def test_dtafile_not_found_gives_empty_json_obj(self):
''' Tests that a DTAFile that doesn't exist (id=2)
returns an empty JSON object as a response
'''
self.assertEqual(views_helpers.get_json_response('api.dtafile_quickinfo', 2), '{}')
def sqtfile_info(sqtfile_pk):
''' View function that displays information about a
SQTFile with id=sqtfile_pk
Looks up associated files via associated tables.
Allows editing/deleting of associated files (or entire dataset)
'''
current_sqtfile = models.SQTFile.query.get_or_404(sqtfile_pk)
# this is performing a second identical DB query... not ideal:
sqtfile_quickinfo_dict = views_helpers.get_json_response('api.sqtfile_quickinfo', sqtfile_pk)
sqtfile_quickinfo_dict = json.loads(sqtfile_quickinfo_dict)
parent_dbsearch = models.DBSearch.query.get_or_404(sqtfile_quickinfo_dict['parent_dbsearch'])
dta_files = parent_dbsearch.dtafiles.all()
parent_dataset = models.Dataset.query.get_or_404(parent_dbsearch.dataset_id)
return render_template( 'data/sqtfile.html',
current_sqtfile=current_sqtfile,
parent_dbsearch=parent_dbsearch,
dta_files=dta_files,
parent_dataset=parent_dataset,
sqtfile_quickinfo_dict=sqtfile_quickinfo_dict)
def test_API():
json_obj = views_helpers.get_json_response('api.json_api')
json_obj = json.loads(json_obj)
# example modifying new dict
json_obj['new'] = 3
return json.dumps(json_obj)
def delete_dataset(dataset_pk):
''' "Deletes" dataset (id=dataset_pk) by setting Dataset.deleted=True
"Recovers" dataset if url is accessed with argument recover=True like this:
/<dataset_pk>/delete?recover=True
'''
# new_status flag sets [model_instance].deleted to True or False
# depending on whether it should be "deleted" or "recovered"
new_status = not request.args.get('recover', None)
dataset_quickinfo_dict = views_helpers.get_json_response('api.dataset_quickinfo', dataset_pk)
dataset_quickinfo_dict = json.loads(dataset_quickinfo_dict)
# "delete" dataset
current_dataset = models.Dataset.query.get(dataset_pk)
current_dataset.deleted = new_status
# "delete" associated MS1 and MS2 files
for ms1_file_id in dataset_quickinfo_dict['ms1_files']:
ms1_file = models.MS1File.query.get(ms1_file_id)
ms1_file.deleted = new_status
for ms2_file_id in dataset_quickinfo_dict['ms2_files']:
ms2_file = models.MS2File.query.get(ms2_file_id)
ms2_file.deleted = new_status
# "delete" associated dbsearches
if dataset_quickinfo_dict['dbsearches']:
all_sqt_files = []
all_dta_files = []
for dbsearch_pk in dataset_quickinfo_dict['dbsearches']:
current_dbsearch = models.DBSearch.query.get(dbsearch_pk)
current_dbsearch.deleted = new_status
for sqt_file in current_dbsearch.sqtfiles.all():
all_sqt_files.append(sqt_file)
for dta_file in current_dbsearch.dtafiles.all():
all_dta_files.append(dta_file)
# "delete" associated SQT and DTA files
for sqt_file in all_sqt_files:
sqt_file.deleted = new_status
for dta_file in all_dta_files:
dta_file.deleted = new_status
db.session.commit()
app.logger.info('Deleted Dataset "{}" (Dataset ID {}) and associated files from database'.format(current_dataset.name, current_dataset.id))
return redirect(url_for('data.document_index')) # pass a message here confirming delete
def test_dtafile_JSON_parser_returns_correct_data(self):
''' Tests that DTASelect file --> JSON parser returns valid data (based on mock DTAFile)
'''
try:
json_resp = json.loads(views_helpers.get_json_response('api.dtafile_json', 1))
except (ValueError, TypeError):
self.fail('Invalid JSON')
self.assertIn('data', json_resp) # JSON response has 'data'
self.assertEqual(len(json_resp['data']), 9) # 9 loci in this "file"
def test_dtafile_found_gives_correct_json_output(self):
''' Tests that a DTAFile exists (id=1),
returns a valid JSON object as a response,
and that this object contains the 'id' key
and that response['id']==1
'''
try:
json_resp = json.loads(views_helpers.get_json_response('api.dtafile_quickinfo', 1))
except (ValueError, TypeError):
self.fail('Invalid JSON')
self.assertIn('id', json_resp)
self.assertEqual(json_resp['id'], 1)
def dataset_info(dataset_pk):
''' View function that displays information about a
Dataset with id=dataset_pk
Looks up associated files via associated tables.
