NMRPCA_timerelax.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This code aims to calculate the relaxation time based on the
PCAlipids analysis.

For details check:

[Principal Component Analysis of Lipid Molecule Conformational
Changes in Molecular Dynamics Simulations
Pavel Buslaev, Valentin Gordeliy, Sergei Grudinin, and Ivan Gushchin
Journal of Chemical Theory and Computation 2016 12 (3), 1019-1028
DOI: 10.1021/acs.jctc.5b01106 ]

and

[Principal component analysis highlights the influence of temperature,
curvature and cholesterol on conformational dynamics of lipids
Pavel Buslaev, Khalid Mustafin, and Ivan Gushchin
Biochimica et Biophysica Acta (BBA) - Biomembranes
Volume 1862, Issue 7, 1 July 2020, 183253
DOI: 10.1016/j.bbamem.2020.183253]

The main idea is to run PCA on concatenated lipid trajectory,
calculate the autocorrelation times, which are linearly related
to equilibration times of individual lipids. The parameters of linear
transform are calculated based on the trajectories from NMRlipids
databank.

The code was developed by
Dr. Pavel Buslaev, pavel.i.buslaev@jyu.fi
Dr. Samuli Olilla
Patrik Kula
Alexander Kuzmin
"""

import gc
import json
import os
import sys
import urllib.request
import warnings

import MDAnalysis as mda
import MDAnalysis.transformations
import numpy as np
import yaml
from MDAnalysis.analysis import align
from MDAnalysis.analysis.base import AnalysisFromFunction
from scipy import signal

sys.path.insert(1, "../BuildDatabank/")

from databankLibrary import databank, download_link, lipids_dict, download_resource_from_uri


SKIPLIPIDS = ["CHOL", "DCHOL"]

mergeCutoff = 2.0
trjSizeCutoff = 5000000000

TAILSN1 = "sn-1"
TAILSN2 = "sn-2"
HEADGRP = "headgroup"

# In case you want to test for a specific coordinate change the flag to yes
TEST = False

"""
Class Parser is a basic class to work with the trajectory. It has basic utility
for preparing trajectories:
    1. Checking if simulation has a correct name
    2. Checking if trajectory is downloaded. Downloading, if not
    3. Calling AmberMerger to merge head groups and tails for Amber
       trajectories
    4. Concatenate trajectories
"""


class Parser:
    """
    Constructor for Parser:
        root          - path to the directory with trajectories
        readme        - simulation data
        eq_time_fname - name of the output file
        path          - name of a particular trajectory
        v             - verbosity for testing
    """

    def __init__(self,
                 root,
                 readme,
                 eq_time_fname="eq_times.json",
                 path=None,
                 v=True):
        # Technical
        self.verbose = v
        self.error = 0

        # Path
        self.root = root
        self.eq_time_fname = eq_time_fname

        # Extracting data from readme
        #self.indexingPath = "/".join(readme["path"].split("/")[4:8])
        self.indexingPath = root + readme["path"]
        print('INdexing path:', self.indexingPath)
        if self.verbose:
            print(f"Parser: Processing trajectory {self.indexingPath}")
        self.doi = readme["DOI"]
        self.soft = readme["SOFTWARE"]
        if self.soft == "openMM" or self.soft == "NAMD":
            try:
                self.trj = readme["TRJ"][0][0]
                self.tpr = readme["PDB"][0][0]
            except Exception:
                print("Parser: Did not find trajectory or pdb for openMM or NAMD")
                self.error = 1
        else:
            try:
                self.trj = readme["TRJ"][0][0]
                self.tpr = readme["TPR"][0][0]
            except Exception:
                print("Parser: Did not find trajectory or tpr for GROMACS")
                self.trj = ""
                self.tpr = ""
                self.error = 1
        self.trj_name = f"{self.root}{self.indexingPath}/{self.trj}"
        self.tpr_name = f"{self.root}{self.indexingPath}/{self.tpr}"
        self.trj_url = download_link(self.doi, self.trj)
        self.tpr_url = download_link(self.doi, self.tpr)
        self.trjLen = readme["TRJLENGTH"] / 1000  # ns
        self.FF = readme.get("FF")

