一个脚本两步计算材料Raman谱(附数据处理和绘图脚本)

news/2024/7/10 18:55:51 标签: 服务器, ffmpeg, centos, linux, python

        在以往推送中已经介绍了相当多的计算材料Raman的方法,使用的软件主要为Phonopy-Spectroscopy,相关软件还有vasp,phonopy,phono3py等。

Phonopy-Spectroscopy计算材料红外和Raman光谱

Phonopy-Spectroscopy 计算红外和拉曼光谱

    也有一些教程介绍了无需额外软件即可得到材料Raman信息的脚本vasp_raman.py,但脚本缺少自由度并且需要额外的数据处理。

    本篇推送即介绍这一脚本计算材料Raman信息的流程。

    脚本和案例可在作者处下载:

https://github.com/ZeliangSu/VaspRoutine/blob/main/raman-sc/

在本文最后也会附上0.6.0版本的脚本

准备工作

计算可得到无虚频的声子谱的结构文件

这里使用单晶硅作案例

   Si1.0     5.4661639157319968    0.0000000000000000    0.0000000000000000     0.0000000000000000    5.4661639157319968    0.0000000000000000     0.0000000000000000    0.0000000000000000    5.4661639157319968     Si   8Direct  0.8750000000000000  0.8750000000000000  0.8750000000000000  0.8750000000000000  0.3750000000000000  0.3750000000000000  0.3750000000000000  0.8750000000000000  0.3750000000000000  0.3750000000000000  0.3750000000000000  0.8750000000000000  0.1250000000000000  0.1250000000000000  0.1250000000000000  0.1250000000000000  0.6250000000000000  0.6250000000000000  0.6250000000000000  0.1250000000000000  0.6250000000000000  0.6250000000000000  0.6250000000000000  0.1250000000000000

图片

这里已将声子谱反折叠为原胞第一布里渊区内的声子色散,并将纵坐标单位设置为(cm-1)

计算第一步:频率信息

计算INCAR参考:

SYSTEM = Si_bulkISTART = 0 # From-scratch; job : 0-new 1-cont 2-samecut NWRITE = 3 Verbosity
! electronic relaxationENCUT = 300.0 # cut-off energyPREC = Accurate # precision : accurate/normal/low ISPIN = 1 # 1 - off, 2 - on (non spin-polarized calculation)ICHARG = 2 # > 10 for non-SC calculationIALGO = 38 # DAVidson, then RMM-DIISEDIFF = 1.0E-8 # defaultISMEAR = 0 # gaussianSIGMA = 0.05
! PAW'sLREAL = .FALSE. # default - Automatic choice of how projection is doneADDGRID = .TRUE.
! phononsIBRION = 5POTIM = 0.01
! parallelisationLPLANE = .FALSE.KPAR=8
! outputLWAVE = .FALSE. # WAVECAR fileLCHARG = .FALSE. # CHCAR fileLELF = .FALSE.LVTOT = .FALSE.

    特别注意参数:NWRITE = 3 Verbosity

计算完成后可自行处理声子色散和相关数据。材料的频率信息在OUTCAR中可自行查看。

计算第二步:宏观介电张量

    将第一步计算结果中的POSCAR复制为POSCAR.phon,将OUTCAR复制为OUTCAR.phon。

第二步INCAR参考:

SYSTEM = Si_bulkISTART = 0 # From-scratch; job : 0-new 1-cont 2-samecut NWRITE = 3 Verbosity
! electronic relaxationENCUT = 300.0 # cut-off energyPREC = Accurate # precision : accurate/normal/low ISPIN = 1 # 1 - off, 2 - on (non spin-polarized calculation)ICHARG = 2 # > 10 for non-SC calculationIALGO = 38 # DAVidson, then RMM-DIISEDIFF = 1.0E-8 # defaultISMEAR = 0 # gaussianSIGMA = 0.05
! PAW'sLREAL = .FALSE. # default - Automatic choice of how projection is doneADDGRID = .TRUE.
! phonons!! IBRION = 5!! POTIM = 0.01
LEPSILON=.TRUE.
! parallelisationLPLANE = .FALSE.KPAR=8
! outputLWAVE = .FALSE. # WAVECAR fileLCHARG = .FALSE. # CHCAR fileLELF = .FALSE.LVTOT = .FALSE.

