora-4af1f417-005https://conference.scipy.org/proceedings/scipy2018/christian_mcdaniel.htmlDavidShupeFrankMasciRussLaherBenRusholmeLeeArmusPractical Applications of Astropy2018414710.25080/Majora-4af1f417-006https://conference.scipy.org/proceedings/scipy2018/david_shupe.htmlDharhasPothinaPhilippRudigerJamesBednarScottChristensenKevinWintersKimberlyPeveyChristopherBallGregoryBrenerEarthSim: Flexible Environmental Simulation Workflows Entirely Within Jupyter Notebooks2018485510.25080/Majora-4af1f417-007https://conference.scipy.org/proceedings/scipy2018/dharhas_pothina.htmlDillonNiederhutSafe handling instructions for missing data2018566010.25080/Majora-4af1f417-008https://conference.scipy.org/proceedings/scipy2018/dillon_niederhut.htmlElizabethSeiverMPacerSebastianBassiText and data mining scientific articles with allofplos2018616410.25080/Majora-4af1f417-009https://conference.scipy.org/proceedings/scipy2018/elizabeth_seiver.htmlHameerAbbasiSparse: A more modern sparse array library2018656810.25080/Majora-4af1f417-00ahttps://conference.scipy.org/proceedings/scipy2018/hameer_abbasi.htmlJonMeaseBringing ipywidgets Support to plotly.py2018697610.25080/Majora-4af1f417-00bhttps://conference.scipy.org/proceedings/scipy2018/jon_mease.htmlKyleSundenBlaiseThompsonJohnWrightWrightSim: Using PyCUDA to Simulate Multidimensional Spectra2018778310.25080/Majora-4af1f417-00chttps://conference.scipy.org/proceedings/scipy2018/kyle_sunden.htmlMarkWickertChiranthSiddappaExploring the Extended Kalman Filter for GPS Positioning Using Simulated User and Satellite Track Data2018849010.25080/Majora-4af1f417-00dhttps://conference.scipy.org/proceedings/scipy2018/mark_wickert_247.htmlMarkWickertReal-Time Digital Signal Processing Using pyaudio\_helper and the ipywidgets2018919810.25080/Majora-4af1f417-00ehttps://conference.scipy.org/proceedings/scipy2018/mark_wickert_250.htmlMatthewShannonChristiaanBoersmaOrganic Molecules in Space: Insights from the NASA Ames Molecular Database in the era of the James Webb Space Telescope20189910510.25080/Majora-4af1f417-00fhttps://conference.scipy.org/proceedings/scipy2018/matthew_shannon.htmlNoelleHeldJaclynSaundersJoeFutrelleMakSaitoHarnessing the Power of Scientific Python to Investigate Biogeochemistry and Metaproteomes of the Central Pacific Ocean201810611210.25080/Majora-4af1f417-010https://conference.scipy.org/proceedings/scipy2018/n_held.htmlProjectJupyterMatthiasBussonnierJessicaFordeJeremyFreemanBrianGrangerTimHeadChrisHoldgrafKyleKelleyGladysNalvarteAndrewOsheroffMPacerYuviPandaFernandoPerezBenjaminRagan-KelleyCarolWillingBinder 2.0 - Reproducible, interactive, sharable environments for science at scale201811312010.25080/Majora-4af1f417-011https://conference.scipy.org/proceedings/scipy2018/project_jupyter.htmlSergioReyElijahKnaapSuHanLeviWolfWeiKangSpatio-temporal analysis of socioeconomic neighborhoods: The Open Source Longitudinal Neighborhood Analysis Package (OSLNAP)201812112810.25080/Majora-4af1f417-012https://conference.scipy.org/proceedings/scipy2018/serge_rey.htmlTylerMartinThomasGartnerRonaldJonesChadSnyderArthiJayaramanDesign and Implementation of pyPRISM: A Polymer Liquid-State Theory Framework201812913610.25080/Majora-4af1f417-013https://conference.scipy.org/proceedings/scipy2018/tyler_martin.htmlKonstantinosVamvourellisMarianneCorvellecA Bayesian’s journey to a better research workflow201813714410.25080/Majora-4af1f417-014https://conference.scipy.org/proceedings/scipy2018/vamvourellis_corvellec.htmlVirginiaNgDanielHofmannScalable Feature Extraction with Aerial and Satellite Imagery201814515110.25080/Majora-4af1f417-015https://conference.scipy.org/proceedings/scipy2018/virginia_ng.htmlVyasRamasubramaniCarlAdorfPaulDoddBradleyDiceSharonGlotzersignac: A Python framework for data and workflow management201815215910.25080/Majora-4af1f417-016https://conference.scipy.org/proceedings/scipy2018/vyas_ramasubramani.htmlPrabhuRamachandranPrathameshSalunkeAnkitJavalkarAdityaPalaparthyMaheshGudiHardikGhaghadaYaksh: Facilitating Learning by Doing201816016710.25080/Majora-4af1f417-017https://conference.scipy.org/proceedings/scipy2018/yaksh.html \ No newline at end of file +Majora.4af1f417-16514c7e1281533673568Dillon Niederhutdniederhut@enthought.comCrossrefPython in Science ConferenceSciPy17thAustin, TexasJuly 9 - July 15 2018Proceedings of the Python in Science ConferenceProceedings of the Python in Science Conference2575-975210.25080/issn.2575-9752https://conference.scipy.org/proceedingsProceedings of the 17th Python in Science ConferenceScientific Computing with PythonSciPy201810.25080/Majora-4af1f417-018https://conference.scipy.org/proceedings/scipy2018AndrewDurdenAllysonLoyBarbaraReavesMojtabaFazliAbigailCourtneyFrederickQuinnSChennubhotlaShannonQuinnDynamic Social Network Modeling of Diffuse Subcellular Morphologies20181710.25080/Majora-4af1f417-000https://conference.scipy.org/proceedings/scipy2018/Andrew_Durden.htmlAdamRichie-HalfordArielRokemCloudknot: A Python Library to Run your Existing Code on AWS Batch201881410.25080/Majora-4af1f417-001https://conference.scipy.org/proceedings/scipy2018/adam_richie-halford.htmlAnthonySuenLauraNorénAlanLiangAndreaTuEquity, Scalability, and Sustainability of Data Science Infrastructure2018151710.25080/Majora-4af1f417-002https://conference.scipy.org/proceedings/scipy2018/anthony_suen_laura_noren_alan_liang_andrea_tu.htmlAntonMalakhovDavidLiuAntonGorshkovTerryWilmarthComposable Multi-Threading and Multi-Processing for Numeric Libraries2018182410.25080/Majora-4af1f417-003https://conference.scipy.org/proceedings/scipy2018/anton_malakhov.htmlChristopherCarrollAlexanderKaufmanJacquelineKazilNathanPalmerMatthewWhiteThe Econ-ARK and HARK: Open Source Tools for Computational Economics2018253010.25080/Majora-4af1f417-004https://conference.scipy.org/proceedings/scipy2018/carroll_et_al.htmlChristianMcDanielShannonQuinnDeveloping a Start-to-Finish Pipeline for Accelerometer-Based Activity Recognition Using Long Short-Term Memory Recurrent Neural Networks2018314010.25080/Majora-4af1f417-005https://conference.scipy.org/proceedings/scipy2018/christian_mcdaniel.htmlDavidShupeFrankMasciRussLaherBenRusholmeLeeArmusPractical Applications of Astropy2018414710.25080/Majora-4af1f417-006https://conference.scipy.org/proceedings/scipy2018/david_shupe.htmlDharhasPothinaPhilippRudigerJamesBednarScottChristensenKevinWintersKimberlyPeveyChristopherBallGregoryBrenerEarthSim: Flexible Environmental Simulation Workflows Entirely Within Jupyter Notebooks2018485510.25080/Majora-4af1f417-007https://conference.scipy.org/proceedings/scipy2018/dharhas_pothina.htmlDillonNiederhutSafe handling instructions for missing data2018566010.25080/Majora-4af1f417-008https://conference.scipy.org/proceedings/scipy2018/dillon_niederhut.htmlElizabethSeiverMPacerSebastianBassiText and data mining scientific articles with allofplos2018616410.25080/Majora-4af1f417-009https://conference.scipy.org/proceedings/scipy2018/elizabeth_seiver.htmlHameerAbbasiSparse: A more 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(OSLNAP)201812112810.25080/Majora-4af1f417-012https://conference.scipy.org/proceedings/scipy2018/serge_rey.htmlTylerMartinThomasGartnerRonaldJonesChadSnyderArthiJayaramanDesign and Implementation of pyPRISM: A Polymer Liquid-State Theory Framework201812913610.25080/Majora-4af1f417-013https://conference.scipy.org/proceedings/scipy2018/tyler_martin.htmlKonstantinosVamvourellisMarianneCorvellecA Bayesian’s journey to a better research workflow201813714410.25080/Majora-4af1f417-014https://conference.scipy.org/proceedings/scipy2018/vamvourellis_corvellec.htmlVirginiaNgDanielHofmannScalable Feature Extraction with Aerial and Satellite Imagery201814515110.25080/Majora-4af1f417-015https://conference.scipy.org/proceedings/scipy2018/virginia_ng.htmlVyasRamasubramaniCarlAdorfPaulDoddBradleyDiceSharonGlotzersignac: A Python framework for data and workflow management201815215910.25080/Majora-4af1f417-016https://conference.scipy.org/proceedings/scipy2018/vyas_ramasubramani.htmlPrabhuRamachandranPrathameshSalunkeAnkitJavalkarAdityaPalaparthyMaheshGudiHardikGhaghadaYaksh: Facilitating Learning by Doing201816016710.25080/Majora-4af1f417-017https://conference.scipy.org/proceedings/scipy2018/yaksh.html diff --git a/publisher/metadata/2018/toc.json b/publisher/metadata/2018/toc.json index 991f101945..da49cfc4a6 100644 --- a/publisher/metadata/2018/toc.json +++ b/publisher/metadata/2018/toc.json @@ -1221,4 +1221,4 @@ "authors": "Prabhu Ramachandran, Prathamesh Salunke, Ankit Javalkar, Aditya Palaparthy, Mahesh Gudi, Hardik Ghaghada" } ] -} \ No newline at end of file +} diff --git a/publisher/metadata/2020/doi_batch_papers.xml b/publisher/metadata/2020/doi_batch_papers.xml index e9e003e2bb..0eab634746 100644 --- a/publisher/metadata/2020/doi_batch_papers.xml +++ b/publisher/metadata/2020/doi_batch_papers.xml @@ -1 +1 @@ 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Applications2020111710.25080/Majora-342d178e-002https://conference.scipy.org/proceedings/scipy2020/75_lu.htmlAntoineDujardinElliottSlaugtherJeffreyDonatelliPeterZwartAmedeoPerazzoChunYoonFluctuation X-ray Scattering real-time app2020182310.25080/Majora-342d178e-003https://conference.scipy.org/proceedings/scipy2020/antoine_dujardin.htmlBrandonButlerhttps://orcid.org/0000-0001-7739-7796VyasRamasubramanihttps://orcid.org/0000-0001-5181-9532JoshuaAndersonSharonGlotzerhttps://orcid.org/0000-0002-7197-0085HOOMD-blue version 3.0 A Modern, Extensible, Flexible, Object-Oriented API for Molecular Simulations2020243110.25080/Majora-342d178e-004https://conference.scipy.org/proceedings/scipy2020/brandon_butler.htmlAdityaBhosalehttps://orcid.org/0000-0003-1876-6138PrabhuRamachandranhttps://orcid.org/0000-0001-6337-1720Compyle: a Python package for parallel computing2020323910.25080/Majora-342d178e-005https://conference.scipy.org/proceedings/scipy2020/compyle_pr_ab.htmlDallinSkousonAndrewKellerhttps://orcid.org/0000-0002-6285-5288MichaelWirthlinhttps://orcid.org/0000-0003-0328-6713Netlist Analysis and Transformations Using SpyDrNet2020404710.25080/Majora-342d178e-006https://conference.scipy.org/proceedings/scipy2020/dallin_skouson.htmlNinaMiolanehttps://orcid.org/0000-0002-1200-9024NicolasGuiguihttps://orcid.org/0000-0002-7901-0732HadiZaatitiChristianShewmakeHatemHajriDanielBrooksAliceLe BrigantJohanMatheBenjaminHouYannThanwerdasStefanHeyderOlivierPeltreNiklasKoepYannCabanesThomasGeraldPaulChauchatBernhardKainzClaireDonnatSusanHolmesXavierPennecIntroduction to Geometric Learning in Python with Geomstats2020485710.25080/Majora-342d178e-007https://conference.scipy.org/proceedings/scipy2020/geomstats.htmlGiancarloPerroneJoseUnpingcoHaw-minnLuNetwork visualizations with Pyvis and VisJS2020586210.25080/Majora-342d178e-008https://conference.scipy.org/proceedings/scipy2020/giancarlo_perrone.htmlHenrySchreinerhttps://orcid.org/0000-0002-7833-783XHansDembinskihttps://orcid.org/0000-0003-3337-3850ShuoLiuhttps://orcid.org/0000-0003-0177-6102JimPivarskihttps://orcid.org/0000-0002-6649-343XBoost-histogram: High-Performance Histograms as Objects2020636910.25080/Majora-342d178e-009https://conference.scipy.org/proceedings/scipy2020/henry_schreiner.htmlJacobMontielhttps://orcid.org/0000-0003-2245-0718Learning from evolving data streams2020707710.25080/Majora-342d178e-00ahttps://conference.scipy.org/proceedings/scipy2020/jacob_montiel.htmlJimPivarskihttps://orcid.org/0000-0002-6649-343XIannaOsbornehttps://orcid.org/0000-0002-6955-1033PratyushDashttps://orcid.org/0000-0001-8140-0097AnishBiswashttps://orcid.org/0000-0001-6149-9739PeterElmerhttps://orcid.org/0000-0001-6830-3356Awkward Array: JSON-like data, NumPy-like idioms2020788410.25080/Majora-342d178e-00bhttps://conference.scipy.org/proceedings/scipy2020/jim_pivarski.htmlValeriaBarrahttps://orcid.org/0000-0003-1129-2056JedBrownhttps://orcid.org/0000-0002-9945-0639JeremyThompsonhttps://orcid.org/0000-0003-2980-0899YohannDudouithttps://orcid.org/0000-0001-5831-561XHigh-performance operator evaluations with ease of use: libCEED's Python interface2020859010.25080/Majora-342d178e-00chttps://conference.scipy.org/proceedings/scipy2020/libceed-paper.htmlMarcusHillMojtabaFazliRachelMattsonMeekailZainAndrewDurdenAllysonLoyBarbaraReavesAbigailCourtneyFrederickQuinnSChennubhotlaShannonQuinnSpectral Analysis of Mitochondrial Dynamics: A Graph-Theoretic Approach to Understanding Subcellular Pathology2020919710.25080/Majora-342d178e-00dhttps://conference.scipy.org/proceedings/scipy2020/marcus_hill.htmlMarkWickerthttps://orcid.org/0000-0002-8302-3835DavidPeckhamMatched Filter Mismatch Losses in MPSK and MQAM Using Semi-Analytic BEP Modeling20209810610.25080/Majora-342d178e-00ehttps://conference.scipy.org/proceedings/scipy2020/mark_wickert_239.htmlMauriceJamiesonNickBrownhttps://orcid.org/0000-0003-2925-7275SihangLiuHaving your cake and eating it: Exploiting Python for programmer productivity and performance on micro-core architectures using ePython202010711510.25080/Majora-342d178e-00fhttps://conference.scipy.org/proceedings/scipy2020/maurice_jamieson.htmlNielsBantilanhttps://orcid.org/0000-0003-1713-5772pandera: Statistical Data Validation of Pandas Dataframes202011612410.25080/Majora-342d178e-010https://conference.scipy.org/proceedings/scipy2020/niels_bantilan.htmlOneyErgehttps://orcid.org/0000-0002-2687-9135Ericvan OortCombining Physics-Based and Data-Driven Modeling for Pressure Prediction in Well Construction202012513110.25080/Majora-342d178e-011https://conference.scipy.org/proceedings/scipy2020/oney_erge.htmlDorotaJareckahttps://orcid.org/0000-0001-8282-2988MathiasGoncalveshttps://orcid.org/0000-0002-7252-7771ChristopherMarkiewiczhttps://orcid.org/0000-0002-6533-164XOscarEstebanhttps://orcid.org/0000-0001-8435-6191NicoleLohttps://orcid.org/0000-0002-7522-686XJakubKaczmarzykhttps://orcid.org/0000-0002-5544-7577SatrajitGhoshhttps://orcid.org/0000-0002-5312-6729Pydra - a flexible and lightweight dataflow engine for scientific analyses202013213910.25080/Majora-342d178e-012https://conference.scipy.org/proceedings/scipy2020/pydra.htmlRalphKubeRChurchillJongChoiRuonanWangScottKlaskyCSChangMinjunChoiJinseopParkLeading magnetic fusion energy science into the big-and-fast data lane202014014710.25080/Majora-342d178e-013https://conference.scipy.org/proceedings/scipy2020/ralph_kube.htmlRyanBunneyAndreasWicenecMarkReynoldsSHADOW: A workflow scheduling algorithm reference and testing framework202014815510.25080/Majora-342d178e-014https://conference.scipy.org/proceedings/scipy2020/ryan_bunney.htmlSebastianBenthallhttps://orcid.org/0000-0002-1789-5109MridulSethSoftware Engineering as Research Method: Aligning Roles in Econ-ARK202015616110.25080/Majora-342d178e-015https://conference.scipy.org/proceedings/scipy2020/sebastian_benthall.htmlZacHatfield-Doddshttps://orcid.org/0000-0002-8646-8362Falsify your Software: validating scientific code with property-based testing202016216510.25080/Majora-342d178e-016https://conference.scipy.org/proceedings/scipy2020/zac_hatfield-dodds.htmlMeekailZainhttps://orcid.org/0000-0002-4624-7960SoniaRaoNathanSafirQuinnWynerIsabellaHumphreyAlexEldridgeChenxiaoLiBahaaEddinAlAilaShannonQuinnTowards an Unsupervised Spatiotemporal Representation of Cilia Video Using A Modular Generative Pipeline202016617510.25080/Majora-342d178e-017https://conference.scipy.org/proceedings/scipy2020/zain.html \ No newline at end of file +Majora.342d178e-1738f0d48071595854479Dillon Niederhutdillon.niederhut@gmail.comCrossrefPython in Science ConferenceSciPy19thAustin, TexasJuly 6 - July 12 2020Proceedings of the Python in Science ConferenceProceedings of the Python in Science Conference2575-975210.25080/issn.2575-9752https://conference.scipy.org/proceedingsProceedings of the 19th Python in Science ConferenceScientific Computing with PythonSciPy202010.25080/Majora-342d178e-02bhttps://conference.scipy.org/proceedings/scipy2020MeghannAgarwalJulieHollekDillonNiederhutPreface20201110.25080/Majora-342d178e-000https://conference.scipy.org/proceedings/scipy2020/00_preface.htmlHaw-minnLuAdrianKwongJoséUnpingcoSecuring Your Collaborative Jupyter Notebooks in the Cloud using Container and Load Balancing Services202021010.25080/Majora-342d178e-001https://conference.scipy.org/proceedings/scipy2020/72_lu.htmlHaw-minnLuQuasi-orthonormal Encoding for Machine Learning Applications2020111710.25080/Majora-342d178e-002https://conference.scipy.org/proceedings/scipy2020/75_lu.htmlAntoineDujardinElliottSlaugtherJeffreyDonatelliPeterZwartAmedeoPerazzoChunYoonFluctuation X-ray Scattering real-time app2020182310.25080/Majora-342d178e-003https://conference.scipy.org/proceedings/scipy2020/antoine_dujardin.htmlBrandonButlerhttps://orcid.org/0000-0001-7739-7796VyasRamasubramanihttps://orcid.org/0000-0001-5181-9532JoshuaAndersonSharonGlotzerhttps://orcid.org/0000-0002-7197-0085HOOMD-blue version 3.0 A Modern, Extensible, Flexible, Object-Oriented API for Molecular Simulations2020243110.25080/Majora-342d178e-004https://conference.scipy.org/proceedings/scipy2020/brandon_butler.htmlAdityaBhosalehttps://orcid.org/0000-0003-1876-6138PrabhuRamachandranhttps://orcid.org/0000-0001-6337-1720Compyle: a Python package for parallel computing2020323910.25080/Majora-342d178e-005https://conference.scipy.org/proceedings/scipy2020/compyle_pr_ab.htmlDallinSkousonAndrewKellerhttps://orcid.org/0000-0002-6285-5288MichaelWirthlinhttps://orcid.org/0000-0003-0328-6713Netlist Analysis and Transformations Using SpyDrNet2020404710.25080/Majora-342d178e-006https://conference.scipy.org/proceedings/scipy2020/dallin_skouson.htmlNinaMiolanehttps://orcid.org/0000-0002-1200-9024NicolasGuiguihttps://orcid.org/0000-0002-7901-0732HadiZaatitiChristianShewmakeHatemHajriDanielBrooksAliceLe BrigantJohanMatheBenjaminHouYannThanwerdasStefanHeyderOlivierPeltreNiklasKoepYannCabanesThomasGeraldPaulChauchatBernhardKainzClaireDonnatSusanHolmesXavierPennecIntroduction to Geometric Learning in Python with Geomstats2020485710.25080/Majora-342d178e-007https://conference.scipy.org/proceedings/scipy2020/geomstats.htmlGiancarloPerroneJoseUnpingcoHaw-minnLuNetwork visualizations with Pyvis and VisJS2020586210.25080/Majora-342d178e-008https://conference.scipy.org/proceedings/scipy2020/giancarlo_perrone.htmlHenrySchreinerhttps://orcid.org/0000-0002-7833-783XHansDembinskihttps://orcid.org/0000-0003-3337-3850ShuoLiuhttps://orcid.org/0000-0003-0177-6102JimPivarskihttps://orcid.org/0000-0002-6649-343XBoost-histogram: High-Performance Histograms as Objects2020636910.25080/Majora-342d178e-009https://conference.scipy.org/proceedings/scipy2020/henry_schreiner.htmlJacobMontielhttps://orcid.org/0000-0003-2245-0718Learning from evolving data streams2020707710.25080/Majora-342d178e-00ahttps://conference.scipy.org/proceedings/scipy2020/jacob_montiel.htmlJimPivarskihttps://orcid.org/0000-0002-6649-343XIannaOsbornehttps://orcid.org/0000-0002-6955-1033PratyushDashttps://orcid.org/0000-0001-8140-0097AnishBiswashttps://orcid.org/0000-0001-6149-9739PeterElmerhttps://orcid.org/0000-0001-6830-3356Awkward Array: JSON-like data, NumPy-like idioms2020788410.25080/Majora-342d178e-00bhttps://conference.scipy.org/proceedings/scipy2020/jim_pivarski.htmlValeriaBarrahttps://orcid.org/0000-0003-1129-2056JedBrownhttps://orcid.org/0000-0002-9945-0639JeremyThompsonhttps://orcid.org/0000-0003-2980-0899YohannDudouithttps://orcid.org/0000-0001-5831-561XHigh-performance operator evaluations with ease of use: libCEED's Python interface2020859010.25080/Majora-342d178e-00chttps://conference.scipy.org/proceedings/scipy2020/libceed-paper.htmlMarcusHillMojtabaFazliRachelMattsonMeekailZainAndrewDurdenAllysonLoyBarbaraReavesAbigailCourtneyFrederickQuinnSChennubhotlaShannonQuinnSpectral Analysis of Mitochondrial Dynamics: A Graph-Theoretic Approach to Understanding Subcellular Pathology2020919710.25080/Majora-342d178e-00dhttps://conference.scipy.org/proceedings/scipy2020/marcus_hill.htmlMarkWickerthttps://orcid.org/0000-0002-8302-3835DavidPeckhamMatched Filter Mismatch Losses in MPSK and MQAM Using Semi-Analytic BEP Modeling20209810610.25080/Majora-342d178e-00ehttps://conference.scipy.org/proceedings/scipy2020/mark_wickert_239.htmlMauriceJamiesonNickBrownhttps://orcid.org/0000-0003-2925-7275SihangLiuHaving your cake and eating it: Exploiting Python for programmer productivity and performance on micro-core architectures using ePython202010711510.25080/Majora-342d178e-00fhttps://conference.scipy.org/proceedings/scipy2020/maurice_jamieson.htmlNielsBantilanhttps://orcid.org/0000-0003-1713-5772pandera: Statistical Data Validation of Pandas Dataframes202011612410.25080/Majora-342d178e-010https://conference.scipy.org/proceedings/scipy2020/niels_bantilan.htmlOneyErgehttps://orcid.org/0000-0002-2687-9135Ericvan OortCombining Physics-Based and Data-Driven Modeling for Pressure Prediction in Well Construction202012513110.25080/Majora-342d178e-011https://conference.scipy.org/proceedings/scipy2020/oney_erge.htmlDorotaJareckahttps://orcid.org/0000-0001-8282-2988MathiasGoncalveshttps://orcid.org/0000-0002-7252-7771ChristopherMarkiewiczhttps://orcid.org/0000-0002-6533-164XOscarEstebanhttps://orcid.org/0000-0001-8435-6191NicoleLohttps://orcid.org/0000-0002-7522-686XJakubKaczmarzykhttps://orcid.org/0000-0002-5544-7577SatrajitGhoshhttps://orcid.org/0000-0002-5312-6729Pydra - a flexible and lightweight dataflow engine for scientific analyses202013213910.25080/Majora-342d178e-012https://conference.scipy.org/proceedings/scipy2020/pydra.htmlRalphKubeRChurchillJongChoiRuonanWangScottKlaskyCSChangMinjunChoiJinseopParkLeading magnetic fusion energy science into the big-and-fast data lane202014014710.25080/Majora-342d178e-013https://conference.scipy.org/proceedings/scipy2020/ralph_kube.htmlRyanBunneyAndreasWicenecMarkReynoldsSHADOW: A workflow scheduling algorithm reference and testing framework202014815510.25080/Majora-342d178e-014https://conference.scipy.org/proceedings/scipy2020/ryan_bunney.htmlSebastianBenthallhttps://orcid.org/0000-0002-1789-5109MridulSethSoftware Engineering as Research Method: Aligning Roles in Econ-ARK202015616110.25080/Majora-342d178e-015https://conference.scipy.org/proceedings/scipy2020/sebastian_benthall.htmlZacHatfield-Doddshttps://orcid.org/0000-0002-8646-8362Falsify your Software: validating scientific code with property-based testing202016216510.25080/Majora-342d178e-016https://conference.scipy.org/proceedings/scipy2020/zac_hatfield-dodds.htmlMeekailZainhttps://orcid.org/0000-0002-4624-7960SoniaRaoNathanSafirQuinnWynerIsabellaHumphreyAlexEldridgeChenxiaoLiBahaaEddinAlAilaShannonQuinnTowards an Unsupervised Spatiotemporal Representation of Cilia Video Using A Modular Generative Pipeline202016617510.25080/Majora-342d178e-017https://conference.scipy.org/proceedings/scipy2020/zain.html diff --git a/publisher/metadata/2020/doi_batch_slides.xml b/publisher/metadata/2020/doi_batch_slides.xml index 33dd673f7f..e1a6d9a422 100644 --- a/publisher/metadata/2020/doi_batch_slides.xml +++ b/publisher/metadata/2020/doi_batch_slides.xml @@ -1 +1 @@ -Majora.342d178e-1738f0d482a1595854479Dillon Niederhutdillon.niederhut@gmail.comCrossrefDillonNiederhutProceedings of the Python in Science ConferenceChristineSmitHailiangZhangMahabaleshwaraHegdeFaithGiguereLongPhamTreating gridded geospatial data as point data to simplify analytics2020Gridded geospatial remote sensing (satellite) data has traditionally been stored in file-based multidimensional arrays to preserve the locality of data. Measurements from locations that are physically next to each other on earth remain next to each other in the arrays. Maintaining this locality is useful when running calculations like reprojection, but unnecessary for many other calculations. This talk will go through a real world example of a tool redesign at the Goddard Earth Sciences Data and Information Services Center (GES DISC), showing the advantages of using the data frame model for calculating summary statistics, where measurement proximity is unimportant.10.25080/issn.2575-975210.25080/Majora-342d178e-019https://zenodo.org/record/3961207BenjaminAlbrechtMichaelMerrillWilliamReusBradChamberlainArkouda: Terascale Data Science at Interactive Rates2020This talk describes Arkouda, a Python package that we have developed for doing exploratory analysis on massive data sets at interactive rates. Arkouda's API is based on NumPy/Pandas, yet its arrays can be transparently distributed across the compute nodes of a cluster or supercomputer to support large-scale analytics. In our work, we have run Arkouda operations from Jupyter notebooks on TB-sized data sets in seconds to small numbers of minutes—achieving scalability and performance that we have not observed with competing technologies.10.25080/issn.2575-975210.25080/Majora-342d178e-01ahttps://zenodo.org/record/3961209HenrySchreinerHansDembinskiJimPivarskiShuoLiuBoost-histogram: High-Performance Histograms as Objects2020Boost-histogram is a new Python library that provides Histograms that can be filled, manipulated, sliced, and projected as objects.10.25080/issn.2575-975210.25080/Majora-342d178e-01bhttps://zenodo.org/record/3961211AnneCarpenterOpen-source bioimage analysis software to accelerate drug discovery202010.25080/issn.2575-975210.25080/Majora-342d178e-01chttps://zenodo.org/record/3961216AdamThompsonMattNicelyGrahamMarkallBradReescuSignal - GPU Accelerating SciPy Signal with Numba and CuPy2020cuSignal is a GPU accelerated signal processing library built around a SciPy Signal-like API, CuPy, and custom Numba and CuPy CUDA kernels. cuSignal is written exclusively in Python and demonstrates GPU speeds without a C++ software layer.10.25080/issn.2575-975210.25080/Majora-342d178e-01dhttps://zenodo.org/record/3961218LillyWinfreeFrictionless Data for Reproducible Biology2020This talk discusses how biologists can make their data more reproducible using Frictionless Data's open source Python libraries10.25080/issn.2575-975210.25080/Majora-342d178e-01ehttps://zenodo.org/record/3961220RollinThomasShaneCanonShreyasCholiaMattHendersonKellyRowlandJonHaysWilliamKrinsmanJustinLeyLabanyaMukhopadhyayTrevorSlatonInteractive Supercomputing with Jupyter at the National Energy Research Scientific Computing Center2020At the National Energy Research Scientific Computing (NERSC) Center, interactive access to high-performance computing and data through Jupyter is a priority. We will discuss the nuts and bolts of how Jupyter is deployed at NERSC, and how we've adapted to engage the Jupyter ecosystem and open-source community to deliver this key capability to our users. Jupyter is a major component in our Superfacility initiative, which aims to connect experimental and observational big data facilities (telescopes, microscopes, genome sequencers, light sources, etc.) with next-generation supercomputing and data capabilities at NERSC.10.25080/issn.2575-975210.25080/Majora-342d178e-01fhttps://zenodo.org/record/3961224C.A.M.GerlachProject Mjolnir: A Modular, Open-source Platform for Developing Scientific IoT Sensor Networks2020From a humble beginning as a side effort using a Raspberry Pi to talk to lightning instruments, Project Mjolnir is evolving into a modular, open source client-server platform for developing scientific IoT sensor networks. Its goal is to enable scientists of many disciplines to employ low-cost hardware to robustly ingest, log and uplink periodic and on-demand science and engineering data and commands, controlled either autonomously or centrally, all with little or no bespoke code. The talk will discuss Mjolnir’s development and future, present examples of current projects built on it, and explore how to leverage it for new applications.10.25080/issn.2575-975210.25080/Majora-342d178e-020https://zenodo.org/record/3961228NielsBantilanPandera: Statistical Data Validation of Pandas Dataframes2020This talk introduces pandera, an open source Python package for pandas data validation. It covers data validation in theory and practice, and goes through a case study analysis of the Fatal Encounters dataset to demonstrate how pandera can be used to make data analysis and machine learning more reproducible, robust, and reliable.10.25080/issn.2575-975210.25080/Majora-342d178e-021https://zenodo.org/record/3961230HoracioGuzmanMolecular infrastructure for modeling viruses with pythonic-mediated packages: pyF4all2020We model full viruses by coupling short highly-detailed molecular dynamics simulations with lower-resolution (but faster) continuum electrostatic models. Such multiscale approach enables to model a full virus in a desktop/small cluster-level infrastructure, which are available for most researchers. Here, we propose a first interfacing of the pythonic-like packages in a multiscale approach that automatizes the access to state-of-the-art biomolecular simulations via Jupyter Notebooks.10.25080/issn.2575-975210.25080/Majora-342d178e-022https://zenodo.org/record/3961232MatthewFeickertpyhf: a pure Python statistical fitting library with tensors and autograd2020pyhf is a pure-Python implementation of the HistFactory statistical model for multi-bin histogram-based analysis with asymptotic interval estimation, and part of the Scikit-HEP project ecosystem. pyhf supports modern computational graph libraries as computational backends in order to make use of features such as auto-differentiation and GPU acceleration. Additionally, the statistical models are defined in a declarative JSON schema, readily enabling preservation and distribution through services such as the Durham High-Energy Physics Database (HEPData).10.25080/issn.2575-975210.25080/Majora-342d178e-023https://zenodo.org/record/3961236JonMeaseBringing GPU Support to Datashader: A RAPIDS Case Study2020A case study on using RAPIDS technologies to add GPU support to the Datashader Python library10.25080/issn.2575-975210.25080/Majora-342d178e-024https://zenodo.org/record/3961238JacobMontielLearning from evolving data streams2020A brief introduction to machine learning for evolving data streams. In this field data is assumed infinite and can change over time. scikit-multiflow, a package for stream learning in Python is also presented.10.25080/issn.2575-975210.25080/Majora-342d178e-025https://zenodo.org/record/3961242DharhasPothinaKimPeveyAdamLewisSpatial Algorithms at Scale with spatialpandas2020How do you analyze 1 trillion rows of geospatial point data? We recently solved this problem using spatialpandas, dask, and parquet file format to efficiently build and execute spatial algorithms at scale. We compare the spatialpandas solution's performance with other cases, and discuss the tradeoffs with various approaches.10.25080/issn.2575-975210.25080/Majora-342d178e-026https://zenodo.org/record/3961244JamesBednarHoloViz: What's new and what's next2020Updates and roadmaps for Panel, hvPlot, HoloViews, GeoViews, Datashader, Param, and Colorcet. The HoloViz suite of tools together form a unified approach for visualization from exploration to sharing applications and dashboards, building on the SciPy ecosystem to support easy visualization of large multidimensional or columnar datasets.10.25080/issn.2575-975210.25080/Majora-342d178e-028https://zenodo.org/record/3961190Elliottde AndradeSciPy Tools Plenary on Matplotlib2020Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. This presentation summarizes changes over the past year, new features, and future plans.10.25080/issn.2575-975210.25080/Majora-342d178e-029https://zenodo.org/record/3961192SiuLamSciPy Tools Plenary on Numba2020Numba is a just-in-time compiler for a subset of Python. This is a short presentation of Numba updates for 2019-2020. 10.25080/issn.2575-975210.25080/Majora-342d178e-02ahttps://zenodo.org/record/3961194MattHaberlandKatyCraigKarthikElamvazhuthiOlgaTuranovaDecentralized, Deterministic Robot Swarm Control using Blob Methods for PDEs2020A Jupyter notebook about robot swarm control, simulation, digital experiments, and computational considerations10.25080/issn.2575-975210.25080/Majora-342d178e-018https://zenodo.org/record/3961198NielsBantilanBuilding an AutoML System for Fun and Non-profit2020This talk introduces metalearn, a MetaRL-based AutoML system that learns to learn how to propose hyperparameter selections that produce high validation scores on meta-test datasets.10.25080/issn.2575-975210.25080/Majora-342d178e-027https://zenodo.org/record/3961204 \ No newline at end of file +Majora.342d178e-1738f0d482a1595854479Dillon Niederhutdillon.niederhut@gmail.comCrossrefDillonNiederhutProceedings of the Python in Science ConferenceChristineSmitHailiangZhangMahabaleshwaraHegdeFaithGiguereLongPhamTreating gridded geospatial data as point data to simplify analytics2020Gridded geospatial remote sensing (satellite) data has traditionally been stored in file-based multidimensional arrays to preserve the locality of data. Measurements from locations that are physically next to each other on earth remain next to each other in the arrays. Maintaining this locality is useful when running calculations like reprojection, but unnecessary for many other calculations. This talk will go through a real world example of a tool redesign at the Goddard Earth Sciences Data and Information Services Center (GES DISC), showing the advantages of using the data frame model for calculating summary statistics, where measurement proximity is unimportant.10.25080/issn.2575-975210.25080/Majora-342d178e-019https://zenodo.org/record/3961207BenjaminAlbrechtMichaelMerrillWilliamReusBradChamberlainArkouda: Terascale Data Science at Interactive Rates2020This talk describes Arkouda, a Python package that we have developed for doing exploratory analysis on massive data sets at interactive rates. Arkouda's API is based on NumPy/Pandas, yet its arrays can be transparently distributed across the compute nodes of a cluster or supercomputer to support large-scale analytics. In our work, we have run Arkouda operations from Jupyter notebooks on TB-sized data sets in seconds to small numbers of minutes—achieving scalability and performance that we have not observed with competing technologies.10.25080/issn.2575-975210.25080/Majora-342d178e-01ahttps://zenodo.org/record/3961209HenrySchreinerHansDembinskiJimPivarskiShuoLiuBoost-histogram: High-Performance Histograms as Objects2020Boost-histogram is a new Python library that provides Histograms that can be filled, manipulated, sliced, and projected as objects.10.25080/issn.2575-975210.25080/Majora-342d178e-01bhttps://zenodo.org/record/3961211AnneCarpenterOpen-source bioimage analysis software to accelerate drug discovery202010.25080/issn.2575-975210.25080/Majora-342d178e-01chttps://zenodo.org/record/3961216AdamThompsonMattNicelyGrahamMarkallBradReescuSignal - GPU Accelerating SciPy Signal with Numba and CuPy2020cuSignal is a GPU accelerated signal processing library built around a SciPy Signal-like API, CuPy, and custom Numba and CuPy CUDA kernels. cuSignal is written exclusively in Python and demonstrates GPU speeds without a C++ software layer.10.25080/issn.2575-975210.25080/Majora-342d178e-01dhttps://zenodo.org/record/3961218LillyWinfreeFrictionless Data for Reproducible Biology2020This talk discusses how biologists can make their data more reproducible using Frictionless Data's open source Python libraries10.25080/issn.2575-975210.25080/Majora-342d178e-01ehttps://zenodo.org/record/3961220RollinThomasShaneCanonShreyasCholiaMattHendersonKellyRowlandJonHaysWilliamKrinsmanJustinLeyLabanyaMukhopadhyayTrevorSlatonInteractive Supercomputing with Jupyter at the National Energy Research Scientific Computing Center2020At the National Energy Research Scientific Computing (NERSC) Center, interactive access to high-performance computing and data through Jupyter is a priority. We will discuss the nuts and bolts of how Jupyter is deployed at NERSC, and how we've adapted to engage the Jupyter ecosystem and open-source community to deliver this key capability to our users. Jupyter is a major component in our Superfacility initiative, which aims to connect experimental and observational big data facilities (telescopes, microscopes, genome sequencers, light sources, etc.) with next-generation supercomputing and data capabilities at NERSC.10.25080/issn.2575-975210.25080/Majora-342d178e-01fhttps://zenodo.org/record/3961224C.A.M.GerlachProject Mjolnir: A Modular, Open-source Platform for Developing Scientific IoT Sensor Networks2020From a humble beginning as a side effort using a Raspberry Pi to talk to lightning instruments, Project Mjolnir is evolving into a modular, open source client-server platform for developing scientific IoT sensor networks. Its goal is to enable scientists of many disciplines to employ low-cost hardware to robustly ingest, log and uplink periodic and on-demand science and engineering data and commands, controlled either autonomously or centrally, all with little or no bespoke code. The talk will discuss Mjolnir’s development and future, present examples of current projects built on it, and explore how to leverage it for new applications.10.25080/issn.2575-975210.25080/Majora-342d178e-020https://zenodo.org/record/3961228NielsBantilanPandera: Statistical Data Validation of Pandas Dataframes2020This talk introduces pandera, an open source Python package for pandas data validation. It covers data validation in theory and practice, and goes through a case study analysis of the Fatal Encounters dataset to demonstrate how pandera can be used to make data analysis and machine learning more reproducible, robust, and reliable.10.25080/issn.2575-975210.25080/Majora-342d178e-021https://zenodo.org/record/3961230HoracioGuzmanMolecular infrastructure for modeling viruses with pythonic-mediated packages: pyF4all2020We model full viruses by coupling short highly-detailed molecular dynamics simulations with lower-resolution (but faster) continuum electrostatic models. Such multiscale approach enables to model a full virus in a desktop/small cluster-level infrastructure, which are available for most researchers. Here, we propose a first interfacing of the pythonic-like packages in a multiscale approach that automatizes the access to state-of-the-art biomolecular simulations via Jupyter Notebooks.10.25080/issn.2575-975210.25080/Majora-342d178e-022https://zenodo.org/record/3961232MatthewFeickertpyhf: a pure Python statistical fitting library with tensors and autograd2020pyhf is a pure-Python implementation of the HistFactory statistical model for multi-bin histogram-based analysis with asymptotic interval estimation, and part of the Scikit-HEP project ecosystem. pyhf supports modern computational graph libraries as computational backends in order to make use of features such as auto-differentiation and GPU acceleration. Additionally, the statistical models are defined in a declarative JSON schema, readily enabling preservation and distribution through services such as the Durham High-Energy Physics Database (HEPData).10.25080/issn.2575-975210.25080/Majora-342d178e-023https://zenodo.org/record/3961236JonMeaseBringing GPU Support to Datashader: A RAPIDS Case Study2020A case study on using RAPIDS technologies to add GPU support to the Datashader Python library10.25080/issn.2575-975210.25080/Majora-342d178e-024https://zenodo.org/record/3961238JacobMontielLearning from evolving data streams2020A brief introduction to machine learning for evolving data streams. In this field data is assumed infinite and can change over time. scikit-multiflow, a package for stream learning in Python is also presented.10.25080/issn.2575-975210.25080/Majora-342d178e-025https://zenodo.org/record/3961242DharhasPothinaKimPeveyAdamLewisSpatial Algorithms at Scale with spatialpandas2020How do you analyze 1 trillion rows of geospatial point data? We recently solved this problem using spatialpandas, dask, and parquet file format to efficiently build and execute spatial algorithms at scale. We compare the spatialpandas solution's performance with other cases, and discuss the tradeoffs with various approaches.10.25080/issn.2575-975210.25080/Majora-342d178e-026https://zenodo.org/record/3961244JamesBednarHoloViz: What's new and what's next2020Updates and roadmaps for Panel, hvPlot, HoloViews, GeoViews, Datashader, Param, and Colorcet. The HoloViz suite of tools together form a unified approach for visualization from exploration to sharing applications and dashboards, building on the SciPy ecosystem to support easy visualization of large multidimensional or columnar datasets.10.25080/issn.2575-975210.25080/Majora-342d178e-028https://zenodo.org/record/3961190Elliottde AndradeSciPy Tools Plenary on Matplotlib2020Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. This presentation summarizes changes over the past year, new features, and future plans.10.25080/issn.2575-975210.25080/Majora-342d178e-029https://zenodo.org/record/3961192SiuLamSciPy Tools Plenary on Numba2020Numba is a just-in-time compiler for a subset of Python. This is a short presentation of Numba updates for 2019-2020. 