        self.size = readme["TRAJECTORY_SIZE"]

        self.composition = readme["COMPOSITION"]
        lipids_list = list(lipids_dict.keys())
        self.lipids = []
        # TODO: add to the lipids those lipids that are indicated in .yaml
        for lipid in lipids_list:
            try:
                if self.composition[lipid] != 0:
                    self.lipids.append(lipid)
            except KeyError:
                continue

        self.path = path

    """
    Path validation. Behaviour depends on the input.
        1. If ther were any errors, validation fails. Currently the only
           error tested is that .xtc and .tpr files are not present. This
           is the case for non-GROMACS trajectories.
        2. If path is not provided, parser is iterating over all trajectories.
        3. If path is provided and current path is equal to the provided one,
           parser reports that it finds the trajectory for the analysis.
    """

    def validatePath(self):
        if self.error > 0:
            # This is an error message. Printing even in silent mode
            print(
                "Parser: Can't read TPR/PDB and TRJ from README for " +
                f"{self.indexingPath}")
        if self.verbose and not self.path:
            print(
                "Parser: Iterating over all trajectories. "
                + f"Current trajectory is {self.indexingPath}"
            )
            #return 
        #if self.path == self.indexingPath:
        if os.path.isfile(f"{self.root}{self.indexingPath}" +
                              f"/{self.eq_time_fname}"):
            if self.verbose:
                print("Parser: Found file with equilibration data. " +
                          "Not processing the trajectory")
                return -1
        if self.verbose:
            print(f"Parser: Found trajectory {self.indexingPath}")
        return 0

        #return -1

    """
    Basic trajectory and TPR download.
    """

    def downloadTraj(self):
        print("Downloading")
        if not os.path.isfile(self.tpr_name):
            # This is a log message. Printing even in silent mode
            print("Parser: Downloading tpr ", self.doi)
            #urllib.request.urlretrieve(self.tpr_url, self.tpr_name)
            download_resource_from_uri(self.tpr_url, self.tpr_name)
        if not os.path.isfile(self.trj_name):
            # This is a log message. Printing even in silent mode
            print("Parser: Downloading trj ", self.doi)
            #urllib.request.urlretrieve(self.trj_url, self.trj_name)
            download_resource_from_uri(self.trj_url, self.trj_name)

    """
    Preparing trajectory. If centered trajectory is found, use it. If whole
    trajectory is found, use it. Otherwise, call gmx trjconv to make whole
    trajectory. The selected trajectory is loaded into Universe.
    """

    def prepareGMXTraj(self):
        # Look for centered.xtc
        if os.path.isfile(f"{self.root}{self.indexingPath}/centered.xtc"):
            print("Parser: Founder centered trajectory: centered.xtc")
            self.trj_name = f"{self.root}{self.indexingPath}/centered.xtc"
        # Look for whole.xtc
        elif os.path.isfile(f"{self.root}{self.indexingPath}/whole.xtc"):
            print("Parser: Founder whole trajectory: whole.xtc")
            self.trj_name = f"{self.root}{self.indexingPath}/whole.xtc"
        # Run gmx trjconv
        else:
            print("Parser: Making trajectory whole")
            trj_out_name = f"{self.root}{self.indexingPath}/whole.xtc"
            os.system(
                f"echo System | gmx trjconv -f {self.trj_name} -s "
                + f"{self.tpr_name} -pbc mol -o {trj_out_name}"
            )
            self.trj_name = trj_out_name

        # Skip frames for large trajectories
        if self.size > trjSizeCutoff:
            trj_out_name = f"{self.root}{self.indexingPath}/short.xtc"
            os.system(
                f"echo System | gmx trjconv -f {self.trj_name} -s "
                + f"{self.tpr_name} -pbc mol -o {trj_out_name} -skip 10"
            )
            self.trj_name = trj_out_name

        # Create .gro file
        if os.path.isfile(f"{self.root}{self.indexingPath}/conf.gro"):
            print("Parser: Founder gro file")
            self.gro_name = f"{self.root}{self.indexingPath}/conf.gro"
        else:
            print("Parser: Making a gro file from the first frame")
            os.system(
                f"echo System | gmx trjconv -s {self.tpr_name}"
                + f" -f {self.trj_name} -dump 0 -o conf.gro"
            )

