def main():
def str2bool(v):
return v.lower() in ("yes", "true", "t", "1")
description = 'Convert single image xrm files into single image hdf5 ' \
'files'
parser = argparse.ArgumentParser(description=description,
formatter_class=RawTextHelpFormatter)
parser.register('type', 'bool', str2bool)
parser.add_argument('txm_txt_script',
metavar='txm_txt_script',
type=str,
help='TXM txt script used to create the '
'xrm files')
parser.add_argument('-s',
'--subfolders',
type='bool',
default='False',
help='- If True: Use subfolders for indexing\n'
'- If False: Use general folder for indexing\n'
'(default: False)')
parser.add_argument('-c',
'--cores',
type=int,
default=-2,
help='Number of cores used for the format conversion\n'
'(default is max of available CPUs: -1)')
parser.add_argument('-u',
'--update_db',
type='bool',
default='True',
help='Update DB with hdf5 records\n'
'(default: True)')
args = parser.parse_args()
db_filename = get_db_path(args.txm_txt_script)
create_db(args.txm_txt_script)
multiple_xrm_2_hdf5(db_filename,
subfolders=args.subfolders,
cores=args.cores,
update_db=args.update_db)
def main():
"""
- Convert from xrm to hdf5 individual image hdf5 files
- Copy raw hdf5 to new files for processingtxm2nexuslib/workflows/magnetism.py:54
- Crop borders
- Normalize
- Create stacks by date, sample, energy and jj position,
with multiple angles in each stack
"""
def str2bool(v):
return v.lower() in ("yes", "true", "t", "1")
description = "energyscan: many repetition images at different energies." \
"Only one FF used for each energy"
parser = argparse.ArgumentParser(description=description,
formatter_class=RawTextHelpFormatter)
parser.register('type', 'bool', str2bool)
parser.add_argument('txm_txt_script', type=str,
help=('TXM txt script containing the commands used ' +
'to perform the image acquisition by the ' +
'BL09 TXM microscope'))
parser.add_argument('--crop', type='bool',
default='True',
help='- If True: Crop images\n'
'- If False: Do not crop images\n'
'(default: True)')
parser.add_argument('--table_for_stack', type=str,
default='hdf5_averages',
help=("DB table of image files to create the stacks" +
"(default: hdf5_averages)"))
parser.add_argument("--db", type=str2bool, nargs='?',
const=True, default=True,
help='- If True: Create database\n'
'- If False: Do not create db\n'
'(default: True)')
parser.add_argument('--e', type=float,
nargs="*", default=[],
help='Energy to pre-process data')
parser.add_argument('--stack', type='bool',
nargs='?',
const=True, default=True,
help='- If True: Calculate stack\n'
'- If False: Do not calculate stack\n'
'(default: True)')
args = parser.parse_args()
print("\nWorkflow for energyscan experiments:\n" +
"xrm -> hdf5 -> crop -> normalize -> align for same energy, "
" and variable repetition ->"
" average all images with same energy ->"
" make normalized stacks")
start_time = time.time()
# Align and average by repetition
variable = "repetition"
db_filename = get_db_path(args.txm_txt_script)
query = Query()
if args.db:
create_db(args.txm_txt_script)
if args.e is not None:
if len(args.e) == 0:
partial_preprocesing_escan(db_filename, variable,
crop=args.crop)
# Average multiple hdf5 files:
# working with many single images files
average_image_groups(db_filename, variable=variable, jj=False)
else:
query_impl = (query.energy==args.e[0])
partial_preprocesing_escan(db_filename, variable, crop=args.crop,
query=query_impl)
# Average multiple hdf5 files:
# working with many single images files
average_image_group_by_energy(db_filename, variable=variable,
energy=args.e[0])
if args.stack:
# Build up hdf5 stacks from individual images.
# Stacks of variable energy: spectrocopy stacks (energyscan)
many_images_to_h5_stack(db_filename, table_name=args.table_for_stack,
type_struct="normalized_spectroscopy",
suffix="_specnorm")
print("spectrocopy preprocessing took %d seconds\n" %
(time.time() - start_time))
def main():
"""
- Convert from xrm to hdf5 individual image hdf5 files
- Copy raw hdf5 to new files for processing
- Crop borders
- Normalize
- Create stacks by date, sample, energy and zpz,
with multiple angles in each stack
"""
def str2bool(v):
return v.lower() in ("yes", "true", "t", "1")
description = "xtend: eXTEnded Depth of field"
parser = argparse.ArgumentParser(description=description,
formatter_class=RawTextHelpFormatter)
parser.register('type', 'bool', str2bool)
parser.add_argument('txm_txt_script', type=str,
help=('TXM txt script containing the commands used ' +
'to perform the image acquisition by the ' +
'BL09 TXM microscope'))
parser.add_argument('--crop', type='bool',
default='True',
help='- If True: Crop images\n'
'- If False: Do not crop images\n'
'(default: True)')
parser.add_argument('--table_for_stack', type=str,
default='hdf5_averages',
help=("DB table of image files to create the stacks" +
"(default: hdf5_averages)"))
parser.add_argument('-z', '--stacks_zp', type='bool',
default='True',
help="Create individual ZP stacks\n"
"(default: True)")
args = parser.parse_args()
print("\nWorkflow with Extended Depth of Field:\n" +
"xrm -> hdf5 -> crop -> normalize -> align for same angle and" +
" variable zpz -> average all images with same angle ->" +
" make normalized stacks")
start_time = time.time()
db_filename = get_db_path(args.txm_txt_script)
create_db(args.txm_txt_script)
# Multiple xrm 2 hdf5 files: working with many single images files
multiple_xrm_2_hdf5(db_filename)
# Copy of multiple hdf5 raw data files to files for processing
copy2proc_multiple(db_filename)
# Multiple files hdf5 images crop: working with single images files
if args.crop:
crop_images(db_filename)
# Normalize multiple hdf5 files: working with many single images files
normalize_images(db_filename)
if args.stacks_zp:
many_images_to_h5_stack(db_filename, table_name="hdf5_proc",
type_struct="normalized", suffix="_stack")
# Align multiple hdf5 files: working with many single images files
align_images(db_filename, align_method='cv2.TM_SQDIFF_NORMED')
# Average multiple hdf5 files: working with many single images files
average_image_groups(db_filename)
# Build up hdf5 stacks from individual images
many_images_to_h5_stack(db_filename, table_name=args.table_for_stack,
type_struct="normalized_multifocus",
suffix="_FS")
print("xtendof took %d seconds\n" % (time.time() - start_time))
def main():
"""
- Convert from xrm to hdf5 individual image hdf5 files
- Copy raw hdf5 to new files for processing
- Crop borders
- Normalize
- Create stacks by date, sample, energy and zpz,
with multiple angles in each stack
"""
def str2bool(v):
return v.lower() in ("yes", "true", "t", "1")
description = "pre-processing for biological samples"
parser = argparse.ArgumentParser(description=description,
formatter_class=RawTextHelpFormatter)
parser.register('type', 'bool', str2bool)
parser.add_argument('txm_txt_script',
type=str,
help=('TXM txt script containing the commands used ' +
'to perform the image acquisition by the ' +
'BL09 TXM microscope'))
parser.add_argument('--crop',
type='bool',
default='True',
help='- If True: Crop images\n'
'- If False: Do not crop images\n'
'(default: True)')
parser.add_argument('--table_for_stack',
type=str,
default='hdf5_averages',
help=("DB table of image files to create the stacks" +
"(default: hdf5_averages)"))
parser.add_argument('-z',
'--stacks_zp',
type='bool',
default='True',
help="Create individual ZP stacks\n"
"(default: True)")
parser.add_argument('-m',
'--hdf_to_mrc',