10.25080/issn.2575-975210.25080/Majora-342d178e-02ahttps://zenodo.org/record/3961194MattHaberlandKatyCraigKarthikElamvazhuthiOlgaTuranovaDecentralized, Deterministic Robot Swarm Control using Blob Methods for PDEs2020A Jupyter notebook about robot swarm control, simulation, digital experiments, and computational considerations10.25080/issn.2575-975210.25080/Majora-342d178e-018https://zenodo.org/record/3961198NielsBantilanBuilding an AutoML System for Fun and Non-profit2020This talk introduces metalearn, a MetaRL-based AutoML system that learns to learn how to propose hyperparameter selections that produce high validation scores on meta-test datasets.10.25080/issn.2575-975210.25080/Majora-342d178e-027https://zenodo.org/record/3961204 diff --git a/publisher/metadata/2020/other.json b/publisher/metadata/2020/other.json index ce6f523977..3158203a1f 100644 --- a/publisher/metadata/2020/other.json +++ b/publisher/metadata/2020/other.json @@ -224,4 +224,4 @@ "doi": "10.25080/Majora-342d178e-027" } ] -} \ No newline at end of file +} diff --git a/publisher/metadata/2020/scipy_proc.json b/publisher/metadata/2020/scipy_proc.json index 770acb44c8..3ddca70149 100644 --- a/publisher/metadata/2020/scipy_proc.json +++ b/publisher/metadata/2020/scipy_proc.json @@ -596,4 +596,4 @@ "dates": "July 6 - July 12", "citation_key": "proc-scipy-2020" } -} \ No newline at end of file +} diff --git a/publisher/metadata/2020/toc.json b/publisher/metadata/2020/toc.json index 64f9218121..597eae180c 100644 --- a/publisher/metadata/2020/toc.json +++ b/publisher/metadata/2020/toc.json @@ -1342,4 +1342,4 @@ "copyright_holder": "Meekail Zain et al." } ] -} \ No newline at end of file +} diff --git a/publisher/metadata/2021/doi_batch_papers.xml b/publisher/metadata/2021/doi_batch_papers.xml index 0532ae1e5a..0e46632ca6 100644 --- a/publisher/metadata/2021/doi_batch_papers.xml +++ b/publisher/metadata/2021/doi_batch_papers.xml @@ -1 +1 @@ -majora.1b6fd038-17b0328486c1627867176Dillon Niederhutdillon.niederhut@gmail.comCrossrefPython in Science ConferenceSciPy20thAustin, TexasJuly 12 - July 18 2021Proceedings of the Python in Science ConferenceProceedings of the Python in Science Conference2575-975210.25080/issn.2575-9752https://conference.scipy.org/proceedingsProceedings of the 20th Python in Science ConferenceScientific Computing with PythonSciPy202110.25080/majora-1b6fd038-02bhttps://conference.scipy.org/proceedings/scipy2021Haw-minnLuJoséUnpingcoHow PDFrw and fillable forms improves throughput at a Covid-19 Vaccine Clinic20211510.25080/majora-1b6fd038-000https://conference.scipy.org/proceedings/scipy2021/111_lu.htmlMarkThorenCristinaSuteuUsing Python for Analysis and Verification of Mixed-mode Signal Chains202161310.25080/majora-1b6fd038-001https://conference.scipy.org/proceedings/scipy2021/adi_sdg.htmlBlaineMooershttps://orcid.org/0000-0001-8181-8987Modernizing computing by structural biologists with Jupyter and Colab2021142210.25080/majora-1b6fd038-002https://conference.scipy.org/proceedings/scipy2021/blaine_mooers.htmlBradleyDiceBrandonButlerVyasRamasubramaniAlyssaTravitzMichaelHenryHardikOjhaKellyWangCarlAdorfEricJankowskiSharonGlotzersignac: Data Management and Workflows for Computational Researchers2021233210.25080/majora-1b6fd038-003https://conference.scipy.org/proceedings/scipy2021/bradley_dice.htmlDanielMargalaLaurieStepheyRollinThomasStephenBaileyAccelerating Spectroscopic Data Processing Using Python and GPUs on NERSC Supercomputers2021333910.25080/majora-1b6fd038-004https://conference.scipy.org/proceedings/scipy2021/daniel_margala.htmlEdisJakupovicOliverBecksteinMPI-parallel Molecular Dynamics Trajectory Analysis with the H5MD Format in the MDAnalysis Python Package2021404810.25080/majora-1b6fd038-005https://conference.scipy.org/proceedings/scipy2021/edis_jakupovic.htmlFrederickReissBryanCutlerZacharyEichenbergerNatural Language Processing with Pandas DataFrames2021495710.25080/majora-1b6fd038-006https://conference.scipy.org/proceedings/scipy2021/fred_reiss.htmlRomeoKienzlerIvanNesichttps://orcid.org/0000-0002-4373-8860CLAIMED, a visual and scalable component library for Trusted AI2021586410.25080/majora-1b6fd038-007https://conference.scipy.org/proceedings/scipy2021/ivan_nesic.htmlJamesFoxTomEverittRyanCareyEricLangloisAlessandroAbateMichaelWooldridgePyCID: A Python Library for Causal Influence Diagrams2021657310.25080/majora-1b6fd038-008https://conference.scipy.org/proceedings/scipy2021/james_fox.htmlJyotikaSinghhttps://orcid.org/0000-0002-5442-3004Social Media Analysis using Natural Language Processing Techniques2021748010.25080/majora-1b6fd038-009https://conference.scipy.org/proceedings/scipy2021/jyotika_singh.htmlKumaranBaskaranJonathanWedellEldonUlrichJefferyHochJohnMarkleyPyBMRB: Data visualization tool for BioMagResBank2021818410.25080/majora-1b6fd038-00ahttps://conference.scipy.org/proceedings/scipy2021/kumaran_baskaran.htmlZoufinéLauer-BaréErichGaertigConformal Mappings with SymPy: Towards Python-driven Analytical Modeling in Physics2021859310.25080/majora-1b6fd038-00bhttps://conference.scipy.org/proceedings/scipy2021/lauer_bare_gaertig.htmlAmandaKraftMatthewWidjajaTrevorSandsBradGalegoProgrammatically Identifying Cognitive Biases Present in Software Development20219410010.25080/majora-1b6fd038-00chttps://conference.scipy.org/proceedings/scipy2021/lm_cognitive.htmlHaw-minnLuJoséUnpingcoHow PDFrw and fillable forms improves throughput at a Covid-19 Vaccine Clinic202110110510.25080/majora-1b6fd038-00dhttps://conference.scipy.org/proceedings/scipy2021/lu.htmlMicheleMartoneSimoneBacchioPyRSB: Portable Performance on Multithreaded Sparse BLAS Operations202110611410.25080/majora-1b6fd038-00ehttps://conference.scipy.org/proceedings/scipy2021/martone_bacchio_pyrsb.htmlNeelimaPulagamMarcusHillMojtabaFazliRachelMattsonMeekailZainAndrewDurdenFrederickQuinnSChennubhotlaShannonQuinnClassification of Diffuse Subcellular Morphologies202111512210.25080/majora-1b6fd038-00fhttps://conference.scipy.org/proceedings/scipy2021/neelima_pulagam.htmlRollinThomashttps://orcid.org/0000-0002-2834-4257LaurieStepheyhttps://orcid.org/0000-0003-3868-6178AnnetteGreinerhttps://orcid.org/0000-0001-6465-7456BrandonCookhttps://orcid.org/0000-0002-4203-4079Monitoring Scientific Python Usage on a Supercomputer202112313110.25080/majora-1b6fd038-010https://conference.scipy.org/proceedings/scipy2021/rollin_thomas.htmlJoesphHoltScottSievertTraining machine learning models faster with Dask202113213910.25080/majora-1b6fd038-011https://conference.scipy.org/proceedings/scipy2021/scott_sievert.htmlToddAndersonTimMattsonMultithreaded parallel Python through OpenMP support in Numba202114014710.25080/majora-1b6fd038-012https://conference.scipy.org/proceedings/scipy2021/tim_mattson.htmlMarian-LeontinPopSzilardMolnarAlexandruPopBenjaminKelenyiLeventeTamasAndreiCozmaCNN Based ToF Image Processing202114815310.25080/majora-1b6fd038-013https://conference.scipy.org/proceedings/scipy2021/trai.htmlVarunKapoorClaudiaCarabañaCell Tracking in 3D using deep learning segmentations202115416110.25080/majora-1b6fd038-014https://conference.scipy.org/proceedings/scipy2021/varun_kapoor.html \ No newline at end of file +majora.1b6fd038-17b0328486c1627867176Dillon Niederhutdillon.niederhut@gmail.comCrossrefPython in Science ConferenceSciPy20thAustin, TexasJuly 12 - July 18 2021Proceedings of the Python in Science ConferenceProceedings of the Python in Science Conference2575-975210.25080/issn.2575-9752https://conference.scipy.org/proceedingsProceedings of the 20th Python in Science ConferenceScientific Computing with PythonSciPy202110.25080/majora-1b6fd038-02bhttps://conference.scipy.org/proceedings/scipy2021Haw-minnLuJoséUnpingcoHow PDFrw and fillable forms improves throughput at a Covid-19 Vaccine Clinic20211510.25080/majora-1b6fd038-000https://conference.scipy.org/proceedings/scipy2021/111_lu.htmlMarkThorenCristinaSuteuUsing Python for Analysis and Verification of Mixed-mode Signal Chains202161310.25080/majora-1b6fd038-001https://conference.scipy.org/proceedings/scipy2021/adi_sdg.htmlBlaineMooershttps://orcid.org/0000-0001-8181-8987Modernizing computing by structural biologists with Jupyter and Colab2021142210.25080/majora-1b6fd038-002https://conference.scipy.org/proceedings/scipy2021/blaine_mooers.htmlBradleyDiceBrandonButlerVyasRamasubramaniAlyssaTravitzMichaelHenryHardikOjhaKellyWangCarlAdorfEricJankowskiSharonGlotzersignac: Data Management and Workflows for Computational Researchers2021233210.25080/majora-1b6fd038-003https://conference.scipy.org/proceedings/scipy2021/bradley_dice.htmlDanielMargalaLaurieStepheyRollinThomasStephenBaileyAccelerating Spectroscopic Data Processing Using Python and GPUs on NERSC Supercomputers2021333910.25080/majora-1b6fd038-004https://conference.scipy.org/proceedings/scipy2021/daniel_margala.htmlEdisJakupovicOliverBecksteinMPI-parallel Molecular Dynamics Trajectory Analysis with the H5MD Format in the MDAnalysis Python Package2021404810.25080/majora-1b6fd038-005https://conference.scipy.org/proceedings/scipy2021/edis_jakupovic.htmlFrederickReissBryanCutlerZacharyEichenbergerNatural Language Processing with Pandas DataFrames2021495710.25080/majora-1b6fd038-006https://conference.scipy.org/proceedings/scipy2021/fred_reiss.htmlRomeoKienzlerIvanNesichttps://orcid.org/0000-0002-4373-8860CLAIMED, a visual and scalable component library for Trusted AI2021586410.25080/majora-1b6fd038-007https://conference.scipy.org/proceedings/scipy2021/ivan_nesic.htmlJamesFoxTomEverittRyanCareyEricLangloisAlessandroAbateMichaelWooldridgePyCID: A Python Library for Causal Influence Diagrams2021657310.25080/majora-1b6fd038-008https://conference.scipy.org/proceedings/scipy2021/james_fox.htmlJyotikaSinghhttps://orcid.org/0000-0002-5442-3004Social Media Analysis using Natural Language Processing Techniques2021748010.25080/majora-1b6fd038-009https://conference.scipy.org/proceedings/scipy2021/jyotika_singh.htmlKumaranBaskaranJonathanWedellEldonUlrichJefferyHochJohnMarkleyPyBMRB: Data visualization tool for BioMagResBank2021818410.25080/majora-1b6fd038-00ahttps://conference.scipy.org/proceedings/scipy2021/kumaran_baskaran.htmlZoufinéLauer-BaréErichGaertigConformal Mappings with SymPy: Towards Python-driven Analytical Modeling in Physics2021859310.25080/majora-1b6fd038-00bhttps://conference.scipy.org/proceedings/scipy2021/lauer_bare_gaertig.htmlAmandaKraftMatthewWidjajaTrevorSandsBradGalegoProgrammatically Identifying Cognitive Biases Present in Software Development20219410010.25080/majora-1b6fd038-00chttps://conference.scipy.org/proceedings/scipy2021/lm_cognitive.htmlHaw-minnLuJoséUnpingcoHow PDFrw and fillable forms improves throughput at a Covid-19 Vaccine Clinic202110110510.25080/majora-1b6fd038-00dhttps://conference.scipy.org/proceedings/scipy2021/lu.htmlMicheleMartoneSimoneBacchioPyRSB: Portable Performance on Multithreaded Sparse BLAS Operations202110611410.25080/majora-1b6fd038-00ehttps://conference.scipy.org/proceedings/scipy2021/martone_bacchio_pyrsb.htmlNeelimaPulagamMarcusHillMojtabaFazliRachelMattsonMeekailZainAndrewDurdenFrederickQuinnSChennubhotlaShannonQuinnClassification of Diffuse Subcellular Morphologies202111512210.25080/majora-1b6fd038-00fhttps://conference.scipy.org/proceedings/scipy2021/neelima_pulagam.htmlRollinThomashttps://orcid.org/0000-0002-2834-4257LaurieStepheyhttps://orcid.org/0000-0003-3868-6178AnnetteGreinerhttps://orcid.org/0000-0001-6465-7456BrandonCookhttps://orcid.org/0000-0002-4203-4079Monitoring Scientific Python Usage on a Supercomputer202112313110.25080/majora-1b6fd038-010https://conference.scipy.org/proceedings/scipy2021/rollin_thomas.htmlJoesphHoltScottSievertTraining machine learning models faster with Dask202113213910.25080/majora-1b6fd038-011https://conference.scipy.org/proceedings/scipy2021/scott_sievert.htmlToddAndersonTimMattsonMultithreaded parallel Python through OpenMP support in Numba202114014710.25080/majora-1b6fd038-012https://conference.scipy.org/proceedings/scipy2021/tim_mattson.htmlMarian-LeontinPopSzilardMolnarAlexandruPopBenjaminKelenyiLeventeTamasAndreiCozmaCNN Based ToF Image Processing202114815310.25080/majora-1b6fd038-013https://conference.scipy.org/proceedings/scipy2021/trai.htmlVarunKapoorClaudiaCarabañaCell Tracking in 3D using deep learning segmentations202115416110.25080/majora-1b6fd038-014https://conference.scipy.org/proceedings/scipy2021/varun_kapoor.html diff --git a/publisher/metadata/2021/doi_batch_slides.xml b/publisher/metadata/2021/doi_batch_slides.xml index b559a64295..374cb84538 100644 --- a/publisher/metadata/2021/doi_batch_slides.xml +++ b/publisher/metadata/2021/doi_batch_slides.xml @@ -2,4 +2,4 @@ In this talk, we'll embrace the fact that a large project's transition toward typing will likely happen over the course of many months, concurrently with ongoing development. However, that doesn't mean that getting started with typing has to be difficult! We'll share with you two examples of adopting typing in existing open-source codebases (100k and 40k lines of Python). We'll particularly focus on the typing experience from the perspective of project maintainers, contributors, and users of these Python libraries. -We will discuss useful tools and strategies, surprising difficulties, the types of bugs and errors we found, and how the addition of typing changes the overall development experience. By the end of this talk, you'll be able to confidently manage the migration toward typing in your own codebase.10.25080/issn.2575-975210.25080/majora-1b6fd038-021https://zenodo.org/record/5151994GregoryLeehttps://orcid.org/0000-0001-8895-2740GigonBaehttps://orcid.org/0000-0003-0648-9735BenjaminZaitlenJohnKirkhamRahulChoudhurycuCIM - A GPU image I/O and processing library2021A presentation introducing RAPIDS cuCIM, a library for image I/O and processing on GPUs10.25080/issn.2575-975210.25080/majora-1b6fd038-022https://zenodo.org/record/5151998MatthewFeickerthttps://orcid.org/0000-0003-4124-7862Distributed statistical inference with pyhf powered by funcX2021In high energy physics (HEP) a core component of analysis of data collected at the Large Hadron Collider is performing statistical inference for binned models to extract physics information. The statistical fitting tools used in HEP have traditionally been implemented in C++, but in recent years pyhf, a pure-Python library with automatic differentiation and hardware acceleration, has grown in use for analysis related statistical inference problems. The fitting of multiple different hypotheses for new physics signatures (signals) is a computational problem that lends itself easily to parallelization, but is hampered on HPC environments by the additional tooling overhead required, which can be very difficult to master. Through use of funcX, a pure-Python high performance function serving system designed to orchestrate scientific workloads across heterogeneous computing resources, pyhf can be used as a highly scalable (fitting) function as a service (FaaS) on HPCs.10.25080/issn.2575-975210.25080/majora-1b6fd038-023https://zenodo.org/record/5152006JimPivarskihttps://orcid.org/0000-0002-6649-343XAwkward Array2021Tools update on Awkward Array.10.25080/issn.2575-975210.25080/majora-1b6fd038-024https://zenodo.org/record/5152018Elliottde Andradehttps://orcid.org/0000-0001-7310-8942SciPy Tools Plenary on Matplotlib2021Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. This presentation summarizes changes over the past year, new features, and future plans.10.25080/issn.2575-975210.25080/majora-1b6fd038-025https://zenodo.org/record/5152026InessaPawsonhttps://orcid.org/0000-0001-7294-122XNumPy – Annual Update2021Presentation about the highlights and milestones of the NumPy project in 2020-202110.25080/issn.2575-975210.25080/majora-1b6fd038-026https://zenodo.org/record/5152034IsabelaPresedo-FloydMatthiasBussonnierhttps://orcid.org/0000-0002-7636-8632SciPy Tools Plenary: Jupyter Updates2021Project Jupyter creates open source software, standards, and services for interactive computing. This presentation covers recent milestones and ideas for people to contribute across the Jupyter ecosystem.10.25080/issn.2575-975210.25080/majora-1b6fd038-027https://zenodo.org/record/5152036K.Millmanhttps://orcid.org/0000-0002-5263-5070Stéfanvan der Walthttps://orcid.org/0000-0001-9276-1891Scientific Python Ecosystem Coordination2021Planning for the Next Decade of Scientific Python: outline of first phase10.25080/issn.2575-975210.25080/majora-1b6fd038-028https://zenodo.org/record/5152046PamphileRoyhttps://orcid.org/0000-0001-9816-1416SciPy: SciPy 2021 Tools Track20212021 updates and outlooks in SciPy10.25080/issn.2575-975210.25080/majora-1b6fd038-029https://zenodo.org/record/5152050GregoryLeehttps://orcid.org/0000-0001-8895-2740SciPy Tools Plenary: scikit-image annual update2021A brief update on recent improvements and future plans for scikit-image.10.25080/issn.2575-975210.25080/majora-1b6fd038-02ahttps://zenodo.org/record/5152054 \ No newline at end of file +We will discuss useful tools and strategies, surprising difficulties, the types of bugs and errors we found, and how the addition of typing changes the overall development experience. By the end of this talk, you'll be able to confidently manage the migration toward typing in your own codebase.10.25080/issn.2575-975210.25080/majora-1b6fd038-021https://zenodo.org/record/5151994GregoryLeehttps://orcid.org/0000-0001-8895-2740GigonBaehttps://orcid.org/0000-0003-0648-9735BenjaminZaitlenJohnKirkhamRahulChoudhurycuCIM - A GPU image I/O and processing library2021A presentation introducing RAPIDS cuCIM, a library for image I/O and processing on GPUs10.25080/issn.2575-975210.25080/majora-1b6fd038-022https://zenodo.org/record/5151998MatthewFeickerthttps://orcid.org/0000-0003-4124-7862Distributed statistical inference with pyhf powered by funcX2021In high energy physics (HEP) a core component of analysis of data collected at the Large Hadron Collider is performing statistical inference for binned models to extract physics information. The statistical fitting tools used in HEP have traditionally been implemented in C++, but in recent years pyhf, a pure-Python library with automatic differentiation and hardware acceleration, has grown in use for analysis related statistical inference problems. The fitting of multiple different hypotheses for new physics signatures (signals) is a computational problem that lends itself easily to parallelization, but is hampered on HPC environments by the additional tooling overhead required, which can be very difficult to master. Through use of funcX, a pure-Python high performance function serving system designed to orchestrate scientific workloads across heterogeneous computing resources, pyhf can be used as a highly scalable (fitting) function as a service (FaaS) on HPCs.10.25080/issn.2575-975210.25080/majora-1b6fd038-023https://zenodo.org/record/5152006JimPivarskihttps://orcid.org/0000-0002-6649-343XAwkward Array2021Tools update on Awkward Array.10.25080/issn.2575-975210.25080/majora-1b6fd038-024https://zenodo.org/record/5152018Elliottde Andradehttps://orcid.org/0000-0001-7310-8942SciPy Tools Plenary on Matplotlib2021Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. This presentation summarizes changes over the past year, new features, and future plans.10.25080/issn.2575-975210.25080/majora-1b6fd038-025https://zenodo.org/record/5152026InessaPawsonhttps://orcid.org/0000-0001-7294-122XNumPy – Annual Update2021Presentation about the highlights and milestones of the NumPy project in 2020-202110.25080/issn.2575-975210.25080/majora-1b6fd038-026https://zenodo.org/record/5152034IsabelaPresedo-FloydMatthiasBussonnierhttps://orcid.org/0000-0002-7636-8632SciPy Tools Plenary: Jupyter Updates2021Project Jupyter creates open source software, standards, and services for interactive computing. This presentation covers recent milestones and ideas for people to contribute across the Jupyter ecosystem.10.25080/issn.2575-975210.25080/majora-1b6fd038-027https://zenodo.org/record/5152036K.Millmanhttps://orcid.org/0000-0002-5263-5070Stéfanvan der Walthttps://orcid.org/0000-0001-9276-1891Scientific Python Ecosystem Coordination2021Planning for the Next Decade of Scientific Python: outline of first phase10.25080/issn.2575-975210.25080/majora-1b6fd038-028https://zenodo.org/record/5152046PamphileRoyhttps://orcid.org/0000-0001-9816-1416SciPy: SciPy 2021 Tools Track20212021 updates and outlooks in SciPy10.25080/issn.2575-975210.25080/majora-1b6fd038-029https://zenodo.org/record/5152050GregoryLeehttps://orcid.org/0000-0001-8895-2740SciPy Tools Plenary: scikit-image annual update2021A brief update on recent improvements and future plans for scikit-image.10.25080/issn.2575-975210.25080/majora-1b6fd038-02ahttps://zenodo.org/record/5152054 diff --git a/publisher/metadata/2021/other.json 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a/publisher/metadata/2022/doi_batch_papers.xml +++ b/publisher/metadata/2022/doi_batch_papers.xml @@ -1 +1 @@ -majora.212e5952-18257e7ec111659353793Dillon Niederhutdillon.niederhut@gmail.comCrossrefPython in Science ConferenceSciPy21stAustin, TexasJuly 11 - July 17 2022Proceedings of the Python in Science ConferenceProceedings of the Python in Science Conference2575-975210.25080/issn.2575-9752https://conference.scipy.org/proceedingsProceedings of the 21st Python in Science ConferenceScientific Computing with PythonSciPy202210.25080/majora-212e5952-046https://conference.scipy.org/proceedings/scipy2022PerryGreenfieldEdwardSlavichWilliamJamiesonNadiaDenchevaThe Advanced Scientific Data Format (ASDF): An Update20221610.25080/majora-212e5952-000https://conference.scipy.org/proceedings/scipy2022/00_greenfield.htmlNathanJessurunDanielCapecciOliviaDizon-ParadisDamonWoodardNavidAsadizanjaniSemi-Supervised Semantic Annotator (S3A): Toward Efficient Semantic Labeling202271210.25080/majora-212e5952-001https://conference.scipy.org/proceedings/scipy2022/221_jessurun.htmlRickMcGeerAndreasBergenMahdiyarBiaziMattHemmingsRobinSchreiberGalyleo: A General-Purpose Extensible Visualization Solution2022132110.25080/majora-212e5952-002https://conference.scipy.org/proceedings/scipy2022/72_galyleo.htmlAmandaCatlettTheresaCoumbeScottChristensenMaryByrantUSACE Coastal Engineering Toolkit and a Method of Creating a Web-Based Application2022222510.25080/majora-212e5952-003https://conference.scipy.org/proceedings/scipy2022/Catlett_Amanda.htmlLuigiCruzWaelFarahRichardElkinsSearch for Extraterrestrial Intelligence: GPU Accelerated TurboSETI2022262710.25080/majora-212e5952-004https://conference.scipy.org/proceedings/scipy2022/Luigi_Cruz.htmlPi-YuehChuangLorenaBarbaExperience report of physics-informed neural networks in fluid simulations: pitfalls and frustration2022283610.25080/majora-212e5952-005https://conference.scipy.org/proceedings/scipy2022/PiYueh_Chuang.htmlTimothyCallowDanielKotikEliKraislerAttilaCangiatoMEC: An open-source average-atom Python code2022374510.25080/majora-212e5952-006https://conference.scipy.org/proceedings/scipy2022/Timothy_Callow.htmlChristophBaumgartenhttps://orcid.org/0000-0002-1168-3359TirthPatelAutomatic random variate generation in Python2022465110.25080/majora-212e5952-007https://conference.scipy.org/proceedings/scipy2022/Tirth_Patel.htmlAlexandrFonariFarshadFallahMichaelRauchUtilizing SciPy and other open source packages to provide a powerful API for materials manipulation in the Schrödinger Materials Suite2022525910.25080/majora-212e5952-008https://conference.scipy.org/proceedings/scipy2022/alexandr_fonari.htmlSeyedVaeziGianniOrlandoMojtabaFazliGaryWardSilviaMorenoShannonQuinnA Novel Pipeline for Cell Instance Segmentation, Tracking and Motility Classification of Toxoplasma Gondii in 3D Space2022606310.25080/majora-212e5952-009https://conference.scipy.org/proceedings/scipy2022/alireza_vaezi.htmlAllanCampopianoThe myth of the normal curve and what to do about it2022646810.25080/majora-212e5952-00ahttps://conference.scipy.org/proceedings/scipy2022/allan_campopiano.htmlAnnaHaenschKarinKnudsonPython for Global Applications: teaching scientific Python in context to law and diplomacy students2022697410.25080/majora-212e5952-00bhttps://conference.scipy.org/proceedings/scipy2022/anna_haensch.htmlMatthiasBussonnierhttps://orcid.org/0000-0002-7636-8632CamilleCarvalhohttps://orcid.org/0000-0002-8426-549XPapyri: better documentation for the scientific ecosystem in Jupyter2022758210.25080/majora-212e5952-00chttps://conference.scipy.org/proceedings/scipy2022/bussonnier.htmlChadFultonBayesian Estimation and Forecasting of Time Series in statsmodels2022838910.25080/majora-212e5952-00dhttps://conference.scipy.org/proceedings/scipy2022/chad_fulton.htmlCliffKerrRobynStuartDinaMistryRomeshAbeysuriyaJamieCohenLaurenGeorgeMichałJastrzebskiMichaelFamulareEdwardWengerDanielKleinPython vs. the pandemic: a case study in high-stakes software development2022909710.25080/majora-212e5952-00ehttps://conference.scipy.org/proceedings/scipy2022/cliff_kerr.htmlAxelDonathhttps://orcid.org/0000-0003-4568-7005AnetaSiemiginowskahttps://orcid.org/0000-0002-0905-7375VinayKashyaphttps://orcid.org/0000-0002-3869-7996DouglasBurkeKarthikSolipuramDavidvan DykPylira: deconvolution of images in the presence of Poisson noise20229810410.25080/majora-212e5952-00fhttps://conference.scipy.org/proceedings/scipy2022/donath.htmlGeoffreyPooreCodebraid Preview for VS Code: Pandoc Markdown Preview with Jupyter Kernels202210510910.25080/majora-212e5952-010https://conference.scipy.org/proceedings/scipy2022/geoffrey_poore.htmlCurtisGodwinMeekailZainNathanSafirBellaHumphreyShannonQuinnIncorporating Task-Agnostic Information in Task-Based Active Learning Using a Variational Autoencoder202211011410.25080/majora-212e5952-011https://conference.scipy.org/proceedings/scipy2022/godwin_zain.htmlHenrySchreinerJimPivarskiEduardoRodriguesAwkward Packaging: building Scikit-HEP202211512010.25080/majora-212e5952-012https://conference.scipy.org/proceedings/scipy2022/henry_schreiner.htmlIdoMichaelKeeping your Jupyter notebook code quality bar high (and production ready) with Ploomber202212112410.25080/majora-212e5952-013https://conference.scipy.org/proceedings/scipy2022/ido_michael.htmlJosephTuccillohttps://orcid.org/0000-0002-5930-0943JamesGaboardihttps://orcid.org/0000-0002-4776-6826Likeness: a toolkit for connecting the social fabric of place to human dynamics202212513510.25080/majora-212e5952-014https://conference.scipy.org/proceedings/scipy2022/james_gaboardi.htmlJuanRodríguezhttps://orcid.org/0000-0002-2187-161XJorgeGarridopoliastro: a Python library for interactive astrodynamics202213614610.25080/majora-212e5952-015https://conference.scipy.org/proceedings/scipy2022/juanluis_cano_poliastro.htmlJustinFisherA New Python API for Webots Robotics Simulations202214715110.25080/majora-212e5952-016https://conference.scipy.org/proceedings/scipy2022/justin_fisher.htmlJyotikaSinghhttps://orcid.org/0000-0002-5442-3004pyAudioProcessing: Audio Processing, Feature Extraction, and Machine Learning Modeling202215215810.25080/majora-212e5952-017https://conference.scipy.org/proceedings/scipy2022/jyotika_singh.htmlAleksandrKoshkarovWanlinLiMy-LinhLuuNadiaTahiriPhylogeography: Analysis of genetic and climatic data of SARS-CoV-2202215916610.25080/majora-212e5952-018https://conference.scipy.org/proceedings/scipy2022/nadia_tahiri.htmlNadiaUdlerGlobal optimization software library for research and education202216717010.25080/majora-212e5952-019https://conference.scipy.org/proceedings/scipy2022/nadiakap.htmlNathanJacobiIvanMoAlbertYouKrishiKishoreZanePageShannonQuinnTimHeckmanTemporal Word Embeddings Analysis for Disease Prevention202217117810.25080/majora-212e5952-01ahttps://conference.scipy.org/proceedings/scipy2022/nathan_jacobi_ivan_mo_albert_you.htmlNathanMartindalehttps://orcid.org/0000-0002-5036-5433JasonHitehttps://orcid.org/0000-0001-8931-5815ScottStewarthttps://orcid.org/0000-0003-4320-5818MarkAdamshttps://orcid.org/0000-0002-5414-2800Design of a Scientific Data Analysis Support Platform202217918610.25080/majora-212e5952-01bhttps://conference.scipy.org/proceedings/scipy2022/nathan_martindale.htmlOrhanErogluAnissaZachariasMichaelaSizemoreAleaKootzHeatherCrakerJohnClyneThe Geoscience Community Analysis Toolkit: An Open Development, Community Driven Toolkit in the Scientific Python Ecosystem202218719310.25080/majora-212e5952-01chttps://conference.scipy.org/proceedings/scipy2022/orhan_eroglu.htmlSimonBrugmanTomasSostakPradyotPatilMaxBaakpopmon: Analysis Package for Dataset Shift Detection202219420110.25080/majora-212e5952-01dhttps://conference.scipy.org/proceedings/scipy2022/popmon.htmlWillyMenachoGonzaloRamírez-ÁvilaHoracioGuzmanhttps://orcid.org/0000-0003-2564-3005pyDAMPF: a Python package for modeling mechanical properties of hygroscopic materials under interaction with a nanoprobe202220220910.25080/majora-212e5952-01ehttps://conference.scipy.org/proceedings/scipy2022/pyDAMPF_HVGuzman.htmlRobertJacksonRebeccaGjiniSriNarayananMattMenickellyPaulHovlandJanHückelheimScottCollisImproving PyDDA's atmospheric wind retrievals using automatic differentiation and Augmented Lagrangian methods202221021610.25080/majora-212e5952-01fhttps://conference.scipy.org/proceedings/scipy2022/robert_jackson.htmlJoãoSoaresMateusJunqueiraOscarRamirezhttps://orcid.org/0000-0001-6904-8959Patrickdos Santos BrandãoAdrianoAntongiovannihttps://orcid.org/0000-0002-3658-0153GuilhermeAlveshttps://orcid.org/0000-0003-3873-2699GiovaniCeottohttps://orcid.org/0000-0002-1614-3028RocketPy: Combining Open-Source and Scientific Libraries to Make the Space Sector More Modern and Accessible202221722510.25080/majora-212e5952-020https://conference.scipy.org/proceedings/scipy2022/rocketpy_team.htmlRohitGoswamihttps://orcid.org/0000-0002-2393-8056Wailord: Parsers and Reproducibility for Quantum Chemistry202222623010.25080/majora-212e5952-021https://conference.scipy.org/proceedings/scipy2022/rohit_goswami_wailord.htmlNathanSafirMeekailZainCurtisGodwinEricMillerBellaHumphreyShannonQuinnVariational Autoencoders For Semi-Supervised Deep Metric Learning202223123910.25080/majora-212e5952-022https://conference.scipy.org/proceedings/scipy2022/safir_zain_22.htmlScottChristensenMarvinBrownRobertHaehnelJoshuaChurchAmandaCatlettDallonSchofieldQuyenBrannonStacySmithA Python Pipeline for Rapid Application Development (RAD)202224024310.25080/majora-212e5952-023https://conference.scipy.org/proceedings/scipy2022/scott_christensen.htmlW.ShambaughMonaco: A Monte Carlo Library for Performing Uncertainty and Sensitivity Analyses202224425010.25080/majora-212e5952-024https://conference.scipy.org/proceedings/scipy2022/scott_shambaugh.htmlZacharydel RosarioEnabling Active Learning Pedagogy and Insight Mining with a Grammar of Model Analysis202225125810.25080/majora-212e5952-025https://conference.scipy.org/proceedings/scipy2022/zachary_delrosario.htmlMeekailZainEricMillerShannonQuinnCeciliaLoLow Level Feature Extraction for Cilia Segmentation202225926410.25080/majora-212e5952-026https://conference.scipy.org/proceedings/scipy2022/zain_miller.html \ No newline at end of file +majora.212e5952-18257e7ec111659353793Dillon Niederhutdillon.niederhut@gmail.comCrossrefPython in Science ConferenceSciPy21stAustin, TexasJuly 11 - July 17 2022Proceedings of the Python in Science ConferenceProceedings of the Python in Science Conference2575-975210.25080/issn.2575-9752https://conference.scipy.org/proceedingsProceedings of the 21st Python in Science ConferenceScientific Computing with PythonSciPy202210.25080/majora-212e5952-046https://conference.scipy.org/proceedings/scipy2022PerryGreenfieldEdwardSlavichWilliamJamiesonNadiaDenchevaThe Advanced Scientific Data Format (ASDF): An Update20221610.25080/majora-212e5952-000https://conference.scipy.org/proceedings/scipy2022/00_greenfield.htmlNathanJessurunDanielCapecciOliviaDizon-ParadisDamonWoodardNavidAsadizanjaniSemi-Supervised Semantic Annotator (S3A): Toward Efficient Semantic Labeling202271210.25080/majora-212e5952-001https://conference.scipy.org/proceedings/scipy2022/221_jessurun.htmlRickMcGeerAndreasBergenMahdiyarBiaziMattHemmingsRobinSchreiberGalyleo: A General-Purpose Extensible Visualization Solution2022132110.25080/majora-212e5952-002https://conference.scipy.org/proceedings/scipy2022/72_galyleo.htmlAmandaCatlettTheresaCoumbeScottChristensenMaryByrantUSACE Coastal Engineering Toolkit and a Method of Creating a Web-Based Application2022222510.25080/majora-212e5952-003https://conference.scipy.org/proceedings/scipy2022/Catlett_Amanda.htmlLuigiCruzWaelFarahRichardElkinsSearch for Extraterrestrial Intelligence: GPU Accelerated TurboSETI2022262710.25080/majora-212e5952-004https://conference.scipy.org/proceedings/scipy2022/Luigi_Cruz.htmlPi-YuehChuangLorenaBarbaExperience report of physics-informed neural networks in fluid simulations: pitfalls and frustration2022283610.25080/majora-212e5952-005https://conference.scipy.org/proceedings/scipy2022/PiYueh_Chuang.htmlTimothyCallowDanielKotikEliKraislerAttilaCangiatoMEC: An open-source average-atom Python code2022374510.25080/majora-212e5952-006https://conference.scipy.org/proceedings/scipy2022/Timothy_Callow.htmlChristophBaumgartenhttps://orcid.org/0000-0002-1168-3359TirthPatelAutomatic random variate generation in Python2022465110.25080/majora-212e5952-007https://conference.scipy.org/proceedings/scipy2022/Tirth_Patel.htmlAlexandrFonariFarshadFallahMichaelRauchUtilizing SciPy and other open source packages to provide a powerful API for materials manipulation in the Schrödinger Materials Suite2022525910.25080/majora-212e5952-008https://conference.scipy.org/proceedings/scipy2022/alexandr_fonari.htmlSeyedVaeziGianniOrlandoMojtabaFazliGaryWardSilviaMorenoShannonQuinnA Novel Pipeline for Cell Instance Segmentation, Tracking and Motility Classification of Toxoplasma Gondii in 3D Space2022606310.25080/majora-212e5952-009https://conference.scipy.org/proceedings/scipy2022/alireza_vaezi.htmlAllanCampopianoThe myth of the normal curve and what to do about it2022646810.25080/majora-212e5952-00ahttps://conference.scipy.org/proceedings/scipy2022/allan_campopiano.htmlAnnaHaenschKarinKnudsonPython for Global Applications: teaching scientific Python in context to law and diplomacy students2022697410.25080/majora-212e5952-00bhttps://conference.scipy.org/proceedings/scipy2022/anna_haensch.htmlMatthiasBussonnierhttps://orcid.org/0000-0002-7636-8632CamilleCarvalhohttps://orcid.org/0000-0002-8426-549XPapyri: better documentation for the scientific ecosystem in Jupyter2022758210.25080/majora-212e5952-00chttps://conference.scipy.org/proceedings/scipy2022/bussonnier.htmlChadFultonBayesian Estimation and Forecasting of Time Series in statsmodels2022838910.25080/majora-212e5952-00dhttps://conference.scipy.org/proceedings/scipy2022/chad_fulton.htmlCliffKerrRobynStuartDinaMistryRomeshAbeysuriyaJamieCohenLaurenGeorgeMichałJastrzebskiMichaelFamulareEdwardWengerDanielKleinPython vs. the pandemic: a case study in high-stakes software development2022909710.25080/majora-212e5952-00ehttps://conference.scipy.org/proceedings/scipy2022/cliff_kerr.htmlAxelDonathhttps://orcid.org/0000-0003-4568-7005AnetaSiemiginowskahttps://orcid.org/0000-0002-0905-7375VinayKashyaphttps://orcid.org/0000-0002-3869-7996DouglasBurkeKarthikSolipuramDavidvan DykPylira: deconvolution of images in the presence of Poisson noise20229810410.25080/majora-212e5952-00fhttps://conference.scipy.org/proceedings/scipy2022/donath.htmlGeoffreyPooreCodebraid Preview for VS Code: Pandoc Markdown Preview with Jupyter Kernels202210510910.25080/majora-212e5952-010https://conference.scipy.org/proceedings/scipy2022/geoffrey_poore.htmlCurtisGodwinMeekailZainNathanSafirBellaHumphreyShannonQuinnIncorporating Task-Agnostic Information in Task-Based Active Learning Using a Variational Autoencoder202211011410.25080/majora-212e5952-011https://conference.scipy.org/proceedings/scipy2022/godwin_zain.htmlHenrySchreinerJimPivarskiEduardoRodriguesAwkward Packaging: building Scikit-HEP202211512010.25080/majora-212e5952-012https://conference.scipy.org/proceedings/scipy2022/henry_schreiner.htmlIdoMichaelKeeping your Jupyter notebook code quality bar high (and production ready) with Ploomber202212112410.25080/majora-212e5952-013https://conference.scipy.org/proceedings/scipy2022/ido_michael.htmlJosephTuccillohttps://orcid.org/0000-0002-5930-0943JamesGaboardihttps://orcid.org/0000-0002-4776-6826Likeness: a toolkit for connecting the social fabric of place to human dynamics202212513510.25080/majora-212e5952-014https://conference.scipy.org/proceedings/scipy2022/james_gaboardi.htmlJuanRodríguezhttps://orcid.org/0000-0002-2187-161XJorgeGarridopoliastro: a Python library for interactive astrodynamics202213614610.25080/majora-212e5952-015https://conference.scipy.org/proceedings/scipy2022/juanluis_cano_poliastro.htmlJustinFisherA New Python API for Webots Robotics Simulations202214715110.25080/majora-212e5952-016https://conference.scipy.org/proceedings/scipy2022/justin_fisher.htmlJyotikaSinghhttps://orcid.org/0000-0002-5442-3004pyAudioProcessing: Audio Processing, Feature Extraction, and Machine Learning Modeling202215215810.25080/majora-212e5952-017https://conference.scipy.org/proceedings/scipy2022/jyotika_singh.htmlAleksandrKoshkarovWanlinLiMy-LinhLuuNadiaTahiriPhylogeography: Analysis of genetic and climatic data of SARS-CoV-2202215916610.25080/majora-212e5952-018https://conference.scipy.org/proceedings/scipy2022/nadia_tahiri.htmlNadiaUdlerGlobal optimization software library for research and education202216717010.25080/majora-212e5952-019https://conference.scipy.org/proceedings/scipy2022/nadiakap.htmlNathanJacobiIvanMoAlbertYouKrishiKishoreZanePageShannonQuinnTimHeckmanTemporal Word Embeddings Analysis for Disease Prevention202217117810.25080/majora-212e5952-01ahttps://conference.scipy.org/proceedings/scipy2022/nathan_jacobi_ivan_mo_albert_you.htmlNathanMartindalehttps://orcid.org/0000-0002-5036-5433JasonHitehttps://orcid.org/0000-0001-8931-5815ScottStewarthttps://orcid.org/0000-0003-4320-5818MarkAdamshttps://orcid.org/0000-0002-5414-2800Design of a Scientific Data Analysis Support Platform202217918610.25080/majora-212e5952-01bhttps://conference.scipy.org/proceedings/scipy2022/nathan_martindale.htmlOrhanErogluAnissaZachariasMichaelaSizemoreAleaKootzHeatherCrakerJohnClyneThe Geoscience Community Analysis Toolkit: An Open Development, Community Driven Toolkit in the Scientific Python Ecosystem202218719310.25080/majora-212e5952-01chttps://conference.scipy.org/proceedings/scipy2022/orhan_eroglu.htmlSimonBrugmanTomasSostakPradyotPatilMaxBaakpopmon: Analysis Package for Dataset Shift Detection202219420110.25080/majora-212e5952-01dhttps://conference.scipy.org/proceedings/scipy2022/popmon.htmlWillyMenachoGonzaloRamírez-ÁvilaHoracioGuzmanhttps://orcid.org/0000-0003-2564-3005pyDAMPF: a Python package for modeling mechanical properties of hygroscopic materials under interaction with a nanoprobe202220220910.25080/majora-212e5952-01ehttps://conference.scipy.org/proceedings/scipy2022/pyDAMPF_HVGuzman.htmlRobertJacksonRebeccaGjiniSriNarayananMattMenickellyPaulHovlandJanHückelheimScottCollisImproving PyDDA's atmospheric wind retrievals using automatic differentiation and Augmented Lagrangian methods202221021610.25080/majora-212e5952-01fhttps://conference.scipy.org/proceedings/scipy2022/robert_jackson.htmlJoãoSoaresMateusJunqueiraOscarRamirezhttps://orcid.org/0000-0001-6904-8959Patrickdos Santos BrandãoAdrianoAntongiovannihttps://orcid.org/0000-0002-3658-0153GuilhermeAlveshttps://orcid.org/0000-0003-3873-2699GiovaniCeottohttps://orcid.org/0000-0002-1614-3028RocketPy: Combining Open-Source and Scientific Libraries to Make the Space Sector More Modern and Accessible202221722510.25080/majora-212e5952-020https://conference.scipy.org/proceedings/scipy2022/rocketpy_team.htmlRohitGoswamihttps://orcid.org/0000-0002-2393-8056Wailord: Parsers and Reproducibility for Quantum Chemistry202222623010.25080/majora-212e5952-021https://conference.scipy.org/proceedings/scipy2022/rohit_goswami_wailord.htmlNathanSafirMeekailZainCurtisGodwinEricMillerBellaHumphreyShannonQuinnVariational Autoencoders For Semi-Supervised Deep Metric Learning202223123910.25080/majora-212e5952-022https://conference.scipy.org/proceedings/scipy2022/safir_zain_22.htmlScottChristensenMarvinBrownRobertHaehnelJoshuaChurchAmandaCatlettDallonSchofieldQuyenBrannonStacySmithA Python Pipeline for Rapid Application Development (RAD)202224024310.25080/majora-212e5952-023https://conference.scipy.org/proceedings/scipy2022/scott_christensen.htmlW.ShambaughMonaco: A Monte Carlo Library for Performing Uncertainty and Sensitivity Analyses202224425010.25080/majora-212e5952-024https://conference.scipy.org/proceedings/scipy2022/scott_shambaugh.htmlZacharydel RosarioEnabling Active Learning Pedagogy and Insight Mining with a Grammar of Model Analysis202225125810.25080/majora-212e5952-025https://conference.scipy.org/proceedings/scipy2022/zachary_delrosario.htmlMeekailZainEricMillerShannonQuinnCeciliaLoLow Level Feature Extraction for Cilia Segmentation202225926410.25080/majora-212e5952-026https://conference.scipy.org/proceedings/scipy2022/zain_miller.html diff --git a/publisher/metadata/2022/doi_batch_slides.xml b/publisher/metadata/2022/doi_batch_slides.xml index 2743a4a728..cd492c8063 100644 --- a/publisher/metadata/2022/doi_batch_slides.xml +++ b/publisher/metadata/2022/doi_batch_slides.xml @@ -1 +1 @@ -majora.212e5952-18257e7ec2c1659353893Dillon Niederhutdillon.niederhut@gmail.comCrossrefDillonNiederhutProceedings of the Python in Science ConferenceDelainaMooreDownsampling Time Series Data for Visualizations2022Exploring the largest triangle three bucket algorithm to downsample time series data.10.25080/issn.2575-975210.25080/majora-212e5952-027https://zenodo.org/record/6946652MatthewFeickerthttps://orcid.org/0000-0003-4124-7862Analysis as Applications: Quick introduction to lockfiles2022An opinionated argument for the use of lockfiles in scientific analysis in a similar manner to Python application deployment. This talk was inspired by Brett Cannon's 'pip-secure-install' project and a Twitter conversation with Dustin Ingram on April 20, 2020.10.25080/issn.2575-975210.25080/majora-212e5952-028https://zenodo.org/record/6946666MattHaberlandhttps://orcid.org/0000-0003-4806-3601NicholasMcKibbenhttps://orcid.org/0000-0003-0034-2230Optimal Review Assignments for the SciPy Conference Using Binary Integer Linear Programming in SciPy 1.92022Each year, the SciPy Conference receives hundreds of submissions, and dozens of volunteers offer to review them to help make selections for the conference. How should submissions be assigned to reviewers to distribute the work fairly while 1) ensuring that each submission receives at least three reviews, 2) preventing conflicts of interest, and 3) respecting reviewers' domains of expertise? Binary integer linear programming is an ideal framework for defining and solving 'scheduling' or 'assignment' problems like this. In this poster, we show how users can formulate and solve problems of this type with new, accessible tools in the scientific Python ecosystem.10.25080/issn.2575-975210.25080/majora-212e5952-029https://zenodo.org/record/6946674DanielMoréhttps://orcid.org/0000-0003-1759-4194Contributing to Open Source Software: From not knowing Python to becoming a Spyder core developer2022Experience overview of becoming an open source developer and updates on the work being done in the Spyder IDE project for 202210.25080/issn.2575-975210.25080/majora-212e5952-02ahttps://zenodo.org/record/6946686NathanJessurunOliviaDizon-ParadisDanCapecciDamonWoodardNavidAsadizanjaniSemi-Supervised Semantic Annotator (S3A): Toward Efficient Semantic Image Labeling2022Python GUI and library for semantic image segmentation and annotation10.25080/issn.2575-975210.25080/majora-212e5952-02bhttps://zenodo.org/record/6946702NezarAbdennurhttps://orcid.org/0000-0001-5814-0864GeoffreyFudenberghttps://orcid.org/0000-0001-5905-6517IlyaFlyamerhttps://orcid.org/0000-0002-4892-4208AleksandraGalitsynahttps://orcid.org/0000-0001-8969-5694AntonGoloborodkohttps://orcid.org/0000-0002-2210-8616MaximImakaevhttps://orcid.org/0000-0002-5320-2728TrevorManzhttps://orcid.org/0000-0001-7694-5164SergeyVenevhttps://orcid.org/0000-0002-1507-7460Bioframe: Operating on Genomic Interval Dataframes2022Python library for working with genomic interval dataframes.10.25080/issn.2575-975210.25080/majora-212e5952-02chttps://zenodo.org/record/6946712JosephTuccillohttps://orcid.org/0000-0002-5930-0943JamesGaboardihttps://orcid.org/0000-0002-4776-6826Likeness: a toolkit for connecting the social fabric of place to human dynamics2022Richly-attributed synthetic population data are crucial for discerning human dynamics while preserving privacy. The Likeness toolkit provides a solution to this problem with a suite of Python packages that generate population data as individual agents in appropriate nighttime locations and allocates them to probable daytime activity spaces. Through a case study utilizing students and faculty as agents, the results of Likeness simulations are shown to recreate high-fidelity school capacities, comparable to empirical data sources.10.25080/issn.2575-975210.25080/majora-212e5952-02dhttps://zenodo.org/record/6946720JyotikaSinghhttps://orcid.org/0000-0002-5442-3004pyAudioProcessing: Audio Processing, Feature Extraction, and Machine Learning Modeling2022pyAudioProcessing is a Python based library for processing audio data, constructing and extracting numerical features from audio, building and testing machine learning models, and classifying data with existing pre-trained audio classification models or custom user-built models. This library contains features built in Python that were originally published in MATLAB. pyAudioProcessing allows the user to compute various features from audio files including Gammatone Frequency Cepstral Coefficients (GFCC), Mel Frequency Cepstral Coefficients (MFCC), spectral features, chroma features, and others such as beat-based and cepstrum-based features from audio. One can use these features along with one’s own classification backend or any of the popular scikit-learn classifiers that have been integrated into pyAudioProcessing. Cleaning functions to strip unwanted portions from the audio are another offering of the library. It further contains integrations with other audio functionalities such as frequency and time-series visualizations and audio format conversions. This software aims to provide machine learning engineers, data scientists, researchers, and students with a set of baseline models to classify audio. The library is available at https://github.com/jsingh811/pyAudioProcessing and is under GPL-3.0 license.10.25080/issn.2575-975210.25080/majora-212e5952-02ehttps://zenodo.org/record/6946724NeelimaPulagamhttps://orcid.org/0000-0002-4584-9513SaiMarasanihttps://orcid.org/0000-0002-8211-7186BrianSassKiwi: Python Tool for Tex Processing and Classification2022A user-friendly desktop tool for text visualization and classification. This allows users within the field to avoid creating boilerplate code for basic NLP tasks and users new to machine learning to plug and play with various models and methods. Our main goal is to make natural language processing accessible and easy.10.25080/issn.2575-975210.25080/majora-212e5952-02fhttps://zenodo.org/record/6946734WanlinLiAleksandrKoshkarovMy-LinhLuuNadiaTahiriPhylogeography: Analysis of genetic and climatic data of SARS-CoV-22022Due to the fact that the SARS-CoV-2 pandemic reaches its peak, researchers around the globe are combining efforts to investigate the genetics of different variants to better deal with its distribution. This paper discusses phylogeographic approaches to examine how patterns of divergence within SARS-CoV-2 coincide with geographic features, such as climatic features. First, we propose a python-based bioinformatic pipeline called **aPhylogeo** for phylogeographic analysis written in Python 3 that help researchers better understand the distribution of the virus in specific regions via a configuration file, and then run all the analysis operations in a single run. In particular, the aPhylogeo tool determines which parts of the genetic sequence undergo a high mutation rate depending on geographic conditions, using a sliding window that moves along the genetic sequence alignment in user-defined steps and a window size. As a Python-based cross-platform program, aPhylogeo works on Windows®, MacOS X® and GNU/Linux. The implementation of this pipeline is publicly available on GitHub (https://github.com/tahiri-lab/aPhylogeo). Second, we present an example of analysis of our new aPhylogeo tool on real data (SARS-CoV-2) to understand the occurrence of different variants.10.25080/issn.2575-975210.25080/majora-212e5952-030https://zenodo.org/record/6946742NathanMartindalehttps://orcid.org/0000-0002-5036-5433JasonHitehttps://orcid.org/0000-0001-8931-5815ScottStewarthttps://orcid.org/0000-0003-4320-5818MarkAdamshttps://orcid.org/0000-0002-5414-2800Design of a Scientific Data Analysis Support Platform2022Studying the design features necessary for a workflow and experiment management system, and presenting Curifactory: an open source package that meets these design features.10.25080/issn.2575-975210.25080/majora-212e5952-031https://zenodo.org/record/6946750ZacharyShermanhttps://orcid.org/0000-0002-6455-8324ScottCollishttps://orcid.org/0000-0002-2303-687XMaxGroverhttps://orcid.org/0000-0002-0370-8974RobertJacksonhttps://orcid.org/0000-0003-2518-1234AdamTheisenhttps://orcid.org/0000-0002-7602-1057Opening ARM: A pivot to community software to meet the needs of users and stakeholders of the planet's largest cloud observatory2022This presentation discusses the evolution (and hurdles that came with) of the Atmospheric Radiation Measurement (ARM) program's open source endeavors, starting with the Python ARM Radar Toolkit to the Atmospheric data Community Toolkit in 2018, the expansion of our open-source presence on Github in 2019 and what is planned for the future.10.25080/issn.2575-975210.25080/majora-212e5952-032https://zenodo.org/record/6946754HenrySchreinerhttps://orcid.org/0000-0002-7833-783XJoeRickerbyRalfGrosse-KunstleveWenzelJakobhttps://orcid.org/0000-0002-6090-1121MatthieuDarboisAaronGokaslanJean-ChristopheFillion-Robinhttps://orcid.org/0000-0002-9688-8950MattMcCormickhttps://orcid.org/0000-0001-9475-3756Building Binary Extensions with pybind11, scikit-build, and cibuildwheel2022Building binary extensions is easier than ever thanks to several key libraries. Pybind11 provides a natural C++ language for extensions without requiring pre-processing or special