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lunwen_2_plot.py
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lunwen_2_plot.py
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import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from matplotlib.patches import ConnectionPatch
import matplotlib.ticker as mtick
import matplotlib.gridspec as gridspec
# import matplotlib as mpl
# mpl.rcParams['font.sans-serif'] = ['FangSong']
# mpl.rcParams['axes.unicode_minus']=False
from matplotlib import cm
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
import xlrd
def zhexiantu_fangshan(mappingNumber, enhanceNumber):
'''
绘制折线图
:return:
'''
fig = plt.figure(figsize=(13, 5))
real_path = 'E:\\yan_2\\CFBLS_LCFBLS复现\\result\\realData\\fangshan\\test\\testReal.csv'
ESN_path = 'E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\ESN\\test\\ESN_resSize_450.csv'
BLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\BLS\\test\\BLS_mapping_{}_enhance_{}.csv".format(mappingNumber,enhanceNumber)
CFBLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\CFBLS\\test\\CFBLS_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
# LCFBLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\LCFBLS\\test\\LCFBLS_mapping_{}_enhance_{}.csv".format(
# mappingNumber, enhanceNumber)
# CEBLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\CEBLS\\test\\CEBLS_mapping_{}_enhance_{}.csv".format(
# mappingNumber, enhanceNumber)
BLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\BLS_ESN\\test\\BLS_ESN_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
CFBLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\CFBLS_ESN\\test\\CFBLS_ESN_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
# LCFBLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\LCFBLS_ESN\\test\\LCFBLS_ESN_mapping_{}_enhance_{}.csv".format(
# mappingNumber, enhanceNumber)
GRU_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\GRU\\test\\GRU_fangshan_predict.csv"
LSTM_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\LSTM\\test\\LSTM_fangshan_predict.csv"
# GRU_path = 'E:\\yan_1\\BLS_self\\真正意义上的BLS和ESN结合(可以用来实验)\\备份好的BLS和增加增强节点的BLS\\result\\csvFile\\fangshan\\GRU\\GRU_result.csv'
# LSTM_path = 'E:\\yan_1\\BLS_self\\真正意义上的BLS和ESN结合(可以用来实验)\\备份好的BLS和增加增强节点的BLS\\result\\csvFile\\fangshan\\LSTM\\LSTM_result.csv'
startPos = 788
endIndex = 858
real_data = pd.read_csv(real_path).values[startPos:endIndex].reshape(-1,1)
ESN_data = pd.read_csv(ESN_path).values[startPos:endIndex].reshape(-1, 1)
BLS_data = pd.read_csv(BLS_path).values[startPos:endIndex].reshape(-1, 1)
CFBLS_data = pd.read_csv(CFBLS_path).values[startPos:endIndex].reshape(-1, 1)
# LCFBLS_data = pd.read_csv(LCFBLS_path).values[startPos:endIndex].reshape(-1, 1)
# CEBLS_data = pd.read_csv(CEBLS_path).values[startPos:endIndex].reshape(-1, 1)
BLS_ESN_data = pd.read_csv(BLS_ESN_path).values[startPos:endIndex].reshape(-1, 1)
CFBLS_ESN_data = pd.read_csv(CFBLS_ESN_path).values[startPos:endIndex].reshape(-1, 1)
# LCFBLS_ESN_data = pd.read_csv(LCFBLS_ESN_path).values[startPos:endIndex].reshape(-1, 1)
GRU_data = pd.read_csv(GRU_path).values[startPos:endIndex].reshape(-1, 1)
LSTM_data = pd.read_csv(LSTM_path).values[startPos:endIndex].reshape(-1, 1)
color = ['#7030A0', '#00B0F0', '#CD853F', '#00B050', '#92D050', '#FFFF00', '#FFC000', '#9DC3E6', '#8B0000', '#FF0000']
x = np.arange(endIndex-startPos)
plt.plot(x, real_data, color='#d71345', label='real')
plt.plot(x, BLS_data, color='#f47920', label='BLS')
plt.plot(x, ESN_data, color='#b2d235', label='ESN')
plt.plot(x, CFBLS_data, color='#8552a1', label='CMBLS')
# plt.plot(x, BLS_ESN_data, color='#005344', label='BLS_ESN')
plt.plot(x, CFBLS_ESN_data, color='#ef5b9c', label='GRU')
plt.plot(x, GRU_data, color='#009ad6', label='CMBESN')
# plt.plot(x, LSTM_data, color='#ea66a6', label='LSTM')
# plt.title("Mapping node:{} Enhance node:{}".format(mappingNumber,enhanceNumber))
# plt.legend(loc='upper left', bbox_to_anchor=(1, 1)) #把图例设置在外边
plt.legend(loc='lower left', prop={'size': 12}) # 把图例设置在外边
plt.ylabel('AQI', fontsize=13)
plt.xlabel('Time steps', fontsize=13)
plt.savefig('E:\\yan_2\\12_5论文\\实验结果图\\fangshan\\第一版修改的图\\ Figure 10.svg', format='svg',
bbox_inches='tight', dpi=600) # 高清图
plt.show()
def plot_ERROR_fangshan(mappingNumber, enhanceNumber):
real_path = 'E:\\yan_2\\CFBLS_LCFBLS复现\\result\\realData\\fangshan\\test\\testReal.csv'
ESN_path = 'E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\ESN\\test\\ESN_resSize_450.csv'
BLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\BLS\\test\\BLS_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
CFBLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\CFBLS\\test\\CFBLS_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
# LCFBLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\LCFBLS\\test\\LCFBLS_mapping_{}_enhance_{}.csv".format(
# mappingNumber, enhanceNumber)
# CEBLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\CEBLS\\test\\CEBLS_mapping_{}_enhance_{}.csv".format(
# mappingNumber, enhanceNumber)
BLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\BLS_ESN\\test\\BLS_ESN_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
CFBLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\CFBLS_ESN\\test\\CFBLS_ESN_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
# LCFBLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\LCFBLS_ESN\\test\\LCFBLS_ESN_mapping_{}_enhance_{}.csv".format(
# mappingNumber, enhanceNumber)
GRU_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\GRU\\test\\GRU_fangshan_predict.csv"
LSTM_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\LSTM\\test\\LSTM_fangshan_predict.csv"
