How to use the multiqc.utils.util_functions.write_data_file function in multiqc

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github ewels / MultiQC / multiqc / View on Github external
config.skip_generalstats = True

    # Write the report sources to disk
    if config.data_dir is not None:
    # Compress the report plot JSON data"Compressing plot data")
    report.plot_compressed_json = report.compress_json(report.plot_data)


    # Data Export / MegaQC integration - save report data to file or send report data to an API endpoint
    if (config.data_dump_file or config.megaqc_url) and config.megaqc_upload:
        multiqc_json_dump = megaqc.multiqc_dump_json(report)
        if config.data_dump_file:
            util_functions.write_data_file(multiqc_json_dump, 'multiqc_data', False, 'json')
        if config.megaqc_url:

    # Make the final report path & data directories
    if filename != 'stdout':
        config.output_fn = os.path.join(config.output_dir, config.output_fn_name)
        config.data_dir = os.path.join(config.output_dir, config.data_dir_name)
        # Check for existing reports and remove if -f was specified
        if os.path.exists(config.output_fn) or (config.make_data_dir and os.path.exists(config.data_dir)):
            if config.force:
                if os.path.exists(config.output_fn):
                    logger.warning("Deleting    : {}   (-f was specified)".format(os.path.relpath(config.output_fn)))
                if config.make_data_dir and os.path.exists(config.data_dir):
                    logger.warning("Deleting    : {}   (-f was specified)".format(os.path.relpath(config.data_dir)))
github ewels / MultiQC / multiqc / plots / View on Github external
fdata[d['name']][pconfig['categories'][i]] = x
                    except (KeyError, IndexError):
                        fdata[d['name']][str(i)] = x

        # Custom tsv output if the x axis varies
        if not sharedcats and config.data_format == 'tsv':
            fout = ''
            for d in pdata:
                fout += "\t"+"\t".join([str(x[0]) for x in d['data']])
                fout += "\n{}\t".format(d['name'])
                fout += "\t".join([str(x[1]) for x in d['data']])
                fout += "\n"
            with (os.path.join(config.data_dir, '{}.txt'.format(pid)), 'w', encoding='utf-8') as f:
                print( fout.encode('utf-8', 'ignore').decode('utf-8'), file=f )
            util_functions.write_data_file(fdata, pid)

        # Set up figure
        fig = plt.figure(figsize=(14, 6), frameon=False)
        axes = fig.add_subplot(111)

        # Go through data series
        for idx, d in enumerate(pdata):

            # Default colour index
            cidx = idx
            while cidx >= len(default_colors):
                cidx -= len(default_colors)

            # Line style
            linestyle = 'solid'
            if d.get('dashStyle', None) == 'Dash':
github ewels / MultiQC / multiqc / plots / View on Github external
        <div class="modal-footer"> <button data-dismiss="modal" class="btn btn-default" type="button">Close</button> </div>
      """.format( tid=table_id, title=table_title, trows=''.join(t_modal_headers.values()) )

    # Save the raw values to a file if requested
    if dt.pconfig.get('save_file') is True:
        fn = dt.pconfig.get('raw_data_fn', 'multiqc_{}'.format(table_id) )
        util_functions.write_data_file(dt.raw_vals, fn )
        report.saved_raw_data[fn] = dt.raw_vals

    return html
github ewels / MultiQC / multiqc / modules / View on Github external
def write_data_file(self, data, fn, sort_cols=False, data_format=None):
        """ Saves raw data to a dictionary for downstream use, then redirects
        to report.write_data_file() to create the file in the report directory """
        report.saved_raw_data[fn] = data
        util_functions.write_data_file(data, fn, sort_cols, data_format)
github ewels / MultiQC / multiqc / plots / View on Github external
name = k+1
            html += '<button data-target="#{pid}" class="btn btn-default btn-sm {a}">{n}</button>\n'.format(a=active, pid=pid, n=name)
        html += '\n\n'

    # Go through datasets creating plots
    for pidx, pdata in enumerate(plotdata):

        # Save plot data to file
        fdata = {}
        for d in pdata:
            for didx, dval in enumerate(d['data']):
                s_name = plotsamples[pidx][didx]
                if s_name not in fdata:
                    fdata[s_name] = dict()
                fdata[s_name][d['name']] = dval
        util_functions.write_data_file(fdata, pids[pidx])

        # Plot percentage as well as counts
        plot_pcts = [False]
        if pconfig.get('cpswitch') is not False:
            plot_pcts = [False, True]

        # Switch out NaN for 0s so that MatPlotLib doesn't ignore stuff
        for idx, d in enumerate(pdata):
            pdata[idx]['data'] = [x if not math.isnan(x) else 0 for x in d['data'] ]

        for plot_pct in plot_pcts:

            # Plot ID
            pid = pids[pidx]
            hide_plot = False
            if plot_pct is True: