Powers often

A talk inspired by Powers of Ten released in 1977 a collaboration between Ray and Charles Eames and IBM. This talk was created using Jupyter Notebooks and Lab. We used RISE for our presentation.

In [1]:
    from pydata.style import *
    %load_ext literacy.template
In [1]:
https://www.youtube.com/embed/0fKBhvDjuy0
In [2]:
## Topics

This presentation is inspired by simple geometries.  Eventually we settled on the following key `topics\`


    = 'design, systems, materials, community, media, culture'
    
with the accompanying `constraints\`

    = 'functional, geographical, physical, people, sensory, time'

Topics

This presentation is inspired by simple geometries. Eventually we settled on the following key topics\

= 'design, systems, materials, community, media, culture'

with the accompanying constraints\

= 'functional, geographical, physical, people, sensory, time'
In [3]:
    
    topics, constraints = map(lambda ___: ___.replace(' ', '').split(','),[topics, constraints])
In [3]:
# [Shapes of the presentation]()
In [4]:
    
    df
Out[4]:
constraints functional geographical physical people sensory time
order topic
0 design systems materials community media culture design
1 systems materials community media culture design systems
2 materials community media culture design systems materials
3 community media culture design systems materials community
4 media culture design systems materials community media
5 culture design systems materials community media culture
In [ ]:
    Import('.Sierpinski Triangle');
In [ ]:
    Import('.shapes');
In [ ]:
# Abstracts
In [ ]:
{% for topic in topics[:2] %}{{the.Path().read_text().loads().get_in(['cells', 0, 'source'])[''.join](topic+'.ipynb')}}
{% endfor %}
In [ ]:
{% for topic in topics[2:4] %}{{the.Path().read_text().loads().get_in(['cells', 0, 'source'])[''.join](topic+'.ipynb')}}

{% endfor %}
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{% for topic in topics[4:] %}{{the.Path().read_text().loads().get_in(['cells', 0, 'source'])[''.join](topic+'.ipynb')}}
{% endfor %}
In [ ]:
## Particles

Particles are standalone articles and presentations that are importable.

Design

Design applies shared experience to forms to functional ideas.

In [2]:
## _Design_ & [Systems]( "1979")

Design & Systems

In [3]:
    Astropy = Import('.Astropy Units')
In [4]:
* https://en.wikipedia.org/wiki/Timeline_of_computing_hardware_before_1950
* https://en.wikipedia.org/wiki/History_of_computing_hardware_(1960s%E2%80%93present)


![](http://i.dailymail.co.uk/i/pix/2017/04/25/16/3F952C0D00000578-4443474-image-a-105_1493135200640.jpg)
In [4]:
https://en.m.wikipedia.org/wiki/Transatlantic_communications_cable#Private_cable_routes
In [4]:
![](http://www.scienceclarified.com/photos/hubble-3225.jpg)
http://www.scienceclarified.com/scitech/Telescopes/Hubble.html
In [4]:
## _Design_ & [Materials]()

* Metals, Polymers, Ceramics
* Function, Structural
* Composite

Design & Materials

  • Metals, Polymers, Ceramics
  • Function, Structural
  • Composite
In [4]:
![](https://i.pinimg.com/736x/f4/97/41/f497413e4278558b62544b6a9ef25de3--tv-quotes-movie-quotes.jpg)
 

In [5]:
    
    %%html
    <div class="github-card" data-user="chrislgarry" data-repo="Apollo-11"></div>
    <script src="https://cdn.jsdelivr.net/gh/lepture/github-cards@1.0.2/jsdelivr/widget.js"></script>
In [5]:
## Hubble Glass

![](http://www.scienceclarified.com/scitech/images/lsts_0001_0001_0_img0028.jpg)
 

Hubble Glass

In [5]:
## [Gorilla Glass](http://www.designlife-cycle.com/corning-gorilla-glass/)
In [5]:
### [Degradation of railway rails from a materials point of view](http://publications.lib.chalmers.se/records/fulltext/173441/173441.pdf#page=15)
In [5]:
## [Advanced High Strength Steels](https://lightmat.org/capabilities/los-alamos-national-laboratory/advanced-high-strength-steel-development)

[![](https://lightmat.org/sites/default/files/capabilities/001_2_LANL.png)](https://lightmat.org/capabilities/los-alamos-national-laboratory/advanced-high-strength-steel-development)
In [5]:
[![](https://image.slidesharecdn.com/flamel-ss-141210102438-conversion-gate02/95/flamel-materials-informatics-course-roundup-15-1024.jpg?cb=1418218370)](https://www.slideshare.net/tonyfast1/flamel-materials-informatics-course-roundup)
 

In [5]:
### [High-Throughput Image Analysis of Fibrillar Materials: A Case Study on Polymer Nanofiber Packing, Alignment, and Defects in Organic Field Effect Transistors](http://pubs.acs.org/doi/abs/10.1021/acsami.7b10510)
 
In [5]:
## _Design_ & [Community]()

> Communities, and their varied forms and structures, are essential to considering other functional constraints.

