def test_para_with_mixture_of_types(self):
img = Img("picture.png")
ahref = Link("http://example.com")
p = Para("Beautiful picture:", img, "See more at", ahref)
self.assertEqual(
p.html(), "<p>Beautiful picture: "
"<img src='picture.png' /> "
"See more at "
"<a href='http://example.com'>"
"http://example.com</a></p>")
def test_img_with_href(self):
img = Img('picture.png', href="http://awesome.pix.com/")
self.assertEqual(
img.html(), "<a href='http://awesome.pix.com/'>"
"<img src='picture.png' /></a>")
def test_img_with_title(self):
img = Img('picture.png', title="An awesome picture")
self.assertEqual(
img.html(), "<img src='picture.png' "
"title='An awesome picture' />")
def test_img_with_alt_text(self):
img = Img('picture.png', alt="An awesome picture")
self.assertEqual(
img.html(), "<img src='picture.png' "
"alt='An awesome picture' />")
def test_img_with_name(self):
img = Img('picture.png', name="awesome-picture")
self.assertEqual(img.html(), "<img id='awesome-picture' "
"src='picture.png' />")
def test_img_with_height_and_width(self):
img = Img('picture.png', height=100, width=150)
self.assertEqual(
img.html(), "<img src='picture.png' "
"height='100' width='150' />")
def test_img(self):
img = Img('picture.png')
self.assertEqual(img.html(), "<img src='picture.png' />")
plot_filen = os.path.join(out_dir, "reads_per_stage.png")
reads_per_stage = \
pd.Series([data.total_reads,],
['Nreads_initial',]).append(
df[cols].sum())
print reads_per_stage
fig = plt.figure()
plot = reads_per_stage.plot.bar(figsize=(6, 4))
labels = ['Initial', 'Assigned', 'Quality filtered', 'Trimmed']
if contaminant_filtered:
labels.append('Uncontaminated')
plot.set_xticklabels(labels, rotation=45)
plot.set_ylabel("#read pairs")
plot.get_figure().savefig(plot_filen, bbox_inches='tight')
general_info.add(
Img(os.path.relpath(plot_filen, os.path.dirname(out_file))))
general_info.add(reads_per_stage.to_frame().to_html(header=False))
toc_list.add_item(Link(general_info.title, general_info))
# Low read counts
low_read_threshold = data.total_assigned_reads / data.total_barcodes / 10
read_counts = report.add_section("Read counts", name="read_counts")
low_read_count = df[['Barcode', 'Nreads']].query(
"Nreads < %d" % low_read_threshold).query("Nreads > 0")
low_reads = len(low_read_count)
no_reads = len(df.query("Nreads == 0"))
tbl = Table(columns=('name', 'value'))
tbl.no_header()
data_items = (('Mean read count per barcode', 'mean_read_count'),
('Median read count per barcode', 'median_read_count'))
for item in data_items: