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astroML: Python Datamining for Astronomy — astroML 0.2 documentation

January 2014: the textbook accompanying astroML is now available! View it on Amazon. November 2013: astroML 0.2 has been released! Get the source on Github. Our Introduction to astroML paper. Received the CIDU 2012 best paper award. If you use the software, please consider citing astroML. AstroML: Machine Learning and Data Mining for Astronomy. Released Versions: Python Package Index. AstroML is a Python module for machine learning and data mining built on numpy. By Zeljko Ivezic, Andrew Connolly, Jacob ...

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Jacob Vanderplas

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University of Washington

Jacob Vanderplas

Box 3●●●●● U.W.

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astroML: Python Datamining for Astronomy — astroML 0.2 documentation | astroml.org Reviews
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January 2014: the textbook accompanying astroML is now available! View it on Amazon. November 2013: astroML 0.2 has been released! Get the source on Github. Our Introduction to astroML paper. Received the CIDU 2012 best paper award. If you use the software, please consider citing astroML. AstroML: Machine Learning and Data Mining for Astronomy. Released Versions: Python Package Index. AstroML is a Python module for machine learning and data mining built on numpy. By Zeljko Ivezic, Andrew Connolly, Jacob ...
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2 book figures
3 examples plots
4 news
5 links
6 astroml mailing list
7 github issue tracker
8 videos
9 15 minute talk
10 20 minute talk
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astroML: Python Datamining for Astronomy — astroML 0.2 documentation | astroml.org Reviews

https://astroml.org

January 2014: the textbook accompanying astroML is now available! View it on Amazon. November 2013: astroML 0.2 has been released! Get the source on Github. Our Introduction to astroML paper. Received the CIDU 2012 best paper award. If you use the software, please consider citing astroML. AstroML: Machine Learning and Data Mining for Astronomy. Released Versions: Python Package Index. AstroML is a Python module for machine learning and data mining built on numpy. By Zeljko Ivezic, Andrew Connolly, Jacob ...

INTERNAL PAGES

astroml.org astroml.org
1

5. Unsupervised Learning: Clustering — astroML 0.2 documentation

http://www.astroml.org/user_guide/clustering.html

4 Unsupervised Learning: Density Estimation. 6 Two-point Correlation Functions. This documentation is for astroML version 0.2. 5 Unsupervised Learning: Clustering. If you use the software, please consider citing astroML. 5 Unsupervised Learning: Clustering. More information will be here soon - check for updates! Some examples of clustering can be found in. Chapter 6: Searching for Structure in Point Data. This documentation is relative to astroML version 0.2. 113 Design by Web y Limonada.

2

1. Introduction — astroML 0.2 documentation

http://www.astroml.org/user_guide/introduction.html

2 Installation of astroML. This documentation is for astroML version 0.2. If you use the software, please consider citing astroML. AstroML is a Python module built within the framework of Python’s Scipy ecosystem, designed as a repository for fast and well-tested code for the analysis of astronomical data. It is envisioned to be a community resource, with the development and submission of new algorithms, data sets, and examples provided by GitHub’s. These figures can be seen in the. Which are used in the...

3

astroML: Python Datamining for Astronomy — astroML 0.2 documentation

http://www.astroml.org/index.html

January 2014: the textbook accompanying astroML is now available! View it on Amazon. November 2013: astroML 0.2 has been released! Get the source on Github. Our Introduction to astroML paper. Received the CIDU 2012 best paper award. If you use the software, please consider citing astroML. AstroML: Machine Learning and Data Mining for Astronomy. Released Versions: Python Package Index. AstroML is a Python module for machine learning and data mining built on numpy. By Zeljko Ivezic, Andrew Connolly, Jacob ...

4

WMAP power spectrum analysis with HealPy — astroML 0.2 documentation

http://www.astroml.org/examples/datasets/plot_wmap_power_spectra.html

Stellar Parameters Hess Diagram. WMAP plotting with HEALPix. This documentation is for astroML version 0.2. WMAP power spectrum analysis with HealPy. If you use the software, please consider citing astroML. WMAP power spectrum analysis with HealPy. This demonstrates how to plot and take a power spectrum of the WMAP data using healpy, the python wrapper for healpix. Healpy is available for download at the github site. Author: Jake VanderPlas vanderplas@astro.washington.edu. Plot the unmasked map.

5

NASA Sloan Atlas — astroML 0.2 documentation

http://www.astroml.org/examples/datasets/plot_nasa_atlas.html

SDSS Stripe 82 M. SDSS Stripe 82 Moving Object Catalog. This documentation is for astroML version 0.2. If you use the software, please consider citing astroML. This shows some visualizations of the data from the NASA SDSS Atlas. Optimization terminated successfully. Current function value: -30227.574882 Iterations: 18 Function evaluations: 50. Author: Jake VanderPlas vanderplas@astro.washington.edu. The figure is an example from astroML: see http:/ astroML.github.com. Convert coordinates to degrees.

