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pspbsf.py
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pspbsf.py
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# This file is part of PySFD.
#
# Copyright (c) 2018 Sebastian Stolzenberg,
# Computational Molecular Biology Group,
# Freie Universitaet Berlin (GER)
#
# for any feedback or questions, please contact the author:
# Sebastian Stolzenberg <ss629@cornell.edu>
#
# PySFD is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
r"""
=======================================
PySFD - Significant Feature Differences Analyzer for Python
Pairwise sparse Pairwise Backbone/Sidechain Features (PsPBSF)
=======================================
"""
# only necessary for Python 2
from __future__ import print_function as _
from __future__ import division as _
from __future__ import absolute_import as _
import warnings as _warnings
import numpy as _np
import pandas as _pd
import itertools as _itertools
from pysfd.features import _feature_agent
class _PsPBSF(_feature_agent.FeatureAgent):
"""
Pairwise sparse Pairwise Backbone/Sidechain Feature (PsPBSF):
Intermediary class between a particular feature class in this module and
_feature_agent.FeatureAgent
in order to bundle common tasks
"""
def __init__(self, feature_name, error_type, df_rgn_seg_res_bb, rgn_agg_func, df_hist_feats = None, max_mom_ord = 1, **params):
super(_PsPBSF, self).__init__(feature_name = feature_name,
error_type = error_type,
max_mom_ord = max_mom_ord,
df_rgn_seg_res_bb = df_rgn_seg_res_bb,
rgn_agg_func = rgn_agg_func,
df_hist_feats = df_hist_feats,
**params)
def get_feature_func(self):
def f(args):
return self._feature_func_engine(self._myf, args, self.params)
f.__name__ = self.feature_name
return f
class _PsPBSF_Correlation(_PsPBSF):
"""
Pairwise Backbone/Sidechain Feature Correlation:
Intermediary class between a particular feature class in this module and
_feature_agent.FeatureAgent
in order to bundle common tasks
"""
def __init__(self, feature_name, partial_corr, error_type,
df_rgn_seg_res_bb, rgn_agg_func, label, **params):
s_coarse = ""
if df_rgn_seg_res_bb is not None:
s_coarse = "coarse."
params["partial_corr"] = partial_corr
super(_PsPBSF_Correlation, self).__init__(feature_name = feature_name + s_coarse + error_type + label,
error_type = error_type,
df_rgn_seg_res_bb = df_rgn_seg_res_bb,
rgn_agg_func = rgn_agg_func,
**params)
@staticmethod
def _feature_func_engine(myf, args, params):
"""
Computes Pairwise sparse Backbone/Sidechain Feature (partial) correlations
for a particular simulation with replica index r
Parameters
----------
* args : tuple (fself, myens, r):
* fself : self pointer to foreign master PySFD object
* myens : string
Name of simulated ensemble
* r : int
replica index
* params : dict, extra parameters as keyword arguments
* error_type : str
compute feature errors as ...
| "std_err" : ... standard errors
| "std_dev" : ... mean standard deviations
* df_rgn_seg_res_bb : optional pandas.DataFrame for coarse-graining that defines
regions by segIDs and resIDs, and optionally backbone/sidechain, e.g.
df_rgn_seg_res_bb = _pd.DataFrame({'rgn' : ["a1", "a2", "b1", "b2", "c"],
'seg' : ["A", "A", "B", "B", "C"],
'res' : [range(4,83), range(83,185), range(4,95), range(95,191), range(102,121)]})
if None, no coarse-graining is performed
!!!
Note: of course, coarse-graining cannot be performed here in
individual frames, but over correlation coefficients
!!!
* rgn_agg_func : function or str for coarse-graining
function that defines how to aggregate from residues (backbone/sidechain) to regions in each frame
this function uses the coarse-graining mapping defined in df_rgn_seg_res_bb
- if a function, it has to be vectorized (i.e. able to be used by, e.g., a 1-dimensional numpy array)
- if a string, it has to be readable by the aggregate function of a pandas Data.Frame,
such as "mean", "std"
* myf : function, with which to compute feature-to-feature distance
Returns
-------
* traj_df : pandas.DataFrame, contains all the feature values accumulated for this replica
* dataflags : dict, contains flags with more information about the data in traj_df
"""
fself, myens, r = args
if params["error_type"] == "std_dev":
print("WARNING: error_type \"std_dev\" not defined in _PsPBSF_Correlation !"