此时需要调用脚本vasp_raman.py 进行第二步计算命令为:

export VASP_RAMAN_RUN='aprun -B /u/afonari/vasp.5.3.2/vasp.5.3/vasp &> job.out'export VASP_RAMAN_PARAMS='01_21_2_0.01'
python vasp_raman.py > vasp_raman.out

    第一行中 VASP_RAMAN_RUN为服务器等计算资源可使用的vasp的执行命令

注意:在集群使用队列资源排队进行计算的时候需要将上面三行命令都填写进队列系统脚本中,并替代原有的执行命令,具体请根据实际计算情况更改

     第二行中VASP_RAMAN_PARAMS参数为Raman计算针对的频率范围和计算设置。

第一个数为起始频率编号,01为起始频率编号;

第二个数为截至频率编号,最大值为POSCAR总原子数×3。

计算总任务数为第二个数减去第一个数再乘第三个数。

计算过程中脚本会读取OUTCAR.phon中的频率信息针对不同频率的声子所对应的原子振动模式对结构施加微扰。

同时会实时将新生成的OUTCAR另存最后统一数据处理。

图片

计算结果保存在vasp_raman.dat

# mode    freq(cm-1)    alpha    beta2    activity001   517.72301  -0.0219904  520.9354925  3646.5702084002   517.71996   0.0177803  521.9308522  3653.5301918003   517.71816  -0.0130798  521.3205054  3649.2512364004   446.13270  -0.0022072   0.0007120   0.0052035005   446.12649  -0.0026568   0.0001072   0.0010683006   446.11229  -0.0091150   0.0020607   0.0181638007   446.10094   0.0056815   0.0001899   0.0027820008   446.09466   0.0108317   0.0016816   0.0170508009   446.08791   0.0082157   0.0009541   0.0097161010   397.45270  -0.0004496   0.0001839   0.0012966011   397.45083  -0.0050684   0.0004061   0.0039986012   397.44702   0.0011036   0.0020508   0.0144103013   397.44649  -0.0029021   0.0011642   0.0085283014   397.44295  -0.0007766   0.0000371   0.0002871015   397.44138   0.0047005   0.0005162   0.0046073016   130.93534  -0.0010219   0.0000026   0.0000654017   130.92851   0.0026977   0.0000319   0.0005511018   130.92701  -0.0001635   0.0000030   0.0000221019   130.92623  -0.0006949   0.0000040   0.0000499020   130.92383  -0.0003270   0.0000030   0.0000261021   130.92351   0.0000817   0.0000035   0.0000247

    可见Raman活性频率为517cm-1,与Si实验值520cm-1相当接近 (J.H. Parker, et al., Phys Rev, 155, 712 (1967))。

    如果吹毛求疵,可以在计算频率或声子时便通过高精度结构优化和使用实验值的晶格常数等方法将所得对应的频率和实验值矫正。

    使用脚本将已经得到Raman信息处理(可自行选择拟合方式Gaussian或Lorentzian)

 python broaden.py vasp_raman.dat

图片

附:

vasp_raman.py脚本

https://github.com/ZeliangSu/VaspRoutine/blob/main/raman-sc/vasp_raman.py

#!/usr/bin/env python## vasp_raman.py v. 0.6.0## Raman off-resonant activity calculator# using VASP as a back-end.## Contributors: Alexandr Fonari (Georgia Tech)# Shannon Stauffer (UT Austin)## URL: http://raman-sc.github.io## MIT license, 2013 - 2016#

import reimport sys

def MAT_m_VEC(m, v):    p = [ 0.0 for i in range(len(v)) ]    for i in range(len(m)):        assert len(v) == len(m[i]), 'Length of the matrix row is not equal to the length of the vector'        p[i] = sum( [ m[i][j]*v[j] for j in range(len(v)) ] )    return p

def T(m):    p = [[ m[i][j] for i in range(len( m[j] )) ] for j in range(len( m )) ]    return p

def parse_poscar(poscar_fh):    # modified subroutine from phonopy 1.8.3 (New BSD license)    #    poscar_fh.seek(0) # just in case    lines = poscar_fh.readlines()    #    print(lines)    scale = float(lines[1])    if scale < 0.0:        print("[parse_poscar]: ERROR negative scale not implemented.")        sys.exit(1)    #    b = []    for i in range(2, 5):        b.append([float(x)*scale for x in lines[i].split()[:3]])    #    vol = b[0][0]*b[1][1]*b[2][2] + b[1][0]*b[2][1]*b[0][2] + b[2][0]*b[0][1]*b[1][2] - \          b[0][2]*b[1][1]*b[2][0] - b[2][1]*b[1][2]*b[0][0] - b[2][2]*b[0][1]*b[1][0]    #    try:        num_atoms = [int(x) for x in lines[5].split()]        line_at = 6    except ValueError:        symbols = [x for x in lines[5].split()]        num_atoms = [int(x) for x in lines[6].split()]        line_at = 7    nat = sum(num_atoms)    #    if lines[line_at][0].lower() == 's':        line_at += 1    #    if (lines[line_at][0].lower() == 'c' or lines[line_at][0].lower() == 'k'):        is_scaled = False    else:        is_scaled = True    #    line_at += 1    #    positions = []    for i in range(line_at, line_at + nat):        pos = [float(x) for x in lines[i].split()[:3]]        #        if is_scaled:            pos = MAT_m_VEC(T(b), pos)        #        positions.append(pos)    #    poscar_header = ''.join(lines[1:line_at-1]) # will add title and 'Cartesian' later    return nat, vol, b, positions, poscar_header

def parse_env_params(params):    tmp = params.strip().split('_')    if len(tmp) != 4:        print("[parse_env_params]: ERROR there should be exactly four parameters")        sys.exit(1)    #    [first, last, nderiv, step_size] = [int(tmp[0]), int(tmp[1]), int(tmp[2]), float(tmp[3])]    #    return first, last, nderiv, step_size

