DOCSLIB.ORG

Ipython Documentation Release 0.10.2

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
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.............................. 89 5 Configuration and customization 95 5.1 Initial configuration of your environment........................... 95 5.2 Customization of IPython................................... 99 5.3 New configuration system................................... 103 6 Frequently asked questions 105 6.1 General questions....................................... 105 i 6.2 Questions about parallel computing with IPython....................... 105 7 History 107 7.1 Origins............................................. 107 7.2 Today and how we got here.................................. 107 8 What’s new 109 8.1 Release 0.10.2......................................... 109 8.2 Release 0.10.1......................................... 110 8.3 Release 0.10.......................................... 111 8.4 Release 0.9.1.......................................... 115 8.5 Release 0.9........................................... 115 8.6 Release 0.8.4.......................................... 119 8.7 Release 0.8.3.......................................... 119 8.8 Release 0.8.2.......................................... 120 8.9 Older releases......................................... 120 9 IPython Developer’s Guide 121 9.1 IPython development guidelines................................ 121 9.2 Coding guide.......................................... 129 9.3 Documenting IPython..................................... 131 9.4 Development roadmap..................................... 132 9.5 IPython.kernel.core.notification blueprint........................... 134 9.6 Notes on the IPython configuration system.......................... 135 10 The IPython API 137 10.1 ColorANSI........................................... 137 10.2 ConfigLoader.......................................... 141 10.3 CrashHandler.......................................... 142 10.4 DPyGetOpt........................................... 143 10.5 Debugger............................................ 146 10.6 Itpl............................................... 149 10.7 Logger............................................. 152 10.8 Magic.............................................. 154 10.9 OInspect............................................ 176 10.10 OutputTrap........................................... 178 10.11 Prompts............................................. 181 10.12 PyColorize........................................... 183 10.13 Shell.............................................. 185 10.14 UserConfig.ipy_user_conf................................... 194 10.15 background_jobs........................................ 194 10.16 clipboard............................................ 198 10.17 completer............................................ 198 10.18 config.api............................................ 201 10.19 config.cutils.......................................... 202 10.20 deep_reload........................................... 203 10.21 demo.............................................. 203 10.22 dtutils.............................................. 212 ii 10.23 excolors............................................. 213 10.24 external.Itpl........................................... 214 10.25 external.argparse........................................ 217 10.26 external.configobj....................................... 223 10.27 external.guid.......................................... 235 10.28 external.mglob......................................... 235 10.29 external.path.......................................... 236 10.30 external.pretty......................................... 245 10.31 external.simplegeneric..................................... 249 10.32 external.validate........................................ 249 10.33 frontend.asyncfrontendbase.................................. 264 10.34 frontend.frontendbase..................................... 265 10.35 frontend.linefrontendbase................................... 267 10.36 frontend.prefilterfrontend................................... 269 10.37 frontend.process.pipedprocess................................. 270 10.38 frontend.wx.console_widget.................................. 271 10.39 frontend.wx.ipythonx..................................... 273 10.40 frontend.wx.wx_frontend................................... 274 10.41 generics............................................. 275 10.42 genutils............................................. 276 10.43 gui.wx.ipshell_nonblocking.................................. 292 10.44 gui.wx.ipython_history..................................... 294 10.45 gui.wx.ipython_view...................................... 296 10.46 gui.wx.thread_ex........................................ 300 10.47 history............................................. 300 10.48 hooks.............................................. 302 10.49 ipapi.............................................. 304 10.50 iplib............................................... 311 10.51 ipmaker............................................. 