Python AlignmentParser Example

说明

python alignmentparser示例是从最受好评的开源项目中提取的实现代码,你可以参考下面示例的使用方式。

编程语言: Python

命名空间/包名称: plastidutilscriptlibargparsers

示例#1
文件: make_wiggle.py项目: joshuagryphon/plastid

def main(argv=sys.argv[1:]):
    """Command-line program
    
    Parameters
    ----------
    argv : list, optional
        A list of command-line arguments, which will be processed
        as if the script were called from the command line if
        :py:func:`main` is called directly.

        Default: sys.argv[1:] (actually command-line arguments)
    """
    ap = AlignmentParser()
    bp = BaseParser()
    parser = argparse.ArgumentParser(description=format_module_docstring(__doc__),
                                     formatter_class=argparse.RawDescriptionHelpFormatter,
                                     parents=[bp.get_parser(),ap.get_parser()])
    parser.add_argument("-o","--out",dest="outbase",type=str,required=True,
                        metavar="FILENAME",
                        help="Base name for output files")
    parser.add_argument("--window_size",default=100000,metavar="N",type=int,
                        help="Size of nucleotides to fetch at once for export. "+\
                             "Large values are faster but require more memory "+\
                             "(Default: 100000)")

    track_opts = parser.add_argument_group(title="Browser track options")
    track_opts.add_argument("--color",type=str,default=None,
                        help="An RGB hex string (`'#NNNNNN'`, `N` in `[0-9,A-F]`) specifying \
                              the track color.")
    track_opts.add_argument("-t","--track_name",dest="track_name",type=str,
                        help="Name to give browser track",
                        default=None)
    track_opts.add_argument("--output_format",choices=("bedgraph","variable_step"),
                        default="bedgraph",
                        help="Format of output file (Default: bedgraph)")

    args = parser.parse_args(argv)
    gnd  = ap.get_genome_array_from_args(args,printer=printer)
    bp.get_base_ops_from_args(args)
    
    if args.track_name is None:
        name = args.outbase
    else:
        name = args.track_name
    
    if args.color is not None:
        fw_color = rc_color = "%s,%s,%s" % tuple(get_rgb255(args.color))
    else:
        fw_color = rc_color = "0,0,0"
    
    if args.output_format == "bedgraph":
        outfn = gnd.to_bedgraph
    elif args.output_format == "variable_step":
        outfn = gnd.to_variable_step

    track_fw = "%s_fw.wig" % args.outbase
    track_rc = "%s_rc.wig" % args.outbase

    with argsopener(track_fw,args,"w") as fw_out:
        printer.write("Writing forward strand track to %s ..." % track_fw)
        outfn(fw_out,"%s_fw" % name,"+",window_size=args.window_size,color=fw_color,
                printer=printer)
        fw_out.close()

    with argsopener(track_rc,args,"w") as rc_out:
        printer.write("Writing reverse strand track to %s ..." % track_rc)
        outfn(rc_out,"%s_rc" % name,"-",window_size=args.window_size,color=rc_color,
                printer=printer)
        rc_out.close()
    
    printer.write("Done!")

示例#2
文件: counts_in_region.py项目: joshuagryphon/plastid

def main(argv=sys.argv[1:]):
    """Command-line program
    
    Parameters
    ----------
    argv : list, optional
        A list of command-line arguments, which will be processed
        as if the script were called from the command line if
        :func:`main` is called directly.

        Default: `sys.argv[1:]`. The command-line arguments, if the script is
        invoked from the command line
    """
    ap = AnnotationParser()
    annotation_file_parser = ap.get_parser(conflict_handler="resolve")
    
    al = AlignmentParser(disabled=_DISABLED)
    alignment_file_parser  = al.get_parser(conflict_handler="resolve")
    
    mp = MaskParser()
    mask_file_parser = mp.get_parser()
    
    bp = BaseParser()
    base_parser = bp.get_parser()
    
    parser = argparse.ArgumentParser(description=format_module_docstring(__doc__),
                                     formatter_class=argparse.RawDescriptionHelpFormatter,
                                     parents=[base_parser,
                                              alignment_file_parser,
                                              annotation_file_parser,
                                              mask_file_parser],
                                     )
    parser.add_argument("outfile",type=str,help="Output filename")
    
    args = parser.parse_args(argv)
    bp.get_base_ops_from_args(args)

