Michael Zingale2013-01-02T12:36:142018-01-22T19:51:53.931863166P2DT9H35M22S126LibreOffice/5.4.4.2$Linux_X86_64 LibreOffice_project/40$Build-2 9701 -3528 31872 21818 view1 true false true true true true false false true 1500 false //////////////////////////////////////////8= //////////////////////////////////////////8= false true false 0 5 false true true 4 0 9701 -3528 48214 21756 2540 2540 254 254 254 1 254 1 false 1500 true false true $(inst)/share/palette%3B$(user)/config/standard.sob 0 $(inst)/share/palette%3B$(user)/config/standard.soc $(inst)/share/palette%3B$(user)/config/standard.sod 1270 false en US $(inst)/share/palette%3B$(user)/config/standard.sog true $(inst)/share/palette%3B$(user)/config/standard.soh false false true true false true false false true false false false false false $(inst)/share/palette%3B$(user)/config/standard.soe false 4 false 0 low-resolution hp 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 false 6 true <number><number><number><number><number><number><number><number><number><number><number><number><number><number><number><number><number><number> Python for Scientific Computing <number> <number><number><number><number><number><number><number><number><number><number><number><number><number><number><number><number><number><number> <number><number><number><number><number><number><number><number><number><number><number><number><number><number><number><number><number><number> <number><number><number><number><number><number><number><number><number><number><number><number><number><number><number><number><number><number> Python for Scientific Computing http://bender.astro.sunysb.edu/classes/python-science Course Goals Simply: to learn how to use python to do Numerical analysis Data analysis Plotting and visualizations Symbol mathematics Write applications ... Class Participation We'll learn by interactively trying out some ideas and looking at code I want to learn from all of you—share you experiences and expertise We'll use slack to communicate out of class Everyone should have received an invite to our slack: https://python-sbu.slack.com Try out ideas and report to the class what you've learned You should have an installation of python on your laptop Alternately, you can use the Virtual SINC site on campus, which has Anaconda Python installed Slack Log onto our slack as soon as possible If you didn’t get an invite, e-mail me, and I’ll add you Slack is a web-based team chat tool I’ve setup a number of channels for us to focus our discussions Everyone is expected to participate Your course grade is based on your participation I have a simple script(https://github.com/zingale/slack_grader ) that I’ll use for grading Good contributions will get a comment on the slack, counting as “+1” The script will record these points over the semester Help, by using slack reactions for useful comments from your classmates In “free” mode, slack only keeps 10k messages—I don’t think we’ll go over that during the semester Why Slack? One of the goals of this class is to teach you tools that are used by computational science groups Slack has gained enormous adoption by research groups over the past few years We’ll see how to integrate github repos to it, so everyone can keep on top of code developments It’s easy to post code snippets in conversations There are lots of integrations that are available to extend its usefulness Why Python? Very high-level language Provides many complex data-structures (lists, dictionaries, ...) Your code is shorter than a comparable algorithm in a compiled language Many powerful libraries to perform complex tasks Parse structured inputs files send e-mail interact with the operating system make plots make GUIs do scientific computations ... Easy to prototype new tools Cross-platform and Free Why Python? Dynamically-typed Object-oriented foundation Extensible (easy to call Fortran, C/C++, ...) Automatic memory management (garbage collection) Ease of readability (whitespace matters) ... and for this course ... 