        # Loading trajectory
        try:
            self.traj = mda.Universe(self.tpr_name, self.trj_name)
        except:
            self.traj = mda.Universe(self.gro_name, self.trj_name)

    def prepareOpenMMTraj(self):
        print("openMM or NAMD")
        if self.size > trjSizeCutoff:
            trj_out_name = f"{self.root}{self.indexingPath}/short.xtc"
            if os.path.isfile(f"{self.root}{self.indexingPath}/{trj_out_name}"):
                print("Parser: Short trajectory is found")
            else:
                u = mda.Universe(self.tpr_name, self.trj_name)
                with mda.Writer(trj_out_name, 
                                u.select_atoms("all").n_atoms) as W:
                    for ts in u.trajectory[::10]:
                        W.write(u.select_atoms("all"))

            self.trj_name = trj_out_name
        
        self.traj = mda.Universe(self.tpr_name, self.trj_name)

    def prepareTraj(self):
        if self.soft == "openMM" or self.soft == "NAMD":
            self.prepareOpenMMTraj()
        else:
            self.prepareGMXTraj()

    """
    Create Concatenator and corresponding concatenated trajectories for
    all lipids available for the trajectory.
    """

    def concatenateTraj(self):
        self.concatenated_trajs = []
        for lipid in self.lipids:
            if lipid in SKIPLIPIDS:
                # We do not treat cholesterols
                continue
            topology = Topology(
                self.FF,
                self.traj,
                self.composition[lipid]["NAME"],
                self.composition[lipid]["MAPPING"]
            )
            concatenator = Concatenator(topology,
                                        self.traj,
                                        lipid,
                                        self.composition[lipid]["NAME"])
            self.concatenated_trajs.append(concatenator.concatenate())

    """
    Write data to json file
    """

    def dumpData(self, data):
        f = open(f"{self.root}{self.indexingPath}/{self.eq_time_fname}", "w")
        json.dump(data, f)
        f.close()


"""
Class Topology is a class needed to extract lipid specific data and also to
merge lipids from Amber trajectories, where lipid is often represented as 3
residue types. It has the following methods:
    1. Simple constructor, which sets the force field,residue names,
       trajectory, and loads mapping_file
    2. Mapping file loader
    3. Function that outputs atomNames
    4. Checker if the merge is needed. Currently defunct
    5. Runner, that returns lists for head, tail1, tail2 orders for merging
       Currently defunct
"""


class Topology:
    """
    Constructor for Topology:
        ff            - force field name
        traj          - MDAnalysis trajectory
        lipid_resname - name of lipid
        mapping_file  - path to maping file
    """

    def __init__(self, ff, traj, lipid_resname, mapping_file):
        self.ff = ff
        self.lipid_resname = lipid_resname
        self.traj = traj
        self.mapping = self.loadMappingFile(mapping_file)

    """
    Basic loader of the mapping file.
    """

    def loadMappingFile(self, mapping_file):
        mapping_dict = {}
        with open(f"../BuildDatabank/mapping_files/{mapping_file}", "r") as yaml_file:
            mapping_dict = yaml.load(yaml_file, Loader=yaml.FullLoader)
        yaml_file.close()
        return mapping_dict

    """
    Extract all names of heavy atoms from the mapping
    """

    def atomNames(self):
        atoms = []
        for key in self.mapping:
            atom = self.mapping[key]["ATOMNAME"]
            if atom[0] == "H":
                continue
            atoms.append(atom)
        return atoms

    """
    Checker for merge. Currently it checks if the RESIDUE key is in the
    mapping file.
    NOTE: This has to be changed if the content of mapping file changes
    """

    def isMergeNeeded(self):
        if "RESIDUE" not in self.mapping[list(self.mapping.keys())[0]].keys():
            return False
        resnames = []
        for key in self.mapping:
            atom = self.mapping[key]["ATOMNAME"]
            atomNames = self.atomNames()
            if atom in atomNames:
                resnames.append(self.mapping[key]["RESIDUE"])
        resnames = set(resnames)
        if len(resnames) > 1:
            return resnames
        return False