Design & Community

Communities, and their varied forms and structures, are essential to considering other functional constraints.

In [5]:
### Philanthropic
* [Human Cell Atlas](https://www.humancellatlas.org/) ([Chan Zuckerberg](https://chanzuckerberg.com/))
In [5]:
### Government
* National Manufacturing Initiatives
* Materials By Design
* [CERN](https://home.cern)

Government

  • National Manufacturing Initiatives
  • Materials By Design
  • CERN
In [5]:
### Public

![](https://github.com/jupyter/notebook/raw/master/notebook/static/base/images/logo.png)

# conda ![](https://conda-forge.org/img/anvil_black.png) forge

Public

conda forge

In [5]:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3878597/
https://en.m.wikipedia.org/wiki/Reproducibility#Reproducible_research
https://en.m.wikipedia.org/wiki/The_Structure_of_Scientific_Revolutions 
https://en.m.wikipedia.org/wiki/LIGO_Scientific_Collaboration#History
https://www.fraunhofer.de/en.html
<IPython.core.display.Markdown object>
In [5]:
## _Design_ & [Media]()

![](https://www.oldtimeradiodownloads.com/assets/img/serie/img_otr.jpg)

Design & Media

In [5]:
> ##  [The First Web Page](http://info.cern.ch/hypertext/WWW/TheProject.html "1990")
In [5]:
## [Django](https://en.m.wikipedia.org/wiki/Django_%28web_framework%29)
In [5]:
## [Particles at NYT](http://nytlabs.com/blog/2015/10/20/particles/)
In [5]:
## [d3: Data-Driven documents](https://d3js.org)
In [5]:
https://getbootstrap.com/
In [5]:
https://reactjs.org/
In [5]:
## _Design_ & [Culture]()

Our complex systems our changing and humans are adapting to displacement}

![](https://cdn.hpm.io/wp-content/uploads/2016/03/Levee-Profile.jpg)

Design & Culture

Our complex systems our changing and humans are adapting to displacement}

In [5]:
https://www.ushahidi.com/ 
In [5]:
https://native-land.ca/

    
inconvenient truth

h
<IPython.core.display.Markdown object>
In [5]:
http://opengeoscience.github.io/geojs/examples/hurricanes/
In [5]:
## _Design_ & [Design]()


![Gorilla glass makes this possible](http://cdn.hexjam.com/editorial_service/bases/images/000/004/947/large/brokenphonefeature.png-1.jpg?1404202657)

Design & Design

Gorilla glass makes this possible

In [6]:
http://www.redharebrewing.com/red-hare-launches-worlds-first-evercan/
In [6]:
## [Design of Experiments](https://en.m.wikipedia.org/wiki/Design_of_experiments#Fisher.27s_principles 1935)
In [7]:
    
    import sklearn.datasets
    print(sklearn.datasets.load_iris().DESCR)
Iris Plants Database
====================

Notes
-----
Data Set Characteristics:
    :Number of Instances: 150 (50 in each of three classes)
    :Number of Attributes: 4 numeric, predictive attributes and the class
    :Attribute Information:
        - sepal length in cm
        - sepal width in cm
        - petal length in cm
        - petal width in cm
        - class:
                - Iris-Setosa
                - Iris-Versicolour
                - Iris-Virginica
    :Summary Statistics:

    ============== ==== ==== ======= ===== ====================
                    Min  Max   Mean    SD   Class Correlation
    ============== ==== ==== ======= ===== ====================
    sepal length:   4.3  7.9   5.84   0.83    0.7826
    sepal width:    2.0  4.4   3.05   0.43   -0.4194
    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)
    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)
    ============== ==== ==== ======= ===== ====================

    :Missing Attribute Values: None
    :Class Distribution: 33.3% for each of 3 classes.
    :Creator: R.A. Fisher
    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
    :Date: July, 1988

This is a copy of UCI ML iris datasets.
http://archive.ics.uci.edu/ml/datasets/Iris

The famous Iris database, first used by Sir R.A Fisher

This is perhaps the best known database to be found in the
pattern recognition literature.  Fisher's paper is a classic in the field and
is referenced frequently to this day.  (See Duda & Hart, for example.)  The
data set contains 3 classes of 50 instances each, where each class refers to a
type of iris plant.  One class is linearly separable from the other 2; the
latter are NOT linearly separable from each other.

