def test_upload_file_already_in_db(self, mock_session):
# Upload the same file but as file instead of frames
mock_session.return_value.__enter__.return_value = self.session
config_path = os.path.join(
self.temp_path,
'config_file.json',
)
config = {
"upload_type": "file",
"microscope": "Mass Spectrometry",
"storage": "local",
"storage_access": self.mount_point
}
json_ops.write_json_file(config, config_path)
data_uploader.upload_data_and_update_db(
csv=self.csv_path,
login=self.credentials_path,
config=config_path,
overwrite=True,
)
# Try uploading a second time
data_uploader.upload_data_and_update_db(
csv=self.csv_path,
login=self.credentials_path,
config=config_path,
overwrite=True,
)
def setUp(self, MockPoolExecutor):
"""
Set up temporary test directory and mock S3 bucket connection
"""
# Magic mocking of multiprocessing
MockPoolExecutor().__enter__().map = map_mock
# Mock S3 directory for upload
self.storage_dir = "raw_frames/SMS-2010-01-01-00-00-00-0001"
# Create temporary directory and write temp image
self.tempdir = TempDirectory()
self.temp_path = self.tempdir.path
# Temporary frame
self.im = np.ones((10, 15), dtype=np.uint16)
self.im[2:5, 3:12] = 10000
# Save test tif files
self.channel_names = ['phase', 'brightfield', '666']
# Write files in dir
for c in self.channel_names:
for z in range(2):
file_name = 'img_{}_t000_p050_z00{}.tif'.format(c, z)
file_path = os.path.join(self.temp_path, file_name)
ijmeta = {"Info": json.dumps({"c": c, "z": z})}
tifffile.imsave(
file_path,
self.im + 5000 * z,
ijmetadata=ijmeta,
)
# Write external metadata in dir
self.meta_dict = {
'Summary': {
'Slices': 26,
'PixelType': 'GRAY16',
'Time': '2018-11-01 19:20:34 -0700',
'z-step_um': 0.5,
'PixelSize_um': 0,
'BitDepth': 16,
'Width': 15,
'Height': 10
},
}
self.json_filename = os.path.join(self.temp_path, 'metadata.txt')
json_ops.write_json_file(self.meta_dict, self.json_filename)
# Setup mock S3 bucket
self.mock = mock_s3()
self.mock.start()
self.conn = boto3.resource('s3', region_name='us-east-1')
self.bucket_name = 'czbiohub-imaging'
self.conn.create_bucket(Bucket=self.bucket_name)
# Instantiate file parser class
storage_class = aux_utils.get_storage_class('s3')
self.frames_inst = tif_splitter.TifFolderSplitter(
data_path=self.temp_path,
storage_dir=self.storage_dir,
storage_class=storage_class,
)
# Upload data
self.frames_inst.get_frames_and_metadata(
filename_parser='parse_sms_name', )
def setUp(self):
super().setUp()
# Create temporary directory and write temp image
self.tempdir = TempDirectory()
self.temp_path = self.tempdir.path
# Mock file storage
self.tempdir.makedir('storage_mount_point')
self.mount_point = os.path.join(self.temp_path, 'storage_mount_point')
self.tempdir.makedir('storage_mount_point/raw_files')
self.tempdir.makedir('storage_mount_point/raw_frames')
# Test metadata parameters
self.nbr_channels = 2
self.nbr_slices = 3
# Mock S3 dir
self.storage_dir = "raw_frames/TEST-2005-06-09-20-00-00-1000"
# Temporary file with 6 frames, tifffile stores channels first
self.im = 50 * np.ones((6, 10, 15), dtype=np.uint16)
self.im[0, :5, 3:12] = 50000
self.im[2, :5, 3:12] = 40000
self.im[4, :5, 3:12] = 30000
# Metadata
self.description = 'ImageJ=1.52e\nimages=6\nchannels=2\nslices=3\nmax=10411.0'
# Save test tif file
self.file_path = os.path.join(self.temp_path, "A1_2_PROTEIN_test.tif")
tifffile.imsave(
self.file_path,
self.im,
description=self.description,
)
self.dataset_serial = 'TEST-2005-06-09-20-00-00-1000'
