def PyExec(self):
""" Alg execution. """
instrument = self.getProperty(INSTRUMENT_PROP).value
run_number = self.getProperty(RUN_NUM_PROP).value
fit_deadtime = self.getProperty(FIT_DEADTIME_PROP).value
fix_phases = self.getProperty(FIX_PHASES_PROP).value
default_level = self.getProperty(DEFAULT_LEVEL).value
sigma_looseness = self.getProperty(SIGMA_LOOSENESS_PROP).value
groupings_file = self.getProperty(GROUPINGS_PROP).value
in_phases_file = self.getProperty(PHASES_PROP).value
in_deadtimes_file = self.getProperty(DEADTIMES_PROP).value
out_phases_file = self.getProperty(PHASES_RESULT_PROP).value
out_deadtimes_file = self.getProperty(DEADTIMES_RESULT_PROP).value
isis = config.getFacility('ISIS')
padding = isis.instrument(instrument).zeroPadding(0)
run_name = instrument + str(run_number).zfill(padding)
try:
run_number = int(run_number)
except:
raise RuntimeError("'%s' is not an integer run number." %
run_number)
try:
run_file_path = FileFinder.findRuns(run_name)[0]
except:
raise RuntimeError("Unable to find file for run %i" % run_number)
if groupings_file == "":
groupings_file = DEFAULT_GROUPINGS_FILENAME % instrument
# Load data and other info from input files.
def temp_hidden_ws_name():
"""Generate a unique name for a temporary, hidden workspace."""
selection = string.ascii_lowercase + string.ascii_uppercase + string.digits
return '__temp_MaxEnt_' + ''.join(
random.choice(selection) for _ in range(20))
input_data_ws_name = temp_hidden_ws_name()
LoadMuonNexus(Filename=run_file_path,
OutputWorkspace=input_data_ws_name)
input_data_ws = mtd[input_data_ws_name]
if isinstance(input_data_ws, WorkspaceGroup):
Logger.get("MaxEnt").warning(
"Multi-period data is not currently supported. Just using first period."
)
input_data_ws = input_data_ws[0]
groupings_ws_name = temp_hidden_ws_name()
LoadDetectorsGroupingFile(InputFile=groupings_file,
OutputWorkspace=groupings_ws_name)
groupings_ws = mtd[groupings_ws_name]
def yield_floats_from_file(path):
"""Given a path to a file with a float on each line, will return
the floats one at a time. Throws otherwise. Strips whitespace
and ignores empty lines."""
with open(path, 'r') as f:
for i, line in enumerate(line.strip() for line in f):
if line == "":
continue
try:
yield float(line)
except:
raise RuntimeError(
"Parsing error in '%s': Line %d: '%s'." %
(path, i, line))
input_phases = np.array(list(yield_floats_from_file(in_phases_file)))
input_phases_size = len(input_phases)
input_deadtimes = np.array(
list(yield_floats_from_file(in_deadtimes_file)))
input_deadtimes_size = len(input_deadtimes)
n_bins = input_data_ws.blocksize()
n_detectors = input_data_ws.getNumberHistograms()
def time_value_to_time_channel_index(value):
"""Given a time value, will return the index of the time channel in
which the value falls."""
bin_width = input_data_ws.readX(0)[1] - input_data_ws.readX(0)[0]
diff = value - input_data_ws.readX(0)[0]
return int(diff / bin_width)
# Mantid corrects for time zero on loading, so we want to find the actual channels
# where 0.0 occurs, and where we have values of 0.1 onwards.
time_zero_channel = time_value_to_time_channel_index(0.0)
first_good_channel = time_value_to_time_channel_index(0.1)
input_data = np.concatenate(
[input_data_ws.readY(i) for i in range(n_detectors)])
groupings = [
groupings_ws.readY(row)[0]
for row in range(groupings_ws.getNumberHistograms())
]
groupings = map(int, groupings)
n_groups = len(set(groupings))
# Cleanup.
input_data_ws.delete()
groupings_ws.delete()