    """
    Helper function that gets the residue names for lipid, if merge is needed
    """

    def getLipidResnames(self):
        resnames = self.isMergeNeeded()
        if self.isMergeNeeded():
            return resnames
        # How can we end up here?
        # Since currently we only call this function when Merge is needed,
        # this is a place for checking if everything is ok and we can raise
        else:
            return self.lipid_resname

    """
    Helper function that finds head, sn-1 and sn-2 tails
    NOTE: currently there are only lipids with 2 different tails available in
    databank: e.g. POPC or POPG. This leads to different names of tails. This
    won't be the case for DOPC. Currently the algorithm is using that all three
    groups differ
    """

    def assignResnames(self, resnames):
        if self.isMergeNeeded():
            resnameDict = {}
            # First find headgroup
            for key in self.mapping:
                resname = self.mapping[key]["RESIDUE"]
                if self.mapping[key]["FRAGMENT"] == HEADGRP:
                    resnameDict[HEADGRP] = resname
                    break
            for key in self.mapping:
                resname = self.mapping[key]["RESIDUE"]
                if (
                    TAILSN1 not in resnameDict
                    and TAILSN1 == self.mapping[key]["FRAGMENT"]
                    and not resnameDict[HEADGRP] == resname
                ):
                    resnameDict[TAILSN1] = resname
                if (
                    TAILSN2 not in resnameDict
                    and TAILSN2 == self.mapping[key]["FRAGMENT"]
                    and not resnameDict[HEADGRP] == resname
                ):
                    resnameDict[TAILSN2] = resname
                if TAILSN1 in resnameDict and TAILSN2 in resnameDict:
                    break
            # TODO: add check that all resnames from input are in the dict
            return resnameDict
        # How can we end up here?
        else:
            return None

    """
    Find lipid tails that correspond to a particular head-group.
    NOTE: currently there are only lipids with 2 different tails available in
    databank: e.g. POPC or POPG. This leads to different names of tails. This
    won't be the case for DOPC. Currently the algorithm is using that all three
    groups differ
    TODO: get the correspondence from structure
    """

    def runMerger(self):
        resnames = self.getLipidResnames()
        resnameDict = self.assignResnames(resnames)
        head_residues = [
            r.atoms.select_atoms("not name H* and mass > 0.8")
            for r in self.traj.select_atoms(
                f"not name H* and resname {resnameDict[HEADGRP]}"
            ).residues
        ]
        sn_1_residues = [
            r.atoms.select_atoms("not name H* and mass > 0.8")
            for r in self.traj.select_atoms(
                f"not name H* and resname {resnameDict[TAILSN1]} and "
                + f"around {mergeCutoff} (resname {resnameDict[HEADGRP]} "
                + "and not name H*)"
            ).residues
        ]
        sn_2_residues = [
            r.atoms.select_atoms("not name H* and mass > 0.8")
            for r in self.traj.select_atoms(
                f"not name H* and resname {resnameDict[TAILSN2]} and "
                + f"around {mergeCutoff} (resname {resnameDict[HEADGRP]} "
                + "and not name H*)"
            ).residues
        ]
        return head_residues, sn_1_residues, sn_2_residues


"""
Class Concatenator is a class needed to concatenate trajectory for lipid types.
It has the following methods:
    1. Simple constructor, which sets the topology,residue names, and
       trajectory
    2. concatenateTraj method to do the basic by lipid concatenation
    3. alignTraj method to perform the alignment of the concatenated trajectory
    4. The enveloping concatenate method
"""


class Concatenator:
    """
    Constructor for Concatenator:
        topology      - topology for lipid
        traj          - MDAnalysis trajectory
        lipid_name    - lipid name in the databank
        lipid_resname - lipid resname in the trajectory
    """

    def __init__(self, topology, traj, lipid_name, lipid_resname):
        self.topology = topology
        self.traj = traj
        self.lipid_name = lipid_name
        self.lipid_resname = lipid_resname

        if self.topology.isMergeNeeded():
            self.headlist, self.tail1list, self.tail2list = \
                self.topology.runMerger()
            # TODO: raise if length of 3 lists is not equal
        else:
            self.headlist = None
            self.tail1list = None
            self.tail2list = None

    """
    concatenateTraj performs basic trajectory concatination. First, it extracts
    coordinates from trajectory, next, it reshapes the coordinate array, swaps
    time and resid axes to obtain continuous trajectories of individual lipids
    (this is needed for autocorrelation time analysis), and finally merges
    individual lipid trajectories.
    """

    def concatenateTraj(self):
        traj = self.traj.trajectory
        n_frames = len(traj)

        heavy_atoms_topology = self.traj.select_atoms(
            f"resname {self.lipid_resname} and not name H*"
        )

        n_atoms_lipid = len(self.topology.atomNames())

        n_lipid = heavy_atoms_topology.n_residues
        if n_lipid * n_atoms_lipid != heavy_atoms_topology.n_atoms:
            n_lipid = heavy_atoms_topology.n_atoms // n_atoms_lipid

        # Get all coordinates n_frames, n_lipid * n_atoms_lipid
        coords = (
            AnalysisFromFunction(lambda ag: ag.positions.copy(),
                                 heavy_atoms_topology)
            .run()
            .results["timeseries"]
        )
        # Adding axis to separate individual lipid trajectories
        coords = coords.reshape((n_frames, n_lipid, n_atoms_lipid, 3))
        # Swapping time frame with lipid axis
        # Now we have continuous lipid trajectory
        coords = np.swapaxes(coords, 0, 1)
        # Reshaping to desired shape
        coords = coords.reshape((n_frames * n_lipid, n_atoms_lipid, 3))

        # Creating new Universe from the concatenated coordinates
        atom_resindex = np.repeat(0, n_atoms_lipid)
        concatenated_traj = mda.Universe.empty(
            n_atoms_lipid, 1, atom_resindex=atom_resindex, trajectory=True
        )
        concatenated_traj.add_TopologyAttr("resname", [self.lipid_resname])
        concatenated_traj.load_new(coords, in_memory=False)

        return concatenated_traj, n_lipid, n_frames * n_lipid

    """
    concatenateTrajWithMerging performs basic trajectory concatination. In
    contrast to basic concatenateTraj it additionally merges splitted lipids.
    First, it creates extracts coordinates from trajectory, next, it reshapes
    the coordinate array, swaps time and resid axes to obtain continuous
    trajectories of individual lipids (this is needed for autocorrelation
    time analysis), and finally merges individual lipid trajectories.
    """

    def concatenateTrajWithMerging(self):
        traj = self.traj.trajectory
        n_frames = len(traj)

        heavy_atoms_topology = \
            self.headlist[0] + \
            self.tail1list[0] + \
            self.tail2list[0]

        for head, sn1, sn2 in zip(
            self.headlist[1:], self.tail1list[1:], self.tail2list[1:]
        ):
            heavy_atoms_topology = heavy_atoms_topology.union(head + sn1 + sn2)

        n_atoms_lipid = len(self.topology.atomNames())

        n_lipid = heavy_atoms_topology.n_atoms // n_atoms_lipid

        # TODO: add check
        # n_atoms_lipid == heavy_atoms_topology.n_atoms

        # Get all coordinates n_frames, n_lipid * n_atoms_lipid
        coords = (
            AnalysisFromFunction(lambda ag: ag.positions.copy(),
                                 heavy_atoms_topology)
            .run()
            .results["timeseries"]
        )
        # Adding axis to separate individual lipid trajectories
        coords = coords.reshape((n_frames, n_lipid, n_atoms_lipid, 3))
        # Swapping time frame with lipid axis
        # Now we have continuous lipid trajectory
        coords = np.swapaxes(coords, 0, 1)
        # Reshaping to desired shape
        coords = coords.reshape((n_frames * n_lipid, n_atoms_lipid, 3))

