Dat Visualizati nLe funzionalità matematiche di Sage per la visualizzazione dei dati

Andrea Lazzarottohttp://andrealazzarotto.com

Caratteristiche

Oltre 90 componenti

Grafica avanzata

Linguaggio Python

Interfaccia web

Non un banale foglio di calcolo

Percorso

Importare i dati

Funzioni built-in

Grafici complessi

Importarei dati

infile = open('bunny.txt','r')d = []for l in infile:

d.append(l.split(' ')) .txt

infile = open('bunny.csv','r')import csvd = list(csv.reader(infile)) .csv

html(table(

[['x','y','z']] +d[:10],header_row = True

))

import urllib2 as Uinfile = U.urlopen('http://example.com/data.txt')d = []for l in infile:

d.append(l.split(' '))

Le liste

a = [1,2,3,4]

b = [[1,3], [6], [1,2,5]]

c = [(5,8,6), 'linux', 7.352]

[i^2 + 3 for i in a]

↓

[4, 7, 12, 19]

Funzioni built-in

v = [random() for i in [0..9]]list_plot(v, transparent=True, size=40)

coords = enumerate(v)line(coords, transparent=True)

scatter_plot(california, edgecolor=[.4,.4,.4,1],\marker='+', markersize=1, aspect_ratio='1',\axes_labels=['Lon','Lat'], transparent=True)

Solo due dimensioni?

Funzioni precedenti → point3d

point3d(bunny[::10], size=20, color='brown')

http://graphics.stanford.edu/data/3Dscanrep/

m = random_matrix(ZZ, 5, 5)matrix_plot(m, cmap='autumn', transparent=True)

Opzioni

color='red'

axes=False

frame=True

thickness=8

...

Esportazione

g = scatter_plot(d)

g.save('graficobellissimo.png')

g.save('graficobellissimo.svg')

g.save('graficobellissimo.pdf')

Grafici complessi

Fitting

v = [(i, random()*0.5-0.25 + i^2+i+1)for i in srange(0,4,0.2)]

# v -> simulazione di x^2 + x + 1

var('a b c')f(x) = a*x^2 + b*x + c

sol = find_fit(v, f, solution_dict=True)

A = scatter_plot(v)B = plot(f.subs(sol), (x, 0, v[-1][0]))

(A+B).show(transparent=True)

Interpolazionepoints = [[x,y] for [x,y,z] in bunny]values = [z for [x,y,z] in bunny]xv = [float(x) for [x,y] in points]yv = [float(y) for [x,y] in points]

import numpy as npgrid_x, grid_y = np.mgrid[min(xv):max(xv):200jr, min(yv):max(yv):200jr]

from scipy.interpolate import griddatagrid_z = griddata(points, values,

(grid_x, grid_y), method='linear')

http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html

matrix_plot(grid_z.T, origin='lower',cmap='BrBG_r', transparent=True)

Matplotlib con Pylab

import matplotlib.pyplot as pltfrom pylab import figure, savefig

def twobar(dA, dB, xlabel, ylabel, ticks, suff):fig = figure()ax = fig.add_subplot(1,1,1)

i = range(len(dA))ax.bar(i, dB, align='center', linewidth=4,

facecolor='#bb0000', edgecolor='white')ax.bar(i, dA, fill=False, align='center',

edgecolor='#666666')

ax.set_xticks(i)ax.set_xticklabels([str(d) + suff

for d in ticks])

ax.set_xlabel(xlabel)ax.set_ylabel(ylabel)ax.xaxis.labelpad = 12

savefig("output.png", transparent=True)

twobar(datiA, datiB, 'Percentuale', 'Elementi',[2,4,6,8], "%")

Conclusione

Numerosi elementi base

Infinite varianti

Vari livelli di complessità

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