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If you have python installed properly, you can start python simply by typing python at your command line prompt The python shell will come up Our hello world program is simply: print("hello, world") Or, in python 2 (more on this later...): print "hello, world" Communities Many scientific disciplines have their own python communities that Provide tutorials Cookbooks Libraries to do standard analysis in the field For the most part, these build on the Open nature of python I've put links on our course page to some information on the python communities for: Astronomy Atmospheric Science Biology Cognitive Science Psychology Let me know of any others! Python in Astro Python is seeing rapidly increasing adoption in Astrophysics Open-source alternative to IDL Python for IDL users: https://www.cfa.harvard.edu/~jbattat/computer/python/science/idl-numpy.html Astropython: http://www.astropython.org/ Lots of tutorials, links to resources, forum, ... Astropy mailing list: http://mail.scipy.org/mailman/listinfo/astropy Python in Biology Biopython (http://biopython.org/wiki/Main_Page) provides tools for using python for computational molecular biology and bioinformatics projects Able to parse many different bioinformatics file formats Access online databases Do analysis, operate on sequences, ... See Python—All a Scientist Needs by J. B. 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Before we get into python, we'll review some of the core ideas about numerical computing Basics of Computation Computers store information and allow us to operate on it. That's basically it. Computers have finite memory, so it is not possible to store the infinite range of numbers that exist in the real world, so approximations are made. You should have some familiarity with how computers store numbers Great floating point reference What Every Computer Scientist Should Know About Floating-Point Arithmetic by D. Goldberg Integers Basic unit of information in a computer is a bit: 0 or 1 8 bits = 1 byte Different types of information require more bits/bytes to store. A logical (T or F) can be stored in a single bit. C/C++: bool datatype Fortran: logical datatype Integers: Standard in many languages is 4-bytes. This allows for 232-1 distinct values. This can store: -2,147,483,648 to 2,147,483,647 (signed) C/C++: int (usually) or int32_t Fortran: integer or integer*4 Or it can store: 0 to 4,294,967,295 (unsigned) C/C++: uint or uint32_t Fortran (as of 95): unsigned Integers Integers (continued): Sometimes 2-bytes. This allows for 216-1 distinct values. This can store: -32,768 to 32,767 (signed) C/C++: short (usually) or int16_t Fortran: integer*2 Or it can store: 0 to 65,535 (unsigned) C/C++: uint16_t Fortran (as of 95): unsigned*2 Note for IDL users: the standard integer in IDL is a 2-byte integer. If you do i = 2 that's 2-bytes. 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Overflow iold = -1 i = 1 type_init = type(i) print "type currently: ", type_init while (i > iold): print i iold = i i *= 2 if (not type(i) == type_init): print "type changed, now: ", type(i) break print i Integers Python allows for the date size of the integer to scale with the size of the number: https://www.python.org/dev/peps/pep-0237/ Initially, it is the largest value supported in hardware on a machine (64-bits on a 64-bit machine—see sys.maxint This prints: def fac(x): if (x == 0): return 1 else: return x*fac(x-1) a = fac(52) print a print a.bit_length() 80658175170943878571660636856403766975289505440883277824000000000000 226 Note: python 3.x does away with the distinction between int and long altogether. Real Numbers Floating point format Infinite real numbers on the number line need to be represented by a finite number of bits Approximations made Finite number of decimal places stored, maximum range of exponents. Not every number can be represented. In base-2 (what a computer uses), 0.1 does not have an exact representation (see S 2.1 of Goldberg) There is