Movatterモバイル変換

Implementing Subplots to our Chart with Matplotlib

In this Matplotlib tutorial, we'll be handling our previous tutorial's code, and implementing the subplot configuration from the previous tutorial. Our starting code:

import matplotlib.pyplot as pltimport matplotlib.dates as mdatesimport matplotlib.ticker as mtickerfrom matplotlib.finance import candlestick_ohlcfrom matplotlib import styleimport numpy as npimport urllibimport datetime as dtstyle.use('fivethirtyeight')print(plt.style.available)print(plt.__file__)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((1,1), (0,0))        stock_price_url = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=1m/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    candlestick_ohlc(ax1, ohlc, width=0.4, colorup='#77d879', colordown='#db3f3f')      for label in ax1.xaxis.get_ticklabels():        label.set_rotation(45)    ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))    ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))    ax1.grid(True)        bbox_props = dict(boxstyle='round',fc='w', ec='k',lw=1)        ax1.annotate(str(closep[-1]), (date[-1], closep[-1]),                 xytext = (date[-1]+4, closep[-1]), bbox=bbox_props)    ##    # Annotation example with arrow##    ax1.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 ##    ax1.text(date[10], closep[1],'Text Example', fontdict=font_dict)    plt.xlabel('Date')    plt.ylabel('Price')    plt.title(stock)    #plt.legend()    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')

The first major change is to modify the axis definitions:

    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)

Now, ax2 is where we'll actually be plotting our stock price data. The top and bottom charts will serve as indicator information shortly.

Further down in the code where we plot data, we need to change ax1 to ax1:

    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)

With these changes, the full code should be:

import matplotlib.pyplot as pltimport matplotlib.dates as mdatesimport matplotlib.ticker as mtickerfrom matplotlib.finance import candlestick_ohlcfrom matplotlib import styleimport numpy as npimport urllibimport datetime as dtstyle.use('fivethirtyeight')print(plt.style.available)print(plt.__file__)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=1m/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    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##    ax1.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 ##    ax1.text(date[10], closep[1],'Text Example', fontdict=font_dict)        #    #plt.legend()    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')

The resulting graph should be:

The next tutorial:More indicator data with Matplotlib