comparison pima_report.py @ 1:67d0939b56b0 draft

Uploaded

0:0a558f444c98

CDC_ADVISORY = 'The analysis and report presented here should be treated as preliminary.  Please contact the CDC/BDRD with any results regarding _Bacillus anthracis_.'


class PimaReport:

    def __init__(self, analysis_name=None, assembly_fasta_file=None, assembly_name=None, feature_bed_files=None, feature_png_files=None,
                 contig_coverage_file=None, dbkey=None, gzipped=None, illumina_fastq_file=None, mutation_regions_bed_file=None,
                 mutation_regions_tsv_files=None, pima_css=None):
        self.ofh = open("process_log.txt", "w")

        self.ofh.write("analysis_name: %s\n" % str(analysis_name))
        self.ofh.write("assembly_fasta_file: %s\n" % str(assembly_fasta_file))
        self.ofh.write("assembly_name: %s\n" % str(assembly_name))
        self.ofh.write("feature_bed_files: %s\n" % str(feature_bed_files))
        self.ofh.write("feature_png_files: %s\n" % str(feature_png_files))
        self.ofh.write("contig_coverage_file: %s\n" % str(contig_coverage_file))
        self.ofh.write("dbkey: %s\n" % str(dbkey))
        self.ofh.write("gzipped: %s\n" % str(gzipped))
        self.ofh.write("illumina_fastq_file: %s\n" % str(illumina_fastq_file))
        self.ofh.write("mutation_regions_bed_file: %s\n" % str(mutation_regions_bed_file))
        self.ofh.write("mutation_regions_tsv_files: %s\n" % str(mutation_regions_tsv_files))
        self.ofh.write("pima_css: %s\n" % str(pima_css))

        # General
        self.doc = None
        self.report_md = 'pima_report.md'

        # Inputs
        self.analysis_name = analysis_name
        self.assembly_fasta_file = assembly_fasta_file
        self.assembly_name = assembly_name
        self.feature_bed_files = feature_bed_files
        self.feature_png_files = feature_png_files
        self.contig_coverage_file = contig_coverage_file
        self.dbkey = dbkey
        self.gzipped = gzipped
        self.illumina_fastq_file = illumina_fastq_file
        self.mutation_regions_bed_file = mutation_regions_bed_file
        self.mutation_regions_tsv_files = mutation_regions_tsv_files
        self.read_type = 'Illumina'
        self.ont_bases = None
        self.ont_n50 = None
        self.ont_read_count = None
        self.pima_css = pima_css

        # Titles
        self.alignment_title = 'Comparison with reference'
        self.alignment_notes_title = 'Alignment notes'
        self.amr_matrix_title = 'AMR matrix'

        self.large_indel_title = 'Large insertions & deletions'
        self.methods_title = 'Methods'
        self.mutation_title = 'Mutations found in the sample'
        self.mutation_methods_title = 'Mutation screening'
        self.plasmid_methods_title = 'Plasmid annotation'
        self.reference_methods_title = 'Reference comparison'
        self.snp_indel_title = 'SNPs and small indels'
        #self.summary_title = 'Summary'
        self.summary_title = 'Analysis of %s' % analysis_name

        # Methods
        self.methods = pandas.Series(dtype='float64')
        self.methods[self.contamination_methods_title] = pandas.Series(dtype='float64')

        self.contig_alignment = pandas.Series(dtype=object)

        # Values
        self.assembly_size = 0
        self.contig_info = None
        self.did_flye_ont_fastq = False
        self.did_medaka_ont_assembly = False
        self.feature_hits = pandas.Series(dtype='float64')
        self.illumina_length_mean = 0
        self.illumina_read_count = 0
        self.illumina_bases = 0
        self.mean_coverage = 0
        self.num_assembly_contigs = 0

        # Actions
        self.did_guppy_ont_fast5 = False
        self.did_qcat_ont_fastq = False
        self.info_illumina_fastq()

    def load_contig_info(self):
        self.contig_info = pandas.Series(dtype=object)
        self.contig_info[self.read_type] = pandas.read_csv(self.contig_coverage_file, header=None, index_col=None, sep='\t').sort_values(1, axis=0, ascending=False)
        self.contig_info[self.read_type].columns = ['contig', 'size', 'coverage']
        self.mean_coverage = (self.contig_info[self.read_type].iloc[:, 1] * self.contig_info[self.read_type].iloc[:, 2]).sum() / self.contig_info[self.read_type].iloc[:, 1].sum()

    def load_fasta(self, fasta):
        sequence = pandas.Series(dtype=object)
        for contig in SeqIO.parse(fasta, 'fasta'):
            sequence[contig.id] = contig

        # self.illumina_length_mean /= len(self.illumina_fastq)
        self.illumina_length_mean /= 1
        self.illumina_bases = self.format_kmg(self.illumina_bases, decimals=1)

    def start_doc(self):
        #header_text = 'Analysis of %s' % self.analysis_name
        self.doc = MdUtils(file_name=self.report_md, title='')

    def add_run_information(self):
        self.ofh.write("\nXXXXXX In add_run_information\n\n")
        self.doc.new_line()

            'Illumina bases',
            '{:s}'.format(self.illumina_bases)
        ]
        self.doc.new_table(columns=2, rows=4, text=Table_List, text_align='left')

    def add_assembly_information(self):
        self.ofh.write("\nXXXXXX In add_assembly_information\n\n")
        if self.assembly_fasta_file is None:
            return
        self.load_assembly()

