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10 Commits
2d98dd5b07
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73adde082b
| Author | SHA1 | Date | |
|---|---|---|---|
| 73adde082b | |||
| 0027293f2b | |||
| ebf86b30eb | |||
| 5be00bb720 | |||
| 2b5bfab114 | |||
| 81565244f1 | |||
| fea2d76548 | |||
| 047c2353b6 | |||
| d2b76e2cd7 | |||
| 67d4c9e5a4 |
@@ -3,11 +3,11 @@
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% 陈数高效法
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clear;clc;
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n=1000 % 积分密度
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n=100 % 积分密度
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delta=2*pi/n;
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C=0;
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for kx=-pi:(2*pi/n):pi
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for ky=-pi:(2*pi/n):pi
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for kx=-pi:(2*pi/n):pi-(2*pi/n)
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for ky=-pi:(2*pi/n):pi-(2*pi/n)
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VV=get_vector(HH(kx,ky));
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Vkx=get_vector(HH(kx+delta,ky)); % 略偏离kx的波函数
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Vky=get_vector(HH(kx,ky+delta)); % 略偏离ky的波函数
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@@ -0,0 +1,63 @@
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"""
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This code is supported by the website: https://www.guanjihuan.com
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The newest version of this code is on the web page: https://www.guanjihuan.com/archives/10890
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"""
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import numpy as np
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a = [[ 0 , 0 , 1.5 , 0.32635182-0.98480775j],
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[0 , 0 , -0.32635182-0.98480775j, 1.5 ],
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[ 1.5 , -0.32635182+0.98480775j ,0, 0 ],
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[ 0.32635182+0.98480775j , 1.5 , 0, 0 ]]
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def Schmidt_orthogonalization(eigenvector):
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num = eigenvector.shape[1]
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for i in range(num):
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for i0 in range(i):
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eigenvector[:, i] = eigenvector[:, i] - eigenvector[:, i0]*np.dot(eigenvector[:, i].transpose().conj(), eigenvector[:, i0])/(np.dot(eigenvector[:, i0].transpose().conj(),eigenvector[:, i0]))
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eigenvector[:, i] = eigenvector[:, i]/np.linalg.norm(eigenvector[:, i])
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return eigenvector
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def verify_orthogonality(vectors):
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identity = np.eye(vectors.shape[1])
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product = np.dot(vectors.T.conj(), vectors)
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return np.allclose(product, identity)
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# 对 np.linalg.eigh() 的特征向量正交化
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E, v = np.linalg.eigh(a)
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print(verify_orthogonality(v))
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v1 = Schmidt_orthogonalization(v)
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print(verify_orthogonality(v1))
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from scipy.linalg import orth
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v2 = orth(v)
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print(verify_orthogonality(v2))
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v3, S, Vt = np.linalg.svd(v)
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print(verify_orthogonality(v3))
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v4, R = np.linalg.qr(v)
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print(verify_orthogonality(v4))
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print()
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# 对 np.linalg.eig() 的特征向量正交化
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E, v = np.linalg.eig(a)
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print(verify_orthogonality(v))
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v1 = Schmidt_orthogonalization(v)
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print(verify_orthogonality(v1))
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from scipy.linalg import orth
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v2 = orth(v)
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print(verify_orthogonality(v2))
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v3, S, Vt = np.linalg.svd(v)
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print(verify_orthogonality(v3))
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v4, R = np.linalg.qr(v)
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print(verify_orthogonality(v4))
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7
2021.05.07_Quantum_Espresso/silicon/command.txt
Normal file
7
2021.05.07_Quantum_Espresso/silicon/command.txt
Normal file
@@ -0,0 +1,7 @@
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pw.x < pw.scf.silicon_bands.in > pw.scf.silicon_bands.out
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pw.x < pw.bands.silicon.in > pw.bands.silicon.out
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bands.x < pp.bands.silicon.in > pp.bands.silicon.out
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python plot_bands.py
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30
2021.05.07_Quantum_Espresso/silicon/plot_bands.py
Normal file
30
2021.05.07_Quantum_Espresso/silicon/plot_bands.py
Normal file
@@ -0,0 +1,30 @@
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import matplotlib.pyplot as plt