References
----------
   - Fisher,R.A. "The use of multiple measurements in taxonomic problems"
     Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
     Mathematical Statistics" (John Wiley, NY, 1950).
   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
   - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
     Structure and Classification Rule for Recognition in Partially Exposed
     Environments".  IEEE Transactions on Pattern Analysis and Machine
     Intelligence, Vol. PAMI-2, No. 1, 67-71.
   - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions
     on Information Theory, May 1972, 431-433.
   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II
     conceptual clustering system finds 3 classes in the data.
   - Many, many more ...

Systems

Systems do work when humans can't or shouldn't.

Remote work, asychnroneous work. A pivotal transition in fiber optics and infrastructure was occuring when Powers of Ten was presented to the public. The explosive growth of bandwith, processing power, sensing capabilities, and miniturization lead to inclusive of technology into our communities, media, cultures, and ultimately design.

In [1]:
    from renci.style import *
    %load_ext literacy.template
In [2]:
## _Systems_ & [Materials]()

Systems & Materials

In [2]:
https://en.m.wikipedia.org/wiki/Three-age_system#Three-age_system_resumptive_table
In [2]:
> [In the 1960s the silicon began](http://www.greatachievements.org/?id=3805)
In [2]:
## _Systems_ & [Community]()

Systems & Community

In [2]:
https://en.m.wikipedia.org/wiki/Trinity_(nuclear_test)
In [3]:
https://en.m.wikipedia.org/wiki/DARPA
In [2]:
# [Apollo 11 Lunar Landing Visualization](https://player.vimeo.com/video/28199826 "1969")
In [2]:
## _Systems_ & [Media]()

Advances in non newtonian physics,information theory, and engineering enabled massively distrbuted systems.

Systems & Media

Advances in non newtonian physics,information theory, and engineering enabled massively distrbuted systems.

In [2]:
# [Xerox PARC](https://www.youtube.com/embed/yJDv-zdhzMY)
In [2]:
# [Powers of Ten](https://www.youtube.com/embed/0fKBhvDjuy0)
In [2]:
https://en.m.wikipedia.org/wiki/Ada_Lovelace#First_computer_program
In [2]:
![](http://sites.harvard.edu/~chsi/markone/images/Bug.jpg)

[The Mark I Computer](http://sites.harvard.edu/~chsi/markone/language.html)
In [2]:
https://player.vimeo.com/video/36579366
In [2]:
## [d3: Data-Driven documents](https://d3js.org)
In [2]:
## _Systems_ & [Culture](https://openculture.com/2013/10/4000-years-of-history-in-histomap-from-1931.html)

Systems & Culture

In [2]:
> [Larger Image](http://www.slate.com/features/2013/08/histomapwider.jpg)
  
In [2]:
[Timeline of Open Source](https://en.m.wikipedia.org/wiki/Timeline_of_open-source_software)
In [3]:
    
    Import('.World Bank');
In [3]:
## _Systems_ & [Design]()

Albers & Johannes Itten

Systems & Design

Albers & Johannes Itten

In [3]:
http://blog.fperez.org/2012/01/ipython-notebook-historical.html
In [2]:
http://printingcode.runemadsen.com/lecture-color/
In [2]:
## _Systems_ & [Systems]()

Just talked about how humans do work and interface.

Automation of human work is prevailing.  


* [Marketing Automation](https://mailchimp.com/features/marketing-automation/)
* [Github Integrations](https://github.com/marketplace)
* ![](https://travis-ci.org/tonyfast/powersoften.svg?branch=master)

Systems & Systems

Automation of human work is prevailing.

In [2]:
[![](http://informationcatalyst.com/wp-content/uploads/2015/09/BD-5Vs.png)](https://www.kdnuggets.com/2017/05/must-know-common-data-quality-issues-big-data.html)

In [2]:
http://www.pewresearch.org/fact-tank/2017/10/04/6-key-findings-on-how-americans-see-the-rise-of-automation/
In [2]:
## Functional + Structural Materials

> There is still a lot work to do.

[![](https://mgi.nist.gov/sites/default/files/uploads/user2/LargerMGIWPImage_0.png)](https://mgi.nist.gov)

Functional + Structural Materials

There is still a lot work to do.

Materials

Everything is made of materials, even data.