type='bool',
default='True',
help="Convert FS hdf5 to mrc")
parser.add_argument('--db',
type=str2bool,
nargs='?',
const=True,
default=False,
help='- If True: Create database\n'
'- If False: Do not create db\n'
'(default: False)')
parser.add_argument('--id', type=float, help='- ID of the record in DB\n')
args = parser.parse_args()
print("\nWorkflow with Extended Depth of Field:\n" +
"xrm -> hdf5 -> crop -> normalize -> align for same angle and" +
" variable zpz -> average all images with same angle ->" +
" make normalized stacks")
start_time = time.time()
# Align and average by repetition
# variable = "sample"
db_filename = get_db_path(args.txm_txt_script)
query = Query()
if args.db:
create_db(args.txm_txt_script)
if args.id:
db = get_db(args.txm_txt_script, use_existing_db=True)
dbsample = db.get(doc_id=args.id)
date = dbsample['date']
energy = dbsample['energy']
sample = dbsample['sample']
query_impl = ((query.date == date) & (query.sample == sample) &
(query.energy == energy))
partial_preprocesing(db_filename,
args.crop,
query=query_impl,
date=date,
sample=sample,
energy=energy,
stacks_zp=args.stacks_zp,
table_name=args.table_for_stack)
print("Execution took %d seconds\n" % (time.time() - start_time))
def main():
"""
- Convert from xrm to hdf5 individual image hdf5 files
- Copy raw hdf5 to new files for processing
- Crop borders
- Normalize
- Create stacks by date, sample, energy and zpz,
with multiple angles in each stack
"""
def str2bool(v):
return v.lower() in ("yes", "true", "t", "1")
description = "pre-processing for biological samples"
parser = argparse.ArgumentParser(description=description,
formatter_class=RawTextHelpFormatter)
parser.register('type', 'bool', str2bool)
parser.add_argument('txm_txt_script',
type=str,
help=('TXM txt script containing the commands used ' +
'to perform the image acquisition by the ' +
'BL09 TXM microscope'))
parser.add_argument('--crop',
type='bool',
default='True',
help='- If True: Crop images\n'
'- If False: Do not crop images\n'
'(default: True)')
parser.add_argument('--table_for_stack',
type=str,
default='hdf5_averages',
help=("DB table of image files to create the stacks" +
"(default: hdf5_averages)"))
parser.add_argument('-z',
'--stacks_zp',
type='bool',
default='True',
help="Create individual ZP stacks\n"
"(default: True)")
parser.add_argument('-m',
'--hdf_to_mrc',
type='bool',
default='True',
help="Convert FS hdf5 to mrc")
args = parser.parse_args()
print("\nWorkflow with Extended Depth of Field:\n" +
"xrm -> hdf5 -> crop -> normalize -> align for same angle and" +
" variable zpz -> average all images with same angle ->" +
" make normalized stacks")
start_time = time.time()
db_filename = get_db_path(args.txm_txt_script)
create_db(args.txm_txt_script)
# Multiple xrm 2 hdf5 files: working with many single images files
multiple_xrm_2_hdf5(db_filename)
# Copy of multiple hdf5 raw data files to files for processing
copy2proc_multiple(db_filename)
# Multiple files hdf5 images crop: working with single images files
if args.crop:
crop_images(db_filename)
# Normalize multiple hdf5 files: working with many single images files
normalize_images(db_filename)
if args.stacks_zp:
many_images_to_h5_stack(db_filename,
table_name="hdf5_proc",
type_struct="normalized",
suffix="_stack")
# Align multiple hdf5 files: working with many single images files
align_images(db_filename, align_method='cv2.TM_SQDIFF_NORMED')
# Average multiple hdf5 files: working with many single images files
average_image_groups(db_filename)
# Build up hdf5 stacks from individual images
many_images_to_h5_stack(db_filename,
table_name=args.table_for_stack,
type_struct="normalized_multifocus",
suffix="_FS")
"""Convert FS stacks from hdf5 to mrc"""
if args.hdf_to_mrc:
db = TinyDB(db_filename)
stack_table = db.table("hdf5_stacks")
print("Converting FS stacks from hdf5 to mrc")
