Tutorial slides (PDF) - Clemson University

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Goal to build a web based application that stores DNA sequences allows upload of DNA sequences allows analysis of DNA sequences (reverse, count, align, etc.) allows plotting of results
Before we start download web2py from web2py.com unzip web2py and click on the executable when it asks for a password choose one visit http://127.0.0.1:8000/admin and login create a new “dna” application by: type “dna” in the apposite box and press [submit]
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Python for Scientific and High Perf
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Overview We seek to cover: Python l
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1. Introduction Introductions Tutor
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Why Use Python for Scientific Compu
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Please login to the Tutorial Enviro
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Interpreters CPython Standard pytho
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CPython Interpreter Notes Compilati
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Built-in Numeric Types int, float,
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Gotchas with Built-in Numeric Types
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Built-in Sequence Types str, unicod
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Built-in Sequence & Mapping Type Go
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Control Structures for - iterative
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List Comprehensions List Comprehens
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File I/O Basics Most I/O in Python
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Let's write ten integers to disk wi
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Modules import - load module, defin
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N-dimensional homogeneous arrays (n
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Slicing Arrays >>> a = np.array([[1
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SciPy Extends NumPy with common sci
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Python Threading Python threads rea
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Implementation Example: Calculating
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Subprocess The subprocess module al
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Calculating pi with multiprocessing
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mpi4py wraps your native mpi prefer
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from mpi4py import MPI import numpy
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Python Best Practices for Performan
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Outline a massively parallel Python
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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: web2py web based IDE web based IDE
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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