324 10.52 ipstruct............................................. 324 10.53 irunner............................................. 328 10.54 kernel.client.......................................... 332 10.55 kernel.clientconnector..................................... 333 10.56 kernel.clientinterfaces..................................... 334 10.57 kernel.codeutil......................................... 335 10.58 kernel.contexts......................................... 336 10.59 kernel.controllerservice.................................... 337 10.60 kernel.core.display_formatter................................. 339 10.61 kernel.core.display_trap.................................... 340 10.62 kernel.core.error........................................ 341 10.63 kernel.core.fd_redirector.................................... 342 10.64 kernel.core.file_like...................................... 343 10.65 kernel.core.history....................................... 344 10.66 kernel.core.interpreter..................................... 346 10.67 kernel.core.macro....................................... 351 10.68 kernel.core.magic....................................... 351 10.69 kernel.core.message_cache................................... 352 10.70 kernel.core.notification..................................... 354 iii 10.71 kernel.core.output_trap..................................... 355 10.72 kernel.core.prompts...................................... 356 10.73 kernel.core.redirector_output_trap............................... 359 10.74 kernel.core.sync_traceback_trap................................ 360 10.75 kernel.core.traceback_formatter................................ 360 10.76 kernel.core.traceback_trap................................... 361 10.77 kernel.core.util......................................... 362 10.78 kernel.engineconnector..................................... 364 10.79 kernel.enginefc......................................... 365 10.80 kernel.engineservice...................................... 369 10.81 kernel.error........................................... 376 10.82 kernel.fcutil........................................... 383 10.83 kernel.magic.......................................... 383 10.84 kernel.map........................................... 383 10.85 kernel.mapper......................................... 384 10.86 kernel.multiengine....................................... 387 10.87 kernel.multiengineclient.................................... 394 10.88 kernel.multienginefc...................................... 402 10.89 kernel.newserialized...................................... 406 10.90 kernel.parallelfunction..................................... 408 10.91 kernel.pbutil.......................................... 410 10.92 kernel.pendingdeferred..................................... 411 10.93 kernel.pickleutil........................................ 412 10.94 kernel.scripts.ipcluster..................................... 413 10.95 kernel.scripts.ipcontroller................................... 418 10.96 kernel.scripts.ipengine....................................
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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/
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • 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,
    [Show full text]
  • 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)
    [Show full text]
  • 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
    [Show full text]
  • Julia: a Modern Language for Modern ML
    Julia: A modern language for modern ML Dr. Viral Shah and Dr. Simon Byrne www.juliacomputing.com What we do: Modernize Technical Computing Today’s technical computing landscape: • Develop new learning algorithms • Run them in parallel on large datasets • Leverage accelerators like GPUs, Xeon Phis • Embed into intelligent products “Business as usual” will simply not do! General Micro-benchmarks: Julia performs almost as fast as C • 10X faster than Python • 100X faster than R & MATLAB Performance benchmark relative to C. A value of 1 means as fast as C. Lower values are better. A real application: Gillespie simulations in systems biology 745x faster than R • Gillespie simulations are used in the field of drug discovery. • Also used for simulations of epidemiological models to study disease propagation • Julia package (Gillespie.jl) is the state of the art in Gillespie simulations • https://github.com/openjournals/joss- papers/blob/master/joss.00042/10.21105.joss.00042.pdf Implementation Time per simulation (ms) R (GillespieSSA) 894.25 R (handcoded) 1087.94 Rcpp (handcoded) 1.31 Julia (Gillespie.jl) 3.99 Julia (Gillespie.jl, passing object) 1.78 Julia (handcoded) 1.2 Those who convert ideas to products fastest will win Computer Quants develop Scientists prepare algorithms The last 25 years for production (Python, R, SAS, DEPLOY (C++, C#, Java) Matlab) Quants and Computer Compress the Scientists DEPLOY innovation cycle collaborate on one platform - JULIA with Julia Julia offers competitive advantages to its users Julia is poised to become one of the Thank you for Julia. Yo u ' v e k i n d l ed leading tools deployed by developers serious excitement.