for record in stack_table.all():
fn_hdf5 = record["filename"]
raw_fname, _ = os.path.splitext(fn_hdf5)
mrc_file = raw_fname + '.mrc'
image_convert = "scipion xmipp_image_convert"
tree_hdf5 = 'TomoNormalized/TomoNormalized@' + fn_hdf5
command = image_convert + " -i " + tree_hdf5 + " -o " + mrc_file
print(command)
print("converting: " + mrc_file)
os.system(command)
time.sleep(2)
""" -------------------------------- """
print("Execution took %d seconds\n" % (time.time() - start_time))
def main():
"""
- Convert from xrm to hdf5 individual image hdf5 files
- Copy raw hdf5 to new files for processingtxm2nexuslib/workflows/magnetism.py:54
- Crop borders
- Normalize
- Create stacks by date, sample, energy and jj position,
with multiple angles in each stack
"""
def str2bool(v):
return v.lower() in ("yes", "true", "t", "1")
description = "magnetism: many repetition images at different angles." \
" Normally using 2 different polarizations by setting the" \
" JJ positions to change to circular left and right" \
" polarizations"
parser = argparse.ArgumentParser(description=description,
formatter_class=RawTextHelpFormatter)
parser.register('type', 'bool', str2bool)
parser.add_argument('txm_txt_script', type=str,
help=('TXM txt script containing the commands used ' +
'to perform the image acquisition by the ' +
'BL09 TXM microscope'))
parser.add_argument('--crop', type='bool',
default='True',
help='- If True: Crop images\n'
'- If False: Do not crop images\n'
'(default: True)')
parser.add_argument('--table_for_stack', type=str,
default='hdf5_averages',
help=("DB table of image files to create the stacks" +
"(default: hdf5_averages)"))
parser.add_argument("--db", type=str2bool, nargs='?',
const=True, default=False,
help='- If True: Create database\n'
'- If False: Do not create db\n'
'(default: False)')
parser.add_argument("--ff", type=str2bool, nargs='?',
const=True, default=False,
help='- If True: Pre-process FF images\n'
'- If False: Do not pre-process FF images\n'
'(default: False)')
parser.add_argument('--th', type=float,
nargs="*",
help=('Angle theta to pre-process data' +
' referred to this angle'))
parser.add_argument('--stack', type='bool',
nargs='?',
const=True, default=False,
help='- If True: Calculate stack\n'
'- If False: Do not calculate stack\n'
'(default: False)')
args = parser.parse_args()
print("\nWorkflow for magnetism experiments:\n" +
"xrm -> hdf5 -> crop -> normalize -> align for same angle, same"
" jj position and variable repetition ->"
" average all images with same angle and"
" same jj position ->" + " make normalized stacks")
start_time = time.time()
# Align and average by repetition
variable = "repetition"
db_filename = get_db_path(args.txm_txt_script)
query = Query()
if args.db:
create_db(args.txm_txt_script)
if args.ff:
partial_preprocesing(db_filename, variable, args.crop,
query.FF==True,
is_ff=True)
if args.th is not None:
if len(args.th) == 0:
partial_preprocesing(db_filename, variable, args.crop,
query.FF==False)
# Average multiple hdf5 files:
# working with many single images files
average_image_groups(db_filename, variable=variable)
else:
partial_preprocesing(db_filename, variable, args.crop,
query.angle==args.th[0])
# Average multiple hdf5 files:
# working with many single images files
average_image_group_by_angle(db_filename, variable=variable,
angle=args.th[0])
if args.stack:
# Build up hdf5 stacks from individual images
# Stack of variable angle. Each of the images has been done by
# averaging many repetitions of the image at the same energy, jj,
# angle... The number of repetitions by each of the images in this
# stack files could be variable.
many_images_to_h5_stack(
db_filename, table_name=args.table_for_stack,
type_struct="normalized_magnetism_many_repetitions",
suffix="_FS")
print("magnetism preprocessing took %d seconds\n" %
(time.time() - start_time))
"""