如何使計算值出現在matplotlib中的每個xtick旁邊？

Question

如果我給代碼提供一個「原始」值，我希望每個x-tick都顯示該值的百分比變化。

I.E如果我設置10作爲原始值我想網格線在5讀取像5(-50%)和20作爲20(+100%)。

括號中術語的顏色將是額外的獎勵。

編輯：源代碼

所以這裏是代碼。它已經多次改變以填補某個角色，所以它不是最好的，但你明白了。這只是一個考慮佣金和股息支付的交易的p/l計算器。

我想圖的中心是「當前股價」和股票價格兩側的上圖從目前的股價「

from colorama import Fore, Style 
import numpy as np 
import matplotlib.pyplot as plt 
from matplotlib.widgets import Cursor 


name='XYZ' 
current_stock_price=8.66 

closing_price=current_stock_price  #price at which you close the position 

#first trade 
entrance_price1=9.55 
number_shares1=1500 
dividend1=0.0 
longshort1='short' #is it a long or short position? 

#second trade 
entrance_price2=8.93 
number_shares2=1200 
dividend2=0.0 
longshort2='short' 

def commission_stock(commission_rate, cost, commission_minimum,  number_shares): 

    com=commission_rate/100*cost 
    #com=commission_rate*number_shares 

    if com<commission_minimum: 
     com=commission_minimum 

    return com 

def pl_calculator(entrance_price, closing_price, number_shares, \ 
       commission_shares, commission_minimum, dividend_amount, name,\ 
       longshort): 

    name=name 
    longshort=longshort 

    entrance_price= entrance_price 
    closing_price= closing_price 

    stock_change=closing_price-entrance_price #change in stock price 

    number_shares=number_shares 

    commission_shares=commission_shares#percent or per/number of shares 
    commission_minimum=commission_minimum 

    dividend_amount=dividend_amount #dollars per share 
    dividend_cost=dividend_amount*number_shares #how much you pay in dividends 

    margin_req=1 

    nominal_value=number_shares*entrance_price #value of shares (not necessarily how much we own if margin is involved) 
    cost_to_exit_share=number_shares*closing_price 

    outlay=nominal_value*margin_req #amount of money required up front 

    movement=float(closing_price)/entrance_price*100-100 #stock price movement 

    stock_pl=number_shares*(closing_price-entrance_price) #numerical value of profit or loss 

    commission_shares_enter=commission_stock(commission_shares, nominal_value, commission_minimum, number_shares) 
    commission_shares_exit=commission_stock(commission_shares, cost_to_exit_share, commission_minimum, number_shares) 
    commission=commission_shares_enter+commission_shares_exit 

    if longshort=='long': 
     stock_pl=stock_pl-commission_shares_enter-  commission_shares_exit+dividend_cost 
    elif longshort=='short': 
     stock_pl=-stock_pl-commission_shares_enter-commission_shares_exit-dividend_cost 
    else: 
     raise AttributeError 

    pl_perc=stock_pl/nominal_value*100 
    pl_perc_outlay=stock_pl/outlay*100 #profit or loss on outlaid cash. If margin=1 then pl_perc_outlay=pl_perc 

    return [stock_pl, pl_perc, commission, dividend_cost] 


def print_graph(current_stock_price, profit_current, profit_chosen, closing_price, name): 

#creates a list of stock prices either side of current stock price and substitutes each of these into the p/l calculator to give the rough idea of a p/l graph. 

    fig=plt.figure() 
    ax=fig.add_subplot(111) 

    a=float(current_stock_price)*0.6 
    b=float(current_stock_price)*1.4 


    #create list of the stockprices to be tested 
    data=np.arange(a, b, 0.25) 

    #create empty list to populate with theh profit value at each point 
    profit_points=np.zeros(len(data), dtype='f') 

    i=0 

    while i <= (len(data)-1): 

     profit1=pl_calculator(entrance_price=entrance_price1, closing_price=data[i], number_shares=number_shares1,\ 
       commission_shares=0.1, commission_minimum=8, dividend_amount=dividend1,\ 
       name=name, longshort=longshort1) 

     profit2=pl_calculator(entrance_price=entrance_price2, closing_price=data[i], number_shares=number_shares2,\ 
       commission_shares=0.1, commission_minimum=8, dividend_amount=dividend2,\ 
       name=name, longshort=longshort2) 


