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Alternatives to Python: Julia
Crossing Language Barriers with , SciPy, and thon Steven G. Johnson MIT Applied Mathemacs Where I’m coming from… [ google “Steven Johnson MIT” ] Computaonal soPware you may know… … mainly C/C++ libraries & soPware … Nanophotonics … oPen with Python interfaces … (& Matlab & Scheme & …) jdj.mit.edu/nlopt www.w.org jdj.mit.edu/meep erf(z) (and erfc, erfi, …) in SciPy 0.12+ & other EM simulators… jdj.mit.edu/book Confession: I’ve used Python’s internal C API more than I’ve coded in Python… A new programming language? Viral Shah Jeff Bezanson Alan Edelman julialang.org Stefan Karpinski [begun 2009, “0.1” in 2013, ~20k commits] [ 17+ developers with 100+ commits ] [ usual fate of all First reacBon: You’re doomed. new languages ] … subsequently: … probably doomed … sll might be doomed but, in the meanBme, I’m having fun with it… … and it solves a real problem with technical compuBng in high-level languages. The “Two-Language” Problem Want a high-level language that you can work with interacBvely = easy development, prototyping, exploraon ⇒ dynamically typed language Plenty to choose from: Python, Matlab / Octave, R, Scilab, … (& some of us even like Scheme / Guile) Historically, can’t write performance-criBcal code (“inner loops”) in these languages… have to switch to C/Fortran/… (stac). [ e.g. SciPy git master is ~70% C/C++/Fortran] Workable, but Python → Python+C = a huge jump in complexity. Just vectorize your code? = rely on mature external libraries, operang on large blocks of data, for performance-criBcal code Good advice! But… • Someone has to write those libraries. • Eventually that person may be you. -
Data Visualization in Python
Data visualization in python Day 2 A variety of packages and philosophies • (today) matplotlib: http://matplotlib.org/ – Gallery: http://matplotlib.org/gallery.html – Frequently used commands: http://matplotlib.org/api/pyplot_summary.html • Seaborn: http://stanford.edu/~mwaskom/software/seaborn/ • ggplot: – R version: http://docs.ggplot2.org/current/ – Python port: http://ggplot.yhathq.com/ • Bokeh (live plots in your browser) – http://bokeh.pydata.org/en/latest/ Biocomputing Bootcamp 2017 Matplotlib • Gallery: http://matplotlib.org/gallery.html • Top commands: http://matplotlib.org/api/pyplot_summary.html • Provides "pylab" API, a mimic of matlab • Many different graph types and options, some obscure Biocomputing Bootcamp 2017 Matplotlib • Resulting plots represented by python objects, from entire figure down to individual points/lines. • Large API allows any aspect to be tweaked • Lengthy coding sometimes required to make a plot "just so" Biocomputing Bootcamp 2017 Seaborn • https://stanford.edu/~mwaskom/software/seaborn/ • Implements more complex plot types – Joint points, clustergrams, fitted linear models • Uses matplotlib "under the hood" Biocomputing Bootcamp 2017 Others • ggplot: – (Original) R version: http://docs.ggplot2.org/current/ – A recent python port: http://ggplot.yhathq.com/ – Elegant syntax for compactly specifying plots – but, they can be hard to tweak – We'll discuss this on the R side tomorrow, both the basics of both work similarly. • Bokeh – Live, clickable plots in your browser! – http://bokeh.pydata.org/en/latest/ -
Using the GNU Compiler Collection (GCC)
Using the GNU Compiler Collection (GCC) Using the GNU Compiler Collection by Richard M. Stallman and the GCC Developer Community Last updated 23 May 2004 for GCC 3.4.6 For GCC Version 3.4.6 Published by: GNU Press Website: www.gnupress.org a division of the General: [email protected] Free Software Foundation Orders: [email protected] 59 Temple Place Suite 330 Tel 617-542-5942 Boston, MA 02111-1307 USA Fax 617-542-2652 Last printed October 2003 for GCC 3.3.1. Printed copies are available for $45 each. Copyright c 1988, 1989, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004 Free Software Foundation, Inc. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with the Invariant Sections being \GNU General Public License" and \Funding Free Software", the Front-Cover texts being (a) (see below), and with the Back-Cover Texts being (b) (see below). A copy of the license is included in the section entitled \GNU Free Documentation License". (a) The FSF's Front-Cover Text is: A GNU Manual (b) The FSF's Back-Cover Text is: You have freedom to copy and modify this GNU Manual, like GNU software. Copies published by the Free Software Foundation raise funds for GNU development. i Short Contents Introduction ...................................... 1 1 Programming Languages Supported by GCC ............ 3 2 Language Standards Supported by GCC ............... 5 3 GCC Command Options ......................... -
Ipython: a System for Interactive Scientific
