def _createLargeImageJob(self, item, fileObj, user, token):
import large_image_tasks.tasks
from girder_worker_utils.transforms.girder_io import GirderUploadToItem
from girder_worker_utils.transforms.contrib.girder_io import GirderFileIdAllowDirect
from girder_worker_utils.transforms.common import TemporaryDirectory
outputName = os.path.splitext(fileObj['name'])[0] + '.tiff'
if outputName == fileObj['name']:
outputName = (os.path.splitext(fileObj['name'])[0] + '.' +
time.strftime('%Y%m%d-%H%M%S') + '.tiff')
try:
localPath = File().getLocalFilePath(fileObj)
except (FilePathException, AttributeError):
localPath = None
job = large_image_tasks.tasks.create_tiff.delay(
girder_job_title=u'TIFF Conversion: %s' % fileObj['name'],
girder_job_other_fields={
'meta': {
'creator': 'large_image',
'itemId': str(item['_id']),
'task': 'createImageItem',
}
},
inputFile=GirderFileIdAllowDirect(str(fileObj['_id']),
fileObj['name'], localPath),
outputName=outputName,
outputDir=TemporaryDirectory(),
girder_result_hooks=[
GirderUploadToItem(str(item['_id']), False),
])
return job.job
def _createLargeImageJob(self, item, fileObj, user, token, **kwargs):
import large_image_tasks.tasks
from girder_worker_utils.transforms.common import TemporaryDirectory
from girder_worker_utils.transforms.contrib.girder_io import GirderFileIdAllowDirect
from girder_worker_utils.transforms.girder_io import GirderUploadToItem
try:
localPath = File().getLocalFilePath(fileObj)
except (FilePathException, AttributeError):
localPath = None
job = large_image_tasks.tasks.create_tiff.apply_async(kwargs=dict(
girder_job_title='TIFF Conversion: %s' % fileObj['name'],
girder_job_other_fields={'meta': {
'creator': 'large_image',
'itemId': str(item['_id']),
'task': 'createImageItem',
}},
inputFile=GirderFileIdAllowDirect(str(fileObj['_id']), fileObj['name'], localPath),
inputName=fileObj['name'],
outputDir=TemporaryDirectory(),
girder_result_hooks=[
GirderUploadToItem(str(item['_id']), False),
],
**kwargs,
), countdown=int(kwargs['countdown']) if kwargs.get('countdown') else None)
return job.job
def asr(
self,
treeFileId,
tableFileId,
selectedColumn,
resultSummaryItemId,
plotItemId
):
result = asr.delay(
GirderFileId(treeFileId),
GirderFileId(tableFileId),
selectedColumn,
girder_result_hooks=[
GirderUploadToItem(resultSummaryItemId),
GirderUploadToItem(plotItemId)
])
return result.job
def run_clip_task(self, item, geometry, name, folder):
girderFile = [i for i in Item().childFiles(item, limit=1)][0]
output = Item().createItem(name,
creator=self.getCurrentUser(),
folder=folder)
result = clip_task.delay(
GirderFileId(str(girderFile['_id'])),
geometry,
name,
girder_result_hooks=[GirderUploadToItem(str(output['_id']))])
return result.job
def pgls(
self,
treeFileId,
tableFileId,
correlation,
independentVariable,
dependentVariable,
modelFitSummaryItemId,
plotItemId
):
result = pgls.delay(
GirderFileId(treeFileId),
GirderFileId(tableFileId),
correlation,
independentVariable,
dependentVariable,
girder_result_hooks=[
GirderUploadToItem(modelFitSummaryItemId),
GirderUploadToItem(plotItemId)
])
return result.job
def createHistogramJob(self,
item,
file_,
user=None,
token=None,
notify=False,
bins=None,
label=False,
bitmask=False):
if bins is None:
bins = Setting().get(PluginSettings.DEFAULT_BINS)
if file_['itemId'] != item['_id']:
raise ValueError('The file must be in the item.')
girder_job_title = 'Histogram computation for item %s' % item['_id']
girder_job_type = 'histogram'
fakeId = uuid.uuid4().hex
other_fields = {
'meta': {
'creator': 'histogram',
'task': 'createHistogram',
'fakeId': fakeId,
}
}
reference = json.dumps({'isHistogram': True, 'fakeId': fakeId})
result = histogramExecutor.delay(
GirderFileId(str(file_['_id'])),
label,
bins,
bitmask,
girder_job_title=girder_job_title,
girder_job_type=girder_job_type,
girder_job_other_fields=other_fields,
girder_result_hooks=[
GirderUploadToItem(str(item['_id']),
delete_file=True,
upload_kwargs={'reference': reference})
])
histogram = {
'expected': True,
'notify': True,
'itemId': item['_id'],
'bins': bins,
'label': label,
'bitmask': bitmask,
# 'jobId': result.job['_id'],
'fakeId': fakeId
}
self.save(histogram)
return histogram
def crop_task(self, item, by_item, name, folder):
target_file = [i for i in Item().childFiles(item, limit=1)][0]
by_file = [i for i in Item().childFiles(by_item, limit=1)][0]
output = Item().createItem(name,
creator=self.getCurrentUser(),
folder=folder)
driver = item.get('geometa', {}).get('driver', None)
if not driver:
raise ValidationException('Unsupported target dataset')
result = crop_task.delay(
GirderFileId(str(target_file['_id'])),
driver,
GirderFileId(str(by_file['_id'])),
name,
girder_result_hooks=[GirderUploadToItem(str(output['_id']))])
return result.job
def generatePCAPlot(self, file):
file_id = str(file['_id'])
item_id = str(file['itemId'])
if file['mimeType'] != 'text/csv':
raise RestException("File must be of type 'text/csv'", code=422)
a = gw_pca.delay(file['name'],
GirderFileId(file_id),
girder_result_hooks=[
GirderItemMetadata(item_id),
GirderUploadToItem(item_id)
])
return a.job
def csv_column_append(self, fileId, itemId):
result = column_append.delay(GirderFileId(fileId),
girder_result_hooks=[GirderUploadToItem(itemId)])
return result.job