Tutorial slides (PDF) - Clemson University

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Classes & Object Orientation >>> class SomeClass: ... """A simple example class""" # docstring ... pi = 3.14159 # attribute ... def __init__(self, ival=89): # init w/ default ... self.i = ival ... def f(self): # class method ... return 'Hello' >>> c = SomeClass(42) # instantiate >>> c.f() # call class method 'hello' >>> c.pi = 3 # change attribute >>> print c.i # print attribute 42
N-dimensional homogeneous arrays (ndarray) Universal functions (ufunc) built-in linear algebra, FFT, PRNGs Tools for integrating with C/C++/Fortran Heavy lifting done by optimized C/Fortran libraries ATLAS or MKL, UMFPACK, FFTW, etc...
Page 1 and 2: Python for Scientific and High Perf
Page 3 and 4: Overview We seek to cover: Python l
Page 5 and 6: 1. Introduction Introductions Tutor
Page 7 and 8: Why Use Python for Scientific Compu
Page 9 and 10: Please login to the Tutorial Enviro
Page 11 and 12: Interpreters CPython Standard pytho
Page 13 and 14: CPython Interpreter Notes Compilati
Page 15 and 16: Built-in Numeric Types int, float,
Page 17 and 18: Gotchas with Built-in Numeric Types
Page 19 and 20: Built-in Sequence Types str, unicod
Page 21 and 22: Built-in Sequence & Mapping Type Go
Page 23 and 24: Control Structures for - iterative
Page 25 and 26: List Comprehensions List Comprehens
Page 27 and 28: File I/O Basics Most I/O in Python
Page 29 and 30: Let's write ten integers to disk wi
Page 31: Modules import - load module, defin
Page 35 and 36: Slicing Arrays >>> a = np.array([[1
Page 37 and 38: SciPy Extends NumPy with common sci
Page 39 and 40: Python Threading Python threads rea
Page 41 and 42: Implementation Example: Calculating
Page 43 and 44: Subprocess The subprocess module al
Page 45 and 46: Calculating pi with multiprocessing
Page 47 and 48: mpi4py wraps your native mpi prefer
Page 49 and 50: from mpi4py import MPI import numpy
Page 51 and 52: Python Best Practices for Performan
Page 53 and 54: Outline a massively parallel Python
Page 55 and 56: GPAW Strong-scaling Results
Page 57 and 58: KS-DFT is a complex algorithm!
Page 59 and 60: Performance Mantra People are able
Page 61 and 62: NumPy - Memory How big is your bina
Page 63 and 64: NumPy - FLOPS WARNING: If you make
Page 65 and 66: Profiling Mixed Python-C code Numbe
Page 67 and 68: Parallel Python Interpreter and Deb
Page 69 and 70: Profiling Mixed Python-C code Flat
Page 71 and 72: Python Interface to BLACS and ScaLA
Page 79 and 80: Python Interface in BLACS and ScaLA
Page 81 and 82: Python Interface in BLACS and ScaLA
Page 83 and 84:
The Bad & Ugly: NumPy cross-compile
Page 85 and 86:
Python for plotting and visualizati
Page 87 and 88:
From Lib to App (overview) Numerica
Page 89 and 90:
From Lib to App (overview) Storage
Page 91 and 92:
Ruby on Rails Django TurboGears Pyl
Page 93 and 94:
matplotlib gallery
Page 95 and 96:
application1 application 2 applicat
Page 97 and 98:
application 1 application=” dna
Page 99 and 100:
web2py and Dispatching hostnam e
Page 101 and 102:
web2py and Dispatching controller
Page 103 and 104:
Upload DNA Seq. {{=form}} web2py a
Page 105 and 106:
web2py and Authentication authentic
Page 107 and 108:
web2py web based IDE web based IDE
Page 109 and 110:
Before we start download web2py fro
Page 111 and 112:
Define some algorithms def find_gen
Page 113 and 114:
Define actions in controllers/defau
Page 115 and 116:
Try it
Page 117 and 118:
Define Actions in controllers/defau
Page 119 and 120:
Try it
Page 121 and 122:
Yet More Resources Tau http://www.c
Page 123 and 124:
Let's review!
Page 125:
This work is supported in part by t
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