二十二、自定义填充、修剪和清除

二十二、自定义填充、修剪和清除

欢迎阅读另一个 Matplotlib 教程！在本教程中，我们将清除图表，然后再做一些自定义。

我们当前的代码是：

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)
    ax2 = plt.subplot2grid((6,1), (1,0), rowspan=4, colspan=1)
    plt.xlabel('Date')
    plt.ylabel('Price')
    ax3 = plt.subplot2grid((6,1), (5,0), rowspan=1, colspan=1)
    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,h_l,'-')
    candlestick_ohlc(ax2, ohlc, width=0.4, colorup='#77d879', colordown='#db3f3f')
    for label in ax2.xaxis.get_ticklabels():
        label.set_rotation(45)
    ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
    ax2.xaxis.set_major_locator(mticker.MaxNLocator(10))
    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:])
    ax3.plot(date[-start:], ma2[-start:])
    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')

现在我认为向我们的移动均值添加自定义填充是一个很好的主意。移动均值通常用于说明价格趋势。这个想法是，你可以计算一个快速和一个慢速的移动均值。一般来说，移动均值用于使价格变得『平滑』。他们总是『滞后』于价格，但是我们的想法是计算不同的速度。移动均值越大就越『慢』。所以这个想法是，如果『较快』的移动均值超过『较慢』的均值，那么价格就会上升，这是一件好事。如果较快的 MA 从较慢的 MA 下方穿过，则这是下降趋势并且通常被视为坏事。我的想法是在快速和慢速 MA 之间填充，『上升』趋势为绿色，然后下降趋势为红色。方法如下：

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)

下面，我们会碰到一些我们可解决的问题：

ax3.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax3.xaxis.set_major_locator(mticker.MaxNLocator(10))
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)

这里，我们剪切和粘贴ax2日期格式，然后我们将x刻度标签设置为false，去掉它们！

我们还可以通过在轴域定义中执行以下操作，为每个轴域提供自定义标签：

fig = plt.figure()
ax1 = plt.subplot2grid((6,1), (0,0), rowspan=1, colspan=1)
plt.title(stock)
ax2 = plt.subplot2grid((6,1), (1,0), rowspan=4, colspan=1)
plt.xlabel('Date')
plt.ylabel('Price')
ax3 = plt.subplot2grid((6,1), (5,0), rowspan=1, colspan=1)

接下来，我们可以看到，我们y刻度有许多数字，经常互相覆盖。我们也看到轴之间互相重叠。我们可以这样：

ax1.yaxis.set_major_locator(mticker.MaxNLocator(nbins=5, prune='lower'))

所以，这里发生的是，我们通过首先将nbins设置为 5 来修改我们的y轴对象。这意味着我们显示的标签最多为 5 个。然后我们还可以『修剪』标签，因此，在我们这里，我们修剪底部标签，这会使它消失，所以现在不会有任何文本重叠。我们仍然可能打算修剪ax2的顶部标签，但这里是我们目前为止的源代码：

当前的源码：

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)
    plt.ylabel('Price')
    ax3 = plt.subplot2grid((6,1), (5,0), rowspan=1, colspan=1)
    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,h_l,'-')
    ax1.yaxis.set_major_locator(mticker.MaxNLocator(nbins=5, prune='lower'))
    candlestick_ohlc(ax2, ohlc, width=0.4, colorup='#77d879', colordown='#db3f3f')
    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))
    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')

看起来好了一些，但是仍然有一些东西需要清除。