# GRU_path = 'E:\\yan_1\\BLS_self\\真正意义上的BLS和ESN结合(可以用来实验)\\备份好的BLS和增加增强节点的BLS\\result\\csvFile\\fangshan\\GRU\\GRU_result.csv'
# LSTM_path = 'E:\\yan_1\\BLS_self\\真正意义上的BLS和ESN结合(可以用来实验)\\备份好的BLS和增加增强节点的BLS\\result\\csvFile\\fangshan\\LSTM\\LSTM_result.csv'
startPos = 300
endIndex = 380
real_data = pd.read_csv(real_path).values[startPos:endIndex].reshape(-1, 1)
ESN_data = pd.read_csv(ESN_path).values[startPos:endIndex].reshape(-1, 1)
BLS_data = pd.read_csv(BLS_path).values[startPos:endIndex].reshape(-1, 1)
CFBLS_data = pd.read_csv(CFBLS_path).values[startPos:endIndex].reshape(-1, 1)
# LCFBLS_data = pd.read_csv(LCFBLS_path).values[startPos:endIndex].reshape(-1, 1)
# CEBLS_data = pd.read_csv(CEBLS_path).values[startPos:endIndex].reshape(-1, 1)
BLS_ESN_data = pd.read_csv(BLS_ESN_path).values[startPos:endIndex].reshape(-1, 1)
CFBLS_ESN_data = pd.read_csv(CFBLS_ESN_path).values[startPos:endIndex].reshape(-1, 1)
# LCFBLS_ESN_data = pd.read_csv(LCFBLS_ESN_path).values[startPos:endIndex].reshape(-1, 1)
GRU_data = pd.read_csv(GRU_path).values[startPos:endIndex].reshape(-1, 1)
LSTM_data = pd.read_csv(LSTM_path).values[startPos:endIndex].reshape(-1, 1)
# plt.plot(x, real_data,'-*',color='#ef5b9c',label='real')
# plt.plot(x, BLS_data,'-3', color='#840228', label='BLS')
# plt.plot(x, ESN_data, '-2', color='#1d953f', label='ESN')
# plt.plot(x, CFBLS_data,'-+', color='#87843b', label='CFBLS')
# # plt.plot(x, LCFBLS_data, color='#d71345', label='LCFBLS')
# # plt.plot(x, CEBLS_data, color='#bed742', label='CEBLS')
# plt.plot(x, BLS_ESN_data,'-.', color='#9b95c9', label='BLS_ESN')
# plt.plot(x, CFBLS_ESN_data,'-s', color='#005344', label='CFBLS_ESN')
# # plt.plot(x, LCFBLS_ESN_data, color='#1d953f', label='LCFBLS_ESN')
# plt.plot(x, GRU_data, '-.x',color='#ffc20e', label='GRU')
# plt.plot(x, LSTM_data, '-.1', color='#8552a1', label='LSTM')
# ed1941 b2d235 ba8448 2b4490 5c7a29 009ad6 2585a6 585eaa ea66a6
x1 = np.arange(endIndex - startPos)
ERROR_ESN = []
ERROR_BLS = []
ERROR_CFBLS = []
ERROR_CFBESN = []
ERROR_GRU = []
for i in range(endIndex - startPos):
ERROR_ESN.append(abs(ESN_data[i] - real_data[i]))
ERROR_BLS.append(abs(BLS_data[i] - real_data[i]))
ERROR_CFBLS.append(abs(CFBLS_data[i] - real_data[i]))
ERROR_CFBESN.append(abs(CFBLS_ESN_data[i] - real_data[i]))
ERROR_GRU.append(abs(GRU_data[i] - real_data[i]))
ERROR_ESN = np.array(ERROR_ESN).reshape(-1, 1)
ERROR_BLS = np.array(ERROR_BLS).reshape(-1, 1)
ERROR_CFBLS = np.array(ERROR_CFBLS).reshape(-1, 1)
ERROR_CFBESN = np.array(ERROR_CFBESN).reshape(-1, 1)
ERROR_GRU = np.array(ERROR_GRU).reshape(-1, 1)
gs = gridspec.GridSpec(2, 6) #创建2行6列的网格
lim_xy_max = max(max(ERROR_ESN), max(ERROR_BLS), max(ERROR_CFBLS), max(ERROR_CFBESN), max(ERROR_GRU)) + 0.1
plt.figure(figsize=(12, 6))
plt.subplot(gs[0, :2]) #gs[哪一行，列的范围]
gs.update(hspace=0.3, wspace=0.8)
plt.scatter(x1, ERROR_ESN, s=40, marker='.', c='#00a6ac', label='ESN')
plt.ylim(0, lim_xy_max)
plt.ylabel('Errors', fontsize=13)
plt.xlabel('Time steps', fontsize=13)
plt.legend(loc='upper right', prop={'size': 11})
plt.subplot(gs[0, 2:4]) #gs[哪一行，列的范围]
plt.scatter(x1, ERROR_BLS, s=40, marker='.', c='#9b95c9', label='BLS')
plt.ylim(0, lim_xy_max)
plt.ylabel('Errors', fontsize=13)
plt.xlabel('Time steps', fontsize=13)
plt.legend(loc='upper right', prop={'size': 11})
plt.subplot(gs[0, 4:6]) #gs[哪一行，列的范围]
plt.scatter(x1, ERROR_CFBLS, s=40, marker='.', c='#fdb933', label='CMBLS')
plt.ylim(0, lim_xy_max)
plt.ylabel('Errors', fontsize=13)
plt.xlabel('Time steps', fontsize=13)
plt.legend(loc='upper right', prop={'size': 11})
plt.subplot(gs[1, 1:3]) #gs[哪一行，列的范围]
plt.scatter(x1, ERROR_CFBESN, s=40, marker='.', c='#a3cf62', label='GRU')
plt.ylim(0, lim_xy_max)
plt.ylabel('Errors', fontsize=13)
plt.xlabel('Time steps', fontsize=13)
plt.legend(loc='upper right', prop={'size': 12})
plt.subplot(gs[1, 3:5]) #gs[哪一行，列的范围]
plt.scatter(x1, ERROR_GRU, s=40, marker='.', c='#f05b72', label='CMBESN')
plt.ylim(0, lim_xy_max)
plt.ylabel('Errors', fontsize=13)
plt.xlabel('Time steps', fontsize=13)
plt.legend(loc='upper right', prop={'size': 12})
# plt.subplots_adjust(hspace=0.3,wspace=0.5) # 调节上下子图之间的行距
plt.savefig('E:\\yan_2\\12_5论文\\实验结果图\\fangshan\\第一版修改的图\\Figure 12.svg', format='svg',
bbox_inches='tight', dpi=600) # 高清图
plt.show()
def fangshan_boxplot(mappingNumber, enhanceNumber):
'''
这个是把多个箱线图画在一个坐标系中的程序,这里实现的是将所有的预测数据用箱线图来展示出来
:return:
'''
fig = plt.figure(figsize=(13, 5))
real_path = 'E:\\yan_2\\CFBLS_LCFBLS复现\\result\\realData\\fangshan\\test\\testReal.csv'
ESN_path = 'E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\ESN\\test\\ESN_resSize_450.csv'
BLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\BLS\\test\\BLS_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
CFBLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\CFBLS\\test\\CFBLS_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
# LCFBLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\LCFBLS\\test\\LCFBLS_mapping_{}_enhance_{}.csv".format(
# mappingNumber, enhanceNumber)
# CEBLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\CEBLS\\test\\CEBLS_mapping_{}_enhance_{}.csv".format(
# mappingNumber, enhanceNumber)
BLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\BLS_ESN\\test\\BLS_ESN_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
CFBLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\CFBLS_ESN\\test\\CFBLS_ESN_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
# LCFBLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\LCFBLS_ESN\\test\\LCFBLS_ESN_mapping_{}_enhance_{}.csv".format(
# mappingNumber, enhanceNumber)
GRU_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\GRU\\test\\GRU_fangshan_predict.csv"
LSTM_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\LSTM\\test\\LSTM_fangshan_predict.csv"
startPos = 100
endIndex = 3000
real_data = pd.read_csv(real_path).values[startPos:endIndex].reshape(1, -1)