# Create csv file for upload
upload_dict = {
'dataset_id': [self.dataset_serial],
'file_name': [self.file_path],
'description': ['Testing'],
'parent_dataset_id': [None],
}
upload_csv = pd.DataFrame.from_dict(upload_dict)
self.csv_path = os.path.join(self.temp_path, "test_upload.csv")
upload_csv.to_csv(self.csv_path)
self.credentials_path = os.path.join(
self.main_dir,
'db_credentials.json',
)
self.config_path = os.path.join(
self.temp_path,
'config_tif_id.json',
)
config = {
"upload_type": "frames",
"frames_format": "tif_id",
"microscope": "Leica microscope CAN bus adapter",
"filename_parser": "parse_ml_name",
"storage": "local",
"storage_access": self.mount_point
}
json_ops.write_json_file(config, self.config_path)
def test_upload_file(self, mock_session):
# Upload the same file but as file instead of frames
mock_session.return_value.__enter__.return_value = self.session
config_path = os.path.join(
self.temp_path,
'config_file.json',
)
config = {
"upload_type": "file",
"microscope": "Mass Spectrometry",
"storage": "s3",
}
json_ops.write_json_file(config, config_path)
data_uploader.upload_data_and_update_db(
csv=self.csv_path,
login=self.credentials_path,
config=config_path,
)
# Query database to find data_set and file_global
datasets = self.session.query(db_ops.DataSet) \
.filter(db_ops.DataSet.dataset_serial == self.dataset_serial)
self.assertEqual(datasets.count(), 1)
dataset = datasets[0]
self.assertEqual(dataset.id, 1)
self.assertFalse(dataset.frames)
self.assertEqual(dataset.dataset_serial, self.dataset_serial)
date_time = dataset.date_time
self.assertEqual(date_time.year, 2005)
self.assertEqual(date_time.month, 6)
self.assertEqual(date_time.day, 9)
self.assertEqual(dataset.microscope, "Mass Spectrometry")
self.assertEqual(dataset.description, 'Testing')
# query file_global
file_global = self.session.query(db_ops.FileGlobal) \
.join(db_ops.DataSet) \
.filter(db_ops.DataSet.dataset_serial == self.dataset_serial) \
.one()
expected_s3 = "raw_files/TEST-2005-06-09-20-00-00-1000"
self.assertEqual(
file_global.storage_dir,
expected_s3,
)
expected_meta = {'file_origin': self.file_path}
self.assertDictEqual(file_global.metadata_json, expected_meta)
self.assertEqual(file_global.data_set, dataset)
sha256 = meta_utils.gen_sha256(self.file_path)
self.assertEqual(file_global.sha256, sha256)
# Check that file has been uploaded
s3_client = boto3.client('s3')
key = os.path.join(expected_s3, "A1_2_PROTEIN_test.tif")
# Just check that the file is there, we've dissected it before
response = s3_client.list_objects_v2(Bucket=self.bucket_name,
Prefix=key)
self.assertEqual(response['KeyCount'], 1)
def test_upload_file(self, mock_session):
# Upload the same file but as file instead of frames
mock_session.return_value.__enter__.return_value = self.session
config_path = os.path.join(
self.temp_path,
'config_file.json',
)
config = {
"upload_type": "file",
"microscope": "Mass Spectrometry",
"storage": "local",
"storage_access": self.mount_point
}
json_ops.write_json_file(config, config_path)
data_uploader.upload_data_and_update_db(
csv=self.csv_path,
login=self.credentials_path,
config=config_path,
)
# Query database to find data_set and file_global
datasets = self.session.query(db_ops.DataSet) \
.filter(db_ops.DataSet.dataset_serial == self.dataset_serial)
self.assertEqual(datasets.count(), 1)
dataset = datasets[0]
self.assertEqual(dataset.id, 1)
self.assertFalse(dataset.frames)
self.assertEqual(dataset.dataset_serial, self.dataset_serial)
