""" MultiQC submodule to parse output from Sentieon AlignmentSummaryMetrics
 (based on the Picard module of the same name) """

import logging
import os
from collections import OrderedDict

from multiqc.plots import bargraph

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


def parse_reports(self):
    """Find Sentieon AlignmentSummaryMetrics reports and parse their data"""

    # Set up vars
    self.sentieon_alignment_metrics = dict()

    # Go through logs and find Metrics
    for f in self.find_log_files("sentieon/alignment_metrics", filehandles=True):
        parsed_data = dict()
        s_name = None
        keys = None
        for l in f["f"]:
            # New log starting
            if s_name is None and "AlignmentStat" in l:
                keys = None
                # Pull sample name from filename
                s_name = os.path.basename(f["s_name"])
                s_name = self.clean_s_name(s_name, f)
                parsed_data[s_name] = dict()

            if s_name is not None:
                if "AlignmentStat" in l and "#SentieonCommandLine" in l:
                    keys = f["f"].readline().strip("\n").split("\t")
                elif keys:
                    vals = l.strip("\n").split("\t")
                    if len(vals) == len(keys):
                        # Ignore the FIRST_OF_PAIR / SECOND_OF_PAIR data
                        # to simplify things
                        if vals[0] == "PAIR" or vals[0] == "UNPAIRED":
                            for i, k in enumerate(keys):
                                try:
                                    parsed_data[s_name][k] = float(vals[i])
                                except ValueError:
                                    parsed_data[s_name][k] = vals[i]
                    else:
                        s_name = None
                        keys = None

        # Remove empty dictionaries
        for s_name in list(parsed_data.keys()):
            if len(parsed_data[s_name]) == 0:
                parsed_data.pop(s_name, None)

        # Manipulate sample names if multiple baits found
        for s_name in parsed_data.keys():
            if s_name in self.sentieon_alignment_metrics:
                log.debug(
                    "Duplicate sample name found in {}!\
                          Overwriting: {}".format(
                        f["fn"], s_name
                    )
                )
            self.add_data_source(f, s_name, section="AlignmentSummaryMetrics")
            self.sentieon_alignment_metrics[s_name] = parsed_data[s_name]

    # Filter to strip out ignored sample names
    self.sentieon_alignment_metrics = self.ignore_samples(self.sentieon_alignment_metrics)

    if len(self.sentieon_alignment_metrics) > 0:

        # Write parsed data to a file
        self.write_data_file(self.sentieon_alignment_metrics, "multiqc_sentieon_AlignmentSummaryMetrics")

        # Add to general stats table
        self.general_stats_headers["PCT_PF_READS_ALIGNED"] = {
            "title": "% Aligned",
            "description": "Percent of aligned reads",
            "max": 100,
            "min": 0,
            "suffix": "%",
            "format": "{:,.0f}",
            "scale": "RdYlGn",
            "modify": lambda x: self.multiply_hundred(x),
        }
        for s_name in self.sentieon_alignment_metrics:
            if s_name not in self.general_stats_data:
                self.general_stats_data[s_name] = dict()
            self.general_stats_data[s_name].update(self.sentieon_alignment_metrics[s_name])

        # Make the bar plot of alignment read count
        pdata = dict()
        for s_name in self.sentieon_alignment_metrics.keys():
            pdata[s_name] = dict()
            # Sentieon reports both reads for PE data.
            # Divide it by two as most people will expect # clusters
            if self.sentieon_alignment_metrics[s_name]["CATEGORY"] == "PAIR":
                pdata[s_name]["total_reads"] = self.sentieon_alignment_metrics[s_name]["TOTAL_READS"] / 2
                pdata[s_name]["aligned_reads"] = self.sentieon_alignment_metrics[s_name]["PF_READS_ALIGNED"] / 2
            else:
                pdata[s_name]["total_reads"] = self.sentieon_alignment_metrics[s_name]["TOTAL_READS"]
                pdata[s_name]["aligned_reads"] = self.sentieon_alignment_metrics[s_name]["PF_READS_ALIGNED"]
                pdata[s_name]["unaligned_reads"] = pdata[s_name]["total_reads"] - pdata[s_name]["aligned_reads"]

        keys = OrderedDict()
        keys["aligned_reads"] = {"name": "Aligned Reads"}
        keys["unaligned_reads"] = {"name": "Unaligned Reads"}

        # Config for the plot
        pconfig = {
            "id": "sentieon_aligned_reads",
            "title": "Sentieon: Aligned Reads",
            "ylab": "# Reads",
            "cpswitch_counts_label": "Number of Reads",
        }

        self.add_section(
            name="Alignment Summary",
            anchor="sentieon-alignmentsummary",
            description="Please note that Sentieon's read counts are divided \
                 by two for paired-end data.",
            plot=bargraph.plot(pdata, keys, pconfig),
        )

    # Return the number of detected samples to the parent module
    return len(self.sentieon_alignment_metrics)
