Plot Python

Graficas en Python

Graficas estadstica y minera de datos con python

Miguel Cardenas Montes

Centro de Investigaciones Energeticas Medioambientales y Tecnologicas,Madrid, Spain

[email protected]

22-26 de Abril de 2013

M. Cardenas (CIEMAT) Graficas 22-26 de Abril de 2013 1 / 24

Tabla de Contenidos

1 Objectivos

2 Introduccion

3 Una Grafica Basica

4 Histograma

5 Boxplot

6 Multiples graficas

7 Aumentando la Complejidad


Objectivos

Realizacion de graficas y analisis simples.

Aspectos Tecnicos

scipy

matplotlib

numpy


Introduccion


Lo que los cientficos necesitan

Manipular y procesar datos.

Visualizar resultados para entender que se esta haciendo.

Comunicar resultados: producir graficas para publicaciones, informes,

presentaciones, etc.


Ladrillos

En Python existe una coleccion de herramientas que cubre la mayor

parte de las necesidades cientficas.

Los tiempos de desarrollos disminuyen considerablemente.

Gran capacidad de reutilizacion y comparticion de codigo existente.


Grafica basica I

Grafica basica relacionando valores en coordenadas y abcisas.

import numpy as np

import pylab as pl

# Make an array of x values

x = [1, 2, 3, 4, 5]

# Make an array of y values for each x value

y = [1, 4, 9, 16, 25]

# use pylab to plot x and y

pl.plot(x, y)

# show the plot on the screen

pl.show()

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Grafica basica II: Puntos en vez de lnea.

import numpy as np

import pylab as pl


x = [1, 2, 3, 4, 5]


y = [1, 4, 9, 16, 25]

# use pylab to plot x and y as red circles

pl.plot(x, y, ro)


pl.show()

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Grafica basica III: Colores

Indicador Color

b blue - azul

g green - verde

r reed - rojo

c cyan -

m magenta -

y yellow - amarillo

k black - negro

w white - blanco


Grafica basica IV: Estilo de la lnea

pl.plot(x, y, o)

Indicador Estilo

- solid line style

dashed line style

-. dash-dot line style

: dotted line style

. point marker

s square marker

p pentagon marker

* star marker

http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot


Grafica basica V: Enriqueciendo la grafica

import numpy as np

import pylab as pl


x = [1, 2, 3, 4, 5]


y = [1, 4, 9, 16, 25]


pl.plot(x, y)

# give plot a title

pl.title(Plot of y vs. x)

# make axis labels

pl.xlabel(x axis)

pl.ylabel(y axis)

# set axis limits

pl.xlim(0.0, 7.0)

pl.ylim(0.0, 30.)


pl.show()

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x axis

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y axis

Plot of y vs. x


Grafica basica VI: Mas de un dibujo por grafica

import numpy as np

import pylab as pl

# Make x, y arrays for each graph

x1 = [1, 2, 3, 4, 5]

y1 = [1, 4, 9, 16, 25]

x2 = [1, 2, 4, 6, 8]

y2 = [2, 4, 8, 12, 16]


pl.plot(x1, y1, r)

pl.plot(x2, y2, g)

# give plot a title


# make axis labels

pl.xlabel(x axis)

pl.ylabel(y axis)

# set axis limits

pl.xlim(0.0, 9.0)

pl.ylim(0.0, 30.)


pl.show()

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x axis

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y axis

Plot of y vs. x


Grafica basica VII: Leyenda

import numpy as np

import pylab as pl

# Make x, y arrays for each graph

x1 = [1, 2, 3, 4, 5]

y1 = [1, 4, 9, 16, 25]

x2 = [1, 2, 4, 6, 8]

y2 = [2, 4, 8, 12, 16]

# use pylab to plot x and y : Give your plots names

plot1 = pl.plot(x1, y1, r)

plot2 = pl.plot(x2, y2, go)

# give plot a title


# make axis labels

pl.xlabel(x axis)

pl.ylabel(y axis)

# set axis limits

pl.xlim(0.0, 9.0)

pl.ylim(0.0, 30.)

# make legend

pl.legend([plot1, plot2], (red line, green circles), best, numpoints=1)


pl.show()

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x axis

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y axis

Plot of y vs. x

red line

green circles


Grafica basica VIII: Leyenda

pl.legend((plot1, plot2), (label1, label2), best, numpoints=1)

El primer parametro es la lista

de dibujos.

El segundo es la lista de

etiquetas.