dependencies. Scikit-build ties the premier C++ build system, CMake, into the Python extension build process. And cibuildwheel makes it easy to build highly compatible wheels for over 80 different platforms using CI or on your local machine.10.25080/issn.2575-975210.25080/majora-212e5952-033https://zenodo.org/record/6946769JacksonMorganhttps://orcid.org/0000-0003-1379-5431KyleNiemeyerhttps://orcid.org/0000-0003-4425-7097Python Development Schemes for Monte Carlo Neutronics on High Performance Computing2022We investigate three methods of hardware accleeration on both GPUs and CPUs for a Monte Carlo neutron transport simulation code writen in Python. The accelerating schemes we examine are Pykokks, Numba, and hardware code generating libraries like PyCUDA. This work was supported by the Center for Exascale Monte-Carlo Neutron Transport (CEMeNT) a PSAAP-III project funded by the Department of Energy, grant number: DE-NA003967.10.25080/issn.2575-975210.25080/majora-212e5952-034https://zenodo.org/record/6946791HenrySchreinerhttps://orcid.org/0000-0002-7833-783XJimPivarskihttps://orcid.org/0000-0002-6649-343XEduardoRodrigueshttps://orcid.org/0000-0003-2846-7625Awkward Packaging: Building scikit-HEP2022Scikit-HEP has grown rapidly over the last few years, not just to serve the needs of the High Energy Physics (HEP) community, but in many ways, the Python ecosystem at large. AwkwardArray, boost-histogram/hist, and iMinuit are examples of libraries that are used beyond the original HEP focus.10.25080/issn.2575-975210.25080/majora-212e5952-035https://zenodo.org/record/6946801MatthewPetroffhttps://orcid.org/0000-0002-4436-4215Development of Accessible, Aesthetically-Pleasing Color Sequences2022Many types of data visualization, e.g., line plots and scatter plots, utilize a discrete palette of colors, a color sequence, to differentiate between the categories of data being plotted. Unfortunately, many commonly-used color sequences offer poor accessibility to individuals with color-vision deficiencies, using colors that such individuals find difficult to differentiate between. Here, the development of new, accessible color sequences is discussed. As new color sequences must be aesthetically pleasing if they are to see widespread adoption, a crowd-sourced survey was used to estimate aesthetic preference, while accessibility aspects were handled via quantitative analysis.10.25080/issn.2575-975210.25080/majora-212e5952-036https://zenodo.org/record/6946815JuliusBuseckehttps://orcid.org/0000-0001-8571-865XCutting Edge Climate Science in the Cloud with Pangeo2022Climate change is one of the most challenging issues of our time. To prevent the worst outcomes, we need to drastically accelerate the creation and distribution of scientific knowledge. But the complex and massive datasets produced by numerical climate models render the common 'download and analyze' workflow inefficient, blocking innovative analysis and fast scientific discoveries. We present python tools and cloud infrastructure developed within the Pangeo community, enabling cutting edge climate science from a web-browser, making it efficient, reproducible, and inclusive. To demonstrate these capabilities we will reproduce a plot from the IPCC report in a live cloud demonstration.10.25080/issn.2575-975210.25080/majora-212e5952-037https://zenodo.org/record/6946825AxelDonathhttps://orcid.org/0000-0003-4568-7005AnetaSiemiginowskahttps://orcid.org/0000-0002-0905-7375VinayKashyaphttps://orcid.org/0000-0002-3869-7996DouglasBurkeKarthikSolipuramDavidvan DykPylira: deconvolution of images in the presence of Poisson noise2022Pylira is Python package for deconvolution for images in the presence of Poisson noise. In this presentation I will explain the method in detail, show the setup and API of the Python package as well as show application examples using real astronomical data.10.25080/issn.2575-975210.25080/majora-212e5952-038https://zenodo.org/record/6946829GTforSDteamAccelerating Science with the Generative Toolkit for Scientific Discovery (GT4SD)2022A presentation about GT4SD: an open-source platform to accelerate hypothesis generation in the scientific discovery process. It provides a library for making state-of-the-art generative AI models easier to use.10.25080/issn.2575-975210.25080/majora-212e5952-039https://zenodo.org/record/6946845PeterSunJohnMarohnMModel: a modular modeling framework for scientific prototyping2022MModel is a Python framework that allows for fast and modular prototyping. The library uses networkx graph for workflow construction and provides built-in toolkits such as subgraph modification and graph visualization with rich metadata.10.25080/issn.2575-975210.25080/majora-212e5952-03ahttps://zenodo.org/record/6946850W.Shambaughhttps://orcid.org/0000-0002-4376-3599Monaco: Quantify Uncertainty and Sensitivities in Your Computational Models with a Monte Carlo Library2022Quantify uncertainty and sensitivities in your existing computational models with the “monaco” library. Users define input variables randomly drawn from any of SciPy's statistical distributions, run their model in parallel anywhere from 1 to millions of times, and postprocess the outputs to obtain meaningful, statistically significant conclusions. This talk goes over why you should always be running Monte Carlo simulations, a demo of how to set up and run a sim, and a crash course in generating relevant plots and statistics.10.25080/issn.2575-975210.25080/majora-212e5952-03bhttps://zenodo.org/record/6946859SebastianBerghttps://orcid.org/0000-0002-1236-259XStéfanvan der Walthttps://orcid.org/0000-0001-9276-1891UFuncs and DTypes: new possibilities in NumPy2022Over the past three years, NumPy has seen large changes to much of its core functionalities including universal functions, casting, and DTypes. The goal of this refactoring was to introduce extensible APIs to improve existing user-defined DTypes and unlock new ones. This refactoring is nearing its conclusion, with the work being surfaced as public-facing API. In this talk we will discuss what has been done, and newly possible applications—such as a custom NumPy DType that is aware of physical units.10.25080/issn.2575-975210.25080/majora-212e5952-03chttps://zenodo.org/record/6946863JuanRodríguezhttps://orcid.org/0000-0002-2187-161XPer Python ad astra: interactive Astrodynamics with poliastro2022This talk presents poliastro, an open-source Python library for interactive Astrodynamics that features an easy-to-use API and tools for quick visualization. poliastro implements core Astrodynamics algorithms and leverages numba and Astropy. During the talk, we will describe the two-layer architecture that allows poliastro to offer an approachable API with good performance, discuss the challenges we faced to validate our code, and comment on the successes and failures of the project in trying to build a rich and diverse community. Source code of poliastro is available at https://github.com/poliastro/poliastro/ and documentation is online at https://docs.poliastro.space/.10.25080/issn.2575-975210.25080/majora-212e5952-03dhttps://zenodo.org/record/6946871RianneSchoutenhttps://orcid.org/0000-0001-5026-4256DavinaZamanzadehhttps://orcid.org/0000-0002-9360-0069PrabhantSinghhttps://orcid.org/0000-0001-7634-2312pyampute: a Python library for data amputation2022Amputation is the opposite of imputation; it is the creation of a missing data mask for complete datasets. Amputation is useful for evaluating the effect of missing values on the outcome of a statistical or machine learning model. In this talk, we present pyampute: the first open-source Python library for data amputation. Our package is compatible with the scikit-learn-style fit and transform paradigm, which allows for seamless integration of amputation in a larger, more complex data processing pipeline.10.25080/issn.2575-975210.25080/majora-212e5952-03ehttps://zenodo.org/record/6946887JuanitaRomerohttps://orcid.org/0000-0003-1593-3404Stéfanvan der Walthttps://orcid.org/0000-0001-9276-1891K.Millmanhttps://orcid.org/0000-0002-5263-5070MelissaMendonçahttps://orcid.org/0000-0002-3212-402XInessaPawsonhttps://orcid.org/0000-0001-7294-122XScientific Python: From GitHub to TikTok2022The Scientific Python project aims to better coordinate the ecosystem and grow the community. This talk focuses on our efforts to expand our community by generating a welcoming and friendly environment where people collaborate, build, and improve together.10.25080/issn.2575-975210.25080/majora-212e5952-03fhttps://zenodo.org/record/6946897PamphileRoyhttps://orcid.org/0000-0001-9816-1416Stéfanvan der Walthttps://orcid.org/0000-0001-9276-1891K.Millmanhttps://orcid.org/0000-0002-5263-5070MelissaMendonçahttps://orcid.org/0000-0002-3212-402XScientific Python: By maintainers, for maintainers2022Tools for maintainers and how we can help each others.10.25080/issn.2575-975210.25080/majora-212e5952-040https://zenodo.org/record/6946917PamphileRoyhttps://orcid.org/0000-0001-9816-1416MattHaberlandhttps://orcid.org/0000-0003-4806-3601ChristophBaumgartenhttps://orcid.org/0000-0002-1168-3359TirthPatelImproving random sampling in Python: scipy.stats.sampling and scipy.stats.qmc2022Why and how to use scipy.stats.sampling and scipy.stats.qmc?10.25080/issn.2575-975210.25080/majora-212e5952-041https://zenodo.org/record/6946927ThomasNicholashttps://orcid.org/0000-0002-2176-0530JuliusBuseckehttps://orcid.org/0000-0001-8571-865XRyanAbernatheyhttps://orcid.org/0000-0001-5999-4917Petabyte-scale ocean data analytics on staggered grids via the grid ufunc protocol in xGCM2022We analysed the highest resolution global ocean simulation to date, using xGCM, xhistogram, and dask.10.25080/issn.2575-975210.25080/majora-212e5952-042https://zenodo.org/record/6946933InessaPawsonhttps://orcid.org/0000-0001-7294-122XSciPy Tools Plenary - CEL team2022Introducing the Contributor Experience Lead team at the SciPy 202210.25080/issn.2575-975210.25080/majora-212e5952-043https://zenodo.org/record/6947063Elliottde Andradehttps://orcid.org/0000-0001-7310-8942SciPy Tools Plenary on Matplotlib2022Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. This presentation summarizes changes over the past year, new features, and future plans.10.25080/issn.2575-975210.25080/majora-212e5952-044https://zenodo.org/record/6946953InessaPawsonhttps://orcid.org/0000-0001-7294-122XSciPy Tools Plenary - NumPy2022Annual update on the NumPy project at SciPy 202210.25080/issn.2575-975210.25080/majora-212e5952-045https://zenodo.org/record/6946961 \ No newline at end of file +majora.212e5952-18257e7ec2c1659353893Dillon Niederhutdillon.niederhut@gmail.comCrossrefDillonNiederhutProceedings of the Python in Science ConferenceDelainaMooreDownsampling Time Series Data for Visualizations2022Exploring the largest triangle three bucket algorithm to downsample time series data.10.25080/issn.2575-975210.25080/majora-212e5952-027https://zenodo.org/record/6946652MatthewFeickerthttps://orcid.org/0000-0003-4124-7862Analysis as Applications: Quick introduction to lockfiles2022An opinionated argument for the use of lockfiles in scientific analysis in a similar manner to Python application deployment. This talk was inspired by Brett Cannon's 'pip-secure-install' project and a Twitter conversation with Dustin Ingram on April 20, 2020.10.25080/issn.2575-975210.25080/majora-212e5952-028https://zenodo.org/record/6946666MattHaberlandhttps://orcid.org/0000-0003-4806-3601NicholasMcKibbenhttps://orcid.org/0000-0003-0034-2230Optimal Review Assignments for the SciPy Conference Using Binary Integer Linear Programming in SciPy 1.92022Each year, the SciPy Conference receives hundreds of submissions, and dozens of volunteers offer to review them to help make selections for the conference. How should submissions be assigned to reviewers to distribute the work fairly while 1) ensuring that each submission receives at least three reviews, 2) preventing conflicts of interest, and 3) respecting reviewers' domains of expertise? Binary integer linear programming is an ideal framework for defining and solving 'scheduling' or 'assignment' problems like this. In this poster, we show how users can formulate and solve problems of this type with new, accessible tools in the scientific Python ecosystem.10.25080/issn.2575-975210.25080/majora-212e5952-029https://zenodo.org/record/6946674DanielMoréhttps://orcid.org/0000-0003-1759-4194Contributing to Open Source Software: From not knowing Python to becoming a Spyder core developer2022Experience overview of becoming an open source developer and updates on the work being done in the Spyder IDE project for 202210.25080/issn.2575-975210.25080/majora-212e5952-02ahttps://zenodo.org/record/6946686NathanJessurunOliviaDizon-ParadisDanCapecciDamonWoodardNavidAsadizanjaniSemi-Supervised Semantic Annotator (S3A): Toward Efficient Semantic Image Labeling2022Python GUI and library for semantic image segmentation and annotation10.25080/issn.2575-975210.25080/majora-212e5952-02bhttps://zenodo.org/record/6946702NezarAbdennurhttps://orcid.org/0000-0001-5814-0864GeoffreyFudenberghttps://orcid.org/0000-0001-5905-6517IlyaFlyamerhttps://orcid.org/0000-0002-4892-4208AleksandraGalitsynahttps://orcid.org/0000-0001-8969-5694AntonGoloborodkohttps://orcid.org/0000-0002-2210-8616MaximImakaevhttps://orcid.org/0000-0002-5320-2728TrevorManzhttps://orcid.org/0000-0001-7694-5164SergeyVenevhttps://orcid.org/0000-0002-1507-7460Bioframe: Operating on Genomic Interval Dataframes2022Python library for working with genomic interval dataframes.10.25080/issn.2575-975210.25080/majora-212e5952-02chttps://zenodo.org/record/6946712JosephTuccillohttps://orcid.org/0000-0002-5930-0943JamesGaboardihttps://orcid.org/0000-0002-4776-6826Likeness: a toolkit for connecting the social fabric of place to human dynamics2022Richly-attributed synthetic population data are crucial for discerning human dynamics while preserving privacy. The Likeness toolkit provides a solution to this problem with a suite of Python packages that generate population data as individual agents in appropriate nighttime locations and allocates them to probable daytime activity spaces. Through a case study utilizing students and faculty as agents, the results of Likeness simulations are shown to recreate high-fidelity school capacities, comparable to empirical data sources.10.25080/issn.2575-975210.25080/majora-212e5952-02dhttps://zenodo.org/record/6946720JyotikaSinghhttps://orcid.org/0000-0002-5442-3004pyAudioProcessing: Audio Processing, Feature Extraction, and Machine Learning Modeling2022pyAudioProcessing is a Python based library for processing audio data, constructing and extracting numerical features from audio, building and testing machine learning models, and classifying data with existing pre-trained audio classification models or custom user-built models. This library contains features built in Python that were originally published in MATLAB. pyAudioProcessing allows the user to compute various features from audio files including Gammatone Frequency Cepstral Coefficients (GFCC), Mel Frequency Cepstral Coefficients (MFCC), spectral features, chroma features, and others such as beat-based and cepstrum-based features from audio. One can use these features along with one’s own classification backend or any of the popular scikit-learn classifiers that have been integrated into pyAudioProcessing. Cleaning functions to strip unwanted portions from the audio are another offering of the library. It further contains integrations with other audio functionalities such as frequency and time-series visualizations and audio format conversions. This software aims to provide machine learning engineers, data scientists, researchers, and students with a set of baseline models to classify audio. The library is available at https://github.com/jsingh811/pyAudioProcessing and is under GPL-3.0 license.10.25080/issn.2575-975210.25080/majora-212e5952-02ehttps://zenodo.org/record/6946724NeelimaPulagamhttps://orcid.org/0000-0002-4584-9513SaiMarasanihttps://orcid.org/0000-0002-8211-7186BrianSassKiwi: Python Tool for Tex Processing and Classification2022A user-friendly desktop tool for text visualization and classification. This allows users within the field to avoid creating boilerplate code for basic NLP tasks and users new to machine learning to plug and play with various models and methods. Our main goal is to make natural language processing accessible and easy.10.25080/issn.2575-975210.25080/majora-212e5952-02fhttps://zenodo.org/record/6946734WanlinLiAleksandrKoshkarovMy-LinhLuuNadiaTahiriPhylogeography: Analysis of genetic and climatic data of SARS-CoV-22022Due to the fact that the SARS-CoV-2 pandemic reaches its peak, researchers around the globe are combining efforts to investigate the genetics of different variants to better deal with its distribution. This paper discusses phylogeographic approaches to examine how patterns of divergence within SARS-CoV-2 coincide with geographic features, such as climatic features. First, we propose a python-based bioinformatic pipeline called **aPhylogeo** for phylogeographic analysis written in Python 3 that help researchers better understand the distribution of the virus in specific regions via a configuration file, and then run all the analysis operations in a single run. In particular, the aPhylogeo tool determines which parts of the genetic sequence undergo a high mutation rate depending on geographic conditions, using a sliding window that moves along the genetic sequence alignment in user-defined steps and a window size. As a Python-based cross-platform program, aPhylogeo works on Windows®, MacOS X® and GNU/Linux. The implementation of this pipeline is publicly available on GitHub (https://github.com/tahiri-lab/aPhylogeo). Second, we present an example of analysis of our new aPhylogeo tool on real data (SARS-CoV-2) to understand the occurrence of different variants.10.25080/issn.2575-975210.25080/majora-212e5952-030https://zenodo.org/record/6946742NathanMartindalehttps://orcid.org/0000-0002-5036-5433JasonHitehttps://orcid.org/0000-0001-8931-5815ScottStewarthttps://orcid.org/0000-0003-4320-5818MarkAdamshttps://orcid.org/0000-0002-5414-2800Design of a Scientific Data Analysis Support Platform2022Studying the design features necessary for a workflow and experiment management system, and presenting Curifactory: an open source package that meets these design features.10.25080/issn.2575-975210.25080/majora-212e5952-031https://zenodo.org/record/6946750ZacharyShermanhttps://orcid.org/0000-0002-6455-8324ScottCollishttps://orcid.org/0000-0002-2303-687XMaxGroverhttps://orcid.org/0000-0002-0370-8974RobertJacksonhttps://orcid.org/0000-0003-2518-1234AdamTheisenhttps://orcid.org/0000-0002-7602-1057Opening ARM: A pivot to community software to meet the needs of users and stakeholders of the planet's largest cloud observatory2022This presentation discusses the evolution (and hurdles that came with) of the Atmospheric Radiation Measurement (ARM) program's open source endeavors, starting with the Python ARM Radar Toolkit to the Atmospheric data Community Toolkit in 2018, the expansion of our open-source presence on Github in 2019 and what is planned for the future.10.25080/issn.2575-975210.25080/majora-212e5952-032https://zenodo.org/record/6946754HenrySchreinerhttps://orcid.org/0000-0002-7833-783XJoeRickerbyRalfGrosse-KunstleveWenzelJakobhttps://orcid.org/0000-0002-6090-1121MatthieuDarboisAaronGokaslanJean-ChristopheFillion-Robinhttps://orcid.org/0000-0002-9688-8950MattMcCormickhttps://orcid.org/0000-0001-9475-3756Building Binary Extensions with pybind11, scikit-build, and cibuildwheel2022Building binary extensions is easier than ever thanks to several key libraries. Pybind11 provides a natural C++ language for extensions without requiring pre-processing or special dependencies. Scikit-build ties the premier C++ build system, CMake, into the Python extension build process. And cibuildwheel makes it easy to build highly compatible wheels for over 80 different platforms using CI or on your local machine.10.25080/issn.2575-975210.25080/majora-212e5952-033https://zenodo.org/record/6946769JacksonMorganhttps://orcid.org/0000-0003-1379-5431KyleNiemeyerhttps://orcid.org/0000-0003-4425-7097Python Development Schemes for Monte Carlo Neutronics on High Performance Computing2022We investigate three methods of hardware accleeration on both GPUs and CPUs for a Monte Carlo neutron transport simulation code writen in Python. The accelerating schemes we examine are Pykokks, Numba, and hardware code generating libraries like PyCUDA. This work was supported by the Center for Exascale Monte-Carlo Neutron Transport (CEMeNT) a PSAAP-III project funded by the Department of Energy, grant number: DE-NA003967.10.25080/issn.2575-975210.25080/majora-212e5952-034https://zenodo.org/record/6946791HenrySchreinerhttps://orcid.org/0000-0002-7833-783XJimPivarskihttps://orcid.org/0000-0002-6649-343XEduardoRodrigueshttps://orcid.org/0000-0003-2846-7625Awkward Packaging: Building scikit-HEP2022Scikit-HEP has grown rapidly over the last few years, not just to serve the needs of the High Energy Physics (HEP) community, but in many ways, the Python ecosystem at large. AwkwardArray, boost-histogram/hist, and iMinuit are examples of libraries that are used beyond the original HEP focus.10.25080/issn.2575-975210.25080/majora-212e5952-035https://zenodo.org/record/6946801MatthewPetroffhttps://orcid.org/0000-0002-4436-4215Development of Accessible, Aesthetically-Pleasing Color Sequences2022Many types of data visualization, e.g., line plots and scatter plots, utilize a discrete palette of colors, a color sequence, to differentiate between the categories of data being plotted. Unfortunately, many commonly-used color sequences offer poor accessibility to individuals with color-vision deficiencies, using colors that such individuals find difficult to differentiate between. Here, the development of new, accessible color sequences is discussed. As new color sequences must be aesthetically pleasing if they are to see widespread adoption, a crowd-sourced survey was used to estimate aesthetic preference, while accessibility aspects were handled via quantitative analysis.10.25080/issn.2575-975210.25080/majora-212e5952-036https://zenodo.org/record/6946815JuliusBuseckehttps://orcid.org/0000-0001-8571-865XCutting Edge Climate Science in the Cloud with Pangeo2022Climate change is one of the most challenging issues of our time. To prevent the worst outcomes, we need to drastically accelerate the creation and distribution of scientific knowledge. But the complex and massive datasets produced by numerical climate models render the common 'download and analyze' workflow inefficient, blocking innovative analysis and fast scientific discoveries. We present python tools and cloud infrastructure developed within the Pangeo community, enabling cutting edge climate science from a web-browser, making it efficient, reproducible, and inclusive. To demonstrate these capabilities we will reproduce a plot from the IPCC report in a live cloud demonstration.10.25080/issn.2575-975210.25080/majora-212e5952-037https://zenodo.org/record/6946825AxelDonathhttps://orcid.org/0000-0003-4568-7005AnetaSiemiginowskahttps://orcid.org/0000-0002-0905-7375VinayKashyaphttps://orcid.org/0000-0002-3869-7996DouglasBurkeKarthikSolipuramDavidvan DykPylira: deconvolution of images in the presence of Poisson noise2022Pylira is Python package for deconvolution for images in the presence of Poisson noise. In this presentation I will explain the method in detail, show the setup and API of the Python package as well as show application examples using real astronomical data.10.25080/issn.2575-975210.25080/majora-212e5952-038https://zenodo.org/record/6946829GTforSDteamAccelerating Science with the Generative Toolkit for Scientific Discovery (GT4SD)2022A presentation about GT4SD: an open-source platform to accelerate hypothesis generation in the scientific discovery process. It provides a library for making state-of-the-art generative AI models easier to use.10.25080/issn.2575-975210.25080/majora-212e5952-039https://zenodo.org/record/6946845PeterSunJohnMarohnMModel: a modular modeling framework for scientific prototyping2022MModel is a Python framework that allows for fast and modular prototyping. The library uses networkx graph for workflow construction and provides built-in toolkits such as subgraph modification and graph visualization with rich metadata.10.25080/issn.2575-975210.25080/majora-212e5952-03ahttps://zenodo.org/record/6946850W.Shambaughhttps://orcid.org/0000-0002-4376-3599Monaco: Quantify Uncertainty and Sensitivities in Your Computational Models with a Monte Carlo Library2022Quantify uncertainty and sensitivities in your existing computational models with the “monaco” library. Users define input variables randomly drawn from any of SciPy's statistical distributions, run their model in parallel anywhere from 1 to millions of times, and postprocess the outputs to obtain meaningful, statistically significant conclusions. This talk goes over why you should always be running Monte Carlo simulations, a demo of how to set up and run a sim, and a crash course in generating relevant plots and statistics.10.25080/issn.2575-975210.25080/majora-212e5952-03bhttps://zenodo.org/record/6946859SebastianBerghttps://orcid.org/0000-0002-1236-259XStéfanvan der Walthttps://orcid.org/0000-0001-9276-1891UFuncs and DTypes: new possibilities in NumPy2022Over the past three years, NumPy has seen large changes to much of its core functionalities including universal functions, casting, and DTypes. The goal of this refactoring was to introduce extensible APIs to improve existing user-defined DTypes and unlock new ones. This refactoring is nearing its conclusion, with the work being surfaced as public-facing API. In this talk we will discuss what has been done, and newly possible applications—such as a custom NumPy DType that is aware of physical units.10.25080/issn.2575-975210.25080/majora-212e5952-03chttps://zenodo.org/record/6946863JuanRodríguezhttps://orcid.org/0000-0002-2187-161XPer Python ad astra: interactive Astrodynamics with poliastro2022This talk presents poliastro, an open-source Python library for interactive Astrodynamics that features an easy-to-use API and tools for quick visualization. poliastro implements core Astrodynamics algorithms and leverages numba and Astropy. During the talk, we will describe the two-layer architecture that allows poliastro to offer an approachable API with good performance, discuss the challenges we faced to validate our code, and comment on the successes and failures of the project in trying to build a rich and diverse community. Source code of poliastro is available at https://github.com/poliastro/poliastro/ and documentation is online at https://docs.poliastro.space/.10.25080/issn.2575-975210.25080/majora-212e5952-03dhttps://zenodo.org/record/6946871RianneSchoutenhttps://orcid.org/0000-0001-5026-4256DavinaZamanzadehhttps://orcid.org/0000-0002-9360-0069PrabhantSinghhttps://orcid.org/0000-0001-7634-2312pyampute: a Python library for data amputation2022Amputation is the opposite of imputation; it is the creation of a missing data mask for complete datasets. Amputation is useful for evaluating the effect of missing values on the outcome of a statistical or machine learning model. In this talk, we present pyampute: the first open-source Python library for data amputation. Our package is compatible with the scikit-learn-style fit and transform paradigm, which allows for seamless integration of amputation in a larger, more complex data processing pipeline.10.25080/issn.2575-975210.25080/majora-212e5952-03ehttps://zenodo.org/record/6946887JuanitaRomerohttps://orcid.org/0000-0003-1593-3404Stéfanvan der Walthttps://orcid.org/0000-0001-9276-1891K.Millmanhttps://orcid.org/0000-0002-5263-5070MelissaMendonçahttps://orcid.org/0000-0002-3212-402XInessaPawsonhttps://orcid.org/0000-0001-7294-122XScientific Python: From GitHub to TikTok2022The Scientific Python project aims to better coordinate the ecosystem and grow the community. This talk focuses on our efforts to expand our community by generating a welcoming and friendly environment where people collaborate, build, and improve together.10.25080/issn.2575-975210.25080/majora-212e5952-03fhttps://zenodo.org/record/6946897PamphileRoyhttps://orcid.org/0000-0001-9816-1416Stéfanvan der Walthttps://orcid.org/0000-0001-9276-1891K.Millmanhttps://orcid.org/0000-0002-5263-5070MelissaMendonçahttps://orcid.org/0000-0002-3212-402XScientific Python: By maintainers, for maintainers2022Tools for maintainers and how we can help each others.10.25080/issn.2575-975210.25080/majora-212e5952-040https://zenodo.org/record/6946917PamphileRoyhttps://orcid.org/0000-0001-9816-1416MattHaberlandhttps://orcid.org/0000-0003-4806-3601ChristophBaumgartenhttps://orcid.org/0000-0002-1168-3359TirthPatelImproving random sampling in Python: scipy.stats.sampling and scipy.stats.qmc2022Why and how to use scipy.stats.sampling and scipy.stats.qmc?10.25080/issn.2575-975210.25080/majora-212e5952-041https://zenodo.org/record/6946927ThomasNicholashttps://orcid.org/0000-0002-2176-0530JuliusBuseckehttps://orcid.org/0000-0001-8571-865XRyanAbernatheyhttps://orcid.org/0000-0001-5999-4917Petabyte-scale ocean data analytics on staggered grids via the grid ufunc protocol in xGCM2022We analysed the highest resolution global ocean simulation to date, using xGCM, xhistogram, and dask.10.25080/issn.2575-975210.25080/majora-212e5952-042https://zenodo.org/record/6946933InessaPawsonhttps://orcid.org/0000-0001-7294-122XSciPy Tools Plenary - CEL team2022Introducing the Contributor Experience Lead team at the SciPy 202210.25080/issn.2575-975210.25080/majora-212e5952-043https://zenodo.org/record/6947063Elliottde Andradehttps://orcid.org/0000-0001-7310-8942SciPy Tools Plenary on Matplotlib2022Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. This presentation summarizes changes over the past year, new features, and future plans.10.25080/issn.2575-975210.25080/majora-212e5952-044https://zenodo.org/record/6946953InessaPawsonhttps://orcid.org/0000-0001-7294-122XSciPy Tools Plenary - NumPy2022Annual update on the NumPy project at SciPy 202210.25080/issn.2575-975210.25080/majora-212e5952-045https://zenodo.org/record/6946961 diff --git a/publisher/metadata/2022/other.json b/publisher/metadata/2022/other.json index 3238f15e0e..0aef6d5d5e 100644 --- a/publisher/metadata/2022/other.json +++ b/publisher/metadata/2022/other.json @@ -706,4 +706,4 @@ "doi": "10.25080/majora-212e5952-045" } ] -} \ No newline at end of file +} diff --git a/publisher/metadata/2022/scipy_proc.json b/publisher/metadata/2022/scipy_proc.json index 3ce9ad3bf2..6e4379e38d 100644 --- a/publisher/metadata/2022/scipy_proc.json +++ b/publisher/metadata/2022/scipy_proc.json @@ -459,4 +459,4 @@ "name": "Zuhal Cakir" } ] -} \ No newline at end of file +} diff --git a/publisher/metadata/2022/toc.json b/publisher/metadata/2022/toc.json index d4f8291ee8..2c56b6f8c7 100644 --- a/publisher/metadata/2022/toc.json +++ b/publisher/metadata/2022/toc.json @@ -1954,4 +1954,4 @@ "doi": "10.25080/majora-212e5952-026" } ] -} \ No newline at end of file +} diff --git a/publisher/tempita/__init__.py b/publisher/tempita/__init__.py index 8e934c476f..3ad84b696b 100644 --- a/publisher/tempita/__init__.py +++ b/publisher/tempita/__init__.py @@ -28,7 +28,7 @@ def foo(bar): If there are syntax errors ``TemplateError`` will be raised. """ -from __future__ import unicode_literals +from __future__ import unicode_literals import re import sys @@ -37,10 +37,10 @@ def foo(bar): import os import tokenize -try: +try: from urllib import quote as url_quote from cStringIO import StringIO -except ImportError: +except ImportError: from urllib.parse import quote as url_quote from io import StringIO diff --git a/publisher/tests/test_options.py b/publisher/tests/test_options.py index 215994dc4c..ef4978dc80 100644 --- a/publisher/tests/test_options.py +++ b/publisher/tests/test_options.py @@ -2,7 +2,7 @@ import os -import options +import options from testpath import tempdir diff --git a/publisher/writer/rstmath.py b/publisher/writer/rstmath.py index 6dc586b23e..c4ca0ef174 100644 --- a/publisher/writer/rstmath.py +++ b/publisher/writer/rstmath.py @@ -78,4 +78,3 @@ def run(self): roles.register_local_role("math", mathRole) directives.register_directive("math", MathDirective) directives.register_directive("latex", LaTeXDirective) - diff --git a/requirements.txt b/requirements.txt index c30d81b054..4c72d3b567 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,4 +4,4 @@ nameparser pygments pytest pyyaml -testpath \ No newline at end of file +testpath diff --git a/reviews/invite-paper-reviews.rst b/reviews/invite-paper-reviews.rst index c525df1420..dd7c8689f1 100644 --- a/reviews/invite-paper-reviews.rst +++ b/reviews/invite-paper-reviews.rst @@ -1,7 +1,7 @@ .. For each conference, update the following section, which will be inserted in the template below. -.. |conference| replace:: SciPy +.. |conference| replace:: SciPy .. |year| replace:: 2010 .. |abstracts| replace:: 32 .. |papers| replace:: 19 diff --git a/reviews/invite-reviewer.txt.in b/reviews/invite-reviewer.txt.in index c209adc25f..8764314079 100644 --- a/reviews/invite-reviewer.txt.in +++ b/reviews/invite-reviewer.txt.in @@ -3,7 +3,7 @@ Hello {{ reviewer }}! The 2017 Scientific Computing with Python Conference (http://scipy2017.scipy.org) is fast approaching, and so too is the deadline for proceedings submissions (May 16th) and consequently the start of the -review process. +review process. We welcome you to be a member of the Proceedings Review Committee this year. This should require a little of your time spread out over the review period @@ -11,15 +11,15 @@ This should require a little of your time spread out over the review period areas of expertise. In the spirit of open source software, the SciPy conference uses a fully open -and collaborative review process to make proceedings acceptance decisions. +and collaborative review process to make proceedings acceptance decisions. To learn more about this, you can read about it on the proceedings github page, -https://github.com/scipy-conference/scipy_proceedings. +https://github.com/scipy-conference/scipy_proceedings. -Please let us know if you wish to participate this year and we will include +Please let us know if you wish to participate this year and we will include you in subsequent communications. Also, please don't hesitate to connect us with any other community members who you think we should consider inviting to -review. +review. -Yours Truly, +Yours Truly, Your SciPy 2017 Proceedings co-chairs, Scott Rostrup and Sebastian Benthall diff --git a/scipy_proc.json b/scipy_proc.json index 714e93c00d..92618240d5 100644 --- a/scipy_proc.json +++ b/scipy_proc.json @@ -264,4 +264,4 @@ "name": "" } ] -} \ No newline at end of file +} From 9c28654fefef8799ef8a5e76ff7d24d3e9f0cbee Mon Sep 17 00:00:00 2001 From: Matthew Feickert Date: Wed, 21 Aug 2024 00:00:12 -0500 Subject: [PATCH 3/4] Add pre-commit/mirrors-prettier Co-authored-by: Tetsuo Koyama --- .pre-commit-config.yaml | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 59dd6d3a7f..8971a64428 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -36,3 +36,9 @@ repos: hooks: - id: check-github-workflows args: ["--verbose"] + + - repo: https://github.com/pre-commit/mirrors-prettier + rev: v3.1.0 + hooks: + - id: prettier + types_or: [yaml, html, css, scss, javascript, json, toml] From fe92e3cc717e261a6184e98db2dbb289a2832699 Mon Sep 17 00:00:00 2001 From: Matthew Feickert Date: Wed, 21 Aug 2024 00:02:35 -0500 Subject: [PATCH 4/4] style: Apply prettier changes * Also fix the following errors: publisher/_static/scipy-proc.css [error] publisher/_static/scipy-proc.css: SyntaxError: CssSyntaxError: Missed semicolon (28:23) [error] 26 | .abstract { [error] 27 | width: 80ex; [error] > 28 | text-align: justify [error] | ^ [error] 29 | padding-left: 1em; [error] 30 | } [error] 31 | publisher/_static/screen.css [error] publisher/_static/screen.css: SyntaxError: CssSyntaxError: Unknown word (57:3) [error] 55 | float: right; [error] 56 | margin: 0.5ex 10px 0.5ex 0px; [error] > 57 | padding 0; [error] | ^ [error] 58 | display: inline; /* forces IE to calc correct margin */ [error] 59 | } [error] 60 | --- .github/workflows/publish.yml | 6 +- papers/00_texderwalt/metadata.yaml | 38 +- presentations/lightning/example/info.json | 18 +- presentations/posters/example/info.json | 28 +- presentations/slides/example/info.json | 18 +- presentations/tools/example/info.json | 18 +- publisher/_static/common.css | 325 +++--- publisher/_static/google_analytics.js | 19 +- publisher/_static/proc_links.js | 36 +- publisher/_static/scipy-proc.css | 10 +- publisher/_static/screen.css | 1218 +++++++++++---------- publisher/metadata/2010/scipy_proc.json | 5 +- publisher/metadata/2010/toc.json | 357 ++---- publisher/metadata/2011/scipy_proc.json | 5 +- publisher/metadata/2011/toc.json | 344 ++---- publisher/metadata/2012/scipy_proc.json | 6 +- publisher/metadata/2012/toc.json | 180 +-- publisher/metadata/2013/scipy_proc.json | 6 +- publisher/metadata/2013/toc.json | 266 ++--- publisher/metadata/2014/scipy_proc.json | 5 +- publisher/metadata/2014/toc.json | 199 +--- publisher/metadata/2015/scipy_proc.json | 5 +- publisher/metadata/2015/toc.json | 395 ++----- publisher/metadata/2016/scipy_proc.json | 5 +- publisher/metadata/2016/toc.json | 228 +--- publisher/metadata/2018/toc.json | 400 ++----- publisher/metadata/2020/other.json | 54 +- publisher/metadata/2020/toc.json | 318 ++---- publisher/metadata/2021/toc.json | 258 +---- publisher/metadata/2022/toc.json | 500 ++------- 30 files changed, 1768 insertions(+), 3502 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index e8464efa4f..f03282cf6d 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -1,7 +1,7 @@ name: curvenote on: pull_request: - branches: ['2024'] + branches: ["2024"] permissions: contents: read pull-requests: write @@ -10,12 +10,12 @@ jobs: uses: curvenote/actions/.github/workflows/publish.yml@v1 with: monorepo: true - id-pattern-regex: '^scipy-2024-(?:[a-zA-Z0-9-_]{3,15})$' + id-pattern-regex: "^scipy-2024-(?:[a-zA-Z0-9-_]{3,15})$" enforce-single-folder: paper preview-label: paper submit-label: true venue: scipy - collection: '2024' + collection: "2024" kind: Article path: papers/* secrets: diff --git a/papers/00_texderwalt/metadata.yaml b/papers/00_texderwalt/metadata.yaml index 0593973197..4c92ec43ba 100644 --- a/papers/00_texderwalt/metadata.yaml +++ b/papers/00_texderwalt/metadata.yaml @@ -1,29 +1,29 @@ --- abstract: - - A short version of the long version that is way too long to be written as a short version anyway. Still, when considering the facts from first principles, we find that the outcomes of this introspective approach is compatible with the guidelines previously established. - - In such an experiment it is then clear that the potential for further development not only depends on previous relationships found but also on connections made during exploitation of this novel new experimental protocol. + - A short version of the long version that is way too long to be written as a short version anyway. Still, when considering the facts from first principles, we find that the outcomes of this introspective approach is compatible with the guidelines previously established. + - In such an experiment it is then clear that the potential for further development not only depends on previous relationships found but also on connections made during exploitation of this novel new experimental protocol. keywords: - terraforming - desert - numerical perspective authors: -- email: jj@rome.it - institution: - - Senate House, S.P.Q.R. - - Egyptian Embassy, S.P.Q.R. - name: Gaius Caesar -- email: mark37@rome.it - institution: - - Egyptian Embassy, S.P.Q.R. - name: Mark Anthony -- email: millman@rome.it - institution: - - Egyptian Embassy, S.P.Q.R. - - Yet another place, S.P.Q.R. - name: Jarrod Millman - corresponding: true -- email: cleopatra@rome.it - name: Cleopatra + - email: jj@rome.it + institution: + - Senate House, S.P.Q.R. + - Egyptian Embassy, S.P.Q.R. + name: Gaius Caesar + - email: mark37@rome.it + institution: + - Egyptian Embassy, S.P.Q.R. + name: Mark Anthony + - email: millman@rome.it + institution: + - Egyptian Embassy, S.P.Q.R. + - Yet another place, S.P.Q.R. + name: Jarrod Millman + corresponding: true + - email: cleopatra@rome.it + name: Cleopatra bibliography: mybib video: "https://www.youtube.com/watch?v=Hz1JWzyvv8A" title: A Numerical Perspective to Terraforming a Desert -- latex edition diff --git a/presentations/lightning/example/info.json b/presentations/lightning/example/info.json index dee98bdeef..46a4b7fb9d 100644 --- a/presentations/lightning/example/info.json +++ b/presentations/lightning/example/info.json @@ -1,11 +1,11 @@ { - "title": "Introducing JOSE: Journal of Open Source Education", - "authors": [ - { - "name": "Kyle Niemeyer", - "affiliation": "Oregon State University", - "orcid": "0000-0003-4425-7097" - } - ], - "description": "A cool presentation about a cooler open source journal" + "title": "Introducing JOSE: Journal of Open Source Education", + "authors": [ + { + "name": "Kyle Niemeyer", + "affiliation": "Oregon State University", + "orcid": "0000-0003-4425-7097" + } + ], + "description": "A cool presentation about a cooler open source journal" } diff --git a/presentations/posters/example/info.json b/presentations/posters/example/info.json index 021d32ddb7..b8ff0777a8 100644 --- a/presentations/posters/example/info.json +++ b/presentations/posters/example/info.json @@ -1,16 +1,16 @@ { - "authors": [ - { - "name": "First Author", - "affiliation": "First Author's Institution", - "orcid": "0101-0101-0101-0103" - }, - { - "name": "Second Author", - "affiliation": "Second Author's Company", - "orcid": "0202-0211-0101-0141" - } - ], - "title": "A test title", - "description": "A brief description of what this object is" + "authors": [ + { + "name": "First Author", + "affiliation": "First Author's Institution", + "orcid": "0101-0101-0101-0103" + }, + { + "name": "Second Author", + "affiliation": "Second Author's Company", + "orcid": "0202-0211-0101-0141" + } + ], + "title": "A test title", + "description": "A brief description of what this object is" } diff --git a/presentations/slides/example/info.json b/presentations/slides/example/info.json index cb4881431c..944ef13ec8 100644 --- a/presentations/slides/example/info.json +++ b/presentations/slides/example/info.json @@ -1,11 +1,11 @@ { - "title": "Safe handling instructions for missing data", - "authors": [ - { - "name": "Dillon Niederhut", - "affiliation": "Enthought, Inc.", - "orcid": "0000-0002-9995-4378" - } - ], - "description": "A presentation about what to do with missing data" + "title": "Safe handling instructions for missing data", + "authors": [ + { + "name": "Dillon Niederhut", + "affiliation": "Enthought, Inc.", + "orcid": "0000-0002-9995-4378" + } + ], + "description": "A presentation about what to do with missing data" } diff --git a/presentations/tools/example/info.json b/presentations/tools/example/info.json index ce522332bf..49f0c3ab31 100644 --- a/presentations/tools/example/info.json +++ b/presentations/tools/example/info.json @@ -1,11 +1,11 @@ { - "title": "SciPy Tools Plenary on Mayavi", - "authors": [ - { - "name": "Prabhu Ramachandran", - "affiliation": "Department of Aerospace Engineering, IIT, Bombay", - "orcid": "0000-0001-6337-1720" - } - ], - "description": "2018 updates in Mayavi, a 3D plotting application" + "title": "SciPy Tools Plenary on Mayavi", + "authors": [ + { + "name": "Prabhu Ramachandran", + "affiliation": "Department of Aerospace Engineering, IIT, Bombay", + "orcid": "0000-0001-6337-1720" + } + ], + "description": "2018 updates in Mayavi, a 3D plotting application" } diff --git a/publisher/_static/common.css b/publisher/_static/common.css index 90c949685f..c43df07e4c 100644 --- a/publisher/_static/common.css +++ b/publisher/_static/common.css @@ -3,252 +3,313 @@ */ html { - background-color: white; - color: black; - font-family: Verdana, Helvetica, Arial, sans-serif; - font-size: 0.75em; - line-height: 1.25em; - margin: 0; - padding: 0; + background-color: white; + color: black; + font-family: Verdana, Helvetica, Arial, sans-serif; + font-size: 0.75em; + line-height: 1.25em; + margin: 0; + padding: 0; } /* Headings */ -h1, h2, h3, h4, h5 { - margin: 1em 0 0.5em 0; - padding: 0px 5px 2px; - font-weight: bold; - color: black; - line-height: 1.2em; - border-bottom: 2px solid rgb(100,135,220); - border-left: 1px solid rgb(100,135,220); +h1, +h2, +h3, +h4, +h5 { + margin: 1em 0 0.5em 0; + padding: 0px 5px 2px; + font-weight: bold; + color: black; + line-height: 1.2em; + border-bottom: 2px solid rgb(100, 135, 220); + border-left: 1px solid rgb(100, 135, 220); } -h1 {font-size: 1.4em;} -h2 {font-size: 1.25em;} -h3 {font-size: 1.15em;} -h4, h5 {font-size: 1em;} +h1 { + font-size: 1.4em; +} +h2 { + font-size: 1.25em; +} +h3 { + font-size: 1.15em; +} +h4, +h5 { + font-size: 1em; +} /* title of the current wiki-page should not be displayed as list */ #pagelocation { - margin: 0; - font-size: 2em; - line-height: 1.0em; + margin: 0; + font-size: 2em; + line-height: 1em; } - #pagelocation li { -/* display: inline;*/ - list-style: none; +#pagelocation li { + /* display: inline;*/ + list-style: none; } li p { - margin: .25em 0; + margin: 0.25em 0; } li.gap { - margin-top: 0.5em; + margin-top: 0.5em; } -a, img, img.drawing { - border: 0; +a, +img, +img.drawing { + border: 0; } dt { - font-weight: bold; + font-weight: bold; } /* fix problem with