def open_sgm_xas(sgm_file, scan_num):
print "Opening scan", str(scan_num)
print "in", sgm_file
f = spec.open(sgm_file)
scan = f[str(scan_num)]
energy_array = scan['Energy']
scaler_array = [[], [], []]
scaler_array[0] = scan['TEY']
scaler_array[1] = scan['I0']
scaler_array[2] = scan['Diode']
mcadata = scan['@A1']
print "Parsing MCAs"
mca_array = [[], [], [], []]
for i in range(0, len(energy_array)):
mca_array[0].append(mcadata[i * 4])
mca_array[1].append(mcadata[i * 4 + 1])
mca_array[2].append(mcadata[i * 4 + 2])
mca_array[3].append(mcadata[i * 4 + 3])
print "Done!"
return energy_array, mca_array, scaler_array
def open_sgm_map(sgm_file, scan_num):
print "Opening scan", str(scan_num)
print "in", sgm_file
f = spec.open(sgm_file)
scan = f[str(scan_num)]
hex_x = scan['Hex_XP']
hex_y = scan['Hex_YP']
scaler_array = [[], [], []]
scaler_array[0] = scan['TEY']
scaler_array[1] = scan['I0']
scaler_array[2] = scan['Diode']
print "Parsing MCAs"
mcadata = scan['@A1']
mca_array = [[], [], [], []]
for i in range(0, len(hex_x)):
mca_array[0].append(mcadata[i * 4])
mca_array[1].append(mcadata[i * 4 + 1])
mca_array[2].append(mcadata[i * 4 + 2])
mca_array[3].append(mcadata[i * 4 + 3])
print "Done!"
return hex_x, hex_y, mca_array, scaler_array, scan_num
def open_sgm_map(sgm_file, scan_num):
print "Opening scan", str(scan_num)
print "in", sgm_file
f = spec.open(sgm_file)
scan=f[str(scan_num)]
hex_x = scan['Hex_XP']
hex_y = scan['Hex_YP']
scaler_array = [[],[],[]]
scaler_array[0] = scan['TEY']
scaler_array[1] = scan['I0']
scaler_array[2] = scan['Diode']
print "Parsing MCAs"
mcadata = scan['@A1']
mca_array = [[],[],[],[]]
for i in range(0,len(hex_x)):
mca_array[0].append(mcadata[i*4])
mca_array[1].append(mcadata[i*4 + 1])
mca_array[2].append(mcadata[i*4 + 2])
mca_array[3].append(mcadata[i*4 + 3])
print "Done!"
return hex_x, hex_y, mca_array, scaler_array, scan_num
def open_sgm_xas(sgm_file, scan_num):
print "Opening scan", str(scan_num)
print "in", sgm_file
f = spec.open(sgm_file)
scan=f[str(scan_num)]
energy_array = scan['Energy']
scaler_array = [[],[],[]]
scaler_array[0] = scan['TEY']
scaler_array[1] = scan['I0']
scaler_array[2] = scan['Diode']
mcadata = scan['@A1']
print "Parsing MCAs"
mca_array = [[],[],[],[]]
for i in range(0,len(energy_array)):
mca_array[0].append(mcadata[i*4])
mca_array[1].append(mcadata[i*4 + 1])
mca_array[2].append(mcadata[i*4 + 2])
mca_array[3].append(mcadata[i*4 + 3])
print "Done!"
return energy_array, mca_array, scaler_array
def convert_to_phynx(
spec_filename, h5_filename=None, force=False, report=False
):
"""convert a spec data file to phynx and return the phynx file object"""
if report: print 'Converting spec file %s to phynx'% spec_filename
if h5_filename is None:
h5_filename = spec_filename + '.h5'
if os.path.exists(h5_filename) and force==False:
raise IOError(
'%s already exists! Use "force" flag to overwrite'%h5_filename
)
if report: print 'making file %s'% h5_filename
h5_file = open(h5_filename, 'w')
spec_file = spec.open(spec_filename)
for scan in spec_file.values():
if len(scan.values()[0]):
convert_scan(scan, h5_file, spec_filename, report=report)
if report: print 'phynx %s complete'% h5_file
return h5_file
def setUp(self):
self.f = spec.open(self.file_name, lock=True)
def open_spec_data_file(file_directory):
opened_file = spec.open(file_directory)
return opened_file
def convert_scan(scan, h5file, spec_filename, report=False):
# access a bunch of metadata before creating an hdf5 group
# if specfile raises an error because the scan is empty,
# we will skip it and move on to the next
if report: print 'converting scan #%s'% scan.name
scan_info = get_scan_metadata(scan.attrs['command'].split())
labels = [label.lower() for label in scan.keys()]
# We need to update time metadata if it was a tseries:
if scan_info['scan_type'] == 'tseries':
scan_info['scan_shape'] = np.array([len(scan.values()[0])])
t = scan['Time'][:]
scan_info['axis_info']['time']['range'] = str((t.min(), t.max()))
# We need to update time metadata if it was a chess_escan:
if scan_info['scan_type'] == 'chess_escan':
scan_info['scan_shape'] = np.array([len(scan.values()[0])])
e = scan['Energy'][:]
scan_info['axis_info']['energy']['range'] = str((e.min(), e.max()))
attrs = {}
attrs['acquisition_name'] = scan.name
attrs['acquisition_id'] = scan.id
attrs['npoints'] = len(scan.values()[0])
attrs['acquisition_command'] = scan.attrs['command']
attrs['source_file'] = scan.attrs['file_origin']
if len(scan_info['scan_shape']) < 2:
if scan_info['scan_shape'] < 1:
# an open-ended scan
scan_info['scan_shape'] = np.array([len(scan.values()[0])])
attrs['acquisition_shape'] = str(tuple(scan_info['scan_shape']))
entry = h5file.create_group(scan.id, type='Entry', **attrs)
measurement = entry.create_group('measurement', type='Measurement')
positioners = measurement.create_group('positioners', type='Positioners')
for motor, pos in scan.attrs['positions'].items():
try:
positioners[motor] = pos
except ValueError:
if report: print (
"""
Invalid spec motor configuration:
"%s" is used to describe more than one positioner.