date_time = dataset.date_time
self.assertEqual(date_time.year, 2005)
self.assertEqual(date_time.month, 6)
self.assertEqual(date_time.day, 9)
self.assertEqual(dataset.microscope, "Mass Spectrometry")
self.assertEqual(dataset.description, 'Testing')
# query file_global
file_global = self.session.query(db_ops.FileGlobal) \
.join(db_ops.DataSet) \
.filter(db_ops.DataSet.dataset_serial == self.dataset_serial) \
.one()
expected_dir = "raw_files/TEST-2005-06-09-20-00-00-1000"
self.assertEqual(
file_global.storage_dir,
expected_dir,
)
expected_meta = {'file_origin': self.file_path}
self.assertDictEqual(file_global.metadata_json, expected_meta)
self.assertEqual(file_global.data_set, dataset)
sha256 = meta_utils.gen_sha256(self.file_path)
self.assertEqual(file_global.sha256, sha256)
# Check that file has been uploaded
file_path = os.path.join(self.mount_point, expected_dir,
'A1_2_PROTEIN_test.tif')
self.assertTrue(os.path.exists(file_path))
def test_write_json_file():
with TempDirectory() as tempdir:
valid_json = {
"drivername": "postgres",
"username": "******",
"password": "******",
"host": "db_host",
"port": 666,
"dbname": "db_name"
}
json_ops.write_json_file(
valid_json,
os.path.join(tempdir.path, 'valid_json_file.json'),
)
json_object = json_ops.read_json_file(os.path.join(
tempdir.path, "valid_json_file.json"),
schema_name="CREDENTIALS_SCHEMA")
nose.tools.assert_equal(json_object, valid_json)
def setUp(self, mock_session):
super().setUp()
mock_session.return_value.__enter__.return_value = self.session
# Create temporary directory and write temp image
self.tempdir = TempDirectory()
self.temp_path = self.tempdir.path
# Mock file storage
self.tempdir.makedir('storage_mount_point')
self.mount_point = os.path.join(self.temp_path, 'storage_mount_point')
self.tempdir.makedir('storage_mount_point/raw_files')
self.tempdir.makedir('storage_mount_point/raw_frames')
# Test metadata parameters
self.nbr_channels = 2
self.nbr_slices = 3
# Mock storage dir
self.dataset_serial = 'FRAMES-2005-06-09-20-00-00-1000'
self.frames_storage_dir = os.path.join('raw_frames',
self.dataset_serial)
# Temporary file with 6 frames, tifffile stores channels first
self.im = 50 * np.ones((6, 10, 15), dtype=np.uint16)
self.im[0, :5, 3:12] = 50000
self.im[2, :5, 3:12] = 40000
self.im[4, :5, 3:12] = 30000
# Metadata
self.description = 'ImageJ=1.52e\nimages=6\nchannels=2\nslices=3\nmax=10411.0'
# Save test tif file
self.file_path = os.path.join(self.temp_path, "A1_2_PROTEIN_test.tif")
tifffile.imsave(
self.file_path,
self.im,
description=self.description,
)
# Create input arguments for data upload
upload_csv = pd.DataFrame(
columns=['dataset_id', 'file_name', 'description'], )
upload_csv = upload_csv.append(
{
'dataset_id': self.dataset_serial,
'file_name': self.file_path,
'description': 'Testing'
},
ignore_index=True,
)
self.csv_path_frames = os.path.join(
self.temp_path,
"test_upload_frames.csv",
)
upload_csv.to_csv(self.csv_path_frames)
self.credentials_path = os.path.join(
self.main_dir,
'db_credentials.json',
)
self.config_path = os.path.join(
self.temp_path,
'config_tif_id.json',
)
config = {
"upload_type": "frames",
"frames_format": "tif_id",
"microscope": "Leica microscope CAN bus adapter",
"filename_parser": "parse_ml_name",
"storage": "local",
"storage_access": self.mount_point
}
json_ops.write_json_file(config, self.config_path)
# Upload frames
data_uploader.upload_data_and_update_db(
csv=self.csv_path_frames,
login=self.credentials_path,
config=self.config_path,