#### subs for the output from VTST toolsdef parse_freqdat(freqdat_fh, nat):    freqdat_fh.seek(0) # just in case    #    eigvals = [ 0.0 for i in range(nat*3) ]    #    for i in range(nat*3): # all frequencies should be supplied, regardless of requested to calculate        tmp = freqdat_fh.readline().split()        eigvals[i] = float(tmp[0])    #    return eigvals#def parse_modesdat(modesdat_fh, nat):    from math import sqrt    modesdat_fh.seek(0) # just in case    #    eigvecs = [ 0.0 for i in range(nat*3) ]    norms =   [ 0.0 for i in range(nat*3) ]    #    for i in range(nat*3): # all frequencies should be supplied, regardless of requested to calculate        eigvec = []        for j in range(nat):            tmp = modesdat_fh.readline().split()            eigvec.append([ float(tmp[x]) for x in range(3) ])        #        modesdat_fh.readline().split() # empty line        eigvecs[i] = eigvec        norms[i] = sqrt( sum( [abs(x)**2 for sublist in eigvec for x in sublist] ) )    #    return eigvecs, norms#### end subs for VTST#def get_modes_from_OUTCAR(outcar_fh, nat):    from math import sqrt    eigvals = [ 0.0 for i in range(nat*3) ]    eigvecs = [ 0.0 for i in range(nat*3) ]    norms   = [ 0.0 for i in range(nat*3) ]    #    outcar_fh.seek(0) # just in case    while True:        line = outcar_fh.readline()        if not line:            break        #        if "Eigenvectors after division by SQRT(mass)" in line:            outcar_fh.readline() # empty line            outcar_fh.readline() # Eigenvectors and eigenvalues of the dynamical matrix            outcar_fh.readline() # ----------------------------------------------------            outcar_fh.readline() # empty line            #            for i in range(nat*3): # all frequencies should be supplied, regardless of those requested to calculate                outcar_fh.readline() # empty line                p = re.search(r'^\s*(\d+).+?([\.\d]+) cm-1', outcar_fh.readline())                eigvals[i] = float(p.group(2))                #                outcar_fh.readline() # X         Y         Z           dx          dy          dz                eigvec = []                #                for j in range(nat):                    tmp = outcar_fh.readline().split()                    #                    eigvec.append([ float(tmp[x]) for x in range(3,6) ])                    #                eigvecs[i] = eigvec                norms[i] = sqrt( sum( [abs(x)**2 for sublist in eigvec for x in sublist] ) )            #            return eigvals, eigvecs, norms        #    print("[get_modes_from_OUTCAR]: ERROR Couldn't find 'Eigenvectors after division by SQRT(mass)' in OUTCAR. Use 'NWRITE=3' in INCAR. Exiting...")    sys.exit(1)#def get_epsilon_from_OUTCAR(outcar_fh):    epsilon = []    #    outcar_fh.seek(0) # just in case    while True:        line = outcar_fh.readline()        if not line:            break        #        if "MACROSCOPIC STATIC DIELECTRIC TENSOR" in line:            try:                outcar_fh.readline()                epsilon.append([float(x) for x in outcar_fh.readline().split()])                epsilon.append([float(x) for x in outcar_fh.readline().split()])                epsilon.append([float(x) for x in outcar_fh.readline().split()])            except:                from lxml import etree as ET                doc = ET.parse('vasprun.xml')                epsilon = [[float(x) for x in c.text.split()] for c in doc.xpath("/modeling/calculation/varray")[3].getchildren()]            return epsilon    #    raise RuntimeError("[get_epsilon_from_OUTCAR]: ERROR Couldn't find dielectric tensor in OUTCAR")    return 1#if __name__ == '__main__':    from math import pi    from shutil import move    import os    import datetime    import time    #import argparse    import optparse    #    print("")    print("    Raman off-resonant activity calculator,")    print("    using VASP as a back-end.")    print("")    print("    Contributors: Alexandr Fonari  (Georgia Tech)")    print("                  Shannon Stauffer (UT Austin)")    print("    MIT License, 2013")    print("    URL: http://raman-sc.github.io")    print("    Started at: "+datetime.datetime.now().strftime("%Y-%m-%d %H:%M"))    print("")    #    description  = "Before run, set environment variables:\n"    description += "    VASP_RAMAN_RUN='mpirun vasp'\n"    description += "    VASP_RAMAN_PARAMS='[first-mode]_[last-mode]_[nderiv]_[step-size]'\n\n"    description += "bash one-liner is:\n"    description += "VASP_RAMAN_RUN='mpirun vasp' VASP_RAMAN_PARAMS='1_2_2_0.01' python vasp_raman.py"    #    parser = optparse.OptionParser(description=description)    parser.add_option('-g', '--gen', help='Generate POSCAR only', action='store_true')    parser.add_option('-u', '--use_poscar', help='Use provided POSCAR in the folder, USE WITH CAUTION!!', action='store_true')    (options, args) = parser.parse_args()    #args = vars(parser.parse_args())    args = vars(options)    #    VASP_RAMAN_RUN = os.environ.get('VASP_RAMAN_RUN')    if VASP_RAMAN_RUN == None:        print("[__main__]: ERROR Set environment variable 'VASP_RAMAN_RUN'")        print("")        parser.print_help()        sys.exit(1)    print("[__main__]: VASP_RAMAN_RUN='"+VASP_RAMAN_RUN+"'")    #    VASP_RAMAN_PARAMS = os.environ.get('VASP_RAMAN_PARAMS')    if VASP_RAMAN_PARAMS == None:        print("[__main__]: ERROR Set environment variable 'VASP_RAMAN_PARAMS'")        print("")        parser.print_help()        sys.exit(1)    print("[__main__]: VASP_RAMAN_PARAMS='"+VASP_RAMAN_PARAMS+"'")    #    first, last, nderiv, step_size = parse_env_params(VASP_RAMAN_PARAMS)    assert first >= 1,    '[__main__]: First mode should be equal or larger than 1'    assert last >= first, '[__main__]: Last mode should be equal or larger than first mode'    if args['gen']: assert last == first, "[__main__]: '-gen' mode -> only generation for the one mode makes sense"    assert nderiv == 2,   '[__main__]: At this time, nderiv = 2 is the only supported'    disps = [-1, 1]      # hardcoded for    coeffs = [-0.5, 0.5] # three point stencil (nderiv=2)    #    try:        poscar_fh = open('POSCAR.phon', 'r')    except IOError:        print("[__main__]: ERROR Couldn't open input file POSCAR.phon, exiting...