def _coverage_vs_quality_plot(contigs, output_dir):
"""
Creates a scatter plot coverage vs quality plot for each contig in the
polished assembly. Each point represents one contig.
:param contigs: (dict) contig id -> ContigInfo object
:param output_dir: (str) path to output directory
:return: (Plot) object that has already been saved as a PNG to output_dir
"""
import pbreports.plot.helper as PH
fig, axes = PH.get_fig_axes_lpr()
axes = fig.add_subplot(111)
axes.set_axisbelow(True)
axes.set_ylabel(
get_plot_ylabel(spec, Constants.PG_COVERAGE, Constants.P_COVERAGE))
axes.set_xlabel(
get_plot_xlabel(spec, Constants.PG_COVERAGE, Constants.P_COVERAGE))
PH.set_tick_label_font_size(axes, 12, 12)
PH.set_axis_label_font_size(axes, 16)
x_vals = [x.mean_coverage for x in contigs.values()]
y_vals = [x.mean_qv for x in contigs.values()]
axes.set_xlim(0, max(x_vals) * 1.2)
axes.set_ylim(0, max(y_vals) * 1.2)
axes.scatter(x_vals, y_vals, s=12)
png_path = os.path.join(output_dir, "polished_coverage_vs_quality.png")
png, thumbpng = PH.save_figure_with_thumbnail(fig, png_path)
return Plot(Constants.P_COVERAGE,
os.path.basename(png),
thumbnail=os.path.basename(thumbpng))
def _coverage_vs_quality_plot(contigs, output_dir):
"""
Creates a scatter plot coverage vs quality plot for each contig in the
polished assembly. Each point represents one contig.
:param contigs: (dict) contig id -> ContigInfo object
:param output_dir: (str) path to output directory
:return: (Plot) object that has already been saved as a PNG to output_dir
"""
fig, axes = PH.get_fig_axes_lpr()
axes = fig.add_subplot(111)
axes.set_axisbelow(True)
axes.set_ylabel("Mean Confidence (QV)")
axes.set_xlabel("Mean Coverage Depth")
PH.set_tick_label_font_size(axes, 12, 12)
PH.set_axis_label_font_size(axes, 16)
x_vals = [x.mean_coverage for x in contigs.values()]
y_vals = [x.mean_qv for x in contigs.values()]
axes.set_xlim(0, max(x_vals) * 1.2)
axes.set_ylim(0, max(y_vals) * 1.2)
axes.scatter(x_vals, y_vals, s=12)
png_path = os.path.join(output_dir, "polished_coverage_vs_quality.png")
png, thumbpng = PH.save_figure_with_thumbnail(fig, png_path)
return Plot('cov_vs_qual', os.path.basename(png),
thumbnail=os.path.basename(thumbpng))
def _apply_plot_data(x_data, y1_data, y2_data, labels, legend_loc=None):
"""Default labels assume y1_data==control, y2_data==sample"""
h1_color = '#5050f0'
h2_color = '#f05050'
# log option isn't really working yet ... funky with polygons
# these are unintuitively inverted b/c we require the sample
# on the left side of the doubleY axis
h2, t = np.histogram(y1_data, bins=x_data)
h1, t = np.histogram(y2_data, bins=x_data)
fig, ax = get_fig_axes_lpr()
x_data = x_data[:-1]
y0 = np.zeros(len(x_data))
# if log:
# h1 = np.log10(h1)
# h2 = np.log10(h2)
ax.fill_between(x_data,
y0,
h1.T,
alpha=0.6,
edgecolor=h1_color,
facecolor=h1_color)
fake_h2 = mp.Rectangle((0.1, 0.1),
0.1,
0.1,
facecolor=h2_color,
edgecolor=h2_color,
alpha=0.6)
fake_h1 = mp.Rectangle((0.1, 0.1),
0.1,
0.1,
facecolor=h1_color,
edgecolor=h1_color,
alpha=0.6)
ax.set_xlabel(labels[0])
ax.set_ylabel('%s (Sample)' % labels[1])
ax.legend([fake_h2, fake_h1], ['Control', 'Sample'], loc=legend_loc)
# gray border around legend
ax.get_legend().get_frame().set_edgecolor('#a0a0a0')
set_tick_label_font_size(ax, 12, 12)
set_axis_label_font_size(ax, 16)
ax2 = ax.twinx()
ax2.fill_between(x_data,
y0,
h2.T,
alpha=0.6,
edgecolor=h2_color,
facecolor=h2_color)
ax2.set_ylabel('%s (Control)' % labels[1])
set_tick_label_font_size(ax2, 12, 12)
set_axis_label_font_size(ax2, 16)
return fig
def _create_fig_template(dims=(8, 6), facecolor='#ffffff', gridcolor='#e0e0e0'):
fig, ax = PH.get_fig_axes_lpr(dims=dims)
ax = fig.add_subplot(111)
ax.axesPatch.set_facecolor(facecolor)
ax.grid(color=gridcolor, linewidth=0.5, linestyle='-')
ax.set_axisbelow(True)
PH.set_tick_label_font_size(ax, 12, 12)
PH.set_axis_label_font_size(ax, 16)
return fig, ax
def _create_fig_template(dims=(8, 6), facecolor='#ffffff',
gridcolor='#e0e0e0'):
fig, ax = PH.get_fig_axes_lpr(dims=dims)
ax = fig.add_subplot(111)
ax.axesPatch.set_facecolor(facecolor)
ax.grid(color=gridcolor, linewidth=0.5, linestyle='-')
ax.set_axisbelow(True)
PH.set_tick_label_font_size(ax, 12, 12)
PH.set_axis_label_font_size(ax, 16)
return fig, ax
def _apply_plot_data(x_data, y1_data, y2_data, labels, legend_loc=None):
"""Default labels assume y1_data==control, y2_data==sample"""
h1_color = '#5050f0'
h2_color = '#f05050'
# log option isn't really working yet ... funky with polygons
# these are unintuitively inverted b/c we require the sample
# on the left side of the doubleY axis
h2, t = np.histogram(y1_data, bins=x_data)
h1, t = np.histogram(y2_data, bins=x_data)
fig, ax = get_fig_axes_lpr()
x_data = x_data[:-1]
y0 = np.zeros(len(x_data))
# if log:
# h1 = np.log10(h1)
# h2 = np.log10(h2)
ax.fill_between(x_data, y0, h1.T, alpha=0.6,
edgecolor=h1_color, facecolor=h1_color)
fake_h2 = mp.Rectangle((0.1, 0.1), 0.1, 0.1, facecolor=h2_color,
edgecolor=h2_color, alpha=0.6)
fake_h1 = mp.Rectangle((0.1, 0.1), 0.1, 0.1, facecolor=h1_color,
edgecolor=h1_color, alpha=0.6)
ax.set_xlabel(labels[0])
ax.set_ylabel('%s (Sample)' % labels[1])
ax.legend([fake_h2, fake_h1],
['Control', 'Sample'], loc=legend_loc)
# gray border around legend
ax.get_legend().get_frame().set_edgecolor('#a0a0a0')
set_tick_label_font_size(ax, 12, 12)
set_axis_label_font_size(ax, 16)
ax2 = ax.twinx()
ax2.fill_between(x_data, y0, h2.T, alpha=0.6,
edgecolor=h2_color, facecolor=h2_color)
ax2.set_ylabel('%s (Control)' % labels[1])
set_tick_label_font_size(ax2, 12, 12)
set_axis_label_font_size(ax2, 16)
return fig
