def test_underscore():
id_str = "ISP_666-12-08-15-45-00-0001"
cli_utils.validate_id(id_str)
def test_validate_id():
id_str = "ISP-2018-06-08-15-45-00-0001"
cli_utils.validate_id(id_str)
def test_invalid_serial():
id_str = "ISP-666-12-08-15-45-00-A"
cli_utils.validate_id(id_str)
def test_invalid_year():
id_str = "ISP-666-12-08-15-45-00-0001"
cli_utils.validate_id(id_str)
def test_invalid_month():
id_str = "ISP-2018-50-08-15-45-00-0001"
cli_utils.validate_id(id_str)
def download_data(dataset_serial,
login,
dest,
storage='local',
storage_access=None,
metadata=True,
download=True,
nbr_workers=None,
positions=None,
times=None,
channels=None,
slices=None):
"""
Find all files associated with unique project identifier and
download them to a local directory.
:param str dataset_serial: Unique dataset identifier
:param str login: Full path to json file containing database login
credentials
:param str dest: Local destination directory name
:param str storage: 'local' (default) - data will be stored locally and
synced to S3 the same day. Or 'S3' - data will be uploaded
directly to S3 then synced with local storage daily.
:param str/None storage_access: If not using predefined storage locations,
this parameter refers to mount_point for local storage and
bucket_name for S3 storage.
:param bool download: Downloads all files associated with dataset (default)
If False, will only write csvs with metadata. Only for
datasets split into frames
:param bool metadata: Writes metadata (default True)
global metadata in json, local for each frame in csv
:param int, None nbr_workers: Number of workers for parallel download
If None, it defaults to number of machine processors * 5
:param list, None positions: Positions (FOVs) as integers (default
None downloads all)
:param list, None times: Timepoints as integers (default None downloads all)
:param list, None channels: Channels as integer indices or strings for channel
names (default None downloads all)
:param list, None slices: Slice (z) integer indices (Default None downloads all)
"""
try:
cli_utils.validate_id(dataset_serial)
except AssertionError as e:
raise AssertionError("Invalid ID:", e)
# Create output directory as a subdirectory in dest named
# dataset_serial. It stops if the subdirectory already exists to avoid
# the risk of overwriting existing data
dest_dir = os.path.join(dest, dataset_serial)
try:
os.makedirs(dest_dir, exist_ok=False)
except FileExistsError as e:
raise FileExistsError("Folder {} already exists, {}".format(
dest_dir, e))
# Get database connection URI
db_connection = db_utils.get_connection_str(login)
db_utils.check_connection(db_connection)
# Instantiate database class
db_inst = db_ops.DatabaseOperations(dataset_serial=dataset_serial, )
# Import local or S3 storage class
storage_class = aux_utils.get_storage_class(storage_type=storage)
if metadata is False:
# Just download file(s)
assert download,\
"You set metadata *and* download to False. You get nothing."
with db_ops.session_scope(db_connection) as session:
storage_dir, file_names = db_inst.get_filenames(session=session, )
else:
# If channels can be converted to ints, they're indices
if channels is not None:
if not isinstance(channels, list):
channels = [channels]
try:
channels = [int(c) for c in channels]
except ValueError:
# Channels are names, not indices
assert all([isinstance(c, str) for c in channels]), \
"channels must be either all str or int"
# Get the metadata from the requested frames
with db_ops.session_scope(db_connection) as session:
global_meta, frames_meta = db_inst.get_frames_meta(
session=session,
positions=positions,
times=times,
channels=channels,
slices=slices,
)
# Write global metadata to destination directory
global_meta_filename = os.path.join(
dest_dir,
"global_metadata.json",
)
json_ops.write_json_file(
meta_dict=global_meta,
json_filename=global_meta_filename,
)
# Write info for each frame to destination directory
local_meta_filename = os.path.join(
dest_dir,
"frames_meta.csv",
)
frames_meta.to_csv(local_meta_filename, sep=",")
# Extract folder and file names if we want to download
storage_dir = global_meta["storage_dir"]
file_names = frames_meta["file_name"]
if download:
if nbr_workers is not None:
assert nbr_workers > 0,\
"Nbr of worker must be >0, not {}".format(nbr_workers)
data_loader = storage_class(
storage_dir=storage_dir,
nbr_workers=nbr_workers,
access_point=storage_access,
)
data_loader.download_files(file_names, dest_dir)
def upload_data_and_update_db(csv,
login,
config,
nbr_workers=None,
overwrite=False):
"""
Takes a csv file in which each row represents a dataset, uploads the data
to storage and metadata to database. If 'frames' is selected as upload
type, each dataset will be split into individual 2D frames before moving
to storage.
TODO: Add logging instead of printing
:param str login: Full path to json file containing login credentials
:param str csv: Full path to csv file containing the following fields
for each file to be uploaded:
str dataset_id: Unique dataset ID <ID>-YYYY-MM-DD-HH-MM-SS-<SSSS>
str file_name: Full path to file to be uploaded
str description: Short description of file
str parent_dataset_id: Parent dataset unique ID if there is one
list positions: Which position files in folder to upload.
Uploads all if left empty and file_name is a folder.
Only valid for ome-tiff uploads.
:param str config: Full path to json config file containing the fields:
str upload_type: Specify if the file should be split prior to upload
Valid options: 'frames' or 'file'
str frames_format: Which file splitter class to use.
Valid options:
'ome_tiff' needs MicroManagerMetadata tag for each frame for metadata
'tif_folder' when each file is already an individual frame
and relies on MicroManager metadata
'tif_id' needs ImageDescription tag in first frame page for metadata
str storage: 'local' (default) - data will be stored locally and
synced to S3 the same day. Or 'S3' - data will be uploaded
directly to S3 then synced with local storage daily.
str storage_access: If not using predefined storage locations,
this parameter refers to mount_point for local storage and
bucket_name for S3 storage. (optional)
str json_meta: If splitting to frames, give full path to json
metadata schema for reading metadata (optional)
:param int, None nbr_workers: Number of workers for parallel uploads
:param bool overwrite: Use with caution if your upload if your upload was
interrupted and you want to overwrite existing data in database
and storage
"""
# Assert that csv file exists and load it
assert os.path.isfile(csv), \
"File doesn't exist: {}".format(csv)
files_data = pd.read_csv(csv)
# Get database connection URI
db_connection = db_utils.get_connection_str(login)
db_utils.check_connection(db_connection)
# Read and validate config json
config_json = json_ops.read_json_file(
json_filename=config,
schema_name="CONFIG_SCHEMA",
)
# Assert that upload type is valid
upload_type = config_json['upload_type'].lower()
assert upload_type in {"file", "frames"}, \
"upload_type should be 'file' or 'frames', not {}".format(
upload_type,
)
if nbr_workers is not None:
assert nbr_workers > 0, \
"Nbr of worker must be >0, not {}".format(nbr_workers)
# Import local or S3 storage class
storage = 'local'
if 'storage' in config_json:
storage = config_json['storage']
storage_class = aux_utils.get_storage_class(storage_type=storage)
storage_access = None
if 'storage_access' in config_json:
storage_access = config_json['storage_access']
# Make sure microscope is a string
microscope = None
if 'microscope' in config_json:
if isinstance(config_json['microscope'], str):
microscope = config_json['microscope']
if upload_type == 'frames':
# If upload type is frames, check from frames format
assert 'frames_format' in config_json, \
'You must specify the type of file(s)'
splitter_class = aux_utils.get_splitter_class(
config_json['frames_format'],
)
# Upload all files
for file_nbr, row in files_data.iterrows():
# Assert that ID is correctly formatted
dataset_serial = row.dataset_id
try:
cli_utils.validate_id(dataset_serial)
except AssertionError as e:
raise AssertionError("Invalid ID:", e)
# Get S3 directory based on upload type
if upload_type == "frames":
storage_dir = "/".join([FRAME_FOLDER_NAME, dataset_serial])
else:
storage_dir = "/".join([FILE_FOLDER_NAME, dataset_serial])
# Instantiate database operations class
db_inst = db_ops.DatabaseOperations(
dataset_serial=dataset_serial,
)
# Make sure dataset is not already in database
if not overwrite:
with db_ops.session_scope(db_connection) as session:
db_inst.assert_unique_id(session)
# Check for parent dataset
parent_dataset_id = 'None'
if 'parent_dataset_id' in row:
parent_dataset_id = row.parent_dataset_id
# Check for dataset description
description = None
if 'description' in row:
if row.description == row.description:
description = row.description
if upload_type == "frames":
# Instantiate splitter class
frames_inst = splitter_class(
data_path=row.file_name,
storage_dir=storage_dir,
storage_class=storage_class,
storage_access=storage_access,
overwrite=overwrite,
file_format=FRAME_FILE_FORMAT,
nbr_workers=nbr_workers,
)
# Get kwargs if any
kwargs = {}
if 'positions' in row:
positions = row['positions']
if not pd.isna(positions):
kwargs['positions'] = positions
if 'schema_filename' in config_json:
kwargs['schema_filename'] = config_json['schema_filename']
if 'filename_parser' in config_json:
filename_parser = config_json['filename_parser']
kwargs['filename_parser'] = filename_parser
# Extract metadata and split file into frames
frames_inst.get_frames_and_metadata(**kwargs)
# Add frames metadata to database
try:
with db_ops.session_scope(db_connection) as session:
db_inst.insert_frames(
session=session,
description=description,
frames_meta=frames_inst.get_frames_meta(),
frames_json_meta=frames_inst.get_frames_json(),
global_meta=frames_inst.get_global_meta(),
global_json_meta=frames_inst.get_global_json(),
microscope=microscope,
parent_dataset=parent_dataset_id,
)
except AssertionError as e:
print("Data set {} already in DB".format(dataset_serial), e)
# File upload
else:
# Just upload file without opening it
assert os.path.isfile(row.file_name), \
"File doesn't exist: {}".format(row.file_name)
data_uploader = storage_class(
storage_dir=storage_dir,
access_point=storage_access,
)
if not overwrite:
data_uploader.assert_unique_id()
try:
data_uploader.upload_file(file_path=row.file_name)
print("File {} uploaded to S3".format(row.file_name))
except AssertionError as e:
print("File already on S3, moving on to DB entry. {}".format(e))
sha = meta_utils.gen_sha256(row.file_name)
# Add file entry to DB once I can get it tested
global_json = {"file_origin": row.file_name}
file_name = row.file_name.split("/")[-1]
try:
with db_ops.session_scope(db_connection) as session:
db_inst.insert_file(
session=session,
description=description,
storage_dir=storage_dir,
file_name=file_name,
global_json_meta=global_json,
microscope=microscope,
parent_dataset=parent_dataset_id,
sha256=sha,
)
print("File info for {} inserted in DB".format(dataset_serial))
except AssertionError as e:
print("File {} already in database".format(dataset_serial))