a finite precision Exact Representations 0.1 as represented by a computer: >>> b = 0.1 >>> print(type(b)) <class 'float'> >>> print("{:30.20}”.format(b)) 0.10000000000000000555 >>> import sys >>> print(sys.float_info) sys.float_info(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.220446049250313e-16, radix=2, rounds=1) Note: in python 2.x, the type() statement would have returned <type 'float'> but this really in a class, in the object-oriented sense. Floating Point In the floating point format, a number is represented by a significand (or mantissa), and exponent, and a sign. Base 2 is used (since we are using bits) 0.1 ~ (1 + 1·2-1 + 0·2-2 + 0·2-3 + 1·2-4 + 1·2-5 + ...) · 2-4 All significand's will start with 1, so it is not stored Single precision: Sign: 1 bit; exponent: 8 bits; significand: 24 bits (23 stored) = 32 bits Range: 27-1 in exponent (because of sign) = 2127 multiplier ~ 1038 Decimal precision: ~6 significant digits Double precision: Sign: 1 bit; exponent: 11 bits; significand: 53 bits (52 stored) = 64 bits Range: 210-1 in exponent = 21023 multiplier ~ 10308 Decimal precision: ~15 significant digits significand Floating Point Overflows and underflows can still occur when you go outside the representable range. The floating-point standard will signal these (and compilers can catch them) Some special numbers: NaN = 0/0 or Inf is for overflows, like 1/0 Both of these allow the program to continue, and both can be trapped (and dealt with) -0 is a valid number, and -0 = 0 in comparison Floating point is governed by an IEEE standard Ensures all machines do the same thing Aggressive compiler optimizations can break the standard iVBORw0KGgoAAAANSUhEUgAAANkAAABkCAYAAAAL6pXdAAAACXBIWXMAAFxWAABcWQHaGE9E AAAFoElEQVR4nO3djW3bSBCGYQZIA7oK7ox04CvBLSQlSCU4JTglJCXEJZxbUAeJLx2oBGcH HgYbakmJ4c7+vg9gxJaEmLD06aOo4erty8vLAMDO29wbALSOkAHGCBlgjJABxggZYIyQAcYI WUHe/f0P76dU5NuP/99ccztCVggXsLvc2wAbhKwc97k3ADYIWQG0xWiyRhGyMtBiDSNkmQVa 7OBeUH/JtT2Ij5Dl57fYMwFrDyHLKNBin3JtC+wQsrz23ve0WKMIWSauxW7cP++9iwhYowhZ Pv5rsdNAyJpFyDLQFvN3Fb+4XcVTru2BLUKWx7TFOODRMEKWGC3WH0KWHi3WGUKWEC3WJ0KW lh8wWqwThCwR12K74feQPdJiaemexCn1352QpSOvxXbez7RYQjrC9tV97dz3/7qgHVP9bkKW QKDF5LXYc67t6Y37+8sT3IN30W7uthYIWRq0WAYuXLfDa7imJ8TKz0+ptoOQGaPF0nN/c5kJ Hb+yI2T2aDFj+npLWmscuk66O3gJITNEi8Xn/qay+3erX0thetbrsweOkNmaPqvSYttJuOYW HZJD84/u68k9mT26QP63cNtkCJktf4TqiRaL4hj4Xv6ux5SH5dcgZEbcs6jsJt54F9FiEbgg fcy9DWsRMjvTFkt2yBhlIWQGaDH4CJkNWgy/ELLIaDFMEbL4/PfFaDEQspi8yYPRY65tQTkI WVwsuY0zhCwSltzGHEIWDy2GIEIWAS2GJYQsDloMswjZRrQYLiFk2/ln39JiOEPINggsVsr7 YjhTbch0fEla5JDxPC2W3MZF1YVMT+mXU9DHBpEH+iHDdrDkNq5SVcj0gS0LVPqjS+/d5R8z PMBpMVylmpDpMl+fh/OFUeRnecAnO2OWFsMaVYRMVyi6X7iJPOBTnpZOi+FqRYdssnt41K99 4Kayvvk+xeFzWgxrFRuyye6hhGdsqlDIxP2Q5sPNp7+fFsOiIkPmfUCANMTBbyh3nXwfCtqN BFPW2zPcrtBipbQYFhUZsuG1lWTX8BBYS0+aY67N5HLLN4RZchurFRkyF6y/Fq57do0iQQp9 mMCdzBJanPJfwpLbug23F29YtlOpi5BaKTJkV5BdxrlP7JDLLdbVKKHFvg8FrO2+lXuy+GC5 W1+aKkMmTeXuKAlSaJ3zvbvuU8yWKaHFVPUBU9LGhKwCcifNfZhA7FGrElps/L37oe6wyf3W 1ZkK1YZMjjjqUcibwNWxR61KaLFxHfjq1oLvXbUhU/LM/jlwebRRK53295ujq2dhbFd7yGTX Q95PC+0+xRq1mi653dWRMWxXdchkd1DfnA7NNW4etWLJbcRQdciUPPDnhoe3jlrxwRHYrPqQ eW0WddSKFkMs1YdMWYxa0WKIoomQ6ajVXJutHrWixRBTEyFT0lZzbbZ21Mr/f460GLZoJmSx Rq0CH3/E+2LYpJmQKQnE1lErltxGVE2FTI4iuiaStvqjUSuW3IaFpkKmtoxa0WKIrrmQ6eDw 6lErWgxWmguZ+pNRK1qsIXoO4Nzr86RaDdnSeVdno1a0WFt02b6HhZvcu9vIMghJ7ucmQ6aj VnPvm4VGrfylDE60WLkC616O5PKd/hs68DX1oC8rxvdAQ2dXRNmjaTJk6qpRq9BipcbbhW3k /lpaTXqtu8m/PgkgIZuzYtSKJbfrJe1jse7ltW14lWZDphZHrfQ9NRYrrYg+Mb7JvR1rNB2y S6NWw+/PVrQYTDQdMrU0auVfTovBRPMhuzBqNaLFYKb5kKm5UasRLQYzXYTswqiV4LA9zHQR MjU3apVtsVL0oaeQzY1a8VoMproJ2cyoFS0Gc92ETE1HrWgxmOsqZDpq9WF4XcPjiRZDCl2F TOj0fTefjYX8ugsZkBohA4wRMsAYIQOMETLAGCEDjBEywBghA4z9BA5lFzvBIwp/AAAAAElF TkSuQmCC Numerical Precision Finite number of bits used to represent #s means there is a finite precision, the machine epsilon, ε One way to find this is to ask when 1 + ε = 1 This gives 2.22044604925e-16 x = 1.0 eps = 1.0 while (not x + eps == x): eps = eps/2.0 # machine precision is 2*eps, since that was the last # value for which 1 + eps was not 1 print 2*eps Numerical Precision This means that most real numbers do not have an exact representation on a computer. 