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added qx to Rlt file

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This commit is contained in:
stahn
2024-08-12 12:26:51 +02:00
parent 516faf860f
commit 05e35ca4e0
3 changed files with 45 additions and 33 deletions
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9
libeos/command_line.py
View File

@@ -24,9 +24,9 @@ def commandLineArgs():
default = Defaults.normalisationFileIdentifier,
nargs = '+',
help = "file number(s) of normalisation measurement")
input_data.add_argument("-nm", "--normalisationMethod",
default = Defaults.normalisationMethod,
help = "normalisation method: overilumination, underillumination, direct_beam")
#input_data.add_argument("-nm", "--normalisationMethod",
# default = Defaults.normalisationMethod,
# help = "normalisation method: overillumination, underillumination, direct_beam")
input_data.add_argument("--raw",
type = str,
default = Defaults.raw,
@@ -198,7 +198,8 @@ def command_line_options():
scale = clas.scale,
subtract = clas.subtract,
normalisationFileIdentifier = clas.normalisationFileIdentifier,
timeSlize = clas.timeSlize
#normalisationMethod = clas.normalisationMethod,
timeSlize = clas.timeSlize,
)
output_config = OutputConfig(
outputFormats = output_format_list(clas.outputFormat),

11
libeos/options.py
View File

@@ -8,11 +8,11 @@ from datetime import datetime
class Defaults:
# fileIdentifier
normalisationFileIdentifier = []
normalisationMethod = 'overilumination'
dataPath = '.'
raw = ['.', './raw', '../raw', '../../raw']
year = datetime.now().year
normalisationFileIdentifier = []
#normalisationMethod = 'overillumination'
# subtract
outputName = "fromEOS"
outputFormat = ['Rqz.ort']
@@ -71,7 +71,7 @@ class ReductionConfig:
autoscale: Optional[Tuple[bool, bool]] = None
subtract: Optional[str] = None
normalisationFileIdentifier: Optional[list] = None
normalisationMethod: str
#normalisationMethod: str
timeSlize: Optional[list] = None
@dataclass
@@ -105,8 +105,8 @@ class EOSConfig:
inpt += f' -Y {datetime.now().year}'
if self.reader_config.dataPath != '.':
inpt += f' --dataPath {self.reader_config.dataPath}'
if self.reader_config.raw != '.':
inpt = f' --rawd {self.reader_config.raw}'
#if self.reader_config.raw != '.':
# inpt = f' --rawd {self.reader_config.raw}'
if self.reduction_config.subtract:
inpt += f' -subtract {self.reduction_config.subtract}'
if self.reduction_config.normalisationFileIdentifier:
@@ -179,6 +179,7 @@ class EOSConfig:
if modl:
mlst += ' ' + modl
print(mlst)
return mlst