                            'END{printf "%d\\t%d\\t%d\\n", n50, (NR - 1), s;exit}\''])
        result = list(re.split('\\t', self.run_command(command)[0]))
        if result[1] == '0':
            self.error_out('No ONT reads found')
        ont_n50, ont_read_count, ont_raw_bases = [int(i) for i in result]

        command = ' '.join([opener,
                            fastq_file,
                            '| awk \'{getline;print length($0);getline;getline;}\''])
        result = self.run_command(command)
        result = list(filter(lambda x: x != '', result))
        ont_read_lengths = [int(i) for i in result]

        return ([ont_n50, ont_read_count, ont_raw_bases, ont_read_lengths])

    def wordwrap_markdown(self, string):
        if string:
            if len(string) < 35:
                return string

            return
        self.doc.new_line()
        self.doc.new_line('<div style="page-break-after: always;"></div>')
        self.doc.new_line()
        self.doc.new_header(2, self.assembly_notes_title)
        #for note in self.analysis.assembly_notes:
        #    self.doc.new_line(note)

    def add_contamination(self):
        self.ofh.write("\nXXXXXX In add_contamination\n\n")
        if len(self.kraken_fracs) == 0:
            return

        self.doc.new_header(level=3, title=self.snp_indel_title)
        Table_1 = [
            "Category",
            "Quantity",
            'SNPs',
            #'{:,}'.format(self.analysis.quast_mismatches),
            'NA'
            'Small indels',
            #'{:,}'.format(self.analysis.quast_indels)
            'NA'
        ]
        self.doc.new_table(columns=2, rows=3, text=Table_1, text_align='left')
        self.doc.new_line('<div style="page-break-after: always;"></div>')
        self.doc.new_line()
        if len(self.alignment_notes) > 0:

                for i in range(contig_features.shape[0]):
                    self.ofh.write("i: %s\n" % str(i))
                    feature = contig_features.iloc[i, :].copy(deep=True)
                    self.ofh.write("feature: %s\n" % str(feature))
                    feature[4] = '{:.3f}'.format(feature[4])
                    Table_List = Table_List + feature[1:].values.tolist()
                self.ofh.write("Table_List: %s\n" % str(Table_List))
                row_count = int(len(Table_List) / 5)
                self.ofh.write("row_count: %s\n" % str(row_count))
                self.doc.new_line()
                self.doc.new_table(columns=7, rows=row_count, text=Table_List, text_align='left')
        blastn_version = 'The genome assembly was queried for features using blastn.'
        bedtools_version = 'Feature hits were clustered using bedtools and the highest scoring hit for each cluster was reported.'
        method = '%s  %s' % (blastn_version, bedtools_version)
        self.methods[self.feature_methods_title] = self.methods[self.feature_methods_title].append(pandas.Series(method))

    def add_mutations(self):
        self.ofh.write("\nXXXXXX In add_mutations\n\n")
        if len(self.mutation_regions_tsv_files) == 0:
            return
        try:
            mutation_regions = pandas.read_csv(self.mutation_regions_bed_file, sep='\t', header=0, index_col=False)
        except Exception:
            # Likely an empty file.
            return
        amr_mutations = pandas.Series(dtype=object)
        for region_i in range(mutation_regions.shape[0]):
            region = mutation_regions.iloc[region_i, :]
            region_name = str(region['name'])
            self.ofh.write("Processing mutations for region %s\n" % region_name)
            region_mutations_tsv_name = '%s_mutations.tsv' % region_name
            if region_mutations_tsv_name not in self.mutation_regions_tsv_files:
                continue
            region_mutations_tsv = self.mutation_regions_tsv_files[region_mutations_tsv_name]
            try:
                region_mutations = pandas.read_csv(region_mutations_tsv, sep='\t', header=0, index_col=False)
            except Exception:
                region_mutations = pandas.DataFrame()
            if region_mutations.shape[0] == 0:
                continue
            # Figure out what kind of mutations are in this region
            region_mutation_types = pandas.Series(['snp'] * region_mutations.shape[0], name='TYPE', index=region_mutations.index)
            region_mutation_types[region_mutations['REF'].str.len() != region_mutations['ALT'].str.len()] = 'small-indel'
            region_mutation_drugs = pandas.Series(region['drug'] * region_mutations.shape[0], name='DRUG', index=region_mutations.index)
            region_notes = pandas.Series(region['note'] * region_mutations.shape[0], name='NOTE', index=region_mutations.index)
            region_mutations = pandas.concat([region_mutations, region_mutation_types, region_mutation_drugs, region_notes], axis=1)

        self.methods[self.mutation_methods_title] = self.methods[self.mutation_methods_title].append(pandas.Series(method))

    def add_amr_matrix(self):
        self.ofh.write("\nXXXXXX In add_amr_matrix\n\n")
        # Make sure that we have an AMR matrix to plot
        #if not getattr(self.analysis, 'did_draw_amr_matrix', False):
        #    return
        #amr_matrix_png = self.analysis.amr_matrix_png
        #self.doc.new_line()
        #self.doc.new_header(level=2, title=self.amr_matrix_title)
        #self.doc.new_line('AMR genes and mutations with their corresponding drugs.')
        #self.doc.new_line(
        #    self.doc.new_inline_image(
        #        text='AMR genes and mutations with their corresponding drugs',
        #        path=amr_matrix_png
        #    )
        #)
        pass

    def add_large_indels(self):
        self.ofh.write("\nXXXXXX In add_large_indels\n\n")
        # Make sure we looked for mutations
        #if len(self.analysis.large_indels) == 0:
        #    return
        #large_indels = self.analysis.large_indels
        #self.doc.new_line()
        #self.doc.new_header(level=2, title=self.large_indel_title)
        #for genome in ['Reference insertions', 'Query insertions']:
        #    genome_indels = large_indels[genome].copy()
        #    self.doc.new_line()
        #    self.doc.new_header(level=3, title=genome)
        #    if (genome_indels.shape[0] == 0):
        #        continue
        #    genome_indels.iloc[:, 1] = genome_indels.iloc[:, 1].apply(lambda x: '{:,}'.format(x))
        #    genome_indels.iloc[:, 2] = genome_indels.iloc[:, 2].apply(lambda x: '{:,}'.format(x))
        #    genome_indels.iloc[:, 3] = genome_indels.iloc[:, 3].apply(lambda x: '{:,}'.format(x))
        #    Table_List = [
        #        'Reference contig', 'Start', 'Stop', 'Size (bp)'
        #    ]
        #    for i in range(genome_indels.shape[0]):
        #        Table_List = Table_List + genome_indels.iloc[i, :].values.tolist()
        #    row_count = int(len(Table_List) / 4)
        #    self.doc.new_table(columns=4, rows=row_count, text=Table_List, text_align='left')
        #method = 'Large insertions or deletions were found as the complement of aligned regions using bedtools.'
        #self.methods[self.reference_methods_title] = self.methods[self.reference_methods_title].append(
        #    pandas.Series(method))
        #self.doc.new_line()
        #self.doc.new_line('<div style="page-break-after: always;"></div>')
        #self.doc.new_line()
        pass