        # Creating new Universe from the concatenated coordinates
        atom_resindex = np.repeat(0, n_atoms_lipid)
        concatenated_traj = mda.Universe.empty(
            n_atoms_lipid, 1, atom_resindex=atom_resindex, trajectory=True
        )
        concatenated_traj.add_TopologyAttr("resname", [self.lipid_resname])
        concatenated_traj.load_new(coords, in_memory=False)

        return concatenated_traj, n_lipid, n_frames * n_lipid

    """
    alignTraj alignes the concatenated trajectory in two steps: (1) it computes
    average structure after alignment to the first frame, and (2) it alignes
    the structure to the calculated average structure in (1).
    """

    def alignTraj(self, concatenated_traj):
        # Compute average structure after alignment to the first frame
        av = align.AverageStructure(concatenated_traj, ref_frame=0).run()
        # Align to average structure
        align.AlignTraj(concatenated_traj, av.results.universe).run()
        # Compute average structure after second alignment
        coords = (
            AnalysisFromFunction(
                lambda ag: ag.positions.copy(),
                concatenated_traj.select_atoms("all")
            )
            .run()
            .results["timeseries"]
        )
        return coords.reshape(-1, 3), coords.mean(axis=0).reshape(1, -1)

    """
    Simple enveloping function to perform concatenation
    """

    def concatenate(self):
        print(f"Concatenator: Concatenating lipid {self.lipid_name}")
        if not self.topology.isMergeNeeded():
            # Merging is not needed
            concatenated_traj, n_lipid, n_frames = self.concatenateTraj()
        else:
            concatenated_traj, n_lipid, n_frames = \
                self.concatenateTrajWithMerging()
        aligned_traj, av_pos = self.alignTraj(concatenated_traj)

        return aligned_traj, av_pos, n_lipid, n_frames, self.lipid_name


"""
Class PCA is a class that actually performs PCA. It has the following methods:
    1. Simple constructor, which sets the aligned trajtory, average coordinates,
       number of lipids, number of frames in the concatenated trajectory and
       trajectory length in ns
    2. PCA prepares the trajectory coordinates for analysis and calculates
       principal components
    3. get_proj projects the trajectory on principal components
    4. get_lipid_autocorrelation calculates the autocorrelation timeseries for
       individual lipid
    5. get_autocorrelations calculates the autocorrelation timeseries for
       trajectory
"""


class PCA:
    """
    Constructor for PCA:
        aligned_traj - np.array with positions of concatenated and aligned
                       trajectory
        av_pos       - np.array with average positions for the lipid
        n_lipid      - number of lipids of particular type in the system
        n_frames     - number of frames in the concatenated trajectory
        traj_time    - trajectory length in ns
    """

    def __init__(self, aligned_traj, av_pos, n_lipid, n_frames, traj_time):
        self.aligned_traj = aligned_traj
        self.av_pos = av_pos
        self.n_lipid = n_lipid
        self.n_frames = n_frames
        self.traj_time = traj_time

    """
    PCA calculates the PCA. First the data is centered and then covariance
    matrix is calculated manually.
    """

    def PCA(self):
        # centering of positions relative to the origin
        X = self.aligned_traj.astype(np.float64)
        X = X.reshape(self.n_frames, self.av_pos.shape[1]) - self.av_pos
        # the sum of all coordinates (to calculate mean)
        X_1 = X.sum(axis=0)
        # production of X and X^T
        X_X = np.tensordot(X, X, axes=(0, 0))
        # covariance matrix calculation
        cov_mat = (
            X_X
            - np.dot(X_1.reshape(len(X_1), 1), (X_1.reshape(len(X_1), 1)).T)
            / self.n_frames
        ) / (self.n_frames - 1)
        # eigenvalues and eigenvectors calculation
        eig_vals, eig_vecs = np.linalg.eigh(cov_mat)
        self.eig_vecs = np.flip(eig_vecs, axis=1).T

        return X

    """
    Projecting the trajectory on the 1st principal component
    """

    def get_proj(self, cdata):
        # projection on PC1
        proj = np.tensordot(cdata, self.eig_vecs[0:1], axes=(1, 1)).T
        proj = np.concatenate(np.array(proj), axis=None)

        self.proj = proj

    """
    Autocorrelation calculation for individual lipid.
    """

    def get_lipid_autocorrelation(self, data, variance, mean):
        # Centering the data
        data -= mean
        # Convolve the data
        r = signal.fftconvolve(data, data[::-1], mode="full")[-len(data):]
        # Weight the correlation to get the result in range -1 to 1
        return r / (variance * (np.arange(len(data), 0, -1)))