\n")        sys.exit(1)    #    # nat, vol, b, poscar_header = parse_poscar_header(poscar_fh)    nat, vol, b, pos, poscar_header = parse_poscar(poscar_fh)    print(pos)    #print poscar_header    #sys.exit(0)    #    # either use modes from vtst tools or VASP    if os.path.isfile('freq.dat') and os.path.isfile('modes_sqrt_amu.dat'):        try:            freqdat_fh = open('freq.dat', 'r')        except IOError:            print("[__main__]: ERROR Couldn't open freq.dat, exiting...\n")            sys.exit(1)        #        eigvals = parse_freqdat(freqdat_fh, nat)        freqdat_fh.close()        #        try:             modes_fh = open('modes_sqrt_amu.dat' , 'r')        except IOError:            print("[__main__]: ERROR Couldn't open modes_sqrt_amu.dat, exiting...\n")            sys.exit(1)        #        eigvecs, norms = parse_modesdat(modes_fh, nat)        modes_fh.close()    #    elif os.path.isfile('OUTCAR.phon'):        try:            outcar_fh = open('OUTCAR.phon', 'r')        except IOError:            print("[__main__]: ERROR Couldn't open OUTCAR.phon, exiting...\n")            sys.exit(1)        #        eigvals, eigvecs, norms = get_modes_from_OUTCAR(outcar_fh, nat)        outcar_fh.close()    #    else:        print("[__main__]: Neither OUTCAR.phon nor freq.dat/modes_sqrt_amu.dat were found, nothing to do, exiting...")        sys.exit(1)    #    output_fh = open('vasp_raman.dat', 'w')    output_fh.write("# mode    freq(cm-1)    alpha    beta2    activity\n")    for i in range(first-1, last):        eigval = eigvals[i]        eigvec = eigvecs[i]        norm = norms[i]        #        print("")        print("[__main__]: Mode #%i: frequency %10.7f cm-1; norm: %10.7f" % ( i+1, eigval, norm ))        #        ra = [[0.0 for x in range(3)] for y in range(3)]        for j in range(len(disps)):            disp_filename = 'OUTCAR.%04d.%+d.out' % (i+1, disps[j])            #            try:                outcar_fh = open(disp_filename, 'r')                print("[__main__]: File "+disp_filename+" exists, parsing...")            except IOError:                if args['use_poscar'] != True:                    print("[__main__]: File "+disp_filename+" not found, preparing displaced POSCAR")                    poscar_fh = open('POSCAR', 'w')                    poscar_fh.write("%s %4.1e \n" % (disp_filename, step_size))                    poscar_fh.write(poscar_header)                    poscar_fh.write("Cartesian\n")                    #                    for k in range(nat):                        pos_disp = [ pos[k][l] + eigvec[k][l]*step_size*disps[j]/norm for l in range(3)]                        poscar_fh.write( '%15.10f %15.10f %15.10f\n' % (pos_disp[0], pos_disp[1], pos_disp[2]) )                        #print '%10.6f %10.6f %10.6f %10.6f %10.6f %10.6f' % (pos[k][0], pos[k][1], pos[k][2], dis[k][0], dis[k][1], dis[k][2])                    poscar_fh.close()                else:                    print("[__main__]: Using provided POSCAR")                #                if args['gen']: # only generate POSCARs                    poscar_fn = 'POSCAR.%+d.out' % disps[j]                    move('POSCAR', poscar_fn)                    print("[__main__]: '-gen' mode -> "+poscar_fn+" with displaced atoms have been generated")                    #                    if j+1 == len(disps): # last iteration for the current displacements list                        print("[__main__]: '-gen' mode -> POSCAR files with displaced atoms have been generated, exiting now")                        sys.exit(0)                else: # run VASP here                    print("[__main__]: Running VASP...")                    os.system(VASP_RAMAN_RUN)                    try:                        move('OUTCAR', disp_filename)                    except IOError:                        print("[__main__]: ERROR Couldn't find OUTCAR file, exiting...")                        sys.exit(1)                    #                    outcar_fh = open(disp_filename, 'r')            #            try:                eps = get_epsilon_from_OUTCAR(outcar_fh)                outcar_fh.close()            except Exception as err:                print(err)                print("[__main__]: Moving "+disp_filename+" back to 'OUTCAR' and exiting...")                move(disp_filename, 'OUTCAR')                sys.exit(1)            #            for m in range(3):                for n in range(3):                    ra[m][n]   += eps[m][n] * coeffs[j]/step_size * norm * vol/(4.0*pi)            #units: A^2/amu^1/2 =         dimless   * 1/A         * 1/amu^1/2  * A^3        #        alpha = (ra[0][0] + ra[1][1] + ra[2][2])/3.0        beta2 = ( (ra[0][0] - ra[1][1])**2 + (ra[0][0] - ra[2][2])**2 + (ra[1][1] - ra[2][2])**2 + 6.0 * (ra[0][1]**2 + ra[0][2]**2 + ra[1][2]**2) )/2.0        print("")        print("! %4i  freq: %10.5f  alpha: %10.7f  beta2: %10.7f  activity: %10.7f " % (i+1, eigval, alpha, beta2, 45.0*alpha**2 + 7.0*beta2))        output_fh.write("%03i  %10.5f  %10.7f  %10.7f  %10.7f\n" % (i+1, eigval, alpha, beta2, 45.0*alpha**2 + 7.0*beta2))        output_fh.flush()    #    output_fh.close()

