def splice_datasets(self, ds):
"""
Combines datasets together.
Parameters
----------
ds: list of `refnx.dataset.Data1D`
The datasets to splice together.
Returns
-------
fname: str
The name of the combined dataset
Notes
-----
The combined dataset is saved as `f"c_{d.filename}.dat"`,
where d is the dataset with the lowest average Q value
from ds.
"""
appended_ds = ReflectDataset()
datasets = []
average_q = []
for d in ds:
dataset = ReflectDataset(d)
average_q.append(np.mean(dataset.x))
datasets.append(dataset)
idxs = np.argsort(average_q)
# sort datasets according to average Q.
datasets = [d for _, d in sorted(zip(idxs, datasets))]
for dataset in datasets:
appended_ds += dataset
fname = datasets[0].filename.rstrip(".dat")
fname = fname.split("_")[0]
fname = f"c_{fname}.dat"
appended_ds.save(fname)
return fname
def reducer(self, callback=None):
"""
Reduce all the entries in reduction_entries
Parameters
----------
callback : callable
Function, `f(percent_finished)` that is called with the current
percentage progress of the reduction
"""
# refnx.reduce.reduce needs you to be in the directory where you're
# going to write files to
if self.output_directory:
os.chdir(self.output_directory)
# if no data directory was specified then assume it's the cwd
data_directory = self.data_directory
if not data_directory:
data_directory = "./"
def full_path(fname):
f = os.path.join(data_directory, fname)
return f
# if the streamed directory isn't mentioned then assume it's the same
# as the data directory
streamed_directory = self.streamed_directory
if not os.path.isdir(streamed_directory):
self.streamed_directory = data_directory
logging.info("-------------------------------------------------------"
"\nStarting reduction run")
logging.info(
"data_folder={data_directory}, trim_trailing=True, "
"lo_wavelength={low_wavelength}, "
"hi_wavelength={high_wavelength}, "
"rebin_percent={rebin_percent}, "
"normalise={monitor_normalisation}, "
"background={background_subtraction} "
"eventmode={streamed_reduction} "
"event_folder={streamed_directory}".format(**self.__dict__))
# sets up time slices for event reduction
if self.streamed_reduction:
eventmode = np.arange(self.stream_start, self.stream_end,
self.stream_duration)
eventmode = np.r_[eventmode, self.stream_end]
else:
eventmode = None
# are you manual beamfinding?
peak_pos = None
if self.manual_beam_find and self.manual_beam_finder is not None:
peak_pos = -1
idx = 0
cached_direct_beams = {}
for row, val in self.reduction_entries.items():
if not val["use"]:
continue
flood = None
if val["flood"]:
flood = full_path(val["flood"])
combined_dataset = None
# process entries one by one
for ref, db in zip(
["reflect-1", "reflect-2", "reflect-3"],
["direct-1", "direct-2", "direct-3"],
):
reflect = val[ref]
direct = val[db]
# if the file doesn't exist there's no point continuing
if (not os.path.isfile(full_path(reflect))) or (
not os.path.isfile(full_path(direct))):
continue
# which of the nspectra to reduce (or all)
ref_pn = PlatypusNexus(full_path(reflect))
if direct not in cached_direct_beams:
cached_direct_beams[direct] = PlatypusReduce(
direct, data_folder=data_directory)
reducer = cached_direct_beams[direct]
try:
reduced = reducer(
ref_pn,
scale=val["scale"],
h5norm=flood,
lo_wavelength=self.low_wavelength,
hi_wavelength=self.high_wavelength,
rebin_percent=self.rebin_percent,
normalise=self.monitor_normalisation,
background=self.background_subtraction,
manual_beam_find=self.manual_beam_finder,
peak_pos=peak_pos,
eventmode=eventmode,
event_folder=streamed_directory,
)
except Exception as e:
# typical Exception would be ValueError for non overlapping
# angles
logging.info(e)
continue
logging.info("Reduced {} vs {}, scale={}, angle={}".format(
reflect,
direct,
val["scale"],
reduced[1]["omega"][0, 0],
))
if combined_dataset is None:
combined_dataset = ReflectDataset()
fname = basename_datafile(reflect)
fname_dat = os.path.join(self.output_directory,
"c_{0}.dat".format(fname))
fname_xml = os.path.join(self.output_directory,
"c_{0}.xml".format(fname))
try:
combined_dataset.add_data(
reducer.data(),
requires_splice=True,
trim_trailing=True,
)
except ValueError as e:
# datasets don't overlap
logging.info(e)
continue
if combined_dataset is not None:
# after you've finished reducing write a combined file.
with open(fname_dat, "wb") as f:
combined_dataset.save(f)
with open(fname_xml, "wb") as f:
combined_dataset.save_xml(f)
logging.info("Written combined files: {} and {}".format(
fname_dat, fname_xml))
# can be used to create a progress bar
idx += 1
if callback is not None:
ok = callback(100 * idx / len(self.reduction_entries))
if not ok:
break
logging.info("\nFinished reduction run"
"-------------------------------------------------------")
def reduce_stitch(reflect_list,
direct_list,
background_list=None,
norm_file_num=None,
data_folder=None,
prefix='PLP',
trim_trailing=True,
save=True,
**kwds):
"""
Reduces a list of reflected beam run numbers and a list of corresponding
direct beam run numbers from the Platypus reflectometer. If there are
multiple reflectivity files they are spliced together.
Parameters
----------
reflect_list : list
Reflected beam run numbers, e.g. `[708, 709, 710]`
708 corresponds to the file PLP0000708.nx.hdf.
direct_list : list
Direct beam run numbers, e.g. `[711, 711, 711]`
background_list : list, optional
List of `bool` to control whether background subtraction is used
for each reduction, e.g. `[False, True, True]`. The default is to do
a background subtraction on all runs.
norm_file_num : int, optional
The run number for the water flood field correction.
data_folder : str, optional
Where is the raw data stored?
prefix : str, optional
The instrument filename prefix.
trim_trailing : bool, optional
When datasets are spliced together do you want to remove points in the
overlap region from the preceding dataset?
save : bool, optional
If `True` then the spliced file is written to a file (in the working
directory) with a name like: `c_PLP0000708.dat`.
kwds : dict, optional
Options passed directly to `refnx.reduce.platypusnexus.process`,
for processing of individual spectra. Look at that method docstring
for specification of options.
Returns
-------
combined_dataset, reduced_filename : refnx.dataset.ReflectDataset, str
The combined dataset and the file name of the reduced data, if it was
saved. If it wasn't saved `reduced_filename` is `None`.
Notes
-----
If `background` is in the supplied `kwds` it is ignored.
The `prefix` is used to specify the run numbers to a filename.
For example a run number of 10, and a prefix of `PLP` resolves to a
NeXus filename of 'PLP0000010.nx.hdf'.
Examples
--------
>>> from refnx.reduce import reduce_stitch
>>> dataset, fname = reduce_stitch([708, 709, 710],
... [711, 711, 711],
... rebin_percent=2)
"""
scale = kwds.get('scale', 1.)
kwds_copy = {}
kwds_copy.update(kwds)
kwds_copy.pop('background', None)
if not background_list:
background_list = [True] * len(reflect_list)
# now reduce all the files.
zipped = zip(reflect_list, direct_list, background_list)
combined_dataset = ReflectDataset()
if data_folder is None:
data_folder = os.getcwd()
if norm_file_num:
norm_datafile = number_datafile(norm_file_num, prefix=prefix)
kwds['h5norm'] = norm_datafile
if prefix == 'PLP':
reducer_klass = PlatypusReduce
else:
raise ValueError("Incorrect prefix specified")
for index, val in enumerate(zipped):
reflect_datafile = os.path.join(data_folder,
number_datafile(val[0], prefix=prefix))
direct_datafile = os.path.join(data_folder,
number_datafile(val[1], prefix=prefix))
reducer = reducer_klass(direct_datafile)
datasets, fnames = reducer.reduce(reflect_datafile,
save=save,
background=val[2],
**kwds_copy)
if not index:
datasets[0].scale(scale)
combined_dataset.add_data(datasets[0].data,
requires_splice=True,
trim_trailing=trim_trailing)
fname_dat = None
if save:
# this will give us <fname>.nx.hdf
# if reflect_list was an integer you'll get PLP0000708.nx.hdf
fname = number_datafile(reflect_list[0], prefix=prefix)
# now chop off .nx.hdf extension
fname = basename_datafile(fname)
fname_dat = 'c_{0}.dat'.format(fname)
with open(fname_dat, 'wb') as f:
combined_dataset.save(f)
fname_xml = 'c_{0}.xml'.format(fname)
with open(fname_xml, 'wb') as f:
combined_dataset.save_xml(f)
return combined_dataset, fname_dat
def reduce_stitch(reflect_list, direct_list, norm_file_num=None,
data_folder=None, trim_trailing=True, save=True, **kwds):
"""
Reduces a list of reflected beam run numbers and a list of corresponding
direct beam run numbers from the Platypus reflectometer. If there are
multiple reflectivity files they are spliced together.