    """
    Autocorrelation calculation for the trajectory.
    """

    def get_autocorrelations(self):
        variance = self.proj.var()
        mean = self.proj.mean()
        # extract the trajectories for individual lipids
        separate_projs = [
            self.proj[
                len(self.proj) * i // self.n_lipid:len(self.proj)
                * (i + 1) // self.n_lipid]
            for i in range(self.n_lipid)
        ]
        # calculate autocorrelations for individual lipids
        R = np.array(
            [self.get_lipid_autocorrelation(x, variance, mean)
                for x in separate_projs]
        )
        R = R.mean(axis=0)
        T = np.arange(len(R)) * self.traj_time / len(R)
        self.autocorrelation = np.array([T, R]).T


"""
Class TimeEstimator is a class that estimates equilbration time from
autocorrelation data. It includes the following methods:
    1. Simple constructor, which sets the autocorrelation data
    2. Helper method get_nearest_value that finds the interval in the data
       where the autocorrelation falls below specific value
    3. timerelax method calculates the logarithms of autocorrelation and
       calculates the decay time
    4. calculate_time method calculates the estimated equilibration time
"""


class TimeEstimator:
    """
    Constructor for PCA:
        autocorrelation - autocorrelation data
    """

    def __init__(self, autocorrelation):
        self.autocorrelation = autocorrelation

    """
    get_nearest_value method return the indices that frame the particular value
    As an input it gets an array, which is expected to decay. The method tries
    to find and index (index_A), for which the array gets below the cutoff value
    for the first time. Then, for index_A-1 the array was larger than the cutoff
    value for the last time. It means that the function, represented by the
    array data is equal to cutoff somewhere between timesteps that correspond to
    index_A-1 and index_A. It can happen, that this index_A is equal 0. Then,
    method searches for the first occurance, where array is smaller than
    array[0]. The method returns the interval inbetween the array gets below
    cutoff.
    """

    def get_nearest_value(self, iterable, value):
        try:
            A = np.where(iterable < value)[0][0]
        except:
            print("TimeEstimator: Autocorrelations do not converge. "
                  + "We shift to extrapolation regime.")
            A = np.where(iterable == np.min(iterable))[0][0]
            return A, A - 1

        if A == 0:
            B = np.where(iterable < iterable[A])[0][0]
        else:
            B = A - 1
        return A, B

    """
    Estimates autocorrelation decay time.
    """

    def timerelax(self):
        time = self.autocorrelation[:, 0]
        autocorrelation = self.autocorrelation[:, 1]

        points = []
        j = 1
        while j < len(autocorrelation):
            points.append(j)
            j = int(1.5 * j) + 1
        # Get timeseries for autocorrelation
        T_pic = np.array([time[i] for i in points if autocorrelation[i] > 0.0])
        R_pic = np.array(
            [autocorrelation[i] for i in points if autocorrelation[i] > 0.0]
        )
        # Calculate logs for time and autocorrelation arrays
        # We use log since in log-log scale autocorrelation is closer to linear
        R_log = np.log(R_pic)
        T_log = np.log(T_pic)
        # data interpolation. We are searching for time interval where
        # autocorrelations decay by e. This is the most stable method.
        # Autocorrelations decay by e is equivalent to
        # log(autocorrelation) < - 1
        power = -1
        A, B = self.get_nearest_value(R_log, power)
        # perform interpolation
        a = (T_log[B] - T_log[A]) / (R_log[B] - R_log[A])
        b = T_log[A] - a * R_log[A]
        t_relax1 = a * power + b
        T_relax1 = np.exp(t_relax1)

        return T_relax1

    """
    Basic enveloping method that estimates equilibration time from
    autocorrelation decay time. They are linearly connected and the
    coefficient is calculated experimentally.
    """

    def calculate_time(self):
        # relaxation time at e^1 decay
        te1 = self.timerelax()

        return te1 * 49  # 49 from sample data average of tkss2/tac1


"""
Main runs the loop over all databank trajectories and does the following:
    1. Calls the parser to check, merge, and concatenate trajectory
    2. Calls PCA to gep PCs, projections, and ACs
    3. Calls Time calculation