绘图脚本:

https://github.com/ZeliangSu/VaspRoutine/blob/main/raman-sc/broaden.py(有修改)

#!/usr/bin/env python#def to_plot(hw,ab,gam=0.001,type='lorentzian'):    import numpy as np    #    fmin = min(hw)    fmax = max(hw)    erange = np.arange(fmin-40*gam,fmax+40*gam,gam/10)#np.arange(fmin-40*gam,fmax+40*gam,gam/10)    spectrum = 0.0*erange    for i in range(len(hw)):        if type=='Gaussian':            spectrum += (2*np.pi)**(-.5)/gam*np.exp(np.clip(-1.0*(hw[i]-erange)**2/(2*gam**2),-300,300))        elif type=='Lorentzian':            spectrum += ab[i]*1/np.pi*gam/((hw[i]-erange)**2+gam**2)    #    return erange, spectrum#if __name__ == '__main__':    import numpy as np    import sys    #    hw=np.genfromtxt(sys.argv[1], dtype=float)    cm1 = hw[:,1]    int1 = hw[:,4]    int1 /= np.max(np.abs(int1),axis=0)    Es1,Spectrum1 = to_plot(cm1, int1, 15.0, 'Lorentzian')#    filename = "new-broaden.dat"    print( "Writing", filename)    f = open(filename,'w')    f.write('# freq/cm-1  Intensity \n')
    for i in range(len(Es1)):        f.write('%.5e   %.5e\n' % (Es1[i],Spectrum1[i]))    f.close()    import matplotlib.pyplot as plt      import numpy as np    
    data = np.loadtxt("new-broaden.dat", unpack=True)    
plt.plot(data[0], data[1])     plt.show()