Parameters
----------
reflect_list : list
Reflected beam run numbers, e.g. `[708, 709, 710]`
708 corresponds to the file PLP0000708.nx.hdf.
direct_list : list
Direct beam run numbers, e.g. `[711, 711, 711]`
norm_file_num : int, optional
The run number for the water flood field correction.
data_folder : str, optional
Where is the raw data stored?
trim_trailing : bool, optional
When datasets are spliced together do you want to remove points in the
overlap region from the preceding dataset?
save : bool, optional
If `True` then the spliced file is written to a file (in the working
directory) with a name like: `c_PLP0000708.dat`.
kwds : dict, optional
Options passed directly to `refnx.reduce.platypusnexus.process`,
for processing of individual spectra. Look at that method docstring
for specification of options.
Returns
-------
combined_dataset, reduced_filename : refnx.dataset.ReflectDataset, str
The combined dataset and the file name of the reduced data, if it was
saved. If it wasn't saved `reduced_filename` is `None`.
"""
scale = kwds.get('scale', 1.)
# now reduce all the files.
zipped = zip(reflect_list, direct_list)
combined_dataset = ReflectDataset()
if data_folder is None:
data_folder = os.getcwd()
if norm_file_num:
norm_datafile = number_datafile(norm_file_num)
kwds['h5norm'] = norm_datafile
for index, val in enumerate(zipped):
reflect_datafile = os.path.join(data_folder,
number_datafile(val[0]))
direct_datafile = os.path.join(data_folder,
number_datafile(val[1]))
reduced = ReducePlatypus(direct_datafile,
reflect=reflect_datafile,
save=save,
**kwds)
if not index:
reduced.scale(scale)
combined_dataset.add_data(reduced.data(),
requires_splice=True,
trim_trailing=trim_trailing)
fname = None
if save:
fname = 'c_PLP{0:07d}.dat'.format(reflect_list[0])
with open(fname, 'wb') as f:
combined_dataset.save(f)
fname = 'c_PLP{0:07d}.xml'.format(reflect_list[0])
with open(fname, 'wb') as f:
combined_dataset.save_xml(f)
return combined_dataset, fname
def _reduce_single_angle(self, scale=1):
"""
Reduce a single angle.
"""
n_spectra = self.reflected_beam.n_spectra
n_tpixels = np.size(self.reflected_beam.m_topandtail, 1)
n_ypixels = np.size(self.reflected_beam.m_topandtail, 2)
# calculate omega and two_theta depending on the mode.
mode = self.reflected_beam.mode
# we'll need the wavelengths to calculate Q.
wavelengths = self.reflected_beam.m_lambda
m_twotheta = np.zeros((n_spectra, n_tpixels, n_ypixels))
if mode in ['FOC', 'POL', 'POLANAL', 'MT']:
detector_z_difference = (self.reflected_beam.detector_z -
self.direct_beam.detector_z)
beampos_z_difference = (self.reflected_beam.m_beampos
- self.direct_beam.m_beampos)
total_z_deflection = (detector_z_difference
+ beampos_z_difference * Y_PIXEL_SPACING)
# omega_nom.shape = (N, )
omega_nom = np.degrees(np.arctan(total_z_deflection
/ self.reflected_beam.detector_y) / 2.)
'''
Wavelength specific angle of incidence correction
This involves:
1) working out the trajectory of the neutrons through the
collimation system.
2) where those neutrons intersect the sample.
3) working out the elevation of the neutrons when they hit the
sample.
4) correcting the angle of incidence.