    def add_plasmids(self):
        self.ofh.write("\nXXXXXX In add_plasmids\n\n")
        """
        if not getattr(self.analysis, 'did_call_plasmids', False):
            return
        # Make sure we looked for mutations
        #plasmids = self.analysis.plasmids
        if plasmids is None:
            return
        plasmids = plasmids.copy()
        self.doc.new_line()
        #self.doc.new_header(level=2, title=self.analysis.plasmid_title)
        if (plasmids.shape[0] == 0):
            self.doc.new_line('None')
            return
        plasmids.iloc[:, 3] = plasmids.iloc[:, 3].apply(lambda x: '{:,}'.format(x))
        plasmids.iloc[:, 4] = plasmids.iloc[:, 4].apply(lambda x: '{:,}'.format(x))
        plasmids.iloc[:, 5] = plasmids.iloc[:, 5].apply(lambda x: '{:,}'.format(x))
        Table_List = [
            'Genome contig',
            'Plasmid hit',
            'Plasmid acc.',
            'Contig size',
            'Aliged',
            'Plasmid size'
        ]
        for i in range(plasmids.shape[0]):
            Table_List = Table_List + plasmids.iloc[i, 0:6].values.tolist()
        row_count = int(len(Table_List) / 6)
        self.doc.new_table(columns=6, rows=row_count, text=Table_List, text_align='left')
        method = 'The plasmid reference database was queried against the genome assembly using minimap2.'
        self.methods[self.plasmid_methods_title] = self.methods[self.plasmid_methods_title].append(pandas.Series(method))
        method = 'The resulting SAM was converted to a PSL using a custom version of sam2psl.'
        self.methods[self.plasmid_methods_title] = self.methods[self.plasmid_methods_title].append(pandas.Series(method))
        method = 'Plasmid-to-genome hits were resolved using the pChunks algorithm.'
        self.methods[self.plasmid_methods_title] = self.methods[self.plasmid_methods_title].append(pandas.Series(method))
        """
        pass

    def add_methods(self):
        self.ofh.write("\nXXXXXX In add_methods\n\n")
        self.doc.new_line('<div style="page-break-after: always;"></div>')
        self.doc.new_line()
        if len(self.methods) == 0:
            return
        self.doc.new_line()
        self.doc.new_header(level=2, title=self.methods_title)

        for methods_section in self.methods.index.tolist():
            if self.methods[methods_section] is None or len(self.methods[methods_section]) == 0:
                continue
            self.doc.new_line()
            self.doc.new_header(level=3, title=methods_section)

            methods += ['ONT reads were basecalled using guppy']
        if self.did_qcat_ont_fastq:
            methods += ['ONT reads were demultiplexed and trimmed using qcat']
        self.methods[self.basecalling_methods_title] = pandas.Series(methods)
        self.add_illumina_library_information()
        self.add_assembly_information()
        self.add_contig_info()
        self.add_assembly_notes()
        if self.did_flye_ont_fastq:
            method = 'ONT reads were assembled using Flye.'
            self.methods[self.assembly_methods_title] = self.methods[self.assembly_methods_title].append(pandas.Series(method))
        if self.did_medaka_ont_assembly:
            method = 'the genome assembly was polished using ont reads and medaka.'
            self.methods[self.assembly_methods_title] = self.methods[self.assembly_methods_title].append(pandas.series(method))

    def make_tex(self):
        self.doc.new_table_of_contents(table_title='detailed run information', depth=2, marker="tableofcontents")
        text = self.doc.file_data_text
        text = text.replace("##--[", "")

        self.add_contamination()
        self.add_alignment()
        self.add_features()
        self.add_feature_plots()
        self.add_mutations()
        # TODO stuff working to here...
        self.add_large_indels()
        self.add_plasmids()
        self.add_amr_matrix()
        # self.add_snps()
        self.add_methods()
        self.make_tex()
        # It took me quite a long time to find out that the value of the -t

        self.ofh.close()


parser = argparse.ArgumentParser()

parser.add_argument('--analysis_name', action='store', dest='analysis_name', help='Sample identifier')
parser.add_argument('--assembly_fasta_file', action='store', dest='assembly_fasta_file', help='Assembly fasta file')
parser.add_argument('--assembly_name', action='store', dest='assembly_name', help='Assembly identifier')
parser.add_argument('--feature_bed_dir', action='store', dest='feature_bed_dir', help='Directory of best feature hits bed files')
parser.add_argument('--feature_png_dir', action='store', dest='feature_png_dir', help='Directory of best feature hits png files')
parser.add_argument('--contig_coverage_file', action='store', dest='contig_coverage_file', help='Contig coverage TSV file')
parser.add_argument('--dbkey', action='store', dest='dbkey', help='Reference genome')
parser.add_argument('--gzipped', action='store_true', dest='gzipped', default=False, help='Input sample is gzipped')
parser.add_argument('--illumina_fastq_file', action='store', dest='illumina_fastq_file', help='Input sample')
parser.add_argument('--mutation_regions_bed_file', action='store', dest='mutation_regions_bed_file', help='AMR mutation regions BRD file')
parser.add_argument('--mutation_regions_dir', action='store', dest='mutation_regions_dir', help='Directory of mutation regions TSV files')
parser.add_argument('--pima_css', action='store', dest='pima_css', help='PIMA css stypesheet')

args = parser.parse_args()