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Can accumulate in a program over many arithmetic operations Round-off Example 1 Imagine that we can only keep track of 4 significant digits Compute Take Keeping only 4 digits each step of the way, We've lost a lot of precision Instead, consider: Then (Yakowitz & Szidarovszky) iVBORw0KGgoAAAANSUhEUgAAAg0AAABsCAYAAADpL4FCAAAACXBIWXMAAFxLAABcUAG0kuVI AAAMJ0lEQVR4nO3dj3XbNhDHcea9LuBO0LrdIB3BGcEewR4hHiEewR4hHsEeodqgdbuBR0hx 8aGBaICk+A844Pt5zy+JolCUBCE/HY7kT9++fesAAADG/JR7BwAAgA2EBgAAMAmhAQAATEJo AAAAkxAaAADAJIQGAAAwCaGhIL//8ivHv6JZf/37z4fc+wBgGKGhEC4wXOTeBwAAhhAayvE5 9w4AADCE0FAArTJQaQAAFI3QUAaqDACA4hEaMotUGW7++vefh1z7AwCIc/P1R/fLn7n3Y6kl TceEhvzCKsMLgQEAyqNf8J5y70duhIaMIlWGu1z7AgAYxDJyR2jI7Tr4PVUGACgQzeo/EBoy cYPw3P1yGdxEYACAMlFlUISGfMJB+NoRGgCgODSrHyM0ZKBVhnBp4sENwtdc+wMASKJZPUBo yKNfZaABEgAKQ7P6e4SGnVFlAAAzaFbvITTsjyoDABSOZvU4QsOOqDIAgBk0q0cQGvYVBgaq DABQIL7gpREaduIG4Vl3PAgfGYSwTCfWV8YxKsQycgKhYT8yCM+CPzMIYZZ2lX91P2fu93+4 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Keeps a separate variable that accumulates small errors Requires that the compiler obey parenthesis Computer Languages You can write any algorithm in any programming language—they all provide the necessary logical constructs However, some languages make things much easier than others C Excellent low-level machine access (operating systems are written in C) Multidimensional arrays are “kludgy” Fortran (Formula Translate) One of the earliest complied languages Large code base of legacy code Modern Fortran offers many more conveniences than old Fortran Great support for arrays Python Offers many high-level data-structures (lists, dictionaries, arrays) Great for quick prototyping, analysis, experimentation Increasingly popular in scientific computing Computer Languages IDL Proprietary Great array language Modern (like object-oriented programming) features break the “clean-ness” of the language C++ Others Ruby, Perl, shell scripts, ... Computer Languages Compiled languages (Fortran, C, C++, ...) Compiled into machine code—machine specific Produce faster code (no interpretation is needed) Can offer lower level system access (especially C) Interpreted languages (python, IDL, perl, Java (kind-of) ...) Not converted into machine code, but instead interpreted by the interpreter Great for prototyping Can modify itself while running Platform independent Often has dynamic typing and scoping Many offer garbage collection http://en.wikipedia.org/wiki/Interpreted_language Computer Languages Vector languages Some languages are designed to operate on entire arrays at once (python + NumPy, many Fortran routines, IDL, ...) For interpreted languages, getting reasonable performance requires operating on arrays instead of explicitly writing loops Low level routines are written in compiled language and do the loop behind the scenes We'll see this in some detail when we discuss python Next-generation computing = GPUs ? Hardware is designed to do the same operations on many pieces of data at the same time Object-Oriented Languages Python is an object-oriented language Think of an object as a container that holds both data and functions (methods) that know how to operate on that date. Objects are build from a datatype called a class—you can create as many instances (objects) from a class as memory allows Each object will have its own memory allocated Objects provide a convenient way to package up data In Fortran, think of a derived type that also has it's own functions In C, think of a struct that, again, also has it's own functions Everything, even integers, etc. is an object 1 + 2 will be interpreted as (1).