def job_status(job_id):
is_job_in_db =job_exists(job_id)
if not is_job_in_db:
return jsonify(status='failure', message='Unable to locate job')
async_id = get_async_id(job_id)
dataset_id = None #request.args.get('dataset_id')
peakfile_id = None # request.args.get('peakfile_id')
if is_job_type_encode(job_id):
data = get_encode_from_jobid(job_id)
dataset_id = data['dataset_id']
peakfile_id = data['peakfile_id']
job_db = get_job_status(job_id)
status = job_db['status']
job = run_job.AsyncResult(async_id)
#job = workflow.AsyncResult(async_id)
#job = run_motif_analysis_job.AsyncResult(async_id)
print 'JOB STATE: {}'.format(job.state)
if status == 'pending':
return json.dumps({'status': 'pending', 'job_id':job_id})
elif status == 'success':
#post_process(async_id, job_id)
images = ['motif{}Combined_plots.png'.format(i) for i in range(1, N_MEME_MOTIFS+1)]
if dataset_id:
metadata = json.loads(get_encode_metadata(peakfile_id))
summary = read_summary('encode/{}/{}'.format(dataset_id, peakfile_id))
motif_occurrences=summary['motif_occurrences']
peaks = summary['peaks']
sorted_mo = sorted(motif_occurrences.items(), key=operator.itemgetter(1), reverse=True)
motifs = [i for i,j in sorted_mo if float(j)/peaks>0.1]
images = ['/static/jobs/encode/{}/{}/{}Combined_plots.png'.format(dataset_id, peakfile_id, i) for i,j in sorted_mo if float(j)/peaks>0.1]
rcimages = ['/static/jobs/encode/{}/{}/{}Combined_plots_rc.png'.format(dataset_id, peakfile_id, i) for i,j in sorted_mo if float(j)/peaks>0.1]
else:
summary = read_summary(job_id)
motif_occurrences=summary['motif_occurrences']
peaks = summary['peaks']
sorted_mo = sorted(motif_occurrences.items(), key=operator.itemgetter(1), reverse=True)
images = {i:'/static/jobs/{}/{}Combined_plots.png'.format(job_id, i) for i,j in sorted_mo if float(j)/peaks>0.1}
motifs = [i for i,j in sorted_mo if float(j)/peaks>0.1]
#images = ['/static/jobs/{}/{}Combined_plots.png'.format(job_id, i) for i,j in sorted_mo if float(j)/peaks>0.1]
rcimages = {i:'/static/jobs/{}/{}Combined_plots_rc.png'.format(job_id, i) for i,j in sorted_mo if float(j)/peaks>0.1}
#rcimages = ['/static/jobs/{}/{}Combined_plots_rc.png'.format(job_id, i) for i,j in sorted_mo if float(j)/peaks>0.1]
metadata = {'filename': get_filename(async_id)}
return jsonify(status=job.status,
job_id=job_id,
motifs=motifs,
images=images,
rcimages=rcimages,
motif_occurrences=dict(sorted_mo),
metadata=metadata,
peaks=summary['peaks'])
else:
exception_log = job_db['exception_log']
if not exception_log:
exception_log="{'stderr':'none', 'stdlog':'none', 'output':'none'}"
status = 'failure'
return jsonify(status=status, message=json.loads(exception_log))
def encodejobs(dataset_id, peakfile_id):
job_status = encode_job_status(peakfile_id)
if job_status == 'success':
summary = read_summary('encode/{}/{}'.format(dataset_id, peakfile_id))
metadata = json.loads(get_encode_metadata(peakfile_id))
motif_occurrences=summary['motif_occurrences']
peaks = summary['peaks']
sorted_mo = sorted(motif_occurrences.items(), key=operator.itemgetter(1), reverse=True)
images = {i:'/static/jobs/encode/{}/{}/{}Combined_plots.png'.format(dataset_id, peakfile_id, i) for i,j in sorted_mo if float(j)/peaks>0.1}
rcimages = {i:'/static/jobs/encode/{}/{}/{}Combined_plots_rc.png'.format(dataset_id, peakfile_id, i) for i,j in sorted_mo if float(j)/peaks>0.1}
data = ({'motifs':images, 'motif_occurrences': summary['motif_occurrences'], 'peaks': summary['peaks'], 'rcimages':rcimages })
if request.method=='POST':
job_id = get_encode_jobid(peakfile_id)
return jsonify(job_id=job_id,
dataset_id=dataset_id,
peakfile_id=peakfile_id,
data=data)
return render_template('encoderesults.html', job_id='none', data=data, metadata=(metadata))#motifs=images, motif_occurrences=summary['motif_occurrences'], peaks=summary['peaks'], job_id='none')
elif job_status == 'pending':
metadata = get_metadata_for_peakfile(dataset_id, peakfile_id)
job_id = get_encode_jobid(peakfile_id)
if request.method=='GET':
return render_template('encoderesults.html', job_id=job_id, dataset_id=dataset_id, peakfile_id=peakfile_id, data=json.dumps({}), metadata=json.dumps(metadata))
else:
return jsonify(job_id=job_id,
dataset_id=dataset_id,
peakfile_id=peakfile_id)
elif job_status == 'inexistent':
metadata = get_metadata_for_peakfile(dataset_id, peakfile_id)
job = process_job(request, metadata)
if request.method=='GET':
return render_template('encoderesults.html', job_id=job.job_id, dataset_id=dataset_id, peakfile_id=peakfile_id, data=json.dumps({}), metadata=json.dumps(metadata))
else:
return jsonify(job_id=job.job_id,
dataset_id=dataset_id,
peakfile_id=peakfile_id)
elif job_status == 'error':
return json.dumps({'status': 'error', 'response': metadata})
