二十三、共享 X 轴

在这个 Matplotlib 数据可视化教程中，我们将讨论sharex选项，它允许我们在图表之间共享x轴。将sharex看做『复制 x』也许更好。

在我们开始之前，首先我们要做些修剪并在另一个轴上设置最大刻度数，如下所示：

ax2.yaxis.set_major_locator(mticker.MaxNLocator(nbins=7, prune='upper'))

以及

ax3.yaxis.set_major_locator(mticker.MaxNLocator(nbins=4, prune='upper'))

现在，让我们共享所有轴域之间的x轴。 为此，我们需要将其添加到轴域定义中：

fig = plt.figure()
ax1 = plt.subplot2grid((6,1), (0,0), rowspan=1, colspan=1)
plt.title(stock)
plt.ylabel('H-L')
ax2 = plt.subplot2grid((6,1), (1,0), rowspan=4, colspan=1, sharex=ax1)
plt.ylabel('Price')
ax3 = plt.subplot2grid((6,1), (5,0), rowspan=1, colspan=1, sharex=ax1)
plt.ylabel('MAvgs')

上面，对于ax2和ax3，我们添加一个新的参数，称为sharex，然后我们说，我们要与ax1共享x轴。

使用这种方式，我们可以加载图表，然后我们可以放大到一个特定的点，结果将是这样：

所以这意味着所有轴域沿着它们的x轴一起移动。 这很酷吧！

接下来，让我们将[-start:]应用到所有数据，所以所有轴域都起始于相同地方。 我们最终的代码为：

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
from matplotlib.finance import candlestick_ohlc
from matplotlib import style
import numpy as np
import urllib
import datetime as dt
style.use('fivethirtyeight')
print(plt.style.available)
print(plt.__file__)
MA1 = 10
MA2 = 30
def moving_average(values, window):
    weights = np.repeat(1.0, window)/window
    smas = np.convolve(values, weights, 'valid')
    return smas
def high_minus_low(highs, lows):
    return highs-lows
def bytespdate2num(fmt, encoding='utf-8'):
    strconverter = mdates.strpdate2num(fmt)
    def bytesconverter(b):
        s = b.decode(encoding)
        return strconverter(s)
    return bytesconverter
def graph_data(stock):
    fig = plt.figure()
    ax1 = plt.subplot2grid((6,1), (0,0), rowspan=1, colspan=1)
    plt.title(stock)
    plt.ylabel('H-L')
    ax2 = plt.subplot2grid((6,1), (1,0), rowspan=4, colspan=1, sharex=ax1)
    plt.ylabel('Price')
    ax3 = plt.subplot2grid((6,1), (5,0), rowspan=1, colspan=1, sharex=ax1)
    plt.ylabel('MAvgs')
    stock_price_url = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=1y/csv'
    source_code = urllib.request.urlopen(stock_price_url).read().decode()
    stock_data = []
    split_source = source_code.split('\n')
    for line in split_source:
        split_line = line.split(',')
        if len(split_line) == 6:
            if 'values' not in line and 'labels' not in line:
                stock_data.append(line)
    date, closep, highp, lowp, openp, volume = np.loadtxt(stock_data,
                                                          delimiter=',',
                                                          unpack=True,
                                                          converters={0: bytespdate2num('%Y%m%d')})
    x = 0
    y = len(date)
    ohlc = []
    while x < y:
        append_me = date[x], openp[x], highp[x], lowp[x], closep[x], volume[x]
        ohlc.append(append_me)
        x+=1
    ma1 = moving_average(closep,MA1)
    ma2 = moving_average(closep,MA2)
    start = len(date[MA2-1:])
    h_l = list(map(high_minus_low, highp, lowp))
    ax1.plot_date(date[-start:],h_l[-start:],'-')
    ax1.yaxis.set_major_locator(mticker.MaxNLocator(nbins=4, prune='lower'))
    candlestick_ohlc(ax2, ohlc[-start:], width=0.4, colorup='#77d879', colordown='#db3f3f')
    ax2.yaxis.set_major_locator(mticker.MaxNLocator(nbins=7, prune='upper'))
    ax2.grid(True)
    bbox_props = dict(boxstyle='round',fc='w', ec='k',lw=1)
    ax2.annotate(str(closep[-1]), (date[-1], closep[-1]),
                 xytext = (date[-1]+4, closep[-1]), bbox=bbox_props)
##    # Annotation example with arrow
##    ax2.annotate('Bad News!',(date[11],highp[11]),
##                 xytext=(0.8, 0.9), textcoords='axes fraction',
##                 arrowprops = dict(facecolor='grey',color='grey'))
##
##    
##    # Font dict example
##    font_dict = {'family':'serif',
##                 'color':'darkred',
##                 'size':15}
##    # Hard coded text 
##    ax2.text(date[10], closep[1],'Text Example', fontdict=font_dict)
    ax3.plot(date[-start:], ma1[-start:], linewidth=1)
    ax3.plot(date[-start:], ma2[-start:], linewidth=1)
    ax3.fill_between(date[-start:], ma2[-start:], ma1[-start:],
                     where=(ma1[-start:] < ma2[-start:]),
                     facecolor='r', edgecolor='r', alpha=0.5)
    ax3.fill_between(date[-start:], ma2[-start:], ma1[-start:],
                     where=(ma1[-start:] > ma2[-start:]),
                     facecolor='g', edgecolor='g', alpha=0.5)
    ax3.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
    ax3.xaxis.set_major_locator(mticker.MaxNLocator(10))
    ax3.yaxis.set_major_locator(mticker.MaxNLocator(nbins=4, prune='upper'))
    for label in ax3.xaxis.get_ticklabels():
        label.set_rotation(45)
    plt.setp(ax1.get_xticklabels(), visible=False)
    plt.setp(ax2.get_xticklabels(), visible=False)
    plt.subplots_adjust(left=0.11, bottom=0.24, right=0.90, top=0.90, wspace=0.2, hspace=0)
    plt.show()
graph_data('EBAY')

下面我们会讨论如何创建多个y轴。