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LoadNMoldyn3Ascii.py
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LoadNMoldyn3Ascii.py
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# Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2018 ISIS Rutherford Appleton Laboratory UKRI,
# NScD Oak Ridge National Laboratory, European Spallation Source,
# Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
# SPDX - License - Identifier: GPL - 3.0 +
#pylint: disable=invalid-name,no-init,too-many-locals,too-many-branches
from mantid.simpleapi import *
from mantid.kernel import *
from mantid.api import *
import ast
import re
import math
import os
import numpy as np
#==============================================================================
def _find_starts(data, c, l1):
for l in range(l1, len(data)):
char = data[l]
if char.startswith(c):
line = l
break
return line
#==============================================================================
def _find_tab_starts(data, c, l1):
for l in range(l1, len(data)):
char = data[l][1:]
if char.startswith(c):
line = l
break
return line
#==============================================================================
def _find_ends(data, c, l1):
for l in range(l1, len(data)):
char = data[l]
if char.endswith(c):
line = l
break
return line
#==============================================================================
def _make_list(a, l1, l2):
data = ''
for m in range(l1, l2 + 1):
data += a[m]
alist = data.split(',')
return alist
#==============================================================================
def _cdl_find_dimensions(data):
"""
Gets the number of Q, time and frequency values in given raw data.
@param data Raw data to search
"""
num_q_values = _find_tab_starts(data, 'NQVALUES', 0)
num_time_values = _find_tab_starts(data, 'NTIMES', 0)
num_freq_values = _find_tab_starts(data, 'NFREQUENCIES', 0)
q_el = data[num_q_values].split()
num_q = int(q_el[2])
t_el = data[num_time_values].split()
num_t = int(t_el[2])
f_el = data[num_freq_values].split()
num_f = int(f_el[2])
logger.debug(data[2][1:-1])
logger.debug(data[3][1:-1])
logger.debug(data[6][1:-1])
return num_q, num_t, num_f
#==============================================================================
class LoadNMoldyn3Ascii(PythonAlgorithm):
_file_name = None
_file_type = None
_functions = None
_out_ws = None
#-------------------------------------------------------------------------------
def category(self):
return 'Inelastic\\DataHandling;Simulation'
#-------------------------------------------------------------------------------
def summary(self):
return 'Imports functions from CDL and ASCII files output by nMOLDYN 3.'
#-------------------------------------------------------------------------------
def PyInit(self):
self.declareProperty(FileProperty('Filename', '',
action=FileAction.Load,
extensions=['.cdl', '.dat']),
doc='File path for data')
self.declareProperty(StringArrayProperty('Functions'),
doc='Names of functions to attempt to load from file')
self.declareProperty(WorkspaceProperty('OutputWorkspace', '',
direction=Direction.Output),
doc='Output workspace name')
#-------------------------------------------------------------------------------
def validateInputs(self):
issues = dict()
sample_filename = self.getPropertyValue('Filename')
file_type = os.path.splitext(sample_filename)[1]
function_list = self.getProperty('Functions').value
if len(function_list) == 0 and file_type == '.cdl':
issues['Functions'] = 'Must specify at least one function when loading a CDL file'
if len(function_list) > 0 and file_type == '.dat':
issues['Functions'] = 'Cannot specify functions when loading an ASCII file'
return issues
#-------------------------------------------------------------------------------
def PyExec(self):
# Do setup
self._setup()
loaded_ws = None
# Run nMOLDYN import
if self._file_type == 'cdl':
loaded_ws = self._cdl_import()
elif self._file_type == 'dat':
loaders = [self._ascii_3d_import, self._ascii_2d_import]
for loader in loaders:
try:
logger.information('Attempting to load with loader {0}'.format(loader))
loaded_ws = loader()
break
except (ValueError, SyntaxError) as err:
logger.information('Loader {0} failed to load data: {1}'.format(loader, err))
else:
raise RuntimeError('Unrecognised file extension: %s' % self._file_type)
if loaded_ws is None:
raise RuntimeError('Failed to load any data, check file format')
# Set the output workspace
self.setProperty('OutputWorkspace', loaded_ws)
#-------------------------------------------------------------------------------
def _setup(self):
"""
Gets algorithm properties.