The parser assumes that the trajectory is downloaded and lipids are made
whole, or GROMACS is installed and available via gmx command.
"""
if __name__ == "__main__":

    warnings.filterwarnings("ignore", category=UserWarning)

    path = "../../Data/Simulations/"
    db_data = databank(path)
    # searches through every subfolder of a path
    # and finds every trajectory in databank
    systems = db_data.get_systems()

    i = 0
    listall = []

    # testTraj = (
    #    "fd8/18f/fd818f1fa1b32dcd80ac3a124e76bd2d73705abe/"
    #    + "fd9cef87eca7bfbaac8581358f2d8f13d8d43cd1"
    # )
    # testTraj = "1c4/77d/1c477da411113327d1c0e43eea10b0f096031d45/" + \
    #           "35a315cecf0156381237417893bf6755b08ab3e8"
    # testTraj = (
    #    "0a4/101/0a41017641414540973e921ed22528d1f3dc414b/"
    #    + "3c0936e61fa40cce74fd1828a2697742709e91fb"
    # )
    # testTraj = (
    #    "813/2bc/8132bc41c60e0156caeb7a99f11efb3206fa1619/" +
    #    "69de81ab0e12664c93685826a4b1734bcde763fe"
    # )
    # testTraj = (
    #    "b86/e1d/b86e1d05121438d07f56d447bf4a0ec1add4af0c/" +
    #    "b7cad43d5423af6dee7da746eabe48b79e79c271"
    #    )
    #testTraj = (
    #    "387/6d8/3876d878edf3efd6e4b40a7e78661581b1dfd387/" +
    #    "b048d322ae8d7ee95e888b26f87dda3ec6fd9350"
    #)
    testTraj = (
        "e69/c46/e69c46a7f69132e734a5dc908ef0bbfa03a91abd/" + 
        "e69c46a7f69132e734a5dc908ef0bbfa03a91abd"
    )
    eq_time_fname = "eq_times.json"

    for readme in systems:
        print(readme)
        # getting data from databank and preprocessing them
        # Start Parser
        if TEST:
            print("TEST")
            parser = Parser(path, readme, eq_time_fname, testTraj)
        else:
            parser = Parser(path, readme, eq_time_fname)
        # Check trajectory
        print(path,testTraj, readme['path'])
        print(parser.indexingPath)
        print(parser.validatePath())
        if parser.validatePath() < 0:
            continue
        # Download files

        eq_time_path = path + readme['path'] + "/eq_times.json"
        print(eq_time_path)
        if os.path.isfile(eq_time_path):
            continue

        #if readme['SOFTWARE'] == 'openMM':
        #    continue

        #if readme['TRAJECTORY_SIZE'] > 5000000000:
            #continue
        
        if ('WARNINGS' in readme and 
            readme['WARNINGS'] is not None and 
            'AMBIGUOUS_ATOMNAMES' in readme['WARNINGS']):
            continue

        if ('WARNINGS' in readme and 
            readme['WARNINGS'] is not None and
            'SKIP_EQTIMES' in readme['WARNINGS']):
            continue

        parser.downloadTraj()
        # Prepare trajectory
        parser.prepareTraj()
        # Concatenate trajectory
        parser.concatenateTraj()
        equilibration_times = {}
        # Iterate over trajectories for different lipids
        for traj in parser.concatenated_trajs:
            print(f"Main: parsing lipid {traj[4]}")
            # Creat PCA for trajectory
            pca_runner = PCA(traj[0], traj[1], traj[2], traj[3], parser.trjLen)
            # Run PCA
            data = pca_runner.PCA()
            print("Main: PCA: done")
            # Project trajectory on PC1
            pca_runner.get_proj(data)
            print("Main: Projections: done")
            # Calculate autocorrelations
            pca_runner.get_autocorrelations()
            print("Main: Autocorrelations: done")
            # Estimate equilibration time
            te2 = TimeEstimator(pca_runner.autocorrelation).calculate_time()
            equilibration_times[traj[4]] = te2 / parser.trjLen
            print("Main: EQ time: done")

            print(te2 / parser.trjLen)
        gc.collect()
        parser.dumpData(equilibration_times)
        if TEST:
            break