In [1]:
    %load_ext literacy.template
    from style import *
In [1]:
## _Materials_ & [Community]()

* Bauhaus
* Arts and Craft Movement
* Industrial Revolution
* Made to Wear clothing

Materials & Community

  • Bauhaus
  • Arts and Craft Movement
  • Industrial Revolution
  • Made to Wear clothing
In [1]:
## _Materials_ & [Media](http://cs-exhibitions.uni-klu.ac.at/index.php?id=187)

Materials & Media

In [1]:
## _Materials_ & [Culture](https://en.m.wikipedia.org/wiki/Three-age_system)

Materials & Culture

In [2]:
## _Materials_ & [Design]()

Better imaging
Faster simulation
Distributed compute
Materials by Design

Materials & Design

Better imaging Faster simulation Distributed compute Materials by Design

In [2]:
## _Materials_ & [Systems]()

> Ignore is Bliss

Materials & Systems

Ignore is Bliss

In [3]:
[`Import('.Bad News').show()`](../particles/Bad News.ipynb)
Loading BokehJS ...
In [3]:
### Good News

Good News

In [3]:
http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.00-Introduction-To-Matplotlib.ipynb#Visualization-with-Matplotlib
In [3]:
## _Materials_ & [Materials]()

How do we use historical information about materials to design new ones
Show pics of materials

Materials & Materials

How do we use historical information about materials to design new ones Show pics of materials

In [3]:
<div class="github-card" data-user="materialsinnovation" data-repo="pymks"></div>
<script src="https://cdn.jsdelivr.net/gh/lepture/github-cards@1.0.2/jsdelivr/widget.js"></script>

In [3]:
https://zoomimgs.github.io/

Community

A community shares resources and experiences.

Communities self-organize to engineer solutions to problems within a set of geographical and material constraints.

In [1]:
## _Community_ & [Media]()

All we know about communities are in the sparse media they have left behind.

Community & Media

All we know about communities are in the sparse media they have left behind.

In [1]:
[Code of Hammurabi](https://en.m.wikipedia.org/wiki/Code_of_Hammurabi "-1754")
In [1]:
[Rosetta Stone](https://en.m.wikipedia.org/wiki/Rosetta_Stone "-196")
In [1]:
[Magna Carta](https://en.m.wikipedia.org/wiki/Magna_Carta "1215")
In [1]:
[Gutenberg bible](https://en.m.wikipedia.org/wiki/Gutenberg_Bible "1450")
In [1]:
[Declaration of Independence](https://en.m.wikipedia.org/wiki/United_States_Declaration_of_Independence "1819")
In [6]:
https://www.high.org/exhibition/basquiat-notebooks/
In [6]:
## _Community_ & [Culture]()

Community & Culture

In [1]:
* Old Time magazines
* Newspapers
* Sears Catalog
* Zines

---


* USENET
* IRC 
* BBS 
* MUDs
  • Old Time magazines
  • Newspapers
  • Sears Catalog
  • Zines

  • USENET
  • IRC
  • BBS
  • MUDs
In [1]:
> ##  [The First Web Page](http://info.cern.ch/hypertext/WWW/TheProject.html "1990")
In [1]:
> ##  The web needs _both raw data_ -- _fresh hypertext_ or old plain text files, or _smart servers_ giving views of existing databases
> – [Helping.html (1990)](http://info.cern.ch/hypertext/WWW/Helping.html)

The web needs both raw data -- fresh hypertext or old plain text files, or smart servers giving views of existing databases

– Helping.html (1990)

In [6]:
## _Community_ & [Design]()

> .. it was Plato (ca. 427 347 BCE) who decreed that all geometric constructions should be done with a straightedge and compass alone.
- excerpt from _Beautiful Geometry_, Maor, Jost; 2014

Community & Design

.. it was Plato (ca. 427 347 BCE) who decreed that all geometric constructions should be done with a straightedge and compass alone.

  • excerpt from Beautiful Geometry, Maor, Jost; 2014
In [6]:
https://en.wikipedia.org/wiki/Form_follows_function#Origins_of_the_phrase
In [6]:
https://player.vimeo.com/video/75234192
In [1]:
## _Community_ & [Systems]()


> The Eagle soars in the summit of Heaven,
The Hunter with his dogs pursues his circuit.
O perpetual revolution of configured stars,
O perpetual recurrence of determined seasons,
O world of spring and autumn, birth and dying!
The endless cycle of idea and action,
Endless invention, endless experiment,
Brings knowledge of motion, but not of stillness;
Knowledge of speech, but not of silence;
Knowledge of words, and ignorance of the Word.
All our knowledge brings us nearer to death,
But nearness to death no nearer to God.
Where is the Life we have lost in living?
Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?
The cycles of Heaven in twenty centuries
Brings us farther from God and nearer to the Dust.

