def save_file_information(request, project, gi, file_id):
"""import file in the synbiowatch and save metadata in the database
"""
data = gi.datasets.show_dataset(dataset_id=file_id)
name_history = gi.histories.show_history(data['history_id'])['name']
project_obj = Project.objects.get(name=project)
try:
runWf = RunWorkflow.objects.get(name=name_history)
except:
messages.warning(
request,
"Workflow data is not linked with run workflow some information are missing.\n"
+
"Please relaunch workflow in a new history with Synbiowatch interface"
)
return redirect("galaxy_workflow")
# TODO test if bigDATA
# recupere et stocke le fichier via l'url api de galaxy dans synbiowatchuser = User.objects.get(username=request.user)
params = urllib.urlencode({'to_ext': "html", 'key': gi.key}, True)
# recupere l'url de telechargement
url = urlparse.urljoin(gi.base_url, data['download_url'])
response = urllib2.urlopen('%s/?%s' % (url, params))
file_info = FileInformation(
name=data['name'],
format=data['data_type'],
type="",
size=data['file_size'],
author="",
# creation_date="",
# file_path="",
project=project_obj)
file_info.file_path.save(
'%s/%s/%s/%s' %
(settings.ANALYSE_FOLDER, project, name_history, data['name']),
ContentFile(response.read()),
save=False)
file_info.save()
worflowdata = WorkflowData(
data=file_info,
input=0,
output=1, # TODO Count
id_run_wf=runWf)
worflowdata.save()
messages.success(request, file_id)
return redirect(request.META.get('HTTP_REFERER'), request)
def save_file_information(request, project, gi, file_id):
"""import file in the synbiowatch and save metadata in the database
"""
data = gi.datasets.show_dataset(dataset_id=file_id)
name_history = gi.histories.show_history(data["history_id"])["name"]
project_obj = Project.objects.get(name=project)
try:
runWf = RunWorkflow.objects.get(name=name_history)
except:
messages.warning(
request,
"Workflow data is not linked with run workflow some information are missing.\n"
+ "Please relaunch workflow in a new history with Synbiowatch interface",
)
return redirect("galaxy_workflow")
# TODO test if bigDATA
# recupere et stocke le fichier via l'url api de galaxy dans synbiowatchuser = User.objects.get(username=request.user)
params = urllib.urlencode({"to_ext": "html", "key": gi.key}, True)
# recupere l'url de telechargement
url = urlparse.urljoin(gi.base_url, data["download_url"])
response = urllib2.urlopen("%s/?%s" % (url, params))
file_info = FileInformation(
name=data["name"],
format=data["data_type"],
type="",
size=data["file_size"],
author="",
# creation_date="",
# file_path="",
project=project_obj,
)
file_info.file_path.save(
"%s/%s/%s/%s" % (settings.ANALYSE_FOLDER, project, name_history, data["name"]),
ContentFile(response.read()),
save=False,
)
file_info.save()
worflowdata = WorkflowData(data=file_info, input=0, output=1, id_run_wf=runWf) # TODO Count
worflowdata.save()
messages.success(request, file_id)
return redirect(request.META.get("HTTP_REFERER"), request)
def save_file_information(request, project, gi, file_id):
"""import file in the synbiowatch and save metadata in the database
"""
data = gi.datasets.show_dataset(dataset_id=file_id)
name_history = gi.histories.show_history(data['history_id'])['name']
project_obj = Project.objects.get(name=project)
try:
runWf = RunWorkflow.objects.get(name=name_history)
except:
messages.warning(request, "Workflow data is not linked with run workflow some information are missing.\n" +
"Please relaunch workflow in a new history with Synbiowatch interface")
return redirect("galaxy_workflow")
# TODO test if bigDATA
# recupere l'url de telechargement
url = urlparse.urljoin(gi.base_url, data['download_url'])
params = urllib.urlencode({'to_ext': "html", 'key': gi.key}, True)
# response = urllib2.urlopen('%s?%s' % (url, params))
r = requests.get('%s?%s' % (url, params))
file_info = FileInformation(name=data['name'],
format=data['data_type'],
type="",
size=data['file_size'],
author="",
# creation_date="",
# file_path="",
project=project_obj
)
file_info._path = '%s/%s/%s' % (settings.ANALYSE_FOLDER, project, name_history)
file_info.file_path.save(data['name'],
ContentFile(r.content),
save=False)
file_info.save()
print file_info
worflowdata = WorkflowData(data=file_info,
input=0,
output=1, # TODO Count
id_run_wf=runWf
)
worflowdata.save()
messages.success(request, file_id)
return redirect('galaxy_history_detail', data['history_id'])
