# Copyright Yair Benita Y.Benita@pharm.uu.nl
# Biopython (http://biopython.org) license applies

"""Simple protein analysis.

Example:

    >>> from Bio.SeqUtils.ProtParam import ProteinAnalysis
    >>> X = ProteinAnalysis("MAEGEITTFTALTEKFNLPPGNYKKPKLLYCSNGGHFLRILPDGTVDGT"
    ...                     "RDRSDQHIQLQLSAESVGEVYIKSTETGQYLAMDTSGLLYGSQTPSEEC"
    ...                     "LFLERLEENHYNTYTSKKHAEKNWFVGLKKNGSCKRGPRTHYGQKAILF"
    ...                     "LPLPV")
    >>> print(X.count_amino_acids()['A'])
    6
    >>> print(X.count_amino_acids()['E'])
    12
    >>> print("%0.2f" % X.get_amino_acids_percent()['A'])
    0.04
    >>> print("%0.2f" % X.get_amino_acids_percent()['L'])
    0.12
    >>> print("%0.2f" % X.molecular_weight())
    17103.16
    >>> print("%0.2f" % X.aromaticity())
    0.10
    >>> print("%0.2f" % X.instability_index())
    41.98
    >>> print("%0.2f" % X.isoelectric_point())
    7.72
    >>> sec_struc = X.secondary_structure_fraction()  # [helix, turn, sheet]
    >>> print("%0.2f" % sec_struc[0])  # helix
    0.28
    >>> epsilon_prot = X.molar_extinction_coefficient()  # [reduced, oxidized]
    >>> print(epsilon_prot[0])  # with reduced cysteines
    17420
    >>> print(epsilon_prot[1])  # with disulfid bridges
    17545

    Other public methods are:
     - gravy
     - protein_scale
     - flexibility
"""

from __future__ import print_function

import sys
from Bio.SeqUtils import ProtParamData  # Local
from Bio.SeqUtils import IsoelectricPoint  # Local
from Bio.Seq import Seq
from Bio.Alphabet import IUPAC
from Bio.Data import IUPACData
from Bio.SeqUtils import molecular_weight


class ProteinAnalysis(object):
    """Class containing methods for protein analysis.

    The constructor takes two arguments.
    The first is the protein sequence as a string, which is then converted to a
    sequence object using the Bio.Seq module. This is done just to make sure
    the sequence is a protein sequence and not anything else.

    The second argument is optional. If set to True, the weight of the amino
    acids will be calculated using their monoisotopic mass (the weight of the
    most abundant isotopes for each element), instead of the average molecular
    mass (the averaged weight of all stable isotopes for each element).
    If set to false (the default value) or left out, the IUPAC average
    molecular mass will be used for the calculation.

    """

    def __init__(self, prot_sequence, monoisotopic=False):
        """Initialize the class."""
        if prot_sequence.islower():
            self.sequence = Seq(prot_sequence.upper(), IUPAC.protein)
        else:
            self.sequence = Seq(prot_sequence, IUPAC.protein)
        self.amino_acids_content = None
        self.amino_acids_percent = None
        self.length = len(self.sequence)
        self.monoisotopic = monoisotopic

    def count_amino_acids(self):
        """Count standard amino acids, return a dict.

        Counts the number times each amino acid is in the protein
        sequence. Returns a dictionary {AminoAcid:Number}.

        The return value is cached in self.amino_acids_content.
        It is not recalculated upon subsequent calls.
        """
        if self.amino_acids_content is None:
            prot_dic = dict((k, 0) for k in IUPACData.protein_letters)
            for aa in prot_dic:
                prot_dic[aa] = self.sequence.count(aa)

            self.amino_acids_content = prot_dic

        return self.amino_acids_content

    def get_amino_acids_percent(self):
        """Calculate the amino acid content in percentages.

        The same as count_amino_acids only returns the Number in percentage of
        entire sequence. Returns a dictionary of {AminoAcid:percentage}.

        The return value is cached in self.amino_acids_percent.

        input is the dictionary self.amino_acids_content.
        output is a dictionary with amino acids as keys.
        """
        if self.amino_acids_percent is None:
            aa_counts = self.count_amino_acids()

            percentages = {}
            for aa in aa_counts:
                percentages[aa] = aa_counts[aa] / float(self.length)

            self.amino_acids_percent = percentages

        return self.amino_acids_percent

    def molecular_weight(self):
        """Calculate MW from Protein sequence."""
        return molecular_weight(self.sequence, monoisotopic=self.monoisotopic)

    def aromaticity(self):
        """Calculate the aromaticity according to Lobry, 1994.

        Calculates the aromaticity value of a protein according to Lobry, 1994.
        It is simply the relative frequency of Phe+Trp+Tyr.
        """
        aromatic_aas = 'YWF'
        aa_percentages = self.get_amino_acids_percent()

        aromaticity = sum(aa_percentages[aa] for aa in aromatic_aas)

        return aromaticity

    def instability_index(self):
        """Calculate the instability index according to Guruprasad et al 1990.

        Implementation of the method of Guruprasad et al. 1990 to test a
        protein for stability. Any value above 40 means the protein is unstable
        (has a short half life).

        See: Guruprasad K., Reddy B.V.B., Pandit M.W.
        Protein Engineering 4:155-161(1990).
        """
        index = ProtParamData.DIWV
        score = 0.0

        for i in range(self.length - 1):
            this, next = self.sequence[i:i + 2]
            dipeptide_value = index[this][next]
            score += dipeptide_value

        return (10.0 / self.length) * score

    def flexibility(self):
        """Calculate the flexibility according to Vihinen, 1994.