small font for inline code */ tt { - font-size: 1.25em; + font-size: 1.25em; } pre { - padding: 5px; - border: 1px solid #c0c0c0; - font-family: courier, monospace; - white-space: pre; - /* begin css 3 or browser specific rules - do not remove! + padding: 5px; + border: 1px solid #c0c0c0; + font-family: courier, monospace; + white-space: pre; + /* begin css 3 or browser specific rules - do not remove! see: http://forums.techguy.org/archive/index.php/t-249849.html */ - white-space: pre-wrap; - word-wrap: break-word; - white-space: -moz-pre-wrap; - white-space: -pre-wrap; - white-space: -o-pre-wrap; - /* end css 3 or browser specific rules */ + white-space: pre-wrap; + word-wrap: break-word; + white-space: -moz-pre-wrap; + white-space: -pre-wrap; + white-space: -o-pre-wrap; + /* end css 3 or browser specific rules */ } table { - margin: 0.5em 0; - font-size: 1em; /* Required to get correct size with GUI editor */ - border-collapse: collapse; + margin: 0.5em 0; + font-size: 1em; /* Required to get correct size with GUI editor */ + border-collapse: collapse; } -td -{ - padding: 0.25em; - border: 1px solid #c0c0c0; +td { + padding: 0.25em; + border: 1px solid #c0c0c0; } td p { - margin: 0; - padding: 0; + margin: 0; + padding: 0; } /* standard rule ---- */ hr { - height: 2px; - background-color: #c0c0c0; - border: none; + height: 2px; + background-color: #c0c0c0; + border: none; } /* custom rules ----- to ---------- */ -.hr1 {height: 3px;} -.hr2 {height: 4px;} -.hr3 {height: 5px;} -.hr4 {height: 6px;} -.hr5 {height: 7px;} -.hr6 {height: 8px;} +.hr1 { + height: 3px; +} +.hr2 { + height: 4px; +} +.hr3 { + height: 5px; +} +.hr4 { + height: 6px; +} +.hr5 { + height: 7px; +} +.hr6 { + height: 8px; +} /* Replacement for deprecated html 3 element and html 4 */ -.u {text-decoration: underline;} -.strike {text-decoration: line-through;} +.u { + text-decoration: underline; +} +.strike { + text-decoration: line-through; +} .footnotes ul { - padding: 0 2em; - margin: 0 0 1em; - list-style: none; + padding: 0 2em; + margin: 0 0 1em; + list-style: none; } .footnotes li { } - /* eye catchers */ -.warning -{ - color: red; +.warning { + color: red; } -.error -{ - color: red; +.error { + color: red; } -strong.highlight -{ - background-color: #ffcc99; - padding: 1pt; +strong.highlight { + background-color: #ffcc99; + padding: 1pt; } - /* Recent changes */ div.recentchanges table { - border: 1px solid #e5e5e5; + border: 1px solid #e5e5e5; } .recentchanges p { - margin: 0.25em; + margin: 0.25em; } .recentchanges td { - border: none; - border-top: 1px solid #e5e5e5; - border-bottom: 1px solid #e5e5e5; 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- padding: 0; - border: 1pt solid #AEBDCC; - background-color: #F3F5F7; - color: black; +div.codearea { + /* the div makes the border */ + margin: 0.5em 0; + padding: 0; + border: 1pt solid #aebdcc; + background-color: #f3f5f7; + color: black; } -div.codearea pre { /* the pre has no border and is inside the div */ - margin: 0; - padding: 10pt; - border: none; +div.codearea pre { + /* the pre has no border and is inside the div */ + margin: 0; + padding: 10pt; + border: none; } -a.codenumbers { /* format of the line numbering link */ - margin: 0 10pt; - font-size: 0.85em; - color: gray; +a.codenumbers { + /* format of the line numbering link */ + margin: 0 10pt; + font-size: 0.85em; + color: gray; } /* format of certain syntax spans */ -div.codearea pre span.LineNumber {color: gray;} -div.codearea pre span.ID {color: #000000;} -div.codearea pre span.Operator {color: #0000C0;} -div.codearea pre span.Char {color: #004080;} -div.codearea pre span.Comment {color: #008000;} -div.codearea pre span.Number {color: #0080C0;} -div.codearea pre span.String {color: #004080;} -div.codearea pre span.SPChar {color: #0000C0;} -div.codearea pre span.ResWord {color: #A00000;} -div.codearea pre span.ConsWord {color: #008080; 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+} +div.codearea pre span.Preprc { + color: #803999; +} /* pageinfo */ .info { - float: right; - font-size: 0.8em; - font-style:italic; - color: gray; + float: right; + font-size: 0.8em; + font-style: italic; + color: gray; } #pageinfo { - margin-top: 2em; + margin-top: 2em; } diff --git a/publisher/_static/google_analytics.js b/publisher/_static/google_analytics.js index 941b1b6f5b..e2c2d9fa59 100644 --- a/publisher/_static/google_analytics.js +++ b/publisher/_static/google_analytics.js @@ -1,9 +1,14 @@ var _gaq = _gaq || []; -_gaq.push(['_setAccount', 'UA-30141106-1']); -_gaq.push(['_trackPageview']); +_gaq.push(["_setAccount", "UA-30141106-1"]); +_gaq.push(["_trackPageview"]); -(function() { - var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; - ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; - var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); - })(); +(function () { + var ga = document.createElement("script"); + ga.type = "text/javascript"; + ga.async = true; + ga.src = + ("https:" == document.location.protocol ? "https://ssl" : "http://www") + + ".google-analytics.com/ga.js"; + var s = document.getElementsByTagName("script")[0]; + s.parentNode.insertBefore(ga, s); +})(); diff --git a/publisher/_static/proc_links.js b/publisher/_static/proc_links.js index 57719fc1b9..4e48fff05b 100644 --- a/publisher/_static/proc_links.js +++ b/publisher/_static/proc_links.js @@ -1,13 +1,31 @@ function proc_versions() { - var versions = ['2023', '2022', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']; - var proc_url = 'https://conference.scipy.org/proceedings/scipy'; - document.write('"); } diff --git a/publisher/_static/scipy-proc.css b/publisher/_static/scipy-proc.css index d0c95ae5ab..a467985caa 100644 --- a/publisher/_static/scipy-proc.css +++ b/publisher/_static/scipy-proc.css @@ -19,12 +19,12 @@ body { } .authors { - font-style: italic; - padding-left: 1em; + font-style: italic; + padding-left: 1em; } .abstract { - width: 80ex; - text-align: justify - padding-left: 1em; + width: 80ex; + text-align: justify; + padding-left: 1em; } diff --git a/publisher/_static/screen.css b/publisher/_static/screen.css index 7cfecb4673..d697143051 100644 --- a/publisher/_static/screen.css +++ b/publisher/_static/screen.css @@ -1,579 +1,639 @@ -/* screen.css of MoinMoin theme "sinorca4moin" by David Linke. - $Id: screen.css 104 2006-08-30 20:41:40Z linke $ -*/ - -/* ##### Common Styles ##### */ - -body { - color: black; - margin: 0; - padding: 0; - line-height: 1.0em; -} - -a {text-decoration: none; -} - - -/* ##### Header ##### */ -.superHeader { - color: white; - background-color: rgb(100,135,220); - height: 2.4em; - font-size: 0.8em; -} - -.superHeader a { - color: white; - text-decoration: none; -} - -.superHeader a:hover { - text-decoration: underline; -} - -.superHeader ul { - display: inline; -} - -.superHeader li { - display: inline; - padding: 0; - margin: -10px; -} - -.superHeader .left { - text-align: left; - float: left; - margin: 0.5ex 0px 0.5ex; - padding: 0 0 0 10px; - display: inline; /* forces IE to calc correct margin */ -} - -.superHeader .right { - text-align: right; - float: right; - margin: 0.5ex 10px 0.5ex 0px; - padding 0; - display: inline; /* forces IE to calc correct margin */ -} - -.midHeader { - color: rgb(39,78,144); -/* background-color: rgb(140,170,230); */ /* should be color of logo background */ - margin: 0; - font-size: 3.2em; - font-weight: normal; - line-height: 1.4em; -} - -.midHeader a { - text-decoration: none; - color: rgb(39,78,144); - margin: 5px 0px 5px 10px; -} - -#logo { - /* contains image and/or text link */ - display: inline; -} - -#logo img { - /* logo image */ - vertical-align: bottom; - margin: 5px 0px 5px 0px; -} - -#username { - white-space: nowrap; -} - -#username ul { - list-style-type: none; - list-style-position: outside; - margin: 0 0 5px 0; - padding: 0; -} - -#username li { - padding: 0.1ex 15px; -} - -.subHeader { - color: white; - background-color: rgb(0,51,153); - font-size: 0.9em; - margin: 0; - padding: 1ex 1ex 1ex 1.5mm; -} - -.subHeader a { - color: white; - background-color: transparent; - text-decoration: none; - font-weight: bold; - margin: 0; - padding: 0 0.75ex 0 0.5ex; -} - -.subHeader a:hover { - text-decoration: underline; -} - -.superHeader .highlight, .subHeader .highlight { - color: rgb(253,160,91); -} - - -/* ##### Side Bar ##### */ - -#sidebar { -/* margin: 5px, 0px;*/ - float: left; -/* background-color: green; for debugging IE surprises*/ - width: 212px; - padding: 0; - overflow: hidden; - display: inline; /* forces IE to calc correct margin */ -} - -#sidebar a { - color: rgb(0,102,204); - background-color: transparent; - text-decoration: none; - margin: 0; - padding: 0.25em 0; - display: block; -} - -#sidebar .current { - color: black; - background-color: white; - border: 1px solid rgb(253,160,91); -} - -#sidebar a:hover { - color: white; - background-color: rgb(100,135,220); - text-decoration: none; -} - -.sidepanel { - margin: 5px; - float: left; - width: 200px; - border: 1px solid #9c9c9c; - background: #e5e5e5; - overflow: hidden; /* new 18:11 */ - display: inline; /* forces IE to calc correct margin */ -} - -.sidepanel h1 { - margin: 0; - padding: 0.4em 10px; - border: none; - font-size: 1em; - color: black; -} - -.sidepanel li { - margin: 0; - padding: 0.1ex 15px; -} - -.sidepanel ul { - list-style-type: none; - list-style-position: outside; - margin: 0 0 5px 0; - padding: 0; -} - -.actionsmenu { - width: 180px; -} - - -/* ===== Iconbar ===== */ -#iconbar { - float: right; - margin: 10px 0px; - padding: 0; - white-space: nowrap; -} - -*[dir="rtl"] #iconbar { - float: right; - margin: 5px 10px 5px 0; -} - -#iconbar li { - display: inline; -} - -#iconbar img { - padding: 1px; -} - - -/* title of the current wiki-page */ -#pagelocation { - padding: 10px 0; - color: rgb(100,135,220); -} - - -/* ##### Main Copy ##### */ - -#page { - color: black; - background-color: transparent; -/* background-color: yellow; for debugging IE surprises*/ -/* text-align: justify;*/ - line-height: 1.4em; - margin: 0 10px 10px 215px; - padding: 0px 0px 10px 0px; -} - -#page p { - margin: 0 0 0.25em 0; - padding: 0; -} - -/* Use same margin for normal list item as for li p (see common.css) */ -#page li { - padding: 0; - margin: 0.25em 0; -} - -#page a { - color: rgb(0,102,204); - background-color: transparent; -} - -#page a.nonexistent, a.badinterwiki { - color: #900000; - border-bottom: dotted 1px; -} - -#page a:hover { - text-decoration: underline; -} - -dl { - margin: 1em 1ex 2em 1ex; - padding: 0; -} - -dt { - font-weight: bold; - margin: 0 0 0 0; - padding: 0; -} - -dd { - margin: 0 0 0.5em 1em; - padding: 0; -} - -pre { - background-color: #FFF8ED; -} - -/* ##### Footer ##### */ - -#footer { - color: white; - background-color: rgb(100,135,220); - font-size: 0.9em; - margin: 0; - padding: 1ex 2.5mm; - clear: both; -} - -#footer a { - color: white; - background-color: transparent; - text-decoration: underline ; -} - -#footer a:hover { - text-decoration: none; -} - - -/* DL addition from rightsidebar/classic/modern screen.css*/ - -#pagetrail { - clear: left; - margin: 0; - padding: 0; - font-size: 0.8em; - height: 2em; - border-bottom: 1px solid rgb(153,153,153); -} - -*[dir="rtl"] #pagetrail { - clear: right; -} - -/* for broken IE */ -* html #pagetrail li { - border-right: 1px solid #AAA; - padding: 0 0.3em 0 0; -} - -#pagetrail li { - float: left; - display: block; - margin: 2px 0 3px 5px; - padding: 0 2px; -} - -*[dir="rtl"] #pagetrail li { - float: right; -} - -#pagetrail a { - text-decoration: none; - color: rgb(0,102,204); -} - -/* XXX Warning: non-ascii characters! */ -#pagetrail li:after { - content: " »"; -} -#pagetrail li:last-child:after { - content: ""; -} - -#searchform { - float: right; - margin: 5px 10px 0px; - padding: 0; - white-space: nowrap; - display: inline; -} - -*[dir="rtl"] #searchform { - float: left; -} - -#searchform form div { - display: inline; -} - -.editbar form, .editbar form div { - display: inline; - text-align: center; -} - -#message { - margin: 10px 10px 5px 215px; - padding: 0.5ex; - background-color: #FFFFA0; - border: 2px solid red; - line-height: 1.2em; -} - -#message p{ - margin: 0; -} - -#message a{ - margin: 0; - color: rgb(0,102,204); -} - -/* We use here dumb css1 ids because of IE suckiness */ -#editor-comment {width: 70%;} -#editor-textarea {width: 99%;} - -#pagebottom { -/* clear: both;*/ -} - -#preview { - border: 2px solid #e5e5e5; - padding: .5em; - background: url(../img/draft.png); -} - -.diff { - width:99%; -} - -.diff-title { - background-color: #C0C0C0; -} - -.diff-added { - background-color: #E0FFE0; - vertical-align: sub; -} - -.diff-removed { - background-color: #FFFFE0; - vertical-align: sub; -} - -.diff-added span { - background-color: #80FF80; -} - -.diff-removed span { - background-color: #FFFF80; -} - -.searchresult dd span { - font-weight: bold; -} - -#version{ - margin: 5px 5px; - padding: 0px; - text-align: center; - font-size: 1em; - color: #6C7680; -} - -#credits{ - margin: 5px 5px; - padding: 0px; - text-align: center; - font-size: 1em; -} - -#timings{ - margin: 5px 0px; - padding: 0; - text-align: center; - font-size: 0.8em; - color: #6C7680; -} -#credits li { - display: inline; - padding: 0 10px; -} - -#timings li { - display: inline; - padding: 0 5px; -} - -#credits img { - vertical-align: middle; -} - -/* MonthCalendar css */ - -/* days without and with pages linked to them */ -a.cal-emptyday { - color: #777777; - text-align: center; -} -a.cal-usedday { - color: #000000; - font-weight: bold; - text-align: center; -} -/* general stuff: workdays, weekend, today */ -td.cal-workday { - background-color: #DDDDFF; - text-align: center; -} -td.cal-weekend { - background-color: #FFDDDD; - text-align: center; -} -td.cal-today { - background-color: #CCFFCC; - border-style: solid; - border-width: 2pt; - text-align: center; -} -/* invalid places on the monthly calendar sheet */ -td.cal-invalidday { - background-color: #CCCCCC; -} -/* links to prev/next month/year */ -a.cal-link { - color: #000000; - text-decoration: none; -} -th.cal-header { - background-color: #DDBBFF; - text-align: center; -} - -/* for MonthCalendar mouseover info boxes */ -TABLE.tip { - color: black; - background-color: #FF8888; - font-size: small; - font-weight: normal; - border-style: solid; - border-width: 1px; -} - -TH.tip { - background-color: #FF4444; - font-weight: bold; - text-align: center; -} - -TD.tip { - text-align: left; -} -*[dir="rtl"] TD.tip { - text-align: right; -} - -/* end MonthCalendar stuff */ - - -/* Spans for line-anchors - uses * html hack so that the rule only applies to - * IE (where omitting the "display: none" triggers rendering bugs). - */ -* html span.anchor { display: none; } - -/* IE6 has a bug with rendering of float elements. We workaround this bug by - * assigning those elements a height attribute because we currently don't know - * a better solution. This results in IE calculating the correct height of the - * characters and displaying them correctly. We don't know any negative side - * effects of this workaround: - */ -/** html div#page { height: 0.001%; } this breaks sinorca4moin on IE6.0! */ - -* html div#header, html form#editor { height: 0.001%; } - -/* Content Sidebar */ - -#content .sidebar {float: right; width: 200px; margin: 0 0 20px 20px; - padding: 0; font-size: 0.85em;} -#content[dir="rtl"] .sidebar {float: left; margin: 0 20px 20px 0;} -#content .sidebar p {text-align: left;} -#content[dir="rtl"] .sidebar p {text-align: right;} -/* All headings use darker blue and some white space above */ -#content .sidebar h3, #content .sidebar h4 {margin: 1.5em 0 0 0; - padding: 2px 8px; background: #B8C5D9;} -#content .sidebar *:first-child {margin-top: 0} -/* all block elements use light blue background */ -#content .sidebar ul, #content .sidebar ol, #content .sidebar p, #content .sidebar table, #content .sidebar div { - background: #F2F4F7;} -#content .sidebar ul, #content .sidebar ol {margin: 0; padding: 0;} -/* Paragraphs and list items separated with bottom border */ -#content .sidebar p, #content .sidebar li {margin: 0; padding: 4px 8px; - border-bottom: 1pt solid #E6EAF0;} -#content .sidebar li { display: block;} -#content .sidebar li p {margin: 0; padding: 0; border: none;} - -/* Content Figures - -Default figure float to the end of the page. Left of right classes float to the left or right :-) -*/ -#content .figure {float: right; margin: 0 0 0 20px; padding: 0; - font-size: 0.85em;} -#content[dir="rtl"] .figure {float: left; margin: 0 20px 0 0;} -#content .figure.left {float: left; margin: 0 20px 0 0;} -#content .figure.right {float: right; margin: 0 0 0 20px;} -#content .figure p {margin: 0; text-align: center; font-weight: bold;} +/* screen.css of MoinMoin theme "sinorca4moin" by David Linke. + $Id: screen.css 104 2006-08-30 20:41:40Z linke $ +*/ + +/* ##### Common Styles ##### */ + +body { + color: black; + margin: 0; + padding: 0; + line-height: 1em; +} + +a { + text-decoration: none; +} + +/* ##### Header ##### */ +.superHeader { + color: white; + background-color: rgb(100, 135, 220); + height: 2.4em; + font-size: 0.8em; +} + +.superHeader a { + color: white; + text-decoration: none; +} + +.superHeader a:hover { + text-decoration: underline; +} + +.superHeader ul { + display: inline; +} + +.superHeader li { + display: inline; + padding: 0; + margin: -10px; +} + +.superHeader .left { + text-align: left; + float: left; + margin: 0.5ex 0px 0.5ex; + padding: 0 0 0 10px; + display: inline; /* forces IE to calc correct margin */ +} + +.superHeader .right { + text-align: right; + float: right; + margin: 0.5ex 10px 0.5ex 0px; + padding: 0; + display: inline; /* forces IE to calc correct margin */ +} + +.midHeader { + color: rgb(39, 78, 144); + /* background-color: rgb(140,170,230); */ /* should be color of logo background */ + margin: 0; + font-size: 3.2em; + font-weight: normal; + line-height: 1.4em; +} + +.midHeader a { + text-decoration: none; + color: rgb(39, 78, 144); + margin: 5px 0px 5px 10px; +} + +#logo { + /* contains image and/or text link */ + display: inline; +} + +#logo img { + /* logo image */ + vertical-align: bottom; + margin: 5px 0px 5px 0px; +} + +#username { + white-space: nowrap; +} + +#username ul { + list-style-type: none; + list-style-position: outside; + margin: 0 0 5px 0; + padding: 0; +} + +#username li { + padding: 0.1ex 15px; +} + +.subHeader { + color: white; + background-color: rgb(0, 51, 153); + font-size: 0.9em; + margin: 0; + padding: 1ex 1ex 1ex 1.5mm; +} + +.subHeader a { + color: white; + background-color: transparent; + text-decoration: none; + font-weight: bold; + margin: 0; + padding: 0 0.75ex 0 0.5ex; +} + +.subHeader a:hover { + text-decoration: underline; +} + +.superHeader .highlight, +.subHeader .highlight { + color: rgb(253, 160, 91); +} + +/* ##### Side Bar ##### */ + +#sidebar { + /* margin: 5px, 0px;*/ + float: left; + /* background-color: green; for debugging IE surprises*/ + width: 212px; + padding: 0; + overflow: hidden; + display: inline; /* forces IE to calc correct margin */ +} + +#sidebar a { + color: rgb(0, 102, 204); + background-color: transparent; + text-decoration: none; + margin: 0; + padding: 0.25em 0; + display: block; +} + +#sidebar .current { + color: black; + background-color: white; + border: 1px solid rgb(253, 160, 91); +} + +#sidebar a:hover { + color: white; + background-color: rgb(100, 135, 220); + text-decoration: none; +} + +.sidepanel { + margin: 5px; + float: left; + width: 200px; + border: 1px solid #9c9c9c; + background: #e5e5e5; + overflow: hidden; /* new 18:11 */ + display: inline; /* forces IE to calc correct margin */ +} + +.sidepanel h1 { + margin: 0; + padding: 0.4em 10px; + border: none; + font-size: 1em; + color: black; +} + +.sidepanel li { + margin: 0; + padding: 0.1ex 15px; +} + +.sidepanel ul { + list-style-type: none; + list-style-position: outside; + margin: 0 0 5px 0; + padding: 0; +} + +.actionsmenu { + width: 180px; +} + +/* ===== Iconbar ===== */ +#iconbar { + float: right; + margin: 10px 0px; + padding: 0; + white-space: nowrap; +} + +*[dir="rtl"] #iconbar { + float: right; + margin: 5px 10px 5px 0; +} + +#iconbar li { + display: inline; +} + +#iconbar img { + padding: 1px; +} + +/* title of the current wiki-page */ +#pagelocation { + padding: 10px 0; + color: rgb(100, 135, 220); +} + +/* ##### Main Copy ##### */ + +#page { + color: black; + background-color: transparent; + /* background-color: yellow; for debugging IE surprises*/ + /* text-align: justify;*/ + line-height: 1.4em; + margin: 0 10px 10px 215px; + padding: 0px 0px 10px 0px; +} + +#page p { + margin: 0 0 0.25em 0; + padding: 0; +} + +/* Use same margin for normal list item as for li p (see common.css) */ +#page li { + padding: 0; + margin: 0.25em 0; +} + +#page a { + color: rgb(0, 102, 204); + background-color: transparent; +} + +#page a.nonexistent, +a.badinterwiki { + color: #900000; + border-bottom: dotted 1px; +} + +#page a:hover { + text-decoration: underline; +} + +dl { + margin: 1em 1ex 2em 1ex; + padding: 0; +} + +dt { + font-weight: bold; + margin: 0 0 0 0; + padding: 0; +} + +dd { + margin: 0 0 0.5em 1em; + padding: 0; +} + +pre { + background-color: #fff8ed; +} + +/* ##### Footer ##### */ + +#footer { + color: white; + background-color: rgb(100, 135, 220); + font-size: 0.9em; + margin: 0; + padding: 1ex 2.5mm; + clear: both; +} + +#footer a { + color: white; + background-color: transparent; + text-decoration: underline; +} + +#footer a:hover { + text-decoration: none; +} + +/* DL addition from rightsidebar/classic/modern screen.css*/ + +#pagetrail { + clear: left; + margin: 0; + padding: 0; + font-size: 0.8em; + height: 2em; + border-bottom: 1px solid rgb(153, 153, 153); +} + +*[dir="rtl"] #pagetrail { + clear: right; +} + +/* for broken IE */ +* html #pagetrail li { + border-right: 1px solid #aaa; + padding: 0 0.3em 0 0; +} + +#pagetrail li { + float: left; + display: block; + margin: 2px 0 3px 5px; + padding: 0 2px; +} + +*[dir="rtl"] #pagetrail li { + float: right; +} + +#pagetrail a { + text-decoration: none; + color: rgb(0, 102, 204); +} + +/* XXX Warning: non-ascii characters! */ +#pagetrail li:after { + content: " »"; +} +#pagetrail li:last-child:after { + content: ""; +} + +#searchform { + float: right; + margin: 5px 10px 0px; + padding: 0; + white-space: nowrap; + display: inline; +} + +*[dir="rtl"] #searchform { + float: left; +} + +#searchform form div { + display: inline; +} + +.editbar form, +.editbar form div { + display: inline; + text-align: center; +} + +#message { + margin: 10px 10px 5px 215px; + padding: 0.5ex; + background-color: #ffffa0; + border: 2px solid red; + line-height: 1.2em; +} + +#message p { + margin: 0; +} + +#message a { + margin: 0; + color: rgb(0, 102, 204); +} + +/* We use here dumb css1 ids because of IE suckiness */ +#editor-comment { + width: 70%; +} +#editor-textarea { + width: 99%; +} + +#pagebottom { + /* clear: both;*/ +} + +#preview { + border: 2px solid #e5e5e5; + padding: 0.5em; + background: url(../img/draft.png); +} + +.diff { + width: 99%; +} + +.diff-title { + background-color: #c0c0c0; +} + +.diff-added { + background-color: #e0ffe0; + vertical-align: sub; +} + +.diff-removed { + background-color: #ffffe0; + vertical-align: sub; +} + +.diff-added span { + background-color: #80ff80; +} + +.diff-removed span { + background-color: #ffff80; +} + +.searchresult dd span { + font-weight: bold; +} + +#version { + margin: 5px 5px; + padding: 0px; + text-align: center; + font-size: 1em; + color: #6c7680; +} + +#credits { + margin: 5px 5px; + padding: 0px; + text-align: center; + font-size: 1em; +} + +#timings { + margin: 5px 0px; + padding: 0; + text-align: center; + font-size: 0.8em; + color: #6c7680; +} +#credits li { + display: inline; + padding: 0 10px; +} + +#timings li { + display: inline; + padding: 0 5px; +} + +#credits img { + vertical-align: middle; +} + +/* MonthCalendar css */ + +/* days without and with pages linked to them */ +a.cal-emptyday { + color: #777777; + text-align: center; +} +a.cal-usedday { + color: #000000; + font-weight: bold; + text-align: center; +} +/* general stuff: workdays, weekend, today */ +td.cal-workday { + background-color: #ddddff; + text-align: center; +} +td.cal-weekend { + background-color: #ffdddd; + text-align: center; +} +td.cal-today { + background-color: #ccffcc; + border-style: solid; + border-width: 2pt; + text-align: center; +} +/* invalid places on the monthly calendar sheet */ +td.cal-invalidday { + background-color: #cccccc; +} +/* links to prev/next month/year */ +a.cal-link { + color: #000000; + text-decoration: none; +} +th.cal-header { + background-color: #ddbbff; + text-align: center; +} + +/* for MonthCalendar mouseover info boxes */ +TABLE.tip { + color: black; + background-color: #ff8888; + font-size: small; + font-weight: normal; + border-style: solid; + border-width: 1px; +} + +TH.tip { + background-color: #ff4444; + font-weight: bold; + text-align: center; +} + +TD.tip { + text-align: left; +} +*[dir="rtl"] TD.tip { + text-align: right; +} + +/* end MonthCalendar stuff */ + +/* Spans for line-anchors - uses * html hack so that the rule only applies to + * IE (where omitting the "display: none" triggers rendering bugs). + */ +* html span.anchor { + display: none; +} + +/* IE6 has a bug with rendering of float elements. We workaround this bug by + * assigning those elements a height attribute because we currently don't know + * a better solution. This results in IE calculating the correct height of the + * characters and displaying them correctly. We don't know any negative side + * effects of this workaround: + */ +/** html div#page { height: 0.001%; } this breaks sinorca4moin on IE6.0! */ + +* html div#header, +html form#editor { + height: 0.001%; +} + +/* Content Sidebar */ + +#content .sidebar { + float: right; + width: 200px; + margin: 0 0 20px 20px; + padding: 0; + font-size: 0.85em; +} +#content[dir="rtl"] .sidebar { + float: left; + margin: 0 20px 20px 0; +} +#content .sidebar p { + text-align: left; +} +#content[dir="rtl"] .sidebar p { + text-align: right; +} +/* All headings use darker blue and some white space above */ +#content .sidebar h3, +#content .sidebar h4 { + margin: 1.5em 0 0 0; + padding: 2px 8px; + background: #b8c5d9; +} +#content .sidebar *:first-child { + margin-top: 0; +} +/* all block elements use light blue background */ +#content .sidebar ul, +#content .sidebar ol, +#content .sidebar p, +#content .sidebar table, +#content .sidebar div { + background: #f2f4f7; +} +#content .sidebar ul, +#content .sidebar ol { + margin: 0; + padding: 0; +} +/* Paragraphs and list items separated with bottom border */ +#content .sidebar p, +#content .sidebar li { + margin: 0; + padding: 4px 8px; + border-bottom: 1pt solid #e6eaf0; +} +#content .sidebar li { + display: block; +} +#content .sidebar li p { + margin: 0; + padding: 0; + border: none; +} + +/* Content Figures + +Default figure float to the end of the page. Left of right classes float to the left or right :-) +*/ +#content .figure { + float: right; + margin: 0 0 0 20px; + padding: 0; + font-size: 0.85em; +} +#content[dir="rtl"] .figure { + float: left; + margin: 0 20px 0 0; +} +#content .figure.left { + float: left; + margin: 0 20px 0 0; +} +#content .figure.right { + float: right; + margin: 0 0 0 20px; +} +#content .figure p { + margin: 0; + text-align: center; + font-weight: bold; +} diff --git a/publisher/metadata/2010/scipy_proc.json b/publisher/metadata/2010/scipy_proc.json index c6b135da6a..72816c0aa8 100644 --- a/publisher/metadata/2010/scipy_proc.json +++ b/publisher/metadata/2010/scipy_proc.json @@ -83,10 +83,7 @@ } }, "proceedings": { - "editor": [ - "Stéfan van der Walt", - "Jarrod Millman" - ], + "editor": ["Stéfan van der Walt", "Jarrod Millman"], "title": { "full": "Proceedings of the 9th Python in Science Conference", "short": "PROC. OF THE 9th PYTHON IN SCIENCE CONF. (SCIPY 2010)", diff --git a/publisher/metadata/2010/toc.json b/publisher/metadata/2010/toc.json index ec30130ac2..175b2de6f5 100644 --- a/publisher/metadata/2010/toc.json +++ b/publisher/metadata/2010/toc.json @@ -3,21 +3,15 @@ { "video": "", "title": "Keeping the Chandra Satellite Cool with Python", - "author_email": [ - "aldcroft@head.cfa.harvard.edu" - ], + "author_email": ["aldcroft@head.cfa.harvard.edu"], "doi": "10.25080/Majora-92bf1922-000", "pages": 5, "bibliography": "", "author_institution_map": { - "Tom Aldcroft": [ - "Smithsonian Astrophysical Observatory" - ] + "Tom Aldcroft": ["Smithsonian Astrophysical Observatory"] }, "copyright_holder": "Tom Aldcroft.", - "author_institution": [ - "Smithsonian Astrophysical Observatory" - ], + "author_institution": ["Smithsonian Astrophysical Observatory"], "authors": "Tom Aldcroft", "paper_id": "aldcroft", "abstract": [ @@ -28,28 +22,20 @@ "start": 1, "stop": 5 }, - "author": [ - "Tom Aldcroft" - ] + "author": ["Tom Aldcroft"] }, { "video": "", "title": "Astrodata", - "author_email": [ - "callen@gemini.edu" - ], + "author_email": ["callen@gemini.edu"], "doi": "10.25080/Majora-92bf1922-001", "pages": 6, "bibliography": "", "author_institution_map": { - "Craig Allen": [ - "Gemini Observatory" - ] + "Craig Allen": ["Gemini Observatory"] }, "copyright_holder": "Craig Allen.", - "author_institution": [ - "Gemini Observatory" - ], + "author_institution": ["Gemini Observatory"], "authors": "Craig Allen", "paper_id": "allen", "abstract": [ @@ -60,38 +46,22 @@ "start": 6, "stop": 11 }, - "author": [ - "Craig Allen" - ] + "author": ["Craig Allen"] }, { "video": "", "title": "Divisi: Learning from Semantic Networks and Sparse SVD", - "author_email": [ - "rspeer@mit.edu", - "kcarnold@mit.edu", - "havasi@mit.edu" - ], + "author_email": ["rspeer@mit.edu", "kcarnold@mit.edu", "havasi@mit.edu"], "doi": "10.25080/Majora-92bf1922-002", "pages": 6, "bibliography": "", "author_institution_map": { - "Rob Speer": [ - "MIT Media Lab" - ], - "Catherine Havasi": [ - "MIT" - ], - "Kenneth Arnold": [ - "MIT" - ] + "Rob Speer": ["MIT Media Lab"], + "Catherine Havasi": ["MIT"], + "Kenneth Arnold": ["MIT"] }, "copyright_holder": "Rob Speer et al.", - "author_institution": [ - "MIT Media Lab", - "MIT", - "MIT" - ], + "author_institution": ["MIT Media Lab", "MIT", "MIT"], "authors": "Rob Speer, Kenneth Arnold, Catherine Havasi", "paper_id": "arnold", "abstract": [ @@ -104,11 +74,7 @@ "start": 12, "stop": 17 }, - "author": [ - "Rob Speer", - "Kenneth Arnold", - "Catherine Havasi" - ] + "author": ["Rob Speer", "Kenneth Arnold", "Catherine Havasi"] }, { "video": "", @@ -128,33 +94,15 @@ "pages": 7, "bibliography": "", "author_institution_map": { - "Frédéric Bastien": [ - "Université de Montréal" - ], - "Pascal Lamblin": [ - "Université de Montréal" - ], - "Guillaume Desjardins": [ - "Université de Montréal" - ], - "David Warde-Farley": [ - "Université de Montréal" - ], - "Yoshua Bengio": [ - "Université de Montréal" - ], - "Razvan Pascanu": [ - "Université de Montréal" - ], - "Joseph Turian": [ - "Université de Montréal" - ], - "Olivier Breuleux": [ - "Université de Montréal" - ], - "James Bergstra": [ - "Université de Montréal" - ] + "Frédéric Bastien": ["Université de Montréal"], + "Pascal Lamblin": ["Université de Montréal"], + "Guillaume Desjardins": ["Université de Montréal"], + "David Warde-Farley": ["Université de Montréal"], + "Yoshua Bengio": ["Université de Montréal"], + "Razvan Pascanu": ["Université de Montréal"], + "Joseph Turian": ["Université de Montréal"], + "Olivier Breuleux": ["Université de Montréal"], + "James Bergstra": ["Université de Montréal"] }, "copyright_holder": "James Bergstra et al.", "author_institution": [ @@ -207,18 +155,12 @@ "Konrad Woellhaf": [ "University of Applied Sciences Ravensburg-Weingarten" ], - "Steven C. Colbert": [ - "University of South Florida" - ], - "Redwan Alqasemi": [ - "University of South Florida" - ], + "Steven C. Colbert": ["University of South Florida"], + "Redwan Alqasemi": ["University of South Florida"], "Gregor Franz": [ "University of Applied Sciences Ravensburg-Weingarten" ], - "Rajiv Dubey": [ - "University of South Florida" - ] + "Rajiv Dubey": ["University of South Florida"] }, "copyright_holder": "Steven C. Colbert et al.", "author_institution": [ @@ -252,25 +194,15 @@ { "video": "", "title": "Unusual Relationships: Python and Weaver Birds", - "author_email": [ - "kadambari.devarajan@gmail.com" - ], + "author_email": ["kadambari.devarajan@gmail.com"], "doi": "10.25080/Majora-92bf1922-005", "pages": 5, "bibliography": "", "author_institution_map": { - "Rajmonda Sulo": [ - "University of Illinois at Chicago" - ], - "Maria A. Echeverry-Galvis": [ - "Princeton University" - ], - "Kadambari Devarajan": [ - "University of Illinois at Chicago" - ], - "Jennifer K. Peterson": [ - "Princeton University" - ] + "Rajmonda Sulo": ["University of Illinois at Chicago"], + "Maria A. Echeverry-Galvis": ["Princeton University"], + "Kadambari Devarajan": ["University of Illinois at Chicago"], + "Jennifer K. Peterson": ["Princeton University"] }, "copyright_holder": "Kadambari Devarajan et al.", "author_institution": [ @@ -300,21 +232,15 @@ { "video": "", "title": "Weather Forecast Accuracy Analysis", - "author_email": [ - "eric@intellovations.com" - ], + "author_email": ["eric@intellovations.com"], "doi": "10.25080/Majora-92bf1922-006", "pages": 4, "bibliography": "", "author_institution_map": { - "Eric Floehr": [ - "Intellovations, LLC" - ] + "Eric Floehr": ["Intellovations, LLC"] }, "copyright_holder": "Eric Floehr.", - "author_institution": [ - "Intellovations, LLC" - ], + "author_institution": ["Intellovations, LLC"], "authors": "Eric Floehr", "paper_id": "floehr", "abstract": [ @@ -325,9 +251,7 @@ "start": 36, "stop": 39 }, - "author": [ - "Eric Floehr" - ] + "author": ["Eric Floehr"] }, { "video": "", @@ -345,27 +269,13 @@ "pages": 5, "bibliography": "", "author_institution_map": { - "Todd Miller": [ - "Space Telescope Science Institute" - ], - "Mark Sienkiewicz": [ - "Space Telescope Science Institute" - ], - "Ivo Busko": [ - "Space Telescope Science Institute" - ], - "Vicki Laidler": [ - "Space Telescope Science Institute" - ], - "Rosa Diaz": [ - "Space Telescope Science Institute" - ], - "Perry Greenfield": [ - "Space Telescope Science Institute" - ], - "Megan Sosey": [ - "Space Telescope Science Institute" - ] + "Todd Miller": ["Space Telescope Science Institute"], + "Mark Sienkiewicz": ["Space Telescope Science Institute"], + "Ivo Busko": ["Space Telescope Science Institute"], + "Vicki Laidler": ["Space Telescope Science Institute"], + "Rosa Diaz": ["Space Telescope Science Institute"], + "Perry Greenfield": ["Space Telescope Science Institute"], + "Megan Sosey": ["Space Telescope Science Institute"] }, "copyright_holder": "Perry Greenfield et al.", "author_institution": [ @@ -412,18 +322,10 @@ "pages": 4, "bibliography": "", "author_institution_map": { - "Warren J. Hack": [ - "Space Telescope Science Institute" - ], - "Babak Saif": [ - "Space Telescope Science Institute" - ], - "Perry Greenfield": [ - "Space Telescope Science Institute" - ], - "Bente Eegholm": [ - "Space Telescope Science Institute" - ] + "Warren J. Hack": ["Space Telescope Science Institute"], + "Babak Saif": ["Space Telescope Science Institute"], + "Perry Greenfield": ["Space Telescope Science Institute"], + "Bente Eegholm": ["Space Telescope Science Institute"] }, "copyright_holder": "Warren J. Hack et al.", "author_institution": [ @@ -461,22 +363,12 @@ "pages": 7, "bibliography": "", "author_institution_map": { - "Sean Davis": [ - "Berea College" - ], - "Andrew Smith": [ - "Berea College" - ], - "Matthew Henderson": [ - "Berea College" - ] + "Sean Davis": ["Berea College"], + "Andrew Smith": ["Berea College"], + "Matthew Henderson": ["Berea College"] }, "copyright_holder": "Sean Davis et al.", - "author_institution": [ - "Berea College", - "Berea College", - "Berea College" - ], + "author_institution": ["Berea College", "Berea College", "Berea College"], "authors": "Sean Davis, Matthew Henderson, Andrew Smith", "paper_id": "henderson", "abstract": [ @@ -488,30 +380,20 @@ "start": 49, "stop": 55 }, - "author": [ - "Sean Davis", - "Matthew Henderson", - "Andrew Smith" - ] + "author": ["Sean Davis", "Matthew Henderson", "Andrew Smith"] }, { "video": "", "title": "Data Structures for Statistical Computing in Python", - "author_email": [ - "wesmckinn@gmail.com" - ], + "author_email": ["wesmckinn@gmail.com"], "doi": "10.25080/Majora-92bf1922-00a", "pages": 6, "bibliography": "", "author_institution_map": { - "Wes McKinney": [ - "AQR Capital Management, LLC" - ] + "Wes McKinney": ["AQR Capital Management, LLC"] }, "copyright_holder": "Wes McKinney.", - "author_institution": [ - "AQR Capital Management, LLC" - ], + "author_institution": ["AQR Capital Management, LLC"], "authors": "Wes McKinney", "paper_id": "mckinney", "abstract": [ @@ -522,28 +404,20 @@ "start": 56, "stop": 61 }, - "author": [ - "Wes McKinney" - ] + "author": ["Wes McKinney"] }, { "video": "", "title": "Protein Folding with Python on Supercomputers", - "author_email": [ - "j.meinke@fz-juelich.de" - ], + "author_email": ["j.meinke@fz-juelich.de"], "doi": "10.25080/Majora-92bf1922-00b", "pages": 5, "bibliography": "", "author_institution_map": { - "Jan H. Meinke": [ - "Jülich Supercomputing Centre" - ] + "Jan H. Meinke": ["Jülich Supercomputing Centre"] }, "copyright_holder": "Jan H. Meinke.", - "author_institution": [ - "Jülich Supercomputing Centre" - ], + "author_institution": ["Jülich Supercomputing Centre"], "authors": "Jan H. Meinke", "paper_id": "meinke", "abstract": [ @@ -556,9 +430,7 @@ "start": 62, "stop": 66 }, - "author": [ - "Jan H. Meinke" - ] + "author": ["Jan H. Meinke"] }, { "video": "", @@ -575,24 +447,12 @@ "pages": 6, "bibliography": "", "author_institution_map": { - "Steven K. Morley": [ - "Los Alamos National Laboratory" - ], - "Jonathan Niehof": [ - "Los Alamos National Laboratory" - ], - "Josef Koller": [ - "Los Alamos National Laboratory" - ], - "Daniel T. Welling": [ - "Los Alamos National Laboratory" - ], - "Michael G. Henderson": [ - "Los Alamos National Laboratory" - ], - "Brian A. Larsen": [ - "Los Alamos National Laboratory" - ] + "Steven K. Morley": ["Los Alamos National Laboratory"], + "Jonathan Niehof": ["Los Alamos National Laboratory"], + "Josef Koller": ["Los Alamos National Laboratory"], + "Daniel T. Welling": ["Los Alamos National Laboratory"], + "Michael G. Henderson": ["Los Alamos National Laboratory"], + "Brian A. Larsen": ["Los Alamos National Laboratory"] }, "copyright_holder": "Steven K. Morley et al.", "author_institution": [ @@ -628,21 +488,15 @@ { "video": "", "title": "Numerical Pyromaniacs: The Use of Python in Fire Research", - "author_email": [ - "koverholt@gmail.com" - ], + "author_email": ["koverholt@gmail.com"], "doi": "10.25080/Majora-92bf1922-00d", "pages": 4, "bibliography": "", "author_institution_map": { - "Kristopher Overholt": [ - "University of Texas at Austin" - ] + "Kristopher Overholt": ["University of Texas at Austin"] }, "copyright_holder": "Kristopher Overholt.", - "author_institution": [ - "University of Texas at Austin" - ], + "author_institution": ["University of Texas at Austin"], "authors": "Kristopher Overholt", "paper_id": "overholt", "abstract": [ @@ -653,27 +507,18 @@ "start": 73, "stop": 76 }, - "author": [ - "Kristopher Overholt" - ] + "author": ["Kristopher Overholt"] }, { "video": "", "title": "A Programmatic Interface for Particle Plasma Simulation in Python", - "author_email": [ - "minrk@berkeley.edu", - "johnv@nuc.berkeley.edu" - ], + "author_email": ["minrk@berkeley.edu", "johnv@nuc.berkeley.edu"], "doi": "10.25080/Majora-92bf1922-00e", "pages": 3, "bibliography": "", "author_institution_map": { - "Min Ragan-Kelley": [ - "University of California, Berkeley" - ], - "John Verboncoeur": [ - "University of California, Berkeley" - ] + "Min Ragan-Kelley": ["University of California, Berkeley"], + "John Verboncoeur": ["University of California, Berkeley"] }, "copyright_holder": "Min Ragan-Kelley et al.", "author_institution": [ @@ -690,34 +535,21 @@ "start": 77, "stop": 79 }, - "author": [ - "Min Ragan-Kelley", - "John Verboncoeur" - ] + "author": ["Min Ragan-Kelley", "John Verboncoeur"] }, { "video": "", "title": "PySPH: A Python Framework for Smoothed Particle Hydrodynamics", - "author_email": [ - "prabhu@aero.iitb.ac.in", - "shekhar@zeusnumerix.com" - ], + "author_email": ["prabhu@aero.iitb.ac.in", "shekhar@zeusnumerix.com"], "doi": "10.25080/Majora-92bf1922-00f", "pages": 5, "bibliography": "", "author_institution_map": { - "Chandrashekhar Kaushik": [ - "IIT Bombay" - ], - "Prabhu Ramachandran": [ - "IIT Bombay" - ] + "Chandrashekhar Kaushik": ["IIT Bombay"], + "Prabhu Ramachandran": ["IIT Bombay"] }, "copyright_holder": "Prabhu Ramachandran et al.", - "author_institution": [ - "IIT Bombay", - "IIT Bombay" - ], + "author_institution": ["IIT Bombay", "IIT Bombay"], "authors": "Prabhu Ramachandran, Chandrashekhar Kaushik", "paper_id": "ramachandran", "abstract": [ @@ -729,10 +561,7 @@ "start": 80, "stop": 84 }, - "author": [ - "Prabhu Ramachandran", - "Chandrashekhar Kaushik" - ] + "author": ["Prabhu Ramachandran", "Chandrashekhar Kaushik"] }, { "video": "", @@ -748,15 +577,9 @@ "bibliography": "", "author_institution_map": { "Helge Reikeras": [], - "Ben Herbst": [ - "Stellenbosch University" - ], - "Herman Engelbrecht": [ - "Stellenbosch University" - ], - "Johan du Preez": [ - "Stellenbosch University" - ] + "Ben Herbst": ["Stellenbosch University"], + "Herman Engelbrecht": ["Stellenbosch University"], + "Johan du Preez": ["Stellenbosch University"] }, "copyright_holder": "Helge Reikeras et al.", "author_institution": [ @@ -785,20 +608,13 @@ { "video": "", "title": "Statsmodels: Econometric and Statistical Modeling with Python", - "author_email": [ - "jsseabold@gmail.com", - "josef.pktd@gmail.com" - ], + "author_email": ["jsseabold@gmail.com", "josef.pktd@gmail.com"], "doi": "10.25080/Majora-92bf1922-011", "pages": 5, "bibliography": "", "author_institution_map": { - "Josef Perktold": [ - "CIRANO, University of North Carolina Chapel Hill" - ], - "Skipper Seabold": [ - "American University" - ] + "Josef Perktold": ["CIRANO, University of North Carolina Chapel Hill"], + "Skipper Seabold": ["American University"] }, "copyright_holder": "Skipper Seabold et al.", "author_institution": [ @@ -815,10 +631,7 @@ "start": 92, "stop": 96 }, - "author": [ - "Skipper Seabold", - "Josef Perktold" - ] + "author": ["Skipper Seabold", "Josef Perktold"] } ] } diff --git a/publisher/metadata/2011/scipy_proc.json b/publisher/metadata/2011/scipy_proc.json index e9a07acefa..37be576225 100644 --- a/publisher/metadata/2011/scipy_proc.json +++ b/publisher/metadata/2011/scipy_proc.json @@ -292,10 +292,7 @@ }, "volume": 3, "citation_key": "proc-scipy-2011", - "editor": [ - "Stéfan van der Walt", - "Jarrod Millman" - ], + "editor": ["Stéfan van der Walt", "Jarrod Millman"], "isbn": "noisbn", "number": 1, "doi": "10.25080/Majora-ebaa42b7-014", diff --git a/publisher/metadata/2011/toc.json b/publisher/metadata/2011/toc.json index 9aed083048..a4767f2f09 100644 --- a/publisher/metadata/2011/toc.json +++ b/publisher/metadata/2011/toc.json @@ -2,16 +2,10 @@ "toc": [ { "author_institution_map": { - "Minesh B. Amin": [ - "MBA Sciences, Inc" - ] + "Minesh B. Amin": ["MBA Sciences, Inc"] }, - "author": [ - "Minesh B. Amin" - ], - "author_email": [ - "mamin@mbasciences.com" - ], + "author": ["Minesh B. Amin"], + "author_email": ["mamin@mbasciences.com"], "abstract": [ "SPM.Python is a scalable, parallel fault-tolerant version of the\nserial Python language, and can be deployed to create parallel\ncapabilities to solve problems in domains spanning finance, life\nsciences, electronic design, IT, visualization, and\nresearch. Software developers may use SPM.Python to augment new or\nexisting (Python) serial scripts for scalability across parallel\nhardware. Alternatively, SPM.Python may be used to better manage\nthe execution of stand-alone (non-Python x86 and GPU) applications\nacross compute resources in a fault-tolerant manner taking into\naccount hard deadlines." ], @@ -23,9 +17,7 @@ "video": "", "doi": "10.25080/Majora-ebaa42b7-000", "pages": 9, - "author_institution": [ - "MBA Sciences, Inc" - ], + "author_institution": ["MBA Sciences, Inc"], "copyright_holder": "Minesh B. Amin.", "page": { "start": 1, @@ -34,18 +26,10 @@ }, { "author_institution_map": { - "Dan Nguyen": [ - "Smithsonian Astrophysical Observatory" - ], - "Brian Refsdal": [ - "Smithsonian Astrophysical Observatory" - ], - "Aneta Siemiginowska": [ - "Smithsonian Astrophysical Observatory" - ], - "Stephen Doe": [ - "Smithsonian Astrophysical Observatory" - ] + "Dan Nguyen": ["Smithsonian Astrophysical Observatory"], + "Brian Refsdal": ["Smithsonian Astrophysical Observatory"], + "Aneta Siemiginowska": ["Smithsonian Astrophysical Observatory"], + "Stephen Doe": ["Smithsonian Astrophysical Observatory"] }, "author": [ "Brian Refsdal", @@ -84,21 +68,11 @@ }, { "author_institution_map": { - "Ricardo Caspirro": [ - "Muricoca Labs" - ], - "Bruno Melo": [ - "Muricoca Labs" - ], - "Marcel Caraciolo": [ - "Muricoca Labs" - ] + "Ricardo Caspirro": ["Muricoca Labs"], + "Bruno Melo": ["Muricoca Labs"], + "Marcel Caraciolo": ["Muricoca Labs"] }, - "author": [ - "Marcel Caraciolo", - "Bruno Melo", - "Ricardo Caspirro" - ], + "author": ["Marcel Caraciolo", "Bruno Melo", "Ricardo Caspirro"], "author_email": [ "marcel@muricoca.com", "bruno@muricoca.com", @@ -115,11 +89,7 @@ "video": "", "doi": "10.25080/Majora-ebaa42b7-002", "pages": 7, - "author_institution": [ - "Muricoca Labs", - "Muricoca Labs", - "Muricoca Labs" - ], + "author_institution": ["Muricoca Labs", "Muricoca Labs", "Muricoca Labs"], "copyright_holder": "Marcel Caraciolo et al.", "page": { "start": 17, @@ -128,21 +98,11 @@ }, { "author_institution_map": { - "Andrew Cron": [ - "Duke University" - ], - "Wes McKinney": [ - "Duke University" - ] + "Andrew Cron": ["Duke University"], + "Wes McKinney": ["Duke University"] }, - "author": [ - "Andrew Cron", - "Wes McKinney" - ], - "author_email": [ - "ajc40@stat.duke.edu", - "wesmckinn@gmail.com" - ], + "author": ["Andrew Cron", "Wes McKinney"], + "author_email": ["ajc40@stat.duke.edu", "wesmckinn@gmail.com"], "abstract": [ "In this work we discuss gpustats, a new Python library for\nassisting in \\textquotedbl{}big data\\textquotedbl{} statistical computing applications,\nparticularly Monte Carlo-based inference algorithms. The library\nprovides a general code generation / metaprogramming framework for\neasily implementing discrete and continuous probability density\nfunctions and random variable samplers. These functions can be\nutilized to achieve more than 100x speedup over their CPU\nequivalents. We demonstrate their use in an Bayesian MCMC\napplication and discuss avenues for future work." ], @@ -154,10 +114,7 @@ "video": "", "doi": "10.25080/Majora-ebaa42b7-003", "pages": 5, - "author_institution": [ - "Duke University", - "Duke University" - ], + "author_institution": ["Duke University", "Duke University"], "copyright_holder": "Andrew Cron et al.", "page": { "start": 24, @@ -166,21 +123,11 @@ }, { "author_institution_map": { - "Jeff Daily": [ - "Pacific Northwest National Laboratory" - ], - "Robert R. Lewis": [ - "Washington State University" - ] + "Jeff Daily": ["Pacific Northwest National Laboratory"], + "Robert R. Lewis": ["Washington State University"] }, - "author": [ - "Jeff Daily", - "Robert R. Lewis" - ], - "author_email": [ - "jeff.daily@pnnl.gov", - "bobl@tricity.wsu.edu" - ], + "author": ["Jeff Daily", "Robert R. Lewis"], + "author_email": ["jeff.daily@pnnl.gov", "bobl@tricity.wsu.edu"], "abstract": [ "Global Arrays (GA) is a software system from Pacific Northwest National\nLaboratory that enables an efficient, portable, and parallel shared-memory\nprogramming interface to manipulate distributed dense arrays. Using a\ncombination of GA and NumPy, we have reimplemented NumPy as a\ndistributed drop-in replacement called Global Arrays in NumPy (GAiN).