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Python for Scientific Computing
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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 ...
Very widely adopted in the scientific community
Mostly due to the very powerful array library NumPy
Python
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(xkcd)
Installing Python
Linux
Python is probably already installed
The dependencies we need for our class should be available through your package manager
OS X / Windows
The easiest way to get everything we need for class is by installing Anaconda: https://www.continuum.io/downloads
You'll have a choice of python 2.7 or 3.6—choose python 3.6
If you run into problems ask on slack (there is an “installation” channel), or come by my office
Hello, World!
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. Lucks (arxiv:0803.1838)
Scientific Python Stack
Most scientific libraries in python build on the Scientific Python stack:
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(scipy.org)
Starters...
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. To get a 4-byte integer, do:
i = 2l
Overflow
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(xkcd)
Overflow example...
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
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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.
Spacing between numbers varies with the size of numbers
Relative spacing is constant
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TexMaths
24§display§\mathrm{relative~roundoff~error}~ = \frac{|\mathrm{true~number} - \mathrm{computer~number}|}{|\mathrm{true~number}|} \le \epsilon§png§600§FALSE§
Round-off Error
Round-off error is the error arising from the fact that no every number is exactly representable in the finite precision floating point format.
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)
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TexMaths
24§display§\sqrt{1985} - \sqrt{1984} = \frac{1}{\sqrt{1985} + \sqrt{1984}} = \frac{1}{44.55 + 44.54} = 0.01122
Round-off Example 2
Consider computing exp(-24) via a truncated Taylor series
The error in this approximation is less than
We can compute S(-24) by adding terms until the term is less than machine precision
We find S(-24) = 3.44305354288101977E-007
But exp(-24) = 3.77513454427909773E-011
The error is far bigger than the true answer!
(Yakowitz & Szidarovszky)
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REFUZSOh2yZqxj5PFQ4AANNEpbv6xt121evFPr5WQ69dcqve/0v4jyxJX7QJ0M95WfzaEbj9
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pqnQXT02fd4AAATO7enu7PJ0ZdfLXb3UsRDhlxU8NXBL8RDBjmAYkrrJlPReAwDQsnPbS7qs
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19bAmdcGAACBobSX+KrZzk1sFK90NzUIIHTjGjFXAQCADjSxDXzrbBObnT3ZO/q5zw7dtvV8
12uSH8w2OGm7N/fb99/Fqi1sI96KlVp4ruRlsahsBU/bFQAAgUGE7gO1FrpbPvZZLHD/ucBd
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KlpBAADowLmhOxZsu15bua6f+9xJXdOaz7/vulGWpJMsST8u1NsO7BLbVRUAAHTg3NAdC7ad
vbFfoJ97lZfFvsrgi92W0I0hoNINAEAHzgrdNQG0y2paK/3cdhk+FuZ3tpZYddvfjjCDvonO
Uej8LAAAuEFN9HRXVyrpssLbSj+36lct2dlaEtxu3tD280Cr8rJgJR4AADrQxOolS30P2l32
dLfVzx0L3YsD1v32tyPIoI+qr83Glva0XWF9W9VKbuDJ6wAAANNGpfvOeq1b1VY/t7WWxKr1
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7qpLn1RfL+uGeq73tZJ1vW4/AAC91NSOlNU37y5CSOw+zg4RtgziwRPMrG91LDfRkir3DbEl
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8SZ3eX0l6bGh+0YPWKCOtU6M5Kq7Ix12RefFJhr6kBt7jq46mHzbVuiea/cgm9ANAIAaCt15
WSyyJF3r+6XmidqbVNhqP3deFs/BaiSSNMmSdOIvx2dJ+qDNSg0rSY+0lVydkQ4YeB1hUvkY
WqjFCbg1K/I09Vp5D5YLrFrmZfHcxP0AADB0TVW6JVfxCiuDbfZyxqpnTfdT38u1Dvgw8Zkl
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i/8aOXj8/vyb/Fqun7qVN/igiuerhb7n+6Y2/7DH+6vym5fFjwueDo6QJemnvgfvJ9aSBwCg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ugEAAICWEboBAACAlhG6AQAAgJYRugEAAICW/T+fgM5kkPsRiQAAAABJRU5ErkJggg==
Round-off Example 2
(Yakowitz & Szidarovszky)
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AP8A/wD/oL2nkwAAAAlwSFlzAAALEwAACxMBAJqcGAAAAAd0SU1FB90BBQAyGJbWsPQAACAA
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X+AorqkcEYrwF/ax6aOpn1mm+WPSUsRmzBIEszTRuBeQrooIyOV8//hMfOPHffZ57nmee6+x