'''
speeds = general.wavelength_velocity(wavelengths)
collimation_distance = self.reflected_beam.cat.collimation_distance
s2_sample_distance = (self.reflected_beam.cat.sample_distance
- self.reflected_beam.cat.slit2_distance)
# work out the trajectories of the neutrons for them to pass
# through the collimation system.
trajectories = pm.find_trajectory(collimation_distance / 1000.,
0, speeds)
# work out where the beam hits the sample
res = pm.parabola_line_intersection_point(s2_sample_distance / 1000,
0,
trajectories,
speeds,
omega_nom[:, np.newaxis])
intersect_x, intersect_y, x_prime, elevation = res
# correct the angle of incidence with a wavelength dependent
# elevation.
omega_corrected = omega_nom[:, np.newaxis] - elevation
elif mode == 'SB' or mode == 'DB':
omega = self.reflected_beam.M_beampos + self.reflected_beam.detectorZ[:, np.newaxis]
omega -= self.direct_beam.M_beampos + self.direct_beam.detectorZ
omega /= 2 * self.reflected_beam.detectorY[:, np.newaxis, np.newaxis]
omega = np.arctan(omega)
m_twotheta += np.arange(n_ypixels * 1.)[np.newaxis, np.newaxis, :] * Y_PIXEL_SPACING
m_twotheta += self.reflected_beam.detectorZ[:, np.newaxis, np.newaxis]
m_twotheta -= self.direct_beam.M_beampos[:, :, np.newaxis] + self.direct_beam.detectorZ
m_twotheta -= self.reflected_beam.detectorY[:, np.newaxis, np.newaxis] * np.tan(omega[:, :, np.newaxis])
m_twotheta /= self.reflected_beam.detectorY[:, np.newaxis, np.newaxis]
m_twotheta = np.arctan(m_twotheta)
m_twotheta += omega[:, :, np.newaxis]
'''
--Specular Reflectivity--
Use the (constant wavelength) spectra that have already been integrated
over 2theta (in processnexus) to calculate the specular reflectivity.
Beware: this is because m_topandtail has already been divided through
by monitor counts and error propagated (at the end of processnexus).
Thus, the 2theta pixels are correlated to some degree. If we use the 2D
plot to calculate reflectivity
(sum {Iref_{2theta, lambda}}/I_direct_{lambda}) then the error bars in
the reflectivity turn out much larger than they should be.
'''
ydata, ydata_sd = EP.EPdiv(self.reflected_beam.m_spec,
self.reflected_beam.m_spec_sd,
self.direct_beam.m_spec,
self.direct_beam.m_spec_sd)
# calculate the 1D Qz values.
xdata = general.q(omega_corrected, wavelengths)
xdata_sd = (self.reflected_beam.m_lambda_fwhm
/ self.reflected_beam.m_lambda) ** 2
xdata_sd += (self.reflected_beam.domega[:, np.newaxis]
/ omega_corrected) ** 2
xdata_sd = np.sqrt(xdata_sd) * xdata
'''
---Offspecular reflectivity---
normalise the counts in the reflected beam by the direct beam
spectrum this gives a reflectivity. Also propagate the errors,
leaving the fractional variance (dr/r)^2.
--Note-- that adjacent y-pixels (same wavelength) are correlated in this
treatment, so you can't just sum over them.
i.e. (c_0 / d) + ... + c_n / d) != (c_0 + ... + c_n) / d
'''
m_ref, m_ref_sd = EP.EPdiv(self.reflected_beam.m_topandtail,
self.reflected_beam.m_topandtail_sd,
self.direct_beam.m_spec[:, :, np.newaxis],
self.direct_beam.m_spec_sd[:, :, np.newaxis])