El tercero es la posicion de las

etiquetas: upper right, upper

left, center, lower left, lower

right, best


Histograma I: uno simple

import numpy as np

import pylab as pl

# make an array of random numbers with

#a gaussian distribution with

# mean = 5.0

# rms = 3.0

# number of points = 1000

data = np.random.normal(5.0, 3.0, 1000)

# make a histogram of the data array

pl.hist(data)

# make plot labels

pl.xlabel(data)

pl.show()

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data

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Histograma II: variar el tamano del bin

import numpy as np

import pylab as pl

# make an array of random numbers with

#a gaussian distribution with

# mean = 5.0

# rms = 3.0

# number of points = 1000

data = np.random.normal(5.0, 3.0, 1000)

# make a histogram of the data array

pl.hist(data)

bins = np.arange(-5., 16., 1.)

pl.hist(data, bins, histtype=stepfilled)

# make plot labels

pl.xlabel(data)

pl.show()

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data

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Histograma II: semitransparente

pl.hist(data)

pl.hist(data, alpha=0.3)

pl.hist(data, bins, histtype=stepfilled)

pl.hist(data, bins, histtype=stepfilled, alpha=0.3)


Histograma III: Anadir textos

import numpy as np

import matplotlib.pyplot as plt

mu, sigma = 100, 15

x = mu + sigma * np.random.randn(10000)

# the histogram of the data

n, bins, patches = plt.hist(x, 50, normed=1, \

facecolor=g, alpha=0.75)

plt.xlabel(Smarts)

plt.ylabel(Probability)

plt.title(Histogram of IQ)

plt.text(60, .025, r$\mu=100,\ \sigma=15$)

plt.axis([40, 160, 0, 0.03])

plt.grid(True)

plt.show()40 60 80 100 120 140 160

Smarts

0.000

0.005

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0.015

0.020

0.025

0.030

Probability

=100,=15

Histogram of IQ


Boxplot I


Boxplot II

import numpy as np

from pylab import *

#gaussian distributions

dataset_1 = np.random.normal(5.0, 3.0, 1000)



datos=[dataset_1, dataset_2, dataset_3]

xticks( arange(3), (Dataset 1, Dataset 2, \

Dataset 3) )

boxplot(datos)

show()Dataset 1 Dataset 2 Dataset 3

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Multiples graficas I

import numpy as np


def f(t):

return np.exp(-t) * np.cos(2*np.pi*t)

t1 = np.arange(0.0, 5.0, 0.1)

t2 = np.arange(0.0, 5.0, 0.02)

plt.figure(1)

plt.subplot(211)

plt.plot(t1, f(t1), bo, t2, f(t2), k)

plt.subplot(212)

plt.plot(t2, np.cos(2*np.pi*t2), r--)

plt.show()

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Aumentando la Complejidad I

import numpy as np


from matplotlib.ticker import NullFormatter

# the random data

x = np.random.randn(1000)

y = np.random.randn(1000)

nullfmt = NullFormatter() # no labels

# definitions for the axes

left, width = 0.1, 0.65

bottom, height = 0.1, 0.65

bottom_h = left_h = left+width+0.02

rect_scatter = [left, bottom, width, height]

rect_histx = [left, bottom_h, width, 0.2]

rect_histy = [left_h, bottom, 0.2, height]


Aumentando la Complejidad II

# start with a rectangular Figure

plt.figure(1, figsize=(8,8))

axScatter = plt.axes(rect_scatter)

axHistx = plt.axes(rect_histx)

axHisty = plt.axes(rect_histy)

# no labels

axHistx.xaxis.set_major_formatter(nullfmt)

axHisty.yaxis.set_major_formatter(nullfmt)

# the scatter plot:

axScatter.scatter(x, y)

# now determine nice limits by hand:

binwidth = 0.25

xymax = np.max( [np.max(np.fabs(x)), np.max(np.fabs(y))] )

lim = ( int(xymax/binwidth) + 1) * binwidth

axScatter.set_xlim( (-lim, lim) )

axScatter.set_ylim( (-lim, lim) )

bins = np.arange(-lim, lim + binwidth, binwidth)

axHistx.hist(x, bins=bins)

axHisty.hist(y, bins=bins, orientation=horizontal)

axHistx.set_xlim( axScatter.get_xlim() )

axHisty.set_ylim( axScatter.get_ylim() )

plt.show()


Gracias

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Table of ContentsObjectivosIntroduccinUna Grfica BsicaHistogramaBoxplotMltiples grficasAumentando la Complejidad

Plot Python

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