diff --git a/bash/DEopt b/bash/DEopt new file mode 100644 index 0000000..d0aab66 --- /dev/null +++ b/bash/DEopt @@ -0,0 +1,450 @@ +#!/bin/bash +# this script runs Differential Evolution (DE) Global optimization algorithm for Quantum Espresso parameters +# as an example the three parameters are used: ecut, alat and kpt +# pseudo potentials are implied to be in the same directory as this script +# QE outdir is defined to be "./tmp" + +# bash part +# creating a template input file +TEMPLATE=Si.scf_template.in +PLACEHOLDER1=@ecutwfc +PLACEHOLDER2=@celldm +PLACEHOLDER3=@kpt +QEOUTDIR_PLACEHOLDER=@outdir +QEPSEUDO_PLACEHOLDER=@pseudo_dir +OUTDIR="" # working directory will be the output directory for the QE +PSEUDO_DIR="" # set pseudo potential directory to the working directory +QEOUTPUT="" # the output is supressed; can be explicitly specified, say Si.scf_template.out, for issues tracking purposes + +WORKDIR="." # set the working directory +PYTHONSCRIPT=DEopt.py + +cd $WORKDIR # change directory to the working directory, if running this script from different directory + +# set parameter search space +PAR1_MIN=30 # ecutwfc +PAR1_MAX=70 +PAR2_MIN=8 # celldm +PAR2_MAX=12 +PAR3_MIN=4 # kpt +PAR3_MAX=14 + + +# DE parameters +POPSIZE=4 # a number of the parameter space vectors in the generation; can't be less than 4 +MUTATION=0.5 # a number F > 0; F in [0,2] +CROSSOVER=0.7 # a crossover constant that is in [0,1] +DE_MAXITER=200 # a maximum number of iterations allowed for DE algorithm +DE_TOL=1e-6 # convergence threshold set for the systems total energy in DE algorithm +INT_PARAM_NUM=1 # number of the integer parameters: it sets that the third parameter, kpt, is integer, i.e. even when it is not then only integer part is used + + +cat > $TEMPLATE << EOF +&control + calculation = 'scf' + restart_mode = 'from_scratch' + prefix = 'Si' + pseudo_dir = $QEPSEUDO_PLACEHOLDER + outdir = $QEOUTDIR_PLACEHOLDER + verbosity = 'high' + wf_collect = .true. +/ +&system + ibrav = 2 + celldm(1) = $PLACEHOLDER2 + nat = 2 + ntyp = 1 + !nosym = .false. + ecutwfc = $PLACEHOLDER1 + nbnd = 10 +/ +&electrons + diago_thr_init=1.0e-3, + electron_maxstep = 60, + diagonalization='david' + mixing_mode = 'plain' + mixing_beta = 0.7 + conv_thr = 1.0d-8, + !startingpot='file', + !startingwfc='atomic', +/ +ATOMIC_SPECIES +Si 28.086 Si.pz-vbc.UPF +ATOMIC_POSITIONS (alat) +Si 0.000000000 0.000000000 0.000000000 +Si 0.250000000 0.250000000 0.250000000 +K_POINTS {automatic} +$PLACEHOLDER3 $PLACEHOLDER3 $PLACEHOLDER3 1 1 1 +EOF + + + +# python part +cat > $PYTHONSCRIPT << EOF +#python script + +import os +import subprocess +import re +from os.path import dirname, abspath + +# QE run with an arbitrary number of the parameters +def QEGenrun(params, + int_param_num = 1, + place_holders = ["@ecutwfc", "@celldm", "@kpt"], + pwx_path = "pw.x", + input_file = "template-scf.in", # must contain two compalsory place holders "@outdir" and "@pseudo_dir" that need not to be specified in the place_holders kwarg. + output_file = "template-scf.out", + working_dir = ".", # working directory is a more descriptive name for this option + outdir = "", + pseudo_dir = ""): + """ + This function runs Quantum Espresso with a given template file as a input. + The given parameter values are substituted into a + template instead of the placeholders starting from '@', e.g. @ecutwfc, + @celldm and etc. Then the function returns the used parameter values together + with a total energy of the system. + + This funciton is to be used as the cost function in the differential evolution + optimization of the parameters of interest. + + """ + plen = len(params) + + if plen != len(place_holders): + raise Exception("The number of optimization parameters must be equal to \ + the number of corresponding place holders. Check place_holders kwarg.") + if int_param_num > plen : + raise Exception("The number of integer parameters cannot exceed the dimensionality \ + of parameter space. Check int_param_num kwarg and the length of the first arg.") + + # set the path to QE installation + if pwx_path == "": + cmd = subprocess.run(['which', 'pw.x'], + stdout=subprocess.PIPE, + universal_newlines=True) + path = cmd.stdout.strip() + + if path == "": + raise Exception("Failed to detect path to pw.x file. Install Quantum Espresso or provide path manually using pwx_path kwarg.") + else: + pwx_path = path + + if (working_dir == "" or working_dir == "." or working_dir == "./"): + working_dir = dirname(abspath(__file__)) + + if pseudo_dir == "": + pseudo_dir = '"' + working_dir + '"' + + if outdir == "": + outdir = '"' + os.path.join(working_dir,"tmp") + '"' + + st = open(os.path.join(working_dir,input_file),'r') + IN = st.read() # this list still contains special characters '\n' + st.close() + + plc_holders = place_holders + ["$QEOUTDIR_PLACEHOLDER", "$QEPSEUDO_PLACEHOLDER"] + paramstr = [] + for i in range(plen): + if i < plen-int_param_num: + paramstr.append(str(params[i])) + else: + paramstr.append(str(int(params[i]))) + + paramstr.append(outdir) + paramstr.append(pseudo_dir) + + for i in range(len(paramstr)): + IN = re.sub(plc_holders[i],paramstr[i],IN) + + # print input file for the testing purposes + # print(IN) + + pwx = subprocess.Popen([pwx_path], + stdin =subprocess.PIPE, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + universal_newlines=True, + bufsize=0) + + # Send qe commands to stdin + pwx.stdin.write(IN) + pwx.stdin.close() + + + # in what follow two option are possible either to save output or avoid this + e_tot = ""; + if output_file != "": + # the output file is produced and saved in the outdir, for testing purposes only + # avoid files overwriting up to 100 runs + outfname = output_file + for i in range(100): + if os.path.isfile(os.path.join(working_dir,outfname)): + outfname = output_file + '_' + str(i) + else: + break + + + f = open(os.path.join(working_dir,outfname),'a') + # this list still contains special characters '\n' + for line in pwx.stdout: + f.write(line) + if line.find("!") != -1: + e_tot = float(re.split(r"\s+",line.strip())[-2]) + + f.close() + else: + # the output file is not produced, should be faster + # this list still contains special characters '\n' + for line in pwx.stdout: + if line.find("!") != -1: + e_tot = float(re.split(r"\s+",line.strip())[-2]) + + if e_tot == "": + raise Exception("Total energy was not found in the Quantum Espresso output, probably due to the lack of self-consistent run convergence or a crash. Check the given script parent directory for the Quantum Espresso CRASH file.") + + return params, e_tot + +# 2023-06-16 +# DE optimization function for an arbitrary number of QE parameters; +# one, few or all parametes can be chosen to be integer, counting from the end of +# the parameter space vector component + +import numpy as np +import time +from datetime import datetime +import matplotlib.pyplot as plt + +def QEDE(param_space, + popsize = 4, # population size cannot be less than 4 + mutation = 0.5, # mutation parameter chosen from [0,2] interval + crossover = 0.7, # crossover probability chosen from [0,1] interval + max_iter = 30, # maximum number of iterations to break the loop in case convergence is too slow + DE_tol = 1e-6, # convergence threshold + int_param_num = 1, # number of the integer parameters + place_holders = ["@ecutwfc", "@celldm", "@kpt"], # place holders used in the QE input file template instead of the parametes of interest + pwx_path = "", # path to QE pw.x is found automatically + input_file = "template-scf.in", # QE input file template; it must contain two compalsory place holders "@outdir" and "@pseudo_dir" that need not to be specified in the place_holders kwarg. + output_file = "", # QE output file; can be used for tracking errors in QE runs + working_dir = ".", # working directory is a more descriptive name for this option + outdir = "", # QE output directory + pseudo_dir = ""): # directory with QE pseudo potentials + """ + This function runs Quantum Espresso within Differential Evolution (DE) Global + optimization algorithm. + The DE algorithm is searching for the Global minimum of the total energy of the + system within the parameter space region specified as param_space. + The parameter