annotate extract_rmsd.py @ 6:be5eaea0b2d1 draft default tip

"planemo upload for repository https://github.com/galaxycomputationalchemistry/galaxy-tools-compchem/ commit f1c3c88c7395f2e84cbc533199406aadb79c5c07"
author chemteam
date Fri, 13 Nov 2020 19:44:38 +0000
parents af9f01ca6a5c
children
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70a2d548e62c "planemo upload for repository https://github.com/galaxycomputationalchemistry/galaxy-tools-compchem/ commit 1b23e024af45cc0999d9142d07de6897d4189ec2"
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1 import argparse
70a2d548e62c "planemo upload for repository https://github.com/galaxycomputationalchemistry/galaxy-tools-compchem/ commit 1b23e024af45cc0999d9142d07de6897d4189ec2"
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2 import json
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4 import MDAnalysis as m
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5 from MDAnalysis.analysis import rms
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7 import numpy as np
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10 def calc_rmsd(str_files, traj_files, str_format, traj_format, filepath_out,
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11 group, start, end, step):
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12 """
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13 the function will cycle through range 0 to no_t and load all files found.
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15 str_files: text file with filepaths for structures, one on each line
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16 traj_files: text file with filepaths for trajectories, one on each line
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17 filepath_in: directory where the files are located
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18 filepath_out: pickle file where results (3D matrix) should be saved to
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20 group: atoms for which RMSD should be calculated;
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21 use the MDAnalysis selection language
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23 start: first trajectory frame to calculate RMSD
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24 end: last trajectory frame to calculate RMSD
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25 step: how frequently frames are sampled between start and end; obviously,
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26 the larger the step, the quicker the script finishes
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27 """
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29 # open list of files
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30 with open(str_files) as f1, open(traj_files) as f2:
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31 str_file_list = f1.read().strip().split('\n')
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32 traj_file_list = f2.read().strip().split('\n')
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34 if sum(1 for line in f1) != sum(1 for line in f2):
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35 raise IOError('Number of structure and trajectory files unequal.')
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37 no_t = len(traj_file_list)
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38
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39 # hard to find array size before loading files
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40 universe_coordinate_data = []
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42 for traj in range(no_t):
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43 # We no longer align here, users should do this themselves.
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44 u = m.Universe(str_file_list[traj], traj_file_list[traj],
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45 format=traj_format, topology_format=str_format)
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46 u.transfer_to_memory()
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47 grp = u.select_atoms(group).universe
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48 coordinates = grp.trajectory.coordinate_array[start:end:step]
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49 universe_coordinate_data.append(coordinates)
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50
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51 universe_coordinate_data = np.array(universe_coordinate_data)
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52 print("All trajs loaded by MDAnalysis")
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53 data = np.zeros((no_t, no_t, universe_coordinate_data.shape[1]))
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55 # calculate differences
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56 for traj1 in range(no_t):
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57 print("Calculating differences for traj {}".format(traj1))
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58 for traj2 in range(traj1):
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59 for frame in range(data.shape[2]):
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60 A = universe_coordinate_data[traj1, frame]
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61 B = universe_coordinate_data[traj2, frame]
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62 r = rms.rmsd(A, B)
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63 data[traj1, traj2, frame] = r
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64 data[traj2, traj1, frame] = r
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65
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66 with open(filepath_out, 'w') as f:
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67 json.dump(data.tolist(), f, indent=4, sort_keys=True)
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68
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69 print("Done!")
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70 return
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73 def main():
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74 parser = argparse.ArgumentParser()
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75 parser.add_argument('--trajs', required=True,
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76 help='File containing trajectory filepaths.')
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77 parser.add_argument("--strs",
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78 help='File containing structure filepaths.')
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79 parser.add_argument('--traj-format', required=True,
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80 help='Trajectory format.')
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81 parser.add_argument("--str-format", help='Structure format.')
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82 parser.add_argument('-o', '--outfile',
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83 help="Path to the output JSON file")
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84 parser.add_argument('--group', help="Atoms for which RMSD should be"
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85 "calculated in MDAnalysis selection language")
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86 parser.add_argument('--start', type=int,
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87 help="First trajectory frame to calculate RMSD")
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88 parser.add_argument('--end', type=int,
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89 help="Last trajectory frame to calculate RMSD")
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90 parser.add_argument('--step', type=int,
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91 help="Frame sampling frequency for RMSD calculation")
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92 args = parser.parse_args()
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93 calc_rmsd(args.strs, args.trajs, args.str_format,
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94 args.traj_format, args.outfile,
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95 args.group, args.start, args.end, args.step)
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96
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97
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98 if __name__ == "__main__":
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99 main()