changed 'Conformation.dissimilarity' method to 'Conformation.identity' method
parent
3662e3a099
commit
4144a2a093
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@ -4,6 +4,7 @@ import pandas as pd
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import numpy as np
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import numpy as np
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import pbxplore as pbx
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import pbxplore as pbx
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import sys
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import sys
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from scipy.spatial.distance import squareform, pdist, jaccard
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class Conformations:
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class Conformations:
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"""
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"""
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@ -18,21 +19,31 @@ class Conformations:
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df : pd.DataFrame object
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df : pd.DataFrame object
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Each row of the dataframe is a conformation and each column a position
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Each row of the dataframe is a conformation and each column a position
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of the sequence.
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of the sequence.
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dfnum : to compute dissimilarity with the function pdist, elements of
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the DataFrame must be float
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"""
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"""
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self.df = pd.DataFrame()
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self.df = pd.DataFrame()
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for chain_name, chain in pbx.chains_from_files([filename]):
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for chain_name, chain in pbx.chains_from_files([filename]):
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dihedrals = chain.get_phi_psi_angles()
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dihedrals = chain.get_phi_psi_angles()
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pb_seq = pbx.assign(dihedrals)
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pb_seq = pbx.assign(dihedrals)
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self.df = self.df.append(pd.Series(list(pb_seq)), ignore_index=True)
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self.df = self.df.append(pd.Series(list(pb_seq)),
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ignore_index=True)
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def dissimilarity(self):
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def identity(self):
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"""
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"""
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computes the dissimilarity matrix of the intance's df
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Returns a matrix of the distance between all conformations based on
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wether the PB at each position is identical or not.
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Returns the matrix as a numpy.ndarray object.
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"""
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"""
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matrix = pd.DataFrame(index=np.arange(self.df.shape[0]),
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dict_str_to_float = {'Z':0,'a':1,'b':2,'c':3,'d':4,'e':5,'f':6,'g':7,
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columns=np.arange(self.df.shape[0]))
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'h':8,'i':9,'j':10,'k':11,'l':12,'m':13,'n':14,
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matrix = matrix.fillna(0)
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'o':15,'p':16} # dict to transform PB to int,
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return matrix
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# necessary for the pdsit function
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dfnum = self.df.replace(dict_str_to_float)
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return squareform(pdist(dfnum, metric ='jaccard'))
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if __name__ == "__main__":
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if __name__ == "__main__":
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if len(sys.argv) != 2:
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if len(sys.argv) != 2:
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