" Falling back to \"std_err\" instead ...")
params["error_type"] = "std_err"
fself.error_type[fself._feature_func_name] = params["error_type"]
fself.partial_corr = params["partial_corr"]
df_rgn_seg_res_bb = params["df_rgn_seg_res_bb"]
rgn_agg_func = params["rgn_agg_func"]
#traj_df, corr, a_0ind1, a_0ind2 = myf(params["sPBSF_class"], args, params)
full_traj, sub_dataflags = myf(params["sPBSF_class"], args)
l_lbl = sub_dataflags["l_lbl"]
traj_df = full_traj.transpose().corr()
dataflags = { "error_type" : fself.error_type[fself._feature_func_name] }
if fself.partial_corr:
cinv = _np.linalg.pinv(traj_df.values)
cinv_diag = _np.diag(cinv)
# square root of self inverse correlations
scinv = _np.sqrt(_np.repeat([cinv_diag], len(cinv_diag), axis = 0))
#pcorr = - cinv[i,j] / _np.sqrt(cinv[i,i] * cinv[j,j])
#corr = - cinv / scinv / scinv.transpose()
traj_df = _pd.DataFrame(- cinv / scinv / scinv.transpose(), index = traj_df.index, columns = traj_df.columns)
traj_df.index.name = "bspair1"
traj_df.columns.name = "bspair2"
traj_df = traj_df.stack(dropna = False).to_frame().reset_index()
traj_df.columns = ["bspair1", "bspair2", "f"]
if df_rgn_seg_res_bb is None:
traj_df.columns = ['bspair1', 'bspair2', 'f']
elif df_rgn_seg_res_bb is not None:
if sub_dataflags["df_rgn_seg_res_bb"] is not None:
raise TypeError("cannot coarse-grain coarse-grained PBSF feature!")
df_rgn_seg_res_bb["res"] = df_rgn_seg_res_bb["res"].astype("str")
if "bb" in df_rgn_seg_res_bb.columns:
df_rgn1_seg1_res1 = df_rgn_seg_res_bb.copy()
df_rgn1_seg1_res1.columns = ["rgn1", "seg1", "res1", "bb1"]
df_rgn2_seg2_res2 = df_rgn_seg_res_bb.copy()
df_rgn2_seg2_res2.columns = ["rgn2", "seg2", "res2", "bb2"]
df_rgn3_seg3_res3 = df_rgn_seg_res_bb.copy()
df_rgn3_seg3_res3.columns = ["rgn3", "seg3", "res3", "bb3"]
df_rgn4_seg4_res4 = df_rgn_seg_res_bb.copy()
df_rgn4_seg4_res4.columns = ["rgn4", "seg4", "res4", "bb4"]
else:
df_rgn1_seg1_res1 = df_rgn_seg_res_bb.copy()
df_rgn1_seg1_res1.columns = ["rgn1", "seg1", "res1"]
df_rgn2_seg2_res2 = df_rgn_seg_res_bb.copy()
df_rgn2_seg2_res2.columns = ["rgn2", "seg2", "res2"]
df_rgn3_seg3_res3 = df_rgn_seg_res_bb.copy()
df_rgn3_seg3_res3.columns = ["rgn3", "seg3", "res3"]
df_rgn4_seg4_res4 = df_rgn_seg_res_bb.copy()
df_rgn4_seg4_res4.columns = ["rgn4", "seg4", "res4"]
if fself.error_type[fself._feature_func_name] == "std_err":
df_tmp = traj_df["bspair1"].str.split('_', 6, expand = True)
df_tmp.columns = ["seg1", "res1", "bb1", "seg2", "res2", "bb2"]
traj_df = _pd.concat([traj_df, df_tmp], axis = 1, copy = True)
df_tmp = traj_df["bspair2"].str.split('_', 6, expand = True)
df_tmp.columns = ["seg3", "res3", "bb3", "seg4", "res4", "bb4"]
traj_df = _pd.concat([traj_df, df_tmp], axis = 1)
if "bb" in df_rgn_seg_res_bb.columns:
traj_df_seg1_res1 = traj_df[["seg1", "res1", "bb1"]].drop_duplicates()
traj_df_seg1_res1.rename(columns={"seg1" : "seg", "res1" : "res", "bb1" : "bb"}, inplace = True)
traj_df_seg2_res2 = traj_df[["seg2", "res2", "bb2"]].drop_duplicates()
traj_df_seg2_res2.rename(columns={"seg2" : "seg", "res2" : "res", "bb2" : "bb"}, inplace = True)
traj_df_seg3_res3 = traj_df[["seg3", "res3", "bb3"]].drop_duplicates()
traj_df_seg3_res3.rename(columns={"seg3" : "seg", "res3" : "res", "bb3" : "bb"}, inplace = True)
traj_df_seg4_res4 = traj_df[["seg4", "res4", "bb4"]].drop_duplicates()
traj_df_seg4_res4.rename(columns={"seg4" : "seg", "res4" : "res", "bb4" : "bb"}, inplace = True)
df_rgn1_seg1_res1 = df_rgn_seg_res_bb.copy()
df_rgn1_seg1_res1.columns = ["rgn1", "seg1", "res1", "bb1"]
df_rgn2_seg2_res2 = df_rgn_seg_res_bb.copy()
df_rgn2_seg2_res2.columns = ["rgn2", "seg2", "res2", "bb2"]
df_rgn3_seg3_res3 = df_rgn_seg_res_bb.copy()
df_rgn3_seg3_res3.columns = ["rgn3", "seg3", "res3", "bb3"]
df_rgn4_seg4_res4 = df_rgn_seg_res_bb.copy()
df_rgn4_seg4_res4.columns = ["rgn4", "seg4", "res4", "bb4"]
else:
traj_df_seg1_res1 = traj_df[["seg1", "res1"]].drop_duplicates()
traj_df_seg1_res1.rename(columns={"seg1" : "seg", "res1" : "res"}, inplace = True)
