def partial_digests_analysis(self, data, clones_observations):
analysis = clones_observations.partial_digests_analysis()
best = max(analysis, key=lambda a: analysis[a]["valid_clones"])
if analysis[best]["valid_clones"] == analysis[()]["valid_clones"]:
return {
"message": "The partial digest analysis did not find any "
"significant results",
"success": "yeah!",
}
ax = clones_observations.plot_partial_digests_analysis(analysis)
validations = analysis[best]["validations"]
observations = ClonesObservations(
clones_observations.clones,
clones_observations.constructs_records,
partial_cutters=best,
)
pdf_data = observations.plot_all_validations_patterns(validations)
return {
"pdf_file": {
"data":
data_to_html_data(pdf_data, "pdf"),
"name":
"digest_validation_assuming_partial_%s.pdf" % "_".join(best),
"mimetype":
"application/pdf",
},
"message":
("The analysis shows that the following enzyme(s) were"
" possibly only partially cutting: %s. <br/>The report below"
" shows the validation under this hypothesis.") %
(", ".join(["<b>%s</b>" % b for b in best])),
"figure_data":
matplotlib_figure_to_svg_base64_data(ax.figure,
bbox_inches="tight"),
"success":
"yeah!",
}
def partial_digests_analysis(self, data, clones_observations):
analysis = clones_observations.partial_digests_analysis()
best = max(analysis, key=lambda a: analysis[a]['valid_clones'])
if analysis[best]['valid_clones'] == analysis[()]['valid_clones']:
return {
'message': 'The partial digest analysis did not find any '
'significant results',
'success': 'yeah!'
}
ax = clones_observations.plot_partial_digests_analysis(analysis)
validations = analysis[best]['validations']
observations = ClonesObservations(
clones_observations.clones,
clones_observations.constructs_records,
partial_cutters=best)
pdf_data = observations.plot_all_validations_patterns(validations)
return {
'pdf_file': {
'data':
data_to_html_data(pdf_data, 'pdf'),
'name':
'digest_validation_assuming_partial_%s.pdf' % "_".join(best),
'mimetype':
'application/pdf'
},
'message':
("The analysis shows that the following enzyme(s) were"
" possibly only partially cutting: %s. <br/>The report below"
" shows the validation under this hypothesis.") %
(", ".join(["<b>%s</b>" % b for b in best])),
'figure_data':
matplotlib_figure_to_svg_base64_data(ax.figure,
bbox_inches='tight'),
'success':
'yeah!'
}
clones_map = {
well.name: well.data.construct
for well in clones_plate.iter_wells()
if 'construct' in well.data
and str(well.data.construct) != 'nan'
}
bands_observations = BandsObservation.from_aati_fa_archive(
data_dir.aati_files_zip._path)
clones = {
well_name: Clone(name=well_name, construct_id=clones_map[well_name],
digestions={("NcoI",): bands_observations[well_name]})
for well_name, construct_id in clones_map.items()
}
# VALIDATE ALL CLONES WITH BANDWITCH
clones_observations = ClonesObservations(clones, constructs_dict)
validations = clones_observations.validate_all_clones(relative_tolerance=0.03)
validations_summary = clones_observations.validations_summary(validations)
# CREATE A FOLDER WITH VALIDATION REPORTS
report_root = flametree.file_tree('.').examples_output._dir('band_validation')
report_root._file('validations.pdf').write(
clones_observations.plot_all_validations_patterns(validations)
)
ax = clones_observations.plot_validations_plate_map(validations)
ax.figure.savefig(report_root._file('success_map.pdf').open('wb'),
format='pdf', bbox_inches='tight')