    ga          = al.get_genome_array_from_args(args,printer=printer)
    transcripts = ap.get_transcripts_from_args(args,printer=printer,return_type=SegmentChain)
    crossmap    = mp.get_genome_hash_from_args(args,printer=printer)
    
    ga_sum = ga.sum()
    normconst = 1000.0*1e6 / ga_sum
    
    with argsopener(args.outfile,args,"w") as fout:
        fout.write("## total_dataset_counts: %s\n" % ga_sum)
        fout.write("region_name\tregion\tcounts\tcounts_per_nucleotide\trpkm\tlength\n")
        for n,ivc in enumerate(transcripts):
            name = ivc.get_name()
            masks = crossmap.get_overlapping_features(ivc)
            ivc.add_masks(*itertools.chain.from_iterable((X for X in masks)))
            if n % 1000 == 0:
                printer.write("Processed %s regions..." % n)
                
            counts = numpy.nansum(ivc.get_masked_counts(ga))
            length = ivc.masked_length
            rpnt = numpy.nan if length == 0 else float(counts)/length
            rpkm = numpy.nan if length == 0 else rpnt * normconst 
            ltmp = [name,
                    str(ivc),
                    "%.8e" % counts,
                    "%.8e" % rpnt,
                    "%.8e" % rpkm,
                    "%d" % length]
            fout.write("%s\n" % "\t".join(ltmp))
    
        fout.close()
        
    printer.write("Processed %s regions total." % n)

    printer.write("Done.")

示例#3
文件: psite.py项目: joshuagryphon/plastid

def main(argv=sys.argv[1:]):
    """Command-line program
    
    Parameters
    ----------
	argv : list, optional
		A list of command-line arguments, which will be processed
		as if the script were called from the command line if
		:py:func:`main` is called directrly.

        Default: `sys.argv[1:]`. The command-line arguments, if the script is
        invoked from the command line
    """
    ap = AlignmentParser(allow_mapping=False,input_choices=["BAM"],
                         disabled=["normalize","big_genome",])
    bp = BaseParser()
    alignment_file_parser = ap.get_parser()
    base_parser = bp.get_parser()
    
    pp = PlottingParser()
    plotting_parser = pp.get_parser()

    parser = argparse.ArgumentParser(description=format_module_docstring(__doc__),
                                     formatter_class=argparse.RawDescriptionHelpFormatter,
                                     parents=[base_parser,
                                              alignment_file_parser,
                                              plotting_parser])
    
    parser.add_argument("--min_counts",type=int,default=10,metavar="N",
                         help="Minimum counts required in normalization region "+
                              "to be included in metagene average (Default: 10)")
    parser.add_argument("--normalize_over",type=int,nargs=2,metavar="N",
                         default=None,
                         #default=(20,50),
                         help="Portion of each window against which its individual raw count profile"+
                              " will be normalized. Specify two integers, in nucleotide"+
                              " distance from landmark (negative for upstream, positive for downstream. Surround negative numbers with quotes.). (Default: 20 50)")
    parser.add_argument("--norm_region",type=int,nargs=2,metavar="N",
                         default=None,
                         help="Deprecated. Use ``--normalize_over`` instead. "+
                              "Formerly, Portion of each window against which its individual raw count profile"+
                              " will be normalized. Specify two integers, in nucleotide"+
                              " distance, from 5\' end of window. (Default: 70 100)")
    parser.add_argument("--require_upstream",default=False,action="store_true",
                        help="If supplied, the P-site offset is taken to be the distance "+
                             "between the largest peak upstream of the start codon and "+
                             "the start codon itself. Otherwise, the P-site offset is taken "+
                             "to be the distance between the largest peak in the entire ROI "+
                             "and the start codon. Ignored if ``--constrain`` is used."
                        )
    parser.add_argument("--constrain",type=int,nargs=2,default=None,metavar="X",
                        help="Constrain P-site offset to be between specified distance from "+
                             "start codon. Useful for noisy data. "+
                             "(Reasonable set: 10 15; default: not constrained)")
    parser.add_argument("--aggregate",default=False,action="store_true",
                        help="Estimate P-site from aggregate reads at each position, instead "+
                             "of median normalized read density. Noisier, but helpful for "+
                             "lower-count data or read lengths with few counts. (Default: False)"
                        ),
    parser.add_argument("--keep",default=False,action="store_true",
                        help="Save intermediate count files. Useful for additional computations (Default: False)")
    parser.add_argument("--default",type=int,default=13,
                        help="Default 5\' P-site offset for read lengths that are not present or evaluated in the dataset. Unaffected by ``--constrain`` (Default: 13)")

    parser.add_argument("roi_file",type=str,
                        help="ROI file surrounding start codons, from ``metagene generate`` subprogram")
    
    parser.add_argument("outbase",type=str,help="Basename for output files")
    
