def plh_sum_proc():
"""Sum all datasets contained in plot 3"""
from Reduction import reduction
from Formats import output
filename = str(plh_file.value)
datasets = Plot3.ds
approach = str(plh_sum_type.value)
if approach == 'Ideal':
newds = reduction.sum_datasets(datasets)
send_to_plot(newds,Plot2,add=False)
else:
newds = reduction.merge_datasets(datasets)
if approach == 'Cluster':
cluster = float(plh_cluster.value)
if cluster > 0:
newds,info_string = reduction.debunch(newds,(cluster,''))
send_to_plot(newds,Plot2,add=False)
n_logger.log_plot(Plot2, footer = Plot2.title)
# Write to file
if filename != '':
output.write_cif_data(newds,filename)
if output_xyd.value:
output.write_xyd_data(newds,filename)
if output_fxye.value:
output.write_fxye_data(newds,filename)
if output_topas.value:
output.write_xyd_data(newds,filename,comment_char="!")
def plh_sum_proc():
"""Sum all datasets contained in plot 3"""
from Reduction import reduction
from Formats import output
filename = str(plh_file.value)
datasets = Plot3.ds
approach = str(plh_sum_type.value)
if approach == 'Ideal':
newds = reduction.sum_datasets(datasets)
send_to_plot(newds, Plot2, add=False)
else:
newds = reduction.merge_datasets(datasets)
if approach == 'Cluster':
cluster = float(plh_cluster.value)
if cluster > 0:
newds = reduction.debunch(newds, cluster)
send_to_plot(newds, Plot2, add=False)
# Write to file
if filename != '':
output.write_cif_data(newds, filename)
if output_xyd.value:
output.write_xyd_data(newds, filename)
if output_fxye.value:
output.write_fxye_data(newds, filename)
def plh_sum_proc():
"""Sum all datasets contained in plot 3"""
from Reduction import reduction
from Formats import output
filename = str(plh_file.value)
datasets = Plot3.ds
approach = str(plh_sum_type.value)
if approach == 'Ideal':
newds = reduction.sum_datasets(datasets)
send_to_plot(newds,Plot2,add=False)
else:
newds = reduction.merge_datasets(datasets)
if approach == 'Cluster':
cluster = float(plh_cluster.value)
if cluster > 0:
newds = reduction.debunch(newds,cluster)
send_to_plot(newds,Plot2,add=False)
# Write to file
if filename != '':
output.write_cif_data(newds,filename)
if output_xyd.value:
output.write_xyd_data(newds,filename)
if output_fxye.value:
output.write_fxye_data(newds,filename)
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')
def __run_script__(fns):
from Reduction import reduction, AddCifMetadata
from os.path import basename
from os.path import join
from Formats import output
import re
df.datasets.clear()
# save user preferences
prof_names,prof_values = save_user_prefs()
# check input
if (fns is None or len(fns) == 0) :
print 'no input datasets'
return
# set the title for Plot2
# Plot2.title = 'Plot 2'
# 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)
norm_tar = norm_target
# 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))
# to avoid complaints in routines that expect it
reduction.AddCifMetadata.add_metadata_methods(bkg)
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 = Dataset(str(eff_map.value))
else:
eff = None
# Check for rescale
if vig_apply_rescale.value:
vig_normalisation = float(vig_rescale_target.value)
else:
vig_normalisation = -1
group_val = grouping_options[str(output_grouping.value)]
# 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 = reduction.AddCifMetadata.extract_metadata(ds)
try:
stth_value = sum(ds.stth)/len(ds.stth) # save for later
all_stth = ds.stth[:] # also save for later
except TypeError:
stth_value = ds.stth
if ds.ndim > 3:
rs = ds.get_reduced()
else:
rs = ds
rs = rs * 1.0 #convert to float
rs.copy_cif_metadata(ds)
# check if normalized is required
if norm_ref:
norm_tar = reduction.applyNormalization(rs, reference=norm_table[norm_ref], target=norm_tar)
