import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import matplotlib.ticker as ticker

#读数据
r = mlab.csv2rec('../data/aapl.csv')
r.sort()
r = r[-30:] # get the last 30 days
N = len(r)
ind = np.arange(N) # the evenly spaced plot indices
def format_date(x, pos=None):
 #保证下标不越界,很重要,越界会导致最终plot坐标轴label无显示
 thisind = np.clip(int(x+0.5), 0, N-1)
 return r.date[thisind].strftime('%Y-%m-%d')

fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.plot(ind, r.adj_close, 'o-')
ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
fig.autofmt_xdate()
plt.show()

import pandas as PD
import numpy as NP
import matplotlib.pyplot as PLT
import matplotlib.ticker as MTK

file = r'vix_series.csv'
df = PD.read_csv(file, parse_dates=[0, 2])
#用下标代理原始时间戳数据
idx_pxy = NP.arange(df.shape[0])
#下标-时间转换func
def x_fmt_func(x, pos=None):
 idx = NP.clip(int(x+0.5), 0, df.shape[0]-1)
 return df['datetime'].iat[idx]
#绘图流程
def decorateAx(ax, xs, ys, x_func):
 ax.plot(xs, ys, color="green", linewidth=1, linestyle="-")
 ax.plot(ax.get_xlim(), [0,0], color="blue", 
   linewidth=0.5, linestyle="--")
 if x_func:
  #set数据代理func
  ax.xaxis.set_major_formatter(MTK.FuncFormatter(x_func))
 ax.grid(True)
 return

fig = PLT.figure()
ax1 = fig.add_subplot(2,1,1)
ax2 = fig.add_subplot(2,1,2)
decorateAx(ax1, df['datetime'], df['vix_all'], None)
decorateAx(ax2, idx_pxy, df['vix_all'], x_fmt_func)
#优化label显示,非必须
fig.autofmt_xdate()
PLT.show()