# We're faced with the problem of providing more than a dozen parameters to
# the Fortran, which can be a bit messy (especially on the Fortran side of
# things where we need to make "Cf2py" declarations). A cleaner way of
# doing this is to simply pass in a few callbacks -- one for each input
# type -- and have the Fortran provide the name of the variable it wants
# to the callback. The callback will then look up the corresponding value
# and feed it back to the Fortran.
#
# We also have a callback for printing to the results log.
self.int_vars = {
"RunNo": run_number,
"frames": FRAMES,
"res": RES,
"Tzeroch": time_zero_channel,
"firstgoodch": first_good_channel,
"ptstofit": POINTS_TO_FIT,
"histolen": n_bins,
"nhisto": n_detectors,
"n_groups": n_groups,
}
self.float_vars = {
"deflevel": default_level,
"sigloose": sigma_looseness,
}
self.bool_vars = {
"fixphase": fix_phases,
"fitdt": fit_deadtime,
}
self._assert_map_values_are_of_expected_type()
def lookup(par_name, par_map, default):
"""The basis of the callbacks passed to the Fortran. Given a parameter
name it will consult the appropriate variable map, and return the
corresponding value of the parameter. Else return a default and log a
warning if a parameter with the name does not exist."""
par_name = par_name.strip()
if par_name in par_map:
return par_map[par_name]
msg = """WARNING: tried to find a value for parameter with name %s but
could not find one. Default of \"%s\" provided.""" % (par_name,
default)
Logger.get("MaxEnt").warning(msg)
return default
def log(priority, message):
"""Log the given message with given priority."""
try:
logger = getattr(Logger.get("MaxEnt"), priority.lower())
except AttributeError:
# If we don't recognise the priority, use warning() as a default.
logger = getattr(Logger.get("MaxEnt"), "warning")
logger(message)
return True
# The Fortran expects arrays to be of a certain size, so any arrays that
# aren't big enough need to be padded.
input_phases = self._pad_to_length_with_zeros(input_phases, MAX_HISTOS)
input_deadtimes = self._pad_to_length_with_zeros(
input_deadtimes, MAX_HISTOS)
input_data = self._pad_to_length_with_zeros(input_data,
MAX_INPUT_DATA_SIZE)
groupings = self._pad_to_length_with_zeros(groupings, MAX_HISTOS)
# TODO: Return the contents of "NNNNN.max", instead of writing to file.
f_out, fchan_out, output_deadtimes, output_phases, chi_sq = maxent.mantid_maxent(
# Input data and other info:
input_data,
groupings,
input_deadtimes,
input_phases,
# Variable-lookup callbacks:
lambda par_name: lookup(par_name, self.int_vars, 0),
lambda par_name: lookup(par_name, self.float_vars, 0.0),
lambda par_name: lookup(par_name, self.bool_vars, False),
# Callback for logging:
log)
def write_items_to_file(path, items):
"""Given a path to a file and a list of items, will write the items
to the file, one on each line."""
with open(path, 'w') as f:
for item in items:
f.write(str(item) + "\n")
# Chop the padded outputs back down to the correct size.
output_phases = output_phases[:input_phases_size]
output_deadtimes = output_deadtimes[:input_deadtimes_size]
input_phases = input_phases[:input_phases_size]
input_deadtimes = input_deadtimes[:input_deadtimes_size]
fchan_out = fchan_out[:n_bins]
f_out = f_out[:n_bins]
write_items_to_file(out_phases_file, output_phases)
write_items_to_file(out_deadtimes_file, output_deadtimes)
log_output = "\nDead times in:\n" + str(input_deadtimes) + "\n" +\
"\nDead times out:\n" + str(output_deadtimes) + "\n" +\
"\nPhases in:\n" + str(input_phases) + "\n" +\
"\nPhases out:\n" + str(output_phases) + "\n" + \
"\nGroupings:\n" + str(groupings) + "\n" +\
"\nChi Squared:\n" + str(chi_sq) + "\n" +\
"\nInput variables:\n"
for type_map in self.int_vars, self.float_vars, self.bool_vars:
for name, value in type_map.items():
log_output += str(name) + " = " + str(value) + "\n"
Logger.get("MaxEnt").notice(log_output)
# Generate our own output ws name if the user has not provided one.
out_ws_name = self.getPropertyValue(OUT_WS_PROP)
if out_ws_name == "":
out_ws_name = run_name + "; MaxEnt"
self.setPropertyValue(OUT_WS_PROP, out_ws_name)
out_ws = CreateWorkspace(OutputWorkspace=out_ws_name,
DataX=fchan_out[:n_bins],
DataY=f_out[:n_bins])
self.setProperty(OUT_WS_PROP, out_ws)