)
# Create input args for file upload
self.dataset_serial_file = 'FILE-2005-06-09-20-00-00-1000'
self.file_storage_dir = os.path.join('raw_files',
self.dataset_serial_file)
self.csv_path_file = os.path.join(
self.temp_path,
"test_upload_file.csv",
)
# Change to unique serial
upload_csv['dataset_id'] = self.dataset_serial_file
upload_csv.to_csv(self.csv_path_file)
config_path = os.path.join(
self.temp_path,
'config_file.json',
)
config = {
"upload_type": "file",
"microscope": "Mass Spectrometry",
"storage": "local",
"storage_access": self.mount_point
}
json_ops.write_json_file(config, config_path)
# Upload file
data_uploader.upload_data_and_update_db(
csv=self.csv_path_file,
login=self.credentials_path,
config=config_path,
)
def test_upload_tiffolder(self, mock_session):
mock_session.return_value.__enter__.return_value = self.session
dataset_serial = 'SMS-2010-01-01-01-00-00-0005'
# Temporary frame
im = np.ones((10, 15), dtype=np.uint8)
# Save test tif files
self.tempdir.makedir('tiffolder')
tif_dir = os.path.join(self.temp_path, 'tiffolder')
channel_names = ['phase', 'brightfield', '666']
# Write files in dir
for c_name in channel_names:
for z in range(2):
file_name = 'img_{}_t060_p050_z00{}.tif'.format(c_name, z)
file_path = os.path.join(tif_dir, file_name)
ijmeta = {"Info": json.dumps({"c": c_name, "z": z})}
tifffile.imsave(
file_path,
im + 50 * z,
ijmetadata=ijmeta,
)
# Write external metadata in dir
self.meta_dict = {
'Summary': {
'Slices': 6,
'PixelType': 'GRAY8',
'Time': '2018-11-01 19:20:34 -0700',
'z-step_um': 0.5,
'PixelSize_um': 0,
'BitDepth': 8,
'Width': 15,
'Height': 10
},
}
self.json_filename = os.path.join(tif_dir, 'metadata.txt')
json_ops.write_json_file(self.meta_dict, self.json_filename)
# Create csv file for upload
upload_dict = {
'dataset_id': [dataset_serial],
'file_name': [tif_dir],
'description': ['Testing tifffolder upload'],
}
upload_csv = pd.DataFrame.from_dict(upload_dict)
csv_path = os.path.join(self.temp_path, "test_tiffolder_upload.csv")
upload_csv.to_csv(csv_path)
config_path = os.path.join(
self.temp_path,
'config_tiffolder.json',
)
config = {
"upload_type": "frames",
"frames_format": "tif_folder",
"microscope": "CZDRAGONFLY-PC",
"filename_parser": "parse_sms_name",
"storage": "local",
"storage_access": self.mount_point
}
json_ops.write_json_file(config, config_path)
# Upload data
data_uploader.upload_data_and_update_db(
csv=csv_path,
login=self.credentials_path,
config=config_path,
)
# Query database to find data_set and frames
datasets = self.session.query(db_ops.DataSet) \
.filter(db_ops.DataSet.dataset_serial == dataset_serial)
self.assertEqual(datasets.count(), 1)
dataset = datasets[0]
self.assertTrue(dataset.frames)
self.assertEqual(dataset.dataset_serial, dataset_serial)
date_time = dataset.date_time
self.assertEqual(date_time.year, 2010)
self.assertEqual(date_time.month, 1)
self.assertEqual(date_time.day, 1)
self.assertEqual(dataset.description, 'Testing tifffolder upload')
# query frames_global
global_query = self.session.query(db_ops.FramesGlobal) \
.join(db_ops.DataSet) \
.filter(db_ops.DataSet.dataset_serial == dataset_serial)
self.assertEqual(
global_query[0].storage_dir,
'raw_frames/' + dataset_serial,
)
self.assertEqual(
global_query[0].nbr_frames,
6,
)
self.assertEqual(
global_query[0].im_width,
15,
)
self.assertEqual(
global_query[0].im_height,
10,
)
self.assertEqual(global_query[0].nbr_slices, 2)
self.assertEqual(
global_query[0].nbr_channels,
3,
)
self.assertEqual(