__add__(2) in python Programming Paradigms iVBORw0KGgoAAAANSUhEUgAAA+0AAAJ8CAYAAACLCkBBAAAABmJLR0QA/wD/AP+gvaeTAAAA CXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH3gEbARwt/PJFzgAAIABJREFUeNrs3XlcTfn/ B/BXWrWvVwqVrSwJkX35UmQrSQwZzIzdzPDDGDS+yTJ2xjZjiBjLF8mIkLKELBOlBjMZkSyV Ni1KSfX7o+nqVvd2b4sWr+fj0YN7z+d87ud8Pp/zued9zzmfI1dQUFAAIiIiIiIiIqp1GrAK iIiIiIiIiBi0ExERERERERGDdiIiIiIiIiIG7URERERERETEoJ2IiIiIiIiIQTsRERERERER MWgnIiIiIiIiIgbtRERERERERHWMgiyJvb29WWNEREREREREFeTi4lJ9QTsA2Nvbs5ZrgL+/ P+ueiDguEBFx3CW2Beu0jtezrHh5PBEREREREVEtxaCdiIiIiIiIiEE7ERERERERETFoJyIi 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24§display§\sin(x) = x - \frac{x^3}{3!} + \frac{x^5}{5!} + \ldots Roundoff vs. Truncation Error Roundoff error is just one of the errors we deal with Translating continuous mathematical expressions into discrete forms introduces truncation error Consider the definition of a derivative: We can't take the limit to 0. Our discrete version will be: If we choose h small enough, this should be a good approximation. 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Potential Topics General python Advanced python / special modules Regular expressions Command line/input file parsing Filesystem operations NumPy SciPy and numerical methods Integration ODEs Curve fitting/optimization Interpolation Signal processing (FFT) Linear algebra Plotting / visualization matplotlib MayaVi Workflow management with IPython Extending python w/ Fortran and C/C++ Symbolic math with SymPy Writing python applications GUI programming Unit testing Git/github Others? Who Are We? Physics Ecology & Evolution Sociology Biology / Applied Ecology / Evolution Cognitive Science Integrative Neuroscience Applied Math & Statistics Mechanical Engineering Psychology Marine and Atmospheric Science Earth & Space Science—Geoscience What Do You Want To Learn? From your posts (in some order by popularity): Replace R / matlab (most popular) General (scientific) programming Data analysis Plotting & Visualization (including 3-d visualization) Interacting with experiments (a la PsychoPy) High performance computing Curve / parameter fitting General scripting / glue for C + Fortran programs Python Shell This is the standard (and most basic) interactive way to use python Runs in a terminal window Provides basic interactivity with the python language Simply type python (or python3) at your command prompt You can scroll back through the history using the arrows IPython Shell Type ipython (or ipython3) at your prompt Like the standard shell, this runs in a terminal, but provides many conveniences (type %quickref to see an overview) Scrollback history preserved between sessions Built-in help with ? function-name? object? Magics (%lsmagic lists all the magic functions) %run script: runs a script %timeit: times a command Lots of magics to deal with command history (including %history) Tab completion Run system commands (prefix with a !) Last 3 output objects are referred to via _, __, ___ Jupyter Notebooks A web-based environment that combines code and output, plots, plain text/stylized headings, LaTeX, ... Notebooks can be saved and shared Viewable on the web via: http://nbviewer.ipython.org/ Provides a complete view of your entire workflow Start with jupyter notebook I'll provide notebooks for a lot of the lectures to follow so you can play along on your own The best way to learn is to experiment—download the notebooks and play around Discuss anything you don't understand in the discussion forum Python Scripts Scripts are a non-interactive means of writing python code filename.py Can be executable by adding: #!