Mercurial > repos > chemteam > biomd_neqgamma

annotate NEQGamma.py @ 1:afcb925def69 draft default tip

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"planemo upload for repository https://github.com/galaxycomputationalchemistry/galaxy-tools-compchem/ commit f1c3c88c7395f2e84cbc533199406aadb79c5c07"
author chemteam
date Fri, 13 Nov 2020 19:38:28 +0000
parents 4f3222cb5cf6
children
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4f3222cb5cf6 "planemo upload for repository https://github.com/galaxycomputationalchemistry/galaxy-tools-compchem/ commit 79589d149a8ff2791d4f71d28b155011672db827"
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1 #!/usr/bin/env python
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3 # coding: utf-8
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4 # This script is a modified version of a script written
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5 # by Steffen Wolf under the GPL v3.0.
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6 # The original version can be accessed at
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7 # https://github.com/moldyn/dcTMD/blob/master/NEQGamma.py
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9 import argparse
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10 import json
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11 import sys
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13 import numpy as np
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15 import pandas as pd
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17 import scipy
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18 import scipy.integrate
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19 from scipy.ndimage.filters import gaussian_filter
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22 def get_file_names(list_file):
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23 with open(list_file) as f:
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24 return [line for line in f.read().split('\n') if line]
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27 def NEQGamma(file_names, output, output_frict, vel, T, av, sigma):
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28 N = len(file_names)
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29 RT = 0.0083144598 * T
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31 sys.stdout.write("reading data...\n")
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32
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33 # read in initial data to get length of necessary array
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34 test_file = pd.read_csv(file_names[0], delim_whitespace=True,
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35 header=None, skiprows=17, dtype=float)
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36 length_data = len(test_file[0].values)
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37 full_force_set = np.zeros((N, length_data))
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38 x = np.zeros(length_data)
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39 t = np.zeros(length_data)
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40 t = test_file[0].values
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41 x = test_file[0].values * vel
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42
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43 # read in data
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44 for i in range(0, N):
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45 current_file_name = file_names[i]
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46 sys.stdout.write("reading file {}\n".format(current_file_name))
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47 input_file_data = pd.read_csv(current_file_name, delim_whitespace=True,
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48 header=None, skiprows=17, dtype=float)
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49 full_force_set[i, :] = input_file_data[1].values
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50
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51 # preprocessing
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52 # * force average: calculate $\left< f_c (t)\right>_N$.
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53 # **Important:** this is an ensemble average over the trajectory ensemble
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54 # $N$, not the time average over $t$
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55 av_force = np.zeros(length_data)
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56 av_forceintegral = np.zeros(length_data)
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57 for i in range(length_data):
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58 av_force[i] = np.mean(full_force_set[:, i])
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59 av_forceintegral[1:] = scipy.integrate.cumtrapz(av_force, x)
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60
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61 # calculate $\delta f_c(t) = f_c(t) - \left< f_c (t) \right>_N$ for all $t$
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62 sys.stdout.write("calculating fluctuations...\n")
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63 delta_force_set = np.zeros((N, length_data))
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64 for i in range(length_data):
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65 delta_force_set[:, i] = full_force_set[:, i] - av_force[i]
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66
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67 # evaluation
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68 # * optimized algorithm for numerical evaluation:
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69 # * integrate: $\int_0^t dt' \delta f_c(t')$ for all $t'$
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70 # * multiply by $\delta f_c(t)$ to yield
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71 # $\int_0^t dt'\delta f_c(t) \delta f_c(t')$ for $t$
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72 # with all $t' \leq t$ each
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73 # * then calculate the ensemble average
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74 # $\left< \int_0^t dt' \delta f_c(t) \delta f_c(t') \right>$
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75 int_delta_force_set = np.zeros((N, length_data))
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76 for n in range(N):
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77 int_delta_force_set[n, 1:] = scipy.integrate.cumtrapz(
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78 delta_force_set[n, :], t)
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79
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80 sys.stdout.write("averaging and integrating...\n")
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81 intcorr = np.zeros((N, length_data))
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82
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83 for n in range(N):
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84 for i in range(length_data):
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85 intcorr[n, i] = delta_force_set[n, i] * int_delta_force_set[n, i]
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86 if i % 1000 == 0:
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87 sys.stdout.write("Trajectory {:2d} {:3.1f} % done\r".format(
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88 n + 1, (i / length_data) * 100))
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89
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90 # shape of $\int_0^t dt' \delta f_c(t) \delta f_c(t')$:
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91 sys.stdout.write("final average...\n")
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92 av_intcorr = np.zeros(length_data)
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93 for i in range(length_data):
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94 av_intcorr[i] = np.mean(intcorr[:, i]) / RT
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95
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96 # autocorrelation function evaluation:
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97 # * calculate $\left< \delta f_c(t) \delta f_c(t') \right>$
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98 # for the last $t$
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99
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100 corr_set = np.zeros((N, length_data))
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101 autocorr_set = np.zeros(length_data)
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102
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103 sys.stdout.write("calculating and processing ACF...\n")
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104 for n in range(N):
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105 for i in range(length_data):
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106 corr_set[n, i] = delta_force_set[
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107 n, i] * delta_force_set[n, length_data - 1]
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108
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109 for i in range(length_data):
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110 autocorr_set[i] = np.mean(corr_set[:, i])