Allows editing/deleting of associated files (or entire dataset)
'''
current_dataset = models.Dataset.query.get_or_404(dataset_pk)
# this is performing a second identical DB query... not ideal:
dataset_quickinfo_dict = views_helpers.get_json_response('api.dataset_quickinfo', dataset_pk)
dataset_quickinfo_dict = json.loads(dataset_quickinfo_dict)
return render_template('data/dataset.html', dataset_id=dataset_pk, current_dataset=current_dataset, dataset_quickinfo_dict=dataset_quickinfo_dict)
def salt_step_peptide_analysis(dtafile_pk):
''' Determines how many filtered peptides are present
per chromatography step and draws a plot
'''
current_dtafile = models.DTAFile.query.get_or_404(dtafile_pk)
dtafile_quickinfo_dict = views_helpers.get_json_response('api.dtafile_quickinfo', dtafile_pk)
dtafile_quickinfo_dict = json.loads(dtafile_quickinfo_dict)
dtafile_json = views_helpers.get_json_response('api.dtafile_json', dtafile_pk)
dtafile_json = json.loads(dtafile_json)
parent_dbsearch = models.DBSearch.query.get_or_404(dtafile_quickinfo_dict['parent_dbsearch'])
sqt_files = parent_dbsearch.sqtfiles.all()
parent_dataset = models.Dataset.query.get_or_404(parent_dbsearch.dataset_id)
def get_distinct_psm_ids(dtaselect_parser):
psms = set()
for locus in dtaselect_parser:
for peptide in locus['peptides']:
psm_id = str(peptide['LCStep'])+'_'+str(peptide['Scan'])+'_'+str(peptide['ChargeState'])
psms.add(psm_id)
return psms
def make_LCStep_histogram(psm_ids_set):
full_lcstep_count = []
for psm in psm_ids_set:
full_lcstep_count.append(psm.split('_')[0])
return Counter(full_lcstep_count)
psm_ids = get_distinct_psm_ids(dtafile_json['data'])
hist = make_LCStep_histogram(psm_ids)
labels, values = zip(*sorted(hist.items(), key=lambda x: int(x[0])))
labels = np.array(labels)
values = np.array(values)
fig = figure(title="Peptides per LC Step", y_range=[0, max(values)*1.25], plot_height=400, plot_width=700)
fig.rect(x=labels, y=values/2, width=0.8, height=values)
fig.xaxis.axis_label = 'chromatography step'
fig.yaxis.axis_label = '# peptides identified'
from bokeh.models import FixedTicker
fig.xaxis[0].ticker.desired_num_ticks = len(labels)
plot_resources = RESOURCES.render(
js_raw=INLINE.js_raw,
css_raw=INLINE.css_raw,
js_files=INLINE.js_files,
css_files=INLINE.css_files,
)
script, div = components(fig, INLINE)
return render_template( 'plots/empty.html',
dtafile_quickinfo_dict=dtafile_quickinfo_dict,
current_dtafile=current_dtafile,
parent_dbsearch=parent_dbsearch,
sqt_files=sqt_files,
parent_dataset=parent_dataset,
plot_script=script,
plot_div=div,
plot_resources=plot_resources,
# color=color,
# _from=_from,
# to=to,
)