# MaxEnt inputs table.
input_table_name = run_name + "; MaxEnt Input"
input_table = CreateEmptyTableWorkspace(
OutputWorkspace=input_table_name)
input_table.addColumn("str", "Name")
input_table.addColumn("str", "Value")
inputs = itertools.chain(self.int_vars.items(),
self.float_vars.items(),
self.bool_vars.items())
for name, value in inputs:
input_table.addRow([str(name), str(value)])
# Deadtimes and phases input/output table.
dead_phases_table_name = run_name + "; MaxEnt Deadtimes & Phases"
dead_phases_table = CreateEmptyTableWorkspace(
OutputWorkspace=dead_phases_table_name)
for column_name in "Deadtimes In", "Deadtimes Out", "Phases In", "Phases Out":
dead_phases_table.addColumn("double", column_name)
for row in zip(input_deadtimes, output_deadtimes, input_phases,
output_phases):
dead_phases_table.addRow(list(map(float, row)))
# Chi-squared output table.
chisq_table_name = run_name + "; MaxEnt Chi^2"
chisq_table = CreateEmptyTableWorkspace(
OutputWorkspace=chisq_table_name)
chisq_table.addColumn("int", "Cycle")
for iteration in range(10):
chisq_table.addColumn("double", "Iter " + str(iteration + 1))
for cycle, data in enumerate(chi_sq):
chisq_table.addRow([cycle + 1] + list(map(float, data)))
all_output_ws = [
input_table_name, dead_phases_table_name, chisq_table_name,
out_ws_name
]
# The output workspaces of this algorithm belong in the same groups
# that are created by the muon interface. If the appropriate group
# doesn't exist already then it needs to be created.
if not run_name in mtd:
GroupWorkspaces(InputWorkspaces=all_output_ws,
OutputWorkspace=run_name)
else:
group = mtd[run_name]
for output_ws in all_output_ws:
if not group.contains(output_ws):
group.add(output_ws)
out_ws.getAxis(0).getUnit().setLabel("Field", "G")
out_ws.setYUnitLabel("P(B)")
if INSIDE_MANTIDPLOT:
mantidplot.plotSpectrum(out_ws, 0)
def PyExec(self):
""" Alg execution. """
instrument = self.getProperty(INSTRUMENT_PROP).value
run_number = self.getProperty(RUN_NUM_PROP).value
fit_deadtime = self.getProperty(FIT_DEADTIME_PROP).value
fix_phases = self.getProperty(FIX_PHASES_PROP).value
default_level = self.getProperty(DEFAULT_LEVEL).value
sigma_looseness = self.getProperty(SIGMA_LOOSENESS_PROP).value
groupings_file = self.getProperty(GROUPINGS_PROP).value
in_phases_file = self.getProperty(PHASES_PROP).value
in_deadtimes_file = self.getProperty(DEADTIMES_PROP).value
out_phases_file = self.getProperty(PHASES_RESULT_PROP).value
out_deadtimes_file = self.getProperty(DEADTIMES_RESULT_PROP).value
isis = config.getFacility('ISIS')
padding = isis.instrument(instrument).zeroPadding(0)
run_name = instrument + str(run_number).zfill(padding)
try:
run_number = int(run_number)
except:
raise RuntimeError("'%s' is not an integer run number." % run_number)
try:
run_file_path = FileFinder.findRuns(run_name)[0]
except:
raise RuntimeError("Unable to find file for run %i" % run_number)
if groupings_file == "":
groupings_file = DEFAULT_GROUPINGS_FILENAME % instrument
# Load data and other info from input files.
def temp_hidden_ws_name():
"""Generate a unique name for a temporary, hidden workspace."""
selection = string.ascii_lowercase + string.ascii_uppercase + string.digits
return '__temp_MaxEnt_' + ''.join(random.choice(selection) for _ in range(20))
input_data_ws_name = temp_hidden_ws_name()
LoadMuonNexus(Filename=run_file_path, OutputWorkspace=input_data_ws_name)
input_data_ws = mtd[input_data_ws_name]
if isinstance(input_data_ws, WorkspaceGroup):
Logger.get("MaxEnt").warning("Multi-period data is not currently supported. Just using first period.")
