def __run_script__(fns):
from Reduction import reduction, AddCifMetadata
from os.path import basename
from os.path import join
import os, re
import time #how fast are we going?
from Formats import output
elapsed = time.clock()
print 'Started working at %f' % (time.clock() - elapsed)
df.datasets.clear()
# save user preferences
prof_names, prof_values = save_user_prefs()
# store current Git versions for data output
code_versions = {
"GUI": __UI_gitversion[4:-1],
"Reduction library": reduction.gitversion[4:-1]
}
# check input
if (fns is None or len(fns) == 0):
print 'no input datasets'
return
# pre-check that we can write the result
# output_destination = out_folder.value
output_destination = get_save_path()
if output_xyd.value or output_fxye.value:
if not os.access(output_destination, os.W_OK):
open_error("Unable to write to folder %s" % output_destination)
return
# check if input needs to be normalized
if norm_apply.value:
# norm_ref is the source of information for normalisation
# norm_tar is the value norm_ref should become,
# by multiplication. If 'auto', the maximum value of norm_ref
# for the first dataset is used, otherwise any number may be entered.
norm_ref = str(norm_reference.value)
if norm_ref.strip() == '':
open_error(
"You have asked to apply normalisation but not specified any normalisation reference"
)
return
norm_tar = str(norm_target).lower()
# check if normalization target needs to be determined
if len(norm_tar) == 0:
norm_ref = None
norm_tar = None
print 'WARNING: no reference for normalization was specified'
elif norm_tar == 'auto':
# set flag
norm_tar = -1
# iterate through input datasets
location = norm_table[norm_ref]
print 'utilized reference value for "' + norm_ref + '" is:', norm_tar
# use provided reference value
else:
norm_tar = float(norm_tar)
else:
norm_ref = None
norm_tar = None
# check if bkg-map needs to be loaded
if bkg_apply.value:
if not bkg_map.value:
bkg = None
print 'WARNING: no bkg-map was specified'
else:
bkg = Dataset(str(bkg_map.value))
else:
bkg = None
# check if eff-map needs to be loaded
if eff_apply.value:
if not eff_map.value:
eff = None
print 'WARNING: no eff-map was specified'
else:
eff_map_canonical = get_calibration_path() + '/' + eff_map.value
if eff_map_canonical[0:5] != 'file:':
eff_map_canonical = 'file:' + eff_map_canonical
if not eff_map_canonical in eff_map_cache:
try:
eff_map_cache[
eff_map_canonical] = reduction.read_efficiency_cif(
eff_map_canonical)
except:
open_error("Failed to read efficiency file %s" %
eff_map_canonical)
return
else:
print 'Found in cache ' + ` eff_map_canonical `
eff = eff_map_cache[eff_map_canonical]
else:
eff = None
# check if vertical tube correction needs to be loaded
if vtc_apply.value:
if not vtc_file.value:
vtc = None
print 'WARNING: no vtc-file was specified'
else:
vtc = get_calibration_path() + '/' + str(vtc_file.value)
else:
vtc = None
# check if horizontal tube correction needs to be loaded
if htc_apply.value:
if not htc_file.value:
htc = None
print 'WARNING: no htc-file was specified'
else:
htc = get_calibration_path() + '/' + str(htc_file.value)
else:
htc = None
reduced_files = []
# iterate through input datasets
# note that the normalisation target (an arbitrary number) is set by
# the first dataset unless it has already been specified.
for fn in fns:
# load dataset
ds = df[fn]
# extract basic metadata
ds = AddCifMetadata.extract_metadata(ds, codeversions=code_versions)
AddCifMetadata.store_reduction_preferences(ds, prof_names, prof_values)
# remove redundant dimensions and convert to floating point
rs = ds.get_reduced() * 1.0
rs.copy_cif_metadata(ds)
# check if normalized is required
if norm_ref:
ds, norm_tar = reduction.applyNormalization(
rs, reference=norm_table[norm_ref], target=norm_tar)
else:
ds = rs
if bkg:
ds = reduction.getBackgroundCorrected(ds, bkg,
norm_table[norm_ref],
norm_tar)
print 'Finished normalisation, background subtraction at %f' % (
time.clock() - elapsed)
# check if vertical tube correction is required
if vtc:
ds = reduction.getVerticallyCorrected(ds, vtc)
print 'Finished vertical offset correction at %f' % (time.clock() -
elapsed)
# check if efficiency correction is required
if eff:
ds = reduction.getEfficiencyCorrected(ds, eff)
print 'Finished efficiency correction at %f' % (time.clock() - elapsed)
# Before fiddling with axes, get the ideal stepsize
stepsize = reduction.get_stepsize(ds)
print 'Ideal stepsize determined to be %f' % stepsize
# check if horizontal tube correction is required
if htc:
ds = reduction.getHorizontallyCorrected(ds, htc)
print 'Finished horizontal correction at %f' % (time.clock() - elapsed)