    
> [T.S. Eliot - The Rock](http://www.tech-samaritan.org/blog/2010/06/16/choruses-from-the-rock-t-s-eliot/)

Community & Systems

The Eagle soars in the summit of Heaven, The Hunter with his dogs pursues his circuit. O perpetual revolution of configured stars, O perpetual recurrence of determined seasons, O world of spring and autumn, birth and dying! The endless cycle of idea and action, Endless invention, endless experiment, Brings knowledge of motion, but not of stillness; Knowledge of speech, but not of silence; Knowledge of words, and ignorance of the Word. All our knowledge brings us nearer to death, But nearness to death no nearer to God. Where is the Life we have lost in living? Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information? The cycles of Heaven in twenty centuries Brings us farther from God and nearer to the Dust.

T.S. Eliot - The Rock

In [1]:
https://en.m.wikipedia.org/wiki/Bauhaus
In [1]:
### [Paul Klee]()

> A line is a dot that went for a walk.

Paul Klee

A line is a dot that went for a walk.

In [2]:
    IFrame("https://www.zpk.org/")
Out[2]:
In [2]:
* Goal is **insight**, not **entertainment**... though **insight** can be _entertaining_
* Communities use systems that do **work** when humans **can't**... or **shouldn't**
  • Goal is insight, not entertainment... though insight can be entertaining
  • Communities use systems that do work when humans can't... or shouldn't
In [2]:
## _Community_ & [Materials]()

Community & Materials

In [2]:
## [Decline of library usage](https://en.m.wikipedia.org/wiki/Trends_in_library_usage#Academic_libraries)
In [2]:
https://www.hermanmiller.com/content/hermanmiller/northamerica/en_us/home/research/research-summaries/the-once-and-future-library.html
In [2]:
<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">fuck paywalls for research papers, fuck paying to publish. these things do nothing good for science. i can&#39;t even access my own work.</p>&mdash; Tony Fast (@DocFast) <a href="https://twitter.com/DocFast/status/922282015281569792?ref_src=twsrc%5Etfw">October 23, 2017</a></blockquote>
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

In [2]:
# [Scihub](https://en.m.wikipedia.org/wiki/Sci-Hub "2011")
In [2]:
* Github
    * Science
    * Students
    * Desktop
  • Github
    • Science
    • Students
    • Desktop
In [2]:
> Integration into the Web of Knowledge?

Integration into the Web of Knowledge?

In [2]:
* Universal Content Identity
    * Zenodo (DOI) - A CERN project
    * Blockchain
    * Interplanetary File System
    * DAT - A Moore Foundation Project
* Publishing
    * Journal of Open Source Science
    * Rescience
  • Universal Content Identity
    • Zenodo (DOI) - A CERN project
    • Blockchain
    • Interplanetary File System
    • DAT - A Moore Foundation Project
  • Publishing
    • Journal of Open Source Science
    • Rescience
In [2]:
## _Community_ & [Community]()

[![](https://www.numfocus.org/wp-content/uploads/NumFocus_LRG.png)](https://www.numfocus.org/)

Community & Community

In [6]:
<script async class="speakerdeck-embed" data-slide="8" data-id="87e03729ebb640f9b990ff079bc49651" data-ratio="1.33333333333333" src="//speakerdeck.com/assets/embed.js"></script>
In [3]:
Anaconda and the founders thought this was important too!

                    __import__('particles.Anaconda_');

[![](https://www.anaconda.com/wp-content/themes/anaconda/images/logo-dark.png)](https://anaconda.org)

Anaconda and the founders thought this was important too!

                __import__('particles.Anaconda_');

There are 476 packages in the Anaconda Distribution 5.0

In [4]:
![](https://conda-forge.org/img/anvil_black.png)
        
        __import__('particles.Conda Forge Packages');

    __import__('particles.Conda Forge Packages');

There are 3246 packages by the Conda Forge community

In [5]:
## [conda ![](https://conda-forge.org/img/anvil_black.png) forge has had rapid growth in the past year and a half]()

