Mercurial > repos > chemteam > biomd_rmsd_clustering

annotate rmsd_clustering.py @ 0:f45b010339f6 draft default tip

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"planemo upload for repository https://github.com/galaxycomputationalchemistry/galaxy-tools-compchem/ commit 1b23e024af45cc0999d9142d07de6897d4189ec2"
author chemteam
date Mon, 24 Aug 2020 10:06:27 +0000
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f45b010339f6 "planemo upload for repository https://github.com/galaxycomputationalchemistry/galaxy-tools-compchem/ commit 1b23e024af45cc0999d9142d07de6897d4189ec2"
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1 import argparse
f45b010339f6 "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 matplotlib.pyplot as plt
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6 import numpy as np
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8 from scipy.cluster.hierarchy import cophenet, dendrogram, linkage
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9 from scipy.spatial.distance import pdist
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12 def json_to_np(fname, start=None, end=None):
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13 """
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14 Load json file and convert to numpy array
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15 """
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16 with open(fname) as f:
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17 k = json.load(f)
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18 print(np.array(k)[:, :, start:end].shape)
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19 return np.array(k)[:, :, start:end]
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22 def flatten_tensor(tensor, normalize=True):
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23 """
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24 Flatten tensor to a 2D matrix along the time axis
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25 """
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26 av = np.mean(tensor, axis=(0, 1)) if normalize else 1
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27 return np.mean(tensor/av, axis=2)
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30 def get_cluster_linkage_array(mat, clustering_method='average'):
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31 Z = linkage(mat, clustering_method)
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32 c, coph_dists = cophenet(Z, pdist(mat))
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33 print('Cophenetic correlation coefficient: {}'.format(c))
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34 return Z
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37 def plot_dist_mat(mat, output, cmap='plasma'):
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38 """
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39 Plot distance matrix as heatmap
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40 """
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41 fig, ax = plt.subplots(1)
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42 p = ax.pcolormesh(mat, cmap=cmap)
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43 plt.xlabel('Trajectory number')
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44 plt.ylabel('Trajectory number')
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45 plt.colorbar(p)
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46 plt.draw()
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47 plt.savefig(output, format='png')
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48
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49
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50 def plot_dendrogram(Z, output):
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51 plt.figure(figsize=(25, 10))
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52 plt.title('Hierarchical Clustering Dendrogram')
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53 plt.xlabel('Trajectory index')
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54 plt.ylabel('distance')
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55 dendrogram(
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56 Z,
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57 leaf_rotation=90., # rotates the x axis labels
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58 leaf_font_size=8., # font size for the x axis labels
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59 )
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60 plt.savefig(output, format='png')
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61
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62
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63 def main():
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64 parser = argparse.ArgumentParser()
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65 parser.add_argument('--json', help='JSON input file (for 3D tensor).')
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66 parser.add_argument('--mat', help='Input tabular file (for 2D matrix).')
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67 parser.add_argument('--outp-mat', help='Tabular output file.')
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68 parser.add_argument('--Z', required=True,
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69 help='File for cluster linkage array.')
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70 parser.add_argument('--dendrogram',
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71 help="Path to the output dendrogram file")
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72 parser.add_argument('--heatmap',
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73 help="Path to the output distance matrix file")
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74 parser.add_argument('--clustering-method', default='average',
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75 choices=['single', 'complete', 'average',
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76 'centroid', 'median', 'ward', 'weighted'],
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77 help="Method to use for clustering.")
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78 parser.add_argument('--cmap', type=str, default='plasma',
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79 help="Matplotlib colormap to use"
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80 "for plotting distance matrix.")
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81 parser.add_argument('--start', type=int,
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82 help="First trajectory frame to"
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83 "calculate distance matrix")
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84 parser.add_argument('--end', type=int,
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85 help="Last trajectory frame to"
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86 "calculate distance matrix")
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87 parser.add_argument('--normalize', action="store_true",
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88 help="Normalize the RMSD variation over"
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89 "the trajectories before averaging.")
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90 args = parser.parse_args()
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91
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92 print(args)
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93 if args.json:
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94 tensor = json_to_np(args.json, args.start, args.end)
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95 mat = flatten_tensor(tensor, args.normalize)
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96 np.savetxt(args.outp_mat, mat)
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97 elif args.mat:
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98 mat = np.loadtxt(args.mat)
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99 else:
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100 print("Either --json or --mat must be specified.")
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101 exit(1)
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102
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103 Z = get_cluster_linkage_array(mat, args.clustering_method)
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104 np.savetxt(args.Z, Z)
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105
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106 if args.heatmap:
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107 plot_dist_mat(mat, args.heatmap, args.cmap)
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108
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109 if args.dendrogram:
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110 plot_dendrogram(Z, args.dendrogram)
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111
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112
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113 if __name__ == "__main__":
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114 main()