# you may have had divide by zero's.
m_ref = np.where(np.isinf(m_ref), 0, m_ref)
m_ref_sd = np.where(np.isinf(m_ref_sd), 0, m_ref_sd)
# calculate the Q values for the detector pixels. Each pixel has
# different 2theta and different wavelength, ASSUME that they have the
# same angle of incidence
qx, qy, qz = general.q2(omega_corrected[:, :, np.newaxis],
m_twotheta,
0,
wavelengths[:, :, np.newaxis])
reduction = {}
reduction['xdata'] = self.xdata = xdata
reduction['xdata_sd'] = self.xdata_sd = xdata_sd
reduction['ydata'] = self.ydata = ydata
reduction['ydata_sd'] = self.ydata_sd = ydata_sd
reduction['m_ref'] = self.m_ref = m_ref
reduction['m_ref_sd'] = self.m_ref_sd = m_ref_sd
reduction['qz'] = self.m_qz = qz
reduction['qy'] = self.m_qy = qy
reduction['nspectra'] = self.n_spectra = n_spectra
reduction['datafile_number'] = self.datafile_number = (
self.reflected_beam.datafile_number)
fnames = []
if self.save:
for i in range(n_spectra):
data_tup = self.data(scanpoint=i)
dataset = ReflectDataset(data_tup)
fname = 'PLP{0:07d}_{1}.dat'.format(self.datafile_number, i)
fnames.append(fname)
with open(fname, 'wb') as f:
dataset.save(f)
fname = 'PLP{0:07d}_{1}.xml'.format(self.datafile_number, i)
with open(fname, 'wb') as f:
dataset.save_xml(f)
reduction['fname'] = fnames
return deepcopy(reduction)
def reduce_stitch(
reflect_list,
direct_list,
data_folder=None,
prefix="PLP",
trim_trailing=True,
save=True,
scale=1.0,
reduction_options=None,
):
"""
Reduces a list of reflected beam run numbers and a list of corresponding
direct beam run numbers from the Platypus/Spatz reflectometers. If there
are multiple reflectivity files they are spliced together.
Parameters
----------
reflect_list : list
Reflected beam run numbers, e.g. `[708, 709, 710]`
708 corresponds to the file PLP0000708.nx.hdf.
direct_list : list
Direct beam run numbers, e.g. `[711, 711, 711]`
data_folder : str, optional
Where is the raw data stored?
prefix : str, optional
The instrument filename prefix.
trim_trailing : bool, optional
When datasets are spliced together do you want to remove points in the
overlap region from the preceding dataset?
save : bool, optional
If `True` then the spliced file is written to a file (in the working
directory) with a name like: `c_PLP0000708.dat`.
scale : float, optional
Scales the data by this value.
reduction_options : None, dict, or list of dict, optional
Options passed directly to `refnx.reduce.PlatypusNexus.process`,
for processing of individual spectra. Look at that method docstring
for specification of options. If an individual dict then the same
options are used to process all datasets. A list (or sequence) of
dict can be used to specify different options for each datasets. If
None, then a default set of reduction options will be used.
Returns
-------
combined_dataset, reduced_filename : refnx.dataset.ReflectDataset, str
The combined dataset and the file name of the reduced data, if it was
saved. If it wasn't saved `reduced_filename` is `None`.
Notes
-----
The `prefix` is used to specify the run numbers to a filename.
For example a run number of 10, and a prefix of `PLP` resolves to a
NeXus filename of 'PLP0000010.nx.hdf'.
Examples
--------
>>> from refnx.reduce import reduce_stitch
>>> dataset, fname = reduce_stitch([708, 709, 710],
... [711, 711, 711],
... reduction_options={"rebin_percent": 2})
"""
options = [ReductionOptions()] * len(reflect_list)
try:
if reduction_options is not None:
options = []
for i in range(len(reflect_list)):
if isinstance(reduction_options[i], dict):
options.append(reduction_options[i])
else:
options.append(ReductionOptions())
except KeyError:
# reduction_options may be an individual dict
if isinstance(reduction_options, dict):
options = [reduction_options] * len(reflect_list)