\nScalability studies will be presented showing the utility of developing\nserial NumPy codes which can later run on more capable clusters or\nsupercomputers." ], @@ -204,16 +151,10 @@ }, { "author_institution_map": { - "Scott Determan": [ - "Vision Spreadsheet" - ] + "Scott Determan": ["Vision Spreadsheet"] }, - "author": [ - "Scott Determan" - ], - "author_email": [ - "scott.determan@gmail.com" - ], + "author": ["Scott Determan"], + "author_email": ["scott.determan@gmail.com"], "abstract": [ "Vision Spreadsheet is an environment for computer vision. It combines a\nspreadsheet with computer vision and scientific python. The cells in the\nspreadsheet are images, computations on images, measurements, and plots. There\nare many built in image processing and machine learning algorithms and it\nextensible by writing python functions and importing them into the\nspreadsheet." ], @@ -225,9 +166,7 @@ "video": "", "doi": "10.25080/Majora-ebaa42b7-005", "pages": 4, - "author_institution": [ - "Vision Spreadsheet" - ], + "author_institution": ["Vision Spreadsheet"], "copyright_holder": "Scott Determan.", "page": { "start": 36, @@ -236,16 +175,10 @@ }, { "author_institution_map": { - "Mark Dewing": [ - "Intel" - ] + "Mark Dewing": ["Intel"] }, - "author": [ - "Mark Dewing" - ], - "author_email": [ - "markdewing@gmail.com" - ], + "author": ["Mark Dewing"], + "author_email": ["markdewing@gmail.com"], "abstract": [ "We describe a method for constructing scientific programs where SymPy is\nused to model the mathematical steps in the derivation. With this workflow,\neach step in the process can be checked by machine, from the derivation of\nthe equations to the generation of the source code. We present an example\nbased on computing the partition function integrals in statistical mechanics." ], @@ -257,9 +190,7 @@ "video": "", "doi": "10.25080/Majora-ebaa42b7-006", "pages": 4, - "author_institution": [ - "Intel" - ], + "author_institution": ["Intel"], "copyright_holder": "Mark Dewing.", "page": { "start": 40, @@ -268,17 +199,10 @@ }, { "author_institution_map": { - "Andrew Wilson": [ - "Texas Water Development Board" - ], - "Dharhas Pothina": [ - "Texas Water Development Board" - ] + "Andrew Wilson": ["Texas Water Development Board"], + "Dharhas Pothina": ["Texas Water Development Board"] }, - "author": [ - "Dharhas Pothina", - "Andrew Wilson" - ], + "author": ["Dharhas Pothina", "Andrew Wilson"], "author_email": [ "dharhas.pothina@twdb.state.tx.us", "andrew.wilson@twdb.state.tx.us" @@ -306,16 +230,10 @@ }, { "author_institution_map": { - "Jonathan Jacky": [ - "University of Washington" - ] + "Jonathan Jacky": ["University of Washington"] }, - "author": [ - "Jonathan Jacky" - ], - "author_email": [ - "jon@uw.edu" - ], + "author": ["Jonathan Jacky"], + "author_email": ["jon@uw.edu"], "abstract": [ "In unit testing, the programmer codes the test cases, and also codes\nassertions that check whether each test case passed. In model-based\ntesting, the programmer codes a \\textquotedbl{}model\\textquotedbl{} that generates as many test\ncases as desired and also acts as the oracle that checks the cases.\nModel-based testing is recommended where so many test cases are needed\nthat it is not feasible to code them all by hand. This need arises\nwhen testing behaviors that exhibit history-dependence and\nnondeterminism, so that many variations (data values, interleavings,\netc.) should be tested for each scenario (or use case). Examples\ninclude communication protocols, web applications, control systems,\nand user interfaces. PyModel is a model-based testing framework in\nPython. PyModel supports on-the-fly testing, which can generate\nindefinitely long nonrepeating tests as the test run executes.\nPyModel can focus test cases on scenarios of interest by composition,\na versatile technique that combines models by synchronizing shared\nactions and interleaving unshared actions. PyModel can guide test\ncoverage according to programmable strategies coded by the programmer." ], @@ -327,9 +245,7 @@ "video": "", "doi": "10.25080/Majora-ebaa42b7-008", "pages": 5, - "author_institution": [ - "University of Washington" - ], + "author_institution": ["University of Washington"], "copyright_holder": "Jonathan Jacky.", "page": { "start": 48, @@ -338,21 +254,11 @@ }, { "author_institution_map": { - "Joseph Koning": [ - "Lawrence Livermore National Laboratory" - ], - "Matthew Terry": [ - "Lawrence Livermore National Laboratory" - ] + "Joseph Koning": ["Lawrence Livermore National Laboratory"], + "Matthew Terry": ["Lawrence Livermore National Laboratory"] }, - "author": [ - "Matthew Terry", - "Joseph Koning" - ], - "author_email": [ - "terry10@llnl.gov", - "koning1@llnl.gov" - ], + "author": ["Matthew Terry", "Joseph Koning"], + "author_email": ["terry10@llnl.gov", "koning1@llnl.gov"], "abstract": [ "The process of tuning an inertial confinement fusion pulse shape to a\nspecific target design is highly iterative process. When done manually,\neach iteration has large latency and is consequently time consuming. We\nhave developed several techniques that can be used to automate much of the\npulse tuning process and significantly accelerate the tuning process by\nremoving the human induced latency. The automated data analysis techniques\nrequire specialized diagnostics to run within the simulation. To\nfacilitate these techniques, we have embedded a loosely coupled Python\ninterpreter within a pre-existing radiation-hydrodynamics code, Hydra. To\nautomate the tuning process we use numerical optimization techniques and\nconstruct objective functions to identify tuned parameters." ], @@ -376,21 +282,11 @@ }, { "author_institution_map": { - "Mark DeMaria": [ - "NOAA/NESDIS/STAR" - ], - "Minwoo Lee": [ - "Colorado State University" - ], - "Charles W. Anderson": [ - "Colorado State University" - ] + "Mark DeMaria": ["NOAA/NESDIS/STAR"], + "Minwoo Lee": ["Colorado State University"], + "Charles W. Anderson": ["Colorado State University"] }, - "author": [ - "Minwoo Lee", - "Charles W. Anderson", - "Mark DeMaria" - ], + "author": ["Minwoo Lee", "Charles W. Anderson", "Mark DeMaria"], "author_email": [ "lemin@cs.colostate.edu", "anderson@cs.colostate.edu", @@ -420,21 +316,11 @@ }, { "author_institution_map": { - "Alex D. Young": [ - "University of Edinburgh" - ], - "Martin J. Ling": [ - "University of Edinburgh" - ] + "Alex D. Young": ["University of Edinburgh"], + "Martin J. Ling": ["University of Edinburgh"] }, - "author": [ - "Martin J. Ling", - "Alex D. Young" - ], - "author_email": [ - "m.j.ling@ed.ac.uk", - "ayoung9@inf.ed.ac.uk" - ], + "author": ["Martin J. Ling", "Alex D. Young"], + "author_email": ["m.j.ling@ed.ac.uk", "ayoung9@inf.ed.ac.uk"], "abstract": [ "IMUSim is a new simulation package developed in Python to model Inertial\nMeasurement Units, i.e. devices which include accelerometers, gyroscopes\nand magnetometers. It was developed in the course of our research into\nalgorithms for IMU-based motion capture, and has now been released under\nthe GPL for the benefit of other researchers and users. The software\ngenerates realistic sensor readings based on trajectory, environment,\nsensor and system models. It includes implementaions of various relevant\nprocessing algorithms and mathematical utilities, some of which may be\nuseful elsewhere. The simulator makes extensive use of NumPy, SciPy, SimPy,\nCython, Matplotlib and Mayavi. The rapid development enabled by these tools\nallowed the project to be completed as a side project by two researchers.\nCareful design of an object-oriented API for the various models involved\nin the simulation allows the software to remain flexible and extensible\nwhile requiring users to write a minimum amount of code to use it." ], @@ -458,18 +344,10 @@ }, { "author_institution_map": { - "Amal Alghamdi": [ - "King Abdullah University of Science and Technology" - ], - "William Scullin": [ - "Argonne National Labs" - ], - "Aron Ahmadia": [ - "King Abdullah University of Science and Technology" - ], - "Kyle T. Mandli": [ - "University of Washington" - ], + "Amal Alghamdi": ["King Abdullah University of Science and Technology"], + "William Scullin": ["Argonne National Labs"], + "Aron Ahmadia": ["King Abdullah University of Science and Technology"], + "Kyle T. Mandli": ["University of Washington"], "David I. Ketcheson": [ "King Abdullah University of Science and Technology" ] @@ -514,21 +392,11 @@ }, { "author_institution_map": { - "Leif Strand": [ - "California Institute of Technology" - ], - "Tim Sullivan": [ - "California Institute of Technology" - ], - "Michael A.G. Aivazis": [ - "California Institute of Technology" - ], - "Alta Fang": [ - "California Institute of Technology" - ], - "Michael M. McKerns": [ - "California Institute of Technology" - ] + "Leif Strand": ["California Institute of Technology"], + "Tim Sullivan": ["California Institute of Technology"], + "Michael A.G. Aivazis": ["California Institute of Technology"], + "Alta Fang": ["California Institute of Technology"], + "Michael M. McKerns": ["California Institute of Technology"] }, "author": [ "Michael M. McKerns", @@ -570,16 +438,10 @@ }, { "author_institution_map": { - "Nick Bray": [ - "Google" - ] + "Nick Bray": ["Google"] }, - "author": [ - "Nick Bray" - ], - "author_email": [ - "ncbray@google.com" - ], + "author": ["Nick Bray"], + "author_email": ["ncbray@google.com"], "abstract": [ "PyStream is a static compiler that can radically transform Python code and run it on a Graphics Processing Unit (GPU). Python compiled to run on the GPU is \\textasciitilde{}100,000x faster than when interpreted on the CPU. The PyStream compiler is specially designed to simplify the development of real-time rendering systems by allowing the entire rendering system to be written in a single, highly productive language. Without PyStream, GPU-accelerated real-time rendering systems must contain two separate code bases written in two separate languages: one for the CPU and one for the GPU. Functions and data structures are not shared between the code bases, and any common functionality must be redundantly written in both languages. PyStream unifies a rendering system into a single, Python code base, allowing functions and data structures to be transparently shared between the CPU and the GPU. A single, unified code base makes it easy to create, maintain, and evolve a high-performance GPU-accelerated application." ], @@ -591,9 +453,7 @@ "video": "", "doi": "10.25080/Majora-ebaa42b7-00e", "pages": 4, - "author_institution": [ - "Google" - ], + "author_institution": ["Google"], "copyright_holder": "Nick Bray.", "page": { "start": 87, @@ -602,21 +462,11 @@ }, { "author_institution_map": { - "Derrick Coetzee": [ - "University of California, Berkeley" - ], - "Armando Fox": [ - "University of California, Berkeley" - ], - "Shoaib Kamil": [ - "University of California, Berkeley" - ] + "Derrick Coetzee": ["University of California, Berkeley"], + "Armando Fox": ["University of California, Berkeley"], + "Shoaib Kamil": ["University of California, Berkeley"] }, - "author": [ - "Shoaib Kamil", - "Derrick Coetzee", - "Armando Fox" - ], + "author": ["Shoaib Kamil", "Derrick Coetzee", "Armando Fox"], "author_email": [ "skamil@cs.berkeley.edu", "dcoetzee@cs.berkeley.edu", @@ -647,21 +497,11 @@ }, { "author_institution_map": { - "Steven E. Gorrell": [ - "Brigham Young University" - ], - "Stephen M. McQuay": [ - "Brigham Young University" - ] + "Steven E. Gorrell": ["Brigham Young University"], + "Stephen M. McQuay": ["Brigham Young University"] }, - "author": [ - "Stephen M. McQuay", - "Steven E. Gorrell" - ], - "author_email": [ - "stephen@mcquay.me", - "sgorrell@byu.edu" - ], + "author": ["Stephen M. McQuay", "Steven E. Gorrell"], + "author_email": ["stephen@mcquay.me", "sgorrell@byu.edu"], "abstract": [ "The research contained herein yielded an open source interpolation library\nimplemented in and designed for use with the Python programming language.\nThis library, named smbinterp, yields an interpolation to an arbitrary\ndegree of accuracy. The smbinterp module was designed to be mesh agnostic.\nA plugin system was implemented that allows end users to conveniently and\nconsistently present their numerical results to the library for rapid\nprototyping and integration. The library includes modules that allow for\nits use in high-performance parallel computing environments. These modules\nwere implemented using built-in Python modules to simplify deployment. This\nimplementation was found to scale linearly to approximately 180\nparticipating compute processes." ], @@ -689,12 +529,8 @@ "University of Memphis - Institute For Intelligent Systems" ] }, - "author": [ - "Douglas A. Starnes" - ], - "author_email": [ - "douglas@poweredbyalt.net" - ], + "author": ["Douglas A. Starnes"], + "author_email": ["douglas@poweredbyalt.net"], "abstract": [ "In recent years, one of the fastest growing trends in information technology has been the move towards cloud computing. The scalable concept of computing resources on demand allows applications to dynamically react to increased usage instead of having to keep resources in reserve that are often not in use but are still paid for. There are several popular entrants into this market including Google App Engine. Modeled after Google's own architecture for building applications, Google App Engine (GAE) provides a scalable solution for web-based applications and services including data storage, communications, application deployment and monitoring, and management tools. With GAE, developers have the option of writing applications using an API exposed to Python. The same benefits of using Python in other applications are available in the cloud." ], @@ -717,21 +553,11 @@ }, { "author_institution_map": { - "Josef Perktold": [ - "University of North Carolina, Chapel Hill" - ], - "Skipper Seabold": [ - "American University" - ], - "Wes McKinney": [ - "Duke University" - ] + "Josef Perktold": ["University of North Carolina, Chapel Hill"], + "Skipper Seabold": ["American University"], + "Wes McKinney": ["Duke University"] }, - "author": [ - "Wes McKinney", - "Josef Perktold", - "Skipper Seabold" - ], + "author": ["Wes McKinney", "Josef Perktold", "Skipper Seabold"], "author_email": [ "wesmckinn@gmail.com", "josef.pktd@gmail.com", @@ -761,21 +587,11 @@ }, { "author_institution_map": { - "Solomon Negusse": [ - "Texas Water Development Board" - ], - "Tyler McEwen": [ - "Texas Water Development Board" - ], - "Dharhas Pothina": [ - "Texas Water Development Board" - ] + "Solomon Negusse": ["Texas Water Development Board"], + "Tyler McEwen": ["Texas Water Development Board"], + "Dharhas Pothina": ["Texas Water Development Board"] }, - "author": [ - "Tyler McEwen", - "Dharhas Pothina", - "Solomon Negusse" - ], + "author": ["Tyler McEwen", "Dharhas Pothina", "Solomon Negusse"], "author_email": [ "tyler.mcewen@twdb.state.tx.us", "dharhas.pothina@twdb.state.tx.us", diff --git a/publisher/metadata/2012/scipy_proc.json b/publisher/metadata/2012/scipy_proc.json index e97bbe34a9..bfb3fb2a04 100644 --- a/publisher/metadata/2012/scipy_proc.json +++ b/publisher/metadata/2012/scipy_proc.json @@ -162,11 +162,7 @@ "citation_key": "proc-scipy-2012", "location": "Austin, Texas", "isbn": "value", - "editor": [ - "Aron Ahmadia", - "Jarrod Millman", - "Stéfan van der Walt" - ] + "editor": ["Aron Ahmadia", "Jarrod Millman", "Stéfan van der Walt"] }, "diversity_scholarship": [ { diff --git a/publisher/metadata/2012/toc.json b/publisher/metadata/2012/toc.json index 668d6da0e1..edd26877b0 100644 --- a/publisher/metadata/2012/toc.json +++ b/publisher/metadata/2012/toc.json @@ -2,18 +2,10 @@ "toc": [ { "author_institution_map": { - "Shoaib Kamil": [ - "University of California, Berkeley" - ], - "David Howard": [ - "University of California, Berkeley" - ], - "Aakash Prasad": [ - "University of California, Berkeley" - ], - "Armando Fox": [ - "University of California, Berkeley" - ] + "Shoaib Kamil": ["University of California, Berkeley"], + "David Howard": ["University of California, Berkeley"], + "Aakash Prasad": ["University of California, Berkeley"], + "Armando Fox": ["University of California, Berkeley"] }, "pages": 5, "page": { @@ -50,32 +42,24 @@ }, { "author_institution_map": { - "Adam Hughes": [ - "The George Washington University" - ] + "Adam Hughes": ["The George Washington University"] }, "pages": 5, "page": { "stop": 10, "start": 6 }, - "author_institution": [ - "The George Washington University" - ], + "author_institution": ["The George Washington University"], "authors": "Adam Hughes", "title": "A Computational Framework for Plasmonic Nanobiosensing", "doi": "10.25080/Majora-54c7f2c8-001", "bibliography": "", - "author_email": [ - "hugadams@gwmail.gwu.edu" - ], + "author_email": ["hugadams@gwmail.gwu.edu"], "abstract": [ "Basic principles in biosensing and nanomaterials precede the introduction of a novel fiber optic sensor. Software limitations in the biosensing domain are presented, followed by the development of a Python-based simulation environment. Finally, the current state of spectral data analysis within the Python ecosystem is discussed." ], "keywords": "gold nanoparticles, fiber optics, biosensor, Python, immunoassay, plasmonics, proteins, metallic colloids, IPython, Traits, Chaco, Pandas, SEM,", - "author": [ - "Adam Hughes" - ], + "author": ["Adam Hughes"], "paper_id": "adam_hughes", "copyright_holder": "Adam Hughes.", "video": "" @@ -85,9 +69,7 @@ "Alejandro Weinstein": [ "the EECS department of the Colorado School of Mines" ], - "Michael Wakin": [ - "the EECS department of the Colorado School of Mines" - ] + "Michael Wakin": ["the EECS department of the Colorado School of Mines"] }, "pages": 5, "page": { @@ -102,18 +84,12 @@ "title": "A Tale of Four Libraries", "doi": "10.25080/Majora-54c7f2c8-002", "bibliography": "", - "author_email": [ - "aweinste@mines.edu", - "mwakin@mines.edu" - ], + "author_email": ["aweinste@mines.edu", "mwakin@mines.edu"], "abstract": [ "This work describes the use some scientific Python tools to solve information\ngathering problems using Reinforcement Learning. In particular, we focus on the\nproblem of designing an agent able to learn how to gather information in linked\ndatasets. We use four different libraries—RL-Glue, Gensim, NetworkX, and\nscikit-learn—during different stages of our research. We show that, by\nusing NumPy arrays as the default vector/matrix format, it is possible to\nintegrate these libraries with minimal effort." ], "keywords": "reinforcement learning, latent semantic analysis, machine learning", - "author": [ - "Alejandro Weinstein", - "Michael Wakin" - ], + "author": ["Alejandro Weinstein", "Michael Wakin"], "paper_id": "alejandro_weinstein", "copyright_holder": "Alejandro Weinstein et al.", "video": "" @@ -136,28 +112,20 @@ "title": "Total Recall: flmake and the Quest for Reproducibility", "doi": "10.25080/Majora-54c7f2c8-003", "bibliography": "", - "author_email": [ - "scopatz@flash.uchicago.edu" - ], + "author_email": ["scopatz@flash.uchicago.edu"], "abstract": [ "FLASH is a high-performance computing (HPC) multi-physics code which is used to perform\nastrophysical and high-energy density physics simulations.\nTo run a FLASH simulation, the user must go through three basic steps: setup, build, and\nexecution. Canonically, each of these tasks are independently handled by the user.\nHowever, with the recent advent of flmake - a Python workflow management utility for\nFLASH - such tasks may now be performed in a fully reproducible way.\nTo achieve such reproducibility a number of developments and abstractions were needed,\nsome only enabled by Python. These methods are widely applicable outside of FLASH.\nThe process of writing flmake opens many questions to what precisely is meant\nby reproducibility in computational science. While posed here, many of these\nquestions remain unanswered." ], "keywords": "FLASH, reproducibility", - "author": [ - "Anthony Scopatz" - ], + "author": ["Anthony Scopatz"], "paper_id": "anthony_scopatz_flmake", "copyright_holder": "Anthony Scopatz.", "video": "" }, { "author_institution_map": { - "Cyrus Harrison": [ - "Lawrence Livermore National Laboratory" - ], - "Harinarayan Krishnan": [ - "Lawrence Berkeley National Laboratory" - ] + "Cyrus Harrison": ["Lawrence Livermore National Laboratory"], + "Harinarayan Krishnan": ["Lawrence Berkeley National Laboratory"] }, "pages": 7, "page": { @@ -172,131 +140,95 @@ "title": "Python's Role in VisIt", "doi": "10.25080/Majora-54c7f2c8-004", "bibliography": "", - "author_email": [ - "cyrush@llnl.gov", - "hkrishnan@lbl.gov" - ], + "author_email": ["cyrush@llnl.gov", "hkrishnan@lbl.gov"], "abstract": [ "VisIt is an open source, turnkey application for scientific data analysis and visualization that runs on a wide variety of platforms from desktops to petascale class supercomputers. VisIt's core software infrastructure is written in C++, however Python plays a vital role in enabling custom workflows. Recent work has extended Python's use in VisIt beyond scripting, enabling custom Python UIs and Python filters for low-level data manipulation. The ultimate goal of this work is to evolve Python into a true peer to our core C++ plugin infrastructure. This paper provides an overview of Python’s role in VisIt with a focus on use cases of scripted rendering, data analysis, and custom application development." ], "keywords": "visualization, hpc, python", - "author": [ - "Cyrus Harrison", - "Harinarayan Krishnan" - ], + "author": ["Cyrus Harrison", "Harinarayan Krishnan"], "paper_id": "cyrus_harrison", "copyright_holder": "Cyrus Harrison et al.", "video": "" }, { "author_institution_map": { - "Geoffrey M. Poore": [ - "Union University" - ] + "Geoffrey M. Poore": ["Union University"] }, "pages": 7, "page": { "stop": 36, "start": 30 }, - "author_institution": [ - "Union University" - ], + "author_institution": ["Union University"], "authors": "Geoffrey M. Poore", "title": "PythonTeX: Fast Access to Python from within LaTeX", "doi": "10.25080/Majora-54c7f2c8-005", "bibliography": "", - "author_email": [ - "gpoore@uu.edu" - ], + "author_email": ["gpoore@uu.edu"], "abstract": [ "PythonTeX is a new LaTeX package that provides access\nto the full power of Python from within LaTeX documents. It allows\nPython code entered within a LaTeX document to be executed, and provides\naccess to the output. PythonTeX also provides syntax highlighting for\nany language supported by the Pygments highlighting engine.", "PythonTeX is fast and user-friendly. Python code is separated into\nuser-defined sessions. Each session is only executed when its code\nis modified. When code is executed, sessions run in parallel. The\ncontents of stdout and stderr are synchronized with the LaTeX document,\nso that printed content is easily accessible and error messages have\nmeaningful line numbering.", "PythonTeX simplifies scientific document creation with LaTeX.\nPlots can be created with matplotlib and then customized in place.\nCalculations can be performed and automatically typeset with NumPy.\nSymPy can be used to automatically create mathematical tables and\nstep-by-step mathematical derivations." ], "keywords": "LaTeX, document preparation, document automation,\nmatplotlib, NumPy, SymPy, Pygments", - "author": [ - "Geoffrey M. Poore" - ], + "author": ["Geoffrey M. Poore"], "paper_id": "geoffrey_poore", "copyright_holder": "Geoffrey M. Poore.", "video": "" }, { "author_institution_map": { - "Iqbal Mohomed": [ - "IBM Research" - ] + "Iqbal Mohomed": ["IBM Research"] }, "pages": 4, "page": { "stop": 40, "start": 37 }, - "author_institution": [ - "IBM Research" - ], + "author_institution": ["IBM Research"], "authors": "Iqbal Mohomed", "title": "Self-driving Lego Mindstorms Robot", "doi": "10.25080/Majora-54c7f2c8-006", "bibliography": "", - "author_email": [ - "iqbal@us.ibm.com" - ], + "author_email": ["iqbal@us.ibm.com"], "abstract": [ "In this paper, I describe the workings of my personal hobby\nproject - a self-driving lego mindstorms robot. The body of the\nrobot is built with Lego Mindstorms. An Android smartphone is used\nto capture the view in front of the robot. A user first teaches the\nrobot how to drive; this is done by making the robot go around a track\na small number of times. The image data, along with the user action is used\nto train a Neural Network. At run-time, images of what is in front of the\nrobot are fed into the neural network and the appropriate driving action\nis selected. This project showcases the power of python's libraries, as they\nenabled me to put together a sophisticated working system in a very short amount\nof time. Specifically, I made use of the Python Image Library to downsample\nimages, as well as the\nPyBrain neural network library. The robot was controlled using the nxt-python library." ], "keywords": "self-driving, neural networks, robotics", - "author": [ - "Iqbal Mohomed" - ], + "author": ["Iqbal Mohomed"], "paper_id": "iqbal_mohomed", "copyright_holder": "Iqbal Mohomed.", "video": "" }, { "author_institution_map": { - "Jacob Barhak": [ - "freelancer" - ] + "Jacob Barhak": ["freelancer"] }, "pages": 5, "page": { "stop": 45, "start": 41 }, - "author_institution": [ - "freelancer" - ], + "author_institution": ["freelancer"], "authors": "Jacob Barhak", "title": "The Reference Model for Disease Progression", "doi": "10.25080/Majora-54c7f2c8-007", "bibliography": "", - "author_email": [ - "jacob.barhak@gmail.com" - ], + "author_email": ["jacob.barhak@gmail.com"], "abstract": [ "The Reference Model for disease progression is based on a modeling framework written in Python. It is a prototype that demonstrates the use of computing power to aid in chronic disease forecast. The model uses references to publicly available data as a source of information, hence the name for the model. The Reference Model also holds this name since it is designed to be used by other models as a reference. The model uses parallel processing and computing power to create a competition among hypothesis of disease progression. The system runs on a multi core machine and is scalable to a SLURM cluster." ], "keywords": "Disease Models, High Performance Computing, Simulation", - "author": [ - "Jacob Barhak" - ], + "author": ["Jacob Barhak"], "paper_id": "jacob_barhak", "copyright_holder": "Jacob Barhak.", "video": "" }, { "author_institution_map": { - "Adam Richards": [ - "Duke University" - ], - "Cliburn Chan": [ - "Duke University" - ], - "Jacob Frelinger": [ - "Duke University" - ] + "Adam Richards": ["Duke University"], + "Cliburn Chan": ["Duke University"], + "Jacob Frelinger": ["Duke University"] }, "pages": 5, "page": { @@ -321,43 +253,31 @@ "Flow cytometry has the ability to measure multiple parameters of a\nheterogeneous mix of cells at single cell resolution. This has lead flow\ncytometry to become an integral tool in immunology and biology. Most flow\ncytometry analysis is performed in expensive proprietary software packages,\nand few opensource tool exist for working with flow cytometry data.\nIn this paper we present fcm, an BSD licensed python library for\ntraditional gating based analysis in addition to newer model based analysis\nmethods." ], "keywords": "Flow Cytometry, Model-based Analysis, Automation, Biology, Immunology", - "author": [ - "Jacob Frelinger", - "Adam Richards", - "Cliburn Chan" - ], + "author": ["Jacob Frelinger", "Adam Richards", "Cliburn Chan"], "paper_id": "jacob_frelinger", "copyright_holder": "Jacob Frelinger et al.", "video": "" }, { "author_institution_map": { - "Matthew Rocklin": [ - "University of Chicago, Computer Science" - ] + "Matthew Rocklin": ["University of Chicago, Computer Science"] }, "pages": 5, "page": { "stop": 55, "start": 51 }, - "author_institution": [ - "University of Chicago, Computer Science" - ], + "author_institution": ["University of Chicago, Computer Science"], "authors": "Matthew Rocklin", "title": "Uncertainty Modeling with SymPy Stats", "doi": "10.25080/Majora-54c7f2c8-009", "bibliography": "", - "author_email": [ - "mrocklin@cs.uchicago.edu" - ], + "author_email": ["mrocklin@cs.uchicago.edu"], "abstract": [ "We add a random variable type to a mathematical modeling language. We\ndemonstrate through examples how this is a highly separable way to introduce\nuncertainty and produce and query stochastic models. We motivate the use of\nsymbolics and thin compilers in scientific computing." ], "keywords": "Symbolics, mathematical modeling, uncertainty, SymPy", - "author": [ - "Matthew Rocklin" - ], + "author": ["Matthew Rocklin"], "paper_id": "matthew_rocklin", "copyright_holder": "Matthew Rocklin.", "video": "" @@ -384,36 +304,22 @@ "title": "QuTiP: A framework for the dynamics of open quantum systems using SciPy and Cython", "doi": "10.25080/Majora-54c7f2c8-00a", "bibliography": "", - "author_email": [ - "robert@riken.jp", - "pnation@riken.jp" - ], + "author_email": ["robert@riken.jp", "pnation@riken.jp"], "abstract": [ "We present QuTiP (http://www.qutip.org), an object-oriented, open-source framework for solving the dynamics of open quantum systems. Written in Python, and using a combination of Cython, NumPy, SciPy, and matplotlib, QuTiP provides an environment for computational quantum mechanics that is both easy and efficient to use. Arbitrary quantum systems, including time-dependent systems, may be built up from operators and states defined by a quantum object class, and then passed on to a choice of unitary or dissipative evolution solvers. Here we give an overview of the basic structure for the framework, and the techniques used in its implementation. We also present a few selected examples from current research on quantum mechanics that illustrate the strengths of the framework, as well as the types of calculations that can be performed. This framework is particularly well suited to the fields of quantum optics, superconducting circuit devices, nanomechanics, and trapped ions, while also being ideal as an educational tool." ], "keywords": "quantum mechanics, master equation, monte-carlo", - "author": [ - "Robert J. Johansson", - "Paul D. Nation" - ], + "author": ["Robert J. Johansson", "Paul D. Nation"], "paper_id": "robert_johansson", "copyright_holder": "Robert J. Johansson et al.", "video": "" }, { "author_institution_map": { - "Mads Ruben Burgdorff Kristensen": [ - "University of Copenhagen" - ], - "Simon Andreas Frimann Lund": [ - "University of Copenhagen" - ], - "Brian Vinter": [ - "University of Copenhagen" - ], - "Troels Blum": [ - "University of Copenhagen" - ] + "Mads Ruben Burgdorff Kristensen": ["University of Copenhagen"], + "Simon Andreas Frimann Lund": ["University of Copenhagen"], + "Brian Vinter": ["University of Copenhagen"], + "Troels Blum": ["University of Copenhagen"] }, "pages": 7, "page": { diff --git a/publisher/metadata/2013/scipy_proc.json b/publisher/metadata/2013/scipy_proc.json index 50cf9aa796..b02b61679f 100644 --- a/publisher/metadata/2013/scipy_proc.json +++ b/publisher/metadata/2013/scipy_proc.json @@ -25,11 +25,7 @@ "volume": 6, "doi": "10.25080/Majora-8b375195-011", "location": "Austin, Texas", - "editor": [ - "Stéfan van der Walt", - "Jarrod Millman", - "Katy Huff" - ], + "editor": ["Stéfan van der Walt", "Jarrod Millman", "Katy Huff"], "number": 1, "xref": { "resource_url": "https://conference.scipy.org/proceedings", diff --git a/publisher/metadata/2013/toc.json b/publisher/metadata/2013/toc.json index 75c446d590..bca386e8d0 100644 --- a/publisher/metadata/2013/toc.json +++ b/publisher/metadata/2013/toc.json @@ -3,10 +3,7 @@ { "authors": "Andy Terrel, Jonathan Rocher", "doi": "10.25080/Majora-8b375195-000", - "author_email": [ - "aterrell@tacc.utexas.edu", - "jrocher@enthought.com" - ], + "author_email": ["aterrell@tacc.utexas.edu", "jrocher@enthought.com"], "page": { "stop": 1, "start": 1 @@ -19,29 +16,19 @@ "University of Texas at Austin", "Enthought, Inc." ], - "author": [ - "Andy Terrel", - "Jonathan Rocher" - ], + "author": ["Andy Terrel", "Jonathan Rocher"], "bibliography": "", "video": "", "author_institution_map": { - "Jonathan Rocher": [ - "Enthought, Inc." - ], - "Andy Terrel": [ - "University of Texas at Austin" - ] + "Jonathan Rocher": ["Enthought, Inc."], + "Andy Terrel": ["University of Texas at Austin"] }, "title": "Preface" }, { "authors": "Kapil Arya, Gene Cooperman", "doi": "10.25080/Majora-8b375195-001", - "author_email": [ - "kapil@ccs.neu.edu", - "gene@ccs.neu.edu" - ], + "author_email": ["kapil@ccs.neu.edu", "gene@ccs.neu.edu"], "page": { "stop": 7, "start": 2 @@ -57,19 +44,12 @@ "Northeastern University", "Northeastern University" ], - "author": [ - "Kapil Arya", - "Gene Cooperman" - ], + "author": ["Kapil Arya", "Gene Cooperman"], "bibliography": "", "video": "http://www.youtube.com/watch?v=1l_wGZz0JEE", "author_institution_map": { - "Kapil Arya": [ - "Northeastern University" - ], - "Gene Cooperman": [ - "Northeastern University" - ] + "Kapil Arya": ["Northeastern University"], + "Gene Cooperman": ["Northeastern University"] }, "title": "DMTCP: Bringing Checkpoint-Restart to Python" }, @@ -109,18 +89,10 @@ "bibliography": "", "video": "http://www.youtube.com/watch?v=47LNpvDlKUk", "author_institution_map": { - "Christopher Beaumont": [ - "University of Hawaii, Harvard University" - ], - "Thomas Robitaille": [ - "Max Planck Institute for Astronomy" - ], - "Alyssa Goodman": [ - "Harvard University" - ], - "Michelle Borkin": [ - "Harvard University" - ] + "Christopher Beaumont": ["University of Hawaii, Harvard University"], + "Thomas Robitaille": ["Max Planck Institute for Astronomy"], + "Alyssa Goodman": ["Harvard University"], + "Michelle Borkin": ["Harvard University"] }, "title": "Multidimensional Data Exploration with Glue" }, @@ -148,23 +120,13 @@ "Massachusetts Institute of Technology", "Harvard University" ], - "author": [ - "James Bergstra", - "Dan Yamins", - "David D. Cox" - ], + "author": ["James Bergstra", "Dan Yamins", "David D. Cox"], "bibliography": "", "video": "http://www.youtube.com/watch?v=Mp1xnPfE4PY", "author_institution_map": { - "James Bergstra": [ - "University of Waterloo" - ], - "Dan Yamins": [ - "Massachusetts Institute of Technology" - ], - "David D. Cox": [ - "Harvard University" - ] + "James Bergstra": ["University of Waterloo"], + "Dan Yamins": ["Massachusetts Institute of Technology"], + "David D. Cox": ["Harvard University"] }, "title": "Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms" }, @@ -192,23 +154,13 @@ "Massachusetts Institute of Technology", "Harvard University" ], - "author": [ - "James Bergstra", - "Nicolas Pinto", - "David D. Cox" - ], + "author": ["James Bergstra", "Nicolas Pinto", "David D. Cox"], "bibliography": "", "video": "http://www.youtube.com/watch?v=u5amehIiImo", "author_institution_map": { - "James Bergstra": [ - "University of Waterloo" - ], - "Nicolas Pinto": [ - "Massachusetts Institute of Technology" - ], - "David D. Cox": [ - "Harvard University" - ] + "James Bergstra": ["University of Waterloo"], + "Nicolas Pinto": ["Massachusetts Institute of Technology"], + "David D. Cox": ["Harvard University"] }, "title": "SkData: Data Sets and Algorithm Evaluation Protocols in Python" }, @@ -254,21 +206,11 @@ "bibliography": "", "video": "", "author_institution_map": { - "Thomas Bensby": [ - "Lund Observatory, Sweden" - ], - "Simone Daflon": [ - "Observatório Nacional, Brazil" - ], - "Sally Oey": [ - "University of Michigan, U. S. A." - ], - "Gregory Walth": [ - "Steward Observatory, U. S. A." - ], - "Gustavo Bragança": [ - "Observatório Nacional, Brazil" - ], + "Thomas Bensby": ["Lund Observatory, Sweden"], + "Simone Daflon": ["Observatório Nacional, Brazil"], + "Sally Oey": ["University of Michigan, U. S. A."], + "Gregory Walth": ["Steward Observatory, U. S. A."], + "Gustavo Bragança": ["Observatório Nacional, Brazil"], "Katia Cunha": [ "Observatório Nacional, Brazil; National Optical Astronomy\nObservatory, University of Arizona, U. S. A." ] @@ -278,10 +220,7 @@ { "authors": "Mark E Fenner, Barbara M. Fenner", "doi": "10.25080/Majora-8b375195-006", - "author_email": [ - "mfenner@gmail.com", - "barb.fenner@gmail.com" - ], + "author_email": ["mfenner@gmail.com", "barb.fenner@gmail.com"], "page": { "stop": 37, "start": 32 @@ -297,28 +236,19 @@ "Coveros, Inc., Fairfax, VA", "King's College, Wilkes-Barre, PA" ], - "author": [ - "Mark E Fenner", - "Barbara M. Fenner" - ], + "author": ["Mark E Fenner", "Barbara M. Fenner"], "bibliography": "", "video": "http://www.youtube.com/watch?v=ar5YtgiXfNI", "author_institution_map": { - "Barbara M. Fenner": [ - "King's College, Wilkes-Barre, PA" - ], - "Mark E Fenner": [ - "Coveros, Inc., Fairfax, VA" - ] + "Barbara M. Fenner": ["King's College, Wilkes-Barre, PA"], + "Mark E Fenner": ["Coveros, Inc., Fairfax, VA"] }, "title": "Automating Quantitative Confocal Microscopy Analysis" }, { "authors": "Patricia Francis-Lyon, Shashank Belvadi, Fu-Yuan Cheng", "doi": "10.25080/Majora-8b375195-007", - "author_email": [ - "pfrancislyon@cs.usfca.edu" - ], + "author_email": ["pfrancislyon@cs.usfca.edu"], "page": { "stop": 43, "start": 38 @@ -337,23 +267,13 @@ "University of San Francisco", "University of San Francisco" ], - "author": [ - "Patricia Francis-Lyon", - "Shashank Belvadi", - "Fu-Yuan Cheng" - ], + "author": ["Patricia Francis-Lyon", "Shashank Belvadi", "Fu-Yuan Cheng"], "bibliography": "", "video": "http://www.youtube.com/wa?v=IA09mZRCCA8", "author_institution_map": { - "Fu-Yuan Cheng": [ - "University of San Francisco" - ], - "Patricia Francis-Lyon": [ - "University of San Francisco" - ], - "Shashank Belvadi": [ - "University of San Francisco" - ] + "Fu-Yuan Cheng": ["University of San Francisco"], + "Patricia Francis-Lyon": ["University of San Francisco"], + "Shashank Belvadi": ["University of San Francisco"] }, "title": "Detection and characterization of interactions of genetic risk factors in disease" }, @@ -397,32 +317,18 @@ "bibliography": "", "video": "http://www.youtube.com/watch?v=KT5-uGEpnGw", "author_institution_map": { - "Alan Raynaud": [ - "Télécom Bretagne, Plouzané, France" - ], - "Adrien Merlini": [ - "Télécom Bretagne, Plouzané, France" - ], - "Serge Guelton": [ - "ENS, Paris, France" - ], - "Pierrick Brunet": [ - "Télécom Bretagne, Plouzané, France" - ], - "Mehdi Amini": [ - "SILKAN, Los Altos, USA" - ] + "Alan Raynaud": ["Télécom Bretagne, Plouzané, France"], + "Adrien Merlini": ["Télécom Bretagne, Plouzané, France"], + "Serge Guelton": ["ENS, Paris, France"], + "Pierrick Brunet": ["Télécom Bretagne, Plouzané, France"], + "Mehdi Amini": ["SILKAN, Los Altos, USA"] }, "title": "Pythran: Enabling Static Optimization of Scientific Python Programs" }, { "authors": "Pedro Henrique Hasselmann, Jorge Márcio Carvano, Daniela Lazzaro", "doi": "10.25080/Majora-8b375195-009", - "author_email": [ - "hasselmann@on.br", - "carvano@on.br", - "lazzaro@on.br" - ], + "author_email": ["hasselmann@on.br", "carvano@on.br", "lazzaro@on.br"], "page": { "stop": 57, "start": 51 @@ -451,9 +357,7 @@ "Pedro Henrique Hasselmann": [ "Observatorio Nacional, Rio de Janeiro, Brazil" ], - "Daniela Lazzaro": [ - "Observatorio Nacional, Rio de Janeiro, Brazil" - ], + "Daniela Lazzaro": ["Observatorio Nacional, Rio de Janeiro, Brazil"], "Jorge Márcio Carvano": [ "Observatorio Nacional, Rio de Janeiro, Brazil" ] @@ -463,9 +367,7 @@ { "authors": "Eric Jeschke", "doi": "10.25080/Majora-8b375195-00a", - "author_email": [ - "eric@naoj.org" - ], + "author_email": ["eric@naoj.org"], "page": { "stop": 64, "start": 58 @@ -482,9 +384,7 @@ "author_institution": [ "Subaru Telescope, National Astronomical Observatory of Japan" ], - "author": [ - "Eric Jeschke" - ], + "author": ["Eric Jeschke"], "bibliography": "", "video": "http://www.youtube.com/watch?v=nZKy_nYUxCs", "author_institution_map": { @@ -538,21 +438,13 @@ "bibliography": "", "video": "http://www.youtube.com/watch?v=ei_pFi2xOUc", "author_institution_map": { - "Brad Whitlock": [ - "Lawrence Livermore National Laboratory" - ], - "David Pugmire": [ - "Oak Ridge National Laboratory" - ], - "Hari Krishnan": [ - "Lawrence Berkeley National Laboratory" - ], + "Brad Whitlock": ["Lawrence Livermore National Laboratory"], + "David Pugmire": ["Oak Ridge National Laboratory"], + "Hari Krishnan": ["Lawrence Berkeley National Laboratory"], "Hank Childs": [ "Lawrence Berkeley National Laboratory/The University of Oregon" ], - "Cyrus Harrison": [ - "Lawrence Livermore National Laboratory" - ] + "Cyrus Harrison": ["Lawrence Livermore National Laboratory"] }, "title": "Exploring Collaborative HPC Visualization Workflows using VisIt and Python" }, @@ -594,30 +486,18 @@ "bibliography": "", "video": "http://www.youtube.com/watch?v=bXPPTCkaVu8", "author_institution_map": { - "Stuart Mumford": [ - "The University of Sheffield" - ], - "Florian Mayer": [ - "Vienna University of Technology" - ], - "David Pérez-Suárez": [ - "Finnish Meteorological Institute" - ], - "Steven Christe": [ - "NASA Goddard Space Flight Center" - ], - "Russell J. Hewett": [ - "Massachusetts Institute of Technology" - ] + "Stuart Mumford": ["The University of Sheffield"], + "Florian Mayer": ["Vienna University of Technology"], + "David Pérez-Suárez": ["Finnish Meteorological Institute"], + "Steven Christe": ["NASA Goddard Space Flight Center"], + "Russell J. Hewett": ["Massachusetts Institute of Technology"] }, "title": "SunPy: Python for Solar Physicists" }, { "authors": "Geoffrey M Poore", "doi": "10.25080/Majora-8b375195-00d", - "author_email": [ - "gpoore@uu.edu" - ], + "author_email": ["gpoore@uu.edu"], "page": { "stop": 80, "start": 74 @@ -629,18 +509,12 @@ ], "keywords": "reproducible science, reproducible documents, dynamic report\ngeneration", "pages": 7, - "author_institution": [ - "Union University" - ], - "author": [ - "Geoffrey M Poore" - ], + "author_institution": ["Union University"], + "author": ["Geoffrey M Poore"], "bibliography": "", "video": "http://www.youtube.com/watch?v=G-UDHc2UVOg", "author_institution_map": { - "Geoffrey M Poore": [ - "Union University" - ] + "Geoffrey M Poore": ["Union University"] }, "title": "Reproducible Documents with PythonTeX" }, @@ -705,9 +579,7 @@ { "authors": "Ramalingam Saravanan", "doi": "10.25080/Majora-8b375195-00f", - "author_email": [ - "sarava@tamu.edu" - ], + "author_email": ["sarava@tamu.edu"], "page": { "stop": 91, "start": 86 @@ -721,18 +593,12 @@ ], "keywords": "GUI, CLI, graphical user interface, command line\ninterface, notebook interface, graphical shell", "pages": 6, - "author_institution": [ - "Texas A\\&M University" - ], - "author": [ - "Ramalingam Saravanan" - ], + "author_institution": ["Texas A\\&M University"], + "author": ["Ramalingam Saravanan"], "bibliography": "", "video": "http://www.youtube.com/watch?v=nO0ceHmTlDQ", "author_institution_map": { - "Ramalingam Saravanan": [ - "Texas A\\&M University" - ] + "Ramalingam Saravanan": ["Texas A\\&M University"] }, "title": "GraphTerm: A notebook-like graphical terminal interface for collaboration and inline data visualization" }, @@ -768,15 +634,9 @@ "bibliography": "", "video": "http://www.youtube.com/watch?v=Ec38h1oB8cc", "author_institution_map": { - "Vanderlei C. Oliveira Jr": [ - "Observatorio Nacional" - ], - "Valéria C. F. Barbosa": [ - "Observatorio Nacional" - ], - "Leonardo Uieda": [ - "Observatorio Nacional" - ] + "Vanderlei C. Oliveira Jr": ["Observatorio Nacional"], + "Valéria C. F. Barbosa": ["Observatorio Nacional"], + "Leonardo Uieda": ["Observatorio Nacional"] }, "title": "Modeling the Earth with Fatiando a Terra" } diff --git a/publisher/metadata/2014/scipy_proc.json b/publisher/metadata/2014/scipy_proc.json index 802ae3514b..5ee47f0c93 100644 --- a/publisher/metadata/2014/scipy_proc.json +++ b/publisher/metadata/2014/scipy_proc.json @@ -7,10 +7,7 @@ "dates": "July 6 - 12", "year": "2014", "location": "Austin, Texas", - "editor": [ - "Stéfan van der Walt", - "James Bergstra" - ], + "editor": ["Stéfan van der Walt", "James Bergstra"], "xref": { "depositor_email": "dillon.niederhut@gmail.com", "registrant": "Crossref", diff --git a/publisher/metadata/2014/toc.json b/publisher/metadata/2014/toc.json index 09bbac817d..a36d194133 100644 --- a/publisher/metadata/2014/toc.json +++ b/publisher/metadata/2014/toc.json @@ -28,18 +28,10 @@ "University of Waterloo" ], "author_institution_map": { - "Andy Terrel": [ - "Continuum Analytics" - ], - "Jonathan Rocher": [ - "Enthought, Inc." - ], - "James Bergstra": [ - "University of Waterloo" - ], - "Stéfan van der Walt": [ - "University of California, Berkeley" - ] + "Andy Terrel": ["Continuum Analytics"], + "Jonathan Rocher": ["Enthought, Inc."], + "James Bergstra": ["University of Waterloo"], + "Stéfan van der Walt": ["University of California, Berkeley"] }, "abstract": [], "title": "Preface", @@ -49,12 +41,8 @@ }, { "pages": 3, - "author_email": [ - "gwb6@psu.edu" - ], - "author": [ - "G William Baxter" - ], + "author_email": ["gwb6@psu.edu"], + "author": ["G William Baxter"], "copyright_holder": "G William Baxter.", "doi": "10.25080/Majora-14bd3278-001", "page": { @@ -87,12 +75,7 @@ "maplabs@light42.com", "rylo@berkeley.edu" ], - "author": [ - "Dav Clark", - "Aaron Culich", - "Brian Hamlin", - "Ryan Lovett" - ], + "author": ["Dav Clark", "Aaron Culich", "Brian Hamlin", "Ryan Lovett"], "copyright_holder": "Dav Clark et al.", "doi": "10.25080/Majora-14bd3278-002", "page": { @@ -109,18 +92,10 @@ "UC Berkeley" ], "author_institution_map": { - "Dav Clark": [ - "UC Berkeley" - ], - "Brian Hamlin": [ - "OSGeo California Chapter" - ], - "Aaron Culich": [ - "UC Berkeley" - ], - "Ryan Lovett": [ - "UC Berkeley" - ] + "Dav Clark": ["UC Berkeley"], + "Brian Hamlin": ["OSGeo California Chapter"], + "Aaron Culich": ["UC Berkeley"], + "Ryan Lovett": ["UC Berkeley"] }, "abstract": [ "There are numerous barriers to the use of scientific computing toolsets. These\nbarriers are becoming more apparent as we\nincreasingly see mixing of different academic backgrounds, and compute ranging\nfrom laptops to cloud platforms.