ESN_data = pd.read_csv(ESN_path).values[startPos:endIndex].reshape(1, -1)
BLS_data = pd.read_csv(BLS_path).values[startPos:endIndex].reshape(1, -1)
CFBLS_data = pd.read_csv(CFBLS_path).values[startPos:endIndex].reshape(1, -1)
BLS_ESN_data = pd.read_csv(BLS_ESN_path).values[startPos:endIndex].reshape(1, -1)
CFBLS_ESN_data = pd.read_csv(CFBLS_ESN_path).values[startPos:endIndex].reshape(1, -1)
GRU_data = pd.read_csv(GRU_path).values[startPos:endIndex].reshape(1, -1)
LSTM_data = pd.read_csv(LSTM_path).values[startPos:endIndex].reshape(1, -1)
SAMPE = {'REAL':real_data[0],'CMBESN':CFBLS_ESN_data[0],'BLS':BLS_data[0],
'ESN':ESN_data[0],'CMBLS':CFBLS_data[0],'GRU':GRU_data[0]}
df = pd.DataFrame(SAMPE)
ff = df.boxplot(showmeans=True, return_type='dict', showfliers=False, vert=True,
sym='o', widths=0.3, whis=True, patch_artist=False, meanline=False,
showcaps=True,flierprops={'markeredgecolor': 'k', 'markersize': 8},
meanprops={'markeredgecolor': 'k', 'markerfacecolor': 'k', 'marker': '*'},figsize=(13, 5))
# color = ['#7030A0', '#00B0F0', '#CD853F', '#00B050', '#92D050', '#FFFF00', '#FFC000', '#9DC3E6', '#8B0000',
# '#FF0000']
# 这里共有5个box
# color = ['#6F933B', '#1B459C', '#16A2FF', '#CE7937', '#FF4025','#7030A0','#9DC3E6'] # 有多少box就对应设置多少颜色
# colors是为了解决上下须不同步，上下帽不同步颜色设置的
colors = ['#6F933B', '#6F933B', '#1B459C', '#1B459C', '#16A2FF', '#16A2FF', '#CE7937',
'#CE7937', '#FF4025', '#FF4025', '#7030A0', '#7030A0', '#9DC3E6', '#9DC3E6']
# ed1941 b2d235 ba8448 2b4490 5c7a29 009ad6 2585a6 585eaa ea66a6
newColor = ['#ef5b9c', '#f47920', '#b2d235', '#817936', '#f15a22', '#009ad6', '#8552a1', '#ffc20e']
newColors = ['#ef5b9c', '#ef5b9c', '#f47920', '#f47920', '#b2d235', '#b2d235', '#817936',
'#817936', '#f15a22', '#f15a22', '#009ad6', '#009ad6', '#8552a1', '#8552a1',
'#ffc20e', '#ffc20e']
# fig = plt.figure(figsize=(13, 5))
linewidth = 1.8
for box, c in zip(ff['boxes'], newColor):
# 箱体边框颜色
box.set(color=c, linewidth=linewidth)
# 箱体内部填充颜色
# box.set(facecolor=c)
# 这里设置的是各个box的其他属性
for whisker, c in zip(ff['whiskers'], newColors):
whisker.set(color=c, linewidth=linewidth)
for cap, c in zip(ff['caps'], newColors):
cap.set(color=c, linewidth=linewidth)
for median, c in zip(ff['medians'], newColor):
median.set(color=c, linewidth=linewidth)
for mean, color in zip(ff['means'], newColor): # 星花是均值
mean.set(markeredgecolor=color, markerfacecolor=color, linewidth=1)
for flier, c in zip(ff['fliers'], newColor):
flier.set(marker='o', color=c, alpha=0.5)
plt.ylabel('AQI')
# plt.savefig('result/SMAPE.png', bbox_inches='tight',dpi=600) # 高清图
# fig.subplots_adjust(right=0.82) # 调整边距和子图的间距
plt.savefig('E:\\yan_2\\12_5论文\\实验结果图\\fangshan\\CM实验结果图\\CM_箱线图.png', bbox_inches='tight', dpi=600)
plt.show()
def plot_3D_weipinwen_fangshan():
'''
没有使用自平稳优化指标体系的3D绘图
:return:
'''
data_path = r'E:\\yan_2\\12_5论文\\实验结果图\\fangshan\\未使用自平稳指标体系的3D图.xlsx'
data = pd.read_excel(data_path).values[:,:]
# print(data)
X = np.linspace(12,20,5,dtype=int)
Y = np.linspace(20,38,10,dtype=int)
z = np.array(data[:, 3:4]).reshape(10,5)
print(Y)
X, Y = np.meshgrid(X, Y)
fig = plt.figure(figsize=(13, 8))
ax = plt.axes(projection='3d')
# ax.plot_wireframe(X, Y, z, rstride = 1, cstride = 1, cmap='RdPu')
ax.plot_surface(X, Y, z, rstride=1, cstride=1, alpha=0.3,cmap="hsv", linewidth=0, antialiased=False)
ax.contour(X, Y, z, zdir='x', offset=11, cmap="hsv_r")
# ax.contour(X, Y, z, zdir='z', offset=0, cmap="hsv_r")
ax.yaxis.set_major_formatter(mtick.FormatStrFormatter('%d'))
ax.xaxis.set_major_formatter(mtick.FormatStrFormatter('%d'))
ax.set_xlabel('mapping node')
ax.set_ylabel('ESN')
ax.set_zlabel('RMSE', rotation=90)
# ax.grid(False, linestyle = "-.", color = "red", linewidth = "1")
ax.view_init(elev=19, azim=35)
# ax.contour(X, Y, z, cmap=cm.coolwarm)
plt.savefig('E:\\yan_2\\12_5论文\\实验结果图\\fangshan\\未使用自平稳指标体系的3D图.png', bbox_inches='tight', dpi=600)
# 调整观察角度和方位角。这里将俯仰角设为60度，把方位角调整为35度
plt.show()
def plot_3D_pinwen_fangshan():
'''
没有使用自平稳优化指标体系的3D绘图
:return:
'''
data_path = r'E:\\yan_2\\12_5论文\\实验结果图\\fangshan\\自平稳指标体系的3D图.xlsx'
data = pd.read_excel(data_path).values[:,:]
# print(data)
X = np.linspace(12,28,9,dtype=int)
Y = np.linspace(20,38,10,dtype=int)
z = np.array(data[:, 3:4]).reshape(10,9)
'''
cmap = "以下的取值"
'CMRmap_r', 'Dark2', 'Dark2_r', 'GnBu', 'GnBu_r', 'Greens', 'Greens_r', 'Greys', 'Greys_r', 'OrRd', 'OrRd_r', 'Oranges',
'Oranges_r', 'PRGn', 'PRGn_r', 'Paired', 'Paired_r', 'Pastel1', 'Pastel1_r', 'Pastel2', 'Pastel2_r', 'PiYG', 'PiYG_r', 'PuBu',
'PuBuGn', 'PuBuGn_r', 'PuBu_r', 'PuOr', 'PuOr_r', 'PuRd', 'PuRd_r', 'Purples', 'Purples_r', 'RdBu', 'RdBu_r', 'RdGy', 'RdGy_r',
'RdPu', 'RdPu_r', 'RdYlBu', 'RdYlBu_r', 'RdYlGn', 'RdYlGn_r', 'Reds', 'Reds_r', 'Set1', 'Set1_r', 'Set2', 'Set2_r', 'Set3',
'Set3_r', 'Spectral', 'Spectral_r', 'Wistia', 'Wistia_r', 'YlGn', 'YlGnBu', 'YlGnBu_r', 'YlGn_r', 'YlOrBr', 'YlOrBr_r', 'YlOrRd',
'YlOrRd_r', 'afmhot', 'afmhot_r', 'autumn', 'autumn_r', 'binary', 'binary_r', 'bone', 'bone_r', 'brg', 'brg_r', 'bwr', 'bwr_r',
'cividis', 'cividis_r', 'cool', 'cool_r', 'coolwarm', 'coolwarm_r', 'copper', 'copper_r', 'cubehelix', 'cubehelix_r', 'flag',
'flag_r', 'gist_earth', 'gist_earth_r', 'gist_gray', 'gist_gray_r', 'gist_heat', 'gist_heat_r', 'gist_ncar', 'gist_ncar_r',
'gist_rainbow', 'gist_rainbow_r', 'gist_stern', 'gist_stern_r', 'gist_yarg', 'gist_yarg_r', 'gnuplot', 'gnuplot2', 'gnuplot2_r',
'gnuplot_r', 'gray', 'gray_r', 'hot', 'hot_r', 'hsv', 'hsv_r', 'inferno', 'inferno_r', 'jet', 'jet_r', 'magma', 'magma_r',
'nipy_spectral', 'nipy_spectral_r', 'ocean', 'ocean_r', 'pink', 'pink_r', 'plasma', 'plasma_r', 'prism', 'prism_r', 'rainbow',
'rainbow_r', 'seismic', 'seismic_r', 'spring', 'spring_r', 'summer', 'summer_r', 'tab10', 'tab10_r', 'tab20', 'tab20_r', 'tab20b',