global_query[0].nbr_positions,
1,
)
self.assertEqual(
global_query[0].nbr_timepoints,
1,
)
self.assertEqual(
global_query[0].im_colors,
1,
)
self.assertEqual(
global_query[0].bit_depth,
'uint8',
)
# query frames
frames = self.session.query(db_ops.Frames) \
.join(db_ops.FramesGlobal) \
.join(db_ops.DataSet) \
.filter(db_ops.DataSet.dataset_serial == dataset_serial) \
.order_by(db_ops.Frames.file_name)
# Validate content
# Channel numbers will be assigned alphabetically
channel_names.sort()
for i, (c, z) in enumerate(itertools.product(range(3), range(2))):
im_name = 'im_c00{}_z00{}_t060_p050.png'.format(c, z)
self.assertEqual(frames[i].file_name, im_name)
self.assertEqual(frames[i].channel_idx, c)
self.assertEqual(frames[i].channel_name, channel_names[c])
self.assertEqual(frames[i].slice_idx, z)
self.assertEqual(frames[i].time_idx, 60)
self.assertEqual(frames[i].pos_idx, 50)
self.assertEqual(
frames[i].sha256,
meta_utils.gen_sha256(im + 50 * z),
)
# # Download frames from storage and compare to originals
for i in range(len(channel_names)):
for z in range(2):
im_name = 'im_c00{}_z00{}_t060_p050.png'.format(i, z)
im_path = os.path.join(
self.mount_point,
'raw_frames',
dataset_serial,
im_name,
)
im_out = cv2.imread(im_path, cv2.IMREAD_ANYDEPTH)
nose.tools.assert_equal(im.dtype, np.uint8)
numpy.testing.assert_array_equal(im_out, im + z * 50)
def test_upload_ometif(self, mock_session):
mock_session.return_value.__enter__.return_value = self.session
dataset_serial = 'ISP-2005-01-01-01-00-00-0001'
# Temporary frame
im = np.ones((10, 15), dtype=np.uint16)
# Metadata
ijmeta = {
"Info":
json.dumps({
"InitialPositionList": [{
"Label": "Pos1"
}, {
"Label": "Pos3"
}]
}),
}
channel_ids = [1, 2]
im_names = ['test_Pos1.ome.tif', 'test_Pos3.ome.tif']
for i, c in enumerate(channel_ids):
mmmetadata = json.dumps({
"ChannelIndex": c,
"Slice": 20,
"FrameIndex": 30,
"PositionIndex": 40,
"Channel": 'channel_{}'.format(c),
})
extra_tags = [('MicroManagerMetadata', 's', 0, mmmetadata, True)]
# Save test ome tif file
file_path = os.path.join(self.temp_path, im_names[i])
tifffile.imsave(
file_path,
im + i * 10000,
ijmetadata=ijmeta,
extratags=extra_tags,
)
schema_file_path = os.path.realpath(
os.path.join(self.main_dir, 'metadata_schema.json'), )
# Create csv file for upload
upload_dict = {
'dataset_id': [dataset_serial],
'file_name': [self.temp_path],
'description': ['Testing'],
'positions': [[1, 3]],
'schema_filename': [schema_file_path],
}
upload_csv = pd.DataFrame.from_dict(upload_dict)
csv_path = os.path.join(self.temp_path, "test_ometif_upload.csv")
upload_csv.to_csv(csv_path)
config_path = os.path.join(
self.temp_path,
'config_ome_tiff.json',
)
config = {
"upload_type": "frames",
"frames_format": "ome_tiff",
"microscope": "",
"schema_filename": "metadata_schema.json",
"storage": "local",
"storage_access": self.mount_point
}
json_ops.write_json_file(config, config_path)
# Upload data
data_uploader.upload_data_and_update_db(
csv=csv_path,
login=self.credentials_path,
config=config_path,
)
# Query database to find data_set and frames
datasets = self.session.query(db_ops.DataSet) \
.filter(db_ops.DataSet.dataset_serial == dataset_serial)
self.assertEqual(datasets.count(), 1)
dataset = datasets[0]
self.assertEqual(dataset.id, 1)
self.assertTrue(dataset.frames)
self.assertEqual(dataset.dataset_serial, dataset_serial)
date_time = dataset.date_time
self.assertEqual(date_time.year, 2005)
self.assertEqual(date_time.month, 1)
self.assertEqual(date_time.day, 1)