http://www.niftyadmin.cn/n/5403166.html

相关文章

道可云元宇宙每日资讯|厦门首个元宇宙办税大厅启用

道可云元宇宙每日简报&#xff08;2024年3月1日&#xff09;讯&#xff0c;今日元宇宙新鲜事有&#xff1a; 中国军号元宇宙发布会即将举行 近日&#xff0c;解放军新闻传播中心中国军号即将正式上线。中国军号元宇宙发布会也将在“云端”与您见面。全方位展现解放军新闻传播…

深入探讨Java集合框架

Java集合框架是Java编程语言中最常用的一部分。无论是在数据存储&#xff0c;还是数据操作过程中&#xff0c;Java集合都担任重要的角色。它提供了一些预定义的类和接口&#xff0c;让我们处理一组对象更加容易。 1. Java集合框架概述 Java的集合框架主要包括两种类型的容器&…

【5G 接口协议】GTP-U协议介绍

博主未授权任何人或组织机构转载博主任何原创文章&#xff0c;感谢各位对原创的支持&#xff01; 博主链接 本人就职于国际知名终端厂商&#xff0c;负责modem芯片研发。 在5G早期负责终端数据业务层、核心网相关的开发工作&#xff0c;目前牵头6G算力网络技术标准研究。 博客…

非专业程序员常用vscode插件

牙叔教程 简单易懂 我常用的脚本语言是js, python. AutoHotkey v2 Language Support vscode-autohotkey-debug 由于工作有写重复, 要用到autohotkey, 所以装这个插件 Black Formatter 格式化python代码 Bookmarks 书签 change-case 命名方式: 小驼峰, 下划线, 等命名风格转…

QT实现机器视觉图片查看窗口

QT实现机器视觉常用图像查看器 在机器视觉行业中最常见的控件就是图像查看器了&#xff0c;使用QT实现其实也非常简单&#xff0c;在我出的项目【降龙&#xff1a;算法软件框架】和【重明&#xff1a;工业相机二次开发】中都有用到。可以说只要你要开发一个和机器视觉相关的软…

Head First Design Patterns - 单例模式

什么是单例模式 单例模式确保应用中&#xff0c;一个类最多只有一个实例单例模式也提供访问此实例的全局访问点单例模式实现中&#xff0c;使用了一个私有构造器&#xff0c;一个静态方法以及一个静态变量 为什么要用单例模式 java中静态全局变量也能做到被实例化一次 但是…

2023年NOC大赛软件创意编程(学而思)赛道图形化小低高组决赛试题

2023NOC 决赛-小低组 每个小朋友从小会有梦寐以求的愿望,或是拥有孙悟空七十二变的能 力,或是像神笔马良一样想要什么画什么,或是成为人见人爱的国宝 大熊猫,或是变身全能机器人……今天,你可以用编程实现自己愿望! 【题目要求】 请以“假如我是_____”为题,自选主…

【Python】进阶学习:pandas--如何根据指定条件筛选数据

【Python】进阶学习&#xff1a;pandas–如何根据指定条件筛选数据 &#x1f308; 个人主页&#xff1a;高斯小哥 &#x1f525; 高质量专栏&#xff1a;Matplotlib之旅&#xff1a;零基础精通数据可视化、Python基础【高质量合集】、PyTorch零基础入门教程&#x1f448; 希望…