import numpy as np
import matplotlib.pyplot as plt
import pylab as pl
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
data1_loss =np.loadtxt("valid_RCSCA_records.txt") 
data2_loss = np.loadtxt("valid_SCRCA_records.txt") 

x = data1_loss[:,0]
y = data1_loss[:,1]
x1 = data2_loss[:,0]
y1 = data2_loss[:,1]

fig = plt.figure(figsize = (7,5))    #figsize是圖片的大小`
ax1 = fig.add_subplot(1, 1, 1) # ax1是子圖的名字`

pl.plot(x,y,'g-',label=u'Dense_Unet(block layer=5)')`
# ‘'g‘'代表“green”,表示畫出的曲線是綠色，“-”代表畫的曲線是實(shí)線，可自行選擇，label代表的是圖例的名稱，一般要在名稱前面加一個(gè)u，如果名稱是中文，會(huì)顯示不出來，目前還不知道怎么解決。
p2 = pl.plot(x1, y1,'r-', label = u'RCSCA_Net')
pl.legend()
#顯示圖例
p3 = pl.plot(x2,y2, 'b-', label = u'SCRCA_Net')
pl.legend()
pl.xlabel(u'iters')
pl.ylabel(u'loss')
plt.title('Compare loss for different models in training')

# plot the box
tx0 = 0
tx1 = 10000
#設(shè)置想放大區(qū)域的橫坐標(biāo)范圍
ty0 = 0.000
ty1 = 0.12
#設(shè)置想放大區(qū)域的縱坐標(biāo)范圍
sx = [tx0,tx1,tx1,tx0,tx0]
sy = [ty0,ty0,ty1,ty1,ty0]
pl.plot(sx,sy,"purple")
axins = inset_axes(ax1, width=1.5, height=1.5, loc='right')
#loc是設(shè)置小圖的放置位置，可以有"lower left,lower right,upper right,upper left,upper #,center,center left,right,center right,lower center,center"
axins.plot(x1,y1 , color='red', ls='-')
axins.plot(x2,y2 , color='blue', ls='-')
axins.axis([0,20000,0.000,0.12])
plt.savefig("train_results_loss.png")
pl.show
#pl.show()也可以