    [Show full text]
  • Python Script Programming and Ipython
    00-python_and_ipython-WORKBOOK In [1]: ## CSS coloring for the dataframe tables from IPython.core.display import HTML # css = open('../style/style-table.css').read() + open('../style/style-no tebook.css').read() HTML('<style>{}</style>'.format(css)) Out[1]: Python Script programming and iPython Table of contents What is python? Python Features Installation Python Interpreter Variables, statements and expressions Comments variables Expressions Input Mathematical Operations Arithmetic Operator Comparison Operators Assignment Operators Bitwise Operators Logical Operators Membership Python Operators Precedence Conditional- and loop statements if statement for statement Looping over list/tuple elements String characters file:///C|/Users/G/Downloads/00-python_and_ipython-WORKBOOK.html[9/5/2019 5:35:30 AM] 00-python_and_ipython-WORKBOOK Looping over dictionary items range() function Exercise while statement range() break and continue statements Lists Tuples Dictionaries Functions Python Built-in function User-defined functions function Function Input Arguments Default arguments Arbitrary arguments : *args and **kwargs Anonymous/Lambda Functions Lambda with filter() Exercise Lambda with map() Exercise assert IPython What is Python ? [[1](#ref1)] "The best things in life are free!" Python is an open source, high-level, object oriented, interpreted, and general purpose dynamic programming language. It was create by Guido van Rossum during 1985-1990. Python interpreter source code is availbale inder the The design philosophy of Python [[1]](#ref1) is: Be consistent, beautiful is better than ugly Use existing libraries, Complex is better than complicated. Keep it simple and stupid ( KISS ), Simple is better than complex. Avoid nested ifs, Flat is better than nested. Be clear, Explicit is better than implicit. Seperate code into modules, Sparse is better than dense.
    [Show full text]
  • Ipython Documentation Release 3.2.3
    IPython Documentation Release 3.2.3 The IPython Development Team August 03, 2016 Contents 1 Introduction 1 2 What’s new in IPython7 3 Installation 269 4 Using IPython for interactive work 275 5 The IPython notebook 317 6 Using IPython for parallel computing 339 7 Configuration and customization 429 8 IPython developer’s guide 447 9 The IPython API 507 10 About IPython 509 Bibliography 523 Python Module Index 525 i ii CHAPTER 1 Introduction 1.1 Overview One of Python’s most useful features is its interactive interpreter. It allows for very fast testing of ideas without the overhead of creating test files as is typical in most programming languages. However, the interpreter supplied with the standard Python distribution is somewhat limited for extended interactive use. The goal of IPython is to create a comprehensive environment for interactive and exploratory computing. To support this goal, IPython has three main components: • An enhanced interactive Python shell. • A decoupled two-process communication model, which allows for multiple clients to connect to a computation kernel, most notably the web-based notebook • An architecture for interactive parallel computing. All of IPython is open source (released under the revised BSD license). 1.2 Enhanced interactive Python shell IPython’s interactive shell (ipython), has the following goals, amongst others: 1. Provide an interactive shell superior to Python’s default. IPython has many features for tab- completion, object introspection, system shell access, command history retrieval across sessions, and its own special command system for adding functionality when working interactively. It tries to be a very efficient environment both for Python code development and for exploration of problems using Python objects (in situations like data analysis).
    [Show full text]
  • Ipython and Matplotlib
    IPYTHON AND MATPLOTLIB Python for computational science 16 – 18 October 2017 CINECA [email protected] Introduction ● plotting the data gives us visual feedback ● Typical workflow: ➔ write a python program to parse data ➔ pass the data to a plot tool to show the results ● with Matplotlib we can achieve the same result in a single script and with more flexibility Ipython (1) ● improves the interactive mode usage – tab completion for functions, modules, variables, files – Introspection, accessibile with “?”, of objects and function – %run, to execute a python script – filesystem navigation (cd, ls, pwd) and bash like behaviour (cat) ● !cmd execute command in the shell – Debugging and profiling Ipython (2) ● improves the interactive mode usage – Search commands (Ctrl-n, Ctrl-p, Ctrl-r) (don't work with notebook) – magic commands: ➢ %magic (list them all) ➢ %whos ➢ %timeit ➢ %logstart Ipython (3) Ipython is recommended over python for interactive usage: ➔ Has a matplotlib support mode $ ipython --pylab ➔ no need to import any modules; merges matplotlib.pyplot (for plotting) and numpy (for mathematical functions) ➔ spawn a thread to handle the GUI and another one to handle the user inputs ● every plot command triggers a plot update Ipython (4) ● HTML notebook – You can share .ipynb files $ ipython notebook – Well integrates with matplotlib %matplotlib inline ● QT GUI console $ ipython qtconsole Matplotlib: ● makes use of Numpy to provide good performance with large data arrays ● allows publication quality plots ● since it's a Python module
    [Show full text]