        __import__('particles.Conda Forge');
In [6]:
    kernels = Import('.Number of Kernels')

There are 95 Jupyter kernels

Jupyter/IPython Version Language(s) Version 3rd party dependencies Example Notebooks Notes
Name
Coarray-Fortran Jupyter 4.0 Fortran 2008/2015 GFortran >= 7.1, OpenCoarrays, MPICH >= 3.2 Demo, Binder demo Docker image
sparkmagic Jupyter >=4.0 Pyspark (Python 2 & 3), Spark (Scala), SparkR (R) Livy Notebooks, Docker Images This kernels are implemented via the magics ma...
sas_kernel Jupyter 4.0 python >= 3.3 SAS 9.4 or higher NaN NaN
IPyKernel Jupyter 4.0 python 2.7, >= 3.3 pyzmq NaN NaN
IJulia NaN julia >= 0.3 NaN NaN NaN
IHaskell NaN ghc >= 7.6 NaN NaN NaN
IRuby NaN ruby >= 2.1 NaN NaN NaN
IJavascript NaN nodejs >= 0.10 NaN NaN NaN
jpCoffeescript NaN coffeescript >= 1.7 NaN NaN NaN
ICSharp Jupyter 4.0 C# 4.0+ scriptcs NaN NaN
IRKernel IPython 3.0 R 3.2 rzmq NaN NaN
SageMath Jupyter 4 Any many NaN NaN
pari_jupyter Jupyter 4 2.8 Cython NaN NaN
IFSharp IPython 2.0 F# NaN Features NaN
gopherlab Jupyter 4.1, JupyterLab Go >= 1.6 ZeroMQ (4.x) examples NaN
Gophernotes Jupyter 4 Go >= 1.4 zeromq 2.2.x examples docker image
IGo NaN Go >= 1.4 NaN NaN NaN
IScala NaN Scala NaN NaN NaN
Jupyter-scala IPython>=3.0 Scala>=2.10 NaN example NaN
IErlang IPython 2.3 Erlang rebar NaN NaN
ITorch IPython >= 2.2 Torch 7 (LuaJIT) NaN NaN NaN
IElixir Jupyter < 6.0 Elixir < 1.5 Erlang OTP <= 19.3, Rebar example IElixir Notebook in Docker
ierl Jupyter >= 4.0 Erlang 19 or 20, Elixir 1.4 or 1.5, LFE 1.2 Erlang, (optional) Elixir NaN NaN
IAldor IPython >= 1 Aldor NaN NaN NaN
IOCaml IPython >= 1.1 OCaml >= 4.01 opam NaN NaN
IForth IPython >= 3 Forth NaN NaN NaN
IPerl NaN Perl 5 NaN NaN NaN
IPerl6 NaN Perl 6 NaN NaN NaN
Jupyter-Perl6 Jupyter Perl 6.C Rakudo Perl 6 NaN NaN
IPHP IPython >= 2 PHP >= 5.4 composer NaN DEPRECATED, use Jupyter-PHP
Jupyter-PHP Jupyter 4.0 PHP >= 7.0.0 composer, php-zmq NaN NaN
IOctave Jupyter Octave NaN Example MetaKernel
IScilab Jupyter Scilab NaN Example MetaKernel
MATLAB Kernel Jupyter Matlab pymatbridge Example MetaKernel
Bash IPython >= 3 bash NaN NaN Wrapper
PowerShell IPython >= 3 Windows NaN NaN Wrapper, Based on Bash Kernel
CloJupyter Jupyter Clojure >= 1.7 NaN NaN NaN
CLJ-Jupyter Jupyter Clojure NaN NaN Abandoned as of 2017-02-12
jupyter-kernel-jsr223 Jupyter>=4.0 Clojure 1.8 clojure-jrs223, Java>=7 NaN Java based JSR223 compliant
Hy Kernel Jupyter Hy NaN Tutorial treats Hy as Python pre-processor
Calysto Hy Jupyter Hy NaN Tutorial based on MetaKernel (magics, shell, parallel, ...
Redis Kernel IPython >= 3 redis NaN NaN Wrapper
jove NaN io.js NaN NaN NaN
jp-babel Jupyter Babel NaN NaN NaN
ICalico IPython >= 2 multiple NaN Index NaN
IMathics NaN Mathics NaN NaN NaN
IWolfram NaN Wolfram Mathematica Wolfram Mathematica(R), Metakernel NaN MetaKernel
Lua Kernel NaN Lua NaN NaN NaN
IPyLua NaN Lua NaN NaN Fork of Lua Kernel
Calysto Scheme NaN Scheme NaN Reference Guide MetaKernel
Calysto Processing NaN Processing.js >= 2 NaN NaN MetaKernel
idl_kernel NaN IDL NaN NaN IDL seem to have a built-in kernel starting wi...
Mochi Kernel NaN Mochi NaN NaN NaN
Lua (used in Splash) NaN Lua NaN NaN NaN
Apache Toree (formerly Spark Kernel) Jupyter Scala, Python, R Spark >= 1.5 Example NaN
Skulpt Python Kernel NaN Skulpt Python NaN Examples MetaKernel
MetaKernel Bash NaN bash NaN NaN MetaKernel
MetaKernel Python NaN python NaN NaN MetaKernel
IVisual NaN VPython NaN Ball-in-Box NaN
IBrainfuck NaN Brainfuck NaN Demo Wrapper
KDB+/Q Kernel (IKdbQ) IPython >= 3.1 Q qzmq, qcrypt NaN NaN
KDB+/Q Kernel (KdbQ Kernel) Jupyter Q NaN NaN NaN
ICryptol NaN Cryptol CVC4 NaN NaN
cling Jupyter 4 C++ NaN Example NaN
Xonsh NaN Xonsh NaN Example MetaKernel
Prolog NaN Prolog NaN NaN MetaKernel
cl-jupyter Jupyter Common Lisp Quicklisp About NaN
Maxima-Jupyter Jupyter Maxima Quicklisp NaN NaN
Calysto LC3 NaN NaN NaN NaN Assembly Language for the Little Computer 3
Yacas NaN YACAS NaN NaN NaN
IJython NaN Jython 2.7 NaN NaN NaN
ROOT Jupyter C++/python ROOT >= 6.05 NaN NaN
Gnuplot Kernel NaN Gnuplot NaN Example MetaKernel
Tcl Jupyter Tcl 8.5 NaN NaN Based on Bash Kernel
J Jupyter J 805 NaN Examples NaN
Jython Jupyter>=4.0 Jython>=2.7.0 Java>=7 NaN Java based JSR223 compliant
C Jupyter C gcc NaN NaN
TaQL Jupyter TaQL python-casacore TaQL tutorial NaN
Coconut Jupyter Coconut NaN NaN NaN
SPARQL Jupyter 4 Python 2.7 or >=3.4 rdflib, SPARQLWrapper Examples Optional GraphViz dependency
AIML chatbot Jupyter 4 Python 2.7 pyAIML Examples NaN
IArm Jupyter 4 ARMv6 THUMB NaN Examples Based off of the ARM Cortex M0+ CPU
SoS Jupyter 4 Python >=3.4 NaN Examples Workflow system, Multi-Kernel support
jupyter-nodejs Jupyter, iPython 3.x NodeJS, Babel, Clojurescript NaN Examples NaN
Pike IPython >= 3 Pike >= 7.8 NaN NaN Wrapper, Based on Bash Kernel
ITypeScript NaN Typescript >= 2.0 Node.js >= 0.10.0 NaN NaN
imatlab ipykernel >= 4.1 MATLAB >= 2016b NaN NaN NaN
jupyter-kotlin Jupyter Kotlin 1.1-M04 EAP Java >= 8 NaN NaN
jupyter_kernel_singular Jupyter Singular 4.1.0 NaN Demo Optional PySingular for better performance, su...
spylon-kernel ipykernel >=4.5 python >= 3.5, scala >= 2.11 Apache Spark >=2.0 Example MetaKernel
mit-scheme-kernel Jupyter 4.0 MIT Scheme 9.2 NaN NaN NaN
elm-kernel Jupyter NaN NaN Examples NaN
SciJava Jupyter Kernel Jupyter 4.3.0 Java + 9 scripting languages Java Examples NaN
Isbt Jupyter 4.3.0 sbt >= 1.0.0 sbt example NaN
BeakerX NaN NaN Groovy, Java, Scala, Clojure, Kotlin, SQL example docker image