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import numpy as np
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plt.rcParams["figure.dpi"]=150
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plt.rcParams["figure.facecolor"]="white"
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plt.rcParams["figure.figsize"]=(8, 6)
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# load data
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data = np.loadtxt('./si_bands.dat.gnu')
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k = np.unique(data[:, 0])
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bands = np.reshape(data[:, 1], (-1, len(k)))
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for band in range(len(bands)):
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plt.plot(k, bands[band, :], linewidth=1, alpha=0.5, color='k')
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plt.xlim(min(k), max(k))
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# Fermi energy
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plt.axhline(6.6416, linestyle=(0, (5, 5)), linewidth=0.75, color='k', alpha=0.5)
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# High symmetry k-points (check bands_pp.out)
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plt.axvline(0.8660, linewidth=0.75, color='k', alpha=0.5)
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plt.axvline(1.8660, linewidth=0.75, color='k', alpha=0.5)
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plt.axvline(2.2196, linewidth=0.75, color='k', alpha=0.5)
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# text labels
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plt.xticks(ticks= [0, 0.8660, 1.8660, 2.2196, 3.2802], \
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labels=['L', '$\Gamma$', 'X', 'U', '$\Gamma$'])
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plt.ylabel("Energy (eV)")
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plt.text(2.3, 5.6, 'Fermi energy')
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plt.savefig('si_bands.jpg')
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plt.show()
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13
2021.05.07_Quantum_Espresso/silicon/pp.bands.silicon.in
Normal file
13
2021.05.07_Quantum_Espresso/silicon/pp.bands.silicon.in
Normal file
@@ -0,0 +1,13 @@
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&BANDS
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prefix = 'silicon'
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outdir = './tmp/'
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filband = 'si_bands.dat'
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/
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K_POINTS {crystal_b}
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5
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0.0000 0.5000 0.0000 20 !L
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0.0000 0.0000 0.0000 30 !G
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-0.500 0.0000 -0.500 10 !X
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-0.375 0.2500 -0.375 30 !U
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0.0000 0.0000 0.0000 20 !G
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38
2021.05.07_Quantum_Espresso/silicon/pw.bands.silicon.in
Normal file
38
2021.05.07_Quantum_Espresso/silicon/pw.bands.silicon.in
Normal file
@@ -0,0 +1,38 @@
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&control
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calculation = 'bands',
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restart_mode = 'from_scratch',
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prefix = 'silicon',
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outdir = './tmp/'
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pseudo_dir = '/opt/qe-7.3.1/pseudo'
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verbosity = 'high'
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/
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&system
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ibrav = 2,
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celldm(1) = 10.2076,
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nat = 2,
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ntyp = 1,
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ecutwfc = 50,
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ecutrho = 400,
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nbnd = 8
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/
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&electrons
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conv_thr = 1e-8,
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mixing_beta = 0.6
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/
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ATOMIC_SPECIES
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Si 28.086 Si.pz-vbc.UPF
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ATOMIC_POSITIONS (alat)
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Si 0.00 0.00 0.00
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Si 0.25 0.25 0.25
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K_POINTS {crystal_b}
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5
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0.0000 0.5000 0.0000 20 !L
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0.0000 0.0000 0.0000 30 !G
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-0.500 0.0000 -0.500 10 !X
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-0.375 0.2500 -0.375 30 !U
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0.0000 0.0000 0.0000 20 !G
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36
2021.05.07_Quantum_Espresso/silicon/pw.scf.silicon_bands.in
Normal file
36
2021.05.07_Quantum_Espresso/silicon/pw.scf.silicon_bands.in
Normal file
@@ -0,0 +1,36 @@
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&CONTROL
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calculation = 'scf',
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restart_mode = 'from_scratch',
