Source code for iss.error_models.basic

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import numpy as np

from iss import util
from iss.error_models import ErrorModel




class BasicErrorModel(ErrorModel):
    """Basic Error Model class

    Basic error model. The phred scores are based on a normal distribution.
    Only substitutions errors occur. The substitution rate is assumed
    equal between all nucleotides.
    """

    def __init__(self, fragment_length=None, fragment_sd=None, store_mutations=False):
        super().__init__()
        self.read_length = 125
        self.insert_size = 200
        self.fragment_length = fragment_length
        self.fragment_sd = fragment_sd
        self.store_mutations = store_mutations
        self.quality_forward = self.quality_reverse = 30
        self.subst_choices_for = self.subst_choices_rev = [
            {
                "A": (["T", "C", "G"], [1 / 3, 1 / 3, 1 / 3]),
                "T": (["A", "C", "G"], [1 / 3, 1 / 3, 1 / 3]),
                "C": (["A", "T", "G"], [1 / 3, 1 / 3, 1 / 3]),
                "G": (["A", "T", "C"], [1 / 3, 1 / 3, 1 / 3]),
            }
            for _ in range(self.read_length)
        ]

        self.ins_for = self.ins_rev = self.del_for = self.del_rev = [
            {"A": 0.0, "T": 0.0, "C": 0.0, "G": 0.0} for _ in range(125)
        ]



    def gen_phred_scores(self, mean_quality, orientation):
        """Generate a normal distribution, transform to phred scores

        Generate a list of phred score according to a normal distribution
        centered around the ErrorModel quality

        Args:
            mean_quality (int): mean phred score

        Returns:
            list: list of phred scores following a normal distribution
        """
        norm = [min(q, 0.9999) for q in np.random.normal(util.phred_to_prob(mean_quality), 0.01, self.read_length)]
        phred = [util.prob_to_phred(p) for p in norm]
        return phred




    def random_insert_size(self):
        """Fake random function returning the default insert size of the
        basic arror model

        Returns:
            int: insert size
        """
        return self.insert_size