""" MultiQC submodule to parse output from GATK BaseRecalibrator """

import logging
from collections import namedtuple
from itertools import groupby

from multiqc.plots import linegraph, scatter

# Initialise the logger
log = logging.getLogger(__name__)

RecalTableType = namedtuple("RecalTableType", "pre_recalibration post_recalibration".split())
recal_table_type = RecalTableType(0, 1)


class BaseRecalibratorMixin:
    def parse_gatk_base_recalibrator(self):
        """Find GATK BaseRecalibrator logs and parse their data"""

        report_table_headers = {
            "#:GATKTable:Arguments:Recalibration argument collection values used in this run": "arguments",
            "#:GATKTable:Quantized:Quality quantization map": "quality_quantization_map",
            "#:GATKTable:RecalTable0:": "recal_table_0",
            "#:GATKTable:RecalTable1:": "recal_table_1",
            "#:GATKTable:RecalTable2:": "recal_table_2",
        }
        samples_kept = {rt_type: set() for rt_type in recal_table_type}
        self.gatk_base_recalibrator = {
            recal_type: {table_name: {} for table_name in report_table_headers.values()}
            for recal_type in recal_table_type
        }

        for f in self.find_log_files("gatk/base_recalibrator", filehandles=True):
            # Check that we're not ignoring this sample name
            if self.is_ignore_sample(f["s_name"]):
                continue

            # Superfluous function call to confirm that it is used in this module
            # Replace None with actual version if it is available
            self.add_software_version(None, f["s_name"])

            parsed_data = self.parse_report(f["f"].readlines(), report_table_headers)
            rt_type = determine_recal_table_type(parsed_data)
            if len(parsed_data) > 0:
                if f["s_name"] in samples_kept[rt_type]:
                    log.debug(f"Duplicate sample name found! Overwriting: {f['s_name']}")
                samples_kept[rt_type].add(f["s_name"])

                self.add_data_source(f, section="base_recalibrator")
                for table_name, sample_tables in parsed_data.items():
                    self.gatk_base_recalibrator[rt_type][table_name][f["s_name"]] = sample_tables

        # Filter to strip out ignored sample names. Again.
        for rt_type in recal_table_type:
            for table_name, sample_tables in self.gatk_base_recalibrator[rt_type].items():
                self.gatk_base_recalibrator[rt_type][table_name] = self.ignore_samples(sample_tables)

        n_reports_found = sum([len(samples_kept[rt_type]) for rt_type in recal_table_type])

        if n_reports_found > 0:
            log.info(f"Found {n_reports_found} BaseRecalibrator reports")

            # Write data to file
            self.write_data_file(self.gatk_base_recalibrator, "gatk_base_recalibrator")

            self.add_quality_score_vs_no_of_observations_section()
            self.add_reported_vs_empirical_section()

        return n_reports_found

    def add_quality_score_vs_no_of_observations_section(self):
        """Add a section for the quality score vs number of observations line plot"""

        sample_data = []
        data_labels = []

        # Loop through the different data types
        for rt_type_name, rt_type in recal_table_type._asdict().items():
            sample_tables = self.gatk_base_recalibrator[rt_type]["quality_quantization_map"]
            if len(sample_tables) == 0:
                continue

            count_data = {}
            pct_data = {}
            for sample, table in sample_tables.items():
                count_data[sample] = {}
                pct_data[sample] = {}

                # Get the total count for this sample
                sample_y_sum = sum(int(y) for y in table["Count"])

                # Collect the data for the plots
                for x, y in zip(table["QualityScore"], table["Count"]):
                    # Quality score counts
                    count_data[sample][int(x)] = int(y)
                    # Quality score percentages
                    try:
                        pct_data[sample][int(x)] = float(y) / sample_y_sum
                    except ZeroDivisionError:
                        pct_data[sample][int(x)] = 0

            # Append the datasets for this data type
            sample_data.append(count_data)
            sample_data.append(pct_data)

            # Build data label configs for this data type
            data_labels.append({"name": f"{rt_type_name.capitalize().replace('_', '-')} Count", "ylab": "Count"})
            data_labels.append({"name": f"{rt_type_name.capitalize().replace('_', '-')} Percent", "ylab": "Percent"})

        plot = linegraph.plot(
            sample_data,
            pconfig={
                "title": "GATK: Observed Quality Score Counts",
                "id": "gatk-base-recalibrator-quality-scores-plot",
                "xlab": "Observed Quality Score",
                "ylab": "Count",
                "xDecimals": False,
                "data_labels": data_labels,
            },
        )

        # Reported vs empirical quality scores
        self.add_section(
            name="Observed Quality Scores",
            anchor="gatk-base-recalibrator-quality-scores",
            description=(
                "This plot shows the distribution of base quality scores in each sample before and "
                "after base quality score recalibration (BQSR). Applying BQSR should broaden the "
                "distribution of base quality scores."
            ),
            helptext=(
                "For more information see "
                "[the Broad's description of BQSR]"
                "(https://gatkforums.broadinstitute.org/gatk/discussion/44/base-quality-score-recalibration-bqsr)"
                "."
            ),
            plot=plot,
        )

    def add_reported_vs_empirical_section(self):
        sample_data = []
        data_labels = []

        # Loop through the different data types
        for (
            rt_type_name,
            rt_type,
        ) in recal_table_type._asdict().items():
            # This table appears to be the correct one to use for reported vs empirical
            # https://github.com/broadinstitute/gatk/blob/853b53ec2a3ac2d90d7d82a6c8451e29a34692d2/src/main/resources/org/broadinstitute/hellbender/utils/recalibration/BQSR.R#L148
            sample_tables = self.gatk_base_recalibrator[rt_type]["recal_table_1"]
            if len(sample_tables) == 0:
                continue

            reported_empirical = {}
            for sample, table in sample_tables.items():
                reported_empirical[sample] = []
                table_rows = [dict(zip(table, r)) for r in zip(*table.values())]
                table_rows.sort(key=lambda r: r["QualityScore"])
                for reported, group in groupby(table_rows, lambda r: r["QualityScore"]):
                    g = list(group)
                    reported_empirical[sample].append(
                        {
                            "x": int(reported),
                            "y": sum(float(r["EmpiricalQuality"]) for r in g) / len(g) if len(g) > 0 else 0,
                        }
                    )

            sample_data.append(reported_empirical)

            # Build data label configs for this data type
            data_labels.append(
                {"name": f"{rt_type_name} Reported vs. Empirical Quality", "ylab": "Empirical quality score"}
            )

        plot = scatter.plot(
            sample_data,
            pconfig={
                "title": "Reported vs. Empirical Quality",
                "id": "gatk-base-recalibrator-reported-empirical-plot",
                "xlab": "Reported quality score",
                "ylab": "Empirical quality score",
                "xDecimals": False,
                "data_labels": data_labels,
                "xmin": 0,
                "ymin": 0,
                "square": True,
            },
        )

        self.add_section(
            name="Reported Quality vs. Empirical Quality",
            anchor="gatk-base-recalibrator-reported-empirical",
            description="Plot shows the reported quality score vs the empirical quality score.",
            plot=plot,
        )


def determine_recal_table_type(parsed_data):
    arguments = dict(zip(parsed_data["arguments"]["Argument"], parsed_data["arguments"]["Value"]))
    if arguments["recalibration_report"] == "null":
        return recal_table_type.pre_recalibration
    else:
        return recal_table_type.post_recalibration