# now reduce all the files.
zipped = zip(reflect_list, direct_list, options)
combined_dataset = ReflectDataset()
if data_folder is None:
data_folder = os.getcwd()
if prefix == "PLP":
reducer_klass = PlatypusReduce
elif prefix == "SPZ":
reducer_klass = SpatzReduce
else:
raise ValueError("Incorrect prefix specified")
for index, val in enumerate(zipped):
reflect_datafile = os.path.join(data_folder,
number_datafile(val[0], prefix=prefix))
direct_datafile = os.path.join(data_folder,
number_datafile(val[1], prefix=prefix))
reducer = reducer_klass(direct_datafile)
datasets, fnames = reducer.reduce(reflect_datafile,
save=save,
**val[2])
if not index:
datasets[0].scale(scale)
combined_dataset.add_data(datasets[0].data,
requires_splice=True,
trim_trailing=trim_trailing)
fname_dat = None
if save:
# this will give us <fname>.nx.hdf
# if reflect_list was an integer you'll get PLP0000708.nx.hdf
fname = number_datafile(reflect_list[0], prefix=prefix)
# now chop off .nx.hdf extension
fname = basename_datafile(fname)
fname_dat = "c_{0}.dat".format(fname)
with open(fname_dat, "wb") as f:
combined_dataset.save(f)
fname_xml = "c_{0}.xml".format(fname)
with open(fname_xml, "wb") as f:
combined_dataset.save_xml(f)
return combined_dataset, fname_dat
def reducer(self, callback=None):
"""
Reduce all the entries in reduction_entries
Parameters
----------
callback : callable
Function, `f(percent_finished)` that is called with the current
percentage progress of the reduction
"""
# refnx.reduce.reduce needs you to be in the directory where you're
# going to write files to
if self.output_directory:
os.chdir(self.output_directory)
# if no data directory was specified then assume it's the cwd
data_directory = self.data_directory
if not data_directory:
data_directory = './'
def full_path(fname):
f = os.path.join(data_directory, fname)
return f
# if the streamed directory isn't mentioned then assume it's the same
# as the data directory
streamed_directory = self.streamed_directory
if not os.path.isdir(streamed_directory):
self.streamed_directory = data_directory
logging.info('-------------------------------------------------------'
'\nStarting reduction run')
logging.info(
'data_folder={data_directory}, trim_trailing=True, '
'lo_wavelength={low_wavelength}, '
'hi_wavelength={high_wavelength}, '
'rebin_percent={rebin_percent}, '
'normalise={monitor_normalisation}, '
'background={background_subtraction} '
'eventmode={streamed_reduction} '
'event_folder={streamed_directory}'.format(**self.__dict__))
# sets up time slices for event reduction
if self.streamed_reduction:
eventmode = np.arange(self.stream_start,
self.stream_end,
self.stream_duration)
eventmode = np.r_[eventmode, self.stream_end]
else:
eventmode = None
# are you manual beamfinding?
peak_pos = None
if (self.manual_beam_find and
self.manual_beam_finder is not None):
peak_pos = -1
idx = 0
cached_direct_beams = {}
for row, val in self.reduction_entries.items():
if not val['use']:
continue
flood = None
if val['flood']:
flood = val['flood']
combined_dataset = None
# process entries one by one
for ref, db in zip(['reflect-1', 'reflect-2', 'reflect-3'],
['direct-1', 'direct-2', 'direct-3']):
reflect = val[ref]
direct = val[db]
# if the file doesn't exist there's no point continuing
if ((not os.path.isfile(full_path(reflect))) or
(not os.path.isfile(full_path(direct)))):
continue
# which of the nspectra to reduce (or all)
ref_pn = PlatypusNexus(reflect)
if direct not in cached_direct_beams:
cached_direct_beams[direct] = PlatypusReduce(
direct,
data_folder=data_directory)
reducer = cached_direct_beams[direct]
reduced = reducer(
ref_pn, scale=val['scale'],
norm_file_num=flood,
lo_wavelength=self.low_wavelength,
hi_wavelength=self.high_wavelength,
rebin_percent=self.rebin_percent,
normalise=self.monitor_normalisation,
background=self.background_subtraction,
manual_beam_find=self.manual_beam_finder,
peak_pos=peak_pos,
eventmode=eventmode,
event_folder=streamed_directory)
logging.info(
'Reduced {} vs {}, scale={}, angle={}'.format(
reflect, direct, val['scale'],
reduced['omega'][0, 0]))
if combined_dataset is None:
combined_dataset = ReflectDataset()
fname = basename_datafile(reflect)
fname_dat = os.path.join(self.output_directory,
'c_{0}.dat'.format(fname))
fname_xml = os.path.join(self.output_directory,
'c_{0}.xml'.format(fname))
combined_dataset.add_data(reducer.data(),
requires_splice=True,
trim_trailing=True)
if combined_dataset is not None:
# after you've finished reducing write a combined file.
with open(fname_dat, 'wb') as f:
combined_dataset.save(f)
with open(fname_xml, 'wb') as f:
combined_dataset.save_xml(f)
logging.info(
'Written combined files: {} and {}'.format(
fname_dat, fname_xml))
# can be used to create a progress bar
idx += 1
if callback is not None:
ok = callback(100 * idx / len(self.reduction_entries))
if not ok:
break
logging.info('\nFinished reduction run'
'-------------------------------------------------------')