input_data_ws = input_data_ws[0]
groupings_ws_name = temp_hidden_ws_name()
LoadDetectorsGroupingFile(InputFile=groupings_file, OutputWorkspace=groupings_ws_name)
groupings_ws = mtd[groupings_ws_name]
def yield_floats_from_file(path):
"""Given a path to a file with a float on each line, will return
the floats one at a time. Throws otherwise. Strips whitespace
and ignores empty lines."""
with open(path, 'r') as f:
for i, line in enumerate(line.strip() for line in f):
if line == "":
continue
try:
yield float(line)
except:
raise RuntimeError("Parsing error in '%s': Line %d: '%s'." %
(path, i, line))
input_phases = np.array(list(yield_floats_from_file(in_phases_file)))
input_phases_size = len(input_phases)
input_deadtimes = np.array(list(yield_floats_from_file(in_deadtimes_file)))
input_deadtimes_size = len(input_deadtimes)
n_bins = input_data_ws.blocksize()
n_detectors = input_data_ws.getNumberHistograms()
def time_value_to_time_channel_index(value):
"""Given a time value, will return the index of the time channel in
which the value falls."""
bin_width = input_data_ws.readX(0)[1] - input_data_ws.readX(0)[0]
diff = value - input_data_ws.readX(0)[0]
return int(diff / bin_width)
# Mantid corrects for time zero on loading, so we want to find the actual channels
# where 0.0 occurs, and where we have values of 0.1 onwards.
time_zero_channel = time_value_to_time_channel_index(0.0)
first_good_channel = time_value_to_time_channel_index(0.1)
input_data = np.concatenate([input_data_ws.readY(i) for i in range(n_detectors)])
groupings = [groupings_ws.readY(row)[0] for row in range(groupings_ws.getNumberHistograms())]
groupings = map(int, groupings)
n_groups = len(set(groupings))
# Cleanup.
input_data_ws.delete()
groupings_ws.delete()
# We're faced with the problem of providing more than a dozen parameters to
# the Fortran, which can be a bit messy (especially on the Fortran side of
# things where we need to make "Cf2py" declarations). A cleaner way of
# doing this is to simply pass in a few callbacks -- one for each input
# type -- and have the Fortran provide the name of the variable it wants
# to the callback. The callback will then look up the corresponding value
# and feed it back to the Fortran.
#
# We also have a callback for printing to the results log.
self.int_vars = {
"RunNo" : run_number,
"frames" : FRAMES,
"res" : RES,
"Tzeroch" : time_zero_channel,
"firstgoodch" : first_good_channel,
"ptstofit" : POINTS_TO_FIT,
"histolen" : n_bins,
"nhisto" : n_detectors,
"n_groups" : n_groups,
}
self.float_vars = {
"deflevel" : default_level,
"sigloose" : sigma_looseness,
}
self.bool_vars = {
"fixphase" : fix_phases,
"fitdt" : fit_deadtime,
}
self._assert_map_values_are_of_expected_type()
def lookup(par_name, par_map, default):
"""The basis of the callbacks passed to the Fortran. Given a parameter
name it will consult the appropriate variable map, and return the
corresponding value of the parameter. Else return a default and log a
warning if a parameter with the name does not exist."""
par_name = par_name.strip()
if par_name in par_map:
return par_map[par_name]
msg = """WARNING: tried to find a value for parameter with name %s but
could not find one. Default of \"%s\" provided.""" % (par_name, default)
Logger.get("MaxEnt").warning(msg)
return default
def log(priority, message):
"""Log the given message with given priority."""
try:
logger = getattr(Logger.get("MaxEnt"), priority.lower())
except AttributeError:
# If we don't recognise the priority, use warning() as a default.
logger = getattr(Logger.get("MaxEnt"), "warning")
logger(message)
return True
# The Fortran expects arrays to be of a certain size, so any arrays that
# aren't big enough need to be padded.
input_phases = self._pad_to_length_with_zeros(input_phases, MAX_HISTOS)
input_deadtimes = self._pad_to_length_with_zeros(input_deadtimes, MAX_HISTOS)
input_data = self._pad_to_length_with_zeros(input_data, MAX_INPUT_DATA_SIZE)
groupings = self._pad_to_length_with_zeros(groupings, MAX_HISTOS)
# TODO: Return the contents of "NNNNN.max", instead of writing to file.
f_out, fchan_out, output_deadtimes, output_phases, chi_sq = maxent.mantid_maxent(
# Input data and other info:
input_data,
groupings,
input_deadtimes,
input_phases,
# Variable-lookup callbacks:
lambda par_name: lookup(par_name, self.int_vars, 0),
lambda par_name: lookup(par_name, self.float_vars, 0.0),
lambda par_name: lookup(par_name, self.bool_vars, False),
# Callback for logging:
log
)
def write_items_to_file(path, items):
"""Given a path to a file and a list of items, will write the items
to the file, one on each line."""
