def post(self, lims_step):
user = self.get_secure_cookie('user')
email = self.get_secure_cookie('email')
a_type = self.get_argument('type', '')
title = self.get_argument('title', '')
url = self.get_argument('url', '')
desc = self.get_argument('desc','')
if not a_type or not title:
self.set_status(400)
self.finish('<html><body>Link title and type is required</body></html>')
else:
p = Process(self.lims, id=lims_step)
p.get(force=True)
links = json.loads(p.udf['Links']) if 'Links' in p.udf else {}
links[str(datetime.datetime.now())] = {'user': user,
'email': email,
'type': a_type,
'title': title,
'url': url,
'desc': desc}
p.udf['Links'] = json.dumps(links)
p.put()
self.set_status(200)
#ajax cries if it does not get anything back
self.set_header("Content-type", "application/json")
self.finish(json.dumps(links))
def delete(self, workset):
note_id=self.get_argument('note_id')
p = Process(self.lims, id=workset)
p.get(force=True)
workset_notes = json.loads(p.udf['Workset Notes']) if 'Workset Notes' in p.udf else {}
try:
self.set_header("Content-type", "application/json")
del workset_notes[note_id]
p.udf['Workset Notes'] = json.dumps(workset_notes)
p.put()
self.set_status(201)
self.write(json.dumps(workset_notes))
except:
self.set_status(400)
self.finish('<html><body>No note found</body></html>')
def post(self, workset):
note = self.get_argument('note', '')
user = self.get_secure_cookie('user')
email = self.get_secure_cookie('email')
if not note:
self.set_status(400)
self.finish('<html><body>No workset id or note parameters found</body></html>')
else:
newNote = {'user': user, 'email': email, 'note': note}
p = Process(self.lims, id=workset)
p.get(force=True)
workset_notes = json.loads(p.udf['Workset Notes']) if 'Workset Notes' in p.udf else {}
workset_notes[str(datetime.datetime.now())] = newNote
p.udf['Workset Notes'] = json.dumps(workset_notes)
p.put()
self.set_status(201)
self.write(json.dumps(newNote))
def post(self, workset):
note = self.get_argument('note', '')
user = self.get_secure_cookie('user')
email = self.get_secure_cookie('email')
if not note:
self.set_status(400)
self.finish(
'<html><body>No workset id or note parameters found</body></html>'
)
else:
newNote = {'user': user, 'email': email, 'note': note}
p = Process(self.lims, id=workset)
p.get(force=True)
workset_notes = json.loads(
p.udf['Workset Notes']) if 'Workset Notes' in p.udf else {}
workset_notes[str(datetime.datetime.now())] = newNote
p.udf['Workset Notes'] = json.dumps(workset_notes)
p.put()
self.set_status(201)
self.write(json.dumps(newNote))
def results(lims, process_id):
"""Upload magnis export to process."""
process = Process(lims, id=process_id)
lot_error = False
for output_file in process.result_files():
if output_file.name == 'Magnis export file':
magnis_xml_file = output_file.files[0]
magnis_data = xmltodict.parse(lims.get_file_contents(magnis_xml_file.id))
# Save lot nunmbers and check sample input strip barcode
for labware in magnis_data['RunInfo']['LabwareInfos']['Labware']:
if labware['@Name'] == 'Probe Input Strip':
process.udf['lot # SureSelect v7'] = labware['@LotNumber']
elif labware['@Name'] == 'Reagent Plate':
process.udf['lot # Magnis Sureselect XT HS reagent plate'] = labware['@LotNumber']
elif labware['@Name'] == 'Beads/Buffers Plate':
process.udf['lot # Magnis SureSelect XT Beads/Buffers Plate'] = labware['@LotNumber']
elif labware['@Name'] == 'Index Strip':
process.udf['lot # Dual BC strip'] = labware['@LotNumber']
index_strip_number = int(labware['@IndexStrip'])
elif labware['@Name'] == 'Reagent Strip':
process.udf['lot # BR Oligo strip (blockers)'] = labware['@LotNumber']
elif (
labware['@Name'] == 'Sample Input Strip' and
process.udf['Barcode sample input strip'] != labware['@BarCode']
):
lot_error = True
# Check sample reagents and fill Lotnr check flag
for output in process.analytes()[0]:
label_index_number = int(output.reagent_labels[0][3:5])
if lot_error or label_index_number != index_strip_number:
output.udf['Lotnr check'] = False
else:
output.udf['Lotnr check'] = True
output.put()
process.put()
def main(lims, args):
log = []
content = None
process = Process(lims, id=args.pid)
# Copy Read and index parameter from the step "Load to Flowcell (NovaSeq 6000 v2.0)"
UDF_to_copy = [
'Read 1 Cycles', 'Read 2 Cycles', 'Index Read 1', 'Index Read 2'
]
for i in UDF_to_copy:
if process.parent_processes()[0].udf.get(i):
process.udf[i] = process.parent_processes()[0].udf[i]
process.put()
# Fetch Flowcell ID
FCID = process.parent_processes()[0].output_containers()[0].name
for outart in process.all_outputs():
if outart.type == 'ResultFile' and outart.name == 'Run Info':
try:
lims.upload_new_file(
outart,
max(glob.glob(
'/srv/mfs/NovaSeq_data/*{}/RunInfo.xml'.format(FCID)),
key=os.path.getctime))
except:
raise RuntimeError("No RunInfo.xml Found!")