"""
self._file_name = self.getPropertyValue('Filename')
self._file_type = os.path.splitext(self._file_name)[1][1:]
self._out_ws = self.getPropertyValue('OutputWorkspace')
raw_functions = self.getProperty('Functions').value
self._functions = [x.strip() for x in raw_functions]
#-------------------------------------------------------------------------------
def _cdl_import(self):
"""
Import data from CDL file.
"""
logger.notice('Loading CDL file')
# Get file base name
base_filename = os.path.basename(self._file_name)
base_name = os.path.splitext(base_filename)[0]
# Open file and get data
data = []
with open(self._file_name, 'r') as handle:
for line in handle:
line = line.rstrip()
data.append(line)
len_data = len(data)
# raw head
num_spec, nT, nF = _cdl_find_dimensions(data)
ldata = _find_starts(data, 'data:', 0)
lq1 = _find_starts(data, ' q =', ldata) # start Q values
lq2 = _find_starts(data, ' q =', lq1 - 1)
Qlist = _make_list(data, lq1, lq2)
if num_spec != len(Qlist):
raise RuntimeError('Error reading Q values')
Qf = Qlist[0].split()
Q = [float(Qf[2]) / 10.0]
for m in range(1, num_spec - 1):
Q.append(float(Qlist[m]) / 10.0)
Q.append(float(Qlist[num_spec - 1][:-1]) / 10.0)
logger.information('Q values = ' + str(Q))
lt1 = _find_starts(data, ' time =', lq2) # start T values
lt2 = _find_ends(data, ';', lt1)
Tlist = _make_list(data, lt1, lt2)
if nT != len(Tlist):
raise RuntimeError('Error reading Time values')
Tf = Tlist[0].split()
T = [float(Tf[2])]
for m in range(1, nT - 1):
T.append(float(Tlist[m]))
T.append(float(Tlist[nT - 1][:-1]))
T.append(2 * T[nT - 1] - T[nT - 2])
logger.information('T values = ' + str(T[:2]) + ' to ' + str(T[-3:]))
lf1 = _find_starts(data, ' frequency =', lq2) # start F values
lf2 = _find_ends(data, ';', lf1)
Flist = _make_list(data, lf1, lf2)
if nF != len(Flist):
raise RuntimeError('Error reading Freq values')
Ff = Flist[0].split()
F = [float(Ff[2])]
for m in range(1, nF - 1):
F.append(float(Flist[m]))
F.append(float(Flist[nF - 1][:-1]))
F.append(2 * F[nF - 1] - T[nF - 2])
logger.information('F values = ' + str(F[:2]) + ' to ' + str(F[-3:]))
# Function
output_ws_list = list()
for func in self._functions:
start = []
lstart = lt2
if func[:3] == 'Fqt':
nP = nT
xEn = np.array(T)
zero_error = np.zeros(nT)
x_unit = 'TOF'
elif func[:3] == 'Sqw':
nP = nF
xEn = np.array(F)
zero_error = np.zeros(nF)
x_unit = 'Energy'
else:
raise RuntimeError('Failed to parse function string ' + func)
for n in range(0, num_spec):
for m in range(lstart, len_data):
char = data[m]
if char.startswith(' // ' + func):
start.append(m)
lstart = m + 1
lend = _find_ends(data, ';', lstart)
start.append(lend + 1)
# Throw error if we couldn't find the function
if len(start) < 2:
raise RuntimeError('Failed to parse function string ' + func)
Qaxis = ''
for n in range(0, num_spec):
logger.information(str(start))
logger.information('Reading : ' + data[start[n]])
Slist = _make_list(data, start[n] + 1, start[n + 1] - 1)
if n == num_spec - 1:
Slist[nP - 1] = Slist[nP - 1][:-1]
S = []
for m in range(0, nP):
S.append(float(Slist[m]))
if nP != len(S):
raise RuntimeError('Error reading S values')
else:
logger.information('S values = ' + str(S[:2]) + ' to ' + str(S[-2:]))
if n == 0:
Qaxis += str(Q[n])
data_x = xEn
data_y = np.array(S)
data_e = zero_error
else:
Qaxis += ',' + str(Q[n])
data_x = np.append(data_x, xEn)
data_y = np.append(data_y, np.array(S))
data_e = np.append(data_e, zero_error)
function_ws_name = base_name + '_' + func
CreateWorkspace(OutputWorkspace=function_ws_name,
DataX=data_x,
DataY=data_y,
DataE=data_e,
Nspec=num_spec,
UnitX=x_unit,
VerticalAxisUnit='MomentumTransfer',
VerticalAxisValues=Qaxis)
output_ws_list.append(function_ws_name)
out_ws = GroupWorkspaces(InputWorkspaces=output_ws_list,
OutputWorkspace=self._out_ws)
return out_ws
#-------------------------------------------------------------------------------
def _ascii_3d_import(self):
"""
Import 3D ASCII data (e.g. I(Q, t)).