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prefix = 'silicon',
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outdir = './tmp/'
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pseudo_dir = '/opt/qe-7.3.1/pseudo'
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verbosity = 'high'
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/
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&SYSTEM
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ibrav = 2,
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celldm(1) = 10.2076,
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nat = 2,
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ntyp = 1,
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ecutwfc = 50,
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ecutrho = 400,
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nbnd = 8,
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! occupations = 'smearing',
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! smearing = 'gaussian',
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! degauss = 0.005
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/
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&ELECTRONS
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conv_thr = 1e-8,
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mixing_beta = 0.6
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/
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ATOMIC_SPECIES
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Si 28.086 Si.pz-vbc.UPF
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ATOMIC_POSITIONS (alat)
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Si 0.0 0.0 0.0
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Si 0.25 0.25 0.25
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K_POINTS (automatic)
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8 8 8 0 0 0
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@@ -1,88 +0,0 @@
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"""
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This code is supported by the website: https://www.guanjihuan.com
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The newest version of this code is on the web page: https://www.guanjihuan.com/archives/13623
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"""
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from bs4 import BeautifulSoup
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from urllib.request import urlopen
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import re
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from collections import Counter
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import datetime
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import random
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import time
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# time.sleep(random.uniform(0,1800)) # 爬虫简单伪装，在固定时间后0到30分钟后开始运行。调试的时候把该语句注释。
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year = datetime.datetime.now().year
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month = datetime.datetime.now().month
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day = datetime.datetime.now().day
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# 获取链接
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try:
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with open('prb_link_list.txt', 'r', encoding='UTF-8') as f: # 如果文件存在
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link_list = f.read().split('\n') # 历史已经访问过的链接（数组类型）
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except:
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with open('prb_link_list.txt', 'w', encoding='UTF-8') as f: # 如果文件不存在
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link_list = []
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f = open('prb_link_list.txt', 'a', encoding='UTF-8') # 打开文件（补充）
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f.write('\nLink list obtained on '+str(year)+'.'+str(month).rjust(2,'0')+'.'+str(day).rjust(2,'0')+':\n')
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match_href = [] # 在本次运行中满足条件的链接
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for loop in range(3):
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if loop == 0:
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start_link = "https://journals.aps.org/prb/recent?page=1" # 看第一页
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elif loop == 1:
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start_link = "https://journals.aps.org/prb/recent?page=2" # 看第二页
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elif loop == 2:
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start_link = "https://journals.aps.org/prb/recent?page=3" # 看第三页（三页基本上覆盖了当天的所有更新）
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html = urlopen(start_link).read().decode('utf-8') # 打开网页
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soup = BeautifulSoup(html, features='lxml') # 放入soup中
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all_a_tag = soup.find_all('a', href=True) # 获取超链接标签
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for a_tag in all_a_tag:
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href = a_tag['href'] # 超链接字符串
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if re.search('/abstract/', href): # 文章的链接
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if re.search('https://journals.aps.org', href)==None: # 如果链接不是完整的，那么补充完整
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href = 'https://journals.aps.org'+ href
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if href not in match_href and href not in link_list and re.search('\?', href)==None: # 链接不重复
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match_href.append(href)
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f.write(href+'\n')
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f.close()
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# 获取摘要
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try:
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f = open('prb_all.txt', 'a', encoding='UTF-8') # 全部记录