if bkg:
rs = reduction.getBackgroundCorrected(rs, bkg, norm_table[norm_ref], norm_tar)
# check if efficiency correction is required
assert rs.dtype == Array([1.2,1.3]).dtype
if eff:
ds = reduction.getEfficiencyCorrected(rs, eff)
else:
ds = rs
# 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 = ds.harvest_metadata("CIF")['_pd_spec_special_details']
name_front = re.sub(r'[^\w+=()*^@~:{}\[\].%-]','_',samplename)
stem = stem.replace('%s',name_front)
if '%t1' in stem:
# get tc1
temperature = df[fn]["/entry1/sample/tc1/sensor/sensorValueA"]
print `temperature`
try:
avetemp = sum(temperature)/len(temperature)
except TypeError:
avetemp = temperature
stem = stem.replace('%t1',"%.0fK" % avetemp)
print 'Filename stem is now ' + stem
# restrict output set of frames
restrict_spec = str(output_restrict.value)
if ':' in restrict_spec:
first,last = map(int,restrict_spec.split(':'))
start_frames = last
current_frame_start = first
frameno = first
else:
start_frames = len(ds)
current_frame_start = 0
frameno = 0
# perform grouping of sequential input frames
while frameno <= start_frames:
if group_val is None:
target_val = ""
final_frame = start_frames-1
frameno = start_frames
else:
stth_value = all_stth[current_frame_start]
target_val = ds[group_val][current_frame_start]
try:
if ds[frameno][group_val] == target_val:
frameno += 1
continue
except: #Assume an exception is due to too large frameno
print 'Exiting frame loop due to error'
# frameno is the first frame with the wrong values
cs = ds.get_section([current_frame_start,0,0],[frameno-current_frame_start,ds.shape[1],ds.shape[2]])
cs.copy_cif_metadata(ds)
print 'Summing frames from %d to %d, shape %s, start 2th %f' % (current_frame_start,frameno-1,cs.shape,stth_value)
if target_val != "":
print 'Corresponding to a target value of ' + `target_val`
# sum the input frames
print 'cs axes: ' + cs.axes[0].title + ' ' + cs.axes[1].title + ' ' + cs.axes[2].title
# es = cs.intg(axis=0)
es = reduction.getSummed(cs,applyStth=stth_value) # does axis correction as well
es.copy_cif_metadata(cs)
print 'es axes: ' + `es.axes[0].title` + es.axes[1].title
Plot1.set_dataset(es)
cs = reduction.getVerticalIntegrated(es, axis=0, normalization=vig_normalisation,
bottom = int(vig_lower_boundary.value),
top=int(vig_upper_boundary.value))
if target_val != "":
cs.title = cs.title + "_" + str(target_val)
try:
send_to_plot(cs,Plot2,add=True,change_title=False)
except IndexError: #catch error from GPlot
send_to_plot(cs,Plot2,add=False,change_title=True)
# Output datasets
filename_base = join(str(out_folder.value),basename(str(fn))[:-7]+'_'+stem+"_"+str(target_val))
if output_cif.value:
output.write_cif_data(cs,filename_base)
if output_xyd.value:
output.write_xyd_data(cs,filename_base)
if output_fxye.value:
output.write_fxye_data(cs,filename_base)
#loop to next group of datasets
current_frame_start = frameno
frameno += 1
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
num_step = 9
prog_bar.max = len(fns) * num_step
prog_bar.selection = 1
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
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)).get_reduced()
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 = str(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 = 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 = str(htc_file.value)
else:
htc = None
# check if gain correction needs to be loaded
regain_data = []
if regain_load.value:
if not regain_load_filename.value:
open_error("You have requested loading of gain correction from a file but no file has been specified")
return
rlf = str(regain_load_filename.value)
regain_data = reduction.load_regain_values(rlf)