def launch_workflow(request, project, gi):
"""
soumet l'execution du workflow a Galaxy
"""
project_obj = Project.objects.get(name=project)
dataset_map = dict()
if request.method == 'POST':
#recuperation des variables POST
wf_launch = request.POST.get('workflow_id')
hist_id = request.POST.get('hist_id')
input_submit = request.POST.getlist('inputs')
#recuperation du noms de l'history
name_history = gi.histories.show_history(hist_id)['name']
#recuperation de la key galaxy pour lancer le workflow
wf_obj = Workflow.objects.get(id=wf_launch)
wf_key = wf_obj.wf_key
#verification 1er lancement du workflows
#Sauvegarde dans la base du workflow lancé dans la base de donnée
runWf, created = RunWorkflow.objects.get_or_create(
name=name_history,
workflow_id=wf_obj,
project_id=project_obj,
)
if not created:
messages.warning(
request,
"Workflow has already run, Please create a new history")
return redirect('galaxy_history_detail', history_id=hist_id)
#recupere les inputs du worflows
wf = gi.workflows.show_workflow(wf_key)
i_inputs = wf['inputs'].keys()
#mappe les inputs du workflow avec les id des fichiers soumis par l'utilisateur
for i, r in zip(i_inputs, input_submit):
dataset_map[i] = {'id': r, 'src': 'hda'}
#donne l'orde a galaxy de lancer le worflow avec les parametres
gi.workflows.run_workflow(workflow_id=wf_key,
history_id=hist_id,
dataset_map=dataset_map)
"""Sauvegarde du workflow lancé dans la base de donnée"""
#sauvergarde des inputs selectionnés pour lancer le workflow
compt = 0
for r in input_submit:
compt += 1
data = gi.datasets.show_dataset(dataset_id=r)
#cree la metadonnée du fichier utiliser avec le workflow
#TODO trouver un moyen de discriminer les fichiers autre que par le nom
file_info = FileInformation(
name=data['name'],
format=data['data_type'],
type="",
size=data['file_size'],
author="",
#creation_date="",
#file_path="",
project=project_obj,
)
file_info.save()
#sauvegarde avec quel workflow le fichier a été utilisé
workflowdata = WorkflowData(
data=file_info,
id_run_wf=runWf,
input=compt,
output=0,
)
workflowdata.save()
print workflowdata
return redirect('galaxy_history_detail', history_id=hist_id)
return redirect("galaxy_workflow")
def launch_workflow(request, project, gi):
"""
soumet l'execution du workflow a Galaxy
"""
project_obj = Project.objects.get(name=project)
dataset_map = dict()
if request.method == 'POST':
#recuperation des variables POST
wf_launch = request.POST.get('workflow_id')
hist_id = request.POST.get('hist_id')
input_submit = request.POST.getlist('inputs')
#recuperation du noms de l'history
name_history = gi.histories.show_history(hist_id)['name']
#recuperation de la key galaxy pour lancer le workflow
wf_obj = Workflow.objects.get(id=wf_launch)
wf_key = wf_obj.wf_key
#verification 1er lancement du workflows
#Sauvegarde dans la base du workflow lancé dans la base de donnée
runWf, created = RunWorkflow.objects.get_or_create(name = name_history,
workflow_id=wf_obj,
project_id = project_obj,
)
if not created:
messages.warning(request,"Workflow has already run, Please create a new history" )
return redirect('galaxy_history_detail',history_id=hist_id)
#recupere les inputs du worflows
wf = gi.workflows.show_workflow(wf_key)
i_inputs = wf['inputs'].keys()
#mappe les inputs du workflow avec les id des fichiers soumis par l'utilisateur
for i, r in zip(i_inputs,input_submit):
dataset_map[i] = {'id':r,'src':'hda'}
#donne l'orde a galaxy de lancer le worflow avec les parametres
gi.workflows.run_workflow(workflow_id=wf_key, history_id=hist_id, dataset_map=dataset_map)
"""Sauvegarde du workflow lancé dans la base de donnée"""
#sauvergarde des inputs selectionnés pour lancer le workflow
compt = 0
for r in input_submit:
compt +=1
data = gi.datasets.show_dataset(dataset_id= r)
#cree la metadonnée du fichier utiliser avec le workflow
#TODO trouver un moyen de discriminer les fichiers autre que par le nom
file_info = FileInformation(name=data['name'],
format=data['data_type'],
type="",
size=data['file_size'],
author="",
#creation_date="",
#file_path="",
project = project_obj,
)
file_info.save()
#sauvegarde avec quel workflow le fichier a été utilisé
workflowdata = WorkflowData (data = file_info,
id_run_wf = runWf,
input = compt,
output = 0,
)
workflowdata.save()
print workflowdata
return redirect('galaxy_history_detail',history_id=hist_id)
return redirect("galaxy_workflow")