"""
logger.notice('Loading ASCII data')
from IndirectCommon import getEfixed
from IndirectNeutron import ChangeAngles, InstrParas
# Read file
data = []
x_axis = ('time', 'ns')
v_axis = ('q', 'ang^-1')
with open(self._file_name, 'r') as handle:
for line in handle:
line = line.strip()
# Ignore empty lines
if line == '':
continue
# Data line (if not comment)
elif line.strip()[0] != '#':
line_values = np.array([ast.literal_eval(t.strip()) if 'nan' not in t.lower() else np.nan for t in line.split()])
data.append(line_values)
if x_axis is None or v_axis is None:
raise ValueError('Data is not in expected format for 3D data')
logger.debug('X axis: {0}'.format(x_axis))
logger.debug('V axis: {0}'.format(v_axis))
# Get axis and Y values
data = np.swapaxes(np.array(data), 0, 1)
x_axis_values = data[0,1:]
v_axis_values = data[1:,0]
y_values = np.ravel(data[1:,1:])
# Create the workspace
wks = CreateWorkspace(OutputWorkspace=self._out_ws,
DataX=x_axis_values,
DataY=y_values,
NSpec=v_axis_values.size,
UnitX=x_axis[1],
EnableLogging=False)
# Load the MolDyn instrument
q_max = v_axis_values[-1]
instrument = 'MolDyn'
reflection = '2' if q_max <= 2.0 else '4'
InstrParas(wks.name(), instrument, 'simul', reflection)
# Process angles
efixed = getEfixed(wks.name())
logger.information('Qmax={0}, Efixed={1}'.format(q_max, efixed))
wave = 1.8 * math.sqrt(25.2429 / efixed)
qw = wave * v_axis_values / (4.0 * math.pi)
theta = 2.0 * np.degrees(np.arcsin(qw))
ChangeAngles(wks.name(), instrument, theta)
return wks
#-------------------------------------------------------------------------------
def _ascii_2d_import(self):
"""
Import 2D ASCII data (e.g. DoS).
"""
logger.notice('Loading ASCII data')
# Regex
x_axis_regex = re.compile(r"\s*columns-1\s*=\s*([A-z0-9\-\s]+)\s*\(([A-z0-9-\s*]+)\)")
y_axis_regex = re.compile(r"\s*columns-2\s*=\s*([A-z\-\s]+)")
# Read file
data = []
x_axis = None
y_axis = None
with open(self._file_name, 'r') as handle:
for line in handle:
line = line.strip()
# Ignore empty lines
if line == "":
continue
x_axis_match = x_axis_regex.match(line)
y_axis_match = y_axis_regex.match(line)
# Line (X) header
if x_axis_match:
x_axis = (x_axis_match.group(1).strip(), x_axis_match.group(2))
# Data (Y) header
elif y_axis_match:
y_axis = y_axis_match.group(1)
# Data line (if not comment)
elif line.strip()[0] != '#':
line_values = np.array([ast.literal_eval(t.strip()) if 'nan' not in t.lower() else np.nan for t in line.split()])
data.append(line_values)
if x_axis is None or y_axis is None:
raise ValueError('Data is not in expected format for 2D data')
logger.debug('X axis: {0}'.format(x_axis))
logger.debug('Y axis: {0}'.format(y_axis))
# Get axis and Y values
data = np.array(data)
x_axis_values = data[:,0]
y_values = data[:,1]
# Create the workspace
wks = CreateWorkspace(OutputWorkspace=self._out_ws,
DataX=x_axis_values,
DataY=y_values,
NSpec=1,
UnitX=x_axis[1],
WorkspaceTitle=y_axis,
YUnitLabel=y_axis,
EnableLogging=False)
return wks
#==============================================================================
# Register algorithm with Mantid
AlgorithmFactory.subscribe(LoadNMoldyn3Ascii)