# Stitching. If we are recalculating gain, this is purely for
# informational purposes. We don't want to take the 100x time penalty of
# multiplying a 2D array by the gain factor for each tube, so we
# stitch on a 1D array after doing the gain re-refinement.
if ds.ndim > 2:
stitched = reduction.getStitched(ds,
ignore=str(asm_drop_frames.value))
# Display dataset
print 'Finished stitching at %f' % (time.clock() - elapsed)
Plot1.set_dataset(stitched)
Plot1.title = stitched.title
# check if we are recalculating gain
if regain_apply.value:
bottom = int(vig_lower_boundary.value)
top = int(vig_upper_boundary.value)
cs, gain, esds, chisquared, no_overlaps = reduction.do_overlap(
ds,
regain_iterno.value,
bottom=bottom,
top=top,
exact_angles=htc,
drop_frames=str(asm_drop_frames.value))
if cs is not None:
print 'Have new gains at %f' % (time.clock() - elapsed)
fg = Dataset(gain)
fg.var = esds
# set horizontal axis (ideal values)
# Plot4.set_dataset(Dataset(chisquared)) #chisquared history
# Plot5.set_dataset(fg) #final gain plot
else:
open_error(
"Cannot do gain recalculation as the scan ranges do not overlap."
)
return
if not vig_apply_rescale.value:
norm_const = -1.0
else:
norm_const = float(vig_rescale_target.value)
# set the cluster value
if str(vig_cluster.value) in ['Merge', 'Sum']:
cluster = (stepsize * 0.6, str(vig_cluster.value)
) #60 percent of ideal
else:
cluster = (0.0, 'None')
if not regain_apply.value: #already done
final_result = reduction.getVerticalIntegrated(
stitched,
axis=0,
normalization=norm_const,
cluster=cluster,
bottom=int(vig_lower_boundary.value),
top=int(vig_upper_boundary.value))
print 'Finished vertical integration at %f' % (time.clock() -
elapsed)
else:
if str(vig_cluster.value
) == 'Sum': #simulate a sum for the gain recalculated value
cs *= no_overlaps
info_string = "\nFinal values were multiplied by %d to simulate summation of individual points." % no_overlaps
cs.add_metadata("_pd_proc_info_data_reduction",
info_string,
append=True)
final_result = cs
# Display reduced dataset
send_to_plot(final_result, Plot2)
if copy_acc.value: #user wants us to accumulate it
plh_copy_proc()
# Output datasets
# Calculate inserted string: %s for sample name, %t for temperature
stem = str(output_stem.value)
stem = re.sub(r'[^\w+=()*^@~:{}\[\].%-]', '_', stem)
if '%s' in stem:
samplename = final_result.harvest_metadata(
"CIF")['_pd_spec_special_details']
name_front = samplename.split()[0]
stem = stem.replace('%s', name_front)
if '%t' in stem:
temperature = 'Unknown_temperature'
stem = stem.replace('%t', temperature)
print 'Filename stem is now ' + stem
filename_base = join(get_save_path(),
basename(str(fn))[:-7] + '_' + stem)
if output_xyd.value or output_fxye.value: #write CIF if other files written
output.write_cif_data(final_result, filename_base)
reduced_files.append(filename_base + '.cif')
if output_xyd.value:
output.write_xyd_data(final_result,
filename_base,
codeversions=code_versions)
reduced_files.append(filename_base + '.xyd')
if output_fxye.value:
output.write_fxye_data(final_result,
filename_base,
codeversions=code_versions)
reduced_files.append(filename_base + '.xye')
# ds.save_copy(join(str(out_folder.value), 'reduced_' + basename(str(fn))))
print 'Finished writing data at %f' % (time.clock() - elapsed)
if len(reduced_files) > 0:
zip_files(reduced_files,
'Echidna_rd_' + str(int(time.time()))[2:] + '.zip')