# Prepare the features BED files.
feature_bed_files = []
for file_name in sorted(os.listdir(args.feature_bed_dir)):
    file_path = os.path.abspath(os.path.join(args.feature_bed_dir, file_name))
    feature_bed_files.append(file_path)

for file_name in sorted(os.listdir(args.mutation_regions_dir)):
    file_path = os.path.abspath(os.path.join(args.feature_png_dir, file_name))
    mutation_regions_files.append(file_path)

markdown_report = PimaReport(args.analysis_name,
                             args.assembly_fasta_file,
                             args.assembly_name,
                             feature_bed_files,
                             feature_png_files,
                             args.contig_coverage_file,
                             args.dbkey,
                             args.gzipped,
                             args.illumina_fastq_file,
                             args.mutation_regions_bed_file,
                             mutation_regions_files,
                             args.pima_css)
markdown_report.make_report()

1:67d0939b56b0

CDC_ADVISORY = 'The analysis and report presented here should be treated as preliminary.  Please contact the CDC/BDRD with any results regarding _Bacillus anthracis_.'


class PimaReport:

    def __init__(self, analysis_name=None, amr_deletions_file=None, amr_matrix_files=None, assembly_fasta_file=None,
                 assembly_name=None, compute_sequence_length_file=None, contig_coverage_file=None, dbkey=None,
                 dnadiff_snps_file=None, feature_bed_files=None, feature_png_files=None, flye_assembly_info_file=None,
                 flye_version=None, genome_insertions_file=None, gzipped=None, illumina_fastq_file=None,
                 mutation_regions_bed_file=None, mutation_regions_tsv_files=None, pima_css=None, plasmids_file=None,
                 reference_insertions_file=None):
        self.ofh = open("process_log.txt", "w")

        self.ofh.write("amr_deletions_file: %s\n" % str(amr_deletions_file))
        self.ofh.write("amr_matrix_files: %s\n" % str(amr_matrix_files))
        self.ofh.write("analysis_name: %s\n" % str(analysis_name))
        self.ofh.write("assembly_fasta_file: %s\n" % str(assembly_fasta_file))
        self.ofh.write("assembly_name: %s\n" % str(assembly_name))
        self.ofh.write("compute_sequence_length_file: %s\n" % str(compute_sequence_length_file))
        self.ofh.write("contig_coverage_file: %s\n" % str(contig_coverage_file))
        self.ofh.write("dbkey: %s\n" % str(dbkey))
        self.ofh.write("dnadiff_snps_file: %s\n" % str(dnadiff_snps_file))
        self.ofh.write("feature_bed_files: %s\n" % str(feature_bed_files))
        self.ofh.write("feature_png_files: %s\n" % str(feature_png_files))
        self.ofh.write("flye_assembly_info_file: %s\n" % str(flye_assembly_info_file))
        self.ofh.write("flye_version: %s\n" % str(flye_version))
        self.ofh.write("gzipped: %s\n" % str(gzipped))
        self.ofh.write("genome_insertions_file: %s\n" % str(genome_insertions_file))
        self.ofh.write("illumina_fastq_file: %s\n" % str(illumina_fastq_file))
        self.ofh.write("mutation_regions_bed_file: %s\n" % str(mutation_regions_bed_file))
        self.ofh.write("mutation_regions_tsv_files: %s\n" % str(mutation_regions_tsv_files))
        self.ofh.write("pima_css: %s\n" % str(pima_css))
        self.ofh.write("plasmids_file: %s\n" % str(plasmids_file))
        # self.ofh.write("reference_genome: %s\n" % str(reference_genome))
        self.ofh.write("reference_insertions_file: %s\n" % str(reference_insertions_file))

        # General
        self.doc = None
        self.report_md = 'pima_report.md'

        # Inputs
        self.amr_deletions_file = amr_deletions_file
        self.amr_matrix_files = amr_matrix_files
        self.analysis_name = analysis_name
        self.assembly_fasta_file = assembly_fasta_file
        self.assembly_name = assembly_name
        self.compute_sequence_length_file = compute_sequence_length_file
        self.contig_coverage_file = contig_coverage_file
        self.dbkey = dbkey
        self.dnadiff_snps_file = dnadiff_snps_file
        self.feature_bed_files = feature_bed_files
        self.feature_png_files = feature_png_files
        self.flye_assembly_info_file = flye_assembly_info_file
        self.flye_version = flye_version
        self.gzipped = gzipped
        self.genome_insertions_file = genome_insertions_file
        self.illumina_fastq_file = illumina_fastq_file
        self.mutation_regions_bed_file = mutation_regions_bed_file
        self.mutation_regions_tsv_files = mutation_regions_tsv_files
        self.read_type = 'Illumina'
        self.ont_bases = None
        self.ont_n50 = None
        self.ont_read_count = None
        self.pima_css = pima_css
        self.plasmids_file = plasmids_file
        # self.reference_genome = reference_genome
        self.reference_insertions_file = reference_insertions_file

        # Titles
        self.alignment_title = 'Comparison with reference'
        self.alignment_notes_title = 'Alignment notes'
        self.amr_matrix_title = 'AMR matrix'

        self.large_indel_title = 'Large insertions & deletions'
        self.methods_title = 'Methods'
        self.mutation_title = 'Mutations found in the sample'
        self.mutation_methods_title = 'Mutation screening'
        self.plasmid_methods_title = 'Plasmid annotation'
        self.plasmid_title = 'Plasmid annotation'
        self.reference_methods_title = 'Reference comparison'
        self.snp_indel_title = 'SNPs and small indels'
        self.summary_title = 'Analysis of %s' % analysis_name

        # Methods
        self.methods = pandas.Series(dtype='float64')
        self.methods[self.contamination_methods_title] = pandas.Series(dtype='float64')

        self.contig_alignment = pandas.Series(dtype=object)

        # Values
        self.assembly_size = 0
        self.contig_info = None
        self.did_medaka_ont_assembly = False
        self.feature_hits = pandas.Series(dtype='float64')
        self.illumina_length_mean = 0
        self.illumina_read_count = 0
        self.illumina_bases = 0
        self.mean_coverage = 0
        self.num_assembly_contigs = 0
        # TODO: should the following 2 values  be passed as  parameters?
        self.ont_n50_min = 2500
        self.ont_coverage_min = 30
        self.quast_indels = 0
        self.quast_mismatches = 0

        # Actions
        self.did_guppy_ont_fast5 = False
        self.did_qcat_ont_fastq = False
        self.info_illumina_fastq()

    def load_contig_info(self):
        self.contig_info = pandas.Series(dtype=object)
        self.contig_info[self.read_type] = pandas.read_csv(self.contig_coverage_file, header=None, index_col=None, sep='\t').sort_values(1, axis=0, ascending=False)
        self.contig_info[self.read_type].columns = ['contig', 'size', 'coverage']
        self.mean_coverage = (self.contig_info[self.read_type].iloc[:, 1] * self.contig_info[self.read_type].iloc[:, 2]).sum() / self.contig_info[self.read_type].iloc[:, 1].sum()
        if self.mean_coverage <= self.ont_coverage_min:
            warning = '%s mean coverage ({:.0f}X) is less than the recommended minimum ({:.0f}X).'.format(self.mean_coverage, self.ont_coverage_min) % self.read_type
            self.assembly_notes = self.assembly_notes.append(pandas.Series(warning))
        # Report if some contigs have low coverage.
        low_coverage = self.contig_info[self.read_type].loc[self.contig_info[self.read_type]['coverage'] < self.ont_coverage_min, :]
        if low_coverage.shape[0] >= 0:
            for contig_i in range(low_coverage.shape[0]):
                warning = '%s coverage of {:s} ({:.0f}X) is less than the recommended minimum ({:.0f}X).'.format(low_coverage.iloc[contig_i, 0], low_coverage.iloc[contig_i, 2], self.ont_coverage_min) % self.read_type
                self.assembly_notes = self.assembly_notes.append(pandas.Series(warning))
        # See if some contigs have anolously low coverage.
        fold_coverage = self.contig_info[self.read_type]['coverage'] / self.mean_coverage
        low_coverage = self.contig_info[self.read_type].loc[fold_coverage < 1 / 5, :]
        if low_coverage.shape[0] >= 0 :
            for contig_i in range(low_coverage.shape[0]):
                warning = '%s coverage of {:s} ({:.0f}X) is less than 1/5 the mean coverage ({:.0f}X).'.format(low_coverage.iloc[contig_i, 0], low_coverage.iloc[contig_i, 2], self.mean_coverage) % self.read_type
                self.assembly_notes = self.assembly_notes.append(pandas.Series(warning))

    def load_fasta(self, fasta):
        sequence = pandas.Series(dtype=object)
        for contig in SeqIO.parse(fasta, 'fasta'):
            sequence[contig.id] = contig

        # self.illumina_length_mean /= len(self.illumina_fastq)
        self.illumina_length_mean /= 1
        self.illumina_bases = self.format_kmg(self.illumina_bases, decimals=1)

    def start_doc(self):
        self.doc = MdUtils(file_name=self.report_md, title='')

    def add_run_information(self):
        self.ofh.write("\nXXXXXX In add_run_information\n\n")
        self.doc.new_line()

            'Illumina bases',
            '{:s}'.format(self.illumina_bases)
        ]
        self.doc.new_table(columns=2, rows=4, text=Table_List, text_align='left')

    def evaluate_assembly(self) :
        assembly_info = pandas.read_csv(self.compute_sequence_length_file, sep='\t', header=None)
        assembly_info.columns = ['contig', 'length']
        self.contig_sizes = assembly_info
        # Take a look at the number of contigs, their sizes,
        # and circularity.  Warn if things don't look good.
        if assembly_info.shape[0] > 4:
            warning = 'Assembly produced {:d} contigs, more than ususally expected; assembly may be fragmented'.format(assembly_info.shape[0])
            self.assembly_notes = self.assembly_notes.append(pandas.Series(warning))
        small_contigs = assembly_info.loc[assembly_info['length'] <= 3000, :]
        if small_contigs.shape[0] > 0:
            warning = 'Assembly produced {:d} small contigs ({:s}); assembly may include spurious sequences.'.format(small_contigs.shape[0], ', '.join(small_contigs['contig']))
            self.assembly_notes = self.assembly_notes.append(pandas.Series(warning))

    def add_assembly_information(self):
        self.ofh.write("\nXXXXXX In add_assembly_information\n\n")
        if self.assembly_fasta_file is None:
            return
        self.load_assembly()

                            'END{printf "%d\\t%d\\t%d\\n", n50, (NR - 1), s;exit}\''])
        result = list(re.split('\\t', self.run_command(command)[0]))
        if result[1] == '0':
            self.error_out('No ONT reads found')
        ont_n50, ont_read_count, ont_raw_bases = [int(i) for i in result]
        command = ' '.join([opener,
                            fastq_file,
                            '| awk \'{getline;print length($0);getline;getline;}\''])
        result = self.run_command(command)
        result = list(filter(lambda x: x != '', result))
        # TODO: the following are not yet used...
        # ont_read_lengths = [int(i) for i in result]
        # ont_bases = self.format_kmg(ont_raw_bases, decimals=1)
        if ont_n50 <= self.ont_n50_min:
            warning = 'ONT N50 (%s) is less than the recommended minimum (%s)' % (str(ont_n50), str(self.ont_n50_min))
            self.assembly_notes = self.assembly_notes.append(pandas.Series(warning))

    def wordwrap_markdown(self, string):
        if string:
            if len(string) < 35:
                return string

            return
        self.doc.new_line()
        self.doc.new_line('<div style="page-break-after: always;"></div>')
        self.doc.new_line()
        self.doc.new_header(2, self.assembly_notes_title)
        for note in self.assembly_notes:
            self.doc.new_line(note)