    # set manual options
    args = parser.parse_args(argv)
    bp.get_base_ops_from_args(args)

    # set defaults
    args.mapping = "fiveprime"
    args.offset  = 0
    args.nibble  = 0

    
    # process arguments
    min_len = args.min_length
    max_len = args.max_length
    profiles = max_len + 1 - min_len
    lengths = list(range(min_len,max_len+1))
    outbase = args.outbase
    title  = "Fiveprime read offsets by length" if args.title is None else args.title
    
    pp.set_style_from_args(args)
    colors = pp.get_colors_from_args(args,profiles)
 
    printer.write("Opening ROI file %s ..." % args.roi_file)
    with opener(args.roi_file) as roi_fh:
        roi_table = pd.read_table(roi_fh,sep="\t",comment="#",index_col=None,header=0)
        roi_fh.close()
        
    printer.write("Opening count files %s ..." % ",".join(args.count_files))
    ga = ap.get_genome_array_from_args(args,printer=printer)

    
    # remove default size filters
    my_filters = ga._filters.keys()
    for f in my_filters:
        ga.remove_filter(f)

    norm_start, norm_end = _get_norm_region(roi_table,args)
    
    # count
    count_dict, norm_count_dict, metagene_profile = do_count(roi_table,
                                                             ga,
                                                             norm_start,
                                                             norm_end,
                                                             args.min_counts,
                                                             min_len,
                                                             max_len,
                                                             aggregate=args.aggregate,
                                                             printer=printer)
    
    # save counts
    profile_fn = "%s_metagene_profiles.txt" % outbase
    with argsopener(profile_fn,args,"w") as metagene_out:
        metagene_profile.to_csv(metagene_out,
                                sep="\t",
                                header=True,
                                index=False,
                                na_rep="nan",
                                columns=["x"]+["%s-mers" % X for X in lengths])
        metagene_out.close()

    if args.keep == True:
        printer.write("Saving raw and normalized counts ...")
        for k in count_dict:
            count_fn     = "%s_%s_rawcounts.txt.gz"  % (outbase,k)
            normcount_fn = "%s_%s_normcounts.txt.gz" % (outbase,k)
            mask_fn      = "%s_%s_mask.txt.gz" % (outbase,k)
            numpy.savetxt(count_fn,count_dict[k],delimiter="\t")
            numpy.savetxt(normcount_fn,norm_count_dict[k],delimiter="\t")
            numpy.savetxt(mask_fn,norm_count_dict[k].mask,delimiter="\t")
    
    # plotting & offsets
    printer.write("Plotting and determining offsets ...")
    offset_dict = OrderedDict() 

    # Determine scaling factor for plotting metagene profiles
    max_y = numpy.nan 
    with warnings.catch_warnings():
        # ignore warnings for slices that contain only NaNs
        warnings.simplefilter("ignore",category=RuntimeWarning)
        for k in lengths:
            max_y = numpy.nanmax([max_y,
                                  numpy.nanmax(metagene_profile["%s-mers"% k].values)])

    if numpy.isnan(max_y) or max_y == 0:
        max_y = 1.0


    # parse arguments & set styles
    mplrc = matplotlib.rcParams
    plt_incr  = 1.2

    # use this figsize if not specified on command line
    figheight = 1.0 + 0.25*(profiles-1) + 0.75*(profiles)
    default_figsize = (7.5,figheight)

    fig = pp.get_figure_from_args(args,figsize=default_figsize)

    ax = plt.gca()
    plt.title(title)
    plt.xlabel("Distance from CDS start, (nt; 5' end mapping)")
    if args.aggregate == True:
        plt.ylabel("Aggregate read counts (au)")
    else:
        plt.ylabel("Median normalized read density (au)")
        