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except:
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f = open('prb_all.txt', 'w', encoding='UTF-8') # 如果文件不存在
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try:
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f_month = open('prb_'+str(year)+'.'+str(month).rjust(2,'0')+'.txt', 'a', encoding='UTF-8') # 一个月的记录
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except:
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f_month = open('prb_'+str(year)+'.'+str(month).rjust(2,'0')+'.txt', 'w', encoding='UTF-8') # 如果文件不存在
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f.write('\n\n['+str(year)+'.'+str(month).rjust(2,'0')+'.'+str(day).rjust(2,'0')+'][total number='+str(len(match_href))+']\n\n\n')
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f_month.write('\n\n['+str(year)+'.'+str(month).rjust(2,'0')+'.'+str(day).rjust(2,'0')+'][total number='+str(len(match_href))+']\n\n\n')
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print('total number=', len(match_href)) # 调试的时候显示这个
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i00 = 0
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for href in match_href:
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i00 += 1
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print('reading number', i00, '...') # 调试的时候显示这个
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# time.sleep(random.uniform(10,110)) # 爬虫简单伪装，休息一分钟左右。如果链接个数有60个，那么程序运行时间延长60分钟。调试的时候把该语句注释。
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try:
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html = urlopen(href).read().decode('utf-8') # 打开文章链接
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soup = BeautifulSoup(html, features='lxml') # 放入soup中
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title = soup.title # 文章标题
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f.write(str(title.get_text())+'\n\n')
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f_month.write(str(title.get_text())+'\n\n')
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f.write(str(href)+'\n\n') # 文章链接
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f_month.write(str(href)+'\n\n')
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abstract = re.findall('"yes"><p>.*</p><div', html, re.S)[0][9:-8] # 文章摘要
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word_list = abstract.split(' ') # 划分单词
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for word in word_list:
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if re.search('<', word)==None and re.search('>', word)==None: # 有些内容满足过滤条件，因此信息可能会丢失。
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f.write(word+' ')
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f_month.write(word+' ')
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f.write('\n\n\n')
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f_month.write('\n\n\n')
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except:
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pass
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f.close()
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@@ -1,88 +0,0 @@
|
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"""
|
||||
This code is supported by the website: https://www.guanjihuan.com
|
||||
The newest version of this code is on the web page: https://www.guanjihuan.com/archives/13623
|
||||
"""
|
||||
|
||||
from bs4 import BeautifulSoup
|
||||
from urllib.request import urlopen
|
||||
import re
|
||||
from collections import Counter
|
||||
import datetime
|
||||
import random
|
||||
import time
|
||||
|
||||
|
||||
# time.sleep(random.uniform(0,1800)) # 爬虫简单伪装，在固定时间后0到30分钟后开始运行。调试的时候把该语句注释。
|
||||
year = datetime.datetime.now().year
|
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month = datetime.datetime.now().month
|
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day = datetime.datetime.now().day
|
||||
|
||||
|
||||
# 获取链接
|
||||
try:
|
||||
with open('prl_link_list.txt', 'r', encoding='UTF-8') as f: # 如果文件存在
|
||||
link_list = f.read().split('\n') # 历史已经访问过的链接（数组类型）
|
||||
except:
|
||||
with open('prl_link_list.txt', 'w', encoding='UTF-8') as f: # 如果文件不存在
|
||||
link_list = []
|
||||
f = open('prl_link_list.txt', 'a', encoding='UTF-8') # 打开文件（补充）
|
||||
f.write('\nLink list obtained on '+str(year)+'.'+str(month).rjust(2,'0')+'.'+str(day).rjust(2,'0')+':\n')
|
||||
match_href = [] # 在本次运行中满足条件的链接
|
||||
for loop in range(3):
|
||||
if loop == 0:
|
||||
start_link = "https://journals.aps.org/prl/recent?page=1" # 看第一页
|
||||
elif loop == 1:
|
||||
start_link = "https://journals.aps.org/prl/recent?page=2" # 看第二页
|
||||
elif loop == 2:
|
||||
start_link = "https://journals.aps.org/prl/recent?page=3" # 看第三页（三页基本上覆盖了当天的所有更新）
|
||||
html = urlopen(start_link).read().decode('utf-8') # 打开网页
|
||||
soup = BeautifulSoup(html, features='lxml') # 放入soup中
|
||||
all_a_tag = soup.find_all('a', href=True) # 获取超链接标签
|
||||
for a_tag in all_a_tag:
|
||||
href = a_tag['href'] # 超链接字符串
|
||||
if re.search('/abstract/', href): # 文章的链接
|
||||
if re.search('https://journals.aps.org', href)==None: # 如果链接不是完整的，那么补充完整
|
||||
href = 'https://journals.aps.org'+ href
|
||||
if href not in match_href and href not in link_list and re.search('\?', href)==None: # 链接不重复
|
||||
match_href.append(href)
|
||||
f.write(href+'\n')
|
||||
f.close()
|
||||
|
||||
|
||||
|
||||
# 获取摘要
|
||||
try:
|
||||
f = open('prl_all.txt', 'a', encoding='UTF-8') # 全部记录
|
||||
except:
|
||||
f = open('prl_all.txt', 'w', encoding='UTF-8') # 如果文件不存在
|
||||
try:
|
||||
f_month = open('prl_'+str(year)+'.'+str(month).rjust(2,'0')+'.txt', 'a', encoding='UTF-8') # 一个月的记录
|
||||
except:
|
||||
f_month = open('prl_'+str(year)+'.'+str(month).rjust(2,'0')+'.txt', 'w', encoding='UTF-8') # 如果文件不存在
|
||||
f.write('\n\n['+str(year)+'.'+str(month).rjust(2,'0')+'.'+str(day).rjust(2,'0')+'][total number='+str(len(match_href))+']\n\n\n')
|
||||
f_month.write('\n\n['+str(year)+'.'+str(month).rjust(2,'0')+'.'+str(day).rjust(2,'0')+'][total number='+str(len(match_href))+']\n\n\n')
|
||||
print('total number=', len(match_href)) # 调试的时候显示这个
|
||||
i00 = 0
|
||||
for href in match_href:
|
||||
i00 += 1