traj_df_seg2_res2 = traj_df[["seg2", "res2"]].drop_duplicates()
traj_df_seg2_res2.rename(columns={"seg2" : "seg", "res2" : "res"}, inplace = True)
traj_df_seg3_res3 = traj_df[["seg3", "res3"]].drop_duplicates()
traj_df_seg3_res3.rename(columns={"seg3" : "seg", "res3" : "res"}, inplace = True)
traj_df_seg4_res4 = traj_df[["seg4", "res4"]].drop_duplicates()
traj_df_seg4_res4.rename(columns={"seg4" : "seg", "res4" : "res"}, inplace = True)
df_rgn1_seg1_res1 = df_rgn_seg_res_bb.copy()
df_rgn1_seg1_res1.columns = ["rgn1", "seg1", "res1"]
df_rgn2_seg2_res2 = df_rgn_seg_res_bb.copy()
df_rgn2_seg2_res2.columns = ["rgn2", "seg2", "res2"]
df_rgn3_seg3_res3 = df_rgn_seg_res_bb.copy()
df_rgn3_seg3_res3.columns = ["rgn3", "seg3", "res3"]
df_rgn4_seg4_res4 = df_rgn_seg_res_bb.copy()
df_rgn4_seg4_res4.columns = ["rgn4", "seg4", "res4"]
traj_df_seg_res = _pd.concat([traj_df_seg1_res1,
traj_df_seg2_res2,
traj_df_seg3_res3,
traj_df_seg4_res4]).drop_duplicates()
df_merge = traj_df_seg_res.merge(df_rgn_seg_res_bb, how = "outer", copy = False, indicator = True)
df_merge = df_merge.query("_merge == 'right_only'")
if len(df_merge) > 0:
warnstr = "df_rgn_seg_res_bb, your coarse-graining definition, has resID entries that are not in your feature list:\n%s" % df_merge
_warnings.warn(warnstr)
traj_df = traj_df.merge(df_rgn1_seg1_res1, copy = False)
traj_df = traj_df.merge(df_rgn2_seg2_res2, copy = False)
traj_df = traj_df.merge(df_rgn3_seg3_res3, copy = False)
traj_df = traj_df.merge(df_rgn4_seg4_res4, copy = False)
traj_df.set_index(["rgn1", "rgn2", "rgn3", "rgn4"] + l_lbl, inplace = True)
traj_df = traj_df.groupby(["rgn1", "rgn2", "rgn3", "rgn4"]).agg({ 'f' : rgn_agg_func })
traj_df.reset_index(inplace = True)
traj_df['r'] = r
#traj_df.reset_index(inplace = True)
return traj_df, dataflags
class sPBSF_Correlation(_PsPBSF_Correlation):
"""
Computes (partial) correlations of sPBSF() (see spbsf module)
for a particular simulation with replica index r
If coarse-graining (via df_rgn_seg_res_bb, see below) into regions,
by default aggregate via rgn_agg_func = "mean"
Parameters
----------
* error_type : str, default="std_err"
compute feature errors as ...
| "std_err" : ... standard errors
| "std_dev" : ... mean standard deviations
* df_rgn_seg_res_bb : optional pandas.DataFrame for coarse-graining that defines
regions by segIDs and resIDs, and optionally backbone/sidechain, e.g.
df_rgn_seg_res_bb = _pd.DataFrame({'rgn' : ["a1", "a2", "b1", "b2", "c"],
'seg' : ["A", "A", "B", "B", "C"],
'res' : [range(4,83), range(83,185), range(4,95), range(95,191), range(102,121)]})
if None, no coarse-graining is performed
!!!
Note: of course, coarse-graining cannot be performed here in
individual frames, but over correlation coefficients
!!!
* rgn_agg_func : function or str for coarse-graining, default = "mean"
function that defines how to aggregate from residues (backbone/sidechain) to regions in each frame
this function uses the coarse-graining mapping defined in df_rgn_seg_res_bb
- if a function, it has to be vectorized (i.e. able to be used by, e.g., a 1-dimensional numpy array)
- if a string, it has to be readable by the aggregate function of a pandas Data.Frame,
such as "mean", "std"
* label : string, user-specific label
"""
def __init__(self, partial_corr = False, error_type = "std_err",
df_rgn_seg_res_bb = None, rgn_agg_func = "mean", label = "", sPBSF_class = None):
if sPBSF_class is None:
raise ValueError("sPBSF_class not defined")
s_pcorr = "partial_" if partial_corr else ""
super(sPBSF_Correlation, self).__init__(
feature_name = "pspbsf." + s_pcorr + "correlation." + sPBSF_class.feature_name + ".",
partial_corr = partial_corr,
error_type = error_type,
df_rgn_seg_res_bb = df_rgn_seg_res_bb,
rgn_agg_func = rgn_agg_func,
label = label,
sPBSF_class = sPBSF_class)
@staticmethod
def _myf(sPBSF_class, args):
sPBSF_class.params["is_correlation"] = True
myfeature_func = sPBSF_class.get_feature_func()
return myfeature_func(args)