\nMembers of the UC\nBerkeley D-Lab, Statistical Computing Facility (SCF), and Berkeley Research\nComputing (BRC) support such use-cases, and have developed\nstrategies that reduce the pain points that arise.\nWe begin by describing the variety of concrete training and research use-cases in which\nour strategy might increase accessibility, productivity, reuse, and reproducibility.\nWe then introduce available tools for the “recipe-based” creation of compute\nenvironments, attempting to demystify and provide a framework for thinking about\nDevOps (along with explaining what “DevOps” means!).\nAs a counterpoint to novel DevOps tools, we'll also examine the success of\nOSGeo-Live OSGL – a project that has\nmanaged to obtain and manage developer contributions for a large number of geospatial projects.\nThis is enabled through the use of commonly\nknown skills like shell scripting, and is a model of complexity that can be\nmanaged without these more recent DevOps tools.\nGiven our evaluation of a variety of technologies and\nuse-cases, we present our current strategy for constructing the Berkeley Common Environment BCE, along with general recommendations for building environments for your own use-cases." @@ -174,21 +149,15 @@ "Scott Collis": [ "Environmental Sciences Division, Argonne National Laboratory." ], - "Ann Fridlind": [ - "NASA Goddard Institute of Space Sciences." - ], + "Ann Fridlind": ["NASA Goddard Institute of Space Sciences."], "Jonathan Helmus": [ "Environmental Sciences Division, Argonne National Laboratory." ], - "Di Wu": [ - "NASA Goddard Space Flight Center." - ], + "Di Wu": ["NASA Goddard Space Flight Center."], "Scott Giangrande": [ "Atmospheric Sciences, Brookhaven National Laboratory." ], - "Marcus van Lier-Walqui": [ - "NASA Goddard Institute of Space Sciences." - ] + "Marcus van Lier-Walqui": ["NASA Goddard Institute of Space Sciences."] }, "abstract": [ "Remote sensing data is complicated, very complicated! It is not only\ngeometrically tricky but also, unlike in-situ methods,\nindirect as the sensor measures the interaction\nof the scattering media (eg raindrops) with the probing radiation, not the geophysics. However the\nproblem is made tractable by the large number of algorithms available in the\nScientific Python community. While SciPy provides many helpful algorithms for\nsignal processing in this domain, a full software stack from highly specialized\nfile formats from specific sensors to interpretable geospatial analysis requires\na common data model for active remote sensing data that can act as a middle layer\nThis paper\nmotivates this work by asking: How big is a rainshaft? What is the natural\nmorphology of rainfall patterns and how well is this represented in fine\nscale atmospheric models. Rather than being specific to the domain of\nmeteorology, we will break down how we approach this problem in terms of the tools\nused from numerous Python packages to read, correct, map and reduce the data\ninto a form better able to answer our science questions. This is a \"how\" paper,\ncovering the Python-ARM Radar Toolkit (Py-ART) containing\nsignal processing using linear programming methods and mapping using k-d\ntrees. We also cover image analysis using SciPy's ndimage sub-module and graphics using\nmatplotlib." @@ -199,12 +168,8 @@ }, { "pages": 6, - "author_email": [ - "david.ketcheson@kaust.edu.sa" - ], - "author": [ - "David I. Ketcheson" - ], + "author_email": ["david.ketcheson@kaust.edu.sa"], + "author": ["David I. Ketcheson"], "copyright_holder": "David I. Ketcheson.", "doi": "10.25080/Majora-14bd3278-004", "page": { @@ -231,12 +196,8 @@ }, { "pages": 7, - "author_email": [ - "jklay@calpoly.edu" - ], - "author": [ - "Jennifer Klay" - ], + "author_email": ["jklay@calpoly.edu"], + "author": ["Jennifer Klay"], "copyright_holder": "Jennifer Klay.", "doi": "10.25080/Majora-14bd3278-005", "page": { @@ -268,11 +229,7 @@ "james.bergstra@uwaterloo.ca", "celiasmith@uwaterloo.ca" ], - "author": [ - "Brent Komer", - "James Bergstra", - "Chris Eliasmith" - ], + "author": ["Brent Komer", "James Bergstra", "Chris Eliasmith"], "copyright_holder": "Brent Komer et al.", "doi": "10.25080/Majora-14bd3278-006", "page": { @@ -335,18 +292,10 @@ "Argonne National Laboratory" ], "author_institution_map": { - "James A. Kuiper": [ - "Argonne National Laboratory" - ], - "Andrew J. Ayers": [ - "Argonne National Laboratory" - ], - "Michael J. Nowak": [ - "Argonne National Laboratory" - ], - "Michael E. Holm": [ - "Argonne National Laboratory" - ] + "James A. Kuiper": ["Argonne National Laboratory"], + "Andrew J. Ayers": ["Argonne National Laboratory"], + "Michael J. Nowak": ["Argonne National Laboratory"], + "Michael E. Holm": ["Argonne National Laboratory"] }, "abstract": [ "Python has powerful capabilities for coding elements of Web-based\nmapping applications. This paper highlights examples of analytical\ngeospatial processing services that we have implemented for several\nOpen Source-based development projects, including the Eastern\nInterconnection States' Planning Council (EISPC) Energy Zones Mapping\nTool (http://eispctools.anl.gov), the Solar Energy Environmental\nMapper (http://solarmapper.anl.gov), and the Ecological Risk\nCalculator (http://bogi.evs.anl.gov/erc/portal). We used common\nOpen Source tools such as GeoServer, PostGIS, GeoExt, and\nOpenLayers for the basic Web-based portal, then added custom\nanalytical tools to support more advanced functionality. The\nanalytical processes were implemented as Web Processing Services\n(WPSs) running on PyWPS, a Python implementation\nof the Open Geospatial Consortium (OGC) WPS. For report tools,\nareas drawn by the user in the map interface are submitted to a\nservice that utilizes the spatial extensions of PostGIS to\ngenerate buffers for use in querying and analyzing the underlying\ndata. Python code then post-processes the results and outputs\nJavaScript Object Notation (JSON)-formatted data for rendering.\nWe made use of PyWPS's integration with the Geographic Resources\nAnalysis Support System (GRASS) to implement flexible,\nuser-adjustable suitability models for several renewable energy\ngeneration technologies. In this paper, we provide details about\nthe processing methods we used within these project examples." @@ -357,14 +306,8 @@ }, { "pages": 6, - "author_email": [ - "jlaura@asu.edu", - "srey@asu.edu" - ], - "author": [ - "Jason Laura", - "Sergio J. Rey" - ], + "author_email": ["jlaura@asu.edu", "srey@asu.edu"], + "author": ["Jason Laura", "Sergio J. Rey"], "copyright_holder": "Jason Laura et al.", "doi": "10.25080/Majora-14bd3278-008", "page": { @@ -395,12 +338,8 @@ }, { "pages": 8, - "author_email": [ - "kannan@iitb.ac.in" - ], - "author": [ - "Kannan M. Moudgalya" - ], + "author_email": ["kannan@iitb.ac.in"], + "author": ["Kannan M. Moudgalya"], "copyright_holder": "Kannan M. Moudgalya.", "doi": "10.25080/Majora-14bd3278-009", "page": { @@ -430,12 +369,8 @@ }, { "pages": 6, - "author_email": [ - "pugh@maths.ox.ac.uk" - ], - "author": [ - "David R. Pugh" - ], + "author_email": ["pugh@maths.ox.ac.uk"], + "author": ["David R. Pugh"], "copyright_holder": "David R. Pugh.", "doi": "10.25080/Majora-14bd3278-00a", "page": { @@ -462,14 +397,8 @@ }, { "pages": 7, - "author_email": [ - "dpsanders@ciencias.unam.mx", - "benet@fis.unam.mx" - ], - "author": [ - "David P. Sanders", - "Luis Benet" - ], + "author_email": ["dpsanders@ciencias.unam.mx", "benet@fis.unam.mx"], + "author": ["David P. Sanders", "Luis Benet"], "copyright_holder": "David P. Sanders et al.", "doi": "10.25080/Majora-14bd3278-00b", "page": { @@ -500,12 +429,8 @@ }, { "pages": 7, - "author_email": [ - "sarava@tamu.edu" - ], - "author": [ - "Ramalingam Saravanan" - ], + "author_email": ["sarava@tamu.edu"], + "author": ["Ramalingam Saravanan"], "copyright_holder": "Ramalingam Saravanan.", "doi": "10.25080/Majora-14bd3278-00c", "page": { @@ -515,13 +440,9 @@ "bibliography": "", "video": "http://www.youtube.com/watch?v=LiZJMYxvJbQ", "paper_id": "saravanan", - "author_institution": [ - "Texas A\\&M University" - ], + "author_institution": ["Texas A\\&M University"], "author_institution_map": { - "Ramalingam Saravanan": [ - "Texas A\\&M University" - ] + "Ramalingam Saravanan": ["Texas A\\&M University"] }, "abstract": [ "With laptops and tablets becoming more powerful and more ubiquitous\nin the classroom, traditional computer labs with rows of expensive\ndesktop computers are slowly beginning to lose their relevance. An\nalternative approach for teaching Python is to use a browser-based\nvirtual computer lab, with a notebook interface. The advantages\nof physical computer labs, such as face-to-face interaction, and\nthe challenge of replicating them in a virtual environment are\ndiscussed. The need for collaborative features like\nterminal/notebook sharing and chatting is emphasized. A virtual computer lab is\nimplemented using the GraphTerm server, with several experimental features\nincluding a virtual dashboard for monitoring tasks and progressively\nfillable notebooks for ensuring step-by-step completion of a\nsequence of tasks." @@ -532,14 +453,8 @@ }, { "pages": 6, - "author_email": [ - "kthyng@tamu.edu", - "hetland@tamu.edu" - ], - "author": [ - "Kristen M. Thyng", - "Robert D. Hetland" - ], + "author_email": ["kthyng@tamu.edu", "hetland@tamu.edu"], + "author": ["Kristen M. Thyng", "Robert D. Hetland"], "copyright_holder": "Kristen M. Thyng et al.", "doi": "10.25080/Majora-14bd3278-00d", "page": { @@ -554,12 +469,8 @@ "Texas A\\&M University" ], "author_institution_map": { - "Robert D. Hetland": [ - "Texas A\\&M University" - ], - "Kristen M. Thyng": [ - "Texas A\\&M University" - ] + "Robert D. Hetland": ["Texas A\\&M University"], + "Kristen M. Thyng": ["Texas A\\&M University"] }, "abstract": [ "Numerical Lagrangian trajectory modeling is a natural method of investigating transport in a circulation system and understanding the physics on the wide range of length scales that are actually experienced by a drifter. A previously-developed tool, TRACMASS, written in Fortran, accomplishes this modeling with a clever algorithm that operates natively on the commonly used staggered Arakawa C grid. TracPy is a Python wrapper written to ease running batches of simulations. Some improvements in TracPy include updating to netCDF4-CLASSIC from netCDF3 for saving drifter trajectories, providing an iPython notebook as a usermanual for using the system, and adding unit tests for stable continued development." @@ -570,12 +481,8 @@ }, { "pages": 9, - "author_email": [ - "jakevdp@cs.washington.edu" - ], - "author": [ - "Jake VanderPlas" - ], + "author_email": ["jakevdp@cs.washington.edu"], + "author": ["Jake VanderPlas"], "copyright_holder": "Jake VanderPlas.", "doi": "10.25080/Majora-14bd3278-00e", "page": { @@ -585,13 +492,9 @@ "bibliography": "", "video": "http://www.youtube.com/watch?v=KhAUfqhLakw", "paper_id": "vanderplas", - "author_institution": [ - "eScience Institute, University of Washington" - ], + "author_institution": ["eScience Institute, University of Washington"], "author_institution_map": { - "Jake VanderPlas": [ - "eScience Institute, University of Washington" - ] + "Jake VanderPlas": ["eScience Institute, University of Washington"] }, "abstract": [ "This paper presents a brief, semi-technical comparison of the essential features of the frequentist and Bayesian approaches to statistical inference, with several illustrative examples implemented in Python. The differences between frequentism and Bayesianism fundamentally stem from differing definitions of probability, a philosophical divide which leads to distinct approaches to the solution of statistical problems as well as contrasting ways of asking and answering questions about unknown parameters. After an example-driven discussion of these differences, we briefly compare several leading Python statistical packages which implement frequentist inference using classical methods and Bayesian inference using Markov Chain Monte Carlo.1" @@ -630,18 +533,10 @@ "Continuum Analytics" ], "author_institution_map": { - "TJ Alumbaugh": [ - "Continuum Analytics" - ], - "Mark Wiebe": [ - "Continuum Analytics" - ], - "Matthew Rocklin": [ - "Continuum Analytics" - ], - "Andy Terrel": [ - "Continuum Analytics" - ] + "TJ Alumbaugh": ["Continuum Analytics"], + "Mark Wiebe": ["Continuum Analytics"], + "Matthew Rocklin": ["Continuum Analytics"], + "Andy Terrel": ["Continuum Analytics"] }, "abstract": [ "We present the motivation and architecture of Blaze, a library for\ncross-backend data-oriented computation. Blaze provides a standard interface\nto connect users familiar with NumPy and Pandas to other data analytics\nlibraries like SQLAlchemy and Spark. We motivate the use of these projects\nthrough Blaze and discuss the benefits of standard interfaces on top of an\nincreasingly varied software ecosystem. We give an overview of the Blaze\narchitecture and then demonstrate its use on a typical problem. We use the\nabstract nature of Blaze to quickly benchmark and compare the performance of a\nvariety of backends on a standard problem." diff --git a/publisher/metadata/2015/scipy_proc.json b/publisher/metadata/2015/scipy_proc.json index 46f2535b1e..413ce37f1c 100644 --- a/publisher/metadata/2015/scipy_proc.json +++ b/publisher/metadata/2015/scipy_proc.json @@ -31,10 +31,7 @@ "volume": 8, "number": 1, "citation_key": "proc-scipy-2015", - "editor": [ - "Kathryn Huff", - "James Bergstra" - ], + "editor": ["Kathryn Huff", "James Bergstra"], "location": "Austin, Texas" }, "organization": [ diff --git a/publisher/metadata/2015/toc.json b/publisher/metadata/2015/toc.json index 4f23662da9..cf4c56a9e9 100644 --- a/publisher/metadata/2015/toc.json +++ b/publisher/metadata/2015/toc.json @@ -10,26 +10,18 @@ "Using data from the National Survey of Family Growth (NSFG),\nwe investigate marriage patterns among women in the United States\nWe describe and predict age at\nfirst marriage for successive generations based on decade of\nbirth. The fraction of women married by age 22 has dropped by 11\npercentage points per decade, from 69\\% for women born in the 1940s\nto 13\\% for women born in the 90s. The fraction of women married by\nage 42 fell more slowly, from 93\\% for women born in the 40s to 82\\%\nfor women born in the 70s. Projections suggest that this\nfraction will be substantially lower for later generations, between\n68\\% and 72\\%. Along with these results, this paper presents an\nintroduction to survival analysis methods and an implementation\nin Python." ], "title": "Will Millennials Ever Get Married?", - "author": [ - "Allen B. Downey" - ], + "author": ["Allen B. Downey"], "authors": "Allen B. Downey", "author_institution_map": { - "Allen B. Downey": [ - "Olin College of Engineering" - ] + "Allen B. Downey": ["Olin College of Engineering"] }, "bibliography": "", "doi": "10.25080/Majora-7b98e3ed-000", "keywords": "Survival analysis, marriage patterns, Python.", - "author_institution": [ - "Olin College of Engineering" - ], + "author_institution": ["Olin College of Engineering"], "video": "https://www.youtube.com/watch?v=XHYFNraQEEo", "copyright_holder": "Allen B. Downey.", - "author_email": [ - "allen.downey@olin.edu" - ], + "author_email": ["allen.downey@olin.edu"], "pages": 5 }, { @@ -44,10 +36,7 @@ "This paper first gives a short introduction to PGMs and various other python\npackages available for working with PGMs. Then we discuss about creating and\ndoing inference over Bayesian Networks and Markov Networks using pgmpy." ], "title": "pgmpy: Probabilistic Graphical Models using Python", - "author": [ - "Ankur Ankan", - "Abinash Panda" - ], + "author": ["Ankur Ankan", "Abinash Panda"], "authors": "Ankur Ankan, Abinash Panda", "author_institution_map": { "Abinash Panda": [], @@ -59,10 +48,7 @@ "author_institution": [], "video": "https://www.youtube.com/watch?v=Vcmjqx7lht0", "copyright_holder": "Ankur Ankan et al.", - "author_email": [ - "ankurankan@gmail.com", - "mailme.abinashpanda@gmail.com" - ], + "author_email": ["ankurankan@gmail.com", "mailme.abinashpanda@gmail.com"], "pages": 6 }, { @@ -75,18 +61,13 @@ "We have been involved with teaching Python to biomedical scientists since 2005. In all, seven courses have been taught: 5 at the University of Pittsburgh, as a required course for biomedical informatics graduate students. Students have primarily been biomedical informatics graduate students with other students coming from human genetics, molecular biology, statistics, and similar fields. The range of prior computing experience has been wide: the majority of students had little or no prior programming experiences while a few students were experienced in other languages such as C/C++ and wanted to learn a scripting language for increased productivity. The semester-long courses have followed a procedural first approach then an introduction to object-oriented programming. By the end of the course students produce an independent programming project on a topic of their own choosing." ], "title": "Python as a First Programming Language for Biomedical Scientists", - "author": [ - "Brian E. Chapman, Ph.D.", - "Jeannie Irwin, Ph.D." - ], + "author": ["Brian E. Chapman, Ph.D.", "Jeannie Irwin, Ph.D."], "authors": "Brian E. Chapman, Ph.D., Jeannie Irwin, Ph.D.", "author_institution_map": { "Brian E. Chapman, Ph.D.": [ "Department of Radiology, University of Utah" ], - "Jeannie Irwin, Ph.D.": [ - "Unaffiliated" - ] + "Jeannie Irwin, Ph.D.": ["Unaffiliated"] }, "bibliography": "", "doi": "10.25080/Majora-7b98e3ed-002", @@ -97,10 +78,7 @@ ], "video": "https://www.youtube.com/watch?v=kP_glnbesJ4", "copyright_holder": "Brian E. Chapman, Ph.D. et al.", - "author_email": [ - "brian.chapman@utah.edu", - "jeannieirwin@gmail.com" - ], + "author_email": ["brian.chapman@utah.edu", "jeannieirwin@gmail.com"], "pages": 6 }, { @@ -124,22 +102,14 @@ ], "authors": "Brian McFee, Colin Raffel, Dawen Liang, Daniel P.W. Ellis, Matt McVicar, Eric Battenberg, Oriol Nieto", "author_institution_map": { - "Daniel P.W. Ellis": [ - "LabROSA, Columbia University" - ], + "Daniel P.W. Ellis": ["LabROSA, Columbia University"], "Brian McFee": [ "Center for Data Science, New York University", "Music and Audio Research Laboratory, New York University" ], - "Colin Raffel": [ - "LabROSA, Columbia University" - ], - "Dawen Liang": [ - "LabROSA, Columbia University" - ], - "Eric Battenberg": [ - "Silicon Valley AI Lab, Baidu, Inc." - ], + "Colin Raffel": ["LabROSA, Columbia University"], + "Dawen Liang": ["LabROSA, Columbia University"], + "Eric Battenberg": ["Silicon Valley AI Lab, Baidu, Inc."], "Oriol Nieto": [ "Music and Audio Research Laboratory, New York University" ], @@ -183,26 +153,18 @@ "This paper introduces PyEDA,\na Python library for electronic design automation (EDA).\nPyEDA provides both a high level interface to the representation of\nBoolean functions,\nand blazingly-fast C extensions for fundamental algorithms where\nperformance is essential.\nPyEDA is a hobby project which has the simple but audacious goal of\nimproving the state of digital design by using Python." ], "title": "PyEDA: Data Structures and Algorithms for Electronic Design Automation", - "author": [ - "Chris Drake" - ], + "author": ["Chris Drake"], "authors": "Chris Drake", "author_institution_map": { - "Chris Drake": [ - "Drake Enterprises" - ] + "Chris Drake": ["Drake Enterprises"] }, "bibliography": "", "doi": "10.25080/Majora-7b98e3ed-004", "keywords": "", - "author_institution": [ - "Drake Enterprises" - ], + "author_institution": ["Drake Enterprises"], "video": "https://www.youtube.com/watch?v=cljDuK0ouRs", "copyright_holder": "Chris Drake.", - "author_email": [ - "cjdrake@gmail.com" - ], + "author_email": ["cjdrake@gmail.com"], "pages": 6 }, { @@ -215,26 +177,18 @@ "VTK and ParaView are leading software packages for data analysis\nand visualization. Since their early years, Python has played an\nimportant role in each package. In many use cases, VTK and ParaView\nserve as modules used by Python applications. In other use cases,\nPython modules are used to generate visualization components within\nVTK. In this paper, we provide an overview of Python integration in\nVTK and ParaView and give some concrete examples of usage. We also\nprovide a roadmap for additional Python integration in VTK and\nParaView in the future." ], "title": "Scientific Data Analysis and Visualization with Python, VTK, and ParaView", - "author": [ - "Cory Quammen" - ], + "author": ["Cory Quammen"], "authors": "Cory Quammen", "author_institution_map": { - "Cory Quammen": [ - "Kitware, Inc." - ] + "Cory Quammen": ["Kitware, Inc."] }, "bibliography": "", "doi": "10.25080/Majora-7b98e3ed-005", "keywords": "data analysis, scientific visualization, VTK, ParaView", - "author_institution": [ - "Kitware, Inc." - ], + "author_institution": ["Kitware, Inc."], "video": "https://www.youtube.com/watch?v=8ugmkKaYKxM", "copyright_holder": "Cory Quammen.", - "author_email": [ - "cory.quammen@kitware.com" - ], + "author_email": ["cory.quammen@kitware.com"], "pages": 8 }, { @@ -247,26 +201,16 @@ "Built on Google App Engine (GAE), RealMassive encountered challenges while attempting to scale its recommendation engine to match its nationwide, multi-market expansion. To address this problem, we borrowed a conceptual model from spectral data processing to transform our domain-specific problem into one that the GAE's search engine could solve. Rather than using a more traditional heuristics-based relevancy ranking, we filtered and scored results using a modified version of a spectral angle. While this approach seems to have little in common with providing a recommendation based on similarity, there are important parallels: filtering to reduce the search space; independent variables that can be resampled into a signature; a signature library to identify meaningful similarities; and an algorithm that lends itself to an accurate but flexible definition of similarity. We implemented this as a web service that provides recommendations in sub-second time. The RealMassive platform currently covers over 4.5 billion square feet of commercial real estate inventory and is expanding quickly." ], "title": "Creating a Real-Time Recommendation Engine using Modified K-Means Clustering and Remote Sensing Signature Matching Algorithms", - "author": [ - "David Lippa", - "Jason Vertrees" - ], + "author": ["David Lippa", "Jason Vertrees"], "authors": "David Lippa, Jason Vertrees", "author_institution_map": { - "David Lippa": [ - "RealMassive, Inc." - ], - "Jason Vertrees": [ - "RealMassive, Inc." - ] + "David Lippa": ["RealMassive, Inc."], + "Jason Vertrees": ["RealMassive, Inc."] }, "bibliography": "", "doi": "10.25080/Majora-7b98e3ed-006", "keywords": "algorithms, clustering, recommendation engine, remote sensing", - "author_institution": [ - "RealMassive, Inc.", - "RealMassive, Inc." - ], + "author_institution": ["RealMassive, Inc.", "RealMassive, Inc."], "video": "", "copyright_holder": "RealMassive, Inc.", "author_email": [ @@ -286,22 +230,12 @@ "Individual steps are written as Python classes that can be invoked\nindividually from within Python or from the stpipe command line. Any\nset of step classes can be configured into a pipeline,\nwith stpipe handling the flow of\ndata between steps. The stpipe environment includes the use of standard\ndata models. The data models, defined using json schema, provide a means of\nvalidating the correct format of the data files presented to the pipeline,\nas well as presenting an abstract interface to isolate the calibration\nsteps from details of how the data are stored on disk." ], "title": "The James Webb Space Telescope Data Calibration Pipeline", - "author": [ - "Howard Bushouse", - "Michael Droettboom", - "Perry Greenfield" - ], + "author": ["Howard Bushouse", "Michael Droettboom", "Perry Greenfield"], "authors": "Howard Bushouse, Michael Droettboom, Perry Greenfield", "author_institution_map": { - "Howard Bushouse": [ - "Space Telescope Science Institute" - ], - "Perry Greenfield": [ - "Space Telescope Science Institute" - ], - "Michael Droettboom": [ - "Space Telescope Science Institute" - ] + "Howard Bushouse": ["Space Telescope Science Institute"], + "Perry Greenfield": ["Space Telescope Science Institute"], + "Michael Droettboom": ["Space Telescope Science Institute"] }, "bibliography": "", "doi": "10.25080/Majora-7b98e3ed-007", @@ -331,26 +265,18 @@ "Cython has existing machinery that allows C-level declarations to be shared between Cython-compiled extension modules without linking against the original libraries.\nThe Cython BLAS and LAPACK API in SciPy uses this functionality to make it so that the same BLAS and LAPACK libraries that were used to compile SciPy can be used in Python extension modules via Cython.\nThis paper will demonstrate how to create and use these APIs for both Fortran and C libraries in a platform-independent manner." ], "title": "Circumventing The Linker: Using SciPy's BLAS and LAPACK Within Cython", - "author": [ - "Ian Henriksen" - ], + "author": ["Ian Henriksen"], "authors": "Ian Henriksen", "author_institution_map": { - "Ian Henriksen": [ - "Brigham Young University Math Department" - ] + "Ian Henriksen": ["Brigham Young University Math Department"] }, "bibliography": "", "doi": "10.25080/Majora-7b98e3ed-008", "keywords": "Cython, BLAS, LAPACK, SciPy", - "author_institution": [ - "Brigham Young University Math Department" - ], + "author_institution": ["Brigham Young University Math Department"], "video": "https://www.youtube.com/watch?v=R4yB-8tB0J0", "copyright_holder": "Ian Henriksen.", - "author_email": [ - "iandh@byu.edu" - ], + "author_email": ["iandh@byu.edu"], "pages": 3 }, { @@ -363,10 +289,7 @@ "Agent-based modeling is a computational methodology used in social science, biology, and other fields, which involves simulating the behavior and interaction of many autonomous entities, or agents, over time. There is currently a hole in this area in Python’s robust and growing scientific ecosystem. Mesa is a new open-source, Apache 2.0 licensed package meant to fill that gap. It allows users to quickly create agent-based models using built-in core components (such as agent schedulers and spatial grids) or customized implementations; visualize them using a browser-based interface; and analyze their results using Python’s data analysis tools. Its goal is to be a Python 3-based alternative to other popular frameworks based in other languages such as NetLogo, Repast, or MASON. Since the framework is being built from scratch it is able to incorporate lessons from other tools. In this paper, we present Mesa's core features and demonstrate them with a simple example model.1" ], "title": "Mesa: An Agent-Based Modeling Framework", - "author": [ - "David Masad", - "Jacqueline Kazil" - ], + "author": ["David Masad", "Jacqueline Kazil"], "authors": "David Masad, Jacqueline Kazil", "author_institution_map": { "David Masad": [ @@ -403,11 +326,7 @@ "HoloViews also provides powerful containers that allow you to\norganize this data for analysis, embedding it whatever\nmultidimensional continuous or discrete space best characterizes\nit. The resulting workflow allows you to focus on exploring,\nanalyzing, and understanding your data and results, while leading\ndirectly to an exportable recipe for reproducible research." ], "title": "HoloViews: Building Complex Visualizations Easily for Reproducible Science", - "author": [ - "Jean-Luc R. Stevens", - "Philipp Rudiger", - "James A. Bednar" - ], + "author": ["Jean-Luc R. Stevens", "Philipp Rudiger", "James A. Bednar"], "authors": "Jean-Luc R. Stevens, Philipp Rudiger, James A. Bednar", "author_institution_map": { "James A. Bednar": [ @@ -447,32 +366,19 @@ "The structural cohesion model is a powerful sociological conception of cohesion in social groups, but its diffusion in empirical literature has been hampered by computational problems. We present useful heuristics for computing structural cohesion that allow a speed-up of one order of magnitude over the algorithms currently available. Both the heuristics and the exact algorithm have been implemented on NetworkX by the first author. Using as examples three large collaboration networks (co-maintenance of Debian packages, co-authorship in Nuclear Theory, and co-authorship in High-Energy Theory) we illustrate our approach to measure structural cohesion in relatively large networks. We also introduce a novel graphical representation of the structural cohesion analysis to quickly spot differences across networks. It is implemented using matplotlib." ], "title": "Structural Cohesion: Visualization and Heuristics for Fast Computation with NetworkX and matplotlib", - "author": [ - "Jordi Torrents", - "Fabrizio Ferraro" - ], + "author": ["Jordi Torrents", "Fabrizio Ferraro"], "authors": "Jordi Torrents, Fabrizio Ferraro", "author_institution_map": { - "Jordi Torrents": [ - "University of Barcelona" - ], - "Fabrizio Ferraro": [ - "IESE Business School" - ] + "Jordi Torrents": ["University of Barcelona"], + "Fabrizio Ferraro": ["IESE Business School"] }, "bibliography": "", "doi": "10.25080/Majora-7b98e3ed-00b", "keywords": "Network Analysis, Sociology, Structural Cohesion, NetworkX, matplotlib", - "author_institution": [ - "University of Barcelona", - "IESE Business School" - ], + "author_institution": ["University of Barcelona", "IESE Business School"], "video": "https://www.youtube.com/watch?v=K8RFIdG3g9Y", "copyright_holder": "Jordi Torrents et al.", - "author_email": [ - "jordi.t21@gmail.com", - "fferraro@iese.edu" - ], + "author_email": ["jordi.t21@gmail.com", "fferraro@iese.edu"], "pages": 10 }, { @@ -485,9 +391,7 @@ "Automated telescopes are capable of generating images more quickly than they can be inspected by a human, but detailed information on the performance of the telescope is valuable for monitoring and tuning of their operation. The IQMon (Image Quality Monitor) package1 was developed to provide basic image quality metrics of automated telescopes in near real time." ], "title": "Automated Image Quality Monitoring with IQMon", - "author": [ - "Josh Walawender" - ], + "author": ["Josh Walawender"], "authors": "Josh Walawender", "author_institution_map": { "Josh Walawender": [ @@ -502,9 +406,7 @@ ], "video": "https://www.youtube.com/watch?v=dGLkDOvYOHA", "copyright_holder": "Josh Walawender.", - "author_email": [ - "joshwalawender@me.com" - ], + "author_email": ["joshwalawender@me.com"], "pages": 7 }, { @@ -517,26 +419,18 @@ "In this work, a new python package, PyRK (Python for Reactor Kinetics), is\nintroduced. PyRK has been designed to simulate, in zero\ndimensions, the transient, coupled, thermal-hydraulics and neutronics of\ntime-dependent behavior in nuclear reactors. PyRK is intended for analysis\nof many commonly studied transient scenarios including normal reactor\nstartup and shutdown as well as abnormal scenarios including Beyond Design\nBasis Events (BDBEs) such as Accident Transients Without Scram (ATWS). For\nrobustness, this package employs various tools within the scientific python\necosystem. For additional ease of use, it employs a reactor-agnostic,\nobject-oriented data model, allowing nuclear engineers to rapidly prototype\nnuclear reactor control and safety systems in the context of their novel\nnuclear reactor designs." ], "title": "PyRK: A Python Package For Nuclear Reactor Kinetics", - "author": [ - "Kathryn Huff" - ], + "author": ["Kathryn Huff"], "authors": "Kathryn Huff", "author_institution_map": { - "Kathryn Huff": [ - "University of California, Berkeley" - ] + "Kathryn Huff": ["University of California, Berkeley"] }, "bibliography": "", "doi": "10.25080/Majora-7b98e3ed-00d", "keywords": "engineering, nuclear reactor, package", - "author_institution": [ - "University of California, Berkeley" - ], + "author_institution": ["University of California, Berkeley"], "video": "https://www.youtube.com/watch?v=2HToG61wMWI", "copyright_holder": "Kathryn Huff.", - "author_email": [ - "katyhuff@gmail.com" - ], + "author_email": ["katyhuff@gmail.com"], "pages": 7 }, { @@ -558,21 +452,13 @@ ], "authors": "Luke Campagnola, Almar Klein, Eric Larson, Cyrille Rossant, Nicolas Rougier", "author_institution_map": { - "Almar Klein": [ - "Continuum Analytics" - ], + "Almar Klein": ["Continuum Analytics"], "Nicolas Rougier": [ "French National Institute for Research in Computer Science and Control" ], - "Luke Campagnola": [ - "University of North Carolina at Chapel Hill" - ], - "Cyrille Rossant": [ - "University College London" - ], - "Eric Larson": [ - "University of Washington" - ] + "Luke Campagnola": ["University of North Carolina at Chapel Hill"], + "Cyrille Rossant": ["University College London"], + "Eric Larson": ["University of Washington"] }, "bibliography": "", "doi": "10.25080/Majora-7b98e3ed-00e", @@ -632,9 +518,7 @@ ], "video": "", "copyright_holder": "Margaret Y Mahan et al.", - "author_email": [ - "mahan027@umn.edu" - ], + "author_email": ["mahan027@umn.edu"], "pages": 8 }, { @@ -647,26 +531,18 @@ "This paper will take the audience through the story of how an electrical and computer\nengineering faculty member has come to embrace Python, in particular IPython Notebook\n(IPython kernel for Jupyter),\nas an analysis and simulation tool for both teaching and research in signal processing\nand communications. Legacy tools such as MATLAB are well established (entrenched) in\nthis discipline, but engineers need to be aware of alternatives, especially in the case\nof Python where there is such a vibrant community of developers.\nIn this paper case studies will also be used to describe domain\nspecific code modules that are being developed to support both lecture and lab oriented\ncourses going through the conversion from MATLAB to Python. These modules in particular\naugment scipy.signal in a very positive way and enable rapid prototyping of\ncommunications and signal processing algorithms. Both student and industry team\nmembers in subcontract work, have responded favorably to the use of Python as an\nengineering problem solving platform. In teaching, IPython notebooks are used to augment\nlecture material with live calculations and simulations. These same notebooks are then\nplaced on the course Web Site so students can download and tinker on their own. This\nactivity also encourages learning more about the language core and Numpy, relative to\nMATLAB. The students quickly mature and are able to turn in homework solutions and\ncomplete computer simulation projects, all in the notebook. Rendering notebooks to\nPDF via LaTeX is also quite popular. The next step is to get other signals and systems faculty\ninvolved." ], "title": "Signal Processing and Communications: Teaching and Research Using IPython Notebook", - "author": [ - "Mark Wickert" - ], + "author": ["Mark Wickert"], "authors": "Mark Wickert", "author_institution_map": { - "Mark Wickert": [ - "University of Colorado Colorado Springs" - ] + "Mark Wickert": ["University of Colorado Colorado Springs"] }, "bibliography": "", "doi": "10.25080/Majora-7b98e3ed-010", "keywords": "numerical computing, signal processing, communications systems, system modeling", - "author_institution": [ - "University of Colorado Colorado Springs" - ], + "author_institution": ["University of Colorado Colorado Springs"], "video": "https://www.youtube.com/watch?v=xWREmn7EajM", "copyright_holder": "Mark Wickert.", - "author_email": [ - "mwickert@uccs.edu" - ], + "author_email": ["mwickert@uccs.edu"], "pages": 8 }, { @@ -689,21 +565,11 @@ ], "authors": "Mattheus P. Ueckermann, Robert D. Chambers, Christopher A. Brooks, William E. Audette III, Jerry Bieszczad", "author_institution_map": { - "William E. Audette III": [ - "Creare LLC, Hanover, NH" - ], - "Christopher A. Brooks": [ - "Creare LLC, Hanover, NH" - ], - "Robert D. Chambers": [ - "Creare LLC, Hanover, NH" - ], - "Jerry Bieszczad": [ - "Creare LLC, Hanover, NH" - ], - "Mattheus P. Ueckermann": [ - "Creare LLC, Hanover, NH" - ] + "William E. Audette III": ["Creare LLC, Hanover, NH"], + "Christopher A. Brooks": ["Creare LLC, Hanover, NH"], + "Robert D. Chambers": ["Creare LLC, Hanover, NH"], + "Jerry Bieszczad": ["Creare LLC, Hanover, NH"], + "Mattheus P. Ueckermann": ["Creare LLC, Hanover, NH"] }, "bibliography": "", "doi": "10.25080/Majora-7b98e3ed-011", @@ -736,9 +602,7 @@ "This paper describes a tool for astronomical research implemented as an\nIPython notebook with a widget interface. The notebook uses Astropy, a\ncommunity-developed package of fundamental tools for astronomy, and\nAstropy affiliated packages, as the back end. The widget interface makes\nAstropy a much more useful tool to undergraduates or other non-experts\ndoing research in astronomy, filling a niche for software that connects\nbeginners to research-grade code." ], "title": "Widgets and Astropy: Accomplishing Productive Research with Undergraduates", - "author": [ - "Matthew Craig" - ], + "author": ["Matthew Craig"], "authors": "Matthew Craig", "author_institution_map": { "Matthew Craig": [ @@ -753,9 +617,7 @@ ], "video": "https://www.youtube.com/watch?v=hyxCDdBH1Mg", "copyright_holder": "Matthew Craig.", - "author_email": [ - "mcraig@mnstate.edu" - ], + "author_email": ["mcraig@mnstate.edu"], "pages": 5 }, { @@ -768,26 +630,18 @@ "Dask enables parallel and out-of-core computation. We couple blocked\nalgorithms with dynamic and memory aware task scheduling to achieve a\nparallel and out-of-core NumPy clone. We show how this extends the\neffective scale of modern hardware to larger datasets and discuss how these\nideas can be more broadly applied to other parallel collections." ], "title": "Dask: Parallel Computation with Blocked algorithms and Task Scheduling", - "author": [ - "Matthew Rocklin" - ], + "author": ["Matthew Rocklin"], "authors": "Matthew Rocklin", "author_institution_map": { - "Matthew Rocklin": [ - "Continuum Analytics" - ] + "Matthew Rocklin": ["Continuum Analytics"] }, "bibliography": "", "doi": "10.25080/Majora-7b98e3ed-013", "keywords": "parallelism, NumPy, scheduling", - "author_institution": [ - "Continuum Analytics" - ], + "author_institution": ["Continuum Analytics"], "video": "https://www.youtube.com/watch?v=1kkFZ4P-XHg", "copyright_holder": "Matthew Rocklin.", - "author_email": [ - "mrocklin@gmail.com" - ], + "author_email": ["mrocklin@gmail.com"], "pages": 7 }, { @@ -800,9 +654,7 @@ "The National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory's HYSPLIT (HYbrid Single Particle Lagrangian Transport) model Drax98, Drax97 uses a hybrid Langrangian and Eulerian calculation method to compute air parcel trajectories and particle dispersion and deposition simulations. Air parcels are hypothetical small volumes of air with uniform characteristics. The HYSPLIT model outputs air parcel paths projected forwards or backwards in time (trajectories) and is used in a variety of scientific contexts. Here we present the first package in the mainstream scientific Python ecosystem designed to facilitate HYSPLIT trajectory analysis workflow by providing an intuitive API for generating, inspecting, and plotting trajectory paths and data." ], "title": "PySPLIT: a Package for the Generation, Analysis, and Visualization of HYSPLIT Air Parcel Trajectories", - "author": [ - "Mellissa Cross" - ], + "author": ["Mellissa Cross"], "authors": "Mellissa Cross", "author_institution_map": { "Mellissa Cross": [ @@ -817,9 +669,7 @@ ], "video": "https://www.youtube.com/watch?v=2mzhTC4Kp-Y", "copyright_holder": "Mellissa Cross.", - "author_email": [ - "cros0324@umn.edu, mellissa.cross@gmail.com" - ], + "author_email": ["cros0324@umn.edu, mellissa.cross@gmail.com"], "pages": 5 }, { @@ -832,9 +682,7 @@ "TrendVis is a plotting package that uses matplotlib to create information-dense, sparkline-like, quantitative visualizations of multiple disparate data sets in a common plot area against a common variable. This plot type is particularly well-suited for time-series data. We discuss the rationale behind and the challenges associated with adapting matplotlib to this particular plot style, the TrendVis API and architecture, and various features available for users to customize and enhance the readability of their figures while walking through a sample workflow." ], "title": "TrendVis: an Elegant Interface for dense, sparkline-like, quantitative visualizations of multiple series using matplotlib", - "author": [ - "Mellissa Cross" - ], + "author": ["Mellissa Cross"], "authors": "Mellissa Cross", "author_institution_map": { "Mellissa Cross": [ @@ -849,9 +697,7 @@ ], "video": "https://www.youtube.com/watch?v=tklAFsce7eg", "copyright_holder": "Mellissa Cross.", - "author_email": [ - "cros0324@umn.edu, mellissa.cross@gmail.com" - ], + "author_email": ["cros0324@umn.edu, mellissa.cross@gmail.com"], "pages": 6 }, { @@ -865,26 +711,18 @@ "This report provides a brief introduction to the formal tools needed to comprehend Bayesian networks, including probability theory and graph theory. Then, it describes Bayesian networks and causal Bayesian networks. It introduces some of the most basic functionality of the extensive NetworkX python package for working with complex graphs and networks networkx. I introduce some utilities I have build on top of NetworkX including conditional graph enumeration and sampling from discrete valued Bayesian networks encoded in NetworkX graphs pacer2015cbnx. I call this Causal Bayesian NetworkX, or cbnx. I conclude by introducing a formal framework for generating causal Bayesian networks called theory based causal induction griffithst09, out of which these utilities emerged. I discuss the background motivations for frameworks of this sort, their use in computational cognitive science, and the use of computational cognitive science for the machine learning community at large." ], "title": "Causal Bayesian NetworkX", - "author": [ - "Michael D. Pacer" - ], + "author": ["Michael D. Pacer"], "authors": "Michael D. Pacer", "author_institution_map": { - "Michael D. Pacer": [ - "University of California at Berkeley" - ] + "Michael D. Pacer": ["University of California at Berkeley"] }, "bibliography": "myBibliography", "doi": "10.25080/Majora-7b98e3ed-016", "keywords": "probabilistic graphical models, causal theories, Bayesian networks, computational cognitive science, networkx", - "author_institution": [ - "University of California at Berkeley" - ], + "author_institution": ["University of California at Berkeley"], "video": "https://www.youtube.com/watch?v=qWAQgWOD_nA", "copyright_holder": "Michael D. Pacer.", - "author_email": [ - "mpacer@berkeley.edu" - ], + "author_email": ["mpacer@berkeley.edu"], "pages": 8 }, { @@ -897,11 +735,7 @@ "The deformation of the Earth surface reflects the action of several forces that act inside the planet. To understand how the Earth surface evolves complex models must be built to reconcile observations with theoretical numerical simulations. Starting from a well known numerical methodology already used among the geodynamic scientific community, PyGmod has been developed from scratch in the last year. The application simulates 2D large scale geodynamic processes by solving the conservation equations of mass, momentum, and energy by a finite difference method with a marker-in-cell technique.