|
||||
print('reading number', i00, '...') # 调试的时候显示这个
|
||||
# time.sleep(random.uniform(10,110)) # 爬虫简单伪装，休息一分钟左右。如果链接个数有60个，那么程序运行时间延长60分钟。调试的时候把该语句注释。
|
||||
try:
|
||||
html = urlopen(href).read().decode('utf-8') # 打开文章链接
|
||||
soup = BeautifulSoup(html, features='lxml') # 放入soup中
|
||||
title = soup.title # 文章标题
|
||||
f.write(str(title.get_text())+'\n\n')
|
||||
f_month.write(str(title.get_text())+'\n\n')
|
||||
f.write(str(href)+'\n\n') # 文章链接
|
||||
f_month.write(str(href)+'\n\n')
|
||||
abstract = re.findall('"yes"><p>.*</p><div', html, re.S)[0][9:-8] # 文章摘要
|
||||
word_list = abstract.split(' ') # 划分单词
|
||||
for word in word_list:
|
||||
if re.search('<', word)==None and re.search('>', word)==None: # 有些内容满足过滤条件，因此信息可能会丢失。
|
||||
f.write(word+' ')
|
||||
f_month.write(word+' ')
|
||||
f.write('\n\n\n')
|
||||
f_month.write('\n\n\n')
|
||||
except:
|
||||
pass
|
||||
f.close()
|
||||
@@ -1,41 +0,0 @@
|
||||
"""
|
||||
This code is supported by the website: https://www.guanjihuan.com
|
||||
The newest version of this code is on the web page: https://www.guanjihuan.com/archives/13623
|
||||
"""
|
||||
|
||||
import re
|
||||
from collections import Counter
|
||||
|
||||
|
||||
def main():
|
||||
file_name = 'prb_all.txt'
|
||||
with open(file_name, 'r', encoding='UTF-8') as f: # 打开文件
|
||||
paper_list = f.read().split('\n\n\n') # 通过三个回车划分不同文章
|
||||
word_list = []
|
||||
ignore = ignore_words() # 过滤常见单词
|
||||
for paper in paper_list:
|
||||
word_list_in_one_paper = []
|
||||
if len(paper)>20: # 通过字符串长度过滤日期
|
||||
content_list = paper.split('\n\n') # 通过两个回车划分内容
|
||||
for content in content_list:
|
||||
if re.search('https://', content)==None: # 过滤文章链接
|
||||
words = content.split(' ') # 通过空格划分单词
|
||||
for word in words:
|
||||
if word not in word_list_in_one_paper: # 一篇文章的某个单词只统计一次
|
||||
if word not in ignore and len(word)>1: # 过滤词汇
|
||||
word_list.append(word)
|
||||
word_list_in_one_paper.append(word)
|
||||
num = 300
|
||||
most_common_words = Counter(word_list).most_common(num) # 统计出现最多的num个词汇
|
||||
print('\n出现频率最高的前', num, '个词汇：')
|
||||
for word in most_common_words:
|
||||
print(word)
|
||||
|
||||
|
||||
def ignore_words(): # 可自行增删
|
||||
ignore = ['Phys.', 'the', 'to', 'of', 'in', 'under', 'and', 'by', 'The', 'at', 'with', 'up', 'be', 'above', 'below', 'are', 'is', 'for', 'that', 'as', 'we', '<a', 'abstract', 'abstract"','<span', 'which', 'We', 'such', 'has', 'two', 'these', 'it', 'all', 'results', 'result', 'each', 'have', 'between', 'on', 'an', 'can', 'also', 'from', 'Our', 'our', 'using', 'where', 'These', 'out', 'both', 'due', 'less', 'along', 'but', 'In', 'show', 'into', 'study', 'find', 'provide', 'change','not', 'open', 'this', 'show', 'into', 'study', 'find', 'provide', 'change', 'present', 'Using', 'large', 'This', 'However', 'appear', 'studied', 'obtain', 'been', 'Both', 'they', 'effects', 'effect', 'compute', 'more', 'does', 'shown', 'Based', 'reveal', 'highly', 'number', 'However,', 'was', 'near', 'full', 'based', 'several', 'suggest', 'agreement', 'predicted', 'values', 'work', 'emphasize', 'without', 'or', 'work,', 'studies', 'future', 'identify', 'present.', 'predict', 'presence', 'their', 'were', 'From', 'its', 'By', 'how', 'ground', 'observed', 'recent', 'For', 'other', 'Here', 'test', 'further', 'Its', 'similar', 'however,', 'range', 'within', 'value', 'possible', 'may', 'than', 'low', 'us', 'obtained', 'around', 'consider', 'about', 'very', 'will', 'when', 'played', 'consist', 'consists', 'Here,', 'observe', 'gives', 'It', 'over', 'cannot', 'As', 'whose', 'new', 'some', 'only', 'from', 'yields', 'shows', 'data', 'direct', 'related', 'different', 'evidence', 'role', 'function', 'origin', 'specific', 'set', 'confirm', 'give', 'Moreover', 'develop', 'including', 'could', 'used', 'means', 'allows', 'make', 'e.g.,', 'provides', 'system', 'systems', 'field', 'fields', 'model', 'model,', 'state', 'states', 'states.', 'state.', 'band', 'bands', 'method', 'methods', 'nature', 'rate', 'zero', 'single', 'theory', 'first', 'one', 'complex', 'approach', 'schemes', 'terms', 'even', 'case', 'analysis', 'weight', 'volume', 'evolution', 'well', 'external', 'measured', 'introducing', 'dependence', 'properties', 'demonstrate', 'remains', 'through', 'measurements', 'samples', 'findings', 'respect', 'investigate', 'behavior', 'importance', 'considered', 'experimental', 'increase', 'propose', 'follows', 'increase', 'emerged', 'interesting', 'behaviors', 'influenced', 'paramount', 'indicate', 'Rev.', 'concepts', 'induced', 'zone', 'regions', 'exact', 'contribution', 'behavior', 'formation', 'measurements.', 'utilizing', 'constant', 'regime', 'features', 'strength', 'compare', 'determined', 'combination', 'compare', 'determined', 'At', 'inside', 'ambient', 'then', 'important', 'report', 'Moreover,', 'Despite', 'found', 'because', 'process', 'and,', 'significantly', 'realized', 'much', 'natural', 'since', 'grows', 'any', 'compared', 'while', 'forms.', 'appears', 'indicating', 'coefficient', 'suggested', 'time', 'exhibits', 'calculations.', 'developed', 'array', 'discuss', 'field', 'becomes', 'allowing', 'indicates', 'via', 'introduce', 'considering', 'times.', 'constructed', 'explain', 'form', 'owing', 'parameters.', 'parameter', 'operation', 'probe', 'experiments', 'interest', 'strategies', 'seen', 'emerge', 'generic', 'geometry', 'numbers', 'observation', 'avenue', 'theretically', 'three', 'excellent', 'amount', 'notable', 'example', 'being', 'promising', 'latter', 'little', 'imposed', 'put', 'resource', 'together', 'produce', 'successfully','there', 'enhanced', 'this', 'great', 'dirven', 'increasing','should', 'otherwise', 'Further', 'field,', 'known', 'changes', 'still', 'beyond', 'various', 'center', 'previously', 'way', 'peculiar', 'detailed', 'understanding', 'good', 'years', 'where', 'Me', 'origins', 'years.', 'attributed', 'known,', 'them', 'reported', 'no', 'systems', 'agree', 'examined', 'rise', 'calculate', 'those', 'particular', 'relation', 'defined', 'either', 'again', 'current', 'exhibit', 'calculated', 'here', 'made', 'Further', 'consisting', 'constitutes', 'originated', 'if', 'exceed', 'access']
|
||||
return ignore
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@@ -1,37 +0,0 @@
|
||||
