#!/usr/bin/env python3

# -*- coding: utf-8 -*-

import matplotlib.pyplot as plt

from .statistic import StatisticHistogram

import singlecellmultiomics.pyutils as pyutils

import collections

import matplotlib

matplotlib.rcParams['figure.dpi'] = 160

matplotlib.use('Agg')

class AlleleHistogram(StatisticHistogram):

    def __init__(self, args):

        StatisticHistogram.__init__(self, args)

        self.histogram = collections.Counter()

    def processRead(self, R1,R2):

        for read in [R1,R2]:

            if read is None:

                continue

            if read.has_tag('DA'):

                self.histogram[read.get_tag('DA')] += 1

    def __repr__(self):

        rt = 'Allele observations:'

        for allele, obs in self.histogram.most_common():

            rt += f'{allele}\t:\t{obs}\n'

        return rt

    def __iter__(self):

        return iter(self.histogram.most_common())

    def plot(self, target_path, title=None):

        d = dict(self)

        fig, ax = plt.subplots()

        ax.scatter(list(d.keys()), list(d.values()))

        plt.subplots_adjust(hspace=1)

        ax.set_yscale('log')

        ax.set_ylabel('# Molecules')

        ax.set_xlabel('Times oversequenced')

        ax.set_xlim(0, 20.5)

        ax.set_ylim((1, None))

        if title is not None:

            plt.title(title)

        plt.tight_layout()

        plt.savefig(target_path)

        plt.close()