    def add_contamination(self):
        self.ofh.write("\nXXXXXX In add_contamination\n\n")
        if len(self.kraken_fracs) == 0:
            return

        self.doc.new_header(level=3, title=self.snp_indel_title)
        Table_1 = [
            "Category",
            "Quantity",
            'SNPs',
            '{:,}'.format(self.quast_mismatches),
            'Small indels',
            '{:,}'.format(self.quast_indels)
        ]
        self.doc.new_table(columns=2, rows=3, text=Table_1, text_align='left')
        self.doc.new_line('<div style="page-break-after: always;"></div>')
        self.doc.new_line()
        if len(self.alignment_notes) > 0:

                for i in range(contig_features.shape[0]):
                    self.ofh.write("i: %s\n" % str(i))
                    feature = contig_features.iloc[i, :].copy(deep=True)
                    self.ofh.write("feature: %s\n" % str(feature))
                    feature[4] = '{:.3f}'.format(feature[4])
                    # FIXME: Uncommenting the following line (which is
                    # https://github.com/appliedbinf/pima_md/blob/main/MarkdownReport.py#L317)
                    # will cause the job to fail with this exception:
                    # ValueError: columns * rows is not equal to text length
                    # Table_List = Table_List + feature[1:].values.tolist()
                self.ofh.write("Table_List: %s\n" % str(Table_List))
                row_count = int(len(Table_List) / 5)
                self.ofh.write("row_count: %s\n" % str(row_count))
                self.doc.new_line()
                self.ofh.write("Before new_table, len(Table_List):: %s\n" % str(len(Table_List)))
                self.doc.new_table(columns=5, rows=row_count, text=Table_List, text_align='left')
        blastn_version = 'The genome assembly was queried for features using blastn.'
        bedtools_version = 'Feature hits were clustered using bedtools and the highest scoring hit for each cluster was reported.'
        method = '%s  %s' % (blastn_version, bedtools_version)
        self.methods[self.feature_methods_title] = self.methods[self.feature_methods_title].append(pandas.Series(method))

    def add_mutations(self):
        self.ofh.write("\nXXXXXX In add_mutations\n\n")
        if len(self.mutation_regions_tsv_files) == 0:
            return
        try :
            mutation_regions = pandas.read_csv(self.mutation_regions_bed_file, sep='\t', header=0, index_col=False)
        except Exception:
            # Likely an empty file.
            return
        # TODO: this is the only place where reference_genome is used,
        # so I'm commenting it out for now.  We need to confirm if these
        # errors that require the reference genmoe being passed are necessary.
        # If so, we'll need to implement data tables in this tool.
        # Make sure that the positions in the BED file fall within
        # the chromosomes provided in the reference sequence.
        """
        for mutation_region in range(mutation_regions.shape[0]):
            mutation_region = mutation_regions.iloc[mutation_region, :]
            if not (mutation_region[0] in self.reference_genome):
                self.ofh.write("\nMutation region: %s not found in reference genome.\n" % ' '.join(mutation_region.astype(str)))
                continue
            if not isinstance(mutation_region[1], int):
                self.ofh.write("\nNon-integer found in mutation region start (column 2): %s.\n" % str(mutation_region[1]))
                break
            elif not isinstance(mutation_region[2], int):
                self.ofh.write("\nNon-integer found in mutation region start (column 3): %s.\n" % str(mutation_region[2]))
                break
            if mutation_region[1] <= 0 or mutation_region[2] <= 0:
                self.ofh.write("\nMutation region %s starts before the reference sequence.\n" % ' '.join(mutation_region.astype(str)))
            if mutation_region[1] > len(self.reference_genome[mutation_region[0]].seq) or mutation_region[2] > len(self.reference_genome[mutation_region[0]].seq):
                self.ofh.write("\nMutation region %s ends after the reference sequence.\n" % ' '.join(mutation_region.astype(str)))
        """
        amr_mutations = pandas.Series(dtype=object)
        for region_i in range(mutation_regions.shape[0]):
            region = mutation_regions.iloc[region_i, :]
            region_name = str(region['name'])
            self.ofh.write("Processing mutations for region %s\n" % region_name)
            region_mutations_tsv_name = '%s_mutations.tsv' % region_name
            if region_mutations_tsv_name not in self.mutation_regions_tsv_files:
                continue
            region_mutations_tsv = self.mutation_regions_tsv_files[region_mutations_tsv_name]
            try :
                region_mutations = pandas.read_csv(region_mutations_tsv, sep='\t', header=0, index_col=False)
            except Exception:
                region_mutations = pandas.DataFrame()
            if region_mutations.shape[0] == 0:
                continue
            # Figure out what kind of mutations are in this region.
            region_mutation_types = pandas.Series(['snp'] * region_mutations.shape[0], name='TYPE', index=region_mutations.index)
            region_mutation_types[region_mutations['REF'].str.len() != region_mutations['ALT'].str.len()] = 'small-indel'
            region_mutation_drugs = pandas.Series(region['drug'] * region_mutations.shape[0], name='DRUG', index=region_mutations.index)
            region_notes = pandas.Series(region['note'] * region_mutations.shape[0], name='NOTE', index=region_mutations.index)
            region_mutations = pandas.concat([region_mutations, region_mutation_types, region_mutation_drugs, region_notes], axis=1)

        self.methods[self.mutation_methods_title] = self.methods[self.mutation_methods_title].append(pandas.Series(method))

    def add_amr_matrix(self):
        self.ofh.write("\nXXXXXX In add_amr_matrix\n\n")
        # Make sure that we have an AMR matrix to plot
        if len(self.amr_matrix_files) == 0:
            return
        self.doc.new_line()
        self.doc.new_header(level=2, title=self.amr_matrix_title)
        self.doc.new_line('AMR genes and mutations with their corresponding drugs')
        for amr_matrix_file in self.amr_matrix_files:
            self.doc.new_line(self.doc.new_inline_image(text='AMR genes and mutations with their corresponding drugs',
                                                        path=os.path.abspath(amr_matrix_file)))

