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| # -*-coding=utf-8-*-
# Usage:提前准备一个文件，存放各个态的信息,现在这个文件就是为了提取K谷处的态的占据数
#pos 记得提前去掉两行
from numpy import *
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
with open("C:\\Users\\14618\\Desktop\\0\\k.txt", 'r', encoding='UTF-8')as f:
with open("C:\\Users\\14618\\Desktop\\0\\pos.txt", 'r', encoding='UTF-8')as f1:
filelist = f.readlines()
filelist_new = np.array(filelist) # 转换成 array类型
num_array = filelist_new.size # 整个文件fx的行数
filelist1 = f1.readlines()
filelist_new1 = np.array(filelist1) # 转换成 array类型
num_array1 = filelist_new1.size # 整个文件fx的行数
# print('整个文件f数据部分的行数:', filelist[0])
important_K = []
for i in range(1, 421):
important_K.append(filelist[82 * i - 52])
i += 1
# print(important[-1])
To_mat_K = zeros((len(important_K), 4))
index = 0
for line in important_K:
x = line.strip('\n').split()
To_mat_K[index:] = x[0:4]
index += 1
#print(To_mat_K)
# 把提取到的占据态的信息To_mat_K储存在2.txt
To_mat1 = zeros((len(filelist_new1), 3))
print(len(To_mat1))
index = 0
for line in filelist_new1:
x = line.strip('\n').split()
To_mat1[index:] = x[0:3]
index += 1
# print(To_mat)
steps = np.linspace(1, num_array-1, num_array-1)
x = 15 * 0.048375 * steps
y = 6*To_mat1[:, 1]
np.savetxt("C:\\Users\\14618\\Desktop\\occ-k.txt", To_mat_K, delimiter=' ', header='整理')
CM1_raw=To_mat_K[:, 2]
CM2_raw=To_mat_K[:, 3]
CM1=CM1_raw[1:]
CM2=CM2_raw[1:]
print('CM1',CM1)
#求平均
CM1_sum = []
CM1_avg = []
CM2_sum = []
CM2_avg = []
for i in range(0,len(CM1)):
CM1_sum.append(np.sum(CM1[:i + 1]))
CM2_sum.append(np.sum(CM2[:i + 1]))
print('CM1_sum',CM1_sum)
print('CM2_sum',CM2_sum)
step=np.linspace(1,419,419)
CM1_avg = np.divide(CM1_sum,step)
CM2_avg = np.divide(CM2_sum,step)
print('CM1_avg',CM1_avg)
print('CM2_avg',CM2_avg)
#去掉列表里的第一个数
print('len',len(CM1_avg))
x=15*0.048378*step
#
# #拟合
def func1(x, a, b, c):
return a * np.exp(-b * x) + c
def func2(x, a, b, c):
return -a * np.exp(-b * x) + c
sigma=np.ones(len(x))
sigma[[0]]=0.01 #设置第一个点的权重，让其通过
#popt_CM1, pcov_CM1 = curve_fit(func1,x,CM1_avg,sigma=sigma)
#popt_CM2, pcov_CM2 = curve_fit(func2,x,CM2_avg,sigma=sigma)
# 画图
figure = plt.figure()
#plt.plot(x, To_mat_K[:, 0], 'b-', label='VM1')
#plt.plot(x, To_mat_K[:, 1], 'r-', label='VM2')
plt.plot(x,CM2_avg, 'r-', label='CM1')
plt.plot(x,CM1_avg, 'b-', label='CM2') #出现能级交叉，因此这里更改了Label
#plt.plot(x, func2(x, *popt_CM2), 'r--',
#label='fit: a=-%5.3f, b=%5.3f, c=%5.3f' % tuple(popt_CM2))
#plt.plot(x, func1(x, *popt_CM1), 'b--',
#label='fit: a=%5.3f, b=%5.3f, c=%5.3f' % tuple(popt_CM1))
print(x)
print(len(y))
plt.plot(x,y[2:]-y[2],'g--',lw=0.5)
#plt.plot(x, 0.2 * sin(0.051699 * x), 'g--', lw=1)
plt.xlabel('Time(fs)')
plt.ylabel('Occupations')
plt.title('Occupations_Average-Time')
plt.xlim([0, 300])
plt.legend(loc=1)
plt.axhline(y=0, ls='--', lw=0.2, c='#808080')
plt.axvline(x=121.53398, ls='--', lw=0.5, c='#808080')
plt.axvline(x=243.06796, ls='--', lw=0.5, c='#808080')
plt.axvline(x=60.76699, ls='--', lw=0.5, c='#808080')
plt.axvline(x=182.30097, ls='--', lw=0.5, c='#808080')
plt.show()
figure.savefig(fname="C:\\Users\\14618\\Desktop\\figure.jpg", dpi=500)
f.close()
f1.close()
|
