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用 Numpy ndarray 作为数据传入 ply
import numpy as npimport matplotlib as mplimport matplotlib.pyplot as pltnp.random.seed(1000)y = np.random.standard_normal(10)print "y = %s"% yx = range(len(y))print "x=%s"% xplt.plot(y)plt.show()
import numpy as npimport matplotlib as mplimport matplotlib.pyplot as pltnp.random.seed(1000)y = np.random.standard_normal(10)plt.plot(y.cumsum())plt.grid(True) ##增加格点plt.axis('tight') # 坐标轴适应数据量 axis 设置坐标轴plt.show()#!/etc/bin/python#coding=utf-8import numpy as npimport matplotlib as mplimport matplotlib.pyplot as pltnp.random.seed(1000)y = np.random.standard_normal(20)plt.plot(y.cumsum())plt.grid(True) ##增加格点plt.xlim(-1,20)plt.ylim(np.min(y.cumsum())- 1, np.max(y.cumsum()) + 1)plt.show()#!/etc/bin/python#coding=utf-8import numpy as npimport matplotlib as mplimport matplotlib.pyplot as pltnp.random.seed(1000)y = np.random.standard_normal(20)plt.figure(figsize=(7,4)) #画布大小plt.plot(y.cumsum(),'b',lw = 1.5) # 蓝色的线plt.plot(y.cumsum(),'ro') #离散的点plt.grid(True)plt.axis('tight')plt.xlabel('index')plt.ylabel('value')plt.title('A simple Plot')plt.show()np.random.seed(2000)y = np.random.standard_normal((10, 2)).cumsum(axis=0) #10行2列 在这个数组上调用cumsum 计算赝本数据在0轴(即第一维)上的总和print y#!/etc/bin/python#coding=utf-8import numpy as npimport matplotlib as mplimport matplotlib.pyplot as pltnp.random.seed(2000)y = np.random.standard_normal((10, 2))plt.figure(figsize=(7,5))plt.plot(y, lw = 1.5)plt.plot(y, 'ro')plt.grid(True)plt.axis('tight')plt.xlabel('index')plt.ylabel('value')plt.title('A simple plot')plt.show()#!/etc/bin/python#coding=utf-8import numpy as npimport matplotlib as mplimport matplotlib.pyplot as pltnp.random.seed(2000)y = np.random.standard_normal((10, 2))plt.figure(figsize=(7,5))plt.plot(y[:,0], lw = 1.5,label = '1st')plt.plot(y[:,1], lw = 1.5, label = '2st')plt.plot(y, 'ro')plt.grid(True)plt.legend(loc = 0) #图例位置自动plt.axis('tight')plt.xlabel('index')plt.ylabel('value')plt.title('A simple plot')plt.show()#!/etc/bin/python#coding=utf-8import numpy as npimport matplotlib as mplimport matplotlib.pyplot as pltnp.random.seed(2000)y = np.random.standard_normal((10, 2))fig, ax1 = plt.subplots() # 关键代码1 plt first data set using first (left) axisplt.plot(y[:,0], lw = 1.5,label = '1st')plt.plot(y[:,0], 'ro')plt.grid(True)plt.legend(loc = 0) #图例位置自动plt.axis('tight')plt.xlabel('index')plt.ylabel('value')plt.title('A simple plot')ax2 = ax1.twinx() #关键代码2 plt second data set using second(right) axisplt.plot(y[:,1],'g', lw = 1.5, label = '2nd')plt.plot(y[:,1], 'ro')plt.legend(loc = 0)plt.ylabel('value 2nd')plt.show()通过使用 plt.subplots 函数,可以直接访问底层绘图对象,例如可以用它生成和第一个子图共享 x 轴的第二个子图.