'tab20b_r', 'tab20c', 'tab20c_r', 'terrain', 'terrain_r', 'turbo', 'turbo_r', 'twilight', 'twilight_r', 'twilight_shifted',
'twilight_shifted_r', 'viridis', 'viridis_r', 'winter', 'winter_r'
'''
X, Y = np.meshgrid(X, Y)
fig = plt.figure(figsize=(13, 8))
ax = plt.axes(projection='3d')
# ax.plot_wireframe(X, Y, z, rstride = 1, cstride = 1, cmap='RdPu')
ax.plot_surface(X, Y, z, rstride=2, cstride=2, alpha=0.45,cmap="Paired", linewidth=0, antialiased=False)
ax.contour(X, Y, z, zdir='x', offset=11, cmap="Paired")
# ax.contour(X, Y, z, zdir='y', offset=20, cmap="Paired")
ax.yaxis.set_major_formatter(mtick.FormatStrFormatter('%d'))
ax.xaxis.set_major_formatter(mtick.FormatStrFormatter('%d'))
ax.set_xlabel('mapping node')
ax.set_ylabel('ESN')
ax.set_zlabel('RMSE', rotation=90)
# ax.grid(False, linestyle = "-.", color = "red", linewidth = "1")
ax.view_init(elev=17, azim=42)
# ax.contour(X, Y, z, cmap=cm.coolwarm)
plt.savefig('E:\\yan_2\\12_5论文\\实验结果图\\fangshan\\使用自平稳指标体系的3D图.png', bbox_inches='tight', dpi=600)
# 调整观察角度和方位角。这里将俯仰角设为60度，把方位角调整为35度
plt.show()
def getStationaryFigure():
#得到自平稳指标体系RMSE曲线图
StationaryPath = "E:\\yan_2\\12_5论文\\实验结果图\\fangshan\\自平稳指标体系实验数据.xlsx"
StationaryData = pd.read_excel(r'E:\\yan_2\\12_5论文\\实验结果图\\fangshan\\自平稳指标体系实验数据.xlsx').values[1:].reshape(-1,1)
fig = plt.figure(figsize=(13, 5))
x = np.arange(len(StationaryData))
plt.plot(x, StationaryData, 'c-*',color='#1B459C')
plt.show()
def zhexiantu_MSO_8(mappingNumber, enhanceNumber):
'''
绘制折线图
:return:
'''
fig = plt.figure(figsize=(13, 5))
real_path = 'E:\\yan_2\\CFBLS_LCFBLS复现\\result\\realData\\MSO_8\\test\\testReal.csv'
ESN_path = 'E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\ESN\\test\\ESN_resSize_920.csv'
BLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\BLS\\test\\BLS_mapping_{}_enhance_{}.csv".format(mappingNumber,enhanceNumber)
CFBLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\CFBLS\\test\\CFBLS_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
# LCFBLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\LCFBLS\\test\\LCFBLS_mapping_{}_enhance_{}.csv".format(
# mappingNumber, enhanceNumber)
# CEBLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\CEBLS\\test\\CEBLS_mapping_{}_enhance_{}.csv".format(
# mappingNumber, enhanceNumber)
BLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\BLS_ESN\\test\\BLS_ESN_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
CFBLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\CFBLS_ESN\\test\\CFBLS_ESN_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
# LCFBLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\LCFBLS_ESN\\test\\LCFBLS_ESN_mapping_{}_enhance_{}.csv".format(
# mappingNumber, enhanceNumber)
GRU_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\GRU\\test\\GRU_MSO_predict.csv"
LSTM_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\LSTM\\test\\LSTM_MSO_predict.csv"
startPos = 1000
endIndex = 1601
real_data = pd.read_csv(real_path).values[startPos:endIndex].reshape(-1,1)
ESN_data = pd.read_csv(ESN_path).values[startPos:endIndex].reshape(-1, 1)
BLS_data = pd.read_csv(BLS_path).values[startPos:endIndex].reshape(-1, 1)
CFBLS_data = pd.read_csv(CFBLS_path).values[startPos:endIndex].reshape(-1, 1)
# LCFBLS_data = pd.read_csv(LCFBLS_path).values[startPos:endIndex].reshape(-1, 1)
# CEBLS_data = pd.read_csv(CEBLS_path).values[startPos:endIndex].reshape(-1, 1)
BLS_ESN_data = pd.read_csv(BLS_ESN_path).values[startPos:endIndex].reshape(-1, 1)
CFBLS_ESN_data = pd.read_csv(CFBLS_ESN_path).values[startPos:endIndex].reshape(-1, 1)
# LCFBLS_ESN_data = pd.read_csv(LCFBLS_ESN_path).values[startPos:endIndex].reshape(-1, 1)
GRU_data = pd.read_csv(GRU_path).values[startPos:endIndex].reshape(-1, 1)
LSTM_data = pd.read_csv(LSTM_path).values[startPos:endIndex].reshape(-1, 1)
color = ['#7030A0', '#00B0F0', '#CD853F', '#00B050', '#92D050', '#FFFF00', '#FFC000', '#9DC3E6', '#8B0000', '#FF0000']
x = np.arange(endIndex-startPos)
plt.plot(x, real_data,color='#ef5b9c',label='real')
plt.plot(x, BLS_data, color='#840228', label='BLS')
plt.plot(x, ESN_data, color='#1d953f', label='ESN')
plt.plot(x, CFBLS_data, color='#87843b', label='CFBLS')
# plt.plot(x, BLS_ESN_data, color='#9b95c9', label='BLS_ESN')
plt.plot(x, CFBLS_ESN_data, color='#005344', label='CFBLS_ESN')
plt.plot(x, GRU_data, color='#ffc20e', label='GRU')
# plt.plot(x, LSTM_data, color='#8552a1', label='LSTM')
#ed1941 b2d235 ba8448 2b4490 5c7a29 009ad6 2585a6 585eaa ea66a6
# plt.title("Mapping node:{} Enhance node:{}".format(mappingNumber,enhanceNumber))
plt.legend(loc='upper left', bbox_to_anchor=(1, 1)) #把图例设置在外边
plt.ylabel('AQI')
fig.subplots_adjust(right=0.90) #调整边距和子图的间距
# plt.savefig('E:\\yan_2\\12_5论文\\实验结果图\\MSO_8\\折线图.png', bbox_inches='tight', dpi=600) # 高清图
plt.show()
def MSO_8_boxplot(mappingNumber, enhanceNumber):
'''
这个是把多个箱线图画在一个坐标系中的程序,这里实现的是将所有的预测数据用箱线图来展示出来
:return:
'''
fig = plt.figure(figsize=(13, 5))
real_path = 'E:\\yan_2\\CFBLS_LCFBLS复现\\result\\realData\\MSO_8\\test\\testReal.csv'
ESN_path = 'E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\ESN\\test\\ESN_resSize_920.csv'
BLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\BLS\\test\\BLS_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
CFBLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\CFBLS\\test\\CFBLS_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
# LCFBLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\LCFBLS\\test\\LCFBLS_mapping_{}_enhance_{}.csv".format(
# mappingNumber, enhanceNumber)
# CEBLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\CEBLS\\test\\CEBLS_mapping_{}_enhance_{}.csv".format(
# mappingNumber, enhanceNumber)
BLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\BLS_ESN\\test\\BLS_ESN_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
CFBLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\CFBLS_ESN\\test\\CFBLS_ESN_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
# LCFBLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\fangshan\\LCFBLS_ESN\\test\\LCFBLS_ESN_mapping_{}_enhance_{}.csv".format(
# mappingNumber, enhanceNumber)
GRU_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\GRU\\test\\GRU_MSO_predict.csv"
LSTM_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\LSTM\\test\\LSTM_MSO_predict.csv"
startPos = 100
endIndex = 3000
real_data = pd.read_csv(real_path).values[startPos:endIndex].reshape(1, -1)
ESN_data = pd.read_csv(ESN_path).values[startPos:endIndex].reshape(1, -1)
BLS_data = pd.read_csv(BLS_path).values[startPos:endIndex].reshape(1, -1)
CFBLS_data = pd.read_csv(CFBLS_path).values[startPos:endIndex].reshape(1, -1)
# LCFBLS_data = pd.read_csv(LCFBLS_path).values[startPos:endIndex].reshape(1, -1)
# CEBLS_data = pd.read_csv(CEBLS_path).values[startPos:endIndex].reshape(1, -1)
BLS_ESN_data = pd.read_csv(BLS_ESN_path).values[startPos:endIndex].reshape(1, -1)
CFBLS_ESN_data = pd.read_csv(CFBLS_ESN_path).values[startPos:endIndex].reshape(1, -1)
# LCFBLS_ESN_data = pd.read_csv(LCFBLS_ESN_path).values[startPos:endIndex].reshape(1, -1)
GRU_data = pd.read_csv(GRU_path).values[startPos:endIndex].reshape(1, -1)
LSTM_data = pd.read_csv(LSTM_path).values[startPos:endIndex].reshape(1, -1)
SAMPE = {'REAL':real_data[0],'CFBESN':CFBLS_ESN_data[0],'BESN':BLS_ESN_data[0],'BLS':BLS_data[0],
'ESN':ESN_data[0],'CFBLS':CFBLS_data[0],'GRU':GRU_data[0],'LSTM':LSTM_data[0]}
df = pd.DataFrame(SAMPE)
ff = df.boxplot(showmeans=True, return_type='dict', showfliers=False, vert=True,
sym='o', widths=0.3, whis=True, patch_artist=False, meanline=False,
showcaps=True,flierprops={'markeredgecolor': 'k', 'markersize': 8},
meanprops={'markeredgecolor': 'k', 'markerfacecolor': 'k', 'marker': '*'},figsize=(13, 5))
# color = ['#7030A0', '#00B0F0', '#CD853F', '#00B050', '#92D050', '#FFFF00', '#FFC000', '#9DC3E6', '#8B0000',
# '#FF0000']
# 这里共有5个box
# color = ['#6F933B', '#1B459C', '#16A2FF', '#CE7937', '#FF4025','#7030A0','#9DC3E6'] # 有多少box就对应设置多少颜色
# colors是为了解决上下须不同步，上下帽不同步颜色设置的
colors = ['#6F933B', '#6F933B', '#1B459C', '#1B459C', '#16A2FF', '#16A2FF', '#CE7937',
'#CE7937', '#FF4025', '#FF4025', '#7030A0', '#7030A0', '#9DC3E6', '#9DC3E6']
# ed1941 b2d235 ba8448 2b4490 5c7a29 009ad6 2585a6 585eaa ea66a6
newColor = ['#ed1941', '#b2d235', '#ba8448', '#2b4490', '#5c7a29', '#009ad6', '#2585a6','#585eaa','#ea66a6']
newColors = ['#ed1941', '#ed1941', '#b2d235', '#b2d235', '#ba8448', '#ba8448', '#2b4490',
'#2b4490', '#5c7a29', '#5c7a29', '#009ad6', '#009ad6', '#2585a6', '#2585a6',
'#585eaa', '#585eaa', '#ea66a6', '#ea66a6']
# fig = plt.figure(figsize=(13, 5))
linewidth = 1.8
for box, c in zip(ff['boxes'], newColor):
# 箱体边框颜色
box.set(color=c, linewidth=linewidth)
# 箱体内部填充颜色
# box.set(facecolor=c)
# 这里设置的是各个box的其他属性
for whisker, c in zip(ff['whiskers'], newColors):
whisker.set(color=c, linewidth=linewidth)
for cap, c in zip(ff['caps'], newColors):
cap.set(color=c, linewidth=linewidth)
for median, c in zip(ff['medians'], newColor):
median.set(color=c, linewidth=linewidth)
for mean, color in zip(ff['means'], newColor): # 星花是均值
mean.set(markeredgecolor=color, markerfacecolor=color, linewidth=1)
for flier, c in zip(ff['fliers'], newColor):
flier.set(marker='o', color=c, alpha=0.5)
plt.ylabel('AQI')
# plt.savefig('result/SMAPE.png', bbox_inches='tight',dpi=600) # 高清图
# fig.subplots_adjust(right=0.82) # 调整边距和子图的间距
plt.savefig('E:\\yan_2\\12_5论文\\实验结果图\\MSO_8\\箱线图.png', bbox_inches='tight', dpi=600)
plt.show()
def jubufangda_MSO_8(mappingNumber, enhanceNumber):
startPos = 270
endIndex = 321
MAX_EPISODES = endIndex - startPos
x_axis_data = []
for l in range(MAX_EPISODES):
x_axis_data.append(l)
real_path = 'E:\\yan_2\\CFBLS_LCFBLS复现\\result\\realData\\MSO_8\\test\\testReal.csv'
ESN_path = 'E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\ESN\\test\\ESN_resSize_920.csv'
BLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\BLS\\test\\BLS_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
CFBLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\CFBLS\\test\\CFBLS_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
BLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\BLS_ESN\\test\\BLS_ESN_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
CFBLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\CFBLS_ESN\\test\\CFBLS_ESN_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
GRU_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\GRU\\test\\GRU_MSO_predict.csv"
LSTM_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\LSTM\\test\\LSTM_MSO_predict.csv"
real_data = pd.read_csv(real_path).values[startPos:endIndex].reshape(-1, 1)
ESN_data = pd.read_csv(ESN_path).values[startPos:endIndex].reshape(-1, 1)
BLS_data = pd.read_csv(BLS_path).values[startPos:endIndex].reshape(-1, 1)
CFBLS_data = pd.read_csv(CFBLS_path).values[startPos:endIndex].reshape(-1, 1)
BLS_ESN_data = pd.read_csv(BLS_ESN_path).values[startPos:endIndex].reshape(-1, 1)
CFBLS_ESN_data = pd.read_csv(CFBLS_ESN_path).values[startPos:endIndex].reshape(-1, 1)
GRU_data = pd.read_csv(GRU_path).values[startPos:endIndex].reshape(-1, 1)
LSTM_data = pd.read_csv(LSTM_path).values[startPos:endIndex].reshape(-1, 1)
fig, ax = plt.subplots(1, 1, figsize=(12, 5))
# plt.plot(x_axis_data, real_data, '-.1', color='#ef5b9c', label='real')
# plt.plot(x_axis_data, BLS_data, '-.2', color='#f47920', label='BLS')
# plt.plot(x_axis_data, ESN_data, '-.3', color='#b2d235', label='ESN')
# plt.plot(x_axis_data, CFBLS_data, '-.4', color='#817936', label='CMBLS')
# # plt.plot(x_axis_data, BLS_ESN_data, '-.>', color='#f15a22', label='BLS_ESN')
# plt.plot(x_axis_data, CFBLS_ESN_data, '-.+', color='#009ad6', label='CMBESN')
# plt.plot(x_axis_data, GRU_data, '-.*', color='#8552a1', label='GRU')
# # plt.plot(x_axis_data, LSTM_data, '-.|', color='#ffc20e', label='LSTM')
plt.plot(x_axis_data, real_data, color='#ef5b9c', label='real')