Media

Media is information that triggers the senses. The best science happens in person

In [1]:
    from renci.style import *
    %load_ext literacy.template
In [2]:
## _Media_ & [Culture]()

We measure cultures based on their articles or remaining media.

    Import('.Moma');

Media & Culture

We measure cultures based on their articles or remaining media.

Import('.Moma');
In [3]:
## _Media_ & [Design]()


* Bauhaus
* Powers of Ten
* Warhol Factory
* Beautiful Losers
* MIT Digital Media Lab

Media & Design

  • Bauhaus
  • Powers of Ten
  • Warhol Factory
  • Beautiful Losers
  • MIT Digital Media Lab
In [2]:
## _Media_ & [Systems]()

Media & Systems

In [3]:
## _Media_ & [Materials]()

Media & Materials

In [3]:
http://www.oreilly.com/programming/free/
In [3]:
# [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook)
In [3]:
http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/00.00-Preface.ipynb#What-Is-Data-Science?
In [3]:
## _Media_ & [Community]()

Media & Community

In [3]:
https://en.m.wikipedia.org/wiki/Library_of_Alexandria
In [3]:
https://www.loc.gov/
In [3]:
http://www.pewinternet.org/2016/09/09/americans-attitudes-toward-public-libraries/
In [3]:
[](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5584989/ "2017")

In [2]:
### Styleguides


# [PEP8](https://www.python.org/dev/peps/pep-0008/)

Styleguides

PEP8

In [3]:
    import this
import this
The Zen of Python, by Tim Peters

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!
In [3]:
https://en.m.wikipedia.org/wiki/The_Elements_of_Style
In [3]:
## [Mail Chimp Style Guide](https://github.com/mailchimp/content-style-guide)
In [3]:
http://speaking.io
In [3]:
## _Media_ & [Media]()