\nUnlike common simulation code written in Fortran or C this code is written in Python. The code implements a new approach that takes advantage of the hybrid architecture of the latest HPC machines. In PyGmod the standard MPI is coupled with a threading architecture to speed up some critical computations. Since the OpenMP API cannot be used with Python, threading is implemented in Cython. In addition a realtime visualization library has been developed to inspect the evolution of the model during the computation." ], "title": "Geodynamic simulations in HPC with Python", - "author": [ - "Nicola Creati", - "Roberto Vidmar", - "Paolo Sterzai" - ], + "author": ["Nicola Creati", "Roberto Vidmar", "Paolo Sterzai"], "authors": "Nicola Creati, Roberto Vidmar, Paolo Sterzai", "author_institution_map": { "Roberto Vidmar": [ @@ -943,26 +777,18 @@ "By providing the database and compute resources at http://qiita.microbio.me\nto the global community of microbiome researchers, Qiita alleviates the\ntechnical burdens, such as familiarity with the command line or access to\ncompute power, that are typically limiting for researchers studying\nmicrobial ecology, while at the same time promoting an open access culture.\nBecause Qiita is entirely open source and highly scalable, developers can\ninspect, customize, and extend it to suit their needs regardless of whether\nit is deployed as a desktop application or as a shared resource." ], "title": "Qiita: report of progress towards an open access microbiome data analysis and visualization platform", - "author": [ - "The Qiita Development Team" - ], + "author": ["The Qiita Development Team"], "authors": "The Qiita Development Team", "author_institution_map": { - "The Qiita Development Team": [ - "University of California, San Diego" - ] + "The Qiita Development Team": ["University of California, San Diego"] }, "bibliography": "", "doi": "10.25080/Majora-7b98e3ed-018", "keywords": "Microbiome, multi-omics, open science, metagenomics, metatranscriptomics,\nmetaproteomics, metabolomics", - "author_institution": [ - "University of California, San Diego" - ], + "author_institution": ["University of California, San Diego"], "video": "https://www.youtube.com/watch?v=TQzXwQ9Vx08", "copyright_holder": "The Qiita Development Team.", - "author_email": [ - "robknight@ucsd.edu" - ], + "author_email": ["robknight@ucsd.edu"], "pages": 6 }, { @@ -975,10 +801,7 @@ "In this paper we demonstrate how Python can be used throughout the\nentire life cycle of a graduate program in Data Science. In\ninterdisciplinary fields, such as Data Science, the students often\ncome from a variety of different backgrounds where, for example,\nsome students may have strong mathematical training but less\nexperience in programming. Python’s ease of use, open source\nlicense, and access to a vast array of libraries make it\nparticularly suited for such students. In particular, we will\ndiscuss how Python, IPython notebooks, scikit-learn, NumPy, SciPy,\nand pandas can be used in several phases of graduate Data Science\neducation, starting from introductory classes (covering topics such\nas data gathering, data cleaning, statistics, regression,\nclassification, machine learning, etc.) and culminating in degree\ncapstone research projects using more advanced ideas such as convex\noptimization, non-linear dimension reduction, and compressed\nsensing. One particular item of note is the scikit-learn library,\nwhich provides numerous routines for machine learning. Having\naccess to such a library allows interesting problems to be addressed\nearly in the educational process and the experience gained with such\n“black box” routines provides a firm foundation for the students own\nsoftware development, analysis, and research later in their academic\nexperience." ], "title": "Python in Data Science Research and Education", - "author": [ - "Randy Paffenroth", - "Xiangnan Kong" - ], + "author": ["Randy Paffenroth", "Xiangnan Kong"], "authors": "Randy Paffenroth, Xiangnan Kong", "author_institution_map": { "Randy Paffenroth": [ @@ -997,10 +820,7 @@ ], "video": "https://www.youtube.com/watch?v=EUEHOYl0mRg", "copyright_holder": "Randy Paffenroth et al.", - "author_email": [ - "rcpaffenroth@wpi.edu", - "xkong@wpi.edu" - ], + "author_email": ["rcpaffenroth@wpi.edu", "xkong@wpi.edu"], "pages": 7 }, { @@ -1011,32 +831,19 @@ }, "abstract": [], "title": "Relation: The Missing Container", - "author": [ - "Scott James", - "James Larkin" - ], + "author": ["Scott James", "James Larkin"], "authors": "Scott James, James Larkin", "author_institution_map": { - "Scott James": [ - "Noblis" - ], - "James Larkin": [ - "Noblis" - ] + "Scott James": ["Noblis"], + "James Larkin": ["Noblis"] }, "bibliography": "", "doi": "10.25080/Majora-7b98e3ed-01a", "keywords": "", - "author_institution": [ - "Noblis", - "Noblis" - ], + "author_institution": ["Noblis", "Noblis"], "video": "", "copyright_holder": "Scott James et al.", - "author_email": [ - "scott.james@noblis.org", - "james.larkin@noblis.org" - ], + "author_email": ["scott.james@noblis.org", "james.larkin@noblis.org"], "pages": 4 }, { @@ -1049,26 +856,18 @@ "We introduce BigBang, a new Python toolkit for analyzing\nonline collaborative communities such as those that\nbuild open source software.\nMailing lists serve as critical communications infrastructure for\nmany communities, including several of the open source software\ndevelopment communities that build scientific Python packages.\nBigBang provides tools for analyzing mailing lists.\nAs a demonstration, in this paper we test a generative\nmodel of network growth on collaborative communities.\nWe derive social networks from archival mailing list history\nand test the Barabási-Alpert model against this data.\nWe find the model does not fit the data, but that mailing list\nsocial networks share statistical regularities.\nThis suggests room for a new generative model of network formation\nin the open collaborative setting." ], "title": "Testing Generative Models of Online Collaboration with BigBang", - "author": [ - "Sebastian Benthall" - ], + "author": ["Sebastian Benthall"], "authors": "Sebastian Benthall", "author_institution_map": { - "Sebastian Benthall": [ - "UC Berkeley School of Information" - ] + "Sebastian Benthall": ["UC Berkeley School of Information"] }, "bibliography": "", "doi": "10.25080/Majora-7b98e3ed-01b", "keywords": "mailing lists, network analysis, assortativity, power law distributions,\ncollaboration", - "author_institution": [ - "UC Berkeley School of Information" - ], + "author_institution": ["UC Berkeley School of Information"], "video": "https://www.youtube.com/watch?v=AQFS_ES7rT0", "copyright_holder": "Sebastian Benthall.", - "author_email": [ - "sb@ischool.berkeley.edu" - ], + "author_email": ["sb@ischool.berkeley.edu"], "pages": 7 }, { @@ -1081,11 +880,7 @@ "This article presents an open-source Python software package, dubbed RTGraph, to visualize, process and record physiological signals (electrocardiography, electromyography, etc.) in real-time. RTGraph has a multiprocess architecture. This allows RTGraph to take advantage of multiple cores and to be able to handle data rates typically encountered during the acquisition and processing of biomedical signals. It also allows RTGraph to have a clean separation between the communication and visualization code. The paper presents the architecture and some programming details of RTGraph. It also includes three examples where RTGraph was adapted to work with (i) signals from a Inertial Measurement Unit (IMU) in the context of a biomechanical experiment; (ii) electromyography signals to estimate muscle fatigue; and (iii) pressure signals from a device used to monitor nutrition disorders in premature infants." ], "title": "Visualizing physiological signals in real-time", - "author": [ - "Sebastián Sepúlveda", - "Pablo Reyes", - "Alejandro Weinstein" - ], + "author": ["Sebastián Sepúlveda", "Pablo Reyes", "Alejandro Weinstein"], "authors": "Sebastián Sepúlveda, Pablo Reyes, Alejandro Weinstein", "author_institution_map": { "Alejandro Weinstein": [ @@ -1127,9 +922,7 @@ "In this paper we define a reproducibility record atom and the cloud\ninfrastructure to support it. We also provide a use case example with the\nevent based simulation management tool Sumatra and the container system\nDocker." ], "title": "Building a Cloud Service for Reproducible Simulation Management", - "author": [ - "Faical Yannick Palingwende Congo" - ], + "author": ["Faical Yannick Palingwende Congo"], "authors": "Faical Yannick Palingwende Congo", "author_institution_map": { "Faical Yannick Palingwende Congo": [ @@ -1144,9 +937,7 @@ ], "video": "https://www.youtube.com/watch?v=euMLYw7SNdk", "copyright_holder": "Faical Yannick Palingwende Congo.", - "author_email": [ - "yannick.congo@gmail.com" - ], + "author_email": ["yannick.congo@gmail.com"], "pages": 7 } ] diff --git a/publisher/metadata/2016/scipy_proc.json b/publisher/metadata/2016/scipy_proc.json index 030f33e36c..b792c0dacd 100644 --- a/publisher/metadata/2016/scipy_proc.json +++ b/publisher/metadata/2016/scipy_proc.json @@ -303,10 +303,7 @@ }, "dates": "July 11 - July 17", "year": "2016", - "editor": [ - "Sebastian Benthall", - "Scott Rostrup" - ], + "editor": ["Sebastian Benthall", "Scott Rostrup"], "xref": { "prefix": "10.25080", "resource_url": "https://conference.scipy.org/proceedings", diff --git a/publisher/metadata/2016/toc.json b/publisher/metadata/2016/toc.json index 5e0e7947f3..69d17e5921 100644 --- a/publisher/metadata/2016/toc.json +++ b/publisher/metadata/2016/toc.json @@ -21,16 +21,12 @@ "Universidad de Valparaiso, Chile", "Advanced Center for Electrical and Electronic Engineering" ], - "Myriam Fuentes": [ - "Universidad de Valparaiso, Chile" - ], + "Myriam Fuentes": ["Universidad de Valparaiso, Chile"], "Alejandro Weinstein": [ "Universidad de Valparaiso, Chile", "Advanced Center for Electrical and Electronic Engineering" ], - "Stéren Chabert": [ - "Universidad de Valparaiso, Chile" - ] + "Stéren Chabert": ["Universidad de Valparaiso, Chile"] }, "paper_id": "alejandro_weinstein", "bibliography": "", @@ -55,22 +51,15 @@ ] }, { - "author": [ - "Andrew M. Fraser", - "Stephen A. Andrews" - ], + "author": ["Andrew M. Fraser", "Stephen A. Andrews"], "author_institution": [ "XCP-8, Los Alamos National Laboratory", "XCP-8, Los Alamos National Laboratory" ], "pages": 8, "author_institution_map": { - "Andrew M. Fraser": [ - "XCP-8, Los Alamos National Laboratory" - ], - "Stephen A. Andrews": [ - "XCP-8, Los Alamos National Laboratory" - ] + "Andrew M. Fraser": ["XCP-8, Los Alamos National Laboratory"], + "Stephen A. Andrews": ["XCP-8, Los Alamos National Laboratory"] }, "paper_id": "andrew_fraser", "bibliography": "", @@ -87,23 +76,14 @@ "start": 7, "stop": 14 }, - "author_email": [ - "afraser@lanl.gov", - "saandrews@lanl.gov" - ] + "author_email": ["afraser@lanl.gov", "saandrews@lanl.gov"] }, { - "author": [ - "Anton Malakhov" - ], - "author_institution": [ - "Intel Corporation" - ], + "author": ["Anton Malakhov"], + "author_institution": ["Intel Corporation"], "pages": 5, "author_institution_map": { - "Anton Malakhov": [ - "Intel Corporation" - ] + "Anton Malakhov": ["Intel Corporation"] }, "paper_id": "anton_malakhov", "bibliography": "", @@ -120,22 +100,14 @@ "start": 15, "stop": 19 }, - "author_email": [ - "Anton.Malakhov@intel.com" - ] + "author_email": ["Anton.Malakhov@intel.com"] }, { - "author": [ - "Ben Lasscock" - ], - "author_institution": [ - "Geotrace Technologies" - ], + "author": ["Ben Lasscock"], + "author_institution": ["Geotrace Technologies"], "pages": 7, "author_institution_map": { - "Ben Lasscock": [ - "Geotrace Technologies" - ] + "Ben Lasscock": ["Geotrace Technologies"] }, "paper_id": "ben_lasscock", "bibliography": "", @@ -152,9 +124,7 @@ "start": 20, "stop": 26 }, - "author_email": [ - "blasscoc@gmail.com" - ] + "author_email": ["blasscoc@gmail.com"] }, { "author": [ @@ -175,15 +145,9 @@ ], "pages": 9, "author_institution_map": { - "Stéfan van der Walt": [ - "University of California, Berkeley" - ], - "Brett Naul": [ - "University of California, Berkeley" - ], - "Arien Crellin-Quick": [ - "University of California, Berkeley" - ], + "Stéfan van der Walt": ["University of California, Berkeley"], + "Brett Naul": ["University of California, Berkeley"], + "Arien Crellin-Quick": ["University of California, Berkeley"], "Joshua S. Bloom": [ "Lawrence Berkeley National Laboratory", "University of California, Berkeley" @@ -217,10 +181,7 @@ ] }, { - "author": [ - "Bryan W. Weber", - "Chih-Jen Sung" - ], + "author": ["Bryan W. Weber", "Chih-Jen Sung"], "author_institution": [ "Mechanical Engineering Department, University of Connecticut, Storrs, CT 06269", "Mechanical Engineering Department, University of Connecticut, Storrs, CT 06269" @@ -249,17 +210,10 @@ "start": 36, "stop": 44 }, - "author_email": [ - "bryan.w.weber@gmail.com", - "chih-jen.sung@uconn.edu" - ] + "author_email": ["bryan.w.weber@gmail.com", "chih-jen.sung@uconn.edu"] }, { - "author": [ - "Christian Schou Oxvig", - "Thomas Arildsen", - "Torben Larsen" - ], + "author": ["Christian Schou Oxvig", "Thomas Arildsen", "Torben Larsen"], "author_institution": [ "Faculty of Engineering and Science, Department of Electronic Systems, Aalborg University, 9220 Aalborg, Denmark", "Faculty of Engineering and Science, Department of Electronic Systems, Aalborg University, 9220 Aalborg, Denmark", @@ -292,11 +246,7 @@ "start": 45, "stop": 50 }, - "author_email": [ - "cso@es.aau.dk", - "tha@es.aau.dk", - "tl@es.aau.dk" - ] + "author_email": ["cso@es.aau.dk", "tha@es.aau.dk", "tl@es.aau.dk"] }, { "author": [ @@ -316,22 +266,14 @@ ], "pages": 6, "author_institution_map": { - "David L. Dotson": [ - "Arizona State University, Tempe, Arizona, USA" - ], - "Oliver Beckstein": [ - "Arizona State University, Tempe, Arizona, USA" - ], + "David L. Dotson": ["Arizona State University, Tempe, Arizona, USA"], + "Oliver Beckstein": ["Arizona State University, Tempe, Arizona, USA"], "Richard J. Gowers": [ "University of Manchester, Manchester, UK", "University of Edinburgh, Edinburgh, UK" ], - "Max Linke": [ - "Max Planck Institut für Biophysik, Frankfurt, Germany" - ], - "Sean L. Seyler": [ - "Arizona State University, Tempe, Arizona, USA" - ] + "Max Linke": ["Max Planck Institut für Biophysik, Frankfurt, Germany"], + "Sean L. Seyler": ["Arizona State University, Tempe, Arizona, USA"] }, "paper_id": "david_dotson", "bibliography": "", @@ -357,9 +299,7 @@ ] }, { - "author": [ - "David Nicholson" - ], + "author": ["David Nicholson"], "author_institution": [ "Emory University, graduate program in Neuroscience, Biology department" ], @@ -384,9 +324,7 @@ "start": 57, "stop": 61 }, - "author_email": [ - "dnicho4@emory.edu" - ] + "author_email": ["dnicho4@emory.edu"] }, { "author": [ @@ -416,27 +354,18 @@ "start": 62, "stop": 68 }, - "author_email": [ - "jonathon.j.smith@jpl.nasa.gov" - ] + "author_email": ["jonathon.j.smith@jpl.nasa.gov"] }, { - "author": [ - "Joy Merwin Monteiro", - "Rodrigo Caballero" - ], + "author": ["Joy Merwin Monteiro", "Rodrigo Caballero"], "author_institution": [ "MISU, Stockholm University", "MISU, Stockholm University" ], "pages": 6, "author_institution_map": { - "Joy Merwin Monteiro": [ - "MISU, Stockholm University" - ], - "Rodrigo Caballero": [ - "MISU, Stockholm University" - ] + "Joy Merwin Monteiro": ["MISU, Stockholm University"], + "Rodrigo Caballero": ["MISU, Stockholm University"] }, "paper_id": "joy_monteiro", "bibliography": "climt", @@ -453,17 +382,10 @@ "start": 69, "stop": 74 }, - "author_email": [ - "joy.merwin@gmail.com", - "rodrigo@misu.su.se" - ] + "author_email": ["joy.merwin@gmail.com", "rodrigo@misu.su.se"] }, { - "author": [ - "Juan Shishido", - "Jaya Narasimhan", - "Matar Haller" - ], + "author": ["Juan Shishido", "Jaya Narasimhan", "Matar Haller"], "author_institution": [ "School of Information, University of California, Berkeley", "Department of Electrical Engineering and Computer Science, University of California, Berkeley", @@ -503,9 +425,7 @@ ] }, { - "author": [ - "Kyle E. Niemeyer" - ], + "author": ["Kyle E. Niemeyer"], "author_institution": [ "School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University" ], @@ -530,27 +450,18 @@ "start": 82, "stop": 89 }, - "author_email": [ - "Kyle.Niemeyer@oregonstate.edu" - ] + "author_email": ["Kyle.Niemeyer@oregonstate.edu"] }, { - "author": [ - "Michael D. Pacer", - "Jordan W. Suchow" - ], + "author": ["Michael D. Pacer", "Jordan W. Suchow"], "author_institution": [ "University of California, Berkeley", "University of California, Berkeley" ], "pages": 8, "author_institution_map": { - "Michael D. Pacer": [ - "University of California, Berkeley" - ], - "Jordan W. Suchow": [ - "University of California, Berkeley" - ] + "Michael D. Pacer": ["University of California, Berkeley"], + "Jordan W. Suchow": ["University of California, Berkeley"] }, "paper_id": "mikejordan_pacersuchow", "bibliography": "mybib", @@ -569,9 +480,7 @@ "start": 90, "stop": 97 }, - "author_email": [ - "mpacer@berkeley.edu" - ] + "author_email": ["mpacer@berkeley.edu"] }, { "author": [ @@ -603,9 +512,7 @@ ], "pages": 8, "author_institution_map": { - "Jan Domański": [ - "University of Oxford, Oxford, UK" - ], + "Jan Domański": ["University of Oxford, Oxford, UK"], "Sébastien Buchoux": [ "Université de Picardie Jules Verne, Amiens, France" ], @@ -616,27 +523,15 @@ "University of Manchester, Manchester, UK", "University of Edinburgh, Edinburgh, UK" ], - "Ian M. Kenney": [ - "Arizona State University, Tempe, Arizona, USA" - ], - "Tyler J. E. Reddy": [ - "University of Oxford, Oxford, UK" - ], - "David L. Dotson": [ - "Arizona State University, Tempe, Arizona, USA" - ], - "Oliver Beckstein": [ - "Arizona State University, Tempe, Arizona, USA" - ], + "Ian M. Kenney": ["Arizona State University, Tempe, Arizona, USA"], + "Tyler J. E. Reddy": ["University of Oxford, Oxford, UK"], + "David L. Dotson": ["Arizona State University, Tempe, Arizona, USA"], + "Oliver Beckstein": ["Arizona State University, Tempe, Arizona, USA"], "Manuel N. Melo": [ "University of Groningen, Groningen, The Netherlands" ], - "Max Linke": [ - "Max Planck Institut für Biophysik, Frankfurt, Germany" - ], - "Sean L. Seyler": [ - "Arizona State University, Tempe, Arizona, USA" - ] + "Max Linke": ["Max Planck Institut für Biophysik, Frankfurt, Germany"], + "Sean L. Seyler": ["Arizona State University, Tempe, Arizona, USA"] }, "paper_id": "oliver_beckstein", "bibliography": "mdanalysis", @@ -718,9 +613,7 @@ ] }, { - "author": [ - "Prabhu Ramachandran" - ], + "author": ["Prabhu Ramachandran"], "author_institution": [ "Department of Aerospace Engineering", "IIT Bombay, Mumbai, India" @@ -748,14 +641,10 @@ "start": 114, "stop": 121 }, - "author_email": [ - "prabhu@aero.iitb.ac.in" - ] + "author_email": ["prabhu@aero.iitb.ac.in"] }, { - "author": [ - "Prabhu Ramachandran" - ], + "author": ["Prabhu Ramachandran"], "author_institution": [ "Department of Aerospace Engineering", "IIT Bombay, Mumbai, India" @@ -783,9 +672,7 @@ "start": 122, "stop": 129 }, - "author_email": [ - "prabhu@aero.iitb.ac.in" - ] + "author_email": ["prabhu@aero.iitb.ac.in"] }, { "author": [ @@ -814,10 +701,7 @@ "kit.plummer@ionchannel.io", "Ion Channel, ionchannel.io" ], - "JC Herz": [ - "jc.herz@ionchannel.io", - "Ion Channel, ionchannel.io" - ], + "JC Herz": ["jc.herz@ionchannel.io", "Ion Channel, ionchannel.io"], "Travis Pinney": [ "Ion Channel, ionchannel.io", "travis.pinney@ionchannel.io" @@ -846,10 +730,7 @@ ] }, { - "author": [ - "Yu Feng", - "Nick Hand" - ], + "author": ["Yu Feng", "Nick Hand"], "author_institution": [ "Berkeley Center for Cosmological Physics, University of California, Berkeley CA 94720", "Berkeley Institute for Data Science, University of California, Berkeley CA 94720", @@ -880,10 +761,7 @@ "start": 137, "stop": 143 }, - "author_email": [ - "yfeng1@berkeley.edu", - "nhand@berkeley.edu" - ] + "author_email": ["yfeng1@berkeley.edu", "nhand@berkeley.edu"] } ] } diff --git a/publisher/metadata/2018/toc.json b/publisher/metadata/2018/toc.json index da49cfc4a6..994d01bf58 100644 --- a/publisher/metadata/2018/toc.json +++ b/publisher/metadata/2018/toc.json @@ -90,26 +90,16 @@ "video": "https://youtu.be/D9LPzqoZ3f8", "doi": "10.25080/Majora-4af1f417-001", "pages": 7, - "author_email": [ - "richiehalford@gmail.com", - "arokem@gmail.com" - ], + "author_email": ["richiehalford@gmail.com", "arokem@gmail.com"], "author_institution": [ "University of Washington, Seattle, WA", "University of Washington, Seattle, WA" ], "copyright_holder": "Adam Richie-Halford et al.", - "author": [ - "Adam Richie-Halford", - "Ariel Rokem" - ], + "author": ["Adam Richie-Halford", "Ariel Rokem"], "author_institution_map": { - "Adam Richie-Halford": [ - "University of Washington, Seattle, WA" - ], - "Ariel Rokem": [ - "University of Washington, Seattle, WA" - ] + "Adam Richie-Halford": ["University of Washington, Seattle, WA"], + "Ariel Rokem": ["University of Washington, Seattle, WA"] }, "bibliography": "mybib", "authors": "Adam Richie-Halford, Ariel Rokem" @@ -141,25 +131,12 @@ "University of California, Berkeley" ], "copyright_holder": "Anthony Suen et al.", - "author": [ - "Anthony Suen", - "Laura Norén", - "Alan Liang", - "Andrea Tu" - ], + "author": ["Anthony Suen", "Laura Norén", "Alan Liang", "Andrea Tu"], "author_institution_map": { - "Laura Norén": [ - "New York University" - ], - "Andrea Tu": [ - "University of California, Berkeley" - ], - "Alan Liang": [ - "University of California, Berkeley" - ], - "Anthony Suen": [ - "University of California, Berkeley" - ] + "Laura Norén": ["New York University"], + "Andrea Tu": ["University of California, Berkeley"], + "Alan Liang": ["University of California, Berkeley"], + "Anthony Suen": ["University of California, Berkeley"] }, "bibliography": "", "authors": "Anthony Suen, Laura Norén, Alan Liang, Andrea Tu" @@ -198,18 +175,10 @@ "Terry Wilmarth" ], "author_institution_map": { - "Anton Malakhov": [ - "Intel Corporation" - ], - "Anton Gorshkov": [ - "Intel Corporation" - ], - "David Liu": [ - "Intel Corporation" - ], - "Terry Wilmarth": [ - "Intel Corporation" - ] + "Anton Malakhov": ["Intel Corporation"], + "Anton Gorshkov": ["Intel Corporation"], + "David Liu": ["Intel Corporation"], + "Terry Wilmarth": ["Intel Corporation"] }, "bibliography": "", "authors": "Anton Malakhov, David Liu, Anton Gorshkov, Terry Wilmarth" @@ -251,21 +220,11 @@ "Matthew N. White" ], "author_institution_map": { - "Alexander M. Kaufman": [ - "Woodrow Wilson School of Public Policy" - ], - "Nathan M. Palmer": [ - "Econ-ARK" - ], - "Matthew N. White": [ - "University of Delaware" - ], - "Christopher D. Carroll": [ - "Johns Hopkins University" - ], - "Jacqueline L. Kazil": [ - "Capital One" - ] + "Alexander M. Kaufman": ["Woodrow Wilson School of Public Policy"], + "Nathan M. Palmer": ["Econ-ARK"], + "Matthew N. White": ["University of Delaware"], + "Christopher D. Carroll": ["Johns Hopkins University"], + "Jacqueline L. Kazil": ["Capital One"] }, "bibliography": "", "authors": "Christopher D. Carroll, Alexander M. Kaufman, Jacqueline L. Kazil, Nathan M. Palmer, Matthew N. White" @@ -284,26 +243,13 @@ "video": "", "doi": "10.25080/Majora-4af1f417-005", "pages": 10, - "author_email": [ - "clm121@uga.edu", - "spq@uga.edu" - ], - "author_institution": [ - "University of Georgia", - "University of Georgia" - ], + "author_email": ["clm121@uga.edu", "spq@uga.edu"], + "author_institution": ["University of Georgia", "University of Georgia"], "copyright_holder": "Christian McDaniel et al.", - "author": [ - "Christian McDaniel", - "Shannon Quinn" - ], + "author": ["Christian McDaniel", "Shannon Quinn"], "author_institution_map": { - "Christian McDaniel": [ - "University of Georgia" - ], - "Shannon Quinn": [ - "University of Georgia" - ] + "Christian McDaniel": ["University of Georgia"], + "Shannon Quinn": ["University of Georgia"] }, "bibliography": "citations", "authors": "Christian McDaniel, Shannon Quinn" @@ -345,21 +291,11 @@ "Lee Armus" ], "author_institution_map": { - "Ben Rusholme": [ - "Caltech/IPAC" - ], - "Russ Laher": [ - "Caltech/IPAC" - ], - "David Shupe": [ - "Caltech/IPAC" - ], - "Lee Armus": [ - "Caltech/IPAC" - ], - "Frank Masci": [ - "Caltech/IPAC" - ] + "Ben Rusholme": ["Caltech/IPAC"], + "Russ Laher": ["Caltech/IPAC"], + "David Shupe": ["Caltech/IPAC"], + "Lee Armus": ["Caltech/IPAC"], + "Frank Masci": ["Caltech/IPAC"] }, "bibliography": "mybib", "authors": "David Shupe, Frank Masci, Russ Laher, Ben Rusholme, Lee Armus" @@ -411,30 +347,16 @@ "Gregory Brener" ], "author_institution_map": { - "Kevin Winters": [ - "US Army Engineer Research and Development Center" - ], - "Christopher E. Ball": [ - "Anaconda, Inc." - ], - "Gregory Brener": [ - "Anaconda, Inc." - ], - "Philipp J. F. Rudiger": [ - "Anaconda, Inc." - ], + "Kevin Winters": ["US Army Engineer Research and Development Center"], + "Christopher E. Ball": ["Anaconda, Inc."], + "Gregory Brener": ["Anaconda, Inc."], + "Philipp J. F. Rudiger": ["Anaconda, Inc."], "Scott Christensen": [ "US Army Engineer Research and Development Center" ], - "James A Bednar": [ - "Anaconda, Inc." - ], - "Kimberly Pevey": [ - "US Army Engineer Research and Development Center" - ], - "Dharhas Pothina": [ - "US Army Engineer Research and Development Center" - ] + "James A Bednar": ["Anaconda, Inc."], + "Kimberly Pevey": ["US Army Engineer Research and Development Center"], + "Dharhas Pothina": ["US Army Engineer Research and Development Center"] }, "bibliography": "", "authors": "Dharhas Pothina, Philipp J. F. Rudiger, James A Bednar, Scott Christensen, Kevin Winters, Kimberly Pevey, Christopher E. Ball, Gregory Brener" @@ -453,20 +375,12 @@ "video": "https://youtu.be/2gkw2T5jAfo", "doi": "10.25080/Majora-4af1f417-008", "pages": 5, - "author_email": [ - "dniederhut@enthought.com" - ], - "author_institution": [ - "Enthought, Inc." - ], + "author_email": ["dniederhut@enthought.com"], + "author_institution": ["Enthought, Inc."], "copyright_holder": "Dillon Niederhut.", - "author": [ - "Dillon Niederhut" - ], + "author": ["Dillon Niederhut"], "author_institution_map": { - "Dillon Niederhut": [ - "Enthought, Inc." - ] + "Dillon Niederhut": ["Enthought, Inc."] }, "bibliography": "", "authors": "Dillon Niederhut" @@ -490,24 +404,13 @@ "mpacer.phd@gmail.com", "sebastian.bassi@globant.com" ], - "author_institution": [ - "Netflix", - "Globant" - ], + "author_institution": ["Netflix", "Globant"], "copyright_holder": "Elizabeth Seiver et al.", - "author": [ - "Elizabeth Seiver", - "M Pacer", - "Sebastian Bassi" - ], + "author": ["Elizabeth Seiver", "M Pacer", "Sebastian Bassi"], "author_institution_map": { "Elizabeth Seiver": [], - "Sebastian Bassi": [ - "Globant" - ], - "M Pacer": [ - "Netflix" - ] + "Sebastian Bassi": ["Globant"], + "M Pacer": ["Netflix"] }, "bibliography": "", "authors": "Elizabeth Seiver, M Pacer, Sebastian Bassi" @@ -526,20 +429,12 @@ "video": "https://youtu.be/xH5eVcb1SlA", "doi": "10.25080/Majora-4af1f417-00a", "pages": 4, - "author_email": [ - "hameerabbasi@yahoo.com" - ], - "author_institution": [ - "TU Darmstadt" - ], + "author_email": ["hameerabbasi@yahoo.com"], + "author_institution": ["TU Darmstadt"], "copyright_holder": "Hameer Abbasi.", - "author": [ - "Hameer Abbasi" - ], + "author": ["Hameer Abbasi"], "author_institution_map": { - "Hameer Abbasi": [ - "TU Darmstadt" - ] + "Hameer Abbasi": ["TU Darmstadt"] }, "bibliography": "references", "authors": "Hameer Abbasi" @@ -558,20 +453,12 @@ "video": "https://youtu.be/1ndo6C1KWjI", "doi": "10.25080/Majora-4af1f417-00b", "pages": 8, - "author_email": [ - "jon.mease@jhuapl.edu" - ], - "author_institution": [ - "Johns Hopkins Applied Physics Laboratory" - ], + "author_email": ["jon.mease@jhuapl.edu"], + "author_institution": ["Johns Hopkins Applied Physics Laboratory"], "copyright_holder": "Jon Mease.", - "author": [ - "Jon Mease" - ], + "author": ["Jon Mease"], "author_institution_map": { - "Jon Mease": [ - "Johns Hopkins Applied Physics Laboratory" - ] + "Jon Mease": ["Johns Hopkins Applied Physics Laboratory"] }, "bibliography": "mybib", "authors": "Jon Mease" @@ -601,21 +488,11 @@ "University of Wisconsin-{}-Madison" ], "copyright_holder": "Kyle F Sunden et al.", - "author": [ - "Kyle F Sunden", - "Blaise J Thompson", - "John C Wright" - ], + "author": ["Kyle F Sunden", "Blaise J Thompson", "John C Wright"], "author_institution_map": { - "Kyle F Sunden": [ - "University of Wisconsin-{}-Madison" - ], - "John C Wright": [ - "University of Wisconsin-{}-Madison" - ], - "Blaise J Thompson": [ - "University of Wisconsin-{}-Madison" - ] + "Kyle F Sunden": ["University of Wisconsin-{}-Madison"], + "John C Wright": ["University of Wisconsin-{}-Madison"], + "Blaise J Thompson": ["University of Wisconsin-{}-Madison"] }, "bibliography": "bib", "authors": "Kyle F Sunden, Blaise J Thompson, John C Wright" @@ -634,26 +511,16 @@ "video": "", "doi": "10.25080/Majora-4af1f417-00d", "pages": 7, - "author_email": [ - "mwickert@uccs.edu", - "csiddapp@uccs.edu" - ], + "author_email": ["mwickert@uccs.edu", "csiddapp@uccs.edu"], "author_institution": [ "University of Colorado Colorado Springs", "University of Colorado Colorado Springs" ], "copyright_holder": "Mark Wickert et al.", - "author": [ - "Mark Wickert", - "Chiranth Siddappa" - ], + "author": ["Mark Wickert", "Chiranth Siddappa"], "author_institution_map": { - "Mark Wickert": [ - "University of Colorado Colorado Springs" - ], - "Chiranth Siddappa": [ - "University of Colorado Colorado Springs" - ] + "Mark Wickert": ["University of Colorado Colorado Springs"], + "Chiranth Siddappa": ["University of Colorado Colorado Springs"] }, "bibliography": "", "authors": "Mark Wickert, Chiranth Siddappa" @@ -672,20 +539,12 @@ "video": "", "doi": "10.25080/Majora-4af1f417-00e", "pages": 8, - "author_email": [ - "mwickert@uccs.edu" - ], - "author_institution": [ - "University of Colorado Colorado Springs" - ], + "author_email": ["mwickert@uccs.edu"], + "author_institution": ["University of Colorado Colorado Springs"], "copyright_holder": "Mark Wickert.", - "author": [ - "Mark Wickert" - ], + "author": ["Mark Wickert"], "author_institution_map": { - "Mark Wickert": [ - "University of Colorado Colorado Springs" - ] + "Mark Wickert": ["University of Colorado Colorado Springs"] }, "bibliography": "", "authors": "Mark Wickert" @@ -715,10 +574,7 @@ "NASA Ames Research Center, MS245-6, Moffett Field, CA 94035-1000" ], "copyright_holder": "Matthew J. Shannon et al.", - "author": [ - "Matthew J. Shannon", - "Christiaan Boersma" - ], + "author": ["Matthew J. Shannon", "Christiaan Boersma"], "author_institution_map": { "Matthew J. Shannon": [ "Universities Space Research Association, Columbia, MD", @@ -780,9 +636,7 @@ "Massachusetts Institute of Technology, Cambridge, MA", "Woods Hole Oceanographic Institution, Woods Hole, MA" ], - "Joe Futrelle": [ - "Woods Hole Oceanographic Institution, Woods Hole, MA" - ] + "Joe Futrelle": ["Woods Hole Oceanographic Institution, Woods Hole, MA"] }, "bibliography": "", "authors": "Noelle A. Held, Jaclyn K. Saunders, Joe Futrelle, Mak A. Saito" @@ -854,51 +708,21 @@ "Carol Willing" ], "author_institution_map": { - "Project Jupyter": [ - "Project Jupyter" - ], - "Carol Willing": [ - "Cal Poly, San Luis Obispo" - ], - "Tim Head": [ - "Wild Tree Tech, Switzerland" - ], - "Chris Holdgraf": [ - "UC Berkeley" - ], - "Fernando Perez": [ - "UC Berkeley" - ], - "Gladys Nalvarte": [ - "Simula Research Lab" - ], - "Benjamin Ragan-Kelley": [ - "Simula Research Lab" - ], - "Brian Granger": [ - "Cal Poly, San Luis Obispo" - ], - "M Pacer": [ - "Netflix" - ], - "Matthias Bussonnier": [ - "UC Berkeley" - ], - "Yuvi Panda": [ - "UC Berkeley" - ], - "Kyle Kelley": [ - "Netflix" - ], - "Jessica Forde": [ - "Project Jupyter" - ], - "Andrew Osheroff": [ - "" - ], - "Jeremy Freeman": [ - "" - ] + "Project Jupyter": ["Project Jupyter"], + "Carol Willing": ["Cal Poly, San Luis Obispo"], + "Tim Head": ["Wild Tree Tech, Switzerland"], + "Chris Holdgraf": ["UC Berkeley"], + "Fernando Perez": ["UC Berkeley"], + "Gladys Nalvarte": ["Simula Research Lab"], + "Benjamin Ragan-Kelley": ["Simula Research Lab"], + "Brian Granger": ["Cal Poly, San Luis Obispo"], + "M Pacer": ["Netflix"], + "Matthias Bussonnier": ["UC Berkeley"], + "Yuvi Panda": ["UC Berkeley"], + "Kyle Kelley": ["Netflix"], + "Jessica Forde": ["Project Jupyter"], + "Andrew Osheroff": [""], + "Jeremy Freeman": [""] }, "bibliography": "binderbib", "authors": "Project Jupyter, Matthias Bussonnier, Jessica Forde, Jeremy Freeman, Brian Granger, Tim Head, Chris Holdgraf, Kyle Kelley, Gladys Nalvarte, Andrew Osheroff, M Pacer, Yuvi Panda, Fernando Perez, Benjamin Ragan-Kelley, Carol Willing" @@ -949,9 +773,7 @@ "Sergio Rey": [ "Center for Geospatial Sciences, University of California, Riverside" ], - "Levi Wolf": [ - "School of Geographical Sciences, University of Bristol" - ], + "Levi Wolf": ["School of Geographical Sciences, University of Bristol"], "Su Han": [ "Center for Geospatial Sciences, University of California, Riverside" ] @@ -1001,15 +823,9 @@ "Chemical and Biomolecular Engineering, University of Delaware", "Materials Science and Engineering, University of Delaware" ], - "Tyler B. Martin": [ - "National Institute of Standards and Technology" - ], - "Chad R. Snyder": [ - "National Institute of Standards and Technology" - ], - "Ronald L. Jones": [ - "National Institute of Standards and Technology" - ], + "Tyler B. Martin": ["National Institute of Standards and Technology"], + "Chad R. Snyder": ["National Institute of Standards and Technology"], + "Ronald L. Jones": ["National Institute of Standards and Technology"], "Thomas E. Gartner III": [ "Chemical and Biomolecular Engineering, University of Delaware" ] @@ -1040,10 +856,7 @@ "Institute for Globally Distributed Open Research and Education (IGDORE)" ], "copyright_holder": "Konstantinos Vamvourellis et al.", - "author": [ - "Konstantinos Vamvourellis", - "Marianne Corvellec" - ], + "author": ["Konstantinos Vamvourellis", "Marianne Corvellec"], "author_institution_map": { "Konstantinos Vamvourellis": [ "London School of Economics and Political Science" @@ -1070,26 +883,13 @@ "video": "https://youtu.be/3AuRW9kq89g", "doi": "10.25080/Majora-4af1f417-015", "pages": 7, - "author_email": [ - "virginia@mapbox.com", - "hofmann@mapbox.com" - ], - "author_institution": [ - "Mapbox", - "Mapbox" - ], + "author_email": ["virginia@mapbox.com", "hofmann@mapbox.com"], + "author_institution": ["Mapbox", "Mapbox"], "copyright_holder": "Virginia Ng et al.", - "author": [ - "Virginia Ng", - "Daniel Hofmann" - ], + "author": ["Virginia Ng", "Daniel Hofmann"], "author_institution_map": { - "Daniel Hofmann": [ - "Mapbox" - ], - "Virginia Ng": [ - "Mapbox" - ] + "Daniel Hofmann": ["Mapbox"], + "Virginia Ng": ["Mapbox"] }, "bibliography": "", "authors": "Virginia Ng, Daniel Hofmann" @@ -1197,25 +997,15 @@ "Hardik Ghaghada" ], "author_institution_map": { - "Ankit Javalkar": [ - "FOSSEE IIT Bombay, Mumbai, India" - ], - "Aditya Palaparthy": [ - "FOSSEE IIT Bombay, Mumbai, India" - ], - "Hardik Ghaghada": [ - "FOSSEE IIT Bombay, Mumbai, India" - ], - "Prathamesh Salunke": [ - "FOSSEE IIT Bombay, Mumbai, India" - ], + "Ankit Javalkar": ["FOSSEE IIT Bombay, Mumbai, India"], + "Aditya Palaparthy": ["FOSSEE IIT Bombay, Mumbai, India"], + "Hardik Ghaghada": ["FOSSEE IIT Bombay, Mumbai, India"], + "Prathamesh Salunke": ["FOSSEE IIT Bombay, Mumbai, India"], "Prabhu Ramachandran": [ "Department of Aerospace Engineering", "IIT Bombay, Mumbai, India" ], - "Mahesh Gudi": [ - "FOSSEE IIT Bombay, Mumbai, India" - ] + "Mahesh Gudi": ["FOSSEE IIT Bombay, Mumbai, India"] }, "bibliography": "", "authors": "Prabhu Ramachandran, Prathamesh Salunke, Ankit Javalkar, Aditya Palaparthy, Mahesh Gudi, Hardik Ghaghada" diff --git a/publisher/metadata/2020/other.json b/publisher/metadata/2020/other.json index 3158203a1f..9a33f7bfb8 100644 --- a/publisher/metadata/2020/other.json +++ b/publisher/metadata/2020/other.json @@ -43,9 +43,7 @@ { "slide_id": "carpenter", "title": "Open-source bioimage analysis software to accelerate drug discovery", - "authors": [ - "Anne Carpenter" - ], + "authors": ["Anne Carpenter"], "description": "", "zenodo_url": "https://zenodo.org/record/3961216", "doi": "10.25080/Majora-342d178e-01c" @@ -66,9 +64,7 @@ { "slide_id": "frictionless_data_slides", "title": "Frictionless Data for Reproducible Biology", - "authors": [ - "Lilly Winfree" - ], + "authors": ["Lilly Winfree"], "description": "This talk discusses how biologists can make their data more reproducible using Frictionless Data's open source Python libraries", "zenodo_url": "https://zenodo.org/record/3961220", "doi": "10.25080/Majora-342d178e-01e" @@ -95,9 +91,7 @@ { "slide_id": "mjolnir", "title": "Project Mjolnir: A Modular, Open-source Platform for Developing Scientific IoT Sensor Networks", - "authors": [ - "C.A.M. Gerlach" - ], + "authors": ["C.A.M. Gerlach"], "description": "From a humble beginning as a side effort using a Raspberry Pi to talk to lightning instruments, Project Mjolnir is evolving into a modular, open source client-server platform for developing scientific IoT sensor networks. Its goal is to enable scientists of many disciplines to employ low-cost hardware to robustly ingest, log and uplink periodic and on-demand science and engineering data and commands, controlled either autonomously or centrally, all with little or no bespoke code. The talk will discuss Mjolnir’s development and future, present examples of current projects built on it, and explore how to leverage it for new applications.", "zenodo_url": "https://zenodo.org/record/3961228", "doi": "10.25080/Majora-342d178e-020" @@ -105,9 +99,7 @@ { "slide_id": "pandera", "title": "Pandera: Statistical Data Validation of Pandas Dataframes", - "authors": [ - "Niels Bantilan" - ], + "authors": ["Niels Bantilan"], "description": "This talk introduces pandera, an open source Python package for pandas data validation. It covers data validation in theory and practice, and goes through a case study analysis of the Fatal Encounters dataset to demonstrate how pandera can be used to make data analysis and machine learning more reproducible, robust, and reliable.", "zenodo_url": "https://zenodo.org/record/3961230", "doi": "10.25080/Majora-342d178e-021" @@ -115,9 +107,7 @@ { "slide_id": "pyF4all", "title": "Molecular infrastructure for modeling viruses with pythonic-mediated packages: pyF4all", - "authors": [ - "Horacio V. Guzman" - ], + "authors": ["Horacio V. Guzman"], "description": "We model full viruses by coupling short highly-detailed molecular dynamics simulations with lower-resolution (but faster) continuum electrostatic models. Such multiscale approach enables to model a full virus in a desktop/small cluster-level infrastructure, which are available for most researchers. Here, we propose a first interfacing of the pythonic-like packages in a multiscale approach that automatizes the access to state-of-the-art biomolecular simulations via Jupyter Notebooks.", "zenodo_url": "https://zenodo.org/record/3961232", "doi": "10.25080/Majora-342d178e-022" @@ -125,9 +115,7 @@ { "slide_id": "pyhf", "title": "pyhf: a pure Python statistical fitting library with tensors and autograd", - "authors": [ - "Matthew Feickert" - ], + "authors": ["Matthew Feickert"], "description": "pyhf is a pure-Python implementation of the HistFactory statistical model for multi-bin histogram-based analysis with asymptotic interval estimation, and part of the Scikit-HEP project ecosystem. pyhf supports modern computational graph libraries as computational backends in order to make use of features such as auto-differentiation and GPU acceleration. Additionally, the statistical models are defined in a declarative JSON schema, readily enabling preservation and distribution through services such as the Durham High-Energy Physics Database (HEPData).", "zenodo_url": "https://zenodo.org/record/3961236", "doi": "10.25080/Majora-342d178e-023" @@ -135,9 +123,7 @@ { "slide_id": "rapids_datashader", "title": "Bringing GPU Support to Datashader: A RAPIDS Case Study", - "authors": [ - "Jon Mease" - ], + "authors": ["Jon Mease"], "description": "A case study on using RAPIDS technologies to add GPU support to the Datashader Python library", "zenodo_url": "https://zenodo.org/record/3961238", "doi": "10.25080/Majora-342d178e-024" @@ -145,9 +131,7 @@ { "slide_id": "scikit-multiflow", "title": "Learning from evolving data streams", - "authors": [ - "Jacob Montiel" - ], + "authors": ["Jacob Montiel"], "description": "A brief introduction to machine learning for evolving data streams. In this field data is assumed infinite and can change over time. scikit-multiflow, a package for stream learning in Python is also presented.", "zenodo_url": "https://zenodo.org/record/3961242", "doi": "10.25080/Majora-342d178e-025" @@ -155,11 +139,7 @@ { "slide_id": "spatial_algorithms_at_scale_with_spatialpandas", "title": "Spatial Algorithms at Scale with spatialpandas", - "authors": [ - "Dharhas Pothina", - "Kim Pevey", - "Adam Lewis" - ], + "authors": ["Dharhas Pothina", "Kim Pevey", "Adam Lewis"], "description": "How do you analyze 1 trillion rows of geospatial point data? We recently solved this problem using spatialpandas, dask, and parquet file format to efficiently build and execute spatial algorithms at scale. We compare the spatialpandas solution's performance with other cases, and discuss the tradeoffs with various approaches.", "zenodo_url": "https://zenodo.org/record/3961244", "doi": "10.25080/Majora-342d178e-026" @@ -169,9 +149,7 @@ { "slide_id": "holoviz", "title": "HoloViz: What's new and what's next", - "authors": [ - "James A. Bednar" - ], + "authors": ["James A. Bednar"], "description": "Updates and roadmaps for Panel, hvPlot, HoloViews, GeoViews, Datashader, Param, and Colorcet. The HoloViz suite of tools together form a unified approach for visualization from exploration to sharing applications and dashboards, building on the SciPy ecosystem to support easy visualization of large multidimensional or columnar datasets.", "zenodo_url": "https://zenodo.org/record/3961190", "doi": "10.25080/Majora-342d178e-028" @@ -179,9 +157,7 @@ { "slide_id": "matplotlib", "title": "SciPy Tools Plenary on Matplotlib", - "authors": [ - "Elliott Sales de Andrade" - ], + "authors": ["Elliott Sales de Andrade"], "description": "Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. This presentation summarizes changes over the past year, new features, and future plans.", "zenodo_url": "https://zenodo.org/record/3961192", "doi": "10.25080/Majora-342d178e-029" @@ -189,9 +165,7 @@ { "slide_id": "numba", "title": "SciPy Tools Plenary on Numba", - "authors": [ - "Siu Kwan Lam" - ], + "authors": ["Siu Kwan Lam"], "description": "Numba is a just-in-time compiler for a subset of Python. This is a short presentation of Numba updates for 2019-2020. ", "zenodo_url": "https://zenodo.org/record/3961194", "doi": "10.25080/Majora-342d178e-02a" @@ -216,9 +190,7 @@ { "slide_id": "metalearn", "title": "Building an AutoML System for Fun and Non-profit", - "authors": [ - "Niels Bantilan" - ], + "authors": ["Niels Bantilan"], "description": "This talk introduces metalearn, a MetaRL-based AutoML system that learns to learn how to propose hyperparameter selections that produce high validation scores on meta-test datasets.", "zenodo_url": "https://zenodo.org/record/3961204", "doi": "10.25080/Majora-342d178e-027" diff --git a/publisher/metadata/2020/toc.json b/publisher/metadata/2020/toc.json index 597eae180c..971a0369c6 100644 --- a/publisher/metadata/2020/toc.json +++ b/publisher/metadata/2020/toc.json @@ -2,15 +2,9 @@ "toc": [ { "author_institution_map": { - "Dillon Niederhut": [ - "Novi Labs" - ], - "Julie Hollek": [ - "Mozilla" - ], - "Meghann Agarwal": [ - "Oracle" - ] + "Dillon Niederhut": ["Novi Labs"], + "Julie Hollek": ["Mozilla"], + "Meghann Agarwal": ["Oracle"] }, "title": "Preface", "doi": "10.25080/Majora-342d178e-000", @@ -24,17 +18,9 @@ "start": 1, "stop": 1 }, - "author": [ - "Meghann Agarwal", - "Julie Hollek", - "Dillon Niederhut" - ], + "author": ["Meghann Agarwal", "Julie Hollek", "Dillon Niederhut"], "keywords": "", - "author_institution": [ - "Oracle", - "Mozilla", - "Novi Labs" - ], + "author_institution": ["Oracle", "Mozilla", "Novi Labs"], "video": "", "bibliography": "", "author_orcid_map": {}, @@ -44,15 +30,9 @@ }, { "author_institution_map": { - "José Unpingco": [ - "Gary and Mary West Health Institute" - ], - "Haw-minn Lu": [ - "Gary and Mary West Health Institute" - ], - "Adrian Kwong": [ - "Gary and Mary West Health Institute" - ] + "José Unpingco": ["Gary and Mary West Health Institute"], + "Haw-minn Lu": ["Gary and Mary West Health Institute"], + "Adrian Kwong": ["Gary and Mary West Health Institute"] }, "title": "Securing Your Collaborative Jupyter Notebooks in the Cloud using Container and Load Balancing Services", "doi": "10.25080/Majora-342d178e-001", @@ -67,11 +47,7 @@ "start": 2, "stop": 10 }, - "author": [ - "Haw-minn Lu", - "Adrian Kwong", - "José Unpingco" - ], + "author": ["Haw-minn Lu", "Adrian Kwong", "José Unpingco"], "keywords": "data science, infrastructure, jupyter, rstudio", "author_institution": [ "Gary and Mary West Health Institute", @@ -89,28 +65,20 @@ }, { "author_institution_map": { - "Haw-minn Lu": [ - "Gary and Mary West Health Institute" - ] + "Haw-minn Lu": ["Gary and Mary West Health Institute"] }, "title": "Quasi-orthonormal Encoding for Machine Learning Applications", "doi": "10.25080/Majora-342d178e-002", "paper_id": "75_lu", - "author_email": [ - "hlu@westhealth.org" - ], + "author_email": ["hlu@westhealth.org"], "pages": 7, "page": { "start": 