elif outart.type == 'ResultFile' and outart.name == 'Run Parameters':
try:
lims.upload_new_file(
outart,
max(glob.glob(
'/srv/mfs/NovaSeq_data/*{}/RunParameters.xml'.format(
FCID)),
key=os.path.getctime))
except:
raise RuntimeError("No RunParameters.xml Found!")
def results(lims, process_id):
"""Upload tecan results to artifacts."""
process = Process(lims, id=process_id)
concentration_range = map(float, re.findall('[\d\.]+', process.udf['Concentratiebereik (ng/ul)']))
# Parse output file
for output in process.all_outputs(unique=True):
if output.name == 'Tecan Spark Output':
tecan_result_file = output.files[0]
tecan_file_order = ['Dx Fluorescentie (nM)', 'sample_name']
tecan_file_part = -1
measurements = {}
sample_measurements = {}
for line in lims.get_file_contents(tecan_result_file.id).data.split('\n'):
if not line.startswith('<>'):
data = line.rstrip().split('\t')
for index, value in enumerate(data[1:]):
value = value.rstrip()
if value:
coordinate = '{0}{1}'.format(data[0], str(index))
if tecan_file_order[tecan_file_part] == 'Dx Fluorescentie (nM)':
measurements[coordinate] = float(value)
elif tecan_file_order[tecan_file_part] == 'sample_name':
if value not in sample_measurements:
sample_measurements[value] = [measurements[coordinate]]
else:
sample_measurements[value].append(measurements[coordinate])
else:
tecan_file_part += 1
# Calculate linear regression for concentration
# Assumes no std duplicates
baseline_fluorescence = sample_measurements['Dx Tecan std 1'][0]
fluorescence_values = [
sample_measurements['Dx Tecan std 1'][0] - baseline_fluorescence,
sample_measurements['Dx Tecan std 2'][0] - baseline_fluorescence,
sample_measurements['Dx Tecan std 3'][0] - baseline_fluorescence,
sample_measurements['Dx Tecan std 4'][0] - baseline_fluorescence,
sample_measurements['Dx Tecan std 5'][0] - baseline_fluorescence,
sample_measurements['Dx Tecan std 6'][0] - baseline_fluorescence,
]
if process.udf['Reagentia kit'] == 'Quant-iT High-Sensitivity dsDNA kit':
ng_values = [0, 5, 10, 20, 40, 60, 80, 100]
fluorescence_values.append(sample_measurements['Dx Tecan std 7'][0] - baseline_fluorescence)
fluorescence_values.append(sample_measurements['Dx Tecan std 8'][0] - baseline_fluorescence)
elif process.udf['Reagentia kit'] == 'Quant-iT Broad Range dsDNA kit':
ng_values = [0, 50, 100, 200, 400, 600]
regression_slope = sum([x*y for x, y in zip(fluorescence_values, ng_values)]) / sum([x**2 for x in fluorescence_values])
rsquared = 1 - (sum([(y - x*regression_slope)**2 for x, y in zip(fluorescence_values, ng_values)]) / sum([y**2 for y in ng_values]))
# Set udf values
process.udf['R-squared waarde'] = rsquared
process.put()
artifact_count = {}
for artifact in process.all_outputs():
if artifact.name not in ['Tecan Spark Output', 'Tecan Spark Samplesheet', 'check gemiddelde concentratie', 'Label plaat']:
if len(artifact.samples) == 1: # Remove 'meet_id' from artifact name if artifact is not a pool
artifact_name = artifact.name.split('_')[0]
else:
artifact_name = artifact.name
# Set Average Concentratie fluorescentie
sample_fluorescence = sum(sample_measurements[artifact_name]) / float(len(sample_measurements[artifact_name]))
sample_concentration = ((sample_fluorescence - baseline_fluorescence) * regression_slope) / 2.0
artifact.udf['Dx Concentratie fluorescentie (ng/ul)'] = sample_concentration
# Set artifact Concentratie fluorescentie
# Get artifact index == count
if artifact_name not in artifact_count:
artifact_count[artifact_name] = 0
else:
artifact_count[artifact_name] += 1
artifact_fluorescence = sample_measurements[artifact_name][artifact_count[artifact_name]]
artifact_concentration = ((artifact_fluorescence - baseline_fluorescence) * regression_slope) / 2.0
artifact.udf['Dx Conc. goedgekeurde meting (ng/ul)'] = artifact_concentration
# Set QC flags
if artifact_name.startswith('Dx Tecan std'):
artifact.qc_flag = 'PASSED'
std_number = int(artifact_name.split(' ')[3])
artifact.udf['Dx Conc. goedgekeurde meting (ng/ul)'] = ng_values[std_number - 1]
artifact.udf['Dx Concentratie fluorescentie (ng/ul)'] = ng_values[std_number - 1]
else:
# Calculate measurement deviation from average.
if concentration_range[0] <= sample_concentration <= concentration_range[1]:
if len(sample_measurements[artifact_name]) == 1:
artifact.qc_flag = 'PASSED'
elif len(sample_measurements[artifact_name]) == 2:
artifact_fluorescence_difference = abs(sample_measurements[artifact_name][0] - sample_measurements[artifact_name][1])
artifact_fluorescence_deviation = artifact_fluorescence_difference / sample_fluorescence
if artifact_fluorescence_deviation <= 0.1:
artifact.qc_flag = 'PASSED'
else:
artifact.qc_flag = 'FAILED'
else:
artifact.qc_flag = 'FAILED'
artifact.put()