from pydata.style import *
%load_ext literacy.template
from pydata.style import *
%load_ext literacy.template
The literacy.template extension is already loaded. To reload it, use: %reload_ext literacy.template
Perhaps one of the earliest multiscale models, Ray and Charles describe the complex relationships across length scales. Their landmark video Powers of Ten illustrated the 1970s state of the art in the design and engineering of optical systems.
df.iloc[[0]]
constraints | functional | geographical | physical | people | sensory | time | |
---|---|---|---|---|---|---|---|
order | topic | ||||||
0 | design | systems | materials | community | media | culture | design |
Astropy = Import('.Astropy Units')
## `Astropy` contains {{len(Astropy.s)}} units
IFrame(Astropy.path)
Astropy
contains {{len(Astropy.s)}} units¶IFrame(Astropy.path)
> There is an interesting region, i.e. find meters
There is an interesting region, i.e. find meters
* https://en.wikipedia.org/wiki/Timeline_of_computing_hardware_before_1950
* https://en.wikipedia.org/wiki/History_of_computing_hardware_(1960s%E2%80%93present)

https://en.m.wikipedia.org/wiki/Transatlantic_communications_cable#Private_cable_routes

http://www.scienceclarified.com/scitech/Telescopes/Hubble.html
## _Design_ & [Materials]()
* Metals, Polymers, Ceramics
* Function, Structural
* Composite

%%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>
## Hubble Glass

## [Gorilla Glass](http://www.designlife-cycle.com/corning-gorilla-glass/)
### [Degradation of railway rails from a materials point of view](http://publications.lib.chalmers.se/records/fulltext/173441/173441.pdf#page=15)
## [Advanced High Strength Steels](https://lightmat.org/capabilities/los-alamos-national-laboratory/advanced-high-strength-steel-development)
[](https://lightmat.org/capabilities/los-alamos-national-laboratory/advanced-high-strength-steel-development)
### [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)
https://zoomimgs.github.io
### Philanthropic
* [Human Cell Atlas](https://www.humancellatlas.org/) ([Chan Zuckerberg](https://chanzuckerberg.com/))
### Public

# conda  forge
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>
> ## [The First Web Page](http://info.cern.ch/hypertext/WWW/TheProject.html "1990")
## [Particles at NYT](http://nytlabs.com/blog/2015/10/20/particles/)
## [d3: Data-Driven documents](https://d3js.org)
https://getbootstrap.com/
https://reactjs.org/
https://www.ushahidi.com/
https://native-land.ca/
inconvenient truth
h
<IPython.core.display.Markdown object>
http://opengeoscience.github.io/geojs/examples/hurricanes/
## _Design_ & [Design]()

## [`ωorldΒank=__import__('particles.World Bank')`](../World Bank.ipynb)
http://www.redharebrewing.com/red-hare-launches-worlds-first-evercan/
## [Design of Experiments](https://en.m.wikipedia.org/wiki/Design_of_experiments#Fisher.27s_principles 1935)
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 ...