11, "stop": 17 }, - "author": [ - "Haw-minn Lu" - ], + "author": ["Haw-minn Lu"], "keywords": "machine learning, classification, categorical encoding", - "author_institution": [ - "Gary and Mary West Health Institute" - ], + "author_institution": ["Gary and Mary West Health Institute"], "video": "", "bibliography": "ourbib", "author_orcid_map": {}, @@ -210,9 +178,7 @@ "title": "HOOMD-blue version 3.0 A Modern, Extensible, Flexible, Object-Oriented API for Molecular Simulations", "doi": "10.25080/Majora-342d178e-004", "paper_id": "brandon_butler", - "author_email": [ - "butlerbr@umich.edu" - ], + "author_email": ["butlerbr@umich.edu"], "pages": 8, "page": { "start": 24, @@ -261,19 +227,13 @@ "title": "Compyle: a Python package for parallel computing", "doi": "10.25080/Majora-342d178e-005", "paper_id": "compyle_pr_ab", - "author_email": [ - "adityapb1546@gmail.com", - "prabhu@aero.iitb.ac.in" - ], + "author_email": ["adityapb1546@gmail.com", "prabhu@aero.iitb.ac.in"], "pages": 8, "page": { "start": 32, "stop": 39 }, - "author": [ - "Aditya Bhosale", - "Prabhu Ramachandran" - ], + "author": ["Aditya Bhosale", "Prabhu Ramachandran"], "keywords": "High-performance computing, multi-core CPUs, GPGPU accelerators, parallel\nalgorithms, transpilation", "author_institution": [ "Department of Aerospace Engineering", @@ -322,11 +282,7 @@ "start": 40, "stop": 47 }, - "author": [ - "Dallin Skouson", - "Andrew Keller", - "Michael Wirthlin" - ], + "author": ["Dallin Skouson", "Andrew Keller", "Michael Wirthlin"], "keywords": "Hardware Design, Netlists, EDA, CAD", "author_institution": [ "NSF Center for Space, High-Performance, and Resilient Computing (SHREC)", @@ -350,9 +306,7 @@ }, { "author_institution_map": { - "Nina Miolane": [ - "Stanford University" - ], + "Nina Miolane": ["Stanford University"], "Xavier Pennec": [], "Alice Le Brigant": [], "Olivier Peltre": [], @@ -365,9 +319,7 @@ "Bernhard Kainz": [], "Hadi Zaatiti": [], "Johan Mathe": [], - "Nicolas Guigui": [ - "Université Côte d'Azur, Inria" - ], + "Nicolas Guigui": ["Université Côte d'Azur, Inria"], "Yann Thanwerdas": [], "Thomas Gerald": [], "Stefan Heyder": [], @@ -446,15 +398,9 @@ }, { "author_institution_map": { - "Jose Unpingco": [ - "Gary and Mary West Health Institute" - ], - "Giancarlo Perrone": [ - "Gary and Mary West Health Institute" - ], - "Haw-minn Lu": [ - "Gary and Mary West Health Institute" - ] + "Jose Unpingco": ["Gary and Mary West Health Institute"], + "Giancarlo Perrone": ["Gary and Mary West Health Institute"], + "Haw-minn Lu": ["Gary and Mary West Health Institute"] }, "title": "Network visualizations with Pyvis and VisJS", "doi": "10.25080/Majora-342d178e-008", @@ -469,11 +415,7 @@ "start": 58, "stop": 62 }, - "author": [ - "Giancarlo Perrone", - "Jose Unpingco", - "Haw-minn Lu" - ], + "author": ["Giancarlo Perrone", "Jose Unpingco", "Haw-minn Lu"], "keywords": "networks, graphs, relationship", "author_institution": [ "Gary and Mary West Health Institute", @@ -491,18 +433,10 @@ }, { "author_institution_map": { - "Jim Pivarski": [ - "Princeton University" - ], - "Hans Dembinski": [ - "TU Dortmund" - ], - "Shuo Liu": [ - "Sun Yat-sen University" - ], - "Henry Schreiner": [ - "Princeton University" - ] + "Jim Pivarski": ["Princeton University"], + "Hans Dembinski": ["TU Dortmund"], + "Shuo Liu": ["Sun Yat-sen University"], + "Henry Schreiner": ["Princeton University"] }, "title": "Boost-histogram: High-Performance Histograms as Objects", "doi": "10.25080/Majora-342d178e-009", @@ -554,17 +488,13 @@ "title": "Learning from evolving data streams", "doi": "10.25080/Majora-342d178e-00a", "paper_id": "jacob_montiel", - "author_email": [ - "jacob.montiel@waikato.ac.nz" - ], + "author_email": ["jacob.montiel@waikato.ac.nz"], "pages": 8, "page": { "start": 70, "stop": 77 }, - "author": [ - "Jacob Montiel" - ], + "author": ["Jacob Montiel"], "keywords": "machine learning, data streams, concept drift, scikit, open-source", "author_institution": [ "Department of Computer Science, University of Waikato" @@ -582,21 +512,11 @@ }, { "author_institution_map": { - "Ianna Osborne": [ - "Princeton University" - ], - "Jim Pivarski": [ - "Princeton University" - ], - "Anish Biswas": [ - "Manipal Institute of Technology" - ], - "Pratyush Das": [ - "Institute of Engineering and Management" - ], - "Peter Elmer": [ - "Princeton University" - ] + "Ianna Osborne": ["Princeton University"], + "Jim Pivarski": ["Princeton University"], + "Anish Biswas": ["Manipal Institute of Technology"], + "Pratyush Das": ["Institute of Engineering and Management"], + "Peter Elmer": ["Princeton University"] }, "title": "Awkward Array: JSON-like data, NumPy-like idioms", "doi": "10.25080/Majora-342d178e-00b", @@ -647,18 +567,10 @@ }, { "author_institution_map": { - "Yohann Dudouit": [ - "Lawrence Livermore National Laboratory" - ], - "Jeremy Thompson": [ - "University of Colorado Boulder" - ], - "Jed Brown": [ - "University of Colorado Boulder" - ], - "Valeria Barra": [ - "University of Colorado Boulder" - ] + "Yohann Dudouit": ["Lawrence Livermore National Laboratory"], + "Jeremy Thompson": ["University of Colorado Boulder"], + "Jed Brown": ["University of Colorado Boulder"], + "Valeria Barra": ["University of Colorado Boulder"] }, "title": "High-performance operator evaluations with ease of use: libCEED's Python interface", "doi": "10.25080/Majora-342d178e-00c", @@ -800,29 +712,19 @@ }, { "author_institution_map": { - "Mark Wickert": [ - "University of Colorado Colorado Springs" - ], - "David Peckham": [ - "University of Colorado Colorado Springs" - ] + "Mark Wickert": ["University of Colorado Colorado Springs"], + "David Peckham": ["University of Colorado Colorado Springs"] }, "title": "Matched Filter Mismatch Losses in MPSK and MQAM Using Semi-Analytic BEP Modeling", "doi": "10.25080/Majora-342d178e-00e", "paper_id": "mark_wickert_239", - "author_email": [ - "mwickert@uccs.edu", - "dpeckham@uccs.edu" - ], + "author_email": ["mwickert@uccs.edu", "dpeckham@uccs.edu"], "pages": 9, "page": { "start": 98, "stop": 106 }, - "author": [ - "Mark Wickert", - "David Peckham" - ], + "author": ["Mark Wickert", "David Peckham"], "keywords": "digital modulation, pulse shaping, phase-shift keying,\nquadrature amplitude modulation", "author_institution": [ "University of Colorado Colorado Springs", @@ -841,33 +743,20 @@ }, { "author_institution_map": { - "Nick Brown": [ - "EPCC at the University of Edinburgh" - ], - "Sihang Liu": [ - "EPCC at the University of Edinburgh" - ], - "Maurice Jamieson": [ - "EPCC at the University of Edinburgh" - ] + "Nick Brown": ["EPCC at the University of Edinburgh"], + "Sihang Liu": ["EPCC at the University of Edinburgh"], + "Maurice Jamieson": ["EPCC at the University of Edinburgh"] }, "title": "Having your cake and eating it: Exploiting Python for programmer productivity and performance on micro-core architectures using ePython", "doi": "10.25080/Majora-342d178e-00f", "paper_id": "maurice_jamieson", - "author_email": [ - "maurice.jamieson@ed.ac.uk", - "n.brown@epcc.ed.ac.uk" - ], + "author_email": ["maurice.jamieson@ed.ac.uk", "n.brown@epcc.ed.ac.uk"], "pages": 9, "page": { "start": 107, "stop": 115 }, - "author": [ - "Maurice Jamieson", - "Nick Brown", - "Sihang Liu" - ], + "author": ["Maurice Jamieson", "Nick Brown", "Sihang Liu"], "keywords": "ePython, micro-cores, RISC-V, MicroBlaze, PicoRV32, Epiphany", "author_institution": [ "EPCC at the University of Edinburgh", @@ -887,30 +776,20 @@ }, { "author_institution_map": { - "Niels Bantilan": [ - "Talkspace", - "pyOpenSci" - ] + "Niels Bantilan": ["Talkspace", "pyOpenSci"] }, "title": "pandera: Statistical Data Validation of Pandas Dataframes", "doi": "10.25080/Majora-342d178e-010", "paper_id": "niels_bantilan", - "author_email": [ - "niels.bantilan@gmail.com" - ], + "author_email": ["niels.bantilan@gmail.com"], "pages": 9, "page": { "start": 116, "stop": 124 }, - "author": [ - "Niels Bantilan" - ], + "author": ["Niels Bantilan"], "keywords": "data validation, data engineering", - "author_institution": [ - "Talkspace", - "pyOpenSci" - ], + "author_institution": ["Talkspace", "pyOpenSci"], "video": "https://youtu.be/PxTLD-ueNd4", "bibliography": "refs", "author_orcid_map": { @@ -924,29 +803,19 @@ }, { "author_institution_map": { - "Eric van Oort": [ - "The University of Texas at Austin" - ], - "Oney Erge": [ - "The University of Texas at Austin" - ] + "Eric van Oort": ["The University of Texas at Austin"], + "Oney Erge": ["The University of Texas at Austin"] }, "title": "Combining Physics-Based and Data-Driven Modeling for Pressure Prediction in Well Construction", "doi": "10.25080/Majora-342d178e-011", "paper_id": "oney_erge", - "author_email": [ - "oneyerge@utexas.edu", - "vanoort@austin.utexas.edu" - ], + "author_email": ["oneyerge@utexas.edu", "vanoort@austin.utexas.edu"], "pages": 7, "page": { "start": 125, "stop": 131 }, - "author": [ - "Oney Erge", - "Eric van Oort" - ], + "author": ["Oney Erge", "Eric van Oort"], "keywords": "deep learning, machine learning, combining physics-based modeling and data-driven modeling, hydraulics modeling, frictional pressure loss modeling.", "author_institution": [ "The University of Texas at Austin", @@ -978,12 +847,8 @@ "Nicole Lo": [ "Massachusetts Institute of Technology, Cambridge, MA, USA" ], - "Christopher J. Markiewicz": [ - "Stanford University, Stanford, CA, USA" - ], - "Oscar Esteban": [ - "Stanford University, Stanford, CA, USA" - ], + "Christopher J. Markiewicz": ["Stanford University, Stanford, CA, USA"], + "Oscar Esteban": ["Stanford University, Stanford, CA, USA"], "Mathias Goncalves": [ "Stanford University, Stanford, CA, USA", "Massachusetts Institute of Technology, Cambridge, MA, USA" @@ -1052,27 +917,15 @@ "Jinseop Park": [ "National Fusion Research Institute, Daejeon 34133, Republic of Korea" ], - "Jong Youl Choi": [ - "Oak Ridge National Laboratory" - ], - "R Michael Churchill": [ - "Princeton Plasma Physics Laboratory" - ], - "CS Chang": [ - "Princeton Plasma Physics Laboratory" - ], - "Ralph Kube": [ - "Princeton Plasma Physics Laboratory" - ], - "Ruonan Wang": [ - "Oak Ridge National Laboratory" - ], + "Jong Youl Choi": ["Oak Ridge National Laboratory"], + "R Michael Churchill": ["Princeton Plasma Physics Laboratory"], + "CS Chang": ["Princeton Plasma Physics Laboratory"], + "Ralph Kube": ["Princeton Plasma Physics Laboratory"], + "Ruonan Wang": ["Oak Ridge National Laboratory"], "Minjun J. Choi": [ "National Fusion Research Institute, Daejeon 34133, Republic of Korea" ], - "Scott Klasky": [ - "Oak Ridge National Laboratory" - ] + "Scott Klasky": ["Oak Ridge National Laboratory"] }, "title": "Leading magnetic fusion energy science into the big-and-fast data lane", "doi": "10.25080/Majora-342d178e-013", @@ -1124,9 +977,7 @@ }, { "author_institution_map": { - "Mark Reynolds": [ - "University of Western Australia" - ], + "Mark Reynolds": ["University of Western Australia"], "Andreas Wicenec": [ "International Centre for Radio Astronomy Research", "University of Western Australia" @@ -1149,11 +1000,7 @@ "start": 148, "stop": 155 }, - "author": [ - "Ryan W. Bunney", - "Andreas Wicenec", - "Mark Reynolds" - ], + "author": ["Ryan W. Bunney", "Andreas Wicenec", "Mark Reynolds"], "keywords": "", "author_institution": [ "International Centre for Radio Astronomy Research", @@ -1173,30 +1020,19 @@ }, { "author_institution_map": { - "Mridul Seth": [ - "Econ-ARK" - ], - "Sebastian Benthall": [ - "New York University School of Law", - "Econ-ARK" - ] + "Mridul Seth": ["Econ-ARK"], + "Sebastian Benthall": ["New York University School of Law", "Econ-ARK"] }, "title": "Software Engineering as Research Method: Aligning Roles in Econ-ARK", "doi": "10.25080/Majora-342d178e-015", "paper_id": "sebastian_benthall", - "author_email": [ - "spb413@nyu.edu", - "mseth@pm.me" - ], + "author_email": ["spb413@nyu.edu", "mseth@pm.me"], "pages": 6, "page": { "start": 156, "stop": 161 }, - "author": [ - "Sebastian Benthall", - "Mridul Seth" - ], + "author": ["Sebastian Benthall", "Mridul Seth"], "keywords": "computational method, computional thinking, constructionist learning, research software engineering", "author_institution": [ "New York University School of Law", @@ -1216,28 +1052,20 @@ }, { "author_institution_map": { - "Zac Hatfield-Dodds": [ - "Australian National University" - ] + "Zac Hatfield-Dodds": ["Australian National University"] }, "title": "Falsify your Software: validating scientific code with property-based testing", "doi": "10.25080/Majora-342d178e-016", "paper_id": "zac_hatfield-dodds", - "author_email": [ - "zac.hatfield.dodds@anu.edu.au" - ], + "author_email": ["zac.hatfield.dodds@anu.edu.au"], "pages": 4, "page": { "start": 162, "stop": 165 }, - "author": [ - "Zac Hatfield-Dodds" - ], + "author": ["Zac Hatfield-Dodds"], "keywords": "methods, software, validation, property-based testing", - "author_institution": [ - "Australian National University" - ], + "author_institution": ["Australian National University"], "video": "", "bibliography": "references", "author_orcid_map": { diff --git a/publisher/metadata/2021/toc.json b/publisher/metadata/2021/toc.json index 97f50cb3ae..cb40b45956 100644 --- a/publisher/metadata/2021/toc.json +++ b/publisher/metadata/2021/toc.json @@ -3,25 +3,15 @@ { "title": "How PDFrw and fillable forms improves throughput at a Covid-19 Vaccine Clinic", "authors": "Haw-minn Lu, José Unpingco", - "author": [ - "Haw-minn Lu", - "José Unpingco" - ], - "author_email": [ - "hlu@westhealth.org", - "jhunpingco@westhealth.org" - ], + "author": ["Haw-minn Lu", "José Unpingco"], + "author_email": ["hlu@westhealth.org", "jhunpingco@westhealth.org"], "author_institution": [ "Gary and Mary West Health Institute", "Gary and Mary West Health Institute" ], "author_institution_map": { - "Haw-minn Lu": [ - "Gary and Mary West Health Institute" - ], - "José Unpingco": [ - "Gary and Mary West Health Institute" - ] + "Haw-minn Lu": ["Gary and Mary West Health Institute"], + "José Unpingco": ["Gary and Mary West Health Institute"] }, "author_orcid_map": {}, "abstract": [ @@ -42,25 +32,12 @@ { "title": "Using Python for Analysis and Verification of Mixed-mode Signal Chains", "authors": "Mark Thoren, Cristina Suteu", - "author": [ - "Mark Thoren", - "Cristina Suteu" - ], - "author_email": [ - "mark.thoren@analog.com", - "cristina.suteu@analog.com" - ], - "author_institution": [ - "Analog Devices, Inc.", - "Analog Devices, Inc." - ], + "author": ["Mark Thoren", "Cristina Suteu"], + "author_email": ["mark.thoren@analog.com", "cristina.suteu@analog.com"], + "author_institution": ["Analog Devices, Inc.", "Analog Devices, Inc."], "author_institution_map": { - "Mark Thoren": [ - "Analog Devices, Inc." - ], - "Cristina Suteu": [ - "Analog Devices, Inc." - ] + "Mark Thoren": ["Analog Devices, Inc."], + "Cristina Suteu": ["Analog Devices, Inc."] }, "author_orcid_map": {}, "abstract": [ @@ -81,12 +58,8 @@ { "title": "Modernizing computing by structural biologists with Jupyter and Colab", "authors": "Blaine H. M. Mooers", - "author": [ - "Blaine H. M. Mooers" - ], - "author_email": [ - "blaine-mooers@ouhsc.edu" - ], + "author": ["Blaine H. M. Mooers"], + "author_email": ["blaine-mooers@ouhsc.edu"], "author_institution": [ "Dept of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 97104", "Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK 97104", @@ -266,25 +239,15 @@ { "title": "MPI-parallel Molecular Dynamics Trajectory Analysis with the H5MD Format in the MDAnalysis Python Package", "authors": "Edis Jakupovic, Oliver Beckstein", - "author": [ - "Edis Jakupovic", - "Oliver Beckstein" - ], - "author_email": [ - "ejakupov@asu.edu", - "obeckste@asu.edu" - ], + "author": ["Edis Jakupovic", "Oliver Beckstein"], + "author_email": ["ejakupov@asu.edu", "obeckste@asu.edu"], "author_institution": [ "Arizona State University", "Arizona State University" ], "author_institution_map": { - "Edis Jakupovic": [ - "Arizona State University" - ], - "Oliver Beckstein": [ - "Arizona State University" - ] + "Edis Jakupovic": ["Arizona State University"], + "Oliver Beckstein": ["Arizona State University"] }, "author_orcid_map": {}, "abstract": [ @@ -305,11 +268,7 @@ { "title": "Natural Language Processing with Pandas DataFrames", "authors": "Frederick Reiss, Bryan Cutler, Zachary Eichenberger", - "author": [ - "Frederick Reiss", - "Bryan Cutler", - "Zachary Eichenberger" - ], + "author": ["Frederick Reiss", "Bryan Cutler", "Zachary Eichenberger"], "author_email": [ "frreiss@us.ibm.com", "bjcutler@us.ibm.com", @@ -322,16 +281,9 @@ "IBM Research" ], "author_institution_map": { - "Frederick Reiss": [ - "IBM Research" - ], - "Bryan Cutler": [ - "IBM" - ], - "Zachary Eichenberger": [ - "University of Michigan", - "IBM Research" - ] + "Frederick Reiss": ["IBM Research"], + "Bryan Cutler": ["IBM"], + "Zachary Eichenberger": ["University of Michigan", "IBM Research"] }, "author_orcid_map": {}, "abstract": [ @@ -354,14 +306,8 @@ { "title": "CLAIMED, a visual and scalable component library for Trusted AI", "authors": "Romeo Kienzler, Ivan Nesic", - "author": [ - "Romeo Kienzler", - "Ivan Nesic" - ], - "author_email": [ - "romeo.kienzler@ch.ibm.com", - "ivan.nesic@usb.ch" - ], + "author": ["Romeo Kienzler", "Ivan Nesic"], + "author_email": ["romeo.kienzler@ch.ibm.com", "ivan.nesic@usb.ch"], "author_institution": [ "IBM, Center for Open Source Data and AI Technologies (CODAIT)", "University Hospital of Basel" @@ -370,9 +316,7 @@ "Romeo Kienzler": [ "IBM, Center for Open Source Data and AI Technologies (CODAIT)" ], - "Ivan Nesic": [ - "University Hospital of Basel" - ] + "Ivan Nesic": ["University Hospital of Basel"] }, "author_orcid_map": { "Ivan Nesic": "0000-0002-4373-8860" @@ -420,24 +364,12 @@ "University of Oxford" ], "author_institution_map": { - "James Fox": [ - "University of Oxford" - ], - "Tom Everitt": [ - "DeepMind" - ], - "Ryan Carey": [ - "University of Oxford" - ], - "Eric Langlois": [ - "University of Toronto" - ], - "Alessandro Abate": [ - "University of Oxford" - ], - "Michael Wooldridge": [ - "University of Oxford" - ] + "James Fox": ["University of Oxford"], + "Tom Everitt": ["DeepMind"], + "Ryan Carey": ["University of Oxford"], + "Eric Langlois": ["University of Toronto"], + "Alessandro Abate": ["University of Oxford"], + "Michael Wooldridge": ["University of Oxford"] }, "author_orcid_map": {}, "abstract": [ @@ -458,19 +390,11 @@ { "title": "Social Media Analysis using Natural Language Processing Techniques", "authors": "Jyotika Singh", - "author": [ - "Jyotika Singh" - ], - "author_email": [ - "singhjyotika811@gmail.com" - ], - "author_institution": [ - "ICX Media, Inc." - ], + "author": ["Jyotika Singh"], + "author_email": ["singhjyotika811@gmail.com"], + "author_institution": ["ICX Media, Inc."], "author_institution_map": { - "Jyotika Singh": [ - "ICX Media, Inc." - ] + "Jyotika Singh": ["ICX Media, Inc."] }, "author_orcid_map": { "Jyotika Singh": "0000-0002-5442-3004" @@ -560,10 +484,7 @@ { "title": "Conformal Mappings with SymPy: Towards Python-driven Analytical Modeling in Physics", "authors": "Zoufiné Lauer-Baré, Erich Gaertig", - "author": [ - "Zoufiné Lauer-Baré", - "Erich Gaertig" - ], + "author": ["Zoufiné Lauer-Baré", "Erich Gaertig"], "author_email": [ "zoufine.lauer-bare@hilite.com", "erich.gaertig@hilite.com" @@ -618,18 +539,10 @@ "Lockheed Martin Advanced Technology Laboratories" ], "author_institution_map": { - "Amanda E. Kraft": [ - "Lockheed Martin Advanced Technology Laboratories" - ], - "Matthew Widjaja": [ - "Lockheed Martin Advanced Technology Laboratories" - ], - "Trevor M. Sands": [ - "Lockheed Martin Advanced Technology Laboratories" - ], - "Brad J. Galego": [ - "Lockheed Martin Advanced Technology Laboratories" - ] + "Amanda E. Kraft": ["Lockheed Martin Advanced Technology Laboratories"], + "Matthew Widjaja": ["Lockheed Martin Advanced Technology Laboratories"], + "Trevor M. Sands": ["Lockheed Martin Advanced Technology Laboratories"], + "Brad J. Galego": ["Lockheed Martin Advanced Technology Laboratories"] }, "author_orcid_map": {}, "abstract": [ @@ -650,25 +563,15 @@ { "title": "How PDFrw and fillable forms improves throughput at a Covid-19 Vaccine Clinic", "authors": "Haw-minn Lu, José Unpingco", - "author": [ - "Haw-minn Lu", - "José Unpingco" - ], - "author_email": [ - "hlu@westhealth.org", - "jhunpingco@westhealth.org" - ], + "author": ["Haw-minn Lu", "José Unpingco"], + "author_email": ["hlu@westhealth.org", "jhunpingco@westhealth.org"], "author_institution": [ "Gary and Mary West Health Institute", "Gary and Mary West Health Institute" ], "author_institution_map": { - "Haw-minn Lu": [ - "Gary and Mary West Health Institute" - ], - "José Unpingco": [ - "Gary and Mary West Health Institute" - ] + "Haw-minn Lu": ["Gary and Mary West Health Institute"], + "José Unpingco": ["Gary and Mary West Health Institute"] }, "author_orcid_map": {}, "abstract": [ @@ -689,14 +592,8 @@ { "title": "PyRSB: Portable Performance on Multithreaded Sparse BLAS Operations", "authors": "Michele Martone, Simone Bacchio", - "author": [ - "Michele Martone", - "Simone Bacchio" - ], - "author_email": [ - "michele.martone@lrz.de", - "s.bacchio@cyi.ac.cy" - ], + "author": ["Michele Martone", "Simone Bacchio"], + "author_email": ["michele.martone@lrz.de", "s.bacchio@cyi.ac.cy"], "author_institution": [ "Leibniz Supercomputing Centre (LRZ), Garching near Munich, Germany", "CaSToRC, The Cyprus Institute, Nicosia, Cyprus" @@ -705,9 +602,7 @@ "Michele Martone": [ "Leibniz Supercomputing Centre (LRZ), Garching near Munich, Germany" ], - "Simone Bacchio": [ - "CaSToRC, The Cyprus Institute, Nicosia, Cyprus" - ] + "Simone Bacchio": ["CaSToRC, The Cyprus Institute, Nicosia, Cyprus"] }, "author_orcid_map": {}, "abstract": [ @@ -867,25 +762,15 @@ { "title": "Training machine learning models faster with Dask", "authors": "Joesph Holt, Scott Sievert", - "author": [ - "Joesph Holt", - "Scott Sievert" - ], - "author_email": [ - "jpholt2@wisc.edu", - "stsievert@wisc.com" - ], + "author": ["Joesph Holt", "Scott Sievert"], + "author_email": ["jpholt2@wisc.edu", "stsievert@wisc.com"], "author_institution": [ "University of Wisconsin–Madison", "University of Wisconsin–Madison" ], "author_institution_map": { - "Joesph Holt": [ - "University of Wisconsin–Madison" - ], - "Scott Sievert": [ - "University of Wisconsin–Madison" - ] + "Joesph Holt": ["University of Wisconsin–Madison"], + "Scott Sievert": ["University of Wisconsin–Madison"] }, "author_orcid_map": {}, "abstract": [ @@ -906,25 +791,15 @@ { "title": "Multithreaded parallel Python through OpenMP support in Numba", "authors": "Todd Anderson, Tim Mattson", - "author": [ - "Todd Anderson", - "Tim Mattson" - ], + "author": ["Todd Anderson", "Tim Mattson"], "author_email": [ "todd.a.anderson@intel.com", "timothy.g.mattson@intel.com" ], - "author_institution": [ - "Intel Corp.", - "Intel Corp." - ], + "author_institution": ["Intel Corp.", "Intel Corp."], "author_institution_map": { - "Todd Anderson": [ - "Intel Corp." - ], - "Tim Mattson": [ - "Intel Corp." - ] + "Todd Anderson": ["Intel Corp."], + "Tim Mattson": ["Intel Corp."] }, "author_orcid_map": {}, "abstract": [ @@ -970,24 +845,12 @@ "Analog Devices International" ], "author_institution_map": { - "Marian-Leontin Pop": [ - "Technical University of Cluj-Napoca" - ], - "Szilard Molnar": [ - "Technical University of Cluj-Napoca" - ], - "Alexandru Pop": [ - "Technical University of Cluj-Napoca" - ], - "Benjamin Kelenyi": [ - "Technical University of Cluj-Napoca" - ], - "Levente Tamas": [ - "Technical University of Cluj-Napoca" - ], - "Andrei Cozma": [ - "Analog Devices International" - ] + "Marian-Leontin Pop": ["Technical University of Cluj-Napoca"], + "Szilard Molnar": ["Technical University of Cluj-Napoca"], + "Alexandru Pop": ["Technical University of Cluj-Napoca"], + "Benjamin Kelenyi": ["Technical University of Cluj-Napoca"], + "Levente Tamas": ["Technical University of Cluj-Napoca"], + "Andrei Cozma": ["Analog Devices International"] }, "author_orcid_map": {}, "abstract": [ @@ -1008,10 +871,7 @@ { "title": "Cell Tracking in 3D using deep learning segmentations", "authors": "Varun Kapoor, Claudia Carabaña", - "author": [ - "Varun Kapoor", - "Claudia Carabaña" - ], + "author": ["Varun Kapoor", "Claudia Carabaña"], "author_email": [ "varun.kapoor@curie.fr", "claudia.carabana-garcia@curie.fr" diff --git a/publisher/metadata/2022/toc.json b/publisher/metadata/2022/toc.json index 2c56b6f8c7..2f02f2c588 100644 --- a/publisher/metadata/2022/toc.json +++ b/publisher/metadata/2022/toc.json @@ -22,18 +22,10 @@ "Space Telescope Science Institute" ], "author_institution_map": { - "Perry Greenfield": [ - "Space Telescope Science Institute" - ], - "Edward Slavich": [ - "Space Telescope Science Institute" - ], - "William Jamieson": [ - "Space Telescope Science Institute" - ], - "Nadia Dencheva": [ - "Space Telescope Science Institute" - ] + "Perry Greenfield": ["Space Telescope Science Institute"], + "Edward Slavich": ["Space Telescope Science Institute"], + "William Jamieson": ["Space Telescope Science Institute"], + "Nadia Dencheva": ["Space Telescope Science Institute"] }, "author_orcid_map": {}, "abstract": [ @@ -76,21 +68,11 @@ "University of Florida" ], "author_institution_map": { - "Nathan Jessurun": [ - "University of Florida" - ], - "Daniel E. Capecci": [ - "University of Florida" - ], - "Olivia P. Dizon-Paradis": [ - "University of Florida" - ], - "Damon L. Woodard": [ - "University of Florida" - ], - "Navid Asadizanjani": [ - "University of Florida" - ] + "Nathan Jessurun": ["University of Florida"], + "Daniel E. Capecci": ["University of Florida"], + "Olivia P. Dizon-Paradis": ["University of Florida"], + "Damon L. Woodard": ["University of Florida"], + "Navid Asadizanjani": ["University of Florida"] }, "author_orcid_map": {}, "abstract": [ @@ -133,21 +115,11 @@ "engageLively" ], "author_institution_map": { - "Rick McGeer": [ - "engageLively" - ], - "Andreas Bergen": [ - "engageLively" - ], - "Mahdiyar Biazi": [ - "engageLively" - ], - "Matt Hemmings": [ - "engageLively" - ], - "Robin Schreiber": [ - "engageLively" - ] + "Rick McGeer": ["engageLively"], + "Andreas Bergen": ["engageLively"], + "Mahdiyar Biazi": ["engageLively"], + "Matt Hemmings": ["engageLively"], + "Robin Schreiber": ["engageLively"] }, "author_orcid_map": {}, "abstract": [ @@ -180,25 +152,12 @@ "scott.d.christensen@erdc.dren.mil", "mary.a.byrant@erdc.dren.mil" ], - "author_institution": [ - "ERDC", - "ERDC", - "ERDC", - "ERDC" - ], + "author_institution": ["ERDC", "ERDC", "ERDC", "ERDC"], "author_institution_map": { - "Amanda Catlett": [ - "ERDC" - ], - "Theresa R. Coumbe": [ - "ERDC" - ], - "Scott D. Christensen": [ - "ERDC" - ], - "Mary A. Byrant": [ - "ERDC" - ] + "Amanda Catlett": ["ERDC"], + "Theresa R. Coumbe": ["ERDC"], + "Scott D. Christensen": ["ERDC"], + "Mary A. Byrant": ["ERDC"] }, "author_orcid_map": {}, "abstract": [ @@ -219,30 +178,17 @@ { "title": "Search for Extraterrestrial Intelligence: GPU Accelerated TurboSETI", "authors": "Luigi Cruz, Wael Farah, Richard Elkins", - "author": [ - "Luigi Cruz", - "Wael Farah", - "Richard Elkins" - ], - "author_email": [ - "lfcruz@seti.org", - "wfarah@seti.org" - ], + "author": ["Luigi Cruz", "Wael Farah", "Richard Elkins"], + "author_email": ["lfcruz@seti.org", "wfarah@seti.org"], "author_institution": [ "SETI Institute", "SETI Institute", "SETI Institute" ], "author_institution_map": { - "Luigi Cruz": [ - "SETI Institute" - ], - "Wael Farah": [ - "SETI Institute" - ], - "Richard Elkins": [ - "SETI Institute" - ] + "Luigi Cruz": ["SETI Institute"], + "Wael Farah": ["SETI Institute"], + "Richard Elkins": ["SETI Institute"] }, "author_orcid_map": {}, "abstract": [ @@ -263,14 +209,8 @@ { "title": "Experience report of physics-informed neural networks in fluid simulations: pitfalls and frustration", "authors": "Pi-Yueh Chuang, Lorena A. Barba", - "author": [ - "Pi-Yueh Chuang", - "Lorena A. Barba" - ], - "author_email": [ - "pychuang@gwu.edu", - "labarba@gwu.edu" - ], + "author": ["Pi-Yueh Chuang", "Lorena A. Barba"], + "author_email": ["pychuang@gwu.edu", "labarba@gwu.edu"], "author_institution": [ "Department of Mechanical and Aerospace Engineering, The George Washington University, Washington, DC 20052, USA", "Department of Mechanical and Aerospace Engineering, The George Washington University, Washington, DC 20052, USA" @@ -359,21 +299,14 @@ { "title": "Automatic random variate generation in Python", "authors": "Christoph Baumgarten, Tirth Patel", - "author": [ - "Christoph Baumgarten", - "Tirth Patel" - ], + "author": ["Christoph Baumgarten", "Tirth Patel"], "author_email": [ "christoph.baumgarten@gmail.com", "tirthasheshpatel@gmail.com" ], - "author_institution": [ - "Unaffiliated" - ], + "author_institution": ["Unaffiliated"], "author_institution_map": { - "Christoph Baumgarten": [ - "Unaffiliated" - ], + "Christoph Baumgarten": ["Unaffiliated"], "Tirth Patel": [] }, "author_orcid_map": { @@ -397,11 +330,7 @@ { "title": "Utilizing SciPy and other open source packages to provide a powerful API for materials manipulation in the Schrödinger Materials Suite", "authors": "Alexandr Fonari, Farshad Fallah, Michael Rauch", - "author": [ - "Alexandr Fonari", - "Farshad Fallah", - "Michael Rauch" - ], + "author": ["Alexandr Fonari", "Farshad Fallah", "Michael Rauch"], "author_email": [ "sasha.fonari@schrodinger.com", "farshad.fallah@schrodinger.com", @@ -467,24 +396,12 @@ "University of Georgia" ], "author_institution_map": { - "Seyed Alireza Vaezi": [ - "University of Georgia" - ], - "Gianni Orlando": [ - "University of Georgia" - ], - "Mojtaba Fazli": [ - "harvard University" - ], - "Gary Ward": [ - "University of Vermont" - ], - "Silvia Moreno": [ - "University of Georgia" - ], - "Shannon Quinn": [ - "University of Georgia" - ] + "Seyed Alireza Vaezi": ["University of Georgia"], + "Gianni Orlando": ["University of Georgia"], + "Mojtaba Fazli": ["harvard University"], + "Gary Ward": ["University of Vermont"], + "Silvia Moreno": ["University of Georgia"], + "Shannon Quinn": ["University of Georgia"] }, "author_orcid_map": {}, "abstract": [ @@ -505,12 +422,8 @@ { "title": "The myth of the normal curve and what to do about it", "authors": "Allan Campopiano", - "author": [ - "Allan Campopiano" - ], - "author_email": [ - "allan@deepnote.com" - ], + "author": ["Allan Campopiano"], + "author_email": ["allan@deepnote.com"], "author_institution": [], "author_institution_map": { "Allan Campopiano": [] @@ -532,14 +445,8 @@ { "title": "Python for Global Applications: teaching scientific Python in context to law and diplomacy students", "authors": "Anna Haensch, Karin Knudson", - "author": [ - "Anna Haensch", - "Karin Knudson" - ], - "author_email": [ - "anna.haensch@tufts.edu", - "karin.knudson@tufts.edu" - ], + "author": ["Anna Haensch", "Karin Knudson"], + "author_email": ["anna.haensch@tufts.edu", "karin.knudson@tufts.edu"], "author_institution": [ "Tufts University", "Data Intensive Studies Center", @@ -547,14 +454,8 @@ "Data Intensive Studies Center" ], "author_institution_map": { - "Anna Haensch": [ - "Tufts University", - "Data Intensive Studies Center" - ], - "Karin Knudson": [ - "Tufts University", - "Data Intensive Studies Center" - ] + "Anna Haensch": ["Tufts University", "Data Intensive Studies Center"], + "Karin Knudson": ["Tufts University", "Data Intensive Studies Center"] }, "author_orcid_map": {}, "abstract": [ @@ -575,14 +476,8 @@ { "title": "Papyri: better documentation for the scientific ecosystem in Jupyter", "authors": "Matthias Bussonnier, Camille Carvalho", - "author": [ - "Matthias Bussonnier", - "Camille Carvalho" - ], - "author_email": [ - "bussonniermatthias@gmail.com", - "" - ], + "author": ["Matthias Bussonnier", "Camille Carvalho"], + "author_email": ["bussonniermatthias@gmail.com", ""], "author_institution": [ "QuanSight, Inc", "Digital Ours Lab, SARL.", @@ -590,10 +485,7 @@ "Univ Lyon, INSA Lyon, UJM, UCBL, ECL, CNRS UMR 5208, ICJ, F-69621, France" ], "author_institution_map": { - "Matthias Bussonnier": [ - "QuanSight, Inc", - "Digital Ours Lab, SARL." - ], + "Matthias Bussonnier": ["QuanSight, Inc", "Digital Ours Lab, SARL."], "Camille Carvalho": [ "University of California Merced, Merced, CA, USA", "Univ Lyon, INSA Lyon, UJM, UCBL, ECL, CNRS UMR 5208, ICJ, F-69621, France" @@ -621,19 +513,11 @@ { "title": "Bayesian Estimation and Forecasting of Time Series in statsmodels", "authors": "Chad Fulton", - "author": [ - "Chad Fulton" - ], - "author_email": [ - "chad.t.fulton@frb.gov" - ], - "author_institution": [ - "Federal Reserve Board of Governors" - ], + "author": ["Chad Fulton"], + "author_email": ["chad.t.fulton@frb.gov"], + "author_institution": ["Federal Reserve Board of Governors"], "author_institution_map": { - "Chad Fulton": [ - "Federal Reserve Board of Governors" - ] + "Chad Fulton": ["Federal Reserve Board of Governors"] }, "author_orcid_map": {}, "abstract": [ @@ -701,21 +585,13 @@ "Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark", "Burnet Institute, Melbourne, Australia" ], - "Dina Mistry": [ - "Twitter, Seattle, USA" - ], - "Romesh G. Abeysuriya": [ - "Burnet Institute, Melbourne, Australia" - ], + "Dina Mistry": ["Twitter, Seattle, USA"], + "Romesh G. Abeysuriya": ["Burnet Institute, Melbourne, Australia"], "Jamie A. Cohen": [ "Institute for Disease Modeling, Bill \\& Melinda Gates Foundation, Seattle, USA" ], - "Lauren George": [ - "Microsoft, Seattle, USA" - ], - "Michał Jastrzebski": [ - "GitHub, San Francisco, USA" - ], + "Lauren George": ["Microsoft, Seattle, USA"], + "Michał Jastrzebski": ["GitHub, San Francisco, USA"], "Michael Famulare": [ "Institute for Disease Modeling, Bill \\& Melinda Gates Foundation, Seattle, USA" ], @@ -768,24 +644,14 @@ "Imperial College London" ], "author_institution_map": { - "Axel Donath": [ - "Center for Astrophysics | Harvard \\& Smithsonian" - ], + "Axel Donath": ["Center for Astrophysics | Harvard \\& Smithsonian"], "Aneta Siemiginowska": [ "Center for Astrophysics | Harvard \\& Smithsonian" ], - "Vinay Kashyap": [ - "Center for Astrophysics | Harvard \\& Smithsonian" - ], - "Douglas Burke": [ - "Center for Astrophysics | Harvard \\& Smithsonian" - ], - "Karthik Reddy Solipuram": [ - "University of Maryland Baltimore County" - ], - "David van Dyk": [ - "Imperial College London" - ] + "Vinay Kashyap": ["Center for Astrophysics | Harvard \\& Smithsonian"], + "Douglas Burke": ["Center for Astrophysics | Harvard \\& Smithsonian"], + "Karthik Reddy Solipuram": ["University of Maryland Baltimore County"], + "David van Dyk": ["Imperial College London"] }, "author_orcid_map": { "Axel Donath": "0000-0003-4568-7005", @@ -810,19 +676,11 @@ { "title": "Codebraid Preview for VS Code: Pandoc Markdown Preview with Jupyter Kernels", "authors": "Geoffrey M. Poore", - "author": [ - "Geoffrey M. Poore" - ], - "author_email": [ - "gpoore@uu.edu" - ], - "author_institution": [ - "Union University" - ], + "author": ["Geoffrey M. Poore"], + "author_email": ["gpoore@uu.edu"], + "author_institution": ["Union University"], "author_institution_map": { - "Geoffrey M. Poore": [ - "Union University" - ] + "Geoffrey M. Poore": ["Union University"] }, "author_orcid_map": {}, "abstract": [ @@ -902,11 +760,7 @@ { "title": "Awkward Packaging: building Scikit-HEP", "authors": "Henry Schreiner, Jim Pivarski, Eduardo Rodrigues", - "author": [ - "Henry Schreiner", - "Jim Pivarski", - "Eduardo Rodrigues" - ], + "author": ["Henry Schreiner", "Jim Pivarski", "Eduardo Rodrigues"], "author_email": [ "henryfs@princeton.edu", "pivarski@princeton.edu", @@ -918,15 +772,9 @@ "University of Liverpool" ], "author_institution_map": { - "Henry Schreiner": [ - "Princeton University" - ], - "Jim Pivarski": [ - "Princeton University" - ], - "Eduardo Rodrigues": [ - "University of Liverpool" - ] + "Henry Schreiner": ["Princeton University"], + "Jim Pivarski": ["Princeton University"], + "Eduardo Rodrigues": ["University of Liverpool"] }, "author_orcid_map": {}, "abstract": [ @@ -947,19 +795,11 @@ { "title": "Keeping your Jupyter notebook code quality bar high (and production ready) with Ploomber", "authors": "Ido Michael", - "author": [ - "Ido Michael" - ], - "author_email": [ - "ido@ploomber.io" - ], - "author_institution": [ - "Ploomber" - ], + "author": ["Ido Michael"], + "author_email": ["ido@ploomber.io"], + "author_institution": ["Ploomber"], "author_institution_map": { - "Ido Michael": [ - "Ploomber" - ] + "Ido Michael": ["Ploomber"] }, "author_orcid_map": {}, "abstract": [], @@ -978,25 +818,15 @@ { "title": "Likeness: a toolkit for connecting the social fabric of place to human dynamics", "authors": "Joseph V. Tuccillo, James D. Gaboardi", - "author": [ - "Joseph V. Tuccillo", - "James D. Gaboardi" - ], - "author_email": [ - "tuccillojv@ornl.gov", - "gaboardijd@ornl.gov" - ], + "author": ["Joseph V. Tuccillo", "James D. Gaboardi"], + "author_email": ["tuccillojv@ornl.gov", "gaboardijd@ornl.gov"], "author_institution": [ "Oak Ridge National Laboratory", "Oak Ridge National Laboratory" ], "author_institution_map": { - "Joseph V. Tuccillo": [ - "Oak Ridge National Laboratory" - ], - "James D. Gaboardi": [ - "Oak Ridge National Laboratory" - ] + "Joseph V. Tuccillo": ["Oak Ridge National Laboratory"], + "James D. Gaboardi": ["Oak Ridge National Laboratory"] }, "author_orcid_map": { "Joseph V. Tuccillo": "0000-0002-5930-0943", @@ -1020,25 +850,12 @@ { "title": "poliastro: a Python library for interactive astrodynamics", "authors": "Juan Luis Cano Rodríguez, Jorge Martínez Garrido", - "author": [ - "Juan Luis Cano Rodríguez", - "Jorge Martínez Garrido" - ], - "author_email": [ - "hello@juanlu.space", - "contact@jorgemartinez.space" - ], - "author_institution": [ - "Unaffiliated", - "Unaffiliated" - ], + "author": ["Juan Luis Cano Rodríguez", "Jorge Martínez Garrido"], + "author_email": ["hello@juanlu.space", "contact@jorgemartinez.space"], + "author_institution": ["Unaffiliated", "Unaffiliated"], "author_institution_map": { - "Juan Luis Cano Rodríguez": [ - "Unaffiliated" - ], - "Jorge Martínez Garrido": [ - "Unaffiliated" - ] + "Juan Luis Cano Rodríguez": ["Unaffiliated"], + "Jorge Martínez Garrido": ["Unaffiliated"] }, "author_orcid_map": { "Juan Luis Cano Rodríguez": "0000-0002-2187-161X" @@ -1064,12 +881,8 @@ { "title": "A New Python API for Webots Robotics Simulations", "authors": "Justin C. Fisher", - "author": [ - "Justin C. Fisher" - ], - "author_email": [ - "fisher@smu.edu" - ], + "author": ["Justin C. Fisher"], + "author_email": ["fisher@smu.edu"], "author_institution": [ "Southern Methodist University, Department of Philosophy" ], @@ -1097,19 +910,11 @@ { "title": "pyAudioProcessing: Audio Processing, Feature Extraction, and Machine Learning Modeling", "authors": "Jyotika Singh", - "author": [ - "Jyotika Singh" - ], - "author_email": [ - "singhjyotika811@gmail.com" - ], - "author_institution": [ - "Placemakr" - ], + "author": ["Jyotika Singh"], + "author_email": ["singhjyotika811@gmail.com"], + "author_institution": ["Placemakr"], "author_institution_map": { - "Jyotika Singh": [ - "Placemakr" - ] + "Jyotika Singh": ["Placemakr"] }, "author_orcid_map": { "Jyotika Singh": "0000-0002-5442-3004" @@ -1189,19 +994,11 @@ { "title": "Global optimization software library for research and education", "authors": "Nadia Udler", - "author": [ - "Nadia Udler" - ], - "author_email": [ - "nadiakap@optonline.net" - ], - "author_institution": [ - "University of Connecticut (Stamford)" - ], + "author": ["Nadia Udler"], + "author_email": ["nadiakap@optonline.net"], + "author_institution": ["University of Connecticut (Stamford)"], "author_institution_map": { - "Nadia Udler": [ - "University of Connecticut (Stamford)" - ] + "Nadia Udler": ["University of Connecticut (Stamford)"] }, "author_orcid_map": {}, "abstract": [ @@ -1252,29 +1049,21 @@ "Public Health Department, University of Georgia" ], "author_institution_map": { - "Nathan Jacobi": [ - "Computer Science Department, University of Georgia" - ], + "Nathan Jacobi": ["Computer Science Department, University of Georgia"], "Ivan Mo": [ "Computer Science Department, University of Georgia", "Linguistics Department, University of Georgia" ], - "Albert You": [ - "Computer Science Department, University of Georgia" - ], + "Albert You": ["Computer Science Department, University of Georgia"], "Krishi Kishore": [ "Computer Science Department, University of Georgia" ], - "Zane Page": [ - "Computer Science Department, University of Georgia" - ], + "Zane Page": ["Computer Science Department, University of Georgia"], "Shannon P. Quinn": [ "Computer Science Department, University of Georgia", "Cellular Biology Department, University of Georgia" ], - "Tim Heckman": [ - "Public Health Department, University of Georgia" - ] + "Tim Heckman": ["Public Health Department, University of Georgia"] }, "author_orcid_map": {}, "abstract": [ @@ -1314,18 +1103,10 @@ "Oak Ridge National Laboratory" ], "author_institution_map": { - "Nathan Martindale": [ - "Oak Ridge National Laboratory" - ], - "Jason Hite": [ - "Oak Ridge National Laboratory" - ], - "Scott Stewart": [ - "Oak Ridge National Laboratory" - ], - "Mark Adams": [ - "Oak Ridge National Laboratory" - ] + "Nathan Martindale": ["Oak Ridge National Laboratory"], + "Jason Hite": ["Oak Ridge National Laboratory"], + "Scott Stewart": ["Oak Ridge National Laboratory"], + "Mark Adams": ["Oak Ridge National Laboratory"] }, "author_orcid_map": { "Nathan Martindale": "0000-0002-5036-5433", @@ -1377,24 +1158,12 @@ "National Center for Atmospheric Research" ], "author_institution_map": { - "Orhan Eroglu": [ - "National Center for Atmospheric Research" - ], - "Anissa Zacharias": [ - "National Center for Atmospheric Research" - ], - "Michaela Sizemore": [ - "National Center for Atmospheric Research" - ], - "Alea Kootz": [ - "National Center for Atmospheric Research" - ], - "Heather Craker": [ - "National Center for Atmospheric Research" - ], - "John Clyne": [ - "National Center for Atmospheric Research" - ] + "Orhan Eroglu": ["National Center for Atmospheric Research"], + "Anissa Zacharias": ["National Center for Atmospheric Research"], + "Michaela Sizemore": ["National Center for Atmospheric Research"], + "Alea Kootz": ["National Center for Atmospheric Research"], + "Heather Craker": ["National Center for Atmospheric Research"], + "John Clyne": ["National Center for Atmospheric Research"] }, "author_orcid_map": {}, "abstract": [ @@ -1415,12 +1184,7 @@ { "title": "popmon: Analysis Package for Dataset Shift Detection", "authors": "Simon Brugman, Tomas Sostak, Pradyot Patil, Max Baak", - "author": [ - "Simon Brugman", - "Tomas Sostak", - "Pradyot Patil", - "Max Baak" - ], + "author": ["Simon Brugman", "Tomas Sostak", "Pradyot Patil", "Max Baak"], "author_email": [ "simon.brugman@ing.com", "tomas.sostak@vinted.com", @@ -1434,18 +1198,10 @@ "ING Analytics Wholesale Banking" ], "author_institution_map": { - "Simon Brugman": [ - "ING Analytics Wholesale Banking" - ], - "Tomas Sostak": [ - "Vinted" - ], - "Pradyot Patil": [ - "ING Analytics Wholesale Banking" - ], - "Max Baak": [ - "ING Analytics Wholesale Banking" - ] + "Simon Brugman": ["ING Analytics Wholesale Banking"], + "Tomas Sostak": ["Vinted"], + "Pradyot Patil": ["ING Analytics Wholesale Banking"], + "Max Baak": ["ING Analytics Wholesale Banking"] }, "author_orcid_map": {}, "abstract": [ @@ -1554,9 +1310,7 @@ "Robert Jackson": [ "Argonne National Laboratory, 9700 Cass Ave., Argonne, IL, 60439" ], - "Rebecca Gjini": [ - "University of California at San Diego" - ], + "Rebecca Gjini": ["University of California at San Diego"], "Sri Hari Krishna Narayanan": [ "Argonne National Laboratory, 9700 Cass Ave., Argonne, IL, 60439" ], @@ -1664,12 +1418,8 @@ { "title": "Wailord: Parsers and Reproducibility for Quantum Chemistry", "authors": "Rohit Goswami", - "author": [ - "Rohit Goswami" - ], - "author_email": [ - "rog32@hi.is" - ], + "author": ["Rohit Goswami"], + "author_email": ["rog32@hi.is"], "author_institution": [ "Science Institute, University of Iceland", "Quansight Austin, TX, USA" @@ -1800,27 +1550,21 @@ "Scott D. Christensen": [ "US Army Engineer Research and Development Center" ], - "Marvin S. Brown": [ - "US Army Engineer Research and Development Center" - ], + "Marvin S. Brown": ["US Army Engineer Research and Development Center"], "Robert B. Haehnel": [ "US Army Engineer Research and Development Center" ], "Joshua Q. Church": [ "US Army Engineer Research and Development Center" ], - "Amanda Catlett": [ - "US Army Engineer Research and Development Center" - ], + "Amanda Catlett": ["US Army Engineer Research and Development Center"], "Dallon C. Schofield": [ "US Army Engineer Research and Development Center" ], "Quyen T. Brannon": [ "US Army Engineer Research and Development Center" ], - "Stacy T. Smith": [ - "US Army Engineer Research and Development Center" - ] + "Stacy T. Smith": ["US Army Engineer Research and Development Center"] }, "author_orcid_map": {}, "abstract": [ @@ -1841,12 +1585,8 @@ { "title": "Monaco: A Monte Carlo Library for Performing Uncertainty and Sensitivity Analyses", "authors": "W. Scott Shambaugh", - "author": [ - "W. Scott Shambaugh" - ], - "author_email": [ - "wsshambaugh@gmail.com" - ], + "author": ["W. Scott Shambaugh"], + "author_email": ["wsshambaugh@gmail.com"], "author_institution": [], "author_institution_map": { "W. Scott Shambaugh": [] @@ -1870,12 +1610,8 @@ { "title": "Enabling Active Learning Pedagogy and Insight Mining with a Grammar of Model Analysis", "authors": "Zachary del Rosario", - "author": [ - "Zachary del Rosario" - ], - "author_email": [ - "zdelrosario@olin.edu" - ], + "author": ["Zachary del Rosario"], + "author_email": ["zdelrosario@olin.edu"], "author_institution": [ "Assistant Professor of Engineering and Applied Statistics, Olin College of Engineering" ],