        No argument to change window size because parameters are specific for
        a window=9. The parameters used are optimized for determining the
        flexibility.
        """
        flexibilities = ProtParamData.Flex
        window_size = 9
        weights = [0.25, 0.4375, 0.625, 0.8125, 1]
        scores = []

        for i in range(self.length - window_size):
            subsequence = self.sequence[i:i + window_size]
            score = 0.0

            for j in range(window_size // 2):
                front = subsequence[j]
                back = subsequence[window_size - j - 1]
                score += (flexibilities[front] +
                          flexibilities[back]) * weights[j]

            middle = subsequence[window_size // 2 + 1]
            score += flexibilities[middle]

            scores.append(score / 5.25)

        return scores

    def gravy(self):
        """Calculate the gravy according to Kyte and Doolittle."""
        total_gravy = sum(ProtParamData.kd[aa] for aa in self.sequence)

        return total_gravy / self.length

    def _weight_list(self, window, edge):
        """Make list of relative weight of window edges (PRIVATE).

        The relative weight of window edges are compared to the window
        center. The weights are linear. It actually generates half a list.
        For a window of size 9 and edge 0.4 you get a list of
        [0.4, 0.55, 0.7, 0.85].
        """
        unit = 2 * (1.0 - edge) / (window - 1)
        weights = [0.0] * (window // 2)

        for i in range(window // 2):
            weights[i] = edge + unit * i

        return weights

    def protein_scale(self, param_dict, window, edge=1.0):
        """Compute a profile by any amino acid scale.

        An amino acid scale is defined by a numerical value assigned to each
        type of amino acid. The most frequently used scales are the
        hydrophobicity or hydrophilicity scales and the secondary structure
        conformational parameters scales, but many other scales exist which
        are based on different chemical and physical properties of the
        amino acids.  You can set several parameters that control the
        computation of a scale profile, such as the window size and the window
        edge relative weight value.

        WindowSize: The window size is the length of the interval to use for
        the profile computation. For a window size n, we use the i-(n-1)/2
        neighboring residues on each side to compute the score for residue i.
        The score for residue i is the sum of the scaled values for these
        amino acids, optionally weighted according to their position in the
        window.

        Edge: The central amino acid of the window always has a weight of 1.
        By default, the amino acids at the remaining window positions have the
        same weight, but you can make the residue at the center of the window
        have a larger weight than the others by setting the edge value for the
        residues at the beginning and end of the interval to a value between
        0 and 1. For instance, for Edge=0.4 and a window size of 5 the weights
        will be: 0.4, 0.7, 1.0, 0.7, 0.4.

        The method returns a list of values which can be plotted to view the
        change along a protein sequence.  Many scales exist. Just add your
        favorites to the ProtParamData modules.

        Similar to expasy's ProtScale:
        http://www.expasy.org/cgi-bin/protscale.pl
        """
        # generate the weights
        #   _weight_list returns only one tail. If the list should be
        #   [0.4,0.7,1.0,0.7,0.4] what you actually get from _weights_list
        #   is [0.4,0.7]. The correct calculation is done in the loop.
        weights = self._weight_list(window, edge)
        scores = []

        # the score in each Window is divided by the sum of weights
        # (* 2 + 1) since the weight list is one sided:
        sum_of_weights = sum(weights) * 2 + 1

        for i in range(self.length - window + 1):
            subsequence = self.sequence[i:i + window]
            score = 0.0

            for j in range(window // 2):
                # walk from the outside of the Window towards the middle.
                # Iddo: try/except clauses added to avoid raising an exception
                # on a non-standard amino acid
                try:
                    front = param_dict[subsequence[j]]
                    back = param_dict[subsequence[window - j - 1]]
                    score += weights[j] * front + weights[j] * back
                except KeyError:
                    sys.stderr.write('warning: %s or %s is not a standard '
                                     'amino acid.\n'
                                     % (subsequence[j],
                                        subsequence[window - j - 1]))

            # Now add the middle value, which always has a weight of 1.
            middle = subsequence[window // 2]
            if middle in param_dict:
                score += param_dict[middle]
            else:
                sys.stderr.write('warning: %s  is not a standard amino acid.\n'
                                 % middle)

            scores.append(score / sum_of_weights)

        return scores

    def isoelectric_point(self):
        """Calculate the isoelectric point.

        Uses the module IsoelectricPoint to calculate the pI of a protein.
        """
        aa_content = self.count_amino_acids()

        ie_point = IsoelectricPoint.IsoelectricPoint(self.sequence, aa_content)
        return ie_point.pi()

    def secondary_structure_fraction(self):
        """Calculate fraction of helix, turn and sheet.

        Returns a list of the fraction of amino acids which tend
        to be in Helix, Turn or Sheet.

        Amino acids in helix: V, I, Y, F, W, L.
        Amino acids in Turn: N, P, G, S.
        Amino acids in sheet: E, M, A, L.

        Returns a tuple of three floats (Helix, Turn, Sheet).
        """
        aa_percentages = self.get_amino_acids_percent()

        helix = sum(aa_percentages[r] for r in 'VIYFWL')
        turn = sum(aa_percentages[r] for r in 'NPGS')
        sheet = sum(aa_percentages[r] for r in 'EMAL')

        return helix, turn, sheet

    def molar_extinction_coefficient(self):
        """Calculate the molar extinction coefficient.

        Calculates the molar extinction coefficient assuming cysteines
        (reduced) and cystines residues (Cys-Cys-bond)
        """
        num_aa = self.count_amino_acids()
        mec_reduced = num_aa['W'] * 5500 + num_aa['Y'] * 1490
        mec_cystines = mec_reduced + (num_aa['C'] // 2) * 125
        return(mec_reduced, mec_cystines)


if __name__ == "__main__":
    from Bio._utils import run_doctest
    run_doctest()
