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用 Numpy ndarray 作為數(shù)據(jù)傳入 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) ##增加格點(diǎn)plt.axis('tight') # 坐標(biāo)軸適應(yīng)數(shù)據(jù)量 axis 設(shè)置坐標(biāo)軸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) ##增加格點(diǎn)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)) #畫布大小plt.plot(y.cumsum(),'b',lw = 1.5) # 藍(lán)色的線plt.plot(y.cumsum(),'ro') #離散的點(diǎn)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 在這個(gè)數(shù)組上調(diào)用cumsum 計(jì)算贗本數(shù)據(jù)在0軸(即第一維)上的總和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) #圖例位置自動(dòng)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() # 關(guān)鍵代碼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) #圖例位置自動(dòng)plt.axis('tight')plt.xlabel('index')plt.ylabel('value')plt.title('A simple plot')ax2 = ax1.twinx() #關(guān)鍵代碼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()通過使用 plt.subplots 函數(shù),可以直接訪問底層繪圖對(duì)象,例如可以用它生成和第一個(gè)子圖共享 x 軸的第二個(gè)子圖.
#!/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) #兩行一列,第一個(gè)圖plt.plot(y[:,0], lw = 1.5,label = '1st')plt.plot(y[:,0], 'ro')plt.grid(True)plt.legend(loc = 0) #圖例位置自動(dòng)plt.axis('tight')plt.ylabel('value')plt.title('A simple plot')plt.subplot(212) #兩行一列.第二個(gè)圖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()有時(shí)候,選擇兩個(gè)不同的圖標(biāo)類型來可視化數(shù)據(jù)可能是必要的或者是理想的.利用子圖方法:
#!/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) #兩行一列,第一個(gè)圖plt.plot(y[:,0], lw = 1.5,label = '1st')plt.plot(y[:,0], 'ro')plt.grid(True)plt.legend(loc = 0) #圖例位置自動(dòng)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. 定義積分函數(shù)def func(x): return 0.5 * np.exp(x)+1#2.定義積分區(qū)間a,b = 0.5, 1.5x = np.linspace(0, 2 )y = func(x)#3.繪制函數(shù)圖形fig, ax = plt.subplots(figsize=(7,5))plt.plot(x,y, 'b',linewidth=2)plt.ylim(ymin=0)#4.核心， 我們使用Polygon函數(shù)生成陰影部分，表示積分面積：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在圖表上添加數(shù)學(xué)公式和一些坐標(biāo)軸標(biāo)簽。plt.text(0.5 *(a+b),1,r"$\int_a^b f(x)\mathrmfbwnfa5ux$", horizontalalignment ='center',fontsize=20)plt.figtext(0.9, 0.075,'$x$')plt.figtext(0.075, 0.9, '$f(x)$')#6. 分別設(shè)置x,y刻度標(biāo)簽的位置。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軸上的日期ax.autoscale_view()plt.setp(plt.gca().get_xticklabels(),rotation=30) #日期傾斜plt.show()該函數(shù)提供了一個(gè)相當(dāng)類似的圖標(biāo)類型,使用方法和 candlestick 函數(shù)相同,使用類似的參數(shù). 這里開盤價(jià)和收盤價(jià)不是由彩色矩形表示,而是由兩條短水平線表示.
#!/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軸上的日期ax.autoscale_view()plt.setp(plt.gca().get_xticklabels(),rotation=30) #日期傾斜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()聯(lián)系客服