Only the first occurance will be saved. Please report
the problem to your beamline scientist
""" % motor
)
attrs = {}
monitor = scan.attrs['monitor']
if monitor:
attrs['monitor'] = monitor
scalar_data = measurement.create_group(
'scalar_data', type='ScalarData', **attrs
)
skipmode = [i for i in scan.attrs['comments'] if i.startswith('SKIPMODE')]
if not skipmode:
skipmode = [i for i in scan.attrs['user_comments']
if i.startswith('SKIPMODE')]
if skipmode:
mon, thresh = skipmode[0].split()[2:]
thresh = int(thresh)
skipped = scan[mon][:] < thresh
kwargs = {'class':'Signal', 'counter':mon, 'threshold':thresh}
masked = scalar_data.create_dataset(
'masked', dtype='uint8', data=skipped.astype('uint8'), **kwargs
)
else:
masked = None
allmotors = scan.attrs['positions'].keys()
for key, val in scan.items():
if key.startswith('@') or key in scalar_data:
continue
val = val[:]
if (key in allmotors) \
or (key.lower() in ('energy', 'time', 'h', 'k', 'l', 'q')):
kwargs = {'class':'Axis'}
kwargs.update(
scan_info['axis_info'].get(key.lower(), {})
)
dset = scalar_data.create_dataset(
key, data=val, dtype='float32', **kwargs
)
elif key.lower() == 'epoch':
kwargs = {'class':'Axis'}
dset = scalar_data.create_dataset(
key,
data=val+scan.attrs['epoch_offset'],
dtype='float64',
**kwargs
)
else:
kwargs = {'class':'Signal'}
dset = scalar_data.create_dataset(
key, data=val, dtype='float32', **kwargs
)
# the last column should always be the primary counter
scalar_data[scan.attrs['labels'][-1]].attrs['signal'] = 1
# and dont forget to include the index
kwargs = {'class':'Axis'}
dset = scalar_data.create_dataset(
'i', data=np.arange(len(dset)), dtype='i', **kwargs
)
# process mca device files:
if [i for i in scan.keys() if i.startswith('@')]:
process_mca(scan, measurement, masked=masked)
# we need to integrate external data files after processing the scan
# in the main file, since we may reference some of that data
dir, spec_filename = os.path.split(spec_filename)
if not dir:
dir = os.getcwd()
for f in sorted(os.listdir(dir)):
if (
f.startswith(spec_filename+'.scan%s.'%scan.name) and
f.endswith('.mca')
):
f = os.path.join(dir, f)
if report: print 'integrating %s'%f
process_mca(
spec.open(f)[scan.id], measurement, masked=masked
)
elif (
f.startswith(spec_filename+'_scan%03d_'%(int(scan.name))) and
f.endswith('.tiff')
):
from praxes.io.tifffile import TIFFfile
f = os.path.join(dir, f)
try:
d = TIFFfile(f).asarray()
except ValueError:
continue
r, c = d.shape
if 'area_detector' not in measurement:
measurement.create_group('area_detector', type='AreaDetector')
ad = measurement['area_detector']
if 'counts' not in ad:
ad.create_dataset(
'counts', shape=(len(scan.data), r, c), dtype='uint32',
maxshape=(None, r, c)
)
dset = ad['counts']
i = os.path.split(f)[-1].replace(
spec_filename+'_scan%03d_'%(int(scan.name)),
''
)
i = int(i.replace('.tiff', ''))
try:
dset[i] = d
except:
dset.resize((i+1, r, c))
dset[i] = d
del d
try:
line = [i for i in scan.attrs['comments']
if i.startswith('subexposures')][0]
n = int(line.split()[1].split('=')[1])
dset.attrs['subexposures'] = n
except IndexError:
pass
if masked is not None and 'masked' not in ad:
ad['masked'] = masked
if report: print 'integrated %s' % f
gc.collect()
elif (
f.startswith(spec_filename+'.%s_'%scan.name) and
f.endswith('.mar3450')
):
f = os.path.join(dir, f)
try:
p = subprocess.Popen(
['marcvt', '-raw32', f],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
res = p.wait()
raw = p.stdout.readline().split()[-1]
d = np.fromfile(raw, dtype='uint32')
os.remove(raw)
d /= 2
p = int(np.sqrt(len(d)))
d.shape = (p, p)
if 'mar345' not in measurement:
measurement.create_group('mar345', type='Mar345')
mar = measurememt['mar345']
if counts not in mar:
mar.create_dataset(
'counts', shape=(scan.lines(), p, p), dtype='uint16'
)
dset = mar['counts']
i = f.replace(spec_filename+'.%s_'%scan_number, '')
i = int(i.replace('.mar3450', ''))
dset[i] = d
del d
if masked is not None and 'masked' not in mar:
mar['masked'] = masked
if report: print 'integrated %s' % f
gc.collect()
except (OSError, ValueError):
if report: sys.stdout.write(
'Found mar image %s but unable to convert it.\n' % f
)
if report: sys.stdout.write(
'marcvt must be installed to do so.\n'
)
def setUp(self):
self.f = spec.open(self.file_name)
def open_spec_data_file(file_directory): opened_file = spec.open(file_directory) return opened_file
def setUp(self):
self.f = spec.open(self.file_name)