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Linking Visualization and Understanding in Astronomy #AAS223 #AASviz

http://www.astrobetter.com/linking-visualization-and-understanding-in-astronomy-aas223

Tips and Tricks for Professional Astronomers. Linking Visualization and Understanding in Astronomy #AAS223 #AASviz. January 5, 2014. This is a guest post by Alyssa Goodman. And accompanies her talk by the same name, presented at the AAS Meeting, at 11:40 AM on Monday. Talk slides will be online. After the talk. Alyssa is a Professor of Astronomy at Harvard University and a Research Associate of the Smithsonian Institution. She studies interstellar gas and star formation. Color Advice for Cartography (and...

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Frequentism and Bayesianism: A Practical Introduction

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Musings and ramblings through the world of Python and beyond. Frequentism and Bayesianism: A Practical Introduction. Mar 11, 2014. This post is part of a 5-part series: Part I. See also Frequentism and Bayesianism: A Python-driven Primer. A peer-reviewed article partially based on this content. Frequentism vs. Bayesianism: a Philosophical Debate ¶. Fundamentally, the disagreement between frequentists and Bayesians concerns the definition of probability. That is, if I measure the photon flux (F ). Flux of...

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The Whys and Hows of Licensing Scientific Code

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Tips and Tricks for Professional Astronomers. The Whys and Hows of Licensing Scientific Code. March 10, 2014. Is the Director of Research in the Physical Sciences at the University of Washington’s eScience Institute. He is a maintainer and/or frequent contributor to many open source Python projects, including scikit-learn, scipy, matplotlib, and others. He occasionally blogs about Python, data visualization, open science, and related topics at Pythonic Perambulations. Or follow him on Twitter at @jakevdp.

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April | 2015 | Data Blast

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Data, Telecom, Maths, Astronomy, Origami, and so on. Monthly Archives: April 2015. April 20, 2015. Lies, Damned Lies, and Statistics – and Visualisation. By chance days ago, I met again with the famous Anscombe’s Quartet, which in case anyone doesn’t know or remember, corresponds to four datasets that seem to be identical when they are examined using simple summary statistics, but our perception changes considerably when they are analyzed visually, i.e. through “graphs”. 8220; According to Business Dicti...

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Remembering “The Way of St. James” ( and The_Way R Shiny App) | Data Blast

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Data, Telecom, Maths, Astronomy, Origami, and so on. Remembering The Way of St. James ( and The Way R Shiny App). September 19, 2014. The Way R Shinny App. Function(index, options, content){ var row = options.data[index] return ' b ' 'Altitude' ' /b ' ' br/ ' 'alt: ' row.alt ' br/ ' }! Vieira) and an arrow. One thought on “ Remembering The Way of St. James ( and The Way R Shiny App). September 23, 2014 at 2:07 am. Leave a Reply Cancel reply. Enter your comment here. Address never made public). Notify me ...

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Discovering RHIPE with SDN-Mininet | Data Blast

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Data, Telecom, Maths, Astronomy, Origami, and so on. Discovering RHIPE with SDN-Mininet. October 31, 2014. However, it’s logical to think all this is the tip of the iceberg of an emerging business that could be very lucrative: sales data well in fact, it already is the case. This reminds me a news of October 2012 where Von McConnell, director of technology at Sprint said, in relation to if Telcos became nothing more than a dumb pipe, we could make a living just out of analytics. For test tree.py). Of) & ...

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Remembering “The Way of St. James” ( and The_Way R Shiny App) | Data Blast

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Data, Telecom, Maths, Astronomy, Origami, and so on. Remembering The Way of St. James ( and The Way R Shiny App). September 19, 2014. The Way R Shinny App. Function(index, options, content){ var row = options.data[index] return ' b ' 'Altitude' ' /b ' ' br/ ' 'alt: ' row.alt ' br/ ' }! Vieira) and an arrow. One thought on “ Remembering The Way of St. James ( and The Way R Shiny App). September 23, 2014 at 2:07 am. Leave a Reply Cancel reply. Enter your comment here. Address never made public). Notify me ...

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Dublin: Venture Capital Fundraising (by using Crunchbase) | Data Blast

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Data, Telecom, Maths, Astronomy, Origami, and so on. Dublin: Venture Capital Fundraising (by using Crunchbase). March 24, 2015. Taking advantage of the Python and R scripts that I developed in a previous post. I wanted to learn about the state of the art of the companies in Dublin according to Crunchbase. Who are the main investors? Which is the order of magnitude of the investments? Which are the most important business areas (“categories”) in the city? A) Total Fundraised vs Year. B) Top 10 Companies.

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Lies, Damned Lies, and Statistics – and Visualisation | Data Blast

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Data, Telecom, Maths, Astronomy, Origami, and so on. Lies, Damned Lies, and Statistics – and Visualisation. April 20, 2015. By chance days ago, I met again with the famous Anscombe’s Quartet, which in case anyone doesn’t know or remember, corresponds to four datasets that seem to be identical when they are examined using simple summary statistics, but our perception changes considerably when they are analyzed visually, i.e. through “graphs”. An hilarious website is “ Spurious Correlation. 8220; According...

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