Media & Media

In [3]:
https://cdn.rawgit.com/ceteri/oriole_jupyterday_atl/master/oriole_talk.slides.html
In [3]:
## [Carol Willing's JupyterCon 2017 Talk](https://www.slideshare.net/willingc/jupyter-and-music)
In [3]:
http://nbviewer.jupyter.org/github/tensorflow/magenta-demos/blob/master/jupyter-notebooks/NSynth.ipynb#Part-2:-Timestretching
In [3]:
## [LIGO Notebooks](https://losc.ligo.org/tutorials/)
In [3]:
<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">The new NBA <a href="https://t.co/RkV6PkwXaD">pic.twitter.com/RkV6PkwXaD</a></p>&mdash; Mark Cuban (@mcuban) <a href="https://twitter.com/mcuban/status/846781342083923969?ref_src=twsrc%5Etfw">March 28, 2017</a></blockquote>
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

Culture

Culture changes with time.

The modern, machine-assisted cultural infrastructure for open knowledge is:

  • persistent
  • (human, computer) language agnostic
  • multi-domain
  • built on volunteer effort
  • decentralized
  • open source

Case study:

In [1]:
    %load_ext literacy.template
    from renci.style import *
In [1]:
## _Culture_ & [Design]()

Culture & Design

In [1]:
> ### [Brand Identities like Facebook and CocaCola are the new colors.](https://brandcolors.net/c/google,home-depot,coca-cola,t-mobile,facebook,mailchimp)
In [2]:
# Color Picker <input type="color" name="favcolor" value="#ff0000">

    __import__("ipywidgets").ColorPicker()

Color Picker

__import__("ipywidgets").ColorPicker()
In [2]:
> Now when I see people with Jupyter on their laptops I ask what they are doing.

Now when I see people with Jupyter on their laptops I ask what they are doing.

In [2]:
## _Culture_ & [Systems]() 

Culture & Systems

In [2]:
https://en.wikipedia.org/wiki/The_Mother_of_All_Demos

So in my office I have a console like this, and there are 12 others that are computer supplies.
And we try now-a-days to do our daily work on here. So this characterizes the way I could sit here and look at a clear blank piece of paper, that's the way I start many projects. So with my system, I start and say "I'd like to load that in." So, sorry about that. So I'm putting in an entity called a "statement." And this is full of other entities called "words." If I make some mistakes I can back up a little bit. So I have a statement with some entities, words, and I can do some operations on these and copy a word, and that word might copy after itself. In fact, a pair word I like to copy after itself. And I can just do this a few times, and get a bit of material there. And there are other materials, like "text." I can copy from that oint to that point. It's copy. So I can get myself some material on my blank piece of paper and say, "Well, this is going to be more important than it looks, so I'd like to set up a file." So I tell the machine, "Output to a file." And it says, "Well, I need a name." I'll give it a name. I'll say it's "sample file." And I'll say "Please output it", and it did! A 7 year old github repo

In [2]:
https://en.wikipedia.org/wiki/Read%E2%80%93eval%E2%80%93print_loop    
In [2]:
https://en.wikipedia.org/wiki/SageMath
In [2]:
#     webrtc = __import__('particles.webrtc')

webrtc = import('particles.webrtc')

In [2]:
## _Culture_ & [Materials]()

![](https://images.gr-assets.com/authors/1340706964p4/3004479.jpg)

> ### The best things in life are free. The second best things are very, very expensive.

Culture & Materials

The best things in life are free. The second best things are very, very expensive.

In [2]:
- Github
- Binder
  • Github
  • Binder
In [2]:
## _Culture_ & [Community]()

### [Contributors](https://github.com/jupyter/notebook/graphs/contributors)

### [Code of Conduct](https://github.com/jupyter/governance/blob/master/conduct/code_of_conduct.md)

### [Internationalization](https://github.com/jupyter/notebook/pull/2140 "JEP")
 
### [https://github.com/JuliaLang/julia/issues/4774](juliacon talk on an issue) 
 
In [2]:
## _Culture_ & [Media]()

Culture & Media

In [2]:
Jupyter days: Atlanta, Boston, Chicago, Hawaii, Philadelphia, Mountain View    

Jupyter days: Atlanta, Boston, Chicago, Hawaii, Philadelphia, Mountain View

In [2]:
Jupytercon

Jupytercon

In [2]:
[nbviewer](https://nbviewer.org)
In [3]:
`Import('.Number of Notebooks').view`

Import('.Number of Notebooks').view

Out[3]:
In [3]:
## _Culture_ & [Culture]()

- Open Source Software and Citizen Science is a life~~style~~.

Culture & Culture

  • Open Source Software and Citizen Science is a lifestyle.
In [3]:
https://en.m.wikipedia.org/wiki/24-hour_news_cycle
In [ ]:
import style