P YTHON: B ATTERIES I NCLUDED IPython: A System for Interactive Scientific Computing Python offers basic facilities for interactive work and a comprehensive library on top of which more sophisticated systems can be built. The IPython project provides an enhanced interactive environment that includes, among other features, support for data visualization and facilities for distributed and parallel computation. he backbone of scientific computing is All these systems offer an interactive command mostly a collection of high-perfor- line in which code can be run immediately, without mance code written in Fortran, C, and having to go through the traditional edit/com- C++ that typically runs in batch mode pile/execute cycle. This flexible style matches well onT large systems, clusters, and supercomputers. the spirit of computing in a scientific context, in However, over the past decade, high-level environ- which determining what computations must be ments that integrate easy-to-use interpreted lan- performed next often requires significant work. An guages, comprehensive numerical libraries, and interactive environment lets scientists look at data, visualization facilities have become extremely popu- test new ideas, combine algorithmic approaches, lar in this field. As hardware becomes faster, the crit- and evaluate their outcome directly. This process ical bottleneck in scientific computing isn’t always the might lead to a final result, or it might clarify how computer’s processing time; the scientist’s time is also they need to build a more static, large-scale pro- a consideration. For this reason, systems that allow duction code. rapid algorithmic exploration, data analysis, and vi- As this article shows, Python (www.python.org) sualization have become a staple of daily scientific is an excellent tool for such a workflow.1 The work. -
Bash Shell Scripts
Bash Shell Scripts Writing Bash shell scripts Bash shell scripts are text files Text files most efficiently built with programming editors (emacs or vi) File must be executable and in search path chmod 700 my_script PATH environment variable may not include .! An example shell script: #!/bin/bash #My first script echo "Hello World!" Bash Shell Scripts Writing Bash shell scripts Compile a Verilog file with vlog #!/bin/bash if [ ! d work ] ; then echo work does not exist, making it vlib work fi if [ ! s adder.v ] ; then vlog adder.v fi work directory must exist before compilation Get scripts via wget, eg: wget http://web.engr.oregonstate.edu/~traylor/ece474/script --- Bash Shell Scripts Writing Bash shell scripts File attribute checking #!/bin/bash if [ ! s junk_dir ] ; then mkdir junk_dir fi Spaces around brackets are needed! File attribute checking d exists and is a directory e, a file exists f exists and is a regular file s file exists and is not empty --- Bash Shell Scripts Writing Bash shell scripts Compile Verilog then run a simultion #!/bin/bash if [ ! -d "work" ] ; then vlib work fi if [ -s "adder.v" ] ; then vlog adder.v #runs simulation with a do file and no GUI vsim adder -do do.do quiet c else echo verilog file missing fi --- Bash Shell Scripts Writing Bash shell scripts vsim command and arguments vsim entity_name do dofile.do quiet -c -quiet (do not report loading file messages) -c (console mode, no GUI) -do (run vsim from a TCL do file) +nowarnTFMPC (don’t warn about mismatched ports, scary) +nowarnTSCALE (don’t warn about timing mismatches) Try vsim help for command line arguements --- Bash Shell Scripts Writing Bash Shell Scripts (TCL Script) In another text file, we create a TCL script with commands for the simulator. -
Introduction to Linux by Lars Eklund Based on Work by Marcus Lundberg
Introduction to Linux By Lars Eklund Based on work by Marcus Lundberg ● What is Linux ● Logging in to UPPMAX ● Navigate the file system ● “Basic toolkit” What is Linux ● The Linux Operating system is a UNIX like UNIX compatible Operating system. ● Linux is a Kernel on which many different programs can run. The shell(bash, sh, ksh, csh, tcsh and many more) is one such program ● Linux has a multiuser platform at its base which means permissions and security comes easy. Many Flavours Connect to UPPMAX ● (Download XQuartz or other X11 server for Mac OS ) ● Linux and MacOS: – start Terminal – $ ssh -X [email protected] Connect to UPPMAX for windows users ● Download a X-server such as GWSL or X-ming or VcXsrv or an other of your choosing ● Install WSL and a Distribution such as ubuntu or a ssh program such as MobaXTerm ● Connect to $ ssh -X [email protected] Windows links ● https://sourceforge.net/projects/vcxsrv/ ● https://mobaxterm.mobatek.net/ ● https://opticos.github.io/gwsl/ ● https://sourceforge.net/projects/xming/ ● https://docs.microsoft.com/en-us/windows/wsl/install-wi n10 ● Don’t forget to update to wsl2 X11-forwarding graphics from the command line ● Graphics can be sent through the SSH connection you’re using to connect - Use ssh -Y or ssh -X ● MacOS users will need to install XQuartz. ● When starting a graphical program, a new window will open, but your terminal will be “locked”. - Run using & at the end to run it as a background proccess e.g. “gedit &” - Alternatively, use ctrl-z to put gedit to sleep and -
