matplotlib 子绘图问题

如何解决matplotlib 子绘图问题

我为子绘图制作了一个代码

fig,axs = plt.subplots(2,2,figsize = (20,10))
candlestick_ohlc(axs[0,0],df.values,width=0.6,colorup='green',colordown='red',alpha=0.5);
candlestick_ohlc(axs[0,df1.values,colorup='red',colordown='green',alpha=0.8);
date_format = mpl_dates.DateFormatter('%d %b %Y')

axs[0,0].xaxis.set_major_formatter(date_format)
axs[0,0].xaxis.set_major_formatter(date_format)

for level in levels:
  axs[0,0].hlines(level[1],xmin=df['Date'][level[0]],\
             xmax=max(df['Date']),colors='black')
fig.autofmt_xdate()

axs[1,1].plot(ichi['Close'],label = 'Close')
axs[1,0].scatter(df.index,df.Close,c = squeeze['signal'])

axs[1,1].fill_between(ichi.index,ichi['ISA_9'],ichi['ISB_26'],where = ichi['ISA_9']>ichi['ISB_26'],facecolor = 'green',alpha = 0.5)

axs[1,where = ichi['ISA_9']<ichi['ISB_26'],facecolor = 'red',alpha = 0.5)
axs[1,1].legend()

我对此很满意

My subplot

但是，我想在 axs[0,1] 处再添加一个图，为此我使用了 Trendln 包来绘制支撑和阻力图

plt.figure(figsize = (20,10))
f= trendln.plot_support_resistance(hist[-100:].Close,accuracy = 10) 
plt.show()
plt.clf() #clear figure

Support resistance plot

有什么方法可以将支撑阻力图合并到我的初始图 axs[0,1]

解决方法

不幸的是，阅读trendln的源代码，他们直接使用plt.plot来做任何事情，所以不容易做到这一点。您必须自己更改源代码。你可以看到源所在的位置：

>>> import trendln
>>> trendln.__file__
'/home/username/.local/lib/python3.8/site-packages/trendln/__init__.py'
>>> 

那么就可以直接将plot_support_resistance函数修改为如下。我基本上让它使用 axs 并在那里绘图而不是 plt;还有一些其他的变化要进行：

def plot_support_resistance(axs,hist,xformatter = None,numbest = 2,fromwindows = True,pctbound=0.1,extmethod = METHOD_NUMDIFF,method=METHOD_NSQUREDLOGN,window=125,errpct = 0.005,hough_scale=0.01,hough_prob_iter=10,sortError=False,accuracy=1):
    import matplotlib.pyplot as plt
    import matplotlib.ticker as ticker
    ret = calc_support_resistance(hist,extmethod,method,window,errpct,hough_scale,hough_prob_iter,sortError,accuracy)
    # plt.clf()
    # plt.subplot(111)

    if len(ret) == 2:
        minimaIdxs,pmin,mintrend,minwindows = ret[0]
        maximaIdxs,pmax,maxtrend,maxwindows = ret[1]
        if type(hist) is tuple and len(hist) == 2 and check_num_alike(hist[0]) and check_num_alike(hist[1]):
            len_h = len(hist[0])
            min_h,max_h = min(min(hist[0]),min(hist[1])),max(max(hist[0]),max(hist[1]))
            disp = [(hist[0],minimaIdxs,'yo','Avg. Support','y--'),(hist[1],maximaIdxs,'bo','Avg. Resistance','b--')]
            dispwin = [(hist[0],minwindows,'Support','g--'),maxwindows,'Resistance','r--')]
            disptrend = [(hist[0],'r--')]
            axs.plot(range(len_h),hist[0],'k--',label='Low Price')
            axs.plot(range(len_h),hist[1],'m--',label='High Price')
        else:
            len_h = len(hist)
            min_h,max_h = min(hist),max(hist)
            disp = [(hist,(hist,'b--')]
            dispwin = [(hist,'r--')]
            disptrend = [(hist,label='Close Price')
    else:
        minimaIdxs,minwindows = ([],[],[]) if hist[0] is None else ret
        maximaIdxs,maxwindows = ([],[]) if hist[1] is None else ret
        len_h = len(hist[1 if hist[0] is None else 0])
        min_h,max_h = min(hist[1 if hist[0] is None else 0]),max(hist[1 if hist[0] is None else 0])
        disp = [(hist[1],'b--') if hist[0] is None else (hist[0],'y--')]
        dispwin = [(hist[1],'r--') if hist[0] is None else (hist[0],'g--')]
        disptrend = [(hist[1],'g--')]
        axs.plot(range(len_h),hist[1 if hist[0] is None else 0],label= ('High' if hist[0] is None else 'Low') + ' Price')
    for h,idxs,pm,clrp,lbl,clrl in disp:
        axs.plot(idxs,[h[x] for x in idxs],clrp)
        axs.plot([0,len_h-1],[pm[1],pm[0] * (len_h-1) + pm[1]],clrl,label=lbl)
    def add_trend(h,trend,clr,bFirst):
        for ln in trend[:numbest]:
            maxx = ln[0][-1]+1
            while maxx < len_h:
                ypred = ln[1][0] * maxx + ln[1][1]
                if (h[maxx] > ypred and h[maxx-1] < ypred or h[maxx] < ypred and h[maxx-1] > ypred or
                    ypred > max_h + (max_h-min_h)*pctbound or ypred < min_h - (max_h-min_h)*pctbound): break
                maxx += 1
            x_vals = np.array((ln[0][0],maxx)) # plt.gca().get_xlim())
            y_vals = ln[1][0] * x_vals + ln[1][1]
            if bFirst:
                axs.plot([ln[0][0],maxx],y_vals,label=lbl)
                bFirst = False
            else: axs.plot([ln[0][0],clr)
        return bFirst
    if fromwindows:
        for h,windows,clr in dispwin:
            bFirst = True
            for trend in windows:
                bFirst = add_trend(h,bFirst)
    else:
        for h,clr in disptrend:
            add_trend(h,True)
    # axs.title('Prices with Support/Resistance Trend Lines')
    #axs.xlabel('Date')
    #axs.ylabel('Price')
    axs.legend()
    #plt.gca()
    axs.xaxis.set_major_locator(ticker.MaxNLocator(6))
    #plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
    #if not xformatter is None: plt.gca().xaxis.set_major_formatter(xformatter)
    #plt.setp(plt.gca().get_xticklabels(),rotation=30,ha='right')
    #plt.gca().set_position([0,1,1])
    #plt.savefig(os.path.join(curdir,'data','suppres.svg'),format='svg',bbox_inches = 'tight')
    #plt.show()
    return None#plt.gcf()

既然已经处理好了，您可以传递其中一个轴来绘制东西：

import matplotlib.pyplot as plt
import trendln
import yfinance as yf

fig,axs = plt.subplots(2,2,figsize = (20,10))
axs[0,1].plot([0,1],[3,4])

tick = yf.Ticker('^GSPC') # S&P500
hist = tick.history(period="max",rounding=True)

f = trendln.plot_support_resistance(axs[0,0],hist[-1000:].Close,accuracy = 2)

plt.show()

我得到：

我希望这会有所帮助。您可能正在寻找另一种选择，但由于他们使用硬编码，这并不容易。我也试过复制plt绘制的轴而不是修改源代码，但没有用。