self.assertEqual(dataset.description, 'Testing')
# query frames_global
global_query = self.session.query(db_ops.FramesGlobal) \
.join(db_ops.DataSet) \
.filter(db_ops.DataSet.dataset_serial == dataset_serial)
self.assertEqual(
global_query[0].storage_dir,
'raw_frames/' + dataset_serial,
)
self.assertEqual(
global_query[0].nbr_frames,
2,
)
im_shape = im.shape
self.assertEqual(
global_query[0].im_width,
im_shape[1],
)
self.assertEqual(
global_query[0].im_height,
im_shape[0],
)
self.assertEqual(global_query[0].nbr_slices, 1)
self.assertEqual(
global_query[0].nbr_channels,
2,
)
self.assertEqual(
global_query[0].nbr_positions,
1,
)
self.assertEqual(
global_query[0].nbr_timepoints,
1,
)
self.assertEqual(
global_query[0].im_colors,
1,
)
self.assertEqual(
global_query[0].bit_depth,
'uint16',
)
# query frames
frames = self.session.query(db_ops.Frames) \
.join(db_ops.FramesGlobal) \
.join(db_ops.DataSet) \
.filter(db_ops.DataSet.dataset_serial == dataset_serial) \
.order_by(db_ops.Frames.file_name)
shas = [meta_utils.gen_sha256(im), meta_utils.gen_sha256(im + 10000)]
for i, c in enumerate(channel_ids):
im_name = 'im_c00{}_z020_t030_p040.png'.format(c)
self.assertEqual(frames[i].file_name, im_name)
self.assertEqual(frames[i].channel_idx, c)
self.assertEqual(frames[i].channel_name, 'channel_{}'.format(c))
self.assertEqual(frames[i].slice_idx, 20)
self.assertEqual(frames[i].time_idx, 30)
self.assertEqual(frames[i].pos_idx, 40)
self.assertEqual(frames[i].sha256, shas[i])
# # Download frames from storage and compare to originals
for i, c in enumerate(channel_ids):
im_name = 'im_c00{}_z020_t030_p040.png'.format(c)
im_path = os.path.join(
self.mount_point,
'raw_frames',
dataset_serial,
im_name,
)
im_out = cv2.imread(im_path, cv2.IMREAD_ANYDEPTH)
nose.tools.assert_equal(im.dtype, np.uint16)
numpy.testing.assert_array_equal(im_out, im + i * 10000)
def test_upload_ometif(self, mock_session):
mock_session.return_value.__enter__.return_value = self.session
dataset_serial = 'ISP-2005-01-01-01-00-00-0001'
# Temporary frame
im = np.ones((10, 15), dtype=np.uint16)
# Metadata
mmmetadata = json.dumps({
"ChannelIndex": 1,
"Slice": 2,
"FrameIndex": 3,
"PositionIndex": 4,
"Channel": 'channel_name',
})
extra_tags = [('MicroManagerMetadata', 's', 0, mmmetadata, True)]
ijmeta = {
"Info":
json.dumps({
"InitialPositionList": [{
"Label": "Pos1"
}, {
"Label": "Pos3"
}]
}),
}
# Save test ome tif file
file_path = os.path.join(self.temp_path, "test_Pos1.ome.tif")
tifffile.imsave(
file_path,
im,
ijmetadata=ijmeta,
extratags=extra_tags,
)
# Get path to json schema file
dir_name = os.path.dirname(__file__)
schema_file_path = os.path.realpath(
os.path.join(dir_name, '..', '..', 'metadata_schema.json'), )
# Create csv file for upload
upload_dict = {
'dataset_id': [dataset_serial],
'file_name': [file_path],
'description': ['Testing'],
'positions': [1],
'schema_filename': [schema_file_path],
}
upload_csv = pd.DataFrame.from_dict(upload_dict)
csv_path = os.path.join(self.temp_path, "test_ometif_upload.csv")
upload_csv.to_csv(csv_path)
config_path = os.path.join(
self.temp_path,
'config_ome_tiff.json',
)
config = {
"upload_type": "frames",
"frames_format": "ome_tiff",
"microscope": "",
"schema_filename": "metadata_schema.json",
"storage": "s3",
}
json_ops.write_json_file(config, config_path)
# Upload data
data_uploader.upload_data_and_update_db(
csv=csv_path,
login=self.credentials_path,
config=config_path,
)
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)