#!/usr/bin/env python

#! encoding=utf-8

#

# sudo apt-get install python-matplotlib

# sudo apt-get install python-numpy python-scipy python-matplotlib ipython ipython-notebook python-pandas python-sympy python-nose

#

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

from matplotlib.ticker import MultipleLocator, FuncFormatter

def make_axis():

x1 = [1, 2, 3, 4, 5]# Make x, y arrays for each graph

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

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

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

plt.plot(x1, y1, 'r')# use pylab to plot x and y

plt.plot(x2, y2, 'g')

plt.title('Plot of y vs. x')# give plot a title

plt.xlabel('x axis')# make axis labels

plt.ylabel('y axis')

plt.xlim(0.0, 9.0)# set axis limits

plt.ylim(0.0, 30.)

plt.savefig('axis.png')

plt.show()# show the plot on the screen

def pi_formatter(x, pos):

"""

比较罗嗦地将数值转换为以pi/4为单位的刻度文本

"""

m = np.round(x / (np.pi/4))

n = 4

if m%2==0: m, n = m/2, n/2

if m%2==0: m, n = m/2, n/2

if m == 0:

return "0"

if m == 1 and n == 1:

return "$\pi$"

if n == 1:

return r"$%d \pi$" % m

if m == 1:

return r"$\frac{\pi}{%d}$" % n

return r"$\frac{%d \pi}{%d}$" % (m,n)

def make_axis_ex():

x = np.arange(0, 4*np.pi, 0.01)

y = np.sin(x)

fig = plt.figure(figsize=(8,4))

fig.patch.set_color("g") # 属性背景颜色

fig.canvas.draw() # 属性修改之后更新显示

plt.plot(x, y)

ax = plt.gca()

# 设置两个坐标轴的范围

plt.ylim(-1.5,1.5)

plt.xlim(0, np.max(x))

# 设置图的底边距

plt.subplots_adjust(bottom = 0.15)

plt.grid() #开启网格

# 主刻度为pi/4

ax.xaxis.set_major_locator( MultipleLocator(np.pi/4) )

# 主刻度文本用pi_formatter函数计算

ax.xaxis.set_major_formatter( FuncFormatter( pi_formatter ) )

# 副刻度为pi/20

ax.xaxis.set_minor_locator( MultipleLocator(np.pi/20) )

# 设置刻度文本的大小

for tick in ax.xaxis.get_major_ticks():

tick.label1.set_fontsize(16)

# 刻度标签

for label in ax.xaxis.get_ticklabels():

label.set_color("yellow")

label.set_rotation(45)

label.set_fontsize(16)

# 刻度线

for line in ax.xaxis.get_ticklines():

line.set_color("red")

line.set_markersize(25)

line.set_markeredgewidth(3)

plt.savefig('axis_ex.png')

plt.show()

def make_curve():

t = np.arange(0.0, 1.01, 0.01)

s = np.sin(2 * 2 * np.pi * t)

plt.fill(t, s * np.exp(-5 * t), 'r')

plt.grid(True)

# save as png(jpeg, pdf, etc)

plt.savefig('curve.png')

plt.show()

def make_curve_ex():

x = np.linspace(0, 10, 1000)

y = np.sin(x)

z = np.cos(x**2)

plt.figure(figsize=(10, 6))

# label : 给所绘制的曲线一个名字，此名字在图示(legend)中显示。

# 只要在字符串前后添加"$"符号，matplotlib就会使用其内嵌的latex引擎绘制的数学公式。

plt.plot(x, y, label="$sin(x)$", color="red", linewidth=2)

plt.plot(x, z, "b--", label="$cos(x^2)$") # "b--"指定曲线的颜色和线型

plt.xlabel("Time(s)")

plt.ylabel("Volt")

plt.title("PyPlot First Example")

plt.ylim(-1.2, 1.2)

plt.legend() # 显示图示

plt.savefig('curve_ex.png')

plt.show()

def make_scatter():

if False:

fig = plt.figure()

ax = fig.add_subplot(2,1,1) # two rows, one column, first plot

t = ax.scatter(np.random.rand(20), np.random.rand(20))

plt.savefig('scatter_one.png')

plt.show()

else:

# Use numpy to load the data contained in the file

# 'fakedata.txt' into a 2-D array called data

data = np.loadtxt('fakedata.txt')

# plot the first column as x, and second column as y

plt.plot(data[:, 0], data[:, 1], 'ro')

plt.xlabel('x')

plt.ylabel('y')

plt.xlim(0.0, 10.)

plt.savefig('scatter_two.png')

plt.show()

def make_pie():

# make a square figure and axes

plt.figure(1, figsize=(6, 6))

ax = plt.axes([0.1, 0.1, 0.8, 0.8])

labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'

fracs = [15, 30, 45, 10]

explode = (0, 0.05, 0, 0)

plt.pie(fracs, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True)

plt.title('Raining Hogs and Dogs', bbox={'facecolor':'0.8', 'pad':10})

plt.savefig('pie.png')

plt.show()

def make_histograms():

# 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

plt.hist(data)

# make plot labels

plt.xlabel('data')

plt.savefig('histograms.png')

plt.show()

def randrange(n, vmin, vmax):

'''

Helper function to make an array of random numbers having shape (n, )

with each number distributed Uniform(vmin, vmax).

'''

return (vmax - vmin) * np.random.rand(n) + vmin

def make_scatter3d():

fig = plt.figure()

ax = fig.add_subplot(111, projection='3d')

n = 100

# For each set of style and range settings, plot n random points in the box

# defined by x in [23, 32], y in [0, 100], z in [zlow, zhigh].

for c, m, zlow, zhigh in [('r', 'o', -50, -25), ('b', '^', -30, -5)]:

xs = randrange(n, 23, 32)

ys = randrange(n, 0, 100)

zs = randrange(n, zlow, zhigh)

ax.scatter(xs, ys, zs, c=c, marker=m)

ax.set_xlabel('X Label')

ax.set_ylabel('Y Label')

ax.set_zlabel('Z Label')

plt.savefig('scatter3d.png')

plt.show()

if __name__ == '__main__':

#make_axis()

#make_axis_ex()

#make_curve()

#make_curve_ex()

#make_scatter()

#make_pie()

#make_histograms()

make_scatter3d()