from bs4 import BeautifulSoup
|
||||
from urllib.request import urlopen
|
||||
import re
|
||||
import datetime
|
||||
|
||||
|
||||
year = datetime.datetime.now().year
|
||||
month = datetime.datetime.now().month
|
||||
day = datetime.datetime.now().day
|
||||
|
||||
|
||||
f = open('nature_physics.html', 'w', encoding='UTF-8')
|
||||
f.write('<meta charset="utf-8"><style type="text/css">a{text-decoration: none;color: #0a5794;}a:hover {text-decoration: underline;color: red; }</style>')
|
||||
f.write('<p>'+str(year)+'.'+str(month).rjust(2,'0')+'.'+str(day).rjust(2,'0')+' 已更新</p>')
|
||||
|
||||
match_href = []
|
||||
start_link = "https://www.nature.com/nphys/research-articles"
|
||||
html = urlopen(start_link).read().decode('utf-8') # 打开网页
|
||||
soup = BeautifulSoup(html, features='lxml') # 放入soup中
|
||||
all_article = soup.find_all('article', {"class":"u-full-height c-card c-card--flush"})
|
||||
for article in all_article:
|
||||
all_a_tag = article.find_all('a', href=True) # 获取超链接标签
|
||||
for a_tag in all_a_tag:
|
||||
href = a_tag['href'] # 超链接字符串
|
||||
if re.search('/articles/', href): # 文章的链接
|
||||
if re.search('https://www.nature.com', href)==None: # 如果链接不是完整的，那么补充完整
|
||||
href = 'https://www.nature.com'+ href
|
||||
if href not in match_href and re.search('\?', href)==None: # 链接不重复
|
||||
match_href.append(href)
|
||||
f.write('<li><a target=\"_blank\" href=\"')
|
||||
f.write(href) # 文章链接
|
||||
f.write('\">')
|
||||
f.write(a_tag.get_text())
|
||||
f.write('</a> ')
|
||||
time = article.find('time', {"class": "c-meta__item c-meta__item--block-at-lg"}).get_text()
|
||||
f.write(time+'</li>')
|
||||
f.close()
|
||||
@@ -1,36 +0,0 @@
|
||||
from bs4 import BeautifulSoup
|
||||
from urllib.request import urlopen
|
||||
import re
|
||||
import datetime
|
||||
|
||||
|
||||
year = datetime.datetime.now().year
|
||||
month = datetime.datetime.now().month
|
||||
day = datetime.datetime.now().day
|
||||
|
||||
f = open('physics_magazine.html', 'w', encoding='UTF-8')
|
||||
f.write('<meta charset="utf-8"><style type="text/css">a{text-decoration: none;color: #0a5794;}a:hover {text-decoration: underline;color: red; }</style>')
|
||||
f.write('<p>'+str(year)+'.'+str(month).rjust(2,'0')+'.'+str(day).rjust(2,'0')+' 已更新</p>')
|
||||
|
||||
match_href = []
|
||||
start_link = "https://physics.aps.org/"
|
||||
html = urlopen(start_link).read().decode('utf-8') # 打开网页
|
||||
soup = BeautifulSoup(html, features='lxml') # 放入soup中
|
||||
all_articles = soup.find_all('div', {"class":"feed-item-details"})
|
||||
for article in all_articles:
|
||||
all_a_tag = article.find_all('a', href=True) # 获取超链接标签
|
||||
for a_tag in all_a_tag:
|
||||
href = a_tag['href'] # 超链接字符串
|
||||
if re.search('/articles/', href): # 文章的链接
|
||||
if re.search('https://physics.aps.org', href)==None: # 如果链接不是完整的，那么补充完整
|
||||
href = 'https://physics.aps.org'+ href
|
||||
if href not in match_href:
|
||||
match_href.append(href)
|
||||
f.write('<li><a target=\"_blank\" href=\"')
|
||||
f.write(href) # 文章链接
|
||||
f.write('\">')
|
||||
f.write(a_tag.get_text())
|
||||
f.write('</a> ')
|
||||
time = article.find('time', {"class": "feed-item-date"}).get_text()
|
||||
f.write(time+'</li>')
|
||||
f.close()
|
||||
@@ -1,42 +0,0 @@
|
||||
from bs4 import BeautifulSoup
|
||||
from urllib.request import urlopen
|
||||
import re
|
||||
import datetime
|
||||
|
||||
|
||||
year = datetime.datetime.now().year
|
||||
month = datetime.datetime.now().month
|
||||
day = datetime.datetime.now().day
|
||||
|
||||
|
||||
f = open('prb.html', 'w', encoding='UTF-8')
|
||||
f.write('<meta charset="utf-8"><style type="text/css">a{text-decoration: none;color: #0a5794;}a:hover {text-decoration: underline;color: red; }</style>')
|
||||
f.write('<p>'+str(year)+'.'+str(month).rjust(2,'0')+'.'+str(day).rjust(2,'0')+' 已更新</p>')
|
||||
|
||||
match_href = []
|
||||
for loop in range(1):
|
||||
if loop == 0:
|
||||
start_link = "https://journals.aps.org/prb/recent" # 看第一页
|
||||
# elif loop == 1:
|
||||
# start_link = "https://journals.aps.org/prb/recent?page=2" # 看第二页
|
||||
html = urlopen(start_link).read().decode('utf-8') # 打开网页
|
||||
soup = BeautifulSoup(html, features='lxml') # 放入soup中
|
||||
all_article = soup.find_all('div', {"class":"article panel article-result"})
|
||||
for article in all_article:
|
||||
all_a_tag = article.find_all('a', href=True) # 获取超链接标签
|
||||
for a_tag in all_a_tag:
|
||||
href = a_tag['href'] # 超链接字符串
|
||||
if re.search('/abstract/', href): # 文章的链接
|
||||
if re.search('https://journals.aps.org', href)==None: # 如果链接不是完整的，那么补充完整
|
||||
href = 'https://journals.aps.org'+ href
|
||||
if href not in match_href and re.search('\?', href)==None: # 链接不重复
|
||||
match_href.append(href)
|
||||
f.write('<li><a target=\"_blank\" href=\"')
|
||||
f.write(href) # 文章链接
|
||||
f.write('\">')
|
||||
f.write(a_tag.get_text())
|
||||
f.write('</a> ')
|
||||
info = article.find('h6', {"class": "pub-info"}).get_text()
|
||||
f.write(re.findall('– Published .*', info, re.S)[0][12:]+'</li>')
|
||||
f.close()
|
||||
|
||||
@@ -1,42 +0,0 @@
|
||||
from bs4 import BeautifulSoup
|
||||
from urllib.request import urlopen
|
||||
import re
|
||||
import datetime
|
||||
|
||||
|
||||
year = datetime.datetime.now().year
|
||||
month = datetime.datetime.now().month
|
||||
day = datetime.datetime.now().day
|
||||
|
||||
|
||||
f = open('prl.html', 'w', encoding='UTF-8')
|
||||
f.write('<meta charset="utf-8"><style type="text/css">a{text-decoration: none;color: #0a5794;}a:hover {text-decoration: underline;color: red; }</style>')
|
||||
f.write('<p>'+str(year)+'.'+str(month).rjust(2,'0')+'.'+str(day).rjust(2,'0')+' 已更新</p>')
|
||||
|
||||
match_href = []
|
||||
for loop in range(1):
|
||||
if loop == 0:
|
||||
start_link = "https://journals.aps.org/prl/recent" # 看第一页
|
||||
# elif loop == 1:
|
||||
# start_link = "https://journals.aps.org/prl/recent?page=2" # 看第二页
|
||||
html = urlopen(start_link).read().decode('utf-8') # 打开网页
|
||||
soup = BeautifulSoup(html, features='lxml') # 放入soup中
|
||||
all_article = soup.find_all('div', {"class":"article panel article-result"})
|
||||
for article in all_article:
|
||||
all_a_tag = article.find_all('a', href=True) # 获取超链接标签
|
||||
for a_tag in all_a_tag:
|
||||
href = a_tag['href'] # 超链接字符串