    plt.axvline(0.0,color=mplrc["axes.edgecolor"],dashes=[3,2])

    x = metagene_profile["x"].values
    xmin = x.min()
    xmax = x.max()
    
    if args.constrain is not None:
        mask = numpy.tile(True,len(x))
        
        zp = (x==0).argmax()
        l,r = args.constrain
        if l == r:
            warnings.warn("Minimum and maximum distance constraints are equal (both '%s'). This is silly." % l,ArgumentWarning)
            
        mindist = min(l,r)
        maxdist = max(l,r)
        
        mask[zp-maxdist:zp-mindist+1] = False
    elif args.require_upstream == True:
        mask = x >= 0
    else:
        mask = numpy.tile(False,len(x))

    for n,k in enumerate(lengths):
        color = colors[n]
        baseline = plt_incr*n
        y = metagene_profile["%s-mers" % k].values
        #ymask = y[mask]
        ymask = numpy.ma.MaskedArray(y,mask=mask)

        if numpy.isnan(y).all():
            plot_y = numpy.zeros_like(x)
        else:
            if args.aggregate == False:
                plot_y = y / max_y
            else:
                plot_y = y.astype(float) / numpy.nanmax(y) * 0.9
 
        # plot metagene profiles on common scale, offset by baseline from bottom to top
        ax.plot(x,baseline + plot_y,color=color)
        ax.text(xmin,baseline,"%s-mers" % k,
                ha="left",
                va="bottom",
                color=color,
                transform=matplotlib.transforms.offset_copy(ax.transData,fig,
                                                            x=6.0,y=3.0,units="points"))

        ymax = baseline + numpy.nanmax(plot_y)

        # if all valid positions are nan, or if all valid positions are <= 0
        if (~mask).sum() == numpy.isnan(ymask).sum() or numpy.nanmax(ymask) == 0:
            offset = args.default
            usedefault = True
        else:
            offset = -x[numpy.ma.argmax(ymask)]
            usedefault = False

        offset_dict[k] = offset
        if usedefault == False:
            yadj = ymax - 0.2 * plt_incr

            ax.plot([-offset,0],[yadj,yadj],color=color,dashes=[3,2])
            ax.text(-offset / 2.0,
                     yadj,
                     "%s nt" % (offset),
                     color=color,
                     ha="center",
                     va="bottom",
                     transform=matplotlib.transforms.offset_copy(ax.transData,fig,
                                                                 x=0.0,y=3.0,units="points")
                    )   

    plt.xlim(xmin,xmax)
    plt.ylim(-0.1,plt_incr+baseline)
    ax.yaxis.set_ticks([])

    # save data as p-site offset table
    fn = "%s_p_offsets.txt" % outbase
    fout = argsopener(fn,args)
    printer.write("Writing offset table to %s ..." % fn)
    fout.write("length\tp_offset\n")
    for k in offset_dict:
        fout.write("%s\t%s\n" % (k,offset_dict[k]))
    
    fout.write("default\t%s" % args.default)
    
    fout.close()

    # save plot
    plot_fn ="%s_p_offsets.%s" % (outbase,args.figformat) 
    printer.write("Saving plot to %s ..." % plot_fn)
    plt.savefig(plot_fn,dpi=args.dpi,bbox_inches="tight")

    printer.write("Done.")

示例#4
文件: get_count_vectors.py项目: joshuagryphon/plastid

def main(args=sys.argv[1:]):
    """Command-line program
    
    Parameters
    ----------
    argv : list, optional
        A list of command-line arguments, which will be processed
        as if the script were called from the command line if
        :func:`main` is called directly.

        Default: `sys.argv[1:]`. The command-line arguments, if the script is
        invoked from the command line
    """
    al = AlignmentParser()
    an = AnnotationParser()
    mp = MaskParser()
    bp = BaseParser()

    alignment_file_parser = al.get_parser(conflict_handler="resolve")
    annotation_file_parser = an.get_parser(conflict_handler="resolve")
    mask_file_parser = mp.get_parser()
    base_parser = bp.get_parser()

    parser = argparse.ArgumentParser(
        description=format_module_docstring(__doc__),
        formatter_class=argparse.RawDescriptionHelpFormatter,
        conflict_handler="resolve",
        parents=[base_parser, alignment_file_parser, annotation_file_parser, mask_file_parser],
    )

    parser.add_argument("out_folder", type=str, help="Folder in which to save output vectors")
    parser.add_argument(
        "--out_prefix", default="", type=str, help="Prefix to prepend to output files (default: no prefix)"
    )
    parser.add_argument(
        "--format", default="%.8f", type=str, help=r"printf-style format string for output (default: '%%.8f')"
    )
    args = parser.parse_args(args)
    bp.get_base_ops_from_args(args)