/usr/bin/env python as the first line and setting the executable bit for the file This is also the way that you write python modules that can be import-ed into other python code (more on that later...) Python 2.x vs. Python 3 See https://wiki.python.org/moin/Python2orPython3 Mostly about cleaning up the language and making things consistent e.g. print is a statement in python 2.x but a function in 3.x Some trivial differences .pyc are now stored in a __pycache__ directory Some gotyas: 1/2 will give different results between python 2 and 3 It's possible to write code that works with both python 2 and 3—often we will do so by importing from __future__ We will focus on python 3.x Python 2.x vs. Python 3 Write for both In python 2.6+, do from __future__ import print_function and then use the new print() style exec cmd becomes exec(cmd) Some small changes to how __init__.py are done (more on this later) One some systems, you can have python 2.x and 3.x installed side by side May need to install packages twice, e.g. python-numpy and python3-numpy Class Organization We’ll work mostly with Jupyter notebooks from now one (with a few exceptions) Each week, I’ll ask you to work through some notebooks on your own, outside of class These will always be posted on the class website Great opportunity to ask questions on slack The hope is that we’ll get a lot of the basic concepts for the week covered by working through the notebooks In class, we’ll work on some exercises together We’ll fine-tune some of this as we go through the semester Let’s Play There are a number of notebooks on our website to demonstrate some core ideas Data types Advanced data types Control flow Functions Classes Modules Lists vs. Arrays Lists (note that python lists are not implemented as a simple linked-list: see http://docs.python.org/2/faq/design.html#how-are-lists-implemented) Implemented as a variable-length array Can store multiple different datatypes in a single list Easy to add items to the end (increasing list length) Accessing a[i] is independent of list length (unlike a traditional linked list) Arrays: Generally used for fixed-length, homogeneous data Less overhead than a list We'll use a special array library (NumPy) for performance later Intro to Python: Functions No distinction between functions and procedures/subroutines Always return something. If not explicitly set, then None Can have default values for arguments, optional arguments, variable number of arguments Can return multiple values, objects Function examples... Intro to Python: Classes Classes are a fundamental concept of object-oriented programming An object is an instance of a class Carries data (state) and has methods that can act on that data Objects are easy to pass around Simplest use is just as a container to carry associated data together (similar to a struct in C) Inheritance, operator overloading, ... all supported. See the tutorial. Classes example... Intro to Python: Modules Modules are a collection of python statements, functions, variables, grouped together in a file ending with .py Can be imported to be used by any routine Python includes a host of useful modules You can define your own. Python will look in the current directory and then in the PYTHONPATH Modules have their own namespace Variables (even global variables) don't clash with those with the same name in the importing program You can change the name of a module on import or import it into the current namespace (*). General practice: put all the imports at the top of your code Intro to Python: Modules Example: myprofile.py—a simple timing module for measuring code performance In the python shell: import myprofile help(myprofile) Note that this module can also be run on its own if __name__ == "__main__": clause at the end Let's look at an example of using this module in a notebook Intro to Python: Exceptions Exceptions are raised if an action you tried (e.g. opening a file) fails. Left alone, the code will abort, printing the exception You can catch the exception, and take appropriate action to fix the situation Built-in exceptions have particular names that let you check for specific failure modes Allows you to write robust code for other users Exceptions example... Intro to Python: I/O Basic I/O File I/O CSV reading INI file reading Caveats Slicing (we'll look at this with arrays soon) Function defaults: default values are only evaluated once. From python tutorial: Prints: Correct way: def f(a, L=[]): L.append(a) return L print f(1) print f(2) print f(3) [1] [1, 2] [1, 2, 3] def f(a, L=None): if L is None: L = [] L.append(a) return L