plt.plot(x_axis_data, BLS_data, color='#f47920', label='BLS')
plt.plot(x_axis_data, ESN_data, color='#b2d235', label='ESN')
plt.plot(x_axis_data, CFBLS_data, color='#817936', label='CMBLS')
# plt.plot(x_axis_data, BLS_ESN_data, '-.>', color='#f15a22', label='BLS_ESN')
plt.plot(x_axis_data, CFBLS_ESN_data, color='#009ad6', label='CMBESN')
plt.plot(x_axis_data, GRU_data, color='#8552a1', label='GRU')
# plt.plot(x_axis_data, LSTM_data, '-.|', color='#ffc20e', label='LSTM')
plt.ylabel('MSO Value', fontsize = 14)
plt.xlabel('Observed Point', fontsize = 14)
# plt.legend(loc='upper left', bbox_to_anchor=(1, 1)) # 把图例设置在外边
plt.legend(loc='upper left', prop={'size' : 14}) # 把图例设置在外边
# plt.ylabel('Death number')
# 嵌入局部放大图的坐标系
axins = inset_axes(ax, width="40%", height="30%", loc='lower left',
bbox_to_anchor=(0.52, 0.62, 0.8, 1.2),
bbox_transform=ax.transAxes)
# 在子坐标系中绘制原始数据
axins.plot(x_axis_data, real_data, color='#ef5b9c', alpha=0.8, linewidth=1.5)
axins.plot(x_axis_data, BLS_data, color='#f47920', alpha=0.8, linewidth=1.5)
axins.plot(x_axis_data, ESN_data, color='#b2d235', alpha=0.8, linewidth=1.5)
axins.plot(x_axis_data, CFBLS_data, color='#817936', alpha=0.8, linewidth=1.5)
axins.plot(x_axis_data, BLS_ESN_data, color='#f15a22', alpha=0.8, linewidth=1.5)
axins.plot(x_axis_data, CFBLS_ESN_data, color='#009ad6', alpha=0.8, linewidth=1.5)
plt.plot(x_axis_data, GRU_data, color='#8552a1', alpha=0.8, linewidth=1.5)
plt.plot(x_axis_data, LSTM_data, color='#ffc20e', alpha=0.8, linewidth=1.5)
# 设置放大区间，调整子坐标系的显示范围
# 设置放大区间
zone_left = 25
zone_right = 29
# 坐标轴的扩展比例（根据实际数据调整）
x_ratio = 0.0 # x轴显示范围的扩展比例
y_ratio = 0.07 # y轴显示范围的扩展比例
# X轴的显示范围
xlim0 = x_axis_data[zone_left] - (x_axis_data[zone_right] - x_axis_data[zone_left]) * x_ratio
xlim1 = x_axis_data[zone_right] + (x_axis_data[zone_right] - x_axis_data[zone_left]) * x_ratio
# Y轴的显示范围
y = np.hstack((real_data[zone_left:zone_right], BLS_data[zone_left:zone_right],
ESN_data[zone_left:zone_right], CFBLS_data[zone_left:zone_right],
CFBLS_ESN_data[zone_left:zone_right],GRU_data[zone_left:zone_right]))
ylim0 = np.min(y) - (np.max(y) - np.min(y)) * y_ratio
ylim1 = np.max(y) + (np.max(y) - np.min(y)) * y_ratio
# 调整子坐标系的显示范围
axins.set_xlim(xlim0, xlim1)
axins.set_ylim(ylim0, ylim1)
# 建立父坐标系与子坐标系的连接线
# 原图中画方框
tx0 = xlim0
tx1 = xlim1
ty0 = ylim0
ty1 = ylim1
sx = [tx0, tx1, tx1, tx0, tx0]
sy = [ty0, ty0, ty1, ty1, ty0]
ax.plot(sx, sy, "#CD853F")
print('xlim0 : ', xlim0)
print('xlim1 : ', xlim1)
print('ylim0 : ', ylim0)
print('ylim1 : ', ylim1)
# 画两条线
xy = (xlim0, ylim1) # (60,-409)
xy2 = (xlim0, ylim0) # (60,409)
con = ConnectionPatch(xyA=xy2, xyB=xy, coordsA="data", coordsB="data",
axesA=axins, axesB=ax, color='#b22c46')
axins.add_artist(con)
xy = (xlim1, ylim1) # (90,-409)
xy2 = (xlim1, ylim0) # (90,-409)
con = ConnectionPatch(xyA=xy2, xyB=xy, coordsA="data", coordsB="data",
axesA=axins, axesB=ax, color='#b22c46')
axins.add_artist(con)
# plt.savefig('E:\\yan_2\\12_5论文\\实验结果图\\MSO_8\\第一版修改的图\\CM_局部放大图_折线图.svg', format='svg', bbox_inches='tight', dpi=800) # 高清图
# plt.show()
x1 = np.arange(endIndex-startPos)
fig1, vax = plt.subplots(1, 1, figsize=(11, 5))
ERROR_ESN = []
ERROR_BLS = []
ERROR_CFBLS = []
ERROR_CFBESN = []
ERROR_GRU = []
for i in range(endIndex-startPos):
ERROR_ESN.append(abs(ESN_data[i] - real_data[i]))
ERROR_BLS.append(abs(BLS_data[i] - real_data[i]))
ERROR_CFBLS.append(abs(CFBLS_data[i] - real_data[i]))
ERROR_CFBESN.append(abs(CFBLS_ESN_data[i] - real_data[i]))
ERROR_GRU.append(abs(GRU_data[i] - real_data[i]))
ERROR_ESN = np.array(ERROR_ESN).reshape(-1, 1)
ERROR_BLS = np.array(ERROR_BLS).reshape(-1, 1)
ERROR_CFBLS = np.array(ERROR_CFBLS).reshape(-1, 1)
ERROR_CFBESN = np.array(ERROR_CFBESN).reshape(-1, 1)
ERROR_GRU = np.array(ERROR_GRU).reshape(-1, 1)
# plt.vlines(x1, [0], ERROR_ESN, colors='#00a6ac', label='ESN')
# plt.vlines(x1, [0], ERROR_BLS, colors='#9b95c9', label='BLS')
# plt.vlines(x1, [0], ERROR_CFBLS, colors='#fdb933', label='CMBLS')
# plt.vlines(x1, [0], ERROR_CFBESN, colors='#a3cf62', label='GRU')
# plt.vlines(x1, [0], ERROR_GRU, colors='#f05b72', label='CMBESN')
# plt.plot(x, ERROR_ESN, color='#b2d235', label='ESN')
# plt.plot(x, ERROR_BLS, color='#f47920', label='BLS')
# plt.plot(x, ERROR_CFBLS, color='#817936', label='CFESN')
# plt.plot(x, ERROR_CFBESN, color='#8552a1', label='CFBESN')
# plt.plot(x, ERROR_GRU, color='#009ad6', label='GRU')
plt.scatter(x1, ERROR_ESN, s=18, marker='.', c='#00a6ac', label='ESN')
plt.scatter(x1, ERROR_BLS, s=18, marker='o', c='#9b95c9', label='BLS')
plt.scatter(x1, ERROR_CFBLS, s=18, marker='v', c='#fdb933', label='CMBLS')
plt.scatter(x1, ERROR_CFBESN, s=18, marker='p', c='#a3cf62', label='GRU')
plt.scatter(x1, ERROR_GRU, s=18, marker='D', c='#f05b72', label='CMBESN')
plt.ylabel('Errors', fontsize = 14)
plt.xlabel('Time steps', fontsize = 14)
# fig1.subplots_adjust(right=0.88) # 调整边距和子图的间距
# plt.legend(loc='lower left') # 把图例设置在外边
# plt.legend(loc='upper left', bbox_to_anchor=(0.995, 1)) # 把图例设置在外边
plt.legend(loc='upper right', prop={'size': 13}) # 把图例设置在外边
# fig.subplots_adjust(right=1.5) # 调整边距和子图的间距
plt.savefig('E:\\yan_2\\12_5论文\\实验结果图\\MSO_8\\第一版修改的图\\CM_所有模型_误差垂线图_补审稿人1_加绝对值.svg', format='svg',bbox_inches='tight', dpi=600) # 高清图
plt.show()
def plot_ERROR_MSO_8(mappingNumber, enhanceNumber):
'''
将所有模型的误差绘制在同一张图上
startPos = 270
endIndex = 321
fig = plt.figure(figsize=(12, 5))
real_path = 'E:\\yan_2\\CFBLS_LCFBLS复现\\result\\realData\\MSO_8\\test\\testReal.csv'
ESN_path = 'E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\ESN\\test\\ESN_resSize_920.csv'
BLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\BLS\\test\\BLS_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
CFBLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\CFBLS\\test\\CFBLS_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
BLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\BLS_ESN\\test\\BLS_ESN_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
CFBLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\CFBLS_ESN\\test\\CFBLS_ESN_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
GRU_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\GRU\\test\\GRU_MSO_predict.csv"
LSTM_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\LSTM\\test\\LSTM_MSO_predict.csv"
real_data = pd.read_csv(real_path).values[startPos:endIndex].reshape(-1, 1)
ESN_data = pd.read_csv(ESN_path).values[startPos:endIndex].reshape(-1, 1)
BLS_data = pd.read_csv(BLS_path).values[startPos:endIndex].reshape(-1, 1)
CFBLS_data = pd.read_csv(CFBLS_path).values[startPos:endIndex].reshape(-1, 1)
BLS_ESN_data = pd.read_csv(BLS_ESN_path).values[startPos:endIndex].reshape(-1, 1)
CFBLS_ESN_data = pd.read_csv(CFBLS_ESN_path).values[startPos:endIndex].reshape(-1, 1)
GRU_data = pd.read_csv(GRU_path).values[startPos:endIndex].reshape(-1, 1)
LSTM_data = pd.read_csv(LSTM_path).values[startPos:endIndex].reshape(-1, 1)
x1 = np.arange(endIndex - startPos)
ERROR_ESN = []
ERROR_BLS = []
ERROR_CFBLS = []
ERROR_CFBESN = []
ERROR_GRU = []
for i in range(endIndex - startPos):
ERROR_ESN.append(abs(ESN_data[i] - real_data[i]))
ERROR_BLS.append(abs(BLS_data[i] - real_data[i]))
ERROR_CFBLS.append(abs(CFBLS_data[i] - real_data[i]))
ERROR_CFBESN.append(abs(CFBLS_ESN_data[i] - real_data[i]))
ERROR_GRU.append(abs(GRU_data[i] - real_data[i]))
ERROR_ESN = np.array(ERROR_ESN).reshape(-1, 1)
ERROR_BLS = np.array(ERROR_BLS).reshape(-1, 1)
ERROR_CFBLS = np.array(ERROR_CFBLS).reshape(-1, 1)
ERROR_CFBESN = np.array(ERROR_CFBESN).reshape(-1, 1)
ERROR_GRU = np.array(ERROR_GRU).reshape(-1, 1)
# plt.vlines(x1, [0], ERROR_ESN, colors='#00a6ac', label='ESN')
# plt.vlines(x1, [0], ERROR_BLS, colors='#9b95c9', label='BLS')
# plt.vlines(x1, [0], ERROR_CFBLS, colors='#fdb933', label='CMBLS')
# plt.vlines(x1, [0], ERROR_CFBESN, colors='#a3cf62', label='GRU')
# plt.vlines(x1, [0], ERROR_GRU, colors='#f05b72', label='CMBESN')
# plt.plot(x, ERROR_ESN, color='#b2d235', label='ESN')
# plt.plot(x, ERROR_BLS, color='#f47920', label='BLS')
# plt.plot(x, ERROR_CFBLS, color='#817936', label='CFESN')
# plt.plot(x, ERROR_CFBESN, color='#8552a1', label='CFBESN')
# plt.plot(x, ERROR_GRU, color='#009ad6', label='GRU')
plt.scatter(x1, ERROR_ESN, s=18, marker='.', c='#00a6ac', label='ESN')
plt.scatter(x1, ERROR_BLS, s=18, marker='o', c='#9b95c9', label='BLS')
plt.scatter(x1, ERROR_CFBLS, s=18, marker='v', c='#fdb933', label='CMBLS')
plt.scatter(x1, ERROR_CFBESN, s=18, marker='p', c='#a3cf62', label='GRU')
plt.scatter(x1, ERROR_GRU, s=18, marker='D', c='#f05b72', label='CMBESN')
plt.ylabel('Errors', fontsize=14)
plt.xlabel('Time steps', fontsize=14)
# fig1.subplots_adjust(right=0.88) # 调整边距和子图的间距
# plt.legend(loc='lower left') # 把图例设置在外边
# plt.legend(loc='upper left', bbox_to_anchor=(0.995, 1)) # 把图例设置在外边
plt.legend(loc='upper right', prop={'size': 13}) # 把图例设置在外边
# fig.subplots_adjust(right=1.5) # 调整边距和子图的间距
# plt.savefig('E:\\yan_2\\12_5论文\\实验结果图\\MSO_8\\第一版修改的图\\CM_所有模型_误差垂线图_补审稿人1_加绝对值.svg', format='svg',
# bbox_inches='tight', dpi=600) # 高清图
plt.show()
'''
startPos = 350
endIndex = 400
real_path = 'E:\\yan_2\\CFBLS_LCFBLS复现\\result\\realData\\MSO_8\\test\\testReal.csv'
ESN_path = 'E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\ESN\\test\\ESN_resSize_920.csv'
BLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\BLS\\test\\BLS_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
CFBLS_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\CFBLS\\test\\CFBLS_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
BLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\BLS_ESN\\test\\BLS_ESN_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
CFBLS_ESN_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\CFBLS_ESN\\test\\CFBLS_ESN_mapping_{}_enhance_{}.csv".format(
mappingNumber, enhanceNumber)
GRU_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\GRU\\test\\GRU_MSO_predict.csv"
LSTM_path = "E:\\yan_2\\CFBLS_LCFBLS复现\\result\\csvFile\\MSO_8\\LSTM\\test\\LSTM_MSO_predict.csv"
real_data = pd.read_csv(real_path).values[startPos:endIndex].reshape(-1, 1)
ESN_data = pd.read_csv(ESN_path).values[startPos:endIndex].reshape(-1, 1)
BLS_data = pd.read_csv(BLS_path).values[startPos:endIndex].reshape(-1, 1)
CFBLS_data = pd.read_csv(CFBLS_path).values[startPos:endIndex].reshape(-1, 1)
BLS_ESN_data = pd.read_csv(BLS_ESN_path).values[startPos:endIndex].reshape(-1, 1)
CFBLS_ESN_data = pd.read_csv(CFBLS_ESN_path).values[startPos:endIndex].reshape(-1, 1)
GRU_data = pd.read_csv(GRU_path).values[startPos:endIndex].reshape(-1, 1)
LSTM_data = pd.read_csv(LSTM_path).values[startPos:endIndex].reshape(-1, 1)
x1 = np.arange(endIndex - startPos)
ERROR_ESN = []
ERROR_BLS = []
ERROR_CFBLS = []
ERROR_CFBESN = []
ERROR_GRU = []
for i in range(endIndex - startPos):
ERROR_ESN.append(abs(ESN_data[i] - real_data[i]))
ERROR_BLS.append(abs(BLS_data[i] - real_data[i]))
ERROR_CFBLS.append(abs(CFBLS_data[i] - real_data[i]))
ERROR_CFBESN.append(abs(CFBLS_ESN_data[i] - real_data[i]))
ERROR_GRU.append(abs(GRU_data[i] - real_data[i]))
ERROR_ESN = np.array(ERROR_ESN).reshape(-1, 1)
ERROR_BLS = np.array(ERROR_BLS).reshape(-1, 1)
ERROR_CFBLS = np.array(ERROR_CFBLS).reshape(-1, 1)
ERROR_CFBESN = np.array(ERROR_CFBESN).reshape(-1, 1)
ERROR_GRU = np.array(ERROR_GRU).reshape(-1, 1)