    # if output folder doesn't exist, create it
    if not os.path.isdir(args.out_folder):
        os.mkdir(args.out_folder)

    # parse args
    ga = al.get_genome_array_from_args(args, printer=printer)
    transcripts = an.get_segmentchains_from_args(args, printer=printer)
    mask_hash = mp.get_genome_hash_from_args(args, printer=printer)

    # evaluate
    for n, tx in enumerate(transcripts):
        if n % 1000 == 0:
            printer.write("Processed %s regions of interest" % n)
        filename = "%s%s.txt" % (args.out_prefix, tx.get_name())
        full_filename = os.path.join(args.out_folder, filename)

        # mask out overlapping masked regions
        overlapping = mask_hash.get_overlapping_features(tx)
        for feature in overlapping:
            tx.add_masks(*feature.segments)

        count_vec = tx.get_masked_counts(ga)
        numpy.savetxt(full_filename, count_vec, fmt=args.format)

示例#5
文件: phase_by_size.py项目: joshuagryphon/plastid

def main(argv=sys.argv[1:]):
    """Command-line program
    
    Parameters
    ----------
	argv : list, optional
		A list of command-line arguments, which will be processed
		as if the script were called from the command line if
		:py:func:`main` is called directly.

        Default: `sys.argv[1:]`. The command-line arguments, if the script is
        invoked from the command line
    """
    al = AlignmentParser(disabled=["normalize","big_genome","spliced_bowtie_files"],
                        input_choices=["BAM"])
    an = AnnotationParser()
    pp = PlottingParser()
    bp = BaseParser()
    plotting_parser = pp.get_parser()
    
    alignment_file_parser = al.get_parser(conflict_handler="resolve")
    annotation_file_parser = an.get_parser(conflict_handler="resolve")
    base_parser = bp.get_parser()

    parser = argparse.ArgumentParser(description=format_module_docstring(__doc__),
                                     formatter_class=argparse.RawDescriptionHelpFormatter,
                                     conflict_handler="resolve",
                                     parents=[base_parser,
                                              annotation_file_parser,
                                              alignment_file_parser,
                                              plotting_parser])
    
    parser.add_argument("roi_file",type=str,nargs="?",default=None,
                        help="Optional. ROI file of maximal spanning windows surrounding start codons, "+\
                             "from ``metagene generate`` subprogram. Using this instead of `--annotation_files` "+\
                             "prevents double-counting of codons when multiple transcript isoforms exist "+\
                             "for a gene. See the documentation for `metagene` for more info about ROI files."+\
                             "If an ROI file is not given, supply an annotation with ``--annotation_files``")
    parser.add_argument("outbase",type=str,help="Required. Basename for output files")
    parser.add_argument("--codon_buffer",type=int,default=5,
                        help="Codons before and after start codon to ignore (Default: 5)")


    args = parser.parse_args(argv)
    bp.get_base_ops_from_args(args)
    pp.set_style_from_args(args)
    gnd = al.get_genome_array_from_args(args,printer=printer)

    read_lengths = list(range(args.min_length,args.max_length+1))
    codon_buffer = args.codon_buffer
    
    dtmp = { "read_length"   : numpy.array(read_lengths),
             "reads_counted" : numpy.zeros_like(read_lengths,dtype=int),
            }

    if args.roi_file is not None:
        using_roi = True
        roi_table = read_pl_table(args.roi_file)
        regions = roi_table.iterrows()
        transform_fn = roi_row_to_cds
        back_buffer = -1
        if len(args.annotation_files) > 0:
            warnings.warn("If an ROI file is given, annotation files are ignored. Pulling regions from '%s'. Ignoring '%s'" % (args.roi_file,
                                                                                                                               ", ".join(args.annotation_files)),
                          ArgumentWarning)
    else:
        using_roi = False
        if len(args.annotation_files) == 0:
            printer.write("Either an ROI file or at least annotation file must be given.")
            sys.exit(1)
        else:
            warnings.warn("Using a transcript annotation file instead of an ROI file can lead to double-counting of codons if the annotation contains multiple transcripts per gene.",
                          ArgumentWarning)        
            regions = an.get_transcripts_from_args(args,printer=printer)
            back_buffer  = -codon_buffer
            transform_fn = lambda x: x.get_cds()
    
    phase_sums = {}
    for k in read_lengths:
        phase_sums[k] = numpy.zeros(3)
    
    for n, roi in enumerate(regions):
        if n % 1000 == 1:
            printer.write("Counted %s ROIs ..." % n)
        
