Tutorial slides (PDF) - Clemson University

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in models/matplotlib_helpers.py import random, cStringIO from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas from matplotlib.figure import Figure def plot(title='title',xlab='x',ylab='y',data={}): fig=Figure() fig.set_facecolor('white') ax=fig.add_subplot(111) if title: ax.set_title(title) if xlab: ax.set_xlabel(xlab) if ylab: ax.set_ylabel(ylab) legend=[] keys=sorted(data) for key in keys: stream = data[key] (x,y)=([],[]) for point in stream: x.append(point[0]) y.append(point[1]) ell=ax.hist(y,20) canvas=FigureCanvas(fig) response.headers['Content-Type']='image/png' stream=cStringIO.StringIO() canvas.print_png(stream) return stream.getvalue()
Define actions in controllers/default.py def index(): rows=db(db.dna.id).select(db.dna.id,db.dna.name) return dict(rows=rows) @auth.requires_login() def gene_size(): dna = db.dna[request.args(0)] or \ redirect(URL(r=request,f='index')) lengths = find_gene_size(dna.sequence) return hist(data={'Lengths':lengths})
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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
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KS-DFT is a complex algorithm!
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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: Define some algorithms def find_gen
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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