Tutorial slides (PDF) - Clemson University

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Dynamic programming language Interpreted & interactive Object-oriented Strongly introspective Provides exception-based error handling Comes with "Batteries included" (extensive standard libraries) Easily extended with C, C++, Fortran, etc... Well documented (http://docs.python.org/)
Why Use Python for Scientific Computing? "Batteries included" + rich scientific computing ecosystem Good balance between computational performance and time investment Similar performance to expensive commercial solutions Many ways to optimize critical components Only spend time on speed if really needed Tools are mostly open source and free (many are MIT/BSD license) Strong community and commercial support options. No license management
Page 1 and 2: Python for Scientific and High Perf
Page 3 and 4: Overview We seek to cover: Python l
Page 5: 1. Introduction Introductions Tutor
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 and 32: Modules import - load module, defin
Page 33 and 34: N-dimensional homogeneous arrays (n
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 73 and 74:
Page 75 and 76:
Page 77 and 78:
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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Tutorial slides (PDF) - Clemson University

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