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111
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112 # * Gauss filter:
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113 sys.stdout.write("applying Gauss filter...\n")
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114 blurr = sigma
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115 blurred = np.zeros(length_data)
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116 blurred = gaussian_filter(av_intcorr, sigma=blurr)
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117
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118 # * sliding window average:
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119 sys.stdout.write("applying sliding window average...\n")
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120 window = av
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121 runn_av = np.zeros(length_data)
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122 runn_av = np.convolve(av_intcorr, np.ones((window,)) / window, mode='same')
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123
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124 # * $W_{diss}$ from integration:
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125 wdiss = np.zeros(length_data)
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126 wdiss[1:] = scipy.integrate.cumtrapz(av_intcorr, x) * vel
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127
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128 sys.stdout.write("writing output...\n")
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129 dist = open(output, "w")
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130 frict = open(output_frict, "w")
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131
1
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132 dist.write('\t'.join(('#x', 'force_integral', 'frict_coeff',
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133 'wdiss', 'corrected_force_integral\n')))
0
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134 for i in range(length_data):
1
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135 dist.write("{:.8f}\t{:.8f}\t{:.8f}\t{:.8f}\t{:.8f}\n".format(
0
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136 x[i], av_forceintegral[i], av_intcorr[i], wdiss[i],
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137 av_forceintegral[i] - wdiss[i]))
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138
1
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139 frict.write('\t'.join(('#x', 'ACF', 'frict_coeff',
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140 'gauss_filtered_frict_coeff',
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141 'av_window_frict_coeff\n')))
0
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142 for i in range(length_data):
1
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143 frict.write("{:.8f}\t{:.8f}\t{:.8f}\t{:.8f}\t{:.8f}\n".format(
0
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144 x[i], autocorr_set[i], av_intcorr[i], blurred[i], runn_av[i]))
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145
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146 dist.close()
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147 frict.close()
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148
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149 sys.stdout.write("Done!\n")
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150
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151
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152 def main():
1
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153 parser = argparse.ArgumentParser(description="""dcTMD friction correction
0
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154 (please cite: Wolf, S., Stock, G. Targeted Molecular Dynamics
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155 Calculations of Free Energy Profiles Using a Nonequilibrium
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156 Friction Correction. J. Chem. Theory Comput. 2018, 14(12), 6175-6182,
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157 DOI: 10.1021/acs.jctc.8b00835). Integrates a constraint force file via
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158 trapezoid rule, calculates the NEQ memory friction kernel and friction
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159 factors, and performs a friction correction. First column: reaction
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160 coordinate in nm calculated via t * vel. Second column: force integral,
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161 i.e. the work profile. Third column: friction factors. Fourth column:
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162 trapezoid integral (final value) of friction work along reaction
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163 coordinate. Fourth column: friction corrected work profile. ATTENTION:
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164 Use with python3 or higher!""")
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165 parser.add_argument('-i', metavar='<xvg force file>', type=str,
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166 help="""List of xvg constraint force files prefix
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167 as given by Gromacs mdrun -pf option before running
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168 number.""")
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169 parser.add_argument('-o', metavar='<combined results>', type=str,
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170 help="""file to write x, dG(x), friction coefficeint by
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171 integration (time resolved), and the friction-corrected
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172 dG(x).""")
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173 parser.add_argument('-ofrict', metavar='<combined friction results>',
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174 type=str,
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175 help="""file to write x, ACF, friction coefficeint by
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176 integration (time resolved), gauss filtered friction
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177 coefficient, and slide window averaged friction.""")
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178 parser.add_argument('-vel', metavar='<pull velocity>', type=float,
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179 help="""pull velocity in nm/ps for converting simulation
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180 time t into distance x""")
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181 parser.add_argument('-T', metavar='<temperature>', type=float,
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182 help='temperature in K')
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183 parser.add_argument('-av', metavar='<average window>', type=int,
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184 help="""size of averaging window for displaying
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185 Gamma(x) (recommended: 4 to 20 per 100 data points)""")
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186 parser.add_argument('-sigma', metavar='<gauss blurr>', type=int,
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187 help="""sigma value for Gauss filter for displaying
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188 Gamma(x) (recommended: 4 per 100 data points)""")
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189 parser.add_argument('-json', metavar='<json>', type=str,
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190 help='JSON file defining cluster membership')
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191
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192 args = parser.parse_args()
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193
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194 file_names = get_file_names(args.i)
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195 if args.json:
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196 with open(args.json) as f:
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197 j = json.load(f)
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198 file_names_dct = {n: [file_names[int(m)] for m in j[n]] for n in j}
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199
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200 for cluster in file_names_dct:
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201 NEQGamma(file_names_dct[cluster],
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202 'cluster{}_{}'.format(cluster, args.o),
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203 'cluster{}_{}'.format(cluster, args.ofrict),
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204 args.vel, args.T, args.av, args.sigma)
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205 else:
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206 NEQGamma(file_names, args.o, args.ofrict, args.vel,
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207 args.T, args.av, args.sigma)
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208
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209
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210 if __name__ == "__main__":
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211 main()