with open(path, 'w') as f:
for item in items:
f.write(str(item) + "\n")
# Chop the padded outputs back down to the correct size.
output_phases = output_phases[:input_phases_size]
output_deadtimes = output_deadtimes[:input_deadtimes_size]
input_phases = input_phases[:input_phases_size]
input_deadtimes = input_deadtimes[:input_deadtimes_size]
fchan_out = fchan_out[:n_bins]
f_out = f_out[:n_bins]
write_items_to_file(out_phases_file, output_phases)
write_items_to_file(out_deadtimes_file, output_deadtimes)
log_output = "\nDead times in:\n" + str(input_deadtimes) + "\n" +\
"\nDead times out:\n" + str(output_deadtimes) + "\n" +\
"\nPhases in:\n" + str(input_phases) + "\n" +\
"\nPhases out:\n" + str(output_phases) + "\n" + \
"\nGroupings:\n" + str(groupings) + "\n" +\
"\nChi Squared:\n" + str(chi_sq) + "\n" +\
"\nInput variables:\n"
for type_map in self.int_vars, self.float_vars, self.bool_vars:
for name, value in type_map.items():
log_output += str(name) + " = " + str(value) + "\n"
Logger.get("MaxEnt").notice(log_output)
# Generate our own output ws name if the user has not provided one.
out_ws_name = self.getPropertyValue(OUT_WS_PROP)
if out_ws_name == "":
out_ws_name = run_name + "; MaxEnt"
self.setPropertyValue(OUT_WS_PROP, out_ws_name)
out_ws = CreateWorkspace(OutputWorkspace=out_ws_name,
DataX=fchan_out[:n_bins],
DataY=f_out[:n_bins])
self.setProperty(OUT_WS_PROP, out_ws)
# MaxEnt inputs table.
input_table_name = run_name + "; MaxEnt Input"
input_table = CreateEmptyTableWorkspace(OutputWorkspace = input_table_name)
input_table.addColumn("str", "Name")
input_table.addColumn("str", "Value")
inputs = itertools.chain(self.int_vars.items(),
self.float_vars.items(),
self.bool_vars.items())
for name, value in inputs:
input_table.addRow([str(name), str(value)])
# Deadtimes and phases input/output table.
dead_phases_table_name = run_name + "; MaxEnt Deadtimes & Phases"
dead_phases_table = CreateEmptyTableWorkspace(OutputWorkspace = dead_phases_table_name)
for column_name in "Deadtimes In", "Deadtimes Out", "Phases In", "Phases Out":
dead_phases_table.addColumn("double", column_name)
for row in zip(input_deadtimes, output_deadtimes, input_phases, output_phases):
dead_phases_table.addRow(list(map(float, row)))
# Chi-squared output table.
chisq_table_name = run_name + "; MaxEnt Chi^2"
chisq_table = CreateEmptyTableWorkspace(OutputWorkspace = chisq_table_name)
chisq_table.addColumn("int", "Cycle")
for iteration in range(10):
chisq_table.addColumn("double", "Iter " + str(iteration + 1))
for cycle, data in enumerate(chi_sq):
chisq_table.addRow([cycle + 1] + list(map(float,data)))
all_output_ws = [input_table_name,
dead_phases_table_name,
chisq_table_name,
out_ws_name]
# The output workspaces of this algorithm belong in the same groups
# that are created by the muon interface. If the appropriate group
# doesn't exist already then it needs to be created.
if not run_name in mtd:
GroupWorkspaces(InputWorkspaces = all_output_ws,
OutputWorkspace = run_name)
else:
group = mtd[run_name]
for output_ws in all_output_ws:
if not group.contains(output_ws):
group.add(output_ws)
out_ws.getAxis(0).getUnit().setLabel("Field", "G")
out_ws.setYUnitLabel("P(B)")
if INSIDE_MANTIDPLOT:
mantidplot.plotSpectrum(out_ws, 0)
