comparison fsd.py @ 22:5e650e66e058 draft

planemo upload for repository https://github.com/monikaheinzl/duplexanalysis_galaxy/tree/master/tools/fsd commit b8a2f7b7615b2bcd3b602027af31f4e677da94f6-dirty

21:89ddbe5ae2aa

                    y[len(y) - 1] = sum(list_to_plot_original[i][list_to_plot_original[i] > 20])
                reads.append(y)
                reads_rel.append(list(numpy.float_(y)) / sum(y))

                x = list(numpy.arange(numpy.amin(unique), numpy.amax(unique) + 1).astype(float))
                x = [xi + barWidth for xi in x]

                if to_plot[l] == "Relative frequencies":
                    counts2_rel = ax.bar(x, list(numpy.float_(y)) / sum(y), align="edge", width=1./(len(list_to_plot) + 1),
                                         edgecolor="black", label=label[i], alpha=0.8, linewidth=1, color=colors[i])
                else:
                    counts2 = ax.bar(x, y, align="edge", width=1./(len(list_to_plot) + 1), edgecolor="black", label=label[i],
                                     alpha=0.8, linewidth=1, color=colors[i])
                if i == len(list_to_plot2):
                    barWidth += 1. / (len(list_to_plot) + 1) + 1. / (len(list_to_plot) + 1)
                else:
                    barWidth += 1. / (len(list_to_plot) + 1)

            if to_plot[l] == "Absolute frequencies":
                ax.legend(loc='upper right', fontsize=14, frameon=True, bbox_to_anchor=(0.9, 1))
            else:
                ax.set_xlabel("Family size", fontsize=14)

            ax.set_xticks(numpy.array(ticks))
            ax.set_xticklabels(ticks1)
            ax.set_ylabel(to_plot[l], fontsize=14)
            if log_axis:
                ax.set_yscale('log')
            ax.grid(b=True, which="major", color="#424242", linestyle=":")

                    output_file.write("{}{}".format(int(reads[n][j]), sep))
            output_file.write("\n")
            j += 1
        output_file.write("sum{}".format(sep))
        if len(label) == 1:
            output_file.write("{}{}".format(int(sum(reads)), sep))
        else:
            for i in reads:
                output_file.write("{}{}".format(int(sum(i)), sep))
        output_file.write("\n")

22:5e650e66e058

                    y[len(y) - 1] = sum(list_to_plot_original[i][list_to_plot_original[i] > 20])
                reads.append(y)
                reads_rel.append(list(numpy.float_(y)) / sum(y))

                x = list(numpy.arange(numpy.amin(unique), numpy.amax(unique) + 1).astype(float))
                if len(list_to_plot2) == 1:
                    x = [xi * 0.5 for xi in x]
                    w = 0.4
                else:
                    x = [xi + barWidth for xi in x]
                    w = 1./(len(list_to_plot) + 1)

                if to_plot[l] == "Relative frequencies":
                    counts2_rel = ax.bar(x, list(numpy.float_(y)) / sum(y), align="edge", width=w,
                                         edgecolor="black", label=label[i], alpha=0.8, linewidth=1, color=colors[i])
                else:
                    counts2 = ax.bar(x, y, align="edge", width=w, edgecolor="black", label=label[i],
                                     alpha=0.8, linewidth=1, color=colors[i])
                if i == len(list_to_plot2):
                    barWidth += 1. / (len(list_to_plot) + 1) + 1. / (len(list_to_plot) + 1)
                else:
                    barWidth += 1. / (len(list_to_plot) + 1)

            if to_plot[l] == "Absolute frequencies":
                ax.legend(loc='upper right', fontsize=14, frameon=True, bbox_to_anchor=(0.9, 1))
            else:
                ax.set_xlabel("Family size", fontsize=14)

            if len(list_to_plot2) == 1:
                ax.set_xticks(numpy.array([xi + 0.2 for xi in x]))
            else:
                ax.set_xticks(numpy.array(ticks))
            ax.set_xticklabels(ticks1)
            ax.set_ylabel(to_plot[l], fontsize=14)
            if log_axis:
                ax.set_yscale('log')
            ax.grid(b=True, which="major", color="#424242", linestyle=":")

                    output_file.write("{}{}".format(int(reads[n][j]), sep))
            output_file.write("\n")
            j += 1
        output_file.write("sum{}".format(sep))
        if len(label) == 1:
            output_file.write("{}{}".format(int(sum(numpy.concatenate(reads))), sep))
        else:
            for i in reads:
                output_file.write("{}{}".format(int(sum(i)), sep))
        output_file.write("\n")