#!/etc/bin/python#coding=utf-8import numpy as npimport matplotlib as mplimport matplotlib.pyplot as pltnp.random.seed(2000)y = np.random.standard_normal((10, 2))plt.figure(figsize=(7,5))plt.subplot(211) #两行一列,第一个图plt.plot(y[:,0], lw = 1.5,label = '1st')plt.plot(y[:,0], 'ro')plt.grid(True)plt.legend(loc = 0) #图例位置自动plt.axis('tight')plt.ylabel('value')plt.title('A simple plot')plt.subplot(212) #两行一列.第二个图plt.plot(y[:,1],'g', lw = 1.5, label = '2nd')plt.plot(y[:,1], 'ro')plt.grid(True)plt.legend(loc = 0)plt.xlabel('index')plt.ylabel('value 2nd')plt.axis('tight')plt.show()有时候,选择两个不同的图标类型来可视化数据可能是必要的或者是理想的.利用子图方法:
#!/etc/bin/python#coding=utf-8import numpy as npimport matplotlib as mplimport matplotlib.pyplot as pltnp.random.seed(2000)y = np.random.standard_normal((10, 2))plt.figure(figsize=(10,5))plt.subplot(121) #两行一列,第一个图plt.plot(y[:,0], lw = 1.5,label = '1st')plt.plot(y[:,0], 'ro')plt.grid(True)plt.legend(loc = 0) #图例位置自动plt.axis('tight')plt.xlabel('index')plt.ylabel('value')plt.title('1st Data Set')plt.subplot(122)plt.bar(np.arange(len(y)), y[:,1],width=0.5, color='g',label = '2nc')plt.grid(True)plt.legend(loc=0)plt.axis('tight')plt.xlabel('index')plt.title('2nd Data Set')plt.show()#!/etc/bin/python#coding=utf-8import numpy as npimport matplotlib as mplimport matplotlib.pyplot as pltnp.random.seed(2000)y = np.random.standard_normal((1000, 2))plt.figure(figsize=(7,5))plt.scatter(y[:,0],y[:,1],marker='o')plt.grid(True)plt.xlabel('1st')plt.ylabel('2nd')plt.title('Scatter Plot')plt.show()#!/etc/bin/python#coding=utf-8import numpy as npimport matplotlib as mplimport matplotlib.pyplot as pltnp.random.seed(2000)y = np.random.standard_normal((1000, 2))plt.figure(figsize=(7,5))plt.hist(y,label=['1st','2nd'],bins=25)plt.grid(True)plt.xlabel('value')plt.ylabel('frequency')plt.title('Histogram')plt.show()#!/etc/bin/python#coding=utf-8import numpy as npimport matplotlib as mplimport matplotlib.pyplot as pltnp.random.seed(2000)y = np.random.standard_normal((1000, 2))plt.figure(figsize=(7,5))plt.hist(y,label=['1st','2nd'],color=['b','g'],stacked=True,bins=20)plt.grid(True)plt.xlabel('value')plt.ylabel('frequency')plt.title('Histogram')plt.show()#!/etc/bin/python#coding=utf-8import numpy as npimport matplotlib as mplimport matplotlib.pyplot as pltnp.random.seed(2000)y = np.random.standard_normal((1000, 2))fig, ax = plt.subplots(figsize=(7,4))plt.boxplot(y)plt.grid(True)plt.setp(ax,xticklabels=['1st' , '2nd'])plt.xlabel('value')plt.ylabel('frequency')plt.title('Histogram')plt.show()from matplotlib.patches import Polygonimport numpy as npimport matplotlib.pyplot as plt#1. 定义积分函数def func(x): return 0.5 * np.exp(x)+1#2.定义积分区间a,b = 0.5, 1.5x = np.linspace(0, 2 )y = func(x)#3.绘制函数图形fig, ax = plt.subplots(figsize=(7,5))plt.plot(x,y, 'b',linewidth=2)plt.ylim(ymin=0)#4.核心， 我们使用Polygon函数生成阴影部分，表示积分面积：Ix = np.linspace(a,b)Iy = func(Ix)verts = [(a,0)] + list(zip(Ix, Iy))+[(b,0)]poly = Polygon(verts,facecolor='0.7',edgecolor = '0.5')ax.add_patch(poly)#5.用plt.text和plt.figtext在图表上添加数学公式和一些坐标轴标签。plt.text(0.5 *(a+b),1,r"$\int_a^b f(x)\mathrm{d}x$", horizontalalignment ='center',fontsize=20)plt.figtext(0.9, 0.075,'$x$')plt.figtext(0.075, 0.9, '$f(x)$')#6. 分别设置x,y刻度标签的位置。ax.set_xticks((a,b))ax.set_xticklabels(('$a$','$b$'))ax.set_yticks([func(a),func(b)])ax.set_yticklabels(('$f(a)$','$f(b)$'))plt.grid(True)#!/etc/bin/python#coding=utf-8import matplotlib.pyplot as pltimport matplotlib.finance as mpfstart = (2014, 5,1)end = (2014, 7,1)quotes = mpf.quotes_historical_yahoo('^GDAXI',start,end)# print quotes[:2]fig, ax = plt.subplots(figsize=(8,5))fig.subplots_adjust(bottom = 0.2)mpf.candlestick(ax, quotes, width=0.6, colorup='b',colordown='r')plt.grid(True)ax.xaxis_date() #x轴上的日期ax.autoscale_view()plt.setp(plt.gca().get_xticklabels(),rotation=30) #日期倾斜plt.show()该函数提供了一个相当类似的图标类型,使用方法和 candlestick 函数相同,使用类似的参数. 这里开盘价和收盘价不是由彩色矩形表示,而是由两条短水平线表示.
#!/etc/bin/python#coding=utf-8import matplotlib.pyplot as pltimport matplotlib.finance as mpfstart = (2014, 5,1)end = (2014, 7,1)quotes = mpf.quotes_historical_yahoo('^GDAXI',start,end)# print quotes[:2]fig, ax = plt.subplots(figsize=(8,5))fig.subplots_adjust(bottom = 0.2)mpf.plot_day_summary(ax, quotes, colorup='b',colordown='r')plt.grid(True)ax.xaxis_date() #x轴上的日期ax.autoscale_view()plt.setp(plt.gca().get_xticklabels(),rotation=30) #日期倾斜plt.show()#!/etc/bin/python#coding=utf-8import matplotlib.pyplot as pltimport numpy as npimport matplotlib.finance as mpfstart = (2014, 5,1)end = (2014, 7,1)quotes = mpf.quotes_historical_yahoo('^GDAXI',start,end)# print quotes[:2]quotes = np.array(quotes)fig, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(8,6))mpf.candlestick(ax1, quotes, width=0.6,colorup='b',colordown='r')ax1.set_title('Yahoo Inc.')ax1.set_ylabel('index level')ax1.grid(True)ax1.xaxis_date()plt.bar(quotes[:,0] - 0.25, quotes[:, 5], width=0.5)ax2.set_ylabel('volume')ax2.grid(True)ax2.autoscale_view()plt.setp(plt.gca().get_xticklabels(),rotation=30)plt.show()#!/etc/bin/python#coding=utf-8import numpy as npimport matplotlib.pyplot as pltstike = np.linspace(50, 150, 24)ttm = np.linspace(0.5, 2.5, 24)stike, ttm = np.meshgrid(stike, ttm)print stike[:2]iv = (stike - 100) ** 2 / (100 * stike) /ttmfrom mpl_toolkits.mplot3d import Axes3Dfig = plt.figure(figsize=(9,6))ax = fig.gca(projection='3d')surf = ax.plot_surface(stike, ttm, iv, rstride=2, cstride=2, cmap=plt.cm.coolwarm, linewidth=0.5, antialiased=True)ax.set_xlabel('strike')ax.set_ylabel('time-to-maturity')ax.set_zlabel('implied volatility')plt.show()联系客服
