Object Manager¶

The ObjectManager makes use of an options dict to specify the current options to manage the file(s):

options[‘project’] = id of the project, as str
options[‘uncompress’] = bool , is the base file an archive uncompressed
if uncompress is True:
    options[‘name’] = name of the base file (‘test.zip’ for ex) to detect the unarchiver to use
    options[‘group’] = bool , if uncompress is True, should all files be a single dataset or one dataset per file (group of files or independent files)
   options[‘files’] = list of file paths to copy in the dataset(s) according to group
else:
    options[‘files’] = list of path to the files to copy in the dataset

options[‘format’] = EDAM format or “auto” . If auto, a call to BioFormat detector will be done to try to detect the format and type
options[‘type’] = EDAM type

Create one dataset in the pairtree¶

To create a projectdata from a file available on local filesystem:

options = {}
options['project'] = project_id  # as str

# optional set status to go directly in STATUS instead of QUEUED
# options["status"] = READY

my_dataset = ObjectManager.add("my file name", options, is_persistent)
# now, my dataset status is "QUEUED"

# Get path to the objects
my_path = my_dataset.get_file_path()
...
write file(s) in my_path
...
# Update the schema according to the files, example:
my_schema = RefData()
my_schema[‘path’] = relative_path_in_directory
my_schema[‘size’] = size_of_the_data_element
my_schema[‘format’] = "EDAM:..."
my_schema[‘type’] = "EDAM:..."

my_dataset.schema(my_schema)
my_dataset.status(ObjectManager.READY)

my_dataset.save_with_history([ List_of_new_or_updated_relative_files_path ], "message why")

Create one dataset with data in an existing directory¶

If the project data must refer to a data located i a specific place, but should not be copied:

options = {}
options['project'] = project_id

options["path"] = path_to_my_job_result_dir # for example

// Optionally set the schema
options["schema"] = StructData(....) or RefData or … (the schema defining the data)
my_dataset = ObjectManager.add("my file name", options, is_persistent)

# Will return path_to_my_job_result_dir
my_path = my_dataset.get_file_path()

...
# Update the schema according to the files
my_dataset.schema(my_schema)

my_dataset.status(ObjectManager.READY)

my_dataset.save_with_history([ List_of_new_or_updated_relative_files_path ], "message why")

Get/update dataset/ dataset info¶

To get an existing dataset (ProjectData)

my_dataset = ObjectManager.get(my_dataset_id) #to get an existing dataset
...
my_path = my_dataset.get_file_path()
# write new files, delete ,... in path
....
# Update the schema according to the files
my_dataset.schema(my_schema)

my_dataset.save_with_history([ List_of_new_or_updated_relative_files_path ], "message why")

Delete dataset¶

Delete dataset in data and all data in the pairtree If path is set (data not in pairtree), data are not deleted

ObjectManager.delete(my_dataset_id)

Request a token¶

To request a token for limited access in time to a file

my_token = manager.get_token(my_dataset_id, file_path, AccessMode.READ or AccessMode.READWRITE)

This will grant access to token my_token during a default period with READ or WRITE access.