X2uxlrA55+yzn7Pfez+P9158hBACDMMwDMMwDMMwDMMwDNMK8eUQMAzDMAzDMAzDMAzDMK0V
fjjGMAzDMAzDMAzDMAzDtFr44RjDMAzDMAzDMAzDMAzTamlj1sRHPz6Gv765AQAwa8Z0REVG
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3t+/4fvwsFAkL13iNh+tNhvezt6MD/buw2cnPmlks9vtWJO2HidP5aHjAx0wJiYGcaNjXcqP
lvTT7PkHRlka/l3ncMDhcCD32D8N3b+K/l+6fBlZm7fgVN5p9A4KxJTJ8ejWtauh52f3nr3Y
sj0Hlbcr8XhQbzz/7Hj8vmdPqfUB4IsvzyF761bkF5xHe39/vJQwAyEDBnByM1JQ59PT9VUr
/yn/ZfJHJT9loPKT2p/W+jL6p7U/Gf2l+mvZ+Dgbr7V/I/xjGGfInC939X+unl8z80dmPJX/
lP5R41X0merPVOOjp/90Rf9U619TGwD0DgrE62nr3PJ8RE98TUWYQGFRkRgWHSOuXrsmiq9e
FUOGjxD5BeeFu1i2cpU4c/asqKqqEqnr0sTU6TNb/L0Sq1VMmBQvAvv0NXR/bL+37Qyjqi96
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cc9NEufy84UQQly9dk0UXfiKE5uRRut8erq+UvlP+U/lj2p+UlD5KbM/rfVl9E+2frWkvzL6
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qvpH6a+MflPx0Rqv5/q66h/DqJx/s1E5v+7MH2fjtfJfpj9R0VdqvJ7+zJX4yOiviv6p1j+z
67+e/sBT+QWY9Jlj5/Lz0eeJJ1BfX495Cxdj8KBIFJwv9MgGv7l8qdlL4gFg+arVGBs3ukWb
6v7Yfm/bGUZVXwAgYXYiLMOG49XFS3Dz5i2v8TkwsBd27NyJqqo7KCwqwpfn8vHtlWJd+aGl
n2bP/wNnPj8LHx9fBPXqZej+VfV/6guT8fLcJOTm5WFlyhokTJ9myvlJWrAIoYOicDIvDykr
lkuv/9XFi6isrMT45+MxdMRIpK5LQ3V1NSczowtn5/Neqa/O/JfNX1fzk8Ko/HS2vqz+UfvT
0l8Z/daKj0p/bpR/DOPq+TK7/3PH+VXNHyo+zvJfVv8ofTVCn7X6M1fjI6O/7ri+Zp1Po+6/
Xe3vvfrhmK20DI91DcD6N95Ely6dMfWFybDabIbNPzDK0uyrJcrLy7EqdQ3mzH650c+35ezA
gx07whIVZcr+2H5v2xlGBmf6AgDRI4Zj3pw52Jadhdrau1iTluY1fq9OTkZpWRmix8Rh8bJk
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RK5CYII=
Look at the terms—we are relying on cancellation to get our result
Round-off Example 2
Instead recognize:
S(-1) is well behaved, since each term is smaller in absolute magnitude than the previous.
S(-1) = 0.36787944117144245
S(-1)24 = 3.77513454427912681E-011
exp(-24) = 3.77513454427909773E-011
(Yakowitz & Szidarovszky)
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Round-off Example 3
In spherically-symmetric codes (like a stellar evolution code), it is common to compute the volume of a spherical shell as
This relies on the cancellation of two very big numbers.
Rewriting reduces round-off errors:
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Comparing Floating Point #s
When comparing two floating point #s, we need to be careful
If you want to check whether 2 computed values are equal, instead of asking if
it is safer to ask whether they agree to within some tolerance
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Associative Property
You learned in grade school that: (a + b) + c = a + (b + c)
Not true with floating point
You can use parenthesizes to force order of operations
If you want to enforce a particular association, use parenthesis
Associative Property
Adding lots of numbers together can compound round-off error
One solution: sort and add starting with the smallest numbers
Kahan summation (see reading list)
Algorithm for adding sequence of numbers while minimizing roundoff accumulation
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
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ESMAAAAASUVORK5CYII=
(Wikipedia)
Arrays and Memory Layout
Building block of many numerical methods
Row vs. Column major: A(m,n)
First index is called the row
Second index is called the column
Multi-dimensional arrays are flattened into a one-dimensional sequence for storage
Row-major (C, python): rows are stored one after the other
Column-major (Fortran, matlab): columns are stored one after the other
Ordering matters for:
Passing arrays between languages
Deciding which index to loop over first
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Row major
Column major
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 Taylor series expansion for sine:
If x is small, then we can neglect the higher-order terms (we truncate the series). This introduces an error.
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TexMaths
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.
(Yakowitz & Szidarovszky)
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Roundoff vs. Truncation Error
(Yakowitz & Szidarovszky)
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Truncation error dominates
Roundoff error dominates
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