58
libeos/reduction.py
View File

@@ -75,7 +75,7 @@ class AmorReduction:
def read_unsliced(self, i):
lamda_e = self.file_reader.lamda_e
detZ_e = self.file_reader.detZ_e
qz_lz, ref_lz, err_lz, res_lz, lamda_lz, theta_lz, int_lz, self.mask_lz = self.project_on_lz(
qz_lz, qx_lz, ref_lz, err_lz, res_lz, lamda_lz, theta_lz, int_lz, self.mask_lz = self.project_on_lz(
self.file_reader, self.norm_lz, self.normAngle, lamda_e, detZ_e)
try:
ref_lz *= self.reduction_config.scale[i]
@@ -130,6 +130,7 @@ class AmorReduction:
fileio.Column('intensity', '', 'filtered neutron events per pixel'),
fileio.Column('norm', '', 'normalisation matrix'),
fileio.Column('mask', '', 'pixels used for calculating R(q_z)'),
fileio.Column('Qx', '1/angstrom', 'parallel momentum transfer'),
]
# data_source = file_reader.data_source
@@ -149,7 +150,8 @@ class AmorReduction:
tindex_lz.T,
int_lz.T,
self.norm_lz.T,
np.where(self.mask_lz, 1, 0).T
np.where(self.mask_lz, 1, 0).T,
qx_lz.T,
):
data = np.array(list(item)).T
headerRlt = self.header.orso_header(columns=columns)
@@ -178,7 +180,7 @@ class AmorReduction:
lamda_e = self.file_reader.lamda_e[filter_e]
detZ_e = self.file_reader.detZ_e[filter_e]
qz_lz, ref_lz, err_lz, res_lz, lamda_lz, theta_lz, int_lz, mask_lz = self.project_on_lz(
qz_lz, qx_lz, ref_lz, err_lz, res_lz, lamda_lz, theta_lz, int_lz, mask_lz = self.project_on_lz(
self.file_reader, self.norm_lz, self.normAngle, lamda_e, detZ_e)
q_q, R_q, dR_q, dq_q = self.project_on_qz(qz_lz, ref_lz, err_lz, res_lz, self.norm_lz, mask_lz)
@@ -349,14 +351,15 @@ class AmorReduction:
def project_on_lz(self, fromHDF, norm_lz, normAngle, lamda_e, detZ_e):
# projection on lambda-z-grid
lamda_l = self.grid.lamda()
if self.experiment_config.incidentAngle == 'alphaF':
theta_z = fromHDF.nu - fromHDF.mu + fromHDF.delta_z
elif self.experiment_config.incidentAngle == 'nu':
theta_z = (fromHDF.nu + fromHDF.delta_z + fromHDF.kap + fromHDF.kad) / 2.
else:
pass
lamda_lz = (self.grid.lz().T*lamda_l[:-1]).T
theta_lz = self.grid.lz()*theta_z
alphaF_z = fromHDF.nu - fromHDF.mu + fromHDF.delta_z
#if self.experiment_config.incidentAngle == 'alphaF':
# alphaF_z = fromHDF.nu - fromHDF.mu + fromHDF.delta_z
#elif self.experiment_config.incidentAngle == 'nu':
# alphaF_z = (fromHDF.nu + fromHDF.delta_z + fromHDF.kap + fromHDF.kad) / 2.
#else:
# pass
lamda_lz = (self.grid.lz().T*lamda_l[:-1]).T
alphaF_lz = self.grid.lz()*alphaF_z
thetaN_z = fromHDF.delta_z + normAngle
thetaN_lz = np.ones(np.shape(norm_lz))*thetaN_z
@@ -364,37 +367,44 @@ class AmorReduction:
mask_lz = np.where(np.isnan(norm_lz), False, True)
mask_lz = np.logical_and(mask_lz, np.where(np.absolute(thetaN_lz)>5e-3, True, False))
mask_lz = np.logical_and(mask_lz, np.where(np.absolute(theta_lz)>5e-3, True, False))
mask_lz = np.logical_and(mask_lz, np.where(np.absolute(alphaF_lz)>5e-3, True, False))
if self.reduction_config.thetaRange[1]<12:
mask_lz = np.logical_and(mask_lz, np.where(theta_lz >= self.reduction_config.thetaRange[0], True, False))
mask_lz = np.logical_and(mask_lz, np.where(theta_lz <= self.reduction_config.thetaRange[1], True, False))
mask_lz = np.logical_and(mask_lz, np.where(alphaF_lz >= self.reduction_config.thetaRange[0], True, False))
mask_lz = np.logical_and(mask_lz, np.where(alphaF_lz <= self.reduction_config.thetaRange[1], True, False))
if self.reduction_config.thetaRangeR[1]<12:
t0 = fromHDF.nu - fromHDF.mu
mask_lz = np.logical_and(mask_lz, np.where(theta_lz-t0 >= self.reduction_config.thetaRangeR[0], True, False))
mask_lz = np.logical_and(mask_lz, np.where(theta_lz-t0 <= self.reduction_config.thetaRangeR[1], True, False))
mask_lz = np.logical_and(mask_lz, np.where(alphaF_lz-t0 >= self.reduction_config.thetaRangeR[0], True, False))
mask_lz = np.logical_and(mask_lz, np.where(alphaF_lz-t0 <= self.reduction_config.thetaRangeR[1], True, False))
if self.experiment_config.lambdaRange[1]<15:
mask_lz = np.logical_and(mask_lz, np.where(lamda_lz >= self.experiment_config.lambdaRange[0], True, False))
mask_lz = np.logical_and(mask_lz, np.where(lamda_lz <= self.experiment_config.lambdaRange[1], True, False))
# gravity correction
#theta_lz += np.rad2deg( np.arctan( 3.07e-10 * (fromHDF.detectorDistance + detXdist_e) * lamda_lz**2 ) )
theta_lz += np.rad2deg( np.arctan( 3.07e-10 * fromHDF.detectorDistance * lamda_lz**2 ) )
#alphaF_lz += np.rad2deg( np.arctan( 3.07e-10 * (fromHDF.detectorDistance + detXdist_e) * lamda_lz**2 ) )
alphaF_lz += np.rad2deg( np.arctan( 3.07e-10 * fromHDF.detectorDistance * lamda_lz**2 ) )
if self.experiment_config.incidentAngle == 'alphaF':
#alphaI_lz = alphaF_lz
qz_lz = 4.0*np.pi * np.sin(np.deg2rad(alphaF_lz)) / lamda_lz
qx_lz = self.grid.lz() * 0.
else:
alphaI_lz = self.grid.lz()*(fromHDF.mu + fromHDF.kap + fromHDF.kad)
qz_lz = 2.0*np.pi * (np.sin(np.deg2rad(alphaF_lz)) + np.sin(np.deg2rad(alphaI_lz))) / lamda_lz
qx_lz = 2.0*np.pi * (np.cos(np.deg2rad(alphaF_lz)) - np.cos(np.deg2rad(alphaI_lz))) / lamda_lz
#z_z = enumerate(theta_z)
qz_lz = 4.0*np.pi * np.sin(np.deg2rad(theta_lz)) / lamda_lz
int_lz, bins_l, bins_z = np.histogram2d(lamda_e, detZ_e, bins = (lamda_l, self.grid.z()))
# cut normalisation sample horizon
int_lz = np.where(mask_lz, int_lz, np.nan)
thetaF_lz = np.where(mask_lz, theta_lz, np.nan)
thetaF_lz = np.where(mask_lz, alphaF_lz, np.nan)
ref_lz = (int_lz * np.absolute(thetaN_lz)) / (norm_lz * np.absolute(thetaF_lz))
err_lz = ref_lz * np.sqrt( 1/(int_lz+.1) + 1/norm_lz )
res_lz = np.ones((np.shape(lamda_l[:-1])[0], np.shape(theta_z)[0])) * 0.022**2
res_lz = res_lz + (0.008/theta_lz)**2
res_lz = np.ones((np.shape(lamda_l[:-1])[0], np.shape(alphaF_z)[0])) * 0.022**2
res_lz = res_lz + (0.008/alphaF_lz)**2
res_lz = qz_lz * np.sqrt(res_lz)
return qz_lz, ref_lz, err_lz, res_lz, lamda_lz, theta_lz, int_lz, mask_lz
return qz_lz, qx_lz, ref_lz, err_lz, res_lz, lamda_lz, alphaF_lz, int_lz, mask_lz
@staticmethod