|
||||
if re.search('/abstract/', href): # 文章的链接
|
||||
if re.search('https://journals.aps.org', href)==None: # 如果链接不是完整的，那么补充完整
|
||||
href = 'https://journals.aps.org'+ href
|
||||
if href not in match_href and re.search('\?', href)==None: # 链接不重复
|
||||
match_href.append(href)
|
||||
f.write('<li><a target=\"_blank\" href=\"')
|
||||
f.write(href) # 文章链接
|
||||
f.write('\">')
|
||||
f.write(a_tag.get_text())
|
||||
f.write('</a> ')
|
||||
info = article.find('h6', {"class": "pub-info"}).get_text()
|
||||
f.write(re.findall('– Published.*', info, re.S)[0][12:]+'</li>')
|
||||
f.close()
|
||||
|
||||
@@ -1,66 +0,0 @@
|
||||
"""
|
||||
This code is supported by the website: https://www.guanjihuan.com
|
||||
The newest version of this code is on the web page: https://www.guanjihuan.com/archives/17937
|
||||
"""
|
||||
|
||||
from bs4 import BeautifulSoup
|
||||
from urllib.request import urlopen
|
||||
import re
|
||||
import datetime
|
||||
|
||||
year = datetime.datetime.now().year
|
||||
month = datetime.datetime.now().month
|
||||
day = datetime.datetime.now().day
|
||||
|
||||
# 获取链接
|
||||
|
||||
# 由于没有模拟登录知乎，因此只能爬取到最新的两篇文章
|
||||
authors = ["https://www.zhihu.com/people/guanjihuan/posts"] # Guan
|
||||
|
||||
match_href = []
|
||||
for i0 in range(len(authors)):
|
||||
start_link = authors[i0]
|
||||
html = urlopen(start_link).read().decode('utf-8') # 打开网页
|
||||
soup = BeautifulSoup(html, features='lxml') # 放入soup中
|
||||
all_a_tag = soup.find_all('a', href=True) # 获取超链接标签
|
||||
for a_tag in all_a_tag:
|
||||
href = a_tag['href'] # 超链接字符串
|
||||
if re.search('//zhuanlan.zhihu.com/p/', href) and not re.search('edit', href): # 文章的链接
|
||||
if re.search('https:', href)==None: # 如果链接不是完整的，那么补充完整
|
||||
href = 'https:'+ href
|
||||
if href not in match_href:
|
||||
match_href.append(href)
|
||||
|
||||
|
||||
# 对链接进行排序
|
||||
numbers = []
|
||||
match_href_new = []
|
||||
for href in match_href:
|
||||
numbers.append(int(href[29:]))
|
||||
numbers.sort(reverse = True)
|
||||
for n in numbers:
|
||||
match_href_new.append('https://zhuanlan.zhihu.com/p/'+str(n))
|
||||
|
||||
|
||||
# 获取内容并写入文件
|
||||
f = open('zhihu.html', 'w', encoding='UTF-8')
|
||||
f.write('<meta charset="utf-8"><style type="text/css">a{text-decoration: none;color: #004e4e;}a:hover {text-decoration: underline;color: red; }</style>')
|
||||
|
||||
f.write('<p>'+str(year)+'.'+str(month).rjust(2,'0')+'.'+str(day).rjust(2,'0')+' 已更新</p>')
|
||||
for href in match_href_new:
|
||||
try:
|
||||
html = urlopen(href).read().decode('utf-8') # 打开文章链接
|
||||
soup = BeautifulSoup(html, features='lxml') # 放入soup中
|
||||
title = soup.title # 文章标题
|
||||
f.write('<li><a target=\"_blank\" href=\"')
|
||||
f.write(str(href)) # 文章链接
|
||||
f.write('\">')
|
||||
f.write(str(title.get_text()[:-5]))
|
||||
f.write('</a> ')
|
||||
author = soup.find("span", {"class": "UserLink AuthorInfo-name"})
|
||||
f.write(str(author.get_text()+' '))
|
||||
post_time = soup.find("div", {"class" : "ContentItem-time"})
|
||||
f.write(str(post_time.get_text()[4:-6])+'</li>')
|
||||
except:
|
||||
pass
|
||||
f.close()
|
||||
@@ -1,7 +1,20 @@
|
||||
import pickle
|
||||
|
||||
data = [1, 2, 3]
|
||||
|
||||
# 保存为文件
|
||||
with open('a.txt', 'wb') as f:
|
||||
pickle.dump(data, f)
|
||||
with open('a.txt', 'rb') as f:
|
||||
data_load = pickle.load(f)
|
||||
print(data_load)
|
||||
data_load_from_file = pickle.load(f)
|
||||
print(data_load_from_file)
|
||||
print()
|
||||
|
||||
# 把对象转换成字节流
|
||||
serialized_data = pickle.dumps(data) # 转换成字节流
|
||||
print(type(serialized_data))
|
||||
print(serialized_data)
|
||||
print()
|
||||
loaded_data = pickle.loads(serialized_data) # 转换成原类型
|
||||
print(type(loaded_data))
|
||||
print(loaded_data)
|
||||
49
2024.11.21_kelly_formula/kelly_formula.py
Normal file
49
2024.11.21_kelly_formula/kelly_formula.py
Normal file
@@ -0,0 +1,49 @@
|
||||
"""
|
||||
This code is supported by the website: https://www.guanjihuan.com
|
||||
The newest version of this code is on the web page: https://www.guanjihuan.com/archives/43508
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
investment_ratio_array = np.arange(0.1, 1.1, 0.1)
|
||||
investment_times = 1000
|
||||
test_times = 100
|
||||
|
||||
# 几个例子：https://www.guanjihuan.com/archives/43412
|
||||
|
||||
# 例子（2）的参数
|
||||
p = 0.6 # 胜率
|
||||
b = 1 # 收益
|
||||
a = 1 # 损失
|
||||
|
||||
# # 例子（3）的参数
|
||||
# p = 0.5
|
||||
# b = 1
|
||||
# a = 0.5
|
||||
|
||||
win_array = [] # 胜出的仓位
|
||||
for i0 in range(test_times):
|
||||
# print(i0)
|
||||
capital_array = []
|
||||
for f in investment_ratio_array:
|
||||
capital = 1
|
||||
for _ in range(investment_times):
|
||||
investment = capital*f
|
||||
if investment>0:
|
||||
random_value = np.random.uniform(0, 1)
|
||||
if random_value<p:
|
||||
capital = capital+investment*b
|
||||
else:
|
||||
capital = capital-investment*a
|
||||
capital_array.append(capital)
|
||||
max_capital_index = capital_array.index(max(capital_array))
|
||||
win_array.append(investment_ratio_array[max_capital_index])
|
||||
|
||||
def kelly_formula(p, b, a):
|
||||
f=(p/a)-((1-p)/b)
|
||||
return f
|
||||
|
||||
print(kelly_formula(p=p, b=b, a=a))
|
||||
plt.hist(win_array, bins=100, color='skyblue')
|
||||
plt.show()
|
||||
33
2024.12.02_MNIST/download_MNIST_and_show_image.py
Normal file
33
2024.12.02_MNIST/download_MNIST_and_show_image.py
Normal file
@@ -0,0 +1,33 @@
|
||||
"""
|
||||
This code is supported by the website: https://www.guanjihuan.com
|
||||
The newest version of this code is on the web page: https://www.guanjihuan.com/archives/43720
|
||||
"""
|
||||
|
||||
from torchvision import datasets, transforms
|
||||
|
||||
transform = transforms.Compose([transforms.ToTensor()]) # 定义数据预处理步骤（转换为Tensor）
|
||||
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform) # 加载 MNIST 数据集，训练集
|
||||
print(type(train_dataset))
|
||||
size_of_train_dataset = len(train_dataset)
|
||||
print(size_of_train_dataset)
|
||||
test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform) # 加载 MNIST 数据集，测试集
|
||||
print(type(test_dataset))
|
||||
size_of_test_dataset = len(test_dataset)
|
||||
print(size_of_test_dataset)
|
||||
|
||||
import random
|
||||
rand_number = random.randint(0, size_of_train_dataset-1)