    def add_large_indels(self):
        self.ofh.write("\nXXXXXX In add_large_indels\n\n")
        large_indels = pandas.Series(dtype='float64')
        # Pull in insertions.
        try:
            reference_insertions = pandas.read_csv(filepath_or_buffer=self.reference_insertions_file, sep='\t', header=None)
        except Exception:
            reference_insertions = pandas.DataFrame()
        try:
            genome_insertions = pandas.read_csv(filepath_or_buffer=self.genome_insertions_file, sep='\t', header=None)
        except Exception:
            genome_insertions = pandas.DataFrame()
        large_indels['Reference insertions'] = reference_insertions
        large_indels['Query insertions'] = genome_insertions
        # TODO: we don't seem to be reporting snps and deletions for some reason...
        # Pull in the number of SNPs and small indels.
        try:
            snps = pandas.read_csv(filepath_or_buffer=self.dnadiff_snps_file, sep='\t', header=None)
            # TODO: the following is not used...
            # small_indels = snps.loc[(snps.iloc[:, 1] == '.') | (snps.iloc[:, 2] == '.'), :]
            snps = snps.loc[(snps.iloc[:, 1] != '.') & (snps.iloc[:, 2] != '.'), :]
        except Exception:
            snps = pandas.DataFrame()
        # Pull in deletions.
        try:
            amr_deletions = pandas.read_csv(filepath_or_buffer=self.amr_deletion_file, sep='\t', header=None)
        except Exception:
            amr_deletions = pandas.DataFrame()
        if amr_deletions.shape[0] > 0:
            amr_deletions.columns = ['contig', 'start', 'stop', 'name', 'type', 'drug', 'note']
            amr_deletions = amr_deletions.loc[amr_deletions['type'].isin(['large-deletion', 'any']), :]
        self.doc.new_line()
        self.doc.new_header(level=2, title=self.large_indel_title)
        for genome in ['Reference insertions', 'Query insertions']:
            genome_indels = large_indels[genome].copy()
            self.doc.new_line()
            self.doc.new_header(level=3, title=genome)
            if (genome_indels.shape[0] == 0):
                continue
            genome_indels.iloc[:, 1] = genome_indels.iloc[:, 1].apply(lambda x: '{:,}'.format(x))
            genome_indels.iloc[:, 2] = genome_indels.iloc[:, 2].apply(lambda x: '{:,}'.format(x))
            genome_indels.iloc[:, 3] = genome_indels.iloc[:, 3].apply(lambda x: '{:,}'.format(x))
            Table_List = [
                'Reference contig', 'Start', 'Stop', 'Size (bp)'
            ]
            for i in range(genome_indels.shape[0]):
                Table_List = Table_List + genome_indels.iloc[i, :].values.tolist()
            row_count = int(len(Table_List) / 4)
            self.doc.new_table(columns=4, rows=row_count, text=Table_List, text_align='left')
        method = 'Large insertions or deletions were found as the complement of aligned regions using bedtools.'
        self.methods[self.reference_methods_title] = self.methods[self.reference_methods_title].append(pandas.Series(method))
        self.doc.new_line()
        self.doc.new_line('<div style="page-break-after: always;"></div>')
        self.doc.new_line()

    def add_plasmids(self):
        try :
            plasmids = pandas.read_csv(filepath_or_buffer=self.plasmids_file, sep='\t', header=0)
        except Exception:
            return
        plasmids = plasmids.copy()
        self.doc.new_line()
        self.doc.new_header(level=2, title=self.plasmid_title)
        if (plasmids.shape[0] == 0):
            self.doc.new_line('None')
            return
        plasmids.iloc[:, 3] = plasmids.iloc[:, 3].apply(lambda x: '{:,}'.format(x))
        plasmids.iloc[:, 4] = plasmids.iloc[:, 4].apply(lambda x: '{:,}'.format(x))
        plasmids.iloc[:, 5] = plasmids.iloc[:, 5].apply(lambda x: '{:,}'.format(x))
        Table_List = ['Genome contig', 'Plasmid hit', 'Plasmid acc.', 'Contig size', 'Aliged', 'Plasmid size']
        for i in range(plasmids.shape[0]):
            Table_List = Table_List + plasmids.iloc[i, 0:6].values.tolist()
        row_count = int(len(Table_List) / 6)
        self.doc.new_table(columns=6, rows=row_count, text=Table_List, text_align='left')
        method = 'The plasmid reference database was queried against the genome assembly using minimap2.'
        self.methods[self.plasmid_methods_title] = self.methods[self.plasmid_methods_title].append(pandas.Series(method))
        method = 'The resulting SAM was converted to a PSL using a custom version of sam2psl.'
        self.methods[self.plasmid_methods_title] = self.methods[self.plasmid_methods_title].append(pandas.Series(method))
        method = 'Plasmid-to-genome hits were resolved using the pChunks algorithm.'
        self.methods[self.plasmid_methods_title] = self.methods[self.plasmid_methods_title].append(pandas.Series(method))

    def add_methods(self):
        self.ofh.write("\nXXXXXX In add_methods\n\n")
        self.doc.new_line('<div style="page-break-after: always;"></div>')
        self.doc.new_line()
        if len(self.methods) == 0:
            return
        self.doc.new_line()
        self.doc.new_header(level=2, title=self.methods_title)
        for methods_section in self.methods.index.tolist():
            if self.methods[methods_section] is None or len(self.methods[methods_section]) == 0:
                continue
            self.doc.new_line()
            self.doc.new_header(level=3, title=methods_section)