Writing Mathematical Expressions with Latex
APPENDIX A Writing Mathematical Expressions with LaTeX LaTeX is extensively used in Python. In this appendix there are many examples that can be useful to represent LaTeX expressions inside Python implementations. This same information can be found at the link http://matplotlib.org/users/mathtext.html. With matplotlib You can enter the LaTeX expression directly as an argument of various functions that can accept it. For example, the title() function that draws a chart title. import matplotlib.pyplot as plt %matplotlib inline plt.title(r'$\alpha > \beta$') With IPython Notebook in a Markdown Cell You can enter the LaTeX expression between two '$$'. $$c = \sqrt{a^2 + b^2}$$ c= a+22b 537 © Fabio Nelli 2018 F. Nelli, Python Data Analytics, https://doi.org/10.1007/978-1-4842-3913-1 APPENDIX A WRITING MaTHEmaTICaL EXPRESSIONS wITH LaTEX With IPython Notebook in a Python 2 Cell You can enter the LaTeX expression within the Math() function. from IPython.display import display, Math, Latex display(Math(r'F(k) = \int_{-\infty}^{\infty} f(x) e^{2\pi i k} dx')) Subscripts and Superscripts To make subscripts and superscripts, use the ‘_’ and ‘^’ symbols: r'$\alpha_i > \beta_i$' abii> This could be very useful when you have to write summations: r'$\sum_{i=0}^\infty x_i$' ¥ åxi i=0 Fractions, Binomials, and Stacked Numbers Fractions, binomials, and stacked numbers can be created with the \frac{}{}, \binom{}{}, and \stackrel{}{} commands, respectively: r'$\frac{3}{4} \binom{3}{4} \stackrel{3}{4}$' 3 3 æ3 ö4 ç ÷ 4 è 4ø Fractions can be arbitrarily nested: 1 5 - x 4 538 APPENDIX A WRITING MaTHEmaTICaL EXPRESSIONS wITH LaTEX Note that special care needs to be taken to place parentheses and brackets around fractions. -
Ipython Documentation Release 0.10.2
IPython Documentation Release 0.10.2 The IPython Development Team April 09, 2011 CONTENTS 1 Introduction 1 1.1 Overview............................................1 1.2 Enhanced interactive Python shell...............................1 1.3 Interactive parallel computing.................................3 2 Installation 5 2.1 Overview............................................5 2.2 Quickstart...........................................5 2.3 Installing IPython itself....................................6 2.4 Basic optional dependencies..................................7 2.5 Dependencies for IPython.kernel (parallel computing)....................8 2.6 Dependencies for IPython.frontend (the IPython GUI).................... 10 3 Using IPython for interactive work 11 3.1 Quick IPython tutorial..................................... 11 3.2 IPython reference........................................ 17 3.3 IPython as a system shell.................................... 42 3.4 IPython extension API..................................... 47 4 Using IPython for parallel computing 53 4.1 Overview and getting started.................................. 53 4.2 Starting the IPython controller and engines.......................... 57 4.3 IPython’s multiengine interface................................ 64 4.4 The IPython task interface................................... 78 4.5 Using MPI with IPython.................................... 80 4.6 Security details of IPython................................... 83 4.7 IPython/Vision Beam Pattern Demo............................. -
Easybuild Documentation Release 20210907.0
EasyBuild Documentation Release 20210907.0 Ghent University Tue, 07 Sep 2021 08:55:41 Contents 1 What is EasyBuild? 3 2 Concepts and terminology 5 2.1 EasyBuild framework..........................................5 2.2 Easyblocks................................................6 2.3 Toolchains................................................7 2.3.1 system toolchain.......................................7 2.3.2 dummy toolchain (DEPRECATED) ..............................7 2.3.3 Common toolchains.......................................7 2.4 Easyconfig files..............................................7 2.5 Extensions................................................8 3 Typical workflow example: building and installing WRF9 3.1 Searching for available easyconfigs files.................................9 3.2 Getting an overview of planned installations.............................. 10 3.3 Installing a software stack........................................ 11 4 Getting started 13 4.1 Installing EasyBuild........................................... 13 4.1.1 Requirements.......................................... 14 4.1.2 Using pip to Install EasyBuild................................. 14 4.1.3 Installing EasyBuild with EasyBuild.............................. 17 4.1.4 Dependencies.......................................... 19 4.1.5 Sources............................................. 21 4.1.6 In case of installation issues. .................................. 22 4.2 Configuring EasyBuild.......................................... 22 4.2.1 Supported configuration -