space vectors are substituted into a + template input file instead of the placeholders that by convention start + from '@', e.g. @ecutwfc, @celldm and etc. The Quantum Espresso is run on this + input file and the total energy is returned. This cycle is repeated within + DE algorithm until maximum number of iterations, max_iter, is achieved or the difference + between total energies of all parameter space vectors in the given generation + becomes lower than the given tolerance, DE_tol. + The function returns an array of the parameter space vectors and their corresponding total + energies for each generation as well as same quantities for last generation. The former + allows one to track the convergence of the DE, while the later to choose between the best found parameters. + + This funciton is to be used for finding optimal parameters of interest. + + """ + start_time = time.time() + + if (working_dir == "" or working_dir == "." or working_dir == "./"): + working_dir = dirname(abspath(__file__)) + + + # Differential Evolution optimization + D = len(param_space) # number optimized parameters, i.e. the dimensionality of parameter space + + # random sampling of the parameters within the zeroth generation + X = np.asarray([(a[1] - a[0]) * np.random.random(popsize) + a[0] for a in param_space]) + # test initial parameter space vectors + print("Initial parameter space vectors (in columns):") + print(X) + + # initializing costs for every parameter space vector in the 0-th generation + costs = np.zeros(popsize) + for i in range(popsize): + params = X[:,i] + costs[i] = QEGenrun(params, + int_param_num, + place_holders, + pwx_path, + input_file, + output_file, + working_dir, + outdir, + pseudo_dir)[1] + + print("Total energies for the initial generation of the parameter space vectors:") + print(costs) + best_idx = np.argmin(costs) + best_cost = costs[best_idx] + best_vector = X[:,best_idx] + + # iterating generation untill the convergence is achieved + # initializing a trial vector and a range of indexes numbering vectors in generation + trial_vector = np.zeros(D) + gen_range = np.arange(popsize) + + # introducing variable to collect the parameter space vectors and total energies thoughout all the iterations + data = np.zeros((max_iter*popsize,D+1)) + + # convergence loop + # set est and k parameters to enter to loop + est = DE_tol + k = 0 + + while (DE_tol <= est) and (k < max_iter): + kp = k*popsize; # alias for k*popsize within the while loop + # iterating through of the generation of parameter vectors choosing each vector to be once a target vector + for i in range(popsize): + # Choose r1, r2, r3 randomly so that they are different from each other and the running index i + r1, r2, r3 = np.random.permutation(np.delete(gen_range,i))[0:3] + + # trial vector is the mixture of the mutant_vector and the target_vector + rnbr = np.random.randint(D) # for the given vector, the random integer between 0 and D - 1 + for j in range(D): + if (np.random.random() > crossover) and (j != rnbr): + # Crossover with a target vector + trial_vector[j] = X[j,i] + else: + # Mutation between three other vectors that are chosen randomly + mutant = X[j, r1] + mutation * (X[j, r2] - X[j, r3]) + # corrections of the parameter value, it must stay within parameter limits + if mutant < param_space[j, 0]: + trial_vector[j] = param_space[j, 0] + elif mutant > param_space[j, 1]: + trial_vector[j] = param_space[j, 1] + else: + trial_vector[j] = mutant + + + print("trial vector = " + str(trial_vector)) + + # Selection + # If the trial vector minimizes the cost function compared to the target vectors than it replaces + # the target vector in the next generation + data[kp+i,:D], data[kp+i,D] = QEGenrun(trial_vector, + int_param_num, + place_holders, + pwx_path, + input_file, + output_file, + working_dir, + outdir, + pseudo_dir) + trial_cost = data[kp+i,D] + + if trial_cost < costs[i]: + X[:,i] = trial_vector + costs[i] = trial_cost + if trial_cost < best_cost: + best_cost = trial_cost + best_vector = trial_vector + else: + data[kp+i,:D] = X[:,i] + data[kp+i,D] = costs[i] + + print(costs) + + k += 1 + print("iteration No. " + str(k)) + # estimating convergence + est = 0 + for i in range(popsize-1): + est += np.abs(costs[0] - costs[i+1]) + print("Total energies spread = " + str(est)) + # clean data array from zeros, outside the while loop + data = data[:k*popsize] + # print the time spent on this search + print("--- %s seconds ---" % (time.time() - start_time)) + + # writing last generation of parameter space vectors into an output file + # this piece of code must be tested 19/06/2023 + date = datetime.now().strftime('%Y-%m-%d_%H_%M_%S') + fname = "QEDE_" + date + ".out" + # writing data to the text file + f = open(os.path.join(working_dir,fname),'w') + f.write('Created by Vasil Saroka 40.ovasil@gmail.com') + # using string literals + f.write('\nToday is ' + date + '\n') + f.writelines('\nDifferential Evolution results:\n') + f.write('\nThe last generation of parameter space vectors:') + + for i in range(popsize): + f.write('\nparameter vector ' + str(i+1) + '\n') + for j in range(D): + f.write('%5.9f\n' % X[j,i]) + f.write('\nThe total energy for vector ' + str(i+1) + ':\n') + f.write('%5.9f\n' % costs[i]) + + f.write('\nThe best vector is\n') + for j in best_vector: + f.write('%5.9f\n' % j) + f.write('\nThe total energy for the best vector:\n') + f.write('%5.9f\n' % best_cost) + if k == max_iter : + f.write("\nThe maximum number of iterations has been achieved. Try to increase this number.\n") + f.write('\nDE is done!\n') + f.close() + + # track convergence of the parameter space vectors during the DE evolution + # create figure object + fig, ax = plt.subplots() + ax.set_xlim(0,k) + ax.set_ylim(min(data[:,3]),max(data[:,3])) + ax.set_xlabel("DE iteration") + ax.set_ylabel(r"\$E_{tot}\$, Ry") + + for i in range(popsize): + v = data[i:-1:popsize,3] + ax.plot(v, alpha=0.9) + + fname ="QEDE_" + date + ".png" + fig.savefig(os.path.join(working_dir,fname),dpi=200,bbox_inches='tight',transparent=False) + + return data, k, X, costs, best_vector, best_cost + +# run DE optimization on the template file created above; review below + + +param_limits = np.array([[$PAR1_MIN, $PAR1_MAX], [$PAR2_MIN, $PAR2_MAX], [$PAR3_MIN, $PAR3_MAX]]) + +# QE run options +placeholders = ["$PLACEHOLDER1", "$PLACEHOLDER2", "$PLACEHOLDER3"] +QEpath = "" # define the path to pw.x automatically +IN = "$TEMPLATE" +OUT = "$QEOUTPUT" # no output file shall be generated to save the space on disk +wk_dir = "$WORKDIR" # +out_dir = "$OUTDIR" # QE outputs for wave function and charge density go to this directory +pp_dir = "$PSEUDO_DIR" # directory with pseudo potentials + +QEDE(param_limits, + popsize = $POPSIZE, # population size cannot be less than 4 + mutation = $MUTATION, # mutation parameter chosen from [0,2] interval + crossover = $CROSSOVER, # crossover probability chosen from [0,1] interval + max_iter = $DE_MAXITER, # maximum number of iterations to break the loop in case convergence is too slow + DE_tol = $DE_TOL, # convergence threshold + int_param_num = $INT_PARAM_NUM, + place_holders = placeholders, + pwx_path = QEpath, + input_file = IN, + output_file = OUT, + working_dir = wk_dir, + outdir = out_dir, + pseudo_dir = pp_dir) +EOF + +#Run python script created above to find optimal parameters +python + +Generated by new atomic code, or converted to UPF format +Author: +Generation date: +Pseudopotential type: NC +Element: Si +Functional: SLA PZ NOGX NOGC +Suggested minimum cutoff for wavefunctions: 0. Ry +Suggested minimum cutoff for charge density: 0. 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+""" +import os +import subprocess +import re +from os.path import dirname, abspath + +# QE run with an arbitrary number of the parameters +def QEGenrun(params, + int_param_num = 1, + place_holders = ["@ecutwfc", "@celldm", "@kpt"], + pwx_path = "pw.x", + input_file = "template-scf.in", # must contain two compalsory place holders "@outdir" and "@pseudo_dir" that need not to be specified in the place_holders kwarg. + output_file = "template-scf.out", + working_dir = ".", # working directory is a more descriptive name for this option + outdir = "", + pseudo_dir = ""): + """ + This function runs Quantum Espresso with a given template file as a input. + The given parameter values are substituted into a + template instead of the placeholders starting from '@', e.g. @ecutwfc, + @celldm and etc. Then the function returns the used parameter values together + with a total energy of the system. + + This funciton is to be used as the cost function in the differential evolution + optimization of the parameters of interest. + + """ + plen = len(params) + + if plen != len(place_holders): + raise Exception("The number of optimization parameters must be equal to \ + the number of corresponding place holders. Check place_holders kwarg.") + if int_param_num > plen : + raise Exception("The number of integer parameters cannot exceed the dimensionality \ + of parameter space. Check int_param_num kwarg and the length of the first arg.") + + # set the path to QE installation + if pwx_path == "": + cmd = subprocess.run(['which', 'pw.x'], + stdout=subprocess.PIPE, + universal_newlines=True) + path = cmd.stdout.strip() + + if path == "": + raise Exception("Failed to detect path to pw.x file. Install Quantum Espresso or provide path manually using pwx_path kwarg.") + else: + pwx_path = path + + if (working_dir == "" or working_dir == "." or working_dir == "./"): + working_dir = dirname(abspath(__file__)) + + if pseudo_dir == "": + pseudo_dir = '"' + working_dir + '"' + + if outdir == "": + outdir = '"' + os.path.join(working_dir,"tmp") + '"' + + st = open(os.path.join(working_dir,input_file),'r') + IN = st.read() # this list still contains special characters '\n' + st.close() + + plc_holders = place_holders + ["@outdir", "@pseudo_dir"] + paramstr = [] + for i in range(plen): + if i < plen-int_param_num: + paramstr.append(str(params[i])) + else: + paramstr.append(str(int(params[i]))) + + paramstr.append(outdir) + paramstr.append(pseudo_dir) + + for i in range(len(paramstr)): + IN = re.sub(plc_holders[i],paramstr[i],IN) + + # print input file for the testing purposes + #print(IN) + + pwx = subprocess.Popen([pwx_path], + stdin =subprocess.PIPE, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + universal_newlines=True, + bufsize=0) + + # Send qe commands to stdin + pwx.stdin.write(IN) + pwx.stdin.close() + + + # in what follow two option are possible either to save output or avoid this + e_tot = ""; + if output_file != "": + # the output file is produced and saved in the outdir, for testing purposes only + # avoid files overwriting up to 100 runs + outfname = output_file + for i in range(100): + if os.path.isfile(os.path.join(working_dir,outfname)): + outfname = output_file + '_' + str(i) + else: + break + + + f = open(os.path.join(working_dir,outfname),'a') + # this list still contains special characters '\n' + for line in pwx.stdout: + f.write(line) + if line.find("!") != -1: + e_tot = float(re.split(r"\s+",line.strip())[-2]) + + f.close() + else: + # the output file is not produced, should be faster + # this list still contains special characters '\n' + for line in pwx.stdout: + if line.find("!") != -1: + e_tot = float(re.split(r"\s+",line.strip())[-2]) + + if e_tot == "": + raise Exception("Total energy was not found in the Quantum Espresso output, probably due to the lack of self-consistent run convergence or a crash. Check the given script parent directory for the Quantum Espresso CRASH file.") + + return params, e_tot + +# 2023-06-16 +# DE optimization function for an arbitrary number of QE parameters; +# one, few or all parametes can be chosen to be integer, counting from the end of +# the parameter space vector + +import numpy as np +import time +from datetime import datetime +import matplotlib.pyplot as plt + +def QEDE(param_space, + popsize = 4, # population size cannot be less than 4 + mutation = 0.5, # mutation parameter chosen from [0,2] interval + crossover = 0.7, # crossover probability chosen from [0,1] interval + max_iter = 30, # maximum number of iterations to break the loop in case convergence is too slow + DE_tol = 1e-6, # convergence threshold + int_param_num = 1, # number of the integer parameters + place_holders = ["@ecutwfc", "@celldm", "@kpt"], # place holders used in the QE input file template instead of the parametes of interest + pwx_path = "", # path to QE pw.x is found automatically + input_file = "template-scf.in", # QE input file template; it must contain two compalsory place holders "@outdir" and "@pseudo_dir" that need not to be specified in the place_holders kwarg. + output_file = "", # QE output file; can be used for tracking errors in QE runs + working_dir = ".", # working directory is a more descriptive name for this option + outdir = "", # QE output directory + pseudo_dir = ""): # directory with QE pseudo potentials + """ + This function runs Quantum Espresso within Differential Evolution (DE) Global + optimization algorithm. + The DE algorithm is searching for the Global minimum of the total energy of the + system within the parameter space region specified as param_space. + The parameter space vectors are substituted into a + template input file instead of the placeholders that by convention start + from '@', e.g. @ecutwfc, @celldm and etc. The Quantum Espresso is run on this + input file and the total energy is returned. This cycle is repeated within + DE algorithm until maximum number of iterations, max_iter, is achieved or the difference + between total energies of all parameter space vectors in the given generation + becomes lower than the given tolerance, DE_tol. + The function returns an array of the parameter space vectors and their corresponding total + energies for each generation as well as same quantities for last generation. The former + allows one to track the convergence of the DE, while the later to choose between the best found parameters. + + This funciton is to be used for finding optimal parameters of interest. + + """ + start_time = time.time() + + if (working_dir == "" or working_dir == "." or working_dir == "./"): + working_dir = dirname(abspath(__file__)) + + # Differential Evolution optimization + D = len(param_space) # number optimized parameters, i.e. the dimensionality of parameter space + + # random sampling of the parameters within the zeroth generation + X = np.asarray([(a[1] - a[0]) * np.random.random(popsize) + a[0] for a in param_space]) + # test initial parameter space vectors + print("Initial parameter space vectors (in columns):") + print(X) + + # initializing costs for every parameter space vector in the 0-th generation + costs = np.zeros(popsize) + for i in range(popsize): + params = X[:,i] + costs[i] = QEGenrun(params, + int_param_num, + place_holders, + pwx_path, + input_file, + output_file, + working_dir, + outdir, + pseudo_dir)[1] + + print("Total energies for the initial generation of the parameter space vectors:") + print(costs) + best_idx = np.argmin(costs) + best_cost = costs[best_idx] + best_vector = X[:,best_idx] + + # iterating generation untill the convergence is achieved + # initializing a trial vector and a range of indexes numbering vectors in generation + trial_vector = np.zeros(D) + gen_range = np.arange(popsize) + + # introducing variable to collect the parameter space vectors and total energies thoughout all the iterations + data = np.zeros((max_iter*popsize,D+1)) + + # convergence loop + # set est and k parameters to enter to loop + est = DE_tol + k = 0 + + while (DE_tol <= est) and (k < max_iter): + kp = k*popsize; # alias for k*popsize within the while loop + # iterating through of the generation of parameter vectors choosing each vector to be once a target vector + for i in range(popsize): + # Choose r1, r2, r3 randomly so that they are different from each other and the running index i + r1, r2, r3 = np.random.permutation(np.delete(gen_range,i))[0:3] + + # trial vector is the mixture of the mutant_vector and the target_vector + rnbr = np.random.randint(D) # for the given vector, the random integer between 0 and D - 1 + for j in range(D): + if (np.random.random() > crossover) and (j != rnbr): + # Crossover with a target vector + trial_vector[j] = X[j,i] + else: + # Mutation between three other vectors that are chosen randomly + mutant = X[j, r1] + mutation * (X[j, r2] - X[j, r3]) + # corrections of the parameter value, it must stay within parameter limits + if mutant < param_space[j, 0]: + trial_vector[j] = param_space[j, 0] + elif mutant > param_space[j, 1]: + trial_vector[j] = param_space[j, 1] + else: + trial_vector[j] = mutant + + + print("trial vector = " + str(trial_vector)) + + # Selection + # If the trial vector minimizes the cost function compared to the target vectors than it replaces + # the target vector in the next generation + data[kp+i,:D], data[kp+i,D] = QEGenrun(trial_vector, + int_param_num, + place_holders, + pwx_path, + input_file, + output_file, + working_dir, + outdir, + pseudo_dir) + trial_cost = data[kp+i,D] + + if trial_cost < costs[i]: + X[:,i] = trial_vector + costs[i] = trial_cost + if trial_cost < best_cost: + best_cost = trial_cost + best_vector = trial_vector + else: + data[kp+i,:D] = X[:,i] + data[kp+i,D] = costs[i] + + print(costs) + + k += 1 + print("iteration No. " + str(k)) + # estimating convergence + est = 0 + for i in range(popsize-1): + est += np.abs(costs[0] - costs[i+1]) + print("Total energies spread = " + str(est)) + # clean data array from zeros, outside the while loop + data = data[:k*popsize] + # print the time spent on this search + print("--- %s seconds ---" % (time.time() - start_time)) + + # writing last generation of parameter space vectors into an output file + # this piece of code must be tested 19/06/2023 + date = datetime.now().strftime('%Y-%m-%d_%H_%M_%S') + fname = "QEDE_" + date + ".out" + # writing data to the text file + f = open(os.path.join(working_dir,fname),'w') + f.write('Created by Vasil Saroka 40.ovasil@gmail.com') + # using string literals + f.write('\nToday is ' + date + '\n') + f.writelines('\nDifferential Evolution results:\n') + f.write('\nThe last generation of parameter space vectors:') + + for i in range(popsize): + f.write('\nparameter vector ' + str(i+1) + '\n') + for j in range(D): + f.write('%5.9f\n' % X[j,i]) + f.write('\nThe total energy for vector ' + str(i+1) + ':\n') + f.write('%5.9f\n' % costs[i]) + + f.write('\nThe best vector is\n') + for j in best_vector: + f.write('%5.9f\n' % j) + f.write('\nThe total energy for the best vector:\n') + f.write('%5.9f\n' % best_cost) + if k == max_iter : + f.write("\nThe maximum number of iterations has been achieved. Try to increase this number.\n") + f.write('\nDE is done!\n') + f.close() + + # track convergence of the parameter space vectors during the DE evolution + # create figure object + fig, ax = plt.subplots() + ax.set_xlim(0,k) + ax.set_ylim(min(data[:,3]),max(data[:,3])) + ax.set_xlabel("DE iteration") + ax.set_ylabel(r"$E_{tot}$, Ry") + + for i in range(popsize): + v = data[i:-1:popsize,3] + ax.plot(v, alpha=0.9) + + fname ="QEDE_" + date + ".png" + fig.savefig(os.path.join(working_dir,fname),dpi=200,bbox_inches='tight',transparent=False) + + return data, k, X, costs, best_vector, best_cost +#%% test on bulk Si + +param_limits = np.array([[30, 80], [5, 15], [4, 16]]) + +# QE run options +placeholders = ["@ecutwfc", "@celldm", "@kpt"] +QEpath = "pw.x" +IN = "Si.scf-template.in" +OUT = "" # no output file shall be generated to save the space on disk +wk_dir = "." # working directory is the directory of this script +pp_dir = "" # implies pseudo potential to be located in the working directory + +data, k, vecs, costs, best_vector, best_cost = QEDE(param_limits, + place_holders = placeholders, + pwx_path = QEpath, + input_file = IN, + output_file= OUT, + working_dir= wk_dir, + pseudo_dir=pp_dir) +#%% +print(vecs) +print(best_vector) + +#%% +print(best_vector) +print(best_cost) + +#%% + +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d.proj3d import proj_transform +from mpl_toolkits.mplot3d.axes3d import Axes3D +from matplotlib.patches import FancyArrowPatch + +class Arrow3D(FancyArrowPatch): + + def __init__(self, x, y, z, dx, dy, dz, *args, **kwargs): + super().