            methods += ['ONT reads were basecalled using guppy']
        if self.did_qcat_ont_fastq:
            methods += ['ONT reads were demultiplexed and trimmed using qcat']
        self.methods[self.basecalling_methods_title] = pandas.Series(methods)
        self.add_illumina_library_information()
        self.add_contig_info()
        self.evaluate_assembly()
        self.add_assembly_information()
        if self.flye_assembly_info_file is not None:
            method = 'ONT reads were assembled using %s' % self.flye_version
            self.methods[self.assembly_methods_title] = self.methods[self.assembly_methods_title].append(pandas.Series(method))
            # Pull in the assembly summary and look at the coverage.
            assembly_info = pandas.read_csv(self.flye_assembly_info_file, header=0, index_col=0, sep='\t')
            # Look for non-circular contigs.
            open_contigs = assembly_info.loc[assembly_info['circ.'] == 'N', :]
            if open_contigs.shape[0] > 0:
                open_contig_ids = open_contigs.index.values
                warning = 'Flye reported {:d} open contigs ({:s}); assembly may be incomplete.'.format(open_contigs.shape[0], ', '.join(open_contig_ids))
                self.assembly_notes = self.assembly_notes.append(pandas.Series(warning))
        if self.did_medaka_ont_assembly:
            method = 'the genome assembly was polished using ont reads and medaka.'
            self.methods[self.assembly_methods_title] = self.methods[self.assembly_methods_title].append(pandas.series(method))
        self.info_ont_fastq(self.illumina_fastq_file)
        self.add_assembly_notes()

    def make_tex(self):
        self.doc.new_table_of_contents(table_title='detailed run information', depth=2, marker="tableofcontents")
        text = self.doc.file_data_text
        text = text.replace("##--[", "")

        self.add_contamination()
        self.add_alignment()
        self.add_features()
        self.add_feature_plots()
        self.add_mutations()
        self.add_large_indels()
        self.add_plasmids()
        # TODO stuff working to here...
        self.add_amr_matrix()
        # self.add_snps()
        self.add_methods()
        self.make_tex()
        # It took me quite a long time to find out that the value of the -t

        self.ofh.close()


parser = argparse.ArgumentParser()

parser.add_argument('--amr_deletions_file', action='store', dest='amr_deletions_file', help='AMR deletions BED file')
parser.add_argument('--amr_matrix_png_dir', action='store', dest='amr_matrix_png_dir', help='Directory of AMR matrix PNG files')
parser.add_argument('--analysis_name', action='store', dest='analysis_name', help='Sample identifier')
parser.add_argument('--assembly_fasta_file', action='store', dest='assembly_fasta_file', help='Assembly fasta file')
parser.add_argument('--assembly_name', action='store', dest='assembly_name', help='Assembly identifier')
parser.add_argument('--compute_sequence_length_file', action='store', dest='compute_sequence_length_file', help='Comnpute sequence length tabular file')
parser.add_argument('--contig_coverage_file', action='store', dest='contig_coverage_file', help='Contig coverage TSV file')
parser.add_argument('--dbkey', action='store', dest='dbkey', help='Reference genome identifier')
parser.add_argument('--dnadiff_snps_file', action='store', dest='dnadiff_snps_file', help='DNAdiff snps tabular file')
parser.add_argument('--feature_bed_dir', action='store', dest='feature_bed_dir', help='Directory of best feature hits bed files')
parser.add_argument('--feature_png_dir', action='store', dest='feature_png_dir', help='Directory of best feature hits png files')
parser.add_argument('--flye_assembly_info_file', action='store', dest='flye_assembly_info_file', default=None, help='Flye assembly info tabular file')
parser.add_argument('--flye_version', action='store', dest='flye_version', default=None, help='Flye version string')
parser.add_argument('--genome_insertions_file', action='store', dest='genome_insertions_file', help='Genome insertions BED file')
parser.add_argument('--gzipped', action='store_true', dest='gzipped', default=False, help='Input sample is gzipped')
parser.add_argument('--illumina_fastq_file', action='store', dest='illumina_fastq_file', help='Input sample')
parser.add_argument('--mutation_regions_bed_file', action='store', dest='mutation_regions_bed_file', help='AMR mutation regions BRD file')
parser.add_argument('--mutation_regions_dir', action='store', dest='mutation_regions_dir', help='Directory of mutation regions TSV files')
parser.add_argument('--pima_css', action='store', dest='pima_css', help='PIMA css stypesheet')
parser.add_argument('--plasmids_file', action='store', dest='plasmids_file', help='pChunks plasmids TSV file')
parser.add_argument('--reference_insertions_file', action='store', dest='reference_insertions_file', help='Reference insertions BED file')
# parser.add_argument('--reference_genome', action='store', dest='reference_genome', help='Reference genome fasta file')

args = parser.parse_args()

# Prepare the AMR matrix PNG files.
amr_matrix_files = []
for file_name in sorted(os.listdir(args.amr_matrix_png_dir)):
    file_path = os.path.abspath(os.path.join(args.amr_matrix_png_dir, file_name))
    amr_matrix_files.append(file_path)
# Prepare the features BED files.
feature_bed_files = []
for file_name in sorted(os.listdir(args.feature_bed_dir)):
    file_path = os.path.abspath(os.path.join(args.feature_bed_dir, file_name))
    feature_bed_files.append(file_path)

for file_name in sorted(os.listdir(args.mutation_regions_dir)):
    file_path = os.path.abspath(os.path.join(args.feature_png_dir, file_name))
    mutation_regions_files.append(file_path)

markdown_report = PimaReport(args.analysis_name,
                             args.amr_deletions_file,
                             amr_matrix_files,
                             args.assembly_fasta_file,
                             args.assembly_name,
                             args.compute_sequence_length_file,
                             args.contig_coverage_file,
                             args.dbkey,
                             args.dnadiff_snps_file,
                             feature_bed_files,
                             feature_png_files,
                             args.flye_assembly_info_file,
                             args.flye_version,
                             args.genome_insertions_file,
                             args.gzipped,
                             args.illumina_fastq_file,
                             args.mutation_regions_bed_file,
                             mutation_regions_files,
                             args.pima_css,
                             args.plasmids_file,
                             args.reference_insertions_file)
markdown_report.make_report()