《NumPy Beginner's Guide》笔记Chapter9

# -*- coding: utf-8 -*-

__author__ = 'ZengDong'
#日期 = 10:34
""" Matplotlib is a very useful Python plotting library. It integrates nicely with NumPy but is a separate open source project. """
import numpy as np
import matplotlib.pyplot as plt
""" 1. Simple plots The matplotlib.pyplot package contains functionality for simple plots. It is important to remember that each subsequent function call changes the state of the current plot. function: poly1d """
#Take the standard input values as polynomial coefficients. Use the NumPy poly1d function
func = np.poly1d(np.array([1, 2, 3, 4]).astype(float))
print(func)

#create the x value with the Numpy linspace function
x= np.linspace(-10, 10, 30)

#calculate the polynomial values using the polynomial
y = func(x)

#plot
plt.plot(x, y)
plt.xlabel("X")
plt.ylabel("Y")
#plt.show()



""" 2. Plot format string The plot function accepts an unlimited number of arguments. In the previous section we gave it two arrays as arguments. We could also specify the line color and style with an optional format string. By default, it is a solid blue line denoted as b-, but you can specify a different color and style such as red dashes. function: poly1d deriv """
#create and differentiate the polynomial
func = np.poly1d(np.array([1, 2, 3, 4]).astype(float))
func1 = func.deriv(m=1)
x = np.linspace(-10, 10, 30)
y = func(x)
y1= func1(x)

#plot different style
plt.clf()
plt.plot(x, y, "ro", x, y1, "g--")
plt.xlabel("X")
plt.ylabel("Y")
#plt.show()



""" 3. Subplots At a certain point you will have too many lines in one plot. Still, you would like to have everything grouped together. We can achieve this with the subplot function. function: subplot """
#create a polynomial and its derivatives using the following code
func = np.poly1d(np.array([1, 2, 3, 4]).astype(float))
x = np.linspace(-10, 10, 30)
y = func(x)

func1 = func.deriv(m=1)
y1= func1(x)

func2 = func.deriv(m=2)
y2 = func2(x)


#create the first subplot of polynomial with subplot function
#The first parameter of this function is the number of rows, the second parameter is the
#number of columns, and the third parameter is an index number starting with 1.
plt.clf()
plt.subplot(311)
plt.plot(x, y, "r--")
plt.title("Polynomial")

plt.subplot(312)
plt.plot(x, y1, "b^")
plt.title("First Derivative")

plt.subplot(313)
plt.plot(x, y2, "go")
plt.title("Second Derivative")
plt.xlabel("X")
plt.ylabel("Y")
#plt.show()







""" 4. Finance Matplotlib can help us monitor our stock investments. The matplotlib.finance package has utilities with which we can download stock quotes from Yahoo Finance function: """
#略过..........




""" 5. Histograms Histograms visualize the distribution of numerical data. Matplotlib has the handy hist function that graphs histograms. The hist function has two arguments—the array containing the data and the number of bars. function: from matplotlib.finance import quotes_historical_yahoo import sys from datetime import date import matplotlib.pyplot as plt import numpy as np today = date.today() start = (today.year - 1, today.month, today.day) symbol = 'DISH' if len(sys.argv) == 2: symbol = sys.argv[1] quotes = quotes_historical_yahoo(symbol, start, today) quotes = np.array(quotes) close = quotes.T[4] plt.hist(close, np.sqrt(len(close))) plt.show() """




""" 5. Three dimensional plots Three-dimensional plots are pretty spectacular so we have to cover them here too. For 3D plots, we need an Axes3D object associated with a 3d projection. function: """
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm

fig = plt.figure()
#We need to use the 3d keyword to specify a three-dimensional projection for the plot.
ax = fig.add_subplot(111, projection="3d")

u = np.linspace(-1, 1, 100)

x, y = np.meshgrid(u, u)
z = x ** 2 + y ** 2
ax.plot_surface(x, y, z, rstride=4, cstride=4, cmap=cm.YlGnBu_r)
#plt.show()




""" 6. Contour plots Matplotlib contour 3D plots come in two flavors—filled and unfilled. We can create normal contour plots with the contour function. For the filled contour plots we can use the contourf function. function: """
fig = plt.figure()
ax = fig.add_subplot(111)
u = np.linspace(-1, 1, 100)
x, y = np.meshgrid(u, u)
z = x**2 + y**2
ax.contourf(x, y, z)
plt.show()


""" 7. Animation Matplotlib offers fancy animation capabilities. Matplotlib has a special animation module. We need to define a callback function that is used to regularly update the screen. We also need a function to generate data to be plotted. function: """
import matplotlib.animation as animation
fig = plt.figure()
ax = fig.add_subplot(111)
N = 10
x = np.random.rand(N)
y = np.random.rand(N)
z = np.random.rand(N)

#We will plot 3 random datasets as circles, dots and triangles in different colors.
circles, triangles, dots = ax.plot(x, 'ro', y, 'g^', z, 'b.')
ax.set_ylim(0, 1)
plt.axis('off')
def update(data):
    circles.set_ydata(data[0])
    triangles.set_ydata(data[1])
    return circles, triangles
def generate():
    while True: yield np.random.rand(2, N)
anim = animation.FuncAnimation(fig, update, generate, interval=150)
plt.show()