     #append the profit at this particular price to a list 
     profit_points[i]=profit1[0]+profit2[0] 



     i+=1 

    ax.plot(data, profit_points) 
    ax.plot(current_stock_price, profit_current, 'ro', label='Current Position')     
    ax.plot(closing_price, profit_chosen, 'g^', label='Chosen Closing Position') 

    cursor = Cursor(ax, useblit=True, color='k', linewidth=1) 

    ax.grid(axis='both') 


    plt.xlabel('%s($)' %name) 
    plt.ylabel('P/L ($)') 
    plt.title('P/L') 
    plt.legend(loc='upper right', numpoints=1) 

    plt.show() 







#run cases for current stock price and chosen stock price 
trade1_current=pl_calculator(entrance_price=entrance_price1,  closing_price=current_stock_price, number_shares=number_shares1,\ 
       commission_shares=0.1, commission_minimum=8, dividend_amount=dividend1,\ 
       name=name, longshort=longshort1) 

trade2_current=pl_calculator(entrance_price=entrance_price2, closing_price=current_stock_price, number_shares=number_shares2,\ 
       commission_shares=0.1, commission_minimum=8, dividend_amount=dividend2,\ 
       name=name, longshort=longshort2) 

trade1_chosen=pl_calculator(entrance_price=entrance_price1, closing_price=closing_price, number_shares=number_shares1,\ 
       commission_shares=0.1, commission_minimum=8, dividend_amount=dividend1,\ 
       name=name, longshort=longshort1) 

trade2_chosen=pl_calculator(entrance_price=entrance_price2, closing_price=closing_price, number_shares=number_shares2,\ 
       commission_shares=0.1, commission_minimum=8, dividend_amount=dividend2,\ 
       name=name, longshort=longshort2) 





profit_current=trade1_current[0]+trade2_current[0] 
profit_chosen=trade1_chosen[0]+trade2_chosen[0] 

dividend=trade1_current[3]+trade2_current[3] 

movement=closing_price/current_stock_price*100-100 

print '%s: $%.2f' %(name, current_stock_price) 
print '@ $%.2f (%.2f%%)' %(closing_price, movement) 
print 'Dividend Payment: $%.2f' %dividend 
print 'Profit: $%.2f'%(trade1_current[0]+trade2_current[0]) 


print_graph(current_stock_price, profit_current, profit_chosen, closing_price, name) 

Answer 1

您顯示百分比變化可以繼承的matplotlib.ticker.ScalarFormatter增加的百分比數如下：

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

class MyPercentageFormatter(matplotlib.ticker.ScalarFormatter): 
    def __init__(self,origval, offset=False, mathText=True): 
     self.origval = float(origval) 
     matplotlib.ticker.ScalarFormatter.__init__(self,useOffset=offset,useMathText=mathText) 
    def pprint_val(self, x): 
     val = matplotlib.ticker.ScalarFormatter.pprint_val(self, x) 
     perc = x/self.origval*100 
     newval = val + "({perc:.0f}%)".format(perc=perc) 
     return newval 


x = np.arange(50) 
y = np.sin(x/10.) 

fig, ax = plt.subplots() 
ax.xaxis.set_major_formatter(MyPercentageFormatter(20)) 
ax.plot(x,y) 

plt.show() 

Answer 2

您需要使用自定義Formatter類做到這一點。我會爲您的特殊情況推薦類似FuncFormatter的東西，因爲您依賴的是外部「原始」價值。你可以這樣做

from matplotlib.ticker import FuncFormatter 
ax.xaxis.set_major_formatter(FuncFormatter(lambda x, pos: '{}({:d}%)'.format(x, int(x/original * 100.0 + 0.5)))) 

顏色可以使用膠乳部分執行，但只有當你使用一個支持支持那種渲染。您也可以在軸上創建自己的標籤並將它們移動以模擬刻度，但我懷疑這對於有限的回報來說太多了。相關帖子在這裏：Partial coloring of text in matplotlib。

小挑逗，考慮使用fig, ax = plt.subplots()而不是兩行。