# iterate through input datasets
# note that the normalisation target (an arbitrary number) is set by
# the first dataset unless it has already been specified.
prog_bar.selection = 2
fn_idx = 0
for fn in fns:
# load dataset
ds = df[fn]
if not norm_uniform.value:
norm_tar = -1 #reinitialise
try:
prog_bar.selection = fn_idx * num_step
# extract basic metadata
print 'Code versions:' + `code_versions`
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:
AddCifMetadata.add_metadata_methods(bkg)
ds = reduction.getBackgroundCorrected(ds, bkg, norm_table[norm_ref], norm_tar)
print 'Finished normalisation, background subtraction at %f' % (time.clock()-elapsed)
prog_bar.selection = fn_idx * num_step + 1
# check that we have the necessary dimensions
dims = ds.shape
if dims[1] != 128:
rebin_factor = int(dims[1]/128)
print 'Need to rebin from %d to 128, factor of %d; stand by...' % (dims[1],rebin_factor)
ds = reduction.rebin(ds,axis=1,factor=rebin_factor)
else:
print 'No need to rebin, dataset shape is ' + repr(dims)
# check if vertical tube correction is required
if vtc:
ds = reduction.getVerticallyCorrected(ds, vtc)
print 'Finished vertical offset correction at %f' % (time.clock()-elapsed)
prog_bar.selection = fn_idx * num_step + 2
# check if efficiency correction is required
if eff:
ds = reduction.getEfficiencyCorrected(ds, eff)
print 'Finished efficiency correction at %f' % (time.clock()-elapsed)
prog_bar.selection = fn_idx * num_step + 3
# Before fiddling with axes, get the ideal stepsize
stepsize = reduction.get_stepsize(ds)
print 'Ideal stepsize determined to be %f' % stepsize
prog_bar.selection = fn_idx * num_step + 4
# check if horizontal tube correction is required
if htc:
ds = reduction.getHorizontallyCorrected(ds, htc)
print 'Finished horizontal correction at %f' % (time.clock()-elapsed)
prog_bar.selection = fn_idx * num_step + 5
# 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 using a 1D array after doing the gain re-refinement.
drop_tubes = str(asm_drop_tubes.value)
if ds.ndim > 2:
# See if we are ignoring any tubes
stitched = reduction.getStitched(ds,ignore=str(asm_drop_frames.value),drop_tubes=drop_tubes)
# Display dataset
print 'Finished stitching at %f' % (time.clock()-elapsed)
prog_bar.selection = fn_idx * num_step + 6
Plot1.set_dataset(stitched)
Plot1.title = stitched.title
n_logger.log_plot(Plot1, footer = Plot1.title)
# check if we are recalculating gain
if regain_apply.value:
bottom = int(vig_lower_boundary.value)
top = int(vig_upper_boundary.value)
dumpfile = None
# if regain_dump_tubes.value:
# dumpfile = filename_base+".tubes"
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),drop_tubes=drop_tubes,use_gains=regain_data,dumpfile=dumpfile,
do_sum=regain_sum.value)
if cs is not None:
print 'Have new gains at %f' % (time.clock() - elapsed)
fg = Dataset(gain)
fg.var = esds**2
# set horizontal axis (ideal values)
Plot4.set_dataset(Dataset(chisquared)) #chisquared history
Plot5.set_dataset(fg) #final gain plot
# now save the file if requested
if regain_store.value and not regain_load.value:
gain_comment = "Gains refined from file %s" % fn
reduction.store_regain_values(str(regain_store_filename.value),gain,gain_comment)
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)
if norm_const > 0: #rescale requested but not performed
reduction.rescale(cs,norm_const)
final_result = cs
prog_bar.selection = fn_idx * num_step + 7
# Display reduced dataset
send_to_plot(final_result,Plot2)
n_logger.log_plot(Plot2, footer = Plot2.title)
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(str(out_folder.value),basename(str(fn))[:-7] + '_' + stem)
if output_xyd.value or output_fxye.value or output_topas.value: #write CIF if other files written
output.write_cif_data(final_result,filename_base)
if output_xyd.value:
add_header = output_naked.value
output.write_xyd_data(final_result,filename_base,codeversions=code_versions,naked=add_header)
if output_fxye.value:
output.write_fxye_data(final_result,filename_base,codeversions=code_versions)
if output_topas.value:
output.write_xyd_data(final_result,filename_base,codeversions=code_versions,comment_char="!",extension='topas')
# ds.save_copy(join(str(out_folder.value), 'reduced_' + basename(str(fn))))
print 'Finished writing data at %f' % (time.clock()-elapsed)
prog_bar.selection = fn_idx * num_step + 8
fn_idx += 1
finally:
df[fn].close()
prog_bar.selection = 0
def __run_script__(fns):
global Plot4,Plot5,Plot6
from Reduction import reduction,AddCifMetadata
from os.path import basename
from os.path import join
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()
# check input
if (fns is None or len(fns) == 0) :
print 'no input datasets'
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)
norm_tar = str(norm_target.value).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 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 = str(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:
eff_map_cache[eff_map_canonical] = reduction.read_efficiency_cif(eff_map_canonical)
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 = str(vtc_file.value)
else:
vtc = None
# 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 = reduction.AddCifMetadata.extract_metadata(ds)
# remove redundant dimensions
rs = ds.get_reduced()
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)
print 'Finished normalisation 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)
# check if we are recalculating gain
if regain_apply.value:
b = ds.intg(axis=1).get_reduced() #reduce dimension
ignore = regain_ignore.value #Ignore first two tubes
# Determine pixels per tube interval
tube_pos = ds.axes[-1]
tubesep = abs(tube_pos[0]-tube_pos[-1])/(len(tube_pos)-1)
tube_steps = ds.axes[0]
bin_size = abs(tube_steps[0]-tube_steps[-1])/(len(tube_steps)-1)
pixel_step = int(round(tubesep/bin_size))
bin_size = tubesep/pixel_step
print '%f tube separation, %d steps before overlap, ideal binsize %f' % (tubesep,pixel_step,bin_size)
# Reshape with individual sections summed
c = b.reshape([b.shape[0]/pixel_step,pixel_step,b.shape[-1]])
print `b.shape` + "->" + `c.shape`
# sum the individual unoverlapped sections
d = c.intg(axis=1)
e = d.transpose()
# we skip the first tubes' data as it is all zero
# Get an initial average to start with
bottom = vig_lower_boundary.value
top = vig_upper_boundary.value
resummed = ds[:,bottom:top,:]
resummed = resummed.intg(axis=1).get_reduced()
first_gain = array.ones(len(b.transpose())-ignore)
first_ave,x,first_var = overlap.apply_gain(resummed.transpose()[ignore:,:],1.0/resummed.transpose().var[ignore:,:],pixel_step,first_gain, calc_var=True)
if regain_unit_weights.value is True:
weights = array.ones_like(e[ignore:])
else:
weights = 1.0/e[ignore:].var
q= iterate_data(e[ignore:],weights,pixel_step=1,iter_no=int(regain_iterno.value))
# Now we actually apply the vertical limits requested
f,x, varf = overlap.apply_gain(resummed.transpose()[ignore:,:],1.0/resummed.transpose().var[ignore:,:],pixel_step,q[0],calc_var=True)
# Get error for full dataset
esds = overlap.calc_error_new(b.transpose()[ignore:,:],f,q[0],pixel_step)
f = Dataset(f)
f.title = "After scaling"
f.var = varf
# construct the ideal axes
axis = arange(len(f))
f.axes[0] = axis*bin_size + ds.axes[0][0] + ignore*pixel_step*bin_size
f.copy_cif_metadata(ds)
print `f.shape` + ' ' + `x.shape`
Plot1.set_dataset(f)
first_ave = Dataset(first_ave)
first_ave.var = first_var
first_ave.title = "Before scaling"
first_ave.axes[0] = f.axes[0]
Plot1.add_dataset(Dataset(first_ave))
Plot4.set_dataset(Dataset(q[4]))
fg = Dataset(q[0])
fg.var = esds
Plot5.set_dataset(fg)
# show old esds
fgold = Dataset(q[0])
fgold.var = q[5]
Plot5.add_dataset(fgold)
residual_map = Dataset(q[3])
try:
Plot6.set_dataset(residual_map)
except:
pass
print 'Finished regain calculation at %f' % (time.clock() - elapsed)
# Output datasets
filename_base = join(str(out_folder.value),str(output_stem.value) + basename(str(fn))[:-7])
if output_cif.value:
output.write_cif_data(f,filename_base)
if output_xyd.value:
output.write_xyd_data(f,filename_base)
if output_fxye.value:
output.write_fxye_data(f,filename_base)
print 'Finished writing data at %f' % (time.clock()-elapsed)