        # transformation needed to extract CDS from transcript or from ROI file window
        cds_part = transform_fn(roi)
        
        # only calculate for coding genes
        if len(cds_part) > 0:

            read_dict     = {}
            count_vectors = {}
            for k in read_lengths:
                read_dict[k]     = []
                count_vectors[k] = []
            
            # for each seg, fetch reads, sort them, and create individual count vectors
            for seg in cds_part:
                reads = gnd.get_reads(seg)
                for read in filter(lambda x: len(x.positions) in read_dict,reads):
                    read_dict[len(read.positions)].append(read)
    
                # map and sort by length
                for read_length in read_dict:
                    count_vector = list(gnd.map_fn(read_dict[read_length],seg)[1])
                    count_vectors[read_length].extend(count_vector)
            
            # add each count vector for each length to total
            for k, vec in count_vectors.items():
                counts = numpy.array(vec)
                if cds_part.strand == "-":
                    counts = counts[::-1]
               
                if len(counts) % 3 == 0:
                    counts = counts.reshape((len(counts)/3,3))
                else:
                    if using_roi == False:
                        message = "Length of '%s' coding region (%s nt) is not divisible by 3. Ignoring last partial codon." % (roi.get_name(),len(counts))
                        warnings.warn(message,DataWarning)
                    newlen = len(counts)//3
                    counts = counts[:3*newlen]
                    counts = counts.reshape(newlen,3)
    
                phase_sums[k] += counts[codon_buffer:back_buffer,:].sum(0)

    printer.write("Counted %s ROIs total." % (n+1))
    for k in dtmp:
        dtmp[k] = numpy.array(dtmp[k])
    
    # total reads counted for each size    
    for k in read_lengths:
        dtmp["reads_counted"][dtmp["read_length"] == k] = phase_sums[k].sum() 
    
    # read length distribution
    dtmp["fraction_reads_counted"] = dtmp["reads_counted"].astype(float) / dtmp["reads_counted"].sum() 
    
    # phase vectors
    phase_vectors = { K : V.astype(float)/V.astype(float).sum() for K,V in phase_sums.items() }
    for i in range(3):
        dtmp["phase%s" % i] = numpy.zeros(len(dtmp["read_length"])) 

    for k, vec in phase_vectors.items():
        for i in range(3):
            dtmp["phase%s" % i][dtmp["read_length"] == k] = vec[i]
    
    # phase table
    fn = "%s_phasing.txt" % args.outbase
    printer.write("Saving phasing table to %s ..." % fn)
    dtmp = pd.DataFrame(dtmp)
    with argsopener(fn,args) as fh:
        dtmp.to_csv(fh,columns=["read_length",
                                "reads_counted",
                                "fraction_reads_counted",
                                "phase0",
                                "phase1",
                                "phase2",
                                ],
                       float_format="%.6f",
                       na_rep="nan",
                       sep="\t",
                       index=False,
                       header=True
                      )
        fh.close()
    
    fig = {}
    if args.figsize is not None:
        fig["figsize"] = tuple(args.figsize)

    colors = pp.get_colors_from_args(args,len(read_lengths))

    fn = "%s_phasing.%s" % (args.outbase,args.figformat)
    printer.write("Plotting to %s ..." % fn)
    plot_counts = numpy.vstack([V for (_,V) in sorted(phase_sums.items())])
    fig, (ax1,_) = phase_plot(plot_counts,labels=read_lengths,lighten_by=0.3,
                                cmap=None,color=colors,fig=fig)

    if args.title is not None:
        ax1.set_title(args.title)
    else:
        ax1.set_title("Phasing stats for %s" % args.outbase)

    fig.savefig(fn,dpi=args.dpi,bbox_inches="tight")

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