Intro to Jupyter Notebook
Evan Williamson University of Idaho Library 20160302 Introducing Jupyter Notebook for Python and R Three questions: http://goo.gl/forms/uYRvebcJkD Try Jupyter https://try.jupyter.org/ Install Jupyter ● Get Python (suggested: Anaconda, Py3, 64bit, https://www.continuum.io/downloads ) ● Manually install (if necessary), http://jupyter.readthedocs.org/en/latest/install.html pip3 install jupyter Install R for Jupyter ● Get R, https://cran.cnr.berkeley.edu/ (suggested: RStudio, https://www.rstudio.com/products/RStudio/#Desktop ) ● Open R console and follow: http://irkernel.github.io/installation/ Start a Notebook ● Open terminal/command prompt jupyter notebook ● Notebook will open at http://127.0.0.1:8888 ● Exit by closing the browser, then typing Ctrl+C in the terminal window Create Slides ● Open terminal/command prompt jupyter nbconvert slideshow.ipynb --to slides --post serve ● Note: “post serve” locally serves the file so you can give a presentation in your browser. If you only want to convert, leave this option off. The resulting HTML file must be served to render correctly. Slides use Reveal.js, http://lab.hakim.se/revealjs/ Reference ● Jupyter docs, http://jupyter.readthedocs.org/en/latest/index.html ● IPython docs, http://ipython.readthedocs.org/en/stable/index.html ● List of kernels, https://github.com/ipython/ipython/wiki/IPythonkernelsforotherlanguages ● A gallery of interesting IPython Notebooks, https://github.com/ipython/ipython/wiki/AgalleryofinterestingIPythonNotebooks ● Markdown basics, -
Numpy for MATLAB Users – Mathesaurus
NumPy for MATLAB users Help MATLAB/Octave Python Description doc help() Browse help interactively help -i % browse with Info help help or doc doc help Help on using help help plot help(plot) or ?plot Help for a function help splines or doc splines help(pylab) Help for a toolbox/library package demo Demonstration examples Searching available documentation MATLAB/Octave Python Description lookfor plot Search help files help help(); modules [Numeric] List available packages which plot help(plot) Locate functions Using interactively MATLAB/Octave Python Description octave -q ipython -pylab Start session TAB or M-? TAB Auto completion foo(.m) execfile('foo.py') or run foo.py Run code from file history hist -n Command history diary on [..] diary off Save command history exit or quit CTRL-D End session CTRL-Z # windows sys.exit() Operators MATLAB/Octave Python Description help - Help on operator syntax Arithmetic operators MATLAB/Octave Python Description a=1; b=2; a=1; b=1 Assignment; defining a number a + b a + b or add(a,b) Addition a - b a - b or subtract(a,b) Subtraction a * b a * b or multiply(a,b) Multiplication a / b a / b or divide(a,b) Division a .^ b a ** b Power, $a^b$ power(a,b) pow(a,b) rem(a,b) a % b Remainder remainder(a,b) fmod(a,b) a+=1 a+=b or add(a,b,a) In place operation to save array creation overhead factorial(a) Factorial, $n!$ Relational operators MATLAB/Octave Python Description a == b a == b or equal(a,b) Equal a < b a < b or less(a,b) Less than a > b a > b or greater(a,b) Greater than a <= b a <= b or less_equal(a,b) -
Sage Tutorial (Pdf)
Sage Tutorial Release 9.4 The Sage Development Team Aug 24, 2021 CONTENTS 1 Introduction 3 1.1 Installation................................................4 1.2 Ways to Use Sage.............................................4 1.3 Longterm Goals for Sage.........................................5 2 A Guided Tour 7 2.1 Assignment, Equality, and Arithmetic..................................7 2.2 Getting Help...............................................9 2.3 Functions, Indentation, and Counting.................................. 10 2.4 Basic Algebra and Calculus....................................... 14 2.5 Plotting.................................................. 20 2.6 Some Common Issues with Functions.................................. 23 2.7 Basic Rings................................................ 26 2.8 Linear Algebra.............................................. 28 2.9 Polynomials............................................... 32 2.10 Parents, Conversion and Coercion.................................... 36 2.11 Finite Groups, Abelian Groups...................................... 42 2.12 Number Theory............................................. 43 2.13 Some More Advanced Mathematics................................... 46 3 The Interactive Shell 55 3.1 Your Sage Session............................................ 55 3.2 Logging Input and Output........................................ 57 3.3 Paste Ignores Prompts.......................................... 58 3.4 Timing Commands............................................ 58 3.5 Other IPython