Tutorial slides (PDF) - Clemson University

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Built-in Mapping Type dict - match any immutable value to an object >>> d = {'a' : 1, 'b' : 'two'} >>> d['b'] # use key 'b' to get object 'two' 'two' # redefine b as a dict with two keys >>> d['b'] = {'foo' : 128.2, 'bar' : 67.3} >>> d {'a': 1, 'b': {'bar': 67.299999999999997, 'foo': 128.19999999999999}} # index nested dict within dict >>> d['b']['foo'] 128.19999999999999 # any immutable type can be an index >>> d['b'][(1,2,3)]='numbers'
Built-in Sequence & Mapping Type Gotchas Python lacks C/C++ or Fortran style arrays. Best that can be done is nested lists or dictionaries Tuples, being immutable are a bad idea You have to be very careful on how you create them It does not pre-allocate memory and this can be a serious source of both annoyance and performance degradation. NumPy gives us a real array type which is generally a better choice
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: Built-in Sequence Types str, unicod
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
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Python Interface to BLACS and ScaLA
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Python Interface in BLACS and ScaLA
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Python Interface in BLACS and ScaLA
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The Bad & Ugly: NumPy cross-compile
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Python for plotting and visualizati
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From Lib to App (overview) Numerica
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From Lib to App (overview) Storage
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Ruby on Rails Django TurboGears Pyl
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matplotlib gallery
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application1 application 2 applicat
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application 1 application=” dna
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web2py and Dispatching hostnam e
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web2py and Dispatching controller
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Upload DNA Seq. {{=form}} web2py a
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web2py and Authentication authentic
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web2py web based IDE web based IDE
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Before we start download web2py fro
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Define some algorithms def find_gen
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Define actions in controllers/defau
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Try it
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Define Actions in controllers/defau
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Try it
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Yet More Resources Tau http://www.c
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Let's review!
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This work is supported in part by t
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Tutorial slides (PDF) - Clemson University

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