gs = gridspec.GridSpec(2, 6) #创建2行6列的网格
lim_xy_max = max(max(ERROR_ESN), max(ERROR_BLS), max(ERROR_CFBLS), max(ERROR_CFBESN), max(ERROR_GRU)) + 0.1
plt.figure(figsize=(12, 6))
plt.subplot(gs[0, :2]) #gs[哪一行，列的范围]
gs.update(hspace=0.3, wspace=0.8)
plt.scatter(x1, ERROR_ESN, s=40, marker='.', c='#00a6ac', label='ESN')
plt.ylim(0, lim_xy_max)
plt.ylabel('Errors', fontsize=13)
plt.xlabel('Time steps', fontsize=13)
plt.legend(loc='upper right', prop={'size': 11})
plt.subplot(gs[0, 2:4]) #gs[哪一行，列的范围]
plt.scatter(x1, ERROR_BLS, s=40, marker='.', c='#9b95c9', label='BLS')
plt.ylim(0, lim_xy_max)
plt.ylabel('Errors', fontsize=13)
plt.xlabel('Time steps', fontsize=13)
plt.legend(loc='upper right', prop={'size': 11})
plt.subplot(gs[0, 4:6]) #gs[哪一行，列的范围]
plt.scatter(x1, ERROR_CFBLS, s=40, marker='.', c='#fdb933', label='CMBLS')
plt.ylim(0, lim_xy_max)
plt.ylabel('Errors', fontsize=13)
plt.xlabel('Time steps', fontsize=13)
plt.legend(loc='upper right', prop={'size': 11})
plt.subplot(gs[1, 1:3]) #gs[哪一行，列的范围]
plt.scatter(x1, ERROR_CFBESN, s=40, marker='.', c='#a3cf62', label='GRU')
plt.ylim(0, lim_xy_max)
plt.ylabel('Errors', fontsize=13)
plt.xlabel('Time steps', fontsize=13)
plt.legend(loc='upper right', prop={'size': 12})
plt.subplot(gs[1, 3:5]) #gs[哪一行，列的范围]
plt.scatter(x1, ERROR_GRU, s=40, marker='.', c='#f05b72', label='CMBESN')
plt.ylim(0, lim_xy_max)
plt.ylabel('Errors', fontsize=13)
plt.xlabel('Time steps', fontsize=13)
plt.legend(loc='upper right', prop={'size': 12})
# plt.subplots_adjust(hspace=0.3,wspace=0.5) # 调节上下子图之间的行距
plt.savefig('E:\\yan_2\\12_5论文\\实验结果图\\MSO_8\\第一版修改的图\\CM_所有模型_误差垂线图_补审稿人1_加绝对值.svg', format='svg',
bbox_inches='tight', dpi=600) # 高清图
plt.show()
def plot_3D_weipinwen_MSO_8():
'''
没有使用自平稳优化指标体系的3D绘图
:return:
'''
data_path = r'E:\\yan_2\\12_5论文\\实验结果图\\MSO_8\\未使用自平稳指标体系的3D图.xlsx'
data = pd.read_excel(data_path).values[:,:]
# print(data)
X = np.linspace(14,22,5)
Y = np.linspace(2,28,14)
z = np.array(data[:,3:4]).reshape(14,5)
# print("x:",x)
# print("y:", y)
# print("z:", z)
'''
cmap = "以下的取值"
'CMRmap_r', 'Dark2', 'Dark2_r', 'GnBu', 'GnBu_r', 'Greens', 'Greens_r', 'Greys', 'Greys_r', 'OrRd', 'OrRd_r', 'Oranges',
'Oranges_r', 'PRGn', 'PRGn_r', 'Paired', 'Paired_r', 'Pastel1', 'Pastel1_r', 'Pastel2', 'Pastel2_r', 'PiYG', 'PiYG_r', 'PuBu',
'PuBuGn', 'PuBuGn_r', 'PuBu_r', 'PuOr', 'PuOr_r', 'PuRd', 'PuRd_r', 'Purples', 'Purples_r', 'RdBu', 'RdBu_r', 'RdGy', 'RdGy_r',
'RdPu', 'RdPu_r', 'RdYlBu', 'RdYlBu_r', 'RdYlGn', 'RdYlGn_r', 'Reds', 'Reds_r', 'Set1', 'Set1_r', 'Set2', 'Set2_r', 'Set3',
'Set3_r', 'Spectral', 'Spectral_r', 'Wistia', 'Wistia_r', 'YlGn', 'YlGnBu', 'YlGnBu_r', 'YlGn_r', 'YlOrBr', 'YlOrBr_r', 'YlOrRd',
'YlOrRd_r', 'afmhot', 'afmhot_r', 'autumn', 'autumn_r', 'binary', 'binary_r', 'bone', 'bone_r', 'brg', 'brg_r', 'bwr', 'bwr_r',
'cividis', 'cividis_r', 'cool', 'cool_r', 'coolwarm', 'coolwarm_r', 'copper', 'copper_r', 'cubehelix', 'cubehelix_r', 'flag',
'flag_r', 'gist_earth', 'gist_earth_r', 'gist_gray', 'gist_gray_r', 'gist_heat', 'gist_heat_r', 'gist_ncar', 'gist_ncar_r',
'gist_rainbow', 'gist_rainbow_r', 'gist_stern', 'gist_stern_r', 'gist_yarg', 'gist_yarg_r', 'gnuplot', 'gnuplot2', 'gnuplot2_r',
'gnuplot_r', 'gray', 'gray_r', 'hot', 'hot_r', 'hsv', 'hsv_r', 'inferno', 'inferno_r', 'jet', 'jet_r', 'magma', 'magma_r',
'nipy_spectral', 'nipy_spectral_r', 'ocean', 'ocean_r', 'pink', 'pink_r', 'plasma', 'plasma_r', 'prism', 'prism_r', 'rainbow',
'rainbow_r', 'seismic', 'seismic_r', 'spring', 'spring_r', 'summer', 'summer_r', 'tab10', 'tab10_r', 'tab20', 'tab20_r', 'tab20b',
'tab20b_r', 'tab20c', 'tab20c_r', 'terrain', 'terrain_r', 'turbo', 'turbo_r', 'twilight', 'twilight_r', 'twilight_shifted',
'twilight_shifted_r', 'viridis', 'viridis_r', 'winter', 'winter_r'
'''
X, Y = np.meshgrid(X, Y)
fig = plt.figure(figsize=(13, 8))
ax = plt.axes(projection='3d')
# ax.plot_wireframe(X, Y, z, rstride = 1, cstride = 1, cmap='RdPu')
ax.plot_surface(X, Y, z, rstride=1, cstride=1, alpha=0.3,cmap="hsv_r", linewidth=0, antialiased=False)
ax.contour(X, Y, z, zdir='x', offset=13, cmap="hsv_r")
# ax.contour(X, Y, z, zdir='y', offset=30, cmap=cm.coolwarm)
ax.set_xlabel('mapping node')
ax.set_ylabel('ESN')
ax.set_zlabel('RMSE', rotation=90)
# ax.grid(False, linestyle = "-.", color = "red", linewidth = "1")
ax.view_init(elev=18, azim=45)
# ax.contour(X, Y, z, cmap=cm.coolwarm)
plt.savefig('E:\\yan_2\\12_5论文\\实验结果图\\MSO_8\\未使用自平稳指标体系的3D图.png', bbox_inches='tight', dpi=600)
# 调整观察角度和方位角。这里将俯仰角设为60度，把方位角调整为35度
plt.show()
def plot_3D_pinwen_MSO_8():
'''
没有使用自平稳优化指标体系的3D绘图
:return:
'''
data_path = r'E:\\yan_2\\12_5论文\\实验结果图\\MSO_8\\自平稳指标体系的3D图.xlsx'
data = pd.read_excel(data_path).values[:,:]
# print(data)
X = np.linspace(14,36,12)
Y = np.linspace(2,28,14)
z = np.array(data[:,3:4]).reshape(14,12)
X, Y = np.meshgrid(X, Y)
fig = plt.figure(figsize=(13, 8))
ax = plt.axes(projection='3d')
# ax.plot_wireframe(X, Y, z, rstride = 1, cstride = 1, cmap='RdPu')
ax.plot_surface(X, Y, z, rstride=2, cstride=2, alpha=0.3,cmap="hsv", linewidth=0, antialiased=False)
ax.contour(X, Y, z, zdir='x', offset=13, cmap="hsv_r")
# ax.contour(X, Y, z, zdir='z', offset=0, cmap="hsv_r")
ax.set_xlabel('mapping node')
ax.set_ylabel('ESN')
ax.set_zlabel('RMSE', rotation=90)
# ax.grid(False, linestyle = "-.", color = "red", linewidth = "1")
ax.view_init(elev=11, azim=49)
# ax.contour(X, Y, z, cmap=cm.coolwarm)
# plt.savefig('E:\\yan_2\\12_5论文\\实验结果图\\MSO_8\\使用自平稳指标体系的3D图.png', bbox_inches='tight', dpi=600)
# 调整观察角度和方位角。这里将俯仰角设为60度，把方位角调整为35度
plt.show()
def plot_real_data_fangshan():
path = 'E:\\yan_1\\BLS_self\\fangshan.csv'
AQI = pd.read_csv(path).values[1000:2000, :1].reshape(-1, 1)
# AQI = pd.read_csv(path).values[1000:15000, :1].reshape(-1, 1)
x = np.arange(AQI.shape[0])
plt.figure(figsize=(13, 6))