__init__((0, 0), (0, 0), *args, **kwargs) + self._xyz = (x, y, z) + self._dxdydz = (dx, dy, dz) + + def draw(self, renderer): + x1, y1, z1 = self._xyz + dx, dy, dz = self._dxdydz + x2, y2, z2 = (x1 + dx, y1 + dy, z1 + dz) + + xs, ys, zs = proj_transform((x1, x2), (y1, y2), (z1, z2), self.axes.M) + self.set_positions((xs[0], ys[0]), (xs[1], ys[1])) + super().draw(renderer) + + def do_3d_projection(self, renderer=None): + x1, y1, z1 = self._xyz + dx, dy, dz = self._dxdydz + x2, y2, z2 = (x1 + dx, y1 + dy, z1 + dz) + + xs, ys, zs = proj_transform((x1, x2), (y1, y2), (z1, z2), self.axes.M) + self.set_positions((xs[0], ys[0]), (xs[1], ys[1])) + + return np.min(zs) + + + +def _arrow3D(ax, x, y, z, dx, dy, dz, *args, **kwargs): + '''Add an 3d arrow to an `Axes3D` instance.''' + + arrow = Arrow3D(x, y, z, dx, dy, dz, *args, **kwargs) + ax.add_artist(arrow) + + +setattr(Axes3D, 'arrow3D', _arrow3D) + +#%% working .gif-animation for QEGenrun + +from matplotlib import pyplot as plt +from celluloid import Camera +import numpy as np + +gsz = int(len(data)/k) +path = dirname(abspath(__file__)) + +# create figure object +fig = plt.figure() +ax = fig.add_subplot(111, projection='3d') + +ax.set_xlim(param_limits[0,0],param_limits[0,1]) +ax.set_ylim(param_limits[1,0],param_limits[1,1]) +ax.set_zlim(param_limits[2,0],param_limits[2,1]) + + + +ax.set_xlabel('$E_{cut}$') +ax.set_ylabel('$a_{lat}$') +ax.set_zlabel('$k_{pt}$') + +camera = Camera(fig) + +for fr in range(k): + # dynamic title for celluloid animation + ax.text(80, 7, 108, str(fr)+'-th generation of parameter vectors', transform=ax.transAxes) + + pv = data[fr*gsz:(fr+1)*gsz] + best_idx = np.argmin(pv[:,3]) + + for i in range(gsz): + if i == best_idx: + ax.arrow3D(0,0,0, + pv[i,0],pv[i,1],pv[i,2], + mutation_scale=20, + ec ='green', + fc='red') + else: + ax.arrow3D(0,0,0, + pv[i,0],pv[i,1],pv[i,2], + mutation_scale=20, + arrowstyle="-|>", + linestyle='dashed') + + + + plt.pause(0.1) + camera.snap() + +animation = camera.animate() +animation.save(os.path.join(path,'animation_Si.gif'), writer='PillowWriter', fps=2, dpi = 200) + + +fig.tight_layout() + +#%% working .mp4-video for QEGenrun +import matplotlib.animation as animation +from IPython.display import HTML + +gsz = int(len(data)/k) +path = dirname(abspath(__file__)) + +fig = plt.figure() + +ax = fig.add_subplot(111,projection='3d') + + +def animate(ind): + ax.clear() + ax.set_xlim(param_limits[0,0],param_limits[0,1]) + ax.set_ylim(param_limits[1,0],param_limits[1,1]) + ax.set_zlim(param_limits[2,0],param_limits[2,1]) + + pv = data[ind*gsz:(ind+1)*gsz] + best_idx = np.argmin(pv[:,3]) + + for i in range(gsz): + if i == best_idx: + ax.arrow3D(0,0,0, + pv[i,0],pv[i,1],pv[i,2], + mutation_scale=20, + ec ='green', + fc='red') + else: + ax.arrow3D(0,0,0, + pv[i,0],pv[i,1],pv[i,2], + mutation_scale=20, + arrowstyle="-|>", + linestyle='dashed') + + + ax.set_title(str(ind)+'-th generation of parameter vectors') + ax.set_xlabel('$E_{cut}$') + ax.set_ylabel('$a_{lat}$') + ax.set_zlabel('$k_{pt}$') + fig.tight_layout() + +anim = animation.FuncAnimation(fig, animate, frames=k, interval=400) + +print(HTML(anim.to_html5_video())) + +plt.show() + +anim.save(os.path.join(path,"video_Si.mp4")) + diff --git a/python/Si.pz-vbc.UPF b/python/Si.pz-vbc.UPF new file mode 100644 index 0000000..a6d8f91 --- /dev/null +++ b/python/Si.pz-vbc.UPF @@ -0,0 +1,939 @@ + + +Generated by new atomic code, or converted to UPF format +Author: +Generation date: +Pseudopotential type: NC +Element: Si +Functional: SLA PZ NOGX NOGC +Suggested minimum cutoff for wavefunctions: 0. Ry +Suggested minimum cutoff for charge density: 0. 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prefix = 'Si' + pseudo_dir = @pseudo_dir + outdir = @outdir + verbosity = 'high' + wf_collect = .true. +/ +&system + ibrav = 2 + celldm(1) = @celldm + nat = 2 + ntyp = 1 + !nosym = .false. + ecutwfc = @ecutwfc + nbnd = 10 +/ +&electrons + diago_thr_init=1.0e-3, + electron_maxstep = 60, + diagonalization='david' + mixing_mode = 'plain' + mixing_beta = 0.7 + conv_thr = 1.0d-8, + !startingpot='file', + !startingwfc='atomic', +/ +ATOMIC_SPECIES +Si 28.086 Si.pz-vbc.UPF +ATOMIC_POSITIONS (alat) +Si 0.000000000 0.000000000 0.000000000 +Si 0.250000000 0.250000000 0.250000000 +K_POINTS {automatic} +@kpt @kpt @kpt 1 1 1