|
||||
image, label = train_dataset[rand_number] # 获取一张图像和标签
|
||||
print(type(image))
|
||||
print(image.shape)
|
||||
image = image.squeeze(0) # 去掉单通道的维度 (1, 28, 28) -> (28, 28)
|
||||
print(type(image))
|
||||
print(image.shape)
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
# import os
|
||||
# os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" # 解决可能的多个 OpenMP 库版本冲突的问题。如果有 OMP 报错，可以试着使用这个解决。
|
||||
plt.imshow(image, cmap='gray') # 显示图像
|
||||
plt.title(f"Label: {label}") # 标签值（理论值）
|
||||
plt.axis('off') # 不显示坐标轴
|
||||
plt.show()
|
||||
100
2024.12.02_MNIST/train_and_predict_MNIST.PY
Normal file
100
2024.12.02_MNIST/train_and_predict_MNIST.PY
Normal file
@@ -0,0 +1,100 @@
|
||||
"""
|
||||
This code is supported by the website: https://www.guanjihuan.com
|
||||
The newest version of this code is on the web page: https://www.guanjihuan.com/archives/43720
|
||||
"""
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.optim as optim
|
||||
from torch.utils.data import DataLoader, TensorDataset
|
||||
from torchvision import datasets, transforms
|
||||
|
||||
transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,), (0.5,))]) # 数据转换（将图片转换为 Tensor 并进行归一化处理，均值和标准差为 0.5）
|
||||
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform) # 下载训练数据集
|
||||
test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform) # 下载测试数据集
|
||||
|
||||
# 训练函数
|
||||
def train(model, train_loader, criterion, optimizer, num_epochs=5):
|
||||
for epoch in range(num_epochs):
|
||||
model.train()
|
||||
running_loss = 0.0
|
||||
correct = 0
|
||||
total = 0
|
||||
for images, labels in train_loader:
|
||||
# print(images.shape)
|
||||
optimizer.zero_grad() # 清除以前的梯度
|
||||
outputs = model(images) # 前向传播
|
||||
loss = criterion(outputs, labels)
|
||||
loss.backward() # 反向传播和优化
|
||||
optimizer.step()
|
||||
running_loss += loss.item()
|
||||
_, predicted = torch.max(outputs, 1) # 计算准确率
|
||||
total += labels.size(0)
|
||||
correct += (predicted == labels).sum().item()
|
||||
avg_loss = running_loss / len(train_loader)
|
||||
accuracy = 100 * correct / total
|
||||
print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {avg_loss:.4f}, Accuracy: {accuracy:.2f}%')
|
||||
|
||||
# 测试函数
|
||||
def test(model, test_loader):
|
||||
model.eval() # 设置为评估模式
|
||||
correct = 0
|
||||
total = 0
|
||||
with torch.no_grad(): # 禁用梯度计算
|
||||
for images, labels in test_loader:
|
||||
outputs = model(images)
|
||||
_, predicted = torch.max(outputs, 1)
|
||||
total += labels.size(0)
|
||||
correct += (predicted == labels).sum().item()
|
||||
accuracy = 100 * correct / total
|
||||
print(f'Test Accuracy: {accuracy:.2f}%')
|
||||
|
||||
# 训练和测试
|
||||
def train_and_test(model, train_loader, test_loader):
|
||||
criterion = nn.CrossEntropyLoss() # 交叉熵损失
|
||||
optimizer = optim.Adam(model.parameters(), lr=0.001)
|
||||
train(model, train_loader, criterion, optimizer, num_epochs=10)
|
||||
test(model, test_loader)
|
||||
|
||||
# 扁平化数据，并重建 DataLoader（用于全连接神经网络输入端的数据处理）
|
||||
def flatten_data(data_loader):
|
||||
images_array = []
|
||||
labels_array = []
|
||||
for images, labels in data_loader:
|
||||
images = torch.flatten(images, start_dim=1) # 除去batch维度后，其他维度展平
|
||||
images_array.append(images)
|
||||
labels_array.append(labels)
|
||||
images_array = torch.cat(images_array, dim=0)
|
||||
labels_array = torch.cat(labels_array, dim=0)
|
||||
dataset_new = TensorDataset(images_array, labels_array)
|
||||
loader_new = DataLoader(dataset_new, batch_size=64, shuffle=True)
|
||||
return loader_new
|
||||
|
||||
# 数据加载器
|
||||
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
|
||||
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
|
||||
|
||||
# 扁平化数据
|
||||
train_loader_new = flatten_data(train_loader)
|
||||
test_loader_new = flatten_data(test_loader)
|
||||
|
||||
# 安装软件包：pip install --upgrade guan
|
||||
import guan
|
||||
|
||||
hidden_size = 64
|
||||
|
||||
print('---全连接神经网络模型（包含一个隐藏层）---')
|
||||
model = guan.fully_connected_neural_network_with_one_hidden_layer(input_size=28*28, hidden_size=hidden_size, output_size=10, activation='relu')
|
||||
train_and_test(model, train_loader_new, test_loader_new)
|
||||
|
||||
print('---全连接神经网络模型（包含两个隐藏层）---')
|
||||
model = guan.fully_connected_neural_network_with_two_hidden_layers(input_size=28*28, hidden_size_1=hidden_size, hidden_size_2=hidden_size, output_size=10, activation_1='relu', activation_2='relu')
|
||||
train_and_test(model, train_loader_new, test_loader_new)
|
||||
|
||||
print('---全连接神经网络模型（包含三个隐藏层）---')
|
||||
model = guan.fully_connected_neural_network_with_three_hidden_layers(input_size=28*28, hidden_size_1=hidden_size, hidden_size_2=hidden_size, hidden_size_3=hidden_size, output_size=10, activation_1='relu', activation_2='relu', activation_3='relu')
|
||||
train_and_test(model, train_loader_new, test_loader_new)
|
||||
|
||||
print('---卷积神经网络模型（包含两个卷积层和两个全连接层）---')
|
||||
model = guan.convolutional_neural_network_with_two_convolutional_layers_and_two_fully_connected_layers(in_channels=1, out_channels_1=32, out_channels_2=64, kernel_size_1=3, kernel_size_2=3, stride_1=1, stride_2=1, padding_1=1, padding_2=1, pooling=1, pooling_kernel_size=2, pooling_stride=2, input_size=7*7*64, hidden_size_1=hidden_size, hidden_size_2=hidden_size, output_size=10)
|
||||
train_and_test(model, train_loader, test_loader)
|
||||
16
2024.12.11_ollama/ollama_with_python.py
Normal file
16
2024.12.11_ollama/ollama_with_python.py
Normal file
@@ -0,0 +1,16 @@
|
||||
# 直接输出
|
||||
import ollama
|
||||
response = ollama.chat(model="llama3.2:latest", messages=[{"role": "user","content": "你好"}], stream=False)
|
||||
print(response['message']['content'])
|
||||
|
||||
# 流式输出
|
||||
import ollama
|
||||
response = ollama.chat(model="llama3.2:latest", messages=[{"role": "user", "content": "你好"}], stream=True)
|
||||
for part in response:
|
||||
print(part['message']['content'], end='', flush=True)
|
||||
|
||||
# 流式输出，且模型后台常驻（需要手动 ollama stop 关闭）
|
||||
import ollama
|
||||
response = ollama.chat(model="llama3.2:latest", messages=[{"role": "user", "content": "你好"}], stream=True, keep_alive=-1)
|
||||
for part in response:
|
||||
print(part['message']['content'], end='', flush=True)
|
||||
Reference in New Issue
Block a user